{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XMM-LSS master catalogue\n",
    "\n",
    "This notebook presents the merge of the pristine catalogues from CFHT Megacam. This has to be conducted separately on XMM-LSS due to the large amount of memory required on this field.\n",
    "\n",
    "This notebook also ingests all the CANDELS-UDS data apart from the photometry that needs to be merged in other 2.x notebooks.\n",
    "\n",
    "Since this is where we ingest all the CANDELS fluxes apert from UKIDSS and IRAC we take the opportunity here to merge them in with SXDS Suprime fluxes.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "0246c5d (Thu Jan 25 17:01:47 2018 +0000) [with local modifications]\n",
      "This notebook was executed on: \n",
      "2018-02-20 16:14:27.605065\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": {},
   "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": [
    "#candels = Table.read(\"{}/CANDELS.fits\".format(TMP_DIR))           # 1.1\n",
    "candels = Table.read(\"{}/CANDELS-UDS.fits\".format(TMP_DIR))           # 1.2\n",
    "cfht_wirds = Table.read(\"{}/CFHT-WIRDS.fits\".format(TMP_DIR))     # 1.3\n",
    "cfhtls_wide = Table.read(\"{}/CFHTLS-WIDE.fits\".format(TMP_DIR))   # 1.4a\n",
    "cfhtls_deep = Table.read(\"{}/CFHTLS-DEEP.fits\".format(TMP_DIR))   # 1.4b\n",
    "#We no longer use CFHTLenS as it is the same raw data set as CFHTLS-WIDE\n",
    "# cfhtlens = Table.read(\"{}/CFHTLENS.fits\".format(TMP_DIR))         # 1.5\n",
    "#decals = Table.read(\"{}/DECaLS.fits\".format(TMP_DIR))             # 1.6\n",
    "#servs = Table.read(\"{}/SERVS.fits\".format(TMP_DIR))               # 1.8\n",
    "#swire = Table.read(\"{}/SWIRE.fits\".format(TMP_DIR))               # 1.7\n",
    "#hsc_wide = Table.read(\"{}/HSC-WIDE.fits\".format(TMP_DIR))         # 1.9a\n",
    "#hsc_deep = Table.read(\"{}/HSC-DEEP.fits\".format(TMP_DIR))         # 1.9b\n",
    "#hsc_udeep = Table.read(\"{}/HSC-UDEEP.fits\".format(TMP_DIR))       # 1.9c\n",
    "#ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR))                   # 1.10\n",
    "sxds = Table.read(\"{}/SXDS.fits\".format(TMP_DIR))                 # 1.11\n",
    "sparcs = Table.read(\"{}/SpARCS.fits\".format(TMP_DIR))             # 1.12\n",
    "#dxs = Table.read(\"{}/UKIDSS-DXS.fits\".format(TMP_DIR))            # 1.13\n",
    "#uds = Table.read(\"{}/UKIDSS-UDS.fits\".format(TMP_DIR))            # 1.14\n",
    "vipers = Table.read(\"{}/VIPERS.fits\".format(TMP_DIR))             # 1.15\n",
    "#vhs = Table.read(\"{}/VISTA-VHS.fits\".format(TMP_DIR))             # 1.16\n",
    "#video = Table.read(\"{}/VISTA-VIDEO.fits\".format(TMP_DIR))         # 1.17\n",
    "#viking = Table.read(\"{}/VISTA-VIKING.fits\".format(TMP_DIR))       # 1.18"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Merging tables\n",
    "\n",
    "We first merge the optical catalogues and then add the infrared ones. We start with PanSTARRS because it coevrs the whole field.\n",
    "\n",
    "At every step, we look at the distribution of the distances separating the sources from one catalogue to the other (within a maximum radius) to determine the best cross-matching radius."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start with CANDELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f_candels-ukidss_j  removed.\n",
      "ferr_candels-ukidss_j  removed.\n",
      "f_candels-ukidss_h  removed.\n",
      "ferr_candels-ukidss_h  removed.\n",
      "f_candels-ukidss_k  removed.\n",
      "ferr_candels-ukidss_k  removed.\n",
      "f_candels-irac_i1  removed.\n",
      "ferr_candels-irac_i1  removed.\n",
      "f_candels-irac_i2  removed.\n",
      "ferr_candels-irac_i2  removed.\n",
      "f_candels-irac_i3  removed.\n",
      "ferr_candels-irac_i3  removed.\n",
      "f_candels-irac_i4  removed.\n",
      "ferr_candels-irac_i4  removed.\n",
      "m_candels-ukidss_j  removed.\n",
      "merr_candels-ukidss_j  removed.\n",
      "flag_candels-ukidss_j  removed.\n",
      "m_candels-ukidss_h  removed.\n",
      "merr_candels-ukidss_h  removed.\n",
      "flag_candels-ukidss_h  removed.\n",
      "m_candels-ukidss_k  removed.\n",
      "merr_candels-ukidss_k  removed.\n",
      "flag_candels-ukidss_k  removed.\n",
      "m_candels-irac_i1  removed.\n",
      "merr_candels-irac_i1  removed.\n",
      "flag_candels-irac_i1  removed.\n",
      "m_candels-irac_i2  removed.\n",
      "merr_candels-irac_i2  removed.\n",
      "flag_candels-irac_i2  removed.\n",
      "m_candels-irac_i3  removed.\n",
      "merr_candels-irac_i3  removed.\n",
      "flag_candels-irac_i3  removed.\n",
      "m_candels-irac_i4  removed.\n",
      "merr_candels-irac_i4  removed.\n",
      "flag_candels-irac_i4  removed.\n"
     ]
    }
   ],
   "source": [
    "master_catalogue = candels\n",
    "master_catalogue['candels_ra'].name = 'ra'\n",
    "master_catalogue['candels_dec'].name = 'dec'\n",
    "del candels\n",
    "unused_bands = [ 'candels-ukidss', 'candels-irac']\n",
    "for col in master_catalogue.colnames:\n",
    "    \n",
    "    for band in unused_bands:\n",
    "        if band in col:\n",
    "            master_catalogue.remove_column(col)\n",
    "            print(col, ' removed.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add CFHTLS-DEEP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HELP Warning: There weren't any cross matches. The two surveys probably don't overlap.\n"
     ]
    }
   ],
   "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": 7,
   "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": [
    "###  Add CFHTLS-WIDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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YeeD1EatruytQer2X1dn+8Wt+LQAAkFwEKw+0D00qUpCrUO7VNwedx4gVAACZi2DlAS96\nWM3LD/gVDPh0jmAFAEDGIVh5oH1wUrXF176+SpLMTKWhXJ0bnPTk9QAAQPIQrK6Rc04dQ5NaVepN\nsJKk0lAuU4EAAGQggtU1Gpmc0/hM1JMeVvMiBblqG5yQc86z1wQAAMuPYHWN2jxstTCvtCBXU7Mx\n9Y6xZyAAAJmEYHWNvGwOOi8SCkiSzg2wzgoAgExCsLpGHUPx8OPlVGBpKN5ygTsDAQDILEsKVma2\ny8yOm1mLmX3uMufdYmZzZvY+70pMbx1Dk8r1+8439vRCaQHBCgCATLRosDIzv6SvSXpA0jZJHzKz\nbZc47y8lPe11kemsY3hKNSVB+Xzm2WsG/D5VhvN0bpBgBQBAJlnKiNWbJbU4504552YkfVfSgxc5\n7/ckfU9Sj4f1pb3OoUlVF3nTHHSh1ZEQLRcAAMgwSwlWqySdW/C4LfHceWa2StJ7Jf2td6Vlhs7h\nKU8Xrs+rj4RYvA4AQIbxavH6FyX9iXMudrmTzOwhM9tvZvt7e3s9unTqRGNO3SNTqi5ehhGr0nx1\nDk9qNnrZtxQAAKSRpQSrdkmrFzyuSzy30E5J3zWzM5LeJ+nrZvZrF76Qc+5R59xO59zOioqKqyw5\nffSPTWsu5lS7DMGqLhJSzL1+1yEAAEh/SwlWL0tqMLN1ZpYr6YOSfrjwBOfcOufcWufcWkn/Kul3\nnHM/8LzaNNMxHO9hVe3RPoEL1UdCkuhlBQBAJslZ7ATn3JyZfVrSU5L8kh53zjWa2cOJ448sc41p\nq2s4HnpqlmMqcD5YcWcgAAAZY9FgJUnOud2Sdl/w3EUDlXPuN6+9rMww33V9OYJVdVFQAb/RywoA\ngAxC5/Vr0DUypdwcnyIeNged5/eZakvyabkAAEAGIVhdg46hSdUUB2XmXXPQheojIZ0bZI0VAACZ\ngmB1DbqGp5ZlGnBeXWlIbYxYAQCQMQhW16BzeEo1y3BH4LzVkXz1j89ofHpu2a4BAAC8Q7C6SvPN\nQZdzxKqeOwMBAMgoBKur1JdoDrqcwWp1Kb2sAADIJEtqt4Bf1jk832phOacC54MVI1YAgNT79t7W\nRc/58K31SagkfRGsrlJnYquZ5dgncF5pKKDCvBxaLgAAlt1SQhMWx1TgVZofsaotWb4RKzNTXWm+\n2lhjBQDw0MxcLNUlZC1GrK5S5/Ck8nJ8Kg0FlvU6qyMhtfYTrAAASzc6Navm7lE1dY2qpWdM+88M\nanRqVqNTcxqdntPMXEzhvBxVFwdVVRRUdXFQ1UXBZe3NuFIQrK5SZ6KH1XJ/A64uDelnJ/rknOOb\nHQDwS3pGp3S4bViH2obV2DGspq5RtS1oLp0f8Ksgz6/CvIBqS/IVDuYoP9evwfEZdQ1P6aVT/ZqL\nOUnS5qqwPnjLauUF/Kn652Q8gtVV6hyeWtb1VfPqI/manI2qf3xG5YV5y349AED6Gp6Y1aH2IR08\nN6TXzg3rSPuwukbiS1NMUnk4T9VFQW2tKVJ1UXwUqiQUuOwf5tGYU//4tJo6R/X00S793Z5T+o23\nrFFJyPvt2lYCgtVV6hqe0q3rIst+nYV3BhKsAGDlmJqN6os/OaG2wQm1DU6qbXBCfWMz54+XF+ap\nrjRfb1pTqlUl+aopCSov58pHmvw+U2U4qMpwfCrw2/ta9fXnT+pjt605/zsIS0ewugrRmFPXSHJG\nrOa/qVsHJnRTfemyXw8AkHwzczE1d4/qSPuwDrYN61DbkI53jZ6fogvn5aguEtLN9aWqKw2prjRf\nwWWYrmuoCuvhOzfom784o//x01N635vqdH1diefXyWYEq6vQNzataMypZhnvCJw33yS0jc2YASAr\nzMzFdLxrVIfbh3W4PT6dd7xrVDPR+J16RcEcXV9XoofuWK/hyVnVlYZUFMxJ2jrbqqKgPnXXRn1r\n71l99+Vzmp6L6Za1yz9Dky0IVlehI9HDqqZo+Ues8nP9Ki/Mo0koAGSgaMypuXtUB88N6VD7sA63\nvTFEBQM+1Zbk69b1Ea0qydeqknyVFuTKlwhRdSmaqCjMy9Enbl+nf/j5GT3V2KXrVhUvywhZNiJY\nXYWu+a7rJcsfrKT4ZszsFwgA6a9nZEqvtA7q23tbdW5wUu2Dk78Uom5bH1FtIkRFCnLT9o7vHL9P\nD+yo1tefP6mfn+zT3VuqUl1SRiBYXYWOJGxns9Dq0pBePTeYlGsBAJZmLhpTU9eoXmkd1IGzg3ql\ndfD83q5+M9WUBHXzmlKtLs3X6khIZWkcoi6lrjSkrTVF+llLn96yvlz5uYxaLYZgdRW6ktQcdF59\nJKR/P9ypuWhMOX6a5QNAKsRHo4b07b1n1TowqfahCc1GE4vLgzlaEwnpuh3Fqo+EVFOSr0CW/Ly+\nd2ulvvLsiH7W0qv7tlWnupy0R7C6Ch1Jag46b3UkX9GYU+fwFLe+AkAS9I1N63BbfHH5obY39ovy\nm6m2JKidayOqLw2pviykkvzL94rKZDXF+dqxqlgvnuzXWzeUqyCP6HA5vDtXoWt4KmnTgNLrdwae\nG5ggWAGAh5xzOjcwqcaOYR3tHFFjx4iOdoy83nTTpPXlBbptfUTX1ZXopvoSHW4bzprRqKW6d0ul\nGtuHtedErx7YUZPqctIaweoqdA5N6rb1ZUm73vkmoSxgB4CrNjMX04me0fPh6WjHiI51jmh0ek5S\nvFFmWUGuakvydVN9iVaV5qu2+I39opo6R1dcqJKkyqKgblhdopdO9ettG8sVDiZnKUwmIlhdoWjM\nqXt0Oml3BEpSTXFQOT5TKy0XAGBJpmajakr0impsH9aRjmEd6xhV1MXXRAX8pprifG2rLVJNcb5q\nS+KbEa/E0LRU92yp1KG2Ib3Q3Ktfvb421eWkLYLVFeodjTcHrU7iVGCO36c1ZSGd6B5L2jUBIFPM\nRmP60k9OqH1wUm1D8e1fukemlGharvyAX6tK8nX7xnLVlgRVW5yvSOHrvaKwNGWFebqpvlR7Tw/o\n7Q0VKs5n1OpiCFZXqHM4fittbRK2s1loS3WRjnQMJ/WaAJBunHPqGJ7Sa61Deu3coF47N6TD7cOa\nmn29V1RdSUhvb6iIN9wszc/qheXJdvfmSr3WOqQXmnv0nhtWpbqctESwukKdiR5WydgncKFNVWHt\nPtKpiZk5hXL5nw3AyjAyNasv/ji+EfG5gfho1PyaqByfqbYk/w3752Vir6hMUlqQqx2rinTw3LB+\n5bpa+X281xfiN/QVmg9WtUmcCpSkzdVhOSed6B7TDavZEBNA9pmei6qpc1QH24Z08NywDrYN6WTv\nmBLLolRemKuNlYWqi4S0ujRf1cVB5fhYE5VsO1YV62DbsE73jWtjZWGqy0k7BKsr1DkUbw5akqTm\noPO2VIclSce7RglWADJeNObU0jOmQ21DOtQ2rENtQzrW+foeeuWFubq+rkTvuaFWg+MzqisN0fU7\nTTRUhhXwmxo7hglWF0GwukKdI1OqLclP+lBzfSSk/IBfTV2jSb0uAFyrC9dFHTwXv0tvYiYqScrL\neX0PvVWl8dGo4gXrosoL81JZPi6Qm+PTpqqwjnaM6N031HITwAUIVleoc2hS1UXJXV8lST6faVNV\noY53jyT92gBwJSZnojrUNqTHf3ZarQMTOjc4qbEF66JqioO6oS7eJ6quJF/l4Tx+OWeYHbXFauwY\nUWv/hNaWF6S6nLRCsLpCXcNTum1D8pqDLrSpKqznjvek5NoAcCm9o9N6+cyA9p8Z1IHWQTW2D2su\n0eugvDBXDayLyjqbq8Py++LTgQSrNyJYXYHzzUGTfEfgvM3VYf2vA23qG5tmaBxASsxvAfOlZ07o\nTP+4zvSNq398RlJ8NKquNKTbN5ZrTSSk1ZEQ+8plqWDAr4bKQjV2jOhd19VwJ+YCfMdfgfnmoMnc\nJ3ChLdVFkqTmrlGVbyRYAVh+zjmd7B3T3tMD2ntqQPtOD5zfRy8/4NeaspBuWRvR2vIC1ZYwGrWS\nbK8tUlPXqNqHJlVXyj628whWV6Aj0Rw0lSNWktTUNaq3bixPSQ0AsttcNKZjnaPad2ZAL58e0Mtn\nBs6PSFWE83TruohuXRdR79iMKlkbtaJtrS6Sz9rV2DFCsFqAYHUFuhI9rFI1YlVemKtIQa6Oc2cg\nAI9MzMzptXNDOnBmUC+fHdQrZwfPLzQvDQW0tqxAd26q0LryAkUWNN9MxU08SC+hvBytLy/UkfZh\nvXNbFdOBCQSrK9AxlNoRKzPT5qqwmroJVgCuziMvnFRr/4TO9o/r7MCEOoYmFXOSSaosytP22iKt\nLS/Q2rIC9oLDorbVFumHBzvUPTpN2E4gWF2BruEpBQPJbw660ObqsP5l/znFYk4+thIAcBnRmNPx\nrlEdODugA2cHtf/soNoG438gzi80v6OhQmvKCuK98mjAiSu0vbZIPzrYocb2YYJVAsHqCnQOT6mm\nOPnNQRfaUh3WxExUbYOTqi9jThvA60amZvVq65AOJKb0Xm0d1HiiCWdFOE8715Tq+roSrYmEVMNC\nc3ggHAyoviykxo4R3bO1KtXlpAWC1RU41TeuNSkOM68vYB8hWAErmHNOrQMT53tHvXJ2UMe7RuUU\nn9arLg5qx6pirSkLqT5SoNJQgDUwWBbba4u1+3Cn+semVUYrIILVUkVjTqd6x3R7ipqDzmuoen3P\nwHdur05pLQCSZ2YupiMdwzpwZlD7E1N7fWPxu/XCwRzdXF+qutJ81UcKtLo0X3kBpvWQHNtri7T7\ncKeOdIzozk0VqS4n5ZYUrMxsl6QvSfJLesw59/kLjn9E0p8o/ofSqKRPOecOelxrSrUPTmp6Lpby\nDScL83K0OpLPAnYgyw2Oz+jA2fho1IEzg3qldfB8N/NIQa7WRBKNOMsKaHuAlCoN5WpVSb4aO4YJ\nVlpCsDIzv6SvSbpPUpukl83sh865owtOOy3pTufcoJk9IOlRSbcuR8Gp0tIbDzKpDlaStLmqiJYL\nQBZxzulM/4T2J7aF2X92QCd7xyXFF5lvX1WsW9dFtKasQGvKQgoHuVsP6WVLTVjPHuvReKJVx0q2\nlBGrN0tqcc6dkiQz+66kByWdD1bOuZ8vOP8lSXVeFpkOTnSPSUqPYLWlOr5n4PRcVHk5DPcDmWZ6\nLqoj7SM6cDYepF5s6Tu/yDwY8GlNpEDv3Fal+rKQ6kpCys1hkTnS26bKsJ451qOWnrFUl5JySwlW\nqySdW/C4TZcfjfqEpCeupah01NIzpvLCXJWEclNdijZVhxWNOZ3sGde22qJUlwNgEfPTevvPDurA\n2QEdbBvWzFxMkrSmLKRNVeHzo1EVTOshA60qzVd+wK8TPcymeLp43czeoXiwetsljj8k6SFJqq+v\n9/LSy66ldywtRquk+IiVJB3vHiFYAWnGOaez/RPaf3ZQ+88M6JmmHvWOTkuS/GaqLQnqzWsjqo+E\nmNZD1vCZaWNloU70jMk5t6LvQF1KsGqXtHrB47rEc29gZtdLekzSA865/ou9kHPuUcXXX2nnzp3u\niqtNEeecWnrG9OCNtakuRZK0rrxAAb+piXVWQMrNRmNq7BhZsD5qUH1j8SBVFMxRTXG+blpdojVl\nBaorzVfAz7QeslNDZaEOtw+rqWtUW2tW7h/9SwlWL0tqMLN1igeqD0r68MITzKxe0vclfcw51+x5\nlSnWOzqt0ak5baxIjxGrgN+nDRWFLGAHUmB4clavJO7U+/fDnWobnNBsNP53YmkooDVlBbp9Yxl3\n62HFmW8HtKe5l2B1Oc65OTP7tKSnFG+38LhzrtHMHk4cf0TSn0oqk/T1xPDfnHNu5/KVnVzzi/E2\nVoZTXMnrtlSHtff0QKrLALKac07nBia1/+xAfH3UmUE194zKOcnvM1UXBbVzbURrywq0JhJSEXvr\nYQUrzg+oqihPLzT36rfv3JDqclJmSWusnHO7Je2+4LlHFnz+SUmf9La09NHSmz53BM7bVB3WD17r\n0PDErIpTuHchkE2iMadjnSPae3pA33+lTa39ExpN3D6el+NTfSSke7ZUnp/W465c4I0aKsPad3pA\nEzNzCuWuzB7kK/NffYVaesZUmJejqqL0adU/v4C9uWdUt6yNpLgaIDPNzMV0qG1Ie08PaN/peDfz\nsUSQKgl0XR98AAAYDUlEQVQFtKGy8Pwi86qiINN6wCIaqgr1s5Y+vXSqX3dvWZl7BxKsluBE95g2\nVBam1V0Om6vj89dNXQQrYKnmojEd6RjR3z7XolN94zrTP35+fVRlOE/baou0tqxAa8tCadFaBcg0\na8sKFAz4tKe5j2CFS2vpHdMdDenVpr+2OKhwMEfHu0ZSXQqQtpxzOt03rp+19OmnJ/r00sn+81N7\nleE8vWlNROvLC7SuvEAFefw4BK5VwO/TrevKtKe5N9WlpAw/SRYxPDmr3tFpNVQld33Vt/e2LnrO\n9XXF2scCduAN/v6np9XSO6YT3aNq6RnT0OSspPgde1tqwtpQUaj1FYUqJEgBy+LOTRX6b/92VOcG\nJrQ6Ekp1OUnHT5ZFnL8jME1aLSz0js2V+n/+/ZjaBidUV7ryvnkBKb7g/FDbkF5o7tWe5l692jok\np/hi8w0VhbpjU4UaKgsVKchNq+l8IFvdkdiI+YXmXn30tjUprib5CFaLONmTfncEzrsrEayeP74y\nv3mxcnUNT2lPc69eONGrF1v6NDQxKzPp+lXFumtzhRoqw1odCcnvI0gBybahokCrSvK1h2CFi2np\nHVNuji8thzM3VBRodSRfzzX1rMhvXqwcU7NR7Ts9oEf3nNKJnlF1j8Q7m4eDOWqoDKuhqlAbKwpZ\nJwWkATPTHZvK9aODnZqNxlbcbgP8FFpES8+Y1pcXpOVfvmamuzdX6p/3n9PUbFTBAD11kB2cczrR\nM6Y9zb3ac6JPe0/1a3ouJr/PtLYspJu2l6qhqlDVRUGm94A0dEdDhb6z75xebR3Sm9etrDvXCVaL\naOkZ03V1xaku45Lu2lKp//mLs3rpVL/u2lyZ6nKAq/bYnlPxRec9Y2rpGdNwYtF5RWGe3rSmVA2V\nhVpXXqjcnJX11y+Qid66sVx+n2lPcy/BCq+bmo3q3OCE3nvTqlSXcklvWV+mYMCn54/3EqyQUaIx\np4NtQ3rheK9eaO7VwXPxRefBgE8bKwp19+ZKbawqVCn9pICMU5wf0E2rS7TnRK/+6P7NqS4nqQhW\nl3Gyd0zOpefC9XnBgF9v3VCuZ5t69Gfv3sa0CNJa7+i0Xmju1fPHe/TTE30anowvOr9xdYnesaVS\nmyoLtaqURedANrhrc4X++9PN6h6ZUlVRMNXlJA3B6jLmWy0ku4fVlXrH5go929SjU33j2pCGbSGw\ncs2PSn3lmRY1d4+qfWhSkhTOy1FDVVibEovOQyw6B7LO/dur9d+fbtbTjV362FvWprqcpOGn2WWc\n7BmTz6R15QWpLuWy4lOAjXquqYdghZQbmZrVT5v79ExTt54/3quB8RmZpPpISO/cVqVNVWHVFLPo\nHMh2DVVhbago0BNHCFZIaOkdU30klPY72K+OhNRQWajnjvfok29fn+pysMJ8e2+r+semdaxrVE1d\nIzrTN66Yk/IDfm2uDuu+rVVqqCpcsTvdAyvZrh3VeuSFUxoYn1GkYGWsl+Qn3WW09Iyl9fqqhe7e\nUqnHXzytsek5turAsovGnF5tHdSPj3Xr+6+0q3c03leqMpynt22s0NaaeINOH6NSwIr2wI4afe25\nk/rJ0W69/5bVqS4nKfgNfAlz0ZhO943rHVsy4067uzZX6u/2nNKLLX26f3t1qstBFhqbntNPm3v1\nk2M9eu54jwbGZ5TjM60pC+nWdRFtqS5aMX+RAlia7bVFqivN15ONXQSrla51YEKzUZeWewRezM61\npQrn5ei5ph6CFTzzteda1NQ1qqbOEZ3qG1c05pQf8GtTVeH59VI0pgVwKWamXdur9c1fnNXo1KzC\nwUCqS1p2BKtLaEnjPQIvJuD36e2byvXc8R4551gYjKsyfxffM8e69cyxHjV1jUqSygpy9Zb1ZdpS\nE9aaSHruRAAgPe3aUa3HfnZazzb16MEb07cvpFcIVpdwIsOClRSfDtx9uEtHO0e0vTZ9u8UjvYxP\nz+mnJ+JTfM8f71Hf2Iz8PtPONaV6YEe1tlQXqSKcl+oyAWSom+tLVRHO01ONXQSrlexkz5iqi4IZ\nNWx51+YKSdLzx3sJVrisjqFJff6JJh1bMMUXDPi0qSqse7ZwFx8A7/h8pvu3V+l7B9o1ORNVfm52\nLx/gJ+clNHWNZtRolSRVhoO6blWxdh/u1O/ctYHpQJznnFNjx4h+cqxbPz7arcaOEUkLpviqw1pT\nxhQfgOWxa3uN/umlVu050Zv164AJVhfRPjSpo50j+uNdmbe/0Udvq9effO+wnjzSpQeuq0l1OUih\nuWhM+04P6Omj8TDVPjQpM+lN9aX63ANbNDUbVWV45WwzASB1bl0fUUkooCePdBGsVqInj3RJivff\nyDS/fnOdHvvpaf3VU8d177YqBfy+VJeEJJqciWrPiV498vxJNXWNanI2qhyfqaGyULfetEpbaorO\n9zkryqBpbgCZLeD36d6tVXqqsUszczHl5mTv7yaC1UU8cbhTW6rDab+VzcXk+H36411b9Fvf3K9/\nfvmcPnrbmlSXhGU2PDGrZ5q69VRjl15o7tXUbEz5Ab+2VIe1rbZIDZXhrP4hBiAzPLCjWv96oE0/\nP9mX2IotOxGsLtA9MqUDrYP67L2bUl3KVbt3a6VuWVuqL/7khN570yoV0Ik96/SMTOnpo/Ew9YuT\n/ZqLOVUXBfX+nat1//ZqneodZ70UgLRy+8ZyFeT69VRjF8FqJXmqsUvOSe+6LnPngM1Mn3tgi379\nb3+hv//ZaX3mnoZUlwQPnBuY0FONXfrHX5xV68CEnOKLz9+6oVzba4u0qjRfPjOd7Z8gVAFIO8GA\nX3dvrdLTjd368wdjysnSpSoEqwvsPtyphspCbawMp7qUa/KmNRHdv71Kf/fCSX341nqVF9KHKBO1\n9IzqicNderKx6/ydfDXFQd2ztVLba4tVGc7j7k8AGePBG2r1o4Md+tGhDr33prpUl7MsCFYL9I5O\na9/pAX36HRtTXYon/njXFv3k2B599dkW/d/v2Z7qcrAE33rprLpGpnSkfUSNHcPqSWxuXB8J6YEd\n1dpeW8x+fAAy1t1bKrW1pkhfeaZF776+NitHrQhWCzx9tEsxp4xpU/Dtva2LnvP+nav1rb1n9fHb\n12pNWeYtxl8JnHM60j6i3Uc69S8vn1P/+IxM0tryAr17XUTbaotVnM8dfAAyn89n+v17GvTwPx3I\n2lErgtUCTx7p0rryAm2pzuxpwIU+e2+DfvBquz7/RJO+/pGbmTZKE7GY06vnhvRUY5d2H+5U2+Ck\n/D7T+vIC3dFQoa21r7dFAIBs8s5tVdpSHdZXnmnRe25YlXVrQvnJnTA4PqOfn+zXb9+xPqvCR2VR\nUL9z1wb99Y+b9V9/dFR/+qvb5Muyb+JMMReNad+ZAT15pEtPNXape2RaAb/pbRvL9Zl7GnTf1io9\nkeihBgDZyucz/cG9DXr4n17Rjw526Nduyq79AwlWCT8+2q1ozOldGTINeCU+ffdGDU/O6rGfndb0\nXFR/8WvXEa6SZH6D4x8f7dETRzo1MRNVwG/aVBXWnZsqtKW6SMGAX3NRR6gCsGK8c1u1tlSH9eVn\nT+jdN9Rm1agVwSph95FO1ZXma3ttUapL8ZyZ6T/9ylYFA3599bkWTc/G9Ffvuz4rFw2mg/ahST1/\nvEc/OdqtF0/2a2YupuL8gDZVhbWtpkibqmjYCWBlm19r9alvvaJ/O9ShB2/MnlErgpWk4clZvdjS\np4/fvi6rpgEXMjP90f2blZfj01//uFnTczF98YM3suWNB6Zmo9p3ekAvNPfqheZetfSMSYrfyfex\n29bo3q1V2rm2VP9rf1uKKwWA9HH/9vio1ZeeOaFfvT57Rq0IVpKeOdat2ajTAzsytynoUv3ePQ0K\nBvz6i93HNDEzp79473WqLclPdVkZZTYa06G2Ib10akC/ONmv/WcHNDUbU47PtLa8QO/aUa2GqvD5\nHlOn+8Z1um881WUDQFrx+UyfuadBv5Nlo1YEK0m7D3eptjioG1eXpLqUpPitO9YrmOvXn//oqO76\nwvP66G1r9Dvv2EAT0UsYn57TwbYhvXJ2UPvODGr/mQFNzEQlSVuqw/rgLfVyTlpXXsAUHwBcgV3b\nq7W5KqwvZ9Go1YoPVrsPd+qZpm791tuz627AeZfqdeU30+/f26Bnm3r0jZ+f1ndfbtUn3rZOn3z7\n+hXdMykaczrZO6bDbcM62DakA2cHdbRjRC5xvDKcp+vrirW+vFBrywtoiQAA18DnM332vvgdgn/8\nr4f0V++7PuPD1Yr+rfBiS5/+4Luv6eb60ozedPlqlYZy9es31+kvf/16/X8/adZXnm3RN148o3u2\nVuqerVW6c3OFioLZG7LGp+d0omdMzV2jauwY1uH2YR3rHNXkbHw0qiDXrxvrS3TX5grVRwq0OpKv\nUO6K/r8MAHhu144a/V/3bdLf/LhZM9GY/ub9N2T0+t8V+1vicNuwHvrmfq0rL9Dj/+ctys/1p7qk\nlNlYWaivffhmferOYf3Di2f0bFO3fvBah3J8ptvWl+nuLZW6sb5Em6vCKsiwEZpYzKlzZEpn+8Z1\npn9CZ/vH42Gqe1Rtg5Pnz8v1+1RTEtRN9SWqLcnXqpJ8VYTz5MvCUUwASDefuadBuTk+ff6JJs3O\nxfTlD92UsUsrMuu3pEdO9Y7pN/9hn0pCufrmJ96s4lD2jsosxcLpwjetKdVN9SVq7Z9QU9eIjnWO\n6mctfZIkM2lNJKStNUXaWlOk1ZF8VRflq6Y4qOrioIKB5IbTiZk59Y/NaGA8/tE5PKWu4cn4f0em\n1DE0qXODk5qZi53/mly/T+vKC3RTfak+sHO1ukemVFkUVKQglxAFACn08J0blOv36b/921F96p8O\n6GsfuTnpv1e8sKRgZWa7JH1Jkl/SY865z19w3BLH3yVpQtJvOude8bhWT3SPTOljf79PkvSPn3iz\nqoqCKa4o/fgsfnfb2vIC7dpRo6GJGW2sLNSxztFE2Bq5aDPLklBAkVCuwvkBFQVzVBQMKBzMUX6u\nX7l+n3JzfMr1+xTI8SknMYfu3OtfH3VOM3MxTc9FNTMXS3we0+j0nMam5jSW+O/o1KwGJmY0NRv7\npRp8JlWGXw96t66LqKwgT5GCXJUV5qo4P/CGAFXGgn0ASBv/4W3rlJvj03/+wRH91jf36wvvu0HV\nxZn1e3rRYGVmfklfk3SfpDZJL5vZD51zRxec9oCkhsTHrZL+NvHftOCc04meMT3X1KPv7GvV0MSM\nvvvQW7S+ojDVpWWEklCu+sZmVBHOU0W4Qm9vqNDMXEzDk7ManpzVyNSsRhKfT8xENTE9p4GxaU3N\nxjQ1F9VsNKa5qFM05uQWv5xMUo7f5PeZAn6f8nL8CgZ8ysuJf15dHNSGikKF8nJUkOtXQV6OCvJy\nVJwfUGFeTsYvfASAleyjt61Rrt+nz33/kN7y+Wf0lvVlevDGWu3aUZMRN1ctZcTqzZJanHOnJMnM\nvivpQUkLg9WDkr7pnHOSXjKzEjOrcc51el7xEk3ORPViS5+eO96j54/3qn0ovp5mS3VYf/+bt+i6\nuuJUlZYVcnN8iaC19BEf55xiLn7nXWzBUNV8DDKLhymCEQCsbO+/ZbVuWRfRD15t1w8PduhPvndY\n/+UHjbprc4V2rCpWRThPlYnfQZXhoMoKc9NmwftSgtUqSecWPG7TL49GXeycVZJSFqyau0f1yW/u\nV0GuX29rKNfv3b1Rd26uUE0xzTBTxczkNxGcAACLWldeoM/et0l/cG+DDrcP6wevduiJI516+mj3\nL537gZ2r9Zfvuz4FVf6ypC5eN7OHJD2UeDhmZseTcd2jkh5NxoXiyiX1Je9yKxbvc3LwPicH73Ny\n8D4nwUdScM2/SnwsszVLOWkpwapd0uoFj+sSz13pOXLOPaqkZpzkM7P9zrmdqa4j2/E+Jwfvc3Lw\nPicH7zOSYSkTki9LajCzdWaWK+mDkn54wTk/lPQbFnebpOFUrq8CAABIhUVHrJxzc2b2aUlPKd5u\n4XHnXKOZPZw4/oik3Yq3WmhRvN3Cx5evZAAAgPS0pDVWzrndioenhc89suBzJ+l3vS0tY2X1VGca\n4X1ODt7n5OB9Tg7eZyw7c24pnYUAAACwmPRo+gAAAJAFCFYeMbNdZnbczFrM7HOpridbmdnjZtZj\nZkdSXUs2M7PVZvacmR01s0Yz+/1U15SNzCxoZvvM7GDiff6vqa4pm5mZ38xeNbN/S3UtyF4EKw8s\n2PbnAUnbJH3IzLaltqqs9Q1Ju1JdxAowJ+kPnXPbJN0m6Xf5nl4W05Luds7dIOlGSbsSd1Zjefy+\npGOpLgLZjWDljfPb/jjnZiTNb/sDjznn9kgaSHUd2c451zm/kbpzblTxX0arUltV9nFxY4mHgcQH\nC1+XgZnVSfoVSY+luhZkN4KVNy61pQ+Q8cxsraSbJO1NbSXZKTE99ZqkHkk/ds7xPi+PL0r6Y0mx\nVBeC7EawAnBJZlYo6XuS/sA5N5LqerKRcy7qnLtR8R0r3mxmO1JdU7Yxs1+V1OOcO5DqWpD9CFbe\nWNKWPkAmMbOA4qHqW86576e6nmznnBuS9JxYQ7gcbpf0HjM7o/hSjbvN7J9SWxKyFcHKG0vZ9gfI\nGGZmkv5e0jHn3N+kup5sZWYVZlaS+Dxf0n2SmlJbVfZxzv1H51ydc26t4j+fn3XOfTTFZSFLEaw8\n4JybkzS/7c8xSf/inGtMbVXZycy+I+kXkjabWZuZfSLVNWWp2yV9TPG/7F9LfLwr1UVloRpJz5nZ\nIcX/QPuxc45WAEAGo/M6AACARxixAgAA8AjBCgAAwCMEKwAAAI8QrAAAADxCsAIAAPAIwQoAAMAj\nBCsAizKzaKKXVaOZHTSzPzQzX+LYTjP78mW+dq2ZfTh51f7StScTe/GlBTP7gJm1mBn9qoAsRLAC\nsBSTzrkbnXPbFe8O/oCkP5Mk59x+59xnLvO1ayWlJFglnEzsxbdkZuZfrmKcc/8s6ZPL9foAUotg\nBeCKOOd6JD0k6dMWd9f86IuZ3bmgU/urZhaW9HlJb08899nEKNJPzeyVxMdbE197l5k9b2b/amZN\nZvatxNY6MrNbzOznidGyfWYWNjO/mX3BzF42s0Nm9ttLqd/MfmBmBxKjbw8teH7MzP7azA5Kessl\nrrk98flriWs2JL72owue/7v5YGZmuxL/xoNm9oyH/zMASFM5qS4AQOZxzp1KhIfKCw79kaTfdc69\naGaFkqYkfU7SHznnflWSzCwk6T7n3FQimHxH0s7E198kabukDkkvSrrdzPZJ+mdJH3DOvWxmRZIm\nJX1C0rBz7hYzy5P0opk97Zw7vUj5/8E5N5DYm+9lM/uec65fUoGkvc65P0zs+dl0kWs+LOlLzrlv\nJc7xm9lWSR+QdLtzbtbMvi7pI2b2hKT/IekO59xpM4tc8RsNIOMQrAB46UVJf2Nm35L0fedcW2LQ\naaGApK+a2Y2SopI2LTi2zznXJkmJdVFrJQ1L6nTOvSxJzrmRxPF3SrrezN6X+NpiSQ2SFgtWnzGz\n9yY+X534mv5ELd9LPL/5Etf8haT/ZGZ1iX/fCTO7R9KbFA9pkpQvqUfSbZL2zAc959zAInUByAIE\nKwBXzMzWKx5EeiRtnX/eOfd5M/t3Se9SfATp/ot8+WcldUu6QfHlCFMLjk0v+Dyqy/+MMkm/55x7\n6grqvkvSvZLe4pybMLPnJQUTh6ecc9HLfb1z7ttmtlfSr0janZh+NEn/0zn3Hy+41ruXWheA7MEa\nKwBXxMwqJD0i6avugl3czWyDc+6wc+4vJb0saYukUUnhBacVKz4aFJP0MUmLLRQ/LqnGzG5JXCNs\nZjmSnpL0KTMLJJ7fZGYFi7xWsaTBRKjaovio0pKvmQiUp5xzX5b0vyVdL+kZSe8zs8rEuREzWyPp\nJUl3mNm6+ecXqQ1AFmDECsBS5Cem5gKS5iT9o6S/uch5f2Bm75AUk9Qo6YnE59HEovBvSPq6pO+Z\n2W9IelLS+OUu7JybMbMPSPpKYl3UpOKjTo8pPlX4SmKRe6+kX1vk3/GkpIfN7Jji4emlK7zm+yV9\nzMxmJXVJ+n8T67X+s6SnLd6CYlbxdWYvJRbHfz/xfI/id1QCyGJ2wR+cAJA1zGytpH9zzu1IcSlv\nkJiSPL+gH0D2YCoQQDaLSiq2NGsQqvio3WCqawHgPUasAAAAPMKIFQAAgEcIVgAAAB4hWAEAAHiE\nYAUAAOARghUAAIBH/n9P3KKxzowzpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4765a22ef0>"
      ]
     },
     "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": 9,
   "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": [
    "### Add SpARCS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0cs6poaM/tnHw8W4diu99t7+xS7VtvSdO4QVMqszPUGpKUEVZsSby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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4765a35ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(sparcs['sparcs_ra'], sparcs['sparcs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, sparcs, \"sparcs_ra\", \"sparcs_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Add CFHT-WIRDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lSRo4fWbgRJgmBavT9E44YH7X9WQqzc+ha0jBSkTOT1vfOF9++ig/f6OHwlwf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\nIiIiImqSYBURERFRkz8B0vfyhwVWh6sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47635e0358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(cfht_wirds['wirds_ra'], cfht_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, \n",
    "                                    cfht_wirds, \n",
    "                                    \"wirds_ra\", \n",
    "                                    \"wirds_dec\", \n",
    "                                    radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add VIPERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WtgEAYB4gWE1Tdo3AyRdfnghL2wAAMD8QrKbpZPegeobSBXVcz2FpGwAA5geC1TQ9nVvK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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f475cca1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(vipers['vipers_ra'], vipers['vipers_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",
    "                                    vipers, \n",
    "                                    \"vipers_ra\", \n",
    "                                    \"vipers_dec\", \n",
    "                                    radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add SXDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R7k6U5JaTlXnLi+o5J1BHIEjpOKfRjza7ogB/MEJb90DcPqaIpJZAMMzXH9vE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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4765c2d278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(sxds['sxds_ra'], sxds['sxds_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",
    "                                    sxds, \n",
    "                                    \"sxds_ra\", \n",
    "                                    \"sxds_dec\", \n",
    "                                    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": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    if \"m_\" in col or \"merr_\" in col or \"f_\" in col or \"ferr_\" in col or \"stellarity\" in col:\n",
    "        master_catalogue[col] = master_catalogue[col].astype(float)\n",
    "        master_catalogue[col].fill_value = np.nan\n",
    "    elif \"flag\" in col:\n",
    "        master_catalogue[col].fill_value = 0\n",
    "    elif \"id\" in col:\n",
    "        master_catalogue[col].fill_value = -1\n",
    "        \n",
    "master_catalogue = master_catalogue.filled()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Since this is not the final merged catalogue. We rename column names to make them unique\n",
    "master_catalogue['ra'].name = 'cfht_ra'\n",
    "master_catalogue['dec'].name = 'cfht_dec'\n",
    "master_catalogue['flag_merged'].name = 'cfht_flag_merged'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=10&gt;\n",
       "<table id=\"table139944626079448-999581\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>candels_id</th><th>cfht_ra</th><th>cfht_dec</th><th>candels_stellarity</th><th>f_acs_f606w</th><th>ferr_acs_f606w</th><th>f_ap_acs_f606w</th><th>ferr_ap_acs_f606w</th><th>f_acs_f814w</th><th>ferr_acs_f814w</th><th>f_ap_acs_f814w</th><th>ferr_ap_acs_f814w</th><th>f_wfc3_f125w</th><th>ferr_wfc3_f125w</th><th>f_ap_wfc3_f125w</th><th>ferr_ap_wfc3_f125w</th><th>f_wfc3_f160w</th><th>ferr_wfc3_f160w</th><th>f_ap_wfc3_f160w</th><th>ferr_ap_wfc3_f160w</th><th>f_candels-megacam_u</th><th>ferr_candels-megacam_u</th><th>f_suprime_b</th><th>ferr_suprime_b</th><th>f_suprime_v</th><th>ferr_suprime_v</th><th>f_suprime_rc</th><th>ferr_suprime_rc</th><th>f_suprime_ip</th><th>ferr_suprime_ip</th><th>f_suprime_zp</th><th>ferr_suprime_zp</th><th>f_hawki_k</th><th>ferr_hawki_y</th><th>ferr_hawki_k</th><th>m_acs_f606w</th><th>merr_acs_f606w</th><th>flag_acs_f606w</th><th>m_ap_acs_f606w</th><th>merr_ap_acs_f606w</th><th>m_acs_f814w</th><th>merr_acs_f814w</th><th>flag_acs_f814w</th><th>m_ap_acs_f814w</th><th>merr_ap_acs_f814w</th><th>m_wfc3_f125w</th><th>merr_wfc3_f125w</th><th>flag_wfc3_f125w</th><th>m_ap_wfc3_f125w</th><th>merr_ap_wfc3_f125w</th><th>m_wfc3_f160w</th><th>merr_wfc3_f160w</th><th>flag_wfc3_f160w</th><th>m_ap_wfc3_f160w</th><th>merr_ap_wfc3_f160w</th><th>m_candels-megacam_u</th><th>merr_candels-megacam_u</th><th>flag_candels-megacam_u</th><th>m_suprime_b</th><th>merr_suprime_b</th><th>flag_suprime_b</th><th>m_suprime_v</th><th>merr_suprime_v</th><th>flag_suprime_v</th><th>m_suprime_rc</th><th>merr_suprime_rc</th><th>flag_suprime_rc</th><th>m_suprime_ip</th><th>merr_suprime_ip</th><th>flag_suprime_ip</th><th>m_suprime_zp</th><th>merr_suprime_zp</th><th>flag_suprime_zp</th><th>m_hawki_k</th><th>merr_hawki_k</th><th>flag_hawki_k</th><th>candels_flag_cleaned</th><th>candels_flag_gaia</th><th>cfht_flag_merged</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_cfhtls-deep_y</th><th>merr_cfhtls-deep_y</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>m_ap_cfhtls-deep_y</th><th>merr_ap_cfhtls-deep_y</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_cfhtls-deep_y</th><th>ferr_cfhtls-deep_y</th><th>flag_cfhtls-deep_y</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>f_ap_cfhtls-deep_y</th><th>ferr_ap_cfhtls-deep_y</th><th>cfhtls-deep_flag_cleaned</th><th>cfhtls-deep_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>sparcs_intid</th><th>sparcs_stellarity</th><th>m_ap_sparcs_u</th><th>merr_ap_sparcs_u</th><th>f_ap_sparcs_u</th><th>ferr_ap_sparcs_u</th><th>m_sparcs_u</th><th>merr_sparcs_u</th><th>f_sparcs_u</th><th>ferr_sparcs_u</th><th>flag_sparcs_u</th><th>m_ap_sparcs_g</th><th>merr_ap_sparcs_g</th><th>f_ap_sparcs_g</th><th>ferr_ap_sparcs_g</th><th>m_sparcs_g</th><th>merr_sparcs_g</th><th>f_sparcs_g</th><th>ferr_sparcs_g</th><th>flag_sparcs_g</th><th>m_ap_sparcs_r</th><th>merr_ap_sparcs_r</th><th>f_ap_sparcs_r</th><th>ferr_ap_sparcs_r</th><th>m_sparcs_r</th><th>merr_sparcs_r</th><th>f_sparcs_r</th><th>ferr_sparcs_r</th><th>flag_sparcs_r</th><th>m_ap_sparcs_z</th><th>merr_ap_sparcs_z</th><th>f_ap_sparcs_z</th><th>ferr_ap_sparcs_z</th><th>m_sparcs_z</th><th>merr_sparcs_z</th><th>f_sparcs_z</th><th>ferr_sparcs_z</th><th>flag_sparcs_z</th><th>m_ap_sparcs_y</th><th>merr_ap_sparcs_y</th><th>f_ap_sparcs_y</th><th>ferr_ap_sparcs_y</th><th>m_sparcs_y</th><th>merr_sparcs_y</th><th>f_sparcs_y</th><th>ferr_sparcs_y</th><th>flag_sparcs_y</th><th>sparcs_flag_cleaned</th><th>sparcs_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>vipers_id</th><th>m_vipers_u</th><th>merr_vipers_u</th><th>m_vipers_g</th><th>merr_vipers_g</th><th>m_vipers_r</th><th>merr_vipers_r</th><th>m_vipers_i</th><th>merr_vipers_i</th><th>m_vipers_y</th><th>merr_vipers_y</th><th>m_vipers_z</th><th>merr_vipers_z</th><th>m_vipers_ks</th><th>merr_vipers_ks</th><th>f_vipers_u</th><th>ferr_vipers_u</th><th>flag_vipers_u</th><th>f_vipers_g</th><th>ferr_vipers_g</th><th>flag_vipers_g</th><th>f_vipers_r</th><th>ferr_vipers_r</th><th>flag_vipers_r</th><th>f_vipers_i</th><th>ferr_vipers_i</th><th>flag_vipers_i</th><th>f_vipers_y</th><th>ferr_vipers_y</th><th>flag_vipers_y</th><th>f_vipers_z</th><th>ferr_vipers_z</th><th>flag_vipers_z</th><th>f_vipers_ks</th><th>ferr_vipers_ks</th><th>flag_vipers_ks</th><th>vipers_flag_cleaned</th><th>vipers_flag_gaia</th><th>sxds_b_id</th><th>m_ap_sxds-suprime_b</th><th>merr_ap_sxds-suprime_b</th><th>m_sxds-suprime_b</th><th>merr_sxds-suprime_b</th><th>sxds_flag_merged</th><th>sxds_v_id</th><th>m_ap_sxds-suprime_v</th><th>merr_ap_sxds-suprime_v</th><th>m_sxds-suprime_v</th><th>merr_sxds-suprime_v</th><th>sxds_r_id</th><th>m_ap_sxds-suprime_rc</th><th>merr_ap_sxds-suprime_rc</th><th>m_sxds-suprime_rc</th><th>merr_sxds-suprime_rc</th><th>sxds_i_id</th><th>m_ap_sxds-suprime_ip</th><th>merr_ap_sxds-suprime_ip</th><th>m_sxds-suprime_ip</th><th>merr_sxds-suprime_ip</th><th>sxds_z_id</th><th>m_ap_sxds-suprime_zp</th><th>merr_ap_sxds-suprime_zp</th><th>m_sxds-suprime_zp</th><th>merr_sxds-suprime_zp</th><th>f_ap_sxds-suprime_b</th><th>ferr_ap_sxds-suprime_b</th><th>f_sxds-suprime_b</th><th>ferr_sxds-suprime_b</th><th>flag_sxds-suprime_b</th><th>f_ap_sxds-suprime_v</th><th>ferr_ap_sxds-suprime_v</th><th>f_sxds-suprime_v</th><th>ferr_sxds-suprime_v</th><th>flag_sxds-suprime_v</th><th>f_ap_sxds-suprime_rc</th><th>ferr_ap_sxds-suprime_rc</th><th>f_sxds-suprime_rc</th><th>ferr_sxds-suprime_rc</th><th>flag_sxds-suprime_rc</th><th>f_ap_sxds-suprime_ip</th><th>ferr_ap_sxds-suprime_ip</th><th>f_sxds-suprime_ip</th><th>ferr_sxds-suprime_ip</th><th>flag_sxds-suprime_ip</th><th>f_ap_sxds-suprime_zp</th><th>ferr_ap_sxds-suprime_zp</th><th>f_sxds-suprime_zp</th><th>ferr_sxds-suprime_zp</th><th>flag_sxds-suprime_zp</th><th>sxds_stellarity</th><th>sxds_flag_cleaned</th><th>sxds_flag_gaia</th><th>sxds_intid</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><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>21539</td><td>34.3795257693</td><td>-5.15683775973</td><td>0.019999999553</td><td>0.0710597</td><td>0.00949195</td><td>0.107001</td><td>0.0121251</td><td>0.142095</td><td>0.0112729</td><td>0.23308</td><td>0.0142605</td><td>0.722495</td><td>0.0232445</td><td>0.991269</td><td>0.028352</td><td>1.09422</td><td>0.017616</td><td>1.50183</td><td>0.0215974</td><td>0.3627532</td><td>0.0066298</td><td>0.4852182</td><td>0.0037431</td><td>0.6368133</td><td>0.0041781</td><td>0.7070535</td><td>0.0061044</td><td>0.9404059</td><td>0.0062788</td><td>1.1989115</td><td>0.0159431</td><td>0.702083</td><td>0.0140271</td><td>0.0295765</td><td>26.7709415759</td><td>0.14502951418</td><td>False</td><td>26.3265304088</td><td>0.123033056292</td><td>26.0185530091</td><td>0.0861353014717</td><td>False</td><td>25.4812374765</td><td>0.0664284415134</td><td>24.2529128852</td><td>0.034930892548</td><td>False</td><td>23.9095211883</td><td>0.0310539246938</td><td>23.8022383788</td><td>0.0174794182002</td><td>False</td><td>23.4584480615</td><td>0.0156136707275</td><td>25.0009718694</td><td>0.0198432815763</td><td>0.0</td><td>24.685157294</td><td>0.00837565282595</td><td>False</td><td>24.3899696871</td><td>0.00712346057644</td><td>False</td><td>24.2763693089</td><td>0.00937378584269</td><td>False</td><td>23.9667116371</td><td>0.00724912559825</td><td>False</td><td>23.703032185</td><td>0.0144380972958</td><td>False</td><td>24.2840288567</td><td>0.0457385762937</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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>False</td><td>0</td><td>-1</td></tr>\n",
       "<tr><td>1</td><td>20563</td><td>34.3530670693</td><td>-5.16239645973</td><td>0.0</td><td>0.133338</td><td>0.00879655</td><td>0.180807</td><td>0.0136602</td><td>0.197151</td><td>0.00897565</td><td>0.310126</td><td>0.0138066</td><td>0.333375</td><td>0.0187768</td><td>0.472117</td><td>0.0292274</td><td>0.538301</td><td>0.0144117</td><td>0.760123</td><td>0.0223591</td><td>0.2452752</td><td>0.0068255</td><td>0.2958344</td><td>0.0043875</td><td>0.4402928</td><td>0.0053329</td><td>0.5652437</td><td>0.007054</td><td>0.9049545</td><td>0.0085324</td><td>1.1906105</td><td>0.0191945</td><td>0.527608</td><td>0.0141214</td><td>0.0304363</td><td>26.0876151584</td><td>0.0716279891101</td><td>False</td><td>25.7569618992</td><td>0.0820287583127</td><td>25.6630025393</td><td>0.0494300722098</td><td>False</td><td>25.1711545558</td><td>0.0483362423164</td><td>25.0926674283</td><td>0.0611523106697</td><td>False</td><td>24.714875903</td><td>0.0672147928394</td><td>24.5724370335</td><td>0.0290679461159</td><td>False</td><td>24.1977903157</td><td>0.0319370475249</td><td>25.4258659037</td><td>0.0302137862514</td><td>0.0</td><td>25.2223783177</td><td>0.0161024803011</td><td>False</td><td>24.790646041</td><td>0.0131506184211</td><td>False</td><td>24.5194106736</td><td>0.0135495241935</td><td>False</td><td>24.0084331401</td><td>0.0102369075942</td><td>False</td><td>23.7105757293</td><td>0.0175037626346</td><td>False</td><td>24.5942215699</td><td>0.0626332293083</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>False</td><td>0</td><td>-1</td></tr>\n",
       "<tr><td>2</td><td>14826</td><td>34.2856193693</td><td>-5.19395515973</td><td>0.0</td><td>0.0491678</td><td>0.0074849</td><td>0.0619168</td><td>0.0160298</td><td>0.0972186</td><td>0.00783663</td><td>0.142508</td><td>0.0157403</td><td>0.264025</td><td>0.0154075</td><td>0.328606</td><td>0.0278024</td><td>0.344238</td><td>0.0123705</td><td>0.467226</td><td>0.0238434</td><td>0.1681561</td><td>0.0073574</td><td>0.2055707</td><td>0.0043688</td><td>0.2400314</td><td>0.0047257</td><td>0.3092149</td><td>0.0067454</td><td>0.3850932</td><td>0.0065248</td><td>0.5510579</td><td>0.0183486</td><td>0.471631</td><td>0.0210946</td><td>0.0445381</td><td>27.1707980592</td><td>0.165283517241</td><td>False</td><td>26.9204787427</td><td>0.281089045542</td><td>26.4306265932</td><td>0.0875193935553</td><td>False</td><td>26.0154018872</td><td>0.119921783926</td><td>25.3458873718</td><td>0.0633594567742</td><td>False</td><td>25.1083112777</td><td>0.0918609893282</td><td>25.0578529754</td><td>0.0390169002869</td><td>False</td><td>24.7261824945</td><td>0.0554071105301</td><td>25.8357184342</td><td>0.0475046433218</td><td>0.0</td><td>25.6175969632</td><td>0.023074126475</td><td>False</td><td>25.4493298545</td><td>0.0213758015944</td><td>False</td><td>25.1743489677</td><td>0.0236849032682</td><td>False</td><td>24.9360853753</td><td>0.0183960962925</td><td>False</td><td>24.5470069179</td><td>0.0361518078711</td><td>False</td><td>24.7159941419</td><td>0.102530638701</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>False</td><td>0</td><td>-1</td></tr>\n",
       "<tr><td>3</td><td>1265</td><td>34.3627124693</td><td>-5.26953255973</td><td>0.019999999553</td><td>0.195174</td><td>0.0114047</td><td>0.277966</td><td>0.0174607</td><td>0.394889</td><td>0.0137767</td><td>0.544339</td><td>0.0199322</td><td>0.665006</td><td>0.0217909</td><td>0.966486</td><td>0.0305951</td><td>0.750229</td><td>0.0151674</td><td>1.11416</td><td>0.0233303</td><td>0.0529733</td><td>0.0075836</td><td>0.0960998</td><td>0.0032959</td><td>0.1398806</td><td>0.0056783</td><td>0.1907871</td><td>0.0052222</td><td>0.4022798</td><td>0.0070876</td><td>0.5167124</td><td>0.0224519</td><td>0.553209</td><td>0.045561</td><td>0.0959896</td><td>25.6739450928</td><td>0.0634433669157</td><td>False</td><td>25.2900208062</td><td>0.0682015575661</td><td>24.9088124094</td><td>0.0378786493726</td><td>False</td><td>24.5603263722</td><td>0.0397566795333</td><td>24.3429360907</td><td>0.0355773768421</td><td>False</td><td>23.9370110814</td><td>0.0343700868488</td><td>24.2120153807</td><td>0.02195035824</td><td>False</td><td>23.7826310935</td><td>0.0227351110952</td><td>27.089857429</td><td>0.155432813935</td><td>0.0</td><td>26.4431937904</td><td>0.0372371009852</td><td>False</td><td>26.0356062838</td><td>0.0440742739985</td><td>False</td><td>25.6986274833</td><td>0.0297186319646</td><td>False</td><td>24.8886794362</td><td>0.0191291333168</td><td>False</td><td>24.6168777905</td><td>0.0471768060832</td><td>False</td><td>24.5427769077</td><td>0.188390615482</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>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>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>False</td><td>0</td><td>-1</td></tr>\n",
       "<tr><td>4</td><td>13273</td><td>34.3243668693</td><td>-5.20170115973</td><td>0.0</td><td>0.113233</td><td>0.00771246</td><td>0.161421</td><td>0.0175376</td><td>0.220957</td><td>0.00871852</td><td>0.318615</td><td>0.0181905</td><td>0.304997</td><td>0.0133402</td><td>0.473484</td><td>0.0277084</td><td>0.332122</td><td>0.00962214</td><td>0.508171</td><td>0.0236617</td><td>0.0365908</td><td>0.0066596</td><td>0.0859443</td><td>0.0048999</td><td>0.1259515</td><td>0.0058358</td><td>0.2384127</td><td>0.0085755</td><td>0.3344948</td><td>0.0111257</td><td>0.4307847</td><td>0.0230394</td><td>0.390239</td><td>0.0194855</td><td>0.0391507</td><td>26.2650674658</td><td>0.0739510306161</td><td>False</td><td>25.8800999164</td><td>0.117959913918</td><td>25.5392305882</td><td>0.0428409727499</td><td>False</td><td>25.141834455</td><td>0.0619873026463</td><td>25.1892610811</td><td>0.0474887888035</td><td>False</td><td>24.7117367303</td><td>0.0635375494334</td><td>25.0967558887</td><td>0.0314556270444</td><td>False</td><td>24.6349753066</td><td>0.0505545659948</td><td>27.4915702381</td><td>0.197606196891</td><td>0.0</td><td>26.5644573033</td><td>0.0619005429062</td><td>False</td><td>26.1494916399</td><td>0.0503061840766</td><td>False</td><td>25.4566765347</td><td>0.0390529985353</td><td>False</td><td>25.0890265733</td><td>0.0361128941116</td><td>False</td><td>24.8143493244</td><td>0.0580677789065</td><td>False</td><td>24.9216733248</td><td>0.108926407744</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>False</td><td>0</td><td>-1</td></tr>\n",
       "<tr><td>5</td><td>9036</td><td>34.2784354693</td><td>-5.22480465973</td><td>0.0</td><td>0.0543055</td><td>0.00773169</td><td>0.102759</td><td>0.0182538</td><td>0.110097</td><td>0.00870382</td><td>0.202327</td><td>0.0191073</td><td>0.262203</td><td>0.015319</td><td>0.379377</td><td>0.0315701</td><td>0.450288</td><td>0.0132094</td><td>0.713741</td><td>0.0276764</td><td>0.1593326</td><td>0.0068346</td><td>0.2187557</td><td>0.0036391</td><td>0.3882704</td><td>0.0065876</td><td>0.6772675</td><td>0.0064811</td><td>1.0186884</td><td>0.008968</td><td>1.122582</td><td>0.0226343</td><td>0.656931</td><td>0.0194469</td><td>0.039726</td><td>27.0628904583</td><td>0.154580581285</td><td>False</td><td>26.3704503268</td><td>0.192866917101</td><td>26.2955612871</td><td>0.0858338782501</td><td>False</td><td>25.6348653948</td><td>0.102534448616</td><td>25.3534058591</td><td>0.0634332670514</td><td>False</td><td>24.9523225052</td><td>0.0903502335614</td><td>24.7662740667</td><td>0.0318505574724</td><td>False</td><td>24.2661483875</td><td>0.0421010835827</td><td>25.8942383924</td><td>0.046572846141</td><td>0.0</td><td>25.5501015549</td><td>0.0180617127816</td><td>False</td><td>24.9271642922</td><td>0.018421172004</td><td>False</td><td>24.3230994109</td><td>0.0103899344301</td><td>False</td><td>23.879896598</td><td>0.0095582538137</td><td>False</td><td>23.7744548143</td><td>0.0218913887621</td><td>False</td><td>24.3562006092</td><td>0.0656567530992</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>False</td><td>0</td><td>-1</td></tr>\n",
       "<tr><td>6</td><td>3353</td><td>34.3749212693</td><td>-5.25793585973</td><td>0.0</td><td>0.0219845</td><td>0.0119491</td><td>0.036657</td><td>0.0189846</td><td>0.0715805</td><td>0.0121569</td><td>0.0942867</td><td>0.0190316</td><td>0.590133</td><td>0.0306195</td><td>0.762989</td><td>0.0439909</td><td>1.32479</td><td>0.0261523</td><td>1.58215</td><td>0.0382111</td><td>-0.0051325</td><td>0.0082125</td><td>0.0002127</td><td>0.0038226</td><td>-0.0133222</td><td>0.0071063</td><td>-0.0022047</td><td>0.0064365</td><td>0.0108566</td><td>0.0077467</td><td>0.1016985</td><td>0.0246658</td><td>0.247723</td><td>0.0177556</td><td>0.0384577</td><td>28.0447085181</td><td>0.590123518128</td><td>False</td><td>27.4896077012</td><td>0.562300994431</td><td>26.7630131808</td><td>0.184396399405</td><td>False</td><td>26.4638739104</td><td>0.219153890787</td><td>24.4726252478</td><td>0.0563342496041</td><td>False</td><td>24.193704308</td><td>0.0625992154669</td><td>23.5946323969</td><td>0.0214332074877</td><td>False</td><td>23.4018808611</td><td>0.0262220236347</td><td>nan</td><td>-1.73728369831</td><td>0.0</td><td>33.0805812752</td><td>19.5126244302</td><td>False</td><td>nan</td><td>-0.579151130584</td><td>False</td><td>nan</td><td>-3.1697469415</td><td>False</td><td>28.8107654075</td><td>0.774724375716</td><td>False</td><td>26.3817136316</td><td>0.263332812965</td><td>False</td><td>25.415084173</td><td>0.168554866693</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>False</td><td>0</td><td>-1</td></tr>\n",
       "<tr><td>7</td><td>30535</td><td>34.3719116693</td><td>-5.21773125973</td><td>0.0</td><td>0.0200661</td><td>0.00917157</td><td>-0.00216399</td><td>0.0189875</td><td>0.0597257</td><td>0.00988745</td><td>0.0881192</td><td>0.0191953</td><td>0.0968641</td><td>0.0228686</td><td>0.213073</td><td>0.0454037</td><td>0.112731</td><td>0.0212121</td><td>0.140503</td><td>0.0393608</td><td>0.0027365</td><td>0.0067958</td><td>0.005866</td><td>0.0037556</td><td>0.0223429</td><td>0.0064162</td><td>0.0384536</td><td>0.0069202</td><td>0.0829306</td><td>0.0096274</td><td>0.0957557</td><td>0.0246016</td><td>0.0821145</td><td>0.0146598</td><td>0.0307014</td><td>28.1438425694</td><td>0.49625515688</td><td>False</td><td>nan</td><td>-9.52657645731</td><td>26.9595968788</td><td>0.179741090313</td><td>False</td><td>26.5373236357</td><td>0.236509548103</td><td>26.4345928809</td><td>0.256330952047</td><td>False</td><td>25.5786789484</td><td>0.231359397577</td><td>26.2698916012</td><td>0.204298240492</td><td>False</td><td>26.0307860067</td><td>0.304160378129</td><td>30.3070113688</td><td>2.69630772896</td><td>0.0</td><td>29.4791448534</td><td>0.695122893043</td><td>False</td><td>28.0271511456</td><td>0.311790351162</td><td>False</td><td>27.437657489</td><td>0.195391632621</td><td>False</td><td>26.6032129813</td><td>0.126042941178</td><td>False</td><td>26.4470884114</td><td>0.278947862268</td><td>False</td><td>26.613950368</td><td>0.405940747575</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>False</td><td>0</td><td>-1</td></tr>\n",
       "<tr><td>8</td><td>10290</td><td>34.3721179693</td><td>-5.21799245973</td><td>0.699999988079</td><td>0.00202651</td><td>0.00576531</td><td>-0.0335463</td><td>0.0190292</td><td>0.00298273</td><td>0.00574464</td><td>0.0125992</td><td>0.0189148</td><td>0.0851452</td><td>0.0152042</td><td>0.075513</td><td>0.0450326</td><td>0.121132</td><td>0.016443</td><td>0.0909148</td><td>0.0397302</td><td>-0.0105424</td><td>0.0064933</td><td>-0.000704</td><td>0.003589</td><td>-0.0016523</td><td>0.006103</td><td>0.002381</td><td>0.0066523</td><td>-0.0106147</td><td>0.0092379</td><td>0.0122139</td><td>0.0235671</td><td>0.065116</td><td>0.0127878</td><td>0.0253353</td><td>30.6331281221</td><td>3.08886005924</td><td>False</td><td>nan</td><td>-0.615885846951</td><td>30.2134651442</td><td>2.09109226491</td><td>False</td><td>28.649142575</td><td>1.62998310732</td><td>26.5745995751</td><td>0.19387763966</td><td>False</td><td>26.7049456891</td><td>0.647484859751</td><td>26.1918527803</td><td>0.147382693383</td><td>False</td><td>26.5034135308</td><td>0.474471885351</td><td>nan</td><td>-0.6687292171</td><td>0.0</td><td>nan</td><td>-5.53509550977</td><td>False</td><td>nan</td><td>-4.01031777379</td><td>False</td><td>30.4581015115</td><td>3.03344937208</td><td>False</td><td>nan</td><td>-0.944908710179</td><td>False</td><td>28.6828641002</td><td>2.09496178216</td><td>False</td><td>26.8657807138</td><td>0.422437687641</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>False</td><td>0</td><td>-1</td></tr>\n",
       "<tr><td>9</td><td>30480</td><td>34.4393352693</td><td>-5.21873475973</td><td>0.709999978542</td><td>-0.00142079</td><td>0.00516246</td><td>-0.0420138</td><td>0.0193282</td><td>-0.00176442</td><td>0.00551065</td><td>-0.0195892</td><td>0.0206293</td><td>0.0360928</td><td>0.0129518</td><td>0.0310641</td><td>0.0448159</td><td>0.0594395</td><td>0.0138861</td><td>0.019035</td><td>0.0400623</td><td>0.0012722</td><td>0.0064643</td><td>-0.002361</td><td>0.003477</td><td>-0.000383</td><td>0.006916</td><td>0.0075941</td><td>0.0064209</td><td>0.0126521</td><td>0.0078891</td><td>0.0375685</td><td>0.0239401</td><td>0.02165</td><td>0.0127359</td><td>0.026029</td><td>nan</td><td>-3.9450374282</td><td>False</td><td>nan</td><td>-0.49948651426</td><td>nan</td><td>-3.39097959485</td><td>False</td><td>nan</td><td>-1.14338400184</td><td>27.5064485626</td><td>0.389613390393</td><td>False</td><td>27.669353061</td><td>1.5663819386</td><td>26.9648121312</td><td>0.253646842805</td><td>False</td><td>28.2011177971</td><td>2.28511108778</td><td>31.1386115219</td><td>5.51684055056</td><td>0.0</td><td>nan</td><td>-1.59894315288</td><td>False</td><td>nan</td><td>-19.605617734</td><td>False</td><td>29.1988092208</td><td>0.91800260691</td><td>False</td><td>28.6445934604</td><td>0.677000774018</td><td>False</td><td>27.4629403617</td><td>0.691873066945</td><td>False</td><td>28.0613552483</td><td>1.30534077015</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>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>False</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>False</td><td>0</td><td>-1</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table139944626079448-999581').dataTable()\");\n",
       "    $('#table139944626079448-999581').dataTable({\n",
       "        \"order\": [],\n",
       "        \"iDisplayLength\": 50,\n",
       "        \"aLengthMenu\": [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        \"pagingType\": \"full_numbers\"\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_catalogue[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(data=np.arange(len(master_catalogue)), \n",
    "                              name=\"cfht_intid\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['candels_id', 'cfhtls-deep_id', 'cfhtls-wide_id', 'sparcs_intid', 'wirds_id', 'vipers_id', 'sxds_b_id', 'sxds_v_id', 'sxds_r_id', 'sxds_i_id', 'sxds_z_id', 'sxds_intid', 'cfht_intid']\n"
     ]
    }
   ],
   "source": [
    "id_names = []\n",
    "for col in master_catalogue.colnames:\n",
    "    if '_id' in col:\n",
    "        id_names += [col]\n",
    "    if '_intid' in col:\n",
    "        id_names += [col]\n",
    "        \n",
    "print(id_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VII - Choosing between multiple values for the same filter\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VII.a CFHT Megacam fluxes: CFHTLS-DEEP, CFHTLS-WIDE SpARCS, CANDELS, CFHT-WIRDS and VIPERS\n",
    "\n",
    "According to Mattia CFHTLenS is built on the same data as CFHTLS-WIDE and should not be included. I have therefore excluded it from the merge above.\n",
    "\n",
    "CFHTLS-DEEP is prefferred to CFHTLS-WIDE which is prefferred to SpARCS... CANDELS... WIRDS... VIPERS\n",
    "\n",
    "| Survey (in HELP use order)            | Bands                 | notes |\n",
    "|:-------------|:------------------|-------------------|\n",
    "| CFHTLS-DEEP | u, g, r, i, z, y ||\n",
    "| CFHTLS-WIDE | u, g, r, i, z    ||\n",
    "| SpARCS      | u, g, r, z, y    ||\n",
    "| CANDELS     | u                | Total fluxes only |\n",
    "| CFHT-WIRDS     | u, g, r, i, z  (+ WIRCAM J, H, Ks)            ||\n",
    "| VIPERS     | u, g, r, i, z, y  (+ WIRCAM Ks)            | Total fluxes only |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "megacam_origin = Table()\n",
    "megacam_origin.add_column(master_catalogue['cfht_intid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(6, 0) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "megacam_stats = Table()\n",
    "megacam_stats.add_column(Column(data=['u','g','r','i','z','y'], name=\"Band\"))\n",
    "for col in [\"CFHTLS-DEEP\", \"CFHTLS-WIDE\", \"SpARCS\", \"CANDELS\", \"CFHT-WIRDS\", \"VIPERS\"]:\n",
    "    megacam_stats.add_column(Column(data=np.full(6, 0), name=\"{}\".format(col)))\n",
    "    megacam_stats.add_column(Column(data=np.full(6, 0), name=\"use {}\".format(col)))\n",
    "    megacam_stats.add_column(Column(data=np.full(6, 0), name=\"{} ap\".format(col)))\n",
    "    megacam_stats.add_column(Column(data=np.full(6, 0), name=\"use {} ap\".format(col)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "megacam_bands = ['u','g','r','i','z','y'] # Lowercase naming convention (k is Ks)\n",
    "for band in megacam_bands:\n",
    "\n",
    "    # Megacam total flux \n",
    "    has_cfhtls_deep = ~np.isnan(master_catalogue['f_cfhtls-deep_' + band])\n",
    "    if band == 'y':\n",
    "        has_cfhtls_wide = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    else:\n",
    "        has_cfhtls_wide = ~np.isnan(master_catalogue['f_cfhtls-wide_' + band])\n",
    "        \n",
    "    if band == 'i':\n",
    "        has_sparcs = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    else:\n",
    "        has_sparcs = ~np.isnan(master_catalogue['f_sparcs_' + band])\n",
    "        \n",
    "    if band == 'u':\n",
    "        has_candels = ~np.isnan(master_catalogue['f_candels-megacam_' + band])\n",
    "    else:\n",
    "        has_candels = np.full(len(master_catalogue), False, dtype=bool)\n",
    "        \n",
    "    if band == 'y':\n",
    "        has_wirds = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    else:\n",
    "        has_wirds = ~np.isnan(master_catalogue['f_wirds_' + band])\n",
    "        \n",
    "    has_vipers  = ~np.isnan(master_catalogue['f_vipers_' + band])\n",
    "    \n",
    "\n",
    "    use_cfhtls_deep = has_cfhtls_deep \n",
    "    use_cfhtls_wide = has_cfhtls_wide & ~has_cfhtls_deep\n",
    "    use_sparcs = has_sparcs & ~has_cfhtls_wide & ~has_cfhtls_deep\n",
    "    use_candels = has_candels & ~has_sparcs & ~has_cfhtls_wide & ~has_cfhtls_deep\n",
    "    use_wirds = has_wirds & ~has_candels & ~has_sparcs & ~has_cfhtls_wide & ~has_cfhtls_deep\n",
    "    use_vipers = has_vipers & ~has_wirds & ~has_candels & ~has_sparcs & ~has_cfhtls_wide & ~has_cfhtls_deep\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",
    "    if not (band == 'y'):\n",
    "        f_megacam[use_cfhtls_wide] = master_catalogue['f_cfhtls-wide_' + band][use_cfhtls_wide]\n",
    "    if not (band == 'i'):\n",
    "        f_megacam[use_sparcs] = master_catalogue['f_sparcs_' + band][use_sparcs]\n",
    "    if band == 'u':\n",
    "        f_megacam[use_candels] = master_catalogue['f_candels-megacam_' + band][use_candels] \n",
    "    if not (band == 'y'):\n",
    "        f_megacam[use_wirds] = master_catalogue['f_wirds_' + band][use_wirds]\n",
    "    f_megacam[use_vipers] = master_catalogue['f_vipers_' + band][use_vipers]    \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",
    "    if not (band == 'y'):\n",
    "        ferr_megacam[use_cfhtls_wide] = master_catalogue['ferr_cfhtls-wide_' + band][use_cfhtls_wide]\n",
    "    if not (band == 'i'):\n",
    "        ferr_megacam[use_sparcs] = master_catalogue['ferr_sparcs_' + band][use_sparcs]\n",
    "    if band == 'u':\n",
    "        ferr_megacam[use_candels] = master_catalogue['ferr_candels-megacam_' + band][use_candels]\n",
    "    if not (band == 'y'):\n",
    "        ferr_megacam[use_wirds] = master_catalogue['ferr_wirds_' + band][use_wirds]\n",
    "    ferr_megacam[use_vipers] = master_catalogue['ferr_vipers_' + band][use_vipers] \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",
    "    if not (band == 'y'):\n",
    "        m_megacam[use_cfhtls_wide] = master_catalogue['m_cfhtls-wide_' + band][use_cfhtls_wide]\n",
    "    if not (band == 'i'):\n",
    "        m_megacam[use_sparcs] = master_catalogue['m_sparcs_' + band][use_sparcs]\n",
    "    if band == 'u':\n",
    "        m_megacam[use_candels] = master_catalogue['m_candels-megacam_' + band][use_candels]\n",
    "    if not (band == 'y'):\n",
    "        m_megacam[use_wirds] = master_catalogue['m_wirds_' + band][use_wirds]\n",
    "    m_megacam[use_vipers] = master_catalogue['m_vipers_' + band][use_vipers] \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",
    "    if not (band == 'y'):\n",
    "        merr_megacam[use_cfhtls_wide] = master_catalogue['merr_cfhtls-wide_' + band][use_cfhtls_wide]\n",
    "    if not (band == 'i'):\n",
    "        merr_megacam[use_sparcs] = master_catalogue['merr_sparcs_' + band][use_sparcs]\n",
    "    if band == 'u':\n",
    "        merr_megacam[use_candels] = master_catalogue['merr_candels-megacam_' + band][use_candels]\n",
    "    if not (band == 'y'):\n",
    "        merr_megacam[use_wirds] = master_catalogue['merr_wirds_' + band][use_wirds]\n",
    "    merr_megacam[use_vipers] = master_catalogue['merr_vipers_' + band][use_vipers] \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",
    "    if not (band == 'y'):\n",
    "        flag_megacam[use_cfhtls_wide] = master_catalogue['flag_cfhtls-wide_' + band][use_cfhtls_wide]\n",
    "    if not (band == 'i'):\n",
    "        flag_megacam[use_sparcs] = master_catalogue['flag_sparcs_' + band][use_sparcs]\n",
    "    if band == 'u':\n",
    "        flag_megacam[use_candels] = master_catalogue['flag_candels-megacam_' + band][use_candels]\n",
    "    if not (band == 'y'):\n",
    "        flag_megacam[use_wirds] = master_catalogue['flag_wirds_' + band][use_wirds]\n",
    "    flag_megacam[use_vipers] = master_catalogue['flag_vipers_' + band][use_vipers] \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",
    "    \n",
    "    old_columns = []\n",
    "    column_types = ['f', 'ferr', 'm', 'merr', 'flag'] \n",
    "    for col_t in column_types:\n",
    "        old_columns += ['{}_cfhtls-deep_{}'.format(col_t, band)]\n",
    "        if not (band == 'y'):\n",
    "            old_columns += ['{}_cfhtls-wide_{}'.format(col_t, band)]\n",
    "        if not (band == 'i'):\n",
    "            old_columns += ['{}_sparcs_{}'.format(col_t, band)]    \n",
    "        if band == 'u':\n",
    "            old_columns += ['{}_candels-megacam_{}'.format(col_t, band)] \n",
    "        if not (band == 'y'):\n",
    "            old_columns += ['{}_wirds_{}'.format(col_t, band)] \n",
    "        old_columns += ['{}_vipers_{}'.format(col_t, band)]     \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_sparcs] = \"SpARCS\"\n",
    "    origin[use_candels] = \"CANDELS\"\n",
    "    origin[use_wirds] = \"WIRDS\"\n",
    "    origin[use_vipers] = \"VIPERS\"\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",
    "    if band == 'y':\n",
    "        has_ap_cfhtls_wide = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    else:\n",
    "        has_ap_cfhtls_wide = ~np.isnan(master_catalogue['f_ap_cfhtls-wide_' + band])\n",
    "    if band == 'i':\n",
    "        has_ap_sparcs = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    else:\n",
    "        has_ap_sparcs = ~np.isnan(master_catalogue['f_ap_sparcs_' + band])\n",
    "    if not (band == 'y'):\n",
    "        has_ap_wirds = ~np.isnan(master_catalogue['f_ap_wirds_' + band])\n",
    "    else:\n",
    "        has_ap_wirds = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    \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_sparcs = has_ap_sparcs & ~has_ap_cfhtls_wide & ~has_ap_cfhtls_deep\n",
    "    use_ap_wirds = has_ap_wirds & ~has_ap_sparcs & ~has_ap_cfhtls_wide & ~has_ap_cfhtls_deep\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",
    "    if not (band == 'y'):\n",
    "        f_ap_megacam[use_ap_cfhtls_wide] = master_catalogue['f_ap_cfhtls-wide_' + band][use_ap_cfhtls_wide]\n",
    "    if not (band == 'i'):\n",
    "        f_ap_megacam[use_ap_sparcs] = master_catalogue['f_ap_sparcs_' + band][use_ap_sparcs]\n",
    "    if not (band == 'y'):\n",
    "        f_ap_megacam[use_ap_wirds] = master_catalogue['f_ap_wirds_' + band][use_ap_wirds]\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",
    "    if not (band == 'y'):\n",
    "        ferr_ap_megacam[use_ap_cfhtls_wide] = master_catalogue['ferr_ap_cfhtls-wide_' + band][use_ap_cfhtls_wide]\n",
    "    if not (band == 'i'):\n",
    "        ferr_ap_megacam[use_ap_sparcs] = master_catalogue['ferr_ap_sparcs_' + band][use_ap_sparcs]\n",
    "    if not (band == 'y'):\n",
    "        ferr_ap_megacam[use_ap_wirds] = master_catalogue['ferr_ap_wirds_' + band][use_ap_wirds]\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",
    "    if not (band == 'y'):\n",
    "        m_ap_megacam[use_ap_cfhtls_wide] = master_catalogue['m_ap_cfhtls-wide_' + band][use_ap_cfhtls_wide]\n",
    "    if not (band == 'i'):\n",
    "        m_ap_megacam[use_ap_sparcs] = master_catalogue['m_ap_sparcs_' + band][use_ap_sparcs]\n",
    "    if not (band == 'y'):\n",
    "        m_ap_megacam[use_ap_wirds] = master_catalogue['m_ap_wirds_' + band][use_ap_wirds]\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",
    "    if not (band == 'y'):\n",
    "        merr_ap_megacam[use_ap_cfhtls_wide] = master_catalogue['merr_ap_cfhtls-wide_' + band][use_ap_cfhtls_wide]\n",
    "    if not (band == 'i'):\n",
    "        merr_ap_megacam[use_ap_sparcs] = master_catalogue['merr_ap_sparcs_' + band][use_ap_sparcs]\n",
    "    if not (band == 'y'):\n",
    "        merr_ap_megacam[use_ap_wirds] = master_catalogue['merr_ap_wirds_' + band][use_ap_wirds]\n",
    "\n",
    "\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",
    "    \n",
    "    old_ap_columns = []\n",
    "    column_types = ['f', 'ferr', 'm', 'merr'] \n",
    "    for col_t in column_types:\n",
    "        old_ap_columns += ['{}_ap_cfhtls-deep_{}'.format(col_t, band)]\n",
    "        if not (band == 'y'):\n",
    "            old_ap_columns += ['{}_ap_cfhtls-wide_{}'.format(col_t, band)]\n",
    "        if not (band == 'i'):\n",
    "            old_ap_columns += ['{}_ap_sparcs_{}'.format(col_t, band)]    \n",
    "        if not (band == 'y'):\n",
    "            old_ap_columns += ['{}_ap_wirds_{}'.format(col_t, 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_sparcs] = \"SpARCS\"\n",
    "    origin_ap[use_ap_wirds] = \"CFHT-WIRDS\"\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['SpARCS'][megacam_stats['Band'] == band] = np.sum(has_sparcs)\n",
    "    megacam_stats['CANDELS'][megacam_stats['Band'] == band] = np.sum(has_candels)\n",
    "    megacam_stats['CFHT-WIRDS'][megacam_stats['Band'] == band] = np.sum(has_wirds)\n",
    "    megacam_stats['VIPERS'][megacam_stats['Band'] == band] = np.sum(has_vipers)\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 SpARCS'][megacam_stats['Band'] == band] = np.sum(use_sparcs)\n",
    "    megacam_stats['use CANDELS'][megacam_stats['Band'] == band] = np.sum(use_candels)\n",
    "    megacam_stats['use CFHT-WIRDS'][megacam_stats['Band'] == band] = np.sum(use_wirds)\n",
    "    megacam_stats['use VIPERS'][megacam_stats['Band'] == band] = np.sum(use_vipers)\n",
    "    \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['SpARCS ap'][megacam_stats['Band'] == band] = np.sum(has_ap_sparcs)\n",
    "    megacam_stats['CFHT-WIRDS ap'][megacam_stats['Band'] == band] = np.sum(has_ap_wirds)\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 SpARCS ap'][megacam_stats['Band'] == band] = np.sum(use_ap_sparcs)\n",
    "    megacam_stats['use CFHT-WIRDS ap'][megacam_stats['Band'] == band] = np.sum(use_ap_wirds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=6&gt;\n",
       "<table id=\"table139944556845432-643227\" 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>SpARCS</th><th>use SpARCS</th><th>SpARCS ap</th><th>use SpARCS ap</th><th>CANDELS</th><th>use CANDELS</th><th>CANDELS ap</th><th>use CANDELS ap</th><th>CFHT-WIRDS</th><th>use CFHT-WIRDS</th><th>CFHT-WIRDS ap</th><th>use CFHT-WIRDS ap</th><th>VIPERS</th><th>use VIPERS</th><th>VIPERS ap</th><th>use VIPERS ap</th></tr></thead>\n",
       "<tr><td>0</td><td>u</td><td>481650.0</td><td>481650.0</td><td>485877.0</td><td>485877.0</td><td>3115218.0</td><td>2928155.0</td><td>3163862.0</td><td>2974025.0</td><td>396423.0</td><td>375948.0</td><td>385887.0</td><td>365727.0</td><td>35931.0</td><td>23992.0</td><td>0.0</td><td>0.0</td><td>134630.0</td><td>24774.0</td><td>135091.0</td><td>24122.0</td><td>908447.0</td><td>3132.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>g</td><td>551595.0</td><td>551595.0</td><td>553969.0</td><td>553969.0</td><td>3367177.0</td><td>3163292.0</td><td>3384048.0</td><td>3179110.0</td><td>436976.0</td><td>411025.0</td><td>429994.0</td><td>404289.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>143047.0</td><td>26012.0</td><td>142074.0</td><td>24642.0</td><td>945986.0</td><td>851.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>2</td><td>r</td><td>561918.0</td><td>561918.0</td><td>565280.0</td><td>565280.0</td><td>3366913.0</td><td>3163239.0</td><td>3397813.0</td><td>3192615.0</td><td>446308.0</td><td>418750.0</td><td>442998.0</td><td>415461.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>144102.0</td><td>26115.0</td><td>143184.0</td><td>24852.0</td><td>949549.0</td><td>479.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>3</td><td>i</td><td>557674.0</td><td>557674.0</td><td>561376.0</td><td>561376.0</td><td>3283920.0</td><td>3084634.0</td><td>3329406.0</td><td>3127407.0</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><td>0.0</td><td>0.0</td><td>144330.0</td><td>26167.0</td><td>143316.0</td><td>24852.0</td><td>728690.0</td><td>234.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>4</td><td>z</td><td>499627.0</td><td>499627.0</td><td>506086.0</td><td>506086.0</td><td>2864911.0</td><td>2688653.0</td><td>2971971.0</td><td>2789427.0</td><td>413627.0</td><td>391475.0</td><td>389357.0</td><td>367615.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>141308.0</td><td>25190.0</td><td>141154.0</td><td>24142.0</td><td>922753.0</td><td>2444.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>5</td><td>y</td><td>522629.0</td><td>522629.0</td><td>528982.0</td><td>528982.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>405761.0</td><td>405761.0</td><td>391629.0</td><td>391629.0</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><td>0.0</td><td>0.0</td><td>221194.0</td><td>205424.0</td><td>0.0</td><td>0.0</td></tr>\n",
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       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table139944556845432-643227').dataTable()\");\n",
       "    $('#table139944556845432-643227').dataTable({\n",
       "        \"order\": [],\n",
       "        \"iDisplayLength\": 50,\n",
       "        \"aLengthMenu\": [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        \"pagingType\": \"full_numbers\"\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "megacam_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "megacam_origin.write(\"{}/xmm-lss_megacam_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VII.b CFHT WIRCAM fluxes: CFHT-WIRDS and VIPERS\n",
    "\n",
    "We take WIRDS over VIPERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    if '_wirds_j' in col:\n",
    "        master_catalogue.rename_column(col, col.replace('_wirds_j', '_wircam_j'))\n",
    "    if '_wirds_h' in col:\n",
    "        master_catalogue.rename_column(col, col.replace('_wirds_h', '_wircam_h'))\n",
    "    if '_wirds_k' in col:\n",
    "        master_catalogue.rename_column(col, col.replace('_wirds_k', '_wircam_ks'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "has_wirds = ~np.isnan(master_catalogue['f_wircam_ks' ])\n",
    "has_vipers = ~np.isnan(master_catalogue['f_vipers_ks' ])\n",
    "\n",
    "use_wirds = has_wirds\n",
    "use_vipers = has_vipers & ~has_wirds\n",
    "\n",
    "master_catalogue['f_wircam_ks'][use_vipers] = master_catalogue['f_vipers_ks'][use_vipers]\n",
    "master_catalogue['ferr_wircam_ks'][use_vipers] = master_catalogue['ferr_vipers_ks'][use_vipers]\n",
    "master_catalogue['m_wircam_ks'][use_vipers] = master_catalogue['m_vipers_ks'][use_vipers]\n",
    "master_catalogue['merr_wircam_ks'][use_vipers] = master_catalogue['merr_vipers_ks'][use_vipers]\n",
    "master_catalogue['flag_wircam_ks'][use_vipers] = master_catalogue['flag_vipers_ks'][use_vipers]\n",
    "\n",
    "master_catalogue.remove_columns(['f_vipers_ks', 'ferr_vipers_ks', 'm_vipers_ks', 'merr_vipers_ks', 'flag_vipers_ks'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 157497 objects with WIRDS Ks fluxes and 644584 with VIPERS Ks.\n",
      "We use all 157497 Ks fluxes and take the remaining 644510 VIPERS Ks fluxes.\n"
     ]
    }
   ],
   "source": [
    "print('There are {} objects with WIRDS Ks fluxes and {} with VIPERS Ks.'.format(np.sum(has_wirds), np.sum(has_vipers)))\n",
    "print('We use all {} Ks fluxes and take the remaining {} VIPERS Ks fluxes.'.format(np.sum(use_wirds), np.sum(use_vipers)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wircam_origin = Table()\n",
    "wircam_origin.add_column(master_catalogue['cfht_intid'])\n",
    "origin = np.full(len(master_catalogue), '     ', dtype='<U5')\n",
    "origin[use_wirds] = \"CFHT-WIRDS\"\n",
    "origin[use_vipers] = \"VIPERS\"\n",
    "wircam_origin.add_column(Column(data=origin, name= 'f_wircam_ks'  ))\n",
    "wircam_origin.write(\"{}/xmm-lss_wircam_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VII.c Subaru SuprimeCam fluxes: SXDS and CANDELS-UDS\n",
    "\n",
    "We take SXDS over CANDELS so that there are not objects with total and ap mags from different source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "suprime_origin = Table()\n",
    "suprime_origin.add_column(master_catalogue['cfht_intid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full(5, 0) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "suprime_stats = Table()\n",
    "suprime_bands = ['b', 'v', 'rc', 'ip', 'zp']\n",
    "suprime_stats.add_column(Column(data=suprime_bands, name=\"Band\"))\n",
    "for col in [\"SXDS\", \"CANDELS-UDS\"]:\n",
    "    suprime_stats.add_column(Column(data=np.full(5, 0), name=\"{}\".format(col)))\n",
    "    suprime_stats.add_column(Column(data=np.full(5, 0), name=\"use {}\".format(col)))\n",
    "    suprime_stats.add_column(Column(data=np.full(5, 0), name=\"{} ap\".format(col)))\n",
    "    suprime_stats.add_column(Column(data=np.full(5, 0), name=\"use {} ap\".format(col)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "suprime_bands = ['b', 'v', 'rc', 'ip', 'zp']\n",
    "for band in suprime_bands:\n",
    "\n",
    "    # SuprimeCam total flux \n",
    "    has_candels = ~np.isnan(master_catalogue['f_suprime_' + band])\n",
    "    has_sxds = ~np.isnan(master_catalogue['f_sxds-suprime_' + band])\n",
    "\n",
    "     \n",
    "    use_sxds = has_sxds \n",
    "    use_candels = has_candels & ~has_sxds\n",
    "\n",
    "\n",
    "    f_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    f_suprime[use_candels] = master_catalogue['f_suprime_' + band][use_candels]\n",
    "    f_suprime[use_sxds] = master_catalogue['f_sxds-suprime_' + band][use_sxds]\n",
    "\n",
    "    ferr_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    ferr_suprime[use_candels] = master_catalogue['ferr_suprime_' + band][use_candels]\n",
    "    ferr_suprime[use_sxds] = master_catalogue['ferr_sxds-suprime_' + band][use_sxds]\n",
    "    \n",
    "    m_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    m_suprime[use_candels] = master_catalogue['m_suprime_' + band][use_candels]\n",
    "    m_suprime[use_sxds] = master_catalogue['m_sxds-suprime_' + band][use_sxds]\n",
    "    \n",
    "    merr_suprime = np.full(len(master_catalogue), np.nan)\n",
    "    merr_suprime[use_candels] = master_catalogue['merr_suprime_' + band][use_candels]\n",
    "    merr_suprime[use_sxds] = master_catalogue['merr_sxds-suprime_' + band][use_sxds]\n",
    "    \n",
    "    flag_suprime = np.full(len(master_catalogue), False, dtype=bool)\n",
    "    flag_suprime[use_candels] = master_catalogue['flag_suprime_' + band][use_candels]\n",
    "    flag_suprime[use_sxds] = master_catalogue['flag_sxds-suprime_' + band][use_sxds]\n",
    "\n",
    "    master_catalogue[\"f_suprime_\" + band][use_sxds] = master_catalogue[\"f_sxds-suprime_\" + band][use_sxds]\n",
    "    master_catalogue[\"ferr_suprime_\" + band][use_sxds] = master_catalogue[\"ferr_sxds-suprime_\" + band][use_sxds]\n",
    "    master_catalogue[\"m_suprime_\" + band][use_sxds] = master_catalogue[\"m_sxds-suprime_\" + band][use_sxds]\n",
    "    master_catalogue[\"merr_suprime_\" + band][use_sxds] = master_catalogue[\"merr_sxds-suprime_\" + band][use_sxds]\n",
    "    master_catalogue[\"flag_suprime_\" + band][use_sxds] = master_catalogue[\"flag_sxds-suprime_\" + band][use_sxds]\n",
    "\n",
    "\n",
    "    old_sxds_columns =      ['f_sxds-suprime_' + band,\n",
    "                               'ferr_sxds-suprime_' + band,\n",
    "                               'm_sxds-suprime_' + band, \n",
    "                               'merr_sxds-suprime_' + band,\n",
    "                               'flag_sxds-suprime_' + band]\n",
    "    \n",
    "\n",
    "    master_catalogue.remove_columns(old_sxds_columns)\n",
    "\n",
    "    origin = np.full(len(master_catalogue), '     ', dtype='<U7')\n",
    "    origin[use_candels] = \"CANDELS\"\n",
    "    origin[use_sxds] = \"SXDS\"\n",
    "\n",
    "    \n",
    "    suprime_origin.add_column(Column(data=origin, name= 'f_suprime_' + band ))\n",
    "        \n",
    "\n",
    "   \n",
    "    suprime_stats['CANDELS-UDS'][suprime_stats['Band'] == band] = np.sum(has_candels)\n",
    "    suprime_stats['SXDS'][suprime_stats['Band'] == band] = np.sum(has_sxds)\n",
    "\n",
    "    suprime_stats['use CANDELS-UDS'][suprime_stats['Band'] == band] = np.sum(use_candels)\n",
    "    suprime_stats['use SXDS'][suprime_stats['Band'] == band] = np.sum(use_sxds)\n",
    "    \n",
    "    suprime_stats['CANDELS-UDS ap'][suprime_stats['Band'] == band] = 0\n",
    "    has_ap_sxds = np.sum( ~np.isnan(master_catalogue['f_ap_sxds-suprime_' + band]))\n",
    "    suprime_stats['SXDS ap'][suprime_stats['Band'] == band] = has_ap_sxds\n",
    "\n",
    "    suprime_stats['use CANDELS-UDS ap'][suprime_stats['Band'] == band] = 0\n",
    "    suprime_stats['use SXDS ap'][suprime_stats['Band'] == band] = has_ap_sxds\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Aperture fluxes all from SXDS\n",
    "for col in master_catalogue.colnames:\n",
    "    if '_ap_sxds-suprime_' in col:\n",
    "        master_catalogue.rename_column(col, col.replace('_ap_sxds-suprime_', '_ap_suprime_'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=5&gt;\n",
       "<table id=\"table139944556969936-760223\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>Band</th><th>SXDS</th><th>use SXDS</th><th>SXDS ap</th><th>use SXDS ap</th><th>CANDELS-UDS</th><th>use CANDELS-UDS</th><th>CANDELS-UDS ap</th><th>use CANDELS-UDS ap</th></tr></thead>\n",
       "<tr><td>0</td><td>b</td><td>906586.0</td><td>906586.0</td><td>883356.0</td><td>883356.0</td><td>35617.0</td><td>9700.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>1</td><td>v</td><td>962918.0</td><td>962918.0</td><td>929147.0</td><td>929147.0</td><td>35617.0</td><td>9119.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>2</td><td>rc</td><td>865238.0</td><td>865238.0</td><td>842176.0</td><td>842176.0</td><td>35617.0</td><td>9742.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>3</td><td>ip</td><td>862149.0</td><td>862149.0</td><td>845761.0</td><td>845761.0</td><td>35617.0</td><td>9730.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "<tr><td>4</td><td>zp</td><td>811734.0</td><td>811734.0</td><td>772022.0</td><td>772022.0</td><td>35617.0</td><td>12297.0</td><td>0.0</td><td>0.0</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table139944556969936-760223').dataTable()\");\n",
       "    $('#table139944556969936-760223').dataTable({\n",
       "        \"order\": [],\n",
       "        \"iDisplayLength\": 50,\n",
       "        \"aLengthMenu\": [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        \"pagingType\": \"full_numbers\"\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suprime_stats.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "suprime_origin.write(\"{}/xmm-lss_suprime_fluxes_origins{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=10&gt;\n",
       "<table id=\"table139944547050216-477817\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>candels_id</th><th>cfht_ra</th><th>cfht_dec</th><th>candels_stellarity</th><th>f_acs_f606w</th><th>ferr_acs_f606w</th><th>f_ap_acs_f606w</th><th>ferr_ap_acs_f606w</th><th>f_acs_f814w</th><th>ferr_acs_f814w</th><th>f_ap_acs_f814w</th><th>ferr_ap_acs_f814w</th><th>f_wfc3_f125w</th><th>ferr_wfc3_f125w</th><th>f_ap_wfc3_f125w</th><th>ferr_ap_wfc3_f125w</th><th>f_wfc3_f160w</th><th>ferr_wfc3_f160w</th><th>f_ap_wfc3_f160w</th><th>ferr_ap_wfc3_f160w</th><th>f_suprime_b</th><th>ferr_suprime_b</th><th>f_suprime_v</th><th>ferr_suprime_v</th><th>f_suprime_rc</th><th>ferr_suprime_rc</th><th>f_suprime_ip</th><th>ferr_suprime_ip</th><th>f_suprime_zp</th><th>ferr_suprime_zp</th><th>f_hawki_k</th><th>ferr_hawki_y</th><th>ferr_hawki_k</th><th>m_acs_f606w</th><th>merr_acs_f606w</th><th>flag_acs_f606w</th><th>m_ap_acs_f606w</th><th>merr_ap_acs_f606w</th><th>m_acs_f814w</th><th>merr_acs_f814w</th><th>flag_acs_f814w</th><th>m_ap_acs_f814w</th><th>merr_ap_acs_f814w</th><th>m_wfc3_f125w</th><th>merr_wfc3_f125w</th><th>flag_wfc3_f125w</th><th>m_ap_wfc3_f125w</th><th>merr_ap_wfc3_f125w</th><th>m_wfc3_f160w</th><th>merr_wfc3_f160w</th><th>flag_wfc3_f160w</th><th>m_ap_wfc3_f160w</th><th>merr_ap_wfc3_f160w</th><th>m_suprime_b</th><th>merr_suprime_b</th><th>flag_suprime_b</th><th>m_suprime_v</th><th>merr_suprime_v</th><th>flag_suprime_v</th><th>m_suprime_rc</th><th>merr_suprime_rc</th><th>flag_suprime_rc</th><th>m_suprime_ip</th><th>merr_suprime_ip</th><th>flag_suprime_ip</th><th>m_suprime_zp</th><th>merr_suprime_zp</th><th>flag_suprime_zp</th><th>m_hawki_k</th><th>merr_hawki_k</th><th>flag_hawki_k</th><th>candels_flag_cleaned</th><th>candels_flag_gaia</th><th>cfht_flag_merged</th><th>cfhtls-deep_id</th><th>cfhtls-deep_stellarity</th><th>cfhtls-deep_flag_cleaned</th><th>cfhtls-deep_flag_gaia</th><th>cfhtls-wide_id</th><th>cfhtls-wide_stellarity</th><th>cfhtls-wide_flag_cleaned</th><th>cfhtls-wide_flag_gaia</th><th>sparcs_intid</th><th>sparcs_stellarity</th><th>sparcs_flag_cleaned</th><th>sparcs_flag_gaia</th><th>wirds_id</th><th>m_wircam_j</th><th>merr_wircam_j</th><th>m_ap_wircam_j</th><th>merr_ap_wircam_j</th><th>m_wircam_h</th><th>merr_wircam_h</th><th>m_ap_wircam_h</th><th>merr_ap_wircam_h</th><th>m_wircam_ks</th><th>merr_wircam_ks</th><th>m_ap_wircam_ks</th><th>merr_ap_wircam_ks</th><th>f_wircam_j</th><th>ferr_wircam_j</th><th>flag_wircam_j</th><th>f_ap_wircam_j</th><th>ferr_ap_wircam_j</th><th>f_wircam_h</th><th>ferr_wircam_h</th><th>flag_wircam_h</th><th>f_ap_wircam_h</th><th>ferr_ap_wircam_h</th><th>f_wircam_ks</th><th>ferr_wircam_ks</th><th>flag_wircam_ks</th><th>f_ap_wircam_ks</th><th>ferr_ap_wircam_ks</th><th>wirds_flag_cleaned</th><th>cfht-wirds_flag_gaia</th><th>vipers_id</th><th>vipers_flag_cleaned</th><th>vipers_flag_gaia</th><th>sxds_b_id</th><th>m_ap_suprime_b</th><th>merr_ap_suprime_b</th><th>sxds_flag_merged</th><th>sxds_v_id</th><th>m_ap_suprime_v</th><th>merr_ap_suprime_v</th><th>sxds_r_id</th><th>m_ap_suprime_rc</th><th>merr_ap_suprime_rc</th><th>sxds_i_id</th><th>m_ap_suprime_ip</th><th>merr_ap_suprime_ip</th><th>sxds_z_id</th><th>m_ap_suprime_zp</th><th>merr_ap_suprime_zp</th><th>f_ap_suprime_b</th><th>ferr_ap_suprime_b</th><th>f_ap_suprime_v</th><th>ferr_ap_suprime_v</th><th>f_ap_suprime_rc</th><th>ferr_ap_suprime_rc</th><th>f_ap_suprime_ip</th><th>ferr_ap_suprime_ip</th><th>f_ap_suprime_zp</th><th>ferr_ap_suprime_zp</th><th>sxds_stellarity</th><th>sxds_flag_cleaned</th><th>sxds_flag_gaia</th><th>sxds_intid</th><th>cfht_intid</th><th>f_megacam_u</th><th>ferr_megacam_u</th><th>m_megacam_u</th><th>merr_megacam_u</th><th>flag_megacam_u</th><th>f_ap_megacam_u</th><th>ferr_ap_megacam_u</th><th>m_ap_megacam_u</th><th>merr_ap_megacam_u</th><th>f_megacam_g</th><th>ferr_megacam_g</th><th>m_megacam_g</th><th>merr_megacam_g</th><th>flag_megacam_g</th><th>f_ap_megacam_g</th><th>ferr_ap_megacam_g</th><th>m_ap_megacam_g</th><th>merr_ap_megacam_g</th><th>f_megacam_r</th><th>ferr_megacam_r</th><th>m_megacam_r</th><th>merr_megacam_r</th><th>flag_megacam_r</th><th>f_ap_megacam_r</th><th>ferr_ap_megacam_r</th><th>m_ap_megacam_r</th><th>merr_ap_megacam_r</th><th>f_megacam_i</th><th>ferr_megacam_i</th><th>m_megacam_i</th><th>merr_megacam_i</th><th>flag_megacam_i</th><th>f_ap_megacam_i</th><th>ferr_ap_megacam_i</th><th>m_ap_megacam_i</th><th>merr_ap_megacam_i</th><th>f_megacam_z</th><th>ferr_megacam_z</th><th>m_megacam_z</th><th>merr_megacam_z</th><th>flag_megacam_z</th><th>f_ap_megacam_z</th><th>ferr_ap_megacam_z</th><th>m_ap_megacam_z</th><th>merr_ap_megacam_z</th><th>f_megacam_y</th><th>ferr_megacam_y</th><th>m_megacam_y</th><th>merr_megacam_y</th><th>flag_megacam_y</th><th>f_ap_megacam_y</th><th>ferr_ap_megacam_y</th><th>m_ap_megacam_y</th><th>merr_ap_megacam_y</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<tr><td>0</td><td>21539</td><td>34.3795257693</td><td>-5.15683775973</td><td>0.019999999553</td><td>0.0710597</td><td>0.00949195</td><td>0.107001</td><td>0.0121251</td><td>0.142095</td><td>0.0112729</td><td>0.23308</td><td>0.0142605</td><td>0.722495</td><td>0.0232445</td><td>0.991269</td><td>0.028352</td><td>1.09422</td><td>0.017616</td><td>1.50183</td><td>0.0215974</td><td>0.4852182</td><td>0.0037431</td><td>0.6368133</td><td>0.0041781</td><td>0.7070535</td><td>0.0061044</td><td>0.9404059</td><td>0.0062788</td><td>1.1989115</td><td>0.0159431</td><td>0.702083</td><td>0.0140271</td><td>0.0295765</td><td>26.7709415759</td><td>0.14502951418</td><td>False</td><td>26.3265304088</td><td>0.123033056292</td><td>26.0185530091</td><td>0.0861353014717</td><td>False</td><td>25.4812374765</td><td>0.0664284415134</td><td>24.2529128852</td><td>0.034930892548</td><td>False</td><td>23.9095211883</td><td>0.0310539246938</td><td>23.8022383788</td><td>0.0174794182002</td><td>False</td><td>23.4584480615</td><td>0.0156136707275</td><td>24.685157294</td><td>0.00837565282595</td><td>False</td><td>24.3899696871</td><td>0.00712346057644</td><td>False</td><td>24.2763693089</td><td>0.00937378584269</td><td>False</td><td>23.9667116371</td><td>0.00724912559825</td><td>False</td><td>23.703032185</td><td>0.0144380972958</td><td>False</td><td>24.2840288567</td><td>0.0457385762937</td><td>False</td><td>False</td><td>0</td><td>True</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>0</td><td>0.3627532</td><td>0.0066298</td><td>25.0009718694</td><td>0.0198432815763</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>1</td><td>20563</td><td>34.3530670693</td><td>-5.16239645973</td><td>0.0</td><td>0.133338</td><td>0.00879655</td><td>0.180807</td><td>0.0136602</td><td>0.197151</td><td>0.00897565</td><td>0.310126</td><td>0.0138066</td><td>0.333375</td><td>0.0187768</td><td>0.472117</td><td>0.0292274</td><td>0.538301</td><td>0.0144117</td><td>0.760123</td><td>0.0223591</td><td>0.2958344</td><td>0.0043875</td><td>0.4402928</td><td>0.0053329</td><td>0.5652437</td><td>0.007054</td><td>0.9049545</td><td>0.0085324</td><td>1.1906105</td><td>0.0191945</td><td>0.527608</td><td>0.0141214</td><td>0.0304363</td><td>26.0876151584</td><td>0.0716279891101</td><td>False</td><td>25.7569618992</td><td>0.0820287583127</td><td>25.6630025393</td><td>0.0494300722098</td><td>False</td><td>25.1711545558</td><td>0.0483362423164</td><td>25.0926674283</td><td>0.0611523106697</td><td>False</td><td>24.714875903</td><td>0.0672147928394</td><td>24.5724370335</td><td>0.0290679461159</td><td>False</td><td>24.1977903157</td><td>0.0319370475249</td><td>25.2223783177</td><td>0.0161024803011</td><td>False</td><td>24.790646041</td><td>0.0131506184211</td><td>False</td><td>24.5194106736</td><td>0.0135495241935</td><td>False</td><td>24.0084331401</td><td>0.0102369075942</td><td>False</td><td>23.7105757293</td><td>0.0175037626346</td><td>False</td><td>24.5942215699</td><td>0.0626332293083</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>1</td><td>0.2452752</td><td>0.0068255</td><td>25.4258659037</td><td>0.0302137862514</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>2</td><td>14826</td><td>34.2856193693</td><td>-5.19395515973</td><td>0.0</td><td>0.0491678</td><td>0.0074849</td><td>0.0619168</td><td>0.0160298</td><td>0.0972186</td><td>0.00783663</td><td>0.142508</td><td>0.0157403</td><td>0.264025</td><td>0.0154075</td><td>0.328606</td><td>0.0278024</td><td>0.344238</td><td>0.0123705</td><td>0.467226</td><td>0.0238434</td><td>0.2055707</td><td>0.0043688</td><td>0.2400314</td><td>0.0047257</td><td>0.3092149</td><td>0.0067454</td><td>0.3850932</td><td>0.0065248</td><td>0.5510579</td><td>0.0183486</td><td>0.471631</td><td>0.0210946</td><td>0.0445381</td><td>27.1707980592</td><td>0.165283517241</td><td>False</td><td>26.9204787427</td><td>0.281089045542</td><td>26.4306265932</td><td>0.0875193935553</td><td>False</td><td>26.0154018872</td><td>0.119921783926</td><td>25.3458873718</td><td>0.0633594567742</td><td>False</td><td>25.1083112777</td><td>0.0918609893282</td><td>25.0578529754</td><td>0.0390169002869</td><td>False</td><td>24.7261824945</td><td>0.0554071105301</td><td>25.6175969632</td><td>0.023074126475</td><td>False</td><td>25.4493298545</td><td>0.0213758015944</td><td>False</td><td>25.1743489677</td><td>0.0236849032682</td><td>False</td><td>24.9360853753</td><td>0.0183960962925</td><td>False</td><td>24.5470069179</td><td>0.0361518078711</td><td>False</td><td>24.7159941419</td><td>0.102530638701</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>2</td><td>0.1681561</td><td>0.0073574</td><td>25.8357184342</td><td>0.0475046433218</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>3</td><td>1265</td><td>34.3627124693</td><td>-5.26953255973</td><td>0.019999999553</td><td>0.195174</td><td>0.0114047</td><td>0.277966</td><td>0.0174607</td><td>0.394889</td><td>0.0137767</td><td>0.544339</td><td>0.0199322</td><td>0.665006</td><td>0.0217909</td><td>0.966486</td><td>0.0305951</td><td>0.750229</td><td>0.0151674</td><td>1.11416</td><td>0.0233303</td><td>0.0960998</td><td>0.0032959</td><td>0.1398806</td><td>0.0056783</td><td>0.1907871</td><td>0.0052222</td><td>0.4022798</td><td>0.0070876</td><td>0.5167124</td><td>0.0224519</td><td>0.553209</td><td>0.045561</td><td>0.0959896</td><td>25.6739450928</td><td>0.0634433669157</td><td>False</td><td>25.2900208062</td><td>0.0682015575661</td><td>24.9088124094</td><td>0.0378786493726</td><td>False</td><td>24.5603263722</td><td>0.0397566795333</td><td>24.3429360907</td><td>0.0355773768421</td><td>False</td><td>23.9370110814</td><td>0.0343700868488</td><td>24.2120153807</td><td>0.02195035824</td><td>False</td><td>23.7826310935</td><td>0.0227351110952</td><td>26.4431937904</td><td>0.0372371009852</td><td>False</td><td>26.0356062838</td><td>0.0440742739985</td><td>False</td><td>25.6986274833</td><td>0.0297186319646</td><td>False</td><td>24.8886794362</td><td>0.0191291333168</td><td>False</td><td>24.6168777905</td><td>0.0471768060832</td><td>False</td><td>24.5427769077</td><td>0.188390615482</td><td>False</td><td>False</td><td>0</td><td>True</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>3</td><td>0.0529733</td><td>0.0075836</td><td>27.089857429</td><td>0.155432813935</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>4</td><td>13273</td><td>34.3243668693</td><td>-5.20170115973</td><td>0.0</td><td>0.113233</td><td>0.00771246</td><td>0.161421</td><td>0.0175376</td><td>0.220957</td><td>0.00871852</td><td>0.318615</td><td>0.0181905</td><td>0.304997</td><td>0.0133402</td><td>0.473484</td><td>0.0277084</td><td>0.332122</td><td>0.00962214</td><td>0.508171</td><td>0.0236617</td><td>0.0859443</td><td>0.0048999</td><td>0.1259515</td><td>0.0058358</td><td>0.2384127</td><td>0.0085755</td><td>0.3344948</td><td>0.0111257</td><td>0.4307847</td><td>0.0230394</td><td>0.390239</td><td>0.0194855</td><td>0.0391507</td><td>26.2650674658</td><td>0.0739510306161</td><td>False</td><td>25.8800999164</td><td>0.117959913918</td><td>25.5392305882</td><td>0.0428409727499</td><td>False</td><td>25.141834455</td><td>0.0619873026463</td><td>25.1892610811</td><td>0.0474887888035</td><td>False</td><td>24.7117367303</td><td>0.0635375494334</td><td>25.0967558887</td><td>0.0314556270444</td><td>False</td><td>24.6349753066</td><td>0.0505545659948</td><td>26.5644573033</td><td>0.0619005429062</td><td>False</td><td>26.1494916399</td><td>0.0503061840766</td><td>False</td><td>25.4566765347</td><td>0.0390529985353</td><td>False</td><td>25.0890265733</td><td>0.0361128941116</td><td>False</td><td>24.8143493244</td><td>0.0580677789065</td><td>False</td><td>24.9216733248</td><td>0.108926407744</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>4</td><td>0.0365908</td><td>0.0066596</td><td>27.4915702381</td><td>0.197606196891</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>5</td><td>9036</td><td>34.2784354693</td><td>-5.22480465973</td><td>0.0</td><td>0.0543055</td><td>0.00773169</td><td>0.102759</td><td>0.0182538</td><td>0.110097</td><td>0.00870382</td><td>0.202327</td><td>0.0191073</td><td>0.262203</td><td>0.015319</td><td>0.379377</td><td>0.0315701</td><td>0.450288</td><td>0.0132094</td><td>0.713741</td><td>0.0276764</td><td>0.2187557</td><td>0.0036391</td><td>0.3882704</td><td>0.0065876</td><td>0.6772675</td><td>0.0064811</td><td>1.0186884</td><td>0.008968</td><td>1.122582</td><td>0.0226343</td><td>0.656931</td><td>0.0194469</td><td>0.039726</td><td>27.0628904583</td><td>0.154580581285</td><td>False</td><td>26.3704503268</td><td>0.192866917101</td><td>26.2955612871</td><td>0.0858338782501</td><td>False</td><td>25.6348653948</td><td>0.102534448616</td><td>25.3534058591</td><td>0.0634332670514</td><td>False</td><td>24.9523225052</td><td>0.0903502335614</td><td>24.7662740667</td><td>0.0318505574724</td><td>False</td><td>24.2661483875</td><td>0.0421010835827</td><td>25.5501015549</td><td>0.0180617127816</td><td>False</td><td>24.9271642922</td><td>0.018421172004</td><td>False</td><td>24.3230994109</td><td>0.0103899344301</td><td>False</td><td>23.879896598</td><td>0.0095582538137</td><td>False</td><td>23.7744548143</td><td>0.0218913887621</td><td>False</td><td>24.3562006092</td><td>0.0656567530992</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>5</td><td>0.1593326</td><td>0.0068346</td><td>25.8942383924</td><td>0.046572846141</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>6</td><td>3353</td><td>34.3749212693</td><td>-5.25793585973</td><td>0.0</td><td>0.0219845</td><td>0.0119491</td><td>0.036657</td><td>0.0189846</td><td>0.0715805</td><td>0.0121569</td><td>0.0942867</td><td>0.0190316</td><td>0.590133</td><td>0.0306195</td><td>0.762989</td><td>0.0439909</td><td>1.32479</td><td>0.0261523</td><td>1.58215</td><td>0.0382111</td><td>0.0002127</td><td>0.0038226</td><td>-0.0133222</td><td>0.0071063</td><td>-0.0022047</td><td>0.0064365</td><td>0.0108566</td><td>0.0077467</td><td>0.1016985</td><td>0.0246658</td><td>0.247723</td><td>0.0177556</td><td>0.0384577</td><td>28.0447085181</td><td>0.590123518128</td><td>False</td><td>27.4896077012</td><td>0.562300994431</td><td>26.7630131808</td><td>0.184396399405</td><td>False</td><td>26.4638739104</td><td>0.219153890787</td><td>24.4726252478</td><td>0.0563342496041</td><td>False</td><td>24.193704308</td><td>0.0625992154669</td><td>23.5946323969</td><td>0.0214332074877</td><td>False</td><td>23.4018808611</td><td>0.0262220236347</td><td>33.0805812752</td><td>19.5126244302</td><td>False</td><td>nan</td><td>-0.579151130584</td><td>False</td><td>nan</td><td>-3.1697469415</td><td>False</td><td>28.8107654075</td><td>0.774724375716</td><td>False</td><td>26.3817136316</td><td>0.263332812965</td><td>False</td><td>25.415084173</td><td>0.168554866693</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>6</td><td>-0.0051325</td><td>0.0082125</td><td>nan</td><td>-1.73728369831</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>7</td><td>30535</td><td>34.3719116693</td><td>-5.21773125973</td><td>0.0</td><td>0.0200661</td><td>0.00917157</td><td>-0.00216399</td><td>0.0189875</td><td>0.0597257</td><td>0.00988745</td><td>0.0881192</td><td>0.0191953</td><td>0.0968641</td><td>0.0228686</td><td>0.213073</td><td>0.0454037</td><td>0.112731</td><td>0.0212121</td><td>0.140503</td><td>0.0393608</td><td>0.005866</td><td>0.0037556</td><td>0.0223429</td><td>0.0064162</td><td>0.0384536</td><td>0.0069202</td><td>0.0829306</td><td>0.0096274</td><td>0.0957557</td><td>0.0246016</td><td>0.0821145</td><td>0.0146598</td><td>0.0307014</td><td>28.1438425694</td><td>0.49625515688</td><td>False</td><td>nan</td><td>-9.52657645731</td><td>26.9595968788</td><td>0.179741090313</td><td>False</td><td>26.5373236357</td><td>0.236509548103</td><td>26.4345928809</td><td>0.256330952047</td><td>False</td><td>25.5786789484</td><td>0.231359397577</td><td>26.2698916012</td><td>0.204298240492</td><td>False</td><td>26.0307860067</td><td>0.304160378129</td><td>29.4791448534</td><td>0.695122893043</td><td>False</td><td>28.0271511456</td><td>0.311790351162</td><td>False</td><td>27.437657489</td><td>0.195391632621</td><td>False</td><td>26.6032129813</td><td>0.126042941178</td><td>False</td><td>26.4470884114</td><td>0.278947862268</td><td>False</td><td>26.613950368</td><td>0.405940747575</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>7</td><td>0.0027365</td><td>0.0067958</td><td>30.3070113688</td><td>2.69630772896</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>8</td><td>10290</td><td>34.3721179693</td><td>-5.21799245973</td><td>0.699999988079</td><td>0.00202651</td><td>0.00576531</td><td>-0.0335463</td><td>0.0190292</td><td>0.00298273</td><td>0.00574464</td><td>0.0125992</td><td>0.0189148</td><td>0.0851452</td><td>0.0152042</td><td>0.075513</td><td>0.0450326</td><td>0.121132</td><td>0.016443</td><td>0.0909148</td><td>0.0397302</td><td>-0.000704</td><td>0.003589</td><td>-0.0016523</td><td>0.006103</td><td>0.002381</td><td>0.0066523</td><td>-0.0106147</td><td>0.0092379</td><td>0.0122139</td><td>0.0235671</td><td>0.065116</td><td>0.0127878</td><td>0.0253353</td><td>30.6331281221</td><td>3.08886005924</td><td>False</td><td>nan</td><td>-0.615885846951</td><td>30.2134651442</td><td>2.09109226491</td><td>False</td><td>28.649142575</td><td>1.62998310732</td><td>26.5745995751</td><td>0.19387763966</td><td>False</td><td>26.7049456891</td><td>0.647484859751</td><td>26.1918527803</td><td>0.147382693383</td><td>False</td><td>26.5034135308</td><td>0.474471885351</td><td>nan</td><td>-5.53509550977</td><td>False</td><td>nan</td><td>-4.01031777379</td><td>False</td><td>30.4581015115</td><td>3.03344937208</td><td>False</td><td>nan</td><td>-0.944908710179</td><td>False</td><td>28.6828641002</td><td>2.09496178216</td><td>False</td><td>26.8657807138</td><td>0.422437687641</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>8</td><td>-0.0105424</td><td>0.0064933</td><td>nan</td><td>-0.6687292171</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>9</td><td>30480</td><td>34.4393352693</td><td>-5.21873475973</td><td>0.709999978542</td><td>-0.00142079</td><td>0.00516246</td><td>-0.0420138</td><td>0.0193282</td><td>-0.00176442</td><td>0.00551065</td><td>-0.0195892</td><td>0.0206293</td><td>0.0360928</td><td>0.0129518</td><td>0.0310641</td><td>0.0448159</td><td>0.0594395</td><td>0.0138861</td><td>0.019035</td><td>0.0400623</td><td>-0.002361</td><td>0.003477</td><td>-0.000383</td><td>0.006916</td><td>0.0075941</td><td>0.0064209</td><td>0.0126521</td><td>0.0078891</td><td>0.0375685</td><td>0.0239401</td><td>0.02165</td><td>0.0127359</td><td>0.026029</td><td>nan</td><td>-3.9450374282</td><td>False</td><td>nan</td><td>-0.49948651426</td><td>nan</td><td>-3.39097959485</td><td>False</td><td>nan</td><td>-1.14338400184</td><td>27.5064485626</td><td>0.389613390393</td><td>False</td><td>27.669353061</td><td>1.5663819386</td><td>26.9648121312</td><td>0.253646842805</td><td>False</td><td>28.2011177971</td><td>2.28511108778</td><td>nan</td><td>-1.59894315288</td><td>False</td><td>nan</td><td>-19.605617734</td><td>False</td><td>29.1988092208</td><td>0.91800260691</td><td>False</td><td>28.6445934604</td><td>0.677000774018</td><td>False</td><td>27.4629403617</td><td>0.691873066945</td><td>False</td><td>28.0613552483</td><td>1.30534077015</td><td>False</td><td>False</td><td>0</td><td>False</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>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>False</td><td>0</td><td>-1</td><td>nan</td><td>nan</td><td>False</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>-1</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>0</td><td>-1</td><td>9</td><td>0.0012722</td><td>0.0064643</td><td>31.1386115219</td><td>5.51684055056</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table139944547050216-477817').dataTable()\");\n",
       "    $('#table139944547050216-477817').dataTable({\n",
       "        \"order\": [],\n",
       "        \"iDisplayLength\": 50,\n",
       "        \"aLengthMenu\": [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        \"pagingType\": \"full_numbers\"\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_catalogue[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XI - Saving the catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.write(\"{}/cfht_merged_catalogue_xmm-lss.fits\".format(TMP_DIR), overwrite=True)"
   ]
  }
 ],
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