{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XMM-LSS master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce the HELP master catalogue on XMM-LSS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "0246c5d (Thu Jan 25 17:01:47 2018 +0000) [with local modifications]\n",
      "This notebook was executed on: \n",
      "2018-05-04 13:04:48.043413\n"
     ]
    }
   ],
   "source": [
    "from herschelhelp_internal import git_version\n",
    "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))\n",
    "import datetime\n",
    "print(\"This notebook was executed on: \\n{}\".format(datetime.datetime.now()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "import os\n",
    "import time\n",
    "import gc\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column, Table, join\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": [
    "# SXDS now merged in with CANDELS in CFHT merge notebook\n",
    "#sxds = Table.read(\"{}/SXDS.fits\".format(TMP_DIR)\n",
    "#                 )[\"sxds_intid\", \n",
    "#                   \"sxds_ra\", \n",
    "#                   \"sxds_dec\",\n",
    "#                  # 'sxds_flag_merged'\n",
    "#                  ]                 # 1.11\n",
    "\n",
    "#WIRCAM:\n",
    "#cfht_wirds = Table.read(\"{}/CFHT-WIRDS.fits\".format(TMP_DIR)\n",
    "#                       )[\"wirds_intid\",\n",
    "#                         \"wirds_ra\",\n",
    "#                         \"wirds_dec\"]     # 1.3\n",
    "\n",
    "#Megacam:\n",
    "#candels = Table.read(\"{}/CANDELS.fits\".format(TMP_DIR)\n",
    "#                    )[\"candels_id\",\n",
    "#                      \"candels_ra\",\n",
    "#                      \"candels_dec\"]           # 1.1\n",
    "#cfhtls_wide = Table.read(\"{}/CFHTLS-WIDE.fits\".format(TMP_DIR)\n",
    "#                        )[\"cfhtls-wide_id\",\n",
    "#                          \"cfhtls-wide_ra\",\n",
    "#                          \"cfhtls-wide_dec\",\n",
    "#                          \"cfhtls-wide_stellarity\"]   # 1.4a\n",
    "#cfhtls_deep = Table.read(\"{}/CFHTLS-DEEP.fits\".format(TMP_DIR)\n",
    "#                        )[\"cfhtls-deep_id\",\n",
    "#                          \"cfhtls-deep_ra\",\n",
    "#                          \"cfhtls-deep_dec\",\n",
    "#                          \"cfhtls-deep_stellarity\"]   # 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",
    "#sparcs = Table.read(\"{}/SpARCS.fits\".format(TMP_DIR)\n",
    "#                   )['sparcs_intid', \n",
    "#                     'sparcs_ra', \n",
    "#                     'sparcs_dec', \n",
    "#                     'sparcs_stellarity']             # 1.12\n",
    "#\n",
    "#vipers = Table.read(\"{}/VIPERS.fits\".format(TMP_DIR)\n",
    "#                   )[\"vipers_id\",\n",
    "#                     \"vipers_ra\",\n",
    "#                     \"vipers_dec\"]             # 1.15\n",
    "cfht = Table.read(\"{}/cfht_merged_catalogue_xmm-lss.fits\".format(TMP_DIR)\n",
    "                   )['cfht_intid', \n",
    "                     \"candels_id\",\n",
    "                     \"cfhtls-wide_id\",\n",
    "                     \"cfhtls-deep_id\",\n",
    "                     'sparcs_intid',\n",
    "                     'wirds_id',\n",
    "                     'vipers_id',\n",
    "                     'sxds_intid',\n",
    "                     'sxds_b_id',\n",
    "                     'sxds_v_id',\n",
    "                     'sxds_r_id',\n",
    "                     'sxds_i_id',\n",
    "                     'sxds_z_id',\n",
    "                     'cfht_ra', \n",
    "                     'cfht_dec']             # 1.12\n",
    "\n",
    "\n",
    "\n",
    "#DECam:\n",
    "#decals = Table.read(\"{}/DECaLS.fits\".format(TMP_DIR)\n",
    "#                   )[\"decals_id\",\n",
    "#                     \"decals_ra\",\n",
    "#                     \"decals_dec\"]             # 1.6\n",
    "decam = Table.read(\"{}/decam_merged_catalogue_xmm-lss.fits\".format(TMP_DIR)\n",
    "                   )[\"decam_intid\",\n",
    "                     \"decals_id\",\n",
    "                     \"des_id\",\n",
    "                     \"decam_ra\",\n",
    "                     \"decam_dec\"]             # 1.6\n",
    "\n",
    "#Spitzer IRAC:\n",
    "#servs = Table.read(\"{}/SERVS.fits\".format(TMP_DIR)\n",
    "#                  )[\"servs_intid\",\n",
    "#                    \"servs_ra\",\n",
    "#                    \"servs_dec\"]               # 1.8\n",
    "#swire = Table.read(\"{}/SWIRE.fits\".format(TMP_DIR)\n",
    "#                  )[\"swire_intid\",\n",
    "#                    \"swire_ra\",\n",
    "#                    \"swire_dec\"]               # 1.7\n",
    "irac = Table.read(\"{}/irac_merged_catalogue_xmm-lss.fits\".format(TMP_DIR)\n",
    "                  )[\"irac_intid\",\n",
    "                    \"servs_intid\",\n",
    "                    \"swire_intid\",\n",
    "                    \"irac_ra\",\n",
    "                    \"irac_dec\"]               # 1.7\n",
    "\n",
    "\n",
    "#Hyper Suprime Cam:\n",
    "#hsc_wide = Table.read(\"{}/HSC-WIDE.fits\".format(TMP_DIR)\n",
    "#                     )[\"hsc-wide_id\",\n",
    "#                       \"hsc-wide_ra\",\n",
    "#                       \"hsc-wide_dec\", \n",
    "#                       \"hsc-wide_stellarity\"]         # 1.9a\n",
    "#hsc_deep = Table.read(\"{}/HSC-DEEP.fits\".format(TMP_DIR)\n",
    "#                     )[\"hsc-deep_id\",\n",
    "#                       \"hsc-deep_ra\",\n",
    "#                       \"hsc-deep_dec\", \n",
    "#                       \"hsc-deep_stellarity\"]         # 1.9b\n",
    "#hsc_udeep = Table.read(\"{}/HSC-UDEEP.fits\".format(TMP_DIR)\n",
    "#                      )[\"hsc-udeep_id\",\n",
    "#                        \"hsc-udeep_ra\",\n",
    "#                        \"hsc-udeep_dec\", \n",
    "#                        \"hsc-udeep_stellarity\"]       # 1.9c\n",
    "hsc = Table.read(\"{}/hsc_merged_catalogue_xmm-lss.fits\".format(TMP_DIR)\n",
    "                      )[\"hsc_intid\",\n",
    "                        \"hsc_ra\",\n",
    "                        \"hsc_dec\", \n",
    "                        \"hsc-wide_id\",\n",
    "                        \"hsc-deep_id\",\n",
    "                        \"hsc-udeep_id\"]       # 1.9c\n",
    "\n",
    "\n",
    "#GPC1:\n",
    "ps1 = Table.read(\"{}/PS1.fits\".format(TMP_DIR)\n",
    "                )[\"ps1_id\",\n",
    "                  \"ps1_ra\",\n",
    "                  \"ps1_dec\"]                   # 1.10\n",
    "\n",
    "# UKIDSS WFCAM:\n",
    "#dxs = Table.read(\"{}/UKIDSS-DXS.fits\".format(TMP_DIR)\n",
    "#                )['dxs_id',\n",
    "#                  'dxs_ra',\n",
    "#                  'dxs_dec', \n",
    "#                  'dxs_stellarity']            # 1.13\n",
    "#uds = Table.read(\"{}/UKIDSS-UDS.fits\".format(TMP_DIR)\n",
    "#                )['uds_id',\n",
    "#                  'uds_ra',\n",
    "#                  'uds_dec',\n",
    "#                  'uds_stellarity']            # 1.14\n",
    "ukidss = Table.read(\"{}/ukidss_merged_catalogue_xmm-lss.fits\".format(TMP_DIR)\n",
    "                )['ukidss_intid',\n",
    "                  'dxs_id',\n",
    "                  'uds_id',\n",
    "                  'ukidss_ra',\n",
    "                  'ukidss_dec',\n",
    "                 # 'ukidss_stellarity'\n",
    "                 ]            # 1.14\n",
    "\n",
    "\n",
    "\n",
    "#VIRCAM:\n",
    "#vhs = Table.read(\"{}/VISTA-VHS.fits\".format(TMP_DIR)\n",
    "#                )[\"vhs_id\",\n",
    "#                  \"vhs_ra\",\n",
    "#                  \"vhs_dec\",\n",
    "#                  \"vhs_stellarity\"]             # 1.16\n",
    "#video = Table.read(\"{}/VISTA-VIDEO.fits\".format(TMP_DIR)\n",
    "#                  )['video_id',\n",
    "#                    'video_ra',\n",
    "#                    'video_dec',\n",
    "#                    'video_stellarity',\n",
    "#                    'video_flag_gaia']         # 1.17\n",
    "#viking = Table.read(\"{}/VISTA-VIKING.fits\".format(TMP_DIR)\n",
    "#                   )[\"viking_id\",\n",
    "#                     \"viking_ra\",\n",
    "#                     \"viking_dec\",\n",
    "#                     \"viking_stellarity\",\n",
    "#                     \"viking_flag_gaia\"]       # 1.18\n",
    "vircam = Table.read(\"{}/vista_merged_catalogue_xmm-lss.fits\".format(TMP_DIR)\n",
    "                   )[\"vircam_intid\",\n",
    "                     \"vhs_id\",\n",
    "                     'video_id',\n",
    "                     \"viking_id\",\n",
    "                     \"vircam_ra\",\n",
    "                     \"vircam_dec\",\n",
    "                     #\"vircam_stellarity\",\n",
    "                     #\"vircam_flag_gaia\"\n",
    "                    ] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Merging tables\n",
    "\n",
    "We first merge the optical catalogues and then add the infrared ones. We start with PanSTARRS because it coevrs the whole field.\n",
    "\n",
    "At every step, we look at the distribution of the distances to the nearest source in the merged catalogue to determine the best crossmatching radius."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add PanSTARRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = ps1\n",
    "master_catalogue['ps1_ra'].name = 'ra'\n",
    "master_catalogue['ps1_dec'].name = 'dec'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CANDELS\n",
    "\n",
    "We now use CANDELS-UDS which must be individually merged with the merged catalogues since it has measurements from different instruments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#nb_merge_dist_plot(\n",
    "#    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "#    SkyCoord(candels['candels_ra'], candels['candels_dec'])\n",
    "#)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "#master_catalogue = merge_catalogues(master_catalogue, candels, \"candels_ra\", \"candels_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add CFHT\n",
    "\n",
    "We independently merge all the CFHT Megacam and WIRCAM and some CANDELS in CFHT_Merge notebook "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bHRvXsd5BLao8t/5ckhQOmVbWlTNXFwAAeYzQlWOvHh/UuEs153hrccLa+rh2M4IRAIC8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efd656e02e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(cfht['cfht_ra'], cfht['cfht_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",
    "                                    cfht, \n",
    "                                    \"cfht_ra\", \n",
    "                                    \"cfht_dec\", \n",
    "                                    radius=0.8*u.arcsec)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add HSC-PSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HiWTm+pl8rFhB9lob9ViJiIi4joJVDuKpNJDHYBX0aytQRETEhRSschBPZoJVPprXIXMy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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efd98491d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(hsc['hsc_ra'], hsc['hsc_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, hsc, \"hsc_ra\", \"hsc_dec\", radius=0.8*u.arcsec)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add DECam (DES and DECaLS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zIaz6cHm1dlSj63oa9cQrZ7V3cEKv2dRU6nJKgmB1CWPTc2VxTuBCNQGfNrfW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\nrAAAADxCsAIAAPAIwQoAAMAj/z+mw1BtIjGixQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efd59650f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(decam['decam_ra'], decam['decam_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, decam, \"decam_ra\", \"decam_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add SXDS\n",
    "\n",
    "This now happens in CFHT merging notebook because CANDELS contains Suprime fluxes which need merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# nb_merge_dist_plot(\n",
    "#     SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "#     SkyCoord(sxds['sxds_ra'], sxds['sxds_dec'])\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is strange that this does not peak at zero. This is bservable in the original band cross match. It implies there is a persistent offset. Perhaps each band should be astrometrically corrected before the original merge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#sxds['flag_merged'].name = 'flag_merged_sxds'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # Given the graph above, we use 0.8 arc-second radius\n",
    "# master_catalogue = merge_catalogues(master_catalogue, sxds, \"sxds_ra\", \"sxds_dec\", radius=1.0*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add UKIDSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nq+or8MzSgTLGcHV7NU8dPMvzR/oUrC5AwWqesh2rgM/JjlWAV7t1rI2IiDjrwKlRvrnz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\nIiKiQf4FNDZltlswXlgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efd5af06160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(ukidss['ukidss_ra'], ukidss['ukidss_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, ukidss, \"ukidss_ra\", \"ukidss_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add VIRCAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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snvTkjkCAgBmV4SBTsYQn309ERET8oWC1RLFEypM7ArOqIiGmFaxERESKmoLVEkXjSU/u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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efd5966ec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(vircam['vircam_ra'], vircam['vircam_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, vircam, \"vircam_ra\", \"vircam_dec\", radius=0.8*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add IRAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lXqfLkiw01pVeoUumIHHYtVPTizC2ruulg7209Q0zo0pNBkUk/Y70hvjaQzt5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efd600340f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(irac['irac_ra'], irac['irac_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 1 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, irac, \"irac_ra\", \"irac_dec\", radius=1.*u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning\n",
    "\n",
    "When we merge the catalogues, astropy masks the non-existent values (e.g. when a row comes only from a catalogue and has no counterparts in the other, the columns from the latest are masked for that row). We indicate to use NaN for masked values for floats columns, False for flag columns and -1 for ID columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    #print(col)\n",
    "    if (col.startswith(\"m_\") or col.startswith(\"merr_\") or col.startswith(\"f_\") or col.startswith(\"ferr_\")):\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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table139618302894600-530728\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>ps1_id</th><th>ra</th><th>dec</th><th>flag_merged</th><th>cfht_intid</th><th>candels_id</th><th>cfhtls-wide_id</th><th>cfhtls-deep_id</th><th>sparcs_intid</th><th>wirds_id</th><th>vipers_id</th><th>sxds_intid</th><th>sxds_b_id</th><th>sxds_v_id</th><th>sxds_r_id</th><th>sxds_i_id</th><th>sxds_z_id</th><th>hsc_intid</th><th>hsc-wide_id</th><th>hsc-deep_id</th><th>hsc-udeep_id</th><th>decam_intid</th><th>decals_id</th><th>des_id</th><th>ukidss_intid</th><th>dxs_id</th><th>uds_id</th><th>vircam_intid</th><th>vhs_id</th><th>video_id</th><th>viking_id</th><th>irac_intid</th><th>servs_intid</th><th>swire_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></tr></thead>\n",
       "<tr><td>0</td><td>104480347938550239</td><td>34.7939347123</td><td>-2.93372783477</td><td>False</td><td>-1</td><td>-1</td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>1</td><td>104470347888590909</td><td>34.7889409923</td><td>-2.94131771477</td><td>False</td><td>-1</td><td>-1</td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>2</td><td>104380348691566216</td><td>34.8690964323</td><td>-3.01188159477</td><td>False</td><td>-1</td><td>-1</td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>3</td><td>103340342264462121</td><td>34.2265307523</td><td>-3.88177687477</td><td>False</td><td>-1</td><td>-1</td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>4</td><td>102150346264006309</td><td>34.6264773123</td><td>-4.87011075477</td><td>False</td><td>-1</td><td>-1</td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>5</td><td>102910369348595147</td><td>36.9348618523</td><td>-4.23782747477</td><td>False</td><td>-1</td><td>-1</td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>6</td><td>104100354643169236</td><td>35.4645593723</td><td>-3.24264912477</td><td>False</td><td>-1</td><td>-1</td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>7</td><td>101280363238941270</td><td>36.3242755923</td><td>-5.59929363477</td><td>False</td><td>-1</td><td>-1</td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>8</td><td>104420348237537625</td><td>34.8238613523</td><td>-2.97741051477</td><td>False</td><td>-1</td><td>-1</td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>9</td><td>102150346263985667</td><td>34.6265266323</td><td>-4.87072229477</td><td>False</td><td>-1</td><td>-1</td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-1</td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td></td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</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",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table139618302894600-530728').dataTable()\");\n",
       "    \n",
       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
       "    \"optionalnum-asc\": astropy_sort_num,\n",
       "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
       "});\n",
       "\n",
       "    $('#table139618302894600-530728').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 1, 2, 3, 5, 6, 9, 10, 11, 12, 19, 20, 21, 23, 24, 26, 27, 29, 30, 31, 33, 34], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_catalogue[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III - Merging flags and stellarity\n",
    "\n",
    "This all happens at the end now after the catalogue has been cut into strips."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV - Adding E(B-V) column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    ebv(master_catalogue['ra'], master_catalogue['dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V - Adding HELP unique identifiers and field columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(gen_help_id(master_catalogue['ra'], master_catalogue['dec']),\n",
    "                                   name=\"help_id\"))\n",
    "master_catalogue.add_column(Column(np.full(len(master_catalogue), \"XMM-LSS\", dtype='<U18'),\n",
    "                                   name=\"field\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK!\n"
     ]
    }
   ],
   "source": [
    "# Check that the HELP Ids are unique\n",
    "if len(master_catalogue) != len(np.unique(master_catalogue['help_id'])):\n",
    "    print(\"The HELP IDs are not unique!!!\")\n",
    "else:\n",
    "    print(\"OK!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VI - Cross-matching with spec-z catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz =  Table.read(\"../../dmu23/dmu23_XMM-LSS/data/XMM-LSS-specz-v2.8.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AMMTA6GREFq4D5GWkYICWPk0FioiIRJI6r8eAvpFJLERsKtDn9ZCd7qNFU4EiIiIRpWAV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\nIiIiavIvpL3vcsSwXpwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efa1e658208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_merge_dist_plot(\n",
    "    SkyCoord(master_catalogue['ra'], master_catalogue['dec']),\n",
    "    SkyCoord(specz['ra'] * u.deg, specz['dec'] * u.deg)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = specz_merge(master_catalogue, specz, radius=1. * u.arcsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII.a Wavelength domain coverage\n",
    "\n",
    "We add a binary `flag_optnir_obs` indicating that a source was observed in a given wavelength domain:\n",
    "\n",
    "- 1 for observation in optical;\n",
    "- 2 for observation in near-infrared;\n",
    "- 4 for observation in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source observed both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: The observation flag is based on the creation of multi-order coverage maps from the catalogues, this may not be accurate, especially on the edges of the coverage.*\n",
    "\n",
    "*Note 2: Being on the observation coverage does not mean having fluxes in that wavelength domain. For sources observed in one domain but having no flux in it, one must take into consideration the different depths in the catalogue we are using.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#candels_moc =   MOC(filename=\"../../dmu0/dmu0_CANDELS-3D-HST/data/CANDELS-3D-HST_XMM-LSS_MOC.fits\") # 1.1\n",
    "candels_moc = MOC(filename=\"../../dmu0/dmu0_CANDELS-UDS/data/hlsp_candels_hst_wfc3_uds-tot-multiband_f160w_v1_MOC.fits\") # 1.2\n",
    "cfht_wirds_moc =  MOC(filename=\"../../dmu0/dmu0_CFHT-WIRDS/data/XMM-LSS_Ks-priors_MOC.fits\")        # 1.3\n",
    "cfhtls_wide_moc = MOC(filename=\"../../dmu0/dmu0_CFHTLS/data/CFHTLS-WIDE_XMM-LSS_MOC.fits\")          # 1.4a\n",
    "cfhtls_deep_moc = MOC(filename=\"../../dmu0/dmu0_CFHTLS/data/CFHTLS-DEEP_XMM-LSS_MOC.fits\")          # 1.4b\n",
    "cfhtls_moc = cfhtls_wide_moc + cfhtls_deep_moc\n",
    "#cfhtlens_moc = MOC(filename=\"../../dmu0/dmu0_CFHTLenS/data/CFHTLenS_XMM-LSS_MOC.fits\")              # 1.5\n",
    "decals_moc =  MOC(filename=\"../../dmu0/dmu0_DECaLS/data/DECaLS_XMM-LSS_MOC.fits\")                   # 1.6\n",
    "des_moc =  MOC(filename=\"../../dmu0/dmu0_DES/data/DES-DR1_XMM-LSS_MOC.fits\")                   # 1.6\n",
    "decam_moc = decals_moc + des_moc\n",
    "servs_moc = MOC(filename=\"../../dmu0/dmu0_DataFusion-Spitzer/data/DF-SERVS_XMM-LSS_MOC.fits\")       # 1.8\n",
    "swire_moc = MOC(filename=\"../../dmu0/dmu0_DataFusion-Spitzer/data/DF-SWIRE_XMM-LSS_MOC.fits\")       # 1.7\n",
    "hsc_wide_moc = MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_wide_XMM-LSS_MOC.fits\")              # 1.9a\n",
    "hsc_deep_moc = MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_deep_XMM-LSS_MOC.fits\")              # 1.9b\n",
    "hsc_udeep_moc = MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_uDeep_XMM-LSS_MOC.fits\")            # 1.9c\n",
    "hsc_moc = hsc_wide_moc + hsc_deep_moc + hsc_udeep_moc\n",
    "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_XMM-LSS_MOC.fits\")       # 1.10\n",
    "sxds_moc = MOC(filename=\"../../dmu0/dmu0_SXDS/data/dmu0_SXDS_MOC.fits\")                             # 1.11\n",
    "sparcs_moc = MOC(filename=\"../../dmu0/dmu0_SpARCS/data/SpARCS_HELP_XMM-LSS_MOC.fits\")               # 1.12\n",
    "dxs_moc = MOC(filename=\"../../dmu0/dmu0_UKIDSS-DXS_DR10plus/data/UKIDSS-DR10plus_XMM-LSS_MOC.fits\") # 1.13\n",
    "uds_moc =  MOC(filename=\"../../dmu0/dmu0_UKIDSS-UDS/data/UKIDSS-UDS_XMM-LSS_MOC.fits\")              # 1.14\n",
    "vipers_moc =  MOC(filename=\"../../dmu0/dmu0_VIPERS-MLS/data/VIPERS-MLS_20160502_MOC.fits\")          # 1.15\n",
    "vhs_moc =   MOC(filename=\"../../dmu0/dmu0_VISTA-VHS/data/VHS_XMM-LSS_MOC.fits\")                     # 1.16\n",
    "video_moc =  MOC(filename=\"../../dmu0/dmu0_VISTA-VIDEO-private/data/VIDEO-all_2016-04-14_fullcat_errfix_XMM-LSS_MOC.fits\")         # 1.17\n",
    "viking_moc = MOC(filename=\"../../dmu0/dmu0_VISTA-VIKING/data/VIKING_XMM-LSS_MOC.fits\")              # 1.18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    (candels_moc + \n",
    "     cfht_wirds_moc + \n",
    "     cfhtls_moc + \n",
    "     #cfhtlens_moc + \n",
    "     sparcs_moc + \n",
    "     decam_moc + \n",
    "     hsc_moc + \n",
    "     ps1_moc) )\n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    dxs_moc + uds_moc + vhs_moc + video_moc + viking_moc\n",
    ")\n",
    "\n",
    "was_observed_mir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    servs_moc + swire_moc\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(\n",
    "    Column(\n",
    "        1 * was_observed_optical + 2 * was_observed_nir + 4 * was_observed_mir,\n",
    "        name=\"flag_optnir_obs\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII.b Wavelength domain detection\n",
    "\n",
    "We add a binary `flag_optnir_det` indicating that a source was detected in a given wavelength domain:\n",
    "\n",
    "- 1 for detection in optical;\n",
    "- 2 for detection in near-infrared;\n",
    "- 4 for detection in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source detected both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: We use the total flux columns to know if the source has flux, in some catalogues, we may have aperture flux and no total flux.*\n",
    "\n",
    "To get rid of artefacts (chip edges, star flares, etc.) we consider that a source is detected in one wavelength domain when it has a flux value in **at least two bands**. That means that good sources will be excluded from this flag when they are on the coverage of only one band."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# SpARCS is a catalogue of sources detected in r (with fluxes measured at \n",
    "# this prior position in the other bands).  Thus, we are only using the r\n",
    "# CFHT band.\n",
    "# Check to use catalogue flags from HSC and PanSTARRS.\n",
    "#nb_optical_flux = np.zeros(len(master_catalogue), dtype=float)\n",
    "\n",
    "#nb_nir_flux = np.zeros(len(master_catalogue), dtype=float)\n",
    "\n",
    "#nb_mir_flux = np.zeros(len(master_catalogue), dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#has_optical_flux = nb_optical_flux >= 2\n",
    "#has_nir_flux = nb_nir_flux >= 2\n",
    "#has_mir_flux = nb_mir_flux >= 2\n",
    "\n",
    "#master_catalogue.add_column(\n",
    "#    Column(\n",
    "#        1 * has_optical_flux + 2 * has_nir_flux + 4 * has_mir_flux,\n",
    "#        name=\"flag_optnir_det\")\n",
    "#)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IX - Cross-identification table\n",
    "\n",
    "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogues. This can be used to easily get additional information from them.\n",
    "\n",
    "For convenience, we also cross-match the master list with the SDSS catalogue and add the objID associated with each source, if any. **TODO: should we correct the astrometry with respect to Gaia positions?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "315 master list rows had multiple associations.\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Addind SDSS ids\n",
    "#\n",
    "sdss = Table.read(\"../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_XMM-LSS.fits\")['objID', 'ra', 'dec']\n",
    "sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)\n",
    "idx_ml, d2d, _ = sdss_coords.match_to_catalog_sky(SkyCoord(master_catalogue['ra'], master_catalogue['dec']))\n",
    "idx_sdss = np.arange(len(sdss))\n",
    "\n",
    "# Limit the cross-match to 1 arcsec\n",
    "mask = d2d <= 1. * u.arcsec\n",
    "idx_ml = idx_ml[mask]\n",
    "idx_sdss = idx_sdss[mask]\n",
    "d2d = d2d[mask]\n",
    "nb_orig_matches = len(idx_ml)\n",
    "\n",
    "# In case of multiple associations of one master list object to an SDSS object, we keep only the\n",
    "# association to the nearest one.\n",
    "sort_idx = np.argsort(d2d)\n",
    "idx_ml = idx_ml[sort_idx]\n",
    "idx_sdss = idx_sdss[sort_idx]\n",
    "_, unique_idx = np.unique(idx_ml, return_index=True)\n",
    "idx_ml = idx_ml[unique_idx]\n",
    "idx_sdss = idx_sdss[unique_idx]\n",
    "print(\"{} master list rows had multiple associations.\".format(nb_orig_matches - len(idx_ml)))\n",
    "\n",
    "# Adding the ObjID to the master list\n",
    "master_catalogue.add_column(Column(data=np.full(len(master_catalogue), -1, dtype='>i8'), name=\"sdss_id\"))\n",
    "master_catalogue['sdss_id'][idx_ml] = sdss['objID'][idx_sdss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ps1_id', 'cfht_intid', 'candels_id', 'cfhtls-wide_id', 'cfhtls-deep_id', 'sparcs_intid', 'wirds_id', 'vipers_id', 'sxds_intid', 'sxds_b_id', 'sxds_v_id', 'sxds_r_id', 'sxds_i_id', 'sxds_z_id', 'hsc_intid', 'hsc-wide_id', 'hsc-deep_id', 'hsc-udeep_id', 'decam_intid', 'decals_id', 'des_id', 'ukidss_intid', 'dxs_id', 'uds_id', 'vircam_intid', 'vhs_id', 'video_id', 'viking_id', 'irac_intid', 'servs_intid', 'swire_intid', 'help_id', 'specz_id', 'sdss_id']\n"
     ]
    }
   ],
   "source": [
    "id_names = []\n",
    "for col in master_catalogue.colnames:\n",
    "    if '_id' in col:\n",
    "        id_names += [col]\n",
    "    if '_intid' in col:\n",
    "        id_names += [col]\n",
    "        \n",
    "print(id_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_xmm-lss{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)\n",
    "id_names.remove('help_id')\n",
    "#master_catalogue.remove_columns(id_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X - Adding HEALPix index\n",
    "\n",
    "We are adding a column with a HEALPix index at order 13 associated with each source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.add_column(Column(\n",
    "    data=coords_to_hpidx(master_catalogue['ra'], master_catalogue['dec'], order=13),\n",
    "    name=\"hp_idx\"\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XI - Saving the catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns = [\"help_id\", \"field\", \"ra\", \"dec\", \"hp_idx\"]\n",
    "\n",
    "bands = [column[5:] for column in master_catalogue.colnames if 'f_ap' in column]\n",
    "for band in bands:\n",
    "    columns += [\"f_ap_{}\".format(band), \"ferr_ap_{}\".format(band),\n",
    "                \"m_ap_{}\".format(band), \"merr_ap_{}\".format(band),\n",
    "                \"f_{}\".format(band), \"ferr_{}\".format(band),\n",
    "                \"m_{}\".format(band), \"merr_{}\".format(band),\n",
    "                \"flag_{}\".format(band)]    \n",
    "    \n",
    "columns += [\"stellarity\", \"stellarity_origin\", \"flag_cleaned\", \"flag_merged\", \"flag_gaia\", \"flag_optnir_obs\", \"flag_optnir_det\", \n",
    "            \"zspec\", \"zspec_qual\", \"zspec_association_flag\", \"ebv\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing columns: {'sparcs_intid', 'candels_id', 'sxds_i_id', 'dxs_id', 'sxds_intid', 'cfhtls-wide_id', 'vhs_id', 'hsc_intid', 'vircam_intid', 'sxds_r_id', 'cfht_intid', 'viking_id', 'sxds_z_id', 'servs_intid', 'sxds_v_id', 'hsc-deep_id', 'cfhtls-deep_id', 'vipers_id', 'swire_intid', 'uds_id', 'hsc-wide_id', 'decals_id', 'wirds_id', 'sdss_id', 'specz_id', 'ukidss_intid', 'sxds_b_id', 'des_id', 'video_id', 'irac_intid', 'decam_intid', 'hsc-udeep_id', 'ps1_id'}\n"
     ]
    }
   ],
   "source": [
    "# We check for columns in the master catalogue that we will not save to disk.\n",
    "print(\"Missing columns: {}\".format(set(master_catalogue.colnames) - set(columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#master_catalogue[columns].write(\"{}/master_catalogue_xmm-lss_low-memory{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XII - folding in the photometry\n",
    "On XMM-LSS there is too much data to load all in to memory at once so we perform the cross matching without photometry columns. Only now do we fold in the photometry data by first cutting the catalogue up in to manageable sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88\n"
     ]
    }
   ],
   "source": [
    "split_length = 100000 #number of sources to include in every sub catalogue\n",
    "num_files = int(np.ceil(len(master_catalogue)/split_length))\n",
    "print(num_files)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Sort catalogue by HELP id so that it is split up in RA strips\n",
    "master_catalogue.sort('help_id')\n",
    "#Remove all the old ids as they interfere with join\n",
    "old_id_names = [\n",
    " 'candels_id',\n",
    " 'cfhtls-wide_id',\n",
    " 'cfhtls-deep_id',\n",
    " 'sparcs_intid',\n",
    " 'wirds_id',\n",
    " 'vipers_id',\n",
    " 'sxds_intid',\n",
    " 'sxds_b_id',\n",
    " 'sxds_v_id',\n",
    " 'sxds_r_id',\n",
    " 'sxds_i_id',\n",
    " 'sxds_z_id',\n",
    "    \n",
    " 'hsc-wide_id',\n",
    " 'hsc-deep_id',\n",
    " 'hsc-udeep_id',\n",
    "    \n",
    " 'des_id',\n",
    " 'decals_id',\n",
    "\n",
    " 'dxs_id',\n",
    " 'uds_id',\n",
    " \n",
    " 'vhs_id',\n",
    " 'video_id',\n",
    " 'viking_id',\n",
    "\n",
    " 'servs_intid',\n",
    " 'swire_intid']\n",
    "master_catalogue.remove_columns(old_id_names)\n",
    "# The old id names will be added back in by \n",
    "# the join so I leave them in the list here to be \n",
    "# removed before the save of the sub catalogue\n",
    "# id_names.remove(old_id_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#There is candels data on three seperate merged catalogues so duplicates must be removed from two\n",
    "irac_merged = Table.read(\"{}/irac_merged_catalogue_xmm-lss.fits\".format(TMP_DIR))\n",
    "irac_merged.remove_columns(['candels_id',\n",
    "                            'candels_stellarity',\n",
    "                            'candels_flag_cleaned',\n",
    "                            'candels_flag_gaia'])\n",
    "\n",
    "ukidss_merged = Table.read(\"{}/ukidss_merged_catalogue_xmm-lss.fits\".format(TMP_DIR))\n",
    "ukidss_merged.remove_columns(['candels_id',\n",
    "                              'candels_stellarity',\n",
    "                              'candels_flag_cleaned',\n",
    "                              'candels_flag_gaia'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "surveys = [\n",
    "    \n",
    "\n",
    "    #['sxds',         \"SXDS.fits\",           Table.read(\"{}/SXDS.fits\".format(TMP_DIR)),          'sxds_intid'], \n",
    "    #['candels',      \"CANDELS.fits\",        Table.read(\"{}/CANDELS.fits\".format(TMP_DIR)),       'candels_id'],\n",
    "    #['cfhtls_wide',  \"CFHTLS-WIDE.fits\",    Table.read(\"{}/CFHTLS-WIDE.fits\".format(TMP_DIR)),   'cfhtls-wide_id'],\n",
    "    #['cfhtls_deep',  \"CFHTLS-DEEP.fits\",    Table.read(\"{}/CFHTLS-DEEP.fits\".format(TMP_DIR)),   'cfhtls-deep_id'],\n",
    "    #['sparcs',       \"SpARCS.fits\",         Table.read(\"{}/SpARCS.fits\".format(TMP_DIR)),        'sparcs_intid'], \n",
    "    #['cfht_wirds',   \"CFHT-WIRDS.fits\",     Table.read(\"{}/CFHT-WIRDS.fits\".format(TMP_DIR)),    'wirds_intid' ],\n",
    "    #['vipers',       \"VIPERS.fits\",         Table.read(\"{}/VIPERS.fits\".format(TMP_DIR)),        'vipers_id'],\n",
    "    ['cfht',       \n",
    "     \"cfht_merged_catalogue_xmm-lss.fits\",        \n",
    "     Table.read(\"{}/cfht_merged_catalogue_xmm-lss.fits\".format(TMP_DIR)),        \n",
    "     'cfht_intid'], \n",
    "    \n",
    "    #['decals',       \"DECaLS.fits\",         Table.read(\"{}/DECaLS.fits\".format(TMP_DIR)),        'decals_id'],\n",
    "    ['decam',       \n",
    "     \"decam_merged_catalogue_xmm-lss.fits\",         \n",
    "     Table.read(\"{}/decam_merged_catalogue_xmm-lss.fits\".format(TMP_DIR)),        \n",
    "     'decam_intid'],\n",
    "    \n",
    "    #['servs',        \"SERVS.fits\",          Table.read(\"{}/SERVS.fits\".format(TMP_DIR)),         'servs_intid'],\n",
    "    #['swire',        \"SWIRE.fits\",          Table.read(\"{}/SWIRE.fits\".format(TMP_DIR)),         'swire_intid'],  \n",
    "    ['irac',       \n",
    "     \"irac_merged_catalogue_xmm-lss.fits\",\n",
    "     irac_merged,        \n",
    "     'irac_intid'], \n",
    "    \n",
    "    #['hsc_wide ',    \"HSC-WIDE.fits\",       Table.read(\"{}/HSC-WIDE.fits\".format(TMP_DIR)),      'hsc-wide_id' ],\n",
    "    #['hsc_deep',     \"HSC-DEEP.fits\",       Table.read(\"{}/HSC-DEEP.fits\".format(TMP_DIR)),      'hsc-deep_id'],\n",
    "    #['hsc_udeep',    \"HSC-UDEEP.fits\",      Table.read(\"{}/HSC-UDEEP.fits\".format(TMP_DIR)),     'hsc-udeep_id'],  \n",
    "    ['hsc',       \n",
    "     \"hsc_merged_catalogue_xmm-lss.fits\",        \n",
    "     Table.read(\"{}/hsc_merged_catalogue_xmm-lss.fits\".format(TMP_DIR)),        \n",
    "     'hsc_intid'], \n",
    "    \n",
    "    ['ps1',          \"PS1.fits\",            Table.read(\"{}/PS1.fits\".format(TMP_DIR)),           'ps1_id'],    \n",
    "    \n",
    "                  \n",
    "    \n",
    "    #['dxs',          \"UKIDSS-DXS.fits\",     Table.read(\"{}/UKIDSS-DXS.fits\".format(TMP_DIR)),    'dxs_id'],          \n",
    "    #['uds',          \"UKIDSS-UDS.fits\",     Table.read(\"{}/UKIDSS-UDS.fits\".format(TMP_DIR)),    'uds_id'],    \n",
    "    ['ukidss',       \n",
    "     \"ukidss_merged_catalogue_xmm-lss.fits\",        \n",
    "     ukidss_merged,         \n",
    "     'ukidss_intid'], \n",
    "    \n",
    "   \n",
    "    \n",
    "    #['vhs',          \"VISTA-VHS.fits\",      Table.read(\"{}/VISTA-VHS.fits\".format(TMP_DIR)),     'vhs_id'],           \n",
    "    #['video',        \"VISTA-VIDEO.fits\",    Table.read(\"{}/VISTA-VIDEO.fits\".format(TMP_DIR)),   'video_id'],       \n",
    "    #['viking',       \"VISTA-VIKING.fits\",   Table.read(\"{}/VISTA-VIKING.fits\".format(TMP_DIR)), 'viking_id']  \n",
    "    ['vircam',       \n",
    "     \"vista_merged_catalogue_xmm-lss.fits\",        \n",
    "     Table.read(\"{}/vista_merged_catalogue_xmm-lss.fits\".format(TMP_DIR)),        \n",
    "     'vircam_intid']\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n=0\n",
    "for sub_file in range(num_files):\n",
    "    \n",
    "    sub_catalogue = master_catalogue[n*split_length:(n+1)*split_length]\n",
    "    #print(n)\n",
    "    for survey in surveys:\n",
    "        #print(survey[0])\n",
    "        sub_catalogue = join(sub_catalogue, \n",
    "                               survey[2], #Table.read(\"{}/{}\".format(TMP_DIR, survey[1])),\n",
    "                               join_type='left',\n",
    "                               metadata_conflicts='silent',\n",
    "                               keys=survey[3]\n",
    "                            )\n",
    "        \n",
    "        \n",
    "        \n",
    "    #Each pristine catalogue contains a flag indicating if the source was associated to a another nearby source that was removed during the cleaning process. We merge these flags in a single one.\n",
    "\n",
    "\n",
    "    flag_cleaned_columns = [column for column in sub_catalogue.colnames\n",
    "                            if 'flag_cleaned' in column]\n",
    "\n",
    "    flag_column = np.zeros(len(sub_catalogue), dtype=bool)\n",
    "    for column in flag_cleaned_columns:\n",
    "        flag_column |= sub_catalogue[column]\n",
    "    \n",
    "    sub_catalogue.add_column(Column(data=flag_column, name=\"flag_cleaned\"))\n",
    "    sub_catalogue.remove_columns(flag_cleaned_columns)\n",
    "    #combining the flag_merged column which contains information regarding multiple associations\n",
    "    \n",
    "    \n",
    "    sub_catalogue['flag_merged'].name = 'flag_merged_tmp'\n",
    "    flag_merged_columns = [column for column in sub_catalogue.colnames\n",
    "                            if 'flag_merged' in column]\n",
    "    \n",
    "    flag_merged_column = np.zeros(len(sub_catalogue), dtype=bool)\n",
    "    for column in flag_merged_columns:\n",
    "        flag_merged_column |= sub_catalogue[column]\n",
    "    \n",
    "    sub_catalogue.add_column(Column(data=flag_merged_column, name=\"flag_merged\"))\n",
    "    sub_catalogue.remove_columns(flag_merged_columns)\n",
    "\n",
    "    #Each pristine catalogue contains a flag indicating the probability of a source being a Gaia object (0: not a Gaia object, 1: possibly, 2: probably, 3: definitely). We merge these flags taking the highest value.\n",
    "\n",
    "\n",
    "    flag_gaia_columns = [column for column in sub_catalogue.colnames\n",
    "                         if 'flag_gaia' in column]\n",
    "\n",
    "    sub_catalogue.add_column(Column(\n",
    "        data=np.max([sub_catalogue[column] for column in flag_gaia_columns], axis=0),\n",
    "        name=\"flag_gaia\"\n",
    "    ))\n",
    "    sub_catalogue.remove_columns(flag_gaia_columns)\n",
    "    #Each prisitine catalogue may contain one or several stellarity columns indicating the #probability (0 to 1) of each source being a star. We merge these columns taking the highest value. We keep trace of the origin of the stellarity.\n",
    "\n",
    "\n",
    "\n",
    "    stellarity_columns = [column for column in sub_catalogue.colnames\n",
    "                          if 'stellarity' in column]\n",
    "\n",
    "    #print(\", \".join(stellarity_columns))\n",
    "\n",
    "\n",
    "\n",
    "    # We create an masked array with all the stellarities and get the maximum value, as well as its\n",
    "    # origin.  Some sources may not have an associated stellarity.\n",
    "    stellarity_array = np.array([sub_catalogue[column] for column in stellarity_columns])\n",
    "    stellarity_array = np.ma.masked_array(stellarity_array, np.isnan(stellarity_array))\n",
    "\n",
    "    max_stellarity = np.max(stellarity_array, axis=0)\n",
    "    max_stellarity.fill_value = np.nan\n",
    "\n",
    "    no_stellarity_mask = max_stellarity.mask\n",
    "\n",
    "    sub_catalogue.add_column(Column(data=max_stellarity.filled(), name=\"stellarity\"))\n",
    "\n",
    "    stellarity_origin = np.full(len(sub_catalogue), \"NO_INFORMATION\", dtype=\"S20\")\n",
    "    stellarity_origin[~no_stellarity_mask] = np.array(stellarity_columns)[np.argmax(stellarity_array, axis=0)[~no_stellarity_mask]]\n",
    "\n",
    "    sub_catalogue.add_column(Column(data=stellarity_origin, name=\"stellarity_origin\"))\n",
    "\n",
    "    sub_catalogue.remove_columns(stellarity_columns)\n",
    "\n",
    "    for col in sub_catalogue.colnames:\n",
    "        #print(col)\n",
    "        if (col.startswith(\"m_\") or col.startswith(\"merr_\") or col.startswith(\"f_\") or col.startswith(\"ferr_\")):\n",
    "            sub_catalogue[col] = sub_catalogue[col].astype(float)\n",
    "            sub_catalogue[col].fill_value = np.nan\n",
    "        elif \"flag\" in col:\n",
    "            sub_catalogue[col].fill_value = 0\n",
    "        elif \"id\" in col:\n",
    "            sub_catalogue[col].fill_value = -1\n",
    "        \n",
    "    sub_catalogue = sub_catalogue.filled()\n",
    "        \n",
    "        \n",
    "    nb_optical_flux = (\n",
    "        # CANDELS\n",
    "        1 * ~np.isnan(sub_catalogue['f_acs_f606w']) + \n",
    "        1 * ~np.isnan(sub_catalogue['f_acs_f814w']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_wfc3_f125w']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_wfc3_f160w']) +\n",
    "\n",
    "        # DES and DECaLS\n",
    "        1 * ~np.isnan(sub_catalogue['f_decam_g']) + \n",
    "        1 * ~np.isnan(sub_catalogue['f_decam_r']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_decam_i']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_decam_z']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_decam_y']) +\n",
    "        # HSC\n",
    "        1 * ~np.isnan(sub_catalogue['f_suprime_g']) + \n",
    "        1 * ~np.isnan(sub_catalogue['f_suprime_r']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_suprime_i']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_suprime_z']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_suprime_y']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_suprime_n921']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_suprime_n816']) +\n",
    "        # PanSTARRS\n",
    "        1 * ~np.isnan(sub_catalogue['f_gpc1_g']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_gpc1_r']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_gpc1_i']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_gpc1_z']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_gpc1_y']) +\n",
    "        # CFHT\n",
    "        1 * ~np.isnan(sub_catalogue['f_megacam_u']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_megacam_g']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_megacam_r']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_megacam_z']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_megacam_y']) \n",
    "    )\n",
    "\n",
    "    nb_nir_flux = (\n",
    "        # CFHT-WIRCAM\n",
    "        1 * ~np.isnan(sub_catalogue['f_wircam_j']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_wircam_h']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_wircam_ks']) +\n",
    "        # UKIDSS\n",
    "        1 * ~np.isnan(sub_catalogue['f_ukidss_j']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_ukidss_h']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_ukidss_k']) +\n",
    "        # VISTA\n",
    "        1 * ~np.isnan(sub_catalogue['f_vista_z']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_vista_y']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_vista_j']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_vista_h']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_vista_ks'])\n",
    "    )\n",
    "\n",
    "    nb_mir_flux = (\n",
    "        1 * ~np.isnan(sub_catalogue['f_irac_i1']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_irac_i2']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_irac_i3']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_irac_i4']) \n",
    "    )\n",
    "        \n",
    "    has_optical_flux = nb_optical_flux >= 2\n",
    "    has_nir_flux = nb_nir_flux >= 2\n",
    "    has_mir_flux = nb_mir_flux >= 2\n",
    "\n",
    "    sub_catalogue.add_column(\n",
    "        Column(\n",
    "            1 * has_optical_flux + 2 * has_nir_flux + 4 * has_mir_flux,\n",
    "            name=\"flag_optnir_det\")\n",
    "    )\n",
    "        \n",
    "    #print('Finished join')\n",
    "    sub_catalogue.remove_columns(id_names)\n",
    "    #sub_catalogue.remove_columns(['sxds_b_id', 'sxds_v_id', 'sxds_r_id', 'sxds_i_id', 'sxds_z_id'])\n",
    "    \n",
    "    for col in sub_catalogue.colnames:\n",
    "        if col.endswith('_ra'):\n",
    "            sub_catalogue.remove_column(col)\n",
    "        if col.endswith('_dec'):\n",
    "            sub_catalogue.remove_column(col)\n",
    "        if '_vircam_' in col:\n",
    "            sub_catalogue.rename_column(col, col.replace('_vircam_', '_vista_'))\n",
    "            \n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    sub_catalogue.write(\"{}/tiles/sub_catalogue_xmm-lss{}_{}.fits\".format(OUT_DIR, SUFFIX, n), overwrite=True)\n",
    "    \n",
    "    \n",
    "    del sub_catalogue\n",
    "    del flag_column\n",
    "    del flag_merged_column\n",
    "    gc.collect()\n",
    "    time.sleep(10)\n",
    "    \n",
    "    n += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to generate final catalogue\n",
    "After this notebook has been run there will be a set of sub catalogues in data/tiles/\n",
    "\n",
    "These need to be stacked using stilts:\n",
    "\n",
    "<pre>\n",
    "suffix=20180111\n",
    "\n",
    "ls ./data/tiles/sub_catalogue_xmm-lss_$suffix_*.fits > ./data/tiles/fits_list_$suffix.lis\n",
    "\n",
    "stilts tcat in=@./data/tiles/fits_list_$suffix.lis out=./data/master_catalogue_xmm-lss_$suffix.fits\n",
    "</pre>\n",
    "\n",
    "For many purposes this file may be too large. In order to run checks and diagnostics we typically take a subset using something like:\n",
    "\n",
    "<pre>\n",
    "stilts tpipe cmd='every 10' ./data/master_catalogue_xmm-lss_$suffix.fits omode=out out=./data/master_catalogue_xmm-lss_RANDOM10PCSAMPLE_$suffix.fits\n",
    "</pre>"
   ]
  }
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