{
 "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",
      "017bb1e (Mon Jun 18 14:58:59 2018 +0100)\n",
      "This notebook was executed on: \n",
      "2019-02-07 12:43:54.870832\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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/pyenv/versions/3.7.2/lib/python3.7/site-packages/matplotlib/__init__.py:855: MatplotlibDeprecationWarning: \n",
      "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n",
      "  \"found relative to the 'datapath' directory.\".format(key))\n",
      "/opt/pyenv/versions/3.7.2/lib/python3.7/site-packages/matplotlib/__init__.py:846: MatplotlibDeprecationWarning: \n",
      "The text.latex.unicode rcparam was deprecated in Matplotlib 2.2 and will be removed in 3.1.\n",
      "  \"2.2\", name=key, obj_type=\"rcparam\", addendum=addendum)\n",
      "/opt/pyenv/versions/3.7.2/lib/python3.7/site-packages/seaborn/apionly.py:9: UserWarning: As seaborn no longer sets a default style on import, the seaborn.apionly module is deprecated. It will be removed in a future version.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "import os\n",
    "import time\n",
    "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 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": [
    "### 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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.sum(master_catalogue['flag_merged'])/len(master_catalogue)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add HSC-PSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.sum(master_catalogue['flag_merged'])/len(master_catalogue)) "
   ]
  },
  {
   "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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.sum(master_catalogue['flag_merged'])/len(master_catalogue)) "
   ]
  },
  {
   "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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.sum(master_catalogue['flag_merged'])/len(master_catalogue)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add VIRCAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.sum(master_catalogue['flag_merged'])/len(master_catalogue)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add IRAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.sum(master_catalogue['flag_merged'])/len(master_catalogue)) "
   ]
  },
  {
   "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=\"table139724562217336-357677\" 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>34.7939347122776</td><td>-2.9337278347732303</td><td>False</td><td>-1</td><td>-1</td><td>0.0</td><td>0.0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</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>-1</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</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>34.7889409922776</td><td>-2.94131771477323</td><td>False</td><td>-1</td><td>-1</td><td>0.0</td><td>0.0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</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>-1</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</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>34.8690964322776</td><td>-3.01188159477323</td><td>False</td><td>-1</td><td>-1</td><td>0.0</td><td>0.0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</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>-1</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</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>34.2265307522776</td><td>-3.88177687477323</td><td>False</td><td>-1</td><td>-1</td><td>0.0</td><td>0.0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</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>-1</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</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>34.6264773122776</td><td>-4.87011075477323</td><td>False</td><td>-1</td><td>-1</td><td>0.0</td><td>0.0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</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>-1</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</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>36.9348618522776</td><td>-4.23782747477323</td><td>False</td><td>-1</td><td>-1</td><td>0.0</td><td>0.0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</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>-1</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</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>35.4645593722776</td><td>-3.24264912477323</td><td>False</td><td>-1</td><td>-1</td><td>0.0</td><td>0.0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</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>-1</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</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>36.3242755922776</td><td>-5.59929363477323</td><td>False</td><td>-1</td><td>-1</td><td>0.0</td><td>0.0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</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>-1</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</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>34.8238613522776</td><td>-2.97741051477323</td><td>False</td><td>-1</td><td>-1</td><td>0.0</td><td>0.0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</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>-1</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</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>34.6265266322776</td><td>-4.8707222947732305</td><td>False</td><td>-1</td><td>-1</td><td>0.0</td><td>0.0</td><td>-1</td><td>-1</td><td>-1</td><td>-1</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>-1</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</td><td>-1</td><td>-1</td><td>0.0</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(\"$('#table139724562217336-357677').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",
       "    $('#table139724562217336-357677').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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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: {'vhs_id', 'irac_intid', 'candels_id', 'decals_id', 'sxds_intid', 'vipers_id', 'des_id', 'swire_intid', 'dxs_id', 'cfhtls-deep_id', 'sxds_z_id', 'vircam_intid', 'sxds_r_id', 'wirds_id', 'sxds_v_id', 'cfhtls-wide_id', 'hsc-wide_id', 'ukidss_intid', 'sxds_i_id', 'video_id', 'ps1_id', 'viking_id', 'servs_intid', 'hsc-deep_id', 'hsc_intid', 'sdss_id', 'sxds_b_id', 'hsc-udeep_id', 'decam_intid', 'cfht_intid', 'specz_id', 'uds_id', 'sparcs_intid'}\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.sum(master_catalogue['flag_merged'])/len(master_catalogue)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n=0\n",
    "for sub_file in range(num_files):\n",
    "    print(\"Run: {}\".format(sub_file))\n",
    "    sub_catalogue = master_catalogue[n*split_length:(n+1)*split_length]\n",
    "    #print(n)\n",
    "    print(np.sum(sub_catalogue['flag_merged'])/len(sub_catalogue)) \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",
    "    print(np.sum(sub_catalogue['flag_merged'])/len(sub_catalogue)) \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",
    "        print(\"flag_merged_count: {}\".format(np.sum(flag_merged_column)))\n",
    "    print(np.sum(sub_catalogue['flag_merged'])/len(sub_catalogue)) \n",
    "    \n",
    "    sub_catalogue.add_column(Column(data=flag_merged_column, name=\"flag_merged\"))\n",
    "    sub_catalogue.remove_columns(flag_merged_columns)\n",
    "    print(np.sum(sub_catalogue['flag_merged'])/len(sub_catalogue)) \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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