{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Herschel Stripe 82 master catalogue\n",
    "\n",
    "This notebook presents the merge of the various pristine catalogues to produce HELP mater catalogue on Herschel Stripe 82."
   ]
  },
  {
   "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",
      "2019-02-05 18:15:34.417063\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",
    "\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",
    "\n",
    "SUFFIX = os.environ.get('SUFFIX', time.strftime(\"_%Y%m%d\"))\n",
    "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\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": [
    "hsc =    Table.read(\"{}/HSC-SSP.fits\".format(TMP_DIR)   )[\"hsc_id\",      \"hsc_ra\",    \"hsc_dec\",\n",
    "                                                          \"hsc_flag_gaia\", \"hsc_stellarity\"]\n",
    "#vhs =    Table.read(\"{}/VISTA-VHS.fits\".format(TMP_DIR) )[\"vhs_id\",      \"vhs_ra\",    \"vhs_dec\",    \n",
    "#                                                          \"vhs_stellarity\", \"vhs_flag_gaia\"]\n",
    "#vics82 = Table.read(\"{}/VICS82.fits\".format(TMP_DIR)    )[\"vics82_id\",   \"vics82_ra\", \"vics82_dec\", \n",
    "#                                                          \"vics82_stellarity\", \"vics82_flag_gaia\"]\n",
    "vista = Table.read(\"{}/vista_merged_catalogue_herschel-stripe-82.fits\".format(TMP_DIR)    )[\n",
    "                                                           \"vics82_id\", \"vhs_id\", \"vista_intid\",  \n",
    "                                                           \"vista_ra\", \"vista_dec\", \n",
    "                                                          \"vista_stellarity\", \"vista_flag_gaia\"]\n",
    "\n",
    "las =    Table.read(\"{}/UKIDSS-LAS.fits\".format(TMP_DIR))[\"las_id\",      \"las_ra\",    \"las_dec\",    \n",
    "                                                          \"las_stellarity\", \"las_flag_gaia\"]\n",
    "  \n",
    "ps1 =    Table.read(\"{}/PS1.fits\".format(TMP_DIR)       )[\"ps1_id\",      \"ps1_ra\",    \"ps1_dec\",\n",
    "                                                         \"ps1_flag_gaia\"]\n",
    "#shela =  Table.read(\"{}/SHELA.fits\".format(TMP_DIR)     )[\"shela_intid\", \"shela_ra\",  \"shela_dec\"]\n",
    "#spies =  Table.read(\"{}/SpIES.fits\".format(TMP_DIR)     )[\"spies_intid\", \"spies_ra\",  \"spies_dec\",  \"spies_stellarity_irac2\"]\n",
    "irac =   Table.read(\"{}/irac_merged_catalogue_herschel-stripe-82.fits\".format(TMP_DIR)      )[\n",
    "                                                          \"irac_intid\",  \"irac_ra\",   \"irac_dec\",   \n",
    "                                                          \"irac_stellarity\", \"irac_flag_gaia\",\n",
    "                                                         \"shela_intid\", \"spies_intid\"]\n",
    "#decals = Table.read(\"{}/DECaLS.fits\".format(TMP_DIR)    )[\"decals_id\",   \"decals_ra\", \"decals_dec\", \n",
    "#                                                          \"decals_stellarity\", \"decals_flag_gaia\"]\n",
    "decam = Table.read(\"{}/decam_merged_catalogue_herschel-stripe-82.fits\".format(TMP_DIR)    )[\n",
    "                                                          \"decam_intid\", \"des_id\", \"decals_id\",   \n",
    "                                                          \"decam_ra\", \"decam_dec\", \n",
    "                                                          \"decam_stellarity\", \"decam_flag_gaia\"]\n",
    "rcs =    Table.read(\"{}/RCSLenS.fits\".format(TMP_DIR)   )[\"rcs_id\",      \"rcs_ra\",    \"rcs_dec\",    \n",
    "                                                          \"rcs_stellarity\", \"rcs_flag_gaia\"]\n",
    "#We choose to use the official SDSS not IAC because it has greater coverage\n",
    "#SDSS-S82_IAC.fits\n",
    "#SDSS-S82.fits\n",
    "sdss =   Table.read(\"{}/SDSS-S82.fits\".format(TMP_DIR)  )[\"sdss_id\",     \"sdss_ra\",   \"sdss_dec\",   \n",
    "                                                          \"sdss_stellarity\", \"sdss_flag_gaia\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Merging tables\n",
    "\n",
    "We first merge the optical catalogues and then add the infrared ones: HSC, VHS, VICS82, UKIDSS-LAS, PanSTARRS, SHELA, SpIES.\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": [
    "### HSC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue = hsc\n",
    "master_catalogue['hsc_ra'].name = 'ra'\n",
    "master_catalogue['hsc_dec'].name = 'dec'\n",
    "del hsc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add VISTA (VHS and VICS82 pre-merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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(vista['vista_ra'], vista['vista_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, vista, \"vista_ra\", \"vista_dec\", radius=0.8*u.arcsec)\n",
    "del vista"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add LAS"
   ]
  },
  {
   "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(las['las_ra'], las['las_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, las, \"las_ra\", \"las_dec\", radius=0.8*u.arcsec)\n",
    "del las"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add PanSTARRS"
   ]
  },
  {
   "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(ps1['ps1_ra'], ps1['ps1_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, ps1, \"ps1_ra\", \"ps1_dec\", radius=0.8*u.arcsec)\n",
    "del ps1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add SDSS\n",
    "We are waiting for a new SDSS-82 catalogue, which does not suffer from the issue of multiple sources per object due to including all exposure extractions."
   ]
  },
  {
   "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(sdss['sdss_ra'], sdss['sdss_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, sdss, \"sdss_ra\", \"sdss_dec\", radius=0.8*u.arcsec)\n",
    "del sdss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add DECam (DECaLS and DES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "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)\n",
    "del decam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add RCSLenS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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(rcs['rcs_ra'], rcs['rcs_dec'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Given the graph above, we use 0.8 arc-second radius\n",
    "master_catalogue = merge_catalogues(master_catalogue, rcs, \"rcs_ra\", \"rcs_dec\", radius=0.8*u.arcsec)\n",
    "del rcs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add IRAC (SHELA and SpIES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "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=1.5*u.arcsec)\n",
    "del irac"
   ]
  },
  {
   "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": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for col in master_catalogue.colnames:\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",
    "    if \"stellarity\" in col:\n",
    "        master_catalogue[col].fill_value = np.nan\n",
    "    elif \"flag\" in col:\n",
    "        master_catalogue[col].fill_value = 0\n",
    "    elif \"id\" in col:\n",
    "        master_catalogue[col].fill_value = -1\n",
    "        \n",
    "master_catalogue = master_catalogue.filled()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table140482000132416-367143\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>hsc_id</th><th>ra</th><th>dec</th><th>hsc_flag_gaia</th><th>hsc_stellarity</th><th>flag_merged</th><th>vics82_id</th><th>vhs_id</th><th>vista_intid</th><th>vista_stellarity</th><th>vista_flag_gaia</th><th>las_id</th><th>las_stellarity</th><th>las_flag_gaia</th><th>ps1_id</th><th>ps1_flag_gaia</th><th>sdss_id</th><th>sdss_stellarity</th><th>sdss_flag_gaia</th><th>decam_intid</th><th>des_id</th><th>decals_id</th><th>decam_stellarity</th><th>decam_flag_gaia</th><th>rcs_id</th><th>rcs_stellarity</th><th>rcs_flag_gaia</th><th>irac_intid</th><th>irac_stellarity</th><th>irac_flag_gaia</th><th>shela_intid</th><th>spies_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></tr></thead>\n",
       "<tr><td>0</td><td>41618864458468676</td><td>351.7078674102876</td><td>-0.8467452047263985</td><td>2</td><td>1.0</td><td>True</td><td>0.0</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>0.0</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>1</td><td>41619564538122633</td><td>350.64946504091785</td><td>-0.45142719892317434</td><td>2</td><td>0.0</td><td>True</td><td>0.0</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>0.0</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>2</td><td>41623687706742799</td><td>352.65351552421663</td><td>-0.2780813675567576</td><td>0</td><td>0.0</td><td>False</td><td>0.0</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>0.0</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>3</td><td>41619281070291584</td><td>351.12786758280777</td><td>-0.752104706105594</td><td>2</td><td>0.0</td><td>True</td><td>0.0</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>0.0</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>4</td><td>42687997782543665</td><td>351.08957782422016</td><td>0.3192921520835247</td><td>0</td><td>0.0</td><td>True</td><td>0.0</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>0.0</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>5</td><td>42692675001946330</td><td>352.27360564225273</td><td>0.6296688128811395</td><td>0</td><td>0.0</td><td>True</td><td>0.0</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>0.0</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>6</td><td>41619156516240934</td><td>351.326841113071</td><td>-0.14068390693201424</td><td>0</td><td>0.0</td><td>False</td><td>0.0</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>0.0</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>7</td><td>42687856048627738</td><td>351.25621500741954</td><td>0.1215166324244826</td><td>0</td><td>0.0</td><td>True</td><td>0.0</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>0.0</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>8</td><td>41619152221266635</td><td>351.37805431803594</td><td>-0.42224428943796183</td><td>0</td><td>0.0</td><td>True</td><td>0.0</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>0.0</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td></tr>\n",
       "<tr><td>9</td><td>41618731314478744</td><td>351.79381153914903</td><td>-0.6987405573794816</td><td>0</td><td>0.0</td><td>False</td><td>0.0</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>0</td><td>-1</td><td>nan</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>nan</td><td>0</td><td>0.0</td><td>nan</td><td>0</td><td>-1</td><td>nan</td><td>0</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(\"$('#table140482000132416-367143').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",
       "    $('#table140482000132416-367143').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 29, 30, 31], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_catalogue[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III - Merging flags and stellarity\n",
    "\n",
    "Each pristine catalogue contains a flag indicating if the source was associated to a another nearby source that was removed during the cleaning process.  We merge these flags in a single one.\n",
    "\n",
    "This now happens in the final merging loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each pristine catalogue contains a flag indicating the probability of a source being a Gaia object (0: not a Gaia object, 1: possibly, 2: probably, 3: definitely).  We merge these flags taking the highest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "flag_gaia_columns = [column for column in master_catalogue.colnames\n",
    "                     if 'flag_gaia' in column]\n",
    "\n",
    "master_catalogue.add_column(Column(\n",
    "    data=np.max([master_catalogue[column] for column in flag_gaia_columns], axis=0),\n",
    "    name=\"flag_gaia\"\n",
    "))\n",
    "master_catalogue.remove_columns(flag_gaia_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each prisitine catalogue may contain one or several stellarity columns indicating the probability (0 to 1) of each source being a star.  We merge these columns taking the highest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hsc_stellarity, vista_stellarity, las_stellarity, sdss_stellarity, decam_stellarity, rcs_stellarity, irac_stellarity\n"
     ]
    }
   ],
   "source": [
    "stellarity_columns = [column for column in master_catalogue.colnames\n",
    "                      if 'stellarity' in column]\n",
    "\n",
    "print(\", \".join(stellarity_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# We create an masked array with all the stellarities and get the maximum value, as well as its\n",
    "# origin.  Some sources may not have an associated stellarity.\n",
    "stellarity_array = np.array([master_catalogue[column] for column in stellarity_columns])\n",
    "stellarity_array = np.ma.masked_array(stellarity_array, np.isnan(stellarity_array))\n",
    "\n",
    "max_stellarity = np.max(stellarity_array, axis=0)\n",
    "max_stellarity.fill_value = np.nan\n",
    "\n",
    "no_stellarity_mask = max_stellarity.mask\n",
    "\n",
    "master_catalogue.add_column(Column(data=max_stellarity.filled(), name=\"stellarity\"))\n",
    "\n",
    "stellarity_origin = np.full(len(master_catalogue), \"NO_INFORMATION\", dtype=\"S20\")\n",
    "stellarity_origin[~no_stellarity_mask] = np.array(stellarity_columns)[np.argmax(stellarity_array, axis=0)[~no_stellarity_mask]]\n",
    "\n",
    "master_catalogue.add_column(Column(data=stellarity_origin, name=\"stellarity_origin\"))\n",
    "\n",
    "master_catalogue.remove_columns(stellarity_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV - Adding E(B-V) column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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), \"Herschel-Stripe-82\", 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": {
    "collapsed": true
   },
   "source": [
    "## VI - Adding spec-z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "specz =  Table.read(\"../../dmu23/dmu23_Herschel-Stripe-82/data/HELP-SPECZ_Herschel-Stripe-82_20170202.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'] , specz['dec'] )\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": [
    "## VII.a Wavelength domain coverage\n",
    "\n",
    "We add a binary `flag_optnir_obs` indicating that a source was observed in a given wavelength domain:\n",
    "\n",
    "- 1 for observation in optical;\n",
    "- 2 for observation in near-infrared;\n",
    "- 4 for observation in mid-infrared (IRAC).\n",
    "\n",
    "It's an integer binary flag, so a source observed both in optical and near-infrared by not in mid-infrared would have this flag at 1 + 2 = 3.\n",
    "\n",
    "*Note 1: The observation flag is based on the creation of multi-order coverage maps from the catalogues, this may not be accurate, especially on the edges of the coverage.*\n",
    "\n",
    "*Note 2: Being on the observation coverage does not mean having fluxes in that wavelength domain. For sources observed in one domain but having no flux in it, one must take into consideration de different depths in the catalogue we are using.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hsc_moc = MOC(filename=\"../../dmu0/dmu0_HSC/data/HSC-PDR1_deep_Herschel-Stripe-82_MOC.fits\")\n",
    "vhs_moc = MOC(filename=\"../../dmu0/dmu0_VISTA-VHS/data/VHS_Herschel-Stripe-82_MOC.fits\")\n",
    "vics82_moc = MOC(filename=\"../../dmu0/dmu0_VICS82/data/VICS82_FULL_SDSS_FEB2017_K22_HELP-coverage_intIDs_MOC.fits\")\n",
    "las_moc = MOC(filename=\"../../dmu0/dmu0_UKIDSS-LAS/data/UKIDSS-LAS_Herschel-Stripe-82_MOC.fits\")\n",
    "ps1_moc = MOC(filename=\"../../dmu0/dmu0_PanSTARRS1-3SS/data/PanSTARRS1-3SS_Herschel-Stripe-82_v2_MOC.fits\")\n",
    "shela_moc = MOC(filename=\"../../dmu0/dmu0_SHELA/data/shela_irac_v1.3_flux_cat_MOC.fits\")\n",
    "spies_moc = MOC(filename=\"../../dmu0/dmu0_SpIES/data/SpIES_ch1andch2_HELP-coverage_MOC.fits\")\n",
    "decals_moc = MOC(filename=\"../../dmu0/dmu0_DECaLS/data/DECaLS_Herschel-Stripe-82_MOC.fits\")\n",
    "des_moc = MOC(filename=\"../../dmu0/dmu0_DES/data/DES-DR1_Herschel-Stripe-82_MOC.fits\")\n",
    "decam_moc = decals_moc + des_moc\n",
    "rcs_moc = MOC(filename=\"../../dmu0/dmu0_RCSLenS/data/RCSLenS_Herschel-Stripe-82_MOC.fits\")\n",
    "sdss_moc = MOC(filename=\"../../dmu0/dmu0_SDSS-S82/data/dmu0_SDSS-S82_MOC.fits\")\n",
    "#sdss_moc = MOC(filename=\"../../dmu0/dmu0_IAC_Stripe82_Legacy_Project/data/dmu0_IAC_Stripe82_Legacy_Project_MOC.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "was_observed_optical = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    hsc_moc + ps1_moc + decam_moc + rcs_moc + sdss_moc) \n",
    "\n",
    "was_observed_nir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    las_moc + vics82_moc + vhs_moc\n",
    ")\n",
    "\n",
    "was_observed_mir = inMoc(\n",
    "    master_catalogue['ra'], master_catalogue['dec'],\n",
    "    shela_moc + spies_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": [
    "## VII.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.\n",
    "\n",
    "This now takes place at the end of the notebook when teh photometry is folded in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VIII - Cross-identification table\n",
    "\n",
    "We are producing a table associating to each HELP identifier, the identifiers of the sources in the pristine catalogue. This can be used to easily get additional information from them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7885 master list rows had multiple associations.\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Addind SDSS ids\n",
    "#\n",
    "sdss_dr13 = Table.read(\"../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_Herschel-Stripe-82.fits\")['objID', 'ra', 'dec']\n",
    "sdss_dr13_coords = SkyCoord(sdss_dr13['ra'] * u.deg, sdss_dr13['dec'] * u.deg)\n",
    "idx_ml, d2d, _ = sdss_dr13_coords.match_to_catalog_sky(SkyCoord(master_catalogue['ra'], master_catalogue['dec']))\n",
    "idx_sdss_dr13 = np.arange(len(sdss_dr13))\n",
    "\n",
    "# Limit the cross-match to 1 arcsec\n",
    "mask = d2d <= 1. * u.arcsec\n",
    "idx_ml = idx_ml[mask]\n",
    "idx_sdss_dr13 = idx_sdss_dr13[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_dr13 = idx_sdss_dr13[sort_idx]\n",
    "_, unique_idx = np.unique(idx_ml, return_index=True)\n",
    "idx_ml = idx_ml[unique_idx]\n",
    "idx_sdss_dr13 = idx_sdss_dr13[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_dr13_id\"))\n",
    "master_catalogue['sdss_dr13_id'][idx_ml] = sdss_dr13['objID'][idx_sdss_dr13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['hsc_id', 'vics82_id', 'vhs_id', 'vista_intid', 'las_id', 'ps1_id', 'sdss_id', 'decam_intid', 'des_id', 'decals_id', 'rcs_id', 'irac_intid', 'shela_intid', 'spies_intid', 'help_id', 'specz_id', 'sdss_dr13_id']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "id_names = []\n",
    "for col in master_catalogue.colnames:\n",
    "    if '_id' in col:\n",
    "        id_names += [col]\n",
    "    if '_intid' in col:\n",
    "        id_names += [col]\n",
    "        \n",
    "print(id_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue[id_names].write(\n",
    "    \"{}/master_list_cross_ident_herschel-stripe-82{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)\n",
    "id_names.remove('help_id')\n",
    "old_id_names =[\"shela_intid\",\"spies_intid\",\"decals_id\",\"des_id\",\"vhs_id\", \"vics82_id\"] \n",
    "#id_names.remove(old_id_names)\n",
    "master_catalogue.remove_columns(old_id_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IX - 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": 37,
   "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": [
    "## X - Saving the catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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\", \"flag_cleaned\", \"flag_merged\", \"flag_gaia\",  \"flag_optnir_obs\",  \"ebv\"] #\"flag_gaia\",\"flag_optnir_det\","
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing columns: {'irac_intid', 'rcs_id', 'specz_id', 'ps1_id', 'las_id', 'stellarity_origin', 'decam_intid', 'zspec_association_flag', 'sdss_dr13_id', 'zspec', 'hsc_id', 'vista_intid', 'sdss_id', 'zspec_qual'}\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": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_catalogue.write(\"{}/temp_{}.fits\".format(OUT_DIR, SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XI - folding in the photometry\n",
    "On HS82 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": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "502\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "surveys = [\n",
    "    ['hsc',    \"HSC-SSP.fits\" ,     Table.read(\"{}/HSC-SSP.fits\".format(TMP_DIR)   ), \"hsc_id\"],   \n",
    "    \n",
    "   # ['vhs',    \"VISTA-VHS.fits\" ,   Table.read(\"{}/VISTA-VHS.fits\".format(TMP_DIR) ), \"vhs_id\"],     \n",
    "   # ['vics82', \"VICS82.fits\" ,      Table.read(\"{}/VICS82.fits\".format(TMP_DIR)    ), \"vics82_id\"],  \n",
    "    ['vista', \n",
    "     \"vista_merged_catalogue_herschel-stripe-82.fits\" ,      \n",
    "     Table.read(\"{}/vista_merged_catalogue_herschel-stripe-82.fits\".format(TMP_DIR)    ), \"vista_intid\"],\n",
    "    \n",
    "    ['las',    \"UKIDSS-LAS.fits\" ,  Table.read(\"{}/UKIDSS-LAS.fits\".format(TMP_DIR)), \"las_id\"], \n",
    "    \n",
    "    ['ps1',    \"PS1.fits\" ,         Table.read(\"{}/PS1.fits\".format(TMP_DIR)       ), \"ps1_id\"],    \n",
    "    \n",
    "    #['shela',  \"SHELA.fits\" ,       Table.read(\"{}/SHELA.fits\".format(TMP_DIR)     ), \"shela_intid\"], \n",
    "    #['spies',  \"SpIES.fits\" ,       Table.read(\"{}/SpIES.fits\".format(TMP_DIR)     ), \"spies_intid\"], \n",
    "    ['irac',   \n",
    "     \"irac_merged_catalogue_herschel-stripe-82.fits\" ,       \n",
    "     Table.read(\"{}/irac_merged_catalogue_herschel-stripe-82.fits\".format(TMP_DIR)      ), \n",
    "     \"irac_intid\"], \n",
    "    \n",
    "    #['decals', \"DECaLS.fits\" ,      Table.read(\"{}/DECaLS.fits\".format(TMP_DIR)    ), \"decals_id\"],  \n",
    "    ['decam', \n",
    "     \"decam_merged_catalogue_herschel-stripe-82.fits\" ,     \n",
    "     Table.read(\"{}/decam_merged_catalogue_herschel-stripe-82.fits\".format(TMP_DIR)    ), \n",
    "     \"decam_intid\"],  \n",
    "    \n",
    "    ['rcs',    \"RCSLenS.fits\" ,     Table.read(\"{}/RCSLenS.fits\".format(TMP_DIR)   ), \"rcs_id\"],    \n",
    "    \n",
    "    ['sdss',   \"SDSS-S82.fits\" ,    Table.read(\"{}/SDSS-S82.fits\".format(TMP_DIR)  ), \"sdss_id\"],  \n",
    "    #['sdss',   \"SDSS-S82_IAC.fits\" ,    Table.read(\"{}/SDSS-S82_IAC.fits\".format(TMP_DIR)  ), \"sdss_id\"],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n=0\n",
    "for sub_file in range(num_files):\n",
    "    # the following used to have a -1 which was wrong as it left out objects\n",
    "    sub_catalogue = master_catalogue[n*split_length:(n+1)*split_length].copy()\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",
    "    #print('Finished join')\n",
    "    \n",
    "    # Combine merge flags\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",
    "    #flag cleaned\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",
    "\n",
    "    #Remove stellarity and gaia flag columns which have been added back in\n",
    "    sub_catalogue.remove_columns(flag_gaia_columns)\n",
    "    sub_catalogue.remove_columns(stellarity_columns)\n",
    "    #sub_catalogue.remove_columns(flag_cleaned_columns)\n",
    "    \n",
    "    #Add flag fill values\n",
    "    \n",
    "    for col in sub_catalogue.colnames:\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",
    "        if \"stellarity\" in col:\n",
    "            sub_catalogue[col].fill_value = np.nan\n",
    "        elif \"flag\" in col:\n",
    "            sub_catalogue[col].fill_value = 0\n",
    "        elif col.endswith(\"id\"):\n",
    "            sub_catalogue[col].fill_value = -1\n",
    "        #Remove residual ra decs from join\n",
    "        if col.endswith('_ra'):\n",
    "            sub_catalogue.remove_column(col)\n",
    "        if col.endswith('_dec'):\n",
    "            sub_catalogue.remove_column(col)\n",
    "        \n",
    "    sub_catalogue = sub_catalogue.filled()\n",
    "    \n",
    "    #Adding detection flag\n",
    "    \n",
    "    nb_optical_flux = (\n",
    "        1 * ~np.isnan(sub_catalogue['f_sdss_u']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_sdss_g']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_sdss_r']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_sdss_i']) +\n",
    "        1 * ~np.isnan(sub_catalogue['f_sdss_z']) +\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",
    "        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",
    "        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",
    "    )\n",
    "\n",
    "    nb_nir_flux = (\n",
    "        #1 * ~np.isnan(sub_catalogue['f_ukidss_y']) +\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",
    "        \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",
    "    )\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",
    "    \n",
    "    # Remove id names and write file\n",
    "    sub_catalogue.remove_columns(id_names)\n",
    "                          \n",
    "    sub_catalogue.write(\"{}/tiles/sub_catalogue_herschel-stripe-82{}_{}.fits\".format(OUT_DIR, SUFFIX, n), overwrite=True)\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_herschel-stripe-82_$suffix_*.fits > ./data/tiles/fits_list_$suffix.lis\n",
    "\n",
    "stilts tcat in=@./data/tiles/fits_list_$suffix.lis out=./data/master_catalogue_herschel-stripe-82_$suffix.fits\n",
    "</pre>\n",
    "\n",
    "For many purposes this file may be too large. In order to run checks and diagnostics we typically take a subset using something like:\n",
    "\n",
    "<pre>\n",
    "stilts tpipe cmd='every 10' ./data/master_catalogue_herschel-stripe-82_$suffix.fits omode=out out=./data/master_catalogue_herschel-stripe-82_RANDOM10PCSAMPLE_$suffix.fits\n",
    "</pre>"
   ]
  }
 ],
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