{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XMM-LSS master catalogue\n",
    "## Preparation of HST CANDELS-3D data\n",
    "\n",
    "THIS IS NO LONGER MERGED INTO THE MASTERLIST. We instead use the CANDELS-UDS catalogue which also includes forced photmetry from other imaging data.\n",
    "\n",
    "The catalogue comes from `dmu0_CANDELS-3D-HST`.\n",
    "\n",
    "In the catalogue, we keep:\n",
    "\n",
    "- The identifier (it's unique in the catalogue);\n",
    "- The position;\n",
    "- The stellarity;\n",
    "- The kron magnitude, there doesn't appear to be aperture magnitudes. This may mean the survey is unusable.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "33f5ec7 (Wed Dec 6 16:56:17 2017 +0000)\n"
     ]
    }
   ],
   "source": [
    "from herschelhelp_internal import git_version\n",
    "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "from collections import OrderedDict\n",
    "import os\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column, Table\n",
    "import numpy as np\n",
    "\n",
    "from herschelhelp_internal.flagging import  gaia_flag_column\n",
    "from herschelhelp_internal.masterlist import nb_astcor_diag_plot, remove_duplicates\n",
    "from herschelhelp_internal.utils import astrometric_correction, flux_to_mag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "OUT_DIR =  os.environ.get('TMP_DIR', \"./data_tmp\")\n",
    "try:\n",
    "    os.makedirs(OUT_DIR)\n",
    "except FileExistsError:\n",
    "    pass\n",
    "\n",
    "RA_COL = \"candels_ra\"\n",
    "DEC_COL = \"candels_dec\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I - Column selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: UnitsWarning: '0.3631uJy' did not parse as fits unit: Numeric factor not supported by FITS [astropy.units.core]\n",
      "WARNING: UnitsWarning: '[Msun]' did not parse as fits unit: Invalid character at col 0 [astropy.units.core]\n"
     ]
    }
   ],
   "source": [
    "imported_columns = OrderedDict({\n",
    "        'ID': \"candels_id\",\n",
    "        'RAJ2000': \"candels_ra\",\n",
    "        'DEJ2000': \"candels_dec\",\n",
    "        'S/G':  \"candels_stellarity\",\n",
    "        'F140Wap': \"f_ap_candels_f140w\",\n",
    "        'e_F140Wap': \"ferr_ap_candels_f140w\",\n",
    "        'F140W': \"f_candels_f140w\",\n",
    "        'e_F140W': \"ferr_candels_f140w\",\n",
    "        'F160Wap': \"f_ap_candels_f160w\",\n",
    "        'e_F160Wap': \"ferr_ap_candels_f160w\",\n",
    "        'F160W': \"f_candels_f160w\",\n",
    "        'e_F160W': \"ferr_candels_f160w\",\n",
    "        'F606W': \"f_candels_f606w\",\n",
    "        'e_F606W': \"ferr_candels_f606w\",\n",
    "        'F814W': \"f_candels_f814w\",\n",
    "        'e_F814W': \"ferr_candels_f814w\",\n",
    "        'F125W': \"f_candels_f125w\",\n",
    "        'e_F125W': \"ferr_candels_f125w\"\n",
    "\n",
    "    })\n",
    "\n",
    "\n",
    "catalogue = Table.read(\"../../dmu0/dmu0_CANDELS-3D-HST/data/CANDELS-3D-HST_XMM-LSS.fits\")[list(imported_columns)]\n",
    "for column in imported_columns:\n",
    "    catalogue[column].name = imported_columns[column]\n",
    "\n",
    "epoch = 2012 #Year of publication\n",
    "\n",
    "# Clean table metadata\n",
    "catalogue.meta = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:76: RuntimeWarning: invalid value encountered in log10\n",
      "  magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n"
     ]
    }
   ],
   "source": [
    "# Adding flux and band-flag columns\n",
    "for col in catalogue.colnames:\n",
    "    if col.startswith('f_'):\n",
    "        \n",
    "        errcol = \"ferr{}\".format(col[1:])\n",
    "        \n",
    "        #Calculate mags, errors including the fact that fluxes are in units of 0.3631 uJy\n",
    "        mag, error = flux_to_mag(np.array(catalogue[col]) * 0.3631e-6, np.array(catalogue[errcol] * 0.3631e-6))\n",
    "        \n",
    "        \n",
    "        # magnitudes are added\n",
    "        catalogue.add_column(Column(mag, name=\"m{}\".format(col[1:])))\n",
    "        catalogue.add_column(Column(error, name=\"m{}\".format(errcol[1:])))\n",
    "        \n",
    "        \n",
    "        #Correct flux units to uJy\n",
    "        catalogue[col] = catalogue[col] * 0.3631\n",
    "        catalogue[col].unit = u.microjansky\n",
    "        catalogue[errcol] = catalogue[errcol] * 0.3631\n",
    "        catalogue[errcol].unit = u.microjansky        \n",
    "\n",
    "        if ('125' in col) or ('814' in col) or ('606' in col) :\n",
    "            # We add nan filled aperture photometry for consistency\n",
    "            catalogue.add_column(Column(np.full(len(catalogue), np.nan), name=\"m_ap{}\".format(col[1:])))\n",
    "            catalogue.add_column(Column(np.full(len(catalogue), np.nan), name=\"merr_ap{}\".format(col[1:])))\n",
    "            catalogue.add_column(Column(np.full(len(catalogue), np.nan), name=\"f_ap{}\".format(col[1:])))\n",
    "            catalogue.add_column(Column(np.full(len(catalogue), np.nan), name=\"ferr_ap{}\".format(col[1:])))\n",
    "\n",
    "        \n",
    "\n",
    "        \n",
    "        # Band-flag column\n",
    "        if \"ap\" not in col:\n",
    "            catalogue.add_column(Column(np.zeros(len(catalogue), dtype=bool), name=\"flag{}\".format(col[1:])))\n",
    "        \n",
    "# TODO: Set to True the flag columns for fluxes that should not be used for SED fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<Table masked=True length=10>\n",
       "<table id=\"table140122973119824-19699\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>candels_id</th><th>candels_ra</th><th>candels_dec</th><th>candels_stellarity</th><th>f_ap_candels_f140w</th><th>ferr_ap_candels_f140w</th><th>f_candels_f140w</th><th>ferr_candels_f140w</th><th>f_ap_candels_f160w</th><th>ferr_ap_candels_f160w</th><th>f_candels_f160w</th><th>ferr_candels_f160w</th><th>f_candels_f606w</th><th>ferr_candels_f606w</th><th>f_candels_f814w</th><th>ferr_candels_f814w</th><th>f_candels_f125w</th><th>ferr_candels_f125w</th><th>m_ap_candels_f140w</th><th>merr_ap_candels_f140w</th><th>m_candels_f140w</th><th>merr_candels_f140w</th><th>flag_candels_f140w</th><th>m_ap_candels_f160w</th><th>merr_ap_candels_f160w</th><th>m_candels_f160w</th><th>merr_candels_f160w</th><th>flag_candels_f160w</th><th>m_candels_f606w</th><th>merr_candels_f606w</th><th>m_ap_candels_f606w</th><th>merr_ap_candels_f606w</th><th>f_ap_candels_f606w</th><th>ferr_ap_candels_f606w</th><th>flag_candels_f606w</th><th>m_candels_f814w</th><th>merr_candels_f814w</th><th>m_ap_candels_f814w</th><th>merr_ap_candels_f814w</th><th>f_ap_candels_f814w</th><th>ferr_ap_candels_f814w</th><th>flag_candels_f814w</th><th>m_candels_f125w</th><th>merr_candels_f125w</th><th>m_ap_candels_f125w</th><th>merr_ap_candels_f125w</th><th>f_ap_candels_f125w</th><th>ferr_ap_candels_f125w</th><th>flag_candels_f125w</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>deg</th><th>deg</th><th></th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><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>1</td><td>34.44606781</td><td>-5.28545713</td><td>2</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.181746</td><td>0.0283218</td><td>0.260964</td><td>0.046662</td><td>0.232061</td><td>0.0186978</td><td>0.256861</td><td>0.0223085</td><td>0.184553</td><td>0.0413063</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>25.7513</td><td>0.169192</td><td>25.3586</td><td>0.194137</td><td>False</td><td>25.486</td><td>0.087481</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>25.3758</td><td>0.0942969</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>25.7347</td><td>0.243007</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>1</td><td>2</td><td>34.44749069</td><td>-5.28481579</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>5.06706</td><td>0.0272383</td><td>7.60331</td><td>0.0859022</td><td>0.215732</td><td>0.0193983</td><td>1.0059</td><td>0.0233462</td><td>5.52239</td><td>0.0378386</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>22.1381</td><td>0.00583644</td><td>21.6975</td><td>0.0122666</td><td>False</td><td>25.5652</td><td>0.0976274</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>23.8936</td><td>0.0251993</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>22.0447</td><td>0.00743932</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>2</td><td>3</td><td>34.43383026</td><td>-5.28518391</td><td>2</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.0387028</td><td>0.0245289</td><td>0.0465894</td><td>0.0295262</td><td>0.0506525</td><td>0.0156108</td><td>0.0854447</td><td>0.0185893</td><td>0.131918</td><td>0.0377769</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>27.4306</td><td>0.688112</td><td>27.2293</td><td>0.68809</td><td>False</td><td>27.1385</td><td>0.334617</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>26.5708</td><td>0.236212</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>26.0992</td><td>0.310919</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>3</td><td>4</td><td>34.43863678</td><td>-5.28336287</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>4</td><td>5</td><td>34.44387817</td><td>-5.28445578</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.737383</td><td>0.0220986</td><td>0.971619</td><td>0.0507214</td><td>0.112626</td><td>0.0169844</td><td>0.165697</td><td>0.0204483</td><td>0.659644</td><td>0.0285219</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>24.2308</td><td>0.0325384</td><td>23.9313</td><td>0.0566787</td><td>False</td><td>26.2709</td><td>0.163732</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>25.8517</td><td>0.133988</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>24.3517</td><td>0.0469454</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>5</td><td>6</td><td>34.43869019</td><td>-5.28433704</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>6</td><td>7</td><td>34.44271088</td><td>-5.284338</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.456017</td><td>0.0258407</td><td>0.629978</td><td>0.093284</td><td>0.289754</td><td>0.017801</td><td>0.299695</td><td>0.0214893</td><td>0.624024</td><td>0.0322701</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>24.7525</td><td>0.0615245</td><td>24.4017</td><td>0.16077</td><td>False</td><td>25.2449</td><td>0.066702</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>25.2083</td><td>0.0778516</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>24.412</td><td>0.0561467</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>7</td><td>8</td><td>34.43680954</td><td>-5.2842989</td><td>2</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.074726</td><td>0.019151</td><td>0.0551258</td><td>0.0540511</td><td>0.0413389</td><td>0.00957967</td><td>0.0552057</td><td>0.0114166</td><td>0.0953718</td><td>0.0159688</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>26.7163</td><td>0.278255</td><td>27.0466</td><td>1.06457</td><td>False</td><td>27.3591</td><td>0.251603</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>27.045</td><td>0.224531</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>26.4515</td><td>0.181792</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>8</td><td>9</td><td>34.43286133</td><td>-5.28434801</td><td>2</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.0703797</td><td>0.0197476</td><td>0.122321</td><td>0.038859</td><td>0.0910909</td><td>0.0226796</td><td>0.0386774</td><td>0.0267089</td><td>0.14711</td><td>0.0424754</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>26.7814</td><td>0.304642</td><td>26.1813</td><td>0.344917</td><td>False</td><td>26.5013</td><td>0.270324</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>27.4314</td><td>0.749761</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>25.9809</td><td>0.313487</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "<tr><td>9</td><td>10</td><td>34.43959045</td><td>-5.28253317</td><td>0</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>2.63331</td><td>0.0237137</td><td>9.67988</td><td>0.228847</td><td>1.9045</td><td>0.0474535</td><td>3.21274</td><td>0.056876</td><td>8.16721</td><td>0.0795697</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>22.8487</td><td>0.00977736</td><td>21.4353</td><td>0.0256685</td><td>False</td><td>23.2006</td><td>0.0270528</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>22.6328</td><td>0.019221</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>21.6198</td><td>0.0105779</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table140122973119824-19699').dataTable()\");\n",
       "    $('#table140122973119824-19699').dataTable({\n",
       "        \"order\": [],\n",
       "        \"iDisplayLength\": 50,\n",
       "        \"aLengthMenu\": [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        \"pagingType\": \"full_numbers\"\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catalogue[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Removal of duplicated sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We remove duplicated objects from the input catalogues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/astropy/table/column.py:1096: MaskedArrayFutureWarning: setting an item on a masked array which has a shared mask will not copy the mask and also change the original mask array in the future.\n",
      "Check the NumPy 1.11 release notes for more information.\n",
      "  ma.MaskedArray.__setitem__(self, index, value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The initial catalogue had 44102 sources.\n",
      "The cleaned catalogue has 43863 sources (239 removed).\n",
      "The cleaned catalogue has 236 sources flagged as having been cleaned\n"
     ]
    }
   ],
   "source": [
    "\n",
    "SORT_COLS = ['merr_candels_f140w', \n",
    "             'merr_candels_f160w', \n",
    "             'merr_candels_f606w', \n",
    "             'merr_candels_f814w',\n",
    "             'merr_candels_f125w']\n",
    "FLAG_NAME = 'candels_flag_cleaned'\n",
    "\n",
    "nb_orig_sources = len(catalogue)\n",
    "\n",
    "catalogue = remove_duplicates(catalogue, RA_COL, DEC_COL, sort_col=SORT_COLS,flag_name=FLAG_NAME)\n",
    "\n",
    "nb_sources = len(catalogue)\n",
    "\n",
    "print(\"The initial catalogue had {} sources.\".format(nb_orig_sources))\n",
    "print(\"The cleaned catalogue has {} sources ({} removed).\".format(nb_sources, nb_orig_sources - nb_sources))\n",
    "print(\"The cleaned catalogue has {} sources flagged as having been cleaned\".format(np.sum(catalogue[FLAG_NAME])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III - Astrometry correction\n",
    "\n",
    "We match the astrometry to the Gaia one. We limit the Gaia catalogue to sources with a g band flux between the 30th and the 70th percentile. Some quick tests show that this give the lower dispersion in the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gaia = Table.read(\"../../dmu0/dmu0_GAIA/data/GAIA_XMM-LSS.fits\")\n",
    "gaia_coords = SkyCoord(gaia['ra'], gaia['dec'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ov6cteFD3Gzhf5QnofLnHQ3GWn4oyB3ar0nUINmpHZDLRCc4eJpcEG0Ua1UnG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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70edfabeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C9vZ2xowZQ6iApy8puTgGazGtHeA42IrSbEcjIiIZkvEBnQd1dnbypz/9iaam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\nEBERkaY4XBAREZGm/gd/TQ8e5JX82wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70ebb8d3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL], \n",
    "                    gaia_coords.ra, gaia_coords.dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RA correction: 0.09405840634997276 arcsec\n",
      "Dec correction: -0.10633781055542357 arcsec\n"
     ]
    }
   ],
   "source": [
    "delta_ra, delta_dec =  astrometric_correction(\n",
    "    SkyCoord(catalogue[RA_COL], catalogue[DEC_COL]),\n",
    "    gaia_coords\n",
    ")\n",
    "\n",
    "print(\"RA correction: {}\".format(delta_ra))\n",
    "print(\"Dec correction: {}\".format(delta_dec))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "catalogue[RA_COL] +=  delta_ra.to(u.deg)\n",
    "catalogue[DEC_COL] += delta_dec.to(u.deg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MzFe5/TqfHXAzLMtHRakDu1XpuQQbtSMymegERg/TigONIo3qJONPRVEoL7RT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70eb949eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aGl577bUj7pPJZHjwwQdZv349jY2NjBkzhoULF3LNNdd0b/POO+/wb//2b/zp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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70eb90e400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL], \n",
    "                    gaia_coords.ra, gaia_coords.dec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV - Flagging Gaia objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "catalogue.add_column(\n",
    "    gaia_flag_column(SkyCoord(catalogue[RA_COL], catalogue[DEC_COL]), epoch, gaia)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "127 sources flagged.\n"
     ]
    }
   ],
   "source": [
    "GAIA_FLAG_NAME = \"candels_flag_gaia\"\n",
    "\n",
    "catalogue['flag_gaia'].name = GAIA_FLAG_NAME\n",
    "print(\"{} sources flagged.\".format(np.sum(catalogue[GAIA_FLAG_NAME] > 0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V - Flagging objects near bright stars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VI - Saving to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "catalogue.write(\"{}/CANDELS.fits\".format(OUT_DIR), overwrite=True)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python (herschelhelp_internal)",
   "language": "python",
   "name": "helpint"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}