{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COSMOS master catalogue\n",
    "## Preparation of HST CANDELS-3D 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",
      "017bb1e (Mon Jun 18 14:58:59 2018 +0100)\n",
      "This notebook was executed on: \n",
      "2018-11-06 12:05:33.270406\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/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/seaborn/apionly.py:6: 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",
    "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": [],
   "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_COSMOS.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:77: RuntimeWarning: divide by zero encountered in log10\n",
      "  magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n",
      "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:81: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  errors = 2.5 / np.log(10) * errors_on_fluxes / fluxes\n",
      "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:81: RuntimeWarning: invalid value encountered in true_divide\n",
      "  errors = 2.5 / np.log(10) * errors_on_fluxes / fluxes\n",
      "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: 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",
    "        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",
    "\n",
    "        mag, error = flux_to_mag(np.array(catalogue[col]) * 1.e-6 , np.array(catalogue[errcol] ) * 1.e-6)\n",
    "        \n",
    "        # Fluxes are added in µJy\n",
    "        catalogue.add_column(Column(mag, name=\"m{}\".format(col[1:])))\n",
    "        catalogue.add_column(Column(error, name=\"m{}\".format(errcol[1:])))\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": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "catalogue['candels_stellarity'] = catalogue['candels_stellarity'].astype(float)\n",
    "catalogue['candels_stellarity'][np.isclose(catalogue['candels_stellarity'], 1.)] = 0.9\n",
    "catalogue['candels_stellarity'][np.isclose(catalogue['candels_stellarity'], 0.)] = 0.05\n",
    "catalogue['candels_stellarity'][np.isclose(catalogue['candels_stellarity'], 2.)] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table masked=True length=10</i>\n",
       "<table id=\"table139733693115808-659755\" 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>flag_candels_f606w</th><th>m_candels_f814w</th><th>merr_candels_f814w</th><th>flag_candels_f814w</th><th>m_candels_f125w</th><th>merr_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></tr></thead>\n",
       "<tr><td>0</td><td>1</td><td>150.07144165</td><td>2.16901445</td><td>0.05</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.8110702</td><td>0.065597646</td><td>2.5702033</td><td>0.11964508</td><td>1.4882016</td><td>0.01217329</td><td>3.0544333</td><td>0.014993488</td><td>2.5597098</td><td>0.058371954</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>23.255165</td><td>0.039325774</td><td>22.875084</td><td>0.050541908</td><td>False</td><td>23.468346</td><td>0.008881177</td><td>False</td><td>22.687675</td><td>0.0053296206</td><td>False</td><td>22.879524</td><td>0.024759267</td><td>False</td></tr>\n",
       "<tr><td>1</td><td>2</td><td>150.07157898</td><td>2.16905951</td><td>0.05</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.7838602</td><td>0.06010394</td><td>1.2548009</td><td>0.09189697</td><td>0.8072076</td><td>0.013738251</td><td>1.5397619</td><td>0.016951323</td><td>1.2588676</td><td>0.06900352</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>24.164406</td><td>0.08325084</td><td>23.653564</td><td>0.07951529</td><td>False</td><td>24.132538</td><td>0.018478662</td><td>False</td><td>23.431366</td><td>0.011952927</td><td>False</td><td>23.650055</td><td>0.059513498</td><td>False</td></tr>\n",
       "<tr><td>2</td><td>3</td><td>150.07551575</td><td>2.17311358</td><td>0.05</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>6.585544</td><td>0.01669897</td><td>21.239897</td><td>0.1982998</td><td>1.7054081</td><td>0.028027687</td><td>7.052854</td><td>0.034523547</td><td>16.211687</td><td>0.037493706</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>21.853523</td><td>0.002753102</td><td>20.582123</td><td>0.010136643</td><td>False</td><td>23.320427</td><td>0.017843632</td><td>False</td><td>21.77909</td><td>0.0053146514</td><td>False</td><td>20.875435</td><td>0.002511045</td><td>False</td></tr>\n",
       "<tr><td>3</td><td>4</td><td>150.06834412</td><td>2.16974521</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.19595417</td><td>0.035028256</td><td>0.2369518</td><td>0.03287689</td><td>0.07290685</td><td>0.011539681</td><td>0.08186816</td><td>0.016118009</td><td>0.075666405</td><td>0.031453174</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>25.669617</td><td>0.19408336</td><td>25.463348</td><td>0.1506451</td><td>False</td><td>26.74308</td><td>0.17185009</td><td>False</td><td>26.617218</td><td>0.21375714</td><td>False</td><td>26.702744</td><td>0.4513211</td><td>False</td></tr>\n",
       "<tr><td>4</td><td>5</td><td>150.07145691</td><td>2.17064571</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.26412258</td><td>0.024869446</td><td>0.29932147</td><td>0.029539999</td><td>0.10092728</td><td>0.009796801</td><td>0.118642926</td><td>0.012246274</td><td>0.3497597</td><td>0.020667652</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>25.345482</td><td>0.10223153</td><td>25.209656</td><td>0.107151166</td><td>False</td><td>26.389977</td><td>0.10539014</td><td>False</td><td>26.214394</td><td>0.11206924</td><td>False</td><td>25.04058</td><td>0.06415725</td><td>False</td></tr>\n",
       "<tr><td>5</td><td>6</td><td>150.06959534</td><td>2.17076755</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.14297426</td><td>0.019896064</td><td>0.17788994</td><td>0.034232702</td><td>0.063669585</td><td>0.010683491</td><td>0.078560315</td><td>0.013464111</td><td>0.15688099</td><td>0.0174898</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>26.011856</td><td>0.15108927</td><td>25.77462</td><td>0.2089364</td><td>False</td><td>26.890175</td><td>0.182182</td><td>False</td><td>26.661995</td><td>0.1860796</td><td>False</td><td>25.91108</td><td>0.121042766</td><td>False</td></tr>\n",
       "<tr><td>6</td><td>7</td><td>150.06835938</td><td>2.17106199</td><td>0.05</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.1017543</td><td>0.024495816</td><td>1.4720073</td><td>0.05348463</td><td>0.47264725</td><td>0.011264088</td><td>0.72147965</td><td>0.014405266</td><td>1.0877386</td><td>0.023628732</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>23.794792</td><td>0.024139678</td><td>23.480225</td><td>0.039449666</td><td>False</td><td>24.713661</td><td>0.025875168</td><td>False</td><td>24.254448</td><td>0.021678116</td><td>False</td><td>23.808685</td><td>0.023585234</td><td>False</td></tr>\n",
       "<tr><td>7</td><td>8</td><td>150.06916809</td><td>2.17103839</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.12354114</td><td>0.020280587</td><td>0.1725197</td><td>0.044588678</td><td>0.040282313</td><td>0.012203065</td><td>0.037588112</td><td>0.015245843</td><td>0.12505889</td><td>0.019706527</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>26.170471</td><td>0.17823508</td><td>25.8079</td><td>0.28061455</td><td>False</td><td>27.387215</td><td>0.3289113</td><td>False</td><td>27.46238</td><td>0.44037756</td><td>False</td><td>26.157219</td><td>0.1710881</td><td>False</td></tr>\n",
       "<tr><td>8</td><td>9</td><td>150.07997131</td><td>2.17103219</td><td>0.05</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.2481389</td><td>0.04266788</td><td>0.46883473</td><td>0.0999106</td><td>0.11358857</td><td>0.016694974</td><td>0.21560514</td><td>0.02044616</td><td>0.44359925</td><td>0.06018745</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>25.413261</td><td>0.18669406</td><td>24.72245</td><td>0.23137483</td><td>False</td><td>26.261665</td><td>0.15957889</td><td>False</td><td>25.56585</td><td>0.102961995</td><td>False</td><td>24.782524</td><td>0.14731245</td><td>False</td></tr>\n",
       "<tr><td>9</td><td>10</td><td>150.068573</td><td>2.17154479</td><td>0.05</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.6297743</td><td>0.020914923</td><td>2.3944266</td><td>0.05305254</td><td>0.8183185</td><td>0.012704506</td><td>1.2700512</td><td>0.015673574</td><td>2.0973744</td><td>0.021401476</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>False</td><td>23.369682</td><td>0.013933271</td><td>22.951996</td><td>0.02405631</td><td>False</td><td>24.117699</td><td>0.016856201</td><td>False</td><td>23.64045</td><td>0.013398961</td><td>False</td><td>23.09581</td><td>0.011078783</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",
       "\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(\"$('#table139733693115808-659755').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",
       "    $('#table139733693115808-659755').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, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The initial catalogue had 33879 sources.\n",
      "The cleaned catalogue has 33869 sources (10 removed).\n",
      "The cleaned catalogue has 10 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": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gaia = Table.read(\"../../dmu0/dmu0_GAIA/data/GAIA_COSMOS.fits\")\n",
    "gaia_coords = SkyCoord(gaia['ra'], gaia['dec'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEQCAYAAABIqvhxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XmUXGWZ+PHve2/tvW9JJ+mELJCQBEOAEGTUsCnOCPw0uOIYWZQ5GRVBc44ao6M4EnBQR1wAmYMEYUSUjDIZiQrhmAwBYQIdDAFCtk7SSTq9V1dV13bvfX9/VNJJk066q7v2fj7nBLq220/frq7nvtvzKq21RgghhEiDke8AhBBCFB9JHkIIIdImyUMIIUTaJHkIIYRImyQPIYQQaXPlO4Bc6OgI5TsEIYQoOg0NFad8TFoeQggh0ibJQwghRNokeQghhEibJA8hhBBpk+QhhBAibZI8hBBCpE2ShxBCiLRJ8hBCCJE2SR5CCCHSJslDCCFE2iR5CCGESJskDyGEKER9YVRPEBwn35EMaVwURhRCiGJitHWgQhFQCnpD2NOnpL4uINLyEEKIQhPqB8NIJYxEMvWvwEjyEEKIAqPc5gk3AJd5yufmi3RbCSEKl2WBBtBgmqmr8UzTGiJRlG2hTReU+fPeRWRPmoDR0Q22xplYn/rZC4wkDyFEYTo2UKyO/se2s5M8YnGUbYMyUI6DjsYg4M/890mH14PT1JjfGIYh3VZCiPFN68EtDZ2/UIqJJA9RmHr7MNo7oT+a70hEvhxrZWhSH/DZ6rrxelLHdxzQDvg82fk+JUa6rUTBUV29GF09gMZ14DD2hHp0bSWUleU7NJFrrhx8RJkmurI81S1mmnkf7ygW0vIQBUdFo2AaqHCq1WFEYxihCCStPEcmSpZSqUQliWPEJHmIwuP1gKNTXQgOaK+XgQFTIURBkG4rUXCc+lqUMlDHrgI9LrRhpJKKEKIgSPIYR1QwjOuNnaA11tmz0DVV+Q5paEqh62vQ9TXQ2Y3rcDvaNNGAM6Eu39EJIZBuq/FDa9wvNmO2dWIe6cLz160FW3DtRK6uXvB6US4XqrM7tWhMCJF3kjzGC8fB6I8dvx2LgVUMYwh68JcyB7/0aA22Tv1fFA1JHuOFaeLUVaf+QLVG11SDu/B7Le2GOrTtoC0LXVscMYs0WA6qLYZxqB91JAZW4beGRYrSuvTTfUdHKN8hFAbHwdy1D7TGPvOMgqyXMyTHye4iMZE/3XGM+PGE4fhMqJGJEYWioaHilI/JZdx4YhjYs2fkO4r0ZaOekSgMb19WUcDLLLR20NrGMNz5DqUgSPIQQuRPhRudiEPSAbcJlQX4wew4qC3NWN0HCTcFcCZPpKZmPkqN74saSR5CiPxxGeiJ/pOLExYQ9/PNGNvfwI2D71CIziU+ot42AmWT0z+Y1hCKpBbB+r1FvXZpfKdOIURhKNDEAWB0BwfiMxIW3vbw6OMNhjFicYxkEtXTV9RTzyV5CCHEaTh1VRjKBSgct4k1pQG/f3R7bSjLGkg8yjQgXnjby46UdFuJ/LJtiERTM6nKsrQBj9YQ7k/9P+Af85aevYkQRxK9mBhM9TfgNYu360EML/l356MDfoxIFGtWExWTJo76WNrtQsUTqSoK2inqbiuZqivyx3FQbR0oFGgHJxCAmsqMfxvVHUTZqemgGp0qezLKboekY/Fa317Mo4OlHuXi7MozMharGAci0dRFU8CXm5LzY1AwU3XXr1/Pk08+yfbt2wmHw8yYMYObbrqJq6+++rSvmzNnzkn3nXvuufzmN7/JVqgiF2JxlEOq81QZqFgMTeaTB0lrYLqvsh207Yy69RFzkugTlrkndPH2WYs8yVYLO8dymjzWrFlDU1MTK1eupKamhk2bNrFixQp6enpYtmzZaV9700038f73v3/gdlmxbAx0dEU3ShX0oGBeeNyk6o0cPS/ZWgTodsGxlodpgDn6ob4y04vXcGNrBweHWncWkt140RfG6OlDl/lTrUFRVHKaPO677z5qa2sHbl988cW0t7fz0EMPDZs8pkyZwsKFC7MdYmY5Tqp+lFKpBOIyx8+CN60hkQBlHE0SQ3C5cGqrUZF+MBS6OjsfxLqmEh2KpG4E/GNK4oYyOLviDDrjvbgNF7UeSR6j0hfGtWMPyjCg3cG2bZyJ9fmOSqQhp59kJyaOY+bOnUt3d3cuw8gdxzn+QaVUUVSxzQitIRxBJS1UPJ4qwngqAR+6oRZdV5O9lodSUFme+jfGwXIAUxlM9NVK4hgDo6cvlTgADAMVlHHJYpP3y+Dm5mZmzZo17PN+8pOfMG/ePC666CJWrlxJb29vDqLLsPHSbWXbx6tMKCXbx4qT6IrA8Yspx0EHSmMcYDzJ61D/Cy+8wIYNG1i9evVpn7d06VIuu+wyamtree2117j33nvZsWMHv/3tbzELuViey5VaBHRszONYrI6TWmVqGFAeKL2kcqybbqDVlfdrFFFgdG01tmWhesPogA9nyuinv2aD1g7asVCG+/iOlmKQvE3VbW1t5WMf+xjnnXceP/vZz9J67caNG/mnf/onfvazn/He97532OcX1FRdrVHtXahjpdFdJrqhQHbHs22IxVNzz8c6hTCZhEQylUD8vtJLkKJk2VaM/tA+NBaG4SFQPh3DLMCaWzlwuqm6ebkk7O3t5eabb2bSpEncfffdab9+yZIlBAIBtm/fnoXossxxUqtMAZRCJZKFsQlOPIGx9wDmoSOYew9Af3Rsx3O7oSww5gFqIXItHutAGepo9VxNIt6V75AKUs6TRzQaZfny5SSTSR544AECgUDaxzjWjCzK5qRhoE+YcaVNsyA+XI1gKHU+DQMMA6O3gFprQuTQidVytdYoVcBd43mU0zEPy7K49dZbaWlp4bHHHqOubnTdNZs2baK/v5/58+dnOMIcUCo1syicmjqqK8vzHFCKNk2Uo8E4Ol4xhrUQ441xuAOjJwimgdU0qWQWgY1XXt8EopH9OHYC0+XH45MpxEPJafK4/fbb2bhxI6tWrSIYDLJ169aBx+bNm4fH4+H6668H4OGHHwbg8ccf57XXXuPiiy+mpqaG119/nfvuu48FCxZw6aWX5jL8zPG4U1uqFhBdW4WOxyEaB58Hp+HkadVFIR7HPNwJONgN9dn/IO+PYnT3pCZDaI15uD21S6MoWobppqxy+Bmg411Ok8fmzZsBuOOOO056bMOGDTQ1NeG8bS3EtGnT+N3vfsef//xnwuEw9fX1fOhDH+LWW28t7JlWxUYpnMmFNeNlNMx9h1BH30OufQexzp6Z3YWZjsOg7e/0OFnLI8Y9KYwoSopr+87jXW6WhTV75qlXuGeC1hj7DmGEw2hl4EyZmLWV8kLkWsEURhQi25zyAEakPzW25POl6lplk1I406fgJJOpFo60hsU4IclDlBRn2mR0TxBsB11Xnf2ZbI6TquHl8YyfumVCIMlDlBqlcjcZIZHA6GwHFBrQEyYW/P4M40I0niq2qRS6omz03ZZapwqbmoZcGAxBzogQo6QiYTBSlZKVYUA4nO+QhOOgQpHjW72GRvk70RrV2Y3R0YVxpDNVeUEMIslDiFHShnG8OoA+ukZG5JfWoPTg26PRH03tPmkevTg4VtJfDJDkIcRoVVah3W60Au31QIXMsso700R7PKmxKMdJTZoYjbd3U8mFwUmkg1bkRjye2s1vrEUSo7HUB0Mh1MxSCl3fkN8YxMmqKtBJK7X8ZrRjUH4fTiyR2o/GMNBVp56yOl5J8hDZ1xfGiMZAge6PpsqzjOaD/9hxDIXuj6W2Ls1EAnEccI6WZMl3QhKZkYkp2jWVlPwiuDGQ5CGyTsXiA81+Zdtoy0pV3R3LcRwbnUiA1zu24KIxjI7uVALxeHAmNUgCEWIEZMxDZN+J/cWa0S+kO6FYo2YMxzmBCoZS/dsuV2o/k7AMjAoxEpI8RNbp6kq0YaKVwqmsGPWceV1diXa50KaBrqrMzJoK9bbEJq0OIUZEuq1E9rlcqdXew9E6tfug2zV0gjFNdE1VRkPTNZXQ0ZPqTvP7oLwso8cXolRJ8hCFwbJQvSGOXv6jqytys1rb40FPmSgDo0KkSbqtRGE4NhhuGKn/R2P5jkgIcRqSPMQg8SSEYqmZqzml3rZaW8lbU4hCJt1WYkBvPxwOpr52mzCjPoe70QZ8aNtGWUm0yyVbuQpR4CR5iAHdkePJwnYgGIXaXI0fKwWV5TL2IESRkL4BMeDEVobWqdaHEEIMRZKHGDCpKpUwFFBTBhWjrCknhCh90m0lBnhcqXEOIYQYjrQ8hBBCpE1aHkKIEUnGe3ESIQxXALe/Lt/hiDyT5CGEGFYyHiQROoAyXFjxIKBx+6WPczyTbishxLCcRBhlpK41lWFiJ6X68HgnLY8ipLp6MY90og2FPW1yale98SIex+jqRXs8qc2gRE4YnnKseA/KMNGOjemWApJ54TjQHwS3B7z5/R1I8ig2loW5/xCYRmqXzb0HsObPzndUuZFM4npzb2ousW3jxGI4TZPyHdW44PZWgXZwkmEZ88gXx8Fo243SDtpxcKoboCJ/XYeSPIpN0kpdfRxb0WfZ+Y0nh1TweNVdTBOjL4yT76DGEbevBnzS2sub/j6UdkAplGliRII4eUweMuZRbHxedHkglUAsG6d2BPtklAhdFjhesVFrtNeT34CEyCW3F+0cv1zSRn5LQCitdcmXE+roCOU7hMzSGtXTBy4TXVme72hySvUEU2MebjPVZZWBrWiFKBqhLlQkCIaBrp0MruxeQDU0VJzyMUkeQgghhnS65CHdVkIIIdImyUMIIUTaJHkIIYRIm0zVFaLUJZIQjwMKAj6ZZCAyQloeQpQyx0H196McjXIciETzHZEoEZI8hChljsOJe/sqLcsqRWZIt5UQpcw00aaB0qQWVnrcQz6t30rQGQ/jNVxM8FWglMptnKLo5LTlsX79epYvX8573vMezjvvPK699lr+53/+J61jfO5zn2POnDk8+uijWYpSiBKiFFSUo70edMA/ZBHNhG3xVvgIvVY/h+NB9vV35yHQ0mBZ/URjndh2PN+hZF1OWx5r1qyhqamJlStXUlNTw6ZNm1ixYgU9PT0sW7Zs2Nc/99xzvPrqqzmIVBQj1d2L0dkDSmFPnghlhVttWGtN2IpgKBdlrixvFq8U+LynfLjPinGsnWEoRWQcfPBlQzwZJBI5iKFMorEOKium4zLTeA/G4pitbWBZ6PIynCkTU7+7ApXT5HHfffdRW1s7cPviiy+mvb2dhx56aNjkkUwmueOOO7jtttv4xje+ke1QRbGxbYxD7aijM4lcrYew5szKc1BD01rTGj1CUltoNJV2OQ3e/BUcLHd5cdCYKLTW+M2hu7bE6SXiQQyVev8ZyiAe78WVxnYJ5pFO0KmipyoSQfWF0FWV2Qp3zHLabXVi4jhm7ty5dHcP30z+5S9/ic/n48Mf/nA2QhPFznYGDwbbhVt1J+EkiTlxDKUwlUGfFc5rPD7TzayyCVSYPmo9ZUwvkx0CR8Mw3Byr9uTgYJqnbu0N6cRKUUoV9HsYCmDAvLm5mVmzTn+F2NHRwb333sv999+PYcgEMTEEjxtdUYYKp6aiOnWFWzrcZZgojndHuFT+111Uun1UurPcfVbiAv5GtLaxnTg+swyf9+SL5dOxG2pT3Va2DQEfuqZwWx2Q5+TxwgsvsGHDBlavXn3a59199928+93v5sILL8xRZKIY2Wc0QaQfDKOgd1c0lclEbx09yT6UggZPeh8yojAppSgvaxr9AcoC2GdNB9sBl1nQ4x2Qx+TR2trKihUruOKKK7j22mtP+bzm5mb+9Kc/8dRTT+UwOlGUlILy4tgetcJdRoVs5SrezjBS/4pAXpJHb28vN998M5MmTeLuu+8+7XNXr17Nxz/+cSoqKujr6xu4PxaLEQqFqKg4dclgIYQQ2ZHz/Tyi0Sg33ngjnZ2dPP7449TVnX4v5EWLFhEKDb0fh2mavP7668N+z4Lez8NxIBYDjwdceR+CEkKIAafbzyOnn1aWZXHrrbfS0tLCY489NmziALj//vux7cH7dH/6059m2bJlXHnlldkKNTcsC+PI4YFZFk79BPDJoKUQovDlNHncfvvtbNy4kVWrVhEMBtm6devAY/PmzcPj8XD99dcD8PDDDwOplsdQpk+fzuLFi7MfdDaFQ6l++qMDYyocQkvyEEIUgZwmj82bNwNwxx13nPTYhg0baGpqwnHGUeE2w0x1WxlGqu6QWRwDZaK4JW2bg7EwCkVToBxTyftOpC+tMY977rmH9vb2IT/8V61aRWNjI7fccktGA8yEgh3z0BrV3QXxGLg96Lr6oplpIYqT1ppXezux0Wit8Rom76iWRYFiaBnbw/ypp55i4cKFQz52/vnny3TadCmFrqtHT25CN0yQxCGyztKamGMBqXUJ/baFk9s5M6JEpNVt1dbWxrRp04Z8bOrUqRw+fDgjQQkhssOlFF7D5FjnsNcwMQp8MVqxs5Jheo68hGNH8fobqZ5wQb5Dyoi0LnV9Ph8dHR1DPtbW1obbLQXVhChkSinmVtZS7fZQ7fYwr1JWt2dbX9ffcOx+QBOLtNIf2pfvkDIireSxcOFC1qxZc9LUWdu2+eUvf3nKLi0hROHwmS5mlVczq7waj+xnnnX6aDchAMrAKZGS92kNmDc3N7Ns2TLOOOMMPvKRjzBx4kTa2tpYu3Yt+/bt45FHHuG8887LZryjUrAD5kKIkheLHKan/SXQNqa7nPopl2MYxbEg+HQD5mmvMN+4cSPf+c53OHjw4MB9U6dO5Zvf/CZLliwZfZRZJMlDCJFPjh3HSoZxe6tRBVBFeaQymjyO2bt3Lz09PdTW1jJ9+vTRxpYTkjyEECJ9WUkexUSShxBCpC9j6zwAdu7cyW233ca73vUu5s+fz/bt2wH48Y9/zAsvvDD6KIUQQhSNtJLHtm3b+OhHP0pzczN/93d/N6iUSDwe57HHHst4gEKIDAr3Y+7ah7lnPyST+Y5GFLG0hvx/8IMfcM455/Dggw9imibr1q0beOycc85h/fr1GQ9QCJEhto17x244ugWuisaw5s+mN5FkW08EB5hbGWCC3zPmb3WsN1zJAsSSlVbL49VXX+XGG2/E6/We9Kaor6+nq6sro8EVo93hLh5qeZl1h96Qsg+isCSSkDy+RktFEwD8taOP7oRFb8Li+Y4gljO2922sr4XwwU2EDz5HMnJkTMcShSutlodSCvMUi4p6e3vxjfNy4m3REHe+8RfaExHQ0B6L8JmZQ5eUF2MUi2O2tKIsC3tCPXrC8HvDjHteD9rvQVkOaI1TkdoGN2o7AyVKko5DwnFwGaObTqqdJIlgC4aZar3EenfiLpuYmfhFQUmr5TF//nz+8Ic/DPnY008/zYIFCzISVLF6ufcgR+Lh1A0FuyLSEssW1579GOF+VCyBa+8BiEbTPoYKhjEOHEYF+4Z/cikwDKx5Z2FPqMOePAF79gwAGv0ebJ2qslvr9eAfw9YAWmsUJ7RcpPFdstJqeXz2s59l+fLl2LbNNddcg1KK1157jSeffJI//OEPPPjgg9mKsyjMr5xAtcdPMBlDa81kn+yvnjWJEwZ7lUJF42i/f8QvVz19uN5qAdOAwx1YZ05D11ZnPs5C43bjTJ006K531lfSEo5hac3Mcv+YxikM04O7YiqJUCug8FXPGmPAolClvc7jiSee4N/+7d/o6zt+tVZeXs7Xv/51rr322owHmAm5XOfxf92tbO7aR5XLx6fOWIh7lM1/cXrG/kOYhzvAUGi3ifWOsyGNOk3m7gMYPcGB205NFfasqdkIdVzSThJQqCIpwyGGlvFFgtFolK1bt9LZ2UlNTQ3nn38+gUBgTEFmkywSLE2qOwiJBLqhNq3EAWAc7sA8cDi1h4rtYE9txJk8IUuRDsNxMA51QCIBfi9OY8PA1sTiuLhtkXBsyl0emcWVI6dLHiO+LEgkEjz11FNs2bJloCz7hAkTuPDCC6UUu8gLXVs16tc6kxrAcVChCLo8kL/EARid3ahYLJUwwlFUT3B8dKGloSse5XA0hFLgMUxmldfmZB8Sx7HojuxDGS5qA9MkaZ1gRC2PN954g8997nO0tbXx9qcrpZg8eTL33Xcfs2fPzlqgYyEtD1HIjEPtqGgMgMNWlKDfpGpCIxN85XmOrHC8FeoamPruaM0UfwVVnuzO7tTaYXfnZmwngUZT5qljak36VcPDySQKRZm7+LrwxtTyCIfDLF++nGg0yle+8hUuu+wyJk+ejNaaQ4cO8eyzz/Lzn/+c5cuXs27dOsrKyjIavBClzqmuwOzv53Ayyi4rjFFTT3u4CwU0SAIBwFTqePJA487Bls1JO0bCiuAyPSgUkUT6syebu3vY398PWnFmRRnzq0ffWi40w/4GnnjiCYLBIP/5n//JjTfeyPTp0/F4PHi9XmbMmMFnPvMZHn30UXp6eli7dm0uYhaiqCVsi55EhIR9dJOggB/7jCa6a8owJjaAaWIqRa9VGpsGZcIUfwUuwwAUE71lBFxjXwU/HJfhGbTvhtsc+Ww+gITjsL+/H49h4DEVeyORklo4PGzy2LRpE0uXLuWss8465XNmz57NBz/4QTZu3JjR4IQoNf1Wgp2RI7TFetkZOUK/lVrljdtFdVUN9tG/SFtrql3e/AVaYHymmzPLazm7so4GX256NwzDxdSa8/G5qvC7a5hanV6XlQGDxmUMpUpqv/hhu6127tzJxz72sWEP9M53vpMNGzZkJCghSlV3Iox59APEBLoTkYGr6EZ/qn85ZMWpcvuky6oAlHlqKasd3T7vLsPgHdVV7AiGMBTMqyqdLisYQfLo6+ujoaFh2AM1NDQQDAaHfZ4Q45nLMHAsjXG0D//tffeN/goaSW9xaX//YSw7RsA/EZercKfMj0fTy8qYXqLjwMMmj3g8PqKpuC6Xi6SUeC4otnZIOjYew1VSzeViNtFbRcK2iDoWFS4PE7yVYzpesG83/f2HUIZJJHqQhroLcZnZ6e6yrAi9nVtxnDiBsjMor5LV4+PZiOaO7du3D4/n9ANULS0tmYhHZEgoGWdb8BCWdvCZbs6rmoI7zYV0hUBrDdpBlchKfaUU08rqM3a8RKJn4NwoDOKxTlxlUzJ2/BP1djZjJVOVJfp6tuPx1eHx5nA9SjSG2XoEHBtdWZFaqyPyZkTJ4ytf+cqwz9FaywKaArI/2oNSCrcysRybg7FeppcVV+XZZLiNeOdraG3hLp+Kr2F+vkPKuWSsm+D+DTh2DH/1bComXTTocZfpx7LjKKVwHAu3e2wtmdOx7eOzv5RhYCXDOU0eZlsHSjupxZTBIFQEoLyAu4QivahkDF1eB67SW0g9bPK48847cxGHyDDF4ERupr/jcN4lenakih4qN8nwAdwVTZi+0hp0HE5f60asRA8A4Y6X8VQ04S0/3rKorj6bYN9uHCeB3z8Rjyd7xTgDZU2Egm+hlIFhePEHGrP2vYZ0wj4jCoWynYIt2qu6D2H0daTK3/R1YE+ZC2bxLRI8nWF/mqVLl+YiDpFhM8tqeS14mJhtUeXxMyVQhOUuCvWTIYe0kzh+QxnYicHVEpQyqa7KTWWHiuo5eHx1WFaEQGBKzose2vU1mAePoLRGB/zoysKdjab6e1OJA0BrVKQXXZm57spCUFqpUAzwmW4W1U7Ldxhj4qmdTbxzGzgOrvLJ467VAeCvPYe+QxsBjctXj7/6zLzG4/XV4yVPH4JVFdjlAbBs8LgLu3ikywOJo3vMaAftLb1ZcKOqqltspLZV8dLaOTpgPn6vc6x4L3a8D0/55HF9HoqKlcToPgC2hVNeBxXFNd54TMZLshcbSR5CCJG+0yWP4htFFUIIkXeSPIQQQqRNkocQQoi0SfIQQgiRNkkeQggh0pbTeX/r16/nySefZPv27YTDYWbMmMFNN93E1VdffcrXdHR08C//8i+88cYbdHV1UVVVxfnnn8+Xv/xlpk+fnrvghRAii2LxHjQav3d0JeBzLafJY82aNTQ1NbFy5UpqamrYtGkTK1asoKenh2XLlg35mlgsRlVVFbfeeiuTJ0+ms7OT+++/n+uvv55169ZRWZm9Wj5CCJEL7T1/I9R/EK00Fb7JTKxdmO+QhpXTdR7d3d3Uvm1jlRUrVtDc3Myzzz474uO0tLTw/ve/n5/85CdceeWVwz6/kNd5aMfGsaIYLn/JVI4VQoyco232HP4z5tEFoLadZOakKwdtgZsvBbPO4+2JA2Du3Ll0d3endZzq6lSdpmLfP8SOBwnu/m+Cu5+kb+8fsK1oVr5PMhkZVBFVCFE4FAaGOn7haCgTpQp/ODrvqa25uZlZs4bfVMZxHGzbpr29nR/96EdMmTKFSy65JAcRZk+s63XQNobpRdsJYp2vU9Z4wbCvi1thukO7QRk0VMw+7eY/XR1biEePAIqqmnmUVUzP3A8ghBgzpRQTa86lq+9N0Jra6jmSPIbzwgsvsGHDBlavXj3sc7/97W/z+OOPAzB16lR+8YtfUF5euFU1R+SEN4jWekTdVlo7HOj+Pzja2xhL9jKj4T1DPjeRCBKPtmOYqY28Qn27JXkIUYDKfBMp803MdxhpyVt6a21tZcWKFVxxxRVce+21wz5/+fLl/Pa3v+Wee+6htraWm266ic7OzhxEmiHawdzzEq7Xn0F1twLga1iIcpejtY3pq8Fff86wh7GcxKAuqIQd5VTDVkq5QB1/zJAxFSFEhuSlMGJvby/XXXcdgUCARx55hEAgvXLF4XCYyy67jE996lPceuutwz6/EAbMXW88i+vANjBMtFIkLvpEaoexNGmt2dvxvzjaQmuNx13GGXXvPOXz+3rfIhLZh6FMqmoX4POV1p4CorA4joVSRlF0u4jhnW7APOfdVtFolOXLl5NMJnnggQfSThwA5eXlTJ06lQMHDmQhwuwwgkfg2F7TjoPRuQ97FMlDKcW0unfSFdmDQlFffvrxosrq2VRW52azIDG+dXS/SiiyF4VBfe1CKsqKez8ZcXo5vTywLItbb72VlpYW/uM//oO6utHVuO/u7mbv3r00NTVlOMLscSoawHGzZdbtAAAbuklEQVQA0IaBUz/6PyyX6WFi5dlMqJxTENP5hEgk+ugL7cFQbpQy6e59Pd8hiSzL6SfP7bffzsaNG1m1ahXBYJCtW7cOPDZv3jw8Hg/XX389AA8//DAAv/jFL2htbWXRokXU1dXR2trKmjVr8Hg8fOITn8hl+GNizbscvOUQ68OePBddLt1HooSctKtfAe/yJzIip8lj8+bNANxxxx0nPbZhwwaamppwjl6dH3P22Wfzl7/8haeeeopIJEJjYyOLFy/m85//PI2NjTmJOyOUgXXmqccmhChmHncFVZUz6QvvRSmT+up5+Q5JZJnsJCjEOGU5mpjtEHAZGBnaD1wGzEtLQQ2YCyHy70g0wfqDvUSSDhP8bj40rRa3MfYEko0xOK01kcgBknYUn6cav7+41kOUKrk8EGIc2tIVIeFo3KaiO57kb92RfId0SpFIK9FYO7YVIRw5QCw2wnJGsThmy37MPXshGBxzHOpIJ65tb6J6pScDpOUhxLh04lWjBswMdVtlg2X3D3SDKWWQtML4GL5sudl6CGVbR78+jF1WBq7RfeSZu/fj2fQiKmmhX32T2AcuRddWj+pYpUJaHgVoV7iTP7bt4K1QR75DESXq4oYKKlwmaGgKeHlHbfrrrXLF66nG0TYAjmPj9dSM7IX28cKpCg2WNeoYXHsOoCwblEJFY7h27Rv1sUqFtDwKzOvBI/xnazO2dtjUuZvrpp7HO6om5TssUWKqvS6um1mPo3XGBsuzxe+fiFJuLCuCx1ONx3PqQdwT6YpKVG8vGAba6wPvCQVEtUb1BsG20BUVgx8bglPmw9R6YEqyU1nkdfUyQJJHgXkj1I6tU9OVHeCNvnZJHiJrCj1xHOPz1cIIuqpO5ExuRJeXgWWhq6sGrUUxOjpQ4UgqsYRCOFOnnrZLK7l4ISppoYJhnMZ67DkzR/ujlAxJHgWm0Vc+cDXoaIeJPrnCEWK0dOUpWimxOBhHx1FQEI1BxWn+1lwmiUsuykKExUuSR4H5u7rpRB2L1v5eJvurWFIvVzhCZJzHA9EoKJWqSu07fbeVOJksEhRCjD9ao7q6U2MelZXg9+c7ooIkiwSFKGXRGK6d+9BeN/bsGUPUmRInUQpdP7rCrCJFkocQxcyy8D7zAkakHzRY3X0kL16Y76jEOCDrPIQoYqqrFyMUTrU2DIXRMcLV10KMkSQPIYqYrqxAezxHb2h0eeEu9hOlRbqthChmfi/xd12Ae2cL2m2SPE9KoYvckOQhRJHTk+pJTJLNxURuSbeVEEKItEnyEEIIkTZJHkKUuN5Egn3hfmK2ne9QRAmRMQ+Rts54mNf6DqNQnF/TRIXLl++QxCkcjER5sy+CyzDYG45yUX01fpeZ77BKm2VhHO6ApAXlAZwJpbkYUVoeBSJqWTzXcZin2w7wSk8HhVo1xtYOf+nYyZF4iLZ4H3/p2JnvkErG4SBsb4MjGaym0xZL4DpWAFApDkfjmTu4GJJxpAuVSKC0k9p1sK80yyNJ8igQb/T1ELNtFIqOWIwDkXC+QxpSzE4SPWGTnUhSPowy4VAvvHoQDvYqmg9A29h3TQXAaxgDFyKW41Dmkj/5rHOc41+bCpUoze5CeScVCJvjLQ0DiGvn1E/OI7/podrjR2uNozUTfCPbmEecXmcEDCNVk8owFJ0Z2lL87KpyarxuvIbB9LIAE/3F0cVYqC3vkXCqK1IJxHEAha4uzW0VZMyjQMwIVNCc6CRhO5S53UwvK8wPZUMp/n7iPN4ItWFiMLeyMd8hlYQqPxzqA0OlPnOqMlTk1WUozq2pHPZ5Wjv0dbxCIt6N6QpQ1XABpiv3lWYtx+ZgNIKlHbyGyWR/edFsWDWgohzb64V4Asr8A/uGlBopyV5Ako5D1LYoc7kxi+0PRoyJ1tDSDb39UFsGZ6S3ad6YRfr20B/cjVKpDzq3r46q+twXWGyPRog6qW4erTVVHi81nuJoLZUiKcleJNyGgdvw5DsMkQdKwYw6IE8Tc7RjAeqE2/nppz+xs1Yphe0UZvetkDEPIYqGrTWhZJJkFj5QA5UzMUw3jpNAa01Z1ayMf4+RqPH40FpjHR3zq/EW+SZNfT2ozjbo7YICHcccLem2EkWjLdpKxIrgMb1M8U/FUONnvULSttneF8JyNArNWRUVVHrcGf0eWjs4dgzD8KCM/HVKaK1x0BgoVDF33/aHMUK9qTEPrXF8AaisyXdUaTldt5W0PPLA1g79VhKn9PN2xvTEu+izgmjlELP7ORI7lO+Qcqo9npoS7TIUpmFwJBbL+PdQysB0BfKaOFJxKExlFHfiALCTxwfLlULlqSswW2TMI8dCyTjPtu8laiepcHu4vGEmfldmryBLUVInMNTxxW62Lq0/xOG4DQNb64GJFEX/wToe+MvQ/RGUUmit0b6yfEeUUdLyyLHtfR3YWuMxXMRthzdCHfkOqShUu2tT3SrawXEsqt05no6UZw1eL9VuD+jUwr8zAkU+FjAeuDzo+kac8kp03QTwldbvTFoeOXIw2sWucDt7I924VQC/mRoYLLo57KOkteb/enZzJB7EZ7q5uHY25WnUxPKYXmaUzabfCuMzA3hMbxajLTxKKWZVpHflah0dWHeV6DqDomC6IFCYa7bGSt5VOZBwLLb27iPmJKj3+ulIdOPgUOXxck7lxHyHlxMtkXYOxXqAVImTvwX3p30Ml+Gm0lMz7hLHaLT29/FKTxuv9LRxsF8mjIjMk5ZHDiRsC1trXIDbMDm7so73T5hXEv3WPf2txJK91Jefhfs0H+oJbWOcsI7AobSmLRYSW2sORyN4jNRstEOxMJOKcaW2KGjS8siBMpeXem85tnawHJspvtqSSBz7u1/mtUP/w+7OzTS3/hbLTpzyubPKJuJ3eUjYFg6a2WWTchjp+KJILTocdHuEr7UdXdR1pUTuSMsjB5RSvLN2Nodj3bgNFxO8VfkOKSPaw29hHO1PT1hhOiN7aKw8e8jnugyTKxreQdiK4Tc9uI3xs0Yj1wylmOqvpDWa6q6aFqgc9mJFa82ekEUooTEMmFHhosIt15bi1CR55IihFFP8pbUpjMcMELdSH1CO41ARcmFEO3HqqsF18lvLUIpKd2HNODnQ383BaBBTKWZXTKDKHch3SBkx0V/GRP/IB9h74g79lsZtppJMW79NRZUkj1yx+7twEiEMTwVmoDg+J+TdIUZt9oTLqfQ24ndXMz/5DqpCBkZvCHNv6+A9DQpU1Eqwt78L0NjaYWd4fE+bls6q/LD7u7CC+9CJPqy+/dj9XfkOaURy2vJYv349Tz75JNu3byccDjNjxgxuuukmrr766lO+Zs+ePTzyyCP89a9/5dChQ9TX13PZZZfxxS9+kcrK4UtN54TjYL6xG6M/ilNVgX3W9MGdziXK567gHVP+HwDmm3sGfmaVSEIiCb7CnhWV1DZKMzAgMJ6L8NX6THoTDn1JjUvBpEARdkrYNioYRvu9UCT7lgA48RDKTJ1vZZg4yRBmvipkpiGn75A1a9bQ1NTEypUrqampYdOmTaxYsYKenh6WLVs25Guef/55XnnlFa677jrmzJnDgQMH+NGPfsTWrVv5zW9+M9Dnnk/m7v2Y3b2gFGZbB9rvw5k6zgaEPe7Uns2ANs3U7QJX4fJR7fHTm+wHYGagIc8R5dfMSjdOsa49sm1cr+1EWRZag33GZHRDcSwkNbwVWLFulOlC2xamuzjWheS0MGJ3dze1tYN/oStWrKC5uZlnn312yNf09PRQXV09aMDvueee4zOf+QyPPPIIixcvHvb7Zrsworl9J2bP8X1D7cZ67DOnZ/V7FpykhdHeCQ44dVWQ5gporVM9XWaOx9G11kSsBC5l4JMyMUVLHenEdfDIQOtXe71Y8/JTGXg07P4unHgIw1tYYx4Fs5/H2xMHwNy5c9mwYcMpX1NTc3IVynnz5gHQ1VUYfYNOYwNGd2+q98NQ2JMm5Duk3HO7cKaMblfBSBQOtYNjg98PUxtz1+unlKLcXdjda2IEPO7UZiBHLz50ke3VbgbqCippjETeOzabm5uZNSu9K4RXXnkFgDPPPDMbIaVN11WTvOAcVG8fuq4GvLKhUzq6elLDDqYJ8TgEQ1BdIMNZojjomirsxihGTx/a5cKe3pTvkEpeXpPHCy+8wIYNG1i9evWIXxONRvnBD37A4sWLOeuss7IYXZoCfrQUqxudE1oZWoMqrotGUSCcpkacptG1fkX68pY8WltbWbFiBVdccQXXXnvtiF6jtWbVqlV0d3fzwAMPZDlCkSsNtXDoCNg2BAJQWVqVq0tGwrZoiXbiaIcmf21ahS1F6clL8ujt7eXmm29m0qRJ3H333SN+3d13383TTz/NQw89xNSpU7MYocglvxdmTTva6ijCiT7jxRuhQ8Sc1Iy6nkQri2tnYkozcdzKefKIRqMsX76cZDLJAw88QCAwshW9a9as4Re/+AU//OEPWbRoUZajFPkgiWMwrTUt/d1ErSTVHj+T/fkta9PvJDCOriu2tEPcThJwyWSD8SqnycOyLG699VZaWlp47LHHqKsb2eyCdevWcdddd/G1r32ND3zgA1mOUpQMy8JsOYRKWjh1VTgTims2S0t/N+2xMIZS9PXH8Bgu6r3569OrMH2ErThKKbymC58pE0PGs5wmj9tvv52NGzeyatUqgsEgW7duHXhs3rx5eDwerr/+egAefvhhAF566SVWrlzJu971LhYuXDjoNY2NjTQ2ygCZGJrZchAjnFoAaLS24fh9kOaGSvkUs62BBXumMohY8bwmj3mVUzgY7cbSDlN8NcW5mFBkTE6Tx+bNmwG44447Tnpsw4YNNDU14bytRMSLL75IMpnkueee47nnnhv02Be+8AVuueWW7AUsipqKJ49/rRSqP4Z+W/LQWtMRj2IqRZ23sGbL1bpTq99dysTWDrWe/CY+QymmFtlaBJE9OV1hni/ZXmEuCpNxuAPjcDvKMNBKYc07E9yDr5e29XYStpI4WtPg8zO74uRFqfnUnYgQshLUuQOymFHkXMGsMBeFpaf3DcKR/Zimlwn1F+JyFU+Xzkg4kxrQfh8qFkuViX9b4ojbNr3JOB7DxFCKjliUs8qrC2qjrlpPWd5bHEIMRebZjVP90SP0Bt/AceIkk310dL2c75CyQldX4DQ2gPvkulUuQw3aGtdtGAWVOIQoZNLyGKcsK4JSx6sQOnYCdagdd8tBHL8P69w5UAAVi7PJVAZzKmvY3x/CAGaWVec7JCGKhiSPcaq8bBrBvrew7RhaO1SqiXiffxllO5hao+Jxku9cmO8ws67e66e+wAbKhSgGkjzGKcNwMWXSe+nvP4zbXUFgbw/KslMr9ZRCBWWSgRDi1Eq7X0KclmG4KC+fitdbjTO5AX1sXMBx0HXShSOEODVpeQgAdGU58csW42o5iA74sOYWz0Y6Qojck+QhBuj6GpL1hbXOQQhRmKTbSgghRNokeQghhEibJA8hhBBpk+QhhBAibZI8hBBCpE2ShxBCiLRJ8hBCCJE2SR5CCCHSJslDCCFE2iR5CCGESJskDyGEEGkbF3uYCyGEyCxpeQghhEibJA8hhBBpk+QhhBAibZI8hBBCpE2ShxBCiLRJ8hBCCJE2SR5CCCHSVpR7mO/bt48HH3yQrVu3snPnThYtWsQjjzwy6DmXX345Bw8eHHRffX09mzdvHnTfrl27+Nd//Ve2bt1KRUUFH/3oR/nCF76AaZqnjSEUCrF69WqeeeYZHMfh0ksv5Rvf+AY1Ncf3AP/a177G7373u5Ne+9RTTzFr1qx0f+yCke/zn0gk+Pd//3deffVVXnvtNeLxODt27Bjyuc888wz33HMPLS0tTJ06lS984Qt84AMfGOVPnn/Fcu5L8b2f73P/t7/9jccee4wtW7bQ3t5OY2Mj11xzDTfffDNer3fQc19++WXuuusuduzYQX19PTfccAOf/vSnx3gGBivK5LFz5042btzIueeeSzKZPOXzrr76apYtWzZw2+12D3o8GAxyww03cOaZZ3Lvvfeyf/9+vve97+E4Dl/60pdOG8Ntt93G3r17+e53v4thGHz/+9/n85//PL/61a8GPW/mzJnceeedg+5ramoa6Y9akPJ9/mOxGE888QQLFizgvPPO469//euQz9uyZQtf/OIX+eQnP8mqVavYuHEjX/7yl6msrOTd7353mj91YSiWcw+l997P97lfv349+/fv5+abb+aMM85gx44d3HPPPezYsYOf/OQnA8/bt28fn/3sZ7n00kv58pe/zLZt27jrrrvw+/189KMfHcMZeBtdhGzbHvj6lltu0Z/61KdOes5ll12m77rrrtMe5/7779eLFi3SoVBo4L4HHnhAL1iwYNB9b/fKK6/o2bNn65deemngvldffVXPnj1bb968eeC+r371q3rp0qUj+pmKSb7Pv9ZaO46jtdb6kUce0bNnzx7yOTfddJNetmzZoPs++9nP6k984hOnPXYhK5ZzX4rv/Xyf+66urpPu+/Wvf61nz56tW1tbB+775je/qa+88kqdTCYH7vvWt76llyxZMvC7y4SiHPMwjMyEvWnTJt797ndTXl4+cN9VV11FLBbjpZdeOu3r6uvrufDCCwfuW7BgAU1NTWzatCkjsRWyfJ9/AKXUaR9PJBK8+OKL/MM//MOg+6+66iq2bt1KKBQafeB5VAznvlTl+9zX1taedN/cuXMB6OrqGnT8973vfbhcxzuWrrrqKtra2njrrbcy8SMAJT5gvnbtWs455xwuuOACvvjFL57UF7lnzx5mzpw56L7Jkyfj9/vZs2fPKY871OsAZs2addLrdu/ezfnnn88555zDddddN+wfZinJ1vkfif3795NMJk86/syZM3Ech717947p+IUun+f+mPH63s/luW9ubsYwjIHj9ff3c/jw4ZOOf2ycKVO/WyjSMY+RuPzyy1m4cCGNjY3s3r2bn/70p/zjP/4j69ato6KiAoC+vr6Br09UWVlJX1/fKY99ute1trYO3J47dy4LFizgzDPPpLu7m4ceeoibbrqJX/3qVyxYsCADP2Xhyub5H4lgMDhwrBNVVVUNfO9Sle9zD+P3vZ/Lc9/R0cH999/PBz/4wYFWzLEW9dvf98duZ/J9X7LJ4xvf+MbA14sWLeK8887jQx/6EGvXruWGG24YeGyoJrjWetim+aled6Lrr79+0O1LL72UD3zgA9x///3ce++9I/kxila2z/9Ivf04x35Hpdz1Ugjnfry+93N17hOJBLfddhuBQICVK1eOOL5Mvu9LutvqRLNnz2bGjBm8/vrrA/dVVlYO2fcdDoeHvDI48XVDZfBQKHRSxj+Rz+fjkksuGRTDeJHJ8z8Sp2phHLs91uMXk1yf+6GM1/d+Ns691pqvfvWr7Nq1iwceeGDgvQ7H39dvP3423vfjJnkcc2LmnTlz5kl9gIcPH6a/v3/IMY0TXzdUn/mpxkJOF8N4k4nzPxLTpk3D7XafdPw9e/ZgGAYzZswY0/GLUa7O/UhjGE8yee5Xr17Nhg0b+NnPfnbSmplAIMCkSZOGfN8f+96ZMm6Sx1tvvcXevXuZP3/+wH1LlizhueeeIxwOD9z31FNP4fP5WLx48SmPtWTJEjo6OtiyZcvAfdu2bePAgQMsWbLklK+LxWJs2rRpUAzjRSbP/0h4PB4uuugi/vjHPw66f/369SxcuHBctTxyfe6HMl7f+5k+9z//+c959NFHufvuu1m0aNGQz1myZAlPP/00tm0POv6kSZOYPXv2GH+i48xvf/vb387Y0XIkGo2yYcMGdu3axXPPPUcwGKSuro5du3YxZcoUNm/ezI9//GPi8Tg9PT1s2rSJb37zm1RXV/Od73wHj8cDwFlnncXjjz/Oiy++yIQJE3j++ef54Q9/yPXXX88ll1wy8P3e97738eabb3LFFVcAMGnSJLZu3coTTzzBpEmT2Lt3L9/+9reZNWsWt912G5BqNt54440kk0lCoRDbtm3jW9/6Fi0tLdx5551MnDgx9ycuQ/J9/gE2btzIm2++ycsvv8zrr7/OWWedxa5du/D7/QNdh1OmTOGnP/0pvb29eL1efvWrX/H73/+e7373u0ybNi23Jy1DiuHcl+p7P9/nft26ddx+++0sXbqUiy66iLa2toF/Ho8Hv98PpFoXDz74ILt27aKqqor169fz4IMP8pWvfCWjybsot6FtbW0d9GY+0YYNGwiHw9x5553s2LGDUChEdXU173nPe/jSl7500ht3165dfOc732Hr1q1UVlbykY98hFtuuWVQmYDLL7+cxYsXc9dddw3c19fXx5133snTTz+N4zhcdtllrFq1amAudjweZ8WKFWzbto2uri68Xi8LFy7klltuYeHChVk4K7lTCOd/qDIQAHfeeSfXXnvtwO1nnnmGH/3oR7S0tNDU1MQtt9zCVVddNdZTkDfFcO5L9b2f73N/qpIvcPL7fsuWLQPlSRoaGrJSnqQok4cQQoj8GjdjHkIIITJHkocQQoi0SfIQQgiRNkkeQggh0ibJQwghRNokeQghhEibJA8hsuS//uu/mDNnzsC/efPmsWTJElauXEl7e/uQr4nFYixatIg5c+bw4osv5jhiIUauZKvqClEovve97zF9+nSi0SjPP//8wD7Y//3f/33SFqV/+tOfBorarV27losuuigfIQsxLGl5CJFlc+bMYeHChVx88cWsWLGCD33oQ+zZs2dQbbRj1q5dS1lZGYsXL+bPf/7zoPpHQhQSSR5C5Nix+kLd3d2D7m9tbeWll17i7//+7/nkJz9JNBrlD3/4Qz5CFGJYkjyEyLFjdaGmT58+6P61a9eitebaa6/liiuuoLq6mrVr1+YhQiGGJ8lDiCyzbRvLsgiHwzzzzDP8+te/5uqrrx5U4dRxHH7/+98zffp0Fi1ahMfj4ZprruHVV19l165deYxeiKFJ8hAiyz784Q8zf/58LrjgAj7/+c8zd+7cQVVqAZ5//nkOHTrE0qVLB70OkNaHKEiSPITIsu9///s88cQTrFmzhg9+8INs2bKF1atXD3rO2rVrUUpx+eWX09fXR19fH1OmTOGss87iySefJJlM5il6IYYmU3WFyLIzzzyTuXPnAnDxxRcTCoV47LHHWLp0KQsWLCAYDPLMM8+gteaaa64Z8hgbN27kve99by7DFuK0JHkIkWNf//rX+d///V/uueceHnzwQdatW0cikWDFihWce+65g56bTCb553/+Z5544glJHqKgSPIQIsemTp3Kxz/+cR599FGam5tZu3YttbW13HDDDQNblZ7oyiuv5I9//CMdHR00NDTkIWIhTiZjHkLkwec+9znKysr44Q9/yOuvv87SpUuHTBwAH//4x7Esi9///vc5jlKIU5NtaIUQQqRNWh5CCCHSJslDCCFE2iR5CCGESJskDyGEEGmT5CGEECJtkjyEEEKkTZKHEEKItEnyEEIIkbb/D+r+D5cyX7TJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RA correction: -0.10791846540882943 arcsec\n",
      "Dec correction: -0.028431532331651965 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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110 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": 16,
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}