{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CDFS-SWIRE master catalogue\n",
    "## Preparation of CANDELS-GOODS-S data\n",
    "\n",
    "CANDELS-GOODS-N catalogue: the catalogue comes from `dmu0_CANDELS-GOODS-S`.\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 total magnitude.\n",
    "\n",
    "We don't know when the maps have been observed. We will use the year of the reference paper."
   ]
  },
  {
   "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": {},
   "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": {},
   "outputs": [],
   "source": [
    "imported_columns = OrderedDict({\n",
    "        'ID': \"candels_id\",\n",
    "        'RA': \"candels_ra\",\n",
    "        'DEC': \"candels_dec\",\n",
    "        'CLASS_STAR':  \"candels_stellarity\",\n",
    "    #HST data\n",
    "        'ACS_F435W_FLUX': \"f_acs_f435w\", \n",
    "        'ACS_F435W_FLUXERR': \"ferr_acs_f435w\", \n",
    "        'ACS_F606W_FLUX': \"f_acs_f606w\", \n",
    "        'ACS_F606W_FLUXERR': \"ferr_acs_f606w\", \n",
    "        'ACS_F775W_FLUX': \"f_acs_f775w\", \n",
    "        'ACS_F775W_FLUXERR': \"ferr_acs_f775w\", \n",
    "        'ACS_F814W_FLUX': \"f_acs_f814w\", \n",
    "        'ACS_F814W_FLUXERR': \"ferr_acs_f814w\", \n",
    "        'ACS_F850LP_FLUX': \"f_acs_f850lp\", \n",
    "        'ACS_F850LP_FLUXERR': \"ferr_acs_f850lp\", \n",
    "    \n",
    "        'WFC3_F098M_FLUX': \"f_wfc3_f098m\", \n",
    "        'WFC3_F098M_FLUXERR': \"ferr_wfc3_f098m\", \n",
    "        'WFC3_F105W_FLUX': \"f_wfc3_f105w\", \n",
    "        'WFC3_F105W_FLUXERR': \"ferr_wfc3_f105w\", \n",
    "        'WFC3_F125W_FLUX': \"f_wfc3_f125w\", \n",
    "        'WFC3_F125W_FLUXERR': \"ferr_wfc3_f125w\", \n",
    "        'WFC3_F160W_FLUX': \"f_wfc3_f160w\", \n",
    "        'WFC3_F160W_FLUXERR': \"ferr_wfc3_f160w\", \n",
    "    #ISAAC?\n",
    "        'ISAAC_KS_FLUX':\"f_isaac_k\",\n",
    "        'ISAAC_KS_FLUXERR':\"ferr_isaac_k\",\n",
    "    #HAWKI WIRCAM\n",
    "        'HAWKI_KS_FLUX': \"f_hawki_k\",# 33 WIRCAM_K_FLUX Flux density (in μJy) in the Ks-band (CFHT/WIRCam) (3)\n",
    "        'HAWKI_KS_FLUXERR': \"ferr_hawki_k\",# 34 WIRCAM_K_FLUXERR \n",
    "    #Spitzer/IRAC\n",
    "        'IRAC_CH1_FLUX': \"f_candels-irac_i1\",# 47 IRAC_CH1_FLUX Flux density (in μJy) in the 3.6μm-band (Spitzer/IRAC) (3)\n",
    "        'IRAC_CH1_FLUXERR': \"ferr_candels-irac_i1\",# 48 IRAC_CH1_FLUXERR Flux uncertainty (in μJy) in the 3.6μm-band (Spitzer/IRAC) (3)\n",
    "        'IRAC_CH2_FLUX': \"f_candels-irac_i2\",# 49 IRAC_CH2_FLUX Flux density (in μJy) in the 4.5μm-band (Spitzer/IRAC) (3)\n",
    "        'IRAC_CH2_FLUXERR': \"ferr_candels-irac_i2\",# 50 IRAC_CH2_FLUXERR Flux uncertainty (in μJy) in the 4.5μm-band (Spitzer/IRAC) (3)\n",
    "        'IRAC_CH3_FLUX': \"f_candels-irac_i3\",# 51 IRAC_CH3_FLUX Flux density (in μJy) in the 5.8μm-band (Spitzer/IRAC) (3)\n",
    "        'IRAC_CH3_FLUXERR': \"ferr_candels-irac_i3\",# 52 IRAC_CH3_FLUXERR Flux uncertainty (in μJy) in the 5.8μm-band (Spitzer/IRAC) (3)\n",
    "        'IRAC_CH4_FLUX': \"f_candels-irac_i4\",# 53 IRAC_CH4_FLUX Flux density (in μJy) in the 8.0μm-band (Spitzer/IRAC) (3)\n",
    "        'IRAC_CH4_FLUXERR': \"ferr_candels-irac_i4\"# 54 IRAC_CH4_FLUXERR\n",
    "    \n",
    "    \n",
    "    })\n",
    "\n",
    "\n",
    "catalogue = Table.read(\"../../dmu0/dmu0_CANDELS-GOODS-S/data/hlsp_candels_hst_wfc3_goodss-tot-multiband_f160w_v1_cat.fits\")[list(imported_columns)]\n",
    "for column in imported_columns:\n",
    "    catalogue[column].name = imported_columns[column]\n",
    "\n",
    "epoch = 2011\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",
      "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:76: 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:80: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  errors = 2.5 / np.log(10) * errors_on_fluxes / fluxes\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",
    "        # Some object have a magnitude to 0, we suppose this means missing value\n",
    "        mask = np.isclose(catalogue[col], -99.) \n",
    "        catalogue[col][mask] = np.nan\n",
    "        catalogue[errcol][mask] = np.nan  \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",
    "\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",
    "catalogue['candels_stellarity'] = catalogue['candels_stellarity'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<Table length=10>\n",
       "<table id=\"table140629291664720-587672\" 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_acs_f435w</th><th>ferr_acs_f435w</th><th>f_acs_f606w</th><th>ferr_acs_f606w</th><th>f_acs_f775w</th><th>ferr_acs_f775w</th><th>f_acs_f814w</th><th>ferr_acs_f814w</th><th>f_acs_f850lp</th><th>ferr_acs_f850lp</th><th>f_wfc3_f098m</th><th>ferr_wfc3_f098m</th><th>f_wfc3_f105w</th><th>ferr_wfc3_f105w</th><th>f_wfc3_f125w</th><th>ferr_wfc3_f125w</th><th>f_wfc3_f160w</th><th>ferr_wfc3_f160w</th><th>f_isaac_k</th><th>ferr_isaac_k</th><th>f_hawki_k</th><th>ferr_hawki_k</th><th>f_candels-irac_i1</th><th>ferr_candels-irac_i1</th><th>f_candels-irac_i2</th><th>ferr_candels-irac_i2</th><th>f_candels-irac_i3</th><th>ferr_candels-irac_i3</th><th>f_candels-irac_i4</th><th>ferr_candels-irac_i4</th><th>m_acs_f435w</th><th>merr_acs_f435w</th><th>flag_acs_f435w</th><th>m_acs_f606w</th><th>merr_acs_f606w</th><th>flag_acs_f606w</th><th>m_acs_f775w</th><th>merr_acs_f775w</th><th>flag_acs_f775w</th><th>m_acs_f814w</th><th>merr_acs_f814w</th><th>flag_acs_f814w</th><th>m_acs_f850lp</th><th>merr_acs_f850lp</th><th>flag_acs_f850lp</th><th>m_wfc3_f098m</th><th>merr_wfc3_f098m</th><th>flag_wfc3_f098m</th><th>m_wfc3_f105w</th><th>merr_wfc3_f105w</th><th>flag_wfc3_f105w</th><th>m_wfc3_f125w</th><th>merr_wfc3_f125w</th><th>flag_wfc3_f125w</th><th>m_wfc3_f160w</th><th>merr_wfc3_f160w</th><th>flag_wfc3_f160w</th><th>m_isaac_k</th><th>merr_isaac_k</th><th>flag_isaac_k</th><th>m_hawki_k</th><th>merr_hawki_k</th><th>flag_hawki_k</th><th>m_candels-irac_i1</th><th>merr_candels-irac_i1</th><th>flag_candels-irac_i1</th><th>m_candels-irac_i2</th><th>merr_candels-irac_i2</th><th>flag_candels-irac_i2</th><th>m_candels-irac_i3</th><th>merr_candels-irac_i3</th><th>flag_candels-irac_i3</th><th>m_candels-irac_i4</th><th>merr_candels-irac_i4</th><th>flag_candels-irac_i4</th></tr></thead>\n",
       "<tr><td>0</td><td>1</td><td>53.0929957</td><td>-27.9545375</td><td>0.0299999993294</td><td>1.57247</td><td>0.0562002</td><td>6.56154</td><td>0.0567839</td><td>15.6708</td><td>0.131342</td><td>17.6684</td><td>0.180936</td><td>23.1631</td><td>0.165851</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>37.9073</td><td>0.0958995</td><td>54.8362</td><td>0.116206</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>76.3619</td><td>0.139008</td><td>58.9298</td><td>0.0822037</td><td>49.5586</td><td>0.591554</td><td>35.9697</td><td>0.523036</td><td>23.4085</td><td>0.0388043</td><td>False</td><td>21.8575</td><td>0.00939602</td><td>False</td><td>20.9123</td><td>0.0090999</td><td>False</td><td>20.782</td><td>0.0111187</td><td>False</td><td>20.488</td><td>0.00777402</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>19.9532</td><td>0.00274674</td><td>False</td><td>19.5523</td><td>0.00230084</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>19.1928</td><td>0.00197646</td><td>False</td><td>19.4742</td><td>0.00151454</td><td>False</td><td>19.6622</td><td>0.0129598</td><td>False</td><td>20.0102</td><td>0.0157877</td><td>False</td></tr>\n",
       "<tr><td>1</td><td>2</td><td>53.1028883</td><td>-27.9596622</td><td>0.509999990463</td><td>nan</td><td>nan</td><td>0.122752</td><td>0.0268322</td><td>nan</td><td>nan</td><td>0.120398</td><td>0.0353473</td><td>0.204252</td><td>0.0639968</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.489016</td><td>0.0583247</td><td>0.376055</td><td>0.101423</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>4.32343</td><td>0.125855</td><td>4.17359</td><td>0.0717704</td><td>1.93559</td><td>0.706397</td><td>1.40668</td><td>0.541094</td><td>nan</td><td>nan</td><td>False</td><td>26.1774</td><td>0.23733</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>26.1984</td><td>0.318758</td><td>False</td><td>25.6246</td><td>0.340186</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>24.6767</td><td>0.129495</td><td>False</td><td>24.9619</td><td>0.292826</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>22.3104</td><td>0.0316058</td><td>False</td><td>22.3487</td><td>0.0186707</td><td>False</td><td>23.183</td><td>0.396241</td><td>False</td><td>23.5295</td><td>0.41764</td><td>False</td></tr>\n",
       "<tr><td>2</td><td>3</td><td>53.1016865</td><td>-27.9584786</td><td>0.0299999993294</td><td>nan</td><td>nan</td><td>3.34779</td><td>0.0594275</td><td>nan</td><td>nan</td><td>5.47988</td><td>0.0956989</td><td>6.13215</td><td>0.155562</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>8.46696</td><td>0.0777481</td><td>10.5502</td><td>0.110988</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>7.49208</td><td>0.135364</td><td>6.48226</td><td>0.0809801</td><td>3.45865</td><td>0.721565</td><td>7.87405</td><td>0.57855</td><td>nan</td><td>nan</td><td>False</td><td>22.5881</td><td>0.0192732</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>22.0531</td><td>0.018961</td><td>False</td><td>21.931</td><td>0.0275432</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>21.5807</td><td>0.0099698</td><td>False</td><td>21.3419</td><td>0.0114219</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>21.7135</td><td>0.0196167</td><td>False</td><td>21.8707</td><td>0.0135636</td><td>False</td><td>22.5527</td><td>0.226513</td><td>False</td><td>21.6595</td><td>0.079775</td><td>False</td></tr>\n",
       "<tr><td>3</td><td>4</td><td>53.1022798</td><td>-27.9586509</td><td>0.00999999977648</td><td>nan</td><td>nan</td><td>0.28294</td><td>0.0769396</td><td>nan</td><td>nan</td><td>0.572661</td><td>0.122873</td><td>0.620282</td><td>0.190909</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.706361</td><td>0.106992</td><td>0.881169</td><td>0.214985</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.71141</td><td>0.129901</td><td>1.37216</td><td>0.0770705</td><td>1.78349</td><td>0.699897</td><td>-0.325973</td><td>0.557147</td><td>nan</td><td>nan</td><td>False</td><td>25.2708</td><td>0.295243</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>24.5053</td><td>0.232961</td><td>False</td><td>24.4185</td><td>0.334165</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>24.2774</td><td>0.164456</td><td>False</td><td>24.0374</td><td>0.264895</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>23.3166</td><td>0.0824105</td><td>False</td><td>23.5565</td><td>0.0609828</td><td>False</td><td>23.2718</td><td>0.426077</td><td>False</td><td>nan</td><td>-1.85572</td><td>False</td></tr>\n",
       "<tr><td>4</td><td>5</td><td>53.0905594</td><td>-27.9584995</td><td>0.0</td><td>0.0616629</td><td>0.0101197</td><td>0.117489</td><td>0.0131163</td><td>0.130316</td><td>0.0221347</td><td>0.114363</td><td>0.0234091</td><td>0.111082</td><td>0.0262729</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.165881</td><td>0.0256818</td><td>0.308574</td><td>0.0219707</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>2.82623</td><td>0.133584</td><td>2.12198</td><td>0.0671409</td><td>4.57958</td><td>0.606722</td><td>2.52612</td><td>0.527717</td><td>26.9249</td><td>0.178184</td><td>False</td><td>26.225</td><td>0.12121</td><td>False</td><td>26.1125</td><td>0.184417</td><td>False</td><td>26.2543</td><td>0.222241</td><td>False</td><td>26.2859</td><td>0.256796</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>25.8505</td><td>0.168094</td><td>False</td><td>25.1766</td><td>0.0773052</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>22.772</td><td>0.0513182</td><td>False</td><td>23.0832</td><td>0.0343534</td><td>False</td><td>22.2479</td><td>0.143843</td><td>False</td><td>22.8939</td><td>0.226815</td><td>False</td></tr>\n",
       "<tr><td>5</td><td>6</td><td>53.0999405</td><td>-27.9582797</td><td>0.0</td><td>nan</td><td>nan</td><td>1.01299</td><td>0.0415012</td><td>nan</td><td>nan</td><td>1.1913</td><td>0.0587654</td><td>1.14286</td><td>0.0900046</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.38284</td><td>0.0363205</td><td>1.37406</td><td>0.063972</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.83482</td><td>0.124917</td><td>1.75306</td><td>0.0726456</td><td>2.2002</td><td>0.645725</td><td>0.479455</td><td>0.521698</td><td>nan</td><td>nan</td><td>False</td><td>23.886</td><td>0.0444815</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>23.7099</td><td>0.0535581</td><td>False</td><td>23.755</td><td>0.0855059</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>23.5481</td><td>0.028517</td><td>False</td><td>23.555</td><td>0.0505485</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>23.241</td><td>0.0739184</td><td>False</td><td>23.2905</td><td>0.0449922</td><td>False</td><td>23.0438</td><td>0.318647</td><td>False</td><td>24.6981</td><td>1.1814</td><td>False</td></tr>\n",
       "<tr><td>6</td><td>7</td><td>53.0961485</td><td>-27.9575924</td><td>0.0</td><td>0.750837</td><td>0.0335719</td><td>1.75813</td><td>0.0326885</td><td>3.87552</td><td>0.0772168</td><td>4.05189</td><td>0.0957558</td><td>5.05038</td><td>0.0863977</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>7.30726</td><td>0.0704524</td><td>9.55474</td><td>0.136626</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>18.5387</td><td>0.162151</td><td>13.7508</td><td>0.0916589</td><td>12.3678</td><td>0.758402</td><td>10.7663</td><td>0.658811</td><td>24.2111</td><td>0.0485461</td><td>False</td><td>23.2874</td><td>0.0201868</td><td>False</td><td>22.4292</td><td>0.0216325</td><td>False</td><td>22.3809</td><td>0.0256585</td><td>False</td><td>22.1417</td><td>0.0185739</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>21.7406</td><td>0.010468</td><td>False</td><td>21.4495</td><td>0.0155253</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>20.7298</td><td>0.00949652</td><td>False</td><td>21.0542</td><td>0.00723721</td><td>False</td><td>21.1693</td><td>0.0665781</td><td>False</td><td>21.3198</td><td>0.0664383</td><td>False</td></tr>\n",
       "<tr><td>7</td><td>8</td><td>53.091539</td><td>-27.9583631</td><td>0.360000014305</td><td>0.0552173</td><td>0.0123191</td><td>0.070271</td><td>0.0116116</td><td>0.0806083</td><td>0.0210433</td><td>0.101759</td><td>0.0273173</td><td>0.106426</td><td>0.027046</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.137755</td><td>0.0281735</td><td>0.146131</td><td>0.0232199</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.80913</td><td>0.183908</td><td>1.05341</td><td>0.0903556</td><td>1.44388</td><td>0.725899</td><td>3.63358</td><td>0.649447</td><td>27.0448</td><td>0.24223</td><td>False</td><td>26.7831</td><td>0.179407</td><td>False</td><td>26.6341</td><td>0.283438</td><td>False</td><td>26.3811</td><td>0.291467</td><td>False</td><td>26.3324</td><td>0.275918</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>26.0522</td><td>0.222054</td><td>False</td><td>25.9882</td><td>0.172521</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>23.2563</td><td>0.110371</td><td>False</td><td>23.8435</td><td>0.0931283</td><td>False</td><td>23.5012</td><td>0.545845</td><td>False</td><td>22.4992</td><td>0.194059</td><td>False</td></tr>\n",
       "<tr><td>8</td><td>9</td><td>53.0895949</td><td>-27.9580295</td><td>0.0</td><td>0.116514</td><td>0.0182176</td><td>0.207775</td><td>0.02597</td><td>0.441355</td><td>0.0446217</td><td>0.412882</td><td>0.038471</td><td>0.439651</td><td>0.0527499</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.475793</td><td>0.0456359</td><td>0.545227</td><td>0.0355639</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.63995</td><td>0.130341</td><td>1.38403</td><td>0.0697079</td><td>1.59181</td><td>0.611055</td><td>0.259089</td><td>0.52638</td><td>26.2341</td><td>0.169761</td><td>False</td><td>25.606</td><td>0.135707</td><td>False</td><td>24.788</td><td>0.10977</td><td>False</td><td>24.8604</td><td>0.101165</td><td>False</td><td>24.7922</td><td>0.130268</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>24.7065</td><td>0.104139</td><td>False</td><td>24.5586</td><td>0.0708201</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>23.3629</td><td>0.0862928</td><td>False</td><td>23.5471</td><td>0.0546841</td><td>False</td><td>23.3953</td><td>0.416786</td><td>False</td><td>25.3664</td><td>2.20584</td><td>False</td></tr>\n",
       "<tr><td>9</td><td>10</td><td>53.0909628</td><td>-27.9578333</td><td>0.0</td><td>0.22139</td><td>0.0216918</td><td>0.357964</td><td>0.0253868</td><td>0.584102</td><td>0.0459367</td><td>0.584998</td><td>0.0523299</td><td>0.616088</td><td>0.0576579</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.703004</td><td>0.0529971</td><td>0.755778</td><td>0.045867</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>2.97497</td><td>0.138839</td><td>1.9797</td><td>0.0711056</td><td>3.01486</td><td>0.606722</td><td>4.98112</td><td>0.521029</td><td>25.5371</td><td>0.10638</td><td>False</td><td>25.0154</td><td>0.0770004</td><td>False</td><td>24.4838</td><td>0.0853877</td><td>False</td><td>24.4821</td><td>0.0971225</td><td>False</td><td>24.4259</td><td>0.101611</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>24.2826</td><td>0.08185</td><td>False</td><td>24.204</td><td>0.0658917</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>nan</td><td>nan</td><td>False</td><td>22.7163</td><td>0.0506703</td><td>False</td><td>23.1585</td><td>0.0389968</td><td>False</td><td>22.7018</td><td>0.218498</td><td>False</td><td>22.1567</td><td>0.113569</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(\"$('#table140629291664720-587672').dataTable()\");\n",
       "    $('#table140629291664720-587672').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": "stdout",
     "output_type": "stream",
     "text": [
      "The initial catalogue had 34930 sources.\n",
      "The cleaned catalogue has 34926 sources (4 removed).\n",
      "The cleaned catalogue has 4 sources flagged as having been cleaned\n"
     ]
    }
   ],
   "source": [
    "SORT_COLS = [\"ferr_acs_f435w\",\"ferr_acs_f606w\",\"ferr_acs_f775w\",\"ferr_acs_f814w\",\"ferr_acs_f850lp\",    \n",
    "             \"ferr_wfc3_f098m\",\"ferr_wfc3_f105w\",\"ferr_wfc3_f125w\",\"ferr_wfc3_f160w\",    \n",
    "             \"ferr_isaac_k\",\"ferr_hawki_k\",    \n",
    "             \"ferr_candels-irac_i1\",\"ferr_candels-irac_i2\",\"ferr_candels-irac_i3\",\"ferr_candels-irac_i4\"]\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_CDFS-SWIRE.fits\")\n",
    "gaia_coords = SkyCoord(gaia['ra'], gaia['dec'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tWV21mmr216MNeI4DOCDfZkCuFah0SWjwxXcAiyFYOdckxK6cm0wbqGnXkLlm\nWgTUemTUeFpWIo6hHKkS9VXGgEpXUK+XwwC7iW835vY+G89zyDACA3KMcPqblxrjNC3PAXkZPPJt\nhphVflPts/H6lpr9omnG2r8jxlci5qrH+CK6iVEl0ikS6fip6LanmYyuXrHGOi7RWHmOA88BhXYD\n8q3BWYA70NIBjDxQGPoSTyBePc4XByAvIzgDqnRJqI9qrvENKtrekgIca5RgbjbjDGPb+03JxJpp\n5uEwm1DXXKeqtblGLp+qWYLUs8/qPb5i0ZnGF3GSLm1UqRQsi9eZUulcsTRSLa4Wa/CnYxsIfHCW\n1DfTCG/zspUks3CxQUDdF2lHxBpabuxpNyDfFlkEMjGDag0D4JMUlNaLsJk4FDmMMAlcyueLA5Br\nEZBjEVDlllDvVWAz8ShyCBC42LPT9nQBbftsR4yvaPedtNCN9ncyumRY6unwp/68Xi8WLVqEqVOn\nYsyYMfj1r3+NjRs3qjr25ptvxpAhQ1TtG3niUzn5kcdHXlmm2qFCGq07qdax6qWrRRvwPAerkcPA\nHCOK803IyRCaZ136tEEqCDwHk8Chl0PAyQcZOSRjUifhwAC4AgwVTkmz88U3Xwj0sBkwJN+EPpkG\nmAQ+5QKOHdW3It8rVV0tY42MS882INrS4Ua1ePFibNu2DStXrsS3336LWbNmYf78+Th06FDc41at\nWoXt27cn9F5aXp2EtLS+4tFDN1Iz3duAazYmtctRieiG/q/tOeOal9S07QcWg/ZtK/DBpdbWD6Gk\nQmR7duc+q5cu+VR0OtSoGhsbsWbNGtxxxx0YMGAAzGYz5syZg0GDBuHtt9+OeVx5eTmefPJJzJ8/\nvwOjJQiCINKBDjWqXbt2QRRFnHnmmS22jxw5Mu5saeHChbjiiivaHEcQBEF0fTrUqOrq6gAA2dnZ\nLbbn5OSgtrY26jHvvPMOysrK8Ic//EH3+AiCIIj0I22e+ou21ltWVoYnn3wSy5cvh9lsPgVREQRB\nEKeaDp1R5eXlAQAaGhpabK+vr0d+fn6b/UNLfqNHj+6Q+AiCIIj0o0ONasSIETCZTPjxxx9bbN+6\ndSvGjh3bYtuJEyewceNGvPvuu5gwYQImTJiAW2+9FQAwYcIErF27tsPiJgiCIE4dHbr053A4MHv2\nbCxbtgzFxcUoLCzE//zP/+DEiROYM2cOSkpKsGDBArz66qsoLCzE119/3eL4bdu24c4778QHH3yA\nrKysjgwaiF97AAAgAElEQVSdIAiCOEV0+D2qBx98EEuXLsXVV18Nt9uNYcOGYcWKFejduzeOHz+O\n0tJSiKIIQRBQWFjY4tjc3FwAaLOdIAiC6Lp0uFGZTCYsXLgQCxcubPPahAkTsG/fvpjHtvc6QRAE\n0fWgwokEQRBEWhNzRlVWVpa0aK9evZI+Nt1Jtp5OPD290DpWvXQ7VxuwhOppqUVp1tUjJanCmKbp\nqZRQjjvNFE/SWfqWXrqUkzY6MY1q6tSpCXcYxhh4nsfu3btTDkwLtEpwGamjVeLM1lmXtY619d96\ntEG6xiorDLLCsKvaj3yrgCKHEQKXuq6kMDT4FFS7RWRZBBj41BLoBgl+5jqPDIHjkGdtW8gwGfyS\nAqdfwerdjZjcz4YBuSZYDKktoDDGICnAgVo/jjSKOL+/DUaeg1FIz37Qus9qqRntPbTQTUVndUl5\nSjFcPrIopeP1JO49qv/6r/9K6Om6hoaGtMkgoUVG5mgp/KNlEU+UaPGkaoLxyg1oods61livpaKb\naqyyEpyV7K32Y2+NHzID9tcC2RY/xvbKCJtLokgyg1dSsKXMh0q3BACodMvIsQgotBvAcckk1G1Z\nJoQBqHLLqPPK6GkLFm1MxrBEWUFABv77x3p8fsgNhQGfHHRjZE8zfjsuFzkZQlKGFZAVVLllrNnr\nRLkr2AY/nPDi3L5WTOpnTaqEiJo+m2rfijZuo72fFrFqrUucJK5RnX322eEf6aqhpqYm7dLUJ2NY\najpPMh2/vX07Ila1g1+tbqKDVI82UBiDwoCjDSJKKn3wyy37YINPweeH3Ci0GzCmlwUWA6/KsGSF\nQVIYfqzw4UiD2GbJr94no9EvIz9DiFsxty0hpbb7SgpwwimjxqOgyCEgw8Cryn4uNc8iP9rnxHu7\nm9qUpS+p9OP2j8pxfn8bbjw7G2aBg0mFYQVkBZ4Aw5r9ThyoDbR4TVSArw57sPmEF78YZMfInhYI\nKrK16z2+2tNNhz5LBpUYMY3qiy++CD8ODgCKoqC8vBy9e/cOb9u7dy8GDx4MgyEok5ubiy+++ELH\ncJNHzZd1Mp1HTcdPVFdtx0/0Kk7N4E/HWOOZq6Qw1LglbC33wRmIX96+wiXh4/0u9M8xYlRPC3ie\ni2pYoZnZnmo/9jXPzGKhMKDKI6POJ6PQZmhnJhTboFrjlxkON0iwGTkUOQww8lxUA1AUBlFh+L9j\nHrz+Y0OMKsQn3/2rw258e8yDXw6x4/LhWTBwHAxRlu5EWYGkAJ8ddGFbha9NteBI3CLDB3ud2HDE\ng0uL7eibbYKRpz6r1XcMEceoIg2prKwM8+bNw/Dhw7F06dLw9sWLF8PpdOKVV15BYWEheJ5vcVw6\nEqvjp7KMFavjp9opY3X8VNezo5mr1rHqoRv5t6QwuAMKfijzosYjq9ZjAErrRRxtEDE034yhBWbw\nCM4CFIVBAXCkPoAdVf42M7N4SApw3CnB7JHD5eVPLgcmXw3YLTIcrBORZeZR6DCEYwWC96EO1gaw\n/Id6HGsSVWsGZIZ3dzvx6UE3rjkrCz/vbwtXXZYVBpkB3x714JsjHojxvb8FtV4Zr29vRN8sIy4b\nYkdOhgCTEJy1UZ/V7n5Wd0TVYvUTTzwBu92Om266qcX2Rx99FDk5OViyZIkuwelJNBNItQNFakRb\nJ09VV6sHOToiVj10geBsxysq+P64B58cdCVkUpHIDNhV7cdH+5w40iiCMYZKt4RPD7rwQ3nb5UO1\n+GWG0gYRhxsCCMhKxJceh0RNKpJGv4L9NQFUeySIMsOJJhGPf12Nh76sSsikInEGFLy0uR53rqtA\nSYUPCmMoqfThmW9r8GVpYiYVydFGES9sqsfq3U54RaXb91ktv2O6K6p+8Ltp0yb8/e9/x9ChQ1ts\nHzRoEB544IE2BtZZCH35a9159OqMesSrZxvoEWtJhRcHagOaPSLulxk2nfDihzJv3OWtRPGIDOVO\nCX0yjZq1AQNQ41Hw1MYK/FTX9p5ZslS4JDy+vhrDe5jBNHzofHe1HzkWHhcOsmteZbgz9Vk9Yu1u\nqJpR+f3+mA1tNBrh9/s1DYog4qHL75jS6xmguMhMnzZIchJJELqjyqjGjRuHZcuWoampqcX2qqoq\nPProoxgzZowuwREEQRCEqqW/+++/H3PnzsU555yDPn36wGazoampCcePH0d2djZef/11veMkCIIg\nuimqjGrAgAFYs2YNVq9ejZ07d6KpqQkDBw7Etddei8svvxx2u13vOAmCIIhuiurs6Tk5Obj55pv1\njIUgCIIg2qDaqLxeL95//33s3r0b1dXVWLx4MfLz87FlyxaMGzdOzxgJgiCIbowqozp27Biuu+46\nVFZWom/fvjh27Bj8fj9KS0tx44034oUXXsD555+vd6wEQRBEN0TVU39LlixBUVERPv/8c3zyyScw\nmUwAgr+jmj9/Pv72t7/pGiRBEATRfVFlVJs2bcL9998ftc7UjBkzsHfvXs0DIwiCIAhApVHxPB/z\nyT5RFOlX1wRBEIRuqDKq008/HS+//HLU11atWoVhw4ZpGlRHo3VpklBuLz10tSakqaW2KDMcbxSx\nryaAGrcYrgibKofqAvjikAs/1QXgSTYRXSsEDjijwIyZQx0YU2SBKcUigCH8koLd1T58tN+JIw0B\nTdqXMYajDQHYTDwG5Bg1i9UkcBhdZMGwAjMKmgs3akFec/mTg/UBuNrJbK8WSWH49qgbT2yowbu7\nGuH0J5frMRp6jIXW2kRyqHqY4pZbbsHvfvc7bNu2DRMnToQkSVi2bBkOHTqEvXv34pVXXtE7Ts1p\nnS1Zi8SZrbMu66mbqmakrlaxygpDtVtCnVcJp/ipciuo9QRQ6DAg08wnpV3pkvDRviaUOaVwotTD\n9SJsJg6FdiPMhiQyXAPol23EqEILDM2ZwwfkmNA/24Td1T7sqw0klVZJUhgqXRIam8ttMAA/lHmx\nu9qPMUUZ6GFX/aBtC6rcEraUeZsNmoPNJGBwHo8mn4xylwQ5CR/gOaA4z4ThPSzg+WDxxwKbgDyr\ngAqXhIY4JUPiYTPxGFNkQYHNAIEDRDlYK8xi4NAr05BU4UbGGHZX+7F2vwt+SYGoADur/Nhd7cfP\nTsvAef1tMCdZwTjWuE238dWd4ZhKq9+0aROWL1+OHTt2wOVyITMzE2eddRbmzZvXqVIoxUvhn0p6\nfy3r2qiJR+9YE9FljKHOK6PKLYPFyEPHATAJQC+HEVaTui+UJr+MTw+4sKfGD1lpqxuKLtvCo4fd\noLp6b09bsIBihoGPWospVIRwW7kPRxrVZSZXGEONWw5nc4/WBgIH5FkFnF0UrDashia/jC1lXtR6\n5Bi5+IJFI2vdUrD9VakCfbOMGN3LAiMfvTKvwoJFI8ucEtwBdaomgcPIHmb0yzGB56JVPWbgwMFh\nDl5gqC1hf7RRxJq9TajzylEzuht4wMBzuGCgFWN7WcOfZ9GiRVi0aFFM3fb6erqN29a8/MX+lI5P\nFr1K1hcUOGK+pvrybvz48Rg/frwmAZ0KEq0qmmilznj7JVqPRu9YE9GNp8kYgyugoNwpQYpiJC32\nRagYoAhrczHAWFfAfknB14fd+L/jwYzmsWY2oc2NPgUNvgDybQLyrULMsvBZZh5jemUgJyN+SXpD\nc0HFsb0zcEYPM7aUeVHljr7ExBhDg09GpStoEvHaQGZAtVvG//7kQp8sA0b2zECGMXob+CQF2yu8\nONYoQYmbhJYDzwEFNgNyrQZUOCXU+2IvhxXYBIzrHXzfeG3AcxxMAoe+WUb4pKBh+aXoUfAcMCTP\nhDMKTs7MYsXKADT5GZz+AHIz+OCsK0YctR4Ja/c7caRBjFtyRFKCFxefHXRjwxEvLh3iwJA8U+wD\noH7c6j2+1OgSCRhVSUkJ+vfvj8zMTADA6tWrsWPHDvzsZz/DtGnTdAswVZK5gtGrUqeajp9qVdFo\nx6baBtGO84pK+MtL/QpZ8IvKLTL81FwMsKfj5ExIVhh+OOHB54c8kBmDpHLlKbRbrVtGnUdGT7uA\nbIsQjjnDwGFUoQW9Mo0QOPVtYOA5ZJoFTO5nQ51HxpZyL5r8J4Ny+RWUOUXIccy0NQxBwzraIOFY\noxPF+SYMy7eEZxeSwrCn2od9NQEwdvKztQvHQeCAXpkG9LALONEktbgvlGnmMbZXBnKs8U26NTzH\nwWoEBuYY4fIrKHdJLc5LvywjRhedXD5VCwNQ51VQ7wugwMojz2oInxd3QMHnP7mwvdIXdSYdC1EJ\n1u1atbMR+VYDmqLcv0rH8UVlQNpHlVF9/PHHuOeee/D2229j5MiRWL58OZ599lmcfvrpWL16NVwu\nFy6//HK9Y00YLaqKttbRu6qoFrGGOr4Wywyt20BUgHKnCHeAIWhRyekyAA1+BY3+AHIzONT5FHy8\n3wWvqCRdsE9pFq5wBZfg+mYZMb53BgbnmWMsRanDwHPItwm4cJAdxxtFfHfcgyP1InwyS7o8SCjW\n/TUBHKwN4MwepmC9rUo/FJZ8yQ2O42AUOPTNNsIvMdR4JAzLN+O0LCP4BEy6lSp4Lmh2drMJdR4Z\n4IAxRe3PzOLBADDWfB/TG0C+VUBJhQ8bjnpSagNRAcpdEraV+/Dm9gbMGOJApjk4c9VqfIW26fEd\nQ7RFlVGtWLECf/jDHzBy5EgwxvDaa6/hlltuwZ133onVq1fjjTfeSDuj0vIqRY+boXotAejV8UNt\n8FNd5EMGqWszALuqAth41IMYK0sJozAgIAMT+1gxKMeU0JV+LHgu+GWdbxOwvyagWe0mufkLeVtF\nAIB2daZ4jkOGkcOUATaYBI0qy3IceAC9Mw0YlGtO2vhbwxBcvvtwrxNHG0XN2lZhwMG6AKzG1Ewq\nEr1mQlo+xNEVUXVXu7S0FJdccgkAYMeOHairq8OVV14JAJg4cSKOHDmiX4RpQqjz6FUNWI/OqUes\nehQYDMhMC89rg83Ia2JSkchK8J6M1rR3fytZBF778ucGnoceT1v7ZUWX4o0Cr9+41VqTnmKPjiqj\nMhqN4Sv0b7/9Fv369UPv3r0BBH/wqyja/EaCIAiCIFqjyqiGDBmCN998EyUlJXjrrbdw4YUXhl/7\n8ssvMWDAAN0CJAiCILo3qozqjjvuwKpVq3DVVVfBYrHgpptuAgD8+9//xl//+ldcf/31ugZJEARB\ndF9UPUwxfvx4rF+/HocOHcLpp5+OjIwMAMDAgQPx0ksvYfLkyboGSRAEQXRfVOccqa2tRVNTU9ik\nAGDLli3o06ePLoERBEEQBKDSqH744QfMnDkTH3zwQYvta9euxaxZs7Bt2zZdgiMIgiAIVUb1zDPP\nYMaMGXj88cdbbF+xYgVmz56NpUuX6hIcQRAEQagyqj179mDevHnhyr4hOI7D9ddfj3379ukSHEEQ\nBEGoMqqMjAxUVlZGfa2iogIWi0XToAiCIAgihCqjuuCCC/DQQw/hq6++Qk1NDbxeLyorK/Hxxx/j\n3nvvxQUXXKB3nARBEEQ3RdXj6QsWLMDtt9+O+fPnt0gdwhjDueeeiwULFugWYCpomTcrsvqnlulT\n9NDVK1ZFYTDygKgkn4w2GlZTMHUMB23TCFW6JBTYBJiE5ArqRcMsBNMSKYxpmu5HYQwctE3NwwEI\nSAy8MfmEvNHwScHCmApjmupmmQ2odkevOZUsAgd4RAabUdu21WOMaVUJuyuiyqjsdjv+8Y9/YOfO\nndixYwecTidyc3MxYsQIDB06VO8Yk0KrJI8dWbVXS10tk9OGBlCNJ7LEQyj+VAZpUCPfasTFg+3Y\nWu5DlVvSxAA4AO/vdWJ/bQCXFtthMXAwpmBYoXY0G3jc9bM8/LvUhR/KEitDEQ2FMQQkhhNNIhiA\n0zKNMBk4TQyAAShtEJGTwaOnzZBC9vQgssIgygxbyrz4aJ8T0093YEi+GYaUcukFiylajRxmDbPj\nWJMFa/Y54Q4kn0UfCBoUzwGT+1lhFrQbC9HGlxbfMQxAY3Nds6EF5qS1uiqqKvw+99xzuOmmm+Bw\nxK7AmM6kWqlTq3pUao7TI9ZkdSMHUJVbjlIfKrLrJBJv7ONqPMGS666AoroeVXsIHDC+dwamDrRB\naC6KqDrSOOerwStj3QEnDtQFEo6VNdfbKnOKcPpbHuww8eiVaUzaAKLNTHkOyLcGy8zzAJCArsIY\nZAXYWenDwRbZ84GedgN+OcSBQoch4Zkrh+aqz5nGcIbz0PttK/Pi05/ckBT1dclCmgIPjCq04Ps3\nn8Ljjz7S4vVUx62W4wsIflaPqKDcKQeTMwMY3kOdUXWnCr+qjGr8+PF46623MGjQoJSD8Xq9eOKJ\nJ7B+/Xo0NjZi8ODB+P3vf49zzz23zb6MMaxYsQLvvvsuysvLYbVaceGFF+Kee+5BVlZWQu+bSAdN\npNOp1U10gGhZVTSZfRWFwSMpqHDK8Lc7xVFrWOr2Y4zhhFPC1jIvRJlpVv7DLATLXozrnRG84o5j\nWIm0VZlTxJq9TlS5pfZnAc1LhhUuCfXe2JV4ASAnQ0Ch3QAhuCbYjrC6pVMDDxTaDXCY+eYs8PHb\nQGHAT3UB7Kz0xf1sg3JN+OVQB+wmvl3D4hC8eChyBOOI1b4BmWHDETc2qqxPZeSB/tkmXFpsR67V\nELcUvR7jKxFdWWEQFYZypwSP2PKDpbtRAfqYVTyjEhbFOpMRWK1WvPXWWxgxYgSys7NTCubhhx/G\njh078Le//Q133nknAOC+++7DxRdfjJycnBb7vvLKK3j99dfx3HPP4eGHH8YFF1yA5cuXY8+ePbjo\noosSel+Oa1vuIFalzmj7JqvbuqMnqqu2qqgaXTVtEFreOd4kocajtuwCh5ZfeK1jaW1Q8WPluGBl\n3dPzTDDyQI1H1uT+ldxcn2h7hQ/ZGQJyLAK4KMthifYDh1nAmF4WFDmMONIgQmHRCioGt9W4ZRxt\nFOEV2/80PomhzisDCNaW4toxFjUoDGjyK3D6FViMfPQSGCx4cVDuFPHNEQ+ONUrtlnep98r4/pgX\njT4F/XNM4ND2QoBDcGbXw8ajT5YRFmNskwKC9wIH5JgwplcG3GKwGGa0y2ojD/SwCfj1iGxM7m9D\nRvPs7KuvvsLPf/7zqNp6jK9oOq3/VpTg7LTcJaHCFf1+XA+busLrW0prVe2nB8N6ar+6ZrPFNmhV\nM6rZs2ejvr4e5eXlsFgssNlsLUU4Dt988027gTQ2NuLcc8/Fs88+i1/84hfh7TNnzsT48ePx4IMP\ntth/48aNMJlMGDduXHjbX/7yF2zYsAEff/xxu+8Xj1gfO9Ubo1pW/2yt2RotdUNlzyucEhr9qa65\nRd6/Sv1eligz7Kr24WBtcNlJq1vOhXYDLhtiR097cNlKi/MlKwxby7z430Mnl60UxuD0yyh3Skkv\nZxp4oMhugMMigOc4zR48sRk59HIYYBCC98UkhaHRJ+OHE140+JIL1sADk/paMbm/DQLHBc0QHHIz\neBTYDEnXCKtxS1i7P1hcUVSCBmUx8JgxxIGh+aY25y3ejKo10caYpuMLwTFW7ZZQ51XinjuaUbVF\nlXUXFxdrEsiuXbsgiiLOPPPMFttHjhyJ7du3t9k/cjlQlmWUlJTgs88+wzXXXJNyLHpWwg3p6qEZ\n+bdWup6Agia/3O4ASkAZJ0sBph6rUeAwqjADxXlmfH3YjaaUjTRIhUvCK1saMDjXhGtGZqX8sAEQ\nnAWMO82Ks4oysGpnA7aU+VDulFQsn8ZHUoBjTRLMbhkDcowwaPQko1tkOFAnwmHiYDHw2FPtR4VL\nSjnWrw57sOmEFzeenYPT80woshthFFJr23ybAdePzsHhhgD+fciNYQUmjOtt1aQ4ppYXlpGaQHB8\neSUFVW5Zl8Kj3QFVRrVkyZKYr4miiLKyMlVvVldXBwBtlg9zcnJQWxt7Gvviiy9i2bJlMJlMmD9/\nPubNm6fq/dTSGarrhjRVTIATpswpwh//dkkSaHXNfxKrkUeh3YAmf0BT3YN1AUgKg9mg3WPsJoFD\ncb4ZH+93pWxSkfhlhoDMYBA0kwQANPgU7K32anrGPCLD8UYRP+9v07TScv9sE24YbdSlaq8e46vW\nI6EpQA6VCimPzJ9++gmzZ89OOZB4ne7WW2/Fjh078Nprr+GDDz7Ao48+mvL7EQRBEJ0DVTMqv9+P\nZ599Fhs2bEB9fX2L1xoaGlBQUKDqzfLy8sLH9OzZM7y9vr4e+fn58QM1GDBq1CjcfffduOOOO3Dn\nnXd22sflCYIgCPWomlE9++yzeO+993D66aejoaEBZ599NoYMGYLGxkZceumlePXVV1W92YgRI2Ay\nmfDjjz+22L5161aMHTu2zf5z587F8uXLW2wLBILLPoKg8doHQRAEkZaoMqpPP/0UTz31FJ5++mkY\njUbce++9WLlyJdatW4d9+/ahsbFR1Zs5HA7Mnj0by5YtQ2lpKbxeL1auXIkTJ05gzpw5KCkpwcUX\nXxy+5zV+/HisXLkSmzdvhizLKC0txfLly3HeeefBarUm/6kJgiCIToOqpb+qqqrwk3+CIIRnNaed\ndhoWLFiAJUuW4J///KeqN3zwwQexdOlSXH311XC73Rg2bBhWrFiB3r174/jx4ygtLYUoigCC96Ys\nFgvuu+8+VFdXIy8vD+effz7uuuuuZD4rQRAE0QlRZVQOhwOVlZXo2bMncnNzcejQoXCWij59+mD/\nfvXP85tMJixcuBALFy5s89qECRNa1LYSBAHz5s3T/Ck/giAIovOgyqgmT56Me++9F6+//jrGjRuH\npUuXwm63Izs7G6+++mr4IQmCIAiC0BpV96juvfde9OzZE4qi4Le//S18Ph9uuukmXH755Vi3bh3u\nuOMOveMkCIIguimqZlQFBQV4/fXXw39/+umn+P777yGKIkaMGIFevXrpFiBBEATRvYk5o7ruuuvQ\n1NQU9TWr1YopU6Zg2rRpLUyqsbER1113nfZRErpiEjhomDgAQDAvhcWgfXYOq5GDw6RdBgkgmJpq\n5/EGiLKGFfsQzPtn1zhWAKg9dgBeZ4PmunqcrwwdNBljqPfKkDpJPiKBD2aLJ5In5oxq8+bNkKTE\ncn6JoojNmzenHFRHEVmlE9Au7VGyNW/UampdDbhPlhEKC1bErU8yEWkkWWYehY5goT6fFCxl4Eux\nVofABTNkD83PhMKATcc9+OqwJ+X0RFV1Tfi/XT/hrc98yLWZsHjWWbhoRFHKiWn31/pxrEnC+QNs\nqHQFS5a4VWRMj4e76ih2/vdiVJash2Aw4Ly5d2PcrN/AYEq90J7AAwNzTQjIwSKOrUtPJEqGgcOv\nR2RherEDPKddn633yvihzIMGnwIDz2F0oQX9slNPp6Tn+CpyGFHoCCbVrfFolU+zexEze/rQoUNR\nVFQEnld/RagoCioqKrBnzx7NAtSDaEaihblEM7xUTVDvWCM1ZCVYK6ncKcEVSNywbEYORQ4DjDwX\nLvEQKr7o9CuodKmo19QKDkCelUeB1QBwJ0uqSwqDrDB8cciNzSe8CVcFbnR5sXn3IVTWN0GKmElZ\nTQL65dnw+OxRGNM/sYeEGGM41iRiW5kPonKyVH2orlNpfQA7Kv3hAnlqCbgasPfdp1H677cBRYYi\nBy8gTRYrjBYrpt32KIb/fCa4BMZqPBTG4BUVnGiSEo5V4ICLT7fjP0Zmw8gjXFU51T7rDijYVu5F\nhatlBWiBAzKMHMb2sqKnveV1t9rs6bGqYicbaywNRQmOhQqXFDczPWVPb0vMGdXtt9+ueSCnmngd\nMJXS0u3pJltmPtYxrWNNRDderALPQQDQJ9MAv8xQpnImZBY4FDkEZBj4tjWImktSZJp5OMwm1Htl\n1VmksyzBJLR8hEGFMDRX6v3FIBsm97Ni7X4Xdlf729X0+gPYtv8IDp2oAWNKmzg8ARl7yptw9csb\nMXFQPh6ZORL98+3t6la5JfxwwguP2LaGF8dxELjgjKV/tgm7qnzYXxtotw3kgA8/ffp37Fv9XwCT\noYgtP1/A50HA58Hap+/GhjeewfQ/PIF+Z53TbqxA/JTBPMfBZhIwOI9Hky9YnkSNX004LQPzxuTA\nZuLbJPhNts8GZIadlT4cqo9e4kVmgCvA8M0RN3IsAsb0zkC2RV3Wmo4eX6GxUWQ3oMDGUO6Uk7og\n7I6oqkfVFUi2w+lRtVet7qmOlQFw+RVUxJgJGXigp01ApkUIlkNUoaswBsaCX+z1McqKRJuZtUdA\nVlDvlfHhXieONbVdspZkGbt+OoGdpSfAWHA21h4Cz8HIc7h8bF/cO/0M5EYp7Nbkl7GlzItaj6x6\nVicrDJLCsK3chyMNYpvXmaLg+HcfYsd/L4YS8EL0eVTpGs0ZOO2Msbjojr+goJ82pXlOFnyUUO2W\no56v4jwTfjc+Fz3tBlhUZqBvr8/KCsOBWj92VfuhMKguj8FzwGmZBpxVmIGljy+OOqNKl3GrKAw+\nue3SOM2o2tLljSqVpbdYHTTVpQG9deNVrE0UpfnYyJkQzwH5VgF5VvUG1RpZCX4BVrikcH2p4MzM\ngAwj12YGpQbGgoUKjzQE8NF+F+q8MhTG8NPxSmzZewSKoiT1wITJwEPgOdw2tRjzzj8dFqMAn6Rg\ne7kXx5qkpIs5Skpwie2HE15UuYN1Vqp3f4eSv/8JntpySCoNKhKe58EbTBj+819h6ryFsOf2aPF6\n0sVX2Mll4QZfMNZCuwHzxuTgjB5mmAT1VXBbyrZddjveJGFruReizBJe0gVOVhL+/NWlWLb0sXAd\nrHQcX9EuCDuDUWlJyPS6pVFp+UCDHtU/9dLVK9bQTMjpl+EwC1FLjSelqzAEFAZRZrCZ+KSNr7Wm\nzIBV28rxlw+3w+cPaPJEX4ZRgMUo4M9XjIMsmMNVkVNFUhgOHTmGFx+cj+oD2yD7vSlrGowmcLyA\ny+55BsOnzgQHTpOb+MH7bQwXDLRhUt9gnalUa02F+myDT8b/HffCHWi7fJoM7764BP9x+wM4r58V\neSZI+Q8AACAASURBVNbgXY50HrcMwQvCUKzt0Z2MSl2LdFLSuWqvXrp6VQPmOQ7ggCyLoGkb8DwH\nMxecTWkWK8+BB/DPb/fD6fFpogkAXlGG0SjAw4wwaHh5Z+A57Pj0bVTu/j8wRZt7FpIYzMfZ/+zz\nAI1MCgj2p8E5JkzqZ4NJoyrDofO+s8qvWfVmIDhzlBmQZzVoPm71qAbMAcjJoKoQ0dD+Rx5E2qBH\nlWE90CtORYff2XDgwsuhmsKYZiYViRaz3jZwgD5NoM/ijh6quo2tLrm+lTpkVARBEERak7JRbd26\nFRs2bNAiFoIgCIJoQ8r3qP70pz/h8OHDaf8jX4IgCKJzkrJRPfHEE/D5tLthTRAEQRCRxFz627Zt\nW7iSb7y8fyNHjsT48eP1iY4gCILo9sTNnl5dXR3+d6xM6gRBEAShJzGX/vLy8jB//nwUFxeDMYaH\nH34YZnPsX0w/9dRTugRIEARBdG9iGtWf//xnLFu2DD/++CM4jsPOnTtjZlLvLL/XIQiCIDofMY1q\nypQpmDJlCoBgyY/33nsPeXmJlT0gCIIgiFSJeY/qkUceCd+XmjVrVtxlP4LoTnSmBYSumckzMTrR\n6epUfasjiWlUq1evDj/59/7778PlciEQCMT8Lx3RMiVLZDVgrXQjtTpbrFrpKs0FECVF25RHssIw\nZ8IAmI1COHt2qvAcB7dPxPE6j+Zl0EefdxGy8gpgzrBqpskZzdj+9ceQZFlDx2IoaxJR5Zbgk7RL\n+RSQFRTYhObkx1qOhWCGfi119R63RFtiZk+fO3cufvjhBwDtJ03lOA67d+/WJ8IU0Ltqb7K67ZX5\nSNdqwFrHygDUeWVUN5cPyTJzKHQYoxZKVEsoc3q5S4LTr6DR48dr3+zHl7tOQJJZ0nn6eJ5DpiMT\np/XpA7PZjHyrgHG9M2A18TBolE9PDPjx6f8sx7t/+08osoSAv/1CkFFjNVlgcBQg+xe3wHLacNgz\nzJgwfCAK87Jg4PmkL9sVxloUUhzfOwPzxubAHqVQolpEOZg5f91BF0oqfOA4oMAmIDdDCF5FJxkr\nB+BfL/0n7nnwYVgMPCyGYAkZi6C+xllrOnJ8qaE7ZU+PaVQNDQ1Ys2YNGhsb8fzzz+PGG2+EzWaL\nKZTOFYGT6UxqjtGyam8i75vMManoal3kkSF2aXoOwdLz+VYDuAQMK1Tmu8otoc7b9kr/RJ0bL32x\nCzuP1cGfwExA4HmYLWb07dsvav8/LdOAMb0yYBRSL3cRwtXYgHdfXIIv3nsNiiRBlqP/hrFNrCYL\nYDAje8rNsA45t805yc+y42dnDkKmLQMGQX2WboUxeAMKTjjblqYXOGDaYDuuOatl6fn2kBUGmTGs\nP+zBt8c8aH1KDHywEq7dzCPYrOralgdgEIBeDiNee+4vuO3eh1q8bjfxKHIIMHDqDUvv8ZXIMZGQ\nUbVi7ty5eOGFF5CZmalddKeAzvaF3l4ceseqta6iMPgkhnJX+yXuBQ7oYROQ3Vz2IJZhRZuZxWP3\niXo8/9kOVDR44RPl2O/P8xAEAX369kVWVlbcz8VzwOBcE0b0tEDgk58NtqbqxBH899IHsf3bLyH6\nfTGXhgSjCQp4ZJ3zazhGXQLOYIyr26dHLiaOGAiz0QAhjmEpLDjbOdEkwiPGb9gMA4crh2dierED\nhjg1qkIz3u0VXnx+yN2ursXAoZfDALMhfjHNULHEIocRWWYeHMfhhScfbWNUIbItPArthnbrqqXL\nuI0GGVUX51Qs5+ml2xmWNBWFQWIM5U4ZrkBi9zWMQvDKunVRxciZWYVLanNF3l6cG/dX4KXPd8MT\nkFoYVvALlkOv3r1RUFCQUDsYeWBETwsG5ZrAc9r9bOOnXdvw6qN/xPFD++D3nqz6y/ECwAvIHHkh\nHD/7NXiLXbUmxwHFfQpx9pB+MAh8i5+eMMYgK0CZU0y4PlRehoAbRmdjbO/gLJOPOF+SAhxuCGBt\nc/XlRLCZgoZl4FsaVuhfBTYB+VZDi9fiGVXo2Hwrj3xb8OFnPs3HV2vIqABMmjQJa9asQU5ODiZN\nmhT3jTiOwzfffJNCqKeG1h89Xat/RtPV6ktQzzZgzSXaK1wSGnyp3XjPaL7HYG5+MMInMZS5JPjb\nmZnFQ5QVrN12BP+9YT9EmUFRGHr06IHCoqK4M432sBo5nF1kQaHDiOBKmDZtunX9p/j74/egsb4W\nkiTDOmA0ss6/EYasHu0LxMAgCDhr8GkY2r8IPM+DMaDKJaHOK6dUGqlfthG/HZuDATkmcFywcu2H\ne5041qRuGTMWoZmQwDcXGrTw6GE3Rr1P2J5RhRA4oKddCBYFbd6WzuMrRDobVch8EiGpCr+TJ0+G\n0RhcQpg0aVKX/FFvqFKnHlV7tUbPKsN6aPolBbUeGQ0+RZNacF6J4VC9CLuJA2OAu50lIzUYBR4z\nxw7AhWeehvs+2AebIwsmkyllXY/IsOGoF6fnyRhVaEGM38knBMdxGHP+xRh17i/w+JPPoI7Phqnn\noJR1JVnGln1HsO9YJSaMHoEmP2t3+VQNRxpEPPh5Fc7ta0WhXcC+WjF1UQANPgWNvgDO629FjkVI\n+iGOSGQGlDlluPwKemcaNS02qcf46o7ENKolS5aE//2f//mfHRIM0bXQyqQiCS4dajvobWYjevYo\nQCCx1ah2cQcUKNC2OqlgMKBw9AVwVTRoqAp4/SLqPHLM7DPJsrfGj0a/AVqeMwYgy6yNSUUiKlRg\nN12JaVSlpaUJCQ0YMCDlYAiCIAiiNTGNavr06QlNV6lwIkEQBKEHqpb+vF4vXn75ZZx99tkYNWoU\nbDYbnE4ntmzZgt27d+OPf/xjhwRLEARBdD9iGtWsWbPC/37ooYdw3XXX4eabb26xz4033ojly5fj\n22+/xYwZM/SLkiAIgui2qLob+dlnn+EXv/hF1NcuuugifP7555oGRRAEQRAhVBmVKIo4cOBA1NcO\nHjwIUdTm0VOCIAiCaE3Mpb9IpkyZggceeAC7d+/G0KFDYbFY4PP5sGPHDrzzzjuYPHmy3nESBEEQ\n3RRVRvXII49g0aJFeOWVV1rMngRBwJQpU/DII4/oFiBBEATRvVFlVHa7HX/961/x2GOP4fDhw3C5\nXLBarejbty/sdvX5xQiCIAgiUVQZVQiLxYKhQ4fqFQvRhfBJ2melYIyhziuB54Bsi0HTtDTn97dj\nR6UPFa7UctFF0jfLiB42Axp87Wd1T4SzBvSE0SBgz/FazTSz7Vb0dBjR4JXblF5JFp4DftYnAwVW\nA7aW+yBr1AY2YzDvo19i8KaQ67E1FgNHFXbTlISMqisRmSwy1UzJsXRDaK2rVe4wPdogIDGUO8Xm\nXHyRbZG8LmMMTX4F5S4RshJUqnLL6OUwwm7iU4w5GOOkflb8rI8V+2r8WHfAmXDW8Eh6OQz45dBM\nFNiCdY962gyodEsJZwyPxfjiXhg9qBDVjW689+0+HK91Jq1lNZsw/owB6N0jBwLPo+D/2zvz8Ciq\ndP9/q6r3JXvIxiK7QBKJgCDLOGRkk0FGcYYMI464MJELznifUbmIGlHH6zaC0cvIJnIZr6PIKqAw\njojguEBYBvzhRmAgQQlZO53eu35/FN30nuruqk4neT/Pkwe6uurt95zz1vnWOVV1Xr0CTRbX5ay4\nsfs4PFeD+aPSka7hwDDAT/vqsfObVnxVF1sySEBYnX5CHx3G9taDY4QV4M12YfX8eJa/UnMMco0c\ndAphhX45+gMpbXZHEi5UFosFzz77LPbv34/m5mYMGDAA999/P8aNGxdy/927d2PVqlU4c+YMjEYj\nJk2ahD/+8Y/QarUx+xAuaOIJpkjpOKSwK6Wv4Y6Px1enm8fFy6uk+9SExzKuiFZ0dtscbtS2OGB3\n8fBIBw/A7Qb+3ey4nK9ICa0y2nXf/EWUZQCWA4b2UGNwlhpfnG/DR9Vm2KIYBqRpWNw0yIgBGWoo\nWP86zNUrkK1ToNbkgCnKVCeBMAwDlYJDfoYR9027Ft9daMTWz79BY6tVtA2lgkPxgJ64uk8eOPaK\n2DMAMnQc0jScIK5t0a2iflWaEr8blY4+aSpofNbiU3HArUONmGjRRb2KOgOgJE+DKQMMULCAkrtS\nr3oVi/4ZKjRZXLhodkU1alOwQs4zz6rpoc4FqbP2kmDFRsKFatmyZfjqq6+wdu1a5OfnY8uWLSgv\nL8e2bdvQr18/v33379+PBx98EC+88AJuvPFGVFdX45577gHHcViyZEnUv91ekHhWOo4mmNrLMeOX\nP0kmu2Jttrd/LL66eR71Zifq2iJN9QUKVvu+2pzCCMps58Pa5eFZVd0Oo4pFrlEJFSemHsKLJssw\nYDlgdC8dRhRo8eH3rfiixhJxdKFRMPhZPz1G5OvCJk5kWQYsgF4pSthcPGpNjrinrRiGgVLBYXBB\nBv74i9E49N0FvF91GhZ7eBFgGAaDe+WgJEQeKp+9wLJAruGKuLY3wszUcph3bZo323GoOlBxLHro\nGdxZko6zTXa8JyIv1cAMFX4+2Ai9ioEqRPZgz++kaQVxrWtzoj5iLApTklk6Dpm6YIHy1oAM55dn\ne7wXr92RhApVc3MzduzYgeXLl3sXsS0rK8Nbb72Ft956K0h8mpubsXDhQkydOhUAMHDgQEyePBmf\nffZZVL8bTcKyaDrraIIt0K5U2T/FBn48voY6jud5NNvc+MEkTBGJ63J9Bcv38xWcbh4/tjqiWnmd\nB9Bid8NUb0OGlkUPvTJMhlnxozoFy0DBMpg8wIAJV+mx82tT0LQVxwBjeukwsa8+YlZbX1iWgYYB\n+qarYLa7UWtywhHnDSyWZcGywHUD83Ft/1x8eOwMPvnqHFwBdnvlZGDMsPYz+3pgGAYKDuiZqgyb\n6VenZPCrYamYMtAgqg4YhoGSA/qlq/Af12Xg+I9W7P2+NchurkGBmwcb0MOgCClQQXXAMAADZOsV\nyNQh7L3GdA2LHE9m3yjPBd/PgcjVxxACooSK53m89tprsFqt+MMf/uDdfv/992Po0KEoLy8X9WMn\nT56Ew+FAUVGR3/bi4mIcO3YsaP8ZM2YEbTt37hzy8sQl5Yono2akYIonuMLZkNtXKex6Prfa3bhg\ncsDhijUtAoPA6UA3z+NSmxN1ZuEKOxa7PIAGixuNVht66DhkeDO+xn6vTMmxUHLArGEpmNjmSf7n\nQFGOGjcNMkLNsX5TUWJgGCHpn0HFYmCmCo0WFy6anXE/bMBxLDiOxeThffGTYb2x/Ytvcaz6R2Sm\nGTC2cACMeg0UMSSEZBkGagWDq9JVsDjcqGlxws3zmDLAgF8Xp0HJCvUUlc3LI8xrcjUoylHjwNk2\nHPx3G3RKFlMHGjAoUx12dNqerywD5BsUyNBy0CsZmB08DCoWeUbhnmEs+abCXRDKdd4S/ogSqtde\new0rV67EH//4R7/tJSUleOWVV8CyLObPn9+unYaGBgBAWlqa3/b09HTU17f/BNOWLVtw4MABvPnm\nm+3uK1XDhwomqezKYROQx1eP3X83R56Oi8Lq5X952JwunG50RDEyCw8PIbPwRbPwpF22XnH5Sa74\n6kDFscgxMJh3bTqcbh4cy4icZgyPR7AytBxULIOzLdKs8KJQcDAoONw27mpcN7QvzE5AwbKI95E2\nlmGgV3EYnqfA70ZmQKdk4s4JJWTmZTChjx7jeuvAMcK0Y7QCFeTr5RFx7zQl3LywBE+8CRETcX5R\nksXQiBKqzZs349lnn/VOwXmYN28eevbsieeff16UUEWivcZZs2YNXn31VSxfvhzFxcWS2IwGObMB\nd4asvR67rXapHzpnYHFeSVsvFW4ARjUneb0qOQQ9KCGFXYtUz277oFJwcIKDIvpBVERyDApJRMoX\nJccIgiJxzLIMAwad67xNBLGkiu9IRAnVDz/8gGHDhoX8btiwYfjhhx9E/VhmZiYAoKmpCTk5Od7t\njY2NyMrKCnmM2+3Go48+iv379+ONN94QLVJEJyNwhi6JoQteeaBqFS7YKL6CEXVJ1KdPH+zfvz/k\ndzt37kTPnj1F/VhhYSFUKhWOHj3qt72qqgojR44Mecxjjz2GY8eOYdOmTSRSBEEQ3RBRI6p58+bh\n0Ucfxeeff47CwkLo9Xq0tLTg8OHDOHjwIJYtWybqx4xGI2bNmoXKykoMGjQIubm5ePPNN1FTU4Oy\nsjIcP34cDz30ENatW4f8/Hzs3bsXe/bswY4dO/xGYARBEET3QZRQ3XrrreA4DmvWrMGePXsACI/E\nDh48GM8//3xUSROXLFmC5557DnPmzIHZbMaQIUOwZs0aFBQU4Pz586iurvYufPvXv/4VJpMpZC6s\n999/HwUFBaJ/lyAIguiciH6PaubMmZg5cyZsNhtaWlqQnp4OhSL617BUKhWWLl2KpUuXBn03evRo\nfP31197P69evj9o+QRAE0bWI6rGd1tZWnDx5ElVVVbDb7QAAl0ua9csIgiAIIhSihkQulwsvvPAC\nNm7cCIfDAYZhsGfPHjQ3N+Ouu+7C+vXr6R4SQRAEIQuiRlSvvvoq3nnnHSxYsAD/93//B41GA0B4\nOCI7OxvLly+X1UmCIAii+yJqRLV161ZUVFQEPTRhMBjwwAMP4L777pPFOYIgCIIQNaKqr6/HNddc\nE/K77OxstLa2SuoUQRAEQXgQJVQFBQU4dOhQyO+OHj2K3NxcSZ1KZqRe4kTOJVPksC3HS/MMIMuq\nFEL55ahfOWpBnjiQY5UDOZY6Ai4vo5WgJYTiRS4/aVWK0Iia+ps0aRKWLVuG2tpajB07FgDwzTff\nYP/+/aisrMTtt98uq5OxItXCtIFBmcwZdkP5KqXd3qlK1JiETLvxn6o8GDDINXDQKCDZwrQshBNe\np2SgUbCwOaVYRFeQJyUHGJQMmmy8ROsTChYytBzsLh7NUaQ3iQTHCHmXJvXT44saK1ps0qSYV7IA\n7+ZhdbqFhW4Rf2y53DzcPHCk1oIMHYc+aSpJ1lP0XeS1s5xfRGhECdXChQtx6dIlvPrqq3jllVfA\n8zz+4z/+AxzHYdasWViwYIHcfkaNFEvoR1ohWa6svXL5Gqu4Bto1qBkMUqnQcDmjaqydNQNAxQnZ\neXUqocMbku3G0R8sON8SWxp0IccQ0CdNieIcjXfR1Fa7kCXYGaO4euzmGhVIVQvZcHN4HnXm9pP0\nReZKmhMFy6BnigrZOjdqTULep3h8HZylwpBsDRQsg9G9dDjxow27vjXB4eJjEiwlK6QlmTHYiAGZ\n6isliCNm3TwPlxv4f3VWfPCd2Zv5uFeKAjdfbUS6lhOViyqQUOeCFAkLE33eEldg+Cik/OLFizhx\n4gRaW1uRmpqKwsJC70KzyY6U2XWj3S9RdtvbV+o6cLmFHFLRdNYMhKv9XKMCKWo2pO0mqwtVtRY0\nWMSnFucYIFvP4do8LYzq4OXCeZ6/nPfJFdWojQGQrWORqVeEnO5yunn8aHKi2RaNYLWfwFFIquiA\n3SVesDgG6JWqwDW5Wr808B4cLh7/PGfGx2fb4HZDVN0qWEDJMpjcX4+SfG3YKb9oY9bu4nHB5MCO\nr024aA79LubQbDWmDzJAo2BE5bsK9KGiogIVFRVx+9pR51d7vPbhNzEdByTn6unZ2caw34leWsLt\ndkOhUGDEiBFITU2VxLFEEm2mTqky4frajNWuFL569pPSV45lkGNQIkPL48dWJ1psbvBhUs0zEKbj\nsnWsT0LD0KRpOJT2M+DHVicO1bbB4uDDdqocI1zpjyzQIksXPpwZhkGGToE0LYdLZicutSOuDIA0\nDYseBsXlnEmhUbAMClKVyHK6ccHkbGck1L5AedCrWAzIUKHFJggWzwupS0LBMUJq9WvztUgJIdIe\nlByDn1xlwMgCHf5xuhVVF6xwu0Pb9Uwdju+jw7je+nZzb4mNWbvLDZPNjR1fm3C6MXL+ra/qbDh1\nyYZR+Vrc2F8PjmGgCOFHvOdXuOPisRvpGEqSGD3tCtU///lPrF69GocOHfKuwWcwGDB+/Hjcc889\nYdN/JCvtZeqMN2uvr514r5o6KhtwtDaVHIOeqUrYnEJqdUtAZ80ASNey6KFXiErX7iHHoMBNA404\n2+zA0QtWON1XBItjhN8dka9FgVEh2meWYdDDoESGThBX/3tCwj0zvZJBXooyqsSIagWLqy6nl79g\ncsDul/04sDbEwTAMUjUcjGoW9W1O1LX5T7VyDKBXCiKdrRe/nJlOyeLng1Mwrrceu74x4ftGO5zu\nK95xLFCco8Gk/gboVdFNvYWLJYeLh8PFY/d3rTj+g1X0KNHNA5/XWHD0Byt+2leP6wq0goiy0p+3\nUpxfnmOk7mO6OxGje82aNXjhhRfQv39/3HHHHcjLy4PT6cSZM2fwj3/8A7Nnz8aSJUswZ86cRPkr\nCYnK1CmFTY+NzuCrWsGi7+XOuvZyZ21UMcg1RtfpB/p5VZoKvVKUOHXJhv9XZwPDAIU91BiYqY75\n6TMFy6AgRYksnTASMjt4qDkG+SlK6JSxJwTUq1j0vzwSumDyTS8fe92yDINsvRIZWgV+NDvQaHFD\nxTG4Nl+DXinKmNstXcvhN9ekoabFge2nTPih1Yl+GUpMH2SMODoVgydmXW4eLp7H/jNt+PRcm1cQ\no8Xm4vHBd63457k2TBtgwNAeau/vxItUAhXOJt2Hip+w0Xjs2DH8+c9/xuLFi3HnnXcGfb906VK8\n/PLLePrppzFs2LCw71klM3JmwpWazpQN2DNt5XTzou4tiIFjGQzrocHADNXlTLvS+OsZCdmdbig5\nRrKOL1XDgQGP8y0u8BI9zs6xDPKNKozI55Cq5qIanUaiIEWJ8lHpMNlcSNHEJ1C+MAyDz8+34ePT\nZskyGLfY3PjbyRY8mJoJo0ba1MVynAty9THdjbBR+de//hXTpk0LKVIAwHEcHnjgAdTU1GDNmjWo\nrKyUy0eiE8IwDCTMVO5FKjFJhF2GYWRJXKziGMlEygPDMCEfQokXNy+MhqSGHuYWRzI+NBELYbuS\nQ4cOYdasWe0aKCsrQ1VVlaROEQRBEISHsEJVV1eHq666ql0DvXr1QmNjo5Q+EQRBEISXsELlcDig\nVqvDfe2F4zh6q5ogCIKQjbBCxTDy3AsgCIIgiGgI+zAFz/OYMWOG6LexCYIgCEIOwgrVLbfckkg/\nCIIgCCIkYYXqmWeeSaQfBEEQBBESGd50IQiCIAjpIKEiCIIgkhoSKsiTtTdwjS+p7MoF+SqtXYeL\nR32bK+yK57GiVzLQKthOUQcAUJitxrxr05BrkG5ppsIeauhU8tWBHNBDZ/EhXfR0MuTO1Om7kGyy\nZxWVIqlcoE1fu/HaDLTr+znZ6sDlFhIqNljcIVZPj91XNccgz8hBq2TBytxeUsZsqoZFiobFvSPS\n8W29Dbu/bUWzLTb57pOmxM2DjUjVcFCw3TdmuyPdTqgirWQsRaZO3+PiXd5fbl99jxWbT0iM3c5U\nB+F8jbYOeJ6PkPXYs+Jf9IKlYIEcPYcUDXc5p1fo9gosi1ifA4+To70YACwHXJ0trHj/ZY0FH1Wb\nRa8BmKXj8PNBRvRM9V+FX86Ylfr88v2OBCt6uo1QiVlqP5bAF2s32gBtb99YOqpo60AqXz3fyelr\nR9UBz/NCqnuTE66Iqe49NsQJFssIHXSmzl+gIvkabcwmur1YhgHLAdf11GJEvgYfVZvx+XlL2KSY\neiWDyQMMKOyh8ctBFcpXseUK9LcjY4tWVRdPlxeqWHLBiAn8aO2KDfxor7bE2O1udSCFr579Ivlq\ncQgCZXOKTxcvRrDSNSxyDAphJNIF20vBMlCwDH7WT49xvXXY9U0rTtbZvN8rWWBCHx3G9taDYyB6\npfjA9gqFHOdXvHajOa670qWFKt4gCBVMsXR44WyGsiuFr76dtRR2w/kaq91E1IGcvjrcwIUWB8wO\nHoJExeJvoGAxMKpY5BkVEUcPYvztLO2l5FgoOeCWoUZMtOix42sTsnQcJg8wQMEippxjgb56zgWp\n60DK80sKO12dLitUUg6r452zDmcTkCcbsMeuHDY7i125fHW63Piu3uEzgorXtnB8uoZFrlERc8Zi\nP4udrL1UHItsPYM7S9LAQxhxxQvDMLKfC1LZ9Nih6cDw0OPpIvEEj9RBJJddOWx2pjqQy1epsvUG\nomAZyS13tvZiGWlEKtCu779S25XaZoRZy24NCRVBEASR1HTZqT+CIIiuTFdJMy8GGlERBEEQSQ0J\nFUEQBJHUkFARBEEQSQ0JFUEQBJHUkFARBEEQSQ0JFUEQBJHUkFARBEEQSQ0JFUEQBJHUJFyoLBYL\nKioqUFpaihEjRmD27Nk4ePBg2P15nsfGjRtRUlKCxYsXJ9DTYD98/012u3LY7Ex1IJevTBTrpEeD\n0y1f+3fn9pLbrtQ2aZm/0CR8ZYply5bhq6++wtq1a5Gfn48tW7agvLwc27ZtQ79+/fz2tdvtuOee\ne8DzPHJzc6P6HSlWdwbCZ0BNdrtSLp6Z6DqIdx21wBW+pfSVYxkMzFThgsmBVns8q6cLMBCSIxrV\nbJDv8frqsdNd20vOOJDz/CKCSeiIqrm5GTt27MCiRYvQt29fqNVqlJWVoX///njrrbeC9rdarRg/\nfjzeeOMNpKWlRf17nhWU28tREw7fIPRd3DLUKtLR2BRjNxZfPZ2Gb7DH46uvL4msg3h8DawDOXxV\ncgx6p6nQL10JrYKNOckHywA5Bg4DM1Uwqrm4ffUc5+tr4P+7Y3vJYdeDHH0MEUxCR1QnT56Ew+FA\nUVGR3/bi4mIcO3YsaP+UlBTMnz8/7t+N9opKzH7RpjwQe9UU7ZWaGLuxpGeIpQ6k8NXzXUe3lxi7\nGiWLfhkqtNrcqDU54IyY4dfHFwCZOhbZ+tBpPTpje4n1QazdzlgHUvtKXCGhQtXQ0AAAQaOj9PR0\n1NfXy/rbYgI/liG4mMCPJSjbC3wpfI1kN1pfIx0rt6/x2JWivQxqFgNVKjRaXLhodsHNhxIsbeyR\nlwAAHr1JREFUHgwYpKiF7L1ikgImog7CHUcxmzwxSyTR6umJarT2hv+x+hEq8OO9akqEr57OWooT\nKFRH1RnqwPM5Hl8ZhkGGToE0LYc6sxP1bW6/RPNaJYt8owJqRfSz7XK2l8eu53NnaK/uGrPdmYQK\nVWZmJgCgqakJOTk53u2NjY3IyspKpCtRT1dEYzeaKQCxNgF5fJXabrRTK7HY9f0shV0p24tlGOQY\nlMjU8fjR5ITVxSPXoIBeFf/t4M5SB742KGZpmk8KEvowRWFhIVQqFY4ePeq3vaqqCiNHjkykK35I\nHUAMw3j/pLYrF+SrtHYVLIOCVCX6pSslEalAOkMdyGFPTtudydfuRkKFymg0YtasWaisrER1dTUs\nFgvWrl2LmpoalJWV4fjx45g6dSpqa2sT6RZBEASRxCT8hd8lS5ZgzJgxmDNnDkaPHo09e/ZgzZo1\nKCgogMViQXV1NRwOBwBg69atKCoqQlFREY4cOYJt27Z5P9fU1CTadYIgCKIDYPhYHv7vQkg5J50I\n5PBXrjro7r7KZbcz+SqXXTE2KyoqUFFRIbndaJGrXl/78BsAXSclfXa2Mex3tNYfQRAEkdSQUBEE\nQRBJDQkVQRAEkdSQUBEEQRBJDQkVQRAEkdSQUBEEQRBJDQkVQRAEhMfIHa5u/bZO0pI0i9J2BJ5X\nyKRei0uOFZJ9bUr5XoZvHUj5roccduVur85WB53FV7nsSmWT53mY7G5cMDnhdAMpaga5BqWoFe7F\n2Pb9tzO9s5lMdEuhCiUkUixIGWhXqoU+A21IYTdcHXQmX6m9pPPVY6e71UGbw43aFgfsriupWVps\nPEw2OzK0Qs4wjo3etlwx213pdkIVLrDjCfxwI6h4T1IxvkYb+JFGe/F0VB1ZB53JV6qD5IhZu4vH\nhRYHzA4ePHggIE8zD6DB4kaj1Y5sHYtMnUK0bTnaq7vTbYRKbHBEE/hip/iiPfljsdvevmL3S4Sv\nYjqqaNpL7P7J1F5S10GyxKyU7SV2v2h8dbp5XGx1osnq9kluGXpfHgDPAxfNbtRb7MgzKmBUsWFt\ny9FehECXFyqxJ6YvYgI/liBrr6OKxVfPvnL7Gnh8PL5G8onqIHl97Wwx6+ubm+dRb3airs0dIvty\nZHgATjdwvtkJFQfkpyihU155Dk2uPoa4QpcVqlhPIF9CBVO8NgPthvsuHpty2vV0MPHYDLQb7rt4\nbMrVXlLblbu9pLAZaDfcd/HYlMNXj+0Wm/CghJtH1CLlCw/A5uJxptEBg1LIN+a5fSVlHZBgBdNl\nhQqQrsF9g0nKIJLDrlzz4HLWgZy+ymW3O/rqsduZ2osHcL7FKYm9y1bBA1BwDBhGvjog/KH3qLow\nneXKTE4/qQ6kh9oL4W5rxQ3pVGhIqAiCIIikhoSKIAiCSGpIqAiCIIikhoSKIAiCSGpIqAiCIIik\nhoSKIAiCSGpIqAiCIIikhoSKIAiCSGpIqAiCIIikhoSKIAgiSegsC3Mkmi4rVG4ecLulW4/EswZX\nuEVEY7Xpa1cq5PA10K6UNjtLHcjpK9WBPDELnodOyUi+4pHFIV8dEMF02UVpv75kR5aORZZeKCIb\n46VKuJQJ8S72GSkVQ6x2Q60YL8WqzJ3RV7nsdqY6kDJmO2N78TwPlmXRN12FVpsbtSYHnO74VlBn\nAKSqWeQYFWAlyislRaaHrk6XFSoeQF2bkKGzh55DqoYDA/GBECl44lntOdIx7eXoiddXuexGYzPS\nMYnwNdrOmtqr87aX73EGNYuBKhUaLS5cNLuiTPnBgwEDnZJBnlEBteLKRJRcdUD402WFyoPTDdSa\nXLjU5kaekYNOwYJlwwdFNMETzckvNoijDfxY7YqxKdau3L5GOkbu9mpvP2qvztNeDMMgQ6dAmpZD\nndmJehFJFBkASo5BvlEJvSr8nRK52osQ6PJC5cHu4nG2yem9KlKyDDgfwYoneCKd/LHabe/kj3W6\nIdLJL7ev8doNPDaeOqD26r7txTIMcgxKZOp4/GhyotkWLFgMAJYB8owKpKjDp58P5auvX1LUK9GN\nhMpDm4PH9w0OpKhZ5BkVYBl4b7TGEzyhTv7A76S0G2+gB578UthNRB3IYbOz2O1MvnrsymFTKrsK\nVsjSm+UUMgC3OTziAmTrWGToFDHf25ajDroz3U6oPLTY3DDZ7OibroBWyUlmV65A9A18qX5DDpty\n2+1MvkpttzP5KpddOWyqFSyuSlehze6G2e5Chk7hN9sSD3LEbHek2woVINxMtbt4aJUd7QlBEB2N\nTsVCq2RIVJKQLvseFUEQBNE1IKEiCIIgkhoSKoIgCCKpIaEiCIIgkppu/TAFQRBEZ+XW4ryOdiFh\n0IiKIAiCSGpIqAiCIIikhoSKIAiCSGoSLlQWiwUVFRUoLS3FiBEjMHv2bBw8eDDs/gcPHkRZWRlG\njhyJiRMn4rHHHoPFYkmgxwRBEERHknChWrZsGY4cOYK1a9fi008/xS233ILy8nKcPn06aN8zZ86g\nvLwc06dPxyeffIINGzbgxIkTWLZsWaLdJgiCIDqIhApVc3MzduzYgUWLFqFv375Qq9UoKytD//79\n8dZbbwXt/7e//Q39+vXD3LlzodVq0atXLyxYsADbt29HQ0ODBB7xaLK44HK5JcsG7Fk00+FwhlxA\nM1bcbh5ut1sWX51O92X70tuVug5cLl6mOnDJ0F7S+uqx67EpfczK0V5yxZa07cXzgo/yxKwbbre0\ncdDdSOjj6SdPnoTD4UBRUZHf9uLiYhw7dixo/6NHj6K4uDhoX6fTiZMnT2LChAkxesKDBWCEEyoH\nj4YGO7RaJXQ6NRgmtoVlPYFttzvR2mqD282DZRkYDGqoVEI1x2qX54G2NhssFgcAQKVSwGBQg2GY\niLm1IuERPpPJBqfTBYYBdDoVtFpVXL4CgNXqgNlsB8/zUChYGAwacFzkPGDt2eV5Hq2tNthsTgCA\nRqOEXq8GgLjsOhwutLba4HK5wbIM9Ho11Or42gsALBY72trsAAClkoPRqIm7vXieh8lkhcPhAiC0\nl04Xf3vZbE6YzULMchwLg0ENhYKLy1cAMJttsFqFmFWrhZgFYq8Dz8VPa6sVTqcbDMNAr1dBoxEW\n65SivXgeUCg4GI1qsGzsMetpr9ZWG+x2IWbj7WO6MwkVKs8oKC0tzW97eno66uvrQ+6fmpoatC+A\nkPv78uGu7Th1Mlj8GABKuKEMkTKNYQClUgHF5QyeYoPJczVmtztDXjWxLAOVSgGWFb/gpecEcjrd\nl0dnwfsoFBxUKi5qX3leEFSXyx30PcMwUKk4cJz4OvD46nIJI8lQdcBxLFQqRVQnqceuw+Hyds6B\nKJUclMpY6oCH3e4KWQfxt5cr5BW5QsFGfdHiewHkdIZuL6WSkzxmhfbiwDDR14HD4bo84gneJ/b2\nai9mFeA4xs/uvn37UFFREdFXl8sNuz10e8UTs3a7UAfBvrbfx4TzuTuTNC/8xppQLhw/u+lm/Oym\nmy9/EoJHBzd0cKG9iySxI6ErV01W2O2hO1JfxIyEQo3MIsEwgFbb/pV1qJFZJDiOhdHY/kjoysjM\nGrIjDUTMSMh/ZGYL2eH5InYkJNSl/8gsEkolB4NBA5aNPAoIHJm1h1argl7ffnsBQFvblZFZJDiO\ngcGggVLJRTw3PNNwra3WsOLvi5iRUKiRWSQYBtDr1e2OhEKNzCKhUAgx6xkJVVRUhOz0PdNxnpFZ\ne3hGQkD7Mes7MouEFLMt3YmEClVmZiYAoKmpCTk5Od7tjY2NyMrKCto/KysLTU1NftsaGxsBANnZ\n2SJ+UYgWNdwwwAVOZCy43TxaWqzeaSuFwj/DZ7QnkAe73YmGBmfYzjpwakMMgvjYYbU6QnbW0Z5A\nHlwuN5qa2sJOW4Wa2hCD1eqA1eoIOW0VrUj7+mIyWdHWJkxbBXbW0Yq0B4fDhcZGs7ezDhxd8Lyn\nw7OJ6vQ9WCye9gqetgo1fSoGl4tHc7PlsrgGT1uFmj4Vg83mhM3mDDttFa1IC8cAra02tLXZQ3bW\noaZPxeB0utHY2Oa9IAysu2gvLD1YLFdiNnBqPFqR9vXFt4+JZ2q8O5BQoSosLIRKpcLRo0cxZcoU\n7/aqqipMnDgxaP+SkhJ8/PHHftsOHz4MlUoVdJ8rGB5K8DDCBQUT201Mp1PorFWqK1fWgHACWSzi\nO/1ArFYHbDaH30jIc5UbzQnkS6jOGoi+0w/E4XChocHTWWu826MV6UDa2uywWByXr6yFMIxWpANx\nudw+nbXm8tSd+JFZOK501sJISLATfafvi0c0LBY79HqNdwrXbnfBbLbC5Yq9vRob26BWK6DXe8QV\nMJuFmI2VK521GlqtEjyPqEZmoQh1QQgAVqsTbW2xx6zngtBud/nZiCdmed5Th1cuCHleiDmTySpa\npAMJ1cfQ6CqYhAqV0WjErFmzUFlZiUGDBiE3NxdvvvkmampqUFZWhuPHj+Ohhx7CunXrkJ+fj7Ky\nMmzcuBHr169HWVkZamtrUVlZiV/+8pcwGo0RfysNTqhiFKhA7Hahs1apFHA6XTGfQL74joQUCi6q\nUUkkfDtrzxSHFAiddSvUagXs9tD3zKLFc3VrsQhXk7F2eIF4RkIqFed9olEKPCMhpVLK9uLR0mLx\ndtKxinQgHnFVqRRh75lFi9BZC+KqULAxX1QF4umspY5Zp9OFhoZWqFSKmC8oAvG9IJQyZn37mNRU\nrSQ2uxIJf49qyZIlGDNmDObMmYPRo0djz549WLNmDQoKCmCxWFBdXQ2HQ7jq6dmzJ1avXo2dO3di\n1KhRmDt3LiZMmIDFixe3+ztSiZQv4W48x4PnhrbUOByhHxSIF5tNGpHyxeVyS3bC+xJ4RS0FwkMY\n0reX0+mWTKR8ES4q5IhZ6dtLjpjleUgmUr7IF7PS+9oVYHipozhJqKszdbQLBEF0IM899yc89NCS\njnYjarKzI88WeehqfVykctNafwRBEERSQ0JFEARBJDUkVARBEERSQ0JFEARBJDUkVARBEERSQ0JF\nEARBJDUkVARBEERSQ0JFEARBJDVd9oVfgiAIomtAIyqCIAgiqSGhIgiCIJIaEiqCIAgiqSGhIgiC\nIJIaEiqCIAgiqSGhIgiCIJIaEiqCIAgiqelSQnXu3DmUl5dj7NixuP7661FeXo5z586JOrapqQnj\nx4/H3LlzZfZSPqItv8lkwmOPPYbx48ejqKgIpaWlWLVqleQZYRNBtGV3Op145ZVXMGnSJAwfPhxT\npkzBxo0bE+ixtMQS+2azGY8//jiuvvpqbN68OUGeSoPFYkFFRQVKS0sxYsQIzJ49GwcPHgy7/8GD\nB1FWVoaRI0di4sSJeOyxx2CxWBLoMREPXUaoHA4H7r33XqSkpOC9997DBx98gPT0dNxzzz3e1PaR\neOqpp2C1WhPgqTzEUv4HHngA1dXVeOedd3D06FE88cQTqKysxNtvv51g7+MjlrKvWLEC7777Lior\nK3H48GE8+OCD+NOf/oQPP/wwwd7HTyzlP336NGbOnAkAnfLCZNmyZThy5AjWrl2LTz/9FLfccgvK\ny8tx+vTpoH3PnDmD8vJyTJ8+HZ988gk2bNiAEydOYNmyZR3gORETfBfhH//4B3/11VfzDQ0N3m2N\njY38kCFD+L1790Y8du/evfyECRP4Z555hr/99tvldlUWYin/li1b+DNnzvhtmzlzJv/444/L6ark\nxFL25cuX8x988IHftptvvpl/8sknZfVVDmIp/+eff87v27ePdzgc/KBBg/h33303Ue7GTVNTEz9s\n2LCgss2cOZN/+umng/b/7//+b/7mm2/227Z3715+6NChfH19vay+EtLQZUZUR48eRe/evZGenu7d\nlpaWhl69euHYsWNhj2tqakJFRQWefPJJ6PX6RLgqC7GU/xe/+AX69OkDALBardi+fTvOnj2L6dOn\nJ8RnqYil7L///e8xefJk72e73Y6LFy8iLy9Pdn+lJpbyX3fddbjhhhsS5aKknDx5Eg6HA0VFRX7b\ni4uLQ5b36NGjKC4uDtrX6XTi5MmTsvpKSIOiox0Qi9PpRFtbW9jvGxsbkZqaGrQ9PT0d9fX1YY97\n8sknMX78eNxwww04fvy4JL7KgVzlB4C77roLBw8eRI8ePfDiiy9i1KhRcfsrJXKWHRCmvh5//HFo\nNBrMnj07Ll/lQO7ydzYaGhoACGLsS7jyNjQ0BNWPR9S7Yv10RTqNUH3xxReYN29e2O8jdTAMw4Tc\n/ve//x1ffPEFdu7cGbd/ciNH+T2sW7cOFosFH330ER5++GE88cQTuOmmm2L2VWrkLLvVasXDDz+M\nf/3rX1i3bh0MBkPMfsqFnOXvakRb3u5WP52VTiNUY8eOxddffx32+xUrVqCpqSloe2NjI7KysoK2\n+075paSkSOqrHEhd/kC0Wi1uuukmVFVVYfXq1UklVHKVvaGhAfPnz4dSqcTbb78tqp46ArnbvrOR\nmZkJQDiHc3JyvNvDlTcrKyuofhobGwEA2dnZMnpKSEWXuUdVUlKCc+fO+Q3lL126hH//+98YOXJk\n0P4fffQRLl26hMWLF2P06NEYPXo01qxZg6qqKowePRoXLlxIpPtxE2356+rqUFpaii+//NJvu91u\nB8dxsvsrJdGWHQBaW1tx9913o1evXnjjjTc6dYceS/k7M4WFhVCpVDh69Kjf9qqqqpDlLSkpCbp3\ndfjwYahUqqD7XERy0mWEaty4cRgwYACefvppNDY2oqGhAU899RQGDRqEsWPHAgA2btzofU9q6tSp\n2LdvH7Zt2+b9KysrQ2FhIbZt24YePXp0ZHGiJtryZ2dno6CgAM899xzOnj0Ll8uFzz77DO+99x6m\nTp3akUWJmmjLDgDLly+HRqPB888/D5VK1VGuS0Is5e/MGI1GzJo1C5WVlaiurobFYsHatWtRU1OD\nsrIyHD9+HFOnTkVtbS0AoKysDOfOncP69ethtVpx+vRpVFZW4pe//CWMRmMHl4YQQ5cRKo7jsGrV\nKlgsFpSWluLGG2+E0+nEqlWrvCOExsZGnD17FoAw1ZWbm+v3ZzAYoFKpkJub2+lGFdGWHwBefvll\nDBkyBLNnz8a1116LiooKLFiwAHfddVdHFSMmYin7m2++iePHj6OkpARFRUXevylTpnRUMWImlvIv\nXboURUVFKCkpAQA8+uijnar8S5YswZgxYzBnzhyMHj0ae/bswZo1a1BQUACLxYLq6mrvO2Q9e/bE\n6tWrsXPnTowaNQpz587FhAkTsHjx4g4uBSEWyvBLEARBJDVdZkRFEARBdE1IqAiCIIikhoSKIAiC\nSGpIqAiCIIikhoSKIAiCSGpIqAiCIIikhoSKkJTFixdj8ODBQX+jRo3C/PnzI65k/9JLL2Hw4MF4\n5JFH4vJh7ty5+NWvfuX9PHjwYLzwwgvezx999BF++tOforCwEIcPHwYgvFc0YsSIqJaOKi0t9Zav\nsyUeDEVlZaW3PF3l5WCia0BCRUhORkYGDhw44P3bv38/Vq5cCafTiblz5+LUqVNBx7hcLmzevBlD\nhgzB7t27Jc2+euDAAdx3333ezy+//DL0ej12796NwsJCnDp1Cu+88w5+85vf4PXXX4/K9l133YUD\nBw4k1dqIseIpy7Rp0zraFYLwg4SKkByWZZGdne39y8nJwciRI1FZWQmlUhky5fsnn3yCS5cu4Zln\nnoHNZsMHH3wgmT/Z2dl+ucaam5sxaNAg9OrVC2q1Gi0tLQCAkSNH+i1yKgadTofs7GxoNBrJ/O0o\n9Hp9lykL0bUgoSIShl6vR+/evUMu+Ltp0yZcf/31GDJkCH7yk5/g3XffFWXz0KFDmDlzJgoLCzFp\n0iRs3bo1aB/P1N/58+cxePBg1NTUYNeuXd4pLs8017333ovS0tK4yvjxxx+jrKwMw4cPR0lJCW69\n9Vbs3bs3yJ81a9bgnnvuQWFhIaqrqwEICf5uv/12DB8+HOPHj8cjjzziXeWb53n85S9/wZQpU1Bc\nXIwxY8Zg4cKFOHfunNeu3W7HSy+9hGnTpqG4uBgTJ07EihUr4HQ6/X5/8+bN+PnPf46ioiJMmjQJ\nK1euhNvtjqvcBCEnJFREwrBarTh//jwKCgr8ttfX12Pfvn249dZbAQCzZs3Cl19+6dcJh6KxsRHl\n5eVISUnBpk2b8NJLL+G9997D999/H3L/vLw8HDhwALm5ufjZz36GAwcOYOXKlaisrAQAvPjii9i0\naVPM5Tt79iwWLFiAQYMGYevWrdi2bRvGjRuH3//+9/jqq6/89n3nnXcwduxYfPDBBygoKMDp06dx\n5513om/fvnjnnXewYsUKHDp0CA888AAAQchfe+01PPjgg3j//fexatUqtLS04He/+53X5hNPPIH1\n69fj7rvvxnvvvYcHHngAGzZswPPPP+/dZ9u2bVi6dCl+9atfefdZuXIl/vKXv8RcboKQm06Tj4ro\n3Fy8eBHPPvssLBYLfv3rX/t9t3XrVmi1WkyaNAkAcMMNNyAjIwNbtmzB/fffH9bm3//+d5hMJjz1\n1FPo06cPAODPf/6zd8XwQDiOQ3Z2NjiOg1qt9uYi8mR/TUlJQUZGRsxlzM3Nxfbt25GXlwedTgcA\nWLhwIVatWoVPP/0UQ4cO9e6bmprqt/jvxo0bodVqUVFR4V1I9rHHHsP27dvR2tqKkydPIi8vDzfe\neCMAID8/H8uXL0dtbS3cbjfq6uqwefNmLFiwALfddhsAoHfv3jh//jz+53/+BwsXLoTRaMTq1atR\nWlqKO+64AwDQp08f1NXVebPmEkQyQkJFSE59fb13VW4AcLvdsFqtKCwsxGuvvYYhQ4b47b9p0yZM\nmzYNarUaAKBUKnHzzTdj69atWLRoUdgsrN988w10Op1XpABBbPr37y9DqdpHrVbju+++w7Jly/D9\n99/DbDZ7vwtM3Dds2DC/z//6178wdOhQv1X7x40bh3HjxgEAJk6ciLfffht33nknZs6ciTFjxiAv\nL88rrCdOnIDb7Q4S6euvvx4rVqzA119/jcLCQnz77beYMWOG3z6//e1v4y88QcgICRUhOWlpafjb\n3/7m/XzkyBE8/PDDuO+++7wdr4eqqiqcPn0ap0+f9jvGw2effYbrr78+5O+YzeaQN/71en3QfZlE\nsHfvXtx///246aabsGjRImRlZYFhGEyePDmkj760tLQgLy8vrO0bbrgBGzZswIYNG/D000/DZDLh\nmmuuwcMPP4wRI0agtbUVgPDkHstemdH3JEeoq6vzPjQS+NsEkeyQUBGSw3Gc3yinT58+eP/99/HE\nE09g9OjRfsnqNm3ahAEDBvi95+ThkUcewbvvvhtWqHQ6HaxWa9B2k8kErVYrQUmiY/v27cjNzcWL\nL77oFYuLFy+KOjYzMxPNzc0R9xk5ciRGjhwJp9OJw4cP45VXXsG9996Lffv2eacvX3jhBQwaNCik\nfbVaDZZl2/0dgkg26GEKIiEsXboUJpPJ78a+2WzG7t27MX36dAwZMiTob8aMGdi7d693tBBI3759\n0dbW5vfwRGNjI06fPi17eULhcDiQkpLiN6LZsmULgCsjm3AUFhbixIkTsNvt3m2fffYZfv3rX+Pi\nxYv45JNP8O233wIAFAoFRo8ejf/6r/+C2WxGdXU1CgsLwXEcfvzxR/Tp08f7l5mZCaVSCYPBAKVS\nicGDB6Oqqsrvt19//fWI9wIJoqMhoSISQs+ePbFgwQK8/fbb+PLLLwEAu3btQltbG6ZPnx7ymGnT\npsFms2Hnzp0hv580aRI0Gg2eeuopnDp1CidOnMB//ud/xvVAhIcXX3wRc+bMieqY4cOH47vvvsOu\nXbtw7tw5rF69GseOHUNeXh5OnToVcXQ1d+5cOBwOPPzwwzhz5gyOHDmCp59+GiqVCj169MDmzZux\ncOFCHDhwALW1tfjmm2/w+uuvIysrC/3790dWVhZuu+02VFZWYseOHTh37hyOHDmC++67D/PmzfNm\nu7377rtx4MABrFq1CjU1Ndi7d693RQqCSFZIqIiEMW/ePPTv3x9Lly6FzWbDu+++i2HDhvlNE/qS\nm5uLkpKSsMsT9ejRA6+88gouXryI2267DX/4wx8wY8YMFBUVxe1rXV2dX+p2Mdxxxx2YMWMGHn/8\nccyaNQvfffcdnnvuOfz2t7/FoUOHIqY+79WrF9atW4cffvgBM2fOxKJFi1BYWIgVK1YAAJ588klc\nf/31eOSRRzB58mTceeedaG1txbp162AwGAAITwn+5je/wUsvvYSpU6eivLwceXl5eOONN6BUKgEA\nM2bMwJNPPonNmzdjypQpePbZZzF//ny/lTsIItmgVPQEESOlpaW45ZZbsGjRoo52RVIWL16Mmpoa\n/O///m9Hu0IQAGhERRBx0dbWhrq6upAPdXQ2zGZzlykL0bUgoSKIOFi3bh3Gjx+PXbt2dbQrceMp\ny+7duzvaFYLwg6b+CIIgiKSGRlQEQRBEUkNCRRAEQSQ1JFQEQRBEUkNCRRAEQSQ1JFQEQRBEUkNC\nRRAEQSQ1/x9aocqrNgpqcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a82516d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BLAN2QxNeTYKXs8ukpYk8EZFJwGls9nvGBBzslnbo7Rt4MDcHr3SmX6OBwa0o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\nF8faQrs4n5/WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a8114ef0>"
      ]
     },
     "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: 0.11352528611894286 arcsec\n",
      "Dec correction: -0.2639050027212875 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].unit = u.deg\n",
    "catalogue[DEC_COL].unit = u.deg\n",
    "catalogue[RA_COL] = catalogue[RA_COL] +  delta_ra.to(u.deg)\n",
    "catalogue[DEC_COL] = catalogue[DEC_COL] + delta_dec.to(u.deg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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41NZVS9O3v9ZlwJg3KBXZRdR5FEaxJjhFhp8rBaTE88hM8g49+hxG6pXjOKQn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\nRrojxxI30WNgZK7hlusN28ukKgzMNNPi9FBV78ThDv1pmu+SYlGGCas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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a7fdbe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GICJSBPROLCOGrR2DjZZjDdiyCN6kcYVu0shklaAqIkOnoRMZOYzBu3BioVsx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\nSJGz4ZCfxAnYQLB3pdFzEQj4i7spyJB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n6ezslKKiIgkLC5P6+nrZuXOnLFmyROrq6qS3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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6a80b2208>"
      ]
     },
     "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": [
      "123 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 - 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
}