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    "# ELAIS-S1 Selection Functions\n",
    "## Depth maps and selection functions\n",
    "\n",
    "The simplest selection function available is the field MOC which specifies the area for which there is Herschel data. Each pristine catalogue also has a MOC defining the area for which that data is available.\n",
    "\n",
    "The next stage is to provide mean flux standard deviations which act as a proxy for the catalogue's 5$\\sigma$ depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "0246c5d (Thu Jan 25 17:01:47 2018 +0000) [with local modifications]\n",
      "This notebook was executed on: \n",
      "2018-02-27 20:54:48.861561\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,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "import os\n",
    "import time\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column, Table, join\n",
    "import numpy as np\n",
    "from pymoc import MOC\n",
    "import healpy as hp\n",
    "#import pandas as pd #Astropy has group_by function so apandas isn't required.\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "#We ignore warnings - this is a little dangerous but a huge number of warnings are generated by empty cells later\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from herschelhelp_internal.utils import inMoc, coords_to_hpidx, flux_to_mag\n",
    "from herschelhelp_internal.masterlist import find_last_ml_suffix, nb_ccplots\n",
    "\n",
    "from astropy.io.votable import parse_single_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "FIELD = 'ELAIS-S1'\n",
    "#FILTERS_DIR = \"/Users/rs548/GitHub/herschelhelp_python/database_builder/filters/\"\n",
    "FILTERS_DIR = \"/opt/herschelhelp_python/database_builder/filters/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth maps produced using: master_catalogue_elais-s1_20180221.fits\n"
     ]
    }
   ],
   "source": [
    "TMP_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n",
    "OUT_DIR = os.environ.get('OUT_DIR', \"./data\")\n",
    "SUFFIX = find_last_ml_suffix()\n",
    "#SUFFIX = \"20171016\"\n",
    "\n",
    "master_catalogue_filename = \"master_catalogue_{}_{}.fits\".format(FIELD.lower(), SUFFIX)\n",
    "master_catalogue = Table.read(\"{}/{}\".format(OUT_DIR, master_catalogue_filename))\n",
    "\n",
    "print(\"Depth maps produced using: {}\".format(master_catalogue_filename))\n",
    "\n",
    "ORDER = 10\n",
    "#TODO write code to decide on appropriate order\n",
    "\n",
    "field_moc = MOC(filename=\"../../dmu2/dmu2_field_coverages/{}_MOC.fits\".format(FIELD))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Remove sources whose signal to noise ratio is less than five as these will have been selected using forced \n",
    "# photometry and so the errors will not refelct the RMS of the map \n",
    "for n,col in enumerate(master_catalogue.colnames):\n",
    "    if col.startswith(\"f_\"):\n",
    "        err_col = \"ferr{}\".format(col[1:])\n",
    "        errs = master_catalogue[err_col]\n",
    "        fluxes = master_catalogue[col]\n",
    "        mask = fluxes/errs < 5.0\n",
    "        master_catalogue[col][mask] = np.nan\n",
    "        master_catalogue[err_col][mask] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## I - Group masterlist objects by healpix cell and calculate depths\n",
    "We add a column to the masterlist catalogue for the target order healpix cell <i>per object</i>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Add a column to the catalogue with the order=ORDER hp_idx\n",
    "master_catalogue.add_column(Column(data=coords_to_hpidx(master_catalogue['ra'],\n",
    "                                                       master_catalogue['dec'],\n",
    "                                                       ORDER), \n",
    "                                   name=\"hp_idx_O_{}\".format(str(ORDER))\n",
    "                                  )\n",
    "                           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Convert catalogue to pandas and group by the order=ORDER pixel\n",
    "\n",
    "group = master_catalogue.group_by([\"hp_idx_O_{}\".format(str(ORDER))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Downgrade the groups from order=ORDER to order=13 and then fill out the appropriate cells\n",
    "#hp.pixelfunc.ud_grade([2599293, 2599294], nside_out=hp.order2nside(13))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## II Create a table of all Order=13 healpix cells in the field and populate it\n",
    "We create a table with every order=13 healpix cell in the field MOC. We then calculate the healpix cell at lower order that the order=13 cell is in. We then fill in the depth at every order=13 cell as calculated for the lower order cell that that the order=13 cell is inside."
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "depths = Table()\n",
    "depths['hp_idx_O_13'] = list(field_moc.flattened(13))"
   ]
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   "execution_count": 9,
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    "depths.add_column(Column(data=hp.pixelfunc.ang2pix(2**ORDER,\n",
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       "<thead><tr><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>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><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>579661755</td><td>9057214</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.102536839434</td><td>1.42655293854</td><td>0.146092349808</td><td>1.70704640509</td><td>0.148692033524</td><td>3.02588974399</td><td>0.25098307305</td><td>3.50846909057</td><td>0.211426879731</td><td>3.91425198708</td><td>0.387636423135</td><td>5.11082549554</td><td>0.398474768073</td><td>5.53783275605</td><td>0.736094149859</td><td>7.02252197078</td><td>0.99731345919</td><td>4.44706032667</td><td>1.55649020812</td><td>5.67653008511</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>1</td><td>579661759</td><td>9057214</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.102536839434</td><td>1.42655293854</td><td>0.146092349808</td><td>1.70704640509</td><td>0.148692033524</td><td>3.02588974399</td><td>0.25098307305</td><td>3.50846909057</td><td>0.211426879731</td><td>3.91425198708</td><td>0.387636423135</td><td>5.11082549554</td><td>0.398474768073</td><td>5.53783275605</td><td>0.736094149859</td><td>7.02252197078</td><td>0.99731345919</td><td>4.44706032667</td><td>1.55649020812</td><td>5.67653008511</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>2</td><td>579661758</td><td>9057214</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.102536839434</td><td>1.42655293854</td><td>0.146092349808</td><td>1.70704640509</td><td>0.148692033524</td><td>3.02588974399</td><td>0.25098307305</td><td>3.50846909057</td><td>0.211426879731</td><td>3.91425198708</td><td>0.387636423135</td><td>5.11082549554</td><td>0.398474768073</td><td>5.53783275605</td><td>0.736094149859</td><td>7.02252197078</td><td>0.99731345919</td><td>4.44706032667</td><td>1.55649020812</td><td>5.67653008511</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>3</td><td>579661757</td><td>9057214</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.102536839434</td><td>1.42655293854</td><td>0.146092349808</td><td>1.70704640509</td><td>0.148692033524</td><td>3.02588974399</td><td>0.25098307305</td><td>3.50846909057</td><td>0.211426879731</td><td>3.91425198708</td><td>0.387636423135</td><td>5.11082549554</td><td>0.398474768073</td><td>5.53783275605</td><td>0.736094149859</td><td>7.02252197078</td><td>0.99731345919</td><td>4.44706032667</td><td>1.55649020812</td><td>5.67653008511</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>4</td><td>579661812</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.3518181818</td><td>2007.48</td><td>9.13181818182</td><td>1205.59</td><td>7.55727272727</td><td>316.98</td><td>6.73454545455</td><td>404.88</td><td>0.108591263566</td><td>3.87500628677</td><td>0.141075619601</td><td>4.91696010165</td><td>0.156677044035</td><td>10.8919869708</td><td>0.246325902184</td><td>13.2261283639</td><td>0.202028980031</td><td>18.9383665576</td><td>0.341492434366</td><td>24.0655213261</td><td>0.387408807647</td><td>24.8179260217</td><td>0.674192219562</td><td>36.4077160483</td><td>1.00001892385</td><td>27.3062159427</td><td>1.48565173976</td><td>39.9055409716</td><td>1.71895652174</td><td>323.86</td><td>1.5052173913</td><td>245.378</td><td>2.04101265823</td><td>173.384</td><td>4.28556962025</td><td>146.396</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.05403289199</td><td>549.846075439</td><td>6.58259902</td><td>537.450134277</td><td>3.82005501348</td><td>529.674230957</td><td>9.35232184654</td><td>544.591699219</td><td>4.31379487779</td><td>381.894152832</td><td>10.5333159871</td><td>468.295074463</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>5</td><td>579661788</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.3518181818</td><td>2007.48</td><td>9.13181818182</td><td>1205.59</td><td>7.55727272727</td><td>316.98</td><td>6.73454545455</td><td>404.88</td><td>0.108591263566</td><td>3.87500628677</td><td>0.141075619601</td><td>4.91696010165</td><td>0.156677044035</td><td>10.8919869708</td><td>0.246325902184</td><td>13.2261283639</td><td>0.202028980031</td><td>18.9383665576</td><td>0.341492434366</td><td>24.0655213261</td><td>0.387408807647</td><td>24.8179260217</td><td>0.674192219562</td><td>36.4077160483</td><td>1.00001892385</td><td>27.3062159427</td><td>1.48565173976</td><td>39.9055409716</td><td>1.71895652174</td><td>323.86</td><td>1.5052173913</td><td>245.378</td><td>2.04101265823</td><td>173.384</td><td>4.28556962025</td><td>146.396</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.05403289199</td><td>549.846075439</td><td>6.58259902</td><td>537.450134277</td><td>3.82005501348</td><td>529.674230957</td><td>9.35232184654</td><td>544.591699219</td><td>4.31379487779</td><td>381.894152832</td><td>10.5333159871</td><td>468.295074463</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>6</td><td>579661822</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.3518181818</td><td>2007.48</td><td>9.13181818182</td><td>1205.59</td><td>7.55727272727</td><td>316.98</td><td>6.73454545455</td><td>404.88</td><td>0.108591263566</td><td>3.87500628677</td><td>0.141075619601</td><td>4.91696010165</td><td>0.156677044035</td><td>10.8919869708</td><td>0.246325902184</td><td>13.2261283639</td><td>0.202028980031</td><td>18.9383665576</td><td>0.341492434366</td><td>24.0655213261</td><td>0.387408807647</td><td>24.8179260217</td><td>0.674192219562</td><td>36.4077160483</td><td>1.00001892385</td><td>27.3062159427</td><td>1.48565173976</td><td>39.9055409716</td><td>1.71895652174</td><td>323.86</td><td>1.5052173913</td><td>245.378</td><td>2.04101265823</td><td>173.384</td><td>4.28556962025</td><td>146.396</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.05403289199</td><td>549.846075439</td><td>6.58259902</td><td>537.450134277</td><td>3.82005501348</td><td>529.674230957</td><td>9.35232184654</td><td>544.591699219</td><td>4.31379487779</td><td>381.894152832</td><td>10.5333159871</td><td>468.295074463</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>7</td><td>579661798</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.3518181818</td><td>2007.48</td><td>9.13181818182</td><td>1205.59</td><td>7.55727272727</td><td>316.98</td><td>6.73454545455</td><td>404.88</td><td>0.108591263566</td><td>3.87500628677</td><td>0.141075619601</td><td>4.91696010165</td><td>0.156677044035</td><td>10.8919869708</td><td>0.246325902184</td><td>13.2261283639</td><td>0.202028980031</td><td>18.9383665576</td><td>0.341492434366</td><td>24.0655213261</td><td>0.387408807647</td><td>24.8179260217</td><td>0.674192219562</td><td>36.4077160483</td><td>1.00001892385</td><td>27.3062159427</td><td>1.48565173976</td><td>39.9055409716</td><td>1.71895652174</td><td>323.86</td><td>1.5052173913</td><td>245.378</td><td>2.04101265823</td><td>173.384</td><td>4.28556962025</td><td>146.396</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.05403289199</td><td>549.846075439</td><td>6.58259902</td><td>537.450134277</td><td>3.82005501348</td><td>529.674230957</td><td>9.35232184654</td><td>544.591699219</td><td>4.31379487779</td><td>381.894152832</td><td>10.5333159871</td><td>468.295074463</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>8</td><td>579661797</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.3518181818</td><td>2007.48</td><td>9.13181818182</td><td>1205.59</td><td>7.55727272727</td><td>316.98</td><td>6.73454545455</td><td>404.88</td><td>0.108591263566</td><td>3.87500628677</td><td>0.141075619601</td><td>4.91696010165</td><td>0.156677044035</td><td>10.8919869708</td><td>0.246325902184</td><td>13.2261283639</td><td>0.202028980031</td><td>18.9383665576</td><td>0.341492434366</td><td>24.0655213261</td><td>0.387408807647</td><td>24.8179260217</td><td>0.674192219562</td><td>36.4077160483</td><td>1.00001892385</td><td>27.3062159427</td><td>1.48565173976</td><td>39.9055409716</td><td>1.71895652174</td><td>323.86</td><td>1.5052173913</td><td>245.378</td><td>2.04101265823</td><td>173.384</td><td>4.28556962025</td><td>146.396</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.05403289199</td><td>549.846075439</td><td>6.58259902</td><td>537.450134277</td><td>3.82005501348</td><td>529.674230957</td><td>9.35232184654</td><td>544.591699219</td><td>4.31379487779</td><td>381.894152832</td><td>10.5333159871</td><td>468.295074463</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td></tr>\n",
       "<tr><td>9</td><td>579661821</td><td>9057215</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>11.3518181818</td><td>2007.48</td><td>9.13181818182</td><td>1205.59</td><td>7.55727272727</td><td>316.98</td><td>6.73454545455</td><td>404.88</td><td>0.108591263566</td><td>3.87500628677</td><td>0.141075619601</td><td>4.91696010165</td><td>0.156677044035</td><td>10.8919869708</td><td>0.246325902184</td><td>13.2261283639</td><td>0.202028980031</td><td>18.9383665576</td><td>0.341492434366</td><td>24.0655213261</td><td>0.387408807647</td><td>24.8179260217</td><td>0.674192219562</td><td>36.4077160483</td><td>1.00001892385</td><td>27.3062159427</td><td>1.48565173976</td><td>39.9055409716</td><td>1.71895652174</td><td>323.86</td><td>1.5052173913</td><td>245.378</td><td>2.04101265823</td><td>173.384</td><td>4.28556962025</td><td>146.396</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>3.05403289199</td><td>549.846075439</td><td>6.58259902</td><td>537.450134277</td><td>3.82005501348</td><td>529.674230957</td><td>9.35232184654</td><td>544.591699219</td><td>4.31379487779</td><td>381.894152832</td><td>10.5333159871</td><td>468.295074463</td><td>nan</td><td>nan</td><td>nan</td><td>nan</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(\"$('#table140291160377328-484610').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",
       "    $('#table140291160377328-484610').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, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in master_catalogue.colnames:\n",
    "    if col.startswith(\"f_\"):\n",
    "        errcol = \"ferr{}\".format(col[1:])\n",
    "        depths = join(depths, \n",
    "                      group[\"hp_idx_O_{}\".format(str(ORDER)), errcol].groups.aggregate(np.nanmean),\n",
    "                     join_type='left')\n",
    "        depths[errcol].name = errcol + \"_mean\"\n",
    "        depths = join(depths, \n",
    "                      group[\"hp_idx_O_{}\".format(str(ORDER)), col].groups.aggregate(lambda x: np.nanpercentile(x, 90.)),\n",
    "                     join_type='left')\n",
    "        depths[col].name = col + \"_p90\"\n",
    "\n",
    "depths[:10].show_in_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## III - Save the depth map table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "depths.write(\"{}/depths_{}_{}.fits\".format(OUT_DIR, FIELD.lower(), SUFFIX), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## IV - Overview plots\n",
    "\n",
    "### IV.a - Filters\n",
    "First we simply plot all the filters available on this field to give an overview of coverage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'decam_g',\n",
       " 'decam_i',\n",
       " 'decam_r',\n",
       " 'decam_y',\n",
       " 'decam_z',\n",
       " 'irac_i1',\n",
       " 'irac_i2',\n",
       " 'irac_i3',\n",
       " 'irac_i4',\n",
       " 'vista_h',\n",
       " 'vista_j',\n",
       " 'vista_ks',\n",
       " 'vista_y',\n",
       " 'vista_z',\n",
       " 'wfi_b',\n",
       " 'wfi_b123',\n",
       " 'wfi_r',\n",
       " 'wfi_v'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot_bands = [column[2:] for column in master_catalogue.colnames \n",
    "             if (column.startswith('f_') & ~column.startswith('f_ap_'))]\n",
    "ap_bands = [column[5:] for column in master_catalogue.colnames \n",
    "            if column.startswith('f_ap_') ]\n",
    "bands = set(tot_bands) | set(ap_bands)\n",
    "bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f979f19ef98>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oaHGoGtZms8FgMECn00Gr1aK+/sy4dXW9Yrl0IiIiusRNnz69+euvv44BgF27dhktFovW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\nIiIiIgqDwTIRERERURgMlomIiIiIwvh/w5EkD7Y7QJ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f979f291ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for b in bands:\n",
    "    plt.plot(Table(data = parse_single_table(FILTERS_DIR + b + '.xml').array.data)['Wavelength']\n",
    "            ,Table(data = parse_single_table(FILTERS_DIR + b + '.xml').array.data)['Transmission']\n",
    "            , label=b)\n",
    "plt.xlabel('Wavelength ($\\AA$)')\n",
    "plt.ylabel('Transmission')\n",
    "plt.xscale('log')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Passbands on {}'.format(FIELD))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### IV.a - Depth overview\n",
    "Then we plot the mean depths available across the area a given band is available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wfi_b: mean flux error: 0.054401807487010956, 3sigma in AB mag (Aperture): 25.868163540031013\n",
      "wfi_b123: mean flux error: 0.05324237421154976, 3sigma in AB mag (Aperture): 25.891553329403074\n",
      "wfi_v: mean flux error: 0.10746744275093079, 3sigma in AB mag (Aperture): 25.129004576412946\n",
      "wfi_r: mean flux error: 0.09391207247972488, 3sigma in AB mag (Aperture): 25.275393301217242\n",
      "irac_i3: mean flux error: 5.8527259007131, 3sigma in AB mag (Aperture): 20.788801399744038\n",
      "irac_i4: mean flux error: 5.78632855538187, 3sigma in AB mag (Aperture): 20.801189138634122\n",
      "decam_g: mean flux error: 0.12168966494844545, 3sigma in AB mag (Aperture): 24.994062624824856\n",
      "decam_r: mean flux error: 0.15984290092380252, 3sigma in AB mag (Aperture): 24.697963481232442\n",
      "decam_i: mean flux error: 0.24659686578722279, 3sigma in AB mag (Aperture): 24.227227982024026\n",
      "decam_z: mean flux error: 0.4820690610782045, 3sigma in AB mag (Aperture): 23.49942371419764\n",
      "decam_y: mean flux error: 1.1746954519327797, 3sigma in AB mag (Aperture): 22.53238364495531\n",
      "irac_i1: mean flux error: 0.8373222812628816, 3sigma in AB mag (Aperture): 22.899965243203603\n",
      "irac_i2: mean flux error: 1.070826479498174, 3sigma in AB mag (Aperture): 22.632899108394376\n",
      "vista_y: mean flux error: 40.19167216093442, 3sigma in AB mag (Aperture): 18.696856675093265\n",
      "vista_j: mean flux error: 23.417885121131587, 3sigma in AB mag (Aperture): 19.283327685217294\n",
      "vista_h: mean flux error: 44.20951206900277, 3sigma in AB mag (Aperture): 18.59340755890998\n",
      "vista_ks: mean flux error: 51.53201321739501, 3sigma in AB mag (Aperture): 18.427004089421224\n",
      "vista_z: mean flux error: 31.054807839908253, 3sigma in AB mag (Aperture): 18.976874747514962\n",
      "wfi_b: mean flux error: 0.07563285529613495, 3sigma in AB mag (Total): 25.51042062265787\n",
      "wfi_b123: mean flux error: 0.07156137377023697, 3sigma in AB mag (Total): 25.570500190285223\n",
      "wfi_v: mean flux error: 0.15549196302890778, 3sigma in AB mag (Total): 24.72792699719563\n",
      "wfi_r: mean flux error: 0.1313200145959854, 3sigma in AB mag (Total): 24.911369557379935\n",
      "irac_i3: mean flux error: 5.839967250197483, 3sigma in AB mag (Total): 20.791170834074983\n",
      "irac_i4: mean flux error: 6.198815773303567, 3sigma in AB mag (Total): 20.726425039553156\n",
      "decam_g: mean flux error: 0.15755736996434655, 3sigma in AB mag (Total): 24.713600056422187\n",
      "decam_r: mean flux error: 0.24832557664463079, 3sigma in AB mag (Total): 24.21964323161631\n",
      "decam_i: mean flux error: 0.41516537499062856, 3sigma in AB mag (Total): 23.661644048291272\n",
      "decam_z: mean flux error: 0.825016533179737, 3sigma in AB mag (Total): 22.916040233653966\n",
      "decam_y: mean flux error: 1.7063437262444947, 3sigma in AB mag (Total): 22.127030563157653\n",
      "irac_i1: mean flux error: 1.0062078712067173, 3sigma in AB mag (Total): 22.70047758736468\n",
      "irac_i2: mean flux error: 1.1883765497745837, 3sigma in AB mag (Total): 22.519811679984663\n",
      "vista_y: mean flux error: 82.88112724821083, 3sigma in AB mag (Total): 17.911057740235215\n",
      "vista_j: mean flux error: 46.338121852285894, 3sigma in AB mag (Total): 18.542350795053558\n",
      "vista_h: mean flux error: 85.47538478432637, 3sigma in AB mag (Total): 17.8775942018623\n",
      "vista_ks: mean flux error: 97.98309534834651, 3sigma in AB mag (Total): 17.729318975760528\n",
      "vista_z: mean flux error: 64.75798996166226, 3sigma in AB mag (Total): 18.178963462893513\n"
     ]
    }
   ],
   "source": [
    "average_depths = []\n",
    "for b in ap_bands:\n",
    "    \n",
    "    mean_err = np.nanmean(depths['ferr_ap_{}_mean'.format(b)])\n",
    "    print(\"{}: mean flux error: {}, 3sigma in AB mag (Aperture): {}\".format(b, mean_err, flux_to_mag(3.0*mean_err*1.e-6)[0]))\n",
    "    average_depths += [('ap_' + b, flux_to_mag(1.0*mean_err*1.e-6)[0], \n",
    "                        flux_to_mag(3.0*mean_err*1.e-6)[0], \n",
    "                        flux_to_mag(5.0*mean_err*1.e-6)[0])]\n",
    "    \n",
    "for b in tot_bands:\n",
    "    \n",
    "    mean_err = np.nanmean(depths['ferr_{}_mean'.format(b)])\n",
    "    print(\"{}: mean flux error: {}, 3sigma in AB mag (Total): {}\".format(b, mean_err, flux_to_mag(3.0*mean_err*1.e-6)[0]))\n",
    "    average_depths += [(b, flux_to_mag(1.0*mean_err*1.e-6)[0], \n",
    "                        flux_to_mag(3.0*mean_err*1.e-6)[0], \n",
    "                        flux_to_mag(5.0*mean_err*1.e-6)[0])]\n",
    "    \n",
    "average_depths = np.array(average_depths,  dtype=[('band', \"<U16\"), ('1s', float), ('3s', float), ('5s', float)])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def FWHM(X,Y):\n",
    "    \n",
    "    half_max = max(Y) / 2.\n",
    "    #find when function crosses line half_max (when sign of diff flips)\n",
    "    #take the 'derivative' of signum(half_max - Y[])\n",
    "    d = half_max - Y\n",
    "    #plot(X,d) #if you are interested\n",
    "    #find the left and right most indexes\n",
    "    low_end = X[np.where(d < 0)[0][0]]\n",
    "    high_end = X[np.where(d < 0)[0][-1]]\n",
    "    return low_end, high_end, (high_end - low_end)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "for b in ap_bands:\n",
    "    data += [['ap_' + b, Table(data = parse_single_table(FILTERS_DIR + b +'.xml').array.data)]]\n",
    "    \n",
    "for b in tot_bands:\n",
    "    data += [[b, Table(data = parse_single_table(FILTERS_DIR + b +'.xml').array.data)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ap_wfi_b (408.5, 505.5, 97.0)\n",
      "ap_wfi_b123 (3875.0, 5105.0, 1230.0)\n",
      "ap_wfi_v (495.0, 583.5, 88.5)\n",
      "ap_wfi_r (570.5, 729.0, 158.5)\n",
      "ap_irac_i3 (50246.301, 64096.699, 13850.398)\n",
      "ap_irac_i4 (64415.199, 92596.797, 28181.598)\n",
      "ap_decam_g (4180.0, 5470.0, 1290.0)\n",
      "ap_decam_r (5680.0, 7150.0, 1470.0)\n",
      "ap_decam_i (7090.0, 8560.0, 1470.0)\n",
      "ap_decam_z (8490.0, 9960.0, 1470.0)\n",
      "ap_decam_y (9510.0, 10170.0, 660.0)\n",
      "ap_irac_i1 (31754.0, 39164.801, 7410.8008)\n",
      "ap_irac_i2 (39980.102, 50052.301, 10072.199)\n",
      "ap_vista_y (9740.0, 10660.0, 920.0)\n",
      "ap_vista_j (11670.0, 13380.0, 1710.0)\n",
      "ap_vista_h (15000.0, 17900.0, 2900.0)\n",
      "ap_vista_ks (19930.0, 23010.0, 3080.0)\n",
      "ap_vista_z (8300.0, 9260.0, 960.0)\n",
      "wfi_b (408.5, 505.5, 97.0)\n",
      "wfi_b123 (3875.0, 5105.0, 1230.0)\n",
      "wfi_v (495.0, 583.5, 88.5)\n",
      "wfi_r (570.5, 729.0, 158.5)\n",
      "irac_i3 (50246.301, 64096.699, 13850.398)\n",
      "irac_i4 (64415.199, 92596.797, 28181.598)\n",
      "decam_g (4180.0, 5470.0, 1290.0)\n",
      "decam_r (5680.0, 7150.0, 1470.0)\n",
      "decam_i (7090.0, 8560.0, 1470.0)\n",
      "decam_z (8490.0, 9960.0, 1470.0)\n",
      "decam_y (9510.0, 10170.0, 660.0)\n",
      "irac_i1 (31754.0, 39164.801, 7410.8008)\n",
      "irac_i2 (39980.102, 50052.301, 10072.199)\n",
      "vista_y (9740.0, 10660.0, 920.0)\n",
      "vista_j (11670.0, 13380.0, 1710.0)\n",
      "vista_h (15000.0, 17900.0, 2900.0)\n",
      "vista_ks (19930.0, 23010.0, 3080.0)\n",
      "vista_z (8300.0, 9260.0, 960.0)\n"
     ]
    }
   ],
   "source": [
    "wav_range = []\n",
    "for dat in data:\n",
    "    print(dat[0], FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission'])))\n",
    "    wav_range += [[dat[0], FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission']))]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f979eba52e8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ApgP4dyHEYwAMaO+CXx6OPIjIuX5x8iscs1iHZa1JXh74deT4YVmLiOg7o5dOtDMUFhYq\nxcXFisFgqFIUxa7X6zVWq/WGpm5PWzFmzJhh/vDDD5WvvvrKLTU1tSE3NzcQgJw3b15jt5N6cP1a\nfdkG8sknn3g8+eSTd+zdu/eko1fD+/n5tc2YMcP8zjvveN91111X+5OP008pEUJ4ASgAsEJKeQXA\nFgDhAO4EUAcgt5t5mUIIgxDCcOHCBWenSUREREQD0NDQoPb29m5TFMVeVlbmbjQaRwOA3W5HXl7e\nbQCwY8cOX71eb+4uRlJSkqWgoMBnwoQJLWq1GmPHjrUdPHjQOzk5eUAd7rq6OreioqLRAJCfn+8z\nbdq0HuOcPHnS7ZFHHpn48ssvfxEfH9/Seb22ttal8zQUi8UiDh48OCYmJqZfxTbg5A63EMIV7cV2\nvpRyDwBIKc93uf8nAIWO5koptwHYBgA6nU46M88TJ34Ns+X4kMZUvGIQFfWLIY1JdLNjx5mI6Psn\nJSWlcdu2bf7h4eFx4eHhV7VabRMAeHh42EtKSkbn5OQE+fr6tu7Zs+fz7mJoNJprUkoxY8YMMwAk\nJiZa6urq3DofZOyvsLCwqy+99NLtmZmZnpGRkVezsrIuAMBvfvOb21966aXAixcvumq12th77rmn\n8Y033vjy5z//+biGhgaXf//3f78DAFxcXOSxY8eOnz171vXxxx+f0NbWBimleOihhy4tXLiw3113\nIaVzatmORzhfAXBJSrmiy/VxUsq6jt9XAviBlPLRnmLpdDppMBickifAgpuIiIiGjhCiVEqpG671\njEZjjVarrR+u9frK09Mzobm5uWyk8xguRqPRT6vVhjm658wO93QA6QA+E0J82nHtGQALhRB3ApAA\nagAscWIOfcLCmIiIiIicxWkFt5TyIwCOdqjvc9aaRERERHRzcNTdTk9PDz1y5Mi3TgxZtmzZ+eXL\nl1/sb3yTyaSeNWuW5vrrH3zwQbWjhx5HEt80SURERETDYteuXX06R7svAgMD26qqqiqHKp4zOf2U\nEiIiIiKi7zMW3ERERERETsSCm4iIiIjIibiH+yZg+u1v0XK8yimxR8VEI/CZZ5wSm4iIiIh6xw43\nEREREd3UamtrXeLj46NjYmJi3333Xa+ZM2dGdL4B8ruAHe6bADvQRERERN0rLCxUYmJirG+88caX\nAHD//fefGumc+oMFNxEREdEt4hd//0XIqcunPIcyZsRtEc2/nv7rsz2NSUpKmlhXV+fW0tKiWrp0\n6fmsrKx6T0/PhIULF9YXFxeP8ff3by0oKPg8KCjIdv3cc+fOucyePTuyoqLi+Mcff+wxbdq02BMn\nTnwWGRl5LSQkZNLLL7/8xXPPPTf+6tWrqujo6NEGg+G4RqOZZDAYjo8bN+6GeE8++WRwSEjItTVr\n1lwAgP/4j/8I8vLyavvVr351fui+lf7hlhIiIiIiGpT8/PyaioqK459++mnl1q1bA0wmk9pqtap0\nOl3TqVOnKqZPn27Ozs4OcjQ3ODjY1tLSorp06ZLq4MGDXnFxcc1FRUVeJ06ccPP19bUlJyc3rVmz\npnb+/PmXq6qqKr28vGRPuaSmpl7as2ePT+ef//KXv9z22GOPXRrqz9wf7HATERER3SJ660Q7y7p1\n6wL27t07FgBMJpNrRUWFu0qlQkZGxiUAWLRo0cUFCxZEdDdfp9NZioqKvD766CPl6aefrnv33Xe9\npZS4++67Lf3NZfr06daLFy+61NTUuNbV1bl4e3u3RUREtA780w0eC+4htK5kHaouDe60kWifaPxM\n/7MhyoiIiIjIuQoLC5Xi4mLFYDBUKYpi1+v1GqvVesMuCiFEtzFmzJhh/vDDD5WvvvrKLTU1tSE3\nNzcQgJw3b17jQHJ68MEHL7/66qu3mUwm1wULFoxodxvglhIiIiIiGoSGhga1t7d3m6Io9rKyMnej\n0TgaAOx2O/Ly8m4DgB07dvjq9XpzdzGSkpIsBQUFPhMmTGhRq9UYO3as7eDBg97Jycn97nADQFpa\n2qWCggKfwsLC29LT0y8P7JMNHXa4hxA700RERPR9k5KS0rht2zb/8PDwuPDw8KtarbYJADw8POwl\nJSWjc3Jygnx9fVv37NnzeXcxNBrNNSmlmDFjhhkAEhMTLXV1dW7+/v5tA8lJp9NdbWpqUgUEBFy7\n4447RnQ7CQAIKXvcd35T0Ol00mAwjHQaRERERL0SQpRKKXXDtZ7RaKzRarX1w7VeX3l6eiY0NzeX\njXQew8VoNPpptdowR/e4pYSIiIiIyIm4pYSIhkXDO6dxrbbJ6eu4BY3G2PkTnb4OERH1zFF3Oz09\nPfTIkSNeXa8tW7bs/PLlyy/2N77JZFLPmjVLc/31Dz74oDowMHBAW1GchQU3EREREQ2LXbt2nRmq\nWIGBgW1VVVWVQxXPmVhwE9GwYNeZiIi+r1hw3wz2ZwOmz5wTO3AyMPd3zolNRERERL3iQ5NERERE\nRE7EDvfNgB1oIiIiolsWO9xEREREdFOrra11iY+Pj46JiYl99913vWbOnBlRX1+vHum8+oodbiIi\nIiK6qRUWFioxMTHWN95440sAuP/++0/1ZZ7dboeUEmr1yNbmLLiJiIiIbhG1z6wNaTl50nMoY46K\njGwO+u3zZ3sak5SUNLGurs6tpaVFtXTp0vNZWVn1np6eCQsXLqwvLi4e4+/v31pQUPB5UFCQ7fq5\n586dc5k9e3ZkRUXF8Y8//thj2rRpsSdOnPgsMjLyWkhIyKSXX375i+eee2781atXVdHR0aMNBsNx\njUYzyWAwHB83btwN8aqrq93mzJkTlZCQYPnss89G79u372RUVNS1ofxO+osF9xD65TsVqKy9MqgY\nsUFj8Nz8uCHKiIiIiMj58vPzawICAtosFotISEiITUtLu2y1WlU6na5p+/btZ7OyssZlZ2cH7dy5\n84ZzuIODg20tLS2qS5cuqQ4ePOgVFxfXXFRU5CWltPj6+tqSk5Ob1qxZU2swGEY7mu/ImTNnRm3f\nvv2L++67r2bIP+wAsOAmomFx6M0TqD9rcfo6fiFemPGjKADA/v37YTKZhnyNwMBAzJ07d8jjEhEN\nVm+daGdZt25dwN69e8cCgMlkcq2oqHBXqVTIyMi4BACLFi26uGDBgoju5ut0OktRUZHXRx99pDz9\n9NN17777rreUEnffffeA/o9j3Lhx1+677z7nv964j1hwDyF2pomIiOj7prCwUCkuLlYMBkOVoih2\nvV6vsVqtNxzMIYToNsaMGTPMH374ofLVV1+5paamNuTm5gYCkPPmzWscSE6enp72gcxzFhbcRDQs\nOrvOw4ldaCIi52toaFB7e3u3KYpiLysrczcajaOB9gcW8/LybsvMzLy8Y8cOX71eb+4uRlJSkuU3\nv/lNsF6vt6jVaowdO9Z28OBB740bN54bvk/iPE47FlAIESKEOCiEqBRCVAghll93f5UQQgoh/JyV\nAxERERE5V0pKSqPNZhPh4eFxq1evDtZqtU0A4OHhYS8pKRkdGRkZ9+GHHyovvPBCXXcxNBrNNSml\nmDFjhhkAEhMTLYqitPn7+7cN1+dwJiGldE5gIcYBGCelPCqEUACUAvihlLJSCBEC4H8ARAOYKqWs\n7ymWTqeTBoPBKXkSERERDSUhRKmUUjdc6xmNxhqtVttjLTUSPD09E5qbm8tGOo/hYjQa/bRabZij\ne07rcEsp66SURzt+NwM4DiC44/bvATwNwDnVPhERERHRTWJY9nALIcIAJAD4RAjxEIBzUkpjT5vn\niYiIiOi7y1F3Oz09PfTIkSNeXa8tW7bs/PLlyy/2N77JZFLPmjVLc/31Dz74oDowMPCm2ori9IJb\nCOEFoADACgA2AM8AmN2HeZkAMgEgNDTUmSkSERER0TDYtWtXn87R7ovAwMC2qqqqyqGK50xO21IC\nAEIIV7QX2/lSyj0AJgKYAMAohKgBMB7AUSFE4PVzpZTbpJQ6KaXO39/fmWkSERERETmN0zrcon2/\nyHYAx6WULwKAlPIzALd3GVMDQNfbQ5NERERERN9VzuxwTweQDuBeIcSnHT//7MT1iIiIiIhuOk7r\ncEspPwLQ41ORUsowZ61PRERERHQzcOoebiIiIiKi3qxYsSLorbfeUpwVPyEhIRoATpw44RYbGxsT\nHR0dGxEREfdf//Vfw/KgIF/tTkREREQjasOGDbWOrttsNri4DL5cLSsrqwKA0NDQ1tLS0ioPDw/Z\n2Nioio2NjfvRj37UEBYW1jroRXrAgpuIiIjoFvG3ncdDLp2zeA5lTJ9gr+b7Hos529OYpKSkiXV1\ndW4tLS2qpUuXns/Kyqr39PRMWLhwYX1xcfEYf3//1oKCgs+DgoJsjuanpKSEzZs3r/GJJ564HBwc\nPPnBBx+8VFxcPGbFihUms9mszsvL829tbRVhYWEtu3fv/kJRFPvZs2ddFi1adMeZM2dGAcDmzZu/\nTE5ObnIUv/Otl+7u7t+8dNFqtQq73T6Yr6bPuKWEiIiIiAYlPz+/pqKi4vinn35auXXr1gCTyaS2\nWq0qnU7XdOrUqYrp06ebs7Ozg/oaz9fX11ZZWXk8MzPzcmpq6uVjx44dr66urtRoNNZNmzb5AcDS\npUtDZ8yYYa6urq6sqKionDJlytW+xD516pRrVFRU7IQJE+Kfeuopk7O72wA73ERERES3jN460c6y\nbt26gL17944FAJPJ5FpRUeGuUqmQkZFxCQAWLVp0ccGCBRF9jffYY49d7vy9tLTU49lnnw02m83q\npqYm9cyZMxsB4PDhw8ru3bu/AAAXFxf4+vr26e2SERERrSdOnKisqalxnT9/fkRaWtrlkJAQh533\nocKCm4ioj06c+DXMluNDEkvxikFU1C+GJBYR0UgqLCxUiouLFYPBUKUoil2v12usVusNuyjaX9HS\nN4qifLPXIzMzc8Lu3btPJSYmWjdt2uRbXFw8JA9XhoWFtUZHR1uLioqUJ5544nLvMwaOBTcR3bIO\n7tiGr7/8fMjiWZu/hK2tGV63qxGZ5D5kcYmIvssaGhrU3t7ebYqi2MvKytyNRuNoALDb7cjLy7st\nMzPz8o4dO3z1er15IPGbm5tVoaGhrS0tLeL111/3GTduXCsATJ8+3ZyTk+P/7LPPfm2z2dDY2Kju\nrct9+vRp14CAAJuXl5e8cOGC+siRI16rV68+P5C8+oMFNxFRH3l43gEAuP32cEydkjnC2RAR3RxS\nUlIat23b5h8eHh4XHh5+VavVNgGAh4eHvaSkZHROTk6Qr69v6549ewbUAcnOzq7V6/UxPj4+tilT\nplgsFosaALZs2XLm8ccfvyMqKspPpVJh8+bNXyYlJTl8aLJTeXm5x89+9rPxQghIKfFv//ZvJr1e\nbx1IXv0hpJS9jxphOp1OGgyGkU6DiIiIqFdCiFIppW641jMajTVarbZ+uNbrq86TQUY6j+FiNBr9\ntFptmKN7PKWEiIiIiMiJuKWEiIiIiIaco+52enp66JEjR7y6Xlu2bNn55cuXXxzseiaTST1r1izN\n9dc/+OCD6sDAwD6dYOIsLLiJiIiIaFjs2rXrjLNiBwYGtlVVVVU6K/5gcEsJEREREZETseAmIiIi\nInIiFtxERERERE7EgpuIiIiIyIlYcBMRERHRiFqxYkXQW2+9NSSvbHckISEhuuufL126pAoICIh/\n7LHHQp21Zlc8pYSIiIiIRtSGDRtqHV232WxwcRl8uVpWVlbV9c+rVq0KHuir5geCBTcRERHRLeK9\nLRtC6s9+6TmUMf1C7mies2zF2Z7GJCUlTayrq3NraWlRLV269HxWVla9p6dnwsKFC+uLi4vH+Pv7\ntxYUFHweFBRkczQ/JSUlbN68eY1PPPHE5eDg4MkPPvjgpeLi4jErVqwwmc1mdV5enn9ra6sICwtr\n2b179xeKotjPnj3rsmjRojvOnDkzCgA2b978ZXJyssNXu3d96+WhQ4c8L1y44Dp79uxGg8EwerDf\nT19wSwkRERERDUp+fn5NRUXF8U8//bRy69atASaTSW21WlU6na7p1KlTFdOnTzdnZ2cH9TWer6+v\nrbKy8nhmZubl1NTUy8eOHTteXV1dqdForJs2bfIDgKVLl4bOmDHDXF1dXVlRUVE5ZcqUq73FbWtr\nw6pVq0Iq8WxOAAAgAElEQVQ2btzY418ghho73ERERES3iN460c6ybt26gL17944FAJPJ5FpRUeGu\nUqmQkZFxCQAWLVp0ccGCBRF9jffYY49d7vy9tLTU49lnnw02m83qpqYm9cyZMxsB4PDhw8ru3bu/\nAAAXFxf4+vr2+jbJdevW+c+ePbth4sSJrf39jIPBgpuIiIiIBqywsFApLi5WDAZDlaIodr1er7Fa\nrTfsohBC9Dmmoij2zt8zMzMn7N69+1RiYqJ106ZNvsXFxQN+uPIf//iH15EjR7zy8vJub25uVrW2\ntqq8vLza/vCHP5wbaMy+4JYSIiIiIhqwhoYGtbe3d5uiKPaysjJ3o9E4GgDsdjvy8vJuA4AdO3b4\nDvQhxebmZlVoaGhrS0uLeP311306r0+fPt2ck5PjD7Q/XHnx4kV1b7HefvvtL+rq6j47d+7cZ7/8\n5S+/WrBgwUVnF9sAC24iIiIiGoSUlJRGm80mwsPD41avXh2s1WqbAMDDw8NeUlIyOjIyMu7DDz9U\nXnjhhbqBxM/Ozq7V6/UxOp0uOjIy8pt92lu2bDlTXFysREVFxU6aNCm2rKzMfag+01ATUsqRzqFX\nOp1OGgyGkU6DiIiIqFdCiFIppW641jMajTVarbZ+uNbrq64ng3wfGI1GP61WG+boHjvcRERERERO\nxIcmiYj6qOGd07hW6/CI135zCxqNsfMnDkksIqKbkaPudnp6euiRI0e8ul5btmzZ+eXLl18c7Hom\nk0k9a9YszfXXP/jgg+rAwMBeTzBxJqcV3EKIEAA7AQQAkAC2SSk3CiF+DeAhAHYAXwN4XErp8O1C\nRERERHTr2LVr1xlnxQ4MDGyrqqqqdFb8wXBmh9sGYJWU8qgQQgFQKoQ4ACBHSvkLABBCPAXgWQBL\nnZgHEdGQYEeaiIgGwml7uKWUdVLKox2/mwEcBxAspbzSZdhotHe/iYiIiIhuScOyh1sIEQYgAcAn\nHX9+HsBjABoB3DMcORARERERjQSnn1IihPACUABgRWd3W0q5VkoZAiAfwL91My9TCGEQQhguXLjg\n7DSJiIiIiJzCqR1uIYQr2ovtfCnlHgdD8gHsA/Dc9TeklNsAbAPaz+F2Zp5ERMPlFye/wjGLdcDz\nJ3l54NeR44cwIyKim0t1dbXbvHnzIk+ePFkx0rkMFad1uIUQAsB2AMellC92uR7ZZdhDAKqclQMR\nERER0UhzZod7OoB0AJ8JIT7tuPYMgMVCCA3ajwX8EjyhhIi+R9idJiJnurT7REirqclzKGO6Bo5u\n9nk46mxPY5KSkibW1dW5tbS0qJYuXXo+Kyur3tPTM2HhwoX1xcXFY/z9/VsLCgo+DwoKsjmaf+jQ\nIc+MjIwwAJg1a9Y3B2zYbDb89Kc/Hf/3v/9duXbtmvjJT37y9erVq+sBYO3atYF//vOffYQQuO++\n+xr/8Ic/nMvNzfXLy8vzb21tFWFhYS27d+/+QlEUe0pKSpi7u7v92LFjnhcvXnT94x//WPPKK6/4\nlpaWjk5ISGgqKCio6e6z/f73v/fbuHFjoKIobXFxcc1ubm5y586d/Tre0JmnlHwkpRRSyngp5Z0d\nP/uklClSykkd1+dLKc85KwciIiIicr78/PyaioqK459++mnl1q1bA0wmk9pqtap0Ol3TqVOnKqZP\nn27Ozs4O6m7+4sWLwzZs2HCmurr6W+dob9iwwc/b27vt2LFjx41G4/FXXnnFv6qqyu3NN98cs2/f\nvrGlpaVV1dXVlc8995wJAFJTUy8fO3bseHV1daVGo7Fu2rTJrzNWY2OjS1lZWdXvfve7s48++mjE\n6tWrz588ebKiqqrK4/Dhwx6O8qqpqXFdv379uE8++eS4wWCoOnnypPtAvh++aZKIiIjoFtFbJ9pZ\n1q1bF7B3796xAGAymVwrKircVSoVMjIyLgHAokWLLi5YsCDC0dz6+nq12WxWz50719I59v333/cG\ngKKiojFVVVWeb7/99m0AYDab1ZWVle4HDhwYk5aWVq8oih0AAgIC2gCgtLTU49lnnw02m83qpqYm\n9cyZMxs713nggQcaVCoVpkyZ0uzr69uq1+utABAVFWU9ffr0qGnTpt3wgM2hQ4dG/+AHPzB3xv+X\nf/mXyydOnOh30c2Cm4iIiIgGrLCwUCkuLlYMBkOVoih2vV6vsVqtN+yiaH+8r3+klCI3N/dMSkpK\n1/e4YP/+/WMcjc/MzJywe/fuU4mJidZNmzb5FhcXK5333N3dJQCo1Wq4ubl9cyCHSqWCzWbrf3L9\n4PRjAYmIiIjo1tXQ0KD29vZuUxTFXlZW5m40GkcDgN1uR15e3m0AsGPHDl+9Xm92NN/Pz69NUZS2\n9957z6tjrE/nveTk5MYtW7b4t7S0CAAoLy8fdeXKFdWcOXOuvPrqq35ms1kFAOfPn1cDQHNzsyo0\nNLS1paVFvP766z6O1uuPf/qnf2r65JNPlAsXLqhbW1vxl7/85baBxGGHm4iIiIgGLCUlpXHbtm3+\n4eHhceHh4Ve1Wm0TAHh4eNhLSkpG5+TkBPn6+rbu2bPn8+5ibN++vSYjIyNMCPGthyZXrlxZX1NT\nM2ry5MkxUkrh4+PTum/fvtMPP/zwlaNHj3reeeedMa6urjIpKalx8+bN57Kzs2v1en2Mj4+PbcqU\nKRaLxaIezGebMGFC68qVK+t0Ol2Mt7e3LSIi4qq3t3dbf+MIKW/+I651Op00GAwjnQYRERFRr4QQ\npVJK3XCtZzQaa7Rabf1wrddXnp6eCc3NzWUjncdgNTY2qry9ve2tra2YM2dOxOOPP17/2GOPNVw/\nzmg0+mm12jBHMbilhIiIiIioG6tXrw6Kjo6OjYqKigsNDW1JS0u7odjuDbeUEBEREdGQc9TdTk9P\nDz1y5IhX12vLli07v3z58ovDl5lj8fHx0deuXftWM3rnzp1fbNu27avBxmbBTURERETDYteuXf16\nYcxwKi8vd9rbz7mlhIiIiIjIiVhwExERERE5EQtuIiIiIiInYsFNRERERDeN6upqt8jIyLiRzmMo\nseAmIiIiIuoDm802oHksuImIiIhoUJKSkibGxcXFRERExK1fv94PaH/xzeLFi0MiIiLiEhMTo2pr\na7s9He/QoUOeGo0mVqPRxL744ou3d1632WxYsmTJ+EmTJsVERUXF5uTk+HXeW7t2bWBUVFSsRqOJ\nffLJJ4MBIDc312/SpEkxGo0mds6cORM7X/2ekpISlpqaGqrVaqPHjx8/ubCwUHnkkUfCwsPD41JS\nUsJ6+myenp4JP/nJT8ZrNJrYv/3tb149je0OjwUkIiIiukW89dZbIV9//bXnUMa8/fbbm3/4wx+e\n7WlMfn5+TUBAQJvFYhEJCQmxaWlpl61Wq0qn0zVt3779bFZW1rjs7OygnTt3OjwWcPHixWEbN248\nM3fuXMuSJUvGd17fsGGDn7e3d9uxY8eOW61Wcdddd0XPnz//Snl5ufu+ffvGlpaWVimKYj9//rwa\nAFJTUy+vWrWqHgCeeuqpoE2bNvmtXbv2awBobGx0KSsrq3rttdfGPvrooxHvv/9+1dSpU63x8fEx\nhw8f9pg2bZrVUW5Wq1X1gx/8oOlPf/rTgM/jZoebiIiIiAZl3bp1ARqNJnbq1KkxJpPJtaKiwl2l\nUiEjI+MSACxatOhiSUmJw+5wfX292mw2q+fOnWvpHNt5r6ioaMybb77pGx0dHZuQkBBz+fJll8rK\nSvcDBw6MSUtLq1cUxQ4AAQEBbQBQWlrqMXXqVE1UVFRsQUGBb0VFhXtnrAceeKBBpVJhypQpzb6+\nvq16vd6qVqsRFRVlPX369KjuPptarcbjjz9+eTDfDzvcRERERLeI3jrRzlBYWKgUFxcrBoOhSlEU\nu16v11it1huaukKIfseWUorc3NwzKSkpV7pe379//xhH4zMzMyfs3r37VGJionXTpk2+xcXFSuc9\nd3d3CbQX0G5ubrLzukqlgs1m6zY5Nzc3u4vL4EpmFtxERMNo//79MJlMA54fGBiIuXPnDmFGRESD\n09DQoPb29m5TFMVeVlbmbjQaRwOA3W5HXl7ebZmZmZd37Njhq9frzY7m+/n5tSmK0vbee+95zZkz\nx7Jjxw6fznvJycmNW7Zs8Z83b5551KhRsry8fFRYWFjrnDlzrjz//PNBmZmZlzq3lAQEBLQ1Nzer\nQkNDW1taWsTrr7/uM27cuNbh+h56woKbiIjIGfZnA6bPhjZm4GRg7u+GNibRIKWkpDRu27bNPzw8\nPC48PPyqVqttAgAPDw97SUnJ6JycnCBfX9/WPXv2fN5djO3bt9dkZGSECSEwa9asb7rZK1eurK+p\nqRk1efLkGCml8PHxad23b9/phx9++MrRo0c977zzzhhXV1eZlJTUuHnz5nPZ2dm1er0+xsfHxzZl\nyhSLxWJRD8d30Bshpex91AjT6XTSYDCMdBpERER9x4L7e0sIUSql1A3XekajsUar1dYP13p95enp\nmdDc3Fw20nkMF6PR6KfVasMc3WOHm4iIyBlYGBNRBxbcRERERDTkHHW309PTQ48cOfKt00qWLVt2\nfvny5RevHzvc4uPjo69du/athz137tz5hV6vd3hcYH+w4CYiIiKiYbFr1y6H53DfDMrLy6ucFZvn\ncBMRERERORELbiIiIiIiJ+KWEiIiIif4xcmvcMwy6K2f3zLJywO/jhzf+0Aiuqmww01ERERE5ERO\n63ALIUIA7AQQAEAC2Cal3CiEyAEwH8A1AKcBPCGlbHBWHkRERCPBWZ3odSXrUHVp6J/tivaJxs/0\nPxvyuET9VV1d7TZv3rzIkydPVox0LgAwc+bMiIKCgi/8/PzaBhrDmR1uG4BVUspYAHcD+KkQIhbA\nAQCTpJTxAE4AWOPEHIiIiIiIBqy4uPjUYIptwIkdbillHYC6jt/NQojjAIKllH/tMuwfAB52Vg5E\nRES3Gnah6WaUlJQ0sa6uzq2lpUW1dOnS81lZWfWenp4JCxcurC8uLh7j7+/fWlBQ8HlQUJDN0fxD\nhw55ZmRkhAH41qvdbTYbfvrTn47/+9//rly7dk385Cc/+Xr16tX1ALB27drAP//5zz5CCNx3332N\nf/jDH87l5ub65eXl+be2toqwsLCW3bt3f6Eoij0lJSXM3d3dfuzYMc+LFy+6/vGPf6x55ZVXfEtL\nS0cnJCQ0FRQU1HT32YKDgycbDIbj48aNc5h7XwzLQ5NCiDAACQA+ue7WIgBvDEcORERERLe6yuM/\nC2mynPAcypijvaKaY2PWne1pTH5+fk1AQECbxWIRCQkJsWlpaZetVqtKp9M1bd++/WxWVta47Ozs\noJ07dzo8h3vx4sVhGzduPDN37lzLkiVLvtmPtWHDBj9vb++2Y8eOHbdareKuu+6Knj9//pXy8nL3\nffv2jS0tLa1SFMV+/vx5NQCkpqZeXrVqVT0APPXUU0GbNm3yW7t27dcA0NjY6FJWVlb12muvjX30\n0Ucj3n///aqpU6da4+PjYw4fPuwxbdq0oX3KuQunF9xCCC8ABQBWSCmvdLm+Fu3bTvK7mZcJIBMA\nQkNDnZ0mEdFN55fvVKCy9orDe7FBY/Dc/LhhzohuBj39ezEY/HeKBmPdunUBe/fuHQsAJpPJtaKi\nwl2lUiEjI+MSACxatOjiggULIhzNra+vV5vNZvXcuXMtnWPff/99bwAoKioaU1VV5fn222/fBgBm\ns1ldWVnpfuDAgTFpaWn1iqLYASAgIKANAEpLSz2effbZYLPZrG5qalLPnDmzsXOdBx54oEGlUmHK\nlCnNvr6+rZ1vkIyKirKePn161He24BZCuKK92M6XUu7pcv1xAPMA3CellI7mSim3AdgGADqdzuEY\nIiIiIvr/9daJdobCwkKluLhYMRgMVYqi2PV6vcZqtd7wnKAQot+xpZQiNzf3TEpKyrf+lrl///4x\njsZnZmZO2L1796nExETrpk2bfIuLi5XOe+7u7hIA1Go13NzcvqktVSoVbDZb/5PrB2eeUiIAbAdw\nXEr5Ypfr9wN4GsBMKWWzs9YnIvquY7eRHOG/F3SzaWhoUHt7e7cpimIvKytzNxqNowHAbrcjLy/v\ntszMzMs7duzw1ev1Zkfz/fz82hRFaXvvvfe85syZY9mxY4dP573k5OTGLVu2+M+bN888atQoWV5e\nPiosLKx1zpw5V55//vmgzMzMS51bSgICAtqam5tVoaGhrS0tLeL111/3GTduXOtwfQ89cWaHezqA\ndACfCSE+7bj2DIBNAEYBONDxN51/SCmXOjEPIiIiInKSlJSUxm3btvmHh4fHhYeHX9VqtU0A4OHh\nYS8pKRmdk5MT5Ovr27pnz57Pu4uxffv2moyMjDAhxLcemly5cmV9TU3NqMmTJ8dIKYWPj0/rvn37\nTj/88MNXjh496nnnnXfGuLq6yqSkpMbNmzefy87OrtXr9TE+Pj62KVOmWCwWi3o4voPeiG52dNxU\ndDqdNBgMI50GERERUa+EEKVSSt1wrWc0Gmu0Wm39cK3XV56engnNzc1lI53HcDEajX5arTbM0T2+\naZKIiIiIyImG5VhAIiIiIvp+cdTdTk9PDz1y5IhX12vLli07v3z58ovDl5lj8fHx0deuXftWM3rn\nzp1fdJ5mMhgsuImIiIhoWOzatcvhOdw3g/Ly8ipnxeaWEiIiIiIiJ2LBTURERETkRCy4iYiIiIic\niAU3EREREZETseAmIiIioptGdXW1W2Rk5C31SlUW3ERERERETtSnglsIMUoI8a9CiGeEEM92/jg7\nOSIiIiK6+SUlJU2Mi4uLiYiIiFu/fr0f0P6mycWLF4dERETEJSYmRtXW1nZ7HPWhQ4c8NRpNrEaj\niX3xxRdv77xus9mwZMmS8ZMmTYqJioqKzcnJ8eu8t3bt2sCoqKhYjUYT++STTwYDQG5urt+kSZNi\nNBpN7Jw5cyaazWYVAKSkpISlpqaGarXa6PHjx08uLCxUHnnkkbDw8PC4lJSUsO7yys/P946Ojo6N\njo6ODQsLmxQcHDx5IN9PX8/h/guARgClAFoGshAREfXPiRO/htly3OE9xSsGUVG/GOaMiOhmt+L4\nmZCqpqueQxkzerR784aY0LM9jcnPz68JCAhos1gsIiEhITYtLe2y1WpV6XS6pu3bt5/Nysoal52d\nHbRz506H53AvXrw4bOPGjWfmzp1rWbJkyfjO6xs2bPDz9vZuO3bs2HGr1Sruuuuu6Pnz518pLy93\n37dv39jS0tIqRVHs58+fVwNAamrq5VWrVtUDwFNPPRW0adMmv7Vr134NAI2NjS5lZWVVr7322thH\nH3004v3336+aOnWqNT4+Pubw4cMe06ZNu+EFN6mpqY2pqamNAPDP//zP4TNmzDAP5Dvsa8E9Xkp5\n/0AWICIiIqJb27p16wL27t07FgBMJpNrRUWFu0qlQkZGxiUAWLRo0cUFCxZEOJpbX1+vNpvN6rlz\n51o6x77//vveAFBUVDSmqqrK8+23374NAMxms7qystL9wIEDY9LS0uoVRbEDQEBAQBsAlJaWejz7\n7LPBZrNZ3dTUpJ45c2Zj5zoPPPBAg0qlwpQpU5p9fX1bO98gGRUVZT19+vQoRwV3p5///OcB7u7u\n9jVr1lwYyPfT14L7sBBispTys4EsQkRE/ccONhH1V2+daGcoLCxUiouLFYPBUKUoil2v12usVusN\n25aFEP2OLaUUubm5Z1JSUq50vb5///4xjsZnZmZO2L1796nExETrpk2bfIuLi5XOe+7u7hIA1Go1\n3NzcZOd1lUoFm83WbXJvvfWW8tZbb/n84x//GPCbKHvcwy2E+EwIUQ7gnwAcFUJUCyHKu1wnIiIi\nou+xhoYGtbe3d5uiKPaysjJ3o9E4GgDsdjvy8vJuA4AdO3b46vV6h9sx/Pz82hRFaXvvvfe8Osb6\ndN5LTk5u3LJli39LS4sAgPLy8lFXrlxRzZkz58qrr77q17lHu3NLSXNzsyo0NLS1paVFvP766z6O\n1uuPEydOuK1YseKOgoKC015eXrL3GY711uGeN9DARERERHTrS0lJady2bZt/eHh4XHh4+FWtVtsE\nAB4eHvaSkpLROTk5Qb6+vq179uz5vLsY27dvr8nIyAgTQmDWrFnfdLNXrlxZX1NTM2ry5MkxUkrh\n4+PTum/fvtMPP/zwlaNHj3reeeedMa6urjIpKalx8+bN57Kzs2v1en2Mj4+PbcqUKRaLxaIezGfb\nunWrb2Njo/qhhx6KAICAgIBrxcXFp/obR0jZe7EuhNglpUzv7Zqz6HQ6aTAYhmMpIiIiokERQpRK\nKXXDtZ7RaKzRarX1w7VeX3l6eiY0NzeXjXQew8VoNPpptdowR/f6eg73tw4fF0KoAUwdZF5ERERE\nRLe8HreUCCHWAHgGgIcQ4gqAzg3l1wBsc3JuRERERPQd5ai7nZ6eHnrkyBGvrteWLVt2fvny5ReH\nLzPH4uPjo69du/atZvTOnTu/6DzNZDB6LLillC8AeEEI8YKUcs1gFyMiIiKi769du3Y5PIf7ZlBe\nXj7gU0h609djAZ8RQixA+2klEsAhKeVbzkqKiIiIiOhW0dc93P8NYCmAzwAcA7BUCPHfTsuKiIiI\niOgW0dcO970AYmTHkSZCiFcAVDgtKyIiIiKiW0RfO9ynAIR2+XNIxzUiIiIiIupBXwtuBcBxIcQH\nQoiDACoBjBFCvC2EeNt56RERERHR90l1dbVbZGRkXO8jvzv6uqXkWadmQURERER0i+pTh1tKWQyg\nBoBrx+8lAI5KKYs7/kxERERE31NJSUkT4+LiYiIiIuLWr1/vB7S/aXLx4sUhERERcYmJiVG1tbXd\nNnoPHTrkqdFoYjUaTeyLL754e+d1m82GJUuWjJ80aVJMVFRUbE5Ojl/nvbVr1wZGRUXFajSa2Cef\nfDIYAHJzc/0mTZoUo9FoYufMmTPRbDarACAlJSUsNTU1VKvVRo8fP35yYWGh8sgjj4SFh4fHpaSk\nhHWX14YNG3wXLVoU0vnn3Nxcv8WLF4d0N747fepwCyF+AiATgA+AiQDGA/gjgPv6uyAREREROcfq\n3caQEyaz51DGjApUmnMe1p7taUx+fn5NQEBAm8ViEQkJCbFpaWmXrVarSqfTNW3fvv1sVlbWuOzs\n7KCdO3c6PId78eLFYRs3bjwzd+5cy5IlS8Z3Xt+wYYOft7d327Fjx45brVZx1113Rc+fP/9KeXm5\n+759+8aWlpZWKYpiP3/+vBoAUlNTL69ataoeAJ566qmgTZs2+a1du/ZrAGhsbHQpKyureu2118Y+\n+uijEe+//37V1KlTrfHx8TGHDx/2mDZt2g0vuHniiScuT5o0aVxLS8tXo0aNkq+++qrf1q1bv+zv\nd9jXPdw/BTAdwBUAkFKeBHB7jzOIiIiI6Hth3bp1ARqNJnbq1KkxJpPJtaKiwl2lUiEjI+MSACxa\ntOhiSUmJl6O59fX1arPZrJ47d66lc2znvaKiojFvvvmmb3R0dGxCQkLM5cuXXSorK90PHDgwJi0t\nrV5RFDsABAQEtAFAaWmpx9SpUzVRUVGxBQUFvhUVFe6dsR544IEGlUqFKVOmNPv6+rbq9XqrWq1G\nVFSU9fTp06Mc5ebt7W2fPn26+Y033vAuKytzb21tFQN582Rf93C3SCmvCdH+ZnchhAvaX4DTLSFE\nCICdAAI6xm6TUm4UQjwC4D8BxADQSykN/U2aiIiIiG7UWyfaGQoLC5Xi4mLFYDBUKYpi1+v1GqvV\nekNTt7OO7A8ppcjNzT2TkpJypev1/fv3j3E0PjMzc8Lu3btPJSYmWjdt2uRbXFysdN5zd3eXAKBW\nq+Hm5vZNHatSqWCz2bpNLjMzs/75558PjIqKupqWllbf7w+Bvne4i4UQzwDwEEIkA/gzgHd6mWMD\nsEpKGQvgbgA/FULEov3FOQsAfDiQhImIiIjo5tHQ0KD29vZuUxTFXlZW5m40GkcDgN1uR15e3m0A\nsGPHDl+9Xm92NN/Pz69NUZS29957z6tjrE/nveTk5MYtW7b4t7S0CAAoLy8fdeXKFdWcOXOuvPrq\nq36de7Q7t5Q0NzerQkNDW1taWsTrr7/u42i9/rr33nub6urq3P73f//Xd/HixZcGEqOvHe5sAIvR\n/qbJJQD2AfifniZIKesA1HX8bhZCHAcQLKU8AAzsbzlEREREdHNJSUlp3LZtm394eHhceHj4Va1W\n2wQAHh4e9pKSktE5OTlBvr6+rXv27Pm8uxjbt2+vycjICBNCYNasWd90s1euXFlfU1MzavLkyTFS\nSuHj49O6b9++0w8//PCVo0ePet55550xrq6uMikpqXHz5s3nsrOza/V6fYyPj49typQpFovFoh6K\nz/jDH/7wcnl5uae/v3/bQOaLjpdH9j5QCH8AkFJe6PciQoShvaM9SUp5pePaBwCy+rKlRKfTSYOB\nO0+IiIjo5ieEKJVS6oZrPaPRWKPVage01cGZPD09E5qbm8tGOo+hcM8990SsWLHi/EMPPeSwSw8A\nRqPRT6vVhjm61+OWEtHuP4UQ9QCqAVQLIS4IIfp8LrcQwgtAAYAVncV2H+dlCiEMQgjDhQv9rvGJ\niIiIiAalvr5eHRYWNsnd3d3eU7Hdm962lKxE++kkd0kpvwAAIUQ4gC1CiJVSyt/3NFkI4Yr2Yjtf\nSrmnP4lJKbcB2Aa0d7j7M5eIiIiIRpaj7nZ6enrokSNHvnVaybJly84vX7784vVjh1t8fHz0tWvX\nvtWM3rlz5xc1NTXHBhu7t4I7HUCylPKb/0whpfxcCJEG4K8Aui24Rfsm7e0AjkspXxxsokRERET0\n3bZr1y6H53DfDMrLy6ucFbu3gtu1a7HdSUp5oaN73ZPpaC/YPxNCfNpx7RkAowC8BMAfwF4hxKdS\nyjn9zJuIiIiI6Duht4L72gDvQUr5EYDujiL5317WJSIiIiK6JfRWcGuFEI4edBQA3B1cJyIiIiKi\nLuH6ujMAACAASURBVHosuKWUQ3J2IRERERHR91Vf3zRJREREROQUK1asCHrrrbeU3kcOTEJCQnTn\n7zNmzIhUFOXOe+65J8JZ612vr2+aJCIiIiJyig0bNtQ6um6z2eDiMvhytays7JsTSLKyskxNTU2q\nP/3pT/6DDtxH7HATERER0aAkJSVNjIuLi4mIiIhbv369H9D+psnFixeHRERExCUmJkbV1tZ2Wzmn\npKSE5eXl3QYAwcHBk5ctWxYcGxsb8/LLL9+W+//Yu/ewqK5zf+DfNYDgwIgKCMGgBFFu6kgdp/Ea\nNeTWE08VkmqKoFEbtWlqqnJik8bkZxItkdiEJqJpogbCiRzvCda0pk3QVhuF4ggDCMaCREFFLgFm\nGIFZvz/EHg7hDntQ/H6eJ0+GvdZ+17vnydaXlbXXfust97FjxwYFBAQEP/LII6Oqq6tVAFBcXGz/\n0EMPjQoICAgOCAgIPnr0qHNb8dVqdeitzz/+8Y+rBw0aZO29q+8YZ7iJiIiI+ouDz/rgao66V2MO\nCzZh7nvF7XVJTk4u9PT0bKypqRGhoaHBCxcurDCbzSqdTlf74YcfFq9du/aedevWeScmJnZqH243\nN7eGnJycXAAoLS21W7NmTRkA/PKXv/SOj493f+mll66uWLFixPTp06vXr1//TUNDA6qqqm7bZw9Z\ncBMRERFRj8TGxnoePnx4MACUlpY6GI1GJ5VKhWXLlpUDwJIlS66Hh4d3es10dHR0xa3PGRkZA9ev\nXz+8urrarra21u6BBx6oAoATJ05o9u7d+y8AsLe3h5ubW2PvXlXvYcFNRERE1F90MBOthNTUVE1a\nWpomPT09T6PRWPV6fYDZbP7esuWbLyHvHI1G8+8lH88888x9e/fuPT958mRzfHy8W1pammIPVyqF\na7iJiIiIqNsqKyvtXF1dGzUajTUzM9PJYDA4A4DVasWtddm7du1y0+v11d2JbzKZVCNGjKi3WCxi\n9+7dQ28dnzp1avXmzZs9gJsPV16/fv22XVLCgpuIiIiIui0iIqKqoaFB+Pn5hcTExAzXarW1ADBw\n4EDrqVOnnEePHh1y7NgxzaZNm0q6E3/dunWX9Xp9kE6nCxw9enTdreMJCQkX09LSNGPGjAkeO3Zs\ncGZmZqdeyjhx4sSAqKgov5MnTw7y9PQcv2/fvkHdyasrhJRS6TF6TKfTyfT09L5Og4iIiKhDQogM\nKaXOVuMZDIZCrVZbZqvxOkutVoeaTKbMvs7DVgwGg7tWq/VtrY0z3ERERERECuJDk0RERETU61qb\n3Y6Kihpx+vRpl+bHVq5ceWXVqlXXezpeaWmp3cyZMwNaHv/qq6/OeXl59ekOJiy4iYiIiMgmkpKS\nOrUPd3d4eXk15uXl5SgVvye4pISIiIiISEEsuImIiIiIFMSCm4iIiIhIQSy4iYiIiIgUxIKbiIiI\niPrU888/733w4EHFXtkeGhoaCAAnTpwYOGHChEB/f/+QMWPGBP/hD38YotSYzXGXEiIiIiLqU2+/\n/fbl1o43NDTA3r7n5WpmZmYeALi4uFiTkpL+NW7cOEthYaHDpEmTgubNm/edu7u7otsGcoabiIiI\niHokLCxsVEhISJC/v39IXFycO3DzTZNLly718ff3D5k8efKYy5cvt1k5R0RE+O7cuXMIAAwfPnzc\nypUrhwcHBwft2LFjyFtvveU+duzYoICAgOBHHnlkVHV1tQoAiouL7R966KFRAQEBwQEBAcFHjx51\nbiu+Wq0OBYDx48dbxo0bZwEAX1/f+qFDhzaUlJQoPgHNGW4iIqK73C8/Xod/1X2jWHy3GuCxqlGK\nxe+KYSP9MGvxM32dhmJe/vvLPucrzqt7M6b/EH/Ta1NfK26vT3JycqGnp2djTU2NCA0NDV64cGGF\n2WxW6XS62g8//LB47dq196xbt847MTGxU/twu7m5NeTk5OQCN19os2bNmjIA+OUvf+kdHx/v/tJL\nL11dsWLFiOnTp1evX7/+m4aGBlRVVdl15bq+/PJLdX19vQgODrZ05bzuYMFNRERERD0SGxvrefjw\n4cEAUFpa6mA0Gp1UKhWWLVtWDgBLliy5Hh4e7t/ZeNHR0RW3PmdkZAxcv3798Orqarva2lq7Bx54\noAoATpw4odm7d++/AMDe3h5ubm6dXhZSVFTk8PTTT/t9+OGH/7Kz61Kd3i0suImIiO5y8Qt/29cp\nUC/paCZaCampqZq0tDRNenp6nkajser1+gCz2fy9ZctCiE7H1Gg01lufn3nmmfv27t17fvLkyeb4\n+Hi3tLS0Hj1cWV5ernrsscf8X3nllUsPPvhgbU9idRYLbiIiIlJU6caNsOTm9XUaAADHoEB4vfhi\nX6fRr1RWVtq5uro2ajQaa2ZmppPBYHAGAKvVip07dw555plnKnbt2uWm1+uruxPfZDKpRowYUW+x\nWMTu3buH3nPPPfUAMHXq1OrNmzd7rF+//uqtJSUdzXLX1dWJ//iP//BfsGDB9aeffrqivb69iQ9N\nEhEREVG3RUREVDU0NAg/P7+QmJiY4VqtthYABg4caD116pTz6NGjQ44dO6bZtGlTSXfir1u37rJe\nrw/S6XSBo0ePrrt1PCEh4WJaWppmzJgxwWPHjg3OzMx06ijWjh07hpw+fdrlv//7v90DAwODAwMD\ng0+cODGwO3l1hZBSKj1Gj+l0Opment7XaRARERF1SAiRIaXU2Wo8g8FQqNVqy2w1Xmep1epQk8mU\n2dd52IrBYHDXarW+rbUpNsMthPARQnwphMgRQhiFEKuajg8VQhwVQhQ0/dsmG44TEREREfUFJddw\nNwBYI6X8pxBCAyBDCHEUwGIAf5FS/lYIsQ7AOgAvKJgHEREREdlYa7PbUVFRI06fPu3S/NjKlSuv\nrFq16npPxystLbWbOXNmQMvjX3311TkvLy9FX2zTEcUKbillCYCSps/VQohcAMMB/BjAzKZuHwH4\nCiy4iYiIiPq9pKSkTu3D3R1eXl6NeXl5OUrF7wmbPDQphPAFEArgawCeTcU4AJQC8LRFDkRERERE\nfUHxglsI4QJgH4DnpZTfNW+TN5/YbPWpTSHEM0KIdCFE+rVr15ROk4iIiIhIEYoW3EIIB9wstpOl\nlPubDl8RQtzT1H4PgKutnSulfF9KqZNS6jw8PJRMk4iIiIhIMUruUiIAfAggV0q5pVnTpwAWNX1e\nBOCQUjkQEREREfU1JWe4pwKIAjBbCHGm6Z8fAfgtgIeEEAUAwpp+JiIiIiL6P+bPnz8yIyOjzRfa\nxMfHuxUWFjrYMqfuUHKXkr8BEG00P6jUuERERETUP6SkpBS11/7xxx+7T5gwwezr61tvq5y6Q8l9\nuImIiIjIhi6/+JKPpaBA3ZsxHUePNnlvfKO4vT5hYWGjSkpKBlgsFtWKFSuurF27tkytVoc+9dRT\nZWlpaYM8PDzq9+3bd8Hb27uh5bmZmZlO0dHR92VlZeUCwLlz5wbMmTPHPz8/P0ev1wfExcUVT5ky\nxTR//nzfs2fPOgshZGRkZNmIESPqs7Oz1dHR0X5OTk7W9PT03FdffdXr888/H2yxWFQ6na4mOTm5\nSKX6/oIOo9Ho+OSTT/rl5OTkAkBWVpbj/Pnz//1zb7PJtoBERERE1H8lJycXGo3G3DNnzuRs377d\ns7S01M5sNqt0Ol3t+fPnjVOnTq1et26dd2vnhoaG1tXX14u8vLwBAJCYmDh07ty5Fc37nDx5Ul1S\nUuJQUFBgzM/Pz3n22WevP/300xVjx441JSYmXsjLy8txcXGRMTExV7Ozs3MLCgqMZrNZtXv3btfW\nxgwJCbFoNJrGEydODASA7du3u0dGRvb45Ttt4Qw3ERERUT/R0Uy0UmJjYz0PHz48GABKS0sdjEaj\nk0qlwrJly8oBYMmSJdfDw8P92zp/7ty55YmJiUM3btxYeuDAgSEpKSkXmrcHBgZaiouLHRctWuQz\nZ86cqnnz5n3XWpwjR45otmzZ4lVXV6eqrKy0Dw4ONgOoaq3v4sWLy/7whz+46/X64kOHDg05ffq0\nIrPbAGe4iYiIiKgHUlNTNWlpaZr09PS8c+fO5QQFBZnNZvP3asybG9i1LioqquLgwYNDzp496yiE\nwLhx4yzN2z08PBqzs7NzZs2aVb1t2zaPBQsW+LaMYTKZxJo1a0bu37//m/z8/JyFCxeW1dXVtVnr\nLlq0qOLLL7903b179+Bx48aZlHz9OwtuIiIiIuq2yspKO1dX10aNRmPNzMx0MhgMzgBgtVqxc+fO\nIQCwa9cuN71eX91WjJCQEItKpcL69eu9582bV96yvaSkxL6xsRGLFy+u3LRp06WsrCw1ALi4uDRW\nVVXZAYDJZFIBgJeXV0NVVZXqs88+G9Je3mq1Wj7wwANVq1evHrF48eKy7n8DHWPBTURERETdFhER\nUdXQ0CD8/PxCYmJihmu12loAGDhwoPXUqVPOo0ePDjl27Jhm06ZNJe3FCQ8PLz906NDQqKioipZt\nhYWFDtOmTQsIDAwMjoqK8tuwYcO3ABAdHV323HPPjQwMDAx2cnKyRkZGXgsKCgqZNWvWmFt5tCc6\nOrpcCIHw8PBWl6j0FnHz7eq3N51OJ9PT0/s6DSIiIqIOCSEypJQ6W41nMBgKtVqtojO03aFWq0NN\nJlNmX+fRnvXr13tWVVXZvfPOO5d7GstgMLhrtVrf1tr40CQRERER3XUeeuihUUVFRY5paWn5So/F\ngpuIiIiIel1rs9tRUVEjTp8+7dL82MqVK6+sWrVKsS352hrz6NGj3yg1ZkssuImIiIjIJpKSki7e\nDWO2xIcmiYiIiIgUxIKbiIiIiEhBLLiJiIiIiBTEgpuIiIiISEEsuImIiIjotjR//vyRGRkZTm21\nx8fHuxUWFjp0J/bzzz/vffDgQU33s+s87lJCRERERLellJSUovbaP/74Y/cJEyaYfX1967sa++23\n3+7xy246iwU3ERERUT/xl8Rcn/JLNerejDl0uIvpweig4vb6hIWFjSopKRlgsVhUK1asuLJ27doy\ntVod+tRTT5WlpaUN8vDwqN+3b98Fb2/vhpbnZmZmOkVHR9+XlZWVCwDnzp0bMGfOHP/8/PwcvV4f\nEBcXVzxlyhTT/Pnzfc+ePesshJCRkZFlI0aMqM/OzlZHR0f7OTk5WdPT03NfffVVr88//3ywxWJR\n6XS6muTk5CKVqvUFHREREb6PP/541dNPP/29V8n3Ni4pISIiIqIeSU5OLjQajblnzpzJ2b59u2dp\naamd2WxW6XS62vPnzxunTp1avW7dOu/Wzg0NDa2rr68XeXl5AwAgMTFx6Ny5c/9PEXzy5El1SUmJ\nQ0FBgTE/Pz/n2Wefvf70009XjB071pSYmHghLy8vx8XFRcbExFzNzs7OLSgoMJrNZtXu3btdbXH9\nHeEMNxEREVE/0dFMtFJiY2M9Dx8+PBgASktLHYxGo5NKpcKyZcvKAWDJkiXXw8PD/ds6f+7cueWJ\niYlDN27cWHrgwIEhKSkpF5q3BwYGWoqLix0XLVrkM2fOnKp58+Z911qcI0eOaLZs2eJVV1enqqys\ntA8ODjYDqOrFS+0WznATERERUbelpqZq0tLSNOnp6Xnnzp3LCQoKMpvN5u/VmEKINmNERUVVHDx4\ncMjZs2cdhRAYN26cpXm7h4dHY3Z2ds6sWbOqt23b5rFgwQLfljFMJpNYs2bNyP3793+Tn5+fs3Dh\nwrK6urrbota9LZIgIiIiojtTZWWlnaura6NGo7FmZmY6GQwGZwCwWq3YuXPnEADYtWuXm16vr24r\nRkhIiEWlUmH9+vXe8+bNK2/ZXlJSYt/Y2IjFixdXbtq06VJWVpYaAFxcXBqrqqrsAMBkMqkAwMvL\nq6Gqqkr12WefDVHieruDBTcRERERdVtERERVQ0OD8PPzC4mJiRmu1WprAWDgwIHWU6dOOY8ePTrk\n2LFjmk2bNpW0Fyc8PLz80KFDQ6Oior73EGNhYaHDtGnTAgIDA4OjoqL8NmzY8C0AREdHlz333HMj\nAwMDg52cnKyRkZHXgoKCQmbNmjXmVh7tEULI7l53VwgpbTJOj+h0Opment7XaRARERF1SAiRIaXU\n2Wo8g8FQqNVqy2w1Xmep1epQk8mU2dd5tGX27Nn+v/rVr67MmTOnzZn3rjAYDO5arda3tTbOcBMR\nERHRXeXJJ5/0NZvNqocffrjGFuNxlxIiIiIi6nWtzW5HRUWNOH36tEvzYytXrryyatWq60rl0Rdj\ntsSCm4iIiIhsIikp6eLdMGZLXFJCRERERKQgxQpuIcQOIcRVIUR2s2NaIcRJIUSWEOIzIcQgpcYn\nIiIiIrodKDnDvQvAoy2OfQBgnZRyHIADAGIUHJ+IiIiIqM8pVnBLKY8BaLlx+RgAx5o+HwUQodT4\nRERERES3A1uv4TYC+HHT5ycB+Nh4fCIiIiK6Q8yfP39kRkaGU1vt8fHxboWFhQ7dia3X6wOOHTum\n7n52nWfrgnsJgJ8LITIAaADcaKujEOIZIUS6ECL92rVrNkuQiIiIiG4PKSkpRRMnTqxrq/3jjz92\nv3jxYrcKbluy6baAUso8AA8DgBBiDID/aKfv+wDeB26+adImCRIRERHdwf6U8LZPWXFRr87auvuM\nND2y8vni9vqEhYWNKikpGWCxWFQrVqy4snbt2jK1Wh361FNPlaWlpQ3y8PCo37dv3wVvb++Gludm\nZmY6RUdH35eVlZULAOfOnRswZ84c//z8/By9Xh8QFxdXPGXKFNP8+fN9z5496yyEkJGRkWUjRoyo\nz87OVkdHR/s5OTlZ09PTc1999VWvzz//fLDFYlHpdLqa5OTkIpWq7fnlTz75ZMizzz47srq62m7b\ntm2Fjz76qCIvwrHpDLcQYljTv1UAfgNgmy3HJyIiIqLel5ycXGg0GnPPnDmTs337ds/S0lI7s9ms\n0ul0tefPnzdOnTq1et26dd6tnRsaGlpXX18v8vLyBgBAYmLi0Llz51Y073Py5El1SUmJQ0FBgTE/\nPz/n2Wefvf70009XjB071pSYmHghLy8vx8XFRcbExFzNzs7OLSgoMJrNZtXu3btd28u7oaFBZGVl\n5cbGxhZv2LCh1fx6g2Iz3EKITwDMBOAuhPgWwCsAXIQQzzZ12Q9gp1LjExEREd1tOpqJVkpsbKzn\n4cOHBwNAaWmpg9FodFKpVFi2bFk5ACxZsuR6eHi4f1vnz507tzwxMXHoxo0bSw8cODAkJSXlQvP2\nwMBAS3FxseOiRYt85syZUzVv3rzvWotz5MgRzZYtW7zq6upUlZWV9sHBwWYAVW2N++STT1YAwJQp\nU2pjYmIGdOPSO0XJXUqeklLeI6V0kFLeK6X8UEr5jpRyTNM/66SUXCpCREREdAdLTU3VpKWladLT\n0/POnTuXExQUZDabzd+rMYUQbcaIioqqOHjw4JCzZ886CiEwbtw4S/N2Dw+Pxuzs7JxZs2ZVb9u2\nzWPBggW+LWOYTCaxZs2akfv37/8mPz8/Z+HChWV1dXXt1rpOTk4SAOzt7dHY2Nh2gj3EN00SERER\nUbdVVlbaubq6Nmo0GmtmZqaTwWBwBgCr1YqdO3cOAYBdu3a56fX66rZihISEWFQqFdavX+89b968\nlttKo6SkxL6xsRGLFy+u3LRp06WsrCw1ALi4uDRWVVXZAYDJZFIBgJeXV0NVVZXqs88+G6LE9XYH\nC24iIiIi6raIiIiqhoYG4efnFxITEzNcq9XWAsDAgQOtp06dch49enTIsWPHNJs2bSppL054eHj5\noUOHhkZFRVW0bCssLHSYNm1aQGBgYHBUVJTfhg0bvgWA6Ojosueee25kYGBgsJOTkzUyMvJaUFBQ\nyKxZs8bcyuN2IO6EVR06nU6mp6f3dRpEREREHRJCZEgpdbYaz2AwFGq12jJbjddZarU61GQyZfZ1\nHrZiMBjctVqtb2ttnOEmIiIiIlKQTffhJiKivnH8f/JRVtz17WXdfVww/SdjFMiIiPq71ma3o6Ki\nRpw+fdql+bGVK1deWbVq1XWl8uiLMVtiwU1ERERENpGUlHTxbhizJRbcRER3Ac5SExH1Ha7hJiIi\nIiJSEAtuIiIiIiIFseAmIiIiIlIQC24iIiIiui3Nnz9/ZEZGhlNb7fHx8W6FhYUO3Ymt1+sDjh07\npu5+dp3HhyaJiIiI6LaUkpJS1F77xx9/7D5hwgSzr69vva1y6g4W3ERERET9RPnefJ/60tpenbV1\n8HI2DX1iTHF7fcLCwkaVlJQMsFgsqhUrVlxZu3ZtmVqtDn3qqafK0tLSBnl4eNTv27fvgre3d0PL\nczMzM52io6Pvy8rKygWAc+fODZgzZ45/fn5+jl6vD4iLiyueMmWKaf78+b5nz551FkLIyMjIshEj\nRtRnZ2ero6Oj/ZycnKzp6em5r776qtfnn38+2GKxqHQ6XU1ycnKRStX+go7Gxkb85Cc/8R0+fPiN\nLVu2XG45ziuvvHK1R18guKSEiIiIiHooOTm50Gg05p45cyZn+/btnqWlpXZms1ml0+lqz58/b5w6\ndWr1unXrvFs7NzQ0tK6+vl7k5eUNAIDExMShc+fOrWje5+TJk+qSkhKHgoICY35+fs6zzz57/emn\nn64YO3asKTEx8UJeXl6Oi4uLjImJuZqdnZ1bUFBgNJvNqt27d7u2l3d9fb2YO3fuff7+/nXx8fGX\nWxunN74fznATERER9RMdzUQrJTY21vPw4cODAaC0tNTBaDQ6qVQqLFu2rBwAlixZcj08PNy/rfPn\nzp1bnpiYOHTjxo2lBw4cGJKSknKheXtgYKCluLjYcdGiRT5z5sypmjdv3netxTly5Ihmy5YtXnV1\ndarKykr74OBgM4Cqtsb9+c9/PnLu3LnlsbGxpV0Zp6tYcBMR2cCRI0dQWlraqb5eXl547LHHFM6I\niKh3pKamatLS0jTp6el5Go3GqtfrA8xm8/dWUQgh2owRFRVV8eSTT/otWLCgQgiBcePGWZq3e3h4\nNGZnZ+ccOHBg0LZt2zxSUlKG7tmzp7B5H5PJJNasWTPy66+/zvH3969fvXq1d11dXburOXQ6Xc3x\n48cHmUymK2q1WnZmnO7gkhIiIiIi6rbKyko7V1fXRo1GY83MzHQyGAzOAGC1WrFz584hALBr1y43\nvV5f3VaMkJAQi0qlwvr1673nzZtX3rK9pKTEvrGxEYsXL67ctGnTpaysLDUAuLi4NFZVVdkBgMlk\nUgGAl5dXQ1VVleqzzz4b0lHuy5cvL3v44YerHn/88VH19fVtjtNTnOEmIrIBzlgTUX8VERFR9f77\n73v4+fmF+Pn51Wm12loAGDhwoPXUqVPOmzdv9nZzc6vfv3//hfbihIeHl7/22mv3xsbGXmrZVlhY\n6LB06VJfq9UqAGDDhg3fAkB0dHTZc889NzImJsaanp6eGxkZeS0oKCjEw8Oj4VYeHXn11Vev/OpX\nv7ILDw+/78UXXyxtbZyeElLK3oijKJ1OJ9PT0/s6DSIiIqIOCSEypJQ6W41nMBgKtVptma3G6yy1\nWh1qMpky+zoPWzEYDO5arda3tTYuKSEiIiIiUhCXlBARERFRr2ttdjsqKmrE6dOnXZofW7ly5ZVV\nq1b1yvZ7remLMVtiwU1ERERENpGUlHTxbhizJS4pISIiIiJSEAtuIiIiIiIFseAmIiIiIlIQ13AT\nEd3mSjduhCU3r90+jkGB8HrxRRtlREREXcEZbiIiIiK6Lc2fP39kRkaGU1vt8fHxboWFhQ62zKk7\nOMNNRHSb48w1Ed2tUlJSitpr//jjj90nTJhg9vX1rbdVTt2hWMEthNgB4HEAV6WUY5uOTQCwDYAT\ngAYAP5dSnlIqByIiIqK7ycGDB32uXr2q7s2Yw4YNM82dO7e4vT5hYWGjSkpKBlgsFtWKFSuurF27\ntkytVoc+9dRTZWlpaYM8PDzq9+3bd8Hb27uh5bmZmZlO0dHR92VlZeUCwLlz5wbMmTPHPz8/P0ev\n1wfExcUVT5kyxTR//nzfs2fPOgshZGRkZNmIESPqs7Oz1dHR0X5OTk7W9PT03FdffdXr888/H2yx\nWFQ6na4mOTm5SKX6/oKOwsJCh0cffXT0rZ8LCgoG5ubmZo0ZM+ZGL3xl36PkDPcuAO8CSGx27E0A\n/09KeUQI8aOmn2cqmAMRESnsy13v42rRhR7HGTbSD7MWP9MLGRGRrSUnJxd6eno21tTUiNDQ0OCF\nCxdWmM1mlU6nq/3www+L165de8+6deu8ExMTv7cndmhoaF19fb3Iy8sbEBgYeCMxMXHo3LlzK5r3\nOXnypLqkpMShoKDACABlZWV27u7ujQkJCcPi4uKKZ8yYYQKAmJiYq3FxcSUAMHfu3Pt2797t+tOf\n/rSq5Zi+vr71eXl5OQCwadMmj+PHj2uUKrYBBQtuKeUxIYRvy8MABjV9dgVwWanxiYiIiO42Hc1E\nKyU2Ntbz8OHDgwGgtLTUwWg0OqlUKixbtqwcAJYsWXI9PDzcv63z586dW56YmDh048aNpQcOHBiS\nkpLyf36LDwwMtBQXFzsuWrTIZ86cOVXz5s37rrU4R44c0WzZssWrrq5OVVlZaR8cHGwG8L2C+5Y/\n//nPzh999JHH119/3f6T6T1k6zXczwP4kxAiDjcf2Jxi4/GJiO5KsadikVfe9b9PAocG4gX9C+32\n4aw00d0tNTVVk5aWpklPT8/TaDRWvV4fYDabv7eOQwjRZoyoqKiKJ5980m/BggUVQgiMGzfO0rzd\nw8OjMTs7O+fAgQODtm3b5pGSkjJ0z549hc37mEwmsWbNmpFff/11jr+/f/3q1au96+rq2twgpKio\nyGH58uW+hw4dOu/q6mrt+pV3nq13KVkJ4FdSSh8AvwLwYVsdhRDPCCHShRDp165ds1mCRERERNR5\nlZWVdq6uro0ajcaamZnpZDAYnAHAarVi586dQwBg165dbnq9vrqtGCEhIRaVSoX169d7z5s311HV\nRAAAIABJREFUr7xle0lJiX1jYyMWL15cuWnTpktZWVlqAHBxcWmsqqqyAwCTyaQCAC8vr4aqqirV\nZ599NqSt8SwWiwgPD/d77bXXLo0fP97SVr/eYusZ7kUAVjV93gPgg7Y6SinfB/A+AOh0Oql8akRE\n/VdHs9RERN0VERFR9f7773v4+fmF+Pn51Wm12loAGDhwoPXUqVPOmzdv9nZzc6vfv39/uw97hIeH\nl7/22mv3xsbGXmrZVlhY6LB06VJfq9UqAGDDhg3fAkB0dHTZc889NzImJsaanp6eGxkZeS0oKCjE\nw8Oj4VYerfniiy+cs7OznV9//XXv119/3RsAPv/88wKldjsRUipXyzat4U5ttktJLoCVUsqvhBAP\nAnhTSjmxozg6nU6mp6crlicRERFRbxFCZEgpdbYaz2AwFGq12jJbjddZarU61GQyZfZ1HrZiMBjc\ntVqtb2ttSm4L+Alu7kDiLoT4FsArAH4G4B0hhD2AOgBc+EdERJ1W+dk3uHG5zUmrNg3wdsbgOaMU\nyIiIqGNK7lLyVBtNHc5oExEREdGdrbXZ7aioqBGnT592aX5s5cqVV1atWnVdqTz6YsyW+KZJIiK6\nY3CWmujOlpSU9L19uPvjmC3ZepcSIiIiIqK7CgtuIiIiIiIFseAmIiIiIlIQC24iIiIiUsyOHTuG\n+Pn5hfzwhz8cc+zYMfXixYt92uq7evVq7/Xr13vaMj9b4EOTRERERKSYnTt3uickJBQ98sgjNQAw\nY8YMU1/nZGuc4SYiIiKibnv55Zc9X3/99WEAsHTpUp/7779/DAB8+umnGiHExIyMDJfly5f7Ll++\n/N7U1FTNrFmz/NuLd/bsWfWECRMCR44cOfatt95yt8U1KI0z3ERENvDlrvdxtajdtxr/27CRfpi1\nmO8FI6Kuy8l9wae2Jl/dmzGdXcaYgoNii9tqnzlzZk1cXJwngKtnzpxR37hxQ2WxWERaWprLm2++\nWbRnzx63uLi44hkzZphSU1M1HY2Xm5s7MCMjI7e6utouNDQ0OCIiokqpV67bCme4iYiIiKjbpk2b\nZsrKynIuLy9XOTo6Sp1OV3P8+HH1yZMnNbNnz67parzHHnus0sXFRd5zzz0NkydP/u748ePOSuRt\nS5zhJiKyAc5YE5EttDcTrRRHR0fp4+Nj2bp1q7ter6/RarXmL774QlNUVOQYGhpa19V4Qoh2f74T\ncYabiIiIiHpk8uTJNe+9957nzJkzq8PCwqo/+ugjj+DgYJNK1fVS88iRI4NNJpMoLS21+8c//qGZ\nNm1arQIp2xQLbiIiIiLqkQceeKD62rVrDrNnz6718fFpcHR0lFOnTu3ychIACAoKMk2ZMiXghz/8\nYdDatWtL7vT12wAgpJR9nUOHdDqdTE9P7+s0iIiIiDokhMiQUupsNZ7BYCjUarVlthqPWmcwGNy1\nWq1va22c4SYiIiIiUhAfmiQiIiIim3rnnXfcEhIS/s8bJSdNmlSTlJR0sa9yUhILbiIiIiKyqVWr\nVl1ftWrV9b7Ow1a4pISIiIiISEGc4SYiut0dWQeUZrXfx2sc8NhvbZMPERF1CQtuIiLqkdKNG2HJ\nzetxHMegQHi9+GIvZEREdHthwU1EdLvjzDUR0R2NBTcREfUIZ6WJqD07duwY8vrrr3t7eHjUb968\n+dsdO3a47dq1q9VX0K9evdrbxcWlccOGDVdatk2fPn30mTNnnHU6Xc2XX355/tbx//zP/7zv7Nmz\nzg4ODnLChAm1H3/8cZGjo6P8+OOPB2/YsMFbpVLB3t5evvXWW8WPPPJIt17G01N8aJKIiIiIFLNz\n5073hISEoq+//jp/xowZpraK7Y6sXbu2dPv27f9qeTwyMrL8woUL2efOnTPW1dWJt99+2x0A5syZ\n811eXl5OXl5ezocffli4YsWKkT29lu5iwU1ERERE3fbyyy97vv7668MAYOnSpT7333//GAD49NNP\nNUKIiRkZGS7Lly/3Xb58+b2pqamaWbNm+bcX7+zZs+oJEyYEjhw5cuxbb73lfuv4j3/84+pBgwZZ\nW/afP39+lUqlgkqlgk6nq/32228HAICrq6tVpbpZ6lZXV6uEEL141V3DJSVERERE/cTzuRd98mrr\n1L0ZM9DZyfR20Ig2Z6VnzpxZExcX5wng6pkzZ9Q3btxQWSwWkZaW5vLmm28W7dmzxy0uLq54xowZ\nptTUVE1H4+Xm5g7MyMjIra6utgsNDQ2OiIio8vX1re/oPIvFIlJSUty2bNny71wTExMHv/LKK8PL\ny8sd9u3bV9Dpi+5lLLiJiOiOcfx/8lFW3PUlmO4+Lpj+kzEKZERE06ZNMy1atMi5vLxc5ejoKMeP\nH19z/Phx9cmTJzW///3vL+7Zs8etK/Eee+yxShcXF+ni4tIwefLk744fP+7s6+tb2dF5ixYtGnH/\n/ffXPProo//+QyI6OroyOjq68siRIy7r168fHhYWlt+da+wpFtxERERE/UR7M9FKcXR0lD4+Ppat\nW7e66/X6Gq1Wa/7iiy80RUVFjqGhoXVdjddy6UdnloKsWbPmnrKyMvs//elP37TW/thjj9X87Gc/\ncywpKbG/5557GrqaU0+x4CYiojsGZ6mJbk+TJ0+uee+99zwTEhIKJ06caH7xxRfvHTt2rOnWGuqu\nOHLkyOA33nij5LvvvlP94x//0Pzud7+71F7/LVu2uP/1r391PX78+Dk7O7t/H8/OznYMDg62qFQq\n/O1vf1PfuHFDeHp62rzYBhQsuIUQOwA8DuCqlHJs07EUAAFNXQYDqJRSTlAqByIiIiJS3gMPPFAd\nHx/vNXv27NpBgwZZHR0d5dSpU7u1BV9QUJBpypQpARUVFfZr164tubV+e+LEiQEXLlxwMpvNdp6e\nnuO3bt1aGBER8d1//dd/jbznnnssOp0uCAAef/zxiri4uJJPPvlkSEpKipu9vb10cnKyJiUlXejO\nLwC9QUgplQksxAwANQASbxXcLdrfAlAlpdzQUSydTifT09MVyJKIiIiodwkhMqSUOluNZzAYCrVa\nbZmtxqPWGQwGd61W69tam2Iz3FLKY0KIVgcVNxfj/ATAbKXGJyIiIiK6HfTVGu7pAK5IKftsexYi\nIiIi6hvvvPOOW0JCgmfzY5MmTapJSkq62Fc5KamvCu6nAHzSXgchxDMAngGAESNG2CInIiIiIrKB\nVatWXV+1atX1vs7DVmy+clwIYQ8gHEBKe/2klO9LKXVSSp2Hh4dtkiMiIiIi6mV98ahmGIA8KeW3\nfTA2EREREZFNKVZwCyE+AXASQIAQ4lshxNKmpgXoYDkJEREREVF/oeQuJU+1cXyxUmMSEREREd1u\n+mb3byIiIiK6K+zYsWOIn59fyA9/+MMxx44dUy9evNinr3OyNb7anYiIiIgUs3PnTveEhISiRx55\npAYAZsyYYerrnGyNM9xERERE1G0vv/yy5+uvvz4MAJYuXepz//33jwGATz/9VCOEmJiRkeGyfPly\n3+XLl9+bmpqqmTVrln9rcRobGzF8+PBxZWVldreOjRw5cmxxcfEdP0F8R1xARkZGmRCiSKHwrgCq\nbsOYPYnR1XO70r+zfd0B3O2vmVXiv63eYMu8+tv91Z3zevv+4r3Fe0upse72ewvovftrZC/E6JaY\nvQaf/NJqdW/GHOOlMW1+QlvcVvvMmTNr4uLiPAFcPXPmjPrGjRsqi8Ui0tLSXN58882iPXv2uMXF\nxRXPmDHDlJqaqmkrjp2dHR5++OHK5OTkwatWrbr+17/+1Xn48OE3fHx8GnrzevrCHVFwSykV24hb\nCPG+lPKZ2y1mT2J09dyu9O9sXyFEupRS19kc+iMl/tvqDbbMq7/dX905r7fvL95bvLeUGutuv7ea\n+t3191d3TJs2zbRo0SLn8vJylaOjoxw/fnzN8ePH1SdPntT8/ve/v7hnzx63zsb66U9/Wr5hwwbv\nVatWXU9OTh4aERFRrmTutnJHFNwK++w2jdmTGF09tyv9lfi++qvb9buyZV797f7qznm8v3rf7fo9\n8d7ivdXn2puJVoqjo6P08fGxbN261V2v19dotVrzF198oSkqKnIMDQ2t60qsBx98sHbp0qWOly9f\ntv/8888Hv/HGG5eVytuW7vo13FLKXr8JeyNmT2J09dyu9Ffi++qvbtfvypZ59bf7qzvn8f7qfbfr\n98R7i/fW3Wzy5Mk17733nufMmTOrw8LCqj/66COP4OBgk0rVtVJTpVLhscceq/z5z3/u4+/vb/by\n8mpUKGWbuusLblLM+32dAFE/xXuLSDm8v7rpgQceqL527ZrD7Nmza318fBocHR3l1KlTa7oTKzIy\nsvzQoUNDn3jiiYrezrOvCCllX+dARERERN1kMBgKtVrt3f4wdZ8zGAzuWq3Wt7U2znATERERESmI\nD00SERERkU298847bgkJCZ7Nj02aNKkmKSnpYl/lpCQuKSEiIiK6g3FJye2BS0qozwkhgoQQ24QQ\ne4UQK/s6H6L+RAjhLIRIF0I83te5EPUXQoiZQojjTX93zezrfOjOxoKbuk0IsUMIcVUIkd3i+KNC\niHNCiPNCiHUAIKXMlVKuAPATAFP7Il+iO0VX7q0mLwD4H9tmSXTn6eK9JQHUAHAC8K2tc6X+hQU3\n9cQuAI82PyCEsAPwHoDHAAQDeEoIEdzU9p8ADgP4o23TJLrj7EIn7y0hxEMAcgBctXWSRHegXej8\n31vHpZSP4eYvtP/PxnlSP8OCm7pNSnkMQMtXruoBnJdSXpBS3gCwG8CPm/p/2vSHV6RtMyW6s3Tx\n3poJ4H4APwXwMyEE/1wnakNX7i0ppbWpvQKAow3TpH6IfzBTbxsOoPlrZb8FMLxpLVy8EGI7OMNN\n1B2t3ltSypeklM8D+G8Af2hWJBBR57T191Z4099ZSQDe7ZPM+okdO3YM8fPzC/nhD3845tixY+rF\nixf79HVOtsZtAckmpJRfAfiqj9Mg6reklLv6Ogei/kRKuR/A/r7Ooz/YuXOne0JCQtEjjzxSAwAz\nZswwdea8+vp6ODg4KJucjXCGm3rbJQDNf3O9t+kYEfUM7y0iZfDe6qGXX37Z8/XXXx8GAEuXLvW5\n//77xwDAp59+qhFCTMzIyHBZvny57/Lly+9NTU3VzJo1y7+tWKtXr/aeO3fufT/4wQ8Cw8PD77PV\nNSiNM9zU204DGC2EuA83/8BagJtrS4moZ3hvESmjf91bB5/1wdUcda/GHBZswtz3ittqnjlzZk1c\nXJwngKtnzpxR37hxQ2WxWERaWprLm2++WbRnzx63uLi44hkzZphSU1M1HQ1XUFDg9PXXX+e5uLj0\nm5fFcIabuk0I8QmAkwAChBDfCiGWSikbAPwCwJ8A5AL4HymlsS/zJLrT8N4iUgbvLWVMmzbNlJWV\n5VxeXq5ydHSUOp2u5vjx4+qTJ09qZs+eXdPVeI8++mhlfyq2Ac5wUw9IKZ9q4/gfwQcjibqN9xaR\nMu6Ke6udmWilODo6Sh8fH8vWrVvd9Xp9jVarNX/xxReaoqIix9DQ0LquxnN2du53D39zhpuIiIiI\nemTy5Mk17733nufMmTOrw8LCqj/66COP4OBgk0rFUhNgwU1EREREPfTAAw9UX7t2zWH27Nm1Pj4+\nDY6OjnLq1KldXk7SXwkp+9USGSIiIqK7isFgKNRqtWV9ncfdzmAwuGu1Wt/W2jjDTURERESkID40\nSUREREQ29c4777glJCR4Nj82adKkmqSkpIt9lZOSuKSEiIiI6A7GJSW3By4pISIiIiLqIyy4iYiI\niIgUxIKbiKgPCCF+L4T4pxBiUl/nQkREymLBTURkY0IIZwDDACwH8Hgfp0NERApjwU1EihJC/E4I\n8Xyzn/8khPig2c9vCSFW9/KYvfqyBSHEYCHEz5v97CuEyO7kuQOFEGlCCLtbx6SUtQDuAfAVgHgh\nxAAhxDEhBHeOIqI7UmhoaKBSsZOTk11ffPFFLwB48803PcaMGRMcGBgYPHHixICMjAwnpcbtTSy4\niUhpfwcwBQCEECoA7gBCmrVPAXCiD/LqisEAft5hr9YtAbBfStl464AQwg2AGkA1gAYp5Q0AfwEw\nv6eJEhH1hczMzLyWx+rr63sldmRkZNXGjRtLAWDZsmXX8/Pzc/Ly8nJWr15d+vzzz/v0yiAKY8FN\nREo7AWBy0+cQANkAqoUQQ4QQjgCCAPxTCHFQCJEhhDAKIZ65dbIQ4rdCiGeb/fyqEGKtEGKhEOKU\nEOKMEGJ78xnkZn1b7dM0Q50rhPhD03h/FkIMbGp7WQhxTgjxNyHEJ0KItQB+C2BUU5zNTeHtWju/\nFZEADrU49hsAcQCM+N9fPg429SUiuuOo1epQAEhNTdVMnDgxYPbs2f6jR48eCwBhYWGjQkJCgvz9\n/UPi4uLcb52zd+/eQcHBwUEBAQHBkydPHtNW7Pj4eLfo6OgRADB06FDrreM1NTV2QgjlLqoX8X9f\nEpGipJSXhRANQogRuDmbfRLAcNwswqsAZEkpbwghlkgpy5sK19NCiH1SyusAUgC8DeC9ppA/wc21\nz/8FYKqUsl4IsRU3i9XEW+MKIYJwc8a4rT6jATwlpfyZEOJ/AEQIIc4BiACgBeAA4J8AMgCsAzBW\nSjmhKbZva+cD+Lj5tQshBgDwk1IWNjvm2/Q9rAYwDTcL7hO4+YsIH6Akoh55+e8v+5yvOK/uzZj+\nQ/xNr019rbiz/XNyctSZmZnGwMDAGwCQnJxc6Onp2VhTUyNCQ0ODFy5cWGG1WsUvfvEL36+++iov\nMDDwxpUrV743adKWTZs2eWzdutWzvr5edfTo0XPduSZbY8FNRLZwAjeLzCkAtuBmwT0FNwvuvzf1\n+aUQYl7TZx/cLGivSykzhRDDhBDeADwAVOBmQTwRNwtzABgI4GqLMR/soM+/pJRnmj5nAPDFzeUu\nh6SUdQDqhBCftXNNrZ3fkjuAyhbHXgewQUophRC5aJrhllI2CiFuCCE0UsrqdsYlIrqtjR8/vvZW\nsQ0AsbGxnocPHx4MAKWlpQ5Go9HpypUr9nq9vvpWP09Pz8a24rX061//+tqvf/3ra9u2bRv6yiuv\n3LN///7CXr+IXsaCm4hs4dY67nG4OZNbDGANgO8A7BRCzAQQBmCylNIkhPgKQPMHYfYAeAKAF27O\neAsAH0kpf93OmB31sTT73IibBXlXdOZ8M5pdhxBiAoBwANOEEO81tWU16+8IoK6LeRAR/VtXZqKV\nolar/73sIzU1VZOWlqZJT0/P02g0Vr1eH2A2m3tlSfPPfvaz8piYmBG9EUtpXMNNRLZwAje3vyuX\nUjZKKctx80HEyU1trgAqmortQAD3tzg/BcAC3Cy69+DmA4ZPCCGGAYAQYqgQYmSLczrTp6W/A5gj\nhHASQrjgf7fsqwag6epFSykrcHOt962iOxbAf0opfaWUvrg5Ux/SlJ8bgDIpZe88ZUREdBuorKy0\nc3V1bdRoNNbMzEwng8HgDAAzZ86sPXXqlCYvL28AAHR2SUlWVpbjrc8pKSmuI0eOtLTX/3bBGW4i\nsoUs3Fxe8d8tjrlIKcuEEJ8DWNG0xOIcgH80P1lKaRRCaABcklKWACgRQvwGwJ+bdj6pB/AsgKJm\n5+R01KclKeVpIcSnAM4CuNKUY5WU8roQ4u9NWwEewf+uJ++MP+PmjLYVgFpK+UWz8a4IIVyEEEMB\nzAJwuAtxiYhuexEREVXvv/++h5+fX4ifn1+dVqutBQBvb++G+Pj4wnnz5vlbrVa4ubnVnzhxoqCj\neFu2bBl2/PjxQfb29tLV1bVh165d/1L+KnpOSCn7OgciotuGEMJFSlkjhFADOAbgGSnlP3sQ7wcA\nfiWljOqg334A66SU+d0di4juTgaDoVCr1Zb1dR53O4PB4K7Van1ba+MMNxHR//W+ECIYN9dXf9ST\nYhsApJT/FEJ8KYSwa74Xd3NNu5kcZLFNRNQ/seAmImpGSvlTBWLu6KD9BpptaUhEdDd655133BIS\nEjybH5s0aVJNUlLSxb7KqbdwSQkRERHRHYxLSm4P7S0p4S4lREREREQKYsFNRERERKQgFtxERERE\nRApiwU1EREREpCAW3ERERETUI6GhoYFKxU5OTnZ98cUXvZof27Vr12AhxMRjx46plRq3N3FbQCIi\nIiLqkczMzLyWx+rr6+Hg4NDj2JGRkVUAqm79XFFRoXr33Xc9x48fX9vj4DbCGW4iIiIi6hG1Wh0K\nAKmpqZqJEycGzJ4923/06NFjASAsLGxUSEhIkL+/f0hcXJz7rXP27t07KDg4OCggICB48uTJY9qK\nHR8f7xYdHT3i1s9r1qwZvnbt2lJHR8c7Zm9rznATERER9ROXX3zJx1JQ0KvLLBxHjzZ5b3yjuLP9\nc3Jy1JmZmcbAwMAbAJCcnFzo6enZWFNTI0JDQ4MXLlxYYbVaxS9+8Qvfr776Ki8wMPDGlStX7DoT\n+29/+5v60qVLAxYsWFC1ZcsWr47PuD2w4CYiIiKiXjN+/PjaW8U2AMTGxnoePnx4MACUlpY6GI1G\npytXrtjr9frqW/08PT0bO4rb2NiI1atX+yQlJf1LueyVwYKbiIiIqJ/oyky0UtRqtfXW59TUVE1a\nWpomPT09T6PRWPV6fYDZbO7WkubKykq7goICp9mzZwcAQFlZmcMTTzzhv3fv3vMzZsww9Vb+SuAa\nbiIiIiJSRGVlpZ2rq2ujRqOxZmZmOhkMBmcAmDlzZu2pU6c0eXl5AwCgM0tK3NzcGisqKgyXLl3K\nunTpUpZWq629E4ptgDPcRERERKSQiIiIqvfff9/Dz88vxM/Pr06r1dYCgLe3d0N8fHzhvHnz/K1W\nK9zc3OpPnDhR0Nf5KkVIecc84ElERERELRgMhkKtVlvW13nc7QwGg7tWq/VtrY1LSoiIiIiIFMQl\nJURERETU59555x23hIQEz+bHJk2aVJOUlHSxr3LqLVxSQkRERHQH45KS2wOXlBARERER9REW3ERE\nRERECmLBTURERESkIBbcREREREQKYsFNRERERL1m9erV3uvXr/fsuOfdgwU3EREREZGCuA83ERER\nUT/xl8Rcn/JLNerejDl0uIvpweig4vb6vPDCC14pKSnubm5u9d7e3jdCQ0NNRqPRccWKFSPKy8vt\nnZycrB988EFRaGhoXXFxsf2SJUtGXrx40REA3n333aKHHnqoNiwsbFRJSckAi8WiWrFixZW1a9eW\nAYBarQ6Nioq69pe//MV12LBh9W+88ca3L7zwgs/ly5cHxMbGXoyMjKxqLafq6mrV/Pnzfc+dOzfQ\nz8+v7sqVKw7vvvvuxRkzZph68/vpDM5wExEREVG3HT9+XH3gwIGhWVlZOUePHi0wGAzOALBs2bKR\nW7duvWg0GnM3b9787cqVK0cAwIoVK0ZMnz69+ty5czlGozHnBz/4QR0AJCcnFxqNxtwzZ87kbN++\n3bO0tNQOAMxms+rBBx/87vz580ZnZ+fG3/zmN8OPHz+ev2fPnvOvvfba8Lby2rx5s8fgwYMbv/nm\nG+PGjRsv5eTkONvi+2gNZ7iJiIiI+omOZqKV8OWXX7r86Ec/qtRoNFYAePjhhyvr6upUmZmZLk8+\n+eSoW/1u3LghAODEiROavXv3/gsA7O3t4ebm1ggAsbGxnocPHx4MAKWlpQ5Go9HJy8ur1sHBQT7x\nxBPfAUBISIjZ0dHR6ujoKPV6vfnSpUsD2srrxIkTLqtWrboKAJMmTaobM2aMzWe2b2HBTURERES9\nymq1QqPRNOTl5eV0pn9qaqomLS1Nk56enqfRaKx6vT7AbDarAMDe3l6qVDcXZahUKjg6OkoAsLOz\nQ2Njo1DsInoRl5QQERERUbfNnj275o9//OPgmpoaUVFRoTp69OhgtVptvffee2/s2LFjCHCzAD95\n8uRAAJg6dWr15s2bPQCgoaEB169ft6usrLRzdXVt1Gg01szMTKdby1J6YvLkyTW7d+8eAgAZGRlO\n+fn5A3sas7tYcBMRERFRt02bNs00b9688rFjx4aEhYWNHj9+fC0AfPLJJxd27tzpHhAQEDx69OiQ\nffv2DQaAhISEi2lpaZoxY8YEjx07NjgzM9MpIiKiqqGhQfj5+YXExMQM12q1tT3NKyYm5tr169ft\nR40aFfLrX/96uL+/f92QIUMaexq3O4SUsi/GJSIiIqJeYDAYCrVabVlf53G7aWhowI0bN4RarZZG\no9Hx4YcfHvPNN99kOzk5KVL8GgwGd61W69taG9dwExEREVG/U11drZo+fXpAfX29kFLid7/7XZFS\nxXZHWHATERER0R1r3759g1566aV7mx/z8fGxHD169Jvs7OzcvsqrORbcRERERHTHioiI+C4iIqJT\nu6H0FT40SURERESkIBbcREREREQKYsFNRERERKQgFtxERERERApiwU1EREREvWb16tXe69ev9+zr\nPG4nLLiJiIiIqF+rr6/v0/G5LSARERFRP/GnhLd9yoqL1L0Z091npOmRlc8Xt9fnhRde8EpJSXF3\nc3Or9/b2vhEaGmoyGo2OK1asGFFeXm7v5ORk/eCDD4pCQ0PriouL7ZcsWTLy4sWLjgDw7rvvFj30\n0EO1YWFho0pKSgZYLBbVihUrrqxdu7YMANRqdWhUVNS1v/zlL67Dhg2rf+ONN7594YUXfC5fvjwg\nNjb2YmRkZFVrOcXHx7sdPHhwiMlkUjU2NorTp0+f683vpSs4w01ERERE3Xb8+HH1gQMHhmZlZeUc\nPXq0wGAwOAPAsmXLRm7duvWi0WjM3bx587crV64cAQArVqwYMX369Opz587lGI3GnB/84Ad1AJCc\nnFxoNBpzz5w5k7N9+3bP0tJSOwAwm82qBx988Lvz588bnZ2dG3/zm98MP378eP6ePXvOv/baa8Pb\ny81oNKoPHTr0TV8W2wBnuImIiIj6jY5mopXw5ZdfuvzoRz+q1Gg0VgB4+OGHK+vq6lQIAT2AAAAg\nAElEQVSZmZkuTz755Khb/W7cuCEA4MSJE5q9e/f+CwDs7e3h5ubWCACxsbGehw8fHgwApaWlDkaj\n0cnLy6vWwcFBPvHEE98BQEhIiNnR0dHq6Ogo9Xq9+dKlSwPay2369OnfeXp6Nipz5Z3HgpuIiIiI\nepXVaoVGo2nIy8vr1BsgU1NTNWlpaZr09PQ8jUZj1ev1AWazWQUA9vb2UqW6uShDpVLB0dFRAoCd\nnR0aGxtFe3HVarW1h5fSK7ikhIiIiIi6bfbs2TV//OMfB9fU1IiKigrV0aNHB6vVauu99957Y8eO\nHUOAmwX4yZMnBwLA1KlTqzdv3uwBAA0NDbh+/bpdZWWlnaura6NGo7FmZmY63VqW0l+w4CYiIiKi\nbps2bZpp3rx55WPHjg0JCwsbPX78+FoA+OSTTy7s3LnTPSAgIHj06NEh+/btGwwACQkJF9PS0jRj\nxowJHjt2bHBmZqZTREREVUNDg/Dz8wuJiYkZrtVqa/v2qnqXkFL2dQ5ERERE1E0Gg6FQq9WW9XUe\ndzuDweCu1Wp9W2vjDDcRERERkYL40CQRERER3bH27ds36KWXXrq3+TEfHx/L0aNHv+mrnFpiwU1E\nREREd6yIiIjvIiIiOrUbSl/hkhIiIiIiIgWx4CYiIiIiUhALbiIiIiIiBbHgJiIiIiJSEAtuIiIi\nIuo1q1ev9l6/fr1nX+dx7Ngx9eLFi336Og+Au5QQERERUT80Y8YM04wZM0x9nQfAgpuIiIio3yjf\nm+9TX1qr7s2YDl7OpqFPjClur88LL7zglZKS4u7m5lbv7e19IzQ01GQ0Gh1XrFgxory83N7Jycn6\nwQcfFIWG/v/27jeoyTPf//hFEghEUxTQpPIfyR8SMYfiUpGy5Vja43ZYR6WddVbarj06oFvnsHY9\nTm0P01mnrVZ65lTbuuxUnf4WjrNbXWvVrjtMVzmOrrtlN6Y0IYjtpoAQKQGihPAnCb8He3CsR6yF\nxKh9vx5JrjvX9b3y6ON3rvu+c4ba29tlzz77bGpbW5tcCCHeeuutLx999FFPcXHx3K6urqjh4WFJ\nRUXFpZ///Oc9QgihUChynnrqqa8+/vjj2NmzZ4++8sorHZs3b07u7OyM2r59e9uqVavcN6rp6NGj\nyjfeeEN14sSJC8H8PSaDIyUAAACYtFOnTikOHToU19TUZKuvr2+1WCzThBBizZo1qe+8806b1Wpt\n3rFjR8e6detShBCioqIipbCw8EpLS4vNarXaHnjggSEhhKirq3NYrdbmc+fO2WpqalROp1MqhBBe\nr1fyyCOPXL5w4YJ12rRp/pdeeinx1KlT599///0LW7duTQzfzm8dHW4AAIB7xDd1okPhxIkT0x9/\n/PF+pVIZEEKIxx57rH9oaEhiNpunP/nkk3PHrxsZGYkQQogzZ84oDxw48HchhJDJZCI+Pt4vhBDb\nt29XHTt2bIYQQjidzkir1RqtVqs9kZGRY0888cRlIYQwGo1euVwekMvlY3l5ed6LFy9G3e79TgaB\nGwAAAEEVCASEUqn02e32W3oD5NGjR5UNDQ3KxsZGu1KpDOTl5em8Xq9ECCFkMtmYRPKPQxkSiUTI\n5fIxIYSQSqXC7/dHhGwTQcSREgAAAEza4sWLBz766KMZAwMDEX19fZL6+voZCoUikJSUNLJ3796Z\nQvwjgP/pT3+KEUKIgoKCKzt27JglhBA+n0+4XC5pf3+/NDY21q9UKgNmszl6/FjKvYLADQAAgEl7\n6KGHBpcvX947b948Y3FxsWb+/PkeIYTYv3//F/v27UvQ6XQGjUZjPHjw4AwhhNi9e3dbQ0ODUqvV\nGubNm2cwm83RpaWlbp/PF5GRkWHctGlToslk8oR3V8EVMTY2Fu4aAAAAMEkWi8VhMpl6wl3Hd53F\nYkkwmUxpNxqjww0AAACEEDdNAgAA4K518ODB+1588cWkaz9LTk4erq+v/zxcNV2PwA0AAIC7Vmlp\n6eXS0tJbehpKuHCkBAAAAAghAjcAAAAQQgRuAAAAIIQI3AAAAEAIEbgBAAAQNBs3bpxTVVWlCncd\ndxICNwAAABBCPBYQAADgHvHBBx8kd3d3K4I55+zZsweXLVvWfrNrNm/erP7Nb36TEB8fPzpnzpyR\nnJycQavVKq+oqEjp7e2VRUdHB959990vc3Jyhtrb22XPPvtsaltbm1wIId56660vH330UU9xcfHc\nrq6uqOHhYUlFRcWln//85z1CCKFQKHKeeuqprz7++OPY2bNnj77yyisdmzdvTu7s7Izavn1726pV\nq9w3qulHP/pRqsVimSaEEJcuXYp89tlnu994442uYP42t4oONwAAACbt1KlTikOHDsU1NTXZ6uvr\nW8dD7po1a1LfeeedNqvV2rxjx46OdevWpQghREVFRUphYeGVlpYWm9VqtT3wwANDQghRV1fnsFqt\nzefOnbPV1NSonE6nVAghvF6v5JFHHrl84cIF67Rp0/wvvfRS4qlTp86///77F7Zu3Zo4UV2/+c1v\nvrTb7bYPP/zwwsyZM33l5eWu2/F73AgdbgAAgHvEN3WiQ+HEiRPTH3/88X6lUhkQQojHHnusf2ho\nSGI2m6c/+eSTc8evGxkZiRBCiDNnzigPHDjwdyGEkMlkIj4+3i+EENu3b1cdO3ZshhBCOJ3OSKvV\nGq1Wqz2RkZFjTzzxxGUhhDAajV65XB6Qy+VjeXl53osXL0bdrLbBwcGI0tLSuf/5n//ZptVqR0Lz\nC3wzAjcAAACCKhAICKVS6bPb7bf0BsijR48qGxoalI2NjXalUhnIy8vTeb1eiRBCyGSyMYnkH4cy\nJBKJkMvlY0IIIZVKhd/vj7jZvE899VTqD3/4w75ly5ZdmeKWpoQjJQAAAJi0xYsXD3z00UczBgYG\nIvr6+iT19fUzFApFICkpaWTv3r0zhfhHAP/Tn/4UI4QQBQUFV3bs2DFLCCF8Pp9wuVzS/v5+aWxs\nrF+pVAbMZnP0+LGUqXjttddmDQwMSF999VXnVOeaKgI3AAAAJu2hhx4aXL58ee+8efOMxcXFmvnz\n53uEEGL//v1f7Nu3L0Gn0xk0Go3x4MGDM4QQYvfu3W0NDQ1KrVZrmDdvnsFsNkeXlpa6fT5fREZG\nhnHTpk2JJpPJM9W63nrrLXVLS0uMXq836PV6w+uvvz5rqnNOVsTY2Fi41gYAAMAUWSwWh8lk6gl3\nHd91FoslwWQypd1ojA43AAAAEELcNAkAAIC71sGDB+978cUXk679LDk5ebi+vv7zcNV0PQI3AAAA\n7lqlpaWXS0tLb+lpKOHCkRIAAAAghAjcAAAAQAgRuAEAAIAQInADAAAgaDZu3DinqqpKFe467iQE\nbgAAACCECNwAAACYks2bN6vT0tLm5ebm6lpbW+VCCGG1WuWFhYUao9GYlZubqzObzdFCCNHe3i57\n9NFH5+p0OoNOpzPU19dPE0KI4uLiuUajMSszM9NYXV2dMD63QqHIKS8vT8rMzDQuWrRIe+LECUVe\nXp4uKSkpu66uLnaimhYsWKA7c+ZMzPjfubm5uvHXy99uPBYQAADgHmFr3pzsGTivCOac06ZrBw1Z\n29snGj916pTi0KFDcU1NTbbR0VHxT//0T4acnJzBNWvWpP7qV7/6Mjs7e/iPf/zjtHXr1qWcPXv2\nfEVFRUphYeGVqqqqz30+n3C73VIhhKirq3OoVCr/wMBARE5OjqGsrKxPrVb7vV6v5JFHHrlcU1PT\n8eijj8596aWXEk+dOnX+b3/7W/Tq1avTV61a5b5RXc8880zPu+++m7Bo0aL2Tz/9VD48PCzJz8/3\nBvO3uVUEbgAAAEzaiRMnpj/++OP9SqUyIIQQjz32WP/Q0JDEbDZPf/LJJ+eOXzcyMhIhhBBnzpxR\nHjhw4O9CCCGTyUR8fLxfCCG2b9+uOnbs2AwhhHA6nZFWqzVarVZ7IiMjx5544onLQghhNBq9crk8\nIJfLx/Ly8rwXL16Mmqiun/zkJ307duy4f3h4uOOXv/xlwo9//OOe0P0KN0fgBgAAuEfcrBN9OwUC\nAaFUKn12u/2WXkhz9OhRZUNDg7KxsdGuVCoDeXl5Oq/XKxFCCJlMNiaR/OMUtEQiEXK5fEwIIaRS\nqfD7/RETzalUKgOFhYWX//u//3vGhx9+GGc2m8P2chzOcAMAAGDSFi9ePPDRRx/NGBgYiOjr65PU\n19fPUCgUgaSkpJG9e/fOFOIfAXz8/HRBQcGVHTt2zBJCCJ/PJ1wul7S/v18aGxvrVyqVAbPZHG2x\nWKYFo7aKioqezZs3J5tMJs+sWbP8wZhzMgjcAAAAmLSHHnpocPny5b3z5s0zFhcXa+bPn+8RQoj9\n+/d/sW/fvgSdTmfQaDTGgwcPzhBCiN27d7c1NDQotVqtYd68eQaz2RxdWlrq9vl8ERkZGcZNmzYl\nmkwmTzBqKywsHJw2bZp/9erVYTtOIoQQEWNjY+FcHwAAAFNgsVgcJpMprIHyTuVwOCKLiop0n3/+\n+WdSqTSka1kslgSTyZR2ozE63AAAALjnvPXWW/ELFy7MqqqquhjqsP1NuGkSAAAAd62DBw/e9+KL\nLyZd+1lycvJwfX39588995wrXHVdi8ANAACAu1Zpaenl0tLSsD2B5FZwpAQAAAAIIQI3AAAAEEIE\nbgAAACCECNwAAABACBG4AQAAMCU5OTn6UM1dV1cXu2XLFrUQQvz+97+fbjAYsmQyWe6+fftmhmrN\nYOMpJQAAAJgSs9lsv/6z0dFRERkZOeW5V61a5RZCuIUQIiMjY2Tfvn2Obdu2qaY88W1EhxsAAABT\nolAocoQQ4ujRo8rc3Fzd4sWLMzUazTwhhCguLp5rNBqzMjMzjdXV1Qnj3zlw4MB9BoMhS6fTGfLz\n87UTzb1z5874p59+OkUIIXQ63ciDDz7olUjurghLhxsAAOAeUdnclmz3DCmCOad+WvTgf2WltN/q\n9TabTWE2m616vX5ECCHq6uocKpXKPzAwEJGTk2MoKyvrCwQCEc8991zayZMn7Xq9fuTSpUvhfRVk\niBG4AQAAEDTz58/3jIdtIYTYvn276tixYzOEEMLpdEZardboS5cuyfLy8q6MX6dSqfzhqvd2IHAD\nAADcI75NJzpUFApFYPzfR48eVTY0NCgbGxvtSqUykJeXp/N6vXfXeZAg+M5tGAAAALdHf3+/NDY2\n1q9UKgNmsznaYrFME0KIoqIiz1/+8hel3W6PEkKIe/1ICYEbAAAAIVFaWur2+XwRGRkZxk2bNiWa\nTCaPEELMmTPHt3PnTsfy5cszdTqdYfny5Rm3Ml9DQ4NCpVLN/+ijj2b+7Gc/S83MzDSGdgfBETE2\nNhbuGgAAADBJFovFYTKZesJdx3edxWJJMJlMaTcao8MNAAAAhBA3TQIAACDs3nzzzfjdu3d/7YU2\n3/ve9wZ+/etft4WrpmDhSAkAAMBdjCMldwaOlAAAAABhQuAGAAAAQojADQAAAIQQgRsAAAAIIQI3\nAAAApiQnJ0cfqrnr6upit2zZohZCiJdfflk1d+5co1arNeTn52vPnz8fFap1g4nADQAAgCkxm832\n6z8bHR0NytyrVq1yv/rqq04hhMjNzR08d+5c8/nz523Lli3r+9nPfpYUlEVCjMANAACAKVEoFDlC\nCHH06FFlbm6ubvHixZkajWaeEEIUFxfPNRqNWZmZmcbq6uqE8e8cOHDgPoPBkKXT6Qz5+fnaiebe\nuXNn/NNPP50ihBA//OEPryiVyoAQQjz00EMDXV1dd0WHmxffAAAA3CM2HbAkn3deUQRzTq1aObjj\nCVP7rV5vs9kUZrPZqtfrR4QQoq6uzqFSqfwDAwMROTk5hrKysr5AIBDx3HPPpZ08edKu1+tHLl26\nJP22ddXU1MwqLi52f9vvhQOBGwAAAEEzf/58z3jYFkKI7du3q44dOzZDCCGcTmek1WqNvnTpkiwv\nL+/K+HUqlcr/bdZ455134iwWi6KmpqYluNWHBoEbAADgHvFtOtGholAoAuP/Pnr0qLKhoUHZ2Nho\nVyqVgby8PJ3X653SkeYPPvhAWV1dff+pU6daYmJi7opXpnOGGwAAACHR398vjY2N9SuVyoDZbI62\nWCzThBCiqKjI85e//EVpt9ujhBDiVo+UnD59OmbDhg2phw8fvpCYmOgLZe3BRIcbAAAAIVFaWur+\n1a9+NSsjI8OYkZExZDKZPEIIMWfOHN/OnTsdy5cvzwwEAiI+Pn70zJkzrd8036ZNm5IHBwelTz75\n5Nz/nWfkj3/844VQ72OqIsbG7opOPAAAAG7AYrE4TCZTT7jr+K6zWCwJJpMp7UZjHCkBAAAAQogj\nJQAAAAi7N998M3737t2qaz/73ve+N/DrX/+6LVw1BQtHSgAAAO5iHCm5M3CkBAAAAAgTAjcAAAAQ\nQgRuAAAAIIQI3AAAAEAIEbgBAAAQVA6HI3LJkiUZE4339PRIt23bNut21hROBG4AAAAEVVpa2ujx\n48e/mGjc5XJJ9+zZM/t21hROBG4AAABM2vr16xNfe+21q93qjRs3zqmqqlJpNBqjEEI0NjZGZ2dn\nZ+n1eoNWqzU0NTXJn3/++aT29na5Xq83lJeXJ7ndbkl+fr7WYDBkabVaQ21t7YyJ1qusrJzzi1/8\n4mpY37BhQ+LWrVvv6PDOc7gBAADuYl97DvcHP00W3TZFUBeYbRgUy95un2j49OnTMZWVlSmffPJJ\nixBCzJ071/j2229/uWHDhtTW1lbrM888k7xw4ULPunXreoeGhiJ8Pp+4ePFiZElJiaa1tdUqhBCj\no6PiypUrkri4uEBXV5fswQcf1Dscjs8kkv/bG25paYlavnz5XJvN1uz3+0VaWtq8Tz75pFmtVvuD\nuu9v6WbP4eZNkwAAAJi0goICr8vlkjkcjsiuri5ZbGysPz09fWR8PD8/31NdXX1/R0dH1MqVK/uy\ns7OHr58jEAhEVFZWJp09e3a6RCIR3d3dUR0dHbKUlBTf9dfqdLqRGTNm+E6fPh3T1dUVaTQaB8Md\ntr8JgRsAAOBecZNOdCgtXbq0r7a2dqbT6YxcsWJF77VjFRUVvYWFhZ5Dhw7FlpSUaHbt2vWlTqf7\nWuiuqamJc7lcsqampma5XD6WmJiY7fV6Jzz6vHr16p533303obu7O3L16tWuUO0rWAjcAAAAmJKy\nsrLetWvXpvX19ckaGhpahoaGIsbHbDZbVFZW1rDRaOxua2uLOnfuXExeXt6gx+O5Gqjdbrc0ISFh\nVC6Xjx05ckTZ2dkZdbP1nnrqqf5XXnkl0efzRZSWlk54c+adgsANAACAKVmwYMGQx+ORqFSqkdTU\n1NGWlpargbm2tjbut7/9bbxMJhubNWvW6NatW7tUKpU/Nzd3QKPRGBcvXux++eWXnT/4wQ8ytVqt\nYf78+YPp6elDN1svOjp6bNGiRZdnzJjhl8nu/DjLTZMAAAB3sa/dNPkd4ff7hdFoNLz//vuf3+hM\neDjc7KZJHgsIAACAu8Zf//rX6NTU1OzCwsLLd0rY/iZ3fg8eAAAA3zlOp1NaVFSku/7zkydPtnR0\ndDSFo6bJInADAADgjqNWq/12u90W7jqCgSMlAAAAQAgRuAEAAIAQInADAAAAIUTgBgAAAEKIwA0A\nAICgcjgckUuWLMmYaLynp0e6bdu2WZOdPycnRz/Z74YDgRsAAABBlZaWNnr8+PEJX7nucrmke/bs\nmT3Z+c1ms32y3w0HAjcAAAAmbf369Ymvvfba1W71xo0b51RVVak0Go1RCCEaGxujs7Ozs/R6vUGr\n1Rqamprkzz//fFJ7e7tcr9cbysvLk9xutyQ/P19rMBiytFqtoba2dsbN1lQoFDmh3lcw8Wp3AACA\nu9i1r3b/j9P/kXyh74IimPNnzswc3FqwtX2i8dOnT8dUVlamfPLJJy1CCDF37lzj22+//eWGDRtS\nW1tbrc8880zywoULPevWresdGhqK8Pl84uLFi5ElJSWa1tZWqxBCjI6OiitXrkji4uICXV1dsgcf\nfFDvcDg+k0hu3BtWKBQ5g4OD5mDuc6pu9mp3XnwDAACASSsoKPC6XC6Zw+GI7OrqksXGxvrT09NH\nxsfz8/M91dXV93d0dEStXLmy70avYw8EAhGVlZVJZ8+enS6RSER3d3dUR0eHLCUlxXd7dxMaBG4A\nAIB7xM060aG0dOnSvtra2plOpzNyxYoVvdeOVVRU9BYWFnoOHToUW1JSotm1a9eXOp3ua6G7pqYm\nzuVyyZqamprlcvlYYmJittfrvWeOPhO4AQAAMCVlZWW9a9euTevr65M1NDS0DA0NRYyP2Wy2qKys\nrGGj0djd1tYWde7cuZi8vLxBj8dzNVC73W5pQkLCqFwuHzty5Iiys7MzKjw7CY175n8OAAAACI8F\nCxYMeTweiUqlGklNTR29dqy2tjZOq9Ua9Xq9obm5Oaa8vNylVqv9ubm5AxqNxlheXp60Zs2aXovF\nMk2r1Rree++9+PT09KGbrRcREXGz4TsON00CAADcxa69afK7wOl0Sh944AFDZ2dnU7hrudbNbpqk\nww0AAIC7gsPhiFy4cGHWT3/600vhruXb4Aw3AAAA7jhOp1NaVFSku/7zs2fPNqvVan84aposAjcA\nAADuOGq12m+3223hriMYOFICAAAAhBCBGwAAAAghAjcAAAAQQgRuAAAAIIQI3AAAAAgqh8MRuWTJ\nkoyJxnt6eqTbtm2bNZm5W1paojQajXHy1d1+BG4AAAAEVVpa2ujx48e/mGjc5XJJ9+zZM/t21hRO\nBG4AAABM2vr16xNfe+21q93qjRs3zqmqqlKNd6EbGxujs7Ozs/R6vUGr1Rqamprkzz//fFJ7e7tc\nr9cbysvLk9xutyQ/P19rMBiytFqtoba2dsbN1vT7/WLlypWpmZmZxoKCAs3AwMAd/a53Xu0OAABw\nF7v21e6dW15MHm5tVQRzfrlGMzjn1VfaJxo/ffp0TGVlZconn3zSIoQQc+fONb799ttfbtiwIbW1\ntdX6zDPPJC9cuNCzbt263qGhoQifzycuXrwYWVJSomltbbUKIcTo6Ki4cuWKJC4uLtDV1SV78MEH\n9Q6H4zOJ5P/2hltaWqKMRmP2//zP/9gWLVrkffzxxzNKSkr6169f3xvMfX9bN3u1Oy++AQAAwKQV\nFBR4XS6XzOFwRHZ1dcliY2P96enpI+Pj+fn5nurq6vs7OjqiVq5c2ZednT18/RyBQCCisrIy6ezZ\ns9MlEono7u6O6ujokKWkpPhutGZiYuLwokWLvEIIkZOTM+hwOOSh2+HUEbgBAADuETfrRIfS0qVL\n+2pra2c6nc7IFStWfK3TXFFR0VtYWOg5dOhQbElJiWbXrl1f6nS6r4XumpqaOJfLJWtqamqWy+Vj\niYmJ2V6vd8Kjz1FRUVePaEil0rGbXXsnIHADAABgSsrKynrXrl2b1tfXJ2toaGgZGhq6eqbaZrNF\nZWVlDRuNxu62traoc+fOxeTl5Q16PJ6rIdntdksTEhJG5XL52JEjR5SdnZ1R4dlJaNzR/xsAAADA\nnW/BggVDHo9HolKpRlJTU0evHautrY3TarVGvV5vaG5ujikvL3ep1Wp/bm7ugEajMZaXlyetWbOm\n12KxTNNqtYb33nsvPj09fShcewkFbpoEAAC4i1170yTC52Y3TdLhBgAAAEKIM9wAAAC44zidTmlR\nUZHu+s9PnjzZolar/eGoabII3AAAALjjqNVqv91ut4W7jmDgSAkAAAAQQgRuAAAAIIQI3AAAAEAI\nEbgBAACAECJwAwAAIKgcDkfkkiVLMiYa7+npkW7btm3WZOZuaWmJ0mg0xslXd/sRuAEAABBUaWlp\no8ePH/9ionGXyyXds2fP7NtZUzjxWEAAAIB7xMf/rzm59+KAIphzxiVOH3zk6az2icbXr1+fmJyc\nPPLCCy98JYQQGzdunDN9+nT//v37E1pbW62NjY3Rq1evTh8dHY0IBALi4MGDn7/wwguJ7e3tcr1e\nb3j44Ycvv/76651LlizJdLvdUp/PF1FVVdVZVlbW/0212Wy2qNLS0sxf/vKXjmnTpgWuXyc7O3s4\nmL/FZNHhBgAAwKStWrWq93e/+13c+N+HDx+euWjRIs/437t27Zq1fv36S3a73fbpp582p6enj7zx\nxhsdycnJw3a73VZTU9OhUCgCx44du2Cz2ZobGhrOb9myJSkQCNx0XYvFIi8tLc3cu3fv3x9++OHB\nG60Twm1/K3S4AQAA7hE360SHSkFBgdflcskcDkdkV1eXLDY21n9t2M3Pz/dUV1ff39HREbVy5cq+\nG3WdA4FARGVlZdLZs2enSyQS0d3dHdXR0SFLSUnx3WjN3t5e2bJlyzIPHDjweW5u7tCtrhMudLgB\nAAAwJUuXLu2rra2dWVdXF7dixYrea8cqKip6Dx8+fCEmJiZQUlKi+fDDD5XXf7+mpibO5XLJmpqa\nmu12uy0+Pn7U6/VOmFOVSqV/zpw5IydOnJj+bdYJFzrcAAAAmJKysrLetWvXpvX19ckaGhpahoaG\nIsbHbDZbVFZW1rDRaOxua2uLOnfuXExeXt6gx+O5Gqjdbrc0ISFhVC6Xjx05ckTZ2dkZdbP1IiMj\nx37/+99//s///M+a6dOnByoqKnpvtM7SpUuvhHLft4rADQAAgClZsGDBkMfjkahUqpHU1NTRlpaW\nq4G5trY27re//W28TCYbmzVr1ujWrVu7VCqVPzc3d0Cj0RgXL17sfvnll50/+MEPMrVarWH+/PmD\n6enpQ9+05n333Rf4wx/+cKGoqEirVCr9Vqs15vp1QrvrWxcxNjYW7hoAAAAwSRaLxWEymXrCXcd3\nncViSTCZTGk3GuMMNwAAABBCHCkBAADAHcfpdEqLiop0139+8uTJFrVa7Q9HTZNF4AYAAMAdR61W\n++12uy3cdQQDR0oAAACAECJwAwAAACFE4AYAAABCiMANAAAAhBCBGwAAAEHlcInb8+cAAAO/SURB\nVDgilyxZkjHReE9Pj3Tbtm2zbmdN4UTgBgAAQFClpaWNHj9+/IuJxl0ul3TPnj2zb2dN4cRjAQEA\nAO4Rf9j9X8k97V8qgjlnQnLq4L+sq2yfaHz9+vWJycnJIy+88MJXQgixcePGOdOnT/fv378/obW1\n1drY2Bi9evXq9NHR0YhAICAOHjz4+QsvvJDY3t4u1+v1hocffvjy66+/3rlkyZJMt9st9fl8EVVV\nVZ1lZWX9N1rv9ddfn7V3795ZQghx5coVaVJS0vCf//zn88Hcc7DR4QYAAMCkrVq1qvd3v/td3Pjf\nhw8fnrlo0SLP+N+7du2atX79+kt2u9326aefNqenp4+88cYbHcnJycN2u91WU1PToVAoAseOHbtg\ns9maGxoazm/ZsiUpEAjccL1///d//8put9ssFkuzWq0e+bd/+7dLt2GbU0KHGwAA4B5xs050qBQU\nFHhdLpfM4XBEdnV1yWJjY/3p6ekj4+P5+fme6urq+zs6OqJWrlzZl52dPXz9HIFAIKKysjLp7Nmz\n0yUSieju7o7q6OiQpaSk+CZa91//9V+Tv//971/58Y9/7A7V3oKFDjcAAACmZOnSpX21tbUz6+rq\n4lasWNF77VhFRUXv4cOHL8TExARKSko0H374ofL679fU1MS5XC5ZU1NTs91ut8XHx496vd4Jc+rO\nnTvjOzo6oqqrqztDsZ9go8MNAACAKSkrK+tdu3ZtWl9fn6yhoaFlaGgoYnzMZrNFZWVlDRuNxu62\ntraoc+fOxeTl5Q16PJ6rgdrtdksTEhJG5XL52JEjR5SdnZ1RE6116tQpxa5du9RnzpyxS6XSUG8t\nKOhwAwAAYEoWLFgw5PF4JCqVaiQ1NXX02rHa2to4rVZr1Ov1hubm5pjy8nKXWq325+bmDmg0GmN5\neXnSmjVrei0WyzStVmt477334tPT04cmWuvNN9+c7Xa7pYWFhTq9Xm/40Y9+lBr6HU5NxNjYWLhr\nAAAAwCRZLBaHyWTqCXcd33UWiyXBZDKl3WiMDjcAAAAQQpzhBgAAwB3H6XRKi4qKdNd/fvLkyRa1\nWu0PR02TReAGAADAHUetVvvtdrst3HUEA0dKAAAA7m6BQCAQ8c2XIVT+9/e/8Zt6BIEbAADgbvfZ\nV199FUvoDo9AIBDx1VdfxQohPpvoGo6UAAAA3MV8Pt8ap9P5rtPpnCdopoZDQAjxmc/nWzPRBTwW\nEAAAAAgh/hcEAAAAhBCBGwAAAAghAjcAAAAQQgRuAAAAIIQI3AAAAEAI/X8btWN7W0w8ggAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f979ec315f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dat in data:\n",
    "    wav_deets = FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission']))\n",
    "    depth = average_depths['5s'][average_depths['band'] == dat[0]]\n",
    "    #print(depth)\n",
    "    plt.plot([wav_deets[0],wav_deets[1]], [depth,depth], label=dat[0])\n",
    "    \n",
    "plt.xlabel('Wavelength ($\\AA$)')\n",
    "plt.ylabel('Depth')\n",
    "plt.xscale('log')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Depths on {}'.format(FIELD))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### IV.c - Depth vs coverage comparison\n",
    "\n",
    "How best to do this? Colour/intensity plot over area? Percentage coverage vs mean depth?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f979e92be48>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KK7BoSy4SIgK9XBnRXS56kjNcf/A08PVvGbaJiOiu1uH9C3uD+2JUWPPkKCzakounxkZi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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f979e9fe6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dat in data:\n",
    "    wav_deets = FWHM(np.array(dat[1]['Wavelength']), np.array(dat[1]['Transmission']))\n",
    "    depth = average_depths['5s'][average_depths['band'] == dat[0]]\n",
    "    #print(depth)\n",
    "    coverage = np.sum(~np.isnan(depths['ferr_{}_mean'.format(dat[0])]))/len(depths)\n",
    "    plt.plot(coverage, depth, 'x', label=dat[0])\n",
    "    \n",
    "plt.xlabel('Coverage')\n",
    "plt.ylabel('Depth')\n",
    "#plt.xscale('log')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Depths (5 $\\sigma$) vs coverage on {}'.format(FIELD))"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}