{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Bootes 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": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was run with herschelhelp_internal version: \n",
      "017bb1e (Mon Jun 18 14:58:59 2018 +0100)\n",
      "This notebook was executed on: \n",
      "2018-06-24 16:05:20.070230\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 = 'Bootes'\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_bootes_20180520.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": 5,
   "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": 6,
   "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": 7,
   "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": 8,
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
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    "depths.add_column(Column(data=hp.pixelfunc.ang2pix(2**ORDER,\n",
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       "<thead><tr><th>idx</th><th>hp_idx_O_13</th><th>hp_idx_O_10</th><th>ferr_ap_lbc_u_mean</th><th>f_ap_lbc_u_p90</th><th>ferr_lbc_u_mean</th><th>f_lbc_u_p90</th><th>ferr_ap_suprime_z_mean</th><th>f_ap_suprime_z_p90</th><th>ferr_suprime_z_mean</th><th>f_suprime_z_p90</th><th>ferr_ap_lbc_y_mean</th><th>f_ap_lbc_y_p90</th><th>ferr_lbc_y_mean</th><th>f_lbc_y_p90</th><th>ferr_ap_gpc1_g_mean</th><th>f_ap_gpc1_g_p90</th><th>ferr_gpc1_g_mean</th><th>f_gpc1_g_p90</th><th>ferr_ap_gpc1_r_mean</th><th>f_ap_gpc1_r_p90</th><th>ferr_gpc1_r_mean</th><th>f_gpc1_r_p90</th><th>ferr_ap_gpc1_i_mean</th><th>f_ap_gpc1_i_p90</th><th>ferr_gpc1_i_mean</th><th>f_gpc1_i_p90</th><th>ferr_ap_gpc1_z_mean</th><th>f_ap_gpc1_z_p90</th><th>ferr_gpc1_z_mean</th><th>f_gpc1_z_p90</th><th>ferr_ap_gpc1_y_mean</th><th>f_ap_gpc1_y_p90</th><th>ferr_gpc1_y_mean</th><th>f_gpc1_y_p90</th><th>ferr_ap_90prime_g_mean</th><th>f_ap_90prime_g_p90</th><th>ferr_90prime_g_mean</th><th>f_90prime_g_p90</th><th>ferr_ap_90prime_r_mean</th><th>f_ap_90prime_r_p90</th><th>ferr_90prime_r_mean</th><th>f_90prime_r_p90</th><th>ferr_ap_mosaic_z_mean</th><th>f_ap_mosaic_z_p90</th><th>ferr_mosaic_z_mean</th><th>f_mosaic_z_p90</th><th>ferr_ap_newfirm_j_mean</th><th>f_ap_newfirm_j_p90</th><th>ferr_newfirm_j_mean</th><th>f_newfirm_j_p90</th><th>ferr_ap_newfirm_h_mean</th><th>f_ap_newfirm_h_p90</th><th>ferr_newfirm_h_mean</th><th>f_newfirm_h_p90</th><th>ferr_ap_newfirm_k_mean</th><th>f_ap_newfirm_k_p90</th><th>ferr_newfirm_k_mean</th><th>f_newfirm_k_p90</th><th>ferr_ap_mosaic_r_mean</th><th>f_ap_mosaic_r_p90</th><th>ferr_mosaic_r_mean</th><th>f_mosaic_r_p90</th><th>ferr_ap_mosaic_i_mean</th><th>f_ap_mosaic_i_p90</th><th>ferr_mosaic_i_mean</th><th>f_mosaic_i_p90</th><th>ferr_ap_mosaic_b_mean</th><th>f_ap_mosaic_b_p90</th><th>ferr_mosaic_b_mean</th><th>f_mosaic_b_p90</th><th>ferr_ap_tifkam_ks_mean</th><th>f_ap_tifkam_ks_p90</th><th>ferr_tifkam_ks_mean</th><th>f_tifkam_ks_p90</th><th>ferr_ap_ukidss_j_mean</th><th>f_ap_ukidss_j_p90</th><th>ferr_ukidss_j_mean</th><th>f_ukidss_j_p90</th><th>ferr_ap_90prime_z_mean</th><th>f_ap_90prime_z_p90</th><th>ferr_90prime_z_mean</th><th>f_90prime_z_p90</th><th>ferr_ap_irac_i1_mean</th><th>f_ap_irac_i1_p90</th><th>ferr_irac_i1_mean</th><th>f_irac_i1_p90</th><th>ferr_ap_irac_i2_mean</th><th>f_ap_irac_i2_p90</th><th>ferr_irac_i2_mean</th><th>f_irac_i2_p90</th><th>ferr_ap_irac_i3_mean</th><th>f_ap_irac_i3_p90</th><th>ferr_irac_i3_mean</th><th>f_irac_i3_p90</th><th>ferr_ap_irac_i4_mean</th><th>f_ap_irac_i4_p90</th><th>ferr_irac_i4_mean</th><th>f_irac_i4_p90</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></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>uJy</th><th>uJy</th><th></th><th></th><th>uJy</th><th>uJy</th><th></th><th></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>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th><th>uJy</th></tr></thead>\n",
       "<tr><td>0</td><td>149674994</td><td>2338671</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>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</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><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>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>1</td><td>149675002</td><td>2338671</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>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</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><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>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>2</td><td>149675001</td><td>2338671</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>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</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><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>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>3</td><td>149675000</td><td>2338671</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>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</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><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>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>4</td><td>149674999</td><td>2338671</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>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</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><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>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>5</td><td>149674998</td><td>2338671</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>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</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><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>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>6</td><td>149674997</td><td>2338671</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>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</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><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>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>7</td><td>149674996</td><td>2338671</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>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</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><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>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>8</td><td>149674995</td><td>2338671</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>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</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><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>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</td></tr>\n",
       "<tr><td>9</td><td>149675006</td><td>2338671</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>0.8317619525740166</td><td>48.49677948513246</td><td>0.8226769024000251</td><td>45.50289763408925</td><td>1.1308060087337373</td><td>89.12552630607229</td><td>1.195489253480359</td><td>87.56453826894781</td><td>0.9634258576783831</td><td>136.4881065796325</td><td>0.9787146599148896</td><td>126.44265562115058</td><td>1.7339707830640172</td><td>152.09440546493528</td><td>1.6402268454227358</td><td>163.97041491309727</td><td>4.078233946872892</td><td>208.31456445753994</td><td>4.561974275186872</td><td>257.3566622492128</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><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>5.863745</td><td>276.65686645507805</td><td>12.023442</td><td>399.30462951660155</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>1.0953219396143807</td><td>64.67883138441027</td><td>1.6320720061318044</td><td>91.63608052080987</td><td>1.4990614661990294</td><td>59.35187823080706</td><td>2.3568757517600463</td><td>84.77261534270869</td><td>6.810147902249709</td><td>91.19888463885421</td><td>9.84281961158783</td><td>138.23880365715047</td><td>7.7550268650629235</td><td>257.2940955631909</td><td>12.209668713390334</td><td>290.18892712436696</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(\"$('#table139825278102048-840943').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",
       "    $('#table139825278102048-840943').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, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "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": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'90prime_g',\n",
       " '90prime_r',\n",
       " '90prime_z',\n",
       " 'gpc1_g',\n",
       " 'gpc1_i',\n",
       " 'gpc1_r',\n",
       " 'gpc1_y',\n",
       " 'gpc1_z',\n",
       " 'irac_i1',\n",
       " 'irac_i2',\n",
       " 'irac_i3',\n",
       " 'irac_i4',\n",
       " 'lbc_u',\n",
       " 'lbc_y',\n",
       " 'mosaic_b',\n",
       " 'mosaic_i',\n",
       " 'mosaic_r',\n",
       " 'mosaic_z',\n",
       " 'newfirm_h',\n",
       " 'newfirm_j',\n",
       " 'newfirm_k',\n",
       " 'suprime_z',\n",
       " 'tifkam_ks',\n",
       " 'ukidss_j'}"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Passbands on Bootes')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aMSAotUGni0NU1DgIvAZ9fRUAgKio8W5j58w+andl1/PHYKpJxLvpNyAaHXhohA2JkZlYV9eCFqsNmWE6HG87iu3kQgAATzncFbEbM2dsGrw3F0SqjrSifE8jqotbMXbGMOz56iROHGjGiQPNOOeSdBjCnd34Jw40oaKoCVGJRibWDMYZwLJly1qWLVvWEqrzs6icgVJ/CLCppuv9IlAGd1HKgePEFMKExDluYwkhyBv3NwDA/vCJCLNYYbBa8ETsdjw0dhpuS03E19PGYs+s8fh0Sg52nXcu7qaviueOOOeMFWoAqD8hxpP0mWywmG2oLWtHWKQo0I2VXW7jzZ1WAIDFZB28STIYjCELE+uB0N0IvHYe8Nek0z2TkFNV/Q6++z4LgtDnskeAXIxOo4kHIWL6Ykz0JNXzDB9+HcZN34LqiBEAgJU9P+DGc/6sOlavj8fTc99AltEAbaT6+c4UOpsd6ZWt9T3obutDToHoMWusdE9V6+kUP2dLb7/qJzAYjLMMJtYD4ehX/TtO4AGbq+gNbcrK/goAaGnZBp434eChe9HbVw9QgEC2rHXgOFGsOc5zdkQ7SUBrZDQA4PABk89rGzUczLzHGgZnBNZem73AbsOJToACyRnRiE4yqlrWpg7RWyPwZ1a2BoPBCA1MrAOltxOQ0936K7gfLgL+mgz0NAPmAeXJDwpWqyMlsKnpGxw6/Fs0NW3C9u3ngkIApKpAgkAwYsS1uPCC/V7zntutPHoMRugjIjBy5Eif1w/XcDALgyvW3X023PTmLuw67liC6jBb8cWBWvQn3dHSyyM2WSyjXF8hfp4xSUakZESh8aS7Zd0nub+FM/wmhcFgBAcm1oHQegJ4Lg3Y87b4evhE/477+i/Apj8Du1YDT8QA5d+K25/PAlZkhmSqwaStbScAQKuNQlfXYbS0bLHvo4IVVOBBKQXP89DrI3xGaXdIbekShg2H1ep7TdbAkUG3rA+easf28hY88tlhAEBLdx9WfnMUv1tXhIomr+WIVbGYbYhJEosc1R8XxTo2ORzJmdHobuuDqdOC3Z8fx8lDYgqr7P5mljWDcXroT4/rgoKCsVu3bg1qcxMZFg0eCM3HxMejXwLT7wR4L4FlP70BHPwEuGszsPMfgzO/ENHatgMaTQRSR9yAyqo1AICwsJHo7T2FPksDGho/R2zsVPA8D41G4/N8HTZRiAw6Hbp532uyWkJgGwTN6rXy6LMKWL2lAq9tEaPazRYeeytbcd3qnfZxjV19GJMcWIS2pdeGyDgDOA1Bd1sfDOFaGCK0SBgRCQCo2NeIPV+eBADc+9pcWPvEz4VnYs1gnBaGWo9rJtaBILu9tdJ6rDIKvGo3kD7D8frLh8RH/syP5m1v/xkxMecgOnqyfVtO9qM4eOge++ujxx4Hz98cmFhrOHT64d7WEQLbIFTaW/7JAew63oKWHsf3WtNuxoe7nfsVdJpteHHzMWQlR6K0rhO/nzfWZ0MRi5mH3qhFZJwBnc29iEkyghCCaMna3vlZhX2s1cLDaresmRucMUT57N40NBYH14pMzjPh6lc9Ngg5evSofv78+dkFBQXd+/btixw3bpzpjjvuaH7qqadSW1patO++++7xvLy8vptuuimzqqrKYDQahTVr1lTOmDHDvHHjxsjly5enA2Jmyo4dO0o5jsP8+fPHdHR0aGw2G3nsscdqb7755nZA7HFtMpmKAOCRRx5J+eSTTxIIIbjooos6Vq1aVeNpju+++27CsmXL0ru7uzVr1qw5MWfOHN+BOX7AxDoQ7GJtEB+VlvXblwBPqJR7tQblezpt9PbWoqfnGIalXIW4uJn27UlJF0OjiQTPO1zCRmOnX2LdbRMFyKDVQvBDrLWDJNYbD6k3fVu/75TT690nWvDO9pP21wunjsTopEiP5+WtAnibAH2YFjHJ4aJYS+vXUfEGcByxizMAdLf2gpc+I+YGZzCcqa6uDvv444+PT506tXLixInjPvjgg4Q9e/aUfvjhh7HPPPPM8NTUVMukSZNM3377bcXnn38e9etf/3pUaWlp8cqVK4e98sorlZdccklPR0cHJ3fe2rhxY3l8fLxQV1ennTFjRu6NN97YznGOFeJPPvkkeuPGjXF79+4tjYqKEhoaGrz+yJlMJq6oqKj0q6++ily8ePGosrIy1T7XgcLEOhCsUo14jSzWigCzaXcAvR1AZx2QrFjqOAMCyDxBKbXX7E5IOB86XQymT/u3ff+FF+zH1m1TodcnwGQ6jmnTPwcvnAClF3i1NK2UQkMADcf5J9bc4Ii1KzNHx6Otx4qjDc7R2uWNzmvWlS0mr2Jt6RPX6PVGDaITRUtaXr/mNBwiYg3oau1FyqhoNJzoREej2X4sE2vGkMWLBRxKUlNT+woKCswAkJOTY547d24nx3E455xzTH/9619H1NTUGNavX18OAFdeeWXX4sWLtS0tLZqZM2d2P/TQQ2mLFi1qveGGG9qysrKEvr4+cv/994/ctWtXJMdxaGxs1J86dUqbnp5uk6+3efPm6Jtvvrk5KipKAICUlBSva3c33nhjKwAsWLCgu7u7m2tubtYkJiYOOAeTBZgFglX6EZUjnZUu7j1vA8+lA6tmAH2KH/fOQa14F1Sam8VAOI7T2yuSRUdPRHS0GFhHCAEhWkQrcqopPYTOzgNez2sRKPSEgPNXrAfBst5f3e70euHUkXjz19Ox+ILRbmObupyzAGo7zG5jlPBW8T1qtBzGFqQgfXw8Rk9x5ObL/5zGzhgGAGhvdHhjmFgzGM7o9XrVftMajQY8z6s2pyKE0Geffbb+zTffrDSbzVxhYeG4oqKisNdffz2+paVFe+jQoZLS0tLihIQEq2tPbEqpz2Uul2t5fd1fmFgHgj1ViwLmdqBV7OMM1zSl/1KkI3U3DsrUQkF7x14AQOGsH7yMotBoIpy2NDZ5rzRmpQK0kljzfgWYIeRiffWr251eT06LRaRBi7wR0W5jlWvagJjS5Q3l1PseXYpZhp+dSohefFseJs1NQ/Z0sUhKa62jyx9bs2YwAmPmzJld77zzTgIAbNiwISouLs4WHx8vHDlyxFBQUGB+5pln6vPz83sOHz4c1tHRoUlMTLQaDAb6xRdfRNXW1updzzd//vzOtWvXJnZ1dXEA4MsNvm7dujgA+PrrryOjoqL4hISEoFQ2Ym7wQJDFmgrA6nOBTmktMzwB6GlSP0a5PSIZMDWLx58B9PSUITJyHAyGFI9jxHKjBKkj3kPR/seRlGhFd7f3IEqLQKHjArSsB/kjS4gQ/2azkyNxQ0E6/nd/DUwW8W+uuVv8d/Dd8gtx2Svb0OGjJKh8py90dcJcVARzURESF99t3z98TCyGjxHblBjCtWiqFj0zeqMWZ1gHWwbjtLNixYraG2+8MTMnJyfPaDQK77777gkA+Nvf/pa8Y8eOaI7jaE5OjnnhwoUd7e3tmgULFoyZMGHCuPHjx5tGjRrV63q+hQsXdu7bty988uTJ43Q6Hb344os7/vGPf3gMMIuLi+OnTJmSKweYBet9MbEOBHmN+sA65+3hiV7EWtH6VWsAdOGAJfA83dOBpa8JBsMwH6PEcqPFxY04Wno+dPk/IcxY5vUIK6XQE25IucFdiZfEWqvh8F/X5qOtx4JNR+oBiJayXsMhKykSMUYd2n3V75amTjsdxU/49nZoYmPdhkYnGtFUJYq1MVLXrwIsDMbZytixYy3KgK3169efVNv33XffVbge+95777mtsRuNRtv+/ftVrQs5EhwAnn322fpnn3223tf8fvrpp6M+30Q/YW7wQGjy8D2EJ3g+Zscrjucd1YDOGNw5hZA+SyMMel91zykIHKJrsSSir68eNpt7CU0ZK+2HZX2axFomKsz5vjbcIHrCYo16tJu9N3KRp05tDlE3FRWpjo1OdJRpNegEMNOawWAATKwDo3SD+vYISax1Ee77lKlbmecDxHdq01CAUh4WSwv0Bu9i3V2vw+7/OQqbVRQsS5/4WXT3HPN4jDXAALP+5lkfOXIEFRVuN9h+4SrWWo1zkEiEXhTvmHAd2nx2xpLmztsDTGEu2u80ovGll1B192JExRvs24SDP8PW5hz4xmAwTj+33HJLem5ubp7yv5dfftmL1TZwmBs8GBjjxMfoEUCLBxfwZS8AU24GXj4zukdZLK0ABBj0yV7HVf8Yg56GdsSOEHPMbTZxfE93GWJjpqoeI1vWhBC/3Lyafor1P//5TwDAE088EfCxseFucSZOhOvFm664cB1ONPd4HSuHKFCbQ6wtlZWO/TYbWl57HQAQdt6v7ds1fC+E3jOr4QuD8Utg7dq1VYN9TWZZ+4u3ntUGKWI4eoTnMeOvcRRTOQOw2kSLrrZT59d4vldMXwoPTwPHGdHT43ndWk7dklMafAk2R+y2aciYnBaLyWmONWQN52xJF4yKd3odbhDvc+MjDDjW0I3OXs/Wtf39SWKtS02Fpdrxt96nsP4NrY5lNUIpwJ0ZnhgGgxFamFj7S69KdTIZWawjFC7jef/lPEa2vkPIrs8q7I0g+kOvlceu4y2objXhhtfEHOunvqzC6h8q8OQX7kV4Opsb0dMgrrGaTohu7/PPvxAREWPQ2XXQ43WsAoWWC0CsAfADWLv1x9VOKUVcuA5/v34S/nr1BLf9V09OxVfLzsf0TPF7DNeJIhofId7MzHtxq++JSG5w/ZgsWKuq7e/bUuUQbl2j0m1PxTsVBoPxi4eJtb/wXtyRBilnVqNwnU5c5Hj+6y/sbSTtTY2DDKUUezdVYuOrByEIgQsbL1DkProJv1qzCy99WwYqiBHrZmsYVmwqxTvbT6LLxXp849477M/7usWAMo7jEBM9GR0d+1Bb+0/Va1mos2XtCw0hcH1LO3fuRH29z+BMAMDmzZt9jhEowBGChVNH4uaZGW77CSEYNzwaYZJIR0iWdaxR/M7rOtwyPuzYM/Uky9owOgtCdzcEKTrcVieWOTVkZ0N7otjlYJ9TZzAYvwCYWPuLt97VahHeBkVXplEXBH8+LmxZ5wjoWr30/wI6lhcoln/iCHjaWtaEzHhRJcw2x3vbV+U52EmrF138hBBkZT0EjSYCLa3q1qZVcKxZA74ta0LEBDH78VYrvv76a7zzzjuq4xsaGrBt2zb767Y23yVfBT+rFBklsZbXrF3d5WpQWXGlaHBdmlg0x9YoFsyx1tSCGI0wTpsKW0UZZl49Guf2fglQylK3GAwGACbW/mPzbDnZ63+3lDu2abwHKAWbI1udc/R7OvwLTLLYBIx7bBM+2+8oi9rU1YesRPG52eZIJfrpRIvqORJHxyI8VnQPVx06AK02EjHRU9Dbq143wEopdIGsWcN5XE+PGNDV16f+Hjdt2oTvvvvO/jpWJZ/ZFcFPj7NRL1vW4uOCfDEPPULvZW1ZfnuyG3ykKNbWBkmsa2uhGz4chjFjIHR1YeKUcETWHARhZjWD4cbTTz+dnJ2dPX7MmDHjn3rqqWRArCpWWFiYnZGRMaGwsDC7qakp4GCP/vSvHkyYWPuLq1hfvdrxfFi++Dj+Gsc2T1ZakOrE+qLqSKtf497YdhwWlfJgKZGisDhZ1pXtuOu9PbjtnZ+cxurD9cieUQgA2PWvdaCUQqePg9WqbtFaqQC9Qhn9CTADHNa1xeI9r7m62rn2gSdRV0IpBReAZW3UiW7w4TFGXDZxOFJiwjweY397coCZJNa2hgYAgLWuDroRI2DIGiPO99gxWOvrAVCWZs1gKPj555/D3n///aR9+/aVlJSUHNm0aVPsoUOHDI8//vjw2bNnd1VWVh6ePXt212OPPearmpMdm/R3OdT6V7vCUrd8UfQ/wP/eC9y8Xnx97jLg0Hpg5HTHmDEXAY+3i0I8Yopz+lZ4ovp5OR0gBKfXtZrYff9+CcYVDvd5rFZSwhsK0rDuJ4fIReh7ASuHXt4RwX7gVLu95KbympOuzAXV5gL7D8FiNqG56iR0ulhYrepuc4tAoQ3IshbhKRXTuBQpUGq4BpT5I9a8QMH5cesa5uIGB8TPkPcSJ2B/f9LitW64+L3YmhyWddi4cTCMzQEAdH3zNWC1ShY5CzBjDE0e3f5oWnlbeVD7WY+JG2N6+tynPXbzOnRjX8IOAAAgAElEQVTokPGcc87pljtgnXvuuV0ff/xx7KZNm2K3bNlyFADuueeelgsvvHAsgJoHH3xwxPHjxw319fW6uro6/X333Ve/fPny5g0bNkQ9/fTTw5OTk63FxcXhFRUVR+T+1Rs2bIh68sknRyQlJVmLi4vDL7300rb8/HzzqlWrUvr6+si///3vivHjx/fV1tZqb7/99oyamho9ALzwwgtVl1xyiWoeZ21trXbhwoWj2tvbtZMnTzb98MMP0Xv37i0ZPny49x8zBcyy9sX/3is+tkv/fsZeCjx4xL1qmWyVZcwCzrlVfP6XeuABlyhqjZQK5a3qWYAc+K7/nepsksg8ceV4p+0amKQGHeL7yhsebRdqAE65xWHRYQpBAqqLD0u9rtW7UQkUAYm1Rhon66Fr8w/X1/0Ra3/XrOUhcgUzANByHGzeumPJu6T3yRmN0MTEwNrQAKG3F3xrK3SpI6CNi4M+IwPt//wUAKCJCOrvIINxxjN58mTz7t27o+rr6zVdXV3c5s2bY6qrq/UtLS3ajIwMKwBkZGRYW1tb7YZoSUmJ8dtvvy3btWtX6fPPPz/i5MmTOgA4ePBgxPPPP19TUVHhlupSWlpqXL16dXVJScmRTz/9NOHYsWNhhw4dKrnllluaV65cmQwA99xzT9qDDz7YcPjw4ZJ///vfFUuWLMn0NO8//vGPIy688MKu4uLikmuvvbatrq4u4HVSZln7i0USJzlX2p+yoWpj5jwC/OsuIDIJ6PYvmtkXtWXqFmzR5ipM+Y90r8fKLnC9xvm+TUv6wHEOsZiVlYDiOkdt653HW2CMjkH4yJMgCusvIjYOtUeLEZcbAUqtEAQrOM45V5uH6HL2O8BMenQEVTtuRjs6OvDiiy/i6quvxrhx43D8+HG34/3p7EWlaHBfyKIsp24BgE5DYPXSHcvJspbMd21KCmwNjbDWipHguhFijn7k7AvR+t77AABNbCyo3/fdDMbg4s0CDhXnnHNO77Jly+rnzp2bEx4eLuTl5Zm0Wu8ytmDBgvbIyEgaGRlpmzVrVue2bdsi4uLi+IkTJ/bk5uaqrqnl5+f3yOKfnp7et2DBgg4AmDRpknnLli1RALB9+/bosrIy+498d3e3pq2tjYuLi3P7Mfjpp58iP/vss3JAbAwSHR0dcCcuZln7i0kKrtKGOT8GysTrgcfaHLnZQeDEATG3es4tubj2946qYTvWl3s6xI6VF6DTuKdRxYUL4DSOm41Yo7PgHqzuABV4EEIBwtkFKWVUFmpKj4DjxJsaQXAPzBOo+A/P/6IozuOU4tvcLL73AwcO4Pvvv8fHH3/sdCwhxC+xFij1K8BMjv62KdzeWg1xeu0KVVrWslgnJ8PW2Ahrjdi5TRbr+NtuAwBEXnwRiIYVRGEwXHnggQeai4uLS/bs2XM0Pj6ez87O7k1ISLBVVlbqAKCyslIXHx9vv8311F86PDzc4x22wWBQ7ZkttfUlgPh7tGfPnpLS0tLi0tLS4sbGxoNqQi2PHShMrH0hrzl3SVawbFkTAtz+FfC7fYGfk+NCUpkqJsmI4VkxAR1jsQnQuVjVJ5+7DGEaCzjOIdZaxZjpmXEobegCFQSAAAQKsR49Bt1trbCYxL8VXnB3QQsQhTHQNWs1y1oWYkIIWlvdg+rCwsL8FGv/LOtfF2ZiQmo0FuQ74gG0HOd1zVp2gxOpnSgAaFOSYWtogKVSLIiizxBzu3XDh2NcaQnS/vEPEI6AUrZmzWAoqamp0QJAWVmZfuPGjbF33nln67x589pff/31BAB4/fXXE+bPn293N3711VexJpOJ1NfXa3bt2hV13nnnea8P7CfnnXde54oVK+z1mHfs2OHR3VpQUNC9du3aeAD417/+Fd3Z2RmwAITUDU4ImQ/gZQAaAG9SSp9z2Z8O4D0AsdKYP1JKvwzlnAJGdmXLLmulRZ1R2P/zcsH/6EdkiylKOTNScGy3GGnM8wI0Gs/3ZFZegF7rvp8XzNAoLGstR/DR4pk43tSDkrpOfFZUAypQ0bKGo8Z3Yqrodjd3iH8Pgsq6tWhZ+18URbZ4eRXLWhZum82GqKgot2PDwsL8qmAmrln7nsuoxAhs+N35TtsIEY/3hMMN7rCsdSkpsLW0wHL8OEh4ODSJKoGIg5Q5wGCcSVx55ZVZ7e3tWq1WS1966aWqpKQk/sknn6y75pprsjIyMhJHjBhh+eyzz+ylAKdMmdJz0UUXZdfW1uofeuihuszMTOvhw4f76Rp1sGbNmuq77rorPScnJ4/neTJjxoyuwsJC1Zrhzz33XO3ChQtH5+Xlxc2aNas7KSnJGhsbG5ArPGRiTQjRAHgVwH8AOAXgZ0LI55RSZYmmRwB8QildTQjJA/AlgMxQzalfyOLc1eD8eqCEQKxl8fuP28fbxfp/Xyhyco27YuGp3bLedP/5duuS583gOEcXMa2GYOboBMwcnYB3tp9AV58NvGxZKxpyRCWJJVdNnWZAC9UgM4ECmkAsaznATHqttKx7e0U3e1VVFaqq3P9OwsLCfEaPi3Pwz7L2ND9vb0HdDZ4CCAJM+/ZBn56ueuNCWKlRBsONvXv3uvUqHjZsGL9z507VVn/Z2dm969atq1Ruu/zyy7suv/xypz6+cv9q133KHtXKfcOHD7dt3LjRPUhGhfj4eH7r1q3HdDodvv3224jt27dHGY3GgHzjobSsCwCUU0qPAwAh5CMAVwFQijUFIC/exgCoxVBDJ4lzoxQwGKxmHJx/DTL8JSpe/SairsJLTXNIlrUk1rnDHOvoAm+GVu+w9pRu8NRY0eJWW7OOiImFVqeHub0bSPSwZh1ggJndDa4SDW42q0ecyxgMBr+jwfurjZwPy1pWa6UbXJeaCgDoKy1F1CWXqB5GOFYWhcE4GygvL9cvWrQoSxAE6HQ6+vrrr58M9ByhFOtUAMpowVMAZriMeQLAN4SQ3wGIAHBxCOfTT1x+wTXBEuvghAvIQtfV6rnCWlt9D+KGqfTahnc3uN7FDS6TGCUHj7mvWRNCEJMyDD1tXTAmAjzvPi+eOn+qfgeYSdKltJR9iXVYWJjf5UYHYll7d4PLTxzR4Ias0fb9+lGj1A8kBCzPmsHoPy+88MKgGoAvv/xywurVq1OU26ZPn969du3aqpKSkmJPx/lDKMVa7VfG9RftBgDvUkpXEkJmAVhLCJlAKXVaZCSELAawGADS072nIgUd15rgmiB9ZCQ4AWbWXtHKnH5ZptP2hX+Yhk9X7AEAnCpt8yjWYoCZ+1fF82ZoNA5rXSnWcXKvZ0pBOEC5Zk0IQUxyCrqbj8GYDQiCu5jSQbas/VuzBrh+mtaEEHi/hPuatXa4I0AtLC9P/bwcxyxrBuMMYtmyZS3Lli1Tr8s8QEIZDX4KQJri9Ui4u7nvBPAJAFBKdwIIA+AWaUMpXUMpnUYpnZaUlOS6O7TYvItBvyHB+eh7e8QqaFEJzm7wpAxHsJXN6llJrLwArYqVL65ZO/KslRHjei0nCg+lAChAiJtl3dnULp1HPXVLg8DXrNUCzHyJtUaj8Sttgg7ADa7hfASYCY4n8nsmhCDullsAAOEF09UPJPb/MRiMXzihFOufAWQTQkYRQvQAfgXgc5cxVQAuAgBCyDiIYt0UwjkFjq0PiB4Z/PMGSaz7pBQpQ7jzGrjSSizdWefxeIGKwWNKKKUQBDM4znEDoOwupdMQe5MJwrm7wWOTh8FiFmsNCMFI3XKpDa50g5tMJqexI0eOxCOPPOI4luP8Emt/U7fU5+fDDS49EoUbHABS/vAwsrf/CG2ceq9zQgjTagaDASCEYk0ptQH4LYCvAZRAjPo+Qgh5ihBypTRsOYC7CSEHAKwDcBsdaj0B+7qA2DTf4wIlSHnWFrPUycno7p43RokC3lrrOa1QrcwmpVZQykOjcVjWSje4XsM5OkIRdcua8nKJUPcCQbxL6pb/bnDPlnWilPqk0+mgrGgkuqj9S93qr1gTlX7bStRStwCAaLXQJnguO0vYmjWDwZAIaVEUSumXlNIcSmkWpfQZadtjlNLPpefFlNJzKaWTKKWTKaXfhHI+AWOzAFYTEBMCsQ6SZW3tE4VLH+Yu/r9+9lz7c95DOUy11pByuhWnWLPmnCxrDsQuzqJlLUMIQfr4SRBksRbcxVq+pv951nKAmYhrgJlGo8FIqZOVa+lBZVqZNwTBvzxr9fmJjx6vI3/0guBfH04Z4h7kwWAwfpmwCmbe6JWK4ITCsg6SWFv6pLaLBnex1ugc16guVm+ZqdYakpeCwjSKNWvlCJ2bZe3sBteFhUFviBTPryLW/Q0wk3tluFrWGo0GOp3oRZAfzz//fEyfPt1vsR5onjUAj9a1HMVOIIAE8L0zy5rBCC1ms5kUFhbm5Obm5r3xxhtx//mf/5mxd+/eIBXTENmwYUPUnDlzxgz0PKyRhzfMkljHDN01azkaXGfw/lV++04x7nrhArftavnFctUxjWLNWilkTmvWBCAu0eAAoNGKaV9qljUfYG1we9ctReqWTqeD1WqF1WqF0WiEXi9GqMtifdFFFwEANm3a5Oea9cDyrAGxzaZG5SSOPh7Oa9Y+IbAvMfjrhWAwGP6zY8eOcKvVSkpLS4sB4O6771bN87TZbG5eu8GGibU3mkrExyEcYCa7wXUqbnAASM6IQmNllz0QzRVBcHdHy25wjTYcgHh+pcYQQpBsE/9Nd1ZFwjV1SzxWzsVWcYOD2gUYCKDrlsKyNhqNsFrFSHitVmsXa41L84tBCTDjZMvao2kNAOLSQQB3BIRIt0SCALCmHowhRu2f/5LWV1YW1D6uhuxs04hnn/HYzevo0aP6BQsWZBcUFHTv2bMnMiUlxfL111+XV1ZW6pcsWZLe2tqqDQsLE958883K/Pz83szMzPyqqqpDra2tmuTk5MkbN248umDBgu6pU6eO/fvf/159++23j2pra9Pm5ubmrV+/vuL222/P/Pvf/159wQUXmMLDw6csXry44fvvv49+/vnnT91xxx2jrrnmmtYff/wxymazkddee63yj3/8Y2plZaXhd7/7XcPDDz/sMzh6y5Yt4UuWLMlcv359eV5enmrHL08wN7g3PpH6Uhtjg3/uIFlKFrtlrf5jXnitd+8Lr2JR8rwYYe3kBneZ7zCL+O+yuzYChLgLok6q/KYWYCZQ2WgMtNyos2UtC7PSDe56Lr/XrAdgvTq6gqnvt19f6Kcb3I9GJAzGL4Wqqqqw++67r7G8vPxITEwM//7778fdddddGatWrao6cuRIyfPPP3/qN7/5TbpWq8WoUaN69+3bF7Z58+bIvLw80w8//BBpNptJfX29fs6cOaZVq1ZVTps2rbu0tLR4/PjxTqkrZrOZmzBhgvngwYOl8+bN6waAtLQ0y/79+0tnzJjRfccdd2R+8cUXFbt37y597rnnRvia9+bNmyOWLl2a8fnnnwcs1ACzrP1jxDniY2zG6Z2HCtY+Hlo957GgR+pY9bQgGUopOBfXLC+VCBUDzMQSua5nF5zKrnJulrVWHwYqcKCC1e16FAgsdct+rDQ/nrcLNM/zTpa1mlj7Ew0eoNHrPD85tcyHZQ30ww0ueS2YE5wx1PBmAYeS1NTUvsLCQjMATJkyxXTy5ElDUVFR5PXXX58lj7FYLAQACgsLu7777ruoEydOGH7/+9/XvfXWW0lbt27tnjRpks/OWxqNBrfddpuTW3zRokXtAJCfn2/q6enh4uLihLi4OMFgMAjNzc2axMRE1Tvr8vLysKVLl2Zu3rz5WGZmplVtjC+YWHtj9GzA0iNWLftjVZCbbwTn59faa/NoVbti7rbAGKl32iZQuK2zCrJlrSg36uoiNoWLKUdpF9Y6Wa8OsdaDCpybG1yWTU0gAWYuXbfk9SOdTofe3l5oNBr7etJALOuBB5ipX8f+2aiF3nuBcASUEPgoj8Zg/KLQ6/X2PzSNRkMbGhq0UVFRNnndWcns2bO7V61aldTQ0KB/4YUXal588cVh3333XdR5553X5TpW5TqC6zq1sq+1ch4cx8FqtXr8405OTrb29fVxu3btCs/MzPTesMEDzA3ujd5OIEzqDx0WA+jVS3b2iyCtWdssArR6/8T67Yd+dNum5v6Vq45pOM9iLedd68JtUFuz5rRaQODc3ODyunP/Aszk+fFuEeCezhWYWPscpgqxi7X6fmVt8EDc4PYbOibWDIZHoqOjhZEjR1refvvtOEDsWbBz504jAMyePbtn3759kRzH0fDwcDp+/HjT+++/nzRnzpzuQZ4j/9VXX5U9/vjjqRs2bHDv5esHTKy9YekG9JGhOXeQ1qxtVgFaXf+/RtU8a0HOs1aKtfMYe/VRArfULQDQaLUeLGsqnc//ftauAWZKyxoQxVq+vppYq213RaD+5327YneDe87dkh4Dc4MTjjh9tgwGQ51169Ydf+eddxLHjh2bl52dPX79+vWxAGA0GumwYcMs06ZN6wGA888/v7unp4crKCgIUR1pz6Slpdk2bNhQfv/996d///33AVt+zA3uDUtP6MQ6SG5w3iY45VOrcd712fjxn2Wq+9TyrAXJsuY4x7q0q5DpFOrtWm4UAGyWPtj6eLTUnADGKeYrR0YrxvpaU3YNMON5HgaDwUmsU1LERjd5Lk0x5PV4b+lP8tz76wbX+IgGdwSYBRoNLh3PAswYDADA2LFjLWVlZUfk10899VSD/Hzbtm2qP3LK/tdLlixpXbJkib3ohLfe1XJ/a5mamppD8vP77ruvBUCL2j5XlNfIzs62lJeXH/E01hvMsvZGXzdgGNqWNW/lfVrWE+d6Tj1TzbOW6nlrNEqxdh6jtTekoE47ZUGsOnwQVCBoPOncD14WLk0A7981wEy2rOWgMq1Wi+HDh+NPf/oTxo0b53SsPzcEdtd8iN3ghPKBucHlz8hD9TkGg/HLgVnWnqBUcoMHcZ3aieC5wX1Z1kqL0tXCVMuzlsWa4xzBaK5WJyEOZVIrigIAAk9AOGcFk2UnkDVr1wAztTVrQGyH6Yo/15At4v62yPRVbtThog/QDe6n54HBYJx+fvrpJ+Ott946SrlNr9cLBw8eLA3G+ZlYe6KvC6C8I8As2AQpwIy3CtAZ9D7H6Y1aWMw2fLn6EC5bOtG+3ZNlTYgWHHF08nJbs5Zfe1izBgDKExCNs4DxdpdzIKlbzgFmsmUtHy+LtRqBiHX/a4N7t6ztcxFoQGKtKI3Wv4kxGIxBo6CgwKwWkR4smFh7wiwta4R77oo0IILlBrf5F2Amd+c6ebDZabtaypIgWJzaYwLu1rc9vow41qzdunfxHDiNB8s6gAAzu+Vqn58AjuPs69EDFWtqd4MPMMDMV+oW5QMKYiPSTQplljWD8YuHrVl7ouJ78VEX1Gp6CgbPDQ4Ac27JVd0uGnsuqVtCLzhO73Q/4WpZc44QZyhTt5zOrWJZK1O3ZPwvN+qI+OY4zp5b7a1mrzLAzBOCwtrvD7IA8x5MayppbcBucE4+nok1g/FLh4m1JzY8ID6aVeu6Dxl4P8V6XOFw1e3qlnUfOM7gdDvhahHaXxJHLrObZS0QEM5ZaBzCGEhRFGmcfX4CCCH2cqMDtaxlkR1oURRfBOwGJ8yyZjAYIkysfTHhutCcl+/zPcaf09gEaLW+v0alkPI2x4+/WplNWayVuFnWyjVrj25wAuJSr8VRwSzwcqOy4ep6LW+WdSDR4APOs/aZuiUE5gZn0eAMBkOCibUnZJUJRRMPAJh8o3SdgX0F/rjBbYINP9f/bH9t6nQUKvFkWWs4g5OwuI5xqolCPIs1pxFQV25PXXRY1ghkzdo5z1q+lj/H+7dmPVA3uPjoMcBMkbrFLGsGo/80NzdrnnvuuSQAOHnypG7+/Pmj5X1XXHHFqJycnLwnn3wyuaCgYOzWrVtDtYZpJ1i9qv2BibUnUs8Ra4OH7PxTgQkLgbjMAZ3GHzf4GwffwB1f32F//f6fd9ifi+VGncerWdauY+ypW8SxZq0Uz/CYWAiCmLpVV+YQa95+vONcgbbIDKS/s3/R4OLjQN3gvlO3AnODE+nugTLLmsEAALS0tGjeeuutZADIzMy0btq06TgAVFVVaffu3Rt57Nix4scff7zx9M4yNLBocE/0dgDRPrueDYwBRoQLAoXAU5/R4NtqtgEA6qIqMLwry2mfILiLlMCrrFlD3bKWTGsvbnCK6KQUx7nloigIYM1aPp/86NIpzJuLO6A861AXRemnG5xZ1oyhyHfvl6S11nQH1XqNT400XXTrOI/dvJYvXz6yurrakJubmzdq1Kje8vJyY1lZ2ZGLL744p7W1VZebm5v30ksvVcnjeZ7H9ddfnzly5EjLK6+8UnvTTTelHzhwIKK3t5e74oor2l588cVaAEhNTc0PZq/qEydO6JcvX54OiH/HO3bsKI2LixvQHzKzrD1hMYWw1KiCAdR9lteeNV7WrAUqoKK9Ajfm3ojlT16vsl+ln7XQJ7XHdOBqEMrHEIipW4D7mq/oBncWcao43l+xdoihOE4OMJNLi44aNcrjsYFEg/d3zVo+yuM17JFxAbrBZcvaVwI3g/ELYeXKlafS0tL6SktLi1966aVT8vYvvviiXN4+f/78bgCwWq3k6quvHpWdnd37yiuv1ALACy+8UHP48OGS0tLSI9u3b4/avXu3vQFCMHtVr1y5ctgrr7xSWVpaWrxr167SyMjIAd9xM8vaE1YToDP6HjcgCBTNjgOGt0pi7cWyruupg8lmwpi4MYgLd19/9xwNnuCSuuXNsnZfs04bPxGd/FEQDYXNolwjdxzf3zxr+VppaWl4/PHHvZ7HnwCzgedZO0eru5/f7r9XdEDxjf19McuaMQTxZgEPBZYuXZpx9dVXt65YsaJe3vbee+/Fv/vuu4k2m400NTXpDhw4EDZjxgwzENxe1TNnzux+6KGH0hYtWtR6ww03tGVlZQ34j5hZ1p6w9YZerAfoBreLtRfLem3xWgDAmFjnGIitx8R1a7U8a1GsnauiuVrfjtQtai83qhTN+b+5HzkzLgThKGwWR+S7o4KZwtoeQDS4L8EfDDe472hw+UK8fR3aL+QbDbZmzWAEzLRp07q3bdsWbTKZCACUlpbq//GPf6Rs2bLl2LFjx4rnzp3b0dvba//xHEivaoPBIOzatcu+JPDss8/Wv/nmm5Vms5krLCwcV1RUFObpeH9hYq0GpaJlrQ21ZY2QuMHre+qxv3E/Ovo68EHJBwCArFhxrVoXLo499EIvLLxF6rrlfF57nrVKgw4ZzsmOdBdrrV6PqLhkEA6wWkyOc8vHB5K6pRINzvnpTh6MADOfBrAidSuQ6H/7UGZZMxgAgJiYGL6np8evP6J77rmn+ZJLLum4/PLLs6xWK9ra2jRGo1GIj4/nq6urtT/88ENQakmr9ao+cuSIoaCgwPzMM8/U5+fn9xw+fHjAYs3c4GrYxBaRQ94NLou1ziEyVZ1VuOzflwEAHp7+MABgwagFiNZHAwCsJscP/+Hmw1LXRvWiKK4zVeJUbtRDz2WN9PnZlGKtkrrlbzS4qxvcH/wSa2FgtcHt1/DwXdovTQWA81zAxdN5mVgzGCLDhg3jp06d2p2dnT1+zJgxPntSP/HEEw0PPPCA5tprrx312WefnZgwYYIpOzt7fHp6et/UqVO7gzUvuVf1ggULssPDw0++99578Tt27IjmOI7m5OSYFy5c2DHQa/gl1oSQYQDSleMppTs8H3GGY5X+DYSs1KjEQN3gNimyWrEO+t6R9+zP//bz3wAASycttW9LTItEc7X4b/Ro21EINMEvsXbruuXDsgYArfT52ayOvyk1y9oXytQtee3Z7/VuPwLMgrZm7Uc0eEC+dmnuAt//GzoG42zjiy++OOG6zbXPtbIvtRzxDQDr168/qXbOYPeqnjt3bo+/78dffLoTCCHPAvgJwF8BPCr990iwJzKksIv1gD0XvhnA77C8lim7wU1WE7488SUuGHmB07hwxU3HvLsm2J8fbT0KQVDLs+6FxpdYK9as1QLMAECrFT+/purjjnMrUrdkAnGDq3X38kZgLTL9OqX7NVzO44qjgllg/awdc2eWNYPxS8cfy/o6ADmU0t5QT2bIMFiWdZCiwTmt+KP+9cmv0W3txu3jb8fWU1vt4yJ0jp7csSmO91TTXePmBqeUeui65XxtjWK7WoAZALt1fnL/Lvu2fq1Z2+eGfou193KjA6wNLjfc8FHBDP0qikIBXjUIlcFgDBKh7lXtD/6I9Qn80gLRbJJYa0NsWQ/UDS65R2XLurS1FOHacExNmYo1/7EGlFKkRaU5ibWSpu5mtzxrQRDTrHxXMFM+V7esOWl9Vtl5S44GF2up+BtgJs1NMTYUAWb9zrN2yQN3RWlZB+QGtxdFYW5wBuN0Eupe1f7gj1h3ASgihHwLwJ6DQyl9MGSzOt0MmmWNoEaDV7RXICs2C4QQzBoxy+fxGYcKUOqSZy0I4lfMafxcsybSKxWxJlL6l1Ks5ber8bO2t3h2hxgGumY9KLXBpUefmioIgbnBOf9uZhgMxtmPP78cmwD8DcA+AEcU/529WKXoZW0YULYZ2PN2iC40MDe4oBDrQ02HsLt+N0bHjPZxlIOcuhkQ0OckfHax9rFmba9g5mXNWs7VJpzjPQouxwP+W9YU/XeDD0ZtcE/fpX3JOcBGHqzrFoPBkPFpWVNK3yKEaAHIVTXKKaW20E7rNCOnGh39Etj1qvg8a+6Am264QTBAy1o89ufGn/DiyecAABOTJvo8Lv/CVBzaUgMAoJzJqaiWQ6y9F0VR5lnLlq+bWBPxHFqDo0+mvShKP1K3hAGsWYe2KIps+avvt6d0BRoN7sd6O4PB+GXgTzT4+QDKAbwF4G0Axwgh5/pzckLIfELIUUJIOSHkjx7GLCKEFBNCjhBCPgxk8iGjr1N8rPjOsa3uYAguNNDULfFH/Nk9zwkLIUoAACAASURBVKC+px4xhhhcm32tz+Mmzk2zP6ek18UNLsYRuq9Ze3GD+7Csw3gLqu4Qu345rNj+FUUJVKwHpTa4PR3ae21w0k83uG//OoPBONvx55fjRQCXUkrPpZQWArgMwMu+DiKEaAC8CmABgDwANxBC8lzGZAP4E4BzKaXjAdwf4PxDQ68k1h3VwOSbARCgMVSxBQNwg0sBZgIRo4XPHXEutJzvMAQ5ehwAKGdWdYO7pm65BZjJNxqKaHC360gBZhRW9OzYKZ4f7pa1z/nKc1VY1sEMMBtonrV8mK/a4LS/bnCWusVg2JkyZUpuqM79wQcfxPz5z38eptz2zjvvxBFCpg5Gf2xv+BNgpqeU2pWKUlpCCNF7O0CiAKLL/DgAEEI+AnAVAKXq3Q3gVUppm3TuodGHtE8qNmPpEfta1x0ASjcAs1WdA/2HkAG5weU8a4HwuD7netyZf6dfxzmVJ+V6XaLBZTe4S9ctN8taCvQCBaBuWTsCzBzbqMKydmzzPxo8FAFm9tzvfuY8ONzg3iuYET7QoiiyG5xZ1gyGTFFRkVu6lM1mg1Y78IKcN910UwcAe7WxtrY27tVXX02eOHFi0IucBIo/724fIeR1AGul1zcBKPLjuFQAyq4spwDMcBmTAwCEkO0QU3efoJRu8uPcoaVG8fYyCoH2SmDXamnNMZhZbAMMMJMsa61Wg0dnPuq3gBmjHPdahJidhJj3GGDmfA5OaVl7coPL93TK1C27Ze2/G1y24mnIiqI4jw0Ue3iZZ9NaulCAbnB7gBkTa8bQ4+vVL6U1V1cG1dpMTMswzfvN/V67eYWHh08xmUxFGzZsiHr66aeHJycnW4uLi8MrKiqOXHzxxVl1dXX6vr4+bsmSJQ0PPfRQMwB8+umn0Y899lgqz/MkPj7etnPnzmNq537llVcS9uzZE/H+++9XAcDy5ctTly9fXv/iiy8OUxs/mPgj1ksA3AfgYYi/S1sB/Lcfx6n98rn+6mgBZAOYDWAkgG2EkAmU0nanExGyGMBiAEhPT/fj0gOEl1o65l4OJI8DYjPEbV11QExq8K4zAMvabDOjqH4/gHAkRyUHJDQcRzBhQQoOf9WAOEo8WNbe16zhodzo0founGjuxrzxwxxBaop/ZbIwKlO3Aum6FdoAs/4WRfHPsg44GtyeusXc4AyGGgcPHowoKio6kpubawGADz744GRKSgrf3d1NpkyZknfzzTe3CYJAfvvb32b+8MMPpbm5uZaGhgaNr/MCwPbt2401NTX6G264oeOMEGupctnfpP8C4RSANMXrkQBqVcbsopRaAZwghByFKN4/u8xhDYA1ADBt2rTQmxl9XeJj3lXiY1yG+NheGVyxHkCA2VuH3sKu42WYhaswIirwf0cJKVEAGnDbqVlOC9L+R4OLEAL78b1WAfNeEiunaTiC/X+ZJI5R/GkIiuP8F1zHsaEQ64HmWdtTy3zUBg84GlwOjmPR4IwhiC8LeDCYOHFijyzUALBixYqUjRs3xgJAfX297siRI2ENDQ3agoKCLnlcSkqKz5KAPM/jgQceSF+7dq1bHfLThcfbfELIOumxiBCyz/U/P879M4BsQsgoaY37VwA+dxnzGYA50nUSIbrFj+N00ymmNSFrrvgYI91zdJwKwcUCv/ew8Bb889g/wVFRBRMjEgM+R3xylP25czS4pwpmni1rIq1Zd/XZoJfWw3mB4kSLeC5lURRl1y37mXxa1g7Ltb8BZt7LjUrXCVEFMyc3eCDLKPa7FOYGZzDUCA8Pt/9hb9iwIWrLli1Re/bsKT169GjxuHHjzGazmQukS59Me3u7pqysLGzu3LljU1NT8w8cOBCxcOHCMaczyMzbL8fvpceFAK5X+c8rUi72bwF8DaAEwCeU0iOEkKcIIVdKw74G0EIIKQbwfwB+TyltUT/jINLTBOgjAFkEI5Kk7c3BvU4/3eBFjUVo7W3F1KSpAID8lPyAzxEe6RBjZWAVFawAHJHcMm6WtfyaUPuatcVGkZUUiXV3zwQAdMn17rRKsZauGVDqljQ3hCjAbKAtMqVHr1pNIEV1B7ZcIR7PxJrB8EV7e7smJiaGj4qKEoqKisIOHDgQAQBz5szp2b17d1RpaakeAPxxgyckJPBtbW0HampqDtXU1ByaNGlSz6efflp+wQUXmHwdGyo8usEppbIZWQugl1JKCSFZAMYC+Mafk1NKvwTwpcu2xxTPKYAHpf+GBn1dYj/rpHGObWGxAKcVRTyo9C/A7Gir2P1tYsIkFJNGXJt9TeCXVlxWKR8CFcVarIPjwC0anDoLHKUUNoEiOcqAaKN4bFcvBx1cK5g51ocDX7MObYDZgFtkeuxnTcXPmCKwAEX5vKyCGYPhk+uuu65jzZo1STk5OXlZWVm9kyZN6gGAESNG2F555ZWT11xzzRhBEJCQkGDdsWNH2emeb6D4E2C2DcAFhJAYAFsgRoL/CsCtoZzYaaO9SnyMGu7YxnGAMQ4wtwX3Wv0Uh6NtR5FoTIQBYdBouX5HMdunodAC2bImLmvW7nnWSmHi7GKdEKlHdJholXf2AgmA078yObA5kJh6u+Ai1GvWAxNrj552Ks1DEAIKU2C1wRkMd0wmUxHg3EsaAIxGI926dauqCC9atKhz0aJFPotluPayllH2xz5d+PObyVFKTRBbZf6DUnoFAN81Lc9UWirEx+jhztvDYoHedvfxA6UfP8Tl7eXIjs2GwFNwmv4JTFSiIo+6wdH9VLasOeLsBicuKmN/pXCD85QiyqBFhEFU5x6LAFDOec16QJZ1aLtu9buRh31p2Us0OBGfsDVrBoPRH/yxrDlCyHQAN0JKn4KjnfHZR5N0AxXrkiIWFgP0driPHxD9c4O3mFtEsW4R+i3WGuVCtdkRHOnvmjWU5UYhB5UBRr0WEVIt8J4+GwAtiMbhIKYKy9rvPOsQR4MHq9yoF9vdYVkHYFoTX2HmDAajX7z88ssJq1evTlFumz59evfatWurTtecfOGPWD8I4EkAGymlhwkhoyG6xs9OZDe4HFQmExYTGjd4P36IO/o6EGuIBS9QcP0tu6Wgb1czcJv43LFm7SrWLgVPpEcxc4tAEESbOUKvgUGrgU5D0N3HAzqNXaypIDgaeQSQuqWMBg9FgJlyTv2B83ENOa6MUhpgnrU4ljXyYDCCy7Jly1qWLVt2+oOZA8DnLwel9HtK6aWU0meI+MvXQCldOghzOz10SKmDYTHO2yOSgh8N3g/LutfWi16+F7FhsRB4Ck0/LWsAODz+W7dt9jVrlwAzd21UWtYENkEUZKNetKojDVr09NlAIIk1IYDN5miRGUjqlsJyHYpr1r681bIXHJT2q+vW2ewG7/m5HjWPbge1shsSBsMb/nTdep8QEk0ICYfYx/oEIWToRG8HG1mso12Kn0QmA931wXVJ9kMc2vvEdfNYQywEvv9ucABoGOEeiyFQKwjRudf5dqsNLj8R16x5QQAFQZhOFOuoMB26eq0A1YLjKCgRLet+dd2Sy432o4KZX123BHlOA4wG95i6JYm0IATkarcHmJ2lYk0Firb1ZaBWAd07ak73dBiMIY0/Prl8SmkngKshpmyNhN1pehbSVS8+xo923h6RJJYcldtnBosAf4dNVjHNL1IXKQWY9d8NLmjcrRkqWN3Wq9VwjQaXc5VlsY40aNHVawNRrFlTG9+vADNZ3pQtMoMbYDawPGtHJ0vPpjWRAszQjxaZZ2u5UVuz2f7cWn/a0lcZjDMCf3459ET0iV4F4DNKqQWOqpFnF1YzYOkWn7u2mjTGio+9wRTrwN3gZl78gQvThoEOIBrccX1nZMva3yPlcqOCQEFBYJAqmEWFaVHZaoIgcCAaadGWtzksawRgHSu8waHpuiVfp7+fpfcKZnKeNaU0sDsCWdjPUsvaWiv+rXFReljrT3tTIwZjSOOPWL8JoApAHIAthJB0AN0hndXpokPhinP9UZXXsIMZEd6PALNem5hmFaYNgyBQR8RwgFBKQS2Oey6+WywN2h/LmoCAl0RUFmudhkN5Yzeau3hwGtENfnLRf9ota43i8+2PZR2SNet+t8j0MYBKVnKAa9bcWe4Gt9T2ABqC8MlJsDaaQG1npw3ACC5PP/10cnZ29vgxY8aMf+qpp5IBsSpZYWFhdkZGxoTCwsLspqamgDOWQtknOxj4E2D2IqV0BP1/9r48Pqr6XP/5njN79oQQ9oQlC2ENCApihboAXrVytbheaxW3lisVq+3t7xar/nq9tIVr7RXFtuoPa9WWVty11spSUVSIEjYNQlgCCYTsmfWc7/f3x1nmzOTMzDkzZ5IA83w+GmbmLN9MZs57nud93+dl7FLZcewIgG+mf2n9gO6m8L+j5UolWFtaEW4+0KrBmpeCNZdEsPbvb8fJp3aCtgbQmCXdoBx8+GMACrNO3CSgOJgxSDlryV0McMoy+FfNkleBIIT7rIOHDuky68StW1KXd/qmbslrStUUJdHULZM56zN9kIfQ3AP7YA8cI3IAkSF0IiOFZxAfn376qWvdunXFO3bs2Lt3797d77zzTn5dXZ3zwQcfHDp37tyuQ4cO7Zo7d27XihUrDE83EgQBgP6c7HQgFAoltV/MqzIh5HrG2IuEkHtibPJ4UmccyNAG4uhgrfqDW205mjyzZqLftAwunPLh1Lo9YEERKANOuk5ieM9w1PlEjO4JScyaOBIeRwUhUHLWWhl86sh8/G1PM3jCg3DhPmtRkx82GqwBiZCmqxo8PCLT0CF1ziEfJ6aDmdxnbTJnrfFyTW5hAxyhkz44RubAPjRLeny8B45h2f28qgyMonX9VyNDTT2WDrawD8nyFl5TEXOaV11dnXvatGndOTk5FADOP//8rpdffjn/nXfeyd+0adOXAHDnnXeeuvDCCysBNC5fvnzYgQMHnE1NTfbjx4877rnnnqb77ruvRW8WtnZO9kMPPTSsuLg4tGfPHs9ll13WNmnSJN+aNWtKAoEAeeWVV76eMGFC4NixY7bvfve7pY2NjQ4AWL169eFLL71UN5+zfPnyYcePH7cfPnzYUVhYKLz++usHzb438ShUgfyzOM42ZxZ8Goey6Itqtnyj1n3CuvMlIYOrOWveBUp9ppi12BVE0y8/AwAU3lAF8gmPU65WAMCxEINw0isxa4MyuFYIp3IRlDJ1a9XiKTjZFUDtppWACDXwKPFMO8/aCDhZeE/P1K3UTFHC3uD6UBzMzOasz+RqcLEzALHVD/uMIbAVuQEbyeStM0iIqVOn+h5++OHhTU1NfFZWFnvvvffypkyZ0nPq1ClbaWlpCABKS0tDra2tamzbu3eve/v27Xu7urr4mpqa6quvvroD6D0LW4t9+/a5169ff2Dw4MFCaWnpJKfT2VJXV7f3kUceGbxq1arBzzzzzJE777xz5PLly5vnz5/fXV9f75g/f375gQMHdsda+86dOz3btm3bl52dndQXOt4gjzXyz58mc+DTEvGYtVu+d/Fa3UefQs5aNJ6zZiJD2/qvAACOsly4Jw4CPiE4mh2+iRVO+cHs5nLWFEQ2RZF+EwevFJjZkeOyYyfhQIjEuoFIBzN1bQZuWCQZnKk3BdbmrOU1pdxnHUMGV7YxmbMmatvZmSeDK9XfztJcEJ7APiQLgQNWOwRmkE7EY8DpwrRp0/zLli1r+uY3v1nh8XhodXW112aLn7ZbuHBhe3Z2NsvOzhZmzZrVuWXLlqyCggIxeha2FpMmTepRgv+oUaMCCxcu7ACAKVOm+DZt2pQDAB9++GFufX29W9mnu7ubb2tr4woKCnS/sAsWLGhPNlADBhzM5IKypQDKtNszxv412ZMOWAQ6oVZoR1+4OQ6we4CQhXk1g8xaaGtD2/N/gLOqEv4R4Zw1owy8PTHDZJSh7ZV6+L9sA3HwGHzXFOn0ICBMU+jlF0BtxqrBw78DB2WQBwDYomR5nnARqfmwW5jx1i1p+/TbjSYvgydwMFMYNaVJMuvk1jWQobRt2Yqla13W9BK0v/o1fLtb4J5gfj57BmcP7r333pZ77723BQCWLl06fMSIEcGioiLh0KFD9tLS0tChQ4fshYWFgrJ9LM8I7SzsaDidTvXLzHEcXC4XU/4tiqLsccTw2Wef7TUagLOyslL6JhvREl8D0ATgtwCe0Px35sHfCdjkOc96uUW7W2rvsgzGLtwnfvUrtKxZg8Z7loE7JsnwDt5huMCs480D8H7WDADImqGxw2WAzx6++WAiM10NzpgUdBmT2LMtSp7mwAFEqgYHNDI4zOWsCcgAHpEZeZxe0PRZkyT6rGMnw08/dH5wBP797Qid9II4eXDZ0mfNM70EnMcG744TCDX14ORvd0LsSa4QJ4MzG42NjTYAqK+vd7z55pv5t912W+v8+fPb165dWwQAa9euLVqwYIGa03z77bfzvV4vaWpq4j/++OOcOXPmWJJvmTNnTufKlSsHK4+3bt3qjrd9qjDiDR5kjK1O5yIGDAJysBb8MYJ1lrXMGoARGbzno4/gLC9HoL4eWfuOADmAjbNJMniCAjOxJ4TurcfUx3kLR0ecOsSHVSAaEk31WVMQMCYXmDEWIYOr2xFOJpNy7lgU5edN5qz7gFknb4qS2MFMPXQS3uBnSoGZ0OpH57sNAABnRQFsg9zq34dz8HBVFcL/ZSs6Nx5B4OsO+Ha1IPvcoXGOmMHZiCuvvHJse3u7zWazsccee+xwcXGx+NBDDx1ftGjR2NLS0kHDhg0Lbtiw4Wtl+5qamp6LLrqo/NixY44f/vCHx8vKykK7du1yxTuHETz99NNHlixZMqqioqJaFEVy7rnnds2ePTttg0CMBOvfEEL+E8C7AALKk4yxnelaVL8h0AXwiZh138rg1OuFcOw48pcuRaixEVn1x4BpAE94Q8zav7cVYMCg2ybCMTwbxKb5vSigvVkI+UVQGoKNz0q8dKlpS87HkpgyOAcCSqAy67y6L4CcIaZz1py81HQ4mFnnDR7PHFzeyMwp1EEeZ0awDh4JGwqJ7X7YB0cWEjtH58G74wQC+yVSJLb6kUEG0di+fXuv2dJDhgwRP/roo6/0ti8vL/e/+OKLh7TPRc/CBmLPydbOsta+NnToUOHNN988YGTNq1evPpZ4q/gwEqwrACwBsBBhFZMB+EaqJx9w8LXFl8EdHiBoJbNO7GAWbGgAADjHjYNr4kRk728AmUbAc7xkN5ogWHt3NIMvcsE5Lr937oZGnn//nlZwgw1Wg6t91uGcNQPpHawJF9G6dc4D9wJPvmjKG1w6DgFFeqZupSqDE8Q/B2MsHKtNMGsuob5+ekHsCsvawgkfXBWFEa8rLVy0W9ou1Jzpu84gAwVGgvViAGWMsUDCLU93dJ+QAjKgr4laLYPHCQ6MMXg/+RTBw9INoWPMaLjGVyHrpR3gZStURmPL4IGGDrT8fhdYiCJn7sg4wY0hZ0w2ug50Y/vXnTjnG8Zy1gDAiCKDh5m1PSoYESgFZtGFZ0T9/Y1Xgw/MAjPtVDBdaOsVzdwQ8ImHkJxOoFE5aD7fGfHYVhSZ8suYpGSQKqxgtGaQzjnZRoL1TgA50EjgZyx6TgKFoxFTq3R4rO2zBiJkcOHkSbRv2IDCG2+Eb9cuHP7Od8KnLisDn58PW0CAk0oXuVhTt8TuoGR8Io8ddJTlxj4/YSg5rwRdByQHWaM5a9m7TG7dUpg1YLf1LjAjmgKz8PNSsDeKZHPWhqZuyS+l2mcdS66OOHUygzwGOLP+W8PfcN+m+/DmojcxKndUzO2ig7UtKlhz7vDlyD21GL6dLWAG6jIyyGCgIJ1zso0E6yIA+wgh2xCZsz6zWrfEEOBvB2zu2BdUq1u3omTwU7/7HVr/3zpwbg/E1vDfu/DWW8E5HOCyJHen7JBk6RkrZ928ejuoV+1cAJ8by5FM2teRGw7OlIbAGSkwk4Oz9D+lGpyDLWo9saRlbZ47mWrw9OSsDR1S5xzSz9jzrFlSzFqpHB/owfrHW34MAPjjvj/ixzN/HHM72hOCrcgF4ZSUi45m1lo4R+fB9/lJiF0B2PJTrgXKIIPTHkaC9c/TvoqBAK/k5AW7K3awdmRZm7PWFJgFjx6Fr24XAKDr/b8DIoVrwgSM/st6dXMuS5LoPYJ8EdfJWYdafGqgdo7JQ+BAB/gc/WBN5JsFmyscnJkJBzPp/1xE4LVHVYNzhItyOwMUojRg7EZpqgVmCRzM5KFjAEwNXlG3HUAy+JHOI3hsx2O4Z9o9ICAYmjUUIpMq/Lcd3xZ3X7EnBC7XCc4ngHoF2Ab3dqoccv85AJMqxwFAbMsE6wwyAIwF660A/IwxRggZC6AS0lzrMwuKM5ktTrC2e4CQlZaIUrDs3vJPHLn99vBSPpKGahTeemvk1rJTj5OGmTXRBEfGGFpf2Ati5zBoySTYilwIHOyMGazBCAhhEdNAqcE+awCSDM405wfARwUjJWetlbw5OXKZCtby8QdigVk4psY+h3pkU6YoAy9nfdkrlwEA/nZIugQ8esGjoIyiLLcM+9v3Y/nG5Vg9V7/Tk3oF2IvdKPjeVECg4By9ByMpeWvldxba/HCOzkvHr5JBBqcVjGiJWwC4CSFDAWwCcDeAZ9K6qv6At0X6aXMmYNbW+xe3//nP6r8LbroJAMAXFKBQk7OW1iZFVTsLt/RomXVgfztCx3uQf+VYOEtzwWc74JmU2A2K10jKlBrMWTM5D014+SHTDaBKzjriOc1mhoNuWk1R5GMmOSJT9R+P3Witad0ycRKlwGyAeKLovYcv7HkBALBk0hIAwHuH3ou9f0AEcdlgH+SGfUj89kAlny22nfmlMhn0H3w+H5k9e3ZFVVVV9W9/+9uCa6+9tnT79u2WSjkNDQ32BQsWjEn1OEaYNccY8xJCbgXwv4yx/yaEfJ7qiQccFBmcd8ZmP45sQAxK+W3ehCVnLBACwQ90ffCB+lTBtYtRcMMNsBUVgs+LZBTELp3TxeRqcFkGF1r96Hi3Ab4vpIlgnprBMAJbSRbQ4o1glFQMmnAwI2BMZkeMqS1MWnAgver1uKgn+ttuNFVv8ERqNWPJMWt1nvUAYdadQalP2kZsEJiUatl1SkrdzB42G3dOvhNrd67FXe/dhSvHXonLxlwWsT8NiiAOg7UGdh58nhOh5h5pwAcB7CWJ+/8zyMAMtm7d6gmFQmTfvn17AOD222/XnYEsCAISeZDHQllZWeidd94x1I8dD0a+ORwhZAaAGwC8IT9nerD3gIfPYM4aAILdFp2UoPOQGwiFUPaX9Rjz9ltwlpfDOWZ0r0ANAMQmBVE7DTNrwhM0/3qHGqjdU4sjjU/igLNLHz6OAJOrpUElDMYdzBiI+l5JdWZ6gUhyMNNWg0cz62Rat9IxdSv51i2FWeu/HkmsTZxEvQkYGNT6hFfqhHj0gkfx5qI3cW3ltQAkN71B7kE4Z8g5AIAPj32IH235Ua/9WVAE5zR+6XCOyYNvZwuaH9uBk7+ts+A3yOB0x5dffukYM2bMhOuuu6503LhxE84///zy7u5usnv3bucFF1xQPmHChPHTp0+vrK2tdQmCgBEjRkyilKKlpYXnOG7622+/nQ0A06dPr/zggw883/3ud0fv27fPXVVVVb17927nzJkzKzdv3uwBAI/HU/ODH/xg2OTJk6vef//97OHDh09aunTp8KlTp1ZNnDhx/D//+U/PnDlzykeOHDnxF7/4RczplF9++aWjvLx8Qqq/u5FbheUAHgLwJmNsFyFkDCRp/MyCyqwdBoJ1T3gKVyogBN3HnHCMGwv3hMR/SyIHV4dGBodfAAuI6jZF11WZXIQkXw8fmoWde1oBiIaqwSFXgzMWzqvqsV3CCMCFp24BkXeIRoN1sqYoRs6Ras46fJw40Tq8GMPHU5WKfo7VlFE8uPVB1AyuAQCUZJVgVO4oTBs8DS9/+TIEKoAQgvGF42MegwkUEBmITp46FtwTiuCtlW4QFKOUDAYONmzYMPLEiROWzrMePHiw96qrroo7zevw4cOuP/zhDwdmz5596LLLLhuzbt26gueff37Q008/fWjSpEmBf/zjH1l33333qI8//vir0aNH+3fs2OGqr693VldXezdu3Jg9d+7cnqamJse8efO8a9asObRq1aqSDz74YH/0eXw+Hzdx4kTfY489pvZqjxw5Mvj555/vu+2220beeuutZdu2bdsnbzfhgQceOGnlexGNhMGaMfYPAP/QPD4A4HvpXFS/INApS+Bc7AuqM1ve1jpm7W+3I/u8Sca2lmVwJWfNRKbm9JwVBciZM9z0+QEGjgB2Jw9wUtA3VmCmVIPz6mO9t42A9Hp+3JEGAJOl1432S0OKeWZlcGXbvvAGjwWt26i5Pmt5/35u3fpn4z+xYf8GbNi/AQAw2COlWaaXTAcAXFNxDQAgzxlWgwpdke5kLCh7wpsJ1hMHoeiWCQjUt6H7w2OgPiGiFzuDsxPDhw8PzJ492wcANTU13oaGBmdtbW32t7/97bHKNsFgkADA7Nmzu95///2cgwcPOu+///7jv//974s3b97cPWXKlITFRzzP45ZbbomQxRcvXtwOAJMmTfL29PRwBQUFtKCggDqdTtrS0sIPGjRI1D9a6jAyInMcJHZdhsgRmZema1H9An8n4MqVqnliMusc6adFMrjQ7Yfo5+GsKDe0vVIN7hA5MMrAGEBP+WAb7EHxrRNNn7+hew94dxc4QuBw8iCclIc01rqlVHiHh03oB9DIqVsAUHos8sY5na1byrbp9AYPD8eKYYpCNUlrM61b6nr6N1g39TRFPB7sloJ1SVYJ3v/2+yhyFamvlXhK0OxtxiB3ZGEjDUrygBkZHADcVYWgXolVU28oE6wHEBIx4HTB4XCoXwie51lzc7MtJydHUPLOWsydO7d7zZo1xc3NzY7Vq1c3/s///M+Q999/P2fOnDld0dvqnIdG56m1ozK1WaQzKwAAIABJREFU6+A4DqFQKK3uPUZu89cD2Avg/wL4qea/MwuBLsCZE9kUGw2nHKwDnfqvm0SwWTqOc8zYBFtKIGo1OFEDg9jmh9tAxbce/KLUM84RAruDB1GYtSEZnEqDPJhSBBUjgLLezJrTBDXjOevkqsGNnCNlb/AEOWsguQIzBf09yEMbrAtdhbBriisHewaD58IB+JcX/hIAYI+64UuGWSvgXNLnnvqEBFtmcDYiNzeXjhgxIvjMM88UAFJ9ykcffeQGgLlz5/bs2LEjm+M45vF42IQJE7zr1q0rnjdvnlXyaJ/BSLCmjLHfMMa2Msa2Kf+lfWV9jUAn4EzArF2ybWcg4U2ZsVMel4K1Y0yZsR00BWaqkQeMV39H44LBi8CoHYQgklkbLDADAEY0MrjOTQ4BkZl1+DWbpmDKeM46shrcaIGZkXNY5w2e2G7UzCAP9WPYD8H6SNcRvPb1awCAZm+z+nyOoi7FQM3gGswbOQ8CjQysSl0FMcmsgbANKfVngnUG+njxxRcPPPvss4MqKyury8vLJ/zlL3/JBwC3282GDBkSPOecc3oA4IILLuju6enhZs6c6evL9ZHo/tUkYERTepUQcgeAVxBpN5qQXhJCFgD4NaTq8d8xxv47xnbXAPgzgBmMsc+MLNxy+DtkGZzFDtYKs/Zbxaw7QHgK+7BhhrZXc9aUgIlygJH7VpMBBx4Ak4xMOKIG65DfSNSSR2QmKDDTY9ZlRw6p/zbeZy3FrHQWmCXrDZ6QWTNtPt/MOfqvdevuv9+NQ52HMC5/XASz9tgS1xPZOFuvYE1lZs0ZbN3SgqjMOm3pwAxOE1RWVgbr6+t3K48ffvhh9U5yy5Yt9Xr7aEdq3nXXXa133XVXq/I43jhMZWSmgsbGRrUl4Z577jkF4JTea9E4ceKELS8vL+UPr5FgvUT+qZW+GYDYjv0ACCE8gCcAXALgKIBPCSGvMcb2RG2XA+AeAP3L1n1twODqBDlrucDMImOUwLF2OHJE4wHL3lsGt2Uln8MjhAMIBUcICEdAiPR5+uefD2L0fyTYWakG13Tx6VaDg4BwANWwzoXvvwVgleZQxqvB0yKDU9bLec0sJOfYOMw6qZy1Zv8+RntAmil97RtSe9YlpZfAY/Pg1km3xtsNgBysWQxmnYwM7paNdzLMOoPTDJs3b/bcfPPNYx566KGjqR7LSDX4yCSPPRPAfrl6HISQlwB8C0B0EcAjAH4B4IdJnsca+NqkdiwqJG7dssByNHjkCLxfNSF/TABGC4iUnLWNElCZWfOuVApuOBBCJYMTjsDOSxdURhMfk0CRtsNFUDFbtwCwGEHKsAyunCWJYM1xXEIZPMVYLd1MxAnWSdmNJpoQkkbwJDKo5jpy8bPZPzO0r52z95bBgynI4JmcdQanAT755BP3zTffPFr7nMPhoA0NDbusOL6hKz0hpApANQDVho0x9scEuw0HoK0WPArg3Kjj1gAYyRh7gxDSf8GaMTlY5wPdJ2MHa94BED5pZt21cSNanliDotuXoOnhR0AcPArLewxTp7AMzqntPKmMDyQyK2aEARwwyiVJzIwauKAq07bUavBYcYhE/Oj1qpmcddpat5KXwLXri3kKzQtmctaqKUofV4MzxtAZ7ESBswBtgTYMcg/CzRNuNry/jbMhRCP7oqnMrM1WgwMyGyeZnHUGAxszZ8706VWkW4WEVw5CyH8CeBrAUwAWAngMwDUGjq139VOvOkSa//c/AO4zsIY7CCGfEUI+O3kyDX3nwW6JUbsLZRk8ZmRJafLWif9eCX9dHRrvWQaxpQXDb58HR44Is8zaLmomRaVACTk10EpS/AinGWZNo2Rwpo50jIDMrLfMi/zI0B7phseUNzjMj8hUzpGodStVZk0IiTMiU/uRSkIG72Nm7RN8EKiA70z4Duq+U4cPFn+AMXnGrY1tpHfOOpUCM8IREJctw6wzOKth5Ip3LYB5AI4zxv4NwBQYY+RHAWgl9BEAjmke5wCYCGAjIaQBwHkAXiOEnBN9IMbY04yxcxhj5xQXx3R1Sx4+ue/dXYA4FFFCkpO3GGMInTihPh78w/uQPdFkhkFHBud48wU7YShWoRQgAOHDzDoh22Wy3WgCZq3K4FGvHb79jvChDMrg6SswYym7lxHEyVlH9Fmbu8kA+j5nrXiAa01OzEBbYBY64YXYE1KZNbEn51TMuW1g/kyBWQZnL4xcOXyMMRGAIBeDNQEwcpv9KYByQshoQogDwHUAXlNeZIx1MMYGMcbKGGNlAD4GcGW/VINrg3W8AjMAcHiSksGFkyfBvGFG7jlXkxEwKoPz0oWOY2G2ZUZV7XU8RQaHrCYQmblQG2r/djjB3lJ0ZgZz1n575JhO344d0utG+6zTLYMbPpo+OELi6iMpFIP3ebTuCHQAkPLUyUAbrJtXb8eJx3dIE7ccnDlvdA04F59h1hmc1TByqa8lhORDGov5GYBPAOxItBNjTACwFMC7kExV/sQY200IeZgQcmUKa7YeZoK1PQsImW/RCx5sAAAU3HA9cq+4Aq4JE6AtzjKC2pYvAAA2cGFmbXBohx6IyqxFgACMU2RwHh+98nX8neVqcGjmWeuzU+k5IUoBUAaVGC8wC8vgZvPLhph1ijo4R+I4mCXbZ21i3rdVaPG14HCXdKOW60wuWPOEB2UULCR9nsSOIFhQTEoCV8C5bJmcdQZnNeLK2US6WvyMMdYO4AlCyLsAchljCYM1ADDG3gLwVtRzK2JsO9fQitMBU8HaDYTM56yDB6UJaUVLloT7qk305ohUxJ3/uBt/AMDTcOtWskwFCAdrEIlZM5lZG64GBwGNyFnHZtbaG5JDZWNRPUp6Dwx7g6tvFTOVr1bOEW/qFmOpD/GIl7OWxoeqzdwmjinv3oeDPK545Qp0hyRzpzxHcjI4z/EQmKB6AQBSgRmXRNuWArEzCKHFByZSkJRSPxmc7njkkUcGr1u3rpgxhptvvvnkihUrTjQ3N/OLFi0a09jY6Bw+fHjg1VdfPVBcXGwqb1JTU1NVW1u7L13rThVxP/VMuqV/Q/N4v9FAfVpBDdb5BmXwZIL1QRCXC7YhQzTPGrtwH+k6gqnPT4UoL4tnJCyDp1ANrkw6pYxKfcKc8WCtm7PWu3FgimFIOOL4srJAO8MucIaZNYtjvhIHxnLWpg6pc44EDmbK8U0M8ugPGVwJ1EDyzJojnMSsNcGa+YWUmLVtsGTGogyuyeDsxKeffupat25d8Y4dO/bu3bt39zvvvJNfV1fnfPDBB4fOnTu369ChQ7vmzp3btWLFiiGJjyZBEKTr3kAO1IAxGfwTQsi0tK+kP+GTDCBUZh0viNo9ScnggYMH4SgriyGDxr4YH+g4gHs/uBcAQOVlcQwQRXkwQgosQ2XWECVmrQZrIxdVuRhNI+XrsVPFZI+RcLD2uz0Qu6VgbSpnLY/ITE+wTlUGJzFjKmNME6vN91nHHL2ZZhQ4kxsDq8rgYvhvLrT5kzJEUaBY6jJhYMz2zqB/UFdX5542bVp3Tk4OtdvtOP/887tefvnl/HfeeSf/zjvvPAUAd95556m33367AACWL18+7Kqrrhp93nnnVZSWlk5ctWrVIAB44403cs4999yKK664YnRlZeUEQJpfrbw2Y8aMyssuu2xMWVnZxO9973vDn3zyycJJkyaNr6ioqN69e7cTAI4dO2abP3/+2IkTJ46fOHHi+L/97W9ZsdZ94YUXjquqqqquqqqqzsnJmfqb3/ymKNa2sRCTQhFCbHLeeQ6A2wkhXwPogVr4ys6cAO5rk8Zj2t3x7UYBWQY3X2AWPNgA18SomdUJZHCf4MN1b1wHn+DDlWOvxGv7XwUgMWsqXwit6LOmcjU4I+GcdUIwgJFwnzWBvqStVoNrhjIHPFmgRw5Kr5swRUkfs069z1oqgIt9jvCITPMyeH8N3fLYkxtVrBiqiGJYhRRa/XAVuGLtkhDK5zwTrAcO9uz90cie7q8snWedlV3hrR6/MuY0r6lTp/oefvjh4U1NTXxWVhZ777338qZMmdJz6tQpW2lpaQgASktLQ62trWps27t3r3v79u17u7q6+Jqamuqrr766AwB27tyZVVtbu7uqqioYfZ59+/a5169ff2Dw4MFCaWnpJKfT2VJXV7f3kUceGbxq1arBzzzzzJE777xz5PLly5vnz5/fXV9f75g/f375gQMHdkcfCwA2bdq0HwC2bNniue2228puuOGGdrPvTTy98xMA0wBcZfagpx387XLbFgzkrD1AyG/q8DQQQKixEXlXXB71Svyr8V/r/wqf4MPDsx/GovJFqG+rByV14ClABStkcKWASQQ4Yk4GV7zBNTlrXWat428dcHtAuyW51XCftcys0xGsreizjsusqbYa3HyBWV/J4CExlHgjA1CmcImCpiBMYCnJ4Gqw7ucJZBn0L6ZNm+ZftmxZ0ze/+c0Kj8dDq6urvdFjLKOxcOHC9uzsbJadnS3MmjWrc8uWLVkFBQXi5MmTe/QCNQBMmjSpRwn+o0aNCixcuLADAKZMmeLbtGlTDgB8+OGHufX19epghu7ubr6trY0rKCjQvaM8fvy47ZZbbhn90ksvfV1UVGS6DzHeb0kAgDGWoCz4DIBiNQoYYNYe0wVmga++AiiFs7Iq8oU4F+MvW7/Eb2p/g5lDZuKqcdL9ko2zgRK5dUtMPVgTuZK7N7M2krNm8o/w+WMN8gAimbXf4wHt6QGTmZfRnLUoj8hMpsAsvjd46gVmXFxmrS0HN3Gevo3VCFLd65ZpcPL3RxAjq7dTkcGhpHuETLAeKIjHgNOJe++9t+Xee+9tAYClS5cOHzFiRLCoqEg4dOiQvbS0NHTo0CF7YWGh+uGLvi4pjz0eT0yZxul0qh80juMi5liLoqjERnz22Wd7s7OzE34oBUHA1VdfPeZHP/rRsRkzZphje8o64rxWTAhZHuu/ZE42YOFrl4rLgPgOZoAsg5vLWXdv3gwAcE2ojnpF/zyfHP8E17x+DQQq4Odzfq5+uHjCg3JyzlqWA1NrOVIunlRaiamctcSswzc2sXLWct4VDOPe/zseX7UGQY+U2qE9PabsRhmQxpy1qUPqnSW2g5m2jzuJnHVfBeto17FkocjgNCpYJ2M1qkBl1mJGBj/b0djYaAOA+vp6x5tvvpl/2223tc6fP7997dq1RQCwdu3aogULFqgy89tvv53v9XpJU1MT//HHH+fMmTPHkklMc+bM6Vy5cqU6n3jr1q0xxx9+//vfH1FdXe2944472pI9XzwKxQPIRup+EQMf/nYgT3YTSxisPYAYAKgIcIkvPjQQwKmn1iLr/PPhGDEixlbhq3GLrwUrtkrdbY+c/wiGZIWLGnmOl5g1DV+0rCgwE6MKzApKcnHqqIhj9XHSKkwa5KGMyCRIVDxFYR8+HCdO+DBcts+lXV3m+qxZemRwMYlj9lqfdK+t+xpjgDLO1pyZS3j/voA2WL90+UtJH0cJ1oIQxaxTCNbIyOAZyLjyyivHtre322w2G3vssccOFxcXiw899NDxRYsWjS0tLR00bNiw4IYNG1RFuKampueiiy4qP3bsmOOHP/zh8bKystCuXbuSL6CQ8fTTTx9ZsmTJqIqKimpRFMm5557bNXv2bF03qaeffrpk3Lhx/qqqqlwA+OlPf9p44403dpg5X7xgfZwx9rCZg5228HUAJROlfxvpswYkdu3MTnjo4IEDYKEQ8q+5uveLUVdjn+DDJesvgZ2zY93CdagZXBOxOQcOIoeInHUqBWbh+zDZFEXusz51LADAhldW7QDyY+yqyODaAjOd+7pwn7V0cyEyhq5CqRAy1NRkbp51kjnrRFO3GEvNCQ6Qp27FIn3acycxyKOvorXIpLTET8/7KSYUTUiwdWwoMri2wAwA+Dxn0sckGRk8Axna+dQKhgwZIn700Udf6W1fXl7uf/HFFw9pn4ueYw2E51fHm3GtfW3o0KHCm2++ecDImhlj241sFw/xrhxnPqNW4O8AXIoBhIFqcMBw3jqwX7rBc4wdq/NqZIHZc7ueg0AF3D/j/l6BGgACYgCUSO1Q6ojMVPRbJtuNMtk6VHYwm3t9+EJdPjgb18/UH13OIDFr1QJUZy2EavLikH5TX650ByC2t4fPnwBau1HLc9aMgU9jNXiEDG5qkEffyuAilf7+Ni6VsauaanAaGaxthRZUg8cxt8kggzMZ8b6VF/XZKvoTlAKBznCwNsOsDSDw9X6A5+EsK+v9oiZA/L7u91jzxRpML5mOb1d8W/dYO1t2SjlrClAh9dYt5V6NQlALzAh4lJ9Tgo0vSDeT7y2/UH9XRuVq8HCwjtdnrWXWgWxJkRA7Ok3bjaYnZ21FgVlsb3DGWPi2OImbq76yGxWYpKykHKw11eAEgH1kDkLHumEflliJiolIgSaDDAxh9erVxxJvZR1+/etfFz355JMl2udmzJjR/fzzzycatpAQMb+VjLHWVA9+WiDQCYClLVgHv/4ajtJSEIcj5jZHu4/hqS+ewiD3IDw0+6GY21UVVoGSXeAYU/usU8tZax3MpJw1IXbwdgPHVAMIiR+so7anAPzZOQCAljVrQBZdZarALF0OZinG6sR91kgiZ616rKa0NMNQmLDCjJOFts/aBiBvQRnsgz3gs+zJH1R5L06TnLUQCoG32UBFEYwx2Owp/O4ZnDZYtmzZqWXLlp1Kx7FTu4U+ExCQxgFCsVY00mcNGJfB6/fDWT4uxqsELTyHhW9dBwB46dKXUJpbGvNYM0pmgHK7JGatDvJIRQZXHLJEgJNy1gT2iHYwKtKYNwRKgVk8GZzRyNYtyoCQR3oPQ0ePmvIGZ2kyRWFWjMgkseVqRhEuvDc1yCO8vr6AkrPmDRROxkM4Zy3ABoDYOPA5sW9WjUD5bJ0OBWbHvtqL9f/3p3C43ehpl4p/y6ZOx7/ccz9cWSmoCxmc1cg44vvlYO1SgrXWyFkHJpg1DQYRPHwYjnH6wTrIKFYMkoqtvjflexibr5fXjjpmdJ91KpVRSs5aHpHJOBEcsUcEw6YDMQoWGTMlgzP5H5QxcDwPR1kZsubMkQ9lXAYfuH3W8c6h7bM2//fq62pwG0ntHl4N1jJTT2XYjIq+Lo1PEp++/le8+NP7EQr44e8O1y81fL4dX27d3I8ry+B0R4ZZ92LWBkxRAEPBuuOVDQClcJWX677+XGsttnjc+PcJt+GOqXcnPB4hBJQDeMrCOeuU/oJyzpqJcjm3ABL1kXhlVS2+/9Q3dfaVxlZQzf0e0XnfonPWVD6rfdgwU61bBBIrTzZnHW/qlhUyOBdn6pZUYCa/aMYTpa9lcGaNDK7kvFUHMyuCtSqDp36odODDP/0BTrcHH//lRXjy8jH/7mUYVjEePW1tKBw+As8suwMHP9+OKZdc1t9LzeA0RYZZ+2Xm6NLK4HEuLja5otWADN7+5z+Dy85G9ty5vV6rPVGL37R8jGl+P+4Yf5Ph5YpEGeQhV4OnkLNWxkuITNTkrKULbdmkBD7zqgIRDra6b1t0sJYrr/n8PIgdHSYHeaTPG9wKGTxeNbi6nRkZXNm/j6K1wqwtk8EVZp1SEaQE5T6wL2d7G8WJhgP4+C8vYdMfnkHQ58O1P1uJMTUz4MrKRtGIkSCEYFhFFb7+bBt+c8tiHN27q7+XnMFpiEywVmRwp9ECM5lZC4kd40JHjiD3isvBeSK97juDnbjzvTsBAP/Z0mZY2mNgoBxAxPBEo5Rm+yp2oyxsN8pBKoQpGp4gt6Yng+sEIqV1S9lGhESSuDwpWBsFB6LmrK2WwaVjmjpk73Mgzp9RM3XL3CCP/mHWqcrg4dYtC5m18l4MwJz19jc3qP8+71+vReGw4b22GVJeCQAI+rzY8dZrfba2MxHa6Vjz5s2LVRB0xiEjgysyuOFqcIVZx5fBxe5uiB0dcAzv/cVd9dkq+AQfflA8G+UHX4LRqzEHTuqzpgxKC6sV86wFJqg5a4VZ2xyJolfYwUxUZmvHa90iYWbNgYDPM8espWrwAT4iM948a3UxJu4KVDaZ/LrMQM1Zp9i6FfYGtzBnPQCrwf3d3Xjm3jvh6+xAzcIrcO5Vi+HJ03cRGlEV9i44WPsZAl4vnB5LB1ZlcIYjw6xNy+BygVkCZh1qbAQA2KOCdUegA28deAvXVFyD24pnyOc0fgGinBKs5datlKrBNXIlgSSDy/dvhUMTM2tAcjATlLXoLIUoErnGFIUjAJ+XL1V2UXpGjMiM62CGsN2oqZy1/PN0qwZXgj1V7iitkMEHWH3ZoZ2f44nbroOvswM2hxPnXrUYWfkFMT9HxaWj8Z1fPYFrH1oJIRTElx9lis2sQFdXF3/JJZeMHTt27IQbbrhhlOKat379+tzq6urxlZWV1bNmzarQ21cURZSWlk48duyYTXk8atSoicePHx+QJHZALqpPEeiUZlnbZCtEi/qs1WAd5Qd+36b7EBADuL7qemD/FlNLZWAqsxbV1q1U7rfkYM1kb3CNDD6mpjh8Xr0AKcvgQLh4SzcfSxU7UlkGZ5JBKZ8nKRnEoCMVISRtIzKtGOQRN2etYYOmctZ9LYNb1GcdaTfKnZHMetuGPwEAplxyGS5e8j1D+wwaWQrGGAaNLMWuD97D5IsWpHOJaccP9h4eua/Hb6k8UJXl8j42fpThaV51dXVZtbW1uyoqKoLf+MY3ytetW1cwf/78rqVLl5Zt3LhxX1VVVbC5uVn3A83zPK655ppTv/vd7wpXrFhx4tVXX80dP368b+jQodZMtLEYGWbt7wyzagAJ7UaVArNEzProUQCRzPqdhnew7fg2zC+bj4oC7c2esQsQAdHI4BY4mFFtgRlUU5Ro7P/sRK/nGJPtRkHiM2vVDISCUgbKoBaYAQCTTSMSQcusB6bdaBwHM2irwc1T674KT2rO2iIZXLSQWWMA9VmHAn4c3bMLM6/6tuFArYAQgopZc3B8/1fwdpqa45CBDiZNmtRTXV0dtNlsWLx4ceuWLVuyN27cmDVz5swuZVZ1SUlJzNnRd999d8tLL71UBADPPPPMoFtuuaWlr9ZuFhlmHeELjsTMmuMkJp6gGjzU2AjidoMvkOZkM8bwx71/BCBN0wKQlLZHOUlaZlTitan0WTNoPJyJZIrCofdN6HvP7kH5jJKonSkYARjjIKg5a50CMyW3DgqRSdYohGiYtcFgrVSDpyVnbdE865jnkO5s5MWYZ9Z9nbNOuXVLrnsQ5RGZVjDrcBtb/wfrk4cawBjFkHG66mpClE2Zhq1/egGH6j7H+PNj2PmeBjDDgNMFvVnVZtS3cePGhQYNGiS89tprObW1tVkbNmwwNJijP5Bh1v6OcI81kDhYA1KRWSg+sw4eOQrHiBHqh2ZL4xbUnqjFD8/5IVwKO48a5JEIigwOkYKKcoVxChdCxsLMGoQAnAiCMLM+57IyaTs9NiO3blFwEBVmrTfIQ86Lc4RCpCyiwEw6uVFmLRVwpWPqlnV2o/qvafusTVfvM9pn1DpdrVuWMGu1Gjz1Q6WKEw3S9bykLLGJkR5KxoyDKzsHh77YYeWyzkrU1dVl7du3zyGKItavX194wQUXdM2bN69n27ZtOfv27XMAQCwZXMGtt956csmSJaOvvPLKVptt4PLXTLD2d0TK4IlMUQCpyExIkLM+chj2UdK0KsooHt/xOIZmDcU1FdeENzLJrAmIpsBMzhin8hdk2px12G5Uwbjpg2PtCenWgQDgIDBFBte5KFOlwEwK1BTStVsbrI0gVbvReKYozKpBHjH7rBnUiGsyEMZtCbMYlrVucZFTt8zk6WNCqYzvZxn8RMMBvP/7NXC4PcgZVJx4Bx1wHI/SSVPRsLN2QPaNn06YOnVq93333TeioqJiwqhRowL/9m//1j5s2DDh8ccfb1i0aNG4ysrK6kWLFo2Jd4zrr7++w+v18nfccUdaPL2twsC9jegrBDqBPE3FNjNw656AWQttbQjU70fW+ZKd5rsN7+LLti/x6AWPIsuepdnSXICQ+qwJCKWqDJ4Ks6YysxaopnVLI4MXDsuKtWu4Gpxx4fx5nNYtQlgEs+aUYC0IxnPWkGRws3e/RnLW9hSlWhLHwUyK00qwNnke0vemKJblrJUbMStkcEL69M7F29mB7tZTyBs8RG2xaty3B5tfeBYAVLOTZFE2ZRq+/GgLWhuPoGiE/gjaDPQRa+60FosXL+5cvHjxHiPH27Ztm7uystJbU1OT2DyjH5EJ1oEuwJkTfmwBsz7xi18CALIumAORilj12SqUF5RjYdnCGHuYyFmTMLPmCMwVLPU6bdgUhcimKFpmrb0YtR7vQeHQcPBmlILKpighOVjruanJ7dVSzpoyiEw2RXE4QDweQDCYswYBZcnL4InsRlN2MEMCBzOlhY03y6yZOnAl3bCsdUvJWTO1YMEaKIULacbJQwfxx/9zH4RQEEUjRuHmX/4GDV/swCv//RAAwJWVjX+55/6UzlFcOhoA0Np4NBOs+xE/+clPhjz33HPFzz777MH+XksiZIK1vzPsXgZYkrP2fvopsi+6CNnnn493Dr6DZm8zltYs7X0RTKbAjACgVCODp8CsaTSzFsDF+Eh88vpBLLhjovqYMQpGuIhqcN2LvFp8JjNrhN28+Lw8MKPMOgW7UZ7nEwRrK/qs47yodTBLQhLuK2ZtdesWpSLAkZTf2/CB+6bn/J8vPw/GKIpHleHk4Qb89nvfRXdbeGLwJXcsRd7gISmdI3/IMABAW1Ofjls+axFvzvR//dd/NfXXuszg7A7WVARCPVHM2kCwtrljtm7RYBChxkbkfetbAIDPT34OALi09FKdrc0VmAEaUxRFBk+FWLPwPOtoU5RofL0jsn0rzKx5hESFkcWWwTkiVYOLDLDJF2++IB8QQobWqiigybRuJWLWzII+a2mQRzwHszi96HEgMevU1mYUVrduCVQZEk2gAAAgAElEQVS0xBdcAYlXxWcBGGNo+vorHNj+CeZcdzPOueJf8be1j2PP5n8AAKb/y1UgHIex55yX8rmcHg/sLje8HW0pHyuDxEjnnOm+wtkdrANyusNssLa7AZ/+lyx0+DDAGBxlpRCogLcPvo1pg6fBY9fxDjDJOBhjKrNmVlSDK8xathsFEWMG6977UjAimaIouUleT+KVz0HkanCBMTVY2/ILjOesZVOUZFq3EsvgFg3yiHEKhrCTm9kCM2X/voBlrVuKg5koWOMLroBLX7DetfHv+OC5teB4G2xOJ2oWXgHeZsPC7y/HN278Ljx5+dYpBDLcObnwdnZaeswMzlyc3dXgSrB2mWzdcmTF7LMOHjokbVJWhk1HNqHV34rrqq6Lf7xk7EYpBUdSlMGlEnCIVGOKgkhTlKrz9OU+Rqk8xINDSFBkcB1mLUbJ4BoDEr6wEAiGDBeYJVsNbixnbeqQvUDieINr/75mW7cIWJ9Xg1vWusWopcxa+pBadzgtat9+HUGfD/7uLpROmgqHy62+Fs9GNBV4cnPh68oE6wyMIROsAR1mneCL6cgCgjGCdUMDAMA+ahSe/OJJFLoKcfGoi2McyLwMLnKQ+qwtqQaX1hC2GxUiqsEB4NxvhbsetEFVCtYk0htcJxCp4zPBQCkgsHDbLV9YABYKpj1nzXFcuDJZByI1f0zd9cWL1SqzHrhfObUa3KKpW0rO2ioQLj2tWyG/HycPh+uLyiZPs/wcenDn5MKXcTHLwCDSeuUghCwghHxJCNlPCPmxzuvLCSF7CCE7CSHvE0JK07meXtAL1onsRgGZWffovhRsaABfVITdwQZ82fYl7ppyF+x8bwtPAKYLzMLe4BRymjmlnDWlDAAf7rPmRBAWudbsApf670O7wikfRkVQotiNyrlOvWpwOUYSQiFQGimDFxYmVQ2eKGcdogwdobC9b+KcdeoxhSBOexiDOsjEfM667/usrWLWAhWs8QUPHzgtMnjzgf1glGLuzUtQOfsbqJg1x/Jz6MGdm5dh1hkYRtqCNSGEB/AEgIUAqgFcTwipjtqsFsA5jLHJANYD+EW61qMLNVibNEWxe2Iy68DBg3CUlWHz0c3gCIf5ZfPjHMikg5mas5YKzFKVwUXKQMBBpCIYRICwmNXgANDTHgivRZbBwThj1eBgECgFA8IyeH6BJPOGEheZmbEbHbnpC1T+cxdOBKTjGpHB9SR8M+C42EGVSfq99MBk65ZUoddHrVsWVYOrOWtGLc9Zp4NZd5xsBgCMnX4uLl/2ADy5eQn2sAZSzjrDrPsD//7v/z58yJAhk5XZ2PHwi1/8ovh///d/i/piXfGQTmY9E8B+xtgBxlgQwEsAvqXdgDH2AWNMiXofAxiBvkRA/qIkI4MLvl4VRaHmZvg+/wKuSRPx1oG3cO6Qc1HoKox9HLPSK1Fy1oopCkmpz5oyBjAOIhNBIfs4s9gXarsz/BqjFJRE2o3yulO3wjnrkPx22TQyOBjAhMRDbjhItzTxZPCf7W/ErI/DPgiTt+7GVTvqDQVrS0ZkxqPAScrg/cKsrZq6xdJQDZ6G98Lb0Q4A8OTrz6JOF1xZ2RACAYgGOyIysA5XXXVV+7Zt2/Ya2faBBx44uXTp0n6vJE9nsB4OQGv0flR+LhZuA/B2GtfTG7rM2mA1ONCryKzzjTcAQUDDReNxtPsovjXuWzo768CoDM403uCW5KwlZi1QAWDKBaO3ZL/wrkkAgPeeCQdCxhRmTSAojEyveEqJkYQhoFSNa2RwAgZqJFjLLmSxgrXIGJ46chIHfcGI5z/u6AExJIOnz8FMYoNy7n4A91kLVABHOAtuXDTe4JYya6RFBu/paIfN4YTd6Uq8sYVwZUskwd/d3afnPd1x//33Dx09evSE2bNnl19xxRWjV6xYUTJz5szKW2+9dWRNTU1VeXn5hA8++MADAB0dHdw111xTVlFRUV1RUVH93HPP5QPARRdd1FNaWmroLmn58uXDVqxYUZJ4y/Qina1bet9S3W8aIeQmAOcA0B1BQwi5A8AdADBqlIVuP345XxRdDZ5IdlTasEI+wJkdPtzefbANG4qt5GvYOBsuLo1VWKYgGbtRqDK4ZIpi6hAREOWcNWUUlEgBk2O9PxLFo8LKQ8AnwOm2gYpUzlmHmbVezhpqNThFSL7QRlSDm2DWigwenbO+Z+8hNAdiH4OoJh299wUsqgZHbMOOiNatAexgRhlNmVUDkQ5mllaDc/FtY5OFr6M9La1ZieDMlq4d/p5uZOUX9Om5rcD9678Y+VVTl6XzrCuG5Hh/ec2UmNO8Nm/e7Hn99dcL6urq9oRCITJ16tTqmpoaLwB4vV6utrZ239tvv519xx13jK6vr9/94x//eGhubq741Vdf7QGAkydPpv4B7yekk1kfBTBS83gEgF52PYSQiwH8HwBXMsYC0a8DAGPsacbYOYyxc4qLkzPP10WgEwABtH7dRnPWQK8is8BXX4GMKcPrB17HjJIZcPLO+MdJ0sGMyA5mqdqNigxSzpqJYDKz1uuzdmeH2Xbt36TWtHA1OIFA48ngypAPhoAcuFUZvEDOWRsY5sHJzDWaWX/R5cWfmtqwqS3SIvgfMyo1O4eDtR6s6LPm4ii0EXajpmdxQ+P5ll6ITEzZEAUIj22llAJmp4zFQbpMUbydHfDk9U2eWgtXlhysM8zaMDZu3Ji9cOHC9uzsbFZQUEAvueSSduW1G264oRUAFi5c2N3d3c21tLTwmzdvzr333ntVR6fi4mJjk4MGINLJrD8FUE4IGQ2gEcB1AG7QbkAIqQGwFsACxtiJ3odIMwJdkgSuvYCaksHD/uA0EEDgwAF8MZahM9CJu6febWAByZuiUJFJd1opjchkIESSwSlTcta9PxI2R/hmVJQTz1LOmpOYtdJnrcuspR8EFEE1cMvMWr5AUgPBmgCgOiMyN7eGg/S8whx8ID+uznbjwoIcbGrrQisvFzzFDNapj8iMm7NmTB2RaT5nzfosWCsyeKpQ2LnI0iCDpyEj0NPRjpzCvq8fUoJ1oOf0DNbxGHC6EE9ZSXW29UBH2pg1Y0wAsBTAuwD2AvgTY2w3IeRhQsiV8ma/BJAN4M+EkM8JIa+laz26CHRFSuCAcVMUIKIiPPBVPSAI2Ow5gkXli1AzOGGRofakBreSZXBRcgPjYE01OGUUTCkwS3D/9vnfj8i5Y5lZszCzPlF7qle1LhHDzDqkMmvZ1YzjwNntYILBYB1lN3o8EMTPDxyX/j13ClZVjozYpyZXUkB6SHxmbU3OOo6DGYM6zW0gM2urZPCIAjMrgzVJTzW4IoP3NZxZYRk8A2OYO3du97vvvpvn9XpJR0cH9/e//139w7344osFAPDuu+9m5+TkiEVFReLcuXM7V69erc76zcjgMcAYe4sxVsEYG8sY+7n83ArG2Gvyvy9mjJUwxqbK/10Z/4gWw98R1WONpAvMfDu/AADsGxzCrGGzjJ3fbJ81U3LWGhk8xZw1AS+1bjFjwRqQWDUA1RRFyVk317Whsb49cls5QBPC1Jy1TRMYid0OJhopMJNuaZTWre/UHUDN1nDBGyEEw1yOiH1m5UsXQ5ogWFvnYBYDWlOUJHLWp5sMruasLTdFsV4GZ4zJMnjfB2tXdkYGN4sLL7zQu2DBgo7q6uoJl1122djJkyf35OXliQBQUFAg1tTUVC1durR07dq1DQDw6KOPHm9vb+fLy8snVFZWVr/11ls5AHDXXXeNKCkpmez3+7mSkpLJy5cvH9aPv5YhZLzBewVrMznrsAzu274d3flOiCW5mDPcrKmCeVMUKjPrVPRbJuesBSaAQaqi1pPBo6EEPSpP3VL6c8F0CoCEMLMOy+Dhlzm7Qw3+8cDJAUtps3q3JbGZhFs+UTB6vnL072PBiEyOxCkwY0zjDT5wmbVVMrg6dctqu9E0BGt/TzeoKMKT2w/B+jSXwfsLDz74YNPq1auPdXV1cbNmzap84IEHml9++eWia6+9tu2JJ55o1G6bl5dH//rXvzZEH+Opp546+tRTTx01cr7Vq1cPiNFomWDtieqDNtJnHcWsGWPo/PQTfDE0iOvGX5+4sExBssxapBBFC0xR5Jy1xKylQBYrWJdNKkJDndRqWLfxsLQelVnHWb8aiBlC8r+1zJpzOAwWmMmHYwxtUeYrD47Vvyl2yTn0kHy+eAVmqea1FIc1XaQ4det0lsFheTW4dYcDND3W/VBgxvE8HG53RgY3iZtuuqm0vr7eHQgEyHXXXXdqzpw5+g5VZxgywTo/uhXMPLMWjh8HTp5C/TQ7flR5rYkFJNG6pXUwS9EURZHBKaOgSjV4DFMU3h5+Tz5c/5W0HiLlrBUZnOj9PkK4dSso6sjgTgdYj751qxYKsxYZw1ZbuHr/lZpxOC8vS3cfN6cE6wQyOLVgRGYiBzOapAx+GlaDE0LAES4NpiiwnFkHvdJ13pml/xlKN5xZ2fB3dyXeMAMVr7/++sHo5z755JMvUz3uj370oyGvvvpqBHv71re+1bpy5coBMe86E6x1C8yM9llLQabnwH4AQMnEc+I7lsWEsQuQ1A8NgMoFZinmrCVTFB4CE0CZIoPr+5jPuHw0vt5xUl6utF6qtm4pzFgnXMsBUpuz1i6ZczjAurrABAHEFvvjqPxJKGUYLZeYj3Y71Ly0Howz69TtRkmManCpGA9qNbhZBk9w+snggFQRng67UauDdSggzaV3ON0JtkwPXFnZGWY9QLBy5cqmgRKY9TBwRwD1BZTWLS0MmaLIX2y5Gnz7jrcAALOmLzJ3fuXCyBLnbBVQTjLYoKIoyeAp2o2q3uBqsHbobls0TBsU5QIzQsCYxh1Mz7xDlIaOcDEKzDinE4wQiO3tvffVQNlDZFQtGFs7oSzuPi45UASQKFhbUA0OfWatVC8TUPO+4JBuck43GRyQgrXlpijEelOUoF8K1nZX37qXKXBlZWdy1hkYwtkbrKkoMePoAjMqAommDimtWyEfKKOo2/keRJ5g5uQF5tZgMlgzxiDKAYiJNGXSIlKociWlUrDmYjDrqJVI/5enbiXMWTMiM2udnLVTyu8Lra1xz2iX9xEYEJTlbU8Cww2PIoMnDNapBwAuRjW48t5wTEzKalT6tU8vGRyQP1ewllkTxcbOQoT8Uiqr34J1dk6mGjwDQzh7ZXA9X3BAlsETBGuOB3gnEOrBCe8J2Dt9oPk54G1GAp32OPLbT42Z6lDQ8HWbUnApXgip0rrFRFAqmcdxMZg1AEy5aCS+eP8ItFdMxogaBPVy1oxSVQb2q0M9wq+rzDpBsLbJO1FC1By0Ryf4fXzeeHTJfduKDB4kBC7EDtYiZerxk4Wk0PYO11RpXWMUiCPzx8LpWA0OSBPYqNV91hwxpUIBAHxtwMdPAnXrgWAPkDUIWLQWGDIRgDTLGui/YO3MMOsMDOLsZdYBufXHGZXzZKIxw227Gwh6caTrCDwBgMtOokBFYfAGg7VaDS7tlDJpkVqWpD5rJVjHylkDwNgaxepVy6w51RQFegVvoggwgONoL1MUIMysEwVrhVlTwqmtWG4dZl3mdmJSjlRTwBMCByEIJmDWImXgU5RrY+WsqVJ8R+Pn5GMeFwCzKIAmgvUyOAWx0G7UsCkKY8D254BfVQIry4DNvwTyhgPjLgZO7QdevhFo2gVAk7N29VPOOjs7w6wzMISzOFjHmGUNJJbBAUkKD4WDtT2ZPk3lwsiMM2sqxxTCaMp5VlHJWWtk8Fg5ayBcEc6YxhRFw6z1dGAmy+AAQ4j1lsF5lwuMEAinEgRr+c5E5DgE5f0TyeAA4OINBmsLHMz01HSVWScbrPswZy0wwVoZnImWXmFIPAN2Bc17gJduAF5fBnQ3AVnFwHfeAL7zOnDVE8CNfwY6GoH/dzng70RQkcH7eOKWAldWNoRQEEIwmHjjDAAANTU1Vek69gsvvJD3k5/8ZAggzbGuqKiorqqqqp4+fXrl9u3b++dDIuPslcH1Jm4pDNcQs/YAwR4c7jyMsUHAlZdEFbgi4xpk1iIVVTZNGLNEBufAQaBBjQwem1nnlygDdrRV3eHWLd3cqijKdp7hQR4Ozbp5txuU4yC0xh8XG2bWkgzOAXAYCLBujoMyHSZmsLagGlwa4dn7eSVYc0kGa45ICkZfQPp8WVgNDquZNfSrwakIiCHA1wo8u0ByJpx6I3DFr6VUk/b9G/0N4JY3gWcuBV66ARy+Cd5uB5dE8Z8VCI/J7EJ2P/iTn46ora3dF/2cIAiwJfH9isaNN97YAaADAJYsWXLqgQceOAlIQfwHP/jByC1bttSnfJIkcRYza0UG15ghMBPB2uEBBD+O9RxDTpAHn52TeJ9oKCzGKLNmVJXBCaNmzbB6QZRlcMqoIRnc4VK+DNJ6K0IuUBCI8XLWjKkFZgF5O22Q5XkejOchNMef46KVwQOEwMMbm7vs4jgE5eu7noMZpVJrVcqtW9DPWStDTggVAXuyOWvOkMtbqrBUBufk1i2bhTcanI4MLgSApy4A/nsk8Oup0uNb3gSuWgPwdv02zJEzgdEXAg1bMPLkG7D3kwQORAbrDIzB4/HUAMAbb7yRc+6551ZcccUVoysrKycAwMUXXzx2woQJ48eNGzfhV7/61SBln/Xr1+dWV1ePr6ysrJ41a1ZFrGM//vjjRTfffPMoACgsLFS/dN3d3Xx/DwTJMOtes6xhnFmHvPAJPngCAJ8Tu983JpQLI03sjQ3IjlDq54WmLN1Sil4FZonsRsfWFKP+UymwloZcOMIi7UZ7L1rKWRPCEGQ6zJrnQXkeoSPxB/go0jnlOARBDEnggJTXDsg3F3rMWmSRM7aTRSJmTUQBxGwBImTpl3CAIACO2CkKKyBQAbyRFJABqK1bqd5RatDLGzzQDXzxInBiN1B1OeDKByZcBZQlsPslBPi3V4CXb0JB/T/hcJVZtkazUPzBfadjsN7w/ZE4scfSedYYXO3FVU8Ynua1c+fOrNra2t1VVVVBAHjhhRcaSkpKxO7ublJTU1N90003tVFKydKlS8s2bty4r6qqKtjc3Gz4Q/7oo48Wr1mzpiQUCnHvvfdeysYrqeDsZdZ+ua/XpWHWStAxcsGyu4GQD37BD2eAgkuKWZsrMBOoADGCWacYrBkDp4zIpEEQagfRC7gafOP6SoTnXvJgIGpA0uPWjIoAI+DAEJDlcofmAs5xHCghCZm1Q1MNHiBEdSdLBC2z1g3W8sU/1QIzjktQDU5DyeWsOSIV8oVCKa3PCEQmWsas1dYtq73Blbe4cTvw6HDgrR8Co2YB1/5BykmXX2LwWDwwfDrctBNuZ/9xFneORBYyzDo5TJ48uUcJ1ACwcuXKksrKyurp06ePb2pqsu/evdu1cePGrJkzZ3Yp25WUlBieaf0f//EfJ48cObLrZz/72dEHH3xwaDp+B6M4e5m1Vy5ocheEn1OZtZFg7QG6TyAY8MIRpOBSYdYG21FEJqrStxXBWnJBC8vghNoTmk54ch0a2Z6HrYeHKIrgAf1AL1IwRsBxNDazJgTCifjB2qaRwc0waxdH4JeFi7jBOmWJi8RIp8oyuJhcsOY4qeK+L4I1ZdRSZi3lrK21G2WUSQVir9wln8gJLFyZnO1ufikAIM9lTNlKB1Rm3XUaBmsTDDhd8Hg86pf6jTfeyNm0aVPOZ599ti8nJ4fOnDmz0ufzcVbMtL799ttb77///mhv6j7F2cusfa1SJTivkSbN5KztbiDkBeuRXMz47CSCNWdSBqdiZDV4in89ybkrLIMTajPkfMqUoRTgkN3sgqiuX2dBlAKUgOdEBOVops1ZK7OphZ4e0Dge4XaNDB5gRLdtSw9unkMgHrNWZHAL+qz1bnTUAjMxyWpwngMjfROsBSpYx6zBQSQWF5hxRPo8/f5SoPUAcNNfgP84CgydktzxsqSUZrYz/fUAseDOzjBrq9De3s7n5eWJOTk5tLa21vXFF19kAcC8efN6tm3blrNv3z4HABiVwevq6tSJTC+//HJeaWlpIN726cbZzay1rBow2bqVLeW9e6TWj76SwW0RwdoCGRw8AlRQmTXtMRIUwjJ4wVEXenLawBiJbYpCCXieqiMytTI4L1fhMkIQOnECztGjdc+omJaIhCAAIN/gnYqb4xBQvMz1grVoVbDWdzBTmDXEUHIFZnLO+nSTwXnCgRKLZXBCgFAA8B+VZO9xF6d2PDlYZ9kNq6KWw+Z0grfZMv7gFuDqq6/uePrpp4srKiqqx44d658yZUoPAAwbNkx4/PHHGxYtWjSOUoqioqLQ1q1bE1Z1r169evCWLVtybTYby8vLE5577rleA0T6EmdvsO45KfVgamGmdctTCPjaQLolZp2aDG6wdYuJ4WpwpN5uJMngUj+sSAMgoh2+upaE+5VNLED9xwAgrT//qBc+t3IXEX0SERA58BxFgPaWwRVmTTkOQnPsYO3QyOB+6Bui6MHNc/DHC9bya+lyMBPVArMQiN18gRnXhzlrK6vBOaRBBucIWMgPOD3AOIO56XiQv/9uPv3vbSwQQiTL0a7E89kzkOD1emsB4PLLL++6/PLLVUnC7XazzZs36wbhxYsXdy5evHhPomPfc889pwCcAoBnn32232V+Lc5eGbynpXewNiODuwsBJiKr2woZ3CCzZoIqg8OyAjMpZ81oEBw1FkxGjpeL8uT3iYGCxPgoMUoBKgXrkI4MrjBrKVjHHnij2o3KMriZnHVAjtHxctapvpeEEFCdpLVaYCaEkqsG57k+y1mHaAh2zvwa9cATHtRqGZwAEELAmHmA3QJ/Co/U1+zm+1XdzPiDZ2AIZy+z9rYAw6JyXUqhlxEZ3COZoOR2S3aFycng5rzBBSpE9FmnWsEsOXfZEBJD4Zy1kf0EJXDwymJAWIyLshysOV4pMCO6zJoRgmBjY8xzavus/cyYexlggFlbVGCW0MFMDIIkUXVMeCLlrIX0F0GFxBDsvEXBGhxEiNbK4IE2qdCiYr41x+Pt8Is2uEj/B2tfd4ZZ9yV+/etfFz355JMl2udmzJjR/fzzzx/urzUlwtkZrBmTmLVnUOTzZmRwtxysfSEAXJJ91srULeMOZtTCnHVQoHBwDgSCAYjUDyJKfbyJqifDBh1KoKWAEqyZzraUgOMpQozBRkiETarCrLniYoTiBOtwNTiBH8xU65ZfXm/cYJ3ie2nnOXWqmBZUNkVBKJikDN53BWZWMmuOcKAkZK0M3nEIDNlA+aWWHI8xBq9gh4v5LDlesnBl56DjxIAdo3xGYtmyZaeWLVsW3zZxgOHslMH9HQANqQUmKsy0bsnMusAvy6g5fVBgxoQoB7PkL4QiZRAog4N3IEiDEIUecKIsLSaoCHfI+Wmivk9ULS7rtasoApQHz4kIMRZRXAaEmTU3dChCjcdinjPCwYyaYNYchxCT5oTpOZhZFawdPIegoBOsFWYd8IO4nL1eTwTCc9IAiz4I1kEatE4Gl3PWsFIG7zoOEDuQa027qxAMwCfa4WBeS46XLNw5OZlq8AwS4uwM1l75hiqaWZvNWQPIVYJ1MjlrkwVmvUxRUmAtSmBx8lKAFkQvOMFYMBFVSVap5GYaZh0ZrpWcNeEkB7PosZYqsy4picuslUEeAschCBhn1nKwEDk+ra1bDhsHygBBjDyHWg0e8IJLwtaSU3LWwT5g1mIIDt4alzSOEal1y2HhJSbQDisvWQGvF17BDjvt33yxVGCWCdYZxMfZGax75IrnaGZNzeescwIM1Marox5NQc1ZG++zDsm7cDSUUgVzOFhL6xZELbOOT62pnLPeni8ZyzBC1Zx1r10FIZyzBpDF6zNrfsgQhI4fj8kgFWYdkv82ZgrMAEDgYwRrq5i1TZ6dHRWslWpw+Lzg3OaLoojtNJXBwYGCgrNbdIlhDCTQAQZrqtUBIODtgVe0wy70b6B05+RCCAXV2doZZKCHszRYn5R+9qoGN+EN7soDA0FWAGBZSQ4CMCmDB8UggnKjNUdDKTHrgCwJO21SABFFL4go33AkkMEFOXB0OOXCHKKpBo/OWQsC8P/bu/PApqp8D+Dfc7O1adMV6A4tpQtpKa1oEUQtgliYUVEYRBY3ZhAdUAR5+hiHcRT11VHHYUYUnAcIosJDB4QiKAiCIsgSoCylBWzZ2kKXhKZJ0yT3vD+S1FK6JG3SJvL7/ENzc5eT0uSX3znnnh+X2OpZM35dsG7MrKOiAIsFDRcutHhNR9BtsM+odmWCGQBYWsmsHV9a5J3srnUc37wrvHEpVpMRrEOZtaRLbt3inMMsujOzFiAyDiZzU3DVXwZEE1qs7NZBpro6GK0ySBpqf/mi3g0cS44a6fYt0oYbM1gbWsmsXekGFySwKlTw71Swdi2zNlgMEOx1dyWiGYK84x+EjqDiJ1EA4BCtBkgV9q78djJrS4MJTGC4qrStOMbxywSz5qt4cbMZjEvAJBxmBgQ0K0UotxenYJG2iZkNZ8+2eE3HcSaZi8FaaDtYmxw9DJ0MKo2Z9XXB2v7YUAehw2PWns+sLfa/QbeNWXNbZu22bnDtOQCO2uju0WCwBWsGETDp3HZeV1Gw7nqzZs2KiYyMzHBU8PIFN2aw1reSWVvt68E7mV1Y/FRQmAAEdWC8GrAtWQoA5vYnuHDOYbQYYbZ/+AliA2SKjgcYR5DylyrsHYsi/KJ62K/V9rHm+npI5VLIJLYvN1ywQLDf9tX0UG61AqIIBltmbWHXd4Mr7MMH1p69bO0qbnlhIanAoACHSWb7vwmROvfam2bkLQdrRw+DezJrU7NgbWmwPZZYGzqWWcuktjHres/OWG4QbX/7bh+zdldmrS0FY7bf5XVlMjvIZDSg3mr/wuyoFdANGoP11e77wnCjGTt2rHbfvn0nXT3O3AXDUa25MW/d0lfYyulJm2U6Vvt/RPPtrWhQqCCvr4YQ1YGZ4AAg9QKdpPgAACAASURBVAPAgIb2g7VZNNtWMJPb/sukohkSv47/9zkyQH+ZHxT2OCUw52aDm031kMgkkAm235fILI2lNSvP16JPmm2xicZskEvBJBxWkSFA2nKwNjPAPzYW9adar0LnD6BeagsmoU4u3bm7xjZ5aF9fNca3kVnLOxusWxmzNhktECQMgtjQocxaUMjAmQCrhxfNMNv/9qWCez4SBEdm7a4x65oSwL4mPez363eWqa7ul2Bt1Hb6fB3lH+SbmfWff/hz3Oma024tkdkvtJ/h1dtebXPlsHnz5kWtW7cuLCoqqiE8PNySlZVl2LJlS0h6erpBo9EE6PV6ydKlS38ePny4QafTCdOmTet99OhRJQDMnz//0mOPPaYdMWJE64UImhk3blx8aGiopaCgQJmRkWH48MMPWx6r87AbNFiXA4ER12+32MdgnVwYwiQPgMRUDRYc3P7OLWEMkAc4lVkb7Ps4grWcm8E6kVkbzbaMMkDmD6VgX3KT2b90tJNam00mSOQSyARb1ykXLGCirS17159F5t29IZH8spCHwKWARISVc4Q1K2bhCNYNDQ0IVath1Bxu9T5vP/DGMetQJzM2x5j1lcBQiGLNdc+bzPZucHcF62aZtclggVwhgKFjC+fIlH4QBSmstU5/tnSI2zNr0Z5Zd+Jv9BraUkDuD1hgi9luOK3J3g0OwFbYp5tQN7jzdu3apdy4cWNoQUHBCbPZzDIzM9VZWVkGADAYDIJGoyn86quvAqdPn55QXFx8/MUXX4wKCgqyFhUVnQCAK1eudOgv58yZM34//PBDkbQDxXjc5cYM1pcLgfB+1293sRu8Xu4PoUGApKPBGrCV2mxo/4PYaLF1g1rt49QyawNYJ8astQbbaw31VyKgMViH2J5sJ7NuqDdCKhMgk5ghwgowbgvIdhve0eDBeYMaM2uBK8EFIIjVIlgSfc25HMHaZDJBdfdI1H79NQw//oiAoUOvu64fF6GzB+sQJ980M+J64r1zl9HToIPo11Y3eCfHrFuZYGYymGEflockxPW/E3mgAmACGjwcrOvMtvMHSAPccj7ByiAysVPzKq5RUwrmlwkY7Iv2uOGUJoMBJtH+n2O8/otcV/ELCARjgs8F6/YyYE/YuXNn4OjRo7WBgYEcAL/77rsbu0QmTZpUDQCjR4/W6/V6obKyUrJr166gzz77rHEiTM+ePTtUteXBBx+s6c5ADdyIY9ZGLVBVDMQOuv65xmDtXHelQaoAb2CQhYR0vD1yJWBufzzSYLFn3/YJVlLRDMvlji/mUGOvrtUrMAgB9r8CKbMHk3Yy63q9HjKlAJlEhGjPrh1LlQb18EPZGR1O/HCp8d5gGWxj+mGWaiibZcwy++sxmUxQ3XMPJOHhqF71cYvX9ROtMEtlYABCnMyse8plCJNJEG6sa3FRFHdl1n729tSbr71Gg8ECucQ+bt2BL3V+Stvvp/6qZ2/raQzWMjcFawuDKLhnbBmAbYKZ0v77c9OYdb2+FqLCfs5uHLNmggA/lQoGHY1Zt6elMrQOzXvjGGPtrsborMDAwO67XcDuxgvWF/bb/o295frnLPYPRKlzmbVOEgRwBrlfJ7IHRRBQ3/54WXW97cNEKpXDIlFAbjFAHtvBiW0AauyZdUJoNAIFR4ENW8nQ9iaY1etrIVFY4SeVokZmGzoQ7BnKhD9lIzRSiR2rCvHTVtsiJzJm6+YLtVYhpNka4oIgwM/PDwaDAYJcjtCHJkC/cycazl//pT3AbEKDVIZgqcSltbyjFDIY/ZSob+E+Vp3R9oUiWNm5WdAh9uO1xmsnoNQbLI3DBRL72KQr5P62L0EmfUOn2tceR7BWytwzBCm3SmES3NRm0QroLgBK+5diNwVrg04LaZB9kmk3ZtYAENSjF3RXKrq1Db4gJydHv3Xr1mCDwcB0Op2wbdu2xkzp008/DQWArVu3BqpUKmt4eLg1Jyfn6jvvvNPLsU9Hu8G9gUeDNWMslzF2ijF2mjH2YgvPKxhja+zP72OMxXuyPQCAku8BQQbEZl//nMm+OILCubHFCtgWRvHjnZicEhwL6FpfucvhsuGyrWlSBRoUwZA1XIXqrt4dvuwlbT2UcgnigsMRZL93Wyq0n1lzzlGnrYbUz4IgfwF7lLbfmcS+oIrCX4rf/DEDAHB4Tw2+zXkPUrltlnmItQaqFjqhQkJCUFNj+7AMeWgiIJGg5uPV1+0XYNCjQZA4PRPcIVgqxcXAENTVXd+VXGNogERgUHWgyEZTYQG2LyvVddcGqAajBTL7EIY0MtLl8yqUtnbVV3t24Y7aBtv5A2Ud/wLYVGC9P2pZXZuZkNOuXgJEM1iA/cuk1T3Buk5bA2VIGOAX3K1j1gAQEhkFbUVZt7bBF9x5552G3NxcnVqtThszZkxiRkZGXXBwsBUAQkNDrVlZWakzZ87ss2TJkhIAeOONN8q0Wq0kKSkpLSUlRb1582YVAMyYMSM2IiIio76+XoiIiMiYM2dOdBuX9QoeC9bMtnD0ewBGA1ADeJgxpm622zQANZzzfgD+DiDPU+1pdPEgEDnA1v3cXL19zEjhXAZ0VW/70FCI7QfbVgXHArrz7aaz5XW2hf6VQgBMsiDILbVgnVh1q7SqDrGh/pAIEsQoFGiAAhLB/jtpoykGnRamujoogg2IVDGESPSwcgaJxR/RSbYvucE9lXj8zWGNx+zfb8teEvXnIeqvv/UhNDQU1dW2D0tZRC8EjRqF6o8+Qt3evdfsp6zVAowhTO5aYN2j1cMokeJqC8H6Sq0JYQHyTneVhSqvD9ZWs4jaqnooTDUQgoIg6cD68Y5gbaz07Him48tgT2XPdvZsHxc5AusUMDNL41yLTqksAgAIYba2iQb33D5Tp62GMjjUtuywvnuz2pCIKFy9crnJUr6kNX/5y1/KS0pKjm3ZsuXM2bNn/QYPHmwAgIceeqhGo9EUFhcXHx8+fLgBAIKDg8UvvviipLi4+PipU6dOPProo1oA+OCDDy5UVFQcFUXxYEVFxdF33nmn1cIEn3/+ecnjjz/evV0v8GxmnQ3gNOf8LOe8AcBnAO5vts/9AD6y/7wOwAjmjgGG1hiqgXN7gZgWxqsB2xtWkNlu63ICKygGB4dfzfb2+45b0zMVMF0Fqk63uduhy4cQHRCFB4t/g3r/HpDrL3V4oQydwYz9JdXIiLW9zt4KhisWAVb72KpV13rJwKK93wMAhOBz0BoY4gQtqsVgMEgagzUAKIPkmDjkIvqUboG1IR5mgwSDrAWo/HcR3pvxLd6b8S1W/2Uv9m08i9jYWFRXV+PcOVt1up5znoMkOBjnHnscl//+LrjFgvLycvhV2QKKs+uCN/elMhwvv/xyY7YnihzHLl1FYs/Oj9P6yyXoEajAmcu/3GJ15XwtrBYRgRePwi8lpUPnDQi2zZ+oqxfQcKETXwrbcUF/AXJBjjC/sE6fy1xhgMpku6e8qt4NhY0uHAAACNF9AADW2s4Ha0tDA65euYKQyGjbZNPKtt9/nhbRNxFcFHH+REG3tsMXTJkypU9qaqo6IyOj/7333lszbNiw7q3E0kU8Ob0tBkDTgccLAAa3tg/n3MIY0wEIB1Dp9tac3g58OQsAty0r+p+nAKvJNqnMarb9e+EA0CMZaCcYiFzEymdzMfjr89D384eElQEf3QtMXgfIXFz/OeluW0GPzyYDEWm2JUhv+QPQezB2rC5E4d5LEM2AGuOgBnAJQEP4AESV70XRrUPQb+cOpzK2K1e24fKVr3Dmsh4ny6oxNdWCW3sH4dAhC8KZDlvqpFhaNRGfKN7A1W/Po8cj13aC1JRdxPo3X0X1pQtQxVigCDfDsJcjWGjAIXMcbhkbh1tGJTTuX5H3JqqXL0dEcAhmPT8RrxTEIzrjDIJ6T4fhcjLAOMBEXDFZECf8NwBg2bJlePzxx9GnTx/Ef/45zv/xj6hasgTb9u1DUWoK9D2jIQD4QavHxMNnsDIj4boqXi35NKMvHj56Fv0u226P/Otf/4onZv0XZq/R4GTZVbw6Nt3p/6623NYvHJuOliE21B9zRqXg2H80ELgVASe+Q9D8uR06pzJYDj+lBHpVHEqnTEH4E08g7JGpbmkvYLvLYPaO2ThQfgBZEVkQnFm9r5ma/xSj/rQW0mAFrLUNEOvM6Is4AMC0rdPwtzv/hoE9B7ZzlmZ2vw1c0thupyzdA/QeCmlULwClqPmiGLIIJbhFRPgUNQQXbg+7XHIWP63/P9Rpa8C5iMjEfkBNGlC8FVj7KNA3B7j5cdfa6gbxmYOgCAjAlsV/R3RSKjjn4I7lj8FgS2EYBo7MRXxmK8nGDWLjxo0/N9/2008/tb5Ag5NeeOGFyA0bNlzzbfX++++vzsvL84r6pZ4M1i1lyM3TT2f2AWNsOoDpANC7dwfHaeu1tu7th1YB+5baxq4lUkDqb7uvWiIHIjOA255t91QCE6CMisPlO1UYOOcvQNlm4EoH/1ZCegNj3wd+WgqUHbF9kUgfZ1ulSeQQW0giyjLikH3LJMjKq8GczDLrTWXQag/AH1akhANKhT8UgghRlKFXrzHIihgI8cpJBEX1Buqs4CK/pptdIpOhR+94hMeHQ5m8DSuLxoFZAnFMDIZarUavmMBrSnbKoqIQ/MAD2HzH3ZjaOwI3Z36Cn7c9BzFQA53SjDBRCYuBQyILRLw6Bk/GP4kvvvgCRqOt21QeG4Pe6/4P26dOxeUI2/yQ36cl4/Xs/thwRYsSo8mpQA0Aw8ODUD48E59VFKKw0DbjNthfBr3Jgr+Nz8C4m2KdOk97/jSmPyxWDqPZaquVbBARYz6NmGeeRMhDD3XonIwxRCWFQhJ1G/xOn2+8d91d/CR+qDPX4a7ed+GZm57p0DlkEQEQjRZYdQ2Q9lJCrDNjUNqdeCbYgK0lW9HTvwNd6zUltmxX5gfE3ASMeBnSMD+ohseh/mQVrFqTbcEVq2s3XTfUG3G59GdIJBJkjMxFQubNgD7G1uNWcazlWzq7gEyuwIMvvow9//cJqi9dAGPsl/c257YPRc5hMt4QSWS3yMvLK/eWwNwS5pYJIC2dmLEhAF7mnN9jf/zfAMA5f6PJPlvt+/zIGJMCKAfQk7fRqJtvvpkfOHDA9QZxbluD28kFTzp0fg/24HsTq9UEsyhFwUUdIoP8EBfm1kWMuowo8k7VBHfqGlYRgjtrOvsYd906QzqPMXaQc35zZ85x5MiRkoEDB7q/55M0OnLkSI+BAwfGN9/uyU+R/QCSGGMJjDE5gIkAvmy2z5cAHrX/PB7At20F6k5hzHOB2nH+G4REooCfTIJb4sN8NlAD8HigBnBDB2rg+ntfCSEd47FucPsY9EwAW2Hrp1rGOT/OGHsFwAHO+ZcA/hfAKsbYaQDVsAV0QgghhDTh0fXTOOebAWxutm1Bk5/rAfzOk20ghBBCfN2N3UdHCCHkhkP1rAkhhBAv19F61t3pxqy6RQghpFMuzf9TnKm42K0zTBVJSYbo11/zmnrWNTU1Qnp6etrZs2ePKRQKXl1dLQwYMKDxsTtes7MoWBNCCPEJXV3POjQ0VBwyZEjt2rVrg6dOnapdtmxZ2JgxY2q6OlADFKwJIYR0QHsZsCd0Rz3r6dOnX8nLy4ucOnWq9uOPP+7x4YcflrjjtbjK54L1wYMHKxljpS4cEgzA04Vi3X2Nzpyvo8e6cpyz+zqzXw94YnlZ79UVf4/OoveG+4/z5vdGHzeeq1t0Rz3rUaNG1c2aNUuRn58faLVa2S233OLZ4vKt8LlgzTl3ae1CxthSzvl0T7XHE9fozPk6eqwrxzm7rzP7McYOdHZVJV/SFX+PzqL3hvuPo/eGZ+Xk5OifeuqpPgaDocxsNrNt27aFPPLII1cAWz3re++9t7aletbLli07D9i6wTuSXU+cOLHq8ccf7zt37txuq2N6I8wG3+iD1+jM+Tp6rCvHObtvV/zufY03/U7oveH+4+i94UHdVc962rRpVVevXpVOmzat2wqfe2xtcEKcQdkDIS3zxveGN6wNrtPphODgYLG2tlYYMmRIygcffFA6Z86cuLfeeuv8HXfc4ZFKJ8uXLw/dsGFDyPr166+r+OVura0N7nPd4ORXZ2l3N4AQL0XvjRZMmTKlT3Fxsb/JZGITJ06s8nQ960cffTRux44dwZs2bSr25HXaQ8GadCvOOX0gEdICem+0rKvrWX/00UfnAXT5zPfmqBucEEKIU7yhG/zXrjtKZBJCCCHEDShYE0IIIV6OgjXxOoyxAMbYQcbYb7u7LYR4C8ZYDmNsN2PsA8ZYTne3h3QtCtbE4xhjyxhjlxljx5ptz2WMnWKMnWaMvdjkqRcArO3aVhLS9Vx8b3AAegB+AC50dVtJ96JgTbrCCgC5TTcwxiQA3gMwGoAawMOMMTVjbCSAEwAqurqRhHSDFXDyvQFgN+d8NGxfZv/axe284WRlZaV2dxuaolu3iMdxzncxxuKbbc4GcJpzfhYAGGOfAbgfQCCAANg+pIyMsc2cc7ELm0tIl3HlvcE5P2F/vgaAossaeYPSaDSFnTnebDZDJpO5qzkUrEm3icG19y5eADCYcz4TABhjjwGopEBNbkAtvjcYYw8CuAdACIB/dUfDmtq+8mRc9UW9W+tZh8UEGkY80r/Ve5pPnTolz83NTcrOztYfOnQosH///oYnnnii8pVXXompqqqSrlix4qxarTZNnjw5/ty5cwp/f39x6dKlpYMHDzbm5+cHzp07tzdgK/KxZ8+eQkEQkJub20+n00ksFgtbsGDBpSlTpmgBQKlUZhkMBg0AvPTSSxFr164NZ4xhxIgRusWLF19sqX3Z2dkp2dnZ+n379gWOGTNG+9e//tVtPYQUrEl3aakUTuNN/5zzFV3XFEK8SovvDc75FwC+6OrGeJvz58/7rVmz5uygQYNKMzIy+q9evTr8wIEDhZ988knIa6+9FhUTE9MwcOBAw7Zt2858+eWXqkcffTShsLDwxNtvvx25aNGi0lGjRtXpdDpBqVSKAJCfn386LCxMLCsrkw4ePDh10qRJWkH4ZYR47dq1Qfn5+aEHDx4sVKlUYkVFRZs1sbVarWT//v2dXqSlOQrWpLtcABDX5HEsgEvd1BZCvIlPvDfayoA9KSYmxpSdnW0EgOTkZONdd911VRAE3HTTTYaFCxdGX7x4UfH555+fBoD77ruvdvr06dKqqirJrbfeqn/++efjJkyYUP3www/XJCYmiiaTic2ePTt27969gYIg4PLly/ILFy5Ie/fubXFc75tvvgmaMmVKpUqlEgEgIiKizapdDz/8sEeKfdAEM9Jd9gNIYowlMMbkACYC+LKb20SIN6D3RhvkcnljD5wgCPDz8+MAIJFIYLVaW1yVkzHGX3/99fJ///vfpUajURg6dGh/jUbjt2TJkrCqqippQUHBycLCwhPh4eFmo9F4TVx0tSa2I6i7GwVr4nGMsU8B/AgghTF2gTE2jXNuATATwFYAJwGs5Zwf7852EtLV6L3hfrfeemvt8uXLwwFg06ZNqtDQUEtYWJh4/PhxRXZ2tvG1114rHzBgQN2xY8f8dDqdpEePHmaFQsE3btyounTpkrz5+XJzc6+uWrWqR21trQAA7XWDewp1gxOP45w/3Mr2zQA2d3FzCPEa9N5wv7y8vEuTJk2KT05OVvv7+4srVqz4GQDefPPNXnv27AkSBIEnJycbx48fr9NqtZLRo0f3S09P75+WlmZISEiob36+8ePHXz106JAyMzOzv0wm4yNHjtT961//anGCmSdRIQ9CCCFOoUIenkeFPAghhBAfRd3ghBBCiAumTp3ae//+/YFNtz311FMVzz77bJWnrknBmhBCCHHBqlWrznX1NakbnBBCCPFyFKwJIYQQL0fBmhBCCPFyFKwJIYQQL0fBmpBfCcbYPxljhxhjt3R3WwjxZrNmzYqJjIzMUCqVWd3dFmdRsCbkV4AxFgCgF4AnAfy2m5tDiFcbO3asdt++fSe7ux2uoFu3iM9hjP0dQCnn/F37460AznPOf29//DaAi5zzd9x4TT3nPLD9PZ0+XwiASZzzxfbH8QA2cc7TnTjWH8AWAHdxzq0AwDmvY4xFAdgJoLe9AMQ2+z6WVk9GSAdtff/duMrzpW6tZ90jro/hnqdmt1nNa968eVHr1q0Li4qKaggPD7dkZWUZtmzZEpKenm7QaDQBer1esnTp0p+HDx9u0Ol0wrRp03ofPXpUCQDz58+/9Nhjj2lHjBhR52ybUlNT1Y6fS0pK/D7//POi3/zmN/qOv8qOoWBNfNEeAL8D8C5jTADQA0BQk+eHApjdHQ1zQQiApwEs7sCxTwD4whGoAYAxFg5ACaAWgJVz3sAY2w7gIQCr3dBeQrrdrl27lBs3bgwtKCg4YTabWWZmpjorK8sAAAaDQdBoNIVfffVV4PTp0xOKi4uPv/jii1FBQUHWoqKiEwBw5coVl4twFBYWngCATz75JPjtt9+OHDlypNOB3p0oWBNf9AOAv9t/TgNwDEAUYywUgAFAfwAaxth62OoC+wH4B+d8KQAwxvJgy8wdWe3LsAW5CgDPAJAD2Afg6aYB0b7vlJb2sWfGXwH4HrYvCxcB3M85NzLG/gxgMoDzACoBHARwM4BExthhAN8AeA+AhDH2YfPjW3j9kwFMarbtJQBvAfgDADVsX2jWA3gDFKyJB7SXAXvCzp07A0ePHq0NDAzkAPjdd9+tdTw3adKkagAYPXq0Xq/XC5WVlZJdu3YFffbZZ2cd+/Ts2bPNWtStKSgoUPzpT3+K3bFjR5FCoeiWgho0Zk18Duf8EgALY6w3bIHtR9gC5xDYguBRznkDgCc454Ps256xZ58A8BlsGafDBAAH7Ntu45xnArDCFhQbMcb6t7NPEoD3OOdpALQAxjHGbgYwDkAWgAftbQGAFwGc4Zxncs7ntXZ889du797uyzkvabIt3v57WANbScU0+1PHANBkM/Kr0VbhqeY1pxljLteibsnVq1eFCRMmJL7//vul8fHx5k6drBMoWBNf9QNsAcoRrH9s8niPfZ9nGGNHAOyFLcNOAgDOuQZAL8ZYNGNsIIAaAAMADAKw357tjgDQt9k1R7Szz8+c88P2nw8CiAcwDMAGzrmRc14LYGMbr6ml45vrAVsgb2ohgFe47ZOsMVjbewUaGGOqNq5JiM/IycnRb926NdhgMDCdTids27YtxPHcp59+GgoAW7duDVSpVNbw8HBrTk7O1XfeeaeXY5+OdINPnDgxfvLkyZW5ubldPk7dFAVr4qv2wBaYB8CWQe6FLbMeCuAHxlgOgJEAhnDOBwLQwNYd7rAOwHjYMuXPADAAH9kz3UzOeQrn/OVm12xvH1OTn62wDTO58rW+peObMzZ9HYyxTNgy9vcYYyUA/oxfMmsAUAC4rkYvIb7ozjvvNOTm5urUanXamDFjEjMyMuqCg4OtABAaGmrNyspKnTlzZp8lS5aUAMAbb7xRptVqJUlJSWkpKSnqzZs3qwBgxowZsRERERn19fVCRERExpw5c6Jbul5RUZF8y5YtoR9//HGP1NRUdWpqqnrXrl1unVTnLKpnTXySPUh9AeAs53ykfdtBADEA0gHcBuD3nPN7GWOpAA4DyOWc77TvmwbgQ9gy1TsBhALYAFsX92XGWBgAFee81L6/HkB2a/s0n83NGHseQCCAfABLYPsSIYUtY/4QwHIAhzjnfez7t3h8C18YwBg7DyCJc15vnwn/Jud8u/25CAAaznm0vdv/e855/079sgmx84Z61jqdTggODhZra2uFIUOGpHzwwQelc+bMiXvrrbfO33HHHYbubJs7tFbPmiaYEV9VAFug/aTZtkDOeSVjbAuAGYyxowBOwZZ5N+KcH7d3D1/knJcBKGOMvQTga/sMczOAPwIobXLMifb2aY5zvp8x9iWAI/b9DgDQcc6rGGM/MMaOwTYx7T0XXvvXAIYxxkQAAY5Abb9eBWMswP5FYjiAzS6clxCvN2XKlD7FxcX+JpOJTZw4sWrYsGE+H6CdQZk1IR7GGAvknOsZY0oAuwBM55wf6sT5sgDM4ZxPbWe/LwD8N+f8VEevRUhT3pBZe8oLL7wQuWHDhrCm2+6///7qvLy88q5sB2XWhHSfpYwxNWxjzR91JlADtglyjLEdjDFJ81vLHOyzxtdToCbEOXl5eeVdHZhdQcGaEA/jnDe/J9od51zWzvMNAFa6+7qEkO5Bs8EJIYQQL0fBmhBCCPFyFKwJIYQQL0fBmhBCiM/IyspK9dS5V69eHTx//vxIAHj55ZcjEhMT05KTk9VDhgxJLioqknvqus6gYE0IIcRnaDSawubbLBb3VIGdPHmy7vXXXy8HgEGDBhkOHz58sqio6MTYsWNrnnvuuVi3XKSDaDY4IYQQl1WvK4ozl9e5delNWWSAIWx8cpvVvJRKZZbBYNBs2rRJ9eqrr0b16tXLfOLECeWZM2eOjxw5MrGsrExuMpmEGTNmVDz//POVALBu3bqgBQsWxFitVhYWFmb58ccfi1o696JFi8IPHDgQsHLlynP33ntvrWP7sGHD9GvWrAlv6ZiuQsGaEEKITzp69GiARqM5npqa2gAAq1evLomIiLDq9XqWlZWlnjJlSo0oimzmzJnxO3fuLExNTW2oqKhwuZjHkiVLeo4cOVLn/lfgPArWhBBCXNZeBtwVMjIy6hyBGgDy8vIi8vPzQwCgvLxcdvz4cb+KigppdnZ2rWO/iIgIl2paL168OOzIkSPKJUuWdOsCQxSsCSGE+CSlUik6ft60aZPqu+++Ux04cKBQpVKJ2dnZKUajUehMTev169er3nrrrajdu3ef8vf379a1uWmCGSGEEJ+n1WolwcHBVpVKJWo0Gr8jR44EAMDw4cPr9u3bpyosLJQDgLPd4D/88IP/rFmz+mzYsOF0TEyMe2awdQJl1oQQQnze+jcECQAAFCtJREFUuHHjdEuXLu2ZnJysTkxMrB84cGAdAERHR1sWLVpU8sADD/QTRRHh4eHmPXv2FLd3vnnz5sUZDAbJ7373u0T7eRq+/fbb055+Ha2hqluEEEKc8muuuuUtWqu6Rd3ghBBCiJejbnBCCCE3lH/84x/h77//fkTTbbfccot+1apV57qrTe2hbnBCCCFOoW5wz6NucEIIIcRHUbAmhBBCvBwFa0IIIcTLUbAmhBBCvBwFa0IIIaQZT9bN7ggK1oQQQkgzLdXN7k50nzUhhBCXrV+/Pu7y5cturWfdq1cvw9ixY1ut5nXq1Cl5bm5uUnZ2tv7QoUOB/fv3NzzxxBOVr7zySkxVVZV0xYoVZ9VqtWny5Mnx586dU/j7+4tLly4tHTx4sDE/Pz9w7ty5vQGAMYY9e/YUCoKA3NzcfjqdTmKxWNiCBQsuTZkyRQv8UjcbAF566aWItWvXhjPGMGLECN3ixYsvNm9bSUmJLDc3N8nxuLi42P/kyZMFycnJDc337QgK1oQQQnzG+fPn/dasWXN20KBBpRkZGf1Xr14dfuDAgcJPPvkk5LXXXouKiYlpGDhwoGHbtm1nvvzyS9Wjjz6aUFhYeOLtt9+OXLRoUemoUaPqdDqd4KjYlZ+ffzosLEwsKyuTDh48OHXSpElaQfil03nt2rVB+fn5oQcPHixUqVRia4VA4uPjzYWFhScA4I033ui5e/dulbsCNUDBmhBCSAe0lQF7UkxMjCk7O9sIAMnJyca77rrrqiAIuOmmmwwLFy6MvnjxouLzzz8/DQD33Xdf7fTp06VVVVWSW2+9Vf/888/HTZgwofrhhx+uSUxMFE0mE5s9e3bs3r17AwVBwOXLl+UXLlyQ9u7du7HK1jfffBM0ZcqUSpVKJQLt18P++uuvA1auXNlz7969bu1GpzFrQgghPkMulzcuuykIAvz8/DgASCQSWK3WFlflZIzx119/vfzf//53qdFoFIYOHdpfo9H4LVmyJKyqqkpaUFBwsrCw8ER4eLjZaDReExddqYddWloqe/LJJ+PXrFlzJjg4WGz/COdRsCaEEPKrceutt9YuX748HAA2bdqkCg0NtYSFhYnHjx9XZGdnG1977bXyAQMG1B07dsxPp9NJevToYVYoFHzjxo2qS5cuyZufLzc39+qqVat61NbWCkDr9bBNJhN78MEH+7766qsXMzIyTO5+XdQNTggh5FcjLy/v0qRJk+KTk5PV/v7+4ooVK34GgDfffLPXnj17ggRB4MnJycbx48frtFqtZPTo0f3S09P7p6WlGRISEuqbn2/8+PFXDx06pMzMzOwvk8n4yJEjdf/617+um2C2bdu2gGPHjgUsXLgweuHChdEAsGXLluL4+HizO14XFfIghBDiFCrk4XlUyIMQQgjxUdQNTgghhLhg6tSpvffv3x/YdNtTTz1V8eyzz1Z56poUrAkhhBAXrFq16lxXX5O6wQkhhBAvR8GaEEII8XIUrAkhhBAvR8GaEEIIaaYjJTJnz54dvX79epUn2kMTzAghhJBmOlIi8913373kibYAlFkTQgjxEadOnZInJCSkPfTQQ32SkpLS7rvvvoT169erbrrpptQ+ffqk79ixQ1lRUSEZOXJkYnJysnrgwIGp+/bt8weA/Pz8wNTUVHVqaqq6f//+6pqaGkGn0wlDhgxJVqvV/ZOTk9Uff/xxiONaSqUyy/HzSy+9FJGcnKxOSUlRP/300zGttW/cuHHxy5cvD/XEa6fMmhBCiMtOnHwhrk5f5NZ61gGByQZ1/7w2q3l5a4lMT6PMmhBCiM9wlMiUSCTXlci8cOGC4qefflJNmzatCrCVyNRqtdeUyFy4cGGvyspKiUwmgyiKbPbs2bHJycnq4cOHJztKZDa9nqslMj2FMmtCCCEuay8D9pT2SmRKJJLrCl44SmSOHTtWt2HDhuChQ4f237JlS9Hu3bsDHCUyFQoFj4mJGdCZEpmeRJk1IYSQX43uKpHpaZRZE0II+dXorhKZnkYlMgkhhDiFSmR6HpXIJIQQQnwUdYMTQgghLqASmYQQQoiXoxKZhBBCCLkOBWtCCCHEy1GwJoQQQrwcBWtCCCHEy1GwJoQQ4tOys7NTdu3adV1RkTvvvLNfZWXldSuOzZkzJ3rBggURnb1uSUmJLDc3t29nz+MMmg1OCCHkV+m777477cnzx8fHm7ds2XLWk9dwoMyaEEKITzh16pQ8KSkpzfF4wYIFEXPmzIl2PLZarXjwwQfjn3nmmWgAiImJGVBWViYFgBdeeCEyPj4+fejQocnFxcUKxzELFy7slZiYmJacnKz+7W9/2xdoufa1M+3xJMqsCSGEuGz2yXNxhXX1bq1nnRrgZ3i3f+8OVfMym81s7NixCWq12piXl1fe9Lndu3cr//Of/4QVFBScMJvNyMzMVGdlZRkAYNGiRZGlpaUF/v7+3NFl3lrt6+5EmTUhhBCf9/TTT/dpKVADwI4dOwLHjBmjValUYlhYmDhq1Cit47mUlBTjAw88kLB48eIwmUzGAaCl2tfdjTJrQgghLutoBtwZUqmUi+IvSW59fX1jwnnzzTfrd+/eHWQwGCqUSmVLNa1bPOeOHTuKv/rqK9X69etD3nzzzeji4uJjLdW+zsrKuq4iV1eizJoQQohPiI2NtVRXV0vLy8slRqORbd26Ndjx3JNPPlk5atQo3W9/+9tEs9l8zXF33XWXPj8/P0Sv17Oamhrhm2++CQFsY9xnzpyR33vvvbWLFy++UFtbK9HpdJKWal938Uu9DmXWhBBCfIJCoeBz584ty87O7h8bG2vq16/fNdnuyy+/XPHcc89JHnzwwYT169f/7Ng+bNgwwwMPPFCdnp6eFhMTY8rOztYDgMViYZMmTUqora2VcM7Zk08+WdGjRw/r3Llzo5vXvm6tTYyxLqkzTfWsCSGEOIXqWV9r9+7dyjlz5sTt37//lLvOSfWsCSGEEDfZtWuXcurUqX1nzpxZ0RXXo25wQgghpA0//fST/yOPPJLQdJtcLhdLSkqOdVUbKFgTQgghbcjOzjYWFhae6M42UDc4IYQQ4uUoWBNCCCFejoI1IYQQ4uUoWBNCCCFejoI1IYSQG5LRaGRDhw5NTk1NVX/44YehDz30UJ+DBw+6dbWyTZs2qYYPH96vs+eh2eCEEEJuSHv27FGazWbmmOn9hz/8oaal/SwWC6TS7g2XlFkTQgjxCadOnZL37ds3beLEiX369euXdttttyXp9Xp2/Phxxe23356UlpbWf9CgQSkajcbPYrEgNjZ2gCiKqKyslAiCMOirr74KBIBBgwal7NixQ/n4448nFBYW+qempqrt64Gn7Nq1SwkASqUya/bs2dEZGRmp27dvD4yJiRkwc+bMmMzMzNT09PT+33//vXLYsGFJcXFx6W+++WbPttpdV1cnyc3N7ZuQkJB23333JTQtRuIsyqwJIYS4bN66I3FF5bVurWedHKky/G38wDareZ07d87v448/Pjt06NDSMWPG9F25cmXoqlWreixdurR0wIABpm+//Tbgqaee6r13796ihISE+kOHDvkVFxcr1Gq1YefOnYE5OTl15eXl8uHDhxsWL15c+vbbb0fs2LHjdPPrGI1GIT093fjuu+9ecmyLi4trOHz4cOG0adPinnjiifh9+/YV2vdL+6//+q8rrbX55MmT/ocPHz4bHx9vHjRoUOo333wTeM899+hd+d1QsCaEEOIzYmJiTEOHDjUCQFZWlqGkpESh0WgCf/e73yU69mloaGAAMHTo0Nrt27erfv75Z8W8efPK/vd//7fnrl279AMHDqxr7zoSiQSPPfbYNd3iEyZM0ALAgAEDDHV1dUJoaKgYGhoqKhQKsbKyUtKjRw9rS+caMGBAXWJiohkA0tLSDGfOnJG7+ropWBNCCHFZexmwp8jl8sbqUxKJhFdUVEhVKpWlpRXGcnJy9IsXL+5ZUVEhf+eddy7+/e9/j9y+fbtq2LBhtU5cR2w+Tu3n58cBQBCEa9ohCALMZnPLBbNhqxbWpM2wWCyt7tsaGrMmhBDis4KCgsTY2NiGZcuWhQKAKIr48ccf/QEgJyen7tChQ4GCIHClUsnT0tIMK1eu7Dl8+HCXuqC9AQVrQgghPu3TTz89u3z58h4pKSnqpKSktM8//zwEAPz9/XlkZGTDzTffXAcAt99+u76urk7Izs42dm+LXUf1rAkhhDiF6ll7HtWzJoQQQnwUTTAjhBBCOqm1mtdHjx4tdMf5KVgTQgghneTpmtfUDU4IIYR4OQrWhBBCiJejYE0IIYR4OQrWhBBCiJejYE0IIcRnKJXKLMB9daJ9BQVrQgghxMtRsCaEEOKTamtrJXfffXdiYmJi2qRJk3pbrbaiV+vWrQtSq9X9U1JS1EOGDElu7fg5c+ZEL1iwIMLxOCkpKe3UqVMuV8TqCnSfNSGEENet/2McLp9waz1r9FIbMPY9p6t5FRQUBGg0mmPJyckNd9xxR9LKlStD77nnntqZM2fG79y5szA1NbWhoqJC4tY2dhMK1oQQQnzSgAED6tRqdQMATJgwoXr37t2BCoVCzM7Ork1NTW0AgIiIiBZrTPsaCtaEEEJc50IG7CmMsesec86v294aqVTKRVFsfGwymVyuM91VaMyaEEKITyooKAgoLCyUW61WrFu3Luz222+vHT58eN2+fftUhYWFcgBoqxs8Pj7edPjw4QAA+P7775UXL15UdFXbXUWZNSGEEJ+UmZmpnzt3bmxhYaH/4MGDa6dOnaqVSCRYtGhRyQMPPNBPFEWEh4eb9+zZU9zS8Y888kjN6tWrw1NTU9WZmZl1ffr0qe/q1+AsqmdNCCHEKVTP2vOonjUhhBDio6gbnBBCyK/aP/7xj/D3338/oum2W265Rb9q1apz3dUmV1E3OCGEEKdQN7jnUTc4IYQQ4qMoWBNCCCFejoI1IYQQ4uUoWBNCCCFejoI1IYQQn5GVlZXqqXOvXr06eP78+ZEA8NVXXwWq1er+Uql00PLly0M9dU1n0a1bhBBCfIZGoylsvs1isUAq7Xw4mzx5sg6ADgD69u3bsHz58pL/+Z//iWjnsC5BwZoQQojL/vzDn+NO15x2a4nMfqH9DK/e9mqbBUKUSmWWwWDQbNq0SfXqq69G9erVy3zixAnlmTNnjo8cOTKxrKxMbjKZhBkzZlQ8//zzlYCtvvWCBQtirFYrCwsLs/z4449FLZ170aJF4QcOHAhYuXLluZSUlAYAEATv6ICmYE0IIcQnHT16NECj0Rx3lMNcvXp1SUREhFWv17OsrCz1lClTakRRZL+G+tYUrAkhhLisvQy4K2RkZNQ5AjUA5OXlReTn54cAQHl5uez48eN+FRUV0l9DfWsK1oQQQnySUqlsLEa9adMm1Xfffac6cOBAoUqlErOzs1OMRqPgSn1rb+YdnfGEEEJIJ2i1WklwcLBVpVKJGo3G78iRIwEA4Ep9a29GwZoQQojPGzdunM5isbDk5GT1/PnzowcOHFgHANHR0RZHfeuUlBT1Aw880NeZ83333XfKiIiIjM2bN4c+99xzffr165fm2VfQNirkQQghxClUyMPzqJAHIYQQ4qNoghkhhJAbii/Wt6ZucEIIIU6hbnDPo25wQgghxEdRsCaEEEK8HAVrQgghxMtRsCaEEEK8HAVrQgghN5RZs2bFREZGZiiVyqzubouzKFgTQgi5oYwdO1a7b9++k93dDlfQfdaEEEJcdmn+n+JMxcVurWetSEoyRL/+WpvVvObNmxe1bt26sKioqIbw8HBLVlaWYcuWLSHp6ekGjUYToNfrJUuXLv15+PDhBp1OJ0ybNq330aNHlQAwf/78S4899ph2xIgRdc626fjx44pJkyYlWK1WNnLkSN3SpUsjDAaDprOv1VWUWRNCCPEJu3btUm7cuDG0oKDgRH5+/pmjR48GOJ4zGAyCRqMpXLRoUen06dMTAODFF1+MCgoKshYVFZ0oKio68Zvf/KbW1WvOnDkz7umnn7587Nixk9HR0WZ3vh5XUGZNCCHEZe1lwJ6wc+fOwNGjR2sDAwM5AH733XdrHc9NmjSpGgBGjx6t1+v1QmVlpWTXrl1Bn3322VnHPj179nS5lrVGown8+uuvTwPA73//+6qXX3451g0vxWWUWRNCCPEJba242bxmNWMMv5Za1gAFa0IIIT4iJydHv3Xr1mCDwcB0Op2wbdu2EMdzn376aSgAbN26NVClUlnDw8OtOTk5V995551ejn2uXLnici3rzMxM/YoVK0IBYNmyZWHueB0dQcGaEEKIT7jzzjsNubm5OrVanTZmzJjEjIyMuuDgYCsAhIaGWrOyslJnzpzZZ8mSJSUA8MYbb5RptVpJUlJSWkpKinrz5s0qAJgxY0ZsRERERn19vRAREZExZ86c6Nau+c9//vP8P//5z4gBAwb0LysrkwUGBrrcle4OVMiDEEKIU7yhkIdOpxOCg4PF2tpaYciQISkffPBB6Zw5c+Leeuut83fccYfB3derra0VAgICREEQsHTp0tA1a9aEbd++/Yy7r+PQWiEPmmBGCCHEZ0yZMqVPcXGxv8lkYhMnTqwaNmyY2wN0Uz/88IPy2Wef7c05R1BQkHXFihUlnrxeayhYE0II8RkbN278ufm2n3766VRnz/vCCy9Ebtiw4Zox6fvvv786Ly+v/NSpUyc6e/7Oom5wQgghTvGGbvBfO6pnTQghpLNEURR/HfdCeSH771Zs6TkK1oQQQpx17MqVK8EUsN1PFEV25cqVYADHWnqexqwJIYQ4xWKx/L68vPzf5eXl6aBkz91EAMcsFsvvW3qSxqwJIYQQL0ffjAghhBAvR8GaEEII8XIUrAkhhBAvR8GaEEII8XIUrAkhhBAv9/+ETdaDm8BFagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lbc_u: mean flux error: 0.06582602533523098, 3sigma in AB mag (Aperture): 25.661202781717414\n",
      "suprime_z: mean flux error: 0.17858133507394464, 3sigma in AB mag (Aperture): 24.577606699635744\n",
      "lbc_y: mean flux error: 0.5812862172771195, 3sigma in AB mag (Aperture): 23.296221799107023\n",
      "gpc1_g: mean flux error: 73.12682863067843, 3sigma in AB mag (Aperture): 18.047005014901465\n",
      "gpc1_r: mean flux error: 5.722261997953992, 3sigma in AB mag (Aperture): 20.813277517162824\n",
      "gpc1_i: mean flux error: 2.730008317477041, 3sigma in AB mag (Aperture): 21.616786937697036\n",
      "gpc1_z: mean flux error: 6.300170123056998, 3sigma in AB mag (Aperture): 20.708816171111586\n",
      "gpc1_y: mean flux error: 151.4673972587472, 3sigma in AB mag (Aperture): 17.25639895625958\n",
      "90prime_g: mean flux error: 0.14448076486587524, 3sigma in AB mag (Aperture): 24.807671782989523\n",
      "90prime_r: mean flux error: 0.2213410586118698, 3sigma in AB mag (Aperture): 24.3445419063922\n",
      "mosaic_z: mean flux error: 0.8128145337104797, 3sigma in AB mag (Aperture): 22.93221821193074\n",
      "newfirm_j: mean flux error: 1.4842238896105333, 3sigma in AB mag (Aperture): 22.27844831925244\n",
      "newfirm_h: mean flux error: 1.665939854796813, 3sigma in AB mag (Aperture): 22.15304856800497\n",
      "newfirm_k: mean flux error: 3.1820827810432313, 3sigma in AB mag (Aperture): 21.45041817941638\n",
      "mosaic_r: mean flux error: 0.09258104917993197, 3sigma in AB mag (Aperture): 25.290891617847713\n",
      "mosaic_i: mean flux error: 0.18261795389985885, 3sigma in AB mag (Aperture): 24.553338181898262\n",
      "mosaic_b: mean flux error: 0.030485112538625268, 3sigma in AB mag (Aperture): 26.496977356702523\n",
      "tifkam_ks: mean flux error: 18.872000889513924, 3sigma in AB mag (Aperture): 19.517651992331842\n",
      "ukidss_j: mean flux error: 6.357367038726807, 3sigma in AB mag (Aperture): 20.69900364849442\n",
      "90prime_z: mean flux error: 0.9037018418312073, 3sigma in AB mag (Aperture): 22.817133944662167\n",
      "irac_i1: mean flux error: 0.8619690718343747, 3sigma in AB mag (Aperture): 22.86846765505623\n",
      "irac_i2: mean flux error: 1.181511486058485, 3sigma in AB mag (Aperture): 22.526101993249036\n",
      "irac_i3: mean flux error: 5.786340766596909, 3sigma in AB mag (Aperture): 20.801186847346266\n",
      "irac_i4: mean flux error: 7.732774418318823, 3sigma in AB mag (Aperture): 20.486358510556805\n",
      "lbc_u: mean flux error: 0.11871575529794391, 3sigma in AB mag (Total): 25.020925963352433\n",
      "suprime_z: mean flux error: 0.3143173649362726, 3sigma in AB mag (Total): 23.963775925867857\n",
      "lbc_y: mean flux error: 1.0797613382098883, 3sigma in AB mag (Total): 22.62387743039219\n",
      "gpc1_g: mean flux error: 58.53693177853172, 3sigma in AB mag (Total): 18.288621975480076\n",
      "gpc1_r: mean flux error: 8.02817246978346, 3sigma in AB mag (Total): 20.445655128465823\n",
      "gpc1_i: mean flux error: 2.8802272151765846, 3sigma in AB mag (Total): 21.55862998893729\n",
      "gpc1_z: mean flux error: 6.612337216168607, 3sigma in AB mag (Total): 20.65630937913334\n",
      "gpc1_y: mean flux error: 31.560301656762157, 3sigma in AB mag (Total): 18.959343999224224\n",
      "90prime_g: mean flux error: 59.86345291137695, 3sigma in AB mag (Total): 18.26429245509825\n",
      "90prime_r: mean flux error: 0.9675227403640747, 3sigma in AB mag (Total): 22.743043909855707\n",
      "mosaic_z: mean flux error: 1.4107751846313477, 3sigma in AB mag (Total): 22.33355233351569\n",
      "newfirm_j: mean flux error: 1.8566344462989504, 3sigma in AB mag (Total): 22.035380853963325\n",
      "newfirm_h: mean flux error: 2.6152105047903254, 3sigma in AB mag (Total): 21.663430233070805\n",
      "newfirm_k: mean flux error: 5.2047704505963805, 3sigma in AB mag (Total): 20.91619291246422\n",
      "mosaic_r: mean flux error: 0.18776629472599635, 3sigma in AB mag (Total): 24.523152772612058\n",
      "mosaic_i: mean flux error: 0.4061876979572217, 3sigma in AB mag (Total): 23.68537994828491\n",
      "mosaic_b: mean flux error: 0.05892621321458029, 3sigma in AB mag (Total): 25.781425531074454\n",
      "tifkam_ks: mean flux error: 30.835213062541975, 3sigma in AB mag (Total): 18.984579479141097\n",
      "ukidss_j: mean flux error: 13.800500869750977, 3sigma in AB mag (Total): 19.857459741215585\n",
      "90prime_z: mean flux error: 1.42275869846344, 3sigma in AB mag (Total): 22.324368739504628\n",
      "irac_i1: mean flux error: 1.514460478111922, 3sigma in AB mag (Total): 22.256552002409087\n",
      "irac_i2: mean flux error: 2.0685033609017034, 3sigma in AB mag (Total): 21.918056286017737\n",
      "irac_i3: mean flux error: 11.279346328376583, 3sigma in AB mag (Total): 20.076487033897443\n",
      "irac_i4: mean flux error: 15.576459790975681, 3sigma in AB mag (Total): 19.726024967335583\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": 18,
   "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": 19,
   "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": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ap_lbc_u (3340.0, 3820.0, 480.0)\n",
      "ap_suprime_z (8540.0, 9280.0, 740.0)\n",
      "ap_lbc_y (9635.0, 11260.0, 1625.0)\n",
      "ap_gpc1_g (4260.0, 5500.0, 1240.0)\n",
      "ap_gpc1_r (5500.0, 6900.0, 1400.0)\n",
      "ap_gpc1_i (6910.0, 8190.0, 1280.0)\n",
      "ap_gpc1_z (8190.0, 9210.0, 1020.0)\n",
      "ap_gpc1_y (9200.0, 9820.0, 620.0)\n",
      "ap_90prime_g (4180.0, 5470.0, 1290.0)\n",
      "ap_90prime_r (5680.0, 7150.0, 1470.0)\n",
      "ap_mosaic_z (8552.0, 10018.0, 1466.0)\n",
      "ap_newfirm_j (11600.0, 13387.0, 1787.0)\n",
      "ap_newfirm_h (14795.0, 17860.0, 3065.0)\n",
      "ap_newfirm_k (19845.0, 23060.0, 3215.0)\n",
      "ap_mosaic_r (5692.0, 7176.0, 1484.0)\n",
      "ap_mosaic_i (7270.0, 9180.0, 1910.0)\n",
      "ap_mosaic_b (3474.0, 4750.0, 1276.0)\n",
      "ap_tifkam_ks (19500.0, 23000.0, 3500.0)\n",
      "ap_ukidss_j (11695.0, 13280.0, 1585.0)\n",
      "ap_90prime_z (8290.0, 12000.0, 3710.0)\n",
      "ap_irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "ap_irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "ap_irac_i3 (50246.3, 64096.7, 13850.398)\n",
      "ap_irac_i4 (64415.2, 92596.8, 28181.598)\n",
      "lbc_u (3340.0, 3820.0, 480.0)\n",
      "suprime_z (8540.0, 9280.0, 740.0)\n",
      "lbc_y (9635.0, 11260.0, 1625.0)\n",
      "gpc1_g (4260.0, 5500.0, 1240.0)\n",
      "gpc1_r (5500.0, 6900.0, 1400.0)\n",
      "gpc1_i (6910.0, 8190.0, 1280.0)\n",
      "gpc1_z (8190.0, 9210.0, 1020.0)\n",
      "gpc1_y (9200.0, 9820.0, 620.0)\n",
      "90prime_g (4180.0, 5470.0, 1290.0)\n",
      "90prime_r (5680.0, 7150.0, 1470.0)\n",
      "mosaic_z (8552.0, 10018.0, 1466.0)\n",
      "newfirm_j (11600.0, 13387.0, 1787.0)\n",
      "newfirm_h (14795.0, 17860.0, 3065.0)\n",
      "newfirm_k (19845.0, 23060.0, 3215.0)\n",
      "mosaic_r (5692.0, 7176.0, 1484.0)\n",
      "mosaic_i (7270.0, 9180.0, 1910.0)\n",
      "mosaic_b (3474.0, 4750.0, 1276.0)\n",
      "tifkam_ks (19500.0, 23000.0, 3500.0)\n",
      "ukidss_j (11695.0, 13280.0, 1585.0)\n",
      "90prime_z (8290.0, 12000.0, 3710.0)\n",
      "irac_i1 (31754.0, 39164.8, 7410.801)\n",
      "irac_i2 (39980.1, 50052.3, 10072.199)\n",
      "irac_i3 (50246.3, 64096.7, 13850.398)\n",
      "irac_i4 (64415.2, 92596.8, 28181.598)\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": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Depths on Bootes')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+ffrc6tix46+58p07dy5av3596yeffLJjp06diufPn3/NGK9lwoQJuWfPnnW48847gwHA0dGxIjo6+qKvr+/vLiJctWpV25ycHJvBgwcHAUDv3r1vbd68udbrC/RhHj0RkZViHn3dJSYm2o0ePTogOTn5rLHbNiXm0RMRETVTVj11T0RE1slUefRBQUEljTmab4zcexZ6IiKyCsbMo28sjZF7z6l7IiIiK8YRvZk79HkSrqcZ5c6O3/D0c8LghwON3i4REZkXjuiJiIisGEf0Zo6jbiIiagiO6ImIyKqFh4f7Vz5Fz9fXt2d6enqzGuSy0BMREVmxZvWthoiI6ueFIy/4ncs+Z9Q8+q7uXQteGfSKyfPoq1uyZInX4cOHXQDg008/vdCjR4/itLQ02xkzZnT85Zdf7AFg9erVqSNGjLhVfd+nn37ax9PTs+yFF164CgBz58719fLyKv373/9+tW6vvvFYdaGvzxXrvBqdiMi8REdHp3h5eZXn5+dLaGhoyJQpU7Ir8+jXrVt3af78+e0WLlzos3HjRoPuo3dxcSk/ffp0/OrVq1vPnTvXb//+/edmz57dYfDgwXkvvvji+bKyMuTk5OhNknnyySevP/DAA11eeOGFq+Xl5di+fbv7zz//HG/cV2xcVl3oiYjIOAwZeZtKQ/Poq3v00UezACAiIiLr73//ux8A/PDDD85btmy5CAC2trZo3bq13qfSBQUFlbi5uZUdOXKkZXp6eovu3bsXGOsJdqZi1YWeI3MiIstmjDz66jSa/+0uInVOdps+ffr1Dz74wPPq1astpk+fbvDjdZsKL8YjIiKz1dA8en02btzoAQAffvihe2ho6C0AGDRoUN6yZcvaAEBZWRmysrJqrI9Tp069uX//fteTJ0+2Cg8Pz2nI62sMVj2iN4WkpFeQl2/a0zHOTt0QGPiCSY9BRGQJGppHr09xcbH06tUruKKiQj777LMLAPDee+/98thjj3UMDAz01Gg0WL16derw4cN/dzEeADg4OKiBAwfmurm5ldvamn8ZNf8eEhFRs9WyZUsVExOTrG/dqlWrrgC4UlsbW7duTan8+fLly6d1P6ZX3cbPz6/su+++O29In8rLy3H8+HGnL774wqDtmxoLfR1xpE1E1HzFxsY6jBs3LiAsLCy7Z8+exU3dH0Ow0BMRkcUxVR59pdvlxF+6dOm0vn3MlckKvYj4AdgIwBtABYC1SqlVVdbPB7AMQBul1HVT9YOIiJoHY+bRN0ZOfGMx5Yi+DMA8pdRxEXEGECsi+5RScbovASMAGO0PhRrR7oVARi1faL17AmFvNE5/iIioRiYr9EqpdOgudlBK5YlIPABfAHEA3gbwLIAvTXV8a2Gqq/xv/lKG6yc712/nGxeAkppXt3UB7vGuX9NERGRcjXKOXkT8AYQCOCoiYwFcVkqdvN0DDkRkJoCZANChQ4dG6CUZrHUtXxA6dgbCZjZOX4iI6LZMXuhFxAnAVgCR0E7nLwZwf237KaXWAlgLAP369avzk4ushcmu8r8DwHjTNE1ERObDpE/GE5EW0Bb5aKXUNgBdAHQCcFJEUgC0B3BcRDjRS0REZm337t1OISEh3WxtbftWPpXPEpjyqnsB8CGAeKXUWwCglDoNoG2VbVIA9ONV9zV7IfkSzuQXGr3dHk4t8UpAe6O3S0RkrTp37lyyYcOGlDfeeMOrqftSF6acuh8EYCqA0yJyQrdskVJqlwmPSUREJnBl0WK/4uRko+bR2wcEFPi89qrJ8+gPHjzoGBER4e/o6FgxYMCA/O+//941OTn5bFRUVOsvv/zSraSkRJOWlmYfHh5+Y8WKFekAsHr16tZRUVFeIoJu3boVbt++/WJQUFAJ8NtQnJqUl5fj0Ucf7fCf//zH2c/Pr7iiogKPPfbYjenTp2fX8W1qMFNedX8YwG3jhJRS/qY6vrXgqJuImruG5tE/8cQTnd59992UESNG3HryySd9q647depUq9OnT591cnKqCA0NDRk3blyOo6NjxfLly9v9+OOPCe3atSvLzMzUm01/Oxs3bnRPS0uzS0xMPHv58mXbHj169HjssceaJOmOT8YjIqJaGTLyNpWG5NFfv37d5tatW5oRI0bcArRZ9Pv27XOrXH/33XfnVubJjxo1KvvAgQNONjY2GDNmTHa7du3KAMDLy6vOefOHDh1ymjBhQraNjQ06dOhQ9oc//MHgdD1jY6EnIiKz1dA8eqVuf9NW9f1EBEqpeuXU1+W4jYl59EREZLYamkffpk2b8latWlV89913rQDgk08+8ai6/vDhwy6ZmZk2+fn5smvXLrehQ4fmjxw5MnfHjh0eGRkZNgBQn6n7wYMH52/fvt29vLwcaWlptkePHnWuaxvGwhE9ERGZLWPk0a9ZsyZl9uzZHR0dHSsGDRqU5+zs/OtUfL9+/fInTpzYKSUlxSE8PPzGkCFDCgBg3rx56YMHDw7WaDSqR48eBVu3bk05ePCg48MPP9w1NzfX5rvvvnN79dVXfc6dO3dW3zEfffTR7G+//dY5MDCwe6dOnYp69+59y83Nrc6nAIxBzGl6oSb9+vVTx44da+puEBFZFBGJVUr1q+/+J0+eTOndu7dZ3v7s6OgYqi/BTp+cnByNq6trBQAsWrTIOz09vcWGDRvSoqKiWh87dqxVTRfxNVTlcTMyMmzuvPPObkeOHEno0KGD3jsDGurkyZOevXv39te3jiN6qrP9H63F1dQavzwDANp27Ix7HuNjcImo6X3++eeuK1asaFdeXi6+vr7FmzZtSmmM444YMSIgNzfXprS0VBYsWJBuqiJfGxZ6IiKyOHXNo4+IiPjd/etPPfXUDQANuuXtp59+ajlt2rROVZfZ2dlVnDp1KuGnn35KbEjbxsJCT3XGkToRmSNj5tEbqn///oXmnlvPq+6JiIisGAs9ERGRFWOhJyIismI8R99MmSoVD2AyHhGROeGInoiIyADMoyeLwhE3EdXFdxvj/bIu5xs1ptbD16ngvmndmiwsp67qm0dfVlYGW9umK7cc0RMRkVkbPnx4l+7du3fr2rVr9+XLl3sC2ifjRUREtA8JCel21113BV65cqXGSnrw4EHHwMDAkD59+gTPmjWrfUBAQHcAiIqKan3fffd1GTx4cIC/v3+PefPmtavcZ/Xq1a0DAwNDgoKCQsaPH98JAIKCgkoGDBhQaEge/VdffeU8YMCAwDFjxnQKCgrq3uA3oQE4oiciolo15cjbEvPoK9v+73//ezY4OLikPvsbCws9ERGZNUvMoweAXr163WrqIg+w0BMRkRmz1Dx6AHB0dKxoaBvGwHP0RERktiw1j96ccERPRERmy1Lz6M0J8+jJpJb+tBQJWQn13j/YIxjP9X/OiD0iaj6YR6/VVHn0jYl59ERE1Gw1VR69uWChJ5PiaJyITMES8ugb0q4xsdATEZFVYB69frzqnoiIyIqx0BMREVkxFnoiIiIrxnP0VGcv7zyLuCu59d4/xMcF/xjTpBkPRETNBkf0REREBqhLHn1KSkqLkSNHdm6svt0OR/RUZxyNW7eGztjow1kcy7f3vZV+19NSjZpH7+nXseCPcyKtMo/e39+/dM+ePTU+ra8xsdCTQV5IvoQz+YVN3Q0AQA+nlngloH1Td4OIGsnw4cO7pKen2xUXF2tmz56dOX/+/OuOjo6hkydPvnbkyBFnV1fX8q1bt17w8fEp07f/wYMHHSMiIvwdHR0rBgwYkP/999+7Jicnn42Kimr95ZdfupWUlGjS0tLsw8PDb6xYsSId0ObRR0VFeYkIunXrVrh9+/aLQUFBJQBgSB59YmKi3ejRowOSk5Ob/BG5LPRmLuO111Acb/znLth3C4b3okVGb5csH0fepE9TjrwtNY/eXLDQk0E4giaipmKpefTmwmSFXkT8AGwE4A2gAsBapdQqEVkGYAyAEgDnAUxXSt00VT8sHUfdRNScWXIevbkw5Yi+DMA8pdRxEXEGECsi+wDsA/C8UqpMRJYCeB4AH4hupW7uPI+SK7fqvb+dTyu4jelixB4RkSWpLY9+5syZ2Ybm0d933323asqjb9WqVcWuXbvcPvjggxRHR8eKBx98sOuiRYsyvb29yzMzM20seVRvskKvlEoHkK77OU9E4gH4KqW+qbLZfwA8aKo+EBGRZWMefcM1Sh69iPgDiAHQQymVW2X5TgCblVL/ut3+zKMnsgK7FwIZp43bpndPIOwN47ZpRZhHr8U8ehMTEScAWwFEVivyi6Gd3o+uYb+ZAGYCQIcOHUzdTSIislLNPY/epCN6EWkB4CsAe5VSb1VZ/iiA2QDuU0oV1NYOR/RERHVnzSN6fW6XR2+qY5pLHn2DR/QiYg8gHIB/1X2UUktus48A+BBAfLUiPxLai++GGlLkiYiIDME8ev0Mnbr/EkAOgFgAxQbuMwjAVACnReSEbtkiAFEA7AHs093W8B+l1GyDe0xNouqDe3KuFaK0uP4XoLawt4Frm5b13p8P+yEiMpyhhb69UmpkXRpWSh0GoO/Gxl11aYcIWReAkiq36FUkAhuO1L4fL9QiIjK40P8gIj2VUka+ZJYsRZOOoE1xtTbV6NDnSbielm/UNj39nDD44UCjtklEhrltoReR0wCUbrvpInIB2ql7AaCUUr1M30Vq9jgqJyKqt9pG9KMbpRdETWTpT0uRkNXwi2ODPYLxXH/reMAjR95E+u3evdtp3rx5fklJSY7r1q27MH369Oym7pMhblvolVKpACAinyilplZdJyKfQHuxHRERWbmsLUl+pRm3jJpH38K7VYHHg4FWmUdvTgw9R/+b3EoRsQHQ1/jdIWpc1jIKJ7JmlphHHxkZ6bNnzx43AMjKyrIdMmRI7pYtW1KM964YrrZz9M9De0tcSxHJxf+uoi8BsNbEfSMCAOz/aC2uptb4GOsate3YGfc8NtMEPSJqfppy5G2JefQrV668AuDKjRs3bO66666gp59++mo9X36D3fZriVLqdaWUM4BlSikXpZSz7r/WSqnnG6mPRETUjC1dutQrKCgopG/fvt1qyqP/6aefnPTtqy+Pvur6yjx6JycnVZlHv3fvXhdj5NFXVFTgwQcf7PSXv/wlc/DgwU32gDhDp+4XicgEAHdDexX+IaXUdtN1i+h/OCq3DvWdmbkdztpYP0vOo583b55Pu3btSkz5CF5D1H6iQesdaJ9NfxrAGQCzReQdk/WKiIgItefRA4ChefQAUFMefX5+vuzatctt6NCh+SNHjszdsWOHR0ZGhg0A1Gfq/tNPP3U9cOCAy/r165v8YkNDR/RDoY2YVQAgIh9DW/SJiAzCkTfVh6Xm0a9cudLr6tWrLfr06dMNAEaOHHlTd96+0Rla6BMBdACQqvvdD8Apk/SIiIhIp2XLliomJiZZ37pVq1ZdAVBr8ezbt29hUlJSHKDNo6/8sgAAnp6eZfou4ps7d+6NuXPn/mbKfejQoQWZmZkG1b6jR48mGbJdYzC00LcGEC8iP+l+vxPAjyKyAwCUUmNN0TkiIqKGau559IYW+hdN2gsiIqI6KCgo+G/1ZbfLo4+IiPjdU+yeeuqpGwAadKGcueTR345BhV4pdVBEOgIIUEp9KyItAdgqpfRe/EBkKW7uPI+SK7dq37AWdj6t4DamixF6RET1xTx6/Qwq9CISAWAmAA8AXQC0B/A+gPtM1zUypobe2sTbmIiILJOhU/d/AdAfwFEAUEoli0hbk/WKjO5j9w445+pf7/27aipwj/G6YzY4Cicia2dooS9WSpVUPlhARGyhfXAOWQi/kB7IyS+s//5OLY3YGyIiaiyGFvqDIlL5zPsRAJ4EsNN03SJjeyWgfVN3oVnLeO01FMfX79oc+27B8F60yMg9IqK6stSYWkOfjLcQwDVoH5IzC8AuAH83VaeIiIjMTWVM7ZgxY5r0kbZ1ZehV9xUish3AdqXUNRP3icjqcEROlm779u1+V69eNWoefdu2bQvGjx9f6yNiLTGmdvz48Z0efPDB7ClTptwEgLFjx3aaOHFi1uTJk3Pq9CYZwW17K1ovich1AAkAEkXkmojwvnoiImoU0dHRKWfPno0/ceJE3Jo1a7wyMjJsKmNq4+Li4gcNGpTvS8YbAAAgAElEQVS3cOFCn5r2f+KJJzq98847qSdOnEiwsbH5zfVlp06davXFF19cOHPmzNkdO3Z4xMTEOB47dsxh+fLl7Q4ePJiUmJgYt2bNmjrfthcREXHto48+ag0AN27csImNjXV6+OGHG73IA7WP6CMBDAJwp1LqIgCISGcA74nI35RSb5u6g0RE1PQMGXmbytKlS72+/vprNwCoKaZ2woQJXfXtqy+mdt++fW6V6ytjagGgMqbWxsYGDY2pHTVqVH5kZGTHy5cv20ZHR7uPGjUqu0WLFnV/8UZQW6GfBmCEUup65QKl1AURmQLgGwAs9EREZDKWHFP78MMP3/jggw88tm7d6rF+/fqUhrZXX7WdaGhRtchX0p2nb5qvJkRE1GxYakwtAMyePfv6mjVrvACgX79+RfVpwxhqG9GX1HMdERFRg1lqTC0A+Pn5lXXp0qVozJgxN435ntRVbYW+t4jk6lkuABxM0B8iIqJfWWpMLQDk5eVpUlJS7B9//PEsQ/cxhdsWeqVUvaYriIiIzEVTxNRu377dec6cOf5z5szJbN26dZ0v5jMmQ5+MR0REZDYsIaZ2/PjxpxvStrGw0BMRkVVgTK1+LPREjWD37t3IyMio177e3t4ICwszco+IqLkw9Fn3REREZIE4ojdDhz5PwvW0fKO36+nnhMEPBxq9XaodR+SmsfSnpUjIql8qYG2CPYLxXP/nTNI2UWPiiJ6IiMiKcURvhsxt1H1z53mUXLlV+4Y6dj6t4Dami1H7kJT0CvLy4+u0j7NTNwQGvmDUfpB54YibDPHjjz+2nDNnTseCggJN+/btS7Zs2XLBw8OjAgCef/557+joaE+NRoMVK1b8Eh4eru/ZMTWKjIz0GTZsWN748eP1PpnPHJis0IuIH4CNALwBVABYq5RaJSIeADYD8AeQAuBhpdTvbnsg8/HmhUwkZBcYvH1wkSNeg3ELPRFRfUVERPgvXbo0bdSoUfkrV65s/fLLL3uvWrXqSmxsrMO2bds8EhMTz6amprYYMWJE4Lhx487Y2hpWGsvKyrBy5cpaH9jT1Ew5oi8DME8pdVxEnAHEisg+AI8B+E4p9YaILASwEAC/lpsx+86uaHFFf2CE3u19XIzeB47MiZpWXPxzfrfyk4yaR9/KKbAgpNtSk+fRp6SkOISFheUDwOjRo3P/+Mc/Bq5aterKli1b3CZMmJDVsmVLFRwcXNKxY8fiAwcOtPLz8ysdOXJkQGho6K0zZ844du7cueiLL75IcXZ2rvD19e35yCOPXN+/f7/LrFmzru7du9d19OjROdOnT8/29fXt+cADD2QdPnzYuaysTN5///3UhQsX+qamptrPnTs389lnn70GAC+88ILXv//9b4+SkhIZNWrUzbfffrvGLwsLFixot2XLFo927dqVtG7duiw0NLRgyZIlmXV5n01W6JVS6QDSdT/niUg8AF8A4wAM0232MYADYKE3a/8Y072pu0BEzVh0dHSKl5dXeX5+voSGhoZMmTIluzKPft26dZfmz5/fbuHChT76HmULAAEBAYWbNm1ymzJlys1//etfHhkZGXYAcPnyZbs//OEPv1757OPjU5KWlmbn5+dXmpKS4rBmzZqU+++//9ZDDz3kv2zZsjaVBdbBwaEiNjY2EQD27t3rWvVYfn5+JSdOnEh4/PHH/WbMmOF/9OjRhMLCQk2PHj26P/vss9e2bdvmcu7cOYdTp07FK6UwfPjwrrt373aq/CJSVUxMjOPOnTvdT58+HVdaWip9+vQJCQ0NNXx6VadRztGLiD+AUABHAXjpvgRAKZUuIm1r2GcmgJkA0KFDh8boJhER1cCQkbepNCSPHgDWr1+f8te//tXv9ddfbzdy5MibLVq0UID+CNvKeFpvb++S+++//xYATJ069UZUVFRbAJkAMG3atBpPNz/88MM3AaBnz54Ft27d0ri7u1e4u7tX2NvbV1y/ft1mz549LjExMS4hISEhAFBQUKBJSEhw0FfoDxw44BQWFnbTyclJAVAjRoyoVziOyQu9iDgB2AogUimVW1NmcHVKqbUA1gJAv379GpwLTERkqIzXXkNxvGlu26sr+27B8F60qKm70WQamkcPAKGhoUVHjhxJBoBTp07Zf/PNN24A0L59+5K0tDS7yu2uXLli1759+1J97VX93dnZuaKmYzk4OCgA0Gg0sLOz+7V2aTQalJaWilIKkZGR6QsWLPhdBHx1+r6I1IdJC72ItIC2yEcrpbbpFmeKSDvdaL4dgKum7IMlqs8V5obgVehEZGlqy6OfOXNm9u3y6AHg8uXLtr6+vmXl5eX4xz/+0e7xxx+/CgDh4eE3J0+e3PnFF1/MTE1NbZGSkuIwbNiwW+fPn7dLT0+3+/bbb1sNHz781qZNmzwGDhxolIebhIWF5b700ks+M2fOzHJ1da24ePFiCzs7O+Xr6/u76wuGDRuWr7tbIL20tFS+/fZbt2nTpl2r6zFNedW9APgQQLxS6q0qq3YAeBTAG7r/f2mqPhAR1UdzHkGbG2Pk0a9fv97jww8/bAsAf/rTn7J1YTbo169f0fjx47MCAwO729jY4K233kqtvOK+c+fORevXr2/95JNPduzUqVPx/Pnz61xg9ZkwYULu2bNnHe68885gAHB0dKyIjo6+qK/QDx06tGDkyJE5ISEh3X19fYt79ep1y9XVtc5JeGKsqYHfNSxyN4BDAE5De3sdACyC9jz95wA6APgFwENKqdtm9fbr108dO3bMJP0kIrJWIhKrlOpX3/1PnjyZ0rt371qnmJuCo6NjqL4EO2NITEy0Gz16dEBycvJZU7RfFzk5ORpXV9eKvLw8zV133RX0/vvvp959992/uyDv5MmTnr179/bX14Ypr7o/DKCmkyb3meq4RERE1mLKlCkdk5OTWxYXF8uf//znG/qKfG34ZDwiIrI4dc2jr0vbQUFBJY05ms/IyLAZNmxYUPXlBw4cSNy5c+fFhrbPQk9ERFahKfLojcHb27vclJn2DLUhIiKyYiz0REREVoxT90RkseqarFgXpkhhJGoKHNETERFZMY7oichiccRNhmjuefQc0RMRkVWLiIjwf/XVVy8lJSXFjR07Nvvll1/2BoCqefR79uxJioyM7FBWpjfpVq/KPPrGKPIVFRUoL6/zQ/EAcERPFmLpT0uRkFW3kJFgj2A8158JyETGEBn/i1/CrSKj5tEHt3IoWNmtA/Poa8ijT0xMtAsLCwsYOHBgXmxsrNOXX355LjAwsKSu7zNH9EREZNaio6NTzp49G3/ixIm4NWvWeGVkZNhU5tHHxcXFDxo0KG/hwoU+Ne1fmUcPANXz6P38/H4tnJV59ID2y8Hs2bOvJSUlxTk7O1csW7asTeV2lXn0M2fO/F1cbWUe/YABA/JnzJjhv3PnzvNHjx5NeOONN3wAoGoefXx8fNyJEyccd+/e7VS9nUopKSkO06dPvxEfHx9XnyIPcERPFoIjc6KmZcjI21Saax49ALRr167kvvvua9CtJSz0RETV7N69GxkZGU3dDQCAt7c3wsLCmrobTaY559ED2nQ7Q7a7HU7dExGR2aotjx4ADMmjBwB9efTbtm3zKCwslISEBLvKPHoAqMyjBwBj59F/8sknnjk5ORoAuHjxYovK/pkKR/RERNU05xG0uWnOefTGYrI8emNiHj0RUd0xj75+zCmP3lBNkkdPRFoNPd/b3M/RElHDsNATEZHFaS559N7e3vV7Sk4VLPREJsbROFHjYB69frzqnoiIyIqx0BMREVkxFnoiIiIrxkJPRERkxVjoiYiIjCQyMtJn+/btzk3dj6p41T0REZGRrFy5Um/kbFNioSciolot2HLSLykjz6h59IHezgXLHuxt8jz6/v37B/Xs2bPg5MmTjllZWbYbNmy4+Oqrr7ZLTExsOW7cuKyoqKgrAPDSSy95RUdHewLA1KlTr7344otXc3NzNWPHju2cnp5uV1FRIc8+++yViIiI7Pnz57fbs2ePW3FxsaZfv3750dHRqRqNBuHh4f6V+fQHDx50jIyM7FBQUKCxs7NTMTExie7u7r8LqZk4cWLHymf4Z2ZmtpgxY8bVFStWpDfs3f0fTt0TEZFZa2gePQDY2dlVHDt2LHH69OnXHnrooa7r1q37JSEh4ezmzZs9MzIybA4dOuS4adOm1rGxsfHHjh2L37hxY5sjR4603LZtm4u3t3dpYmJiXHJy8tkJEybkAsCCBQuunjlzJj45OflsYWGh5rPPPnOteryioiKZPHlyl5UrV/6SmJgYd/DgwUQnJye9SXSbN29OTUhIiNuxY8c5Nze3slmzZtXpAT+14YieiIhqZcjI21QamkcPAA888MBNAOjdu3dh165dCzt27FgKAH5+fsUXLlywO3DggNOf/vSnmy4uLhUAMGrUqOz9+/c7jx07Nmfx4sV+c+bM8R03blzOyJEj8wFg9+7dzm+99ZZ3UVGR5ubNm7YhISGFAHIqj3fq1CmHtm3blg4dOrQAADw8PG4bN1tQUCDh4eFd3n777V8CAwNL6v1m6cFCT8a1eyGQcVr/Ou+eQNgbjdsfc3C798QQzfV9I4Jx8uiB3+bE29vb/yYnvqysrMaAt169ehUfP348buvWra6LFy/2/fbbb3OXLFmSMW/evI5Hjx6N69q1a+kzzzzjU1RU9Js+KaUgIganxk2dOrXjmDFjssePH19j3G59ceqeiIjMljHy6A1x77335u/atcstLy9Pk5ubq9m1a5f7Pffck5eSktLC2dm54sknn8yKjIzMPHHihGNBQYEGALy9vctycnI0O3fudK/eXu/evYsyMzPtDh486AgA2dnZmtLSUr3Hfv3119vk5+fbvPbaa/VPv7oNjujJuDjy/D2+J0T1Zow8ekPcfffdBZMmTbpxxx13dAO0F+MNGjSocOvWrS7PP/98e41GA1tbW/Xuu++menp6lk+ePPlaSEhI9/bt25dU9qkqBwcHFR0dff6pp57qUFRUpHFwcKiIiYlJcnV1/d0U/urVq71btGihgoODQwBgxowZ15599tlrDXk9VTGPnojISjGPvvm4XR49p+6JiIisGKfuiYjI4pgyj95Utm7d6rJ48eL2VZf5+fkV79u377wpj8tCT0REVsHc8+jDw8Nzw8PDTZY7XxOTTd2LyHoRuSoiZ6os6yMi/xGREyJyTET6m+r4REREZNpz9B8BGFlt2ZsAXlZK9QHwou53IiIiMhGTFXqlVAyArOqLAbjofnYFYHYP/yciIrImjX2OPhLAXhFZDu2XjIE1bSgiMwHMBIAOHTo0Tu+IiIisTGPfXjcHwN+UUn4A/gbgw5o2VEqtVUr1U0r1a9OmTaN1kIiIqDb//Oc/23bu3Ln72LFjO0VHR7suWrTI29jHmDhxYsfY2FiHhrbT2CP6RwE8rfv5CwAfNPLxiYiIGuzDDz9ss3v37uTg4ODKAJqc6tuUlpaiRYsW9T7G5s2bU+u9cxWNXeivABgK4ACAewEkN/LxiYioPrb/xQ9X44yaR4+2IQUY/06j5NH37ds3//Dhwy55eXk277//fsrIkSPzy8rK8Je//KX9kSNHnEtKSiQiIuLqggULrk+ZMqVDWFhYzuTJk3NGjBjRxc3NrfyLL75Iefvttz0vXrxod/36ddtLly7Zjx07tuvkyZOvu7u7lx87dqzVxo0bfwkPD/d3d3cvO336tGOvXr0KnJ2dK1JSUuwyMzNbpKSkOLz22mtpP/74o9P333/v4uXlVfrtt9+eqxqyU73fy5cvTxsyZEhBQ95mU95e9ymAHwEEicglEXkcQASAFSJyEsBr0J2DJyIiqokx8ujLysrk9OnT8UuXLk1bsmSJDwCsXLnS09XVtfzMmTPxJ0+ejP/444/bJCQk2A0ZMiQvJibGGQAyMjLskpKSHADgyJEjTkOHDs3ftGnTL23bti09ePBg0j/+8Y+r1Y91/vx5hyNHjiStW7fuEgCkpqbaf//99+e2bNlybvbs2Z3uvffe3KSkpDgHB4eKzz//3LX6/sZmshG9UuqRGlb1NdUxG8PSn5bih3M/oKTEqHHBv9EGbfCI1yMICwsz2TGIiOrEgJG3qRgjj/6hhx7KBoCBAwfeWrBggR0AfPvtty4JCQmOO3bscAeAvLw8m7i4OIcRI0bkv/POO16xsbEOgYGBhTdv3rRJTU1tERsb22rdunW1PpRnwoQJ2ba2/yuvw4cPz7G3t1f9+/cvLC8vlwcffDAXALp371548eJFu3q+LQbjk/GIiMhsGTuP3tbWFuXl5QIASilZsWLFL+Hh4bnVt8/JybHduXOn6+DBg/OysrJsN27c6N6qVasKd3f336XPVefk5PSbbSqn5m1sbGBra6s0Gm33NRoNysrKbt9xI2Chr6Pn+j8H8Hl+RESNorY8+pkzZ2bXN49+xIgROe+9916b0aNH59nb26tTp07Z+/v7l7q4uFT07ds3f82aNW337duXdPXqVdtJkyZ1GTVqVLbxX6HpsdATEZHZMmUe/d/+9rfrKSkp9j179uymlBIPD4/SXbt2nQeAu+++O//QoUMuPXr0KC4uLi7JycmxGTJkSJ2/TDRUbTMVBrXBPHoiIuvEPHrLFhgYGLJjx45zVW7hq9Ht8ug5oq+HjNdeQ3F8gkmPYd8tGN6LFpn0GEREZJ4GDhwYEBQUVGhIka8NCz0REVkcS8yjr8mIESO6pKWl2Vdd9uqrr17Sd5FgfbDQ1wNH2kRE5sfc8+hrsm/fvvOmbL+xn3VPREREjYiFnoiIyIqx0BMREVkxFnoiIiIrxkJPRERUR42RR+/r69szPT29wRfN86p7IiKiOmqMPHpjYaEnIqJavXDkBb9z2eeMmkff1b1rwSuDXmEefQ159ADw5ptvtt27d69rWVmZbN68+UJoaGhRXd9nTt0TEZFZa8559J6enmVxcXHxM2bMuPbGG2941ef944ieiIhqZcjI21Sacx79pEmTsgGgf//+BZX9rCsWeiIiMlvNPY++Sr9VfbPrOXVPRERmq7Y8egBoaB59cXGxAMCpU6fsc3NzNQBQmUc/fPjw/GHDhuW/88473gMGDMg35mtrLBzRExGR2WruefTGwDx6IiIrxTz65uN2efScuiciIrJinLono9q9ezcyMjL0rvP29kZYWFgj94iIrBHz6A3HQk9ERFaBefT6sdCTUXHETkRkXniOnoiIyIqx0BMREVkxFnoiIiIrxkJPRERUR8yjJyIismLMoyciIqtyZdFiv+LkZKPm0dsHBBT4vPYq8+hvk0cPAPn5+RIWFtZ1/Pjx2fPmzavzkwo5dU9ERGatOefR5+bmau6///6AiRMnZtWnyAMc0RMRkQEMGXmbSnPOox87dmzXyMjIjDlz5mTVdtyaWEShj42NvS4iqdUWu0LPORETMfaxjNFefduo63512d7QbT0BmGVQRiNozL+3huJnyXo/Sx0NPJ7Zau559HfeeWf+nj17XGfNmpVVuV9dWUShV0q1qb5MRNYqpWY2xvGNfSxjtFffNuq6X122N3RbETnWkEQtS9aYf28Nxc8SP0vmrLY8+pkzZ2Y3NI9+9OjRefb29urUqVP2/v7+pS4uLhWVefT79u1Lunr1qu2kSZO6jBo1Ktv4r/D2li1bdmXRokXtpk6d2iE6Orpej/i15HP0Oy34WMZor75t1HW/umzfmH8mlsoc3yN+lhpnP36W6iE8PDynrKxMAgMDQxYtWuSjJ4++W0xMjPPrr7+eXte2//a3v10PDg4u6tmzZ7eAgIDuERERHUtLSwXQ5tGXl5dLjx49igcNGlTQlHn0H374YZruQsT29dnfIvLoybo0h1EIUWMw9WeJefSWg3n0ZG7WNnUHiKwEP0tUK4s4R0/WRSnFf5yIjKA5f5aYR284FnoiIrIKzKPXj1P3REREVoyFnoiIyIqx0FOTE5FWIhIrIqObui9ElkpEhonIIRF5X0SGNXV/yHyw0JPRich6EbkqImeqLR8pIokick5EFlZZ9RyAzxu3l0Tmr46fJQUgH4ADgEuN3VcyXyz0ZAofARhZdYGI2AB4B0AYgBAAj4hIiIgMBxAHILOxO0lkAT6CgZ8lAIeUUmHQfnF+uZH7STqRkZE+27dvd27qflTFq+7J6JRSMSLiX21xfwDnlFIXAEBEPgMwDoATgFbQ/oNVKCK7lFK1PkuaqDmoy2dJKRWnW58NwB5G9t3GeL+sy/lGjan18HUquG9atyYLyzGFlStXXmnI/qbIsOeInhqLL4CqH+hLAHyVUouVUpEANgFYxyJPVCu9nyURmSAiawB8AmB1k/TMRIYPH96le/fu3bp27dp9+fLlnoD2yXgRERHtQ0JCut11112BV65cqXHg2r9//6DHH3/cr1+/fkGdO3fufvDgQcf777+/S8eOHXs89dRTv8bbvvTSS14BAQHdAwICui9ZsqQtoI2JHTZsWNegoKCQgICA7uvWrXMHgPnz57fr0aNHt4CAgO6PPPJIx4oK7T9d4eHh/hs2bHAHgIMHDzqGhoYGBwUFhfTs2bNbdna23pobFRXVOiwsrPO9997bdfDgwYFGe+N0OKKnxqIvoenX5y8rpT5qvK4QWTS9nyWl1DYA20x10KYceUdHR6d4eXmV5+fnS2hoaMiUKVOyK/Po161bd2n+/PntFi5c6LNx48Ya76O3s7OrOHbsWOIrr7zS9qGHHur6888/x7dt27bM39+/56JFizKTk5PtN23a1Do2NjZeKYW+fft2u++++/KSk5Ptvb29Sw8cOHAOAG7cuGEDAAsWLLi6fPnydAAYP358p88++8x10qRJvyYOFhUVyeTJk7tER0efHzp0aEFWVpameqpdVcePH3c6derUWS8vr3LjvXNaHNFTY7kEwK/K7+0BNGiKi6iZanafpaVLl3oFBQWF9O3bt1tNefQ//fST0+3aeOCBB24CQO/evQu7du1a2LFjx9KWLVsqPz+/4gsXLtgdOHDA6U9/+tNNFxeXCldX14pRo0Zl79+/3/mOO+4oPHTokMucOXN89+zZ49S6detyANi9e7dzr169ggMDA0N++OEH5zNnzrSserxTp045tG3btnTo0KEFAODh4VFxuyn5wYMH55qiyAMs9NR4fgYQICKdRMQOwJ8B7GjiPhFZomb1WaqaR5+YmBjXrVu3wobk0Ws0ml/z4St/LysrqzHgrVevXsXHjx+P69mzZ+HixYt958+f366goEDmzZvXcdu2beeTkpLipkyZcr2oqOg3fVJKQUQMTo1zdHQ02WlLFnoyOhH5FMCPAIJE5JKIPK6UKgPwVwB7AcQD+FwpdbYp+0lk7vhZqj2PHgDqm0df1b333pu/a9cut7y8PE1ubq5m165d7vfcc09eSkpKC2dn54onn3wyKzIyMvPEiROOBQUFGgDw9vYuy8nJ0ezcudO9enu9e/cuyszMtDt48KAjAGRnZ2tKS0sb0sV64zl6Mjql1CM1LN8FYFcjd4fIYvGzpM2jX7t2bZvAwMCQLl26FOnJo/d2dnYu37Zt24WGHOfuu+8umDRp0o077rijGwBMnTr12qBBgwq3bt3q8vzzz7fXaDSwtbVV7777bqqnp2f55MmTr4WEhHRv3759SWWfqnJwcFDR0dHnn3rqqQ5FRUUaBweHipiYmCRXV9dGv+CYefRERKQX8+gtB/PoiYiImilO3RMRkcWxxDz6rVu3uixevLh91WV+fn7Fpo6p5dQ9ERHpZc5T9/RbnLonIiJqpljoiYiIrBgLPRERkRVjoSciIrJiLPREFkxE/k9EjovInU3dFyKqXx59TEyM42OPPeZX+5b1w9vriCyUiLQC0BbALACjoX0GOpFJ7H1vpd/1tFSj5tF7+nUs+OOcyGafRz9kyJCCIUOGFJiiPwBH9GQhRORtEYms8vteEfmgyu8rROQZIx8z38jtuYnIk1V+9xeRMwbu21JEDoqITeUypdQtAO0AHAAQJSJ2IhIjIvwCT1bF2vPov/rqK+d77rmnq9HesGr4DwJZih8APARgpYhoAHgCcKmyfiCASH07mhE3AE8CeLce+84AsE0p9WuMpYi0BuAIIA9AuVKqRES+AzARQLQR+kv0q6YceTeHPHpT4oieLMURaIs5AHQHcAZAnoi4i4g9gG4A/isi20UkVkTOisjMyp1FZGm10fRLIjJPRKaIyE8ickJE1lQdMVfZVu82uhF5vIis0x3vGxFpqVv3gogkiMg+EflUROYDeANAF107y3TN2+jbX4/JAL6stuzvAJYDOAsgRLdsu25bIqvRHPLoTYmFniyCUuoKgDIR6QBtwf8RwFEAdwHoB+CUUqoEwAylVF/dsqd0o14A+AzakW6lhwEc0y0bpJTqA6Ac1YqkiHSrZZsAAO8opboDuAkgXET6AQgHEApggq4vALAQwHmlVB+l1IKa9q/+2nWZ452VUilVlvnr3ofN0EaVdtetOgOAF+aR1WguefSmxEJPlqRyVF9Z6H+s8vsPum2eEpGTAP4DwA/aQgql1H8BtBURHxHpDSAbQE8AfQH8LCInANwHoHO1Y95XyzYXlVIndD/HAvAHcDeAL5VShUqpPAA7b/Oa9O1fnSe0XwKq+ieAJUr7r9OvhV43tV8iInW66pfIXDGPvuF4jp4syQ/QFvWe0I5c0wDMA5ALYL2IDAMwHMBdSqkCETkAwKHK/lsAPAjAG9oRvgD4WCn1/G2OWds2xVV+LgfQUrePofTtX10hqrwOEekD7UzB3SLyjm7d6Srb2wMoqkMfiMwW8+gbjqE2ZDF0BW4bgAtKqeG6ZbEAfAH0ADAIwBNKqTEiEgzgBICRSqkDum27A1gH7Qh5KAB3aM97D1JKXRURDwDOSqlU3fb5APrXtI1u+vwrpVQP3fbzATgB+BrAGmi/lNhCO1JfB2ADgONKqY667fXur5R6Sc9rTwMQoGUz2DMAACAASURBVJQqEpG9AN5USn2nW+cF4L9KKR/dqYrDSqluDXqziWDeoTbMo/8thtqQtTgNbZH+T7VlOUqp6wD2ALAVkVMAXqm2HZRSZwE4A7islEpXSsVBe0HbN7p99kF7u1rVfWrdpjql1M8AdgA4Ce0Xk2O6Pt4AcEREzlS5GM9Q30A7gr8XQKvKIq87XiaAVrovIfcA2FXHtonIinFET2QCIuKklMoXEUcAMQBmKqWON6C9UADPKKWm1rLdNgDPK6US63ssokrmPKLXpznn0d9uRM9CT2QCIrIJ2lveHKA9x/+6EdqcoWurvIb1dgD+rJTa2NBjEQGWV+ibs9sVel6MR2QCSqlJJmhzfS3rSwCwyBPRb/AcPRERkRVjoSciIrJiLPRERERWjIWeiIjISOqTRx8VFdV62rRpHUzVJ16MR0REtcrakuRXmnHLqHn0LbxbFXg8GNjs8+hNjSN6IiIya9aeRw8Aly9fbjF48OAAf3//HvPmzbvtQ7nqiiN6IiKqVVOOvJtDHv2pU6danT59+qyTk1NFaGhoyLhx43KGDBlSYIz3jyN6IiIya80hj/7uu+/O9fb2LndyclKjRo3KPnDgwG1fT12w0BMRkdlqLnn01ftf2+upCxZ6IiIyW80lj/7w4cMumZmZNvn5+bJr1y63oUOH5jfk9VTFc/RERGS2mksefb9+/fInTpzYKSUlxSE8PPyGsc7PAwy1ISKiGphzqA3z6H+LefRERETNFKfuiYjI4ugbzTfnPPrb4dQ9ERHpZc5T9/RbnLonIiJqpljoiYiIrBgLPRERkRVjoSciIrJiLPRERGTVoqKiWqekpPz6oPmJEyd2jI2NdQCA9evXu3fu3Ln7gAEDAk2dC1+Vr69vz/T09Ea584231xERUa22b9/ud/XqVaPm0bdt27Zg/PjxJk/F+9e//uXZp0+fQn9//1IA2Lx5c2rlug0bNniuWrXqlzFjxuRFRUW1NnVfmgJH9EREZNYakke/YcMG9zNnzjhOmzatc3BwcEh+fr70798/KCYmxnH+/PntYmNjnebOndtx1qxZv7m//bPPPnPt06dPcHp6uu2mTZtce/XqFdytW7eQgQMHBqalpdkCwDPPPOMzYcIE/0GDBgX4+vr2/Pjjj91mz57dPjAwMGTw4MEBxcXFtSbT5Ofny+DBgwNWrFjhmZubqxk2bFjXoKCgkICAgO7r1q373TP064MjeiIiqlVjjLxr0pA8+unTp2e/9957bZcvX55W/fnxy5cvT4+JiXGpXFc5ot+4caPbqlWrvPbt25fcpk2b8hEjRuT/+c9/TtBoNHjrrbc8lyxZ4r1u3bpLAJCammr/ww8/JB0/ftzh3nvvDf7444/Pv//++5dGjBjR5fPPP3edOnXqzZpeV25uriY8PLzzpEmTbvz1r3+98dFHH7l5e3uXHjhw4BwA3Lhxw8YY7x8LPRERmbWlS5d6ff31124AUFMe/YQJE7oa41g//PCD88mTJx3379+f5OHhUQEAFy9etBs/fnz7a9eutSgpKdH4+fkVV24/fPjwHHt7e9W/f//C8vJyefDBB3MBoHv37oUXL160u92xxo4d2zUyMjJjzpw5WQBwxx13FC5evNhvzpw5vuPGjcsZOXKkURLsOHVPRERmy1h59Ibq0KFD8a1bt2zOnPl/9u48Lqp6/x/4exa2YQZlR9ZBYBiGRZCEJM01BSuvilm53dRSMypFM7OblWamptfIn6aVmmY3u2imeNWLpUiSpoiJwLCIILIpqzMMwiz8/vCOX7LRQZiBM8Pr+Xj4eOBw5pzPmX9efD5zznldsda+lpCQ4L1gwYKbBQUFuZs3by5taWm5d3xttz2HwyEul9vGZt/9lbbn/mHHGjRokPzYsWN9NJq7hXZhYWEtFy9ezA0NDW1+9913PZYsWdLPEOeEoAcAAMYyRB89n89XNzY2dmgZ3NPTs3X//v1Fs2bN8r1w4YI1EZFMJuN4e3srtcfq+lndtX79+goHBwfVjBkzvImISkpKLAQCgWbBggV1CxcurL506ZJBLn5E0AMAAGPFx8c3qlQqlkgkkixfvtxdRx990OnTpwVr1qypfNA+Zs6cWfP666/7aC/G03fMAQMGtOzevbv4+eef98vJybF69913K1588UW/yMjIQEdHR5Uhz+/rr78ua2lpYc+fP98zMzPTJjw8PEgsFkvWrl3bb8WKFQ88p0eBUhsAANCJyaU26KP/M5TaAAAA9FK46h4AAEyOqfTRP/XUU35lZWVW7V9bvXr1jfj4+NvdNQYEPQAAmIU9e/b85T76npaamnq1p8eApXsAAAAzhqAHAAAwYwh6AAAAM4agBwCAXulBtbTr1q1z3rx5818ejJOfn28ZEBAQbIhjt6/KNTZcjAcAANDO0qVLbxn7GO2rco0NQQ8AAHrl5r3t1SQvMGgfvS1fpJAErdXbijd69Gi/yspKy/89Qa56yZIlNTweL2LatGm3zpw5I+jTp496//79xe7u7jqfWhcVFRWobairrKzkPvbYY0Hl5eXZ7bf5/vvv+3zyySf9jh49WrR+/XoXPp+vXrlyZXV6ejrv5ZdfFtrY2Giio6PvlcxcuHDBetasWb5KpZKl0Who//79V318fJTjx4/vX1lZaanRaFhLly6teOWVV+r1jelRP7dHhaV7AABgtL1795bk5OTkXbp0KXfbtm2uVVVVHG1NbW5ubt4TTzwhW7ZsmXtn97979+6+69evd0tNTS3s16/fn/5YmDNnjnDjxo3XL126JG3/+ueff+68YMGCaqlUmnv58uU8X1/f1gMHDti5ubkp8/PzcwsLC3MmTZrUbffKPwxm9AAAoFdHZt7GYsyaWl21tFq1tbUcmUzGefrpp+Xa4/zyyy99iIgGDx7c9Omnn/a7ceOG5QsvvFAfGhraYqya2a7CjB4AABjLEDW1XC63Ta1WExGRQqH404a6amm12traHrjf+fPn1/30009FNjY2mri4ONGhQ4cExqqZ7SoEPQAAMJYhamq9vLxafv/9d1sior1799q3/52uWlotJycnNZ/PVx8/fpz/v+M4aH+Xm5trGRQU1PKPf/zj5pgxYxouXbpkY6ya2a7C0j0AADBWfHx84/bt251FIpHEz8/vjo6aWjeBQKA+cOBA8YP2sWzZsurnn3++//fff+84dOjQv3xv3r6W9tChQ0Xtf/f111+XaC/GGzly5L337tmzx+Hf//63I5fLbXN2dlauWbOm4tdff7V95513PNlsNnG53LYtW7Y89Mr6h61CGBJqagEAQCfU1BqPSCSSHDp0qEgsFrcaYn+oqQUAAGCImJiYgMDAwGZDhbw+WLoHAACTg5rajkPQAwCAWUBNrW5YugcAADBjCHoAAAAzhqAHAAAwYwh6AAAAM4agBwAAs/bbb7/ZhIeHi0UikWTkyJH+dXV197LvnXfecfP29g4RCoUh+/fvt3vUfS9cuND94MGDAsOO2LAQ9AAAYNZeeeUV4erVq28UFBTkjh8/vv7DDz90IyLKzMy0PnDggEN+fn7OsWPHChYuXOitUulsutVJpVLRpk2bKiZMmPDAx+8yAW6vAwAAvRbmXfeSNt0x6LPbxbbWik1B3kbvoy8pKbGOi4uTExE988wzt8eOHSv67LPPKpKTk/tOmjSpzsbGpk0sFrf6+Pi0nDp1ytbLy0sZGxsbEBER0XTlyhVe//797/z73/8uEQgEGg8Pj9AXX3yx5uTJk3bz5s27efz48T7PPPNM46xZs+o9PDxCJ06cWPfrr78KVCoV64svvihdtmyZR2lpqdXrr79evXTp0ltERO+9957rjz/+6NDa2sp6+umnG/75z39W6Br3unXrnHfs2OFMRCSTyTienp4t586dK3jUzxkzegAAYLSu9tEHBAQ0f/fdd32JiL799luHqqoqSyKi8vJySy8vr3tPp3N3d28tKyuzJLr7x8H8+fNvFRQU5AoEAs369eudtdtZW1trMjMz8+fOnVt//7G8vLxaL126JI2OjpbPnj1bePjw4avnzp2TfvLJJ+5ERAcOHLArKiqyvnz5cl5eXl7upUuXeEePHuXfvx8ioqVLl96SSqW5f/zxR56bm1vrm2++Wd2Zzw8zegAA0KsjM29j6Wof/Y4dO0oSEhK81qxZ0y82NrbBwsKijehuDe39WCxWGxGRm5tb65gxY5qIiGbMmFGblJTkQkTVREQzZ878S8BrTZkypYGIKDQ0VNHU1MS2t7fX2Nvba6ysrDQ1NTWcY8eO2Z0+fdpOIpFIiIgUCgVbKpXeW3HQZc6cOV5PPvmkbOrUqY36PitdEPQAAMBY7fvoBQKBJioqKvBR++gjIiLunDlzppCI6PLly1b//e9/+xLdrajVzuCJiCoqKiw9PT2VuvbX/v8CgUDzoGNZW1u3ERGx2WyytLS895cEm80mpVLJamtro4ULF1a+9dZbHSoLSkpKcrxx44blN9980+mn/mHpHgAAGMsQffTl5eVcIiK1Wk3vv/9+vzlz5twkIoqPj284cOCAQ3NzM0sqlVqWlJRYDx8+vImIqLKy0vLEiRO2RETfffedQ0xMzANn3I8iLi7u9p49e5waGxvZRETXrl2z0I7vfunp6bzPP//c7d///vc1DofT6WNiRg8AAIxliD76HTt2OHz99dcuRETjxo2rf+ONN2qJiB577LE7EyZMqBOJRMEcDoc2btxYyuXejcX+/fvf2bFjh+OCBQt8fH19W5YsWXLLEOczadKk2zk5OdaDBg0SExHxeDzN3r17r3l4ePzlQsLPPvvMpbGxkTN06NBAIqIBAwY07du376Ed97qgjx4AAHTqrX30+fn5ls8880xAYWFhjjH2bwzoowcAAOilsHQPAAAmx5h99IGBga3dOZuvqqriDB8+PPD+10+dOpXv5uam7ur+EfQAAGAWmNhH3xFubm5qqVSaa6z9Y+keAADAjCHoAQAAzBiCHgAAwIwh6AEAAMwYgh4AAMyasTvjIyIixNqfhw4dGiAQCMJHjBjxwGfvdzdcdQ8AAGZt06ZNOmtgVSoVaZ+E1xVZWVlS7c9LliypampqYn/55ZfOD3tPd0LQAwCAXm8l/+FVUCUzaB+9yE2gWD95gNH76OPj44XtO+Pb98nLZDLOzp07nZVKJUsoFLYkJydfEwgEmrKyMu7s2bN9rl+/bkVEtHnz5tKnnnqqSdf+2z+l729/+5ssJSXFaKsHnYGlewAAYLSu9tHfr32f/LRp0+qvXLmSl5+fnxsYGNiclJTkREQ0f/5876FDh8ry8/Nzc3JycgcOHHjHeGdoXJjRAwCAXh2ZeRtLV/vo79e+Tz4zM9NmxYoVHjKZjNPU1MQZNmxYIxFRRkaGIDk5+RoREZfLJUdHxy4/oa6nYEYPAACM1b6PPj8/PzcoKKj5Ufvo79e+T37u3Lm+mzdvvl5QUJD79ttvV7S0tJhdLprdCQEAgPkwRB/9wygUCra3t7eypaWF9f333ztoX3/iiSdk69evdya6e9FeXV2dyealyQ4cAADMX3x8fKNKpWKJRCLJ8uXL3XX00QedPn1asGbNmsrO7H/ZsmUVUVFRQUOHDhUFBATc+x5+69at19PS0gQikUgSEhIiuXjxok1H9hcZGRk4Y8aM/r/99pudq6tr2P79++06My5DQh89AADo1Fv76E0R+ugBAAB6KVx1DwAAJseYffS6GLsz3pgQ9AAAYBaM2Udv7M54Y8LSPQAAgBlD0AMAAJgxBD0AAIAZQ9ADAACYMQQ9AACYte7qo8/IyLAJDw8X+/v7B4tEIsmXX35pb6xjPgo8MAcAAHRi8gNzDMFQffRaly9ftmKxWBQaGtpSUlJiMWjQoKC8vLwcJycno99+97AH5uD2OgAA0O/ga150M9egffTkIlHQhP9nNn30YWFhLdrXhEKh0sHBQVVZWcntjqB/GCzdAwAAo5liH/3Jkyd5SqWSJZFIWvRvbVyY0QMAgH4dmHkbi6n10ZeWllrMmjWr/9dff32Nw+F09G1Ggxk9AAAwlqn10dfV1bHj4uL8V6xYUT5q1CidS/3dDUEPAACMZUp99Hfu3GE9/fTT/i+88ELt7Nmz6/Vt310Q9AAAwFim1Ee/Y8cO+/Pnz/O/++47J7FYLBGLxZKMjIwO9dgbE26vAwAAnZh8ex366P8MffQAAAC9FK66BwAAk4M++o5D0AMAgFlAH71uWLoHAAAwYwh6AAAAM4agBwAAMGMIegAAADOGoAcAALPWXX30BQUFlsHBwUFisVji7+8fvG7dOmdjHfNR4IE5AACgE5MfmGMIhu6jv3PnDqutrY1sbGzaGhsb2RKJJPjMmTNSoVCoNNhBHgB99AAA0CXvnXnPq6i+yKB99P72/opVT6wymz56a2vrezPn5uZmlkaj0bV5t8PSPQAAMJop9dEXFRVZiEQiia+vb9gbb7xR1R2zeX0wowcAAL06MvM2FlPqo/f391cWFBTklpSUWDz77LP+06dPr/fy8tK50tBdMKMHAADGMrU+ei2hUKgMDAxsPnHihNEuAuwoBD0AADCWKfXRX7161UIul7OIiG7dusW5cOECPzg4uENL/saEpXsAAGCs+Pj4xu3btzuLRCKJn5/fHR199G4CgUB94MCB4s7sX9tH7+Hh0RoUFKSQy+Ucort99C+99JKPSCRyYrPZtHnz5tLRo0frvBhP6/LlyzZvv/22J4vFora2NkpISKiKiopq7sy4DAm31wEAgE5Mvr0OffR/hj56AACAXgpL9wAAYHLQR99xCHoAADAL6KPXDUv3AAAAZgxBDwAAYMYQ9AAAAGYMQQ8AAGDGEPQAAGDWuquPXquuro7t4uISNnPmTG9jHfNR4Kp7AAAwa5s2barQ9bqh+uizsrKk7f+/ePFij+jo6E49ktcYEPQAAKBXxfJ3vVoKCw3aR28VEKBw/3i12fTRExGlp6fzbt26ZTFmzJjGCxcu2Hbl8zEULN0DAACjmUofvVqtpsWLF3tt2rSpxyp9dcGMHgAA9OrIzNtYTKWPfu3atc5jxoxp8Pf3Vz7qORoTgh4AABirfR+9QCDQREVFBRq6jz45Oblo8ODBzUlJSY5paWmdvmjv7Nmz/PPnz/N37tzpolAo2Eqlks3n89Vbtmwp7+w+DQFL9wAAwFim1Ed/6NCha5WVldnl5eXZH3744Y1JkybV9nTIEyHoAQCAweLj4xtVKhVLJBJJli9f7q6jjz7o9OnTgjVr1lR2Zv/aPvqhQ4eKAgIC7n0Pv3Xr1utpaWkCkUgkCQkJkVy8eNHGUOfU3dBHDwAAOqGP3nSgjx4AAKCXwsV4AABgctBH33EIegAAMAvoo9cNS/cAAABmDEEPAABgxhD0AAAAZgxBDwAAYMYQ9AAAwFg8Hi+C6O6jcEeMGNHh59nD/0HQAwAAmDHcXgcAAHr9vDvPq65cbtA+egcPvmLUzKAOt+LJZDLOU0895VdcXGwdHR0t27Nnz3UOh0PJycl2K1as8FCr1SwHBwfVb7/9VqDr/YmJie58Pl+9cuXKaiKigICA4JSUlMLAwMBWQ50TEyHoAQDAJGRnZ9tmZWVdEYlErU8++WTA7t277ceOHStLSEgQnjp1SioWi1urq6s5PT1OpkHQAwCAXo8y8zaW0NDQJolE0kpENGXKlLr09HS+lZWVJioqSiYWi1uJiFxdXRn9lLqegO/oAQDAJNzfOc9isaitra3DXfRcLrdNo7lXRU8tLS0dL7E3YQh6AAAwCdnZ2bZSqdRSrVZTcnKyw9ChQ2UjRoxoOnfunEAqlVoSET1s6V4oFLZcunTJlojo119/5ZWXl1t119h7EpbuAQDAJISHh8sXL17sKZVKbaKjo2UzZsxo4HA4lJSUVDJx4kR/jUZDjo6OyoyMjEJd7585c2b93r17HcVisSQ8PLzJx8fnjq7tzA366AEAQCcm99HDn6GPHgAAoJfC0j0AAJiVzz77zHHr1q2u7V8bNGiQ3Jg1tkyGpXsAANAJS/emA0v3AAAAvRSCHgAAwIwh6AEAAMwYgh4AAMCMIegBAKDXiYiIEPf0GLoLbq8DAAC9jm/d5FVTVmrQmlonLx/F2FcXdmtZjkqlIi6XS1lZWdLuPG5PwoweAAAY6/bt2+zhw4f7BwYGSgICAoK//PJLew8Pj9DKykouEdHp06d5UVFRgUR3++YnTJjg+/jjj4t8fHxCNmzY4ERElJKSIoiOjhY9++yzvoGBgcFERDweL0L7u0GDBgWOGzeuv1AoDFmwYIHH1q1bHUJDQ4NEIpEkJyfHioiooqKCO3bsWL+QkJCgkJCQoP/+97+2DxrzsGHD/MVisUQsFksEAkH4559/7mjsz+lhMKMHAAC9unvmrXXgwAE7Nzc35alTp4qIiGprazkffPDBA7fPy8uzyczMzJPJZJyIiAhJfHx8IxHR5cuXbbOysnK0dbbtSaVSm+Tk5GIXFxeVj49PqJWVVU12dnbeqlWrXDZs2OCyY8eOsnnz5nklJiZWjx07Vl5YWGg5duzYgOLi4hxdY0hLSysiIkpPT+fNmTNHOHXq1AZDfBadhaAHAADGGjhwYPO7777r9eqrr3r87W9/a4yNjZU/bPu4uLgGPp/fxufzVYMHD76dnp5ua29vrw4LC2vSFfJEd3vufXx8lERE3t7eLXFxcY1ERAMGDGhOS0sTEBGdOXPGrrCw0Eb7Hrlczqmvr2fb29trdO2zsrKS+9JLL/l+//33Vx0dHdWdPX9DQNADAABjhYWFtVy8eDF3//79fd59912PEydO3OZwOPd65Zubm//0FbSuznoiIh6PpzOQiYisrKzuPSKWzWaTtbV1m/ZntVrNIiJqa2ujCxcu5PH5fL2Pk1WpVBQfH9//7bffrhg0aFCPN+ThO3oAAGCskpISC4FAoFmwYEHdwoULqy9dusTz9PRsPXPmDI+I6IcffrBvv/3Ro0f7KhQKVlVVFefs2bOCIUOGNBliHEOGDLm9du1aF+3/MzIybB607WuvveYpkUgUc+fOrTfEsbsKM3oAAGCszMxMm3feeceTzWYTl8tt27JlS6lCoWDPnz9fuHbtWmVkZOSfgjwiIqJp1KhRARUVFZZLliypFAqFyitXrlh3dRzbt28ve/nll71FIpFErVazoqOjZTExMTpLcrZv3+7q7+9/RywW2xERvffee+XTpk1r7OoYOgulNgAAoJOpldokJia68/l89cqVK6t7eizdDaU2AAAAvRSW7gEAwCxs3LixojuPZyq991i6BwAAnUxt6b43w9I9AABAL4WgBwAAMGMIegAAADOGoAcAADBjCHoAAGCs9i1zI0aM8O/p8Zgi3F4HAAB61SUXeCmrmgzaR2/hZqtwmCzqkVa83gQzegAAMAkymYzz1FNP+fn5+QVPnTrVW62+WwqXnJxsJ5FIggIDAyWDBw8W6XqvWq0mHx+fkIqKCq72/97e3iHaXntzZvYnCAAAXceEmXd2drZtVlbWFZFI1Prkk08G7N69237s2LGyhIQE4alTp6Risbi1urqao+u9HA6HJk+eXPvVV185rFix4uZPP/1kFxQU1NyvXz9Vd59Hd8OMHgAATEJoaGiTRCJp5XK5NGXKlLr09HT+qVOnbKOiomTarnlXV9cHdr+/+uqrNd9//70jEdGOHTucXnrppV7xMCAEPQAAmARdXfNtbW1/ef1B/P39lU5OTqpDhw4JsrKybJ977rkea5TrTgh6AAAwCdnZ2bZSqdRSrVZTcnKyw9ChQ2UjRoxoOnfunEAqlVoSET1o6V5r9uzZt15++WXf8ePH13G5vePbawQ9AACYhPDwcPnixYs9RSJRsLe3d8uMGTMa3N3dVUlJSSUTJ070DwwMlEycOLH/w/bx4osvNioUCs7cuXNru2vcPQ2lNgAAoJM5ltqcPn2at2jRIq/MzMz8nh6LIT2s1KZ3rFsAAECvt3z5crddu3Y579y581pPj6U7IegBAMCsPKwn/uOPP67qqXH1FCzdAwCATua4dG+u0EcPAADQSyHoAQAAzBiCHgAAwIwh6AEAAMwYgh4AAMze66+/7uHm5ham7bfvTXB7HQAA6HXw4EGvmzdvGrSP3sXFRTFhwoRuacWbMGFCw5IlS24GBQWFdMfxmARBDwAAjPbWW2/1S05OdujXr1+ro6OjKiIiQnHs2LG+ISEhiqysLFu5XM7Zvn37tREjRigaGxvZc+bM8b58+TKPiGj58uUVL730UsOoUaOaOnq8nJwcq6lTp/qq1WrW6NGjG7dv3+6qUCiyjHeGxoWgBwAAvbpr5n2/06dP8w4fPmyfnZ2dq1QqWeHh4ZKIiAgFEZFCoWBnZWVJjx49yp87d65vYWFhzrJly/rZ2dmpCwoKcomIbt269dCSG10SEhK8FixYcHPevHl169atczb0OXU3fEcPAACMderUKX5cXFwDn89vs7e31zz11FMN2t9NnTq1jogoLi5OLpfL2TU1NZzTp0/bLVq06KZ2G2dn5wf20z9IVlYWf/bs2XVERC+//LLJl98g6AEAgLEe9vTWrvbT9xYIegAAYKzhw4fLjx8/3kehULAaGxvZJ06c6Kv93b/+9S97IqLjx4/zBQKB2tHRUT18+PDbGzdudNFu05ml+/DwcPmuXbvsiYh27NjhYIjz6EkIegAAYKxhw4YpYmNjGyUSSfC4ceP8wsLCmvr06aMmIrK3t1dHRESIExISfLZt21ZCRLRmzZrKhoYGTkBAQHBgYKDkP//5j4CIaP78+Z6urq5hd+7cYbu6uoYlJia6P+iYn3/+ednnn3/uGhoaGlRZWWnB5/MfefmfSVBqAwAAOjGl1KaxsZHdp08fjUwmYw8ePDjwiy++KE1MTPT69NNPy5588kmFoY8nk8nYtra2GjabTdu3b7fft2+fw88//3zV0McxJPTRAwCAyZo+fbpPYWGhQEUbkwAAIABJREFUTUtLC+uFF16oHTJkiMHDvb0zZ87w3nzzTe+2tjays7NT79q1q8SYxzM2BD0AADDa4cOHr93/2u+//57f1f2+/fbbbj/99NOfvoP/29/+Vrd27dqq/Pz83K7unymwdA8AADoxZeke9EMfPQAAQC+FoAcAADBjCHoAAAAzhqAHAACz15trahH0AABg9iZMmNBw7ty5vEd9n1KpNMZwuhVurwMAAL1y8972apIXGLSP3pYvUkiC1uptxevumtr4+Hihvb29Kjs7mxcWFqb48ssvb3TlPHsagh4AABirJ2pqiYiuXr1qfebMmQIu1/Rj0vTPAAAAjK4jM29jaF9TS0RtHamp/f7774u123SmppaIaNKkSfXmEPJE+I4eAAAYrKdqavl8vqbLO2EIBD0AADBWT9TUmhsEPQAAMFZP1NSaGzzrHgAAdGLKs+67u6bWFKGmFgAATFZ319SaGwQ9AAAwWk/U1HZ130yCpXsAANCJKUv3oB9qagEAAHopBD0AAIAZQ9ADAACYMQQ9AACAGUPQAwCA2XuUPvp169Y5b9682bE7xtUdcHsdAACYvQkTJjQsWbLkZlBQUIi+bZcuXXqrO8bUXRD0AACg18K8617SpjsG7aMX21orNgV5M66PPjEx0Z3P56tXrlxZ3ZXzYwoEPQAAMFZP9dGbEwQ9AADo1ZGZtzH0VB+9OcHFeAAAwFg91UdvThD0AADAWOij7zoEPQAAMBb66LsOpTYAAKATU0pt0EevH/roAQDAZKGPvmsQ9AAAwGjoo+8aLN0DAIBOTFm6B/3QRw8AANBLIegBAADMGIIeAADAjCHoAQAAzBiCHgAAzN6j9NGbGwQ9AACYvQkTJjScO3cur6fH0RNwHz0AAOj1VvIfXgVVMoP20YvcBIr1kwcwro9eLBZLtD+XlJRY79+/v+Dpp5+Wd+4sex6CHgAAGKsn+uilUmkuEdF3333XZ8OGDW6jR4/u8B8JTISgBwAAvToy8zaGnuqjz87Otnr33Xc9T548WWBlZWXST5bDd/QAAMBYPdFHf/v2bfaUKVP8tm7dWioUCpVd2hkDIOgBAICxeqKP/oUXXhBOmzatJjY21mS/l28PQQ8AAIzV3X30BQUFlseOHbP/9ttvncRisUQsFktOnz5t0IsQuxtKbQAAQCemlNqgj14/9NEDAIDJQh991yDoAQCA0dBH3zVYugcAAJ2YsnQP+qGPHgAAoJdC0AMAAJgxBD0AAIAZQ9ADAACYMQQ9AACYPfTRAwAAmDH00QMAADzMwde86GauYR8F6yJR0IT/x6g++vr6enZISEhwcXHxFSsrq7a6ujp2aGjovf939ZR7AoIeAAAYq7v76O3t7TWDBw+W/fDDD31mzJjRsGPHDodx48bVm2rIEyHoAQCgIzow8zaGnuijnzt37q21a9e6zZgxo+Hbb791+vLLL0sMcS49Bd/RAwAAY/VEH/2YMWOabty4YXXkyBG+Wq1mDRo06E6XdtjDEPQAAMBYPdFHT0T0wgsv1M6aNav/9OnTTf4RwAh6AABgrO7uo9eaM2dO7e3bt7lz5sypM/pJGhlKbQAAQCemlNr0RB/9zp077X/66ae+Bw8e/EtzHhOhjx4AAExWd/fR//3vf/c6efJkn5SUlEJjHqe7IOgBAIDRuruP/ptvvikjoh65y8AYsHQPAAA6MWXpHvRDHz0AAEAvhaAHAAAwYwh6AAAAM4agBwAAMGMIegAAYLRVq1a5BAQEBPv7+wevXLnShYiourqaExMTE+Dj4xMSExMT0Jkn4EVERIgNP1rmQdADAABjnT9/3nr37t3OFy9ezMvLy8s5duxY3+zsbKv333+/3/Dhw2WlpaVXhg8fLluxYoVbR/epUqmIiCgrK0tqtIEzCO6jBwAAvd47855XUX2RQfvo/e39FaueWPXQ+9Wzs7NtBg4cKBcIBBoioieeeEK2b9++vseOHeublpaWT0Q0b9682mHDhgUSUXliYqJ7cXGxVVVVlUVlZaXlG2+8UbV48eKalJQUwapVq/q5uLgoc3NzeVevXs3h8XgRCoUiKyUlRfDhhx+6Ozs7K3Nzc3njxo2rDw0Nbd6yZYtrS0sL68cff7waHBzcUlFRwZ01a5ZPeXm5JRHRxo0br48ZM0Znz31FRQV38uTJvg0NDdzw8HDFqVOn7DIzM/P69eunMuRn2BGY0QMAAGOFh4c3nzt3TlBVVcWRyWTs1NTUPmVlZZa1tbVcHx8fJRGRj4+Psq6u7t7ENS8vz+bEiROFZ8+ela5fv969pKTEgojo8uXLtuvXry+/evVqzv3HkUqlNlu3bi3Ly8vLSU5OdiwoKLDOzs7OmzFjRs2GDRtciIjmzZvnlZiYWH3lypW8H3/88er8+fOFDxr3smXL3IcNGybLzc3NmzRpUn1lZaWlwT+cDsKMHgAA9NI38zaWgQMH3nnzzTerRo4cKeLxeBqJRKLgch8eXdr+ej6frxo8ePDt9PR0W3t7e3VYWFiTWCxu1fWe0NDQJu0fDt7e3i1xcXGNREQDBgxoTktLExARnTlzxq6wsNBG+x65XM6pr69n29vba+7f3++//84/ePBgERHR5MmTb9vZ2ak7/SF0EYIeAAAYbdGiRTWLFi2qISJKSEjw8PT0bHV0dFSVlpZa+Pj4KEtLSy0cHBzuLYnr6qknIuLxeH8JZC0rK6t7j4lls9lkbW3dpv1ZrVaziIja2trowoULeXw+X+8jZZn01Fks3QMAAKOVl5dziYgKCwstjxw50nfOnDl1Y8eObdi2bZsjEdG2bdscY2NjG7TbHz16tK9CoWBVVVVxzp49KxgyZIjO79Ef1ZAhQ26vXbv2Xtd9RkaGzYO2jYqKku/Zs8eBiOjAgQN2t2/ffuS7AgwFM3oAAGC08ePH+zU0NHC5XG7bpk2brjs7O6s//PDDyokTJ/r5+Pg4ubu7tx48ePCqdvuIiIimUaNGBVRUVFguWbKkUigUKq9cuWLd1XFs37697OWXX/YWiUQStVrNio6OlsXExFzXte0nn3xSMXny5P4SicR+8ODBcmdnZ2Xfvn17ZPkepTYAAKCTKZbaJCYmuvP5fPXKlSure3Iczc3NLC6X22ZhYUEnTpywTUhI8JFKpbnGOh766AEAALpRUVGR5ZQpU/w0Gg1ZWFi0bdu2raSnxoKgBwAAs7Fx48aK7jzeZ5995rh161bX9q8NGjRIvmfPnut5eXlGm8E/CizdAwCATqa4dN9boY8eAACgl0LQAwAAmDEEPQAAgBlD0AMAAJgxBD0AADAa+ui7BkEPAACMZQ599EqlsjsO80C4jx4AAPSqWP6uV0thoUH76K0CAhTuH682yz76xMRE98rKSovr169bOjg4qA4fPnzNkJ/do0DQAwAAY4WHhzevXLnSo6qqimNra9uWmpraZ8CAAU36+ugzMzPzZDIZJyIiQhIfH99IdLePPisrK0dXVa1UKrVJTk4udnFxUfn4+IRaWVnVZGdn561atcplw4YNLjt27CjT9tGPHTtWXlhYaDl27NiA4uLiv3Tba12+fJl37tw5aUfa7owJQQ8AAHrpm3kbi6n20RMRxcbGNvR0yBMh6AEAgOFMsY+eiMjW1vaBx+tOuBgPAAAYzRT76JkEM3oAAGA0U+yjZxKU2gAAgE6mWGrDlD767oZSGwAAgF4KS/cAAGA2mNRH353jeBgs3QMAgE6muHTfW2HpHgAAoJdC0AMAAJgxBD0AAIAZQ9ADAACYMQQ9AAAAmW8/PYIeAACAuq+fvrvhPnoAANDr5915XnXlcoP20Tt48BWjZgY9tBUvPz/fMjY2NiAqKkp+8eJFflBQkGL27Nk1K1eu9KitreXu2rWrWCKRtEybNk14/fp1KxsbG8327dtLo6Ojm48cOcJfvHixN9HdYpuMjAwpm82m2NhY/8bGRo5KpWKtWLGiYvr06Q1ERNp+eiKif/zjH64//PCDI4vFolGjRjVu2bKl/P6xlZSUWMTGxgZo/19YWGiTl5eXLRKJdDbk9RQEPQAAMFpZWZn1vn37iiMjI0vDwsKC9u7d63jhwgXpd99913f16tX9PDw8WgcMGKA4ceLE1UOHDgn+/ve/+0ql0twNGza4JSUllY4ZM6apsbGRrW2vO3LkSJGDg4OmsrKSGx0dLZ46dWoDm/1/C9w//PCD3ZEjR+wzMzOlAoFAU11dzdE1LqFQqJRKpblERGvWrHFOT08XMC3kiRD0AADQAfpm3sbk4eHREhUV1UxEJBKJmkeOHHmbzWbTwIEDFR999JF7eXm51f79+4uIiMaPHy+bO3cut7a2lvP444/LlyxZ4jVlypS6F198sd7Pz0/T0tLCWrhwoefZs2f5bDabbt68aXnjxg2ut7f3vZrb1NRUu+nTp9cIBAINEZGrq6v6YeP773//a7t7927ns2fPMnLpH9/RAwAAo1laWursiudwOKRWq3U+4ZXFYrV9/PHHVV999VVpc3MzOyYmJigrK8t627ZtDrW1tdzs7Ow8qVSa6+joqGxubv5TFra1tf2l0/5BSktLLebNmyfct2/f1T59+jCif/5+CHoAADBpjz/+uGznzp2OREQpKSkCe3t7lYODgyYnJ8cqKiqqefXq1VWhoaFNV65csW5sbOQ4OTkprays2g4fPiyoqKiwvH9/sbGxt/fs2eMkk8nYREQPWrpvaWlhTZo0qf+qVavKw8LCWox7lp2HpXsAADBpa9eurZg6dapQJBJJbGxsNLt27bpGRLRu3TqXjIwMOzab3SYSiZonT57c2NDQwImLi/MPCQkJCg4OVvj6+t65f3+TJ0++ffHiRV54eHiQhYVF2+jRoxs3b978l4vxTpw4YXvlyhXbjz76yP2jjz5yJyI6duxYoVAoVBr/rDsOpTYAAKATSm1MB0ptAAAAeiks3QMAAOgxY8YM7/Pnz/Pbv/bqq69Wv/nmm7U9NaaOQtADAADosWfPnus9PYbOwtI9AACAGUPQAwAAmDEEPQAAgBlD0AMAAJgxBD0AAPQazc3NrJiYGJFYLJZ8+eWX9s8//7xPZmamtSGP8b9Wu/6G3GdX4Kp7AADoNTIyMnhKpZKlbZ175ZVX6nVtp1KpiMvtXEQKhULlsWPHirswTINC0AMAgF7Ht27yqikrNWgfvZOXj2Lsqwv19tHHxcUFREVFyS9cuMB3dXVtPX78eFFpaanl/Pnzvevq6rjW1taar776qjQ0NPSOUCgMvX79enZdXR3HxcUl/MiRI/lxcXHyyMjIwE8//bRs1qxZvvX19VyxWCzZv3//1VmzZgk//fTTsieffFLB4/Ei5s6dW/3LL7/YrV+//sbs2bN9J06cWPfrr78KVCoV64svvihdtmyZR2lpqdXrr79evXTp0lsPGvMzzzwTUFhYmGPIz6uzsHQPAACMdv36des33njjZlFRUU6fPn3Uu3fvtn/55Zd9tmzZcj0nJydv/fr1N1599VVvLpdLvr6+dy5evGidmprKl0gkilOnTvGbm5tZVVVVliNGjFBs2bKl9LHHHpNLpdLc4ODgPxXRNDc3s0NCQpovX74sHTt2rJyIyMvLq/XSpUvS6Oho+ezZs4WHDx++eu7cOeknn3zi3jOfxqPDjB4AAPTSN/M2Jg8Pj5aYmJhmIqKIiAhFSUmJVVZWFv+5557z027T2trKIiKKiYmR/fzzz4Jr165ZvfXWW5Vff/218+nTp+UDBgxo0nccDodDL7300p+W8qdMmdJARBQaGqpoampi29vba+zt7TVWVlaampoajpOT00O76pkAM3oAAGC09n30HA6nra6ujiMQCFRSqTRX+6+4uDiHiGj48OHyX3/9lX/x4kXb5557rvH27ducn3/+WTBkyBBZB46juf97eWtr6zYiIjab/adxsNlsUiqVHSut72EIegAAMCl2dnYaT0/P1h07dtgTEWk0Gvrtt99siIiGDx/edPHiRT6bzW7j8XhtwcHBit27dzuPGDFC3rOj7jkIegAAMDn/+te/infu3OkUGBgoCQgICN6/f39fIiIbG5s2Nze31scee6yJiGjo0KHypqYmdlRUVHN3jo/FYjGmAx599AAAoBP66DsnPT2dl5iY6HX+/Pn87jom+ugBAAC6wenTp3kzZszon5CQUN3TY9HCVfcAAACd8Pvvv9vMnDnTt/1rlpaWmpKSkis9NSZdEPQAAACdEBUV1ax9wh6TYekeAADAjCHoAQAAzBiCHgAAwIwh6AEAAMwYgh4AAHqN7uijT0lJEYwYMcLfkPvsClx1DwAAetUlF3gpq5oMWlNr4WarcJgs6taynO7oo2cazOgBAICx8vPzLfv37x/8wgsv+Pj7+wc/8cQTAXK5nJWTk2M1dOjQgODg4KDIyMjArKwsa5VKRZ6enqEajYZqamo4bDY78ujRo3wiosjIyMCTJ0/yZs2a5SuVSm3EYrEkJyfHKioqKvD06dM8IiIejxexcOFC97CwMPHPP//M9/DwCE1ISPAIDw8Xh4SEBP3666+8IUOGBHh5eYWsW7fO+WHjbmpq4sTGxvb39fUNHj9+vK9Go+mOj0sn8/hzBQAAjKq7Z97tXb9+3frbb78tjomJKR03blz/3bt32+/Zs8dp+/btpaGhoS2//PKL7auvvup99uzZAm0ffWFhoZW2j3748OFN7fvoN2zY4Hry5Mmi+4+j7aPftGlThfY1bR/9nDlzvGbPni08d+6c9H/bBS9duvTWg8acl5dnc+nSpWKhUKiMjIwUp6am8rUd990NQQ8AAIxmin30oaGhTX5+fkoiouDgYMXVq1ctO/8JdA2W7gEAgNFMsY/eysqq/ZhJpVL1WHc9gh4AAEwK+ugfDYIeAABMDtP76JkEffQAAKAT+uhNB/roAQAAeilcdQ8AANAJD+qjv3z5srSnxqQLgh4AAKAT0EcPAAAAPQ5BDwAAYMYQ9AAAAGYMQQ8AAGDGEPQAANBroI8eAABAh4MHD3rdvHnToH30Li4uigkTJqCP3sgwowcAAMYy1T56rbS0NF5QUJAkNze3x9rrzOPPFQAAMKrunnm3Z4p99EREqamptgsXLvQ+dOhQUUBAQKsxPpuOQNADAACjmWIffVFRkfWCBQuEqampBUKhUNmV8+8qLN0DAACjmWIfvYuLi9LKykpz9uxZg17X0BkIegAAMCmm0EdvZ2enPnr0aOH777/vkZKSIujOY98PQQ8AACbHFProvby8VCkpKUULFy70/uWXX2y7+/ha6KMHAACd0EdvOtBHDwAA0EvhqnsAAIBOQB89AACAGUMfPQAAAPQ4BD0AAIAZQ9ADAACYMQQ9AACAGUPQAwAAEFFERIS4p8dgDLjqHgAA9MrNe9urSV5g0Oe22/JFCknQ2h5rxbtfVlZWl26LUyqVZGFhYajhGAxm9AAAwFj5+fmWvr6+wc8//7xPQEBA8Pjx430PHjwoGDhwoNjHxyfk5MmTvOrqas7o0aP9RCKRZMCAAeJz587ZEBEdOXKELxaLJWKxWBIUFCSpr69nNzY2sgcPHiySSCRBIpFI8u233/bVHovH40Vof/7HP/7hKhKJJIGBgZIFCxZ4PGh8UVFRgQkJCR6DBg0K/Oijj1yN+2l0Dmb0AACgV0/OvMvKyqz37dtXHBkZWRoWFha0d+9exwsXLki/++67vqtXr+7n4eHROmDAAMWJEyeuHjp0SPD3v//dVyqV5m7YsMEtKSmpdMyYMU2NjY1sHo+nISI6cuRIkYODg6ayspIbHR0tnjp1agOb/X/z3h9++MHuyJEj9pmZmVKBQKCprq7mPGx8DQ0NnPPnz+cb+WPoNMzoAQCA0Tw8PFqioqKaORwOiUSi5pEjR95ms9k0cOBAxY0bN6x+//13wZw5c2qJiMaPHy9raGjg1tbWch5//HH5kiVLvD766COXmpoajoWFBWk0GtbChQs9RSKRZMSIEaKbN29a3rhx40+T3tTUVLvp06fXCAQCDRGRq6urzs55rRdffLHOeGffdQh6AABgtPt74LUd8RwOh9Rqtc5yNhaL1fbxxx9XffXVV6XNzc3smJiYoKysLOtt27Y51NbWcrOzs/OkUmmuo6Ojsrm5+U9Z2NbWRizWA6vm/0L7BwFTIegBAMCkPf7447KdO3c6EhGlpKQI7O3tVQ4ODpqcnByrqKio5tWrV1eFhoY2XblyxbqxsZHj5OSktLKyajt8+LCgoqLC8v79xcbG3t6zZ4+TTCZjExHpW7pnOnxHDwAAJm3t2rUVU6dOFYpEIomNjY1m165d14iI1q1b55KRkWHHZrPbRCJR8+TJkxsbGho4cXFx/iEhIUHBwcEKX1/fO/fvb/LkybcvXrzICw8PD7KwsGgbPXp04+bNm8u7/8wMA330AACgE/roTQf66AEAAHopLN0DAADoMWPGDO/z58/z27/26quvVr/55pu1PTWmjkLQAwAA6LFnz57rPT2GzsLSPQAAgBlD0AMAAJgxBD0AAIAZQ9ADAABQ52pqFy5c6H7w4EGBMcZjKLgYDwAAgDpXU7tp06YKY4zFkBD0AACg18K8617SpjsG7aMX21orNgV5P7QVLz8/3zI2NjYgKipKfvHiRX5QUJBi9uzZNStXrvSora3l7tq1q1gikbRMmzZNeP36dSsbGxvN9u3bS6Ojo5uPHDnCX7x4sTcREYvFooyMDCmbzabY2Fj/xsZGjkqlYq1YsaJi+vTpDUR3a2oVCkUW0d2a2h9++MGRxWLRqFGjGrds2aLzyXjx8fHCZ555pnHWrFn1hvxsDAlBDwAAjMb0mlqmQ9ADAIBe+mbexqStqSWiv9TUfvTRR+7l5eVW+/fvLyK6W1M7d+7cP9XUTpkype7FF1+s9/Pz07S0tLAWLlzoefbsWT6bzSZtTa23t7dKe7xHrallOlyMBwAAjMb0mlqmQ9ADAIBJQ03tw2HpHgAATBpqah8ONbUAAKATampNB2pqAQAAeiks3QMAAOiBmloAAAAzhppaAAAAYCQEPQAAgBlD0AMAAJgxBD0AAIAZQ9ADAABQ5/roo6KiAk+fPm3QVj9DQ9ADAABQ5/roTQFurwMAAL3eSv7Dq6BKZtCZq8hNoFg/eYBJ99ETEe3atcvxzTff9JbL5Zzt27dfGzFihMKQn1NXIegBAIDRmN5Hr1Ao2FlZWdKjR4/y586d61tYWJhj5I/kkSDoAQBAL30zb2Nieh/91KlT64iI4uLi5HK5nF1TU8NxcnJiTIc9vqMHAABGY3of/f3bMq3LHkEPAAAmraf76P/1r3/ZExEdP36cLxAI1I6OjoyZzRNh6R4AAExcT/fR29vbqyMiIsTai/GMea6dgT56AADQCX30pgN99AAAAL0Ulu4BAAD0QB89AACAGUMfPQAAADASgh4AAMCMIegBAADMGIIeAADAjCHoAQCAsWpqajiffPKJMxFRSUmJRWxsbH/t75599llfkUgk+fDDD126qxc+JSVFMGLECH9jH8eQEPQAAMBYtbW1nK+//tqFiEgoFCqPHTtWTER0/fp1bmZmJr+goCD3/fffv9mzo2Q23F4HAAD6HXzNi27mGnbG7CJR0IT/99BWvMWLF3uWlZVZicViia+v752ioiKbwsLCnNGjR4vq6uosxGKxZNOmTfdufVOr1fTcc88JPT09W5OSkiqmTZvm/ccff9jeuXOH/eyzz9b/85//rCAi8vDwCJ04cWLdr7/+KlCpVKwvvviidNmyZR6lpaVWr7/+evXSpUtv6Rt+Wloab/78+cL9+/cXXbt2zXLx4sXeRHdLbTIyMqT29vaarn5EhoAZPQAAMNaGDRtueHl5tUil0txNmzbd0L5++PDhIu3rsbGxciIipVLJmjBhgm9AQMCdpKSkCiKijRs3ll+5ciVPKpXmnDlzRnDu3Dkb7T68vLxaL126JI2OjpbPnj1bePjw4avnzp2TfvLJJ+76xpWammq7YMECn0OHDhVJJJLWDRs2uCUlJZVKpdLcs2fPSvl8PiNCnggzegAA6Ag9M28mWLBggc+ECRPq1q5dW6V97ZtvvnHYtWuXk0qlYt26dcvijz/+sI6Ojm4mIpoyZUoDEVFoaKiiqamJbW9vr7G3t9dYWVlpHtYpX1RUZL1gwQJhampqgVAoVBIRPf744/IlS5Z4TZkype7FF1+s9/PzY0zQY0YPAABm4bHHHpOnp6fbKRQKFhGRVCq13Lx5s2taWlpBQUFB7siRIxvv3LlzL/e0vfZsNvsvnfdKpfKBpfIuLi5KKysrzdmzZ+99lfHxxx9XffXVV6XNzc3smJiYoKysLGvjnOWjQ9ADAABj9enTR93U1NShrJo3b17NmDFjGp955hk/pVJJ9fX1HBsbG42Dg4O6rKyMe+rUqT6GGJOdnZ366NGjhe+//75HSkqKgIgoJyfHKioqqnn16tVVoaGhTVeuXGFM0GPpHgAAGMvNzU0dGRkpDwgICPb392/Wt/0HH3xQvWjRIs6kSZN8Dx48eC0kJEQREBAQ7O3t3RIZGSk31Li8vLxUKSkpRXFxcQE8Hq/km2++ccjIyLBjs9ltIpGoefLkyY2GOlZXoY8eAAB0Qh+96UAfPQAAQC+FpXsAAID7/P777zYzZ870bf+apaWl5vLly9KeGlNnIegBAADuExUV1SyVSnN7ehyGgKV7AAAAM4agBwAAMGMIegAAADOGoAcAADBjCHoAADA5D+qfHzZsmH9NTQ3n/tcTExPdV6xY4drV45aUlFjExsb27+p+uhOuugcAALORlpZWZMz9C4VC5bFjx4qNeQxDQ9ADAIBe7515z6uovsigffT+9v6KVU+semgrXn5+vuUzzzwTUFhYmENEtGLFCle5XH5vxn5//7yHh0fohQsX8vr166d6++233fbt2+fk7u7e6ujoqIyIiFAQEX300UcuO3fudOZwOG0ikehOSkpK8ZEjR/gd6ZPRnPx7AAANLklEQVS/fzymAEEPAAAmSds/L5FImttX0xIRpaen83788UeH7OzsXKVSSeHh4RJt0CclJbmVlpZm29jYtGmX+bV98mPGjGlqbGxk83g8xtTMdhWCHgAA9NI38+4JuvrntU6ePMkfN25cg0Ag0BARjRkzpkH7u8DAwOaJEyf6jh8/vmHatGkNRMzuk+8qXIwHAACMxeVy2zSa/8vc9n3y9/fP34/F0l0pf/LkycLXXnvtVmZmpu2AAQMkSqWS0X3yXYWgBwAAxvL09FTV1dVxq6qqOM3Nzazjx4/f65S/v3++vZEjR8qPHDnSVy6Xs+rr69mpqal9ie5+p3/16lXLZ599VrZly5YbMpmM09jYyGFyn3xXYekeAAAYy8rKqm3x4sWVUVFRQZ6eni3+/v532v/+/v557etDhgxRTJw4sS4kJCTYw8OjJSoqSk5EpFKpWFOnTvWVyWSctrY21rx586qdnJzUixcvdu9onzyLxTKpfnf00QMAgE7oo/+r9PR0XmJiotf58+fze3os7aGPHgAAoItOnz7NmzFjRv+EhITqnh7Lo8DSPQAAwH0e1EdfUlJypafG1FkIegAAgPugjx4AAABMAoIeAADAjCHoAQAAzBiCHgAAwIwh6AEAgNFWrVrlEhAQEOzv7x+8cuVKFyKi6upqTkxMTICPj09ITExMwK1bt/7SQa9PRESE2PCjZR4EPQAAMNb58+etd+/e7Xzx4sW8vLy8nGPHjvXNzs62ev/99/sNHz5cVlpaemX48OGyFStWuHV0nyqVioiIsrKypEYbOIPg9joAANCrYvm7Xi2FhQbto7cKCFC4f7z6oa142dnZNgMHDpRrW+ieeOIJ2b59+/oeO3asb1paWj4R0bx582qHDRsWSETliYmJ7sXFxVZVVVUWlZWVlm+88UbV4sWLa1JSUgSrVq3q5+LioszNzeVdvXo1h8fjRSgUiqyUlBTBhx9+6O7s7KzMzc3ljRs3rj40NLR5y5Ytri0tLawff/zxanBwcEtFRQV31qxZPuXl5ZZERBs3brw+ZsyYJl3jHjZsmH91dbUlEVF5ebnlxx9/XPb666/XGvLz6ygEPQAAMFZ4eHjzypUrPaqqqji2trZtqampfQYMGNBUW1vL9fHxURIR+fj4KOvq6u7lWV5enk1mZmaeTCbjRERESOLj4xuJiC5fvmyblZWVIxaLW+8/jlQqtUlOTi52cXFR+fj4hFpZWdVkZ2fnrVq1ymXDhg0uO3bsKJs3b55XYmJi9dixY+WFhYWWY8eODSguLs7RNe60tLQioruPzJ0zZ45w6tSpDbq26w4IegAA0EvfzNtYBg4ceOfNN9+sGjlypIjH42kkEomCy314dMXFxTXw+fw2Pp+vGjx48O309HRbe3t7dVhYWJOukCciCg0NbdL+4eDt7d0SFxfXSEQ0YMCA5rS0NAER0ZkzZ+wKCwtttO+Ry+Wc+vp6tr29vc7u+srKSu5LL73k+/333191dHRUd/Ij6DIEPQAAMNqiRYtqFi1aVENElJCQ4OHp6dnq6OioKi0ttfDx8VGWlpZaODg4qLTb399Dr/0/j8fTGchEd1vytD+z2WyytrZu0/6sVqtZRERtbW104cKFPD6fr7cNTqVSUXx8fP+33367YtCgQXf0bW9MuBgPAAAYrby8nEtEVFhYaHnkyJG+c+bMqRs7dmzDtm3bHImItm3b5hgbG3tvafzo0aN9FQoFq6qqinP27FnBkCFDdH6P/qiGDBlye+3atS7a/2dkZNg8aNvXXnvNUyKRKObOnVtviGN3BWb0AADAaOPHj/draGjgcrnctk2bNl13dnZWf/jhh5UTJ0708/HxcXJ3d289ePDgVe32ERERTaNGjQqoqKiwXLJkSaVQKFReuXLFuqvj2L59e9nLL7/sLRKJJGq1mhUdHS2LiYm5/oBtXf39/e+IxWI7IqL33nuvfNq0aQ/suDcm9NEDAIBOpthHn5iY6M7n89UrV640qSrZrkIfPQAAQC+FpXsAADAbGzdurOjO43322WeOW7dudW3/2qBBg+R79uzRuaTfE7B0DwAAOpni0n1vhaV7AACAXgpBDwAAYMYQ9AAAAGYMQQ8AAGDGEPQAAMBYxuyM37t3b5/ly5e7EREdPXqUL5FIgrhcbuTOnTvtjXXMnoDb6wAAgLF0dcarVCrSV2zTEf97Ul0jEVH//v1bd+7cWfLJJ5+46nmbyUHQAwCAXj/vzvOqK5cbtI/ewYOvGDUz6KGteO074+/vkx89erRfZWWlZUtLC3v+/PnVS5YsqSEiSk5OtluxYoWHWq1mOTg4qH777bcCXftOSkpyvHDhgu3u3buvBwYGthLdLbExNwh6AAAwCff3ye/du7fE1dVVLZfLWREREZLp06fXazQaVkJCgvDUqVNSsVjcWl1dzenpcfc0BD0AAOilb+bdHe7vk1+7dq3rkSNH+hIRVVVVWeTk5FhXV1dzo6KiZNrtXF1de6wHnikQ9AAAYBLa98mnpKQI0tLSBBcuXJAKBAJNVFRUYHNzM7utre0vffS9nfl9GQEAAGavoaGB06dPH7VAINBkZWVZ//HHH7ZERCNGjGg6d+6cQCqVWhIRYekeQQ8AACYoPj6+UaVSsUQikWT58uXuAwYMaCIicnd3VyUlJZVMnDjRPzAwUDJx4sT+HdlfWloaz9XVNew///mP/aJFi3z8/f2DjXsG3QelNgAAoBNKbUwHSm0AAAB6KVyMBwAAZs0UOuONCUv3AACgE5buTQeW7gEAAHopBD0AAIAZQ9ADAACYMQQ9AACAGUPQAwAAY3VXH/0HH3zg6ufnFywSiSSDBw8WFRQUWBrruN0NQQ8AAIz1oD56Q5g2bVrjxx9/XEVEFBkZqbh06VJeQUFB7oQJE+oXLVrkaZCDMADuowcAAL2Ob93kVVNWatA+eicvH8XYVxcyoo/+2WeflWlfHzJkiHzfvn2OhjzXnoSgBwAAk9BdffTbtm1zHj16dKPhz6BnIOgBAEAvfTPv7tAdffRbtmxx+OOPP3jbtm3LN+zoew6CHgAATIKx++gPHjwo+PTTT/ulp6fn29jYmM1jY3ExHgAAmBxD99GfOXPG5vXXX/f56aefijw8PAxztR9DYEYPAAAmJz4+vnH79u3OIpFI4ufnd0dXH71GoyFHR0dlRkZGob79vfXWW14KhYLz3HPP+f1vP62//PJLkbHPozug1AYAAHRCqY3pQKkNAABAL4WlewAAMGvoo8fSPQAA6ICle9OBpXsAAIBeCkEPAABgxhD0AAAAZgxBDwAAYMYQ9AAAwFjd1Ue/bt06Z5FIJBGLxZLIyMjAzMxMa2Mdt7vhqnsAANCp/VX3dckFXsqqJoPW1Fq42SocJoseuSxHpVIRl2vYu8Pr6urYDg4OGqK7fwB88cUXLunp6XqfqMcUuOoeAABMEo/HiyC6W2ITHR0tevbZZ30DAwODiYhGjx7tFxwcHOTv7x/86aefOmnfk5ycbCeRSIICAwMlgwcPFj1o30lJSY4zZ870JiLShjwRkVwu53S2GIeJ8MAcAADQqzMzb0Mzdh/9mjVrnLds2eKqVCrZqampZlNTixk9AACYBF199IGBgZLIyMggbR/9qVOnbDvbR//OO+/cKisru/LBBx/ceP/99/sZ4xx6AoIeAABMwoP66PPz83ODgoKau9pHr/XKK6/Upaam9u3ygBkCQQ8AACbH0H302dnZVtqf9+3b18fHx6fFOCPvfviOHgAATI6h++g3btzokp6ebsflctv69Omj2rVr1zXjn0X3wO11AACgE0ptTAdurwMAAOilsHQPAAD/v707Zk0cjOM4/o8tZ0uHXhGkINehFLpUM9zu4CtwcBHXvgHJC3FyO3BwdXDxDQjOEuJw4HHbA9EM0aGd4sNNwlHiSbmYJk+/n02NgtPP54fhZzT26KnuAQAxqO7zg+oeAIBPiqAHAMBgBD0AAAYj6AEAMBhBDwDIrLT26PcGg8GNZVnfp9NpopO8H4nb6wAAR43H42/r9TrR8CuXy6/NZvOfq3jz+fzn2+eS2qPvdDpbEdnuH4dhWOj3++Varfby3x+eIZzoAQCZldYevYiI4zgVx3H8YrFo1H3nnOgBAEcdO3mn4ZR79LPZ7FIp9aXdbm97vd7t8XfkB0EPAMiFuD36yWTyVURkv0e/Wq3O37tHv9vtpNvt3g2HQ2OGbP5GdQ8AyIVT7dFvNpuz5XJ50Wg0HiuVStV13atWq/Vgyh/yCHoAQO4kuUdfKpV2YRi6SilPKeXZtv0yGo1+1ev111N/jzRQ3QMAcifpPXqTMWoDAIjFqE1+MGoDAMAnRXUPADAae/RU9wCAGK7r/q5Wq2GhUCAoMkxrbXmed2Pb9n3c61T3AIBDFkEQXGut33e/GlKjtbaCILgWkcWha6juAQCxoih69n3/h+/7T8LBMKu0iCyiKHo+dAHVPQAABuMXGgAABiPoAQAwGEEPAIDBCHoAAAxG0AMAYLA/cOVkbmzje+oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Depths (5 $\\\\sigma$) vs coverage on Bootes')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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
}