PTF_A_Fig_1_stars_day_averaged.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "from os import listdir\n",
    "import pandas as pd \n",
    "from astropy.table import Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resulting PTF stellar lightcurves from  S82"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, find how many stars we have from this query, in  ../IPAC_output_tables/Ipac_PTF_lightcurves_stars_S82.tbl "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# all lightcurves (may take a minute to read as the file has size of 82 MB ... )\n",
    "lcs = Table.read('../IPAC_output_tables/Ipac_PTF_lightcurves_stars_S82.tbl', format='ipac')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "913975"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This gives total number of epochs : \n",
    "len(lcs)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 38776 stars to start with \n"
     ]
    }
   ],
   "source": [
    "# Need to group by SDSS ra to get info about how many objects we have:\n",
    "print('We have %d stars to start with ' % len(np.unique(lcs['ra'])))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove empty files and those with less than 10 points "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three equivalent ways of testing for the number of empty files.  Those files should be removed from the list of accepted stars/ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since I selected the objects from PTF asking them to have at least \n",
    "# ngoodobs > 10, there are definitely no objects with less than 10 epochs : \n",
    "np.sum(lcs['ngoodobs'] < 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics on the raw  LC (before day-averaging)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group by ra : the SDSS ra, since ra_01 : the PTF ra , may differ from chip to chip,  just as oid .  For these stars there seems to be only one edge-case ( number of unique SDSS  ra is 38776, and PTF oid : 38886, PTF ra_01: 38892)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# drop some pesky 'masked' row... \n",
    "lcs_new = lcs[lcs['ra'] != np.unique(lcs['ra'])[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# group by ra to calculate stats on each object...\n",
    "grouped_lcs = lcs_new.group_by('ra')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38775"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(grouped_lcs.groups.keys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I capitalize on what stats are **really** needed. I'm not making a Fig.1 for PTF. If needed in the future, I may worry about that, but for now, I'll get some simple-to-do stats  (per object-grouped group :  max(mjd) - min(mjd),  mean(mag), mean(err) ) .  Means are built-in as group-aggregates , and the max-min  statistic I'll try to implement as a function.  \n",
    "\n",
    "Either way,  stats are not as needed as just making the master files  (delta_t, delta_mag, error),  and the PTF-SDSS matched catalogs, to use SDSS r-mag for magnitude cuts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=6&gt;\n",
       "<table id=\"table4678309592\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>cntr_01</th><th>dist_x</th><th>pang_x</th><th>ra_01</th><th>dec_01</th><th>obsmjd</th><th>mag_autocorr</th><th>magerr_auto</th><th>oid</th><th>ra</th><th>dec</th><th>fid</th><th>transient_flag</th><th>astrometricrms</th><th>nobs</th><th>ngoodobs</th><th>nbestobs</th><th>referencemag</th><th>referencemagerr</th><th>medianmag</th><th>bestmedianmag</th><th>meanmag</th><th>bestmeanmag</th><th>magrms</th><th>bestmagrms</th><th>sid</th><th>s_ra</th><th>s_dec</th><th>ptffield</th><th>ccdid</th><th>hmjd</th><th>nid</th><th>mag_auto</th><th>limitmag</th><th>absphotzp</th><th>fwhmsex</th><th>fwhm_image</th><th>qaflags</th><th>flags</th><th>photcalflag</th><th>goodflag</th></tr></thead>\n",
       "<thead><tr><th></th><th>arcsec</th><th>deg</th><th>degrees</th><th>degrees</th><th>days</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>degrees</th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>days</th><th></th><th>mag</th><th>mag</th><th>mag</th><th>arcsec</th><th>pixels</th><th></th><th></th><th>0=No, 1=Yes</th><th>0=No, 1=Yes</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>18239</td><td>0.0</td><td>0.0</td><td>0.054504</td><td>-1.149916</td><td>56942.3257272</td><td>15.854</td><td>0.032</td><td>25921000000030</td><td>0.054504</td><td>-1.149916</td><td>1</td><td>0</td><td>3.837622e-07</td><td>170</td><td>168</td><td>168</td><td>15.848</td><td>0.005</td><td>15.85</td><td>15.85</td><td>15.853</td><td>15.853</td><td>0.022</td><td>0.022</td><td>21771458000909</td><td>0.054509</td><td>-1.149944</td><td>2592</td><td>0</td><td>56942.3312272</td><td>2006</td><td>15.856</td><td>20.798</td><td>28.006</td><td>2.45</td><td>2.963</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>18239</td><td>0.0</td><td>0.0</td><td>0.054504</td><td>-1.149916</td><td>56942.3708872</td><td>15.859</td><td>0.032</td><td>25921000000030</td><td>0.054504</td><td>-1.149916</td><td>1</td><td>0</td><td>3.837622e-07</td><td>170</td><td>168</td><td>168</td><td>15.848</td><td>0.005</td><td>15.85</td><td>15.85</td><td>15.853</td><td>15.853</td><td>0.022</td><td>0.022</td><td>21771594000870</td><td>0.054502</td><td>-1.14994</td><td>2592</td><td>0</td><td>56942.3763972</td><td>2006</td><td>15.848</td><td>20.692</td><td>27.969</td><td>2.45</td><td>2.768</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>18239</td><td>0.0</td><td>0.0</td><td>0.054504</td><td>-1.149916</td><td>56943.2970072</td><td>15.846</td><td>0.034</td><td>25921000000030</td><td>0.054504</td><td>-1.149916</td><td>1</td><td>0</td><td>3.837622e-07</td><td>170</td><td>168</td><td>168</td><td>15.848</td><td>0.005</td><td>15.85</td><td>15.85</td><td>15.853</td><td>15.853</td><td>0.022</td><td>0.022</td><td>21786591001041</td><td>0.054499</td><td>-1.149921</td><td>2592</td><td>0</td><td>56943.3024772</td><td>2002</td><td>15.835</td><td>20.998</td><td>28.033</td><td>2.65</td><td>2.751</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>18239</td><td>0.0</td><td>0.0</td><td>0.054504</td><td>-1.149916</td><td>56943.3422372</td><td>15.86</td><td>0.034</td><td>25921000000030</td><td>0.054504</td><td>-1.149916</td><td>1</td><td>0</td><td>3.837622e-07</td><td>170</td><td>168</td><td>168</td><td>15.848</td><td>0.005</td><td>15.85</td><td>15.85</td><td>15.853</td><td>15.853</td><td>0.022</td><td>0.022</td><td>21786687000844</td><td>0.054498</td><td>-1.149918</td><td>2592</td><td>0</td><td>56943.3477072</td><td>2002</td><td>15.854</td><td>20.646</td><td>28.002</td><td>3.35</td><td>3.451</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>18239</td><td>0.0</td><td>0.0</td><td>0.054504</td><td>-1.149916</td><td>56951.2557972</td><td>15.852</td><td>0.037</td><td>25921000000030</td><td>0.054504</td><td>-1.149916</td><td>1</td><td>0</td><td>3.837622e-07</td><td>170</td><td>168</td><td>168</td><td>15.848</td><td>0.005</td><td>15.85</td><td>15.85</td><td>15.853</td><td>15.853</td><td>0.022</td><td>0.022</td><td>21903205001471</td><td>0.054499</td><td>-1.149919</td><td>2592</td><td>0</td><td>56951.2609672</td><td>2013</td><td>15.862</td><td>21.398</td><td>28.044</td><td>3.45</td><td>3.938</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>18239</td><td>0.0</td><td>0.0</td><td>0.054504</td><td>-1.149916</td><td>56952.2262372</td><td>15.86</td><td>0.031</td><td>25921000000030</td><td>0.054504</td><td>-1.149916</td><td>1</td><td>0</td><td>3.837622e-07</td><td>170</td><td>168</td><td>168</td><td>15.848</td><td>0.005</td><td>15.85</td><td>15.85</td><td>15.853</td><td>15.853</td><td>0.022</td><td>0.022</td><td>21922072001453</td><td>0.054499</td><td>-1.149916</td><td>2592</td><td>0</td><td>56952.2313672</td><td>2014</td><td>15.862</td><td>21.6</td><td>28.072</td><td>2.65</td><td>2.701</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=6>\n",
       "cntr_01  dist_x  pang_x  ra_01   ... qaflags flags  photcalflag   goodflag \n",
       "         arcsec   deg   degrees  ...                0=No, 1=Yes 0=No, 1=Yes\n",
       " int64  float64 float64 float64  ...  int64  int64     int64       int64   \n",
       "------- ------- ------- -------- ... ------- ------ ----------- -----------\n",
       "  18239     0.0     0.0 0.054504 ...       0 131072           0           1\n",
       "  18239     0.0     0.0 0.054504 ...       0 131072           0           1\n",
       "  18239     0.0     0.0 0.054504 ...       0 131072           0           1\n",
       "  18239     0.0     0.0 0.054504 ...       0 131072           0           1\n",
       "  18239     0.0     0.0 0.054504 ...       0 131072           0           1\n",
       "  18239     0.0     0.0 0.054504 ...       0 131072           0           1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate some aggregate info...\n",
    "\n",
    "# ra = grouped_lcs.groups.keys[0]\n",
    "#http://docs.astropy.org/en/stable/table/operations.html \n",
    "\n",
    "# display first 10 rows of the first group\n",
    "grouped_lcs.groups[0][:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# All the quick stats below : test on few objects... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#n_groups = 2\n",
    "test_group = grouped_lcs.groups[:2]\n",
    "#test_agg_info = grouped_lcs.groups.keys[:2]\n",
    "test_agg_info = test_group[['ra','dec', 'oid', 'magerr_auto','mag_autocorr']].groups.aggregate(np.mean)\n",
    "\n",
    "def agg_func(array):\n",
    "    'return max(array)-min(array)'\n",
    "    return max(array) - min(array)\n",
    "\n",
    "test_agg_info['mjd_span'] =  test_group['obsmjd'].groups.aggregate(agg_func)\n",
    "test_agg_info['lc_length'] = test_group['ra'].groups.aggregate(np.count_nonzero)\n",
    "\n",
    "# rename columns, since aggregate leaves the columns unchanged...\n",
    "test_agg_info.rename_column('magerr_auto', 'avg_err')\n",
    "test_agg_info.rename_column('mag_autocorr', 'avg_mag')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The cell below may take a minute to run... After all its 38775 lightcurves.... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# And do on all objects.... \n",
    "raw_agg_info = grouped_lcs[['ra','dec', 'oid', 'magerr_auto','mag_autocorr']].groups.aggregate(np.mean)\n",
    "\n",
    "def agg_func(array):\n",
    "    'return max(array)-min(array)'\n",
    "    return max(array) - min(array)\n",
    "\n",
    "raw_agg_info['mjd_span'] =  grouped_lcs['obsmjd'].groups.aggregate(agg_func)\n",
    "raw_agg_info['lc_length'] = grouped_lcs['ra'].groups.aggregate(np.count_nonzero)\n",
    "\n",
    "# rename columns, since aggregate leaves the columns unchanged...\n",
    "raw_agg_info.rename_column('magerr_auto', 'avg_err')\n",
    "raw_agg_info.rename_column('mag_autocorr', 'avg_mag')\n",
    "raw_agg_info.rename_column('ra', 'ra_sdss')\n",
    "raw_agg_info.rename_column('dec', 'dec_sdss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Save that info as something that could be easily accessed to make PTF - SDSS matched catalog \n",
    "raw_agg_info.write('../data_products/PTF_stars_raw_agg_info.dat', format='ascii', overwrite='True')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 20357.,    380.,   6459.,    674.,    946.,    458.,    838.,\n",
       "          1592.,   1873.,   5198.]),\n",
       " array([    0.      ,   193.190252,   386.380504,   579.570756,\n",
       "          772.761008,   965.95126 ,  1159.141512,  1352.331764,\n",
       "         1545.522016,  1738.712268,  1931.90252 ]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QJDUzNCRJzQwNSVIzQ0OS1MzQkCQ1MzQkSc0MDUlSM0NDktTM0JAkNTM0JEnNDA1JUjND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\nS7wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e2af9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Show some stats....\n",
    "plt.hist(raw_agg_info['mjd_span'], histtype='step', lw=4)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11eccb2e8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pUh0u7TrX90zIx0JQRKGJWi7ZtEkvlQt1GHPzeqpFro8cSiaB+b0uzOMmFoIi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\nfBAh3RRbCGwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21fb9de80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(raw_agg_info['ra_sdss'], raw_agg_info['dec_sdss'], lw=0, alpha=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing : Make day-averaged light curves : needs be RUN ONLY ONCE  to make them!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make day-averaged lightcurves, save only if more than 10 observing days (points in the day-averaged light curve). \n",
    "Save MJD, avg_mag, error, N_obs that day. For our 38776 lightcurves it takes about few mins on my workstation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test on a few lightcurves : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Input lightcurves are lcs_qso_new , and it is an AstroPy Table \n",
    "# Each lightcurve is most easily accessed as a grouped object  by 'ra' : \n",
    "# object_lc = grouped_lcs.groups[i] \n",
    "# \n",
    "object_lc = grouped_lcs.groups[0] \n",
    "mjd = np.array(object_lc['obsmjd'])\n",
    "mags = np.array(object_lc['mag_autocorr'])\n",
    "errs = np.array(object_lc['magerr_auto'])\n",
    "\n",
    "days = np.unique([int(day) for day in mjd])\n",
    "\n",
    "# store values for a given object... \n",
    "mjd_arr = np.zeros_like(days).astype(float)\n",
    "avg_mags = np.zeros_like(days).astype(float)\n",
    "err_weights = np.zeros_like(days).astype(float)  \n",
    "\n",
    "for i in range(len(days)):\n",
    "    day = days[i]\n",
    "    int_mjd = np.require(mjd,int)       # forcing mjd array -> integers\n",
    "    condition = (int_mjd == day)        # finding where int(mjd) = day\n",
    "\n",
    "    w=1.0 / ( errs[condition] * errs[condition]) \n",
    "    avgmag = np.average(mags[condition], weights = w)       \n",
    "    avg_mags[i] = avgmag\n",
    "    error_weights = 1.0 / np.sqrt(np.sum(w))\n",
    "\n",
    "    # increase error if too small \n",
    "    if error_weights < 0.02 : \n",
    "        error_weights = np.sqrt(error_weights**2.0 + 0.01**2.0)  \n",
    "    err_weights[i] = error_weights\n",
    "\n",
    "    mjd_arr[i] = np.mean(mjd[condition])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ee2e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the original (blue) and the averaged lightcurve (red)...\n",
    "plt.scatter(mjd,mags)\n",
    "plt.errorbar(mjd,mags,errs, ls=\"None\")\n",
    "plt.scatter(mjd_arr, avg_mags, color='red')\n",
    "plt.errorbar(mjd_arr, avg_mags, err_weights, color='red', ls='None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38775"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(grouped_lcs.groups.keys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run on all 38775 lightcurves : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from CRTS_paper_modules import update_progress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=6&gt;\n",
       "<table id=\"table4810995976\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>cntr_01</th><th>dist_x</th><th>pang_x</th><th>ra_01</th><th>dec_01</th><th>obsmjd</th><th>mag_autocorr</th><th>magerr_auto</th><th>oid</th><th>ra</th><th>dec</th><th>fid</th><th>transient_flag</th><th>astrometricrms</th><th>nobs</th><th>ngoodobs</th><th>nbestobs</th><th>referencemag</th><th>referencemagerr</th><th>medianmag</th><th>bestmedianmag</th><th>meanmag</th><th>bestmeanmag</th><th>magrms</th><th>bestmagrms</th><th>sid</th><th>s_ra</th><th>s_dec</th><th>ptffield</th><th>ccdid</th><th>hmjd</th><th>nid</th><th>mag_auto</th><th>limitmag</th><th>absphotzp</th><th>fwhmsex</th><th>fwhm_image</th><th>qaflags</th><th>flags</th><th>photcalflag</th><th>goodflag</th></tr></thead>\n",
       "<thead><tr><th></th><th>arcsec</th><th>deg</th><th>degrees</th><th>degrees</th><th>days</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>degrees</th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>days</th><th></th><th>mag</th><th>mag</th><th>mag</th><th>arcsec</th><th>pixels</th><th></th><th></th><th>0=No, 1=Yes</th><th>0=No, 1=Yes</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>18239</td><td>0.0</td><td>0.0</td><td>0.054504</td><td>-1.149916</td><td>56942.3257272</td><td>15.854</td><td>0.032</td><td>25921000000030</td><td>0.054504</td><td>-1.149916</td><td>1</td><td>0</td><td>3.837622e-07</td><td>170</td><td>168</td><td>168</td><td>15.848</td><td>0.005</td><td>15.85</td><td>15.85</td><td>15.853</td><td>15.853</td><td>0.022</td><td>0.022</td><td>21771458000909</td><td>0.054509</td><td>-1.149944</td><td>2592</td><td>0</td><td>56942.3312272</td><td>2006</td><td>15.856</td><td>20.798</td><td>28.006</td><td>2.45</td><td>2.963</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>18239</td><td>0.0</td><td>0.0</td><td>0.054504</td><td>-1.149916</td><td>56942.3708872</td><td>15.859</td><td>0.032</td><td>25921000000030</td><td>0.054504</td><td>-1.149916</td><td>1</td><td>0</td><td>3.837622e-07</td><td>170</td><td>168</td><td>168</td><td>15.848</td><td>0.005</td><td>15.85</td><td>15.85</td><td>15.853</td><td>15.853</td><td>0.022</td><td>0.022</td><td>21771594000870</td><td>0.054502</td><td>-1.14994</td><td>2592</td><td>0</td><td>56942.3763972</td><td>2006</td><td>15.848</td><td>20.692</td><td>27.969</td><td>2.45</td><td>2.768</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>18239</td><td>0.0</td><td>0.0</td><td>0.054504</td><td>-1.149916</td><td>56943.2970072</td><td>15.846</td><td>0.034</td><td>25921000000030</td><td>0.054504</td><td>-1.149916</td><td>1</td><td>0</td><td>3.837622e-07</td><td>170</td><td>168</td><td>168</td><td>15.848</td><td>0.005</td><td>15.85</td><td>15.85</td><td>15.853</td><td>15.853</td><td>0.022</td><td>0.022</td><td>21786591001041</td><td>0.054499</td><td>-1.149921</td><td>2592</td><td>0</td><td>56943.3024772</td><td>2002</td><td>15.835</td><td>20.998</td><td>28.033</td><td>2.65</td><td>2.751</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>18239</td><td>0.0</td><td>0.0</td><td>0.054504</td><td>-1.149916</td><td>56943.3422372</td><td>15.86</td><td>0.034</td><td>25921000000030</td><td>0.054504</td><td>-1.149916</td><td>1</td><td>0</td><td>3.837622e-07</td><td>170</td><td>168</td><td>168</td><td>15.848</td><td>0.005</td><td>15.85</td><td>15.85</td><td>15.853</td><td>15.853</td><td>0.022</td><td>0.022</td><td>21786687000844</td><td>0.054498</td><td>-1.149918</td><td>2592</td><td>0</td><td>56943.3477072</td><td>2002</td><td>15.854</td><td>20.646</td><td>28.002</td><td>3.35</td><td>3.451</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>18239</td><td>0.0</td><td>0.0</td><td>0.054504</td><td>-1.149916</td><td>56951.2557972</td><td>15.852</td><td>0.037</td><td>25921000000030</td><td>0.054504</td><td>-1.149916</td><td>1</td><td>0</td><td>3.837622e-07</td><td>170</td><td>168</td><td>168</td><td>15.848</td><td>0.005</td><td>15.85</td><td>15.85</td><td>15.853</td><td>15.853</td><td>0.022</td><td>0.022</td><td>21903205001471</td><td>0.054499</td><td>-1.149919</td><td>2592</td><td>0</td><td>56951.2609672</td><td>2013</td><td>15.862</td><td>21.398</td><td>28.044</td><td>3.45</td><td>3.938</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>18239</td><td>0.0</td><td>0.0</td><td>0.054504</td><td>-1.149916</td><td>56952.2262372</td><td>15.86</td><td>0.031</td><td>25921000000030</td><td>0.054504</td><td>-1.149916</td><td>1</td><td>0</td><td>3.837622e-07</td><td>170</td><td>168</td><td>168</td><td>15.848</td><td>0.005</td><td>15.85</td><td>15.85</td><td>15.853</td><td>15.853</td><td>0.022</td><td>0.022</td><td>21922072001453</td><td>0.054499</td><td>-1.149916</td><td>2592</td><td>0</td><td>56952.2313672</td><td>2014</td><td>15.862</td><td>21.6</td><td>28.072</td><td>2.65</td><td>2.701</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=6>\n",
       "cntr_01  dist_x  pang_x  ra_01   ... qaflags flags  photcalflag   goodflag \n",
       "         arcsec   deg   degrees  ...                0=No, 1=Yes 0=No, 1=Yes\n",
       " int64  float64 float64 float64  ...  int64  int64     int64       int64   \n",
       "------- ------- ------- -------- ... ------- ------ ----------- -----------\n",
       "  18239     0.0     0.0 0.054504 ...       0 131072           0           1\n",
       "  18239     0.0     0.0 0.054504 ...       0 131072           0           1\n",
       "  18239     0.0     0.0 0.054504 ...       0 131072           0           1\n",
       "  18239     0.0     0.0 0.054504 ...       0 131072           0           1\n",
       "  18239     0.0     0.0 0.054504 ...       0 131072           0           1\n",
       "  18239     0.0     0.0 0.054504 ...       0 131072           0           1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_lcs.groups[0][:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below takes about 5 mins on the mac ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%finished\n"
     ]
    }
   ],
   "source": [
    "dir_out = '../proc_LC_PTF/stars/'\n",
    "number_of_lightcurves = len(grouped_lcs.groups.keys)\n",
    "keys = np.array(grouped_lcs.groups.keys)\n",
    "\n",
    "for k in range(number_of_lightcurves):\n",
    "    \n",
    "    percent = 100*(k / number_of_lightcurves)\n",
    "    if (k % 10) == 0 : # every tenth loop.. \n",
    "        update_progress(int(percent))\n",
    "    \n",
    "    object_lc = grouped_lcs.groups[k] \n",
    "    mjd = np.array(object_lc['obsmjd'])\n",
    "    mags = np.array(object_lc['mag_autocorr'])\n",
    "    errs = np.array(object_lc['magerr_auto'])\n",
    "\n",
    "    days = np.unique([int(day) for day in mjd])\n",
    "\n",
    "    # store values for a given object... \n",
    "    mjd_arr = np.zeros_like(days).astype(float)\n",
    "    avg_mags = np.zeros_like(days).astype(float)\n",
    "    err_weights = np.zeros_like(days).astype(float)  \n",
    "    N_per_day = np.zeros_like(days).astype(float)\n",
    "    \n",
    "    for i in range(len(days)):\n",
    "        day = days[i]\n",
    "        int_mjd = np.require(mjd,int)       # forcing mjd array -> integers\n",
    "        condition = (int_mjd == day)        # finding where int(mjd) = day\n",
    "        \n",
    "        # number of obs in that night \n",
    "        N_per_day[i] = float(len(mags[condition]))\n",
    "        \n",
    "        w=1.0 / ( errs[condition] * errs[condition]) \n",
    "        avgmag = np.average(mags[condition], weights = w)       \n",
    "        avg_mags[i] = avgmag\n",
    "        error_weights = 1.0 / np.sqrt(np.sum(w))\n",
    "\n",
    "        # increase error if too small \n",
    "        if error_weights < 0.02 : \n",
    "            error_weights = np.sqrt(error_weights**2.0 + 0.01**2.0)  \n",
    "        err_weights[i] = error_weights\n",
    "\n",
    "        mjd_arr[i] = np.mean(mjd[condition])\n",
    "\n",
    "      \n",
    "    # Save the output... \n",
    "    # Once per lightcurve \n",
    "    # ONLY IF THERE IS MORE THAN 10 OBS ! \n",
    "    \n",
    "    if( len(mjd_arr) >  10 ) :\n",
    "        object_name = str(keys[k][0])\n",
    "        name_out = dir_out  + object_name +'.txt'\n",
    "        np.savetxt(name_out, np.column_stack((mjd_arr,avg_mags, err_weights, N_per_day)),fmt='%11.4f')\n",
    "        \n",
    "print('\\nfinished')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics on the day-averaged LC "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I add to the raw LC statistics (agg_info), the statistics on day-averaged lightcurves.\n",
    "Thus necessarily I select only those rows that correspond to objects \n",
    "that had a day-averaged lightcurve longer than 10 days, so that they made it to \n",
    "proc_LC_PTF/  directory . "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "avg_err :  average of raw PTF errors over all epochs\n",
    "\n",
    "avg_mag : average of raw PTF magnitudes over all epochs\n",
    "\n",
    "mjd_span : min(mjd) - max(mjd), before day-averaging \n",
    " \n",
    "lc_length : number of epochs before day-averaging "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert the ra_sdss to strings to easily compare to ra of objects that made it to \n",
    "# proc_LC_PTF/   ,  i.e. they met the criteria explained above  \n",
    "ra_info = np.array(agg_info['ra_sdss']).astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# all LCs that had more than 10 separate days of obs. \n",
    "dir_in = '../proc_LC_PTF/stars/'\n",
    "files = np.array(listdir(dir_in))\n",
    "\n",
    "# take only the ra part  (cut off the .txt part )\n",
    "ra_files = np.array([name[:-4] for name in files])\n",
    "\n",
    "# Choose only those PTF aggregate info about lightcurves that \n",
    "# during day-averaging were found to have at least day-averaged 10 epochs  (10 separate days of observations)\n",
    "raw_agg_info_select = raw_agg_info[np.in1d(ra_info, ra_files)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Only 15714 stars out of 38775 have more than 10 days of obs \n"
     ]
    }
   ],
   "source": [
    "print('Only %d stars out of %d have more than 10 days of obs '%(len(raw_agg_info_select),len(raw_agg_info)))\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%"
     ]
    }
   ],
   "source": [
    "# make new columns:\n",
    "# avg_day_err :  the average of processed lightcurves errors \n",
    "# avg_day_mag : the average of processed lightcurves magnitudes \n",
    "# \n",
    "# make an array for storing total timespan of obs per object, \n",
    "# as well as the total number of nights per object\n",
    "\n",
    "proc_mjd_span = np.zeros_like(files, dtype=float) # store what was the total  timespan of observations per object\n",
    "proc_lc_length=np.zeros_like(files, dtype=float) # number of points in the lightcurve - equal to the number of obs nights\n",
    "avg_N_per_day = np.zeros_like(files, dtype=float)\n",
    "avg_day_mag = np.zeros_like(files, dtype=float)\n",
    "avg_day_err = np.zeros_like(files, dtype=float)\n",
    "avg_mjd_diff= np.zeros_like(files, dtype=float)\n",
    "\n",
    "k = 0\n",
    "total = float(len(files))\n",
    "\n",
    "for i in range(len(files)):\n",
    "    percent = 100*(k / total)\n",
    "    if (k % 10) == 0 : # every tenth loop.. \n",
    "        update_progress(int(percent))\n",
    "    k += 1\n",
    "    # choose processed lightcurves according to \n",
    "    # order of the aggregate info \n",
    "    # then rows will match . \n",
    "    \n",
    "    ra_sdss = str(agg_info_select['ra_sdss'][i])\n",
    "    address=dir_in+files[ra_files == ra_sdss][0]\n",
    "    \n",
    "    data=np.loadtxt(address) \n",
    "    proc_lc_length[i] = sum(1 for line in open(address))\n",
    "    avg_day_mag[i] = np.mean(data[:,1])\n",
    "    avg_day_err[i] = np.mean(data[:,2])\n",
    "    avg_N_per_day[i] = np.mean(data[:,3])\n",
    "    proc_mjd_span[i] = data[-1,0]-data[0,0]  # gives number of days between first and last obs\n",
    "    \n",
    "    #mjd_diff = np.zeros(len(data[:,0]))\n",
    "    #for j in range(len(data)-1):\n",
    "    #    mjd_diff[j] = data[j+1,0] - data[j,0]\n",
    "    #avg_mjd_diff[i] = np.mean(mjd_diff)     # avg span between obs in MJD    \n",
    "    \n",
    "keys = ['ra_files', 'proc_mjd_span', 'proc_lc_length', 'avg_N_per_day', 'avg_day_mag', 'avg_day_err']\n",
    "values = [np.array(ra_files).astype(float), proc_mjd_span,proc_lc_length,avg_N_per_day,avg_mag_ttl,avg_err_ttl]\n",
    "\n",
    "\n",
    "stats_proc_lc ={}\n",
    "for key, value in zip(keys, values):\n",
    "    stats_proc_lc[key] = value\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=6&gt;\n",
       "<table id=\"table4846451008\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>ra_files</th><th>proc_lc_length</th><th>avg_day_err</th><th>avg_day_mag</th><th>proc_mjd_span</th><th>avg_N_per_day</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>0.054504</td><td>35.0</td><td>0.0370542857143</td><td>15.85392</td><td>1122.904</td><td>1.94285714286</td></tr>\n",
       "<tr><td>0.078347</td><td>29.0</td><td>0.137420689655</td><td>20.9523068966</td><td>54.8939</td><td>1.55172413793</td></tr>\n",
       "<tr><td>0.093096</td><td>35.0</td><td>0.0352342857143</td><td>15.1224171429</td><td>54.8939</td><td>1.85714285714</td></tr>\n",
       "<tr><td>0.132509</td><td>35.0</td><td>0.0400514285714</td><td>17.6477142857</td><td>54.8939</td><td>1.8</td></tr>\n",
       "<tr><td>0.262434</td><td>35.0</td><td>0.0490171428571</td><td>18.7640628571</td><td>54.8939</td><td>1.77142857143</td></tr>\n",
       "<tr><td>0.28241</td><td>35.0</td><td>0.0490228571429</td><td>18.7135885714</td><td>54.8939</td><td>1.77142857143</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=6>\n",
       "ra_files proc_lc_length   avg_day_err   ... proc_mjd_span avg_N_per_day\n",
       "float64     float64         float64     ...    float64       float64   \n",
       "-------- -------------- --------------- ... ------------- -------------\n",
       "0.054504           35.0 0.0370542857143 ...      1122.904 1.94285714286\n",
       "0.078347           29.0  0.137420689655 ...       54.8939 1.55172413793\n",
       "0.093096           35.0 0.0352342857143 ...       54.8939 1.85714285714\n",
       "0.132509           35.0 0.0400514285714 ...       54.8939           1.8\n",
       "0.262434           35.0 0.0490171428571 ...       54.8939 1.77142857143\n",
       " 0.28241           35.0 0.0490228571429 ...       54.8939 1.77142857143"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make an Astropy table from these stats, and add it to agg_info_selected \n",
    "\n",
    "stats = Table(stats_proc_lc)\n",
    "stats[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stats.rename_column('ra_files', 'ra_sdss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15714"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.in1d(raw_agg_info_select['ra_sdss'], stats['ra_sdss']))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#http://docs.astropy.org/en/stable/api/astropy.table.join.html\n",
    "# beautiful examples \n",
    "# https://star.herts.ac.uk/~gb/python/14-astropy-tables.html  \n",
    "\n",
    "from astropy.table import join\n",
    "agg_info_merged = join(left=raw_agg_info_select, right = stats, keys='ra_sdss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  Save the combined catalog, with both raw lightcurve aggregates, and processed lightcurve aggregates \n",
    "# this is the master catalog combining all the PTF information about stars in one place \n",
    "\n",
    "agg_info_merged.write('../data_products/PTF_stars_raw_and_proc_combined_agg_info_15714.dat', \n",
    "                      format='ascii', overwrite='True')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare before and after day-averaging: error and mag distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x120ce3a58>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ba1+SJWFcXliIiDtJOtRNyy0is36uMAx5lpd1z5o1lT/P0m2Z5hAxoPoESgenQ3j8UdJh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\nMzPLxAnDzMwyccIwM7NM/hetc0BOUoKFDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120cdcd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = (stats_proc_lc['mag']>15)*(stats_proc_lc['mag']<22)\n",
    "m1 = (stats_proc_lc['avg_mag']>15)*(stats_proc_lc['avg_mag']<22)\n",
    "#plt.hist(mag_list[m])\n",
    "\n",
    "#sns.set_context('talk')\n",
    "fig,ax = plt.subplots(1,1)\n",
    "nbins=30\n",
    "# Calculate the histogram1 : individual epoch brightness measurements \n",
    "hist1, bin_edges = np.histogram(stats_proc_lc['mag'][m], bins=nbins, density=True)\n",
    "bin_centres1 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "# Calculate the histogram2 : day-averaged brightness measurements \n",
    "hist2, bin_edges = np.histogram(stats_proc_lc['avg_mag'][m1], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label=r'$m_{i}$')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label=r'$\\langle m_{i}\\rangle_{day}$')\n",
    "plt.legend(loc='best')\n",
    "ax.set_ylabel('Density (normalized)')\n",
    "ax.set_xlabel('PTF Brightness at each epoch [mag]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['lc_length', 'avg_N_day', 'avg_err', 'err', 'mag', 'timespan_obs', 'avg_mjd_diff', 'avg_mag'])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_proc_lc.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 126.27  ,  126.354 ,  126.368 ,  126.456 ,  126.4365,  126.276 ,\n",
       "        126.964 ])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = stats_proc_lc['err'] > 1 \n",
    "stats_proc_lc['mag'][m]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11ede69e8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9OSKeSNePBq5My31cUhk4nCQZDBdPxTJJW4BVwHkN9qsoRXMj/z4SEZW7k5+T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\nAP4/ETD5zOqitpQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1189a3940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import seaborn as sns\n",
    "#sns.set_context('talk')\n",
    "fig,ax = plt.subplots(1,1)\n",
    "nbins=30\n",
    "# Calculate the histogram1 : new day-averages\n",
    "hist1, bin_edges = np.histogram(stats_proc_lc['err'][stats_proc_lc['err']<0.6], bins=nbins, density=True)\n",
    "bin_centres1 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "# Calculate the histogram2 : old day-averages\n",
    "hist2, bin_edges = np.histogram(stats_proc_lc['avg_err'][stats_proc_lc['avg_err']<1], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label=r'$ e_{i} $')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label=r'$\\langle e_{i} \\rangle $')\n",
    "plt.legend(loc='best')\n",
    "ax.set_ylabel('Density (normalized)')\n",
    "ax.set_xlabel('Photometric error PTF [mag]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare PTF r mag  with SDSS r mag  for stars "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zipping CRTS-SDSS stars catalog...\n",
      "Read in catalog for 48250 stars from CRTS \n"
     ]
    }
   ],
   "source": [
    "import CRTS_paper_modules as mod\n",
    "cols1, stars_cat = mod.get_stars_catalog() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "address = '../data_products/PTF_stars_aggregate_info.dat'\n",
    "ptf_agg_info = Table.read(address,format='ascii' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38775"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ptf_agg_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21fbb4c18>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6wG1wRdjZiEgixF0cgiWll4DbghmEl9W0lMdeSpwgXqoJEyYUb+fk5JCTk1OV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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ec27d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# SDSS r-mag \n",
    "r = np.array(stars_cat['r_mMed']).astype(float)\n",
    "m = (r > 15)*(r<22)\n",
    "plt.hist(r[m], histtype='step', lw=3, label='SDSS')\n",
    "\n",
    "# CRTS white light \n",
    "avg_mag = np.array(stars_cat['CRTS_M']).astype(float)\n",
    "plt.hist(avg_mag,histtype='step', lw=3, label='CRTS')\n",
    "\n",
    "#PTF r mag \n",
    "ptf_mag = np.array(ptf_agg_info['avg_mag'])\n",
    "m = (ptf_mag > 15)*(ptf_mag<22)\n",
    "\n",
    "plt.hist(ptf_mag[m],histtype='step', lw=3, label='PTF')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Fig.1 : statistical properties of those stars I use in my analysis (2016/06)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['avg_err', 'avg_N_day', 'err', 'avg_mag', 'mag', 'timespan_obs', 'lc_length', 'avg_mjd_diff'])"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_proc_lc.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1502e7a90>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BxYsX8eOPP+L9999HYmIiNmzYgH79+uHSpUuVeh1CSN2iwJJUaO3atdDT00NmZiZWrVpV\n4+9nZGQEe3t7qW26urpo2rQpAMgdGiN55gQAXr58KZPeo0cPuYFSz549YW5ujtevX/NfWuHh4cjM\nzIS+vj4GDhwot4wuLi7Q0NBAWloaHjx4IJPetWvXCmooq6CgAGFhYQDKnm+Vx9zcHIMGDQJjDGfP\nnlXq9d+UkJCAp0+fAih7HvNNWlpaGD9+vNy8+/fvx71792SCMwldXV0AZUO6ypMMcQ0NDZU6RsXF\nxQgJCQHHcXwQBJQFI4wx+Pr64vjx41J5TExM4O/vj5UrV1ZqwqJbt24hNzcXmpqamDx5stx9hg8f\nDhMTE5SWlspMWCUZoivvvSRDr+Pj4xEXFwcA/JDPf/75B76+vkhNTZXKs3nzZqxbtw62trZ8G0jO\nvxEjRsgtn62tLUQiERhjcp9dbt++Pd/25XXr1g1WVlYoLCzEmTNnpNICAwMBAMOGDeOHtal6/l+8\neBGvX79Gq1at8P7778vksbCwQO/eveW+HiHvMsn1QSAQ4H//+x/WrFmj8FGEN3EcB0dHR5nPfvl1\njOV9Z0oepyh/XW3atCmuXbuGiIgIqR9hJUpLS/ngtfz1XfJ9NHjwYP57urxOnTqhc+fOlaoPUBYY\njxgxAhs3bsSQIUMAlP3glZmZWenXIITUHfoJmVTIwsICX375JdasWYNTp07B2dkZgwYNqrH3MzU1\nlbtd0pMl74urfE8Ie2OdPI7jZHrWyhMKhUhKSuJ7gWJiYgCUfZEpCkKAsiVZSktL8eTJE5lAuHyg\nWxkJCQkoKSl5a1k7duyIEydO8EGhqiT59fX1FbZ3+/btFebX1NREbm4uIiIiEBcXh4SEBMTFxSEq\nKgoZGRkAINOT2717d1hZWSE+Ph4hISF84Hru3Dnk5uZCJBLBzs6O39/DwwNnzpxBeno6Fi1aBIFA\ngI4dO6J3797o27ev3EknFHny5AmAsoCvfE/wm+zs7PD8+XO57avouJiamqJx48Z4+fIlHj9+jNat\nW8PJyQmOjo64efMmNm3ahE2bNqFt27bo1asXPvzwQ/Ts2VMqSH327BmKi4vBcRxWrFihMFhOTk4G\nY4yvT3ktWrRQWK9x48Zh06ZNOH78OD+ZR2lpKU6dOgWO46R+XFD1/Je0mSRYlkckEuHChQsK0wl5\nFzk7OyMmJgYPHjzA5s2b4efnhxEjRsDV1VXq+UdF5F3DJd+XQMXfmW9+XwJlgV1ycjLu37+PZ8+e\nITExEY8fP0ZUVBRevXoFjuOkru+PHj0Cx3Eyk8mV17FjR9y9e/etdWGM4eLFizhw4AAuXryI0tJS\naGpqon///koFp4SQukOBJXmrqVOn4vTp07h58yZWrFght0eiupSfEEAeeRO/vE1FwYS+vj4YYygo\nKAAAfihtcXExwsPD3/ra8obelp+5tTLK/2r85iRCb5YVAPLz85V6/TdJhmBW1NaKJhvKz8/HmjVr\ncPz4cYjFYn7opZaWFuzs7GBnZ4eLFy/KzTtmzBj8/PPPCA4O5gPLoKAgmd5KAHjvvfcQFBQEX19f\nhISEIC0tDXfv3kVERAS2b9+OVq1aYdmyZQp71cqTtK/k13ZFDAwMwBiT275vO4devnzJ/4qvqamJ\n3377DXv37kVgYCAePXqEp0+f4smTJ9i7dy8MDAzg4eHBz75b/hyS1wNeHsdxcs85eb2VEqNGjcLP\nP/+M69evIyMjA82bN8fly5eRmZkJCwsLqQlBVD3/JedUo0aNFO5bfhgxIaRM37590bdvX6lZYA8c\nOICAgAB07twZrq6uGD58uMIfnKrzO/Pp06fw8vLCzZs3AYC/vhsYGKB79+54/vw5P9Rf4sWLFwAq\n/uy/7dqblZWFw4cP488//0RSUhI4joOxsTEmTJgAV1dXhT+AEkLUDwWWpFLWrl2LkSNH8kNiFQ2F\nlJD3SygAPoCrTRUFYnl5eeA4jr/plXxJd+jQAYcPH66V8pUPWvLy8uT+wgz8d/NeUZBTGZJndipq\nlzeHskrMmzcPYWFh0NPTw7Rp0+Dg4AAbGxu0bt0ampqaOHToUIWB5ZYtW3D79m2kpqZCW1sbV65c\ngUAgkDsEtGnTpliyZAmWLFmCmJgYhIWF4fr167h8+TKSkpKwYMECBAQEyPQYv0nSXvKGSZeXm5sL\njuPktu+bzyeWJwmuygfjAoEA06dPx/Tp05GWloYbN27gxo0buHDhAjIzM/HLL79AV1cX06dPl7oh\nCw8PrzBIVIWJiQl69+6Ny5cv4+TJk3B3d0dwcLBMbyWg+vlflXOKEFI2ZLRTp05YsmQJDh06hAMH\nDvA/pq1btw5jxoyBq6trjc2unJWVhSlTpuDFixcwMzODq6sr2rdvj7Zt2/JDa7/66itERUVJ5ZNc\nvyq6viq6LoSFhSEgIABnz57lR+107twZkydPhrOzs1TPKyGkfqBnLEmlSIbEMsbw119/4fTp03L3\nkyzVoGhSlefPn9dYGeVhjFU4dFTyJSlZE1HyLGZcXJzMcM7ybty4gWfPnlXLos6Wlpb80KSKeqz+\n/fdfAKjyjYWkjgUFBXj27JncfWJjY2W23b17l19mxNfXF19//TWGDh0Ka2tr/ri/+TxheZIAR/Kc\n6Pnz5yEWizFgwAB+shkJSTBWVFQEoGyIpWR9U8n6p6WlpZWayKht27YAyo6pohuc8hNZyGtfecNP\ngbJnKyWvKTmHcnNzcffuXb4tTExMMHLkSHz//fe4cOEC+vfvD8YYjh07BqDssyVpv/KTYrzp/v37\nePToUYVBriJjx44FYwynT59GQUEBzp8/D47jZNZ+VfX8l+R7szejPHnnFCFEmrGxMWbPno1z585h\n+/bt6N27N/Ly8vD7779j2LBhlRpSqorDhw8jKysLhoaGCAwMxOzZs/Hhhx9KPa+ZlpYms2xIu3bt\nwBhDdHS0wteWd13w9fXFtGnTEBISAk1NTYwdOxaHDx/GgQMHMHLkSAoqCamnKLAklTZ16lR+ltj9\n+/fL3UcSIOTk5CArK0sm/c0JRGrDpUuXkJ6eLrP977//5nvOJBOLdO/eHY0bN0Z+fj6OHDki9/WO\nHTsGd3d3DB8+vMJAqrL09PTwwQcfgDGG3bt3y90nISEBoaGh4Diuymt7tWrVin+eMSAgQCadMSa3\n7omJifzf5Z+HlCgsLMSJEycAAGKxWO57jx8/Howx/P333zh37pzcYbClpaUYPXo0pk+fLveZvKZN\nm/JBXGlpqaJq8rp16wZDQ0OUlpZi3759cvc5ceIE0tPTwXEcPvzwQ5n0oKAgub3we/bsAVDWHpIJ\nMZYtW4aPP/4YO3fulNlfU1OTn5hDUnZ9fX04OjqCMYY//vhDbvkSEhIwadIkjBw5UuGPOhUZOHAg\nDA0NERERgYMHD6KgoAA9evSQWYhc1fPfyckJAoGAX+vyTenp6XwwSwh5O47j4OTkBH9/f/z111+Y\nNm0amjRpUqmZsFUhub6bmZnJHbYeGxvLD48vf90dPHgwgLKJ2bKzs2XyPX78GLdv35bZXlxcDAsL\nCyxatAgXLlzA2rVrK5xjgBBSP1BgSZQimSVWEQcHBwgEAjDGsHbtWr7HSSwWY/fu3Th06FCt31wW\nFhZi7ty5UstIXL16FcuXLwfHcXB3d+eHn+rp6WH27NlgjOH777/H0aNHpQKKs2fPYsWKFeA4Ds7O\nzlLLolTFp59+CoFAgCtXrsDLy0uqZ+3hw4eYNWsWioqK0L59e4waNarK7yfpff7jjz+we/duvo6F\nhYVYvnw57t+/L5NH0vMHANu3b5e6uYiNjYWHhwffA6po2KOTkxOMjY1x69YtXLlyBc2aNZMJlDU1\nNeHi4sIfgzfLcubMGVy+fBkA0K9fv7fWVVdXlz+mW7Zswf79+6WO6enTp/Hdd9+B4zh8/PHHsLKy\nknmNBw8eYNmyZfxQbsYYfv/9d+zbtw8cx2HhwoX8vpLjc+DAAZnlVWJiYrBnzx5wHIf+/fvz2z/9\n9FNoamrixIkTWL9+vVSv5KNHjzB79myIxWKYm5tLLR0AoFKfJ8lMi69fv8aWLVvkDoMFVD//DQ0N\nMXPmTDDGsGzZMly7do3Pk5ycjPnz59NQWEJUZGVlhWXLluHixYsVTqxWFZLre3R0tMwPwBcvXoSH\nhwd/zS//SIuTkxM6dOiAvLw8zJ8/X+rH1kePHmH+/Plyf5SbNGkS/v77b8yYMYOevyakAaFnLIlS\nLCws8NVXX2H16tVy05s0aQIPDw/s2LEDJ06cwKVLl2Bubo6kpCTk5ORg0qRJOHfuXK0OiR0wYACu\nXLmCIUOGwMbGhh8CynEcBg8ejAULFkjtP2vWLCQkJODQoUNYtmwZfvrpJ5ibmyMtLQ3Pnz8Hx3Ho\n1q0b1qxZU21l7Ny5M77//nt8++23OHToEI4dOwZra2u8evUKT58+5Wfd27p1a7UMEerduzcWLVqE\njRs3Yt26dfDz84OpqSmePHmCV69eYciQITI3F+3bt8fw4cPx119/4bfffkNgYCBatWqF7OxsJCYm\nguM49O7dG1euXEF+fj7y8/NlnlfU0tLCiBEjsGfPHrx+/RqjRo2SO7nE559/jtu3byMqKgoTJkyA\nmZkZmjZtiufPn/PHYPLkyejTp0+l6jtz5kwkJSUhICAAq1atwtatW2FhYYHU1FT+9YYNG4alS5fK\nzT906FAEBgbizJkzaNOmDVJTU5GRkQENDQ0sWrRIqpdz8ODBmDhxIg4dOoTFixfjhx9+gKmpKV6+\nfIn4+HgAZT/AzJkzh8/TtWtXrFmzBl5eXti9ezcOHDgAa2tr5Ofn88F6ixYt8Ntvv8kcf0XPM79p\n7Nix2Lt3L/Lz82FgYMBP5f8mVc////3vf3j69Cn+/vtvfPLJJ7CysoK+vj4ePXoETU1N9OvXT+5S\nKYSQynnz+evKfvYrY/z48QgICEB8fDw+++wz/pqbkpKCzMxMaGlpwdHRETdu3JAKHjU0NLB582a4\nubkhPDwcgwYNgq2tLcRiMWJjY9GkSRNYWVnh2bNnUjO4v7lOJiGkYaAeSyKlMr0fU6ZMgaOjIziO\nk7v/woULsWHDBrz//vsQi8WIi4tDmzZtsGHDBnh5eSl8H0WvV9myKXrNrl274sCBA+jduzeSkpLw\n/PlzdO7cGevWrcOWLVv459vKW7VqFfz9/TFkyBAIBAJ+qvXOnTtj+fLl+P3336t9kpVRo0YhODgY\nEyZMQIsWLRAbG4vs7Gx069YNXl5eOHjwoNTzLm+r+9vMnDkTe/bsgZOTE4CyXkdra2ts2rQJ7u7u\nco/Hxo0bsWrVKnTq1AmvX79GdHQ0SkpKMHDgQOzYsQP+/v4wMzMDx3Ey60FKlB/6+uYwWIlGjRph\n7969+Oyzz9CxY0fk5ubi4cOHYIxh8ODB2LFjB7799lul6uvl5QV/f38MHjwYmpqaiIqKAsdxGDhw\nILy9vbF582a5My9yHIcZM2bg559/Rtu2bfH48WOUlJRgwIAB2Lt3Lz755BOZPKtWrcK6devQo0cP\n/vmj7OxsvP/++/Dy8sK+fftkZlEcM2YMgoODMXHiRLRs2RKxsbFITU2FjY0NZs2aheDgYFhaWsot\nX2WOv52dHUQiETiOw/DhwyucvViV819bWxtbtmzBunXr0KVLF2RlZSExMRG9e/dGQEAAHBwcKl1W\nQsjbqfKdWD6tfLqBgQGOHDmC2bNnw9bWFi9evEBsbCwMDAwwYcIEBAYG4vvvvwdQ1hNZPri0tLRE\ncHAw3N3dYWZmhidPniArKwujRo3C0aNH+e+t6v7OJISoH45V509ehBDyFufPn8e8efPg4OCAP//8\ns66LQwghpAaNHz8eDx48wIYNG/DRRx/VdXEIITWIeiwJIbXqzz//BMdxmDBhQl0XhRBCSBUcOXIE\nw4YN43sz35SWlsbPCitv0jdCSMNCz1gSQmrU69ev8fDhQxgaGuLo0aP4559/0Lx5c4wcObKui0YI\nIaQKOnbsiLi4OCQmJsLBwUFqcrHExER89dVXEIvF6NWrF78sESGk4aKhsISQGmdvb8+vechxHDZu\n3Ijhw4fXcakIIYRU1bp167Bnzx4wxtCsWTOYmpoiLy8PCQkJYIzBxsYGO3fu5JdkIoQ0XBRYEkJq\n3CeffILoxG/CAAAgAElEQVTbt2/DzMwMc+bMkbvUBSGEkPrp+vXr2LdvH6Kjo/H8+XM0atQIlpaW\ncHZ2hqura4WThRFCGg4KLBuwtLQ0+Pv74/Lly/wajubm5ujfvz9mzJjBr91YXmFhIfz8/HDq1Ckk\nJiZCX18fHTp0gLu7u8x6g+qitLQUEydOxIMHD7B+/XqMHj1aZp/6UK9r165h3759iIiIQHZ2NoyN\njdGjRw/MnTsX1tbWcvOoe72ys7Ph6+uL0NBQJCcnQ0tLC7a2thg7diwmTJigcNbC7OxseHt7IzQ0\nFKmpqTA0NETXrl3h4eEBBweHWq7Ffw4cOIAVK1ZgzZo1GD9+vNx9VD0m6lpnorzKnCeAap/5+qii\n9nBycpJaY7gijo6O2LNnT00UsVZV5vxQ9dpZH1WmPZKTk+Hj44PLly8jIyMDzZs3R5cuXTBz5sx6\n/+zmu3KvVlmqtMebKnsNJtVPc8WKFSvquhCk+t26dQtTp05FWFgY8vLyYG5ujkaNGiEhIQG3bt1C\ncHAw+vTpI7WWVEFBAdzd3XHixAnk5+fD1tYWpaWliIqKwvHjxwGUfbGrG29vb5w8eRIcx2HQoEEQ\niURS6fWhXhs2bMB3332Hp0+fwsDAAK1atUJaWhoiIyNx5MgRdO7cmV+MXkLd65WcnIyJEyfi0qVL\nePnyJVq3bg0NDQ3Exsbi/PnziIyMhLOzs8w6lpmZmfj4449x6dIllJSUoF27dsjPz8eDBw9w9OhR\nmJqa1tgi4RW5d+8evv76a4jFYjg5Ocm9mVH1mKhrnYnyKnOeAKp95uujt7VHeHg4DA0NYWZmJvef\nkZERMjIy+HVyBwwYUEc1qR6VOT9UvXbWR5Vpj2vXrmHatGkIDw9HQUEBf129desWDh48CAMDA3Tu\n3LkOSl9179K9WmWo0h5vquw1mNQQRhqc3Nxc1qNHDyYSidjs2bNZRkYGn5aQkMAmT57MhEIhGzRo\nECsqKuLTFi9ezIRCIRszZgxLTU3ltwcHB7MOHTowkUjErl69Wqt1eZvIyEi+bCKRiAUGBsrso+71\nOnToEBMKhaxjx45S5c/JyWFz5sxhQqGQ9e7dmxUUFEjlU/d6TZs2jQmFQjZixAgWHx/Pbz9//jzr\n1KkTE4lEzMfHRybf1KlTmUgkYjNnzmS5ubn8dj8/P76dHj9+XCt1kLh+/TpzdHRkQqGQiUQidujQ\nIbn7qXpM1LHORHmVPU9U/czXN5Vtj4osXLiQCYVCNm7cOKnvq/qosu2h6rWzvqlMe6SmprL333+f\niUQi5urqylJSUvi0q1ev8mknT56szaJXi3fpXq0yVG2P8qrjmkOqhgLLBmjXrl1MKBSyvn37svz8\nfJn0zMxM5ujoyEQiETt27BhjjLH4+HhmZ2fH7Ozs5N7Abt68mQmFQjZ58uQaL39lFRcXMxcXF2Zn\nZ8fs7e3lBpbqXq+ioiL+WBw4cEAmPS8vj3Xt2lXmi1Pd65WSksJf2CMiImTSt2zZwn9BlHfjxg0m\nFApZ165d2YsXL2TyffXVV0wkErFFixbVWNnLKyoqYlu2bGF2dnZMJBJV+GWl6jFRtzoT5Slznqj6\nma9PlGmPivz555/8ZyMhIaGGSlvzlGkPVa+d9Yky7bF27VomFApZnz59WE5Ojkz6gQMHmFAoZB9+\n+GG9++HhXblXqyxV2kOiuq45pOrq/zgKIiMsLAwcx2HAgAFo1KiRTHrTpk3RpUsXAMD9+/cBAEFB\nQSgtLUXnzp3Rtm1bmTyTJk0CANy5cwepqak1WPrK++WXXxAbGws3Nzc0b95c7j7BwcFqXa/Q0FDk\n5OTAysoKEydOlEk3MDDAt99+i8WLF0tN1a7u9Sr/nkKhUCbd3t4eAJCSkiK1PTAwEBzHYeDAgTAy\nMpLJN2nSJDDGcPbsWRQXF1dzqaXFx8djyJAh2L59OziOw8KFC2FmZqZwf1WPiTrVmShP2fNE1c98\nfaFseyiSkZGB9evXg+M4fPXVVzA3N6+B0tY8ZdtD1WtnfaFse1y8eJFf97hJkyYy6RMmTICenh7S\n09Nx5cqVmix6tXtX7tUqS5X2AKrvmkOqBwWWDdD8+fPxww8/YOzYsQr3Yf8/Z9Pr168BABEREeA4\nDt26dZO7v4mJCf9BDQsLq+YSKy8iIgK7du1CmzZt8MUXXyjcLzw8XK3rdfXqVT6oUDQZw+jRozF9\n+nSpZ+zUvV6mpqb835GRkTLpkgWzW7VqJbU9PDwcABTWq1OnThAIBCgoKJD6YqkJqampSEtLQ5cu\nXXDo0CHMmTOnwv1VPSbqVGeiPGXPE1U/8/WFsu2hyMaNG/Hq1SvY29vzN8v1kbLtoeq1s75Qtj0k\nk7d06NBBbrqGhgasrKwAlN0X1Cfvwr2aMlRpD6D6rjmkegjqugCk+nXs2BEdO3ZUmP7ixQv+gmNr\nawug7BcfABVOFtGqVSukpKQgLi6u+gqrgqKiIixevBhA2fpZ2traCvdV93pFR0cD+O84nDlzBufP\nn+dnBe3Tpw9Gjx4NgUD6o6ru9TIxMcHAgQMRGhqKFStWYPv27XxZr127Bl9fX3Ach08++YTPwxhD\nYmIiAMDS0lLu6woEArRs2ZKvl6Iv1+rw3nvvYceOHZWeYU+VY6JudSbKU/Y8UfUzX18o2x7yREZG\nIigoCBzHYenSpdVYutqnbHuocu2sT5RtD8mPLxV9HsRiMQAgKSmp6gWsRQ39Xk1ZyrSHjY0Nv706\nrjmk+tTPby5SJWvWrEFBQQH09PQwePBgAGWzUgKocBpnY2NjAGUf7rr0008/4dmzZ5g5c+Zbl2FQ\n93pJvgg1NTUxZcoU3L59W6oXIyQkBHv27IGvry/ee+89fru61wsom/Vy2bJlCAkJgbOzM1q3bo3C\nwkIkJibC0NAQX3zxBVxdXfn9c3JyIBaLwXEcX3Z5jIyMkJKSUuP1srS0VBjsyaPKMVG3OhPlKXue\nqPqZry+UbQ95fHx8wBhDr169+KFv9ZUq7aHstbM+UbY9LCwsEBsbi4cPH6Jfv34y6cXFxUhISAAA\n5ObmVls51UF9v1erbuXbY8iQIfz26rjmkOpDQ2HfMeWX5vD09OQvToWFhQBQ4SLGOjo6YIzx+9aF\nGzduYN++fbCxscGCBQveur+61ys/Px9AWc/rgwcPsHz5cly9ehURERHw8fGBubk5Hj16hLlz5/K/\nygLqXy8JkUgEQ0NDlJaW4vHjx0hKSgLHcWjcuLFM2cuXs6J66erqyuyvDlQ5JvW9zkR5qn7m3xVJ\nSUk4d+4cOI7DvHnz6ro4dUaZa2dD5uTkBMYY9u/fj5ycHJl0f39/FBUVAQBKSkpqu3g1pr7fq1U3\nRe1B1A8Flu+Qbdu2YcuWLfzzPTNnzuTTJOthVbTosmRse10tzJyfn4+lS5dCU1MTa9eurXAIrIS6\n10vyhfjixQts2rQJU6dOhbGxMXR0dNCvXz/4+flBIBAgOjoagYGBfD51r9fLly8xbdo0bN68Ga1a\ntcKePXtw9+5dXL9+HatXr0ZeXh6+/fZbrFq1is9Tfk02da1XRVQ5JvW9zkR5qn7m3xV79+7lJyfp\n3r17XRen1qly7WzI3N3d0bx5c6SlpWHq1Km4du0aiouLkZWVBV9fX2zfvh1NmzYFY6zeDh9/U32/\nV6tuFbUHUT8UWL4DSktL4eXlhW3btoHjOHz44YfYtGmT1D76+voA/rvpkae4uBgcx/G9J7Vt7dq1\nSElJwYwZM9CpU6dK5VH3ekneUyQSwcnJSSa9TZs2cHFxAWMM586d47ere7127tyJ+/fv47333sPu\n3bvRvXt3aGtrw9DQEOPHj8fOnTuhqamJgIAA3Lx5EwCkZoGrqF6StLo6DxVR5ZjU9zoT5an6mX9X\nhISEgOO4CifwaMhUuXY2ZE2bNoWvry9MTEwQGxuLTz75BJ06dUKvXr2wefNmTJw4EaNHjwZQNqNy\nfdZQ7tWqS2Xag6gfCiwbuJcvX2LWrFk4ePAgOI7D8OHD4e3tLdPbJ1nmIDs7W+FrScbr18UQhAsX\nLuDIkSOwtrbGp59+Wul86l4vyfTpIpFI4T6Sh/Ylk7wA6l+v06dPg+M4uLm5yf2yt7e3R//+/QEA\nx48fB1D2hSk5LyuqlyStWbNm1VzqqlHlmNT3OhPlqfqZfxf8+++/SElJgUAgwNChQ+u6OHVClWtn\nQ2dnZ4dTp05h8eLFGDhwIHr37o0pU6bgzz//hJeXF168eAGO42BiYlLXRVVZQ7lXqy6VbQ+ifiiw\nbMBSU1MxadIkfnp7Dw8PbNy4Ue5wEWtrawAV38hIJp2oi7XVQkJCAACxsbGwt7eHSCSS+ieZknzJ\nkiUQiUSYNm0aAPWvl6R8Fa1PqKmpCQBSF1R1r5fkeMhbZ0vCxsYGjDG+nBzH8WVVVC+xWIznz58D\nAFq3bl2NJa46VY5Jfa8zUZ6qn/l3gaSHtk+fPnLXLHwXqHLtfBfo6+tj+vTp2L59O/z9/fHtt9/y\nI5cePHgAAGjXrl1dFlFlDelerToo0x5E/VBg2UClp6fDzc0NMTExEAgEWLlyJb788kuF+zs4OIAx\npnAdqLS0NP4Lry5m6WvdujW6deum8J/kBkyyn2RhaXWvl6R8Fa1P+OTJEwDS04ure70kv7Snp6cr\n3Ecyu51kaA/w9nrdu3cPYrEYOjo6sLOzq8YSV52qx6Q+15koT9XP/LtAMkNur1696roodUbVa2dD\ndevWLfz++++4du2a3PSEhAQ8evQIANCzZ8/aLFq1aGj3alWlbHsQ9UOBZQNUUlKCOXPmICEhAdra\n2vjll18wceLECvMMGzYMQNmCuvLWPtq/fz8AwNHRkV98tzbNmTMH+/btU/ivefPmAIC5c+di3759\n+OabbwCof71cXFwAlH05nj17ViY9KyuLnwlNUhdA/evVo0cPMMZw+PBhfiKB8nJycnD27FmZm0hn\nZ2cAZcPB5E0dL6nX8OHD1a43R9VjUp/rTJSn6me+oWOM8T1P9vb2dVyauqPqtbOhCgsLw/r167Fl\nyxa56d7e3gCAQYMGwdTUtDaLVmUN8V6tKlRpD6J+KLBsgHx9fREZGQmO4+Dl5YWBAwe+NY+VlRVc\nXFwgFovh6enJL8ILAMHBwfD396+X07+re73atm2LCRMmgDGGpUuX4vz583xaeno6FixYgPz8fIhE\nIn4dK0D96zVnzhxoaWnh/v37+Oqrr6TW00pISMDs2bORnZ0NCwsLqUk6evbsiW7duiE3Nxfz58/n\nf5lnjMHPzw8nTpyAlpYWPDw8ar1Ob6PqManPdSbKU/Uz39A9e/aMX4pF8ozpu0jVa2dDNWLECAgE\nAkRERMDX15cPtouLi/Hzzz8jMDAQOjo6lVp+TN3QvZo0VdqDqB8asNzAlJSUYPfu3QDKntM5evQo\njh49qnD/fv36Yc6cOQCA5cuX49GjR4iJiYGzszPatWuHnJwcJCcng+M4fP755+jRo0et1KM6qXu9\nli9fjvT0dFy4cAHz5s2DqakpjI2NERMTA7FYDHNzc2zevFnm+QJ1rle7du2wadMmfP311zh16hTO\nnDkDa2trfk02xhjMzc2xY8cOmV649evXw83NDbdv38aAAQNga2uLtLQ0ZGRkQENDA+vXr6/w+aO6\npOoxqc91JspT9TPfkKWlpQEo+956F4Z4KlKVa2dDZGFhgaVLl2LNmjXYtGkT9uzZAxMTE8THxyMv\nLw+6urrw9vaGjY1NXRdVKXSvJq0q7UHUy7vzrfWOePToEfLy8sBxHEpLSxEeHl7h/lZWVvzfRkZG\nOHjwIHbu3ImQkBA8efIEAoEAH3zwAdzc3DBo0KCaLn6VKFqzSd3rpaOjAx8fH5w4cQKHDx9GVFQU\nsrOzYWlpiaFDh8Ld3R2GhoYy+dS9XoMHD8axY8fw22+/4erVq3j69Ck0NTX5nhhFsx5aWFggKCgI\nPj4+CA0NRUxMDPT09NCvXz94eHio9dp2qh6T+lxnojxVP/MNWVZWFjiOe2cn7SlP1WtnQzVlyhRY\nW1tj165diIiIQHR0NFq0aIGhQ4fCw8OjXk5q9i7fq8lTlfYg6oVj8gbxE0IIIYQQQgghlUTPWBJC\nCCGEEEIIqRIKLAkhhBBCCCGEVAkFloQQQgghhBBCqoQCS0IIIYQQQgghVaLWs8KmpaXB398fly9f\nRnJyMgDA3Nwc/fv3x4wZM9C0aVOZPIWFhfDz88OpU6eQmJgIfX19dOjQAe7u7ujbt6/C98rOzoa3\ntzdCQ0ORmpoKQ0NDdO3aFR4eHnBwcFCYLyUlBdu2bcPly5eRmZmJpk2bomfPnpg9ezasra2r3giE\nEEIIIYSQKnFycuLjibdxdHTEnj17+P+nOKFy1HZW2Fu3bmH+/PnIzc2FQCCApaUlSktLkZSUBLFY\njObNm+O3335Du3bt+DwFBQVwd3fHvXv3oKWlBVtbW2RnZyMlJQWMMXh6esLT01PmvTIzM+Hq6oqE\nhATo6enB2toaqampyMzMhIaGBlavXi13IeInT55g0qRJyM3NRePGjWFlZYWEhARkZ2dDR0cH3t7e\n6N27d422EyGEEEIIIaRiCxcuRHp6usL0V69eISoqChzHYcKECVi1ahUAihOUwtRQbm4u69GjBxOJ\nRGz27NksIyODT0tISGCTJ09mQqGQDRo0iBUVFfFpixcvZkKhkI0ZM4alpqby24ODg1mHDh2YSCRi\nV69elXm/qVOnMpFIxGbOnMlyc3P57X5+fkwoFLKOHTuyx48fS+URi8Vs8ODBTCQSscWLF/PlKCkp\nYatXr2ZCoZA5Ojqy7OzsamsXQgghhBBCSPVbuHAhEwqFbNy4cVLxBcUJlae5YsWKFXUd3L4pICAA\nZ8+ehYmJCf744w+phaKbNGmCAQMG4PDhw0hPT0fbtm0hFAqRkJAALy8vaGho4Pfff4eFhQWfRygU\nori4GLdu3UJiYiLGjRvHp4WFhWH79u3Q19fHvn37pN6ra9euePbsGR4+fIi8vDwMHjyYTwsKCsLR\no0dhZmaGXbt2QUtLCwCgoaGBvn374tq1a4iLi4NAIECPHj1qsrkIIYQQQgghKjp48CD8/Pygr6+P\n3bt3w9jYGADFCcpSy8l7wsLCwHEcBgwYgEaNGsmkN23aFF26dAEA3L9/H0DZASwtLUXnzp3Rtm1b\nmTyTJk0CANy5cwepqan89sDAQHAch4EDB8LIyEhuPsYYzp49i+LiYpl8o0aNgkAg+6iqq6srGGM4\nefKkkrUnhBBCCCGE1IaMjAysX78eHMfhq6++grm5OZ9GcYJy1DKwnD9/Pn744Qe545Ul2P8/Gvr6\n9WsAQEREBDiOQ7du3eTub2JiAjMzMwBlgatEeHg4ACjM16lTJwgEAhQUFPBBLGOM/1tRvq5duwIA\nEhISkJaWprAehBBCCCGEkLqxceNGvHr1Cvb29nxHlATFCcpRy8CyY8eOGDlyJDp16iQ3/cWLF3xw\naGtrCwCIj48HAKkhsG9q1aoVACAuLg5A2YFPTEwEAFhaWsrNIxAI0LJlS6l8aWlpKCwsrDCfqakp\nNDU1AQBPnz5VWCZCCCGEEEJI7YuMjERQUBA4jsPSpUul0ihOUJ5aBpZvs2bNGhQUFEBXV5cfz5yZ\nmQkAcpcgkZCMl37x4gUAICcnB2KxWCpNHknXtySf5L0qej8NDQ0YGBhI5SOEEEIIIYSoBx8fHzDG\n0LNnT/4xOwmKE5RX7wJLb29vnDx5EhzHwdPTkz9gkl8GdHR0FObV0dEBY4zfV/Lft+XT1dWV2r+g\noIBP09bWrnQ+QgghhBBCSN1LSkrCuXPnwHEc5s2bJ5NOcYLy6lVguW3bNmzZsoV/iHbmzJl8moZG\nWVU4jlOYX/JcpmQfSR5l80m6rpXNRwghhBBCCKl7e/fu5Sf+7N69u0w6xQnKqxeBZWlpKby8vLBt\n2zZwHIcPP/wQmzZtktpHX18fAFBUVKTwdYqLi8FxHP8LQfkZZyvKJ0mraj5CCCGEEEJI3QsJCQHH\ncQonC6U4QXlqH1i+fPkSs2bNwsGDB8FxHIYPHw5vb2+ZrmXJ+Obs7GyFryUZwywZPquvr8+/TkX5\nJGnNmjUDID3OWlG+0tJSvHz5UiofIYQQQgghpG79+++/SElJgUAgwNChQ+XuQ3GC8tQ6sExNTcWk\nSZNw9epVcBwHDw8PbNy4Ue56MNbW1gDAz94kT1JSEgCgTZs2AMq6niV/K8onFovx/PlzAEDr1q0B\nAC1btkTjxo0rzJeSkoLS0lKpfIQQQgghhJC6de7cOQBAnz590KRJE7n7UJygPNkITU2kp6fDzc0N\nCQkJEAgE8PLywsSJExXu7+DggNDQUERERMhNT0tLQ3JyMjiOk5r1ycHBAdHR0YiIiMC4ceNk8t27\ndw9isRi6urqws7Pjt3fq1AlXr15FREQEevToIZNPsu6NmZkZWrRoUel6vyk9PU/lvJVhZFTWXZ+d\n/apG36e2Ub3qF6oXqSktWjSu6yLIqOnren1Anw1p1B7SqD2kUXtIq47r+u3bt8FxHHr16lXhfuoe\nJ6gbtQwsS0pKMGfOHCQkJEBbWxubN2/GwIEDK8wzbNgwbN68GWFhYYiLi5OJ/vfv3w8AcHR0hJmZ\nGb/d2dkZBw8exOnTp7Fo0SKZXy0k+YYPHy41/HbYsGG4cuUKjh49Cg8PD5le1ICAgArHbRNCCCGE\nEFJeWFQaAi89RWGxmN+m8f+Tu7z+/8leytPVFmDMh23g2N6k1spY3zHG8ODBAwCAvb19hftSnKAc\ntRwK6+vri8jISHAcBy8vr7cGlQBgZWUFFxcXiMVieHp6Ij4+nk8LDg6Gv7+/3OmEe/bsiW7duiE3\nNxfz58/n155hjMHPzw8nTpyAlpYWPDw8pPKNGjUKlpaWiI+PxxdffIH8/HwAZUHxmjVrcOfOHTRp\n0gRTp06tanMQQgghhJB3QOClp0jLeoWcl8X8vxd5RXiRVyS1TfIvLesVAi89reti1yvPnj3j79tt\nbW0r3JfiBOVwjMn5+aMOlZSUoHfv3sjNzYVAIECnTp0q3L9fv36YM2cOgLIHZKdNm4aYmBhoaGig\nXbt2yMnJ4YfAfv7555g9e7bMayQkJMDNzQ1paWnQ0tKCra0t0tLSkJGRAQ0NDfz000/46KOPZPLd\nv38fM2fORF5eHvT09NC2bVskJCQgJycH2tra8Pf3lzt9sTJoKKxqqF71C9WL1BQaCque6LMhjdpD\n2rvcHp9vu4ycl8XgADQxKOsBU9RjmfuyGAyAoYE2Nnv2qeWS1p2qXtdv3LgBd3d3CAQC/Pvvv2/d\nX53jBHWjdkNhHz16hLy8PHAch9LSUn4MsiJWVlb830ZGRjh48CB27tyJkJAQPHnyBAKBAB988AHc\n3NwwaNAgua9hYWGBoKAg+Pj4IDQ0FDExMdDT00O/fv3g4eGh8KDb29sjODgY3t7euHz5MqKjo9G4\ncWMMGzYMc+fOhUgkUr0hCCGEEELIO6lJuWBRUaAtCUKJcrKyssBxnMJJe95EcULlqV2PJZFGPZaq\noXrVL1QvUlOox1I90WdDGrWHtHe5PSTBoqESgSX1WBJ1oZbPWBJCCCGEEEIIqT8osCSEEEIIIYQQ\nUiUUWBJCCCGEEEIIqRIKLAkhhBBCCCGEVAkFloQQQgghhBBCqoQCS0IIIYQQQgghVUKBJSGEEEII\nIYSQKhHUdQGI8sKi0hB46SkKi8WVzqOrLcCYD9vAsb1JDZaMEEIIIYQQ8i6iwLIeCrz0FGlZyi0a\nnINiBF56SoElIYQQQgghpNpRYFkPSXoqOQBNDLTfun/uy2KwcvkIIYQQQkjDkPuyGJ9vu/zW/Wj0\nGqlpFFjWY00MtLHZs89b9/t822XkvCyuhRIRQgghhDRsyj6SVFMBna62ADko6zyozH0ejV4jNY0C\nS0IIIYQQQipJ2UeSaiqgG/Nhm0oHuJLRa8+zXlWqdxOgHk6iPAosCSGEEEIIqSRlHklSNqDLVWKE\nmWN7k0oHfUt9ryMt61WlezcB6uEkyqPAkhBCCCGEECVV5pEkVQI6oKy3sDop07sJ0PwcRDUUWBJC\nCCGEEFIDlA3ogP+GoFYnZXo3gYY9P8e1a9ewb98+REREIDs7G8bGxujRowfmzp0La2truXkKCwvh\n5+eHU6dOITExEfr6+ujQoQPc3d3Rt29fhe+VnZ0Nb29vhIaGIjU1FYaGhujatSs8PDzg4OBQU1Ws\nMxRYEkIIIYQQUgOUDehIzdqwYQN27twJjuPQokUL2NjY4OnTpzh+/DjOnDkDHx8f9OzZUypPQUEB\n3N3dce/ePWhpacHW1hbZ2dm4evUqrly5Ak9PT3h6esq8V2ZmJlxdXZGQkAA9PT2IRCKkpqbi7Nmz\nCA0NxerVqzF27Njaqnqt0KjrAhBCCCGEEEJITTp8+DB27twJLS0trFu3DhcvXkRQUBAuXbqE/v37\no6ioCIsWLUJhYaFUvpUrV+LevXuws7PD2bNncfToUYSGhuKHH36AQCDA9u3bce3aNZn3W7hwIRIT\nE9GnTx9cvHgRhw8fxuXLl/Hll1+itLQU3333HZ48eVJb1a8VFFgSQgghhBBCGqzi4mL89NNP4DgO\ny5cvx+jRo/m0Jk2aYMOGDdDX10dmZiZCQ0P5tISEBBw/fhyamprYsGEDTEz+630eOXIkPDw8wBjD\ntm3bpN4vLCwMN2/eRKNGjbBhwwY0btyYT/Pw8MCIESMgFovh4+NTg7WufRRYEkIIIYQQQhqs0NBQ\n5OTkwMrKChMnTpRJNzAwwLfffovFixejTZv/nm8NDg5GaWkpOnfujLZt28rkmzRpEgDgzp07SE1N\n5bcHBgaC4zgMHDgQRkZGcvMxxnD27FkUFzecZ1kpsCSEEEIIIYQ0WFevXuUDPY7j5O4zevRoTJ8+\nHe3bt+e3hYeHg+M4dOvWTW4eExMTmJmZASjrpSyfD4DCfJ06dYJAIEBBQQHu37+vUp3UEU3eQwgh\nhEIoiwYAACAASURBVBBCCGmwoqOjAQC2trYAgDNnzuD8+fP8TK19+vTB6NGjIRBIh0bx8fEAAAsL\nC4Wv3apVK6SkpCAuLg4AwBhDYmIiAMDS0lJuHoFAgJYtW/L5FAWg9Q0FloQQQgghhJAGKykpCQCg\nqamJKVOm4Pbt21I9lyEhIdizZw98fX3x3nvv8dszMzMBAE2bNlX42sbGxgCAFy9eAABycnIgFovB\ncRyfJo+RkRFSUlL4fA0BDYUlhBBCCCGENFj5+fkAgHXr1uHBgwdYvnw5rl69ioiICPj4+MDc3ByP\nHj3C3LlzIRb/t+aoZIZYHR0dha+to6MDxhi/b/lZZSvKp6urK7N/fUc9loQQQkgtMjJqVNdFqHMC\nQdnv2tQWZag9pKl7e2j8f0+XBsfVShnroj1qu441raioCEBZr+L27dvh5OTEp/Xr1w+WlpYYMWIE\noqOjERgYiAkTJgAANDQ0UFpaqvC5TKBs6CsAfh8Njf/67ZTJ1xBQjyUhhBBCCCGkwZL0DopEIqmg\nUqJNmzZwcXEBYwznzp3jt+vr6wP4LzCVp7i4GBzH8e/RqNF/gXhF+SRpknwNAfVYEkIIIbUoO/tV\nXRehzkl6QKgtylB7SFP39nj9/z1NrxmrlTLWRXvUdh2V0aJF47fv9IYmTZqgoKAAIpFI4T6SiX0k\nE+8AZc9B5ubmIjs7W2E+yTOSkucw9fX1oa2tjZKSkgrzSdKaNWtW+YqoOeqxJIQQQgghhDRY1tbW\nAFDhmpGampoAAG1tbZl85YPNN0kmBpKsf8lxHP+3onxisRjPnz8HALRu3boyVagXKLAkhBBCCCGE\nNFgODg5gjFW4ZuSTJ08ASC8tIskXEREhN09aWhqSk5MBAF26dKl0vnv37kEsFkNHRwd2dnZK10dd\nUWBJCCGEEEIIabBcXFwAAAkJCTh79qxMelZWFk6ePAmO4zBs2DB+u+TvsLAwfp3K8vbv3w8AcHR0\nhJmZGb/d2dkZAHD69Gnk5uYqzDd8+HCpHtL6jgJLQgghhBBCSIPVtm1bTJgwAYwxLF26FOfPn+fT\n0tPTsWDBAuTn50MkEmHw4MF8mpWVFVxcXCAWi+Hp6Yn4+Hg+LTg4GP7+/uA4DvPmzZN6v549e6Jb\nt27Izc3F/Pnz+fUwGWPw8/PDiRMnoKWlBQ8Pjxquee2iyXsIIYQQQgghDdry5cuRnp6OCxcuYN68\neTA1NYWxsTFiYmIgFothbm6OzZs3QyAQyOR79OgRYmJi4OzsjHbt2iEnJwfJycngOA6ff/45evTo\nIfN+69evh5ubG27fvo0BAwbA1tYWaWlpyMjIgIaGBtavX4+2bdvWVvVrBQWWhBBCCCGEkAZNR0cH\nPj4+OHHiBA4fPoyoqChkZ2fD0tISQ4cOhbu7OwwNDWXyGRkZ4eDBg9i5cydCQkLw5MkTCAQCfPDB\nB3Bzc8OgQYPkvp+FhQWCgoLg4+OD0NBQxMTEQE9PD/369YOHhwe6d+9e01WudRRYEkIIIYQQQt4J\nLi4u/DOXlaWrqwtPT094enoqlc/IyAhLlizBkiVLlMpXX9EzloQQQgghhBBCqoQCS0IIIYQQQggh\nVUKBJSGEEEIIIYSQKqHAkhBCCCGEEEJIldDkPYQQQggh/8fevUdHVaV5H/+epFIJCSEFDEQit4DQ\ngEiAtCAjyuBtuIrXHlARWyIiE1vAV5uZYRq5LI3d6WG6DXQcsAddXphoE6LIgKTTS4m4jA0yYVoE\negCTgqSUSKoMJqlUqPePWAVFUklV7qn8Pmu5xNr7ObX3wcB5zj5nPyJSj6PCyYqM/Cb7RZlN3H1T\nIpNGx7fDqKSz6lKJ5fbt23nuuefYsGED9913X4N9brnlFs6ePdvocf785z/Ts2dPn8/Ky8vZvHkz\neXl5lJaWEhcXx8SJE0lJSSEpKcnvsUpKSsjIyCA/P5+ysjL69OnDlClTWLJkCcOHDw9+kiIiIiLS\nIgVHbWTvP0WV0xVwjJKjS6LMJuw4cQP2CmeT/e04yd5/Sueum+syiWVhYSG/+tWvMAzDb5+KigrO\nnj2LyWTymwwahkF4eLjPZ2VlZcyfP5/i4mJ69OjBqFGjKC0tJTc3l7y8PNavX88999xT71gnT55k\nwYIFOBwOYmNjGT16NMXFxeTk5LBnzx42b97MjTfe2LKJi4iIiEhQsvefwvbt90HFKDm65O6bEgNO\nzB0VdQloMEm8hKYukVh++umn/OxnP+PChQuNJpZffvklAEOGDOGNN94I+PjLly/HarUydepUNm7c\nSGxsLABbt24lPT2dNWvWMH78eIYNG+aNqa2tZenSpTgcDubNm8e6deswm824XC7S0tJ4/fXXWbly\nJR988EGDxVZFREREpG14khwD6NXT3GR/JUe+Jo2ODzjBXpGRH9CqpoS+Tp1YOp1OXn75ZTIzM7l4\n8WKT/T2J5ciRIwP+joKCAj777DNiYmJIT0/3JpUAKSkpHDt2jF27dpGZmckvf/lLb1tOTg5FRUUk\nJCSwYcMGTKa6U2kymVi9ejVHjx7l0KFDbNu2jaeeeirg8YiIiIhI6+jV08zG1KlN9lNyJNJynXZX\n2KKiIu644w42bdqEYRgsX76chISERmO+/PJLDMNgxIgRAX9PdnY2hmFw6623YrFY6rUvWLAAt9tN\nbm4uTqezXty8efO8SeXl5s+fj9vt5v333w94LCIiIiIiIl1Rp12xLC0txWazMWHCBH7xi18wevRo\n/uu//qvRmOasWH7++ecAJCcnN9g+btw4TCYTlZWVHDlyhOTkZNxuN0eOHGk0buLEiQAUFxdjs9mI\nj9fz+iIiIiKXa2iTnbAfXnu66HbX668NdkQ6r06bWF511VW8/PLL3HzzzQH1v3jxIn/9618B6N+/\nP9u2bePgwYN89913xMfHM23aNGbOnOnzjqbb7cZqtQIwePDgBo9rMpno378/JSUlnD59muTkZGw2\nG1VVVRiG4TduwIABhIeHc/HiRU6dOqXEUkREROQKwW6yow12RDqvTptYDh482G/S1pBTp055k72f\n/vSnfP+97x9SOTk5bNu2jU2bNtGvXz8A7HY7LpcLwzDo3bu332NbLBZKSko4f/48ULeLrEefPn0a\njAkLC6Nnz544HA5vnIiIiIhc0tAmO/5WLNtjg51A6jY69C6mSIM6bWIZrGPHjnl/nZSURGpqKtde\ney3V1dV89NFHvPjiixQWFvL444+TlZWFyWSiqqrKGxMZGen32FFRUQDe/pWVld42s9n/TmNRUVE4\nHA6f7xERERERX5dvsmOxRANQXu67SNCWG+wEW7fREyNdT3Nq3qvefWBC5idiwIABLFy4EJPJxM9/\n/nPv55GRkcyZM4exY8dy9913c/ToUd555x3mz59PWNilvYsaK2Pi/uGOmafP5XUwg4kTERERkc4n\nmLqNcOldT+lamlPzXvXuAxcyieWECROYMGGC3/ahQ4cyd+5csrKy2LdvH/Pnzyc6OtrbXl1d7TfW\n0+ZZubwyLiIiIqC45vDctbuc5xGRMMNosD2Y/iZTmN/v6co0r65F8xIRkY4UTN1G6bqaU/Ne9e4D\n12nLjbSF0aNHA3g37ImJifE+ylpeXu43ztPWt29fAJ/3Mf3F1dbWUlFR4RMnIiIiIiIdI9gKEp56\n99HR0Q3Wu587dy4ul4vMzEyfOE+9+wEDBrBhwwZvvuGpd5+cnIzD4WDbtm2tM7FOImRWLKHu0VOX\ny+V3BfHixYsA3nbDMEhMTOT48eNYrVYmT55cL8blcvH1118DdaueULfrbGxsLBUVFVitVgYOHFgv\nrqSkhNraWgzD8MY1x5XvF8Cll9kvut0NtgfT3997DF2d5tW1aF7SVvr1i226k4iIdAvB1rwPpN79\ne++9561370kgA6l3f/DgQd5//32eeuqplk2qEwmZxPLBBx/k8OHDPPDAA/zLv/xLg32++OILAJ+X\nZZOSkjh27BiHDx/m3nvvrRdTWFiIy+UiKiqKMWPGeD8fN24cBw4c4PDhw9xwww314jz1MRMSEry7\n0ErbubIOVmM1sDxUC0tERESk+wh2xVL17oMTMonlyJEjOXjwIPv27WPFihU+70ECnDlzht27d2MY\nBnPmzPF+PnPmTLKysti7dy/PPPMMvXr18ol78803AZg1a5bPDrAzZszg448/ZseOHaSkpNS7G/HW\nW29hGEaDL/NK6wu2DhaoFpaIiHRfV96QbYpuxkpXF2zNe9W7D17IJJaPPPIIf/jDH7DZbCxfvpzn\nn3+ev/mbvwHq7k4sX76cqqoqrr/+em6//XZv3JQpU0hOTubgwYMsW7aM3/zmN/Tt2xe3283WrVvZ\ntWsXERERpKSk+HzfvHnz2LJlC0VFRaxcuZIXXniBmJgYampqePHFFzl06BBxcXE89NBD7Xoeuqsr\n62A1tWLZHrWwREREOqtgb8h2ppuxqjUpzRFszXvVuw9eyCSWQ4YMIT09nWeffZb9+/czffp0hg4d\nSm1tLSdPnsQwDK677joyMjLqxaalpbFw4UIOHjzI9OnTGTFiBDabjXPnzhEWFkZaWprPTk9QV7/y\n17/+NYsXL2bfvn3k5+czbNgwiouLsdvtmM1mMjIyQmqnp67AUwerqXfb2rIWloiISGd35Q3ZxnSW\nm7GqNSktEWzNe9W7D15I/bTdcccdjBw5kt///vccOHCAr776iqioKCZMmMDcuXPr1a70GDRoEDt3\n7iQzM5O8vDxOnDhBjx49mDZtGikpKVx//fUNft91111HTk4OmzdvJj8/n2PHjhEbG8uMGTNYunQp\no0aNauspi4iIiDSb54ZsYzrLzVjVmpSWCLbm/S233OLto3r3gelSiWVeXl6TfYYOHcq6deuCPrbF\nYmHVqlWsWrUqqLgBAwawfv36oL9PRES6J9U1VY3XK3XE+QimJnaw9bNbOg5/5+OOKYncMaX7JYqd\n/eelLf//aE3B1ry/fE+WzlrvvrPpUomliIiIiHRe+f9zlu37jlFZHdiqYnmF/wt2kfZ2ec17T737\nmpqaFtW779mzZ72YUK13r8RSRESkHamuqWq8XqkjzkcwNbGD6fvG3i+D3qUdwBwR7j22/v/w1dnP\nR7D11VuqJfWJg6l53xXq3Xc29V84FBERERFphss3BYrraQ7on/g+0XoXUtrcgw8+yNixY/nlL3/p\nt4+n5v0111wD1G3y43a7OXz4cIP9PfXuIyMj69W7B/zGhWq9e61YioiIiEirCmRTIJH2FEzN+9mz\nZwOqdx8srViKiIiIiEhIe+SRRzCbzd6a9+fOnfO2ffnllyxevLhezXtPvXuHw8GyZcu8NSrdbjdb\ntmxptN794MGDvfXuL1y4AEBNTQ0bNmzg0KFD9OrVK+Tq3WvFUkREROQyBUdtzSprMWl0fBuPTESa\nq7k171XvPnBKLEVEREQuk73/VFAb0Nhxkr3/lBJL6dYcFU5WZOQ32a8jb8Q0p+a96t0HTomliIiI\nyGUu34CmV09zo30dFU7cl8WIdDdRZhN26n4O7BXOJvt39I2Y5tS8V737wCixFBEREWlAIBvQrMjI\nD+hiWiRU3X1TYsCPjutGTGhTYikiIiIiIs0yaXR8wKuPuhET2rQrrIiIiIiIiLSIEksRERERERFp\nESWWIiIiIiIi0iJKLEVERERERKRFlFiKiIiIiIhIiyixFBERERERkRZRYikiIiIiIiItosRSRERE\nREREWkSJpYiIiIiIiLSIqaMHICIiItLVOSqcrMjID6hvlNnE3TclMml0fBuPSkSk/SixFBEREWmm\nKLMJO07cgL3CGVCMHSeZOX/hrT+eACDMMAC46Ha3eCxKWEWkoyixFBEREWmmu29KJHv/KaqcroD6\nX558BpqIBsqOk+z9p9ossQxkVdbRynMSka5DiaWIiIhIM00aHR9UIldw1FYvEW2NFUtHRd2qaaAJ\nbjCasyobZdYlpnQNtbW1/OQnP+Evf/kLaWlp3HXXXfX6VFVVsWXLFnbv3o3VaiUmJoZrr72WRYsW\ncfPNN/s9dnl5OZs3byYvL4/S0lLi4uKYOHEiKSkpJCUlteW0OoR+6kVERETaSUOJqMUSDUB5+ffN\nPu6KjPxWXwH1CHZV1vNIrkhX8Lvf/Y6//OUvGD/c4LlSZWUlixYtorCwkIiICEaMGEF5eTkHDhzg\n448/JjU1ldTU1HpxZWVlzJ8/n+LiYnr06MGoUaMoLS0lNzeXvLw81q9fzz333NPW02tXSixFRERE\nxK9gV2VFuoqjR4+SmZnpN6kEWLt2LYWFhYwZM4bf/e53xMfX/Sy8++67/PM//zObNm0iOTmZKVOm\n+MQtX74cq9XK1KlT2bhxI7GxsQBs3bqV9PR01qxZw/jx4xk2bFjbTbCdqdyIiIiIiIh0KzU1NTz7\n7LO43W4iIiIa7FNcXMx7771HeHg46enp3qQS4M477yQlJQW3201GRoZPXEFBAZ999hnR0dGkp6d7\nk0qAlJQU5s6di8vlIjMzs20m10GUWIqIiIiISLfym9/8hr/+9a8sXLiQv/mbv2mwT05ODrW1tX5X\nFhcsWADAoUOHKC0t9X6enZ2NYRjceuutWCyWBuPcbje5ubk4naGz4ZUSSxERERER6TYOHz7Mf/7n\nf5KYmMjKlSv99vv8888xDIPk5OQG2+Pj40lISADqVikvjwP8xo0bNw6TyURlZSVHjhxp7jQ6Hb1j\nKSIiIhIiAikJ4ukn0h1VV1fz85//HIAXXngBs9nst29RUREAgwYN8tvn6quvpqSkhNOnTwPgdrux\nWq0ADB48uMEYk8lE//79vXH+EtCuRomliIiISBfXnJIgnjiR7uRXv/oVX331FYsXL26y5EdZWRkA\nffr08dund+/eAJw/fx4Au92Oy+XCMAxvW0MsFgslJSXeuFCgP01EREREurhgS4KAyoJI9/Ppp5/y\nxhtvcM011/DUU0812b+qqgqAyMhIv30iIyNxu93evp5/NxUXFRVVr39Xp8RSRESkHXlqFnZnJlPd\nFg+d9VyE/VB6IMww2mWMrXE+7piSyB1TQiNJ7Oz/f7S3UDof7f2zdbkLFy7wT//0T4SHh/P88883\n+gisR1hYGLW1tY2WI3G73QDePmFhl7awCSYuFCixFBERkU4h/3/Osn3fMSqrA1t16xFpYv7tP2Jq\nUkIbj0xEurrnn3+ekpISHnvsMcaNGxdQTExMDA6Hg+rqar99nE4nhmF4VyCjoy8lzI3Fedo8caFA\niaWIiEg7Ki//vqOH0OE8KxVXnos39n6J7dvAz8/576p5Y++XjB1Sfzv/KxUctQX8qKhnY5uLbne7\n/H75Ox/dlc6Hr1A6Hxd/WKVryc9Wv36xTXe6wocffsgf/vAHrrnmGp588smA4ywWCw6Hg/Lycr99\nPO9Iet7DjImJwWw2U1NT02icp61v374Bj6ezU2IpIiIinYIn6TOAXj0bf0zNUVG3UU2g7xRm7z8V\nVNIK2thGJFTs2bMHgL/+9a9cd911DfYxDINVq1axatUqJk2axGuvvcbw4cMpKiry7vLakDNnzgCQ\nmJjoPU5iYiLHjx/HarUyefLkejEul4uvv/4agKFDh7Zkap2K/sQUERGRTqVXTzMbU6c22mdFRn5Q\nu58Gk7SCNrYRCSVDhw5ttKTHkSNHqKmpYejQofTt25cf/ehHACQlJZGXl8fhw4cbjLPZbJw9exbD\nMJgwYYL386SkJI4dO8bhw4e5995768UVFhbicrmIiopizJgxLZxd56HEUkRERLqNQJJWEQktjz/+\nOI8//rjf9ltuuYWSkhKWLl3KXXfd5f18xowZbNy4kYKCAk6fPl1vdfHNN98EYNKkSSQkXHrXe+bM\nmWRlZbF3716eeeYZevXq1WDcrFmzAtpEqKsIa7qLiIiIiIhI9zJkyBDmzJmDy+UiNTWVoqIib1tO\nTg6vvPIKhmHwxBNP+MRNmTKF5ORkHA4Hy5Yt89bDdLvdbNmyhV27dhEREUFKSkq7zqetdakVy+3b\nt/Pcc8+xYcMG7rvvvgb7VFVVsWXLFnbv3o3VaiUmJoZrr72WRYsWcfPNN/s9dnl5OZs3byYvL4/S\n0lLi4uKYOHEiKSkpjRZPLSkpISMjg/z8fMrKyujTpw9TpkxhyZIlDB8+vMVzFhERERGRjrF69WqO\nHz/OiRMnmDlzJiNHjsRut3sfgV2xYgU33HBDvbi0tDQWLlzIwYMHmT59OiNGjMBms3Hu3DnCwsJI\nS0tj2LBhHTCjttNlViwLCwv51a9+1Witl8rKSh5++GE2bdqE1WplxIgRREdHc+DAAZYsWUJGRkaD\ncWVlZdx///289tprlJWVMWrUKAzDIDc3lwceeIAdO3Y0GHfy5EnuuusuduzYQWVlJaNHj8bpdJKT\nk8M999zDxx9/3CpzFxERERGRtuMvx7BYLGRlZfGP//iPJCYmcvLkSex2O5MnT+all15iyZIlDcYN\nGjSInTt3smjRIq666ipOnDhBTU0N06ZN49VXX2X27NltOZ0O0SVWLD/99FN+9rOfceHChUYTy7Vr\n11JYWMiYMWP43e9+R3x8PADvvvsu//zP/8ymTZtITk5mypQpPnHLly/HarUydepUNm7cSGxs3TbG\nW7duJT09nTVr1jB+/Hifuwq1tbUsXboUh8PBvHnzWLduHWazGZfLRVpaGq+//jorV67kgw8+IC4u\nrg3OioiIiIiItFReXl6j7VFRUaSmppKamhrUcS0Wi3en2e6gU69YOp1OXnrpJR599FEcDkejfYuL\ni3nvvfcIDw8nPT3dm1QC3HnnnaSkpOB2u+utWhYUFPDZZ58RHR1Nenq6N6kESElJYe7cubhcLjIz\nM33icnJyKCoqYsCAAWzYsMH74q3JZGL16tXe56q3bdvWwrMgIiIiIiLSuXXaxLKoqIg77riDTZs2\nYRgGy5cv99lt6Uo5OTnU1tbWW1n0WLBgAQCHDh2itLTU+3l2djaGYXDrrbdisdQvsLxgwQLcbje5\nubk4nc56cfPmzcNkqr/wO3/+fNxuN++//35Q8xYREREREelqOm1iWVpais1mY8KECbz99tuNbhEM\n8Pnnn2MYht8aNfHx8d7EtKCgwCcO8Bs3btw4TCYTlZWVHDlyBKjb0cnza39xEydOBOpWUm02W6Nj\nFxERERER6co6bWJ51VVX8fLLL/PWW28xevToJvt7tv8dNGiQ3z5XX301AKdPnwbqEkSr1QrA4MGD\nG4wxmUz079/fJ85ms1FVVdVo3IABAwgPDwfg1KlTTY5fRERERESkq2py855AkrqmGIbBF198EVTM\n4MGD/SZtDfHUh+nTp4/fPr179wbg/PnzANjtdlwuF4ZheNsaYrFYKCkp8cZ5vqux7wsLC6Nnz544\nHA5vnIiIiIiISKA6KhdrjiYTS7fb3eaDaA2eFcTIyEi/fSIjI3G73d6+nn83FRcVFeXTv7Ky0tvm\n2bTHX5zD4fD5HhEREWk9jgonKzLyA+onIp1DoD+3AFFmE3fflMik0fFNdw5BXSUXgwASy9deey3o\ng+7du5c333wTqDsZ119/ffAjC1JYWBi1tbWNliPx/MZ4+oSFXXoSOJg4zyOuwcaJiIhI64gym7Dj\nxA3Yg0gao8xdotKaSEhqzs+tHSd7Pi3qtollV8nFIIDEctKkSQEfrLy8nHXr1vHf//3fuN1uevTo\nwf/7f/+PBx98sEWDDERMTAwOh4Pq6mq/fZxOJ4ZheFcgo6OjvW2NxXna/MVFREQEFNccFkt0vc/C\nPImxYTTYHkx/kynM7/d0JVfOsal5BXsOO4tQ+f26kuYlIsG6+6ZEsvefosrpCjjGs/IhIh2juT+3\nMyYH/npcqOkquRgEkFgGKjc3l+eee46ysjJvZvz88883uplOa7JYLDgcDsrLy/328bzr6HkvMiYm\nBrPZTE1NTaNxnra+ffsC+LyPWV5eTs+ePevF1NbWUlFR4RMnIiLS3RQctdW7kPTc3Lt4xSNewTyu\nOml0fLddwRDpqvRz23Y6OheDVkgs7XY769atY/fu3d7M+Omnn+ahhx5qjfEFbPjw4RQVFXl3eW3I\nmTNnAEhMrLtbaRgGiYmJHD9+HKvVyuTJk+vFuFwuvv76awCGDh0KQP/+/YmNjaWiogKr1crAgQPr\nxZWUlHgfzfXENUd5+ff1PvP8RXzR7W6wPZj+npWUQI7TmV05x6bmFew57CxC5ffrSpqXtJV+/WI7\neghdQkPJX2OCeecpe/8pbN8G9zOgx1VFRALTWXIxaGFi+cc//pE1a9Z0aGbskZSURF5eHocPH26w\n3WazcfbsWQzDYMKECT5xx44d4/Dhw9x777314goLC3G5XERFRTFmzBjv5+PGjePAgQMcPnyYG264\noV6cpz5mQkIC/fr1a+n0WkVDL0o3dNe4s7wkHcyFjjZlEBFpvmCTPztOsvefCujvCc+f4QbQq2fd\nhnf+VixBj6uKiASqM+Vi0MzE0uFwsH79enbt2tXhmbHHjBkz2LhxIwUFBZw+fbreKqHnBdZJkyaR\nkJDg/XzmzJlkZWWxd+9ennnmGXr16tVg3KxZs3x2gJ0xYwYff/wxO3bsICUlBZPJ91S+9dZbGIbB\nPffc05rTbJZgX5QO5oKhLekut4hI+2go+fPHUVH390kw70jxw3E3pk4FtJovItISnTEXg2Ykln/6\n05/4xS9+wblz5zpFZuwxZMgQ5syZw65du0hNTWXz5s3eOpg5OTm88sorGIbBE0884RM3ZcoUkpOT\nOXjwIMuWLeM3v/kNffv2xe12s3XrVnbt2kVERAQpKSk+cfPmzWPLli0UFRWxcuVKXnjhBWJiYqip\nqeHFF1/k0KFDxMXFdfhvMDT+ovSVd42be8HQFoK50AHd5RYRaanLkz9/VmTkB7ULq4iItJ7OmotB\nEInld999x4YNG3j33Xe9mfHKlStZuHBhW44vKKtXr+b48eOcOHGCmTNnMnLkSOx2u/cR2BUrBMLP\ngAAAIABJREFUVjT42GpaWhoLFy7k4MGDTJ8+nREjRmCz2Th37hxhYWGkpaUxbNgwnxiz2cyvf/1r\nFi9ezL59+8jPz2fYsGEUFxdjt9sxm81kZGQQFxfXXtP3q7EXpa+8a9wZLxgCudAREREREWnKV199\nxcsvv8wnn3zCN998g8ViISkpiQcffJC//du/bTCmvLyczZs3k5eXR2lpKXFxcUycOJGUlBSSkpL8\nfldJSQkZGRnk5+dTVlZGnz59mDJlCkuWLGH48OFBjbsr5GIBJZYffvgh//qv/8o333yD2+3mxz/+\nMS+88EKnyIwvZ7FYyMrKYuvWrezZs4eTJ09iMpmYPHkyCxcu5LbbbmswbtCgQezcuZPMzEzy8vI4\nceIEPXr0YNq0aaSkpPit/XLdddeRk5PD5s2byc/P59ixY8TGxjJjxgyWLl3KqFGj2nK6IiIiHSbQ\nAud6B15EOov9+/fz5JNPUl1dTVRUFCNGjODbb78lLy+PP/7xjyxevJhnnnnGJ6asrIz58+dTXFxM\njx49GDVqFKWlpeTm5pKXl8f69esbfPXt5MmTLFiwAIfDQWxsLKNHj6a4uJicnBz27NnD5s2bufHG\nGwMad1fJxZpMLP/pn/6JnTt34na7CQsL48EHH+Thhx8GoLi4OOAvao2J5+XlNdknKiqK1NRUUlNT\ngzq2xWJh1apVrFq1Kqi4AQMGsH79+qBiREREuqrmFDj3xImIdJTz58/z9NNPU11dzezZs3nuuee8\nJQPff/99nn32WX7/+98zfvx4br/9dm/c8uXLsVqtTJ06lY0bNxIbW7fb+NatW0lPT2fNmjWMHz/e\n5+nG2tpali5disPhYN68eaxbtw6z2YzL5SItLY3XX3+dlStX8sEHHzT5dGNnysWa0uSf8tnZ2RiG\ngWEYuN1u3njjDd54442gvsQwDL744otmD1JEREQ6h+YWONc78CLSkd5++20cDgcDBw7khRdeICIi\nwts2e/ZsPvvsM7Zv38727du9iWVBQQGfffYZMTExpKene5NKgJSUFI4dO8auXbvIzMzkl7/8pbct\nJyeHoqIiEhIS2LBhg3eTT5PJxOrVqzl69CiHDh1i27ZtPPXUU42OuyvlYgHdPnQ3sB14MFoaLyIi\nIp2DCpyLSFc0cOBA5syZw5gxY3ySSo8f/ehHwKW693Apqbv11luxWCz1YhYsWMB7771Hbm4uTqfT\nW0HCEzdv3rx6lSMA5s+fz8GDB3n//febTCyh6+RiTSaWf/zjH9tjHCIiIiIiIm1i1qxZzJo1y2/7\nkSNHAHxKFnrq0icnJzcYM27cOEwmE5WVlRw5coTk5GTcbrf3WP7iJk6cCNQ9ymqz2YiP93+zrivl\nYk0mlldffXV7jENERERERKRdfffdd7z66qtkZ2djMpm8JQbdbjdWqxXAW8LwSiaTif79+1NSUsLp\n06dJTk7GZrNRVVWFYRh+4wYMGEB4eDgXL17k1KlTjSaWXSkX05v0IiIiIiLSrXzwwQf89re/paio\nCKfTSUJCAmvWrOHHP/4xAHa7HZfLhWEY9O7d2+9xLBYLJSUlnD9/HqjbRdajT58+DcaEhYXRs2dP\nHA6HNy4UhHX0AERERERERNpTYWEh//d//0dNTQ2GYWC32/nTn/7EhQsXAKiqqvL2jYyM9HucqKgo\nn/6VlZXeNs87l4HEhQIlliIiIiIi0q0sXLiQzz//nP379/PCCy8QFRXF9u3beeSRR7h48SJhYZfS\nJMMw/B7HszGOp094eHiz4kKBHoWVbi3QAt+erfK1E6KItJTFEt3RQ6gn7IcLmzDDaJfxmUx1F2yd\n8Vx0BJ0PXzofvnQ+2obnvcaoqCjuuusukpKSuOuuu/jf//1f3n33XW677TZv3+rqar/H8bR5ViCj\no6N92hragbahuFCgxFK6pWALfNtxkr3/lBJLEeky8v/nLNv3HaOyuul6k+UV/i+aRES6g8TERG6/\n/XZ27drFp59+yrx58zCbzdTU1FBeXu43ztPWt29fAJ/3McvLy+nZs2e9mNraWioqKnziQoESS+mW\nginw7aioS0CDKQYuIuJPefn37fI9b+z9Etu3wX2XOSK8XcbnWXlpr3PR2el8+NL58KXz4atfv9hm\nxdntdqxWKwkJCX434/HswHru3DkMwyAxMZHjx49jtVqZPHlyvf4ul4uvv/4auFSmpH///sTGxlJR\nUYHVamXgwIH14kpKSqitrcUwDJ/yJl2dEkvploIp8L0iIz+gVU0Rkc7EczPMAHr19L+BhIfnkX8R\nkVB07733YrVaefbZZ3n00Ucb7HPmzBng0mOySUlJHDt2jMOHD3PvvffW619YWIjL5SIqKooxY8Z4\nPx83bhwHDhzg8OHD3HDDDfXiPPUxExIS6NevX4vn1lkosRQREQlhvXqa2Zg6taOHISLSoaZOncr2\n7dvJyspi0aJFPpvsAFitVnJzczEMg1tuuQWAmTNnkpWVxd69e3nmmWfo1auXT8ybb74JwKxZs3x2\ngJ0xYwYff/wxO3bsICUlBZPJN+V66623MAyDe+65py2m2mG0K6yIiIiIiIS0xYsXExUVxVdffcXK\nlSt96kd+8cUXLF68mOrqaq6//npvYjllyhSSk5NxOBwsW7bMW6PS7XazZcsWdu3aRUREBCkpKT7f\nNW/ePAYPHkxRURErV670ljCpqalhw4YNHDp0iF69evHQQw+10+zbh1YsRUREREQkpA0aNIh///d/\nZ8WKFXzwwQf86U9/IjExkerqak6fPo1hGIwfP57f/va3PnFpaWksXLiQgwcPMn36dEaMGIHNZuPc\nuXOEhYWRlpbGsGHDfGLMZjO//vWvWbx4Mfv27SM/P59hw4ZRXFyM3W7HbDaTkZFBXFxce56CNqcV\nSxERERERCXl/93d/x7vvvstPfvIT+vfvz8mTJ/n222/58Y9/zLp163j99dexWCw+MYMGDWLnzp0s\nWrSIq666ihMnTlBTU8O0adN49dVXmT17doPfdd1115GTk8N9991HXFwcx44dIzw8nBkzZpCVlcX1\n11/fHlNuV1qxFBERERGRbmHQoEGsXbs2qBiLxcKqVatYtWpVUHEDBgxg/fr1QcV0ZUospUGOCicr\nMvID6uvZSVA1HkVEREREuiclluIjymzCTl3dxkBLbNhxkr3/lBJLEZE2VHDUFnD9Xai7QSgiItJe\nlFiKj7tvSgz6wsUNAfcXEZHmyd5/Ctu3wRdIjzLrr3oREWl7+ttGfEwaHR/UyuOKjPyAVzZFRKT5\nPDfwDOpqUwbC86qCiIhIW1NiKSIi0oX06mlmY+rUjh6GiIiID5UbERERERERkRZRYikiIiIiIiIt\nosRSREREREREWkSJpYiIiIiIiLSIEksRERERERFpEe0KKyIi0kEKjtoCrh3sUGknERHpxJRYioiI\ndJDs/aewfft9UDFRZv3VLSIinY/+dhIREekgnpVKg7r6lE2JMpu4+6bENh6ViIhI8JRYioiIdLBe\nPc1sTJ3a0cMQERFpNiWWIiIiIiLSLdhsNl555RXy8/M5e/YsAAMHDuTv/u7vePTRR+nTp0+9mKqq\nKrZs2cLu3buxWq3ExMRw7bXXsmjRIm6++Wa/31VeXs7mzZvJy8ujtLSUuLg4Jk6cSEpKCklJSW02\nx46iXWFFRERERCTk/fnPf2bu3Lm89tprFBUVkZCQQHx8PKdPn2br1q3ceeedHD9+3CemsrKShx9+\nmE2bNmG1WhkxYgTR0dEcOHCAJUuWkJGR0eB3lZWVcf/99/Paa69RVlbGqFGjMAyD3NxcHnjgAXbs\n2NEeU25XSixFRERERCSkfffddzz55JN89913TJs2jQ8//JDdu3ezd+9e9uzZQ3JyMufOneMf//Ef\ncTov7cK9du1aCgsLGTNmDLm5uezYsYO8vDxefPFFTCYTmzZt4pNPPqn3fcuXL8dqtTJ16lQ++ugj\n3nnnHfLz83n66aepra1lzZo1nDx5sj1PQZtTYikiIiIiIiHtD3/4A+fPn6d///5s3LiRvn37etsG\nDhzISy+9RFxcHFarlb179wJQXFzMe++9R3h4OOnp6cTHx3tj7rzzTlJSUnC73fVWLQsKCvjss8+I\njo4mPT2d2NhYb1tKSgpz587F5XKRmZnZxrNuX3rHUtpNMPXaQDXbRERERKR1FBQUYBgG06dPJzo6\nul57nz59mDBhAh9++CFHjhxh7ty57Ny5k9raWpKTkxk2bFi9mAULFpCZmcmhQ4coLS3lqquuAiA7\nOxvDMLj11luxWCwNxr333nvk5ubidDoxm5veFbwrUGIp7aY59dpANdtEREREpGWWLVvGjBkzGDp0\nqN8+brcbgIsXLwJw+PBhDMMgOTm5wf7x8fEkJCRQUlJCQUEBd955JwCff/45gN+4cePGYTKZqKys\n5MiRI377dTW6Ypd2E2y9NlDNNhERERFpubFjxzJ27Fi/7efPn6egoACAESNGAFBUVATAoEGD/MZd\nffXVlJSUcPr0aaAuObVarQAMHjy4wRiTyUT//v29cUosRS7jqHCyIiO/yT6gem0iIiIi0rls2LCB\nyspKevTowe233w7U7ewKNFiCxKN3795AXWIKYLfbcblcGIbhbWuIxWKhpKTEGxcKQi6xvOWWW7w1\nafz585//TM+ePb3/3dwaMyUlJWRkZJCfn09ZWRl9+vRhypQpLFmyhOHDh7fanDqzKLMJO07cgD3A\ndyL1aKuIiIiIdBabN2/m/fffxzAMUlNTvYlkVVUVAJGRkX5jIyMjcbvd3r6efzcVFxUVVa9/VxdS\nV/gVFRWcPXsWk8nkNyE0DIPw8HDvf5eVlTF//nyKi4vp0aMHo0aNorS0lNzcXPLy8li/fj333HNP\nveOcPHmSBQsW4HA4iI2NZfTo0RQXF5OTk8OePXvYvHkzN954Y5vNtbO4+6bEoDbk0aOtIiIiItJZ\nZGRkkJGR4d1sZ/Hixd62sLAwamtrMQzDb7znvUxPn7CwS0U3gokLBSGVWH755ZcADBkyhDfeeCOg\nmMtrzGzcuNG7HfDWrVtJT09nzZo1jB8/3mcnqNraWpYuXYrD4WDevHmsW7cOs9mMy+UiLS2N119/\nnZUrV/LBBx8QFxfX+hPtRCaNjmfS6PimO4qICAAWy6XdCMM8FyKG4fN5qDOZ6i68utOcG6Pz4Uvn\nw5fOR9uora1l7dq1ZGVlYRgGN910E//2b//m0ycmJgaHw0F1dbXf4zidTgzD8K5AXr7jbGNxnjZP\nXCgIqTqWnsRy5MiRAfVvbo2ZnJwcioqKGDBgABs2bPBuEWwymVi9ejXJyck4HA62bdvWOhMTERER\nEZFWUVFRwWOPPeZNKmfNmsXmzZvrlf3wlAopLy/3eyzPO5Kex2djYmK8x2ksztN2eT3Nri7kViwN\nw/Du5NSU5taY8cTNmzcPk6n+KZw/fz4HDx7k/fff56mnnmrZpEREJKSUl18qu3TRs7W92+3zeajz\nrLx0pzk3RufDl86HL50PX/36xTbdqRGlpaU89thjnDhxAsMwSElJ4emnn26w7/DhwykqKvLu8tqQ\nM2fOAJCYWPeql2EYJCYmcvz4caxWK5MnT64X43K5+PrrrwEaLX/S1XTrFctga8xA3fPQnl/7i5s4\ncSIAxcXF2Gy2wCcgIiIiIiJt4ptvvmHhwoWcOHECk8nE2rVr/SaVAElJSbjdbg4fPtxgu81m824a\nOmHChIDjCgsLcblcREZGMmbMmBbMqHMJmcTy4sWL/PWvfwWgf//+bNu2jSeffJJHHnmEn//85+ze\nvdv7kiwEV2MG8Namsdls3t2b/MUNGDDAu0HQqVOnWj45ERERERFptpqaGh5//HGKi4sxm8385je/\n4Sc/+UmjMTNmzADqXp/z5AKXe/PNNwGYNGkSCQkJ3s9nzpwJwN69e3E4HH7jZs2aVe/x264sZBLL\nU6dOeRO+n/70p7z44ovk5uby6aef8u6777Jy5Ur+4R/+gW+++Qa4VGMGaLLGDFx6ftpTzwb817QJ\nCwvzljMJpdo0IiIiIiJd0X/8x3/wxRdfYBgGv/jFL7j11lubjBkyZAhz5szB5XKRmppKUVGRty0n\nJ4dXXnkFwzB44oknfOKmTJni3XNl2bJl3vzB7XazZcsWdu3aRUREBCkpKa07yQ4WMu9YHjt2zPvr\npKQkUlNTufbaa6muruajjz7ixRdfpLCwkMcff5ysrKxm15iprKz0tjV2hyEqKgqHwxFStWlERERE\nRLqampoaXn31VQDCw8PZsWMHO3bs8Nt/2rRpPP744wCsXr2a48ePc+LECWbOnMnIkSOx2+2cPXsW\nwzBYsWIFN9xwQ71jpKWlsXDhQg4ePMj06dMZMWIENpuNc+fOERYWRlpamk/ViVAQMonlgAEDWLhw\nISaTiZ///OfezyMjI5kzZw5jx47l7rvv5ujRo7zzzjvccsst3j7B1Ji5vAZmd6tNIyIiIiLS1Rw/\nfpzvvvsOwzCora317rPiz5AhQ7y/tlgsZGVlsXXrVvbs2cPJkycxmUxMnjyZhQsXcttttzV4jEGD\nBrFz504yMzPJy8vjxIkT9OjRg2nTppGSksL111/fqnPsDEImsZwwYYLPS7NXGjp0KHPnziUrK4t9\n+/YxZ84cb1swNWaurE0TERERUFxzNVSzqDXrnoVqbaTWnFdnqjOn36+uJVTnJSIi0pVce+21HD16\ntNnxUVFRpKamkpqaGlScxWJh1apVrFq1qtnf3ZWEzDuWgRg9ejQAVqu12TVmLn8f019cbW0tFRUV\nPnEiIiIiIiKhKmRWLKHu8VOXy+V3FfHixYsARERENLvGTP/+/YmNjaWiogKr1crAgQPrxZWUlFBb\nW4thGC2uTdNQzaLWrHsWqrWRWnNenanOnH6/upZQnVdX0tJ6ZyIiIhKYkFmxfPDBBxk7diy//OUv\n/fb54osvALjmmmuA5teYGTduHIDfOM9z2wkJCfTr1y/4yYiIiIiIiHQhIZNYjhw5ktraWvbt28f3\n39dfHThz5gy7d+/GMAxmz54NNL/GzIwZM3C73ezYscNbsuRyb731FoZhcM8997TK3ERERERERDqz\nkEksH3nkEcxmMzabjeXLl3Pu3Dlv25dffsnixYupqqri+uuv5/bbbweaX2Nm3rx5DB48mKKiIlau\nXMmFCxeAuq2MN2zYwKFDh+jVqxcPPfRQO81eRERERESk44TMO5ZDhgwhPT2dZ599lv379zN9+nSG\nDh1KbW0tJ0+exDAMrrvuOjIyMnzimlNjxmw28+tf/5rFixezb98+8vPzGTZsGMXFxdjtdsxmMxkZ\nGcTFxbXnKRAREREREekQIbNiCXDHHXewc+dO7r//fuLj4/nqq684d+4cEyZM4F//9V/Zvn07vXr1\n8onx1JhZtGgRV111FSdOnKCmpoZp06bx6quveh+bvdJ1111HTk4O9913H3FxcRw7dozw8HBmzJhB\nVlZWSNamERERERERaUjIrFh6DB06lHXr1gUV09waMwMGDGD9+vVBxYiIiIiIiISakFqxFBERERER\nkfYXciuWIh2p4KiN7P2nqHLW3y3YnyizibtvSmTS6Pg2HJmIiIiISNtRYinSirL3n8L2bf1yN42x\n4yR7/yklliIiIiLSZSmxFGlFnpVKA+jV09x4Z8BR4cR9WZyIiIiISFekxFKkDfTqaWZj6tQm+63I\nyMde4WyHEYmIiIiItB1t3iMiIiIiIiItosRSRERERES6pe3btzNq1Cjeeecdv32qqqp46aWXmDlz\nJtdddx033HADixcv5qOPPmr02OXl5Tz//PPcdtttjB07lhtvvJEnn3yS//mf/2ntaXQKSixFRERE\nRKTbKSws5Fe/+hWGYfjtU1lZycMPP8ymTZuwWq2MGDGC6OhoDhw4wJIlS8jIyGgwrqysjPvvv5/X\nXnuNsrIyRo0ahWEY5Obm8sADD7Bjx462mlaHUWIpIiIiIiLdyqeffspjjz3GhQsXGu23du1aCgsL\nGTNmDLm5uezYsYO8vDxefPFFTCYTmzZt4pNPPqkXt3z5cqxWK1OnTuWjjz7inXfeIT8/n6effpra\n2lrWrFnDyZMn22p6HUKJpYiIiIiIdAtOp5OXXnqJRx99FIfD0Wjf4uJi3nvvPcLDw0lPTyc+/lJp\nuDvvvJOUlBTcbne9VcuCggI+++wzoqOjSU9PJzY21tuWkpLC3LlzcblcZGZmtu7kOpgSSxERERER\nCXlFRUXccccdbNq0CcMwWL58OQkJCX775+TkUFtby/jx4xk2bFi99gULFgBw6NAhSktLvZ9nZ2dj\nGAa33norFoulwTi3201ubi5OZ+hUB1BiKSIiIiIiIa+0tBSbzcaECRN4++23efzxxxvt//nnn2MY\nBsnJyQ22x8fHexPTgoICnzjAb9y4ceMwmUxUVlZy5MiR5kylU1JiKSIiIiIiIe+qq67i5Zdf5q23\n3mL06NFN9i8qKgJg0KBBfvtcffXVAJw+fRoAt9uN1WoFYPDgwQ3GmEwm+vfv7xMXCkwdPQARERER\nEZG2NnjwYL/JXkPKysoA6NOnj98+vXv3BuD8+fMA2O12XC4XhmF42xpisVgoKSnxxoUCrViKiIiI\niIhcoaqqCoDIyEi/fSIjI3G73d6+nn83FRcVFVWvf1enFUsREZF2ZLFEe38d9kPttDDD8Pk81JlM\ndfe1u9OcG6Pz4Uvnw5fOR8cJCwujtra20TqXbrcbwNsnLOzSul0wcaFAK5YiIiIiIiJXiImJAaC6\nutpvH6fTiWEY3hXI6OhLNwAai/O0eeJCgVYsRURE2lF5+ffeX1/84Y71Rbfb5/NQ51l56U5zbozO\nhy+dD186H7769YttulMrsVgsOBwOysvL/fbxvCPpeQ8zJiYGs9lMTU1No3Getr59+7biiDuWVixF\nRERERESuMHz4cADvLq8NOXPmDACJiYlA3aOtnl/7i3O5XHz99dcADB06tLWG2+GUWIqIiIiIiFwh\nKSkJt9vN4cOHG2y32WycPXsWgAkTJgQcV1hYiMvlIjIykjFjxrT+wDuIHoUVCZCjwsmKjPwm+4iI\niIhI1zdjxgw2btxIQUEBp0+frre6+OabbwIwadIkEhISvJ/PnDmTrKws9u7dyzPPPEOvXr0ajJs1\naxZms7ltJ9GOtGIp0oQoc939Fzdgr3A2+o/7ihgRERER6ZqGDBnCnDlzcLlcpKamUlRU5G3Lycnh\nlVdewTAMnnjiCZ+4KVOmkJycjMPhYNmyZd56mG63my1btrBr1y4iIiJISUlp1/m0NV39ijTh7psS\nyd5/iiqnK6D+UWYTd9+U2MajEhEREZG2tnr1ao4fP86JEyeYOXMmI0eOxG63c/bsWQzDYMWKFdxw\nww314tLS0li4cCEHDx5k+vTpjBgxApvNxrlz5wgLCyMtLY1hw4Z1wIzajhJLkSZMGh3PpNHxHT0M\nEREREWlnFouFrKwstm7dyp49ezh58iQmk4nJkyezcOFCbrvttgbjBg0axM6dO8nMzCQvL48TJ07Q\no0cPpk2bRkpKCtdff307z6TtKbEUEREREZFuKS8vr8k+UVFRpKamkpqaGtSxLRYLq1atYtWqVc0d\nXpeidyxFRERERESkRZRYioiIiIiISIsosRQREREREZEWUWIpIiIiIiIiLaLEUkRERERERFpEiaWI\niIiIiIi0iBJLERERERERaRElliIiIiIiItIiSixFRERERESkRZRYioiIiIiISIuYOnoAIgKOCicr\nMvKb7BcTFcH823/E2CGWdhiViIiIiEhgtGIp0oGizHX3dtyAvcLZ5D9nz11g+75jHTtoEREREZEr\naMVSpAPdfVMi2ftPUeV0NdnXUeHEDVRWN91XRERERKQ9KbFsoaqqKrZs2cLu3buxWq3ExMRw7bXX\nsmjRIm6++eaOHp50cpNGxzNpdHxAfVdk5GOvcLbxiERERERCk67b25YSyxaorKxk0aJFFBYWEhER\nwYgRIygvL+fAgQN8/PHHpKamkpqa2tHDFBERERHp1nTd3vb0jmULrF27lsLCQsaMGUNubi47duwg\nLy+PF198EZPJxKZNm/jkk086epgiIiIiIt2artvbnlYsm6m4uJj33nuP8PBw0tPTiY+/9DjjnXfe\nycmTJ8nMzCQjI4MpU6Z04EhFRBpWcNQW8Du+ULfZ1N03JQb8+HZbCWbcr6+d2Q4jEhGRzkzX7e1D\nK5bNlJOTQ21tLePHj2fYsGH12hcsWADAoUOHKC0tbe/hiYg0KXv/KWzffh/QjsT2Cie2b78ne/+p\njh52UOMWERHRdXv70IplM33++ecYhkFycnKD7fHx8SQkJFBSUkJBQQF33nlnO49QRKRxnhU/A+jV\n09xoX8+uxIGubgYj2JVTxw8JYyDjFhER0XV7+1Bi2UxFRUUADBo0yG+fq6++mpKSEk6fPt1OoxIR\nCV6vnmY2pk5ttI9nV2JHhZMVGfkBHTfQR2c9K5DB6t8nmheW3BB0nIiIdC+6bm8fSiybqaysDIA+\nffr47dO7d28Azp8/3y5jEhEJZvXPEcSjolFmE3bqVi0DfcTUjpPMnL/w1h9PBDSOYFYgPUmriIhI\nU3Td3j6UWDZTVVUVAJGRkX77REZG4na7vX1FWkN5RXWrrxgF+yhiawszDAAuut2dZoOYYPg7f5fP\nq700573CKHPTfxXcfVNiUP+PXD6OQMekFUgREWkLum5vH0osmyksLIza2lqMHy4cG+L+4WKysT4i\ngfKuGLlbf8WoM21yEuiYO5POdP4uFxfA6l+gK3+TRscHlew3d8dZERGR1qbr9vahxLKZYmJicDgc\nVFdX++3jdDoxDIOoqKhmf0+/frH1PmuL7fMb+p5QEErz2vovt3f0EEQCNrtfLLNvvqajh9EpXf7n\nUncvhxJKf0a3Bp0PXzofvnQ+mq+9rtu7O5UbaSaLxQJAeXm53z6eZ7Qbe55bRERERET70juAAAAg\nAElEQVTajq7b24cSy2YaPnw4AFar1W+fM2fOAJCYqMe7REREREQ6gq7b24cSy2ZKSkrC7XZz+PDh\nBtttNhtnz54FYMKECe05NBERERER+YGu29uHEstmmjFjBgAFBQUN1rt58803AZg0aRIJCQntOTQR\nEREREfmBrtvbhxLLZhoyZAhz5szB5XKRmprqLbwKkJOTwyuvvIJhGDzxxBMdOEoRERERke5N1+3t\nw3C727HAWogpLy/n4Ycf5sSJE4SFhTFy5Ejsdjtnz57FMAxWrFjBkiVLOnqYIiIiIiLdmq7b254S\nyxaqqqpi69at7Nmzh+LiYkwmE2PHjmXhwoXcdtttHT08ERERERFB1+1tTYmliIiIiIiItIjesRQR\nEREREZEWUWIpIiIiIiIiLaLEUkRERERERFpEiaWIiIiIiIi0iBJLERERERERaRElliIiIiIiItIi\nSixFRERERESkRZRYioiIiIiISIsosRQREREREZEWUWIpIiIiIiIiLaLEUkRERERERFpEiaWIiIiI\niIi0iBJLERERERERaRElliIiIiIiItIiSixFRERERESkRZRYioiIiIiISIuYOnoAHW379u0899xz\nbNiwgfvuuy/o+PLycjZv3kxeXh6lpaXExcUxceJEUlJSSEpKaoMRi4iIiIhIc3z11Ve8/PLLfPLJ\nJ3zzzTdYLBaSkpJ48MEH+du//dsGY5p7vV9SUkJGRgb5+fmUlZXRp08fpkyZwpIlSxg+fLjfuBMn\nTrB582YKCgpwOBz069ePadOmsXTpUuLj41t8DtqK4Xa73R09iI5SWFjIT3/6U77//nvWr18fdGJZ\nVlbG/PnzKS4upkePHgwfPpzS0lLKysoICwtj/fr13HPPPW00ehERERERCdT+/ft58sknqa6uJioq\niqFDh/Ltt9/y9ddf43a7Wbx4Mc8884xPTHOv90+ePMmCBQtwOBzExsYyZMgQiouLKS8vJzIyks2b\nN3PjjTfWi/vzn//M4sWLcTqd9O7dm4SEBE6dOsWFCxeIi4vj1VdfZdSoUW12jlqi2z4K++mnn/LY\nY49x4cKFZh9j+fLlWK1Wpk6dykcffcQ777xDfn4+Tz/9NLW1taxZs4aTJ0+24qhFRERERCRY58+f\n5+mnn6a6uprZs2ezf/9+srOz+fDDD0lPTyc8PJzf//737Nu3zyeuOdf7tbW1LF26FIfDwbx588jP\nz+ftt98mPz+fhx56iOrqalauXIndbveJs9vtLFu2DKfTyZIlS8jPz+edd95h//79/P3f/z12u52f\n/exnuFyuNj9fzdHtEkun08lLL73Eo48+isPhaPZxCgoK+Oyzz4iOjiY9PZ3Y2FhvW0pKCnPnzsXl\ncpGZmdkawxYRERERkWZ6++23cTgcXH311bzwwgv07NnT2zZ79mzuv/9+3G4327dv937e3Ov9nJwc\nioqKGDBgABs2bMBsNgNgMplYvXo1ycnJOBwOtm3b5hP32muv4XA4GD9+PCtWrCAsrC5V83z/wIED\nKS4uZufOna19elpFt0osi4qKuOOOO9i0aROGYbB8+XISEhKadazs7GwMw+DWW2/FYrHUa1+wYAFu\nt5vc3FycTmdLhy4iIiIiIs00cOBA5syZwwMPPEBERES99h/96EcAnDlzxvtZc6/3PXHz5s3DZKq/\npc38+fNxu928//77Pp974u699956MREREdx3330NxnUW3SqxLC0txWazMWHCBN5++20ef/zxZh/r\n888/ByA5ObnB9nHjxmEymaisrOTIkSPN/h4REREREWmZWbNmkZ6ezqOPPtpgu+d6fejQod7PmnO9\n73a7vb/2Fzdx4kQAiouLsdlsAHzzzTecPXs2oLhDhw5RW1vb8EQ7ULdKLK+66ipefvll3nrrLUaP\nHt3s47jdbqxWKwCDBw9usI/JZKJ///4AnD59utnfJSIiIiIibeO7774jIyOD7OxsTCYTKSkpQPOv\n9202G1VVVY3GDRgwgPDwcABOnToF1O1WC2AYBgMHDmww7uqrrwbqXu3zJKGdSbcqNzJ48GC/v8HB\nsNvtuFwuDMOgd+/efvtZLBZKSko4f/58i79TRERERERaxwcffMBvf/tbioqKcDqdJCQksGbNGn78\n4x8Dzb/eLysr87b16dOnwZiwsDB69uyJw+Hwxn377bcA9OzZs8FHdT3f5XH+/HkGDRoUxIzbXrda\nsWwtnrsQAJGRkX77RUVF1esvIiIiIiIdq7CwkP/7v/+jpqYGwzCw2+386U9/8laMaO71fmVlpbfN\ns2lPMHGBxFw5vs5CiWUzeHZogrrlan88JUIb6yMiIv+fvXuPj6q69///2pPJBZKQiCUxsRAuDT9A\nIEDK7StKEfUkCKRQ2wNVREsOIsYW8FStpSqXhwYNpT2ENBb0gR5vpQoGkYLQ9CFEWqJcDD3KxQbM\nhFwEJBkCuc1k//6IM5ImkztJYN7Px6OPjnt9PmuvvTum+WTtvZaIiEjHmjNnDocOHWLv3r0899xz\nBAQE8NZbb3H//fdTU1PT6t/3XY+4tjavuXXD5ePrKrreiK4C3bt3d3+urKz0GOdqu/yvCyIiIiIi\n0rnCw8MJCAjgO9/5Dj/84Q957bXX8Pf355///Cdbt25t9e/7bc1rbDeJ5s6idhYVlq0QGBjonqYu\nKSnxGOdqu/766ztkXCIiIiIi0nL9+vXjjjvuwDRN9u/f3+rf9y9/H9NTntPppKysrMG8srIyjyu+\nXr5uS1esL7xq8Z72YhgG/fr14/jx4+Tn5zN27Nh6MQ6Hg6+++gqou2xxS1VX136xrFYLDkcNJSWX\nmp27ODWL0rIqQoL8WJM0odVj6Oi+RUQa05KfP716BTfa3hnOnLnQ2UPodKGhtX+Zb8n/p13LdD/q\n0v2oS/ejrtb+XC8tLSU/P5/IyEiPi/G4Vl09e/Zsq3/fDwsLIzg4mLKyMvLz8xtc4bWwsBCn04lh\nGO68AQMGAFBTU0NBQUGDC/O49tj09/cnIiKiZTegA2jGspViYmIwTZPDhw832J6Tk4PD4cDf358h\nQ4Z08OhERERERMTlRz/6ET/60Y/YsmWLxxhX4RYeHg60/vf94cOHA3jMc+2PGRkZSa9evQDo0aMH\nUVFRzcqLiYnpkmu4qLBspfj4eAB27tyJ3W6v1/7GG28AtZuxNra6k4iIiIiIXFkTJtQ+4bJp06YG\nHzXNz89n9+7dGIbBbbfdBrT+9/24uDhM02Tz5s04HI56eW+++SaGYTBz5sw6x+Pj4zFNk02bNtXL\nqa6u5p133sEwDGbMmNHcy+5QKiybUFhYSG5uLoWFhXWOjx8/ntjYWOx2OwsXLnTvWWOaJuvXr2fb\ntm34+vq6N1kVEREREZHOMW/ePAICAvjyyy9ZsmRJnfcVP/vsM+bNm0dlZSWjR492F5at/X0/ISGB\nPn36kJeXx5IlS9xbmFRXV7Ny5UoOHjxIjx49uPfee+vk3XfffYSEhPDJJ5+wcuVKqqurAbh48SKP\nPvooNpuNPn36MG3atCt2n9pC71g24bHHHuPjjz9mzJgxvPrqq3XakpOTmTNnDgcOHGDSpElER0dT\nXFzM2bNnsVgsJCcn079//04auYiIiIiIAPTu3Zvf/e53LF68mA8++IC//e1v9OvXj8rKSk6dOoVh\nGIwYMYL/+Z//qZPXmt/3/fz8WL16NfPmzWPXrl1kZWXRv39/bDYbpaWl+Pn5kZqaSkhISJ28nj17\nsmrVKn7+85/z+uuv895779G7d29OnjzJxYsXCQkJIS0trc6WJl2JZiybwTCMBp9j7t27N++++y5z\n587lhhtu4MSJE1RXVzNx4kReeeUV7rrrrk4YrYiIiIiI/Lsf/OAHbN26lZ/85CeEhYWRm5vL119/\nzfe//32WL1/Oa6+9RmhoaJ2c1v6+P2zYMDIyMrj77rsJCQnh2LFj+Pj4EBcXx6ZNmxg9erTHMb7z\nzjtMmTIFX19fjh49SmBgIDNnzuSdd95xL/LTFRmma3dO6ZK0KqyISF1aFfbqp1Uu69L9qEv3oy7d\nj7q64s91qaUZSxEREREREWkTFZYiIiIiIiLSJiosRUREREREpE1UWIqIiIiIiEibqLAUERERERGR\nNlFhKSIiIiIiIm2iwlJERERERETaRIWliIiIiIiItIkKSxEREREREWkTFZYiIiIiIiLSJiosRURE\nREREpE1UWIqIiIiIiEibWDt7ACIiIiIil8v6tIDXdx6losrRZGyAn5UZt/RjzODwDhiZiHiiwlJE\nREREupS3dh2j+OtLzYotpYote0+qsBTpZCosRURERKRLKa+snak0gB5Bfh7j7GVVmNCsmU0RubJU\nWIqIiIhIl9QjyI81SRM8ti9OzaK0rKoDRyQinmjxHhEREREREWkTFZYiIiIiIiLSJiosRURERERE\npE30jqWIiIiIiHiF4uJiXnrpJbKysigoKADgu9/9Lj/4wQ/42c9+Rs+ePevl3Hbbbe5YTz755BOC\ngoLqHCspKSEtLY3MzEyKiooICQlh1KhRJCYmEhMT47GvwsJCUlNTycrK4ty5c/Ts2ZPx48czf/58\nBgwY0Iqr7hgqLEVERERE5Jr3ySefsHDhQux2O1arlT59+uB0Ojl16hQbNmzg3Xff5eWXX2bgwIHu\nnLKyMgoKCrBarR6LQcMw8PHxqXPs3LlzzJo1C5vNRrdu3Rg0aBBFRUXs3r2bzMxMVqxYwcyZM+v1\nlZuby+zZs7Hb7QQHBzN48GBsNhsZGRns2LGDtLQ0br755va9Me1EhaWIiIiIiFzTLly4wCOPPMKF\nCxeYOHEizz77LNdffz0A+fn5PP744xw4cICHH36Y999/Hz+/2m1ujh49CkBUVBSvv/56s8+3aNEi\n8vPzmTBhAmvWrCE4OBiADRs2kJKSwtNPP82IESPo37+/O8fpdLJgwQLsdjsJCQksX74cPz8/HA4H\nycnJvPbaayxZsoQPPviAkJCQ9ro17UbvWIqIiIiIyDXtnXfe4fz584SFhbFmzRp3UQm1j8KuXbuW\nkJAQ8vPz2blzp7vNVVhePovZlOzsbD7++GO6d+9OSkqKu6gESExMZNq0aTgcDtLT0+vkZWRkkJeX\nR0REBCtXrnQXt1arlaVLlxIbG4vdbmfjxo2tuQVXnApLERERERG5pmVnZ2MYBpMmTaJ79+712nv2\n7MnIkSMBOHLkiPv40aNHMQyD6OjoZp9ry5YtGIbB5MmTCQ0Nrdc+e/ZsTNNk9+7dVFVV1ctLSEjA\naq3/YOmsWbMwTZP333+/2WPpSHoUVkRERERErmkLFy4kLi6Ovn37eowxTROAmpoa97HWzFgeOnQI\ngNjY2Abbhw8fjtVqpby8nCNHjhAbG4tpmu6C1lPeqFGjALDZbBQXFxMeHt7sMXUEFZYiIiIiInJN\nGzp0KEOHDvXYfv78ebKzswH43ve+B9QWmF988QUAYWFhbNy4kQMHDnDhwgXCw8OZOHEi8fHxGIbh\n7sc0TfLz8wHo06dPg+eyWq2EhYVRWFjIqVOniI2Npbi4mIqKCgzD8JgXERGBj48PNTU1nDx5UoVl\nZ6qoqGD9+vVs376d/Px8AgMDuemmm5g7dy633nprq/r84osvePHFF9m/fz9ff/01wcHBxMTEMHfu\nXMaPH9/OVyAiIiIiIu1t5cqVlJeX061bN+68804ATp486S72HnjgAS5dulQnJyMjg40bN7Ju3Tp6\n9eoFQGlpKQ6HA8MwuO666zyeLzQ0lMLCQs6fPw/UriLr0tCWJwAWi4WgoCDsdrs7ryvxmncsy8vL\nue+++1i3bh35+flER0fTvXt39u3bx/z580lNTW1xnx9++CE/+tGP2LZtGxcuXHA/e/3hhx/ywAMP\n8Mc//rG9L0NERERERNpRWloa77//PoZhkJSU5C7sjh075o6JiYnh9ddf5/Dhw+zfv58XXniBXr16\nkZOTw4MPPojD4QBqJ7Jc/P39PZ4zICCgTnx5ebm7zbVoT3PyuhKvmbFctmwZOTk5DBkyhD/84Q/u\nqeOtW7fy5JNPsm7dOmJjY5s9y3jhwgUee+wxqqqqiIuLY8WKFQQFBWGaJuvWrSM1NZU1a9bw/e9/\n3/08tIiISGho/UUjvI3VWvt3bd2LWrofdVmtFlxPFloMo9H7YvkmsKm4q5m+H1dWamoqqamp7sV2\n5s2b526LiIhgzpw5WK1WHn/8cfdxf39/pk6dytChQ5kxYwaff/45b7/9NrNmzcJi+Xbe7vJHZP+d\n631OV8zl+2C2JK8r8YoZS5vNxnvvvYePjw8pKSl1nkeePn06iYmJmKbZolnLv/3tb5SWltKjRw+S\nk5MJCgoCcP+lY/To0UDt0sYiIiIiItJ1OJ1OnnrqKXdRecstt/Db3/62TszIkSP59a9/XaeovFzf\nvn2ZNm0apmmya9cugDorzlZWVno8v6vNNQPZ2ryuxCtmLDMyMnA6ncTGxtbZhNRl9uzZpKenc/Dg\nQYqKirjhhhua7LOoqAiofSm3oWnuoUOH8vHHH1NYWNj2CxARkWtGScmlpoOuca6ZF92LWrofdYWG\nduebSRlqTLPR+1LjWsWzibirmb4fdfXqFdx0UBPKysr4+c9/zr59+zAMgylTprBq1aoGt/hoyuDB\ngwHcC/YEBgbi5+dHdXU1JSUlHvNcba79NC9/H7OkpMQ9aXU5p9NJWVlZnbyuxCtmLA8dOoRhGB6X\n7g0PDycyMhLAvRpUUyIiIgD48ssvG3zG+fjx4wDceOONrRmyiIiIiIi0s6KiImbPnu0uKhMTE1m9\nerXHotI0Taqrqz3259qaxNfXF6h9erFfv37At8Xmv3M4HHz11VcA7u1PwsLCCA4ObjSvsLAQp9NZ\nJ68r8YrCMi8vD4DevXt7jHEVgKdOnWpWn7fffjthYWFcuHCBJ5980v3XA4CXX36Zjz76CD8/P+65\n557WD1xERERERNrFmTNnmDNnDidOnMBqtbJs2TIeffRRj/H33HMPQ4cO5fnnn/cY89lnnwEwYMAA\n97GYmBhM0+Tw4cMN5uTk5OBwOPD392fIkCHu48OHDwfwmOfaHzMyMtK9Cm1X4hWFpWv5Xk9L98K3\n08/NXbq3W7duvPLKKwwZMoTt27dzyy23MGPGDG6++Waef/55+vbty/r16xk0aFDbL0BERERERFqt\nurqaBx98EJvNhp+fH7///e/5yU9+0mjOwIEDcTqd7Nq1q95WIwCnT59m+/btGIbB1KlT3cfj4+MB\n2LlzJ3a7vV7eG2+8AcCUKVPqrAAbFxeHaZps3rzZvcrs5d58800Mw2DmzJnNu+gO5hWFpetR1caW\n/PX398c0zRYt3evv78+IESPw8fGhoqKCo0eP8vXXX2MYRpd87llERERExBv98Y9/5LPPPsMwDJ56\n6ikmT57cZM7999+Pn58fxcXFLFq0iLNnz7rbjh49yrx586ioqGD06NHccccd7rbx48cTGxuL3W5n\n4cKF7kku0zRZv34927Ztw9fXl8TExDrnS0hIoE+fPuTl5bFkyRIuXrwI1BbFK1eu5ODBg/To0YN7\n7723PW5Ju/OKxXssFgtOp7Ndl+49duwY999/PyUlJUyZMoWFCxcSFRXFV199xVtvvcWGDRv42c9+\nxgsvvMCUKVPa5TpERERERKRlqqureeWVV4DabT02b97M5s2bPcZPnDiRBx98kKioKFJSUnjsscfY\nu3cvkyZNom/fvjidTnJzczEMg2HDhjW4s0RycjJz5szhwIEDTJo0iejoaIqLizl79iwWi4Xk5OR6\ni4r6+fmxevVq5s2bx65du8jKyqJ///7YbDZKS0vx8/MjNTWVkJCQ9r1B7cQrCsvAwEDsdnujS/dW\nVVVhGEazl+5dtmwZJSUlTJw4kdWrV7uPR0ZGsmTJEnr27ElycjLPPPMMt956a4MrOzWHa+8i1+eW\n7GF0Jfd28oZ9o0Ska9LPHxERaYnjx49z4cIFDMPA6XS631X0JCoqyv35zjvvZODAgbz88svs27eP\nL7/8koCAAEaOHMm0adPq7V3p0rt3b959913S09PJzMzkxIkTdOvWjYkTJ5KYmOjemvDfDRs2jIyM\nDNLS0sjKyuLYsWMEBwcTFxfHggULuvRrdl5RWIaGhmK32xtd8tf1bmVj72G6nD17loMHD2IYBg8/\n/HCDMffddx/p6emUlpby4Ycfctddd7Vu8CIiIiIi0mo33XQTn3/+eavz+/bty/Lly1ucFxoayhNP\nPMETTzzRoryIiAhWrFjR4vN1Nq8oLAcMGEBeXp7HpXuh9uVbwL08cGMKCgrcnxvaFxNqH7/t27cv\nn376aaPnbYrDUbuEsdVqweGoadEeRldybydv2DdKRLqmlvz8aY/9zkRERKRpXrF4T1NL/hYXF7uL\nxZEjRzbZX2BgoPvzmTNnPMZ9/fXXAK1+DFZERERERORq4BWFZVxcHADZ2dkN7lPpWvJ3zJgxREZG\nNtnfgAEDCAsLA+BPf/pTgzHZ2dnu/TPHjRvXmmGLiIiIiIhcFbyisIyKimLq1Kk4HA6SkpLcBR9A\nRkYGL730EoZh8NBDD9XLtdls5Obm1puZTEpKwjRNXn31VTZs2EBVVZW7bf/+/SxZsgTDMLjrrrvq\nbJgqIiIiIiJyrfGKdywBli5dyvHjxzlx4gTx8fEMHDiQ0tJSCgoKMAyDxYsXNzizOHfuXAoKCpgx\nYwbPPfec+/hPfvITbDYbGzZsICUlhRdffJGoqCjOnz/P6dOnMQyD8ePHs3Llyo68TBERERERkQ7n\nNYVlaGgomzZtYsOGDezYsYPc3FysVitjx45lzpw53H777R5zDcNocH/LRx99lFtvvZXXXnuNgwcP\ncvToUQIDAxk3bhw//OEPSUhIaPa+mCIiIiIiIlcrryksAQICAkhKSiIpKanZOZmZmY22jx492uM+\nNCIiIiIiIt7AK96xFBERERERkStHhaWIiIiIiIi0iQpLERERERERaRMVliIiIiIiItImKixFRERE\nRESkTVRYioiIiIiISJuosBQREREREZE2UWEpIiIiIiIibaLCUkRERERERNpEhaWIiIiIiIi0iQpL\nERERERERaRMVliIiIiIiItIm1s4egIiIiIiISEcoLi7mpZdeIisri4KCAgC++93v8oMf/ICf/exn\n9OzZs15ORUUF69evZ/v27eTn5xMYGMhNN93E3LlzufXWWz2eq6SkhLS0NDIzMykqKiIkJIRRo0aR\nmJhITEyMx7zCwkJSU1PJysri3Llz9OzZk/HjxzN//nwGDBjQ9ptwhWjGUkRERERErnmffPIJ06ZN\n49VXXyUvL4/IyEjCw8M5deoUGzZsYPr06Rw/frxOTnl5Offddx/r1q0jPz+f6Ohounfvzr59+5g/\nfz6pqakNnuvcuXP8+Mc/5tVXX+XcuXMMGjQIwzDYvXs3P/3pT9m8eXODebm5ufzwhz9k8+bNlJeX\nM3jwYKqqqsjIyGDmzJl89NFH7X5f2osKSxERERERuaZduHCBRx55hAsXLjBx4kQ+/PBDtm/fzs6d\nO9mxYwexsbGcPXuWhx9+mKqqKnfesmXLyMnJYciQIezevZvNmzeTmZnJqlWrsFqtrFu3jr///e/1\nzrdo0SLy8/OZMGECe/bs4e233yYrK4tHH30Up9PJ008/TW5ubp0cp9PJggULsNvtJCQkkJWVxZ//\n/GeysrK49957qaysZMmSJZSWll7x+9UaKixFREREROSa9s4773D+/HnCwsJYs2YN119/vbvtu9/9\nLmvXriUkJIT8/Hx27twJgM1m47333sPHx4eUlBTCw8PdOdOnTycxMRHTNOvNWmZnZ/Pxxx/TvXt3\nUlJSCA4OdrclJiYybdo0HA4H6enpdfIyMjLIy8sjIiKClStX4ufnB4DVamXp0qXExsZit9vZuHFj\ne9+edqHCUkRERERErmnZ2dkYhsGkSZPo3r17vfaePXsycuRIAI4cOQLAu+++i9PpZMSIEfTv379e\nzuzZswE4ePAgRUVF7uNbtmzBMAwmT55MaGhog3mmabJ79+46s6OuvISEBKzW+kvhzJo1C9M0ef/9\n91t49R1DhaWIiIiIiFzTFi5cyKpVq5g5c6bHGNM0AaipqQHg8OHDGIZBbGxsg/Hh4eFERkYCtYWr\ny6FDhwA85g0fPhyr1Up5ebm7iDVN0/3ZU96oUaOA2pnU4uJij9fRWVRYioiIiIjINW3o0KFMnz6d\n4cOHN9h+/vx5d3EYHR0NQF5eHgC9e/f22O+NN94IwKlTp4DaAjE/Px+APn36NJhjtVoJCwurk1dc\nXExFRUWjeREREfj4+ABw8uRJj2PqLCosRURERETEq61cuZLy8nICAgK44447gNqVXYEGtyBxue66\n64DawhSgtLQUh8NRp60hrkdkXXmuczV2PovFQlBQUJ28rkSFpYiIiIiIeK20tDTef/99DMMgKSnJ\nXdi5ZhD9/f095vr7+2OapjvW9d9N5QUEBNSJLy8vd7e5Fu1pTl5XUv+tUBEREbliQkPrLxrhbazW\n2r9r617U0v2oy2q1YBi1ny2G0eh9sXwT2FTc1UzfjysrNTWV1NRU92I78+bNc7dZLBacTieG6wvZ\nANd7ma4Yi+XbebuW5LkecW1pXleiwlJERERERLyK0+lk2bJlbNq0CcMwuOWWW/jtb39bJyYwMBC7\n3U5lZaXHfqqqqjAMwz2TePmKs43ludo85fn6+jYrrytRYSkiItKBSkoudfYQOp1r5kX3opbuR12h\nod35ZlKGGtNs9L7UuFbxbCLuaqbvR129egU3HdSEsrIyfv7zn7Nv3z4Mw2DKlCmsWrWq3hYfoaGh\n2O12SkpKPPbletfR9fhsYGAgfn5+VFdXN5rnanPtp3n5+5glJSXudykv53Q6KSsrq5PXlegdSxER\nERER8QpFRUXMnj3bXVQmJiayevXqBveNHDBgAIB7ldeGnD59GoB+/foBtY+ougY7kxoAACAASURB\nVD57ynM4HHz11VcA9O3bF4CwsDCCg4MbzSssLMTpdNbJ60pUWIqIiIiIyDXvzJkzzJkzhxMnTmC1\nWlm2bBmPPvqox/iYmBhM0+Tw4cMNthcXF1NQUADAyJEjm52Xk5ODw+HA39+fIUOGuI+7tkLxlOfa\nHzMyMpJevXo1cqWdQ4WliIiIiIhc06qrq3nwwQex2Wz4+fnx+9//np/85CeN5sTFxQGQnZ3t3m/y\ncm+88QYAY8aMITIy0n08Pj4egJ07d2K32z3mTZkypc4KsHFxcZimyebNm91bllzuzTffxDAMZs6c\n2cTVdg6vKiwrKipYu3Yt8fHxDBs2jHHjxjFv3jz27NnT6j5N0+TPf/4z99xzD2PHjmXYsGH8x3/8\nB88//3yDXyQREREREelYf/zjH/nss88wDIOnnnqKyZMnN5kTFRXF1KlTcTgcJCUlkZeX527LyMjg\npZdewjAMHnrooTp548ePJzY2FrvdzsKFC917VJqmyfr169m2bRu+vr4kJibWyUtISKBPnz7k5eWx\nZMkSLl68CNQWxStXruTgwYP06NGDe++9t62344rwmsV7ysvLmTt3Ljk5Ofj6+hIdHU1JSQn79u3j\no48+IikpiaSkpBb3uWDBAvbv349hGPTt25eePXuSl5fHyy+/zAcffMDrr79OeHj4FboqERERERFp\nTHV1Na+88gpQu63H5s2b2bx5s8f4iRMn8uCDDwKwdOlSjh8/zokTJ4iPj2fgwIGUlpZSUFCAYRgs\nXryYcePG1esjOTmZOXPmcODAASZNmkR0dDTFxcWcPXsWi8VCcnIy/fv3r5Pj5+fH6tWrmTdvHrt2\n7SIrK4v+/ftjs9koLS3Fz8+P1NRUQkJC2vHutB+vKSyXLVtGTk4OQ4YM4Q9/+IO72Nu6dStPPvkk\n69atIzY2lvHjxze7z6effpr9+/dzww03kJqaytChQwE4ceKE+68aTz/9NOnp6VfkmkREREREpHHH\njx/nwoULGIaB0+l0v6voSVRUlPtzaGgomzZtYsOGDezYsYPc3FysVitjx45lzpw53H777Q320bt3\nb959913S09PJzMzkxIkTdOvWjYkTJ5KYmMjo0aMbzBs2bBgZGRmkpaWRlZXFsWPHCA4OJi4ujgUL\nFjBo0KDW34grzCsKS5vNxnvvvYePjw8pKSl1ZhCnT59Obm4u6enppKamNruwzMnJYevWrVitVjZs\n2MD3vvc9d1t0dDTLly9n7ty57Nmzh6+++oqwsLB2vy4REREREWncTTfdxOeff97q/ICAgFY93Rga\nGsoTTzzBE0880aK8iIgIVqxY0aKcrsAr3rHMyMjA6XQyYsSIelPOALNnzwbg4MGDFBUVNavPLVu2\nYBgGM2bMqFNUuowdO5bFixfz61//Gh8fn7ZdgIiIiIiISBfmFTOWhw4dwjAMYmNjG2wPDw8nMjKS\nwsJCsrOzmT59epN97tu3D8Dj9DfgfjZbRERERETkWuYVhaVrBafevXt7jLnxxhspLCxscCnhf1dR\nUYHNZgNqH3u9ePEiGRkZ7N+/H7vdTmRkJPHx8UyYMKFdxi8iIiIiItKVeUVh6Vrit2fPnh5jrrvu\nOgDOnz/fZH+FhYXU1NRgGAaFhYXcc8899R6hfeedd5gyZQqrVq3C19e3DaMXERERERHp2rziHcuK\nigoA/P39Pcb4+/tjmqY7tjGuPWUAHnnkESwWC2lpaRw+fJh//OMf/OY3v6Fbt2785S9/4dlnn237\nBYiIiIiIiHRhXlFYWiy1l2kYhscY0zSbjHFxFZ+maVJdXc3rr7/OpEmT8Pf3JyQkhJ/+9Kf85je/\nwTRNNm3a1KzHa0VERERERK5WXvEobGBgIHa7ncrKSo8xVVVVGIZBQEBAk/1169YNqC1CZ86cyQ03\n3FAvZubMmaSmplJYWMjf/vY3HnjggVaN3Wq11PkcGtq92bmWb4pki2G0KK+z+xYRaYx+/oiIiHQ9\nXjFjGRoaCkBJSYnHGNe7lY29h+nSo0cP9+fBgwd7jIuOjgZwL/QjIiIiIiJyLfKKGcsBAwaQl5dH\nfn6+x5jTp08D0K9fvyb7u/HGGwkICKCyspKqqiqPca79K/38/Fo44m85HDVA7Wylw1FDScmlZufW\nfPN4b41ptiivs/sWEWlMS37+9OoV3BFDEhER8XpeMWMZExODaZocPny4wfbi4mIKCgoAGDlyZJP9\nWSwWhg4dCsCnn37qMe7kyZNA49uciIiIiIiIXO28orCMi4sDIDs7u8GFdN544w0AxowZQ2RkZLP6\nnDZtGqZp8pe//IXi4uJ67R9++CEnT57EYrFwxx13tH7wIiIiIiIiXZxXFJZRUVFMnToVh8NBUlIS\neXl57raMjAxeeuklDMPgoYceqpdrs9nIzc3lzJkzdY7PnDmT6OhoLl26RGJiIv/617/cbf/85z95\n6qmnMAyD//zP/yQsLOzKXZyIiIiIiEgn84p3LAGWLl3K8ePHOXHiBPHx8QwcOJDS0lIKCgowDIPF\nixczbty4enlz586loKCAGTNm8Nxzz7mP+/r68oc//IF58+bxxRdfMHXqVPr3749hGHzxxRcYhsH/\n+3//j8cee6wjL1NERERERKTDeU1hGRoayqZNm9iwYQM7duwgNzcXq9XK2LFjmTNnDrfffrvHXMMw\nGtzf8rvf/S5bt27llVdeYceOHZw6dQqr1cqIESOYMWMGP/7xj917aIqIiIiIiFyrvKawBAgICCAp\nKYmkpKRm52RmZjba7u/vz/z585k/f35bhyciIiIiInJV0nSaiIiIiIiItIkKSxEREREREWkTFZYi\nIiIiIiLSJiosRUREREREpE28avEeERERERERl7feeotnnnmGlStXcvfddzcYc9ttt1FQUNBoP598\n8glBQUF1jpWUlJCWlkZmZiZFRUWEhIQwatQoEhMTiYmJ8dhXYWEhqampZGVlce7cOXr27Mn48eOZ\nP38+AwYMaPlFdhAVliIiIiIi4nVycnJ44YUXGtxW0KWsrIyCggKsVqvHYtAwDHx8fOocO3fuHLNm\nzcJms9GtWzcGDRpEUVERu3fvJjMzkxUrVjBz5sx6feXm5jJ79mzsdjvBwcEMHjwYm81GRkYGO3bs\nIC0tjZtvvrltF36FqLAUERERERGvsn//fn7+859z8eLFRgvLo0ePAhAVFcXrr7/e7P4XLVpEfn4+\nEyZMYM2aNQQHBwOwYcMGUlJSePrppxkxYgT9+/d35zidThYsWIDdbichIYHly5fj5+eHw+EgOTmZ\n1157jSVLlvDBBx8QEhLSyiu/cvSOpYiIiIiIeIWqqirWrl3Lz372M+x2e5PxrsJy4MCBzT5HdnY2\nH3/8Md27dyclJcVdVAIkJiYybdo0HA4H6enpdfIyMjLIy8sjIiKClStX4ufnB4DVamXp0qXExsZi\nt9vZuHFjs8fSkVRYioiIiIjINS8vL48777yTdevWYRgGixYtIjIystGco0ePYhgG0dHRzT7Pli1b\nMAyDyZMnExoaWq999uzZmKbJ7t27qaqqqpeXkJCA1Vr/wdJZs2Zhmibvv/9+s8fSkfQorIiIiIhc\n1exlVSxOzfLYHuBnZcYt/RgzOLwDRyVdTVFREcXFxYwcOZKnnnqKwYMH86c//anRnNbMWB46dAiA\n2NjYBtuHDx+O1WqlvLycI0eOEBsbi2maHDlypNG8UaNGAWCz2SguLiY8vGt9nzulsBw8eHCb+zAM\ng88++6wdRiMiIiIiV6MAPyulVGECpWVVHuNKqWLL3pMqLL3cDTfcwIsvvsitt97arPiamhq++OIL\nAMLCwti4cSMHDhzgwoULhIeHM3HiROLj4+u8o2maJvn5+QD06dOnwX6tVithYWEUFhZy6tQpYmNj\nKS4upqKiAsMwPOZFRETg4+NDTU0NJ0+ebLSw7Ix6q1MKS9M0O+O0IiIiInINmXFLP7bsPUlFlcNj\njL2stvBsLEa8Q58+fTwWbQ05efKku9h74IEHuHTpUp32jIwMNm7cyLp16+jVqxcApaWlOBwODMPg\nuuuu89h3aGgohYWFnD9/HqhdRdalZ8+eDeZYLBaCgoKw2+3uPE86o97qlMLy1VdfbXHOzp07eeON\nN4DaGzV69Oj2HpaIiIiIXEXGDA5vchZycWpWo7OZIp4cO3bM/TkmJoakpCRuuukmKisr2bNnD6tW\nrSInJ4cHH3yQTZs2YbVaqaiocOf4+/t77DsgIADAHV9eXu5ucy3a4ynPbrfXOU9DOqPe6pTCcsyY\nMc2OLSkpYfny5fzlL3/BNE26devGf//3f3PPPfdcwRGKiIhcGaGh3Tt7CJ3Oaq1dO1D3opbuR11W\nqwXXk4UWw2jzfbF801l79NUZ9P3oPBEREcyZMwer1crjjz/uPu7v78/UqVMZOnQoM2bM4PPPP+ft\nt99m1qxZWCzfro3a2DYmrhlFV8zl+2C2JM+Tzqi3uvTiPbt37+aZZ57h3Llz7qr52WefpXfv3p09\nNBERERERuYaNHDmSkSNHemzv27cv06ZNY9OmTezatYtZs2bRvfu3fwCorKz0mOtqc81c/nuer69v\ns/Laqj3rrS5ZWJaWlrJ8+XK2b9/urpofffRR7r333s4emoiISJuUlFxqOuga55p50b2opftRV2ho\nd1yvh9WYZpvvS803nbVHX51B34+6evUKbjqoA7kWyXEt2BMYGIifnx/V1dWUlJR4zHO1XX/99QB1\n3scsKSkhKCioXo7T6aSsrKxOXmtdiXqry+1j+de//pW77rrLfZGjR49m69atKipFRERERKRDmaZJ\ndXW1x/aamhoA9wyjYRj069cP+LbY/HcOh4OvvvoKqJ31hNpVZ4ODgxvNKywsxOl01slrjStVb3WZ\nwtJut/PLX/6SpKQkzp49S0BAAEuXLuV///d/9eiriIiIiIh0qHvuuYehQ4fy/PPPe4xxbccxYMAA\n97GYmBhM0+Tw4cMN5uTk5OBwOPD392fIkCHu48OHDwfwmOfaHzMyMtK9Cm1LXOl6q0sUln/729+4\n66672LZtm2YpRURERESk0w0cOBCn08muXbvqbTUCcPr0abZv345hGEydOtV9PD4+HqhdZdVut9fL\nc628OmXKlDorwMbFxWGaJps3b8bhqL89zptvvolhGMycObPF19IR9VanvmN54cIFVq5cydatW93P\n9i5ZsoQ5c+Z05rBERERE5ArJ/ry40b0nLYZBSZnnRU9EOsr999/PO++8Q3FxMYsWLeLZZ5/lO9/5\nDgBHjx5l0aJFVFRUMHr0aO644w533vjx44mNjeXAgQMsXLiQ3//+91x//fWYpsmGDRvYtm0bvr6+\nJCYm1jlfQkIC69evJy8vjyVLlvDcc88RGBhIdXU1q1at4uDBg4SEhLSoGOzIeqvTCssPP/yQ3/zm\nN5w5cwbTNPn+97/Pc889p8deRURERK5hW/aepPjr5i1EE+DXJdeZFC8RFRVFSkoKjz32GHv37mXS\npEn07dsXp9NJbm4uhmEwbNgwUlNT6+UmJyczZ84cDhw4wKRJk4iOjqa4uJizZ89isVhITk6mf//+\ndXL8/PxYvXo18+bNY9euXWRlZdG/f39sNhulpaX4+fmRmppKSEhIs8bf0fVWp/zb+qtf/Yp3330X\n0zSxWCzcc8893HfffQDYbLZm96MiVEREROTq4pqpNIAeQfU3gnftO+nn68OMW/p15NBE6rnzzjsZ\nOHAgL7/8Mvv27ePLL78kICCAkSNHMm3atHp7V7r07t2bd999l/T0dDIzMzlx4gTdunVj4sSJJCYm\nMnr06AbPN2zYMDIyMkhLSyMrK4tjx44RHBxMXFwcCxYsYNCgQc0ad2fUW4bp2mWzAw0aNKjJTT2b\nYhiG+2XZa1l1de3KT1arBYejpkVLTS9OzaK0rIqQID/WJE1o13Fdyb5FRBrTkp8/XW1ZeoAzZy50\n9hA6nbZPqMvb7kdT/w639/3oqr+zNPVIsIvFMOjmb2X6zX0ZMzi8g0bXdXXFn+tdUWfUW532fEFb\n69lOqIdFRERERNpFSx4JPn+hki17T6qwlBbp6HqrUwrLv/71r51xWhERERGRLqGpR4Jd7BerME2a\nnNkUuVxn1FudUljeeOONnXFaEREREZEupUcTj+g+uu4jzl/QKrnSMp1Rb3WJfSxFRERERETk6uVV\nhWVFRQVr164lPj6eYcOGMW7cOObNm8eePXva7RyFhYXExsYyaNAgCgoK2q1fERERERGRrsprNgcq\nLy9n7ty55OTk4OvrS3R0NCUlJezbt4+PPvqIpKQkkpKS2nyeX/3qV1y8eLHNqzCJiIiIiIhcLbxm\nxnLZsmXk5OQwZMgQdu/ezebNm8nMzGTVqlVYrVbWrVvH3//+9zad47XXXuMf//hHO41YRERERETk\n6uAVhaXNZuO9997Dx8eHlJQUwsO/Xap5+vTpJCYmYpomqamprT7Hl19+yerVq+nWrVt7DFlERERE\nROSq4RWFZUZGBk6nkxEjRtC/f/967bNnzwbg4MGDFBUVtbh/0zR54oknqKioYMmSJW0er4iIiIiI\nyNXEKwrLQ4cOYRgGsbGxDbaHh4cTGRkJQHZ2dov737BhA4cOHSIhIYHbbrutTWMVERERERG52nhF\nYZmXlwdA7969Pca49no5depUi/o+ceIEa9euJTw8nF//+tetHqOIiIiIiMjVyisKy3PnzgHQs2dP\njzHXXXcdAOfPn292v06nk8cff5zq6mqWL19OcHBw2wYqIiIiIiJyFfKKwrKiogIAf39/jzH+/v6Y\npumObY60tDQ+++wzZsyYwcSJE9s8ThERERERkauRV+xjabFYcDqdje4taZomQLP3n/y///s/Xnzx\nRSIiInjyySfbZZwNsVotdT6HhnZvdq7lm2uxGEaL8jq7bxGRxujnj4iISNfjFTOWgYGBAFRWVnqM\nqaqqwjAMAgICmuyvqqqKxx9/HKfTyYoVKwgKCmq3sYqIiIiIiFxtvGLGMjQ0FLvdTklJiccY17uV\njb2H6fL73/+eL774grvvvpsJEybUaXPNfLYXh6MGqJ2tdDhqKCm51Ozcmm/GUmOaLcrr7L5FRBrT\nkp8/vXrp3XcREZGO4BWF5YABA8jLyyM/P99jzOnTpwHo169fk/395S9/wTAM3n77bd5++22Pca6t\nR5KSkkhKSmrhqEVERERERK4OXlFYxsTEkJmZyeHDhxtsLy4upqCgAMMwGDlyZJP9DR8+nIiIiAbb\nqqqqOHLkCADDhg3Dz8/PvUemiIiIiIjItcgrCsu4uDjWrFlDdnY2p06dom/fvnXa33jjDQDGjBnT\nrCLwd7/7nce206dPM3nyZKD2kVkVlSIiIiIicq3zisV7oqKimDp1Kg6Hg6SkJPLy8txtGRkZvPTS\nSxiGwUMPPVQv12azkZuby5kzZzpyyCIiIiIicoW99dZbDBo0qNHX2yoqKli7di3x8fEMGzaMcePG\nMW/ePPbs2dNo3yUlJTz77LPcfvvtDB06lJtvvplHHnmETz/9tNG8wsJCfv3rXzNx4kSGDh3Krbfe\nyuOPP86//vWvVl1jR/GKGUuApUuXcvz4cU6cOEF8fDwDBw6ktLTU/Qjs4sWLGTduXL28uXPnUlBQ\nwIwZM3juuec6YeQiIiIiItLecnJyeOGFFxrdbrC8vJy5c+eSk5ODr68v0dHRlJSUsG/fPj766COP\na6mcO3eOWbNmYbPZ6NatG4MGDaKoqIjdu3eTmZnJihUrmDlzZr283NxcZs+ejd1uJzg4mMGDB2Oz\n2cjIyGDHjh2kpaVx8803t+t9aC9eMWMJtSvDbtq0iYcffph+/fqRm5tLaWkpY8eOZe3atcyfP99j\nrmEYzd7f8vIcERERERHpevbv389//dd/cfHixUbjli1bRk5ODkOGDGH37t1s3ryZzMxMVq1ahdVq\nZd26dfz973+vl7do0SLy8/OZMGECe/bs4e233yYrK4tHH30Up9PJ008/TW5ubp0cp9PJggULsNvt\nJCQkkJWVxZ///GeysrK49957qaysZMmSJZSWlrbrvWgvXjNjCRAQENDiFVozMzNbdI4bb7yRo0eP\ntnRoIiIiIiJyhVVVVfHiiy+Snp5OTU1No7E2m4333nsPHx8fUlJSCA8Pd7dNnz6d3Nxc0tPTSU1N\nZfz48e627OxsPv74YwIDA0lJSSE4+NutrxITEzl27Bjbtm0jPT2d559/3t2WkZFBXl4ekZGRrFy5\nEqu1tlSzWq0sXbqUzz//nIMHD7Jx40Z+8YtftNctaTdeM2MpIiIiIiLeKy8vjzvvvJN169ZhGAaL\nFi1qdKHNjIwMnE4nI0aMoH///vXaZ8+eDcDBgwcpKipyH9+yZQuGYTB58mRCQ0MbzDNNk927d1NV\nVVUvLyEhwV1UXm7WrFmYpsn777/fouvuKF41YykiIiJtl/15MVv2nqSiytFkbICflRm39GPM4PAm\nY0VErqSioiKKi4sZOXIkTz31FIMHD+ZPf/qTx/hDhw5hGAaxsbENtoeHhxMZGUlhYSHZ2dlMnz7d\nnQd4zBs+fDhWq5Xy8nKOHDlCbGwspmm6tyz0lDdq1Cigdia1uLi4zgxqV6DCUkRERFpky96TFH99\nqVmxpVSxZe9JFZYi0uluuOEGXnzxRW699dZmxbt2kujdu7fHmBtvvJHCwkJOnToFgGma5OfnA9Cn\nT58Gc6xWK2FhYe682NhYiouLqaiowDAMj3kRERH4+PhQU1PDyZMnVViKiIjI1c01U2kAPYL8PMbZ\ny6owL4sXEelMffr08Vi0NeTcuXMA9OzZ02PMddddB8D58+cBKC0txeFwYBiGu60hoaGhFBYWuvNc\n52rsfBaLhaCgIOx2uzuvK1FhKSIiIq3SI8iPNUkTPLYvTs2itKzKY7uISFdWUVEBgL+/v8cYf39/\nTNN0x7r+u6m8gICAOvHl5eXuNj8/z3+wCwgIwG631zlPV6HCUkREpAOFhnbv7CG0meWbLbUshtHo\n9XiKs1pr1w68Fu5Fe/C2+9HU96e970dzv68drbnjcu1g19XG7w0sFgtOp7PRbQRN0wS+3WrQYvl2\nbdSW5Pn4+LQqryvRqrAiIiIiIiL/JjAwEIDKykqPMVVVVRiG4Z6B7N792+K/sTxXW1vzuhLNWIqI\niHSgkpLmLXrTldV88xfzGtNs9Ho8xblmXa6Fe9EevO1+NPX9ae/70dzva0dr7ri+Cety4+8svXoF\nNx3UTkJDQ7Hb7ZSUlHiMcb3r6HovMjAwED8/P6qrqxvNc7Vdf/31AHXexywpKSEoKKhejtPppKys\nrE5eV6IZSxERERERkX8zYMAAAPcqrw05ffo0AP369QNqH1F1ffaU53A4+OqrrwDo27cvAGFhYQQH\nBzeaV1hYiNPprJPXlaiwFBERERER+TcxMTGYpsnhw4cbbC8uLqagoACAkSNHNjsvJycHh8OBv78/\nQ4YMcR8fPnw4gMc81/6YkZGR9OrVq+UXdIWpsBQREREREfk3cXFxAGRnZ7v3qbzcG2+8AcCYMWOI\njIx0H4+Pjwdg586d2O12j3lTpkypswJsXFwcpmmyefNmHI762zS9+eabGIbBzJkzW39RV5AKSxER\nERERkX8TFRXF1KlTcTgcJCUlkZeX527LyMjgpZdewjAMHnrooTp548ePJzY2FrvdzsKFC917VJqm\nyfr169m2bRu+vr4kJibWyUtISKBPnz7k5eWxZMkSLl68CEB1dTUrV67k4MGD9OjRg3vvvfcKX3nr\naPEeERERERGRBixdupTjx49z4sQJ4uPjGThwIKWlpRQUFGAYBosXL2bcuHH18pKTk5kzZw4HDhxg\n0qRJREdHU1xczNmzZ7FYLCQnJ9O/f/86OX5+fqxevZp58+axa9cusrKy6N+/PzabjdLSUvz8/EhN\nTSUkJKSjLr9FNGMpIiIiIiLSgNDQUDZt2sTDDz9Mv379yM3NpbS0lLFjx7J27Vrmz5/fYF7v3r15\n9913mTt3LjfccAMnTpygurqaiRMn8sorr3DXXXc1mDds2DAyMjK4++67CQkJ4dixY/j4+BAXF8em\nTZsYPXr0lbzcNtGMpYiIiIiIeKXMzMwmYwICAkhKSiIpKalFfYeGhvLEE0/wxBNPtCgvIiKCFStW\ntCinK9CMpYiIiIiIiLSJCksRERERERFpExWWIiIiIiIi0iYqLEVERERERKRNVFiKiIiIiIhIm6iw\nFBERERERkTZRYSkiIiIiIiJtosJSRERERERE2kSFpYiIiIiIiLSJCksRERERERFpExWWIiIiIiIi\n0ibWzh5AR6qoqGD9+vVs376d/Px8AgMDuemmm5g7dy633nprq/rMycnhlVde4eDBg5w5c4aAgAC+\n973vcddddzFr1ix8fX3b+SpERERE2kf258Vs2XuSiipHo3EBflZm3NKPMYPDO2hkInK18ZrCsry8\nnLlz55KTk4Ovry/R0dGUlJSwb98+PvroI5KSkkhKSmpRn6+88gqrVq3CNE13QXn+/Hk+/fRTDh8+\nzLZt23j55ZcJDAy8QlclIiIi0npb9p6k+OtLTcaVUsWWvSdVWIqIR15TWC5btoycnByGDBnCH/7w\nB8LDa38wbt26lSeffJJ169YRGxvL+PHjm9XfwYMHSU5OBuC//uu/eOSRR9yzk9nZ2fzyl78kJyeH\np556itWrV1+ZixIRERFpA9dMpQH0CPJrMMZeVoV5Wax0DntZFYtTsxqN0cyydCavKCxtNhvvvfce\nPj4+pKSkuItKgOnTp5Obm0t6ejqpqanNLiw3bNiAaZpMnjyZJUuW1GkbM2YMycnJPPDAA2zfvp3H\nHnuszjlFREREupIeQX6sSZrQYNvi1CxKy6o6eETi0s3fyvkLlZjQ5P8OmlmWzuQVhWVGRgZOp5PY\n2Fj69+9fr3327Nmkp6dz8OBBioqKuOGGG5rsMzs7G8MwuOuuuxpsHz9+cdDRlAAAIABJREFUPIGB\ngVy6dIkjR46osBQRERHpwrrq+6az7vj/eGvXMS5WVDcap5ll6WxeUVgeOnQIwzCIjY1tsD08PJzI\nyEgKCwvJzs5m+vTpjfZnmia/+93vKCws9NinaZruzzU1Na0fvIiIiIhccV31fdMJMZFMiImkpKTx\nsWlmWTqbVxSWeXl5APTu3dtjzI033khhYSGnTp1qsj/DMJgwoeHHRVz27NnDxYsXMQyD733vey0a\nr4iIiIh0LL1vKtI2XlFYnjt3DoCePXt6jLnuuusAOH/+fJvPd+nSJffCPsOGDWvw8VsRERER6Xr0\nvqlI63hFYVlRUQGAv7+/xxh/f39M03THtlZ1dTW/+MUvOHnyJFarlSeffLJN/YmIiIiISPu47bbb\nKCgoaDTmk08+ISgoyP3PJSUlpKWlkZmZSVFRESEhIYwaNYrExERiYmI89lNYWEhqaipZWVmcO3eO\nnj17Mn78eObPn8+AAQPa7Zq6Cq8oLC0WC06nE8MwPMa43olsLKYplZWVPPLII+zduxfDMPjlL3/J\niBEjWt2fiIiIiIi0j7KyMgoKCrBarR4LQsMw8PHxcf/zuXPnmDVrFjabjW7dujFo0CCKiorYvXs3\nmZmZrFixgpkzZ9brJzc3l9mzZ2O32wkODmbw4MHYbDYyMjLYsWMHaWlp3HzzzVfsWjuDVxSWgYGB\n2O12KisrPcZUVVVhGAYBAQGtOsfXX3/NQw89xKeffophGDz88MPMnTu3tUN2s1otdT6HhnZvdq7l\nmyLZYhgtyuvsvkVEGqOfPyIinjW136U373V59OhRAKKionj99deblbNo0SLy8/OZMGECa9asITg4\nGKjdejAlJYWnn36aESNG1Hn1zel0smDBAux2OwkJCSxfvhw/Pz8cDgfJycm89tprLFmyhA8++ICQ\nkJD2v9BOYmk65OoXGhoK1E5je+J6t7Kx9zA9+de//sXdd9/Np59+isVi4Ve/+hVJSUmtG6yIiIiI\nSAsF+NXOF7n2u/T0n+KvL7Fl78nOHWwncRWWAwcObFZ8dnY2H3/8Md27dyclJcVdVAIkJiYybdo0\nHA4H6enpdfIyMjLIy8sjIiKClStX4udXuxiU1Wpl6dKlxMbGYrfb2bhxY/tcWBfhFTOWAwYMIC8v\nj/z8fI8xp0+fBqBfv34t6nv//v088sgj2O12AgICeOGFF7jjjjvaNN7LORy1W5VYrRYcjpoml5q+\nXM03j/fWmGaL8jq7bxGRxrTk50+vXsGNtouIXCtm3NKvyX04vX1V26NHj2IYBtHR0c2K37JlC4Zh\nMHnyZPdE1eVmz57Ne++9x+7du6mqqnIXkK68hIQErNb65dasWbM4cOAA77//Pr/4xS/adlFdiFcU\nljExMWRmZnL48OEG24uLiykoKMAwDEaOHNnsfrOzs3nwwQepqKjguuuu48UXX2T48OHtNWwRERER\nkWYZMzi8ycdbvX1V25bOWB46dAjA4771w4cPx2q1Ul5ezpEjR4iNjcU0TY4cOdJo3qhRowCw2WwU\nFxcTHn5tPJbsFY/CxsXFAbWFYEP7VL7xxhsAjBkzhsjIyGb1abPZePjhh6msrCQyMpK33npLRaWI\niIiISBdUU1PDF198AUBYWBgbN27kkUce4f777+fxxx9n+/bt7sU8oXZhT9fTjn369GmwT6vVSlhY\nGIC7xiguLnbvMuEpLyIiwr1A0MmT185jyV4xYxkVFcXUqVPZtm0bSUlJpKWluf+HzsjI4KWXXsIw\nDB566KF6uTabjerqaoKDg+nVq5f7+NKlS7lw4QLdunUjPT2dqKioDrseERERka4m+/PiJh/FhNrH\nMUU62smTJ6moqMAwDB544AEuXar7KkVGRgYbN25k3bp19OrVi9LSUhwOB4ZhuPe7b0hoaCiFhYXu\n9VrOnTvnbvO0dovFYiEoKAi73e7OuxZ4RWEJtYXg8ePHOXHiBPHx8QwcOJDS0lL3I7CLFy9m3Lhx\n9fLmzp1LQUEBM2bM4LnnngPgn//8J/v37wdq97985plnGj33Qw89xC233NLu1yQiIiLSVWzZe5Li\nr5u/7oJrsRmRjnDs2DH355iYGJKSkrjpppuorKxkz549rFq1ipycHB588EE2bdpUZ297f39/j/26\ndpRwxZeXl7vbXO9cesqz2+11znO185p/o0NDQ9m0aRMbNmxgx44d5ObmYrVaGTt2LHPmzOH222/3\nmGsYRp39LT/++GP3P5eWlrqfv/bk8r9ciIiIiFyLXDOVBtAjyPMv1PDtlhciHSUiIoI5c+ZgtVp5\n/PHH3cf9/f2ZOnUqQ4cOZcaMGXz++ef8/+3deVxU1f8/8NeFAURAEVNyBQGZYXElKTRFcPkgouaW\noKKVgJpWan0zzY+f3CstS4lMxcxcSE3AhTSVXNASd0xxBWNAGAWBAWSb4fz+4De3GZiBAWZY38/H\ng8cD7r3nzrmHM3fue8528OBBeHt788dUtc69ovus4hjlNTBrkq45aDGBJVD+zcD8+fNrtBRIbGxs\npW1vv/023n77bV1mjRBCSAvRHNbe1HYtUU3HKdZobg5loQsNWR7a/C9r+v+2tDBB+Ke1nyFf1+VR\n1/pam3PpMl+6LI+WvA5wv379qpyk09bWFmPGjMH+/ftx8uRJ+Pn58fuKi4s1plPsU7Rctm7dWmWf\nkZGRVumagxYVWBJCCCGEEKJvcTefIOLkPRQWax5vmpOvOVghDcPJyQkAkJqaCjMzMxgbG6O0tBQ5\nOTka0yj2tW/fHgBUxmPm5OTA3Ny8Uhq5XI78/HyVdM0BBZaEEEJIPWoOa/9qu5aopuMULSXNoSx0\noSHLQ5v/ZV3/3zWl6/LQZf61PdeeE3e1Hm9qbGRY5bl0WR7NYR3yuqxPzBiDTCbT2IpYVla+fryR\nkRE4jkOPHj1w//59pKam4tVXX610vEwmw9OnTwGUt3gC5TPOWlhYID8/H6mpqejatWuldOnp6ZDL\n5eA4jk/XHLSI5UYIIYQQQgipL8rjTduaG2v8sbZqTWNN68m0adPg6uqKL7/8UuMxd+7cAQA4ODgA\nKJ/khzGGGzduqD0+ISEBMpkMJiYmcHZ25rcrliDUlE4xP0vnzp1VVp1o6qjFkhBCCCGEkBqQ5pdg\nYWhclfuB8kmMNs5/vb6yRarg6OiIq1ev4uTJk1i4cKHKWEgASEtLQ0xMDDiOw+jRowEAo0aNwv79\n+3HixAn83//9H9q0aaOSZu/evQAAX19flRlgfXx8cOHCBRw6dAhBQUEQCFRDrn379oHjOEyYMEEf\nl9pgqMWSEEIIIYQQLSiWSGEAcvNLNP6wCseThvfWW2/B2NgYEokECxYsQGZmJr/v7t27mDVrFoqK\nijBgwACMGFE++ZSHhwfc3NwglUrx7rvv8is9MMawbds2HD16FEZGRggKClJ5rXHjxqF79+5ISUnB\nokWLUFBQAAAoLS3F6tWrce3aNbRp0wbTp0+vp6uvH1TbCSGEEEII0cL4wT0QeT6Z7+paFVpSpXGx\nsbHBhg0b8PHHH+P8+fPw8vKCra0t5HI5kpKSwHEcevXqhdDQUJV0n3/+OQIDA3H16lV4eXmhZ8+e\nkEgkyMzMhIGBAT7//HPY2dmppDE2NsZXX32FWbNm4eTJk4iLi4OdnR3EYjFyc3NhbGyM0NBQtG3b\ntj6LQO8osCSEEEKIXlXsNqhY8kAxkYjiAdzdybpB8keIttydrKmeNmEjR46Eo6MjduzYgYsXL+Kf\nf/5Bq1at0K9fP4wZMwb+/v4wMFDt0NmtWzdERUVhy5YtiI2NxYMHD2BqagpPT08EBQVhwIABal+r\nV69eiI6ORlhYGOLi4nDv3j1YWFjAx8cHc+bMgUgkqo9LrlcUWBJCCCFEL1oZC5CLEr7boCa5KEHk\n+WR6YCeE6J2trS1WrlxZozSWlpb45JNP8Mknn9QoXadOnbBq1aoapWnKKLAkhBBCiF5o6jao3GIp\n/f/j0bTpWkgI0Y3qJh8CqCcBqTkKLAkhhBCiF5q6DSqvy7cwNK7K1syaiE+U1Hj8W1N8aNb2Opvy\nNeqDtjO5Nmfa9iIAynsSHL+UQvWHaI0CS0IIIYQ0C5Hnk7VelL4pd7/V9jqb8jXqUk2CKcXxzVVN\nJx/yebV7PeSKNBfN951DCCGEkBZFeVH6NubGGo9r6t1vtbnOpn6NukQzuf6LJh8i+kSBJSGEEEKa\nleoWpddl99uGVNV1Npdr1AUKpgipHxRYEkIIIYTUUksZ10kIIdWhwJIQQgghpJZayrhOQgipDgWW\nhBBCCCG11FLGdRJCSHUosCSEEEIIqaOWMq6TEEI0MWjoDBBCCCGEEEIIadoosCSEEEIIIYQQUicU\nWBJCCCGEEEIIqRMKLAkhhBBCCCGE1AkFloQQQgghhBBC6oRmhSWEEEKasPhECSLPJ2u1jEUrYwHG\nD+5B6yiSWpHml2BhaFyV+wkhLRcFloQQQkgTFnk+GZLnL7Q6NhcliDyfTIElqZFWxgLkonwdTm2W\nTGllTI+XhLRE9M4nhBBCmjBFSyWH8rUUNZHmlwcG2rRskoahbetzfbcMjh/co8at4oSQlocCS0II\nIaQZaGNujI3zX9e4f2FonFatTaTh1KT1Gai/lkF3J2tq5SaEVIsCS0IIIYSQeqJunKIBxwEAcvKK\nAVTf+gxQyyAhpPFpUYFlUVERtm3bhpiYGKSmpsLMzAwuLi6YOXMmhgwZUqtzpqenIzQ0FHFxccjK\nyoKVlRU8PDwQEhICe3t7HV8BIYQQQpqimoxT7GjVGutCXtPJ69KEO4T8Sx+xAPlXiwksCwsLMXPm\nTCQkJMDIyAg9e/ZETk4OLl68iAsXLmD+/PmYP39+jc6ZlJSEgIAASKVSWFhYwMnJCWKxGNHR0Th+\n/DjCwsIwaNAgPV0RIYQQQpqKqsYpKlosyxjTWUskTbhDiCp9xAJEVYu5i6xYsQIJCQlwdnbG999/\nD2vr8rEChw8fxtKlS/Hdd9/Bzc0NHh4eWp1PLpdjzpw5kEqlGDduHFauXAljY2PIZDJ8/vnn2L17\nNxYtWoTff/8dbdu21eelEUIIIaQWqmvN0+XyLFWNU7S0bA0AyMnRfnxldWjCHUJU6ToWIJW1iMBS\nLBbjyJEjMDQ0xIYNG/iKBABjx45FUlIStmzZgtDQUK0rU3R0NFJSUtC5c2esXr0aAkF5UQoEAixb\ntgyJiYm4du0adu7ciQ8++EAv10UIIYSQmtO2Na8pL89CE+4Q8i99xAKkshYRWEZHR0Mul8PNzQ12\ndnaV9gcEBGDLli24du0aMjIy8PLLL1d7zsjISHAch3HjxvFBpTJ/f39cvXoVx44do8CSEEIIqUZ1\nrYeA7loQtWnNo+VZCGk+9BELkMpaRGB5/fp1cBwHNzc3tfutra3RuXNnpKenIz4+HmPHjq3yfIwx\n3Lp1CwA0nrN///4Ayr8hkUgkKt+MEEIIIaRcTcYC6qoFUZvWPFqehZDmQ9exAFGvRQSWKSkpAIBu\n3bppPKZLly5IT0/H48ePqz2fRCJBUVEROI5D9+7d1R7TqVMnGBoaoqysDMnJyRRYEkIIaRTqc1yh\nNrQdC0gtiISQ2tJ1LEDUaxGBZVZWFgDAyspK4zHt2rUDAGRnZ2t9vqrOaWBgAHNzc0ilUq3OSQgh\nhOhTTcYVbom+jX2nH2g8RpdLVGg7FlDRglhVYKyPpTNouQ5Cmj5dxwJEvRYRWBYVFQEATExMNB5j\nYmICxhh/bFUKCwv5342NNS9g3KpVK0ilUq3OSQghhOiTNi2DygFnY1uioiZdZnWRL1qug5DmQ9ex\nAFGvRdwFDQwMIJfLwf3/daLUYYwBQJXHKBgaGvK/6+qchBBCiD5p0zIYnyhptEtUaNtlVlf5ouU6\nCGk+dB0LEPVaRGBpZmYGqVSK4uJijceUlJSA4zi0atWq2vO1bt2a/724uBhGRkZqj1O8njbn1MTI\nyFDl9w4dLLROu3vFqFq/bkOemxBCqtLU7z81uY9rQ5flMbqDBUYPcdDZ+apTk7Ko77zV9+sBuq8b\nTR2Vhyoqj9rTdSxA1DNo6AzUB0tLSwBATk6OxmMU/amr6nutoOiDXdU55XI58vPzAQDt27fXOq+E\nEEIIIYQQ3dF1LEDUaxGBpb29PQAgNTVV4zFpaWkAgB49qu/K0rFjR1hYWFR5zvT0dMjlcgCAra1t\nTbJLCCGEEEII0RFdxwJEvRYRWPbp0weMMdy4cUPtfolEgidPngAA+vXrp9U5e/fuDQAaz3n9+nUA\nQOfOndGhQ4eaZpkQQgghhBCiA/qIBUhlLSKw9PHxAQDEx8erXZtm7969AAB3d3d07txZ63MyxnDo\n0CHIZJUH9u/btw8cx2HChAm1zzghhBBCCCGkTvQRC5DKWkRgaWNjAz8/P8hkMsyfP59fJBUAoqOj\nER4eDo7jMHfu3EppxWIxkpKS8OzZM5Xt48aNQ/fu3ZGSkoJFixahoKAAAFBaWorVq1fj2rVraNOm\nDaZPn67fiyOEEEIIIYRoVJdYgGiPY4q5dZu5nJwczJgxAw8ePICBgQEcHR2Rm5uLJ0+egOM4LFy4\nECEhIZXSeXt748mTJxg/fjzWrVunsu/WrVuYNWsW8vLyYGpqCjs7O4jFYuTm5sLY2Bjh4eEYMGBA\nfV0iIYQQQgghRI3axgJEey0msATKF0fdvn07jh8/DrFYDIFAAFdXVwQGBmL48OFq03h7eyM9PR3j\nx4/H2rVrK+1PT09HWFgY4uLikJmZCQsLC7i7u2POnDkQiUT6viRCCCGEEEKIFmoTCxDttajAkhBC\nCCGEEEKI7rWIMZaEEEIIIYQQQvSHAktCCCGEEEIIIXVCgSUhhBBCCCGEkDqhwJIQQgghhBBCSJ0I\nGjoDLUFRURG+//577Nu3D7m5uSr7OI6DmZkZevfujfbt2+P27dtITU2FmZkZXFxcMHPmTAwZMkTt\neRMSEvDVV1/h+vXrKC4uBgAYGRmhV69eeO+99/Dee++hoKAAsbGxtNgrIYToSFFREbZt24aYmBit\n79fVSU9PR2hoKOLi4pCVlQUrKyt4eHggJCQE9vb2Or4C3dJ1eaSlpWHYsGFVHuPk5ITIyMjaZrne\nRURE4LPPPsPq1asxadKkGqfPyclBWFgYYmNjkZGRgbZt26J///4ICgpCnz599JBj/apLechkMvTt\n2xcymUzjMW3atEF8fHxds6k3EokE4eHhiIuLw5MnTwAAXbt2xdChQ/HOO+/AysqqRudr6vVDl+XR\nHOpHU0azwupZYWEhAgMDcevWLa2ONzQ0hEgkQk5ODtLT08EYw/z58zF//nyV43766SesW7cOVf37\nOI4Dx3E4ffo0BZaEEKIDhYWFmDlzJhISEmBkZISePXtWe7+uTlJSEgICAiCVSmFhYQEbGxuIxWLk\n5OTAxMQEYWFhGDRokJ6uqG70UR6nT5/GvHnzYGlpqTGotrW1xZo1a3RxCXqXkJCAt99+Gy9evMCq\nVatqHEhlZWXB398fYrEYpqamsLe3R0ZGBrKysmBgYIBVq1ZhwoQJesq97tW1PO7evYs33ngDpqam\ncHZ2VnuMubk5fvjhB11kV+euXLmCd999F1KpFAKBAN27d4dcLkdaWhpkMhleeukl7NixA46Ojlqd\nr6nXD12XR1OvH00eI3q1ePFi5ujoyP+88sorTCgUMm9vbyYSiVT2OTo6MpFIxC5evMgYYyw6Opq5\nuLiobGOMsatXrzKhUKg2bVRUFHNxcVHZlpaW1lCXTwghzcrixYuZUChk48ePZxkZGfx2Tffr6shk\nMjZixAgmEonY4sWLWXFxMWOMsdLSUrZq1SomFAqZu7s7y8nJ0fm16IKuy4MxxkJDQ5lQKGSfffaZ\nrrNb7/766y/m7u7OhEIhE4lE7MCBAzU+x/Tp05lIJGKzZs1iUqmU375t2zYmFAqZq6sre/TokS6z\nrTe6KI/IyEgmFApZcHCwHnKoX1KplL322mtMJBKxkJAQlpmZye8Ti8Vs6tSpTCgUsuHDh/P3guo0\n5fqhj/JoyvWjOaAxlnokFotx+PBh/m8DAwMUFBTA0NAQ27Ztw+zZs1WO79ChAxhjCA0NBQCMHTsW\nQUFBKtsAYPv27SotlQYGBuA4DgBgbW0NA4N//62MGqQJIUQnxGIxjhw5AkNDQ2zYsAHW1tb8Pk33\n6+pER0cjJSUFnTp1wurVq2FsbAwAEAgEWLZsGdzc3CCVSrFz505dX06d6aM8AODevXvgOE7rForG\nqKSkBJs3b8Y777wDqVRa6/PEx8fj8uXLaN26NTZs2AALCwt+X1BQEMaMGQOZTIYtW7boItt6o6vy\nAMrrB4AmWT9+/fVXZGdno2PHjti4cSPat2/P7+vatSs2b96Mtm3bIjU1FSdOnKj2fE29fui6PICm\nXT+aAwos9Sg6OhpyuZz/++WXX0ZZWRn69u0LOzs7BAQEqByfmZkJALh27RoyMjIAgD9GedvFixf5\nNObm5li+fDmA8iDyyy+/RGlpKR9cUmBJCCG6obinK+7hFam7X1cnMjISHMdh3LhxEAgqT3vg7+8P\nxhiOHTtWt8zrgT7KAyjvygYAPXv21E1G61lKSgpGjhyJ7777DhzHYcGCBbUejqKoH8OGDYOlpWWl\n/QEBAWCM4dSpUygpKalr1vVCl+UBlNcPjuOaZP2Ij48Hx3Hw8vJC69atK+23srJCv379AECrIVRN\nvX7oujyApl0/mgMKLPXo+vXr/O8cx/Eti25ubgDKWxdNTExU0ihuDIpBxdbW1vwNOD4+HowxjBw5\nEhzH4T//+Q9+//13lckR7ty5g7Fjx6KsrEyv10YIIS3N9evXVe7hFVW8X1eHMcY/LGk6Z//+/QGU\ntw5KJJLaZFtvdF0eAPDixQuIxWIATbfFISMjAxKJBP369cOBAwcq9U6qCcVzhKYy7t27NwQCAQoL\nC7V+8K5vuiwP4N8vHoRCoS6yV6/effddfPHFF1WOeVQ0CGjzHNfU64euywNo2vWjOaBZYfUoJSVF\n5e8XL14AALp166YxTevWrZGbm4vHjx/z27p06YL09HQ8fvwYHMdh4sSJCA4O5r+NSUtL4994VlZW\nSE5O5tMqusgSQgipG8U9vap7uPL9ujoSiQRFRUXgOA7du3dXe0ynTp1gaGiIsrIyJCcnq3Q3bWi6\nLg+gvBsbYwzW1tZ4/vw5wsPDkZiYCLlcDltbW4wePZoPthurl19+GT/88EOtZwhWYIwhNTUVADTW\nD4FAgI4dO/JlrCnAaEi6Kg8AePr0KbKzsyEQCNC6dWt8//33SEhIQFFREbp27YoRI0bo5HX0xdXV\nFa6urhr3Z2dn81/CODg4VHmu5lA/dFkeQNOvH80BBZZ6lJWVpfK3IrBUnja54nTIii6s2dnZ/LZ2\n7dqpbHv11VdV0ih3t83Ozsbz58/5vymwJIQQ3VDc06ua+r7i/Vqb81V1TgMDA5ibm0MqlWp1zvqk\n6/IA/m1tyM3NxejRo1VaKS5evIg9e/Zg4sSJWLlyJQwNDWubdb3q3r27xgf9msjNzYVMJgPHcXw5\nqmNpaYn09PRGVz8UdFUewL/1AwDGjBlTqXvngQMHMHToUHz11VcwMzPTyWvWp9WrV6OwsBCmpqYY\nOXJklcc2l/pRlZqUB9D860dTQF1h9aioqEjlb0UFV+7+qvyhqWh1ZIyppDUxMam0TdnPP/9c6RyE\nEEJ0S3EPrjiEQVl192tlhYWF/O+KSXvUadWqlcrrNxa6Lg/g3wfDkpISTJkyBUePHsWtW7fwxx9/\nYMGCBTAyMsKhQ4eazFIjdVHxOUCTxlo/9EFRP+RyOYYNG4ZDhw7h5s2buHDhApYvXw5zc3OcOXMG\nixYtauCc1lxYWBiOHTsGjuMwf/78atdubO71o6blATTv+tFUUIulHhkYGKi0JhoYGKCsrEyrVkTl\nYxTBorp0t2/fxt69e9W+dllZGQWahBCiI4p7elX38Kru1xUpt7jp6pz1SdflAQADBgwAYwwuLi6Y\nMmUKv/3ll1/G7Nmz0aVLF3z00UeIiIjAtGnTNK5z2Rwoz/DeFOuHPgiFQkydOhUdOnTA3Llz+e1W\nVlYICAiAo6Mjpk+fjnPnzuH8+fMYPHhwA+ZWe6GhoQgNDeUn4pk1a1a1aZpz/ahNeQDNt340JRRY\n6pGZmRlycnL4v01MTFBYWIji4mJ+m0AgQGlpKYB/3/Qcx/HfMAHl39xW3KbYvnjxYpVWTwMDA8yb\nNw/m5uZYt24dGGN48uRJnWZgI4QQUn5Pl0qlKvfwijTdr9VRngWxuLgYRkZGao9TvJ4256xPui4P\nAPDz84Ofn1+V+zdv3oyUlBScPn26WQeWFeuHJo21fuiDp6cnPD09Ne53c3PDwIEDcfHiRZw8ebLR\nBw5yuRwrVqzA/v37wXEcBg8ejK+//lqrtM2xftSlPIDmVz+aIuoKq0cVp342NTUFAJVgU/kbJ+Df\nrrHKTf6KfvEVuwF8++23ePjwoUoXiLlz52L+/PkYPnw4v60xzgRGCCFNjeKernwPr0jT/Vod5XFR\nms4pl8uRn58PACprvDUGui4PbTk7OwMAP3FJc2VmZsZ3ka6qjBX7Glv9aChOTk5gjCEtLa2hs1Kl\n/Px8BAcH80GUr68vwsLCquwWr6y51Y+6loe2mkr9aKoosNSjit+kKhauVf4wrNg1QTHBT48ePfht\nisqvvO3p06c4ePAgGGMq43TCwsIgEolUAsvPP/8cIpGoxotUE0II+Zfinl5VQKPufq1Jx44d1X4u\nKEtPT+eHVNja2tYku3qn6/JQkMlkVS4toNinbt3P5oTjOL7cNJWxTCbD06dPATS++qEvZWVlKsOM\n1O0HGnf9yMjIQEBAAC5evAiO4xAUFISvvvqqRnluTvVDF+Wh0BzqR1NGgaUe9enTh/+dMcaPebxx\n4waAf6eaV6b4ZkmxIKxEIsGTJ09UtmVkZGDIkCH8scrjdFxdXeH2AL9GAAAgAElEQVTm5oZevXrx\n27p16wY3NzfqDksIIXXQp08flXt4Reru19Xp3bs3AGg8p2Kdus6dO6NDhw41zbJe6bo8pFIp3N3d\n4erqitjYWI3HJSYmAqj85W1zVF0ZJyQkQCaTwcTEhG/Jbc68vb3h4uKiMmlhRXfu3AHHcY22fjx7\n9gyBgYF48OABBAIBVqxYgQ8//LBW52oO9UOX5dEc6kdTR4GlHvn4+Kj8rfhGKT4+Ho8fP6406U7H\njh0BAO7u7nwQqDhGeVtpaSnf0tm6dWvs3LkTHMfBwMAAGzduxJ49ezBu3Dj+vJs2bcKePXuqXICW\nEEJI1RT3dMU9vCJ192ttzskYw6FDhyotPwUA+/btA8dxjfL+revyaNOmDTp06ACO43Do0CG1xxw/\nfhwpKSkwMjLSavmBpm7UqFEAgBMnTkAqlVbaryhjX19fnXcZbIwcHBzAGMPhw4fVtmonJCTg0qVL\nAIDRo0fXd/aqVVpaitmzZ0MsFsPY2Bjffvst3nzzzVqfr6nXD12XR1OvH80BBZZ6ZGNjgzFjxvB/\ny+VytG7dGjKZDDNmzMDWrVtVjpdIJOA4DnPnzoVYLMb69euxdetWMMYwYsQIJCUl4dmzZ1i2bBn/\nhikqKsKZM2dUZn+NiYnBhg0bAJR3lag41pMQQkjN2djYwM/PDzKZDPPnz0dKSgq/Lzo6GuHh4fw9\nvCKxWMzfw5WNGzcO3bt3R0pKChYtWoSCggIA5Q9cq1evxrVr19CmTRtMnz5dvxdXC/ooj5CQEDDG\n8Mcff+Drr79WWYfu+PHjWLp0KTiOQ3BwcKNrwa2L9PR0JCUlIT09XWW7h4cH3NzcIJVK8e677/Jr\nhzLGsG3bNhw9ehRGRkYICgpqiGzrjabyCA4OBsdxSExMxKeffsqPPwaAS5cu4d133wVQ/r5ycXGp\n1zxrY+vWrXyL2fLlyzFs2DCt0jXX+qHr8mjq9aM54BitR6FXOTk5mDZtGh4+fKjV8QYGBhCJRLh3\n755KH/HWrVujsLAQXl5e+OOPPwCUzzJbsSutQCBQ+dab4zjExsZSN1hCCNGBnJwczJgxAw8ePICB\ngQEcHR2Rm5uLJ0+egOM4LFy4ECEhIZXSeXt748mTJxg/fjzWrVunsu/WrVuYNWsW8vLyYGpqCjs7\nO4jFYuTm5sLY2Bjh4eEYMGBAfV1ijeijPNavX48dO3aAMQYzMzPY2NggKyuL//J18uTJWLlyZX1d\nok54e3sjPT0dq1atwqRJkyrtDwwMxOXLl+Hu7o5du3ap7BOLxQgMDIREIoGRkRF69uwJiUSCzMxM\nGBgYYP369U2u9aUu5bF79258/vnnkMvlMDExQY8ePZCXl4fU1FRwHAdPT09s2rSp0bXQlZaWYtCg\nQZBKpRAIBHw3eE08PT0xe/ZsAM2zfuirPJpq/WguaOSqnllaWuLXX3/Fli1bEBERwc+Qp8BxHExM\nTNC7d2+89NJLePDgAZKSktQ24XMch8zMTL4brGIad+XvBip2pWpK6xYRQkhjZ2lpif3792P79u04\nfvw4kpKSIBAI8OqrryIwMFBl4rSKOI5Te0/u1asXoqOjERYWhri4ONy7dw8WFhbw8fHBnDlzIBKJ\n9HlJdaKP8vi///s/vP7669izZw9u3LiB+/fvw9LSEt7e3vD398eQIUP0eUkNRlN5dOvWDVFRUdiy\nZQtiY2Px4MEDmJqawtPTE0FBQY32S4e60lQe06dPR9++fbFz505cuXIFDx8+hJmZGQYOHIgJEyZU\nuVxNQ7p//z7y8vLAcRzkcjk/floTGxsblb+bW/3QV3k01frRXFCLJSGEEEIIIYSQOqExloQQQggh\nhBBC6oQCS0IIIYQQQgghdUKBJSGEEEIIIYSQOqHAkhBCCCGEEEJInVBgSQghhBBCCCGkTiiwJIQQ\nQgghhBBSJxRYEkIIIYQQQgipEwosCSGEEEIIIYTUCQWWhBBCCCGEEELqhAJLQgghpImLjIyESCTC\n0KFDGzorvPPnz8PJyQlHjhypl9fz9vaGSCTCwYMH6+X1mjIqK+1cuXIFIpEIBw4cqFV6kUik8jNs\n2DCt0zal/5Hi/qP88+233zZ0tkgDEDR0BgghhBDSvEilUixZsgR9+vTBmDFj6u11OY6rt9dq6qis\nqvfKK69g1KhRWLt2LV577TV069atxufgOA49e/aEhYUFOnbsWOO0TUH79u3h5uYGALh//z7y8/Mb\nOEekoVBgSQghhBCd2rBhA7KysrBp06aGzgohdfLhhx/i5MmTWLFiBbZv316rcyxZsgQeHh46zlnj\nMWTIEAwZMgQAEBgYiCtXrjRwjkhDoa6whDQT58+fR2pqakNnQy9yc3Nx/PhxyGSyhs4KIaQaDx48\nwMGDB+Hh4YH+/fvX6hx5eXk4fPiwjnNGWiKxWIzz58/XOn3Xrl0xbtw4XLhwoU7nIaQloMCSkCYs\nJycH27dvx/DhwxEcHIz09PSGzpJe5OfnY8GCBfDy8sKmTZsgkUgaOkuEEA1CQ0PBGMOUKVNqnPbO\nnTv49NNPMWTIEBqjRXQiPT0dwcHBGDlyJMLDw5GTk1Pjc/j7+4MxhtDQUD3kkJDmg7rCEtIEJSQk\nYM+ePTh+/DiKi4vBcRzs7e0rjd+4fPkyfv75Z1y7dg05OTlo27Yt+vbti8DAQLz22muVzuvt7Y30\n9HRERUXhwIEDOHz4MEpLS2FnZ4cff/wRP/30E7777juEhISgd+/eWL9+PZ48eYIOHTrgo48+gq+v\nLwCguLgY+/btQ0xMDB49eoTS0lJYW1tj4MCBeOedd2BjY6PyuvHx8ZgxYwb69u2LNWvWYNmyZbh9\n+zbMzc3xxhtvYPbs2XB1dcXt27cRFhaGH374Ad7e3pg6dWqz7l5EiK78+eef2Lt3L65fv46cnBxY\nWFjA1dUVb775JkaMGKE2TVFREfbt24fo6GikpKTAyMgI/fv3x7vvvouHDx9iyZIlGD9+PNatW8en\nkUgkOH36NMzMzLSeqKSkpATHjh3D3r17cevWLXAcB0NDQ7i7u1c69tmzZ9ixYwdiY2ORkZEBKysr\n+Pj4YN68eVW+RkpKCn7++WfEx8fjyZMnKCwshLm5OYRCIfz8/DBx4kQYGJR/1x4XF4egoCAIBAKc\nO3cOVlZWlc5XXFyMgQMHoqCgAPv370fv3r21ulagvAVt9+7dOHfuHNLT02FoaIiePXti4sSJmDRp\nUqVxdc+ePcPu3btx8eJFpKSkoKCgAGZmZujRowf+85//YOrUqTAxMdFZWZWUlFS6f3fq1AlDhw7F\nrFmz0KFDB5Xjle/fu3fvxq5duxAdHY1//vkHRkZGcHFxQWBgoMb6kJmZiT179uD06dNITU0FYww9\nevTA6NGjERgYCGNj40ppTp06hf379+Pvv/+GVCqFlZUVBgwYgFmzZsHZ2Vnl2I4dO8LOzg7JyclY\nv349Nm3ahFGjRmHq1Kla/9969eoFOzs7JCQkICEhoUb/76o09vp8/vx57NmzBwkJCZBKpTA3N0fP\nnj0xatQoTJ48GUZGRjopB9J8UGBJSBNRVFSEI0eOYO/evUhMTATHcTA2Noafnx+mTJmCAQMGqBy/\nYcMGbN++HRzHoU2bNhCJRJBIJIiNjcXp06cRHByMDz/8UO1rrVixAtevX4eDgwNevHgBExMTtGnT\nBhzHgeM4xMfHIzw8HJaWlnBwcMDDhw/5D/OMjAy8/fbbSE5OBsdxsLW1hZmZGR49eoRffvkFUVFR\nWLduHR+EKnv+/Dneeust5Ofnw8HBASkpKejRowfatm2LgwcP4s6dO4iIiMCxY8dw6tQpnDx5Era2\ntvD398eECRPQpk0b3Rc8IU3cqlWrsGfPHnAcB0tLSzg7O0MikSAuLg7nz5/HqFGjsGHDBhgaGvJp\ncnJyEBwcjFu3bsHAwAD29vbgOA5nz57F+fPnMWTIELUTi5w4cQIymQwDBw6EQFD1I4ZYLMbevXtx\n6NAh5ObmguM4dOrUCZMmTcKkSZNgbW2tcvzdu3cRFBSEzMxMGBsbo2fPnpBKpdi5cyfi4uJQVFSk\n9nVOnTqFRYsWobS0FK1bt0a3bt1QVlaG1NRUxMfH49KlS4iPj8f69esBAIMGDUKnTp2QkZGBY8eO\nITAwsNI5f//9dxQUFKBnz541CjJOnjyJxYsX48WLF2jVqhXs7e2Rl5eHmzdv4saNG7h8+TK+/PJL\n/vgbN24gJCQEUqkUrVq1Qrdu3WBkZITU1FQ+TWxsLHbt2qXy/6htWT179gzBwcG4e/cuDAwM0Llz\nZ7Rt2xaPHj3Czp07ERkZie+//15tF+eSkhIEBwfjzz//hJWVFezt7ZGcnIy//voLf/31F1asWFGp\nFfvq1at4//33kZWVBYFAAAcHB5SUlODu3bu4c+cOzp07h/DwcL4uyeVyLF68GEePHgXHcWjfvj2c\nnJwgFosRExOD48ePY+nSpZg2bRr/Gra2toiJicHly5cRERGBkydPIjo6GlFRUXB2dkZAQADGjBmD\nVq1aVfm/Gzx4MJKTkxETE6OTwLKx1+ddu3Zh7dq14DgOHTt2hJOTE7Kzs3HlyhVcvnwZx48fx08/\n/dRkJhgi9YQRQhq1pKQktnr1ajZgwAAmFAqZSCRiPj4+7Mcff2TZ2dlq0+zbt48JhULm7u7Ojh49\nqrIvJiaG9e3bl4lEInbw4EGVfV5eXvxr/Pbbb/x2xets3ryZ3//ee++xkpISxhhjz58/Z4wxJpfL\n2bhx45hQKGS+vr7s3r17/Dny8/PZf//7XyYUCpmrqyu7efMmv+/SpUtMKBQyoVDIRo4cyZ49e8YY\nY+zFixesuLi40vUVFBSwiIgINn78eCYSiZhQKGR9+vRhS5cuZbdu3dK6bAlpLg4dOsSEQiHz9PRU\n2R4eHs6EQiFzcXFh+/btU9l3/Phx1r9/fyYSidiaNWtU9i1atIh/Pyq/j5OSkpifnx9/H/jkk09U\n0s2ePZuJRCL2448/qs2nXC5np06dYu+88w7/3nV2dmZz5sxhf/zxBysrK1ObTiaTMV9fXyYSidjb\nb7/N33MYY+zcuXPMzc2Nz9OBAwf4fbm5uWzAgAFMJBKxlStXsqKiIn5fYWEhW7NmDZ/u4cOH/L5v\nvvmGCYVCNnHiRLX5UeR/x44daverk5KSwt97lyxZwvLy8vh958+fZ3369GEikYjt37+fL6vhw4fz\n91upVKpSHtu2bePzfubMmTqXFWOMTZkyhQmFQjZ9+nT2+PFjfnteXh779NNPmVAoZB4eHiwzM5Pf\np3z/7t+/Pzt27JhKurfeeosJhUL22muvMblczu/Lzc1lgwYNYiKRiM2ePZtlZWXx+/7++2/m4eHB\nRCIR+/rrr/ntGzZsYEKhkHl5ebELFy7w28vKytju3buZi4sLc3JyYhcvXtT4f3j+/DkLDw9n//nP\nf/g6OGDAALZmzRqWlJSkMd2JEyeYUChkfn5+Go+pSFHOFfPT2OuzVCplvXv3ZiKRiMXExKgce+HC\nBb6uKv+vFaZPn85EIhH75ptvtC4n0nxQYElII6b4MBAKhaxXr15s0aJF7NKlS1WmKSkpYQMHDmQi\nkYidOnVK7TGKwNPT01Plg97Ly4uJRCI2ffp0temUA0uxWFxp/7Fjx/ggLzU1Ve05goKC+A9TBcWD\niUgkYocOHary+ipKSEhgn376Kevbty9fVhUfdglp7tQFlsXFxfxD6NatW9WmU7xnXVxcWFpaGmOM\nsYcPHzKhUMicnJzYgwcPKqVJS0vjHyyV32tlZWV8oKr80K9s6NCh/Hvd09OTbd68maWnp1d7fYp8\nuru7qwRYCpGRkWofxE+fPs369+/PBg8erHKvU5DJZMzV1bXSQ3JKSgoTiURMJBKxR48eqaTJyMhg\nTk5OzMXFRSXAqs7y5cuZUChk/v7+avdv2bKFiUQiNmXKFMYYY7dv32bu7u6sT58+LCcnR20aReCp\n/P+tbVmdOnWKr0PKAYuyN998k4lEIrZhwwZ+m/L9e+fOnZXS3L59W22w88MPPzChUMiGDRum9gvE\nI0eOMJFIxIYOHcoYYywrK4v16tWLiUQilpiYqDZ/69evZ0KhkL355ptq91f0559/sgULFjBXV1f+\n82PTpk1qj01OTuavQzkIroqmwLKx1+ebN2/y+VMnLCyMffDBB+zkyZOV9lFg2bLR5D2ENGIZGRkA\nAIFAgHnz5mH16tVqxx0pu379OrKysqoc4+Tn5wcDAwNIJBLcvn270v7qZnJ86aWX0LVr10rbY2Nj\nAZSP1ezSpYvatG+//TYYY4iPj1e71lVNZ5Hs1asXli9fjsWLF8PExAQcxyEtLa1G5yCkObpy5Qqk\nUikMDQ0xdepUtcf4+vrC2toacrkcf/zxB4DyrnYA4ObmBgcHh0ppOnfujGHDhoExprI9OzsbBQUF\nAIDu3burfb309HS+y+sXX3yB+fPn4+WXX672Ws6ePQuO4zBs2DBYWFhU2u/n56d2u7e3N65evYpT\np07xY86UFRUVoW3btvzvCt26deOHF0RHR6ukiY6ORllZGYYMGYL27dtXm3eFM2fOgOM4TJ48We3+\n6dOn4+jRo/j5558BAM7Ozny3RkUelZWUlKjNe23L6tSpU3w6dWM2AWDs2LFgjPF1pSIvL69K2+zt\n7fnfpVIp/7uiPMaNG6d2HKWPjw8iIyNx/Phx/rpKSkrg4OAAkUik9vXHjRsHoHwegufPn6s9Rtlr\nr72GNWvWYO7cuTAwMADHcXjy5InaY21sbPg6VNcZ2Bt7fe7atSsEAgGkUik++eQT3L17VyXN3Llz\n8c0332D48OE1vHLS3NEYS0IasVGjRuHBgwe4ffs2Nm7ciG3btmHMmDHw9/eHUChUm+bBgwcAgNLS\nUo0PkwBgYGAAuVyOpKQk9OrVS2VfdYs4a9qflJQEjuPg4uKiMa1in1wuR0pKSqWJFipODFEVsViM\nffv24dChQ8jJyQHHcejRowcmTZqk9TkIaa6SkpIAgB/nrImzszOePn2K5ORkAOULnHMcp/EeA5R/\noXPs2DGVbcoP8prGOwcFBeHXX39Feno6Zs6cCXt7e0yZMgXjx49X+yCtoMibo6Oj2v2K8Xk3btxQ\nu9/Y2Bh3797F3bt3kZqaipSUFDx8+BD379+HTCYDx3EoKytTSTNx4kTEx8fjyJEjWLhwIb89Ojoa\nHMdhwoQJGvNbUUlJCSQSCTiO0xgUmZmZqQRhynlPTk7GnTt3kJKSArFYjIcPH+LevXv85G3Kea9t\nWd2/fx9A+ReEiYmJatMqAsPHjx+r3V9xXCwAlSBVLpfzv6ekpACAxvIQCAQq+xT5y8jI0PjZplwO\nSUlJaieqUbh79y727duHo0ePoqCgABzHwdXVVe34fwDgOA7m5ubIy8tDVlaWxvNqo7HXZysrKwQF\nBeGHH35AVFQUoqKi8NJLL8HDwwODBg2Cp6cn2rVrV6cyIM0TBZaENGKKRYeVZ4GNiIjAvn370Ldv\nX/j7+8PX11fl2968vDwA5Q8y169fr/Y1FMcr0/RttYKmSQ4UrRVVPSCam5tXOl6bcyvI5XLExsYi\nIiICFy9eBAAYGhpixIgRNEssIUoUPQKU33PqmJubgzHGvx8VyzFUFYyq25ebm8v/rul9/NFHH+H9\n999HTEwM9uzZg7///htr167F119/jVGjRiEgIEDtxCiKc7du3VpjntS16gHlrUNr1qzhAxnFZCMd\nO3aEr68vzp49q9KSpuDj44NVq1YhPT0d8fHxcHd3x99//41Hjx7ByspKpXXuhx9+wNmzZ9W+/ubN\nm1XW4K3qGiq6efMmPvvsMz7QU+S9Xbt28PT0xJ07dyr10KhtWSnqS0ZGBt9bRh1F0KKYnVZZdbOE\nKrdyK+qZtuWhyF9+fn6Vn22KSebUfbaVlJQgJiYGERERuHHjBj8J3htvvKHVLLGmpqbIy8tTqeu1\n0djrMwAsWLAArq6u2LNnDy5fvoysrCwcPXoUR44cgUAggK+vL5YvX17t/YW0LBRYEtIE9O7dG717\n98Ynn3yCAwcOICIigp8RcN26dRg/fjz8/f1ha2sLU1NTAOUtgwcPHqzXfCoeMtR9oCsofyBX9eBa\nkUQiwS+//IKDBw/i6dOn4DgOVlZWmDx5Mvz9/bXqTkdIS6J4f6nrcq5MKpWC4zj+eMU9pKp06r4U\nUv5CKi8vT2M3UcWD/BtvvIFbt25h7969iImJQVRUFCIjI+Hs7IwpU6ZgzJgx/IN3u3btkJKSUmWe\n1M2i+ddff2Hu3LlgjKFfv37w8/ODUCiEnZ0d35o1ZMgQteczMTGBr68v9u/fjyNHjsDd3R1RUVEA\nyruEKs+i+/jxY7XBDsdxKC4uVvmyTV3ZqfPo0SPMnDkTxcXF/HIkQqFQZWmpgICASoFlbctK8X9f\ntmyZyqyq+mJqaor8/Hyty0ORv5EjR9Z4jdPk5GREREQgKiqKn4G4c+fOCAgIwKRJk7RufVMEbNV9\nAVqdxl6fFYYPH47hw4ejoKAAly9fxqVLl3Du3DkkJSXh8OHDyM/PR1hYWG2KgDRTNMaSkCakXbt2\nCAkJwenTp/Hdd99h0KBByMvLw86dO+Hj44ObN2+iR48eAMofdCp2hVF26dIl/PPPPygtLdVZ/nr0\n6AHGmNpxmwp///03gPIHLk3jsCqSSCTw8vJCWFgYnj17hr59++LLL7/EmTNnsGDBAgoqCVHDzs4O\nQPm9QNPDO2MMd+7cAVDeZRYo757HGMO9e/c0nlvdPuVu7NnZ2VrlsVevXli3bh3Onj2LDz/8EF27\ndkViYiL+97//YeLEifxxinuLIq/qPHz4sNK27du3gzEGDw8P7N27F9OmTcMrr7zCP4SXlJRUmVdF\nt/rTp0+jrKwMp0+fBsdxGD9+vMpx69atQ2JiYqWfO3fuoHPnzrCwsOADbcVwhYqePXuGN998Ex9+\n+CHy8/Px008/oaioCHZ2djh48CDeeusteHh4qAxFkEgklc5T27JSfHao26eQkZGBmzdv4tmzZxqP\n0Zaivim6uFYkk8kQEBCA999/H6mpqVrlr6ioCJcvX4ZYLOY//27cuIFRo0Zh165dyMvLw+uvv46w\nsDB+2S1tg8ri4mIUFhYCKJ9noC4ae30uLi7GvXv3+LGVZmZmGDp0KBYvXoxjx47xXWn/+OOPar+4\nIi0LBZaENEEcx8Hb2xvh4eH47bffMGPGDLRp0wYlJSUYMGAALCwsUFBQgF9//VVt+sOHD2PmzJnw\n9fWtsstTTXl7ewMoH6OjaQKdn376CQDQr18/rbvQyGQymJiYYNKkSfj1118RERGBMWPG0OLMhFTB\nzc0Nbdu2hVwux549e9Qec/ToUTx79gwcx2Hw4MEAyluEgPKJwBRjwZQ9f/4cJ0+erLR+nbW1Nd/C\nWNP7iqWlJYKCgnDq1CmEhYXh9ddfVxmPp8hTbGwsnj59Wil9bGwsMjMzK21XTLIiFArVrrcXFRXF\nf7mm/HoKvXv3Rs+ePZGdnY1du3YhPT0dzs7OVY4/1WTIkCFgjGm8L//2229ISEjAjRs3YG5ujrS0\nNHAcB3t7e7XDEy5cuMBPNKOLsvLy8gJjDDExMRonvlmyZAmmTJmCRYsWVX/B1fD09ARjDEeOHFHp\nKqxw9uxZXL9+HXFxcXjppZfg6ekJQ0NDJCUl4c8//1R7zh9//BGBgYEYP348HwSWlJSgTZs2mDFj\nBn777Tds374dXl5eNV5/UblOK760qa3GXp9/+eUXjBs3Dh9//LHa/A8aNIj/Xd3rkJaLAktCmjgb\nGxssXboU586dg5OTE0xNTRESEgLGGNasWYNDhw6pjGs5deoUPvvsM3Ach1GjRqFbt246y4uPjw+E\nQiGKiooQFBSkMpNcQUEB/vvf/+LChQsQCAT46KOPtD5vhw4dcPbsWaxevbrSZD+EEPVatWrF3ws2\nbdqEvXv3qtwLTpw4gf/973/gOA5TpkyBjY0NgPLJVIYPHw65XI558+bh0aNHfJonT55g7ty5Glsp\n+vXrBwBaje/WxMvLC9u3b8fu3bv5bUOHDkX//v3x4sULzJkzB2KxmN935coVLFu2TO2Dtp2dHRhj\nOHr0qMp1lJSUYPfu3VizZg2fThGIVDRx4kS+DGs6aY+yoKAgmJiY4MqVK1i1apVKV8dz587hm2++\nAcdxCA4OVsl7XFwcrl69yh8rl8tx9OhRLFq0SG3ea1tWvr6+cHR0RG5uLmbNmqXSYlZQUIDPPvsM\nf/75JwwMDDBnzpxalYGyqVOnwtLSEikpKVi0aJHKMImEhAT+c2ratGlo1aoVOnfujMmTJ4MxhoUL\nF6rMTMsYw4EDB/Ddd9/xaRRdu52dnXHu3DksWbKEr+O1ce3aNQDlMx5XNSmQNhp7fR41ahSMjIzw\n4MEDrF27VuVcz58/x9dffw0A6NOnj8axoKRlojGWhDQTymM+goODIRaLceDAASxduhTr169H165d\nIZFI+PGJbm5uWL16tU7zYGhoiLCwMISEhCApKQlvvPEGPyPlo0ePUFRUBFNTU6xYsYJ/ANWGsbGx\n2unoCSFVmzVrFtLS0rBv3z6sXLkSmzdvRrdu3ZCRkcHfC3x8fLBkyRKVdKtWrcI///yDhw8fws/P\nDw4ODjA0NMSDBw9gZGQEkUiEe/fuQSBQfYwYPHgwLly4oBII1ZZyl0+O4/DVV18hODgYiYmJ8PHx\ngaOjIwoLC/H48WN069YN1tbWlZZFmDdvHv78809kZmZi7NixsLW1hbGxMf755x+8ePEC7du3R8eO\nHZGYmKi2WylQPv5sw4YNKCwshJGREfz8/Gp1Pfb29vjiiy+wePFi7N27F5GRkbCzs0NWVha/DMuE\nCRPg7+8PAHjnnXdw7NgxZGdnY9q0abCxsYG5uTlSU1ORm5sLMzMz9O3bF9evX1fJe23LSiAQ4Lvv\nvkNISAju3r0LPz8/9OjRA6ampkhOTkZhYSE4jsPSpUtVWiibXqgAAAUPSURBVKxqy8rKCqGhoZg3\nbx5OnjyJM2fOwMHBAXl5eXzL3ODBg/Hee+/xaZYuXQqJRIIzZ85g7ty56NixI6ytrZGWlobnz5/z\n9XnBggV8Gl1NLqOo00OHDq3zuRp7fe7QoQPWrl2Ljz/+GLt27cLBgwfRvXt3fjb3kpIStG/fHmvW\nrKlzWZDmhVosCWmmVq5cifDwcIwcORICgQCJiYl48eIF+vbti2XLlmHnzp21moBAMeOeJl26dMGv\nv/6Kjz/+GH369EFmZiaSkpLQuXNnvPXWW4iOjsbYsWNrfF5CSNU0vYeWL1+O8PBwjBgxAoaGhkhM\nTOTX0AsLC8PGjRsrfXHTrl07HDhwAPPmzYO9vT3EYjHS09MxbNgw/PLLL+jduzcYY5XuIaNHj4ZA\nIMCNGzd0PvaqU6dOiIiIwPvvvw87Ozs8fvwY+fn5mDx5Mn755RdYWlpWSuPi4sLfc7p06QKxWAyx\nWAwbGxvMnTsXR44cQWBgIDiO0zirq5WVFd9tc9iwYXVqofHx8UF0dDQmT56Mdu3a4f79+8jPz4eH\nhwe+/fZblQf1Tp064fDhwwgICECPHj0gkUiQnJyMDh06YMaMGTh8+DAWLFgAjuNw6dIllRbQ2pQV\nUL7eYWRkJD7++GP07dsXmZmZuH//PiwsLDBq1Cjs3r0b06dPr5ROm/u3uv2vvPIKjhw5gpkzZ6JL\nly5ISkpCVlYW+vTpg5UrV2Lr1q0qddPY2Bjff/89Nm7ciCFDhkAmkyExMRFyuRyvvfYavvjiC2zc\nuFHnnyWMMVy4cAEcx6n9/KqNxl6fx4wZg59//hk+Pj5o27YtHj16hLS0NNja2mL27Nk4duyY2uVx\nSMvGsYorHBNCCCGEVOGDDz7A77//joULFyIkJERl35IlSxAVFYX//ve/Va6l25RMmDABiYmJ2Lp1\nKz8WlbQcZ8+exezZs+Hh4YEff/xR63QikQgcx2HHjh2NaiksfdbnwMBAXLlyBXPmzMEHH3yg03OT\nxo9aLAkhhBDCu3jxIoYPH66yiLqyoqIixMfHA4DaMc9z586FgYEBDhw4oNd81pfbt2/jzp076NSp\nE15//fWGzg5pAL/88gs4jsO8efMaOit1RvWZ6BMFloQQQgjhOTk5QSKR4Pjx49ixY4fKrI9ZWVlY\ntGgRsrOzYW9vr7YVpnv37hg/fjzu3r2rcfbOxi41NRVisRjXr1/nJ8mZMWMGdddvgR49eoSzZ89i\n4MCBeOWVVxo6O7VC9ZnUF+oKSwghhBAVO3fuxBdffAHGGNq2bYuuXbuisLAQKSkpkMvl6NSpE7Zu\n3QoHBwe16fPy8jBmzBi0a9cOkZGR9Zz7utu6dSs/8yXHcXB0dMTBgwdpiaMWaM6cObhy5QqOHDmC\nTp061Sitoiusg4MDLCws0KFDB3z77bd6yqlm+q7P586dw/fffw+O43D//n0UFBRQV9gWimaFJYQQ\nQoiKt956CwMGDMBPP/2EW7duISkpCcbGxnB0dMSIESMwderUKiexsbCwwNq1axEUFITIyMhKC7A3\ndkKhEG3btoVMJsOgQYPwv//9j4LKFig+Ph5nz57FmjVrahxUAv9OWKRYuqVLly46zZ+29F2fs7Ky\ncOPGDZ2djzRd1GJJCCGEEEIIIaROaIwlIYQQQgghhJA6ocCSEEIIIYQQQkidUGBJCCGEEEIIIaRO\nKLAkhBBCCCGEEFInFFgSQgghhBBCCKmT/wcwBRnSfr6a7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106249d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_context('poster')\n",
    "from matplotlib import rcParams\n",
    "rcParams['ytick.labelsize'] = 25\n",
    "rcParams['xtick.labelsize'] = 25\n",
    "rcParams['axes.labelsize'] = 25\n",
    "rcParams['axes.linewidth'] = 3\n",
    "rcParams['font.size'] = 25\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.subplots_adjust(hspace=0.15)\n",
    "fig.subplots_adjust(wspace=0.1)\n",
    "\n",
    "# Set number of histogram bins \n",
    "nbins = 30\n",
    "\n",
    "\n",
    "# Panel 1 \n",
    "ax1 = fig.add_subplot(221)\n",
    "ax1.set_ylabel('N')\n",
    "ax1.set_xlabel('Number of days observed')\n",
    "ax1.set_xticks([0,20,40,60,80,100])\n",
    "ax1.set_xticklabels(['0','20','40','60','80','100'])\n",
    "\n",
    "ax1.xaxis.set_label_position('top')\n",
    "ax1.xaxis.tick_top()\n",
    "hist2, bin_edges = np.histogram(stats_proc_lc['lc_length'], bins=nbins, density=False)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "hist2[0]=0\n",
    "ax1.set_xlim(xmin = 10, xmax = max(bin_centres2))\n",
    "ax1.plot(bin_centres2, hist2, ls = 'steps')\n",
    "\n",
    "# Panel 2 \n",
    "ax2 = fig.add_subplot(222)\n",
    "ax2.set_ylabel('N')\n",
    "ax2.set_xlabel('<mag>')\n",
    "ax2.xaxis.set_label_position('top')\n",
    "ax2.xaxis.tick_top()\n",
    "ax2.yaxis.tick_right()\n",
    "ax2.yaxis.set_label_position(\"right\")\n",
    "a = stats_proc_lc['avg_mag']\n",
    "m = (a > 16) * (a < 21)\n",
    "hist2, bin_edges = np.histogram(a[m], bins=nbins, density=False)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "hist2[0]=0  # start at 0 \n",
    "\n",
    "ax2.plot(bin_centres2, hist2, ls = 'steps')\n",
    "\n",
    "# Panel 3 \n",
    "ax3 = fig.add_subplot(223)\n",
    "ax3.set_ylabel('N')\n",
    "ax3.set_xlabel('<error>')\n",
    "ax3.set_xticks([0,0.1,0.2,0.3,0.4])\n",
    "ax3.set_xticklabels(['0','0.1','0.2','0.3','0.4'])\n",
    "\n",
    "hist2, bin_edges = np.histogram(stats_proc_lc['avg_err'], bins=nbins, density=False)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "ax3.set_xlim(xmin = 0, xmax = max(bin_centres2))\n",
    "hist2[0]=0\n",
    "\n",
    "ax3.plot(bin_centres2, hist2, ls = 'steps')\n",
    "\n",
    "# Panel 4 \n",
    "ax4 = fig.add_subplot(224)\n",
    "ax4.set_ylabel('N')\n",
    "ax4.set_xlabel('log(<day-cadence>) [days]')  # time between consecutive observations\n",
    "ax4.yaxis.tick_right()\n",
    "ax4.yaxis.set_label_position(\"right\")\n",
    "\n",
    "hist2, bin_edges = np.histogram(np.log10(stats_proc_lc['avg_mjd_diff']), bins=nbins, density=False)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "hist2[0]=0\n",
    "\n",
    "ax4.plot(bin_centres2, hist2, ls = 'steps')\n",
    "\n",
    "\n",
    "# Save\n",
    "\n",
    "#plt.savefig('../data_products/PTF_Fig_1_QSO_avg_stats_four_panels.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}