A_Fig_1_QSO_CRTS_day_averaged_stats.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 datetime\n",
    "from CRTS_paper_modules import update_progress\n",
    "import os \n",
    "from astropy.table import join\n",
    "from astropy.table import Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Sample of S82 CRTS QSO  from Branimir "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, find how many quasars we got from Branimir : in /local/tmp/suberlak/CRTS_PROJECT/raw_LC_CRTS \n",
    "    QSO_CRTS.tar.gz  `12MB    (7932 files,  remove empty : 7753,  remove N<10 :  7707, remove Nday<10  7601)\n",
    "      (same as    /astro/users/ivezic/suberlak/QSO.tar.gz   ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 7933 CRTS qso files to start with \n"
     ]
    }
   ],
   "source": [
    "dir_in = '../raw_LC_CRTS/QSO_CRTS/'\n",
    "QSO_arr = np.array(listdir(dir_in))\n",
    "print('We have %d CRTS qso files to start with ' % len(QSO_arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# ignore the two files that are not lightcurves\n",
    "\n",
    "a = np.array([len(entry) for entry in QSO_arr])\n",
    "#np.unique(a)\n",
    "#QSO_arr[a == 9 ]\n",
    "#QSO_arr[a == 11 ]\n",
    "QSO_list = list(QSO_arr)\n",
    "QSO_list.remove('message.txt')\n",
    "QSO_list.remove('file.list')\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 QSO/ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 179 empty files\n"
     ]
    }
   ],
   "source": [
    "c = 0 \n",
    "empty_files = []\n",
    "for obj in QSO_list:\n",
    "    address = dir_in + obj\n",
    "    f = open(address, 'r')\n",
    "    if f.readline() == '' :\n",
    "        c += 1\n",
    "        empty_files.append(obj)\n",
    "        \n",
    "print('We have %d empty files' % c)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7752"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QSO_nonempty = np.array(QSO_list)[~np.in1d(QSO_list, empty_files)]  \n",
    "len(QSO_nonempty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove those files that have less than 10 points : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 45  files with less than 10 pts \n"
     ]
    }
   ],
   "source": [
    "c = 0 \n",
    "short_files = []\n",
    "for obj in QSO_nonempty:\n",
    "    address = dir_in + obj\n",
    "    f = open(address, 'r')\n",
    "    num_lines = sum(1 for line in open(dir_in+obj))\n",
    "    if num_lines < 10 :\n",
    "        c += 1\n",
    "        short_files.append(obj)\n",
    "        \n",
    "print('We have %d  files with less than 10 pts ' % c) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7707"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QSO_10_pts_plus = QSO_nonempty[~np.in1d(QSO_nonempty, short_files)]\n",
    "len(QSO_10_pts_plus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a directory to save stats and figures  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today we will save our Fig_1 files in /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_1_data/2017-02-24/\n"
     ]
    }
   ],
   "source": [
    "outDir = os.path.join(os.getcwd()[:-4],'data_products/'+'Fig_1_data', \n",
    "                      datetime.datetime.now().strftime('%Y-%m-%d')+ '/')\n",
    "if not os.path.exists(outDir): os.system('mkdir %s' % outDir)\n",
    "\n",
    "# set in stone just for today since I'm continuing from yesterday\n",
    "#outDir = os.path.join(os.getcwd()[:-4],'data_products/'+'Fig_1_data/'+'2017-01-24/')\n",
    "                      \n",
    "print('Today we will save our Fig_1 files in %s'%outDir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics on the raw  QSO  light curves \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['000011.96+000225.3.dat', '000014.82-011030.6.dat',\n",
       "       '000017.88+002612.6.dat', ..., '235944.13-005738.2.dat',\n",
       "       '235945.49-005819.6.dat', '235948.09+001229.5.dat'], \n",
       "      dtype='<U22')"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QSO_10_pts_plus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%"
     ]
    }
   ],
   "source": [
    "dir_in = '../raw_LC_CRTS/QSO_CRTS/'\n",
    "files = QSO_10_pts_plus\n",
    "\n",
    "\n",
    "timespan_obs = np.zeros_like(files, dtype=float) # store what was the total  timespan of observations per object\n",
    "lc_length=np.zeros_like(files, dtype=float)      # number of points in the lightcurve : epochs before day-averaging\n",
    "mean_N_day = np.zeros_like(files, dtype=float)    # average nunmber of observations per day                          \n",
    "mean_mag = np.zeros_like(files, dtype=float)   # mean magnitude over all epochs \n",
    "mean_err = np.zeros_like(files, dtype=float)   # mean error over all epochs \n",
    "qso_name = np.zeros_like(files)\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",
    "    \n",
    "    address=dir_in+files[i]  \n",
    "    data=np.loadtxt(address) \n",
    "    \n",
    "    lc_length[i] = sum(1 for line in open(address))\n",
    "    mean_mag[i] = np.mean(data[:,1])\n",
    "    mean_err[i] = np.mean(data[:,2])\n",
    "    #mean_N_day[i] = np.mean(data[:,3])\n",
    "    timespan_obs[i] = data[-1,0]-data[0,0]  \n",
    "    qso_name[i] = files[i][:-4]\n",
    "    \n",
    "keys = ['CRTS_name', 'raw_timespan_obs', 'raw_lc_length', 'raw_mean_mag', 'raw_mean_err']\n",
    "values = [qso_name, timespan_obs, lc_length, mean_mag, mean_err]\n",
    "\n",
    "stats_before={}\n",
    "for key, value in zip(keys, values):\n",
    "    stats_before[key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# save the raw CRTS LC  stats...\n",
    "outDir = '../data_products/'\n",
    "savefile = outDir+'Fig_1_raw_CRTS_QSO_LC_stats'\n",
    "np.save(savefile, stats_before)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing : 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 7707 lightcurves it takes about 1.5 mins on my workstation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%"
     ]
    }
   ],
   "source": [
    "dir_in = '../raw_LC_CRTS/QSO_CRTS/'\n",
    "dir_out = '../proc_LC_CRTS/qso/'\n",
    "lc_files = QSO_10_pts_plus\n",
    "\n",
    "k = 0\n",
    "total = float(len(lc_files))\n",
    "\n",
    "for obj in lc_files:\n",
    "    percent = 100*(k / total)\n",
    "    if (k % 10) == 0 : # every tenth loop.. \n",
    "        update_progress(int(percent))\n",
    "    k += 1\n",
    "    \n",
    "    address=dir_in+obj\n",
    "    data=np.loadtxt(address)\n",
    "\n",
    "    averages=np.zeros(shape=(len(data),3))\n",
    "\n",
    "    mjd = data[:,0]\n",
    "    mags = data[:,1]\n",
    "    errs = data[:,2]\n",
    "   \n",
    "    days = np.unique([int(day) for day in data[:,0]])\n",
    "    \n",
    "    # storage arrays  for each qso (all N's)\n",
    "    mjd_arr = np.zeros_like(days).astype(float)\n",
    "    avg_mags = np.zeros_like(days).astype(float)\n",
    "    avg_err_weights = np.zeros_like(days).astype(float)  \n",
    "    Nobs = np.zeros_like(days).astype(float)\n",
    "\n",
    "    # loop through days calculating mean, etc. \n",
    "    # day-averaging\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 = float(len(mags[condition]))            # number of obs in that night \n",
    "        Nobs[i] = 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",
    "        avg_err_weights[i] = error_weights\n",
    "        mjd_arr[i] = np.mean(mjd[condition])\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",
    "            name_out = dir_out  + 'out_'+obj[:18]+'.txt'\n",
    "            # So now we are saving all the day-averaged LC's, even if the resulting length was less than 10 days... \n",
    "            # Easier to explain stats if they are ignored later. \n",
    "\n",
    "            # Then select only those that have more than 10 days of observations, and \n",
    "            # compare to content of the QSO_CRTS_processed_err_w/ directory, to ensure that they are the same\n",
    "            np.savetxt(name_out, np.column_stack((mjd_arr, avg_mags, avg_err_weights, Nobs)),fmt='%11.4f')\n",
    "    \n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics on the day-averaged LC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%"
     ]
    }
   ],
   "source": [
    "dir_in = '../proc_LC_CRTS/qso/'\n",
    "files = np.array(listdir(dir_in))\n",
    "\n",
    "mjd_span = np.zeros_like(files, dtype=float) # store what was the total  timespan of observations per object\n",
    "lc_length=np.zeros_like(files, dtype=float) # number of points in the lightcurve - equal to the number of obs nights\n",
    "mean_N_day = np.zeros_like(files, dtype=float)\n",
    "mean_mag = np.zeros_like(files, dtype=float)\n",
    "mean_err = np.zeros_like(files, dtype=float)\n",
    "qso_name = np.zeros_like(files)\n",
    "#mean_delta_t = np.zeros_like(files, dtype=float)\n",
    "median_pairwise_dt = np.zeros_like(files, dtype=float)\n",
    "\n",
    "l = 0\n",
    "total = float(len(files))\n",
    "\n",
    "#delta_t_stack = np.zeros(0, dtype=float)\n",
    "\n",
    "for i in range(len(files)):\n",
    "    percent = 100*(l / total)\n",
    "    if (l % 10) == 0 : # every tenth loop.. \n",
    "        update_progress(int(percent))\n",
    "    l += 1\n",
    "\n",
    "    address=dir_in+files[i]  \n",
    "\n",
    "    mjd,flx,err, N_obs =np.loadtxt(address, usecols=(0,1,2,3), unpack=True) \n",
    "    \n",
    "    lc_length[i]  = len(mjd)  # sum(1 for line in open(address))\n",
    "    mean_mag[i]   = np.mean(flx)\n",
    "    mean_err[i]   = np.mean(err)\n",
    "    mean_N_day[i] = np.mean(N_obs)\n",
    "    mjd_span[i]   = mjd[-1]-mjd[0]  # gives number of days between first and last obs\n",
    "    qso_name[i]   = files[i][4:-4]\n",
    "            \n",
    "    # Sort according to mjd's  : these are individual days\n",
    "    # on which the object was observed,  so each element of \n",
    "    # mjd is a mean mjd of day-averaged day. \n",
    "    ind = np.argsort(mjd)\n",
    "    mjd = mjd[ind]  \n",
    "\n",
    "    \n",
    "    # Calculate tau, mag difference and err difference for each pair (k,j), \n",
    "    # where tau=t_j-t_k (j>k) \n",
    "    #  i.e.  t5-t4, t5-t3, t5-t2, t5-t1,   t4-t3,  t4-t2, t4-t1, ....\n",
    "    \n",
    "    #delta_t = []\n",
    "    #for j in range(len(mjd)-1):\n",
    "    #    # j from 1 and k<j\n",
    "    #    for k in range(j+1):     \n",
    "    #        delta_t.append(mjd[j+1]-mjd[k]) \n",
    "    #\n",
    "    #delta_t_stack = np.append(delta_t_stack, delta_t)    # this really slows it down...     takes about 10 mins\n",
    "    #mean_delta_t[i] = np.mean(np.array(delta_t))        # it's like 18 mln lines altogether! \n",
    "    \n",
    "    # NOTE  2/24/2017 , IN RESPONSE TO MATTHEW GRAHAM'S COMMENTS \n",
    "    #\n",
    "    # This is not needed, because people find it confusing. Replace \n",
    "    # time-differences calculated against all pairs of points, \n",
    "    # with simple pairwise time difference \n",
    "    # i.e.    t5-t4,  t4-t3,  t3-t2,  t2-t1.... \n",
    "    # and per lightcurve, calculate the median, not mean, because \n",
    "    # it is more robust against outliers... \n",
    "    \n",
    "    \n",
    "    # this dt is calculated between days of observations per \n",
    "    # lightcurve \n",
    "    dt = []\n",
    "    for j in range(len(mjd)-1):\n",
    "        dt.append(mjd[j+1]-mjd[j])\n",
    "    median_pairwise_dt[i] = np.median(dt)\n",
    "    \n",
    "keys = ['CRTS_name', 'proc_mjd_span', 'proc_lc_length', 'proc_mean_N_day', 'proc_mean_mag', \n",
    "        'proc_mean_err', 'proc_median_dt'] #'proc_mean_delta_t'\n",
    "values = [qso_name, mjd_span, lc_length, mean_N_day, mean_mag, mean_err, median_pairwise_dt]\n",
    "\n",
    "\n",
    "stats_after={}\n",
    "for key, value in zip(keys, values):\n",
    "    stats_after[key] = value\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_1_data/2017-02-24/'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outDir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save the day-averaged (processed ) CRTS LC  stats...\n",
    "#outDir = '../data_products/Fig_1_data/'\n",
    "savefile = outDir+'Fig_1_proc_CRTS_QSO_LC_stats'\n",
    "np.save(savefile, stats_after)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine before and after  day-averaging aggregate properties of CRTS QSO into one master catalog..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.table import join\n",
    "from astropy.table import Table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read the raw LC agg info \n",
    "# 2/24/2017 - I use info from 2/9/2017,  because the raw lightcurve stats are fine \n",
    "raw_lc_agg = np.load('../data_products/Fig_1_data/2017-02-09/' + 'Fig_1_raw_CRTS_QSO_LC_stats.npy').item()\n",
    "# convert a dictionary to an AstroPy Table \n",
    "raw_lc_agg_table = Table(raw_lc_agg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read the processed LC agg info\n",
    "proc_lc_agg = np.load(outDir + 'Fig_1_proc_CRTS_QSO_LC_stats.npy').item()\n",
    "proc_lc_agg_table = Table(proc_lc_agg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Join the two tables on CRTS_name  :\n",
    "\n",
    "from astropy.table import join\n",
    "agg_combined = join(left=raw_lc_agg_table, right = proc_lc_agg_table, keys='CRTS_name')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "&lt;Table length=5&gt;\n",
       "<table id=\"table4676556616\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>raw_timespan_obs</th><th>raw_mean_err</th><th>raw_lc_length</th><th>raw_mean_mag</th><th>CRTS_name</th><th>proc_mean_N_day</th><th>proc_lc_length</th><th>proc_mean_mag</th><th>proc_mjd_span</th><th>proc_mean_err</th><th>proc_median_dt</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str26</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>3020.85593</td><td>0.0683333333333</td><td>138.0</td><td>17.4757971014</td><td>000011.96+000225.3</td><td>3.45</td><td>40.0</td><td>17.48116</td><td>3020.8438</td><td>0.0382175</td><td>22.837</td></tr>\n",
       "<tr><td>2892.13763</td><td>0.107023809524</td><td>84.0</td><td>19.3313095238</td><td>000014.82-011030.6</td><td>4.0</td><td>21.0</td><td>19.3280952381</td><td>2892.1202</td><td>0.0533952380952</td><td>29.38975</td></tr>\n",
       "<tr><td>3020.85593</td><td>0.123284671533</td><td>137.0</td><td>19.6811678832</td><td>000017.88+002612.6</td><td>3.425</td><td>40.0</td><td>19.671925</td><td>3020.8438</td><td>0.06977</td><td>22.837</td></tr>\n",
       "<tr><td>2889.07825</td><td>0.152266666667</td><td>150.0</td><td>20.1694666667</td><td>000038.65+011426.3</td><td>4.05405405405</td><td>37.0</td><td>20.1422432432</td><td>2889.0603</td><td>0.0745783783784</td><td>23.4111</td></tr>\n",
       "<tr><td>3020.85593</td><td>0.0779710144928</td><td>138.0</td><td>18.1111594203</td><td>000042.89+005539.5</td><td>3.45</td><td>40.0</td><td>18.115225</td><td>3020.8438</td><td>0.0439675</td><td>22.837</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=5>\n",
       "raw_timespan_obs   raw_mean_err  ...  proc_mean_err  proc_median_dt\n",
       "    float64          float64     ...     float64        float64    \n",
       "---------------- --------------- ... --------------- --------------\n",
       "      3020.85593 0.0683333333333 ...       0.0382175         22.837\n",
       "      2892.13763  0.107023809524 ... 0.0533952380952       29.38975\n",
       "      3020.85593  0.123284671533 ...         0.06977         22.837\n",
       "      2889.07825  0.152266666667 ... 0.0745783783784        23.4111\n",
       "      3020.85593 0.0779710144928 ...       0.0439675         22.837"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_combined[:5]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save the master CRTS catalog \n",
    "\n",
    "address = '../data_products/CRTS_qso_raw_and_proc_combined_agg.dat'\n",
    "agg_combined.write(address,format='ascii', overwrite='True')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Fig. 1  : statistics of the day-averaged sample of CRTS QSO .... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the master catalog, to investigate the properties of light curves both before and after day-averaging (this pertains to section 2.2, and Fig.1 in the paper )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "address = '../data_products/CRTS_qso_raw_and_proc_combined_agg.dat'\n",
    "stats = Table.read(address, format='ascii')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=7601&gt;\n",
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       "<thead><tr><th>raw_timespan_obs</th><th>raw_mean_err</th><th>raw_lc_length</th><th>raw_mean_mag</th><th>CRTS_name</th><th>proc_mean_N_day</th><th>proc_lc_length</th><th>proc_mean_mag</th><th>proc_mjd_span</th><th>proc_mean_err</th><th>proc_median_dt</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str18</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
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       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>2997.83977</td><td>0.195294117647</td><td>187.0</td><td>19.1704278075</td><td>235926.25-004750.3</td><td>2.83333333333</td><td>66.0</td><td>19.1414621212</td><td>2997.8292</td><td>0.123856060606</td><td>17.8653</td></tr>\n",
       "<tr><td>2997.83266</td><td>0.253255813953</td><td>129.0</td><td>19.613875969</td><td>235927.16-000204.8</td><td>2.43396226415</td><td>53.0</td><td>19.4636377358</td><td>2997.8116</td><td>0.175881132075</td><td>19.492</td></tr>\n",
       "<tr><td>2907.97692</td><td>0.149591836735</td><td>196.0</td><td>18.5085204082</td><td>235930.42+003733.5</td><td>3.01538461538</td><td>65.0</td><td>18.48888</td><td>2907.9643</td><td>0.0906492307692</td><td>19.8652</td></tr>\n",
       "<tr><td>2971.99552</td><td>0.361666666667</td><td>18.0</td><td>20.3138888889</td><td>235936.81-003112.7</td><td>1.05882352941</td><td>17.0</td><td>20.2967588235</td><td>2971.9955</td><td>0.353658823529</td><td>148.01035</td></tr>\n",
       "<tr><td>2888.1414</td><td>0.334909090909</td><td>55.0</td><td>20.0429090909</td><td>235937.58+004223.2</td><td>1.44736842105</td><td>38.0</td><td>20.0088736842</td><td>2888.1414</td><td>0.30165</td><td>39.7743</td></tr>\n",
       "<tr><td>2997.83977</td><td>0.104473684211</td><td>228.0</td><td>17.6592982456</td><td>235938.40-003448.4</td><td>3.40298507463</td><td>67.0</td><td>17.6405701493</td><td>2997.8165</td><td>0.0584746268657</td><td>17.42085</td></tr>\n",
       "<tr><td>2899.16162</td><td>0.234357142857</td><td>140.0</td><td>19.2548571429</td><td>235942.35+001338.2</td><td>2.5</td><td>56.0</td><td>18.9737196429</td><td>2899.1532</td><td>0.1614375</td><td>21.0185</td></tr>\n",
       "<tr><td>2997.83977</td><td>0.166736842105</td><td>190.0</td><td>18.8172631579</td><td>235944.13-005738.2</td><td>3.11475409836</td><td>61.0</td><td>18.7805409836</td><td>2997.8165</td><td>0.0990655737705</td><td>20.50825</td></tr>\n",
       "<tr><td>2997.83977</td><td>0.134521276596</td><td>188.0</td><td>18.3442553191</td><td>235945.49-005819.6</td><td>3.24137931034</td><td>58.0</td><td>18.3226741379</td><td>2997.8165</td><td>0.0786672413793</td><td>24.0973</td></tr>\n",
       "<tr><td>2997.82796</td><td>0.247755102041</td><td>98.0</td><td>19.3225510204</td><td>235948.09+001229.5</td><td>2.04166666667</td><td>48.0</td><td>19.0252083333</td><td>2997.8126</td><td>0.188660416667</td><td>25.0373</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=7601>\n",
       "raw_timespan_obs   raw_mean_err  ...  proc_mean_err  proc_median_dt\n",
       "    float64          float64     ...     float64        float64    \n",
       "---------------- --------------- ... --------------- --------------\n",
       "      3020.85593 0.0683333333333 ...       0.0382175         22.837\n",
       "      2892.13763  0.107023809524 ... 0.0533952380952       29.38975\n",
       "      3020.85593  0.123284671533 ...         0.06977         22.837\n",
       "      2889.07825  0.152266666667 ... 0.0745783783784        23.4111\n",
       "      3020.85593 0.0779710144928 ...       0.0439675         22.837\n",
       "      2889.07825  0.142635135135 ... 0.0711243243243        23.4111\n",
       "      3020.85593  0.154057971014 ...       0.0860325         22.837\n",
       "      3020.85593  0.122028985507 ...         0.06792         22.837\n",
       "      3020.85593  0.105869565217 ...        0.059445         22.837\n",
       "      3020.85593  0.104460431655 ...       0.0581875         22.837\n",
       "             ...             ... ...             ...            ...\n",
       "      2997.83977  0.195294117647 ...  0.123856060606        17.8653\n",
       "      2997.83266  0.253255813953 ...  0.175881132075         19.492\n",
       "      2907.97692  0.149591836735 ... 0.0906492307692        19.8652\n",
       "      2971.99552  0.361666666667 ...  0.353658823529      148.01035\n",
       "       2888.1414  0.334909090909 ...         0.30165        39.7743\n",
       "      2997.83977  0.104473684211 ... 0.0584746268657       17.42085\n",
       "      2899.16162  0.234357142857 ...       0.1614375        21.0185\n",
       "      2997.83977  0.166736842105 ... 0.0990655737705       20.50825\n",
       "      2997.83977  0.134521276596 ... 0.0786672413793        24.0973\n",
       "      2997.82796  0.247755102041 ...  0.188660416667        25.0373"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's verify the statement in the second paragraph of section 2.2 : \"mean interval between two succesive observing nights of 10.26 days\". It pertains to light curves after day-averaging - we take a light curve, day-average, and then calculate per lightcurve the median of pairwise time difference between consecutive observations. Eg. with a QSO observed on days  t1,  t2, t3, t4 ,  we calculate dt = [t4-t3,  t3-t2,  t2-t1],  and then calculate PER light curve  np.median(dt), and call it 'proc_median_dt'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.456490935403231"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(stats['proc_median_dt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.525649999999587"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(stats['proc_median_dt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 7601 day-averaged quasar lightcurves\n"
     ]
    }
   ],
   "source": [
    "print('We have %d day-averaged quasar lightcurves'%len(stats['proc_lc_length']))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_1_data/2017-02-24/'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outDir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['proc_mean_N_day', 'proc_mean_err', 'proc_mean_mag', 'CRTS_name', 'proc_mjd_span', 'proc_lc_length', 'proc_median_dt'])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_after.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Axz8r7Odx4KdY8sYLsWB332vG+Qj7KY3vImwV1gwsHsty4FONlANbvro8nPO/h79/XqZ9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e84550>"
      ]
     },
     "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'] = 20\n",
    "rcParams['xtick.labelsize'] = 20\n",
    "rcParams['axes.labelsize'] = 20\n",
    "rcParams['axes.linewidth'] = 2\n",
    "rcParams['font.size'] = 20\n",
    "lw = 2 \n",
    "\n",
    "fig, axs = plt.subplots(2,2, figsize=(8,6))\n",
    "\n",
    "ax = np.ravel(axs)\n",
    "\n",
    "# Set number of histogram bins \n",
    "nbins = 30\n",
    "lcolor = 'black'\n",
    "density = False \n",
    "\n",
    "# Panel 1 \n",
    "#ax[0].set_ylabel('count')\n",
    "ax[0].set_xlabel(r'$ days / lightcurve$')\n",
    "ax[0].set_xticks([0,20,40,60,80,100])\n",
    "ax[0].set_xticklabels(['0','20','40','60','80','100'])\n",
    "ax[0].xaxis.set_label_position('top') \n",
    "ax[0].xaxis.tick_top()\n",
    "\n",
    "hist, bin_edges = np.histogram(stats_after['proc_lc_length'], bins=nbins, density=density)\n",
    "bin_centres = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "hist[0]=0\n",
    "ax[0].set_xlim(xmin = 5, xmax = max(bin_centres))\n",
    "ax[0].plot(bin_centres, hist, ls = 'steps', lw=lw, color=lcolor)\n",
    "\n",
    "#ax[0].hist(stats_after['lc_length'], bins=nbins, histtype='step', lw=lw, normed=False)\n",
    "# make ticks less frequent \n",
    "ticks = ax[0].get_yticks()\n",
    "ax[0].set_yticks(ticks[::2])\n",
    "    \n",
    "    \n",
    "# Panel 2\n",
    "#ax[1].set_ylabel('count')\n",
    "ax[1].set_xlabel(r'$\\langle m_{j} \\rangle$')\n",
    "ax[1].xaxis.set_label_position('top')\n",
    "ax[1].xaxis.tick_top()\n",
    "ax[1].yaxis.tick_right()\n",
    "ax[1].yaxis.set_label_position(\"right\")\n",
    "a = stats_after['proc_mean_mag']\n",
    "m = (a > 16) * (a < 21)\n",
    "hist2, bin_edges = np.histogram(a[m], bins=nbins, density=density)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "hist2[0]=0  # start at 0 \n",
    "ax[1].plot(bin_centres2, hist2, ls = 'steps', lw=lw, color=lcolor)\n",
    "ticks = ax[1].get_yticks()\n",
    "ax[1].set_yticks(ticks[::2])\n",
    "\n",
    "ticks = ax[1].get_xticks()\n",
    "ax[1].set_xticks(ticks[1:-1])\n",
    "\n",
    "\n",
    "\n",
    "# Panel 3 \n",
    "#ax[2].set_ylabel('count')\n",
    "ax[2].set_xlabel(r'$\\langle e_{j} \\rangle$')\n",
    "ax[2].set_xticks([0,0.1,0.2,0.3,0.4])\n",
    "ax[2].set_xticklabels(['0','0.1','0.2','0.3','0.4'])\n",
    "hist3, bin_edges = np.histogram(stats_after['proc_mean_err'], bins=nbins, density=density)\n",
    "bin_centres3 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "ax[2].set_xlim(xmin = 0, xmax = max(bin_centres3))\n",
    "hist3[0]=0\n",
    "ax[2].plot(bin_centres3, hist3, ls = 'steps', lw=lw, color=lcolor)\n",
    "ticks = ax[2].get_yticks()\n",
    "ax[2].set_yticks(ticks[::2])\n",
    "\n",
    "\n",
    "# Panel 4 \n",
    "#ax[3].set_ylabel('count')\n",
    "ax[3].set_xlabel(r'$ \\log_{10} (\\langle \\Delta {t} \\rangle) [days] $')  \n",
    "ax[3].yaxis.tick_right()\n",
    "ax[3].yaxis.set_label_position(\"right\")\n",
    "\n",
    "\n",
    "hist4, bin_edges = np.histogram(np.log10(stats_after['proc_median_dt']), bins=nbins, density=density)\n",
    "bin_centres4 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "hist4[0]=0\n",
    "\n",
    "ax[3].plot(bin_centres4, hist4, ls = 'steps', lw=lw, color = lcolor)\n",
    "ax[3].set_xlim(0.9,2.6)\n",
    "#ax[3].set_ylim(0,2100)\n",
    "\n",
    "xticks = ax[3].get_xticks()\n",
    "ax[3].set_xticks(xticks[1::2])\n",
    "\n",
    "ticks = ax[3].get_yticks()\n",
    "ax[3].set_yticks(ticks[:-1])\n",
    "\n",
    "fig.text(0.03, 0.5, r'$N$', ha='center', va='center', rotation='vertical',fontsize=20)\n",
    "#fig.text(0.96, 0.5, r'$N$', ha='center', va='center', rotation='vertical',fontsize=20)\n",
    "\n",
    "# gridding... \n",
    "grid= True\n",
    "if grid == True : \n",
    "    for i in range(4) :  \n",
    "        ax[i].grid()\n",
    "        \n",
    "\n",
    "# Save\n",
    "plt.tight_layout()\n",
    "fig.subplots_adjust(hspace=0.1)\n",
    "fig.subplots_adjust(wspace=0.05)\n",
    "plt.savefig(outDir + 'Fig_1_QSO_CRTS_proc_stats.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../data_products/'"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outDir\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare before and after preprocessing: error and mag distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1QtHVjmLzVXHncgreZ3K+Aujp6WHZstzznTt3cNZZYyNdxx67mN27d3P88Ybt\n221N5zAVWSIidcZ1XY488khee+2Pozd4vOqq9Ye06+7uZv/+NwmHw6PbkskkyWSSSA1dVyAiMlli\nYHjGAgsAj1yB5VMqOMBwcHAWkUkh8uWrW2+9ffT1FStOZ8WK00efX3HFutHH+/e/WdM5TEWWiEgd\nWr16LRs33s71139qyjbBYJBQaB6xWIzDDstNq9iyZTOrV6+pVJgiIgXLeh5eLEbLUB8Aq888hrZ5\nzXnbJt4I8eC+vbknC1xSrW1cunIx4dbgIW17Bnv5+m+/STZQvpkGcig/+SqfWs9hKrJEROpQa2sr\nra2tDA0lp5yXHotFmT9/Pn19fRx22OEA9PQc4Kijjq5kqCIivmU9jzc2fo9kb5Tlb+aKrMz+dpJT\nXLuaygSBkZvdNjnQ7Oauo29vObRty4AKrCrwk6/yqfUcpiJLRKROrV9/zbSvL1p0DOvWbZiw7WMf\n+0QZIxIRmZ10IoEXjxe173CTFvOpVTPlq3xqPYepyBIRkYZmjDkduBlYAKSAr1prN05qczXwz8Dv\ngINXd++01k5c1kpEKmb7cWeTCs5jyXnH0dl26PQ/gHhsCO56GYBsnlUFRcpFRZaIiDQsY0wQuAf4\njLX2p8aYxcCvjTHPWmtfmtT8KWvt+ysfpYjkkwrOY6ilHbezk6Y80/8A3KwWsZDqUEk/g0zWoT8T\nJB4bItY7OOG/St1BW0REymYVkLXW/hTAWrsTeAC4sqpRiYjInKaRrGl4Xob/k1zCQLZldKh5rvJz\nw77xOlqbcTWsLiL17wRgx6Rt24FT8rRdZIx5AFgM/B64wVr772WOT0RE5iAVWdPo7xvOFVgz6OgM\n0R6eud1UvEQi7/aM65BimOFoP5mRG5e6HR04rlvY8X3esG+8jrYgV19oVGiJSL1rAybPJxoc2T7e\nq8C9wD9Ya/cbY/4zsMUY8y5rbawCcYqIyByiIsun1WsWE3nbgryvtYdbcKdYOtSPtzbdnXe74zgc\nCLoMpzyy2ZEiKxzm8PUbCiq0fN+wb/w+/SkSA8NEppjjLCJSJ/qAyXcqbRvZPspa+wTwxLjn3zTG\n/DVwNrDF78kCAWfmRnPAwX6oP7Wr3vo0vj+O4zD6P8fBDTi4bv5+jt/uOA5M0378RyvXZcpjjpdx\nczEABNyp45ipT5NjLSSGWlFvv3Mwu76oyPKpoz1IZ5f/O4bPxO3owA2HC16G1IvH8RIJmoq8k/Wl\nKxfT0ZoX8XFyAAAgAElEQVT/hn2QK8jueXRnUccWEZmDXgL+atK2JcAL4zcYY44GUtbaN8ZtdgD/\n87CBSGTyANncpv7UvnrrU2e4lf1BFxyHpqBLJugS6WqjOzzF8uyZsQ/Jzc0uTNM+FRwb1O6MtNHd\n3j5jPCmGORDMVWddkTaC3TPvM9noe5SZ+IHebwy1pt5+54qlIqtKHNfl8PUbppwqCLlvRLoibfRG\n+xmOxqcc8SpER2uzRqdERMY8AqSNMVdba+8wxpwEnA/cMKndXwCnGmMusdYmjTHXAB7wZCEni0b7\nyWTm/s1OAwGHSKRN/alh9dang/2JxQcYTnkMpzOkUx7DAY9obz+k03n3i8XGCqfhYQ9gyvaxgf6x\nx9F+gqmZv1wfjvYznModtzfaTzNTf5E9VZ8OvkfjYy0khlpRb79zMNanYqjIqiLHdacdkXJdh2B3\nO800j16TJSIipWOtTRtjPgh8yxhzA7nrsa611r5qjPkK0Get/Qrwd8A/Ar8xxgwD+4DV1tq+KQ+e\nRyaTxaujv+fqT+2rtz5lMlmy2Syj/8tm8abp4/jt2WwWpmnveRMf+/m5Zbzs6CUdGa+4n/XB92jy\nvn5jqDX19jtXLBVZIiLS0Ky1L5C7tmry9s+NezwEfLKScYlIeUy12nI8OXb9enwgRXNmSKstS9FU\nZImIiIhIw5jq2vNUIAFdY22CmTe12rIUTb8xIiIiIlL32uf5v17qoIOrLYsUSiNZIiIiIlL3Lv2T\nY3Fapv7oeyDZwzdG1hW94D2LeGRrb4Uik3qkIktERERE6l4gEKBzmhWWhwPB0cdtRYx6iYynIkum\n5Hd4XBeFioiIiIiMUZElU/J7U2JdFCoiIiIiMkafimWCjtZmOtqCMzccRxeFioiIiIiMqfhIljGm\nC7gVOBNIAXdYa/++0nFIfm4gwNUXGl9FU2Jg2Pdol4iIiIhIo6jGdMHvAb+z1i4yxswH7jHG3GWt\nfbUKsVRMIpac8rX2cAuuWzuDim4gQGSaC0NFRERERGRqFS2yjDFHAhcCbwOw1h4AVlYyhmrZ/KPn\np3ytozPElde9p6YKLRERERERKc6MRZYxpgX4CHA+8G5gIeAA+4EXgJ8Bd1trh3yc72TgTeBaY8x6\nwANutdb+f8WFX9vawy10dIamHcWC3ChXX3yIzq55FYpMRGTuKnFeEhERKblpiyxjzCeAGwEXeAy4\nF3gLyJJLau8GvgF8zRjzRWvtrTOcrws4HBi01p5ojHk38EtjzA5r7S9m15Xa47oBrrzuPfTF8+f5\nRCw57QiXiIhMVIa8JCI1wMtkpr0e3A040NREvG+IYS9D2stWMDqRwk1ZZBlj7gGOA/4L8FNrbWaK\ndg65bxRvMMZ8wFp72TTniwIZ4J8ArLW/McY8AFwE+CqyAgHHT7OScFxnwmPXLfzcruvSvaB1itec\nCY8nH/9gXwMBh4Dr4DgjzwuMxQ2M7esGiutHpY47vs+NQn1uHI3a71IpU14SkSrzMhnueMiS6E9N\n2cZxHJqDLoF4jGVv9uU2HlHYeXoGe0m1DEz9erK3sAOKTGO6kawdwEettdMuM2etzQI/McZsAmZa\nJfBVoBloA0b+hZAF0v7ChUikzW/TWRvujY8+7gzPo7u7vbQnyIx90OrsbKW7O3/fIpE2UpkUB4Iu\nAF2RNoKFxNLURPPIvpGuNrrDoeJjrsRxqez7XCvU58bRqP0ugXLkJRGpssTA8LQF1nQ62oJ0tDZP\n+bqXGfuIefO220iFpi6yREppuiLrJmCeMWbGC4WstfGRpPfXM7Tbbox5Avgc8DljzDHkRrE+6Dfg\naLSfTKYyQ8Sx+OCEx809fdO0LuL4sXHHjw1AYGK/AgGHSKSNaLSfoWg/wykPgN5oP81M/Qdlsmhi\naHTfaG8/pH3XtBU/7vg+V+p9rjb1uTH6DI3b7xJ+QVXyvCQiteXSlYvzFk1uwCHS1caBPa8R3f8k\nAEvOO47IkQtwA1MvHBYbShQcw/xQF53BMLllBESKM12RFSU3yuSHW8A5/wz4rjFmD7nRrL+21j7u\nd+dMJotXoXm42XHnyXqlP+/443nTHD+TyZLxsmSzudczBcbiZcb29Ur48yvXcaGy73OtUJ8bR6P2\nuwTKlZdEpEZ0tDbnvY2M6zp0h0MMt7fQP7Iac2dbcNoCa7I/W3oFRx22cMZ2XaFO+gY8/0GL5DFd\nkXXeuMcnAJ8Cvgu8BASAk4ANwJcLOaG1dg+wqpB9REREKFNeEpHGEAmGWdg632fr6hdZfq8R6wp1\n0hSoxq1vZTpTviPW2kcPPjbGfBX4iLXWjmvyM2PMg8B3gLvKF6KIiIjykog0lpu33ear3fxQF58/\n87MqtGqM3zHW5cDuPNt3jLwmIiJSScpLIiLAgWQvvclYtcOQSfyWvK8AXzfGfMlaux/AGLMAuIFc\nQhMREakk5SURqVufPvk62iOHXps2Xk+y1/dol1Se3yLreuBu4D8aYwbIXVDcAhwA1pQpNhERkako\nL4lI3eqe10Vn64wLqUoN81VkWWufNcYsBk4HjiKXyF4DnrLWDpUxPhERkUMoL4mISC3zve7lyM0d\n3wKi1tq7rLWPAcXdOU5ERGSWlJdERKRW+SqyjDFHG2MeA14FtoxsOwrYYYx5VxnjExEROYTykoiI\n1DK/I1n/L7CH3JSMgzeCfJ3cfPibSx+WiIjItJSXRESkZvktss4FPmWtfZ2RZGatzQBfBM4oT2gi\nIiJTOhflJRERqVF+i6xBIJNnexhoLl04IiIivigviYhIzfK7hPvjwDeNMZ89uMEYswz4R+Dn5QhM\nRERkGspLInUi63m0DPUB4MVipNPBQ9pkXIcUw3iJeKXDIzEwPOVrXn+KYS/3fU+sP0UknMEN+F5X\nTuqY3yLr08C/AvuBgDFmaGTfR4G/KFNsModM9wdovI7WZv3xEZFSUF4SqQNZzyPxkx+wfNfrACT2\nbyXpHvo5wXEcDgRdhlNepUPknkd3Tvlay1Afy9/MFYgvPrKDYPcBrr7Q6LOO+L5P1uvA6caYU4Hj\nyE3T2GGtfbmcwcncMd0foPE62oL64yMis6a8JFIfvESCTF/ho1NuOIzb0VGGiHI6WpvpaAuS6C/s\nrhCJ/hSJgWEi7S1likzmCl9FljHmfcBWa+2zwLOTXvuctfYr5QhOalsxf4D0x0dESkF5SaT+bD/u\nbJZccCKdbYdOFwy4Dl2RNnqj/WS8LG5HB47rli0WNxDg6gvNjDN1vFiMxP6tpL0sL5YtGpmL/E4X\nfBT4tTHmI9ba30167W8BJbMG5PcPEOSmE/od7RIR8UF5SaTOpILzcDs7acrzRazrOgS722mmGc/L\n5tm79NxAYMYvhdPp4Mj0xgzB1CAw9XVlE47d0QGu34/hMhf5fXdTwFbgWWPMBmvt5nGvOaUPq/Ek\nYslDtrmuAxmHWGyQZi/fIlrV5+cPkIhIGSgviTQwz8vQFx+asd1Awt8146Vw/I4ngKmvKxvPDYd5\n24ZrKhGWVInfIitjrf20MeZR4PvGmNuAvxm5J0llvk6oc5t/9Py0r7d3BDk36xBw9OMWEUF5SaRh\neV6Gu257Ou8X1JXmdnTghsMM90YL2s+Lx0knErCws0yRSbUVNE5prf0XY8w24MfAI8aYK8sTVmNo\nD7fQ0Rny9UeiL5FiMNRMm1PYBZgiIvVMeUmk8fTFhwousFLBAVo7Sn8LPcd1OXz9Bnr2HeDFR3YA\nsOS84/JeVwa5hT7e2nR3yeOQ2uO3yBqdemGt3WmMOYvcvUi2oZs+Fs11A1x53XsmDHdnMhn6kunc\n646Dg8O9338GgOFMhuFshlh/Crcpt4+WRBeRBqW8JCKsueIkOjpDU77eM9jLzdtuYzg4SMA9oywx\nOK6L29nJUEs7wJTXlUlj8VtkfXn8E2ttCviPI9M0ri95VA3EdQN0ds0DwMtkuOMhO7pan+M4Ez4p\n7DswQEsmyYuP7Bj9h6wl0UWkQSkviQgdnaHRz1H5pFoGSIUGKhiRSI7f+2TdNMX2H5OboiElkBgY\n1v0YRER8UF4SEZFaNmWRZYx53Vr7tpHHvUx9IXHWWju/HME1sktXLibSHsTJOmy8JbdazRHzW2lz\nmlhy3nEMNLdqSXQRaSjKSyIiMldMN5L138Y9/ku0WlNFdbQ2E+logczYSsTNgQDNgQCdbUHcJl1y\nICINR3lJRETmhCmLLGvtneMef68i0YiIiExBeUlEROaK6aYL3uP3INbaS0sTjoiISH7KSyIiMldM\nN10wVrEoREREZlaWvGSMOR24GVgApICvWms3TtP+TOBx4Fpr7ffLEZOIiMxt000XvMbPAYwxF5cu\nHBERkfzKkZeMMUHgHuAz1tqfGmMWA782xjxrrX0pT/sW4NvAH/2eQ0TKp2ewl1TL1Eu09yR7KxiN\nyBi/98nCGNMBLAHG3/Ht7eSSTXuJ4xIREZlWifLSKnKrEf4URm9s/ABwJfC3edp/GdgMnFls3CIy\nO14mPfr45m236T5YUpN8FVnGmIuAnwBt5FZzOrjk3TBw51T7iYiIlEMJ89IJwI5J27YDp+Q551nA\nB4DTgJ8VGLKIlEhsKFHwPvNDXXSFOssQjUh+fkeyvkLuG727gN+R+6bwTOAa4MbyhCYiIjKlUuWl\nNmBw0rbBke2jjDEh4DbgamvtsDGmyLBFGoOXyZAYGJ65XX+KtFf83Rj+bOkVHHXYwhnbdYU6aQr4\nnsAlMmt+f9veBdxsrc0aY7DW9gBbjDE9wO3A+WWLsMSynoeX8PcNiNffX+ZoRESkSKXKS33AvEnb\n2ka2j3cTcK+19pnZBB0IODM3mgMO9kP9qV3V7JOXybDxYUu8f+YiqyXZx7IDA4CDg4MbcHDdQ2Me\n3x/XHds+f16YIzoWlCr0orkBB8dxRh/n6wNAxh1rN/k9Gr+P6059jLE2Ex/P1L7c6vnfUTH8FllR\n4DDgDSBujFlkrf098GsKmJdujHkHsBt4ZWSTQ26axzkjCbKssp7HGxu/hxeP+2rfnwkCy8oblIiI\nFKMkeQl4CfirSduWAC9M2nYp4BhjriKXu44AlhljTrLWTt5/SpFI28yN5hD1p/ZVo0898SSDwxma\ng+6MbZsyLs7IB9nurnm846guXDcwZftIpI2OeOvo845wK93dNbA0QFPTaH8jXW10h0N5m6UY5sBI\nu85wrh+j71Fm7AN9Z2cr3d3Tv3ep4NggfGekje72Gvg5UJ//jorht8j6V+BxY8wpwCPAncaY24D3\nAnsLPGfWWru0wH1KwkskfBdYkwXa9AsjIlJDSpWXHgHSxpirrbV3GGNOIjcKdsP4Rtbad45/box5\nBLi90CXco9F+Mpnip0bVikDAIRJpU39qWDX7FE0MMZzyALjs3GMJtwanbOvFosRey33GOuOcY4jF\n8i9iMb4/ifhYm0R8gJ6eyQPPlTe+z9Hefkin87YbjvaPtovFB1jY3TX6HsViY0XTH3/fM+XP4qCe\nwR6cjEM2kCUW7SeYmjwoX1n1/O+oGH6LrM8AnwUGgP8M/Jjc6k27geuLOnOVLfjw5bgdHdO2iceG\n4K6XAXDcmb+NkdlLZ9Ls69tPbKAfz8vfRvOqRYQS5SVrbdoY80HgW8aYG8hdj3WttfZVY8xXgD5r\n7Vfy7FrUJ4hMJos3i+tPao36U/uq0ScvkyWbzZ2zLdRMxzRFVjoVpP/gNLesM2Osuf6MO5dHTbxn\n4/vsTfMzz3hj7Q4WIgffo/H73PuDbb7Oe1xwJTtOfLRmfg5Qn/+OiuHrk6q1NkVuPjrkpmacO4tz\nOsaYO4BTySWzm621FV+h0O3ooCkSmb5NdvK10FJO6UyaL239GgdmuKfF/FAXnz/zsyq0RBpYKfOS\ntfYF4Ow82z83zT7vL/Z8IiL5tIdb6OgMkYglfe8TTLXSXOURLMmvkPtknQIcx8T7kQBQwHSJPuC7\nwC3W2heMMWcDPzPG7LHWPu43FqlPvcnYjAUWwIFkL73JGAtb51cgKhGpVSXKSyIiNcF1A1x53Xvo\niw/N2DYRS7L5R89XICoplt/7ZN0CfApIcehSt1nAVzKz1h4APj7u+RPGmPuAtYCvIms2q3xMWNHF\n16otha3yMluTV6YJBBwymbHXB2nByTok+lIMBgI46dyLA/EhIu3BKS8U9bviTTn5iWH8jMzPnHY9\nXS0TRxoPJKP84zO3jrat9io6pVKPq/HMpBH7DI3b73IoVV4SEaklrhugs0sjU/XA70jWeuBia+2D\nszmZMaYL6LbW7hy3OUDu5pG+zGbFkvErunRF2gjOtBpNgau8zNqklWki4RA9b40tI/+r5LtyD+78\nLQDNI9v/9fvPEumex6f++/txm/IUWj5XvCkrHzGMXyXnmMPfzhHtE+97sa9v/+jjWlpFp1QacTWe\nRuwzNG6/S6wkeUlERKQcClnC/f+U4HzvBb5rjDndWvtHY8xy4EJgld8DzGbFkvEruvRG+2keLVPy\nG7/KSyw2AIHyXsQ3eWWaQMajMzKPcCREPDr9/NxozyC//92BvN9++F3xppz8xBAbGCso4/EBgqm+\nKV+vhVV0SqUeV+OZSSP2GRq332VaXrlUeUlERKTk/BZZfwt8wxhzk7X29WJPZq3dMrJa08+NMVkg\nCXzMWvtrv8eYzYolE1Z08WY+zvjXJ6/6Ug6TV6bJZLK4TQH+9BNn0PO7N3lr091AbmXE/qYQ9zy6\nE2c4Q/Pevmlj9LviTTmNjyHal8LL8wGzN5kafRxLDBF2PdzA2MhcLa4mVEqNuBpPI/YZGrffJVaS\nvCQiIlIOfousOLkbMX7CGHPIi9Za3+ubW2tvAW7x215y83PDnS0MBnJFSLizhUBTCJrd4tYQrrJ7\nHt2Zd3sqkICu3OO7/20X3aEoV19oJhRaIiIjSpaXRERESs1vkfVPwC+AB8jdk0SkIB2tzXS0BUn0\np2ZuPCLRnyIxMEykvaWMkYnIHKW8JCIiNctvkdUBbLDWVv5iHqkLbiDA1RcaEgNTr3FyINnDN16o\nYFAiMpcpL4mISM3yW2TdDnwIuLuMsUidcwOBaUelhgNT3xFeRGQS5SUREalZfousNuBbxpi/Af4A\nZMa/aK29tNSBiYiITEN5SUREapbfIqsZuL+cgYiIiBRAeUlERGqW3yLrL6210bJGIiIi4p/ykoiI\n1Cy/RdYfjDFha+1cXDFcGkw6k6Y3GZu2TVeok6aA319/EalByksiIlKz/H7K/BHwX4wx37TWZmZs\nLVIl6UyaL239GgeSvdO2mx/q4vNnflaFlsjcpbwkIiI1y+8nzGPIreJ0ozHmj8CEmx1Za08tcVwi\nRelNxmYssAAOJHvpTcZY2Dq/AlGJSBkcg/KSiIjUKL9F1uMj/4nMGZ8++Tq6Q10TtvUke7l5221V\nikhESkh5SUREapavIsta+8VyByL+eYkEXiBFy1Af2fTY5QhePE7aGRp97nZ04LhuNUKsCd2hLo1U\nidQp5SWR+uN5Gfp7B/O+5roOZBxisUEGEsMVjkykcL4vSDHGrAOuABYDWWAH8F1r7X1lik2m8Nam\nuxn2Mix/s4+hQIjtHWeMbh8MjM2YccNhDl+/oUpRioiUl/KSSP3IZB1+8qNX6EukZm5c4xIDUxeB\nXn+KYS93GWm8b4gFni4prVe+iixjzF8DfwP8GHh0ZPNy4C5jzMestXeVKT4Z4XZ04IbDePG47328\neBwvkYCmeWWMTESk8pSXROrLYLa54AIrFRygtaO5TBEV755Hd075WstQH8vf7APg4V+8SvjlOFeu\nWgw4FYpOKsXvSNb1wCXW2l+O32iM2Qh8HVAyKzPHdTl8/YZc0QTE+lO8+MiO3HTBfWkAFnz4csKd\nLXiJBG9turua4YqIlJvykkidWnPFSXR0hiZsc12Hzs5WYrEB9vf1cPO22xgODhJwz6hSlBN1tDbT\n0RYk0V9YoRjtGyLRP0xHa7BMkUm1+C2yDgOeyLP9EXIrPEkFOK5LUyQCgNs0xFBLOwQ8guRGt9xw\nmKaIRq1EpCEoL4nUqY7OEJ1dEz/PuK5Dd3cbBLIMNrWQCg1UKbr83ECAqy80004VBPBiMRL7t5L2\nsrxUodikOvwWWTuB9wP/e9L2c4A/ljQiERGRmSkvidSorOfRMpSbEufFYqTTU4/SHJyhUw/cQIBI\ne8u0bdLpIEk3AOharHrnt8j6OnCfMebHMFp4vxu4HPhv5QhMZDo9U9wLa6rtIlJ3lJdEalDW80j8\n5Acs3/U6AIn9W0eKCpHG4ncJ9zuMMW8A1wHrgBZy3yKut9beU8b4RPLSva5EGpvykkht8hIJMn3+\nF+k6KNDeDsnc457BXlItE6cDui6kgoPEBvr1harMCb6XcLfWPgQ8VMZYRKbVFepkfqiLAz7+uM4P\nddEV6qxAVCJSLcpLIrVt+3Fns+SCE+lsm3lRh9iwA999Dsh9kVpr11yJFMrvEu7NwEeBJcAhKytY\naz9T4rikBqUzaXqTsWnbdIU6aQr4rt0L0hRo4vNnfnbGGModh4hUn/KSSO1LBefhdnbSNMN1SgDx\n118v+Pj6QlVqmd9PoXcCa4EXgMlfLWRLGpEULRHLjbN78SH6M7lvjeKxIfqbHMhmwSn+HgzpTJov\nbf3ajKNI80NdfP7Mz+YtcGYq0vwM/zcFmljYOn/mgEWk3ikvidSpP1t6BUcdtnDCNteFzkgbsWg/\nnpfbpi9UpZb5/c28EHiPtfY35QxGZmfzj54f92xZ7v/uehmA5qYAw4vCRR+7NxnzNU3vQLKX3mTs\nkELIb5EmIuKT8pJInYoEw4d8jnBdh+72doKpeXievkeR2ue3yIoB28sZiBSpKUBbRwv9iaFpmznp\nDKRLs1zop0++ju5Q14RtPcne0cUo8o1I9SR7fRdY8+d109zTOvtARaSeKS+JiEjN8ltk/QNwozHm\n76y1XjkDkgI5DhevO5mmcfWTF4/z1qa7AWg5fy1bNu8s6Sm7Q13TTtmbaeW/fEXaQa4LXe0LuOWu\n58lqxo+ITE15SUREapbfImsNcBrwSWPM74EJCc1ae2qpAxP/XDdAZ+fYRaVpZ4jBQAqAee0zr+hT\nCn5X/psf6mJx5Jgp51C7rgOaXy0iM1NeEhGRmuX30+yTI/+J5OV35T9dpCoiJaK8JCIiNWvKT7vG\nmMXW2p0A1tov+jmYMeZYa+2uUgUnc4tW/hORclJeEqkeL5MhMTA8c7v+FGktTCEy7UjWE8aYzwPf\nnWm+uzEmAHwM+CJwZAnjExEROUh5SaQKvEyGOx6yJPpTM7ZtGepj+YH+CkQlUtumK7JWAT8G/sYY\n8x3gfwMvWGsHAYwx84B3A/+BXCLrH9lHatSw08+BZA/DgfzXaTXaVL7p7svVaD8LkTlCeUmkChID\nw74KrMna5gXpaG0uQ0QitW/KT5HW2peMMScD1wB/Afw9kDXGHFwrvAVwgBeBrwDfs9amyxyvzMJr\nnb/gGy9snvL16W4kXI+mWwWx0X4WInOB8pJI9V26cvG0hZMXi5HYvxWA089bjBsIVCo0kZoy7SfI\nkeT0beDbxpjDgeXAwYtuDgAvWWv3FXNiY0wn8FvgYWvttcUcQ0prqhsJ1xO/qyA2ws9CZC4qZ14S\nkZl1tDYTaW+Z8vV0OkjSzRVWKrCkkfn+mt5a+wbwRgnPfTMwWMLjiQ/XnnANiyKHTdg2/kbC9W6m\nVRAb6WchMteVIS+JiIiURFXmQhljLgGOBe4EFlUjhkYVCUYafnRGqyCKiIiISDlVfBzXGNMF/CO5\nOfVa41NEREREROpKNUay/hG4xVr7qjGmCqdvXM2peQzEU8TcibM0+waHcDIO2YBqXhERERGR2fJV\nZBlj/h/gDmvtttmczBizBjjWWnt1sccIBJyiz59xHRwnt3/AdXDd6Y81/nXXR/vZcgNj8bkBZ7Sv\n+fo8ue342Mb30xm3/Z2vnMHjr+wGdh9yvOOCK9lx4qO4Lnn76boTH5frZxEIOGQAHHBwDulbuVWq\nn+NN9z7Xq0bsMzRuv8uhVHlJRESkHPyOZC0DnjbGvAJsBO601u4t4nz/F/BOY8wucsvsRgDXGGOs\ntWf7OUAk0lbEaXNSDHMgmPsU3RVpI9jdPv0OmbEPQp2drXR3F39uX5qaaB6JL9LVRiQcyj3O1+dJ\nbbtH2sLEfh59VIS2cJD++PT3twimWmlOzWO4OUkqeOh6JMPDydHHnZE2uttn+NnNQk88SXNz/r6V\n2/i+l7ufk83md3uuasQ+Q+P2u8RKlZcwxpxObjGmBUAK+Kq1dmOedp8hd/8tgD7gb6y1vyjmnCIi\nUt98FVnW2g8YY7qBS4HLgS8ZY34JfB/4l4M3gvRxnPXjnxtjbgTeUcgS7tFoP5lMcdPahqP9DKc8\nAHqj/TTTjOdl6IsP5W2fiI0VFrHYAJR5Ol00MTQaX7S3n0DGIxJpy9vnyW1Jj90KZnw/44lBzvnI\nMdzy77kV8z629Bre0X34aNtELMm9Pxj7IvhL//bNGeOMRfsJpuYV2cvpBQIOBFyGhz3IHtq3cosN\njN2lvpz9HC8QcKZ8n+tVI/YZGrff3TN9oVWEUuUlY0wQuAf4jLX2p8aYxcCvjTHPWmtfGtfug8B/\nAt5jrX3LGHM58C/GmCP9nktERBpHIUu49wDfAb4zsnjFeuAW4J+NMT8Evmat3VmeMMdkMlk8r7gP\nJxkvSzabHX2cSnncddvTE4qpqXhe8ef1y8uMxedlsqMfwvL1eXLb8a9P7mfWcUiFBgAItQdpHzcy\nVGif5oe6CDd3lvdnEQCykM1mD+lbuXnexMeVPPdsfrfnqkbsMzRuv0utRHlpFZC11v505Jg7jTEP\nAFcCfzuu3avAVdbat0aePwSEgaOB7aXqk4g0jubhQbJJFy8WJZ0Kztje7ejAGX9dg9S0gha+MMa4\nwIXAOmAt8BpwB3AYuW/+rrHW3uv3eNbaLxZy/lLriw/5KrA6OkO0h6e+8V69+PTJ19Eemb6fXaFO\nmglZYbkAACAASURBVAJVWflfROQQJchLJwA7Jm3bDpwyfsOkUa0AuVGt58kVXyIiBTt++xM4AYfY\na230+7gG3A2HOXz9BhVac4TfhS9OI/cN4RXAPOAnwAXW2sfHtXkAuBXwXWTVkjVXnERHZ/5rf9rD\nLbhu7d61PJ1Js3+gb/R5ZjBOyhsG4MBgL9HszIUkQPe8Ljpbyz89TkRktkqYl9qAydP9Bke25zvv\njcBfAHuBj1prM8X2QUQaj9vRgRsOM9wbLXhfLx7HSyRoikTKEJmUmt8hiaeAfwM+C9xtrR2Y3MBa\n+/+zd+9x7lX1vf9f2TvJZJLJZDIDgtbihcICQUCrQKUtchC1VutPbKuoFGx7qHpazvFCa6UHTz1H\na1v1p/RiS2vVIoqAgCIee46K2KLosQr8QF18AS+Hm9/bTCaTTCbJ3vn9sTMzmZlcdmaSTJJ5P3nw\n+GaSlb3Xyt7J3p+91/qs/2WMCXc2P4TSmQSZ7OgFGDU8PnjPh5gvr39Zk8se587nAfjyd/6a4qSu\neIjI2OnVcWmJIEhrlKo/v0W9B8afGmNeAvyrMea51tqHwlZ6XDJLjlumzHFrD/S2Te0yGm/WbSbn\ntXV0yO47NtvIjfKki1/P4ccO8i9feYBozOWZZz+dqWTr7oJefpEDn/kMsP6Zbv58BpURuZ2x2UYN\ndtKWsEHWW621H9z8pDEmBfyhtfadANbaE7ddE9mWilPcEGC1E/VSZOLTLV9v13Vy2O/micie06vj\n0n3AWzc9dyJwz6blng0UrLXfri/3C8aYHxGM6QodZI1bZkm1Z/j1pE1tMhpv1nUm57qDS8m1x+np\nZMuEOeOyjSITE/jpA5SB7DFPav+ZHp5nYfNn6m88+R90RuR2xmUb7VTbIKuedWkSeI8x5iMEadcb\nnQj8EfDO/lRPunHpaZcwm8ji5xYp3n89AM969m+wFEtw4+0PEvOTbcdT3XLt3S1fS2cSXHDJ6Qq0\nRGRX9eG4dBtQNcZcZK39uDHmVOA84PJN5U4HXmOMOcdau2CMORk4DvhuN/Ufl8yS45Ypc9zaA71t\nU7uMxps1y+QcRn6xuOHx4cMbbyaP2zZayK9QqXjEYi6LuWLXn2kut7GX86AyIrczbtsI1tu0HZ3u\nZL0R+ED9cavbJXdsa83Sc7OJLEcm56iWXapu8KM2N5llIjpJ3N/f9D1T0xOkM4mOCUDyuRJLiysj\n2aVSRMZKT49L1tpqPT37h40xlxOMx/pta+0Dxpj3AEvW2vfU1zkL3FXvglgF/vPqna2wxi2zpNoz\n/HrRpnYZjbesb1OG47DrDpvdd1y2kefXoN6MTm1q9pluyTo94IzI7YzLNtqptkGWtfZDxphrCLI1\nvbBJkWW6vIonw8V1HS645PS2c4W1u8MlIjJI/TguWWvvAc5q8vw7Gh57wB/X/xcREWmr45is+qSL\nx1lrfzKICsngua6jO1QiMjJ0XBIRkWHXMsgyxlxrrX11/c8PGmNaLsRae36vKyYiItJIxyURERkV\n7e5kLTY8zvW7IiIiIh3ouCQiIiOhZZBlrb2k4fHrB1Mdke55vk++WAldPp2M4TrKkigyanRcEhGR\nURFqnixjzDTwDmvt2+t/vwn4PWAf8PvW2sf7V0WR1jzf5+NftOQL5dDvSafiXPRio0BLZITpuCQi\nIsMs7GTEVxHMS4Ix5jnAB4H/AZwMfAh4VV9qJ9JBvljpKsACyBfK5IsVZqYm+lQrERkAHZdERoDn\n+S0zGDcq5sP3SBEZBWGDrBcAx9Yfvwa41Vr7rvqVxH19qZlIl84/+1jSydaTHuaLFW68/cEB1khE\n+kjHJZEh53k+11/1rY5zcYqMo7BBVoz1AccvAv6i/ngJ2N40yCI9lk7GdHdKZO/QcUlkyBWWKl0H\nWOV4kWS69QVTkVERNsi6F/iv9Vnunwp8rv78y4Af9b5aIiIibem4JDJCXvbqU0lnEi1fP7w8z5V3\nXUUlvozjnjHAmon0R9gg683A1cAMcKm1dt4YMwd8GnhdvyonIiLSgo5LIiMknUmQyU62fL08UaSc\nKA6wRiL9FSrIstZ+CzCbnjtkjDnWWvtIX2omIiLSgo5LIiIyzMLeycIYcwzBAW1y0/M/b639XPN3\niYiI9IeOSyIiMqzCzpP1xwSpcSNNXq4Bbi8rNW7CTpbbzYS6IiJ7mY5LIiIyzMLeyfovBJM8XmOt\nXe5jfcbOdibLleFwuDTf9vVsIkPUCX0zWER6S8clERkLi8Uynl9r+bpXKFPxfAByhTIz0/6gqiY7\n0E0K949aa71+VmYcbWey3HQq3na+p254+TyeU2ZiZSn4O5ejWo03Leum00RcXfxddeVdV7V9fS6R\n5YozL1OgJbI7dFwSkbHwma8+RK3WOsiaWFni5P3Bedy9t+0jPnuIV5xxzKCqJ9sU9uzwOuClwGf7\nWJex12my3FXpZAzXcXqyzoM33UDF89e+nPkDd1Jymy/bnZ7mqAsv3tOBVjaRYS6R5VCHu1gAh0rz\nzJdyHJmcG0DNRGQTHZdEZGSlkzGmUzGWK93flcoXyiyVqn2olfRS2CDrUeDvjDGXAA8AG64cWmvf\n0uuKjaNBTZbrptO409N4i4udCzfwFhfx8nmiMzN9qtnwizpRrjjzMuZLuZZlDpfmO97lEpG+03FJ\nZABqnoeXy4XqEQNBDxrpzHUcLn7JCbjxOAvzhfbdBXM58gfupOrVuHeAdZSdCRtknQP8AEgCp2x6\nrfVeIbsi4rocdeHFaz90uUKZe2/bB8CJ5xxHJrXxx9HL5zl40w0Dr+ewijpR3Z0SGX46Lon0Wc3z\n+OnVH6M0vxCqR4x0x3UcZqcTUK3iea1/tqrVeP0z11isURJ2nqxz+l0R6a2I667dkXKjK6xMTAWP\nMxmiA7ibtle0S46hxBgi/aPjkkj/efl8171iVrnT0zipVI9rJDI6upknaxr4deAYa+1/qz93nLV2\nX5/qJjL02nUbVGIMkf7ScUlkcO4/7izK8cmmPWKacdNpFheVWVn2rrDzZJ0N3AI8BjwF+G/GmKcA\n3zXGvNJa+y99rKPIUAmbHEOJMUT6R8clkcEqxydZmZjado+Yw8vzlCeKrV8PkXBKZJSEvcT+l8Af\nW2v/xhizDGCt/bEx5iLg3YAOZrJndEqOocQYIgOh45LICLnyrqsoJ1oHWSLjJmyQdRLw9/XHjSPz\nbgI+1ssKiYwCJccQ2XU6LomMoblElmwis9vVENmxsEHWT4EnAv930/PPBAo9rZFs22KxTMxf2fJ8\nvljZhdqIiPSVjksiI+bS0y5hNpFtW0ZJo2RchN2LbwJuNMa8G4gYY54HPAv4I+AT/aqcBKp+lYPF\nhQ3PLZbKlJ08VWf9XOLG2x8k7u8fdPVERHaDjksiI2Y2kVUvENkzwgZZfwy8B/goMAH8G3AA+Bvg\nz7pZoTHmxcB/B1IEXTz+3lp7ZTfL2EuqXpV33vEXzZMstL8YtEU6FSedjPWmYiIiu6tnxyUREZFe\nCztPVhl4mzHmMuAJwLK1tuuJE4wxRwHXA+dZa+80xjwNuMsY8+/W2ju6Xd5ecHB5vmMWO4BsfIbX\nn/estrfY08kYrqMJBEVk9PXquCQiItIPYVO4Hwm8DHg64AHfM8bcaq1d6nJ9NeA11to7Aay1PzTG\n7CMYwKwgq4N2fZnVh1lE9pIeHpdERER6ruNZuTHmd4ArgRJwf/09bwVWjDG/Z629IezKrLX7CeY1\nWV32fwCOAf53l/Xek9SXWUSkt8clERGRfmgbZNUHEv8V8AfAR621fv35SYLBxf9sjHl49c5UWMaY\nXyFIvZsA3mCt/WHY9zpOpJtVbeC7ESKR4P2OG8GtrS/LdSO47vaX3YrrrK/Tdbpbx+a2ui59qeOW\nz8Xd+Lk0Pu7H+hs5TgQfIAIRIh0/s24+351si2647sbHndazup13sm+Pmr3YZti77e6lfh2XRERE\neqnTnaxLgf9urf1I45PW2mXgvxljVoDLCbpshGat/Z/AMcYYA3zeGBOz1n46zHtnZlLdrGqDMhUO\nxYMz4OxMipgfX3stk0kyO7v9ZbcUjRKrr3Mmm2J2OtHV2x9fWp+4LzOTYnZqqqfVg62fS3y2YR3+\n+slg3z6jTQ4vlojFQn5m3Xy+O9wWYZXjy2uPu9lmO9m3R9VebDPs3Xb3SF+OSyJ7jef7Had48Qpl\nKp5P1au1LSciW3UKsn4ReEeb168C3hZ2ZcaY4wFjrb0FwFprjTGfA34NCBVkLSwU8P3tfdkrCwUq\nZQ+A+YUCxVp17bVcrghO739EFvIra+tcmC9AtdrhHescJ7JhC+UWCsTLk72u4pbPJcZ6BsJcbj1g\nePgnh4PPqYWp6Qlcd2eJNRwnAo5LpeJBrfNn1s3n21j2J4/MszAfb1l2VTrVfbKQXHE9rX6YbeY4\nEWZmUjvat0fNXmwz7N12z8729OJQT49LInuR5/t8/IuWfKHcttzEyhIn768Pczx6ABUTGSOdgqxZ\n4EetXrTWHjLGJLtYXxa4xhhzlrX2/zPGzAAvAD4edgG+X8Pb5hUV36tRq9XWHnu19eV43vaX247n\nr6/T20HdATyPvtRxy+fibfxcVt18zV0dl/Xq330uTotAK3QQ5gA1qNVqHT+zbj7fxrI33PZg53oQ\npL2/6MWmq0DL8zY+DrvNdrJvj6q92GbYu+3ukV4fl0T2nHyx0jHAamZ1KhjP81laXOm8nlxpO9UT\nGQsdE1+s9ndvI/SZgrX2m8aYNwHXG2McIAJ8FvhQ2GXIYE1NT5DOJEL/UF77j/+n5WvpTIILLjl9\nx3e7tiudjJFOxbs6sOQLZfLFCjNTE32smYh0o5fHJZG97vyzj205h6aXy5E/EAxvPPGc45h54hFQ\ng09d9S0FUCIddAqyosaYPyAIhlpx27y2hbX2E8AnunmP7B7XdbjgktPbXrHyPb9tcLUqnyuxtLhC\nJtv7Lo9huI7DRS82HfugQ3CV78bbw93tEpGB6vlxSWQvSydjLS8kVqtxSvULo5lUHNdxyM0vdx1g\nleNFKg3jlUX2gk5B1qPAW0KUkTHmuk7HwOiSy365ZSCWz5W45dq71x63EnQn7O+5kes4uislMtp0\nXBIZEi979amkM+2TSB1enuf93/sQtT6MexcZZm2DLGvtUwdUDxlxYQIxYC3YaiadSfC6N56BqzmV\nRaQFHZdEhkc6k+h47C9PFBVgyZ6k01npu7Djuta6Ex7ZOeufiIiI7K7Dy/OUJ1pnHQY4XJofUG1E\nhouCLOm7TuO6GrsTioiIyGi48q6rKCfaB1kie5WCLBmIsN0Jx027K3jZRIaoo6+giIiMv7lElmwi\ns9vVEBkYneHJUMnnSuC6UPGgViOfKxGpBNmaezHZ8aBdeddVLV+bS2S54szLcN3mqXNFRESG3aWn\nXcJsItuxnC4syl6jvV2GyuqEx6thx+ev/u7aa7s9z1ZY2USGuUSWQx36oR8qzTNfynF07IgB1UxE\nRKS3ZhNZjkzO7XY1RIaOgizZdV0nxhjybodRJ8oVZ17GfCnX9PXDpfm2d7hEZLCMMc8FrgSOAMrA\ne621VzcpdylwCcE8XEXgj6y1XxpkXUVEZDQoyJJd15gYw3Uj4Lr80+fupVarcf7ZxxKp+COXGCPq\nRHVlT2QEGGPiwI3AW6y11xtjjgW+bYz5jrX2voZyLwX+EHiutfYxY8xvADcYY55grS3vTu1FRGRY\nKciSDbx8PnRZN50m0qPJg1cTY7huBKJRiLlQq5HOJNbGZImI9MG5QM1aez2AtfZBY8ytwAXAnzSU\newD4TWvtY/W/bwGmgacA+wZYXxERGQEKsmSDgzfdELqsOz3NURde3LNAay86XJrHdaEcXyZXLOB5\n3b1fA4lFduwEtgZJ9wPPanzCWvuDTWVeCTwMPNS/qomIyKjS2ZngptO409N4i4tdvc9bXMTL54nO\nzPSpZuNvp2OzVjMUKtAS2bYUsLzpueX6800ZY54PfBB4lbW2y0sjIiKyF+jMTIi4LkddeHHoroJe\nPt/VHS/ZKGz2wTBWMxRq/JfIti0Bm7PppOrPb2GM+S3gLwi6Dt7W7cocJ9J1BYfRajvUnuHVrk2u\nEyESiaw9dt3m7fbd9XKOG5TbXNZ1afn+Xtpr26jR6nZY+y8SwY3sznZoZ5y30XYoyBIgCLR0R2ow\nNmcfdF3IzKTILYTvLqgMhSI9cx/w1k3PnQjcs7mgMeZ3gHcAZ1tr7XZWNjPT8gbZSFJ7hl/TNkWj\nxOJBV/+ZbIrZ6UTT95apcKheLjuTIj47Bf7Gk87MTIrZqaneVrqNPbONGqxth0iEaNzFj7tMZzZe\nGxr0dmhnHLfRdijIEtkFjdkHXTfC7NQU8fIknlfb5ZqJ7Dm3AVVjzEXW2o8bY04FzgMubyxkjHkG\n8F6C7II/2u7KFhYK+P7of88dJ8LMTErtGWLt2rSQX6FSDq7qLcwXoFptuozKQmGt3PxCgRgxcrmN\nvWtzCwXi5f5PrbLXtlGj1e1QqfpUyx4Vx2Nxl7ZDO+O8jbZDQZaIiOxZ1tqqMeblwIeNMZcTjMf6\nbWvtA8aY9wBL1tr3AJcCceALxhiACFAjSP3+xbDr8/3aWF1MUXuGX7M2eX6NWq229rhVm31vvZzv\nBeW2LMtjoJ/ZXtlGG16vb4e1/2o1vNrubod2xnEbbYeCLBER2dOstfcAZzV5/h0Nj98AvGGQ9RLp\nh5rn4eVyTKwEww69XI5qNd60bDfTuojIRgqyhkDVr66Nz2nkulCplHahRiIiIjJuap7HT6/+GKX5\nBU7eHwRZ+QN3UnKdXa6ZyPhRkLXLqn6Vd935lz3JNCciIiLSipfPdz1dCwTzYrrpdB9qJN2Kl4Ox\nWH7DXcZEycPPLVItb5y31E2nNZfpLlKQtcvmS7lQAdZcIks2kRlAjURERGTc3X/cWZTjk5x4znFk\nUs27C67SyfrwOH7fHQAs7Z8FngnA8+4uUPzx9VTd2Iay7vQ0R114sbbdLlGQNUQuPe0SZhPZtb8b\nU3tPxzKacFZERER6ohyfZGViCjeTITo1sdvVkTbcdBp3eprK/EJX7/MWF/HyeU3Rs0t01j5EZhPZ\nDZPKKrW3iIiIyN4WcV2OuvBiDj9+iHtv2wfArzznKXDzAwB8/dQUp//CbzA3GVyo9/J5Dt50w67V\nVwIKsmSk5HMbE4Hki2WoBHN4eJ6/G1USERER6auI6+JmMqxMBBMOOw1j5EoJl4UJDycenA/50Spl\nrwLAoeX5tecBsgn1jBoUfcoyUm659u4tz632JL/1E3fx2jecgdvjLEn5YiVUuXQyhusoQ5OIiIgM\n1pV3XbX2OLnsce58kBjjy9/5a4qT62Oy5hJZrjjzMgVaA6BPWIbe1PQE6Uxiy12szQr5FZYWV8hk\nezvj+Y23PxiqXDoV56IXGwVaIiIiMpQOleaZL+U2DE+R/lCQJUPPdR0uuOR0lhZXtry2WCxz05f2\nEXusPt9Hm0Bsanoi9F2udDJGOhUnXyiHrme+UCZfrDCjAcQiIiIyIJeedglTM+vnHn5ukeL91wPw\nrGf/Bk5mmsOl+Q13u6T/FGTJSHBdp+kdqlrMoRZbD5yadSdclc4kuOCS00MFWq7jcNGLTaiugvli\nJfTdLhEREZFemp3MkkmunyNVy+5aOve5ySzRpLIL7gYFWTLUOgU5+WIFog61qEOk2j7xRT5X6qo7\noes4uislIiIjwfP9DcdM14lANMpCfgXPDzIUe4UyFc+nqozFY2VzLx5vcYWCH4xYX8yt4NaWWVpe\nIeJHqDna9oOiIEuGWqg7RJEIlWOmOf+spzGd3DqhYj5XanuHS0REZJR5vs/Hv2g3dHGPRCLE4i6V\nsketFpxYT6wscfL+oHs9R29ahuc37Za/Wafx0TJ4zc9xTgr++dT31545Ln42+065fTCVEgVZMnwy\nqTjTqRi5pfDjodJTEzzpiWklnRARkT0nX6x0NYZ4VToVJ52M4Xk+n7rqWwqgRkhyKh4qKVijeDlJ\nrNzb5GDS2sCDLGPMucC7gQzgAB+21n5w0PWQ4eW6Dhe/5AQW8l0EWUqfLiIiwvlnH1s/JkaYyaZY\nmC+sdxfM5cgfuBOAE885jpknHoHrOOTml7sOsMrxIpX4cs/rL+G0SwrmLS6uTUZ8xCt+nWItrh49\nu2CgQZYx5ijgZuBl1tqvGmOeDtxljPmGtfabg6yL9IaXz4cq56bTRFy3c8HV8hoPJSIi0rV0MsbM\n1ASuG2F2OgHVKl59DFa1GqdUT/6UScWbXpz85Vc8jWQ61nYdC+VFPvLARzW+Z5e1SgpWjayw7AQX\nqqczE7g1nU/thkHfyfKA11lrvwpgrX3IGPM94BRAQdYIWr1S0ok7Pc1RF17cVaAlIiIig/WPD36U\ncqLYuaA6j4i0NdAgy1p7EPjs6t/GmGMJRubdMch6yM646TTu9DTe4mLo93iLi3j5PNGZ4U0jGmbQ\nbzdzbQ3K4dJ829eziYxmdhcRkZ6bS2TJJjK7XQ2RobRrZ17GmCcDnwP+3Fr7vd2qh3Qv4rocdeHF\noboKevl86LtduynsoN9u5toalE6TC84lslxx5mUKtEREJJRLT7uE2US2YzldxBNpbVe+GcaYZxOM\nzbrSWvu+bt7rOJFtr9d3I0QiwfsdN4JbW1+W60Zw3e0vuxXXWV+n62xdR2PvOddlw+urbd1Jm/vG\njRKd6/wDXNn8mXf4jPvR5sZ1ttrOS4vlUIN+87kSy4Xyhj7QnbZxJ9tp8xGpGeYSWQ51uIsFcKg0\nz2Ilx5HJI7qqVz8N9b7dR3u13SIyWmYTWY5Mzu12NURG2m5kF3w2cCvwRmvtzd2+f2Ymte11l6lw\nKB5ENdmZFDF/fU6lTCbJ7Oz2l91SNEqsvs6ZbCoYhNpYp4bMPJmZFLNTU1sWsZM277bNn3l8dmv7\nmulpm/3Ixsd+kxPchucufMMvbBlImptf5uq/+wbQZF/psI3D6rbNf/XSd3FwuXWQdbBwiHd99UNA\n631rt43yvr0Te7XdIiIie8WgswtOANcBb9pOgAWwsFDA97eXzaayUKBS9gCYXyhQrFXXXsvlitCH\nLDkL+ZW1dS7MF6Ba3fB6rlhYf7xQIN4wf4HjRJiZSe2ozbtt82ceo33Gon60OZdbD2RXA6X2laht\n3Rca/t68r3Taxh1Xt4M2x2k930Wssh7sbd63dts47NvbsVfbPRvy4oqIiISTL1ZavuYVylQ8H4Bc\noYyTaH/uJf0x6DtZrwCeArzbGPOe+nM14Fpr7bvCLMD3a2upSLvle7W1Wc99r4ZXW1+O521/ue14\n/vo6vSZ197yNj5vVYSdt3m1bPvOQ7ehlmydT4SfsS2cSTKbiTbZT632l0zYOq9fbOcy+tdtGed/e\nib3abhER6Y0bb3+w5WsTK0ucvH8JgHtv20csNT2oakmDQWcXvBa4dpDrFGk3Yd9mw5g9UERERCSd\njJFOxckXyl29b2m5QrxzMekxpYSRPaHVhH0iIiIio8B1HC56sWnbVRDAy+XIH7iTqlfj3gHVTbZS\nkCUiIiIiMgJcx2FmaqJtmWo1Tsl1AH8wlZKmFGSJ9FCnq0ur0skYrqNuiSIiIiLjSEGWSA+1G4ja\nKJ2Kc9GLzcACrcNt5tPSZJIiIiIivaUzK5Ed2s5A1HyhTL5Y6XjLv1euvOuqlq/NJbJcceZlCrRE\nREREekRnVSI7FHYgKgTdCcPe7dqpbCLDXCLLoTZ3sQAOleaZL+U4Mjk3kHqJiIiIjDsFWTIwXj7f\nsYzvRihToeY5wOiMWQozEHXQok6UK868jPlSrunrh0vzbe9wiYjI8Kt5Hl4ux8RKMC+Sl8tRrcbX\njqeVhQJ+fV6+MMdhEekNBVkyMAdvuqFjmUgkwqG4i59I8YTXXUTEdQdQs/EVdaK6QyUiMqZqnsdP\nr/4YpfmFtcln8wfupOQ6a8fTStmjVtPk5yKDNjq3CmQkuek07nT3M417i4u64iYiItKGl8/jLS52\n/T53eho3ne5DjURkle5kSV9FXJejLrw4dMBUK+ZZuOXmPtdKRERkvNx/3FmU45OceM5xZFJxHDdC\ndibFfEN3wVVuOq2eIiJ9piBL+i7iukRnZkKV9d1In2sjIiIyfsrxSVYmpnAzGaJTE7huhPjsFDFi\neJ66C4oMmroLioiIiIiI9JDuZPVZ1a9SdoKucodKh6k48Q2vt5skVkRERERERo+CrD6q+lU+eM+H\nmM8uAPCBe3a5QiIiIiIi0nfqLthH86Uc8+WFUGXnElmyiUyfayQiIiIiIv2mO1kD8uTcufzmL53C\ndDLe9PVsIkPU0eYQERHZ6zzfJ1+sdC5XKFPxfKpKbCEydHRWPyBRP8VcYpaZ5MRuV2VkdDNP1iDT\n0eZzpZavTU1P4Lq6QSwiItvj+T4f/6IlXyh3LDuxsrQ2CTFHN1lW1efQoTzzxfZzaRXznQM6EemO\ngiwZWgdvuiF0WXd6mqMuvHgggdYt197d8rV0JsEFl5yuQEtERLYlX6yECrCaSafipJMxADzP56/f\n+xVy88u9rJ6IhKQgS4ZKNJ0mlslQOXC4q/d5i4t4+Xzo+bi6NTU9QTqTaHsXC4K7XEuLK2Syk32p\nh4iI7B3nn33sWtDUjJfLkT9wJwAnnnMcM088AtcJLvItLa50HWCV40XS0wmNERfpAQVZMlQirsvP\nvekN7P/xY1tmqG/Gy+e7uuO1Xa7rcMElp7O0uNL09Xyu1PYOl4gML2PMc4ErgSOAMvBea+3VTcol\ngPcBbwKeY639zkArKntOOhljZqr1MINqNU6p3nMik4qvBVib/fCEb3LBaS9nJj7ddn3JdIy51As1\nRlykB/Qt2qYwg1IXS9u73b/XRVyX2MzM0M1Q77qO7lCJjBljTBy4EXiLtfZ6Y8yxwLeNMd+x1t63\nqfg3gWuB4fpxEumgEl/myU84kiOTc7tdFZE9Q0HWNoQdlFp28pAdUKVkaLTrUpgvlqHirZWLeI4f\nRgAAIABJREFUVHxcNwJ+hFxumclUXOO5RAbrXKBmrb0ewFr7oDHmVuAC4E82lX2jtfbrxph3D7qS\nIiIyWhRkbcN2BqVOJWNt+1XL+OjUbXA1if/nr/7ulteUOENk4E4A9m167n7gWZsLWmu/PpAaiYj0\nSLy8TM1Zv/meKHn4uUWq5a2JwgaZqXkvUJC1Q+0GpR4qHeYD9wSPX/HLT2/ZV1pGX9jEGJ0ocYbI\nwKWAzdkBluvPi4iMtOP33cGKk+D+9BkAPO/uAsUfX0/V3XruOshMzXuBgqwdajcoteKsTzzsRhRg\njbNOiTFWLRbL3Hj7g0AQoE8n42vdBa/+u29sKR92QspV6WRMwbxId5aAzVc1UvXne85xIv1Y7MCt\ntkPt6T3XiRCJRNYeu27rOvnuelnH3Vg2sultrkvbZQ27YdpGvdKvNjkz00QzGaqHF4BI/f8GEdb2\nm0Z+Pk+tuLTtTM3jvI22Q0GWSI+ESYxRizkQC64QpTMJMlMTa0HWZt1MSLkqnYpz0YuNAi2R8O4D\n3rrpuROBe/qxspmZ8bpBpvb0QTRKLB4cJ2ayKWanEy2LlqlwqF42O5MiPju1/uKm40pmJsXs1BSj\nbii2UY/1o02zb/599j+yny/dch+1ig//N7gI/PVTU7zwJRfxhNR6EpTKYo4ff+KTQJP9aBvGcRtt\nh4IskSG1nbF/+UKZfLHSNuVvM4dL8y1fyyYySucr4+w2oGqMucha+3FjzKnAecDl/VjZwkIB3x/9\n5ISOE2FmJqX29MFCfoVKOUiQtDBfgGp1w+ue57OwUGBxZRE/v0Sh4ANQsA/hpNdPjotLG9+XWygQ\nL49uV/Rh2ka90u82FWpxCs4kOB4xgiCrlHApRKIssd5dsOJH1/a5+YUCMbaXQ2Cct9F26MxJZAR0\nmpAyX6ysdUPcjivvuqrla3OJLFeceZkCLRlL1tqqMeblwIeNMZcTjMf6bWvtA8aY9wBL1tr3GGPO\nBD5af1sNuMEYUwL+2Fr72bDr8/3a0E1PsRNqT+9VK1XipTwA5fkFVsrrQw88z+e6a7/PUr6xG/mp\nwT83PNp2uZ7HrretF4ZhG/Vav9rk+TVqtRrUNi57877ge/Vy9cc7rcs4bqPt0FmTyAjoNCHldmQT\nGeYSWQ61uYsFcKg0z3wpp/lVZGxZa+8Bzmry/DsaHt9J0I1QpG9qnkf+ums4+aEgYMofuHNtsmGA\ngh9nqXRSV8ssx4ukpxNkE5me1lVE2lOQJbJHRZ0oV5x5GfOlXNPXD5fm297hEhGR3vLyefylxVBl\njy59madOzRB3opBKMvmrv0KkYTyu60J6Okm1ViGbeKF6I4gM2J7+xq2m3V59LLLXRJ2o7lCJiAyh\n+487ixNfdAqZ1Hp3wcXcCnzq+wB86xR43i+cz9xktun8Rq4bYXZ2isOHl9R1SzZYLJaJ+evZkL1C\nmYoXjO3LFcrMTPtKoNUDuxJkGWMuAT4AXGGt/cBu1AHW026vPhYREREZBuX4JG4mQ7Shq7hbW5/S\nrZRwcTLTRJPbS7cte9eNtz9I3N+/9vfEyhIn7w9mrbj3tn3EZw8pU3EPDDzIMsb8NXAE8P1Br7sZ\nBVd7T83z8PL50OU1A7qIiIiMmnQyRjoVJ7+web719rabqVg22o07WZ+01n7dGHPbLqxb9ria5/HT\nqz+GtxiuzztoBnQREREZPa7jcNGLDY8+lufzV3937fnzzz6WucTs2t9eLkf+wJ1UvRr37kZFx9TA\ngyxr7dcHvU6RVV4+31WABeAtLuLl89ueAV1ERERkN7iOw3QyvuG56WScmeT6XapqNV7PYukPuHbj\nbeQSXzhOpHOhFnw3QiQSvN9xI7ju9pblOuvLcZ3Wy2m88eG6dL2+1bbupM2jpts2d7tNG8sf+cpX\n4qanW5b18osc+MxnQi87jGb7juNE8Bt+19z6usLuZ62Wu+O67nD/bWcv7tuwd9ststd5vk++WOlc\nrlCmqiQVImNh5IKs7c66DFCmwqF4cOaYnUkRn53q8I4WolFi9eXMZFPMTieary++3gc2M5Nidmp7\n69tJm0dV2DZ3u00byx/xs08kPpttXfbwPAu92F8atdh3Dh8srBXJZJLMzqZC72ftlrsTvdp/29mL\n+zbs3XaL7EWe7/PxL1ryhXLHshMrS5x8qNCxnIgMv5ELshYWCvj+9q7yVBYKVMoeAPMLBWLEtleH\n/MrachbmC1CtNi2XK67/UOYWCsTLk12tx3EizMykdtTmUdNtm7vdpt2U79X+0qjZvrP5rkYuVwSn\nFno/27zcnzwyz8J8vGXZVelUrG3moJ3uv+3sxX0b9m67Z3txgUJkROWLlVAB1mapyTjp5M6POyKy\nO0YuyPL92rbne/C9GrVabe3xdpfj+evL8drUx/M2Pt52vXfQ5lEVts2N27SysIjf4T1ePh96H+jV\n/rJh/S33nfVAy6uva3PZctljaXFl8yKBYM6LWjkIwm74ygMQ6dwdLZ2Kt03R2rj/HijMb/h7s2wi\ns62JLvfivg17t90ie935Zx/bNnBaTUAA8NxzjlUKbZERNtAgyxjjAPcBNeAY4ERjzO8CN1lrLx9k\nXWT8HLzphr4te7dTvnuez6eu+hb5XKllmdV7V7WoQ+WY6Y6BVjcpWq+866q2r88lslxx5mXbCrRE\nRPaKdDLW9jd3PQEBCrBERtxAz4istT5w4iDXKePNTadxp6e7zhjoTk/jptOhy3cTwPUj5Xtxqdw2\nwGoUqfqcf9bTSGeaj8vKFyvcePuDHZeTTWSYS2Q5VJrvWPZQaZ75Uo4jk3Oh6igiIiIyznTZWUZa\nxHU56sKLu7rTBOHuNm03gNtpyvfVYCpfLEMl6KNXyK93E3zZq09tGkDlcyVuufZuIEjPmtnhJIJR\nJ8oVZ17GfCnXsszh0nzHu1wiIiIyGuLlIOmVl8tRrbYf392PnjvjREGWjLyI6/ZlDqtuAzgvn+9J\nl8XVQAnWuwDe9pPvrT2XziTIZHuXhKKdqBPV3SkREZE94vh9dwCQP3DnWtfVVvrRc2ecKMjaoapf\n5UBxqelrh0N0s5Lhtt0ArlVg5hXKTKwE+8vqVSJnZpqZbJp0JtGxS2A6k2Bqemd3qERERERWrfbc\nqcwvdPW+nfbcGXcKsnaghscH7/kQ8+XudkoZf63uaFU8n5P3B0HW/OPfIO9GggmR3/B7vPR1p5Fv\nyB5YWK7wL9/6CRBkpJpOxpmansDtcGVJRESGR83ztlxca6Xbru8ivbDac+fw44e497Z9ABxz5lNJ\nTDbPhFkr5Fm69WYAcoUyM9O+ErU0oSBrBypOMVSANZfIkk1kBlAj2U3djuF6fHXCyf0F/tcnv0HB\nmVxL2b4mFtyCT2cSOx5j1W/t7txuN8W7iMgoq3ke+euu4eSHHgXCdcES2Q0R18XNZFiZCOY1vOm7\nB1uWnVhZWrtgfO9t+4jPHgqmhFG3wQ101tMjl552CbOJbNPXdIK5N4QZw+X5Pv9624MUlsvEy8tr\nfZ/bSadGY0LKdgkwlOJdRPYiL5/HX+oueRJ0nwFXpBfSyRjpVLzrybNXp4SZyyjIaqQznh6ZTWSV\nIEA6juGKAq95xQz5YiWYdDJ3NxEinHH2MRR8F8/f+p6pySi1xUWqDF8mn7Bp3pXiXUT2uvuPO4sT\nX3QKmdTW7oKe55PLL5NfCXo4RFJJfvTTn24pV8xX+l5P2btcx+GiFxvyxfb72eqk2VWvxr0Dqtso\nUpAlMmCu4zAzNbE26WQkEmHh5huolL2t3QWBxlQYO83k43l+kFyjnhr+8cfz5Cc3JttITsW3jPtK\nJ2NN+1t3SvOuFO8iIoFyfBI3kyG6qeu35/lcd9U3WcqttHinyOCsnqO0sz5pdpMrw7JGQZbILlkd\nw+V3MdB5J5l8PM/nU1d9i3yu1JAafus1qFrUoXLMNEQia8+lU/Ggv3WLQEt3qEREtmdpcaXrAKsc\nL5KeTmi8t8gQU5AlsktWx3DViktkZ1LMLxTwva13sqA3c3AtLa50TBEPEKn6UPXXkm7Aen/rTle3\nRET2As/3O3apgmDajmqL3/VmfnjCN7ngtJczE59uWy6ZjjGXeqHGuYoMMX07RXbR6hiu+OwUMWJ4\nIQ7GLefgakj/7i0uUo2stBzD9auveiaR+MavfyG/wm03B5Men3/2scG8XcUKN97+YDdNEhEZa57v\n8/Ev2lDJASZWljh5NZNsCJX4Mk9+wpHqHSAyBhRkiYyYVne0Cn4cOGmtzLJTbjmGKzOTJJOd3PBc\nbn557fF0Mr4lZXyYq7bQevyWiMg4yBcrobOv1fDxqQIQS65QiixyoLjx93FpWWOxRMaRgiyREdDt\nHFzFWjwYj7pQIv7wQdzp6VBdBdsJe0er3fgtEZFxcv7Zx7acYqNcKfORf/swuXhwJ+sBbuPPb//a\nlnKx8iRP44y+1lNEBm9PBVmrJ6qrj0VGRZg5uBZzK/Cp7wPwjZXj1l+oP7cd25kzQ+O3RGSc1TyP\niZVgItZkpchUtXlK9s9+6j5mlk7n4XqngWPu77zsmYlpJbMQGRN7KshaPVFdfSwySjrNwZVJ+6Qz\nP+x4x2oqHSfhL1Nd2NhFpXFM16qwc2YAGr8lImOv5nnkr7uGkx96FID8gTvrqaw3KvhxiqWTulp2\ncjrG23/595XMQmRM7LlvsoIrGVeu63DBJaeztLiCt7jYcuzWZLXCwWu+ueX5xjFdG5YbYs4MEZG9\nwMvn8ZfCddtedXTpyxz/xGP4mVde0PYcZGp6YsschSIyuvZckCUyzlzXIZOdpDYdpzyTCD2GS0RE\nunP/cWdx4otOIZPa2l2wsfv2t06Bs857OTPpqUFXUUR2kYIskTEUZgxXIy+fp/CZz/W5ViIio83z\nfAp+nJITY8n1eXRlmcVIdUu5Ymm9i3Up4aoXjcgepCBLZEx1GsPVzsKjB/EWt16dBUhNxXBdp+Uc\nXCIio8bzfQ4vlljIr+D5LSaF93w+/8nvUaw8E9IQewz+7bofDrimIjt3uDQfqlw2kek4RjBeDqZ/\n8XI5KrUSZSpUFgr4Teb93GvnDQqyRGSLL9zSOoFFMrLCf0h8n1gm3XQOrrA8z297QrOZ5t8SkX7w\nfJ+r/8WyXPGplD1qtRa/SRWPeGHrXat2yvEi6emEMgbKULnyrqtClZtLZLnizMvaBlrH77sDgPnH\nv8GS6/B4zKVS8aix9XvkTE2T/s3XMp1O7InjuYIsEcFNp5nKJEiurFCstU9yUaxNsFyL4SwuUn70\n0bXpELxCeS2tsZfLUa3G8ZcXSS57QJCVa5Xn+/zNDXdz4HCx9QnNJpp/S0T6IV+ssFioEIuHv2B0\ndOnLPH7iGZz3S8/CJdKyXDIdYy71QmUMlJF0qDTPfCnHkcm5Dc+vTolUmV9Ye+7xQwUgQsSJUPNr\n0CTIYv8S997yXeKzs3vieK5vvYgQcV2e+FsX8+qFHIWl5una80vlLXe4GjMYVjyfk/cHQdZqWuOy\nV+Hc+WBc2PzD18BrXkvEdSmUyvy0cICq41Or1Yj5SSK0P8HR/Fsi0m+vfP7TSSWaTy6cz5X4/NXf\nBSBaK/DyXzqRJz3pZwZZPZEd+91jX08y3XwfX7VQXuQjD3yUmtP8IujquO9yLse/3vYgheVgLs0I\nEaJxl2p5452seHl57Y4X7J3juYIsEQGCH82JuVkm5pq/7s4vA0GQ5UxNQfFwV8v/4SPf5x+/9ucU\nJ+vBVGr9tWx8hv9yyn9uerVX82+JSD/VPI+J0hJR3yVVKTIVbT4e1a9unEvQjYz3VXgZT1+7Kdw4\nwuPiZ7PvlNtbvh5xXSZmZ3nNK2bW5tJ0nQgz2RQL84UNQwG8XI587m6qXo17d1b9kaIgS0S6dsT/\n80qmohvHJuQKZe69bR8AJ55zHJlUnKpf5aav/xXP+NZjACRLNcDbvDhWlg/hFfYzM5lde67ZANkw\nkyL3k8aFiYyX1cmFT3roMSJOhNwjKQpu8+5/reYSFBl2U9MTpDMJ8rlS6PfEy0li5cmO5Rrn0nTd\nCLPTCahW8RoSX1Sr8fqk3X7XdR9lCrJEpGtLSxUimcSG54pEWHEmIepQjCVxo0F3hNed9nss/OCf\nAXh6/QKa7/scyJXwa1WWoweD999/PVV3vQuDOz3NURdevGEdu31HS+PCRMbLdiYXBihNOESmUp0L\nigwB13W44JLTWVpc6Vg2nytxy7V3D6BW409Bloh0rdUPcByoRR1u/OoDEAmuBkd8n5PyLhPlYkPJ\nCI4TazYsdo1XT6wxmUox65TX+ny3Uo4lqYUIfiK+T7xS7Fiu6TpWYOGxg00nH90glWJpZesVO9eJ\nQDS6Jaui7pCJ7L6HTvhFTnrBSUxNNv9+N04w/PVTUjx/D6WiltHnug6ZbOc7U9I7CrJEJJSw3Q0i\nVR+qPsSCE5Ca43DfM16wIbBZHRwbiRb4/sStADzr2b/B3GQWL59fS6ix+u85QNVr381gNTVsu5Ty\nq12Dur1yXfVq9cxJ60k9Wq4DeHj/Encffw41Z2O3o2aDgsuxJFPphO6QifSQ5/uhuhd7hTJVr4YP\nLLkej6wsk6w1T9PeOMEwTuuMgiIioCBLRELq1N2gsYvB+WcfS3pTd8INy6oPjn184TH+/RtfBGBh\nwsOJe9RmJqgkJ6jllza8J+ZGibRJlczyEulamehU6wmYqwsLFJeXcNsESc2F70de9Xyqns9J3/9y\nk1e3prddiSe57xkv2BOZlkS2I2zAtFa+UuX6W+8KVTZeXubYQwXuT/88/CTK136iyYVFpDcUZIlI\naGG7G0QqPpFK88BkanqCeNxldjrBgcX1oKlxcsTIU2tMbupuNzsxw5uf/cYtGQgb73x144hX/Pra\nHF+dNCb1OObMp5KYbJH+1vdY/vQ1a38ePZci2jCIPkKEWMNEjat3yKYKh1h8/CBeq+U2yDwhSzTW\nuZzIsOomaOomYAKI+DVO+v6XObmL+pScGFVnKnR5TTAsImEoyNqBmJ8kG5/BcSL6sRVp0G7QbDqT\n4HVvPAOAbGKGuUSWQ6X5DWVqTmQ91XtdkTz5RIQjk63vVHn5fNt6Nb7uptNEZ1ovq5EbXWFlIjgJ\nu+m7B9uWjfzsucSPDrpGrmZZXOW4EbIzKeYXClQWFnnshusAOH7fHfykYQ6RdvzkFM97+6UKtGQk\nVSsVPvW5uzaMsazVas2SjhLxaxz34Nc5ruG5oJtt+zvLJWf9u3F0Nk2nTOv5qru2yGe/7EiekM22\nLa8JhmWviJUneXj/AZbi7RNmBN+JLK6r41Kjgf9CGGOeC1wJHAGUgfdaa68edD12Ip2Mka6fOL3x\nzLfhOo5+bGXPCztmK58rsbS4wpFHQtSJcsWZlzFfyrUsf7g0v+EuVzvbuaMVxup3Pl9on3wDgjFo\nKxNTpFNxZp54xIZxVq4bIT47RYwYkeQUEzMZooeKHcebNXKKSzz64/1MH31EqHprnFdnYY9Lxpjf\nAt5OcOw8BPyBtfbbg6zrKKt5Ho9+7GM87aFH157ziXB/+rlUnK13yGvA/ekzdrTOfc2HV7X0xCOy\nHHPUk3a0TpFx8bQfnMGdP3gceLxtuXK8yKHT7+FPf+mywVRsRAw0MjDGxIEbgbdYa683xhwLfNsY\n8x1r7X2DrMtOuI7DRS82a49FpLsxW/lcicMHC+Ryy3hejTjJlsuNl1aIl4LXV6+oJdMxnPq4qprr\nUUlOEC2W2o/Zaqzr9HToroKw/p3vZlxIpwAn4roc/VuvZzaXY2m585ng0sF5/u+11wLwpa99n3K8\nc7fN1GScC37tNN31aiPscckYcwrwIeDnrbUPGWN+E7jRGPN0a22Xp/Ljo+Z5rBxeCLUP1wp5yrn1\nCypHz6VYiUxQqQxHxrNyvMhkWt8V2dumpieYykywlOuc7n1VvJwkv1hivrTAEwjXQ2QvGPTtl3OB\nmrX2egBr7YPGmFuBC4A/GXBddkTBlchWYcds3XxN+DEWAMfzfADuvCe4olaOF9l3yu3UnCB5ROSp\nNX6G6aZjtprWs8lExx3f0zDhYq9EXJeJ2VnCLHVqMspjrkPV8zk+ZNdCgB/98Hayr3otOJ3b60xN\nhfpcxuwOWdjj0muBz1trH6qXu84Y837g+cCXBlvl/ql5HuUWgX+lWmF/bgEANwLJyTi5mz/Pwfnl\n7lbixHjgqc9k+oynU1guwm1B5s5Hn/Z1qrGtyyrFnS3Z/P7Tab+DS+t91cPjb+76SFfVymSTHJF6\nEW3nlhAZc67r8JpLziC3UCC30r4LfjFf4Ws3rSeLOVRa4PGlA+SKBbyGLsD+8iJlr0LF84lUDhLH\nDzUOufGYNIrHnUEHWScA+zY9dz/wrAHXQ0QGbDszzodVcyI83GHMVtWvrndLXFloWiabyAxt1994\nJsNTjn0iKwutu1auakw5//ihAo//bbjulgD3nXjultTzm6Um4/zmOccSz2S6DlaHUNjj0gnA5q6B\n+4CT2GaQ5Xk+pZVlXNchMRHubk61UiG3f75tmcZgqCu+j/+/v8TBha2BTi0S4YGpX2jypmdB+JvC\n6w7BHV84sOGp8884nyc/4ciObw37Pf3Lo/+kbVfkRq4Lxz7xySzmSnieoizZ21zXYXYuzWyHL3du\nfhkIgqxYeZK/vaP56J9EyeN5C8Ex6ZjFH+EQ4cf3HdWx98lq9t3VOTAvfJHB6dP0Cf0I4gZ9NpEC\nNv96L9efF5Ex1tid0HUjZDJJcrliqBMaz69uuKIWdBcMxmo0jtk6XGp+8unVqvz3b76/43rmElmu\nOPOyoQy0VrsXdkruAUH2tju/so+n/vsXu15P89TzG0VdhwOPTBGdnuaoCy8e9UAr7HGpp8cvz/P5\n5N9/k6XFFX5m+fP87K9diBtrv9/Vqh4HPvP59mVaBkNhbTNo2qHqRIln/MzTmYh1mOi7C1EnypHJ\nuVBlXTdC1B2+773IqHjaD9qPn3y4fh0p6i/xs6X/GWqZE+UiU4VDa93jr/vcv++ojpuVY8m1AC6d\nivd8vspB/6IsAZsv16Xqz4fSrwh2GK22VW0eb3upza7rMntEEseJMDOTwomC74e7anwk0y2Wuf44\nbIKM9nWM4PZpW+x4W7tRonPtM5+tet1vZFl80Sn4S52DMnyf3HWfDF2NaDRChAiRSATXjRBxR3rf\nDXtc2vHxC9a3/dJieW384iOTL+WR/93+7tSa9PO6WV1f/MKvzBGPuiSTExSLK9QSkyST8R19b550\n5JE9DbC6NY6/w+PWpnFrD4x+mzLZRNc9VOLTGZ79usuJt7mo5C8tkf/8zQA8OXc3D+8v7LiuzZTj\nk9x30nnUHAcn0vz4v5NtM+gg6z7grZueOxG4J+T7IzMze++ml9q8N6jN2zM7O8V1r/pwD2ozOIPa\n1kceMQ08IVzh097Z17oMsbDHpfsAs+m5E5qUa2ftGDY7O8UV739ZF2+VQRjH3+Fxa9O4tQdGu01v\nvuK8/iz4eaeuPXxuf9bQd4MeQXYbUDXGXARgjDkVOA/4xIDrISIiAuGPS58AXmKMOale7j8CeeBr\nA6yriIiMiEitNtgBnvU0uB8GjiToz/5Oa+3NA62EiIhIXavjkjHmPcCStfY99XKvAv4rEAMeA95k\nrf3eLlVbRESG2MCDLBERERERkXE2WgnnRUREREREhpyCLBERERERkR5SkCUiIiIiItJDCrJERERE\nRER6SEGWiIiIiIhIDw16MuK2jDHPBa4EjgDKwHuttVc3KfdbwNsJ6n8I+ANr7bcHWdde6aLNCeB9\nwJuA51hrvzPQivZQF22+FLgEcIEi8EfW2i8Nsq691EW73wL8bv3PJeCPrbVfHlhFeyhsmxvKnwn8\nG/Db1tp/HkwteytMm+tzMv0t8GNgdTr5B621Lx1kXXuli337NIJU6U8gSJX+Dmvt5wZZ10ExxlwC\nfAC4wlr7gfpzPyLY3oX6vzXgLdbaL+5SNbvSrE31598DvBaoAt8EftdaW9ydWoa3uT3GmBjB5NKN\naZdTQNlae9xu1LFbLfa7JwN/QzCZtgfcTrDflXatoiG1ac9fAScRTKfwD6vTLAwzY8y5wLuBDMFN\njg9baz9ojJkDPgKcTLB9brHWvm33ahpeqzbVX/tF4J+Be621v7Z7tQyvzTZ6OvAh4OcIYo+vAv/J\nWltut7yhuZNljIkDNwIfqP+Y/Rpw5erEjw3lTiFo6EuttccD/y9wozFmqALGMMK2ue6bwCNs/PEf\nOV1s55cCfwicZ609EXgvcEP9/SOni3a/HPh94Jettc8A/gL4jDFmctB13qku92+MMRPAPwAPD66W\nvdXtd9pa+wxr7Yn1/0c1wAq7byeBW4H3WWuPBX4PuNQYMzTHoV4xxvw18B+A7296qQa8rmG7P2OE\nAqymbTLGvLn+/AnAcUAF+NWBV7BLzdpjra00bJdn1H+DbycIUIZem/3uH4H7rbUnAKcCxwNXDLh6\nXWvTnmuAH9fPAZ8NXGiMecWg69cNY8xRwM3A2+vnNL8CvKt+YfHvgEestT8HPAs42xjzht2rbTht\n2nRGfU7BDwJ37GYdu9FhG10HfLv+/CkE36M3d1rmMB3czgVq1trrAay1DxIckC/YVO61wOettQ/V\ny11HcEXw+YOras+EbTPAG621f8b6Ve9RFbbNDwC/aa19rP73LcA08JRBVbTHumn3a6y1B+t/f5Gg\n3T87qIr2UDf7N8D/INjODw2men3RbZvHQdg2/xqw31r7mXq5O6y1L7DW+gOt7WB80lr7aoI70ZuN\n6m94qzb9HvBn1tpla61vrb1odV8Ycu22EQDGmBcQnFBdObBa7UyrNp0K/C8Aa20VuI3grsmw29Ie\nY8wU8EsEF9ux1s4DVwGv2ZUahucRXGD5KkD9HPZ7wOnAy4H3158vAn8PvG53qtmVVm06BdgHPI/g\nnGZUtGrPM4E/Y30bLQNfIWhnW8N09+cEgo3S6H6CqH5zuc1dA/cR3DYeta5kYduMtfa1qIJvAAAX\npklEQVTrA6lR/4Vqs7X2B5vKvJLgDseonoCHbfd9q4/rV/h/H7ib0fqhWhV6/zbGPA94IfAc6icD\nIyp0m4FjjDG3AscCPwEut9b+nz7Xrx/CtvlZwI+MMf9IcJL0OPAn1tp/7X8VB6vD7/WbjTHvB5IE\nV03fWT/xHWrN2lS/O3k8wb78dWAW+AJBN9Ch7ooW8pj6XoLv5UhcCGjTpluBVxljvgJMAi8CPjWw\nim1Ti/bUAJ+N56+LBPvh0KpfOP3s6t/GmGMJzlu/U3+98dzm/vprQ61Nm+6w1n6v/twu1a57YdpT\nfz4OvIQQd7iH6U5WiqCPfqPl+vPbKTcKxqktYXXdZmPM8wluO19srfX6V7W+6qrdxph3AvsJ7ty+\ndlQO8puEanN9vOFVBOOwKgOqW7+E3c4PEJxgX1zvwnMr8AVjTKb/Vey5sG3OAucAV1lrDcEYhM8Z\nY2b7X8WhcT1wtbX2uQQXFV5OML54VM3U/30OcDbwCwQB9OW7VqMeqXdZj1hrb93tuvTAHwHPJRjD\nvh/IEXTNHjnW2gLBeJi3G2Oi9S5erwcSu1qxLtTHlH0O+PP6U5vH9YzceWBjmxoDklHVqj31AOuT\nBMN3On6HhinIWiK4wtIoxdbb3mHLjYJxaktYXbW5nuTkWoKug1/pc936qat2W2v/1Fp7BMHB8V/r\ngy5HTdg2vxu42Vr77wOpVX+FanO9q9zbrLUH6n9/iCBpwFkDqWVvhd3OC8D/sdZ+C8AGiU2KBF1K\n9gRr7R9aa2+sP36EYPD+SAwIb2Gh/u/f18czrXbd+pVdrFOvvB5omaRnxNwK3GitnSHofv4wwXF1\nVF1IkJjgPoLECrcC87tao5CMMc8Gvg581Fr7Pwh+Jyc2FRup88AmbRpprdpjjDmCoMecB7w8zMXv\nYQqy7mPr7d4TCTL9bC63+f7jCU3KjYKwbR4nodtsjPkd4J3A2dba2wZQt34K1W5jzNnGmOes/m2t\n/QLwI4JxL6Mm7LY+H3idMeYhY8wPgTOB99W7VI2asNv5Z+tXYBtFCJIGjJqw2/kB1u98rKoRBJdj\nzxgzYYx55qanHUZzmwNr40ceI7hLuWrkt2m9G+SLCa5kj7R65rrTCZJfrI7JuobgTupIstY+Zq39\ndWutsda+iOB7NPQX6eon77cCl1pr31d/+n7AM8b8XEPRkTkPbNGmkdWqPcaYGYIA61+tta+yHbIK\nrhqmIOs2oFpPbYwx5lTgPOATm8p9AnjJauYqY8x/BPLA1wZY114J2+ZxEqrNxphnEPSHP9daawde\ny94Lu61PB/6h/oXGGHMyQcau7w6wrr0Sqs3W2qdZa59qrX26tfZpwJ3A26y1bx14jXcu7Hb+A+Ca\neldJjDGvJ7g69o0B1rVXwrb508DxxpgX1su9nKCLzyi2eTvSwDeMMecBGGOywH8EPrOrtdq5fwLe\naoyJ17OgXgx8fnertGM/T9BVcFTHADc6DDxKMK4ZY0yEoJvqXbtZqZ0wxny6ntUSY8xTCb5HQ939\nsZ499zrgTdbam1efr1+ouAF4R73cDMFUPf+0G/XsRqs2jaoO7flb4HZrbVddoSO12vBkBK+nZ/8w\ncCRBn9R3WmtvNsEcHEu2Pg9CPTXkfyWYH+Exgg9kJPuAhmlzPX3kR+tvOZ5gbp0SwfxJn2223GEW\nss1/R5Cd7JH620ZuTpnNQrbbJciydwHBNq4C77fWfrTVcodZ2O/0pvd8BfiYHd15ssJs5wmCcYYv\nILiT8TjwZmvt3btV753o4rf7XIKsYBMEJ39vHqOkPsBawpr7CH6vjiHo9nMYuAn4MsE4jCmCwfvX\nA+8a9jGXHdr0pwTb/oUE8399EfjDsFd6d0O79lhrLzfGvBp4j7V2ZLppd9hGnyHIjPbEevH7gf9s\nrf3hLlQ1lA7t+TRBEDJD8Pt5xbBntKzvU1cTJAlazTBaI+i2+SGCMaqnERzzP2Wt/dPdqGc3OrTp\neIKLFXMEF9MeJkhTf94uVDWUNu35GkEg/yOC8XO1+usd57YcqiBLRERERERk1A1Td0EREREREZGR\npyBLRERERESkhxRkiYiIiIiI9JCCLBERERERkR5SkCUiIiIiItJDCrJERERERER6SEGWiIiIiIhI\nDynIktCMMe80xnxjt+vRT8aYq4wx1+52PfYKY8yPjDFlY0xxF9b9L8aYkjHGN8YcM+j1i0jvdDo+\nGWMu79Xxyxhzdv13Y7r+97Ix5ldDvve2+iTd2133bcaYD3T5nh8YY94UsuxHjTGf3F7tJIz6Z1w1\nxhSNMb844HVfXt9ffWPMbw1y3XtRdLcrIMPBGPMj4EkEs41DMIv6/cD7rLWfbii67dmrjTER4G3W\n2r/c7jJ2Isz6rbWXDLBKfWOMiQL/BXgd8HNACbgP+Btr///2zjzOiurK419gxKBR4q5xYVzi0cgk\nBpeQxA0TMRONImoU465RIsa4EsclRoKCqHFcE3cE1I+o4AouoHFLXMZEDS4/ERd0BsVdFFwinT/O\nLbqorve6Gl53m8n5fj58eK/urXtv1X19Tp17zj2l8anO1sC9qSzjc/yt5pdJOjfVuxPYCp/7f0v/\nPsbfeN4E9Jf0oJmtB/wG2AZYHpgL3AOcIOmFGkNtAg6SNLYR190WJG1vZr2AFzu67yAIyjGzR4HH\nJA3JHVsaeAcYIek3ueNr4fLq++lQTf0k6TTgtNy5+wGTJc1exKEu6EtSj0VsowVJJvUt6N3FQtIG\njWrLzHoCe0q6uFFtNhIzGwAcAfTBddQrwLXAmZL+nurMx59xPs+d+jpwK3CSpDlmdiJwEj7PXYHu\nwKfpexMwXNLpZrYUcCowAFgtlT0OnC7prjpDHSPpwMZcdXWyvwMze6mj+/5XJDxZQUYTcIykpSQt\nBawInAmMMbPtGtRHH+CEBrX1z9h/h2BmXYHbcAPrMGAZYG3gKuAKM/tVrnoTsEpu3nsCRwHDzOwQ\ncGNEUo9U/jPgvVS/R/r/waRo7gXeA/qkuv+BK6V7zKx7nSF3aeDlB0Hwz80koH/hWD/gk5Lj2wMf\nAA+0pQMz6wacA6y6iGNsT3YD9uzsQdThB8Dgzh5EGckwugz4Az63K+H67ADglkL13TK9l/TVj/DF\nxIvBjZGc3jNcV26W03uZN/Ia4NvADpK+nPq9BbjVzL7ZntcbfPEJT1aQZ8HDrqTPgPFmtg+wK3B3\nVmZm++MrNysDE4H9JX2ayvYDjgHWBWYDF0saaWZbAlOBbik0bJCkm2vVT22dAnw3nXdcGt8J+Mrl\nucAawOTU1ufpnIPxVax18JWpsyX9vqx/YGNgi9TensCauOLtKWlgam8P4GSgF+7ZGyppatnNS27/\n04Fv4J6e64GjJX2WvEZTcOV0FrA/8BV8pWwscCywpaQnK9yThcYs6b3CUPbFlcV6kv4vHZuDG1gf\nAMVVzfy8zwemmNkYYCBwSdm1ltAb94Seka0MS5plZj8HfgwsgRtcdUmra2elc7YAXgB2Bw7FFeWn\n+ByMTfV7AecDfVMfjwFDJE1P5f+Or2J+E3gOGArcBWws6amK1xYEQccxCTjZzNaWlK229wdGA4eY\nWU9J76fj2wFTJf3dzIDa+inJzp0l9QE+wuXFI2Z2kaRj6snv1gacPCMDJN1iZssDVwNb416UI4Ab\ngH0kZQ/63czsHGC/9H2kpFHJSDg1tTkXWF3SuyVdLmlml+MG2Tu4l+aidN69uBzcCugqafMkV8+R\ndJ6ZfQm4FJfvs3EdNBS4XNJ5uWs6Ho+GWDqVHWlmewNXAl3T+L4L/BI3gN8EDkn39RpJh+faOiFd\n6xrAS8Apkm5MZV8DLgQ2ww2Zh4FDJb1qZisDv0/X0h14AvhFmew2s3XSvfuJpAm5oqkplPMYM/tK\nTl8utLgn6TkzGw5cZWZdJBW9ol2K5yT6p/E+n9r5CPidmb0GlM1dC8zsStyz9hGu5+YAQ4BlgeH4\ns8JYSb9I9bsDvwN2TmUv4BEjk1N5pTkO2p/wZAWt0Y3mEEKArwHrp399cRf5TwDM7Ie4QDwa957s\nC/yXme0t6QHcC/J+WgW6uV79XH+b4g/Wq+EP/GfhwnpTXMDvAuyU+t8RGIUL+mVwQ2akmfUr6z+1\nvzHwDLBs0Vgxsz64Qjka9/BcBExMgp9C3VWB24FxwHLp3myBC7eMLrgi+Wqu/xWBLwPLJwOryj3J\nxtyzxMACN4on5gysBUi6QdLwknOKfKlCnTwv4vN0qpmtlOtvjqRrkuKpyhD8+lcHlsSN42n4Q9No\n3MDOuAx/IFoT/428A1yeK78ZV/4r40bpCBYj5DUIgnbnMeAt3EuV0R+Xr9NIoYEp/Pv76XhGTf1U\nwHB5vHkysKrI76pcCayAL8z1wx9uly7U2Ru4D5dLQ4ERZrZWCuUaC9yS9FSth/S9gDtw/XEicIGZ\nbZIrH4RHpmxecu4ZwPfwhbGNcUOtV6FOP+BDXK7uChxhZltIGoc/9D+ZxvdEqj8QmIXL7D2Aw7K9\nRmZ2OK57B+A67WTg6hReDnABboyunM5/BY+iIfXVI41vBXyhstbC3y7ArIKBBYCk6ZIG19CXeXrg\nhmlbdMQzwOFmtmGhz/GSZrahnV2Ah/A5vQ/3qH0H/60OAoaY2bdS3ePwv49NcUNsNL4ovkwqrzLH\nQQcQnqyglLQSMgDYllwcO250nZw8R0+a2VPA11PZIcB1kqak7w+Y2XX4w+24km6q1P9c0n+nMd0K\n/ArfV/QR8DczexFXrOCCfIykh9P3B82TWByAh7KVsQRwbg2hui9wXy6u+nIzm0P5Q/og4CVJmQJ4\n2cxG4UbfKelYF3yv07zcecsCo7JY8Yr3JBvz/BrXtC7QluQdC1bn0gpZ/9TfoVUbkPSWmf0U9yrt\nY2bT8BCeWyXdXf/sFkyW9Ewaz/3AtpJGp++3AEPNbOXkMdsR6CLpk1Q+AVc4mNnqeMjiYEkfAs+b\n2QXAFW0cTxAEHYSkprQPtD/wh+St7gXcj+/x7A9MADbHV/En506vp5/KyGRfFfndKsnw2x44QNKb\n6dhwmveMZfxF0k2pfBxuOGwAVH0of0LS9enzNWZ2Eh7u9ng69pSkh2qc+5+4Hnop9X8ULfelviXp\ngvT5LjObjd/HB2u0+WbmSQPuNrM3gQ1T/Z/h+urZVD7RzO7D9euvSZ6Y5DH8DPh5rt2e6dgnaU5/\nm/6VsS4wvUZZXdK8fQOPlCl7VqnHfrhhPM18b/tDeLTEjQVd3xozJd2QxnM7/ps8Lem2yWb2Kf6s\n81d8sfDcpNcws2vxKJwNgUepNsdBBxBGVpDnTDMbmT5/AjyLxy3nhfXMLDQvMY9mr8c6tBRQ04Et\na/RXpX7eGzO3xrGs//WBH2Z7iWhWoA9Tm9daMVYW2hyqlDSihPWB3tYyS14X8yQUGS8XyudJeiv3\nvco9qTfmjG6tlC8YHzArC7XBPUcz8JC7NmWYkjTBzG7CV4C3wldDDzOzh4DtlEJKK/Bq7vNc4H8L\n36F5zr+FrwJvnMbejWa5tipuEOfn8BFiD1gQfNGZhBtYXfGQwD9JmmdmU3BPP+n4U5Jm5c6rp5/q\nUVd+5xbBWmN5PKytKHOKzMg+SPo4yd+2RA88XdLe6rnv9ZIarEbugVvSy8mIKh1forX7WExsNBf3\nCoHf25HWnFExC7vL9PipwDgz+xHunRsvKVsUHYXvb3o1Gd435aJAyqiq98A9P5keXQL33J0PDGtD\nG0h6GuhjZr1xnbcl/hsdYWbbZqHrFSjqPQrRKPk5WAk418z64Qu14LouK68yx0EHEEZWkOdYSee3\nUqfew/2SNY7Xcr1XqV/WX60xzAOGpZCLqtR78J9P9ZDaecC9kkqThOSMmGJ/xe9V7klrxoqAjVqp\nk293FUlzAMxsBL4f6tqK5y/csRt/96d/w5Px8ygeQlI1g2Bxfkvn2zx98mRgDL4f4n0zG4jvpYDm\nucvfr9aM0yAIOp87gaXwcKntad4T/ACwmpmti3uHJhXOW9S/77ryuw1UlTmLK4eK53dh4Syx9XRE\n15LySjK3Dp/XKZsHHF5r0U7SJDNbA9gBD/2/zcwulDRU0uNpX+32eNTCaDO7W1JZCKiAgTX2U5Wx\nu6RbYcFWhxvxfU9VDeridUzDw1nPT/vyHgKOBw6q2ERb7vl1+DxuJmlm6i+/WFtljoMOIPZkBXkW\nd4V/Bh6elac3tV34ba3fGtPx+OMFmNlXC56ktjADj4fOt3dY2qhb1nfvtPKa1V0uFyPdlj4X956M\nB3Yys/WLBWY20MweSOERGfnPp+KGXq2QjFLMbCczO654PMXsv46vvDWaDfFVvDPVvBG+b658Nn5t\n6+SOfZvYkxUEX2jSXqRHcENqa5KRlcKv/oyHQ/WlpZG1qDRKfr+NGxxFmdNoismL1mNhT0g9ZpMb\nn3ka/PbMsjgdjzhYgJmtmekgM1tB0lxJ10vaBw8XPDSV9QTmS7pN0mA80cNuZrZcST8T8f10BxQL\nzKyX+bvC8vuS8gmf7gBuIoWa16CF3jCzjczs/PzvJrX3Dr63sD30Hvhv6pLcnq++hfKOnuOgBuHJ\nChrJFcBYM7sCj8Xuh2+4HJTK5wFLm9ma+KpLa/XLqGcIXojHg++KJzzYCA81OAUXnsX+q1zP/+Ta\n2wMYiQvzItfgm3RPSzH4y+Ap02fhCThaG3u+z7bek4WQdG3aH3WPmQ3GQzB6pDbOAo5M+x5ajCmF\nrgwG7jCzGyU9VrHbD4HfpvCLq9IerRVwhbk8fv8aRTbmmfjq3PdSmOIu+D4NzGw1Sa+Y2Qx8D9cB\nePbDyvvMgiDoVCbjD8xdJD2eOz4VOBwPqfrTIrad7ZVZP+3rrSK/i7SQ55Lmp/1GvzTP8tcdzxTb\nloWdebjB1xOYq/LshpuZJ3qajCf2WI9yvVTGVOBgMxsPvI+H5H3QxvGtkuT7hxXqX4iHtt2ORzhs\niRs0u5jZw8B0a0693g3YhOZFxYeBCWlOPsONi7fKEoIkj87JwIXJOL4c9+ZsgyetelDSK3XGeSTw\nrJkdI+nskvIy/f06/lywjJkNw8M0l8S9cjvTfqnuXwK+Y77nfFP87+QzmkNGF3eOgwYRnqwgY1FX\n9/MvZJyAZ0q6CM/ydjZwsJrT1k7B06A/D+xVoX6Vceb7vx9/qB+BC5Qb8c2ho8v6b+3Ckvt/t9Te\ne3jGuwGFPQBZ3ffxcIZt8FWkv+CC8LA6Yy/rc1HuSRk741n4srHPwDNA7Swpn/ihxZjkKerHAVea\n2RJVOpN0D65YfgA8bWbz8D19m+Kp6Ysx/rWo8jtsSn3OwlPfn4c/DG2DJ2v5WxrDSrjhtQHwBm7A\nDsOVZYROBMEXm0n4avyUwvG78QQAd1YIC8teHLsQKWnODXgI8wUV5XdZ22Wf98dlzKt4xsKRLCxz\nysacP3Y1nuhjJs1JnYp1L8ONq3fxxFQHSnoxV16v/WNxI+Y53IgZi+uIejIxf/5E/IH+FdzLWLe+\npDH4Q/4YPDX5RXj44B8lfYzL6ANxffcafs0/Tafvju/xfR3PErsjHs5eiqRRuG4fmNp6A78/Z0ja\nv8b1ZOe+id+bYTWiVcrOeRvPcgzwR9zofAN/N9fBkhYp7L4G+f6H4HsS38WjTo7CdfalltLV0/Y5\nDtqBLk1NETkTBEHnYP7+llOSIm6vPhZsXDdPK3wfsKKkd1P4yIvA2mpbut0gCIJSzKy7mt8duQZu\nMG0i6a+dOzKnML6u+PuZ9pJU1RsWLAbm78VqknRgO/ZRd447QvcGES4YBMH/Y8zsLuBDM9sXl3dD\ngT/nwk1qvWAyCIKgzZjZJXi43074g+2v8ayy0zpzXBnmLwY+0My2xSMAjseTZtzfqQMLGkbM8ReH\nCBcMgqCzubQkdXKjOAg3rmbiYaJNpFCUlBL4OSKEIgiCxnEc7h1/Dn/9xLp4mHnZ3qrO4Ex8L9ej\neAjeDngY+dudOqp/PfYxs7kpuqLR1JxjMzsxhfOv1Q79BgUiXDAIgiAIgiAIgqCBhCcrCIIgCIIg\nCIKggYSRFQRBEARBEARB0EDCyAqCIAiCIAiCIGggYWQFQRAEQRAEQRA0kDCygiAIgiAIgiAIGkgY\nWUEQBEEQBEEQBA3kH2YibLgMa8yEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb824960090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "fig,ax = plt.subplots(1,2, figsize=(12,6))\n",
    "nbins=30\n",
    "\n",
    "\n",
    "# ERROR  \n",
    "\n",
    "# Calculate the histogram1 : raw errors\n",
    "hist1, bin_edges = np.histogram(stats_before['err'][stats_before['err']<0.6], bins=nbins, density=True)\n",
    "bin_centres1 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "# Calculate the histogram2 : raw errors LC averaged \n",
    "hist2, bin_edges = np.histogram(stats_before['avg_err'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax[0].plot(bin_centres1, hist1, ls = 'steps',alpha=0.7, label=r'$ e_{ij} $')\n",
    "ax[0].plot(bin_centres2, hist2, ls = 'steps', label=r'$\\langle e_{ij} \\rangle $')\n",
    "\n",
    "# Calculate the histogram1 : processed errors per day\n",
    "hist1, bin_edges = np.histogram(stats_after['err'][stats_after['err']<0.6], bins=nbins, density=True)\n",
    "bin_centres1 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "# Calculate the histogram2 : processed ; average LC error \n",
    "hist2, bin_edges = np.histogram(stats_after['avg_err'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax[0].plot(bin_centres1, hist1, ls = 'steps', alpha=0.7,label=r'$ e_{i} $')\n",
    "ax[0].plot(bin_centres2, hist2, ls = 'steps', label=r'$\\langle e_{i} \\rangle $')\n",
    "ax[0].legend(loc='best')\n",
    "ax[0].set_ylabel('Density (normalized)')\n",
    "ax[0].set_xlabel('Photometric error CRTS [mag]')\n",
    "ax[0].set_xlim(0,0.6)\n",
    "\n",
    "# MAGNITUDE \n",
    "\n",
    "m = (stats_before['mag']>15)*(stats_before['mag']<22)\n",
    "\n",
    "# Calculate the histogram1 : raw magnitudes\n",
    "hist1, bin_edges = np.histogram(stats_before['mag'][m], bins=nbins, density=True)\n",
    "bin_centres1 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "# Calculate the histogram2 : average lightcurve raw magnitudes\n",
    "hist2, bin_edges = np.histogram(stats_before['avg_mag'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax[1].plot(bin_centres1, hist1, ls = 'steps', alpha=0.7,label=r'$m_{ij}$')\n",
    "ax[1].plot(bin_centres2, hist2, ls = 'steps', label=r'$\\langle m_{ij}\\rangle$')\n",
    "\n",
    "m = (stats_after['mag']>15)*(stats_after['mag']<22)\n",
    "\n",
    "# Calculate the histogram1 : processed day averaged magnitudes\n",
    "hist1, bin_edges = np.histogram(stats_after['mag'][m], bins=nbins, density=True)\n",
    "bin_centres1 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "# Calculate the histogram2 : processed LC averages\n",
    "hist2, bin_edges = np.histogram(stats_after['avg_mag'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax[1].plot(bin_centres1, hist1, ls = 'steps', alpha=0.7, label=r'$m_{i}$')\n",
    "ax[1].plot(bin_centres2, hist2, ls = 'steps', label=r'$\\langle m_{i}\\rangle$')\n",
    "ax[1].legend(loc='best')\n",
    "ax[1].set_ylabel('Density (normalized)')\n",
    "ax[1].set_xlabel('White light brightness CRTS [mag]')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../Fig_1_extended_err_mag_CRTS_qso_properties.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7108"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(stats_after['avg_err'] < 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22069133056605572"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(stats_before['avg_err'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare CRTS white light mag and SDSS r mag  for QSO (2016/06/11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zipping CRTS-SDSS quasars catalog from  ../data_products/CRTS_SDSS_cross_matched_qso_DB_QSO_catalog.txt  ...\n",
      "Read in  7601 , quasars from CRTS\n"
     ]
    }
   ],
   "source": [
    "import CRTS_paper_modules as mod\n",
    "cols1, qso_cat = mod.get_qso_catalog() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['N_rows',\n",
       " 'z',\n",
       " 'g',\n",
       " 'redshift',\n",
       " 'i',\n",
       " 'ra_SDSS',\n",
       " 'dec_CRTS',\n",
       " 'mjd_uniq_N',\n",
       " 'r',\n",
       " 'u',\n",
       " 'ra_CRTS',\n",
       " 'CRTS_avg_m',\n",
       " 'dec_SDSS',\n",
       " 'CRTS_id',\n",
       " 'm_ang_deg',\n",
       " 'mjd_span',\n",
       " 'CRTS_avg_e']"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qso_cat.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.00000000e+00,   7.00000000e+00,   3.10000000e+01,\n",
       "          8.60000000e+01,   3.00000000e+02,   8.02000000e+02,\n",
       "          1.67800000e+03,   2.74400000e+03,   1.89900000e+03,\n",
       "          5.30000000e+01]),\n",
       " array([ 15.00910971,  15.63279457,  16.25647943,  16.8801643 ,\n",
       "         17.50384916,  18.12753402,  18.75121888,  19.37490375,\n",
       "         19.99858861,  20.62227347,  21.24595833]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B0iw8BlCSJKkwBkBJkqTCGAAlSZIKYwCUJEkqjAFQkiSpMAZASZKkwhgAJUmSCmMAlCRJ\nKowBUJIkqTAGQEmSpMIYACVJkgpjAJQkSSqMAVCSJKkwBkBJkqTCGAAlSZIKYwCUJEkqzMpeF4yI\n3wQuB96XmZdXY98F+oBHq387wLsy84sR0QdcCpxdjQ8B52fmSLXum4FLqhoeAt6RmXfV87IkSZI0\nk54CYER8HHgmMDxlqgOcl5l3TLPaRcBZwOmZ+VhE/DFwNfCGiNgGXAn8TGbuiYhfBW6OiOdl5pPz\nfTGSJEmaW6+7gD+VmW8EDk4z1zfDOucBOzLzser+FcBrI2IN8CbgtszcA5CZu6rHeVmvhUuSJGl+\netoCmJlfm2X6nRFxGbAWuAV4f7UVbzNw76Tl7qcbODdVc1N39+4GtgJf7q10SZIkzUfPxwDO4Cbg\nG5l5c0RsBL4EHAL+AOivbgOQmZ2IeKIaf9pc5VA13pNWa6YNj+rVRA/t5cLZy/qU2svSXq+Wnlar\njxUrjv77sNTP+NGwkB4uKABm5nsn3d4bER8DzqcbAA8CaybmI6IFrK7GnzZX6Wf6XczTGhzsOStq\nDvayPvayPqX1cmBgbdMlqHADA2vZsOG4RXu+0j7jS828A2BErAY2Zea3Jw23gMPV7SEggIkTRDZX\nczlpbrLNwD29Pv/+/Y/SbnfmUbkmtFp9DA7228sa2Mv6lNrL0dGxpktQ4UZHxxgZ6Xk7zLyV+hk/\nGiZ6OR8L2QJ4PPD1iHhdZv5lRKwHLgBuqOavAy6OiF3AAbqXfLkxMx+PiBuAOyNia2YORcQF1TJf\n7fXJ2+0O4+O+cepgL+tjL+tTWi/9RaimLfZnrrTP+FIzZwCsdt0O0b3ky0nAloh4K/BZutf4+3BE\nXAW06R4T+FGAzLwmIk7mqZM97qJ7aRgyczgiLgR2RsQqYB9wTma263xxkiRJ+nFzBsAqlG2ZZZEX\nzbLudmD7DHM7gZ1zPb8kSZLq5VfBSZIkFcYAKEmSVBgDoCRJUmEMgJIkSYUxAEqSJBXGAChJklQY\nA6AkSVJhDICSJEmFMQBKkiQVxgAoSZJUGAOgJElSYQyAkiRJhTEASpIkFcYAKEmSVBgDoCRJUmEM\ngJIkSYUxAEqSJBXGAChJklQYA6AkSVJhDICSJEmFMQBKkiQVxgAoSZJUGAOgJElSYQyAkiRJhTEA\nSpIkFcYAKEmSVBgDoCRJUmEMgJIkSYUxAEqSJBXGAChJklQYA6AkSVJhDICSJEmFMQBKkiQVxgAo\nSZJUGAOgJElSYQyAkiRJhTEASpIkFcYAKEmSVBgDoCRJUmEMgJIkSYUxAEqSJBXGAChJklQYA6Ak\nSVJhDICSJEmFMQBKkiQVxgAoSZJUGAOgJElSYQyAkiRJhTEASpIkFcYAKEmSVBgDoCRJUmEMgJIk\nSYUxAEqSJBXGAChJklQYA6AkSVJhDICSJEmFWdnrghHxm8DlwPsy8/Jq7BnAtcBpwDhwa2a+p5rr\nAy4FzgY6wBBwfmaOVPNvBi6pangIeEdm3lXT65IkSdIMetoCGBEfB34eGJ4ytQPYm5mnAmcCL42I\nt1VzFwFnAadn5ibge8DV1eNtA64EXl3NXQHcHBE9B1JJkiTNT6+7gD+VmW8EDk4MRMRxwDnAZQCZ\nOUY3EJ5bLXIesCMzH6vuXwG8NiLWAG8CbsvMPdW6u4A+4GULejWSJEmaU08BMDO/Ns3wTwOdiRBX\nuRfYWt3eXN2fcH/1fJummQPYPWldSZIkHSULOQmkH3hiytihanxi/tDERGZ2quX7p85Ns64kSZKO\nkoUcc3cQWD1lrJ+ndhMfBNZMTEREq1r+4NS5adadU6vVd4TlaqqJHtrLhbOX9Sm1l6W9Xi09rVYf\nK1Yc/fdhqZ/xo2EhPVxIALwXGI+IUzPzvmrs+cA91e0hIIA7qvubgcNATpqbbPOkdec0OOjGwrrY\ny/rYy/qU1suBgbVNl6DCDQysZcOG4xbt+Ur7jC818w6AmTkWEZ8BtgNviYhB4ELgI9Ui1wEXR8Qu\n4ADdS77cmJmPR8QNwJ0RsTUzhyLigmqZr/b6/Pv3P0q73Zlv+aL7l8PgYL+9rIG9rE+pvRwdHWu6\nBBVudHSMkZGed8TNW6mf8aNhopfzMWcArHbdDtG9lt9JwJaIeCvwWbqXerk2Iu4DnqQb8K4HyMxr\nIuJkYOLafndVy5OZwxFxIbAzIlYB+4BzMrPda+Htdofxcd84dbCX9bGX9Smtl/4iVNMW+zNX2md8\nqZkzAFahbMssi7x+lnW3091CON3cTmDnXM8vSZKkevlVcJIkSYUxAEqSJBXGAChJklQYA6AkSVJh\nDICSJEmFMQBKkiQVxgAoSZJUGAOgJElSYQyAkiRJhTEASpIkFcYAKEmSVBgDoCRJUmEMgJIkSYUx\nAEqSJBXGAChJklQYA6AkSVJhDICSJEmFMQBKkiQVxgAoSZJUGAOgJElSYQyAkiRJhVnZdAGSNF+H\nDx9meHio6TJqsXt3Nl2CpIIYACUtW8PDQ2y/5QOs27i+6VIWbN/dD/CsM09qugxJhTAASlrW1m1c\nz/qTT2i6jAV7ZO/DTZcgqSAeAyhJklQYA6AkSVJhDICSJEmFMQBKkiQVxgAoSZJUGAOgJElSYQyA\nkiRJhTEASpIkFcYAKEmSVBgDoCRJUmEMgJIkSYUxAEqSJBXGAChJklQYA6AkSVJhDICSJEmFMQBK\nkiQVxgAoSZJUGAOgJElSYQyAkiRJhTEASpIkFcYAKEmSVBgDoCRJUmEMgJIkSYUxAEqSJBXGAChJ\nklQYA6AkSVJhDICSJEmFMQBKkiQVxgAoSZJUGAOgJElSYQyAkiRJhTEASpIkFcYAKEmSVJiVC1k5\nIp4D/APwf6uhPqADvKS6fS1wGjAO3JqZ76nW6wMuBc6ulh8Czs/MkYXUI0mSpLnVsQWwk5nPr/7b\nUv07AuwA9mbmqcCZwEsj4m3VOhcBZwGnZ+Ym4HvA1TXUIkmSpDkclV3AEXEccA5wGUBmjtENhOdW\ni5wH7MjMx6r7VwCvi4g1R6MeSZIkPWVBu4ArfRHxX4EXAoeAq+ju0u1k5p5Jy90LbK1ub67uT7if\nbhjdBPx9DTVJkiRpBgsNgAfpHuf3scy8JyJ+DvgS8CvAE1OWPQT0V7f7q/sAZGYnIh6fND+nVqtv\nIXWLp3poLxfOXtbnSHppv6X6tFp9rFhx9D9T/rysz0J6uKAAmJkPARdMun9nRNwKvA9YPWXxfrqB\nkerfH+3ujYhWtfxBejQ42HNW1BzsZX3sZX166eXAwNpFqEQqw8DAWjZsOG7Rns+fl81a6FnA64EN\nmXn/pOEW3d24L4mIUzPzvmr8+cA91e0hIIA7qvubgcNA9vrc+/c/SrvdWUj5xWu1+hgc7LeXNbCX\n9TmSXo6Oji1SVdKxb3R0jJGRnrfDzJs/L+sz0cv5WOgu4J8Fro2IF2XmgxFxGvBK4BXAicB24C0R\nMQhcCHykWu864OKI2AUcAC4BbszMx3t94na7w/i4b5w62Mv62Mv69NJLf3lI9Vnsn1/+vGzWgs4C\nzswvAB8C/jIivgNcD7w1M/83cDGwLiLuA74BfCYzr6/Wuwb4AnAX3a1+K4HfXkgtkiRJ6s2CzwLO\nzI8BH5tmfD/w+lnW2053C6EkSZIWkV8FJ0mSVBgDoCRJUmEMgJIkSYUxAEqSJBXGAChJklQYA6Ak\nSVJhDICSJEmFMQBKkiQVxgAoSZJUGAOgJElSYQyAkiRJhTEASpIkFcYAKEmSVBgDoCRJUmEMgJIk\nSYUxAEqSJBXGAChJklQYA6AkSVJhDICSJEmFMQBKkiQVxgAoSZJUmJVNFyBp8Rw+fJjh4aGmy5hV\nq9XHwMBaRkfHaLc7sy67e3cuUlWSdGwxAEoFGR4eYvstH2DdxvVNl1KLfXc/wLPOPKnpMiRp2TEA\nSoVZt3E9608+oekyavHI3oebLkGSliWPAZQkSSqMAVCSJKkwBkBJkqTCGAAlSZIKYwCUJEkqjAFQ\nkiSpMAZASZKkwngdQEmSCtdujy/aN+scybf9zMeWLVtZtWpV7Y97rDEASpJUuLH9+9jx+X0cv+GR\npktZkAMjD3Lpu2HbtjOaLmXJMwBKkiSO3/BsBk48pekytEg8BlCSJKkwBkBJkqTCGAAlSZIKYwCU\nJEkqjAFQkiSpMAZASZKkwhgAJUmSCmMAlCRJKowBUJIkqTAGQEmSpMIYACVJkgpjAJQkSSqMAVCS\nJKkwBkBJkqTCGAAlSZIKs7LpAqSl7PDhwwwPD825XKvVx8DAWkZHx2i3O4tQ2fzs3p1NlyBJWgIM\ngNIshoeH2H7LB1i3cX3TpdRi390P8KwzT2q6DElSwwyA0hzWbVzP+pNPaLqMWjyy9+GmS5AkLQEe\nAyhJklQYA6AkSVJh3AUsSVJB2uNtDo48+LSxsdHvN1RNPY5/5nNorTDSHAm7JUlSQQ7+0yirTvl7\nVm984EdjP3UawAPVf8vLI3sfhh++ioETT2m6lGWlsQAYES8CrgKeCTwB/FFmfqKpetSMXi+z0hQv\nmyLpWHQsndwG8Ljntx2xRgJgRPwEcDPwrsy8KSJOAe6KiG9m5tJNA5rTkQa63buTKz7x1/QPnHgU\nq5q/sdHv81OvaboKSZLq1dQWwJcDncy8CSAz74+I24F/C/ynhmpSDeZz3byTXgNLdbfDE3c/AHjd\nPEnSsaWpALgZ2D1l7F7gzAZqUc2OpV0LXjdPknQsaioA9gOHpowdqsaLM5/j4EZGRjhw4MCCnrfV\nguOPX8OBA4dotxf0UD/yj//4XVhTz2NJknQk2u3xY+rY7S1btrJq1aqj8thNBcCD/HhM6K/G5zQ2\nNsYrX/UKnnj88doLa8K6gUF+uP4R1p5wfNOlLNhDu7/PP9v6z5suozaP/mBhIXup8fUsXcfSawFf\nz1J2LL0W6O6pOVxd1ub//eO3uPy6w6xZ962Gq1q4Q4/8gB1/+Fu84AVnzLhMq9U378dvKgAOAe+e\nMrYFuKeXldeuXdv3v77+jdqLkiRJKkFT3wTy18CTEfHrABHxAuAXgBsaqkeSJKkYfZ1Op5Enjoht\nwNXACXSP/3t/Zt7SSDGSJEkFaSwASpIkqRlN7QKWJElSQwyAkiRJhTEASpIkFcYAKEmSVBgDoCRJ\nUmGauhD0EYmI3wQuB96XmZdXY98F+oBHq387wLsy84sNlbksTNfLavxDwJuAJ4G/Bd6amWPNVLk8\nTO1lRKyiezHzyafW9wNPZOZPN1HjcjHDZ/zZwB8DAYwDf0P3M/5YY4UuA7P08mPAVmAVcE1mfqi5\nKpe2iHg58EFggO6Gkqsz86MR8QzgWuA0uu/JWzPzPc1VuvTN1Mtq7iXA9cD/ycyzm6tyeZjlffk8\n4ErgVLq57ivARZn5xGyPt+QDYER8HHgmMDxlqgOcl5l3LH5Vy9NMvYyIdwI/D2wGHgf+HHgVcNNi\n17hcTNfLzDxM9xttJi93PfDNxa1ueZnlM/5nwLcz85yIWAl8EXgfsH2RS1w2ZunlJ4G7M/N1EbEe\n+FpEDGfmZxe9yCUuIk4EbgFek5lfqX65fisivkH3G6z2ZuZrI2It8DcR8bbM/NMma16qZunl14Hn\nAv8BuJNuoNEs5nhffpzuHyOviYg1dP9Yfifw4dkecznsAv5UZr6R6b8neP5fglemmXr574E/zMxD\nmdnOzF/PTMPf7GZ7XwIQEa8AtgFXLVpVy9NMvXwB8N8BMvNJut8gdNoi17bc/FgvI+I44Cy6WwjI\nzIeB/wz8WiMVLn3jwLmZ+RWAzNwDfAd4MXAOcFk1PgbsAM5tpsxlYaZebgN2A/8auK+x6paXmXp5\nOvCHPPW+PAT8Fd0ez2rJbwHMzK/NMv3OiLgMWEs3Gb+/+kWhaUzXy+qv2E3ASRHxNWAD8AVgu7va\nZjbH+3LCHwG/k5nto13PcjZLL28H3hARfwWsAX4JuHHRCluGZuhlB2jz9J/3j9D93GuKzPwh8LmJ\n+xFxCt1d59+s5vdMWvzeak7TmKWXd2bmd6qxhqpbXnrpZTX+E8Cv0D18ZlbLYQvgTG4CPpGZLwJ+\nke5fZpc0W9KyNFj9+y+AlwI/S3drwe80VtExICJeDfRl5u1N17KM/UfgRcBDwA+AUeCaRitahjLz\nUbrHBF0r4ry8AAACsUlEQVQSESurXUm/Afxko4UtA9Wxk5/nqV1pU4+pOkT3OF/NYXIvJwcWHbmZ\nelmFv08Be+nhZ+WyDYCZ+d7MvLm6vZfuAc4eRHrk9lf/7sjMw5N2D/1ygzUdC34D+ETTRSxztwM3\nZ+YgsA54EPh0syUtW+fRPc5qiO5B97cDDzda0RIXES8Evgb8eWb+Ad3d6qunLNbPLIeBqGuaXmqe\nZuplRDwT+DLdXcXn9LLnaVkGwIhYHRGnTxluAYebqGc5q45j2QesnzTcoXs2sOah2q3+Srp/oWke\nqrMtX0z3RJCJYwA/SXdrv45QZu7LzNdnZmTmL9H9efl3Tde1VFW/ZG8HfiszL62G7wXGI+LUSYtu\noXvmv2YwQy81DzP1MiIG6Ya/OzLzDXOd/TthWQZA4Hjg6xHxCwDVWW0XAH/RaFXL138B3h0RP1Gd\nQfTvgNuaLWlZ+xm6u3/3zLmkZjICfA/4NwAR0Uf3MI9vNVnUchURO6uz/YmI59L9eenu9GlExGpg\nF/D2zLxlYrz6Y/kzVGehV790307356emMVMvdeTm6OWfAH+TmUd06FZfp9OZe6mGRESL7i6LDnAS\n3U3tI8Bngf9B97iM4+ge4HwT8HsecD+9OXr5u8DVdLeuPEr3chvv7fWviNLM1svM/J2IeCPwocx8\nXoNlLgtzvC//gu6Zbc+qFr8X+O3M/IcGSl3y5ujlTrpBZZDunpL3eab/9KrP7yfonqU6caWJDt3D\nD66kex3AM+juJbkxM3+3iTqXgzl6uYnuH8vPoHs86oN0L7HzCw2UuuTN0suv0v2D7rt0j1HtVPP3\nZ+arZ3vMJR0AJUmSVL/lugtYkiRJ82QAlCRJKowBUJIkqTAGQEmSpMIYACVJkgpjAJQkSSqMAVCS\nJKkwBkBJkqTCGAAlSZIK8/8BZmhNZGS8picAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb824182490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = np.array(qso_cat['r']).astype(float)\n",
    "m = (r > 15)*(r<22)\n",
    "plt.hist(r[m])\n",
    "\n",
    "avg_mag = np.array(qso_cat['CRTS_avg_m']).astype(float)\n",
    "plt.hist(avg_mag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7584"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r7dXNnEtERGJR9kTybBzPVH7MWfkAbNi1A19wNnd8x7Xh11Yl+Xhy7IUsOeo8\ngt4kindsYujXb9RTWbn/LaczV/qHfZmuRrvTHRRf4uzcMycc56j+a8Y4qr9rucO5404NudtZfYQf\nsCXxIdy/gq8Bo4CvgdLIxREREZEDre3Un2dPv5mtHXrsX1sw7lK6r5lL1p5tLiYTkeYUblN+DGCs\ntVsiGUZERES+s7qz4c8X3U/Qm1Rn3e9LZUP/Yxg+738uJROR5hbu9BULVEQyiIiIiNTVZ9sq+m1d\nWWetdfEuxr9+nxpykRYm3Cvl1wLPGGOmAluBOneXWGs/a+5gIiIiic5LkCtnPMafrniEKl8q/ZZ+\nyFGf/pvUihK3o4lIMwu3KT8HOBv4fj3HgkBSPesiIiISpqK0DDLKQ2+i7bhnG5d9+BRbSgrotn6h\nC8lEJBrC3b5yO3AD0BXIPuhXu8hEExERafnKk9N44ZSf8burn2BPena9Ncct+0gNuUgLF+6V8iJg\nsrW2stFKERFpkOM54kBegoxMy52Y4XaEqFrWcyTPnXYDuzNzAXhh/M+58c0H8LicS0SiL9ym/C7g\nHmPMvdba4kgGEhGRw+O0gc/NdThTmdj5BmFo8ExH9R15x1H9T7a9En7xs+GXlnhbM2XCVXw06LQ6\n64v6jWXJ8OM5bs3nIR9zqj/88wNkZjqrdzjqu+ZuszA996MLHZ16FcZR/f033OOoHmdj0OEmh/Ui\nTRBuU/57oAtwpzGmiNAbPbWFRUREJEzbUjvxSb9TQtY7FW6lQ3G+C4lExG3hNuX3RzSFiIhIAulX\ntpYffDON10edD4AnGOCsxW9y0fwppFZrp6hIIgqrKbfWPhfpICIiIonk/K+nMq/XWACu/2QSA/JW\nuZxIRNwU7pVyERERcagwKYOvso7mjN0zQ46l+Ku4a/r/0ba0gBR/lQvpRCSWqCkXERFpZkFgVtY4\nnup6HYXJbcmpyueIokUhdblFedEPJyIxKdw55SIiIhKGAl8WD/T8NX/pdSeFyW0BmNTtRkq9rVxO\nJiKxLKwr5caYW6y1j9azng78ylo7sdmTiUhc0yxugSb8Obg+vv85Wdl6APf0/j1FvrrzCHem5PBW\nh7O4KO81l5KJSKxrsCk3xqQArYD/Z4yZDCHPMxgE/BqI76+iIiJRFGvffMRUHofzo3/OU47qb/zm\nGWcv8JiD2o3Q3beZ1G6VFB3wr6svUMXFq6Zy3uppNftaaq0ocRbliKOd1XO5s/LgJc7qp7WbEHbt\nlcunOjpZU22/AAAgAElEQVT3+MHO5snH3Nzx7Ink2aIIv4i0NI1tX7ke2A2kAnuAgoN+fQHMjWRA\nERGReJFeXcpN3zy+//3+Bat45NNfcNG3r+ELOnz6j4gklAavlFtr/26MeRHYApxeT0kZsDASwURE\nRGJZkNAfHwOMzl/IhPXT6Viaxw/XvkFSMFBPlYhIXY3uKbfW7jTG9LfWboxGIBERkVgWwMP7yafx\nUfIp3F/6e5KpDqm5YYmzbTUiIuGORCwyxvweGAykHXzQWntus6YSERGJQds9HZnU6kYW+4YDMDX1\nQi6vmOJyKhFpCcJtyl8ChgGfA4WRiyMiIhJ7Anh4J/lMnkv7MRWe765NvZpyHsdUfUnfwDoX04lI\nSxBuU34SYKy1GyKYRUREJCYt8B3BP1tdF7KeQiXbvJ3VlIvIYQu3KV8P7IxgDhERiQNNmT/fEoyu\n/pojq+YxL/nI/WsjqxdxU9kTdAzqqZwicvg8wWCw0SJjzJnAD4DfWWt3RTxVE+TnFzX+GxERkcPi\ntCm/MNjLUX0HnP0T8/icXzqq51Nn5bz53Zu7kttx4/BJBIGrN07m9PwP6k5fcXqxfJjD+iRn5RWv\nOqt/uvU1jur/wc/Crl065cjGiw400lm54znlBXc7/IAYm+cvMS0nJ6O+wUyNOuSVcmPMNuo85oBM\n4DpjTBFQZ76TtbZdU15cREQklviDXtZW96Z/8pqQY+2rdvPrb/9Ct/It5FTqh8ci0rwa2r7ym6il\nEBERcdn6qp78fe/NbKzuzqT2t9LFtz2kZtTeb1xIJiKJ4JBNubX2uWgGERERcUN1MIlXS87jlZIL\nqCYZgEl7b+K+7D/gRTsjRSQ6wrrR0xjzvwYOB6h54udb1toPmiWViIhIFGyo7s6Dhb9gXXXvOutL\nq4byftlpTGCGS8lEJNF4w6zbApwAjAAqgXJgKHAsUEzNQ4XeNcZcH4mQIiIikeDDz5bqLiHr41Jn\nc3TqHBcSiUiiCnckYjkwCbjHWrv/Z3nGmN8BAWvtlcaY04FHgSebP6aIiEjz6+rbymVtXmJy8ZUA\ntPXu4ecZTzEu7Ut3g4lIwgm3Kb8a6HxgQ17rr8AG4AFgJtC1GbOJiIhE3Dmt32R2+bF08W3l2oxn\nyPIWuR1JRBJQuE15kJrtKwfvGT8aaF379glA6K3qIiLScmRPdFT+ynJnp58x+HhnHzA7vLIVFYYv\nyo/l6tmTOXiAcBIB7vP+gbRARegH1rN0SFc6qAXwOysP/spZfWnrVo7qb5rytLMXcOIyh3PBr3f2\n54yLIe9uJ99Maea4xJ5wm/K/UrNn/BNgLTX7yvsA44FHjTGpwAzgtkiEFBERaYryQCrP772Mt0rO\nJoiXAW1Xcfye0E6+3oZcRCSKwmrKrbV/NsasAiZQs0XFC+QB1wEvWmuDxpiTrbVfhHM+Y8xPgYeA\nP1prH6pd60bNvvUhQDLwL2vt/U5/QyIiIgBLKoYwqeAmtvk771/7R9efMrxoCVn+vS4mExEJFe6V\ncqy104BpDRwPtyF/DOgArDjo0IvAQmvtj4wx2cAXxpgVta8rIiIStq/KjuK+3b8NWd/ry2JO1lGc\nvlsTfEUkthyyKTfGvGytvbj27YbmlGOtPdfBa06x1n5hjPn4gNdqAxxP7Y48a22BMeafwKU08I2A\niIhIfY5IW0hX32a2VHfbv5abtINbVj3OiOLFLiYTEalfQ3PKD/zZXmEjv8J2iCvqQWoeQnTgNwl7\ngQFOzi0iIgKQ4qni1raT8BAA4Oz0d3gs91Y15CISsw55pdxa+9MD3r4qkiGstSW1N5H+xhjzM6A9\ncBWQFsnXFRGR+FccSKeNtyRkfVCq5crM/zAgZRVDUx2OgRERibKGtq/cEuY5gtbaSc2Q5cfU3Oi5\nDFgPvAOc0wznFRGRBuTmPhR+scORiJFUXprKw7tvYWnlUCbl3kJrb3lIzbkZr7uQTBKdo79TQF6e\nRjRKwzd63h7mOYLUNNOHxVq7DTh/3/u1Twv9+nDPKyIizaf9zi2O6t/wOru2Mu6h8L7sf1V2FM/v\nuZqCQDsA/rPkx/x8y78a/8DQC+oN6+2gNsvZqWf/arSj+i5sdVR/HWF8Pg50k7NyR5zOHcfp3HGR\n+NfQ9hUnX4oOmzFmKvCVtfZhY0wvasYt/jCaGUREJLYV+jP4Z+F1fFZ2Qp31dzqcxbg9XzCsZJlL\nyUREDk/YIxGbgzHGS832lCDQAxhkjLmWmgkr9wH/NsbcCFQBd1prF0Uzn4iIxLZvq/qHNOQAPco2\n0CpQ5kIiEZHmEdWm3FobAAY1UDImWllERCT+jElbwCmtPuKjslMASApWc/6O/3JR3qskB6tdTici\n0nRRbcpFREQO17Vt/83CipFkJ+3h1qWT6FO+zu1IIiKHTU25iIjEnF3+bJZXDOb41rNDjmV4i7mv\nwx/p7NuGr9zvQjoRkeYXdlNujPEAJwM9rbWTa9cyrbV7G/5IERGR8ASD8GHpKTxdeDXlwTS6JW+h\nd/L6kLruyZujH05EJILCasqNMcOAN4EcIBmYXDshZaEx5jRr7fzIRRQRSWxOZx7zYuzMEndib0Em\nd+/6A19XfDcq8JGCm3kw51f4PLoiLiItW7hXyv8OTAV+DxQBWGvXG2P+ADwInBiZeCIiEnEOmvg7\nvOE+wqLGuM/Cmzv+9Y5R/H3+dZRVt66zvraqLx/lnczpvg/q/8CRjuLA0c7KF57f0GyCuhYzzNG5\n3+VMR/XLGeyofmm7Ix3VO3axg9on73Z+/rvj94E6ehiQNIU3zLojgYnW2mpqxhnu8w9gRLOnEhGR\nhNIzcyOeOv+8QAoVXON7hvFJH7mUSkQkesJtyvcAbepZ7w7oZ4oiInJYOrTaxdVDJ+9/f4hnKZNS\nbuWHvrdI8gRcTCYiEh3hbl+ZCfzHGPM7AGNMF2AUNQ/8eSNC2UREpAUKBsHjCV0/vccHfL14NCO8\ni5mQ9B5eTzC0SESkhQr3SvltQBnwNZAGbAJeBxbUHhMREWmQP+jl9TXf5//m/I5gPf22xwN3Jf+Z\ns3zT1ZCLSMIJ60p57djD840x7YF+1DTo64BiICNy8UREpCXYVNSVRxfezMqCgQC8t+F0JvSaEVJX\n3xV0EZFEEO5IxDxrba61dhew64D1LGAt0D5C+UREJI75A16mrTmHKSsvoSqQsn998rIrGZ27gNzW\nO11MJyISOxpsyo0xZwDfA7KNMfUNyu3d2DlERCRxfbjpFJ5b/pOQda8nwJbirmrK5ZCczueP1zGE\njp9D0ATx+rlJNI011NuBJGr2no+q53gZcE1zhxIRkcPgcHb3DYPDbwp+89ojjs7t3+Rluu8MVlf3\n3782NmUO16f/g/bfFsC3jk4X6sfOygtGtHJUv9PBD4Kf5wpH5/7whrMc1XOcs/KmyLNFETlvLg4f\naNWUueYica7Bptxa+w1wizEmzVr70yhlEhGRFiLJE+C2NpO4bc+DtPKU8dP0pzkx9TPtHRcROcgh\nm3JjzFBr7dLadx8zxgw/VK21dnGzJxMRkbhRFfCxpawrvdI3hBzr6dvInRkPMih5BdneQhfSiYjE\nvoaulM8D9v2cbxE1T/Ks79pGkJotLiIikoBWFffj76tvprAqi8dH3kxWcugWiGNTv3IhmYhI/Gio\nKTcHvN070kFERCS+VAaSmbLpYqZt/SGB2msz/1p/LXf0f9jlZCIi8eeQTbm1duMBb4f+PFJERBLW\nquJ+PLz6VjaXda+z/unOEzmh/SyOajfPpWQiIvEp3DnlI4G/AYOpeaJnHdbads2cS0REYlhVIJkt\nZV1D1k/J+YjBmctdSCQiEt/CnTE+BdgE3EPNGEQREWmiXBP/D0IekrmCszu9w1vbvw9Ah5Sd3Njn\nCcZkL3A5mUSLoz/HF0cuh0hLEW5T3gs40lpbEsEsIiJSn+udzXhuP3CLo/rxfBB+8QH3a14RfIH5\njGE4S7iq8lnSbWk9YRxFgTOcla8b0dlR/VQuclT/t8AdYdfu6hD6k4MGPeas3LHHIO+0yMwdd6wJ\nc8cT5YE3ifL7lMaF25QvoOZLq5pyEZEEsnVVV/I35jLi1IUhx9I8FTwS/AWtPfoBqojI4WpoTvmA\nA959AHjGGPMksBYIHFirOeUiIi1LZVkK894ey6qvBgPQsc82OvXZHlKnhlxEpHk0dKV8JaGzyccf\n8Pa+Y5pTLiLSgmxe0Z3Zr55AaWGb/Wuzp57IOb/8r4upRERatoaacs0mFxFJMPargXzx6okh63t3\nZbF1lcM90yIiEraG5pTvn01ujFkIzARmAJ9bayuikE1ERKKs57B1LJh+JOXFrfevZeUUMO6iT+nY\ne4eLyUREWrZwb/R8HDgBeAbIMcbMpqZJn2mtDb37R0RE4lJaegXHnDeLj587HY8nwJCTFjPqjPn4\nkv1uRxMRadHCasqttU8DTwMYY3oDJwLHA9cYY9paaztGLqKIiESCt9xPIC30lqBew9cx7JSF9By2\njpwe+S4kE4ktubkPOf4YjToUp8K9Ug6AMSYDMMCA2l8dgWURyCUi0nLNclbudO74PO+RDR4vKsrg\n1f9dxK7dHfjFLX+h37TQ85/LdPiaml8H+qWjKJDnrNzp3PG/Ef4ccYDPON5RvePZ407cBHk2/Dni\nTWkMiWRjWOBs9riaVJGGhdWUG2MeoebKeHdgDjAb+C0wV/vLRUTiQzAICxaO4bVpF1FSWjNZ5aNP\nTqUfz7mcTEREwr1S/nNgNfAo8Ckwx1pbGbFUIiLSrAr3ZvLKa5ewZNnIOuvTZ5zNhOEf0r31ZpeS\niYgIhN+UZwPHACcB9wIjjDHfAJ8Bn1lrZ0YmnoiINIdly4eFNOQAnTttdSGNiIgcLNwbPcuAj2p/\nYYxJAy4BfkXNNhY9PEhEJIYdM3Y2CxaOYdXqgQD4fFVMOP1tTjnpA7q/6WzPuoiINL9w95R7gOHA\nOOC42v+mA59Qs6VFRERimMcDl1z4Ag/87fd06byVSy96nk4dt7sdS0REaoW7fWVPbe1s4EPgQWCB\ntTYYqWAiIuJc6u4KluUPYcig0MFY7dvv4rabHqRL5y14vfryLSISS8Jtyn8IfKFJKyISr5o0Ti5c\n1090Vn9TBDIEgnT8Mp+e727mWc81/PbO/yM7uyCkrFtX3dCZSCL6515EmlW4e8o/jnQQEZFEMXTw\nPEf1J/B5g8erdvrY/UouFWtaAVBBK958/jwmnv1/eDxhvMAgB2EKHdQCp45421H9h1POclQ//tJ3\nHNUv9bzrqB6c1SfaLO5E+f0myu9T3OV1O4CIiDRdycI2bP9b9/0N+T5fbxjN1xtGu5RKRESccvRE\nTxERiS3JHSsJ+uteDm+VXMrV455ldM+DH8cpIiKxSk25iEgcS+lSSeb4AvbObAdAminlsaNvITdj\np8vJRETECUdNuTFmDtADGGqt3RWZSCIiUp9gkHr3iGedWkDFmlakH1lE+pFF5K5XQy4iEm/C3lNu\njBkPdAS+An4WsUQiIlJH0A+FM9uy+5Wceo97fJB7w1baHFUU3o2dIiISc5zc6HkrNQ8K+itwvTFG\nT/EUEYmwlC1VbH+kG4XvtadkbiZly1vXW6dmXEQkvoX7RM9+wPHAZdbaImPMFuAC4OVIhhMRSVjV\nQdp+UETWh8VUBVL3L+9+NYfOv9qEt1XAxXByKLkmw1F9ni2KUJLY4vTzAs4+N02Zx64xhxJrwt1T\nfgvwrLX7/4Y8AtyGmnIRaakcPBCo/WNbHJ16W6BLozVdP95K25nFIespgUqOzZtNp57bD/mxC3s7\nGTwObdkTdu3TXOvo3IsCIx3Vc9ndjso/vMxZc5XLfGd5nMh2+BAph9REirRsjW5fMcZkApdT04jv\n8yrQ2RhzVKSCiYgksm3HdaQ8O6XO2oBRK7jszskNNuQiIhKfwtlTfi3wkbV2w74Fa60fmETN1XIR\nEWlmgdQk1lzQC4DWGcWcddU0vnf5O7RuU+ZuMBERiYhGt69Yax8CQjZrWWv/FpFEIiIJxFvpJ2Vv\nFeUd0kKO7e2fyeqLevGXoXeQ1rrchXQiIhIt4WxfGWCMOame9euNMd0ikkpEJAFkrC1i+MPLGfjv\n1Xir6r9xM39MBzXkIiIJoMGm3BjTn5q55OfUc/hU4AtjTMdIBBMRaam8FX56vb6RoU9aWu2soFV+\nOd1mbHU7loiIuKixK+W/B96y1t5+8AFr7XnA57U1IiIShsw1RYx4aBmdZ+fVWe/y6XbSN5W4lEpE\nRNzW2J7yk4GTGjj+R2AmcHNzBRKRxNSUOcPxyFdcRdruypD1HUfnUJYTuq9cGpcof3acivTsbkfn\nj/C4SJGWoLGmvL21du2hDlpr12j7iojECycNxyU574RdewOPh10bHAlLvxnJ/CVjAchtv4NrLnyK\nQf1WHPJjpnBp2OcHeJ3Bjup30j7s2l10cHTuXR26OqqPhsjN+06MBwE1ScHdEZ2zrhnu0hI01pQX\nGGO6WGvr3exojOkLFDp9UWPMT6mZ6PLH2uku1N40+jhgAD/wKfALa63ucBKRFsPjgSvOnYxdO4hj\nR8/i/O+9QmpqhduxRETEZY3tKX8f+E0Dxx8CZjh5QWPMY8ApwMGXhZ4GVllrBwIjgAHUbI8REYk7\nm5b3wH5Z/5M122YW8pe7bueyc55XQy4iIkDjV8rvBb42xvQCHgVWAUnAEODO2v+OcfiaU6y1Xxhj\nPj5ofQTwIIC1trr2+FiH5xYRcVV5SSpzXh/H2gX9SfJV06nvVrJyQ3+gmN6q1IV0IiISqxq8Um6t\nXQecAGRSc0V8HbAaeAMoB46z1q538oLW2i8Ocegd4CJjTJIxpg1wBjDdyblFRNy0fnFvXv/rhaxd\n0B8Af7WPWVNPIhDwuJxMRERiXThP9FwKnGSM6QD0qV1eZa3d08xZfg18BOwCUoAPgX8182uIiETE\nNx+MYsH0o0LW8zfmkr8hl469d7iQSkRE4kWjT/Tcx1q701o7t/bX/obcGPOHZsryDvA/a21baq7M\nbwZebqZzi4hEVO+Ra/ClVNVZy+68i7NvnaaGXEREGtXolfIw3AX83+GcwBjTHjgKOA/27yl/EXj3\n8OOJiNTInZgRdu34J5ydO7PDXo6YMJe5b4zDm+RnxKkLGHbKIpJ8AYcpJdKczu/WuD0RiYbmaMqb\nY7PkbmArNU35o8YYD3AOsKgZzi0iccBp45Nrwm+wAbjYWfkhBYNQCaR+96WviJos3Y/byM68XPqO\nW0VW50JKSa/3FOvp5egllzucO/7EFIdN5E0Oap1+HgvudvgBkW2CE+lBQ/pmQiS+NEdTHgy30Bjj\nBZbVfkwPYJAx5lpgGvAD4EFjzA215auAnzRDPhGR5rEnSMprAfBA5dXemqHjB/B4gxxx/jyXwomI\nSDxrjqY8bNbaAFD/4N4aJ0cri4hI2IJBkuYESX4rgKf2cWZJC4L4R2uqioiINI8Gm3JjTDg/54tq\nYy8iEk2e3UGSXw2QtKruDwWTpwXw9/dAphpzERE5fI011KPCOMfs5ggiIhKLkuYFQxpygEBn8FQ7\n2L8nIiLSgAabcmuttpOISEKrHu8haTF4t9e8H0yBqjO9+Md5wKur5CIi0jwanVNujDnBGDOunvU+\nxpgLIhNLRCRG+DxUXpRE0AP+fh4q7kjCf7xXDbmIiDSrcPaD+4CngQEHrd8DLG72RCIiLkj3FJHi\nqaQg0D7kWLCHh4qbkwh2R814AnIyRjGexxAm0rhIkVjUaFNurf3IGFNmjPm+tfYtAGNMV+AsnE23\nFRFxzdAnDjGq0B8k89Nist8vwp/uZcuduQRbefkR0+rW9Tz0uecy1lGWLmx1VP+Ep8hRPdc7K3c0\nS/xJh43n3fHbpErj4vmbEJFY0+j2lVqPAr884P2bgFestXuaP5KISHQkb6+i86SdtHunCE81+AoD\ntHt7r9uxREQkAYU7zvAF4H5jzEhqHupzHXB8xFKJiERYxhcltHu9EI//oPWvSik6ujV0dyeXiIgk\nprCulFtrK4CnqLlafiWwwFq7IoK5REQiqqp9UkhD7m/lIf/StlR2S3YnlIiIJCwnD/55AlhNzRXy\nGyITR0QkOspNGkVHtSZjbikAJcPS2H1uFv7MJJeTiYhIIgq7KbfWbjfG3AR0tta+G8FMIiJRsfsH\nmaRsqaLwlDaUjkgDjyariIiIO5xcKcda+2yEcoiINDtvkp/+IyzBYP3NdrCVl223d1AzLiIirnPU\nlIuIREpufoaj+qG7DzHisFbqhkraT91Dyo5qgl5YsnEQJd3S6y8Ohi79w/uzsLN0JC/sWoDTb/jc\nUT04GFkI8LLD0zuRPZFc46DeybjFJnIyli+RRvg1Ze54In1+RGJNuCMRRUTigqcyQPabhXSatJOU\nHdU1awHoO3U9nuqAy+lERETqp6ZcRFqU7Lf3kvVpCZ6Drn4nl1aTtqvCnVAiIiKNaFJTbozxGWPa\nNXcYEZHDVXhqBv5WdfeIFx3VmkV3DKGsYyuXUomIiDSswabcGJNkjPmLMebEA9ZuBPYC+caY94wx\nbSIdUkQkXP7MJHb/MAuA6uwktv+0Hbsuaou/lW6hERGR2NXYlfLfAxcDpQDGmD7A34HHgdOBNOAP\nkQwoIlIfT3mApD3+eo+VjG7Frh9lseWOHMpNWpSTiYiIONfYpaPLgQuttfvGHFwIrLPW3glgjNkO\nvA78OnIRRUTqytmcR9f/5tdcCb+hPXgPGmno8VB03CEmrYiIiMSgxprybsDcA94/GXjvgPeXA12b\nO5SIxL9c42zEIbMaL/FVVDF47lJ6rNpY8/4ePxmzSyg6XrvoxH0aQRg/9P9KYlFjTXkZkAxUGGOS\ngLHAMwccTwGqIpRNRBLJooYP5xbuYPiGRaRVlddZz363iLLBaVS3b/jLWWfvVkdxli4/MvxaR2cG\njnNaP5G804rCLs/NdT4bPNyGI5Lnlualz7tIfGlsT/lyavaOA1wAtAY+OOD4scCGCOQSEakjvbwo\npCEPeqDo2Nb4M5NcSiUiItI8GrtSPgl4yRgzBzga+Le1djeAMWYc8E/g35GNKCIC63L70qVgK9kl\nBQBUdvSx86K2VPZMcTmZiIjI4WvwSrm1dirwY8BSczPnzQccPouaXaB/iVg6EZF9PB6+6TmKam8S\n33bqz9Zf5KghFxGRFqPRwb3W2mnAtHrWfxuRRCKSuIJBOhdsJejxsD27S8jh4lYZfDjsNKp8qQz1\nzavnBCIiIvGpsYcHrYhWEBFJbKlV5YxZO5fR6+YzfMMiUg7aP75PlS81yslEREQir7EbPXtFI4SI\nJLIgXftu5MRlH9Fpz3YAUvxVDN24xOVcIiIi0dPY9pVgVFKIiCuaMqs3bNkTwyobfNRS+gxZCwc9\nnLNj4XbSy4spSdMM8miI6J8FERFpVGNNebIxptGv1NZaDUMVkbrCeBgQwNa8rvR+ey2eAy4BFKRn\n803PkQ025NsCoXvOG7LrJofPOXvSwTzuML8B2a/A+axvHMycdjqf2mlDrvnX9dPnJX7o/5XEosaa\nci8wqpEaXU0XkSbbk9uOtUP70XfJavweL7brINbm9gWPx+1oIiIiUdNYU15hrT05KklEpGULBvH6\nAwR8oQ/6sUcMJGVLBas7D9B2FRERSUiNjkQUETlcrfeWMHzWIoqz2rB03IiQ4wFfEt/0PsKFZCIi\nIrGhsaZcPz8WkaYLBum9bC3m6xX4qv102LaTbb27sKtLjtvJREREYkpjIxFNVFKISIuTXljMse/M\nYsicpfiqvxutMnzWIpKqql1MJiIiEnsavFJurd1ojMkFTqldetdau3ffcWOMF/ittfbeCGYUkTjU\nZ8lq2u3YHbJempFOUnU1/mTtnhMREdmnwX8VjTFHAe8BSbW1JcaYE621K4wxw4Bnge6AmnIRqWPl\nkYPpuGk7aaUVAFSl+Fg+diib+vfQZJUE0pT55/E6ri7XZDj+mDxbFIEkIhKPPMHgoScaGmNmAIut\ntXcYY5KBvwNdgK+B3wGvArdba3dGI2xD8vOLNJpRJIJyZzprOC689Dmql/qonNwG76AqUi4oxZt1\n6L+mr0z5ibNAYc5B3+8mh/VAXk54DVM0HrwTt42qmvIGqSkXaXlycjKadOWpsZ8fjwLOA7DWVhlj\n/gDkA0OB71trZzblRUWk5cgoLQSPh6JWmSHHfEOr8dxYhLe3XxfHRUREGtDYjZ7p1n73bby1dhdQ\nCQxVQy6S2DyBAAO2ruT4FZ8yct3XeAKBeuuS+qghFxERaUxT7rQKWGvLmz2JiMSNrJI9jNiwkMyy\nmvu+s8r20m/7t3zbRQObREREmkLjD0TEkb7bV2G2rMRL3f3h/bdbNrfvTllqa5eSiYiIxK/GmvIU\nY8z/Gluz1p7bvLFEJFZV+FJDGvIKXypLegxXQy4iItJEjTXlz4e5JiIJYnP7HnQp2Eru3rya99t1\nY1n3YVT5UlxOJiIiEr8ae3jQVdEKIiKhHI+Ty57orL7g7vBrX6w9t8fD4p4jGPvtl6zoOoS8tp2c\nvaaIiIiEaHBOOYAxZgDQxVr7yUHr1wNvW2s3RS5e+DSnXFoit5ryJJ+Xgcd1pnh3ORsW76pZvP7g\ncweBBsaqOJ0LPsTBNwgQ2W9AasXrvGwREXFPU+eUNzgS0RjTH/gKOKeew6cCs40xHZvywiISm9p3\nb8MJVxh6H5HDoBO60CrzUNtSNOdQRESkuTQ2p/z3wFvW2tsPPmCtPQ/4vLZGROJcUrKXoeO7ccwF\n/UhvmwqALyWJ4ad1dzmZiIhIy9dYU34y0NDPfP8InNV8cUTELSPP6EGvER1C1ltnpZDWJtmFRCIi\nIomjsaa8vbV27aEOWmvXANq+ItIC2C+38//bu/M4O6v68OOfO0v2fYOQEEJCchIg7GBB9lXAiqgt\nVEFAKW211Zdal2oLxSLaWmlt+WG1yhZk0SooEmPYEQMxhD0kJwnZyL6vk1nv/f3x3MAks2WSmfvc\nuaHvnDEAACAASURBVPN5v168ZuY85z73e+fhZr73POd8T0P9nrtyLnllPc9NjVTvqEspKkmSuoe2\nkvLNIYRDWjoYQhgPbO3YkCSlYcfGaha+uDb5fnM1Mx9ayNynV9JQl23jkZIk6UC1Vaf8d8DXgM+1\ncPw2YEaHRiSpU1X2qIUM1NU0XcD59ktryTZkWfraBrL1FjSSJKlQ2krKbwHmhBDGAv8FLADKgaOA\nL+e/ntSZAUrqOAePWcXRp77OxjXDeOXZpm/dXBYWz1mfQmSSupp2l2xtJ0uSqrvZlzrlRwO3A2fC\nHntrPwF8PsY4v/PC23fWKZdatq2qlvsfX8Af5617t+2zl0/h4jPat0Fve/5I7s8fbP8IS12HSbnU\nvP2tU97WSDkxxjeBs0MIw4Bx+eYFMcYt+/OEkgrrj/PWct+MBezYtedizakzIpW9yqmrbkgpMkmS\ntFubSfluMcYNwIaOeNIQwg0k89FvjDHeFkKoBF5nz5H4vkBtjHFCRzyn1F0tXrWtSUJeXpbh7OMO\n4YHa2SlFJUmSGtvnpLyjhBBuB4YB83a3xRjrgMl79bsXeLmw0Uml5/Izx/Hqog2s27wLgDEH9eNT\nl0xmzEH9uSHrrC9JkopBWyURO8P9McYrgR0tdQghnA8cQ7K4VNIB6FlZznUXT6KyooyPnDmOf/zk\nSYw5qH/aYUmSpEYKPlIeY5y5D92+A3wjxmiBZGkf5HI5nn99NSMG9yaMGdzkeBgzmH/7m9MY2Ldp\nGURJkpS+giflbQkhfBDIxBgfSzsWqSvYuLWae6bP580lmxgxqDc3f/oUelaWN+lnQi5JUvEquqQc\nuA5oX502KSWdXfZvRGhtmkmOMWEpR548l4rKpILKui27uGjqO7x16JR2x6XCsVykJGlvRZWUhxD6\nAB8g2ZhIUiuOP/NlRo1f0aR9zIZlLDp4IrWVPVOI6j0mkVJp8z0udaw0Fnq25kSSqSuL0w5EKnar\nlh7SpG1jv6E8d+Q5qSfkkiSpfQo6Uh5CKAPmktQjHwNMDiFcDzwcY/wGMApYU8iYpK5q7fKRrFw8\nilHjVlJfV868l45i2WfHQma/NhKTJEkpKmhSnq+mMrmV4w8CDxYuIqkLyOTIkCOXa3pja+6Lydzx\n+XOOZNeOPmA+LklSl1Rs01ckNdJv4Dbef+lzjJ+ysNnjtTU9eeXZk5KEXJIkdVlFtdBTUqIhm2X6\nrOWccdlSysuzDBiyjTXLR7Jjy4C0Q5MkSZ3AkXKpyKxYt4Nb7p3DL55dTHl5sn9WeXmW405/hUzG\n/bQkSSpFjpRLReb/nn2bZWu2N2mvrupFeUUD9XXpfZZub31tS6ZJkrRvTMqlInPVhRP5p5/8kZra\nZEOg2uoevPniFFYtGUWHr+T86U2su6DpB4Dm7M+GN2qeH1YkSXtz+opUZIYN7M2fnz0egFVLDuGZ\nh89l1ZLRWFpFkqTS5Ui5lJLFq7YxsG8Phg7s1eTYWceP4qAhfTj7osNSiEySJBWaI+VSgdXWNfCz\npxfxrakvcff0+eRyuSZ9yjIZjhw7JIXoJElSGkzKpQIaPLIPN901m+mzlpPLwdwlm3j+9dVphyVJ\nklJmUi4VyKQzRnLalRNYu6lqj/YHn1pEVXVdSlFJkqRi4JxyqUAaarNkMnsu1hwyoCfXfGASfXpV\nphSVJEkqBiblKnkjQv929V8X961EYHstmr2WgycMZOCIPgAsmz+W6S8dyb3fNyGXJKm7yzS3yKwr\nWr9+e2m8EHW4zkzKR9zUvnMPmLGVE86ezRsvHMvG1cPbfsCV7To9AOtu7pwPFZIkqW3Dh/ffrxrG\njpRLHaiCOo6snMvahoNYmx3Z5Pi2TQN55uHzIGfNcUmS9B6TcqmDjChby5Qer9I7U82I8jVsqh5K\nHT2adjQhlyRJe7H6inSAKqnl2MqXOaXni/TOVAPQK1PDkZVvphyZJEnqKhwplw7QyT1mMaR8U5P2\nQWWbKaeOBlzIKUmSWudIuXSAYv3kPX7O5jIsqpvA72vONiGXJEn7xJFy6QBtzA5jaf1YxlYsZVu2\nP6/VHs/W3OC0w5IkSV2ISbm0lxEjbmu2vUefCuprG8jWN6q++Tc3ATCv7kh25fqwuH48uS50A6ql\n19qSdeu+2EmRSMXJ94ikQjEpV8lrV93xFv4Aj5o0mKPOGcU7czcx77lV7x14MPnSQCVvM+FAwmze\ng523mZEkSSoeJuVSK3r1q+To80Zz8PiBAIw7YTirF25hy+qqlCOTJEmlpOvcZ5cKbPRRQzjrk5Pe\nTcgBMmUZjr1wDGXl1hqXJEkdx6RcasGIwwdQ2at8j7aG+iwr5m4il8218ChJkqT2c/qK1II3n1zB\nsEP70aN38jbZtHIHr814h52ba1KOTJIklRpHyqUW1O6q582nVtBQl2Xu0yuZ+bNFJuSSJKlTOFKu\nbi2by/H0yyuZfNhgDhnWt8nxVXELG1fsoGZnfQrRSZKk7sKkXCWvpTKHfQf14JgLxzB0dD82r9rJ\nHx5a2Gw/E3JJktTZMrlcaSxYW79+e2m8EHW4Jkl5Bg4/fjiTTh9JecV7M7jmPrOSJUs+076Tb765\n3fG4uYgkSaVr+PD++1WizZFydTvv+8h4hh/Wv0n7+JNGsGx5A9mG8mYeJUmS1Hlc6KluZ+3bW5u0\nrVuyjefvX2BCLkmSUuFIubqdpa9uYOTEQQwd3Y/a6nreemYlK97anBwcnG5skiSpezIpV8mqb8hS\nXtb8tK7XZywnvH8kc59Z6UJOSZKUOpNylaRla7Zz57R5XHjyoc0e37mllpcfW1bgqCRJkppnUq6S\nUlef5dGZS5j2wnKyuRwPPLGQnn0rHA2XJElFzaRcJWPxqm3cNW0eKzfsfLetqqaeKecdyku/XpJi\nZHtqqW56SyyhKElS6TMpV0nI5XJM/V3cIyHfraGugbLyDNmGfShlv/nmdibB7UuY25uQS5Kk7sGS\niCoJmUyGay+eRFnmvYWdA/v14O8+OoVXfrt83xJySZKklDhSrpJx2MH9ufhPxvDYC8s4fcpIrjjv\nCPr2qkw7LEmSpDaZlKvLWfDOFkYP70ufZhLuD73/cI48bDCTxw5JITJJkqT94/QVdRnVtfVMnRH5\nzk9f5sGnFjXbp7KizIRckiR1OY6Uq0uYu3QTd0+bz8Zt1QA8//pqTpk0gqPHDU05MkmSpAPnSLmK\nWjaX457p8/neg6++m5Dvds/0SH1DNqXIJEmSOo4j5WrT/pTx66ja2o2rqTQ2YlBvrrtkEhXlfq6U\nJEldn0m5it6fn3MEr7+9kc3ba8gA5590KB85axw9K8vTDq3d3AhIkiQ1x6RcRa93zwquvXgSDz65\nkOsumcwRowamHZIkSVKHMilXUdheVcsDTyzk/JMOZdwhA5ocnzJuKEeOHUx5mdNVJElS6TEpV+pe\nmr+OqTMi26vqWL5uBzddezKVFU2TbxNySZJUqsxylJqtO2u54+E3uOORN9leVQfAqg07eXTmkpQj\nkyRJKiyTcqUim83xnZ++zEtxfZNjc5dsstShJEnqVkzKlYqysgwfev/YPdrKyzJcfsbh/MNVJ1rq\nUJIkdSuZXC6XdgwdYv367aXxQorQ/tQp31cnX3Y4B40fyJY1O3ntd++wfWN12w+ifaUFOzN+sMyh\nJEl6z/Dh/ZvfZKUNLvRUm9qbdO6dBPfsW0FNVT0087HpjSdXsHHFDpa8vJ4S+XwoSZLUbs4RUKca\nM2UoZ187mcOPG97s8eoddSyeY0IuSZK6t1RGykMINwC3ATfGGG9r1H4r8AmgHpgFXB9jrEojRh2Y\n3gN6cMwFhzL8sP4ATDp9JGuXbKVqS23KkUmSJBWfgo+UhxBuB84F5u3V/oV8+yRgAlAHXFro+HRg\nsrkchx07jLM+Gd5NyAHKK8s49sIxKUYmSZJUvNIYKb8/xjgzhPD0Xu1/BXw1xrgr//M1BY5LHSEH\noyYNoqJH+R7N9bUNrIpbUgpKkiSpuBU8KY8xzty7LYTQB5gIjAkhzASGANOAr8cY960ch4pCWVmG\n12a8w5lXB8rzu3KuX7ad1x9/h13bnLoiSZLUnGJZ6Dko//Uk4CzgVOAM4BupRaT9tnNzDXHmaupq\nGnj98XeY9Yu3TcglSZJaUSxJ+e55DT+MMdbFGDcDPwIuTjEmtaIhm2X6rOVs3VHT7PElc9bz7D3z\nWf7GxgJHJkmS1PUURZ3yGGNVCGE1MLhRc46kCouKzIr1O7jzsXksXbOdRSu38tnLjyaTea9OfrFt\nplNs8UiSJO2tKJLyvDuBL4UQHgfKgWuB36QakfZQ35Bl2ovLePQPS2nIJoXFX16wntnz13HK5INS\njk6SJKnrKmhSHkIoA+aSjIKPASaHEK4HHgZuBn4AvA3sBKYD/1bI+NSy+oYst06dw9I125sc+9Xz\nSzhp0gjKMvu1q6wkSVK3V9CkPMaYBSa30uXThYpF7VNRXsbksYObJOUnhuFcdWEwIZckSToAxTR9\nRUXuw6cfzqsLN7B6YxX9+1Ry9YWBkyaNSDssSZKkLi+Ty+XSjqFDrF+/vTReSBFoyGYpL2u+MM+i\nlVt5as4K/uL8CfTv06PAkUmSJBW34cP779f0AZNy7WHRyq3cNW0eH79gIkeNHZJ2OJIkSV3K/ibl\nxVKnXCmrqWvgwScX8u2pc1i9sYq7p82nutaKlJIkSYVgUi7i8s3c9JM/MmP2O+y+3bBxWzX/98zb\nqcYlSZLUXbjQs5urq8/yo0ffYvP2pjtzNmRzZHM5K6tIkiR1MkfKu7nKijI+eVHYo23ogF586crj\nuOYDk0zIJUmSCsCRcnHsEcM49aiDeWHuGs49YRQfPWs8vXv6v4YkSVKhWH2lG5m3bDMTRg+korzp\nDZIdu+pYuX4HYczgFCKTJEkqDVZfUYt2Vtfxk8fe4rsPvMJvZi5ttk+/3pUm5JIkSSlxjkKJe2Xh\neu79XWTrjloAHnthGSdMHM6Yg/qnHJkkSZJ2c/pKiaqrb+CuafN58a21TY5tWVPF8/cvaNf51q37\nYkeFJkmSVLKcvqI9VJSXsbO66eY/o4b15Y0n30khIkmSJLXEpLxEZTIZrvlAoHfPcgDKMhk+eNpY\nbrz2ZLau3ZVydJIkSWrMOeUlbMiAXlxx7gSenLOCT10ymcMOdh65JElSMTIp7+K27KjhwScX8pGz\nxjNiUO8mx884ZiSnHX1ws2UQJUmSVBxMyruoXC7HzDfX8MATC6mqqWfbzlr+/i+Ob7IDZyaToaLc\nXTklSZKKmUl5F7RpWzX3TI+8sXjju23zl2/huVdXcfbxo1KMTJIkSfvDOQ1dTE1dA9+8e/YeCflu\ncxasp1RKXEqSJHUnJuVdTM/Kci44+dA92ioryrji3CP4wp8dSybjVBVJkqSuxs2DuqCGbJZv3TuH\npWu2M3H0QK67ZDIHDemTdliSJEnd3v5uHmRSXsS27qhhYL+ezR5bsX4HcfkWzjlhVJPFnZIkSUqH\nO3qWkGw2x4zZ7/DV/3mBl+ava7bP6OH9OO/E0SbkkiRJJcDqK0Vm9cad3DVtPotWbgXgvhmRMGYQ\n/fv0SDkySZIkdRZHyotEQzbLb19cxk13zn43IQfYVlXHA08sTDEySZIkdTaT8iLR0JDjuddWUd+Q\n3aO9d88KJo8dbKlDSZKkEmZSXiR6VJZz3SWTaTxD/LgjhnHL9e/jjGMOsdShJElSCXNOeRGZeOgg\nzjtxNC/MXcPHL5jInxx5kMm4JElSN2BJxAKrb8jy+EvvcM7xo+jVo+lnopraBqpr61sshShJkqTi\ntb8lER0pL6Alq7dx57R5rFy/k03bavjEBROb9OnZo5yePcpTiE6SJElpMSkvgLr6Bh55fgnTZy1n\n942JJ+es4ORJI5h46KB0g5MkSVLqXOjZyXbV1PPPd83mty++l5Dv9stn304nKEmSJBUVk/JO1rtn\nBYcd3L9J+2lHH8zffvSYFCKSJElSsXH6SgF8/PyJvLVkE9uq6hjcvyfXfCBwzPhhaYclSZKkImFS\n3oGy2RxlZU0X3PbrXclVFwbeWLyRK86dQJ9e/tolSZL0HksidpC3lm5i6owFfPbDRzN6RL80Q5Ek\nSVJK9rckonPKD9CumnrumT6ff3/wVdZuquIn0+bRkM2mHZYkSZK6EOdRHIA3F2/k7unz2bSt5t22\nZWu2M33Wci49dWx6gUmSJKlLMSnfTzur67jjkTeprm3Yoz0DVFXXpxOUJEmSuiSnr+ynvr0q+fNz\njtij7aAhffjaVSfwZ3u1S5IkSa1xpPwAnHXcIcyev475yzdz0Slj+PDph9OjsjztsCRJktTFWH1l\nH8xftpkwZhCZTNPFtOu37GJbVS3jDxnYWU8vSZKkLsLqK51g285a7njkTf7tgVd47rVVzfYZPqi3\nCbkkSZIOiNNXmpHL5Zg1by33P76QHbvqAHjoqUVMGTeUIQN6pRydJEmSSo0j5Xupqq7j9l++wY9+\n/da7CTlAdW0D981YkGJkkiRJKlUm5XvpUVm+R93x3Q47uD+XnzkuhYgkSZJU6kzK91JRXsZ1l0yi\nvCyT/znDR88axz9+8kQOHdEv5egkSZJUipxT3owxB/Xn0lMPY+6STVx3yWQOGdY37ZAkSZJUwrpt\nScQNW3fxi2cX84kLJtKvd2WT4/UNWcoyGcrK9quqjSRJkrqh/S2J2O1GyrO5HM+8spKfP/M2NbUN\nlGUy/OWfHtmkX0W5M3skSZJUGN0qKV+3uYq7fzuf+cu3vNv2wtw1nDJ5BMceMSzFyCRJktSddZuk\nfOvOWm66czY1dQ1Njs2at7ZbJeUjRtzW7sesW/fFTohEkiRJ0I2qrwzs24P3Tzl4j7aePcq5+qLA\n9R9sOn1FkiRJKpRuM1IO8LGzx/Paoo1s3FbNUWMHc83Fkxg2sHfaYUmSJKmbSyUpDyHcANwG3Bhj\nvC3fthTIADvzX3PAF2OM09t7/m1VtQzo06NJe68eFXzqkkls2FrN6ceMJJOxsookSZLSV/CkPIRw\nOzAMmLfXoRxwdYzx9/t77vqGLL99cRm/eWEZf3/lcUwYPahJn8ljh+zv6SVJkqROkcac8vtjjFcC\nO5o5tt9D18vXbueWe1/i4d8voa4+y53T5lPbzKJOSZIkqdgUPCmPMc5s5fAXQgizQwhzQwjfCiHs\n80j+v9zzEsvXvpfnr91Uxa+eX3IgoUqSJEkFUUzVV34OTI0xngxcCFwGfG1fH9yQ3XNDz369Kxk7\nckCHBihJkiR1hqKpvhJj/Eqj71eGEP4b+DRwS3vPdcrkEXz8gonNLvaUJEmSik1RJOUhhJ7AxBjj\nG42ay4C6fT3Ho9+7zFIq+yiXuyntECRJktRIsUxf6Q+8EEK4ACCEMBj4S+AXqUYlSZIkFUAml8u1\n3auDhBDKgLkk5Q/HkFRg2QQ8DDwJ/CvQD8iSzDH/ZowxW7AAJUmSpBQUNCmXJEmS1FSxTF+RJEmS\nui2TckmSJCllJuWSJElSykzKJUmSpJSZlEuSJEkpK4rNg/ZHCOEG4Dbgxhjjbfm2pUAG2Jn/mgO+\nGGOcnlKYJau533++/VbgE0A9MAu4PsZYlU6UpW3vaxBCqAReJ/n/fre+QG2McUIaMZa6Fv4dGg38\nPyAADcCzJP8OVacWaAlr5Rr8N3AUUAn8b4zx1vSiLE0hhPOAbwEDSQb5fhBj/M8QwlDgJ8DRJO+B\nR2OMf59epKWrpWuQP3Y6cC/wZozxQ+lFWdpaeR+MA74PHEGSbz8DfDbGWNvSubpkUh5CuB0YBszb\n61AOuDrG+PvCR9V9tPT7DyF8ATgXmATUAHcBl5LUnFcHau4axBjrgMl79bsXeLmw0XUPrfw79GPg\njRjjZSGECmA6cCPw9QKHWPJauQY/BV6JMV6e34xuZghhXozx4YIHWaJCCAcBjwB/GmN8Jp+AvBpC\neBH4ErAyxvjhEEIf4NkQwl/HGP8nzZhLTSvX4AVgLPBl4A8kyaI6QRvvg9tJPpD+aQihN8kAzRdI\n9uRpVledvnJ/jPFKks2H9pYpdDDdUEu//78Cvh1j3BVjzMYYr4kxmpB3jtbeAwCEEM4HjgH+q2BR\ndS8tXYNjgRkAMcZ64GmSEUN1vCbXIITQDziDZISKGONm4EfAx1OJsHQ1AFfFGJ8BiDEuBt4CTgEu\nA76Xb68CfghclU6YJa2la3AMsBA4DViUWnTdQ0vXYArwbd57H+wCniK5Ni3qkiPlMcaZrRz+Qgjh\ne0Afkk8vN+X/MKqDNPf7z4+GTATGhBBmAkOAacDXvW3f8dp4D+z2HeAb7orbOVq5Bo8BV4QQngJ6\nAxcBDxQssG6khWuQI9kVuvHft20k/z6pg8QYNwC/2v1zCGE8yXShl/PHFzfqviB/TB2olWvwhxjj\nW/m2lKLrHvblGuTbewCXkExtbFFXHSlvyc+BqTHGk4ELST6tfy3dkLqNQfmvJwFnAaeSjFZ9I7WI\nurEQwgeBTIzxsbRj6Ya+CpwMbATWAVuB/001om4kxriTZO7m10IIFfnby9cBvVINrITl5/D/mvdu\ny+89Z3YXyfoWdZLG16BxMqjCaeka5BPy+4GVtPG3oKSS8hjjV2KMv8x/v5JkoY+LGwpjS/7rD2OM\ndY1uGV+cYkzd2XXA1LSD6KYeA34ZYxwEDABWAA+mG1K3czXJPNq5JAvdHgM2pxpRiQohnADMBO6K\nMd5CMpWo517d+tLKVDsdmGaugQqspWsQQhgGPEEyzeWytu5cl0xSHkLoGUKYsldzGVCXRjzdTX7e\n4GpgcKPmHEkVFhVQfirRB0g+sauA8lUnTiFZ7Ll7TvlPSe7cqUBijKtjjB+LMYYY40UkfwvmpB1X\nqcknIo8Bn4sx/nu+eQHQEEI4olHXySSVodTBWrgGKqCWrkEIYRBJQv77GOMVrVVd2a1kknKgP/BC\nCOECgPyK+78EfpFqVN3LncCXQgg98iuNrwV+k25I3dKJJFNXFrfZUx1tE7AK+ChACCFDMo3u1TSD\n6m5CCA/lq0ERQhhL8rfAKUQdKITQE/gZ8JkY4yO72/MDNP9HvtpQPjH5DMnfB3Wglq6BCqeNa3AH\n8GyMcZ+n8WZyuVzbvYpICKGM5JZkDhhDcktsE/Aw8CTJnLZ+JAt9fg5804VuHaeN3//NwA9IRgV3\nkpSC+8q+fDrUvmvtGsQYvxFCuBK4NcY4LsUwS1ob74NfkKy4H5nvvgD4fIxxSQqhlqw2rsFDJEng\nIJK7pTdaCapj5f+dmUpS5WN31bMcyVSt75PUKT+O5G7pAzHGm9OIs5S1cQ0mkgzQDCVZT7GCpEzl\nBSmEWrJauQbPkQwGLCVZY5HLH387xvjBls7X5ZJySZIkqdSU0vQVSZIkqUsyKZckSZJSZlIuSZIk\npcykXJIkSUqZSbkkSZKUMpNySZIkKWUm5ZIkSVLKTMolSZKklFWkHYAkdTchhHLgH4C/INmNcvfu\nlP8RY3wg3+ca4C6gOv+weiCS7Fj5n/ntzHefbwTJjroXA8NJdpCbCdwUY3ypUb8rgL8DjgR6k+zy\ndx9wS4yxobNerySpbY6US1LhfQ/4OPAJYCAwBPgv4K4QwqWN+m2JMfaJMfYBDgW+AlwKvBhCGNCo\n3+9IkvHTY4x9gXHAPOCpfMJOCOFykq3P/xUYCfQFrgY+DXy7s16oJGnfOFIuSYV3IfBAjPHV/M81\nwH0hhM3A2809IMa4FXg6hHA+8ArwT8CX80n3scDnYowr8n03hxC+DLwKZPOnuACYFWN8tNFpXwwh\nfBTo09xzhhDOAp4A/hr4d+DaGOOv9upzDfB14BaS5L4/8B/Ar4AfAwF4AfhY/jUQQvhb4G+BUcAG\n4I4Y43cbnfOrwJeBDHAn0AsYHWO8vLk4JakUOFIuSYX3FvDJEML7GjfGGB+LMc5v7YExxl3A/wBX\n5Js2AOuBr4YQxjTql4sx3hdj3NDoOf8khHBlfvrM7n6zY4zPtvKUGeBk4JC9E/JGDgGOIhmh/wrJ\nB4Z/Bi4iScqPBz4FEEJ4P0nSfk2MsT9wJXBLCOGc/PHLgX/Jt48EdpBM88m19nuRpK7OpFySCu/z\nwDvAzBDCihDCQyGEG0IIg/bx8fOAUSGEyhhjFvgYSfK7OITwVgjhRyGEy0MIje+G/hCYCtwLbAwh\n/C6E8LUQwrg2nisD/Dj/YaAlfUjmpdcCv84/5qEY44YY40pgDjABIMb4B2BojHFW/udZwCKSxB+S\nefHPxBifyJ/vm8DWffu1SFLXZVIuSQUWY1wZYzyXJFG9mWT6yq0kSfWp+3CKCpKR44b8+X4fY5wA\nnEqyOHQk8ADwSghhaL5PXYzxr4GDgRuABSTzyReEED7bxvMtbeP49hjjjvz3uxegrmp0vIpkCgoh\nhDLgGyGEpSGEnSGEXSQfKHrl+44EFu9+YIwxB8xu4/klqctzTrkkpSTGuJgkAf3fEEIvkgWbtwDn\ntfHQk4FF+VHyxuebTZLAfjeEcFj++8+QTAfZ3WcT8LP8f4QQvpvv/8MYY30Lz1fbRjzZfWwDuBG4\nBvgQMDvGmAshvN7oeFkzz9fSuSSpZDhSLkkFFEIYFUK4Y++pKjHGauBZkioqrT1+CMkiyan5n08P\nIdy6d78Y4zJg/u7zhRBuzc/n3tt0oCcwoJljneF9wG9ijH/MJ+RDgCMaHV9HMje9sZMKFJskpcaR\nckkqrLUkI+H35yukzAPKgTNJppP8d3MPyk/7OJVkkeRCkmooABuBz+UXb34/xrgqhNCfZHHkSST1\n0CGZFnJ3COEGkhrmtST1yv8JeDw/gt6czAG81uYsAU4KIfQDhpGUaFxCUokF4EmSOwdnAi+SVGHp\n38ExSFLRcaRckgooP0XkDJJE9FGSRYybSJLTf4kxfqdR94EhhKoQQhVJFZIfA78Bzokx1uTPNw84\nCxgPvJTvuwz4M+CD+YWVANeTlBf8Pslo9E7gEWAWyULRluxP1ZPWHnMrsAtYTfL6f0C+bnsITh+w\n8AAAAJ5JREFU4dsxxvtIarY/mn8dWZJpPU5hkVTSMrmcVaYkScUjhNBz94eO/M+PA2/FGD+fYliS\n1KmcviJJKhohhNNINkm6EHiOZNOjs0juJEhSyXKkXJJUVEIIfwN8kaR840qSufI/SDcqSepcJuWS\nJElSylzoKUmSJKXMpFySJElKmUm5JEmSlDKTckmSJCllJuWSJElSykzKJUmSpJT9f3E7NYho3J6O\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb823bf5cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = np.array(qso_cat['r']).astype(float)\n",
    "m0 = (r > 15)*(r<22)\n",
    "\n",
    "avg_mag = np.array(qso_cat['CRTS_avg_m']).astype(float)\n",
    "m1 = (avg_mag>15)*(avg_mag<22)\n",
    "\n",
    "m = m0 * m1 \n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "H, xedges, yedges = np.histogram2d(r[m],avg_mag[m], bins=50)\n",
    "H = np.rot90(H)\n",
    "H = np.flipud(H)\n",
    "# Mask zeros\n",
    "Hmasked = np.ma.masked_where(H==0,H) # Mask pixels with a value of zeropltssssss\n",
    "# Plot 2D histogram using pcolor\n",
    "ax.pcolormesh(xedges,yedges,np.log10(Hmasked), cmap='jet')\n",
    "ax.plot(np.linspace(15,22), np.linspace(15,22), ls='--', lw=4)\n",
    "ax.set_xlabel('SDSS r mag')\n",
    "ax.set_ylabel('CRTS <LC> white light mag')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../Fig_1_QSO_CRTS_vs_SDSS_r_magnitudes.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zHpBk8yQnTkw/Mclew+97JLk6yUmjmbXWPyY5ZaLMBbXWH4yVuSjJ0rEyAABAZ2Y9R6WU\n8pQ1XWit9YNzKHPOambfaXhcNjH97CQ7lFJuljac66Ja6x9mKPOg4fcdk8z0Omcn2Wm2OgIAANMx\nl5Ppj53jslaO/T5rUJnF1klW1lpXTExfPjwuGspcNsNzlw/zM8cyAABAZ2YNKrXWWYeHDSevvz7t\n/I8fr3u1+rfttltOuwoMNt20vUW1SV+0y/qxcOHm064CTM3ChZtP9TvE91iftMvGY53OUSmlLCil\n/F1aONk/ycvTrtC1ri5JsqCUstXE9G3G5l+SmXtFthnmZY5lAACAzqz1fVRKKQ9I8o60YHJckpfX\nWs9bT/U6c3jcMcn3x6bvlOTntdYVpZQzk2xXSlk0cUL+Tkl+NLach8yw/J3STqhfaxdf/Pt1eTrr\n0eiIijbpi3ZZP5Yvv3zaVYCpWb788ql+h/ge65N26dPixVuv92WucY9KKeWWpZQPJfla2nkpf1Fr\nfcp6DClJ8o0kv0+7SeO4fZJ8fvj9hOHxmjJDD8yDxsp8KcnNSyn3HStzuyR3GysDAAB0Zs49KqWU\nTZK8OMlhafc9eUGS99RaV672iate3s2TbJZkwTBpYSnllsPvFyZ5Q5KDSyk/TLsr/YFJbp3kjUm7\nalgp5dgkbyilnJfk3LQ72V+W5L1Dma+XUk5O8p5SyrOSXJ52l/ofJPnU2tQbAADY8OYUVEopD0nb\nwf+zJP+c5JW11t+s42t/Kslfjv390iQvS+uluUNa6FiQ5N1Jts9w75Na67Kx5zwvydFJPp5kYVpP\nzB611t+Nldk/yVvT7sGySZLjkzyx1nr1OtYfAADYQOZyH5WPpe3sfzvJfWqtp62PF661PngOxV49\n/KxqGVek9fK8eDVlLk7y1DWuIAAAMDVz6VF53PB46ySfLKXM+oRa6x3XpVIAAMDGbS5B5YgNXgsA\nAIAxc7nho6ACAADMq3W64SMAAMCGIKgAAADdEVQAAIDuCCoAAEB3BBUAAKA7ggoAANAdQQUAAOiO\noAIAAHRHUAEAALojqAAAAN0RVAAAgO4IKgAAQHcEFQAAoDuCCgAA0B1BBQAA6I6gAgAAdEdQAQAA\nuiOoAAAA3RFUAACA7ggqAABAdwQVAACgO4IKAADQHUEFAADojqACAAB0R1ABAAC6I6gAAADdEVQA\nAIDuCCoAAEB3BBUAAKA7ggoAANAdQQUAAOiOoAIAAHRHUAEAALojqAAAAN0RVAAAgO4IKgAAQHcE\nFQAAoDuCCgAA0B1BBQAA6I6gAgAAdEdQAQAAuiOoAAAA3RFUAACA7ggqAABAdwQVAACgO4IKAADQ\nHUEFAADojqACAAB0R1ABAAC6I6gAAADdEVQAAIDuCCoAAEB3BBUAAKA7ggoAANAdQQUAAOiOoAIA\nAHRHUAEAALqz6bQrALA6K1asyNKlp067GlNzxhmnT7sKADAVggrQtaVLT80hb/5kFi1eMu2qTMW5\nZ34rO+x0v2lXAwDmnaACdG/R4iXZ/rY7T7saU3HphcumXQUAmArnqAAAAN0RVAAAgO4IKgAAQHcE\nFQAAoDuCCgAA0B1BBQAA6I6gAgAAdEdQAQAAuiOoAAAA3RFUAACA7ggqAABAdwQVAACgO4IKAADQ\nHUEFAADojqACAAB0R1ABAAC6I6gAAADdEVQAAIDuCCoAAEB3BBUAAKA7ggoAANAdQQUAAOiOoAIA\nAHRHUAEAALqz6bQrAKzeihUrsnTpqdOuxhpbuHDzJMny5Zev03LOOOP09VEdAOAGRlCBzi1demoO\nefMns2jxkmlXZSrOPfNb2WGn+027GsA8u/qqK6d+oGJ9HXBZW7vscq9sscUWU3lt6IGgAjcAixYv\nyfa33Xna1ZiKSy9cNu0qAFOw/Le/ynEnJItOm05ImLZLL1yWow5Mdtvt/tOuCkyNoAIAdGljPkgD\nOJkeAADokKACAAB0R1ABAAC6I6gAAADdEVQAAIDuCCoAAEB3BBUAAKA7ggoAANAdQQUAAOiOoAIA\nAHRHUAEAALojqAAAAN0RVAAAgO4IKgAAQHcEFQAAoDuCCgAA0B1BBQAA6I6gAgAAdGfTaVdgNqWU\nZUluNzF5ZZJ31lpfVEq5SZIjkzwlyS2SnJ7kFbXWL48tY8skb0qyX5Ktk3w3yUtqradt8BUAAADW\n2A2hR2VlkqOT3GrsZ4ckhwzzX5/kWUmen+QeSU5I8vlSyl3HlnFskj2TPD7JrknOSnJSKWXxPNQf\nAABYQ933qAwuq7VeMDmxlLJ1khek9aB8Zph8aCll7yQHJXl6KeXOSR6X5NG11q8Mz3tOkkekhZsj\n5mMFWHsrVqzI0qWnrnL+woWbJ0mWL798vqo0r8444/RpVwEAYN7dUILKqjwgyeZJTpyYfmKSJw2/\n75Hk6iQnjWbWWv9YSjklyV4RVLq3dOmpOeTNn8yixUumXZWpOPfMb2WHne437WoAAMyrG3pQudPw\nuGxi+tlJdiil3CzJjkkuqrX+YYYyD9qgtWO9WbR4Sba/7c7TrsZUXHrhsmlXAQBg3t1Qgsp9SinH\nJ7l7ksuSfCjJUWknxq+sta6YKL98eFw0lLlshmUuH+YDAACduSEElQuS3CzJPyY5N8kDh9+XJPnJ\n9KoFAABsKN0HlVrr/5mYdHopZZskr0k7v2RBKWWrWut4r8k2w+Mlw89MPSfbDPPWyrbbbrm2T2UN\njU6WB4CNycKFm9vfmMGmm7aL1to2N343hMsTz2Tp8Dg672THifk7Jfn5MCTszCTblVImw8pOSX60\n4aoIAACsra57VIZLC78yyeG11p+Nzdo1yVVJjktyWJK9k3x/bP4+ST4//H7C8Lh3ko8Ny90q7UT6\n165t3S6++Pdr+1TW0I31ssMAsDrLl19uf2MGo54U26Yvixdvvd6X2XVQSfKLJH+Z5KOllJcl+WVa\nwDgoyTG11nNLKW9IcnAp5Ydpd6U/MMmtk7wxSWqt55RSjk3yhlLKeWnnuRyZdoL9e+d3dQAAgLno\neujXcEnhByX5aZKPpg3VOjTtZPoXDsWOTPKWJO9O8sMk90myV6112diinpfk00k+njZsbNske9Ra\nf7fBVwIAAFhjvfeopNZ6TpInrmb+yiSvHn5WVeaKJC8efgAAgM513aMCAABsnAQVAACgO4IKAADQ\nHUEFAADojqACAAB0R1ABAAC6I6gAAADdEVQAAIDuCCoAAEB3ur8zPc3Rb3l7lv3y/GlXYyouPP9X\nyTb3mnY1AACYR4LKDcSZy87NH2+157SrMRWXrzhj2lUAAGCeGfoFAAB0R1ABAAC6I6gAAADdEVQA\nAIDuCCoAAEB3BBUAAKA7ggoAANAdQQUAAOiOoAIAAHRHUAEAALojqAAAAN0RVAAAgO4IKgAAQHcE\nFQAAoDuCCgAA0B1BBQAA6I6gAgAAdEdQAQAAuiOoAAAA3RFUAACA7ggqAABAdwQVAACgO4IKAADQ\nHUEFAADojqACAAB0R1ABAAC6I6gAAADdEVQAAIDuCCoAAEB3BBUAAKA7ggoAANAdQQUAAOiOoAIA\nAHRHUAEAALojqAAAAN0RVAAAgO4IKgAAQHcEFQAAoDuCCgAA0B1BBQAA6I6gAgAAdEdQAQAAuiOo\nAAAA3RFUAACA7ggqAABAdwQVAACgO4IKAADQHUEFAADojqACAAB0R1ABAAC6I6gAAADdEVQAAIDu\nCCoAAEB3BBUAAKA7ggoAANAdQQUAAOiOoAIAAHRHUAEAALojqAAAAN0RVAAAgO4IKgAAQHcEFQAA\noDuCCgAA0J1Np10BAACu6+qrrswZZ5w+7WpMzS673CtbbLHFtKvBlAkqAACdWf7bX+W4E5JFp10+\n7arMu0svXJajDkx22+3+064KUyaoAAB0aNHiJdn+tjtPuxowNc5RAQAAuiOoAAAA3RFUAACA7ggq\nAABAdwQVAACgO4IKAADQHUEFAADojqACAAB0R1ABAAC6I6gAAADdEVQAAIDuCCoAAEB3BBUAAKA7\nggoAANAdQQUAAOiOoAIAAHRHUAEAALojqAAAAN0RVAAAgO4IKgAAQHcEFQAAoDuCCgAA0B1BBQAA\n6I6gAgAAdEdQAQAAuiOoAAAA3RFUAACA7ggqAABAdwQVAACgO5tOuwIAADBy9VVX5owzTl/l/IUL\nN0+SLF9++XxVad7tssu9ssUWW0y7GlMnqAAA0I3lv/1VjjshWXTajTeIrM6lFy7LUQcmu+12/2lX\nZeoEFQAAurJo8ZJsf9udp10Npsw5KgAAQHc2mh6VUsqBSV6Q5DZJfprkNbXWj063VgAAwEw2ih6V\nUsrzk7wuyeFJ7pbkfUmOK6XsNc16AQAAM9tYelQOSfKuWuuHhr/fWkp5YJJDk5w4vWoBAAAzudH3\nqJRS/ixtuNdkIDkxye6llM3nv1YAAMDq3OiDSpI7JVmZZNnE9LPT1v+O810hAABg9TaGoLL18HjZ\nxPTlw+OieawLAAAwBxtDUAEAAG5gFqxcuXLaddigSikPT/LZJHevtZ4xw/Sda60/nuPibtwbCwAA\n1t2C9bGQjaFH5cy0jbXjxPSdklyZdk8VAACgIzf6oFJrPTPtxPm9J2Y9IsmXa61Xzn+tAACA1dlY\n7qNyRJJjSinfSnJKkickeVCSv1jD5ayXbiwAAGD1bvTnqIyUUp6b5KAkt07ykySH1Fq/MN1aAQAA\nM9loggoAAHDDcaM/RwUAALjhEVQAAIDuCCoAAEB3BBUAAKA7ggoAANAdQQUAAOjOxnLDx3VWSjkw\nyQuS3CbJT5O8ptb60enWauNSSrlpkkOTPDGtHZYleWet9d3D/JskOTLJU5LcIsnpSV5Ra/3yVCq8\nkSmlbJ3kx0kur7XecZimTaaolHK/JEcnuVeS/03ygSSvrLWu1DbTMXyPHZJ24+ElSS5I8v4kr6+1\nXqFd5kcpZUGSw5O8KskRtdZXj82btQ1KKVsmeVOS/ZJsneS7SV5Saz1tvtbhxmiWdtk6rV32TbI4\nSU373HxirIx22QBW1y4T5W6X5IdJvltrfcjY9LVuFz0qc1BKeX6S16U10t2SvC/JcaWUvaZZr43Q\nW5O8MO2f/Kgd3lFKedow//VJnpXk+UnukeSEJJ8vpdx1/qu6UXptku0npmmTKRm28QlJPp/kLkle\nnPb5eflQRNtMxz8meUmSg3NtuxyY9j8m0S4bXCll+yRfSvL4JFfNUGQubXBskj2HZeya5KwkJ5VS\nFm+4mt+4zaFd/j3JI5M8O8kuae3ysVLKg8fKHBvtsl7NoV3GvSMzd4Icm7VsFz0qc3NIknfVWj80\n/P3WUsoD047unzi9am08SimLkjwzyctqrZ8aJr+tlPLwJAeUUj6Z1uP1ilrrZ4b5h5ZS9k5yUJKn\nz3ulNyKllHuntc9HkjxwmLZ1tMk0vSrJF2qtRw1/n1NKuTjJJdpmqg5I8q+11k8Pfy8rpfxlkieV\nUo6IdpkPByS5Isl9kpw/PmMun41Syp2TPC7Jo2utXxme95wkj0gLN0fMy1rc+KyuXe6a5GFJHlNr\nPWmYfGgpZb8kT0ryVe2ywayyXcaVUh6bZLckn0uy3dj0nbIO7aJHZRallD9LG2Y0GUhOTLJ7KWXz\n+a/VxqfWemmSWyc5ZmLW+Wld8w9Isnlmbic9XxvQMEziPWlHipeNzdo92mQqhm76R6QdgbxGrfWk\nWut/x+dlmlbm+kclrxima5f58Z+11n2H/yuT5tIGeyS5Oslohzm11j8mOSXaaV2ssl1qrT9MskOS\nL0zMGu0DJNplQ1nd5yVJUkrZKm3Uy0FJfjcxe8+sQ7sIKrO7U9o/kGUT089O2353nO8Kbaxqrb+p\nta4Y/V1KuVmShyT5Tlo7JTO30w5DWTaMFyZZmOSoiek7Do/LJqZrkw1vSVqb/L6U8rFSynmllLNK\nKS8a5vu8TM+70nqB750kpZSd044IvyfaZV7UWs9Zzey5tMGOSS6qtf5hhjI7rZdKboRmaZfUWi+o\ntV4T8kspt0py7yTfHiZplw1gtnYZHJnkrFrrB2aYt07tIqjMbuvh8bKJ6cuHx0XzWBeu611Jtkkb\nT7x1kpXjQWagnTagUsptkrw6yfNqrVdOzNYm07M4yYIkb0k7ivWwtBO2/6mUcki0zdTUWo9I8qkk\n3y2lXJ5zr67NAAAcJklEQVTkB0k+O5ycql2mby5tsHWuv08wKqON5kEpZZMkH0pycZJ3D5O1yxSU\nUu6Zdk7Xc1dRZJ3axTkq3CCVUt6ddvWvv6q1/qyUMu0qbazemtYt/NVpV4TruOnweFyt9X3D798r\npdwlyYuSvH061aKUcnCSv067otT30i4M8qZSykVJJo84AhNKKZsl+USS+ybZq9Z6yZSrtNEahhm/\nJ8k/1Vp/siFeQ4/K7EYfgMnUt83EfOZBKeUmpZQPpv2T33/sZMdLkiwYxkmO004bSCnlkUn+MslL\nxyYvGPtdm0zPaIzw5KUfv5HkT4bftc08K6XcPO3E0cNqrcfVWn9Qa/1wklemXQXsptEu0zaX761L\nMvOR4G2ijTaoYejd55PcL8metdbvjs3WLvPv79K2+WtXU2ad2kVQmd2ZaTtfO05M3ynJlWn3VGH+\nvDPJo5I8tNb6ubHpZw6PM7XTz2foxmfd7Zd2ZY9zSylXllKuTPL3SZaUUq7ItVf90Cbz76dpJy9u\nNzF99J3//az6e03bbDg7poWROjH9rCSb5NoeFe0yPXP5X3Jmku2Gq1FOlvnRBq7fRmu4cMvH0y7r\nvftwYZBx2mX+7Zd2XtdlY/sBT0nyoFLKFaWUA7KO7SKozKLWembaCT97T8x6RJIvzzAunw2klPLs\nJE9Lsm+t9ZsTs7+R5Pe5fjvtk3b0hfXvlUnunnafgdHPe5L8avj9A9EmU1FrvSzJN9NujDZu97QQ\nc1LamGFtM79+OTzeeWL6XYbH4+IzM21z+V9ywvB4TZmhB+ZB0U4b0pFpPSl71Fonw36iXabh6bnu\nPsA9knwmyX+P/b5O7eIclbk5IskxpZRvpV1O7QlpG/gvplmpjcnwpj4q7YTgn5RSbjk+v9Z6finl\nDUkOLqX8MO1OwgemXdL4jfNd341BrfXcJOeOTyulXJDkylrrj4a/tcn0HJHk+OGciI+lhZa/Srvw\nwQptM/9qrecN93w6rJRyXlrP1s5p97w5vtZ6rnbZ8IYheJvl2qGqC8f+p1yYZLVtUGs9p5RybJI3\nDO14btpO9GVJ3jtf63FjM0u7bJHkZWm99hdP7ANcVWu9SLtsGLO0yy9qrVdPlL84yTaj/YAkl65L\nuwgqc1Br/dCwo3x42pfVT9JuOvSdqVZs47Jrkm2TPG/4GVmQdvnoTdLe+AvSrgCyfZKlaSfaLZvX\nmjJOm0xJrfUrpZS/Sgss/5Dk12kh5V+GItpmOp6a1h7vTDtf6IK0q4AdOszXLhvep9LOrxt5adpO\n8Mokd8jc2uB5SY5OG4q0MK0nZo9a6+Q9JJi71bXL4Wn/5183/Iw7J9feKkK7rH+zfV5+PodlrHW7\nLFi5cuWaVhgAAGCDco4KAADQHUEFAADojqACAAB0R1ABAAC6I6gAAADdEVQAAIDuCCoAAEB33PAR\nOlFKKUkOSvKQJDsk+V2Ss5L8W5Jjaq1XrKfXOTzJYUmW1FrncqOmG6RSyiFJXp52R92daq2/nmJd\nDs9GsM03tFLK7ZP8LMnTaq0fnOfXvk3a53PvJLdL8ocky9JuYPauWuulY2VPznVvkDbyhyQ1yQeT\nvL3WetXYOs1mWa31jsPyN0/y7CQHpN3obtskFyc5Lckbaq0nzXGddknykiQPTHKroX5nJPlI2nfO\nlXNZzo1RKeXqJFcluVet9QczzD88yVNrrXdYT693cpLbjdoYaPSoQAdKKX+d5HtJ7p3ktUn2SPKs\nJD9M8pYkXy2lbLOeXm7l8HOjVUrZOslrkpyUFvwuKqXca9j5mIYb/TafJ79O+4x8dj5ftJTywLQd\n+EcleXuSvZI8OcnJaQH0O6WUPx17ysqhrrsO9R397Jfku0nelOS9Q9lfT5S59zD9sxPP33eoy4Ik\nX0y7O/eJSf46LWgclGRxki+WUh4+h3V6XpL/SXLntLt+PyTJ3yT5dpI3JvlWKWX7uWyfG7GbpLX3\nTNb3Z9r3A8xAjwpMWSnlzkmOTfLVJPvWWv84NvvTpZQvJfn3tB2Tv5v/Gt4gbZe2k/G5Wut3kmt2\nNu0M3IANR/hPnc/XHHbWP57kp0keWGtdPjb7C6WUT6Z9dt+d5JFj866otZ42wyKPH4L000spr6q1\nnpeJdWqdq/nNKp7/gCQPSvLCWus7x6b/VynlE0l+kuSlSb6wmnV6QNoO+Adrrc+YmH1iKeXDSb6W\n1vPziFUtZyPwsSR/XUp5Yq31w9OuDGyMBBWYvlck2STJMydCSpKk1vqJUsozknx9NK2UsjjJEUke\nk7ZT/uskn0ly2MQQlN3Sdkj+PMn5Sd4xUwVKKXdNclTacJXNk/woydG11o+OlTk5bRjVc9N6ee6T\n5H+TfDjJobXWq8fKPjctVO2Y5FdJPpTkdaP1G44KH5TkGUnukDZs5fNJDq61XrC6jVVKuUeSVw91\n3Sxt2Mx7a61vH+b/Q5J/SAsl/1pK+Ze0Ha6nJlk59KqcXGt9yFD+UUkOTrJLkivSjigfWms9dew1\nr07y+iQLkzwtyeNrrTPuCK7BNn9Qklclue8w6cwkb6y1fmSY/54kT09yq1rr/048tya5sNa6+2q2\n01bD8p+YNqzn/CRfSdvG55VS7pS2U3t4rfXVE899YZK3Jtm11npaKWXbtPfHo5LcIskv0rbpeJse\nnta7cP8k/5Lk0lrrbsO8/dLa+x5JLk/rnTii1nri2GtuOrzGk5NsNdT1uWm9iv9Ra33G5NCvsb+f\nm/YZ+r9Jbj1sy5fXWk8YW/5uae/buyc5N609t0zy5qx+SN7zk2yf5BETISVJUmv9r1LKk4ZtOVf/\nleTxaUPIzluD5yVt/VYmOWeGuiwvpdx5pnpOOCzJb7OKAx9Dm78+yatLKfettX53CPpfzcSwu1LK\nU5P8a5IH1Vq/Nky7Q1qP5sPSPjM/T/ueOGp8CGsp5aC09/jtk/w+yXfSPnvfHyszl/fOyWnfW+9K\ncnRaAHv5TOtWSjk2yVNqrXMZUfLFJFskeUMp5dO11svm8JxZDd/nhya5TdpQwINXUe7ZaW1U0oYC\nn5a2ff5nmP/jJMtrrfeeeN4OSX6Ztr1fNQTTI9K24c3SQvc7a63vWx/rAxuSoV8wffskOWV151DU\nWj9Qaz17bNKnkzwubcfswWk7W8/NtcNJRkeCv5C20/fEYf4D0naQMlbudkm+keS2aTvhj0zy/5J8\nuJTyxLGiK9N22D6Y5ANpR1q/knYeyHPGlvfSJO9M8sm08fxvTfun/OaxZb0pyZFpY+H3SNsR2TPJ\nl0spN13Vdiil3DLJl9N2vB+ftiN0cpK3llJGdXhv2jCZBWmB5T5pw+k+N8zfdVTfUspjkvxH2k7f\nI9LG/G+V5OShp2vcnklumeShaTtUM9Vvrtv8z9N2gq5MC5uPSnJ2kn8rpTxsKPahtINJfzPx3J2T\n7DTMX51jkrwobTs/MG3n9FFp7ZJa61lJlg6vP2m/JD8Zdlg3SXJCksembc89h2UfnLZjODLqrXrd\nUG60jR+W1iNxZlp7/dWw3p8btsPIkWk9AR8Y6vmdJJ9K27GazZPT2u/v0rb1wiT/UUrZbqjDn6Rt\n722Hsi9O8sy0YVOz9bI9PMlPa63/vaoCtdZPjO9cz8E9htedy7kpk05Pe28fWUr5sxnqstqQUkq5\nWdp3xqdqrX9YTdEPDa8z3ku0qm11zfRSymZpn9H7JvnbtM/3v6W9J44YK/fCtM/l+9KG0j0tydZp\n3wFbDWUemrm9d1Ym2SbXtv97VrNeF2bNtvuBaQeD/mENnrNKpZS9kvxzWjs+Ku3zcmSSJRPlnp22\nHiemDct7etr3z/HD92DS2uiew/mN4/YfzR9CyxeTXJq2bfZOOyj0rlLKE9bHOsGGpEcFpqiUsiht\np/sja/CcbdN6KY4Z6/H41nDE+HGllE1qrVel7XRvk+ThtdZvD889Kdf/J31I2g7Jw2qtFw3TvjIc\ncX9N2pHQkTsleWit9cvD8v4n7Z/fQ5K8ewgZrxzqdvjwnK+VUpakDaF4RdrO4t+l9R6Mdly+UUr5\nWZJTkjwhLQzN5I5pR6MPH/V4lFK+meTRQz3eO/QWnD6UP2es3G+SdrR4bHmvTfKtWus1/7BLKV8b\nttHBaT0+I3dOsvssFzWY6za/Y1rIe9YooJZSvju2HsfXWr85bJMDct0dr8el7ax9fFWVGHqsrkry\nmlrrMcPkb5dSdkpycCnl9rXWczIMKSyl3G7UozD01v1FWq/V6PV2TbLPWA/F10s7ofuwUsprh2Ul\nbYfx+FrreN3+NC28PXN0cnYp5Zy0I8n7Jzm9lHKTtJPDv1xrfcXwvK8On4//s6r1HHObtCP6Vw3L\nv2VaYL1f2k7Zk5MsSvLIWus3hzLfTguHsylpPQnrbAhMf522U/5vtdYL13QZtdYfllLekha2flhK\n+U5aWP9akq/WWlfMsog7pf3vP2OW1/l5KeWSJHcdm7xgDlW8fdr5dseM9Tr+1xBYH5/2fZO0wPv9\nWutbRk8c1uXpaW11WVqP02rfO2OvW5LcfzTUczXrdVDagZE5qbUuK6UcneQVpZT311rrXJ+7Cs9P\n60H+m1rr5UlSSvlBWnssGyt3iyQfqbW+bDShlHJ5kuPTwsYH0oLKq9Pe368ae+5+SZbWWmsp5bFp\nB04Oq7WOttc3SilnZc1782De6VGB6Vo4PM42VOMatdaLa61/VWv914lZZ6ftgOww/H3vJCtGO8zD\nc/+YdoRu3F5JvjEWUkY+m2TJsHM1cvkopAzL+32Si5LcfJh0n7Qg8pWJOr+s1nq7ofxD0obpfHLi\n9b6R5JIku8285kmt9Vu11keND8uqtY6OTP/pqp43k1LKbZPcJa1HZfw1Lk0bZjdZj2/PElKSOW7z\nWutnaq2PGO9FG7bNeRPrcVyS+w1Bb2S/tDDw22E9Nhn/GZa1stb65Frr6yfqN9oxH73Gx9J2Ph87\nVuaxw7RReN4rbXjJCbmuz6b9D5kMEqdMrOs/11r3rde9gtRkPW6bGd43mb3XaOT4UUgZLBvWYfS+\n3DntvfvNsXpdlLbTN5uFWYPP55glpZSrx3/S2vewtN7FZ67FMpMktdb/m9bT8+m0dXt5WiC7sJTy\nplLKFqt5+qLhcS7rtDytl2NN6nZmrfWx9fpDI8/Odd/b5yf581LK347qW2u9qNZ6dK313OHvubx3\nRi6fLaSsg6PShguu6sT6NXHvJN8ZhZQkqbX+KNcNKam1vq7W+qSJ515n3YeDC19P671NkpRSbpF2\noOG4YdL5aZ+FVw2956Pl/0utdb0EcNiQ9KjAdI3OJ7n5aktNGMaLvzRteMUtcu1Bh5Vjv/9Jkt/M\n8PTJIWa3SXKHMvMVsVYO80fnjZw/Q5krx15zh+E5qztSfJu0f5z/ff0RC9e83ioN47ufmRYyth2b\ntWx1z1tFPZI2/vzoGerxu4lpczn6PadtPgSKF6f1luyYa3cGV6ZdknrkQ2k7tgekDfW5U5K7pfUE\njd4HXx2et2B43GSYd4+0858eONRrk7HXuEmS1Fp/NvSKPSZtiF7Sgsr3aq1nDn/fJsnWs7w/xl1n\nOw1DjQ5OG7Zz+1w7lGvyvZpc//11ZuZm8sjwaMd2fPmTQXyuy780a/j5HPw6bVjnqBfilmlh4sNj\nvUZrrdZ6fNowoE3Sdn73SDsP6yVpvSaPWsVTR985265i/rhtcv3PwayGIZUvSBviNn7lsPGhY69I\nG8L43iRvL6V8I62X8ANjPQ1zee+MzPS5Wy9qrStKKf83ySdLKfvXWicPsqyJP8nM3yW/Tjv/KMk1\nw0hfmdaOt0k7Bye5/rp/MMkxpZTda63fyLVDOT861P2/SimvTPL3ab3aP047n/F9E8OJoUt6VGCK\nhvHkv8y1lySdVSll17Qj9EvSdnZ3SzsR/L0TRUc7rpNm+tyfmLZTscvEzz3ThlmMzDaef7Qzu9ks\n5Vam7RDP9HoHrupJpZSXpI3vviDJk9KGJO2Sdk7N2nrdKuoxeR+MudxTYq7b/J/SLgF7atoQlnsO\nr3udHe5a60+TfCttXTOU/V3ajkaS/PdYfUePo96ir6edH/PK4XGXzDzO/mNJHlBK2a60S2A/JNcd\n7pe0E69X9f74t4myk9vpI0MdPpu24z567vgwotH7ZW2vyjbb8zZbh2X/MMm91uJ5V9Raf1Br/f7w\nc2LaRQaeN8M5BWut1npVrfU7tdbXpYXYE5M8olz3csnjzkryx8yyTqWdEL8wswwRy8RwsCGkfCrJ\nTdMusX7vtPa+ziWla63/W2t9cNrBljemXVr5PUm+W0rZcig2l/fOyAa950ut9T/Stu0/zdJjNZu5\nfkecmOR5Sd6fNkxul7RetMl1/0TaRQZG3xH7pZ3zeO5Y3Y9KC3rPTWv/0bDBvdZhPWBe6FGB6ftM\n2s7LXYYhANdTSnljkvNqrW9M6+bfJMn+Y0e9R1dNGndR2hj9Sbef+PsXSbaqM9zUbC38Iu0f6Q7j\nE4e6bZW2k/2LYfLyumYnICetZ+HXtdbxoUopa3ePmVE9shb1WJW5bvMD0s6N+dvRhOHI+ExH7j+Y\nduLr3dOOLP/H6DyEYbjYTHV/TNr23r9e9+pI+85Q9mNJ3pB2AYKr095b/z42/xdpO0p1DkPfrqO0\ny/Dum+Sj470IpZRbTxQdXdXsFhPT77Qmr7ca/5vrHtkf2WkOz/1Mkn8spexTa/3iTAVKu4DEnyQ5\npI5d/W4G/5C2Q/nmtJ3ONVbaTRp3mKkutdYrSrtc8p5p5779YoYyfyjtKlmPKqVsW2u9eBUv9aS0\nHepRKB6t1+TFLnaY+PvJaaFhn+H9Oar3lplBrfX/pR1o+PtSypPTzr34m9IutTyX9858elHa+Td/\nn3aFwLVxUa7/Pk/acK7ReTh/nhZMjhpCRobp1xuGV2u9tJTy6ST7lVIOS2v7Z81Q7sK0Cxe8b9iG\n3047r2VyKDB0RY8KTN8b0y7N+a9luNrNuNJuBvmStCOOybUHGM4dK7ND2knPybVDfE5NsmVpl6Yc\nldsi7Yj5uJOS7DYcQR1/3b8ddsDWxA/SwsijJ6YfljY0Y5u0oUpX59ojgKPXW1RK+efSLpW8KjfN\nRK9DaTe3u1OuXe9VWTmUX5Akw/khP04bDnGd78JSyj+WUh55/UXMaq7b/KYZa7/Bc9IuhTq5Hv+e\ntgNzSFoPxnGZ3Whncvw9slWuvTjANa8xjHP/TtoJuo9JC1DjO7gnDeUnr1x2v+F8iEVZtU3Tguvk\nur5koh4/Tbsr+mQv1gGrWfaaOD3JzYbeyCTXDK156Byee0yG8xNKKbeanFlKGV14YMksISW11vPT\nrnj3sFLKPmtQ/3GvShuCtKoQ9+C0nejVnfR9VNpww/ePzmsaN4Shg5J8YQgSybVhcjJ0T4bfTdMu\nTT0eUu6eNgQxpZQFpZRNSyn/VK69wt3I6Mp8N8/c3zvzZjiR/m1pV1tc2xB9apLdx3tlSin3zHWH\nUF7v8zt4YcaGd475UNr/h7el9ZZ9amzZf1NKOXJiPX6ddqnj7dZyHWDe6FGBKRuuKvOEtB3SpaWU\nN6UdJd8ubXjUk9NOPB9d1eWUtPHfbyulvD/tPiSvShv69fIkTyrtXgEfTrvj9L+UUg5OGx7w0rTz\nAMZPkP/HtJ3QE0opL08bP71H2pCLN67hulxeSnld2pWk3ph2ovo9h9d9Tx3uB1JKeXuSF5d2VaFP\npI3ff8WwLoet5iVOSfL8UsqBaTvXD0gLPB9JCxz7ZuJk7jGjf/qHllJOr7V+Ou2yyZ9Iu5v3G4b5\nz0i7MtPJa7Lug7lu81OS7FNKeVraUIyHpw3h+//tnVuIVWUYhp8uokAvDKmbooiCLxJLCCSobjzk\nhQZS0cE8d5FByiRWToZlFx6TSqwwu5DRQlIrTTpRDloSVJZQJB8xSGEg3ZQZlA01XbxrN9vdntkz\n7ilXe97nRtS1/nX619r/+//f936dwLiImAAczMxTmflTROxFNsXHkfVrIw6gQd6aUD2Mi1Afebn4\n8/aI+K4ILQOtqrSjOPj2mrZ2oQrmz0fESGRpPAZZzXZlVd2eWjLzx5AD26yI+BjNJs9A9Uu6kFHA\nTZn5YajI4LxiVvgAqsdyXV9tD5JtqD+/FKqz01Nc5yGKAXQ/13Ai5Jz0FvBFRKxHfW8EssxdUPz9\nH7PYfbAWidL1EfFejQnAQFiGVu0ORsQG9GxOooHuTGQnvKTBc+mMiMeQq9+nEbERifYR6N1/AOXv\nzKna7QgyrbgvIr5G4ZdzgVqXsf3AtIhYhUK2rkHiYhNy+5uPVmmuRN+q5WiCYwRyxDoJ7B5M3xnE\nvausTt+WmZc33Lg+T6Jvzgzq1LIZAJtRGNvOiHgWra48ju53RaAcQd/hRRHRhcTHAlSr53dgYkTs\nqRKR76LncQewKzOr84q60TdvFBIwv6J362aGyHLZmH8Tr6gYUwIycy8qELgPWIKW4zehPJQZmXln\nxfkmM19Dg8RJwDvoB2w+SrD+DA3AbilmzaYhm89XUOXst1HMc/Wxv0UD/q9Q/sf76AevLTMfrTnV\nhnUUMnMNCpGYWrS1FM3gtlVtsxiJqilo4L0ZDYJuyH7qyaCQi+30xq2PR4P89UiIdKDk9HrnugmF\nbSwv9icz30CrPyORg9Ju5EBVG+bT08+1/81A7zkakO1DBQh3IaE2HQnDP5Hwqk52rrhfbU+5nDU6\nj0OoXwTqI8uK634CDWrmcbrr1KsoNOp8amyPi4H0ZGALEnadRVvbGFjV8rvQwGsLEkonUH9dgwRU\nJR+mDT2/B9E9uYreGjLV11x7/X09m+o+eRTF7p+L+s9K1Gf2125bj1QNlTHFud6LnmkH6n8LgUl1\nhEHdNlNFA1egZ7Owj0P22d+KWf3xaGJjJhp8dqJwsu7iXJ6ut29NOyvRYPU4Ek8fob4ypTi/67Nw\nliu2/wN9F7rQ+9qBBs6rTm+ZjaiO0lwk7qajCZd1aJXnGeQUdzfq5+3oXdiGhPLkKgHdqO9U50cN\nNAdpNANzCOzr/v+Cvl2DOWb1/rvR93EMEmxLUd//stJeEdp5KzI+2IG+XZ9kZju69mvpNb+oPJuK\nS99pK67F78U9yJHxdfT+zwYeysy1GFNyzunpOdP8QmOMMf8FoSr2HwDjhiiX6H9BsYLzM7AhM9sa\nbX8G7T+FhNHofnI1hgURsRUJwwsz88TZPh8zOIqVwvuBS1KW6Ma0BF5RMcaY8tOOQsFaVqRERFtE\nbK7554or0eEm274iIrZGxMSa/5oEHBvuIqVgBwoHP+P6LubsEBEXIJHyokWKaTWco2KMMSWkSFIf\ni0JdJtBPIcwW4RQwPyK6URjLZcBq5Fy1s8m2vwduREnMj6B4/lno/i5usu2WIDP3hCrDrwhVQD8M\nfJOZPzTYddhTuHSd13BDOFodTjcEx70YuBqF6nWjED5jWgoLFWOMKSdjUVL5MZT820ytmNKTmS9E\nRA9KuJ6DkrQ7gYeLvIBm2v6tMChYjWL7R6GcqEWZ+VxzZ95STEP5VOuQcJxKb7FX0zdvApc23Eq5\nYR1DeNzZyBDhc2Bis++JMWXEOSrGGGOMMcaY0uEcFWOMMcYYY0zpsFAxxhhjjDHGlA4LFWOMMcYY\nY0zpsFAxxhhjjDHGlA4LFWOMMcYYY0zpsFAxxhhjjDHGlI6/AB2Nk3XF+5VAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa25c1af950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_context('poster')\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Cadence after day-averaging CRTS Quasar : N_days')\n",
    "ax.hist(stats_dict['lc_length'])\n",
    "plt.savefig('../QSO_CRTS_AVG_stats_lc_length_NEW.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qauXUqgOSfKe19u0kZ4zLDpnY7q5JDkxy+rjoo0l2r6qHTLS5d5L7T7QBAAA6mllPSFXt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\nBAAA6EoIAQAAuhJCAACArv5/Q1aVhW2MLAMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa259d761d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_context('poster')\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average Nobs / night')\n",
    "ax.hist(stats_dict['avg_N_day'])\n",
    "plt.savefig('../QSO_CRTS_AVG_stats_N_days_per_QSO.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics of the day-averaged light curves (read-in previously made LCs) ( 2015/12 ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out what are the stats for the 7601 quasar light curves that are longer than 10 days. \n",
    "Each lightcurve has three columns :\n",
    "MJD, average_magnitude,  error (as calculated above), N_points_per_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%\n"
     ]
    }
   ],
   "source": [
    "dir_err_w = '../../SDSS_CRTS/QSO_CRTS_LC_day_averaged_err_w/'\n",
    "files = np.array(listdir(dir_err_w))\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",
    "timespan_obs = np.zeros_like(files, dtype=float) # store what was the total  timespan of observations per object\n",
    "lc_length=np.zeros_like(files, dtype=float) # number of points in the lightcurve - equal to the number of obs nights\n",
    "avg_N_day = np.zeros_like(files, dtype=float)\n",
    "avg_mag_ttl= np.zeros_like(files, dtype=float)\n",
    "avg_err_ttl= 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",
    "    \n",
    "    address=dir_err_w+files[i]  \n",
    "    data=np.loadtxt(address) \n",
    "    \n",
    "    lc_length[i] = sum(1 for line in open(address))\n",
    "    avg_mag_ttl[i] = np.mean(data[:,1])\n",
    "    avg_err_ttl[i] = np.mean(data[:,2])\n",
    "    avg_N_day[i] = np.mean(data[:,3])\n",
    "    timespan_obs[i] = data[-1,0]-data[0,0]  # gives number of days between first and last obs\n",
    "    \n",
    "    \n",
    "    # the \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 = ['timespan_obs', 'lc_length', 'avg_N_day', 'avg_mag_ttl', 'avg_err_ttl', 'avg_mjd_diff']\n",
    "values = [timespan_obs,lc_length,avg_N_day,avg_mag_ttl,avg_err_ttl,avg_mjd_diff]\n",
    "\n",
    "\n",
    "stats_dict1={}\n",
    "for key, value in zip(keys, values):\n",
    "    stats_dict1[key] = value\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare 2016/06  with 2015/05 .... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Dh3jqqeepX78BrVrdzpEjh6hevSZHjhyiU6f7uP76f9C7dz/++9/9/P3vtTO0MSEhgREj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\ni8dzI9EKvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa25c93ad10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_context('poster')\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Number of days of observations per CRTS Quasar')\n",
    "\n",
    "nbins=100\n",
    "# Calculate the histogram1 : new day-averages\n",
    "hist1, bin_edges = np.histogram(stats_dict['lc_length'], 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_dict1['lc_length'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label='2016/06, weighted by 1/ (err**2)')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label='2015/12, weighted by err ')\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('../QSO_CRTS_AVG_stats_lc_length_COMP.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G+fPnOHhwP82atSjQOYOCgvHx8WH37l3Osl27dlCz5g3s3h3hLIuI2EGjRo2p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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa25c2aa090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_context('poster')\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average CRTS white light magnitudes per lightcurve')\n",
    "\n",
    "nbins=100\n",
    "# Calculate the histogram1 : new day-averages\n",
    "hist1, bin_edges = np.histogram(stats_dict['avg_mag_ttl'], 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_dict1['avg_mag_ttl'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label='2016/06, weighted by 1/ (err**2)')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label='2015/12, weighted by err ')\n",
    "ax.legend(loc='best')\n",
    "\n",
    "plt.savefig('../QSO_CRTS_AVG_stats_avg_mjd_COMP.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "So there is a difference !   Must redo all calculations with NEW data   - because now I know exactly what's going on. Plot other statistics below... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.4616339218\n",
      "19.5995789474\n"
     ]
    }
   ],
   "source": [
    "# What is the mean and median magnitude ? \n",
    "print(np.mean(stats_dict['avg_mag_ttl']))\n",
    "print(np.median(stats_dict['avg_mag_ttl']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some old things are below (Dec 2015 - February  2016 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lOTPJJ0spqwbKvD/Jg5Mck+Q+Sb6V5Jw+nKSUsiTJJ5IcmOThSR7Yb3d2KWW3RTgsAABg\nAcw6olJKedp8d1Zr/eA8ih+T5Mokj05yxdBrTyX54yQvrbWe3i9+eSnlYUlenOTYUspdkzwhyWOm\nR1lKKc9O8ogkz0k3UvOQJPdLcu9a6wV9mWOTfLd//dXzPUYAAGDxzXXq1+p57mtLkvkElY/VWt+e\nJKWU4XX3T3LLJGcNLT8nye/2/z6yf82zp1fWWjeXUj6XLqC8ui/z/emQ0pf5YSnla32Z1fOoLwAA\nsJ3MGlRqrVs9LayUsk+SNyU5Nsl/zeeFa63fnWP1XfrntUPLv51kv1LK7n2ZK2qt1w6VuTTJEQP7\nuWyG/V+a5KD51BcAANh+RppM38/9eG6S1yRZmu50rHcsYL32TLKl1nrd0PKN/fNUX+aaGbbd2K+f\n3s/VWykDAAA0Zt5BpZTygCTvSnLPJKekuxLXuoWu2DYanpi/IJYt2yUrV+6+GLteVMt2XTruKsBY\nLF26yw77dztuy5Z1g+rabv603bbRfqPTdqPTdttmuv0WfL83t2Ap5XZJ/jLJU5J8LckDa63/uii1\nStYnWVJK2aPWOjgismJg/frMPCqyol83Xe5Os5S5aoHqCgAALLCtBpVSyrIkL0jyF+kuI/zcJCfX\nWhdl1KJ3Sf98YJILBpYflOSyWuu1pZRLktymlDJVa90wVOaigf08aIb9H5Tk/BmWz2nz5htz1VUz\nnW3Wts03/HTcVYDtbvP11+X887+Wn/70xmzcuGnc1RmLgw9elampFVsvOIPpbxV3xP/zxk3bbRvt\nNzptNzptt21Wrtw9uy7CGTxzBpVSypFJ3pmkJHlfklfUWq9c8Frc1BfSzS05On1Q6efFHJ3kU32Z\ns9Kd4nV0ko/0ZfZIcniS1/dlzkjy0lLKb9Ra/60vc4ckdx8oA+yErlm/Lu89fV2mvrhx64V3Qhuu\nWJsTXpgccsih464KAIxkrvuofCzJ45N8Ocn9aq3/uZAvXEq5d5KV/Y+7JLlTKeXw/ucvJTkxXchY\nk+6u9Mcl2S/Jm5Ok1rq2lLI6yYmllMuTXJ7kdekmyp/clzmvlPKZJCeXUp6ZZFOSk5JcmOS0hTwe\noD1T+xyQvfZftfWCAEBz5hpReXz/fPskp81wr5NhW2qtd57Ha78tyWHT2yZ5ev/Ykm5eyevSBZiT\nk+yV7lSto2qtawf28Zx0weXjSZYn+XySI2utPxko84R0VyQ7J90Vys5Mckyt9cZ51BUAANiO5goq\nr5nnvuY1Z6XWesTWS+X4/jHbPjYleX7/mK3Mj5M8bT51AwAAxmuuGz6+ejvWAwAA4GcW56LHAAAA\n20BQAQAAmiOoAAAAzRFUAACA5ggqAABAcwQVAACgOYIKAADQHEEFAABojqACAAA0R1ABAACaI6gA\nAADNEVQAAIDmCCoAAEBzBBUAAKA5ggoAANAcQQUAAGiOoAIAADRHUAEAAJojqAAAAM0RVAAAgOYI\nKgAAQHMEFQAAoDmCCgAA0BxBBQAAaI6gAgAANEdQAQAAmiOoAAAAzRFUAACA5ggqAABAcwQVAACg\nOYIKAADQHEEFAABojqACAAA0R1ABAACaI6gAAADNEVQAAIDmCCoAAEBzBBUAAKA5ggoAANCcZeOu\nAAALb/P112XNmgtH3n758t2SJBs3blqoKm13Bx+8KlNTK8ZdDQBGJKgA7ISuWb8up5y1LlPn77hB\nY1tsuGJtTnhhcsghh467KgCMSFAB2ElN7XNA9tp/1birAQAjMUcFAABojqACAAA0R1ABAACaI6gA\nAADNEVQAAIDmCCoAAEBzBBUAAKA5ggoAANAcQQUAAGiOoAIAADRHUAEAAJojqAAAAM0RVAAAgOYI\nKgAAQHMEFQAAoDmCCgAA0BxBBQAAaI6gAgAANEdQAQAAmiOoAAAAzRFUAACA5ggqAABAcwQVAACg\nOYIKAADQHEEFAABojqACAAA0R1ABAACaI6gAAADNEVQAAIDmCCoAAEBzBBUAAKA5ggoAANAcQQUA\nAGiOoAIAADRHUAEAAJojqAAAAM0RVAAAgOYIKgAAQHMEFQAAoDnLxl0BAFhom6+/LmvWXDiW116+\nfLckycaNm8by+kly8MGrMjW1YmyvD7AQBBUAdjrXrF+XU85al6nzxxcWxmXDFWtzwguTQw45dNxV\nAdgmggoAO6WpfQ7IXvuvGnc1ABiROSoAAEBzBBUAAKA5ggoAANAcQQUAAGiOoAIAADRHUAEAAJoj\nqAAAAM0RVAAAgOYIKgAAQHMEFQAAoDmCCgAA0BxBBQAAaI6gAgAANEdQAQAAmiOoAAAAzRFUAACA\n5ggqAABAcwQVAACgOYIKAADQHEEFAABojqACAAA0R1ABAACaI6gAAADNEVQAAIDmLBt3BeZSSlmb\n5A4zrHpXrfX5pZSlSV6b5GlJ9k6yJslLaq3nDuxjjyRvSfK4JHsm+UqS42qt5y9u7QEAgFG1PqKy\nJclfJrnd0ONl/foTkjwryXOT3CvJmUk+WUpZNbCP9yd5cJJjktwnybeSnFNK2Xd7HAAAADB/TY+o\n9DbWWn8wvLCUMpXkj5O8tNZ6er/45aWUhyV5cZJjSyl3TfKEJI+ZHmUppTw7ySOSPCfJq7dD/QEA\ngHlqfURlLvdPcsskZw0tPyfJQ/p/H5luVObs6ZW11s1JPjdQBgAAaMyOHFTu0j+vHVr+7ST7lVJ2\n78tcUWu9dqjMpUkOWtzqAQAAo2r91K8lSe5bSjkzyT2TXJ3kQ+nmpuyZZEut9bqhbTb2z1N9mWtm\n2O/Gfj0AANCg1oPKD5LcKsmbklye5PAkb0xyQJKLx1YrAABgUTUdVGqt9xtadGE/if516SbCLyml\n7FFrvXqgzIr+eX3/mGnkZEWSq+Zbn2XLdsnKlbvPd7OxW7br0nFXAYDtaPny3XbI96uke69NssPW\nf5y03ei03baZbr+FtiPOUfl6/zx9yteBQ+sPSnJZPy/lkiS36cPNcJmLFq+KAADAtmh2RKWUUtLd\nL+U1tdZvD6z6tSSbk5yS5JVJjk5yQb/Nkv7nT/Vlz0p31a+jk3ykL7NHulPIXj/fOm3efGOuumqm\nKS9t23zDT8ddBQC2o40bN+2Q71fJz7/R3lHrP07abnTabtusXLl7dl2EM3iaDSpJvpPksCSnllJe\nlOR/+59fnOR9tdbvlVJOTPLSUsqadHelPy7JfknenCS11rWllNVJTiylXJ5unsvr0k2mP3k7Hw8A\nAHAzNXvqV3/q1hFJ/jvJqelO1Xp5khPT3egx6ULH29OFjm8muW+So2qtawd29Zwkpyf5eLrTxlYm\nObLW+pPFPwoAAGAULY+opA8cx8yxfkuS4/vHbGU2JXl+/wAAAHYAzY6oAAAAk0tQAQAAmiOoAAAA\nzRFUAACA5ggqAABAcwQVAACgOYIKAADQHEEFAABojqACAAA0R1ABAACaI6gAAADNEVQAAIDmCCoA\nAEBzBBUAAKA5y8ZdAQBg4Wy+/rqsWXPhuKsxsuXLd0uSbNy4aeR9HHzwqkxNrVioKgFjIqgAwE7k\nmvXrcspZ6zJ1/ugf9HdkG65YmxNemBxyyKHjrgqwjQQVANjJTO1zQPbaf9W4qwGwTcxRAQAAmiOo\nAAAAzRFUAACA5ggqAABAcwQVAACgOYIKAADQHEEFAABojqACAAA0R1ABAACaI6gAAADNEVQAAIDm\nCCoAAEBzBBUAAKA5ggoAANAcQQUAAGiOoAIAADRHUAEAAJojqAAAAM0RVAAAgOYIKgAAQHMEFQAA\noDmCCgAA0BxBBQAAaI6gAgAANEdQAQAAmiOoAAAAzRFUAACA5ggqAABAcwQVAACgOYIKAADQHEEF\nAABojqACAAA0R1ABAACaI6gAAADNEVQAAIDmCCoAAEBzBBUAAKA5ggoAANAcQQUAAGiOoAIAADRH\nUAEAAJqzbNwVAABYKJuvvy5r1lw47mqMxfLlu+Ue97hnkl3HXRVYEIIKALDTuGb9upxy1rpMnb9p\n3FXZ7jZcsTYnvTK5+91/fdxVgQUhqAAAO5WpfQ7IXvuvGnc1gG1kjgoAANAcQQUAAGiOoAIAADRH\nUAEAAJojqAAAAM0RVAAAgOYIKgAAQHMEFQAAoDmCCgAA0BxBBQAAaI6gAgAANEdQAQAAmiOoAAAA\nzRFUAACA5ggqAABAcwQVAACgOYIKAADQHEEFAABojqACAAA0R1ABAACaI6gAAADNEVQAAIDmLBt3\nBQAA2Habr78uF1xwQTZu3DTuqozNwQevytTUinFXgwUiqAAA7ASuWb8u7z19Xab22TjuqozFhivW\n5oQXJocccui4q8ICEVQAAHYSU/sckL32XzXuasCCMEcFAABojqACAAA0R1ABAACaI6gAAADNEVQA\nAIDmCCoAAEBzBBUAAKA5ggoAANAcQQUAAGiOoAIAADRHUAEAAJojqAAAAM0RVAAAgOYIKgAAQHME\nFQAAoDmCCgAA0BxBBQAAaI6gAgAANEdQAQAAmiOoAAAAzRFUAACA5ggqAABAcwQVAACgOcvGXQEA\nANhWm6+/LmvWXDjStsuX75Yk2bhx00JWabs7+OBVmZpaMe5qLJiJCSqllBcmeV6S2yf5dpLX1FpP\nHW+tAABYCNesX5dTzlqXqfN37LAxqg1XrM0JL0wOOeTQcVdlwUxEUCmlPCfJG5I8O8mXkjw8ySml\nlB/VWs8aa+UAAFgQU/sckL32XzXuarBAdvqgUkpZkuTlSd5Ta/1gv/gdpZTD+uWCCgAANGYSJtPf\nLd3pXsOB5JwkDyil7Lb9qwQAAMxlEoLKXfrntUPLv53u+O+8XWsDAABs1SQElT3756uHlm/sn6e2\nY10AAICbYRKCCgAAsIPZ6SfTJ1nfPw+PnKwYWr9Vy5btkpUrd1+QSm1Pt566Ra7+7jivGbCkf96y\n/V/5R9/JhqX7bffXbcXVV10+7iqMzSQfe+L4J/n4J/nYk8k+/kk+9sTxb7hibZYvP3Qsn1WXLVuc\nsY9JCCqX9M8HJlkzsPygJDck+e+bu6MlS5Ys2XXXpQtYte3j5JPePO4qAADAvOz0p37VWi9ON3H+\n6KFVj0hyTq31hu1fKwAAYC6TMKKSJMcneV8p5YtJzkvy5CSHJ3ngOCsFAADMbMmWLdt/3sA4lFL+\nKMmLkvxSkprk5bXWT463VgAAwEwmJqgAAAA7jp1+jgoAALDjEVQAAIDmCCoAAEBzBBUAAKA5ggoA\nANAcQQUAAGjOpNzw8SZKKS9M8rwkt0935/rX1FpPnaP8fZK8Jcl9k1yd5GNJ/rTWeu1AmaOSnJDk\n4CQ/SvL+JK+ste5U14Be6LYrpaxO8rQZNr2w1nrPha39eM237fptfivJqUk21VrvNMP6ieh3ycK3\nn74359/tg9PdLPfuSTYkOTvJS2utPxgoMxF9b6HbbpL6XTJS+z01yZ8kuWuS65J8Ot17xncGyuh7\nM5efs+0mqe+N8n4xsO1JSZ6b5PBa63kDyyei3yUL1n5H1Fo/1y9bnRH63kSOqJRSnpPkDUleneQe\nSf46ySl9B5yp/H5Jzkn3i7pvkt9J8pAk7x0oc+8k/5zuDeneSf4oybOSvHaxjmMcFqPtel9Mcruh\nx2ELfwTjM9+267d5WZJPpfuwc5P/CCel3yWL0349fe+m5Q9N8i9JvpzkPkmemuQBST46UGYi+t5i\ntF1vp+93yUjtd0y6D3//N13Qe1ySeyY5vZSypC+j781cfqtt19vp+94o7xcD2943yR9k6D1jUvpd\nsqDtN/y+O+++N3E3fOz/WP8nyUdrrX8ysPwfktym1nr4DNu8IcnvJdm/1rq5X/boJP+U5M611rWl\nlA8nuVut9dcGtnt+ul/0vrXWaxbxsLaLRWy71UnuWGs9YvGPYjxGbLuVSS5I92bz+0keNsOIwE7f\n75JFbb/V0fcOn2GbjyY5sNb66wPLnpzkw0nuUGv930noe4vYdquzk/e7ZOT2+8ckG2utTx1YdkyS\nv0tSaq2X6Hvb1Hars5P3vVHabqDM0iT/nuQr6ULIz0ZUJqHfJYvafqszQt+bxBGVu6UbxjpraPk5\nSR5QStlthm2OTPLZ6Q/avXPTJcUHD5SZaZ+7Jzl0WyvdiMVqu0kwSttdneTXaq3/kWTJDOuTyeh3\nyeK13yQiTdsMAAAVC0lEQVQYpe2OTTL8zdn0KV9798+T0PcWq+0mxbzbr9b62MEP2r3pv9/p9xF9\nb/S2mwSj/N1Oe16SWyV56wzrJqHfJYvXfiOZxKByl/557dDyb6drjzvPsM2Bw+VrrVene/M5qJSy\nPMk+s+wzSQ4aubZtWfC2W9jqNW3ebVdrvaHW+sPZdlhK2TOT0e+SRWi/CTJK211Ta71yaPGjkqxP\nctEE9b0Fb7uFrmDjRnnP+AWllHskeVmSj9VaL9X3Rm+7Ba1d20Zqu1LK/unmlv1RkuuH1k1Kv0sW\nof22xSROpt+zf756aPnG/nlqlm2Gy09vMzXbPmut15VSfjrLPndEi9F20/YtpXwo3bncuyU5M93k\n0++PXt2mjNJ2I+1zJ+x3yeK03zR9bytKKUem+6bsZbXWTaWUvWba507Y9xa87QZW7ez9LtmG9uvP\nkX9rkl2TvDvdBPFZ96nv/dwcbTdtZ+97o7bdO5P8U631s6WUA27OPnfCfpcsTvtNm3ffm8QRFdow\neCrOVelOBfuXJI9I8sdJHpjkM1sZYoRtpe9tRemuYPWJJKfVWt887vrsSOZoO/1u605JNxH8d5I8\nPMk/DE0IZ3ZztZ2+N4NSyqOS/FaSPx13XXZEN7P9Rup7kziisr5/Hk6EK4bWD28zU4Jc2a/bMNM+\nSyl7JFk6yz53RAvZdlPT5Wutxw2t+2YpZV2SLyR5QrqJgDu6Udpuayal3yWL03763i+uv4n+zeej\nST6S7qIY0yal7y1G201Kv0u2of1qrRvS9bOLSykXJflGkkenm+N4k33qez83S9v9drpvuyeh782r\n7fq+864kL5nhdOHpgDcp/+cli9N+I/+/N4kjKpf0zwcOLT8oyQ1J/nuWbe4yuKCUcuskeyW5qJ9z\ncflwmfz8nMWd5bzkhWy7vTN3u3y9f77d/KvZpFHabk611o2ZjH6XLEL7zUHfy8/uP/OxJO+ptR5b\na71xet0E9b0Fb7s57Gz9Lpln+5VSdimlPLGUcveh8hcluTHdlav0vdHa7q5zvNbO1vfm+3d7nyS/\nnOSvSyk3lFJuGNjHuaWUiyeo3yWL0H5zvNZW+97EBZVa68XpJgQdPbTqEUnOqbXeMMNmZyQ5rJRy\ny4FlD0/3x3/mQJmHzrDPq9JdN3qHtxhtV0pZVkp5TynlkUPbTV/+b64OvsMYse1ujp2+3yWL0376\n3uxtV7r7H/1jkv87eHnKITt931uMtpuUfpfMv/36QPe2JC8eKr8q3eeV7/Y/63sjtN2k9L0R/m7/\nPd19Z+418Hh4v+73B/690/e7ZHHab1v63iSe+pV0VyV4Xynli0nOS/LkJIenO1cupZQTkvxqrfVh\nffl3p5sM+bellFcl2T/JG5OcXGtd15c5Mcl/llL+si9/z3T/YbxuGz6EtmjB266Ucpt+/XOTfDXd\nNz/vTDdc/antdFzbw7zarpRy2yS/0m97+yS3LKUclm4o9dJa62WZnH6XLEL76Xuz/t2+Jt1drU8o\npQx/03VVrfW6TE7fW/C2m6B+l8y//d6U5G2llG8kOT3JbZO8Pcn30t1/K9H3Rmq7WuvmCep7N7vt\n+vuffHNw41LK9D1RLq21fqv/96T0u2QR2m/UvjdxIypJUmv9UJLj0t1xsyZ5SpLH1Fq/3Be5XQYu\nv1Zr/VG662ffPt0N5E7tH8cNlKnp0udh6YYA35nkjbXWv1zkw9muFqPtkjwjyep0/8lelOQD6c5Z\nPLLW+tPFO5rta75tl64/fbp/PDLJvkk+0//89H6fE9HvksVpv+h7s7Xdkf2yy9J9yBl8PKnf50T0\nvcVou0xIv0tGes84KckL0rXRN9K9X9Qkh/anWet729B2mZC+N8Lf7Ux+4Y7ok9LvksVpv4zY9ybu\nzvQAAED7JnJEBQAAaJugAgAANEdQAQAAmiOoAAAAzRFUAACA5ggqAABAcwQVAACgOZN6Z3pggpVS\n7pvk39LdQXy/Wuv6MVeJbVBK2T/Ji9LdjO2Xk1ybZG2SjyX5q1rrhoGyn03yWzPs5tp0Nzb7YJKT\naq0/LaW8Oslf3IwqvLrW+pp+/welu1HaQ9Ld6HZZku8mOTvJq2qt378Zx7M0yVP7xz2T7Jnkh0k+\n39ftizejTgA7PCMqwCT6vST/k+QWSZ485rqwDUophyVZk+TRSU5KFxCemuRz6ULGv5VSfnlos+8l\nuc/Q43FJ/j3JW5P8TV/ur4fKHN8vf/bQ8r/p63L3JP+ZLgi9Md1d6h+Z5ENJfjfJ50opU1s5nj2S\nnNHv8+IkT09yeLogdpskXyilHD/rDgB2IkZUgIlSSrllkt9J8t4khyQ5Nt0H0u31+stqrZu31+st\nltmOo5SytNb60xH3uTTJjbXWLTez/N7pRk3+O8lhtdafDKz+VCnltCSfTvKedIFh2vW11v+cYZdn\nllKWJ3lGKeXPa62XJ7l84PXu2f+zzrL9C5Ps2tflRwPLzymlXJjk1CSPTfKBOQ7rremCycNrrWcP\nLP9yklNLKa9P8spSyppa60fn2E9zSilLkuwyav8AJo+gAkyaxyVZmeTjSS5L8q5SSqm11iQppfxB\nuuByRK31c4Mb9h98D0ty2/7UoFVJ3pDuG/TdklyU5M211lMHtvlsv+5d6T6E/nOS3+8/lL84ye8n\nuUOSK9N9GP2zWuslA9vv12/7kCSbkpyS5MPpTl37vVrr6r7crfu6PDrJ3ulGjD6Y5A1bC0allAek\nGy24X7/oa0mOr7WeM1BmbZIvpRsxeHmStyV5TSnlxiRvSnKr/liem+SD/cjA8UmemOR2SX6c5Mwk\nL6u1fq/f5+HpgsSTkvxhkgcmOSjJd/r9fqDW+ow5qv5H/bE+ciikJElqrf9aSvk/6U7purm+lOSY\ndKeQXb6VssNun2TDUEiZdlqSW89Uz2n9yM/vJ/nroZAy6JXp+vCrk3y03251kqfVWn/hLIm+792x\n1nqngWWPS/KSdKeUbUo3GnX84OuVUu6Qri8dnmSvJOv6+r+i1rqpL7M8ySvSjRTdNsn30/0uX1pr\nXdeXOSDJt5M8L93fyKOSPCzJeaWUZyR5QZI7J9mc5OvpTqH7hb85YLI59QuYNM9Ickmt9d/TfdDb\nnG5UZdpp/bLHDG5UStk93Yesj/ch5Y5JvpBk/36fj0z3If7DpZTfHdh0S5Ll6T6sPSXdKUFJ8tok\nr0/y/nQfCI9L8utJziil3Gpg+39IclSSP0v3AXqvJO/o193Y121ZuhDw2HQfYB+c5H1JXppuNGFW\n/XydT/f1fEL/uDLJv/SnVQ0ex52SPDxdGFo9sO6wvh2OSnLWQL2fnS6cPTjdh+Mjknxm6PiS5E/S\nnap1WJIf9MsuS3LFXHVP8ogk36q1fmW2ArXWj9VaL9jKfgbdM127XjqPbaZdkGTvUspb+/4yWI8t\nc4WU3sPSvS+fMluBWuuNSf4+yd1KKXceWDXbKNTPlpdSHpouoH+7f60nJbkhyT+XUu4xsM0/JrlX\nkj9O9zt7U7oAddJAmb9JN4L0hnS/t1el6xf/MEMdfjfdHJsHJbmglPKoJH+bLrQfne70y6vS9f07\nzbA9MKGMqAATo/+m+Ij0cw1qrT8spZyR5KmllFfUWm+stf6olHJukt9O90Fs2tHpRg3+vv/5pf3z\nw2qt0x+oP11KOTBdCPm7ftmSJKuSPLDW+q8D+9s9yXtqrW/of/5SKWVFutGc+6Wbz/DrSX4jyV/U\nWqcDxzmllC8NHdrj082VOLrWema/7POllFskeVUp5fW11rWzNMtr0o2+PLLWel3fTuck+Wa60HPE\nwHHcJ8kBtdb/HdrHqnQjUNPftv9WuhGgF9Rapz/cfr6U8oMkn0r3wfT9A9tfXWt97eAOB0cB5lDS\nhaz5WnKTHZVy23SjP8cm+fDA73Q+3pKu3xyXbtTs3CTnJfn0zQxLv9I/X7iVctP7Ojhd6EhmOKYZ\n/HKSf0k3Ejf9u16bbsTp8Um+UUq5TZJfTfL8Wus/9dt9uS+3X7/NknRh7g211pMHytwlyctKKXes\ntV428Lp71VqfO/1DKeUhSX5ca/3zgWWfSxdYb3EzjgOYEIIKMEmOzU2/sT4l3WjIQ9KNSiTdSMvf\nllLuXWv9Wr/s8Um+N3BqylFJvjDDB9r/l+QtpZR9a63TowPXDYWU1FqPm6F+0x86pyd/371/Pneo\n3CnpAsy0o5JsHAgpg3V5dV927fCLlVJ2TRdE3jv9wbWv20/7APesoU2+NUNISZIvTYeU3nS4OX2o\n3DnpvsF/QH4xqAwf3821R5KNI2x3x/7UsmFXJnl7ulPb5q3W+oNSyq+lO/3tmHSh5TFJUkq5ON2p\nU6fNsYvpifZbO6bp9XvOs37vSzfSNmi6z+3fP1/T7/8PSilfrrX+R7/tGQP72ZLk/8zwEoP9dzCo\nfGao3Lokty6lvDRdWF/f958T5nM8wM5PUAEmQv8t8LFJzk+yoZ+InXRzEq7t101/0P/HJCen+5D5\ntX5k4hHpTleZ9ktJ7jTLB94t/frpoHLlDPW5U5KXJXloujkcuw6snj4td9/+efiStt8a+vmXkiyf\noy63n2F50p1Gdoskzy2lPHeG9VtKKbeutf64/3m2UYbh5dOv993BhbXWzaWUH85Qn1FGL5JkQ5Jb\nj7Dd99Kdwjbtdkk+meTvaq0vGbEuSZJa6zVJ3pzkzaWUvdKFskelG0X6WCnlSbXWj8+y+fRllFem\nm9MzmxX989ZOJfsF/elof5bulK87pBshnLZLX//rSilPSjfh/yullHXpRmFW11o/P7Cve/X7Oixd\nP106vK8Bw7/ft6QbtXlDunlOX0n3N/fewUtJA5ijAkyKw5MckO4D0hXpQsQP0n3ze6skv92fepVa\n61Xp7nvx2H7bh6T79vrvB/a3Jd18jHvP8PjVdJeWnXbDYEX6icjnpZsP8ua+bvdO8gdDdZ4+Debm\nXAXrx3PU5cNb2Xb1HNsOfrt/w022nHn5XPWdPm1oru1vrm+mm9czX9fXWi8YeJyVLoQ+p5RytxHr\nchO11itrrafXWp+Z7gpz1yR5zhybXNQ/b+2Y7tU/f3Mr5YZPB/twkj9PN9J2dH7+Ox6u9xnp/lae\nlK6P/3a6UxFflfxs0v/nk9y/39/9+329apZ6/MLvt9a6qdb6xHSnDB6fLuS8OcmFpZRf2soxARPE\niAowKX4vyfXpRkmuH1pXkrw73bfe05cq/miS1f3Ix+OT/Hc/AX/a/yRZPs+J2tOOSDcK8oe11ul7\ndmRoQnPy82/V984vTu6+y1C5/0k3Yf2/aq3DxzaXH6a76eWuIx7HbKZPD9s/A6cA9SNTe2dopGUb\nfCLJiaWUh9daPzVTgVLKn6b7xv/lW7ks7qvSTfp+W7oP8fPSXyDgsCSX1VovGl5fa72wlPKNdFfI\nms2/pAtxz0h3mtxMr7M03eW119Rap0+1mr6owvClofcb2G4q3WT3UwdHjWYLBrXWa9NNvP94/3s7\nPcnLSylvTPc3tDzJE/qQN72vR81xbDO9xkXpLijx+v7CDZ9JdwGGm3OTTWACGFEBdnr9h7THJflE\nrfWMWuunhx7vSfeB+tiBzU5PF2iOTnfqzqlDuz0nySFDV15KKeWZ/YfjuUyf5jV4j46l6eY2JD8/\njWZ6UvXg1beS7uphg85O9//5MUN1+c3+ClQrMoP+ssWfTfKoMnQjwlLKS0spT9/Kccxm+lK3jxta\n/tB0X5CNOidl2HvTncZ1UinldsMrSykPTHdhgztt7d4dtbtj/FuTPLSU8vC5ys5ieZJ/SvKOUspN\n3lv7+v1Kfj4RfqY6/G+6+UdPLqU8YZZir0p3CefBmz5OB9oDBl7v4CQHDpSZ/mLye0P7e0H/vLTf\n7j6llPcPXpmtD79np+u3yzNz/90j3ZXBfrav2ZRSXtVfNnrQeelOZRvlVD5gJ2VEBZgET053etfq\nOcp8KMmfl9LdU6XWur6UcmaSP003l+Pvh8q/qd/vWaWUF+fnl199RZK/HCo7fArOl9ONZPx5KeXq\nvm4vTncFq0OSPLKU8q/pLn98cborKV2Z5Dvp7lS+aWh/pyX5jyR/1X9g/Hq6K0K9Jt1I0Po5jvtV\n6U7jObeU8hfp5us8Nt3llP9wju1mVWv9t1LK/0s3/2D6HhkHJXldkq+m+6Z+Tv1Vpj5Wa33xHK+z\nvpTy2HQjEeeXUt6S5CvpJtkf1df/K0meObTpbFfIOjHdN/pvKaWctbX7zwzV5YpSyp+lG5E5r5Ty\nN+kuYLAs3UURXpAu+M52etS0F6T73X2klPKBdHM3rkw3Qf3p6S4rfMLQPJd/TneFuneVUk5MN2r1\n8nR9Yt++fj8q3U0nn9ZfNe7KdIF3976eh5ZSDkk3GvbEJPuXUt6Z7jTJO/f7P7PWemV/ha4keWM/\nwrJvulPATk13NbzHl1KmR3tmcpskLy6l7Jvu97MsXci+VW76hQAwwYyoAJPg2HTf/p4xR5npu4UP\njiJ8JN29Qy6stf7CfID+8qsPSDfq8bfpRlielOSFtdbBq0ZtydCcjf6Gh09JN+/ln9MFmw/1l2v9\nYLpRnOP6e2Y8Kt0H/XenC1rfSjdKML3v9KMFR/XrX5HuFJq/SPft/JyjA/3pbEeku4/FR9K10f2T\nPGXwtLThY7gZfifdBQlelK5tXpPuHhsPHhrdmG2/d0gXEOfU139VuiD5zHSh5YPpLvH8/CRHDk3Q\nvsnvY2BfV6cbqbhrunuIzGTWdqi1viPdRRd+nC7InptuPsiz0gWOe9ZaL55t+34f69P1q5enuxz0\nR9IF1velO8XrobXWVwxt85l0AeFu6S4K8KJ0QXP4ogvHpJsH84F0l89en+40szelOyXtff2ozhHp\n5pW8P11fem26UyF/p3+9r6a72ebd0l2A4hXpAtgr0/2un9G/1mxt9Sfp+vwfppsDc1q60y8fOXx1\nPGCyLdmyZb7vPQCMUz8X4PQkjxu41wU7oX6E7Iokn+wnoANMDCMqAI0qpSwtpbynlPLCoVUP7p8X\ncgI8DepHec5I8ohSyv5bKw+wMzGiAtCwUspH0l0e9iXp7gFzaLrTk86otT5mnHVj+yil3D3d7/7r\n6U6vurrWet54awWw+IyoALTtGenmp/xJuvP//zjdJZSHr/zFTqrWemG6uS9T6U75e/t4awSwfRhR\nAQAAmmNEBQAAaI6gAgAANEdQAQAAmiOoAAAAzRFUAACA5ggqAABAc/4/ba2e+OOoWogAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5895d3090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average error: CRTS Quasars')\n",
    "ax.hist(stats_dict['avg_err_ttl'])\n",
    "plt.savefig('QSO_CRTS_AVG_stats_avg_err.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OFesDFmTm+kbNicD3qa6x8k3gUeCYzNw6Ga9NkiRJ0o5pc+J8P9VV30daPwhcUN9GqtkM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe58945a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average timespan between consecutive observations: CRTS Quasars [days]')\n",
    "ax.hist(stats_dict['avg_mjd_diff'])\n",
    "plt.savefig('QSO_CRTS_AVG_observations_cadence.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pmTlMGWB+b59lhoCZzc9rAfesoIwkSZKkCgY5fNwKrE65E9Zi4BXACcC6wDGjLAf9B68/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\nY8ys8G+8YGbETP9aTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5892e6a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average number of observations per day: CRTS Quasars')\n",
    "ax.hist(stats_dict['avg_N_day'])\n",
    "plt.savefig('QSO_CRTS_AVG_N_obs_per_day.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "293"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many lightcurves have on average more than 4 day of obs per day ? \n",
    "np.sum(avg_N_day > 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many lightcurves have less than 10 days of obs ? \n",
    "np.sum(lc_length < 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7035"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many lightcurves have between 1 and 4 obs ? \n",
    "np.sum((avg_N_day < 4) * (avg_N_day >1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7707"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(avg_N_day)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanity check : plot light curves... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For diagnostics : print the averaged LC  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QSO_CRTS_LC_day_averaged_err_w/out_235948.09+001229.5.txt\n"
     ]
    },
    {
     "data": {
      "image/png": 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pIklT4dgN6JabIpIkbaoSEZFYVDhERCQWFQ4REYlFhUNERGJR4RARkVhUOEREJBYVDhER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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4748b04550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "i=0\n",
    "address=dir_err_w+files[i]  \n",
    "data=np.loadtxt(address) \n",
    "plt.scatter(data[:,0], data[:,1])\n",
    "print address"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And print the original LC.... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f4748b3e550>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mdnu2IYqISL/Wa9nfAeypLzCznwP2Aj/m7i8E3pNRbCIikpK2yd7d7wOeaCh+HfAOd38q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\nyV5EJABK9iIiAVCyFxEJwP8DjXiG1Sdn9DYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4748ced390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "address = 'QSO_CRTS_LC_longer_10/' + files[i][4:-3]+'dat'\n",
    "data=np.loadtxt(address) \n",
    "plt.scatter(data[:,0], data[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Check whether this happens often... calculate the scatter of points...\n",
    "\n",
    "rms = np.sqrt(np.mean(np.square(data[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scatt = np.zeros_like(files, dtype=float)\n",
    "\n",
    "for i in range(len(files)):\n",
    "    address=dir_err_w+files[i]  \n",
    "    data=np.loadtxt(address) \n",
    "    scatt[i] = np.mean(data[:,1]) - min(data[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of light curves affected :\n",
      "353\n"
     ]
    },
    {
     "data": {
      "image/png": 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isZ4EHJdGO5YoTY9Knz8GrOfuY3o9YBER6XdaNokCpGS4XSeftwNHpT8REZFe\n1bJtoiIiIq1OSVRERKQkJVEREZGSlERFRERKUhIVEREpSUlURESkJCVRERGRkpRERURESlISFRER\nKUlJVEREpCQlURERkZKUREVEREpSEhURESlJSVRERKQkJVEREZGSlERFRERKUhIVEREpSUlURESk\npIFVByBS9P777/HMM6N79DfmnHMWACZM+KTuOF/72tIMGTJXj8YhIn2bkqi0nGeeGc0hp13NkPmG\nVxbD++PGcPzPYOWVv11ZDCLS+pREZYrJn05k9Oinp2sajZTwujJ69NMMmW8483xp6emKRUSkpymJ\nyhQfvfc6l9zyOkMeK58Au8PYZx9kwSVWqTQGEZFGKIlKB61QAnx/3JhKf19EpFHqnSsiIlKSkqiI\niEhJSqIiIiIlKYmKiIiUpI5FIjV0x+U+3WGVVVZirrl0wweRVqUkKlJDK1zu8/64MfxmzllYbbXV\nK4tBRDqnJCpSRytc7iMirU1toiIiIiWpJCrSoiZ/OpEnn3wSmL7bKE4v3YhfpL4ZIoma2c+AnwJf\nBJ4Hjnb3K6qNSmT6fPTe65x37esMeWBCZTHoRvwinevzSdTM9gSOA34MPAh8F7jEzN5x91sqDU5k\nOqldVqS19ekkamZtwKHAOe5+URp8hpmtkYYriYpMh0Yv9emOp/d0RdXK0or6dBIFliKqcIvJ8jbg\nTDObxd2rfSSJSB/WCpf6gKqVpXX19SS6eHodUxj+PNHz+CvAv3szIJEZjaqUW8f777/HM8+MrjoM\nIG4EMu+8c1cdRuX6ehIdnF4/LAzPemIM6cVYRER61DPPjOaQ065myHzDK40juxHImmuOqDSOVtDX\nk2i3aX/1VqC9st8f8NEY3h83oLLfB/hw/NhKfz/TCnEohtaJAeKg/fzzc05pey1rwIC4NP6zzz7v\njrB6TL04n3/eqwinpizG/q6vJ9H30muxxDlX4fMu3XDFWW3dEpGISA9Zc80R7Ltv1VFIXl8/lXg2\nvS5WGL4EMAn4X++GIyIi/UmfTqLu/l+iE9GGhY82Am5z90m9H5WIiPQXfb06F+Ao4Pdm9gBwD7At\nMALQoy9ERKRHtbW3V9eZpruY2R7AAcBCgAOHuvuN1UYlIiIzuhkiiYqIiFShT7eJioiIVElJVERE\npCQlURERkZKUREVEREpSEhURESlJSVRERKSkGeFmC6WZ2czAIcD2xDWmY4Cz3f2cKuMqMrOhwNHA\nZsD8wMum6L4VAAAboUlEQVTABcBx7t5y1yiZ2RDiEXSfuvuiVceTZ2ZjgC/X+Ogsd9+nd6PpnJmt\nAvwa+AbwLvBH4LBWWedmNpy4Y1g9w939pV4Kp1NpXz8Y2A4YDowDfg+c4O6fVhhaB2Y2EDgS2BkY\nRuxHh7j73yuOawAwEjgMOMrdjyp8dgywEzAvMBo4yN1vryDUXtffS6KnA/sAhwJfB84FzjKzXSuN\nalpXAusSO5YBpxFJ9WdVBtWJY4mdqSUO9gXtRGJaoPB3aJVBFZnZ14BbgRuBrwL7EdvqQVXGVfAS\n0y7HBYATiJuevFpdaNM4gdhfDiGW577p/fFVBlXDqcD+wC+JOO8ArjWz5aoKyMzmJ7bFLYDJTLtf\nHw/sDuwFLAvcDNxoZv3iIbT9tiRqZnMBuwEHuPvVafCZZvZd4AdESa9yZrYwsBKwnbvfmQafbWab\nAlsRO13LMLMVgR8ClxG3X2xFE9z9zaqD6MIvgRvd/bj0/kUze5cmnkzU09z9c6DDckwH3D2BHd39\ns0oCq21H4AJ3/2t6P8bMVgd2AH5eXVhTmdlswI+BU9z9j2nwz83sO8TJ0/YVhbYd8DawKVGCnyLV\nOu0NHOzu16bBh5rZBsCBwC69GGcl+m0Sdff3zGxB4KPCR28Cy1QQUk3u/jJQ6/HxbcSTalpGqtb5\nHXASEZ+UYGYzAd8lTkamcPfbqomoKb8CHnX366sOpOBzoJjUP03DW8XiwCDg3sLw64lEVZWr3P10\nADMrfrYqMCtwS2H4rURhZIbXb5MogLu/nX9vZrMDaxFVaC3JzAYRG+dqwDYVh1O0NzAHUb1zWMWx\n9GXDgcHAh2Z2FfEwhQnAme5+ZpWBdcbMFiHaxTaoOpYafgv8xMyuBB4BvkaUQn9XaVQdDUivkwvD\n3wLmNbM53X1CL8eEu3dWLb94eh1TGP4CsKCZzebuH/dIYC2iv7eJFp1NPOD7hKoDqSU9qeZjIklt\n20pn+2a2ENFOu2eLP4KuDVjJzG42s7Fm9pyZHZk6nrSK+dLrGcQZ/frAH4BTzOyQyqLq2v7AU+5+\nR9WBFKWOMNcADwMTgaeA6/MdZFrAc0RpecXC8GXT6+DeDachg4F2d59YGJ4l+yG9HE+v69cl0YyZ\ntRFnqjsAW7v7CxWHVM/3gXmIXrpXmtmP3P2yimPKnAlc14oH0II3gdmAE4GxRLvtCUTpr1U6lA1K\nr5e4+7np/yfMbCmiQ0yrdYbJ2vN+SHR+ajlmdjCx/+wMPE402ZxiZm+5+xGVBpe4+wQzuwT4mZnd\nCfwT2Bz4HtGZp5VPTvutfp9EUzveBcCWwJatVLorcvdXgFeIA+qcwG+IDjyVMrONge8ALd8bz92/\nWRj0dOoccayZHebur1URV8EH6fXRwvD7gR+Y2bAW7Bi1LjA7LdgUYmZzE7Uk+7r7xWnwU2Y2K3CO\nmZ3u7u9UF2EH+wJDgQeI9tq7iWcmn0Fc5tRq3gPazGwOd/8wN3yu3OczNFXnwllEr7P1WzGBmtki\nZrZLun4sbzTwBTMbVkVcBVsQnZ9eNbNJZjaJ6F26SHp/eLXhdemJ9LpgpVFM9T/iAFrsUJbtr+/3\nbjgN2ZToUNRqyR1gMaLA8J/C8P+l4S1zLbO7v+/umxOXiM3n7msTVaL/brHezpln0+tiheFLAC/W\nqOad4fTrJGpmuxNVeJu6+31Vx1PHEkR72OqF4V8neha3whn04UQ8y+b+RgGvpf9bovOGhQvN7CuF\nj5Yn2qI6u3FAr0mdR+4nElPeqsBzLXpgWgN4sOog6nglvRa7li6VXl/uxVg6ZWZbmNmK7v6Ou7+b\nmpq2Aa7t6rsVuQ/4ENgwG5Bi3hC4qaqgelO/rc5N1aEnAOcDbmYL5D9399crCWxadwD/As41s72J\nzgcjgJ8A57t7sSdfr0tVoB2qQc1sHDDJ3Z+pJqqaXiIO9leY2QHEwXUN4nq289y9larLjgJuSW15\nVwEbE216e1QaVQ2pSWRRokdmy3H3sWZ2NXCEmY0lOhV9jagt+XuLlZ53AJYzs52AN4hrWOcnqnMr\nkW70MDS9nQlY1MxGpPcPEpe0HWxmo4kasv2IWp2TeznUSvTnkugKxIaxB9HB5LXcX8vcaSVd0L4x\ncA9x27eniDutjKR171gE0RGipe5YlLrar0lU411B3FLtUOIgUOV1eNNIHbS2Ji6wH0102NnD3c+v\nNLDahhDHklasZs7sAlxCdCB0onbkGuLEpJX8CHiIKHk+DiwCjChejtfLTiNO5u8gCl47p/9vJxL8\nscTd30YBzxA3h1nP3cdUEWxva2tvb6njnIiISJ/Rn0uiIiIi00VJVEREpCQlURERkZKUREVEREpS\nEhURESlJSVRERKQkJVEREZGSGrpjkZmNBI4A/unu36ozzqzEHTYGA7vmnszeLdIdMu4gLjy+pzun\nXaXcsh3u7i9VHE6flLs12g+BbwBzAq8Tj706O7+95Lajos+BccQNv49196fT+I08tLnd3Qek8Wci\nnqn5Q+KWjfMQj4V6OsVyZYlZzOL4o7u3ypNm+iWth+bkjm+Q20/6AjO7i3iwBsDd7r5mrfGaKYl+\nCqxoZkvU+XxjYBZ67k41/yKes1d8soX0IDN7Kt2CrCWlG/NfBVxK3HlqV2Ad4BjigcF3mtkvanx1\nJLE9ZX+rE7eBWxF4wMy+msbLj7NS+o1HagzP/AE4j7jbzE7ETrgXsU9cbmY/mY7Z1Z1RWoPWQ/OK\n+0lf8H9EzI/SyTpv5t65Y4mbdO/I1DOLvB2Ix/eMaGKaDUs35VYC7QWpZDcT8TijrxEPsp7eaQ7q\noYd1H0Y8RWYXd78oN/x+M/sjcWu348zsTnd/OPf5GHcvbk8PmtndxG3h9gV+UhwnPaHmgxrfxcwW\nJhLnqe5+QO6jh8zsKuL5kD8nbo8mXTCzAS365JJ+pTvWQ639pdW5+7MAZjahs/GavQH99USy7JBE\nzWwosAFxQBtR+GwAcYPvHwFfBt4m7g35i1yQZwC7A0u7+/Np2EDgMaKabQVgNXLVuWa2JnHvxo2I\nJwb8gDjYX0zcAPnHwEFEddo/gB+6+8tp2ncBi7h7h0cgmdkY4IWs2J4OqAOZeuBbknj0z+7E/XXP\nJUow7wCnuPuZnS08M1uFeHj1/yOqvmveVNrMlgaOI0oxsxD3eD3Z3a/IjfMiUdoZRdz7dQniROdk\nd/9tbrw5ifWyA3GfyzfScjw4u8m+mQ0nnmDy0/Sbm6RlemeazAVmdgFxk/FdqVH9bGYXAju5+0zp\n/cg03srEPX9nJj0uycxWI26wnj3b83HgKHe/rbPlV2M5zQHsT9xE/KLi5+7+mZntSZRURzcyTXd/\n1szeIbbVZn0xvb5YY7qTzWwNd+/0/rJpuz+Z2J5nI57mMs1N57tar2a2EHHD/VPd/cDCd7clnkO7\nXmfLPFV9/5KpJYhngV+7++Xp80uJB8TPm+5LnH1vALEtPuTum6ZhmwKHEE/1+ZQ4BhyaP7imqtIT\n03z/iCjBX5Qe9zcy/dbcxP2trwOOyC9PM1uZuIfrMkR1/vHEM05PAxZ19xfTeI3sXw2thzrLbUni\nHr2rEE84uRL4E9FUsJa731W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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f473a40d910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cutoff = 2 # how many mags away from the mean can the farthest point be? \n",
    "print('Number of light curves affected :')\n",
    "print(np.sum(scatt>cutoff))\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('Departure from the mean > '+str(cutoff)+' mag')\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Maximum departure, CRTS day-averaged quasars [mags]')\n",
    "ax.hist(scatt[scatt>cutoff])\n",
    "plt.savefig('QSO_CRTS_AVG_scatter_from_the_mean.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Sanity check : compare to stats of  QSO_CRTS_processed_err_w LC's"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the statistical info of those processed day-averaged light curves that I used before, from QSO_CRTS_processed_err_w.tar.gz . It is exactly the same number of lightcurves, so the selection works identically and predictably. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%\n"
     ]
    }
   ],
   "source": [
    "dir_err_w = 'QSO_CRTS_processed_err_w/'\n",
    "files = np.array(listdir(dir_err_w))\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",
    "timespan_obs = np.zeros_like(files, dtype=float) # store what was the total  timespan of observations per object\n",
    "lc_length=np.zeros_like(files, dtype=float) # number of points in the lightcurve - equal to the number of obs nights\n",
    "#avg_N_day = np.zeros_like(files, dtype=float)\n",
    "avg_mag_ttl= np.zeros_like(files, dtype=float)\n",
    "avg_err_ttl= 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",
    "    \n",
    "    address=dir_err_w+files[i]  \n",
    "    data=np.loadtxt(address) \n",
    "    \n",
    "    lc_length[i] = sum(1 for line in open(address))\n",
    "    avg_mag_ttl[i] = np.mean(data[:,1])\n",
    "    avg_err_ttl[i] = np.mean(data[:,2])\n",
    "    #avg_N_day[i] = np.mean(data[:,3])\n",
    "    assert data[-1,0] == max(data[:,0])\n",
    "    assert data[0,0] == min(data[:,0])\n",
    "    timespan_obs[i] = data[-1,0]-data[0,0]  # gives number of days between first and last obs\n",
    "    # we assume it's sorted! \n",
    "    \n",
    "    \n",
    "    # the \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 : <cadence>       \n",
    "\n",
    "\n",
    "stats_dict_old={'timespan_obs':timespan_obs,\n",
    "               'lc_length' : lc_length,   # Number of epochs \n",
    "               'avg_mag_ttl': avg_mag_ttl,\n",
    "               'avg_err_ttl' :avg_err_ttl,\n",
    "              'avg_mjd_diff' :  avg_mjd_diff }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66.1345875543\n"
     ]
    },
    {
     "data": {
      "image/png": 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fbffFSJIkSeo0uVzDcgUwKYqil4B/kF4Yvz8wGCCKomuAPUMIh2TK30r6XSz3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5894f3690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_context('poster')\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Number of days of observations per CRTS Quasar')\n",
    "\n",
    "# Calculate the histogram1 : new day-averages\n",
    "hist1, bin_edges = np.histogram(stats_dict['lc_length'], 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_dict_old['lc_length'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label='New calculations')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label='Old calculations')\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('QSO_CRTS_AVG_stats_lc_length_comp.png')\n",
    "\n",
    "# What is the mean number of days per Quasar ? \n",
    "print(np.mean(lc_length))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RIYT++fXjGjjmAGBPYKsY4yf5dT8EdgfaA5XrondJkiRJqyrkZVG9yF0KVT9E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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5896e90d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average magnitude: CRTS Quasars')\n",
    "\n",
    "nbins = 30\n",
    "\n",
    "# Calculate the histogram1 : new day-averages\n",
    "hist1, bin_edges = np.histogram(stats_dict['avg_mag_ttl'], 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_dict_old['avg_mag_ttl'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label='New calculations')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label='Old calculations')\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('QSO_CRTS_AVG_stats_avg_mag_comp.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SJEmRGSAkSZIkRWaAkCRJkhSZAUKSJElSZAYISZIkSZEZICRJkiRFZoCQJEmS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\nRBAEQRAEQRAEdRMGRBAEQRAEQRAEdfMfTY58vaip5JQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5893c9690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average error: CRTS Quasars')\n",
    "\n",
    "nbins = 30\n",
    "\n",
    "# Calculate the histogram1 : new day-averages\n",
    "hist1, bin_edges = np.histogram(stats_dict['avg_err_ttl'], 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_dict_old['avg_err_ttl'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label='New calculations')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label='Old calculations')\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('QSO_CRTS_AVG_stats_avg_err_comp.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KomAzLZIkSZJUG4YWSZIkSYlmaJEkSZKUaIYWSZIkSYlmaJEkSZKUaIYWSZIk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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5892baed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average timespan between consecutive observations: CRTS Quasars [days]')\n",
    "\n",
    "nbins = 30\n",
    "\n",
    "# Calculate the histogram1 : new day-averages\n",
    "hist1, bin_edges = np.histogram(stats_dict['avg_mjd_diff'], 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_dict_old['avg_mjd_diff'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label='New calculations')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label='Old calculations')\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('QSO_CRTS_AVG_observations_cadence_comp.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note : Nobs per day cannot be compared between the old and new preprocessing (making day-averaged error-weigted light curves), because this information was not stored anywhere when making them for the first time... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But otherwise, it looks pretty nice ! It seems that I am getting almost exactly the same with my current day-averaging... However, I am suprised that they are not exactly the same..e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a four-panel stats plot only about data that were actually used... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CRTS Quasars: sample of 7601\n"
     ]
    },
    {
     "data": {
      "image/png": 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XtUvrOp9qxyN7Fk4pkf+v3P/9SpSpZaodj+1SWmq/9BUp3bEOBaFuOU8rQ6Xj\nAS3jAbE3+6vufsHCVURnIuczNYy7/5owMyvFYrRcw+yHtmVKJ5Sp9zSxIXg4cMeitLG9MbOVgQsJ\nu/SjgbVLFK23fu5AXK+H8pq0Vqi3Pr6U0kFm1rvwpZBe+MsTWpaX09e12seTq5wQt7Ufw4jxKFrP\n3WeY2SvE6vxwwnxLtD/VXu+dU7qQUNggVDseRUmm0j8n7vHjF7lVXUe145E9C83Mmoos+qyW0mYq\nMFuuQaodj5VTupDjscSrhDlpT2Ao8NQitK1T6Y7ztHK0cTwADmqPZ45YdCQY1jeZOd5c4GkzWwxY\nl3jZlPOGNyWlG3Rc09rMZcQK6vHu/qaZLSQY1mk/N07pKzDfk+EehOnENMIr6W+yB2Od9nEc4Ulz\nM2K/yLGZEGxmSxKOChYHHnf3mr5fq3lBtbUfZjaI8GLZWr3JhGBYC9e4IWnDhGT+7zntF9qV2BO0\nIrHS/4DnHCzVG+04QTuK2HN5j7s/007H7HTaMB43EmaRQ4Afmdk5mXCYTIsvTOVuSeZ1dUUbxuMz\nYhtFzxLH+9zMPiCcE61DHQmGFdCI87RFYYHxyL6UUFg7yJS0TkkrsdnehRvc/R1ikrlk+u7DMtXf\nT+nyHdS8NmFm3wC+Bjzt7mPKFK3Hfmbumj8yszuAh4mQDnsDRwC3An81s6xc3fUxTXx2JgTEw4G3\nzGycmY0D3kzf3UUIxFCHfSxBW/vRP5dXz/3vjmS/00+J3/JviX1B3yQsHX5rZuOTO/puSbIQOI2Y\nAHerPbLJPHBb4BngbOB1M7vfzP5EmMztROyrO7zrWtmp/COlGxfLTM7Iij0X65pGnKctCiXGQ9QY\nEgzrkKR9uYVYSXqTFgcVfXLFym3czfL6lCnTqZjZcoSDgjmEoFSOeuxntufkeGLT+YGEe/fewGjg\ndWB94Hfp+tZjHyGcENxOTARWIDbZj0z/O/AQYSYM9dvHQtraj0bpf3ck+z2fQ2g59iS8bi4LHEzs\nJd6a0nuqugMHEb/7B939pdYKNyCvEyZ1/yEsQ3Yk9s73JbwvPlrFtoJ65/cpPTiFJCjkTFos2Opt\nj2FRGnGetiiUGQ9RY8iUtM5Iq7B3ESuO04Dd3b2YW996c4F9ETHBOsfdX26tcI566edSKR0EbOru\nk3J595rZZMIMcwNiY/pDufx66SPERHhv4DkiwH1mKrIlcB7hsnw3M9u9oF499bEcbe1HE6FZEfVB\n9nteFljBOPisAAAgAElEQVTP3d/P5d1oZh8B9wIjzWwbd3+i01vY9ZyQ0rqMTbcomNlShJfpEURo\nij0JpxtLEZrEC4lQFV9296NLHqhxuBQ4ktB+jTezw4jxWJ1YLD2CWExchwbwRtnA87Q2UcV4iBpA\nGsM6Inm2fJj4cX3AwgFCZ+b+L2rLn1g6pTXhNtzMdiBWl18F/l8FVeqxn1k8xkcLhEIAPIJgP5I+\n7kId9jEFt9+b0JaMdPdH3X12+nuM0Bz+GxgF7M+Cba6LPpagrdeq7q6xmE/2e/5NgVAIgLv/jhZH\nG6M6rVU1gpltReyjeh94oJXijcj3CKHwn0QQ+z+7+2fJ+/TviUWzGcB3zWxEVza0M0hCwJ7EvGVD\nYsFwFmFFcihwGPFugDp/zjXqPK2tVDAeosaQYFgnpL1nEwgvhv8AvuTuzxcUm0GYYkI4QSjFSild\naELT2aSVpCuJjciHV2haU3f9pGUvwRtlyrya0jVSrK966+NhKb3N3T8uzEz7bu5NHw+s0z4Wo639\nyMf+quf+d0eq+T2v3sFtqUX2S+ld+dic3YjsWXhVPmZfRtpblS0EHtRprepC3H0CsVhwIuGc5yZi\nD6q5+y2ENQ2UDvFR8zTqPK2tVDgeosaQKWkdYGYDCdPCwcRK2675oKEZ7t5sZpMId89rAY+WOGTm\n6fPFdm9s9exF9OsT4HIzK8zP7OtXNbMXCHO7wwnvnptSP/3MXJKvVKbMnILP9XYtswnw62XKZJvN\nM3ft9dbHhWjr787d3zazjwkvvGtRekJU0/3vprxCBPau5vfcndg1pQ92aSu6jmqehYPKlGko0oLh\nQqbFZrY08QxsBv7W2e1qDxp8nlY1lY6HqD2kMaxxzKwvEeR9MPGS3b6VH9eTKS1qnpI2AA8vKNuV\nLJ7SpQmPZYV/a6b8JYFN0l8fWuL/1Es/sz1GGyX39sXI+ppNJurtWmYmMgPLlBmQ0sw8pt76WIq2\n9mMCsc+kVL3MfXsztd3/7kb2ex5apkzh77lbkEIMrUHcs491cXO6irY8C7szI4l3/N/c/d2ubky1\ndIN5WlW0YTxEDSHBsPa5hNCMTQS+5u6tbcy+NaWjzaxfkfy9Ca9oHxI/3C7F3W9w98XcffFif4QX\nN4ApuXKPUWf9JOIyvUGsJI8uzEwCwA7pY+Z4pt76+OeU7pJiNRVj65RmgdrrrY+laGs/snoHmFkx\nC47Mnf1L7l6XK8cNyn3Af4GtzWzzwkwzyxaxmlnQkVR3IBuP99x9ape2pOvInoWFTraA+RP/Yenj\nxGJlGgkzO8DM/mJmdxfJawK+nz7e2Lktazcaep7WBqodD1FDSDCsYcxsOOH6fA6wv7vPKl9jvh3/\nOOKhcksKppsdbyjh/RPgrDoJKFpUu1Zv/Uwx/s5OH8eY2WZZXorhdDWhNX2L9NKotz4SHkfnAesB\nF6TJDwBmtriZnUVMhuYCV0Bd9rG978fbgZeJuHhX5IVDM9uecOndDJzenp0QFVPqek8HLk4fb8rF\nH80Wea5OH19w90YSDCvxorhhSv9RtlRjUGo8fpHSkWZ2Yt5KJJlNXg6sSmgLr+/QFnYupcbjZWAz\nYE8zm7+nMj3vfgZsQzhruqzDW9jOaJ62IG0Zj1boFp5ba4mm5mZ5SK9VUiD0bxBOKia0Uvwhd/9F\nqrcSYbe+LuEa+FniAZStUN7s7nWx4d3MtiM8Wk1x9zUL8uqun2Z2GXAUIUBNJEyOhhFx0aYBo9z9\nqVz5uuqjmR0JjCFMhD8g4nU1EyZ3KxJC4bHuflWuTk310cxWJIJP59mGCLzs6S9jkX93ZrZeqrci\n8B4xZisSK67NwLnufkY7dE0UYRGu9+LA3YRW6DPCKmAeYQLWk1jk2d7d60pAaut45OpfTThfudfd\nF7KOqDcW4f44h5YFnbeJvWJLEhrVfsS++m+5+30d1/r2ZxHG42eEt1aIveVvEFr1lYB3id/KQh67\nax3N0xakLeNhZhsRMWHz7EAslj/Pgk6+bnP329urvWJh5HymthlATAyXp4RJCi3xz+ab7Lj7O2b2\nReAHhHOXrYnVm8cJL2n1FHS55MpFPfbT3Y8xsz8BRxMrqL2JCeTNwPnu/kZB+brqo7tfaWZPEzHM\nvkyL6eh/CJOYS9z9hYI6tdbHXsAeLHzvNRMv8XVpx9+du7+aTA9/SIQq2ZZYMLgfuNTdx7Vf10QR\n2nq9Pyc0IIcSLveHAj0IJ0L3AhfWqSllm8YjR9+UN7NIXj3S1vvjR2Y2DjgG2ArYDvic2HN6M3Cx\nu79G/dHW8TjZzJ4hFkbXI6wk3iS0Y+e5+4fUJ5qnLUhbxmMFSt9Tm6a/rM5z7dxeUYA0hkIIIYQQ\nQgjRzdEeQyGEEEIIIYTo5kgwFEIIIYQQQohujgRDIYQQQgghhOjmSDAUQgghhBBCiG5OTXslNbMN\ngBOB7YmYP/MIt7XjCO+GC3n0MrPliWCpewCDCff4LwM3AVcmT3LFzrUacCqwEzAImEW4yb26jr1D\nCSGEEEII0TCY2TrAycBIQj74hJizX+bud5eoI/mgAmrWK6mZ7UsEfl0SeIeI7dVExABagbgw++Rj\nAJnZWkRcmFWBj4i4MEsTrqKXIATKPd19TsG5hgF/JGLJZefqD2yRznmdux/WMT0VQgghhBBCtIaZ\n7QrcScSL/ZCY6y9PzNkhQsH8T0EdyQcVUpOmpGa2CnANIRReDqzh7qPcfRdgDSIG0NLAjWa2TKrT\nBNxIXPS7gVXdfWd335aImTOZkPZPKzhXT+DXxEUfAwxK5xpOXPiPgEPMrO4CjQohhBBCCNEImNnK\nwG2EUHgPMDjN2bckYoXOBE4ys9G5OpIPqqAmBUNgN+KiTwdOdPfPsgx3nwUcSwS67AfsmLJGEpL/\nVOBAd5+dq/MaEWQW4GQz65U71wGESnkScIK7z8vVe44IOg1wZnt1TgghhBBCCFEVRwJ9CE3hAe7+\n3yzD3R8Hfpw+5ufskg+qoFYFw2VS+ra7zy3MdPfpwPuEGrdf+nqflN7p7p8UqfNAqtObEDwpqHej\nuxezq72F2Ns4OKmUhRBCCCGEEJ3Ldim9Ny8U5vgV8DmwiZkNSd9JPqiCWhUMX0rpIDPrXZiZJPrl\nCa3hy+nrLVM6ocxxn07p8Nx3w9JxitZz9xnAK4QQOrxYGSGEEEIIIUSHsnJKF3I+CeDuHwNvEnP2\nTC6QfFAFtSoYjiM8/vQBLjKzJbOM9P//AYsD4939aTNbDFiXuICTyxx3Sko3SMcalM5BK/WyvA2q\n64YQQgghhBCiHci2lvUsU+adlK6b9hdKPqiCmgxX4e7NZrYzsVn0cGC0mT2fsjclPALdBWSegPoQ\njmqaCbvjUryf0uVT2j+XV009IYQQQgghROfxD0II27hYZhIEB6eP/ZF8UDW1qjEE+AC4nbgJViA2\nj45M/zvwEDAjle2Tqzeb0mR5fQrSausJIYQQQgghOo/fp3Q3M1u7SP4xwMD0fy8kH1RNLQuGtwHX\nEq5ntyc2hfZO/08nwlj8Lq0O5Cn8XCltrSeEEEIIIYToWG4G/klsJ3vYzEaaWU8zW93MzgIuJPb9\nwcICneSDCqhJU9IU3H5vIkbIyLSZNOMxMxtJOJ0ZBexPxDLJKGd3vHRKM03jzIJ6C3krKlGvUop5\nMRJCCLFodOWLWs91IYRof1p9rrv7bDPbHbifMBn9Yy77c+B0YG3C3HQGC8/zS9HZ8kHNUqsaw2zv\n4G0FQiEAyd3svenjgckz0Jz0ecUyx10ppZlN8Ae5vGrqCSGEEEIIIToRd3dC8DsCuA64FTgb2NDd\nLwRWS0WnSD6onprUGAKrp/T1MmUyr0PZDTAJGAqsBTxaok5mj/wigLu/bWYfE7EQ16LFK1HZekII\nIYQQQojOJwWpvyb9FZI5pvlbSiUfVEGtCoaZCndgmTIDUpqpb58kLvwIitwoKczF8FzZjAmESeoI\nwqFNYb2BwDqE+dCThfmV8v77Ha9lXnbZXgB8/HEpjXf9oz42Dt2hn92hj53FgAF9u7oJC9EZz/Va\nR/f4gmg8FkTjsSAajwVp7+e6mW0MrEp4En02fV3T8kGtUaumpH9O6S4pRmExtk7pxJTemtLRZtav\nSPm9gb7EzXJ/7vus3gFmVkxQPjylL7l7w6wICCGEEEIIUS+Y2Y5m9rSZPVOiyCkpvdXd52X/p1Ty\nQQXUqmB4OTAPWA+4oCDA/eLJ89AwYC5wBYC7TwDGERf3FjPrnaszFLgofTzL3efmznU74chmCHBF\n/uKb2fbAD4nVgNPbuY9CCCGEEEKIyniJ0P5tYWZnZF+aWZOZnQTsRziuPCfLk3xQHU3NzbXpYM3M\njgTGEC5pPwBeIC7AUGIj6FzgWHe/KldnJcJ+eF1gGqFG7ksIkQA3u/tBRc61Xqq3IvBeOteKwKbp\nnOe6+xmF9Spg/uDKlLR9UB8bh+7Qz+7Qx86iwOSoJrySypRU93ghGo8F0XgsiMZjQdryXDez44BL\n0scpwN8BA9Ygtpft4e6PFdSpNfmgZqlZwRDmS/InAF8GVk5f/wcYD1zi7i8UqdMH+AGwF+HKdg5x\nIa9y99vKnGtFQvrfhXBoM5MwU73U3ce1sQsSDNsZ9bH9mDjpXcaOn8zsOXNbL5zo2WMJRo8YwrD1\ny23/rQxdS1ENEgxrE93jC6LxWBCNx4JoPBakrc/1FLbuREJZtALhkHIccJ67/7tEnVqSD2qWmhYM\nGwAJhu2M+th+nHbV07w7tfpzDOzfi/O+M7z1gq2gaymqQYJhbaJ7fEE0Hgui8VgQjceC1NBzXSRq\n1SupEKKDyTSFTcAyfXq0Wn76zDk05+oJIYQQQojGQYKhEN2cZfr04OJjt2m13EljnmDazDmtlhNC\nCCGEEPWHBEMhhBBCCCE6kWr3+bfnHn8hSiHBUAghhBBCiE5k7PjJVe3zn8Ycxo6fLMFQdCgSDIUQ\nQgghhOhEqtnnrz3+orOQYCiEEEIIIUQXUMk+f+3xF52FBEMhhBBCCCEWkdb2DS7WFBEZ5jU3M12C\nnqhBJBgKIYQQQgixiFS7bxDCqYyoDjPbgAhwvz2wKjAPeIMIcn+Ju79WpM7ywPeBPYgA93OBl4Gb\ngCvd/fMS51oNOBXYCRgEzAKeB65299vatWM1wGJd3QAhhBBCCCHqnfy+wX59eiz0t1zfpViu71Lz\nPw/s34vRI4Z0baPrDDPblxDMDgd6AY8AjwP9geOAF81st4I6awEvAKcAA1P554HNgTHAfWa20EZP\nMxsGvAgclc71MODAtsAtZnZNB3SxS9EyhRBCCCGEEO1EqX2Dyy7bC4CPP65OqygCM1sFuAZYErgc\nOMndP0t5SwNXAvsDN5rZYHefbmZNwI2EZvFuYH93n53qrAk8SGgDTwPOzp2rJ/BrYBlCeDzR3eel\nvC+keoeY2ePufkOHd76TqEnB0MweBb5cYfEb3P2QXF2pioUQQgghhGgsdgN6AtMJQW3+Zk53n2Vm\nxwL7Af2AHYHfACOBrYCpwIGZUJjqvGZmxwB/AE42swvdPZPaDyDkiEnACe7enKv3nJn9kBBOzwQa\nRjCsVVPS8cA9rfxNTmVnZJWkKhZCCCGEEKIhWSalb+eFwgx3nw68T7LmTV/vk9I7c0Jfvs4DqU5v\nQvCkoN6NeaEwxy3E3sbBSY5oCGpSY+juZ5TLN7N+wN+B2cBF6TupioUQQgghhGhMXkrpIDPr7e7/\nzWeaWS9geaCZsBYE2DKlE8oc92lgd2A4cEf6blg6TtF67j7DzF4BNkr1JlbXldqkVjWGrXEuMAC4\n0N2npO/KqoqBY9LHk9ONk1GoKp6Xq/cc8MP08cz274YQQgghhBCiAsYRloB9gIvMbMksI/3/f8Di\nwHh3f9rMFgPWJQS8yUWOlzElpRukYw1K56CVelneBtV1o3apO8EwafG+C7wOnJfLkqpYCCGEEEKI\nBiTN03cmBMTDgbfMbJyZjQPeTN/dRfgZgRDuMuHxwzKHfj+ly6e0fy6vmnp1T90JhsBlhO3wKXmt\nIJWriiFUvhmtqoqBV9I5hxcrI4QQQgghhOhwPgBuB/4BrEBYDI5M/zvwEC3+R/rk6uVlhkKyvD4F\nabX16p6a3GNYCjMbTQiAz7n7HbnvF1VV3Fq9yYQNccOoioUQQiwamev57swSS8T6ssYi0HgsSHcb\nj8Wamuanxfrc1vFo7bjdjNuAvYHniAD3mdJnS8KS8HJgNzPbvaBeUxvP10TICd2CuhEMk3OZH6eP\n/1uQnamKm5GqWAghhBBCiIYiBbffG/gIGOnuH+eyHzOzkYTTmVFEPMN7cvk9yxx66ZRmmsaZBfVK\nBZ4srFf31I1gSNgUbwi86u6/L8iTqlgIIUSnoiDVCthdiMZjQbrbeMxrbp6fFutzW8ejtePWKwMG\n9K22ymEpva1AKATA3T8xs3uB4wlHlDeZ2RxCebQisTWsGCulNFMEfZDLW5EWi8PW6tU99SQYnpDS\nMa2Uq0lVcWeo/ruDyYb62H5Ua5rS3qYsupZCCCGEqILVU/p6mTLvpHS1lE4ChgJrAY+WqLN2Sl8E\ncPe3zexjIhbiWpQWDBeo1wjUhWBoZqsCOwCfETEHCylU+ZZCqmIhhBBCCFEREye9y9jxk5k9Z6F4\n6gsxfeacTmhRtyabtw8sU2ZASrM5+5OEYDgCuKawcApzMTxXNmMCYZI6gnBoU1hvILAOoVR6sjC/\nXqkLwRDYl9DoPeTuU4vkzwBqWlXcGar/7mCyoT62H9WaprS3KYuupaiGNpgcCSHEIjN2/GTenVrd\nM7xnj3qZXtcdfwY2BXYxs+/nY4/n2DqlWcD5W4GjgdFmdoK7TysovzfQl/A1cn/u+1sJwfAAMzvH\n3QtXBg5P6Uvu3jAaw3oJV7FrSh8slpnimkwihMe1yhxnIVUxkNkoV1xPCCGEEEI0PpmmsAno16dH\nq38D+/di9IghXdvoxuVyIrb4esAFBQHuFzezs4gwdHOBKwDcfQIR97AvcIuZ9c7VGQpclD6eVSD8\n3U44shkCXGFmS+TqbQ/8kNAWnt7OfexSan5Jw8x6AV8iBv+xMkWlKhZCCCGEEO3OMn16cPGx23R1\nM7o17v6CmR1D+Bv5HnCgmb1AzNGHEtZ/c4Fj3f2lXNVDiP2Fo4A3zexZQlAclvJvcvfLCs71uZl9\nM9U7DNg9nWtFQmvZDJxbxCFmXVMPGsONaQlFUcpEFELlC6Eq7lckv5yqGEJVXExQbkhVsRBCCCGE\nEPWEu18JfBG4HphOmI5uQ+w/vAEY5u5XFdR5J9X5CfB2qrMe8Diwv7sfVOJcrwKbAL9I59qWcGpz\nPzDK3c9o5+51OTWvMSRCVAC87e4lQ0q4+wQzGwfsRKiK93H3/0JFquLT0nmuMLOjsvxGVhULIYQQ\nQghRb7j7X4FDq6wzEzgj/VVT7z0iMsIJrZVtBOpBMFw5peUC0GdIVSyEEEIIIYQQVVIPpqSZK7qZ\nZUshVbEQQgghhBBCtIWa1xi6+6nAqVWUl6pYCCGEEEIIIaqg5gVDIURtMX3mHE4a80Sr5Xr2WILR\nI4YwbP1ycWiFEEIIIUQtIMFQCFERPXsswTTm0AxMmzmn1fLTmMPY8ZMlGAohhBBC1AESDIUQFTF6\nxBDGjp88P9hvOabPDAGykrJCCCGEEKLrkWAohKiIYesPrFj7d9KYJyrSKgohhBBCVIKZPQp8ucLi\nN7j7Ibm6ywPfB/YABgNzgZeBm4Ar3f3zEudcjfB1shMwCJgFPA9c7e63takjNYwEQyGEEEIIIUSt\nMx6Y2kqZocAQYEb2hZmtRYSlWxX4iIhWsDSwFbAlEapuT3dfYEXbzIYBfwSWAd4BHgb6E9ELtjOz\nke5+2KJ3q3aQYCiEEEIIIYSoaVoLH2dm/YC/A7OBi9J3TcCNhFB4NxG+bnbKWxN4kNAGngacnTtW\nT+DXhFA4BjjR3eelvC+keoeY2ePufkM7drNLqYc4hkIIIYQQQghRjnOBAcCF7j4lfTeS0AxOBQ7M\nhEIAd38NOCZ9PNnMeuWOdQBhcjoJOCETClO954Afpo9ntn83ug4JhkIIIYQQQoi6JWnxvgu8DpyX\ny9onpXe6+yeF9dz9AeB9oDewW5F6N7p7c5FT3gLMAwYnk9OGQIKhEEIIIYQQop65DGgCTslrBYk9\nhAATytR9OqXDc98NA5pL1XP3GcAr6ZzDi5WpR2p+j6GZbUZ4EdqO2PD5H+AR4GJ3f7FIeXkdEkII\nIYQQohtgZqMJAfA5d78j9/1iwLqEgDe5zCGmpHSDVG8Q0KeCepOBjbJ6jUBNawzNbD/gGeBbhJr3\nEcKL0MHAs2a2Y0H5tYAXgFOAgYTXoeeBzYmNo/eZWY8i5xkGvAgcBfQivA454XXoFjO7pgO6J4QQ\nQgghhGgjybnMj9PH/y3I7gMsmf7/sMxh3k/p8intn8urpl7dU7OCoZltDFwLfAqMcveh7r4LsDpw\nA9ADuDYT9Ip4HVrV3Xd2922B9QipPvM6lD9PodehQe4+yt2HA1sQbm0PMbODOrrPQgghhBBCiIrZ\nGdgQeNXdf1+Q1yf3/2xKk+X1KUirrVf31LIp6TmElH9S2hgKgLvPMbPjgJUJl7QrAG/TitchMzsG\n+APhdejC3AbUQq9Dzbl6z5nZD4HLCa9DDeOOVgghxKKx7LK9Wi/U4CyxRKwvaywCjceCNMJ4LNbU\nND9d1H60dTzasw0NyAkpHdNKuaY2Hr+JMCntFtSkxtDMVgB2JbR1Vxfmu/vMpA083t3fTl/L65AQ\nQgghhBDdADNbFdgB+Iyw/itkZu7/nmUOtXRKZyxivbqnVjWGOxBC60Pu/lmFdSr1OrQ74T0o25za\nqtchM3uF2Fw6HJhYYXuEEEI0MB9/vNAaZLcj015oLAKNx4I0wnjMa26eny5qP9o6Hu3ZhlpiwIC+\ni3qIfQmN3kPuPrVI/gxgDmGBuCLhRbQYK6U02zP4QS5vRVqc07RWr+6pVcFw45S+AmBm2xFeRlcB\nphFOaH7j7nNTvrwOCSGEEEII0X3YNaUPFst092YzmwQMBdYCHi1xnLVT+mKq97aZfQz0S/WmVFKv\nEahVwXBISj8yszuAbxTkHwGcYWa7uftkWrwONSOvQ0IIIYQQoggTJ73L2PGTmT1nbkXlp8+c08Et\nEm3BzHoBXyLm/o+VKfokIRiOABaKMmBmS9ISh/DJXNYEYFSq91CRegOBddL5nyzMr1dqco8h4SEU\n4HjCocyBwABif+Bo4HVgfeB36YLK65AQQgghhCjL2PGTeXfqJ0ybOaeiv8z5RM8etapL6bZsTItS\nqJSJKMCtKR1tZv2K5O8N9CUURPcXqXeAmRW7+Ien9KVicdXrlVoVDJdK6SBgR3e/2d0/dPdZ7n4v\nYVbaTJh27sOC3oIWxeuQEEIIIYRoUDJNYRPQr0+Piv4G9u/F6BFDyh9YdDYbpvTtfCSCQtx9AjCO\nEP5uMbPeWZ6ZDQUuSh/PyraoJW4HXiasGK/IC4dmtj3wQ0L+OL0d+lIz1Oryx6yUPurukwoz3f1v\nZvYIsD2wC3BvLntRvA6V2tG7yF6HOsO9cCO4hW4N9bH96Ej315UcW9dSCCFEV7FMnx5cfOw2Xd0M\n0XZWTmm5rWAZhxD7C0cBb5rZs4SgmEUbuMndL8tXcPfPzeybqd5hwO5m9gLhjGZTQig8t0jsxLqm\nVjWG2UV+o0yZV1O6hrtnXocgLlgpWvM6VGk9IYQQQgghRNeQuTSdWbYU4O7vAF8EfkLEPt8aWA94\nHNjf3Q8qUe9VYBPgF8B0YFtgNcLkdJS7n7GIfag5alVjmNkKr1SmTOFu4Jr2OtQZ7oUbwS10a6iP\n5almU322ob4j3F9X4lpb11JUQzu4NRdCCNEguPupwKlVlJ8JnJH+qjnPe8AJ6a/hqVWN4RMp3cjM\nSu39WzOlr6c08wg0oljhVrwONZWp15Beh0RjUs2mem2oF0IIIYQQGbUqGD5FmJGuTnghXYAkrO2Q\nPmYuZOV1SHR7qt1Urw31QgghhBACatSUNAWkPBv4FTDGzCa7+/MAZrYMcDXhEOYtkmDn7hPMbByw\nE+F1aB93/2+q05rXodMI70ZXmNlRWX4jex0SjY021QshhBBCiGqoScEQwN2vNbPNgaOAZ81sIrHB\ndBgR53AasLe7z8pVk9chIYQQQgghhKiSWjUlBcDdjwG+DjwCrEvsA/wAuBzYxN2fKigvr0NCCCGE\nEEIIUSU1qzHMcPexwNgqysvrkBBCCCGEEEJUQU1rDIUQQgghhBBCdDw1rzEUQgghhBBCiAwz2wz4\nPrAd0B/4D7H17OJiUQTMbPlUfg9gMDAXeBm4CbjS3T8vcZ7ViHiJOwGDgFnA88DV7n5bu3aqBpDG\nUAghhBBCCFEXmNl+wDPAt4D3CYFwaeBgwmHljgXl1wJeAE4BBhK+R54HNgfGAPeZWY8i5xkGvEg4\nwuwFPAw44YvkFjO7pgO616VIMBRCCCGEEELUPGa2MXAt8CnhIHKou+9CxD6/AegBXJsJembWBNwI\nrArcDazq7ju7+7aEg8rJhDbwtILz9AR+TURCGAMMcvdR7j4c2AL4CDjEzIo6tqxXJBgKIYQQQggh\n6oFzgCWBH7j7A9mX7j4HOA74IyEArpCyRgJbAVOBA919dq7Oa8Ax6ePJZtYrd54DCJPTScAJ7j4v\nV+85Is45wJnt1rMaQIKhEEIIIYQQoqYxsxWAXQlt3dWF+e4+M2kDj3f3t9PX+6T0Tnf/pEidBwhz\n1N7AbrmsrN6N7t5cpDm3APOAwcnktCGQYCiEEEIIIYSodXYgZJeH3P2zCutsmdIJZco8ndLhue+G\nAc2l6rn7DOAVoKmgXl0jr6RCCCGEEEKIWmfjlL4CYGbbEV5GVwGmEU5ofuPuc1P+YsC6hIA3ucxx\np6R0g1RvENCngnqTgY2yeo2ANIZCCCGEEEKIWmdISj8yszsIL6EnAnsDRwC3An81s6xcH2I/IsCH\nZf2Nk+EAACAASURBVI77fkqXT2n/XF419eoeCYZCCCGEEEKIWmeZlB5POJQ5EBhA7A8cDbwOrA/8\nzsyWJATDjNmUJsvrU5BWW6/uqVlT0qQefriCon9x9y0K6iqIpRBCiA5l2WV7tV6owVliiVhf1lgE\nGo8FqcXxWKypaX7a2e1q63h0ZZtrjKVSOgjY1N0n5fLuNbPJxLx9A8J5zEO5/KY2nrOJMCntFtSs\nYJhjFjCuTP4/8x9SEMtHiXglHxFBLJcmVha2BHY3sz2TW9t8vWGEi9tlgHcIobQ/EcRyOzMb6e6H\ntUeHhOguTJ85h5PGPFE0L3vRzWtupmePJRg9YgjD1h/Ymc0TQgghRP0wK6WPFgiFALj738zsEWB7\nYBfg3lx2zzLHXTqlM1I6s6DeQt5MS9Sre+pBMHzX3feqpGCRIJb7Z/FKzGxN4EFaglienatXGMTy\nxCxeiZl9IdU7xMwed/cb2qtjQjQqPXsswTTm0AxMmzmn1fLTmMPY8ZMlGIq64uOPS80Vug+Z9kJj\nEWg8FqQWx2Nec/P8tLPb1dbxyNr88YxPOeScP7Zavl4WWwcM6FttlWy/3xtlyrxKCIZruPsMM5tD\n7DNckeS0pggrpTTbM/hBLm9FWpzTtFav7mm0PYYKYilEDTB6xBAG9u9Fvz49Sv4t13cpluu71Hzb\njtlz5nZpm4UQQohapGeP0ONki62t/b079RPGji/nTLNuyQS7lcqUKVyNnkSYg65Vps7aKX0RIMVA\n/Dh9V3G9RqAeNIbV0GoQSzN7n9iouhtwR0G9ckEsx5CCWLr7xHZutxANxbD1B7a6UpmtnB5yzh8r\n0ioKIYQQ3ZHRI4YwdvzkihZQp88Ma50GXWzN9qZsZGZNJebsa6b09ZQ+CQwFRgDXFBZOTmqG58pm\nTABGpXoPFak3EFiHkNefLMyvVxpNMKw0iOXuxE2QCYatBrE0s1eIWCXDAQmGQgghhBA1wMRJ71Yl\nONUblSy2Zpw05olGXmx9ijAjXZ3wQnp3PjMJazukj5kwdytwNDDazE5w92kFx9wb6EuYqd6f+/5W\nQjA8wMzOyWIj5jg8pS+5e8NoDOvClNTMljOzo8zsWjO7w8wuNbNdC8osahBLWqmX5TVMEEshhBBC\niHpn7PjJvDv1k4rMLDMVU2aeKeqHpCHMfISMMbPNsjwzWwa4mnAI8xYh2OHuEwgnln2BW8ysd67O\nUOCi9PGsAuHvdiKiwRDgCjNbIldve2KbWTNwenv2sauph1/FQOA1oF/B98ea2VPAaHd/j5Ygls0o\niKUQQgghRLcg0xQ2Acv06dFq+cw5i6g/3P1aM9scOAp41swmEl5EhxFOJKcBe7v7rFy1Q4iIBaOA\nN83sWUJQHJbyb3L3ywrO87mZfTPVO4yIavAC4YxmU0LeONfdf98hHe0i6kEw7AncB/wM+BshAO4J\nXEg4mvmtmX0JBbEUQgghhOi2LNOnBxcfu01XN0N0MO5+jJn9iTAR3YwIcP8WcDNwvru/UVD+HTP7\nIvADYC9ga8JJzePAVaVilbv7q2a2CaEd3IUIYTeTMDm91N3LhdOrS2pZMHwZ2Bf4yN3zvnk/Ba41\nsynAnwhp/xvA+FyZmgti2RkBSWsxkGx7oz6Wp56C4Gb9rKc2V0t3uF+FEEKIzsbdxwJjqyg/Ezgj\n/VVznveAE9Jfw1OzgqG7v0/Y95bKf9jMHgW2I5zJ5DeMKoilEEIIIYQQQlRIzQqGFfIsIRgOqfUg\nlp0RRLUWA8m2N+pjeboycG+1ZP2spzZXS3e4XzuLNgRCFkIIIUQV1IVX0jJkJqNZQHoFsRRCCCGE\nEEKIKqlZwdDMtjWzA81stTLFstARWSiJLMDkiBLHLBfEsqlMvYYMYimEEEIIIYQQUMOCIeF19Hrg\ne8UyzWwNWoJYZvsLb03paDMrDG8B5YNYQgSxLGZe25BBLIUQQgghhBACalswvDKlx5nZYfmMJBSO\nJfYTvgTcAQpiKYQQQgghhBBtoWadz7j7NWY2AjgQuNrMTgdeBZYFtiDa/k/ga+4+L1dVQSyFEEII\nIYRoMMxsO+DhCor+xd23KKi7PPB9YA9gMDCXUAzdBFzp7p+XOOdqwKnATsAgYBbwPHD1/2fv7uPk\nqqr8338qJiGETogJSfBHUEKAZSISVGxgICL8QDQSNF6B0SFIBpy5PDgBLwjIBWEGcX7qRUeCDHBx\nfiSGB72SUUEICGJCYoTXBBwewhodEhWREAjdSUtCE9L3j71P+qS6qrqqu6rrVNX3/Xr1a6fr7HXq\nnOpOUqv2wypWA7FRZXnEEHc/E5gL/AwYDRxPWFf4H4QilYe6+/N5MS8BhwFfBV4kFLF8N6GI5enu\n/rkiz/UccAhwPbCZUMRyX8KU09nuXlHdExERERERqYmtwL+X+PpFurOZTQOeBL4ETCbkBU8AHwAW\nAveY2cj8JzGzdsLGk+cQcpGHASfkCUvM7NYa3FvdZHbEMOHuPwZ+XGGMiliKiIiIiDSnDe7+qXI6\nmlkOWATsA9xNGCjaFo/tDzxIGA28DLg6FTcKuBMYS0geL0hmKZrZ+2PcfDNb7u63VevG6inTI4Yi\nIiIiIiKDcDxwJLAJOCNJCgHizMPz4rcXmdnoVNw8wpTTtcCC9NI1d19D2IME4MraXfrQUmIoIiIi\nIiLN6rTY/tDdX88/6O73AxuBPYCTCsQtcveeAuddQqilvl+cctrwlBiKiIiIiEizOjy2q0r0WR3b\nI1KPtRM2oCwY5+5bgGcJtdCPKNSn0WR+jaGIiIiIiEiamb0d+GtCtYI24CVgWbqKgJkNAw4iJHjr\nSpxufWxnxLgp8Zz9xa0DDk7iGp0SQxERERERaSSTgeeBPfMeP9/MfgXMjZtKthHqnvcAr5Y438bY\nTojt+NSxSuIamqaSioiIiIhIIxkFPECYJro7MBE4G3iNsNHMT+JoYVsqZlv+SQoca8trK41raBox\nFBERGYBx40b336nJDR8ePl/WaxHo9djVUL0ew3K5nW2WX/uheD0a5bUYhGeAzwCvufsDqcffAL5n\nZuuBnxPWB34aWJHqkxvgc+YII45NT4mhiIiIiIhknrtvBO4qcfxhM3sE+DAwB7gvdXhUiVPvHtst\nse3Ki+uzm2mRuIamxFBERGQAOjqKvU9oHcmIhF6LQK/Hrobq9djR07OzzfJrPxSvR6O8FgATJ46p\n1akfJySGU919i5l1E9YZTiLsIlrI3rFN1gy+kjo2id7NafqLa2hKDEVEREQkUx5bu4GlK9axrXt7\nv303d3UPwRVJA0mmjCYF6dcCM4FpwCNFYg6I7VMA7v6imXUQNreZRvHEcJe4RtdQiaGZvY1QZ+QD\nwO/dfWqBPhOAi4GTgf2A7YT5yIuBm9z9rSLn3he4FDgRmAJsBZ4AbnH3O6p+MyIiIiJS0NIV69iw\nqbIRr1EjG+ptrQyAmR0DvAv4hbv/sUi3pHREUmZiJSExnAXcWuCcI+itQ7gydWgVMDvGPVQgbjJw\nIGH94cr8442o0XYlvZCQFEKBRaBmNg14EvgSYRvb5YTk7gPAQuAeMxtZIK6dkOmfA4wGHgYcOAZY\nYmZ9folEREREpDaSkcIcsGfbyH6/Jo8fzdxZfcYLpPl8A/jfwBcLHTSzdwEnxG+T9YW3x3aumeWX\ntwA4FRhDKEuRXpOYxM0zs0KfOpwd26fdXSOGQykmff9IGMnbvcDxHLAI2Ae4Gzjd3bfFY/sDDxJG\nAy8Drk7FjQLuBMYSkscL3H1HPPb+GDffzJa7+201u0ERKUsl04sgfII8d9ZU2qdPrvGViYhItY1t\nG8m3zj+63pch2XETcBjwBTN72t13Dt7EpHApYT3h08APANx9lZktI+QBS8zsNHf/S4yZCVwXT3GV\nu6ffXNxFyBveA9xoZuckx83sOOBywkDVl2t2t0OskUYMbybsCnR9kePHE+qWbALOSJJCAHd/Hjgv\nfnuRmaX3751HmHK6FliQJIUxbg3hhw5wZRXuQUQGKZle1NnVXdbXhk2vs3TFuv5PLCIiIpkWE8FF\nhBzmFjP7bzO718xWAr8FDgV+B3wy/Z4emA/8F2Fq6Atm9oCZ/QpYA+wFLHb3G/Ke6y3gFOBl4KwY\nd7+ZrSGUxNgNuNbd763hLQ+phkgMzexs4FjgHnYd4k07LbY/dPc+k9Ld/X7CjkF7ACcViFvk7oVq\nlCwhLF7dL045FZE6qmR6US4vRkRERBqbu58JzAV+RlgCdjxhXeF/AJcAh8ZBoXTMS4SRxq8CLwJH\nAe8mLDs73d0/V+S5ngMOIQxMbSYsM9uXkI/Mdvcrqnx7dVVyKqmZ7WBwBR1zQI+7v22gJzCzdxDm\nE28BzqV39598h8d2VYnTrSbUNDmCOLxMKIDZUywubnP7LHBwjHuskusXkdooZ3rRhQsfpVO71YmI\niDQVd/8x8OMKY7qAK+JXJXEvAwviV0WykEtVopwRw9wgvqrhBsJWsZe7+wuFOpjZMOAgwgtfas7Y\n+tjOiHFTgLb4WKm45NiMEn1ERERERETS6p1Lla3kiKG7VzzVNCZpXyBs8DKWsFnMgJjZp4FPAqvd\nfWGJrm2EhaY9hB2FikmKT06I7fjUsUriRIZEpRutqJaTiIhklWoTSqupdy5VqaruSmpmRxFG+A6J\nD/07cMEAz/V2wnzebuDz/XRvS/15W9Fevcfa8tpK40SGxEDqOIFqOYmISPaoNqFIadXMpQaiKn/b\nzGwi8HXgDMKw5++Af4gbvgzUdYRahNe4+zMVxA102DXH4OYAlzRu3Oj+Ow3S8OHDhuy56qXV7rH7\nzbcAyOVgXNtuZcXvvttwPvMRy/xrlNznsFxuZ1vONVfSv9JzV1sr/L6KiJQrvXnY2LY+ZaX7SMoN\niTS7GuVSFRtUYhiHOs8B/gkYRxjq/BrwdXcf8BwAMzsB+BzwXDx3f7pSfx5Vol9S/3BLkbhiH2Pl\nx4kMqXFtu3Hr5Sf031FERCTjVJtQJKhVLjVQA04MzewIwlDn++JDPyHUAfz9YC4o1hi8CdgOnO3u\nb5YRtoUw5XQEMAl4tki/vWObrBl8JXVsEr2b0/QXV7GOjsqnA1YqGZUYiueql1a7xx09YRB7R09P\n091zcp/JPXZseYP51zzQb1yy7qSc16Ter18r/L4OlYkTx9T7EkRERKqmVrnUYFScGJrZXsD/As4k\nDHU+Txjq/FmVrulThILzrwPfNbP848k6v33M7EnC9M+zCcngocA04JEi505KXTwF4O4vmlkHYdfT\naRRPDHeJE5HqGTVyOJ100wMVlZbQuhMREREBMLO3EcrSfQD4vbv3mYNsZhOAi4GTCbnGduAZYDFw\nUyxoX+jc+wKXAicCUwijek8At7j7HQO41lrnUgNW9jsrM8sBf08oDPl2woYs/wz8L3d/o4rXlNTp\n2B14b4HjyRrCEYSFmT2EZHEVITGcBdxa4PpHEOoQAqxMHVoFzI5xDxWImwwcGJ9nZf5xERmcubOm\nVrTzKmjdiYiIiOziQkJSCAX2DDGzZOBoH+A1QmH73YEjCbXQ55jZJ/Knb5pZO/AAYXfQl4CHCVUN\njgE+bGbHu/tZ5VzgEOZSA1ZWYmhmHwS+S+8Lfg8hs11f7Qty99uA20pcyzHAL4D17r5/6vE3gXOB\nuWa2wN0780JPBcYQylLcl3r8dkJiOM/MrnH3/HenZ8f2aXfXiKFIlbVPn0z79Mn1vgwRERFpQDHp\n+0fCSN7uBY7ngEWEpPBu4HR33xaP7Q88SBgNvIxQIiKJGwXcSUgKFwIXuPuOeOz9MW6+mS2P+Uup\naxyyXGowStbWMLPxZnYzvUOz64CT3f3kOt5IwV1H3X0VsIyQ/C0xsz2SY2Y2k7DLKcBVecnfXYRh\n5KnAjWY2PBV3HHA54ZOHL1fzJkREREREZNBuJmwieX2R48cTRgY3AWckSSGAuz8PnBe/vSjudZKY\nR5hyupaw9m9HKm4NIUcAuLLYhWU0lyqqv6KL/0UYMXuDkEHPcPd7an5VAzefcM2zgRfM7AEz+xWw\nBtgLWOzuN6QD4nziU4CXgbNi3P1mtgb4ObAbcK273zuE9yEiIiIiIiWY2dnAsYQRuPuKdDsttj90\n9z67wcWSEBuBPYCTCsQtcvdCJe2WADuA/eKU00IaKpfqbyrp+NiOImTDVxbYDKZf7v62/nuVrWit\nQXd/ycwOAy4hbGJzFGG30uXAzcUWiLr7c2Z2CCHz/xhh3nAX4RfsO+6+rIrXLyIiIiIig2Bm7wC+\nQahOcC69m0XmOzy2q0qcbjUwh7AfyQ/iY+2EvKNgnLtvMbNngYNj3GMFumUxlyqqkm39Blo4vqrc\n/ZeUGOl09y7givhVyXlfBhbELxERERERya4bCJUF/sHdXzCzPolhrBN4ECHBW1fiXOtjOyPGTSFs\nbtlf3DpCYjijjOvNRC5VSsnE0N37m2oqIiIiIiIyZMzs08AngdXuvrBE1zZCJYMewgaUxSS1yifE\ndnzqWCVxu2i0XKqhLlZERERERFqXmb2dsNFMN/D5frq3pf68rWiv3mNteW2lcQ1NFaJFREQGYNy4\n0f13anLDh4fPl/VaBHo9dpX/egzL5Xa2rfgaDcXvR4u8xtcBk4Fr3P2ZCuIGOpUzR4k9TpqJEkMR\nERERGZBHf/Midz7obH0jvww0xByFnviWuqMrEzW8pYGZ2QnA54DngH8qI6Qr9edRJfol9Q+3FInr\ns5tpkbiGpsRQRERkADo6ir1PaB3JiIRei6AVX48ly55jw6bK7nfkiLe11GuUGIrfjx0xC9/R05P5\n13jixDEV9Y81Bm8CtgNnu/ubZYRtIUw5HQFMAp4t0m/v2CZrBl9JHZtE7+Y0/cU1NCWGIiIiIjIg\n27rDSGEOGNs2cpdjybTGJFkBGDVyOHNnTR2y65Om8ilCwfnXge8WKPuQrPPbx8yeJEz/PJuQDB4K\nTAMeKXLuZEfTpwDc/UUz6yDsejqN4onhLnGNTomhiIiIiAzK2LaRfOv8o3d5rBVHUKWmklp+uwPv\nLXA8WUM4AjiEkBi2EeoQHgrMAm7NDzKzEYQ6hAArU4dWAbNj3EMF4iYDB8bnWZl/vBEpMRQRERER\nkUxz99uA24odN7NjgF8A6919/9TjbwLnAnPNbIG7d+aFngqMIZSluC/1+O2ExHCemV3j7vkLac+O\n7dPu3hQjhipXISIiIiIija7grqPuvgpYRkj+lpjZHskxM5tJ2OUU4Kq85O8u4BlgKnCjmQ1PxR0H\nXE4YLfxyNW+injI9Ymhm7wK+CJwIvIvw4v8BeAD4trs/XyRuAnAxcDJhLvJ2wg92MXCTu79VJG5f\n4NL4fFOArcATwC3ufkfVbkxERERERIbKfML6wtnAC2b2OCFRbI/HF7v7DekAd3/LzE6JcWcBc+La\nxUmEqak9wLXufu+Q3MEQyOyIoZmdSEjmvgDsBfyKkKTtA5wP/MbMji8QNw14EvgSocbJ8hj3AWAh\ncI+ZjSwQ105YOHoOMBp4GHDgGMKnC33mJIuIiIiISCYUrTXo7i8BhwFfBV4EjgLeTcgTTnf3zxWJ\ne46wXvF6YDMhL9iXMOV0trtfUc0bqLdMjhia2d7ADwgJ2i3ABe6+NR6bFI99CPi+me3n7tvisRyw\niJA83k34QSfH9gceJIwGXgZcnXq+UcCdwFhC8niBu++Ix94f4+ab2fI4v1lkp8fWbmDpinU7d2Yr\nR7IrW/v0yTW8MhEREZHW4O6/pMSgl7t3AVfEr0rO+zKwIH41tayOGM4nDO86cE6SFMLOH06y2HMi\ncGwq7njgSGATcEaSFMa454Hz4rcXxVooiXmEKadrgQVJUhjj1hDmEANcOeg7k6azdMU6Nmx6nc6u\n7rK/Nmx6naUr1tX70kVEREREgIyOGAJ/JOw69Kt0kpZw99/F2iLjgHemDp0W2x+6e5+9kd39fjPb\nSEgoTyKMPKbjFrl7oWHoJYSRxP3MrN3dHxvITUlzKlXDqZDNXd30AC9vep0LFz7a53i67tPmru5q\nXqqIiIiISEGZTAzd/fvA94sdN7Nh9F77n1KHDo/tqhKnXw3MIdQrSRLDdsK85IJx7r7FzJ4FDo5x\nSgylj0I1nAq57ObVbNj0Oj1AZ5mJ36iRmfyrKiIiIiJNolHfbX6aULCyi1hQMiaLBxESvFJz9NbH\ndkaMmxLP1V/cOkJiOGMQ1y3C3FlTS65JTI8YQu96RBERERGRWmmYxNDMRhB2AfobQkmJN4Cz3P21\n2KUNGEFI8F4tcaqNsZ0Q2/GpY5XEiQxI+/TJJTedGTcuLH/t6OgzG1pEREREpCYaIjE0s/X0riV8\nkzDN9Ovu7qlubak/b6O45FhbXltpnIiIiIiISFNoiMQQWAa8A9gbeB9hF9HxZnaxu/+uQP/cAJ8n\nR4kaKIORjALV0vDhw4bsueoli/eYTP0clstV5bqyeI+1MBT3We2fTaVa5WcpIiIija8hEkN3//vk\nz2a2F3AVcC4wy8wOc/f1hPWGiVElTrd7bLfENj+u2Py9/DgRERERERlCZvYu4IuE2uTvIgzq/AF4\nAPh2LFFXKG4CcDFwMqFM3XbgGWAxcJO7v1Ukbl/CMrYTgSnAVuAJ4BZ3v6NqN5YBDZEYprn7K8D5\nZvYe4Bjgy8DfERK2bsI6w0nAs0VOsXdskzWDr6SOTaJ3c5r+4ioyFOvFWmFtWhbvMdkkZkdPT1Wu\nK4v3WAtDcZ/V/tlUqlV+lkNh4sQx9b4EERGpMzM7EfgRMJpQt/xXwG7AIcD5wHwzm+vuP8+LmwY8\nAuwDvAYsJwz6HEmoajDHzD7h7t15ce2EhHMs8BLwMGF/kmOAD5vZ8e5+Vm3uduhltcB9OR6M7ZEA\nsf7gWsJ00Gkl4g6I7VMx7kWgIz5WdpyIiIiIiAwNM9ubUGpuNHALsK+7H+fuRxHewy8H9gC+b2aj\nUnE5YBEhKbwb2MfdP+ruxwDvJlQeOBG4LO/5RgF3EpLChcAUd5/t7kcAHyQkmPPN7HM1vO0hlcnE\n0Mx+ama/NbO/KdFtXGx3pB5bGdtZRc47glCHMN0XQv3CXIm4ycCBhKHqlYX6iIiIiIhIzcwHxgAO\nnOPuW5MD7v4ycHb8diJwbCrueMJA0ibgDHfflop7HjgvfnuRmaU3BZhHmHK6Fljg7jtScWuAy+O3\nVw76zjIik4khYc7vNOCMQgdj5v/h+G16BO/22M41sz0LhJ5K+IV6FbivQNw8Mys0vTb5RXva3TVi\nKCIiIiIytP4I3AZ8K52kJeKGlB2EwZ53pg6dFtsfunuftR3ufj9hqdgewEkF4hbFmYn5lhAGqPaL\nU04bXlYTw3+N7Qlm9jUzG5kciMO63wAOI4zg3ZIcc/dVhB1MxwBLzGyPVNxM4Lr47VXunq4ufhdh\n8elU4MZ0cmhmxxE+EeghrGcUEREREZEh5O7fd/f57n5zoeNmNoze/VP+lDp0eGxXlTj96tgekXqs\nnfD+v2Ccu28h7GmSy4trWJncfMbdl5nZpcC1wCXA35rZk4RdQ99DWPS5A/iKu/8yL3w+YXHpbOAF\nM3uckCgmmfxid78h7/neMrNTYtxZhAWoTxI2ozmU8EtxrbvfW+17FRERERGRQfs0od54F3HpV0wW\nDyK8l19XInZ9bGfEuCnxXP3FrQMOTuIaXSYTQwB3/7qZ/Rz4AmHt39GEjHwDYVTwu+7eZ72fu79k\nZocREspPAUcRditdDtxcbFtZd3/OzA4hjA5+jLDbUBdhyul33H1ZlW9RREREJHMeW7uBpSvWsa17\ne799N3d199tHpFbi/iH7An9DKCnxBnCWu78Wu7QRKhb0EJaSFZNUHZgQ2/GpY5XENbTMJoawc2Hn\n/AHEdQFXxK9K4l4GFsQvERERkZazdMU6NmyqrMzOqJGZfkspTcjM1tO7lvBN4PvA193dU93aUn/e\nRnHJsba8ttK4hqa/xSIiIgOQ1KlsZcOHh60K9FoEzfJ6dL8Z6nzncjCubbd+++++23A+8xHrc9/N\n8npUy1C8HsNyuZ1tC7zuy4B3EGqNv4+wi+h4M7s4bkSTLzfA58kRRhybnhJDEREREeljXNtu3Hr5\nCfW+DJGC3P3vkz+b2V7AVcC5wCwzO8zd1xOWhSVGUdzusd0S2/y4YkPo+XENTYmhiDS9zV3dXLjw\n0aqfd9TI4cydNZX26ZOrfm7Jvo6OyqbaNaNkREKvRdAsr8eOnp6d7WDupVlej2oZitejWj+7oTBx\n4piqncvdXwHON7P3EPYJ+TLwd4SErZuwznASYRfRQvaObbJm8JXUsUn0bk7TX1xDU2IoLaOSxfSV\n0ML77Bo1cjiddNMDdNbg59RJN0tXrFNiKCKZpw1lpEU8SEgMjwRw9x4zWwvMJNRIf6RI3AGxfSrG\nvWhmHcCeMW59OXGNTomhtIyBLKavhBbeZ8/cWVNr8mEAhDdOPVCTc4uIVJs2lJFGZ2Y/Bd5NqEe+\npEi3cbHdkXpsJSExnAXcWuC8I+itQ5iueLCKUP5uFvBQgbjJwIGE9Yd9KiU0Iv2Nl5aRvIHPAWPb\nRlb13MmUQsmW9umTazaad+HCR2syCikiUguV/h+o/9ckg7YTRu/OAPokhmaWAz4cv02P4N1OWHs4\n18wWuHtnXuiphJrnrxLK1KXjZgPzzOwad8//JPjs2D7t7hoxFGlEY9tG8q3zj673ZYiIiAw5/R8o\nDexfgU8AJ5jZ14CvuHs3gJmNAq4BDiOM4N2SBLn7KjNbBpwILDGz09z9LzFuJnBd7HpVXvJ3F3AZ\n8B7gRjM7JzluZscRap/3ENYzNgUlhiIiIiIikmnuvszMLgWuBS4B/tbMniTsGvoeQlH6HYSE8Zd5\n4fMJ6wtnAy+Y2eOEUcL2eHyxu9+Q93xvmdkpMe4sYE58vknAoYSk8Fp3v7fa91ovSgwlU/pbHJ/U\n50l23dKukCIiIiKtwd2/bmY/B75AWPt3NGGG9AZCXcPvunuf9X7u/pKZHUZIKD8FHEXYrXQ5FGeU\nvwAAIABJREFUcLO731Hk+Z4zs0MIo4MfI2xs00WYcvodd19W5VusKyWGkimVLo7XrpAiIiIiuyq3\nTFMjfsDu7msII4CVxnUBV8SvSuJeBhbEr6aW6cQw7vZzITAHSFZA/x64H/imu/+pQMwE4GLgZGA/\nwkLVZ4DFwE3u/laR59oXuJQw/3gKsBV4Aril2KcIUn39LY5PjxhqV0gRERGRXpWWadIH7JKW2cTQ\nzA4H7gEmEIpTPg68jTCndwFwhpkd5+6/ScUk9Un2AV4jDA/vTqhlcjhhbvAnkoWqqbh24AFgLPAS\n8DBhnvIxwIfN7Hh3P6t2dyv5ii2OTxeH1a6QIiIiIr0qKdOkD9glXyYTQzMbA9xNSAp/Cpzp7q/F\nY+8A/h34ILCIUJck2aJ2ESEpvBs43d23xWP7EwpenkjYXejq1HONAu4kJIULgQvcfUc89v4YN9/M\nlrv7bbW9cxERERGRgamkTJM+YJd8w+p9AUV8FngHoZ7IZ5OkEMDd/0yoRQJwcNxmFuB4wsjgJuCM\nJCmMMc8D58VvLzKz0annmkeYcroWWJAkhTFuDWGxKcCV1bk1ERERERGRbMlqYthJqB1yQ1JnJM9v\nCGsHc8CB8bHTYvtDd++ze4m73w9sBPYATkodSuIWuXtPgedaQtj6dr845VRERERERKSpZDIxdPc7\n3f0z7n5VkS7D6J0Gm0yMPjy2q0qcenVsj0g91k6oQ1Iwzt23AM8SktAjCvURERERERFpZJlcY1iG\nj8R2O7DazIYBBxESvHUl4tbHdgaAmU0B2sqIWwccnMSJiIiIiMjQU9WC2mm4xDBuFvPP8dvbYsHK\nscAIQoL3aonwjbGdENvxqWOVxLW0/orQ52vEGjkiIiJZV8n/x5u1yYg0AVUtqK2GSgzNbARhzd8M\n4AV6N4ZpS3Xblh9X4FhbXltpXF309x9AusYf1C4hUxF6ERGR+qv0/2MI7w1EGpGqFtRew/zrEHcS\n/RHhh9cJzHH3lwt0zQ3wKXKEEceaSOrvDcZPVq6vOCH7ycr1fOTIqf13rkD3m2G0PZeDcW27lezb\n0fUGPT0hppzXIEluh+VyBfsPHx6WxY4bN7rfvpWeOyvS99jMGv0+y/l9avR7FJFsSz4ozhHq//Yn\n+cBYpEHlVy3YuUGlu//ZzM4ljCAebGYz46hhyaoFZnYe8DNC1YJvpDawzK9a0JOKW2NmlwPfJVQt\nUGI4lMxsL8KwcTvwCnBieogY6Er9eVSJU+0e2y1F4oplXflxdbH1jfgfQJGELL5PpaenNyFLYmph\nXNtu3Hr5CSX7nPXVB3ltyxs1uwYREZFWN7ZtJN86/+h6X4ZIrSVVC7yfqgXDCVULfkMZVQvMbCMw\nkVC14AfxUDlVCxYSqxa4+2MDvKdMyXxiaGZTgWXAAcBvgY+7++/yum0BugnrDCcRdhEtZO/YJmsG\nX0kdm0Tv5jT9xVWso6OyqR6FJFNEx+4xkv/nvKP6HE9GJTo6Xt9ZtHRHT09VnrvQdZRz7kr6ltM/\nfY/VPndWpO+xmTX6fZbz+9To95glEyeOqfcliIhIHbn7nYTpncUMpmrBHEL1gSQx7LdqgZk9S9ic\n8ghAiWGtxV2HHiIM5a4mJIWv5fdz9x4zW0uYT5wsMC3kgNg+FeNeNLMOYM8Yt76cOBGRSmnTJhER\nkZpS1YJBymQdQ9i5wPQ+QlL4IHBcoaQwZWVsZxU53wh66xCuTB1aRZieXyxuMmE4uicvTkSkbMkm\nEZ1d3WV9bdj0OktXlPr/SERERKBw1QJCcjciPqaqBWXIbGII/Ath69nHgE+mF4sWcXts55rZngWO\nnwqMIfyA7ysQN8/MCo2gnh3bp91dI4YiMiDpTSL2bBtZ8iuXFyMiIiKFtXrVgmrK5FRSMzsCOJOw\nbvB0d9/aX4y7rzKzZYRdS5eY2WnJwlQzmwlcF7te5e7pd1t3EbaofQ9wo5mdkxw3s+MIv1w9wJer\ncnMi0tLK2SQiWSMs2abdZrXzbr6hfj2yvtu2fj92lbXXI+u/P+Vo9KoFWZPJxBD4Ymw3A980s1J9\nH3L36+Of5xPWF84GXjCzxwmjhO3x+GJ3vyEd7O5vmdkpMe4sQpHLJwmb0RxK+GW41t3vHexNiUjz\n2dzVzYULHy14LF1bVMWlRUREqkdVC6ovq4nhREJCNoGwS1AhSQa/KXnA3V8ys8OAS4BPAUcRRh2X\nAze7+x2FTuTuz5nZIYTRwY8BxxB+Ke4DvuPuy6pxU1lV6aYYeoMrEjaH6aSbHqhodE/FpZuHdpvV\nzrv5hvr1yPpu2/r92FXWXo96//4MZrfpZqlakDWZfIfi7scOIrYLuCJ+VRL3MrAgfg2JrCRkyaYY\nldIbXGllc2dN7ffvb3rEEFRcWkREZLBUtaB29M6+jrKSkKU3xRjbNrLsa9AbXGll7dMn91tKImuf\nDouIiDSyAlULPtHPBpUrCYnhLODWAucrVbVgdox7qEBcU1YtUGJYR1lLyMrZFENEREREpE4GUrXg\nXELVggXu3pl3vFTVgtmEqgXX5G1cCU1atUCJYQbUMiErtTFGuo+IiIiISFapakHtKTFsUgPZGENr\nBkVEROonK3sPiGSUqhbUmDKBJlXOxhhptV4zWM7IZdJPRESkFWVl7wGRjFLVghrTvyRNqpyNMYbC\nUGzpr6RTRESaQdb2HhDJklapWlBPSgylpioduYTy/5NTHTkREWlG2gxOROpB75Clpmo5clnLpFNE\nREREpJUoMZSGlZXpsiIiIiIija4hEkMzm0soSjkOuNrdry7RdwJwMXAyofjlduAZYDFwk7u/VSRu\nX+BSwna2U4CtwBPALcUWpYqIiIiIiDSDYfW+gFLMbJSZ3QD8iJAUQthpqFj/acCTwJeAyYTdhp4A\nPgAsBO4xsz6ruc2sHXgKOAcYDTwMOGH3oSVmdmu17klERERERCRrMjtiaGbTCcUlDwZWA2OBGSX6\n54BFwD7A3YTCl9visf2BBwmjgZcBV6fiRgF3xvMvBC5w9x3x2Ptj3HwzW+7ut1X5NkVERKSJVVKb\nULtni0g9ZTYxJNQaORj4F8LU0J/30/944EhC3ZIzkqQQwN2fN7PzgJ8BF5nZN9w9KRQ0jzDldC2w\nwN17UnFrzOxy4LvAlYASQxERESnbQGoTavdskf5pqVn1ZXkqaQfwaXe/0N3L2XbytNj+MJX07eTu\n9wMbgT2AkwrELUonhSlLgB3AfnHKqYiIiEhZ0rUJ92wb2e/X5PGjtXu2SAlaalY7Wf5I6qIyE8LE\n4bFdVaLPamAOcATwg/hYO+GXqWCcu28xs2cJo5dHAI9VcE0iIiIiqk0oUgVaalZbmR0xrCQpNLNh\nwEGEBG9dia7rYzsjxk0B2uJjpeKSY0V/8UREREREpKbSS80+BLzST/+SS82A8+K3F5nZ6FRc/lKz\nHam4NcDl8dsrB3wnGZTlEcNKtAEjCInhqyX6bYzthNiOTx2rJE5ERERaVLENZYblcgDs6Omd1aYN\nZUSqKllqdjeAmfXXv9+lZma2EZhIWGr2g7y4UkvNFhKXmrl7U8wobKbEMLGtaK/eY215baVxIiIi\n0qK0oYxI3WipWQ01479SuUHEFV24Oljjxo3u81jyyeKwXK7g8UoNHz6s6HM1C91j82iF+xzoPVb7\n3wYRqa70hjJj23r3rCg0YgghKdSGMiKDN8RLzfqLW0dIDJtmqVmzJIZdqT+PKtFv99huKRJX7OO/\n/DgRERFpcfkbyiQf5HR0VDaaKCI1oaVmFWqWxHAL0E344U8Cni3Sb+/YJj/I9ILVSfR+YtBfXMUK\n/SeRfKK4o6enKv+JtMJ/SLrH5tEK9znQe6z2vw3NYOLEMfW+BBERaSxaalahzO5KWom4KHQtYVbH\ntBJdD4jtUzHuRcIiViqJExERERGRhjGYpWYto1lGDAFWAjOBWUCfgpNmNoKwODTpm1gFzI5xDxWI\nmwwcSBiGXpl/XEREWpPWf7bGWuFCiq0DbtXXoxi9HrvK2uvRAuvZtdSsQs2UGN4OnAvMNbMF7t6Z\nd/xUYAxhrvB9eXGzgXlmdk2BRa1nx/Zpd9eIoYgMmc1d3Vy48NF++yUbW7RPnzwEVyUiItIQMr/U\nLGsaMTEsOKTr7qvMbBlwIrDEzE5z978AmNlM4LrY9aq85O8u4DLgPcCNZnZOctzMjiMUsOwBvlyT\nuxERyTNq5HA66aYH6CyjBlon3SxdsU6J4RDT+s/WWCtcSLF1wK36ehSj12NXWXs96r2evdZrx929\nx8zWEmYUTgMeKdK1z1IzM+sA9oxx68uJawaZTAzNbBJwc97DB8f2r83sfanHH3L36+Of5xN+6LOB\nF8zsccIoYXs8vtjdb0if1N3fMrNTYtxZwBwze5LwCcGhhKTwWne/txr3JiLSn7mzphYsnl3I5q6Q\nQJbTV0SKK1a0vhAVrRdpGFpqVoFMJobAaOBk+tYV7CHUIzmI3rqDm5KD7v6SmR0GXAJ8CjiKMIS8\nHLjZ3e8o9GTu/pyZHUIYHfwYcAxhfvF9wHfcfVn1bk1EpLT26ZPLHv27cOGjZY0qikhpKlov0pS0\n1KwCmfwXzd3XM8AdU929C7giflUS9zKwIH6JiIhICylWtL4YFa0XyRQtNauCTCaGIiIiIvWQX7Re\nRLJBS81qT4mhiIiIiIhknZaa1ZgSQxERERERyTQtNas9JYYiIiLSlLTTqIhI+ZQYioiISFPSTqMi\nIuXTv34iIiLSlLTTqIhI+ZQYioiISFPTTqMixW3u6ubChY/22y/54KTcOrvSeJQYioiISMPQukGR\n6hg1cjiddNMDdJbxd6WTbpauWKfEsIkpMRQREZGGoXWDItUxd9bUij5k6YGy+krj0r+UIiIiUlcD\nGQXUukGRwWmfPrns0b8LFz5a1qiiNDYlhpGZ7QFcAHwaOIBQJ+W3wF3At919ax0vT0SkpHLXiIDW\niUj2DGQUcNL40Xzt746o0RWJSFbpPXvtKDEEzGwv4GHgYOAvwK/joSOBrwKfNrP/6e4ddbpEEZGC\nKl0jAlonItmj3UNFpBx6z15bSgyD6wm/YCuBT7j7JgAzmwjcCxwGfAuYX7crFBEpoJI1IqB1IpJt\n2j1URPqh9+w11PKJoZkdCJwGvAl8NvkFA3D3jWb2OeAZ4Awzu8rdf1+nSxUR6aOSNSJQ2TqRStZ9\nJTRNVaDy3x3tHioi/dF79tpr+cSQ8AsG8LC7/zH/oLuvNbPHgQ/Gvl8fyosTEamFctYkDmSjAU1T\nFRjYmkHQ7qEiUpLes9eY/gWGw2O7qkSf1YRfMq1yF5GGNpA1iQB7lrHuK5mm+vKm11UsucVVumYQ\ntG5QpBGUu9FZjf5913v2GlNiCNNju65En/WxnVHbSxERqa1K1yRW8p/7ZTevZsOm1ysqlvyvP36G\nOx76bb99v3/1x8q53KY1kGm9tVDpmz2tGRRpDpV+qNhJN/f/+g/VTgz1nr3GlBjC+Ni+WqLPxthO\nqPG1iIjUVKVrEitRSdKZfmOh2lj9G+jUzGrLT+aH5XIA7Ojp2aWf1gyKNJeBfKj40cPfWe3L0Hv2\nGlNiCG0QNukr0Wdbqq+IiBRQSdKZlRGwaqvVfVVa1L0WBpLMa82gSHOo5YeKFdB79hrTv9i9crU8\n+cSJY/o8VqupUYWeq9noHptHK9xnK9xjpT4+cQwf/9AB9b6MQSn0c22G+5LB09/5Xen12JVej0Gr\n6Xv2Vjas3heQAV2EX7BRJfrsHtsttb8cERERERHJo/fsNabEEF6J7aQSffaO7cYSfUREREREpDb0\nnr3GNJU0FMI8AJhWok8yL+ipCs+toW4Rkeaif9dFROqjlu/ZBY0YAqyM7awSfZJjK0v0ERERERGR\n2tB79hpTYgh3AjuAo82sz44BZnYU8G7gTeCuIb42ERERERHRe/aaa/nE0N1fAG4hvBa3m9nE5JiZ\nvQu4NX77HXd/uQ6XKCIiIiLS0vSevfZyPXlFaVuRme0BLAP+CtgK/JrwS3ckMAL4OTDH3d+o20WK\niIiIiLQwvWevLSWGkZmNABYAnwEOIhTQfAZYDPyru++o4+WJiIiIiLQ8vWevHSWGIiIiIiIiLa7l\n1xiKiIiIiIi0OiWGIiIiIiIiLU6JoYiIiIiISItTYigiIiIiItLilBiKiIiIiIi0OCWGIiIiIiIi\nLU6JoYiIiIiISItTYigiIiIiItLilBiKiIiIiIi0OCWGIiIiIiIiLU6JoYiIiIiISItTYigiIiIi\nItLilBiKiIiIiIi0OCWGIiIiIiIiLU6JoYiIiIiISIsbXu8LKMTMHgE+VGb329x9fip2AnAxcDKw\nH7AdeAZYDNzk7m8Vec59gUuBE4EpwFbgCeAWd79jQDciIiIiIiJVY2aTgQuBOcDU+PDvgfuBb7r7\nn1J9zwS+V8Zpf+TupxR4rgHlB2Y2A7gEOBaYDGwBfg1c7+73l3E9dZHr6emp9zX0YWb/BLynn24z\nCb8MC939H2LcNOARYB/gNeBxYHfgSEISvAz4hLt35z1fO/AAMBZ4CXgSGA98EMgB/+buZ1Xj3kRE\nREREpHJmdjhwDzCBkGw9CbwNOBQYTXj/f5y7/yb2P5OQGL4CPFri1Kvc/Zt5zzWg/MDMTgJ+BIwA\n1gNrgf9ByF0ArnL3f6zszodGJhPD/pjZnsB/AWOAGe6+3sxyhB/4kcDdwOnuvi323x94kJBIXu3u\nV6fONQp4ljC6uBC4wN13xGPvj3FvB+a7+21Dc4ciIiIiIpIwszHAc8A7gJ8CZ7r7a/HYO4B/JyRt\nT7n7zPj4mYTE8BF3P66C5xpQfmBmk+jNUS5192+kjp0I/BgYCRzr7r+s/FWorUZdY3gtMBH4hruv\nj48dT0gKNwFnJEkhgLs/D5wXv73IzEanzjWP8ENfCyxIfugxbg1wefz2yurfhoiIiIiIlOGzhKTw\nVeCzSVII4O5/Bs6N3x5sZjMLxFdioPnBAsII48/TSWGMWwZ8O377lUFeX000XGIYs/T/E/gD8LXU\nodNi+0N3fz0/Ls7n3QjsAZxUIG6RuxcaPl0C7AD2i0PKIiIiIiIytDqBu4Ab3P0vBY7/hrC3SA44\ncJDPNdD8IIkrtq4xGV38kJntPchrrLqGSwyBGwg/8C+lRwWBw2O7qkTs6tgekXqsHegpFufuWwhD\nybm8OBERERERGQLufqe7f8bdryrSZRi9G2tuH+TTVZwfxA0w9y8VR5gKuyVea+YGnDK5K2kxZjaX\nkACucfcfpB4fBhxE+EGsK3GK9bGdEeOmAG1lxK0DDk7iREREREQkUz4S2+30DgbtFNchfoawCcxu\nwB+Bn7r78rx+A80PpifP7+5/LBTg7j1mth54b4z7SX83NZQaJjGMm8skO/jkz8ttI+z800OYd1zM\nxthOiO341LFK4kREREREJAPiZjH/HL+9zd1fyusyE/hvYFTe4/+Xmf2UsGYxmZ460Pwgievo53Iz\nm1c00lTSjxJKWDzn7vfmHWtL/XkbxSXH2vLaSuNERERERKTOzGwEYc3fDOAFejeGgTBoBLAn8L+B\nQwijhfsAFxHe48+hd+0fDD6vKBVTKC4zGmbEkLDLD4QtY0vJDfD8OXp/eaql8WqBiIhk30D/na8G\n/bsuIlJ9A/p3PVYa+BGhAH0nMMfdX051+SXw18Cf3H1l6vE/A9eZWQfw/wKfMrMPuvvjBa6r0n/3\n6/l/1KA0RGJoZvsAJwBvAncW6NKV+nP+EHHa7rHdUiSuz26mReJERERERKROzGwvQrH7dkIB+xOT\nwvaJWNZufYnT/BvwZcKmMZ8AHmfg+UESVyoXKRSXGY0ylfQzhOz7IXffVOD4FqA7/nlSifMk28Im\nc3tfSR2rJE5EREREROrAzKYSdv5sB34L/JW7P1HpeWIpiv+I3+4X24HmB0nc2+PGmOXGZUZDjBgC\nH4/tg4UOxh1+1hIWlk4DHilyngNi+1SMezEOIe8Z49aXEzcQGzdm7kOBzBs3bjQAHR3FPqiRUvT6\nDY5ev8Gr5ms4ceKYQZ+j2prx3/VW+L3XPTa+Zr8/aI17HOi/62Y2GXiIkMitBj6eLnY/AMnUzx0w\nqPzgmdgOA6YSNrvJv/a30ZuADjivqJXMJ4Zx7vBfEeb3/rJE15WExHAWcGuB84ygtw5heo7xKmB2\njHuoQNxkQpHMnrw4ERGRIfHY2g0sXbGObd39l+YaNXI4c2dNpX365CG4MhGRoWNmY4D7CMnVg8An\n8uqap/uOAD4EvBO42907i5w2KTeRLk1RcX7g7p1m9mw83ywKJIbABwhTSd8EHit2n/XSCFNJ30tv\nKYpnS/S7PbZzzWzPAsdPBcYQtp29r0DcPDMrlCifHdun3T1zmb2IiDS/pSvWsWHT63R2dff7tWHT\n6yxdUar0lohIw/oX4FBCUvXJYklhtAP4PmHA6MxCHczsSEIi10N18oMk7m+LXNPnY3t/keVxddUI\nieF7YvtiqR++u68ClhGSvyVmtkdyzMxmAtfFb69y9/RHrncRhn6nAjemf/hmdhxhy9sewsJUERGR\nIZeMFOaAPdtGFv3K5fUXEWkWZnYEIcHrBk53962l+rv7W4QdRwG+amYn5Z3vEOCO+O0D7r46dXig\n+cFC4GXgaDPbpe66mX2WkDBuB64oebN1kvmppMA7YluqwGRiPmF94WzgBTN7nJAotsfji939hnSA\nu79lZqfEuLOAOWb2JGGx6aGEH/q1BWonioiIDKmxbSP51vlHFz1+4cJH6ezqLnpcRKSBfTG2m4Fv\nmlmpvg+5+/XANYQ84ATgJ3Gq53pgMvB+wudtjwOnp4MHmh+4++YY9zPgK2Y2H1gLvAt4N2EU8zx3\n/8+BvAC11ggjhsnK1K6SvQB3fwk4DPgq8CJwFOGHsJzwycLnisQ9Ryh4eT3hl+0YYF/CkPJsd89k\nVi8iIiIi0iImEhKyCYSi9IW+To7t+wDc/Q3go4TBo18QEsITCCOBK4BzgKPcvc8A1EDzA3dfEeP+\nLT50LPB2Qr3Fv3L3WwbxGtRU5kcM3f1S4NIK+ncRhmcrSuZiMcwF8UtSKtn0ALTxgYiIiIhUl7sf\nO8C4HuC2+FVp7IDyA3dfRxhpbCiZTwyl/pJND8rVSTdLV6xTYigiIiIi0iCUGEq/0psejG0bWbLv\n5q5uetDGByIiIiIijUSJoZStv00PQBsfiIiIiIg0okbYfEZERERERERqSImhiIiIiIhIi1NiKCIi\nIiIi0uKUGIqIiIiIiLQ4JYYiIiIiIiItTomhiIiIiIhIi1NiKCIiIiIi0uKUGIqIiIiIiLQ4Fbhv\nUY+t3cDSFevY1r29376bVbBeRERERKSpKTFsUUtXrGPDptcrihk1Ur8uIiIiIiLNSO/0W1QyUpgD\nxraN7Lf/qJHDmTtrao2vSkRERERE6iHziaGZvQ+4GPgwMB74M/AL4Fvu/lSB/hNi/5OB/YDtwDPA\nYuAmd3+ryPPsC1wKnAhMAbYCTwC3uPsdVb2pDBnbNpJvnX90vS9DRERERETqKNObz5jZ3wC/Bv4a\n2EhICHcHzgQeN7OP5PWfBjwJfAmYDCwnJHcfABYC95hZn+ExM2sHngLOAUYDDwMOHAMsMbNba3B7\nIiIiIiIimZDZEUMzey/wPeAN4GR3vz8+PhK4Cfgc8D0z29/du80sBywC9gHuBk53920xZn/gQcJo\n4GXA1annGQXcCYwlJI8XuPuOeOz9MW6+mS1399tqf+fNYXNXNxcufLTffskU1fbpk4fgqkRERERE\npJAsjxheA4wALkmSQgB37wa+ADxASAD3ioeOB44ENgFnJElhjHkeOC9+e5GZjU49zzzClNO1wIIk\nKYxxa4DL47dXVu3OmliyQU0P0NnV3e/Xhk2vs3TFuvpetIiIiIhIi8vkiKGZ7QV8HHgNuCX/uLt3\nAR/Ne/i02P7Q3ftst+nu95vZRmAicBLwg7y4Re7eU+BylhBGEvczs3Z3f6zS+2klc2dNragMRg+U\n1VdERERERGonk4khcAJhNPMhd3+zzJjDY7uqRJ/VwBzgCHoTw3bCAFfBOHffYmbPAgfHOCWGJbRP\nn1z2tNALFz5Kp2okioiIiIjUXVYTw/fG9lkAM/swYZfR/wF0Ejah+f/cfXs8Pgw4iJDglZqXuD62\nM2LcFKCtjLh1hMRwRsV3IiIiIiIiknFZXWOYFMx7zcx+QNgl9ALgVODzwO3Ab8ws6ddGWI8I8GqJ\n826M7YTYjk8dqyRORERERESkaWQ1MRwb238gbChzBmFt4B7AXOAPwHTgp2Y2gpAYJrZRXHKsLa+t\nNE5ERERERKRpZHUq6W6xnQIc6u5rU8d+bGbrCPUJZxA2j3kodTw3wOfMEaaU1sS4caP77zSEhuVy\nO9t6XVt/1zB8ePjcImuvXaPQ6zc4ev0GT6+hiIhI48jqiOHW2D6SlxQC4O7/SVhnCPAxoCt1eFSJ\n8+4e2y2xHWiciIiIiIhI08jqiGGy3u+PJfo8BxwHvCvuHNpNWGc4ibhpTQF7xzZZM/hK6tgkejen\n6S+uYh0dfSpo1NWOnp6dbb2urb9rSEYZsvbaNQq9foOj12/wqvkaTpw4ZtDnEBERkeKyOmKYJHZ7\nl+iTX+dgLWE66LQSMQfE9ikAd38R6IiPlR0nIiIiIiLSTLI6YvhobA82s1yRwvP7x/YPsV0JzARm\nAbfmd46b1ByR6ptYBcyOcQ8ViJsMHEhYf7gy/7iIiIiIiAyN+N78QkJt8qRCwe+B+4FvuvufCsRM\nAC4mlL/bD9gOPAMsBm5y97eKPNe+wKXAiYS9T7YS9jm5xd3vKHGNM4BLgGOByYTlaL8Grnf3+yu7\n46GT1RHDXxGmkb6TsAvpLuIvxAnx2ySZuz22c81szwLnPBUYQ5imel/q8SRunpkVSpTPju3T7q4R\nQxERERGROjCzw4GngS8RErXHgTXAvsAC4D/NbGZezDTgyRgzGVhOSO4+ACwE7jGzkQVZv7aUAAAg\nAElEQVSeq50wW/AcYDShfJ4DxwBLzKzPQFSMOymefx4hAf05Ia/5GPAzM7ty4K9AbWUyMYwjhFfH\nbxea2fuSY2Y2FriFsCHMn4iJnbuvApYRkr8lZrZHKmYmcF389ip33556ursInxhMBW5MJ4dmdhxw\nOWG08MvVvEcRERERESmPmY0B7ibUFf8psJ+7H+PuRxNm9z0OvB1YlIrJxe/3ibH7uPtH3f0Y4N3A\nOsJo4GV5zzUKuJNQQm8hMMXdZ7v7EcAHgdeA+Wb2uby4ScD3CbMyL3H3/d394+7+PkJi2A1cZWbH\nVPGlqZpMJoYA7v494EbCOsPHzWyVmT1AmDp6EtAJnOruW1Nh84H/IkwNfcHMHjCzXxE+SdgLWOzu\nN+Q9z1vAKcDLwFkx7n4zW0PI8HcDrnX3e2t4uyIiIiIiUtxngXcQZv991t1fSw64+5+Bc+O3B6dG\nDY8n1ETfBJzh7ttSMc8D58VvLzKzdG2leYQpp2uBBe6+IxW3hjBwBJA/+reAkEz+3N2/kT7g7suA\nb8dvv1LmPQ+pzCaGAO5+HvB/EEpTHERYB/gK8F3gEHf/VV7/l4DDgK8CLwJHET4NWA6c7u67ZPWp\nuOeAQ4Drgc2EIeJ9CVNOZ7v7FVW/ORERERERKVcnYabfDe7+lwLHf0OYupkjjCBCqHcO8EN377NF\ndlzvtxHYgzDwRF7coiJ7nSwBdgD7xSmn+XHfK3IPt8X2Q2ZWapPNusjq5jM7uftSYGkF/buAK+JX\nJc/zMiHLX1DRBYqIiIiISE25+52E6Z3FDKM3t0mWjR0e21Ul4lYTNrI5AvhBfKydsJSsYFwslfcs\ncHCMeyxucLN/qThCub0thKVv7cBPSlzXkMv0iKGIiIiIiEgZPhLb7cBqMxtGmHHYQ1hLWMz62M4A\nMLMpQFt8rFRccmxGbKcnz+/uBWuxx9HHXZ4vS5QYioiIiIhIw4qbxfxz/Pa2uLysDRgRH3u1RPjG\n2E6I7fjUsYHEdRToWyouM5QYioiIiIhIQ4q1ypcQRuBeoHdjmLZUt235cQWOteW1A40rFVMoLjMy\nv8ZQmt/mrm4uXPhon8eH5XIA7OjpYdTI4cydNZX26ZOH+vJERAoaN250/52qJPn3cFguV/J5y+1X\nzPDh4fPioby3oaZ7bHzNfn/QGvdYDXEn0R8RSk50AnPiviH5cgN8ihxhKmqlMQ1JiaHUzaiRw+mk\nmx6gs6u7ZN9Oulm6Yp0SQxERERHBzPYC7iFs4vIKcKK7/ybVpSv151ElTrV7bLcUieuzm2k/caWe\nq1BcZigxlLqZO2sqS1esY1v39oLHk0++O7a8QQ8U7SciUg8dHcXeK1Tfjp6enW2p5y23XzHJ6MRQ\n3ttQ0z02vma/P2iNe5w4ccyAY81sKrAMOAD4LfBxd/9dXrcthILyI4BJwLNFTpeUjUjW/r2SOjaJ\n3s1iyo17u5kNS9c+7CcuM5QYSt20T59ccgQw+Udx/jUP9DuiKCIiIiLNz8wmAw8RCtCvJiSFr+X3\nc/ceM1sLzASmAY8UOeUBsX0qxr1oZh3AnjFufTlxwDOxHQZMBf67wLW/LV53Oi4ztPmMiIiIiIhk\nnpmNAe4jJFcPAscVSgpTVsZ2VpHzjSDUIUz3hVCHMFcibjJwIGH94UoAd+8kjEoWjQM+QJhK+ibw\nWInrrgslhiIiIiIi0gj+BTiUkFR90t372wH09tjONbM9Cxw/lVBs/lVCwpkfN8/MCs2wPDu2T7t7\neuQvifvbItfz+dje7+6bSl55HSgxFBERERGRTDOzI4AzCesGT3f3rf3FuPsqwlrEMcASM9sjdb6Z\nwHXx26vcPb2ZxV2EqaFTgRvTyaGZHUcoidEDfDnvKRcCLwNHm9lX8q7/s4SEcTtwRX/XXg9aYygi\nIiIiIln3xdhuBr5pZqX6PuTu18c/zyesL5wNvGBmjxMSxfZ4fLG735AOdve3zOyUGHcWMMfMniRs\nRnMoISm81t3vzYvbHON+BnzFzOYDa4F3Ae8GdgDnuft/VnjvQ0KJoYiIiIiIZN1EQkI2AZhTpE9S\nd3DnNE13f8nMDgMuAT4FHEUYdVwO3OzudxQ6kbs/Z2aHEEYHPwYcQyhJcR/wHXdfViRuRYz7v4H/\nCRwLvEaot/gNd8/c2sKEEkMREREREck0dz92ELFdhOmbFU3hdPeXgQXxq5K4dYSRxoaS2cTQzD4M\nPFxG1/9w9w/mxU4ALgZOJuxatJ0wT3gxcJO7v1XkOfcFLgVOBKYAW4EngFuKfZogIiIiIiLS6DKb\nGKZsJSwaLWaXYpZmltQp2YcwbLucsC3skcDhhDnCn3D37ry4duABYCzwEiEpHU8YNv6wmR3v7g2X\n+YuIiIiIiPSnERLDDe7+qXI6mlkOWERICu8m7Fi0LR7bn1Dv5ETgMuDqVNwo4E5CUrgQuMDdd8Rj\n749x881subvfVq0bExERERERyYJmK1dxPGFkcBNwRrq2ibs/D5wXv73IzEan4uYRppyuBRYkSWGM\nW0NYdApwZe0uXURE/n/27jxejqrO///rhiRcQgIhMQkKDIksH0AEVAhBiCziIAyLYXBBAckExy+C\nE6MoKoJhBnDUGRUJIiAqYFj0JxkFhBBAJBBZRsRhCZ8RSRwRCYGsl5BcIPf3xzmVFJ1e7+2lqvv9\nfDz6cdJd51TXqe70rU+dTURERFojDy2GtfhwTH/m7msKN7r77Wa2lDCr0dHATwvKXePufUX2O5vQ\nkjjezCZmeTYhERGRVT29zJh1X9k83UMHM2XyBCbuPq5JRyUiIlnWbi2G+8d0QZk8D8R0Uuq1iYSp\nbYuWc/fVwJOEKXAnFcsjIiLSat1Dw/3ePmBlT2/Zx5Jla5gzf1FrD1hERDIjFy2GZrYN8BFgP2A4\nYXKYuelFJc1sELAr4e9hub90i2O6Ryy3fdxnpXKLgD2TciIiIlkzZfIE5sxfxNre18rmW9XTSx9U\nzCciIp0jD4HhOOAZYOuC1880s98CU+IaI8OBIYQA76Uy+1sa09ExHZXaVks5abJqukaBukeJSOea\nuPu4qn77Zsy6j5U9vRXziYhI58hDV9JuwjIS+xOWnRgDnEZYiuIA4JextXB4qszawp0U2Ta8IK21\nnDRJLV2j1D1KRERERKR2WW4xfAI4EVju7nekXl8H/NDMFgN3EsYHngDMT+Xp6ud7dhHij7obOXJY\n5UxNNKira0OatWNLDB4c7lt87IjduGGe88q6yl2eVvSso68Pel99PbP1apbk/HX6eegvnb+B0zkU\nERHJj8wGhu6+FLixzPa7zewe4BDgGOC21ObuMrveIqarY9pTUG6T2UxLlJMmOWjvt3DQ3m+pKu+0\nC+exfPW6Bh+RiIiIiEh7yWxgWKWHCYHhBHdfbWa9hHGGYwmziBazbUyTMYMvpraNZePkNJXK1WTF\nilLxZmus7+vbkGbt2BJJK0Mtx5eHejVLf86fbKTzN3D1PIdjxowY8D5ERESktDyMMSwn6TKaLEi/\nML62U5kyO8f0MQB3fw5YEV+rupyIiIiIiEi7yGxgaGYHm9kpZrZDmWzJ0hHJTCP3x3RyiX0OYeM6\nhPenNi0gBJSlyo0DdiGMP7y/WB4REREREZG8ymxgCHwT+DHw2WIbzWxH4H3xaTK+8LqYTjGzwuUt\nAD4EjCAsS5Eek5iUO9nMinWvPS2mj7u7WgxFRERERKStZDkwvDymnzazaekNMSicQxhP+DjwUwB3\nXwDMJQR/s81sy1SZvYFvxacz3T09xeWNhFlQJwCXpYNDMzsMOIfQWvjlutVOREREREQkIzI7+Yy7\nX2Vmk4FTgCvN7MvAU8BIYD/CsT8NfMDd16eKTgXuAY4CnjWzhwmB4sS4/Vp3v7TgvV43sw/GctOA\nY8zsUcJkNPsQgsKL3P3WRtRVRERERESklbLcYoi7nwpMAX4FDAMOJ4wr/B1wNrCPuz9TUOZ5YF/g\nQuA54EBgN+Be4CR3/3iJ93oK2Au4BFgFHAzsQOhyepS7n1vn6omIiIiIiGRCZlsME+7+C+AXNZbp\nAc6Nj1rKvQBMjw8REREREZGOkOkWQxEREREREWk8BYYiIiIiIiIdToGhiIiIiIhIh1NgKCIiIiIi\n0uEUGIqIiIiIiHQ4BYYiIiIiIiIdToGhiIiIiIhIh8v8OoYi/bGqp5cZs+6rmK976GCmTJ7AxN3H\nNeGoRERERKQezGwKcBUwEjjf3c8vkudU4IdV7O7n7v7BIuV3AL4IHAFsD7wC/B640t2vL3NsewBn\nA4cC44DVwIPAJe5+exXH0xIKDKWtdA8dzEp66QNW9vRWzL+SXubMX6TAUERERCQHzKwb+E/g9NTL\nfRWKvQiUazF4sMj7TATuALYCngfuBkYBBwOHmNnh7j6tSLmjgZ8DQ4DFwJ3AW4AjgSPNbKa7/2uF\n420JBYbSVqZMnsCc+YtY2/taxbyrekIAWU1eEREREWktM9sduBHYE3iAELTtUUXRx939+Brepxu4\nIe5/FvAZd18ft70TmAdMNbN73f3qVLmxwE8IMdbZ7v7N1LYjgF8AM83sN+7+m2qPp1kUGEpbmbj7\nuKpb/2bMuq+qVkURERERyYSzCUHhxcDnCa1xjXAyMB5YCEx39w0tku7+iJmdA3wPOA+4OlVuOiGY\nnJcOCmO5uWb2HeALwFeBwxp07P2myWdERERERCQPVgAnuPsMd29kl68Px/SadFCYMhtYD4yPXU4L\ny5Ua15gEke8xs20Hfpj1pcBQRERERETy4Cx3v6kJ7zORMG5xQbGN7r4aeBLoAiYBmNlo4K3lygFP\nESaiGRTfI1PUlVRERERERDJvIK2EZvZm4ERgb2Bz4C/Aze5+b0G+7YHhhABvUZldLiJ0a03GOO4e\n09fc/S8ljr/PzBYDb4/lftmfujRKrgJDM9uMMND0XcCf3X1CkTyjCX2OjyX0DX4NeAK4Frjc3V8v\nse9+TUcrIiIiIiKZtjfwJ6C74PXPmdnNwEfd/eX42qjU9pfK7HNpTEcXlFtR4VgKy2VG3rqSziAE\nhVBkWloz2wl4lDCocxxwLyG4exdhRqFbzGxokXITgccI094OI0xH64TpaGeb2VV1r4mIiIiIiDRS\nEi9sDfwY2IvQWrgdcBawFjiGN04gMzz177Vl9p1sG16QlitTrFxm5KbFMAZ9/0poyduiyPYu4BrC\nB30TcJK7r43b3kqYVvYI4EvA+aly/ZqOVkREREREMu03wEeAv7r7/anX/wZ8y8xWAD8Ajjez/dz9\n4YLyXVReI7FQV7+PtsVyExgCVxCaf79BaBEsdDhwALAMOCUJCgHc/RkzOwP4FXCWmX3T3dfEzf2d\njlZERDrYyJHDmvZeg7q6NqT1eN9S+xs8OHQkambdmk11zL92rx90Rh2bwd0XExaZL+VHwJcJk8Yc\nBzwM9KS2dwNripSDjQ1Vq2OalCvsrlqpXGbkoiupmZ0GHArcAtxWIlsyPezPUkHfBu5+O6FP75bA\n0UXK1TodrYiIiIiI5FS89v9dfDo+pi+msowtUzxZbiIZM5iU28bMysVYheUyI/MthnEGoW8SoupP\nATuXyLp/TEtNDwth4ppjCNPK/jS+VnE6WjN7kjDr0CTgoVqOX7JvVU8vM2bdVzZP99DBTJk8gYm7\nj2vSUYlI1q1YUeomcv2t7+vbkNbjfUvtL2mdaGbdmk11zL92rx90Rh3HjBnR6kNIJF0/1wO4+3Ox\ni+nWwE6UbnFMYpLHYvpETAcBEwiT3bxBnEhzfEG5zMhDi+GlhA/mHHd/tliGGJXvSuVpZRfHdI9Y\nLpmOlgrlkm17lMkjOdM9NNwX6QNW9vSWfSxZtoY588t9RUREREQkK8xsiJm918ymmtnWZbIm1/fp\nC70FhIBxcol9jwN2IVxG3g/g7ivZuLZh0XKECTG3AF4lg41NmQ4MzewE4APAA+4+q0zW4cCQ+O/+\nTCtbazlpA1MmT2DcqGFsPXxo2UdyG2ltb7+XzhERERGR5loP/AS4Cji1WAYzO4AQGPbxxuFq18X0\nZDMr1sPytJg+7u7plr+k3D+VOKZPxPR2d19W9uhbILNdSc1sG+ASoJeNJ7GUgU4rW2s5aQMTdx9X\nVdfQGbPuY2VPbxOOSERERET6YZOZQN39dTP7AXAOcKGZ/cndb0m2m9leQLJW+R3u/kCq+I2ElQze\nBlxmZqe7+2ux3GFxn32EiWvSZgH/AhxkZl919/RKCB8lBIyvAecOqLYNktnAEPgWYS3CC9z9iUqZ\nU/o7RWx/pqOtWtZmlar3DHeNkJUZufJwrorJyvnLK52/gdM5FBGRejGzsYRVCtL2jOlHzOwdqdfv\ncvdLgAsI84m8D/hlnDdkMSHGeCfh+v9h4KT0TmNQ+UHgHmAacIyZPUqYjGYfQsxwkbvfWlBuVSz3\nK+CrZjaVsPLBjsBuhFbMM9z9f/p7Hhopk11Jzex9wMeBp4B/q6JI4bSypZSaVrbWciIiIiIi0jzD\ngGMJE0kmj20IQdqu8Xmy/R0A7r4OeD8wFfg1ISB8H2FymPnA6cCB7r7JkDJ3fwrYi9CDcRVwMLAD\nocvpUe5etNXP3efHcj+KLx0aj/PnwLvd/coBnIOGylyLoZkNAy4nNLOe5u6vVlFsNaHL6RBCJP9k\niXylppUllltcZbmaZW1WqXrPcNcIWZmRKw/nqpisnL+80vkbuHqewwzNXiciIi0Q1ySsuVErLklx\nNf1Yj9zdXwCmx0ct5RYRWhpzJXOBIXA8YRrXNcD3zKxwezLOb7vYpNtHGAD6JKFpdydCs28xb5hW\ndgDT0YqIiIiIiLSNLAaGm8V0C+DtRbYnYwiHEJpp+wjB4gJCYDiZMPvQG5jZEMI6hBCnlY0WAEfF\ncncVKbfJdLTSmapZ7xC05qGIiIiI5E/mAkN3L9vUa2YHE/oIL3b3t6ZefxX4FDDFzKbHtUTSPgSM\nICxLUTgd7VGE6WgvSGYcSik1HW0mPbRwCXPmL6q4tMIqzbJZte6hg1lJ74b1DitZSS9z5i9SYCgi\nIiIiuZHJyWcqKDrrqLsvAOYSgr/ZZrZlss3M9ibMcgowsyD4uxF4gjAI9bL0WiUVpqPNpDnzF7Fk\n2ZqKC7Yn068mi7xLadWud6g1D0VEREQkr9otKphKGF94FPCsmT1MCBQnxu3Xuvul6QL9nY42q5KA\npAvYavjQsnmTLo9SXrXrHYLWPBQRERGRfMpjYFhyrUF3f97M9gXOJkxicyBhttJ7gSvc/foS5Z6K\ni1yeAxxJmI62h9Dl9LvuPre+VWi8rYYP5dtnHtTqwxARERERkRzIXWDo7r+hTBdYd+8Bzo2PWvbb\nr+loRURERERE8i6PYwxFRERERESkjhQYioiIiIiIdLiyXUnNbD1lxvRVoQvoc/fNKuYUERERERHp\nYK2Mv6oZY1h0eQgRERERERGpu5bEX2UDQ3evuaupmQ0CPg2cD2wFvNK/QxMREREREekcrYy/6jrG\n0MwOBB4Bvh0P6r+APer5HiIiIiIiIlLf+Ksuy1WY2RjgG8AphKbPp4F/cffb67F/ERERERERCRoR\nfw0oMIzNlqcD/waMJDRbfg34hrv3DmTfIiIiIiIislEj469+B4ZmNgm4FHhHfOmXwHR3//NADkhE\nRERERETeqNHxV82BoZm9Cfg6cCqh2fIZQrPlr+pxQCIiIiIiIhI0K/6qOjA0sy7gk8CFwDbAWuDf\nga+7+7p6HpSIiIiIiEgna3b8VVVgaGb7Ad8D3hVfuoUQpS6u9wGJiIiIiIh0slbEX2UDQzMbRYhK\npxGaLRcR+rHe0qgDEhERERER6UStjL8qtRj+LzCK0Gz5deDf1W1URERERESkIVoWf1UKDEfFtBs4\nDzjPzGp+E3ffrOZCgJntCHwWOALYEegD/g+4A/iOuz9Totxo4PPAscB44DXgCeBa4HJ3f71EuR2A\nL8b3254w/evvgSvd/fr+1EFERERERKRKLYu/BtWQt2sAj5qZ2RGEYO7TwJuA3xKCtO2AM4E/mNnh\nRcrtBDwKfAEYB9wby70LmAXcYmZDi5SbCDxGWBdkGHA34MDBwGwzu6o/9RAREREREemHpsZfZVsM\n3b2WwLFuzGxb4KeEAO1K4DPu/krcNjZuew/wEzMb7+5r47Yu4BpC8HgTcFJq21uBeYTWwC8B56fe\nrxu4AdiKEDx+xt3Xx23vjOWmmtm97n51g6svIiIiIiIdqFXxF9TWYthMU4ERhBa705OgEMDdXwBO\ni0/HAIemyh0OHAAsA05JgsJY7hngjPj0LDMblip3MqHL6ULC4M71qXKPAOfEp+cNuGYiIiIiIiIZ\nk9XA8C/A1cC300Fawt2fBlYQmkn/LrXpwzH9mbuvKVLudmApsCVwdJFy17h7X5HjmQ2sB8bHLqci\nIiIiIiJtI5OBobv/xN2nuvsVxbab2SA2doP9a2rT/jFdUGb3D8R0Uuq1iYSJbYqWc/fVwJOEQHRS\nsTwiIiIiIiJ5VdUC9xl0AjAc6AHuhw3B4q6EAG9RmbKLY7pHLLd93FelcouAPZNyIiIiIiLSGmY2\nBbgKGAmc7+7nl8nb1BULzGwP4GzCkLdxwGrgQeCS2IMxk3ITGJrZEGAH4GOED2gdMM3dl8csw4Eh\nhADvpTK7WhrT0TEdldpWSzmRklb19DJj1n0V83UPHcyUyROYuPu4JhyViIiISL7FSSP/k7CSQKLY\nULAk/07APYTJKZcTVizYgjAvyf7AMWZ2nLv3FpSbSFgibyvgecKKBaMIKxYcYmaHu/u0Iu93NPBz\nQlyyGLgTeAtwJHCkmc1093+tueJNkMmupIXMbDEhEHwa+AphBtF93P1nqWzDU/9eS2nJtuEFaa3l\nRDbRPTTca+kDVvb0VnwsWbaGOfPLNVSLiIiICICZ7Q48RAgKHyAM9SqXv3DFgu3c/f3ufjCwG6FH\nYLJiQbpc4YoF27v7Ue4+CdiPEGBONbOPF5QbC/yE0Ph2tru/1d3/wd3fQQgMe4GZZnbwAE5Dw+Sl\nxXAu8GZgW+AdhFlER5nZ5+NENIX6tXZHLFfyjsNAjBw5rHKmOhjU1bUhbdZ7NsrgweG+RZ7q8bEj\nduOGec4r616rmHdFzzr6+uCF5Wv43KX3V8y/xeaD+cj7jIP2fktVx5LH85clOn8Dp3MoIiJ1djZh\naNfFhK6hd1bIX3bFAjM7A/gVYcWCb6YmryxcsaAvVe4RMzsH+B5hxYL0UnbTCcHkPHf/ZvpA3H2u\nmX2HsNb6V4HDaql4M+QiMHT3Tyb/NrM3ATOBTwGTzWxfd19MGG+Y6C6zuy1iujqmheU2mc20RDmR\nTRy091uqDtzO/I9f89yLL9PXB8tXr6uYf/nqddwwz6vev4iIiEibWQGc4O43AZhZpfwVVywws6WE\nJfCOJqyVni5XbsWCWcQVC9z9oYJyPyxxPFcTAsP3mNm27v58pQo0Uy4CwzR3fxE408zeRujj+2Xg\nnwkBWy+hP+9YSjctbxvTZMzgi6ltY9k4OU2lcjVZsaJUvFlf6/v6NqTNes9GSVoZ8l6PUo49cDxz\n5i9ibW/l1sVVPb30AX978WWmXnBH2bzJuMW/P2AC0L7nr9Ha/fvXDPU8h2PGjBjwPkREJPfOcvfK\nF04bVbtiwTGElQeSwLDiigVm9iSh9XIS8FCc4Oat5coBTxFilhHxPX5ZdU2aIHeBYco8QmB4AIC7\n95nZQmBvIBlkWszOMX0slnvOzFYAW8dyi6spJzJQE3cfV/WkM1+64gGWLFuzYexiOSvpZc78RRsC\nQxEREZF2UEtQ2IIVC3aP6Wvu/pdiBWK8shh4eyyXqcAwk5PPmNnNZvZHM/tYmWwjY7o+9VoyUGty\nif0OYeM6hOlBXQsI4wtLlRsH7EL4glQeDCZSZ1MmT2DcqGFsPXxo2UcyuLaaVkgRERGRNpasWADN\nWbEgKbeiwnFldqWDrLYYvkZovTuF0If3DeIMQ4fEp+kWvOsIYw+nmNl0d19ZUPRDhKbbl4DbCsod\nBZxsZhcUuRtxWkwfd3e1GErTVdu6OGPWfRVbFEVEREQ6QLNXLBhe8Hq15TIjq4Hh94HjgPeZ2deA\nryZri8TpYy8A9iW04F2ZFHL3BWY2lzDt7Gwz+7C7vxzL7Q18K2adWRD83UiYpvZtwGVmdnqy3cwO\nA86J7/XlRlVYRETypZmzrdZ7xulS++uEmWRVx/xr9/pBZ9SxyZq5YkF/36vlMhkYxulcvwhcRJiW\n9p/M7FHCrKFvIzTVricEjL8pKD6VML7wKOBZM3uYjQM8Aa5190sL3u91M/tgLDeNsNDlo4TJaPYh\nfCEucvdb611XkUZY1dPLtAvnARsnJCommaim2rGOIiIiIjnR7BULknLl3qtYuczIZGAI4O7fMLM7\ngU8Txv4dRIjAlxDWNfyeu28y3s/dnzezfQkB5fHAgYTZSu8FrnD360u831NmthehdfBIwsQ2PYQu\np99197l1rqJI3XUPHcxKwgym1SyBkUxUo8BQpHbNnLG23jNOl9pfJ8zGqzrmX7vXDzqjjk2YbbrZ\nKxYk5bYxs0Huvr5ImWLlMiOzgSGEBSQJLYC1lusBzo2PWsq9QFiYcnqt7ymSBVMmT9iwBEbSVaxU\ni2GyBIYmqhEREZF204IVC56I6SBgAvCnwgJmthkwvqBcZmQ6MBSR2qQnqal0t1ET1YiIiEibu58Q\nGE4GrircWGHFgqNiubuKlNtkxQJ3XxnXNtwjltskMATeRehK+irwUL9q1ECZXK5CRERERERkgK6L\n6RQz27rI9nIrFkBYsaBYQ1qpFQuScv9U4ng+EdPb3X1Z2SNvAQWGIiIiIiKSZ0VnAnX3BYS5SUYQ\nVizYMtlWxYoFTxC6hF6WDg4rrFgwC3gBOMjMvpreYGYfJQSMr1HjcLdmUVdSERERERHJNDMbC1xR\n8PKeMf2Imb0j9fpd7n5J/HfTVixw91Wx3K+Ar5rZVGAhsCOwG2FVhTPc/X/6cdLXxZkAACAASURB\nVAoaTi2GIiIiIiKSdcOAY4FjUo9tCEHarvF5sn1DkOjuzxPWP78QeI6wYsFuhBULTnL3jxd7M3d/\nCtgLuARYRVixYAdCl9Oj3L1oq5+7z4/lfhRfOjQe58+Bd7v7lcXKZYFaDEVEREREJNPcfTH9bNRq\n9ooF7r6I0NKYK2oxFBERERER6XAKDEVERERERDqcAkMREREREZEOpzGGIh1uVU8vM2bdVzFf99DB\nTJk8gYm7j2vCUYmIiIhIMykwFOlQ3UMHs5Je+oCVPb0V86+klznzFykwFBEREWlDCgxFOtSUyROY\nM38Ra3tfq5h3VU8IIKvJKyIiIiL5o8BQpENN3H1c1a1/M2bdV1WrooiIiIjkU6YDQzMbB8wgLFQ5\nIb78Z+B24D/c/a9FyowGPk9Y4HI88BrwBHAtcLm7v17ivXYAvggcAWwPvAL8HrjS3a+vX61E8kvj\nEUVERETaU2ZnJTWz/YHHgS8QArWHgUeAHQiLTP6Pme1dUGYn4NFYZhxwLyG4excwC7jFzIYWea+J\nwGPA6cAw4G7AgYOB2WZ2VQOqKJIb3UPDPaRkPGKlx5Jla5gzf1FrD1pEREREqpbJwNDMRgA3AaOB\nm4Hx7n6wux8E7EIIErcBrkmV6YrPt4tlt3P397v7wcBuwCJCa+CXCt6rG7gB2IoQPG7v7ke5+yRg\nP2A5MNXMPt7AKotk2pTJExg3ahhbDx9a8dEVy2g8ooiIiEh+ZLUr6UeBNwMvAR9195eTDe7+NzP7\nFCE43NPM9nb3PwCHAwcAy4BT3H1tqswzZnYG8CvgLDP7pruviZtPJnQ5XQhMd/e+VLlHzOwc4HvA\necDVDauxSIZpPKKIiIhIe8tkiyGwErgRuDQdFKb8gTB2sIvQggjw4Zj+LBX0beDutwNLgS2Bo1Ob\nknLXpIPClNnAemB87HIqIiIiIiLSVjIZGLr7De5+orvPLJFlEBtbO5P+avvHdEGZXT8Q00mp1yYS\nhk4VLefuq4EnCUHopGJ5RERERERE8iyTgWEV/j6mrwEPmNkgYFdCgFduxovFMd0DwMy2B4bH18qV\nS7bt0Z+DFRERERERybKsjjEsKU4W8+/x6dXu/ryZbQUMIQSGL5UpvjSmo2M6KrWtlnIiMkAPLVzC\nnPmLqp6kRktgiIiIiDROrgJDMxtCGPO3B/AscE7cNDyVbW1huSLbhhektZYTkQGaM38RS5ZtMhy4\npJX0Mmf+IgWGIiIiIg2Qm8DQzIYBPycsObESOMbdXyiStavIa9XoIrQ4NsTIkcMates3GNTVtSFt\n1ns2yuDBoadz3uvRKq06f8l3cNXLvXzu0vtL5lvRsw6Ari4YOXzzsvtc0bOOvj7offX1ptVH37+B\n0zkUERHJj1wEhmb2JuAWwkQxLwJHxCUqEj2pf3eX2dUWMV1dolyp5ovCciJSwhabD2b56hDILV+9\nrmL+N4/ekllnHVo2z7QL51W1LxERERHpn8wHhmY2AZgL7Az8EfgHd3+6INtqoJcwznAsYRbRYraN\naTJm8MXUtrFsnJymUrmarVhRfZe5gVjf17chbdZ7NkrSypD3erRKq87fsQeOr3rsYPfQwRx74PiK\nx9iK77W+fwNXz3M4ZsyIAe9DNrWqp5cZs+7b8Dxp8U/+z2lsr4hI58h0YGhm44C7CAvQP0AICpcX\n5nP3PjNbCOwN7ATcU2KXO8f0sVjuOTNbAWwdyy2uppyIlDZx93G6iBTJuO6hg1lJL33Ayp7ekvk0\ntldEpHNkdrkKMxsB3EYICucBhxULClOSwUyTS+xvCBvXIUwPfFpAGF9Yqtw4YBfC+MPSA6ZERERy\nYsrkCYwbNYythw99w2ObEZuzzYjN2Xr40A0D9qudOVhERPItyy2GFwP7AA8BH3D3crOGAlwHfAqY\nYmbT3X1lwfYPASMIy1LcVlDuKOBkM7vA3Qv/Ap4W08fdXS2GIiKSe6Va9tPdf2fMuq9sa6KIiLSX\nTLYYmtkk4FTCuMGT3P2VSmXcfQFhLOIIYLaZbZna397At+LTmQXB343AE8AE4DIzG5wqdxhhSYw+\n4MsDqZOIiIiIiEhWZbXF8LMxXQX8h5mVy3uXu18S/z2VML7wKOBZM3uYEChOjNuvdfdL04Xd/XUz\n+2AsNw04xsweJUxGsw8hKLzI3W8daKVERERERESyKKuB4RhCQDYaOKZEnmTdwWXJC+7+vJntC5wN\nHA8cSGh1vBe4wt2vL7Yjd3/KzPYitA4eCRxMWMriNuC77j63HpUSERERERHJokwGhu5eflGz8mV7\ngHPjo5ZyLwDT40NERERERKRjZHKMoYiIiIiIiDRPJlsMRURERERE0szsEODuKrL+zt33Kyg7Gvg8\ncCxhObzXCBNQXgtc7u6vl3jPHYAvAkcA2wOvAL8Hriw1TC2vFBiKiIiIiEievEJYjaCUp9NPzGwn\nwkST2wHLCfOPbAEcAOxPmHzyOHfvLSg3EbgD2Ap4nhCUjiLMR3KImR3u7tPqUaEsUGAoIiIiIiJ5\nssTdj68mo5l1AdcQgsKbCEvhrY3b3grMI7QGfgk4P1WuG7iBEBTOAj7j7uvjtnfGclPN7F53v7pe\nFWsljTEUEREREZF2dTihZXAZcEoSFAK4+zPAGfHpWWY2LFXuZEKX04XA9CQojOUeIaxmAHBe4w69\nuRQYioiIiIhIu/pwTH/m7msKN7r77cBSYEvg6CLlrnH3viL7nQ2sB8bHLqe5p8BQRERERETa1f4x\nXVAmzwMxnZR6bSJhzfSi5dx9NfAkYW31ScXy5I3GGIqIiIiISK6Y2TbAR4D9gOGEyWHmuvutqTyD\ngF0JAd6iMrtbHNM9Yrnt4z4rlVsE7JmUyzsFhiKSG6t6epkx676K+bqHDmbK5AlM3H1cE45KRERE\nmmwc8AywdcHrZ5rZb4Ep7v4CIbgbQgjwXiqzv6UxHR3TUalttZTLNXUlFZHM6x4a7mH1ASt7eis+\nlixbw5z55W7wiYiISI51E5aR2J+w7MQY4DTCUhQHAL+MrYXDU2XWFu6kyLbhBWmt5XJNLYYiknlT\nJk9gzvxFrO19rWLeVT299EFVeUUGYuTIYZUz1cmgrq4NaSPfd/DgcL945MhhTXvPZkvXsV21ex3b\nvX7QGXXspyeAE4Hl7n5H6vV1wA/NbDFwJ2F84AnA/FSern6+Zxfh3nTbU2AoIpk3cfdxVXcLnTHr\nPlb29FbOKCIiIrni7kuBG8tsv9vM7gEOAY4Bbktt7i6z6y1iujqmPQXlNpnNtES5XFNgKCJtqdrx\niKUkrSXr+8JNQo1blEIrVpS6Tqi/5Hu4vq+voe+btE6sWLGmae/ZbOk6tqt2r2O71w86o45jxoxo\n1K4fJgSGE9x9tZn1EsYZjiXMIlrMtjFNxgy+mNo2lo2T01Qql2sKDEWkrXQPHcxKejeMR6yXlfQy\nZ/4iBYYiIiLZlnQZTRakXwjsDewE3FOizM4xfQzA3Z8zsxWEyW12onRg+IZyeZeLwNDMpgBXASOB\n8939/DJ5RwOfB44FxgOvEfojXwtc7u6vlyi3A/BF4Ahge+AV4PfAle5+fd0qIyINVct4xHLSLYYa\ntygiItJ6ZnYwsCPwa3f/S4lsydIRySx09xMCw8mEeKJwn0PYuA7h/alNC4CjYrm7ipQbB+xCGH94\nf+H2PMp0YGhm3cB/AqenXi45+NPMkjsB2xFmJbqX0Pf3AMKsRceY2XHu3ltQbiJhZqOtCGug3E2Y\npvZg4BAzO9zdp9WpWiLSQLWMRywn3Y1H4xZFREQy4ZvAvsDFwIzCjWa2I/C++DQZX3gd8ClgiplN\nd/eVBcU+BIwgLEuRHpN4HSEwPNnMLnD3wrvDp8X0cXdvixbDzC5XYWa7Aw8RgsIHKN0nOMnfBVxD\nCApvArZz9/e7+8HAboS7BkcAXyoo1w3cQAgKZwHbu/tR7j6JsGDmcmCqmX28jtUTEREREZHaXB7T\nT5vZGxptYlA4hzCe8HHgpwDuvgCYSwj+ZpvZlqkyewPfik9nFgR/NxJ6HU4ALjOzwalyhwHnEBqs\nvly32rVYZgND4GxgT8IdgffwxkGgxRxOaBlcBpzi7hvWHHH3Z4Az4tOzzCw99+/JhC6nC4Hp7r4+\nVe4RwocOcF6/ayIiIiIiIgPi7lcRGoIGAVea2Z/M7FYzux/4I7AP8DTwgfQ1PTAV+F9CC+CzZnaH\nmf0WeAR4E3Ctu19a8F6vAx8EXgCmxXK3m9kjhCUxNgcucvdbG1jlpspyYLgCOMHdZxRpui3mwzH9\nmbtvMoWTu99OmDFoS+DoIuWucfdi3VRnEwavjo9dTkVEREREpAXc/VRgCvArYBihcWgP4HeEhqV9\nYqNQuszzhC6oFwLPAQcSehTeC5zk7kV7Brr7U8BewCXAKsIwsx0IXU6Pcvdz61y9lsryGMOzqgwI\nE/vHdEGZPA8Q1jSZRGxeJiyA2VeqXJzm9klC6+UkQvdWERERERFpAXf/BfCLGsv0AOfGRy3lXgCm\nx0dby2xgWEtQaGaDgF0JAd6iMlkXx3SPWG57YHgV5RYRAsM9yuQRERFpO9WsCap1PkVE8i+zgWGN\nhhMGmvYRZhQqJVl8cnRMR6W21VJORESkrdWyJqjW+RQRyb92CgwTa0vm2rhteEFaazkREZG2Vu2a\noFrnU0SkPbRLYJjWNYByJddIHKhkTbRGSxblHtTV1bT3bJTBg8PcSHmvR6vo/A1M+vy10/+rZtJ3\nMN+qXRNU63yKiLSHLM9KWoue1L+7y+TbIqarB1hORERERESkbbRLi+FqoJcwznAs8GSJfNvGNBkz\nmF4bcSwbJ6epVK5mK1ZssoJGQ6zv69uQNus9GyVpZch7PVpF529g0uevlv9XDy1cUlX3u0Q7T9pR\nz+/gmDEjBrwPERERKa0tWgzj+oMLCd1BdyqTdeeYPhbLPUdYL5FayomIlDJn/iKWLFvDyp7eqh5L\nlq1hzvxykyKLiIiINF5bBIbR/TGdXGyjmQ0hrEOYzgth/cKuMuXGAbsQxh/eXyyPiEgiaSnsArYe\nPrTso6ugjIiIiEirtEtXUoDrgE8BU8xsuruvLNj+IWAEYVmK2wrKHQWcbGYXFFk/8bSYPu7uajEU\n6WDVrOe2Kk7CsdXwoXz7zIPK5tWkHSIiIpIVeWwxLDrrqLsvAOYSgr/ZZrZlss3M9ga+FZ/OLAj+\nbgSeACYAl5nZ4FS5w4BzCK2FX65nJUQkP7qHhp+FZD23co++gjIiIiIieZDJKxczGwtcUfDynjH9\niJm9I/X6Xe5+Sfz3VOAeQgvgs2b2MCFQnBi3X+vul6Z36u6vm9kHY7lpwDFm9ihhMpp9CNeCF7n7\nrfWom4jkT7XruSWSCWVERERE8iKTgSEwDDiWTdcV7AN2jY9k3cFlyUZ3f97M9gXOBo4HDiTMVnov\ncIW7X1/szdz9KTPbi9A6eCRwMGEpi9uA77r73PpVTUTyptr13ERERETyKpOBobsvpp/dXN29Bzg3\nPmop9wIwPT5EREREREQ6RiYDQxEREcmXaiZnaud1O0VE8k6BoYhIxj20cEm/xjjq4luaoXvoYFbS\nu2FypnJW0suc+Yv03RQRySAFhiIiGTdn/iKWLFtTdX5dfEszVTs506o4a6/W7RQRySYFhiIiGZdc\nSHcR1kcsRxff0mzVTs6kdTtFRLJNgaGISItVGpu1Kl5MbzV8KN8+86Cy+9LFt4iIiPSHAkMRkRap\nZWxWkl8k7yrdCNEYWRGR1tBVhohIi1Q7Ngs2XiyL5FW1N0JW0sv3f/EE19/1x4r7UwApIlI/CgxF\nRFqk2rFZIu2gmhsh6YBRM5yKiDSXAkMRERFpuGpuhFS7NIsmWRIRqT8FhiIibaiaxcZB3fEkWzTD\nqYhI6ygwFBFpI7VOaKPueCIiIgIKDEVE2kotE9qoO56IiIgkFBiKiLSRWia0UXc8aWelxisO6uoC\nYH1f34bX1KVaRESB4QZmtiXwGeAEYGdgEPBH4EbgO+7+SgsPT0RERGowZ/4ilixbU1VedakWyQ9d\nszeOAkPAzN4E3A3sCbwMPBg3HQBcCJxgZu919xUtOkQRkYapZqIatahIFpX77q6KreFdwFbDh254\nvbDFsJ5dqqudVRX0f0qkP3TN3lgKDINLCF+w+4Hj3H0ZgJmNAW4F9gW+DUxt2RGKiNRZLRPVqEVF\nsqSW7+7YUcP42j9P2vB85MhhAKxYEVoTq+1SXU3QV0vX7Gb/n6o2aFXAKhmna/YG6vjA0Mx2AT4M\nvAp8NPmCAbj7UjP7OPAEcIqZzXT3P7foUEVE6qraiWo0SY1kTbXf3STIqUallvNax+NunWqlLPZe\nfcALy9Y0rbW+2q61ugkkWaVr9sbr+MCQ8AUDuNvd/1K40d0XmtnDwH4x7zeaeXAiIo2iNeMkr2qZ\nZKmSWpd4gfJBXzWB3JeueIAly9ZU3Vr//V88wfV3/bGqY4PiE+yU6lqbpptAknG6Zm8wBYawf0wX\nlMnzAOFLNqlMHhGRtlbNWETYeGH89wdU11Ij0kq1LPFSr9a7at8zHTTW6+ZMYdfaNN0EkozTNXuD\nKTCE3WO6qEyexTHdo7GHIiKSPbW2qCRd0RQYSh7Us/Wx3u9Zy2Q2acVaDKH6rrXV3gSqhsYsSh3p\nmr3BFBjCqJi+VCbP0piObvCxiIhkTi0tKumxU9MunAdsenGapotGkdL6G7QWTrBTrf50q61EYxal\njnTN3mAKDGE4hC71ZfKsTeUVEekotVycpsdOLV+9rmL+asdP/eT8I6t6fxHpv1puAlWj2kl2qrlB\nlG49LdUiWgvdlMolXbM3mALDjbpafQAiInmXvrCsdPHWiPFTItJ/9e5WW+0kO9XcIKr3b0R/JvVp\npnoEv9C2AbCu2RtEgSH0ACOB7jJ5tojp6v6+yZgxI/pbtCbteFe9WeeuXen8DYzOX23+YcwI/uE9\nO7f6MJqimd+NZv+2d8L3XnVsvB+c876Wvr+0naZcs3eyQa0+gAx4MaZjy+TZNqZLy+QREREREZHG\n0DV7gykwDAthAuxUJk9y+/uxBh+LiIiIiIhsStfsDaaupHA/cBwwuUyeyam8tVAfaBGR9qLfdRGR\n1mjkNbugFkOAG4D1wEFmtsnAGDM7ENgNeBW4scnHJiIiIiIiumZvuI4PDN39WeBKwrm4zszGJNvM\nbEfgqvj0u+7+QgsOUURERESko+mavfG6+gY4DW47MLMtgbnAu4FXgAcJX7oDgCHAncAx7l55US4R\nEREREak7XbM3lgLDyMyGANOBE4FdCQtoPgFcC3zf3de38PBERERERDqertkbR4GhiIiIiIhIh+v4\nMYYiIiIiIiKdToGhiIiIiIhIh1NgKCIiIiIi0uEUGIqIiIiIiHQ4BYYiIiIiIiIdbnCrD6DdxPVV\nPgOcAOxMCL7/CNwIfMfdX2nh4bVMo86LmW0GfAU4N+7zUHf/TV0OOkMacf7MbC/gs8ChwLbAOmAh\n8P8Bl7j72vocfes16Py9G/g0cBAwjrCekgNzgFnuvro+R996zfhdM7PtgSeB4cDV7j51oPusl7z/\nrpvZIcDdVWT9nbvvV1B2NPB54FhgPPAaG6eFv9zdX6/rwdbAzKYQFrQeCZzv7ueXyduvepjZDsAX\ngSOA7Qn/z38PXOnu19etMsXfu2L9zOxU4IdV7O7n7v7BIuVbUj8zGwfMAI4BJsSX/wzcDvyHu/+1\nSJncfIa11C/Hn+GOhGuII4AdCUtG/B9wB+F38ZkS5XLzOXYiLVdRR2b2JsIf3z2BlwmLbkJYdHML\nwhf4ve6+ojVH2BqNOi/xQnI2MDm+1EcIDO+tx3FnRSPOn5l9EpgFbAY8DzwOvAnYi3DR+yhwiLuv\nqlM1WqZB5+9cILlI+zMhIBwX32Mz4Bng4GIXN3nTrN81M7sFOCo+/bG7/9NA9lcv7fC7ngoMXyEs\nDF3K0+7+hVS5nYB7gO2A5cDDhDofQLixPBc4zt17G3LgJZhZN/CfwOmpl2e6+7+WyN+vepjZRMJF\n7laE38lHgVHAfkAX8CN3n1a3im1836rrlwoqXgTuK7PbBe7+HwVlW1W//YFbgNHA6vi+mwH7AMMI\nn9Fh7v6HVJncfIa11i+nn+ERwM8J9VkG/A+wOeEaYkvCb+UUd7+zoFxuPsdOpRbD+rqEcPFwP+GL\nvQzAzMYAtwL7At8GMnMnvEnqfl7M7FjCD+k2hDuq0wg/Du2orufPzPYDvhefng9ckNyhM7N9CT/M\n+wAXAP9Sv2q0TL3P31GE87Ye+H/u/oPUtn2AXwFvJZzj4+pXjZZp+O+amZ1ICApfIVwkZEk7/a4v\ncffjq8loZl3ANYQLuJuAk5JeBGb2VmAe4c79l9h4k6ThzGx3QkvtnsADhAvFPcrk71c9YnB2Q9z/\nLOAzyaLZZvbOWG6qmd3r7le3qn4pj1f72cb3aVX9RhA+h9HAzcCp7r48bnsz8F+Ei/1rgL3j67n5\nDPtTv5S8fIbbAj8lBIVXxvd9JW4bG7e9B/iJmY1PfVa5+Rw7mcYY1omZ7QJ8GHgV+Ghy8QDg7kuB\nj8enp8Tm947QwPPyXaAbmOrun4ivtV3zd4PO32cJQfQ8dz8/3W3D3f8buCg+PXGgx99qDTp/02P6\no3RQGPf5KHBefHpkvEjIrWb8rsUWuYsJd9Z/PKADrrMO/10/nHAXfxlwSrpreewidkZ8epaZDWvi\ncZ1NCJouJlx8vlghf3/rcTKhm9tCYHpyIRrLPQKcE5+eR33VWr/+alX9Pgq8GXiJ8H9qeep9/wZ8\nKj7d08ySwClPn2F/6tdfrfoMpwIjCD1lTk93pXf3F4DT4tMxhKEqiTx9jh1LgWH9fDimd7v7Xwo3\nuvtCQpN5VypvJ2jUeXkK2N/drxn4IWZaI85fMg7uihLb/zumo+JFe5414vw9CFxH6SDmoZhuBryl\n6iPNpmb8rn2H0I35fOCFfu6jUTr5dz2pz8/cfU3hRne/HVhK6DZ2dBOPawVwgrvPcPfXqsjf33ok\n5a5x92I3HWcTeg2Mj93c6qXW+vVXq+q3ktAieqm7v1xk+x8IY866gF0KjjUPn2F/6tdfrfoM/wJc\nDXw7HaQl3P1pwve4C/i7Isebh8+xYykwrJ/9Y7qgTJ4HYjqpwceSJY06L0e7+xP9O6Rcqfv5c/eZ\n7v6P7n5TiSxDUv9u5IVJMzTi/J3n7ie5+/0lsmye+nfexxg29HfNzI4k3GH/HSFAzFp38E7+Xc9q\n3c8q89tVTH/rMZHQC6VoOQ+TSz1J+M7Ws/611q+/WlI/d7/B3U9095klsgxi4zCn5O9Pbj7Dftav\nv1r1Gf7E3ae6e9Gby2aWrmP6b2BuPsdOpjGG9bN7TBeVybM4ptWMF2gXDTkvDb6TmiWt+F69P6ZP\neYYn1KhSK85fMtbsAXfvqdM+W6Vh5y92s/0+4eLoE+6+3sxqPsAGa7vfdTPbBvgIYZzTcMJEDnPd\n/dZUnkHAroSLsUzVvZbf/v7WI05sNryKcosI3T7rVv+B/G2LY9hOJIxd25zQsnOzF0zI1sr6VeHv\nY/oa8EAeP8MK3lC/wo1t8hmeQDi2HsLY7Fz+X+xUCgzrZ1RMXyqTZ2lMRzf4WLJE52Vgmnr+zGxX\nNo6BuKhc3pxoyvmL4yF2A84ETgX+BmRiVs0BauT5+xqwA/CNODYzi9rt92scYcbcrQteP9PMfkuY\nRfAFwoXYEMLFWJ7r3t96jEpty0v99wb+RBh7n/Y5M7uZMN4t6dqYyfrFSUb+PT692t2fN7OtaJPP\nsFj9CrLk9jM0syGE3/OPEZaUWAdMS42x7KT/i7mmrqT1k9zRKLf229pU3k6h8zIwTTt/8U7lzYQ/\nSne4++yB7C8jGnr+zGy8ma0n3Bn9b8K6TOcDe7q717q/DGrI+TOzAwk3IP4EzOzvwTVBu/1+dROm\nfN+fMPvrGMJEEcsJk0L8Mt7ZT9clz3Xvbz3yVP9kzNXWhHHPexFamrYDziIc4zGEMWGJzNUvBhaz\nCS0+z7JxQpG2+AzL1A9y/hma2WJCIPg0YV3pG4B93P1nqWxt8Tl2ArUY1l/Wxshkhc7LwDT0/JnZ\nzoQLxvHAY8Ami+jmXKPO38vALwgX3DsSWg0/S5i45yvePovc1+38mdnmwA8IF0OfTM9Ml2F5//16\ngtA9bbm735F6fR3ww3hhdydhLM8JwPxUnrzXPdHfenSR7Rmvf0PoGvzXgnHPfwO+ZWYrCP/fjjez\n/dz94YLyLa9f7HHxc8JSBSuBY2LLdaFcfoZV1C/vn+Fcwkys2wLvIMwiOsrMPh8noimUy8+xUygw\nrJ8eYCSbdgFIS9bnapeLxWrovAxMw89fXLvwVkLrwcPA+9sooGno+YtLFkxJnseuuFcCnwb2N7OD\ncj4ethHn71zACIvY3z2AY2uGtvj9it/TG8tsv9vM7gEOIbRM3JbanOe6p8f41lKPwnKbzKBYolzT\nuftiNo7LKuZHwJcJa6seR/iNz0z94szXtxBuSrwIHOGphe3J+WdYRf1y/xm6+yeTf8f6ziT0CJls\nZvvG+uX6c+wk6kpaP8laQ2PL5Nk2pkvL5Gk3Oi8D09DzZ2bvB+4hBIW3Aoel111qA039/rn7/xK6\nk64kXAh8aKD7bLG6nj8z2wv4AvAc8LmBHVpTdNLvV9IKMSHeGOqNz/Nc9/7WI712YJ7rT5ze/3fx\n6fiYZqJ+ZjaBMNPkROCPwLvd/fcF2XL7GVZZv4qy/BkWcvcX3f1MQivoKEJACzn+HDuNAsP6SZZO\n2KlMnp1j+liDjyVLdF4GpmHnz8zeA/wX4U7b94FjS6y7lGdN//65+0o2rgV5QD322UL1Pn//Quip\nMhj4tZk9mn4A/y/mOy6+VvNFVJ110u9X0r0rWZdsYXwtt3WPF9Q118PdnyOsw0Yt5TLsDZ9tFupn\nZuOAu+J7PABMKtbtMK+fYbX1q0HmPsMK5sX0AMjv59iJFBjWT9IvfHKZtjohmgAAFohJREFUPMm2\nUuuftSOdl4FpyPkzs7cRxsYNBS5y90+VWDg27+p6/sxsezN7yMz+bGbjy2RNZn3cZPHfnKn392+z\nmI4B3l7kkdwR3oYwAcNetRxsA7TF75eZHWxmp5jZDmWyJdO8J1PCl617nExjUkHeLOpvPRYQLmJL\nlRtHWKC8jxbV38yGmNl7zWyqmRXONJtW+NlCC+sXl6q5jdD6NY/KPVVy9RnWUr88foZmdrOZ/dHM\nPlYm28iYpv8G5upz7FQKDOvnBsJ/gIPiRB5vEGfh2w14lTJjPdqQzsvA1P38xck/fkYIXi5193Pr\nd7iZU+/z91fCOI8dCAuzb8LMxgJvi0/zfveyrucvLoo8yN03K/YgzOgKYfzhoPhaK7XL79c3CbMd\nfrbYRjPbEXhffJqML7wuplNKXLB+CBhBmEL+tiLbs6K/9UjKnWxmxeZjOC2mj7t7q/6frwd+AlxF\nWCZnE2Z2ACGo6CM79bsY2Ad4CPhAFRNQ5e0zrKV+efwMXyO03p1SbKOZdRHGK8Mb/wbm7XPsSAoM\n68TdnyVMOjEIuM7MxiTb4h/dq+LT75aYbastDeS8mNldZrbQzE6u4S3bZQY9oGHn7zOEi9n/BWY0\n7OAzoN7nL7aqXhGfnmtmHyoo82bClORbEJYASE/XnTud/v+3jX7XL4/pp81sWnpDrMccwhpjjwM/\nBXD3BYTZBkcAs81sy1SZvYFvxaczMzLBUtHvzgDqcSOhK/EE4LL0BamZHUZYbqCPjWOoGm2T+rn7\n64TZKgEuNLOj09vjmN7r49M73D29oHpL6mdmkwgBUC9wkru/UqlMnj7DWuuXx8+QMPQE4H1m9jUz\nG5p6327Cjah943tfmWzL0+fYybr6+tqx91hrxC/5XODdwCvAg4QLigMIf3TvJExTvK5lB9kC/T0v\nFqZQ/zvgM+7+3dTrhxHGKqUdG9P7eeMiqBe7+6/rVZdWaMD5ewF4E/AUITgsR+dv0/M3mHDx/IH4\n0tOEiQXGAHsSZk7rAf7R3eeRc/U+fxXeayZwHqHF8J/qU4OBaZffdTP7MRvv8C8i/P8fCexHGPP5\nNGFG4mdSZbYlTE61K2FCpYcJF3UTY5afuPvHm3D4yfGMZeONmcRBhEkuPD4Sd7n7JbFcv+phZrvF\ncmOBF4BH47/3IVyIXlTPHhf9qV/sAfJLNrb4PkmY4XIc8E5CQPkwcJS7v2GB8GbXL77nTwlLorxI\n6CJYTh4/w5rrl7fPML7vF4CLCL+FS+P7dhN6y4witIR+1d0vLCiXi8+xk2m5ijpy95fN7FBgOmHN\nqP0IX9hHgGuB77t73scc1WwA56WP4mvW7EAIBPsK8kK4eION693cNOAKtFgDzt+b4usWH8Xo/JU4\nf/FO5vFmNoVwZ3hf4L2E7oTPEAKF78QpunOvAd+/cvpTpqHa5Xfd3U81sznAJ4B3AYcTpn7/HeH/\n+aXuvqagzPMWlrM5GzgeOJDQEnIvcIW7X09zDWPT337i813jI/ntWpZs7G893P2p2GJzDnAkcDDh\nps9thFbiufWrGtCP+rn7OguzS58SH3sRxlutJqxHeT1wVbFW3RbUD8INtD5gNGFplGLy/BnWXL8c\nfoa4+zfM7E7C0kyTCTcwuoAlhBtp3/M3rsmYlMvL59ix1GIoIiIiIiLS4TTGUEREREREpMMpMBQR\nEREREelwCgxFREREREQ6nAJDERERERGRDqfAUEREREREpMMpMBQREREREelwCgxFREREREQ6nAJD\nERERERGRDqfAUEREREREpMMNbvUBiIiIiHQyM1sf/3mou/8mvjYTOA/4jbsf2qpjKyd13ImV7r5N\nlWUXA38HTHX3q+t8aHVlZiuArdKvubsaV6TtKDAUERGRljOz/YCpwLuBCcCWwFrgr8B/Aze4+82t\nO8KG64uPxELgv4DHW3M4NbkDWAO83I+yfZWztNwtwLD4+HvyccwiNVNgKCIiIi1lZt8FzoxPlwGP\nAKsIrTTvAE4ETjSzW4B/dPdXW3KgTeTuNwI3tvo4qtAH/LO7/1+rD6RR3P0kADPbEVjU4sMRaRg1\ng4tkkJlt3upjaAQzG9rqYxCRbDGzMwhB4RrgZGCMux/q7sfFLpTbAp8F1gNHA+e37GCl03W1+gBE\nGkmBoUhGmNlgMzvRzB4EPtLq42mQd5vZk2b2z2bW3eqDEZFMOD2mF7v7bHd/Qzc9d1/r7t8Bvh5f\nOqNdb56JiLSSupKKtJiZjQI+CZwBvIXQLWdVQZ73xu0HAKOBlcCjwA/d/foi+7wHeA/hgutJ4NvA\nHsA0d7/ezE4Ffgg8CBwBXAa8H3jI3Y9M7WdrYDrwAWBnYCiwBJgPfNvdf1fwvuOBZ+LTLYAvAp8A\nRrn7MML4k12A7wMXmdnlwKXu/lwNp0xE2stOMf2fCvm+BdwDPOru69IbzGwwMI3Q5fTthC6oLwOP\nAT9y9x8W7szMfgycQmiB/A4wk/Bbty1hXONs4Hx3X29mBwJfAfYjjH38A3Ceu99RsM/FhAlVjgH+\nF/g34FBgJOG38xex3PIKdS05+UzqPd4fj+MrwD8A2xH+dvwa+JK7/6nIPveM9Z0MjCD8Xv+Q8Dfi\n68DngKvdfWql46uGmX0g7nMfQovvQsJv/rUVyr2f8HdxIjAGeDUe6xzg6+7+cirv74G9ga+5+zkl\n9rcd8Jf4dF93f8TMuoCPAafG8iOB5cCfgRuAy929px/VFskttRiKtIiZ7REDo2eBC4FRhD/Qb3f3\nOal8FwDzCBcsy4G7CBcY7wVmm9kNZlbq//JowoXIlsCdsXyhWYSLigcJQWTyvjsSxvnMBHYDfh/3\n0Qd8FHjQzEpdPPQRLtK+AjhwH4C7P0wIDC8GhgBfAhaZ2ew48YSIdJ6lMX13uUzu/pK7z3P3penX\n4+/fzYQbXAcBfwLmEi7wDwR+YGY/KrPrwcBthG6sC4GngLcC5wLfMLPDgbsJN+7+mzAGciJwi5nt\nVWR/fYARflMPB54AfguMI9zg+2286VatYhOd9BECpgWE32MHfgcMB04A7jWzkekC8Tf2IWAK4Sbf\nb4DVwDeA69nYWFCXiVXM7HTgJsJnsJRwQ3Ew8GMz+7cy5b4E/Ao4jhDczyPcNNiF8DdlgZkNTxW5\nKqYfK3M4/xjTx939kfjvy4BrgEMIQeNcwnncC/gmcJeZjaimriLtQi2GIk0U71AeAXyGMLMZwAuE\nP8yXFrngmQJ8mXAhMsXd56e2TQZ+CnyIcNFxcZG3nAbc6O6nF9kG4UJnNGDuvqRg27WEmQEfBo5N\ntsc6fIVw1/l7Znafu/+xoGwX8C/AYeljBvD/v71zD/ZruuL456o0aGdaTZuEiBKRr3FFSmNCESUe\n8WjaZtQjHiH1pp4R8RpUUEaoelRar1CGdkJiJhlVFUqReEzqVYvWsykyKkKJirj9Y+3jnpx7zu/e\nX3Jz5d67PjOZk9/ZZz9+v3Nnn7P2Xuu7zF4FTpJ0Dr6b+DOahSUexVfu7zSzpRVjDoKga/FnYCxw\nnKSPcW+Et+qoPwqfVxfj6R7mZgVpDp0GjJV0vZk9XFL/UFz589vZTpSkE/BdtCNxo+KILKVCipX+\nC24cHgUcU2ivAd/puxs4zMw+SfU2Af4KDMIXxSbW8R2LNOALinPwdA8fpT6EG4jr4Ibulbk6U4A1\nUp3dzey9VGcbYCbw3gqMZxkk9caNK4BfmtnJubJhqb+1Sup9C/g5bpyeYGZX5coG4s+6wfgzdFIq\nuhW4FOgv6ftm9kDJkPZOx+webgIcgaveDjezJ3L99MVVSLfCn1GX1fPdg6AzEzuGQdABSFpL0lH4\njtws3Ch8Hn8wrW9m5xaNwsRZ6XhKiYH1EDA+fTypouveuWvK6I+73yxjFKYH93b4w/ngfLmZNZnZ\n+fhuYk/8xaiMOcUxF8b/vplNxt3I9scN0G1wFb6XJY2vc1U9CILOyVn4jk0DcCrwL0kPSPq5pBGS\n1mylfhO+83NZ3igESN4Xc9LHkRX1ewPj8u6JwG9w18evAM/k8+wlQy9zTd2ios2lwFGZUZjqvQBM\nTh9r7W61lZ7AIZlRmPow/BkDMCw7L2kI7s7ZBByXGYWpzqO4uM8G7TCmjH1ww+9tYEK+wMzmABfi\nRmqR3vhvOw24ulDvH8B16eNuufML8bQeDbhr8DIkQ29b4FPgd+n0d9LxmbxRmNp7C19UPR03soOg\n2xCGYRB0DNOBa/CV4vuAPcxsMzO7Lv/ikEfSuvhLR1OqX8aMdOwvaeOS8jmFl50iTbibTpHsBerp\n9KJRRubuOqKivKzdFpjZUjO7w8yG4XGR0/FYmUtwt6cgCLowZjYf3327BTeoVsPngrPweeQ9SfdI\nOkDSl0rqzzCzQ8zs7IousvQCfSrKHy/GOZvZYppdXO8uqfNaOn6jos1ZeYMtRxaTuK6k/hV128qM\nNM4iWZz3N3Pntk3HBcXY8MRtuFtpe7FdOt5vZp+WlP+hrJKZPWdmR5rZPkURokR2L/sWzmeG+ugS\nYbPRuNF4b26RMwur2FTS4JJx/M3MLjazB8vGGQRdlXAlDYKOIUvTcLaZXdjGOtnDqgGY6h5CpSzB\n4/UGAUWXzvlt6KdM+GXTdKyVWDkzGMsG1tTGvpdt0N28HpY0Dheo6VFvG0EQdD7SC/tYSafiMXA7\n48ZFb3we2DX9myjph2b2crGNJBCzI7AeHn+XGZFbpmMLozJRlZcuE7h5tUZZ1Rz1TMX5/Lg3oFkQ\nZXloIS6T+Dgd8+mBNkzH4jMC8F1QSU8BO6zAePJkgkJV/b0h6UN8R7YFaaFzL3zcfWn+LuunY/Fe\n/gn/Lfvjfz95UbZl3EgT9+Nxi5sDcyVNwxdaZ5vZOzW/WRB0YcIwDIKOIXvQTJI0Fo9dmVqx2puR\nX4n+QSvtN+EqfEVaU777wMw+KzmfiRbUijlZlI5fltSzqBLYhr6XQVIP3P3oZJrds/5TTxtBEHRu\nzGwBHgs3BUDSINzYOwgXp2kEZkoanO1ESVobj7cuei9kO06t5Z4r9dqoo7yMqvlvUe7/X624pq0U\n59xaZG75teb0Ypz5ipD1936NaxZRYhhKugxXw87ft5r30syaJE3Fd5kPIhmGKWZxOH4/ZuSuXyJp\nV9w1dS9cwGdMqjMXNyKvr/LoCYKuShiGQdABmNnekvbEA+ZH4G6lkyRdC1xVIbSQPQg/MbPlzflX\nZvS1pbwtqnT5B3RZO631DXz+UncELkKTpeuYg4vplLobBUHQPTCzF/G0D1MkHY+LUwnYFxcdAY8H\nHIG7Qp6Hu6PPzxarkiLp2A4eepV4Vj6Ep01zZDuRzde15vZ2USOto78WO7iSjsWfk024YvaNwEtZ\n2oi0sFqlMHsjbhjuLKlP2oX+Ef6b31E08tIixChJm+K7jLvjLs3Zv2Mk7VKnEFIQdGrCMAyCDsLM\nZuIr3Y34g+8AXHF0vKTbceGEfB6vbJexh6ReZtaRu2fZavfaNa7Jyhab2ZJ6O5C0Ef47HIqLFCzB\nhWeuMLPH6m0vCIKujZn9KhmHA/Cdw2xHaHS65AQzu6mkagv1yw6gSjgrf35RxTUrgywfX630C71X\nQn9lniyZuvXXS4qOTMdbzOz4kvLKe2lmr0iaje8w74Mrsu6biqfWqPc8LgZ3QVqoHIMrvjbiYkHt\nIRQUBJ2CEJ8Jgg4mBdcfjsdCnI0bgAcD8yTdJ2mPdGk+RmWzDh5m1ndZjq6MbEy14hBbIGl7SXfh\nuwDH4vEwFwMDzGz/MAqDoPsg6ceSpkg6rY1V3k3HLI5uAL471YTnoSu2vxqw9QoPtH4aK84PzP2/\nRZzkSiQTy9mwrDCl4PhuO/aXxW1uVFE+EFdVLZKJqLW4l4mauS5ZVoSmD56j0JISaquY2UIzu5rk\nVkq1uFoQdEnCMAyCL4iUrPkCXIDgQDxdw0540uQDkxvMk/hLz+FlbUgaJGmepDPaeXiZ3Hlj2uEs\n9rsazQmD72lroyn34oN44uIXgKOB9czs9KRMGARB92IoPr9NbE2lU1I/YAhuBD6aTud33crSWozF\nF+GgY72k9pRUZvhkaRZeSa6MHUWWkmF9lSuZ7U/F7t5yki3w7VimJAvsV1Evu58t7mXKPfiT9LHq\nXk5LbWyPhyesRsluoaTjJN2V3EjLeL1qHEHQlQnDMAi+YMzsUzO7LaVr2A6Pq8tiTzIF0zEp4fLn\npGS/0/BdvRWVPS+OaR6+YtsA3CCpV67f1fEdvk1wIYNr62z+HmCkmTWa2RQz+7jVGkEQdFUmA2/i\nLpb3S2qhiimpQdKO+NzRA3jMzLJ0OP/EYwsbgOMKdQ7Fk5Pflk5vshzjW964uzWAK5OoVjamIbi4\nFlTHya0sHsWNnQbginxuyJTgfjIVCqLLye/x8IB+eNzn50gaDpxCuTDNvHT8aT7thKTv4c+km9Op\nPpJauKKm58nt+PvtqXis5y0l/WyML1Bel/Ic5sfXk+YcwrOrv2IQdD0ixjAIViHM7BHgkdznuyT9\nApgIXC7pJODveH6qLfCH31NAW92w6mEcLum9FfCapGdx961GoBfwETCmIjC/SjnuIaAy6X0QBN0L\nM3s3qUPeib+sz5b0Nj7PfYirM2cpC8Dnj9G5+kskXQhcBJwoaSRuADUC6+CJyt/GXQOHSnoMuNnM\nrmnjEFtTNK3iEtwIHCVpHq6+uQ0+Z8+jOdF9e/TVKma2NC0u3omn/XhD0hN4nN9QXNSnF82unCva\n36uSJuFG4RmS9sNTHK2D7/pemsYxpFD1XGAX/Ld6WdLTuHHZiLuJnoZ72KwBPCHpXjM7ptDGDXis\nYg/gvgpvlPPw3dut8d9iHrAAj2HcAt89/TfNhnwQdAtixzAIVnHM7Aw8p9d0PJfTTvjDey4u3rKN\nmRVXXptYQfU5M3sTV2Y7B19J3jR9znYJNzezKjfS9lS3C4KgC2Nmz+Exy4fghssH+ILUbrhBsBDf\n9dnLzHYoCnGZ2cXABHyeGoDngJ0H7GRmU9M89WvcxXAQzYvibZknq8pbm+Pm4wbXH9N3G4rn2bsc\nGF7hKVFss1bfdc/vZjYDGIm7868ODMO9Uw42s/E0q4TWLSZW0d/5ePz8XKAP7hGzFDjazE4DFhfH\nmmLMR+HK1F/D/w4+AMaZ2WFmthA4Bt9l7kuJgI2ZPU5zTOVNFWN7F3+eTUjj64fHE26J70JPAgaX\n5csMgq5MQ1NTvL8FQRAEQRCsKJJexZOwH2JmN9e+etVC0izccLzIzM5sY53PcONuQzN7vbXrO4Kk\neP0iLlTUrz1zEUraABcNajKzstjJIOjUhCtpEARBEARBFyfl0h0IzDKzlwpla+Lum+CiZ52Zs3G3\n3GsjQX0Q1Ee4kgZBEARBEHR9JuCurL+V9LkCaUpVcSXuurkAmLkcba+0+Mh6kHQi7r76Dh7HGARB\nHcSOYRAEQRAEQdfnWFxlczgwX9KTuBvoYFzkZzEeb/i/OtttwI3Nj4D/mtmB7TjmVklqr+fj8aOD\n8BjJQ81sUc2K9fVxKy4gtFZ7tRkEqyJhGAZBEARBELQPrQnDfGGY2bOStsKVPUfg4jNNuPrmdOBS\nM3thOZpuSu014OJkHc1aeGzkUjx/4plm1t5pJvagffM8BsEqSYjPBEEQBEEQBEEQdHMixjAIgiAI\ngiAIgqCbE4ZhEARBEARBEARBNycMwyAIgiAIgiAIgm5OGIZBEARBEARBEATdnDAMgyAIgiAIgiAI\nujn/B+WGOYKRPeq2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2af8497050>"
      ]
     },
     "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",
    "print('CRTS Quasars: sample of 7601')\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_dict_old['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_dict_old['avg_mag_ttl']\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_dict_old['avg_err_ttl'], 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('Sampling [days]')  # time between consecutive observations\n",
    "ax4.yaxis.tick_right()\n",
    "ax4.yaxis.set_label_position(\"right\")\n",
    "\n",
    "hist2, bin_edges = np.histogram(stats_dict_old['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', label='Old calculations')\n",
    "\n",
    "\n",
    "# Save\n",
    "\n",
    "plt.savefig('QSO_CRTS_AVG_stats_USED_data_four_panels.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MISFILTERING ? From original to day-averaged , longer than 10 days... Compare the files.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "CRTS  : compare  QSO_CRTS_processed_err_w/   to QSO_CRTS_LC_day_averaged_err_w/   -  ought be exactly the same, and yet the stats are not exactly the same... check why!  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'/astro/users/suberlak/Desktop/SDSS'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dir1 = 'QSO_CRTS_processed_err_w/'\n",
    "dir2 = 'QSO_CRTS_LC_day_averaged_err_w/'\n",
    "qso_old = listdir(dir1)\n",
    "qso_new = listdir(dir2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(~np.in1d(qso_new, qso_old))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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