{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline \n",
    "import matplotlib as mpl\n",
    "import seaborn\n",
    "mpl.rcParams['figure.figsize']=(15.0,8.0)    \n",
    "mpl.rcParams['font.size']=12                #10 \n",
    "mpl.rcParams['savefig.dpi']=100             #72 \n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import stingray as sr\n",
    "\n",
    "from stingray import Lightcurve, Powerspectrum, AveragedPowerspectrum, Crossspectrum, AveragedCrossspectrum\n",
    "from stingray import events\n",
    "from stingray.events import EventList\n",
    "import glob\n",
    "import numpy as np\n",
    "from astropy.modeling import models, fitting\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# R.m.s. - intensity diagram\n",
    "\n",
    "This diagram is used to characterize the variability of black hole binaries and AGN (see e.g. Plant et al., arXiv:1404.7498; McHardy 2010 2010LNP...794..203M for a review).\n",
    "\n",
    "In Stingray it is very easy to calculate.\n",
    "\n",
    "## Setup: simulate a light curve with a variable rms and rate\n",
    "We simulate a light curve with powerlaw variability, and then we rescale\n",
    "it so that it has increasing flux and r.m.s. variability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stingray.simulator.simulator import Simulator\n",
    "from scipy.ndimage.filters import gaussian_filter1d\n",
    "from stingray.utils import baseline_als\n",
    "from scipy.interpolate import interp1d\n",
    "\n",
    "\n",
    "np.random.seed(1034232)\n",
    "# Simulate a light curve with increasing variability and flux\n",
    "length = 10000\n",
    "dt = 0.1\n",
    "times = np.arange(0, length, dt)\n",
    "\n",
    "# Create a light curve with powerlaw variability (index 1), \n",
    "# and smooth it to eliminate some Gaussian noise. We will simulate proper\n",
    "# noise with the `np.random.poisson` function.\n",
    "# Both should not be used together, because they alter the noise properties.\n",
    "sim = Simulator(dt=dt, N=int(length/dt), mean=50, rms=0.4)\n",
    "counts_cont = sim.simulate(1).counts\n",
    "counts_cont_init = gaussian_filter1d(counts_cont, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[52.83292539 52.83104461 52.82542772 ... 64.26625716 64.25516327\n",
      " 64.24864925]\n"
     ]
    }
   ],
   "source": [
    "# ---------------------\n",
    "# Renormalize so that the light curve has increasing flux and r.m.s. \n",
    "# variability.\n",
    "# ---------------------\n",
    "\n",
    "\n",
    "# The baseline function cannot be used with too large arrays. \n",
    "# Since it's just an approximation, we will just use one every\n",
    "# ten array elements to calculate the baseline\n",
    "mask = np.zeros_like(times, dtype=bool)\n",
    "mask[::10] = True\n",
    "print (counts_cont_init[mask])\n",
    "\n",
    "baseline = baseline_als(times[mask], counts_cont_init[mask], 1e10, 0.001)\n",
    "base_func = interp1d(times[mask], baseline, bounds_error=False, fill_value='extrapolate')\n",
    "\n",
    "counts_cont = counts_cont_init - base_func(times)\n",
    "\n",
    "counts_cont -= np.min(counts_cont)\n",
    "counts_cont += 1\n",
    "counts_cont *= times * 0.003\n",
    "# counts_cont += 500\n",
    "counts_cont += 500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x106983978>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118aa73c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finally, Poissonize it!\n",
    "counts = np.random.poisson(counts_cont)\n",
    "plt.plot(times, counts_cont, zorder=10, label='Continuous light curve')\n",
    "plt.plot(times, counts, label='Final light curve')\n",
    "\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## R.m.s. - intensity diagram\n",
    "\n",
    "We use the `analyze_lc_chunks` method in `Lightcurve` to calculate two quantities: the rate and the excess variance, normalized as $F_{\\rm var}$ (Vaughan et al. 2010).\n",
    "`analyze_lc_chunks()` requires an input function that just accepts a light curve. Therefore, we create the two functions `rate` and `excvar` that wrap the existing functionality in Stingray.\n",
    "\n",
    "Then, we plot the results.\n",
    "\n",
    "Done!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function can be found in stingray.utils\n",
    "def excess_variance(lc, normalization='fvar'):\n",
    "    \"\"\"Calculate the excess variance.\n",
    "\n",
    "    Vaughan et al. 2003, MNRAS 345, 1271 give three measurements of source\n",
    "    intrinsic variance: the *excess variance*, defined as\n",
    "    \n",
    "    .. math:: \\sigma_{XS} = S^2 - \\overline{\\sigma_{err}^2}\n",
    "    \n",
    "    the *normalized excess variance*, defined as\n",
    "    \n",
    "    .. math:: \\sigma_{NXS} = \\sigma_{XS} / \\overline{x^2}\n",
    "    \n",
    "    and the *fractional mean square variability amplitude*, or \n",
    "    :math:`F_{var}`, defined as\n",
    "    \n",
    "    .. math:: F_{var} = \\sqrt{\\dfrac{\\sigma_{XS}}{\\overline{x^2}}}\n",
    "    \n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    lc : a :class:`Lightcurve` object\n",
    "    normalization : str\n",
    "        if 'fvar', return the fractional mean square variability :math:`F_{var}`. \n",
    "        If 'none', return the unnormalized excess variance variance \n",
    "        :math:`\\sigma_{XS}`. If 'norm_xs', return the normalized excess variance\n",
    "        :math:`\\sigma_{XS}`\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    var_xs : float\n",
    "    var_xs_err : float\n",
    "    \"\"\"\n",
    "    lc_mean_var = np.mean(lc.counts_err ** 2)\n",
    "    lc_actual_var = np.var(lc.counts)\n",
    "    var_xs = lc_actual_var - lc_mean_var\n",
    "    mean_lc = np.mean(lc.counts)\n",
    "    mean_ctvar = mean_lc ** 2\n",
    "    var_nxs = var_xs / mean_lc ** 2\n",
    "\n",
    "    fvar = np.sqrt(var_xs / mean_ctvar)\n",
    "\n",
    "    N = len(lc.counts)\n",
    "    var_nxs_err_A = np.sqrt(2 / N) * lc_mean_var / mean_lc ** 2\n",
    "    var_nxs_err_B = np.sqrt(mean_lc ** 2 / N) * 2 * fvar / mean_lc\n",
    "    var_nxs_err = np.sqrt(var_nxs_err_A ** 2 + var_nxs_err_B ** 2)\n",
    "\n",
    "    fvar_err = var_nxs_err / (2 * fvar)\n",
    "\n",
    "    if normalization == 'fvar':\n",
    "        return fvar, fvar_err\n",
    "    elif normalization == 'norm_xs':\n",
    "        return var_nxs, var_nxs_err\n",
    "    elif normalization == 'none' or normalization is None:\n",
    "        return var_xs, var_nxs_err * mean_lc **2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1140ab588>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1140a16d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fvar_fun(lc):\n",
    "    return excess_variance(lc, normalization='fvar')\n",
    "\n",
    "def norm_exc_var_fun(lc):\n",
    "    return excess_variance(lc, normalization='norm_xs')\n",
    "\n",
    "def exc_var_fun(lc):\n",
    "    return excess_variance(lc, normalization='none')\n",
    "\n",
    "def rate_fun(lc):\n",
    "    return lc.meancounts, np.std(lc.counts)\n",
    "\n",
    "lc = Lightcurve(times, counts, gti=[[-0.5*dt, length - 0.5*dt]], dt=dt)\n",
    "\n",
    "start, stop, res = lc.analyze_lc_chunks(1000, np.var)\n",
    "var = res\n",
    "\n",
    "start, stop, res = lc.analyze_lc_chunks(1000, rate_fun)\n",
    "rate, rate_err = res\n",
    "\n",
    "start, stop, res = lc.analyze_lc_chunks(1000, fvar_fun)\n",
    "fvar, fvar_err = res\n",
    "\n",
    "start, stop, res = lc.analyze_lc_chunks(1000, exc_var_fun)\n",
    "evar, evar_err = res\n",
    "\n",
    "start, stop, res = lc.analyze_lc_chunks(1000, norm_exc_var_fun)\n",
    "nvar, nvar_err = res\n",
    "\n",
    "plt.errorbar(rate, fvar, xerr=rate_err, yerr=fvar_err, fmt='none')\n",
    "plt.loglog()\n",
    "plt.xlabel('Count rate')\n",
    "plt.ylabel(r'$F_{\\rm var}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmean = (start + stop)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x118bf6eb8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/VgBXQDNquFcUxSYATYIgZAIYBeCw+Sk1IiOD4evr48i+d1hsbJiru0BezJvfX7padov/Nhq+PjJcNqmvfl+jwahebGwYUjNL8OmvZzBmcHejc4SEBXX49f+jTRhC3quqUYmoqBBEhNp2v9Hw7y06OhRR4YFW27+tfQ9fd9lg9IwJ7XhHvZg3f4aR6/H95R4s/R5CQjx7rMeW7wF34g7BYC00U0TTAcQAWABgpiiKutsCNQAioAkUqwyO0223qKKi3tpul4mNDUNJSY2ru0Feqqu8v1Z+fwIAMLp/lH7blgO5+sfvf3MMvx8+DwA4m2e81mv1Dyec0EPyVP9ZvR++PjLcceVQDOkTiciwADQpLI8WGv69lZXVoqWp7dFBACgtrYUf74Cb6SqfYeQafH+5D0u/h7q6Jif3xL7Ky+ts/h5wFms3QNxhmugjAH4TRXEwgNHQrB80XJQTBqASQLX2sel2IvIyeUU1uP/NJJw613aB8G92ZGD7EU3QZ7jeTxcIStl1/ELnO0leTdmixppfzuCxlXsBAJuSrdc+JCIi+/hhV7aru9ApERZyC7grdwgGK9A64lcOwA9AiiAIs7Xb5gPYA+AQgBmCIAQKghABYCg0yWWIyMtsO5iHZqVKn0K6oqYJjc3SqaZ/P3weX/+RgaZOrPMiasvWg5aTA53ILHViT4iIiOzHHaaJvgNgnSAIe6AZEXwawBEAawRB8AeQBuB7URRbBEFYDk1gKAfwjCiKltMDEpFHqmtU4IA2oUtxRQM2JZ/DxuRzCA7wxYpHZlo8btO+c8g4X2VxP1FHtbVO9L3vU/WPq2qbERbsBx+5O9xrJSJyH43NSuxMaS0bUdugwJ5UztRxNZcHg6Io1gK4XmLXLIm2awCscXiniMjpquubERbkh/2nLhpt36idnlffpMQFC5k8AWDrAZZ1IMewlEFWygvrNTnN3lo8HZFh1pMgcLkgEXUF1fXNOJFRit2pF5BVUK3f/tm2dBwRS1zYMwLcY5ooEXVxR8USLFmejJ/2nINMJrPYjmv9yFO8/e1xAMChtCKs/PEkyqsbkZxaKJky/afd2fhxd5bRtpSzJcgvqXVKX4mIHCU1qwxLlifjk63pRoEgAAaCbsLlI4NE5PlOnytHZFgAesaEoLZBgbWbz+DqGf3RLy5csv3xzFIcOH0R9/51OPaeKsQnW9IBADuOnse1MwdYfB1dohgid1dQWodmRQs+/Pk0AODoWc1FT0Roa2KB1Kwy/LArCykZmjWHuve+QqnC+z+eBACsWzrXmd0mIuqUi+X1KK5owKgB0QCAI2Kxi3tEbWEwSESdolKr8ZZ2FGTd0rn4/XAeTmSVISO/ymyNX2FZHVZvOo28Is2Ix9xxCdhtMNrX0NQCKwODRB7l/rd2mW2rrG1Nmf7dzkzJ41Qqzh8lIvdTWtmA4soGDOsXZbHN0x8dAAB88OhMBPr7Ijm10Fndow7iNFEi6hyT69aWFs0GRYvKrOnqn1sDQZ3aBuNaPFxHRd5s4x7rJSoKSmqxZvMZo20q7R+FWq3G57+JOJ1T7rD+ERFZ8uSH+/HmN8dR32ie3bu+UYE3vk7RP1e28MvcUzAYJOrCpNYvtfscBtHgqewyfQp+w3OXVzcir6gGjRKFu4sqGoyef7n9bKf7ROSuKmosF1M+X1yL/6w9hGNnW9fR3Pnan7h72U4oW1TIuViDnSkFeOub487oKhGRpGal+Xf5OxtOIC23wmibUuKmsLfz9/W80IrTRIm6qN0nLmD91nT8794piIsKtvm47UfOIzI0ABOGdDfb9/Z3J/SPDe8KPv7BPgBA98ggo/bLDVLyE3V1r3x+xOK++iYlWgymjzY0KREUwK9wInI+qfvIpslhPt2arl8r3VX857YJEBJj0NzQ7OqutIvnha9EZBfrt2qSthzU1vTTKa6oN5u6aejrPzLwwcZT+ufWBhdN7wqaLgesb5IuJE/UFTUrLN9FT8+twG+HWsunvPrFUavnUqnUyL1Yw/WHRORwUp8zXS0QnD+5D/rHhyMi1HpJIXfEYJCIjCxdfQAPv7fH5vbZF6ot7vvid5Mpn8wOQ9QhH/58GkcN0rDnl7TWPqxtUBglpgGA3w7l4YX1h7H1YK7T+khEXU91XTOe/+Swq7vheh58ecNgkKiLU6vVSDpegOfWHoJCafv8/oqaJiiULXjty2MW2xxOL7K4j4g6R9miQnl1Ix5+bw8eXbFXv726vhkbkjR1C09mM9kMETnOp9vSWRMVgMyDo0EuOCAifLZNBADkXLQ8ync6pxw/7motjP3Yyr2Ij7a+1tB02ltReX0neklEhpZ9dcxsnQ4ArPs1TbJ9Q5MSMhkQ6M+vfiLqOMMEcWVVjS7sCdkDvxGISM/a+iKpDIaFZdaDuxaVuktmEyNyBtNAsKKmCQfPFOFCaesUUsN71Yvf2Q2AheyJSFpDkxIBfj6Qy1s/OeoaFQgJ9DNqV17dhNWbTuO6WQOMpqx3ZZ68CobBIFEXkllQBZVKjcG9u0nu33/a/tM6730jye7nJCJzj63c23YjIiIJCqUKi9/ZjT49QvH8HZMAAJuSz2Fj8jk8duMYxEW2zgR69YujUANWl4mQ5+CaQSIvse9UIb40Tdhi4n+fH7X64V1e3Trdo4BrAIi6hBOZpZzqRdRFFVfU4/0fUlFQqvnOzytq/e7//fB5AMCJjFJ8uzNTv505ir0Lg0EiL/Hx5jTsOJYPhUQxWFMpBimfN+3N0T82/IBf8eNJ/bTRxmaWgCDyROL5SjyyIhlvfJ0iub+0qgHvfZ+KJ1ftk9yvUquxbksaTucwEQ2Rt1Gp1Fi3JR0pGaX4anuGxXYtajVauOTDzKIZ/T16eqgOp4kSeRlrdf903v/xpOR2w7VGDU1K3P36TgBARKi/XfpGRM5XVduMqtrWIsgVNU34cXcWrpmRiLoGzY0eNYD6RgW+2ZGJK6f2RVyUZkpYZn4VklMLkZxayLWGRF4g6XgBFAoVLp/YG/9evgd1jZrPAGvZxHceK3BW9zxCZFgAoiMCsXB6f2xKzoEaao8OChkMEnmYwrI6+PnKERMRZPdzV9S01iqrrm8tPG94IUlEnu2L30WkZJSirkGJqy/pr9++eX8ukk8WIutCFV65ZwoATRIonfziWoQE+SEyzPOKKhORhi57+OUTe+sDQVMqtRq7UgpQ39Q1ZwVdPqE3th85b3H/W4un6x+rvWDSLINBIg/zzJqDAIwzAtY3Kiw1R7OiBQ+9tweXT+jt8L4Rkfurrtfc3DmTW24U7DU2a6aY1zVIf548t+4QAGYjJfJGhkFNxvlKfN5GDgJvFhrsJ7n9wUUjjGZQGfPcoUEGg0ReICO/Sv/Y8B7VhdI6bNiZCYVShS0Hcp3fMSJyO7qSFM0KFU5ml5nt9/z73ETUGblFXTuBnNxCXDdhSHezbX+fPRDf7czE+MGxDu6V4zCBDJGXKiipxbMfH8SJLPOLPSIiW9RbmEZGRO6lRaXCc2sP4tf9OZ0+1zc7LCeT8WaXTUgAAIweGINLxyfYdMxfJvfBR0/MRt+4MEd2zaEYDBJ5mXJtivgLbRSEJyIylJSiSRJhmIRq5U/SyaaIyPnOnq/EoTTpesDl1U3IL6nDD7uybTpXUbnxNUJeFx8NBICbLhuMVY/NQkJsKG6+fDAmD+th03G+Pp4dTnl274kIgPG0ruQTBTgqFkNtS1pRIiITtRbWDBKRa7325TF8+PPpdh+363gBVvx40ui6QLcGmDRevHMSACDAz0e/7d6/DsOKJTNc1SWnsfuaQUEQ4kVRLBQEYQaAUQDWi6JoabUlEXWSQqnCnhMX9M8/25IGALj/6uGu6hIRERHZ0YHTFzt87KfaDKJFFQ36bdZKSXQ19y0cjoTuoWbbZTIZggP98NRNY/UJtryRXYNBQRBWAVAJgrASwFcAfgcwF8B19nwdImr1++E8pGSUmm0/k1Phgt4QkTdY++sZhARKZ9TTKa5swHNrD+LOK4di4pDukHlyoS0iN5ZfXIuPfjnToWObFa1BzNMfHbBXlzxefHQwquuaMW9SH0yUSAxjSOgT6aReuYa9RwYnAZgA4L8A1oqi+LwgCIft/BpEbk/ZonL4HPLCsjrER4fgTwvFYHcbjBYSEbXH3pNtj0Is/XA/AODDn0/jiFiCBxeNcHS3iLqkmg5O3f50Wzp2Hee1gBRdLVWy/5pBH+05rwawVRCEYAAhdn4NIreWlFKAe99IQqZBuQdbncouQ522ZmD2hWoUV2qmdBSU1mH1ptNG00R09QYNC8UTEbnCkfRiAEBpVQMyC9r/2UdElnV0zJ2BINnC3sHgZwAKAeSIongQwFEAq+38GkRu7cfdmkxe+8+0b35/Wm4F3v7uBN785jgA4OXPjujvvL/73XEcPFNkNk1k/dY0O/SYiMh2d772J4oqpLMVP7lqP/73+VG0qLgeicheTGdgM0Ec2ZO9p4n+BuA9URR1E5RnABho7QBBEG4HcLv2aSCAMQAuAfAuNEkSTwFYLIqiShCEewDcB0AJ4GVRFDfbuf9ELlOsvbjKvVhjtq/WQq2v3ScKHdonIiIpK360XnJCpQI8PNs6kdtSq80DREOlVQ1QqRgwWnLtzERXd8Gt2CUYFARhOjRTRD8GcJcgCLq3qC+ADwEMtnSsKIrrAazXnmclgHUAngPwrCiKSYIgfAjgakEQ9gN4GJo1iYEAkgVB2C6KIufIkXtq5+cwky8Qkaeoqbd9DdPrXx2DXC7D4zeOdWCPiLoOlVoNuZXJo0+u2u/E3ri3l+6ejNSsUmzYmQUAiAwLwIJp/VzbKTdjr5HBywHMAhAP4EWD7UrYOE1UEIQJAIaLorhYEIT/Atil3bUVwBUAWgDs1QZ/TYIgZEJTuoIJasit2COme/4T1v8hIvdVXddstm3LgVz94/veTMLr909FTLcgpOdVOrNrRF7HlpvFvJ0srVdMCHrFhOCKib0hg4w/KAl2CQZFUXweAARBuFUUxc87eJqnAbygfSwTRVE3rlIDIAJAOADDVem67RZFRgbD19fHWhOXiY0Nc3UXyEHkcs0nTWCQH+qUavzvk0N4/JbxGNxGauKI8ED947yiWv3j9dtENHlxfRtyHwtnJmKTds0rUXt9n5Rl9PzJD/fj5zcW6p9b+95Tq9UoKq9Hj6jgNtsSdZanvb+Ka4xvvkRHh8Lfz/j6duXGU87skkcY3KebS37Xnvb+sveawd2CILwBIAoGsbcoindaO0gQhG4ABFEUd2o3Ga48DwNQCaBa+9h0u0UVFha4u1psbBhKSszXhZF30M3T37ovB7uP5aOuUYnl36bgv7dPtHpcjYWsoLuPS5eOIGrLjFHx2JNq+7rSxg6mLyeyxPC7rqi4GjV1zYBMhoNnijBnbC/4+crx+6E8fPNnJgDglisG44Z5Q/kdSQ7jaddg9Y0KLF2ZbLTtXF45Vm08BWWLGk/fOg55RbU4qs3oS8DYQTFIySjFkN7dnP67dtf3l7UA1d7B4HcA9mj/tWfF1EwAOwyepwiCMFsUxSQA8wHsBHAIwCuCIAQCCAAwFJrkMkQuV9eo0Bdolhlt1yZ+MfhrKK1qwIofT+KO+UPRNy5Mf3xBaS2I7GXK8B64Ye7AdgWD8g7Mce4VG4KCkrp2H0ddz93Ldho9V6vVmDepjz4QBIBjZ0tww7yhzu4akUudzC5DkL8vBiaYT3iTqvm59tc0ZF2oBgCczqnAp1vTHd5Hd9IrJgQFpZa/d+68aigOninCjFHxTuyV57J3MOgniuLjHThOAGA4N+kxAGsEQfAHkAbge1EUWwRBWA5NoCkH8Iwoio2d7jFRJ72w/jByL9bgH5cOwuUTe0u2UWujwabmFnyzIxN5RbV4Yf1h3LNgGNZsPiN5DFF8dDCG94vCVVP7QqUGHlu5V7Kd1AjgvX8dDrVajZGJ0ejdPdRoPZcltsaC0RGBePmuyahvUqKxWamveQkA8yf3wdaDebadiLxek8LyFPfyauZ/IwKAd747AQBYt3SuTe3Tciv0j5c1q1oxAAAgAElEQVR/n+qQPrmru64aiukj4/Hr/hz8sKs1dOgfH45zhZoAOSTQD3PHJbioh57H3sFgsiAIfwXwmyiK5qvLLRBF8Q2T52ehSUhj2m4NgDWd7iVRByiUKvj5mudK15WC+HpHBrpHBkleUecV1eJwejFWmczpZyBI1rxyzxTJ7eHBfqjWZnMc0qcbbps/RHIEUCaT4ZHrRwOAjcGgbdHg+ufmoaSkBgH+PgAC8PLdk/Hsx5qAMDTYD+OFWPj7yrH/dJFN59O5ff4QrG/jDnegvw8auYbWYyx+Z7fFfYfSi7BoRn+z7Wq1Gp9sScPIxGhMGNJdv31DUiZiIoIwZ2wvh/SVyN0UlNTi7HkmYJLiI2+9Hps2Ig7zp/TFfz4+aOUIssTeVYD+BuBnAI2CIKi0//itTR4hObVQXzDe1PniWtz3ZhJ+2Zdj9RzvfZ+qKQAkwTQQJGqv6SPjzKYRTRsR36HpnT5y82P6x4dLtk2IDdUfc9dV5lP4esaE4PUHpmLBtH64dFwCFl8zEv+cN6TdfZoxKh73LRxutc2oAdHtPi+5p6raZnwscUOsqLwee1IL8YHJZ+bWA3n4/DfRWd0jciiVWo31W9Nw6lyZftuxsyX6vAN5RTX4z9pDOHq2xFVd9Bh/mdzH1V3waHYNBkVR7CmKotzkn3um8yQysW5LGjZbCPaOZ5YCAH7anQ21hWCPyNHuumoYnr5lvP55TEQgpo+M69C5Hr1hjNF57v3rMIwXYvH32QO0I36twkM062GnjojD9JHSazBiIoJw7cxEfYY7H5/WYLNfnPHC9R5Rwbh9vnmwKJPJ0CsmxGq/b7p8MOZN6o23Fk+32g4Alt48rs025FoZ+VVGz+salJL30/i5S97mXGE1dp8oxNvfntBvW/HjSdz9+k6UVjXg+U9YOc1WCbGhFm/EU9vsOk1UEITnpLaLovii1HYiT7HjaL7+8V3LduLvcwZg/uS+khco1e0oxkze4Z2HLsH2w+dtmopp6qW7J0tObVl2/9Q2jx2RGG3z1M6gAF8M6xspeZf59Qem6R/Pn9IXGflV+hsgAHDnlUOx5UAurr7EfEqfJb4+rfcan7t9Iu5ethMqtRpXTe2L62YNMLob3h7hwf64Ye4gm9oO7t2tQ69BrpNbZJyFT61Wo6ZegSXvJ1s4gshz7DyWD38/H0wfGa8fAZRi6cY0ta7EUbcrTyVZY+9pojKDf/4AFgLoYefXIHI60wLLG3Zq6mmdK3S/9MHkXGufmoOIEH9cOysRM0e3jprNGdsLl45vXcA+f4r0NBbDkbD/u2UcAvx9MKBXOGK7BVl8zSBt5lp/iTWslqxYMgOLrx1pc3tDUeGBuOUKAWHB/h06XhK/x8kGGflVEE3WTL306WG88XWKi3pEZF1pVQPKqqTzG37++1ms/TUNADQF0C3YfcL2LNCkwa+UjrP3NNEXDP49C2A6gBH2fA0iZ1IoVRbTFxeW1eH7pEzJfeR5+vQIldx+7cxEq8fpRubkMhmG9I3Ub791noDEnq1r8BZdkmhxpOqeBcOwYFo/DErohpWPzDSaCirl4etGYsqwHlgwrZ/VdlL91AWsCbHWp2PaW6B26qnhiKGhoQY/O0sG9DJe0zgiMard/Rg3OLbdx5DjKFpUVve/9uUx1NQb34w7V1hjlE2RyJ08uWo/nli1r+2G7V/qTWgNon3lJt8ljAY7zN7ZRE2FAuCqTnJ7lqZrrPzpJFKzpKezvfzZETQ0MT+St+gXF4a8IvNajwum9bOYWMiWqZw6fr5yjB4YLZkZbuqI1nV/tiSDiY8Owb1tJFqx5Pb5Q3HLFYLFoMxRnvjHWGzen4PLJ2jKr/Tu3hp8TxsRJ5mYBtAUIa+qbcZfp/czS3qzcHp/nMouR3iIv370fubonth94oLFfvzr2pG487U/zbbbksmU7K9JIjNsSWW90fMvfj/rrO4QkYeYObonMvIrMY/JYzrN3msGz6E1NpcD6AbgDctHEDlGRU0Tth7Mxd9nD5QsB2HqcHqx/rFarUZFTROiwgMtBoIAGAh6kVEDorFoRmK7p+aYTuW0Nu0HAIb1jQKQhSsm9sbvh8+3t5t2owsEH71hNLqFBjjlNfvGhWHxNa3TVCNCAzC0byTScisQHx3cuvbR4Ec4f3Ifq7WiBvaKwP1XD8eghG5obFaiuKIBowfGGAWDz/5zApQtKrz25TH9tlfvnYL/++iA/vnIxGiMF2IZDLqJZ2wZVSHyIJkFVWaF4Tkw2EHaH1yAvw8eNPhOiQoPBGDbLBMyZu+RwdkGj9UAKkVRrLbzaxC1SVecOzm1EB88alay0kxtQ2vSl98Oncd3OzNxjUT9K/JOS/4+2i7nGTsoBiP6R+Ey7eiXqb5xYVixZAaCAnztHgzet3A4DqUVISWjtO3GWiP6S5dpcFbmxgcWjcARsRjTR7SutewZE4Kxg2IwcWh3TBnWdqbUSUN1y9IDEB9tPvVVN1X3sRvHIMBXM1W1R1QwYiICUapd1yP06YYAbRbUiBB/VNXZXCaXXOjs+Uo0NCkxemCM5P7q+maEBfnZnGSJyFE+/PkUyqub9M837snGsH7tn+ZO5tmpdYIDfbHykZlm2bCpbfYOBvMA3A/gUu25/xQEYYUoitYXBRBZsP3weRSW1+Of84QOHW+tOPUv+3LQ0KjE9XMHGm3fd+oiAOCnPec69JrkvW6dJ+jrnN10mXlGS38/H6OSDVKCtclf7G3ysB6YPKwHGpuVbY5QtsdDHUw6Y4vQID/MHmNcQFwuk+Gh60bZ/bWGm1x4xUeH6INBQDNauvzfMxDo74N730iy++uT/elGe9c+NQcfbz4DoU8kZo7uCUAzEvO/z4/i8gm98Q+Jv1UiZzL9TN60N4f1AyWsenQWmpQt8JXL8a93dxvtu2ZmImaN7onwEMuJzIICHL36zTvZe9HI6wDmAfgMwCcA5gJ4286vQV3I1zsykJRS0GY7tVoNhbJ90zZ/2p2NbYfyoFCq0GxwLG8iez7D9WjtMW2E9ZGoOWN7Wd1vKlj7xSRV4N1RAv197XZntG+PMIz1wIQrDywagSdutB6U37XAfI1iaJCf09dSUuc1NLVg/+kio2m+Z3LKAQDbj7huOjZ5v9oGBd7dcAKZBVVW25nmOgGAghLp5HRdWYC/D8KD/REc2BrU6b6XB/WKsBoIUsfZO4S+AsBY3UigIAi/Ajhp59cg0hPzKhARGoAvfxdxOqcCqx+fBT9f6xfCLSqVUZKO+95McnAvydmeuXU8vtx+FntSjdcAhgT6oq5RqX/eMyYE4w2CnbsXDNOPDBt67+FLOpSobOSAaFw7MxHjBfOAasWSGWhScNKEI0wc0r3NNuFWymT4yGVoUakRGuSHQQkRRlNvw4P9WEuUiAAAvx3KQ2pWGVKzyrD2qTloMUhGdzyzFGO0U5g5VbnVtBFxkt+zltxx5RAsmNYPcVHBDuxV12bvYNBX+6/Z4DmzbJDDLPvKuNbUuxtS8cAi82omp86VISWjFLdcPhj3vJ5klqLeUIuVQrDkGfz9fBBjkNzlzQenobSqEQmxoahvUuDJVfsBAM/fMdGmkaCO1teTy2QWyz8EB/ohOLBDpyU7GTsoBikZpehjYSR51pieuG7WAJzMLsM7350AANw2fwgC/X3x9R9nkc87+26BxafJVVpaWt97dy3babRv+fepAIArp/RFcUWDU/vlzkYNiLYpGJwgxKKipgk+cjkDQQezdzD4JYAkQRC+1j7/B4Cv7Pwa1EVl5FficFoxbrxskMX0+2m5Fdi8L8ds+9vfai7kdOuTsgos5zW6YKGuIHmuqPBAfaYxw+knHblZe/Plg/Hl9rMYO8jzpk+SsfsWDkd+SR36xxsnJBg7KAZHxBL06aHZPjIxGuuWzjVqc9mE3vppiX17hCG3qMY5nSYzD727x9VdILJoy4FcV3fBrST2DMd/bpuAlz49YrWdYaZQciy7LY4QBCESwBoAL0FTW/B2AKtEUfyfvV6DurZXvziGP47mQ8ytwDc7MpB7UfriS6G0PPXu6z9Yr8pTxUc7587g3QuGIjrccrmFS8cnYO1TcxAd4b3DerpF+EEB3p2Vzd/PB4k9w82mcN21YBieumksJkhM79UZ0b81Ic2D14xw6rpQsiznovSNPpVajcz8KijbKHJPZKi8uhFF5fVtNySbRYQEoH986+ysF++c5MLeEGCnkUFBEMYC2ALgDlEUtwLYKgjC/wC8JgjCCVEUU+3xOkQAcPRsCf48VmAxNX+zSSKZNG0iAQBIzzMv+E3u7+On5uB8US1eWH8Yf5nUB9sO5dnlvFJZN6eNiEeQvy/e//EkBiZESB/n5es/bpg7EHK5DIs6WV7lP7dNQFiQY7KnOlKAnw+EPtZrVUWFB+Ljp+boZym88eA0FFc0GNUzlBIS6AuZTGZUzobs58X1R/DW4unIL67Vb1O2qJCcWojPtJmAn7t9AvrFWV4qQKTz+AeampemMwMAoKiiHrWN/DtuL13t59vnD0F9oxIJHUz4RvZjr2mibwL4hyiKSboNoig+LQjCLmiyiV5mp9chQpPC+jLUvSeN56K/8c1xR3aHHOjluyejoVkJuUyGvnFhWPPkbPjI5fpgcNSAaKRmlQEAIkL9seRvoyFvzwiNhaZjBsXgoWtHYnCfbp39X/BIEaEBuHvBsE6fx/DurzcynK7eLTQA3UIDcMmoeCSnFsJHLsOVU/ril305eOIfY/HG15r1ze8vmYmPNp3GgTNFruq219PVmdV54K1dCDTIsPvi+iNmF/eFZXWI7RZkcQ2xskWFksoGyVqW5Ll2n7iAH3dn45V7JiPEoOzPa18cxdl8yxlC1Wo1/m/1AWd00WvpysCQ69lrmmikYSCoI4ribwCkq8EStUMd7755pY+emC25vW9cGJ6/YyJ6xoRgQM/W0Tkfk/zcc8cl6B8/e+sE9I0L05eV6MysPZlMhrGDY40uDohsEWGQ+vyamYlY/fgsDNKOMCf21ATHt3awbip1TItKbZRFWKe8uhFLV+/Hxj3ZeGbNQaz55YzFc6z7NQ3PrDkIMa/CkV0lJ/pxdxbWb01HdV0zTmWXo6m5BVV1mvyHlgLBi+X12HYwjymLyKvYa2TQTxAEuWlxeUEQ5ABYFIQ67XBasf6xPQtqk2v5+sjRvVsQiiuNM6399/aJ7T5XlMk6vzljeyE9rxILp/frTBeJ2mVkYjR+3Z+LKyb2BgB9qZtVj86Cn5/mZkZ7CiP3jAlhUisHSMspx1d/ZKC4ogGb9uYAAA6nF2NBcS16dw9F0vECCL276UcCdSO52YXVbU4hJs+weV9rYhc11Hh05V40NCnx1E1jzdpW1jbh7PlKfPjzaQBoV2kE0njixjHtm7lDTmOvYHAXgP9q/xl6FoD1dEFENsgzyNSXfLLQSktyZ4uvGYE9qYX6qZ0AMGN0PH7Yld3pc5uu4wsO9MNjN0gXHr/pskHILqy2mJWWqKMG9+6G5f+egZBA46/XAH/jZDwJsSFmpSkeum4kBvfuhpYWNT7YeArXzUrE5n25DAYdwNLygfVb0xEW7Kf/jDKdTnpMLEF6biVmjIrHBBvqWZJnqKxpRkOTZvRYKtB7dIXx1OP8klqzNiTt+TsmoqiiAUP7RbXdmFzCXsHg/wHYIgjCzQAOQ7MSZxyAYgAL7fQa5IF+2ZcDhVKFa2cmQtmiwoHTRRg3OAb+fpoLI2s13pqaW9cGJh2/4PC+kv2Fh/ijuq5Z/zwuKhhThvcwCgY7YuUjMwEAvj4yDOgZjkvHJ7RxhLHLJvTu1OsTWRNqQ9KcR64fY7a2LSosUD81eenN47RbmZbeudRWP5+yLmiylZ7MLpNMKkLuo7K2CWk5FZgyvAdkMhk+/11Er5gQ3DBvqFnb73Zm6h/vSeUNZ3vq0yNMX6aH3JNdgkFRFGsEQZgJYA6AsQBUAFaKosjiP13cT7s1Iz7XzkzEH0fy8d3OTKRkxODUuXK0tKjx8VNz9G2PisUY2CsCEaEB2JlSgM+1md/IvY0ZGIPjmaWS+2aO7ilZ97GzDKfZPfPPCXY/P5GjRYYFQOjdDQMTIvDrfk3AJzVQzcFr11IoVfrsh+RY9Y0KrNuSjgXT+tol2+trXxxDcWUDQoP9MDIxGjuPFQAA5k7q1+lzk7TVj8/GfW8mubob1E52KzoviqIawJ/af+Tlvk/KQq+YEEwdEWdTe2WLSn/n7VxhtVktwBU/nsSxsyUAgKdvGW+xbAS5n9EDo3HXgqGShZ8H9orAmw9Ow++Hz+OIWIzukcE4X2x5es2l4xIwzkptNyJv8pR29E8XDEoZ1i+q0yPpZDu1SWaQI+nFmDK8h2s6o3XqXBkOpxXjtvlDvHpq+/Yj+Th2tgRpueVY+cisTp9Ptxa9rLrRaPvaX07hrvlDOn3+rmpYv0icyTFPpLTs/qlGN07eXzIDjU3Ws7+Te7BbMEhdy5YDmosXW4PBU9nlVvfrAkEA+N8XRzveMXK6+OgQs6ybIYG+ePzGsejTIxQymQw3XjoIN146CADMsrAF+vvqj7n5isHO6DKRx7hsQgIGJUTgpU+5/N4Zci7WGD1fs/kM1myWzjK642g+hvePQlxUsEP79Pa3JwBoZloM6CVd+9QbKFs0N4mbFao2WrbPZ9tEjBvcepOxoUmJpz7cZ9fX6OrkMhliuwUZbQsJ9GNGbg/BYJDsrqC0DkH+PogKD9RvO3u+tdi7aTBwJsd6oEju6+lbx2OgycXJ4mtGQOgTadO6KQCYOToeF8vqMXtcL0d0kchj+Ehk2pPLZBbrNa5bOhd3vqaZjCOVlZcc68vtZwFofg9qtRoymUz/X0d4/esUrH58tkPO7ena+rnnFFbrHx9LL7bYjtpmOnoOdH46+5sPTmuzhjQ5DifCk9395+ODePwD47tuuiLhAFBV25pQ5HROOd5kUXi389p9U2xqZxoIAppC49YCwdgIzd3DnjGalO1+vj64+YrB6BXDYs7UNT1+4xhcMbG3/m+iI26bP8Ri3U6dmy8fjBvmDuzwa5C0b//MwF3LduJ8cS2WvJ+MO1/7U5+Z0p5Ml1eQxvqtafj38mSopKIUrcMMAO0mwM+n7UbtFBUeqC/jQs7HYJA6JbOgCtsO5rXd0IK3GAi6pe6Rwfj4yTltNzTw8HWjcNn4BESGBVhtNzAhAo9cP9ogWyJR1zasXxRuvHSQ1ZGN+GjrUxF7xoTA10dutd2l4xNY58sBfjukWeP+33WHUFOvAABs3p9j9Zjy6kZU1zdbbdMVFJbVGWUOB4AWlRq1DQrUNSpQYsNo9+4ThahtUEBhZXrp3pOsC2gvt0gs5xjSt7X25iv3TO5QrWByHbeYJioIwv9BU4LCH8AH0NQtXA/NjMJTABaLoqgSBOEeAPcBUAJ4WRTFza7pcddw8EwRggN9MTIx2mKb/32uWd83cUh3REcEWmxHnsf0onFIn25Iz2ud7juiv3HNoDGDYjBmUIxN57b2niIicy/cOQnPrT2Ei+X1AICocOmbLpeMjMeGpCyjbT5yWevfs+XBE7KjmjpNUJhXVIPQID+jZRMA9LNnDMtTlFU1IjTYz+rIy4akTPx9tneM7pZXN+KZNQfRKzYEL9012Wjfw+/tgVwmg0qtNivh8eoXRzGgZwSuNxnl/u8nhzCsbyRLUTmIbuaBr48c7/xrOr74/SwWzeiPwrJ6DOvXGgxyhM/zuHxkUBCE2QCmAZgOYBaA3gDeBvCsKIozoKlZeLUgCHEAHta2mwfgVUEQrA9BUKes3nQa73x3wqa2yhYV1Go1yg2ydj3/ySFHdY2cbNVjs/DI9aONtvWK5Qc+kbP4+sixxOBv8PX7pwEA7vnrMEwfGYfwYM3UbKnRxVWPzdLX5iTnOJRehPpGBZ7/5DAe/2Af3ttwAkdFy1MVG5qUeGLVPjy39qDV82490PGZOO5Gl+WzoKQOaokpnpamfWbkVxktPdEprmhgIOggIYG+8PWR62tDR4QGYPG1I9ErNhQThnRHMBPFeDR3GBmcB+AkgJ8AhAN4AsA90IwOAsBWAFcAaAGwVxTFJgBNgiBkAhgFTZF7sqC6vhlhQX4OW9Bu6M9jBfoF9QCQV2S5hAB5FkesESCi9uneLQj3LhyG/nHh+pG+qcPjMHV4a1bnbmH+RsdMGd5DfwFHztOsUOFfBuV2TmSV4USWdKH6Mznl+Px3zXdnSWWj2f62KJQqKJQqyGTA3pOFmD4y3qgWa2ufWtCiUkvua8umvedQWduMf84T9NsKSmrx6TYRd101FD06mVFVl6HcEt21THpua0kDtVqNmgZFp16XpE0d3gMTh/RAZFgA8ktqMaRPZNsHkcdyh2AwBkBfAAsA9AewCYBcW7cQAGoAREATKFYZHKfbblFkZDB8fd3zIjY2Nkxy+5lzZVC2qDBqYOdrrZ3OLsPSlclYNGsA7lo4wmK7+kYFggJ8LQaMhn39NTkb2Reqzdo0qmAUCJJnM31/Sr1fg4MDLL6Pyfvxd+8af51l/ed+5YxQyHx8sPqnkwCAmMhgo9/V1XMG4XhWGeoaFcgzKaNAjvdHygVcPTNR/9wnwM8siVpbf1uxsWFY/m0KBvSKwFWXJOK2F7ahvLoJ86b0xW8HclFep8Div402O27RE5vQolLjl7eubne/N+45BwB47JYJSBGLERLkhy+2ZyCzoAobdmfjhXumtvucJQbJ5H7YlW2xXV5ZPZ5fcwBR4YFGs4/+vTwZtQwG7eaxm8fjrS81S39CggNw+bT+AIAJruyUh/K070d3CAbLAKSLotgMQBQEoRGaqaI6YQAqAVRrH5tut6iiot7OXbWP2NgwlJRIfwk/tSIZACTvHrZXcko+AODn3VlYOLWvZJvK2iY8umIvJgixuHfhcBxOK8bw/lEIC24d8v/k55PILKjCTZcNwofaCwxTL3x8oNP9JfuYP7Uftu7PadcxKx+ZiRaVGntPFmLsoBiz96fueVCArz5LXkN9s8X3MXk3a59h5HqThVh8HuCL+iYlGhsUZr+rJ24cg292ZCDvYg0C/X3Q2MyU7s7y1W/p+Oq3dP3zT342/05t62/rQmElth/Kw3YAk4RYlFc3AQDO5mrKNOUUVKKkpAYqtRotLWp9IfAWldqm81tTUlKD5z7aDwDoH6+5JCurbEBJSQ1qGxQ4mV2GyUN7mK05b1a0wN9khkllpW3XaLuOahL0lJsUj2cgaD8+chmG9Gq9xG5sNP/cINu46/ejtQDVHYLBZAD/FgThbQDxAEIA7BAEYbYoikkA5gPYCeAQgFcEQQgEEABgKDTJZbxWXaMC6bkVGDc4tt3TPKvrm3E8o1Ryn0qlBmSa+lUFJXUAgCNiCfoeytPfnRtqkBnqx92abbrEBeTeHrhuFE5nl1qcpjsiMQqnso1rO+qmDc2b1MfquZ+7bQL+7yMG/kRE9mCtHALQWojd0H1v7pJoCZwrNL4AfX7dIeSX1OGluyY5JODXvV7uxRrkF9fii+1ncfZ8JQ6eKcK/rh2pn558rrAaL316BNfMTMRfp/Vr9+vsPFZgz26TiaF9I/Gva0ca1w9k0uEuxeULCbQZQVOgCfZ+AbAYwGMAXhAEYT80GUa/F0XxIoDlAPYA+BPAM6Iotn9yvQd57/tUrPzpFI6KJe0+9tXPjyK/RDoYeOi9PXh6tfkF/YXSOv3jNIN5+TrFFSxo7Amkbhzcs2CY/vGIflFm+23VIyoYDy4aAV8fGWaO6dnh8xCR+3j2nxNw7cxE/OOyQUbbRw9g1l/Hs37Vfe8bSR0+c772Zu9/1h7CK9rM31IKy+qQV6QJ7DYkZeJIB2ryPbfuEM6e10zWSs0qw71vJCE1qxQqtRop2hvTG3dntxn8kvPFdgs0W0fKWLBrcYeRQYii+KTE5lkS7dYAWOP4HrmHzHzNEsliK3V21Gq1/uLf8HGRYeBm8tnb0KREQ5PSLHvX/tNFdug1uQXtr3bMwBg8/LdRADQjgtX1CqTltI4KvnrfFPhbWVf71uLpaFYY31GeMKQ7Jgzpbv8+E5HdjB4Yg/2nL6JvnPTUIMOP/8Se4UjsGQ4AiI0IwvIfUvWfHVsP5JqVqiD72X/avP6d4Xd5W84Vmq/hT8+rlMzOqd+fW4Hq+maMF2LhI5fjmTWaDKarH5+tz1Zqj6Uq725IBQD9aKAawN3LduLqS/pj4fR+nT4/2YcuEPRhDdIuyy2CQeoYMa8Cy75KwZK/j0K/uHAseT8Z181KxFVT+9l0/F3LduLKKdJrCcmzDe7TDXnFtfoLPAAIC/ZHWLC/UTDYI9J6Bri2CsgTkXu6fb6AGaPiMbhPN6vtTGOO0QOj8dRNY9EvTvPZMW1EnFEwOHtsLySlcNqevSiU5tNAH1mxF9V1thWkf+nTI5Lb71q20+Ixr3+dAgC4fs5A/GWy4dKA1gDywbd3Yf6UvqiubcbCS/rpt5/KLrOpX4ZM32M/J59D1oUqo+Uo5BqThnbHAm2wLpPJEBHqj6raZrPfGXk3BoNuqq6x7YXRWw9q7uBt3HMOV0zS5Nz5YVe2zcEg0HY6Z/JMf589ECP6RxsVgtUZOSAa+CMDi2b0d0HPiMgZ/Hx9MKQDF9symQyCQRr5iNAALL15HF778hgAoFcM64s6mq2BYGdlFlQZPTccjWxsbsFP2nwBO47l67e/bWPtYUNinnmuv1PZ5WZr18m5Hr1+NEYkmkwF198PYDTYlbh8zSBJe+T9vR0+1jTDFmfoe7Z1S+fixbsmYaLE1Mx+cWGYNLS7WQF4P185Rg2Ilqwv1iMyGGuenI2F0xkMEnVVE4dqPk9s+RwY3Lt1dLFfvGelTCfLjp0twafb0ttu2EnieauJ38mB7r96uOWdVuI9jgx2LRwZdKGa+mb4+sglC8BKZRBrbFZix9F8zBjdE+HBxoWFZQZ/1Q+/t8f0UJRXN+LxD/bZodfkCgmxoZ8Wo20AACAASURBVHhg0Qgcfu1P/bYVS2YiOFDz3jmUVoQPfz6NO68catP5fOS8D0TUlQ3sFYHVj8+CXwdq8V41tS9+3W95VklkWAAqapo60z1ykl3HL+gfl1V5dU6+Lkkmk8FHLoNabZ65ViYRDXLwoGviFaEL/Xt5Mha/s9vm9r/szcEPu7Kx7tc0o+1qAM1K62mjdxzNt7qfPI8uEASASUN7YOUjM3HJqHgX9oiIPEl7AsEHF43A+MGx6B8XjvFCrNn+vj1aRwz7WUhaQ+6NZYM812wr2b0/eHQWVj02C927BRltDwyQ+PvXBowcGOxaGAx6kDJtwdWL5fXYdbwAqVmahdy5F2vwyRbrUz1OdmDRNzmXv58cM9oI5j56YrbFfVIjzERE9jBhSHcsvnakWTFxAPjPbROw5PrR+uc3XDrIrA0ROY6/n+UbO36+cvj5yvHyPZOxYslM/fbesaGWT8h5ol0Krx7dRHVdMz7efEZy3/dJWZg/uQ+aFZqpo8UVDfh0m9iu8+vqDZF7MkzjHejvi+1Hzku28/WRQyYzTgtPRORMvbuHYnDvbhgzMAZzxvZCgL/xhajpCAQROc/IxGj9AECCQT4BXx85fH3k+OiJ2ahvUkoGkNNHxmPrwTwMl0g+R96LwaCLGKaTziqosloQFrCeJpq8S1s35FYsmYlmiXTkRETO4COXY+nN41zdDSKS8LfZA/TBYHy0efZfXx+5Wd4JnetmD8Cssb14Q6eL4TRRF8jKr8R9bybpn7cVCJJ3iwq3XMtv/GDztTlBAb6ICJH+ICciIiLvddn4BIv7TEfp20sukzEQ7IIYDLrAf9fsd3UXyE307h6KJ24ca7Qt2GDt370LraSFJiIioi5Fat3uLG0CmTvmD4Ga60ionRgMukBVrXMKypL7ePT60fjLpD5m2/8+ZwB6RAUbbbtiUm9cOi4Br947BX6+/BMlIs8wtG8kosMDJfddP2eg2bawYD9Hd4nI6/TpYZ74JT46BOuWzsWkoT1c0CPydLzSdLLdJy603Yi8TmR4oFlheEC6zk+gvy9uvmKwWZBIROTOnvjHWLz+wFTJfZeOT8Dssb2Mtj38t1HO6BaR13jsxjGYMjzOaNt1sxJd1BvyFkwg42Trt1ovAUHeIyo8AOXVmsLLPaODER8djLUmNSKJiLyJzEoGrH/OE3DL5YNR26hA3sUaDOgZ4cSeEXmGyyYk4I8j0rWhh/eLMnr+8ZNzzKaNcpYotRdHBolsMKJ/VNuNTMgNLopkMhnkMhkeum4kLhlpUEuQpXyIyAvFR2tmNowXYnHp+AT9lHe5XIbwYH+MSIw2at+XheqJMHV4HEIC254+rSsZIbV+MCSQ4zzUPnzHELXhziuHYuKQ7njg7V2dPtfYQbEYOygWyScL7dAzIiL39ORN45CWU47Jw3pYHS0MDvBFfZMSz9w6HofSivDdzixU13FdPZE1z985CS0t0iWmYroF4V/XjkTv7laKyhMZ4MggkRXXzxmIS0bFt91QQs8Y8zWCpjgwSETeKCLEH1OGx1kNBAHg3YcvwarHZsHXR45pI+JZNoe6hIEJ0lOkxw2OxTCTgu83zjVPviSXyeDna7mMxLjBsYhliQiyEUcGiSxYt3Su/rG/nxzjhVgIvbuhV0wI3vjmuNVj/3HZIEwdHoeH39vj6G4SEXksXx85DK9pe0QG4Xxxres6ROQgN8wdCJVajWnD4xARGoD84lpsTD6HY2dLAACjBkRjvKCpLfzOQ5cgJNAXjc0tOJNT7spuUxfAYJDIBjKZDIuvGWm2ffm/Z0ChVOGJD/ZBZbBq+/IJvQEAS28eh7LqRsvntX9XiYg81j//MgTDB8YiK6+C0+nJq8wzKS+V0D0UU4fH6YNBw1IruhHy0CBO4CPH47uMujRLC637x4fbdHxokB8iwwLwyr2TMX5wrNn+wb27YapJGmgiIpIWGuSHv80dhH9cNggLp/fDXVcNNWtjWKJiynDWVSPPpUu0BEiXmiJyBgaD1KU9fet4s20+chn+PntAu87TIzIYC6b1a38H2lhPQ0TUFQUF+GLRjERMHRGHhdP76bffs2AYbr1isP75+MHdXdA7Ivswyi1g4XLA38/y2kAie2AwSF3KUzeN1T/uHx+G+OgQPLhohH7ba/dPxZon52BI30ipw+1Gd3HTj+nUiYgskstkWDSjtah27+6hRklpxg2Owf/dMs4VXSOy6v6rh9vUbvE1mmuQueN6Se4flRiNv0zug//ePtFufSMyxDWD1KUM6BWBF++ahI83n8FD140CAESGB+j3d+9E9i01bK/0umhGIq6+pH+bmfaIiMjcdbMSERcVDJlMhkEJ3VzdHSIzk4b2QHREIF757KjVduOF7lj71ByL1wNyuQzXzzHPKEpkLxwZJK/12n1TJLcnxIbi+TsmoVuoJgiMj9JM05jRjhISz98xEa9aOL+tGAgSEdlm2gjN2uuYboEAgKum9sN4wXyK6LxJveHrw89Wcg8DekqXkDDF6wFyJY4Mktd5a/F0hIf4wUcux6XjExAS6ItNe3MASC/RCw70xZonZ8NHbvu9kT49zKd3qm0fGCQiona4e8Ew3HHlkDY/p/8yuS9yCmsgnq90Us+oK3nqprFY9lWK5L41T87GPa8nmW1/+pbxCDXIFErkbhgMkteJDGud9nnz5ZpEA/pg0MIK7fYEgkRE5HzWPqeX3T8VF0rrEBHij0B/JtygzgsL9kNNvcJoWz8rmcYtvT8tFZgnche8Aiav8v6SGVb3t2ddHxEReYbYbkEYPTDG5vZrnpyNv0zu03ZD6rKumNjbbFuAhcyefzPJQB7AGxLkQTgySF4lJFB6KsbNlw9G1oUqjgASERF85HIM7t0N2w7mubor5IaW/3sGQoP8MHdcApoVLTh6tgQzRvUEoAn0mppbJI/7v1vGoaq2uV03JohczS2CQUEQjgGo1j49B+AVAOsBqAGcArBYFEWVIAj3ALgPgBLAy6IobnZBd8lNPHbDGLz17XGb2l46PgGXjk9waH8s3TEkIiLnuX7uQJTXNKGhSYnSqkb9dt16r5GJ0S7sHXmC0CDNjeWgAF8EBfhi7jjr1w+6BHTMbEueyOXBoCAIgQBkoijONti2CcCzoigmCYLwIYCrBUHYD+BhABMABAJIFgRhuyiKTa7oN9nffQuHY/Wm0za37909FADQIyoY04b3wIBerp2X///s3XucXXV97//X3DKTy8wwSSYkGEggYT5yC8RgCJeQAFFucrGUYqMWRAEpFa0tooL1cvBYrXAUW2gboGitv0OL5Rwbi6DiBbBCxXh+UPQbo2jLUWQIk8yEmEmYzPlj7xkmyZ5JJrNvM+v1fDx4OOu71t77M5OPe+/3Wt+11kEzp7J61eF0HOyHgSRVypwZU/no5Uv5hwfX880fPgvAogUziEPa+NQ7T+SAgfPKC5w1cP7J8wfPMZcKGbjywPJFc5h5wGRWHncQzVMmVbQmaSwqHgaBY4EpEfEguXo+CCwBvpNffz/weqAPeDQf/nojYgOwCPj38pesUmiZOolPXLWMA6Y2cvUt3ym4zR3Xn8ba7/2CJR3tu2xfLfPzVx2/5zkGkqTye+OphzG5qZ4zlsyldWruy/rMIfeSbWzY87SBN5xkGMyqE448kMee/s0+b980qZ7zTppfuoKkMqmGMLgV+DRwB3A4ufBXk1Ia2GfXA7QCLcDmIY8bGB9WW9sU6uurIyRo79rapnD0gtw8+0/90XLe95cPs2LxXL6z7tnBbQ6c1cLbL1w0uNzevuctHuTfRaVnj6mUitVfVx3cNuy6GTOm8csXtnLiMXPoeWk7v3yuhzmzvfJjVt349mWc9yf/Gxi5/2prc8cGJ09p8H1QBY23vqiGMLge2JAPf+sjYiO5I4MDmoFN5M4pbC4wPqyurq1FLlWjdcf7TuPBf/8v/vFbG/a6bffm39LZ2QPAzGkN3PX+03m2c8tgGDw+2gfXa3jt7c3+nVRS9phKqZz9ddbxuXPBWhsnM3f6ZDo7ezhg2iQ2bdk+7GPOWDKXbz7x7LDrVR2mtzTyYnfhM4mWHjGLI+a18fmvpcGxoT03Uv8dfeh0Hv/x87Q3N/o+qD1U6+fjiDs4yljHcC4HbgaIiIPIHQF8MCJW5tefDTwMPA4sj4imiGgFjiB3cRlVsdraGs464RCuvvDoguvramsGT9RunbbnnPtXzZzK6197MH/ypuP4wzceU9JaJUn66OVL+cBbXjO4fOD0KbusH7h/rarbcBeNm9pUzzsvOJoVx71qj3UXrTiMq84/asTnfds5R/AnbzqOE4+eXZQ6pUqrhjB4J3BARDwC3EMuHL4b+Gj+ojGTgHtTSs8Bt5ILhg8BN6SUtg3znKoyr331rILjF61YwH97xwn86ZuO48C2KXusr6mp4U1nHM5R86eXukRJkmieMmmXq0LOnTmVO9532i7bHL7bjcSvcWdlVbju9xfvdZs3n3XEHmNHzs9NJz73xPmccOSBIz6+saGOo+ZPp7amZsTtpPGi4tNEU0rbgdUFVq0osO0aYE3Ji1JZfPiy1zJvdu6wdetUw54kqXr8+VXL+Mdv/YzfX3X44HlihTRPaWCE1SqTy85+NUfMa2POjCksO/LAgleL/dvrVjJnduse0/heNXNamaqUqk/Fw6DGh7raGvp2FnhnHcbrX3sw0wcu372bT//hSfz8V92DQVCSpGozq20Kf/Q7rxzxe/PrOvhNgWsRfPiy1/KL514JF2efcAjnn3IoP312E7fc83/KUut49bHLl/Jndz0+7PpXzZzK/33hpRGf46rzj2Lnzv7BI3ofv2IZAPd//5eD2xw4fQq/eXEr9XW7Togb+G7zuysP299fQRr3DIPaJx97+1JuWPPY4PINf7CEj3/hCQCueMORLDvqQO5/7D9Z/1+beOcFR9E0ac/W+vBlr6VrSy/TW5qY3tJUttolSRqrQuegHTGvjektTTzz61fCYGNDHY0NdRx96Aze/LoO/uHr68tZ5rhz+NxWfvrs5oLr6ur2fsh16RGzqCkwZXPo7uub3rGUHS/v3GObNbtN/5WyqBrOGVQVO3ROM5+4chlzZkzlr//klZm7UxpfCXsnHj2bmpoazlk2j/dcfGzBIAgwb3Yzxy2cWfKaJUkqp7mzpr6yMCSXnLFkLh+69PjyFzSOLHzV8LfzmNRQx2Vnv5r3v/k1HDqnBYDjFs7kjNe8EswLBcHd1dXWDvvdRMo6/5+hEX3o0tcO/jyp4ZV7NjbUux9BkiSAA9umcNnZr+b+7/+SFccetMu6Q+e08Ikrl/GBv/1+haqrHqctfhXfWvd/B5cLXUV8qBrg1Pzf8wNveQ09W3fQlj8F5Zs/HPn2HgNXKp/eUviUFUk5hkGNyo1/cDzP/LqbA6b55ipJ0oBTjz1oMLjsbvfbU3zkbbnzDO++/ycAHHXodP7jmRcH15930nx+8VwPT/58Y+kKLrO3nf1qlkQ7LVMnsXzRHHbu7Kd5yithsKG+lj9903HcufbHPL/pt3s8vr6udjAI7ouTjp7Npi29LDvKW0BII/HwjgB4+7l7Xmq5kMMOahn23j2SJGXBRSsWUFdbw4XLD93nx1xwyivbHnJgMycOCSnv+p1db01RUwMLDmoZ9rkuXrlgFNXum7eeGXuMvf61Bxft+ZcfexBTmhq44JRDmd7SxMwDJu+xzeFzD+DjV56wT8+3aMEMjpjXNuz6+rpazj/5UGYVeB1JrzAMitMWv4qTj5nDeSfN3+fHDNws/iRvuipJypiOgw9gzftO2+V+hKPVUF/Lxact4L2/dyyTGuq46/2n77L+9UuHD2J1Be5l0TJ15CmXd73/dG6+5mSuPP/IgutPW7zrTdjPPXEebzrj8D3qGs7ZJxwy7LrVqw7fp+eA3Pl9rz5k73/X91x87D7dV1DSyAyDE8hn3nXKPm133e8v5rb3ngrk9qwN7A3c9xtH5E7Y/uy1p/CONxT+UJEkSSM7+4R5HH3YjMHlRQtyPx80c+ouFzy58/pXrnp57onzmD1j12mnAJ+4clnBWT6rVx3OFfnP6rbmRpYduetO3AuXH8od1+95Vc3Z0/d8jZFcfNpC5rZP3WP8TWcczqrj9+8Io7dvlErPcwbHkcZJdfRu7xt2/b5cghkYnFZxx/WnUTvkKlyvffUs1n7vF4PLF54y8vSXfbmClyRJ2jfvvOAoNjy7maMOnb7L+NDP24tWLGDnzn5WHHcQxxw2g7/85ycBmNxYz4lHz6Zl6iQ65h7Ar198iSlNDXudJnnGkrm7fBfYHwfPyt20/fo3v4Z3febhXdbNanOaplTNPDI4jtz+3hUjrt+Xt/Khl7je/c3/4FnT+PgVr8zVP38vYVCSJO2bgamdu9/4fKimSfUcfdiMve5sra2t4dKzXs1rOtp3Ha+p4ZjDZtA4qY75s1v2GgTPPuEQpjY1DC4P/Q4wGgPTNYc+16olc3nXRcdw7IIZwz1s79zpLJWcRwbHsbbmRrp6evd5+zuvP22vHzDTm3M3gx/ppGxJkjQ6ZyyZyzO/7uYNozg//0OXHs/O/txJHDe8dQm1Bc4VHIvdn2/OjD2neQ7V2FBH46Q6ul/avsv4wG0cIHcl1H/53i9YdtRsDhvhIji71zD0PMhTjz2In/znpj1u0yGp+AyDZTa3fSrPdr406sctO/JAIHf0rx+Igw/g+je/hrvv/wnf/T+/ym+165v6Ocvm8a/f/+Xg8r5M62ycVMdt7z11l3sKSpKksZncWM+7Llo0qscM3GgdYMEwN2evq62hb+dozvofvXdddAyP/P+/5g/feDQbnt3MJ7+0jsmN9Rxz2HSOXThzl23feOphnHXCIUxu3LevmGcuPYRfPtfDhcsPGxxbdtRsjl04c5+fQ9L+8/9lZTZ0CsWAmhro38v7+EUrcpeRXji3lZ8+u3lw5sQFpxzKcxtf4k2rDt9jNsXQPX5vHMXlr4eetC5JkqrXbe89lZ07S/saiw9vZ/HhuSmpHQcfwGVnv5oj57UVvD0EMKoQN21yA++95LgxPYek/ec5g2XWn09982Y3Dxnc++NmtOambx4fswBYkv/ftuZG3v+WJcyfvedUjDj4lUsze9NVSZImnob63NTNcqmpqeHUYw8aNghKGl/c7VJmA7lvf2f9rzp+LscunEF7gTfhpkl1LHxVK0fOb+OkY+Z4o1VJkiRJwzIMltlAGGwashev/YDJPL/pt4PLk+pr2f5y4TkfNTU1zGorfO+fmpoaPvjWJbuMnX/yfL731HO0NTeOrXBJkiRJE4rTRMstnwZramr42NuXcs6yeXzosuM5a+khg5scNHPkq3mNxoXLD+NTV5804qWsJUlSdlx5Xu4m9KccM6fClUiqNBNCmZ1/8nwAzj1xHnPbp/G7KxcwtamB3zt94eA2HQcfwAffuoTfO23hMM8iSZK0f5YdNZu73n86B07fc6bRxStzF6w7cv70PdZJmnicJlpmRx82g698+nxeeGHLsNuceNRs5s1upnd7HwCHzmkedltJkqRiOXvZPM464ZB9uh2VpPHPMFgBw73B3nn9aezs76euNnfA9sj5bbzn4mNZ8Kq937RVkiSpGAyCUnYYBqtITU0NdUPegGtqali0YEYFK5IkSZI0UXnOoCRJkiRlkGFQkiRJkjLIMChJkiRJGWQYlCRJkqQMMgxKkiRJUgYZBiVJkiQpg2r6+/srXYMkSZIkqcw8MihJkiRJGWQYlCRJkqQMMgxKkiRJUgYZBiVJkiQpgwyDkiRJkpRBhkFJkiRJyiDDoCRJkiRlUH2lC8iSiKgFbgOOBXqBd6SUNlS2Ko0XEdEA3AXMBxqBm4CngbuBfuAp4JqU0s6IuAK4CngZuCmltDYiJgNfBGYBPcClKaXOcv8eqm4RMQt4Angduf65G/tLRRARHwDOByaR+yz8DvaXiiD/+fh5cp+PfcAV+P6lIoiIE4BPppRWRsRCxthTEbEM+Gx+2wdTSh8t/2+1K48MlteFQFNK6UTg/cDNFa5H48tbgI0ppeXAWcBfArcAN+bHaoALImI2cC1wMnAm8ImIaASuBp7Mb/sF4MYK/A6qYvkvVH8D/DY/ZH+pKCJiJXASub5ZARyM/aXiOQeoTymdBHwM+Dj2l8YoIt4H3AE05YeK0VN/DawGTgFOiIjF5fp9hmMYLK9TgK8BpJS+Dxxf2XI0zvwT8KH8zzXk9iotIbd3HeB+YBWwFHg0pdSbUtoMbAAWMaT/hmwrDfVpch9Uv8ov218qljOBJ4H7gH8B1mJ/qXjWA/X5GVgtwA7sL43dz4DfGbI8pp6KiBagMaX0s5RSP/AAVdBrhsHyagE2D1nuiwin6mqfpJS2pJR6IqIZuJfcXqaa/BsK5KYhtLJnnxUaHxiTAIiIy4DOlNIDQ4btLxXLTHI7QC8G3gn8A1Brf6lItpCbIvoTYA1wK75/aYxSSl8mt2NhwFh7qgXoLrBtRRkGy6sbaB6yXJtSerlSxWj8iYiDgW8Bf59S+hKwc8jqZmATe/ZZofGBMWnA5cDrIuLbwHHkprXMGrLe/tJYbAQeSCltTyklYBu7fgmyvzQWf0yuvzrIXZfh8+TOTR1gf6kYxvqda7htK8owWF6PkpvXTv4E0icrW47Gk4g4EHgQuD6ldFd+eF3+XByAs4GHgceB5RHRFBGtwBHkTnQe7L8h20oApJROTSmtSCmtBH4E/AFwv/2lInkEOCsiaiLiIGAq8E37S0XSxStHYV4EGvDzUcU3pp5KKXUD2yNiQUTUkJs+X/Feq+nv79/7ViqKIVcTXUTunK+3pZR+UtmqNF5ExGeBS8hNgxnwbnLTYSYBPwauSCn15a9sdSW5HT7/PaX05YiYQm5v6RxgO7A6pfRcOX8HjQ/5o4PvJLcXdA32l4ogIj4FnEaubz4IPIP9pSKIiGnkrrY9h1w/fRb4AfaXxigi5gP/M6W0LCI6GGNP5Q8GfQaoI3c10RvK/1vtyjAoSZIkSRnkNFFJkiRJyiDDoCRJkiRlkGFQkiRJkjLIMChJkiRJGWQYlCRJkqQMMgxKkiRJUgYZBiVJkiQpgwyDkiRJkpRBhkFJkiRJyiDDoCRJkiRlkGFQkiRJkjLIMChJkiRJGWQYlCRJkqQMMgxKkiRJUgYZBiVJkiQpgwyDkiRJkpRBhkFJkiRJyiDDoCRJkiRlkGFQkiRJkjLIMChJkiRJGWQYlCRJkqQMMgxKkiRJUgbVV7qAUurs7OmvdA2FtLVNoatra6XL0ARlf6nU7DGVkv2lUrK/VErV2l/t7c01w63zyGAF1NfXVboETWD2l0rNHlMp2V8qJftLpTQe+8swKEmSJEkZZBiUJEmSpAwyDEqSJEkaF3p39PF811Z6d/RVupQJYUJfQEaSxqveHX1s3tJL67RGGhvG3zkIkiQVU9/Ondzz0AbWre/kxe5eprc0srijnUtOX0hdrce39pdhUJKqiB92kiTt6Z6HNvCNHzw7uLyxu3dwefWqjkqVNe75zUKSqsjAh93G7l76eeXD7p6HNlS6NEmSKqJ3Rx/r1ncWXLdu/QtOGR2Dih8ZjIjLgMvyi03AccCJwFrgp/nx21NK90TEFcBVwMvATSmlteWtVpJKZ28fdhetWOCUUUlS5mze0suL3b0F13X1bGPzll5mtU0pc1UTQ8XDYErpbuBugIj4K+AuYAlwS0rp5oHtImI2cC1wPLnQ+EhEfD2lVLgzJGmc8cNOkqQ9tU5rZHpLIxsLfEa2NTfROq2xAlVNDFUzTTQijgeOSin9LbkweG5EfDci7oyIZmAp8GhKqTeltBnYACyqYMmSVFQDH3aF+GEnScqqxoY6Fne0F1y3uGOms2bGoOJHBof4IPDR/M+PA3eklJ6IiBuADwM/AjYP2b4HaB3pCdvaplBfX53N0d7eXOkSNIHZX+PXyce+iq88/PMC4wcx96ADKlBRYfaYSsn+UinZX+PTH/3eYqZMnsT3n/o1L2z6LTMPmMyyo+dw+XlHUVdXNce3xl1/VUUYjIgDgEgpfSs/dF9KadPAz8DngO8CQ/+6zcAmRtDVtbXYpRZFe3sznZ09lS5DE5T9Nb6dd+IhbP3tdtatf4Gunm20NTexuGMm5514SNX8u1Zjj3krjomjGvtLE4f9Nb5dePJ8zl568C7v9y+++FKlyxpUrf01UkCtijAInAp8c8jyAxHxrpTS48AZwBPkjhZ+PCKagEbgCOCpslcqSSVUV1vL6lUdXLRigeFmH3grDknKlsaGOs+fL6JqCYMBDJ0XdTXwuYjYATwHXJlS6o6IW4GHyZ3reENKaVv5S5Wk0vPDbt943ylJkvZfVYTBlNJf7Lb8Q+DkAtutAdaUqy5JUvXyVhySJI2Nc2gkSePSvtyKQ5IkDc8wKEkal7wVhyRJY2MYlCSNS953SpKksamKcwYlSdofl5y+EGCPW3EMjEuSpOEZBiVJ45a34pAkaf8ZBiVJ45634pAkafQ8Z1CSJEmSMsgwKEmSJEkZZBiUJEmSpAwyDEqSJElSBhkGJUmSJCmDDIOSJEmSlEGGQUmSJEnKIMOgJEmSJGWQYVCSJEmSMsgwKEmSJEkZZBiUJEmSpAwyDEqSJElSBhkGJUmSJCmDDIOSJEmSlEGGQUmSJEnKIMOgJEmSJGWQYVCSJEmSMsgwKEmSJEkZZBiUJEmSpAwyDEqSJElSBhkGJUmSJCmDDIOSJEmSlEGGQUmSJEnKIMOgJEmSJGVQfaULiIjLgMvyi03AccApwGeAfuAp4JqU0s6IuAK4CngZuCmltLbsBUuSJEnSBFDxI4MppbtTSitTSiuBJ4BrgT8DbkwpLQdqgAsiYnZ+3cnAmcAnIqKxQmVLkiRJ0rhW8TA4ICKOB45KKf0tsAT4Tn7V/cAqYCnwaEqpN6W0GdgALKpIsZJGrXdHH893baV3R1+lS5EkSRJVF/EP/QAAIABJREFUME10iA8CH83/XJNS6s//3AO0Ai3A5iHbD4wPq61tCvX1dcWusyja25srXYImsGrqr76+ndz1L//B95/6NZ2bfkv7AZNZdvQcLj/vKOrqqmZ/lEapmnpME4/9pVKyv1RK462/qiIMRsQBQKSUvpUf2jlkdTOwCejO/7z7+LC6urYWs8yiaW9vprOzp9JlaIKqtv760jfW840fPDu4/HzXb/nKwz9n62+3s3pVRwUr0/6qth7TxGJ/qZTsL5VStfbXSAG1WnbLnwp8c8jyuohYmf/5bOBh4HFgeUQ0RUQrcAS5i8tIqlK9O/pYt76z4Lp1619wyqgkSVIFVUsYDODnQ5b/BPhoRPwbMAm4N6X0HHAruWD4EHBDSmlb2SuVtM82b+nlxe7eguu6eraxeUvhdZIkSSq9qpgmmlL6i92W1wMrCmy3BlhTrrokjU3rtEamtzSysUAgbGtuonWaFwSWJEmqlGo5MihpAmpsqGNxR3vBdYs7ZtLYUJ0XeJIkScqCqjgyKGniuuT0hUDuHMGunm20NTexuGPm4LgkSZIqwzAoqaTqamtZvaqDi1YsYPOWXlqnNXpEUJIkqQoYBiWVRWNDHbPaplS6DEmSJOV5zqAkSZIkZZBhUJIkSZIyyDAoSZIkSRlkGJQkSZKkDDIMSpIkSVIGGQYlSZIkKYMMg5IkSZKUQYZBSZIkScogw6AkSZIkZZBhUJIkSZIyyDAoSZIkSRlkGJQkSZKkDDIMSpIkSVIGGQYlSZIkKYMMg5IkZUzvjj6e79pK746+SpciSaqg+koXIEmSyqNv507ueWgD69Z38mJ3L9NbGlnc0c4lpy+krtb9w5KUNYZBSZIy4p6HNvCNHzw7uLyxu3dwefWqjkqVJUmqEHcDSpKUAb07+li3vrPgunXrX3DKqCRlkGFQkqQM2Lyllxe7ewuu6+rZxuYthddJkiYuw6AkSRnQOq2R6S2NBde1NTfROq3wOknSxFX0MBgRc/L/uzwiromIqcV+DUmSNDqNDXUs7mgvuG5xx0waG+rKXJEkqdKKegGZiLgd2BkRfwV8CXgQOB24qJivI0mSRu+S0xcCuXMEu3q20dbcxOKOmYPjkqRsKfbVRJcCxwMfBu5MKX0kIv69yK8hSZL2Q11tLatXdXDRigVs3tJL67RGjwhKUoYVe5poXf45LwDuj4gpgNNEJUmqIo0Ndcxqm2IQlKSMK3YY/ALwa+AXKaXHgCeAvynya0iSJEmSxqjY00QfAD6bUhq4WdFywBMRJEmSJKnKFCUMRsTJ5KaI3gG8PSJqhjz/XwMde3n8B4DzgUnAbcAPgbXAT/Ob3J5SuicirgCuAl4GbkoprS1G/ZIkSZKUNcU6Mvg6YAUwB/jYkPGX2cs00YhYCZwEnAxMAf4UqAFuSSndPGS72cC15C5Q0wQ8EhFfTyl5l1xJkiRJGqWihMGU0kcAIuKtKaW/H+XDzwSeBO4DWoDrgLfnni4uIHd08D3krlT6aD789UbEBmAR4NVKJUmSJGmUin3O4Hcj4i+A6eSO7gGQUrp8hMfMBOYBbwAOBb4C/DlwR0rpiYi4gdytKn4EbB7yuB6gdaRi2tqmUF9fnVdKa29vrnQJmsDsL5WaPaZSsr9USvaXSmm89Vexw+A/Ag/n/+vfx8dsBH6SUtoOpIjYBnw1pfR8fv19wOeA7wJD/7rNwKaRnrira+soSi+f9vZmOjt7Kl2GJij7S6Vmj6mU7C+Vkv2lUqrW/hopoBY7DDaklP50lI95BHh3RNxC7pzDqcBXI+KalNLjwBnkblHxOPDxiGgCGoEjgKeKV7okSZIkZUexw+AjEXEe8ED+SN9epZTWRsSp5MJeLXAN0Al8LiJ2AM8BV6aUuiPiVnJHHWuBG1JK24pcvyRJkiRlQrHD4O8CfwQQEQNj/SmlEU/cSym9r8DwyQW2WwOsGWONkiRJkpR5RQ2DKaWDivl8kiRJkqTSKGoYjIg/KzSeUvpYoXFJkiRJUmXUFvn5aob8Nwk4HziwyK8hSZIkSRqjYk8T/ejQ5Yj4b8CDxXwNSZIkSdLYFfvI4O6mAYeU+DUkSZIkSaNU7HMGn+GVm83XAgcAf1HM15AkSZIkjV2xby2xcsjP/cCmlFJ3kV9DkiRJkjRGxZ4m+p/AOcDNwK3AZRFR6qmokiRJkqRRKvaRwU8BhwN3kbui6NuAw4D3FPl1JEmSJEljUOww+HpgcUppJ0BEfBV4ssivIUmSJEkao2JP4axn14BZD/QV+TUkSZIkSWNU7COD/wB8OyL+v/zy7wNfKvJrSJIkSZLGqGhhMCLagDXAOuD0/H+fSSn9fbFeQ5IkSZJUHEWZJhoRi4GngSUppftTStcBDwB/HhGLivEakiRJkqTiKdY5g58Gfj+l9LWBgZTSB4HLgVuK9BqSJEmSpCIpVhhsSyl9e/fBlNIDwMwivYYkSZIkqUiKFQYbCt1cPj82qUivIUmSJEkqkmKFwe8AHy4wfiPwgyK9hiRJkiSpSIp1NdEPAP8aEW8G/h2oAV4DPA+cX6TXkCRJkiQVSVHCYEqpJyJOBU4DFgM7gb9KKT1cjOeXJEmSJBVX0e4zmFLqBx7K/ydJkiRJqmLFOmdQkiRJkjSOGAal/dC7o4/nu7bSu6Ov0qVIkqRxzO8UqqSiTROVsqBv507ueWgD69Z38mJ3L9NbGlnc0c4lpy+krtZ9K5Ikad/4nULVwDAojcI9D23gGz94dnB5Y3fv4PLqVR2VKkuSJI0zfqdQNXC3g7SPenf0sW59Z8F169a/4PQOSZK0T/xOoWphGJT20eYtvbzY3VtwXVfPNjZvKbxOkiRpKL9TqFoYBqV91DqtkektjQXXtTU30Tqt8DpJkqSh/E6hamEYlPZRY0MdizvaC65b3DGTxoa6MlckSZLGI79TqFp4ARlpFC45fSGQm8/f1bONtuYmFnfMHByXJEnaF36nUDWoijAYER8AzgcmAbcB3wHuBvqBp4BrUko7I+IK4CrgZeCmlNLaylSsrKqrrWX1qg4uWrGAzVt6aZ3W6N47SZI0an6nUDWo+DTRiFgJnAScDKwADgZuAW5MKS0HaoALImI2cG1+uzOBT0SEE6pVEY0Ndcxqm+KbtiRJGhO/U6iSKh4GyQW7J4H7gH8B1gJLyB0dBLgfWAUsBR5NKfWmlDYDG4BF5S9XkiRJksa/apgmOhOYB7wBOBT4ClCbUurPr+8BWoEWYPOQxw2MD6utbQr19dW5l6W9vbnSJWgCs79UavaYSsn+UinZXyql8dZf1RAGNwI/SSltB1JEbCM3VXRAM7AJ6M7/vPv4sLq6tha51OJob2+ms7On0mVogrK/VGr2mErJ/lIp2V8qpWrtr5ECajVME30EOCsiaiLiIGAq8M38uYQAZwMPA48DyyOiKSJagSPIXVxGkiRJkjRKFT8ymFJaGxGnkgt7tcA1wDPAmoiYBPwYuDel1BcRt5ILhrXADSmlbZWqW5IkSZLGs4qHQYCU0vsKDK8osN0aYE3pK5IkSZKkia0apolKkiRJksrMMChJkiRJGWQYlCRJkqQMMgxKkiRJUgYZBiVJkiQpgwyDkiRJkpRBhkFJkiRJyiDDoCRJkiRlkGFQkiRJkjLIMChJkiRJGWQYlCRJkqQMMgxKkiRJUgYZBiVJkiQpgwyDkiRJkpRBhkFJkiRJyiDDoCRJkiRlkGFQkiRJkjLIMChJkiRJGWQYlCRJkqQMMgxKkiRJUgYZBiVJkiQpgwyDkiRJI+jd0cfzXVvp3dFX6VIkqajqK12AJElSNerbuZN7HtrAuvWdvNjdy/SWRhZ3tHPJ6Qupq3V/uqTxzzAoSZJUwD0PbeAbP3h2cHljd+/g8upVHZUqS5KKxt1akiRJu+nd0ce69Z0F161b/4JTRiVNCIZBSZKk3Wze0suL3b0F13X1bGPzlsLrJGk8MQxKkiTtpnVaI9NbGguua2tuonVa4XWSNJ4YBiVJknbT2FDH4o72gusWd8yksaGuzBVJUvF5ARlJkqQCLjl9IZA7R7CrZxttzU0s7pg5OC5J451hUJIkqYC62lpWr+rgohUL2Lyll9ZpjR4RlDShVEUYjIgfAt35xWeAW4G1wE/zY7enlO6JiCuAq4CXgZtSSmvLXqwkScqUxoY6ZrVNqXQZklR0FQ+DEdEE1KSUVg4ZewdwS0rp5iFjs4FrgeOBJuCRiPh6SsnLeUmSJEnSKFU8DALHAlMi4kFy9XwQWAJERFxA7ujge4ClwKP58NcbERuARcC/V6ZsSZIkSRq/qiEMbgU+DdwBHA7cD/w5cEdK6YmIuAH4MPAjYPOQx/UArSM9cVvbFOrrq3Nuf3t7c6VL0ARmf6nU7DGVkv2lUrK/VErjrb+qIQyuBzaklPqB9RGxEfhaSum/8uvvAz4HfBcY+tdtBjaN9MRdXVtLUO7Ytbc309nZU+kyNEHZXyo1e0ylZH+plOwvlVK19tdIAbUa7jN4OXAzQEQcBLQA/ysilubXnwE8ATwOLI+IpohoBY4AnqpAvZIkSZI07lXDkcE7gbsj4hGgn1w43AZ8LiJ2AM8BV6aUuiPiVuBhciH2hpTStkoVLUmSJEnjWcXDYEppO7C6wKqTC2y7BlhT8qIkSZIkaYKrhmmikiRJkqQyMwxKkiRJUgYZBiVJkiQpgwyDkiRJkpRBhkFJkiRJyiDDoCRJkoqmd0cfz3dtpXdHX6VLkbQXFb+1hKpD744+Nm/ppXVaI40NdZUuR5IkjTN9O3dyz0MbWLe+kxe7e5ne0sjijnYuOX0hdbUef5CqkWEw43zjliRJxXDPQxv4xg+eHVze2N07uLx6VUelypI0Ar/tZ9zAG/fG7l76eeWN+56HNlS6NEmSNE707uhj3frOguvWrX/BKaNSlTIMZphv3JIkqRg2b+nlxe7eguu6eraxeUvhdZIqyzCYYb5xS5KkYmid1sj0lsaC69qam2idVnidpMoyDGaYb9ySJKkYGhvqWNzRXnDd4o6ZXpxOqlKGwQzzjVuSJBXLJacvZNXxc5nR0kRtDcxoaWLV8XO55PSFlS5N0jC8mmjGDbxBr1v/Al0922hrbmJxx0zfuCVJ0qjU1dayelUHF61Y4O2qpHHCMJhxvnFLkqRiamyoY1bblEqXIWkfGAYF+MYtSZIkZY3nDEqSJElSBhkGJUmSJCmDDIOSJEmSlEE1/f39la5BkiRJklRmHhmUJEmSpAwyDEqSJElSBhkGJUmSJCmDDIOSJEmSlEGGQUmSJEnKIMOgJEmSJGWQYVCSJEmSMqi+0gVkSUTUArcBxwK9wDtSShsqW5XGi4hoAO4C5gONwE3A08DdQD/wFHBNSmlnRFwBXAW8DNyUUlobEZOBLwKzgB7g0pRSZ7l/D1W3iJgFPAG8jlz/3I39pSKIiA8A5wOTyH0Wfgf7S0WQ/3z8PLnPxz7gCnz/UhFExAnAJ1NKKyNiIWPsqYhYBnw2v+2DKaWPlv+32pVHBsvrQqAppXQi8H7g5grXo/HlLcDGlNJy4CzgL4FbgBvzYzXABRExG7gWOBk4E/hERDQCVwNP5rf9AnBjBX4HVbH8F6q/AX6bH7K/VBQRsRI4iVzfrAAOxv5S8ZwD1KeUTgI+Bnwc+0tjFBHvA+4AmvJDxeipvwZWA6cAJ0TE4nL9PsMxDJbXKcDXAFJK3weOr2w5Gmf+CfhQ/ucacnuVlpDbuw5wP7AKWAo8mlLqTSltBjYAixjSf0O2lYb6NLkPql/ll+0vFcuZwJPAfcC/AGuxv1Q864H6/AysFmAH9pfG7mfA7wxZHlNPRUQL0JhS+llKqR94gCroNcNgebUAm4cs90WEU3W1T1JKW1JKPRHRDNxLbi9TTf4NBXLTEFrZs88KjQ+MSQBExGVAZ0rpgSHD9peKZSa5HaAXA+8E/gGotb9UJFvITRH9CbAGuBXfvzRGKaUvk9uxMGCsPdUCdBfYtqIMg+XVDTQPWa5NKb1cqWI0/kTEwcC3gL9PKX0J2DlkdTOwiT37rND4wJg04HLgdRHxbeA4ctNaZg1Zb39pLDYCD6SUtqeUErCNXb8E2V8aiz8m118d5K7L8Hly56YOsL9UDGP9zjXcthVlGCyvR8nNayd/AumTlS1H40lEHAg8CFyfUrorP7wufy4OwNnAw8DjwPKIaIqIVuAIcic6D/bfkG0lAFJKp6aUVqSUVgI/Av4AuN/+UpE8ApwVETURcRAwFfim/aUi6eKVozAvAg34+ajiG1NPpZS6ge0RsSAiashNn694r9X09/fvfSsVxZCriS4id87X21JKP6lsVRovIuKzwCXkpsEMeDe56TCTgB8DV6SU+vJXtrqS3A6f/55S+nJETCG3t3QOsB1YnVJ6rpy/g8aH/NHBd5LbC7oG+0tFEBGfAk4j1zcfBJ7B/lIRRMQ0clfbnkOunz4L/AD7S2MUEfOB/5lSWhYRHYyxp/IHgz4D1JG7mugN5f+tdmUYlCRJkqQMcpqoJEmSJGWQYVCSJEmSMsgwKEmSJEkZZBiUJEmSpAwyDEqSJElSBhkGJUmSJCmDDIOSJEmSlEGGQUmSJEnKIMOgJEmSJGWQYVCSJEmSMsgwKEmSJEkZZBiUJEmSpAwyDEqSJElSBhkGJUmSJCmDDIOSJEmSlEGGQUmSJEnKIMOgJEmSJGWQYVCSJEmSMsgwKEmSJEkZZBiUJEmSpAwyDEqSJElSBtVXuoBS6uzs6a90DYW0tU2hq2trpcvQBGV/qdTsMZWS/aVSsr9UStXaX+3tzTXDrStbGIyIOmANEEA/8E5gG3B3fvkp4JqU0s6IuAK4CngZuCmltDYiJgNfBGYBPcClKaXOctVfTPX1dZUuQROY/aVSs8dUSvaXSsn+UimNx/4q5zTR8wBSSicDNwIfB24BbkwpLQdqgAsiYjZwLXAycCbwiYhoBK4Gnsxv+4X8c0iSJEmS9kPZwmBK6X8BV+YX5wGbgCXAd/Jj9wOrgKXAoyml3pTSZmADsAg4BfjabttKkiRJkvZDWc8ZTCm9HBGfB94I/C7wupTSwHl9PUAr0AJsHvKwQuMDYyNqa5tStYdr29ubK12CJjD7S6Vmj6mU7C+Vkv2lUhpv/VX2C8iklC6NiOuBx4DJQ1Y1kzta2J3/eaTxgbERVeMJnJBrks7OnkqXoQnK/lKp2WMqJftLpWR/qZSqtb9GCqhlmyYaEW+NiA/kF7cCO4EfRMTK/NjZwMPA48DyiGiKiFbgCHIXl3kUOGe3bSVJkiRJ+6GcRwb/Gfi7iPgu0AC8B/gxsCYiJuV/vjel1BcRt5ILe7XADSmlbRFxO/D5iHgE2A6sLmPtkiRJkjSh1PT3V+Wt+IqiWu8zWK2HkDUx2F8qNXtMpWR/qZTsL5VStfbXSPcZLOetJSRJkiRpv1132/e47rbvVbqMCcMwKEmSJEkZZBiUJEmSpAwyDEqSJElSBhkGJUmSJCmDDIOSJEmSlEGGQUmSJEnKIMOgJEmSJGWQYVCSJEmSMsgwKEmSJEkZZBiUJEmSpAwyDEqSJElSBhkGJUmSJCmDDIOSJEmSlEGGQUmSJEnKIMOgJEmSJGWQYVCSJEmSMsgwKEmSJEkZZBiUJEmSpAwyDEqSJElSBhkGJUmSJCmD6sv1QhHRANwFzAcagZuA/wLWAj/Nb3Z7SumeiLgCuAp4GbgppbQ2IiYDXwRmAT3ApSmlznLVL0mSJEkTSdnCIPAWYGNK6a0RMR34EfAx4JaU0s0DG0XEbOBa4HigCXgkIr4OXA08mVL6SES8CbgReHcZ65ckSZKkCaOcYfCfgHvzP9eQO+q3BIiIuIDc0cH3AEuBR1NKvUBvRGwAFgGnAJ/KP/5+4ENlrF2SJEmSJpSyhcGU0haAiGgmFwpvJDdd9I6U0hMRcQPwYXJHDDcPeWgP0Aq0DBkfGBtRW9sU6uvrivY7FFN7e3OlS9AEZn+p1OwxlZL9pVKyv8a3uroaoHr/Hau1ruGU88ggEXEwcB9wW0rpSxFxQEppU371fcDngO8CQ/+KzcAmoHvI+MDYiLq6thar9KJqb2+ms7On0mVogrK/VGr2mErJ/lIp2V/jX19fP0BV/jtWa3+NFFDLdjXRiDgQeBC4PqV0V374gYhYmv/5DOAJ4HFgeUQ0RUQrcATwFPAocE5+27OBh8tVuyRJkiRNNOU8MvhBoA34UEQMnO/3XuB/RMQO4DngypRSd0TcSi7s1QI3pJS2RcTtwOcj4hFgO7C6jLVLkiRJ0oRSznMG303hq3+eXGDbNcCa3ca2AheXpjpJkiRJyhZvOi9JkiRJGWQYlCRJkqQMMgxKkiRJUgYZBiVJkiQpgwyDkiRJkpRBhkFJkiRJyiDDoCRJkiRlkGFQkiRJkjLIMChJkiRJGWQYlCRJklT1Hnv6N2za0svG7m382Z2P8djTv6l0SeNefaULkCRJkqSRPPb0b/ibr/zH4PKznS8NLp9w5IGVKmvc88igJEmSpKr21X/7xTDjvyxrHRONYVCSJElSVfvVC1sLjv9640tlrmRiMQxKkiRJqmoHzZxScHzOjKllrmRiMQxKkiRJqmrnnjh/mPF55S1kgvECMpIkSZKq2sBFYu5Y+zR9O/uZ2z6Nc0+c58VjxsgwKEmSJKnqnXDkgdz77Z8B8LG3L61wNROD00QlSZIkKYMMg5IkSZKUQYZBSZIkScogw6AkSZIkZVDZLiATEQ3AXcB8oBG4CXgauBvoB54Crkkp7YyIK4CrgJeBm1JKayNiMvBFYBbQA1yaUuosV/2SJEmSNJGU88jgW4CNKaXlwFnAXwK3ADfmx2qACyJiNnAtcDJwJvCJiGgErgaezG/7BeDGMtYuSZIkSRNKOcPgPwEfyv9cQ+6o3xLgO/mx+4FVwFLg0ZRSb0ppM7ABWAScAnxtt20lSZIkSfuhbNNEU0pbACKiGbiX3JG9T6eU+vOb9ACtQAuwechDC40PjI2orW0K9fV1Ram/2NrbmytdgiYw+0ulZo+plOwvlZL9Nb7V1dUA1fvvWK11DaesN52PiIOB+4DbUkpfiohPDVndDGwCuvM/jzQ+MDairq6txSi76Nrbm+ns7Kl0GZqg7C+Vmj2mUrK/VEr21/jX15c7jlSN/47V2l8jBdSyTRONiAOBB4HrU0p35YfXRcTK/M9nAw8DjwPLI6IpIlqBI8hdXOZR4JzdtpUkSZIk7YdyHhn8INAGfCgiBs4dfDdwa0RMAn4M3JtS6ouIW8mFvVrghpTStoi4Hfh8RDwCbAdWl7F2SZIkSZpQynnO4LvJhb/drSiw7RpgzW5jW4GLS1OdJEmSJGWLN52XJEmSpAwyDEqSJElSBhkGJUmSJCmDDIOSJEmSlEGGQUmSJEnKIMOgJEmSJGWQYVCSJEmSMsgwKEmSJEkZZBiUJEmSpAwyDEqSJElSBhkGJUmSJCmDDIOSJEmSlEGGQUmSJEnKIMOgJEmSJGWQYVCSJEmSMsgwKEmSJEkZZBiUJEmSpAwyDEqSJElSBhkGJUmSpAq57rbvcd1t36t0Gcoow6AkSZIkZVD9aDaOiOuB21NK3RFRD5wMPJNS+s9RPMcJwCdTSisjYjGwFvhpfvXtKaV7IuIK4CrgZeCmlNLaiJgMfBGYBfQAl6aUOkdTvyRJkiQpZ1RhELgwpfTJ/M9/D0wCpkTEP6aU/m5vD46I9wFvBV7KDy0Bbkkp3Txkm9nAtcDxQBPwSER8HbgaeDKl9JGIeBNwI/DuUdYvSZIkSWL000R7ASKiHXhtSuki4Fzgbfv4+J8BvzNkeQlwbkR8NyLujIhmYCnwaEqpN6W0GdgALAJOAb6Wf9z9wKpR1i5JkiRJyhvtkcG7I+KrwGTg7wBSSjvzU0b3KqX05YiYP2ToceCOlNITEXED8GHgR8DmIdv0AK1Ay5DxgbERtbVNob6+bl9KK7v29uZKl6AJzP5SqdljKiX7S6VUbf1VV1cDVF9d1eruD59Z6RJGNN7+HUcVBlNKd0fEg8DclNLjABFxGPDsfr7+fSmlTQM/A58DvgsM/Ss2A5uA7iHjA2Mj6uraup9llVZ7ezOdnT2VLkMTlP2lUrPHVEr2l0qpGvurr68foOrq0uhVY3/ByAF1tBeQWZBS+hnwq4GxlNLPgd/bz9oeiIh35YPlGcAT5I4WfjwimoBG4AjgKeBR4Jz8+rOBh/fzNSVJkiQp80Z7zuBtEXHl0IGIqI+I393P178a+B8R8W1yVya9KaX0HHArubD3EHBDSmkbcDtwVEQ8AlwJfHQ/X1OSJEmSMm+05wyeDbwvIr4MfAa4CDia3BTPfZJS+gWwLP/zD8mFwN23WQOs2W1sK3DxKOuVJEmSJBUw2jA4jdxFXA4ldwGZL6aUvKqnJEmSJI0zo50m+s/kwuBJwDHA9Ij464iYUvTKJEmSJEklM9oweBrwDXJHBrenlK4lN0X0/mIXJkmSJEkqndGGwRrgGeBfgV9FxHUppQeANxa9MkmSJElSyYw2DPYAh6WUDgWOBU6IiD9OKb1Y/NIkSZIkSaUy2jD485TSbwDyt4B4M7C66FVJkqSSue6273Hdbd+rdBmSpAobbRh8JiLeMWR5J9BSxHokSZIkSWUw2ltL/BHwzxFxDbAOOA54tOhVSZIkSZJKalRhMKX0K2BZRJxI7pzBfwX+dykKkyRJkiSVzmiPDAKQUvo34N+KXIskSZIkqUxGe86gJEmSJGkCMAxKkiRJFfDY079h05ZeNnZv48/ufIzHnv5NpUtSxuzXNFFJkiRJ+++xp3/D33zlPwaXn+18aXD5hCMPrFRZyhiPDEqSJEll9tV/+8Uw478sax3KNsOgJEmSVGa/emFrwfFfb3ypzJUoywyDkiRJUpkdNHNKwfE5M6aWuRJlmWFQkiRJKrNzT5w/zPi88haiTPMCMpIkSVKZDVwk5o61T9O3s5+57dM498R5XjxGZWVJHZxhAAAgAElEQVQYlCRJkirghCMP5N5v/wyAj719aYWrURY5TVSSJEmSMsgwKEmSJEkZVPZpohFxAvDJlNLKiFgI3A30A08B16SUdkbEFcBVwMvATSmltRExGfgiMAv+H3v3HmVXed55/luqAhShkhCiLMMiLeWCn0ieMXYbIwpjIB1jx8jYTDLuTDOZ2ASMMZ52YhLFTuJWt1nOpRtj4iRTMTc1uXVuOKwREIMnFxIjiXLiXIZQ8DjChgkXqwu1bggjoqqaP84p+SCqiirq7LP3Ofv7WUtr1X732aqnznlUql+9734PB4H3Z+Z4p+uXJEmSpF7Q0ZnBiPgZ4FZgaXPos8AnM/NtQB/w3oh4LfBR4K3AO4FfiogTgA8DDzUf+1vAJztZuyRJkiT1kk4vE30M+KGW4zcDf9n8+IvA24Gzge2ZeTgz9wO7gDcA5wH3HvNYSZIkSdKr0NFlopn5hYhY1zLUl5lTzY8PAiuBFcD+lsfMND49NqdVq5YxMNC/2LILMTQ0WHYJ6mH2l4pmj3W3/v4+oLqvY1XrUm+oWn9V/d+jFqbbXsey31pisuXjQWAfcKD58Vzj02Nz2rv3+fZU2WZDQ4OMjx8suwz1KPtLRbPHutvo2G727H+BickpPvzLf8qm4XWVel8z+0tFqmJ/TUw05kWqVpcWror9BXMH1LJ3E/27iLiw+fG7gC8DXwHeFhFLI2IlsJ7G5jLbgYuPeaxUis0jO9g8sqPsMiRpQUbHdnPTtoeZmGz88Pnk+CFu2vYwo2O7S65MklSGssPgTwGfioidwPHAHZn5TeBXaYS9Pwd+PjNfAH4DeH1EPABcBXyqpJolSepK9+x8fJbxJzpahySpGjq+TDQzHwfOaX78NeCCGR5zC3DLMWPPA+/rQImSJPWkp5+d+faJZ/Yc6nAlkqQqKHtmUJIkdchppyybcfzU1Sd2uBJJUhUYBiVJqolNw+tmGV/b2UIkSZVQ9m6ikiSpQ6Z3Db317jEmJqc4fWg5m4bXVmo3UUlS5xgGJUmqkY0b1nDH/Y8BcN0VZ5dcTXeY3j36+mvOLbkSSWovl4lKkiRJUg0ZBiVJkiSphgyDkiRJklRDhkFJHbF5ZMfR+24kSZJUPsOgJEmSJNWQu4lKkiRJJXGXWpXJmUFJkiRJqiHDoCRJkiTVkGFQWqDRsd3se+4wew68wJbbRhkd2112SZIkSdKCec+gtACjY7u5advDR4+fHD909HjjhjVllSVJkiQtmDOD0gLcs/PxWcaf6GgdkiRJ0mIZBqUFePrZ52ccf2bPoQ5XIknqBG8NkNTLDIPSApx2yrIZx09dfWKHK5EkFW361oCJySng27cGGAgl9QrDoLQAm4bXzTK+trOFSJIK560BknqdG8hICzC9Scytd48xMTnF6UPL2TS81s1jJKkHeWuApF7nzKC0QBs3rOGk5SewesVSrrvibIOgJPUobw2Q1OsMg5IkSTPw1gBJva4Sy0Qj4m+BA83DbwC/ANwOTAH/CHwkMycj4oPAh4AjwKcz8+4SypUkSTXgrQGSel3pM4MRsRToy8wLm38uBz4LfDIz3wb0Ae+NiNcCHwXeCrwT+KWIOKG0wnvM5pEdbB7ZUXYZ6lFuzS6pW3lrgKReVoWZwTOBZRHxJRr1/BzwZuAvm+e/CLwDmAC2Z+Zh4HBE7ALeAPx150uWNF/TW7NPm96aHfCHKqkk119zbtklSJIqoAph8HngM8CtwBk0wl9fZk41zx8EVgIrgP0t102Pz2rVqmUMDPS3veB2GBoaLLuEl+jv7wOqV1dVVf35qlJd9/3138wy/s+8+4Lv7XA1apcq9Zh6T9X6q+rf87Uwvo4qUrf1VxXC4NeAXc3w97WI2ENjZnDaILCPxj2FgzOMz2rv3pm3hC7b0NAg4+MHyy7jJSYmGtm7anVVVZWfr6r11//3zZlr+efdBytVp+avaj2m3lLF/qry93wtTBX7S72jqv01V0At/Z5B4MeBGwAi4jQaM4BfiogLm+ffBXwZ+ArwtohYGhErgfU0NpeRVGFuzS5JklRNVQiDtwEnRcQDwB/QCIc/AXwqInYCxwN3ZOY3gV+lEQz/HPj5zHyhpJolzZNbs0uSJFVT6ctEM/NF4LIZTl0ww2NvAW4pvChJbePW7JIkSdVUehiU1Ps2bljDHfc/BsB1V5xdcjWSJEmCaiwTVcl8DzhJkiSpfpwZrDnfA06SJEmqJ2cGa+6enY/PMv5ER+uQpMXYPLKDzSM7yi5DkqSu4sxgzT397MzvxfjMnkMdrqS7XH/NuWWXIEmSJC2KYbDmTjtlGU+Ovzz4+R5wkiQ1+AtASb3KZaI153vASZIkSfXkzGDN+R5wkiRJUj0ZBuV7wEmSJEk15DJRSZIkSaohw6AkSZIk1ZBhUJLU1UbHdrPvucPsOfACW24bZXRsd9klSZLUFbxnUJLUtUbHdnPTtoePHj85fujosRthSZI0N8OgpI7wfbpUhHt2Pj7L+BOGQUmSXoHLRCVJXevpZ5+fcfyZPYc6XIkkSd3HmUEBztpI6k6nnbKMJ8dfHvxOXX1iCdVIAtg8sgPwZwupGzgzKEkVtHlkx9EfqDS7TcPrZhlf29lCJEnqQoZBSVLX2rhhDR96z+vpX9IHwOlDy/nQe17v/YJSSdzdV+ouhkFJqhh/mFqYjRvWcNLyE1i9YinXXXG2QVAqyfTuvhOTU8C3d/f1e5hUXYZBSaoQf5iS1K3m2t1XUjV11QYyEbEEGAHOBA4DV2bmrnKrkqT28a0SJHUrd/eVuk+3zQxeCizNzGHgE8ANJdcjSW3lD1OSutVppyybcdzdfaXq6rYweB5wL0BmPgicVW45ktRe/jAlqVu5u6/UfbpqmSiwAtjfcjwREQOZeWSmB69atYyBgf7OVLZAQ0ODZZegHmZ/da9/987v4/rf+eoM41Gp17VKtQD09zd2E61aXXp1fB2707svGGTFiqXc+Ht/y5GJKdaduoL3/cAZnP+m08su7SXsLxWp2/qr28LgAaD1GV4yWxAE2Lt35uVWZRsaGmR8/GDZZahH2V/dbf3pK/nQe17PrXePMTE5xelDy9k0vJb1p6+szOtaxR6bmGhsuFO1urRwVewvzd/601ey8sQTANjy/sYCriq9nvaXilTV/poroHZbGNwOXAL8YUScAzxUcj2S1HYbN6zhjvsfA+C6K84uuZrucP0155ZdgqQm/z1K3aPbwuCdwEURsQPoAy4vuR5JKoQ/TEmSpKJ1VRjMzEng6rLrkCRJkqRu1227iUqSJEmS2sAwKEmSJEk1ZBiUJEmSpBoyDEqSJElSDRkGJUmSJKmGDIOSJEmSVEOGQUmSJEmqob6pqamya5AkSZIkdZgzg5IkSZJUQ4ZBSZIkSaohw6AkSZIk1ZBhUJIkSZJqyDAoSZIkSTVkGJQkSZKkGjIMSpIkSVINDZRdQJ1ExBJgBDgTOAxcmZm7yq1K3SIijgO2AuuAE4BPA2PA7cAU8I/ARzJzMiI+CHwIOAJ8OjPvjojvAH4HeA1wEHh/Zo53+utQtUXEa4CvAhfR6J/bsb/UBhHxs8B7gONp/F/4l9hfaoPm/4+/SeP/xwngg/j9S20QERuB/5yZF0bE97LInoqIc4DPNR/7pcz8VOe/qpdyZrCzLgWWZuYw8AnghpLrUXf5UWBPZr4N+EHg14HPAp9sjvUB742I1wIfBd4KvBP4pYg4Afgw8FDzsb8FfLKEr0EV1vyB6ibgW80h+0ttEREXAufS6JsLgO/E/lL7XAwMZOa5wHXAL2B/aZEi4meAW4GlzaF29NTngcuA84CNEfGmTn09szEMdtZ5wL0AmfkgcFa55ajL/BHwH5of99H4rdKbafx2HeCLwNuBs4HtmXk4M/cDu4A30NJ/LY+VWn2Gxn9UTzeP7S+1yzuBh4A7gbuAu7G/1D5fAwaaK7BWAP+C/aXFewz4oZbjRfVURKwATsjMxzJzCriPCvSaYbCzVgD7W44nIsKlupqXzHwuMw9GxCBwB43fMvU1v6FAYxnCSl7eZzONT49JAETEB4DxzLyvZdj+UrucQuMXoO8DrgZ+F1hif6lNnqOxRPRR4BbgV/H7lxYpM79A4xcL0xbbUyuAAzM8tlSGwc46AAy2HC/JzCNlFaPuExHfCfwF8NuZ+d+AyZbTg8A+Xt5nM41Pj0nTfhy4KCLuB95IY1nLa1rO219ajD3AfZn5YmYm8AIv/SHI/tJifIxGf72Oxr4Mv0nj3tRp9pfaYbE/c8322FIZBjtrO4117TRvIH2o3HLUTSJiDfAl4OOZubU5/HfNe3EA3gV8GfgK8LaIWBoRK4H1NG50Ptp/LY+VAMjM8zPzgsy8EPh74MeAL9pfapMHgB+MiL6IOA04Efgz+0ttspdvz8L8D+A4/P9R7beonsrMA8CLEfE9EdFHY/l86b3WNzU19cqPUlu07Cb6Bhr3fF2emY+WW5W6RUR8DvgRGstgpv0EjeUwxwOPAB/MzInmzlZX0fiFzy9m5hciYhmN35aeCrwIXJaZ3+zk16Du0JwdvJrGb0Fvwf5SG0TEfwG+n0bf/BzwDewvtUFELKex2/apNPrpc8DfYH9pkSJiHfD7mXlORLyORfZUczLoV4B+GruJ/nznv6qXMgxKkiRJUg25TFSSJEmSasgwKEmSJEk1ZBiUJEmSpBoyDEqSJElSDRkGJUmSJKmGDIOSJEmSVEOGQUmSJEmqIcOgJEmSJNWQYVCSJEmSasgwKEmSJEk1ZBiUJEmSpBoyDEqSJElSDRkGJUmSJKmGDIOSJEmSVEOGQUmSJEmqIcOgJEmSJNWQYVCSJEmSasgwKEmSJEk1ZBiUJEmSpBoyDEqSJElSDRkGJUmSJKmGBsouoEjj4wenyq5hJqtWLWPv3ufLLkM9yv5S0ewxFcn+UpHsLxWpqv01NDTYN9s5ZwZLMDDQX3YJ6mH2l4pmj6lI9peKZH+pSN3YX4ZBSZIkSaohw6AkSZIk1ZBhUJIkSZJqyDAoSZIkSTVkGJQkSZKkGjIMSpIkSVINGQYlSZIkqYYMg5IkSZK6wuaRHWwe2VF2GT1joMi/PCKWACPAmcBh4MrM3NVy/hJgC3AE2JqZt8x2TUS8Cbgb+Kfm5b+RmX9QZP2SJEmS1KsKDYPApcDSzByOiHOAG4D3AkTEccCNwFuAQ8D2iNgGvHWWa94MfDYzbyi4ZkmSJEnqeUUvEz0PuBcgMx8Ezmo5tx7YlZl7M/NF4AHg/DmueTOwKSL+KiJui4jBgmuXJEmSpJ5V9MzgCmB/y/FERAxk5pEZzh0EVs52DfAV4NbM/GpE/DzwH4GfnuuTr1q1jIGB/jZ8Ge03NGSWVXHsLxXNHlOR7C8Vyf7qbv39fUB1X8eq1jWbosPgAaD1GVnSDIIznRsE9s12TUTcmZn7mmN3Ar/2Sp98797nX3XhRRoaGmR8/GDZZahH2V8qmj2mItlfKpL91f0mJqYAKvk6VrW/5gqoRS8T3Q5cDNC8/++hlnOPAGdExMkRcTyNJaI757jmvog4u/nxDwBfLbh2SZIkSepZRc8M3glcFBE7gD7g8oi4DFiemTdHxLXAfTRC6dbMfCoiXnZN8+/6MPBrEfEvwDeBqwquXZIkSZJ6VqFhMDMngauPGX605fxdwF3zuIbM/FsaO41KkiRJkhbJN52XJEmSpBoyDEqSJElSDRkGJUmSJKmGDIOSJEmSVEOGQUmSJEmqIcOgJEmSJNWQYVCSJEmSasgwKEmSJEk1ZBiUJEmSpBoyDEqSJElSDRkGJUmSJKmGDIOSJEmSVEOGQUmSJEmqIcOgJEmSJNWQYVCSJEmSasgwKEmSJEk1ZBiUJEmSpBoyDEqSJElSDRkGJUmSJKmGDIOSJElSSTaP7GDzyI6yy1BNGQYlSZIkqYYMg5IkSZJUQ4ZBSZIkSaohw6AkSZIk1ZBhUJIkSZJqaKDIvzwilgAjwJnAYeDKzNzVcv4SYAtwBNiambfM45rLgH+fmcNF1i5JkiRJvazomcFLgaXN4PYJ4IbpExFxHHAj8A7gAuCqiFjzCte8CbgC6Cu4bkmSJEnqaUWHwfOAewEy80HgrJZz64Fdmbk3M18EHgDOn+2aiFgN/CLwkwXXLEmSJEk9r9BlosAKYH/L8UREDGTmkRnOHQRWznLNCcBtwLXAt+b7yVetWsbAQP+rrb1QQ0ODZZegHmZ/qWj2mIpkf6lIVeuv/v7Ggreq1VVVVX++qlrXbIoOgweA1mdkSTMIznRuENg30zU07h88A/gNYCmwISJ+JTPnnCXcu/f5xVVfkKGhQcbHD5ZdhnqU/aWi2WMqkv2lIlWxvyYmpgAqV1cVjY7tZs/+F5iYnOLDv/ynbBpex8YNa8ou66gq9hfMHVCLDoPbgUuAP4yIc4CHWs49ApwREScDz9FYIvoZYOrYazLzK8DrASJiHfD7rxQEJUmSJPWG0bHd3LTt4aPHT44fOnpcpUDYbYq+Z/BO4IWI2EFjs5iPRcRlEXFVZv4LjWWf9wE7aewm+tRM1xRcoyRJkqQKu2fn47OMP9HROnpNoTODmTkJXH3M8KMt5+8C7prHNa3nHwfOaV+VkiRJkqrs6Wdnvv3rmT2HOlxJb/FN5yVJkiRV2mmnLJtx/NTVJ3a4kt5iGJQkSZJUaZuG180yvrazhfSYojeQkSRJkqRFmd4k5ta7x5iYnOL0oeVsGl7r5jGLZBiUJEmSVHkbN6zhjvsfA+C6K84uuZre4DJRSZIkSaohw6AkSZIk1ZBhUJIkSZJqyDAoSZIkSTVkGJQkSZKkGjIMSpIkSVINGQYlSZIkqYYMg5IkSZJUQ4ZBSZIkSaohw6AkSZIk1ZBhUJIkSZJqyDAoSZIkSTVkGJQkSZKkGjIMSpIkSVINGQYlSZIkqYYMg5IkSVIJRsd2s++5w+w58AJbbhtldGx32SWpZgbKLkCSJEmqm9Gx3dy07eGjx0+OHzp6vHHDmrLKUs04MyhJkiR12D07H59l/ImO1qF6MwxKkiRJHfb0s8/POP7MnkMdrkR1ZhiUJEmSOuy0U5bNOH7q6hM7XInqzDAoSZIkddim4XWzjK/tbCGqtUI3kImIJcAIcCZwGLgyM3e1nL8E2AIcAbZm5i2zXRMRG4CbgT7gn5rjR4qsX5IkSSrC9CYxt949xsTkFKcPLWfT8Fo3j1FHFT0zeCmwNDOHgU8AN0yfiIjjgBuBdwAXAFdFxJo5rvlF4Ocy863N40sKrl2SJEkqzMYNazhp+QmsXrGU66442yCojis6DJ4H3AuQmQ8CZ7WcWw/sysy9mfki8ABw/hzX/HBm/lVEHA+8FthfcO2SJPWkzSM72Dyyo+wyJEklK/p9Blfw0tA2EREDzeWdx547CKyc65qIWAv8afP8P7zSJ1+1ahkDA/2L/RoKMTQ0WHYJ6mH2l4pmj3W3/v4+oLqvY1XrUm+oWn9V/d9j1VT9+apqXbMpOgweAFqfkSUt9/kde24Q2DfXNZn5BHBGRFwJfBZ4/1yffO/embfsLdvQ0CDj4wfLLkM9yv5S0eyx7jcxMQVQydfR/lKRqthfVf73WEVVfr6q2F8wd0AtepnoduBigIg4B3io5dwjNILdyc2ln+cDO2e7JiK2RcQZzWsPApMF1y5JkiRJPavomcE7gYsiYgeNXUAvj4jLgOWZeXNEXAvcRyOUbs3MpyLiZdc0/65fBm6PiBeB54ErC65dkiRJknpWoWEwMyeBq48ZfrTl/F3AXfO4hszcAbz12HFJkiRJ0sK94jLRiPh4RKxofjwQERdExL8qvjRJkiRJUlHmc8/gpZl5oPnxbwMfBW6KiMvnuEaSJEmSVGHzCYOHASJiCHhLZv4wsIlv38snSZIkSeoy8wmDt0fEPcAfAP8Vjt7XV/TmM5IkSaXbPLKDzSM7yi5DktruFQNdZt4eEV8CTs/MrwBExHcDTxZdnCRJkiSpGK8YBiPiezLzMeDp6bHM/Drwb4ssTJIkSZJUnPks9RyJiC9k5s3TAxExQGNjmTuKK02SJEmSvu36a84tu4SeMp8w+C7gZyLiC8CvAD8M/E803lBeqqXpe0f8hiRJkqRuNZ8wuBzYD3wXjQ1kficz315oVZIkSZKkQs1nN9HfoxEGzwX+Z+DkiPh8RCwrtDJJkiRJUmHms5vopmOGPhoR7wS+CFxQSFWSJEmSpELNZzfRTwGPAI8CmZnfysz7IuKvC69OkiRJklSI+dwz+CXg+4D/HTgjIk4EXgC+BvxUgbVJkiRJkgoyn2Wi24HtEXE8jfsG/w2wEfhXBdcmSZIkSSrIfJaJfhw4C5gAHgTuyMwtRRcmSZIkSSrOfHYT/WlgTfPjk4Dvi4g3RMQJxZUlSZIkSSrSfO4ZXA28D3hd83g98IHm8buLKUuSJEmSVKT5hEGArwP/HVgG3JCZ1xZXkiRJkiSpaPNZJnoQ+O7M/C7gTODsiPhYsWVJkiRJkoo0nzD49czcDZCZ36TxFhOXFVqVJEmSJKlQ8wmD34iIK1uOJ4EVBdUjSZIkSeqA+dwz+H8CfxwRHwH+DngjsL3QqiRJUiFGx3az77nDTExOseW2UTYNr2PjhjWvfKEkqefM503nnwbOiYhhGvcM/gnwfxddmCRJaq/Rsd3ctO3ho8dPjh86emwglKT6me9uomTmTmBngbVIkqQC3bPz8VnGnzAMSlINzTsMvhoRsQQYoTGjeBi4MjN3tZy/BNgCHAG2ZuYts10TEW8Efg2YaI7/2PTGNpIk6ZU9/ezzM44/s+dQhyuRJFXBfDaQWYxLgaWZOQx8Arhh+kREHAfcCLwDuAC4KiLWzHHN54B/n5kXAn8MfLzg2iVJ6imnnbJsxvFTV5/Y4UokSVVQdBg8D7gXIDMfBM5qObce2JWZezPzReAB4Pw5rvnfMvPvmx8PAC8UXLskST1l0/C6WcbXdrYQSUddf825XH/NuWWXoZoqdJkojbeg2N9yPBERA5l5ZIZzB4GVc1zzDEBEnEtjh9PzX+mTr1q1jIGB/kV+CcUYGhosuwQtQn9/H1Dd17Gqdal32GPd6d0XDLJixVJu/L2/5cjEFOtOXcH7fuAMzn/T6WWX9hJV66+qf8/Xwvg6qkjd1l9Fh8EDQOszsqQZBGc6Nwjsm+uaiPgR4OeBTZk5/kqffO/eme+NKNvQ0CDj4wfLLkOLMDExBVDJ19H+UtHsse62/vSVrDzxBAC2vL+x+KZKr2fV+mt0bDd79r/AxOQUH/7lP/WtOLpc1fpLvaWq/TVXQC16meh24GKAiDgHeKjl3CPAGRFxckQcT2Omb+ds10TEj9KYEbwwM79ecN2SJKnmpt+KY2Ky8QvA6bfiGB1z/zpJvaHoMHgn8EJE7KCxWczHIuKyiLgqM/8FuBa4j0YI3JqZT81yTT/wqzRmDP84Iu6PiE8VXLskSaqxud6KQ5J6QaHLRDNzErj6mOFHW87fBdw1j2sATm57gdKrMDq2m33PHWZicoott426ZEiSepRvxSGp1xU9M6gusXlkB5tHdpRdRuW5ZEiS6sO34pDU6wyD0gK4ZEiS6sO34pDU64reTVTqKS4ZkqT6mL4F4Na7x5iYnOL0oeVsGl7rrQGSeoYzg9ICuGRIkupl44Y1nLT8BFavWMp1V5xtEJTUUwyD0gK4ZEiSJEm9wmWi0gK4ZEiSJEm9wjAoLdDGDWu44/7HALjuirNLrkaSJEl6dVwmKqkjfPsSSZKkajEMSpIkSVINGQYlSZIkqYYMg5IkSZJUQ4ZBSZIkSaohw6AkSZIk1ZBhUJIkSZJqyDAoSZIkSTVkGJQkSZKkGjIMSpIkSVINGQYlSZIkqYYMg5IkSZJUQ4ZBMTq2m33PHWbPgRfYctsoo2O7yy5JkiRJUsEGyi5A5Rod281N2x4+evzk+KGjxxs3rCmrLEmSJEkFc2aw5u7Z+fgs4090tA5JkiRJneXMYM09/ezzM44/s+dQhyuRJHXK9decW3YJkqQKcGaw5k47ZdmM46euPrHDlUiSJEnqJMNgzW0aXjfL+NrOFiJJkiSpowpdJhoRS4AR4EzgMHBlZu5qOX8JsAU4AmzNzFvmcc2NQGbm54usvS6mN4m59e4xJianOH1oOZuG17p5jNpqesfaickpttw2yqbhdfaYJElSyYqeGbwUWJqZw8AngBumT0TEccCNwDuAC4CrImLNbNdExFBEfBF4T8E1187GDWs4afkJrF6xlOuuONsf0tVW0zvWTkxOAd/esda3MJEkSSpX0WHwPOBegMx8EDir5dx6YFdm7s3MF4EHgPPnuGY58J+A3y64Zklt5I61krrd9dec66Y7knpS0buJrgD2txxPRMRAZh6Z4dxBYOUc13wD+EZEvGu+n3zVqmUMDPS/+uoLNDQ0WHYJL9Hf3wdUr66qqvrzVaW6nt4z+461VapTC+NrpyLZXyqS/aUidVt/FR0GDwCtz8iSZhCc6dwgsO8VrlmQvXtn/iG0bENDg4yPHyy7jJeYmGgs4ataXVVV5eerav112uplPDn+8rcqOXX1iZWqU/NXtR5Tb7G/VCT7S0Wqan/NFVCLXia6HbgYICLOAR5qOfcIcEZEnBwRx9NYIrrzFa6RKsElQ/PnjrWSJEnVVPTM4J3ARRGxA+gDLo+Iy4DlmXlzRFwL3EcjlG7NzKci4mXXFFyjpAK5Y60kSVI1FRoGM3MSuPqY4Udbzt8F3DWPa1rP/6c2liipAzZuWMMd9z8GwHVXnF1yNZIkSQLfdF6SJEmSaskwKEmSJEk1ZBiUJEmSpBoyDEqSJElSDRkGJUmSJKmGDIOSJEmSVEOGQUmSJEmqIcOgJKnrbR7ZweaRHWWXIUlSVzEMSpIkSVINGQYlSZIkqYYMg5IkSWobl21L3WOg7AJUDddfc27ZJUiSJEnqIGcGJUmSJKmGDIOSJEmSVEOGQUmSJEmqIe8ZlNQR3peqoirsALsAACAASURBVIyO7Wbfc4eZmJxiy22jbBpex8YNa8ouS5KkyjMMSpK61ujYbm7a9vDR4yfHDx09NhBKkjQ3l4lKkrrWPTsfn2X8iY7WIalheqZ+z4EX2HLbKKNju8suSdIcnBmUJHWtp599fsbxZ/Yc6nAlkpypl7qPM4OSpK512inLZhw/dfWJHa5EkjP1UvcxDEpSBW0e2cHmkR1ll1F5m4bXzTK+trOFSHKmXupCLhOVJHWt6aVnt949xsTkFKcPLWfT8FqXpEklOO2UZTw5/vLg50y9VF3ODEpSxbgBw8Js3LCGk5afwOoVS7nuirMNglJJnKmXuo8zg5JUIW7AIKlbOVMvdZ9Cw2BELAFGgDOBw8CVmbmr5fwlwBbgCLA1M2+Z7ZqI+F7gdmAK+EfgI5k5WWT9ktRpc23A4A9Ukqpu44Y13HH/YwBcd8XZJVcj6ZUUvUz0UmBpZg4DnwBumD4REccBNwLvAC4AroqINXNc81ngk5n5NqAPeG/BtUtSx7kBgyRJ6pSiw+B5wL0AmfkgcFbLufXArszcm5kvAg8A589xzZuBv2x+/EXg7QXXLkkd51slSJKkTin6nsEVwP6W44mIGMjMIzOcOwisnO0aoC8zp4557JxWrVrGwED/YuovzNDQYNklqIfZX93r373z+7j+d746w3hU6nWtUi0A/f19QPXq0qvj69jdqv7vsap1qTd0W38VHQYPAK3PyJJmEJzp3CCwb7ZrImJyhsfOae/emZdblW1oaJDx8YNll6EeZX91t/Wnr+RD73n9yzZgWH/6ysq8rlXssYmJxu8Kq1aXFq6K/aWFqfK/R/tLRapqf80VUIsOg9uBS4A/jIhzgIdazj0CnBERJwPP0Vgi+hkaG8TMdM3fRcSFmXk/8C7gLwquXZJK4QYMC3f9NeeWXYIkSV2n6DB4J3BRROygsenL5RFxGbA8M2+OiGuB+2jcu7g1M5+KiJdd0/y7fgq4JSKOpxEk7yi4dkkqjeFGUrfy+5fUPfqmpqZe+VFdanz8YCW/uKpOIas32F8qmj2mItlfKpL9pSJVtb+Ghgb7ZjtX9G6ikiRJkqQKMgxKkiRJUg0ZBiVJkiSphgyDkiRJklRDhkFJkiRJqiHDoCRJkiTVkGFQkiRJkmqop99nUJIkSZI0M2cGJUmSJKmGDIOSJEmSVEOGQUmSJEmqIcOgJEmSJNWQYVCSJEmSasgwKEmSJEk1NFB2AXUSEUuAEeBM4DBwZWbuKrcqdYuIOA7YCqwDTgA+DYwBtwNTwD8CH8nMyYj4IPAh4Ajw6cy8OyK+A/gd4DXAQeD9mTne6a9D1RYRrwG+ClxEo39ux/5SG0TEzwLvAY6n8X/hX2J/qQ2a/z/+Jo3/HyeAD+L3L7VBRGwE/nNmXhgR38sieyoizgE+13zslzLzU53/ql7KmcHOuhRYmpnDwCeAG0quR93lR4E9mfk24AeBXwc+C3yyOdYHvDciXgt8FHgr8E7glyLiBODDwEPNx/4W8MkSvgZVWPMHqpuAbzWH7C+1RURcCJxLo28uAL4T+0vtczEwkJnnAtcBv4D9pUWKiJ8BbgWWNofa0VOfBy4DzgM2RsSbOvX1zMYw2FnnAfcCZOaDwFnllqMu80fAf2h+3Efjt0pvpvHbdYAvAm8Hzga2Z+bhzNwP7ALeQEv/tTxWavUZGv9RPd08tr/ULu8EHgLuBO4C7sb+Uvt8DRhorsBaAfwL9pcW7zHgh1qOF9VTEbECOCEzH8vMKeA+KtBrhsHOWgHsbzmeiAiX6mpeMvO5zDwYEYPAHTR+y9TX/IYCjWUIK3l5n800Pj0mARARHwDGM/O+lmH7S+1yCo1fgL4PuBr4XWCJ/aU2eY7GEtFHgVuAX8XvX1qkzPwCjV8sTFtsT60ADszw2FIZBjvrADDYcrwkM4+UVYy6T0R8J/AXwG9n5n8DJltODwL7eHmfzTQ+PSZN+3Hgooi4H3gjjWUtr2k5b39pMfYA92Xmi5mZwAu89Icg+0uL8TEa/fU6Gvsy/CaNe1On2V9qh8X+zDXbY0tlGOys7TTWtdO8gfShcstRN4mINcCXgI9n5tbm8N8178UBeBfwZeArwNsiYmlErATW07jR+Wj/tTxWAiAzz8/MCzLzQuDvgR8Dvmh/qU0eAH4wIvoi4jTgRODP7C+1yV6+PQvzP4Dj8P9Htd+ieiozDwAvRsT3REQfjeXzpfda39TU1Cs/Sm3RspvoG2jc83V5Zj5ablXqFhHxOeBHaCyDmfYTNJbDHA88AnwwMyeaO1tdReMXPr+YmV+IiGU0flt6KvAicFlmfrOTX4O6Q3N28GoavwW9BftLbRAR/wX4fhp983PAN7C/1AYRsZzGbtun0uinzwF/g/2lRYqIdcDvZ+Y5EfE6FtlTzcmgXwH6aewm+vOd/6peyjAoSZIkSTXkMlFJkiRJqiHDoCRJkiTVkGFQkiRJkmrIMChJkiRJNWQYlCRJkqQaMgxKkiRJUg0ZBiVJkiSphgyDkiRJklRDhkFJkiRJqiHDoCRJkiTVkGFQkiRJkmrIMChJkiRJNWQYlCRJkqQaMgxKkiRJUg0ZBiVJkiSphgyDkiRJklRDhkFJkiRJqiHDoCRJkiTVkGFQkiRJkmrIMChJkiRJNWQYlCRJkqQaMgxKkiRJUg0NlF1AkcbHD06VXcNMVq1axt69z5ddhnqU/aWi2WMqkv2lItlfKlJV+2toaLBvtnPODJZgYKC/7BLUw+wvFc0eU5HsLxXJ/lKRurG/DIOSJEmSVEOGQUmSJEmqIcOgJEmSJNWQYVCSJEmSasgwKEmSJEk1ZBiUJEmSpBoyDEqSJElSDRkGJUmSJHWFzSM72Dyyo+wyeoZhUJIkSZJqaKCMTxoRS4AR4EzgMHBlZu5qOX8JsAU4AmzNzFsi4gPAB5oPWQq8EXhtZu7rYOmSJEmS1BNKCYPApcDSzByOiHOAG4D3AkTEccCNwFuAQ8D2iNiWmbcDtzcf83/RCIkGQUmSJEl6FcpaJnoecC9AZj4InNVybj2wKzP3ZuaLwAPA+dMnI+Is4PWZeXMH65UkSZKknlLWzOAKYH/L8UREDGTmkRnOHQRWthz/HPCp+XySVauWMTDQv9haCzE0NFh2Ceph9peKZo+pSPaXimR/dbf+/j6guq9jVeuaTVlh8ADQ+kwtaQbBmc4NAvsAIuIkIDLzL+bzSfbufb4Npbbf0NAg4+MHyy5DPcr+UtHsMRXJ/lKR7K/uNzExBVDJ17Gq/TVXQC1rmeh24GKA5j2DD7WcewQ4IyJOjojjaSwR3dk8dz7wZ50sVJIkSZJ6UVkzg3cCF0XEDqAPuDwiLgOWZ+bNEXEtcB+NsLo1M59qXhfA10upWJIkSZJ6SClhMDMngauPGX605fxdwF0zXHd9waVJkiRJUi34pvOSJEmSVEOGQUmSJEmqIcOgJEmSJNWQYVCSJEmSasgwKEmSJJVk88gONo/sKLsM1ZRhUJIkSZJqyDAoSZIkSTVkGJQkSZKkGjIMSpIkSVINGQYlSZIkqYYMg5IkSZIqb3RsN/ueO8yeAy+w5bZRRsd2l11S1xsouwBJkiRJmsvo2G5u2vbw0eMnxw8dPd64YU1ZZXU9ZwYlSZIkVdo9Ox+fZfyJjtbRawyDkiRJkirt6Wefn3H8mT2HOlxJbzEMSpIkSaq0005ZNuP4qatP7HAlvaWUewYjYgkwApwJHAauzMxdLecvAbYAR4CtmXlLc/xngfcAxwMjmXlbp2uXJEmS1Fmbhte95J7Bb4+vLaGa3lHWBjKXAkszczgizgFuAN4LEBHHATcCbwEOAdsjYhuwHjgXeCuwDPjpMgqXJEmS1FnTm8TcevcYE5NTnD60nE3Da908ZpHKWiZ6HnAvQGY+CJzVcm49sCsz92bmi8ADwPnAO4GHgDuBu4C7O1qxJEmSpNJs3LCGk5afwOoVS7nuirMNgm1Q1szgCmB/y/FERAxk5pEZzh0EVgKnAGuBdwPfBWyLiO/LzKnZPsmqVcsYGOhve/HtMDQ0WHYJ6mH2l4pmj6lI9peKVLX+6u/vA6pXV1VV/fmqal2zKSsMHgBan6klzSA407lBYB+wB3i0OVuYEfECMAT899k+yd69M+86VLahoUHGxw+WXYZ6lP2lotljKpL9pSJVsb8mJhrzGlWrq6qq/HxVsb9g7oBa1jLR7cDFAM17Bh9qOfcIcEZEnBwRx9NYIrqTxnLRH4yIvog4DTiRRkCUJEmSJC1QWTODdwIXRcQOoA+4PCIuA5Zn5s0RcS1wH42wujUznwKeiojzga80xz+SmRMl1S9JkiRJXa2UMJiZk8DVxww/2nL+LhqbxBx73c8UXJokSZIk1YJvOi9JkiRJNWQYlCRJkkowOrabfc8dZs+BF9hy2yijY7vLLkk1U9Y9g5IkSVJtjY7t5qZtDx89fnL80NFj3z9PneLMoCRJktRh9+x8fJbxJzpah+rNMChJkiR12NPPzvx+2M/sOdThSlRnhkFJkmpm88gONo/sKLsMqdZOO2XZjOOnrj6xw5WozgyDkiRJUodtGl43y/jazhaiWnMDGUmSJKnDpjeJufXuMSYmpzh9aDmbhte6eYw6yjAoSZIklWDjhjXccf9jAFx3xdklV6M6cpmoJEmSJNWQYVCSJGkObrgjqVcZBiVJkiSphrxnUJIkSVJXuP6ac8suoac4Myi9Ci4ZkiRJUrczDEqSJElSDZWyTDQilgAjwJnAYeDKzNzVcv4SYAtwBNiambc0x/8WONB82Dcy8/KOFi5JkiRJPaKsewYvBZZm5nBEnAPcALwXICKOA24E3gIcArZHxDZgP9CXmReWU7IkSZIk9Y6ylomeB9wLkJkPAme1nFsP7MrMvZn5IvAAcD6NWcRlEfGliPjzZoiUJEmSJL0KZc0MrqAx0zdtIiIGMvPIDOcOAiuB54HPALcCZwBfjIhoXjOjVauWMTDQ3/bi22FoaLDsErQI/f19QHVfx6rWpd5hj3U3v4ctTNWfLy1M1V5H+6u3dNvrWFYYPAC0PlNLWkLdsecGgX3A12jMGE4BX4uIPcCpwD/P9kn27n2+rUW3y9DQIOPjB8suQ4swMTEFUMnX0f5S0eyx7uf3sIWp8vOlhbG/VKQq9hfMHVDLWia6HbgYoLnc86GWc48AZ0TEyRFxPI0lojuBH6dxbyERcRqNGcRnOlm0JEmSJPWKsmYG7wQuiogdQB9weURcBizPzJsj4lrgPhphdWtmPhURtwG3R8QDwBTw43MtEZUkSZIkza6UMJiZk8DVxww/2nL+LuCuY655Ebis+OokSZIkqff5pvOSJEmSVEOGQUmSJEmqIcOgJEk1Mjq2m33PHWbPgRfYctsoo2O7yy5JklSSsjaQkSRJHTY6tpubtj189PjJ8UNHjzduWFNWWZKkkhgGJUmqiXt2Pj7L+BOGQakk119zbtklqMZcJipJUk08/ezzM44/s+dQhyuRJFWBYVCSpJo47ZRlM46fuvrEDlciSaoCw6AkSTWxaXjdLONrO1tIF3HDHUm9zHsGBcDmkR2A69YlqZdN3xd4691jTExOcfrQcjYNr/V+wVm44Y6kXufMoCRJNbJxwxpOWn4Cq1cs5borzjbUzGGuDXckqRcYBqUFcsmQJNWDG+5I6nWGQWkBppcMTUxOAd9eMmQglKTe44Y7knqdYVBaAJcMSVJ9uOGOpF7nBjLSArhkSJLqww13JPU6ZwalBXDJ0Ku3eWTH0V1rJalbuOGOpF5WysxgRCwBRoAzgcPAlZm5q+X8JcAW4AiwNTNvaTn3GuCrwEWZ+WhHC1ftbRpe95Jtxr897pIhSZIkdZeyZgYvBZZm5jDwCeCG6RMRcRxwI/AO4ALgqohY03LuJuBbHa9YovEb4g+95/X0L+kD4PSh5XzoPa/3N8WSJEnqOmWFwfOAewEy80HgrJZz64Fdmbk3M18EHgDOb577DPB54OkO1iq9hEuGJEmS1AvK2kBmBbC/5XgiIgYy88gM5w4CKyPiA8B4Zt4XET87n0+yatUyBgb621VzWw0NDZZdwkv09zdmuqpWV1VV/fmqYl1Vf860ML6O3a3q/x6rVlfVny8tjK+jitRt/VVWGDwAtD5TS5pBcKZzg8A+4KPAVES8HXgj8FsR8Z7M/OZsn2Tv3pl3fizb0NAg4+MHyy7jJSYmGu+bV7W6qqrKz1cV+wuq/ZxpYaraY5q/Kv97rGJ/Vfn50sJUsb/UO6raX3MF1LLC4HbgEuAPI+Ic4KGWc48AZ0TEycBzNJaIfiYz75h+QETcD1w9VxCUJEmSJM1uUWEwIvqAD2Xm5xd46Z3ARRGxA+gDLo+Iy4DlmXlzRFwL3EfjnsatmfnUYuqUJEmSJL3UosJgZk5FxHkRcXNmTi7gukng6mOGH205fxdw1xzXX7jQWiVJkiRJ39aOZaLfDXw5In4L+Hvg4cx8rg1/ryRJkiSpIO14a4m3Aj9G4+0evh/49Tb8nZIkSZKkArVjZvB44CTgb5rLOyVJkiRJFdeOmcH/B3g3sDMitkfEF9vwd0qSJEmSCtSOmcElmfmpiLgQuBi4rg1/pyRJKsj115xbdgmSpApox8zgP0TEMuC/09hM5sI2/J3qoNGx3ex77jB7DrzAlttGGR3bXXZJkiRJkgrWjjC4C5gEfhr4GPAbbfg71SGjY7u5advDTExOAfDk+CFu2vawgVCSJEnqce1YJvpPwO8DDwI/6dtKdJd7dj4+y/gTbNywpqO1SJIkSeqcRc8MZubdmXkpsBO4JSK2LL4sdcrTzz4/4/gzew51uBJJkiRJnbTomcGI+FHgdc0/gzTed1Bd4rRTlvHk+MuD36mrTyyhGkmSJEmdsuAwGBEXZ+aftAxNAduAzMyDbatMHbFpeB03bXt4hvG1JVQjSVL1uPuqpF71amYGPw38CUBEvDczf7e9JamTpu8LvPXuMSYmpzh9aDmbhtd6v6DaanrH2onJKbbcNsqm4XX2mCRJUslezT2DfS0f/8d2FaLybNywhpOWn8DqFUu57oqz/SFdbeWOtZIkSdX0amYGp1o+7pv1UVIPc8nQ/LljrSRJUjW9mjD4vRFxG/DXwHdExEBmHmlzXZJ6hDvWSpIkVdOrCYMXA/8aOAd4EdgfEY8B/wD8v5l5/Sv9BRGxBBgBzgQOA1dm5q6W85cAW4AjwNbMvCUi+oFbgKAxO3l1Zv7jq6hfUge5Y60kSVI1LTgMZuYDwAPTxxFxAo1Q96+bf+bjUmBpZg5HxDnADcB7m3/fccCNwFuAQ8D2iNgGDDc//1sj4kLgF6avkVRd7lgrSZJUTYt+n8HMPAx8pflnvs4D7m1e/2BEnNVybj2wKzP3AkTEA8D5mflHEXF38zFrgX2LrV1S8dyxVpIkqZoWHQZfpRXA/pbjiZZ7D489dxBYCZCZRyLiN4H/BfhfX+mTrFq1jIGB/vZV3UZDQ4Nll/AS/f2NvYCqVpdenaq9ju++YJA7v/x1AH7jEz9QcjVqh6r1mHqL/aUi2V8qUrf1V1lh8ADQ+kwtadmE5thzg7TMAmbm+yPi48BoRGzIzFl3odi7d+aNK8o2NDTI+PjBsst4iYmJxiaxVatLC1fF/gJ7rJdUtcfUG+wvFcn+UpGq2l9zBdRX8z6D7bCdxkY0NO8ZfKjl3CPAGRFxckQcD5wP7IyI/yMifrb5mOeByeYfSZIkSdIClTUzeCdwUUTsoPFehZdHxGXA8sy8OSKuBe6jEVa3ZuZTEfHHwH+NiL8CjgN+MjO/VVL9kiRJktTVSgmDmTkJXH3M8KMt5+8C7jrmmkPAvy2+OkmSJEnqfWUtE9X/397dxshVnncYv/bF2DVeuwaWLSgISy25hT+EVEXY6xpMpZoQLFoUqapkRWkJL3FoFBQkUtqCpSLaKiWUUkVWwbBK0jaqGpAlTBSMFIWq2LBRq1ZysviubJVIgcTauOs3CHa93n44s2Yway/2zs6ZM+f6fdrzPHO098zcuzP/Oec8I0mSJEklMgxKkirv/i27uH/LrrLLkCSpUgyDkiRJklRDhkFJkiRJqiHDoCRJkiTVkGFQkiRJkmrIMChJkqSWcUEnqToMg5IkSZJUQ6V86bw6z6P3rCm7BEmSJElt5JFBSZIkSaohw6AkSZIk1ZBhUJIkSZJqyDAoSZIkSTVkGJQkSZKkGiplNdGI6AW2ANcAx4A7M3Nv0/ytwGbgBDCSmVsjYgEwAqwAFgKPZObz7a5d0vlxxVpJkqTOUtaRwduARZk5DDwAPDY90Qh9jwM3AeuAuyNiCPg0cCAzrwduBr7W9qolSZIkqUuUFQbXAi8CZOZrwLVNc1cDezNzIjOPA68ANwDfBh5q3KaH4qihJEmSJOk8lPWl80uBQ03bkxHRn5knZpg7AizLzKMAETEAPAs8ONsvWb58Mf39fa2ruoUGBwfKLkFdzP7SfOu0Huvr6wE6ry6dH5/Hauv0v8dOrUvdoWr9VVYYPAw0P1K9jSA409wAcBAgIq4AtgFbMvNbs/2SiYl3WlNtiw0ODjA+fqTsMtSl7C/Nt07sscnJKYCOq0vnrhP7S+emk/8e7S/Np07tr7MF1LLC4E7gVuBfImI1sLtp7nXgqoi4CDhKcYroVxvXDb4EfCEzv9fugiVJkiSpm5QVBrcB6yNiF8X1f7dHxEZgSWY+FRH3ATsormkcycw3I+IJYDnwUERMXzv4ycz8RRl3QJIkSZKqrJQwmJkngU2nDe9pmt8ObD9tn3uBe+e/OklSlYyO7efg0WNMnpxi8zOjbBhewaqVQ2WXJUlSxyvryKAk6Szu37IL8PsZZzM6tp8nn//Rqe2fjL99attAKEnS2ZX11RKSJM3Zd1594wzjP25rHZIkVZFhUJJUWW/9fOZVo3964O02VyJJUvUYBiVJlXX5JYtnHL/s4gvbXIkkSdVjGJQkVdaG4RVnGL+yvYVIAt5b0OnA4XfZ/Mwoo2P7yy5J0lm4gIwkqbKmF4l5+oUxJk9O8ZHBJWwYvtLFY6QSuKCTVD0eGZQkVdqqlUP88pKFXLx0EQ/fcZ1vOqWSuKCTVD2GQUmSJM2ZCzpJ1WMYlCRJ0py5oJNUPYZBSZIkzZkLOknV4wIykiRJmjMXdJKqxzAoSZKklli1cohnX94HwMN3XFdyNZJm42mikiRJklRDhkFJkiRJqiHDoCRJkiTVUCnXDEZEL7AFuAY4BtyZmXub5m8FNgMngJHM3No0twr4Smbe2NaiJUmSJKmLlHVk8DZgUWYOAw8Aj01PRMQC4HHgJmAdcHdEDDXmvgw8DSxqe8WSJEmS1EXKCoNrgRcBMvM14NqmuauBvZk5kZnHgVeAGxpz+4BPtbNQSZIkSepGZX21xFLgUNP2ZET0Z+aJGeaOAMsAMvO5iFjxYX/J8uWL6e/va0G5rTc4OFB2Cepi9lf19fX1AJ37XHZaXZ3+eOnc+DxWW6f/PXZqXeoOVeuvssLgYaD5keptBMGZ5gaAg+fzSyYm3jm/6ubZ4OAA4+NHyi5DXcr+6g6Tk1MAHflcdmKPdfLjpXPTif2lc9PJf4/2l+ZTp/bX2QJqWaeJ7gRuAYiI1cDuprnXgasi4qKIuIDiFNFX21+iJJVjdGw/B48e48Dhd9n8zCijY/vLLkmSJHWhso4MbgPWR8QuoAe4PSI2Aksy86mIuA/YQRFWRzLzzZLqlKS2Gh3bz5PP/+jU9k/G3z61vWrlUFllSZKkLlRKGMzMk8Cm04b3NM1vB7afYd83gNXzVpwkleg7r75xhvEfGwYlSVJL+aXzktRB3vr5zNc6//TA222uRJIkdTvDoCR1kMsvWTzj+GUXX9jmSiRJUrcr65pBSdIMNgyveN81g++NX1lCNdXx6D1ryi5BkqTKMQxKUgeZvi7w6RfGmDw5xUcGl7Bh+EqvF5QkSS1nGJSkDrNq5RDPvrwPgIfvuK7kaiRJUrfymkFJkiRJqiHDoCRJkiTVkKeJSpIkqWVc0EmqDo8MSpIkSVINGQYlSZIkqYYMg5IkSZJUQ4ZBSZIkSaohw6AkSZIk1ZBhUJIkSZJqyDAoSZIkSTVUyvcMRkQvsAW4BjgG3JmZe5vmbwU2AyeAkczcOts+kiRJkqQPr6wjg7cBizJzGHgAeGx6IiIWAI8DNwHrgLsjYuhs+0iSJEmSzk1ZYXAt8CJAZr4GXNs0dzWwNzMnMvM48Apwwyz7SJIkSZLOQSmniQJLgUNN25MR0Z+ZJ2aYOwIsm2WfGS1fvpj+/r4Wlt06g4MDZZegLmZ/VV9fXw/Quc9lp9al7mB/aT7ZX5pPVeuvssLgYaD5keptCnWnzw0AB2fZZ0YTE++0oNTWGxwcYHz8SNllqEvZX91hcnIKoCOfS3tM88n+0nyyvzSfOrW/zhZQyzpNdCdwC0BErAZ2N829DlwVERdFxAUUp4i+Oss+kiRJkqRzUNaRwW3A+ojYBfQAt0fERmBJZj4VEfcBOyjC6khmvhkRH9inpNolSZIkqfJKCYOZeRLYdNrwnqb57cD2D7GPJEmSJOk8lHVkUJJ0Fo/es6bsEiRJUpcr65pBSZIkSVKJDIOSJEmSVEOGQUmSJEmqIcOgJEmSJNWQYVCSJEmSasgwKEmSJEk1ZBiUJEmSpBoyDEqSJElSDfVMTU2VXYMkSZIkqc08MihJkiRJNWQYlCRJkqQaMgxKkiRJUg0ZBiVJkiSphgyDkiRJklRDhkFJkiRJqqH+sguok4joBbYA1wDHgDszc2+5VakqImIBMAKsABYCjwBjwNeBKeCHwB9l5smIuAv4HHACeCQzX4iIXwL+EbgUOAL8QWaOt/t+qLNFxKXAfwDrKfrn69hfaoGI+BPgd4ALKF4L/xX7Sy3QeH38BsXr4yRwF/7/UgtExCrgK5l5Y0T8GnPsqYhYDTzRuO1Lmfnn7b9X7+eRwfa6DViUmcPAA8BjJdejHHwWgQAAAxlJREFUavk0cCAzrwduBr4G/A3wYGOsB/jdiPgV4IvAbwKfAP4qIhYCnwd2N277TeDBEu6DOljjDdWTwC8aQ/aXWiIibgTWUPTNOuAK7C+1zi1Af2auAR4G/gL7S3MUEV8GngYWNYZa0VN/D2wE1gKrIuLX23V/zsQw2F5rgRcBMvM14Npyy1HFfBt4qPFzD8WnSr9B8ek6wHeB3wauA3Zm5rHMPATsBT5GU/813VZq9lWKF6q3Gtv2l1rlE8BuYBuwHXgB+0ut899Af+MMrKXA/2F/ae72AZ9q2p5TT0XEUmBhZu7LzClgBx3Qa4bB9loKHGranowIT9XVh5KZRzPzSEQMAM9SfMrU0/iHAsVpCMv4YJ/NND49JgEQEX8IjGfmjqZh+0utcgnFB6C/B2wC/gnotb/UIkcpThHdA2wF/g7/f2mOMvM5ig8Wps21p5YCh2e4bakMg+11GBho2u7NzBNlFaPqiYgrgO8D/5CZ3wJONk0PAAf5YJ/NND49Jk37LLA+Il4GPk5xWsulTfP2l+biALAjM49nZgLv8v43QfaX5uJLFP31UYp1Gb5BcW3qNPtLrTDX91xnum2pDIPttZPivHYaF5DuLrccVUlEDAEvAX+cmSON4f9sXIsD8Eng34AfANdHxKKIWAZcTXGh86n+a7qtBEBm3pCZ6zLzRuC/gM8A37W/1CKvADdHRE9EXA5cCHzP/lKLTPDeUZj/BRbg66Nab049lZmHgeMR8asR0UNx+nzpvdYzNTU1+63UEk2riX6M4pqv2zNzT7lVqSoi4gng9ylOg5l2L8XpMBcArwN3ZeZkY2Wruyk+8PnLzHwuIhZTfFp6GXAc2JiZP2vnfVA1NI4ObqL4FHQr9pdaICL+Gvgtir75U+B/sL/UAhGxhGK17cso+ukJ4N+xvzRHEbEC+OfMXB0RH2WOPdU4GPS3QB/FaqJ/1v579X6GQUmSJEmqIU8TlSRJkqQaMgxKkiRJUg0ZBiVJkiSphgyDkiRJklRDhkFJkiRJqiHDoCRJkiTVkGFQkiRJkmrIMChJkiRJNfT/RHyrlUNUVfcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ae1e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.gridspec import GridSpec\n",
    "plt.figure(figsize=(15, 20))\n",
    "gs = GridSpec(5, 1)\n",
    "ax_lc = plt.subplot(gs[0])\n",
    "ax_mean = plt.subplot(gs[1], sharex=ax_lc)\n",
    "ax_evar = plt.subplot(gs[2], sharex=ax_lc)\n",
    "ax_nvar = plt.subplot(gs[3], sharex=ax_lc)\n",
    "ax_fvar = plt.subplot(gs[4], sharex=ax_lc)\n",
    "\n",
    "ax_lc.plot(lc.time, lc.counts)\n",
    "ax_lc.set_ylabel('Counts')\n",
    "ax_mean.scatter(tmean, rate)\n",
    "ax_mean.set_ylabel('Counts')\n",
    "\n",
    "ax_evar.errorbar(tmean, evar, yerr=evar_err, fmt='o')\n",
    "ax_evar.set_ylabel(r'$\\sigma_{XS}$')\n",
    "\n",
    "ax_fvar.errorbar(tmean, fvar, yerr=fvar_err, fmt='o')\n",
    "ax_fvar.set_ylabel(r'$F_{var}$')\n",
    "\n",
    "ax_nvar.errorbar(tmean, nvar, yerr=nvar_err, fmt='o')\n",
    "ax_nvar.set_ylabel(r'$\\sigma_{NXS}$')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}