{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $H\\rightarrow \\gamma \\gamma$ Project\n",
    "\n",
    "For this problem, we take the $H\\rightarrow \\gamma \\gamma$ CMS data at 7TeV and 8TeV and attempt to extract a maximum likelihood estimiate of the Higgs Boson mass and width. Our data model consists of the following.\n",
    "\n",
    "\\begin{equation}\n",
    "p(n|\\theta) = \\prod_{i=1}^{M}\\textrm{Poisson}(n_i,a_i(\\theta))\n",
    "\\end{equation}\n",
    "\n",
    "Where $n_i$ is the number of observed events in bin $i$, and $a_i$ is the expected number of events. We estimate the model parameters, $\\theta$, by minimizing the negative log-likelihood (nll).\n",
    "\n",
    "\\begin{align}\n",
    "\\textrm{nll}(\\theta) &= -\\ln(\\mathcal{L}(\\theta|n) \\\\\n",
    "             &= \\sum_{i=1}^{M}\\left( a_i - n_i\\ln(a_i) + \\ln(n!) \\right)\n",
    "\\end{align}\n",
    "\n",
    "The signal model is taken to be simply a gaussian with mean, $m_H$, and width $\\sigma$. The background model is more complicated. A first attempt would be to simply use a decaying exponential distribution, but it turns out that this function does not appropriately describe the background, leading to an background overestimate in the region of interest(120-130GeV), giving the signal no possible excess to fit to. A better background model is the sum of two exponentials with different decay rates. Given this, we can write down $a_i$ as:\n",
    "\n",
    "\\begin{equation}\n",
    "a_i(x_i) = \\left(\\frac{s}{\\sqrt{2\\pi \\sigma^2}}\\exp{\\left(\\frac{(x_i - M_H)^2}{2\\sigma^2}\\right)} + b_1\\exp{\\left(-r_1 x_i\\right)} + b_2\\exp{\\left(-r_2 x_i\\right)}\\right) \\Delta x\n",
    "\\end{equation}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm, poisson\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.dpi']=150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ROOT\n",
    "def setup_minuit(params):\n",
    "    from ctypes import c_int\n",
    "    PRINT_LEVEL = -1\n",
    "    UP = 1.0\n",
    "    npar = len(params)\n",
    "    minuit = ROOT.TMinuit(npar)\n",
    "    minuit.SetFCN(nll)\n",
    "    minuit.SetErrorDef(UP)\n",
    "    minuit.SetPrintLevel(PRINT_LEVEL)\n",
    "    \n",
    "    status = c_int()\n",
    "    print(\"{:<10s}{:>10s}{:>10s}{:>10s}{:>10s}\".format('param', 'guess', 'step', 'min', 'max'))\n",
    "    for ii, t in enumerate(params):\n",
    "        name, guess, step, pmin, pmax = t\n",
    "        print(f\"{name:<10s}{guess:10.3e}{step:10.3e}{pmin:10.3e}{pmax:10.3e}\")\n",
    "        minuit.mnparm(ii, name, guess, step, pmin, pmax, status)\n",
    "        if status.value != 0:\n",
    "            raise ValueError(f\"** mnparm({name}) status = {status.value}\")\n",
    "    return minuit\n",
    "\n",
    "def perform_fit(minuit, params):\n",
    "    from array import array\n",
    "    from ctypes import c_int, c_double\n",
    "    \n",
    "    LINE = '='*79\n",
    "    print(LINE)\n",
    "    print('Running Minuit')\n",
    "    \n",
    "    # define fit control parameters\n",
    "    MAXITER = 10000\n",
    "    TOLERANCE = 1e-5\n",
    "    args = array('d')\n",
    "    args.append(MAXITER)\n",
    "    args.append(TOLERANCE)\n",
    "    \n",
    "    swatch = ROOT.TStopwatch()\n",
    "    swatch.Start()\n",
    "    \n",
    "    # run MIGRAD minimizer\n",
    "    status = c_int()\n",
    "    minuit.mnexcm('MIGRAD', args, 2, status)\n",
    "    if status.value != 0:\n",
    "        raise ValueError(f'** mnexcm status = {status.value}')\n",
    "    print(f'real time: {swatch.RealTime():10.3f} s')\n",
    "    \n",
    "    # print results\n",
    "    out = open('fit.txt', 'w')\n",
    "    value = c_double()\n",
    "    error = c_double()\n",
    "    results = []\n",
    "    print('{:<10s}{:>11s}{:>13s}'.format('name', 'value', 'uncertainty'))\n",
    "    for ii, t in enumerate(params):\n",
    "        name = t[0]\n",
    "        minuit.GetParameter(ii, value, error)\n",
    "        record = f'{name:10s}{value.value:11.3f}{error.value:13.3f}'\n",
    "        out.write(record + '\\n')\n",
    "        print(record)\n",
    "        results.append((value.value, error.value))\n",
    "    out.close()\n",
    "    print(LINE)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "use_8TeV = True\n",
    "\n",
    "def parse(fname):\n",
    "    with open(fname) as f:\n",
    "        rows = [r.split() for r in f.readlines()[1:]]\n",
    "    bins = [(float(r[0]), sum(map(int, r[1:]))) for r in rows]\n",
    "    bin_centers, bin_counts = map(np.array, zip(*bins))\n",
    "    return bin_centers, bin_counts\n",
    "    \n",
    "bin_centers, bin_counts = parse('Hgg_7TeV.txt')\n",
    "\n",
    "if use_8TeV:\n",
    "    bin_counts += parse('Hgg_8TeV.txt')[1]\n",
    "    \n",
    "total_count = sum(bin_counts)\n",
    "# Here, we normalize the distribution to ease the task of selecting fit parameters.\n",
    "# We will multiply again by total_count later to restore the scale.\n",
    "bin_counts = bin_counts / total_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f_s(x_i, s, m_H, w):\n",
    "    return s*norm.pdf(x_i, m_H, w)\n",
    "\n",
    "def f_b(x_i, b1, r1, b2, r2):\n",
    "    return b1*np.exp(-x_i*r1) + b2*np.exp(-x_i*r2)\n",
    "\n",
    "def a(x_i, t_s, t_b):\n",
    "    return (f_s(x_i, *t_s) + f_b(x_i, *t_b))*0.8\n",
    "\n",
    "def nll(npar, grad, fval, xval, flag):\n",
    "    t_s = [xval[i] for i in range(3)]\n",
    "    t_b = [xval[i] for i in range(3,7)]\n",
    "    as_ = [a(x_i, t_s, t_b) for x_i in bin_centers]\n",
    "    fval[0] = sum(a_i - D_i*np.log(a_i) for D_i, a_i in zip(bin_counts, as_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "param          guess      step       min       max\n",
      "s          1.000e-01 1.000e-03 0.000e+00 1.000e+00\n",
      "m_H        1.250e+02 1.000e-01 1.200e+02 1.300e+02\n",
      "w          4.000e+00 1.000e-02 1.000e+00 3.000e+01\n",
      "b1         1.400e-01 1.000e-02 1.000e-02 1.000e+01\n",
      "r1         2.000e-02 1.000e-03 1.000e-03 5.000e-01\n",
      "b2         9.800e+00 1.000e-02 1.000e-02 1.000e+01\n",
      "r2         6.000e-02 1.000e-03 1.000e-03 5.000e-01\n",
      "===============================================================================\n",
      "Running Minuit\n",
      "real time:      3.751 s\n",
      "name            value  uncertainty\n",
      "s               0.007        0.668\n",
      "m_H           120.369        6.023\n",
      "w              14.362       21.159\n",
      "b1              0.122        6.129\n",
      "r1              0.022        0.061\n",
      "b2              9.961        7.394\n",
      "r2              0.059        0.047\n",
      "===============================================================================\n"
     ]
    },
    {
     "data": {
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IyMigXbt21RpHXYnLZAf4EoiWaASA6/0/l/pVgJlNBm4HvgcWAS2BVOACMxvmnJsffjEz\nuwLIwZsFewv4GvgpMMPMejvnxkSIOR54D+gAfOKP4zTgd8D5Znauc25fld6xiIiI1KkZM2aUafvs\ns8+45557mD17Nueeey7vvPMOJ598ct0PrhL27dvH/fffz8knn8zgwYNLnbv88su5/PLLI8b95je/\nYcmSJVxxxRW0bt261LmPPvqI8847j3379jFu3Djuuece2rRpU6pPXl4ekydPJjs7u0bjHzlyJJMn\nTyYnJ4dHH32U5s2bR+y3YsUKNm7cSMeOHUlNTa3RPWvDxx9/zGOPPUbbtm1577336NatW8m5KVOm\nMGbMGH71q1+VSXZeffVVrrjiCgKBAA8//DA333wzLVq0KNVn7dq1PPjgg6xcubJGYxw5ciTz5s3j\n2WefLTfZWbBgAYWFhZx66ql07969pP3uu+/mpZdeYsKECUyYMKFGYznY4nIZm3PuU+dcWqQvYKbf\n7QvgzWCMmZ2Hl+hsB3o75y53zg0GBgDFQLaZtQ29j/86Gy+BGuqcG+ScGwqcCPwHuN3Mzo0wxOl4\nic6jzrlezrkRQDdgPtAPuKeWPoq4kjxuIcnjFjLo50/zZN9hLOg+gCf7DmPQz5+O9dBEREQq5fjj\nj2fWrFlkZGTw3XffkV6PN8ueM2cO27Zt44Ybbqh0jHOO559/Hig7S+Kc4/rrr2fv3r088MADjB8/\nvkyiA5CcnMyjjz7K0qVLazT+k08+mZNOOokdO3aUmZkKFZzVueaaawgEAtW+X3BpX03HHVx2N2LE\niFKJDsDtt9/O4YcfzqZNm9i2bVtJ+549e7jxxhs5cOAAzzzzDGPGjCmT6AD07NmTv//97zz77LM1\nGuNFF11E+/btWbt2bbmzb9FmzPr27UuXLl2YPn06+/fvr9FYDra4THYqEJzVec45F7pYdKx//JNz\nLjfY6Jx7D3gKSADC/8X6md/+D+fcvJCYrcBd/stSMztmdjpeArUtpA/OuSJgNPADcIuZNavWu4tz\nw1YvZsm00Yz+YA6Xfvo2oz+Yw5Jpo6GGv/0RERGpSw8//DCtWrVi1apVLFu2rNS5hQsXkp6eTvfu\n3TnssMNo1aoVvXv35oEHHmDfvtILOwYNGsSoUaMA+OMf/1jqeZngzFIwAbn66qs54YQTaNWqVckS\npyeeeCLqszHTpk3DzLjmmmsq/b6WLl3KF198wZFHHsn5559f6twrr7zC2rVrOeaYY7jrrruiXOFH\np556asT2vLw8fvGLX5CcnEyLFi3o0KEDQ4cOZfXq1WX6Bn/IjvbDfXFxMS+88ALw4zKrWIuUpIQL\nBAIkJCSUvJ4xYwZfffUVZ599dqX+e0X7bNesWcN1111H586dadGiBZ06dWLUqFHk5eWV6te8eXOG\nDx8ORP9sg0lmIBDg6quvLnP+mmuu4euvv2b+/DILo+qVBpXsmFkr4DL/5bMh7S3xlp4BzIkQGmy7\nNKz9krDzoRYCe/GWpbWMELMgfKmanyS9DbQFzor+Thqm5B0FjH/tMZq60v8oN3UHIDMTcnOjRIqI\niNQvCQkJXHjhhQBlHhjPyMggJyeHhIQEBg8ezDnnnMMXX3zBvffey0UXXURxcXFJ38GDB3PWWd6P\nBL179+bGG28s+erSpQvgLUe79tprWbRoEYmJiVx66aWceeaZrF27lptvvjni7NKuXbt4++23OfHE\nEznyyCMr/b6CP/hGmiUJzq4MHTqUJk2q9yPksmXL6N27N08//TStW7dmyJAhdO3alXnz5tG3b98y\nn+V1111HkyZNePnll9m5c2eZ6y1ZsoStW7fSvXv3qAlAXTv33HNp2rQps2bNYsOGDaXOTZkyhW+/\n/ZYRI0aUSoqCn+2IESOqfd+5c+dy2mmnMXPmTI466iiGDBnCkUceyYwZMzjttNNYu3Ztqf7BRPL5\n55+PmDDn5OSwf/9+LrjgAjp27Fjm/KBBgwAvua/PGlSyA1wJtAJWOedC/4ueCLQAvnLO5UeI+9A/\npoS1p4SdL+Gc24/3PE5LvCVqQb2jxYS1945yvsEasXpxmUSnRHExTJ9etwMSERGpgeCzOuvXry/V\n/tRTT/Hll1/y/vvvM3v2bF577TU2bdrEJZdcwr/+9a+SpUEA48aN42c/+xngPUcTfLh/xowZnH32\n2QA0bdqUuXPn8uWXX7Js2TJeeOEFlixZQl5eHqeddhrPPPNMmapp77zzDsXFxZx++umVfj979+5l\n7ty5QOQH/YPLnfr06VPpa4bauXMnw4YN4/vvvycnJ4dPPvmEnJwc3n33XRYtWkRxcTEjR44stSyq\nc+fOnHvuuezdu5c5c8r+7jmYnNWXWR2ALl26MGnSJL799lt69erF+eefz4gRI+jVqxd33XUX1113\nHX/9619LxdT0s/3vf//LDTfcwCGHHMKbb77JypUrycnJ4cMPP+SZZ55h+/btJTOIQf369aNLly4U\nFBREXLpX0Wd7+umn06RJk0pVy4ulhpbsBP9r/D2s/Wj/GCnRwTm3B/gWaGtmbQDM7DDg8PLiQtqP\nDmkr915RYhqFpMKt5XcIm2IVERGpz4444gjAK+Ub6vLLL6dVq1al2tq0acOUKVMAqlxFq2nTplx5\n5ZVlHtDv0KED48ePj3jN4JKw8GdGyvPSSy9RWFhIz549I/7Q/fXXXwM/vu9waWlpZb4++OCDkvPT\np0/nyy+/5I477mDo0KGlYs8//3xuuukmCgoKePnll0udCyZeoUkieOWvX3zxxXpXchrgtttuY+bM\nmTRr1ozXX3+d2bNn88knn3D00Udz3nnnlSn8UN5n+/XXX0f8bD/77LOSPn/+85/57rvvmDhxIgMG\nDCgVf8MNN3D55ZezYsUKPvyw9O/ig4lM+Ge7adMm3nnnHVq3bh21kEWbNm046qijyMvLizjrVl/E\nazW2MszsSLylasXA82Gng99R35VziT14yU1rYFdITHlxe8KuX5l7RYqJyszWRjl1fGXi65P8hLJT\noKUkJ9fJOERERGqDcw7w9uUJl5ubyyuvvMJ//vMf9uzZw4EDB0r651Zz2fZHH33EokWL2LRpE999\n9x3OOXbt2hXxmsGH39u2bVvmOtEEf5MfrXxzee8X4JlnyhbKHTx4MGeeeSYAixcvBoj6w/PZZ5/N\nI488wooVK7jyyitL2q+66ipuuukmli5dSkFBAZ07dwbgxRdfZPfu3QwYMIBjjjmmMm8R8JKHO+64\no0z7Rx99BMCDDz5YphLfEUccwaRJkyp1feccY8aM4ZFHHuGXv/wlY8aMoVOnTqxdu5axY8eSkZHB\nunXrIl4v0me7e/fuiJ/tL3/5S44/3vtxMPjZXnbZZWX6gffZvvjii6xYsaJUOfGRI0dy3333MXfu\nXB5//HFatvSezHjuuedwznHllVdy6KGHRn2v7dq1o6CggK+++orDDjusnE8ldhpMsgNci1c17TXn\n3Jdh54LfOa6c+PDvrsh/kyvuU9G9KnPdBmlWSiqZy+dFXsoWCEA9rmgjIiISLvjb+NB9Rpxz3HHH\nHUyZMqUkOQgXTFAqa//+/aSlpZVUSavMNQsLCwEiVkuLZPv27bz22ms0adKEa6+9NmKfI444gg0b\nNpS873Ch7zctLa3MD+jBh+SDyU804dcPzi7MnDmT559/viRRqe7eOtGSh6B//vOfZdqOOeaYSic7\nzzzzDI888giXX345Tz75ZEn7GWecwSuvvEL37t2ZMmUKP/vZzzjxxBMBaN++PQUFBXz99deccMIJ\npa6XnJxc6rMdNGgQb775Zqk+wc+2ouezwj/b4447jv79+/Puu+/y8ssvl8y4VfazDSY4we+3+qgh\nJTvRlrCBN1MD3vM80QTT1t1hMcFzkebnwmMqc69IMVE553pGavdnfHpU5hr1RV67ztw9+JYyRQqK\nrAlNp02Drl1jODoREZGqCc4E9Ojx4/+OZ82axeTJk0lKSuKRRx6hX79+dOjQgWbNmrF//35atGgR\nNQmKZvLkyTz//POcdNJJPPTQQ5xyyim0bduWZs2asXHjRrp161bmmsFKX5VdXjRr1ix++OEHzj33\nXH7yk59E7NO7d2/eeecdPvzww6gJUXmChRmGDRtW7mxBpGRo5MiRzJw5k2effZY77riDr776ikWL\nFtGiRYsyS+IqEp48BN1333388Y9/5I033ih5+L46/v5370fRSONq06YNgwcPJisri7feeqsk2end\nuzcFBQV8+OGH9O/fv8r3LC4uxswqLDPes2fZHytHjhzJu+++y7PPPsvQoUNZtWoV69ato1OnTpx3\n3nnlXi+Y5IRWlqtvGkSyY2bdgT54CcSLEbps9o9JUeJb4S1h+9Y5twvAObfTzArxSk8nAesihAav\ntzmkbbM/loj3ihLTaOSkpLIiqQfD1ywmqXAb+QmJzO6VytK0tFgPTUREpNIKCwt57bXXAK/6VlCw\nDO+TTz5ZZtPIzz//vFr3Cl4zmPBU5pqJiYmAVz64MipawgZw4YUX8sQTTzBnzhwmTpxY5YpsSUlJ\nbNiwgd/+9rekpITXhCpfamoqRx55JB9//DFr167ljTfeoKioiMsvv5zDDz+84gvUofx87/HsaMu6\ngu2h/20uvPBCXnnlFWbNmsWvfvWrKt8zKSmJzz77jEcffbTKy8lGjBjBbbfdxquvvso333xTMqtz\n7bXXVvjfOPi8WocOHao85rrSUAoUBP9mznPORXpWZgOwD+hgZpGSkODixfAC7x+HnS/h75Nzkn/d\nDZWJqeBejUZeu85MHJjGrUPuYuLANPLadY71kERERKpk7Nix7Nmzh9NPP51+/fqVtAd/+Is0OzJ7\n9uyI1woWHigqKop4vjrX7N3bK/r66aefRnsLJT7//HPee+89DjnkEK666qqo/S666CK6d+/Opk2b\nmDBhQoXXDRfct+fFFyP9Xrp8gUCgZP+ZZ599tl5WYQsKLiVbuXJlxPMrVqwAvBmmoLS0NNq3b8+y\nZcvKXa4YTU0+27Zt23LxxRezf/9+XnjhhZL7V/TZ7ty5ky1btnDsscfW2+d1oAEkO+Y9yRWcS420\nhA3n3PfAv/yXkeY6g20vh7UvDDsf6hK8stOvO+f2Roi51MxK7SplZh2Bc4BCoPQOZCIiIlLvff75\n54wYMYKsrCxatWpFVlZWqfPB5y2efvrpUkul3n77bR566KGI1+zUqRNAmT1Zwq/51FNPlWqfM2cO\nf/vb3yLG9O/fn0AgwPLlyyt8T8HE4bLLLiv3h9YmTZrw97//nRYtWnDvvfdy9913R3z+aNOmTWzc\nuLFM+y9+8Qs6dOjAAw88QHZ2dpmlZHv27OFvf/tbycxIuOCs09SpU/nggw9o3749F110UYXvr64F\nCzBMnjy5zOf/l7/8hWXLltGmTRsuuOCCkvbWrVuTnZ2NmXHjjTcyZcqUMhvQAqxbt46CgoIy7WPH\njuWQQw7h9ttvZ8GCBWXO79ixgyeeeILvv/8+4piDn+3vfvc7tmzZQq9evUoS5mhWrFiBc45zzjmn\n3H6x1hCWsZ0DHANs4ceEJpLJwIXAb81soXMuF8DM+gG/wHsmJyssZhpwL3CZmV3pnJvnxyQCE0Ou\nW8I5t9zM3sHbNHQC8Gs/pinwBNAMeMw590P13q6IiIjUhTR/ifWBAwfYuXMnGzdu5NNPP8U5R9eu\nXZk5cya9evUqFXPrrbcyY8YMnnjiCZYuXUpKSgoFBQUsW7aMsWPHRnzIvW/fviQmJjJnzhwGDRrE\ncccdR5MmTUhPT6d///7cddddvPbaa4wbN46cnBxOOOEEcnNzWblyJXfccUfEa7Zp04ZzzjmHpUuX\nkp+fT1JStNX1VXvQ/9RTT2XJkiUMHTqUBx98kEceeYQzzjiDTp068f3335Ofn8+qVas4cOAAPXv2\nLNmLCLwZhPnz5zNkyBDS09P54x//yEknnUSLFi3YvHkz69evZ8+ePaxatSriePv06UPPnj1LNscc\nPnw4zZo1q3DMdW306NHMmzePZcuW0a9fP/r161dSjW3dunUEAgEef/zxUoUtAC699FLmzJlDWloa\nY8aM4Q9/+ANnnHEGiYmJ7Nq1i02bNrFmzRoAzjrrLI499tiS2K5du/Lss89y/fXXM2TIELp160b3\n7t1xzrFp0ybWrVvH/v37ufbaaznkkEPKjPniiy+mXbt2bN++HajcjFlwb576mHCGsqo+JFffmNnT\nQCbwkHPurgr6PgLchlcWejEQrpSaAAAgAElEQVTQHEjFm+Ea7pybGyHmKmA2XhW1N4GvgfPxnvF5\n1Dl3W4SYrsB7QHtgDd7zPqcDxwEfAIPCZoOqzMzW9ujRo0f4brh1KXlc7e2Ym/fgxbV2LRERqdiB\nAwdKZhK6detW5ecvGrLw8r9NmzblsMMOo1OnTpx66qkMGTKEIUOG0LRp5N8Zr1+/nt/85jd88MEH\n7N69m27dujF69GgyMzMxM4455piS6llBK1eu5J577mH58uXs3LkT5xzZ2dklCdf777/Pvffey6pV\nqygqKqJXr16MHTuWU045hWOPPZaBAweW2Rhy5syZXHfddUycOJE777wz4liXL1/OmWeeSYcOHdiy\nZUvU9xRuz549TJ06lQULFvDJJ5/wzTff0KJFC5KSkjj99NMZNmwYF110EYFAoEzsli1bmDx5MgsX\nLmTTpk0EAgE6depEnz59uPLKK7n88svL7CkUNGHCBMaNGwd4G6dW52H+aGqrQAF4FfT+8pe/MGvW\nLNavX8/3339Phw4dOOussxg7dix9+/aNGrt9+3aefPJJXn31VTZs2EBhYSGHHnooycnJ9O3bl2uu\nuSbq+HJzc3n44YdZvHgxBQUFtGzZkk6dOtG3b1+uuuoqLrrooqilw0ePHs1TTz1FkyZN2LRpU7kJ\nMnibpxYWFlJQUBD1v1dQVf+96dmzJ+vWrVsXrVBXVcR1suMvE/t/QFugt3OuwudgzCwN+BXQHfgB\neB/4k3Mu6rIyMzsL+C3QFy9BWg887pzLLifmJ8D9wGCgHfAF3v4/D/jL6mpEyY6IiNSEkp2Gb9++\nfRxzzDEkJiaWbDIqUhvee++9klnHyjy/FctkJ66XsTnn9uElElWJmQHMqGLMO3hL4KoS8wUwqiox\njV5uLmRlQV6et8FoRobKUYuIiFRTixYt+P3vf8/NN9/MK6+8Uu+XG0n8ePDBBzn88MO5665yF1XV\nC/o1jtQP2dnQvTtMmACzZnnH7t29dhEREamWn//855xwwgncf//9sR6KNBCrVq3ipZde4u6776Z9\n+/axHk6F4npmRxqG5B0FkHkT+JuNlSguhsxMOPtszfCIiIhUQ9OmTaNWeROpjj59+lR5Y9xY0syO\nxNyI1YvLJjpBxcUwfXrdDkhEREREGgQlOxJzSYVby+8QVrFGRERERKQylOxIzOUndCy/Q8gOwyIi\nIiIilaVkR2JuVkoqRRblWzEQgPT0uh2QiIiIiDQISnYk5vLadebuwbeUSXiKrAlMm6biBCIiB0Ho\nxoIHDhyI4UhEpKEL/Tcm2qamB4uqsUm9kJOSyoqkHgxfs5ikwm3kJyQyu1cqS/2do0VEpHaZGc2b\nN2f//v3s2bOHhISEWA9JRBqoPXv2ANC8eXMlO9J45bXrzMSBabEehohIo9GmTRu2b9/O1q1eoZhW\nrVpVuLO5iEhlHThwgD179pT8G9OmTZs6H4OSHRERkUaqffv27Nmzh71797Jly5ZYD0dEGrCWLVvG\nZBNSJTsiIiKNVCAQ4Oijj2b79u3s2rWL/fv3x3pIItLANG/enDZt2tC+fXsCgUCd31/JjoiISCMW\nCARITEwkMTER51xc7YwuIvWbmdX5MzrhlOyIiIgIUD9+MBERqU1KdqT+y82FrCzIy/M2GM3IUDlq\nEREREamQkh2p37KzITMTiot/bJs0CaZOhVGjYjcuEREREan3VF9S6q3kHQVlEx3wXmdmejM+IiIi\nIiJRKNmRemvE6sVlE52g4mKYPr1uByQiIiIicUXJjtRbSYVby++Ql1cn4xARERGR+KRkR+qt/ISO\n5XdITq6TcYiIiIhIfFKyI/XWrJRUiizKt2ggAOnpdTsgEREREYkrSnak3spr15m7B99SJuEpsiYw\nbZrKT4uIiIhIuVR6Wuq1nJRUViT1YPiaxSQVbiM/IZHZvVJZmpYW66GJiIiISD2nZEfqvbx2nZk4\nMC3WwxARERGROKNlbCIiIiIi0iAp2RERERERkQZJyY6IiIiIiDRIemZH4ltuLmRleRuMJidDRoaq\ntImIiIgIoGRH4lTyuIUMW72Y8a89RlN3oKS9aOJDNM2aBqNGxXB0IiIiIlIfaBmbxKXkHQVlEh3A\ne52Z6c34iIiIiEijpmRH4tKI1YvLJDolioth+vS6HZCIiIiI1DtKdiQuJRVuLb9DXl6djENERERE\n6i8lOxKX8hM6lt8hOblOxiEiIiIi9ZeSHYlLs1JSKbIo376BAKSn1+2ARERERKTeUbIjcSmvXWfu\nHnxLmYSnyJrAtGkqPy0iIiIiKj0t8SsnJZUVST0YvmYxSYXbyE9IZHavVJampcV6aCIiIiJSDyjZ\nkbiW164zEwemxXoYIiIiIlIPaRmbiIiIiIg0SEp2RERERESkQdIyNmmYcnMhK8vbbyc5GTIyVLRA\nREREpJFRsiMNT3Y2ZGZCcfGPbZMmwdSpMGpU7MYlIiIiInUq7pexmdmRZjbFzDaa2fdmtsPM/m1m\nE6P0v8TM3jSzQjPb6f/5kgru0d3McszsK/8ea8zsdrNoG72AmR1uZo+Y2SYz2+cf/2xmh9f0PUt0\nyTsKyiY64L3OzPRmfERERESkUYjrZMfM+gHrgV8DPwAvAe8D7YExEfrfCiwA+gPvAv8CTgcW+Oci\n3aMvsBIYCnzu3+MIYDIw28wsQkx7YDlwG1AEvAjsAm4FVvjn5SAYsXpx2UQnqLgYpk+v2wGJiIiI\nSMzE7TI2M+sEvAK0AK50zs0PO39G2OsTgIeBfcC5zrn3QtrfBR42s1edc7khMU2BZ4FDgTHOuSl+\ne2tgEXAVkAZkhw1vCtAVmAeMcM4V+XGPArfgJUo31vAjkAiSCreW3yEvr07GISIiIiKxF88zOw8C\nhwN3hSc6AM655WFNt+Eld08FEx2/30bg//xz4bM7VwDHAx8HEx0/Zjdws/+y1AySmR0JXIc303RT\nMNHx3Ql8BVxnZh0r+T6lCvITKvhYk5PrZBwiIiIiEntxmeyYWVtgOFAITKtkWPC5nDkRzuX4x0sr\nG+OcW4W3rO0kM0sOOXUh3uf6lnNua1jMPrxldAG/n9SyWSmpEAhEPhkIQHp63Q5IRERERGImLpMd\n4Cy85WvLgB/MbKhfDOBxM7slfNbELwpwtP9yVfjFnHP5wNfAMWaWEHKqt3/8MMo4PgzrV90YqSV5\n7Tp7VdfCE55AAKZNU/lpERERkUYkXp/Z6ekftwJvA/3Czo83s1HOueCMTTDR+cY5tyfKNfPxCg8c\nDawJi8svJya0X3VjojKztVFOHV+Z+MYoeUMiyelPMHzNYpIKt5GfkMjsXqksTUuL9dBEREREpA7F\na7LT1j/egFdwIAOvSlprvAIAY4BnzWyDc2613w7wXTnXDCZBrUPaKoqrrRipZXntOjNxYFqshyEi\nIiIiMRSvyU5wjVJT4GbnXLCe8NfAWDM7Gq9U9F3A9UCwPLQr55plSkiHiBYXKaaie5V3n7I3dq5n\npHZ/xqdHVa4lIiIiItKYxOszO7v84wHgmQjng8nPoLD+rcq55qH+cXdIW/DP0eIixVR0r0gxIiIi\nIiJSy+J1ZifPP37pVziLdj7RP272j23NrFWU53aSwvoG/9zWP7e6CjGh5yoTI3UlNxeysrz9dpKT\nISNDRQtEREREGqh4ndkJVlRra2aRloW194+7AZxz3/JjctEnvLOZJeEVJ9jsnCsMOfWxfzwlyjiC\n7aGJUHVipC5kZ0P37jBhAsya5R27d/faRURERKTBictkxzm3BvgvcAhwZoQug/xjaPnnhf5xaIT+\nw/zjy2HtUWPMrA9wHLDOOfffkFOv4S2vO8fMEsNiWuDt5XMAeDXCOOQgSd5RAJmZUFxc+kRxsdee\nmxubgYmIiIjIQROXyY5vgn981MyOCDaa2anAWP/lUyH9/wwUA780s74h/bsC9/rnHg27x3y8pKq3\nmd0eEtMKeNx/OTk0wDn3/4DngebAE2YWulRwItABmOmc+7Lyb1VqasTqxWUTnaDiYpg+PfI5ERER\nEYlb8frMDsBU4Kd4szIbzOxdvHLO/fESjanOuTnBzs65DWZ2J15y8raZLQb2AxfgzRCNcc5tCL2B\nc+4HM7seWAJMNrMRwCbgHOAo4EUg0hqoXwN9gauAT81sJd7eQCcBnwG3R4iRgyipcGv5HfLy6mQc\nIiIiIlJ34nZmxzl3ALgauBkvATkPOB1YCdzgnPt5hJgpwBDgPbyE5afAv4HL/HOR7vOuf925QBfg\nMuAb4A5gqD+O8Jiv/ZjH8BKvK4AE4C/AGf55qUP5CR3L75CcXCfjEBEREZG6E88zO8GE5wn/q7Ix\nC4AFVbzPWiI/61NezDfArf6XxNislFRGr5wfeSlbIADp6XU/KBERERE5qOJ2ZkekKvLadYapU73E\nJlQgANOmqfy0iIiISAMU1zM7IlUyahScfbZXjCC4z056uhIdERERkQZKyY40Ll27wvjxsR6FiIiI\niNQBJTvSaCSPWxixPe/Bi+t4JCIiIiJSF5TsiATl5kJW1o9L3DIytMRNREREJI4p2REByM6GzMzS\n1domTfKKGowaFbtxiYiIiEi1qRqbSG5u2UQHvNeZmd55EREREYk7SnZEsrIi778DXvv06XU7HhER\nERGpFUp2RPLyanZeREREROolJTsiyck1Oy8iIiIi9ZKSHWn0Bu04jiKL8lchEPA2HhURERGRuKNk\nRxq9vHaduXvwLWUSniJrAtOmqfy0iIiISJxS6WkRICcllRVJPRi+ZjFJhdvIT0hkdq9UlqalxXpo\nIiIiIlJNSnZEfHntOjNxYFqshyEiIiIitUTL2EREREREpEHSzI5IRXJzvb148vK8ymwZGXqOR0RE\nRCQOKNkRKU92NmRmlt50dNIkmDoVRo2K3bhEREREpEJaxiYSRfKOAooyflY60QHvdWamN+MjIiIi\nIvWWkh2RKEasXkxTdyDyyeJimD69bgckIiIiIlWiZEckiqTCreV3yMurk3GIiIiISPUo2RGJIj+h\nY/kdkpPrZBwiIiIiUj1KdkSimJWSSpFF+SsSCEB6et0OSERERESqRMmOSBR57Tpz9+BbyiQ8RdYE\npk1T+WkRERGRek6lp0XKkZOSyoqkHgxfs5ikwm3kJyQyu1cqS9PSYj00EREREamAkh2RCuS168zE\ngWmxHoaIiIiIVJGWsYmIiIiISIOkmR2R6srNhawsrwR1cjJkZOg5HhEREZF6RMmOSHVkZ0Nmpre5\naNCkSTB1KowaFbtxiYiIiEgJLWMTqaLkHQVlEx3wXmdmejM+IiIiIhJzSnZEqmjE6sVlE52g4mKY\nPr1uByQiIiIiESnZEamipMKt5XfIy6uTcYiIiIhI+ZTsiFRRfkLH8jskJ9fJOERERESkfEp2RKpo\nVkoqBAKRTwYCkJ5etwMSERERkYiU7IhUUV67ztx5wa8ostJ/fYqsCUybpvLTIiIiIvWESk+LVENO\nSiorknowfM1ikgq3kZ+QyOxeqSxNS4v10ERERETEp2RHpJry2nVm4sC0WA9DRERERKJQsiNyMOTm\nQlaWV5ktORkyMrS8TURERKSOKdkRqW3Z2WU3HZ00CaZOhVGjYjcuERERkUZGBQpEalNubtlEB7zX\nmZneeRERERGpE3Gb7JjZUjNz5XwNjhJ3g5ktN7PdZrbDzF4xs/4V3Ku/32+HH7fczG6sICbJzKab\n2RYz22tmG83sfjNrWZP3LfXbkyPvKZvoBBUXw/TpdTsgERERkUasISxjmwvsjtBeEN5gZpOB24Hv\ngUVASyAVuMDMhjnn5keIuQLIwUsM3wK+Bn4KzDCz3s65MRFijgfeAzoAnwBvA6cBvwPON7NznXP7\nqvFepZ5LKtxafoe8vDoZh4iIiIg0jGTnDudcXkWdzOw8vERnO9DPOZfrt/cDlgLZZrbUOfdNSExb\nIBsIAFc55+b57R2BZcDtZrbAOfdG2O2m4yU6jzrnbvNjmgKzgSuAe4A/VPsdS72Vn9Cx/A7JyXUy\nDhERERGJ42Vs1TDWP/4pmOgAOOfeA54CEoD0sJif+e3/CCY6fsxW4C7/ZamZHTM7HRgAbAvpg3Ou\nCBgN/ADcYmbNauE9ST0zKyW1zGajJQIBSA//FhMRERGRg6VRJDv+czI/9V/OidAl2HZpWPsl5cQs\nBPbiLUsLfQ4nGLMgfKmanyS9DbQFzqrc6CWe5LXrzN2DbymT8BRZE5g2TeWnRUREROpQQ1jGlmFm\n7YEDwEbgRefc5rA+JwItgK+cc/kRrvGhf0wJa08JO1/CObffzD7BexanG/Cxf6p3tJiQ9vP8fkuj\n9JE4lpOSyoqkHgxfs5ikwm3kJyQyu1cqS9PSYj00ERERkUalISQ7vw17PcnM/tc5978hbUf7x0iJ\nDs65PWb2LdDWzNo453aZ2WHA4eXF+e2n+dcPJjvl3iuk/ego50sxs7VRTh1fmXiJjbx2nZk4MC3W\nwxARERFp1OJ5GdtbwEi8H/oPxZtduRcoAu43s9tC+rb2j9+Vc709YX1bh5yLFhceU5l7RYqRxiI3\nF8aNg6uv9o7ad0dERETkoInbmR3n3O/DmjYCD5jZSuCfwB/N7Gnn3PeABcPKuaRV8LoyMaFt0e5V\nmeuWcM71jHgRb8anR1WuJTGWnV12w9FJk2DqVBg1KnbjEhEREWmg4nlmJyLn3CJgJV4Vtb5+8y7/\n2Kqc0EP9Y3DPnl0RzlUUU5l7RYqRBi55R0HZRAe815mZmuEREREROQgaXLLjC/7keJR/DBYsSIrU\n2cxa4T2f861zbheAc24nUFheXEh7aEGEcu8VJUYauBGrF5dNdIKKi2H69LodkIiIiEgj0FCTnbb+\nMTh7sgHYB3Qws0hJyCn+cXVY+8dh50v4++Sc5F93Q2ViKriXNGBJhVvL75CXVyfjEBEREWlMGlyy\nY2YdgHP8lx8C+M/t/MtvGxohLNj2clj7wnJiLgFaAq875/ZGiLnUzFqEja2jP7ZCYFn570QakvyE\njuV3SE6uk3GIiIiINCZxmeyYWV8zO9fMLKw9GZiP97zMS2F76kz2j781s64hMf2AXwA7gaywW03z\n2y8zsytDYhKBiWHXBcA5txx4B0gEJoTENAWeAJoBjznnfqjCW5Y4NyslFQKByCcDAUhPr9sBiYiI\niDQCcZns4G0S+i+gwMyWmtkLZrYMWA+cBawFMkMDnHNLgD8D7YGPzOxFM3sFr4R1MyDdObcjLGYH\nkI63YekcM3vDzHLwlq11AR51zr0eYXyjgO3AbWa22sxe8GOuBD4A/q9WPgWJG3ntOnPnBb+iyEr/\nlSuyJjBtGnTtGiVSRERERKorXktPfwA8CZyJV375LLz9az4CcoAn/aVrpTjnfm1mHwG/AlKBH4DX\ngT855yIuK3POzTWzAXibl/YFmuMlVY8757KjxOSaWR/gfmAwcAXwBfAn4IGwZW/SSOSkpLIiqQfD\n1ywmqXAb+QmJzO6VytK0tFgPTURERKRBistkxzm3HripmrEzgBlVjHkHuLCKMV/gzfCIlMhr15mJ\nA9PKnsjNhawsr1BBcjJkZGi2R0RERKSG4jLZEWlQtNmoiIiIyEERr8/siDQI2mxURERE5OBRsiMS\nQ9psVEREROTgUbIjEkPabFRERETk4FGyIxJD2mxURERE5OBRsiMSQ9psVEREROTgUbIjEkN57Tp7\nVdfCE55AQJuNioiIiNSQSk+LxFjyhkSS058os9lo3qcdyIv14ERERETimJIdkXog2majyeMWkryj\ngBGrF5NUuJX8hI7MSkll6dM/r/tBioiIiMQZJTsi9diw1YsZ/9pjNHUHStoyl8+Dfs204aiIiIhI\nBfTMjkg9lbyjoEyiA3ivteGoiIiISIWU7IjUUyNWLy6T6JTQhqMiIiIiFVKyI1JPacNRERERkZpR\nsiNST2nDUREREZGaUbIjUk/NSkmlyKL8FdWGoyIiIiIVUjU2kXoqr11n7h58S5kiBUXWhHH/cwtz\nsjYCG0vHPHhxHY9SREREpP5SsiNSj+WkpLIiqUfZDUfbdY710ERERETqPSU7IvVc1A1HI2w2KiIi\nIiI/qtVkx8yOBnY753ZU0K8t0MY5t7k27y/SWGizUREREZGK1XaBgv8CD1Wi30Tg81q+t0ijoM1G\nRURERCqntpMd878q21dEqkibjYqIiIhUTqxKTx8BfB+je4vENW02KiIiIlI5NX5mx8wGhDUdGaEt\n9H7dgMHAJzW9t0hjpM1GRURERCqnNgoULAVcyOv/8b+iMb//w7Vwb5FGZ1ZKKpnL50VeyqbNRkVE\nRERK1Eay8zd+THZuBD4D3onSdz+wBVjgnPuwFu4t0uiUt9lo02nToGvXGI5OREREpP6ocbLjnEsL\n/tnMbgSWOef0q2WRgyjaZqNL09K8amxZWd6zO8nJkJGhBEhEREQapVrdZ8c5F6uCByKNTsTNRrOz\nvfLTxcU/tk2aBFOnav8dERERaXSUnIg0EMk7CsomOuC91v47IiIi0gjV6swOgJm1AK4BBgBHAS2i\ndHXOuZ/W9v1FGqsRqxeXTXSCgvvvjB9ft4MSERERiaFaTXbMrDPwOtCVijcNdRWcF5Eq0P47IiIi\nIqXV9szOQ8AJwLt4paU3Artr+R4iEoH23xEREREprbaTnf8BNgPnO+f21vK1RaQcs1JSGb1yfuSl\nbNp/R0RERBqh2i5Q0AJYoURHpO7ltevsVV0LBEqfCARA+++IiIhII1TbMztrgKRavqaIVNaoUXD2\n2V4xguA+O+npPyY62oNHREREGpHaTnYmAHPMbKBz7s1avraIVEbXrpGrrmkPHhEREWlkajvZ+RCv\nMMECM5sMLAbyiVJ5zTm3uZbvLyKR5OaWvwfP2WdrhkdEREQanNpOdvLwEhsDfud/ReMOwv1FJJKs\nLO3BIyIiIo1ObScbb6H9c0Tqn4r22NEePCIiItIA1Wqy45wbVJvXE5GqSR63MGL7b/KKGV1uYPLB\nGI6IiIhITNV26WkRqYdmpaSWLUkdpD14REREpIFqEMmOmbUzs21m5szs0wr63mBmy81st5ntMLNX\nzKx/BTH9/X47/LjlZnZjBTFJZjbdzLaY2V4z22hm95tZy+q8R5GaqHAPHoBx4+Dqq71jbm7dD1JE\nRESkltXqMjYz+30Vujvn3P/W0q0nA0dU1MmvEHc78D2wCGgJpAIXmNkw59z8CDFXADl4ieFbwNfA\nT4EZZtbbOTcmQszxwHtAB+AT4G3gNLyCDeeb2bnOuX3VeaMi1RZtD55ly6B7d5WkFhERkQbHnKu9\negJmdoAfq7FFEryZ4SU7UdbVVOmePwWWAE8DPwc2OOdOjNDvPOB1YDvQzzmX67f3A5biJUDHOue+\nCYlpC/wXSACucs7N89s7AsuALsB5zrk3wu71JjAAeNQ5d5vf1hSYDVwB3O+c+0MN3/faHj169Fi7\ndm1NLlMj0Z4PkfiRvKOApdNvilypLRCA9etVklpERETqVM+ePVm3bt0651zPml6rtpexjQLS/WPo\nVwZwH/A+XqLzuN+vRszsEOApYB0wqYLuY/3jn4KJDoBz7j3/GgkRxvQzv/0fwUTHj9kK3OW/LDWz\nY2an4yU620L64JwrAkYDPwC3mFmzSrxFkYNqxOrFFZekFhEREYlTtV2N7ZkKutxvZncD9+LNxNTU\nH4DjgUF4SURE/nMyP/VfzonQZQ5wK3Ap3qaoQZeUE7MQ2Iu3LK2lc25vWMyC8KVqzrmtZvY2cB5w\nFt6MkkjMJBVuLb+DSlKLiIhIHKvzAgXOufFAPvBATa5jZil4szXZzrm3Kuh+ItAC+Mo5lx/h/If+\nMSWsPSXsfAnn3H6853FaAt1CTvWOFhPW3jvKeZE6k5/QsfwOKkktIiIicSxW1djWAGdXN9jMmgBT\ngW8JWSpWjqP9Y6REB+fcHv9abc2sjX+Pw4DDy4sLaT86pK3ce0WJicrM1kb6wpvREqmRWSmpFFmU\nfwZUklpERETiXKySneOp2RK6W4AzgDudc9sr0b+1f/yunD57wvq2DjkXLS48pjL3ihQjEhN57Tpz\n9+BbyiQ8RdbEK0mt4gQiIiISx2r1mZ2KmNnheOWXTwbeqKB7tGv8BPgT8KZzbkZlw/xjeaXnwivI\nRasoV1Gfiu5VmeuWiFaFwp/d6VGVa4lEkpOSyoqkHgxfs5ikwm3kJyQyu1cqS9PSvP12srJ+LFWd\nkaEESEREROJGbe+z83k5p1sD7fF+2P8euLuat3kCaI5X2ayydvnHVuX0OdQ/7g6LCZ7bWYmYytwr\nUoxITOW168zEgWmlG7OzITNT+++IiIhI3KrtmZ3kcs79AHwBvAlMcM6tq+Y9LsF7vuZJs1KTJC39\n49FmtjTY1zm3G9jsv06KdEEza4X3fM63zrldAM65nWZWiFd6OgmvvHW44PU2h7RtBvpEu1eUGJF6\nJXlHAWRG2H+nuNhLgM4+WzM8IiIiUu/VdunpunoG6HBgYJRzh4ScC76/DcA+oIOZJUWoyHaKf1wd\n1v4x3p45pxCW7Pj75JzkX3dDWMxlIdcMF+1eIvVGpfbfGT++bgclIiIiUkWxKlBQbc45i/QFHOt3\n2RDS/q0f8z3wL//80AiXDba9HNa+sJyYS/Bmk14P2WMnNOZSM2sRGmBmHYFzgEJgWfnvVCR2tP+O\niIiINAQHPdkxszZmVh8qj032j781s5L1N2bWD/gF3jM5WWEx0/z2y8zsypCYRGBi2HUBcM4tB94B\nEoEJITFN8Z43agY85pyLugmqSKxp/x0RERFpCA5KsmNmg83sFf+Zl2+BQjPbaWYLzWzwwbhnRZxz\nS4A/4xVJ+MjMXjSzV4C38BKQdOfcjrCYHUA6cACYY2ZvmFkO3rK1LsCjzrnXI9xuFLAduM3MVpvZ\nC37MlcAHwP8dlDcpUktmpaR6++xEov13REREJE7UerJjZpPxlnINBtrgzYzsxKvGdiGw0O9T55xz\nv8ZLRNYDqUB/4HVgoLk/4AwAACAASURBVHNubpSYuXjP7fwTr2T2RcBneMnRbVFicvGKFMwAOsD/\nb+/e4+WsykOP/57sIAjIJRWoJdKtiEAioaCcAiIoGoUKIgpCbVUg0h6sqKhoqOjxQkUoclTqpcrN\niu0J0KjVIBjBKFBqQIRAghDRlIsWxEgId5I854/3nWT2ZC579p69Z8/s3/fzmc/LrPWued9ZTPae\nZ6+1nsWRFKmozwBeVTPtTZpwVkzbkVNf++66++988HUnF8kJli+HuXPh2GOL4/LlXbpbSZKk+iKz\n2dYzbb5YxDHAvwEPUnyx/0ZmrirrtgLeBpxOMcXrLzPz0o5dfJKJiKUzZsyYsXTp0q7dw+DcBa1P\nUk8bXHn/RvvvrJi2Iyt2fXDjtNQDA6alliRJozZz5kyWLVu2rNF+k+3odOrpdwFPAgdm5l3VFZn5\nCPDFiFgI3FKea7AjTWD19t8xLbUkSeoVnZ7GtidwTW2gU62su4ZiSpikHjOstNSSJEkTQKeDnWcB\njw3jvMfKcyX1GNNSS5KkXtHpYOdu4KCI2LzRCWXdQeW5knqMaaklSVKv6HSwcylF8oH5EfHC2sqI\n2BmYT5GhbF6Hry1pHLRMS33wwWZpkyRJE0KnExScAxwBvBa4MyIWAyso0i6/APhfwABwE/DZDl9b\n0jiopKU+88rzmJrr1peviSlMfcc74NBDh67pOeccs7RJkqSu6Giwk5lPRMQrgTMpNuPcr3xUPAFc\nCJyWmU908tqSxs9ls2Zz4/QZQ9JSX7/TLL5+0ceHBECAWdokSVLXdHpkh8x8FDg5Ij4MvBT4k7Lq\nN8DPMvPxTl9T0virTUv94UUXbxzoVFSytJ155vjcnCRJEh0IdiLiYGA6cFNmLquUl0HNtTXnzoiI\nlwH3ZuaPRnttSROHWdokSdJEM6pgJyKeDywA7qUYxWnlXuBbwPSI2CUzfzOa60uaOMzSJkmSJprR\nZmN7J8V+OR/KzNWtTi7PORV4NjBnlNeWNIHMmzWbNdHgR8rAAJxwwvjekCRJmvRGG+zMBn6Xmd8e\nboPM/A/gAeDQUV5b0gSyYtqOnHbIyRsFPGtiCpx/fvHElNSSJGkcjXbNzm7A9SNodxOw/yivLWmC\nqZel7dI9ZrMoE3bf3ZTUkiRpXI022NkCWDWCdquALUd5bUkTUG2WtsGV9xepp6sDHTAltSRJGnOj\nncb2B6DFquS6dijbSupzxyxZuHGgU1FJSS1JkjQGRhvsLAP2jYhnD7dBRGxOsdHoslbnSup9pqSW\nJEndMtpg57sUU9lOb6PN6RTZ2L47ymtL6gGmpJYkSd0y2mDnn4H/AeZGxOkRjfLOQkRMiYiPAnMp\nsrH98yivLakHNEtJvSamFCmply83U5skSeq4USUoyMzHI+JNwNXAJ4ATI+Iy4Gbgd+Vp2wF7A0cD\n04EngTdn5uOjubak3lBJSX3mlecxNdetL18TU5h76Hs457rrNk5gYKY2SZLUAaPNxkZm/ldE7Adc\nArwEOKXOaVEelwJ/nZm3jva6knpHo5TUgJnaJEnSmBl1sAOQmUuAWRHxOuD1wF7AH1EEOQ8BtwAL\nMvPKTlxPUu+pTUkN8OFFF7fO1HbmmWN+b5IkqT91JNipyMyrgKs6+ZqS+peZ2iRJ0lgabYICSRox\nM7VJkqSxZLAjqWvmzZoNAwP1KwcG4OCDzdImSZJGrKPT2CSpHSum7VhkXatNUjAwAO94Bxx6qFna\nJEnSiBnsSOqu448vsq5deGGxRmdwsBjRqQ10wCxtkiSpLQY7krpvl12GZl2bO9csbZIkadRcsyNp\n4mmVhc0sbZIkaRgc2ZHUVYNzF2xU9uEVazmpaaPBsbodSZLURxzZkTThzJs1mzXR4MeTWdokSdIw\nGexImnBWTNuR0w45eaOAZ01M2ZCl7ayzYN684rj77nDRRV26W0mSNFE5jU3ShHTZrNncOH0Gb7lt\nIdNXPch9W2/P9TvN4ptf/4RZ2iRJ0rAY7EiasFZM25GzDzpu/fMPL7rYLG2SJGnYnMYmqWdMX/VA\n8xPM0iZJkqoY7EjqGfdtvUPzE8zSJkmSqhjsSOoZzbK0rYkpcMIJRWY2M7VJkiQMdiT1kGZZ2uYe\n+h647roiM5uZ2iRJEiYokNRj6mVpu3SP2UXliSeaqU2SJK1nsCOp59RmaQMztUmSpI317DS2iHh/\nRMyPiOURsSoinoqI/46Ir0fEzCbt3h4RiyPi0YhYGRFXRMT+La61f3neyrLd4oh4R4s20yPiwoj4\nTUQ8GRF3RcQnI2Kzkb5nSY2ZqU2SJNXq5ZGdvwe2AJYAt5VlM4G3A8dGxBsz8/vVDSLiXOAU4Ang\nB8BmwGzgtRFxdGZ+q/YiEXEkcBlFYPgT4CHg1cDFEbFnZr6/TpudgRuA7YDbgWuBlwEfBV4TEa/K\nzKdG+f4lVWmVqe2bd/yBR/Y9mumrHuC+rXdg3qzZLPrq34zT3UmSpG7o5WDnCOBnmflkdWFEnAR8\nCTg/InbKzLVl+cEUgc7vgf0yc3lZvh+wCLgoIhZl5h+qXmtb4CJgAHhzZs4vy3cArgNOiYjvZuaP\nau7tQopA5wuZ+d6yzVTgUuBIikDt/3SsJyQxb9ZsTrrpW3Wnsq0lOHbJQgZy3fqyExfPh/02geOP\nH8/blCRJ46hnp7Fl5vW1gU5Z/mXgl8CfALtWVX2gPJ5RCXTK828AvgJsDZxQ83LvLMu/Uwl0yjYP\nAB8qnw4Z2YmIfYADgQerziEz1wAnAc8AJ0fEJsN+s5JaWjFtR/ja12BgYGjFlCkQDAl0AKbmuiJx\ngampJUnqWz0b7LRQ+dPu0wDlOplXl2WX1zm/UnZ4TflhTdosAJ6kmJZWvQ6n0ua7tVPVyiDpWmBb\n4OUt3oOkdh1/PNxxx9B9dubMYSCz/vmVxAWSJKkv9V2wExFvpxjRuQv4VVm8G7Ap8LvMvK9Os5vL\n46ya8lk19etl5tMU63E2Y+gI0p6N2tSU79mgXtIIDc5dwOAFdzGYBzD4p3/NYB7Ad6/9RfNGJi6Q\nJKlv9fKaHQAi4lSKxARbALuX//0b4K2Z6+et7FQe6wU6ZOZjEfEwsG1EPCczV0fEVsA2zdqV5S8r\nX//W4VyrqnynBvVDRMTSBlU7D6e9NNm1SlzAVlsVI0ArVsDgIMyZ4348kiT1iZ4PdoDXsWGKGsC9\nwNsy82dVZVuWx8ebvM5jFMHNlsDqqjbN2j1W8/rDuVa9NpLGyLxZszlx8fxijU6tCLjggqFJDc45\np1j7Y+ICSZJ6Xs9PY8vM12RmUKyDORC4E1gUER+pOi0qpzd5qWjxfDhthnOt4bzuepk5s94DuLud\n15EmqxXTduS0Q05mTQz9cbeGKBb31WZvW7uWNXPeaeICSZL6QD+M7ACQmQ8D10bEX1DscfOpiPhB\nZt5IMVIDxVS3RjYvj4+Wx9U1dY8Mo011u0bXqtdG0hi6bNZsbpw+g7fctpDpqx7kvq23Z5snVvPW\nW6+qe/7UXFckLjjzzHG+U0mS1El9E+xUZOYzETEPeClFdrUbgXvK6un12kTEFhRT2B7OzNXl6zwS\nEasoUk9PB5bVaVp5vXuqyu4B9mp0rQZtJI2xFdN25OyDjlv//LzvnNX0/P/4zn/ynlyw8et85vWd\nvjVJkjRG+i7YKT1UHrcrj3cCTwHbRcT0OhnZ9i6PS2rKb6WYGrc3NcFOuU/OS8rXvbOmzRFVr1mr\n0bUkjaNWiQvu23p7BlfezzFLFjJ91QPct/UOzJs1e5zuTpIkdULPr9lp4KDyeDdAZj4BXFOWHVXn\n/ErZ92rKF9TUVzuMIu301TWbm1baHB4Rm1Y3iIgdgFcAq4DrWrwHSWNo3qzZG63jqVgTU3jkWZvz\nw/NP4qSfXs7hv7iWk356OT88/yS46KJxvlNJkjRSPRnsRMQrIuKYiJhaU75JRJwMvA14AphXVX1u\neTw9InaparMf8LcUa3IuqLnU+WX5ERHxpqo22wNn17wuAJm5GLge2B44q6rNVOBLwCbAeZn5TFtv\nWlJHNUxcEFM458C38cFrL9kog9vUXMeaOe/klX/z1WJPn6qHJEmaeHp1GtvOwEXAQxHxM+D3wHOB\nPYDnAU8Cx2XmvZUGmfnDiPg88F7glohYCDwLmE0R9P1VZq6svkhmroyIE4BLgcsj4scUU+ReQ7HG\n5wuZeXWd+zueIknCeyPiYIopcPsALwR+CvxDZ7pB0mjUS1xw6R6zOWbJwvqpqikCnrfctnDI+h9J\nkjQx9Wqw82Pg0xTT1WZRBDpPAyuAyymCkF/WNsrM90XELcC7KYKcZ4CrgTMys+60ssz894g4EDgd\n2JciQLoD+GJm1p3PkpnLI2Iv4JPAIcCRFPv/nAF8umbam6Quqk1cADB91QNN20xf9eAY3pEkSeqU\nngx2MvPXwEdanli/7cXAxW22uR44tM0291KM8EjqMa2SF6x+1rP58KKLTVwgSdIE15PBjiSNpXmz\nZnPi4vl1p7KtJTh2yUIGqupOXDwf9tsEjvfvG5IkTSQGO5JUo5K84MwrzxsS8KwhiGBIoAMbEhe8\n5oZnWDFtx41fz715JEnqCoMdSaqjXvKCbZ5YzVtvvaru+SYukCRp4jHYkaQGapMXnPedsxqfjIkL\nJEmaaAx2JGmYRpK4oN60NkmSND4MdiRpmEaSuOC0Q04GXLMjSVI3TGl9iiQJNiQuWBNDf3SuIaBB\n4oIzrzwPli8fz9uUJEklR3YkqQ0jSVzAhRfCmWeO851KkiSDHUlqU7uJC1ixohjdueCC4r8HB2HO\nHNhllzG8S0mSZLAjSaPUKnEBjzwCu+8Oa9duKDvnHPja19yIVJKkMeSaHUkapXmzZm+0jqdiDQFX\nXTU00IHi+Yknup5HkqQxZLAjSaPUMHFBTOEnL3zpxoFOxdq1xXoeSZI0JpzGJkkdUC9xwaV7zOYD\n117SvOGKFeNyf5IkTUYGO5LUIbWJC2AY63m22grmzjVxgSRJY8BgR5LGULONSIkoMrSZuECSpDHh\nmh1JGkPNNyINExdIkjSGHNmRpDHW7kak6xMXuBGpJEmjYrAjSePAjUglSRp/BjuS1AVuRCpJ0thz\nzY4kdUGzjUiZMsWNSCVJ6gCDHUnqgmYbkXLIIW5EKklSBziNTZK6pNFGpIseuaZ5w9tuc28eSZKG\nwWBHkrqo3kakTBts3uiKK2DBgg3PXcsjSVJdTmOTpIlmzhwYGGhcnzn0uWt5JEmqy2BHkiaaXXYp\nRmpqA56Ixm1cyyNJ0kacxiZJE8zg3AXA9gye8KUh63l2e3AFB//qpsYNXcsjSdIQBjuSNEHVruf5\n8KKLmwc7ruWRJGkIp7FJUo9oujcPuJZHkqQaBjuS1CMa7c2zrlmjylqe5cuLKW7HHlscDYAkSZOA\n09gkqYfU25un5Vqea66Bf/zHoRuVOsVNkjQJGOxIUo9pey3PjTc2nuJ2wAEmMZAk9S2nsUlSj2u6\nlidi40CnwnTVkqQ+Z7AjST2u0VqeNTEF9tmneeNKumrX8kiS+pDT2CSpD9Rby3PpHrNZNO1XsHhx\n44amq5Yk9TGDHUnqE7VreQCY86oigKlOTlDNtTySpD7mNDZJ6me77FKM1AwMDC2PaNzGtTySpD7h\nyI4k9bHBuQuA7Rk84UvtpauurOVZsQIGB2HOHEd6JEk9x2BHkiaBttNVu5ZHktQHnMYmSZNQs3TV\nCY3X8pitTZLUQ3oy2ImIzSPijRFxQUQsiYhHIuKxiLg1Ij4WEVs2afv2iFgcEY9GxMqIuCIi9m9x\nvf3L81aW7RZHxDtatJkeERdGxG8i4smIuCsiPhkRm430fUtSpzRKV70OaLiap7KWZ/ly01VLknpC\nZKPN5iawiHgn8LXy6VJgGbAVsD/wHOAXwEGZ+WBNu3OBU4AngB8AmwGvpvjdfnRmfqvOtY4ELqMI\nDH8CPFS22Qb4v5n5/jptdgZuALYDbi/v72XAC8vyV2XmUyPvAYiIpTNmzJixdOnS0bzMqBRrAST1\nssGV97e1lufnz3sxe/zPL5ma69aXrYkpTL3gfKe4SZI6YubMmSxbtmxZZs4c7Wv15MgO8DTwZeDF\nmfmSzHxLZh4C7Ar8HNgN+Fx1g4g4mCLQ+T2wZ2a+sWxzILAWuCgitq1psy1wETAAHJWZr8zMo8rX\n/yVwSkS8qs79XUgR6HwhM/fIzGPKe/sWsB/w9x3pBUkapcpanve84UOcfdBx3LndYNPz9/zt8iGB\nDlA8d4qbJGkC6slgJzP/JTPflZnLa8p/C/xd+fRNEfGsquoPlMczqttl5g3AV4CtgRNqLvXOsvw7\nmTm/qs0DwIfKp0NGdiJiH4oA6sGqc8jMNcBJwDPAyRGxyfDfsSSNj2ZredYBU2gwG2Dt2iKJgdPb\nJEkTSE8GOy3cWh43Bf4IoFwn8+qy/PI6bSplh9eUH9akzQLgSeA1NetwKm2+WztVrQySrgW2BV7e\n/G1I0vhrtJZnTUzh1ue9uHnjr30NzjoL5s0rjrvvDhddNIZ3K0lSc/2YevqF5fEZYGX537tRBD+/\ny8z76rS5uTzOqimfVVO/XmY+HRG3U6zF2ZUNQdaejdpUlR9cnreo4buQpC65bNZsbpw+Y8hankv3\nmM0xSxay12/vatywUQa3Aw5wjx5JUlf0Y7Dz3vJ4ZdXIyk7lsV6gQ2Y+FhEPA9tGxHMyc3VEbEWR\nhKBhu7L8ZeXrV4KdpteqKt+pQf0QEdEoA8HOw2kvSSNRuy8PFFPcTlw8f6M1O01Vprdtu60blEqS\nxl1fTWOLiL8A5lCM6ny0qqqSivrxJs0fqzm3On11o3a1bYZzrXptJGnCa5auuimnt0mSuqRvRnYi\nYnfgEoo00qdm5q3V1eWxWZ7t2q0lGm410eKcVtcazuuu1yjlXjniM6Od15Kk0ao3xW2bJ1bz1luv\natzI6W2SpC7pi2AnIqYDV1Is/D83Mz9fc8rq8rhFk5fZvDw+WtOmUvfIMNoM51r12khSz6id4ja4\n8n7esmRh+9PbLrwQTjgBLrjAKW6SpDHR89PYIuK5wEKKNTAXAR+sc9o95XF6g9fYgmJ9zsOZuRog\nMx8BVjVrV1V+T1VZ02s1aCNJPWvE09uuuaaY0uYUN0nSGOnpkZ2IeA7wfYpsa/OBEzNr50sAcCfw\nFLBdREyvk5Ft7/K4pKb8Voo9c/YGltVcexPgJeXr3lnT5oiq16zV6FqS1LNGNL3txhud4iZJGlM9\nG+xExKbAdyiyoV0F/GVmrq13bmY+ERHXAIcCRwGfqznlqPL4vZryBRTBzlEU64GqHQZsBlyRmU/W\ntPkYcHhEbFq9105E7AC8gmLE6LrhvE9J6hVtTW+L2DjQqTCDmySpQ3pyGltEDAD/BryKYpPON2Xm\n0y2anVseT4+I9b8xI2I/4G8p1uRcUNPm/LL8iIh4U1Wb7YGza14XgMxcDFwPbA+cVdVmKvAlYBPg\nvMx8pvU7laTe1WyDUvbZp3ljM7hJkjqgV0d23g0cWf73Q8CXIuomOftgZj4EkJk/jIjPU+zDc0tE\nLASeBcymCPr+KjNXVjfOzJURcQJwKXB5RPy4vN5rKNb4fCEzr65z3eOBG4D3RsTBFFPg9qHY8PSn\nwD+M+J1LUg9ptEHpomm/gsWLGzd0epskqQN6NdjZtuq/j2x4FnycIjgBIDPfFxG3UARLsyn247ka\nOCMz604ry8x/j4gDgdOBfSkCpDuAL2Zm3T8zZubyiNgL+CRwSHmP9wJnAJ+umfYmSX2t3galr1wJ\nP4wpblAqSRpTUX89vya6iFg6Y8aMGUuXLu3aPQzOXdC1a0vqfUcvWciZV543JOBZR4v51bVrfQYG\niilvxx8/VrcpSRpnM2fOZNmyZcsa7TfZjl4d2ZEk9Tg3KJUkjTWDHUlS17hBqSRpLPVkNjZJUn9y\ng1JJUic5siNJmlDcoFSS1CkGO5KkCaed6W3rgCluUCpJqsNpbJKkCa/ZBqW3Pu/FTduu++pXh0xv\nW7Prbk5vk6RJwpEdSVJPaLRB6TFLFrLXb+9q2K72r3pTc10xvW36dLj6akd8JKmPGexIknpGvQ1K\n582azYmL57efwe11rxu6zuecc9yzR5L6jNPYJEk9bcQZ3BolNFi+vKP3J0nqHkd2JEk9b0QZ3Opx\nzx5J6isGO5KkvtCRDUqh2LPnH/+xCHwqnOImST3JaWySpL400ult6xbfODTQgQ1T3BYuhLlz4dhj\ni6NT3iRpQnNkR5LUt+pNb7t+p1l8/bKPN96zhyZ79pjUQJJ6isGOJKmv1cvgdtohJ3PmlecNCXjW\nxBRu++MXNU1j3TCpgWmsJWlCMtiRJE06I92zpy5HfCRpwjLYkSRNSh3bswcaj/gccIAjPJLURSYo\nkCSpNOI9e+qppLFevtykBpLUJY7sSJJUpd2kBk2ZxlqSuspgR5KkGsNNalBkb2vixhtNaiBJXWSw\nI0nSMLQ94hOxcaBTYVIDSRoXBjuSJA2TaawlqbcY7EiSNAqmsZakictgR5KkURq3NNYAF1zgqI8k\nDZOppyVJGgMdT2P9vvfB7rvDWWfBvHnFcffd4aKLOnK/ktSPHNmRJGmMdDSN9fe/7zofSWqTwY4k\nSWOoY2mszewmSW0z2JEkaZy1O+LTdiDkiI8kAQY7kiR1RTtprH/ygr05+Fc3tXcBR3wkyWBHkqSJ\nolEaa4ADf31zRzK7rZnzTqaa2U3SJGGwI0nSBFJvxAdGuM6njqm5rsjsdtVVxehPRWXU54ADDIIk\n9Q2DHUmSesC4ZHabMwemTKkfBDn1TVIPMtiRJKlHjHlmt8yhgQ6Y7EBSTzPYkSSph3U8s1s9JjuQ\n1KMMdiRJ6nFjntkNGqa3fuUNzwBwzJKFTF/1APdtvQPzZs1m0Vf/pv1rSFKHGexIktSHOp7ZrZ61\na/nY1V/b6PVOXDwf9tvEUR9JXWewI0lSn2ons9sagqlTAta1FwS98lc3bTQtbmqua7jOZ/CCuxrf\n72de39a1JakVgx1JkiaZRqM+i/bbpAhSqpMURDROaECT9T8N1vkc/dp3c+P0GRtNe1sxbceOvDdJ\nqmawI0nSJFRv1GfwThg84UudS3ZQZ53PZ77/BYhgoGba22mHnAw4siOpswx2JEnSemOd7GCA3CgI\nmprrOPPK82D53xUFbmoqqUMMdiRJUlPjkexgaq6D970Prrqq/qamBxxgECSpbZFN5uFOZBHxUmA2\n8L+APwf+BHgqMzdr0e7twLuBGcDTwH8BZ2TmfzZpsz9wOrAv8CxgGfDFzPx6kzbTgU8ChwDTgHuA\n/wd8OjOfHObbbPY+ls6YMWPG0qVLR/tSIzY4d0HXri1JmhiOXrKw/U1NG2m0PigCpkwZGgQNDLjP\nj9SnZs6cybJly5Zl5szRvlYvj+x8FDiinQYRcS5wCvAE8ANgM4qA6bURcXRmfqtOmyOByyh+bv8E\neAh4NXBxROyZme+v02Zn4AZgO+B24FrgZeU9vyYiXpWZT7Vz75IkTUTtbmraVKM/wGYODXRg/T4/\n9TK+OeIjqaKXR3Y+DGwO3Fg+/ocmIzsRcTBwNfB7YL/MXF6W7wcsogiAXpCZf6hqsy3wa2Br4M2Z\nOb8s3wG4DngRcHBm/qjmWj8GDgS+kJnvLcumApcCRwKfzMz/M8r378iOJGnCqjfis4YgAgbqfPfo\n2GhQ9YjP8uVOfZN6UCdHdno22KkVEUnzYGcB8BfAKZn5uZq6zwPvAT6YmZ+tKj8VOBv4Tma+sabN\nkcB84HuZeXhV+T7AYuBBYKfqEZwySLoXeBTYITOfGcX7NdiRJE1ogyvv32idzz73LetYsoOGBgb4\nzMv/ig9ee8lG15l6wfmu/5EmOKextSkiNqOYegZweZ1TLqcIdg4HPltVfliTNguAJymmpW1WtQ6n\n0ua7tVPVMvOBiLgWOBh4OcWIkiRJfaleZrcV03Yc82QHrF3Lh37yDaawcdY35szZeP1PJQmC63+k\nvjMpgh1gN2BT4HeZeV+d+pvL46ya8lk19etl5tMRcTvFWpxdgVvLqj0btakqP7g8b9Fwbl6SpH5S\nLwiCBimum0x9a6Y20FnP9T/SpDJZgp2dymO9QIfMfCwiHga2jYjnZObqiNgK2KZZu7L8ZeXrV4Kd\npteqKt+pQf0QEdFontrOw2kvSVKvaJTiut7UtxGv8Wlk7Vp43euGrv+pHvFx/Y/UkyZLsLNleXy8\nyTmPUQQ3WwKrq9o0a/dYzesP51r12kiSJIY/9a1ZxrcRB0K1o0eVEZ8HHoDTT3f/H6kHTZZgJ8pj\nszHwaPF8OG2Gc63hvO56jRZmlSM+M9p5LUmSelW9IKjutLeYwjkHvo0P/uQbHVv/w9//ff1AqNn6\nH4MgaUKYLMHO6vK4RZNzNi+Pj9a0qdQ9Mow2w7lWvTaSJKlNjaa9rZi2I7/ffJuOrf9pe/+fVkkQ\nnBInjZvJEuzcUx6n16uMiC0oprA9nJmrATLzkYhYRbHHznRgWZ2mlde7p6rsHmCvRtdq0EaSJI1A\no2QHXV3/0ywJglPipHE1WYKdO4GngO0iYnqdjGx7l8clNeW3UmwOujc1wU5EbAK8pHzdO2vaHFH1\nmrUaXUuSJHVQV9f/1DPSKXGmxJZGrKN/yJioMvMJ4Jry6VF1TqmUfa+mfEFNfbXDgM2Aq6v22Klu\nc3hEbFrdoNxU9BXAKuC64d29JEnqpEoQ9J43fIizDzqO61+wN6cdcjJrYujXojUxhbMPegcMDHTu\n4u1OiTvxRFi4EObOhWOPLY7Ll3fufqQ+N1lGdgDOBQ4FTo+IBZm5HCAi9gP+lmJNzgU1bc4HPgIc\nERFvysz5ZZvtgbOrXne9zFwcEddTbBp6FvC+ss1U4EvAJsB5mflM59+iJEkaiXFb/9MuU2JLoxI5\n1v9Ix0hEvB74aFXRn1NkQFtcVfapzFxQ1eZzwHsp0kIvBJ4FzKYY4XpLZv57neu8GbiUIovaj4GH\ngNdQrPH5Qma+MjvJFgAAFbFJREFUt06bXYAbgD8CbqOYArcP8ELgp8Ara0aD2hYRS2fMmDFj6dJG\n2/CMvcG5C1qfJElSHxhcef+w1v80C4I6OiVuYADOOGPj9T8DA67/Uc+bOXMmy5YtW9YoK3E7ejnY\nOQ64qMVpx2fmxXXavRvYHXgG+C/gjMxsOK0sIl4OnA7sSxEg3QF8MTMbXj8ing98EjgEmAbcC/wb\n8OlyWt2oGOxIktR9ww6COp0SG1hHMKXeThcRG6//qQRBjgapBxjsyGBHkqQJrF4QtGLajhy9ZGHd\n0aCpUwLWdSYIasjRIPUIgx0Z7EiS1KPqBUKL9tukSEZQHYRENE5oMEIjGg0yCNI4M9iRwY4kSX2m\nNggat5TYTawlIIKBmil5Uy84v/mUOKfKaRQ6GexMpmxskiRJE1a9fYFOO+TkcVn/08gAudHo0tRc\n13yD1Le/Hf7lX9w4VROCIzs9ypEdSZImh3bX/4xLNjhof5qdiRM0TI7sSJIkTRL1Rnyg8d5A45UN\nru31RM02Tm00SuRokEbJkZ0e5ciOJElqpFOjQeOm0SiRiRMmJRMUyGBHkiSNyITbIHUkRjolzqly\nPcFgRwY7kiSpozq1QWoCMY73PUSzvYTqJU5wlGhCMtiRwY4kSRoXbU2Jiyn8+0tezZtvv9rECRox\ngx0Z7EiSpK5rFAh1apSoq5qNEjUbDTI4GjWDHRnsSJKkntP3iROaTZVzlGjYDHZksCNJkvpKXyRO\naMRRorYY7MhgR5IkTQp9kTgBxm+UqA8CJIMdGexIkqRJrecSJ3TKJMg4Z7Ajgx1JkqQG+jpxAvR9\nxrlOBjtTO3FDkiRJ0kSxYtqOnH3QccMqXzFtR26cPqNucPT7zbdpa81Qo6lyHR8lanewInNooAPF\n8xNPhAce2HiU6JxzNgRCPc6RnR7lyI4kSdLYa2c0qO5UuV4dJRoYgDvu6MoIjyM7kiRJ0jhodzTo\ny/se1R+jRGvXwoUXwplndvJq485gR5IkSWpTO1PlAC6bNbtugNTJUaKOZ5xbsaKTr9YVBjuSJEnS\nOBjTUaIRZJxrmehgcHCU77j7XLPTo1yzI0mSNHmNecY51+xIkiRJ6oYxzTgXU5h6/vkTdh+edhjs\nSJIkSX2u3bVEi47b+NxeZLAjSZIkTWKNAqF+0NHMdZIkSZI0URjsSJIkSepLBjuSJEmS+pLBjiRJ\nkqS+ZLAjSZIkqS8Z7EiSJEnqSwY7kiRJkvqSwY4kSZKkvmSwI0mSJKkvGexIkiRJ6ksGO5IkSZL6\nksGOJEmSpL5ksCNJkiSpLxnsSJIkSepLBjuSJEmS+pLBzhiKiM0i4hMRcVdEPBkRv4mICyNierfv\nTZIkSep3BjtjJCI2A64GPgZsCXwHuBc4Hrg5Inbu4u1JkiRJfc9gZ+z8PbA/cAPw4sw8JjP/HPgA\nsB1wYTdvTpIkSep3BjtjICI2AU4un/5dZj5aqcvMc4ElwIER8dJu3J8kSZI0GRjsjI0DgG2AuzPz\n53XqLy+Ph4/fLUmSJEmTi8HO2NizPN7coP7mmvMkSZIkddjUbt9An9qpPN7XoP6+mvMaioilDap2\nu/vuu5k5c2a799Yxv3ng0dYnSZIkqefM/O6WXbv23XffDfD8TryWwc7YqHw6Hm9Q/1jNeSOx7qmn\nnnps2bJl947iNUajkk3u7i5dv5fZdyNn342O/Tdy9t3I2XcjZ9+Njv03cjsv+z3Qvb57Po2/R7fF\nYGdsRHnMFvUtZWb3hm6aqIw4TdT7m8jsu5Gz70bH/hs5+27k7LuRs+9Gx/4buX7qO9fsjI3V5XGL\nBvWbl0fngUmSJEljxGBnbNxTHqc3qJ9ec54kSZKkDjPYGRu3lse9G9RXypeMw71IkiRJk5LBzti4\nHlgF7BwRe9WpP6o8fm/8bkmSJEmaXAx2xkBmPg38U/n0nyJi/dqdiHg/MAu4LjNv7Mb9SZIkSZNB\nZDZKGKbRiIjNgEXAnwO/Ba4F/rR8/ntg38z8ZdduUJIkSepzBjtjKCKeDZwGvJUiX/gfgCuBj2Zm\nt/bHkSRJkiYFgx1JkiRJfck1O5IkSZL6ksGOJEmSpL5ksCNJkiSpLxnsSJIkSepLBjuSJEmS+pLB\njiRJkqS+ZLAzCUXESyNibkTMj4j7IyIj4slhtHt7RCyOiEcjYmVEXBER+7dos3953sqy3eKIeEfn\n3s34arfvIuL5EfGuiPh6RNwREevKNvsO41p91XfQXv9FxJSIeEVEnB0RP42IByPiqYi4OyK+EhEv\naHGtvuq/EXz23lB+7m6LiIci4pmyD6+IiNe3uNak7rsGr/HDsl1GxB83OW9S911EfLyqn+o9PtOk\nbV/1HYzq9+2mEfHBiLgpIh4p++POiLggInZs0Gb3iLgsIn4XEU+U//ZPiYie/K43gs9es89d5XFN\ng7Z99dkbyecuInaMiC9HxK/K37WPRsTNEXFqRGzapN1hEfHjiFhVflZ/HBGHdf5djUJm+phkD+Db\nQNY8nmzR5tzyvMfL9lcCzwBrgCMbtDmyrF8HLAIup9hYNYFzu90P49F3wPvqnJ/Avi2u03d9127/\nAS+qOue+su388r8TeAQ4YLL03wg+e5eX7/82YAHw/4D/qmr7Sftu2O2PK9usK49/bN81PP/j5TnX\nARfXeRw9WfpupJ89YHtgSXnub8ufe/Oryjb6uQfsCzxW1v8UmFe2zbIvo9t9MQ6fvXqft8rjd2X7\nT0yGz94I+u7FVX10d9kH3wdWl2XXApvUafeesv6Z8vxvU3xPTOA93e6H9ffZ7Rvw0YX/6fBh4BPA\nYcAOw/hHcHB5zkPALlXl+wFPAQ8D29a02bYsT+BNVeU7AMvL8ld1uy/Goe/eQBEo/iXFl/dFtAh2\n+rXv2u0/YGeKoPrAmvJNgYvKtv9d+wO4X/tvBJ+9vYA/qlP+5+UvsHXADPuuZdvtyp99VwEraBDs\n2Hfrz/94ec5xbVyjL/tuhP03BbihPO8MYGpN/QuB59aUTQV+WbY5pap8S+A/y/Lju90XY913TV5n\nG+DJsv0uNXV9+dkbwedufnnOecBAVfn2wF31PkMUAdIzZd/uV1P+UFm3Syff14j7o9s34KP7j2H8\nI1hQnvO+OnWfL+s+UFN+aln+7Tptjizrvtvt9z7WfVfn/EW0DnYmRd+NpP+q2m1W9QvqoMnYfyPt\nu7Lt18r277LvWp77TeAJiuB7BY2DHfsuRxzsTIq+G2b/nVCec3kbr3l02eaWOnV7lXW3dfu9j3Xf\nNWl3Ytn2hjp1k+KzN4zP3UPlOTvUqavMUPlSTfkXy/LP1WlzSll3Xrffe2a6ZkfNRcRmwKvLp5fX\nOaVSdnhN+WE19dUWUPwl4DXl62so+66FzHyS4q9NAH9SU23/tba2PD5dU27fVYmI1wFvBf4hM+9u\ncbp9N3L23QZ/Wx4/20abhv2XmT8HfgW8JCIGR3Vnveuvy+M36tT52Ss8NYxzVtY8b9Z3l5XH2u+G\nXWGwo1Z2o5g29LvMvK9O/c3lcVZN+aya+vUy82ngdoq/zu/aofvsJ/ZdCxExAPxp+fR/aqrtvyYi\nYhZwDMUUg6trqu27UkRsDnwF+AVw9jCa2HdDHRwRn4simcjpEfHSJufad0BEPAd4GcU0059GxH4R\ncWZE/HNEfCQiXtKg6Z7lcaP+qynfs0F934qInYBXUPy8m1fnFD97hYXl8SPVCS0iYnvgXRRrmr5Z\nVb4NsFP59Oe1L1Z+X3wI+NOI2Hqsbnq4DHbUSuXDXC/QITMfo1yzU/6gJiK2opgj27BdVflODeon\nJftu2I6lmEv8O4o56YD9V09EHB4RF0fENyPiWopfTM8G/iYzf111nn031KeAQeCk8ktPQ/ZdXW8D\n3ksxUvEp4KaIuDwitqw+yb4bYgbF97JfAl+g+Nk2F/gbivU7SyKiXuDd9Pc0k6f/6vkrIIDvZ+bv\nqyv87A1xGrAUOBlYXmb1u4IiWcGzgDdm5h1V51f64w/l98B6JkzfGeyolcovpsebnFP5oG9Zc2zW\nrraNCvZdCxHxfOBz5dOPZWb18Lv9t7E9gXdQTMc6gGLq2nuBf6k5z74rRcTeFH309cxcNIwm9t0G\nvwQ+CMykeK/Pp/jCeT/wZjaeSmTfbbBtedwD+DvgHOAFFEkyTqRYO3ZqRPzvmnatfk9Plv6rp9kU\nNj97pcz8LXAQxQjPC4GjgEOBLSjWGi+raTKS74ZdY7CjVqI85jDOafR8OG1UsO+aiIgtgG8Bz6VY\nUPqV2lOG8zIdv7EJLDPPyMygGM3ZA7gQ+DLwnYh4VtWp9h3rp0h+jWLE+oPDbdahc3peZl6SmZ/N\nzGWZ+Vhm3peZ/wrsA/weeGMM3Z/NvttgoDxOBf4tM0/NzBWZ+VBmng98qKz/SIP2jX5PT5b+G6L8\no8UMin/L3613ynBepqM3NUGV05tvocikdgRF4D2dItHAW4AbImLn6iblsZ3vhl1jsKNWVpfHLZqc\ns3l5fLSmTXVdqzYq2HcNRMQmwL8DL6XYw+OtdU6z/xrIzCcz8/bM/DuK9KKHUUxZqLDvCu8D9gY+\nlJkPDbONfddC+Zfji8qnr6uqsu82qO6LC+vUX0Tx5XJ6RLyoqrzSL41+T0+W/qtVGdW5rGYGQIWf\nPdb/br2MItnPkZn5H5n5cGben5mfB06nSF/9qapmI/lu2DUGO2rlnvI4vV5l+Zf2bYCHM3M1QGY+\nAqxq1q6q/J4G9ZOSfVdfuWDyEoovSbcCh2fmE7Xn2X/Ddkl5PKJSYN+tdzjFF8p3RMSi6gfwx+U5\n88uyA8C+a8Py8vi8SoF9N8SKqv/+79rKzHycYp0iFGsWK5r+nmby9N965QjtseXTS+qd42dvvX0p\nRnR+VWbvq3VpeXxlVVmlP7YtvwfWM2H6zmBHrdxJkZJwu4io98Ng7/K4pKb81pr69cq/IrykfN07\nO3Sf/cS+29iXKIbS7wJem5kPNznX/mutMmKxXU25fVcI4ECKOezVj03L+v3K58+tamPftVZZk1L7\nl177DsjMeyim+gFMq60v/+hTWVBf3YcN+6+mvPb3dD97NUVQ/d/AtU3O87O3ISh5pEF9pXz9Z7L8\nHVwJYvaqbVB+X3wucE9mrqqtH28GO2qq/Ov5NeXTo+qcUin7Xk35giZtDqNI5Xh1uV+KhrLvqkTE\npykyOt0DzM7MB1s0sf9aO6g81u4dM+n7LjNfmZlR78GGv7Y/ryz7dlXTSd93zUREUGzSCPCzmmr7\nboPK2pJX1anbnyIz1hMUKdErGvZfROxFseB8WXX2xUmgMoXtkix3uWzAz96G7Rt2rWTVrbFPeVxR\nU96s744uj7XfDbuj27ua+uj+g9Y7676mPOchYJeq8v0oNtxaBUyraTOtLE/gTVXl21NMZUjg1d1+\n72Pdd3XOX1S22bfJOZOi74b52Xt/ec5vqz97LV5zUvRfs74r3+sHgG3q1M0GHizbv9m+a6vdirLt\nH/u5q1v3XODtwKY15VtS7FlU+be8+WTsu+F89igW1K+hGOHZq6Yvfla2/6eaNptQbByawClV5VtQ\npK9OYE633/tY913VeZtTrClJYLcW506Kz16Lf7ebAQ+U53y9+t8vxTqeJWXdGTXtdi0/q09S9Z0G\n2IXi++IaYNduv/fMNNiZjA/g9cB/VT0SWFdT9vqaNp8rz3sM+DZwBcUmXWup+cJU1ebNZf064EcU\nC+D+UL7O57vdD+PRdxTD6NV1j5Rtbq8q++hk6Lt2+w/4s7Iuy1/YFzd4HDAZ+q/Nvhss6x8HfgL8\nK/Ad4I6yPIFzG1xnUvddi9dZQYNgx74b8rlbVZZfCvyA4otPln3x8snSdyP97FEkDkmKL5E/ohjt\nWVmW/Qx4Tp3r7F/+e8/yNecBvymffwuY0u2+GI++K9u9tTx38TCv03efvXb7DngjxXe6pNgf59vA\nVWz4zvIzYMs61zmlrH+G4nvht6s+h6eM1/tt2R/dvgEfXfifDsex4QtPo8dxDdrdRBHwPAxcSZ0v\nmjVtXg58v/zB8VjZ/vhu98F49R0bfvk3e1w8Gfqu3f6jWAzZ6ty6n9V+7L82+25z4FSKaQYryl8+\nTwK/Bv4NeGWLa03avmvxOitoEuxM9r4DngN8hmIE+77yM/cYxR93zgF2nEyfu9F89oDXUgSKD5f9\nuBT4GDWjYjVtZgKXUwSXlTYfAAa63Q/j3HdXlHXvaeNaffXZG0nfUay9+SZwL8V+bI9SbEJ9GvDs\nJtc6nOKPaqvLx7XAG7rdB9WPKG9UkiRJkvqKCQokSZIk9SWDHUmSJEl9yWBHkiRJUl8y2JEkSZLU\nlwx2JEmSJPUlgx1JkiRJfclgR5IkSVJfMtiRJEmS1JcMdiRJkiT1JYMdSZIkSX3JYEeSJElSXzLY\nkSRJktSXDHYkSZIk9SWDHUlS34uIwYjIiLgmIraJiM9GxK8j4omIuCUiDq06960R8Z8RsToi/jsi\nPhURU7t5/5KkkTHYkSRNBn9WHh8BbgHeCPwUuAvYE/hWRLwoIi4Bvgo8BCwC/gQ4HXjXeN+wJGn0\nDHYkSZNBJdg5Ajgf2CUzjy3LrwA2Ba4EdgZ2zsw3ZObhwFvLdm8e5/uVJHWAwY4kaTLYszz+a2ae\nkZnrADIzKYIcgG2AN2fmA1XtKnXPG5/blCR1ksGOJGkyqIzsnFGnbuvyeEFm/qambpvy+OCY3JUk\naUwZ7EiS+lpEbA0MAr/MzDvqnFIZ9fmPOnV7lMfbx+DWJEljzGBHktTvKsHMTQ3q/wxYB/y8QR0U\nSQ0kST3GYEeS1O8qActGwUxEPIciKcGdmfl4k7a3VLX5WUT8IiJ2rHmtkyPifyLi5y3qtxrVu5Ek\nDZvBjiSp3zUMdihGfaJBXaV+HbCkquwvgT8C3lQpiIjtgU8CpwLHNKvPzEdG9C4kSW1zkzRJUr+r\nTGOrNxWt4TS1iNgCeBGwvHrUJzPviogfAzOqTj8buD0zv1G2bVovSRofBjuSpL4VEVOBmcD9mfm7\nOqc0G/WZRTEDol6QdAdwYHmN/YG/Al7WRr0kaRw4jU2S1M92o9gwtFGCgWYJCJqNCP0C2D0iBoAv\nAl/OzFvbqJckjYMo9lOTJEnDFREvpcju9gngJGDXzHx4uPWSpPFhsCNJUpvK9TyrgQTmZObF7dRL\nksaHwY4kSSMQEfcA9wEvzzq/TFvVS5LGnmt2JEkamWcDFzUJZFrVS5LGmMGOJEltioidgOcCN4+k\nXpI0Pgx2JElq317AM8BtI6yXJI0D1+xIkiRJ6kuO7EiSJEnqSwY7kiRJkvqSwY4kSZKkvmSwI0mS\nJKkvGexIkiRJ6ksGO5IkSZL6ksGOJEmSpL5ksCNJkiSpLxnsSJIkSepLBjuSJEmS+pLBjiRJkqS+\nZLAjSZIkqS8Z7EiSJEnqSwY7kiRJkvqSwY4kSZKkvmSwI0mSJKkv/X/bZAuJDcZsHQAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95c6c7b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " **********\n",
      " **    1 **SET ERRDEF           1\n",
      " **********\n"
     ]
    }
   ],
   "source": [
    "params = [\n",
    "    # Signal Parameters\n",
    "    ('s',   0.1,    1e-3, 0,     1),\n",
    "    ('m_H', 125,    1e-1, 120,   130),\n",
    "    ('w',    4,     1e-2, 1,     30),\n",
    "    # Background Parameters\n",
    "    ('b1',   0.14,   1e-2, 1e-2,  10),\n",
    "    ('r1',   2e-2,   1e-3, 1e-3,  0.5),\n",
    "    ('b2',   9.8,    1e-2, 1e-2,  10),\n",
    "    ('r2',   6e-2,   1e-3, 1e-3,  0.5),\n",
    "          ]\n",
    "minuit = setup_minuit(params)\n",
    "\n",
    "    \n",
    "results = perform_fit(minuit, params)\n",
    "for ((name, *_), (val, _)) in zip(params, results):\n",
    "    locals()[name] = val\n",
    " \n",
    "t_s = (s, m_H, w)\n",
    "t_b = (b1, r1, b2, r2)\n",
    "ys = np.array([a(x, t_s, t_b) for x in bin_centers])\n",
    "plt.bar(bin_centers, bin_counts*total_count,\n",
    "        label=r'Data(7GeV + 8GeV)' if use_8TeV else r'Data(7GeV)')\n",
    "plt.plot(bin_centers, ys*total_count, 'r.', label=r'Fit')\n",
    "plt.xlabel(r'$m_{\\gamma\\gamma}$')\n",
    "plt.ylabel('Count');\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we can see the result of the fit. The background seems to be described quite well, but, for reasons unclear to me, the fit of the clear H peak around 125GeV is quite poor. In fact, the central value of $M_H$ brushes up against the lower limit of 120GeV. (I'll be interested to see the solution and how this problem can be avoided) In any case, we can still press on and calculate the local p-values for each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z4QGrK5N0bEh5wnLZb4QF/H5GQV+3HZtySeW4Y0PgBoCcUNgABfczv/cFfeqZ\nTf3qJ57WJy7dmPXDWSjb+/l0bMJzbOjYYPHFhYzt4MTCVLQwPEBiLw1AfihsgIJ7amPn8NdXbu3N\n8JEsHtuxOT7Bjg1xz1g2cSGz10gvTuxzolJKCA+gywkgJxQ2QMFt7tQPf82p9vkKd2zyGUWjY4Nl\nEHds9hrpHRsbgV4tO5VLTs40bejYAMgLhQ1QYPvNlrbNxTeL6fnaDnZsGEUDhjXKjk2zFR7QKSkI\nEOCGDYC8UNgABba50wjermfMsWN0O/v5dGxsKhqjaFgG+63hOjbttpedNOs+V8rRIZ0AkAcKG6DA\nbmzXg7e5s5mv7WDHZvyOTc10bOp0bLAE9hvxjk1yYdOIzqjpdmwqjG8CmAIKG6DAbkaFDRfN+cqv\nY8MBnVgu/aloya9N8c2Yw1E0njMApoDCBiiwm9EoWtYcO0a3Y+4yT7JjU4nGarIOKwQWwbDhAXEw\nQLdTY58zhAcAyAuFDVBgN3biUTQuAPK0s98bRZukY1OrxPG1fJ2w2OK456E7NqX+jg0jtgDyQmED\nFNhmPIpGxyZXNnFudYIdm/4DB7lQw2KLU9FSOzbtlI5NmfAAAPmjsAEKLO7YUNjkyx7QuTrJOTZR\nx4bCBouubxQtrWPTDJ8L3ZsANhWNDieAvFDYAAUWhwcwipav4IDOlQkKm1L4UkrIAxZd3yjaEKlo\n1bKT65zMaZ8z3AgAkBcKG6DA4vAAOjb5sjs2E42ilaNRNO5AY8ENm4pmi5aKKWbCs594vgDIB4UN\nUGA3o1G0+FA8jK/d9kEq2lqOOzbx+A2waIbdsbFdZlvM2Ij0Bjs2AHJCYQMUWFzYNOjY5Gav2ZI3\n11PHJ9ixcc6pWmZnAMtj+FS03vvtQbZV4p4BTAGFDVBgN7ejUTQuAHJj92ukyXZspHDMhp0BLLqh\nz7Ex3RjbsbHhARzQCSAv489eRJxzFUkXJJ2XtCHpSe99M/M3AUhVb7Z1Z7/Z9z7kY2e/dyHmnHSs\nMllhUy077XbqUEIesOj6dmwagzs2tvi359hwIwBAXnLp2Djn/oGkK5K+IOnDkj4vacM59z7n3Bvy\n+DuWkXPujHPugnPugqRqm/GWpbIZjaFJXDDnadtGPVfLKkV7MqMKDxzk64TFFqeg7TXTdmx6RYs9\nyDYID+B7G4CcTFzYOOf+L0k/LumsDgqa35D0QUlNSX9V0iedc+92zlUn/buW0EOSLnV+PLixsTHj\nh4OjFJ9hI9GxyVNwhs3K5M1rTlLHMolfi1JH0YKOjQkPKPF8AZC/PDo2f1uSl/RXvPev9t7/Ze/9\nX5R0l6RvkfSopL8n6f3OOXaQKEh3AAAgAElEQVR6RvMuSfd1flxcX1+f8cPBUYr3a6T0BV2MbtuM\nok1yOGcX8bVYJsPGPduixSahVXm+AJiCPAqNl0r6r977f2/f6Q/8rqSvkfSrkv57Sf9rDn/f0vDe\nb3rvL3vvL0tqlErUhcskTkSTGHHKkw0PmOQMm64q8bVYInEqWnp4QHhAZ5ctcggPAJCXPK6Ur0u6\nlvZB731b0t/pfN7fyeHvA5bCje2EUTQKm9zYUbS1HDo2QdwznTUsuDgsYJi4Z1v8V4K4ZwobAPnI\no7D5sKSvzdqh8d7vS/qvkr4sh78PWApJ4QHs2ORn23ZsctixCeKeWYbGguuPex5iFC3YsSE8AED+\n8ihs3inplKR3D/i8M5K2cvj7gKVwI2HHhsImPzv7YSrapKom8anOHWgsMO99X/c4Tknrst2YoGND\n2AaAKcijsPkFSX8q6W87537POffV8Sc4575B0tdJ+o85/H3AUkiOe+YCIC/Bjs2Eh3NKnKSO5ZE0\ndjbcKJpL/DXPFwB5yeOAzreYX3+jpL/gnHtG0mOSbukg0evP6SAG+v/M4e8DlgJxz9MV7tjkMIpW\nZmcAyyGpiEkLDwgO6Ax2bAgPAJC/PAqbl0p6vaTXmR/3SXpF9HlfKelfOecek/THkh7z3l/J4e8H\nFtLNlPAA772cm+wwScQ7NnmEB9hRNApQLK44EU3KSkWzo2g2FY0dGwD5m7iw6RQnv9v5IUlyzp2S\n9FqFxc6X6aDg+R90cO6NnHMveu/vnfQxAIvo5k7/jo10MI5Wq1DYTMru2OTRsbGFDaM1WGRxIpqU\nPooWHtBJKhqA6cqjY9PHe39LBylo/7X7PudcTdKr1St0Xt95G0CCpI6NdNANqFU402hS4Tk2+cY9\nM1qDRZZUxDTbXs1WOxg3k8IgDcID5s/WTkN/71cf1dZuQ//3X3+tHjh/ctYPCch0ZFdH3vu69/5R\n7/0vee+/x3v/Zkk8Q4AEjVZbt01HwWLPZjhPb+yolVFg5H1AZ4VRNCyJpFG0g/cnFDxp4QHEPc+F\n//ip5/SxJzf0uedv6Vc+ennWDwcYaKa3fb333KYBEtxMCA7oanDRPNCP/Obn9LU/9SG97ec/qnZK\ncbNtwwNyT0XjpQ2LK23sLGnPxnYvK8GOjR3d5PlSVNfv7B/++sqt/YzPBIqBeRaggG6aM2zisTM6\nNoP9u0eflSQ9/tyWPvt88vFZO/v5dmyqwWgNXyMsrqQdG0naS3htsq9XdsemSnjAXLDF6nbKFAFQ\nJBQ2QAHZjs36Wi24CEi7W4qeXTNm9vzmXuLn2I5NHjs27AxgWaSOoiV2bHqvV/YmTZkO51yw329s\nRD5QVBQ2QAHZ4IAzqzXV6AYMrdlqB+MvL27tJn7e7jTDA/gaYYGlj6Il7diYUbRS8ihag7CNwrLd\nuTt0bDAHKGyAArJRz+fWqqqaO52MomWLF/dfTJkLD3dsGEUDhpVa2CR0cmz30hYz4U4az5eist05\nG7gCFBWFDVBAdhQt7tiQuJUtnv9P6ti02j64u5zPKFrvQo070FhkSSNnB+/vf22yRX6N8IC5Y4tY\nOjaYB1M5xwb5cM6dkXSm82a1zYLl0rhhRtHOrdaC2fQGHZtM8d3kF7b6d2ziWfE8DugMxgX5GmGB\njdKxsTs2QccmuBHA86Wowh2blrz3co4DolFcdGyK7SFJlzo/HtzY2Jjxw8FRsR2bs2thYbNPxyZT\nvNh85VZ/YbMbjVQcz6NjYxKfOKATiyytsEnu2CTv2NjwgKzzpjBbNhWt1faE16DwKGyK7V2S7uv8\nuLi+vj7jh4OjYsMDzq5Ww1E0vrFkSurYxEdmbZvCplxyWqlM/lIYjKJRfGKBpR/QmdCxCQ7o7D3P\n7I0AUgSLK349ZRwNRccoWoF57zclbUqSc65RKlGHLoswPCAaReOiOVN813i/2dbmTkNn12qH77Pn\nMazWyrmMVpBch2WReo5Nwu6NLVqqKaNohAcUV1ys7uy3pBMzejDAELhSBgooMzyAjk2mpLvGL0bj\naDbdJ4/9Gins2LAMjUWWOoqW8H5b5FfSwgMYRSusuIilY4Oio7ABCigOD6hS2Awt6eLqxa24sMn3\ncE4pvBtNch0WWdooWlLHxhYttktj457pcBZX/HrKIZ0oOgoboGAarbZu7/W+eZxdqzKKNoKki644\nGc12bFZX8ips6NhgOaTdXBkU9xzs2JhfEx5QXPHrKR0bFB2FDVAwm2a/RpLORnHPpNJkS7q4ikfR\nwh2bnEbRglQ0vkZYXGMf0GmeI+WgY0NhU1R7jbhjwyGdKDYKG6BgNs1+Ta1S0mqtzAGdI0geRQsP\n6Qx3bHLq2FTs14gLNSyu1MIm4aZCmIrmEn/NjYDiomODeUNhAxRMvF/jnIsO6OSiOcvIo2g5dWzs\nzgApT1hk+2aXxp5Nkxj3bMbMKilxz4xuFpP3/efW7FDYoOAobICCCRPRqpIUdWwYBciSNP8fH9I5\njfCACnHPWBL2YvfU8erhr5M6Nvb5mNax4flSTI2WV3QEWHAGGFBEFDZAwcRn2EhStdK7CCAVLVvS\nmEzcsdneN6NoKzl1bAgPwJKwnZnTQWGT1LEZHB5A3HMxJXXgtunYoOAobICCsaNoZ1cPCptauddV\nYNE2W1Jhc3uvGXxDnnbcc4OdASywtI5N0nOvGYQHuMRft9pePm4NYOaSvp4UNig6ChugYGx4wNm1\nzigaqWhD20+4ayyFyWh2nCK/jg17UFgONnnw1LHe8yepY1NPjXt2wefRtSmepK8no2goOgoboGBu\nbJtRtMOODaNow0or/OwhnbumY3O8mteODSlPWA5po2iDOjbVlPCA+PNQDHRsMI/yuVWJqXDOnZF0\npvNmtc3F0lIIwwM6hQ0HdA4trbCxezbhjk1Oo2gl+zXiIg2LK3UUbcCOTSUlPEA6GN88rnyei8hH\n0plgxD2j6OjYFNtDki51fjy4sbEx44eDo2ALm254gC1s6NhkS1p4lcKzbMIdm5xG0SqkPGE52MLm\ndEYqmvc+KPJt8W/DAyQ6NkWU9FrKAZ0oOgqbYnuXpPs6Py6ur6/P+OHgKNzc7o97rnJA59CS7jJK\nWTs2OY2icS4HloTtzJw6ZkfRwoveeG/GFv82PODgc3ldKxpG0TCPGEUrMO/9pqRNSXLONUol6tBl\nkBT3zCja8Ow347tO1HT9zkGhGO7Y9C7AjlfzeRmsRcWn917OuYzfAcyncBTNhgeEr01xgW+L/77C\nhpsBhZNY2NQpbFBsXCkDBdJstbW12ytsenHPpKINy941fsW51cNfBzs25ptzbh2baGegRcoTFlB8\nGn0wihZ1bOLYc7tXU6awKbzEVLR9RtFQbBQ2QIFsmqJGks6yYzMye9H1yvW1w19fMaNoO+abc147\nNsTXYhnEo7BBKtqgjo25QeOcCwodzn4qHkbRMI8obIACsWfY1MolrXUOj7QdG0bRstmLq1eu9zo2\n1+/Utd9sqdFqBxdneXVsatEyNLtQWETxxa7dsdlrtoKDNuPXqjgJjb20YktKudtvttXktQ0FRmED\nFIg9w+bMavVwR4OOzfDSRtEk6eqt/b5Un/w6Noub8tSm+4SOuCtjOzbeh1HnfYVNtCdqu5zcsCme\ntLFnDulEkVHYAAWSFPUskYo2CvvN+MRKRXed6P1/fPHWXhD1LEmrtbxS0aJzORbk6/TI5Rv66n/y\n+/qmf/GHumrG+bCc4uQze46NFO7Z2OK+5KRSKe7Y9N5mJ614UgsbxtFQYBQ2QIHYqOducIAUpaLR\nsclkvxmvVMu65/Sxw7df2NoLll9r5VJQNE4i/nMWpbD5lY9e0sZ2Xf/tyh397AcvzvrhYMbs86ta\ndn03BuzCuX0OJD3PbJeTuOfiSTsTLL45BBQJhQ1QIDdMx+bsWu9OaDCKtiAXzNNi58JXKiXdc+r4\n4dsvbu2Gh3PmtF8jHaQ82RvSizKK9tzN3sGm//7R57S108j4bCw6Owq7UilrpRJeRthRteBwzoTC\nplpyiZ+LYojju7vukIyGAqOwAQpkc6c/6lki7nkUQcemUtI9p1cO335xaz/o2KzltF/TVV3AkIer\nt/cPf73baOnX/ujpGT4azFr8/HLOBTde7F1+24WJUwMP3kd4QJGldWwYRUORUdgUmHPujHPugnPu\ngqRqm1b9wrsxzCjaglwwT8t+dEf5padNx+bWrnYbvW/Kx3Par+kKC5v5v1Brt72umcJGkt77sacK\nn4rkvddnn9vSHS7Achd3RCXpmHl92kvp2FQSDpiuEPdcaHFQRBeFDYqMwqbYHpJ0qfPjwY2NjRk/\nHEzbZjCKltyxIRUtm73LuFIt6Z5T6Ts2azkXNouW8nRzp953Hs9zm7v6L5+/MqNHNJyf/r0v6C/9\n3Ef053/qQxQ3OYt32OzPBx9P3rGpJXRsbEpaawFuBCya9FQ0nlMoLgqbYnuXpPs6Py6ur6/P+OFg\n2mzH5pzZsamaO6Jtr8LfMZ+VdtsHd4kPRtF6hc2VrTAVLa+o567qgi1DX426NV2/8tHLR/tARvQb\nn35e0sHZRR974vqMH81iiUfRJOlYNbljY8fL4jh06WAv7fBzF+D5smjSR9HYsUFxUdgUmPd+03t/\n2Xt/WVKjlNDKx2KxOzZnUnZspMUYc5qGOFhhpRKmol25va/be73CJq/DObsWbRn6iol3tuOQn7x8\nQ599bmsWD2koG3d6Nwium19jckFH9HAULaVjM2DHplperOfLoiHuGfOIK2WgQGwq2rmUHRuJcbQ0\n8Ux4PIrWans9fWPn8O3cOzYLtgtlOzavufe0XvslZw7fLmrXZrfeCg5h3biT3HXCeOxzbKXSHUUb\n3LGJD+eUiHsuOrtPZXFAJ4qMwgYoiFbba2t3cCqaJO23+MaSJB6dWKmUtLZS0cljvQLmi9e2D3+d\n1+GcXfbAwUVIebLBAedPrejtf+7C4du/+enn+4IFimBjO3xM1ylschXu2PR3bFLPsakkpKItWIdz\n0cRnFnXRsUGRUdgABbG125A339vTzrGRuAhIE49OdAvCl5pxtCev3Tn89TR3bBaiY2NG0c6fPKZv\nfvVL9ZJTB/HZ9VZbv/qJp2b10FJtRKNn17cZRctT0iia7djY56B9DiSlolWnFPe832zp/Z96Tp96\nZjO3P3MZ2e6cvdHGAZ0oMgoboCBscEC17HRipXfRXS65YNGWUbRk8UWXcwf/z+4xkc8vbPUu1nPf\nsVmwuGc7inb3yRVVyyX9T1/zysP3/ZuPP526YDwrN6JC5noBu0rzLI5Tl9I7NsEoWsKOjX1Na+U4\nivZzH3hC//v7PqW3/fxH9cTV27n9ucvGPrfPmZRODuhEkVHYAAVho57PrNYOL8q77IUBhU2yvUZ/\nYpMk3XNqJenTc+/Y2AXpRdgZsIXN+ZMH/w+/842vOOwgXr+zr9/+zAszeWxp4tGzDTo2ubJ38WuH\nqWg2PCC5Y1NNSEWbVnjAJy71jkb4+Bdv5PbnLhv7tbSFzQ6jaCgwChtMpNX2wR06jC+IejZt/67a\ngo05TUPSGRtS2LGx8t6xsQvSi1B82lS0850QhvUTK/rLr7338P2//NFL8r443am+jg07NrlKHEUL\nDui0qWjZcc92PC3PGwG75jHYFESMJq2w4WwoFBmFTYE558445y445y5IqrYLdgf4ha1dvfnHP6Cv\nfufv6xNf5PDQSd0MOjbVvo/XKsl3RdGTdNElhTs2Vu6FTcV2bIpzsT8O733QsXmJ6Xq9/a0XDn/9\n2edu6Y+eunmUDy1TXNhs7jS4EZCjpHNs7E2EMBXNdGxKCeEBU+rY7JrUrjv7jYzPRBZbpK6bwoYD\nOlFkFDbF9pCkS50fD25sFKt4+NWPP62rt/d1e7+p9z58edYPZ+7dNGfY2LtjXTVG0QayF121YBQt\nubBZW8l5FM3egZ7zi+lbu83g39n5k73/h3/2nlN685f2Dgx+78cuH+VDy5R0bk1c7GB8wc2DTkFz\nLAgPSN6xST7HZjrhAba4ukPHZmxhx6Z3Y2OHHRsUGIVNsb1L0n2dHxfX19cHfPrR+ow5oM9G6GI8\nN83F19mkwmbBzkiZhqQzNiQFh3RauXdszIVafc7DA67e7o2hVctOZ6Mu4nd9zSsOf/3pZ4uTPnVj\nu3/0jHG0/Own7LGtVJI7NvUBOzbTCg9gFC0f9hybcyalk1E0FFm+tyuRK+/9pqRNSXLONUoJcZmz\n4r0PTh6/vLGtdturlDBugOHYUbT4IlIKCxs6NslGHUXLu2Njl6HnvWMTJKKdWOkLs/iSs6uHvy7S\nxWNSWEBSFwfjSRpFCzo2qaloA8IDchzdtCNUt7kIH1tqx4YDOlFgxblSxlx5bnM3GO/Ya7R1xdzh\nxehubCcfztkVdgPm+6J5WpIuuiTp9PFq8HbX8WrOB3QuUMCD7djcnTDKd+p4r/i+vdcsTIBAfI7N\nwfvo2OQlvHlQDn4++LjZsTFdmErSjs0URje991HHhh2bcTRb7WBP0J6rtl0vzvMdiFHYYCy2W9N1\n+frODB7J4rAdm8QdG0bRBkpLRXPOJXZtptmxmfdzbK7cMsEBJ/vjsk8e6/2/a7V9Ye7ibjCKNlXh\nc6y/Y2O7JcEoWsKNhWmEB+w328FBx4xNjSe+ebZuOjbeh+N+QJFQ2GAsjycVNhvs2UwiHEXLjnsm\nFS2ZHYOJOzRJezZrU4x7nvdzbK6awuZ8wjlAtrCRijGOtlNvBjseXUldHIwnaccm7RybYBQtoWMT\nhAfk9HyJjx8gPGA88fMovtlGwYiiorDBWB5/7lbf+y5fp7CZxCjhAezYJEsbRZOSk9FW8+7YVBan\nY2NH0WwiWtdKpRz8m5x05Gdrt6HfffwFbe2M/+ekFTDX6NjkJnkULbljY8fLks+xsTtp+Txf4k5C\nEQrueWS/ztLBOK+tTUlGQ1ERHoCRee/1eEIKEh2b8bXaXlu7dscmITygTGEzSFjYhN2YpEM6c9+x\nKS3OuKANDzifMIomSaeOVQ4X829NcAHpvdd3/dLH9dnnbunLX3pKv/19b+0LKxhGUnCARMcmT3ZE\nKaljs9dMO6Azaccm/3OfdqORSMIDxmM7cyV3MGa7Vqsc/v+kY4OiomNTYEU9oPO5zd3gzJUudmzG\nd2u3Ift9nbjn8YRnbIQvb/GOzbFqKYibzUO4YzPfX6Nrt7NH0STp5DEbIDB+p+XmTkOf7XSBP//C\nrdQCZZC0kAB2bPITjKJVu3HPpcSPN+y5Ukkdm3L+4QHxCFW92e7rPmCw+CaRcy7YSSzKTh0Qo7Ap\ntkIe0GmDA+yFYTfyGaO7YfZrKiWnkwkjUoyiDZY0/9/1kmgUba2Wf8N6WgcOzsLVW9mjaNJBx6Zr\nko7NdnT3d9wxF1sQ2W4AHZv8JHVF0zo2tgtTSTiuoDKFuOekpXb2bEaXdJNobaX3dY6fs0BRUNgU\nWyEP6LTBAW994K7DX+8323rxFpHP47BjaKePVxPHcIh7HiwckwnHzOKOzepKvmNoUhz3PL+FzZ39\nprbNHdlpd2y2683Mt4dlC5j77lrrvX97n3janCSdFRWmopmOTbBjk/CaNoW45zg8QGLPZhxJ+4q2\nYzPucxSYNgqbAvPeb3rvL3vvL0sqzAGdNjjgTV96Tned6F30sGczHnuHOi2COOjYUNgkyurYxIXN\nNDo2tQUZRbPdmpILo14tm4w2ycXjdtSh2RnzoumGiXr+M/ecPPx1o+V1a5cLsTwkdWyCc2xSD+hM\n2LEpTyE8IGFEin2Q0e01+kMi7GsmHRsUVTGulDE34uCAV997Wvfd1TuBnD2b8dgLudWUCGLCAwbL\n2rFZP7ESjE4ezznqWYp2BgqyEzcOGxxwV/T/zQoLm/E7NnEhExc6w7Idmy+9ay0YRyMZLR9JOzZB\nx6aZ3LGpDkpFm+IoGh2b0dmv87GEUbQ7pKKhoChsMJI4OODV957WhfXeyAcdm/HYRcyhOjYUNomy\nUtHKJRccNDmNjo29UJvnUbSrQwQHSPEoWo47NuOOopkdm7tOrGj9RC+EIy1YAMPz3ieOotnnWr3Z\nPhz7C1PRBoQHTOkcG2nyKPJllPRaGoQH0LFBQVHYYCQ2OOBLzh3XmdWaLphZ9kucZTOW7RE7NvM8\n5jRNWefYSOEhnWn/nyexKMl1wwQHSNMbRRu3Y3PDFDbn1mrBCN11AgQm1mz7IL3xcBQt6o52n4d2\nbybpgM5p3AhIKmwYRRtdUgG7am4G3WHHBgVFYZMz59zXOufe75x7yjnnnXP/eII/q3BxzzY44NX3\nnpakoGPzFB2bsdgdm9TCho7NQPuN/m/Gli1s0jpjk6gEy9CL0bF5SUbH5pTp2NzazW8UbeyOjenK\nrJ+o6S7TodvYpmMzqf3odSfpHBupV1wMGkWrTiHumVG0fAQ3iTqF6wkzisYBnSgqCpv8nZD0eUn/\nQNKLE/5ZhYt7/syzvcLmK7qFjdmxeWpjh8jnMQSjaCkjUqSiDRZ+M+4vEO890zuk8/Tx/kNQJ2WX\noef5a2Q7NncfRccmWviO3x6G9z4YRVtfW9Fd5jyo67cpbCa1HxUNtcNRtOSOje3CJB7QWZ7GAZ39\nzzs6NqPbTwgPWCU8AHNgrgsb59wbnHP/0Dn368655zodkoF5w865Y865H3HO/Tfn3J5z7nnn3C87\n514+6WPy3v+O9/4HvPe/JmnS76SFinv23gejaK+594yksGOz32zrBSKfR2bvUKcttYcdG4rHJING\n0f7aV3+JTh+v6tSxir799RM/3fvUpnAHehaCHZuTw+3Y3Jok7rlvx2b0wma73gq+/nHH5vqYh36i\nJ61jUyuXZBPqux0buzeTHB6Qfzx6Usdmkn+by2ov4bX0BHHPmAP5z2IcrXdI+rZRfoNz7pikD0h6\ni6QXJL1f0gVJb5f0l5xzb/beP5nz4xyL935T0qYkOedmHvccBwd8xb2nJB2M9Nx9cuXwpPKnrm8H\nd8YxmP0mQdzz+OxceC2hsHng/El98h/9BbXb00pFy/8O9CwMW9icmlbc8xh3g2+YHRrnpLOrNa3T\nscmVLWzKJXe4/O+c07FK+bCo6O3Y2AM6s3dsWlMMD+CAztGFqWidjk1wQCejaCimeS9sHpb0aUmP\ndH4MM/r1gzooah6W9E3e+zuS5Jz7fkk/I+mXJX1d95Odc+cknRvwZ97y3l8d+dHPmaTggK771tcO\nC5tLG9t6izm4E4PZO9Tp4QFmzKnJN5UkWefY9N6ff0HTVV2QSO4gPOBU1ihaPgd09sU9j9GxuW52\naM6u1lQuueCMrQ06NhNLWig/fLtaOixsusWFvQFTTXg+TuMcG8ID8pH0taZjg3kw14WN9/4n7dtJ\np7VHH69K+t7Om9/TLWo6f9Y/d879TUlf65x7g/f+0c6Hvk/SDw94KO+V9N0jPPS5ZPdrusEBXRfu\nWtUnL9+QJF0mGW1kwQGdKTs2YeLW/HYDpikr7vkoVBegY7PXaOmWucOdFR5gd2zu7DfVbnuVUs68\nyRJfeI4THmA7Nuc6nRob93yduOeJZd04OFYpSzoobvca/R2basLEgb0R0MipY0N4QD6Sxno5oBPz\nYK53bMbwVklnJD3pvf/jhI//u87P32re96OSqgN+/K1pPeAiCRPRzgQfCyOfOaRzVNvD7NiUw7Mi\n0C/rjvJRCHcG5vNrdPVWrwBwTkHXI3bKBDC0/fh3ceOdmnHGXGzqWXcELejYEPc8sawbBzbyufs8\ntHtmieEBpfw7NrsJ3T5G0UYXHnbMKBrmx1x3bMbwlZ2fH0v5+GPR58l735Z0ZFcozrnPpXzo/qN6\nDEni4IC+jg2RzxMJD+hMLmyqwSjafF40T5u98DpWPfrCJoyvnc+OzdXbvTG0c6u1xKXvLtuxkQ7u\njNvxtGHlcUBnkIh2or+wubPf1F6j1RdNjOGFF7tJHZsD3Y6NPaAzMTygTHhAUSV15xhFwzxYto7N\nKzo/P5vy8WejzxuZc+6Ec+61zrnXSqpJuqfz9peP+2cWQVpwQFdQ2Nwg8nlU4Y7NMKNoFDYx731Q\n8M16FG1ev0Y2OODujOAA6eBi1RaQ4478xBdJ4+zYbCSMop0z4QES42iTyhxFM/8Oks+xmWF4AGNT\nI0tKRSPuGfNg2To2Jzo/p81KbUefN46vkvQh8/bf7fx4Sgfpa5m8969Ken+nkzOz4ujxZ9ODA6Tw\nLJt6s63nt3b18rOrwnDsHephDuiMY1eRHkV7lIKdgXktbIYMDug6eayqvcZBwTBugEB82N9YqWjR\nGTbSwXPm9PGqtjqHh16/U+d1aQKZo2jm7eRUtKMKD+AcmzzYc2y6XU7bsWm0Dm4kJaVPArO0bP8i\nu6+iaa+go2+9Rrz3f+C9dwk/Lkz6Z8/S4xljaNLBnRwbC3uZPZuR2Hnl1I4NB3Rm6i9sjr5jM40L\ntaM2bNRzVx6HdMYdm3HOsbHdGBsacJf59QYdm4kMSkXrGrZjc5ThAd7P5/NxVpLCA1ajMWm6Niii\nZStsbnd+Xkv5ePdW3p2Ujy+trOCALhsgcJk9m5HsBufYDO7YzGs3YJr2owjseAfgKFQXoPi0hU1W\nIlpXHod0xh2bceb3kzo2krRu9mwYRZtMMOoZPb+SOjZhYZN0QOfRhAe02j6xk4N0YRF78LWNEzvZ\ns0ERLVth83Tn57Qjx18efR40ODig6z6zZ0Pk8/Daba+dxmhxz4QH9NuPLlxqGUvv0xKEB8zpntkV\nO4p2cvAomj2k89YYHRvvfX/HZozEJVvY2N2au4PChmS0SWSNosU7Nu22l30KJKWixc+XPLoqSTs2\n0mTnLC2j/YQitlxywdeZZDQU0bIVNp/u/Pz6lI933/+ZI3gsc2NQcEAXHZvx7DVbst/P0+KeF+Xw\nx2mxHZJauTTWeSqTCpeh/VyGaFwbcRTt1ISHdO412or/N9Vb7ZH+jXvvg/AAO37GWTb5yRpFs2lz\n+41W32hZcipa+BzN4wz74kMAACAASURBVGZA0iiaJN1mbGokaUERJKOh6JatsPmopC1J9zvnXpfw\n8e/o/PxbR/eQim9QcEDXhfXeUu7lDXZshhXf9Urt2ER3N+fxonmashKbjkp88ZbX3sBRCnZshhpF\nm2zHJu3iKGmkKM2d/WZQ2NqODWfZ5CfrObYShZvEo2WVhBsN5eh9rQlf07z36YUNZ9mMJOkcG4lk\nNGuv0dJHn7g+0msVpm+pChvvfV3Suztvvts5d9hicM59v6TXSPqI9/6RWTy+orL7Na9J2a+Rwo7N\n0xs7E3+TWhb2RdG59PNX4guJed3hmJasMzaOSrwgPW8BAvVmOxjpGmYULSxsRu/YpF0cjXI32BYs\nJafg5gsdm/xkj6LZc2xafXuA1YSbDdUoKW3S3cF6qx10v+1rJod0jmYvpYhdsx2bJR5F897rb73n\nEX3XL31C3/4vP0Y4RYHMdWHjnPsW59zHuz86767Z9znnviX6be+U9AlJb5F00Tn3a53f+zOSNiS9\n/ej+C+aDLWy+ImW/RpJeaTo29VZbz2/uTvVxLQp7AbdaLcu55BGquBtAYRPKuug6KpXoazRvhU18\n4T/oHBspDA8Yq2OTcnE0yiGd9nDOs6u1oBNAxyY/mQd0Bjs27b4DN+MiRkoYRZvw+bJXD18T7b/f\nO/vs2Iwi7fX0hAm3WeaOzbU7+/rYkxuSpM+/cEvP3uR6pyjmurCRdLekN5kf0kFks33f3fY3eO/3\nJH29pB/TwXk2b9PB+TLvlfQ67/0TR/HA58WwwQHSQYvapiixZzOc4AyblfSjpeLzAhrs2QSS4kmP\nWhxYMG/Fpx1DO328GtyFTzPpKFpaATPK3eCNlKhnKdy3oWMzmaznWJiK1lIzGsMcFB4gTT66GY+h\n2R2xcYItllnaPpUdRRvl5sOieeJqGJ7bPSsLszfXhY33/j0pZ8bYH+9J+H273vsf8t4/4L1f8d7f\n473/bu/9MzP4zyi0W3tNveLc6uEFW1pwQNcFm4zGns1Q7JkdaynBAVJ/YTNvF83TZg+Um9Whcf3L\n0PP1NQoT0QZ3a6QwPODWGN/c0w5PHGUULS0RTQo7Njd26ozITiDcsclKRWur0Ry8YxO/b9KOjS1s\nKiWns2YkkVG00dgi1n5tbXjAnSUeRaOwKa7028OADu7avv/vv1X1ZltfvH4nNTig67671vSJSzck\nEfk8LHtn+nhKcIDUfxFAMloojCed0Sha9DWKL+6KbtTgACmPjk3KKNooHRt7hs2J8HHbt70/KIKG\nGbFDv8wDOqOOje2+VMsuccQ27/AAu694rFrWiRwOj11G3vvwzKKKDQ9gFE3qL2w2dyhsimKuOzY4\nOrVKSX/2nuxujRRFPlPYDMW287M6Ns65wh3SGX8DnKUijKI554IAgXlLRbs24hk2UrxjM/vwgPWo\nY7NWKwd3nDe2GUcbl32OxV3RuGNjuy+VhP0a6eD5Ym8GTPqattcMCxtbdLNjM7z96DXd7lOtEfcs\niY5NkVHYIFc28vkSOzZD2TZ3GbN2bCRpxcykx998jtpOvalv/tmP6A3v/C/6wJ9cmeljkbLvJh8l\nexE3b+EBs+jYpBU2aZ2cJLZYWV8LH7dzLnjf9dsECIwr6+ZBkIrWDFPRkvZrkj426Tk2e3Xb/S7p\nxMpkwRbLKj7s2HZs1ggPkCRdjDs2u7yuFAWFDXJlOzbP3CDyeRi7Q3ZspDAyddadkv/y+Sv6kxdu\n6fZeU7/04UszfSxSMc6xkcLI5yJ01UYRFDZDdmzsjs2denPk85W2UwqYUS6agh2bE/3jsneZ0TM6\nNuNLO9tEis6xabSDf/txqIZl09Imfb7YHZvjUceGAzqHZ7/OUkbc85Ke37K12wgOMpakLUbRCoPC\nBrl65bleYdNoeSKfhxDu2GQXNvYCIY5TPWr2hf2Frdl/nYsQ9yyFSU/zV9iMER5wvHeh4/1BcTOK\ntGSlUTo21zNG0STpLvO++IIEw8s8oDPq2Njuy9AdmxzDA+LChvCA4fWNotnChgM6+8bQJEbRioTC\nBrk6XivrnlO9O72X2LMZKNyxyR5FqxWoY2NHO4pwsViYUbSgYzNfHcsrt2zHZrjC5kQ0PjlqMlpa\nrPNoqWh2FC2hsLFn2WwzMjKurJsHWR2btB0bKTz7adIUQRsesBJ3bMbY/1pW9rW0VikFwQ+2YzNK\nwMcieTKhsCE8oDgobJC7C3f19myeYs9moJ1gx2bAKJq5aK63ZvtN5Za5UNiut2Z+9y5MRZvlKJrd\nsZmfjk2r7YPzYM6fGm4UrVIuBUlJo+4ypO7YDHnR5L0PRtHic2zi910vQBE+r7JuHtgdm/1mK+i+\nZMWv2/CAiQ/oNK8Bx6vlYMcmLVYc/fYyOnN2XHpZ/59evHq7733s2BQHhQ1yd5/Zs7l0nbNsBgkK\nm+qgjk3vm0p9xlHC8QXsrA8/zDpj4ygFo2hztGO2cWdf9uEO27GRJgsQsP/+7UXUsB2bW3vNoDMW\nhwdIdGzyknXz4Jh5zu31dWxmEB5QLQfdRMIDhheeYRO+lgYdmyVNRUseRVvO/xdFRGGD3IWHdNKx\nGcTesV4b0LEJRtFm3A2IR45mPY5WmFE0G19bkCjsYdjggBMrleACZpBJIp9tAWPPlxm2Y2O7NeWS\n0+nj1b7PseEBsy7A51nWzQNb6Ow3W0GxWZlFeECNHZtx2cOO+zo2HNCpJ64lFDY73DApCgob5O6V\n65xlM4qgYzNox8aOohVox0YqQmFTlFS0/HYGjtI4wQFdk3RsbGFv/95hOzZ2fO7sak2lhO6ADQ+w\nZ95gNMOOojVaPvjc6gzCA/rOsRkjsW9ZZb2W2ptvy9ix2a239OzN/rCcTcIDCoPCBrmzo2hP39iZ\nqz2DWQjCA0bo2Mw6cet2dODdtVmPogVjMrMcRbN7UPNzIWWDA+4esbCxkc+3Ru3YmLu+QcdmyFQ0\nO1qWFBwghR2ba3f25f38fF2KJHsULXzb7l9UMzo2wblPOYYHHKuWgk6i98t9oOQowgI2GkWr2VG0\n1tIVi09eu6Okl4+demvmNxtxgMIGuXvFuV54QLPtdYVl3Uw70Vx4Fhv3POsX0Vu7BevYZIxPHKV5\nDQ+4emv04ICuyXZskkfRhg2jsB2YpOAAKSx46s320i49T6qelYoWvXbZ0a8j27FppO/YSMu77D6q\nrAI2HlHdaSzXONqTZgztleZAconI56KgsEHujtfKOmcuJF7gLJtM20HHJnsUrVqgwibepZh5YVOQ\nUbQ8R2uO0mSjaON3bOyc/t0negXVsB0bG/V8LqVjc3a1JnttfT1hHO3FrT2975NP68Wtvb6P4aBI\nt4VH3yha9Pb20B2bHFPRosKmVikFj5MAgeFknVcUTxXMOg3zqNnggFffezro0FPYFAOFDabipad7\nFyjPc6GQyS5Jrw46oLMg4QHe+8Lt2GTdTT5KQfE5Tx0b8/V7yalRR9Hy6dicP7WS+P4stkix6WdW\nqeR0zqSlbURjk41WW9/1Sx/XP/z1x/Udv/AxtZZsvGYY8b/l+IK3Ui6pbIqU2+aCN/uAzumFB0iT\ndROXlR1Fi1PRauVSUIwuWxfs4pVeYfPg+ZNBWMkWkc+FQGGDqXjZmeOHv36ejk2mkcIDCnJA526j\n1Tc2MvsdG3s4H6Noo7KFzfmTRzOK1m774N//3SfsKNroqWhpHRtJusueZRP9W/39z1/Rk9cOgk6e\nvbmrF7Z4zYrZu/hS8h6b7drYUbSsjk11iFG0m9v14OucZrfRH1McRj5zR30YWefYOOeW+pBOm4j2\nwPkTQWHDIZ3FQGGDqXiZ6dgwipau1fbBXcZBHZuVgnRski5eZ92xKcwoWim/nYGjdO1WPqNoo1w8\n7kbz+XbHZrfRGqpzsmFG0dJ2bKSwmxOPov2/n3w6eJs7+/32m9kdGym8ux+GB2R0bErZNwI+9cym\n3vRPP6Cv+acf0KNP3cx8jHv1/k6D/be5bN2FcWWFB0jLe0hno9UOkl4fOH9CZ1Z7rzmMohUDhQ2m\n4qWmY/PcJqNoaeILu1F2bGZ5RkrSxev1O/szTcjZL8ooWkGKz1G02z7s2Iw6ina89+82Pt8oSzyf\nH/+98fMjSRAeMEbH5umNHX344vXgcyls+tmL3ZJLDgSwxY694LXFS8wWPY2EHZv3f+o51Vtt1Vtt\n/cannst8jLuN/iAW27HhLJvhDLpJtKyHdD61sX14s6pccrpw1yodmwKisMFU2FE0xjrSxd8UBu7Y\nFGR/I+mU5UbLz/SOVVEO6KzmuAx9VG7u1IPu0t2jjqKt2I7N8Bc62+YOe7nkdHY1LEx2hrgbHMQ9\np+zYxB+zxdD7Hnm673NHKc6WRXzjwLn+wsZ2bG4POYpm93KSOnQ3zdd30OtLEB5QO/g72bEZXVYq\nmiStBod0Ls//Uxsc8Mpzq1qplHXGFja8bhQChQ2mIhhFIzwglZ1PLrnBF+RF2bFJGzea5Z5NkOQz\nwx2bPJehj4r9uq1USkEYwDDGvXi0HZvVWlkrlXABfXtAMlq77YML3+wdGzuKdvDf22i19W//6Nm+\nz43PaMJwz69aSscm+4BO83xJOMfGXiwOuoiOD+iUpBP23+YSXYRPwkbnH0vofp8IDulcnh0bW9jc\nf/6EJOn0qkmEpLApBAobTIXt2NzYrgcHp6EniHquVRLvglpFSdxKu3id5Z5NYUbRgsJmPjo24Rk2\nKwP/HcbG3bGxhc2JlYN//7ZrOShK9tZeI+g0ZY2i2f2bbsfm9z9/pS9IQOo/o2ne3Niu6+c/9IQ+\n+KdXcvszh+mIBjs2e8Olog3qcNrxnlsDiuY47lkKD48lPGA4gzo29pDOZYp7vnjVJqJ1CptgFI1U\ntCKgsMFUnD+5EpwbwThasiARbWXwxXhROjZpZ5XMtrApyChacI7NfHRs7H7N3RnjXGlsx2a7PtzS\nvxQnAh78+49PNs9ix9AqJRdcxMbuTujYxKEBXfN+Afzjv/Mn+qn//AX9L+/9I128cjuXP9Ne7NZS\nC5vRd2wqA1IE7fjZoB2Z3YTwAHZsRrfXGBAesGILm+W5aWk7Ng90ChtG0YqHwgZTUSmX9BJzevnz\nBAgkGiXqWYpS0WY6ilasjo33fqgLr6NgL+LmZhRtgqhnSX0FxbAXkEmH09oCf3vAYrLdlTm7VlMp\n44T79Sg8IA4N6N6BleZ/F+Oxpw/Sw7zv/XpSg5Ky4vfbwibr+RiEByQUxLawyRoR9N4nhwewYxOo\nN9t69uZO5ucMDg8Y/jm6KNptryevJRQ2pKIVDoUNpiY4y2YOOjbee20dcarJTrRjMEi1IPsbabPE\ns9qxabS8vLkmmm0qWvaFWhFdvW2inkdMRJPCi0cpvaMXi3ds7M/S4DMybtio54wxNCncsbm119S/\nevjy4dv3372mb/iy8+bj832BYse34mjrcWWdRt91LGX3JilBrSsrPKDd9sF4T1bBXG+1ZX970gGd\ny7TonmSn3tTX//Qf6K0/+SH94h8+mfp5AwubJRxFe25zNzjf5/67+0fRjvr6AckobDA1Lz1tOzbj\nFzatttcnL90IloTz1mp7ffu//Jhe92O/l/mCnze7HL02RMemVpAo4Wl1bOxd4Ul+32xT0YoRyT2K\nSUfRyiUXjPwMX9j0vm7d3287lwM7NkEiWnZhEwcL/OonemNo3/nGVwRdp0G7HEXmvQ9GYvLqog5z\nTlTaDYVKRipaVofzTr0ZFCt39pvyPvlmwV50gGjiAZ1LchGe5g+/cE3Pdb4X/9s/eib188LDjgeN\noi3H/1M7hnbvmeOH/w9seACjaMVAYYOpuddGPk8wivZPfvtP9Fd/8WH9+Z/+g6ndcXv0qZt67OlN\ntb30Kx+9PJW/I8muuXAbasemXJRRtN4LuD1UcZKLqO/9//5YX/HD/1nv/uDFkX9v3+GBM0xFs121\neTmg89oEZ9h0jZOMZuPOuwWNPfxvUOhIeIZN9uM+Vi0Hj7E7tlSrlPTtr395kAQ3z+lGt/aaQecj\nry7qMKNoaR2bWlZ4QDk9PCC+A95o+b7netdedOYR4QH9Lm/0RtCyzlyx3bmkr+lqcEDncuzYJCWi\nSeGOzdZuI7XwxtGhsMHUBB2bCUbRfufxFyQdvGh88tLGxI8ryYvm1PXrd/aP7MVpO2F5Okt4+OPs\nXkDtHe377147/PW4hc3l69v6zU8/r0bL62c/+MTIY3bxxU4t4w7xtFWCAwfno2Mz6Y6NNF5hYy+K\nunP79oyMQR2bG0NGPXfdldCN+uavuEdn12pRstv83oWOO9vXp9GxSSlgjiXc3ZcGdGyCGwHh8yVp\nZyHta7MbnYnULZjsmOSyhwc8fWP78Ne39tIvwgclTJ5YwgM6g+CAu3uFjR1Fa7X90o87FgGFDabm\npXbHZsxRtFbbB3ccp3Wyr/3m32z7IxtZCHdshhhFK2DH5kvNi7zd1RjFhtmVqDfbemoje7k1Zs9d\nqJRc5oXUtFXnsLC5agp724EbxTiRzztR3PnBz8Pv2Nio5rsGjKKlfc7feNMrJUmnjo8+SldEN6PI\n2dw6NkPs2KS9P2vHxsY9x/HoSa/3af+2gjNsKqXDyPJFPqCz0Wrr8We3hk5fvHy997raaIVhC1aY\nipZ9QOeyjKJdvNpLF3zgfHJhI03vGgXDo7DB1ASjaFt7Y3VBNrb3g7GKaaWOxN/8p7nPY4U7NoM7\nNmEq2uxGAOwFwpfe1evY3NxpjFVwxRccT1wdLaJ2mPn/oxKMos3BOTbb+83g3+H5sQub0S8g7Y7N\n6hg7NmHHZvDjjsfV7r97TV994aykuDCb34u1+MIqv47NMKNoye+vDtuxiS7QN3f7X4fT7ogHiWjm\ntdR2F3YbrbmJYB/Ee6+//v98XN/67o/o7/7rR4f6PU/fCG8YpX0/Hdyxsaloiz+K5r0POjYPvqRX\n2FTKJZ00/8ZIRps9ChtMjR1F26m3xnrC24MDpem9aMTf/G8e0V0XG/d8fIiOTVEOf7Q7CPebjo0U\ndl+GFV9IXrxyJ+Uzk4VjMrNLRJPCC7VZBjwMy46hlZy0PkZ4gBTuMgy7oxJ2bDrn2KwM37EJdmyG\n6dicDD/nO9/4isM7+4uyixF3bG7tNfv2T8YxzM2DcQobm4oW76Qlvd6njZPZ/0b7OE6uhHfUF+Xc\nlee39vToUwdR3h/406tBkZ9kv9nqGwlPO4g2DA9I2rFZro7NtTv7wfj1A9H3vFPRng1mi8IGU3Nu\nrRZ8AxznLJsrt8Lfs2gdm6QLuyxFOaDTFiJ3n1wJ7liNs2fTV9hcHbGwGTA6cZRqGcvQRWT/7Z9b\nWwkuNEcRdGyGvNixd9/XJk1FG2LHxnZsuqEBXfbx7zXaM31+TSLppsxGDq9nw+zYpI6ijRkekDTW\nk5ZYt5dwho00fhR50cXBCl+8lv2a+ezNXcVDE8N1bPq/pieWbBTNdmvW12o6G73WnLHJaIyizRyF\nDabGORecZfPCGAECLx5RYXM9Lmx2jmgULWEUJ0sR4p7bba875oLz1LHqxMlo8R3ykQubAo2i2fja\neBm6iGxXdNwxNGncHZv+8IBgxyZjzKXd9sHzdJhO06vvPX3467e99mXBBcqpaFZ+Xrs2mwmvXXlE\nPtcHjCdJ6d3SakZhk/V8SezYpI2i1Xu/146ilUsuSvFajAvx+P/NF69tp3zmgacT9hbTOqthKtqA\nuOd6a+GTwNIS0bpsYUPHZvYobDBVLzsz2Vk2V6JRtLTW+aTib/yD2vp52W2M1rGxFwizuqN8e78Z\n3Pk7dbyiuyYubMKv6xev3ek7rC/LoJnwo2TvTs8yuW5Ykx7O2WU7HsOeA7OdEJ4x7GLy1m4j+Dcy\nTCraN/zZ8/rRb3uVvu8bHtAPf+urgo+t1cqyzap5Pcsm6aZMHns24Y7NqOEB6ZcaYdhG3LHp/28Z\nLjwgfA1YxACBuPP05ICOzVMb/YVP0kW4937g19p+r2q10yO4F0WwX5NQ2NgAgaS9sGn51w9f1lt+\n/AP66f/8hSP7O+cBhQ2m6qWnTTLa1uijaFejjs00zpdot30wqy8dXTvZdmyOjxoeMKOOTXxhcWKl\nEtzpH6ewie+i7jfbevbm8Mlo9SHGZI5KLWMZuoiuTXg4Z9epccIDTOevO95iD6rN6tjYXa5q2QV/\nf5pSyel/fvMFff83/XfBXWfpoMMcHOZYkI6N916PXL6hx5/dGurOeNIoWh7JaGEq2pTCA4bp2KTF\nPdvCJnottV/XO/vF+LpOKv5e+OSAjs1TNxI6Ngn/xhstHxyKmvS1jp87iz6OFkQ9JxY2vZsq8Yjg\ntLTbXj/xu3+q57f29O4PPaFL17O//suEwgZT9bLTk3Zspj+KtrXb6FtavXFEo2hJcbdZauXeN5l6\nsz2TEQB70bpWK6tSLoWjaGNcRCV9gx0lQGCYu8lHpRIEPBS/sLmaw+Gc0pijaHYUs9Y9x8YmLqVf\nMNmbEefWaochAJOw42hFubP/m595QX/lFx7Wt777I3r4ycHneCV1OfLp2Axxjk3Kcy9rFC0IDxhi\nxyZtlGzPBrFEj29REu+svlG069mvl0mjaEnfT/ejtM2k19PjUQGbdQNiEVwcUNjMYhTt9l6YZjnM\na8OyoLApMOfcGefcBefcBUnV9hzM68eCHZuxwgOmn4qWdCGedHEwDeGOzTAHdIYXCLNIRrN3CrsX\nDJPv2PRfbIyyZ1OkUbSsZegiuppXx8aeAzPk89QWLmtJHZuMBKtRo56HcXKMZLdp+9CfXj389e//\nydWMzzxwc3tKHZthRtHGOKAzeL4MkYo2aniAtKijaOF/x9MbO5k3UhI7Ngmj3fFYWVIRWyq5YBxt\nUfaWkmztNoLvackdm6MPD4hvBj78RQqbLgqbYntI0qXOjwc3NubvH25wSOcY4QHxgY/TKGyS7mYe\n3Y6NvWM92gGd0mw6AvbCoHvBYC+I8wgPkMID0QYpUipaEMk9BzcjrgUdm2MZn5lt1LvizVZbe2a8\n6bBjUxuuY3P9/2fvveMty6py0W/udHKOlVNX7G46003uAC0giKBeEBTBK/cqooJyVZ5PBS+m6wMx\noRJ8qM/7ruFy9XExkASa0KQGOldVV3VVdeUT6uRzdlzvj73X3mPMPedac4Wdzpnf71e/OufscPZZ\ne+255hhfGAET0UwQRk7XaNAmi0mUupKxiaWwMZCihWBsWHiAPMcmAGOjm2MDyFK09nhfo0IuvAsl\nB88oihegLFuSZ9gAOsZGKmw07/VWGdJJZWj9XSlMK9bI4RZ4bOT37sHTc5s+xMEUtrBpb3wAwL7K\nv5NjY2MtfjnBsYOEB1xe3AhkCM8XS5iVvC/r+WLspnlVN1PV9WwE6AUhaNwz0JoAAdopcqU7UaVo\nqk3kU2EZmxZ7bOiU9Xyh/S80MzQ8IFIqWrCiYE2arVL12HRxj43uYj0fcIaNCRhj0yYemwWygTEp\nUJQemzikaHn/5EF9Klq48ADVxtskPEDejPNzsz3e16hQMYq6ZLQryxvKa4WysCHHMZ0U2vj3fikZ\nbTPCcRx8/KHz1e8PTPYrJa9citacIk9+/2eWszhtfTYAbGHT1nAcZ8FxnDOO45wBkE94JMu0K2h4\nQKHkBOoc6i7GcbM2qt/TjLhnOU3GJO5Z3iC0IkBAydjEHB4AlAsb0w5UW0nRUnozdLuhUCyxGScT\nMcU9r+eLvmyiLDNzGUvTxKV5wl6YJKKZgMnp2oSxoWZkOeRExka+yDb4LuQGURj4DW0EgG7Nz1Me\ns5F0cc+6v8VkQGc9Y1M7N3WP7zSoCm+dz+aswl+jew7TtZQxq5uUsfnol57G33ztXPX75x1QN5fZ\ngM4mydhV+6AHrRwNgC1sLBqMvq4Uk3dcCBAgIAcHuIi9sFExNmu5htO6a5LMplfT7aSQpWitYGxo\nx1PlsVnLFQNf6FQd/rVc0ThJr53CA9IJGh7Q3ozN3GqORXdPDkSRovHC3G8DSWVm6aSospFyga8z\nJgcdzmmCwTb02ARhbHT6/lgYGyMpWphUNLUnTXf89XNstpjHRsEMnLqq7tirggPKz1F/jDcMZb19\nm1yK9i+PXMJv/vMT1e9v3DGEt917nfK+wyQVbaFJ64aqKH3w9HxTfne7wxY2Fg1H2AABOTjARdyF\nzexyfYclX3QaTq/LGzaT8IBEQvBZNi1gbGgn2y1ax/q62AyQIBspx3FYsUSZ/pNXzHw2VCYjy/Wa\njRST1rQ3Y0OHcw50pYwix3Xoz6TYe+e3gWQyTLJJkjeluk1TY8ID2msDXCo5zDMzv5rzlPPqmOaV\nbIFt/MOAFja6z5iOyfEsbBhjU/vbdBtE3ftC/Vqehc0m2YSrroNaxma+VvBsI0mlqsLGdNhx3yZm\nbL519hre/rffqTZ9dgz34KNvul3rg6VStLVc/HJ5FXSMjfXZ2MLGogmghU2QyGc5OMBF3J1UnSfk\nWoMDBOjFIJUQdWyMDnST0C6MTTIh2OYyiM8mWygxZuPQ5ED1a1OfjenFuBlIszk27X2RoZ+xKDI0\noFx0U929n0eFJgLSJLRkQrCNqZaxkeKe48BgiMjqRmIlV2AzRUqOt0yW3ib7paIGCJgEdOgYm5RX\neABjbGqfY10Dy2hAZ2YLeGxUUjSNx4ZK0W7cMVT92i88QOeZAiTGZhN5bM7MruItf/XN6nEY7E7h\nL3/8Dk82m6aiAc2JfFb9DuuzKcMWNhYNx3YSIBAkGa1ZUjTdjIdG+2zohq03kzSew0G7pa1gBJYU\nHhsgvM9GvkDfume4+rXpLBsuRWufuOdcsTWzhkzBhnNGLGwAScrls4GkUsxeaSPaZzDLhknRYgsP\nMC/MmgHVsD8vnw2Vok0MdGGEdJKjRj6bNA+0Azo9/KEsPIAyNuRvoWveSrag/EyxwkZ6fcxjs0nY\nBVWDb241pzxnaCIaLWxWc8W6JDrThMn+TShFm1/N4c0f+0aVDU4nBT70xttxHWm2qdCbSbLzuBmF\njUqKCFifDWALpSd5nAAAIABJREFUG4smgAYIBJGiXV5sUniA5oLf6MhnXtj4Bwe4yLSYsaEXVGqa\nDFvYUC9GMiFw4w5S2BhGPrcrYwMgUBJgs3E1pqhnF0GkXCsaKRrAPw+qWTalksMaD7F5bNpsQKfK\nM+PFvNBjMtKbiRzqQWHisdF99uT5WxS6uGcqwds5UruGlBw1i+cZHkDOy80QHpAvlrQsySmFHI0x\nNjuH2G1ySIYpY8M+o5uAsdnIF/Gf/uqbeJowHr/3gzfhrv3+abRCCAwRn81iEyKf6T6IJtfZQZ22\nsLFoAnaEnGWjk6LFWdgUSw4rYIIM2lrLFfAjH/ka7nvf5/Hw+YXAv5t2ok38NS5aL0Wr99gA4WfZ\nyClrh6ZqA9BMk9FYFG2r454l2Y08dLCdwKRoEYZzughS2NDNUJ90/vvNslnayLOCcawBHpu2YGwU\na51nYUPWsuHeNMbJexpZimaQipbQSGpTHoyNLjyA/u07R3rZY1Ssy1YKD5D/hv3jfdWvZTna4lqe\nHctj2weZH1I+x0ybRP3kM7sZWLD3feo4vnn2WvX7d95/CN9/yw7jxw+RRMVmDOmk69Od+0arXz94\ner6tVQLNgC1sLBoOala8GCg8oHbfqcHaBTrOwubaGjfj0k21H2Pzie9exJeemsWpmVX8+RdOB/7d\naxqPgR/oxaY1cc/UYxNdikYv0v1dKTbZeWmjYPRcbSVFkzZxrXiPTMGHc8ZR2Jh7VKh8RWYs+Syb\n+k0TlaGlEoLFNEdB0CGjjYZq2J9XdDOdYRMnY1MsOcwH57XhVd1mPKCzpC5sJge6WMGkOrfYgE65\nsOnaXOEBlDVPCM7CnJ7hjA0NDujNJDHR38WYSVnSZpqK1uvzGe00/Otjl6tfv/b2Xfjpe9QJaDoM\n91LGprkem5ccm6p+PbuSxSmN12qrwBY2Fg0HDQ+YXcmyTagXaCraoamaxjXORYN2MTOpBHaN1jqD\nqgneFJTepxcPU9CLQZA0Kqo3b82ATsrYaKRoAbrDchjBcG+GdZpPGgQItJUUTfr97RwgwKRoMXhs\nGOPhM6iOhwd4MDYKKRptOoz0ZYz9aX4YlDr7re58qjq/c8ZStPgYG3md8WoeqORLKdO45xKVotX+\n9uGeNJOTqYpOmopWHx5QW6dyhZLxNahdsSjJgQ9M1JpBp+TChlyndo/2QgjB1m1vxsYsPGBF8Rnt\nJJRKDi6T0QI/8YJ9gdeU4QBqjzhAC9I9Y704Ml3bI211n40tbCwajqnBbhYDe9lgNslGvsgW3IOT\nDSpsSNTzRH8XRknXZd6nsKGF11VNNLUXmBQnQGHDpGgtZ2yie2xUAz8PEtbGJPKZ68JbLEWThhFG\nDXh48vISXv3BL+Otf/Ot2Ddk9LyNOzzAj7Ghhb3ssenL+DA2ZJMel78G4OdzseS03DsQVIrGioEY\nGRv5vPP6jKmGdHoxNvLcJ7eYpHHPQz1pXznZhgdj0x9wxlK7g8qQBrt5YSNL0WhwwJ6xcuOOSq5l\nyaWJ5BDg16y1DmfB5lZzjJHcRpqxpmAy9qYwNrVjPtSTZl4gW9hYWDQYmVSC6fdN5GhyoUDlSXEW\nNjMrtdcyPtCFEbJJurbq/XuoP2F2JRvYJM49NgHCA1qYipYrlFhnlEqAQntssvWenYNEEmjG2LSR\nFE3qTkd9jz7476fw7XML+OdHLuN/PXQh0nNROI7DmLUowzldBPEyrHoUNtRzpmJsGpGIBtQPGW21\nHE3FGnulojHGpk9mbMIbmrN1jE1AKZqhxwaohW3QdX64N81SuGRPh+M4PBVNKmz6MknWXOt0Twhl\nQwd7Utg/UfPYnJ1bY9eis3O1QmfPWF/1MS7qGBvKfBkzNp19PC8R7+9AV4qda6YY6m3ecF/HcXiI\nT7dc2Gxtn40tbCyagqCzbK6QomG8P8M2L3EuGnWMDS1sfBmb2mssOcFT1NY8pDheaGUqmtyF1zE2\nsytZlAwLPRUDxBgbk8Im3z5StGRCMHNuVCka7bg+fGEx0nNRLK0X2PkTjxSNMDZZH8Ymy+POKfwY\nm3k2wyae4ACgvCGmjYNWBwgETUVrGGOT5+uM18wtuahIiHKogA66sI1Fsv4O9WZY0SkzLvmiwzbz\nMmMjBJ+x1OqCNSoWJTZr33hftXDLFUs4f43IpCUpmvsYF7Jk1JT9pp9RXSR7p+ASUZFMD4Vr8AyT\nVDQ/GXtUZAslptYoMzaj1XNgq/tsbGFj0RTQWTaXDJLReHBAN1uI42VsqBQnw+Y++BUqV6WNgi7F\nTYewcc/pFnps6IYgIXhBRjdRhZJjTMfL4QEA2NyAU4E9Nq1lbADuKYjK2NDz8Phls/hrE9DzNZ0U\nbHp2WISNe5Y7pIyxUcjBGGMToxQNaK8hnarPkHd4AI97HicNoUgemyI3lHv5D+TGgpe/Bqhnc9zP\nC/3bh3vSbBaNXHBStgaoL2wAKUCgwwsbWYrWnU5iOxmrQOVoKimat8fGLDyAJhmqItk7CVQeH0aG\nBkipaA1mbOT3bLCn7E09Mj1Y/dlXt7AczRY2Fk0BnWVzwUCKRv0rjSxs6HDOif4ujPTSrov+92zk\ni3W3y4WOH7wGFHqBMTZNNqYvSewK3eAMdqdYt9u0Q6xKWaNStLnVnKdhGjDXhTcLmST3DUQBjfA9\ncXk5NonBjHTux2HA53HJ5nHPdalobI6NgrFZpYxN3IWNeQBCo6Eatji7klWeA8WSw9bGkd40azas\n5YqhByluBGBEZcbGi90BgKRGikbXV9ljI0ufslJh052p/51BEvvaHbIMCQCTo7kBAhv5Ii6TJuGe\n0fJ9vK6n/L3eGlI0OoZiW8h5Xs1MRaPvfyaVqH7m7tpPY59tYWNh0VBQKZoJY3NVinqmC/Farhib\nt4QyNrLHZn4tp91EqjbtMwEDBFbZHI+Qcc8tZGxkP4IQIpTPhj9n+X0e6+Ps2VM+rE2ujVLRAG6W\n3ohg+M8WisyDtJwt4KJB+IYJaCEeR3AAILEdPhd37rHxmmOjYmxqrz3uwqadZtmo4p6zBfVwxqX1\nPOhyNdybwWhvhnlLwrI2pkMbgfrCRpaayahnbByUSg479sO93uEBlLFJCHUx1e9RGHUamBStsk7y\nZLQyY3P+2lr1nEglRFU5MWgaHuDJ2NSOZ7ZQYsNVOw2XY5CiUY+NqiERJ2Qpogvqs/na6bkt67Np\n/Q7AYktgO1ksLhkxNrX7TA5wxgaIryMid60pY5MrlOokDqrX5yKoFG2dxj37bBYo6Ka5lR4b2gF1\nwSOfzY4H3WS4mxchBAuM8PPZtJsUbWow2Pmug4o1PH55KfTzUbDhnDEEBwDclOzH2FD2QJ7j5DvH\nhsixxmMMDwD4pq/VkiUdazyraBpQGVoyITDYnUIqmWBSvbA+G9PNrup2r+GcgMpjU6pEbdd+NtyT\n4eEBHoVNdzqpZB8305DOJcWQ5AMTdEhneb2k/podIz1VWaDXHBtTj40sH/X7vDcbpZJjvLGnHptt\nYQubJqaicSli7X24cx/12eTqor+3CmxhY9EUBA4PkKRovZkki9GNq7CZlRgb2Weg89moZGdBpWhs\njkdXAClaC1PRWBpPdz3LFMasvKRhgajPxo+xaac5NgDYPCSqcQ8KVQLW8cvxXKziHs4JBB3QSaRo\nXoyNzxybOMMDgPZhbBxH71OjjJWLa9LcF3dzH8csmyCfr3opmjdjI8ejF4oOY6qEKL8n9NySGZf1\nnD7q2YVXqlqngUnRelwpGol8ni0zNmcUwQGAHB4QLhVtsDvFmDE/uXAz8eiFRTzvdz+H7/nAF42u\nQ1RFEtZjMyzJ+xrJlugYm3qfzXzDXkM7o/U7AIstgW0kPGA5W/DdMFyRpGhCiNh9NsWSwzZIE/1d\nSCcTbGOj65gqGZuAUjTusQkX99zsOTayx0ZGmMJmeaNeLw7wZDSvwqZQLLFEpHbw2OyOqbBRFdbx\nMTacrYwD9LOTLZQ8GUU2x8bLYyMxNo7jMHYifo9NezA2G3l+/EYZ81J/XtAkJtqgiSMZLWvouyjf\nHiw8QAjBipt8scTW3cHuNBIJwaRkXuEBcmHlYqBN3tc4oNrYUo/NzHIWyxt5nGNRz7U1iTalPMMD\nPNZSIQRjS6Ok7sWNP/3CKVxa3MCJKyv4u28+43nfUsnBlcXaaw/L2FCPTbHkNLR4plK3QUnNYn02\ntrCxaBLG+7qYhMpPniOnogHehscwmFvNgiYSj1c2AFSOpmNsriiKmKBSNO6xCRIeULtvKz02SsYm\noseGbl74LBt9Glj9jI3WS9F2jdS6fs9EKWwUsaFPxpSMRgvxRjA2gDdr48VYes2xWdoosECGuFPR\nGGPThEF7Osj+Gio18mNs6BpGGZuZkLNsgoRzBPXYAGXpnAs5BMEt0gY8GBc2nFMTxMKlaB0eHqBo\nBk1XlA0uTs+s4ixNRButnT98QKdH3LPPWjrOpMftU9g8RmLx5YGlMubXcqxBGNZjI18PvcKHooK+\nZ7JM3/psbGFj0SQkEoIlo130CBBYyRbYpt/ddHnpgsOAzrDpSSer0cUjBrNsrio9NsEWdi6fCBL3\nTDw2LWRs5E4RwLvDpsdDlYoGAAeJFO3KUlZbzAYZHtgs7B6LibFRbBZOz6zGIkGMezgnUI7/psoi\nXWc8J81hqBvQ6cHY0GZDMiHqLuxREYSx+e4zC/jIA6cDNzVMQDdGvZkkWz9nfRkbWtjE4bExl6LJ\nhY/XcM7qfaQUQTnqGfD2yNAkr3aQoj11dQU/+tGv4Zf+4eGGGMnlAZ1AmUHZN058NrMrOEelaJSx\n8ZBN0YQ5v/c6rgGwcWIlW2AFnV9jiTZZ+7tS7PMfBKlkghXfjUxGW1Sk4rmwPhtb2Fg0EZTi9fLZ\nULYmmRAY66svbOJYNHgiWqaqSR8lMo5rAT02QbojXqlQXuhq6YBOfSoaEFz2ki+W2KaEdvynBrvY\nhUInR8tKqWNtUdgQKdqFhfXQiUHzik1RrljCmdnow9docR5XKpo8CFEnOV2XUr3q4571qWhUyz/S\nm/Ec/hgGpp39uZUsXvehB/HeTz6BX/i778b6GgBp2GZPmg0pVjM2dIaNfnBuGATp4su300aMDpTV\nKZRKdcM5AXiHB+SoFE39+W9meMAHPnMCD5ycxd9+8xm8/iMPBh7e7AV56jwt7KnP5uSVFTxzrX6G\njfyYYslh0etBiti45iTFieOXl1jwhF9jifprwrI1LlgyWgMLG937Dyh8Nqe2nhyt9TsAiy2DHTTy\n2UOKRgubif6uqkyBSdFi6ILRZCHaeWJStAAem1yhFGjuBZ+8HtJj08JUNFVnayKgNEHeoNDNhxAC\nB5jPRi3Bovr/hPDX9DcDO0dqm4hiyWGpO0Ewr9jAAtHlaBv5IpMzTMZU2ABmqWIrEgvTl5GlaLXz\nIFcoMYaqkcM5AV5ceyU9PXJhsert+PJTs0wOFQcW1/nm3i8EgEnR+jRStNAem9rflvENDwiWiibf\nJ190lB4Sz/AAA4+NV2EUN05cqX0+H7u4hNd/+MHYNv7y1Hm6DlO54pdPzTHJJm22yGs3Pd5Bor3Z\nOdkmHpvHL3IP4uWlDc/PJp3zE9Zf44IlozVQiqYLD3Bx257h6td+wTubEa3fAVhsGdAAAS8p2lWW\niFZbOOlk3zi6IfRCQ70hdFOwoJGiqQobwNxnky/yi1OQAZ3pGKfaBwUt3JSMDTmOC2v5OjZFhrzx\n7ZcKPBogcPKKjrFpr6hnoLy5ouduWJ+NrtNLN05hIG9wx2MKDwDMktHo0M2uVKKuGJULHdpRbuRw\nTkAuzPTrDF0DSg5wPCbvkwuZseHd8XDhAU1JRZMZGwOPDb1PQQoPUEnRVrIFFhjCPDYG4QGNTLtz\nHAcXrvHr25OXl/G6Dz2olDAHhSzDHtQwNg+fX6h+PTHQxZpnmVSCHSdW2NAi0ee9DtrIagYev1T/\nOTx/Tb/+XlyIr7AZbhZjwyTh9ddh2lgL21TrZNjCpo0hhBgWQuwVQuwFkC6VOncAFmAe+cxm2JB5\nIHGHB9DNHTVBUhmHamMpd7tp5KWpr2RNktfIqVBeaCljkzVPRQPUccUUdIHu70rVyYpogMBTGq1w\nEGNzMxFHMho9/yjjGZWxoefpSG/atwsfBDwuWd0Z9xtOKzOY1GfDCpuYZ9gAcniAvrMvB4g8fime\ntDoXC5KB3pexWa3df1QXHhBQLusikBRN9tgYMKhciubU/e1A/dwUKuVlfkWD8ICGJlat55UDVJ+6\nuoLXfuhBowHVfs/vgk6dB4D9xGND3+Y9ZC1yoYt8Ds3YtE1hU/85pPN8ZFxmUrRwUc8uhntIU1Qx\nXDcuLK7rwwMAXqDZwsai3fB2AE9X/h2cm+tsreR2smh4fdjohmG6gYWNGWNT/3sooyQEcGi6tvk2\nZWxkQ7TuYqxCK+OeWSqaolPUnU6yDYSf9MXPs0MDBMwYm/ZZ0uKYZUM38TTtJio7wGbYxBQc4GLQ\nwMtAGRsVW5lJJVgXnyaj0WK5EVK0QcNZPDJr+9jFRc09w4GxFr1pjJE1StUwuKYJD6DNhmyhFGpT\nHygVLSWnogWTohVKDvvb3XW/X1of6Lm1UTBhbJpT2JwnbE1POomfu+9g9funZ1fx2j9/0JNB8IMq\nEc0FjXymoMEB1cdqFBDBPDZUitb68IBiyVHG4Xutv3EM53QxGLNcXoclj/AAACxoxBY2Fu2GDwDY\nV/l3cmxszOfu7Q0qRbu0sIFSSd05vLLMZ9i4iJ2xWdExNt5xz/T1jfVl2CJiOsuGbtQyyUSgjnm6\nheEBdEFVMTZAsAABusFQFTbXESnahYV1Nq3eRZAZG81EPIxN7Xg/90Dt839ufq2uOA6CmWXKisY9\n4NK/MKDvu9yJd6FLRqPG+UZI0eh5uJoraoMf5MJG1vZHBfPY9GSYFG1xPV/32deFB4z0ZlhSXRif\nDf+MBU1FMwgPSHApmsocnU4mmH+H+mTWc2SopIHHZnmj0LAY3AtEjbBjpAfveMkh/JfvOVz92bn5\nNbz2zx8MLU9VJaK56M2klJtzGvXsQhf5vBEgFW1igAdaRDmmjuPg8YtLkda1p2dXWRiNC6/1N06P\nTdOkaIoBrRT075hdyTZ9n9Bq2MKmjeE4zoLjOGccxzkDIJ8wMGG2M6gULVcsMRMwxdUmSdFoh2lC\nEx6g8tgwqdxANzNem0rRTKQTOnS1iLFxHMeXYQGkWTY+8oRln4GfO4Z7WAdWFV3JusltxNjQwibM\nJqZU4oMob9o1zDZ2JzQMlgkaMZzThYmUi0oxdf4yloyWVXtsxmJ+7UD9RkHX3ZelaE9eXma+j6iQ\nGRvZB0WPQ3loqTo8IJkQ7DiFieUNIkWrZ2xMUtHkuGc1+8QDBGp/r1F4ADkviyVHuQGOA9Rf48pH\nf/qe6/ArLz9au8/COv7bvx0P9fx+xnEVa7NHxdh0119PC8USCmzYsbkUTQ59CIrf/dfjePkfPoC7\nf+/zoYubJzRy0HMaKZrjOBJjE1WK1vjwgGLJwXLWW4o2NcgLNJ0neLOifXYBFpseg91p1jXTaY2v\nsPCA2gc07jk2dNNNO08jfcRjoyxseLgBlfKYdkNZ1HPAwibTIsZmPV9kFz1d3j8tRqNK0RIJgQOT\ntQu1urChmvD2WdKiMjZLG3m2UZ7o78KhqZo070QEORp9XyZiZmxMpFw86lzD2HRpGJsGS9FkBkkn\np5M3C2u5Is7ORY/hdiEb6LvTSfbaqJR2PV9kawHtHAPRk9GCNA/kwsLEY5OW4p7los7FQJfav2US\nHiCvV40a0kkZG9rMe8sL9zPm5rvPLCAMvKRoAHCABAi4UEnRVB4buVHm914P9aTZexfFZ/NP37kA\noNx0CRtRTP019LOiW3/nV3PscxM57pkWNg3y2MjnrYqxyaQS7DO/1eRo7bMLsNgS2D7sPcvGcRy2\nYWiUFC1fLLFu+ER/7XVR4+1GvlQ3c+Mqk8p1MylPGI9Nr2Zjp0OrUtHkDZ4RY+Nb2PhL26hpXu6S\nA/Kmqz2laNfW8oGTmORBlAPdKRwmhU2UAIFmMTa6ooBKCnXBGbTgb2YqWjLBZ/Go1ppCsaTcxD0W\noxxNZaDXzQ25JnWHqYkZiJ6MFqR5ECo8gEnReHgAXfcp68KlaJQBV/++rlSC/Z7lBvlsKGOzc4Qz\nAHcfnqh+HXa2jZ8MiQYIuFCFB6jmwmUlFkvHfrkQQrANdNAh1S4cx2HnpWqdNwGVg95zZLL69bn5\nNaX0nW74ezNJ5g8MAy5Fa8z5RdcjIXixT8EDBKIFVnQabGFj0VRQqleVVLK4nmcX0akBtRRtNVeM\ntKmfX82x1JhxwthQ6QPAtesA99FMDoaToq1m/aU4OrQqFY0WIZlkQnvRC+KxoRtfndeCsnYqSj2I\n/r+ZmBjoYq8nqByNbnzcQZSHp2uFzfEr4TfRV5fVcs84wDw2WQ1jQ89/zXBalcfGcRwuRWtAYQP4\nByDMruSgUp3FmYzGhlRWChWdpIwOEu7vStV59qIOUgziY6uTohl5bIh3Jlvg7BNZ93VF87oBYyOE\n4AECDZplwzw2w7ywoUXASrYQavYRl6LVr5n7JcamvyulbACoFBDZQjDGBpCT0cIVa4vreTZzJ6x0\nikrRXnr9dPXrbKGklEXLwQHukO6wGCINhUXNqIioYCMXFEmiLrZyMlr77AIstgSObqtNxP3iyZm6\n22mnJpNMsA6IrCWNIkejG+6+TLIu459usuXOGvfYSFI0w07TuoHHQIdWFTaLHqZViiCzDaicRNct\n8ysc2zUVTQgRyWfDmYnyuc8Km8vhPTY8Fa35jM1azoCx6ar32KxkC0wu0wjGBvCfeaLbeMUZIODH\n2MyRz5ZOuuUiSLNBhSBSNJmxMUpF85Az0Q04G7JJiuYNA48NwBkf3bkZFRel8ACKEalppvOZeoGF\nByhYbtljs3u0V7lh5+EBbmFTO44JYVaUsqI5JGMjv+emygf5Oej14Y59I2x9UMnRaNRzVH8NIEvR\nGiN1ZIWt4rPughY2l21hY2HRONx3tEYPf+30fN2mgc+w6WILcn9XCkmy0EaRo+kS0VxQn41sAuRS\nOS5FW84W6qRrKqwabOx0YKloxcYk+6hgIhsD4k1FA+BbOLarFA2I5rNRSa5oYTO7kmWbW1MUSw7r\nrMqzh6LCpLBZCcnYUH9NQtSzq3GBFu6qv0Fb2MTE2GQLRSa/c4uVMc3cEJ6IVn9MJiLOG4kyoDMT\nMDyAvsc96SQrVFh4gMZj41XYDHSpwwfiwnquyIoVmbHJpBKsgRPm8+sXHrB9qIeFjKiCAwDeSHKf\nc0Ni5kwYjDhm2cwsy83D4M9D2Zrx/nLDkUbuqxQilMmI6q8BeFNhLVdsSONx0Sfq2cU0i3y2UjQL\ni4bh1t0j1SjSQsnBF09w1kYuGiiEEMrFOAxmfTwGLPJZlqKRx04NdmGsLwO6/pt0m9biSkUrBJcy\nhMWyAbsC1HtsvCJATYqlCR8PU65NwwOAaLNs5pjkqnwMJvq7WBcyzDyb+dUcCyWIn7HxD/mgjE2/\nCWNT+bzMSfK8pEFHOQz8IquvkDXgCCk2Z5azobrNMuS1zd3Ajmtm2SywGTbxMza5AEMbu0MwNjQS\nmm6O5Y17vyY8wESKBnDGRjc8NgqoDC2VEHXXMED/HpqCT52vf68TCYG9YzXWRhUcAMjhAeVjEWbY\n8XhE/5bqcWGkaJQtPbqt/Jn0ayzRwmZ7zIUN0JjIZ/r+qwpbF1aKZmHRJCQTgpn6PvvEVXa7XDTI\niCtAgHas5RhVQB/5vJYrsA3+1GA3UslEdeMJmPlsTKQ4OrRqQOdSCMZmPV9UTuF2YRIfLUvR5EKp\nXaVogHxhDdY1o74Jl0EUQuDQVE1Df/xK8MKGbmx7pKStODDkw3YAssfGYI5NhdlrdHCAC7/I6itk\no3Bs+yA7R+OQo9HhfplkorpZp7KfGU14gIqxieqDCPIZk1lTs7hndWEjbxR1QzZZYePRKKJG60Z4\nbGhhMz3UrSy8x6icMIwUzScVDQBu3ztS/fqWXSPK+yjDA0KspUHCYnSoL2yiMTbHtpcl7zQ04Zwi\nsZAyGdMxSNF60kmWEteIwsaUsbGFjYVFE/Hio1PVr//9+FU2AE+eESMjrsKGxd2qpGjkgko3UzQ4\nQIiaeZltvg0WZRPztA5paeZDs2BShADlDSe9nl/16L7RbrhJeMBarlg3VyTIjI1mIz6PTe38OjJd\n86mFYWwoozAx0BXZMCuDFr25YklpkF4ziDtnc2wqxfF8g4dzuvCLrJaZZXcjBcQjR1uQdPTue6Tr\n9uuGc7qQGZuggxSjeGwyRh6b2n1o4SV3pLnMkcyxMRjQWf/4BhQ2ihk2MmgTrBFSNAB4+4sP4U3P\n3YtffOlh3H9sSnkfpccmb3YcKThjE84wLxc2c6vZwOFA9HN3rOLl9WNsLkvhAVEhhOABAg2IfFYN\nr1WBeoa22pBOW9hYNB0vODhe7WosrOXx0Llanj/dMKg0r3HNsqELqZKx6aOMTe330Nc33t9VvSAH\njXyOi7EplpxYhwJ6YdmgUwjUDwT0YrB4saR+ztHeDDOxys+XDTApu9mgMpDz19YCvVdUAjlKNqvU\nZxMm8vlqA4MDgPqiV7WBZHHPAebYMHlef5MYGx8p2vRgd3UjBcTD2MgzbFzQFLhZbXiAN2OTK5YC\ny7CCpKLJn8GUwWDplEaKJjM2/cwjU/sbsiGkaI3w2FxYqG2e5eAAF6NRGRuDEJfx/i68+/uux1vv\nvk6bmkWvpWuVlNEww46jJu4BfFg2ADhOsOfayBdxaqbGyFQLmzE9Y143nHM4nnRIes42YkinaXjA\n1FDtM+844QIZOhXttQuw2BIY6E7jrv1j1e8/+8SV6tfy8EsZvMsUvuNGGRsa9eyCeWwoY6ORygWN\nfF6N4LGRO6DN6sSwmEmfvP+pQbPjsWLAAiUS0qwEiRFr1wGdALBrpHZhzRcdXA6gHWeMDfn76ZDO\nk1eWlfOsixnAAAAgAElEQVQZvMAS0WIezgmUN5ZUgqNiPOj536dhLBljU2E4+XDO+F+7C7rpUxVm\nV6VZW9dvH6p+H09ho/bM0O74/Gqu+t77MTbDPWlWPASVDAX5jAkh2KbYSIpGih96vL0YmxWNx0b2\n+PDHqwujuGDC2IxrilMTlEqOcYPJD/KxlUctmLLfExKLGJQNBNTHIUiS18krK9WmUSaVwL7KLB/K\n2MyuZFlD5doa/3u3DUaXogHxzttTwSRJFCi/f7To3EpytPbaBVhsGdxHfDafIYUN2zA0UIpGF1Jl\neAC5+NBNwxXN66OyORMpGk1O00lxdKgrbJrkszFNRQPk46FeUEslBys5ukjrn9OLEWtnKVpPJslk\nQOcUyTw68E187XykjM1qrsh0/SaY8QnOiAp5XoiqMFgjG4xeDWOpSkVrhcdG9fovs/RGLkV7em6V\nbaDCgMuNan/nOCnmCiWnej/msVEcl0RCMIYr6IY6aCefypjSBoWN7j4y+6SKa84XSyiQ4t6rUaQL\nH4gLXjNsXIxFCA9YyRXY/CQvKZIf+jK8AbG0nmeyUXPGRmIDQwymVI0FCOKzefzSYvXrI9MDVSXF\n1GA3u14+c622/lJ/TU866TnCIAgow9pwxsbn/Z/eoj4bW9hYtAT3EZ/NqZlVPD27ilLJ4TIZRaIM\nK2wiLBq+cc+k60kLm6uabndQKRrd+OjM0zrIw/eaxdiwVDSfi4AJY7OSK7AhqV4skJeHKYx8opkI\n67PRRfj2d6XYRPOgcrRGDud0MUpe70VF4UW75TpvlWqOTbOkaIMec2w28kW2YZka7Mae0d7qPCrH\nCScRpNDNpRnsSbEiYK7iOeIMj/q4RElGC9o8oJ/DdMA5NhR1jA05V5Yr59C65OHykqINNnhA58WF\n2mdLJ0Xj4QHB3gdZfu3HnHtBlTIahv0ektnAEHI01fybINKpJy7VPm9HiQcxmRDYOaoeCn5pgcvQ\n4vIaUnlYI2bZsPAAv8KGsFCXt1Dkc/vtAiy2BHaN9uIwkdR89okrmF/Lsc5bo1LRcoUS2zioutZ0\nY3ZtVe2xmWSMTbBNw1pMAzqB5jE2pqloADBhwNjInfB+r8KGbMDrGBui/5ePTTsgzCybjTyfYyJv\n4uln50TAZDS/4Iw4cGRb7fU9enGR3eY4jtH5r2ZsmhMe4MXYyJ/vyYEuJBKCDR+OGiCwQEzHtAMs\nhGASPHf+B0vQ0+juw84bcRyHxz0HZGyMBnRqfDj1qWj1oQ4bUuqi6YDOsH4QHQrFEmPyTMID5gMy\nNpQN6cskjY6tF2RpN/UqyfOIdJClwkGLZsdxlKEDQSKfqfyTsqeAvrF0aSne4AAXQzH5gHVYDlDY\nbCe+IVp0b3a03y7AYsuADuv8zBNX2ELWm1HH0MZR2MhdMmUqmkaKdpV5gGqLxkRAj02U8IBkQrDU\nsXwLGJsgHhudpIBK27rTCc/OrpeHqZ2laEC4WTbzkqlY3uBFCRC42oTC5sYdw9WvH7nAN/k5STqk\nCw9QzbGhG8HGFjb6zcllFiCSqZ6312+PL0BAx9gA3BM4t5pFQQoDUMU9A+FjebPS+mJW2BDGxmDW\nkG7Cfd0cG7LubORLyBdLjLERwvv1UX/a45eWPBMbg+Ly0gYLB9mulaIRSeBqME9KEBmSCeTI57B+\nRXpOBi0YlzYKyuacqRTNcRxl1LOLPZr1lzIY0zH5awBguIcGD8WfimYa9wxwKVoQz1KnwxY2Fi3D\ni0kM5TfOXMPJKyvV76cG1dRwHIUNTWAZ6EopO3x0c7CWK1a1x1eWuWnYBWVv5ldzvvKwKIwN0JpZ\nNksBFlTmsdFIClYMEtGUz9fBUrQwhc1AV6quYKOFzfHL5ptox3HY8WtEKhoA3LijZqZ/5PwC27zR\nqHPAkLHJFuA4jnJoaSMgz+Khr1/H2vJkNM5SBQVPPuKFCv27Z5ezdeugakAnEH6QYn1hYyJFox6b\nYHHPFHSTCNQ3VFazBWzQiOJU0lNSdGzbYJVJcRzgM9IctSigwQHj/V1a5oj65XKFUqAQA7/hnEEh\nswthm0Rh2UCv+5syNuevrVdliQAfmAvwxpJOirY9pkQ0QEpFi5mxcRzHeEAnIM2yibGIb3e03y7A\nogohxLAQYq8QYi+AdKm0uXLIb945XE3tKJYc/O03nqnepttwxVHYzKyQjqvm98ibA7eDasLYAP6L\nu0ncrReoIbIVHhs/xsZkrk/o5/MKD2izVDQgnMdmjiWi1XfgaWFzembV+BxYzRVZh1s1KyoO3LCj\ntsm/tpZnpmrZWK8LD6BM5lq+PL+IvteNjXvms3jo79UlN9JO8ZOXl9l8rqDQxT0D0iyb1RxjlFMJ\nofUshWdseCFq8hk7SIbIHpjs97hnGfrwAP63y+z28kbBeDgnUJbyvYQ01D71+GXf12YKFhyg8dcA\nZQ8UJaiCBAgEaS6ZgD5HmbEJ1ySKVNhozkWTEB4AeIywo7tHe+uaZFopGmEwVKMlwqKRqWjr+SKb\nXedf2NTOw0sBQ2Y6Ge23C7CgeDuApyv/Ds7NzbX45cSLRELgnsM1OdpXT9f+vimNqTmOOTaUsdGl\nQnWnk6yTPL+aw2q2wLprdLPdnU4yI6afHG0tQtwz0HzGJkiCGcDfv+VsgUnvXATx7LBwBpmxCTBj\noxWgF9a51ZxRh5Z7Juo38PvH+6vynULJwenZlbr7qEClNwnRODnXcG8Gu4hp99ELNQaDnftSNDQF\nHVzrOHzjKIRechUH5PObnqtXNbO2Dk0NVP+WbKGEp2frJ52bgm6I6qRoUrrZNWmGjY6xCDtIkX6+\nALOBm++8/zB+7Dl78F9fdT1u3jXse3+dx0beuCUTgqVILm8UWMKkV3CAi/uvrxU2X3lqThlHHgaU\nsdmpkaEB5b+Bfu6CBAgEMY6bQL6ebuSDealcsMJmOZj8ip6LdCm4YhgeQGVoR7cN1N2+Z6yv+vUz\nZJbY5UZ5bMjnNUrAkQpy4pxfiA/9u2ZWgg897VTYwqa98QEA+yr/To6NjfncvfNA09EoVMEBAL/Q\nLWcLoYZT8kQ0/eaIbpwW1nKsWEkIHtsJcIO7V0c0V5A8BgE9NkDzGZvlLE8w81tQx/szoPsrVfdt\n2TCPH6gvlOhmpt2laJMDXawQNWFtuOSq/hzNpBI4MFHrhB839NnQc3i8v0tbVMQBJkcjhc0KYyv1\nG1H5c/EMGbA33JNu6GvvTieY74Oeq5c1UrTudBLXkfckSoAASzmT5Fi8O55jRfBon36zG4fHJpNM\naAc+Umwf7sF7XnUDfvQ5e41+hzYVTSGrkwMENgxn2Lh49t7R6nUkVyzhCydmjF6jH2jh7SdtonLC\nQIxNgGRKE9DnWNqQGBuDItEFLbaDpqJRhuc6wu4trPH3Vgf6OTu2bajudtpgcWeJOY7D0hrj9dg0\nTopGC9vudMK3kUevm+UhnfEGZrQr2m8XYFGF4zgLjuOccRznDIB8wmCCc6fhBQfHlR1AHWMjX+jC\nsDamczxGyCZhfi3HNL8TA/WbQi+5FIXMXvR6bO50YIxNMwobqaupk7u4SCUT7OKtWlDpZtHv+cb6\npEKJHN9swMSmZiORENhFpCkmPhvG2GhYlUPMZ2NW2DQjEc3FDaywqW0+WHCGx/venU6w95wWhI0M\nDgAqUbgadpjNspLWKSpHeyxkgECx5LANrMzYyPNoFiTGRocJKXTAdLBrMxoHKilaMiFYvLMLGiCw\nkg0mRQPKaxMNrvnUY1c87m0Okxk2Ljhj0zopmiybyjK/kvl7PRHSvyXfnyYLAmYFuB9j05tJsWbA\nubk1LEjDOeP02MjHNMzAUh2CBAcA5WYLPde2ihyt/XYBFlsKfV0pPOdAPROlK2z6MylGV4fRsNKF\ndNyrsKGRz2t5zw0NYOYrAfjUdQDoDdAZc0ENuUHp5XyxFJjpokWIacyoX6G3kqVStPCFEvfYtJ8U\nDQjus/FjbABukg3D2DQqOMAFZWwevbBYvcDT8ACdvwYoFxeUtaHD9RoZHOBCF/lMP9vTQ/x18ACB\ncIWN3KyRmznj0oBHPu/Ii7GprVn5omO8djbDw5ZUNO2GetJKWd2AVNhshIgovv/YdPXrf3/yaizN\nISpF2zHS63FPaZZNgEJgKeZUNB4eUAi9lk4wKVr4wmZ6qJuxP5d9DO+L63mcJ8ddTkRzsWeMBris\nMn9NdzoRy7F0QZsLxZITKBzCD2He/21bcEinLWwsWo4Xk+6ZC11hk0iIuojKoDDtWrPCZjXnmybF\nZ63oF/d10rHOpBKhZhGEZWyeuLSE5/z2Z/Gc3/4szgTwANAF1c8P48Iv8nk5QCoaoC8csyGmZTcb\nQZPRuLxIXdjQWTamkc/n5mrv+fRQfPILFW7YXits5ldzuFi5qPKoc+/NE/W50YKwkcEBLugGmnps\ndKlogBT5fGkpVLeWyldUrIXM2FCPjZfvaLAnxdhx0856MzxsKsZGt3Gj7O5SwPAAFy88NF5dK5az\nBTx4Opp/1XGcQIyNLCc0RdypaLGFB0j+rSDnvaygoJ8pv2S0JwlbM9id0h53ef29vFR7r7YN9cQ2\nnNN9HRQLMfpswnistm3ByOf23AVYbCncq/DZ6Dw2QPTUEVPGhm4o51dzvhPb+ZBO/QJCO9Z+Gzsd\nwoYH/OVXzmB2pewX+thXzhg/LkiCmQu/yOegzznJCqXOkaIBwWfZzBtI0Wgy2oWFdaPO4HEyzPPw\nlH9aVRSM9GWwk0jwHjlf9tkESQSkt1OPTaOlaADf9Lnn6vJGnjGucgOGSmnmV3PGszgoqL9GxVrQ\n7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3ZEOdpihMKWMlNXljb/kM723wlYWEhIJAQGyIXNtLBhPgPDzV0UKdpGXk3HU8YmdHgA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\n+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f95d41eb4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def p_value(n, x_i, t_b):\n",
    "    a_i = f_b(x_i, *t_b)*total_count*0.8\n",
    "    n = n*total_count\n",
    "    p = 1 - poisson.cdf(n, a_i)\n",
    "    return p\n",
    "\n",
    "p_values = p_value(bin_counts, bin_centers, t_b)\n",
    "plt.plot(bin_centers, p_values)\n",
    "plt.yscale('log')\n",
    "plt.xlabel(r'$m_{\\gamma\\gamma}$')\n",
    "plt.ylabel(r'$p$')\n",
    "plt.ylim((1, 1e-3));"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
