{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binomial, Poisson and Gaussian distributions\n",
    "\n",
    "### Authors: \n",
    "- Christian Michelsen (Niels Bohr Institute)\n",
    "- Troels C. Petersen (Niels Bohr Institute)\n",
    "\n",
    "### Date:    \n",
    "- 07-11-2018 (latest update)\n",
    "\n",
    "***\n",
    "\n",
    "Python notebook for illustrating the Binomial, Poisson and Gaussian distributions and how they transform into each other (well, into the Gaussian).\n",
    "\n",
    "Reference:\n",
    "-   Barlow, chapter 3\n",
    "\n",
    "***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                                     # Matlab like syntax for linear algebra and functions\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt                        # Plots and figures like you know them from Matlab\n",
    "#import seaborn as sns                                  # Make the plots nicer to look at\n",
    "from iminuit import Minuit                             # The actual fitting tool, better than scipy's\n",
    "from probfit import BinnedLH, Chi2Regression, Extended # Helper tool for fitting\n",
    "import sys                                             # Modules to see files and folders in directories\n",
    "\n",
    "from scipy import stats\n",
    "from scipy.stats import binom, poisson, norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set up the parameters for the program:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# General settings:\n",
    "r = np.random                       # Random generator\n",
    "r.seed(42)                          # Fixed order of random numbers\n",
    "\n",
    "save_plots = False\n",
    "verbose = True\n",
    "N_verbose = 10\n",
    "\n",
    "# Set plotting parameters:\n",
    "mpl.rcParams['font.size'] = 18      # Set the general plotting font size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting PDFs:\n",
    "### Problem parameters:\n",
    "* The Binomial PDF needs two parameters: Number of trials (n) and probability of succes (p).\n",
    "* The Poisson PDF needs one parameter:  The expected number (lambda - but in Python we write it \"Lambda\", as \"lambda\" is reserved!).\n",
    "* The Gaussian PDF needs two parameters: Mean (mu) and width (sigma)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func_binomial_pmf(x, n, p):\n",
    "    return binom.pmf(x, n, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func_poisson_pmf(x, lamb):\n",
    "    return poisson.pmf(x, lamb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def func_gaussian_pdf(x, mu, sigma) :\n",
    "    return norm.pdf(x, mu, sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Range parameters:\n",
    "xmin = -1.5\n",
    "xmax = 80.5\n",
    "\n",
    "# Function parameters:\n",
    "n = 10\n",
    "p = 0.2\n",
    "Lambda = n * p               # We choose this, as this is the expected number of successes.\n",
    "mu = Lambda                  # Same here - the central value is n*p.\n",
    "sigma = np.sqrt(n*p*(1-p))   # For a Binomial process, the variance is n*p*(1-p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "xaxis = np.linspace(xmin, xmax, 1000)\n",
    "yaxis_binom = func_binomial_pmf(np.floor(xaxis+0.5), n, p)      # np.floor: See below\n",
    "yaxis_poiss = func_poisson_pmf(np.floor(xaxis+0.5), Lambda)     # np.floor: See below\n",
    "yaxis_gauss = func_gaussian_pdf(xaxis, n*p, np.sqrt(n*p*(1-p)))\n",
    "\n",
    "# Note that the np.floor function takes the integer/rounded (towards zero) value of numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AWKXUCNNnaMO0DmX1zOncfP9NrdfeNP1dGY/jteriMX6MaGP6+2berxDGdybLmD6lVFOlVJvMP/SYAsoWppf/mNLKKKWes/deuLl2oCvXZl5+vkIIB2QyFiHEPU1rfU0p1Q1jooDhQFel1FaMKdCjMCZN+UBrvdhBEZOBiaZf5fdgdJ2rg3EzNUBrbT3BxUSMxcMfBf5USm3EuLGKwliG4DTG2n2O5ORYuWk+xkLHK5VScZgm6NBa97WT9zNuBq253ZqH1jpBKTUYYzmDRUqptRiTYVTCWGMvBehlnvnzDvhEKWU962ZJjC5svhjjhgY42jGXDMeYybQR8JdSahVGi2s0xsQeu4CX7nAdnBmNsdD1GKVUV4wb9jCgHkaXvTdz6TgfYMw0+zwQrZRKwOjK2BhjgpL6OO5qmSNa6yNKqQHANIzF5V/B6KpbCaN1ahIw6BbKnaWUSsUYuzYSeFMptQmj1a0AxkLo1TCurTMY6xCa992qlFqM8aPKdqXUSozvZX2M1jC7ddJaJymlvsaYNGm1aZbWG0BtjLF0CzGW1LBWDYgBLiiltgAnMbp+1sPohbCfmz9AFMEIGD9TSm3D+G56mMp4yFTHES6cnjz7fIUQjkmLnhDinmeaqr46xg2KD8b4kOoYC1B30Fq/5WT3uRg3JBcxfrGugtFtsJXW2mYSEq31XxiTL/yEsZRCO4yb4A+Blpi6SObGsXLZMIwWzSsYXcKeI9OYHCu/mZ4vYizOneu01pMwApufMW4eu2IE5t8BkVrrhXfiuCadMYL1ZzBmF6yNcaP7X6DK7Uwl7wpTq15zjGVE/jL9/2MYN98jgHpa6zN3sg7Z1O8HU51+x5i8ox3GvUKvbL5HOT3OWowAeylGYNMOY1bcoRifS64yfb9aYXzfqmCc8wumtHm3Ue4PGH8D3sSYjCkc4zvWEiOwWYJp4iat9Y+Zdu+EMQnQAaApxndiLUYL41Ynh30R41o5atqnOkbPhnqm95TZrxgt1gmm+nU25T2EMbNmpNU43L8xrs1lpvq3M72XNIyWxqpa641O6gbk/ecrhLBP5Wy5ICGEEPczpdQwjFadiVrrV/K7PkIIIYS4NRLoCSGEACyTOezB+AW+oqkFUwghhBD3IBmjJ4QQ/3JKqdeBqhgLdQcDn0uQJ4QQQtzbpEVPCCH+5ZRS8RgTypzEmCjhP1rr6/laKSGEEELcFgn0hBBCCCGEEOI+I7NuCiGEEEIIIcR9Rsbo5aOgoCAdFhaW39UQQgghhBBC3KW2bNlyRmsdnNP9JNDLR2FhYWzevDm/qyGEEEIIIYS4SymlDt3KftJ1UwghhBBCCCHuMxLoCSGEEEIIIcR9RgI9IYQQQgghhLjPSKAnhBBCCCGEEPcZCfSEEEIIIYQQ4j4jgZ4QQgghhBBC3Gck0BNCCCGEEEKI+4ysoyeEEOKedenSJU6dOkVqamp+V0UIIYRwmaenJ8WKFcPf3/+OHUMCPSGEEPekS5cucfLkSUJDQ/Hx8UEpld9VEkIIIbKltebatWscPXoU4I4Fe9J1UwghxD3p1KlThIaG4uvrK0GeEEKIe4ZSCl9fX0JDQzl16tQdO44EekIIIe5Jqamp+Pj45Hc1hBBCiFvi4+NzR4ceSKAnhBDiniUteUIIIe5Vd/rfMAn0hBBCCCGEEOI+I4GeEEIIIYQQQtxnJNATQggh7jNKKXr37p3f1ciR261zdHQ0YWFhuVYfe5KSklBKMXLkyDuSX9x58fHxKKWYPn16fldFiDtOAj0hhBDiLma+MbV+eHt7U65cOfr06cOePXvyu4r/ahcuXGDkyJHEx8fnd1Vu2/Hjxxk2bBitW7cmODjYpeD7//7v/6hevTo+Pj4UL16cvn37cvr06byp8H1m8eLF1K9fHz8/P4oWLUrXrl05ePCgS/ueP3+eCRMm0LJlS0qXLo2Pjw/h4eH079+fw4cP293n4sWLDBo0iNDQULy9valcuTKff/45WusseTMyMoiJiaFixYp4e3tTunRphgwZwtWrV2/rPYs7S9bRE0IIIe4B3bt3p23btgBcu3aNHTt2MHXqVObOncvOnTspU6aMJe+1a9dwd3fPr6reknuhzmXKlOHatWt4eNy8fbpw4QLvvfceYLQq3sv27dvHmDFjKF26NJGRkSxZssRp/piYGF577TWioqKYMGECR44cYdy4caxbt46NGzfi5+eXRzW/982bN48uXbrw8MMP8/HHH3Px4kXGjx9PgwYN2Lx5MyVLlnS6/4YNGxgyZAjNmjVj4MCBBAUFsWvXLr788kt++OEH1q5dS6VKlSz5b9y4QYsWLdi2bRuDBg0iIiKCJUuWMGDAAE6ePJmlFXrw4MFMnDiRjh07MmTIEPbs2cPEiRPZtm0bK1aswM1N2o7uRhLo3Q+05sCudVw+f5Lr+gbXM25wLT2FGzqNNJ1Gmk4nTaeTqtNI1xmk6nTSdbox049WKBSgcAOUmztFSjyIr09BCrh54eXuhZe7N17uBYxnNy+83b0ILFyEAp5y+QghRF6pUaMGPXv2tEmrUKECr7zyCvPmzWPw4MGWdG9v77yu3m27F+psbk29X9WsWZNTp04RHBzMmTNnCA4Odpj3zJkzDB8+nMjISFauXGkJ0iMjI2nfvj0TJkzg7bffzquq39NSU1MZNGgQpUuX5vfff6dgwYIAtGnThpo1azJy5EhiY2OdllGxYkX27dtH+fLlbdIfffRRWrRowbvvvstPP/1kSZ86dSqbNm1i4sSJDBo0CIB+/frRuXNnxowZQ58+fSw/HiUmJjJp0iQ6derE3LlzLWWULVuWl19+me+//54ePXrkyrkQuUvu1O9SV1Ovcjr5NIkJS0jZNImLbhlccIcLboqL7ooLbnDVTZHsBskKrrnB1dz6NcWFHheeGRpfPPDLcMdPu+GrjWfz60IZ7hTWHvh7h1CvWV9KFCxOAXdP+4W5FwD/UJBp0oUQIkfMv/IXKFDAJl0pxTPPPGMzDsmc9vzzz/Pmm2+yefNmvL296dixI+PHj7fcXJrt2LGDESNGsGbNGq5evUq5cuXo3bs3Q4YMsWl56927NzNmzODMmTMMGTKEX3/9lZSUFJo1a8YXX3xBSEgIsbGxxMTEcPDgQcLCwvjwww/p0KFDtnWeM2cOs2bNIiEhgZMnT1KoUCEaNmzIqFGjqFat2i2dsyZNmnDw4EGSkpIsabNnz6ZHjx5Uq1aN7du3W9I///xzBgwYwPr166lTpw5JSUmULVuWESNGWLprNmnSBID33nvP0rJXpkwZm/IBfv31V9577z127txJkSJF6NmzJ2PHjrVpHXQkp5/drSpUqBCFChVyKe+CBQtITk5m0KBBNtdDu3btKFeuHN9++222gV5GRgZjx45l2bJl7N+/n3PnzhESEsKjjz7K6NGjCQwMtOS1Pve1atVy6VwuXLiQkSNHsmfPHoKDg+nduzeNGzd2+XyYr+1Tp04xZMgQFi1axPXr16lbty4ff/wxNWrUcLksZ1avXs2xY8cYNWqUzWf5yCOPEB0dzZw5c/jss8/w9HRwHwUOx6c2b96cokWLsmvXLpv07777Dl9fX/r162eT/uqrrzJv3jzmzJnDf/7zH8D4fmitefXVV23y9uvXjzfffJNvv/1WAr27lAR6+ehq6lUW/LWAE1dPGI/kE5y8epITV09wJfXKzYzB7oDxR9Q7w42CGe4UzHDHR7sRmOZGqHajgG8IQYEP4OPmha+7Nz5uXhRQHngoD9yVO57KHU83DzyVBx5u7njghga05b/G/6WnpXD5+F7S09O4QTqppJNiftZppJJBCmncuHKQVJ3MZbdUrrjBZTfFcXe44gnJbtYB23FY8xIA/unpBJseJdLSKZmWRmhqGqFp6bgXqU350o/g5iTY864QjVuFZrn9MQghxD0hOTmZM2fOAEY3x127djFs2DCCgoLo3LmzS2UkJCTw2GOP0adPH3r06EF8fDxff/01bm5uNi0GmzdvJioqCk9PT1566SVCQkL45ZdfeOONN9i+fTuzZs3KUnbr1q0pVaoUo0aN4q+//rJ08+rUqROxsbE899xzeHt7M3HiRLp06cL+/fspW7as0/pOnjyZwMBA+vfvT0hICH///TexsbE0aNCArVu3UqFChRycQUPTpk159913+fvvvy2tHytXrsTNzY2dO3dy5swZgoKCAIiLi8Pf359atWrZLSsiIoKYmBgGDx5sea9AlsBr8eLFTJkyhRdeeIFnn32WhQsX8sknn1CkSBGXW71c/exSUlK4fPmyS2W6u7tTpEgRl/JmtmnTJgDq1auXZVvdunWZPXs2V65ccRqE3rhxg48//pjOnTvToUMH/Pz82LRpE19//TX/+9//2LJlS5YfMVw9l/Pnz6dz586EhYXx7rvv4uHhwbRp01i0aFGO32vr1q0pWrQoI0eO5MSJE0yePJmoqCjWrVtHlSpVLPnOnz9Penq6S2UWKlQILy8vIPtzGRcXx/79+6lcuXKO637x4kUuX75sU8+MjAy2bt1KjRo1srRQ165dG6WUpU7m+rm5uVG7dm2bvN7e3jzyyCM2ecXdRQK9fJR0KYl3/ngHgKLeRQnxC+GBQg9QO6Q2IX4hBPkEcXLtIuof/gmf59ZSMqgMXu5e+Vzr7KVlpHH++nlOXzvNHwlrOHIkgUskc9n9Glfcr3GUZBK5wmWuWe11CM/TSZRINQLAMqlphKWmEXbDeC6Wng4bJ5ER9BBKOWm59AtEtf0U/J30ZVdu4JU7v34KIUReGTFiBCNGjLBJq1SpEr///jshISEulbFjxw7WrVtHnTp1AHj++ee5dOkS06ZNY9y4cZab8ldeeYWUlBTWrVtnaTkbOHAg3bp147vvvuPZZ5+lWTPbH95q167NZ599ZpMWExPD0aNH2bVrF/7+/oARaD388MPExsYyduxYp/VdunRplnFevXr14pFHHiEmJoYpU6a49L6tmQO9uLg4S6AXFxdHjx49+Pbbb4mLi+OJJ55Aa018fDyNGzd2OHawePHiPP744wwePJhq1apl6VprlpiYSGJioqXV5YUXXqBq1apMmjTJ5UDP1c9u9uzZ9OnTx6Uy7bU8uurYsWMAhIaGZtkWGhqK1ppjx47x0EMPOSzDy8uL48eP4+PjY0l74YUXqF+/Pn379mXBggU88cQTNvu4ci7T09N55ZVXKFq0KBs3brQE7s8///wttQSXKVOGuXPnWha37tSpE5GRkQwdOpSlS5da8lWvXp1Dhw65VOa0adMsE91kdy4Bjh49ekuB3n//+19SU1N55plnLGnnz5/n2rVrdo/n5eVFUFAQR48etaQdO3aMoKAgS2CauX5r167lxo0bWYJykf8k0MtHYf5hLO64mGJ+xRwGcOvXbabSjVSu+jrOc7fxcPMg2DeYYN9gKjWr5DBfSnoKx64c48D5wyzfv5szKSe4mHqKpNQTbL1xhBsZNwNBT+VD0WsQekNTLEVR4oYnJVM8KJTuZhpjaGjgnkDAlDrZV7LWs/CwC90MileGAr7Z5xNC3DXe+yWR3ccu5Xc1bFQq6c+Idjm/SbPWv39/unbtCsD169fZvXs3n376KW3btmXVqlU2k7E4Uq9ePUugYNa0aVMWL15MUlISVapU4dSpU6xdu5aOHTva3BQrpRg2bBg//vgj8+fPzxLoZe7W1ahRI2JiYujVq5clyAOoVq0a/v7+/Pnnn9nW1xzkaa25fPkyN27cIDg4mPDwcDZs2JDt/vbUrl2bggULEhcXR79+/Th06BAHDx5k8uTJJCQksHLlSp544glL617Tpk1v6TjWHn/8cZuudUopmjRpwuTJk7Nt9TJz5bMDaNWqFb/99ptL9bIOsHIqOTkZwO7Nv7mVyJzHEaWUpQ7p6elcvnyZtLQ0yznfsGFDlkDPlXO5ZcsWDh8+zNChQy1BHkBAQAAvvPBCjscO/uc//7EEeWCMZWzRogUrVqyw+fxmzZrFtWvXHBVjwzpoy41zac9PP/3EJ598QuvWrW2Cf2fHMx/T+njJyclO85rzSKB395FALx/5efpR2r90flcj33i5e1E2oCxlA8rSLMy2z7zWmlPJpzh46SAHLx7kwIUDbD66j73Jh9iacc6Uwdo6AAAgAElEQVSSz9stgEDPsgR6hlHUM4xPt1+go8dhivk5DopLpR6k/uZvYPM32VcyOAKavAU4GT8YEAqhNbMvSwghbkOFChVo3ry55fVjjz1GVFQUdevW5Y033uD777/Ptoxy5cplSTOPgzp79iyAZTp3e60HERERuLm5ceDAgWzLNncJtNc9s0iRIpbjObNt2zbeeecd4uPjs0zjnl23T0c8PT1p2LAhq1atAoxumx4eHjRu3NgSOIHRygfkSqCX3Xl3JdBz5bMDKFGiBCVKlLjVqrrM19f4ETQlJSVLwHj9+nWbPM788MMPfPrpp2zbto3U1FSbbefPn8+S35Vzab4+K1asmCWv9cyTroqIiLBbzvLlyzl06JDlu9KgQYMclw225zKznJxLa4sXL+app56iZs2azJkzxyZQdXY88zGtj+fr68upU6cc5r2V+om8IYGeuCsppSjuV5zifsWpW6KuzbZLNy6x/9x+9p3fx95ze9l3bh+7L/xKWkYaPACxugCe6WXwSitLgfQwCqSVxV0HWPY/cukqLQJaMLJFKafzv6grJ/D77T+oH3plX+Hot6Gwk6DdvQA81Fq6jAqRB2635exeUqdOHQICAixBSXacLV9gb+2snHBUtqP07I73zz//0LhxY/z9/XnnnXcIDw/Hz88PpRSvvvoqV65ccbq/M02bNmXp0qUkJiYSFxdHZGQkBQsWpGnTpkycOJF//vmHuLg4goKCbnnSF2u5cd5dLePatWtcvHjR5TKdzazpjHkioKNHj/Lggw/abDt69ChKqWyXBJg3bx7dunWjdu3aTJgwgdKlS+Pt7U16ejqtW7cmIyPDbp0dud1r+HadPn3a5TF6AQEBlgDZ+lxmDirNXSjtdbN0ZOnSpXTq1InKlSuzfPlymxZ1MH5o8fHxsemeaZaSksKZM2eIioqypJUsWZLdu3eTkpKSpWXv6NGjBAUFSWveXUoCPXHP8S/gT62QWtQKuTk4PjU9lQMXD7Dv/D4SzySy88xO9pyL43JGGgAhfiFUC6pGteBqnD1TkolLijNvVtZ/QGwVo/fDPzCyibN/BDUseRPix2Rf8bBG8Eg23UVL1oBiWX+BFEIIR9LS0hz+Mn8rzC1liYmJWbbt3buXjIwMu60quW3+/PlcuXKFn3/+2TKzpdnZs2cddiVzhbmVbuXKlcTFxfHcc88Bxjp47u7uLF++nDVr1tCiRQublhB7stue1+bMmZMnY/QiIyOJjY1l3bp1WQK99evXEx4enm1L5cyZM/H29mbVqlU2LUJ79+69pTqZma9Pe+Xs3r07x+Xt2bOHunVtf3TevXs37u7uNl2mIyMjb2mMXmRkJADr1q2zabUH41z6+/s7HetobenSpTz++ONUrFiRFStW2J1sx83NjRo1arBt27YswdvGjRvRWttMQBQZGcny5cvZuHEjjRo1sqRfv36dhISEHM1kKvKWBHp3u3z+depe4enuSXjRcMKLhtO+fHvAGAO499xedpzewY7TO9h5ZifLDy0HoGhlb0p6V6S0T1VK+1QhxLsC7sp22uL4faf4LvEsA9tVIMDH8ZTG7k8vxO3yEecV/PM3WPw6JP2ezRvxg0c/BU8n4yZ8ixpB4112cyGEyHu//fYbV69eveUuY/YUK1aM+vXr88svv7Br1y7L2C+ttWXylI4dO+ba8Rwxt9xkbqX56quvOHHihEtjEh2pXr06RYoU4YsvvuD48eOWwC8gIIAaNWoQExPDxYsXXeq2aQ5mzp07l03OvJFXY/Q6dOjAyy+/zOTJk+nRo4fl8/rll184cOAA77//frZluLu7o5SyabnTWjN69OhbrhcYY+hKlSrFtGnTeOONNyzj9C5dusQXX3yR4/I++ugjm8lYtm7dyooVK2jevLlNMHurY/SioqIoUaIEU6dOZfDgwZYyt2/fTnx8PH369LFZWuH48eNcvHiRBx54wCZAXr58OR07diQ8PJyVK1dStGhRh8fv3r07f/zxB7GxsZZ19ADGjx+Ph4cH3bp1s6R169aNMWPGMH78eJtA76uvviI5OZmnnnrKpfcs8p4EevcMuanPKS93Lx4OfpiHgx+2pJ25doatJ7ey+eRmNp/czP/OzgTA292bh4MfplZILeqWqEuVoCrULFOEVZP+R63RK5wep0SAN2v+0wRPdyezgdbuB5Ueh9SrjvNcvwjfdoYFL2T/5hq/DuWinWRQUKoWeNwbE/gIIbK3detWvv32W8DoXpWYmEhsbCyenp63fWOc2YQJE4iKiqJRo0aW5RV+/fVXli1bRo8ePbJMxHIntGnTBl9fX55++mkGDhxIkSJF+OOPP1i8eDHly5cnLS3tlst2c3MjKiqKBQsW4O3tTf369S3bmjZtyocffmj5/+wEBgby4IMP8v3331O+fHmKFy+On58f7dq1u+X63Y7bHaNnvpbMk3Hs2LHDkta4cWNL601wcDDvv/8+Q4cOpXnz5nTv3p2jR4/y6aefUrFixSyT89jTpUsX5s6dS9OmTenVqxepqamW9fluh7u7OzExMTzxxBPUrl2bfv364eHhwTfffENgYCD//PNPjso7dOgQrVq1on379hw/fpzJkyfj4+PDxx9/bJPvVn9w8fT0ZMKECXTr1o1GjRrRr18/Ll26RExMDMHBwZb1Gc3eeustZsyYwapVq4iOjgaMJVE6dOiA1po+ffqwZMmSLMexnhW2X79+TJs2jddee42kpCQiIiJYvHgx8+fPZ/jw4TYT3lStWpWXXnqJyZMn06lTJ9q2bcuePXuYOHEiUVFRsobeXUwCvXuENN7kjiCfIFqGtaRlWEsAzl8/bxP4TUmYwmcJn1HIsxB1S9ale7OK+OvKFPIoZre8TUnnWb3/NNdT050HegAFg4FsxkIM2goXs2kdjHsf1nxsPJwJawQdPsv+4ilUEtzlT4EQd7vZs2cze/ZswAhUAgMDadmyJW+99Zal61duqVWrFmvXrmXEiBFMmTLFsmD6hx9+yJAhQ3L1WI6UL1+eJUuW8PbbbzNmzBjc3d1p0KABq1evZuDAgbfc5dCsadOmLFiwgPr169t0XWvWrBkffvghoaGhhIeHu1TWrFmzGDx4MG+//TbJycmUKVMm3wK92/XOO+/YvN62bRvbtm0DjCU+rLvpDRkyhMDAQGJiYnj55Zfx9/fniSee4IMPPnBpgpknn3ySy5cvExMTw9ChQylSpAjt2rXjgw8+sFks/VZ06dKFn376iVGjRjFy5EiKFStmWTC9ZcuWOSpr6dKlvPbaa4wYMYJr165ZFkzPjfGbZl27dsXHx4fRo0czdOhQvLy8bK7F7OzatcsyMcrgwYPt5rEO9AoUKMCKFSsYPnw4s2fP5uzZs5QvX55Jkybx0ksvZdl3/PjxhIWFERsby6JFiwgKCmLQoEGMGjUKN7ds7n9EvlH5PXD136xWrVp68+bNTvOsn/kudf+eQPLQf/AtGOA0r7h9F1Musv74etYeW8sfR//gZPJJwFgKo0FoA+qXrE/tkNp4exjTCU/9/QCjF+1h58iWFPJ23L0zV6WnwuGNoJ0M+D6yCVaOcq280Frw7FJwz6P6C5FL9uzZY3c2PCGEyA29e/dmxowZ+T7Ji7i/ufJvmVJqi9a6ltNMdsjP+EJYCfAKoFVYK1qFtUJrzYGLB/jj6B+sPbaWn/b/xKw9s/Dx8KFeiXo0eaAJyem3Pkbklrl7Qlg23UPCGkGJh+HyCef5Lh6B+LEwpqSxkLwjBYvD0/MhsHzO6yuEEEIIIfKcBHpCOKCUonzh8pQvXJ5elXtxPe06m09uJv5wPPGH44k7HIfCDZ8yDzBr73Fal21OWEBYflfboBQ82Dz7fABFysKprLPrWWgNW2bAjHYQUMrJMd2h8RDXjyuEEEIIIe4YCfTudtJd4K7h7eFNw9CGNAxtyLA6w9hzbg+f/m8e666v4bPtE/hs+wTKBpSlZZmWtAprRYUiFfK7yq55uFv2eco2hvVTQDtZkuLMXzC3X/ZLSITWhCqdclZHIYQQQgiRIxLoCXELlFJUCqxEvaLerFxXneWvV2HTqf8R908cX+38ii93fEn5gPKWbqDlCt/5NafuqAotjIczp/bAd0/A5mmO8+h0WDcZUi5BISezwnn5wwN1ZRYiIYQQ+Wb69OlMnz49v6shxC2TQO8eoZyNnxL5LsSvJE9FPMVTEU9x5toZVhxawbKkZXy+/XOmbJ9ChSIVaFWmFa3LtqaMfz6M68sLxSLg1Z3O89y4ClPqwS+vZF9ey/9C3QHZ55PZvoQQQgghspBAT4hcFuQTxJMVn+TJik9yKvkUvx36jeVJy5mcMJnJCZOpFlyN9uXa07psawK8/mUzqRbwgxf+B2f/dJ5v1RhYPsx4OOPmAY/FQI1euVdHIYQQQoj7gAR6TiilvIDJQDOMBdCOA5O01pOs8ngAnwJPA27AXOAlrfX1vK+xuNsU8y1maek7cfUESw8u5ecDPzN6w2g+2PQB0aWiaVe+HY1CG+H5b1newNvfGKfnTMdY2PZ/kHbDeb4/l8PSt2DvIuf5yjWBui4sRC+EEEIIcZ+QQM85D+AE0BI4AFQDlimlTmqtfzDleRtoAlQFbgA/Ax8BL+d9dcXdLMQvhN5VetO7Sm/2ndvHz3//zKIDi1jxzwoKexWmTdk2dCjfgUqBlVD/9rFpfoHQ0P6CrzaqPWF0A7183HGe65dg/1JjfKCvkwV4fQONGUP/7edeCCGEEPcFCfSc0FpfBd6xSkpQSv0MNATMgV5f4D9a66MASqmRwI9KqcFaO1vRWvybhRcN5/WirzO45mDWHVvHL3//wrw/5zF772wqFq1IlwpdaFuuLYUKFMrvqt7dipaFZ352niflCnzREJa9nX15j34KtZ5znkcCQSGEEELcA+76QE8p9RZQA6gJlAUOaa3DHOR1A14BngfCgNMYAdm7pqDtduviCTQCPjG9LgyUBhKssm0FCpmO//ftHlMWV7i/ebh50KhUIxqVasTlG5dZcnAJP+7/kdEbRvPplk9pHdaazg91plpQNWnlu1VeBWHAOuetfgALB8KiIcbDmag3oIkLQaMQQgghRD666wM9YAxwDiOAKpxN3hiMLpPzMcbNRZheV1dKNdf65iJgSqnvAWcLiDXRWsdnSpsMXAb+z/Ta3NxywSrPhUzbcoXc5N/dcmO5w0IFCvFE+BN0fagru8/u5sf9P7L44GLm/zWfCkUq0LlCZ9qVb4d/Af/bP9i/jacPFM1miYsu38C2mZDhpCH+8EZY8wlcOAzOZsItFgH1XpLWPyGEEELkm3sh0CuvtT4AoJTaBRS0l0kpVRkYBMzTWne2Sj8ITASeBL6z2qUfMNDJcS9mKn8cUA9oqrU2zxBx2fQcgDGWD24Go5cR4hYopagcVJnKQZV5PfJ1lhxcwk/7f+KDjR8wYesE2pVrR4+IHpQvXD6/q3p/KRQCjV93nufqWZjzFBxc4zhP+g1I+NYILks84jifpw8Ur3RrdRVCCCGEyMZdH+iZgzwXdAcUMD5T+lfAB0BPrAI9rfVlXAzGlFLjMWbebKq1PmNVxgWl1GHgEWCfKbm6qdwkF+st7mF3uqXVz9OPLg91octDXdh9djez985mwV8L+GH/D9QpUYceFXsQVSoKdzf3O1oPYeIXCM8udZ4nPRU+bwCLXsu+vFZjjJY/IfLB9OnT6dOnD6tWrSI6Ojq/q5OvevfuzYwZM9Auds/IaX577pbzHx0dTVJSEklJSXdFOUKI3HPXB3o5EAlkAButE7XW15VSCabtOaaUmgg0xejKedpOlqnAW0qp34FUYCQwXSZiEbmtUmAl3m/wPq/VfI25f87l+73f88qqVwgtGMqT4U/SsULHf9+6fHcjd094bhkc3uQ837pJEDcads1zns/NHaL+Y8wIKv6V4uPjadKkiU2an58f4eHh9OrVi4EDB+LuLj/25LYFCxaQkJDAyJEj87sqIhs///wz8+fPZ926dRw+fJiAgAAqVarE0KFDad26tcvlpKSk8N///peZM2dy7NgxSpUqRZ8+fXjjjTfw9My6BNL//d//ERMTw969e/H396ddu3aMHTuW4ODg3Hx7Qtyy+ynQKwmc0Vqn2Nl2FKivlCpg1e0yW0qpMhjdQVOAg1atN79rrduY/n8MEAQkYqyj9xPwhpMy+wP9AR544AFXqyKERRHvIvSt2pfelXuz6vAqvtvzHZ9u+ZTPEj6jw4Md6FWpFw/4y7WVr3yKwEMtnecJqgC/vQs3spkn6vQ+Y6KYVmOcj/krXsUoU9y3unfvTtu2bdFac+zYMaZPn86rr75KYmIisbGxOS7v6aef5sknn6RAgQJ3oLb3lq+++oovvvjCJm3BggXMmDFDAr17QP/+/fH396dDhw6Eh4dz7tw5pk2bRps2bRg9ejTDhg1zqZxu3bqxcOFCnn32WerVq8e6det45513+Ouvv5g+fbpN3piYGF577TWioqKYMGECR44cYdy4caxbt46NGzfi5+d3B96pEDlzPwV6vhgBmT3XrfK4HOhprQ9hdAd1licNY8IXl9bN01rHArEAtWrVyr7Px835Y4Sw4eHmQYsyLWhRpgX7zu1j1p5ZzPtzHj/s+4HmZZrTu3JvqgVXy+9qCkeKloVuM7PPd2gdzGgHP/Vxns/DB17eCv4lc6d+4q5To0YNevbsaXn94osvEhERwdSpU3n//fcpXrx4jspzd3eXlkATT09Puy024t7w3Xff0bRpU5u0gQMHUr16dd577z0GDBhAkSJFnJaxePFiFi5cyGuvvcann34KQN++fSlcuDDjxo2jf//+1K9fH4AzZ84wfPhwIiMjWblypeV7FBkZSfv27ZkwYQJvvy2zM4v852TauHtOMuDlYJu3VR4hcl8+r4MRXjScUQ1GsazzMp6r+hzrj6/nqcVP8cySZ4g/HE+G/GBw7ypTDwbvggHrHT96L4aMNPiiEUx4xPHjszpwYmd+vyORS/z9/alXrx5aaw4cuDmcPSkpiaeffprixYvj5eVF+fLlefvtt0lOtv0ncPr06SiliI+Pt6Rdv36dkSNHEh4ejq+vL4ULF6Zq1aq8/rrtREWLFi0iKiqKoKAgfHx8eOCBB+jUqRP79++3ybdjxw46duxIYGAg3t7eVKpUiY8++oj0dNvRDb1790YpxcWLF3nxxRcpVqwY3t7eNGjQgA0bNmR7LlavXo1SimnTptmkh4eHo5Ri/vz5NukhISG0adPG8tp8fLPo6GhmzJgBGGOxzY/MrTq3Wl9HLl++zPDhw6lTpw5BQUF4eXnx4IMP8uabb2b5/OLj4y11mjJlCuHh4Xh7e1O1alV+/fVXAHbu3Enr1q3x9/cnMDCQl19+mdTUVLvHPnDgAB06dCAgIAB/f386duxoc12ZnT9/nn79+hEUFISfnx/R0dFs2bLFbpnLly+nW7dulCtXDh8fHwoXLkzLli1ZvXr1LZ8jezIHeQC+vr489thjpKamsm/fPjt72fruO2Mah1dffdUm3fz622+/taQtWLCA5ORkBg0aZPNjSbt27ShXrpxNXiHy0/3UoncMqKSU8rLTfTMUo1uny615dx2Zpl24INg3mFdqvELfqn2Z9+c8Zu6eyaC4QZQLKMczlZ/hsXKPUcBdumndcwqFGA9nOn0J+7KZKObP5bBoKNR8xnm+B+pmvxyFyHdaa/766y8AgoKCADh06BC1a9fm4sWLDBgwgAoVKhAfH8/YsWP5448/WLlyJR4ejv/pf+mll/jmm2/o1asXr732Gmlpafz555/ExcVZ8qxevZr27dtTpUoV3nrrLQoXLsyxY8dYsWIFf/31Fw899BAAmzdvJioqCk9PT1566SVCQkL45ZdfeOONN9i+fTuzZs3KcvxWrVoRHBzMu+++y9mzZxk3bhyPPvooBw8epFAhx6sW1atXDx8fH+Li4ujTx2j9PnLkCPv378fNzY24uDg6duwIQGJiIidPnrQbHJgNGzaMjIwMfv/9d2bOvNnybm7Rud36OnL06FGmTp1K586d6dGjBx4eHqxevZqPPvqIbdu2sWzZsiz7fPbZZ5w/f56+ffvi7e3NxIkT6dixIz/++CP9+vWje/fuPP744yxfvpxJkyZRrFgxhg8fblPG1atXiY6Opk6dOowdO5Y///yTKVOmsH79erZt20ZIiPH3JzU1lVatWrFp0yaefvpp6tatS0JCAs2bNycwMDBL3aZPn865c+fo1asXpUqVsry/Zs2asWrVKho1amTJm5ycnCWYdcTT05OAgOzHpB85cgTApdbuTZs2ERoaSunSpW3SS5cuTcmSJdm0aZNNXjCuu8zq1q3L7NmzuXLlCgUL2p0oXoi8o7W+Zx7ALiDJwbbRGO0qjTKlewNXgSX5Xf/Mj5o1a+rsrJ3+ttYj/PW15CvZ5hV57+vfD+gyb/yqL1y9kd9VsetG+g3969+/6i4/d9FVplfRzX5opmftnqWvpV7L76qJ/LBuitYj/LN/TKqldXpaftc2W7t3787vKuSJVatWaUC/9957+vTp0/rUqVN6+/btum/fvhrQdevWteTt0aOHBvSiRYtsyhg6dKgG9NSpUy1p06ZN04BetWqVJa1IkSK6TZs2TuszePBgDeiTJ086zVe/fn3t7u6ut2/fbknLyMjQXbt21YBesWKFJf2ZZ57RgH7xxRdtyvjhhx80oL/44gunx9Ja6+bNm+vQ0FDL6xkzZmh3d3fdvXt3HRERYUmfOHGiBvTmzZuzHN+avbTcrK+985+SkqJv3Mj678nw4cM1oDds2GBJM18XJUuW1BcuXLCkb9++XQNaKaXnzp1rU06NGjV0SEiITVpUVJQG9CuvvGKTPm/ePA3o559/3pL25ZdfakC/++67NnljYmI0oMuUKWOTfuVK1nuXEydO6MDAwCzX2YgRI7TpPi7bR1RUVJZyM0tISNAeHh66UaNG2ebVWuuCBQvq2rVr290WGRmpS5QoYXn92GOPaUAnJydnyfv6669rQO/bt8+l4wrhyr9lwGZ9C7HG/dSiNwd4G3gV+N0qvR/G2LysPx0KcZ/zdPPk0XKP0rZsW9YeW0vsjljGbhzLVzu/onfl3nR9qCu+nr75XU2RV+q8ABHtjG6ejhz8HX4eCOOrGTOIOlKwOPT8Cbxy3mpxxy158+7rohpSFdp8cFtFjBgxghEjRlheu7m50b59e8tELBkZGfz8889Ur16dtm3b2uz71ltvMW7cOObPn89zzz3n8BgBAQEkJiaya9cuqlSp4jAPwNy5c+nXr5/dFsJTp06xdu1aOnbsSLVqN8cKK6UYNmwYP/74I/Pnz6dZs2Y2+w0ePNjmtbnV7c8//3RYZ+u8K1asYN++fYSHhxMXF0eNGjXo3Lkzs2fP5vjx45QoUYJVq1ZRpEgRqlevnm2Z2bmd+tpjPTFOWloaly9fJj09nebNmzN69Gg2bNhA7dq1bfbp3bu3TetWtWrV8Pf3p1ChQnTq1Mkmb8OGDZk4caLd1qY333zT5nXHjh0JDw9nwYIFlolqFixYgLu7O0OGDLHJ++KLL9pcm2bWE5JcuXKFlJQU3N3dqVOnDuvXr7fJ26tXLxo2bOjw3FjLbrzd6dOn6dSpEz4+PkydOtWlMpOTk/Hysj8CyNvb26a10fz/9vJ7e3vb5BEiP931gZ5S6mmgjOllMFBAKWXuc3BIaz0TQGu9Uyn1GTBQKTUPWAxEYEySshrbxdKF+FdRStEgtAH1S9Zn88nNfLnjSz7Z/Alf7/yaXpV78WT4kxQsIF1M7ntKQUAp53kKl4FzB+DiEcd5MlIhcT4sHw7lop2X5+kLD7YAt/tpSHj+6N+/P127dkUphZ+fHw899BBFixa1bD99+jRXrlyhcuXKWfYtWrQoJUqUsDvmytr48eN5+umnqVq1KuXKlaNJkya0a9eOdu3a4Wb6DAcOHMjChQsZMGAAb7zxBg0bNqR169Z0797dMq38wYMHAezWJSIiAjc3N7t1KVfOtsuwuTvg2bNnndYbbgZZcXFxhIeHs2rVKrp3706TJk1QShEXF0f37t1ZvXo1UVFRlvdzO26nvo5MmTKFL774gsTERDIybMdXnz9/Pts6gBEIZe6CaE4318860CtcuLCle6a1iIgIFixYwNWrV/Hz8+PAgQOUKFECf39/m3xeXl6UK1cuS/3+/vtvhg0bxrJly7hw4YLNtszr0JYrV87ue8mpc+fO0aJFC44dO8aiRYssXYmz4+vrS0qK/Tn9rl+/jq+vr01eMJZj8PHxyZLXOo8Q+emuD/SA54CoTGnvm55XA9bT1r2KsVB5f+BR4AwwCXhX63t1Nop8nuVD3FeUUkSGRBIZEknCqQRid8QyYesEvtn1DT0jevJ0pacpVOAubKEReUcpaJ71l/ks0lNhy3TjkZ0OU6D6U7dbM9fdZsvZ3apChQo0b35n11Ps0KEDSUlJLF68mNWrV7NixQq+/vprGjVqxIoVKyhQoACBgYFs2rSJ33//nd9++401a9YwePBgRowYweLFi+2OW3KVo1lAtQsLk9eqVQt/f3/i4uJo0aIF//zzD02bNqVo0aI8/PDDrFy5koiICM6dO+d0fF5e1deecePGMWTIEFq2bMnLL79MyZIlKVCgAEePHqV3795ZAj9ndXA2o+qt1i8nrly5QuPGjbl69SqvvvoqVatWpVChQri5uTF27FibcZ/m/FeuXHGp7AIFCtj8yGF27tw5mjdvzt69e1m4cGGOPueSJUty9OhRu9uOHj1KaGioTV5z+oMPPpglr1LKkkeI/HTXB3pa6+gc5E0HPjU9hBBOPFLsEaY0n0Li2URit8fy+fbPmbVnFn2q9KFHxR7SpVM41/lrOH8w+3zzn4dlb8GmbLpPRfbN22DwPhQcHEyhQoVITEzMsu38+fMcP36cRx55JNtyihYtSs+ePenZsydaa3pr0d8AACAASURBVN58800++ugjFi5cSNeuXQEjiIiOjiY6OhowZtesWbMmo0ePZtGiRZQtWxbAbl327t1LRkZGrrTeWHN3d6dx48asWrXKEpSauwI2a9aMn376ydLC6EoAkLnFKS/MnDmTsLAwlixZYtPiuHRpNhMt3aYLFy5w4sSJLK16e/bsoVixYpYumOXKlWP58uVcunTJplUvJSWFAwcO2HSpXLlyJceOHeObb76xTJBjlnkyGIBPPvmE9957z6X6RkVF2cwWCzeDvN27dzN//nxatWrlUllmkZGRzJo1i8OHD9u0hh4+fJhjx47Rvn17m7yxsbGsW7cuS6C3fv16wsPDZSIWcVeQvjT3iPz4B0e4Tt/DLa+VAyszoekEfnjsB6oXq86ErRNoM68NM3fPJCXd0dKU4l/P0xuKRWT/aPsJlK4LvoGOH9fOwbK3IekP+GeD48c5FwLLfzE3NzfatWvHtm3bsgQGH3zwARkZGZaZJ+1JT0+3273OPJbt3LlzgLGGWGYVK1bEx8fHkqdYsWLUr1+fX375hV27dlnyaa0ZO3YsgNO63KqmTZty9uxZJk6cSN26dS3d55o2bcqhQ4f45ptvKF68uN0upZmZb9TN7ykvuLu7o5SyaXFLS0vjgw/ufCt15mPMnz+fffv28fjjj1vSOnToQHp6umWdObPPP/+cS5cu2aSZWxQztx4uX77c7hIUvXr14rfffnPpkfn458+fp0WLFiQmJjJ37lybpTPs2bt3L3///bdNWvfu3QGj+7I18+unnrr5Q1SHDh3w8fFh8uTJNkuF/PLLLxw4cMAmrxD56a5v0RPibnY/xd8RgRFMbjaZhFMJTE6YzEebPmJ64nSer/Y8HR/siKeziTmEcKR0bXjqB+d5jiVAbBRMb+s8n7uXsSh8duMM/8XGjBnDb7/9xuOPP86AAQN48MEHWbNmDXPmzKFx48Y884zjpTUuX75MiRIlaN++PdWrV6dYsWIcPHiQzz//nCJFitCuXTsA+vXrx5EjR2jZsiVlypTh2rVrzJkzh8uXL9OrVy9LeRMmTCAqKopGjRpZllf49ddfWbZsGT169MgyEUtuMLfU7dmzh27dulnSGzdujIeHB7t37+bJJ590qay6desyefJkBgwYwKOPPoqnpyd16tSxtFbeCV26dOGtt96iTZs2dOrUiUuXLvHdd9/d8cXcg4KCmDdvHseOHSM6OtqyvELx4sUZOXKkJV+fPn2IjY1l1KhRHDx4kHr16rFt2zZ+/PFHypcvT1razYmeGjZsSEhICEOGDCEpKYlSpUqRkJDAzJkzqVq1Kjt32k6YdDtj9Fq0aMHWrVvp3r0758+fz7KOXf369W3KjoiIoEyZMiQlJVnSHn30UR577DHGjRvHxYsXqVevHuvWrePrr7+mZ8+eNhPFBAcH8/777zN06FCaN2/O/7N33+FRVlkcx783PSEkEEIJAUJRCUWkN6kRsIC6qAiIoEjRRUB6lbKKFKUJiIg0ARVldS1roxcFpQqioCJFmtJ7AoTc/WOSbALJpBCYSfL7PM/7wLxz5p0zM5lkztz7ntu2bVsOHTrEhAkTiIyMvG4tPhFXUaEnIslULlSZWc1mseHIBqZuncrL37/MnB1z6Fa5G81LNcfTI/XzPkQypWhleHYNXLh+pCjR5Qvw72dgUTsoED9VqlRHOLkveZwxkLcwePndtHTdWUREBD/88APDhw9n4cKFnD59mmLFijF48GBefPFFp2voBQQE0KtXL5YvX86yZcs4f/58YuE3ePDgxHOO2rdvz7x583jnnXc4duwYQUFBlC9fnn//+988+uijicerXr0669atY8SIEUyfPp0LFy5QunRpxo0bd13XxqxSqVIlQkNDOX78eLLpmYGBgdSsWZN169al+7yttm3bsnXrVhYtWsTixYuJi4tj7ty5N7XQ69+/P9ZaZs+ezQsvvECRIkVo3bo1HTt2pHz58jftfvPkycOKFSvo3bs3gwYNwlrLfffdx4QJEwgLC0uM8/HxYenSpfTv359PPvmEjz76iBo1arB06VL69euXrHDKly8f33zzDQMGDGDq1KnExsZSrVo1vvzyS2bPnn1doXcjEhZsf//993n//fevu37u3LnpKiIXL17MqFGjWLhwIQsWLCA8PJyXXnrpuo6kAH379qVAgQJMmjSJnj17EhQUxOOPP87YsWM1bVPchrkVJ+RKyqpXr243bdrkNGb9O0Oos/cNLg06gq+fzplyN3O/28u/Pv+FH4c3JV9AzluI3FrLt4e+ZerWqew8uZOy+cvSp1of6obXTfvGIllt9auwbVHixZ21xlOu5DWdAq9eBr8gR/dQZ4xHzhqSFxGRbGnnzp2UK1fOaYwxZrO1tnpGj60RPRFJlTGG+sXqc3f43SzZt4TJWybz7LJnqRNWhz7V+xAZEunqFCU3aTjAsSXYuRMKX/PH8ewROP8X/LXd+bF8Ah0jgyr2REQkh1Kh5+aMBlzFDXgYD+4rdR9RJaL48NcPmbF9Bo9//jgtSrege5XuFA1UG2lxE4GFwMMLp0vTXIl2NIA5dyQ+NhUenuAfomJQRESyJRV62YQxapDqznLLDGgfTx+eLP8kD932ELN/ms3CXxbyzb5vaFeuHZ3u7ESwb7CrU5TczsMTAgs6j4mLg8vn4fzfaR/PeIB//rTjRERE3IwKPRHJsCCfIHpX602bsm2Y9uM05v08j//s/g/dK3fn0TsexcvZKImIq3l4OJZ+sNcvPp3M8d/g9AE4l0ZBaAzkKwHe/lmXo4iIyA3SMJHIDcjtE7rCAsN4pd4rfPjgh9ye/3ZG/TCKx//7OD8cuX6NJBG3Yjwc0zadbcHFwTcQvHycb7ExcPawY0qosy3uatp5iYiIZBF97S4iNywyJJLZzWaz7M9lTNg0gc5LOtOkRBP6VO9D8bzFXZ2eSOb45nVsaUloAHPsrPM4n0AIvT1rchMREUmDCj0RyRLGGJpGNKV+eH3m/zKfWT/NYs0na+hQoQOd7+xMHu88rk5R5OYILJz2tM3L5+HCMThzAIyTtSg9vCBPQTWAERGRG6ZCz81Z0jiHRMTN+Hn50bVSVx4u8zCvb3mdWT/N4tPdn9K7Wm9alG6B0QdYyWk8PMA/n/MY37wQcxYunHASFN/VycsX/NTYSEREbowKPZEskEuabmZI4TyFGV1/NG0i2zB2w1iGfDuEj3//mBdrv0iZfGVcnZ7IreXhCYXLO4+xcXB0J5za73zZB4DgcBWDIiLilJqxZBMaBZHsqlLBSix8YCHD6wznt1O/8dhnjzFp8yQuXrno6tRE3IvxiG8AEwTeAalvNi6dzV9iXf2IRETEhTSiJ3IDVICnj4fxoNUdrbinxD1M3jyZOTvm8OXeLxlUYxBRJaL0PIok8AtybM5cPAmn98OxXc7jPH0cy0hoHVYRkVxJhZ6I3DIhfiG8dPdLtLy9JS9//zK9VvWifnh9BtcarO6cIunln98xFdTZOoCxl+DcEThz0FHwOePpAwEhWZujiIi4nL7mE5FbrkqhKnzY4kP6V+/P5r830/LTlszYNoPLVy+7OjUR92eM4/w8//ypb4GFHdM8L55wFHzOttP74fIFlzyUffv2YYxh5MiRLrn/rGCM4emnn75p8ZJ7jBw5EmMM+/bty5LjzZs3D2MMq1atypLjSfajQs/dWbX5kJzJy8OLDhU68Nk/PqNR8Ua88eMbPP754/x49EdXpybilmJiYpg+fTpRUVEULFgQb29v8uXLR40aNRg4cCC7diWZymkMhN4BYXc53wrf6Vju4dQ+OP6bk+13lxWD2dHIkSP55JNPXJ2GW4iJieHtt9/m4YcfpmTJkvj7+1O6dGnatm3Lzp07M3Ssw4cP06FDBwoWLIi/vz/Vq1dn8eLFKcZeunSJ4cOHU6pUKXx9fSlTpgyjRo3iypUrWfGwxE2NGTOGVq1aUbp0aYwxlCxZMkO3P3ToEGPGjKFhw4aEhYWRJ08eKlSoQP/+/TlxIvWuyevXr+fhhx8mNDQUPz8/SpUqRdu2bbl82bVfYGvqpoi4VOE8hRnfcDwPlXmIl79/mQ5fdaBNZBteqPqC1t4Tibdnzx5atGjBzp07adiwIb179yYsLIzz58/z448/MmfOHMaPH8+ff/5JeHi440bGAGmc/+rp4ejgefGk87gr0Y7Rv/ylnMcZjwytARgREUF0dDReXtn340h0dDSensnXRvzXv/7FU089xT/+8Q8XZeU+9u3bR9euXalXrx6dOnWiaNGi7NmzhzfffJOPP/6Yr7/+msaNG6d5nJMnT1KvXj2OHj1Knz59KFasGO+99x6PP/44c+bMoWPHjsniW7duzaeffsozzzxDnTp1WL9+PcOGDWP37t3MmzfvJj1a99K+fXvatGmDj08a07dzkCFDhhASEkLVqlU5ffp0hm//+eefM3LkSJo3b07//v3JmzcvGzZsYPLkySxatIiNGzdSpEiRZLeZO3cunTt3platWgwePJh8+fJx+PBh1q5dS2xsrEuf/+z7mzWXUbMK92Y18nrDGhRrwCcPf8LUrVN5b+d7rDywkmG1h9GgWANXpybiUtHR0TRv3pw//viDjz/+mJYtW14XExMTw6RJkzL3tyKggGNz5txfjkLvr+3O43wCIfT2dN+1MQY/P790x7uj7J7/zVawYEG2bt1K5cqVk+1v164dVapUoX///mzatCnN44wdO5a9e/fy2Wef8eCDDwLQqVMn6tSpQ79+/WjVqhWBgYEAfPnll3z66af06dOHCRMmANC5c2fy5cvHxIkT6dq1K3Xr1s3iR+p+PD09r/sSIqf7448/KF26NAAVK1bk/PnzGbp9/fr12b9/f7JirkuXLtSqVYsuXbowfvx4xo8fn3jdL7/8wnPPPUfHjh15++233e7zuqZuitwAN3s/Z3t5vPMwqOYg5t8/nzxeeXh++fMMWDOAE9HOFpkWydlmzZrFrl276N+/f4pFHjiKjcGDB1O0aNHEfYcPH6Zv375UrlyZ/Pnz4+fnR/ny5Rk3bhxXr15Ndntn5waVLFmSRg+2hqBwyFsU8hbli+9+puHj3Qi9swn+ZepSotaDPPLsYH7b+TNcOAbRZzjw+8880+FJIkoUx9fXl0KFClK3di3emTUj8dipnaM3ffp0mjVrRnh4OD4+PoSFhfHkk0+mmF/COW/r16+nYcOG5MmThwIFCtC5c+d0fcjr2LEjfn5+xMTEJO5bv349xhhCQkKIi/t/05uvvvoKYwwffPDBdfef9PEAvPPOOxhjErdrZTZfiH9NGjVi27ZtNGnShMDAQAoVKkTfvn2JjY0lJiaGfv36ER4ejp+fHw0aNLhummSar3mjRunKJS0FChS4rsgDKF++PBUrVmTHjh3pOs57771HmTJlEos8cBQyPXr04OTJk3z55ZfJYgF69eqV7BgJlxcuXJjm/WXk/ZNwLtyKFSsYP348ZcqUwdfXlzvuuIN33nnnumPHxcUxZswYSpUqhZ+fHxUrVuTdd99N1/OQYN26ddx///0UKVIEPz8/wsPDeeCBB/j++++vy+vac/T27dvHo48+SlBQEEFBQTz88MPs3bs3xdc94ed7xYoV1KlTh4CAAIoVK8a4ceMAOHXqFJ06daJQoUIEBATQokULDh8+nOnn8kYlFHmZVaFChetG7MAxQgxc9/M6fvx4rLW8+uqrGGO4cOECsbHus7SNRvRExO1ULlSZxQ8uZtaOWczcPpN1h9cxoMYAHiz9oNt9WyZys/373/8GHCMSGbF9+/bEEcAyZcpw5coVvv76awYNGsSePXt46623MnA0A4GFAFi9ejUPtXmKihUrMnjIkMRpSsuWLWX3n4e5o8xBYmNjaXr/4xz66yjdnmrFHaUjOHP2PNt3/s7aFd/w1JNPOF1GYvz48dSuXZuePXsSEhLCjh07mDVrFitWrOCnn36iQIHkI5A//vgjLVq0oGPHjjzxxBOsWrWK2bNn4+HhwcyZM50+sqioKObNm8d3333HPffcA8Dy5cvx8PDg1KlTbN26lWrVqgGwYsUKjDGpTjUsWLAgCxYsoH379tSvX5+uXbumGHcj+SY4ePAgTZs2pXXr1jz22GMsWbKEiRMn4uXlxc8//0x0dDSDBg3i+PHjjB8/nn/84x/s3LkTD4/Mfcd/5syZdJ/fFhAQQEBAgNOYuLg4jhw5QuHChdM83pEjRzh06BDt2rW77rratWsDsHHjRh5//PHE/4eHh1O8ePJuzsWLF6do0aJs3LgxzfvMzPtnyJAhREdH8+yzz+Lr68ubb77J008/zW233cbdd9+dGNenTx9ef/11GjRoQO/evTl69CjPP/98uouUX3/9laZNm1KkSBFeeOEFChcuzN9//823337Ltm3bEp+TlJw4cYL69evz999/89xzz1GuXDnWrl1L48aNuXAh5fNwt27dyueff07Xrl3p0KEDH374IYMGDcLPz4933nmHkiVLMnLkSHbv3s2UKVPo0KEDy5Yty9RzGRcXx8mTaUwlTyIkJCTTP9MZcfDgQYDrfl6/+uorIiMjWb16Nf379+ePP/7A29ubJk2a8Prrr3P77emf4XBTWGu1uWirVq2aTcu6OQOsHRFkr1y+lGas3HrvrNtrIwb+1x4/F+PqVHKs3ad223ZftLMV51W0zy551h45f8TVKYmb+OWXX1ydwi0REhJig4KCrtsfGxtrjx07lmy7ePFi4vUXL160cXFx193uySeftB4eHvbw4cOJ+0aMGGEBu3fv3uviIyIibMOGDRMv9+7d2wL277//vj7Z2MvWXrpgt2363gJ23CsvW3vpQvLtyE/W/v2LtacP2L3b1lnAjhjY29rTBxK384d+dcQmsWzZMscxx41Lth+wxhj7/fffJ9v/wAMPWC8vL3vu3Lnr80zi4MGDFrBDhgxJ3Ne4cWP70EMP2bx58ya7v6pVq9qKFSted/9PPfVUmvuyKl9rHa8JYD/88MNk+6tWrWqNMfahhx5K9tq//vrrFrBff/114r6MvObWWtuwYUMLpGsbMWJEmo/hjTfesIAdNmxYmrGbNm2ygB0wYMB11124cMECtm3bton7AgMDbc2aNVM8Vo0aNWxYWFia95mR98/cuXMtYCtXrmwvXfr/57WDBw9aHx8f26ZNm8R9u3btssYYGxUVZWNjYxP3b9682RpjUn1Nkkp4PX/44QencQl5rVy5MnFf//79LWAXLlyYLDZh/7Wve0o/r5cuXbJFihSxxhjbo0ePZPEJvx927dqVuC8jz+XevXvT/XOW1nNVoUIFGxER4eQZSr9WrVpZwC5fvjxx3+nTpy1gQ0JCrJeXl+3Vq5f9+OOP7ciRI62vr68tVKiQPXIk7c8s6flbBmyymag1NKInIm6tTL4yzL9/Pot2LWLylsk88ukjDKg5gIfLPKzRPUnRuA3j2HUyjcXEb7HIkEgG1hyYqduePXs2xalEO3fu5M4770y277XXXqNfv34A+Pv7J+6/fPky58+fJy4ujnvvvZeFCxeyadOmZNPg0is4OBiAjz76iC5duiRvpOLpDZ7eBIc68l259jue7tyVQoUK/T8mb2E4e8TRACYmvlnClehkDWHyEAdnDhJX4DbOnTvHlStXuOuuuwgODuaHH364Lqc6depQq1atZPuioqL48ssv2bdvHxUrVkz18YSHh3PHHXewYsUKwHG+4/r165kwYQJxcXEsX76cAQMGcPr0aX788Ue6d++eoecrJTeSb9K8W7VqlWxfvXr12LJlCz169Ej2+7F+/foA/P7779x7772ZynnChAmcOnUqXbFpjUytW7eOPn36cNdddzFkyJA0j3fx4kUAfH19r7su4RzJhJiE/6cUmxCfNDY1mXn/dOvWLVnjjYSfrd9//z1x36effoq1lj59+iQ7f65q1ao0bdqUJUuWpJlbwnvw008/pVKlShk6T/Tzzz8nLCyMtm3bJtvfr18/XnvttRRvc+3Pq4+PDzVr1uSzzz6jZ8+eyWLr16/PpEmT+P333ylbtiyQseeySJEiLF26NN2PJ6XfjVltwoQJLF68mK5duxIVFZW4/9y5c4CjUdDQoUMZNWoUAC1btiQiIoKOHTsyadKkxGmurqBCT0Tcnofx4IlyT1A/vD4vfvciw74bxvL9yxleZzgFAwq6Oj2RmyooKIizZ89et79UqVKJH4i2bduWWOAliI2NZezYscyfP5/du3df1zQqvR/ar9W9e3c+/fRTunXrxsCBA6lXrx733Xcfbdu2pWBBx/sxIiKCoUOHMmbMGMLCwqhcuTL33HMPrVq1okaNGpAn/n17KX76Zt7CEFYp8T5WfPERL70yhh+27iAm5lLyvI8egmPxhbyX4wNkSoVFwvROZy3RE0RFRTFr1izOnTvHxo0biYmJISoqikuXLvHiiy9y+fJlVq1aRVxcXLIPepl1o/mC4/W/Vv78+VO8LmF/eo+dkoTpqzdq8+bNNG/enKJFi/LFF1+kq0hJmAZ66dKl665LOLcy6VTRgICAFGMT4tOaVgqZe/+k9rru378/8fKePXsAiIyMvC62fPny6Sr02rRpw8KFCxk9ejSTJk2idu3a3HvvvbRp04aIiAint927dy81a9a8brpjoUKFyJcvX4q3SelxZeRnLSPPpZ+fH02aNHH6GG6lWbNm0b9/f5o3b860adOSXZe0gL12bcx27drRuXNnl69hqEIvmzBGfXPcmXpu3hrFg4oz9765LPxlIVO2TuEfn/6DobWGcn+p+zW6J4kyO3LmripWrMiaNWvYu3dvsg9VefLkSfxAlNLyBH369GHq1Km0bt2aoUOHUqhQIby9vdmyZQsDBw5M1mTE2fvn2sYCBQoUYOPGjaxdu5alS5eyZs0aevfuzYgRI/jyyy+pU6cOAKNGjeKZZ57hiy++YO3atcyaNYvXXnuNAQMGOP2Ge+PGjTR7uDW3lY5g7LD+lCpRDH9/P4wxtOnSi7g4Cx7eEHcVoh2jgJ6eHmDjkh8o/rKNu+pYk9bJY4yKimLGjBmsWbOG9evXU7RoUSIjI7l06RIXL17k+++/Z8WKFXh6etKwYcNUj5NezjohXvshODPHSO26pMfOyGsOjlGL9K4JFhgYmNgBM6ktW7bQtGlTgoODWbly5f+XAklDQpOhQ4cOXXddwr6kxypatGiKsQnx6bnfjLx/EqTnec8Kvr6+LF26lA0bNvDNN9+wZs0ahg8fzsiRI3nvvfdSbdqUWTf6s5aR5/Lq1ascO3Ys3bkVLFjwpnUWnTNnDl27dqVZs2Z89NFHeHt7J7s+JCSEgIAALl68eN3Iore3N6GhoZn+Qi2rqNATkWzFw3jQoUIH6hdzjO4NXDuQZX8uY2itoRTwT6NFvEg29Nhjj7FmzRpmzZrFK6+8ku7bLViwgAYNGrBo0aJk+3fv3n1dbEhICOD4MJ90geGYmBiOHDnCbbfdlize09OTRo0aJXbo2759O9WqVWPUqFF88cUXiXGlS5emR48e9OjRg5iYGO69915effVV+vbtm3w6ZxLvvfceV69e5atvliUrbC9cuMCp0x3B2x8KlIG4ODj6s+PKi6fgyLbkBzp9wPHviT/gr1AoFAmeKa9n1bhxY4wxLF++nPXr1yeO2lWqVInQ0FCWL1/OypUrqVKlSqqjHtlNRl/zRx55hNWrV6fr2CNGjLiuk+qWLVto0qQJefPmZeXKlWmOPCUVFhZGeHh4so6SCRL2Va9ePXFfjRo1ePfddzlw4ECyhiwHDhzg8OHDPPTQQ2neZ0bePxmRMDq2a9cuypQpk+y6X375JUPHqlmzJjVr1gQcj61KlSq8+OKLTgu9kiVLsnv3buLi4pKN6h09ejRT686lR0aeywMHDqQ4Wp2ahG6hWW3OnDl07tyZJk2a8Mknn6Q4FdgYQ/Xq1VmzZg0HDx5MNkp76dIljh07dt376FZToSdyAzSG5Dqlgksx/775vPPLO0zbOo1Nf21iWJ1hNI1o6urURLJU586dmT59Oq+99hrVq1dP8UNcSiMGnp6e1+2/cOECkyZNui72jjvuAGDZsmVUrVo1cf+kSZOuG7k4fvw4oaGhyfZFRkbi7++f2C3vzJkzBAQEJPsG3M/Pj3LlyrFmzRpOnTqVaqGX8O38tbmPHj06eS4eHv9fwN3bH/KGJT+Qf3xBFhAC9qpjLUD//CneZ2heX+6sWIH//ve/7N27N7FbZkKHzcWLFycucZEegYGBGeoc6AoZec3hxs7R27p1K02bNiUwMJCVK1c6/SB/8eJF/vzzT4KDgwkL+/9r2rZtW8aPH8/nn3+eeD7X1atXmTp1Kvny5eOBBx5IFvvuu+8yefLkxHX0ACZPngyQYvfOa2Xk/ZMRDz30EIMGDWLixIncd999iT/vW7ZsSdap0pmU3oPFihWjYMGCaf7cPfjgg4wfP573338/2fOQdG24rJaR5/JWnqP3559/cvHiRcqUKZPsd9W8efPo0qULUVFRfPrpp06nF7dv3541a9bw5ptv8vrrryfunzlzJnFxccl+Ll1BhZ6IZFueHp48U/EZGoQ3YOh3Q+mzqg8tSrdgSK0h5PXJ6+r0RLKEv78/X3zxBS1atOCRRx6hUaNGNGvWjCJFinD27Fl27drFBx98gKenZ7LRi8cee4y33nqL1q1b06RJE/7++2/mzJlz3dIEAE2aNKFs2bIMHz6cEydOUKpUKb799lu+//776z5QdunShYMHD9KsWTMiIiKIjo7mgw8+4Ny5c3To0AGAlStX0rVrVx599FHKli1LYGAgmzdvZtasWdSqVSuxSUNKWrZsyaRJk3jggQfo2rUrPj4+LF26lO3bt1+XC77x0wO9/SHvNR/4/BwNKwgo4Pj/xROOLRVRtSoxeZZj/bWk5+FFRUWxePHi6/Y7U7t2bZYtW8a4ceMoUaKEY9ppmzbpuu2tSZWNDwAAIABJREFUkpHXHDJ/jt7+/ftp2rQpp06domfPnqxbt45169Yli2nZsiV58uQBYMOGDTRu3JinnnqKefPmJcYMGjSIxYsX88QTT9CnTx/Cw8N5//332bhxI7NmzSJv3v//zm/evDktWrRg4sSJnDlzhjp16rB+/Xpmz57Nk08+Sb169dLMOyPvn4yIjIzk+eefZ9q0aURFRfHoo49y9OhRpk2bxl133cXWrVvTPMaoUaNYsmQJLVq0oFSpUlhr+fzzz9m1axcDBgxwetuBAwfy3nvv0bFjRzZs2EBkZCRr165l3bp1hIaG3pTTIDLyXN7oOXoLFixIPCfy2LFjXL58ObFJSkREBO3bt0+M7dChA6tXr042KvjZZ5/RqVMngoKCaN26NR999FGy4wcGBvKPf/wj8XLHjh2ZP38+U6ZM4fjx49SvX5+ffvqJt956iwoVKlzXrOaWy0yrTm23fnmF2CtX0oyVW29+/PIKx7S8gstdvnrZTt863d71zl222eJmdvNfm12dktxkuWV5hQQXL16006ZNs40aNbIFChSwXl5eNjg42FarVs32798/WTtzax1t5/v162dLlChhfX197W233WbHjBmTuEzB3Llzk8X/+uuv9t5777X+/v42ODjYtmrVyh48ePC6VvsfffSRffDBB214eLj18fGxoaGhtkGDBvbf//53YsyePXvss88+ayMjI23evHltQECAjYyMtMOGDbOnT59OjEtopX5tO/7//Oc/tmrVqjYgIMAWKFDAtm7d2u7fvz/Ftv+kspRBstbyV2OtjTnrdPts4UwL2NKlIqw991fi9ttWxxIQ3t7e9sLfe6yNPpPm/f/222+2adOmNm/evIlt4DOUbxpSeh6sTX3JhNSe5/S+5jdi5cqVGWqRnxCf0nN08OBB++STT9oCBQpYX19fW6VKFbto0aIU7zc6OtoOHTrURkREWB8fH1uqVCn70ksv2cuXL6cr74y8f5y9dg0bNryuxf/Vq1ftqFGjbIkSJayPj4+tUKGCXbhwodMlL5JauXKlffzxx21ERIT18/Oz+fPntzVr1rRvv/12smUMUstrz549tmXLljYwMNDmzZvXPvTQQ3bPnj22QIEC9v77708Wm9pr8dRTTyX7uU6a27XPT0Z/F90IZ8uApLZkSNLnO+E1SG1LabmG8+fP24EDB9qIiAjr7e1tixYtap9//nl78uTJdOV8M5dXMI7biitUr17dbtq0yWnM+jkDqPPnW1x98QSeKZxsL661YP0+hn36M5tebEJoYMqtnOXW+vHojwxeO5jDFw7T+c7OPHfXc3h7eKd9Q8l2du7cSbly5VydhuQUly/C8d9Iu72WgcIVwVN/kyXnOHHiBKGhoTz77LPMmDHD1enkKun5W2aM2Wytre40KAX6LSWSBfR9ifuoXKgy/37o34z5YQwzt89k/eH1jKk/hoig9J/4LyK5kE+AY4kHZ7/QY2McxeDJPak2dgHAw0DecBWD4paio6OTLQ0AMHbsWACaNtV57jmJfgOlwRgzD3gCSNpT+DFr7dfx14cB04CGOHpzrAW6W2sPZnEeWXk4kRwtj3ceRtUbRYNiDfjX+n/R6vNWDKo5iJa3tdR7SURSZzycd9nyyeM45+/SeYi7fgmCRFcvgYcPBIWlHiPiIg888AARERFUrVqVuLg4li9fzn//+1/q1q2b7Pwzyf5U6KXPTGtt91Sum47jeSwFXAXeBuYAzW5RbuJKKhrcWrOSzahUsBIvfvsiI9aNYM3BNYysM5J8fjmjPbqIuEC+EmnHnPgDzh+FmDTa1ecJ/f/i8SK3SIsWLZg/fz7/+c9/iI6OplixYvTt25cRI0bctDXpxDW0CveNKwMsttaes9ZeBN4DKrk4JxGJVyRPEWY2m0nfan1ZfXA1j372KBv/2ujqtEQkJwsKA78g8PJLfbMWzh5xLPxubdqbSBbp27cv27Zt48yZM1y+fJk9e/Ywfvz4ZJ1LJWdw+xE9Y8xgoCpQDceo2X5rbclUYj2AF4BngZLAMeBDYLi19sINpNHOGPME8DewEBhnrU2YszEReMwY8xmOEb32wOc3cF8iksU8jAdPV3ya2kVr0391fzov6cxzlZ6ja6WueHro20sRyWLeARCSxqLPl87Bid3w1/a0j+ebFwq4duFlEcl+ssOI3mggCvgDSGulzkk4Cq9fgB7AYqAn8Hl8EZjIGLPIGGOdbI3iQ6cAZYFQHEXc08CIJIf6FsgHnAROx8cOyeyDvZZJs/uXiKRXZEgkH7T4gOalmjN923S6LO3C0YtHXZ2WiORGPoEQXNyx/p+zzS+foyiMPgNXolPfYi+5+hGJiJtx+xE9oIy1dg+AMWYHEJhSkDGmAo7i7mNr7aNJ9u/FUay1wTGtMkEXILXz7gDOAFhrtyTZt8kYMwL4FzAsvnhcBnwMPIBjRG8AsMoYU9laeyUjDzQlKvNEslaAdwCj64+mVlgtXvnhFVp93opX6r1CvfC0F9AV92OtVYMdyZ6McZyjl5a4WPj7LJzak3Zs6B2OhjEiki3c7GXu3H5EL6HIS4e2OHplTb5m/9vAReDJa457zlp73MmWWpEWx/97coUAEcAUa+15a200jhHF8jjO3csy+iDj3qxK8mzn4dseZlHzRYT4hfDPZf9k4uaJXIm74e9m5Bby8vIiNtZJ50ORnMDDC0Jvh/wlnW/GE84dgejTzreres+IuIvY2Fi8buI62dlhRC+9auAowjYk3WmtjTHG/Bh/fYYZY1oDXwNngTtxTNtcHH/s48aY3cDzxpjhOEb0XsAxxXRf5h6GZCcqv7O30vlK837z93l146vM3TGXzX9v5tUGrxIeGO7q1CQd/Pz8OH/+PPnz53d1KiI3l3eAY3Pm8kW4cNQxzdMZv2AIKZ11uYlIpp07dw4/P7+bdvycVOgVBY5ba1OapH4IqGuM8bHWXk7heme6ATMAb+AIsAAYk+T6h3GM4h3EMUK6A2hhrY1J6WDGmK5AV4ASJdLRollEbio/Lz+G1xlOzSI1Gbl+JK0+b8Wou0cRVSLK1alJGgoWLMiff/6Jr68v/v7+mvkguVtQUQgIcR5z4ThcPA7n/sLpV5VevuCvZWhEbhZrLdHR0Rw/fvym1gM5qdALAFI7EzkmSUyGCj1rbcM0rv8FuC8Dx5sJzASoXr265vuJuIn7St1HhQIV6LemHy+sfIFnKj5Djyo98PLISb8mcxY/Pz8KFy7MX3/9xaVLakQhkqa4WDh3HGw6mlAFFXVMGxWRm8LX15fChQtrRC+dLgKFUrnOL0mMiEiKigcVZ/798xm3YRxzdszhp+M/8WqDVwn1T0fDBHGJ4OBggoODXZ2GSPZxNdJR8KXm3F8wtRoUqwGFIlOP8/SB+n0dnUFFxC3lpELvMFDeGOObwvTNcBzTOjM6bdP1tEiqyC3l6+nL8DrDuavgXbz8/cs8/vnjTGg0gSqFqrg6NRGRG+fp7dhSE1IKanaFnz+GU3tTjzt/FDy84b7RWZ+jiGSJnFTobQSaATWBtQk7jTF+QGVgjYvyyho698S9qR7PcR6+7WEiQyLpvao3z3z9DH2r96VduXY6D0xEcr77xzo2ZxZ3hM3z4NhO53HlHoLqHbMsNRFJP7dfXiEDPsDxcbvXNfu74Dg3791bnlEW0odLkVuvbEhZFrVYRL1i9Ri3cRz91/TnwpULrk5LRMT16veFsLscXT5T2479CkuGwaXzrs5WJFdy+xE9Y0x7HGvVARQEfIwxL8Zf3m+tXQBgrf3JGPMG0N0Y8zHwJVAO6AmsJvli6SJZQvV3zhfkE8TrjV9n7o65TNk6hd9O/cbkRpMpnU/tyUUkFytSEZ75ynnMgQ0wuylMrwPe/s5jw6tByzezLj8Rcf9CD+gEXNv58uX4f1fjWO4gQS8c69d1BZoDx4GpwHBrbdzNTVNEcioP40GnOztxZ+id9F/TnzZftOGVeq/QNKKpq1MTEXFfxWo4Rv5O/OE87vzfsO09qNkFwqvemtxEcgG3L/SstY0yEHsVmBC/iYhkqZphNfmwxYf0Wd2HPqv60LVSV56v/DweJifNghcRySLGwD3D046LOQMTysGc+xxr+KXGyxee/BjCKmVdjiI5mNsXeqIuHyLupHCewsy9dy6v/PAKM7fP5NeTvzKm/hjy+uR1dWoiItmTXzC0nAF/rncet2U+rJ8GLSY7j/P217kVIqjQE8kSKsdzFx9PH0bWGUm5kHKM2zCOJ754gilRUygVXMrVqYmIZE/lH3JszsRegk2zYfsHzuOqdYQH0ygGRXIBFXoiIplgjKFNZBvK5CtDv9X9eOKLJxjXYBwNijVwdWoiIjlTo8GOdf7irqYes3c1/Piu43w/n8DU4/yCwD9/1uco4kZU6IncAIOmhuR2NYrUYFHzRbyw8gW6L+9O9yrd6XJnFy2JIiKS1QILQt0ezmPuuBem14Y36zqP884DL2xzHFMkh1KhJyJyg8ICw3jn/ncYuW4kU7dOZdfJXYy6exQB3gGuTk1EJHcpVA7a/wfOHkk95tI5+HogrHsdKjySepyHJxSu6PhXJBtSoScikgX8vfwZW38s5QuUZ+Lmiew/u5+pUVMpGljU1amJiOQuZaLSjtn1X1g31bE50+RfUK9X1uQlcoup0BMRySLGGJ6q8BS357udfqv70faLtkyJmsJdBe9ydWoiIpLUY3Pg0BbnMWteczR/Sc9yDuHVHN1DRdyICj03Z6z6OYpkN3XD67LwgYV0X9GdZ75+hpfufonmpZu7Oi0REUkQWAjK3uc8JjYGFj8FC1qmfbwKj0CruVmTm0gWUaGXDcRZg5Zjdm+qx+VapfOV5r0H3qPXql4MWjuIvWf20q1yNy2uLiKSXZR/GLqucizr4MyWBbB9EWz/EDy9U4/LV8Ix8idyi6jQE7kBaqwozuTzy8fbTd/m5e9f5q3tb7H3zF5G1RuFv5e/q1MTEZG0GANFq6QdFxAK296Hj7s4j/Pwht4/Q97CWZOfSBpU6ImI3ETent78q+6/KBVcikmbJ3Ho/CGmRE2hUEAhV6cmIiJZIfQ26PUTXDqbesz5v2H+w7BsBETcnXqcp7djJNFbXwjKjVOhJyJykxlj6FixIyWDSjJw7UDaftGWqVFTKV+gvKtTExGRrBAcDoSnfn2hcnBbE8fI37b3nR/rzEFo0C9L05PcSYWeiMgt0rhEYxbcv4DuK7rz9NdPM7b+WKJKpKMNuIiIZH9t3neM7Dnzn+dg01zwzes8rkSd9HUDlVxNhZ6bs6jLh0hOUjakLO83f58ey3vQa2UvBtYcSLty7VydloiI3GxePpCvuPOYOt1g0RPw1QDncflKQM9t4KEGX5I6FXoiWUAFuWREqH8os++dzcC1Axm7YSyHzx+mb/W+6sgpIpLbRTaHQQcgLjb1mF+/gk+7wTdDIG+R1OMCQqBKe3WOy8VU6GUDKiFEcp4A7wAmN5rMqxtfZf4v8zly4Qij643Gz8vP1amJiIgr+QU5v/7Ox2D1WPjhzbSPFVTUcW6g5Eoq9ERugL4jkxvh6eHJoJqDCA8MZ/ym8Ry9eJQpUVMI8QtxdWoiIuKuvHyh54/O1/e7ehmmVIGVo+HQltTjjIE7W0H+klmeprieCj0RERcyxtChQgfCAsMYvHYw7b9sz5tN3qREUAlXpyYiIu7KwxN8ApwEBECt52DVaDi02fmx/voJHp+fpemJe1ChJyLiBppGNKWgf0F6rOhBuy/bMTVqKpULVXZ1WiIikl01Gpj2Mg1LhsGGmbD8ZUjrPPE77oNi1bIuP7npVOi5O6sz9ERyi8qFKrPwgYV0W9aNzks6M6b+GJpGNHV1WiIikl15eDq/vkYn2LoA1k5I40AWfvkEnt+g5i7ZiAo9kSygelyySkRQBAsfWEiPFT3ou6ovg2sNpm1kW1enJSIiOVGBMjD4QNpxWxbAZ93hi77gny/1uLxhUKOzikE3oUIvG7Bq+SGSq+T3y8+sZrPov6Y/o38YzfHo43Sv3B2jP5wiIuIKFR+BteNhyzupx1gL9ioUqQQlat263CRVKvREboA+d8vN4uflx6RGkxj1/Shmbp/JiegTvFj7Rbw89GtbRERuMZ888MI25zGXzsOEsrB0GJSsl3qc8YRqT0FwsazNUa6jTwwiIm7Ky8OLEXVGUMC/ADO3z+RkzElebfCq1toTERH34xsINbvAuqnOO33GxcK5I/DwtFuXWy6lQk9ExI0ZY+hRpQcF/AowdsNYnl36LFOiphDsG+zq1ERERJJrMtKxOfPp87DjYwgs7DyuWA0oe1/W5JVLqdATEckGnij3BCH+IQxeO5inv36aGU1mUDhPGn8kRURE3E3NrvDzJ/DtpNRjbJxjumjfXx0jhZIpKvTcnto5iojDfSXvI59vPnqt7EX7r9ozo+kMSgeXdnVaIiIi6Rd2Fww55Dzmz+9hzr3w314Q4uTvnE8eqPVP8PLJ2hxzCBV62YC6bro/leNyq9QOq82ce+fwz2X/5KmvnmL6PdO5s+Cdrk5LREQk6xSv5dh+Wpx2bJ6CUPmJm59TNqRCT+QGGBXh4gLlC5Rn4f0L6bq0K52WdGJa1DRqhtV0dVoiIiJZwxjotMR5jLUwtRp8NwXOpDFC6OkFVZ+CgJCsyzEbUKEnIpINFQ8qzvz759N1aVf+ueyfjG84nsYlGrs6LRERkVvDGKjTzbGI+8pRacfHnEm7UUwOo0JPRCSbKhhQkHn3zeOfy/5J71W9GVVvFC1Kt3B1WiIiIrdGjc5Q9em04z5oB1vfBf/8zuNK1IXiNbIkNXegQi8NxphSwBSgLhALzAGGWmvjksQ0B14GygLngAnW2tdckK6I5DLBvsG83exteq7oyZC1Qzh/+TxtItu4Oi0REZFbwzMd5UzNrvDuY7B0uPO4vEWh9w7w8Mya3FxMhZ4TxhhP4HPgG+AxoBDwX+A0MC4+phkwE+gArAYCgBJZloRVmw8RcS6Pdx6mN5lOv1X9eOWHVzh/5Tyd7+zs6rRERETcw233wJAjYK+mHvPrV/BRJ1g2AoKdfJT3zwd3tnJMHXVzKvScKwuUA2pYay8BB4wxk4ARxBd6OEbyXrbWLo+/fBbYccszFZeyKsjFxXw9fZnYeCLDvhvG61te59zlc/Sq2guTDf4QiYiI3HTefs6vL/cQBBeHdVPTPlaeUCgTlTV53URuX+gZYwYDVYFqQClgv7W2ZCqxHsALwLNASeAY8CEw3Fp7ITN3n2RLuq+kMSYIuArUAL4yxuwC8gM/AC9Ya/dm4v5SpBJCRNLD28Ob0fVGE+gdyJwdczh/+TxDaw/Fw3i4OjURERH35uUDPTbDpfOpx8Rdgem1Ye1E550+jYHb74XAglmfZwa4faEHjAZOAluAfGnETgJ6Av8BJuAYjesJVDHGNLnmvLpFQGsnx2oMfAv8AYyOLziLAL3jrw9KOBTwKHAfcBSYDHxsjKlqNcyT82mwRNyMh/FgaK2hBHoHMnvHbM5fOc+oeqPw9vB2dWoiIiLuzcvXsTlT5UnHqN++tc7j7nwcHn0763LLhOxQ6JWx1u4BMMbsAAJTCjLGVAB6AB9bax9Nsn8vjmYqbYD3ktykC9Ddyf2esdbGGmMexFFA7sdRcM7GMW3zFP9//l631u6Lv78hOEYSiwN/ZuiRiohkAWMMvar1Iq9PXiZvmcylq5d4rcFreHuq2BMREbkhTV6CWs8576Oxaizs+Df8dK/zxi4hZSCsUtbnGM/tC72EIi8d2uIYX5l8zf63gbHAkyQp9Ky153B0yEzr/ncB9ydcNsY8D2xMmApqjNmPZleKiBvqdGcn/Lz8GLthLL1W9WJio4n4eqbxTaWIiIikzsMDgos5j6nVFX5819HcxRnvPNDvV/DNm3X5JeH2hV4G1ADigA1Jd1prY4wxP8Zfn2HGmErAHiAGx3TOocBTSUJmAC8YY5bgGMl7Gdhsrc2S0TyjGlJEbkC7cu3w8fThpfUv0XNFTyY3noy/l7+r0xIREcm5wu6CXj/BZSfn+x37FRY/Bd9Ogoi6qcfdwBe0OanQKwocj++Oea1DQF1jjI+19nIGj9sK6Ab4AbuALtbapUmufxVHE5YtgAeO8/oeyWjyqVGZlz3obExxZ63uaIWX8WLEuhF0X96dqVFTCfAOcHVaIiIiOVe+4s6vLxgJBcvB2gmO7SbISYVeAJBSkQeO0biEmAwVetbaYcAwJ9fHAQPjtzQZY7oCXQFKlEjvcnvq+CEiN6bl7S3x8fRh6LdDeW7Zc0y/ZzqBPime8iwiIiI3mzHw9H/hZBpnqX3cFdiWqbvISYXeRRwLmqfEL0mMS1lrZ+JYYJ3q1atrHCibUwku2Unz0s3x9vBm4JqBdF3alTebvEmwb7Cr0xIREcmd8oQ6NmcqtyOzhV5OWlzpMBBqjElpIms4jmmdGZ22KSKSozQr2YwJjSaw8+ROuizpwumY065OSURERFJTtX2mb5qTCr2NOB5PzaQ7jTF+QGVgkyuSEhFxN1ElopjSeAp/nP6DZ5Y8w4noE65OSURERFKSt0imb5qTCr0PcPQu6XXN/i44zs1795ZnJCLipuoXq88bTd7gwNkDdPqmE8ejj7s6JREREclCbl/oGWPaG2NeNMa8CBQEghMuG2MSxzKttT8BbwCPGGM+NsZ0NsZMACYCq0m+WHr2obP4ROQmqR1Wm+lNpnP4wmE6f9NZI3siIiI5iNsXekAnHGvTvYyj2Uq+JJevXYWwF9APqICj6GsDTAVaxHfHzJasWn6IyE1So0gN3rjnDQ6dP0TnJZ05GXPS1SmJiIhIFnD7Qs9a28haa1LZGl0Te9VaO8FaW9Za62utDbfW9rHWOlmtUCTzjFERLtlfjSI1mHbPNA6eO6hiT0REJIdw+0JPRERuvlphtZh6z1T+PPsnnZd05lTMKVenJCIiIjdAhZ6IiACOc/amRv2/2NPSCyIiItmXCj0REUlUp2gdpkRNYd+ZfSr2REREsjEVem4v2/aQEZFsqm7RukyNmsreM3vpsrQLZy6dcXVKIiIikkEq9ESygNUyGJLD1A2vy5SoKew5vYcuS1TsiYiIZDcq9LIB1RAi4gp3h9/N61Gvs/v0bros6cLZy2ddnZKIiIikkwo9kRugxRUkp6sXXo/JjSfz++nf+eeyf3LhygVXpyQiIiLpoEJPREScalCsAeMbjufn4z/TfXl3omOjXZ2SiIiIpEGFnoiIpOmeEvcwut5oNv+9mV4re3H56mVXpyQiIiJOqNATEZF0eaD0A/yr7r9Yd3gd/Vb340rcFVenJCIiIqlQoefmjNo5iogbaXl7S4bUGsLKAysZsnYIV+OuujolERERSYGXqxOQtFm1/HB7Vr1RJRdpG9mWmNgYJm6eiK+nLy/d/RIeRt8bioiIuBMVeiI3wKgGl1yqY8WOxMTGMH3bdPy8/BhaayhGbwgRERG3oUJPREQy5bm7niM6Npq5P8/Fz9OPvtX7qtgTERFxEyr0REQkU4wx9K7Wm+jYaN755R38vf15vvLzrk5LREREUKEnIiI3wBjD4FqDuXT1EjO2zSDAK4COFTu6Oi0REZFcT4WeiIjcEA/jwYg6I4iOjWbi5okE+QTx6B2PujotERGRXE2FXjagrpvuT6tgSG7n6eHJ6HqjOXflHC99/xJ5ffLSrGQzV6clIiKSa6kftsgNUN8Jkf/z9vRmUqNJVAqtxMC1A1l3eJ2rUxIREcm1VOiJiEiW8ffyZ9o90ygdXJpeK3ux7dg2V6ckIiKSK6nQExGRLBXsG8xbTd8i1D+Ubsu68fup312dkoiISK6jQk9ERLJcqH8oM5vOxNfTl2eXPsvBcwddnZKIiEiukuFCzxizzBjT2hjjczMSkmvYOFdnICKSKcXyFuOtpm9x6eolui7tyvHo465OSUREJNfIzIheFeA94LAxZrIx5s4szkkk21HTTZGU3Z7/dqY3mc7x6OM8u/RZzl4+6+qUREREcoXMFHphQDtgK9AD+NEY84MxposxJjBLsxMRkWzvroJ3MbnxZPac2cPzy57n4pWLrk5JREQkx8twoWetvWytXWStbQqUBkYBhYG3gCPGmNnGmLuzOE8Rt2S0xqFIutQtWpdx9cex/fh2+qzuw5W4K65OSUREJEe7oWYs1tr91toRQCngPmAl8DSwxhjzizGml0b5REQEoFnJZgyrPYzvDn3HyHUjsVaTnkVERG6WrOq6WRl4CKgPGOAPIA6YCPxujKmbRfcjIiLZ2GN3PEa3yt347I/PeH3L665OR0REJMfyyuwNjTH5cJyr1wm4C7gCfALMtNauiI+JAt4G3sDRxEVERHK55yo9x7GLx5i9YzYFAwrSrlw7V6ckIiKS42S40DPG3AM8A7QE/IDfgAHAPGvtiaSx1toVxpixOAo9ERERjDEMrTWUE9EnGLdhHKH+odxb8l5XpyUiIpKjZGbq5lLgEeA/QGNrbaS1dsK1RV4Su4HvMpuggFXDD7enc41EMsbTw5NxDcZRuVBlBq8dzMa/Nro6JRERkRwlM4VeHyDcWtvOWrs6rWBr7UprbeNM3I+I2zOqwUUyzc/Lj6lRUymetzg9V/Tk15O/ujolERGRHCMzhV4QUDS1K40xFYwxwzOf0q1ljHncGPOtMea8MWZfCtdPN8YcMMacNcYcil8k3ieFOH9jzG5jzPlbkriISA4Q7BvMjCYzCPAOoNuybhw+f9jVKYmIiOQImSn0RgCVnFxfMT4muzgFTAOGpnL9NCDSWhuEo+nMXcCQFOJeAvbflAxFRHKwsMAwZjSZQXRsNM8te47TMaddnZKIiEi2l5lCL63Jan5AbCaO6xLW2qXW2kWkUqRZa3+x1l6Iv2hwLBtxe9IYY0w1HOsIjruZuYqI5FS357+dKVFTOHTuEN1XdCc6NtrVKYmIiGRr6Sr0jDFBxpgSxpgRiGd9AAAgAElEQVQS8bsKJFy+ZquMY8mFA+lNwBgz2Biz2BizxxhjU5o+mSTWwxjT2xizyxgTEz+lcoIxJk967y8zjDGD4qdkHsUxojc5yXVeOJaQeB64nPX3riYfIpI7VC9SnbENxrL92HYGrBlAbFy2+c5QRETE7aR3RK83sDd+szgKnb0pbJuBJsCMDOQwGojCscj6qTRiJ+FYhP0XoAewGOgJfG6MSfZYjDGL4gvH1LZG6U3QWjvWWhsIlMfx2I4kubo/sNVauya9x8sodd10fyrHRbJG04imDKk1hFUHVjHmhzHqaCsiIpJJ6V1Hb1X8vwYYjmNphe3XxFjgPPC9tXZdBnIoY63dA2CM2QEEphRkjKmAo7j72Fr7aJL9e4EpQBvgvSQ36QJ0d3K/ZzKQIwDW2p3GmG3AAqCxMeY24Dm0GLyISJZpE9mGwxcOM3fHXIrlLUbHih1dnZKIiEi2k65CL34ZhdUAxpgIYIa19oesSCChyEuHtjgKzcnX7H8bGAs8SZJCz1p7DjiXFTlewxu4I/7/9YDCwG/G0WffG8hjjDkOPHIzR/lERHKyXlV7ceT8ESZunkjRwKJaUF1ERCSD0juil8ha66qvVmvgaISyIelOa22MMebH+OszzBjjiaNA83ZcNH6Ow9pLxphgoCXwCY4RwDuBF4Fv4m/+IbAsyeHqAPOAysCxzOQjIiLgYTwYVW8Uf1/8myFrh1AooBBVCmnyhIiISHqleY7eNU1YEi+ntd2EXIsCx621l1K47hAQmtL6dunQHojGUbSViP9/wqq9FsdI4R4co4OfAF/imEKKtfaitfZgwoajuLPxl1PKU0RE0snX05cpjacQFhhGzxU92X9WK9iIiIikV3qasewD9iQpovaRciOWa7esFgCkVjzFJInJEGvtPGutuWYrGX/dWWttE2ttiLU20Fpb2lrbL8lyC9cea1V805ZUGWO6GmM2GWM2HTuWjkE/NSIQkVwsn18+pt8zHYOh27JunIpJq2eXiIiIQPqmbr6EY2Qr9prLt9pFoFAq1/kliXFr1tqZwEyA6tWrq4rLIVSPi9w8JYJKMCVqCp2XdKbHih7MajYLPy+/tG8oIiKSi6VZ6FlrRzq7fAsdBsobY3xTmBYZjmNa501Yx871VEOISG5XuVBlxtQfQ99VfRny7RDGNxyPh0nvCkEiIiK5T3b6K7kRR741k+6Mb55SGdjkiqQkd4vvtioit0DTiKb0rd6XpfuXMmnzJFenIyIi4tayU6H3AY7BrV7X7O+C49y8d295RiIickt1KN+BNmXbMO/neSzatcjV6YiIiLitNKduGmPiyPjsQWutTdfSDcaY9kBE/MWCgI8x5sX4y/uttQviD/iTMeYNoLsx5mMc3S/LAT1xrPH3HiIikqMZYxhUcxB/XfiLMRvGEJYnjIbFG7o6LREREbeTnmJsPjf3NLFOwLV/pV+O/3c1sCDJ/l44un52BZoDx4GpwHBrbdxNzFFERNyEp4cn4xqMo+M3Hem/pj8L7l9A2ZCyrk5LRETEraSnGcvTNzMBa22jDMReBSbEb7mEWrGIiFwrwDuAqVFTaftFW7qv6M77zd8n1D/U1WmJiIi4jex0jl6uZdXwIxtQQS5yqxUKKMS0qGmcuXSGnit6EhMbk/aNREREcgkVeiI3QCW4iGuVK1COMfXHsOP4Dl787kXiNItfREQESF8zlr1AHBBprb1ijNmTjuNaa22ZG85OREQkDfeUuIfe1XozcfNESgWX4vnKz7s6JREREZdLTzOW/TjmpSXMTfsTzVMTERE38nSFp9lzZg8zts0gIiiCFqVbuDolERERl0pPM5ZGzi6LiIi4mjGG4bWHc+DcAYZ/N5xigcWoXKiyq9MSERFxGZ2j5+6sBk9FRNLD29ObyY0mUyRPEV5Y+QKHzh9ydUoiIiIuk+lCzxjja4y51xjzz/jtXmOMX1YmJ5JdqB4XcQ/5/sfenYdZVZ35Hv++yKQioIJRcZ6HqKio4KyJ0cQkdsdoa1oztNF0EjWiGAcQp0QlCs4ZHKKJidF4tW29bTqdwRETBY1xngEVJ1BEEFCG9/5xDrdPygKqiqra5+z6fp5nP4dae9Wpt9Y+VNXv7L3X6t2fKz51BfMXzefYPx3L7I9mF12SJEmFaFPQi4ivAlOBu4Arq9tdwNSI+Hq7Vacq53asV658IdWfjfptxLi9xzFp5iROvu9kFixaUHRJkiR1ulYHvYj4F+B6YDYwEvin6jaq2nZttY8kSYUYutZQRg4dyQNTH+CiiRcVXY4kSZ2uJbNuNnU68CwwNDPfr2m/IyJ+DDxU7XNzO9QnSVKbHLLZIbz83sv86plfsUn/TfjyZl8uuiRJkjpNWy7d3By4rknIAyAzZwLXAZstb2GSJC2vk4acxG5r78YPH/ohj771aNHlSJLUadoS9N5cxv4E3mrD80qS1K66d+vOmD3HMKjPIIbfM5w3Zr9RdEmSJHWKtgS964FvRESfpjsioi/wDSpn9dQOwrXpG4JHSapf/Xr147J9L+OjhR9x/N3HM2f+nKJLkiSpwy0z6EXEnrUbcB8wB3giIk6OiC9Ut+8Df6cyIcv9HVt212KIkKTls1G/jRiz5xiee/c5zhh/BumaKJKkkmvJZCz38PGssXhS+TE1+xa3rQ/8AVhheYuT6l249IXUMPZcZ0+G7ziccY+M4+onruaYbY8puiRJkjpMS4LeNzq8CkmSOsHXt/46z894nsv/djmb9N+Efdfbt+iSJEnqEMsMepn5i84oRJKkjhYRnDnsTCbNnMRp95/Grz73KzZdddOiy5Ikqd21ZTIWSZIaVu/uvbl0n0tZqcdKHPfn43hv3ntFlyRJUrtrc9CLiE9ExIERcUREfLXp1p5FdmlOGCBJ7e4TK3+CS/a5hLfnvM2Ie0cwf9H8okuSJKldteQevX8QEd2AK4FvsvSg+Mu2FqV/lE74UffM41Lj2W7gdpw57ExGjR/FRRMu4rRdTiu6JEmS2k1bzuiNAL4F/Ab4GpXZNk8Fvgu8AEwE9muvAqV6FmZwqaEdtMlBfHWrr3Ljszdy2wu3FV2OJEntpi1B72vAf2fmV4HfVdseycyfAjsCA6qPkiTVveE7DmfXtXflB3/9AY9Pe7zociRJahdtCXobAf9d/fei6mMPgMz8ALiOymWdkiTVve7dujNmjzGssdIaDL9nONPnTi+6JEmSlltbgt5cYPFd67OpLJi+Rs3+N4F1l7MuSZI6Tf/e/blkn0t4/8P3Oemek5ycRZLU8NoS9KYAGwNk5nzgReCAmv2fBt5a/tJU4SwfktQZtlhtC87a9SwefftRxk4cW3Q5kiQtl7YEvT8D/1zz8Q3A4RFxd0TcAxwC/LYdapMaRhrIpVI4cKMDOWLLI/j1M7/mzpfuLLocSZLarNXLKwAXAf8TEb0y80PgfCqXbh4BLASuAs5svxLl8gr1yyMjlc+JQ07kuRnPcfZfzmbj/huz1epbFV2SJEmt1uozepn5Rmb+vhryyMyFmXl8Zq6WmQMz89uZOa/9S5UkqeP16NaDC/e8kP69+jP87uHMmDej6JIkSWq1tly6KUlSqa2+4upcss8lTJ87nZPvO5kFixYUXZIkSa3S5qAXEYdGxG8i4qHq9puIOLQ9i5MkqSifHPBJRg0dxUNvPMRlj15WdDmSJLVKq+/Ri4iVgduBfancovRedddOwKER8S3gi9U19SRJalj/vOk/89Q7T3HdU9ex1epbccCGByz7kyRJqgNtOaP3Q+BTwOXA2tV781YD1q627VPtI3UZ6aSbUmmdstMpbL/G9ox+cDTPz3i+6HIkSWqRtgS9fwFuycwTMvPNxY2Z+WZmngDcWu3TMKqXoT4QEbMjYnIz+2c32eZHxOPVfb0i4uqIeDkiZkXE8xFxXDtX2L5PJ0lqsR4r9GDsXmPp06MPJ9x9Au9/9H7RJUmStExtCXp9gbuXsv/P1T6NZAZwBTCyuZ2Z2ad2A54Bbqru7g68CXwG6AccCozyfsWuIczgUpcwcKWBjN17LG/MfoNRD4wiPY0vSapzbQl6jwObLmX/psATbSunGJn5h8y8CZiyrL4RsTOwFXB99XM/yMwzMvPFzFyUmY8BdwC7d2TNkqTOtf0a23PSkJO4+9W7ue6p64ouR5KkpWpL0BsFHB0RX2i6IyIOAr4JnN6aJ4yI0yLilurlj9nc5ZM1fbtFxPCIeDYi5kXEqxExtjpJTGc4CvhdZr6+hPp6AHtQCcSSpBL51y3/lf032J9LH72UCW9OKLocSZKWaJmzbkbEz5tpngTcHhHPUbmMEWBLYHMqZ/P+lcolnC11HvAu8CjQfxl9LwaOB/4DGFv9uscD20fEpzNzUU3tN7H0+wX3ycx7WlpkNUweBnx1Kd2uAGYBv2zp80qSGkNEcPauZ/P8jOcZce8IbvnCLayx0hpFlyVJ0se0ZHmFry9l3xbVrda2wDZUzny11MaZ+TJARDwJ9GmuU0RsDRwH3JaZB9e0TwIuoxLCbqz5lKOBY5fydWe2okaAQ4A5wH8tob5xwDBg38z8qJXP3bz/za2SpDqwco+VuXjvizn8vw7n5HtP5pr9r6FHtx5FlyVJ0j9Y5qWbmdmtDdsKrSlicchrgcOpTEF5SZP2q6kEsCOaPO+szJy+lG1+a+qkclnqLzJzQdMdEXEJsB/wqcyc3srnXSpv+a9/zssgdS0b99+Ys4adxaNvP8oljzT9lSRJUvHaco9ekXYCFgEP1zZm5jzgser+VouIFSKiN9Cj8mH0joheTfpsDuwKXNvM518GfJrKmbxpbalBjcppN6Wu6nMbfY6vbPEVfvn0L/mfyf9TdDmSJP2DNge9qNghIr5c3XaI6PDJ5tcGpmfmh83smwoMiIiebXjeI4G5wG+B9ar/fq5Jn6OA+zPzhdrGiFifyuWkmwCTatba+11zXygijomIiRExcdo0M6EkNbIRQ0aw7cBtOWP8GUyaOanociRJ+v/aFPQi4gDgJWACcHN1mwC8GBH7t195H7MS0FzIA5hX06dVMvP6zIwm2wZN+nw/M/dq5nOnVPv3brLe3meX8LWuyswhmTlk4MCBrS1VklRHFi+m3muFXpx4z4nMmT+n6JIkSQLaEPQiYjcq68StClwKHFPdLq223RERu7ZnkTXmAL2WsK93TR9JkjrFmiuvyZg9x/DSey9x9l/OdjF1SVJdaMsZvdHAm8BWmXliZl5b3U4EtgbeqvbpCK9TuTyzubA3iMplne0z26UkSS00bO1hHLv9sdw16S5ufu7mosuRJKlNQW8X4KrMfKPpjmrb1cDQ5S1sCSZQqXnn2sbqRCqDgYkd9HWlpUrnRpW6vG9u8032XGdPxkwYwxPTnii6HElSF9eWoNeTyoLgS/J+tU9HuJnKagMnNGk/msq9eb/uoK9bqHRmx7rV4dMPSWoY3aIb5+1+HmusuAYn33cyMz9s7VKtkiS1n7YEvWeAwyLiY4utV9v+pdqnxSLiyIgYFRGjgIFAv8UfR8SRi/tl5hPAlcCXIuK2iPhmRIwFxgH38o+LpUuS1Kn69erHhXtdyFsfvMXo8aO9X0+SVJi2BL2fULl8808RcWBEbFjdPg/8qbrvx618zqOAc6vbGkD/mo+PatL3BGAElfsBrwQOAy4HPp+Zi9rw/UiS1G62Hbgtw3cczp9f/TO/euZXRZcjSeqiPnZWblky85qI2JRK2Nq9mS4XZubHFhVfxnPu3Yq+C4Gx1U2SpLpz5FZHMvGtiYx7ZByDBw5mm4HbFF2SJKmLadM6epl5CrAlcCrws+p2CrBlZp7afuVJktR4IoJzdzuXNVZcgxH3jvB+PUlSp2tV0IuIXhGxZ0RsmpnPZ+aFmfmd6nZRZj7fUYV2Xd7f0Qi8DUdSU/169eOivS7i7blvc8b4M7xfT5LUqVp7Rm8hlfvwPtsBtWgJnHVTkhrTNgO34aQdT+LuV+/2fj1JUqdqVdDLzAVUFks3eUj4H0HSsv3rlv/Kvuvuy7iJ43h82uNFlyNJ6iLaco/eLcChEdGm+/skSepKIoJzdjuHT6z8CU6+1/X1JEmdoy1h7Roqi5P/ISK+EBFbRMR6Tbd2rlOSpIZVe7/eqPGjvF9PktTh2hL0ngS2A/YBbgeeAiY1s0mSpKpPDvgkI4aM4J5X7+GGp28ouhxJUsm1eh094BycCrLz+K6vJJXGV7b4ChPenMDFj1zMdmtsx3YDtyu6JElSSbUq6EXEQOB3wPTMfKljSpIkqZwW36936J2HcvK9J3PLF26hX69+RZclSSqhFl26GRHdIuKnwBvAg8DzEfFANfhJXVaE825Kap2+Pfsydq+xTJs7jdHjR3u/niSpQ7T0Hr1jgWOoLK1wG/AEsCvwsw6qS5Kk0tp6wNYM32E4f371z9z83M1FlyNJKqGWBr2vAs8AW2bmIZk5GLgW+EJE9O+w6iRJKqkjtjqC3QftzoUTLuT5Gc8XXY4kqWRaGvQ2B67PzFk1bZcDKwCbtXtVkiSVXLfoxg92+wF9e/Xl5HtPZu6CuUWXJEkqkZYGvZWB15u0vV6zTx0knOBUkkpr9RVX5/w9zmfSzEmMeXhM0eVIkkqkNevoNU0ciz92Ngp1ec6lIKmthq41lKO2OYpbX7iV30/+fdHlSJJKojXLK3wuItas+XglKmHvkIgY3KRvZubFy12dAEizdN3yyEhqD98Z/B0efvNhzn7wbD454JMM6jOo6JIkSQ2uNUHvK9WtqW8105aAQU+SpBbo0a0HY/YYwyF3HsIp953CdQdcR49uPYouS5LUwFoa9Pbp0CokSeri1lllHc4cdiYn33cyP3nsJxy/w/FFlyRJamAtCnqZeW9HFyJJUld3wIYH8Jc3/sI1T1zDLmvtwi5r7VJ0SZKkBtWayVgkSVIHO2WnU9ig3wacdv9pvDvv3aLLkSQ1KINevXM6x4aQLoMhqZ2s1GMlLtzzQmZ+OJMzxp9B+ntAktQGBr0G4KybktS1bL7a5ozYaQT3vXYfv3rmV0WXI0lqQAY9aTmEGVxSBzls88PYZ919GPfIOJ5+5+miy5EkNRiDniRJdSgiOGfXc1it92qcct8pzF0wt+iSJEkNxKAnSVKd6t+7P+fvfj5T3p/CRRMuKrocSVIDMehJklTHdl5rZ77+ya/z2+d/y92v3F10OZKkBmHQq3vOtiZJXd1xg49jy9W25MwHz2T63OlFlyNJagAGPakdOPu5pI7UY4UeXLDHBcxdMJdRD4xiUS4quiRJUp0z6EnLwVk3JXWWjfpvxIghIxj/+nh+8+xvii5HklTnDHqSJDWIQzc/lL3W2YtxE8fxwowXii5HklTHDHqSJDWIiODsXc9mlZ6rcMr9p/Dhwg+LLkmSVKcMepIkNZDVV1ydc3c7lxdmvMClj15adDmSpDrVpYNeRPSKiKsj4uWImBURz0fEcU36HBoRD0TE7IiY3MxzdI+ISyPi3Yh4LyKujYjenfZNSJK6nD3W2YOvbPEVbnj6Bh6c+mDR5UiS6lCXDnpAd+BN4DNAP+BQYFREHFrTZwZwBTByCc9xOrAPsA2wKbAV8KN2q9DpHBuCR0lSZxu+43A26b8JI8ePZMa8GUWXI0mqM1066GXmB5l5Rma+mJmLMvMx4A5g95o+f8jMm4ApS3iabwLnZebUzJwGnAV8PSJWaLc6cWrHehUeG0kF6d29NxfscQEzP5zJmQ+eSfrGoCSpRuFBLyJOi4hbqpdPZnOXR9b07RYRwyPi2YiYFxGvRsTYiFi5nWrpAewBPN7C/v2BdYHHapofBVYBNmiPmiRJWpLNV9uc7+3wPe5+9W5ufeHWosuRJNWRwoMecB6wL/ASlcskl+ZiYBzwNHAccAtwPHBnRPzD9xIRN1WD45K2vZt5/iuAWcAvW1j7KtXH92ra3muyT5KkDnPkVkcydK2h/GjCj5g8c3LR5UiS6kQ9BL2NM3P1zNwPeH1JnSJiayrh7rbM/FJmXp2ZJwInUrlH7rAmn3I0MHAp2/gmzz8OGAZ8NjM/amHts6qP/Wra+jfZJ0lSh+kW3fjh7j+k5wo9OfX+U5m/aH7RJUmS6kDhQS8zX25h18OBAC5p0n41MAc4osnzzsrM6UvZ/v9vwoi4BNgP+FRmTm9F7e8BrwKDa5q3pxLyJrf0eSRJWh5rrLQGZw47k6feeYprHr+m6HIkSXWg8KDXCjsBi4CHaxszcx6Ve+R2asuTRsRlwKeBfauTqTTdv0J1uYQelQ+jd0T0qulyDXBaRKwdEQOpTMZyfWYubEs9akxOgiCpaPutvx9f2OgL/Ozxn/Hk9CeLLkeSVLBGCnprA9Mz88Nm9k0FBkREz9Y8YUSsT+Vy0E2ASdW18mZHxO9quh0JzAV+C6xX/fdzNfvPA+4DngJeBJ4BTlnK1zwmIiZGxMRp0z6WK5vlrJuSpJY4dZdTGbDiAE67/zTmLphbdDmSpAI1UtBbCWgu5AHMq+nTYpk5JTMjM3tnZp+a7bM1fa6v9qndNqjZvyAzj8/MVTOzX2YelZlL/O2amVdl5pDMHDJw4MDWlKt6ZAaXVEf69uzLD3b/AZPfn8wljzS900GS1JU0UtCbA/Rawr7eNX0kSeqyhq41lCO2PIIbn72Rv7z+l6LLkSQVpJGC3utULs9sLuwNonJZZ0tny5QkqbS+t8P32LDfhpwx/gze/+j9osuRJBWgkYLeBCr17lzbWJ0oZTAwsYiiJEmqN7279+b83c/nnbnvcN5D5xVdjiSpAI0U9G4GEjihSfvRVO7N+3WnV9QJAmdzlCS13tYDtuaY7Y7hv17+L34/+fdFlyNJ6mTdiy4gIo4E1q9+OBDoGRGjqh9PycwbADLziYi4Ejg2Im4D7gK2BI4H7gVu7NzKpf9lHJdUj47e5mjuf+1+zv3rueywxg4MXMlJwCSpq6iHM3pHAedWtzWA/jUfH9Wk7wnACGBr4ErgMOBy4POZuaizCu58Tu1YrzwykupZ927d+eHuP+TDBR8y+sHRrvkpSV1I4UEvM/duZvmCxdveTfouzMyxmbl5ZvbKzEGZeWJmzi6ofEmS6tqG/TZk+I7DeWDqA9zy/C1FlyNJ6iSFBz1JktSxDtviMIatNYyLJl7EK++/UnQ5kqROYNCTJKnkukU3ztntHLp3687IB0aycNHCokuSJHUwg54kSV3AmiuvychdRvLYtMe47qnrii5HktTBDHr1zhvnG4KHSVIj+NyGn2P/Dfbnyseu5Ll3nyu6HElSBzLoNQAzRP2KcN5NSY0jIhi5y0j69uzLqPGjmL9oftElSZI6iEFPkqQuZNXeqzJ66GieffdZrnn8mqLLkSR1EIOeJEldzKfW/xQHbnQgVz1+Fc+880zR5UiSOoBBT5KkLui0nU9j1d6rMnL8SOYv9BJOSSobg54kSV1Qv179OHPYmbww4wV++vhPiy5HktTODHp1z6lYJEkdY6919+KgjQ/i2ieu5anpTxVdjiSpHRn0GkA6s2MDMJBLakzf3/n7rL7i6ox8YCQfLfyo6HIkSe3EoCctByO4pEbXt2dfzt71bF6a+RI/fuzHRZcjSWonBj1Jkrq43QftzsGbHsx1T13H49MeL7ocSVI7MOhJkiRGDBnBGiutwajxo5i3YF7R5UiSlpNBT5Ik0adnH87Z9RwmzZzEFX+7ouhyJEnLyaBX95zkQ5LUOYatPYxDNzuUXz79S/729t+KLkeStBwMelI7SPO4pJI4cciJrN1nbUY9MIq5C+YWXY4kqY0Meg3BuR3rlStfSCqblXuszDm7nsMrs17hskcvK7ocSVIbGfQkSdI/2HmtnTl8i8P51TO/4pG3Him6HElSGxj0JEnSx5ywwwkM6jOIMx8801k4JakBGfQkSdLHrNRjJc7a9SymvD+FH//dhdQlqdEY9CRJUrOGrjWUgzc9mF889QuenP5k0eVIklrBoFfnwukcG4JHSVJZnTTkJAasOIAzxp/B/IXziy5HktRCBr0GYIioX+GMqJJKbpWeqzB66GhefO9Frn7i6qLLkSS1kEFPkiQt1V7r7sWBGx3I1Y9fzXPvPld0OZKkFjDoSZKkZTp1p1Pp26svox8czYJFC4ouR5K0DAY9SZK0TP1792fkLiN5+p2n+cVTvyi6HEnSMhj0JElSi3xmg8/w6fU+zY8f+zGTZk4quhxJ0lIY9OqeU7FIkurHyKEj6d29N6PHj2bhooVFlyNJWgKDXgNIZ3ase66CIamrGLDiAE7d+VQem/YYNz13U9HlSJKWwKAnLYcwg0vqgj6/0efZfdDuXPropbw267Wiy5EkNcOgJ0mSWiUiOHPYmXSLbpz1l7NIL2uQpLrTpYNeRPSKiKsj4uWImBURz0fEcUvou2JEvBgRs5u0rxURt0bE9Ih4JyJuj4h1Ouc7kCSpGGuuvCYnDTmJh954iNteuK3ociRJTXTpoAd0B94EPgP0Aw4FRkXEoc30PQeY0kz7j4GewIbAusAHwM87pFpJkurIlzf9MjuvuTMXTbyINz94s+hyJEk1unTQy8wPMvOMzHwxMxdl5mPAHcDutf0iYkfgAGBMM0+zMXBLZs7KzDnAjcC2HV27JElFiwjO2vUsFixawHkPneclnJJURwoPehFxWkTcUr18MiNi8lL6douI4RHxbETMi4hXI2JsRKzcTrX0APYAHq9p6w5cDXwX+KiZTxsHfDki+kfEKsCRwJ3tUY8ah3/cSOqq1l1lXb47+Lvc/erd/PGVPxZdjiSpqvCgB5wH7Au8BMxYRt+LqQSrp4HjgFuA44E7I+IfvpeIuKkaHJe07d3M818BzAJ+WdN2MvC3zLxvCTU9APQH3gXeAzYHTl/G99FKTu1YrzwykgRHbHUEW662Jec9dB4zP5xZdDmSJOoj6G2cmatn5n7A60vqFBFbUwl3t2XmlzLz6sw8ETgR2Ac4rMmnHOgb7ToAACAASURBVA0MXMo2vsnzjwOGAZ/NzI+qbZsA/04l7DVXUzfgj8BEoC/QB7gduKd6dlCSpNLr3q07Z+96NjPmzeDiRy4uuhxJEnUQ9DLz5RZ2PZzKCZRLmrRfDcwBjmjyvLMyc/pStvmL+0bEJcB+wKcyc3rN0+wOfAJ4PiKmA/8JrFydYXNPYDVgfeCyzJydmXOpnHHcisq9e5IkdQlbrr4lX93qq9z6wq1MeHNC0eVIUpdXeNBrhZ2ARcDDtY2ZOQ94rLq/1SLiMuDTwL6ZOa3J7t8CmwCDq9s3qYTKwcBD1VD4IvDd6vILPYHvUbkEdXJb6pEkqVF9e/C3WafPOpz9l7P5cOGHRZcjSV1aIwW9tYHpmdncb46pwIBq0GqxiFifyuWgmwCTImJ2dfsdQGbOyczXFm/AtEpzvlZTx0HANsBrwFvA/sDnqwG0ua95TERMjIiJ06Y1zZWSJDWuFbuvyOhho5ny/hR+9vefFV2OJHVp3YsuoBVWApb09uC8mj7NzYzZrMycQivm08jMe6jch1fb9jSVpRda+hxXAVcBDBkyZNlTNTqbY0PwKElSxbC1h3HQxgdx3ZPXsf8G+7P5apsXXZIkdUmNdEZvDtBrCft61/QpnXRux/rloZGkjxkxZAR9e/XlrAfPYuGihUWXI0ldUiMFvdepXJ7ZXNgbROWyzhafzZMkSR2jf+/+nLrzqTz5zpPc+OyNRZcjSV1SIwW9CVTq3bm2MSJ6U5kcZWIRRUmSpI87YIMD2GPQHlz+t8uZOntq0eVIUpfTSEHvZiq3Qp3QpP1oKvfm/brTK5IkSc2KCM4YegYA5/71XNJ7ziWpUxU+GUtEHEllLTqoLGTeMyJGVT+ekpk3AGTmExFxJXBsRNwG3AVsCRwP3At4bYgkSXVkrT5r8b0dvscFD1/AXZPu4sCNDiy6JEnqMgoPesBRwF5N2s6tPt4L3FDTfgKV9emOAQ4EpgOXA6Mzc1HHllmMcD5HSVIDO2zzw7jr5bsY8/AYdl17V1btvWrRJUlSl1D4pZuZuXdmxhK2vZv0XZiZYzNz88zslZmDMvPEzJxdUPkS4CoYkrQkK3RbgTN3PZNZH83iookXFV2OJHUZhQc9qZGF6ytI0jJttupm/Ns2/8YdL93Bg68/WHQ5ktQlGPQkSVKHO2bbY9ig7wb84K8/YN6CeUWXI0mlZ9CTJEkdrtcKvThj6Bm8OutVrnr8qqLLkaTSM+hJkqROsfNaO/PFjb/IdU9dx0vvvVR0OZJUagY9SZLUaU4achIr91iZc/5yDovKOWG2JNUFg17dczrHRpAeJ0lqkdV6r8ZJO57Eo28/yn+++J9FlyNJpWXQawAZzuxYrzw0ktR6/7TJP7HDGjsw9pGxvDvv3aLLkaRSMuhJkqROFRGMHjaaD+Z/wNiJY4suR5JKyaAnSZI63cb9N+YbW3+DO166g4feeKjociSpdAx6kiSpEMdsewzrrrIu5/71XD5c+GHR5UhSqRj0JElSIXp3782ooaOY8v4Urn3i2qLLkaRSMejVu3Q2x4bgYZKkNtl17V353Iaf45onrmHSzElFlyNJpWHQawCJUzvWK4+MJC2/k3c6md7de3PuX88lfYNTktqFQU+SJBVqwIoDGL7jcCa8OYE7Xrqj6HIkqRQMepIkqXAHb3owgwcO5qKJFzFj3oyiy5GkhmfQkyRJhesW3Rg9bDSzP5rNuEfGFV2OJDU8g54kSaoLm666KV/b+mvc/uLtTHhzQtHlSFJDM+jVuXA6R0lSF/Kt7b7FoD6D+OFff8j8hfOLLkeSGpZBT2oHxnFJah8rdl+R03Y+jZdmvsQNz9xQdDmS1LAMeg3BSfzrVYTHRpLa217r7sU+6+7DT//+U96Y/UbR5UhSQzLoSZKkunPqzqeSmYyZMKboUiSpIRn0JElS3Vm7z9p8a7tv8adX/sR9r91XdDmS1HAMepIkqS59bauvsWG/DTn/ofOZt2Be0eVIUkMx6EmSpLrUY4UejNxlJK/Nfo1rn7y26HIkqaEY9Oqe8zk2gvQwSVKH2GWtXfjchp/j50/8nCnvTym6HElqGAa9BmCGqF9OuilJHW/EkBH0XKEn5z90Puk7a5LUIgY9SZJU1wauNJBjtz+W8a+P5w9T/lB0OZLUEAx6kiSp7v3L5v/CFqttwZgJY/hg/gdFlyNJdc+gJ0mS6l73bt0ZuctI3p7zNj/9+0+LLkeS6p5BT5IkNYTBawzm4E0P5oanb+CFGS8UXY4k1TWDXr3znvOGkB4oSeoUJ+xwAqv0XIUf/PUHTswiSUvRpYNeRPSKiKsj4uWImBURz0fEcU36rBURt0bE9Ih4JyJuj4h1mvQ5MCIejYgPIuLNiDi5nStt36dTu/HISFLn6t+7PyfscAKPvv0od758Z9HlSFLd6tJBD+gOvAl8BugHHAqMiohDa/r8GOgJbAisC3wA/Hzxzoj4DHAVcHL1OTYDftcZxUuS1BX986b/zLYDt2XsxLHM/HBm0eVIUl3q0kEvMz/IzDMy88XMXJSZjwF3ALvXdNsYuCUzZ2XmHOBGYNua/ecC52bmnzJzQWa+n5lPdt53IUlS19ItunHG0DN478P3uPxvlxddjiTVpcKDXkScFhG3VC+fzIiYvJS+3SJieEQ8GxHzIuLViBgbESu3Uy09gD2Ax2uaxwFfjoj+EbEKcCRwZ7X/ysBOwJrVmt6KiDsiYsP2qEeSJDVvi9W24PAtDue3z/2Wp995uuhyJKnuFB70gPOAfYGXgBnL6HsxleD1NHAccAtwPHBnRPzD9xIRN1WD45K2vZt5/iuAWcAva9oeAPoD7wLvAZsDp1f3rUrlNq2DgQOoXN75JnBbRHj7liRJHeg7g7/Dqr1X5byHzmNRLiq6HEmqK/UQ9DbOzNUzcz/g9SV1ioitqYS72zLzS5l5dWaeCJwI7AMc1uRTjgYGLmUb3+T5xwHDgM9m5kfVtm7AH4GJQF+gD3A7cE/17N+s6qdfmpmTq5d2ng4MpnI/nyRJ6iB9e/blhB1O4O/T/s7/ffn/Fl2OJNWVwoNeZr7cwq6HUzl7dkmT9quBOcARTZ53VmZOX8o2f3HfiLgE2A/4VGZOr3ma1YD1gcsyc3ZmzqVyRnErKgF1JjCFDl0EwamjG4EzfEtSMQ7a5CC2HbAt4yaOY9ZHs5b9CZLURRQe9FphJ2AR8HBtY2bOAx6r7m+1iLgM+DSwb2ZOa/Lc04EXge9GxIoR0RP4HpVLTCdXu/0U+F5ErBsRvalMzvJIZr7Slnqak07iX7e8QFeSitUtunH6Lqfz7rx3+cnff1J0OZJUNxop6K0NTM/MD5vZNxUYUA1iLRYR61O5HHQTYFJEzK5utcsjHARsA7wGvAXsD3y+GjABfkRlOYVHq3WsDXypNXVIkqS223rA1hy82cHc+MyNvDjjxaLLkaS60EhBbyWguZAHMK+mT4tl5pTMjMzsnZl9arbP1vR5OjMPqN5HuGpm7pGZD9bsX5SZp2TmwGqfg5Z2Ni8ijomIiRExcdq0aUvqJkmSWuH47Y9n5R4rc/7D55NeTy9JDRX05gC9lrCvd02fupaZV2XmkMwcMnDgwKLLkSSpFFbtvSrHb388D7/5ML+f8vuiy5GkwjVS0HudyuWZzYW9QVQu6/yok2uSJEl14subfZktVtuCiyZcxJz5df/eryR1qEYKehOo1LtzbWN1ApTBVJZAKJ1w1s2G4FGSpOKt0G0FTt/ldN6a8xZXP3F10eVIUqEaKejdTOXv6ROatB9N5d68X3d6RZ3GqR3rl8dGkurJ9mtszxc3/iLXP3U9k2dOLrocSSpM4UEvIo6MiFERMYrKQub9Fn8cEUcu7peZTwBXAl+KiNsi4psRMZbKunb3AjcW8g1IkqS6MnzH4fRaoRcXTLjAiVkkdVndiy4AOArYq0nbudXHe4EbatpPoLJ+3THAgcB04HJgdGYu6tgyJUlSIxiw4gC+s913uHDihdzz6j3ss94+RZckSZ2u8DN6mbl3dYmD5ra9m/RdmJljM3PzzOyVmYMy88TMnF1Q+ZIkqQ4dvuXhbNxvY8ZMGMO8BfOW/QmSVDKFBz1JkqT21qNbD07b5TSmzp7KdU9dV3Q5ktTpDHr1znsLGoL3gEhS/dllrV3Yf4P9ufaJa5k6e2rR5UhSpzLoNQAjRP0KJ92UpLo2YsgIukU3LpxwYdGlSFKnMuhJkqTSWnPlNTlm22P40yt/YvzU8UWXI0mdxqAnSZJK7atbfZX1VlmPCx6+gPmL5hddjiR1CoOeJEkqtZ4r9OT7O32fye9P5qZnbyq6HEnqFAY9SZJUenuusye7DdqNnzz2E96d927R5UhShzPoSZKk0osIvj/k+8xdMJfL/3Z50eVIUocz6NU959xsBB4lSap/G/XfiMO2OIxbn7+VZ999tuhyJKlDGfQaQDqHf93yyEhSY/n24G/Tv1d/Lnj4AtdAlVRqBj1JktRl9O3Zl+N2OI5H3nqE30/5fdHlSFKHMehJkqQu5UubfInNV92ccRPHMXfB3KLLkaQOYdCTJEldygrdVuDUnU/ljQ/e4Pqnri+6HEnqEAY9SZLU5QxZcwj7b7A/P3/i57z5wZtFlyNJ7c6gJ7UH7+eXpIZz4o4nkiTjJo4ruhRJancGvYbg3I71KpwRVZIa1tp91uYbn/wGv5v8Ox5565Giy5GkdmXQkyRJXda/ffLf+MRKn2DMw2NYuGhh0eVIUrsx6EmSpC5rxe4rctKQk3jm3We4/cXbiy5HktqNQU+SJHVpB2xwANuvsT2X/e0yZn00q+hyJKldGPQkSVKXFhGcuvOpzJg3g5/9/WdFlyNJ7cKgV+fC6RwbQnqcJKmhbbX6Vnxp0y/x62d+zaSZk4ouR5KWm0GvAaSzbtYtj4wklcex2x9L7+69uXDChUWXIknLzaAnSZIEDFhxAP++3b9z/9T7ue+1+4ouR5KWi0FPkiSp6itbfIUN+m7AhRMuZP6i+UWXI0ltZtCTJEmq6rFCD07e6WQmvz+Zm569qehyJKnNDHqSJEk19hi0B7utvRs/eewnvDvv3aLLkaQ2MehJkiTViAi+v9P3mbNgDlf+7cqiy5GkNjHo1bt02v5G4GGSpHLZqP9GHLbFYfyfF/4Pz737XNHlSFKrGfSk5RCuryBJpfXt7b5N3559+dGEH5G+oyepwRj0JEmSmtGvVz++O/i7PPzmw/zplT8VXY4ktYpBT5IkaQm+vNmX2aT/Jlw08SI+XPhh0eVIUosZ9CRJkpage7funLLzKUydPZUbnr6h6HIkqcUMepIkSUsxdK2h7LPuPlz1+FVMmzOt6HIkqUUMenUu8ObvRuA9+pJUbiOGjGDBogVc+uilRZciSS1i0GsAiVM71qvw2EhSl7Be3/U4Yqsj+M+X/pMnpz9ZdDmStEwGPUmSpBY4ZptjWL336lzw8AUutyCp7hn0JEmSWqBPzz58b4fv8fdpf+euSXcVXY4kLZVBT5IkqYUO2uQgtlxtSy5+5GLmzJ9TdDmStEQGPUmSpBbqFt04dedTeWvOW1z31HVFlyNJS2TQk9qBd2pIUtexwyd2YL/19+MXT/3Ce/Uk1a3wB1RxImIaMKUNnzoAmN7O5ajlHP/iOPbFceyL49gXy/EvjmNfHMe+WE3Hf/3MHNjaJzHoNaCImJiZQ4quo6ty/Ivj2BfHsS+OY18sx784jn1xHPtitdf4e+mmJEmSJJWMQU+SJEmSSsag15iuKrqALs7xL45jXxzHvjiOfbEc/+I49sVx7IvVLuPvPXqSJEmSVDKe0ZMkSZKkkjHoSZIkSVLJGPQaRER0i4jhEfFsRMyLiFcjYmxErFx0bWUREadFxC0R8XJEZERMXkb/XSLijxExKyLej4j/jojBnVRuqUTEZhFxTkT8NSKmVcf0sYgY2dxrPCI2j4jbI2JGRHwQEfdHxL5F1N7oqmP564h4JiJmRsSc6s+ZcRGx1hL6O/YdJCJWqvkZdEUz+x3/dlQd5+a22c30dezbWUSsFhEXRcSL1b9tpkXE3RGxR5N+/r5tJxFx1lJe9xkR85v093XfziKiT0ScHhFPVF/T0yPiwYj4ekREk77L9drv3v7lq4NcDBwP/AcwFtiy+vH2EfHpzFxUZHElcR7wLvAo0H9pHSNiKHAPMBUYXW0+Frg/InbNzCc6sM4y+jfgu8AdwK+B+cA+wA+AQyNiaGbOBYiIjYEHgQXAj4CZwNHA7yPis5n5xwLqb2TrAGtR+dnyGpVx3QY4BjgsIgZn5tvg2HeSc4BmF8V1/DvM/Xx84oOmf+w69u0sItan8nu0D3At8DzQD9gWGFTTz9+37es24MVm2rcFTgbuXNzg6779RUQ34HfArsAvgMuBlYDDgeuo/H1/SrXv8r/2M9Otzjdga2ARcGuT9uOABL5SdI1l2ICNav79JDB5KX0fBt4HBtW0Daq2/U/R30ujbcAQoF8z7T+ovsaPrWn7LbAQGFzT1geYAjxHdZIpt+U+JodUx/77jn2njfkOVP6gOrE69lc02e/4t/+YJ3B9C/o59u0/9vcDrwJrLaOfv28753j8rPr/4cCaNl/37T/Ow6rjfHGT9p7Ay8B7NW3L/dr30s3GcDgQwCVN2q8G5gBHdHpFJZSZL7ekX0RsAuwE3JKZU2s+fypwC/DpiFizY6osp8ycmJkzm9l1c/XxkwDVyzi/CNyTmY/VfP5s4BpgMyrHRstvSvVxVXDsO1pErEDlZ/p/U3nHvel+x78DRUTPiOizhH2OfTuLiD2B3YEfZeYbEdEjIlZqpp+/bztB9TV+GJWrOv67ps3XffvrW318vbYxMz8CpgMfQPu99g16jWEnKmf0Hq5tzMx5wGP4H62zLR7vvzSz769UQvmOnVdOqa1TfXyr+rgt0Isljz34/6FNIqJ3RAyIiHUi4jNU3t0FuKv66Nh3rOHAFlQuy2mO499xvkzlTdNZEfF2RFweEf1q9jv27e9z1cdXIuJOYC7wQUQ8HxG1b177+7ZzHEIlgFyfmQurbb7uO8bDwHvA9yPikIhYLyK2iIjzqbyWz6r2a5fXvvfoNYa1gemZ+WEz+6YCu0ZEz+q7Aep4a1cfpzazb3HboGb2qRWqZzjOoHIp243VZse+43yTyr0Ci00GjsjM+6sfO/YdJCI2BM4GzsnMyRGxQTPdHP+O8TCVd8dfpPKH7ueohO29qvfAzMax7wibVx+vBl4Avkbl0rWTgBsiokdmXodj31mOonI54c9r2hz7DpCZMyLii1TOiv62Ztcs4ODMvL36cbuMv0GvMawENBfyAObV9DHodY7Fl5c0d0zmNemjtruEyrXsp2fmc9U2x77j3A48S+X+i+2pXLIzoGa/Y99xfkrl3oxxS+nj+HeAzNylSdMvI+Jx4IfA96qPjn37W6X6OAvYZ/Eb1RFxO5X/C+dFxC9w7DtcRGxO5TLaP2XmpJpdjn3HmU1lLog7qEx2sxqVCelujIiDMvMPtNP4G/QawxxgjSXs613TR51j8Vj3amafx6MdRMS5VN5Vvyozz6/Z5dh3kMx8jcr9GQC3R8StwISIWKl6DBz7DlC9TG0/YM/MnL+Uro5/57kQOBM4kErQc+zb39zq429qr0aqnu24A/gqlbN+jn3HO6r6eE2Tdse+A0TENlTC3fDM/GlN+2+ohL+rq7Odtsv4e49eY3gdGBARzR3sQVQu6/RsXudZfANtc6fMF7c1d6pdLRARZwGjqEwz/O9Ndjv2nSQzHwf+Bnyn2uTYt7Pqz/RxVO6DfDMiNqnegL9+tUu/alt/HP9OUw3cr/O/Z7Qd+/a3+E2lN5vZ90b1cVUc+w4VEd2phOp3qCyxU8ux7xjDqQS1W2obM3MO8F9Ufv5vQDuNv0GvMUygcqx2rm2MiN7AYGBiEUV1YROqj8Oa2TeUynXuj3ReOeVRDXlnUllb5ptZnUu4xhNULmNY0tiD/x/a04pULikBx74jrEhlzbwDqdyntHi7p7r/iOrH38Tx7zTV363r8L+TQDn27W/x5HLrNLNvcdvb+Pu2o30B+ATwq2bmgfB13zEWh7QVmtnXveaxXV77Br3GcDOVA3pCk/ajqVyf++tOr6gLy8wXqfxwOyQiFt8sS/XfhwB/zszm3qXUUkTEaCoh7wbg3zJzUdM+1YkR7gT2jojtaj63D5U/hl+gyey0WrolTc8cEftQWdbir+DYd5APqPzMaLotPov639WP73D8219ErL6EXedS+UPrTvC130Fup3J/3hG1y1pExFrAPwHPZ+aL/r7tcIsv27y26Q5f9x3m6erj12sbq1duHATMANrttR8ff8Nc9SgiLqdyz9J/ULnMZ0vgeGA8sG9zfxSrdSLiSP73kqnjqMwANrb68ZTMvKGm767A3VQuP7m85nM+AeyWmX/vlKJLIiK+C1wBvEJlps2mr+e3qjcnL15b5mFgPnAxlYVDjwa2obLQ6+87q+4yiIj/ANYC/kxl7bzeVKZsPozK9f97L15DybHvHNVZNycBV2bmsTXtjn87ioiLqbwzfjeVnz19qMy6uQ/wEJVJQuZW+zr27SwijqGyjMtTVGZ77Al8m8rPo89n5v9U+/n7tgNUA8MrwCPNTEq0uI+v+3YWEesDj1K5NPnXVP6OX43KuG4AfDczf1ztu/yv/SJXh3dr+UblFO9JwHNUTqVPpXJvR5+iayvLRuVyqVzCdk8z/YcBf6Iye9Is4PfADkV/H424AdcvZew/Nv5U3uj4Typr0cwBHgA+XfT30YgbcCjwf4FXqczkNZfK7JuXA+s109+x7/hjskH1dX+F49+h43xQ9ef21Opr/wMqa9OeDvR27DvlGHyJylUDH1R/j/5P9Q/Ypv38fdv+Y3969efM0cvo5+u+/cd+Yyq3qLxGJUS/D9wHfKmZvsv12veMniRJkiSVjPfoSZIkSVLJGPQkSZIkqWQMepIkSZJUMgY9SZIkSSoZg54kSZIklYxBT5IkSZJKxqAnSZIkSSVj0JMkSZKkkjHoSZIkSVLJGPQkSZIkqWQMepIkSZJUMgY9SZIKEhHdI2J8RHwQEVs02XdMRGREnFNUfZKkxhWZWXQNkiR1WRGxPvAYMAXYJTM/jIitgQnAI8DembmwyBolSY3HM3qSJBUoM6cARwHbAWMjYkXgZmAe8K+GPElSW3hGT5KkOhARPwa+DTwI7AocnJm3FVuVJKlRGfQkSaoDEdEbeBLYGLg6M48puCRJUgPz0k1JkurDdsB61X9/MiK6F1mMJKmxGfQkSSpYRPQFfgNMB0YCw4CzCy1KktTQfLdQkqTiXQWsD+yXmX+OiO2BUyPij5l5d8G1SZIakPfoSZJUoIg4CrgGOC8zR1bb+lNZcqEHsG1mvlNgiZKkBmTQkySpINVF0h+hEur2yswFNfuGAfcBv8vMLxZUoiSpQRn0JEmSJKlknIxFkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkiSpZAx6kiRJklQyBj1JkiRJKhmDniRJkiSVjEFPkiRJkkrGoCdJkiRJJWPQkyRJkqSSMehJkiRJUskY9CRJkvT/2rl7VLuqMADD3zKk0hgkxkZnoIiY1gGIhUPICByBkxALO7uUKtiLla2gVg5ACIiKvwEVk2WRg4RLbO7Fe/A9zwObBWs1X/uy9t5AjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIqvY/LAAAEL1JREFUPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihBwAAECP0AAAAYoQeAABAjNADAACIEXoAAAAxQg8AACBG6AEAAMQIPQAAgBihdwFrrStrrY/XWn+stfZa66+11udrrZvHng0AADhda+997Bn+t9ZaX8zMKzNzd2Y+nZkXZ+bVmflxZm7uve8fcTwAAOBEudE7p7XWm3OIvL3383vv23vvWzPz4cw8MzPvHnVAAADgZLnRO6e11mcz89rMvLX3fu+R/esz89PMfLf3fu5Y8wEAAKdL6J3TWuv7mbkxM0/vvX89c/bLzDy193ZjCgAAXDohcn5PzcyDs5F38PPMrLXWk5c8EwAAgNC7gCsz82/XoX8e1mfPHqy17qy17h2eO//ZdAAAwMny6uY5rbV+n5mre+8rjzn7ZmZemIevb9679OEAAICT5kbv/H6bmSfWWtcec3Z9ZrbIAwAAjkHond/Xh/X2o5uHv25em5kfLn0iAACAEXoX8c5hffvM/vuH9YNLnAUAAOAfvtG7gLXWVzPz8szcnZlPZualmbk1D/+6eWPvff+I4wEAACdK6F3AWuvqzHw0M6/PzNWZeTAzX87MG3vvb485GwAAcLqEHgAAQIxv9AAAAGKEHgAAQIzQAwAAiBF6AAAAMUIPAAAgRugBAADECD0AAIAYoQcAABAj9AAAAGKEHgAAQMzfdLBQ8xEs49YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig0, ax0 = plt.subplots(figsize=(14, 8))\n",
    "ax0.plot(xaxis, yaxis_binom, '-', label=f'Binomial with n={n:2d} and p={p:.3f}')\n",
    "ax0.plot(xaxis, yaxis_poiss, '-', label=f'Poisson with lambda={Lambda:.2f}')\n",
    "ax0.plot(xaxis, yaxis_gauss, '-', label=f'Gaussian with mu={mu:.2f} and sigma={sigma:.2f}')\n",
    "ax0.set(xlim=(xmin, xmax),\n",
    "        title='Probability for Binomial and Gaussian', \n",
    "        xlabel='x', \n",
    "        ylabel='Probability')\n",
    "# ax0.set_yscale('log')\n",
    "# ax0.set_ylim(1e-12, 1.0)\n",
    "ax0.legend(loc='upper right');\n",
    "ax0.set_yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looping over processes:\n",
    "In the following we simulate a Binomial/Poisson process with given parameters, i.e. number of trials and probability of success. For the Poisson, these can not be specified, but the resulting expected number is naturally lambda = n * p.\n",
    "\n",
    "After having simulated the process, we fit the result with the three distributions in question, and test to what extend they match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  With N_trials = 10 and p_success = 0.2000, the average number of successes is lambda = 2.00\n"
     ]
    }
   ],
   "source": [
    "# Simulation parameters:\n",
    "N_experiments = 1000              # Number of simulations/experiments to perform\n",
    "\n",
    "N_trials = 10                     # Number of trials in each experiment\n",
    "p_success = 0.2                   # Chance of succes in each trial\n",
    "Lambda = N_trials * p_success     # This is the mean and the one parameter by which the Poisson is defined!\n",
    "\n",
    "print(f\"  With N_trials = {N_trials:d} and p_success = {p_success:.4f}, the average number of successes is lambda = {Lambda:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_success:    3\n",
      "n_success:    3\n",
      "n_success:    3\n",
      "n_success:    3\n",
      "n_success:    3\n",
      "n_success:    3\n",
      "n_success:    2\n",
      "n_success:    4\n",
      "n_success:    1\n",
      "n_success:    3\n"
     ]
    }
   ],
   "source": [
    "all_n_success = np.zeros(N_experiments)\n",
    "\n",
    "# Run the experiments, and fill the histogram from above:\n",
    "for iexp in range(N_experiments): \n",
    "    \n",
    "    # Simulating process defined:\n",
    "    n_success = 0\n",
    "    for i in range(N_trials): \n",
    "        x = r.uniform()\n",
    "        if (x < p_success): \n",
    "            n_success += 1\n",
    "\n",
    "    # Record result:\n",
    "    if (verbose and iexp < N_verbose): \n",
    "        print(f\"n_success: {n_success:4d}\")\n",
    "        \n",
    "    # Save Result\n",
    "    all_n_success[iexp] = n_success"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot result:\n",
    "\n",
    "Define a histogram with the \"data\" (note and think about the binning!). Also, ask yourself what uncertainty to assign to each bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts, bin_edges = np.histogram(all_n_success, bins=N_trials+1, range=(-0.5, N_trials+0.5))\n",
    "bin_centers = (bin_edges[1:] + bin_edges[:-1])/2\n",
    "s_counts = np.sqrt(counts)                         # NOTE: We (naturally) assume that the bin count is Poisson distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We remove any bins, which don't have any counts in them:\n",
    "x = bin_centers[counts>0]\n",
    "y = counts[counts>0]\n",
    "sy = s_counts[counts>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "ax.errorbar(x, y, yerr=sy, xerr=0.5, label='Distribution of nSuccesses', fmt='.k',  ecolor='k', elinewidth=1, capsize=1, capthick=1)\n",
    "ax.set(xlim=(-0.5, 30.5), xlabel='Number of successes', ylabel='Frequency');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binomial:\n",
    "\n",
    "First define the (fitting) function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func_binomial(x, N, n, p):\n",
    "    return N * binom.pmf(x, n, p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then fit it with a $\\chi^2$-fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<hr>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td title=\"Minimum value of function\">FCN = 3.180736675000052</td>\n",
       "        <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 118</td>\n",
       "        <td title=\"Number of call in last migrad\">NCALLS = 118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"Estimated distance to minimum\">EDM = 3.0714039131056e-05</td>\n",
       "        <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 1e-05</td>\n",
       "        <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
       "        UP = 1.0</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<table>\n",
       "    <tr>\n",
       "        <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
       "        <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
       "        <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
       "        <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
       "        <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">Made PosDef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
       "        <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
       "        <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
       "        <td align=\"center\"></td>\n",
       "        <td align=\"center\" title=\"Did last migrad call reach max call limit?\">Reach calllim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "        <td align=\"center\"></td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td><a href=\"#\" onclick=\"$('#jYAKfgqMRZ').toggle()\">+</a></td>\n",
       "        <td title=\"Variable name\">Name</td>\n",
       "        <td title=\"Value of parameter\">Value</td>\n",
       "        <td title=\"Hesse error\">Hesse Error</td>\n",
       "        <td title=\"Minos lower error\">Minos Error-</td>\n",
       "        <td title=\"Minos upper error\">Minos Error+</td>\n",
       "        <td title=\"Lower limit of the parameter\">Limit-</td>\n",
       "        <td title=\"Upper limit of the parameter\">Limit+</td>\n",
       "        <td title=\"Is the parameter fixed in the fit\">Fixed?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>0</td>\n",
       "        <td>N</td>\n",
       "        <td>996.836</td>\n",
       "        <td>31.5765</td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1</td>\n",
       "        <td>n</td>\n",
       "        <td>6.7319</td>\n",
       "        <td>0.717964</td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>2</td>\n",
       "        <td>p</td>\n",
       "        <td>0.30271</td>\n",
       "        <td>0.0320674</td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>No</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<pre id=\"jYAKfgqMRZ\" style=\"display:none;\">\n",
       "<textarea rows=\"12\" cols=\"50\" onclick=\"this.select()\" readonly>\n",
       "\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
       "\\hline\n",
       " & Name & Value & Hesse Error & Minos Error- & Minos Error+ & Limit- & Limit+ & Fixed?\\\\\n",
       "\\hline\n",
       "0 & N & 996.836 & 31.5765 &  &  &  &  & No\\\\\n",
       "\\hline\n",
       "1 & n & 6.7319 & 0.717964 &  &  &  &  & No\\\\\n",
       "\\hline\n",
       "2 & p & 0.30271 & 0.0320674 &  &  &  &  & No\\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "</textarea>\n",
       "</pre>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<hr>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chi2_bin = Chi2Regression(func_binomial, x, y, sy)\n",
    "minuit_bin = Minuit(chi2_bin, pedantic=False, N=N_experiments, n=N_trials, p=p_success) #   \n",
    "minuit_bin.migrad();  # perform the actual fit\n",
    "Ndof_bin = len(x) - 3  # 3 parameters in fit\n",
    "Prob_bin = stats.chi2.sf(minuit_bin.fval, Ndof_bin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And plot it on the figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xaxis = np.linspace(-0.5, N_trials+0.5, 1000)                  # This way we include all possibilties!\n",
    "yaxis = func_binomial(np.floor(xaxis+0.5), *minuit_bin.args)\n",
    "ax.plot(xaxis, yaxis, '-', label=f'Binomial distribution  p(Chi2={minuit_bin.fval:.1f},Ndof={Ndof_bin:d}) = {Prob_bin:.3f}')\n",
    "ax.legend()\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Poisson:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func_poisson(x, N, mu) :\n",
    "    return N * poisson.pmf(x, mu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<hr>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td title=\"Minimum value of function\">FCN = 68.2645091204848</td>\n",
       "        <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 30</td>\n",
       "        <td title=\"Number of call in last migrad\">NCALLS = 30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"Estimated distance to minimum\">EDM = 2.1490308882736347e-06</td>\n",
       "        <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 1e-05</td>\n",
       "        <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
       "        UP = 1.0</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<table>\n",
       "    <tr>\n",
       "        <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
       "        <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
       "        <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
       "        <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
       "        <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">Made PosDef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
       "        <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
       "        <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
       "        <td align=\"center\"></td>\n",
       "        <td align=\"center\" title=\"Did last migrad call reach max call limit?\">Reach calllim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "        <td align=\"center\"></td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td><a href=\"#\" onclick=\"$('#dsxzNAyRzU').toggle()\">+</a></td>\n",
       "        <td title=\"Variable name\">Name</td>\n",
       "        <td title=\"Value of parameter\">Value</td>\n",
       "        <td title=\"Hesse error\">Hesse Error</td>\n",
       "        <td title=\"Minos lower error\">Minos Error-</td>\n",
       "        <td title=\"Minos upper error\">Minos Error+</td>\n",
       "        <td title=\"Lower limit of the parameter\">Limit-</td>\n",
       "        <td title=\"Upper limit of the parameter\">Limit+</td>\n",
       "        <td title=\"Is the parameter fixed in the fit\">Fixed?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>0</td>\n",
       "        <td>N</td>\n",
       "        <td>936.188</td>\n",
       "        <td>30.6717</td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1</td>\n",
       "        <td>mu</td>\n",
       "        <td>2.01472</td>\n",
       "        <td>0.035826</td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>No</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<pre id=\"dsxzNAyRzU\" style=\"display:none;\">\n",
       "<textarea rows=\"10\" cols=\"50\" onclick=\"this.select()\" readonly>\n",
       "\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
       "\\hline\n",
       " & Name & Value & Hesse Error & Minos Error- & Minos Error+ & Limit- & Limit+ & Fixed?\\\\\n",
       "\\hline\n",
       "0 & N & 936.188 & 30.6717 &  &  &  &  & No\\\\\n",
       "\\hline\n",
       "1 & $\\mu$ & 2.01472 & 0.035826 &  &  &  &  & No\\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "</textarea>\n",
       "</pre>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<hr>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chi2_poisson = Chi2Regression(func_poisson, x, y, sy)\n",
    "minuit_poisson = Minuit(chi2_poisson, pedantic=False, N=N_experiments, mu=Lambda) #   \n",
    "minuit_poisson.migrad();  # perform the actual fit\n",
    "Ndof_poi = len(x) - 2     # 2 parameters in fit\n",
    "Prob_poi = stats.chi2.sf(minuit_poisson.fval, Ndof_poi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yaxis = func_poisson(np.floor(xaxis+0.5), *minuit_poisson.args)\n",
    "ax.plot(xaxis, yaxis, '-', label=f'Poisson distribution  p(Chi2={minuit_poisson.fval:.1f},Ndof={Ndof_poi:d}) = {Prob_poi:.3f}')\n",
    "ax.legend()\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def func_gaussian(x, N, mu, sigma) :\n",
    "    return N * norm.pdf(x, mu, sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<hr>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td title=\"Minimum value of function\">FCN = 5.589836934256331</td>\n",
       "        <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 55</td>\n",
       "        <td title=\"Number of call in last migrad\">NCALLS = 55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"Estimated distance to minimum\">EDM = 1.579801066730104e-05</td>\n",
       "        <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 1e-05</td>\n",
       "        <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
       "        UP = 1.0</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<table>\n",
       "    <tr>\n",
       "        <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
       "        <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
       "        <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
       "        <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
       "        <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">Made PosDef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
       "        <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
       "        <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
       "        <td align=\"center\"></td>\n",
       "        <td align=\"center\" title=\"Did last migrad call reach max call limit?\">Reach calllim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "        <td align=\"center\"></td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td><a href=\"#\" onclick=\"$('#qBtcPuxaBb').toggle()\">+</a></td>\n",
       "        <td title=\"Variable name\">Name</td>\n",
       "        <td title=\"Value of parameter\">Value</td>\n",
       "        <td title=\"Hesse error\">Hesse Error</td>\n",
       "        <td title=\"Minos lower error\">Minos Error-</td>\n",
       "        <td title=\"Minos upper error\">Minos Error+</td>\n",
       "        <td title=\"Lower limit of the parameter\">Limit-</td>\n",
       "        <td title=\"Upper limit of the parameter\">Limit+</td>\n",
       "        <td title=\"Is the parameter fixed in the fit\">Fixed?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>0</td>\n",
       "        <td>N</td>\n",
       "        <td>1015.74</td>\n",
       "        <td>32.4804</td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1</td>\n",
       "        <td>mu</td>\n",
       "        <td>1.96568</td>\n",
       "        <td>0.0422074</td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>2</td>\n",
       "        <td>sigma</td>\n",
       "        <td>1.24548</td>\n",
       "        <td>0.0392525</td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>No</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<pre id=\"qBtcPuxaBb\" style=\"display:none;\">\n",
       "<textarea rows=\"12\" cols=\"50\" onclick=\"this.select()\" readonly>\n",
       "\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
       "\\hline\n",
       " & Name & Value & Hesse Error & Minos Error- & Minos Error+ & Limit- & Limit+ & Fixed?\\\\\n",
       "\\hline\n",
       "0 & N & 1015.74 & 32.4804 &  &  &  &  & No\\\\\n",
       "\\hline\n",
       "1 & $\\mu$ & 1.96568 & 0.0422074 &  &  &  &  & No\\\\\n",
       "\\hline\n",
       "2 & $\\sigma$ & 1.24548 & 0.0392525 &  &  &  &  & No\\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "</textarea>\n",
       "</pre>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<hr>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chi2_gaussian = Chi2Regression(func_gaussian, x, y, sy)\n",
    "minuit_gaussian = Minuit(chi2_gaussian, pedantic=False, N=N_experiments, mu=Lambda, sigma=np.sqrt(Lambda)) #   \n",
    "minuit_gaussian.migrad();  # perform the actual fit\n",
    "Ndof_gau = len(x) - 3  # 3 parameters in fit\n",
    "Prob_gau = stats.chi2.sf(minuit_gaussian.fval, Ndof_gau)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yaxis = func_gaussian(xaxis, *minuit_gaussian.args)\n",
    "ax.plot(xaxis, yaxis, '-', label=f'Gaussian distribution  p(Chi2={minuit_gaussian.fval:.1f},Ndof={Ndof_gau:d}) = {Prob_gau:.3f}')\n",
    "\n",
    "ax.legend()\n",
    "fig.tight_layout()\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And save the figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "if save_plots: \n",
    "    fig.savefig(\"BinomialPoissonGaussian.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculation of Binomial $\\chi^2$-value:\n",
    "\n",
    "Here comes your calculation of ChiSquare values! Above, we have (using Minuit) *fitted* the distribution, but as we know the initial values, I would like you to calculate the $\\chi^2$-value between the data and the binomial they were generated from, i.e. with NO free parameters.\n",
    "\n",
    "I suggest you use Pearson's $\\chi^2$, and require `N_obs` and/or `N_exp` > e.g. 0.1. Remember that your choice should ensure, that there is no division by zero (which is a cardinal sin in programming)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_bins = len(x)                # Just to know how many bins to loop over\n",
    "chi2_bino = 0.0                # This you'll add to\n",
    "N_dof = 0                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for N_obs, x_i in zip(y, x):\n",
    "    N_exp = func_binomial(x_i, *minuit_bin.args)\n",
    "    if (N_obs > 0) :\n",
    "        chi2_bino += 0.0       # Write the expression yourself!\n",
    "\n",
    "# Also calculate Ndof and Prob:\n",
    "Ndof_bino = 1                  # Think about the number of degrees of freedom when there are no free parameters!\n",
    "Prob_bino = stats.chi2.sf(chi2_bino, Ndof_bino)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Just a test printout, change to match your own results! \n",
      "\n",
      "Binomial:   chi2 = 9999.00   N_dof = 9999   Prob = 9999.0000\n"
     ]
    }
   ],
   "source": [
    "print(f\"Just a test printout, change to match your own results! \\n\")\n",
    "print(f\"Binomial:   chi2 = {9999:.2f}   N_dof = {9999:d}   Prob = {9999:6.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "\n",
    "# Questions:\n",
    "\n",
    "Important: Make sure you understand what process yields a Binomial, a Poisson and a Gaussian distribution. Without this knowledge, a large fraction of the course will be lost on you!\n",
    "\n",
    "1. Plot a Binomial ($N=10$, $p=0.2$), Poisson ($\\lambda = 2$), and Gaussian ($\\mu=2$, $\\sigma=\\sqrt{2}$), i.e. same means and widths. Which distribution has the longest tail towards high values? And which one has the longest tail the other way? Does this pattern depend on the parameters (given same means and widths)? Play around with the settings (remember also to change the scale of the plot accordingly), and challenge/confirm the statements in todays lecture. Possibly also do logarithmic plots.\n",
    "\n",
    "2. Producing binomially distributed numbers (using `N_experiments=1000`, `N_trials=10` and `p_success=0.2`), do $\\chi^2$ fits of the resulting distribution with a Binomial, a Poisson, and a Gaussian distribution. Do you get acceptable fit probabilities with all of these? If not, investigate in what cases you do.\n",
    "\n",
    "3. Calculate \"by hand\" (i.e. using a loop) the $\\chi^2$ between the data and the original Binomial distribution (which the data is generated from). Since you are not fitting anything, what is the number of degrees of freedom? Does it give a reasonable $\\chi^2$-probability?\n",
    "\n",
    "4. In all of the above $\\chi^2$ fits, we have _assumed_ that the uncertainty on the count in each bin is Gaussianly distributed! Ask yourself to what extend this requirement is fulfilled? Does changing the parameters (`N_experiments`, `N_trials` and `p_success`) \"help\" fulfilling this requirement, and if so, which and how?\n",
    "\n",
    "### Advanced questions:\n",
    "\n",
    "5. Using `N_experiments=1000`, `N_trials=1000` and `p_success=1/60`, is the skewness consistent with zero (as the Gaussian should have)? Does that contradict the conclusion from above?"
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