04.Hyperfine_Splitting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "99e31eb0-2e42-44b0-99d1-bf56f2a7138b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#lmfit provides curve fiting using non-linear least squares minimization\n",
    "#lmfit was developed by Matt Newville: https://github.com/newville\n",
    "\n",
    "#install lmfit\n",
    "\n",
    "#uncomment the following lines to install lmfit\n",
    "\n",
    "# from subprocess import call\n",
    "# call(['pip', 'install', 'lmfit'])\n",
    "# call(['pip', 'install', 'matplotlib'])\n",
    "\n",
    "#import libraries\n",
    "from numpy import sqrt, pi, exp, linspace, loadtxt\n",
    "from lmfit.models import GaussianModel, VoigtModel, LorentzianModel, PseudoVoigtModel, MoffatModel, StudentsTModel\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import matplotlib.lines as mlines\n",
    "import scipy.signal\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d185d24-27ec-4f48-b785-bc765934fff2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Load the data\n",
    "\n",
    "#to get a better measure of the center, the data was split and loaded into chunks\n",
    "#each peak of interest was fit individually and the whole spectrum plotted on a single graph\n",
    "\n",
    "#The data was cleaned and prepared using external spreadsheet applications\n",
    "#The data includes estimated errors based on instruments' accuracy\n",
    "\n",
    "\n",
    "data1 = loadtxt('./Data/s1.csv')\n",
    "x1 = data1[:, 0]\n",
    "y1 = data1[:, 1]\n",
    "yerr1 = data1[:, 2]\n",
    "\n",
    "#-------------------------------\n",
    "\n",
    "data2 = loadtxt('./Data/s2.csv')\n",
    "x2 = data2[:, 0]\n",
    "y2 = data2[:, 1]\n",
    "yerr2 = data2[:, 2]\n",
    "\n",
    "#-------------------------------\n",
    "\n",
    "data3 = loadtxt('./Data/s3.csv')\n",
    "x3 = data3[:, 0]\n",
    "y3 = data3[:, 1]\n",
    "yerr3 = data3[:, 2]\n",
    "\n",
    "#-------------------------------\n",
    "\n",
    "data4 = loadtxt('./Data/s4.csv')\n",
    "x4 = data4[:, 0]\n",
    "y4 = data4[:, 1]\n",
    "yerr4 = data4[:, 2]\n",
    "\n",
    "#-------------------------------\n",
    "\n",
    "data5 = loadtxt('./Data/s5.csv')\n",
    "x5 = data5[:, 0]\n",
    "y5 = data5[:, 1]\n",
    "yerr5 = data5[:, 2]\n",
    "\n",
    "#-------------------------------\n",
    "\n",
    "data6 = loadtxt('./Data/s6.csv')\n",
    "x6 = data6[:, 0]\n",
    "y6 = data6[:, 1]\n",
    "yerr6 = data6[:, 2]\n",
    "\n",
    "#-------------------------------\n",
    "\n",
    "data7 = loadtxt('./Data/s7.csv')\n",
    "x7 = data7[:, 0]\n",
    "y7 = data7[:, 1]\n",
    "yerr7 = data7[:, 2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "615b3af5-4437-4206-bad7-07f5490ba3c4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#define the fit for each peak using builtin models\n",
    "#the suffixes (2, 4, 6) indicate the data chunk they are fiiting\n",
    "#specify an alias(prefix) for each model:\n",
    "\n",
    "mod2 = MoffatModel(prefix='g2_')\n",
    "mod4 = MoffatModel(prefix='g4_')\n",
    "mod6 = MoffatModel(prefix='g6_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "94ab7b12-31c7-460c-9915-f3056ddad9ea",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#initialize the parameters to be optimized\n",
    "\n",
    "pars2  = mod2.guess(y2, x=x2)#g2_amplitude=0.0093, g2_center=47.8, g2_sigma=1.088, g2_beta=1)\n",
    "pars4  = mod4.guess(y4, x=x4)#g4_amplitude=0.0128, g4_center=74.95, g4_sigma=10.01, g4_beta=1)\n",
    "pars6  = mod6.guess(y6, x=x6)#g6_amplitude=0.011994, g6_center=120.1133928, g6_sigma=1.27, g6_beta=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "30fdd56e-fb95-409f-9335-67a77d1e9ec8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#fit with weights=1/yerr\n",
    "\n",
    "result2 = mod2.fit(y2, pars2, x=x2, weights=1./yerr2)\n",
    "result4 = mod4.fit(y4, pars4, x=x4, weights=1./yerr4)\n",
    "result6 = mod6.fit(y6, pars6, x=x6, weights=1./yerr6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4724abad-bd69-4269-9e9f-cc3fd5bde7ba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1m1st Peak\n",
      "\u001b[0m\n",
      "[[Model]]\n",
      "    Model(moffat, prefix='g2_')\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 74\n",
      "    # data points      = 190\n",
      "    # variables        = 4\n",
      "    chi-square         = 189.449454\n",
      "    reduced chi-square = 1.01854545\n",
      "    Akaike info crit   = 7.44865445\n",
      "    Bayesian info crit = 20.4367507\n",
      "    R-squared          = -211916.131\n",
      "[[Variables]]\n",
      "    g2_amplitude:  0.01248919 +/- 2.5772e-04 (2.06%) (init = 0.57105)\n",
      "    g2_center:     126.995557 +/- 1.27421847 (1.00%) (init = 127.7964)\n",
      "    g2_sigma:      84.2746916 +/- 173.816861 (206.25%) (init = 38.07)\n",
      "    g2_beta:       1.77307990 +/- 6.69254409 (377.45%) (init = 1)\n",
      "    g2_fwhm:       116.573873 +/- 26.4240040 (22.67%) == '2*g2_sigma*sqrt(2**(1.0/max(1e-3, g2_beta))-1)'\n",
      "    g2_height:     0.01248919 +/- 2.5772e-04 (2.06%) == 'g2_amplitude'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(g2_sigma, g2_beta)      = +0.9996\n",
      "    C(g2_amplitude, g2_sigma) = -0.5845\n",
      "    C(g2_amplitude, g2_beta)  = -0.5675\n",
      "    C(g2_center, g2_beta)     = -0.1481\n",
      "    C(g2_center, g2_sigma)    = -0.1433\n",
      "\n",
      "\u001b[1m2nd Peak\n",
      "\u001b[0m\n",
      "[[Model]]\n",
      "    Model(moffat, prefix='g4_')\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 987\n",
      "    # data points      = 147\n",
      "    # variables        = 4\n",
      "    chi-square         = 164.900878\n",
      "    reduced chi-square = 1.15315299\n",
      "    Akaike info crit   = 24.8920588\n",
      "    Bayesian info crit = 36.8537892\n",
      "    R-squared          = -99675.0955\n",
      "[[Variables]]\n",
      "    g4_amplitude:  0.02616973 +/- 2.5964e-04 (0.99%) (init = 0.58254)\n",
      "    g4_center:     230.001248 +/- 1.13589787 (0.49%) (init = 225.3396)\n",
      "    g4_sigma:      10318.1992 +/- 2.9577e+08 (2866471.20%) (init = 27.74)\n",
      "    g4_beta:       27134.4207 +/- 1.5382e+09 (5668863.73%) (init = 1)\n",
      "    g4_fwhm:       104.301159 +/- 33096.4728 (31731.64%) == '2*g4_sigma*sqrt(2**(1.0/max(1e-3, g4_beta))-1)'\n",
      "    g4_height:     0.02616973 +/- 2.5964e-04 (0.99%) == 'g4_amplitude'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(g4_sigma, g4_beta)       = +1.0000\n",
      "    C(g4_amplitude, g4_sigma)  = -0.3774\n",
      "    C(g4_amplitude, g4_beta)   = -0.3774\n",
      "    C(g4_amplitude, g4_center) = -0.1328\n",
      "    C(g4_center, g4_beta)      = -0.1288\n",
      "    C(g4_center, g4_sigma)     = -0.1288\n",
      "\n",
      "\u001b[1m3rd Peak\n",
      "\u001b[0m\n",
      "[[Model]]\n",
      "    Model(moffat, prefix='g6_')\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 851\n",
      "    # data points      = 222\n",
      "    # variables        = 4\n",
      "    chi-square         = 240.845868\n",
      "    reduced chi-square = 1.10479756\n",
      "    Akaike info crit   = 26.0885147\n",
      "    Bayesian info crit = 39.6992243\n",
      "    R-squared          = -47604.7955\n",
      "[[Variables]]\n",
      "    g6_amplitude:  0.01812014 +/- 2.4557e-04 (1.36%) (init = 1.166715)\n",
      "    g6_center:     372.866865 +/- 0.96106665 (0.26%) (init = 373.6551)\n",
      "    g6_sigma:      9962.18219 +/- 3.6579e+08 (3671770.22%) (init = 35.355)\n",
      "    g6_beta:       32931.4594 +/- 2.4183e+09 (7343429.58%) (init = 1)\n",
      "    g6_fwhm:       91.4100626 +/- 131.780654 (144.16%) == '2*g6_sigma*sqrt(2**(1.0/max(1e-3, g6_beta))-1)'\n",
      "    g6_height:     0.01812014 +/- 2.4557e-04 (1.36%) == 'g6_amplitude'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(g6_sigma, g6_beta)       = +1.0000\n",
      "    C(g6_amplitude, g6_center) = +0.1540\n",
      "    C(g6_center, g6_beta)      = +0.1057\n",
      "    C(g6_center, g6_sigma)     = +0.1057\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#print the fit statistics and estimated parameters\n",
    "print()\n",
    "print('\\033[1m' + \"1st Peak\")\n",
    "print('\\033[0m')\n",
    "print(result2.fit_report())\n",
    "print()\n",
    "print('\\033[1m' + \"2nd Peak\")\n",
    "print('\\033[0m')\n",
    "print(result4.fit_report())\n",
    "print()\n",
    "print('\\033[1m' + \"3rd Peak\")\n",
    "print('\\033[0m')\n",
    "print(result6.fit_report())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a46aa07c-28e3-40c2-9223-84f29ca6a3bb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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eLyHKqs33VF5eHiNGjMDb25vvvvsOFxeXhjilZkVGloRoQDUZgo+NjcXFxYVNmzbZjklJSeHw4cO8/vrrdd5XUbPXS4jqqOl7SqfTMXz4cNzc3Fi3bh3u7u513UWBBEtCNKiaDMH7+fkxadIkHnvsMVq0aEFgYCCPP/44Xbt2td0dJ+pWTS+ZpKamkpqaasunc+jQIXx8fIiMjJTLeQKo2XsqLy+PYcOGodfrWblyJTqdDp1OB4BGo8HJyakuu9qsSbAkRBPyzjvv4OzszO23305hYSHXXnstK1askD+SjdSyZct44YUXbNtXXXUVAJ988gn33ntvA/VKNFX79u3jzz//BKBdu3Z2+xISEmjdunUD9Kp5UCmKojR0J5o6nU6Hn5+f7RZOIYQQQjR+1f38lgneQgghhBBVkGBJCCGEEKIKEiwJIYQQQlRBgiUhhBBCiCpIsCSEEEIIUQUJloRopoYPH05QUBAvv/yyrWzFihUMGDCAQYMGsX//fgCys7MZMGAAQ4cOpW/fvvz2228O23N0rKMysNwCPWzYMK6++mr+85//AHD69GlUKhUrV6601Zs0aRLR0dG2/eXzSZW/fVrUnKP3AVjWHQsICLC9Hvv27WPgwIFcddVVXHPNNZw6dapCW4888gj9+vWjT58+rFq1ylbu6PWurLyq94G8B0SDUcQly83NVQAlNze3obsiRLUlJiYqn3zyifLSSy8piqIoWVlZSq9evZTi4mIlISFBGThwoKIoimIymRSj0agoiqKcPHlS6d27d4W2HB1bWXsGg0G57rrrFJ1OZ9dGQkKC0qtXL+Wmm25SFEVRioqKlOuvv15p27atbf+1115rd4x1n7h45d8HVjNnzlRuvPFG5fPPP1cURVHOnTtne81++ukn5a677rKrf+jQIeXqq69WFEVR8vLybK9NZa/3xbwP5D0galt1P79lZEmIZioiIsJu+88//2Tw4MG4uLjQunVr8vPzMRgMqNVqW9LLnJwcunXrVqEtR8du3brVYXu7du3C29ubO++8k6uvvpqtW7fa2gkICMDZ2Zn09HR+/PFHRo0adcHzWL58OUOHDmXo0KG0aNGCH3744RJ/M81L+fcBwMmTJ8nMzCQ2NtZWFhoaio+PDwCurq4VEqGGhYXh6elJSUkJeXl5tqzUlb3e8j4QTYlk8BZCAJCVlYW/v79t28/Pj6ysLEJDQzl79ix33HEHx44d45NPPqnWsfn5+Q7bO3fuHAcPHiQuLo68vDyuvfZajhw5Yqs3btw4vvnmG7Zu3cq7777LkiVLbPv27dvH0KFDbdsmk4lJkyYxadIkNmzYwNKlSxk5cmSt/D6as2eeeYaXXnqJzz//vMK+goIC5s6dy0cffWRXHhAQQIcOHYiJiUGv1/PBBx8AVPp6X+z7wNF7AJD3gahTEiwJIQDLGlU5OTm27dzcXNvoQGRkJNu3b+f06dMMHTqUG2+88YLHent7O2wvMDCQAQMG4Ovri6+vL0FBQWi1Wlu9MWPGcN111xEQEGBbmd0qNjaWX3/91bZtna+yb98+XnvtNX744QecneXP2qXYtWsXLVq0oG3bthX2lZSUMH78eJ566im6d+9ut2/Tpk2cOnWKEydOoNPpGDx4MMOGDav09b7Y90Fl7wGQ94GoO03uMtzSpUuJjo7G3d2d2NhYtm3bVmX9LVu2EBsbi7u7O23atGHZsmV2+1esWIFKparwKCoqqsvTEKLR6du3L9u3b6ekpISzZ8/i7e2Nm5sbBoPBVsfX19d2KeZCx1511VUO2+vbty/x8fEYjUby8vLQarUEBQXZ2vLw8ODmm29m2rRp1ep3QkICjz76KF999RVeXl6X/oto5v766y/+/vtvRowYwcqVK3njjTfYs2cPZrOZu+66i7FjxzJ27NgKxymKQkBAAE5OTvj4+FBSUoLRaKz09Zb3gWhKmlTo/fXXXzNz5kyWLl3KwIED+e9//8vIkSP5999/iYyMrFA/ISGBUaNG8cADD7By5Up27NjBtGnT0Gg03HrrrbZ6vr6+HDt2zO5Yd3f3Oj8fIRrSAw88wM6dOzEYDOzdu5fvv/+eadOmMWTIEFQqFe+++y4ABw8e5PHHH8fJyYmSkhIWLlxoa+POO+/kiy++ICAgoMKxjsoA/P39mT59OkOHDqWkpITXXnsNtdr+e9vjjz9e7fN4+eWX0Wq13H777bbtQYMGXeJvp/lw9D6YMWMGAM8//zzt2rWjT58+fPXVV/z000+kpaWxcuVKunbtyuLFiwHL++Dzzz/nq6++YtCgQRgMBqZMmWILrB293vI+EE1Jk1pIt2/fvvTq1Yv333/fVtapUyfGjh3LggULKtSfM2cO69ats7sOPmXKFA4ePMiuXbsAy8jSzJkz7S4X1JQspCtqi6IolJSUAODi4oJKpWrgHomqyOslapu8p+rXZbeQbnFxsS0nR1nDhg1j586dDo/ZtWtXhfrDhw9n7969tjcjQH5+PlFRUURERHDjjTdy4MCBKvtiMBjQ6XR2DyFqQ0lJCS+88AIvvPCC3XtUNE7yeonaJu+pxqnJBEsZGRmYTCZCQkLsykNCQkhNTXV4TGpqqsP6RqORjIwMADp27MiKFStYt24dq1atwt3dnYEDB3L8+PFK+7JgwQL8/Pxsj1atWl3i2QkhhBCisWoywZJV+SFJRVGqHKZ0VL9seb9+/bjrrrvo3r07gwcP5ptvviEmJsZ2Ld6RuXPnkpuba3skJiZe7OkIcdHS09Pt7h4StevcuXNkZWXVSluKonDkyBHMZnOttCeapuzsbJKTkxu6G+IiNJlgKSgoCCcnpwqjSOnp6RVGj6xatmzpsL6zszMtWrRweIxarebKK6+scmTJzc3Ndrur9SFEfUlOTmbGjBlERkYyd+7chu7OZevBBx8kKiqKefPm2Uaia0pRFH766Sf69OlD586dOXr0aC33UjQlb775JlFRUdx///2cPHmyobsjaqDJBEuurq7ExsayadMmu/JNmzYxYMAAh8f079+/Qv2NGzfSu3dvXFxcHB6jKApxcXEV8rsI0dCsQVLbtm1ZuXIlzzzzDG+//XZDd+uytWLFCh555BEWLVpEdHR0jYKmskHSjTfeiLu7O7/++iudO3eu416Lxuzpp5/mzTff5Oeff6ZDhw4SNDUhTSp1wOzZs7n77rvp3bs3/fv354MPPuDs2bNMmTIFsFweS05O5rPPPgMsd74tWbKE2bNn88ADD7Br1y6WL19ut8DjCy+8QL9+/Wjfvj06nY5FixYRFxfHe++91yDnKER5xcXFjBw5kq1bt+Li4sLAgQPp27cvJpPJ7jZ+Ufs8PDx4+OGH2blzJ2+99RZvvfUWo0aN4ttvv6308v/333/Pww8/zLlz54iMjGTixIlER0ezY8cOduzYUc9nIBqjyZMns3fvXlavXs2nn35K79692bhxI35+fg3dNVGJJhUsjR8/nszMTF588UVSUlLo0qUL69evJyoqCoCUlBTOnj1rqx8dHc369euZNWsW7733HmFhYSxatMgux1JOTg4PPvggqamp+Pn50bNnT7Zu3UqfPn3q/fyEcCQ9PZ2tW7diNBpxcXHh8OHDdukwRN0zGo2oVCoMBgM///yz7bVw5Ntvv+XcuXOoVCpyc3PZsGGD3P4tKjCZTJjNZsxmM3v37uXYsWPyudOY1eFivs1GdVctFuJCDAaDMm/ePGXevHmKwWCwlefm5irz589XAgMDFVdXV+Xhhx9WEhMTG7CnzcPx48eViRMnKk5OTkpoaKjy7rvvKnq93ra/stdr7969yujRoxVAiYmJUVauXKkYjcaGOAXRyKSnpytz5sxRvLy8FG9vb2XevHlKRkaGbX9l7ylRN6r7+d1k5iwJ0Zz5+voyb948EhISeO6551i1ahVt27a1S9AqatdLL71Ex44d2bhxI2+//TYnT55kxowZeHh4XPDY2NhY1q1bx969e+nQoQN33XUXV1xxRaVpTkTz8M033xAdHc17773Ho48+yunTp5k/f36lNxyJxqNJXYYTormzBk3Tp09n2bJl8ke2DoWFhfHOO+8wefLkagVIjliDpn379vHZZ5/J4q7NnK+vL3PmzGHatGnyb7eJkX+5QjRBPj4+PPHEEw3djcvapEmTaq2t2NhYYmNja6090TSNGDGCESNGNHQ3xEWQy3BCCCGEEFWQYEkIIYQQogoSLAkhhBBCVEGCJSGEEEKIKkiwJIQQQghRBQmWhBBCCCGqIMGSEEIIIUQVJFgSQgghhKiCBEtCCCGEEFWQYEkIIYQQogoSLAkhhBBCVEGCJSGEEEKIKkiwJIQQQghRBQmWhBBCCCGqIMGSEEIIIUQVJFgSQgghhKiCBEtCCCGEEFWQYEkIIYQQogoSLAkhhBBCVEGCJSGEEEKIKkiwJIQQQghRBQmWhBBCCCGqIMGSEEIIIUQVJFgSQgghhKiCBEtCCCGEEFWQYEkIIYQQogoSLAkhhBBCVEGCJSGEEEKIKkiwJIQQQghRBQmWhBBCCCGqIMGSEEIIIUQVJFgSQgghhKiCBEtCCCGEEFWQYEkIIYQQogoSLAkhhBBCVEGCJSGEEEKIKkiwJIQQQghRBQmWhBBCCCGqIMGSEEIIIUQVmlywtHTpUqKjo3F3dyc2NpZt27ZVWX/Lli3Exsbi7u5OmzZtWLZsWaV1v/rqK1QqFWPHjq3lXgshhBCiqWpSwdLXX3/NzJkzeeqppzhw4ACDBw9m5MiRnD171mH9hIQERo0axeDBgzlw4ADz5s1jxowZrF27tkLdM2fO8PjjjzN48OC6Pg0hhBBCNCFNKlh6++23mTRpEpMnT6ZTp04sXLiQVq1a8f777zusv2zZMiIjI1m4cCGdOnVi8uTJ3H///bz55pt29UwmE3feeScvvPACbdq0qY9TEUIIIUQT0WSCpeLiYvbt28ewYcPsyocNG8bOnTsdHrNr164K9YcPH87evXspKSmxlb344otoNBomTZpUrb4YDAZ0Op3dQwghhBCXpyYTLGVkZGAymQgJCbErDwkJITU11eExqampDusbjUYyMjIA2LFjB8uXL+fDDz+sdl8WLFiAn5+f7dGqVasano0QQgghmoomEyxZqVQqu21FUSqUXai+tTwvL4+77rqLDz/8kKCgoGr3Ye7cueTm5toeiYmJNTgDIYRoGuLjpxMXN5y0tFW27fj46Xb7rY+0tFXExQ1vqK4KUaecG7oD1RUUFISTk1OFUaT09PQKo0dWLVu2dFjf2dmZFi1a8M8//3D69GlGjx5t2282mwFwdnbm2LFjtG3btkK7bm5uuLm5XeopCSFEo6bVrsZozMbJyY2QkAlotasBiIlZbNtvZTCcITd3c0N0U4g6V61gadGiRTVu+L777sPHx6fGx1XG1dWV2NhYNm3axM0332wr37RpEzfddJPDY/r3788PP/xgV7Zx40Z69+6Ni4sLHTt25NChQ3b7n376afLy8nj33Xfl8poQQgghqhcszZw5k4iICJycnKrVaGJiIjfeeGOtBksAs2fP5u6776Z3797079+fDz74gLNnzzJlyhTAcnksOTmZzz77DIApU6awZMkSZs+ezQMPPMCuXbtYvnw5q1ZZhpTd3d3p0qWL3XP4+/sDVCgXQgghRPNU7ctwe/fuJTg4uFp1aztIsho/fjyZmZm8+OKLpKSk0KVLF9avX09UVBQAKSkpdjmXoqOjWb9+PbNmzeK9994jLCyMRYsWceutt9ZJ/4QQQghx+alWsPTcc8/h7e1d7UbnzZtHYGDgRXeqKtOmTWPatGkO961YsaJC2ZAhQ9i/f3+123fUhhBCCCGar2oHSzUxd+7ci+qMEEIIIURjU+PUAYWFhej1etv2mTNnWLhwIRs3bqzVjgkhhBBCNAY1DpZuuukm2wTqnJwc+vbty1tvvcVNN91U6bIjQgghmpaSEtDp/MjL88dstuSr0+u9MBjcATAYLPt1Oj/MZhU6nRclJS4N2WUh6kyN8yzt37+fd955B4A1a9YQEhLCgQMHWLt2Lc8++yxTp06t9U4KIYSoX9Onw6lTr5CermHChD1ERMCUKT/SokUae/bAAw9ASsoC0tIi6Np1D3/+OYauXa/iuusauudC1L4ajyzp9Xrb3W4bN27klltuQa1W069fP86cOVPrHRRCCFH/kpPh2Wcf5uabl2IyqUlMhMGDf8bV1QCA0Qj/+c/jjBmzkuJiN665ZheFhdW/EUiIpqTGwVK7du34/vvvSUxMZMOGDbaFatPT0/H19a31DgohhKh75ZcyKSg4hHW1KKMxm61bn0WjSUFRTADk5e3FbLYETopixNk5A4PBw67NskugpKWtsi2bIkRTU+Ng6dlnn+Xxxx+ndevW9O3bl/79+wOWUaaePXvWegeFEELUPa12dbnlS1JsP5eUZHP6dBbBwSmAZUmooqKzmM2F52uYUKkyKC52t2szPX2VbQmU9PRVpKdLsCSaphrPWbrtttsYNGgQKSkpdO/e3VZ+7bXXcsstt9Rq54QQQtQ/nQ68vPLsyjIywujW7Uilx6hUSl13S4gGU+ORpfvvvx8vLy969uyJWl16+BVXXMFrr71Wq50TQghR/5KSQKNJsSvTasMrlJUnAZO4XNU4WPr0008pLCysUF5YWGhLKSCEEKLpSk6uGCzpdC3w88uq8jhFUdVlt4RoMNW+DKfT6VAUBUVRyMvLw9299Nq0yWRi/fr11V47TgghROOVlMT5+UmlFEVlm/AtRHNT7WDJ398flUqFSqUiJiamwn6VSsULL7xQq50TQghR/5KTKwZLQjRn1Q6W/vjjDxRF4ZprrmHt2rV2C+W6uroSFRVFWFhYnXRSCCFE/UlKgi5dUtBoxuHpGYxW64KnZx4azTjU6tKrCq6uofj4XIlabcTZuQRn5xYN2Gsh6k61g6UhQ4YAkJCQQGRkJCoZjxVCiMtCXh4sX/447dsfZuBASE2FgAAtMTGLadMGvvnmEO3bbyAmZjEZGad47jk4ebIz3t6dCQ29myNHjhEVtR1///62Nr/9Fv7443ZGj95CYiK8994E+vePo2vXBjxRIS5StYKlv//+my5duqBWq8nNzeXQoUOV1u3WrVutdU4IIUTdO3kStNpQTpzozNy5YDaDs7Ml+eSoUWA2f4S390rgcd544zUiIv5Lu3YP4enpx623gofHUiIiUlmwYBKKAioVfPIJeHoGkpDQGaMRzp4No7jYlYkTG/ZchbgY1QqWevToQWpqKsHBwfTo0QOVSoWiVLxFVKVSYTKZar2TQggh6o5OB+HhCeTm+p7P4r0Ys7kIAA8P6NgxgaysfACiolLo2hUKCo4BffDysuwHcHWF4mJwc7METJ06nSQjIxy1+n0CAsIBS6bwmJjFDXGaQly0agVLCQkJaDQa289CCCEuH3l54OWVj6KYz2fxXlwmO3f1eXhAYaElWAIIDs4kPj4cN7ftwHjAkilcgiXR1FQrWIqKinL4sxBCiKbPUcbui+HpaQmW/P0t2yEhmezY0YqIiIxLbluIhlTj5U4A4uPj2bx5M+np6ZjNZrt9zz77bK10TAghRP3Q6cDTM/+S27GOLFmFhGSSkdEXOHjJbQvRkGocLH344YdMnTqVoKAgWrZsaXdXnEqlkmBJCCGaGOtluEvl4QF6fem2r28+Ol1g5QcI0UTUOFh6+eWXmT9/PnPmzKmL/gghhKhnOh1ERV36ZbjyI0sqlawXJy4PNV4bLjs7m3HjxtVFX4QQQjSAuroMZyVrxommrsbB0rhx49i4cWNd9EUIIUQDsFyGq/2RJQBv7xzy830uuW0hGlKNL8O1a9eOZ555ht27d9O1a1dcXFzs9s+YMaPWOieEEKLuWS7DXV9uKZPS5auCgydgMhlsPwNoNOPw8xtgV2a9G67scRERKrTaMDw9O+DmJnn4RNNU42Dpgw8+wNvbmy1btrBlyxa7fSqVSoIlIYRoYgwG6Nr1beAv5sz5lJYtwdu7dF2SkJAJhIRMsP0M2OVKspaVn+AdEjKBXr1gy5YPCQvrgLMzLFz4EgMH1v05CVGbahwsSVJKIYS4/KjVsGTJS+h0exg4MJUqVrWqlIcHZGbal0VEWJZSCQuzLKOSlNSmdjosRD2q8ZwlIYQQTVNa2ioA4uKG27bj46eTm7urVtr38IAzZ1YDkJu7i7S0VYSHQ3p6GNnZmwEwGEov9aWlrbL1QYjGrMYjS/fff3+V+z/++OOL7owQQoi6k56+ipCQCeTmbrZt63R7MBqH10r7lpGlIwAYjZmkp68iImICGRktKSzcBwylqMjDrj8AOt0eWQJFNGo1Dpays7PttktKSjh8+DA5OTlcc801tdYxIYQQTYunJ3bBEEBwMOTmliamLC52L3+YEI1ejYOl7777rkKZ2Wxm2rRptGkj16KFEKIpUWoxZ6SHh/1lNgAnJ/D3L10brvx+IZqCWpmzpFarmTVrFu+8805tNCeEEKKeGAweuLnpL1yxGhwFSwDBwSllnk+CJdH01NoE75MnT2I0GmurOSGEEPWgoMC7VhJSQuXBkkYjwZJo2mp8GW727Nl224qikJKSwk8//cTEiRNrrWNCCCHqXkGBD56etRcsOZqTpNGkYDZbvpuXn9MkRFNQ42DpwIEDdttqtRqNRsNbb711wTvlhBBCNC56vTeenrpaacvRBG+wBEtpaeEAFBd7oCiWRXaFaCpqHCz98ccfddEPIYQQdWzDhoEkJVmCmr//hu++u574eDX+/mcAy/Ikbm5Rtp9rysUFUlIiWb4c0tJa2crbtYsiLa10mkZREezeDTt2XEO3bsfw8PC6xDMTom5JUkohhGgmPv/8JubPt/y8YgUYjU507bqHa6/9GrAsT2LNd2RdwqQmVCqYOvUlgoJgypR5tvL777+PoUN/sm0XFsLrr8NPPw3l669Hcvhw74s/KSHqgQRLQgjRxFmzYFszdJctK5ut288vjfBwKCz0JDv7d7p1+4bBgzfQokVqrfXlyiu3cdNNcOWVv9rKfH3B3z8Tvf4oYAmWXFxAozkGWJZDkSzeojGTYEkIIZq49PRVaLWrbRmxy5aVzdZtNhcRHg4ZGeEUFsZTXJxer/3U64+d/79l22QqACzBkla7ul77IkRNSLAkhBDNSEQEZGSENWgfCgvtt7Xalg3TESGqSYIlIYRoRsLDQasNb9A+VAyWGjZ4E+JCai1Y2rt3L1u3bq2t5oQQQtSBiIiGD07KB0vp6aEN0xEhqqnGqQMqc/fddxMfH4/JZKqtJoUQQtQy65wlV9eG60P5YCk7W9MwHRGimmptZOm3337j1KlTtdVcpZYuXUp0dDTu7u7Exsaybdu2Kutv2bKF2NhY3N3dadOmDcuWLbPb/+2339K7d2/8/f3x8vKiR48efP7553V5CkII0WDCwiAzs2FHcvTllqJzcjJiMLg1TGeEqIZaC5bCwsKIioqqreYc+vrrr5k5cyZPPfUUBw4cYPDgwYwcOZKzZ886rJ+QkMCoUaMYPHgwBw4cYN68ecyYMYO1a9fa6gQGBvLUU0+xa9cu/v77b+677z7uu+8+NmzYUKfnIoQQDcHVFUpKGnBYiYojS0FBqWRkyCRv0Xhd1GU4k8nEd999x5EjR1CpVHTs2JGxY8fi7FxrV/Ucevvtt5k0aRKTJ08GYOHChWzYsIH333+fBQsWVKi/bNkyIiMjWbhwIQCdOnVi7969vPnmm9x6660ADB061O6YRx99lE8//ZTt27czfPjwOj0fIYSoT2q1Zd02F5cWQCJqtRsazTj0+vhaew6NZhwAfn5D7bKABwZa/p46O5dQWOgCgErljKdnB1q10qPVyrwl0XjVOLo5fPgwN910E6mpqXTo0AGA+Ph4NBoN69ato2vXrrXeSYDi4mL27dvHk08+aVc+bNgwdu7c6fCYXbt2MWzYMLuy4cOHs3z5ckpKSnBxcbHbpygKv//+O8eOHeO1116rtC8GgwGDwWDb1ulqZ10lIYSoqTNn4N57F+DhkURoqMLq1fDii/Dbb89QUFDEffc9TY8eMHHiAtq0SQKgV69+bNjgyoQJS20Zu2uLtb0ePexH57t0eZq4uHiio5MpLGx9fn04Jzw9O9Cxo0zyFo1bjS/DTZ48mSuuuIKkpCT279/P/v37SUxMpFu3bjz44IN10UcAMjIyMJlMhISE2JWHhISQmuo4+2xqaqrD+kajkYyMDFtZbm4u3t7euLq6csMNN7B48WKuv/76SvuyYMEC/Pz8bI9WrVpVWlcI0XhUliW6bHla2iq7TNjVlZ6+2na8I9Zya2btqtSkD7m5EBt7mNdem4hWGwDA8eOwYMFb3HXXYo4di6WwENq2TeS5594D4J134MMPRxEUVH9JKVu1gi+/fJynn36/wmU4SzoDSR8gGq8aB0sHDx5kwYIFBAQE2MoCAgKYP38+cXFxtdk3h1TllqpWFKVC2YXqly/38fEhLi6Ov/76i/nz5zN79mw2b95caZtz584lNzfX9khMTLyIMxFC1LfKskSXLU9PX2WXCbv6bX9jO94Ra7k1s3ZVyvfBGjxZgyzr0ibWgKq4WAuAyZQHgE63B7DOBQrj5Ml5lJRk1fic6oKbm4HExJ9t23r9MXx8dqLVtiQubrjDIPFiA1ghakuNL8N16NCBtLQ0rrjiCrvy9PR02rVrV2sdKy8oKAgnJ6cKo0jp6ekVRo+sWrZs6bC+s7MzLVq0sJWp1Wpb33v06MGRI0dYsGBBhflMVm5ubri5yZ0bQoj6YQ2cdLo9xMQsRqtdjcFw5vzeCbYgyWwuArDtCw4+h1YbTmbmp5jNbeq93464uxdz5kwWgYGQlWUJltzcdpGRMZDc3M04OblVWMTXev4Xs7ivELWhWiNLOp3O9njllVeYMWMGa9asISkpiaSkJNasWcPMmTOrnOdzqVxdXYmNjWXTpk125Zs2bWLAgAEOj+nfv3+F+hs3bqR3794V5iuVpSiK3ZwkIYRoiry88iko8G3obthxcysmKSmaiIjSMlfXEoqLG/YOPSGqUq2RJX9/f7vLVoqicPvtt9vKrJe2Ro8eXadJKWfPns3dd99N79696d+/Px988AFnz55lypQpgOXyWHJyMp999hkAU6ZMYcmSJcyePZsHHniAXbt2sXz5clatKh3OXbBgAb1796Zt27YUFxezfv16PvvsM95///06Ow8hhGiu3N0NJCVFEx4Of/9ddk/l0ymEaGjVCpb++OOPuu5HtYwfP57MzExefPFFUlJS6NKlC+vXr7fld0pJSbHLuRQdHc369euZNWsW7733HmFhYSxatMiWNgCgoKCAadOmkZSUhIeHBx07dmTlypWMHz++3s9PCCHqgqI0nkDE3b2YjIxQu5ElALXahNHo1DCdEuICqhUsDRkyBACj0cj8+fO5//77G+wOsGnTpjFt2jSH+1asWFGhbMiQIezfv7/S9l5++WVefvnl2uqeEEI0Kr6+meh0AReuWE/c3CxTHMoHS4GB6WRnhxAc3ACdEuICanQ3nLOzM2+++aas/yaEEE2ERpPcqBI+OjubcXIqITzcvjw4OAWtNtzxQUI0sBqnDrj22murvK1eCCFE49HYgiUAb28dmnJr52o0EiyJxqvGqQNGjhzJ3LlzOXz4MLGxsXh5edntHzNmTK11TgghaktycuX7tFrLumSnTsHu3d3p2fPfarVpNpf+fPRoNHo9+FZy89nhw+3Q6yEvLxB398rbjI+H3bu70bv3YQCMRvjzz24EB2eSkdEVlQpSUlrx77/d6dHjCG5ulmVDNJpxFBQEcOCApR2N5jYURUNUVAAbN3bCyanIbvkR67Ik9cn6/OHhp1GrW+DuHk1qqmU+Vbt27fnxxxh6946j7EIQycmW16Rr19pbkkWImqpxsDR16lTAsk5beSqVSi7RCSEapUmT4JlnHO976aUljB0L8+fD33/fzqOPfkbv3hdu8+TJ0p/ffvs+DAb44gvHdV955SHc3SEs7G7uuedQpW0++SQkJd1OSEgmPXtaljP573/H4+WlJytLhUoF0dGPkJzci4cf/oL27cHTs5iYmMWMGfMwkycPJChIRXDwbfj53UbLlnD69FN07BhHSMhPtuep7WVOqsOaJ2nGjFHAembNGsC6dR/QunUy3bu/yZ4981i27P8YNar0mPfeg02bbufuu9fRr1+9d1kI4CIuw5nN5kofEigJIRqL8lmfyy/hGB8/nbi44cTHTyc9vSWKArm5u2jbdo9tv7Wd8j9b287LK23Pxyef1q0r9iEuzrKArL9/ImHnV/TIyGhZIQu39TkMhkTat//btrhtRsYPtG9vucfe1zebqChQFBPt2/9NYeEp/vrrYzSaFACGD19L166g0wVw4sQT54+B225bTpcu+y72V1nrunSx3HQzYADceeeP+PoW4O0NN964vELdgoJ/bOcvREOpcbAkhBBNQfklQ8oHS1rtanJzN6PVrqaoyJ2cHCgqSsBkKrDtt7ZT/mdr22WDpcr6kJu7GSjNrm1pO9SunbLPZzAkYTLlU1JiWbctM/MHTKZ8u9v/FcWEyZSP0ZjByZNnCA5Ose2zrLPWkoyM76v3i2qEyq6dV1DwLyZTfgP2RoiLuAwHltxEW7Zs4ezZsxQXF9vtmzFjRq10TAghalP5YKm8pKTab7MyFzPhurDQHT+/AodtDRhQulJBRMTFtd+YaLWrG+QyoRCVqXGwdODAAUaNGoVer6egoIDAwEAyMjLw9PQkODhYgiUhRKN0ocCmqgnglcm/yAGP9PSwGh9TUOBBWFie3aRysARGGk3pGpjh4aDTBV5cx4QQDtX4MtysWbMYPXo0WVlZeHh4sHv3bs6cOUNsbCxvvvlmXfRRCCEuiaJwwUtm9Tuy1LLGxxQUeOLpWXFkqajIAw8PvW27fLJHIcSlq3GwFBcXx2OPPYaTkxNOTk4YDAZatWrF66+/zrx58+qij0IIcUkKCqgwIlOWWm2+qJGlvDwXvvvuFp577jnM5uov1ZGZWfNgKT/fEy+vC0R8UCHZoxDi0tU4WHJxcbEtoBsSEmJbi83Pz89uXTYhhGgsLjQCFBiYdlEjS/n5KgoLfXB1daUmC8GaTDVfA62gwANPT/tgyWxWo1IpdmUaDTg7288lFUJcmhrPWerZsyd79+4lJiaGq6++mmeffZaMjAw+//xzupbNJCaEEI3EhYIlf38t6ekXbsftVB7hGwvwT9iP+3Ed85KCeMqgB0/4Qgkk06s9oW+chdt/hPNrajqmYDbXbHHboiJ3vLzsJ0nl5AQREJBhV6ZWQ1BQWo3aFkJUrcbB0iuvvELe+Yv/L730EhMnTmTq1Km0a9eOTz75pNY7KIQQl+pC85XUanOll+nU+SWEfVkAD3Qm5siR86XlZnYXQjjJhBclw+fA56PB3Z2IYUGUDDfDVfajP/7+meTmtsDfv2bnUf4ynFYbgUaTUiEbt0Zzrtx2/WfrrkrZ/pTNKu7rOwh39+gK9Z2cvPHz61QvfRPCkRoHS73LpLXVaDSsX7++VjskhBCX6uRJeOSRp7n++p3nkzRayq0f0v/+C3PmfAoUo1K5UFLijbMz/PVXf666ah1nzoynODuOvuufI/L1HfgY84EjlT6fQ0VFBKxLImAd8O31xOTfRFYQuLtHExmZxObNN/DXX7dy220b6NoV1q27kx9+gGPHWtOy5QnAg6NH4YUXHuaKK/YCEBraC1dX+PPP6xg1agM//PAg99//FjEx/7U7v86dS3ByKk0S3Nhuwy/bH2tWb4CuXdfi4QFr197HwIGwaBH89lt/rrlmN/7+VzVEVxu1+Pjpje61vVxdVJ4lIYRozM6dg4iIVA4fbk98/HR0OssHip/fAOLjp/PVV2244YaDDBiwCmfnAHx9+9C16yB27OjPZ59Ox3/zn4z9cw/Oeb/i46D9TI8Aclr7s193JYpHO24YeZhda5xpVZhETN5fqEz2I0n89huf8Bu/Zh9g7aB3CIpuw4EDSURGpnDiRBRxcY/x9dcP0b07fPPNANaseYbc3HB2717C1Ve7cuedP/LJJ5Po3Plh7r8fDh16FIB77nn+/BNYgiXrB+dXXw1ixw49TdUvv4zj7bdhxw5YvfpRfvppaEN3qVGSfFT1p1oTvHv16kV2dna1Gx00aBDJF3NriRBC1DKtdrVtZMmagfvs2Xy7rNdWar2Zezev4eFfHyEwz36iU4Z7OG94z+TWDiuZesOzrJk4iHci7+GrrvOJv0/FyzH/4YnBf/Ln+iCOvuzHyfbBlL+yd13iJ7y8ritdUn9Fr/e2lR85sov09HAUBYqLzxEcnIVW25KcnC12x1e2SO/lpLAwAa225fl0D/vJz7csKJyc/EGlx5Rf2kaI2latkaW4uDgOHjxIYGD1Ep3FxcVhMBguqWNCCFFb8vLA1bXQtq3VhleY18OhQ/SYlIVHotauWOseSM5MI08cPMX6XxU0eSkMVP0PAJVKwcMDDAY3W32Tl5rMoe5sUQ3E/bQ3Pdee4Oqsnbb9LQoSuX35MLLC72Nn2Ojz/QmlsNDLFtSFhGSwZ09YhcnbzSFYKipKoLCwFzk5lnXyiostQa1OtwN40OEx1iVjyl7SE6I2Vfsy3LXXXouiKBeuCLbUAkII0RjodNgldMzJCSYgIMM2qdt7Rzo8MQCP/NJ5PkacOHurL7NzFvDEjc+SttmVoKAEtNrS7NuKosLDA4qKXB0+r7ZFIE91mMfULu8xavVOWuTkAqBSFKYmfUyvbUdZc8O9aLWhhIaeISkpCoCQkEy02nbExNgvINscgiUruTghGpNqBUsJCQk1bjhC0sgKIRoJnc7+TjKzWY1arWA2Q/CPhbR+Yw+UmWeU4RnEfe7LmDbpKYredQfAYIBOnfbw++/j7dq2BEtuVMbTM4/tqkHkzPGg7X9DGHX6fdu+vmd3ovley+Lht9O5836OHbMES8HBmWRkVExc6e1doeiydTF5r4SoK9UKlqKiouq6H0IIUWfKB0tWYd+aaL/Ifm7SPxHteLfv82xYdyNTzM/Y7evU6S9bsGQyOaFWm/D0BIPB8cgSQFDQOf79txeDBm3gv93Wkhzel0l7HkRdYkkc2SblONO/+5zl4/QcO3YzAK6uRoqL7dv08MhHrW4+0ZKMLInGpMYZvIUQoqnJyyu9DFdY6Im7u57Q1XraLzLZ1Usd7cGSEfdQ4OaDWm0iN9d+nmarVsfx9s45344XHh75VV6GA9Bokvn3315oNJa5NzvaTST+w61kUtp2m9yzPPbTR/ibMittpzpLnVwu3N31MrIkGhUJloQQl728PPDwsARLWm1L/i9vDW3eLRd8PPssJ//jg1ltWYpEo0kmPT3UropGk2S7i06v98bLK6/CBO/yNJok9HpvvL1LR7CcB/ZlqPoPsjxKA6bAlDxWZozAqcByOdDJyWS3LEpzCpY0mmQJlkSjIsGSEOKyZzaDWm0ZRfL5XeGB/d/Y7U+b1gFeeAHK3JwSFJRMRoZ9sBQUdI6gIEuwVFDgjaen7nywVPVlOI3mXNmm8fGBw+ZuvDx8AVk+LWzl3Uv20mWeEZXBRIsW6eTl+dv2eXqWyxp+Gavu8jNC1BdJSimEuCx5enbAaFSRmhqOwWC5c033i4phn+xFXebO3ozpfeCpmYAlA7aXV1fOnfMiJCSbEyc6o1I5odGMw8MD/P0LiYx0R6VyIiOjI/7+f+PhAQkJEZSUuKAokJhoWa4jIyMUL68A2raNRKNJtVviw8UF3NyKKQi+ig9vX8Hkz+6mRUkOAP4HFdwW5BAVGUhubgBBQbEEB/vYBUvWJULc3CqfT9rYljiproICD1Qqy4R6vd4btdodX98+GAx7HdbPzYUzZ0IJD29+6+EVFno2dBeajYseWSouLiYpKYmzZ8/aPYQQojHw9OyAp2cMjz22ihEj4NYrP6Prs5txNRlLK02bRtC7u235eWJiFnPrraMJDNRxxx27UavNXHXVJmJiFjNnDjg5ufPIIwMYMmQTXl7O9O79G56esGDBQwwatI/x4+H77yfy/fcTSU5uR48eGVx33RfcffeiCpmW/fxc8fTsSJrvjTzT90kICLDt8/h2JxPP7CcnpwU+PrGEhExg3LgPbftDQiYQEjKBmJjFlWZwboqZnb29ISrqHCNGHOa226Bdu39wc3MiOLgvBoO7w2OeeQb+858niItrXmvHJSfDokUvNHQ3mo0ajywdP36c+++/n507d9qVK4qCSqXCZDJVcqQQQtQea8bm3NydxMQsJi1tFbm5O9Hr40lMHEl2dg+cnMKIiDjBrAfDye/5Nd5FZQKlW26xLD5WLi/cwIHg6/sxAG3bfmorHzPGsvzGkCHg7PwngwcrZGXtJfP8nOy77vqBrl0fIDr6eQCMxmwCA4fj5ARXXrnV1k5h4SnS0w/g63sr2dl/4O4eSqJvGHz/PVx3HZSUANDzf8/TovdzpIevAW5j0KBNtfsLbIRcXGDWLMvvvGvXGXTq9AoA4eGwa1eow2PMZujd+zBmc/OaVZKfD/n5fg3djWajxsHSvffei7OzMz/++COhoaGSgFII0SCsWZt1uj3ExCwmPX0VOt0ejMZscnLcKSz0wsmpABQT3H8/3sdLAyVdBxW+K1eCk1NlzVebh0fN6hcVJZCRsRYfn1spLIzHbM4FjHDVVfDRRzBxoq3uNfv/ZNOVBuC2S+5nUxYeDhkZLYmPn46f3wBbgBwXN5ycnKdQXyBOKrvg7OWy+GxhoWXenKgfNQ6W4uLi2LdvHx07dqyL/gghRK26OfVn2Fk6obu4hZp/Xnaif02jnEpcbDMOs3Hfcw+Jv06l1eeWRXBdzSW4phRiWwelmQoPh/T0MLTa5zEYztgC5NzczRQWjsfLq+qluMouOHu5LD5bWAiFhRIs1Zcaj1t27tyZjIyMC1cUQogGFpV9iukJH9m2zS5qjsz3o1hTeyPitRosAWcne8OwYbZtVYkZ7rvv4p7kMmFJ/Ol4zlJzZRlZ8mnobjQbNQ6WXnvtNf7zn/+wefNmMjMz0el0dg8hhGgM3EuKmLnzVVyV0stvqY91Jr9L5bf5XwzPi7whyaeyzzknFXz5JZRdOeHbb6FYFicXpfR6CZbqU40vw1133XWAZWHdsmSCtxCi0VAUHty/ktD8c7aizMFuZE5oDXnaWn2qSxlZysqqZGeLFrBmDcVXPo2iAlasANc5F9nDy4NKpWA2yxxZK5mzVL9qHCz98ccfddEPIYSoNR32JDE48UBpQVQUx+fq8aqDG1LqJFgC6N2bb9uOxiss2TLpe0fzDpYCA7VkZwfh79/QPWkcrHOWFKXCDZ2iDtQ4WBoyZEhd9EMIIWqFa7pCv9WHbNtGlRPOX32FyTS2Tp7vYoOlSi/DlXEmuiXt1DJHFCAoKBWtNozo6IbuSeNQWAiurgYKC50v+lKwqL6LyuCdk5PD8uXLOXLkCCqVis6dO3P//ffj5yc5H4QQDUhR6PCGEbfC0gzdy1tN4KF+/WBH3TxlbU/wLisgIBOjsXkttGDNTg72Wcjbtg1Dq20JZFc4xtOzI/7+jWP+jjU1gTUPmDXhafn9l6qwEAICtOh0XhIs1YMa/yvcu3cvw4cPx8PDgz59+qAoCm+//Tbz589n48aN9OrVqy76KYQQNunpMH/+Q8TG/kNRUQj79sGOHVMYeiyAgQc/s9WLc+vBlxF38xCWD14/vwG4uUWh18dX2X5ly4lYP7ytbZlMBtzd7Y+x1tHr4yuUFRVBfHwHVCqFgABwcSnmzz+707Hj7grPARAWFkpubmaF8stZ2eCibFDRq9cd/PDDSV55pRdFRbfwxReQlbUAsARLgYGO09msWwfe3paFjlNT4eDBPgwcWHf9t6YmsOYBKx8slU1dUFlAVRVrsKXXQ4sW6Rw8+BktWz7rsK34+Om2n6sK4MSF1ThYmjVrFmPGjOHDDz/E2dlyuNFoZPLkycycOZOtW7deoAUhhLh48fHTOXPmBoqKitmwoROJiSNo1w7mjv+Yfuu+s9UzuaooXNaaBTEfA8NsH1DV+aCw1ilf19pG+bZ+/z2SkJCzdvscHff113DgwDx8fQuYMAEiI19l4sT9+PoWABMqHP/886PZvXs2MPeyyA10KcLD4eTJTnh4uDFx4rtceeW93HZbZ4KDq56wv3IljB3bCoAzZ+DPP69m2rT66PGFVRZQVcUabFlHlpKStlfalla72vZzVQGcuLAapw7Yu3cvc+bMsQVKAM7OzvznP/9h717HCx0KIURt0WpXk5X1C+7uaWRk+JKX54eTWqHHks04F5lt9c4+5IV3rAkfH32d98nVtbha9fz9ISgoBwBnZ3B2NtGyZSaenkWV1vfxyaudTjZxERGWxJQuLskEB6cQEQEBAWno9ZY7wrKyfiMtbZVtNMU6ipKRkYJW29JWrtdbLtfFxQ23q9fUFBZCaKif7XxE3apxsOTr6+twwdzExER8qjNjUQghaklGRjiFhd5cm7gC778ybeW5PVw4N04mclxOAgIgL88fk6nAVubpmW/LYp2T8zvp6atsoynWUZSsrBy02jBbuTU3UW7uZrt6TU1hIXToMEjSB9STGgdL48ePZ9KkSXz99dckJiaSlJTEV199xeTJk5kwQYb2hBD1R6sNxyO/kPv+fdxWZnKFE0/6glrup76cOLo93tNTd8FgoaDA4/zEcOv25RFcFBZCSIgkpqwvNZ6z9Oabb6JSqbjnnnswGi2ZcV1cXJg6dSqvvvpqrXdQCCEqU1zswVvOj+FbUpqw6MxEJ4oimtcdZM2Fj0+O3baXV94FgwW1WkGrDbVtWy/bNXV6vSVYOnFCgqX6UOO/KK6urrz77rssWLCAkydPoigK7dq1w1PuXRRC1LPr2MQEY+kiuQVtnUkar8KpAfsk6k5w8Dm7bcvIUtXBQlBQNpmZwbbty2UkpqjIEizp9V4N3ZVm4aK/fnl6etK1a9fa7IsQQlSbi7GY95lq21ZUcOIJHxTngiqOEk1ZUFAqhYWls0c8PfNsc5YcURRwcSmhpMTFVna5jCyZzZYbAGSCd/2o1pylW265xbZI7i233FLlo64tXbqU6Oho3N3diY2NZdu2bVXW37JlC7Gxsbi7u9OmTRuWLVtmt//DDz9k8ODBBAQEEBAQwHXXXceePXvq8hSEELVgzNGNtOOkbTtrfOtaXyRXNC7BwSl2215eeeTnVx4sFBaCh4f9AsSXy8gSWLLAX07n05hVK1jy8/NDdX52na+vL35+fpU+6tLXX3/NzJkzeeqppzhw4ACDBw9m5MiRDu/OA0hISGDUqFEMHjyYAwcOMG/ePGbMmMHatWttdTZv3syECRP4448/2LVrF5GRkQwbNozk5OQ6PRchxMXzSs/lpqMbbdup6lBSZzhOSiguHxqNfbDk6anDaKw8QNbpwMurECcnoy0T+uUUXPj6Xl7n05hV6zLcJ598Yvt5xYoVddWXC3r77beZNGkSkydPBmDhwoVs2LCB999/nwULFlSov2zZMiIjI1m4cCEAnTp1Yu/evbz55pvceuutAHzxxRd2x3z44YesWbOG3377jXvuucdhPwwGAwZD6bcV66ibEKLuKQp0/3Q7rqYSW9mLPm8y1fsruAz/Kbq5hTd0FxqNmJjO7NvnZMtmHhISY9tnNDqhlK5yg9GoJisLvLz0REQEkZkZTHi4ZfFZk8lS//w9SpVKS1tFSMgE4uOn4+c3wJbM0ZqbKTd3p8NkofHx0ykpcUKtVirsK6ukxAmVSrEdU76t9PTVuLioKySRTEtbhcEwGB+fCPR6L4xGS1suLqZy7VsuP6rVJkwmx3VE9dQ4dcA111xDTk5OhXKdTsc111xTG31yqLi4mH379jFs2DC78mHDhrFz506Hx+zatatC/eHDh7N3715KSkocHqPX6ykpKSEwMLDSvixYsMBuNK1Vq1Y1PBshxMV44w3Y9dxNtNp3wlaWFB3Dd+4TCA6ecH4ZkqFoNOPQaMYRHDzBbq2xulKTpUjK9qk6fWzV6vFK9zU31133OD167LUFFb17LwWgWzdYtmwC7757Dx9++B8A7rzzTa64wjKyFBMzmOzsIFs7+fnw4IN/0r9/1c9nzcGk1a62y8eUnr7KLqeT1apV8McfN3LixK/cdNNSHn10nt3+O+6AL7+0zLNTFBg9ehmTJ8+3PUd5Wu03ds87ffp6Tp1qRWLim+j1R3F2BpPJmauvtrSVn29ZQDo+fjp79qxj4sTfeOGFpTz66NeMHQtjxixGp/OyS9zZVJNy1rcaT/DevHkzxcUVs9UWFRVdcP7QpcjIyMBkMhESEmJXHhISQmpqqsNjUlNTHdY3Go1kZGQQGhpa4Zgnn3yS8PBwrrvuukr7MnfuXGbPnm3b1ul0EjAJUcvKf9NOS1vFvl0evPnnOluZooaTs76BV1SVfsuvDzV53srWPqtO/ebO3x+uueYH27Z1MeJrroG8vDV8993155eOgZCQTM6ebYe3tx61GqA0UdOZM9C+fRyurrV7k1J2NqSkRGIy7aZPn7/JzPS323/8OISGtrFtd+iQgNlc/Xxgx48X0L07GAzJmM2WrO/WdQbbtz9NcbHlbkGtdjWZmUYGDDjOI4+8wX/+8xEqFfTosRWj0emC69eJiqodLP3999+2n//991+7AMVkMvHLL78QHl73w8WqcpnJFEWpUHah+o7KAV5//XVWrVrF5s2bcbeujumAm5sbbm5uNem2EKKGyi44CpZv84P2BRFRUPq3J+VmD+jW3WF9cfmzBkunTj0LuJCS4omTkw9paaswm0MpKABn5+MAmM0GsrI2AsP47bdP0GjSyM21BB5xccPp0WODw0thNVFYCOnpFb+EW7m5QUnJxX92pKeHXfSx4tJUO1jq0aMHKpUKlUrl8HKbh4cHixfX3R+qoKAgnJycKowipaenVxg9smrZsqXD+s7OzrRo0cKu/M033+SVV17h119/pVu3brXbeSHEJXNOK+S+xK9s28YAV85O8rZ9YIrmx80NXFwgM/MnYCzp6RoCA1NJT1+B2TztfB3LDUDWYMnTcxiHDmURFHTOFiwVFBwGLj3gLiyEjIwLBUsXf8dm2Uzk1rlOon5Ue85SQkKCLQnlnj17SEhIsD2Sk5PR6XTcf//9ddZRV1dXYmNj2bRpk135pk2bGDBggMNj+vfvX6H+xo0b6d27Ny4upXk33njjDV566SV++eUXevfuXfudF0JcstB3juClFNq2U2d0xOSrlmCpGVOpLLfPW2Vnh1BYaJ+k0cMj327b1xcSEmLQaGr/jufqjCwVF19csKQo2DKRG41OqFTmCxwhalO1R5aioqIAMJsb7gWaPXs2d999N71796Z///588MEHnD17lilTpgCWuUTJycl89tlnAEyZMoUlS5Ywe/ZsHnjgAXbt2sXy5ctZtap0Qtvrr7/OM888w5dffknr1q1tI1He3t54e18eycuEaPJ278Z/femH27mIVmTfHAn5acj63c1b2WDZ2bkEDw+93X4vLx1FRaXbPj5w5kx7NJqkWu+LXl/1rfyurlSZ6qAq2dlgMFhWyigudsfNrfACR4jadFEZvI8dO8bixYs5cuQIKpWKjh078sgjj9CxY93mORk/fjyZmZm8+OKLpKSk0KVLF9avX28L5FJSUuxyLkVHR7N+/XpmzZrFe++9R1hYGIsWLbKlDQBLksvi4mJuu+02u+d67rnneP755+v0fIQQ1aAoUOaGCoBfbxxLT/VpABlZaubKvv5BQecqjLh4eurIzLSvf/BgG4KC7JdOqQ2FheDuXojJ5Pij9VJGlsqm/jMYPHBz01deWdS6GgdLa9asYcKECbbRHYDdu3fTtWtXvvzyS8aNq/4ttBdj2rRpTJs2zeE+RzmghgwZwv79+ytt7/Tp07XUMyFEnVi9Gnbtsm3+4D2S5MjW9OQ0YPkAcpXE3c1W2WBJo0lCUexv3vHyynNY399fW+t9KSyE0NCzZGVpHO6vzpwlFxcXnnvuOQCOHi39Ep+UBH5+lqjPYHDHza3I4fGibtQ4WPrPf/7D3LlzefHFF+3Kn3vuOebMmVPnwZIQohkpKoI5c2ybxWpnXvaax1hKlyQqP29FNC9lX3uNJpnCQvsceZ6eeRXqazSpF0wYeTEKC6FVq1N2E7HLqvRuOLMZj1NGyPoB1dmzuBYUgJMTLbJPUxzhCd4JJJ2NsmUwtwRLchmuPtU4WEpNTXWY2fquu+7ijTfeqJVOCSEEAIsWQZnR3+19unDqRFvAfv1GuRTXfJRPAFr+MlxqqjclJaUfbR4elmDJxUVjqx8UlIJabUkP4+YWjtGYbauflraKlJQVeHrG2JI8OlJ2nzWxY1HRBNq2PY1W6/gWf7vLcHl5jEj5gQHa7RB0iF7Z2cAYu/qlyXjaMMErhFYeXXDZm0VSrygJlupZjTN4Dx061GHyye3btzN48OBa6ZQQonnT6+HU7s4wf76tLNM5kPibvSkp8cXXt78tYzdAHS9LKRqR8rf2l15Wu4rg4HQCAjQsWzaeoiLLCJPr+Wu0v/76IOfOBePra1mQNyTkHnQ6+O67BzCbVRw/DhkZIaSnryI3dzNa7Wp+/HEor71mybpdXGwJwHQ6+PjjW9i5sxMHDlimolgzeisKBAefqzCyZM2YbTQeJUx/DiZNwhziz3+OvsKgzK2W2dsX4F2QxoiM37j2v6cYO/0vJsT9BKmpKIpl+RKzuQhFgZ9/fpmjR9vz9dcDbcdmZ2uwTuc9frw1Op0/27bdwocfXsuff3az9U9UrsYjS2PGjGHOnDns27ePfv36AZY5S6tXr+aFF15g3bp1dnWFEKI6rH+wY2IWk5wMgYucLJ9M1v13tSOiczwGgxd+fv0JCelvyzz86aeW7Mii+Zk/H44dSyY2dggtW97PN9+8yk8/teb995+nX78+HD/uztSp8Mwz+Rw6FENMDIwc+TQxMYdZtgymTu1J794a/vwT1OputGmTa2v7l1/uZtEimDu3D9nZCYAl+/c//7Tn4MF7SUmJ4ZFH7Pvj4VGAweBhV6bVribG60nu/n0qfVO2wccmnC7hnP2K8xl+cDe0bcuDATfws+9Aij3Tyc/34/vvXcnJGY+rq44777TkJZs371m6dNnA00/Dr7/2p3fvBHx9T3LoUBvOnOlCp053SkLXC6hxsGSdXL106VKWLl3qcB9YMmSbTLJgnxBQGgj4+Q2wLcthXaTTOoTfnJccsKxR9TlqtTsxMYtxPf4PI87+btt/2qcL5ntP42ootLsN3Co4WIKl5kqjgfh48PAAT88CfH3B2Tmf6Ohk2/tCo4HWrZM5dCiEgoKDBARkANCxI3TocIqMjDD++WcLXcutfuLurmfgQGjRIr3cc2Zy9GgQubkV1xD197ckbfbwaIubmzuYTISu0cPyTgzMy6tQP8/Zj63eN6K5fSdOsYkYwwMocs+hR9c8Hhi3nGCdM0smFfPnyxvpnfsDTmXf/3o9E/WruTFjO18MuJXMzLZotS1xcdERHp5AVFQobm798PU9Q9eu4OTkiUrVCa22Jb6+JwkKSqW4uE2FPomKanwZzmw2V+shgZIQpbTa1bbFOK0LZlrXZbIO4Tdn6emrMJtL52AEvfYETpTeAv7JFW+BswpXVwNVrG4kBD4+oNc7Xq7KZMonP/9vu7KQkEy02gjOnCmo0fNoteEUFNjn4lOpIDLSkubCw6Md4SYzDBlCm4V5UC5Qyumq4vkr5jNxWBqP+K9kT9fu5HdQY/RXg7MK3NzIcfPjsF93eOQRXr5yHX+t05D8TFcKou3HpVoYUpjxxxL6vZNMYYr9BL6yI0ZOTl5Am0rnVInK1ShYKikp4eqrryY+Pr6u+iNqQK4zi8vShg14bf3ZtpnZV0Vc8DDA8mFUxbKNQuDrCwUF1Z/xbwmWwmscQGRkhFFQ4INSyU113c+s492tPWDHDrvy/eqe8OuvxL3rzNbgazA6uaHTlWbnrorJU03WuNbEfdqCI087QRv7UaEOBxP56MB0onMSK23jQlnGhWM1CpZcXFw4fPhwlQvXivpjHaEQ4rJhVOCxx2ybZjWcmmo/W8DDo/xBQpTy9YXCwurnkggJySQjI6zS2/0rU1jog6urAYOhtExRLP8Z++/PPLLpJnxKykzc9vHhh2Hj6a/aCddei3WIVFHAbK5esGSjVpF+nRMcPswXre7BpCodaYo0JjH/j7fpceSIw0P1esjIqNm5iou4DHfPPfewfPnyuuiLEKKZC/mpEP75x7Z98poQ9K3tv5xJsCSqUtOcW8HBmaSmRpGVFVzj52rd+njZexBwNhfT7uV7+b9D6+zq5XV2gb//5s8rh2BW2Qf/RUXQti0XDNYcjlF4ePBhm2k8MWg3haGllwRdzUbu/OknNB/EU37oq7CwQpGohhpP8C4uLuajjz5i06ZN9O7dGy8v+0UL33777VrrnBCi+XAqUIj8qHTeSC6+/DGkNx2wXwxbgiVRlZrm3PLzyyMhoTNOTsYaP1dU1HF0up6WDb2Rp/8aQ7B2g32l2bM5NGYlA1q3dthGXp5lonlycuUjSyYTqKsY2jjh35sTX1/FuREGhut/s5W3XHIMjNNgSml0ZDSCs3PNz7W5q3GwdPjwYXr16gVQYe6SXJ4TQlwK7TB3wr8tgZIS3vV6hOIiHR3K1ZFgSVSlpsGSSgVFRV4EBaXV6DhX10JCQxPR6cCtqITIR/7CR1u6CJ1R5czSbv9lxlv3o+z4otJ2dDrL3ZwJCZUvg5KfDxdc193fiXu9P2aO6k1mFpRJA7BsGeEprWBt6U1X7u6FFBbKQvE1UeNg6Y8//qiLfgghmjmTl4rT030If2kbuc++xXdxN9A3o+LfGwmWRFUuJpt7UFAyZnPN7hzQaJLx8tJRkJZPh7l/4nWwdH6S3tmdpcPWs9XpamZcoJ2iogv3Waer3nkFBafxsepOXF0zeWj/KpzOX28L/F8i3H03auWe831PISND7oiriRrPWSorKSmJ5LJLIQshxKVq3570F5fhF5JFenrFP+gSLInyrJncNZpxtjlLwcET7Pb5+vbHycnbrsxaLyzMhEaTip/fQCqj1x8lPn46R48+hMlUQFBQMn5u2XScO9YuUCrxDeTloY8QF9DK7vi4uOEYjTkV2i0pycDHB9RqMyaT44/kagdLQaloNKn83ro/K8eMwexc5mrPqlU8GLcYFOV8sBReeUOigovKs/Tiiy/i5+dHVFQUkZGR+Pv789JLL2E2my/cgKgVe/fCrFlfNXQ3RDV8/z3cd98mvvxyGrffvpA5cz7lxhst++bPh9tue5d//mnXoH1sSGPHwvTpTzF16ja7Sa6tWvmQmtqKefPWoiiWNbz8/IZWGiyVXzNMNB/WXEIxMYsJDobevbfYkrxa911xxQDi47vTqlWqXe6hkJAJ/N//XUNU1HEMhilMn/4U8+atZc6cT/Hzy7DVKy5OQ6tdTX7+X6jV2Qzs8xN3/bQOzcHSOUK5rt78s2QzJwIiKCw8CcD08xleli2LZf36a2jRIpuzZ+Ghh3YRE3Mak8mSSLNXr+089dRaHn74O3buvIEjR57EycmI2VxEXh4UFHzi8NwVRU1AgOXnfv1+p0+fzZw40RXtIE/OLrzSsiDdecNOr2dq4jyuuGIfu3cPJzz8NEZjNosWvQBYArr4+Om2/8fHTz+fMNY+D5yjsstdjS/DPfXUUyxfvpxXX32VgQMHoigKO3bs4Pnnn6eoqIj5ZdZyEnXn33/hr7+GNnQ3RDVkZcGgQRs5c6Yd3bodZcaM+3n99VQA0tOhd+9D5Od7Ojw2Pn66Ldt3bu5O9Pp4evTY4LBOeWUzhDvKDl7+uMraqamy7ZTtgzVzubWOldm8mMWL5/P++51ITGxDfPx0MjOH4eXVnjfeGIrRmM2QIQYOHQqna9d1eHpCSsqnwES755XlGgSApycsXDgeSLUrHzIEPv74qfNbT9jtu+ceOHw4lTZtYPHi+WRlbcDZOeD8Arv3VngOF6cinj39FiFHSwcIDB7OvHrVJEa37QqUZp9fvx5uv11FdraGuXNn8d//vkN6uoaBA3/g4Yf3sW5df3x94eGHc7nllps5duwa/vyzD+HhWwgK0lBQ0BqdDtTqg2g04/DzG4CbWxR6vWXO8JIlL9G16zoOHYL77jOi1a5mzJjvcXEJxKPV4xDpjXLzWFQmS1/vOfcq2lMD6LRwOEbjAF58sS/79t0AQG7uZgoKDmI0ZlNQcNByXoYzgP0KA9Ykus1p1YEajyx9+umnfPTRR0ydOpVu3brRvXt3pk2bxocffsiKFSvqoIvCEZ0OXF0drPvQTNVXgs7qPE/5Orm5f+LhkU1BQQ3vaQa7bN9a7WpyczdXWqe8shnCq2r7Qu3UVNl2yvahbLk1o3nZsqCgZLTaULTa1WRm/khh4VGH7Xt4QF7enlrpqxBlZWdvwWAonVqiVlvmMbm7t7H9DHDb378Tsqk0UCpSu/L95EGkhgaeP670LvHMTMjODrJtK4oKnQ48PUszevv6lgb7wcEpaLURpKeHodFkoFa7o9OBl5eOmJjFhIRMICZmcYUvTVDahrd3V9zcwi3BzOjRqFZ8aldP88ZOQjaYcHMLJSMjnIyMEOTCUNVqHCxlZWXRsWPHCuUdO3YkKyurVjrVHFU2rFn2g7fsz9Z/bI6GR61Dp5fSh0tppyHUV4LO6jxP+Tp5eX/i6ZmJXi93n1RFo0muVmI+mbMk6kpubvlgyfJm8/cfjFptuZzVdkcmo//+yVbHgCsv9JjJ6fAI3NwsX2BdXS3BkaJYUgOUf19bgx+rsvORAgK0ZGWFkJHREo0mxVa/bHBVY3fdBe++a1cU84YJ37+1aLWhmM1OpKdXcqwALiJY6t69O0uWLKlQvmTJErp3714rnWqOKlsfrPy3cSvLPx5dhWOs3+AvZq2xsn24lHZERZ6euosaWWpOJFgSjZ1vXDEDV9gvJXIfn7A/qCsGgwduboV2+/T6itm5VSqlQvBTNpGms7MJs9kJrTaU4OBaCpYAZsxgXY9nbJtqI3Satw2fDD3R0ceQe7WqVuM5S6+//jo33HADv/76K/3790elUrFz504SExNZv359XfRROFD+m4lo3FxciikpqTyPioCgoHNkZIRcsJ4ES6IheKUY6DgvBydTaYJH/ZznWfXaHQxjJQaDB66u9sGSTgetWlUcWcrLswY/lvEKR3e6abWhBAWl2OoHB1/63/v/9XyBFpnHGZhouTnIJdfA/xjLi+0fJSmpE/7+l/wUl60ajywNGTKE+Ph4br75ZnJycsjKyuKWW27h2LFjDB48uC76KByw/GOTYElcPlxcSqoVUEqwJOqba0kBfV49jYuuNFDSXu+Oy4vP2rYNBnfc3fV2x+l00KmTfbCkVitkZ1d+GQ7AxcVAWloYAQEZtnYueWQJQKVixVUfQ9++tqIu/MMLxxeQnCiTlqpS45ElgLCwMLnrrYFZ/vHkN3Q3hKhlF14FwMMDCgouWE2I2qEo3LVlMn5nS2+oOREcQ/qTOQxwLX2/Ggzu+Pvbjyzl5UHnznDkSBh+fkkAuLqWoNVCRIQO8Acqrmen0ZzjwIFOODlZApjavJLg4usB339PUfdQ3M/PU+p0KoHkb16GrrXyFJelGo8s/fLLL2zfvt22/d5779GjRw/uuOMOsrOzqzhS1CadDnx8Shq6G6JGZPXKC3FyMmI0Vv0dTkaWRL16912uPFGa064gwIWl1z2G4mYf2BsM7hXuUM7Lg8hIyMvzs5W5uJSQkVH6ZdfdvRAXF/unDAo6h4dH6TcC6xzV2uDhAbRsyeH5zpjcnGzl12x7noA/ZXSpMjUOlp544gl055dZPnToELNnz2bUqFGcOnWK2bNn13oHhWM6Hfj5uV24omg0PDz0F67UzLVokU5mZtWrv0uwJOqL74FiePxx27bZGf6YFk2ep3+FugaDe4UJ3ori6BKb8XywZPkcdXSFQKNJQqMpzRNVUECFS3wXy/rvp6C9mhNPXGkrV6PQab4RtxRTJUc2bzUOlhISEujcuTMAa9euZfTo0bzyyissXbqUn3/+udY72Bxs3w6bNvVHcTDwcPp0ewCOlks5k58PaWlB/PtvW1vZoUPw88+D+fXXm8jM9K9RH37/Hf74ow8AZ8+WtnP6dONfP6ikBBITo+v8eRSl9PWoStk68fFw6FAMzs4B+Pv74uHR3pZp2rocg6dnBwICrqnQzrFjlv+fOlX6eqSnR9jVOXIEh+8bgN27u7FuXfX6efq05Y/9pcrMhKys0pwy27f34pdf4NSpCFs/8/Lg119v4tdfb+LwYcui3MHBE/DzG0rr1oGcONGZrVstf8QtSfiG2uqABEviwirL5h4cPMH2PirPxyeWffu6UFho+XfgkmWiw3O5YCoNHn4ePogfM251ePyJE11swVJCguXfqaIYzgdLKs6etSwJ7epaQnLyWdscJC+vinORgoLO2SZ35+T4kpFhWfC3NpT996MdHs23YTfYtl10EDYDtmy4hV9/vYnk5Eg2bBjIn3+WjkCtXz+fn38eTGrqaaD5ZPOucbDk6uqKXm+JcH/99VeGDRsGQGBgoG3ESdTMiy/C55+PRa+v+CmwaNGLADz6qH1569YwcuQWPvzwdlvZwoWQn+/J4cNXsmtXjxr34b//HQ/Ad9/B8eNRpKVF8L//XVejdhpCfDx8/fVDdf48JhPcdde2C9azvmYAK1ZAREQqAwcWEBp6BZ6e7W2J46zZbysLlmbNApNJzapVcPZsGBkZLfnuuyl2dZ55BnS6AIf9+O9//48XX3S4C4AFC96x/dypE/zww50XPLcL2bwZtm0bUaYP43nxRXj77XvJzbUk7IuLg+3bh6MoKj791DIaHRIygR49NtCmzWj0em9atMjhlls22iXfs/6+RoyAPn22XHJfxeWrsmzuISETKs06ff/9E+jY8ZRlw6zQ/iUdrlmll6UO9Ijhr9jOBAcnMmZMaYbuf/6BcePeZdiwNURH/0O3bnDjjZu5444fURQjPj7wwAOvMmzYF7RsmYSrawn5+UW2+UjTpz9XoS9XXLGbCRPeB+DBB7/hmWcqVKlSVUFhfv63gCXhZtu2ozG8kk1hj9K7UMNSMhj26wESEjqwdu09rF49hOXLH7Tl+fvkkxB++OFqDh2yfJE+dWpes0gxU+NgadCgQcyePZuXXnqJPXv2cMMNlqg0Pj6eiIiICxwtHHF3h7CwNIf7rIkMy8ehbm4wfPj2CvWvvXYXXbvWPLuxry8EB5cmFe3T52/69/+1xu00hLw8KCz0unDFGir7jSk+fjrp6RXX4itfB7BLPpmVtZHBg/fi75+Hry/k5JQGW9b62dmbyc7+nbS0VRw//phtv9EI2dkaMjJ+oEePNdx445doteG2tZsA8vMPkJbWwm4NJ2u7Hh6phJUbGLTuUxQ4c6Y9imIpc3MrpqjICUfKZyS3bpf/RpmWtoqcnG22fFLx8dPx99cRFAQpKV7o9d6kpa0iMXEh7dsf4Prrv0etNtiOBcu3Zx+fNK69dhVt2iQ57E9QELa7hISoLeHhMGrUVjw8imi1ykzAX8WlO7t1o+itHK4f9j+uvfYbWrc+Z9vVuTO0b/831133P9zdC/H2trQTE2NZJsTXFwYM6Mw116zBx0eDv38MxcVu+PkNJTh4AtHR8ba2/PyGotGMIyhoICEh59BoxjF48D5Gjqy839agqOzCwVUFhUbjbgBCQu4hNHQCke1O4vHDXtBobHX6/XWM21y+QVFMhIcfQa022eX5i44u/bdZXFz6u7ic1ThYWrJkCc7OzqxZs4b333+f8HDLysU///wzI0aMuMDRoqasHzx5tXDXaHnWD73yH4bp6WsoKkqwKyub2btsWflAwfphbf0wd5QJvDrZwWsytKvTUeOEj9Vpv2ySTkuCzorBUvk6YN8Xvf6I7WdfX2yLa5atX1h4nOzs30lPX0VGxnd27Wu1oRQUHKakRIuXVx4FBX52y54YDMmkpQXZEoiWXVbEbK64HI51n8EA+fl+5ORYylxd8ygqcjyxurJlUconUk1PX0Vu7nYKC70rHJeeHkxBgTfp6avIyfkDRTGe32O2HWulKMV2WZSFqE+BR/2I/rjMRGdPT/j6a8zlJnSXvdSnVrvj5hbusL2yS5m4uYUTGNgZZ+cSevTYUCGg6dFjg91oanXWO7S2UX7h4MpYs4zb1YuIgM8/t6t33Zf7aaGXm7asahwsRUZG8uOPP3Lw4EEmTZpkK3/nnXdYtGhRrXauOTGZCjEYztk+wOPihpOWtoqCgoojS/Hx08nPP2Q7ztGHflFRosMRgbJl1g+z8h+G+flxlJRk2pU5WmfMUaBQfg0zR5nAq5MdvLKM5o5cTLBUk/Zryvqaleco8dyFpKeXzfzruE5GRs3nlVnfT9asvW5uxRgMtTMZqPz5FxVBbq4GvV4ymIvGzSm3mLbPJKIqk3iSpUvBwRJfZYMNtdqj0mCpfFoANzfw8mq4tC+Vrik6fDhnJ5SGBO76Emb8+Tlqs0z4hosIlgBMJhNr1qzhpZde4uWXX2bNmjUYjcYLHygqZTbrKS4+Z/sAz83dTHr6KtsHTNlgSatdbRv6NJsLHX7oG43ZDkcE6msNtfpkCZYaz7prRUWeOPrnUP6PZnVotRUDofITurVax3+kq2JdiDnp/Gi6m1sxxcW1FSzZn6jluQpluRfRuCkK4c8dxDWlzB1td90F99xzSc2W/5JkCZbq4FJBNZW/Y6+s05OcyOtcmsegU8Ypxh37oT661ejVOFg6fPgw7du3Z+LEiXz33Xd8++233HvvvbRv355Dhw7VRR+bNYPBneJi+8twJSUuODtLjiUrnY56HbWo7O6zsvIdfHG8mJElrbZluTYyK0zovthgqVWrk7aRJXf3YoqK6iZYAoiMPNaoAlohKnjvPfx+L71dn/btLaNKl3gbWvkvSa6uDZtQ2HoZzhHFWcWx5/0wlpkCOu7oD/TK/bseeta41TiD9+TJk+nSpQv79u0jIMDyRzs7O5t7772XBx98kF27dtV6J5u7lBTLYoxWer13gw7jNjZ5ebVz23t1GQyWSflV0emosM5ShXwr2WZiEn+jx6lfidWdodV3SYRlaenv8xcYjRz4ewwlR3IwtlDwSEvFX1tEV7eDaNNC8GpzytZOVpZ9QFUdOh20bn2c5OQr6NgR3N0NtXYZTq+vONm+desjdhPfhWhUTp2Cx0pvrsDVFb755uKGg8twd9fj7Gz/78HNrWGDJXf3ykeWAAxhThx7wpkrnrcMj6tRePb4W5zSOb4BpLmocbB08OBB9u7dawuUAAICApg/fz5XXnllFUeKi1V+NeiCAp/aWSfoMqHT1V7CturIy6tesFRecOEZrj+9Bcb9xIoN2whYp6UPjlIzbALAtnpTOnAEWAdv8DbFG5wpjDHBLc/TUefLfvUVF3UOUVHH7eYs1dZlOEejfJGRR+UynGi8oqNhyRLM06egNpjhrbegR49LbtYSFGnsyhr+MlzlI0tWGUPVHB7Qmi47TwOgKc5C9YYbjGi+qxDUOFjq0KEDaWlpXHGF/R/o9PR02rVrV2sda27Uak9cXR1N1FXb5pVY774oKPDGyyuP4OD/Q622/4ALCroFX98cMjN317gPbm7h528//Qe12pXAwMF4enaucTv1TacDX98cSkqosGxAXT2fRnPhOgAt8rPosHYvbf/8B/eTrbkKIA4cZ0aqHleDEddDwKEXeB/Icvaj5BUDBbdlUBDbolpt6HQQEXGKuDjLtru7gYKCursMFxV1lISEvg5qC9EIqFTwwAOcaPEFAf9LRPPww7XSrKOgqCkESwDbbulKwCE94XmWBeSC/jDAypV12bVGrVrBUtlkk6+88gozZszg+eefp1+/fgDs3r2bF198kddee61uetkMODl54OYWBtgPkapULrZgyXr3hV7vg6dnASEh/4eTk/1lz+DgWwgLg6ysfTXug5tbxPnbT59GpXKldeun8Kr99EW1zvLB70ReHgQG1s/zVcVF5YTXj1/DvPd5Z0v1kicWu7uihLhg8CnGVzMInJ35Z18hrkXnCDGn41tU+bB9oDEX1gPrd2GI8CR1hAraplZa33oOPj65tsu7bm7FZGXVzqVMR3OToqKOcvjwtbXSvhB1xW/gQ5gHUmvpsh1dbmvoy3DVDZaMrs4s6ns3839biLNy/o64hx9GM2ge+R5BVR98GarWBG9/f38CAgIICAhg9OjR/Pvvv9x+++1ERUURFRXF7bffzuHDhxk9enRd9/ey5OcHLVrkMH3606SmBnHsGMye/QsrV1p+n2Uvw/34I7z33rOEhCShUkFSUggvvjjNrj1/f1izZjIbN95sK3v4YZg582uKi10ByzIZW7aMRFHg889L0wksWQIbNoyrMCdqzpzHuLm0ObZtg0mTXmb9+qsAWLnyEaZOhenTn2LWrK/IzGzJ4cPwwAMvMmfOpyxadBcATz5pqfPoo99w8mQrh7+P116ztJ2cXPUaYVZ6PbRr17PauahmzLD0q6Sk8u8Kzz4Lkye/RGamH3v3ws8/j+PBBy3psK3P8847ln6ePh3GyZOw/aeR8NZbHMjrRvcF/weVBUqBgWwPHUfCI94sHrOJ2XfMo3f7v3l90nzeGPugZe2ZjRuZO3Abb971PldGbueLj2I58914jj3Riy8DxnHSM8ph025JeqI+KoCoKOacWEJ43lFbsrrdu2HfvoGAJVgKC7NcNv/ii4fZtasnzs4Vfx8vv2y/fegQ7Np1LYpieR2ff97yDXzRIsvvIiGhFYWFgRQWwtdfP4ibWzh9+kBgYB7ff38/U6c+x4cfziEw0IRGM47s7FYYDHa/GlaufI6EhEGVZiCGypezEOJSVJXM8WLExFS86SkkBEJDz9bac9REZCR4e+dWq66XVx5rE+7mf72uLy3My2P6no9QjLmYzU5s2/Y0jz223rb6A2CXINfqclgSpVojS3/88Udd96NZ+/xzOHToYxYvvhOdzpv9+6FPn40kJFgCkaQyiYxPn4bJk1+nT58tODn9l9WrZzJ9+lN27Q0bBq+8ch8ff1w6YTExEVq2TCI/3zLLOC0NDh3qQ0YGHDjQn5Dz2e737oV33hmPr2+OXZt6vbvdJa6kJOjQIYGkpJbk5cGPP95Bz56wePF8PvywB2fOdMTPD4YO3cPo0c/y1FP/A+Dffy11PvigJ1lZfjhy+DB07HiK3NzqTQhWFEvAWd3Vdk6fhsDA3CqDpYMHISbmNAUFnhw/DidOXEGnTicZMWIbOt3DtnPp2PEU+SnuFH/1MtPXfAXFRTgMAdu1gzvv5GDEYrrfl85rY52YM74lWxZex7RJk4n+x50ff7walcr+Uux//zuMHTssE7ijBu6FsZBxU0uSjNm0jT7DicVdMK2JpN3pIzhR5i6A4mLGpG9gTHoniiZdydH7lvDz349SXBx5PlM4jBw5C5UKTp1qy/Ll9/D5548Alj9subk70evj+eyzDVx9Nbbyb789x4kTfTl+fDqLF59h2rQZAOzatY02bY6h1XpRUODO5s0vc+RIV9RqSyD1xx9GSkqcmTz5Wdq334evbx9iYtbx1Vdj6Np1HdYbae+4A7p2tURoVX1oVSdZnxANbcaM5wD7pZj69wez+X/Af6s8tvwaktY1Ei/Fc8/Bjh323yrLfvHw8xuKp2cMen08gwbF8PnnY3FRB5D7oBN+f1tGlzplnuT206v5s00Ee/ce58orTRw/Xrq+nFa7GoPBkr3c+m/Ymt6mNgPR+latYGnIkCHVaizOOgFCXJLkZAgKSibzfF7IssFSdQUGppOdbT+xxssrz+6OJK22ZYXJ4zpdzYeIk5MtbVlvqQ8KSr2oRInx8dPtPgRLSrSkpa2q8A/MWs/6//z8Q/j6duXw4bcJDg4lJGSC7QPf2l75tgHy8/+p0B5YPohzc3fh5ZWPXn+Mv/9ej6IYMZny8fIq5NixFdx4473o0v/HmMSvufnbb3EvKKa8ApUXO6J7EvTYzfSaOgtUKvJ3LAMnJ/z9IT/fl8zMw7Zh8ZQUd1xcQomLG46nZwywuOpvY5GRpI31ZHfLu/j5oyu4z+UDrk7YQ1h+ul0195/+osN6uD66hB3DupCevors7OFl7s4zoVKdoajIMuqYnr4KnW4PBQV6Tp0qTZWQnr6Ks2d7otV2Qat9BV/fPrYs4UVFp3BxKUBRFMxmNX//fQxFaXc+E3cErq6W34/RqK38fIRoxKxBhV4fX2HU07Lg8wBMJsswqXW/Tle69JR1eZPK2i2v/BqS1qzeta3s38Xyz3HokCXoOf5MIj0mZuKst/wxGPXnzxwKfQhth1A0miROneKyd1FJKcvKzc1l6dKl9OrVi9jY2NroU7OXlAQaTTImUwFubsUkJ1vy4KSlrSI9fTVms/0Hs8lk+aaQm7uLkyfnAuDkZMZsVhMXN7zMWmE5FBT4kJa2iqys39BqQ/nzz2VYl5wwGJIwGsHZuTSjYlbWRuLjp9uew7K8xipSUlZQUpKB0ahj8+YnMRg8yc6G/PxDBAefQ6sNJylpsV0frf2wZh8vLLT8CysdtrVPt19cnO4w4Wb5zOPFxefw8YHk5L12mcbLJuB0lIyzuDilQnvWn43GDEymfIqL0zl9OhtFMWEy5ePtrSc15TB8/z3vbprMnQe+qhAonVOHsum612itPsmHvcZxuuWpCnMgwsMtS5mUXVcpPV2DVtuS3NzNdhnRLyQkJJN/cnrwv47XMXvk89zh+wlpMfajdioFBp36i8c++JSQhUcw5brYpTJwcyvCYHC1OyYzMwyTCbuUAlptBBkZVacqcHUt5vTpDhfstxBNSUzMYttSJOW/wMXELLYtBg2OL+c5Os56bGNnCHXi1OzSGzeczGYmbvyO3JRAgoJkbbgq/f7779x1112EhoayePFiRo0axd69e2uzb82SXn+co0cPEBR0DpOpAA8PHefOgadnDunpq8jPP4Ci2H84l367TyAjY53dvrIfvB4e2RQU+Jxfn8sSLJ04cRJFsQZLFdfj0uuPotWutj2HwZB8fuRhNyZTAUajjjNntLRqdYJz5yyBi0aTilYbQU5O6Zwds7nILrgBbEuqWJc+MZurzv9RFV/fuktMqdWWLjkSmpPIfd98DzffTMtC+8VcM738SHquG0Nbbefz0P+QYQqhMhERkJYWgVpdeuksIyMMDw99tZJelhUSkklGhuW5FJWar/Pv4tNJo3n3/+5mv2a4XV1ns5ngj0/wwbZ5+P78tW3YSK02YTbbB3TW0cGyE7azskJwcak4ilaWl5ee06djanYSQohGTTvcne2hpaNg4fmp3LB5iwRLjiQlJfHyyy/Tpk0bJkyYQEBAACUlJaxdu5aXX36Znj171lU/m42SknTy8jJsKem9vfWUlFzc3ROursUYDG62bS8vnV3CwIyMlqSl1Tz7c3labTidOx+g+PxnaIsWaWRm1jxR4sVSqZTzwVLd3Lqn1YbiZjRw54G1jH3uYzonnrTbr/dx4Y1WM3hq3H/IvjUKNx8j//5bdZvh4XDsWDcCA0svmSmKmsjIE+Tn+9eofy1aZGMylV5RN5udiY/vSmF3Fc/3+4VTH/cn9wr7QCi4OBPnu/6Pl3ddS1R+0vnnt6+j1YbTsqV9KgCz2Qm12lRlQOflVciZM+1rdA5CiEZOpWJpt2VkupeOWo86/TsxJxIbsFP1p9rB0qhRo+jcuTP//vsvixcv5ty5cyxe3PiHD5um0k8iLy9L0HQxSSiDglLJzCydO+TpqbMbfSkpcePkyZonNCxPq42gc+f9tm0XlxKMxnpIeIQlm7azcwm+vjVfTLe6Op87xpsbX+fGY7+hNpWOBBlVTvzQ4TpWPnslS1SPYHSyBCxeXnqOHq26zYgI+PffXmg0pbf4BwamEhqaWOPlS1xcTHZBF8A//8Ta2i7oHcSBxc5McPmCbF/731G3zD9YsvMZ2n6RibPZfgkdy+taccTOxyeXvDzHk/MBvL0LOX1agiUhLjf5roEs6X23XVnXhXr8irMbqEf1p9rB0saNG5k8eTIvvPACN9xwA05ODZP6fOnSpURHR+Pu7k5sbCzbtm2rsv6WLVuIjY3F3d2dNm3asGzZMrv9//zzD7feeiutW7dGpVKxcOHCOuz9hVm+uZd+IFuDpYtZ3kSjSbGbaO3pmW8XUHh4FHDiRGe7deZqegkILJdmOnQ4WPMDa4FOZ5m47uNT+8GSV3EBvZel80PRzYQUZNrvHD6cR656ni963EqJhzP5+aXBg5tbMYWFVWcVDw+3BksptrKgoHMEBaVe1FpvZYMud/cCjhzpgUZTOjyeX+DP/5zH8M4D95D+YHuKVaUjUS6KifYrs/jgwETL7ZDnabVhdO5cehmuuNgZF5diNJoUu0uT5Xl5VX7e5UevhBBNy9/BHTl9c+mXbLdshZn/vH5xHx5NSLWDpW3btpGXl0fv3r3p27cvS5YsQaut3ztbvv76a2bOnMlTTz3FgQMHGDx4MCNHjuTsWcc5KxISEhg1ahSDBw/mwIEDzJs3jxkzZrB27VpbHb1eT5s2bXj11Vdp2bL+Lh2VFxw8AS+vK8jNjSAgQIuPT3+cnLxwdS3GxcUS2Fg5O7cod1tpxcBVoxlHmzaRdh+8fn6uwIAydc7h7FyCSmV5k5vNKtQXMYvNZHImNNR+KNbZuYSSEtdKjqg91mDJchmu9tYeG5i1h7d+eYEO2+2DpOIQD57q8jT8/DNJ3paAoaDAFy+v0rwF1suCZYOV8kJCIC/Pj+Dg0joaTbJtcnxNlX2uoKBz5Of7EhBQOqdKqw0nOPgcJS4upD3Skbt6vAejRtm10bbgJPTtS8t3/kVlUNBqw+nYsfT3mp7egqCgc9UKlsqeV1lFRU0gy6kQokrH74vljG/p36kB2u3w0UcN2KO6V+2Pxv79+/Phhx+SkpLCQw89xFdffUV4eDhms5lNmzaRV92MgJfg7bffZtKkSUyePJlOnTqxcOFCWrVqxfvvv++w/rJly4iMjGThwoV06tSJyZMnc//99/Pmm2/a6lx55ZW88cYb/N///R9ubm4O26kPISET8PLqQkZGFBpNKl26rMLJyQuVSiE8HLvRJg+P1na3lapUFV/GmJjFXHnlRLsPXo0mFHf3m2zb4eFmgoNTUKstmZsVRYWPjyXQqng7q+U53NxK21OpnHB29j3/fFfj4gJqtTsazThCQ9VkZobg4xN7vq3SgE6tdsfbuztqdeW/b0/Pjri6XjgpZV6eZdSttoIldaECU6bw2pGXCSiyT9y0of0Qjn8/lC1+g+zubtNqw+1GdoDzwVLlWbSdnKBFi1SCgkrrWAKRixtZCg4uHaHSaJIICkpDrS79pqfVRtiNYiV7hMGPP/Jmry/JdSnzezOb0Xxykh6TMmmdk4xGUzpil5bWgqCg5AsGS97eervnKquuJuELIeqP2dWZz4bdTIm6TPahmTPh2LEG61Ndq/E4gqenJ/fffz/bt2/n0KFDPPbYY7z66qsEBwczZsyYuugjAMXFxezbt49hw4bZlQ8bNoydO3c6PGbXrl0V6g8fPpy9e/dSUlLi8JjqMBgM6HQ6u0dt+fffnrZv5RkZASiKmogISyATF9eRc+ciKxxTVOTJzp1UyKsUHg7x8T05dKg3eXng6VnI8eMQF9eRoiI32rfvQFBQCgUFoRSevxHN17f0FlmwTG4+dKg3RqMHRiPExw9Fp/MiMbEtKpUzaWntcXEppmPHRYSFgVrtcf722jFkZLTExyf2fC6kAA4d6k12tqWORnMLKpUrJhPk5gZw+HA7UlJa2/ru6dmRhIQ+6PX2S3BYBzMzMiwjYZa8UHm2OUsnTrQiLg7y8jxtxxiNoNP527Xj7h6FWu1Bfj4UFXlgNkPaT23odGcB/Nc+WVySVygzey3g8z6TCIq+m8JCr/O/7xCcnLw5dqwfGk0Knp5dOHSoPUajMxoN+PllkZ3dxq6tskFoWFgJwcHn0GjG4e9/FcHBaXTsGMuJE905dKg3ubmlx5Q9zpLTZajt5+DgCbRurUGlciIrK5rg4Bxbu2az5fU+dGgAGk0qnp59iIvriEplBpWK34InMKHbZ6Rdbf/e8Txt4qNDs+n+y2s4MYjUVNi3r8v50a/SYMn63svM1Jx/XVri5VWIRpNKTo6GkhL7uWuFhS3sAm4hRNOhKNhu3jF0gO/7llkIXK/H+H93kp7Ukl27XCgoSCMubjiKAnv2qDl7tvIvWE1BjRfSLatDhw68/vrrLFiwgB9++IGPP/64tvpVQUZGBiaTiZAQ+9uxQ0JCSE11/A0+NTXVYX2j0UhGRgahoRf34i1YsIAXXnjhoo6tyk03QUbGWfr1+40WLaBHjyO0bp1M9+5XsmfP5+zbdz3+/om0b3/G7rhhw9aydetgevTYhY9PaSr71q2hU6e/iIvrx513Wr7xf/MNHD9+L126xNOpEwwZ8j4nTtxGp05X8OGH2OXeiYyE6OhjxMX1Y9Sor+ja9WqefnoqV1xxAmfnBIYPX8OhQ3dy9dWfAjczf37pseHhsHt36e/36qt/IC6uH+PPZ8X39LQENHv2wGefPcqBA8Px9BzD//2fZf+NN8Lrr/fCw6OIvmXWXw0Ohu3bLQvZLlw4kE6dLHf9ubuDweDO889PR1EgKuoh7rrrNGDJCP7ZZ48ycmRpO4GBrSkuDuDTT0GXPYTem15h9Gt7cMFkq6OoYFPXgaRPV8PhUEb0/puQkDcYNmwWW7cOolu3HVx1lYEtWwJp1SqBzMzlvPRSByZPXk1sbCz7939JYuIIQkNLL1eXzamydGk0JlMeMTGL8fICF5cn6d9/F7GxM4mL68eECRWPKb9t/fnBB2HHjus4ePBuevb8l6KiQ8TErOehh+C3367A0/MYrVrtIjt7IT/8cAXjxs0DxjBpEuzalc25uwbw0YP38sSpKbhmpQHgohjp8MmT3N96MMtT+6Er9uLaa3/D2/sBNmzwIzh4AsOHr2Hr1mvo2nUvPXr8zsGDtzJ48Eny848QF3cVUVHOQC80mnH88QcUFOymVatnyryeE+z+X/5nIUTj8eCD8NNP+wgMzKVv3z8p7OJJZpETLQ5Y/m46x+3DOHcMi71m8+ijL9G+/R9kZ8Prrz9LUBDccEPV7VuTEDtKRtzQLilYsnJycmLs2LGMHTu2Npqrkqpccj9FUSqUXai+o/KamDt3LrNnz7Zt63Q6WrVyvM5ZTVx5JRQXLwEsiy3ed993AHTtCs7Ov+Lrq0On24Ovbx+742644SsGDlzIjh1L7cqdnWHcuMU4OwcwcODT7N9vGT664orjtufo0OEQV17pQdeuD/Hhh+BT5iqJjw/cddcS2/bAgf/l00/PkJoaxNNPT8fbO48rr/yWrCzLyN7QobBjh6VuRIRlgrDVuHHLz7fxNDt2WIKpbdta2rKTt2mTSG5u6bys2FgYMuSvKn9frq7WRcUU21WxgIBcfH0hJSWI9HTL8ysKFS4bWZNC5h8+zZ0//4LfmU/t9hdp4NyC/nh1PEE00L1v6Ty3G29cxcCB77Bly9sEBg4nMnIPR492JzMT2rc/w7BhO+naFVxcNnP99ZVPdu7fv/T3FR4OrVoloFbDLbe8b3vNrMuAXEhMDGi1h+nd29JPnc5yd+Lo0dC69Vqysjawc+cEsrKO0qXLX/TpswmA226DDh2+B+Cv8LH8MPkwbV77ip4J/9jajjy9jdmfdubwQ53Q+6fTKvI5FMXyXrvhhm8ZOHCp7b3Xr9+m889/kD59Si+zWkYrYceOLLs/gtafHZUJ0dRVlp27Idc2rMlzW7+4uLlZ1qIcOBCCglbh7t6VDh2uRqtdzdmXhuJy8358Syx3xN2a8AP/Do6wa6dNm0MUFna84POlp1uCJOv/G5NLzuBdX4KCgnBycqowipSenl5h9MiqZcuWDus7OzvTokWLi+6Lm5sbvr6+do+mwMXFeME61TmV3FzLpOqqWIKllqSnr3G4PzwcMjJCOXBgLYpixGzW4+GRT16e/RIBRUWJxMdPt8tEXlJiSejk4ZFJdnbpuoWKYrIlz0xN9SYjoyXx8dPJzPwFrTbYdry1f5qNhTzyURciz6TZd278ePZ97EJBn5qtrK3XH8Vsrjw4agz0+qMYjY5fOw8POKs/xPvXjuH4PF+MnmX2lRRy5ZL9dH7OiDo7E0UxVSu7uBDNWWXZuRsya3dNntuaibzs1Axv76527XS+7leWXfmJ7Rg1CtP/+gK3/KqT1zY1TSZYcnV1JTY2lk2bNtmVb9q0iQEDBjg8pn///hXqb9y4kd69e+PiUj95gBqjkhLLUh55efsr7NPrP3dwhD1FKS6/ekcFtpGb/AOV7k9PDyUhIf38UiKFaDTJJCdjW4QRwGjMQqtdbZeJvKDAMlncySmPnJyyizybMZuLUBTQ6XxITw9Dq11NVtYvtp8BvEpyuOGLOxj99Q68jKWjWXpnd+Kf9oVVqzD61HzkUa8/isnUuIOlqnh4WC5lolKRPsqDfctdMPYdaFdHs1Uh4sbudEmPb6BeCiEam73hN3FqWOmXy8CiXAZ/+s9llU6gyQRLALNnz+ajjz7i448/5siRI8yaNYuzZ88yZcoUwHJ57J577rHVnzJlCmfOnGH27NkcOXKEjz/+mOXLl/P444/b6hQXFxMXF0dcXBzFxcUkJycTFxfHiRMn6v386ovJVIDJlO8wkDGZfr/g8a6uRRes4+UFJlPlubg0GsjJaWF3eSwo6FyFhX0dudAdVYWFEB39r90aZpmZoZblPLZsYdHmboRtsR8VyezoyaNDnkE7wqPCOm7NhS1YOq8oVIVq6xY+67QAo7r0tXROTebZbe8RsvAIZROoCiGar99GXclpj0627dZx6YT+aK7iiKalSQVL48ePZ+HChbz44ov06NGDrVu3sn79eqKiLNdTU1JS7HIuRUdHs379ejZv3kyPHj146aWXWLRoEbfeequtzrlz5+jZsyc9e/YkJSWFN998k549ezJ58uR6P7/G4EKX18CSD6g6qrp1Xq223OGn1bYsUz/JNoepKtW5/bxDh332C76WqAh91whXX42mqDQnlBEn/rimC9tfbke6V80uu11uLMGSh12Zk6sTq9s+ydPXPkpuWOk+NQrBH5+AzCyIl1EmIZq7lNwIvr3tSwyU5tdru8SE+tiRBuxV7WlSwRLAtGnTOH36NAaDgX379nHVVVfZ9q1YsYLNmzfb1R8yZAj79+/HYDCQkJBgG4Wyat26NYqiVHiUb6e58PQsuGCd6i6cWFVSRquyAY1GU72RpYKCC0+sCgtLsN227pOcxS76025tht2w8BnXCAaynW1DOqM4Nc/RpLI8PS1pFBxJCIhgw/yunBtd7k+G0QjffVcPvRNCNGZabSitRvfgSV61lTkZwPuhO3AxX3yqnsaiyQVL4tIpSuUve22OLJVNlOiIm1sRRmPpDZkaTXI1R5YuHCwFBZ1DMUPL7/Rc/+QqYik3P2vyZEa1XMfJFq0v/ITNRPnLcFbWq5ImNyeOP+ZM6vvfked6fva3qwuUuawthGietNpQunWD910eIi6k9M4350Nx3HvM8Y0+TUmtpA4QtafsbZ1l881YEhEOwM0tCj+/AQ6PcXRLqJ/fUDw9Y+zaKyryxc9PDRhs7QK4uoZVCJbKt+nuHo1Gk2Er9/MbgMlkcFi/qstwlv6cIyWlNMnm/7d372FRVQv/wL/DbQYGGOQ6IKigecs7GEGBl0ysTD32HFTMMs0TlprHtzfvhV1Eq2PHjqWmhWap+DuJ2fmRiqmYCgkISurRymsGIir3m8B6/yC2bGfYXFJmwO/neebR2XvtNWvPUvm69t5rOTtno54ps2QaM7LkozmP988sRuekO8Kfiwve9V2PhevGwKb/EbjZNeID7xO2tkBFhQYqlfJTLCXDx+CNYfPwfvkngK6yZjpyIrqvXbvmCW9vwMUtBx/3j8BHiW/DtqhmRGnsxd1Is38MQKByJWaMYcnM1H2ss+48E3WXN6nvGGOPhPbrt9ugvpISJzg7VwMolx2jVreHnZ08XNxZp62tHzw8ctG162bFdgKAn58XcnNvALgzRNX8vlMnD/z++++wsNDC0tIWLi6PorLO7AaOjkHIzT1hcE75+d0AANbWNcuhODj0B1CzjIpKVXPpzc31Bh4szpcdd8KrG/qk7sePL9XcVO7jY428vN9hZeUMALC0tKsTAgfD3X2CNL9I3YBqrEx2to9UR20odXP7q+zJPmPqm/OkdpbrlpygsXZkSaNpeEb6G7ZOuLRsIFBnmgciun+VldlBq625opBnq8PByb0Qtur2Q0Tz//sWcO4ZwM9PoRbzxbB0n/H1BeztC3Dw4BBERPxHtq9dO8DWVvnRd60W8PBo3GW4oKAJ2L275hKNsVmnH3nkL8jK+hCJieHo0+eMLNgBgJtbEFavtkZi4tg/piuouXEwL69mno9Vq97Do4+mwccnHABw7lxv6PW58PIC7P3KEB/+JSZsegrlKhts85+Era6P4f/XmbV94MCB+Pnnf2LbtlnQaseiU6dSqW21bVEKp3XLlJUBBw9egb//T9IxXbv+C1evKs9FVPd7qRvCPD0n1/v59ak7Slgb8oCawFVVVQ5X10AcPDgQYWHZ0nIptftrPhfYv38kRo/+DG5uf0VJSc2N27a2QFLSU4iI2AedbjCsrYGjR0fi888dMH16xzvafvvz7xwBrVuOiNqOsrJLEKJmFm9v73PQaIqxMOmf8AwLR5/dNfPY2VcVAeHhOP5JO9g6dUdJyVnY2XWVftXpgvH++8/A2hp48cVGzsbbglRCtKGJEEykoKAAOp0O+fn5rWKCykOH9NBoHoG1dSX69v1Gtu/wYT0eeUT50lRjyjS27KFDetjaBsPSshr9+u3A2LFAfv432L59NBwdgf37a34YV1behJVVO2g0F/HuuwfQo8dgWFquQ1aWGwIDx+CVV4Bjx8YCEBgwIA6HD+tx5Eg2go/0gm6cHpXd7fHKKy/j8OHhGDUK2Lmz5l7vw4drvouCglQ4O/dHv347GnVexqSmPgMrq6o/Vce9JASQlvaM0X6v3b9/f0dYW1cgJOT2/WZVVcC+fQ7Q64egd++dAIC0tLFQqYABA7a3WPuJyPRq/wPo4TEBZ8/ORMeO/8KoUYdQVJSHw4dH4uzZmcjK2oSMjEk4d8YSC7+PgfvZ26PVV8ZY4MJrbtK/6bW/Ojo+hMjImXByehyvv67GoEHl9TXhrmrsz2/e4H0fUqkAtfqWbFV6U7bFxqYSlpY183F4eQHXr7tL+9TqctnLxQW4etUVlZX/gZVVJaqqilBQkAqgZoZya+sqWf3HH+8M8aCdVH9GRhiAmr/wKtXt70KtLpfKNJdafetP13EvNdTvtd/3nfstLWvW36vLxqayUTPCE1HbUjurN2D81o+uXf8FKysNfH0rcfVGB+ycEIIi29tP2bbfUQ3X743P1adSVZvtLZAMS2RWamf+/uWX/zW638EBuHrVBUIkA8Afk2umNrr+/PwDAMClOoiI7iF39xvIzfXCuYou+HZcCKpxe3qWLssKYHvR9P9ZbwqGJTIrNWvKeSI3d4fR/Y6OQE6OC7TaoibXrdUWori44QktiYjoz9FqS1FS4lCz7FWwBTZ1eEHaZ1kq0GvRLVgWmu9I/J0YlsistG8P3Lqlrne/VgvcumXVqPmg7uTmloVr19r/meYREVETXLumh5tbFjZ2mgoMHSptt7sMdF2SD1S1jhEmhiUyK97eyvtVKsDevqTZYSk3l2GJiKglqFQC1697wNk5B9UqSyA2FqV1VqFyTq6A72dV9VdgRhiWyKy0b0SW0WpLDeaDagx3d44sERG1FJ0uFzdvut5+8MXVFSfftUJVnYUCOmyu/4Zvc8KwRGZFqwXs7fMaKFPaqDXs7uTqyrBERNRS3NyuQAj5upvFnS3w80KdbFuXpfnQnJZPImxuOCnlfaju5IXG9jXm+KZ8VlPb4ulZYlCmduIyAHBysmvWZTiOLBERtRxX19+NrhF6fYgGFycVouOmmhEny3Kg46yjcPXJhbne8s2wdB8yNjdGY/Y1pUxjyxrb7+fnJf3eze2vBmVcXFyh1RbCwSEAZWW/AaiZIfbOtfQAwMlpCNzdPQAAVlZJuHq1A5ycICtX35p7TdWSS5MQEZmLsjI1qqsNJ0hyc7sCN7ffAQC3blnjyhXg2jUvWFnpkDVSA5v/3oRnSs2ElTZXy/Be4ZtY7Pp0i7a9sRiWyOy89BKg0ZQCMB6mpk0DbG2LMXToQBw7louePX8BYHyNOh+f2fCoyUrw8JiAXr2i0L59mEG5piwrUp+7Uce91lCgq28ksHYdvMbWQ0T3B2trwNs7C+3a/QDgCdm+Hj1S4ORU+cfvf8U77wzA77/Ph4WFGtXV5djuXo13HJbCr/AyAKBrya+YmxaOwls1T8hlZITB03OyWfzbyuVO7oLWttxJa9DQMim1+zMzRwGAtAxHXe+/Dzz8MBAScntbYmLNNPqZmaOMHkNERE2TmTkKRUWZCAo6DwA4e3YmdLpgZGVtQHHxcWlR8d69dyIxUS1b5gS/5eGB5+zhXnFdqu/KCFu0jy9G4kENnJ3D7um/1VzuhAjAjRt7G1zMloiI/hx7+97S77t2/Rc8PCZIi40r3Y5RqRf43x5vo9iyzpIou0qBBQvuXWObgWGJ2rSbN/dyaRMiIjP2i7YzFnWbB2FZ58m5Zcvg85X5zMHEsEREREQmY2NTjmTHgfjtrb6y7X7rquC89byJWiXHsEREREQm4+ZWM71A3tM+2Dd2lWxf+6U/ARs2mKBVcgxLREREZDJubrcf5jn/5Cs4ENZfXmDKFGDjxhZulRzDEhEREZlM7VxMAODoCHw/MBiYO/d2ASGAF14A4uJM0LoaDEtERERkMnVn+XZwAIqLHYDoaFwOrxNR+vQBQkNN0LoanJSSzFJjlkkBmj45ok43uFnHERHRveHqejssOToCJSVaQKXCuemWsFV3gusJB+D77wEXF5O1kWGJzFJjl0lRmtnVwgKIjX0SanUFev8xBUjtvB/mMCMsERHV3LNkZVUBoDYsOWDHjuXYsmU5/jImCUOjnoC7CYMSwLBEbVhkJNC162a4uOSZuilERG2W0kj9nVcBdLrBsLPrKi2OXvvr4sWRcHdfgfJyoLq6P7ZvT4cQwPET7fFgrzi444UWOZf6MCxRm6XVAp06/d5wQSIiajalkfo7rwLUju7f6dq1/wcPjwnIywMsLEKRk3P1j7IOd7u5zcIbvImIiMgs2NsDhYVAYaEO9vb5pm6OhGGJiIiIzIKVFVD1xyonKpUwbWPqYFgiIiIisyHMJyNJGJaIiIjIbOTnAw4O5nMJDmBYIiIiIjNSWnp7vThzwbBEREREZsPRUb5enDlgWCIiIiKz4eAgXy/OHHCeJWrTuKwJEVHrUjuydPWqvambImFYojaNy5oQEbUuw4YB7u7nAOgQH/8SHB0zpSWrTIVhiYiIiEyq7uLpzz0HHD5cgIAAX/TrNw9qdXsAc0zXOPCeJSIiIjIxY4un9+u3+4+gZHoMS0REREQKGJaIiIiIFDAsERERESlgWCIiIiJS0OrC0ieffAJfX19oNBr4+/vjhx9+UCyfmJgIf39/aDQa+Pn5Yc2aNQZlvv76a/Ts2RNqtRo9e/ZEXFzcvWo+ERERtTKtKizFxsZi9uzZWLhwIdLT0xESEoInnngCly5dMlr+/PnzePLJJxESEoL09HQsWLAAs2bNwtdffy2VSUpKwrhx4zBp0iQcP34ckyZNQnh4OH788ceWOi0iIiIyYyohhDB1IxorMDAQAwYMwOrVq6VtPXr0wJgxYxAdHW1Qfu7cudi5cydOnz4tbYuMjMTx48eRlJQEABg3bhwKCgrw3XffSWVGjBiBdu3aYcuWLY1qV0FBAXQ6HfLz8+Ho6Njc0yMiIiIAhw/r8cgj2cjMHAUA6N175z35nMb+/G41I0sVFRVIS0vD8OHDZduHDx+OI0eOGD0mKSnJoHxYWBhSU1Nx69YtxTL11QkA5eXlKCgokL2IiIiobWo1YSk3NxdVVVXw8PCQbffw8EB2tvHVibOzs42Wr6ysRG5urmKZ+uoEgOjoaOh0Ounl4+PTnFMiIiIiI2pn9HZ3n2AWa3y2mrBUS6VSyd4LIQy2NVT+zu1NrXP+/PnIz8+XXpcvX250+4mIiEhZ7YzeHh4TzGKNz1azNpyrqyssLS0NRnxycnIMRoZq6fV6o+WtrKzg4uKiWKa+OgFArVZDrVY35zSIiIiolWk1I0s2Njbw9/dHQkKCbHtCQgKCg4ONHhMUFGRQfs+ePQgICIC1tbVimfrqJCIiovtLqxlZAoA5c+Zg0qRJCAgIQFBQED799FNcunQJkZGRAGouj125cgVffPEFgJon31atWoU5c+Zg2rRpSEpKwmeffSZ7yu3VV19FaGgoli9fjtGjR+Obb77B3r17cejQIZOcIxEREZmXVhWWxo0bh+vXr+Ott95CVlYWevXqhfj4eHTs2BEAkJWVJZtzydfXF/Hx8fj73/+Ojz/+GF5eXvjoo4/wzDPPSGWCg4OxdetWLFq0CIsXL0bnzp0RGxuLwMDAFj8/IiIiMj+tap4lc8V5loiIiFqfNjfPEhEREZEpMCwRERERKWBYIiIiIlLAsERERESkgGGJiIiISAHDEhEREZEChiUiIiIiBQxLRERERAoYloiIiIgUMCwRERERKWBYIiIiIlLAsERERESkgGGJiIiISAHDEhEREZEChiUiIiIiBQxLRERERAoYloiIiIgUMCwRERERKWBYIiIiIlLAsERERESkgGGJiIiISAHDEhEREZEChiUiIiIiBQxLRERERAoYloiIiIgUMCwRERERKWBYIiIiIlLAsERERESkgGGJiIiISAHDEhEREZEChiUiIiIiBQxLRERERAoYloiIiIgUMCwRERERKWBYIiIiIlLAsERERESkgGGJiIiISAHDEhEREZEChiUiIiIiBQxLRERERAoYloiIiIgUMCwRERERKWg1YenmzZuYNGkSdDoddDodJk2ahLy8PMVjhBCIioqCl5cXbG1tMXjwYJw8eVJW5tNPP8XgwYPh6OgIlUrVYJ1ERER0f2k1YSkiIgIZGRnYtWsXdu3ahYyMDEyaNEnxmPfeew8rVqzAqlWrkJKSAr1ej8cffxyFhYVSmZKSEowYMQILFiy416dARERErZBKCCFM3YiGnD59Gj179kRycjICAwMBAMnJyQgKCsJ///tfdOvWzeAYIQS8vLwwe/ZszJ07FwBQXl4ODw8PLF++HC+99JKs/IEDBzBkyBDcvHkTTk5OTWpfQUEBdDod8vPz4ejo2LyTJCIiohbV2J/fVi3YpmZLSkqCTqeTghIAPPzww9DpdDhy5IjRsHT+/HlkZ2dj+PDh0ja1Wo1BgwbhyJEjBmGpKcrLy1FeXi69z8/PB1DzpRMREVHrUPtzu6Fxo1YRlrKzs+Hu7m6w3d3dHdnZ2fUeAwAeHh6y7R4eHrh48eKfak90dDSWLFlisN3Hx+dP1UtEREQtr7CwEDqdrt79Jg1LUVFRRkNHXSkpKQAAlUplsE8IYXR7XXfub8wxDZk/fz7mzJkjva+ursaNGzfg4uLSrLoLCgrg4+ODy5cv8zKeGWL/mDf2j/ljH5m3+7l/hBAoLCyEl5eXYjmThqUZM2Zg/PjximU6deqEEydO4OrVqwb7rl27ZjByVEuv1wOoGWHy9PSUtufk5NR7TGOp1Wqo1WrZtqbe52SMo6PjffcHtTVh/5g39o/5Yx+Zt/u1f5RGlGqZNCy5urrC1dW1wXJBQUHIz8/H0aNH8dBDDwEAfvzxR+Tn5yM4ONjoMb6+vtDr9UhISED//v0BABUVFUhMTMTy5cvv3kkQERFRm9Yqpg7o0aMHRowYgWnTpiE5ORnJycmYNm0aRo4cKbu5u3v37oiLiwNQc/lt9uzZWLp0KeLi4vDTTz9h8uTJsLOzQ0REhHRMdnY2MjIy8MsvvwAAMjMzkZGRgRs3brTsSRIREZFZahU3eAPAV199hVmzZklPt40aNQqrVq2SlTlz5oz0ZBoAvP766ygtLcXLL7+MmzdvIjAwEHv27IGDg4NUZs2aNbL7pkJDQwEAMTExmDx58j08o9vUajXefPNNg0t7ZB7YP+aN/WP+2Efmjf3TsFYxzxIRERGRqbSKy3BEREREpsKwRERERKSAYYmIiIhIAcMSERERkQKGJRP75JNP4OvrC41GA39/f/zwww+mbtJ94eDBg3j66afh5eUFlUqFHTt2yPYLIRAVFQUvLy/Y2tpi8ODBOHnypKxMeXk5Zs6cCVdXV2i1WowaNQq//fZbC55F2xUdHY2BAwfCwcEB7u7uGDNmDM6cOSMrwz4yrdWrV6NPnz7SRIZBQUH47rvvpP3sH/MSHR0tTalTi33UeAxLJhQbG4vZs2dj4cKFSE9PR0hICJ544glcunTJ1E1r84qLi9G3b1+D6Sdqvffee1ixYgVWrVqFlJQU6PV6PP744ygsLJTKzJ49G3Fxcdi6dSsOHTqEoqIijBw5ElVVVS11Gm1WYmIiXnnlFSQnJyMhIQGVlZUYPnw4iouLpTLsI9Py9vbGsmXLkJqaitTUVAwdOhSjR4+Wftiyf8xHSkoKPv30U/Tp00e2nX3UBIJM5qGHHhKRkZGybd27dxfz5s0zUYvuTwBEXFyc9L66ulro9XqxbNkyaVtZWZnQ6XRizZo1Qggh8vLyhLW1tdi6datU5sqVK8LCwkLs2rWrxdp+v8jJyREARGJiohCCfWSu2rVrJ9avX8/+MSOFhYXigQceEAkJCWLQoEHi1VdfFULw71BTcWTJRCoqKpCWliZNsllr+PDhOHLkiIlaRQBw/vx5ZGdny/pGrVZj0KBBUt+kpaXh1q1bsjJeXl7o1asX++8eqJ1s1tnZGQD7yNxUVVVh69atKC4uRlBQEPvHjLzyyit46qmnMGzYMNl29lHTtJoZvNua3NxcVFVVGSzq6+HhgezsbBO1igBI37+xvrl48aJUxsbGBu3atTMow/67u4QQmDNnDh599FH06tULAPvIXGRmZiIoKAhlZWWwt7dHXFwcevbsKf0gZf+Y1tatW3Hs2DGkpKQY7OPfoaZhWDIxlUoley+EMNhGptGcvmH/3X0zZszAiRMncOjQIYN97CPT6tatGzIyMpCXl4evv/4azz//PBITE6X97B/TuXz5Ml599VXs2bMHGo2m3nLso8bhZTgTcXV1haWlpUE6z8nJMUj61LL0ej0AKPaNXq9HRUUFbt68WW8Z+vNmzpyJnTt3Yv/+/fD29pa2s4/Mg42NDbp06YKAgABER0ejb9++WLlyJfvHDKSlpSEnJwf+/v6wsrKClZUVEhMT8dFHH8HKykr6jtlHjcOwZCI2Njbw9/dHQkKCbHtCQgKCg4NN1CoCAF9fX+j1elnfVFRUIDExUeobf39/WFtby8pkZWXhp59+Yv/dBUIIzJgxA9u3b8e+ffvg6+sr288+Mk9CCJSXl7N/zMBjjz2GzMxMZGRkSK+AgABMnDgRGRkZ8PPzYx81hWnuKychhNi6dauwtrYWn332mTh16pSYPXu20Gq14sKFC6ZuWptXWFgo0tPTRXp6ugAgVqxYIdLT08XFixeFEEIsW7ZM6HQ6sX37dpGZmSkmTJggPD09RUFBgVRHZGSk8Pb2Fnv37hXHjh0TQ4cOFX379hWVlZWmOq02Y/r06UKn04kDBw6IrKws6VVSUiKVYR+Z1vz588XBgwfF+fPnxYkTJ8SCBQuEhYWF2LNnjxCC/WOO6j4NJwT7qCkYlkzs448/Fh07dhQ2NjZiwIAB0qPRdG/t379fADB4Pf/880KImsdq33zzTaHX64VarRahoaEiMzNTVkdpaamYMWOGcHZ2Fra2tmLkyJHi0qVLJjibtsdY3wAQMTExUhn2kWlNmTJF+rfLzc1NPPbYY1JQEoL9Y47uDEvso8ZTCSGEaca0iIiIiMwf71kiIiIiUsCwRERERKSAYYmIiIhIAcMSERERkQKGJSIiIiIFDEtEREREChiWiIiIiBQwLBEREREpYFgiIgDAhQsXoFKpkJGRYRb10N2lUqmgUqng5ORk6qZg8uTJUnt27Nhh6uYQNYhhiagNqPvDx8rKCh06dMD06dMNVgu/F587ZswY2TYfHx9kZWWhV69e9/Sza8+37uvRRx+9p5/Z2sXExODs2bPS+w0bNkClUqFHjx4GZbdt2waVSoVOnTrJytcXtpoSfFauXImsrKymNJ3IpKxM3QAiujtGjBiBmJgYVFZW4tSpU5gyZQry8vKwZcuWFm2HpaUl9Hp9i3xWTEwMRowYIb23sbExWu7WrVuwtrZukTaZMycnJ7i7u8u2abVa5OTkICkpCUFBQdL2zz//HB06dLgn7dDpdNDpdPekbqJ7gSNLRG2EWq2GXq+Ht7c3hg8fjnHjxmHPnj2yMjExMejRowc0Gg26d++OTz75pN76qqqqMHXqVPj6+sLW1hbdunXDypUrpf1RUVHYuHEjvvnmG2lk58CBA7LLcNXV1fD29saaNWtkdR87dgwqlQrnzp0DAOTn5+Nvf/sb3N3d4ejoiKFDh+L48eMNnrOTkxP0er30cnZ2lj5/27ZtGDx4MDQaDb788stGnf/Ro0fRv39/aDQaBAQEIC4uTnZJ0djIyo4dO6BSqWTbvv32W/j7+0Oj0cDPzw9LlixBZWWltF+lUmH9+vX4y1/+Ajs7OzzwwAPYuXOnrI6TJ0/iqaeegqOjIxwcHBASEoJff/0VBw8ehLW1NbKzs2Xl/+d//gehoaENfmd3srKyQkREBD7//HNp22+//YYDBw4gIiKiyfUBNX82jI38bdiwoVn1EZkawxJRG3Tu3Dns2rVLNpqybt06LFy4EO+++y5Onz6NpUuXYvHixdi4caPROmqDzrZt23Dq1Cm88cYbWLBgAbZt2wYAeO211xAeHo4RI0YgKysLWVlZCA4OltVhYWGB8ePH46uvvpJt37x5M4KCguDn5wchBJ566ilkZ2cjPj4eaWlpGDBgAB577DHcuHGj2d/B3LlzMWvWLJw+fRphYWENnn9xcTFGjhyJbt26IS0tDVFRUXjttdea/Lm7d+/Gs88+i1mzZuHUqVNYu3YtNmzYgHfffVdWbsmSJQgPD8eJEyfw5JNPYuLEidL5XrlyBaGhodBoNNi3bx/S0tIwZcoUVFZWIjQ0FH5+fti0aZNUV2VlJb788ku88MILzfqupk6ditjYWJSUlACoCYUjRoyAh4dHs+p77bXXpD8TWVlZ+OCDD2BnZ4eAgIBm1UdkcoKIWr3nn39eWFpaCq1WKzQajQAgAIgVK1ZIZXx8fMTmzZtlx7399tsiKChICCHE+fPnBQCRnp5e7+e8/PLL4plnnpF97ujRo2Vl7qzn2LFjQqVSiQsXLgghhKiqqhLt27cXH3/8sRBCiO+//144OjqKsrIyWT2dO3cWa9eurbctAIRGoxFarVZ6xcXFSZ//z3/+U1a+ofNfu3atcHZ2FsXFxdL+1atXy84lJiZG6HQ6WR1xcXGi7j+lISEhYunSpbIymzZtEp6enrK2L1q0SHpfVFQkVCqV+O6774QQQsyfP1/4+vqKiooKo+e+fPly0aNHD+n9jh07hL29vSgqKjJavvYz4+LiZNvqnk+/fv3Exo0bRXV1tejcubP45ptvxIcffig6duwoKw9A9p3XvozVL4QQSUlJQqPRiNjY2Ea1icgc8Z4lojZiyJAhWL16NUpKSrB+/XqcPXsWM2fOBABcu3YNly9fxtSpUzFt2jTpmMrKSsV7R9asWYP169fj4sWLKC0tRUVFBfr169ekdvXv3x/du3fHli1bMG/ePCQmJiInJwfh4eEAgLS0NBQVFcHFxUV2XGlpKX799VfFuj/88EMMGzZMeu/p6Ylr164BgGwUozHnf/r0afTt2xd2dnbS/rr38DRWWloaUlJSZCNJVVVVKCsrQ0lJiVR/nz59pP1arRYODg7IyckBAGRkZCAkJKTe+6wmT56MRYsWITk5GQ8//DA+//xzhIeHQ6vVNrm9taZMmYKYmBh06NABRUVFePLJJ7Fq1SqDcg4ODjh27JjB9gceeMBg26VLlzBmzBhpFJKotWJYImojtFotunTpAgD46KOPMGTIECxZsgRvv/02qqurAdRcigsMDJQdZ2lpabS+bdu24e9//zv+8Y9/ICgoCA4ODnj//ffx448/NrltEydOxObNmzFv3jxs3rwZYWFhcHV1BVBzuc/T0xMHDhwwOK6hx9z1er10zrVqw1Ld4NCY8xdCNHgeFhYWBuVu3bole19dXY0lS5Zg7NixBsdrNBrp93cGIZVKJbXT1tZWsR3u7u54+umnERMTAz8/P8THxxv9/ppi4sSJeP311xEVFYXnnnsOVlbGfzxYWFgYfOfGFBcXY9SoUQgKCsJbb731p9pGZGoMS0Rt1JtvvoknnngC06dPh5eXF9q3b49z585h4sSJjTr+hx9+QHBwMF5++WVp250jPTY2NqiqqmqwroiICCxatAhpaWn497//jdWrV0v7BgwYgOzsbFhZWckeU7+bPDw8Gjz/nj17YtOmTSgtLZXCSnJysqyMm5sbCgsLUVxcLIWxO+eTGjBgAM6cOdOoQFGfPn36YOPGjYpP8b344osYP348vL290blzZzzyyCPN/jwAcHZ2xqhRo7Bt2zaDG/KbSgiBZ599FtXV1di0aZPBDfBErQ1v8CZqowYPHowHH3wQS5cuBVDzhFJ0dDRWrlyJs2fPIjMzEzExMVixYoXR47t06YLU1FTs3r0bZ8+exeLFi5GSkiIr06lTJ5w4cQJnzpxBbm6uwShLLV9fXwQHB2Pq1KmorKzE6NGjpX3Dhg1DUFAQxowZg927d+PChQs4cuQIFi1ahNTU1Lv0bTR8/hEREbCwsMDUqVNx6tQpxMfH44MPPpDVERgYCDs7OyxYsAC//PILNm/ebPCE1xtvvIEvvvgCUVFROHnyJE6fPo3Y2FgsWrSo0W2dMWMGCgoKMH78eKSmpuLnn3/Gpk2bcObMGalMWFgYdDod3nnnnWbf2H2nDRs2IDc3F927d/9T9URFRWHv3r1Yu3YtioqKkJ2djezsbJSWlt6VdhK1NIYlojZszpw5WLduHS5fvowXX3wR69evx4YNG9C7d28MGjQIGzZsgK+vr9FjIyMjMXbsWIwbNw6BgYG4fv26bJQJAKZNm4Zu3bohICAAbm5uOHz4cL1tmThxIo4fP46xY8fKLjOpVCrEx8cjNDQUU6ZMQdeuXTF+/HhcuHCh2U9jGdPQ+dvb2+Pbb7/FqVOn0L9/fyxcuBDLly+X1eHs7Iwvv/wS8fHx6N27N7Zs2YKoqChZmbCwMPznP/9BQkICBg4ciIcffhgrVqxAx44dG91WFxcX7Nu3D0VFRRg0aBD8/f2xbt062SiThYUFJk+ejKqqKjz33HPN/2LqsLW1Nbh3rDkSExNRVFSE4OBgeHp6Sq/Y2Ni70EqilqcSjblQT0R0H7pw4QJ8fX2Rnp7e5BvbW8K0adNw9epVgzmajFGpVIiLizOYcd2UzLFNRMZwZImIqJXJz8/H3r178dVXX0lPPDbGhAkT4O3tfQ9b1jiRkZGwt7c3dTOIGo03eBMRtTKjR4/G0aNH8dJLL+Hxxx9v1DE///wzgPqffmxJb731ljThp6enp4lbQ9QwXoYjIiIiUsDLcEREREQKGJaIiIiIFDAsERERESlgWCIiIiJSwLBEREREpIBhiYiIiEgBwxIRERGRAoYlIiIiIgX/B63HEDCCnpCdAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot data, model and errors\n",
    "\n",
    "# Plot the data points with error bars in yellow ('y')\n",
    "plt.errorbar(x1, y1, yerr=yerr1,fmt='none', ecolor='y', linewidth=0.5 )\n",
    "# Plot the data points in blue ('b') with a thin line\n",
    "plt.plot(x1, y1, 'b-', linewidth=0.5)\n",
    "\n",
    "# Repeat the same for the second set of data (x2, y2)\n",
    "plt.errorbar(x2, y2, yerr=yerr2,fmt='none', ecolor='y', linewidth=0.5 )\n",
    "plt.plot(x2, y2, 'b-', linewidth=0.5)\n",
    "# Plot the model (result2.best_fit) in red ('r') with a thicker line\n",
    "plt.plot(x2, result2.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "\n",
    "# Repeat the above steps for the next four sets of data (x3, y3) to (x6, y6)\n",
    "# Each set is plotted with error bars and blue data points, and the model is plotted in red\n",
    "plt.errorbar(x3, y3, yerr=yerr3,fmt='none', ecolor='y', linewidth=0.5 )\n",
    "plt.plot(x3, y3, 'b-', linewidth=0.5)\n",
    "\n",
    "plt.errorbar(x4, y4, yerr=yerr4,fmt='none', ecolor='y', linewidth=0.5 )\n",
    "plt.plot(x4, y4, 'b-', linewidth=0.5)\n",
    "plt.plot(x4, result4.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "plt.errorbar(x5, y5, yerr=yerr5,fmt='none', ecolor='y', linewidth=0.5 )\n",
    "plt.plot(x5, y5, 'b-', linewidth=0.5)\n",
    "\n",
    "plt.errorbar(x6, y6, yerr=yerr6,fmt='none', ecolor='y', linewidth=0.5 )\n",
    "plt.plot(x6, y6, 'b-', linewidth=0.5)\n",
    "plt.plot(x6, result6.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "# Define a function 'annotate_dim' to annotate dimensions with arrows\n",
    "def annotate_dim(ax,xyfrom,xyto,text='$248.013 \\pm 0.895$'):\n",
    "   ax.annotate(\"\",xyfrom,xyto,arrowprops=dict(arrowstyle='<->'))   \n",
    "\n",
    "# Define coordinates (xa, ya), (xb, yb), and (xc, yc) to create a gray line with arrows\n",
    "# These coordinates are used to indicate the peak centers in the plot\n",
    "xa, ya = [126.995112,126.995112], [0.013, 0.05]\n",
    "xb, yb = [230.001078, 230.001078], [0.027, 0.05]\n",
    "xc, yc = [372.867051, 372.867051], [0.019, 0.05]\n",
    "plt.plot(xa, ya, xb, yb, xc, yc, color='#7a7a7a')\n",
    "\n",
    "# Annotate dimensions between xa and xb, xb and xc with arrows and text\n",
    "annotate_dim(plt.gca(),[126.995112, 0.049],[230.001078, 0.049])\n",
    "plt.text(177, 0.0505, '$103.006$MHz',horizontalalignment=\"center\", fontsize=6.5)\n",
    "\n",
    "annotate_dim(plt.gca(),[230.001078, 0.049],[372.867051, 0.049])\n",
    "plt.text(301, 0.0505, '$142.866$MHz', horizontalalignment=\"center\", fontsize=6.5)\n",
    "\n",
    "# Add text labels at peak centers in the plot\n",
    "plt.text(372.867051, 0.05305, 'F=2',horizontalalignment=\"center\", fontsize=10)\n",
    "plt.text(230.001078, 0.05305, 'F=1',horizontalalignment=\"center\", fontsize=10)\n",
    "plt.text(126.995112, 0.05305, 'F=0',horizontalalignment=\"center\", fontsize=10)\n",
    "\n",
    "# Set the y-axis limits to (-0.01, 0.06) and x-axis limits to (-15, 460)\n",
    "plt.ylim(-0.01, 0.06)\n",
    "plt.xlim(-15, 460)\n",
    "\n",
    "# Label the y-axis as 'Absorption [arb. units]' and the x-axis as 'Relative Frequency [MHz]'\n",
    "plt.ylabel('Absorption [arb. units]')\n",
    "plt.xlabel('Relative Frequency [MHz]')\n",
    "\n",
    "# Create legend entries for the blue and red lines (data and model) and display the legend\n",
    "blue_line = mlines.Line2D([], [], color='blue', label='Data')\n",
    "\n",
    "red_line = mlines.Line2D([], [], color='red', label='Model', linewidth=2.5)\n",
    "\n",
    "plt.legend([blue_line, red_line], ['Data', 'Model'], loc='lower center', bbox_to_anchor= (0.5, 1.005), ncol=2,\n",
    "            borderaxespad=0, frameon=False)\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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