ivvv/pyxspec_demo1.ipynb

## pyxspec_demo1.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example on using pyXspec to fit a spectrum\n",
    "\n",
    "It is a very simple example on using pyXspec. We'll use Kepler SNR observation with XMM-Newton.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import xspec\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "from astropy.io import fits\n",
    "from astropy import units as u\n",
    "from astropy.constants import b_wien\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "plt.rc('text', usetex=False)\n",
    "plt.rc('font', family='serif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "home = os.path.expanduser('~')\n",
    "#\n",
    "wdir = f'{home}/Dropbox/Work/XMM/energy_scale_works'\n",
    "spec_dir = f\"{wdir}/Kepler/0084100101\"\n",
    "spec_file = f\"{spec_dir}/pn_spectrum_grp0.fits\"\n",
    "#\n",
    "# need to go to the spec dir to read the arf and rmf files\n",
    "# first, store the current wd to get back to it at the end\n",
    "#\n",
    "current = os.getcwd()\n",
    "os.chdir(spec_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "logfile = xspec.Xset.openLog(f\"xspec.log\")\n",
    "\n",
    "try:\n",
    "    s = xspec.Spectrum(spec_file)\n",
    "except:\n",
    "    print (f\"Cannot read {spec_file} in XSPEC\")\n",
    "    raise Exception"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the spectrum using XSPEC and matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bbody ['kT', 'norm']\n",
      "gaussian ['LineE', 'Sigma', 'norm']\n"
     ]
    }
   ],
   "source": [
    "#model = xspec.Model(\"po + ga\")\n",
    "model = xspec.Model(\"bbody + ga\")\n",
    "#\n",
    "# initial parameters\n",
    "#\n",
    "ncomp = len(model.componentNames)\n",
    "for icomp in model.componentNames:\n",
    "    print (icomp,eval(f'model.{icomp}.parameterNames'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# the BlackBody peak at 6.5  keV \n",
    "#\n",
    "# Wien's law, the temperature in kT units [keV] of a BB peaking at 6.5 keV\n",
    "#\n",
    "model.bbody.kT = 6.5/1.59362\n",
    "# and fix it\n",
    "model.bbody.kT.frozen = True\n",
    "#\n",
    "# initial sigma of the line is set to 50 eV, but can be between 1 and 100 eV\n",
    "model.gaussian.Sigma = [5.0e-2,0.001,1.0e-3,1.0e-3,1.0e-1,1.0e-1]\n",
    "#\n",
    "line_c = 6.4 # keV\n",
    "# set the initial line energy and the range where to lok for a C-stat minimum\n",
    "model.gaussian.LineE = [line_c,0.001,line_c-0.2,line_c-0.1,line_c+0.1,line_c+0.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# the fitting cell\n",
    "#\n",
    "xspec.Fit.statMethod = \"cstat\"\n",
    "xspec.Xset.abund = \"wilm\"\n",
    "#\n",
    "#\n",
    "# will constrain the spectrum in [4.5-8] keV\n",
    "#\n",
    "startE = 4.5 # keV\n",
    "endE = 8.0 # keV\n",
    "#\n",
    "s.notice(\"all\")\n",
    "s.ignore(f\"**-{startE} {endE}-**\")\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "xspec.Fit.nIterations = 100\n",
    "xspec.Fit.query = 'yes'\n",
    "xspec.Fit.perform()\n",
    "xspec.Fit.show()\n",
    "cstat = xspec.Fit.statistic\n",
    "dof = xspec.Fit.dof\n",
    "chi2r = xspec.Fit.testStatistic/dof\n",
    "#xspec.Fit.error(\"2.706 3 6\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print the fit results\n",
    "\n",
    "The XSPEC output is stored in the log file but also in the stdout/stderr (notebok terminal). But there are methods to get them out of the Fit class.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the data and the model with XSPEC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot in Xserver\n",
    "xspec.Plot.device = '/xs'\n",
    "# or save to postScript\n",
    "#xspec.Plot.device = f'{psfile}/cps'\n",
    "xspec.Plot.xAxis = \"keV\"\n",
    "xspec.Plot.setRebin(minSig=30.0,maxBins=4)\n",
    "#xspec.Plot.addCommand(\"viewport 0.2 0.2 0.9 0.9\")\n",
    "xspec.Plot.addCommand(\"csize 1.2\")\n",
    "xspec.Plot.addCommand(\"lwidth 4\")\n",
    "xspec.Plot.addCommand(\"rescale y 0.001 0.1\")\n",
    "xspec.Plot.addCommand(\"lab top \\\"Test plot\\\"\")\n",
    "xspec.Plot('ldata','ratio')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot command list is now empty\n"
     ]
    }
   ],
   "source": [
    "# reset the plot\n",
    "xspec.Plot.commands = ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the data and the model with matplotlib\n",
    "\n",
    "The idea on how to plot the model components is from the XSPEC facebook group, idea from Andy Beardmore. Saving in QDP file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_data = 'specfit.qdp'\n",
    "if (os.path.isfile(save_data)):\n",
    "    os.remove(save_data)\n",
    "# following Andy Beardmore idea\n",
    "xspec.Plot.device = '/null'\n",
    "xspec.Plot.add = True\n",
    "xspec.Plot.addCommand(f'wd {save_data}')\n",
    "xspec.Plot(\"ld\") \n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>e</th>\n",
       "      <th>de</th>\n",
       "      <th>rate</th>\n",
       "      <th>rate_err</th>\n",
       "      <th>total</th>\n",
       "      <th>model0</th>\n",
       "      <th>model1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.5100</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.018456</td>\n",
       "      <td>0.006395</td>\n",
       "      <td>0.004903</td>\n",
       "      <td>0.004756</td>\n",
       "      <td>0.000147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.5300</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.011073</td>\n",
       "      <td>0.005222</td>\n",
       "      <td>0.004947</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>0.000185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.5500</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.016611</td>\n",
       "      <td>0.006123</td>\n",
       "      <td>0.004996</td>\n",
       "      <td>0.004768</td>\n",
       "      <td>0.000228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.5700</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.014767</td>\n",
       "      <td>0.005221</td>\n",
       "      <td>0.005049</td>\n",
       "      <td>0.004775</td>\n",
       "      <td>0.000274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.5900</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.007383</td>\n",
       "      <td>0.003692</td>\n",
       "      <td>0.005100</td>\n",
       "      <td>0.004782</td>\n",
       "      <td>0.000318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>7.9100</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.001846</td>\n",
       "      <td>0.001846</td>\n",
       "      <td>0.003077</td>\n",
       "      <td>0.003077</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>7.9300</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.003689</td>\n",
       "      <td>0.003693</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>7.9500</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003039</td>\n",
       "      <td>0.003039</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>7.9700</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>-0.001848</td>\n",
       "      <td>0.001848</td>\n",
       "      <td>0.003022</td>\n",
       "      <td>0.003022</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>7.9875</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>-0.002464</td>\n",
       "      <td>0.002464</td>\n",
       "      <td>0.003007</td>\n",
       "      <td>0.003007</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>175 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          e      de      rate  rate_err     total    model0    model1\n",
       "0    4.5100  0.0100  0.018456  0.006395  0.004903  0.004756  0.000147\n",
       "1    4.5300  0.0100  0.011073  0.005222  0.004947  0.004762  0.000185\n",
       "2    4.5500  0.0100  0.016611  0.006123  0.004996  0.004768  0.000228\n",
       "3    4.5700  0.0100  0.014767  0.005221  0.005049  0.004775  0.000274\n",
       "4    4.5900  0.0100  0.007383  0.003692  0.005100  0.004782  0.000318\n",
       "..      ...     ...       ...       ...       ...       ...       ...\n",
       "170  7.9100  0.0100  0.001846  0.001846  0.003077  0.003077  0.000000\n",
       "171  7.9300  0.0100  0.003689  0.003693  0.003058  0.003058  0.000000\n",
       "172  7.9500  0.0100  0.000000  0.000000  0.003039  0.003039  0.000000\n",
       "173  7.9700  0.0100 -0.001848  0.001848  0.003022  0.003022  0.000000\n",
       "174  7.9875  0.0075 -0.002464  0.002464  0.003007  0.003007  0.000000\n",
       "\n",
       "[175 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "names = ['e','de','rate','rate_err','total']\n",
    "for j in range(ncomp):\n",
    "    names.append(f'model{j}')\n",
    "#print (names)\n",
    "df = pd.read_table('specfit.qdp',skiprows=3,names=names, delimiter=' ')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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58sknmTlzJjNnzmTKlCl84hOf4POf/3wuT3vESfAmhBA55PUGUXQdV7AbiNW8xZqBoroZvH3mmd8DsX5vhgH9NBMJMdZ98Ytf5Itf/GK/z/XnrLPO4qyzzuqzPD8/n3vuuSftNrW1tVRWVlJSUsL06dOHVd6xSoI3IYTIIU/tPtxBLyoGVFXRXjoZVTf79Oi6Qd0hR0NBAXR1UdjZQkFnC11F5TkutRBj34oVK5gxYwaXX355rosy4iR4E0KIHPLW7ccd8JoPTjkFFCXR5003DAxVg6OPhrfNbNOS5noJ3oTIwFNPPZXrImSNJCwIIUQOedu9uIOx4G3xYgBURcGiqeixGjiOOCKxfllz3WgXUQgxxkjwJoQQOeQN6eTHa95mzUosd9q1nuDt8MMTy0sleBPioCfBmxBC5JAHS0+z6aGHYmAQ0jow1Ag+rZGQ1tEreKvPTUGFEGOGBG9CCJEjjm4PXquTvICPsNWGXlmB5ci3qC16Bl+0izABuuwfS/AmhEghwZsQQmTRyvvWsfK+dX3+BihubsBnzyM/4KGtdDJ7u/bzUetOzj7yNGaUTkGLuOi2NaQGb0115nAhQoghu+aaaxJTa41nErwJIUSO5LfuR1c13EEfbWVT2dq8HYBlR36KfKcdJeokrHlocoDf6QbAHvLj7mrLZbGFGLeuv/76CTHHqQwVIoQQOeLqbIVCzJq38ilsbf6YMlcJ5XmlOGwWiFgB2Lh/KzPLq6ja+yEgTadifHvwwQd58cUXOeKII1AUhRdffJFvfetbrFu3jhkzZhAMBikqKuLb3/42bW1t3HTTTXR1dfHggw/y3HPP8dOf/pS77rqLk046iZqaGh588EGqq6vZs2cPF198MYsWLeKvf/0rmzZtorS0lJ07d3LTTTexd+9e7rzzTk499VT+8z//k7feeouHHnqI6dOns2/fPr74xS8yb9483nnnHW666SZmzJhBXl4eH3zwAZ/+9Ke55pprcn3pEiR4E0KIHLF3dQKQF/DRXHYsW5u3M3vSMQA47RYMXcUSdbGx8QMqyqcmBW+ScSqG57Vdb/HqrjcHXS8ajaJp2pD2/akZCzh1xsC1Wtu2beOBBx7g5ZdfxuFwcPfddzN9+nS+8IUvUF5ezhlnnAHAN7/5TTZu3Mjs2bNZvnx5YhaFZcuW8Ze//CWxv//5n//h3HPP5T/+4z/YtWsXH330EQB33nkn//znPyktLeX5558nGAxy0kknceqppya2dTgc3HDDDUyZMoW2tjYuueQSnnvuOebMmcPXv/51/vu//5unnnqKUCjEokWLuOKKK8bM/KgSvAkhRI5YfV2AWfP2TnUFncF6Zpab/dscdgu6YeAMT2Zz4zaOr6hKbFfaIjVvYnxav349s2bNwuFwACRqujRNY9++fdxwww3k5eVRV1fHrl27mD179oD7O+200/jpT3/Kpk2bWLZsWSL4O+OMM7jkkks4//zzOeuss6iqquqz7aRJk3jggQfQNA1VVdm9e3fK88ceeyw2mw2bzUZhYSHt7e0SvAkhxMFODfgBc1L62qnml9nMCnNAXqfdgq4buMJTaAzvYPsRFSx9ztyurElq3sTwnDrj5EFrxwB8Ph95eXkjfnzDMFDSzM37zDPP8I9//IPHHnsMTdO4/vrr0XUdAEVREn8DhMPhxN8XXXQRn/rUp3j22Wf53ve+x8UXX8w3v/lNbr/9dj7++GOeeuopLrzwQu655x5OPPHElGP+8Ic/ZOHChVx22WUArF69OuV5m82W+FvTtJQy5JokLAghRA6o0QhGOAqYNW8tJWEKHQVMdlcA4LRpGAY4QhVEWifzon0e517zOJdd/ls+dpTlsuhCDNtJJ53E+++/TyAQAKCmpgaAjo4O8vPzE021DQ0NiW3Kyspobm4GIBgMsmvXrsRzd9xxB8XFxSxfvpzrr7+ejRs3AvCTn/yEww8/nGuuuYbzzjuPDz74oE9Z2tvbKSoq6nO88UBq3oQQIgcK25vw2V0ARG12uu0tzCs/OlEr4bCbt+euToNI2ycwdBUUaC6o4M9zL6RkQy1L5lXnrPxCDMfRRx/NFVdcweWXX87MmTNRVRWr1cp5553HK6+8wre//W2mTp1KV1cXTz75JHPmzOHQQw9l9uzZfPe73+WII45gxowZrF69munTp5Ofn8/3v/99Jk2aRH19PStWrADMYGzVqlU4nU5aWlq46qqrWL9+Pa+99hoA8+fP53vf+x533XUXmzdvTjSH/vrXv+bss8/mySefpLGxkTfeeIO9e/fS0dHBb37zG+64446cXbtkErwJIUQOlLQ04HO4sUZC1E6fSkTr5pjynjlM48FbU3vADNyShKx2Vj+zRYI3MS5NmzaNhx56CIA///nPaJpGfn4+v//97/vd5s4770z8feWVVyb+jgdrvd177719lp100kn87W9/S1m2cOHCxN8/+MEPEn//8Y9/TFnni1/8Yr9lywUJ3oQQIgdKWhqod7hxB7xs/sQhAIlkBTCbTQEi0fT9bFq6gtkvpBBZ8Mgjj7B27VoURaGzs5Mbb7wx10UadyR4E0KIHChpaWCbfSruoI+PDy9B1W1UF05JPO+M1bxpqkJU7zujQpl17HSeFmIo0tWKiaGRhAUhhMiBkpYGvA437oCHhslWHJFyVKXnlhxvNlX7JuZhDwdYruwepZIKIcYaCd6EECIHSlrN4C0v6MObp2OLFKU8/8GuVgDCUQNNVVC0MGCAYRC02FndXcmamtrRL7gQIuckeBNCiBwoba7Ha3djNQIYClj1/MRza2pqefTl7YnHUd3AiGqgGKAooCg0W/K459GNEsAJcRCS4E0IIUZbeztOvxePw41iN/uzWaPuxNOrn9tKONK7T5sKRuotOxiOsvq5rdkurThIrbxvHSvvW5frYog0JHgTQojRtnMnEVXDb3ehF5mjuFv1nuCtpd2f8a6Gsq4QYmKQ4E0IIUbbzp347ObUQ6ESOxgqmt4zZ2JZcebzJw5lXSHGkv3793PFFVdw6aWXDrje+vXrB13nYCPBmxBCjLadO/HazZo2f6GGVc9DSbodL182E7tV67udktqUatfMdYUYaes27mPbnnY272jlsttezErfykmTJvG1r31txPd7MJBx3oQQYrTt2YPHaQZv3nwDS1J/N4Alc82ZE379yHuEIzrlxU7sVpWmvLdh13RC5OH2e/jWTGtiXSFGypqaWn77ZE+/y+Z2P/c8as4ZOhLvtwcffJAXXniBQw89lLw8swY6Eolw9dVXM2PGDILBIEVFRXz729+mo6OD1atXs3v3bm699VZOOOEEli1blnbdg4kEb0IIMdr27UvUvHU5gjj00j6rLJlbzQvr9wBw+4pFrLxvHd6Cbsonv8L2fedw3jtPseTQE0e12GL8e2XDXv759t60z+2s7wTAH4jQe1joYDjKL//yDvc/vgmAQ6cW9tn+M/On8el50wY8/rZt23jggQd4+eWXcTgc3H333Ynnzj//fM444wwAvvnNb7Jx40Zmz57N8uXLueeee1i1ahVgBnr9rXuwkOBNCCFGW4M5xhtAQAuSH80fZAOTVc+no7gV+96AOal9Q0M2SykOUn3n8xh4+VCsX7+eWbNm4XA4AJg3bx7vvPMOmqaxb98+brjhBvLy8qirq2PXrl1pA7KhrDtRjVrw1tDQwG233UZZWRlNTU388Ic/pLo6tfrVMAzuvPNOWltb8Xq9nHbaaVxwwQUANDU1sWrVKqZMmYLX66W0tJRrr70WRUkz/LgQQoxlDQ14y44DQLGEsfrdg2xgskbzac8DV7DbTHiQ4E0M0afnDV47dtltL9KcJou5vNjJ7288/YCObxhG2u/tZ555hn/84x889thjaJrG9ddfj66nnwJuKOtOVKOWsHDLLbdwwQUXcOutt/KFL3yBm266qc86zz//PHv27OGOO+7gl7/8Jffccw91dXUA/Pa3v2XSpEmsWrWKn//857z22mu89tpro1V8IYQYEYquw/79iWZTtDAWPfPgDQXsUT/ddhfU12expOJgtXzZTGzW1PDAbtVGJDnmpJNO4v333ycQCABQU1MDQEdHB/n5+WiamajTkPTDxG63E41GAXjssccGXPdgMSrBW3t7O+vWrWPx4sUALFiwgA0bNtDY2Jiy3hNPPMEpp5wCmC/W/PnzeeaZZwCoqKigtdWcLiYQCOD1eqXWTQgx7rh8nRCJ4HG4sehBFNVIGaB3IPFZGCyE8Nmk5k1kx5K51Xzz3JlYLWaIUF7s5OqLZo9IssLRRx/NFVdcweWXX86tt96K1+tl9+7dRKNRrFYr3/72t7njjjvo6uriySefZPfu3Rx11FEYhsHKlSvZv38/5513Xr/rHixGpdm0oaEBl8uF3W4HwGazUVBQQH19PZWVlYn16uvrKS3t6bhbWlqaqHm7/PLL+cEPfsCVV15JW1sbF154IaeeeupoFF8IIUZMfmcra45azAuf+AwRxYa+cQldhVGK8q2DbmuN943TIvgcsT5vRmzKLCFG0KLZk1nz3n7ATJgZSZdeemnKuG033ngjAF/5ylf63eavf/1ryuPf//73I1qm8WZUgrf+2rgzqTmLr3P33XeTl5fHL37xC0KhEN/61rfYtGkTxx13XMbl2Lx5c5/avpEWrwIW/ZNrNDi5RgMbT9fH4/EAZpk9Hg9qaxf3nH4VQavZYVsPOqhv8uH3B/qcV+9tAZRCK7pdpzvkhkCA9159lWhh38y/8XSNcuFguj4WiwWfzzfk7W78ygkAw9r2YDCS1yUUCqW8J5ubmwdcf1SCt6lTp+Lz+QgGg9jtdsLhMF1dXUyZMqXPevGmUYDW1lamT58OwCuvvMI111wDmDV3xxxzDI8++uiQgrdZs2ZRVVV14CfUj5qaGubOnZu1/U8Eco0GJ9doYOPt+vxtvTk35Ny5c/nb+nXscFYS1Bwp6xhAl9/oc169twXoNAoIuhSiIRcAx1dUwKxZKduNt2s02g6267N169bEeGqZ8vl8Q97mYDLS18dms6Vky8ZbHfszKn3eiouLWbhwIWvXrgXgjTfeYM6cOVRWVvLSSy8lflGee+65vP766wAEg0HefvttzjrrLACmT5/Oxx9/nNjnjh07mDx58mgUXwghRoxPtadd3nci+vSsUTeBPMVMWAB+/98vjljZhBDjw6gNFXLzzTfzk5/8hNdff52mpiZ+/OMfA/CrX/2KW265hXnz5rFs2TI2bdrEtddei9frZcWKFYnhRG644QZ+/OMfc+utt+Lz+cjPz5dpNYQQ405ByEeXvW+CQnmGc5Rao/mEXN2ELXbCmoWCjoGbV4SA/rsvidwbzjAnoxa8VVVVcf/99/dZ/vTTTyf+VhSF66+/Pu32U6dO5YEHHsha+YQQYjQs2/4aj808nYjWk6AwlGEYrNF8sHYB4LPlkd/ZOsgW4mDncDhobW2ltLRUArgxxDAMwuEwjY2NQ26ClRkWhBBiFJ26bR2biqbz4dSZGIpCaaGDr551bMbDMFh0N2hhALrtLgo6W7JZXDEBVFVVUVdXN2gn+GShUAibzZbFUo1vI3V9LBYLhYWFlJWVDW27Az6yEEKIjOV3tdKaX8bU4Ee0LdrD779wD6qSefdji+5E0SIAeO15EryJQVmtVmbMmDGkbWpqag6q6aaGKtfXZ9RmWBBCiIOdousEotBUWInD1oxVdw8pcAPQdCdYzOCt2+6ioMMM3lbet46V960b8TILIcYeCd6EEGKUuHydfDjlaAAiJR60DKfFSqZiQYtNEe6TmjchDkoSvAkhxCjJ72zlg6kzcQa76a4IYdFdw9qPRTWTHbptLtxdbRCb91EIcXCQ4E0IIUZJQWcLW6Yew8x9H+ItsGPRMxsepDdNMTtK++x5aHoUmppGsphCiDFOEhaEECJL1tTUsm1PO+GIzpduepagbTqhMist+WWE2zQ0x/CCN6tiBwx8sYF6ZYJ6IQ4uUvMmhBBZsKamlnse3ZiYOcHTHSYUb+605xHefSz+9qH3eQOwGE5zcnp7bGwoCd6EOKhI8CaEEFmw+rmtBMMD9EXTLXQ0p58qazDx4UI63Pnmgvr6Ye1HCDE+SfAmhBBZ0NLuH3SdSGR4+7boLtAidLgLzAVS8ybEQUWCNyGEyIKyDOYqtVqGdwvWDCeKFsbjjDW7SvAmxEFFgjchhMiC5ctmYrdq/a+gRKksGV7CQrzmLdHnTZpNhTioSPAmhBBZsGRuNVdfNJv4POD5Tiv5/i4UQ8eOB+e0nRTlO4a1b013oFgi+K2xbFMJ3oQ4qMhQIUIIkSVL5lbz23+8Tziq85f/dyJUVABw3R0X01g8CzzD26+Cit0KQUss+GtsHKESCyHGA6l5E0KILDIMUFBS+qV1lOSZc5QeAKfTSkSxmxNltbSg6DLLghAHCwnehBAiiwzDMJtOY8Gbrih05NmGPbtCXL7ThoFK0GIDXcfl6xqB0gohxgMJ3oQQIosMQFEU2LcPgM5CJ4aqHHDNW77LbDKNJy24u9oOaH9CiPFDgjchhMiiRM3b/v0AtBebSQYHWvNW7Da373KZw4W4Pe0HtD8hxPghwZsQQmSRYWAGb7HJ49uKzZoyzTiw4K3EbQZtjaUlgARvQhxMJHgTQogsSiQsxIK3jkTNm+uA9luWb06N1VhaCkjwJsTBRII3IYTIokSzaVLNm4I5VttQ3L5iEbevWJR4XFFQCEBrUREAbo/0eRPiYCHBmxBCZFEiYSFe81bkokBzohzg7XdyUTEAbbEgLs/TcUD7E0KMHxK8CSFEFvWteXNR5Mg/4P3Ga9468sx95UvNmxAHDQnehBAiiwwjdqNtaQHMmrcSd+kB79dptwA6XS4zeJOaNyEOHhK8CSFEFhmGgTUSgqg5A0JbiZsiV/EB71dRFBQtitch47wJcbCR4E0IIbLIMMAWDgDm7AqdhU6KnYUjsm9F1fFbzSFH3N5282BCiAlPgjchhMgSwzAwAEfIDN7isysUO0YmeFM1CFrM4M0aDmEPdI/IfoUQY5sl1wUQQoiJKqqbNWH2oB/omV3BrHnzDrp98tAg6ZapqkIEG36HFWcgLGO9CXGQkJo3IYTIkkhEB8AZ8AG9g7cDp6kKRsSS2K+M9SbEwUGCNyGEyJJIrOatJ3gzkwtGLnhTIWqlq8BsOpWMUyEODhK8CSFElvTUvJlNpO1FZg1Zkb1gRPavqRpG1EJHYSxpQWrehDgoSJ83IYTIkkjUDN5c/p6aN1tIxaId+K13TU0tni5At/Dr+asIdP5e+rwJcZCQmjchhMiSnuDNA5g1b7aw9YD3u6amlnse3YiuAyh4bMXcc/pV7LYWHfC+hRBjnwRvQgiRJeFYs6mruwuArkLnkCekT2f1c1sJhqMpy4JWB69PPv6A9y2EGPskeBNCiCyJ17y5fZ0AdBY4UZS8A95vS7s/7fKOEZgzVQgx9knwJoQQWRKNmtmmed1ms6k5SO+BB1hlxc60y0t90udNiIOBBG9CCJEl8Zo3ix4hYLcQdFjRjPSB11AsXzYTu1VLWabqEb70r78e8L6FEGOfBG9CCJEl4aTgLT4Wm2YceJ+3JXOrufqi2VgtKmAAUXRF4zdLV3DZj19g0y7fAR9DCDF2yVAhQgiRJfFx3izRpOBNtwPpp74aiiVzq3lh/R7qgh/TUV8AKAA0dwR46u0gM2bUsmRu9QEdQwgxNknNmxBCZEmi2TQaobPQrHEbiZq3ZJ79BWCkNqGGowarn9s6oscRQowdErwJIUSWRGIJC9ZohK78eM3byAZv0Uj623h/GalCiPFPgjchhMiS5ISFzsLsBG+Wfjq/9JeRKoQY/yR4E0KILIkHb1osYUGLgDrCXY3LSi2gRlKWWTWF5ctmjuhxhBBjhwRvQgiRJckJC52FTmxhbZAthq7E7cY6fQuOiBeAIkKcM79IkhWEmMAkeBNCiCyJ17xZY9mmlqhtxI+hYiGvtJEFnscAuKrj3xw348BncRBCjF0SvAkhRJYkj/PWWeBEHYEBetMpUh0EY/GazyOJCkJMdBK8CSFElsSnx4o3myqqOyvHKbC56S4wb+e+7lBWjiGEGDskeBNCiCyJ+AMAaNEIXQUOULITvBXmFeErMPvTdYeiWTmGEGLskBkWhBAiSyJdZhJByKEStWicO28mZx91YDMrpFOYX8L2Igf2+gC+sAGGMeLHEEKMHVLzJoQQI2DlfetYed+6lGVhj9esdYuN8VZgz8/KsQvziukqcOAKduOzOtE8nqwcRwgxNkjwJoQQWRLxdqdMSl/oyFLwZs/HUFWc4W667S4sbW1ZOY4QYmyQZlMhhMiSqK8bSzRKZzx4y1bNm6MAALsewGfLw9renpXjCCHGBgnehBAiS8LdAaxRJTE1VtaaTWM1ehYjhM/hwiLBmxATmjSbCiFElkT8gZRm0wJ7lrJNY8GbpoTotuVJs6kQE5zUvAkhRJZEAkG0qEJXgYM8Q8OiZeeWW2Q3m00VNYzP5pJmUyEmuAFr3u66667RKocQQkw4kWDYnF2h0EmB5sjacVw2J4oOhjUiCQtCHAQGDN4eeeQRbrzxRtatW4eu66NVJiGEmBAioUhiXtNCW3aaTAFURcUeUtFtOiGLnd3bmvoMWyKEmDgGrMP/1Kc+xdVXX81zzz3H6tWrmTRpEsuWLePkk09GUZQhHaihoYHbbruNsrIympqa+OEPf0h1dXXKOoZhcOedd9La2orX6+W0007jggsuSDz/zDPPUFNTA8D27dv5zne+w9y5c4dUDiGEGC2RcASLZs5rOsVVlNVjWSNWIg4DdNBCMkWWEBPZgMHbHXfcAcDXvvY1vva1r1FfX8+zzz7L73//e6qqqli2bBnz58/P6EC33HILF198MUuXLmXNmjXcdNNN/PGPf0xZ5/nnn2fPnj3ce++9BIPBxP6rqqrYsmUL7733HqtWrQLMYFDTtGGcshBCjALDIBKJYlEitBU6mZlfmtXDaYaDkDMCPtDCErwJMZENKdt06tSpfOMb3+C///u/mTlzJldeeSWLFy8edLv29nbWrVuXWHfBggVs2LCBxsbGlPWeeOIJTjnlFADsdjvz58/nmWeeAeDBBx9k0qRJ/PKXv+RHP/oR7777LpWVlUMpvhBCjJ6ODiKqhqZH8bodFOQVZfVwCnkE3LEWkWgkq8cSQuTWkFKfampqeO6553j++edpbW1l9uzZ/Md//Meg2zU0NOByubDb7QDYbDYKCgqor69PCcDq6+spLe35dVpaWkpdXR0AO3bsYN++ffz+978nGo3yxS9+EbvdztKlS4dyCkIIMTqamoioFlQiGKqStdkV4gw1n+58LzSCboAqAZwQE9aAwds3vvEN/vM//5Nnn32W559/nsbGRo477jguv/xyli1blnHNl2EYafvIZdJvLr6Oz+fjnHPOQdM0NE3j9NNP59lnnx1S8LZ58+Y+tX0jLd4nT/RPrtHg5BoNbCxeH09sPtF42dzvvktYs2DXzSbMlvpmarpGttzJxwwHNaIOM7HMb3OhNzaMyes0Vsi1GZxco4Fl8/o0NzcP+PyAwdvatWtZt24ds2bNYvny5SxbtozJkycPuRBTp07F5/MRDAax2+2Ew2G6urqYMmVKn/VaW1sTj1tbW5k+fToAkyZNSunjZrVaCQaDQyrHrFmzqKqqGnL5M1VTUyMJFIOQazQ4uUYDG6vX52/rzezORNl27yaiebEpBgAnHDObYyuOzNoxXe9twaOZtW3dNheVRMbkdRoLxup7aCyRazSwbF+feKtjfwbs83b44Yfz0ksv8eijj3LZZZcNK3ADKC4uZuHChaxduxaAN954gzlz5lBZWclLL72U+PV47rnn8vrrrwMQDAZ5++23OeusswBYtmwZ69evT+xzw4YNLFy4cFjlEUKIrGtqIqpa0GO/ObM1r2mcpttRYsGbz55HnkcG6hViohqw5u2uu+5i6tSpKctaWlpoaWnhqKOOQtf1jDM+b775Zn7yk5/w+uuv09TUxI9//GMAfvWrX3HLLbcwb948li1bxqZNm7j22mvxer2sWLEiMZzIBRdcwJ49e1i1ahW6rjNjxgy+8IUvDOechRAi+5qbiWgVxEfILMhynzeL7gAtAoaOz+7CLcGbEBPWgMHb0Ucfnfi7qamJ6667jn/9619UVVXx+OOP8+Uvf5nbbruN4447btADVVVVcf/99/dZ/vTTTyf+VhSF66+/Pu32mqbx/e9/f9DjCCHEmNDURFidQkRTUAxw21xZPZxmOFAUsOkhuu15TPJ0ZPV4QojcyXiokJtvvpnPfOYzrF+/nsmTJ1NQUMAf/vAHfvnLX2azfEIIMT41NRHRLESsUIAVVRnSyExDpurm9FtWI4jXnkeeV2rehJioMh4qxOfz8aUvfQnoyQAtLS2VabOEECKdpiYi5RYiNoUCizPrh1PRsIYBQnTbXeS17s76MYUQuZHxT8FQKMSePXtSljU0NBAOh0e8UEIIMe7FEhbCNjXryQpxtrCGqobptrlwS82bEBNWxjVvV111Feeffz6f+MQn2LlzJ1dccQUbN27kF7/4RTbLJ4QQ41NTE2HVQsiuUuAuGJVDWqI2wpaIZJsKMcFlHLwtXryYJ554gmeeeYYZM2YwefJkbrzxxqyOmyaEEONSJILR2krEYiXoUCnM8rymcarhRLdF8dnzcEvCghATVsbB28MPP8wXvvAFrrjiipTl3/nOd/jVr3410uUSQojxq6WFqGoOoxSxqxS6CrNymNtXLEpdoLqJ2A0ZKkSICS7jPm8PPvggkUjPXHmRSITf/OY3vPzyy1kpmBBCjFvNzUTU2G9jRadglPq8oeQTdup021zYAz4Y4iw0QojxIePgLRAIsHLlSkKhEFu2bOHCCy/kzTffTAyiK4QQIiY2TAiAohhZn5Q+TsUBlihRzULQYodB5kcUQoxPGTeb/vrXv0bTNL7+9a+zdetWrr76ar7yla9QX1+fzfIJIcT409TUq+bNPSqH1XQHiiU+RZYLR1MTSL9kISacjGvejj32WI4++mhWrVrFggUL+NKXvoSiKNx8883ZLJ8QQow/STVvqPqoDRWi6Q7QzOGbuu150NTEyvvWsfK+daNyfCHE6Bh0eqz4gLzJDMNg9uzZGIaR9nkhhDioJQdvipH1eU3jNCN5cnoXNDUBo1PrJ4QYPQMGb7Nnzx5w+ivDMPje97434oUSQohxLTbGG4AFA6fFkfVD3r5iER2BLr7+0JsA+Ox5Zp8356FZP7YQYnQNGLwtXrwYTdOYNGlSv+v89re/HfFCCSHEuJZU8+ZStVFroci35aHGat66bbGat0NG5dBCiFE0YPAWCAS44ooriEajLFq0iMWLFzNv3jxsNltincLC7IxfJIQQ48Wamlq27WknHNG57LYXWa6XUhWrecuz2gbZeuRoqkaeahAgVvMmwZsQE9KACQvf//73+cc//sH//u//cvjhh/PII4/wmc98hiuuuII///nP1NbWjlY5hRBiTFpTU8s9j24kHNEBaG73c8+hy3j70HkAuB3ZbzJNVmQzBwdOBG9CiAkno6FCKioquPDCC7nwwgvRdZ333nuPtWvXcs011+D1elm0aBE33nhjtssqhBBjzurnthIMR1OWBS02npt9JgAu1+gmDBQ6nIBuJiw0143qsYUQoyPjcd7iVFVlzpw5zJkzh//3//4fbW1trFsnaehCiINTS7s/7fIOVxHAqE1KH1eYV4iqhOm2u6BOat6EmIgyHudt48aN3HPPPYRCIerq6rj00ku55JJLaGlp4dxzz81mGYUQYswqK3amXV7g7zL/d+aNZnEoLCgDSwSfLdZsahijenwhRPZlHLz9+te/5phjjsFqtXLXXXdxxBFHcNFFF3Hrrbdms3xCCDGmLV82E7tVS1lmDwc4cbc5ZEe+wzWq5Sl0l2BYo3gdeeD3YwumrxkUQoxfGTeb6rrOpz/9aTweDxs2bOCVV17BZrPx2GOPZbN8Qggxpi2Za87vfPdf30E3oNAKlz9zL02V5sC8Rc7R7fO2b68Nw5/HhhnzuOzy31LS1oV1yugGkEKI7Mq45s3v99PW1saf//xnli5dis1mQ9d1IpFINssnhBBj3pK51RTl2wG4arKHJdvW4skzm1NHM3hbU1PLq6/5wdBAUWguqGC7X6PDExi1Mgghsi/jmrfLL7+cs88+m6KiIv7whz/Q3NzMt771LU466aRslk8IIcYFPda1rLvdA4A3FrwVjmLwtvq5rYQjqX3cdFQa26TpVIiJZMDg7Y033uDEE0/EZrOxdOlSli5dmvL8448/ntXCCSHEeKHHojd/lw8An9Mc363INXrZpv1lvsbHoBNCTAwDNpv+7W9/4/TTT+db3/oWDz30EHv27BmtcgkhxLhixLI6u71mE2V3LHhz2UZvhoX+Ml+tlox7yAghxoEBa97uvvtuDMNg48aNvP7663z/+9+nq6srMVXWySefjGOURw8XQoixKF7z1u0Pmf/HgjeLNnqB0/JlM7nn0Y0pgwZbohEqK2QaQyEmkkH7vCmKwvHHH8/xxx/Pt7/9bdrb21m3bh3PPvssP/rRj5gxYwaLFy/ma1/72miUVwghxqR4nzd/0Ezi8tvtEBnd4C2e+frLR97AiDjJC3g4c/e/2HbkF0etDEKI7BvyDAvFxcWcc845nHPOOQBs3ryZtWvXjnjBhBBiPNHjzaaxhIGe4E0Z1XIsmVvN6nfeoP3DhSz66E0W1m1gGxK8CTGRZPyTcNeuXfzjH/8gEonQ3t7OypUrufbaaykrK+PKK6/MZhmFEGLMSyQs6OZtNWC3oSgGijK6wRuAGrVj07qoK6kiz9M+6scXQmRXxsHbHXfcgcfjQVEUfvGLX+DxeJg2bRo//OEPs1k+IYQYF+IJC36rA12BkNWGOsq1bj1lcaI6fdSWVOH2SvAmxESTcbNpMBjk0ksvJRAI8Oqrr/LCCy/gdrv58pe/nM3yCSHEuJAY583mxJdnR0dlFLu7pZZFySOS7yfgLSQajsr8pkJMMBnfWrq7u4lEIjz11FMsWLAAt9sceNKQm4IQQvQ0m9pcdBY4wVBHNVkhlQs93xzzraFoMg6/N0flEEJkQ8Y1bxdccAELFy5EVVUeeugh2tvbuemmm5g+fXoWiyeEEGNfJNozCG63zUlXoRMMJWfjq6lRO0q+OVhwXWkVbun3JsSEknHw9vnPf55zzjkHTdOw2+1EIhG++tWvMmvWrGyWTwghxrxgyBxXTTEM/DanWfOm567mTY3aUGx+rJGg2e9NgjchJpSM7yzf+c53cLlc2O3m5MsWi4Xnn3+elStXZq1wQggxHgRC5thuzkiQgM1Je6ELDBWbZcijMY0INWpHUaDU30ytZJwKMeFkHLy1tbX1WXbjjTeyb9++ES2QEEKMN/EZDQpCZt+ytsJC0BVsFi0n5VF1c0quwnALdVLzJsSEM+jPwk9/+tMoikJLSwunnXZaynOBQIAjjjgia4UTQojxIN5sWuD3sD+vjLbCQjS0nPZ5A8gz2vmo4DhsjRtyUg4hRHYMGrz97Gc/wzAMbr/9dm644YaU5/Ly8jj66KOzVjghhBgP4jVvJb42KJtBe0E+Fqw56/OmGBa0CNgsnRiKSjAsowIIMZEMGrzNnz8fMCepnzFjRp/nd+zYwWGHHTbyJRNCiHEiXvNW2tUCQKcrHw0rWs6GCgF7SEV1mM24XnLTfCuEyI6Me9POmDGD5uZm6uvrCYfDieU//elP+fvf/56VwgkhxHgQr3mr6NgPQFeeGzVixZrD4M0StbHfOQWiBo8ccyav3vYiy5fNTExeL4QYvzIO3u6++27+93//l6KiImw2W2J5a2trVgomhBDjRTBoBm9lXc0A+Bx5WHwWLDnq8wYQ7JhKkz4DYnOrNrf7uefRjQASwAkxzmUcvL3wwgu8+uqrlJeXpyz/0Y9+NOKFEkKI8SQYNocKKfZ1mI8tDqxoWHI0tymAp/kQjF63+GA4yurntkrwJsQ4l/HPwqOOOqpP4Abw2c9+diTLI4QQ4068z1uRLzYkR9SCktPpsSAasaZd3tLuH+WSCCFGWsY1b4ceeihXXHEFixYtSsxrCvDb3/6WZ599NiuFE0KI8SDe5624uwMAI6rR5Q3ntNlUs+pEw30TFcqKnTkojRBiJGUcvD300EMcffTRvPDCCynLW1paRrxQQggxnsRr3pwhP5ZoCKIWMBQsau6Ct6LyIK37baD33ObtVo3ly2bmrExCiJGRcfB29tlnc/PNN/dZfuedd45ogYQQYrwJhKJoehTN0LHqIUJ6LHjLYc1bYYGFLscWrB8eRrfNTYld4WsXzpb+bkJMABnfWdIFbgBnnXXWiBVGCCHGo2A4ilU3kxYshlnzZhhKThMWNN2OpWwf5330ZwBWHhqQwE2ICSLjmrd///vfaZfLOG9CiINdMBTFFgkBoClhiFjAMHKasKDpDgB0mxlUdrV05qwsQoiRlXHw9vWvf53y8nIMw5xmxePx4Pf7qayszFrhhBBiPAiGo9gjQfOBFkGJuNANcja3KYBmmPObRlxm8NbZ4ctZWYQQIyvj4O2zn/0st956a8qyt99+my1btox4oYQQYjwJhqI4QgHzgSUKUSsG5LTmTUEjT9cIucwf3F2eQM7KIoQYWRnfWXoHbmDOe/rKK6+MaIGEEGK8CYYjuCPm+Gm6LYoeNcdY03LY5w2gQLXjK7BgiwTp6g4PvoEQYlzIuObtH//4R8rjUCjERx99REdHxwgXSQghxpdAMIKz22yWDNsNDI8ZvOVyblOAQrubrgInBY0eOkN6TssihBg5GQdvP/nJTzj66KMTj202G1OnTuW//uu/slIwIYQYL4L+IEWRILoCYQcYnWbQlstmU4ACVxH7Cp0U7O6iK6qx8r51ANy+YlFOyyWEODAZB28XX3wxP/jBD7JZFiGEGJeCgTD2SBCv22H2eYvJ5ThvAIWFZXxY4GRydxddDhc2XcfI4cDBQoiRkfGn+Ac/+AG6rrNhwwaeeeYZampq0HWphhdCiGAogj0cpLPQiaJFEstzXfNWmFeEJ99BQaCLLkc+Lp8MFyLERJBxzVttbS3f/OY3aWhooKCggK6uLqZOncoDDzzAtGnTsllGIYQY04JhHXskSGeBE1KCt9wkLHxtaQVz587l+e1rMFQFR9RHl6OA/K5WfPnFOSmTEGLkZPyz8Ec/+hFXXnkl7777LmvXruXdd9/lyiuvTJuFKoQQB5OgDo5wYMzVvBXY8wGwGX58jjzyutpyWh4hxMjI+M4SDAY599xzUWP9JVRV5ZxzziEYDGatcEIIMdZFozoRVOzhEJ2FTlCT+rzlutnUYQZvmmLep62erlwWRwgxQjK+s4TDYfbu3ZuyrLa2lkgk0s8WqRoaGlixYgWrVq3iiiuuoLa2ts86hmHw85//nOuuu46rrrqKxx9/vM86dXV1zJkzJ+1zQggx2oJhM1izR4J0FThRk2vecp2wEKt5Uy2xqbu6e2ZZWHnfukT2qRBifMm4z9uKFSs477zzmD17NiUlJbS1tbF582Z+/etfZ7T9LbfcwsUXX8zSpUtZs2YNN910E3/84x9T1nn++efZs2cP9957L8FgkGXLljF//nyqqqoAM7j7xS9+wZQpUzI/QyGEyKJgKBa8hYM0FrpwWzXicxnkuuatIFbzFp/fVAnILAtCTAQZ31lOOeUUnnzySU4++WQKCgo4+eSTefzxx1mwYMGg27a3t7Nu3ToWL14MwIIFC9iwYQONjY0p6z3xxBOccsopANjtdubPn88zzzyTeP6hhx7izDPPpLhYOtwKIcaGlJq3QgeFTnviuVwP0uu2ucCAiNMsoxGWWRaEmAgyvrPcc889XHrppZx00knccsstfOYzn+Hqq69mw4YNg27b0NCAy+XCbjdvajabjYKCAurr61PWq6+vp7S0NPG4tLSUuro6AHbv3s3WrVs544wzMi2yEEJkXTBo1mqZCQsuigryE8/lenosVVGxhTXCeeawTtGIDO8kxESQcbPpunXrePrpp3G73QAcdthhrF69mquvvpqHHnpowG0Nw0BR+t7E0i1Lt46u69x11138+Mc/zrS4aW3evLlPbd9Iq6mpyer+JwK5RoOTazSwsXR9GnabGZz2SIjOIhel4Z4+bx9v/wh/my0n5YpfIzVixR+LJ0M6eDweampq8Hg8KesdbA7W8x4KuUYDy+b1aW5uHvD5jIM3q9WaCNziioqKMgrApk6dis/nIxgMYrfbCYfDdHV19em7NnXqVFpbWxOPW1tbmT59Otu2bSMYDHL33XcDsGvXLv7+97+zY8eOIc36MGvWrET/uWyoqalh7ty5Wdv/RCDXaHByjQY21q6Ptf3fQHdskF4X86um864ChgGfmHUMM6YUjnqZkq+RtvkfeArsuJs8hFQL+fn5zJ07l7+tN5MVxtK1HC1j7T00Fsk1Gli2r0+81bE/GTebOhwOnnjiicSsCrqu88QTT+BwOAbdtri4mIULF7J27VoA3njjDebMmUNlZSUvvfRS4hfgueeey+uvvw6YQ5O8/fbbnHXWWcycOZP/+Z//4dZbb+XWW29lxowZnH/++TJdlxAi54JNsR+cSoSgTaPAkY+mmj9qc52wAKCQR0eRiwJ/Fz6bC2vQn+siCSEOUMY1b/EhPm688UYKCwvp6upi2rRp3H///Rltf/PNN/OTn/yE119/naampkQT6K9+9StuueUW5s2bx7Jly9i0aRPXXnstXq+XFStWUF1dndhHJBLhpz/9Kbt37+bJJ58kGo1y0UUXDfGUhRBiZKy8bx0l726BgsMJO8yArchRgKq2QNQYE8GbZjjpKMpjkt9Dl7OAQhmoV4hxL+Pgrbq6mqeeeor33nuP/fv3M3nyZGbPnp0YtHcwVVVVaQO9p59+OvG3oihcf/31/RfWYmHVqlWsWrUq02ILIURWWQLdUABBpxm8FdjzURWzNi7XCQsAmu4gmGfBHfTQmldGVVfr4BsJIca0jIM3MGdVmDNnTrbKIoQQ444laI6dFnSZP2SLkppNcz1UCIBFdwJg17vpdBXg7mrIcYmEEAcq93cWIYQYx9SQOfVUt1sDzIFx1XiftxzPsABmsymAlYA5OX2n1LwJMd7l/s4ihBDjmBIb+NZbYDZkbNziwes3l337F2tYU9N3KsDRpMVq3lQtRMRixeaT+U2FGO+G1GwqhBAilRKJYImE6SpyYWmbxv3vvo9hmM+1dPi559GNACyZWz3AXrLHopsjAhjW+Pym3TkphxBi5EjNmxBCHABD17FHgrQXufDXHp6YLisuGI6y+rmtOSodqIYdRYeoIzZ4cKyZVwgxfknwJoQQw2QLdBNRNBzhAB0leUSC1rTrtbTnbmw1BQVbxEo4z6wO1JNmgBBCjE8SvAkhxDDld7UStNrNmrfSfByu9HOHlhU7R7lkPW5fsYiq4jL8+WbwJtObCjH+SfAmhBDDlN/ZStBixx4O0ZFv5/g5CnarlrKO3aqxfNnMHJXQVJRfgq/QvN2H0CAaHWQLIcRYJsGbEEIMU7zmzaKHiFhUTji2kKsvmo01NkRIebGTqy+anbNkhbhiVzGNlkPBMPjDKV/hsttepMMTyGmZhBDDJ9mmQggxTAWdrQQtU1AVsx9ZkaOQBUdW88L6PYDZZDkWtDW46Wo6ARRz/LnmrhCKJ5TjUgkhhktq3oQQYpjiNW+KYjZDFjsLclyi9Da9o4Ke+lvdMKCxTSapF2I8kuBNCCGGKd7nDc3MAih2FOa4ROl5vOmzFMKSvSDEuCTNpkIIMUQr71sHwGVdrQSsdvIMMwgqco7N4K24wEp7V7jPcusYmL5LCDF08skVQohhyu80m02jVgOnasVhsee6SGl97jMzQE0d301RoLIkd0OYCCGGT4I3IYQYpoLOFoIWO2G7QpF9bPZ3A1h20uFYp28hL9xBYu4uoK7Jx2W3vZjz+VeFEEMjwZsQQgyDFg5h9/sIW2yEnArF7pJcF6lfVs1KUUUbS72/43vP/hLF0BMxXHO7Of+qBHBCjB8SvAkhxDAUdLYQstgACOZpY7a/W1yRI5+OIherF1+KoaTe+nM9/6oQYmgkYUEIIYahqK3RzDQFAi51zGaaxhW7S2kvzqOFsrTP53L+VSHE0EjNmxBCDENReyMBqxm8RazGmB3jLa7QWUh7iZsyT0va53M5/6oQYmgkeBNCiGEobG8iGAveUKMUjfWaN0cBHYUOLl37IPZw6tRYY2H+VSFE5iR4E0KIYahT8rjpwh8BEN4zk927clygQRQ5ColYNU7au56rX7wXNTY23ViZf1UIkTkJ3oQQYog6PAGemb6Q9niGacTOEy80jemMzXizbnuxiyXb1lLV3YLTbuH3N54ugZsQ44wEb0IIMUSNbX7CFmvKslBYH9MZm0UOM3jrKHIB4Aj5MZLGfBNCjB8SvAkhxBD1NyfoWM7YjA9l0h4L3lwBH7oEb0KMSxK8CSHEENm09MvHcsZm75o3l9+LxG5CjE8SvAkhxBAdrvjGXcZmntWFoquJ4M3d3YWuS/QmxHgkwZsQQgzRTE89l659MPbIwO6MjPmMTUVR0AwnbcV5gNlsKn3ehBifJHgTQoghKmxvpCDoBcB9xDqWnR8e04FbnKY7aSkvAsAWCaIbhgRwQoxDErwJIcQQFbU3sWXqMbiCPiJFPorH+LymcRbdRWupGwBbJAQoRKXpVIhxR4I3IYQYoqK2Rj6YegyHNW9HURjzsyvEWXQXHUV2DMAWDQMQCkdzWyghxJBJ8CaEEEOk+TzUllZT7dkJMObnNY2z6C7CVhVfni1W8wZBCd6EGHckeBNCiCHabzdr2spCdQAUj6OaN4DWUjf2SBCAcDj9mHVCiLFLgjchhBiCNRv28rsFy8EwePyY5URaJlPqKs51sTJi0c1M09ZSt9S8CTGOSfAmhBAZWlNTyz2PbsRvd4Gi4LUWEd49iw3vt+e6aBmJ17y1lLqxRqTPmxDjlSXXBRBCiPFi9XNbCfaeGkvXePD5rXxqXs9QIbevWDTKJcuMpjvAUGgryaNsu9lsGpJmUyHGHal5E0KIDPU3d+lYntM0mYKKRXfSUlGQaDYNebw5LpUQYqgkeBNCiAz1N3fpWJ7TtDeL7qJ1cmnPUCH7GnNcIiHEUEnwJoQQGVq+bCYWI7WZUVH0MT2naW9abKDeRM1bU0uOSySEGCoJ3oQQIkNL5laz2GuO7YZhoNj8FE/2j4upscDsi/ep446kNd+KNTZUSLCpNcelEkIMlQRvQggxBJVtDQDc/vxVOI5/jaICa45LNDRlrmLCmkLYrgAQbmrOcYmEEEMlwZsQYtSsvG8dK+9bl+tiHBCvP0xewEtbmTlmmiWal+MSDU18TDpvoRl0BlvaUp6fCK+REBOdBG9CCJEprxevoeEOeGkuM6fEio+dNl6UOs3gzVNgAyDU2pHD0gghhkOCNyGEyNSOHXjtbvIDXuqnVqDqdtRxNlxmvOats9gBQLjTA4aRyyIJIYZIgjchhMjU9u14HW7cQS+NlUXjrtYNoNCRj6ZqtE0qwhIJm4MOt7UNvqEQYsyQ4E0IITK1fTsehxt3wEtbSV5irtDxRFVUSpxFtBxSiT0SImSxwccf57pYQoghkOBthElnXyEmsO3b8dnN4K0r34J1nCUrxJU6i2irKMQWCRKy2GHHjlwXSQgxBBK8CSFEhoxYzZs96idsMwe8HY9KXcW0FtqxRUKELFapeRNinJHgTQiRc+Olxjqway9RzYKqmbMTWMdt8FZCqw2s0bDZbCo1b0KMK+MrTUoIIXKlqwtPVzcAEYeZnXn1Zz/JUWWH5bJUw1LqLCKiGFiMMEEJ3oQYd6TmTQghMrFjBz67GwBvoTnMRpmrJJclGrb4cCGKEjX7vEmzqRDjigRvQgiRie3b8TjN4M1T6gRDodhRmONCDU9ZLHhDMwhrVmhsBK83t4USQmRMgjchhMjE9u14YzVvnhIbFt2Jqo7PW2i85k13WsxmU4CdO3NYIiHEUIzPO884NF46ZAsh+hHLNAXwFmnjcoy3uHy7G4tqQXdZzIQFkKZTIcYRCd6EECIT27fjiwVv3fn6uJxdIU5VVEpdxYSdWk/wJkkLQowbEryNU1KTJ8QoizWbatEIAWdwXNe8AUxylxG0K1LzJsQ4JMGbEEIMprMTmpvxONzkhXygGVii47fmDaAyrxy/JSI1b0KMQxK8CSHEYGK1Ul6HG4cSARj/NW/55UTUCEGL3VwgwZsQ48aoDdLb0NDAbbfdRllZGU1NTfzwhz+kuro6ZR3DMLjzzjtpbW3F6/Vy2mmnccEFFwDw29/+lo8//piSkhJ27tzJpZdeyuLFi0er+EKIA7SmppZte9oJR3Quu+1Fli+byZK51YNvOBZs3w6YwZvFZg7Qa9Xzc1miA1bpLgc1SlSzEFVUtL17WbN+1/h9jYQ4iIxa8HbLLbdw8cUXs3TpUtasWcNNN93EH//4x5R1nn/+efbs2cO9995LMBhk2bJlzJ8/n6qqKtauXcsf/vAHLBYLH330EZ///Od56623sNvto3UKQohhWlNTyz2PbiQc0QFobvdzz6MbAcZHcBAP3uxuFCdggCXqzm2ZDtAkdzmo5usR1qysPWw+9zz+PuGIGZyOu9dIiIPIqDSbtre3s27dukRN2YIFC9iwYQONjY0p6z3xxBOccsopANjtdubPn88zzzwDwJ/+9CcsFjPWrK6upru7G4/HMxrFH1ckkSG35Pqnt/q5rQTD0ZRlwXCU1c9tzVGJhigWvHkcbnQXlOWVcMeKU3NcqANTmVeGopivSdBiY/XiSwnGAre4cfUaCXEQGZXgraGhAZfLlagls9lsFBQUUF9fn7JefX09paWlicelpaXU1dWZBU0aDHPNmjV85jOfoaysbBRKL4Q4UC3t/iEtH3OSmk1D9giT88tzXKADZ7PYcDnMe3LYYqMlP/39dNy8RkIcREal2dQwDBRF6bM83bLB1mloaOCRRx7hl7/85ZDLsXnz5j61fSMtXhtYU1PT7/I/vNQEwNeWVmT1OMlG4piZyOQ4vcs2kaS7/sO59umuUfJ+RuL1HK33BECBS6OzO5p2eU1NTb/v2/5k8z3U57oYBrM3b0ZRVHwON1G1Cc2vjvn3cSblcyoWOoGQxUaZp4Xmgr7vBVWFq+94Dhid98poGeuv31gg12hg2bw+zc3NAz4/KsHb1KlT8fl8BINB7HY74XCYrq4upkyZ0me91tbWxOPW1lamT5+eeFxfX89Pf/pT7rrrLoqLi4dcjlmzZlFVVTXs8xhMTU0N+flmJ+a5c+emPPe39esSy5P/Hq7+9jHU5SNtsOPU1NRkvQy5lO78h3rt+7tGo/UeyobLMfu8JTed2q0al392NnPnVg+pLNl+D/Upy65d4PUmZleIWoIcd+g85h41dt/HmV6jybu3sR+z2XT52gf59ZnfNuc6jVEUmFyWR36+Axid98pomOj3oZEg12hg2b4+8VbH/oxKs2lxcTELFy5k7dq1ALzxxhvMmTOHyspKXnrppcSv7nPPPZfXX38dgGAwyNtvv81ZZ50FwN69e7n99tu57bbbKC0t5dlnn+Wdd94ZjeJnbNMuH9v2tLN5RyuX3fYia2pqc10kIcaEJXOrufqi2YnHJQUOrr5oNkvmVieyUMfs5+a99wASwRuWMJPcE6MGqtRdCIDX5WLJtrWcuen5xHNlRQ6mludRFAvchBBjx6iN83bzzTfz2GOPsWrVKh5++GF+/OMfA/CrX/2Kbdu2AbBs2TKqq6u59tprueaaa1ixYkViOJHLL7+c9evXc/bZZ7Nw4UJuuukmwuHwaBV/UGtqannq7Y4+2XRj7otIiBxZMrcai2Z2g1j19ZMSgVu6LNQx9bmJBW/xSekVLcykCdDnDaDCXQRA09GHA3BI857Ecyu/Ml8CNyHGqFEbKqSqqor777+/z/Knn3468beiKFx//fVpt3/xxRezVraRsPq5rYSj6TO1JM1eCFNUNz8j3UFzoNuBslDHzOcmHrzFat4Ua4TKvImRLFWRXwy00HjkofAieB09Y9fVNko2vxBjlcywMELGfTadEFkWDEcxYr9v/LHgbVx8bnoFbyVuF9akfmHj2aQCM7u/eUolAF5HHqqhowB1Td4clkwIMRAJ3kZIWbFzSMuFONh4u0OJv7sDZvA25j83bW2wdy8AnS6zf9ikwqIcFmhkFbrM+VlbC83A1OPIxxXyY7NqUvMmxBgmwdsIWb5sJlYtdVgTu1Vj+bKZOSrR2DeeB7Qdz2XPFa+/p4+qP2D+vXzZTOxWLWW9TD43o3b9N25M/Lmv3GzGnVpc2t/a447NYl77DqvZg8Zrd1Pg78Ru06hrGl/Bm3wmxcFEgrcRsmRuNefML0JVzQCutLD/bLoOT+CAjjXms/OESMPbnRS8xZpNl8yt5qrP9WShFufbE5+bMSHWZAqwr2IqqBGmFk2MTFMAWyxw7oiGweHA63BT0N2FmzD7WrvRDWOQPQghckGCtxF03Iw8qirM5ocbvjq/32y6+mbfsAO4cZGdJ0Qa6ZpNAU7+xOTE3ys+N4YCN4B330382VRcClqEyfkTKXgzvwK8wQCR4z6B1+HGHfAwydOMrhuEwn0HVhZC5J4EbyPMajEvaXOsw3W6bDrDgMa24XXIHvdzRIqDlidNzRtAd6BneVN796iWaVBJNW+dLjeKJcxk98QYJgRINFkbUYXmEz+Bx+HGHfAxvXEnAMGQBG9CjEUSvI2wRPDWYX4J9Zc1F685G6pxkZ0nRBrxPm+qklrz5kvqC9c8lt7HgQBs7flRFLBYUSxhKibIMCEAmqaiKoCusf/YGfjsbvIDHo7cuwWgzw9FIcTYMGrjvB0sNFVBVaCp3c/K+9ZhsahpA7V4kJdOvNPt7SsW9XmurNiZ9gtuzGTniQTpPJ0q3mxqsai9at56/h5TwdsHH0AkVrbDDiNiKKiagUWbWLdNm1UjrGvsq67A93Ee7qCPaQ0fUX6mU2rehBijpOZthCmKgtWi0Rxr/qkscaKpSq91zOXDMdzsPCFyzesPo6kKmqqmNJX21MgpY6vZNKnJlOOPR4+qiYSkicRu1bBgZY/LDoA74KWyYSfV5W4J3oQYoyR4ywKrVaUpVoNQlO/giOqilOfNATB9w8oU7Z2dV1Y0+ByRI5WdOlgq/sGeBZsuq7jDExjyNfnDS019rnPyvr9007Ns3dV2QNd5oNcqW0MueLvN4E1VlcQMC9DT581h12ju8OdkyIf4MVOuy2YXa45aDMCrx5yKEbYSaiuecO9tq1XDZXGzx9cFmMGbLRyEbh+BUJTNO1oT/y677UWuvOOlCVerLMOMiPFmYtX/jxE2i0pzu59DJptTzYQiOm6nlYI8Gw0tPmIzBCUyRYEhZdjNP3ZS4u/bVyxiUmlev1moW3e18vKGuj7Lh3rMwfR3/JE+zliV7vzBDNTjgy0M95r03ndyx//h7DNXr5XHH0LTFDRVSWk29cWaTR02jbauIFPK8nJSw9XhCaReF8XJPadfxdYpR/OSvxrz1Zx47227VcXQ8qhr3wlMwx3wsuaoxWyq8xE/57jmdj/KxKt8FGLckZq3LLBaNDzdIXTdwDAM6pq82G1mrUJvw8kUTR7stNMbBPrPQn1+/d5RyU492LNg050/9ARuccO5Jv3te7j7zNVr5esOo6lm02NyP7fu2PvZbjN/Sw43medANbb5+14Xq4PnZ59JSE+NWCbSe9tm1bCrDvx+89zzAx5WL76UCOmjtAPJlhdCjAwJ3rIgnowQikQJR3RC4Sh2q9bvl9JQM0WTx8vq8oUG3Ieupx9kc6SzUw/2LNihnOdQr0km64/E8bP9Wnm6zZo3tU/NWzx4M/tyhiO56WfV3+dTV9LfJifKe9tm1bBgx4ia87W6gz5a8gfOqM1VgC2EMEnwlgXx4C0c0RO/5O02rd8M06FmiiaPVN/pDQ24j/6an0Y6O3XMz1GZZUM5z6Fek0zWH4njZ/u1SiQsKAr+QBgjNnq/L7bclvS5yYX+Pp9Kn/pT00R5b9ssGhpWiJg1n/ZomDJPy4DbDJQtL4TIPvkEZoHVmhS8hXqCt8oS54hkiiY3m8Zr3pYvm9nnhmq3apx50jS0UZhzdfmymVgO4rldly+b2ec6Q+8eQ8O7Jule2wPZZy4ylg3DwOsPc9qJ0/iPhTPQjZ4xxLoDEVRVwWJRURSzj2gupMsMt4cDzAh30LsBfCK9t21WlUgUXEohAMUnHsfytQ9iD6efBeZAsuWFECNDgrcssGpmv55w2Aze8l02LJpKUb6ZGRrv8Fte7BzWPI6elGZTs8/bkrnVnLP40MTy8iJz31d+7nhmH94zIvxwjzmYJXOrmXVoz4Tdgx1nPGempiv7krnVHHd4T1NTkdtOVUUe5UU9X3IOmzasa79kbjXnnXpY4nG+y5r4eziv55K51Xzh9CMTj0sKsj+fqD8YQdcN3E4bLodZw+OP9XuL18ipikJJgWPQmrdNu3xZee8U5TsoLXQkHucFvFz1z/spmFYGGCiqWa5sfYZyxWbVCIWj5GmFKFoU6+JFLNm2lqtfvJfCoLfP+pUlToryHWn2lDvj+X4ixHBItmkWKIpCWaGDYNjs83bIpILEc0vmVvO/T23B2x3i9zeePqz9J8bFUpVEzRvAtMr8xN8/u3oRFcUuoKcvkaYqwz5mJuJ1E1aLOuBxxnNm6kBlt1k0LJpCJGpw7imH8s62JlpiSSoWTeWYGaXDPr9DYq/tEdVF/PI7p/Klm54lHNGH/XpOLnUn/l751fkcfUjJsPaTqXiGbL7LiiVWi+gPRijGHCokXuNVXuSkttHT737W1NTy1NsdhKPmu20k3ztR3aClw8/MUDMd3REOadnDEd2NPGLVAZWyyQEqHVPTDp49ntksGuGwjpN80AKEFy/ECizZtpZjOvZy96qHAPP12lHXiUXTBt7hKBvP9xMhhktq3rKkvNiVaDatTgqqwBxKJBI1UjpmD2WcIW93CCW2n+Tgrd0TTPydPFJ9PMs1qhspHcVHSrzstY3mr/RoNH0fobhsZjumu44HMoZT720HKntzhx+H3YLdqrFlZytgNglaLSouhyUxZdpwxF/beNO0qipE+0lGGUjitWrqCZCS+1AeiIGuczzJxu2y4rKbvxnjGafxZlOAitjnpj+rn9uaCNziBnvvZPr6dwfC6AYct3cTx9R9wJapx/DxUXPwBM3XrcCWP8gexiebVSUYjqIZDtBC1M88BKxm7W5F4x7yPO2AWXPstFtSBljOhqF+Xkcje3qkxoEbS+PJjaWyiKGT4C1LyoudBEJRorpBdaU75bl4/6WWjvR9Sgbj9YfN8bI0NTFUCEC7p2d/zUkj1Te3dye+HJuzNIJ9NKrT1hVAVRV0wxgwY3A8Z6YOVPbm9m5sFhWX08LW3W0YhoHPHybPYcVqMQdujnfSH6q2rgCK0pOAYtHUYQVvccm1W8nZy9kSry12u2y4HGZg0B00l/kC5hAiYH5uwhG93+uUzfdOdyCCqsDC917i2PoP8DgL2HjkSfj9oDp9ONSJGbzZY82mRsSKYomwN9AK8+cnnp/+sVmLpSgKM2eU4POP/A/AAzGe7ydCDJcEb1lSUexKDNNRVZF607dazGaH4dbEeLpDaKqKRUttNm3vCmLRzJc0PsNDIBSh0xtK9DNqytINLf7LN36c5HL1Np4zU/srY2mRE093GKtFI89hpTsQocsXIqobuJwWrBaVYCg64HUZSIfHfG0VpafmTdeNYQdwdU1enHbzfZicAJMt8do9t9OK057a583nD7P4BLM5srzYhQFE+qm9zeZ7x+cPc2hlHjP2buXY+g8A2FZxOOFuG8VlEZR+xj0b72xWDV8gTO1+H6olwp6OOjjllMTzM2LBG8CxM0oJhqNEomNnqJCxcj+RmiwxmiR4y5LkjupVFb1q3mLZqE3DHOjS2x2reVN7N5sGsFlVNFVJNJXG+1zlxWo70g0UPBLiWbXx4wwUpIzn+Vn7y+o9a+F0gEQTKfQ0Xec5rIlhMIZ7/ds9gURgDiT6iPmGEXjFB452xl4rzwg1mw7E6zffD/munoSF7lgSgz8YSSyriH3h9ldzu3zZTLRed62ReO/ohlmOYzGniJrcsY+ikJcmv44RtTC9auz/sBguq1XFMMzuDm6Xlb2d9SnB2/QdScFbLCkpeZDlXBvP9xMhhkuCtxF2+4pF3L5iEftafYllN9z3Bh1JTZrxL/90TZiZZE3Fs/MsmoLXH078Cm7vMr/gzblVzX3Ha9qciYBieLV9g5UrGI5i0ZRErUpyc25vS+ZWp2Td2qzqAWXvjeYv3iVzqzntxJ5ylhaYGcTTJ5vDLFgtKjarRr7LSiAW0O7e15X4u7/rnzy3Zl1LqM91busKpgSNWiyCGU6TZ3zgaIdNQ1WURGAF2cnaW3nfOh59eTvQq+YtGMEfjGAYPUH/7n1m8LSzvivt8ZfMrebYaT2BVH+Zn0N5T8TP2TDg1YYIa45ajAKU23t+lHy4yZ7yGZ5I4oFPJGpQnOdiT0c9LFgAsabsyXUf4/J2ALCvxezXune/Z1SyOjN5HeP3k7jSQseQ7ieSqSq1huORZJtmwZqaWp5euzPxuLkjdT5AVTEDr961MH3mVuwna8rbHTIHO419gXu6QxTnO2j3BBNfjPEgIV77Y7OoWGNzrg7nfAYrVzAUZXKZG4vFPNHBmgeXzK3mnr9tJBiKUlniGldZYWVJtar/+fnjmXt0Jc/9azdg1mJ0eAIp/YLCET3xWg90/eOvf7xFKvk6d3gCqcFbrOZtOE2eibEHrRqapiSaNLOZtRfVdSyaamY+xz4L3YFIYnYFl8PKmppaHv7ntsQ2/R2/yN1z2/rVNUsoyLMNu1zxc44n2XSp9sR8prvoqTH3+Qy6u3397WZcsyXVWpUV5rM50EWnDQpPOgn+9S9Uw+DYja/zz+NO54HH30+sO5ayOpPne77tigV9uqr0RzJVxXglNW9ZsPq5rX0GGu09H6DVoiVqx+LSzq2YJmvKTFhQE5mHXd4QgVCE7kDErHmLBWmGYZjJCopZI2R2mh96zVsm2VzBcJTqSneiaS+Tvl2R2DVqaPaNqT40g0menDueYdvc3o2mKlg1lcY2P3qvDvfxhwP1Oez39X92K57ucEqzafy19wyj5i151g9N7Qnespm1F2+SUxQFu1VDVcyat3izr9tpNT834dT3Qbrjdwd71hloWJFMpD3n2HymkV7d7ibqnJ62pB8Fk4uKANjb2QAXX5xYftyGlzO+P+VCR1Km/VCadA/2OZnF+CU1b1nQX5ZT8hAI8QAruao6k7lPo1Gd7kCEz55yGMceVsoP73+TLl8oMZabRVNQFJVAKMq196xlf0s3JYVOFMWcfqi5o+eYmY5XNVg2l24YhMI61RX5iebS+LRd/YlEdaK6gdWiEo7oXPubtdhtWp8yJZd1qOXOVjNAc7sfh00jFNapa/LEhknxUFpkXueBhrp4taaWHfUdac+hv+3itXaWpC/ZeNbpcIb5CIaiFOSZA0drqpIIAAd7nYd6/ZNFdYNCt1lDpigKToeV7kA48UXrclj6PX7v2srugI6qgG6YiRcPPb912OXqd07gfuYznYhzeibXvE0rKYdO2NW+l09cdBH6d7+Lahgcuv29Qe9PB/L+GIp0x2nr6mnSHko/0IHe8/2dz3DvQwdyXUbq2o7WaySyT2resqC/LKfkZq94IJU8JEImc58mD7lQkGcHzFqu9q7YOGCxGjaI9W2KRBPJE1arRmtnYMjDVQyWzRWK/XKtqnCjKAqaqiRmfuhPPMhzO82+Tr1//Y5lTe3dWC0adptGXZNZ8xaO6D3XuZ/X0QxU+z/P/rYrzjdf55SaN3X4fd6C4WgiiUbT1MR7KptZe1Fdx+3sad50OSwpzaZ5TmtGnxswa94cdguKAnVNB1bz1u+59ZNYOhHn9EwO3soLC5jkLufD5h0wdSp7Dj0OANXQcUfS9/kbC1niyWNc+oYwDt1YyVQVYqgm3p1oDOgv++nbFx+feGy1mjVOyQPaVpY4URWlz3bJWVM9wZs10den0xekLdaZ2qKpiZtxOKITjuiJmRasFhVdN4ZcezBYNle8D1VVbDBirdcQJunEg828ePAWGh/Bm2EYNLX7sVpU7FYt0WxnXmfzhp9uDltFgaMPKe7TLJisssSZEqCBeZ1POWEqYE67FqdqB9bnLT5wtNls2jM/bray9uLNpnFOuyWl2TTPaU17/HTzaHYHo2b/uaTrP1zpsodtkRDF+XZs1tTlE3VOz+Rrnu+yckz5EWxt+Rjd0Nk099OJ585/7+kxm9WZPMblUMahM7OXD945mcX4JcFbFsSzn+JfCuky4uJjvYUiUTo8AbbtaaeuyYduGIn+VOnmwkyMVO/sCd66fCE6Ys0GVq2n5i0Um56rvDi1RmiowduSudVcelbPzaw4v2cuzDU1tdQ3mx25f/KHt+mIDWkxWPAWDzZtVpXSQgfB0IEPPdA7a6zDE0hc2+FkkqXLQuvyhQiFo2bwZtPo8oWIxILk8liQHJ/DNn69rRaVqeV5zD6ynKhuJMb/660o38HsI3rmRy3Is3H1RbOZGut8HU8GATPpRVWGFrytqanlw93tRHWDdRsb6PAEzISF2D56v87xTNpMOm4PlrEX1Y1ELSuAy27BH4jQ7Y8nLFgSn5t4AJvnsDC1PK/PPJq+oI6mKthtGrVNfefeHEq5lsyt5tPzqswHhkF5VxMLO7YztSKf0z+Vj2Izm9XKi51pyzIRJAevbqeNmeVH4At1U9e5j80nLEk0IX9u7Z+5eum0xPo2y4FliaeT7jOcifiPQWBIM0AsmVvN4VWFicfp7tXDLVNvYymrdaTOSeSO9HnLkiVzq3lh/R4gff+C+A2wwxOk3ROkd0um1aJw6NTCPjfGnjkizT5LeQ4Lnd4gkYiOqiposRoZm1VLTIVVUezkwz3mHIYwvH47h08tSvz9rQuOY+FxUxKZWvFgpCWWVdt72q50Es28mkp1RT7b9rYNuUzJ0mXqgtn6Fb+0Q8kk6y8L7XOnHQGY1zceSsWDn4piJx/sIrH/+OsfV15kBncDXf8uXwibRSEUMTh70aEsmVvNX174EEWhT62cqqoZJyzEzyeeGOLzh+kOhMl3Wc1p3MJR7FaNmUlznH7/y3OZdVhZf7vss++BMvaiukG+q6fZ1JxmKYIv1uctPlRI/Lpt39vBUdNLEk3ycYZh0B3UcTlUFMVMFCkrdCT6ACbLNHs7L9CNJRLmb7/5PJqmctttj5vHKq7FddwGprdfyM9WnDJhh1JIbjZ1u6zMdJrv8Q+at+MtKGHnkSdw+LYaVMNgyeZXeOGQeezd7yHPaR3xwK3366X003zdW3wcxEhUH/LYh8FYbXi6uZ/TvYcyLdNg+8lVVutInZPILal5y4HbVyzitm8tAKDDG+oTuMUHzKxt9PYZf8eb1MwEUJBnZ+179by4fg9FbhuKoqAoCuVFzsRNrLzYxe0rFvHTFQsBs7ZvqFKn2zIDo3SZWoZhdkgfrM9bvJnDYlGpqnQTDEWHPXUUpM/UhJ7ALS7TTLL+stCeig0BE695g57a0Hhw1lt87L/BBqCN6gY76jtx2BSsFjXRn6vdE6Qgz8bPrlqc+CFw+4pFVFW4M05Y6O+1ilcCxs8hueN3pgMKD5axZxhmbWNKzZvDSnfQDCCTm/oTzzstfBibYiyZL2COC6dpZs2bYfTfXzLT7MjajduZ2tGAZuhw5pnceN053L5iEZubtuGMVKDEbpPx13GiSW4KdTmslLtKKHUV80Hzdm5fsYjDv/etnpUfftjcxqbR2OrrE1wfiP7eo41t/kHHIWvvCmK1KKiqMqQ+b1HdoKHZi0L6uZ/TvYeGk3U8ljJ1R+qcRG5J8JYjebHBSvtrQtMNcxiI3kNo+Lp7RqoHKHDbiEQNIlGd4oKeJp2KYmdiiqF4s6nDZqEgzzasmrf4EBfx2g7oP1MrEJsGaqBgrL0rgKYqqIpCVUU+usEBDRcylHPKZM7D/taJ1yjGh16xWbVEQF0+SCfneLNq72Fk4vyBMLpu4LCagWFdbBiS9q4AxWma69wua8bNpv2dTzz4i/+f3PE702FlBstSjU/hlde7z1sggtcfTgnq4uJTjAV69YXsiiW6aLE+b9B/f8lMsrfZt4+6jiBVrXXm469+FYBmXytNvlYc4cq0+5hI4n37VNVMNlIUxez31vyx+Rm+4AKwxBpp1q9n2s73sVs1dAMaWkZu7LtMsvT7E69501RlSEOFNLd3E4rouGLvwd7v+f6OPdR7aEbvxVEyUuckckuCtxxRFIWKYme/1dXxTrS9v5g8SQkLYPaLikbNPlfF+Y6eWp6Snlqg5Km6KoqdhAfoNN+f5g4/mqpgs2iJGpn+MrLynFYiUWPAm2i7p2ce1upKM/PxQJIWhpIFmEkmWX/rOO2WxPhoiqJQVe5OCZIHqp0pLTQDsP5ukr7YxOh2qzkWWl2zF103aPcEEhmnyfJdtoyzTfs7n0K3ud9482u876SmKhkP6DxYxl48KSe52dTlsNAdNMcmjE+NlczljE2h1Ss4jQfPFk3BZjPHi+uv5i2T7O3QrbfRmF9OVXsdDVMPh/PPB2BL00cAOMOT0u5jIonXeiZ33J9ZfgSdgS72eZugtBS+9KXEc6c9+4dErfOBZvsmyzTbOJ32riAWixm8DaXZNJ7wEv8B0fs9P1Dm+FBk8l4cLSN1TiK35NXKofJiV59MJzBrt8qLzC/63l9M3u4wTruWCHwK8pJq3pK+4OMBm6YquBw9NRvlxa5E8JDcFDFYs0RTezdWa+pAv/3N87n4+CmAmQXbn/auQGLcsupYh/x48DacqVriY4gNpr9Mst7HXL5sZsrgpWC+LoZhxIJu83WrigWemqrgsA3chdSiqVg0NRE89z5mtz/MjKmFqLHO+KFwlB/85nV2NXSl1KrGuZ3WPvOSxvfZ+7W1W9U+2ZN2q8bZi2YAPc3xbZ6gGaRbM5+NY7As1aiuJ8ob53T0ZJu60tS82SwaFcXORJ+4uETwpqqoikJlaV6/QX9lSd8fR8nl+sXNf6H2sefQVY3q1jpeOO+KxJRQmxu3UWB3Y4sW9t7thJMI3rSei3VM+eEAbG0ypzXjxhsT1+bIrf/msPptKAr84aktaT+r/X2GB/psp8v8zCTDNxrV6fSZPwYHazbt/bn47T/MGSPiP4Z7T19XWeLs059SoadMmd6r0v34Goms1uHcK/NdfT9v8es81P1le33RPwnecigUiSZqbeLiWW2lRU7sNq1vzVt3iLyk8bIK8+xEdd2clzDpC76ls6fJKjmzKRiKEAxH2byjlW172jPOMmpu92OzaOa8qbG+EUvmVrPwuCkpZb/6otmcdOxkYOBZFsyaN/Om+N5HzQDsa+1OyXxKzoj60k3PsnVXW59Mrfg6LR09NUZxvW+6dmvf7N3+LJlbnQhswKxxm1qeB0pq37Z4INz7OqezpqaWaFSnwxvsc54f7mnHF4jQ0OzD64+mNAlGInram3+eM/Nm06J8B8s+OT3xOJFVN8fMtIzX4MWD6t4zgAyUKdd7rlqLpqZkI+/ZZ9Zu/PqR9xLbuewWDMPsY5eXpuYNzJrKLl/qPK+diWZT82COWMZv8jrxstY1+WJ9MM39uRyWlNf/M0/9jn1F5nsVdx4fHTMfMAP0LU0fcUzFkSj9Dfg2gby9ZT8AgWA0cQ0n51dSaM/ng+ZY8HbEESm1b6c/9wfKi12JH5cjkb24ZG41h04tSFmmKAp1Tb7EvSrd+7DTZ/YbTjSbDmGokGAoSqHbhiNWkxhvVUh+D+m6kfIuMIC6Jl+/96p096fkbicAZUOYf3WkslTj+2nrCqKQeq/sfZ3F2CfZpjmypqaWLTtaU5YpivnrM56lOLXcTVNb6i9Bnz+c8supIM+WSHgoiX3Br6mp5eV/93zA45lNW3e1svHjlsTycESnvtk36M0gPs1WntOKpim0d/kJBCM4kpoQZ84oSTQXfrS3HUgN3uK/tj53khPDMGjvCnDWokM5dEpBIusqXlZFMc8zOSMquYYp+Xxe3lCX0gypGwYFeVa6fOE+/QnLihxDyuyKB8MOm8bksjxcDgv7Wrpjgc/xrKmp5b/+770+5YK+GWTxTLrkzNfk84xPFeYPRggEwWY3v4C6AxGMpLIky3fZCIWjhMLRPh3+0zGTWWDm9BLuuHox0DMafTwIbPcEEsPNxAeRfu2dukEz5ZbMrea+xzbiD0Zx2i2JwM2cq9U86w5PMLGdM1Yb3NzhZ0p5Xp+ydngC7G9NTZK559GNzD/W7IP2kysW8tbmffzq4XdT1vnFX97Boil9fhRpCsw5qqLndXnjDY5791X+evLFKIbOxk+dn/hm3e9tptXfzvkVZ/LqoFd1fFtTU8vvnticeJz82s6M9XtLuPFG+MtfQNc56oP1VNsibPFGRzR7MRjWyXdZcTosNLX5E5/hcESnrsnHf/3fu4nXtncG+NfOPpY3329g667MM9eD4SgzphSixKYQbGrz98l6BUCBYrcNT3c45fjp7lUD3Z/iN4DrvnIiRydldvdnpOZe7b0fA/Neme8ya++Tr3P8OyGz2WFFrkjNW46sfm5r4kstzjBIyT6qrsjv02zq6Q6ljFSfPCl3UewLfvVzW/v0qwqGozy/fm/KoMDpjpmOpztMIGSObRYfbiT+C7WuyZvo/9K7TPHO5b11ByKEYrVJ/WWYtXtCA866ED+fdNt2+dLXRu1rHdq8rrWNXjRVIT/Pxu6GTnNQZd1IJCasfm5rnySL/jLIhnKeBtDSEaAgz5ZoAkpX8xZv6sm09u2DXa047ZaUGkmn3YKq9ATH8b6IVotKMJZ4kun8j/EBiD3dITq9wQG3c9rN340+fzgxTEiyxjZ/nwAsGI6yYWsTFs3Mdkz3GQL6bGcYoENiNgzq6+GiiwCoK6miPNrNvhnHJNbf0rQNgFkVR/bZ90Qz0Gt0TMURtHS30eyL/cg86ij44hcT61VtfItgODpi2YvRqE5Ds3k/SR63LVm698TT68wM8OICeyzRJbPPg2EYBEM9s42YP1i6B/yspntvpfsM93t/iv1fl+Hg0iM192p/59S720V8ucztOvZJ8JYjg2XogdmRPxzRU2qQvP5wykj18Q7n0PMF3+98jf1ktg6W8RTvBxLPsDSXxYM3T5/+TsmDB/f2h5eauOm/3zTLW+A4oGyr/s5noPWjQ8horW30YLdp5Dks6EbPlF7xGSsyeQ0HWjaQcESnujI/kfSRtuYtFsRnMtabrhvsqOvsEyipqkKe00x8MGtEg1gsSqK/X3OHP6PzDIajRHUjkXxQ2+gZcLvkJAVXmuCtv6QOfzCCy66hKMqQrqlhQH2zl6iv20xK2LcPgD1lh1B92OSUdTc3bqPYWcjk/ImfaTrQa3RMuVmjtXF/0hf5jTcmBu2t3vQvDGPkshf3t3UTiRrYrdqQto3Po1yS78DlsMSGkxn83hCNGkR1IzHbiM2i0dTuz7iv52AGuj/VNg48uHTcUO4xw9nPSK0/UYynPnkSvOVIJnPqVcU78if9YvJ2h1I6fifXvJXEvuD723e6gUwHWj8u3l/DZtUSwVtTezddvhCd3lCfmjen3YJ1gIF6w7EAqjjffkDZVv2dz0BauzLvz1HX5MVu1XA6rKiqkgje4skgQ5kXcajnabWoiRoBSF/zFh96I5Ox3roDETO4cvbtKREfcsTrDxOJ6mbNWywgb27vzug84wF+/IdFXZN3wO3iNW/QM2Zhsv4y32wWFZdd7XP8wWiqQjii07TiGvj3vwEIqxZqS6upnl6RWC+qR9nctI1jK45KJKVMZAO9RtWFU6h0l/Ov2pqeJ44+mtc+Y/Z9iw+vYjOGlu3bn3jmp92mDWnbeJ/Jonyz5k3XjT5DzKQTv6/GE6asVpW2Tv+IZV0OdH+qG2RmkLiRmns12+uL0SfBW470N49jcvZRVZohNLzd4ZQhFwqSsiyLYl/w/WX/nXnStEGPmU5zR2rNm6oqNHf4e262ffapUJBnS/wi7i3ev6ukwNHvdSjKtw3YVTx+Pr1nHVAUKM639d1n/Fwy/EXZ6Q3i6Q4l+vQdNrUQf9B8HeI1b0OZC3TA8+x1ovFstniNAPQE5snyE8Hb4DVvvkAYVSHtsBz5Live7nBigF6LllrDmsn8j/Hrmuewxqat8pgZu/1cH1dy8JamTOnmh7VbNUoKHIngLd01NcvfN2OxIvZdVLv2ncTy//vCtehKapD8fuOHdAW9nFx1Qp/9TkQDvYcVRWHhtLlsbtpGZ6Ar8fxLZ3+dXYcdR1WbGbzN3fE2veOU4cwDGw9o7FYt7esf32/vsh5RXYTbacVm1RI/BDJpOu2Zk7mn2VQ3oLTA3vcz2d99pZ97Vfz+1DsQVBSzD21thkOsjNR8w8uXzeybNTvAOcncrmOfBG850nv+0/j8l8mdUD/e2wGYN7XLbnuRf769h1BET2k23bS9JwFhxc9fYU1Nbb9zq175ueNTloPZnPTbf7yfNpMzrrndj82q8fOrF/OzqxZTWuigqb07McZT75o3MGsE+6t5iyTVvPUuq1ko6PCEMCDxpZDvsib+LnLbE+dz4syeWpN4pu7Uivw+17Yi9kXSeygASJ/NlVwLED+fuOvvXTfgdU7XkTjdeRrx80zKiCwvdnL+J4u5/7qltHX1BJpX3/lqn9cl3vcx3uct+Tzi/+JZus3tflRVwZPmNXE7bXj9ITpi/YxWfG42P796MXab2Yy0ZG51ygDEZUV9zzNeOxuvMaxr9LJkbjXnLj40sU7y9UluKu3dbHr7ikXcf91Srr5oduILJ56dpyhKIniLX9P4OhZNpaoijws/fUTKMY9Vurjpd98FoK7UzK5ds+Jm/jblJAAeeu5DzjjpEG5fsYjXd68nz+bihMnHJsoyEWdViBvsPbygeh6GYfBWbU9iyE/+cwkz1jzDu7NOQdF1/nXYyegGqMnzmRg9GZm9My/7u8/UNnooKXCgaWqf+YETuzV6foipqsLVF83G5bRSXGD+cI13C0jXDzT5+Hc8Ws++VnOA4evvWccZJx3Ct84/DjCTaSqTx8ns574SP8/4vSr+GY7PSXzl547n1DlT++wnP8+W8ewUS+ZWs+LC4xKPtdg5xxOCMs1CXTK3msI8W8p9pr9zivd527Rr4AGYh5oFO5bmdp0IJNs0h9LNfxnX4Qlw/+ObEo+b2/385pH3AHDHat4GyhTrb27VJXOr+b+XtlHf5Evcantncv7yr+/wfy9t4/7rlrLyvnXs3e+hvKhnbLOKYhdvb9nP+x+3pDSlJivMs/P+jhZW3reuz5dfJKpjtaiJX8nxsnZ4Auxr6U7phG4AVRV53H/dUr7369f4aE8HXzrjqMSXi8ViHv+oQ4q5fcWiRH+F3tdW1w0a2/yJIAPM/g0dngDN7YGUbK5f/vUdCmPBmt2qxTIfe7Zr7hj8Oqcz0HmSdJ41NTWsqanl6bW7+j0m9NS8ebrD6TPkSH1tI1GD+ua+N2S3y8q+Vh9tsSECivPtiSnWXq2pZXtteyyAVwmFdW746okcUV2cso/kfpGTC128+X4DK+9bxydic6POnFHMz68+JbF+arNp+tvQkrnV/O2V7ezZ7+EHl87jmBmlPPD4JqaVOlLWeezVj9m9ryvxxRivSTjV2s733/8L+qOPohoGRb52akuquP9z1/Fy/jyisS/PDq+ZBRuKhnm7/j1OnX4yVq1vU+5ENdB7eFrRVKoKJvNm7QbOOOLUxPI1jQb3LPkGRtLbzRIJU27V2ac4E/294p+n3z+1me5AtN+syZX3rWNHXSdHVBehx/qrpbtXAaBAgcvMKH/6jZ3UNno5vKoISB7cOXW4kN6fD3+4Z4/xz9aXzjgKMPvqxe9ph04t4L+++6k+95X+PsMAZy+ckfiMuuzmj86ZM0r42VWLY/ecILoB192zFofdknLfSncPOWq6mZUa//w98+Yu/u+lbX3uW/1loa68bx2RqE67J0hliSsxmHjyOfW+zs3tfv7+Lz9v73iJ+69b2md//d03498bB7K+yIzUvGXRcH+1375iEcGwnjY7CMBNBLq6WP3MB8PKRGps8/eZ87P3cZIzxcKRaGJeTjB/tcUnM68qd6ftG1SQZ0sMztpbfNyy3ts1tvnTZuDGy2LVVFQFapP6i9Q1edLW/PWmqmZTbu/5OvvLlOvyhRJ9b8zMx8yySpP19/oPdp5g/vLtPY1W72O6HFYUBbz+9Bmh6aTLAnQ7zWbTeIZfvIm2othFOBKlO2j2l4svT9dfp7nD7CukKEpKos2Wna04bBqamnqrSW6+TdfnLS7+2tY2eglHdHyBSKLmrfe+uv0hpuzdxgf/fJtKTxPfv+Nr8MgjqLEPTnXnPt6duYB/Hrow7efmj89uIRQNc8r0k/otz8FowbR5fNi8g9bu9sSy1c9tJdjr4x2y2NgXtfbpqD9QRmby/LfBcDSlqwCkv1cZBolEHp8/kphdBnpq3noP1DvY5yMYjvLUOvPHUigSxReIoCipPzJ6lyvdZxhS70+1jR5sNi3lXhd/T2fyeYWezNT45687EBnyXKnxmUrSdZuIn0+f60z/GcNDzTCW+VRHntS8jbRIBFpawOMBrzf1/zTLLqrZiT3YjS3oxx7ohvsU8HhoufC/+nbwiMn/woWwdxMt1zwOSt/4u6WtG+bO5VsdYcI2Ozw/GVwucDrB5SJs/0y/+44Lh6PwzDMcueVDttunU+5vh1dfBVWl3BMkEomiRcJUlWrk7TKHVuCtnrdTgdeDHo5QvWsLrLea/wM+v58t9ikUl7jh7bcT61ft2crmSDmk6ekWjkRhwwaq9m6j1iiidns9bAgSNaC+sYvJRjdT9u6DGidT9sbK8o6r5+8Yf/5kmvbsh3fNb53JtR+xOVKS9pi6YTDVrTG5fgdbIkVp12lp98OmTUyqj42FtSk2wGh/11ZRqGzYyeZIQf/nuWULjh07aGnvm6CQOOYHH4BiTpeeZ1Px1jcOKTssHInCtp5rkx/w4POHaNuzD5tFwblnJygK5WqI90MRjJYWFGzMCLSwHye1W/dCYepQDk0NrTiNMCXN9VQdbjY52Rr38UHIQZkaprilAXbuTKxvNQwUDAwUXC2NQKf5hGH0fAsaBlNa6viYPOo2foSn3bzOpbs+gtW10NQE+/fzpTXv8btZ5+Ks72bFIz/kkisfZNGu91LKt+2Yk6laupAtH3ag95PJ6PFEme4u58jSQ9M+f7BaMG0uj2x+in/VvsPZR50GDJDRnuZ+NJD4fiJRA103qK5ws2d/T/+6/rJOI1EDp91CdyAcm9c51mzaT5+3TD4frR1+Ct3m3M/+2LRt/SWtDJQNmzxlWG0s6SmZ3aqhKJlPBxgPBovcdlo6Avj84SHPleoLRLBZVJz9BG9DzRjO9nIxOAneRpBr61Y47zxzHKkMzelneZmnmeaCirTPuQO+2Dotadcp8zSjb3qPaYoCCug73wMFjNjj8i+fQHN++YDlKve1EF7+HS7WrDx+6R8pffKvhH76JChQcsSnME66jAAak5/5Cxd98CSGohD4K2ZMoii4jjmHyLHnc+ljq/A/pvM1zOWGAj/41M0U7PuA7j89kFj2VUVhw5LbaHGV9j0ffxver/8nX1EUfMdeytbA4Xi+8U0aHWWEFtzM6Vuf4FP7/4XnRfhqbJvkv+PB1N3HXkaDq4Kuv3wLMJ9/9+SbaXH0HSxT1XUmffgmX/vwId6bvyrtOqWBNrouv8Y8N6Drn6SLyVJcBmycd1Pa/ZUF2un62jlUA6Unpl+nNNBG11fOSjx2nXgj7a++TWnBobQ4Bx/0E6Csuw3feUsAUAwD24yl6MdeRO0/11CUP5nAf1wOhkHxkWcRPvZ8HA17mGZx8N1Hb+b/nfET9jz1DsHrfmPuzDDAgKZz7mR2y8f8vzfup/ahKXDuHUypWcN7M8/gS6/ey4Ld6wnfDWCgmCOEknfp/+B15OP47DL0jv2xAdkMlNj/GPA9YM+X76ZuZytd6x6E5f/F4X/9E2z/V+J8ZgPH51Xz9PFnsaPyMHyOPI6t34KuqKgXfY5fH3oG+6qP5JNHTkb/oB2LpvapSQVQbAEWHzL/oMgyHYop+ZXMKKrmzb0bEsFbWbEzbfKPaujoSppEA8Mw7z+9xLMaE8kDFak1b1aLmvYL3mpRmTm9hPd3tGAYJGre4jVLvec37a+8vdcpdNupb/ISCEVT+nmmO366cqkK1Dd5ieoGoXCUlg5/or9tYh1VMWenCGU2E0RtoweLpmCxqBw5rYhdDV39Hr+/LFFfIMyRhxSnfW6g8xloHtRsri8GJ8HbCCp87TXaji7Ad8HMRBADsaBJBVASyw0l9XEiuMJ8vCy6mb+GFxOmp0lJI0oUjfYrP8lO7ROcGfqQh7tLCCe9jFYinDn5Q3bf8tl+y3lmcCsP+4pTtktmIcKZlVup/e4ZNEXzoRO0RZOpO+0zZvFCUyDWMuBcUEr9kk/32YcemALdsONrSylQU4fnaG0vo7rcx/5jF6YsXxbcwsO+BX3OZ1nZFpouNqcuKvRbaPWXUHfhJ/k4XAlecM0votk6r9/zjXP5rDQHy2g+f26icmxZcDMP+RZhJPUgsBIhrFooOkSj5eg5LAtuTl+u0s20nDf0rMRM9jfgOp/tCfntndBeMJlltr7nkY6VCMvKt9B46YLEsmhwCvhgz5QZFKh+9l1mzr5gCZaBD7aVHcFi+4c0fGsJpZ4we4oOo35Fz2uuGwot7WUcV95I3dzPEDVU1HadV49ZAgYUnj+ZWvXMPmWxdSigQ9tViwmp/cyDqxsU+WBX5Ci2HXMmBKD7wlnsVktigZ65TqVuJ2JY+fOXzMDcesFhvOc8jIqKIi5QPkBRP+SD94qBY5hn28Fb/ukkR9qaGkWt+oiZWzUat31ofiZVFUVRe/6P/VPU2N8pzyt9nk+s1+/fSt91VRWF1H33PK+k7j/5+V7rqt0dRDqb05RTBVVD0Szm/xkGqgumzePPm/7Ofk8Tk/IrWL5sJvc8ujGlKUxR4GhPHTtckwhae2qObeEAn/rgVV447syUWmm7orO8qANeeAFnYzOQT5U9wu3f+iRoZgBYWeKkvtmXqIyNH6eyxMkxh5bwzrYmgD4JC73nxF2+bCa/fuS9fmt64hmcb76/j49rO1L2BX37olWWOGluD/Q5/wK3jQ5PiOb27sQQPsk1b/H93PI//2LLztQZdnqL90kLhaOJptZjDy3lg11tTCnLY19r6nXpLws1qhsEglGOnVHKll09x0w+p3TXOb48nYpiJw3NqX0RB8owLnLbaO5I/R4YTkay6CHB2whqO+sspv11JzZPyLz5WKxgsZg3Ss38G4sVrOY/xWJlZ3M3Uc3CETMqUaxWsNnAZuNTNjuO1m7+stWJVzdvItHYF/M94Qu46PAQS6t0XPU6D70XwqdbKXXoXDTdx4LSw1Aih/DWe7Vo0SgnHlEG4TBKOAzhCNV793OWsoFXHZ+gU3XhJoiuG3Sr5q9Xw4AHvYv5u+dEIoYKGjzVNYfCzm4WebfxlnsGxCp5/tExF3eLj0WebSjxj7Ju4K04ForhpvaLcEfNaZZ8Fpf5y1zVeLf7EOZ+/BGLOz5EMQwa27o52dhJ8YwOHqw8hXZHIWXhLs7e9iLz92+issRFY2s3U8otcMIcWBcgmG/ANKj+Rw22sJ/KkjyS7z6NbWYNZWWJOf3SoZWTeG36MUQf2UPtIceyuupTtDmLALBGQ4RjndR1A1BhXdeRHPneTi5oeZXSsmZWV3+aNmcR5cFOLtn9Mqc2m5Nax6cwqyhxJWqi+urpwL1YeZ/SIxpZPX1pbH8dnPv+s5xU/y7lRU4CwSAX2LdTWt7I6hmn0eYspjzYwSU7X+LUpk0956hAyXEL6LbYOfu9V/jLogVYoyECFnusBkTFHe7GAHxWF2XBTr6055+c0mqW2/yxYFBVbIGjF9IWzePwllomv7kDgO6p0yCWLPeet5oj393EVPcuPqhehO3J7VjQyc+z0WbNR5+lMm37Hipad7GueBZUG/hVO6oRZfcGhZPbPybPaTWvgqLwRuGxdE5xggq/bD6Ti1rWsND3AagqhqokfuB0h6JMLm3gvaLptO/XoAiKOrpxu1wYTge4XOxqD7HLcgiE4R0OQ8XgPevRzLS2MKWoGHQdw9Bp7DKbteOBm5UIYTSKrUGUqm1UFjVS1G4jZOhg6Bi6+X98e/O17VmeeN4wkv4eG81AhcDe1zNYUTXvT8sjBlE09vx6NYoWD+7M5xTNwhEqaDZ4+Jk7+IJazkzNwqWHFvDw9iK8uo1Sh84n7PUcOsnH4pIgD78foVNxxQJUJRG4WaNhwpqVokAHX976GPOfWM+Lk09k1/FfBovB929/lkveeYRT62vAYuWnEZW1h5zEY8edQ5urmPJQF6d/8DLHN2/n39vOhOLZAPzvQ2+i/PEjTrW0oamn0P3iy/Dm/yXup0tsNt51lfBKlxMMA5cSJWJACI1yl8ryT+SxRGnm1baO2IUxaGjspMqtmt1cbDbzvh3ru1mU7+DzS49KBITlxU7sVnNO4A5PiNpGTyLjtb8+uYFQNJERHh+H8bLbXmT5spmJpAPDMKhr8ibmXo33I25oMe9timK+LR02jas+1zfTfU1NLR/tMfsqPv/WbvJdVory+w47FF/W2OZPCXDjGcPxMsUzRuPrqErsfon5U2iw9TVVSfQVVGPzqfY+51xJLutYKdNAFCOToajHubq6Ok477TRefvllqqqqsnacmpoa5s6dO6RtBsoyij/f4QnQ0NydyMKC1EnW+9vHYMvj4plHA2VQxY952rwqXtpQl5Lm3nvC9zU1tQP+yh3sHAb6OxCK8HFtJ9d8cQ6bd7Tw7w8aE+M0DXb+b2xq4Gd/+jcVxU46vaE+v5qL3DY6veERu87pZHKeye+jwfZ9x+p/s6uhk+98cQ4/+PVaqivdKbNuDJbJBrB5Rwsr73sDgLMWzuCKC45jTU0tv3nkvZSkCUUxZ/To8AQ5oroIu03j9hWL2LqrjWvvWcvNl5+MtzuUtkZmanleIqssnvkXHOA9lHy9Or1Bahu9FBfYae8K8v3zJ3PqovmJda6846W0tTODHTO+zkUXOXng3w/xvYXf5KQDHN/NiAV4PcHdAAGfEYUBAsHkgNBIDiLT7iv1+d27dnLIIdP6rJtYLxrBiEZAj2BEo7zx3l5UQ+ekmWXmc3rUfD4awdDNdf+mdPGmFuQGr4PiqLmP9k4fqqGT71AJ+IOoRLEo5jlvCE7vUxtsIUIEC191v8YJtj1sCE5PW8P8hbw3mWffnf4i6wYb/IfwcHBR6nZGmEuir/CodgrzIh/xxcArKFE98e8veUt5y3ksv6m9EzWqo0R0SDxv8K+C4/ifQz5HVO3Zpy0a4hsf/IVF9W+b66EQ0RV0VJwOO55AFB2N4qI8mjrDdNncXHv6D/lqw1p81jweKzueW167BzSNE2ZVgc3GGns1/2U5hkg/teR2xeDqQ4LUb/wIr9XJ064j+ITehqoqbNWKSe4qp2Jg1xSKCp389tpPmRUEsdrNgd7z6TJC4zo8Afa1dqdMpRi/97+8oa7P/grdNjpjw6Vksr7bYcHTKxu4v8//aBnKPSluON/3QzFY3CI1b+NAY5s/JaCAnsyikXyzp8ug6n3M59fv7ZNN1rss6eZW7W9/Qz0Hu9UcNLeuyUNdk5epSQOsDiaeMdvaGUibKdbu6TsGWjau80jKd5mTZX8Qa4JJN09oJvuIi8/kkC7b1TB6BgQOhiKJGoX4IM7lxU7ue2zjoFllA83XmO46x5ud4sM/OHtlmza2+fs092RyTMMwp2R6ZPPLHFEynflTj+9z7KEym081QCNN169REwrnU3B85l8s//rI/PI+++z+f4Bc0t3OW8+s4l+z5/GtEy8B4P5+foDccO/rfLi3A6NXNXQECxgGfuNIpjR7eY45hLXUr6EwFp5rmcWZ6x8DTcXQFAxNxdDU2GOVZ48+l7Ct13aKlSc5GVc0gD9qRY1EMTQV3WrFUBUa7BVUKp0Ep5cl9pe8z//rOpuonrrPkGbjL7M/x5GnDtwdIZ5fagfc7X4+nDGJbt1GWdRD0Tn5ENXZHW1Cier8PrKo38ANIGgo/PHjAHe2/YOt1kN42nUEy/b8nf+degEhW+q6OgpKoJt9Uaif9wmK/R0oioaiavzpUz8i2KsfrGGAb3c9xmeWgs2OYreDzcZFuzuJWKxENSv3HHchUVteapnCUZ7/1270Xp16DQM6u4J9+jMGw1Gef2sPvb9ODIM+gVt8/VzeZ4d6TxoLJHjLsUxqa4aaWTTYvuPLe9fAZRJwZTI/6lAyH+PrDlbW5L+v+NnLvPDWHnz+MJ856ZCU7K7+toWemREGClAHKmN/hjIczEgP+Op2WenyhfjbK9uZUpaHZRgdgJMHfY7PodrfOcd/jQfDPe+V1c+awxOUFzn73S75vTWU+RpvX7GIUDjKRSufJhiOoqlKn9keIhlksvV3zEhEp83fwX+e/DVJVBhEqauYpYcu4p87Xuf8mWdQ4S5LeT75vW0oKuFIP58zRWHfkfNwfPkKWr/3RNpVWp0lFLxYw+0PvIY1HMISCWEJh7j6nKMhEKDtufQDyLZbCphhCaJTxeTiUyEUSvxrppxpTbvp2BqmukCnsTmAFo1wRIULgkE6FuSlTTjqiOYx9YHXQDeDQcPSN/BL/ld13EJaFSs+i4OqUB0Fmz4217VoGJpK+wz3oIlNrZZCug+vZLdyOEQgf1Yerb6itOt2W80fpW999j84wdYztmVLW/oEhXZXIbuWFJoPIlGUSDclh2oo0QhKJESX4Uq7nW6Qttz93U113Ujp49izQfrlLW3d1B02k6iiMW1qKYrNBrHgcuPeTqKalTmfqEo0hZP0PDYbT/+7jqhm5bylM1Oe+9M/txO1WLnswjlpt739r+/R3J7+vjmW53iV4G0cGGpm0UgfJ5mqKmkDuOSyZJLZlW67TFVXuvn3B43mpNIV7n6Dt94KYiOMK9DnF+FIl3G0JM+ycPKsyYlR44e0jzQ1b/29hmXF5gCsyZly4YgZVLkc1n63S84qG2jf6disGpUlZgdtTet70z+QYyq2AMdPOoZjK45Me2yR6rMzz+Dlnet4fOvzXHHilwdct7/7iaJAXWxi9gHfC6WldBX3yqZfZAaIZW+92M92Ltyl5XRHdbj60sRynz9M243PYp05h78uWMjnTnLyt/WpPxzLbutnn/+/vTsPj6q6Gzj+nUkme1gSQhJ2REFkE1kLkReJrYRNaMVSBPRlEYu+PCIq74uVRrSR0McKJI8vD8YuRtpXZK02RKE0aF2gCTQ2rCVsCSHJJCHbJJnJzNz3j8kMM8xMMgmQMMzv8zw+5N45995zz5x7/c0995wTEUJgQZkl6DAaQa+3BYQb0r7G39jIy3OHseWjI/gbG+nXpy8HL9RhQs2E+7oT+aN1DkFk1HkTWlPz/+uNMtZRfaiOK/cGE9JTz4gvrxI1pBptUGentJG6CmoDwyg+1kiP3CxbcNnt0ekue+9H6ivpeuCEcxDq74fipyKiZzUV/s7HUaM4PXkDUKtuzfoufjoMix8A4AI0BZZmVCYDXe4LRGUyU2C8bGm+1puhzmT522hp+h6jUcBkpjTzS4fm8hlN/15743paTIrt8/8ymzk78Q3Kgl2UVV05hkF9be9+4uePOigE9bM/hxEjnNK3JwnevICrnk1tmd+uLcex5+49hhvz4qonmrv9teUcekeH811eMQC9osMh76pH26lUKsvcrCrLJOX2T+Cs77zVNZhueznfStanZooCD/SPbFPwZh13yn7IBVffobUs/vqPAs5crrCttx+R3l0vRPteZc3t251e0WGW4E3t/Au5rcdEZca/11nmD/9PhGciQroQPyCOA+e+ZOq9k5tN665HZkiQP4XaWsxmhUUJg9n0f8cdrkVPrrnm6tDX3xdxtczxOmhuKj9P9mnLfFNnM6uqiGjLH0OGcLW3pWPAD4bGYrxomfmm98MjYXQfx+O4eL/KnkoFixY9zB8H9eFCUZVlIPRNWS63U6kguH8fenUOJivkMa489gRJi8eAwcDTxwvZsv88jfbvrvnBMz/oQddZWyzBpF4Pej2f7M/D39iIv7GRkRWX+Hv3oejtmnYDFRPxjQUc1PTGYPc+gMZoYOzl42T3fQi93awkgUYDjxQe4UCfCZjU19MHGPX8x+WvOdxnIgZ/ux7JJgPzzu4l4tq/rgeV/nZPNpsCS6Xp6aXlSablX3Owv2MzuHV7tep6YOpinlx7CfoTbNfFYb5h1IGEqBMULrphJAOTmR5v/g+qj3Y3u8/bTYI3L+CqZ9Pt6Alz43HCQzS2XlD2xxzcP7LZvFj/drUfq5s5B/uxoHp1D2tVU6TG349Gowm1umkoMQVbT7Eu4UE8Nq7vbS9nV9ranBpu1+Q55J5I/pp9uU378VOrMJoU25ALN36H9mVx5tI1vj+nxdrXyWA0E+CvbnE7K0/S3KhX93D+cbLEadL5th6zc7gfdVG5dA0LoV/XO/OdljvVEw9M40jhcd795n2CVJNRK67fs3TXI9OsQJFWh7bSMm/uR5mnKG0a4f/G787dKx7NfefHz2qdhgopKLk+6b07bamXrjjen8KdPm/p/tijW6htGi69wWTbh6v8We9bQ/pHkvvvMoyKyjIge0gIkx/pQvaVBg4ft4w72tz5zJ1v+ddazi+M68um/zuGydw0n+q8MUwe9WNMO47z+ZHLtv0FakKpGjSNF8b1ZfPHxzGaFMKCNSyf8xCTR80lN+kAxeV1tu820C8Ybf9Z/Nfo3qTsO4XBrKAC7gk2My1pHakfHcHP1MjPZwx2eFr5p8++x9/YyNyH+zmst//v639cwM/YyPiBkQ7rT/+7GD9jI/dGh6A0NqKYGsFo+VcxGamprWeC/7/407TxaBQFvV8A/mYjy3M/JK4kpylovP6EEpNCwP0PogQ599ptTxK8eYnWzKF5K4/jqqeiJ3lxt5+amhrCw8Nv6hysQ4CAZVLpRdM8u8Fm5RRQ39BoazLt3jWY7hEhbudE9YYJyf/dNCYVwGv/+zWBAWqXQwE0JyunAGPTr/NXtnxlK093ZVGvN2JW4MT5Cua/noHeYEJvMDl0r29t/WhJXb2lo0RNXSPv7r3KUgqcgjNPj6n3q6Ss20FCG0LpVjXJZVrhXqegcFb9YCmJf3uX4NDviK51//25ug9YB9AtKKlpmrKugW5dgomODGnVNefuOw8N1jjMsPA/7/2d4nId/n5qAjTNvxN6K67/Au311zje/sNRnp72gNP9yVW5FGlrqajWc0Wrcxg+5MiJq2TlFLi8Jm1jwTUNOn3q4jWH6zCgqYPX4P4RrS7bnQfzqG/0Q1tZz+jBlieMRrOCn1rF/f262uZqtT+fM5euMfy+bkwe1Rt9owltZT2RXYKIiQy15dcITI4byOffl3KltJZrNXpOG4KYv6OAOnN3TIrCP/5ucBhu5JMoDY1GM/tLXAegWTkFbME56LYf+cB+vTXf9j/WASK6BmNWFK5V6/mPPdsJwuTQTI7BYBkuZuBAOHbM4/K8HSR482GuOgPcCZrLS1ZOATsP/du27GrCdnfbpX6S6/Cum7ay3nYz9+T8b3UZ3ewxs3IK2Hs437asrawnUOPHTx8d5FAWLe3DWn7WfTRXnlk5BXz5z+sziNhPfG8/OfatlJVTwKGcQttyVZ3Jo+/cFZNKT3H4YcL8A9kw5xUiQrrcyqx6rdbW7fuj7mX+8Mf5KHcPk8Y3On3eXAekap2Bp9btp7C0Br+md2itE8rfCqFBGssPDLOCuqlzi95gokdUKBuetwxAnZOTc0uu5xvvoTdeT2WVDR7V1cqaBq7VXB+o2v660tUb3e7DeswU+2vY7josLK31aO5nV+cDEBKsQblWz8mLFYx9IIaT5ysICb4+bZhT+iB/Tp6vQFEUzl66htGkEOJmsOPKmgYqa12fs/UcTl0o56/ZhQ4T2t9YFtYyvzFNc9taj2+/HVjufxHhgZjMCme0DYy4r/mZiDqSzE0hvMqH+09haHR8CdqTSeLdDRXhzRMjuxqSxZOyuHEfrZnguqVhYFp7fE/zeLPnCVBSq6Wo018xqut4eeJyCdxu0oxBjzKqxzA+/OcuvjjnyYjAFp1CA+gUGkBhaS0nLpSjVrmfML0tQoP9LZPX6683neobTfRqxbBCbdXa68nK1XA3nu7Dck90ccyMUxSW1rQqeLtRSKA//n4qTp4vp6K6gavlumaHIwoN1lBZq6eoTMeJC+WoVBDq5rv15Jwzj1xusTzdlXlL25ZU1Lv8f0KVrhGVCtvwS3cqCd6EV2nNMBOefO7NEyO3tSxuZh+e7PtWd6+/Fed5rOhf/PcXb2NU64ipmcTAbjL5/M1Sq9SsHL+YETEPkJbzJ35/bAcms2eTrfeODqegpIaT5yvo16Ozy44obWV90lPX1DxrVhQMjWZ6u3j/7FZra1315D7U2n1rK+upqWts9j2/lqjVKu7t1YUT58tt03mFNBO8WYNwa/q+MZ3w83P93d7uoala2tbd8Y0mM/1iOzlMJXYn8olmU5PJckMpLi6+rcfRarUUFha2nLAVnp/VD7CMtqyrLrP97S7NrWB/HHfHdLe+pf3U19aiVvRtzmuYpo6Kqgan9RGdg5rdp7vt/P3UbTq328nTetTWsmjNPm4sC3fpb9xWV604bOeKp+Xs6Xm62l9ZXQWfn/uSby9n06tTLMEFQ0AJ7LDvtr3cjnuROwv6zyKkPoC9RzM4mX+anwyZRs9OMbbPXX0vnTR1ZJ8qpdFoZtLInhSUVjqlsefuHudq3/U15TTWVZB/8RKGunAqy0torKshSK2zpWuufG7m+m/NNWl/HEVfidHUfDDT2muyU0gA1XUGzGEGdNW6Vp9PfU05akXPPT06c+DoJToH1KNurMKsN6MzqFyej6IoBKLjm+xT/OtsKeOHxVJUVuWQXytPzlmlVqG4CMLsyzPMv46Kaufzt/agd7WtrlpB0Ve5PL6/n5peXUL5e24+ly73cRpX0up2X2PWeMUav9zIJ6bHys7O5qmnnurobAghhBBCeGz79u2MHj3aab1PBG8NDQ3k5eURFRWFn18HzlkjhBBCCNECk8mEVqtl6NChBLkYlsQngjchhBBCiLuFdFgQQgghhPAiErwJIYQQQngRCd6EEEIIIbyIBG9CCCGEEF5EgjchhBBCCC8iwZsQQgghhBeR4E0IIYQQwov4xPRYt8Nvf/tbkpOTOXPmjNNnu3fvZsOGDWg0ljngYmJi2LVrV3tnscM8/fTTnDt3zra8ePFilixZ4pBGURR+/etfU15eTm1tLfHx8fz4xz9u76x2GE/KyJfrUUNDAykpKRiNRqqrqykuLuZ3v/udQxpfr0OelJGv1qHCwkJmzpxJSEiIbV1VVRWHDx8mMjLSts6X65CnZeSrdQjgwIED7NixgwEDBnDx4kUWLFhAXFycQ5oOq0OKaLVz584py5YtUwYOHOjy8127dinfffddO+fqzrFmzZoW02RkZCgrVqxQFEVRGhoalEceeUQpKCi43Vm7Y3hSRr5cj371q18peXl5tuWcnBynNL5ehzwpI1+tQ0VFRcq2bdtsy2VlZcry5cud0vlyHfK0jHy1DpnNZmXUqFFKbm6uoiiKkpubq4wfP94pXUfVIXny1komk4lNmzbx0ksvcfjwYbfpdu7cSVZWFg0NDcybN49Bgwa1Yy47Vl1dHcnJySiKQkhICMuWLSM4ONghzb59+3jkkUcACAwMZOzYsfzlL39h+fLlHZHldudJGYFv1qOGhgaysrK47777yMzMRKfT8fTTTzul8+U65GkZgW/WodjYWJYtW2Zb3rFjB0888YRTOl+uQ56WEfhmHVKpVHTr1o2ysjIAysrKUKmcJ6nvqDokwVsrvf/++8ydO5ewsDC3aQYOHMiAAQMYMWIEly9fZt68eezZs4fo6Oh2zGnHmTJlCj/84Q8JDQ1l69atrFmzhi1btjikuXLlisOj+cjISAoLC9s7qx3GkzLy1Xp05coVLl26hEqlYvXq1eTl5bFo0SL279/v0MTjy3XI0zLy1Tpkz2Qy8dVXX/Hss886febLdchec2Xky3UoNTWVV199lYMHD5KXl+d0j4aOq0PSYaEVTp8+TUlJCZMmTWo23dChQxkxYgQAffr04f777ycrK6sdcnhnmD17NqGhoQDMmTOHAwcOoNfrW9zO1a+au5UnZeSr9Uin0wEwdepUANvEzDk5OS1u6yt1yNMy8tU6ZO/QoUNMnjwZPz8/j9L7Sh2y11wZ+WodamhoYPny5bz++uskJSWRlJTE5s2bMRqNLW7bHnVIgrdWOHToEDqdjnXr1vHuu+8CsG7dOg4ePOiQ7sKFCw7LGo2GhoaGdstnRzIYDBQVFdmWNRoNZrPZKTDp2bMn5eXltuXy8nJ69OjRbvnsSJ6Wka/WI+sverX6+u0pICBA6pAdT8vIV+uQvZ07d7ptDvTlOmSvuTLy1Tp09uxZqqqqGDlyJGAJYvPz8/n+++8d0nVUHZLgrRVWrFjBxo0bWb9+PatWrQJg/fr1PProoxw8eJCamhoA3nrrLaqqqgDLu015eXmMHTu2w/LdnkpLS0lOTrYtf/fddwwZMoROnTo5lNGsWbP48ssvAdDr9Rw9epTp06d3SJ7bm6dl5Kv1KDo6mlGjRnH06FHA8q6JVqtl5MiRUoeaeFpGvlqHrPLz8+natSsRERG2dVKHHLVURr5ah3r16oXBYKC4uBiwXGM1NTVER0ffEXVIpSiKctuPcpfJzs7mk08+Ye/evTz11FP87Gc/Y9WqVSQmJjJ69Gg+/PBDvvnmG/r27UtBQQE/+tGPmD17dkdnu13U1tby2muvERISQmhoKEVFRbzyyiv079+fGTNm2MpIURSSk5OpqKigtraWKVOmuP3ld7fxtIx8uR4VFhayceNGYmNjKSoqYt68eUycOFHqkB1PysiX6xBYAo9Zs2YxfPhw2zqpQ45aKiNfrkMZGRns2bOH/v37c/78eRISEvjJT35yR9QhCd6EEEIIIbyINJsKIYQQQngRCd6EEEIIIbyIBG9CCCGEEF5EgjchhBBCCC8iwZsQQgghhBeR6bGEEHe0r7/+mo0bN3L69GnGjBnjNHp5enp6B+WsdfLz88nKyiIhIYEXX3yR3Nxczpw54/H2p06d4uWXX+bcuXPExcXxwQcf2D579dVX+dvf/sbcuXOJiopi0qRJDBgw4HachhDiDiBDhQgh7nhHjhxh0aJFnDhxAn//6785Fy5c6BXBW0VFBUuXLiUtLY2IiAgKCwuJj49vVfAG2Lb7+OOPefDBB23rr169SkpKCklJSVy7do0lS5bYjiWEuPtIs6kQwmu98sorHZ0Fj6SkpDB16tSbDqZ69erFsGHDyMjIcFi/f/9+2zynXbt2ZerUqaSkpNzUsYQQdy4J3oQQXufIkSOkpKTYRoW3jna+adMmVq5cyWOPPcZvfvMbh23S0tJ48sknmT9/Pm+++SYGgwGAF154gWHDhpGWlsZzzz3HQw89xO7du6mrq2P16tXMnj2bZ599lg0bNjB69GgSExN55plnGDRoEMuXLwdg7969xMXFsXLlSqe8ms1mvvjiC8aMGePyXPLy8pgyZQoJCQn88Y9/BOCrr77ipz/9KQsXLuS5556jpKTElj4hIYHMzEzsG02+/fZbJkyYYFseO3Ysn3/+OSaTqS3FK4S4w0nwJoTwGs888wwLFy4kKSnJYX1iYiKDBw/m5MmTbN68mfT0dD744ANb0PPnP/+ZXbt28Yc//IHt27dTVlZGWloaAKmpqURFRZGfn8/WrVt57733iIiIIDU1laqqKnbv3k1qairHjx9n8ODBJCYm8v7779OlSxdb8DZ79myGDh3K5s2bnfJcUlJCWVkZvXv3dnlO/v7+jBw5kn379jF//nwKCgpYuXIlb7/9Nunp6Tz88MOsWbPGlj4hIYHS0lJycnIAS1NqTEyMQ3Nynz59KC8vt83LKIS4u0jwJoTwGr///e9JT09n7dq1Lj+Pi4tDpVLRvXt3unTpwpUrVwDYs2cP06dPJzg4GJVKxYwZM9i3b5/DtvHx8QCMHz+eyZMnk5mZyYwZM1Cr1QQEBNiaJQE0Gg3Tp09n7969AJw8eZJBgwY5daYAy4TWAKGhoU6fnT17lrfeeoukpCQCAgIA+Oyzzxg6dCj33HMPYJln8ttvv6W0tBSAHj16MHz4cFvTaUZGBgkJCQ77DQsLA6C8vNxdUQohvJj0NhVCeJ1x48Yxbtw4p/XWoAUgMDCQxsZGAIqLi/n00085cuQIAHq9HrXa8bdreHi4w7JWq6Vr16625c6dOzt8Pnv2bJYuXcovfvEL9u3bx5NPPukyr9bmTVeBXUpKCidPnuTs2bMMGzbMltf8/HwWLlxoS9ezZ0/Ky8vp3r07YHn6lpaWxmuvvcbRo0dZsmSJy2ObzWaX64UQ3k2CNyGE1zp69Chjx45tMV1sbCwTJkxg6dKltnUVFRXNbhMVFcW1a9dsy5WVlQ6fDx8+nMjISA4cOMDFixfdDs0RGRkJgE6nIygoyOGzjRs38vHHH7N27Vp27dpFQEAAsbGxDB06lG3bttnSVVVVOQSmCQkJJCcns3PnTnr27Imfn5/DfnU6ncOxhRB3F2k2FUJ4LU97VM6ZM4fMzEz0ej1g6fDwy1/+stltEhIS+PTTTzGbzRgMBg4ePOiU5vHHH+ftt99m4sSJbvcTHR3t0IRrLzg4mEWLFhEaGsrWrVsBmD59Orm5ubb05eXlLFiwwOEpWkxMDA8++CDJyclOTaYAV65coXPnzsTExDR7jkII7yRP3oQQd7Ts7GxbkLZq1SqXzY8bN27k1KlTaLVa+vfvz759+9BqtSQlJfHOO+8wc+ZMtFotCxYsIDg4mLCwMNavXw9YBri1pl26dCkzZ84E4Pnnn+f1119nzpw5xMbGMmbMGI4dO+Zw3Mcff5zU1FSmT5/uNv/+/v7Ex8dz7Ngxhg8fTmVlJS+99BIAixcv5o033qCqqopt27Zx7tw5tmzZwjvvvMPq1avRaDSoVCrefPNNNBqNw34TEhIoKChw+eQxOzub+Ph4p22EEHcHGaRXCCFc0Ol0aDQaW0eCtLQ08vLy2LRpky1NdXU1a9euJTU1tdl9FRcXs2LFCtLT0112XLiVdDodCxcu5L333pMnb0LcpaTZVAghXMjIyGD37t0AGAwGMjMziYuLA+Dw4cNUVFTw2WefMW3atBb3FRMTQ2JiYrvMBrF9+3YSExMlcBPiLiZP3oQQwoUTJ06QlJSEWq1Gp9MRFxfHiy++iFqtJj09ne3bt9OvXz9SU1MdxlgTQojbTYI3IYQQQggvIs2mQgghhBBeRII3IYQQQggvIsGbEEIIIYQXkeBNCCGEEMKLSPAmhBBCCOFFJHgTQgghhPAi/w9C8is06RSQIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "# # Plot using Matplotlib:\n",
    "ax.errorbar(df.e, df.rate, xerr=df.de,yerr=df.rate_err,fmt='o-',label='data')\n",
    "ax.plot(df.e, df.total, color='red',label='Total model',linewidth=3)\n",
    "for j in range(ncomp):\n",
    "    ax.plot(df.e, df[f'model{j}'],label=f'{model.componentNames[j]}')\n",
    "#ax.plot(x, y)\n",
    "ax.set_xlabel('Energy (keV)')\n",
    "ax.set_ylabel(r'counts/s/keV')\n",
    "ax.set_xscale(\"linear\")\n",
    "ax.set_yscale(\"linear\")\n",
    "ax.set_ylim((0.001,0.1))\n",
    "ax.grid()\n",
    "ax.legend()\n",
    "plt.show();\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot command list is now empty\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# clear all, data, plot commands and close the logfile\n",
    "#\n",
    "s = None\n",
    "xspec.Plot.commands = ()\n",
    "xspec.AllData.clear()\n",
    "xspec.Xset.closeLog()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}