1.6.Scattering_at_100deg.ipynb

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    "** ** \n",
    "\n",
    "<figure>\n",
    "<div style=\"text-align:center\"><img src=\"./img/compton_setup.png\" /></div>\n",
    "<p><center>The experimental setup.</center></p>\n",
    "</figure>\n",
    "\n",
    "# Compton gamma-ray scattering at a 100-degree angle\n",
    "\n",
    "** ** \n",
    "\n",
    "* This project uses the lmfit package. Lmfit provides curve fiting using non-linear least squares minimization and was developed by Matt Newville: https://github.com/newville."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a2ba1a3-90c8-4e51-9b5c-ee4e2752dc47",
   "metadata": {},
   "source": [
    "## Install and import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e39f1c7-de86-43c2-a90c-b369f7495ee5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#uncomment the following line to install lmfit\n",
    "#!pip install lmfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e53f6d8-c3aa-44c4-a10c-64f76c6e361e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#import libraries\n",
    "from numpy import sqrt, pi, exp, linspace, loadtxt\n",
    "from lmfit.models import MoffatModel, PolynomialModel\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import matplotlib.lines as mlines\n",
    "import pandas as pd\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.signal import find_peaks\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dee50b56-adba-4b44-891a-8c25bfb119f1",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "907e6f02-3654-49fb-b3a4-716fe6bd6dd1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Load data\n",
    "\n",
    "#Load channel counts of scattering from large aluminum target at 18deg\n",
    "df_100 = pd.read_csv(\"./Data/180912-1437_Cs-137_LgHole_LgAl_100-degrees.csv\")"
   ]
  },
  {
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   "execution_count": 4,
   "id": "a6c9720c-4b60-49e7-9638-b6e399e68f9f",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "   0\n",
       "0  0\n",
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     "execution_count": 4,
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   "source": [
    "df_100.head()"
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       "   Channel  0\n",
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    "#force start the index at 1 and add it as a column to match the channel numbers\n",
    "# Set the index to start at 1\n",
    "df_100.index = range(1, len(df_100) + 1)\n",
    "\n",
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    "\n",
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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#preview the data \n",
    "\n",
    "#Uncomment the next line to make the graph interactive\n",
    "#%matplotlib widget\n",
    "\n",
    "#rename the second column\n",
    "df_100.rename(columns={\"0\": \"Counts\"}, inplace=True)\n",
    "\n",
    "#remove unnecessary data\n",
    "df_100 = df_100[df_100['Channel'] <= 700]\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "#preview the spectrum\n",
    "plt.plot('Channel', 'Counts', data = df_100, marker = \".\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55c5918c-620c-4d5a-9869-c27dae6a9805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>Channel</th>\n",
       "      <th>Counts</th>\n",
       "      <th>Channel Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>177</td>\n",
       "      <td>94</td>\n",
       "      <td>Range 176-214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>178</td>\n",
       "      <td>101</td>\n",
       "      <td>Range 176-214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>179</td>\n",
       "      <td>98</td>\n",
       "      <td>Range 176-214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>180</td>\n",
       "      <td>103</td>\n",
       "      <td>Range 176-214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>181</td>\n",
       "      <td>117</td>\n",
       "      <td>Range 176-214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Channel  Counts  Channel Range\n",
       "177      177      94  Range 176-214\n",
       "178      178     101  Range 176-214\n",
       "179      179      98  Range 176-214\n",
       "180      180     103  Range 176-214\n",
       "181      181     117  Range 176-214"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the channel ranges\n",
    "channel_ranges = [0, 176, 214, 700]\n",
    "\n",
    "# Create labels for the ranges\n",
    "labels = [f\"Range {i}-{j}\" for i, j in zip(channel_ranges[:-1], channel_ranges[1:])]\n",
    "\n",
    "# Use pd.cut() to categorize the data into the specified ranges\n",
    "df_100['Channel Range'] = pd.cut(df_100['Channel'], bins=channel_ranges, labels=labels, include_lowest=True)\n",
    "\n",
    "# Create a dictionary of DataFrames, one for each channel range\n",
    "channel_range_dfs = {label: group for label, group in df_100.groupby('Channel Range')}\n",
    "\n",
    "# Access individual DataFrames using keys (e.g., channel_range_dfs['Range 0-40'])\n",
    "channel_range_dfs['Range 176-214'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5234dd82-70d8-49f5-ae15-1076b5315cee",
   "metadata": {},
   "source": [
    "## Define the model, fit, and print the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "406ce5e6-7458-4a28-b3cc-72d5cd79a401",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#use builtin NameModel, specify prefix:\n",
    "mod2 = MoffatModel(prefix='g2_') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "149f5e88-b6b1-48ce-8677-b0b3436fc431",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#initialize the parameters to be optimized\n",
    "pars2  = mod2.make_params(g2_amplitude=158.0, g2_center=194.0, g2_sigma=6.34, g2_beta=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2d0188b7-5b03-4a1c-87e8-118cb1009f0e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "[[Model]]\n",
      "    Model(moffat, prefix='g2_')\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 62\n",
      "    # data points      = 38\n",
      "    # variables        = 4\n",
      "    chi-square         = 34.0020892\n",
      "    reduced chi-square = 1.00006145\n",
      "    Akaike info crit   = 3.77576080\n",
      "    Bayesian info crit = 10.3261054\n",
      "    R-squared          = 0.99781152\n",
      "[[Variables]]\n",
      "    g2_amplitude:  148.539247 +/- 3.24108170 (2.18%) (init = 158)\n",
      "    g2_center:     196.178318 +/- 0.48241950 (0.25%) (init = 194)\n",
      "    g2_sigma:      16.6419683 +/- 9.32592672 (56.04%) (init = 6.34)\n",
      "    g2_beta:       0.56893545 +/- 0.45910477 (80.70%) (init = 1)\n",
      "    g2_fwhm:       51.3642693 +/- 7.85235343 (15.29%) == '2*g2_sigma*sqrt(2**(1.0/max(1e-3, g2_beta))-1)'\n",
      "    g2_height:     148.539247 +/- 3.24108170 (2.18%) == 'g2_amplitude'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(g2_sigma, g2_beta)      = +0.9943\n",
      "    C(g2_amplitude, g2_sigma) = -0.6168\n",
      "    C(g2_amplitude, g2_beta)  = -0.5528\n",
      "------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#fit with weights=1./yerr\n",
    "result2 = mod2.fit(channel_range_dfs['Range 176-214']['Counts'], pars2, x = channel_range_dfs['Range 176-214']['Channel'],\n",
    "                   weights = 1 / (channel_range_dfs['Range 176-214'][\"Channel\"] * 0.046905))\n",
    "\n",
    "#print the Fit Statistics and parameters\n",
    "print('------------------------------------------------------')\n",
    "print(result2.fit_report())\n",
    "print('------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4948fa2f-affb-492f-b279-74b90541ab92",
   "metadata": {},
   "source": [
    "## Plot the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a6ec5ef-37e4-4acc-8a34-fada8924ac6d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MVJ1T3u7O4UP2XpdOp1P9PRBCiLvuuktERkaqArPKykrRtm1b1Xfk1KlTIigoSDz11FOqxxcUFIjExEQxYsQI4zYOH/JPHD5EtVanTp0QEhKCsWPH4vPPP8exY8csjqmsrERFRYXxR6/Xu/x8o0aNwvbt27FmzRo899xzeOutt/DUU09ZPT4nJwdLly7FoEGD0KhRI7vn37RpEwBgxIgRqu3Dhw9XDXOQzZ49G9deey3CwsIQFBSE4OBg/PTTTzhw4IDFsUOGDFENr2jTpg0AWAydkbefOnVKtb1Tp06q1yAf17dvX0RERFhsVw4NunLlCv7v//4PKSkpCAoKQlBQEOrUqYPCwkLNtmq59dZbERwcbLF98eLF6NmzJ+rUqWN8Dz799FPVeQMCAvDkk09i5cqVxtd19OhRrF69GuPGjbNZMceRz9iqVasQFhaG0aNH23wNu3btwq233orY2FgEBgYiODgYDz74ICorK3Ho0CGH3gctJSUl+Omnn3D77bcjIiJC9XkfMmQISkpKsG3bNtVj7rzzTtX9LVu2ICcnByNHjrT4vgwaNAjbt29HYWEhCgsLsX37dtxxxx2qalpRUVG45ZZbHGrvhQsX8Nhjj6FJkybG31mzZs0AwOHPgztY+72bb7f1+ZD3bd26FSUlJfjHP/6h2t+jRw/ja5OtXLkS7du3R6dOnVTv9cCBA1VDpKz9Pbj33nvtvzgHmH8Gfv75ZwBQDUkDgG7duqFNmzYWw8gaNGiAtLQ04/2YmBjUr18fnTp1QsOGDY3btf4mWOPI91nWr18/REVFGe8nJCSgfv36Dj3P3LlzodfrVd/Z0aNHo7Cw0FgYwp2ceV033ngjoqOjVds2bdqEG2+8EXFxccZtAQEBFp+NNWvWoKKiAg8++KDqsxUWFoY+ffr4TZECso5BAdUYcXFxiIiIwPHjxx06vkWLFli/fj3q16+PJ554Ai1atECLFi3w7rvvGo/p378/goODjT/2Om62JCYmokuXLkhPT8cbb7yBV199FR988IFqrKbS/PnzUVpaiocfftih82dnZwMw/OemFBQUhNjYWNW2GTNm4PHHH0f37t3x3XffYdu2bdi+fTsGDRqE4uJii3PHxMSo7oeEhNjcXlJS4rbH33ffffjggw/w8MMPY82aNfjjjz+wfft2xMfHa7ZVS4MGDSy2LVmyBCNGjECjRo0wf/58bN26Fdu3b8fo0aMt2j969GiEh4dj9uzZAAzj+8PDw+1+Hhz5jF28eBENGza0Oab91KlTuOGGG3D27Fm8++672Lx5M7Zv324c1+vo+6AlOzsbFRUVeP/991Wf9eDgYGMVHuV4fcDy/Tx//jwAQwBqfo4333wTQgjk5OQgNzcXer0eiYmJFu3Q2mZOr9cjPT0dS5YswXPPPYeffvoJf/zxhzFoqcr74Cj5uyR/35RycnJUn+nY2FjN4woLC1FWVmY8Vj7Gkffl/Pnz2Lt3r8X7HBUVBSGE8XeVnZ2NoKAgi++Y+d8HV5l/BuTXoPVda9iwocX7YN4uwPD9d/Rvijlnvs8ALP4mAkBoaKjdz5Ber8e8efPQsGFDpKWl4fLly7h8+TJuuukmREZG4tNPP7X5eGc5+7q03v/s7GzN37v5Nvl73LVrV4vP1zfffGPxd4D8j+XlRSIfFRgYiP79+2PVqlU4c+YMGjdubPcxN9xwA2644QZUVlZix44deP/99zF+/HgkJCTgnnvuwUcffYSCggLj8corLVXVrVs3AMChQ4fQuXNni/2ffvopEhISMHToUIfOJ/8nd/78edVV+YqKCov/kOfPn4++ffti1qxZqu3K1+oL8vLysHLlSrzyyit4/vnnjdtLS0udWm9C62rt/PnzkZycjG+++Ua137ykJADodDqMHDkSn3zyCZ555hnMnTsX9913H+rVq2f3ue19xuLj4/Hrr79Cr9dbDQyWLl2KwsJCLFmyRHXlePfu3fZfvB3R0dEIDAzEAw88gCeeeELzmOTkZNV98/dT/l68//77uO666zTPkZCQgPLyckiShKysLIv9WtvM7du3D3v27MG8efMwcuRI43Z5onN1aN++PQBDjXrz0qWZmZnG/QCQmpqKhQsXIisrS9W5z8zMVJ1L/u5ae1/kSaeA4b0ODw/HZ599ptk++XcRGxuLiooKi0DFkffZEeafAfk1nDt3zuJv799//+3Wv51anPk+V8X69euN2QStwGLbtm34888/0bZtW7c8n7OvS+tvXWxsrLHDr2T+WZB/R99++61FhooIYKaAapgXXngBQgg88sgjKCsrs9hfXl6OFStWWGwPDAxE9+7djVded+7cCQBo3bo1unTpYvxR/udcVXK6PSUlxWLfjh07sHfvXowcOVJz6I+W3r17A4BF+vrbb7+1WCRNkiSL6kx79+7F1q1bHW5/dZAkCUIIi7Z+8sknqKysrPK5Q0JCVP+JZmVlaVb1AIB//vOfuHTpEoYPH47Lly/jySefdOr5rH3GBg8ejJKSEps1yuU2Kt8HIQQ+/vhji2OtXe2UH2u+LyIiAv369cOuXbvQoUMH1edd/tHq/Cj17NkT9erVw59//qn5+C5duiAkJMRYYWrJkiWqq5wFBQWa30tH3gcA+Oijjxx+vVXVqFEjdOvWDfPnz1d9Brdt24aDBw/ijjvuMG4bNmwYJEkyVoCRzZs3D+Hh4Rg0aBAA4LrrrkNYWBi++uor1XFbtmyxGM4ydOhQHD16FLGxsZrvs/w3qk+fPgAs/x4sXLiwam+AFXJ1tPnz56u2b9++HQcOHED//v098rwyZ7/Prvr0008REBCApUuX4ueff1b9fPnllwBgNWCzxdr31h2vq0+fPtiwYYPqSr9er8fixYtVxw0cOBBBQUE4evSo1e+xsr1A9WTnyHcwU0A1yvXXX49Zs2Zh3LhxSEtLw+OPP4527dqhvLwcu3btwpw5c9C+fXvccsstmD17NjZs2ICbb74ZTZs2RUlJifGP+U033WT3uU6ePInt27cDMIwxBwwdcMBQYk7+A/rKK6/g/Pnz6N27Nxo1aoTLly9j9erV+Pjjj3HXXXepxtXK5BS0MwuttWvXDvfeey+mT5+OwMBA3Hjjjdi/fz+mT58OnU6nugo9dOhQvPbaa3jllVfQp08fHDx4EK+++iqSk5MtAghvqlu3Lnr37o233noLcXFxSEpKwqZNm/Dpp586dJXelqFDh2LJkiUYN24chg8fjtOnT+O1115DgwYNcPjwYYvjW7VqhUGDBmHVqlXo1auXzQXrZI58xu69917MnTsXjz32GA4ePIh+/fpBr9fj999/R5s2bXDPPfdgwIABCAkJwb333ovnnnsOJSUlmDVrFnJzcy2eMzU1FUuWLMGsWbOQlpaGgIAAdOnSBVFRUWjWrBmWLVuG/v37IyYmxvievvvuu+jVqxduuOEGPP7440hKSkJBQQGOHDmCFStWYMOGDTZfZ506dfD+++9j5MiRyMnJwfDhw1G/fn1cvHgRe/bswcWLF41Zqddeew2DBg3CgAEDMHHiRFRWVuLNN99EZGSk3ezPNddcgxYtWuD555+HEAIxMTFYsWIF1q1bp/k+AMC7776LkSNHIjg4GK1bt1aNIzcnf3/luR87duxAnTp1ABiGRsnefPNNDBgwAHfddRfGjRuHCxcu4Pnnn0f79u1V5UfbtWuHMWPG4JVXXkFgYCC6du2KtWvXYs6cOXj99deNV/Cjo6PxzDPP4PXXX8fDDz+Mu+66C6dPn8akSZMshg+NHz8e3333HXr37o0JEyagQ4cO0Ov1OHXqFNauXYuJEyeie/fuGDRoEHr27ImJEyciPz8faWlp2Lp1q3GRraqWVDbXunVrjB07Fu+//z4CAgIwePBgnDhxAi+99BKaNGmCCRMmuPX5zDn7fXZFdnY2li1bhoEDB2LYsGGax7zzzjv44osvMHXqVM15TNbIWaVvvvkGzZs3R1hYGFJTU93yul588UWsWLEC/fv3x4svvmgcCimXsJU/C0lJSXj11Vfx4osv4tixYxg0aBCio6Nx/vx5/PHHH4iMjDQuWCZ/v958800MHjwYgYGB6NChg3G4F9VS3pzlTOSq3bt3i5EjR4qmTZuKkJAQERkZKTp37ixefvllceHCBSGEEFu3bhW33367aNasmQgNDRWxsbGiT58+NivfKMlVOLR+lJV+li9fLm666SaRkJAggoKCRJ06dUS3bt3Ee++9p7noU1FRkdDpdBYVXhxRUlIinn76aVG/fn0RFhYmrrvuOrF161ah0+nEhAkTjMeVlpaKZ555RjRq1EiEhYWJa6+9VixdulSMHDlSVblGrt7z1ltvqZ5HrkKzePFizfdEWRmlWbNm4uabb7ZoKzQqm2g935kzZ8Sdd94poqOjRVRUlBg0aJDYt2+faNasmc2KSrbaL3vjjTdEUlKSCA0NFW3atBEff/yxzUW05s2bJwCIhQsX2nxemaOfseLiYvHyyy+Lli1bipCQEBEbGytuvPFGsWXLFuMxK1asEB07dhRhYWGiUaNG4tlnnxWrVq0SAFRlZHNycsTw4cNFvXr1hCRJqteyfv160blzZxEaGmrxOT1+/LgYPXq0aNSokQgODhbx8fGiR48eqnK41n7vsk2bNombb75ZxMTEiODgYNGoUSNx8803Wxy/fPly0aFDBxESEiKaNm0q3njjDYcXL/vzzz/FgAEDRFRUlIiOjhZ33XWXOHXqlGZFnRdeeEE0bNhQBAQEWLxPWqx9n7XatXbtWnHdddeJsLAwERMTIx588EFx/vx5i+PKysrEK6+8Yvxb1KpVK81KOnq9XkydOlU0adJEhISEiA4dOogVK1ZoVq+5cuWK+M9//iNat24tQkJCjOVkJ0yYILKysozH5eTkiIceekjUq1dPREREiAEDBoht27YJAHYrd8nvh7XqQ+bVj4QwVLR58803RatWrURwcLCIi4sT999/vzh9+rTquD59+oh27dpZPN6ZvxVaHP0+Wzufvb8pctW1pUuXWj1m9uzZAoD47rvvhBCOVx86ceKESE9PF1FRUcYy0O56XUIIsXnzZtG9e3cRGhoqEhMTxbPPPivefPNNAUBcvnxZdezSpUtFv379RN26dUVoaKho1qyZGD58uKq0cGlpqXj44YdFfHy88e+MK5W+qGaRhDBbsYeIapQtW7agZ8+e+Oqrr3Dfffd5uzk12p133olt27bhxIkTTl0FJPIVCxYswD/+8Q/89ttv6NGjh7ebQ16Unp6OEydOVKl6GfkXDh8iqkHWrVuHrVu3Ii0tDeHh4dizZw/eeOMNtGzZUjXWmRxXWlqKnTt34o8//sD333+PGTNmMCCgGuHrr7/G2bNnkZqaioCAAGzbtg1vvfUWevfuzYDAzzz99NPo3LkzmjRpgpycHHz11VdYt26d26slUe3GoICoBqlbty7Wrl2LmTNnoqCgAHFxcRg8eDCmTp2qqgtPjjt37hx69OiBunXr4tFHH7W5tgSRL4mKisLChQvx+uuvo7CwEA0aNMCoUaPw+uuve7tpVM0qKyvx8ssvIysrC5IkoW3btvjyyy9x//33e7tpVINw+BARERERkZ9jSVIiIiIiIj/HoICIiIiIyM8xKCAiIiIi8nMMCoiIiIiI/ByDAiIiIiIiP8eggIiIiIjIzzEoICIiIiLycwwKiIiIiIj8HIMCIiIiIiI/x6CAiIiIiMjPMSggIiIiIvJzDAqIiIiIiPwcgwIiIiIiIj/HoICIiIiIyM8xKCAiIiIi8nMMCoiIiDzkt99+Q2pqKoKDg3HbbbdZ3VYd+vbti/Hjx1fb8xFRzcKggIjIi0aNGgVJkiBJEoKDg5GQkIABAwbgs88+g16vd/g88+bNQ7169TzXUD8g/y4ee+wxi33jxo2DJEkYNWqUU+d8+umn0alTJxw/fhzz5s2zus2RtjkSQCg/T8qfI0eOYMmSJXjttdeMxyYlJWHmzJlOvR4iqr0YFBARedmgQYNw7tw5nDhxAqtWrUK/fv3wr3/9C0OHDkVFRYW3m+dXmjRpgoULF6K4uNi4raSkBF9//TWaNm3q9PmOHj2KG2+8EY0bNzYGbVrb3En+PCl/kpOTERMTg6ioKLc/HxHVDpIQQni7EURE7iSEQHm5d547OBiQJMnh40eNGoXLly9j6dKlqu0bNmxA//798fHHH+Phhx/GjBkzMHfuXBw7dgwxMTG45ZZbMG3aNNSpUwcbN25Ev379VI9/5ZVXMGnSJMyfPx8zZ87EwYMHERkZiRtvvBEzZ85E/fr13fFyHZeXB2RmVu9zylJTAZ3O7mHy7+LYsWP4v//7P/zjH/8AACxYsABvvPEGmjdvjnr16hmv7peWluLZZ5/FwoULkZ+fjy5duuCdd95B165dceLECSQnJ6vOP3fuXDz00EMW2x544AGMHTsWGzZsQFZWFpo2bYpx48bhX//6FwBg0qRJmDx5supxP//8M/r27Wv1NZh/ngDD8KFOnTph5syZ6Nu3LzZt2qTaz+4AkX8L8nYDiIjcrbwceO0973RwXvqnhJCQqp/nxhtvRMeOHbFkyRI8/PDDCAgIwHvvvYekpCQcP34c48aNw3PPPYf//e9/6NGjB2bOnImXX34ZBw8eBADUqVMHAFBWVobXXnsNrVu3xoULFzBhwgSMGjUKP/74Y9Ub6YzMTOCGG6r3OWWbNwO9ejl8+EMPPYS5c+cag4LPPvsMo0ePxsaNG1XHPffcc/juu+/w+eefo1mzZpg2bRoGDhyII0eOoEmTJjh37hxat26NV199FXfffTeioqIwaNAg1TadTge9Xo/GjRtj0aJFiIuLw5YtWzB27Fg0aNAAI0aMwDPPPIMDBw4gPz8fc+fOBQDExMRU6S1ZsmQJOnbsiLFjx+KRRx6p0rmIqHZgUEBE5KOuueYa7N27FwBUE0STk5Px2muv4fHHH8f//vc/hISEQKfTQZIkJCYmqs4xevRo4+3mzZvjvffeQ7du3XDlyhVj4EBqDzzwAF544QWcOHECkiTht99+w8KFC1VBQWFhIWbNmoV58+Zh8ODBAICPP/4Y69atw6effopnn30WiYmJkCQJOp3O+HuJjIy02AZAlQlITk7Gli1bsGjRIowYMQJ16tRBeHg4SktLLX6/WlauXKn63Q4ePBiLFy9WHRMTE4PAwEBERUU5dE4iqv0YFBBRrRMcbLhi763ndhchhHEo0s8//4wpU6bgzz//RH5+PioqKlBSUoLCwkJERkZaPceuXbswadIk7N69Gzk5OcbJy6dOnULbtm3d19haJC4uDjfffDM+//xzCCFw8803Iy4uTnXM0aNHUV5ejp49exq3BQcHo1u3bjhw4IDTzzl79mx88sknOHnyJIqLi1FWVoZOnTq51P5+/fph1qxZxvu2Ph9ERDIGBURU60iSe4bweNuBAweQnJyMkydPYsiQIXjsscfw2muvISYmBr/++ivGjBmDchuTJwoLC5Geno709HTMnz8f8fHxOHXqFAYOHIiysrJqfCUwjOvfvLl6n1P53E4aPXo0nnzySQDAhx9+aLFfHn9vPn9EGcg5atGiRZgwYQKmT5+O66+/HlFRUXjrrbfw+++/O91uwBAEpKSkuPRYIvJfDAqIiHzQhg0bkJmZiQkTJmDHjh2oqKjA9OnTERBgKBq3aNEi1fEhISGorKxUbfvrr79w6dIlvPHGG2jSpAkAYMeOHdXzAszpdE6N6/e2QYMGGQOngQMHWuxPSUlBSEgIfv31V9x3330AgPLycuzYscPptQA2b96MHj16YNy4ccZtR48eVR2j9futKk+ck4hqLpYkJSLystLSUmRlZeHs2bPYuXMnpkyZgmHDhmHo0KF48MEH0aJFC1RUVOD999/HsWPH8OWXX2L27NmqcyQlJeHKlSv46aefcOnSJRQVFaFp06YICQkxPm758uWqOvVkXWBgIA4cOIADBw4gMDDQYn9kZCQef/xxPPvss1i9ejX+/PNPPPLIIygqKsKYMWOceq6UlBTs2LEDa9aswaFDh/DSSy9h+/btqmOSkpKwd+9eHDx4EJcuXbKZIXJUUlISfvnlF5w9exaXLl2q8vmIqGZjUEBE5GWrV69GgwYNkJSUhEGDBuHnn3/Ge++9h2XLliEwMBCdOnXCjBkz8Oabb6J9+/b46quvMHXqVNU5evTogcceewx333034uPjMW3aNMTHx2PevHlYvHgx2rZtizfeeANvv/22l15lzVO3bl3UrVvX6v433ngDd955Jx544AFce+21OHLkCNasWYPo6Ginnuexxx7DHXfcgbvvvhvdu3dHdna2KmsAAI888ghat26NLl26ID4+Hr/99ptLr0np1VdfxYkTJ9CiRQvEx8dX+XxEVLNxnQIiIiIiIj/HTAERERERkZ9jUEBERERE5OcYFBARERER+TkGBUREREREfo5BARERERGRn2NQQERERETk5xgUEBERERH5OQYFRERERER+LsjbDfAFer0ef//9N6KioiBJkrebQ0RERERUZUIIFBQUoGHDhggIsJ0LYFAA4O+//0aTJk283QwiIiIiIrc7ffo0GjdubPMYBgUAoqKiABjesLp163q5NUREREREVZefn48mTZoY+7q2MCgAjEOG6taty6CAiIiIiGoVR4bHc6IxEREREZGfY1BAREREROTnGBQQEREREfk5BgVERERERH6OQQERERERkZ9jUEBERERE5OcYFBARERER+TkGBUREREREfo5BARERERGRn2NQQERERETk5xgUEBERERH5OQYFRERERER+jkEBEREREZGfY1BAREREROTnGBQQEREREfk5BgVU7fIKBI6dEsgrEN5uChEREREBCPJ2A8i/ZGQKLFsrIAQgScCwdCAtVfJ2s4iIiIj8GjMFVG3yCkwBAQAIASxby4wBERERkbcxKKBqk50LY0AgEwLIyfVOe4iIiIjIgEEBVZvYaMOQISVJAmKivdMeIiIiIjJgUEDVRhclYVi6OioYli5BF8U5BURERETexKCAqpVyUnF0PU4yJiIiIvIFDArIa4L46SMiIiLyCeyWERERERH5OQYF5D0cOURERETkExgUkNcwJiAiIiLyDQwKyHsYFRARERH5BAYFRERERER+jkEBeQ0TBURERES+gUEBVavL+XpvN4GIiIiIzAR5uwHkPzIyBZatNd0vLvFeW4iIiIjIhJkCqhZ5BQLL1ggIYdqWf8WwnYiIiIi8i0EBVYvsXECr+5+TW+1NISIiIiIzDAqoWsRGa2+PsbKdiIiIiKoPgwKqFrooy1pDkRHa24mIiIioejEoIK8J5jR3IiIiIp/AoIC8pqLS2y0gIiIiIoBBAVWTigrLacaVDAqIiIiIfAKDAqoWBYWW2xgUEBEREfkGBgVULbbvYaaAiIiIyFcxKCCPyysQ2PyH5fZKPSAEFy8jIiIi8jYGBeRx2TYWKGO2gIiIiMj7GBSQx1lbuAwwZAuIiIiIyLsYFJDH6aIkdGijva+ionrbQkRERESWGBRQtWicqL1yMTMFRERERN7HoICqhd5K57+SmQIiIiIir2NQQNXCWkaggpkCIiIiIq9jUEDVwmqmoNJQsvTYKYG8ApYnJSIiIvKGIG83gPyDXq/d4d93UOCX3wEhAEkChqUDaana8w+IiIiIyDOYKaBqUVyivX3TNkNAABj+XbaWGQMiIiKi6saggKpFYbFjxwkB5NhY7IyIiIiI3I9BAVWLkGDHjpMkIMbGYmdERERE5H4MCqhaBDsQFBjmFEjQRXFOAREREVF1YlBA1UKuPtS5PWCty//IPZxkTEREROQNDAqoWhhLkgrA2jTiSs4vJiIiIvIKBgVULeSgoE6k9WNyLldLU4iIiIjIDIMCqhZyUBARLmFgH+1j6tapvvYQERERkQkXL6NqUXk1KAgIAHqkBQDQY80m9THhYZxPQEREROQNzBRQtZAzBQFX+/29ugagSQPtY4iIiIioejEooGqhvzqJOEDxiQsPUx9TyaCAiIiIyCsYFFC1kLMAgYGmbUFB2scQERERUfViUEDVwnz4EGAZFFRWVl97iIiIiMiEQQFVC71iorEsmJkCIiIiIp/AoICqhSNBAecUEBEREXkHgwKqFlpBAYcPEREREfkGBgVULRwJCjh8iIiIiMg7GBRQtTBWH1IFBerFyjh8iIiIiMg7GBRQtajUWKeAE42JiIiIfAODAqoW+qvzBWxONOacAiIiIiKvYFBA1cK4orFynYJAs2OYKSAiIiLyCgYFVC2ME41trGjMOQVERERE3sGggKqF1orGwcHqYxgUEBEREXkHgwLyuLwCgbIyw21VSVLz4UOcU0BERETkFUH2DyFyXUamwLK1AuLqnIK/jgo0TDCkCyxXNBYA1GVKiYiIiMjzmCkgj8krUAcEALBxq2E7wMXLiIiIiHyFV4OCqVOnomvXroiKikL9+vVx22234eDBg6pjhBCYNGkSGjZsiPDwcPTt2xf79+9XHVNaWoqnnnoKcXFxiIyMxK233oozZ85U50shDdm5UAUEgOF+Tq7htsVEYw4fIiIiIvIKrwYFmzZtwhNPPIFt27Zh3bp1qKioQHp6OgoLC43HTJs2DTNmzMAHH3yA7du3IzExEQMGDEBBQYHxmPHjx+P777/HwoUL8euvv+LKlSsYOnQoKtnL9KrYaMttkgTEXN3OxcuIiIiIfIMkhPm1XO+5ePEi6tevj02bNqF3794QQqBhw4YYP348/u///g+AISuQkJCAN998E48++ijy8vIQHx+PL7/8EnfffTcA4O+//0aTJk3w448/YuDAgXafNz8/HzqdDnl5eahbt65HX6O/WbFejz92m+4P7AP06mqIRfOvCLw12/Tx69oRuHUAR7QRERERuYMzfVyf6oHl5eUBAGJiYgAAx48fR1ZWFtLT043HhIaGok+fPtiyZQsAICMjA+Xl5apjGjZsiPbt2xuPMVdaWor8/HzVD3nGNS3UE4c7tjXd18oU5BUIHDsljPMOiIiIiMjzfKb6kBACTz/9NHr16oX27dsDALKysgAACQkJqmMTEhJw8uRJ4zEhISGIjo62OEZ+vLmpU6di8uTJ7n4JpCHQrOyorRWNz18C3v7IEAxIEjAsHUhLZTUiIiIiIk/zmUzBk08+ib179+Lrr7+22CdJ6o6hEMJimzlbx7zwwgvIy8sz/pw+fdr1hpNN5r8C5ToFew6oswFnzpluCwEsW8uMAREREVF18Img4KmnnsLy5cvx888/o3HjxsbtiYmJAGBxxf/ChQvG7EFiYiLKysqQm5tr9RhzoaGhqFu3ruqHPMN8xkrg1U9cXoHA8nX2H5uTa/sYIiIiIqo6rwYFQgg8+eSTWLJkCTZs2IDk5GTV/uTkZCQmJmLdOlPvsaysDJs2bUKPHj0AAGlpaQgODlYdc+7cOezbt894DHmPeVAgZwq0ypWaU1YqIiIiIiLP8eqcgieeeAILFizAsmXLEBUVZcwI6HQ6hIeHQ5IkjB8/HlOmTEHLli3RsmVLTJkyBREREbjvvvuMx44ZMwYTJ05EbGwsYmJi8MwzzyA1NRU33XSTN18ewbLMqBwUxEYbOv3WAgPDnAIJuijOKSAiIiLyNK8GBbNmzQIA9O3bV7V97ty5GDVqFADgueeeQ3FxMcaNG4fc3Fx0794da9euRVRUlPH4d955B0FBQRgxYgSKi4vRv39/zJs3D4Hms1yp2pl3+uU5BrooCcPSYbHisWziWAYERERERNXFp9Yp8BauU+A5B48KzP/e9BF77Rn1iLW8AoHtewQ2bVM/zvw4IiIiInJOjV2ngGofvZ2QUxcloUF9ZgSIiIiIvIlBAXmUI3moAH4KiYiIiLyK3THyKPOJxloC+SkkIiIi8ip2x8ijHMkUcD44ERERkXcxKCCP4vAhIiIiIt/H7hh5lEOZAo1PoZ5FsYiIiIiqDYMC8ih71YcA7UyBvtL9bSEiIiIibQwKyKNcnVNQ6cAEZSIiIiJyDwYF5FHmQUFegWWUoJUpqGSmgIiIiKjaMCggjzIPCqbPEcjIVG88cMQyUHCklCkRERERuQeDAvKowiJ1h18IYNlaYcwY5BUIbPjN8nHbdnKiMREREVF1YVBAHnWl0HKbEEBOruF2dq72vINfftceakRERERE7seggDwqItxymyQBMdGG27HR2o8TMAUORERERORZDArIo8JCJdV9SQKGpUvQRRm266IkDOyj/dgrRYLZAiIiIqJqEOTtBlDtJq9TcE0K0ONaCTHRMAYEsl5dAwDosfYX9VCiRSsBSRIYlg6kpaofQ0RERETuw0wBeZS4WkUoLARIbipZBASyXl0DMHGsZDHcyHxiMhERERG5H4MC8ig5U6C1FoE5XZSEII2FzJQTk4mIiIjI/RgUkEfJw4EkB0f/BAdbblNOTCYiIiIi92NQQB7lbFAQYhYUmE9MJiIiIiL340Rj8ii9k0FBoFmYOnEsAwIiIiIiT2OmgDxKXE0VBDgaFJjNKWBAQEREROR5DArIo5wdPuTIhGQiIiIici92wcijjEGBg58080yBubwCgWOnWKKUiIiIyJ04p4A8Sn91nQJHBwGZzylQysgUWLZWQAh5AjIXNSMiIiJyB2YKyKOEE+sUAECAlUxBXoEpIJDPy0XNiIiIiNyDQQF5lLNzCqxlCrJzTedSnpuLmhERERFVHYMC8iingwJFpkCZXYjVWLyMi5oRERERuQeDAvKoqqxToLyti5JwXWfTfS5qRkREROQ+DArIo8TVicaOrlOgZD4PoUWS6SQTx0qcZExERETkJgwKyKPkaQCSg6mCSr3ptnlQoJxTEFWnau0iIiIiIhMGBeRRxpKkDn7SKisdO07o7R9DRERERI5hUEAeZSxJ6uBIH71e+7byXFr7iIiIiMh1DArIo5ytPqQcPmQra6Dn8gREREREbsOggDxK72ymQBEIVDJTQERERFQtGBSQR1UlUyAEoLeSEmBQQEREROQ+DArIo4xBgYsTjZX3VfMNOHyIiIiIyG0YFJBHGasPOXh8/hX1/TW/COQVGCKAShuTkImIiIjIdQwKyKOM1Ycc+KTlFQjk5qm3/b4LmD5HICNTqOYbMCgg8l8nTpyAJEnYvXu3t5tCRFRrMCggj3JmTkF2rvVzLFsrUFhsGjPEoID8hRAC3333HYYNG4amTZuifv366Nq1K1599VXk5mp/ac6ePYv7778fsbGxiIiIQKdOnZCRkaE65sCBA7j11luh0+kQFRWF6667DqdOnTLunzNnDvr27Yu6detCkiRcvnzZofbae+5Ro0ZBkiTVz3XXXWfcL3f4tX4WL17sxDunraysDHFxcXj99dc190+dOhVxcXEoKyur8nMREdUkDArIo5wJCmKjrR8nhHpoEYMC8gfZ2dm46aab8Nxzz6FPnz6YP38+NmzYgJdeegl//vkn2rZti23btqkek5ubi549eyI4OBirVq3Cn3/+ienTp6NevXrGY44ePYpevXrhmmuuwcaNG7Fnzx689NJLCAsLMx5TVFSEQYMG4d///rfD7XXkuQFg0KBBOHfunPHnxx9/NO5r0qSJat+5c+cwefJkREZGYvDgwc69gRpCQkJw//33Y968eRDCcnLS3Llz8cADDyAkJKTKz0VEVJMwKCCP0jsRFOiiJAxL1z5QkoDwUMvzEskKCgrwj3/8A5GRkWjQoAHeeecd9O3bF+PHjwcAzJ8/H126dEFUVBQSExNx33334cKFC8bHb9y4EZIkYc2aNejcuTPCw8Nx44034sKFC1i1ahXatGmDunXr4t5770VRUZHxcX379sVTTz2F8ePHIzo6GgkJCZgzZw4KCwvx0EMPISoqCi1atMCqVauMj6msrMSYMWOQnJyM8PBwtG7dGu+++67q9VRUVGDYsGFISkrCX3/9haeffhq9e/dG+/btceutt2LhwoX45JNPcPvtt+PcuXPGx7355pto0qQJ5s6di27duiEpKQn9+/dHixYtjMe8+OKLGDJkCKZNm4bOnTujefPmuPnmm1G/fn3jMePHj8fzzz+vuopvjyPPDQChoaFITEw0/sTExBj3BQYGqvYlJibi+++/x9133406depoPq9er8cjjzyCVq1a4eTJkwCAFStWIC0tDWFhYWjevDkmT56MiooKAMCYMWNw9OhR/PLLL6rzbN68GYcPH8aYMWMcfs1ERLUFgwLyKGdXNE5LlfDMoxKCgtTbh6VLCAkxnYSZAjL39NNP47fffsPy5cuxbt06bN68GTt37jTuLysrw2uvvYY9e/Zg6dKlOH78OEaNGmVxnkmTJuGDDz7Ali1bcPr0aYwYMQIzZ87EggUL8MMPP2DdunV4//33VY/5/PPPERcXhz/++ANPPfUUHn/8cdx1113o0aMHdu7ciYEDB+KBBx4wBhN6vR6NGzfGokWL8Oeff+Lll1/Gv//9byxatMh4zrlz50IIgTlz5kAIgQkTJqBJkyZo27YtFi1ahDp16uDmm2/Gww8/jMmTJxsft3z5cnTp0gV33XUX6tevj86dO+Pjjz827tfr9fjhhx/QqlUrDBw4EPXr10f37t2xdOnSKv8O7D23bOPGjahfvz5atWqFRx55RBWcmcvIyMDu3butdtTLysowYsQI7NixA7/++iuaNWuGNWvW4P7778c///lP/Pnnn/joo48wb948/Pe//wUApKamomvXrpg7d67qXJ999hm6deuG9u3bV+FdICKqoQSJvLw8AUDk5eV5uym1zrzFleI/b1WKXfv0Tj1u2mzD4/7zVqX4aH6lEEKIjdv0xm1nzjl3Pqrd8vPzRXBwsFi8eLFx2+XLl0VERIT417/+pfmYP/74QwAQBQUFQgghfv75ZwFArF+/3njM1KlTBQBx9OhR47ZHH31UDBw40Hi/T58+olevXsb7FRUVIjIyUjzwwAPGbefOnRMAxNatW62+hnHjxok777zTeL9nz55ixYoVQgghXn/9dZGSkiLWrVsnfvvtN9G9e3ch//k+cuSISEhIMD4uNDRUhIaGihdeeEHs3LlTzJ49W4SFhYnPP/9c1ZaIiAgxY8YMsWvXLjF16lQhSZLYuHGjRbvk9yU3N9dq2x19biGEWLhwoVi5cqXIzMwUy5cvFx07dhTt2rUTJSUlmud8/PHHRZs2bVTbjh8/LgCIzZs3i5tuukn07NlTXL582bj/hhtuEFOmTFE95ssvvxQNGjQw3p81a5aIjIw0/v4LCgpEZGSk+Oijj+y+TiKimsKZPm6QtWCByB2MJUmdzEkFKo4PDVWfC+DwIVI7duwYysvL0a1bN+M2nU6H1q1bG+/v2rULkyZNwu7du5GTkwP91Q/UqVOn0LZtW+NxHTp0MN5OSEhAREQEmjdvrtr2xx9/qJ5f+ZjAwEDExsYiNTVV9RgAqivis2fPxieffIKTJ0+iuLgYZWVl6NSpk3H/3r170aNHDwDAsmXLMGnSJNx0000AgLfffhs33HADAKBBgwaqCcd6vR5dunTBlClTAACdO3fG/v37MWvWLDz44IPG1z1s2DBMmDABANCpUyds2bIFs2fPRp8+fay9zXbZe24AuPvuu43Ht2/fHl26dEGzZs3www8/4I477lCdr7i4GAsWLMBLL72k+Xz33nsvGjdujJ9++gkRERHG7RkZGdi+fbsxMwAYhmyVlJSgqKgIERERuPfee/H000/jm2++wZgxY/DNN99ACIF77rnH5ddPRFSTcfgQeZSzKxrLAgMtb+srWX2ItImrHzTJ7IMmby8sLER6ejrq1KmD+fPnY/v27fj+++8BwKLKTHBwsPG2JEmq+/I2vdkHUOsY8/MAMD5u0aJFmDBhAkaPHo21a9di9+7deOihh1RtqaioME78LSsrQ2RkpHGfcmz9nj17VGP2GzRooApyAKBNmzbGykJxcXEICgqyeYyr7D23tcc0a9YMhw8fttj37bffoqioyBhQmBsyZAj27t1rMdlar9dj8uTJ2L17t/EnMzMThw8fNr6nOp0Ow4cPNw4hmjt3LoYPH466des69ZqJiGoLZgrIY/IKBIqKDbcdnVMgUxYFkbMGysXLBIMCUmjRogWCg4Pxxx9/oEmTJgCA/Px8HD58GH369MFff/2FS5cu4Y033jDu37Fjh9fau3nzZvTo0QPjxo0zbjt69KjqmJSUFOzduxfXXXcdevfujZkzZ+KGG25AWFgY3njjDQCGDMmTTz6Jp59+2vi4nj174uDBg6pzHTp0CM2aNQNgqL7TtWtXm8e4yt5za8nOzsbp06fRoEEDi32ffvopbr31VsTHx2s+9vHHHzdOvP7hhx+MWY5rr70WBw8eREpKis32jhkzBn379sXKlSvx22+/GTMcRET+iJkC8oiMTIHpcwTOXzLcP3bKufE+lYqFyuSFzzh8iKyJiorCyJEj8eyzz+Lnn3/G/v37MXr0aAQEBECSJDRt2hQhISF4//33cezYMSxfvhyvvfaa19qbkpKCHTt2YM2aNTh06BBeeuklbN++XXXM7bffbpzQ/Morr0CSJMTHxyMuLg7XXHMNAEMnfNSoUXj44YeNj5swYQK2bduGKVOm4MiRI1iwYAHmzJmDJ554wnjMs88+i2+++QYff/wxjhw5gg8++AArVqxQBSlZWVnYvXs3jhw5AgDIzMw0Dr2S9e/fHx988IHDz33lyhU888wz2Lp1K06cOIGNGzfilltuQVxcHG6//XbV6z9y5Ah++eUX1WvT8tRTT+H111/H0KFD8euvvwIAXn75ZXzxxReYNGkS9u/fjwMHDuCbb77Bf/7zH9Vj+/Tpg5SUFDz44INISUlB7969bT4XEVFtxqCA3C6vQGDZWqG62r9jr2G7o8orTLflDIEqKGCmgMzMmDED119/PYYOHYqbbroJPXv2RJs2bRAWFob4+HjMmzcPixcvRtu2bfHGG2/g7bff9lpbH3vsMdxxxx24++670b17d2RnZ6s65IChg71p0ybMnj0bsbGx+Pnnn3Hx4kVcvHgRkyZNwtmzZ/H333/jqaeeUj2ua9eu+P777/H111+jffv2eO211zBz5kz84x//MB5z++23Y/bs2Zg2bRpSU1PxySef4LvvvkOvXr2Mx8yePRudO3fGI488AgDo3bs3OnfujOXLlxuPOXr0KC5duuTwcwcGBiIzMxPDhg1Dq1atMHLkSLRq1Qpbt25FVFSU6nV89tlnaNSoEdLT0+2+n+PHj8fkyZMxZMgQbNmyBQMHDsTKlSuxbt06dO3aFddddx1mzJihmbEYPXo0cnNzMXr0aLvPQ0RUm0lCaKze4mfy8/Oh0+mQl5fH8aRucOyUwNxFlh+r0SMkJDd1bBzR6+/pUXp1eHXLZODBOwOwYr0ef+w2bHvgDgmtmjs5Jon8SmFhIRo1aoTp06fX2Lrze/bswZAhQ9C7d2/885//RNeuXREUFITjx49jzpw52LVrF1avXu3tZhIRkY9ypo/LTAG5ndbKxJIExEQ7fo4KxfChq+sNqbIDlcwUkJldu3bh66+/xtGjR7Fz507j1elhw4Z5uWWu69ixI/bs2YPExETcfvvtCAkJQUhICDp37oyLFy9izpw53m4iERHVEpxoTG6ni5IwsLfA6k2mbdd1Nmx3lHJOgTyUSLmN+S3S8vbbb+PgwYMICQlBWloaNm/ejLi4OG83q0ri4uLwzjvvYMaMGbh06RLKysqQmJiIQGWJLiIioipiUEAecU2KhNWbTD33qgz1kTMFlZxTQDZ07twZGRkZ3m6Gx8gTjYmIiDyBw4fII8rK1fedXadAqVxj+BCDAiIiIiL3YVBAHlHuxqCgrMwwebm01LSNQQHVRgUFBfjvf/+L+Ph4fP31195uDhER+REOHyKPUJYUBZxbvCwjUz1hoKAQFtWMGBRQbVJYWIgPP/wQ06ZNQ0FBAR577DEMGTLE280iIiI/wqCAPMLV4UPyGgf2cPEyqg2Ki4sxe/ZsvPHGG8jNzcXDDz+Mf//732jcuLG3m0ZERH6GQQF5hEVQ4OBAtexcxyoLMVNANVlpaSk+/vhjTJkyBRcuXMBDDz2EF198EUlJSd5uGhER+SnOKSCPcHVOgdYaB1oYFFBNVFZWho8++ggpKSn417/+hfT0dBw8eBAff/wxAwIiIvIqBgXkEeaZAkfnFOiiJAxLl+wGBhw+RDVJeXk5PvvsM7Ru3RqPP/44evfujQMHDmDevHlo0aKFt5tHRETE4UPkGVUpSZqWKiElCbiUKzBvkfYxzBRQTVBZWYkFCxZg8uTJOHr0KO666y6sXLkS7dq183bTiIiIVJgpII8oL1dfyne2JKkuSkKLptY/ngwKyJfp9XosXLgQ7dq1w4MPPojU1FTs2bMHixYtYkBAREQ+iUEBeYT5nAJnSpI6gsOHyBfp9Xp899136NChA+69916kpKRgx44d+P7779GhQwdvN4+IiMgqBgXkEe5c0VgLMwXkS4QQWL58Oa699loMHz4cjRo1wrZt27By5UqkpaV5u3lERER2MSggj3C1JKmj9EwVkA8QQmDVqlXo1q0bhg0bhpiYGGzevBlr1qxB9+7dvd08IiIihzEoII9wx/Ah85WNlZgpIG8SQmDdunXo0aMHhgwZgtDQUGzYsAEbNmxAr169vN08IiIipzEoII8or1Dfd3b4kL2VjR1Z4IzIEzZt2oQ+ffogPT0dQgisXbsWmzdvRr9+/bzdNCIiIpcxKCCPqOqcAnsrGzNTQNXtt99+Q//+/dG3b18UFRXhhx9+wNatWzFgwABI7p40Q0REVM0YFJBHFJeo7zs7fMjeysYMCnxTXoHAsVMCeQW1J5Xzxx9/YNCgQejVqxcuXbqEpUuXYvv27RgyZAiDASIiqjW4eBm5XUamwKUc9TZX1ikYlg4sWys0MwYMCnxPRqYw/r4kCRiWbliIrqbauXMnXnnlFaxcuRJt27bF4sWLcccddyAggNdSiIio9uH/buRW1uYCXCly/spxWqqEiWMltG1puY/Fh3yL/HuXAzghDAFdTcwY7N27F3fccQfS0tJw6NAhLFiwAHv37sXw4cMZEBARUa3F/+HIrazNBbic59r5dFESYupZbmemwLdo/d6FAHJyvdMeV/z555+4++670bFjR+zZsweff/459u/fj3vvvReBgYHebh4REZFHMSggt7I2FyAm2vVzavXHGBT4Fq3fuyRV7fdeXQ4dOoT7778f7du3x++//45PPvkEf/31Fx588EEEBXGEJRER+QcGBeRWhrkAllGBLsr1seUBGrOUL9WgK9D+oofZwr3D0qUq/d497dixY3jooYfQpk0bbNy4Ef/73/9w6NAhjBkzBsHBwd5uHhERUbXiZTByu7RUCcvXCtW4/6oUaSmvsByPdOacYRy7L3c6/YVygrGsSwffnWR88uRJ/Pe//8XcuXMRFxeHmTNn4pFHHkFYWJi3m0ZEROQ1DArII8zHl7uyorGstER7e04uoIty/bxUdeYTjH3Z2bNn8d///heffPIJ6tWrhzfffBOPPfYYIiIivN00IiIir2NQQG6n1wuY9xGrkimoU0d7e00Yr17bWZtYbr5OhTdlZWVh6tSp+OijjxAZGYnXXnsNTzzxBOpY+2ARERH5IQYF5HZak4ClKsxeqRMhAWZhRly0oUMKcAiRN8kTjM0Dg9BQ77RH6eLFi5g2bRo+/PBDhIaG4j//+Q/++c9/om7dut5uGhERkc9hUEBupxkUVKHfrlV96FIuMHeRqBWLZNVk8iJzS9eoo4IQL87Tzc7OxvTp0/Hee+8hICAAzz77LCZMmIB69ep5r1FEREQ+jtWHyO0qNYKCgiuuDzoPtPEprcmLZNUWWgFZaSlw7FT1/l4uX76Ml19+GcnJyXjvvffwz3/+E8ePH8fkyZMZEBAREdnh1aDgl19+wS233IKGDRtCkiQsXbpUtX/UqFGQJEn1c91116mOKS0txVNPPYW4uDhERkbi1ltvxZkzZ6rxVZA5rUzBjI8NVWpcYW/dqKoskpVXIKq981ob2Hvfdu03ZHKmzxEu/94dlZ+fj9deew1JSUl4++238eijj+L48eOYMmUKYmNjPfrcREREtYVXhw8VFhaiY8eOeOihh3DnnXdqHjNo0CDMnTvXeD8kJES1f/z48VixYgUWLlyI2NhYTJw4EUOHDkVGRgZXIfUSvUYfUL6in5Lk/JoFAQ6ErmfPCyQ3de68ylKaHIbkOPP37ZabrHf6q/J7t+fKlSv44IMP8NZbb6GwsBCPPfYYnn/+eSQmJrr1eYiIiPyBV4OCwYMHY/DgwTaPCQ0NtfqffF5eHj799FN8+eWXuOmmmwAA8+fPR5MmTbB+/XoMHDhQ83GlpaUoLS013s/Pz3fxFZAWfaX2dvmKvrNlRIMciO3W/gKkXuP4pGPzUpqe7LzWJmfO6bFsjWnatxDAivW2H+Pq792aoqIizJo1C2+++SYuX76MRx55BC+88AIaN27snicgIiLyQz4/p2Djxo2oX78+WrVqhUceeQQXLlww7svIyEB5eTnS09ON2xo2bIj27dtjy5YtVs85depU6HQ640+TJk08+hr8jdbwIcBwVdmVMqLKhI+15I+zQ4i0SmlWZRiSP8jIFPjoK/M6UNolSZVc+b2fOHECbdu2xd69e43bSkpK8N5776FFixZ4/vnncdttt+Hw4cP48MMPGRAQERFVkU8HBYMHD8ZXX32FDRs2YPr06di+fTtuvPFG41X+rKwshISEIDpa3eNISEhAVlaW1fO+8MILyMvLM/6cPn3ao6/D38jDh4KCTFWHDMNzJJeuwiuHD4WGaB/jbMczVuNYV4MWfyBnVrQkHd+MgSufQ+ruhQioLFftc+X3Xl5ejnvuuQfFxcVo1qwZysrKMGvWLKSkpGDChAkYNGgQDh48iDlz5qBZs2ZVel1ERERk4NMlSe+++27j7fbt26NLly5o1qwZfvjhB9xxxx1WHyeEgGSjBmZoaChCfaGQei1VeXX4UEgwMG6MhJxcQ2fb1WE5yuxARYXlflc6nrooCZ3bC+za5/o5/Im1Rcp6bH4Xg5c/bbx/NOUzfPHwD9AHGmqS3n0L0K6Vc+/piy++iIyMDGzatAmLFy/Ga6+9htOnT+O+++7Dyy+/jFatWlXptRAREZEln84UmGvQoAGaNWuGw4cPAwASExNRVlaG3Fz1mI8LFy4gISHBG00kmDIFAZKh853ctGqdbWVJ0rJyy/1j73NtgnBSY9NjJo6VOMnYBnmRMqWEc5kYtGKialuLIz+h18a3jfdDgp17T1etWoW33noLd955Jx544AE88sgjuO6667Bv3z7Mnz+fAQEREZGH1KigIDs7G6dPn0aDBg0AAGlpaQgODsa6deuMx5w7dw779u1Djx49vNVMvyfPKQhwU/Ene0Wkystd7MwrrnwzQ2CbLkrCkH7qbX3XvwZJI33Qe8NUhJQUANAO4qw5ffo07r77bkREROCbb75BamoqduzYgc8//xzXXHNNVZpPREREdng1KLhy5Qp2796N3bt3AwCOHz+O3bt349SpU7hy5QqeeeYZbN26FSdOnMDGjRtxyy23IC4uDrfffjsAQKfTYcyYMZg4cSJ++ukn7Nq1C/fffz9SU1ON1Yio+smLlwW4qZ9ta/EygPMAqkv71qZfaJ38LLTNXKJ5XGhZIdplfgsAKL8aFDiyHsTQoUNRUFCAoqIiAMCyZcvQpUsXhIeH47bbbnPPiyAiIiJNXp1TsGPHDvTrZ7r8+PTThrHJI0eOxKxZs5CZmYkvvvgCly9fRoMGDdCvXz988803iIoy1TZ85513EBQUhBEjRqC4uBj9+/fHvHnzuEaBFxkzBW4KOZW/yvatgP2HTePbY+o5dpU/r0AgO9cwDEY+nsuVOUc5n6PN/qUIUGQJPh/zA+5acD8iig1D+Trv+BK7uj6EsnJg8x96rP3FcJyt9SDeeustbN68GUlJSQgICFD9dOzY0aOvjYiIyN95NSjo27cvhI16hmvWrLF7jrCwMLz//vt4//333dk0qgI5KLB3hd9RyvMkN5UwqB+wa5/AT78BkeH2H+/IImV6IRBgY3I6AeWK9SeUWYIrderjaKsB2NdxBLpt+wgA0Oz4ZoQVX8aho/Vw8LjpcbbWg0hPT1eVFyYiIqLqU6PmFFDN4O5MgXJuQnCQoTPZoL6kei5rrC1SZj6Mxd55yJQpCC4rQtKxX4zbD7S7FSIgEAfa3WrcFiD0SD66URUQyLgeBBERke9hUEBu5/bhQ4rzBAapz11pZwyQo4uUMSiwr+JqpqDxqW0IUqxHcLj1IADAyeY3oOJqKVIAaH74J83zcD0IIiIi38OggNzOk3MK5ABB3mavM69VSlOrU8qgwD45U5B0bLNq+8nkXgCA8pBInG5mqvrV/MjPmudJ781qT0RERL6GQQG5XaUH5xTIgYZc2cheZ14XJWFYuroDKi9SpswgMCiwTysouJDQFkV14o33TzTvbbxd/8IBhBbnqc4xsA/Qqyv/7BAREfka/u9MbufJTEFxiVCdW6+3X+5SOam4Xl3TfWtBga3zOVJas7aqqAQkvR6Nzmw3bjtxNUsgO920m+p+o9OmY+NjGRAQERH5Kq9WH6Layd1Bwa79pttLVwNCCCTEGe4XlwDT59iuLKQUrPjEKwMBeRVmW5WKHKliVJtVVACxlw4htPSKcdvZJl1Ux5w1Cwoan96OY60Ma4b4zztFRERU8/CyHbmdO4MCuXqQTMBQPaio2LCtuAR2KwspKdukN8sU2KpUlFcgsHSN/SpGtVl5BdDwTIZq29+N0gAAndoZ7hdFxiEnprlxf+NTvxtvV3KIFhERkc9iUEBuV+nGoMBa9aCCQu3j7ZW7VA5F0ivq7uv1wMatwmqlomyNc/pbac2KCqDhmZ3G++VBobiQaIgGEuJMeQBl9qDB33uMtysV7zcRERH5FgYF5HbuzBRYqx4UrdM+3l65S2WblFeu/9gjsGOv9fPFapzT30prVlQCDc/uMt4/36AD9FdLkCqHZWU1NK0+XO/yKYQVXwYAlJRWSzOJiIjIBQwKyO3cuaKxXD1IDgwMY/klY0nLoED18cp9WpRtUs4p+G275bEA0DPN0AZdlIS6Uabt5u3wBxXlAglZmcb75xSd/xDT8gQ416CD6nEJ5wyPKSmFXw23IiIiqkk40Zjczt0TjdNSJaQkGYbqxEQbOum5lw2dS2UWIamx/Ym/qjkFevsd1OvSTOcLDwXyCwy3J471r4AAAALO/42Iohzj/fOJqcbbO/eZ3svzZkFB4rm9ONn8BgCG36EuCkRERORjGBSQ27k7KADkq/Wm+8aSpIp+vfJqtZJQTBRQzimwN/H1mhbqRbaU8w38LSAAgKAD+1T3zzdob7x94oxpe76uEYoiYowBRKJiXoE/DbciIiKqSTh8iNzO3YuXaVGuUyCTrDxfhWKCa4CiL29vwbLkpuqOv/kkZH8TelAdFFxIbK99oCSpsgjx5w8YNsM/gykiIqKagEEBuZ189d6dmQJz8rmVHXVlh1+5yJi8Ei8ABCirD9kJCvSV6vM4MNqoVos+YwoKCqISURQZZ/XYCwltjLfrX/gTEAIC6qwNERER+Q4OHyK301deXXXYgxeFtQIOeX6B+SJj6b0VxyjbaScoOHVOYO1mGM8TEVblZtdodU//Zbx9IaGtat81KcDBo6b3KrRjG2CrYV948WXUKTiPK3UToderh3ARERGRb2BQQG5XnZkC821aC5Ct/UXRNr32bS0HDptuCwEUFrve3hpPCNQ5YwoKYq+/RrX75hslDO2vmAz+Rztgtml//IU/caVuIioZFBAREfkkDh8it/PERGNz1jIF1hY7kyknF3OFXSecP4+Qwjzj3Xrd2qg698FBhvkCyU2vVmVq00b18PjzhoCCC5gRERH5JgYF5HZyx88bmYLYaPUQIUBdttSZTAEpHDigvt+mjWqNCIvKTw0aADrTCnP1z/8JgO85ERGRr2JQQG5XLcOHNOYrBEiGq9W9u5u2SRLQs4uibU5kCho3UJ8nLNS1ttYKf/2lvn/NNaqgIMh8IKIkAa1bG+/GXToEgNkZIiIiX8WggNzOnSsaWyNJkkXQIQ8TatXcFDE8/gDQXFFa1JlMQZ0I0+0+12l0fP2JIiiojKwDNGyoej8kSSNKUwQFsZcMEzQ4fIiIiMg3MSggtzPNKfBsTXrzoEC+Cq3s7IeFSigrtzzG/DgtysdVVPjnOgVySdZyxazr0matAEmyHyS1amW8WS/3FILLijh8iIiIyEf587VP8pDqmGgMWA4hkp9XuZ5AWTlU6xQ4kykoKzPdzr/if0GBsrTr+J2HEHt1+5GQlijPFParCCmCAgCIuXQElZUdPdJWIiIiqhpmCsjtqmNFY8CxTEFZOVBeYXmM+W0tpYpMwYVL/jVJVlnaNbCiDPVyTxj3Zce1xLK1QvXHI69AI2JSDB8CDPMKOKeAiIjINzEoILertkyB2fn1V8erK8etl5sFBa5mCrIuAiWlrrWzJlKWdo3OOY5AvelNzY5rCSGAohLT8dPnCGRkmgUGKSmqu3EXD3FOARERkY9iUEBuV11BgXkmotLK8KFys7kBMntBQWmZ7f21WWy0qZRrzKXDqn3Z8S0BGIZUyYQAlq0V6oxBZCTQqJHxbnT2UWYKiIiIfBSDAnI7b2UKysqAY6cErhSaOqZl5cCps6b7l/NhvKLtTKbA3+iiJAxLN0QFcRfVQUFOfEtVmVeZEIYVjVUU2YLYS0ehZ6aAiIjIJzEoILer9FJQcPocMHeRwPJ1pm15+QIHj6mPk69o2wsK9H42sdhcWqohKIjJPmLcVhQRg8f/FYPr0ySYVyGVJCAm2uwkLVsab8ZkH2GmgIiIyEcxKCC381amQEtevuU2+Yp2VTqomhNra6mY7KPG27n1W6Je3QBjJkEODCQJGJYuQRdlFikoMgV1889BrxxzRERERD6DJUnJ7bxVklRLcLDlNvmKdlWqCeXkAroo1x9fUwQGGIb9yAoSmhtvp6VKSEkyvBcx0bAMCACLycaBJ44CHTp5qLVERETkKmYKyO2qY0VjwLGgIyAAaFBfvU2+ol2VTEFMtGlhr9qcNQgLLIfu8knj/cKG6k6+LkpCclONDIHMLCgIOn5Y+zgiIiLyKmYKyO2qrfqQvcWzYJhoHBlhuh8UaBor72imIC4GuJSj3nb4uGHughDy0BnTeWuTuPyTqnKkRY1aOHeCFurjg08etXIgEREReZNL3badO3ciMzPTeH/ZsmW47bbb8O9//xtl/lyyhQAYOuIAUFLq2SvojgwfKiwytQcwTB6Wr/Ary5PakhBnuU0OCAAr5ThridhcdSe+tImTQUGdOkBCgvFuyCkGBURERL7IpaDg0UcfxaFDhwAAx44dwz333IOIiAgsXrwYzz33nFsbSDVLRqZA1kXD7aWrYbmglRs5kom4UqguLarXGxbamrtIIK/AseeJCLfcJoTlfYtynLWA7ry6E1+Z5GRQAKiyBSGnj9k4kIiIiLzFpaDg0KFD6NSpEwBg8eLF6N27NxYsWIB58+bhu+++c2f7qAbJKxBYttbUWxbw7BV0R4ICKUCdKQAsO/T2hIZYDlVyqBxnLVBXERSUhtaBlFDfxtFWKIKC0NPMFBAREfkil4ICIQT0Vwdkr1+/HkOGDAEANGnSBJcuXXJf66hGyc6t3ivoV4rsH1NUBJSUVu15ggKBYLPZN0NuVN/XLMdZC0RdMF3Zz41pjqyLLrxGZabg/Gn/XhWOiIjIR7kUFHTp0gWvv/46vvzyS2zatAk333wzAOD48eNIUIwfJv8SG119V9DzCoTF5F8tWReBouKqPVdQkIQgs0xBxzamF9q2Ze2cZJxXIFDvoikoyIltjsMngF+3O1m2SREUSHo9cOKEexpIREREbuNSUPDOO+9g586dePLJJ/Hiiy8i5WrZwW+//RY9evRwawOp5tBFSRjSz3Tf6oJWbpBdjeP3gwIthw8pMyIhGmsh1AbZOQLR2cqgwNC5X/uLk4u3NW+uvn+UQ4iIiIh8jUslSTt27KiqPiR76623EBTEKqf+rGWyBEAgKBAY/7DnhtTEVuP4/aAgy6BAWc7UPDtSW8SVZSGk3DRGKycmGYBpSJjDi7eZlSXFMU42JiIi8jUuZQqaN2+O7Oxsi+0lJSVo1apVlRtFNVfh1T5kVB0rK9y6iS5KsliUzFMCNTIFJ87UvvKj5upkqTvvcqbA6SFh9eujIizSdJ9BARERkc9xKSg4ceIEKisrLbaXlpbizJkzVW4U1VyFV8fvKxcM8xRnsgWBAep1DZo1dvyxWpmCb1Y4/viaSn9E3XnPjW3u2pAwSUJxQ8UQIgYFREREPsepsT7Lly833l6zZg10Op3xfmVlJX766SckJye7r3VU48iZgkiN2v7u5syKyWFhprYBQKME4KSD8WtQIBBk47nMS57WFkLReRcBAbjtkSTE1HdtSFhxw2REHTMMOaw8cgwOLEZNRERE1cipoOC2224DAEiShJEjR6r2BQcHIykpCdOnT3db46jmMQYF1ZAp0DtRBCckGChU3K8bZZj74AitTIFSra2weVRxRb9pUyS3CHH5VNn1mkMe7VVx+Bh279UjrYNLiUoiIiLyAKeCAnltguTkZGzfvh1xcXEeaRTVXDmXDR3t6phv7sz6AyFm/VlnMhla1YeUQkMdP1dNohw+JJlXEHJCXoHAkaAWaHP1fmjpFaz7/iJSkuvXyrUdiIiIaiKXLtUdP36cAQFZyMgU2LnPcPuP3Yb7nlTqxBV686zC3+cdb1tpqbAZFEgScOyU51Zu9oY/dulRetAUFFys5/qwwOxcIDdW/fjoS0c9tqgdEREROc/l67k//fQTfvrpJ1y4cMGYQZB99tlnVW4Y1Sx5BQLL1qo7xcvWCqQkea4KkTOZgotmxbK27XL8sd+sABJsVDrK/AvI/EtcnYRb8xcyyysQWL2qCN3yzxm37S5ujm4FwqXfZWy0YTVkpZicY4iJvr7KbSUiIiL3cClTMHnyZKSnp+Onn37CpUuXkJubq/oh/5Odq17QCzDVs/cUZzIF5szbavNYAFkXHDvnsrU1P2OQnQvUyz2h2pYTk+zy71IXJaHRdc2gVyzo0C3qOLJznVwEjYiIiDzGpUzB7NmzMW/ePDzwwAPubg/VULHRhmE0ys620/XsnVRUZP8YLQGSoaPvTGDgKKcX9vJBsdFQrWQMAJdjk6v0u2zWIhwFdRtBl2co+ZS98xi+X1R7sitEREQ1nUuZgrKyMvTo0cPdbaEaTBcl4dYBpvsu1bN3Ql6BQIXlUhkOCQkxtM0TKxF7OhCqDrooCY2Ljqu2dR+eUqXfZUAAkBNrGkIUnW04f23JrhAREdV0LgUFDz/8MBYsWODutlAN166VqdM4frRnr/5mV2FYUkiwoW0Tx0pok6Le17MrMOFh187r6UCoOsXnmTIFpeF10alXbJXOFxgA5MaYJhtH5yjWQPDwMDMiIiKyz6XhQyUlJZgzZw7Wr1+PDh06IDg4WLV/xowZbmkc1SxXrg7nCQ0BYqI9W4Nea7iSLb26Ar9uN9xWlks9cER93JYdwHWd1dse/QcwZ4H95xrSD4jWGbIYNTUwyCsQyM4Foi6YOu358c0RX8W0SkAAkK2YbFw37wwCK0pRGRRaK7IrRERENZ1LQcHevXvRqVMnAMC+fftU+yRPjMmgGqGoGhcu00VJGJZuGHpirbOuXJ4sqbGEX7dfXUPhanlRrWyDEEDuZdMjg4OAxg0CMCxd2HwuAPhhAwDU3HHyGZmm1/jkWdPwofz6yYiv4rkNw4dMmYIAIRCdcwLZ9VvXmuwKERFRTeZSUPDzzz+7ux1UC1ypxqAAMHS6U5IMQ08+W2TZWw8NNZUtVbYp4Gr/05HJ0aEh6uf6dKFAbp7tdsnj5D1ZjtXd5JKy4uoMbOXwnsuxri9cJgsMtCxLWi/nOLqMaF3jgiciIqLayLNjPMivZOcaetfmqwd7ki5KQo6VTnqooh3HTpl6/lmXDFfFdVESBvRSP8b8qrVytWJdlIQwB1cvrmnj5JUlZesUnEdIebFxX45iLoCrAs0mGgNATM5xhIYwICAiIvIFLmUK+vXrZ3OY0IYNG1xuENVMGZkC6zYbbh89YbhfHVeAtRZNkwUoQt71v6r3yVfyu3aSsHaz4fFhYZZDfkLNApwAB8PomjZOPlbRVmWWAACyo6ueKQgIAArr1EdZcARCyg0ppejsY6ioqPKpiYiIyA1cyhR06tQJHTt2NP60bdsWZWVl2LlzJ1JTU93dRvJx1lYzro4yk1qLpskqFSVLrS2spsxqVJRbnsMiKHAgzpGrEAGGDEVNKLepi5LQtaPhdkz2UdW+i/VaVPn8xSUCkCTkKrIFMTnHcKXQ998bIiIif+BSpuCdd97R3D5p0iRcuXKlSg2imsfWasaeXsTLVhUieT6BFvlKfoBkmlSste5BqNlwIcmBMHpof8O/0+cYxujXlInHSU0kbN8jEJNtmmSslwKQHdW0yue+Umj4NycmGQlZhuIE0dnH8Rf/XBAREfkEt84puP/++/HZZ5+585RUA8gdc6XqGj5jqEJkWohM2YwyjSv/gHPrCZi/LkcyBVcKgWVrTJWKtBboyisQPpdFEHrDv8pMQV69JqgMCqlyO+vVM/ybE2vKOsTkHEVIkO+8fiIiIn/mUqbAmq1btyIsLMydp6QaQC4PunSNoYMnoXoX8VJWITqTJbD2F+vHDu4LtGttu20ZmaaO6oHD6vkRjlTcvZhjKoUqU2ZOlKU/fSmLoL8aFETnmDIFcid++hxRpXbq6hgyMsrhQ6GlVxB25RKABFebTERERG7iUlBwxx13qO4LIXDu3Dns2LEDL730klsaRjVLWqqEvX8JHDsJDOhd/Z1cXZQEXRRwOR+Qu+TKdQoAQwfcXkBgbX6EXF7UkcXSCqwMiQkOFsgrUK+t4EvlS+XhU8pMgbwKcVXbKU/QNq9kFHr6KBgUEBEReZ9LQYFOp1PdDwgIQOvWrfHqq68iPT3dLQ2jmifwal+xToT3OreBgabbvbsDv/wBxRV57YAgKNDUIbY3P8KRoODkWe3tcxYAPdIsF0CrrvkX9lRUAMFlRYgqyDJuU17Zr0o75aAgN1Y9abn84DEAPVxpLhEREbmRS0HB3Llz3d0OqgUqrw4/CQi0fZwnKYOCdq0ldO1k6MjGRFu/wh0SAlRcLctvb0EzfRWGwAsB/LbDcruvlC8tr7AsR6pcW6Aq7QyUg4KYJOglCQFX32Bx5BjyCoTXsyRERET+rkpzCjIyMnDgwAFIkoS2bduic+fO7moX1UDymHRHJuN6ijIoCJBMw4psCTb7FgxLl8zG/JsyDPJk3KpomQQcPmG47cykZ08rLxeIuaQuR5oTZ7iyX9V2ykFBZVAo8nWNUe/yaQBAdPZRn8iSEBER+TuXgoILFy7gnnvuwcaNG1GvXj0IIZCXl4d+/fph4cKFiI+Pd3c7qQaQMwWB3swUKOppObrQWKWio2+YUCth4lhJM8NQlUyBLOjqty4sFHhylG8EBIAhUxCTrc4U3DuhBXIh2cy0OEKZPcqJbWEMCmJyjiPaB7IkRERE/s6lkqRPPfUU8vPzsX//fuTk5CA3Nxf79u1Dfn4+/vnPf7q7jVRD6K+Oyw90a6Fb56gyBQ60I69AGGvoA6YJtQCQ3NSyw+6OoODAEdNtXwkIAMOcAuUk4+LwaNRrGqP5PjhLmT1SDkmKyz3qU+8BERGRv3Kp+7Z69WrMmjULbdq0MW5r27YtPvzwQ6xatcptjaOapfJqh9nRK/Se4GymIDvXcps8oVaL3s7wodbNbe935lzVzXxOQW581VcylimDNeVk4zqX/waKi932PEREROQal7pver0ewcHBFtuDg4Oh97WeDnmUchEuY6bARyYaOxIUOLvwmr05Bdc6UYq10se+KubDh/LinYhw7FD+LpSZAgA4/dtRn1rEjYiIyB+5FBTceOON+Ne//oW///7buO3s2bOYMGEC+vfv77bGkW/LyBSYPkdg7iLDv4VXL/j6ykRjR4YxWayIbGdCrb3hQyGWsbKKJAHNGhtuV1Ya1vjwFRWlFaiXe8J4P7++GzMFqqBAfd5f5h/G9DlCtWgcERERVS+XJhp/8MEHGDZsGJKSktCkSRNIkoRTp04hNTUV8+fPd3cbyQfJi3wpF+EqLDLc9mZJUmVA4ugwJuWKyPYm1FZW2j5XSIh63QNzw4cABxVzefXCtL6Dt4VmnUZQZbnxfm58C7eVCzWfaKwUk33MpxZxIyIi8kcuBQVNmjTBzp07sW7dOvz1118QQqBt27a46aab3N0+8lFai3zJvDnRWDkU6EqRQES4Yx1MR0qXAoYhNraUlQkE2ggKwsMklJWZ3rjKSu++X0oRZ4+o7p+JbHG1GlPVV6gOkCRIEBAASsLroVwXi+C8bACmyc2+sogbERGRP3KqO7Jhwwa0bdsW+fn5AIABAwbgqaeewj//+U907doV7dq1w+bNmz3SUPItsTbKSHpzovGfh00d7g/mwe1DUuwNjfr8W6C0zPp+vR4oK1ff9xUR58zWKIhNMV7Bd8uYf8V7V5FkyhbEZBuCEV9ZxI2IiMgfOdV9mzlzJh555BHUrVvXYp9Op8Ojjz6KGTNmuK1x5Lt0URLapJjuS5JpPL23rnznFQhs2GK679YOrayKI1v0AihTBA2VlerJ2t4UpQgKyoLDUVC3AQDb1ZicocwsBaSYgoLYS0d9ahE3IiIif+RU923Pnj0YNGiQ1f3p6enIyMiocqOoZmjcwNSBmzjWNFnXW5kCrSFN7urQyqp6Zb+yUp0p2LVfPVnbm5Nt6543BQW5sS2MY7E8cQU/oJUpotRdPolH7iyv8hAlIiIicp1T3bfz589rliKVBQUF4eLFi1VuFNUMyko/deuYOszeyhQ4W17UFcqgw/y5HHGlSKiCgjWboJqs7fbMhhN0F0xBgVw21FNX8JVBQaC+EnWyT7n1/EREROQcp7pvjRo1QmZmptX9e/fuRYMGDarcKKoZlJ3/Sr2p7r63qg85W17UFcpMgfK5ZJIE3DZQwoih2o8vuKLOFJhzd2bDYXo96l00TTTOjk1Bu1aGDJAnruAfClRXIDq16ZDbn4OIiIgc51T1oSFDhuDll1/G4MGDERYWptpXXFyMV155BUOHWukNUa2jHCZUXu79TAHgXHlRVygzBSlJhk5zTi4QHCxQXi4ZnzOvAJAkYTGcKSJMPafAnNcm2/79N4LLS4x3s+NSEB/rufKgy463RBvF/TObjyLpEfeUPyUiIiLnORUU/Oc//8GSJUvQqlUrPPnkk2jdujUkScKBAwfw4YcforKyEi+++KKn2ko+RnmVXHn125vVhwDHy4u6okJRktRQrlO+kq7uzBqyFlCt5QAAwcESKiq1hwd5c7LtX6sP4hrF/ez4logN8Vw7CsNjURymQ3hJHgAg5tIRliMlIiLyIqeCgoSEBGzZsgWPP/44XnjhBeNqrJIkYeDAgfjf//6HhIQEjzSUfI9yKE1pqem2t4MCT8krEMYhUgDsLrilzFps/kPg8AmgpNTiMKOJY00BQV6BQHauYZ6Ep4OEvAKBg2sOq4OCuJbQ6wWqXG7JCilAQk5cChqdMRQmiL10mOVIiYiIvMjpxcuaNWuGH3/8Ebm5uThy5AiEEGjZsiWio/k/ur9RBQWKITG+shiXu2VrjPW3t+CWnLX4Y48hgD5+2vokYrnzn5EpsGyNYaEvQ/ag6ouH2XLqrEDMRdMk4/KgMBTUbYRSG3MfqmpYuoTs+aagoFHBEURy6BAREZHXuLSiMQBER0eja9eu7mwL1TDKoEB5BdxbE409Ta5uZF6ByJEr3HL25MgJ28flFQjDkKOr9+1lI6oqI1Ng6Rrg3kuHjdtyYltABAQgtp7bn84oLVVCSXpLYLfhfkTWccPEFBvVzYiIiMhzvHpN95dffsEtt9yChg0bQpIkLF26VLVfCIFJkyahYcOGCA8PR9++fbF//37VMaWlpXjqqacQFxeHyMhI3HrrrThz5kw1vgr/VWklU2Bv1d+aqirVjRzNnlTHWgsyOQABgNhLispDcYZyofXqevYXGda+lfG2VFkJHD/u0ecjIiIi67waFBQWFqJjx4744IMPNPdPmzYNM2bMwAcffIDt27cjMTERAwYMQEFBgfGY8ePH4/vvv8fChQvx66+/4sqVKxg6dCgqKyur62X4La3hQ4EBhjkmtVVaqoSJYyWMHiE5Va7T0XkWsdGWo/g9VZFIDkAkfSVishVBQbyhsx4a4v7nVGnVSn3/8GHt44iIiMjjXB4+5A6DBw/G4MGDNfcJITBz5ky8+OKLuOOOOwAAn3/+ORISErBgwQI8+uijyMvLw6effoovv/wSN910EwBg/vz5aNKkCdavX4+BAwdqnru0tBSlipmx+fn5bn5l/qFSb7qkfTlfnnTurdZUH1eqG8lBQXRdINfKx62y0lCSs3tngW27DNtcqUjk6CRleTiU7vJpBFeYvg+X4lsCAEI8HRS0bKm6m/37QQT1HsKypERERF7gs1NCjx8/jqysLKSnpxu3hYaGok+fPtiyZQsAICMjA+Xl5apjGjZsiPbt2xuP0TJ16lTodDrjT5MmTTz3QmoxZaZg41bDvxWVhnHqpCYHBbbmW8jZlmaNTZ3iJ0c5N8k4I1Ng+hyBuYsM/9r6XeiiJAztb6j8o5QdZ+isezxTEBODkjoxxrtH1h+222YiIiLyDJ8NCrKysgDAosRpQkKCcV9WVhZCQkIsKh8pj9HywgsvIC8vz/hz+vRpN7fePyiDAqVlawXyCtixU5LnFNgqSXr4uOF9K1dU/akT4VyGQLkugjxJ2dbvok2KhNiL5kFBNQ0fApCXaBpCFHfxsENtJiIiIvfz6vAhR5iPTxdC2B2zbu+Y0NBQhIaGuqV9/sxaUGCvTKc/CnAgKPj2R8MqyKmKBQOsvcdabE1Stva7KCoB4i4dMt4vCY3ClShDIF4dhYByE1sh4cg2AEDcxYMA+PkhIiLyBp/NFCQmJgKAxRX/CxcuGLMHiYmJKCsrQ25urtVjyHOsdVg9NTG2JpODAnvz34UA9h4w3dc7ccFcniOgZO13kVcgcOyUwMkzArEXTUFBdlxL40kKrjj+3K660tiUKdDlnUFwWSE/P0RERF7gs0FBcnIyEhMTsW7dOuO2srIybNq0CT169AAApKWlITg4WHXMuXPnsG/fPuMx5DmVVoICZyfG+gNXV3l2JFOQVyCQ+Zcep84KpPdW79P6XSjnHaxYD8RfOGjcd6l+a+Pt6hjfX9xUXYEo7tIhfn6IiIi8wKvDh65cuYIjR0ylEI8fP47du3cjJiYGTZs2xfjx4zFlyhS0bNkSLVu2xJQpUxAREYH77rsPAKDT6TBmzBhMnDgRsbGxiImJwTPPPIPU1FRjNSLyHK0Oaz2dZ1ffrakCAiQAznew7QUFhsXHtM9br67l78J83kFQeTF0l08a91+KNwUFnl44DQCuNG6tuj+qw2FEpKZ55LmIiIjIOq8GBTt27EC/fv2M959++mkAwMiRIzFv3jw899xzKC4uxrhx45Cbm4vu3btj7dq1iIoyDTZ+5513EBQUhBEjRqC4uBj9+/fHvHnzEBhYS5fV9SFaHdYQn5+l4h3OZAoS6wNZFwy3le+xeanRvALrAQGgXR7WfN5B7KUjCFBsuBSvvnLv6fH9RY1aQC9JxjZEnDpk5xFERETkCV7twvXt2xfCfGakgiRJmDRpEiZNmmT1mLCwMLz//vt4//33PdBCskVr+JCtkpv+zNEVjQEgWPEeykFBRqbpCr9h7QIgWmf7PFpfLXnegbxPntwru5Rwjeq+u8b3mw9DysgUSEuVIIWHI69eM0TnnjDsOHjQ8sFERETkcT47p4B8n1amIJAjhzSZZwqCbARPys68XlgvNRoSbHs4klZQoIuSMCzd9EuKu6DuhF9/dytjhsGVhdO0yO1XksuOBgQYshMFUYk4364P0L59lZ6LiIiIXMPBHuQyraCgqKT621ETBJj1q8PCgCuFlsc1aQCUV5ju6/XWS42Wl0vo0kFgx17TduXMBWshQ1qqhM2/C2RfBuIv/GXcXtqgKa7tFokWbQRycg0ZAnfMJbBVKjUgAJg/ejn0gcFo3xq4+xZepyAiIvIG/g9MLisps9yWmwcuPKXBfIpLeJjpti4KuP5aw+2oOkCxIrDS622XGm2YYNoREQZMfNR038bIPJReXSAtThEUnKp7DfIKBHRREpKbuq8CkK32BwYA+kDDggicBkREROQ9DArIZaVWFuLKydXe7s/Mhw8pg4LQUFPn/lIOkFdg2vfnYWEx5AcwDetRLoYWEKi+sm8zKCgFJL1elSm4WL+NR353cvu1hiUp35dg5i2JiIi8hv8Nk8usjYvnwlOWzIcPhSsW1A4OAkJCDLcvZKuP+3U70L2zYVKuXGlIWWq0pNTU8zdfGM1aUKDXC5RXGBYLCykvMm6/VL812nnod5eWKiElCRbDkpRBATMFRERE3sOggFwmd+iU1Wwiwz1X074mM6/KpLxfUQGUl2v34LVKgio7zyWKoUYWqyVbCQpKrw77ir9wQLW99c1tPfq700VJFqVNla/F1uRrIiIi8iwOHyKXySVJb1GsExfEMFOTeabgwGHT7fOXgO9+1H6cVklQ5fj8YsXwoYpKqEr8Whs9JA85Ug4dAoDWQ9taeYTnBCjeGGYKiIiIvIddOHKZXH2oTqSp5k2F+dVqAqAe5qPF2t7O7UwLlcmUQUGJ2aTkCkXlIvPhQ/LiZ5WVhh3x5xWZgthYID7eZhs9Qbl+QxDr2RIREXkNgwJymRwUKDt2FkNYCABQWGT/GC0x9UwLl8mUk4tLzCZ7lyoqQimDAtXiZ1e37eswHFJCfXQJ/guIiHCtgVWkCgr414iIiMhr+N8wuUwePqScLMpMgTbzsfSOCggAlq0RqkxCwRUYS4cW2wgK5KDNYvGzq/uPtboJed1vQpcx3htFGKDKFJiyGbFuWiOBiIiIHMOggFymmSmo0D7W30XVUS4rZsna3pNntbfLk49LzBaLUwYFctCmtXiYLDvXkEWQqxlVN2VQcDZLYNVGQ1sNZUvhtXYRERH5G040JpfpNTIFAly8TIvyPVKuUSBTvmMR4UDThobbB49qn0+efFxsKyioNJQfjbVTZnTZWuG135lycvGeA6bgRQjvtouIiMjfMCgglxmDArOqMdPnCGRksjOnpMymyGsSWFM3yvYxdSIMQ2v+2K03ZgNkZWarTFdUGo5tmWT9fHLZU28wX9RNyZvtIiIi8jcMCshlcoe0qEgdAPAqryVVpiBUXUEIUN8PDbYsYapUJ9KQjVn5k+W+nDz1ey5XI2rcwPr5tMqeVpdAO3+BuBAeERFR9WBQQC6TMwUFhZb7eJVXTRkURIQDw9IlYyAgSUBHxRIBJ88C+Vesnys4yPo8gdzL6vvl8hwP8yhEYVi65LVJvcr3JVhjhtORE9XWFCIiIr/GicbkMjkoiNGpVzUGvHv12RepOr/Bhgm0KUmGwCk4WGDOAvXxWRetn0uSDNV5tCYnhwSr78uZgpIS61mba9vba73nKN8Xrbhl2VqBlCRWIiIiIvI0ZgrIZXJQUDdKsrjy7c2rz75I2fmVO+66KAnJTSWUlUtWqwNpKa8wZAo6ttPYKVkeC1iuZ6Ck11vf52nKicZa5WyZcSIiIqoezBSQy+SFygID1Fe+Y1hj3oIyKAg1m0QcG22ZabHl3AVg7iLTwY0SDVWHLuVoTDS+GhSYr2egOqZS3TmvTso5BVrBCTNORERE1YOZAnKZ/mq/VO7wyle+GRBYCtTIFMh0GpmWJjYmBpurG2UKNErNgoK8fMOEb/O5BkoVXlxbwlb1IYAZJyIiourCTAG5TH81U2BekpQsmc8pMGeeadnwm8Dpc46fP+jq78B8mNDCFYCtRdMAU8bHG+wFBVy8jIiIqHowKCCX6IUwdjVtlc8kA/WcAu03TBclQRdluF1R4fgkg+i6QOnVYKCo2Pm2aY3lry72SpISERFR9eB/yeQSvaIj6a3x6DVJoI05BVrKyh0/d7ROMmYKCoscf5w8XMmbQQGzTERERL6BQQG5RDkplJkC+7SqD9kSHu74uSPCgcCrOT9nKglFhBn+9ebwIX50iIiIfAODAnKJXjG6hVd77bM3p8BceKjj59brhTET4UyGIeRqxsKbE431dkZJ6Z2p1UpEREQuY1BALlFeXbY3WZRslyS1d3yThraPXbIKyMs33DZfXTpaZ/1x8rAvbw4f+uuI7U5/qY1SqkREROQ+7M6RS+QrvBKAAK2laElF2ckvL7d/9Vt5vDz52BoBWK1UlF9g/XHyb81bw4fyCgR+/cP2MbYWXSMiIiL3YVBALmE5Uufs2mcKBBauADIybQcGynkaQVV4jyttzDG4mGP411uZguxce8VSGRQQERFVFwYF5BLjhFZhuOJL1uUVCCxfZ7ovBLBsrbD5vgUoogJH5iBUxfmL3vn9ySs525Kdw88WERFRdWBQQC7Z85ehs1apB6bPEXavfPuz7FxDIKAkhGGhMmuUw4eC7KwmIgFolGj7mHatgMF9tfet/9V+5sITzFdyBiznp3yz0jttIyIi8jcMCshpeQUCG34z3Xfkyrc/07oiLkmGlYutUQUFdoYPCQAJ8baPiYoE2rWWrF6Z99bvLy1VwsSxEkaPkPDMo5LmYmb8bBEREXkegwJymitXvv2Z+RVxSQKGpUvQRVkfO6MMCi5m238OYTZ3wLzzHxpqaofm4734+9NFSUhuang/tNZZ4GeLiIjI8+wMTCCyFKtxhdvelW9/l5YqISXJ0LmNiYbNgABQBwV/HbV9bklSB2kjhgKNGwAzPjZtCwuRjO1IiBOY85V6kq/P/P403hafaRsREVEtxkwBOU0XJaFhgum+I1e+SX1F3B5HV4mW3/vzl0zbFv8AbNiiPi7rkikEaNwgAMMGOpe58CZfbhsREVFtwUwBOS0jU+Dv86b76b0NV6DJfewtCKerC9w5SDJeQV+6xrRPCGD3fvXxew8AA24Qxs61s5mL6mI+fKhpQ362iIiIqgMzBeSUvAKBZWvVEwrW/sKypO6mDAquTdWYIxAMY9Yh24Hx9lrj8p3JXFSHvAJhMVfl1N/8bBEREVUHBgXkFE4yrh7KoKBVsqlCj9Z+R+r9A8DZ877dubYW3PCzRURE5HkMCsgpnGRcPZSd/uAg01V9mbJ0p1Z1o7atLM/p6xkda8ENP1tERESexzkF5JQjJ9T3JXAiqCcoJxprLV5mPufAfI5Adi7w5yF1ACBndHRR7m+vOxiCG8O6BHI2KtaH5jsQERHVZgwKyGFa8wkEgJQkrzSnVrO3orHWRGRdlKTo8AuLUqU1IaMjBzd7/hRYtxkIDfF2i4iIiPwDhw+Rw7TmEwAc8+0J5sOHzGkt8qXkyoJpvkIXJaFpQ0M7y8q93BgiIiI/wUwBOexvjYmqNeHqc02kGj4UaPg3I9P0/p8+Z7hvq1ynr5YddUTI1QxBWZl320FEROQvmCkgh+QVCKz9xXJ7eu+a1dmsKcwzBVpDt5atFXYnDvta2VFHhQQb/mWmgIiIqHowKCCHWBs61CihZnU2awrzOQX+VgrWmCkoB4TWB4+IiIjcikEBOUSrXCSHDnmOcs5AUJD/vf9ypkCvB3IvCxw7ZT8rQkRERK5jUEAO0UVJuOUm0/2aNHG1JlIGBfI6BTV14rArgoNNt2d+BsxdJDB9jlDNqyAiIiL34URjckhegUCdSNP98WOAmHq1s0PqCyoVQUFBoUC9ulKNnjjsrMAACYGBApWVpmFTQhjmUaQk1e7XTkRE5A0MCsiujEyhWlAqIACIqcckkycdOWG6Ij7jY2BYuqHSkHotgtotKBCorFRv8/UF2IiIiGoq9uzIJrnqjXKup14Pju/2oLwCge17TPflK+T+9p5rrc9Qm+dREBEReRODArKJC5ZVv2yN97Y2VxrSkpEpcKXIcnttnkdBRETkTQwKyCatqjcAr9Z6kr9VGjKntSYDALRrDZuLtREREZHrGBSQTXLVG6W4Wj7J1dv8rdKQOWvZqQuXOGyNiIjIUzjRmOxKS5WwdI2pMxYf68XG+Al/qjRkTs6UmAcGF7OB6XMEhqUzY0BERORuzBSQ00JDvN0C/6CLkpDc1H8yBDLzTImSv066JiIi8jQGBeS00FBvt4Bqu7RUCT27aO/zt0nXRERE1YFBATktjEEBeVhegcBvO7T3+dOkayIiourCoIDs0uvVQzWCAv1rOAtVP2uTjQH/mnRNRERUXRgUkF3lFer7hUUcz02eZa0UbtOGQEKcwLFTlvMK8gq0txMREZF9rD5EdpWXq+9v2wUk1hesAEMeY5hsDIvVtE/9DXz0FQCIq6VaDfMPMjJNK28rtxMREZFjmCkgu3IuW155ZQUY8rS0VAkTx0q4eyig1b2XKxGdOadXBQ+sUEREROQ8BgVk16Ucy22sAEPVQRclISJCgrXuvRDAqbOW8w/4+SQiInIOgwKyKzLCchsrwFB1sTa/ADBsb9rIcj8/n0RERM5hUEB2hYaqe1yGMdusAEPVQ17MzJz8OWzcIABD+llu5+eTiIjIcZxoTHbJE43jY4Fb+kuIiQY7XFSt0lIl/HlI4NBx07aJY00d/9RrJPywwTCGiJOMiYiInMdMAdkllyQNDwWSm/IKLHlHfKz6vvJzWFFp2h4UxM8nERGRsxgUkF1ypiA42LvtIP+WbTZxOCPTNLu4UhEUlJVVU4OIiIhqEQYFZJecKQjmYDPykrwCgb+Oqrcpy45WKBbYK2VQQERE5DQGBWQXMwXkbeZZAkBddlQ5fKi0jOsTEBEROYtBAdlVxkwBeZlWWVJl2dFKvWk7hw8RERE5j0EBWZVXIHDslEB2juHKq54XYMlL5LKkysCgd3dDBiGvQKCSw4eIiIiqhNd+SVNGpsCytUK1Uuzu/UBSY8Fyj+QVaakSUpKAdz8VKK8Afvkd2LRNQJKA6681HceggIiIyHnMFNRi8pV+eTKmM48zDwhkysmdRNVNFyUZh7HJn08hgK07Tcdw+BAREZHzmCmopZRX+g0rvDq+oFN2LjQDAsA0uVMX5cbGEjlB0riUofy8lpZXX1uIiIhqC2YKaiHzK/1COHeFPzba+j7l5E4ibwgNsdymnGvA4UNERETOY1BQC2ld6VeWb7RHFyWhTYr2vmHpXNGYvCss1HJbp3am2xw+RERE5DwGBbWQvfKNjoiM0N6ekuRys4jcwrw0bmJ9oEkD0we+qBic90JEROQkBgW1kFy+UcnZK/z5BdrbHc02EHlKkPlMKKFevKykFJg+RyAjk4EBERGRoxgU1FLKScVtWzo+yVimNdGY8wnIF5ivrJ11EThyTP2BdXYeDRERkb9j9SE/oDUG2x7lCrGyW24C5xOQ92n08w+d0DjMOI9G4NRZw4OaNuKcGCIiIi0+nSmYNGkSJElS/SQmJhr3CyEwadIkNGzYEOHh4ejbty/279/vxRb7DqG41G8+v8ARJaXq+5IEdO3o0x8X8hOVlfaPAQyf2bPnBd7+SGDRSmDRSuDtjzisiIiISIvP9/LatWuHc+fOGX8yMzON+6ZNm4YZM2bggw8+wPbt25GYmIgBAwagoMDKgHg/ohxj7UpQUFyivm9t3QKi6mZtErySJAHpvYE1myz3KYcVubrAHxERUW3j88OHgoKCVNkBmRACM2fOxIsvvog77rgDAPD5558jISEBCxYswKOPPlrdTfUp5YoFnNyRKSDyFVpBQf044MIl0/3hQ4A6kRK0xhrJw4qOnHB9gT8iIqLaxuczBYcPH0bDhg2RnJyMe+65B8eOHQMAHD9+HFlZWUhPTzceGxoaij59+mDLli02z1laWor8/HzVT21TpggKHB1uIRNCaAYFvJpKvkA530WuRGSeyQoMlKwuwidJQHBw1Rb4IyIiqm18Oijo3r07vvjiC6xZswYff/wxsrKy0KNHD2RnZyMrKwsAkJCQoHpMQkKCcZ81U6dOhU6nM/40adLEY6/BW5RBQXmF84/VGi7EMo/kC46fNt2uuPrZvpitPqak1Pqk+GHpEsrKpSot8EdERFTb+HRQMHjwYNx5551ITU3FTTfdhB9++AGAYZiQTDIbGyOEsNhm7oUXXkBeXp7x5/Tp0zaPr4mUw4fkjpOj46dLrs4nMH8beTWVvC2vQOD8RfvHXc4zfNbNtUo2DBHSyiKw5C4REfkzn59ToBQZGYnU1FQcPnwYt912GwAgKysLDRo0MB5z4cIFi+yBudDQUISGulCnswZRBgXFJcDqjXr8tsNw39r46bwCgexc4OBRQ2dKK1sgX03VRXmq5UTWZTt4JX/jNmDjNssPsPy110VJSG0tkHnQcN/wnWC5UiIi8l8+nSkwV1paigMHDqBBgwZITk5GYmIi1q1bZ9xfVlaGTZs2oUePHl5spW9QDh86cQbGgADQvuKfkSkwfY7A3EUCWzKsn5dXU8mbYqNtT5wPC7P9+ADFX7xGDUwnGjWck4yJiMi/+XRQ8Mwzz2DTpk04fvw4fv/9dwwfPhz5+fkYOXIkJEnC+PHjMWXKFHz//ffYt28fRo0ahYiICNx3333ebrrXKYMCLcrx02fO6bFsjbBbdpRXU8nbdFEShqVLVgODcDsJwADF45TfEQF+pomIyL/59PChM2fO4N5778WlS5cQHx+P6667Dtu2bUOzZs0AAM899xyKi4sxbtw45Obmonv37li7di2ioji2pdxOUCBf8c/IFFi6xv75RtwCNG3IgIC8Ly1VQkqSIajdtkvgz8OmfbooIDfP+mOV63eUlZmi4Pwr7m8nERFRTeLTQcHChQtt7pckCZMmTcKkSZOqp0E1iK2KQ/LCTn8dEVj5k+3zyNmB1NYMBsh36KIk6KKAg8fU6S1dXduPU34vlJmCk2cEmje1XrFIJs+7iY22fywREVFN4tNBAbnO1vCh9q20V3rV0iWVY63JdwWZ/QUrLLR9vDKDVlZmup2RCezcJ2wuYJaRycXOiIio9vLpOQXkOltBgVxxxRE7MrloGfmu4CB1p/zISdvHKzMFhUXqfbZK7uYVcLEzIiKq3RgU1FLl5e7prHBBJ/Jl5pkCeyqUQUGx5X5rn/fsXMsSvfxuEBFRbcKgoJa6YmcYhaNYgpR8WUWFc8GvKoOmMfLH2uddqxQqvxtERFSbMCiohTIyBXb/6dpj60aZOj8sQUq+rqTUueOVcwr0lep98ucdgMXK33IpVCV+N4iIqDbhRONaRh777KjYaPUqsdE6YOx9EnJyDVdB2ekhX2at2lB4OFCsMTxIOWTIfN7N+DHA8dPA9Dnak4nTUiUsXWP6bnGSMRER1SbMFNQyWmOfbTE/NjTYEAgkN+VVUPJ95p9RSQJuGyghwsrKxhUVhsX6jp0SFlmGkhJwMjEREfktZgpqGXnss6OBgfkiZyEh7m8TkacEBarvj38YiNFJ2L7H+hdgzleAgOX+vy9Yn0ys43qIRERUyzFTUMtojX22pdjsamkogwKqQYKD1LdjdIY/aRHh1h9jHg6EXc0qhARzMjEREfkvBgW1UFqq5PCVzQqzlY+ZKaCaRFmSVPnZDQl2/Bx1rgYQAhJuHaDex8nERETkLxgU1FKlNhYvsyXUic4UkbcpgwJllkt5+95hwOgRkkUWQBZVx/Dv6bMCSY1N23t15WRiIiLyH5xTUEuVlbn2uNAQdoKo5gi2kikIVgS3CXESYqMlhIZYTi4GDBWHAOD33cAfe0zbbX0X+C0hIqLahpmCWqiyUkCvd+2xwcwUUA0SbCVTEKDotUdGGP4NNJuUrEU50bi0jFWHiIjIfzBTUAuVKrIEzlQiAoCSUgFeB6WaIshsorGsXDFXpqRUYP8hoLDIuXOXuphtc1ZegUB2rqFyGOcvEBGRtzAoqIXkoUNBgcD4hw0LkQUHC1zOAxb9YDtIWP8rUCdScCw11QjKoCBAkffMumi6PX0OYFlzyD5nV0t2RUamMK6NYL5YGhERUXXi8KFaSL7CGRJiWoiscYMAtL8mAMPSrU+4lHHBJqoplEFB4NW/ZnkFAmfOVf3cpbaCAjf02+XVx7lYGhER+QJmCmohufKQ1poDaakSUpIMCzItWilwRWNIBRdsopoiQJIgZwHkTEF2rnvO7enhQ1qrj/O7R0RE3sKgoBYqU2QKtOiiDOsYhIZqBwVcsIlqIjkocHZVb2sKi4FjpwRir34XLuW49wq+Vjtr83ePcyeIiHwbg4JaSL7CaW91Yq0FngzjmrlgE9U8clBgWNUbZmP1JRw5IbDvoPoxkgR0bAvs3m95vks5wNxFnhvKI7dz6RrTc9TW7x7nThAR+T4GBbWQMSiwU15UGRSkpQId20iI4VU8qqEqK023lcPk5M901kVT5zslCejdTTJeld/zp3AqsyCEKYtQle9LWqpkDAoS42tnR9na3ImUJP6tISLyJQwKaqG8fMP/vvYmFCv3BwcByU35HzTVLBmZpp78voNASpKpcpY8TE6mzJxFhJs+78dOORcQyOQswsA+Ar26Vr1mg73va03FuRNERDUDqw/VMhmZAhu2GG4fOq7uNJkfd+KM6f62XdaPJfJF8hVoJVvVe0KCJcVt0/bYKo7hX7MJ+HW7i6sFKri64KCvk+dOKNXmuRNERDUVg4JaxNFOUl6BUI1jtnUska+ydQVaizJToFzoTBcloWeXqrVl7S+o8nenspYGBYa5E+qooLbOnSAiqskYFNQijnaSrJVstNWhIvI1zl6BVgUFZvNtrk+zv36HLe747tTWTAGgnitRW+dOEBHVdAwKahFHO0nWhkswpU81iXwFWv7M26ucFaLKFKiP0bqa7Qyt705egcCxU45n32zNa3D2XL7MPCAjIiLfwInGtYh5iUMJ2p0kXZSE2waqSyFaO5bIl2lVGbLGVqZAea5tGQK/ZTi+zoFWMOJoCU6heBJrw4dqWznPAF6KIiLySQwKapm0VAl/7BH4Owu4ZYD1zoPcATp9VkBIQNOGDAioZjKvMmRNiJU5BebnGthXwnVpAjm5wIr1AhdzbJ93YB/198yZEpzKIUPmw4fyCgROnRVYthZeK+fprgXHlMFPoJ8FBVy0jYhqCgYFtVBFueHfmHq2/wPSRUnQXcP/pMg/KNftsDeERQ40IiPsBwXmQ5GcKcFZoVhbQRkUKLMD5qqrnKc7MxRl5abb/pQpqG1ZHiKq3fzoz7P/KCk1/BsW6t12EPkSa9WHbAkLs39M2dXFAuVx/yHBQnPSstZ8nYoK0205KDDPNJgzn7/gifkG1rIdrj6H/B75CmvvmTvfS3e/h0REnsZMQS1UXGL4N9yBDg2Rv1AOHyopFTDMpLEt3IHAurRcICMTWLZGQO7utWkJHDisPu7ICcPK4UrKTEH51QBBK9OgpJy/sGOvHsvXAgLuvRLtzgXH5I62TBkIeWNoTUamMP6ulO+Zu6/qO/seyu9FSLBAWbnE4UZEVO0YFNQyFZXC2LlgpoDIZN9BUw9t2VpAkoTdTp8j36GCK8CmbaaAALAMCAzPaTkXQNlBrqw0fH/tLaaWkmT413Al2rTdnfMN5Epmyk6tK9XJtIZByYGQN4bWGK/eX70vv2cJce6fu+HMe2j5PgkONyKiasfhQ7VMaanpdiiDAiIAhs7givXqbY4M5QgLs98hu1LkWKUirbUMlJkCACgvN3RCWyZZP498Dq31RuTnsDcMxt5+dyw4Zm0YVEkpcOac3nC1XtEJX7pG4Mw5vcU55HbmFQhk/qVH5l96h4fgyI8/c06PY6cME7e1rt6fOuv4QniODjHSRUkY0Mt031rJXGvvE4cbEVF1Y6aglpHnE4SGAIEBvMJEBLg+HEY5D8EaRxc907pKrMwUAIYJueFhtgN6+RxaGQVJAs6eF5i7GFavwDt6hf7a9sDSNYbb7Vo5f8Xa2jCoK4XAR19pP2bOV8CwgcJiOI8lgdsG2m6T1tV3LZIENG3k2FV9Z7MbLZtLWLvZcNIRQ4H2rS2PtTVcrLomlRMRAcwU1DryfAIOHSIycXb1Y+UxjmiTYv8YravE5pmC7Fxh83nr1jENZ9FFSWjaUN3W9N7A2l8sh8HIV5udmfyqrBgUGa7dHltXzUOCtXu6JaWamw3tgaE9Z87pbU62Bkzt1mqDvcnaMvnqfeMGARaZkfTehg67rfdu6RphM3OhytyGWGYIbE1Ml9vHBSXJG2rTgonkOGYKahlWHiKyJC/sp77Ka384zJlz9v9DLCsDmjeVcOCI9WPrRGhfUa40CwrmLTJcKTfPIMiCzP5ix0YDp/423J44Vrp61VndDuXVZmcyJvIFBkA7SLF11dywT/s12GNtOI/WccqF5pRtsDdZW/bY/UDDBEObOysyI5ERcnBlGtsfrdM+56KVhvkpWlkDZQBUrvidmr937VoB+w6qz+voZ5TI3VhK138xKKgF5EWOioqBSznq7fwPhcjAmdWPAVwdw27/vOXlcjUj60rLTd9HZcWdvHyzDjwMgUujBCvnMbvKruykGl6PsDkMRmvyK6B9NVoZFJSbBSnWrprLk6AduUpvjbXhPFqUK08rJwdby1IAhppT8t7QEMn491MZiBUWmW7L5x17n/U2KZ8bgPH3W6ooxSq/h1rv3f5Dlud8+hGgXl3tz6jyMwQAp84aTta0EYMIqhpnFl+k2odBQQ2XkSmwdI3l/1LnLwHT52hfvSLyV46ufgxoT+TVUlpue0gMYAgcps8R6NhWYM+fpivbrZtbHiuEYfKylhKzev/Kb35lpTBmROS/CRLUV5t1URIG9RFYtVF9Hq1yqUXFivabBQXWrsRvyxBo2VyqUkBgGM4joWeaHr/usH5szy7Ab2b75ayHUHX91VLbAHsPGG7v3i+wcZv9dgkBlJer31utY8wzFx3amPaXXx2OZS1bY+7QMaBbJ8vtVZ1rQWSLO8sRU83DOQU1WF6BdkAgY/UKItdpzUPQUlKivqou69JRvRKCEMDu/eor238d1T5nqZXFviorgYoK0xj3giumfdmXDWOAU5IAucZAr26WHcSgQMvzav2dsJYpyCsQKCzS/pvy6w7gYrbrf2/uuw24nCfw+y496kRaP65HF6B9a+vzRKyVdY2MMAwDkjkSEMiCgwWubW/7mF93qH+/e/407ZMzEY5+rlb+BIvfSV6BUFVt0qJVxYnIUa7Ov6LagZmCGsyRK5mM8IlcYz4PwZrSMu1MQZMGwI49rj23VpChfL49B4RqsTQA+GCeaQy83F693jS8EADyCoA1myzPaSq/aTr27/Om/XKH9tftes3HK638yfZ+W7763rHjtuwAtmYYAgPlEC95cnBsNNCgPnDugvpxhUXAJicCAaU5C4ChN5re8fqxwIVs0/52rbSHAcmUgVVivLptDRPU7zdwdVjRQYF2rU3DNrZmCCv5D7O2Kqo4ETnDPNsIcG6LP2FQUIPZW+QIYIRPVBXKeQjBwQJzFlim1isrDdkCc9+v9kybLuZcXWjLbLvyCrXs9N/AbzvsdyPlUqafLdLen5PnWEBQnYRQT85t0Uw9OdiRv4/OPt/KDab75uVqbQUEgCEosDbc0zwgkK3aCKzeZBgGmpJkOVzKalvBceDkurRUyfg57dyOw9H8CYcP1WC6KAldOljfz+oVRFWni5KQ3NRUtlJOrcv/VlRanwPgCYeOOlZZBzBVJrInvbd2BkGWk2t7v7co34ejJ9WBkbLogieer6DQuccWFhnmArjynEvXmDI4zjxOufgaS0ySKxxZq8WcOz9r/NxWL2YKarjEeMOEukaJhrS5chhDem9G+ETupMwcnPpbYP2vhu15BdXXhs3b3Xu+jm3kspz8T9ce5dAsZ4OCgivWgzn53W+ZBJw8q14jQnb8lGMVmZRtlbPELDFJrnL2r4I7P2v83FY/ZgpquCtXJ/zFRVuOa177i+VENSKqGl2UhJho4Kff7B8rScDAPp5vU1UEB7s+1KY2rocSXVd7IrAkAd07m+6brzFhj/kaE0otkw3/RkYYVrTWsiMTSLMz0VmpR5rhX2vlY/l/g+v86uq1Ey/RmcURXTkXP7eex6CghjPW09b4T8w8fUxE7mFrcSz5qygP3+vVNQCJ9W2fL7iacrYSDKsiA0BSY8O/xSWGQKdTW+fPZ68Uq/F5bVzcM9/XMNHymC4dgRG3ACOHO942V8XHGhaCa9xAvX3svUDjBq5fpTxzzvo+ORAICQZCrAzXEAKItFGRydxvOwxlcLdmaE+UX7qanStXZGQKTJ8jMHeR4d+MzNr9Pjrz6myVM3WWrbLH5DkMCmo4OSiIrccyYkTVxVZZSQFDB3biWMmY6o4Mt30+83UAlP5xu2bM75InHzJ1OuXsQEmp4apcoFmpUmtXrF0xagQweoT2q7h9oKFSkKx7R9NxdaOAZx6VMGxAAFJbByAuxjNDB65VrNEQHHw1G1RPfUxAoKQ5odxRuXnW91VczTqEhFgPECUJCHDyf2whrE9OPnISXi1dWhOvtrvzSrgv0+sVr8eJl6aVcXS1H2Ite/lbBkdAeBKDghqu8OoCQ3ExksUkSE4yJvIMQ9k+69+tOuHq7561q7+OCA2WMGyg+rkS4lw7V0iwZFwDoe7V9mXn4uoVT/WxtsqiOiuunoQcK53imGgJurqm+8qsSnyMunqOK8OVHPkL2K6l4ncVbPjXfIJlYRFQ7GBmxFnyQnEhwZLmOhKA4e+53sU+fJSVDMOps66dr6pq6tV2d14J92WVLn7OdFGSai2PqvRDdFESWllZ3LG2vd++hEFBDZdvXLzIUJN64lgJo0dIqquUROR+KUna27WujIUGu/Yc8rnSUiXc0M2wrV0rxyebmispBcquBgXy2iWX810/n8PPW2a98k55uYBQdELiYxU7zf6EhYQ4tvCXkgAw9Cbrj5MAhIea2hZsIyjIy3fvGxVxNRsjZxFCQqzPPUhLlUzDRZ0gSbDIesiaNnL+fEDVrvK7erXdFzIL/rKwl7PzZZSUQ+y6drD+d9IRzZtYbquJ77cvfHYdxepDNVhGpkDuZcPtxT8AZeWGwIALlRF5nrXFA3umWdaGV3YwE+OArEv2z29+lS022lCjJuuiYwsXaikutgwKqkN+gfXA44vvDIUSZL/vMh149ITh75x8gSNAkhAWKpzOYlRWWH9+AWDO15bbw0LVFZl+3yVwJsvx5wwMtN65kiSgY1vDCteAITADDFkKa5kCIYTqOK0KRVp6pAFnNdrdqR3QuIHz1wW379Fj+TrDbVcqwti62m7+mcwrEMjOBc5kCaz7xbS9ZxeB69OqPxOui5Iw9EaBFVcX6KutGXnl59b8ayP/TmKjtdfAqFAMhfxjD/6/vXsPjqLK+wb+7ZDJlckIBEICBKIgAcI1QQiwIF6SRXBBn11ZZV0pdl9EUHHlqXJ1axd9qlasdd1ntVZQwBe1ZAupF0G8QAgIAWUBCSDhKuFOJERuIVwkIXPeP5qe6e7p7umZTDIzzPdTRZFM386c7syc37ni290i6FmD4uO1f4PB5Le/9NrdJ1jqtUmiYQYlBgVRSqltUSi1LVyshqhlKLWG+gLO0Hzfvz9196Hszv6DguGDgcJB2i+/1iny/8EGBIDcsqgk105Q4HIGPt3qwD7Azr3a19q3kyBJxgNehQB+VK0poF8PQf+5lpQYWNcmSZJrxO1O56l00dG3FAQSEADmAUGPbsA9w+UVkvVu3BCmLQVbdgpUHpN/thsQANoxBXd0kwOtzA7Af40JPCCorROegAAI7nvnhzO+N8Go9lc9HaXeN9uBzeXBFzabIi9Xwmfr5ERNKAYG5d1637fq7kPq59jOFKF1V7Q3rCllE/VznpQEPP1EYAGBnfQ257SntXXyyvOKaCinsftQlIqVvo1EkUoZV6AexzOh2PhLKzFBPXhWwoRi6y+EzQaDQ90hWEdAKeBLkAfxGlEPdI0L4nsro73xQeq8CoT+cy3QAdAjh8Cz8JwdSsDhCLLLlz/t2wH1DZJhYbe2zrz70KqvjF8H7Hep6tBW/t8d5KNkFJAKAZysstc9orZOYM1G39eLRmoLSfouRkbCNchXXROu/ru2I9zdSOxeXx0IKIPgzaYIrTjg1pyv7jJ8BFs2qW9QFajdxgVps/dk1k3t1Gm3Z//mHjh+7oLxyvORXE5jS0GUMqqljMa+dkTRTL2YWVuLpmd1rXNSgve4k1UCZ84BG/6j3d+oO0V9CAa5Kl92CQlAxQHjLz5na+D8RfnnC5e8r2ekA4PygFUbzM/viJfn2td7Y77A+CJ5rNPJKoGlXwS3CBcQ+GDjjVuANi65C9Kp0wLbd1vvv/d7ufbw6MnACgZprdVjvMwlJUmmrUzfbAeys4yPs0rN/T+DYWFbTwl0gh1EbjYjjHw/hd+aVrNpJjtlSLb20zPrdtScbqgKzA0BtNiEeyGuQK6vfo9KgGB2T5Z+DkiSt9XGKJgOtmyizt+GBuUZ86bZ6j2ZVZzOXwwIyM/qsHz7XdmCEcrZmFoKWwqilMspoXik9/dbtW8jUaRzOSXkZFv/7am7DykBgsspIS83DgX9fGvQjb44sjKanlalpSA+Hvh0jfE+SkCgFx8P9OlpXduflOgdPKum1MABQF5unO1WA6PPNaOgQFkkzuicAt7av4x0e5+PK0oEvttna1cPOwEBACQnWs9edeKHwK4LAKWb7O3X5WbAce2avf1r6+SaYKU22OWUcHu27352a1rtDtRt18berFH6Y61qja1qyAPZri6o2u3G5a9G2ur6oWhdCLRG3KilwGqBQ/X59FMbN6Vsos5rtwAuXBS2a/nNpo1W3rEyXW9zDhx3OSX0VM2gFA3lNLYURLHed0pYXSbQKg74w/+J7AeNKJapWwoSdYVauYAIXY2X79/z8SZMH5mcJNcOK0GB/ovbjqpqoPKYnDazrh3x8TCtYVfXwOlbWCqPad9/0Ui59tio9eXyFe151eMvkpO8g/qMrn3ugnZbQX95rIa+pSYQSo2j2XoAeqdrBAB5drgzPwr8Z0fw11bYbXXJzpIHbjbckGd9cjjMvzPUAyRvXgUTioHWqulN7xkOfKVb2duqptXllDC6UOCrzd7XRtzle49dTgl9ewns3m/+XvR/J2a1xv5qyAPdPnKINw12WwqsuvtWHjO/fqhaFwIZ3A1ogwLlZ5dTQrs2wnRMk3I+dfeq1GTgqd8GXzbRB13//L/eFil/tfwup4ShA/3/fWV3Ao6fkn+WEPpCe2YH4OAR+eeZU5QJIyIXg4IopkTwymI7RBSZElRN6kkGaxb464akH+CpmPQQsHi5+TWVL1WXUw4KlL6siUF+8n+6RmDWVLkb0N/fNSiJSsA33xofq6+Bczm9M6Xl94Wtbli1dcKnJn3zdqBwkHIeCRnp4mYXAe21HQ6Brbu0x5bvBqY+BpRt8S1g2BmYnOYEpj4mp3WzycrBejv3AvcMl2vdu3VBUEGBXLQPjMMh99GOk+Ra15qzAudr5bNkd5ILQsosLAkOgRUlvudYUSI8K2EDwJUrvvvI1xJQ6vr1M7vc3lXCV5u9qb+9i/F+/r7RZk31Ft7Mao0z0gU+XePbkqEM9Dx12o1PS7S1x+rtykBR9faNW71p0BdazWaxMetG4nD4pm9FiUCCQ6CNC4bb1GlT7lV9g2Q5c46/7sb6dJsNNE5N8Y4rMTqfwyE0i/S5hTatSj7YTbc+f9V5YRaEq589dWXM6GHA+s2++6vX6rh3ROi7dKn/TuUZzSIbg4IopkTkZlPYEVFksGopUKgLyXpm/XmtBqylOYGzN2f1UQ5VvmR/vCBPSblrr+GhppSauJxs42KpcBsXVu00m1u9f4XZQFd1jWfnzDiML9bXsEqov9knWX9sQ4NxSw0ATUFTecfqwpBSIwnItbhGrRRW6c3soM1HSQJy7wD2V1qfY3iBvLJrIOtLNDQA/1ggj/uobwDeWaxJFQb0kbtM+TvnWdVMUfogSzH/38D4IgEh5GBWXdPdWjfmpL7Bt0a8f2+B7yxaCQBtQdKsJvxElXlt8qGjbsMudOr7YzZQ1Jt2bwF0+263z3tVCpgup4SsDIEfzsjHWT2TgNxP38yWcoH0dtC11lmP51BaI9XPp/L3aNQaoV7XQj2+QL2eyLj7gM9UFRX9e8v3XZs/1rNI+Uu3v5aY3O7AAd3fyruLgQnF8sU2bPG+fv6CPF5HX6mgTlerVqEvtKvfw/V64zFXkYRBQRRTIvhgugIQUctRjylouOEtSNhlVtOX3cm81jg1GVBmPj3zo+/2XXuBJycBP1TDM++62r0jgHVfa19T1y7Gt9IWGAC5Ntqohn3qY9pFjYKl9DXXtwLo+wAbtbzU1gnT2tKcbOOWGvVrgPxzm9sE3pgv/64e32DWSqGnTq+6K05Ge+DxhyV8tw/YX2ldMh+aL2Fovpweh0P4FMbMCGHeD95ugHjZxgJqSq22/rVP1wiMuVu778VLAqs3aGuB7aTF7Raou+KtdTbSqpXx38fxKoF13xgdob0/ZjX8SlobblbMyS0V3n3UNf7ZneRnST1rltLKYfRM+vP1dhi8I+81M9KF4foT+X0lzz1JTZF/N2th+a8x3uPq64GKA3I0oH52et0h4bNSbzqMAsrGRn3w4kvfOqPWcMP4GEC+D+1uM962okT4jBX4bj+Q19P8fIAyfsN4vYJA1zJQ9q9TtaYpq8lHMgYFUUz5Qjabwo6IIsP3R7zfiu8tkWtRA2mmNht30DlTws/vdhvOCGRnDMLeg0Dx3XFoFe9bs57fV4Iz1fd1ywHV8doxB+p0hoLLKaFopECJaqYdszTpWx78jd0waqnwPQcgFzPlQlIrXdlLaaVQF4j1az2MHOItVKj7zJ/5UR5bYWe60MpjUC1UKWF8kfFYikgjBHC+VvvaxdrACsWK/+wQKCmzPvZzg2AXgGlAAPg+E/oCeE5n4MhJ+WelFtisn71c4++bwEa3QMUBgavXgH69EPCgdivvLgaKR7kxYrD24XSrHiwlz8xaWC6qZh07c9a45UK/fonZfWjKLFJWA7nHF0k4csL85EbXvepngP2WHcCWHb6tF4GO7Siv0HY7U9QzKKDmxJYCoshXWyewVlXjblUzZsVs3EFhvoRVG4IrEH5TDgzNF6bnNnu9tk74tBIAclHZ7jStwUoKcJ0CtVCkrbzCm9cHDmtXXFaucfmK95536wLUXgKO3RzMuHGrPEVq927eGZkUn64RGDrIfxr0z4/dVopwkyTf76uEBPsLy6n5CwiC0bu7/z7lSkAAeAutVjPzGPnfBQEmLEDyAoDawOAn1ZTGStdjs5aQlGT/1zh5OrSZX3VG3OyW6GUVFOT3lVB5LLA0mC0oqKf+jAZg2Jpi9vntaX0xOG80tBRwStIopvxhOxgUEEWsUC40aDT9qaRvJw+AOh1mU6savW5WM9pQb32upjIacB3oYkNNSZt+JXmz66e3Vd0f4Q0IAG+h4kSVb7cKIYCrNrvn6J8fuZUiuAXiWkrRSPhELXGSdkYfu0IdEABASoD9va/crHkO1XMe6BocVtZshOa5VNeS1zcAZ8+74XJK6NpZe1zRSCDRxoBYZUadUCkpg89CaEpLjNm4Sf1MZFa6ZAZWKPfOWGb8+b33oPHnjtUaGwwKqFkpNXWt2N5DFLHszs0eDsGmw2wO8LMXtDXpoRbuldztXl89mPCGGz6EkDshGT0XdtY7MLtv+X3lmaGmPCLhyUnw1HRGik4ZkqbGGgBO/iDP/hQJAi20ma33EGxg5q97Se4d9s+lfy71C9a9tUj+W3Xoyg9rNgKHbdTAHz5mPy12Lf1cXuhQ+QxRWgqMgrWtO92aYFtPfw9SU4ErNgJu9fFt25h/1q3aoE2rwqrVyO66FuHEoCCKecYUsKWAKGIpC1UpXyyhXsDGbi25foGvpqTDavGtQGvuAxHuAMvu9dVBQVpr42O6dPJ9LopGAkdPwpCkOtbqviktIc7WEiqP2XpbLULJJ6Vwmnqzi0rl8dBfKz3I5+HyFTR5oTAAGDU0uOP8jScx6rJnRv9c/njeeOB3tW4SAiGAHXvsXyfUlAHTFQfcnkK0fsYqAPjCZLyIoq9uUPH16/YGySuKRnpbRMeMNk+r/vPO5ZRMg/FoaClgHXMUa7zZfYhjCogiW3P2szfrygN4+2qrBw/3zRUhSUd+XwkJDuEzCNFqUaSmsrvQW3Oxe311ISYl2fwY/foM8r30LRlOHCcHEYHcN7PnYsgA71SirjR5vIOVgn7A9t3W+7Rq5b+/9i/ul9P903X5/aU5vd1vzEiSHDwEUpgD7A3WNnL0pLz4njKYtF+un+u4letpL9i+nXeAcscOQHVNcOnRCyTI0z+X6qlkFUIE1gUnEMGME1FTf66kGoxxsDq1JAG7D2hfO3PWfnqys+QWE2WhtNGF5vvqP+/KK4TpfWJQQM2KLQVE0cPOPPzBMGuuliR5KtCGBu3KwKFMR3YnCZKk7Rvf3DX3zT2QORTXV/cNv3xFYNTQONNjtPfDeNrULjcXFwvkvplNY9s50xsUpLcB2qTBsBvGfSOAAX28s69YFajsDODs30ueAlOZfSjZz2DWRAfwzBQJn6wSuHzC//nVzl8MbH89pba60WJKTMDbHUTfjai6RtWXP8CAJhCTHgISHRIcDoGGBgnHqgS++kZ+xtS11cr0mC3JmWreFe7uoUBGOvCxxXoManbK8pIkl4UabgQ385Caei0DIYD1FiueK4u2HTkhT41rtPaFor5eINDpqFsag4Io1sgpSYlinsspYUKxdl54CaGdCtTq2uGouW+uACtU11d3v9iyE+jYQaimELU+b6jy0+xcHdIBpZiVnGRee3pbWuALs1nZtktgdZn39yN+ug2lpMrXT04KTVe0YGqujdbvUFMGwur7qqtbBuyMEQlWokO6OWOPfJ+OnpLf4PkLcn/38UVybbdVQbW5JCebv/eyLcCwAvvn8tdCojzb674WlmsbmHHEW6+JoA+sPQsaSupF2/w/XJySlJpVA1c0JiJ4a69PVgkICcjOarkuNeGuuY80ZjMU2Z2CNpT5abaImyI5Sa7R3fu977EdO2jPU1Ut8O13QSdFExDYcdvNAEo/ENZMVoa8mN+WHb7blFazi7X2a6ftuF4v3299N6hT1f6P/eUDcnel8gr/+/7ifvjMugVoW+Rq6wQ2qGq0jRaQa0lWrUcCwDfbQ3Od1GTgqd/Kn3dbdwrNYmF2BRJIqMv+4+7xHziqnbso36dI/ozkQOMoxnUKiEjhckrIy41D355xLf6l01xTkEajUMyQFMr81J9L3T+7ocG8W4XcL167r5VQ33llPQqHw97+XTKBKwYFQu8CenHIy43DhOLQpfRGI/D3dwXWbNTecP1MP0aWrbLXnz8lGbijq2+aB+VpA0arqTBDqUc3e/vZmWlnRACtBWbiHd58cLZu+vkCca42sP0PHzeesSiSMCiIYjca5QeLLQVERJEh3DMk+fOdahXlXfvk7k1G4lRvorZO+F11VwB45EHgyUnBT8mpnjN//yF5LIPR+ID/flLCxHHAaNUsPw4HUHHQd9+pj8Fncbn/flLCg/cGl0Yjp04HfowQ9ub6v3oNWPu1byGyz53aTDabOjMUklVjZA4ds3dM/XXr7ZIE9OnZ9DTXXvLOwFZzrmnnCtTmIFo7jGYsiiQMCqIYWwqIiCJLc09B2xRGXZus9lWcu2BvsGfrZLlGXv3+A3FcN+B5RYnAYZOxB3m5cejd03sRs5ldGhp8E+JySrhrYBycqYGnMRwqDvi+dvmq9o4YTRNcPMpeC44kAROKJQw3qbm/ZlDA93d/f/LTf75oJFDfIIWkdeP8Bfl5vWiz5j7vzqZfsylacm2VQHFMQRS7wYHGREQRJ1LHWQTSxUQZqJrfVzKcyUhP3RqivP/V6wX2GIxXaCplCshK1SJbRuMd/LXQSGGuFm3KtJ3LV8uDW/WtID+eE/hmuzzGosRgHMdvHgI+Wu79vXcP4IF75KD1x/PAN9vtJeiRsfL/S78I7j10ypBnRTPLg66dgKEDgSs/AZ+vtT6Xdzpfe9q1bfqUqU0RSS2HemwpiGI3PAONI+MLh4iIZJE4zsKsi4nRa+puDkatHwP6+F8IzywgkCT7C3wZpa1tG7lmeM1G62MfvM86IGvO6ULNqPPsrgFNO5dRN5TMDvIFfjhjfEycrtSXke7NozSbffKVaXLzcoNrFVIKxT7PlWofZ2u5NWjIgDjcbbFOQM/b5fME0n1q41a5paK5ultZiaSWQyOsY45iXKeAiIjsMpumtHs3YO9BgVUbtPurF2Yyav24b4T5QnhWNbfKQnpJie6bi0R5p3f8bp92wT3Am171+zhywnrtBAAY3N+83rO8QgS0QrAddhZ6EwIYPhgoHCQvRrd1p++bSEzQdofqkgWc/MFnN8OFAs+ctc6UD5dpf1//H8CVJrc4JCbICxJaDRLWF2qV58Lo+Qnk+PMXgAuXBJavlvdLSfIec1d/CRv+I7+vNrcBo4YAK0rkbcogbP2zDQCD+wM5XWC4wGKnDAmzpsrXrTojPM9hKBWPkmfREpK8Hoh+zZhIxKAginnGFPAuEhGRDWZdm/r0BFaXWS9Ep1+fwWq9BrMuR09Ogmf9jBGD43xW2DYKNLp3A+b/W+BSnXyO2jphujib+nez6R8DGVuhJsF6bEXfnt6gIDXZfMXmzduBwkFAQoLxdv34iFOn5YXK/r1ce339/amtE9i0zc+bMKCeMjclBag36Zv/yIPG0x27nJLh86M+BjDvTqc8R47TgGcNDdUsWSmqn4UbyMrw3gl5fQDfAEM9Ba/ZAovKdXOytSu9110WeHex/3wD5G5Ox6uMt/XNjdwWATPsPhTF2FJARESBMuraFOoB0kbnm1AsD0S2SotZ2tTz3r8xX6DyGAzTq554w2z6x0Cn75QkYHiB/8HW7/8/1TEWpSulhj/RJCgw2j/RIWF8sfX9CXbVYiU95RXGg3WVe2c13bHZ/VaOsdOdLjXF+3NKkne/Vqou0vUNwPFT3juxagM099joebLzXKuP65wZZzroWi+ni/m2SB1MbIV1zFFMWYKdsw8REVFThXqAdKjOV1snNKsGK+MdZk2VMGuq5Dk/oF00S9lPv3CcWSvGuPuMB7VOfQxwtpawudy6y5J6m9UaBEpNdZzN7FD2z8m2zs92QQ5elSTA4RCeLjl6Ux+DrdXRm3q/1Wto1Dd4WwDUrtcDX67XvuZvccBg0lWY7/9+A3J3JiORPJjYClsKohhnHyIiolAK9QDpUJzPqAbc25/ee36r/fRpMqrVbt/WOI0NDZLhMZ06Bv5e1DXV6u5Dkmq71SBuq/x0OSWfxdkkaM/nkx7I5683mLpVYTStq5mm3G/1OhPrvoFhK09jY3CLAwaaLv39Boynd11RIuevmpKn0dZ1CGBLQVRj9yEiIrrVmY0f0NfE2t0PsOp/bn68/pjP1wY+LkFd6777gPd4Ae8AZH+DuK0oaTxZJSAk7xgA9fkA3+1mi2m1VI230TgPfy0AiuZKo/5+A3K+Lf3c25VMCHlw/JOTgIu10ORpNGJQEMUaGRQQEdEtzmzWJKN+4Xb2U++vHzjt73jlmNo6gQOHA38vSq17bZ3AZ6XabcoAZKO0BcLllODKNR7M6/ndYPuEYnnBOEVL1ngbjfPwtgCYB1/NPcWnPt/OpQBClx4h5PualxudgYAag4IodoNjCoiIKAbY7Rfe1H7tdo8PZlCvukbbqhAcbDDQVGatDC3BqpXHLK/H3A306dmytfKBtEZFI44piGLKXMLXr4dpWT4iIqIWYrdfeFPHMdg53t9CcP7GBhgdHwmFS5dTXpTMaqah5rqu2SxBZnnV0gGBv3TeCiQhwrXQc2jNnTsXr7/+Ok6fPo0+ffrgn//8J372s5/ZOvbSpUtwuVyora1FWlpaM6c0NMorhKeZTwIwvljSLHdOREREzae8QhguBKcfp2DW6mB0fKx/j5vlV6TlldV9jTSBlHFviaDg448/xuOPP465c+di+PDhePfdd7Fw4ULs27cP2dnZfo8Pd1BQWydwoFLg7Hl5nt7kJOP9rv0EXLkqdxf6Zrt2myQBs6beOtEqERFRpGtq4TCaCpfhxrwKTswFBUOGDMGgQYMwb948z2u9evXChAkTMGfOHL/HhzMoKNvixtqvQ3OuKY/ITZ5ERERERIGUcaN+TEF9fT3Ky8tRVFSkeb2oqAibN282POb69eu4dOmS5l841NaJkAUEkdAXkYiIiIiiU9QHBWfPnkVjYyMyMjI0r2dkZKC6utrwmDlz5sDlcnn+delisU51Mwp2SXIjw/PZnEZEREREwYn6oEAh6YamCyF8XlO8+OKLqK2t9fw7efJkSyTRR7BLkhsZms+AgIiIiIiCE/VBQXp6Olq1auXTKlBTU+PTeqBITExEWlqa5l84GC1JHigJ8vLsbCUgIiIiomBF/eJlCQkJyM/PR2lpKR566CHP66WlpRg/fnwYU2aPsljIwUqBHy8ArVOAJD+zDyn7pCRH93LaRERERBQZoj4oAIDnn38ejz/+OAoKClBYWIj58+fjxIkTmDZtWriTZovLKeGugSzYExEREVF43BJBwcSJE3Hu3Dn8z//8D06fPo28vDx8+eWX6Nq1a7iTRkREREQU8W6JdQqaKtyLlxERERERhVpMrVNARERERERNw6CAiIiIiCjGMSggIiIiIopxDAqIiIiIiGIcgwIiIiIiohjHoICIiIiIKMYxKCAiIiIiinEMCoiIiIiIYhyDAiIiIiKiGMeggIiIiIgoxjEoICIiIiKKcQwKiIiIiIhiHIMCIiIiIqIYFx/uBEQCIQQA4NKlS2FOCRERERFRaChlW6Wsa4VBAYC6ujoAQJcuXcKcEiIiIiKi0Kqrq4PL5bLcRxJ2QodbnNvtxg8//ACn0wlJklr02pcuXUKXLl1w8uRJpKWltei1YwnzueUwr1sO87rlMK9bBvO55TCvW04481oIgbq6OmRlZSEuznrUAFsKAMTFxaFz585hTUNaWhr/KFsA87nlMK9bDvO65TCvWwbzueUwr1tOuPLaXwuBggONiYiIiIhiHIMCIiIiIqIYx6AgzBITEzF79mwkJiaGOym3NOZzy2FetxzmdcthXrcM5nPLYV63nGjJaw40JiIiIiKKcWwpICIiIiKKcQwKiIiIiIhiHIMCIiIiIqIYx6CAiIiIiCjGMSgIo7lz5yInJwdJSUnIz8/Hpk2bwp2kqLNx40Y8+OCDyMrKgiRJWLFihWa7EAIvv/wysrKykJycjLvvvht79+7V7HP9+nU888wzSE9PR2pqKn7xi1/g1KlTLfguIt+cOXMwePBgOJ1OdOjQARMmTMDBgwc1+zCvQ2PevHno16+fZ5GbwsJCrFq1yrOd+dw85syZA0mS8Nxzz3leY16HxssvvwxJkjT/Onbs6NnOfA6tqqoq/OY3v0G7du2QkpKCAQMGoLy83LOd+R0a3bp183muJUnCjBkzAERpPgsKiyVLlgiHwyEWLFgg9u3bJ2bOnClSU1PF8ePHw520qPLll1+KP/3pT2LZsmUCgFi+fLlm+2uvvSacTqdYtmyZqKioEBMnThSZmZni0qVLnn2mTZsmOnXqJEpLS8WOHTvE6NGjRf/+/cWNGzda+N1EruLiYrFo0SKxZ88esWvXLjF27FiRnZ0tLl++7NmHeR0aK1euFF988YU4ePCgOHjwoHjppZeEw+EQe/bsEUIwn5vDtm3bRLdu3US/fv3EzJkzPa8zr0Nj9uzZok+fPuL06dOefzU1NZ7tzOfQOX/+vOjatauYPHmy2Lp1qzh69KhYu3atqKys9OzD/A6NmpoazTNdWloqAIj169cLIaIznxkUhMldd90lpk2bpnktNzdX/PGPfwxTiqKfPihwu92iY8eO4rXXXvO89tNPPwmXyyXeeecdIYQQFy9eFA6HQyxZssSzT1VVlYiLixOrV69usbRHm5qaGgFAlJWVCSGY182tTZs2YuHChcznZlBXVyd69OghSktLxahRozxBAfM6dGbPni369+9vuI35HFovvPCCGDFihOl25nfzmTlzprjjjjuE2+2O2nxm96EwqK+vR3l5OYqKijSvFxUVYfPmzWFK1a3n6NGjqK6u1uRzYmIiRo0a5cnn8vJyNDQ0aPbJyspCXl4e74WF2tpaAEDbtm0BMK+bS2NjI5YsWYIrV66gsLCQ+dwMZsyYgbFjx+K+++7TvM68Dq1Dhw4hKysLOTk5+PWvf40jR44AYD6H2sqVK1FQUIBf/epX6NChAwYOHIgFCxZ4tjO/m0d9fT0++ugjTJkyBZIkRW0+MygIg7Nnz6KxsREZGRma1zMyMlBdXR2mVN16lLy0yufq6mokJCSgTZs2pvuQlhACzz//PEaMGIG8vDwAzOtQq6ioQOvWrZGYmIhp06Zh+fLl6N27N/M5xJYsWYIdO3Zgzpw5PtuY16EzZMgQfPjhhygpKcGCBQtQXV2NYcOG4dy5c8znEDty5AjmzZuHHj16oKSkBNOmTcOzzz6LDz/8EACf6+ayYsUKXLx4EZMnTwYQvfkcH5arEgBAkiTN70IIn9eo6YLJZ94Lc08//TR2796Nr7/+2mcb8zo0evbsiV27duHixYtYtmwZnnjiCZSVlXm2M5+b7uTJk5g5cybWrFmDpKQk0/2Y1003ZswYz899+/ZFYWEh7rjjDnzwwQcYOnQoAOZzqLjdbhQUFODVV18FAAwcOBB79+7FvHnz8Nvf/tazH/M7tN577z2MGTMGWVlZmtejLZ/ZUhAG6enpaNWqlU8kWFNT4xNVUvCU2S2s8rljx46or6/HhQsXTPchr2eeeQYrV67E+vXr0blzZ8/rzOvQSkhIQPfu3VFQUIA5c+agf//+ePPNN5nPIVReXo6amhrk5+cjPj4e8fHxKCsrw1tvvYX4+HhPXjGvQy81NRV9+/bFoUOH+EyHWGZmJnr37q15rVevXjhx4gQAflY3h+PHj2Pt2rX4/e9/73ktWvOZQUEYJCQkID8/H6WlpZrXS0tLMWzYsDCl6taTk5ODjh07avK5vr4eZWVlnnzOz8+Hw+HQ7HP69Gns2bOH90JFCIGnn34an3zyCb766ivk5ORotjOvm5cQAtevX2c+h9C9996LiooK7Nq1y/OvoKAAkyZNwq5du3D77bczr5vJ9evXsX//fmRmZvKZDrHhw4f7TBf9/fffo2vXrgD4Wd0cFi1ahA4dOmDs2LGe16I2n1t6ZDPJlClJ33vvPbFv3z7x3HPPidTUVHHs2LFwJy2q1NXViZ07d4qdO3cKAOIf//iH2Llzp2dq19dee024XC7xySefiIqKCvHoo48aTgnWuXNnsXbtWrFjxw5xzz33cOo1naeeekq4XC6xYcMGzRRsV69e9ezDvA6NF198UWzcuFEcPXpU7N69W7z00ksiLi5OrFmzRgjBfG5O6tmHhGBeh8qsWbPEhg0bxJEjR8SWLVvEuHHjhNPp9HzfMZ9DZ9u2bSI+Pl789a9/FYcOHRKLFy8WKSkp4qOPPvLsw/wOncbGRpGdnS1eeOEFn23RmM8MCsLo7bffFl27dhUJCQli0KBBnukdyb7169cLAD7/nnjiCSGEPP3a7NmzRceOHUViYqIYOXKkqKio0Jzj2rVr4umnnxZt27YVycnJYty4ceLEiRNheDeRyyiPAYhFixZ59mFeh8aUKVM8nwvt27cX9957rycgEIL53Jz0QQHzOjSU+dkdDofIysoSDz/8sNi7d69nO/M5tD777DORl5cnEhMTRW5urpg/f75mO/M7dEpKSgQAcfDgQZ9t0ZjPkhBChKWJgoiIiIiIIgLHFBARERERxTgGBUREREREMY5BARERERFRjGNQQEREREQU4xgUEBERERHFOAYFREREREQxjkEBEREREVGMY1BARERERBTjGBQQEZFtkiRhxYoV4U6GLZMnT8aECRPCnQwioqjAoICIiDyqq6vxzDPP4Pbbb0diYiK6dOmCBx98EOvWrQt30oiIqBnFhzsBREQUGY4dO4bhw4fjtttuw9/+9jf069cPDQ0NKCkpwYwZM3DgwIFwJ5GIiJoJWwqIiAgAMH36dEiShG3btuGXv/wl7rzzTvTp0wfPP/88tmzZ4tnv7NmzeOihh5CSkoIePXpg5cqVnm2NjY343e9+h5ycHCQnJ6Nnz5548803NddRuvX8/e9/R2ZmJtq1a4cZM2agoaHBs0+3bt3w6quvYsqUKXA6ncjOzsb8+fM156mqqsLEiRPRpk0btGvXDuPHj8exY8eaJ3OIiG5xDAqIiAjnz5/H6tWrMWPGDKSmpvpsv+222zw/v/LKK3jkkUewe/duPPDAA5g0aRLOnz8PAHC73ejcuTOWLl2Kffv24S9/+QteeuklLF26VHO+9evX4/Dhw1i/fj0++OADvP/++3j//fc1+7zxxhsoKCjAzp07MX36dDz11FOe1oqrV69i9OjRaN26NTZu3Iivv/4arVu3xs9//nPU19eHNnOIiGIAgwIiIkJlZSWEEMjNzfW77+TJk/Hoo4+ie/fuePXVV3HlyhVs27YNAOBwOPDKK69g8ODByMnJwaRJkzB58mSfoKBNmzb417/+hdzcXIwbNw5jx471GbfwwAMPYPr06ejevTteeOEFpKenY8OGDQCAJUuWIC4uDgsXLkTfvn3Rq1cvLFq0CCdOnPDsQ0RE9nFMARERQQgBQJ5dyJ9+/fp5fk5NTYXT6URNTY3ntXfeeQcLFy7E8ePHce3aNdTX12PAgAGac/Tp0wetWrXy/J6ZmYmKigrT60iShI4dO3quU15ejsrKSjidTs0xP/30Ew4fPuz3PRARkRaDAiIiQo8ePSBJEvbv3+93Gk+Hw6H5XZIkuN1uAMDSpUvxhz/8AW+88QYKCwvhdDrx+uuvY+vWrbbPYWcft9uN/Px8LF682Cd97du3t0w/ERH5YlBARERo27YtiouL8fbbb+PZZ5/1GVdw8eJFzbgCM5s2bcKwYcMwffp0z2vNUXM/aNAgfPzxx+jQoQPS0tJCfn4ioljDMQVERAQAmDt3LhobG3HXXXdh2bJlOHToEPbv34+33noLhYWFts7RvXt3bN++HSUlJfj+++/x5z//Gd9++23I0zpp0iSkp6dj/Pjx2LRpE44ePYqysjLMnDkTp06dCvn1iIhudQwKiIgIAJCTk4MdO3Zg9OjRmDVrFvLy8nD//fdj3bp1mDdvnq1zTJs2DQ8//DAmTpyIIUOG4Ny5c5pWg1BJSUnBxo0bkZ2djYcffhi9evXClClTcO3aNbYcEBEFQRLK6DIiIiIiIopJbCkgIiIiIopxDAqIiIiIiGIcgwIiIiIiohjHoICIiIiIKMYxKCAiIiIiinEMCoiIiIiIYhyDAiIiIiKiGMeggIiIiIgoxjEoICIiIiKKcQwKiIiIiIhiHIMCIiIiIqIY9/8BV4L5CmaLaw0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Uncomment the line below to make the graph interactive\n",
    "#%matplotlib widget\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "#plot data and models\n",
    "plt.plot('Channel', 'Counts', data = channel_range_dfs['Range 0-176'], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.plot('Channel', 'Counts', data = channel_range_dfs['Range 176-214'], color = '#7c8bfd', marker = \".\")\n",
    "plt.plot(channel_range_dfs['Range 176-214']['Channel'], result2.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "plt.plot('Channel', 'Counts', data = channel_range_dfs['Range 214-700'], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.ylabel('Counts')\n",
    "plt.xlabel('Channel')\n",
    "\n",
    "plt.annotate('gamma@661.657keV', xy=(211, 146), xytext=(230, 170),\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc\"),\n",
    "            )\n",
    "\n",
    "\n",
    "# Create legend entries for the blue and red lines (data and model) and display the legend\n",
    "blue_line = mlines.Line2D([], [], color='#7c8bfd', label='Data')\n",
    "\n",
    "red_line = mlines.Line2D([], [], color='red', label='Moffat Fit', linewidth=2.5)\n",
    "\n",
    "plt.legend([blue_line, red_line], ['Data', 'Moffat Fit'], loc='lower center', bbox_to_anchor= (0.5, 1.005), ncol=2,\n",
    "            borderaxespad=0, frameon=False)\n",
    "\n",
    "plt.title('Cs-137 gamma ray scattered at 100deg from an Al target ', y = 1.07)\n",
    "\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd2b1027-4a59-4481-a1c1-3fbcebed488d",
   "metadata": {},
   "source": [
    "# Estimate the measured energy using the calibration curve (prepared in notebook 5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24a2edda-9e8b-4159-ab0a-3abaf36f6a0c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear Model Parameters\n",
      "-------------------------------\n",
      "Parameter    Value       Stderr\n",
      "p_r1_c0   -27.70990     4.87254\n",
      "p_r1_c1     1.45191     0.12099\n",
      "\n",
      "Linear Model Fit Stats\n",
      "-------------------------------\n",
      "Reduced Chi-squared =  1.0038505734658223\n",
      "r-squared =  0.9999951130945643\n",
      "\n",
      "Quadratic Model Parameters\n",
      "-------------------------------\n",
      "Parameter    Value       Stderr\n",
      "p_r2_c0   -35.38041     7.32770\n",
      "p_r2_c1     1.71396     0.22612\n",
      "p_r2_c2    -0.00061     0.00046\n",
      "\n",
      "Quadratic Model Fit Stats\n",
      "-------------------------------\n",
      "Reduced Chi-squared =  1.00411106030073\n",
      "r-squared =  0.9999959265220606\n"
     ]
    }
   ],
   "source": [
    "# Data\n",
    "x_r = np.array([29.3812658, 70.4034958, 100.839961, 102.324468, 254.677094, 350.450834, 638.540552, 738.972592])\n",
    "y_r = np.array([14.4136, 80.998, 122.060, 160.605, 356.005, 511.006, 661.6570, 1274.54])\n",
    "yerr_r = np.array([0.0003, 0.008, 0.010, 0.015, 0.017, 0.008, 0.003, 0.008])\n",
    "\n",
    "#use builtin NameModel, specify prefix for each:\n",
    "mod_r1 = PolynomialModel(1, prefix='p_r1_')\n",
    "mod_r2 = PolynomialModel(2, prefix='p_r2_')\n",
    "\n",
    "#set initial parameters\n",
    "pars_r1 = mod_r1.guess(y_r, x=x_r)\n",
    "pars_r2 = mod_r2.guess(y_r, x=x_r)    \n",
    "\n",
    "#fit with weights=1./yerr\n",
    "result_r1 = mod_r1.fit(y_r, pars_r1, x=x_r, weights = 1. / (y_r * np.mean(yerr_r / y_r) + y_r * 0.198))\n",
    "result_r2 = mod_r2.fit(y_r, pars_r2, x=x_r, weights = 1. / (y_r * np.mean(yerr_r / y_r) + y_r * 0.186))\n",
    "\n",
    "\n",
    "print()\n",
    "print(\"Linear Model Parameters\")\n",
    "print('-------------------------------')\n",
    "print('Parameter    Value       Stderr')\n",
    "for name, param in result_r1.params.items():\n",
    "    print(f'{name:7s} {param.value:11.5f} {param.stderr:11.5f}')\n",
    "\n",
    "print()\n",
    "print(\"Linear Model Fit Stats\")\n",
    "print('-------------------------------')\n",
    "print(\"Reduced Chi-squared = \", result_r1.redchi)\n",
    "print(\"r-squared = \", result_r1.rsquared)\n",
    "    \n",
    "print()\n",
    "print(\"Quadratic Model Parameters\")\n",
    "print('-------------------------------')\n",
    "print('Parameter    Value       Stderr')\n",
    "for name, param in result_r2.params.items():\n",
    "    print(f'{name:7s} {param.value:11.5f} {param.stderr:11.5f}')    \n",
    "\n",
    "print()\n",
    "print(\"Quadratic Model Fit Stats\")\n",
    "print('-------------------------------')\n",
    "print(\"Reduced Chi-squared = \", result_r2.redchi)\n",
    "print(\"r-squared = \", result_r2.rsquared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cea7815a-bb99-4ffa-a35f-2de39f25864c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196.17831784018657\n"
     ]
    }
   ],
   "source": [
    "#Print the new position\n",
    "varys = result2.params.valuesdict()\n",
    "\n",
    "position = varys['g2_center']\n",
    "\n",
    "print(position)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95593d9f-fdd4-4e12-b3c7-0f72c9fd9f06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear model estimation\n",
      "----------------------------\n",
      "265.5295708642683\n",
      "\n",
      "Quadratic model estimation\n",
      "----------------------------\n",
      "234.55790093504157\n"
     ]
    }
   ],
   "source": [
    "print()\n",
    "print(\"Linear model estimation\")\n",
    "print('----------------------------')\n",
    "print(result_r1.eval(pars_r1, x = position))\n",
    "\n",
    "print()\n",
    "print(\"Quadratic model estimation\")\n",
    "print('----------------------------')\n",
    "print(result_r2.eval(pars_r2, x = position))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "969a385a-5c37-4672-bf26-e5d460cae7fb",
   "metadata": {},
   "source": [
    "## Creating a new table row by plugging the result into the Compton formula\n",
    "\n",
    "![](./img/Compton_formula.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "501a2c0e-0544-446f-a1fb-d1a90ba16730",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'x': 1.1736481776669303, 'y': 4.26333965308182}\n"
     ]
    }
   ],
   "source": [
    "#Extract the energy from the model\n",
    "energy = result_r2.eval(pars_r2, x = position) \n",
    "\n",
    "#Calculate the variables (to be compared in the last step) according to the Compton model\n",
    "x = 1 - np.cos(np.deg2rad(100))\n",
    "y = 1 / (energy / 1000) #convert the energy to MeV\n",
    " \n",
    "#Add the data to a dictionary    \n",
    "Compton_data = {\"x\" : x, \"y\" : y}\n",
    "\n",
    "#Preview the created row of data\n",
    "print(Compton_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2738414-b582-4159-90e1-b7ee68300f95",
   "metadata": {},
   "source": [
    "### Append the values to a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "38522db9-61ec-4252-ac8d-641520fd8ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import csv\n",
    "\n",
    "# with open(r'./Data/Compton_data.csv', 'a', newline='') as csvfile:\n",
    "#     fieldnames = ['x','y']\n",
    "#     writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
    "\n",
    "#     writer.writerow(Compton_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6912d8bd-d7b7-418a-af6a-bf560f4c0ec1",
   "metadata": {},
   "source": [
    "## Author\n",
    "** **\n",
    "\n",
    "### <a href=\"https://www.linkedin.com/in/alin-airinei/\">Alin Airinei</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7173483a-36be-414b-aa06-d5c97da0b04c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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