0.7.Scattering_at_20deg .ipynb

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   "source": [
    "** ** \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 20-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": "a11974c0-c4cc-474e-97f3-884840b527ef",
   "metadata": {},
   "source": [
    "## Install and import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b8e0f64-b12b-4ef6-bec2-057dfd7c1bb9",
   "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": "8c9d2cad-61da-41f3-9c09-e3646a5838e1",
   "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_20 = pd.read_csv(\"./Data/180912-1259_Cs-137_LgHole_LgAl_020-degrees.csv\")"
   ]
  },
  {
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   "execution_count": 4,
   "id": "a6c9720c-4b60-49e7-9638-b6e399e68f9f",
   "metadata": {
    "tags": []
   },
   "outputs": [
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    "df_20.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_20.index = range(1, len(df_20) + 1)\n",
    "\n",
    "# Add the index as the first column\n",
    "df_20.insert(0, 'Channel', df_20.index)\n",
    "\n",
    "df_20.head(15)"
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e/XxBZpoSgtC5B0D/QtAHACDE+lq6k+N0qHhBoaysb0i96twDoH8h6AMAEGJ9Ld2RpIVT83XP/DMlSadlpmnWGcODMjYA8YugDwBAiB2xg37vSncsh480SZL2VNdrxooNdN4B0CmCPgAAIXbUrtHvXemO5O2888jbX9iXW+i8A6ALBH0AAEIsGKU7dN4B0FMEfQAAQqymwVty03iipdf3QecdAD1F0AcAIIRWl1Zqz+F6SdKS1WW9rqu3Ou9YEgw67wDoHEEfAIAQCfaOtgun5mvG6GGSpDsvGaeFU/ODMk4A8YmgDwBAiISirj7LOVCSZBpdHAig3yPoAwAQIqGoqx/iSJYk1TYc78vQAPQDBH0AAEIkx+nQ7XPG2pcTDaPPdfVDUr0tOt2epj6PD0B869vOHQAAoFMzTs+UXt+tzLRkvXbLzD4vnrWCPjP6ALrCjD4AACFktdYcMXhgUDrkOB0EfQDdQ9AHACCErECekdb7XXHbGpLaWqPvIegD6BxBHwCAEKptndG3FtH21ZDWGf1DdZ5et+kE0D8Q9AEACKGa1hl9q7a+r/7+RbUk6Zv645qxYkOvN+ACEP8I+gAAhJDbE7yg73J79J9v7LYvt/RxAy4A8Y2gDwBACFmLcTNS+166E4oNuADEL4I+AAAh9LX7mCTJNM0ujuxaKDbgAhC/CPoAAITI6tJKban4VpK0fN2uPtfT5zgdKl5QaF9OMNTnDbgAxC+CPgAAIeBye7RsTbl92QxSPf3CqfkanzNYkrRiwUQtnJrfp/sDEL8I+gAAhEAo6+lHpKdIkhLa1/EAQBt9CvrFxcUyDENLliyxrzNNU/fcc49yc3PlcDh03nnnaefOnT4/19jYqJtvvlmZmZlKS0vT/PnzdfDgQZ9jampqtHjxYjmdTjmdTi1evFi1tbV9GS4AAGETynp6q4OP1aMfAALpddAvLS3V448/rokTJ/pcf9999+n+++/XQw89pNLSUmVnZ+viiy/WkSNH7GOWLFmitWvXatWqVdq0aZOOHj2qefPmqbm52T5m0aJFKisrU0lJiUpKSlRWVqbFixf3drgAAIRVjtOh310xwb4czHp6Z+umWW52xwXQiV4F/aNHj+onP/mJVq5cqYyMDPt60zT14IMP6u6779aCBQs0YcIEPfPMM2poaNALL7wgSXK73XryySf1X//1X7rooos0efJkPffccyovL9ebb74pSfr0009VUlKiJ554QkVFRSoqKtLKlSv1l7/8Rbt37w44JgAAos1lhbn292/ffl7Q6umt3XFrGwj6ADrWq6B/44036rLLLtNFF13kc31FRYWqqqo0Z84c+7qUlBTNnj1bmzdvliRt375dx48f9zkmNzdXEyZMsI9577335HQ6NW3aNPuYc889V06n0z6mvcbGRtXV1fl8AQAQSUcavUE8OSlB+cPSgna/ztae/LXM6APoRFJPf2DVqlX64IMPVFpa6ndbVVWVJCkrK8vn+qysLO3fv98+Jjk52eeTAOsY6+erqqo0YsQIv/sfMWKEfUx7xcXF+u1vf9vTXwcAgJCpb/SWpA5K6fHLbadOzuhTow+gYz2a0T9w4IBuvfVWPffccxo4cGCHxxmG7+oj0zT9rmuv/TGBju/sfpYtWya3221/HThwoNPHAwAg1I42npAkpaUkBvV+rcW4X9Z4+tyuE0D86lHQ3759uw4dOqQpU6YoKSlJSUlJ2rhxo/74xz8qKSnJnslvP+t+6NAh+7bs7Gw1NTWppqam02O+/vprv8c/fPiw36cFlpSUFA0ePNjnCwCASKpvDfqDUgYE9X7f3+/dhGtvdb1mrNjQ5424AMSnHgX9Cy+8UOXl5SorK7O/zjnnHP3kJz9RWVmZTjvtNGVnZ2v9+vX2zzQ1NWnjxo2aPn26JGnKlCkaMGCAzzEul0s7duywjykqKpLb7da2bdvsY7Zu3Sq3220fAwBAtDtqB/3gzei73B49tnGvfbklSBtxAYg/PSoaTE9P14QJE3yuS0tL07Bhw+zrlyxZouXLl2vMmDEaM2aMli9frtTUVC1atEiS5HQ6dfXVV+u2227TsGHDNHToUN1+++0qLCy0F/eOHz9el156qa655ho99thjkqRrr71W8+bN09ixY/v8SwMAEA4nS3eCV6Pf2UZcwWjdCSB+BHd1kKQ77rhDHo9HN9xwg2pqajRt2jS98cYbSk9Pt4954IEHlJSUpB/+8IfyeDy68MIL9fTTTysx8eSMx/PPP69bbrnF7s4zf/58PfTQQ8EeLgAAIVMfgqBvbcTVNuwHayMuAPHFME3T7Pqw2FNXVyen0ym32029PgAgIv73/3yu/1r/mX40NU8rrpzY9Q9006ptlVq6plySdyOu4gWFQevRDyD6dTfn9npnXAAA0LmjTcGf0ZekH303X86B3vt87upphHwAARH0AQAIkVCU7lgGt7bYHJgc3NadAOIHQR8AgBA5XNcoSWppaQn6fVstO48eOxH0+wYQHwj6AACEwOrSSr3+iXdPmIff2hP0XvfprZ8SWJ19AKA9gj4AAEHmcnu0rHWxrCSZCn6ve2u3XWb0AXSEoA8AQJB11us+WAYN9JbuHGFGH0AHCPoAAASZ1eu+rWD3uh9kle4wow+gAwR9AACCLMfpUPGCQvtygiEtXzAhqDvXpg+0avSPB+0+AcQXgj4AACHwj1Py7O9fu3lm0HvdD2IxLoAuEPQBAAiBOs/JmfYzstKDfv8ng35z0O8bQHwg6AMAEALu1qCfmpyoAYnBf7kd1Fq6U33kmDbvqQ5qRx8A8SH4W/UBAAA76A9xDAjJ/Vt99N/b+63e27tVCYZUvKAw6CVCAGIXM/oAAIRAbWvQHxyioL/xs8M+l1vM4PfqBxDbCPoAAISANaPvDEHQd7k9WlV6wO/6YPfqBxDbCPoAAISAXbqTGvygX1FdH/D6BENB7dUPILYR9AEACIGD33rD+IBEo4sjey7QhlySdOfccUHt1Q8gthH0AQAIstWllXr8nQpJ0l8/rtLq0sqg3r+1IVfbsL9s7jhdN2t0UB8HQGwj6AMAEEQut0fL1pTLbL1sKjSLZBdOzdfLN8yQJCUa0nWzCfkAfBH0AQAIoorqerWYvteFapHsyGFprfcvNZ1oCfr9A4htBH0AAIIoUP18omGEZJGsIznR/t5znB1yAfgi6AMAEERW/bwlwZCWL5gQkkWyAxINJba+q/A0EfQB+CLoAwAQZAun5mvkUG+w/+OPJodst1rDMJQ6wDur39B0IiSPASB2EfQBAAiBY60186My00L6OANby3co3QHQHkEfAIAQqG/0Bu9BKUkhfZxUK+hTugOgHYI+AABB1tJi6mijt5QmLcRB32GX7hD0Afgi6AMAEGT1berl0weGOOhTugOgAwR9AACCzCrbSUowlJIU2pdaSncAdISgDwBAkB1tPC5JGjQwSYZhdHF031ilO8zoA2iPoA8AQJAdOdZan58c2rIdSXK0PgY1+gDaI+gDABBkVulOqOvzJdl99D300QfQDkEfAIAgs0t3QtxxR2IxLoCOEfQBAAgyq3RnUBhm9K2gT+kOgPYI+gAABNlXtR5JUkKIF+JKJxfj7quul8vtCfnjAYgdBH0AAIJodWmlHnzzc0nShl2HtLq0MqSPt7vqiCTprd2HNWPFhpA/HoDYQdAHACBIXG6Plq0pl9nmurvW7AjZTLvL7dHfyl325RYztI8HILYQ9AEACJKK6nq1mL7XNZum9lU3hOzx2j1cSB8PQGwh6AMAECQFmWlKaFeWn2gYGpWZGhePByC2EPQBAAiSHKdDxQsK7cuGIS1fMEE5TkfIHu8//qHN40m649KxIXs8ALGFoA8AQBAtnJqv8dnpkqR7F0zUwqn5IX28H383X3kZ3mBvSrq3ZBcLcgFIIugDABB0x060SJIKhqeF/LFcbo8O1pxcfMuCXAAWgj4AAEHm9nh3xh08cEDIH4sFuQA6QtAHACCITNO0g77TEfqgX5CZpvbbcrEgF4BE0AcAIKjqm5rV3NpjMxxBP8fp0FUzC+zLiYYR0gXAAGIHQR8AgCCyZvOTExM0cEB4XmavOCtXkpSROkCblp4f8gXAAGIDQR8AgCByN7TW5zsGyDDaF9WERkZqsiSpoamZmXwANoI+AABBVHfMqs9PCttjDkn1lgg1nmiRp6k5bI8LILoR9AEACCKrdCfBUNhaXA5KSVJS6xa5tZ6msDwmgOhH0AcAIIjWf/K1JOnzQ/WasWJDWDavMgzDntWvqT8e8scDEBsI+gAABInL7dFL2w/al8O5edWQ1jr92gZm9AF4EfQBAAiSSG5elZacKEnaW10f8scCEBsI+gAABEmkNq9aXVqpjw66JUm/fnlHWMqFAEQ/gj4AAEGS43So6LSh9uVwbF7lcnu0bE25fdlU+MqFAES38PX+AgCgHzh1aKq091stnJqnJReNCXlf+4rqerW0qxeyyoXoqQ/0b8zoAwAQRJ7jLZKksVnpYQnaBZlpSmhXLxSOciEA0Y+gDwBAEB077t2wytG6ODbUcpwOFS8oVNtNeENdLgQgNhD0AQAIIivoDxwQvpfYhVPztfyKCZKkwlOcWjg1P2yPDSB6EfQBAAgiT1PrjP6A8MzoW0ZlDpIk1TedCOvjAoheBH0AAILo2AlrRj+8QT8jzbszbm0DO+MC8CLoAwAQRNaMftiDfpudcVvat+EB0C8R9AEACKJjrV13wl26MyTVO6PfYkpHjlG+A4CgDwBAUJ1cjBveoJ+SlKjU1k4/tZ6msD42gOhE0AcAIIg8xyOzGFeShji8s/o11OkDEEEfAICgMU3TDvoDk8P/EpuW4t3wfs/ho2F/bADRh6APAECQNDW3yGxdBxvu0p3VpZX6/JA34N/+fz/S6tLKsD4+gOhD0AcAIEiONbXY34ezdMfl9mjZmnL7sinprjU75HJ7wjYGANGHoA8AQJBYZTtJCYYGJIbvJbaiul7tO2o2m6b2VTeEbQwAog9BHwCAIIlUx52CzDQlGL7XJRqGRmWmhnUcAKILQR8AgCDxRCjo5zgdKl5QKKM17BuSli+YoBynI6zjABBdehT0H330UU2cOFGDBw/W4MGDVVRUpHXr1tm3m6ape+65R7m5uXI4HDrvvPO0c+dOn/tobGzUzTffrMzMTKWlpWn+/Pk6ePCgzzE1NTVavHixnE6nnE6nFi9erNra2t7/lgAAhMHJoB/+ebSFU/P1/35/vCRp6qgMLZyaH/YxAIguPXomOvXUU7VixQq9//77ev/993XBBRfo8ssvt8P8fffdp/vvv18PPfSQSktLlZ2drYsvvlhHjhyx72PJkiVau3atVq1apU2bNuno0aOaN2+empub7WMWLVqksrIylZSUqKSkRGVlZVq8eHGQfmUAAELjWAR76EtSwfA0SZLneEsXRwLoD5J6cvAPfvADn8v/8R//oUcffVRbtmzRmWeeqQcffFB33323FixYIEl65plnlJWVpRdeeEHXXXed3G63nnzyST377LO66KKLJEnPPfec8vLy9Oabb+qSSy7Rp59+qpKSEm3ZskXTpk2TJK1cuVJFRUXavXu3xo4dG4zfGwCAoLOCfnOLKZfbE/bSmSGpyZKkmgZ2xgXQhxr95uZmrVq1SvX19SoqKlJFRYWqqqo0Z84c+5iUlBTNnj1bmzdvliRt375dx48f9zkmNzdXEyZMsI9577335HQ67ZAvSeeee66cTqd9TCCNjY2qq6vz+QIAIJz+59NDkqS91fWasWJD2HvZWzvj1rIzLgD1IuiXl5dr0KBBSklJ0fXXX6+1a9fqzDPPVFVVlSQpKyvL5/isrCz7tqqqKiUnJysjI6PTY0aMGOH3uCNGjLCPCaS4uNiu6Xc6ncrLy+vprwYAQK+53B69sPVksG8xw9/LPqN1Rv9o4wk1naB8B+jvehz0x44dq7KyMm3ZskW//OUv9dOf/lSffPKJfbth+Pb3Mk3T77r22h8T6Piu7mfZsmVyu93214EDB7r7KwEA0GcV1fVq18o+7L3sBzsG2J13aj2U7wD9XY+DfnJysk4//XSdc845Ki4u1qRJk/SHP/xB2dnZkuQ3637o0CF7lj87O1tNTU2qqanp9Jivv/7a73EPHz7s92lBWykpKXY3IOsLAIBwKchMU/vpqHD3sk9MMORsLd9xU74D9Ht97v9lmqYaGxtVUFCg7OxsrV+/3r6tqalJGzdu1PTp0yVJU6ZM0YABA3yOcblc2rFjh31MUVGR3G63tm3bZh+zdetWud1u+xgAAKJNjtOhotHD7MuJhhGRXvbpKd4+G18cOhrWxwUQfXrUdeeuu+7S3LlzlZeXpyNHjmjVqlV6++23VVJSIsMwtGTJEi1fvlxjxozRmDFjtHz5cqWmpmrRokWSJKfTqauvvlq33Xabhg0bpqFDh+r2229XYWGh3YVn/PjxuvTSS3XNNdfosccekyRde+21mjdvHh13AABRbUR6iiTpJ9PydNMFY8Ie8leXVupAjXdNwA3Pf6AVVxbSTx/ox3oU9L/++mstXrxYLpdLTqdTEydOVElJiS6++GJJ0h133CGPx6MbbrhBNTU1mjZtmt544w2lp6fb9/HAAw8oKSlJP/zhD+XxeHThhRfq6aefVmLiyZ7Dzz//vG655Ra7O8/8+fP10EMPBeP3BQAgZGpay2UmnZoR9pDvcnu0bE25fdmUdzHwrDOGs0Mu0E8Zpmm2XzsUF+rq6uR0OuV2u6nXBwCExeUPbdJHB916fPEUzflOdlgfe/Oeai1audXv+hevOdenpAhA7Otuzg3/Ht0AAMQpa0Y/Iy057I9dkJmmhHargcO9GBhAdCHoAwAQJNaOtBmpA8L+2DlOh4oXFNqdfwwpIouBAUQPgj4AAEFworlFR46dkCQNSQ3/jL4kLZyar9vmnCFJmnVGJgtxgX6OoA8AQBDUek72rR/iCP+MvmXksDRJkuc4O+MC/R1BHwCAIKhtLdtxDEjQ4aONERtHRuunCWyYBYCgDwBAEPz39oOSvDPpM1Zs0OrSyoiMY0jr+gBrvQCA/ougDwBAH7ncHj32zl77covp7WHvcnvCPhYr6Nc2HFecdtAG0E0EfQAA+qiiul7tM3WzaWpfdUPYx2KV7jQ1t2jv4fqwPz6A6EHQBwCgjwoy09SuhX3Eeti/9tFX9vcXP7AxYiVEACKPoA8AQB/lOB36fmGOfTnRMCLSw97l9uiuteX25UiWEAGIvKRIDwAAgHgwJmuQVC5dOH6EfndFZDaqqqiuV0sHJURsnAX0P8zoAwAQBA1NzZKk0cMHRSxUF2SmKaFdDVGkSogARB5BHwCAIKhv9O6Km5qcGLEx5DgdKl5QaF82DEWkhAhAdCDoAwAQBFbQT0uObFXswqn5mnNmliTp5vNP18Kp+REdD4DIIegDABAE9a2lO6kpkZvRt4wYnOL9xmjfCwhAf0LQBwAgCBqavDP6g1Ii3+di8EDvpllHjh2P8EgARBJBHwCAIKhvbJ3Rj3DpjiSltwb9Os+JCI8EQCQR9AEACAJrRj8tgotxLekDvW82mNEH+jeCPgAAQWDP6EdD6Y6jdUafoA/0awR9AACCoN6u0Y+mGX1Kd4D+jKAPAEAQNERRjb61GJcZfaB/I+gDANBHTSda1NTcIinyffQlaTAz+gBE0AcAoM88rT30pejoo2/X6HuO66vahgiPBkCkEPQBAOijo631+UkJhqqPNkZ4NNIbO6skSS2mNPPet7S6tDLCIwIQCQR9AAD66KXtByRJJ1pMzVixIaLB2uX26Dev7rQvt5jSXWt2yOX2RGxMACKDoA8AQB+43B498Obn9uVIB+uK6nq1mL7XNZum9lVTwgP0NwR9AAD6oKK6XmYUBeuCzDQlGL7XJRqGRmWmRmQ8ACKHoA8AQB8UZKapXa6OaLDOcTpUvKDQvpxgSMsXTFCO0xGR8QCIHII+AAB9kON06JIJ2fblRMOIeLBeODVfs8/IlCT96uIztHBqfsTGAiByIt/sFwCAGHfqEG+o/8HEHN112fiomD3PHeL9ROFE+4J9AP0GM/oAAPTRN/VNkqQJpzijIuRL0tA0by/9mtaxAeh/CPoAAPSRFfSHDUqJ8EhOGprmHcs3BH2g3yLoAwDQR9+0bpI1bFByhEdykjWj/y1BH+i3CPoAAPTR4SPeoG+277MZQdaMPkEf6L8I+gAA9MGqbZU61Br0f/HM+xHdFbetYWneTxeq3MfYFRfopwj6AAD0ksvt0V1ry+3Lkd4Vt61NX1RLkmo9xzVjxYaoeQMCIHwI+gAA9FJFdb3ad6+M5K64Fpfbo/tKdtmXo+kNCIDwIegDANBLBZlpSmi3LW4kd8W1ROsbEADhRdAHAKCXcpwO3XD+6fblaNgVV4reNyAAwougDwBAH8waM1ySlDN4oDYtPV8Lp+ZHeETeNyDFCwplZX1Dioo3IADCi6APAEAfHDveLEkakpYcVUF64dR83XDeaEnS2fkZmnXG8AiPCEC4EfQBAOgDT2vQdwyIvpfUI8dOSJK2V9bQeQfoh6LvWQkAgBhizegPHJAY4ZH4crk9em7rfvsynXeA/oegDwBAHxyzZ/SjK+jTeQcAQR8AgD44drxFUvTN6NN5BwBBHwCAPrBm9FOirEbf6rxjSTDovAP0N9H1rAQAQIzxRGnpjuTtvDMiPUWS9MRPz4mK1p8AwoegDwBAH0Rr6Y4lIzVZkpSSFJ3jAxA6BH0AAPogWhfjWtIHJkmS6jzHIzwSAOFG0AcAoA9OtteMzpfUwY4Bkk721AfQf0TnsxIAADHCE6V99C32jP4xZvSB/oagDwBAH0TrhlmWwQO9M/p1zOgD/Q5BHwCAPoj2xbjU6AP9F0EfAIA+8FCjDyBKReezEgAAMaIxyrvunCzdYUYf6G8I+gAA9EGsLMY98G2DXG5PhEcDIJwI+gAA9EG01+h/eKBWkrSr6ohmrNig1aWVkR0QgLAh6AMA0AfRXKPvcnv01N8r7MstpnTXmh3M7AP9RPQ9KwEAEEMamryLXKNxsWtFdb1M0/e6ZtPUvuqGyAwIQFgR9AEA6KXVpZWqb/TO6C9auSXqymIKMtOUYPhel2gYGpWZGpkBAQgrgj4AAL3gcnu0bE25fTkay2JynA792w/OtC8nGNLyBROU43REcFQAwoWgDwBAL1RU16slBspiFp87yv7+LzfP1MKp+ZEbDICwIugDANALsVIWk5hgKDXZ2xEoLSUpwqMBEE4EfQAAeiHH6dB1s0+zLycaRtSWxQxqDfhHG6NvwTCA0OGtPQAAvXR2/lBJezV6eJqe+8W0qAz5kjRoYJIOHWnU0SjsDAQgdJjRBwCgl2rqmyRJeUNTozbkS8zoA/0VQR8AgF76tsEb9IemJkd4JJ0j6AP9U4+CfnFxsaZOnar09HSNGDFCV1xxhXbv3u1zjGmauueee5SbmyuHw6HzzjtPO3fu9DmmsbFRN998szIzM5WWlqb58+fr4MGDPsfU1NRo8eLFcjqdcjqdWrx4sWpra3v3WwIAEALWjH5GWnQH/TSCPtAv9Sjob9y4UTfeeKO2bNmi9evX68SJE5ozZ47q6+vtY+677z7df//9euihh1RaWqrs7GxdfPHFOnLkiH3MkiVLtHbtWq1atUqbNm3S0aNHNW/ePDU3N9vHLFq0SGVlZSopKVFJSYnKysq0ePHiIPzKAAAEx7etQX9olAf99NagX0/QB/qVHi3GLSkp8bn81FNPacSIEdq+fbtmzZol0zT14IMP6u6779aCBQskSc8884yysrL0wgsv6LrrrpPb7daTTz6pZ599VhdddJEk6bnnnlNeXp7efPNNXXLJJfr0009VUlKiLVu2aNq0aZKklStXqqioSLt379bYsWOD8bsDANAn1uZYCYbRxZGRZc/osxgX6Ff6VKPvdrslSUOHDpUkVVRUqKqqSnPmzLGPSUlJ0ezZs7V582ZJ0vbt23X8+HGfY3JzczVhwgT7mPfee09Op9MO+ZJ07rnnyul02se019jYqLq6Op8vAABCZXVppTZ98Y0k6b6SXVpdWhnhEXVs0EBv0D/CjD7Qr/Q66JumqV/96leaOXOmJkyYIEmqqqqSJGVlZfkcm5WVZd9WVVWl5ORkZWRkdHrMiBEj/B5zxIgR9jHtFRcX2/X8TqdTeXl5vf3VAADolMvt0bI15fZlU9Jda3bYM/zRZhClO0C/1Ougf9NNN+njjz/Wiy++6Heb0e4jTNM0/a5rr/0xgY7v7H6WLVsmt9ttfx04cKA7vwYAAD1WUV2vFtP3umbT1L7qhsgMqAtW0N//TX3UvhkBEHy9Cvo333yzXn31Vb311ls69dRT7euzs7MlyW/W/dChQ/Ysf3Z2tpqamlRTU9PpMV9//bXf4x4+fNjv0wJLSkqKBg8e7PMFAEAoFGSmKaHdvFOiYWhUZmpkBtSFHV95S223VtRoevEGPbZxT4RHBCAcehT0TdPUTTfdpDVr1mjDhg0qKCjwub2goEDZ2dlav369fV1TU5M2btyo6dOnS5KmTJmiAQMG+Bzjcrm0Y8cO+5iioiK53W5t27bNPmbr1q1yu932MQAAREqO06G7vz/evpxgSMsXTIjKTbNcbo/++/2TLaxNScXrdumxdwj7QLzrUdedG2+8US+88IJeeeUVpaen2zP3TqdTDodDhmFoyZIlWr58ucaMGaMxY8Zo+fLlSk1N1aJFi+xjr776at12220aNmyYhg4dqttvv12FhYV2F57x48fr0ksv1TXXXKPHHntMknTttddq3rx5dNwBAESF2WOH63/99VOlJSfqzdtmR2XIl7xlRmaA6+9dt0vzJ+VG7bgB9F2Pgv6jjz4qSTrvvPN8rn/qqaf0s5/9TJJ0xx13yOPx6IYbblBNTY2mTZumN954Q+np6fbxDzzwgJKSkvTDH/5QHo9HF154oZ5++mklJibaxzz//PO65ZZb7O488+fP10MPPdSb3xEAgKD7tv64JGnE4IFRHZYLMtNkSH5hv8WU9lU3RPXYAfSNYZpmoDf6Ma+urk5Op1Nut5t6fQBA0JXsqNL1z23X2flDtOaGGZEeTqce27hHxet2+VyXaBjatPR8gj4Qg7qbc/vURx8AgP6qpiE2dsWVpOtmj9a13zvNvpxoGFG7pgBA8BD0AQDohW/rvUE/IzX6g74k3XLRGPv7N/6fWVo4NT+CowEQDgR9AAB6oaY+dmb0JW8v/bRk71q4Lra2ARAnCPoICpfbo817qtmIBUC/cbDW+3yX2L6hfhTLcg6UJFXVHYvwSACEQ4+67gCBrC6t1LI15Woxvb2kixcU8pEwgLi2urRSJTu8LaYffXuPRg5LjYnnvaz0gdp7uF5//7xaBZlp1OgDcY4ZffSJy+2xQ77kbdd215odzOwDiFvW857FVOw87x070SxJevjtPZqxYoNWl1ZGeEQAQomgjz6pqK63Q76l2TS1r7ohMgMCgBCL1ec9l9ujsspa+zITM0D8I+ijT6yNWNrb9MXhsI8FAMKhIDNN7cvyEw1DozJTIzOgbgq0Q24svEEB0HsEffRJjtOh747K8Lv+4bf26LF39kRgRAAQWjlOh+6cO86+nGAoJnrSB5qYiYU3KAB6j6CPPjM66Dhx77pdfCQMIC6dM9I7wZE5KFl/X3pBTCzEzXE69POZBfZlNs0C4h9BH33W0Ngc8PoWU3wkDCAufVXrbU952vBBMRWU/2HyKZIkp2OANi09PybeoADoPYI++uzQkcaA1/ORMIB4tctVJ0nKcAyI8Eh6Ztgg7+Ze9Y0nlD14YIRHAyDUCProkxe3VQbceMWQdMelY2NqpgsAumN1aaUeedu7BumNT76OqRaV1i6+J1pM1XlORHg0AEKNoI9ec7k9unttecDbTEn3luyKqRdAAOiK1UPf6l4TSz30JSklKVGDUrx7ZX5TH/jTWADxg6CPXgvUS7qtFlNauqY8Zl4AAaArsdpDvy1rVv/b+qYIjwRAqBH00Wsd9dBvyzSlD/bXhGU8ABBqsdpDv630gd4Z/S8OHY3wSACEGkEfvZbjdOiCcSPsyx2FfrOTWX8AiCU5ToeW/0OhfTlWeuhbVpdWaudX3oXEy9aUU14JxDmCPvpkUOvM0I+/m6eXb5weMOxPCbChFgDEqu9PzLG/33DbeTHTotJaX2CJtfUFAHqOoI8++exr70e/E09xalJehlZcWehzuyHpnc8OR2BkABAa1a0thQelJGlUZlqER9N98bC+AEDPJEV6AIhdq0sr9WlrL+m7Xt6hhARDs84YrgRD9ouJNWM064zhMfPRNgB0pvqodxFrZmtP+lhhrS9oG/ZjbX0BgJ5hRh+94vcRsOkN9Nv31zBjBCCuVR/1zuhnDkqJ8Eh6JsfpUPGCQp/FxEsuGsMkDBDHCProlY4+ApapmO9IAQCd2XPYW7Jo9aOPJQun5uvOuePsy/e/+RkLcoE4RtBHrxRkpskIEOinjMpQ8YJCe1GuodjqSAEAnVldWqn73/hMkvT2Z4djLiS73B7du26Xfdn6NJYFuUB8IuijV3KcDl0+Kde+nGgYdqBfODVfPy0aJUlacPYpMdORAgA6035XXCn2QjILcoH+JfY+d0TUyB3inaWfc2aWfnv5d3xm7U/J8H7f3NnWuQAQQzoLybHyqSULcoH+hRl99Nrew/WSpNEjBvm9yA12eN9Duj3Hwz4uAAiFeNgVN9CCXMorgfhF0EevrC6tVMnOKknSn97e41en6nQMkCTVHTsR9rHFE5fbo817qmOqNACIV1ZItsTarriWhVPz9X9+NlWSlJGaTHklEMcI+uix7uyuOHhga9BnRr/b2of6x97Zo+krNmjRyq2asWJDzC36A+LRP07Js2fDX75xRsyG5LPyhkiSahqatK+6PrKDARAy1Oijx7pTpzq4dUaf0p3uWV1aqWVrytXS2p70ku9ka92OKvv2FvPkxmOS99+gIDMt5mYSgVj3TX2j/f/pmTmDIz2cXitp8/xywX+9reIFhTH7pgVAxwj66LHuLOY6WbpD0O+Ky+3R0pdOdvJoMeUT8i3NpqmnNu3TE5v2qsX0ti695nsFumpmAYEfCJNDdSc3y0pKjM0PxV1uj+5ae/JT2bYTCTyXAPElNp+lEFE5Tod+Pe9M+3KgOlVrRv/Y8RY1nmgO+xhjyf/ZVKHu9CYyJDvkS96SqcffraCsBwijT111kqShackRHknv0WIT6D8I+uiVS76TLUlKSjD096UX+H3km56SZG+oVedhQW5HXG6PVr5b0a1jp48e6vfiLJ2cjWPBLhBaq0srdcd/fyxJ2lV1JGbfYMdD9yAA3UPQR6/UN3rD+6CBSQE/6k1IMJSanCjp5Hbx8FfRg0Vwf9/zbYe3MRsHhFY8bJZlCdRi845Lx1K2A8Qhgj56pb7JW46Tlhx4mcfq0krVN3qP+fHKLTE78xVqBZlpQbmfBInZOCCE4q3cZeHUfP1iZoF9+d6SXTxPA3GIoI9eaWid0U9LSfS7za/9pikte6k8Jme+Qm3Dp4eCcj+mpHc+OyyJ3vtAKMRbuYvL7dETm06WDVICCMQngj56xZrRTw0wox9o5qtF0lOb9oV+YDHE5fbo/31lR1Duy5S09KVy3fLiB5peTO99INjab5ZlxOhmWZZ4+4QCQGAEffRKfScz+gWZaTL8rvV2jGG2yMvl9uh3f/lEZnfa7XSTKenVj1w+bTr5JAUInoVT8zUuO12S9J//OCmm+87H2ycUAAIj6KNX6pu8QT/QjH6O06Frvlfgd32LKWaL5F2/ML14g/5a7t8rP9j4JAUILuu5r2B4cNbXREq8fUIBIDCCPnqloXWh7aCUwItxr5pZwGxRAB8dqNHSdp07OpNgSMvmjtOL15yrV26c3qvH5JMUIHhqG7ybAFqbAsayhVPzdVmht1XydbNOi+lPKAAERtBHrxxttGb0/Ut3pJOzRVbWN8Rs0erSSl3x8OYelev88UeTdd3s0SoaPcxeF9FTLab04tZKwj7QR80tpo4c8z73DYmDoC9JuUO8z8nNgTbpABDzCProlYYmq0Y/8Iy+5J0t+vnMUZKki8aPUN7Q1H4bNgP14O5KomFoyqgM+3Kgmtru+uOGL1RUvEGPbdzTuzsAoDrPcfv7eJjRl6Qhqd4dfq1PKgDEF4I+euVk153AM/qWM3OckqT1nx7q151gAnW4aCtB0k3nj7aDfKJh+H0CYn1Kkti65XCiYejKs0+xL3dH8bpd+v3ru3rzKwD9Xm1r0E9PSVJSYny8fA5J9b5hqSHoA3Gp4+lYoBP2zridzOhL0geVNT6XrV7Ns84Y3q/KeKxORB1l/RZJM04frp+cO1L7qhs0KjM14PlZODVfs84Y7nPM7ZeM1b7qBn38Za3u/dsutXQxloff2qOahibdfMEYSd43IQWZaf3q3wPojdqGJkmSMzU+ZvMlKaN1Rt/taYrwSACEAkEfvWLtehuo647F5fboha3+s/dWr+b+FCxznA795Nx8Pbcl8KcZ1kLlHKejy/PS/hjrctHoYTpliEM3vfBhl+N5YesBvbj1gCTvm48EQypeUMhiPKAT1ox+UqIhl9sTF89h1loDSneA+BQfnz0i7GoaGiVJx5s7XiBaUV3f4Qz2x1/WBn9QUe6UIYE7DgUq0+mtKSMzAu5hEIgp+fXc/+hATWc/AvRrr+/wtsTdV90QN2WIVo0+pTtAfCLoo8dWl1Zq+/5aSdI9r33S4YtdZ4tH71u3u18tzF1dWqn7Sk7Wxl87q0Cv3DhdL15zrjYtPT9oM+kd7WHQHS2Srnhkc1yEFyDYXG6PVpcesC9bZYix/jx2ska/UV/Vss8JEG8I+ugRq3uMxezkxc5aPBroj6w/bbUeqOPOk+/u04jBA1U0eljQP/4PtIdBt2f5TWnpS+X6y8dfdTvAuNwebd5THfOBB+hMoE8o4+F57H8+/VqS1Nwizbz3Ld7oA3GGoI8eeWpThV/3mM5e7BZOzdfaG6f7Bc3+tHlWoI47oQwIgbrzrLiyUBeOH9Gtnzcl3fTCh3ZpQmdBfnVppWas2NCvOyqhfyjI9N8JN9afx1xuj37z6k77crx8SgHgJBbjottcbo9Wvlvhd32CoU5f7CblZeie+d+xX1AS+tlW656mE34dd0IdENp355GkpW0+iemOltbZfcPwft92wa7L7dH7+7717vJrnjy+P3ZUQv+Q43RoTNYgff71UUnBXVsTKZ1NQsTy7wXgJII+uq2jxbW/mHlaly8K/1I0Uve8ulOmpJdvnKGJpw4JxRCjisvt0R3//bHe/bza5/pwBYS23Xk276nu0Y68FlPyCfLLXirXRwfdWrWtMuC+AM2mqe37ajRvEiEB8cf6lGzZ3HGaf1ZuzIdhax1V2/+XE9T5xA2A2ELpDrot0OLaBElXte5+2xnDMDS4tY1bV5tsxYPVpZUqKt7gF/IlafG5+WFvY9mXXXXbapH0wtbAId9yy6oPKeFBXKqqOyZJOm/siJgP+dLJMr+2TEnvfHY4MgMCEHQEfXRb+xeFBEMqvrKw2y94gx3eD5DcnhMhGV+0cLk9nZbJPLNlf9hrYNvX7YdST+t8WcyLWHDseLPdaz578MAIjyZ4Zp0x3GcNlSnq9IF4QukOeuSfpuTpzpe8IfbVm2ZqwinObv9sesoASR4dORbf/Zorqus7LZMxTUWkBtaq29++r0a3rPqw01n5vmq72LiznXdXl1Zq2ZpyvzUA0c7l9vR5R+G29yGxQ3G0O1Tn3TtkQIKh+qbjcbM7bmfdhPhbBGIfQR89crTp5Gz86SMG9ehn0wd6/9yOHIvvGf1A3TnaimSnjhynQ/MmOVTfdELLXipXS4geJ0HeTdEWPbFFpult73nN9wp01cwCOzxYbUdbOljMG4wwHQrdfXPS2fjb3oc1m9p+h+Lu/P7Reo7i0Qvb9kuSjreYmnnvWzHzprQrger0Y72bEICTCProESukJycmaOCAntXaWzX6dXE+o5/jdCgvw6EDNf4ffUdLx6GFU/M1LjtdVzyy2efThwRJ/3vRZD22cY8+/rKu1/ffImnF33bZM4WmpMffrdATmyp056XjVHiqU9/WN3XY8eOdzw5HdKa/fYC2LqclJ3b65sTS0ZuBQN2K2p4C6/5qPcd177pd9s9b56zt7H/5l26fY+IleEYjl9ujxzbutS/HU4cpq6zP+qQ2Wp6jAAQHQR89YpXdWLPzPdFfZvQlqdYT+M3MH380WfMm5YZ5NIFNysvQigWFumvNDjWbpt0N6LKJuVq17UDXd9CFQJVBLaZUvM67Q7DR+tW+7WhqckK3wnSotA/p/zD5FK398MsOS53adxoK9EnFnS+V68C3DXrk7T1dlkw1m6bPm6S250zyP2fWMfESPKNRvJe3LJyar9c+cmnTF9X61ZyxvGEE4ghBHz1ihfTeBP3BA70z+q5a7+LLeCg3aDvTW9/UrILMNL2+syrgm5lEw9CUURkRGGXH2vfbt2avN33h3y0o2Ez579h79cxRWv3+gYAz/e3DdGclK70taQkU0l/64Msuf+6WVR+qvumEZp0xXC9s3R8wzD/01p5uj6Oz9wId3RZPwTPaFGSmhX0vjHA7bXiaNn1RrYbG+J+IAfoTgj565OSMfs8Xog1ufXPwzHv79cx7+2O+3KDtzK+lo5420fxxeNt++1LH+yWEQvvHeTzAhmwWK0xL6rSsp7OymfZvytr/ewTaQKg7rM3FwnXeAqH/eejkOB2aeKpTHx10S4qPzbLas36Xjw+65XJ74up3A/ozgj56pC8z+u070cRyuUH7mV9LR0Evmkp2uhJocV5vjB6eqj2HG4IzKJ0sgWk7s2pt4jUuO12T8jI6XOBb29Cke0t2+24M1MGbgN7+7pEM+dbjv/PZ4Zh94xztUlrXJN1ywen68bT8mHvO6sr+b+olSZu+qNaMFRtiehIGwEkEffRIXR+CftnBWr/rYrXcoCczv9FYstMZa3GeVbufoNYdctscY0gyugjEwQz5bfnVp0u6/OHNWvTdPH1d1xiw7Kd43W6/+7HeJLStnTcknT1yiLbvrw3J2EPJlPeNUNOJFl10ZlbM/T8V7b5u3Sxr5pjYm5joisvt0er3T67LieVJGAC+CProkd6W7rjcnoC7xBqKzXKDnsx63zF3bMy9WLav3X/ns8N+i3ZnnTFcf/3Ypd/99dNID1eS9EIvFhC3yLd23pRiMuS39etXdurXr+zUsrnjdN3s0ZEeTlwwTdMO+vG0WZYl0N4fsToJA8AXQR894qr1vtgl9HCD1Yrq+g5vi8VygxynQ7+ed6Z++9onXR478ZQhoR9QCLSt3Q+0aFeSLpuYo+V/+zSkm2+hd4rX7ZIM6bpZhP2+qvOc0LHj3l0nWjrbDS9GdTRx8fGXtSoaPSwygwIQFAmRHgBix+rSSj27xbtpzP/3/kGtLq3s9s9aLyTtxfJ266OGdb4xluR9QxSLn1gEkuN0qGj0MJ8ZPqvMpztPJIakC8cND9n4oon1p56gwAu0F03L03vLLtBDiyYH/PkLx43Qw4sm66bzR/f4TXVb967bFZP/b0Wbp987uUj8gv96u0fPfbEgx+nQnZeO87v+vnW7+fsBYhwz+ugWa5GjxQro3a3htAJhoN1YY/Ej4tWllVr6Unmnxxitiz1j6ffqjYVT85WWkqSbXviww2OunVWgq2YUSJI2FG+I+MLVUHv5xulqaGrxK3tKkHRnm5KaKSPlN5OaIOl3/+Dt6HLZxFz95NyR9icpb37ytX79ys5uj6PFVMz9vxVtXG6PHnzzc/tyvNavF57q9LsuFp+bAfgi6KNbAi0+7emLgL0b68Ob/YJeNH9EHGiX1GVrArdSNCTdfskZGjUsTWePzOg3L5BTRmYE3MhJkl65cbom5Z1cjLziysKIt6LsrUTD0PXnnaaHO+mJb0jaVXXELkfrqOxJ8l/4HKhtY9sSqovOzNK/vbKz2+cu3nq9R0J/qV8PVL7D3w8Q+wj66JZgvQhMysvQj6bm6cVS34WT963brfmTcsP6wtmdTZUC9WRPS0nqsCbdlHR2/tCofdMSKjlOh1+ANwxpxYJCn5AvnQy+2/fVqNbTpIzUZA0ckKBf/Hm7T6Bq29kn0TB0x6Vj9c3RJj2xaW+XawI6etPRF9ZeCAun5muwY4DuXbfL7tTT9rECfdrVfq+Ctjp7I9BeR+d56aXjdMqQgbr5xTKfsdx60Zi4CqORUJDpX6IXjwHYetN5Z+snldG89weA7iPoo1ve+eyw78ZQfXgROCt/iF/QD/UMWftQ39GmSu1/pn1P9q5mouMxAHRX2wBvGOr0E40cp8Pe5dayIsDMdqAAfNXMUdpX3aCGpuN+bw4SJP3vRZN19sgM3frih9q2r6bTMRuSLhg3XP+z63CXv1/bvRCumzVa8yflal91g76pb/QrW+rp33NnbwTa6+w8/+6vu1TV2h1Gkh548zNlDU6JucXu0WTjbt+/jb4890W7hVPz9eK2Ayo7UKvf/OBM/m6AONDjxbjvvPOOfvCDHyg3N1eGYejll1/2ud00Td1zzz3Kzc2Vw+HQeeedp507fWtKGxsbdfPNNyszM1NpaWmaP3++Dh486HNMTU2NFi9eLKfTKafTqcWLF6u2trbHvyD6rn19viQZpjTrjN4trCw8xb8WNBQB2eX2aPOeaj32zh7NWLFBi1Zu1fTiDbprzcd+AX7ZS+X66ECNz89t31/T7Q2xrN8hXgNAd3kDfK4um9jzT2cWTs3XpqXn68VrztWmpedr4dT8DhcAF40epgvHZ2vFgkIlGt7VqomGoeIrC+3HnnjqEJ/7tz4hkLxPfNfOKtDmZRfod/9Q2OWC10B7IVjjmDIyw+/nQ/2Gr6PzfKLFdwWMacbuYvdo4HJ7dNfa4D33xYIzsgZJksoP1vF3A8SBHs/o19fXa9KkSbrqqqt05ZVX+t1+33336f7779fTTz+tM844Q7/73e908cUXa/fu3UpPT5ckLVmyRK+99ppWrVqlYcOG6bbbbtO8efO0fft2JSZ6dx9ctGiRDh48qJKSEknStddeq8WLF+u1117ry++LXghUn9+i3i/yG5c92O+6YAZkl9uj/7OpQk9sqvCrrTUVuN+6tenS9ydkq2RnlV2S0d0SEGuxaX8O+cHQk5ltqfOyF5f75My2IWnp3HGaf1ZuwGPb18lfMTlXL3/4VYd18+3H3FWdfbgcbTzhd1081pOHS7Cf+2KB2+PdK+W/PzioNR8eZIdcIMb1OOjPnTtXc+fODXibaZp68MEHdffdd2vBggWSpGeeeUZZWVl64YUXdN1118ntduvJJ5/Us88+q4suukiS9NxzzykvL09vvvmmLrnkEn366acqKSnRli1bNG3aNEnSypUrVVRUpN27d2vs2LG9/X3RC8FepJUQYPp0ysihvR2eT1nOO58d7tNCz7/tqLK/78l9PPnuPrurDMIr0JsDl9ujv5W77MumpPtKdmv+WbkB108EesNw+yVju1U339HPh5vL7bF7vbcVTy1ew60gM83vzX48l+e53B69sfNr+3K8dhgC+pOg9tGvqKhQVVWV5syZY1+XkpKi2bNna/PmzZKk7du36/jx4z7H5ObmasKECfYx7733npxOpx3yJencc8+V0+m0j2mvsbFRdXV1Pl8IDmvG0hKKRVoXP7CxV72pV22r1PTWspyi4g0R6+ZizZoiOlRU1/v9HXT1b9S+TChQ2VBnenp8sHW0Kd2PWkug0HM5TocumZBtX4738rze/H8DILoFNehXVXlnQ7Oysnyuz8rKsm+rqqpScnKyMjIyOj1mxIgRfvc/YsQI+5j2iouL7Xp+p9OpvLy8Pv8+OOnys06xv//rLd/r00e5gQJ9b2qJXW6Plq0t9ynPiVTLxnie5YtFgTZoi/d/o442pXtxW6Ue29hxO1B0LnvwQEnS/Em59tqReNUf/78B4l1IdsY1DN9nCtM0/a5rr/0xgY7v7H6WLVsmt9ttfx044F+Hjd47WOMN4AOTEuR09L5ZU6CFvZbuzBxZC2Wtcp1o2I0+nrtwxCrrU6i2C3Xj/d+oo12KTUnF63bpsXcI+71x6Ih3rcfZ+UPi+u9H8v4N/cc/hPbTWwDhFdT2mtnZ3o84q6qqlJOTY19/6NAhe5Y/OztbTU1Nqqmp8ZnVP3TokKZPn24f8/XXX6u9w4cP+31aYElJSVFKSkrQfhf4en7rfknSsRMtmnnvW71eoBVocZulq5mj9i0xf9m6u2g4/eslZ+g/X//M55ODeO/CEauioW4+3DrbpfjedbvCvldFPPi6rlGSlNU6sx/vfvzdfP3hzc9VVXdMf1o8RXPOzO76hwBEraDO6BcUFCg7O1vr16+3r2tqatLGjRvtED9lyhQNGDDA5xiXy6UdO3bYxxQVFcntdmvbtm32MVu3bpXb7baPQfi43B49/fd99uWWPrTs66i8QJLuuHRshyHkowM1WvqSb0vMh98O/wxlSlKiX3mQ1YUD0SfSdfORYO1S3F6LKb24tZKWiT30Za33fCV21YM1jpya4f3/5fiJKPjIFECf9DjoHz16VGVlZSorK5PkXYBbVlamyspKGYahJUuWaPny5Vq7dq127Nihn/3sZ0pNTdWiRYskSU6nU1dffbVuu+02/c///I8+/PBD/fM//7MKCwvtLjzjx4/XpZdeqmuuuUZbtmzRli1bdM0112jevHl03ImAYC7Qal9S0da9JbsC1u+vLq3U5Q9vjlj9vSXRMDR1VPh7pgM9keN0aOnccQFv++OGLzS9eEOvFr73R6u2VaqqtU3r9c9t7zfnLdvp/fTivdYySQCxq8dB//3339fkyZM1efJkSdKvfvUrTZ48Wf/2b/8mSbrjjju0ZMkS3XDDDTrnnHP05Zdf6o033rB76EvSAw88oCuuuEI//OEPNWPGDKWmpuq1116ze+hL0vPPP6/CwkLNmTNHc+bM0cSJE/Xss8/29fdFL3xYWet3XV/CrbUx0kM/nuwTmq2Nq9q+sLjcHi19KXBNf09cOG5El5sidcaqVZ2Ul9Hvar8Re66bPVrLvh847Jvy7vBsbRCHwKzF/pa+fJIZa+pae+k/t7VSM1bwxhCIZYZpRsNyxuCrq6uT0+mU2+3W4MH+GzShe1xuj6av2OC36HXZ98fpull9q5HfvKdai1Zu9bt+0Xfztby1nedfPv4qYL1xR66aPkrPvLfPr+f/pqXnS5L++rFLv/vrpwEfc3XpATWbprfswfB2AkqQ9IsAm2G53J5+VfuN2HTtn9/XG5/4r3eSvIvIV7AZUoeW//UTPf5uhd/1L15zbsC9GOKFy+3R9OINfnsHbFp6Ps91QBTpbs4N6mJcxJ+OOttMPGVIn+870GY0kvTCtkrVN53QmbmDVfy3Xd2+v0TD0LWzT9O4nPQOdym9bGKOlv/tU783AjdfeLpuvvB0O7xL6jTI93QHVyDcXG6P1ncQ8qWTLW3ZDMmfy+3RygAhvz9sPtZZqSZ/J0DsIeijUwWZaX7XBasmPcfp0DXfKwg4a/ZK2Vd6peyrbt9X2zZwnXVbsdYIdPRGoP2xQKwKFNjaI8AF1tG5+8XM0+L+XAXaCT1B8f8GB4hXBH10KsfpUEFmmr3rZrBr0q+aWaCV71b0aaHttQFKazqbce+PbRfR/wQKbO0lSPqmvlEut4f/D9oI9GljgqSrZo6KzIDCyJoMubPN2ihT0jufHabMC4hBIdkwC/Gl8XizJOnfL/9O0HeG7KxDSHckSH4hv7uP29/aLqJ/6azDlcWUdNMLH7Lgsp13Pjvsu1eGIRVfWdhvni/a7wtiqv8sRAbiDUEfnWo80ayv3NbOkBkheaG7bvZo3Xh+7xb20sMe6FjbDleB4r4VZvtTR5muBNq9u79timd9gttWb1sqA4gsgj461XZB2vyHNoVs1u9fLxnXYTvAtuhhD/RMjtOhoYOSu12v398F2r27v00oFGSmqf0HQTzXArGJoI8Oudwe/dcbu+3LoZ71u27WaL1yY8c7H1/7vdPoYQ/0Qmc7Ulv6Q0eZ7rDq89vqbyE3x+nQ/7p8gn3ZkHiuBWIUi3HRoUCtNUPdpWNSXoaWzR2n4nW+bTWthXA5TgcLaYEesur1l71UrpYOjvnleaPtko3+/P9VoPr8/hhyByT2YYdBAFGDoI8OpSUn+l0Xjpmt62aPlgzp3nW71GL6z9zTwx7ouYVT85WWkhRwA7pEQ3r07T16+K09SjCk4n66kRb1+V7tz4Mp767l7LkAxB6CPgJaXVqppS+1e8EL48zWdbNGa/6kXGbugSCaMjIj4CZ1zW2uaOnHG2l1Vp/fn85FR+fhqU37dNdl4yMyJgC9Q40+/LjcHi19qdx/8V6YZ7ZogQkEl7VJXVf668JcFqF6BVqnIElPbNpLZyYgxhD0+zmX26PNe6p9nryf2hR4AytT/avzBBCPrppZ0PXCXPXPhbnvfHbYZ11Sf63P7+gNYYvJawAQayjd6Sdcbo/e3/etaj3HlZGarCkjM/TOZ4e1bE25WkxvV4Wlc8fp3NOG6vE2LTXboisHEPu6szDXW6ZRoatm9nwzulhFfb6vq2YW6IlNFX4lPB9/Waui0cMiMygAPUbQ7wesevv2s/Rta3VNya/TTftjixf0n50hgXjW2cJcy+PvVuiJTRX9ZmEu9fm+cpwO3Xmpfwe0+9bt1vxJuf3ynACxiNKdONdhvb38F+R1xJD08o3T+8WLPdBfWAtzO9OfdswtP+j2u64/1ue3VXiq0++6/rp+A4hVBP04t31/TbcDfSCGpBVXFmpSXkawhgQgCuQ4HVo6t+vdqJtNUy9urYzrsO9ye7QiwCead8wd269nrjvaaO3jL2vDPhYAvUPQj2OrSys7/Wi+u/prjSoQ766bPVrLvj+uy8W5f9zwhYqKN+j3Jbv8Fu/Hg4rq+oATIhNPGRLuoUQVq3ynvfvW7Y67vwEgXhH049RHB2p0Z7s++L1Bpx0gvl03a7T+vvQCPfTjyX6tJdt7+O09WrRyq2as2KDVpZXhGWAYFGSm+V3X38t2LJTvALGNoB+HXtxWqcsf3hyU++LFDoh/OU6H5k3K1YoFhd16UYin2n2X26OK6nq/9Qr9sa1mIIHKd3hdAGIHQT9GBep/b13fvkVcWwmG9LOikd16jETD4MUO6EcWTs3XHxdN7tax8TCru7q0UjNWbNCilVv7tJYpnlntWK2sb4g3QUAsob1mDFpdWunT//6a7xXoqpnezU3+8vFXHf6c1SIzb2iqnn5vf6eP8evLxuv7E3N4Mgf6mSkjM5RgyK/VZHuxPqvrcnu0dE25zwZZbd21ZodmnTGc50B53wDuch3RU5v36YrJuXRgA2IIQT/GWDP21ouwKW+/65Wtm1x19tr88o3TNSkvQy63p9MX8kTDIOQD/ZQ1g3vXmh1q7iAFx8OOsU9tqugw5EsnP7GI5d8xmMZkpUuS6jwnIjwSAD1B0I8xgTZ1kbruiW9IGjF4oCT/F3Kj9QDTpFwHgHcGd9YZw7WvukGpyQl6ZvM+rfnw5KeFZ+UNUd7QVLncHr/nCqvmvSAzLeqeR6yxpSUn2pMjHWEncF85Tu/rx6dVdfrLx19pysiMqPv3BeCPoB8D2r5wBtrUpTus7jnWE3PbF3Lrxcz6nidvADlOh3KcDrncHr1c5lsS+GFlrRat3CpD0g3njda4nHTVeo7rU9cRrdpWqRbTG5SjaVfd9iWPXU2O3Dl3HM+FbXx0sFaS9FXtMd30wof2HivR8u8LIDCCfpRr/+LUW4Hqaa0X8raXAaCtjj5FlLxh+eG39wS8zerMY+3DEclZ/kAlj51ZNnecrps1OuTjihUut0d/ePNzn+tMScvWlLOOAYhyBP0o1tMXp44kxEE9LYDIsNordrU4N5Bm09RTm/bpiU17/ZoHhPP5qLM3K5YrzsrRxWdm62xKUvx0tKFYiynWMQBRjvaaUaw7L07d8ccfTebjVQC9Yq3pSexqN60OPP7uXr/mAdNXbNBjG/fotY++1F8+/qpP/fjbtxoO1Hq4IDOty09EF04dqcsm5hJaA+jo/LGOAYh+zOhHsYLMNBmti2R7K9EwNGVURvAGBaDfsdb0/OHNz7Sq9GCf7880peJ1u+zLVr33rDOG2yU+km+5T9uFtPVNzSrITNM7nx32KW383phMbfqi2r68dO44XTd7tN757HCnn4jGeqvQUMtxOrTiykItfancPo9Wu2beGAHRjaAf5S4an6X1n3zd5XHWR+vWrIspOugACK7/+37fQ34gpqQ7Xyr3WSRrfW8F+Hc/r/YJ622f66z/vvN5tc99Fq/bpQ/2f6s3PjnU4WNT2tg91pu9m1/4QO/vr9UN543mk2IgBhD0o1TbRbidsTpb0EEHQCgFq5SwM2aA79sH+EDHdub1TkK+JK29wbu/CLqW43TonFHD9P7+WtU3NUd6OAC6gaAfhdovwu2IId8XKTroAAiVvizKjVaGpF1VRwj6PZA7xNtPf8eX7oD7KACILizGjUJPbaroVshfcWUhL1AAwqL9olxD3h1y23+fIKnotKGRGGKPmfK2AO3LYuD+5otDRyVJ7++v0YwVG7S6tDLCIwLQGWb0o4zL7elyx0ZD0ss38nEzgPDqbKO9tt/nOB36/eu79PBbgXvsR5Nm06RFZDe53B49u2W/fbnFlJa9RC99IJoR9KNMR/2KLdYCW0I+gEjobKO9tt//6yXjNNgxQPeu29Xt3WgjgY473VdRXe/XBa5F0lOb9umuy8ZHZEwAOkfpTpSx6mADSZC05oYiOh0AiAnXzRqtvy+9QC9ec65evnF6h89twfL7fyzUQz+e3Olz6JVnn2KXH9GZrGc66qe/8t29lD8BUYoZ/SiT43Tox9/N1/Nb/eseWyQ1NLWEf1AA0EttPwEoXlCou9bsULNpKkHSnXPH6dzThuqKRzZ3uV9IgqSVP52irXtr7J1227ry7FP0T+d4J0Hqm07Yj5NoGLrj0rGaeOoQu6zo9kvG0pmsF3KcDl3zvQI93q681BSz+kC0MkyzL9sxRa+6ujo5nU653W4NHjw40sPpttWllT6bkrSVaBjatPR8XpgAxCyX2+MXsleXVmrZS+XqaBrDmnm3Ps207qOh6bj2VTfonFEZfuWMgR4HfedyezS9eIPfa1SCIf196QWcayBMuptzmdGPIlZbzY5CPh8xA4h17Wv8Je8i33HZ6X4z+wmS/veiyTp7ZIbfWoCungu7cwx6rqNZ/RZTLGoGohA1+lGkow1pfn3ZeG1aej61+QDi1qS8DK1o074z0TBUfGWhLpuYS3iMMlfNLPBbB5EgsagZiELM6EeRQBvSJBqGvj8xhxc6AHGvfftOnveik7Wnwp0vldvXmZLe+ewwE1JAlGFGP4pYT56WBEOU6wDoV3KcDhWNHsbzXpSbdcZwnw48prw99em+A0QXgn6UsWpVJen3/ziJ2REAQNQJtOeL1VMfQPQg6EchazFatnNgZAcCAEAAHfXUf2ITPfWBaELQj0JNzd4mc8lJ/PMAAKKP1X2nPav7DoDoQJKMQk0nvEE/haAPAIhSV80s8JvVTzQMuu8AUYQkGYUaTzRLYkYfABC9cpwO3XbJWPuyIemOS8eykBqIIiTJKNTYOqOfnMg/DwAgev1y9mi7p74p6d6SXVpdWhnRMQE4iSQZhezSnQGJER4JAAAde+LdvT57v7SYtNkEoglBP8qYpsmMPgAg6rncHq1Yt8vvetpsAtGDJBlljjefnBqhRh8AEK0C9dK30GYTiA4kyShjtdaU6LoDAIheBZlpdn1+e7TZBKIDSTLKNB5vtr+ndAcAEK1ynA4VLygMGCQSJNpsAlGAJBllrBn9AYmGEjqaKgEAIAosnJqvvy+7QNd+7zSf61skvVr2VWQGBcBG0I8yJzfLouMOACD65TgdumrmKL8ynuJ1u/T71/0X6wIIH4J+lLE77lCfDwCIERXV9T5tNi0Pv7WHsA9EEGkyyjTRWhMAEGMKMtPUUbHpw2/t0WPv7AnreAB4kSajTKO9WRb/NACA2JDjdGjp3HEd3r7ib7v00YGaMI4IgETQjzrM6AMAYtF1s0frxvNHB7zNlHTFI5u1urQyvIMC+jnSZJRpPOFtr0mNPgAg1vzrJeM6DvumdNeaHWykBYQRaTLKnOy6wz8NACD2dBb2m02TjbSAMCJNRhmrjz4z+gCAWDXj9MwOb9v0xeEwjgTo30iTUabxuBX06aMPAIhNBZlpfn31LbTcBMKHoB9lrBl9SncAALEqx+lQ8YLCTltuEvaB0CNNRpkmNswCAMSBhVPz9e9XfKfD2x9+a49uXfUhi3OBECJNRhmr604K7TUBADHuovFZnd7+StlXKireoN+X7NJrH32pv3z8FcEfCKKkSA8AvprYMAsAECdynA7de2Wh7nypvNPjHn7bd+fcG88brX+9tOMNuAB0D0E/yrBhFgAgniycmq9ZZwzXvet26eWyr7r1Mw+/vUd/31Ot387/juqbmpWWnKj6pmYVZKYpx+kI8YiB+BH1Qf+RRx7R73//e7lcLn3nO9/Rgw8+qO9973uRHlbIfFPfJOnkolwAAGJdjtOhB380WadkOPTwW3u6/gFJZQfcuvzhzX7XX3FWjk7PStc3R5s0LC1ZztQBkqSM1GRNGZkhSXp/37eq9RyX23Pc77hAMlKTlZfhUOW3Dar1HA94u2NAgsoO1ColKTHgfbW/j87us+39jUgfqIvOzFKO0yGX26OK6nqlJSfaP9f+dwh0v9Yxp2Wm6aIzs3zOQdvj93/b0OF9GYahvAxHwDdUbcfV9vZA421/Pj46WKuK6ga/f6v2jxvouI7+bTq7znpj2Pbfoe3fhfV4bR870L9Z+9va//tNGZkRE286DdM0zUgPoiOrV6/W4sWL9cgjj2jGjBl67LHH9MQTT+iTTz5Rfn5+pz9bV1cnp9Mpt9utwYMHh2nEJ7ncHvt/MknderLZvq/Gnu0wJK24slALp3b+ewIAEEsee2ePiv9Gx532zspz6qMDbkVLKJsxeqiKRmfqi0NH9UrZV37jOivPqbID7oiMLVos+m6ebr5wTEQCf3dzblQH/WnTpunss8/Wo48+al83fvx4XXHFFSouLu70ZyMZ9FeXVmrpS+V9/p/VMKTNSy+IiXeMAAB0l8vt6VEpDxCtIjUx292cG7WF4E1NTdq+fbvmzJnjc/2cOXO0ebP/R3mNjY2qq6vz+YoEl9sTlJAvSaYpfbC/Jgj3BABA9LBKeZZ9f1yHG2sBscCUtGxNedR2i4raGv3q6mo1NzcrK8u3NVdWVpaqqqr8ji8uLtZvf/vbcA2vQxXV9UH92C16P28BAKBvrps1WvMn5WpfdYNGZabq+S379VA3a/iBaNFiSvuqG6KyAiNqZ/QthuH7Vt80Tb/rJGnZsmVyu93214EDB8I1RB8FmWkd7gTYU4akKaMygnRvAABEnxynQ0WjhynH6dDtl4zTe8su0KLv5jPTj5iRYEijMlMjPYyAonZGPzMzU4mJiX6z94cOHfKb5ZeklJQUpaSkhGt4HcpxOrTiysKglO+suLIwKt8dAgAQKjlOh5YvKNTNF56ufdUNSk1OUENTi1KTE3TgW49qPU0nG1wMSpbTMUDv76vxWzB6xVk5GmN152k9LpD/3n7QZ1HpWac69Y/nnGpffn9fjV796Cu1tN75JeOz9L2xmT730f6YtgxJl5+Vo3NGDe3y2EA/1/Z3+Pzro/bPtr1ft+e43tvzjTZ98Y3PfZyV59THB9328XPOHKFTMlL97st6vL7mlitax9P+d5wxeqimn55p/1t19Lhtj2t/rgxJkwIsAL7irBxJRrfOaVe/Z9vbOvq+/aJpw5CKF0RvXov6xbhTpkzRI488Yl935pln6vLLL4/qxbiSt1Z/+74a1Xq87TLbPyl1JiM1WWfHSNsmAACigfW6axjq8WvoRwdq9P6+Gp0zKkOT8vw/SXe5PXZ5UUf3ax3T9o1JQ1NLwJ9pe3+S7LyQkZqsUzMcHf5cV2MJdA66Or7tOKzxf3zQrYrqejuztB1X2zdclkC5paeP293ft6N/547OvzVe6/i2j2ed+45u62iMfflbC5a46Lpjtdf805/+pKKiIj3++ONauXKldu7cqZEjR3b6s5EO+gAAAEAodDfnRm3pjiQtXLhQ33zzjf793/9dLpdLEyZM0N/+9rcuQz4AAADQ30X1jH5fMKMPAACAeBTzffQBAAAA9B5BHwAAAIhDBH0AAAAgDhH0AQAAgDhE0AcAAADiEEEfAAAAiEMEfQAAACAOEfQBAACAOETQBwAAAOIQQR8AAACIQwR9AAAAIA4R9AEAAIA4RNAHAAAA4lBSpAcQKqZpSpLq6uoiPBIAAAAgeKx8a+XdjsRt0D9y5IgkKS8vL8IjAQAAAILvyJEjcjqdHd5umF29FYhRLS0t+uqrr5Seni7DMML62HV1dcrLy9OBAwc0ePDgsD52f8O5Dg/Oc/hwrsOHcx0+nOvw4DyHT6TPtWmaOnLkiHJzc5WQ0HElftzO6CckJOjUU0+N6BgGDx7M/2hhwrkOD85z+HCuw4dzHT6c6/DgPIdPJM91ZzP5FhbjAgAAAHGIoA8AAADEIYJ+CKSkpOg3v/mNUlJSIj2UuMe5Dg/Oc/hwrsOHcx0+nOvw4DyHT6yc67hdjAsAAAD0Z8zoAwAAAHGIoA8AAADEIYI+AAAAEIcI+gAAAEAcIugDAAAAcYigH2SPPPKICgoKNHDgQE2ZMkXvvvtupIcUc9555x394Ac/UG5urgzD0Msvv+xzu2mauueee5SbmyuHw6HzzjtPO3fu9DmmsbFRN998szIzM5WWlqb58+fr4MGDYfwtol9xcbGmTp2q9PR0jRgxQldccYV2797tcwznOjgeffRRTZw40d5BsaioSOvWrbNv5zyHRnFxsQzD0JIlS+zrONfBcc8998gwDJ+v7Oxs+3bOc3B9+eWX+ud//mcNGzZMqampOuuss7R9+3b7ds53cIwaNcrv79owDN14442SYvQ8mwiaVatWmQMGDDBXrlxpfvLJJ+att95qpqWlmfv374/00GLK3/72N/Puu+82X3rpJVOSuXbtWp/bV6xYYaanp5svvfSSWV5ebi5cuNDMyckx6+rq7GOuv/5685RTTjHXr19vfvDBB+b5559vTpo0yTxx4kSYf5vodckll5hPPfWUuWPHDrOsrMy87LLLzPz8fPPo0aP2MZzr4Hj11VfNv/71r+bu3bvN3bt3m3fddZc5YMAAc8eOHaZpcp5DYdu2beaoUaPMiRMnmrfeeqt9Pec6OH7zm9+Y3/nOd0yXy2V/HTp0yL6d8xw83377rTly5EjzZz/7mbl161azoqLCfPPNN80vvvjCPobzHRyHDh3y+Ztev369Kcl86623TNOMzfNM0A+i7373u+b111/vc924cePMpUuXRmhEsa990G9paTGzs7PNFStW2NcdO3bMdDqd5p/+9CfTNE2ztrbWHDBggLlq1Sr7mC+//NJMSEgwS0pKwjb2WHPo0CFTkrlx40bTNDnXoZaRkWE+8cQTnOcQOHLkiDlmzBhz/fr15uzZs+2gz7kOnt/85jfmpEmTAt7GeQ6uO++805w5c2aHt3O+Q+fWW281R48ebba0tMTseaZ0J0iampq0fft2zZkzx+f6OXPmaPPmzREaVfypqKhQVVWVz3lOSUnR7Nmz7fO8fft2HT9+3OeY3NxcTZgwgX+LTrjdbknS0KFDJXGuQ6W5uVmrVq1SfX29ioqKOM8hcOONN+qyyy7TRRdd5HM95zq4Pv/8c+Xm5qqgoEA/+tGPtHfvXkmc52B79dVXdc455+if/umfNGLECE2ePFkrV660b+d8h0ZTU5Oee+45/fznP5dhGDF7ngn6QVJdXa3m5mZlZWX5XJ+VlaWqqqoIjSr+WOeys/NcVVWl5ORkZWRkdHgMfJmmqV/96leaOXOmJkyYIIlzHWzl5eUaNGiQUlJSdP3112vt2rU688wzOc9BtmrVKn3wwQcqLi72u41zHTzTpk3Tn//8Z73++utauXKlqqqqNH36dH3zzTec5yDbu3evHn30UY0ZM0avv/66rr/+et1yyy3685//LIm/61B5+eWXVVtbq5/97GeSYvc8J0XkUeOYYRg+l03T9LsOfdeb88y/Rcduuukmffzxx9q0aZPfbZzr4Bg7dqzKyspUW1url156ST/96U+1ceNG+3bOc98dOHBAt956q9544w0NHDiww+M41303d+5c+/vCwkIVFRVp9OjReuaZZ3TuuedK4jwHS0tLi8455xwtX75ckjR58mTt3LlTjz76qP7lX/7FPo7zHVxPPvmk5s6dq9zcXJ/rY+08M6MfJJmZmUpMTPR7x3bo0CG/d3/oPaurQ2fnOTs7W01NTaqpqenwGJx0880369VXX9Vbb72lU0891b6ecx1cycnJOv3003XOOeeouLhYkyZN0h/+8AfOcxBt375dhw4d0pQpU5SUlKSkpCRt3LhRf/zjH5WUlGSfK8518KWlpamwsFCff/45f9NBlpOTozPPPNPnuvHjx6uyslISz9WhsH//fr355pv6xS9+YV8Xq+eZoB8kycnJmjJlitavX+9z/fr16zV9+vQIjSr+FBQUKDs72+c8NzU1aePGjfZ5njJligYMGOBzjMvl0o4dO/i3aMM0Td10001as2aNNmzYoIKCAp/bOdehZZqmGhsbOc9BdOGFF6q8vFxlZWX21znnnKOf/OQnKisr02mnnca5DpHGxkZ9+umnysnJ4W86yGbMmOHX+vizzz7TyJEjJfFcHQpPPfWURowYocsuu8y+LmbPc7hX/8Yzq73mk08+aX7yySfmkiVLzLS0NHPfvn2RHlpMOXLkiPnhhx+aH374oSnJvP/++80PP/zQblO6YsUK0+l0mmvWrDHLy8vNH//4xwHbW5166qnmm2++aX7wwQfmBRdcQBuxdn75y1+aTqfTfPvtt33aiTU0NNjHcK6DY9myZeY777xjVlRUmB9//LF51113mQkJCeYbb7xhmibnOZTadt0xTc51sNx2223m22+/be7du9fcsmWLOW/ePDM9Pd1+veM8B8+2bdvMpKQk8z/+4z/Mzz//3Hz++efN1NRU87nnnrOP4XwHT3Nzs5mfn2/eeeedfrfF4nkm6AfZww8/bI4cOdJMTk42zz77bLtVIbrvrbfeMiX5ff30pz81TdPbSuw3v/mNmZ2dbaakpJizZs0yy8vLfe7D4/GYN910kzl06FDT4XCY8+bNMysrKyPw20SvQOdYkvnUU0/Zx3Cug+PnP/+5/bwwfPhw88ILL7RDvmlynkOpfdDnXAeH1T98wIABZm5urrlgwQJz586d9u2c5+B67bXXzAkTJpgpKSnmuHHjzMcff9znds538Lz++uumJHP37t1+t8XieTZM0zQj8lECAAAAgJChRh8AAACIQwR9AAAAIA4R9AEAAIA4RNAHAAAA4hBBHwAAAIhDBH0AAAAgDhH0AQAAgDhE0AcAAADiEEEfAAAAiEMEfQAAACAOEfQBAACAOPT/A6ORvoQK6WHlAAAAAElFTkSuQmCC",
      "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",
    "\n",
    "#rename the second column\n",
    "df_20.rename(columns={\"0\": \"Counts\"}, inplace=True)\n",
    "\n",
    "#remove unnecessary data\n",
    "df_20 = df_20[df_20['Channel'] <= 700]\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "#preview the spectrum\n",
    "plt.plot('Channel', 'Counts', data = df_20, 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>415</th>\n",
       "      <td>415</td>\n",
       "      <td>1484</td>\n",
       "      <td>Range 414-474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>416</td>\n",
       "      <td>1598</td>\n",
       "      <td>Range 414-474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>417</td>\n",
       "      <td>1728</td>\n",
       "      <td>Range 414-474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>418</td>\n",
       "      <td>1828</td>\n",
       "      <td>Range 414-474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>419</td>\n",
       "      <td>1979</td>\n",
       "      <td>Range 414-474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Channel  Counts  Channel Range\n",
       "415      415    1484  Range 414-474\n",
       "416      416    1598  Range 414-474\n",
       "417      417    1728  Range 414-474\n",
       "418      418    1828  Range 414-474\n",
       "419      419    1979  Range 414-474"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the channel ranges\n",
    "channel_ranges = [0, 414, 474, 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_20['Channel Range'] = pd.cut(df_20['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_20.groupby('Channel Range')}\n",
    "\n",
    "# Access individual DataFrames using keys (e.g., channel_range_dfs['Range 0-40'])\n",
    "channel_range_dfs['Range 414-474'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff93bd10-d553-455e-b486-f552ee396d56",
   "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=5370.0, g2_center=440.0, g2_sigma=10.0, g2_beta=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0eb74cfa-f6c0-4eb6-b07e-627301d6d6c7",
   "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   = 46\n",
      "    # data points      = 60\n",
      "    # variables        = 4\n",
      "    chi-square         = 56.3037868\n",
      "    reduced chi-square = 1.00542476\n",
      "    Akaike info crit   = 4.18503386\n",
      "    Bayesian info crit = 12.5624121\n",
      "    R-squared          = 0.99999946\n",
      "[[Variables]]\n",
      "    g2_amplitude:  5267.80682 +/- 40.8443525 (0.78%) (init = 5370)\n",
      "    g2_center:     441.442922 +/- 0.13441777 (0.03%) (init = 440)\n",
      "    g2_sigma:      35.0457150 +/- 4.93732923 (14.09%) (init = 10)\n",
      "    g2_beta:       2.49920876 +/- 0.58644731 (23.47%) (init = 1)\n",
      "    g2_fwhm:       39.6263836 +/- 0.47672284 (1.20%) == '2*g2_sigma*sqrt(2**(1.0/max(1e-3, g2_beta))-1)'\n",
      "    g2_height:     5267.80682 +/- 40.8443525 (0.78%) == 'g2_amplitude'\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(g2_sigma, g2_beta)      = +0.9973\n",
      "    C(g2_amplitude, g2_sigma) = -0.5486\n",
      "    C(g2_amplitude, g2_beta)  = -0.5080\n",
      "    C(g2_center, g2_beta)     = +0.1421\n",
      "    C(g2_center, g2_sigma)    = +0.1390\n",
      "------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#fit with weights=1./yerr\n",
    "result2 = mod2.fit(channel_range_dfs['Range 414-474']['Counts'], pars2, x = channel_range_dfs['Range 414-474']['Channel'], weights = 1 / (np.std(channel_range_dfs['Range 414-474'][\"Channel\"]) + (channel_range_dfs['Range 414-474'][\"Channel\"] * 0.281)))\n",
    "\n",
    "#print the Fit Statistics and parameters\n",
    "print('------------------------------------------------------')\n",
    "print(result2.fit_report())\n",
    "print('------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fc3fa2a-6e67-4fba-bcce-21f1b5719959",
   "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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",
      "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 0-414'], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.plot('Channel', 'Counts', data = channel_range_dfs['Range 414-474'], color = '#7c8bfd', marker = \".\")\n",
    "plt.plot(channel_range_dfs['Range 414-474']['Channel'], result2.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "plt.plot('Channel', 'Counts', data = channel_range_dfs['Range 474-700'], color = '#7c8bfd', marker = \".\")\n",
    "\n",
    "plt.ylabel('Counts')\n",
    "plt.xlabel('Channel')\n",
    "\n",
    "plt.annotate('gamma@661.657keV', xy=(453, 4300), xytext=(500, 5000),\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 20deg 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": "85130509-67e9-4fc3-9d82-191bc9b28625",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "441.4429223389423\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",
      "627.5368334144723\n",
      "\n",
      "Quadratic model estimation\n",
      "----------------------------\n",
      "572.595576187621\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": "329785f6-12da-41ab-94f8-9b38d49af11a",
   "metadata": {
    "tags": []
   },
   "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": "e3464cc4-9232-4504-9f02-3f4165bcd21f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'x': 0.06030737921409157, 'y': 1.7464333319828733}\n"
     ]
    }
   ],
   "source": [
    "#Extract the energy from the model\n",
    "energy = result_r2.eval(pars_r2, x = position) \n",
    "\n",
    "#Calculate the varibles (to be compared in the last step) according to the Compton model\n",
    "x = 1 - np.cos(np.deg2rad(20))\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": "cb825c85-0ca1-4fe0-926a-5199d468a94f",
   "metadata": {},
   "source": [
    "### Append the values to a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c2e06451-a8ba-4a6f-8837-8413f10e0237",
   "metadata": {
    "tags": []
   },
   "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": "4b64aa2d-1f3a-4e9c-8973-c7b1b1f1b09b",
   "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": "853f2a9f-b95d-4a48-ac6a-980daf2c0864",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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