CS666_Assignment_3.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shivangraikar/cs666student/blob/main/CS666_Assignment_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hev7LYxba6Db"
      },
      "source": [
        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ULlgDLOEpqHAMgifU4GLAeX6JDRDUlLJ6CRCS+AD+2TJ9xuSjD4phepnITVkMc7KkLEA6sPGRWPm5sNT6tvDL8B5E5Lt44qo3KSBEeBnMfQZFoGfSFJF/eHqnhYABPheYIUHsqU7k9vaAKmWwkaeJ0CJ/K8NJjGvmBA/xSbZdEaYvISKrCdBYjkQZODGgkTJi84hYUhEfxYz1jYrNka+JXC3qA2AEc2tsYKiNhzXhKL9IamB/AkWTsg1JYANayciMA8qJcqbIkiKfXVUcX4Fe6F7b+KOcoYsd1ob3oOYPDFNZRO9fhKbeChfBGmCBHckQFSr4/INZIoXLglD5/wxpJSA2TuXBtyRiUdzDG5i7Qs94a2N/WoeQOiGtrAAaIS+CqeiZbCWIHX2lia0pjaQlzxrqmx63Xn+Vge/jxcmpXu0cxhRs1vgX4cLMvNEyAsEvW18nI4n64uYj5rmi8HFzQIwUvh97KCArvgXvBVd6i+iTM1pv69Ahf0FrBCl2J9YD5IE1F3ZZ/J2ima8tNJsoN5a/5IZLphDp4ZpgsWsKxZYPNBM3VuUrIG312uAok01nbcfUKDONisAYKsszwyjL9iESaC3YEKSpE2PxhInC33Sht5dfkBz6sSMIeX4zM4A2vfDZhf2VHWt42ypZrazgJ0ep6UUEtkBH4wf6fGiwuvlPA40Xn6WomZ7VR6rhfwpmGcSNSJGrdr7KmZJWYpwkWoxpIf1FCVEaEKOsXWxhPsg5dZp80gbO0baAds2QNRtRX/YUvo2obs6pJdU1EBzlrSJHauVqajIjR0CFhBbeA06a3YtK6+jgSAUbooReDjg1Y45qk/Uv3S/ZozA1IuFflsiBhqoJkob0EhWQ3ai6AU4UosF43eDt1nNKWJbogm2o5aS3QgD+oydN4PsD5a3JeIAwSUIepYVh3mkfDPSFUIwgp4ss4uT1yjMUj2tLMib3WNDBA8zGMET67mYT4uoe7oNP5AGwEU5oUCPVWDMGFc2nqjKdKb7YMbuTp0uuYUS8ZS28+A3ZUdhmwWikJXLW8MDNFo9nrIHulEbqHNiM4WloHzjoZKNG3KxYs8gS7eG+gTvAeXhtmm0s+lkQvhyFYOOlZpwyp6TaWS4uUQB9Nj57TL3QgYBBhvtKxGRnCwvhQEuuRNEhalqFDgCdHLVRupGmj7CrBR6zlvkAFLjCzA9+cqyalOPXa2h6YIv1KPqqFjj5RGrg3BUvzU0B7NeVzwvYOBuDr86tbEF67E6WEngyJEudnHeEKeaIIUwsHQ6ucaQE6PD5htBtc0VdBuCw+sNGbZno6ihZ3RZxqlvxn3IJX4b+B80IWQM/qcm2BU8IS3Z+c1/WF1GGpNo4u8BJwDzxcsHvb5Jmlq+ob1aw5RKyaVEDUNFsX7FgM4ioVA0HJHbsmyZAxssuynV7WaEUH4ooLES4V+BNeQlMVi2Hk69MW4AP4Hta3mQOxIJAG0Jt4oc26kjKTrTFyqyJ5pngnQdW3b8HgBYOVxEJMnOpwdD6GtJboW8kHkR0nQrEFaz9oYm3dU9OJF53soCUqOvkeeKYUn8CkU7dNq5kGdErsrEcRf5xZU5YMgri4NORGRRiWWVK72P2gdSuZqYgSOAph9OQ3dwOx1MbxQzwCOx+n2FC55Qt/2PIySwVMlhPTR4DkM1S0SH78MT0igkjVEGC5FTVU1nh1D70VrL+EbQnIhWykYgIewYUTzu0sTTfSdNCIK8IJTfhIo/VHaOeFw195Ne9CgPJmlHBKNPjzkhKI/M9Bf70ZiyO0Ddghd4XSuh3vh+4pOfBReF2iL9emp2ivUjG39WEl6DCzu0BKAiRFG7NMPWIeTnMqoivV3orOhPrH9vNRmfUdJgEIQCzjPVd2jvQ2p0DWjhqkB+aBDSFQ2jQBRIDBwI8trdnpI/lr3IV0UBilx2siI2a+i203sELehtYv6aB/tQc7UBJK98nfVxWBqctcaIrylRBSQQhrhA0+kFblFCfPQYxReA+D1qO1MHKSGIxJAcWaCA2IAWKFTXWhpYAPBfTUhhsOeyLE3FAraSDiU5xTK0czb7QShVu2U8MOC3IOw5G1B0jrRZ0inpO73Gt3dwFspgYdtZQSeOF2N9WNxqG2Iq/NoVL5GfZDsHAWKI4gHBdS/prMjSOGLrj1OcolkEAcievRAHs5KHiE3SaGGJCNP6AjBopqXqplVKLjxwjws5pxUEGMCVWGvg71sn01yxzKrNviORnmJKKProLBAnLj+2yKzBXkhepo2kQeyhH46sLcvUCMAeqWMnaYHVTmi0NfQ/AGTWOQXRtHpBdojB0lVTBGE0j7bB1iVPjXXOLJ9INV2hqhN1wN7Ylkye8E3AAj9jzoaiYrD8vGtcyZy+eITb6WnNFOh7gAQDOUBj3RaAKwHs2yrrLVRB9K9bMmBrrcHAl/OeSdaStpJI0uUy8F10c00zqfXJLHS0UGPFTy6Coz1cp+hwRmmoRW42mPAgZ2MympkNb4TdzKJkg7QP5it6R1rgHmHxqXoxeklY/qI8BM1hk5FNIE8uChInQ7ACPkIlEwYwN7YKg13uigPmUCuUJiFTJkGYkCthxQIo8ZRT+thsU27lAflhQOG0TFQS1DIjVyQ5cn1oPJprIAJ4gdFZwqGCGNhrngvDg3e04bwe740Ue4olTa1bY4JmtsdVFZG/p+uNXMFVIdGC/NZMrXVx5JR5hqzIpM/jizzA2TUg50e4LXyvgSvnrDbXpbvmfdI0x70NS0IrMnTQZxV+1o8gKZJ3HmR0aHozeNwY0f+ugTtyxAOCRn0YsS3VVEK2n9L9IB1s0kui2wCBQ4Uv/IsXD9Ol+GSqOnk+FjjdxLlalZz0ztcI5rDh+GpKRjiwb0QWEho/Cm3K5DtbHW6ogmpdhylprr2DlJc4ksdHGBBlOvizQMIkSYDS7LMGrrxRacMoI5C3C6pDFpM9AOaByDg0cB/jWZxqmjNqu3JLvskiNJJBuoHTOCHg/cuzRIkIkhs026Zj4hPAh0py24C0oMypAyutjJml5Jc79BiHUdMvYk4VetlRIBxl9Z8WHgtkFO0B6pNaoU6xS3XnoYOhKCTkHnaUF9SX/gevjzALM+HaKGKXQb3aFRaC8RDKSxpMGBcrD7epAzdoJHzespDZyvjeRUmqtJ2ic6E5Oiw1kh5HJCmzV1bWn5pNkpHxviG0GSGf5D4wc/oV432NV2znVjEbjjQjvJHx1bNLd+KNfxB3Sfdf+uQwdV04AnREY2fHhQc4kgTdM0hUCLzFWu+bj+XKPNEOaPmtAtl8j1X0KLJPWxpMmArPJbAAiS5+paxPBgPftDQH061CpBoB0UTyqA5uw5sNsXlNY3GM5FlgKOAMkoZVxRFvBp5jVmR5m1kt7UlvWhXcT39bA0E00ShVY22kP68C9+/aUauwCuDDzhTUouMmuqTFc2ai/kmX6hjiBcnheYGQPGxvC5rcohk0wgSAsEvaHSE1dDu9ymkZ+AAcLU6U+DeHBtDwYK40dsPDVNgj64sKSWMSaHmdWoJGdLldtszQfGjfW+ht0u/Tm0/NFTGyqNBdPSkaxMWSsVlgKXo7/MCst4+4NY2LDKy4YB1uGVqk6UPQ43UmnQOaL1FJ9zRJe+059B2F/AU0LEfcrJ8jk7fARXpwkyLi3oaJU3rDs2FwofOwVpsnaaC9D5yGE+scY52gzBlEFk4hW7GBpQ7eQixiaHHcCsXGiFr0PFeXF6HizUA5SGOBie4I9wrfalnx19sUsjjw5Zj8plLo+Qk1iKOfMIiCFAwi/utN+7QGTh6hXwRzway8POGht4ajlFxaGhxDQ8AAMLMrGwg77kQmVoal1Qwx7ek/d5LSJrUt7y/Ngy2DqNgQRt5yI9UvGpiCCi2Dh8mxyVU9GjKimXTTrrxmFU7wl5zEZAXT+QtbBAsaoMCWVbQHInb8xbtEMQVgwuG6uyaUB58ZRd6yvdrW59WpsAQVuhPyjVrTw9zim45r1G5LY5NmSPEDgV3NOeis7TzQt/A5kUnO/MhiEHHeUBW7UagslDOJNN/jsVhsXQUW+GlssHcSa9o3q9Tq1WaE1bGumSxmkb+uWRNoRa1E9BAE8lfRsfjXFwslJw1uXArwTrgCIpzpHeeQNayAaYy+gmiVBkrMYvMT7AK8kO44lg1nGxBb4w7SB4j+fAOUScVsIwJw0eS0RGUZ29T24mgS0KS6kwQAnVQNhJlOGMdM135sQj+slVsK2WDW4E1ApGdOgmKjERn4iDw9/rFCxZ30LeA/jU4fqsFr//Mm60C/ihJspz3Z6iDpL0yzyFnuZer4+A42Y2G/XgxH9RR8EZDoqOAWYZOh4JHGCZWmVHpNfJQW1sPjefW+RUcn4RWoXeOwLEVFAZVBXm3R8zoBMHGrbgjiZ6qQwAtoOIMWv8UtM5myPBfDZJpQWlfUggKvH8QvI5h2tlgSnjt6sRJJ0YCEqBV4wfNTyDF3nXsKL1Z89WxCYpAkyCqFHCe2pFeuEH6U2e0VmflqERgEV7vGl0exSV+LAxCRBt9N2pQApKHhQsE8RB72jF+U+vjT/CuofOybC/8s4JoJ6oTkAfzoinJ9JQ00DZFkmRRoIEEyjlouerMooGL03Er+kZHsnygcAiNnB8gB5HDUjN0Hd0d4gzNj99BxtPJ04I9NL/UIcGJGE3w2SEvU5MaTe+R4BiYpX0z6VUdrCJZku0wDpZUs5vVoWa4cbyNap1ydHDXwhHpmNhG/gZu02QSh+YsppmmTtyg0i7Pt4d2EwuwASXTEkPberyFOnSoIwLIssjLAXaiiICOWwC0IU6Q+VsHHMKRrqYsNWTBxk/tl3UNnpD+qyUnu0vNbKQ/skoDcOQ+jkHnYLKODEiVCoAR16SckKCwZya9wFTSBF02vQ9s1jbeqAN1VLLO+2BWaXnZKCsBksCO5t21vVixC3i3u847NKv0Lp4dl22kX3W+dVgYQqYLKUWWsClQvq1zoQC5j3paJILOaGQhHVjYB1KV3kC7aC/kqZGuM5Q5V4+iQCXo5O6pEI12J1ln2z+dw0ZGVwR/kHak2zX08TIETrs0ppNHQIgfcmY6Ta1DH+hyrwgZlXyOL1tGtGkPHNdPSRBq/XLIvd0wMA5LgQcERN9h4QULYEceymCY9VJsdXtiDITqH+9BZ+8iLfEmw7J1frv6dsWbRnsNIoG4QksESme5pwal+v0xTZg+SiQjl/Tyz2mWL0RAKurRtN8tJNP4UBv6hFBHGuA7LJP4yXQywb8jWFMHNa9oD0oyzOXWOTFEeXY6mYADQDLQarCEJnn4Hj/GO7MOrqAkzpU+DX8Mx1mVNozRZ4C9Nr+P+5wlOggXlhE7YUFRQOqYxrjL2znQ+7UVtdEBBCRrxoSyJBsFDdg0Cl0doaXnBfNp3aNjj2gbBQMZZcRr37fnCENOnXSuFHkkp8C9jsCUhtjXqa2wHHz/gSCUPjLftN9Ti2aynw28ly/Mb3rKer1SQnwTaU0ttOGGqwai0dkIvD+0NvHQ0TAE8NXeouYdJnFM3oLsouWoI9z6XaarqZt9bXga5nglHb+oGmJg5YfOPF35vndaUQfudGIkYqJvzRhMaBaaWuq1EnRoEmXfYnf4aJ1TJp46Vg9e67B+CinpqF44TceMsM0HI56kTaUx+xzagqDrXiFpn7roVD0dGKNUTKDo6ogLWYt308yCNOTzzjP2d7tt9tlyBv5xrXIBqfWWSnfIcOosYIgMtbpIhwwJVakJ6+wz6RQ/Nt7e8e6k4XK62jHma/3STD/ZdNwVhGSlWLwoMUpDaB9C6u6Zfx125ifJtM/JDY9GyUikq50eegGxKR83eB4322LpVUcDi36zAp7A2qORTSfv+9Bw6WIN4HSdUGS1mAuqGVWsDsT8eP1aYkXWwJrv+SVs8GoFN4cr0baOBJKOkRjiCP5Cg2M6ycBOy5sOiWUYR6N7fIhTmieKkddWuXLalGugU3zQqRhZUw1nuAEkjN+DS00qSoVJgEyljzs4jlqRztnAfSmsDHYYqbfNRbJEWo86YNi1TXK0kcs9t5z51W84ZFYpKYEsBrMxBKvJSuW+dd4beUYbUWfaTtAWFlYfjdLfSfPN874D/FakEEeQRUnQFVkDUafQEG5G8eL0pn4JphNI/T5M1y95mLhpatxNnOiZNfU7RRDQGPhiUDU0hxaNGrKiarUvJlFr2jnWL0Pp2Dk/vO8wyIrkTdPYO8hfEr3SGbK4yJuQHdZQzIHrS35h9SxQTdiUEMk9hhMspAKmfmVHs68y9as7UCcmkchE/c7P4AHpNdrG60CFupByvDr3S7VQxzp4IF3yWtAkhmToi07WN42NCg6zF8q2UfoAW1pFZ6R4OUIeEVlIkbZkTJvpqMebtO8KzPHo2qo7XYe7YO5hoKRkPtL7OIgftY5li7Jd73avO3WQ8eqAiWKjE3DvF53IDCCggmrgIlUOKhcdgUwO3aFfREQWYQpA5Kqx4NSx2PcrDP/yxMXPn93ffeH/60KXgnf/B8yIgCkpCahVAAABhGlDQ1BJQ0MgcHJvZmlsZQAAeJx9kT1Iw0AcxV9TxSJVEYuIKGSoThZERRy1CkWoEGqFVh1MLv2CJg1Jiouj4Fpw8GOx6uDirKuDqyAIfoA4OjkpukiJ/0sKLWI9OO7Hu3uPu3eAUC0yzWobBzTdNhOxqJhKr4odrwhgGN3oR6/MLGNOkuJoOb7u4ePrXYRntT735+hSMxYDfCLxLDNMm3iDeHrTNjjvE4dYXlaJz4nHTLog8SPXFY/fOOdcFnhmyEwm5olDxGKuiZUmZnlTI54iDquaTvlCymOV8xZnrVhm9XvyFwYz+soy12kOIYZFLEGCCAVlFFCEjQitOikWErQfbeEfdP0SuRRyFcDIsYASNMiuH/wPfndrZScnvKRgFGh/cZyPEaBjF6hVHOf72HFqJ4D/GbjSG/5SFZj5JL3S0MJHQM82cHHd0JQ94HIHGHgyZFN2JT9NIZsF3s/om9JA3y3Queb1Vt/H6QOQpK7iN8DBITCao+z1Fu8ONPf275l6fz9QcXKZnLSHWwAAAAZiS0dEAMYAxgDGeDMrcwAAAAlwSFlzAAALEwAACxMBAJqcGAAAAAd0SU1FB+YIHQ4WGJugljcAACAASURBVHja7L13QBPZ9z6cCoHQO4hSpCqCoiBWsGDBjoVVbCuKsuK69q7Yu1jXVbGBXUFFwYKCIAgCCtINvfcaIJDG+8f8vvPezySZhJBQdJ6/MjN35szc3Hnm3HNPweEwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMDQG4DHugBDZ0EgEMaOHTtx4sThw4ebmpoaGBhQKBToUG1tbUFBQXJycnR09KtXr2prayUu3cbGxtnZedSoUWZmZgMGDFBSUoL20+n0goKCjIwMSHRhYaHERRsaGrq4uDg4OAwePHjAgAEaGhrQ/vb29oKCgqysrLi4uNDQ0JSUFGyQYMCAQQLQ1tY+duxYRUVFhwhgsVjPnj2zs7OTiGgqlbpp0yYajdYhGj59+jRz5kyJiCYSiUuXLv369auIotPS0jw8PEgkEjZgMGDAICZIJNLevXubm5s7Ogkul+vv76+mptYV6cuXLy8vL+/oPMLDw01MTLoiesKECRkZGWKIzsjIGDt2LDZyMGDA0Gno6+vHx8d3dAFFRUUjRowQQ7SCgsKzZ8+6IrqpqWn+/PnimVBOnjzJ4XDEFs1isXbu3ImNHwwYMHQCgwYNKi0t7egy6HT6hAkTOiVaXV3927dvXRfNZrM9PT07JZpMJgcGBnZIApcuXcJGEQYMGESCoaGhRAgXQmNj49ChQ0UUTaVSu6hcg+BwOAsWLBBRNB6Pf/DgQYfk4OPjg40lDBgwCIGMjIwEWQ9Cdna2oqKiKNL9/f0lK7qpqUlE2+6WLVskK5rL5U6bNg0bURgwYEDD3r17O6SA8+fPCxU9a9YsaYiOiIgQKtrExKStrU3ioouLi6lUKjaoMGDAwB/a2tpieCmIAiaTOXDgQBTRBAJBdJ+wzmLWrFnoDy4pMy4v9u7di40rDBgw8MfBgwc7ZajNz89ns9kSUXVdXV1FF81gMPLz81tbWyWi6pqamoruqMDhcPLz8xsaGkRsX15eLiMjgw0tDBgw8EFBQYEoPBIUFDR8+HDoFCqVunXrVlEm5tXV1UQiUZDoly9fiiL669evkydPJhAIOByOTCYvXry4urpaFNOqoaGhINGHDh0SRXR+fr6bmxscfTdp0qTMzExRTpw9ezY2tDBgwIDEsGHDRGGQffv28Z7r7OzMZDKFnjt+/Hi+ouXl5RkMhtDTHz16xMvapqamotDuhg0bBD14SkqK0NMTEhJUVFQQJyorK4tyrp+fHza6MGDAgIS3t7dQ+nj+/HlX7BK7du3ie+64ceOEnpuZmSkrKyu2XeLhw4d8z1VRURFqWGhsbNTX1+d7uqWlZXt7u9A7x0YXBgwYkPDz8xNqykTxu5KTk6uvrxeqqPI9d8OGDUJJc968eSg3L9S/TRDxiUL36J62N27cENpvsEUCw++JvpqGA4/Hk8lkBQWF/v37GxkZDRo0aNiwYQMHDtTV1VVWViaTyW1tbQ0NDaWlpTQa7fv371lZWQUFBaWlpQwGg81mY388OgwMDNAbxMTE5OTkCDrKYDACAwM9PDzEECFUdF1d3atXr1AaBAQEoCfWESQCxc4L4+7duyhH7927t3r1apQGBAJhwIABNBoNG2MY5/YltiWRSCoqKnZ2dpMmTYIS6ykpKeHx/5OXUkFBQUFBQV9ff+TIkUuWLKmtrU1OTo6JiQkPD//x40drayuHw8H+fkGAsxSicC56g9jYWHTOFSRCU1MT/cpfv35F/2rGxsaiX0FOTo5Kpba0tHRWdFlZWX5+PrqKzWaz0TOKaWhoYJyLcW6fAZFIVFZWHjJkyIoVK6ZMmaKpqSmK8w2RSNTS0nJ2dh47dqy7u/uLFy8ePHiQm5vb0tLS0dGBDQJeyMvLozcoKipCbyA0fa0gEXJycl0ULbQBJJ2Xc7v+1AwGo7q6WldXtyt9iwHj3N4CWVnZgQMH/vnnn+7u7ujDWpCCLC8vb2ZmtmXLltmzZ9+8efPBgweVlZWYwstnWAjL/drU1ITegE6niyei66KFNhAkBcV9TfQrNzU1oQ9OoVIwYJzbK+wJcnJy9vb269evnz17dhcdy4lEorm5+d69ew0NDa9fv56RkYFZeBEQqv5zudwuXuGXFN0V6Rh+ExD6BOEqKChMnz793Llzc+fOlUgkDx6PV1ZW/vPPP0+fPj1y5EgymYwNBckSB+S3gNJAUNmerotmsVjoWnZ7ezvfBhKhy7q6ui42wIDpuT1MuFQqderUqadOnTIyMpLsxeXl5SdOnKiiorJp0yZo9QMbEJJin+zs7KlTp+rp6QlqEBcXJyXRXC53woQJVlZWghrQaLTm5mYpce7SpUsFhXvgcLjq6uqEhARsgGGc23tBoVDGjRu3Y8cOoS5EYj4/iWRtbb179+79+/cnJyeLMnnEOFdEhIWF9ZTob9++ffv2rUe6Lj8/H923AQNmW+i9IJPJVlZWO3bssLW1hcLqpUTrEyZM2L59u6D4IgzYlwYDhl+fc/F4vLq6+urVq8eNGyc9woWNDC4uLsuWLcPSPvU4+/yeojFgtoWeh5ycnKOj49y5c6VNuBCoVOqSJUvCw8Pj4uK6/93T19cfMWKEgYEBlDylrKwsLy8vMTGxsbFRvAtqamo6ODgYGBioq6tDF6TRaN++feNrx8SID+NcDL875xKJxIEDB65Zs0ZoaJDEFH4CwdjYeN26dbm5uVVVVd32XfH09Fy9ejXfBR82mx0ZGfnvv/8GBQWJ/hRLlizx8vIaPXo079H29vY3b96cPXs2OjpaUuyjpaW1dOlSZ2dnCwsLDQ0NGRmZsrKy8vLy1NTU6Ojo4ODgzn42RBdNpVIXL17s7Ow8YsQIFRUVNTW1goKC2trarKys6OjoV69elZaWSm98zps3b/r06WPGjFFWVtbR0SkrK6utrc3NzY2JiXn9+nVWVhZGLhj6EhQUFNauXVtbW9vRjeBwODk5OS4uLogwYilh0qRJhYWFotxYTEyMsbGx0Auam5snJCSIcsH79+8rKSmhXCojIwP9CosXL5aRkTl58iR6svD29vY7d+6YmpqK3i0PHz5EF33ixAkcDuft7V1VVYWeJ/f169d8vz2CsHPnTnTRb968weFws2bNys7OFprv0dXVFXuRMfQN4PF4AwODFy9ecLncju5FS0vL5cuXhYafdh2rV68WvapCR0dHbW3tqFGjUC7o4OBQV1cn+gVTU1O1tbUFXS09PV1ooQcR+R1iXh8fHxHjr4QW3A0MDOxU+Zzbt2+LWPVyx44d6JdKTk4+ffq06MMyPDy8f//+2BuNobeDTCY7OTnR6fSObgeXy83JyRElv1RXMHfuXNELwMCor683Nzfne0FDQ0Mx5gSJiYmC8gqmpaVJvG8/fPggCvfdv39f4qKzsrJE+U+3b98ucdE1NTVjxozBXmoM/78BsBfeE4VCGTVqlIKCQo+o2BoaGugaZRehqal569YtMRYGVVRUHjx4wPfEmzdvqqmpdfaCw4cPP3jwYBeNqp2ypYSFhQnN8CIN0ebm5p8/fx4wYED3i1ZXV3///j1Guxh6NedSqdROmeEkC1lZWal6p+3atUtVVZXvoezs7MDAwDdv3giKi7W1tXV3d0fsnDhx4sSJE/m2Ly4ufv78eWhoaENDA98G//zzT3d6JY8cOfLevXs98rfq6+uHhIT0SE4veXn54OBgaU+eMGAQ9yNAIAwaNOjnz58dPQQWixUZGamsrCwlQudbJrahoWHOnDlwMxkZGR8fH752Q97ksPfu3eNtxmaz//77b/jLIScnd/nyZb7Pe+DAAd77FKW0l9jw8vJC6aKAgADpif7vv/9QRG/dulV6omNiYrrH6xEDhs5BRkZm8uTJxcXFPcW5bDY7KSlJkOW0i3B2duYrdOrUqbyNz5w5w9fijFj74rt8f+jQId4LPnv2jLcl3xjZHz9+SK+HGxoatLS0BHWRv7+/VF1TRo4cKUj0li1bpDq01qxZg73gGHrdh5dIJOrq6vZgzSgozS5KcpaugG/NmC9fvrx79453/5EjR9ra2nhvb8SIEfBmv379eF2YWSzWuXPneC+4f/9+3p3W1ta8vS3V6ABlZeVt27YJOipV0QQC4fDhwz0iGup/LNARQ6/jXAKBQKVShSaulrauTaVSpXFlvka9iIgIvo0bGhq+f//Oux/Mr8bXGpuens7XgJuRkVFSUoLYSSKReBeXRGefvLy8devWmZubKykpmZube3p65ubmCj3L09NTUA+LLrqurm7Pnj1DhgxRUlIyMjJyc3P7+vWrKFMNlJRjIoLJZPr6+o4cOVJDQ6Nfv35Tpkx5+fKl0LP09fUXLlyIkQ7Gub0LeDyeQqH0rOWLSCRKSdHmayaurq4W1J7vISg+GAJfV2JBS3A4HK64uBj9gp1CRETE0KFDr127RqPR6HQ6jUa7cePGsGHDoqKi0E9UUlJCr90rFDk5Oba2tseOHUtLS6PT6QUFBU+ePBkzZszNmzeFnrtixYqu0H1TU5Ojo+PmzZvj4+Nra2vLysrCwsLmzp27efNmsUVjwDi3JwEFC/TgDXC5XCnl0uWbHB2lOBDf3JLgB4nX+AAxmqALtre389XrxWCfioqK+fPn8yb/ptPpCxYsqK+vRz991qxZYhMfm812dXXlLbnG4XDWrVv348cP6YmGlHS+yX99fX0fPHiAfq6jo6OIARoYflX0unwLXC6XwWD0bI0yNpvNYDB6Q298/PhRVVW1paUFUngbGhoaGhoCAgLgBpWVlbxnDRkyRF5evrW1lfcQX47mfVhR2OfUqVOCiLW6uvrChQs+Pj4opzs5OYlNfPfu3UtNTRX03/n4+Dx//hzldHNzcz09vbKyMjFEJyUlPX78WNDRffv2/fHHHyizNBkZmbFjx0IxxBgwzu0tnFtfX89isXrqBqBYVaFqWvfgypUrV65cQWlQWFjY3NyMiB+hUCgeHh6XLl3ibe/h4WFmZoZgYV7XBVEqg6F72gYEBKBzrpaW1oABA0Sp0cv34ihHX79+XV9fL8gJGsLw4cN5ObfrovPy8r58+TJ27Fh00RjnYraF3mVYKC4u5q2D3Z2c29TUxLvW1DvB5XLDw8N59x85cmTIkCG8+8vKyj79LwTVyEFHZmYmihkaYh+h5db5OuQJpXsWi8XrpIwYQp8/f5aGaBwOJ9RULWhFFF00BoxzewwcDqeqqqqmpqYHWayiogKdUHoV+GpeSkpK79+/t7GxEfvDg94gOztb6EXy8vLQG/D14hAquqSkRKjlR0qiRXlw8URjwDi3x9DR0UGn05OSknrqBlgsVmJiYg8aNzqL58+fZ2Zm8u7X0dGJiopycXGRBueKkmJYaPpavjm3JCJaqN1APNEMBqOpqUkaojFgnNuTaG1tjYiI6BHW6+joaG5u/vDhQx/6Czkczt9//82XL5SUlIKDg/fs2dPZjMBC2Yfv2l1nyVE84hNFtNA20qN7oaL19PSwIGCMc3sXmExmWlqaKK71EgebzU5PT//582ff+hc/fPhw4cIFvoeIROKRI0dCQ0M1NDQkKLGurk5oG6EGIvHS60hEdL9+/XpKNJlMRslcjAHj3J4xL1RWVr569aqbVV3IrPHy5UsRi4b1Kmzbtg1lNXzatGlJSUnjxo2TlJ7L1+cMAaFVefj6EXePaL7BKd0jGofqQI0B49ye4dympqZ3797RaLRuVnJ//PgRFhYmpYAIad+8q6trcHAwilIZHh6+detWiXAu39gKBIQ6n/ANouse0TIyMrwT/O4RLejBMWCc25NgsVhpaWnv378XRbOQFNE3NDS8fv1a6Lpzr0VbW5urq6uvr6+gBiQS6fTp048ePep64nAmkynKZ6CnOFeoaCjEXBqioexlGOdi6GOcCxU1uXXrFlQkphskMpnM2NjYu3fvivJe9VpwOJzNmzcvX76cbxAaBDc3t0+fPqGbFIX2uSirQELb8I2EFgpR6qqJcntipPgSsaSbUOlYdjGMc3spfeTm5l68eFGUdWqJyDp//jxKdpg+hICAAAcHB74OZBDs7Ow+f/6Mso7EN88DCFlZWaG3ITRPEF99UKiSKBHRfAVJRLSsrKxQL5G+aLzC8OtzLjRZfv/+/b1793gTqUgQXC63srLyxo0b6NFNfQupqal2dna3b98W1MDU1PTt27eCFnOEmg5EKXIjNB8m3ylF94jmK73bRItimcGAcW7PWBiqq6vPnz9/69YtKUUDc7ncqqoqX1/f69evd5vtuHvQ0tKyatUqd3d3QV8sKysrQckcxHM56Gwbvh0uVLQoebmEiuZwOLzE1z2icfySCmHAOLe3gMvllpeXnz9//smTJ83NzZK17XK53Orqaj8/v+vXr6MYQPs0Hjx4YG9vn5WVxffo0qVLJ0yYwLtfaOizKCl3hZYi5tvnQkWjJ68RUTRf1use0YIeHAPGub2IdouLi48ePXrz5s2amhqhpkYRwWKx8vPzfX19z58/LzSgs08jKyvLwcEhOjqa79G9e/fy7hSankZdXV2oXKGe/3z9oHuzaFVVVaG2WlHiHXowhRMGjHNFArTGtW/fvq1bt2ZkZLS1tXVF4eVyuU1NTV++fPHy8jpz5syvsW6GjsbGxmnTpvFN5j1hwgQdHR3EzrS0NPQLWlpaChVqbW2N3oDvpy49PR39LFNTU6H+A+KJLikpEVSUHgKJREJkwhRDtCDpGDDO7Vbg8Xg8Hk8gEMhksqysLIVCkZOTk5eXB+v00On0R48ebdiwISAgoKqqqrPlJKCiue3t7fn5+VeuXNm4cWNYWBi8VE0kEiGJcnJyFApFRkaGRCIRCAToxn6Bf7qlpcXd3Z13xRyPx48ZM6aznDt06FB046aurq6FhQX6RcrLy3l3FhQUoC+ZKigo2Nraoo8lR0dHMUSL8uBCY/n42mpANDQ0YPbc3xk9nLMc4lkSiaSgoKCgoKChoWFubm5gYKCnp6ekpCQrK1tVVXX79u2MjAxolZnJZEZFRf348ePJkyfr1q2zs7NTU1ODeFkQOUJUy+VyW1paKisrIyMjr1279vPnT9imhsfjqVSqra3typUr5eXlGQxGQ0NDaWlpQUHBz58/GxoampubW1pa2Gy2pMwaosPb29vd3R1aa6qsrGQwGGw2+8KFCzA1aGlp5ebmInKWx8fH860onp6e/v79e95MYyYmJrzE19bWhuJxJSsr6+rqevfuXUEN0MslQMjPzxdkDOFbIBnG4sWLExISUFhPaNlmFNHoGccXL17s5+eH8qURyrmCRGPAIH2+J5FUVVVHjhzp6el579699PR0yGgAor29PTY2dtGiRVQqFUGpFApl+PDhe/bsef78+ffv3ysqKlpaWtra2tra2trb29va2hgMRnNzc0lJSWxs7IMHD9avX29mZoaYlhIIBA0NDS8vr8zMTC6XC4rmcrnNzc3fvn27du2au7v70KFDFRQUupgRKigoqIMH3t7enWoP1l+wsLDgbSBIicPhcAcPHuRtf/z4cd6WMTExHajIzMwU5NuvqKhYUlLSIQyC6Onff/9FP5FOp+vq6gr6ikdGRgoV/c8///A9fe3atULPRSHly5cvCz0dxYEPA6bnSlG3VVRUtLW1XbRo0ejRo42NjQV5NcrIyNjZ2R09etTAwODevXvV1dUcDgcyKUBFZZKTk9XV1fv376+np6ejo6OpqamkpEQmk9vb2xsaGiorKysqKkpLS0tLSxsaGkBbBB6Pl5GR6d+//5o1a1asWMG79AHrv8OGDXNzc/v582dUVNSTJ0+gb4N4Oi9fx0wU6yTfQ+BF+Lrxo4SW8nXP4vssUVFRo0ePRnkWCwuLixcvenl5ISw8RCLx5s2bQhN3tbe3CyqNHhUV5eXlhW5euH///owZM3gn6fv37x8/frzQP0JQuQehZSBwOJy/v//o0aMrKioQ++fNm4d+26KLwIBBYiAQCCoqKo6OjgEBARUVFSwWi1cR4HK5HA6HDYDFYtXV1YWEhMyYMUNVVVWQsgmZgykUCoVCkZWVhQyyglRsXV3dpUuXfvnyBbIbgOBwOAidF1a6CwoKLl26ZG1tLS8vL4adl68Gx1fNhPD161fe9uvWrQMns3w7UNDi/qVLl3jb8018M3LkyA4REBwcbGVlBZ81bNiwjx8/inIiShY0ZWVl3hkPLxISEkBN2dDQ8Pbt26KILikpQfnvsrKyhF6hsLBwwYIFsJqvrq5+8OBBvoMZATabLUhDx4BB8iCTyWZmZnv37s3Ly+M7HNvb25uamoqLi3/8+PHly5fIyMgIAOHh4YGBgUuWLNHW1u7KuhaRSDQ0NFy3bt3bt28j/hdRUVFxcXHp6enl5eUtLS1MJhNSqxH3mZKS4uXlpaen19nb2LJlC1/u4NtYQ0Ojvb2dt/3kyZPBzwyDweBts3TpUr4zjJ8/f/I2njt3Lt/GGRkZHaKhvLycRqNVVFR0iAw3NzeUjnr06JGI16mtraXRaEVFRaKLPnr0KIroXbt2iXgdOp1Oo9Hy8/Oh5VxR8Pr1a4wHfnN004o8Ho8nk8mWlpaHDh1ycnKCY3UgpYzNZjc3N2dkZMTExHz//r2yspJOp7e1tcFmBBBsNru2tpZOp4vtLkYgEJSVldXU1Hi1YDweTyKR5OTklJSU+vXrZ29vP2rUKDMzMzk5OSKRCLeHUvCEhITs27evvLxc9Mrwo0ePjomJ4d2/YMGCwMBAxM6rV6+CKi0EFoulqakJRkx9+fJl1KhRiGY0Gm3YsGEI3/vVq1ffuHED0bKjo6Nfv358TcCenp7Xrl2TxnjIzc21sLBASTswcuTI2NhYaXiMMBiMgQMHopi8NTQ08vPzEcuSkoKTk1NkZCTGOxikTrjKysqurq7Jycmg2shms+vq6r58+XL48GFLS0sKhUIkEnuPYxbEv1Qq1c7O7tKlSz9+/Ghubgbvn8lkRkZGjh8/XpTUJzDdl5aW8qo/LS0tK1euhJ9dQUHh/PnzfBWlt2/fiqiXRUREwBVoCATCunXr+GrNKFkmSCRSZmZmhxSwYMECoX317NkzaYg+fPiwUNH79++XhmiU7MYYMEgSCgoK7u7uaWlpsJGUy+UyGIykpKRt27aJ4uLe45CRkRk2bNixY8dycnLAiSSTyfzy5cuUKVNE92fYs2ePoHeyoqIiIiLi8+fPra2tgtpMnz4dcUEdHR2+5oWOjg6oLHlERATKrH/JkiUodztq1CjRJ84i4tGjR6J0lK6ubk1NjWRFJycni/KBlJGRSU5Olqzo6upq8QoCYcDQOVAolIkTJ+bm5sKEy+FwampqAgICRo0aJbqG2BvUXgUFhWnTpr1586a1tRV+HKhOsKWlpYi0S6VSCwoKxHtvBa07HTp0SLwLxsXFCb3tDRs2SJB6vn37JkrmLQiTJk1iMpmSEl1eXm5gYCCi6IEDB1ZXV0tKNIPBEBqmgQGDBCArKztp0qSkpCRQ86LRaFu2bOlDbItgXjU1tZMnT1ZWVoK0Gx4ebmVlJSLtjho1SpBmir5WzhukC4FMJn/+/LmzF6ypqeGNhuCLnTt3SoR64uLiREkTA2Lu3Lmi+DAIRVFRkbm5eadEDx06tLKysuuim5qanJ2dMTbAIHUQiUQLC4unT5/CqgqLxUpPT/fw8BAlD2lvpl11dfWdO3eCllk6nX716lXRa+u6uLi0tLSI/t7m5+ejR/qrqqrGxsZ2aqqLHuuFwIIFCyAHZ7Hx33//ifehtbe3z8/P74rod+/eaWpqiiHawMAgLi6uK6JTU1MHDRqEsQGG7oCqqurevXth0ySUp2bZsmW/QDEoPB6vqqq6efPmxsZG2EJdXV3t4eEhumF3yJAh379/F+W9ffLkiShsTqFQLly4IIr5NSoqytDQsLNPraure/fuXVG8UHmd4ZycnLrS4VQq9fjx4536SkHIy8tbtmxZF1UHb29vMRTempqaHTt2iFd/CAOGToNMJo8fPx4OAOVwOKWlpZs3b/6VnlFeXv706dPgRyUtLW3gwIGiX4FAICxatOjjx498iaypqQmqstOpu7K0tPzvv//42iKZTOabN29mz57dlac2NjaG1hJF8Zz19/cXmn9AdGhoaGzbtu3bt29CRbe0tAQHBy9YsEBSlEehUDw8PD59+iTUvsxisSIjIz09PaXkbYahz2tsUlIDdXR0Dhw44OHhQSKRcDhcU1PTkydP9u7d2w3FzboNBAJh4MCB586dc3FxgdTb5uZmX1/fo0ePdraQJZVKHTJkyMCBA+HuysvLS01N7UpWncGDB5uYmEDJxdlsdm5ubkpKigSzZRsbGw8bNmzQoEH9+/cnk8lKSkpQisLi4uKCgoLv37+npKRIKSuQlpbWyJEjIdFUKhUWXVlZWVJSkpiYmJycLKWqH4qKig4ODlZWVgMGDFBRUaFQKFwul8lk1tXVlZaW/vjxIz4+XqqlpDBg4K8UzJw5Ew55YjKZ0dHRDg4Ov0ZSRBAyMjIuLi7Z2dmwx3F8fPzw4cOxMYABAwb+upo0lFxlZWUnJydjY2NoD51ODwoKio+P756q6d0JJpP5+fPnFy9eQI8GLRtOmDABs+JhwIChm0AkEq2srL58+QJbOVNSUvT19X/ZrxaBYGNjA6/pM5nMkJAQUerHYMCAAdNzJQASiWRiYgJn8m9ubg4NDS0rK/tVe5DL5ebn58NhnWQy2d7eXgyvAAwYMGCcKw7k5ORsbW0hT0xo8ToiIqL7Kyx0J9ra2t6/fw8vT8nLy/Mt04ABAwYMkudcCoViY2MD/WaxWIWFhULLqf4Cqm5eXl5RURGs6Y8YMQIbWxgwYJA65+LxeHl5eTjIkslk0mi0mpqaX55zy8vLs7OzoU0ikTho0CDI6wsDBgwYQIjKC3g8nkgkkslkMpmMEmpFIBB0dXXh9LhcLpdOpxOJRMhLxEECigAAIABJREFU9NcGXEAbj8crKSkZGBigV3GH6l9wOBy+aYIlDi0trRMnTjg6Oubl5e3Zsyc+Pv43HO59uhOIRKKRkZG+vn5TU1N+fn59fT3fZnJycocPH545c2Z9ff3x48exBJK9DcIdZvF4vKysrL6+/syZM6dMmaKtrY3CuXg8Xk5OzsjICHKW4nA4VVVVVVVVv56XGO/HRk9PD47QbW9vz8nJYbFYKKcwmcySkpKQkJDQ0NCamhqUBN4IZGdnI3LTcLncoqKi1tbWxMTEN2/eBAYG8oqOi4uDrcxNTU2Wlpa/6sKmqqpqYWEhXAp+48aNFy9e7A2doKKiUlRUBN/Yhg0bLl++LMqJpqamu3fvdnV1hbUZHA739etXX1/fx48fIxrfuXNnxYoV0G8OhzN27Ni4uLje/zmpqKiAX59Dhw4dOHCg94+0sLAwuGhLaGjojBkzJHNdZWVlDw+P9PR03io1GLoOJpMZExPj4uIiehLh5uZm9GsWFBQg8hsMHDgQ0Wb16tW/6vevf//+4JMeO3asl3RCv379QOlHjhwR5az58+ej/OOPHz+mUCjgtx+Rhu3s2bO9/y+TkZEB71lK1UkkjrS0NPiev3//Lqp+hn6YQqGMHz9+//79gwYN6mKZcQx8QSaTHRwcjh8/PnjwYEld08DAICwsDMxuzjvP+LU9SfiiL3bC6NGjHzx4gJJxeNGiRSBDQemWfvM/urfPidHny/r6+uvXrx8wYEBfeR6oZjCEvjLaCASCpaXl1q1bJZhxjUQi3bt3T1lZGdrMy8uLjo6Gj9bX1/+GxRD7YidcuHABri6Mw+FaW1s/f/6ck5MDtlm+fPnEiRNhzr1//z58iMViPXz4EKO5PsO5FAplxIgRQ4YM6SsP09LSEhkZGRQUFBQU9PLly/j4eNGNpD1uz7K3tx82bFhnTwwNDcXj8Xg83tjY2NPTs6GhAT6kpqa2Zs0aeHPOnDn//vtvbGxsYGCgk5NTVVXVbzjc4U549uxZ7+8EGxsb0OkwNTXVxMRk/PjxpqamGzduBFtu2LAB/u3t7X348OHY2NjQ0NDp06eLPufF0PNQVlbevn17U1NTnzCMQvFg06ZNU1FRUVFR0dbWXrlyJVQeuE/cfHl5+eLFi0X5X0DrXlBQEHhowoQJ4GU/fPjwG45bQfbcHkdn7bl//fUX2B5eroEQHh4OHwK/tX0RmD33/5/zysnJ9RU/U4i56HR6w/8BSnHdV4YdmUzuum0hIiIiKSkJ3jQ1NcVUh74LRCkmhPtBeHg4qB7BdiQMvRxC+PTXy77YmyGR3k5NTYVtFGDxMSsrKzjJZGNj44sXL3jPHTJkyPTp0y0tLTU0NGpqasrKysLDwz9+/MjbcurUqTAjpKSkJCUljRo1asGCBSYmJo2NjdHR0ffu3YODoS0tLRcvXmxubs5kMjMyMm7fvl1RUSHo/seOHTt58mQDAwPoHmg02tu3b8EPCQKDBw92c3MzMTEhEAipqal37txB6RxROmHYsGHTpk0zMzODbqC4uPjjx4+RkZHoCqyrq+vQoUM1NTVra2sLCwvfvn3bdQ8thCsLYrOxsRExK4X29O/fHzbvstnsJ0+eIBwHjY2N3d3dzczMKBQKjUYLCAjIysqaNGkSnIiKRqPFxsZCv8eMGQM7JhYVFUVERJibmy9dutTU1JTL5UKn5+bmCnoELS0tV1fX4cOHa2trQymGw8LCPn36JFllZcaMGU5OTtra2kQisbq6Ojo6+uXLl7ypok1MTMaMGQP9bmtre/z4sZqa2sqVK21sbBQUFIqKip4+ffrlyxe+UtTV1ZcsWWJra6uiopKXl/f48WOpOHerqqr6+Pig1P3uylSazWYzmUw2mw1XchTUDAocEHpNqPYP3KeysrILFy4U0TAC3QyLxUK/GRaLBd2zNDqkpqbGw8Oji7YFHA53+/Zt+CiYIf7OnTugRAS/a2trBwcH87239PR03lDmsrIyuEFYWJi/vz/irOzsbCiZ56lTpxB/X0NDA9+ajBYWFl+/fhVU0IxvofLDhw8j/g4Gg3H48GFBtoW7d++idEK/fv3evn3L9wa+f/8OR7SDoFAovr6+fItHREZGIlIdddG2MGfOHPDo6NGjw8PDIyIiIiIiXr16Bfs2IAqGIqqxeXl5tbe3I7wV16xZk5ubC+/5/PkzqFzD+3/+/Ll+/XrEw7a3t8PuwP+jzZFIhw4d4ltrNTExEXFXYtsWXFxcioqKeEVUVVXx1mQ6ffo02Gb+/Pk1NTWIE69cucKr+kyfPr2urg7R8vr162ABb8nYzaXEuRwOp7y8/OrVq56enleuXCkvLxdU44RGo507d2737t2hoaF1dXXozCse50KF3/38/JYtW7Z///6cnBy+tMtms7Ozs48ePbp27dqHDx8K9ZDtQc6F8hTzvjwIugE9/7S1tdGLPLa1tY0fPx6UIuhfA/Hjx4/9+/fzPdTY2Kinp4dQV9FrXBYXFyMygopYBB6Fc8FO0NPT4/v2gvV+EHWSZGVlw8LC0At9guadznKutbU12P7nz59qampChweCc0EfRGdnZ77DG1Ju4E3Qu0PQVxDxdiCWfwkEwtOnT9FrIcOpB8Xm3OXLl6Nzwo4dO1A4VxDWr1+PmPyJUnm6V3MunU6/ePGinJwcHo/X1NQ8cuQIL4VxOJyioiIPDw8o1FhXV3fTpk1xcXGtra2CVFExOJfJZObk5OzduxcqXUWlUr28vGpra/kucK1bt45MJkNlh6Ak5b2Qcx0dHcH+2b17tyh0ExQUJPQOS0pKwGrNonAuOs6cOQMaVZKTk4WeEhoaCp8iLy9fX18vQc599eqV0EsVFBSAAQgHDhwQ5VWE9SYxYiIQtZxFSUWNwrki1jDuLOd2dHQ8ffoUvIf169cLPSU3Nxd2gxODc42NjYVSE4fDAT+TInJuaWkpaMYRNP8Tm3N7IMyhtbU1KioKmnTQ6fSkpCTevAQQ537//h2a70N68Z9//rlv3z4ajcZgMODvW2fX2aB/or29vaamJiAgYPXq1b6+vs3NzTgcDrI28lZsg24gOTkZqhTZ0NAQERHRS0zAVCrV0NDQ0NDQzMzM29v75cuX8BteW1sr4tidO3cu+LAXLlyYN2/e3r17oW6B+cLNzU3QRfLy8tasWbNs2bLMzEzEITqdvmvXrvnz5yMC/+fNmwf/njhxIjhzZzKZR44ccXV1PXPmDIfDAad4lpaW0O8pU6aASTza29uPHDmycOFCX19fMRwETUxMZs6cCY6TS5cuzZs378CBA6BZ0MDAYP78+fBHHeGwdffu3UWLFh09epTJZILWYTA4pbPw9vYGe2DIkCEJCQmOjo7ircghUow+ffrU1dV169at6IlBQNTV1W3dutXV1fXJkyeIOT7MU3g8fuvWreDRoKCgRYsW7du3r6WlBRx4f/zxh9g9s2HDBnDNOScn588//1y1alVWVhaobiNUXQTJnD592tXV9fjx42An6+npwUZ/BQUFFxcX8KwnT54sXLhwy5YtUkndJSU9t6KiAh7fFApl1qxZoC0Jnq2kpaVNmzYNsW6Ax+NVVFTmzJnj5+cXHx9fXFzMYDDYbDaHw2Gz2Tk5OaNHj4ZfiQULFjQ0NECHmExmfX19Xl5eXFzc/fv3165dC5cOAlWnlStXVlRU8N5MfHw8PBWSlZX19PTsJXouykR40qRJCEbgq+KtWrUK3H/8+HH4FFdXV/CQv78/Xz2XxWLByywGBgYIex9sViMSienp6eAjw5kHDh06BJ7y999/w4L27dsHHvLy8oL2+/j4CJoPHjlypLN67urVqwXFy7q7u4OHrl69CptTwf3gupC3tzd46Pz582LruTgcbvPmzbxmt127dglacRWk5zo7OwuaNCAOoei5cEoBPB4P14KBAFtRTE1NEbo53M9ubm7goXv37omt56ampoJjycLCAl61A22vzc3NsHSEnnvixAn4aqdOnQIPrVq1Cto/atQocD+oN4wdO1Y8PbeX+oERiURjY+O1a9fW1NQkJSVB82VIAWloaHj58uWbN2+MjIxMTU2NjY2NjIx0dXU1NDQaGxvhDymXy62oqAgJCWGxWNXV1dXV1RUVFaWlpUVFRUVFRbx1eWVkZIYNG+bp6amlpdXX/R84HM7kyZPhpWd0IOyqYM6U58+fNzY2wk5IiJYwEhMT4ciowsLC+Ph42MLD4XACAwPh38HBwfDiCWRZgkrk6urqghcEI6kePnx46NAheBNuiah+BN72/fv39+zZ06keQ9wAzAWQMnjnzh3YY1JTUxMeYCDPXrp0Cf5969YtX19f+JQuhnGeO3dOQUHh4MGD4PLUsWPHxo4du3TpUkHZxfiqUIKeMSwsrL6+HtGAF42NjTBTQwQ0atQo+ChsayaRSGDP+Pv7wxGhT58+vXbtGjyiutIzWVlZsKZZUFAAq7dVVVXBwcHwsh6VSlVXV6+urua9wqNHj+DfL1++3LZtG++zIAzo4DCLjo4uLCw0MDDo7J33Xt9bOTk5FxcXQ0NDPz+/4ODgiooKyMwPTz9//vz58+dPyIlYVlZWVla2o6MDHoJsNjshISEzMxOyJDCZTHD6ACrOBAJBTU1t8uTJu3fvHjRo0C/gHkckEm/fvu3s7FxcXCy0MVTRAwboXQ/1J/yGIFrCQFhjwM3m5mZwbo6I+4Lnhogr19XVwb8RnAJbABFVPsFmoElERIDxtYirMZnMixcvqqmp0en02tpa2JcoLi5uwoQJgqxnDQ0NcJYs0A4uHg4dOpSbm3v9+nXwUi4uLl+/fp05cyaNRhPpVf9fR3tExzY0NAjl3OrqatCaB/5N4J+YmZkpqGcgMx08olDySAjFwoULBR0qLS1F2N/4ci44UBHWFfhZEMMMEXsixkjr1ZwLvQlDhw49d+7c8uXLb9++HR0dnZeX19bWBiZS4HK5LS0toJ0I5ov29nZefRZkWzKZrKmpaW9vv3z5cmdn566MgJ5CXFzcrl27oA/y2LFj169fD9GHubn5q1ev7O3tQduilICwqoP/DsohXN/x/t6yZYvQNpqamvAsnkAgCPo+iY379++npKQ8evQI9LIyNTX9/PnzuHHjRKRdlH9NFCD+PhGvoK6uPmjQINhICKajlBQoFMrgwYNhU9XAgQOl1AMSibHqAzFmMjIydnZ2gwYNSk9Pj42NffXqVVJSEp1OBw0OQi8Cv96QYisjI2Nqajp9+vSxY8fa2toippZ9COXl5fA8Ligo6OPHj69evYIe1sbGZv369b6+vn3uoSDHNb7aWW+Durr6li1b3N3duyEPVGpqqp2d3aVLl2BrI2S+fPfu3ZAhQ8TTuaQHJSWljRs3Ll++HJHrWbJwc3Nbt27d+PHj+1DWw74R14vH4xUUFEaOHGlra7ts2bKysrKvX7+Gh4dnZWXV19czGAwmkwlFK0B+DvDrSqFQoNoWMjIyFApFVVXV3NzcwcFh5MiRAwYMUFJSkpWV/ZVi7UJCQt6/fz916lRo86+//uqLnCuGjaxHMGnSpCdPnojiMysptLa2enh4xMTEXL16FbaHGBoa+vj4IFwFehb29vYvXryQqiqjpKQUFBSEWCjGOFfyIJPJ6urq6urqQ4YM8fDwYDAYRUVFBQUFVVVVlZWVpaWlgYGBJSUlEOEOHjx42bJlmpqaampqmpqa/fr109HR+eXLlH38+BHmXBMTk/79+4ti1cXQWfTv3//58+fwfLY7cevWrerq6hcvXsDKnaen5549e1Asad0JVVXV4OBgbW1tqUq5fv16XyTcPsC50ByTryoK1bu0sLCA3ES4XG5BQUFiYiLEuUQi0czMzNPTU5S3Ap7J/gI6LyKVgbGxcZ/j3GPHjgkyQ799+7aX3OTff/8NDq2KiorDhw8nJiZWVVURCITv3793PekMmDmByWQ+fvwYnsO9evXq9u3bsHOhoqKijY1NLynv5unpCRJufX394cOH4+LiysvLcTjcp0+fuj6PMTMzW7RoEfj+3rhxIzAwMDc3l8Ph7Nq1y9PTE+NccdiWwWAUFxe3t7dra2trampKyWTDZrPLy8urqqoUFBQMDAzAQKO+CMT9S3wxpxuwd+/e3p8QbsqUKfBvDofj7OyclpYG75HI0qW7uzvoLh0TE1NUVARvBgcHgw7diCRkvaRncDjcvHnzwAxBvMvdYsDZ2RlUjy5evPjPP//Am708LXLvNTzT6fSHDx8uWrRoxowZGzdujIiIgKIzJCuFyWQmJiZ6eHhMmjTJzc3t+fPn3bDQL1Td7gqGDh0Kboruv/n/BsT/fth6ZGkCIVRbW1voh1Cy94mY7pSUlMCu7zdv3oQnEHCDzMxMkHAlNWFqa2sDNxEzNgaDgbIpdHQhvKAQm10B2DMVFRWIlGwS6RlEQBMiKE56g1YiV+6lnMtms/Py8vz9/VNTU0tKSgIDA//666/9+/dnZWXByTi6Qm1QBHBdXd2jR4+8vb0/fPjQ2NiYmZn54MEDhHNf34KWlhYYT8nhcBB1XPh+28BN0M9GUVER1J7gYvLS+L4KeqOsra3Ly8uhSHEul+vq6grtR2TqAzN4ieFCgHIDysrK4FoQ7FAIuvQioo1VVFSE+rqKAoTj89ixY8FNRCCv0ALGCMcGMFecjo6OoIAXMYDSMzIyMnzzw3VFBK8UIyMjST0LyjAjkUjidVov5VzIsAArtmw2m0ajnT171sHBwcvL6/Xr17m5uQwGA3YXE5FnuVxuW1tbSUlJbGzsxYsXHR0d//zzz2/fvsE+Z+3t7X2lnA9Ca8Dj8Y6OjmFhYeAyenx8vFA9F5H8dN++fbDj/bFjx8DBnZeXJ6WnyM/PBzcPHToEKRSysrJnzpwBnxEOPUJ8Gg8cOAA1k5GR2b9/f2dvgLcTYM366NGjoHYDG8dB93grKyszMzNYFTp58qToVZxREBMTA27u378ffuetrKw2b94MH6qurs7IyOhUJ2/atAkyFquoqPj5+UlQNwR7Rl9fH/w2HDx4UCL+uYjYBPhLDH2Z4JwYXQfiS/b333/DAZBbtmwR78vaS+25JBLJ2Nh4woQJGRkZkI4Dq1o3b9589uyZtbU15LQ7aNAgIyMjVVVVPB6PoGBImWUymY2NjdXV1cXFxfn5+Tk5ORkZGWlpaeXl5SC9QpkcnJ2dheZt6lWYO3cuylfn9OnTQq8QHh7e2toK8+y4ceOys7O/fv1qaWkJx7BDePXqlZSeIjQ09NSpUzC3/vHHHzY2NsnJyWPGjAGV1rq6OjgROJgTAIfDubu729raQqnTxVBzEJ3g6OiYlZX16dOnYcOGWVtbgy3fvHkD/UhPT4fDxEkkUlxc3N27d1taWlxcXMSoa8cXRUVFHz9+hFfn9fT00tLS3rx5IyMjM3XqVNBS7+fnJ1T5yMzMrKyshFe3FBUVP378WFlZqaamJkHDAg6HS0tLAwM3Pnz4cPfu3bq6usmTJ4Oxwl1Beno6uLljxw5LS8vv37+bmZktXLhQgo+TkZFRW1sL86ypqWlmZuaHDx8GDhxob28vJrn1WvVNU1Pzn3/+0dDQgHLswoG/UMqFqKiomJgYRUVFZWVlRUVFFRUVHR0dAoEA62IsFuvLly+LFi2i0+nNzc1tbW0t/wfeIGAikaihobF+/XpPT08JFt/tWdy5c+f58+dCm9XX1/v6+oIJCvT09MCkXxCioqLev38vpVvNyMh4/PgxaBWxtLSEU4jBOHv2LGxt//r1a3JyMmi85nuKiODtBAMDA95U3BEREXBFnMDAQDDCVVVVFVzGkRQ2b94cGxsLfwyoVOqCBQsQbfLy8k6dOiX0UhwO5+rVqz4+PuBOaXh0QVnE4E0FBQVERtqu4/3793Q6HTRwz549e/bs2RJ/Fi6X6+fnByYn09TUFLFuYR+zLUBzND09vc2bN4eEhGzYsMHc3FxGRgY0wHM4nIaGhsLCwrS0tOjo6GfPnj158gT2lOJyuWVlZWFhYXFxcWlpaTk5OeXl5U1NTQjChdjW1dXV399/165dv0ZRqY6OjnPnzoFFf9Fx4MAB3tznCE4E3yJpwNPTEz0pz8uXLxHMsmbNGt76K2JDaCekpqaC2Sxv3LiBmPvDqKmpkdRKbEpKCqQ3CGpQUFAwc+ZMEWtQnjhxgm/tGUjhlVRPPn36FExaBqKpqUki8XJ1dXUoAdlCTdudwpEjR4TabX4RzoVnbVZWVkeOHLl+/fquXbscHBygLOZdWf2EipOTSCQdHZ1FixZduHDh0qVLU6ZM6SvhEijeNjQa7dq1a9bW1lu2bEEYpsGzEB4gHA5n/vz5np6evAtudXV1x44ds7e3R7yT4NUQ94NyCKRIKOEkvEmn0x0dHXft2sVbKq24uHjjxo2urq6IJ0pMTJwwYUJqaiq4MzY2dty4ceCXFRQqtBPWrl3LW92rurr64MGD9vb2YKoUJpM5derUkydPIpYWo6KiRo8eDb72sNC2tjZBN4aCkJCQ4cOHP378GMHjzc3NFy5cGDFiBCJnMfiMHR0doJT29nZnZ+fDhw/DPmeVlZXHjx+fNWsW2LfgbxH/TfAol8udN2/e/v37EYljvn37Nn78ePBu4VM4HA7opCGKP9mNGzdmzpyJ+PdrampWrVp1+fJlUFGFPTrAy7LZbFAiYjCALZubm52cnJ49ewYKKi0tdXV1TUhI6NQ9/z/+QTmmqqq6cePG7du3S3a6XVlZuXr16tevX+NwOAqF4uzsfP78ed5stry6W3t7e11dXXJy8tu3bz9//lxRUdHa2trW1gaW2ENYtcDlFzKZLCsrKycnp6KiMnjw4AkTJkyZMkVbW1tRURF9AYHD4Xz//n3dunVQikxZWdkVK1ZIthx0R0dHXV3d9u3bb9261bOEbmxsbGJioqurW1dXV1xc/OPHj+53lbWysoJqUFZXV+fn5/PmQUdg8ODBFhYWRCIxNTVVaGNRMHDgQBMTEx0dndra2qKiopSUFJTGcnJyNjY25ubm9fX1aWlp0ltplJeXHzZsWP/+/VksVnFxcVJSEqK4ZKdApVJlZGSgJVYikdjS0gIbiIODgxG118SDrKystbW1hYVFc3NzRkbGz58/pdEtFhYWgwYNkpOTy87O7mKfoENXV3fYsGGampqZmZldEdRnAmHxeDyFQtHT09PT05s+fTqDwUhPT09PT6fRaMXFxQ0NDY2NjY2Njbm5udAHh0AgqKqqWlpaKigoKCgoqKio6OnpQYQyaNAgibjy/JLIy8uTHmuIiLS0NIS7KzqgkSDBG8jNzUWpZYsAg8GIi4vrepVfoWhtbRVkzRBvtgSrZtbW1uCKnKTsDO3t7QkJCaAyKA1kZWWBtSGkh/LyciiUrqtz977IC1DUr52dnZ2dHVQhkU6nNzY25uXl7dy5Mzk5GTJK2Nvbnzx5Ul1dnUqlUqnUXz7TAgYM4gGRH+fbt29Yn0gPvZqGoLo4bDZbVlZW0PQfj8fLycnJyclpaGgQiUTYZR1KRWZoaChKvgUWi9XW1kYmk/t64C8GDJ2CmZnZgQMHlixZAu/hcrnSc1DB0Ks5l81m5+bmBgcHNzY2Dh8+fOrUqV3Pt8+X1puamp49e/bt2zd9ff1FixZJNd0nBgy9Ae/evVNRUTE0NOStRPXmzRtE9IQEYWpq2q9fv6SkpMbGRoxze52GW1FRcfbsWX9/fxaLpaen5+rq6uXlZWZmJsGAGS6XW19f/99//509e7a+vl5RUbG6unr//v2/ibV36tSpcGjv9+/fEUvAvznGjh0rqNxAeXn5ly9feluO8E4BkSMGRktLC6KYsaSAx+OvXbsG+S/S6fQ//vhDkD8Zxrk9puTm5+fHx8dDKUFLSkquXLny+PHjuXPnrlq1ysjISE1NTezwSiiwuKSkJDIy8saNG0lJSZBzDIPB+PbtW3l5+W/Cuc+ePVNQUIB+BwUFSTBi8hfAmTNnEAkNQDAYjDt37uzatetX0tcaGhrc3NxEXzzsFGbOnAk7jCsqKt68eVNfX59viUKMc3sGUF1ITU1NPB4P+SpxOJzKyko/P7+QkJAxY8aMGTNm1KhRZmZmCHMtr2MTvAcyIxQUFPz48SMpKSk+Pj4hIQF0RSSRSPr6+oiCsr8wQE3nVyqW0Q2Qk5Pz8vKaPHnypEmT+mJKeA6HAy4p5+XlPXv27Ny5cxKMjEAAEQ+to6Ojo6PTp/NJ/WqcSyQSjYyM1q9fX1paSqPR4O8hh8MpKSl58uRJSEiItra2rq7u4MGDra2tjYyM9PT0mpqaYKc5qGZtQkICg8GASqxnZ2cXFBTU19dXV1fT6XTEN5ZMJltaWnp7e8PFtDFgQIepqenz588dHBz6Vl4kHA43cuRIKNdMa2trbm4uInhBGkA4IDY1NfGtxYtxbk9CXl5+9uzZNjY2V65cef36dU5ODsySHR0dzc3Nzc3Nubm5iFwnMJhM5ocPHz58+CCKTq2pqTlt2rQ9e/aYmppiVIIBATqdHhgYCP22sbEBVbbhw4d7eHhINkCmGwBF93Qnnjx5smbNmvHjx+NwOC6Xu23bth5MVI1xLhobQvX1Zs+e/fTp05CQkMLCQkTB5y5OrqlU6vjx45cvXz5p0iQNDQ2MXzDwoqKi4s8//4Q3d+/effToUXhz/fr1fY5zux9MJnPixIlTp07V1NRMTEyUbAwLxrmSBORmO27cOFtbWy8vr0+fPj148ODnz59Q1K8Y/IvH42VkZOTl5TU0NFxcXGbPnj148GDpFf7B8Ovh2LFjM2fOhNMSDhkyRF9fH6rChwEFHA7nt/VV6EucCzIvlC3Xw8MjOzs7Jibm69evxcXFlZWVNTU1jY2NYJpdBMNSqVQlJSVVVVVNTU0dHR1ra+sRI0YMHTr018gi1m2QlZXV1dUtLCzkm4GhUwWGFRQUlJWVRV9C0dbWptPpYmQRMzQ0rKysFFq3prN4/PgxmAp28ODBXeFcBQV5t51LAAAYUElEQVQFNTU1sNaZ8PeWRNLX1y8rKxN9hk6hUHR0dIqKiiQ4U5Qq5OTklJWVeXMe8QIKNO3lZdBEgqqqqo+PD5RxR4KoqKiYOXMmPA5mzZqVm5srxnXa29tLS0u/f//+/v37J0+eXLt2bc+ePXBJURKJZGtre/ny5YcPH7569SoyMjIlJaWsrKy9vb2zgthsdnx8vK2tLUw9np6eku0TLpcLpUTqzv+3ubkZvgEwjeGQIUMiAFhaWs6ZM+fLly9QSvjW1taAgAC4Ksn48eNDQ0OZTGZHRweLxXr//j1Y9AUBOzu7+/fv19TUwE+dmJi4ZcsW2GUNARMTk8ePH8P3mZaW9scff+jq6n78+BG+vWnTpiHOGjBggK+vL41Gg5+upqbm0aNHiNo26IiLi4NPp9FoiKMzZ84E/z44oerJkyfhG7ty5QoOh1u0aFFaWlpHR0d5eTniVqdMmRIUFFRfXw9fJzMz8/Tp0yjVawYNGnTjxo2ioiL4lLKyMn9/f3hw8sLe3v7+/fuVlZXwKbm5uZcvXxaUVYpMJq9cufL9+/eNjY1Q+5ycnNu3bw8fPpxv+9GjRwcEBEBfYihVU2ho6OLFi3ldOR89egR3zvbt2+H98+fPh/eHhYUpKyuvWLEiPT0dumBzc/P9+/f51lsik8m7du2C2aO+vv7SpUvKysqnTp2CLwjmGMM4t6ucy8uMNBpt9OjRMDMuXLiwqalJIlf+rTh39uzZ4L19+fKF94aLior09PRWrFgBl6eDwWAwxo0bx3dKzuFw+D5+Tk6OlZUVov3QoUPr6up4Gz9+/BjcBEvUQBxHp9MF9fOFCxdENCKhcy6ii2DX5q9fv8I7f/786erqCj4yXACcTCb7+/sLusnGxkbenPE4HG7Dhg3Qt40XHA7n0KFDvKecOHEC+lLyorW1dfny5Yj2Ojo68fHxgoYoWIQYmkT6+voKeorPnz8jXIDA/wXMpn/w4EHwxBcvXvBerbS0FEG7BAIhNDSUt2ViYmJOTg682T3pb34dzmWz2SUlJWlpaQ0NDUIvy+FwcnNzx4wZ01nOhWqtJyQkFBcXY5zLSyiCAPnh8T2UnZ2NyCi0f/9+9KuVlpaC+h0ej4fSSAoFyLlOTk4sFgu9/bFjx7rOuVu3bgWvCX9jQM4tKyvLz88Hm8GDEyqlgwImkwkt8cNwd3cX2hW7du0CT9m1a5fQV2bGjBkgiwkiXL4idu/ejd44NjYW1HZF5FxBQBQ92bBhgyhnYZzbCc5ta2t79+7dlClThg0btmLFipSUFGlwLpvN/vHjx7Rp0wYNGjR79mzwTcM4F0RZWZkgOqutreVVLcGX2djYGKGgsdns6upqxCl3794F2ZP3XwDn1Hw5l3eQlJSUIEQzmUxBQb0ici6RSAQFsVgs2DYCci7vXwytH4wcORKhe7a1tZWVlSHa//jxA7RsVlRUIBoUFBQg5g0MBgOu5qenp4d4c9lsNmwBAO0MMC0uXLgQPESn0x8/fhwaGgr+7y0tLVCKBmVlZXD8dHR0hIWFPX78uKqqCtwJqtKd4tzCwkLE07HZbLDO7s+fPxGnVFZWtrS09H7O7aWL9Vwut6io6Pz582FhYUlJSffu3Zs2bZq3t3d0dHRdXZ1EFgGYTCaNRrty5cq8efPevXuXkZHx/v37q1ev1tXVYctlIFpaWiZMmKCnp2dkZMSbdnrTpk0aGhqamppPnjwB9zs5OYE6GlgW8MOHD+rq6pqamkuXLgUjUxYtWgSnhXN0dASvlpiYaGBgMGDAABsbG0E5TK2trYcMGQJv1tbWOjg46Ovr6+rqgomyyGQyWGKns6BQKP/99x8o6MOHD+i5F2pqagoKCvLz86FA4SVLloBRf0+fPtXS0tLT0xs3bhxYpBl8nEmTJoGFy/Ly8iwsLAwNDQ0MDJKSksB7g60c8+bNA0sNQB1oYGAwePDggoIC8HMILwaCn8n29nYHBwc3NzcXF5cZM2bAb5y8vLyLiwv0B8F/Fg6H27Fjh7Ozs5ubm6WlJZgiR4yA8pKSksGDBxsYGBgaGoKMSSQSYcuhrq4uXGgZh8OxWKz58+dra2tramrev3+/l79QvbfWektLC6Sl4nA4DodTVlZ29erVhQsXbtq06fr164mJiaIXwwDB4XCKi4tDQkKOHz++bNmy7du35+XlQVK4XG5zczOU4QEDjH///ffTp0/QywBWacThcPHx8efPn4c0tb///hv8FoKvBGKFZ8eOHRD73L9/H9RtKRSKjY0N9BteC4Wwdu1ayMkhJSWFr+ESIilw88iRI1+/foXId9WqVeC9IVoKhYGBAbQgExsbW1VVtXr1anCgnjhxQtCJjY2NkEeqkZERrFyDdTMZDMaaNWug6j7R0dGIR4Nbwt0CWzagj19JSQmi6h1chRPxjHAHZmRkgBUVwYuDfR4REQG70L5//x5MIQ/FDYGNOzo6oAVDqLf9/f35DgMR4ePjA9UfKy4u3r9/P3gIXveDa85DuHXrFjRRa21tXbdunXjM0G3ovfkW9PT0bG1tExMTYRLkcrkVFRUBAQHPnz/X1dU1MjKCvL6srKz69+9PoVD41lpnsVitra1lZWXZ2dnp6ekpKSk0Gq2iogJRKBCPxysqKjo4OGCREQhAzAWTLIJz4d+VlZXFxcXwqwj64Z09e/bFixfQ77a2NjAI6tOnT6BFBV51AdNosFgsUJsTlFFbTU1N0G2XlpaWl5fD9mJES6GQkZEB1XYQ586di4yMFHTi1q1beXPRgtJzcnLALDmIKpxwS0TSJbDbk5KSOBwObB+A5xOgFEQHJiYm8pUCvjuI5a/z58/D9uWIiAhEYzwer6GhATNdUFAQPAzEmNqDfxxUfwAGPCoQji5ghzQ3N2dnZ4MfNoxzRQJUa93Hx0dPT+/27dv5+flgIgU6nU6n02k02rt376BpGlS+l0qlwt58TCbz/fv3tra2UNkeMNMNn14gkaD0DuvXr8fKSfDaFuDfgsoO8h4FuzE6OlpQiDaioi3MHeDsG3KNADcFjRlwE1GuCtyUSEIfDodz+vTp3bt3oxivHjx4wFefEOUmwZYoj8blckeOHEmlUiH3XtjwgtKBCClwy7y8PLh6/PDhw0+dOrVnzx6o8e3bt2/fvg2ehcix++DBAzc3N8hJOSUlBQzb6ywQRTNF6RCEk7L0SqL9ypwLdauamtrGjRsdHR2fP38eGBgIrQLzWiGgUYXwnYayiCFqsvKVoqKiMnfu3MWLFzs6OmKEK1Voa2u7uLgMHTpUSUlJX1+fRCJJL6OQnZ0dqA11JeE95D8Dc1xRUVFCQoK/vz960bbS0lIJloJHgdBSOiQSCdTT4aTJCAQHB3t4eMCb27Zt++OPPy5cuHDz5k3eWu6RkZENDQ0qKirQ5ujRo7Ozs+/cuXPx4kWJlAH9hdHbKUZOTs7BwcHa2nrJkiUfPnx49uxZRkZGW1sb4tPd6ccmkWRlZbW0tKZMmeLm5mZjY6OqqorlM5QedHR0Tp06tXjx4m77qv3777+SulR+fr4Ydsneo22RyWTIIICOV69evX37Fgzc6N+//5kzZ3x8fO7cuXPixAkwbrC5uXnnzp3//fcfvIdCoaxbt27dunVv3rw5ceJEVFQUNuz7JOfi/i8TzdChQ21sbLy9vb99+/bp06dv374VFhZWVlbW1taCdepRLqKoqKiqqqqurt6vXz9LS8tRo0Y5OjpCH2qMbaWK/v37f/nyBXZjwtA7AQV3+Pn5wWF1EBQUFLy9vVevXr17925fX194P5TZ5/z584gqgtOnT58+ffqzZ8/WrFnDqyBj6EtTaajcOpStvKWlpbCwsKysrLy8vKysrLKysrq6uqqq6tu3b5DDDYFA0NfXHzdunJqamrq6OuTPpKGhoa2traenp6ysjMfjMartHly8eBFBuBUVFdDqio6OjoWFRTffz2+bRVBob7S2ti5ZsuTRo0dHjhwB/eEgNfbcuXNaWlpgWMS1a9fCw8OPHTvm6uqKCPBbsGCBsbHx+PHje7kXAca5IoFAICgqKlpZWQ0ePBiHw7HZbBaLxWQy8/Pzvby8oHVPMplsa2t7+vRpJSUlEolEIpGgMdFreRZy4f71RpiWltasWbPgzba2tqVLl8LpaOfOnYuIL5IU5s6dC4YVgPjdXLDb29tRPmxlZWWIPcHBwcHBwRMnTly/fv3s2bNBc9DOnTs/fPjw8eNHeE92dvbChQuNjIy8vLw8PDxAfwlbW9tTp06tX78e49k+z7mg5gvRK1QmXVlZGYw1JJPJCgoKoOc2hu7HiBEjwD/Fz88PJlyporS0FHT+/4Uxf/58eLUwOTmZ90vD5XLF6Irw8PDw8HATE5O7d+/CwQg4HG7t2rUg50LIz8/fvn27j4/P4cOHwcjAlStXbt68Wdo+731rwtoDnIvH42VlZUFmlOC6ipR6n0QiwZ6PkIkDI1PR9VxwE8EI4EgAATqEycrKysnJwfkY4bVyBBDFlhD/0erVq/v16wdlMuzZnNngfYKhYrybYEkqQY9GIBCePn0KD/v79+8vXboUcQqZTCYQCHBUiJyc3KZNm6B4bhwOFxISUlNTg8PhNmzYAGupAQEBUDWdnJycGTNmFBQUwA7XkM1h8uTJcJx9YmJiSEgIZJrYsmWLsbHx3LlzoUPy8vIDBw6EYhwkCMT6JGJICBohvy/nUqlUFxeX8PBwOp2uqak5ZswYMK6xd5oyBgwYMGbMmIyMjNbWVl1dXThfBIbOvh4IR6WJEycKnf4TCIRFixbBEWuLFi0SxWIwYcIE2CnYwsLi+vXrMDGdPXt269atPdUhEMFBMDU1BfOdT548GWwJlylD1CtzcnKCY71GjRoF6hlwhAXYGyQSafz48VAwIQ6HW7p0KVjnYu7cuS9fvsThcCdOnIA96pqbm8+ePQv9bmhoKC0thTkXErdy5Up3d3doT0xMDMS5ENLT02HOlZIaBAZJ43A4Nze3y5cvQ6Y5W1tbExMTjHP/B/Ly8vPmzZOVlc3IyLCwsJg5c6YgZaf3WDBUVVU3bdpkZGRUXl7u4OCAyAaAAQWIUrJ//fXX69evk5OTZWRkNm7cCMbRgkhNTQU3r169amhomJGR4eTktG7dOr6nINxUd+7cmZubGxoaCuWcBd98oQ6tUkViYiIcekAikQIDA729vaurq52dnXfu3Am2TEhIgE8B9585c6ampubTp092dna3bt0CD8Euw4iKZ35+fn/++WdqaurkyZNPnz4N7+/o6IBbFhYWwqHDHh4ed+/ehT4Pw4cPB13loGLsoLFi5MiREyZMgNzR5OTkwESULBarU7nYRURWVhaLxYKnnmPGjHn9+vWdO3f09PS2b9/eh9fGpZRXDE6zxGazBSX3FO+C5eXl//zzj7Ozs7Ozs4uLy+HDhyV78xK/Z/DK1dXVXYneEQMi5hUDHTbV1dXBQ4hUAxkZGfAhWMdUUlLiTUhWWlqKyEoFwdXVFTqrX79+vJl5heZyREnrBWanBQOLBQE9l6MgIPLn8m0zZMgQQXmEEWmLQeMAlBUEHXQ6HZ4yamhoiJLIFFZ+cTjc0aNHwUN1dXWPHj16+PAh4jorV67E4XAjRowAXwQOh/P27dvbt2+DuWs7OjqCg4Ph64uYVwxMpj5gwADwEKiev3z5so/mcuyxHDd4PJ5IJErwi4TH43V0dM6cORMaGhoaGvry5cvdu3cjDGS97Z5/BzQ1NQUEBCB26unpoa9tlpaWCirsiBJr4O3tLbQMz65duxABx92M1NTU8+fPo7dpaWkBl/tZLNZff/2FsOryYseOHfCsoqamBpHLhhft7e2gjeXkyZNgRXRVVVU3N7c//vgD/ERFRUVBZo3ExEQ/Pz/Q/jN16tSVK1eCeTIbGhq2bdsmpW48ePCgoCjwXh77+6sVXiQSiaT/A1ZWspfg77//5hsHxWAweOkYxpYtW0DtG0JxcTFvdQNwMj5z5kxBFbTYbPbu3bslGJ8mNrZt23bmzBlBroFlZWUzZswAs9LgcLi3b9+6ubmBCXFAtLW1bdiwAfFoV69e3bhxoyBn5Lq6OldXV9Bq0dTU5OTkFBMTI+i2nz17NmvWLHgtzsvLy9fXV1BiVRqNNmnSJEHKftfx/fv35cuXI6KrORzOli1bBPkI/r72XAy9BP/++y+c7gCcY6anp9+5cwe0nYGv5bVr12D7O4JJ/f39zc3Nee2Jzc3NkyZNWrBgwfLly62trbW0tIqKimJiYk6dOsXhcED1Dcxg0NbWNn/+fCcnp3nz5unq6ra0tCQkJAQEBCBSRiEU2/DwcDMzs1WrVk2bNs3CwkJXVxcKlomIiLh586boqQAePXoEN87OzhbxrICAAHiNHmWxnsvlbtu27c6dOx4eHk5OTlpaWmpqapWVlRkZGW/evLlz5w7fhLyBgYGRkZFr1qyZMmWKoaEhVPSztLQ0IiLi2rVrfItgXrx48cWLF2vXrp04caKurm6/fv1KSkry8/PDwsL8/Pyqq6t5P2njxo2bMmXK3LlzbW1tIZ+T4uLixMTER48eIbLKcTiczZs3//fff8uWLRsxYoS5uTkej6+vr09LSwsJCXn+/DmC7q9evQqPNzAZW1RUFDze2Gw2uABQXV3t5+cH+zUhUrg9fPgwMjJy2bJlFhYWVCo1Pz//3r17qampYNYIiZcf7cP2XLHtnu3t7S2SQFtbW696ru635/ZRLF++nG/9RwwYoJkuuFQgSqIJTM9FA4vFio+PDwgI6GL4poyMzLhx4yBPRgx9C4jquTk5OVifYIAxbtw4cKmgFw6PPsa5BAJBQUEhJycnMjJS7EhZPB7fr1+/OXPmYAO0z2H27Nmgf25bW1tKSgrWLRggaGhoINYnwQzoGOeKdbskkpmZ2bJly1JTU3mtUSJCTk5u8uTJdnZ22Bjt/aBQKA4ODjgcrn///nPmzHF1dQX9RgIDA7FaSr85rK2t1dTU5OXlx4wZs2bNGjAjc1tbm5SyefxGnAsz5sSJEx8/fiyepqytrT1jxgysBk+fwNSpU+G6PgiwWKwzZ85gXfSbIywsDBFfDuP69euIEL5eMVnvc10M+eH+888/gtLdo4NMJo8aNWrKlCmYm21f+bsFHfLx8UHUy8KAjRAYGRkZKJWTMM7ttIXB3Nx8zZo1YoQ8qKiorF69GlHDDkPfQmNjo7e397Fjx7CuwMAXQUFBTk5OvTN1b1/1z1VUVJwzZ87Hjx/BEEmhkJGRGT16tL29PTYo+wpKS0vz8vKg8JaSkpLs7OzIyMigoKCejSXD0HuQnJwMlX9vbGwsKChITEwMCgqSeCazbuLcjo4OJpMpNOKwp1RdS0vLtWvXFhYWilj/A4/H6+vrr1mzpndm1GWz2X3Mf7tbkJCQAIaT/n/t3T1P8lAYxnFaWlDDi4MJQeOgEwkbi0waE8PmxM7myHcwcdPVycVv4MIkE1/AhEHCRkJEBkMCSTFtQtvjcJIG+4SGJ7xY8f9bewgnUG4Opee+AJ9SqfTLfqYHHJtMJr1e7/PzM5y/xLe3ty8vLxVFmbUh0kdV1Ww2WywWQ3glVwgxGo263S4fIWCzBdVcy7KazWa32w1nf1vZYtFr4jmncDZhcF233W77GhgC2DzR4MWXYRij0ejs7MxrZhzCyvtfwllwO51OtVpdRadRAL+m5kYiEdu2+/3+eDw+Pj7e3d3l/qqls2272Wze3Nw0Go2NzKAE8G2ZOM9CMpFI5PP5crlcKpUymcx0pOCCS9QFH76zs+NlQ9m2bRiGr7OcpmnJZFJeTxBCmKYZkv+phBCyhX6tVnt6eup0OuynAqi53wpcNBpVVVXX9aXU3DkLbsCwdDp9fX1dqVRUVXUc5/X19erqarrtnq7rJycnDw8P2Ww2EokYhnF/f397exuSl17mw7uuK/vtcy4Cf8G89+cKIWRX9gUbei3z60JRvMnI6cmrz9M1dzwee/e6CSEsy5rzxjIA+Mma+yMlVcY9zBqwtbU1nX8uI9ynd6ZpmhaLxbyVsqIouq4HbF2ThXtW4AcAbGzNjcVi+Xz+4uJib29v1uWFeDxeKBTkUUVR9vf3q9WqL6P74OAglUp548/PzwMyhh3HeXt7e35+lnl/nBwA/gRVVY+Ojur1ummak9l8EbwylNc3wJcd6zjOJNBwOHx8fAznRjUAWM3aW9NOT0//DeheT0bO+/v74eEh7wKAlawpQzgnuRdjMBis/6ld1/34+KDvAYAViYZzWpZlmaaZy+VSqdTaNmLYtt1qte7u7l5eXmYlSAMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA5vcF8Lx7fp5W+AsAAAAASUVORK5CYII=)\n",
        "#Assignment 3"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##\n",
        "## Shivang Raikar\n",
        "## "
      ],
      "metadata": {
        "id": "A-s34Q59Fo9t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ux1g-TwrEe5N",
        "outputId": "7c5ba29b-6094-4b28-f7db-1d43387e74a6"
      },
      "source": [
        "%pylab inline"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cl4dZERpcBEh"
      },
      "source": [
        "**Task 1:** Download and install 3D Slicer [10 Points]\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "37bn9S-dMn0p"
      },
      "source": [
        "# 3D Slicer is available for Windows, Linux, and Mac here: https://slicer.org\n",
        "# Please use the stable version 5.2.2 !"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFEDA1lMhZPM"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8pSy9pW5cKO_"
      },
      "source": [
        "**Task 2:** Let's load some Ultrasound data! [10 Points]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpcL98RXOBZO"
      },
      "source": [
        "# Please download this file: https://cs666.org/data/ultrasound.nrrd !\n",
        "# Then, drag and drop the file into 3D Slicer and click OK."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99OwONlCchwX"
      },
      "source": [
        "# TODO Is this a traditional 3D ultrasound image?\n",
        "# TODO: Yes, the image is a traditional ultrasound image represented by the B-mode."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGFoOHsrcy2L"
      },
      "source": [
        "**Task 3:** Visualize the ultrasound data! [15 Points]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DoZQlOqmUkWg"
      },
      "source": [
        "# Please use the Volume Rendering module in 3D slicer to render the data.\n",
        "# Then, activate Cropping and adjust the bounding box to show the fetus.\n",
        "# Hint: The MRI Default color map creates a nice visual."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_DnKFQyMdPDF"
      },
      "source": [
        "# TODO Please post a screenshot of your rendering in the text box below by \n",
        "# using the Upload image button after double-click."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "![picture](https://drive.google.com/uc?export=view&id=1totPY8cSu8h_4nSFDB6USpxL51tM0Ugx)"
      ],
      "metadata": {
        "id": "eIJZ0lrgBaKS"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rLSbojsodr9X"
      },
      "source": [
        "**Task 4:** Segment the fetus! [25 Points]\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPrMJuVcgsD4"
      },
      "source": [
        "# Switch to the Segment Editor module and Add to create a new segment.\n",
        "# Select the PAINT ICON and activate Masking -> Editable Intensity Range.\n",
        "# Hint: Adjust the DIAMETER for a smaller or larger brush.\n",
        "# Hint: Adjust the lower range of the intensity slider under masking to not include the black background.\n",
        "# Hint: Switch to 'Red Slice only' view and use the arrow keys to navigate through the slices.\n",
        "# \n",
        "# TODO: Mark the Fetus by drawing on each slice!\n",
        "# Hint: Start at Slice ~83mm to ~112mm\n",
        "# TODO: Take a screenshot of one slice after painting and upload it below.\n",
        "# Hint: You can use the SPHERE BRUSH for 3D labeling but be careful!"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NN8fOim3y3kq"
      },
      "source": [
        "![picture](https://drive.google.com/uc?export=view&id=1yHYeBweW0hoT4irIPEJ2y736FprWBZMW)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWLWC3Nty-Iz"
      },
      "source": [
        "# TODO: Click on Show 3D.\n",
        "# This will create a 3D mesh of your segmentation.\n",
        "# TODO: Switch to '3D Only' view and take a screenshot of the segmented fetus in 3D!\n",
        "# TODO: Upload the screenshot below."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "![picture](https://drive.google.com/uc?export=view&id=1B27RXyVxdECoGxqCBkDQ159FAHXS8erP)"
      ],
      "metadata": {
        "id": "pX_WhdDTID3E"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NmHxBDVMzhZE"
      },
      "source": [
        "**Task 5:** Let's look at some Twins! The segmentation was actually made from MRI data rather than US! [10 Points]\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EdpGq1eRzi3u"
      },
      "source": [
        "# Please download the twin babies labelmap from https://cs666.org/data/labelmap2.nii ! \n",
        "# Load the data in 3D Slicer ..\n",
        "# IMPORTANT: Make sure to check the LABELMAP checkbox during loading\n",
        "# ..and convert it into a segmentation node with the \n",
        "# Data Module by right-clicking on it."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: How many different labels are part of this data?\n",
        "# TODO: 21 labels "
      ],
      "metadata": {
        "id": "hogloixdvwg_"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Task 6:** Find the babies! [15 Points]\n"
      ],
      "metadata": {
        "id": "LEtzXQAKv-uG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: Please identify the labelmap values for the body of each baby!\n",
        "# TODO: Baby 1: 2\n",
        "# TODO: Baby 2: 102"
      ],
      "metadata": {
        "id": "md0jxc_gwG6p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: Use the Segmentation module to show the 3D models of Baby 1 and 2. \n",
        "# TODO: Take a screenshot of the 3D view and upload it below."
      ],
      "metadata": {
        "id": "YuaO4QLIwcm2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "![picture](https://drive.google.com/uc?export=view&id=1K9g5bg9UbbNK4UbIjpfVRxIDFhY4ozzb)"
      ],
      "metadata": {
        "id": "8EgDFEHIwn1y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Task 7:** Find and visualize some more structures! [15 Points]"
      ],
      "metadata": {
        "id": "Tkqu-0J_wGPO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Now, we will look at some internal structures of each baby.\n",
        "# TODO: Identify the labelmap values for the following structures:\n",
        "#\n",
        "# Baby 1:\n",
        "#    Brain/Spinal Cord: 129\n",
        "#    Eyes: 111\n",
        "#    Lung: 112\n",
        "#\n",
        "# Baby 2:\n",
        "#    Brain/Spinal Cord: 29\n",
        "#    Eyes: 11\n",
        "#    Lung: 12\n"
      ],
      "metadata": {
        "id": "TkzKQe-bwTuY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: Show the 3D models of the above structures and upload a screenshot below!"
      ],
      "metadata": {
        "id": "lzfMEECYw9NI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "![picture](https://drive.google.com/uc?export=view&id=1tHlP673yU98n5W2GnJmI2bCpyqcHsfA2)"
      ],
      "metadata": {
        "id": "e2RNb_q4w9cm"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4yr4Boxce011"
      },
      "source": [
        "**Bonus Task:** Use Slicer Python to analyze the image data from Task 3 (ultrasound.nrrd). [33 Points]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Activate the console with View -> Python Interactor.\n",
        "# Now, you can run Python commands in 3D Slicer (don't run the commands here but in 3D Slicer!).\n",
        "#\n",
        "\n",
        "# \n",
        "# 1) Grab the image data as a numpy array.\n",
        "# Hint: You need to replace $MRML_ID with the correct value.\n",
        "# Hint: You can find that value in the DATA module, if you activate 'Show MRML IDs'\n",
        "node = slicer.mrmlScene.GetNodeByID('vtkMRMLScalarVolumeNode1')\n",
        "data_as_array = slicer.util.arrayFromVolume(node)\n",
        "\n",
        "#\n",
        "# 2) Now, data_as_array is a numpy array containing the image data.\n",
        "#    We can print the shape and also the max value.\n",
        "print(data_as_array.shape)\n",
        "# TODO: (143, 224, 238)\n",
        "print(data_as_array.max())\n",
        "# TODO: 254\n",
        "\n",
        "#\n",
        "# 3) Please write numpy code to find the coordinates of the single pixel\n",
        "# with the maximum value.\n",
        "import numpy as np\n",
        "\n",
        "np.where(data_as_array == data_as_array.max())"
      ],
      "metadata": {
        "id": "KdUvQr_VYUrA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7V4io1ngtUR"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHfGf23KfHb-"
      },
      "source": [
        "# You did it!!\n",
        "#     ,------.\n",
        "#     `-____-'        ,-----------.\n",
        "#      ,i--i.         |           |\n",
        "#     / @  @ \\       /  HEE HEE!  |\n",
        "#    | -.__.- | ___-'             J\n",
        "#     \\.    ,/ \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"'\n",
        "#     ,\\\"\"\"\"/.\n",
        "#   ,'  `--'  `.\n",
        "#  (_,i'    `i._)\n",
        "#     |      |\n",
        "#     |  ,.  |\n",
        "#     | |  | |\n",
        "#     `-'  `-'  -bodom-"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}