numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[1,2],[3,4]], dtype=np.float64)\n",
    "b = np.array([[5,6],[7,8]], dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         1.41421356]\n",
      " [1.73205081 2.        ]]\n",
      "[[ 6.  8.]\n",
      " [10. 12.]]\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(a))\n",
    "print(np.add(a,b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59\n",
      "[[36 41]\n",
      " [64 73]]\n",
      "[33 59]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[2,3],[4,5]])\n",
    "y = np.array([[6,7],[8,9]])\n",
    "\n",
    "v = np.array([6,7])\n",
    "u = np.array([4,5])\n",
    "\n",
    "print(np.dot(v,u))\n",
    "print(x.dot(y))\n",
    "print(np.dot(x,v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  2  4]\n",
      " [ 5  5  7]\n",
      " [ 8  8 10]\n",
      " [11 11 13]]\n",
      "[[1 0 1]\n",
      " [1 0 1]\n",
      " [1 0 1]\n",
      " [1 0 1]]\n",
      "[[ 2  2  4]\n",
      " [ 5  5  7]\n",
      " [ 8  8 10]\n",
      " [11 11 13]]\n"
     ]
    }
   ],
   "source": [
    "p = np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])\n",
    "w = np.array([1,0,1])\n",
    "q = np.empty_like(p)\n",
    "\n",
    "for i in range(4):\n",
    "    q[i, :] = p[i, :] + w\n",
    "    \n",
    "print(q)\n",
    "ww = np.tile(w,(4,1))\n",
    "print(ww)\n",
    "print(np.add(p,ww))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  2  4]\n",
      " [ 5  5  7]\n",
      " [ 8  8 10]\n",
      " [11 11 13]]\n"
     ]
    }
   ],
   "source": [
    "g = p + w\n",
    "print(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,3])\n",
    "b = np.array([4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4, 10, 18])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a*b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SciPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.misc import imread, imsave, imresize, imshow\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: `imread` is deprecated!\n",
      "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imread`` instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "img = imread('cat.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint8 (340, 325, 3)\n"
     ]
    }
   ],
   "source": [
    "print(img.dtype, img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "img_tinted = img*[1, 0.95, 0.90]\n",
    "img_tinted_resize = imresize(img_tinted, (300,300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb17a53198>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yu7uLlJK9vT2khOc+8UkeffRhdKfPqxs3CaVHatKxfZTZ1BaA53k4jjN0zmFjMlZNzxaAu5l6arUhUSw4WoDv+aRaESnFD/3ZP4MQgoVTy/i+z2f/5Pext98mihKqYYXf+I3fYLO9S82voNK4MGuKnPG7OO/GNfVB1dO7b5j9btJE7+44O3xKVN5Ye2zcecf0kBa3xC4yvV6P5557DikllUqI53mk2pYl0pkXGfSBuHdZimqtkUojHYc0ialk0th1XaIoYvP2OisrK9x+513q1Qb9XrfU//IwmKKCjYChuPw0NO2Y3RHpYcmqMVSDkCiJEY7D2toqi8tL1Ot1nnr2YywvL+NVqvTihGq9RhLFvHr1FYSQxCrONKV7Q8NIDZP1lw9MiN23zK7RhWf1UOlxjFDONFR2wB2nuOKdUKHmCYHSitnZWTrdPm5YIaj4uBpQGp0qK3W0KjG7RY0LASqNcXAxRqDQOI4g9H36/R5CWvsewMHwV//yX+IX/vnP89q3/gDH91FxnK04A9+wweBIqwpjTCHZpw29jaq4ZcjnUWM8dpzGhNscRBE+S43GlQ6n11Zpd7t88ctf4vLlyzxy6VFmZ2dp9S1GoZek1Go1giDgzdff4OWXX8Z1JcJAnKZ4mYYwcMBN308jDpptefT2D5s+FN74w6Cx03p3p6FJxQXvxSAVz1OaucYYzp45i+t7LC4uUqvVkNItoLM5fHagZqribylliZk00mikEPS6XarVKg6CWlghDEOSqMfly5cJqxUuPfYYrWgg2XNvfK5aloE0dwNGO43HOu/HNKATIayRkY9PEAQsrazwiU98gs9//vM88+zHSJKE/f190jhBq5Q46hH1umxvbjE/O8eFc+dxpYNJFb4shbSOoElMPE0YUvPBFxK5LyT7OA9onuU0DGM9SLpksw6y0qYP04zP5c6cMUd49UdV3uMwgiucoh1tFFJKPM/j4UuP4PsBflixXnUkQmsUesDQrovRKcYIkiTCaIMxEikESdTHcRy00URa47oucdTDkQ5xFlZyXcPO7hZf+cqX+Km//X/Ycc4qmAojcYSxDq4MUeY6LhjD0tIS77zzzrG950POqTG/H+bAykNwZYdd+Z25vkMnjqm6Ho6G5VPL/Nf/zX/F7NwcSilarX2CwEeplDSKiwhGogyu6/Jbv/VbXH/rLWSaUnEd4jQt5l1R4HKMN33gohs46gQljaVksYyOzWi9hdExmTSLyk5jp1Sm7EGcfUo6gJEfiQpMS8eVeMqooeuCIKBarfLYY48RxzHGGBzHw3XdQlSM02KG7Gili49lXI1SCW5W3cZOOolQhqjXxwt8XN+jUq0iXeeAPZ5P0nH+gfI5H0QevDTjoalJkuACSZLQTxKefvZj1BsN0jSl3W6DNjjCqugV38N3XFzpIA1U/IAXv/4NOu02wmTn5W0zKEwx7bu9k1E4StU3I/+fhO4LyT6OhkAS93gy3WmtuZOQRA5JtCiKiOOY559/nkqtRlitgXAgP8dkDC4lxkhSZUCl1ksvhbWllclQYClxlKD6GsdxcF0fPwjAsSAaR8Dm5gZXPvIRLj72KO2dFteufgcXi8iLTTIIcwlpTYXsHjmG/A+DRt+TlBLHkegkYX5xgb/wn/4En//+7yeOrUe9Wq0WEtf3PHQSW1ixdki1YnXtFC/93u9S8T2U1mgdE0hJLG3EIU4TK02PgOEO9bGk4Y27Tg8UgtJzfTB0R29NCPGOEOJbQojfF0K8lB2bF0L8mhDijez/uTvtiBGi2N/rbtNo4sVRNdEOXH8XEGV5MosxhuVTKzSbs9TrdaR06fftxB2dRJ60kjjnuzRNiaKIJIkxqUKiCTw/k7zWoee7WSrsxjpCCN56923+l7/1t/jsn/xeKrUqnhegtcGRXuEfkFIW0s1xnOLvu2XDH4fcbIEEisSfWqNOtV7jl375qzzzzDPs7+9jBNki5xIGAV5mjxspcAMfI6DaqJMajRe4pFrheM6QHZ3DZgXDCMOjqBx7h1KUQIwPsU1j3wshkMLOUnkHY343lujPGWOeLtXA+mvAvzbGPAr86+z7kSQYnkDHzSa6UypP3GmdfnfK5Plz5hh1rTVxbO1Kz7Mq/JHFEOSwhqC1RimVZcxZrLdOFWmaFii5er0OWNX3/PnzPPfcczZn3nHQmW/gMPjnB83k5fvni3G+EIVhyI//+I/z+uuv05yZKVJWRcYcaZoWC6rdbMKQpilSStqdDq7rFuZLCgSV0J5nDoNwTaaxGzTepaG60zG/F2r8DwCfzf7+B8BvAv/t0ZfpkRURCgVHDGya0ccdLZ5vyRSLR3HEmEExyHJIqAirZJNIjlloxtx40g4xU6+eWZjNly6xTgjDkEuPP8Fer8vc6VVibYjj2EJapARtMCq1fdMajMFoTRrFONJuySSkQaVghAXCRH2NK31UnBC4Hr3WJqQtgkqNGI3vuGxv7/Ndn3iei5ef5DuvXsVEXeKkT7XiERuDMRov9Oj2I7q9tn0vQmO0wCkMTYvLz8e5GPMxc1PlB4eG+ODCrjCF/0SS5YIbTQL4UrKyskytUeen/8HfwXEcdnf3CHyHVivGc6yG4voBrpdJaWNwlI9RBl96zFbq7O7u4qQWOJSkCUtzc2zu7GQ4elEUdXTGqeOMrxwjR5x6AG4pzGiwJbYEg+w9MTpWo07jMl9k/ycnWIruVLIb4FeFEL8r7BbMACvGmJsA2f/L0zR02Op33LzfqcEzo/dhOv/AJOz0cfqZ90dK25pRGt/38Dxv6PeypM7V+dFPcVznk9J2JPAcHGFj5EqnmT1vSOOENO6jU0W/3yUMQ5544gn6cVTYuHEvwShr81uzwDKNPGIcD4t0HIeEsNtclSMsGpit1ki0JgxDnnnmGWq1Guvr62hlAUllLaccrszNIGNMoTH93u/9HkLYvIDV1VW63S6+40w1B47q+1F0mGFwmGp/JzH7O5XsnzLG3BBCLAO/JoR4bdoLxcj+7DlNepBJjDSudtiBtMcpBl8YUzhjBsfGncdBKZ+fdwwtSymFIx3SNMWTLr0kZnNzE1c6BSRVJSlKKUycABmOXlspRZb84nkeGMvsXhAgopik30ebGJLMYZd6pFGEG4ZExkJwXSFwHcF+e5/9SoUv/sCf4t9+4//j2ndidNsQuB5pGuO5Hr1eDweJVqC0Dck5I89bXijF0DsZpmknqtAGIW04MWdUIQSdXpeV5WX++v/4P/D444+ztbWB7wdI4eI4Hmmq8YMQIZzsXQ4iCDmuv9vtMj8/zy/90i+RJAlnz57lnXffLVJNRxfzkzB/edEZDT2O+ujKzK2g2LSy/Pvd2Gb8jiS7MeZG9v868C+ATwC3hRCnAbL/1ydc+3eNMR83xnxccO897ofRNPXcYRhdd6eksVJToUh1Si0IWV5eJo6tx1joTBXUBkdkfcyPqYGNDxTl9FVq6HQ6GXLOJU0i0iTC6JQ46Wd2u0bFCa6UCGM1ht3WPo9feZzv/8qXWFxaAiFJU2UBPVogpZUJeQWdoTHJQ4AmQ7MdUEGHP9OT1UiKuv5S0leK5uws5x+6wMWLF4ni2C5E0kMpg5QS1/EQYhi/L6W0Icy8ZWN45513ePvttzl//jzr6+tIIaj6PlXfL/Xg7s7JaQE445x5Rgx/TkInZnYhRE0I0cj/Bj4PfBv4JeDHstN+DPjFador2+TTPMskZFsZ2DDS34ntlO8P4yfmaB5yPsFPWlpIIknTFAeJ7/r0o34RC9dJCmgEmjRN6Xd7xP0eSiVgDEKY4rwkim3hCg1KGer1JlLacJnneQVYR2bQJTKPdtyP2Lx5G0fbEB7S8GM//heozs7SnF8irDYRQhKliVV7MYS1KrI86iL3X+uh95aPyx2NjxhEAxzXtci4xQVOrZ7mZ/7B36fT7dJut6nXmvT7Mb7vkyRqSAvI1fhclY+iyFb38Tz+1b/6VywuLvLWW2/R7fVQxvpI+hnwaLBx4vEeYHSOCCEOqN4DwJhV0sqfMuVMrzj4MXBslf5O1PgV4F9kD+cC/9gY8ytCiBeBfyqE+HHgPeCHjmqoYHRR6nzpKUbZdCKTj6jvQ/cwBx0qxgxe5ZAtOsUIjmvvWORYt5POpHQC9Pt9XNeh3W4zOzODUgqVRLhOltGVpb8qpTIPslOk8xpjcD0XlUSoNMURhjhJiOOY2EClVmdmZgYjrTahEoVA40hQGUMLR3P63BlkP6G322J7q49UECcpEocoioqQlooTXNcp8PpSuiitrDcfa4pEKsYTLjJzTJk8lDfiwJJSFh50ZRSeYxepNE2pVCq0um0WlpdYPbPKn/9PfqzIw9dakySKMAxJUw1G4Phe0WZZlXYcB8fziZIUjeDqG2/ynbffAekUnDZcqloMMfykajqjx8ep7kfRsK9yylk1tH3sdJecmNmNMW8BT405vgV870nbHaXyo09b9vnE95owaKMb+02s5nKMe5kstCOxk98FZpt1m9kmDP1+n4of0DIGo1MLpklTEDb3HZMWzJM78UitCaCQSNfF80MCKdne3aERVIlVCiolDEJQ0Kw06HZ6NOfn2Nvdwg0C/ru//t/zs3/77/HPf+6f0pydJYoiqp4kiiLOP/QQb7z1ZsHwSdzHQeA7Lj2VZOmwkgRF6IUFQ0rP4vu73S4mNz1K/pR88RJCIIwgVSlVv8rs3Bz1ep1lKbjykSf5G3/zf6bf73Pz1i0qlYqVotnCIcVAXXe9IIu0DMpuCWEQwqHT6XD69GmuXr1KGIZT15s7Dk1i9HH7sUPmmc9BTNnBkzrwDqP7F0E3+l2I8Q6zk7RdVrXy/83B3+QxFs+jvNSj5DgOOk0RjoMj4ezaeT796U8Xnvd+t0fFr4A2xL1+wdTCAL6PyIAyRg1ClknUB88Bt4IJfOrNWaTr4M/Ok0Yx1669x8riEsHCHK6O6XR6JP2IrnRYXJgnSRPOP3KBjz3/SarNOl/9+X/G2uop0jSlWq3iVUIcP8ALfKRWpMkAkFSvNuj3+ywvL9PtdolVSqpSlFaIWJOqFEc66DwlNR95Y/92hMT3feI4xnEkrudx+swaZ86d5dLjj/ODP/iD7Ozu0m63qdVqxFGURTLsUuz5Pq7r4nkBThZTtzH2HIyk0VpRqVSKhJl7DfUtClKWZk+uwufhN8j3srd/i8w5d9hurvk5x6UPBTY+t4/vdpsFSGQMo4+jURu0uP6EfcjRbUrZHV7m5izYMAfACENmiyZEUYRK7HGdSfBchc5DbzpbAPpJTFCpk0oHJ6ighUut2aBer6N0SqvdtQzT69OsVghdh7jfJYlidnZ3ufyRy/TiiEtPPM7y6VM8+vhj4EiCSohSVm0uA2+kEOx1W8zMzKAFdPq9DKtubWRkZreKCaCTbDr7mXMsCAIWlpeYnZ/DGMOP/MiPEKd2DAC0UnieR+AOVPa8nHa5uMZwqFLg+z5pmnL79m3ee+89er3e8Pu4iw65cTb/OGk9LuQ86re6W0x630h2LYZ3ZC3T3VCbJ9EkRh+956Q+yBGHzHElheu6pEmSbfTgUqlUiKIIiUPqpYRhyEa/i1YK3/dtP3Vqa8QLWcSMtdZ4riBGsLC0DK5HdWYe13Vpui5Rr02lUmF5cYEd6VFrzNCp7rG1sc3K6WW6+23SwEFUAj76sae5efM2Wzeu8Nu//ds8dOEcn/zu57j66it88ctf4qWvf93Wb/MDeq02AN/z2e+h3W5z/fp10jTl4sWL3N5Y58KFC7z66qtUKhV6vR5ulrJbHu9arUa73S7U8EuXLrFydo2ZuVk+/4Uv0OnZNN1Wa59KpYLR2m7qoHXB4LZiroUQp0oNUISSwhPfbrdRSvHSSy9liUbOYEEYYs6Tz6yj3r8Z87cQJdwFg0WhHKK7G/Xp7hvJPrT1cmlEjmOnW6XO7hFmioLvxsamjC48+I4ZfDwEHqL4Xs7nToUpPrlntLwS5w4+U/o+7Ce1H5Gpafn9JeAmmgCHJFFUqnVWz5/loYcfRWhDIBykViRRB4mm3YrBuLjaIFOV7Q3moWWAwUUIjzAIiMMlnJlVNroxkSNZWJyj6jvErX2WZ2a4fXsD4bi019fZb+1SXZjlxv42qe8hUpA9heykRL0+Tz71Uc4Pw3EUAAAgAElEQVRdPMOly48wtzBLtV7BDwM++annOX3+PCsPP8xHn/9u+qHPjlE8dOUJZL3K0vmzRMLgN2ssLC1y/vx5Ti0uMFuvEQiB57k89NAFGs0qvmtI0gjpGKTvIEMft9ng7ONPUFta5Hu+/AUufeQyTuijMbi+D1LiuB4pkBiNEA5aQ6JSlElJTUqiYuI0QqsEtME1Ah9JlEoSI/nOm2/b6IrSOEYXirYBdI52K7FlroqP7qU+qbDlKJUx9/l98oq3DhKMtIAoLcZ62vN2nezjSokQg8+0dN9I9nF00pVIQjFS4hBAwpAfYOgdZXalObkz5MC9RvrgeR57SZ+aF+C6Ls8//zwvv/Itrjz5UeI4QimNTlJ8P8R1BFGvQ8WtIYyyzigDSiXEcYJA4zqCrlE8cvlx5pwFlNG8+I2v4wrJp7/r43zrm99kaWWZb7z4EhcfexwpJdvb2zzyyCPs7OywsrZK3I/Y2d0iuFVnZs4m43z0ox+l3W7T3tvniSuXuXTpErVajZ//+V/gylNP89Lv/T6f/O5P8fSzz/H0s5/gxRdfRLgBUko+9Sc+xz/+h/+Izzz/KXrtDr/5m7/B+bU1vErApz79Gb75zX/HQ80mt9Zv85FLT3H95k1WFhb5yle+QmT6PPHEE4UEDoMQIaw0TyIbHnMc1+baY30gMlPhC6Zz5JBqn2Pe9/f3iVRCgDO2ZvthW4MdRffCBzAKDjvpPe4LZh+Njd8tkM3oIlvcY6RpMyKtJYMCA8IuwyfziOT3HXNtK+kzE1aJ0wStU1v5tFoljnq4jp/FhROUspO91+9iVILRBukDxhaWtLhvBUaQpH3qtQr7UQ8pXZaXl+nst/jqV3+Zz3z6U7zxxlU837exZycL8c3O0osji+TzPJIoIkltuq3rOniey+zsLK7r0mt3AJiZnWd5ZYWnnn6a7/389/HpT3+G02fPsbS0xE6ry0OPXuLq1at8+rPfy8uvvI4TVpBKU5tfZPHUaeZmm/jVCnNLyziOw8rpNZCChx9+GOkHnDq9zMbuJtVqlXa7TeB6Q7DgPAMvj2Hnz5M7MdPUIg8LzLkjkQaiJObUii3AUQsq6MQ6EO8GDaPkRqX/vXMCHofEHyZyLScphHFLDJczRy7ZxZghm6TGAxihi3bKtlD5vCJV0pRio6XXVNhNY9Syw510B8dzHJY+NQrf8RCO5MKFc/ylv/JfElQrhGEVR3okSUoc24ytdHeTWzevEUrwHEG9OYtXaYCwcFujNBKNkgEEPmcuPYzJKtOkScJsvcbrr13lkUce5hvf+AYXHn2YmXqN2dlZbt+6xexsk1QZOv0eS6dOE9RqdsSUDUt1+zG7u7u02x2SJGF17SxSWo93szFLt9vFqzRskcxaSKfTYWZmhps3bzIzM8Pm+gaOkPzcz/0cjopYW1tDpTH7+/s89dRH+PrXv87KygrLy8ssrSzTbDZxA5/V1VWibg/P87KYelI43PJ3K6RdGIWb5aDHMZ1OB5MqHMehEoQEQYDWmr1eRKu1x8/+zE/zf//T/4tGrUGn0yq849aJKDK46kE1vvx9lMZJ//y8cmWacl2lg8eO1mbHzcfE6N8tZZ1OpPtCspfpboTXpvWuF+cXjrXBknK3CvZP2iTCFTaHvRv3WV9fZ2Njg0uXLqGMGUpHtWqsNTeiXgeTAWekXyF3PGuTolJFog0Ohu3NTRpzs7RabQLf5/bt28zPz9Pudnj0sUtsbG/heQ4LjkOcRty4dZOzZ8+y126hkpR+r5dJeRvyczMn18LCAknmSFzf3GZxcZnNnW2azSZKJWgt6HQUru+zs7eH43l0ej3qzQaVSoXnnv9uqr6LVondarpuC3TMzs+xemaNOI5txEApmkFI1O0dQKQBRW19qwFqMCBTU3jlfd8nland/cUZgHh29veYn2nyK7/yK1SCCt1ul0n0QRczmZbKdQ2OS/cFs4/yt7Wl70zjGC34D2UJXr6XzP/IkFSDVVpitxjO+zTax3E0Gs4bShYpx+1dSSvu8/nPfZZqtUoS93n/2rucWTtHrdlga3MHx/Ho9no4cYIxil6nDWFAHFVww4REG5QGzwsQniZIJd0kplmtsru1TWNulm67VySAnDt3nuu3bvDo4gLvv/8+127eoFqvE8cxm9vbLC8vsr5xg0qlxsLiMsJo0jjB8XwqgYcRTuHZnp2dBTRhtUKqFVLHNGdm0EC1HtLtG8Cj1WqBNrT2u3ziu562oJ/MU379+nXifpdnP/kcjrQIwsXFRdbXb1OtVoqwY76bbf4OjbYJQFaie0PxcmMMrpC4nl/ksdutpw1zc3NsbK7z3Kee59/82q+SGFU4hgv05YT3Oam+Qjn5J99tpuzdPzg/svef/Tx6pubgPDugfX+YbXYY8cBnoIEyBPY4dJQqZEqBTG2GK8CMZis5HHwhh9FEFN7IcaUUzUqNl156iStPPkG9Xmd/d5edWo2b6xusrZ7F8yvEiaLX7+P7IYnrWtU8Te3GD25AUGkgtX2cuufy3o3ryFoVv1phc30DrTVX33+f0yun+MY3vs6Vj36El197jYWFeSqVANCErkNrf5f33n8H13XppApHwMxsA43GJAmuFDa/3nWoVeuo/Rah76O1IU0japUAFXUwUtBtRUVYqxF49LpdjE6Iey1wPYLQItwee/xRut0uWmt67Q6+59Dv96yan6SYzPlmjMYW10yK8FyaSXTVS4uYP0rT6XTotm1RinqzMVSjrx9FnDp1im9+85skaYInHLRRh76nAmtfLAbZbJjwnrXJNqw4oWZwwHy4i2b2fRF6mzQsB3DHY/5Nak8wwSQYI5EnrdoDe/zOVLoDmWBZLC5JEh5++GGuXLmCELaCTKvVwvf9oixSmqaYrA5c/skdUFprer2ePaYM/W7EwsIC/W6Pzc1NlhYWQGuM0rz11ls0m01e+Ldf56mnniJNLbgmL8yYS8eKH+AIQ6/TIo1iUDqL+wvSNKbTauMIY/HuSWKr3OoUjLIZdioh6nVRSZxthWTP9aS0telVik5stprKwDG+7xOGIdVqtYDOFhBgPcjbz9V0kRXHLJJ8pLRIwG6XuB/RaDRoNBpUghDPcYtxm5mZ4db67SzLTRKb6Z1zo/Nm2uX/ToA6dzvr7r6R7KM0GsOEYeadlv2GrxlsJVWUcTJm/L1KedTZFcfp/vi+lNqu1epstXZ47rnnOHV6GSkE7V6bNNGsnX2I7e1tFpd83MAnzcovrXc6oBXVeoPZmWWkH7Lb7oHjZqgyh2i/y+LKMl4YsrW1ZdNm+32MUmxu3GZ+fpZOv4fje1SDkG5vnyAI2N7Zx3c9Go063U6HbqdH3I/wPI9+u8VsvUYfQ7fXhjjGFRqTRnbr4ySm3U1sKa0sPBbHMTMzM6hYIYIQYwytVoso7tBszhJ6AWnUJwgq1s4WDtKB06dPs7ezi3QgVbGNQRuDcGSWuy9RxgAyS6CxJZ99P8RzfOphHZ2V0BZGkCSWoTXQTXr8+q//OqlWeELgmPGe8nHOuAMCAXGgJPRAKzTj1e/jzpe77De4LyR7mSaCaMxAzT4MwHBSKpi/BLG8l9Rqtaj7obVpM9KpsiGuXo9arUairUfZ9X1SpelHMVrbiix5/LhSqSAdFyEd+v2YarXKzs4OlUoFpRR7O7s0ajVu3bpFt9slTayK3ev12NnfwxhDL+qilK1T5/s+tVoNlcQF0+bVWo3SVMMK3W6bXqeFMIo07tPrtGwxDsdB4BTVdvK36WRhMYktr6V1muWeSxzHhg8bzVpRJ65SqRSx8fK7yBmovAj7vk/o+fiuV9zHzT4ARlkvfhonbG5u8s4776B0pj1MK51L0RqYbN+Pu+YkdCdmwGF03zH7oAxRCYFmLAZJmsFHZB+JKT4GnX0AIYaT/eVAOxgtplAYBYXXxECWwpp/yveRwvYvV1GlMbgG3Cwmn8ftpbGhPch2RhWiwBAEno+RDvtaEXs+7VZE1EqYnV0gcjXz55eJ4n2i7hZJv0NrdwuhNaQJoeMT92PQgkpYtfeSEuM5tnJPJ+b22+/xyPmHmZ1bYKfX5dylR7l1e4P129v829/+bQLp0t3fo7W3T8WrEPUiWrt7pEmC4wgasw02N64TRy2i/j7t/R2iuE+tXqW938Iozfb6Bt39fZJeDxEbXPJijw6BGxB3O8S9LkIqFDGJE3NqYQUda5LI4tIdV+L4kkil4Do4QYBXq+P6HkiBFglCKqRQpEkfYyIcYRcMpRTsb9FZv8at999i/ea77Gxt4GhwNMg0Qacx2sQoExN3E379l3+VGiDShEZWJ97u4u5kW0kNGHqw/dPBjSiFsDXqyqhIYczQ98N2EyqbiOV7DbazKrV7l4TcfcfsH3Y6bEUv255R0rfhoJ0d9vf3AYvdzqV2mqa09/dp7bbY399nf38fkZV57vf7haMuL9KgBXQ6Hfb29kAKKpUKL7/8MlJKao06nudRqVTo9/vMzMxQq1eKxJNbWcqo53ns7u7yzjvvWITf3h6O49DvxaxvbbK1tcXe3h5KKYtCi/pF7DsPmeXOMs/zUBmePy+kkVfMLYpSODlOICs6oU1hi+dS2sJhdaGaF2CabCzt/UVRJTZ/Ttd1SZIElWT7zgvB9evXrT8EqPsBfaMtiwsnExPTV5T9IPEpg2Qec0da54eK2curWi73h37ncFt+3ApZLmg4es7o4I6uqmXTYug+I6CbcXafi8AHzp89x/z8PPudNqkw+NUKjUaD0K/w7T94mTSK+c5rr9Pe2QNtUElCr9MtJnqiUlKjMUbbDDMoHHj7+/u2GKPWKKN57PITVGpVAt+6ahYWFmjWbTbcbKNJEASF+t/tdokSRS9KWFk9zfr6JpevPMn7164TBAH1Rg2tNWHg8e47bxHFPV557VW2t7fxAh8jBWlimdRzfCpByPzMLO12myiK0KlCGEUSxZa5sag4R9rsBsfzCqedteuzWvY4RaELYxSpsiWgHdcnrNaZm5tD6YSo36XX6xAnfZIkQpuUN77zJlESUwtDenFEdaS4pwEcZ1DkMicz4te5246zD4o+VMxepoke/BOoONPYR2UnoRBiCIUnJzF9aeEoPMmZpznAJXS8Ioc7Uilhrc7y6hoIh9dee43HLl7ijVdeR8cJSRSj09QyibD2eZwm4AiMMHR6XdyKh/Rc1tdvo4zhzJlVorjH7OwsjmMhpU88+QTdbpdr770PWrG5uYnSCUopVldXefe994rQ3rnzD7Gxuc3C4gpr58/zrZdf5fHLT9Lt96hUKly9+ipCGObnZ9nc3uby5cfJqkiwub2F7/v0+xbHnsYJOlVUqgH1StXm4Wf4fxOn1rzJUHK5RJeuzVFHiqIIRf7xfd964v0Ax/Vx/IBK1dbD7/V6tNt2I0eTJgSez2yzyb/7/d8nNZpEKVzPo58kKAxKaxC2GtBhG0J82Bn+Q8XsoyrMKIsexehHqUNDwA1jhpgVJsfQh9rIob5jzs0BOkYKGlW748v29jZbuzskWpFohet7tNttwrBKr9PFd1y63S5JPFDdc1MABqWm83zvvdYujufy/rX3qNfrdmPHOCLRilTFhGHI/u4Om5vr1jHWaBSps/n+7WAZJkkSZmdnafe6uI7P/OIC12/eoNaY4caNG8W2x7du3cLzLXJtbm4Ox3FI4tQuakFAp9MpIK82BdU+h9VUUruVdAa2KbaV9qxabrK01fx7OaklSRLWt7ZpdXu4pe2ku+0WnU6HVNkNNxzXLhALCwvMzc3Rz0yPFIodb4r3Lw8u2h9Gxh5H913obTjskR+z5I5h5iGGzb20E86bVsU3Y1Z3iTiwukhpEyzKxwUUx/Jc97zdwmMtIEoVD19+nEefuIwTuuzeXuexRx5lr7WPElCvN9iXDsuLS7z36jeZn5vJpLti+XSDSq2KEg79OKJarzEzP8P2jZvMLMzzwgsv8fQzz3Br4zanz53hD17+No9dfoLNWzd5//13McbQabXYuH2TlZUVbt++zezsDNevX2dtbY2lxXmuXbsGwOuvv8Hyfof5xQV291ucPXuWxcUlfvfFb3Dh/AU2t7dQKqFSCel0WuggJFGK2YV5TGq3UqjX6/R6Pfr9PkrE7OzsEFbrOI6DF/gIY0FGcRyjs4XMD4LCHxD4tgSV57i0Wi27wMURymjOnrvA3NwcN2/fsNGH3U32NrYQaFZOn7LlvBBEScLq6irPPvssqnMR1evjui5BrYFwrX8iinp85603WN/eBezmEMoYXClJtcaRklSbokzZOJTmcWiQmDXs5Z/22mPXTjjW2R8QiaGIeH7s3tO0gzcxZXbS+VmWFpk6r1QKrodCECmNrwxBFotO05SgViWKeviVkL2tbRaWFhFaoTH4jt2bbG+3xezyMrHWxGnKXmuXhx65yGuvXKU522Bnf4/X33+HL59ZZWdnh52tDcAuUHMzDRxhiKKInZ0dZmZmePfddzl9+jSrq6u8+sq3WVtbY3+/zfz8HNVqlX4/JqzWcf2Q116/il8JuXX7NotzDWbnF2jv7WMQ+EFoEW5xghbgOhIv8O1+ao6D73oEQUAc9wuUXb3ZKNRnW8kWUpNBZIVEC400hn4ckSibIRj41i/g4bO9vW0r6PjWZ7FyagmdqsI8albsnnee57GxscHvv/A7zGT+jV6iUBrqzQanTi3z9NNPc/3mbW7dusX+/n7hH5BCoLTGc5ys5r8cKxTuZ7rvmH3sbi4fwH1PkmAgdGljiayQxWF2keM4GGljgT3H4/Vr1/hYonDrNTQSo+2+2/1+n26nxexMhc5eyNKpJbrtNv1dxU5rH7O+ybOPXWG328Vv1uj22jiu4FtXX+WZjz2NiTUvvPACZ86c4Xd+53c4vWald7NaxQ1Dep0up1dOZVlpTdbXb7O2tkavZ0EnQRBwa/02S7PzzM/NIVyP/XaHuaUm3V6fsw9dxPMc+t0O6zevMT87R2tnm36/z2rD7rdWrTXYXt8gkQlBYItPhNUAR7gElQpxq8Xe3g7zi0tZYotCuA6O5+L4LkFQod1uWxte2Nz1vHYcZBEIBb1eB2MMKo3Z63dxJMVmG7HSBLUq1eYcrU6bM2tr3Fo7w5thCErZTDdjQTndbpfXX3+d116/CkKgjeHK5csFvHZjawu3VAVXaz2UlVnMo2PG7k9KJ7n+Q2Wz3ykdFa88bljjsLLB5ZZtySRBP+qjlGJtbY2Ljz/On/zyl3nsyhXCqi3z7Ps+aEMQWE/0zVu3CCphVqkFi/wKLLS03bVVUnd3d6nWaqysrKClYWZulkSlnDl3FscVLC7O06w3kAg8x2F/dw+DptGsgzCEQUDU7xNHEVEUcfHiRQu57cWcPn3awltNSr1ep91u0+/3adSbuH4ISIJKjVRbBsizyMqhNilsCAwp8UIrTf0wsNI8Y4w0TdHCah1pjn7La+gLkNKxjrRiw8pB9luSJPT7fZrNJnMzs/i+z8zMDGEY0mg0WF5eZr/d4tWrV3nrrbf4xV/8RZIoopckpEmCMAZlUlRqnYTVwLemhBB8+5VX+NrXvsalS5d47hOfQGV9U8YUYcuD733U3h/5mBPnsdwx3TfMPt6bPdb8Hnvd3ULVHcXsow44k729wxaKHJUnXRfP86hWq5y6+BAXn/wIQaOBcF26XQtp9R2XjY0NHE/ihR591SdVil7UpRP1ka5Lp9dlY2ODGzdvIl2Xd999l83tbS5deYLvvPsOW/vb1GeaLC4tcePGDSqVCs16Dc9xWVmyTqq3336bc+fOFRpNt9tlptlkd3eXTqfD008/zdtvvYnRdg9433eJk4R+krK1u8eFiw+zdv4Cnh9ghMO1999le2sDz7X13vv9PkYKKrUqxpWkWoEUtLsta6cLweLiIvV6PYPCWvtdGZ1538k87kHhhPQ8awLkmXdpmtLrtjE6ZXd7i/39Xa5dex/P86g16szMLdBPUvpxyuLSaXa2tvnTX/lTJFmCk8zCohZOY0t0x1GMk28skc2vF154ga+/8AKB73P+/HnOnTlTbCYxXhOdAmF3RH7HUXSS645kdiHEzwgh1oUQ3y4dG7sHu7D0vwsh3hRC/IEQ4mPH7hE5A5/kyvuDRruep2nmgJJOp4MXBCwsLRJlxRbiOKbT7jE3N4fnO3S7XWp1C33d3t7GSGE3eTCGqJRIMj8/z9LSUrbJgyRKbE54t9tld3eX8+fPs729TaPRsIuI4xCGIZ7nsbOzw+bmJs1mk+3tbTY2Nmg2mywvL3P9+nVOn1qm1+vR7dqCj92urUobRRGbm5vMzMxkC5PL9uYWSZIUC24/jlCp3UQxl4JC2Aqvxhj29vYK73qezJJHGJSxXntynH3Wbt5GkeUGBbZgZmaGWq2GMXZPN6VUhsWPqdUaLC4vceHCBd544w28EpJRinwb6GyXHcgSi6xdHiU2LOlKST+OeeM730Frzdrp0x/ATLq7NI1k//vAF0aOTdqD/YvAo9nnLwI/NW1H8oKPuTdbmFJhSGEL7Q02G5pcWKKoYlL65IUoR7clsl5ze5KQIKQNE4uRz9BecBn8tlyAUpU+WgiUEOhsQjoYPBI8kdrtmjSE86v8e5/7LIuLi3i9PkEc4biaLhEdrQhFiBsp4nabhfkmiTR09jvsvr9J79Ye/e0dHFezfGaeXrxHt7dHveKz/d776F6bXrdFperSaAQ4JPT2t7n21pvMNGq89957JL0ujtGkGQJuZ2cH1/PZ2d1jY3ub3VaLWrPJ6ceeoXbqAk5thvXtPS49+giLs01WlxcJpEfUjrj8xLMYVaHZnGdpfhmVClKlqNYComSXza3rtHY28aVD3NagBUZpq8kIG1IzQiAcWyW3EoT40kWKFJVGgCao1sD10dIF1ycVmtQkSN9Qq1UQwiBdjzCss7C4QqezTxS3ac56hFUBIsaRmq12m8c/+hGUsVLclQ6ugJSEauCCUVRcK+ddBEIbW4y09LcvJDdu3GB7w/oFFhcWaDYadotpTFGoVGVzI5VkwGpBDsDWpe95jKiYVxz8lH/PPyfBzh/poDPGfE0IcWHk8KQ92H8A+Flj9dmvCyFmhRCnTbaF892ksVtAndAWEmLCBhSTvO5jjudqvcgy8YX9AoCTlXs2UqCM5sy5C4SVgNbeLr3WHqIIyms6nRa+70Lq0kn6Wey8w5UnLvP2t16l3+qgHdjpd2hn+PLV1VXijo2LN5tN9vb2qVarpGnK3t4e58+ft5tIpho3jrm9tU290aDXb1ENQ04tnymk5/buDlGqEK7h3ZvXufTEYxaW69hdYXzfJwkcTl95hCiJaVck7ZrgyflPEFSqOEEFpQy9bp/QC0liq85HScLMTJ29rX3m5+c5d+G8lfjSZqzliT06TW2xzThBKAOpwsk0IkdKUpWioxRHOAhpEL5kaWWJ9fUt+v0+QhpmZ5tEvT6t3T38ag1chXThh3/4h/nZn/lpZufn6G5v4TjWiWeA1KbH003VREbSGKQBTzrEaYIrJOtbmwC4WdEKnYV4jfXZjp8rjN/4If+NCb/fafHTk9rsk/ZgXwPeL513LTt2V+luM/ph3w+7T66yOpT8BSXNIqdelJAkKUFgq66eu3CeSiUgCDz8wCUMfcA6m/IsszS14JJavcLqmTUilbK+u42o+FTnZnjyqY/SarfZ2t1BpWkBOe3st6hXqsUuLmfOnOH9a9dY39jAC3xOrZ6mOT9HrFIq9Trdbo+t7W3AFmRcXj7F7MI8q2fPUK/XefPNN+2GEt0+geezMDfPzes3Cjz9wqklHrv8OJVGg3OPPILJsu88L6DX6xFHtoxVv98HKYo93HJcfKfTKTDvnufhODl23inMnlx1z7WxAdTZGUg6KVlcXKTRaPDaa6/x2muvsLlxm3arhSMMjVod6fncunWLL33xK3gZHkAIie+GmfMzxBED2HX5Ayaz7AFjq+EoY+vXu0KizTCy/qj5eBTjHTXHj0q0GUd3O/Q2jlPGPrYo7c8+7YI17YONMqxTvK4xXvNcIh+Cbx/3vejTyHELtLAb/+QT03Mcm5xSq1IJa/TSmN/49V/j9MoSizMV0jjCD1wcVxAnfVQc2Sw+oUkVVGs1pBB8/LOfsWmtMw36QtNXCTMzM2xvb+Mj8QOvkND5ls/9fp9Tp07RiyPWtzaZm5uj141xXIft7W3q9Tr9Xg/X9YmiiFpthjSB/b0up5YatNrb/NZXf5UoikiiyIaeHIfPfd/3EAe7eDrBlxKxdgZZreGj6EW7aCXpdiKk0FbiOg6tbgspJfttGy5zPI9KpWbrxAkJ0gEUqTL4nke/16fXj/FnKkhhN3/QgqK4pON57GxvUK3WmVuao9fusLOzjee49FOFSlLae7ssnFoFpfnWt1/m4598jp/6m/8ToSP5zGc+x69/7bdQaR/fDehFfYuPP2ReaTPYbst3PaI0KTIk87mW57mXdwguU/79sC2eJtGd7NN+Usk+aQ/2a8DZ0nlngBvjGjCl/dlP2AeAA1L00HPLWPXsM44k03n4x0Fi8zNl6RXnZaITZaGuH/vYx/jkdz3L6upqAX0VQhAENjEjiiKSJNsqWZBVfK3z2EeusHrhHM2lBaTr0JydKTLCwtCqwnNzc2itmZubQwhBo9nEGEOtVrOlqpPEOujcgLXVs4Sez2yjiUpsLnuaaBqNJtVqjZ2tXTqtLo1qg+X5JZbnlmgGdbau3ebv/52/x7/8J/8Mp6+ZcQK8sIJ0PITr4gd2V1Vb/DFkf3/fai1JgjIa3/fxs00VldE4MofCekVJaMf30MaQGI3jeziei8qkp9IaJSzseGFhgSBD24H12bzyyivFGOSx+V6vx8MPP8x+q8Xnv/gF4ijl33ztN/ni93+RtZU16zz0Qjx3MkvkTO7IrJJtmiCxan05MSvX9AZzYvjfuGrD46T0USmyx40+nVSy53uw/w2G92D/JeAnhRD/BPgksHdcewExIXUAACAASURBVH2QJXbQchoFvhyQvqXElKHrSthnMXL+aKx8km00hMkXooDElvszqFIy/IqiJGY/jqnPzCGN4a2332VmvoHnu0ijUCrB82zpZ8+zqr7SCt8LLErMqxDW6+xGXYQrMI5AGAcJ1CtVYhnh///kvXmQZdld3/k559z1bflyray9t6ruqu7W1uqWhIRAG2g0NkKShcAYBpshIMBhTDgIZobw2GNMEHiMMWE7ZmQDBowxAwIBkloCyZJaaoTWllrqrq7u6q69a8v9rXc758wf596bL7Oyuqu6tZRmTsSLzHx5333v3Xt+5/yW7+/7DUOSbMxwOCTPNUurK0x1u47/PQzKBaeJkJJIBvT7jnAiS1KKTBPFTbrTcyws7uXZS0tEsc/xs+c5e/YsV569SKvRIhKKtZVVLp47x99+2/eQDTP+l5/9JzzwwP286YfeTRw1SZKENM1JdEEQN1hdvkwjCknSFQ5OdymyDD+UGK3pdmcwFkZpQqfTJctzfD/EC3ySQhN1OnTn5pEYR+xhTU3uEcYRSZ4xWFt1La2+z6A/wgrJO971TtZXVzh/4Vmi2AlOBmFMlm0AcOzYMQ4c3MfM9BwPPfQQ/WRApzXFxmCDKPDqCeTAPo5EsrJkgasQTNJLaGNqA96cg1ervO1EGb19rlk70dlpNz2E7Tu/tSVd+g3E8c9r7EKI/4ZLxs0JIc4D/wxn5DtpsD8IvA14GhgBf//6P8qNjet1q7e85kWcs2Irqw39ec5vRIXlt65ttOQ/D4KAdrvtMPLGUqQZuijwpMVKyEuACcI1zvi+62RTSjmSRdzi5UnH+hIEAXEUMR4Ma3SXCnz8kpix0kOr4mJwpUDfV4ShT+jPu+4yPyRutXjqqadpzy1glc/HPvMwd999L69/6cs4d+Ys60tr3Lp/Lwv79/B7f/RHvObVD7C4bz8f/cQnOPqdrybwQvJUk5XikybLwDqiSqHA5FU7roMFB5HbJat+Bik9lBcghcKPPJqqibCG/sYGnVazbgKKGjGD8QiBotFqobUBHHXXrkOH6K8ssd7rE4YxrakOWa4RvmZ+YY67jhzmA//POsuXLhD757jltsNcWVpidn6O7FRCmqUoIbduKtfAod+oR71T8q0mUBHVMZtA8cn3/XpQm980IhHxxMroVklzlQszubPvZJjqecx5u9suSmO81vkmjXpS462KxTfPQV23FUIDrkVTCJe9FdK6hopmh/bsIne89H7ufslh4jCkGUmMzhA2L41c0YhD0AW2yAh9hfRiEC6TL4TAFJoid2SQRVE4vreZafoba7RaLYJSG73ZbG4LRyY75Ypy8QkpioJRasmsYXk94aOffJiLV5b44Z/6CXq9Hv/6N36D3sYGWIHyFS+76wivf+A+jn/1q7Q8xcED+2gqOHj7IV77PW/jyvoGCIPIhwz6G/RXllnYNYctNGHcpNlsMjU94+ryyqPRaNFotgFJGDlk3jjPaDcjMNBfX0OUare+75PkmQsZlGSwsUqj0WI4GGO1hizjyvlzFFni0HdBSNydJYgidNDmd37zP/HBP/w9Lpw/R8P3HZIO2L93H3v37uaLX/wCumTrVULVbLE7q89ePa43aeV4lXYaJZ3WxIlqyrSJo1TNqAMZfHuKRFzPuFY54zlfc4Pn22nU5bXti8a24xxVdTlJpJsqSaFR4zG21+O+++4DlRMGAZDXKDzXoQVYF78aUwAOAz7ZYmWtxUO4mr7Wjr45GTsop9ZkNqvliScZXsBJKCU6c3zwmaKJc0NzJM2pLrN7Z/njBz/KS+67n/NnznPl8jK9pTVQHvgeujB86ctf5nWvfoCZhV187qFPIjzF3tjntIVXvHaIEIK0yLFZUQNlhr0+87NzGOmw/x1bKs/6DkzjylZuh7bWEiqP9eUNTjx1nBNPHAMM7UaT6elpbjt0mKABuTZ4XlCTdcRxk43+kDiOGaHdJfM8CmMIpAMqhaHP2fPnaAhBkruYuxFEnH32PEtLrkfg/LkLWzLr14tWu9HK2HPt1lU4ObkZX6ssd73j28rYBddnmM9njFv+d71Z9utw3aEsA1UxV1nG0VozO9VBxi1mFveRZRnd2aZr7UzHYAxIiS4sfqgojMZKCLwArN50c5WPlOBbQVFosOB7HkYXSE8gpU9WFLRaDfworCmusixz2W/loYREexbhKyfo0GkSN1u0uwsUViDjae58yUtI8oJiecSzx08iColSAUWaIxoxQRjxzMkz3LZ/L4uLu/n0Jz/Ne974WshyTp14mgN33wWpJdMpU+0ZPKMZ9jfI04x4aoqscBLVURSBdEk5ay2mrCBoLJHn8Ud/9udOsHKj72C4yne17WcvEnc6dKamiDoRw8EY3w9J05Q4jhFF6voH0jG5MfhhBELwyCNf4vOf/azTddcFHg4ElecpCsiynCuXLxJHMaNktJnHucYM2h4vV97dtebI9SwZkwvLtZzuFyV4+q0eLkR1ZI7CGqzVO9cZJy76ZI1bbUmX7FQj3XlYazcJKSkJIiv01MSdnETLGbGVBMOUj+ozeYiaCNP9dLRM+aigt7bOxTMnOf7Y58jzDZBjCqHJJeRC4gXlbm61o1ySgsLzKZShEJosS1wCTBeY0MOEHsL3HGS0gAzwoog8z+mvbzDq9Rn1NtDJmGI8YrC+yqC3Rv/SMk0vot1o0081l9b6qHab5uw8s7OzvPX1r6dtoXNoLzOH9mONpuj1CVWTlmxQGMX/8K4fYHmcUgQx62mByWH54mUe/fzDMO4R2oypTovBYESvN0Rn4Fmf5dUVR8wxHhP44SbFlIDcaIY6gcDjd37vL9hoTbMyN88J3+PRjRGtuVtRwTyPPX2G5fUV1tYvkyTg+Q1QEi9QiFgStBp4jRjphTQ6Uxg0mS74y7/8KF/50tcItY+wFiUlubRkyoKQSDzIFNoaoiimwGyZV1cRQbpJtKW75Xqy45ULP7lDbyLjNrF1EosQOz+qyX0jQJubwthvdLzYZpdv6PvYzeRO9bPSbssyJ5B46ulnuHz5MqOy3gxuAal24EliyqrnG23q4yrGl+1JNwl4UtWvkVI6wx8MGAyHtfSSlJL19TWKPGduusv8zDTjfo8iGVPkKXfccRt3HT7ERm+NxcVFDt19hFe+7nXkJqc/7PG///N/yg++83scrZQp6HQ6FIXjkhv0ethCIxHkSYrOS0ELLInOaURN+hs98jSn1WzSbXcQVjpYswVpJOlgxFNnzjI1PcPy5WV++z/+Nv/5N3+LX/lX/ydPnDpJd9cuurNzSBmUybRy7pfYe6UcgWSz08b3Q5Qfoq1hbrrLy156L9PT0wTSJzObAqCTI8/zGs/vlww4/18YN42xV2b1fLXDb4ahb8/E3sh7umPlVc8JIVicX+DAgQPs37+fPYu7abVadZumBMcBX3HVCVEvEH6JJpukohoPR6TjpHTPFY0wwit52Ys0I01cggol64dRAutJUE4MctBb4+yJpxitLNO/fJm8t0aEZu/iLG/93jewsbKEpOBHfuSH+eH/6e9iRE44FXH2/Gn+8++/j431Fc6efIb5mQ4nzp0k0SlREPDFz36G/uoKxWjk+sz7G6wNesjII88Lms0WS1dWOPnUSYb9Efk4QRlJoAJ8K1i9ssSqlHz6C4/w6U9/hnx9hCrg0RNP8Q9+9md4YnWZM5fX6Q8MvpL4nkcY+vi+u05+FBJFEV7Zkef5IRcvL3H2zCkuPnuOi6vPUpi8DgsnQzRdPrfR22Cq2a6vN1yNWd9OXXatZPdkDf6aKkbXwIu8GBDN9nHTGPv1jm9W9eDFLyou0aRLP0vbUqygKBgPR06TbDSqjRU2qbPq3b3Q2EJvEUPEuFlRtX0WRQHaZemtztF5Sp45RtXBoIcrSTWI4qbrLdcWoSDLEkyR4wsoxmOK0YjVS5eQJkfYnKlui1ff/0p6a2sYm3H23EkeeN1rePvbvw+jcz79yU9g84zZqQ5CW/wwYHpmiiQdceHceS6dO8+g32N9dYVca1qdDuvDPiur66SJQ/71R2MajRZaW7S2oI2L27Ulmuq6HIXwyLKCLC0YZTn+VJczFy+z2h8iVECSjNBZCsZ1sEnr+uIDP3KLoxT0RyOWVlYosoR+v0e30SEHWiUl1uZwZizK+1B11OWlTNT2ZpQbHd9qLrubKkH3fAbmQKjbV9Bvjkt//cPlWF0mVTjhAKUQVjAYDDDjIZmw3PGSw8zOzro6uICg2STPc5R0xq4AvySClOV31to4IULjWkdtoTHaUIgChYLc9b0bgUPU+R6e8hG+5wwhLBVPTUIrbtCIYxoqwuqc5fPnaE91eObY19h7+x34ccw73vgm3vra1/Irv/ZrXFpeoRXA2ZNPsL60QmQ0e+fnedldR2k1IvYtNLj11ls5ceIZbJFy+ukTNJcvMzXdZXH3AsoLyIqc7tw847wAY/C9kKdOnKDRaIHnMAg2TRmORzS9gPbcAktLl8mEhcAnX+/TnJ3hoYf+hpfecYQvHTvB/PQ9jihDuDgcJRxZCIoscYIapAXNVodWo8FL7r2bLzz0SWLPJ82LTV4CDFZItHU031ONNuvr646nTvnkuqi357oOfp0z4kaMXCG2lHndCcpQ70VO9ZvG2Lcj0ewOz7mq+HNnSKtjdzrvtY7dbKyYeP0W933rzdqOcNryu9h2XJnQ8zzF9PQs0wtz7L399pqZJkmSmozBGEPg++X7b75P5UZaTC3D5EtHSx2EXhkr950nYC1FmfSKSgFE33c16UowcjwqHC98kpB5MbPT0yzumndpAVOwsbaKnzSIQoNF8sM/+B5OnT3Hk6dPs7y8zGBxjekgIvY8dnenKfIUbJ/HnzjG3MwsZ8+eZ21tjbu697K6usr0rsWawGN5eZlut0voR6AkC7sXGY9Tev0+8/PzKM9jOB7TsIJCCuZ2LUCW4M3O8zM/84/4uZ//ab7re99FFDboCcdBPzc3x8r6Co2mk6a2wkcpat65MAxr+Oy438NIRZImREoRxxGDcYKQgtwYAhkQh+46FaYgkI7q+3rm2jWP2WG+XMultlhU+d+rtOQm0J0vxL2/aYy9MrrruXgvpJf3+d57E6Lw9TiXwFow0jhQjedRFAUXL15gtb/Ban/A0mjdxe97XFOgMQZhS0on6WrilcaaoAQTmc2OOlti+pJRWneFCWXIC02z2SyZUxtYK5BembTTliJ30FgvbpGORyRpytLKCu2SO67fg7jdwZMw7KXEzQazzYjZe4/y0nvvwuaWbDhA5Dk6ycA6Ysf//okPogKfc8+eRynBS1/+Ck6dOcd9978SnWZYBOMsYX5uL7Ozswx6facLv7zM3NwCfhDQG/TxwoDp2WnmvbPc/er7+eyxr/DWv/d3+eiDf8X3vePdNAJ465vfQjIasLG+xvmLl5B+wMFbD7Cx4TTtPenV0GMjJO1Wg7uPHOUre/dy8umE+17zGj71yY8ReyHJeIQCPM8nzzK0LRhnlkzn+NJjnIydvrv5JoWPE56r2AF4861ohPmGjJsBzQc74d2vf0xm06tHmqYYAZ1Oh25nila7wcxUlzgIa3YXv9Qin2RhqYYScsKVd/G7tbaWM65aY0ejkUPYGUNW5IRhSKPVBFxFABzVk5Ru8dEWWu0plB8gvVICyRiKPCVPM6LAI/CUo6aiQGqNLw3dVpNOIyZuhOQ259LyJUwJizUCXvaKl3P+/HkOHjyIMYaN1XXCIEAZSZ5mpOOEdDRmsNHDE5J+fwPlOUKJoiiIwpCX33MXX/ybT0OW8apX3s8PvOvv8GM//j/zi//ivew7sJ9AKZLeOrpsuAHHbSeEqDnvQ88n8PzSwzLMzu/izW/5XlrtKeam5+mlIxpxAwGMsow48JmenybXhUvAibKHXW5LuPLNCx6/ngZ60+zsk+Na5rW9zv6cB7+Q97VX39jq+esfmxBccHX5RhQhhGCjt8F6v8cwT7nl6J20Wi2Xkbe6pG/y0HlKqnOEcIk7YUGX63tRFAhjQRgkCqlkbdy20M5dLVlco6ixiR+Qsu4sAzCFpDvXJU1TRNk1Z8v+8k4UM9jo4fsppqEZ5SNypZBakSQp5JrReo/xeEyap1zpr7PR7zEcDpFSMr+wyKXlFfw44skTT5E9VnDffQ+gjERkltHKKtn6BnNzc47qar1H1Igp2glhFGGKDK01+/dPca8+iAhDvvrkGe47cg9v/R/fyTBPCSLBl973Pkbnz5O97CjtqRlyC3GzTZ5m+H7Z3puOEH5AM4pIkjEHbjvEFz/3eZ4+c47OzDzD4Zg0TdFAqBR5nrOysoIUEl8qcl0gELWmXn2Hy9t73SqwO0SDZR/VxJzejOy/UYm8m9LYv5mjCh2cQX9j1mvnUvos7lqk3Z2iNTNNt9ul0Wi4eFxU+mw5ElOGNBaJozS2pmzD0U6myPe9ekdfXV0lGycEQUAY+bRaLff6slW0Ku1pYaCs0Udhg+HA7X7NVkwrdm2oynckE570kSgG6RCd9PFbLfKxptOawsoCHfgMhwNGeYoNBKLhc/DgrfT7GzTbLdbX19m/fz9BFDPdmSEdJ3z645/ila98gHNnTrvGnFKMMWrEtOOIS+fOEUQhtxy6naWVFXJfs3dhBiF97njTnYyM4r987GNkoeR9f/z7/OR3vIa77zrCG974ZjzPccuPx05P3hrjlG2kqvsYIj+g1e7y6u94Lbt37+K3/8O/I260GK4neCjuPHyY1d46565cRBtLrgsC6ZB9Sm7KQn09S2E7jUkKyqqTTl4TR39j46YxdjPRCCMsGMwWEgEhBGpLd8C1z6XFZqdQXb+fwLVXmGM7sQlXrbWTzTb1IrBtDagTJ+JqyKSp3tuC0OU/lQKhWO/36A0HtPo90gCSdMxtt92Gpzy0LbBojBCOLcUKEBZhLTbwUFWDkLEkwwGBdMITM+0GaSBrZhptBXHcxqLIrXK0yGHgar0l9lybEcIGBKFibeUKwyBg965FjHELU6PpElrpsE9uDHmaMT09zXBtjeFwTGE0vXSE53m0Ug8KiVrcTTi/QDFKaKuYlf6YRtwgp2Bhusl60zK8fIIgGyLyFHlpyB133sHGcIXZecW6VURhxJWLK8zM7mFlY512d4bheIDfyJkPLT/9tvt47KtfJb73EPff/3L2HdgPXo4WAmUNAYZxmqC1xQpJYd13ltZ1Hu7Z1UapaU49/SRRowVqFRBIKRinOeefvYzymmBH7uYaNyuN1hO77tY5WU/Hba3Sk0PVljpxfHmm7bv4pFFXWfmvV3R70xj79gz3Tnvsjkwz17Eb3wiX3DdiVHF7Z7rr4vaZaVpTXaw2DAaDsjvNQ0pHelgUBRKB8nykkljr2E6F1qiySjAcj0p0Wl4SX4SkuSaMQrSxWGkIPc8l6qIIhCEZjcpEoMd4nLjXxU201ly4sownHLVTkmYMBgO0dsg/6ftcvHiRuYVdXLx8mV27dhP6Ae12E9+fdXpwiUsm9tccFbWxljQZEyrJ2ZOn8Izg8rOXMb1lpBAUnSanz15geu9eemNNP8tICogjTWoNnfYMRZ4y151ifTwgzz1acZPvev0b+O7Xv4G0zJBLDEWas95bxxYav6S10lojPCcRXRhDlmUEQcjFi5cJw5CFhQWKZMR3vOIlfOHzn+XKxUuu/Kbzb1g8PllJ2qmTTmwLAa81rjeRvX3cNMZ+vePrnYm/1qgXnbINdscGh+tgsBGUkkYlHLbX6+GHAbct7KLVatKMG6Xo4aa2OUKArfjmAWXxlaNWxri2ViklRkmMLtlPPEWz3YIyNhdCbLK3jMdYq9G6QAlBGERIoUjTlCx38eigt8HMzAzLa6vsXdyN53k88siXsNbSmZ4mjBxIZWFhAWMK5udnnd66lGRJwkyni8kLRFYwHo/xlWTUX6fdXeBMfwBpTohgOlJkhcWTPkp6eH6M8EIanisNeianf/lZOmGDsBHRiCIGyQid5WRSIW2CNYY4dsnNQW+A0dqpwVqNFrYE1GjyUijSKg8QXLhwgZMnT/OBD3yAp48f55Z9e3j44YfBanrJmE6jxfpogP9NmmOTBv9c8/qFGvf28W1l7IKroYnXuggvJou5/ZzXJKy42sPHYGu3q+qfN1q72DvPUUqxsrLCV77wJaamu9x552Gm52bxQ5chD3yFXzKtSgRGu6Ygqw2mcBNaSvD9kKjRIsscJj1qxCTa0orDsqFiU95YGIunFLEXYIyhNxwhrQP9eEGIMQWtdhsL6KLg9OnTCCE4dPh25ufn+cKXvuwaV6KYmZkZpPTob2zQajUc15vWfOj972dtbY0f/4mfpNW6hUtLV5iamkIiyMYZ506cZM/MDGvnHufw0Xu5ZWEXNohpt6bQUpEOh9g8I1KKbNjnrz/2IG9629toBLM0wwgtBP2NHqkU5MmQdjN0SMKiVInRzuVWSpEXKUIowiBESEmBK01u9IdMz87z3W94E03f59TxJ8jSFExOAIxHA9fItIPhvdik2ZZuNuw1jXty3m71PMXm6+wmmcqNjJvG2Cd3Uvclr4Ez3gnIsm3UGlzXyLm5OvWNjZ128etdbXUZ8+XJ2DGtiJhnn32WjY0NfN9jv9HsP7ivFkZAlYaKKCGyLmNeFBlKyFoeCZy0sZI+WEkch6jAp9PpOqnmcVJmkgEjQThIacP3nbY7ZX5ASKwH1mhMnuMpRZYlfPnLx5mbm+PlL38Fvh/wtceOoZTi4MFbWV5eJgx9Lj57geXlZY7ceRjfDzlz5gx7D+5naqpNu90kHydMzUyz1mmzsrpOKzPkScbyhUscPHSItlL005TAaBrKZc7FOKNIRvQ21pje67wMJaTj1ysKRkUK1pClCVEQYIWgKHMtVV+BnWABrMqVt912B8vLy9x///0MV5eZa7c48/RjdV+5pySZ3qSY+nqN51oobki59UV+rpuCqUYJYaNtCTqXjd523Bb3+DncnvKFNS+Y3XSt6xbVCokkth5TIekmr4rG7siCsyNST2x2p8ntnogUeKGjpZKxy8Qv7tvLwVsPcvCO29A6d1RRQYDCbnKJC2fwnidRJT95lmVY4TDgRjjhQ11m4OPAxarpOHEwWSnwpVMfHY1GbKwvE0URQRQShmHd2ZXnOYP1tfo9tPKRnmOSyQvNnn37mZqa4tFHv0a32yUZDWm1WvT7fQ7s30sYhjxx/Cle8or7eORrj3Ln4SPMdLuY3gClLY9+9nNc+fLjPPnYo5x68hjf++bv5j0/9ANYT5L5EXFrmgurfaSMuDi8wpH7XsFtdx2lbwVCeq6H3xhMOqLIhiAKTOEuksMNGAI/Imw0yHKNFh5BGDJKcobDIR/+6Mc49rVjfPxjn2D18gWyUY+Q3O14CgrthD52yuVsZY6p7rm8rgV/pxLdizXcah7D9TPVfFsa+06qmZv6WhYpq5KFuKqrCbZeKOTVxi6FKLPXtj5+Jzd+a1xfDrFZormqe66siwdxRHdmnvn5eQh9ujPTvPSVL2eUjAkbIY3SWEXJwCKFcDRSSqCUU1QJAqeMmmu3kERRjFaK0FPovICylz4ZjeummjRNy0UoJ2o4zfPeaIi1EMXub2sMoXRJrsxzIJVmo4X0fLLClQX37Tvg+vR9nzzNKIqCXGcIIdi//yAraxtMz84wNT1NHMdsXFkmUJKsP0T2IV26xImHP8Yd+xZphhDGMXmrS2N2kUdOXWb+wCFENOLWu44yNTvLynCMQZCMUhphQBwFjJO+u89lE1At+ugptLEuE4+H5/vkGsbjMY895lhnH//q45w7eYJ0MOSxR/6GjbVVBuMhxoIp0XLbF/ItzDXfpsZ+07jxL3RUSY5vRUfRtRJ0kzh7aTdvjNEu0hJ5Tn9jwHg85tbDdxCGIUmS0Gw2McLV0ouiQJU96a5zzSdLhhSFpjvVJk1yVwv2G1jpUGNB3GCcpgS+5/TXxyk6T0u2Woeg8zwPz7pY1+HlQywgfa8UYpAY7fj/PM9DSUdnXemlh2HsJJlDl7DbWFt3/eyp5tTJMzTCNs+cOoUKfMJmg1tuuYWFmRmyJKMzM82l3hqL+/extLCLtf6AdtTFIJC+R2ENh+85ig7aGD9EeIpRMiZJR1ikY8oVEIU+UvlodEkGarHC0XQJC14YYHNNlpetqUVOliTEoY+SPrfccgsffP/7eOaJ42Cc8qySApQgLwz+N4kv4XrHzvPsxuf7t9XO7tmtO2U1HB59oqwhN3fWyZ29es1kg8FkY8H2nb06txFbQ4jtn2FLHkEYVBlHTCq9aixCueeDOOLOQ/cwOz9H0AwJGjHz+xYZJWNmF2aR1uCVwBNjDFGjSRwGJOMBaTpGUlDkOVgnrBAGMY12izTN8KWkyFOKLMHqgrwkxNgUSHTwV8/3UX6ADTw8P2R6fgGlFK1Wm8iP8JWHipzqrLUCP4hYXl5FIGm32zx5/DgzMzP01tY4cOAAIvDI05zISlTpPmc2xwp45vRJZmZmOHLnIfqjnI6BYHmF08ceZzaOUb5HGgbM7t/HkrHM7N6DbDRoddoU1jAY9SmyHJNqfKlod6fxm02yPCcd9Mt7quseAQsIoTAGhJRcvrzEysoKn//s5zh+/EkefvgzNDwPD8OlcycxRpNZ7TBLfogsrm58eTE7+41Y2PUuM/r/lzv7NtjsJGPstUa1217/+Z/7fDuVRurYv4TgGrsZy6dpyvHjxzHHDEdedjeL+/ZSFAWzs7Pu85UEjNa638MgxrCpRFLJJ7mJ7SGV0xpLrKNotkXZBqsURfk9fd+jUbLNhmHMytoq0guY370bFUY0Wi1ybSk0LPd7rgMvLAUWEocAvLK8hu/7zKezzM/PMzMzw3RnivF4xExnAVEMiVWEZwXNZosUgxWGo0ePukSjlMSdmKI3JIojmjPz5GlK1J5ijKGQCik1KItVHqMsrfXYHamHo4mqhDEKW3EQGxxDkER5TkZKa0uaFUghqq6XWgAAIABJREFUWFla5vTp04SBR7c7xZ2H7uDRL32J3vqKw/y7GwY45Fy07X4KIb5+yJZv4bhpjH070m2nUdTIOIESZQcY5X0SAms1VjhqI8DdILHZHyyqLjQBwlRYpU2cnhHuL11BI0UV+4Oe8CRk1aE38dk2IfWbjSzGVUmoCP03CRYEKpIEyrVfri4v0+lOsb6yxvziAt1uB4Rhdm6GNE0ZbKzU3oqUHsqPsdYRTiplKPIxy5c3iIKQtFSSUaUXEcUNsJYiN1gNWZ5xyy23EJYQ2ZWLl4mbDdL+sNRal2Ta1d57fUOYFQgM49GAVgBSambaMUJ5CFswGLsknV3rE0rBpaUL7N6/jy8f+yoLCwsc2LePwfl1ZqdnSC/28TLNFZvQ67Ro332Ukx//AoenppjqxqRa0ZjrEEctMmMZrK5j8oIsT1w4oyRxHJOaDPIAUxRkhYPICk+5ey19ChSZLigKwzh3klVBEHDi6VOcfPoZLp09zW17FvEP7AEMQkmiVpMwjohbTcZ9J1OdJAnj8biunFQeYSXiOIlq35Iz2rY/e+XraoYbAZMZvxcSNJjJuu91rkPXIxLx28DfAq5Ya+8pn/vnwE8AS+Vh/5u19sHyf/8r8OO4UuA/stb+5fV/hc1R9ZTf0PHPM7aAXSoXnG3P7eSu16/fISEHODHprWHFteL5CoKbjMY1r3uj2WBjY4Moimrpp4O37OfMmTMoJfCRbnJIkNbtOtJustUIIfCjkCIr6r7x6uHANZooihiPxyzu2e202FdWkVIyO7dAVuS1q+8FPgqLNYJGI3K9+CX7q8I6SK52SqcXL16kPTXNysoKUzJAKM+10/oecbOxRSfeVx5WGyIkzVZMM2rQ9WO67RatdoPEVxTCkA+Hjv01jlxjj1QYW9TzofpeSjkocNUjIK0qG4cso9EIzyt798djNjY28Dzfsfp2u/SWG3ztsUexm36Bu+0ShKewmSEMQ2655Rba7Tb33H03QRCwvr7O0tISZ589T+g5TflxlhJ6PnmRY8Ddq+pefwvySM81rmdn/x3g3wO/t+35X7fW/uvJJ4QQR4EfBO4G9gAfE0IcttY+LwaguiyTJqJL41LPZfRVn3F1yAS4eLvBbSGqqHbf8njptmCY+J+YOIdyJ6zPJcWkmzdxg3cA+2x3Ca21FFmOjQwXzp8jjBvk1rHbvPl73sK5c8+6Fs3IR2tDFLnsuFdqjFmjXVecEBRFVjdpBM1oS+deRS6ZZQNmZmY4duwY589fYGFhBt/3QUKajvGCsG6cyY1GSA/lSbR2GfyDBxwG4PyZ0wyHQ+ZnF7h4+Qrtdptms0kURYTSpzCaRjRNqzvNAT9AGst4NGbvgf2MB0MkUFiFn0Grl+PbHD/PyfOEcSNERx5IQTpMGIyGdDottDXEcQxApguMFehi09h9rwx5jEYqD09C2G4zGIxoNpsMBkNG5cLaiGJWr1zh8cceI5IKYwRg8IRweupYdK7xlAM4nThxwt17pUjyjMh3HPUzU11e97rXkaYpf/Wxj5KWmm+SrdUhWcf0X3+j/4aQV9id9dmvNd4O/KG1NgVOCSGeBh4A/ub5Xng92Pgtx29LulHWzoW4uqyxc1y/3TCvfsctpbOraQRcLInZ4oBYu0P8jtjyvQTQaTVY31hlbmGevbsXWe31OHrPPZw8eZK9B/YhpYfAocDcIiUxZe3dYPGlh1QKT0psCRrRFqRUiBJYoqTb9e6+5yWsrq4y1Z2hOz1LOu6RZRmxisvmGSiweCUopdCWXFsCz2N2dpaiKOj3+3Vb6tLSEp7nMTU1xShxCUDheSg/IIwaDMcj+sMBPpIsyRAO8u/ChEIgjWC4tMrK2jrraysEnZgsmkZ4MXEjRhiJsfkmeUdJz+V7IZQNQNX9sUWOQ8hbMptgBWSjBN8PWVlZJcsyDt56C595+K+Zm5lGpym7F3fTW7lCYXJ8JNo6E7VYhACtXeVCliGZKTSBUK6CYaG/0eMjD36Y6dkZXv3Aq1haWuLpUycBJ/JY5Rng5trdX0zM/g+FED8KfBH4J9baNZwW+2cnjrlhffadzE6XO7KkjFvZPGh7Lbs+ycRztfs+sWtX/9u+Y0/u6lv+nti9Nw3cOuOqXm80pk6ubdZqt0N8wcFfQ89nZWkZrTV3HD3Chz70IQ4duZPdZm+Jpy/dVuVQdX5JxGBLHjpjQXo+MgjxcbkMadl05aVleqrLlZVV0nRMuzvFqD+g0+kwGA1JksS52GGECiOSLMUUhvbUtGOt2Vjj/Pnz3HrLAbrdLmdPnaTRaDDsjzh4621cuXKFwrjFUkUN5mZnGYxHtOIWGEFecrxlozHCwvLlZUaJJigK4tGIpL/B2uoSJpTsXuyi0wLpFVgUjU4D31d1DV1rDVIgRYAS1M02Aih0GYZIhdZjLK6E6Hkew+GQ//6xT7C+tsb7/uC/ko6H7Nu9WKMaK1X1zfzP9pr61ntYPbTWLC8vs7a2RqYLWnGDubk51lfXGA6HaKuRSKf4WpY9v57jhcDBX6ix/1/AL+HM6peAXwP+ATtvyDsubWKbPvsWQ5g8jh3c8XJJkAi80ghtmbAwxtScX9X/Kk3tKmk1mUCx8ip9zM2ynRD1OiBKsT9rXdtp9XvVdCKsca2sZtPYjdk8dzVBqt/HSUaqc7771a/i9Jmz9Ho97r33pTSnmrXUsC2z654fbHm953moQNS/Vxn+Sna5kntuN5uMxmM8pWo5qdZUB2FSokaMseAFEcoPHH5ceTSnZljvDdz9MIbFxUUuXryItZapqSnSNOXIkSNcurJEGIa0Gq6RR3khnWaL/obb0WVhyMcp+TghH2UuXg8CFnftIQ4kDakJleDCmZM88+RTDL/wKLv27kPtkXizASbPyPFcc0zgWF7TsrHHlAuMwFUrKArH+ONRIi4cHfeTTzzB+voG+/buJgp9jt51mCceP8azZ88RBhKTuSSrFE5HT0pJZgwBEo2jA/OkR2ZylFUTGXmHqKyYfXypSJKE8+fPo61h3+IepJRcuXKFrMjwqjlsbZlMfvHJ/cmE4PWOF2Ts1trL9ZsK8Z+AD5Z/3pA+O/AfwdXZr/o/13blqy+6Pbau65+mypZvK5+wzdB3uOJim1dQ/bwqKVc+t0kjJZ3BC1WXhqoFQSnFIEvoNlqOSEJrEpNz9K6jfOUrj5LlOaMsZ2Z+Di/yOHPmDAsL8ywuLjjFValQUtULiNYaz3MQ1wo55vs+vvIcQ23ZSDMej93iJyUV8w3g9NG0QQmnRWetxQ8CorhBnqd4vsQagRJOOqr6ntXvGxsbTE1NAZBkGfPz8/R7I3zl0W23sIVmqtXm9KUrTvJJ+ShP0ohikBYZeGhPkCtJZ3EXe7OCv/nEQygUXqeDaUdY6wgzkyRD+Y56yvdDZEmiWVNrl3kJawVZmhLFTZTyGQ2HzMzMMD8/jwD++uHPMD87R/uBV/LoVx5hbW2lVv4prEEqRaY1URBiCl2TPmamQEkPPeGaX2vuAMR+yPlLFwiVz/z8POPxmP7GWj2nrxExvqBxoyHCCzJ2IcRuu6m7/g7gsfL3vwD+QAjxb3AJukPA55/vfFUK46prUJa4d8puV0arJoQTgDqmnYzPJ0to9XtOuGf179te49x8N9En4YnGOpVWNwHV1oQfcpPnPc+xxpDpgmbkZIbDMCTPM176sldy+vRpGu0WYRDSnZ9F+R6tqQ5xHGGtJkld//l4XDjxA98v38vWi0ZUtp46Tnq3oxtdtrMiSuba0liU+64GhZIu855kBVYI2u02aZYDFmkNUklarQ7r6+u1uszGukPLae1IJuM4ZteuWQaDAXEQoywYbXj6ySfBOgVbz/OJmk2khWa3TaYUmcnwwgaZr5DRLPtmZ3hzs8NTx46xsnqF9lRA2GgSWqf/5qFQfkgYRwCOhQZX7rTSQxdOClqV1Frj8ZhkNObf/fq/4fbbb+c3/u2/Jdc573jb3+Kuu+7iyF2HGQ169Ho9Hn74YS6urBBK4XZvnLeQFBm+UMjKo5uYU27O7pyAzfKMQDqBzQuXLuBJz2naATbL0Pa5yax2quLstLBUZcAbMfcXqs/+3UKIl5XvdRr4yfJDPS6E+CPgGFAAP3M9mfhqTBp8HX4LZ4Q7leImm1Ncy6b7fYuGlt3KPLNThh4m3gPqbLaaaGSRymmdW2MQspQcEgIrHKVz9dmkFJu6XUYDLrmWFTnSUyAFe/bt5ckTz3DPPQ5FFzUbNJpNgshnaW2JOI436aWMRuLVrLKCzZ+yXIiAWq1VSgleKf9kodCZA9hI0CWmQAuQypFlSGkIgsgtTlajhAJPIkuG1lqIgs0F9cqVKyzu2UuSJOTaAW2GWZ8w8DhzymXsZ2ZmkMLRQSnlaJ2s5/j0fKmQVlJoML5PYQ3zt+7nyvoqKMjTESpwQo1hGGNlqVZrBEjneuuiQAjnZgdRgLai1J332VhbZ2Vlhbd8z5v48/f/Gf/3f/j3/MIv/AIfevBDgGFxYRdveMN3MT03w/e/6x2cPXOOD//lX7o6eG4JgwDfevV8yXTuvMktididwVQSQWEcd50nvS2h3Ld63BRwWTkBl4XKLb/6c/m2KoMJfCndrjuRgLNWY6zBCHVVuas65/bS22RGfnK3V4gapaaUQkQxtmxMqUpabmf3UXJzgbDehERTyXLqeR7rGz3aJdNrs9lkbEMWFxd52StezszcLO1u2+26NgM07U4TnaUk6Rhl3eethB+CIHDEEcrfUnd25SiNEm6nz5KRW8R07sKNcvfTflx7CUi3+1c1dhciuEUj005euVKS1VlKp9Ph0oXLTM/O0e/3yQpDq9UiGydEUYxEMDs7S1xi553unBOtaLVabKQj2pkiEj5EEYOOT2o1TZ0jxgkPf+KjHL77MHFnkbAREwQRftzAIhmniQtXfIU1Tmcuy9xCpJQiCDz6/T7v/5M/Zd+ePRx77Gvcsv8Av/1bv4Xv+5w4ccJ5ROXu6pWxc6vV5O/96I8wMz3Hr/yrX3VQ5HKeWSxqW4a9mi+VsU+CagLPJy0yBOCX9wdTlOGCrev51myd7zc6Crk5d3N7fXDZm4pK+kbHc8VQW3bw51jQtmdZr3VOpZRLwCl19fFi63PVJK/i+eFwSHeqw3g8JooiLl2+Qp7nrG2s14mdz3zmMzz11FMMh8P6NdoUtWHX4oLG1lTSWZ6ilHIKKOXEq/TiwGnHhaFftrGqTTKL8jtqu9kxJqV0mu1lq2sV+4uyHFcJWQghXI0emJ2dpdFoEEUR09PTgGUwGjoRiDii2W4Rxq6VtlEi1DrTHYTVxH5It9UGIfGikObsLIWE6ZmZ+v2CINiyAAdBUC9MlfE1Go2auLMinDxy5AiPPvpliqLgve99LwcPHmR9dY1//HM/h7ZOHy4O3PcJQ5/RaMR73/tePvjBD/Iv/+W/JPB852WWOPvcaAfoYTPk3O5lViMvchQSiaTQBdpsOrbi2i97QWMnl/85j78Zdna1bWeH0gi3JcuU1TXxYiDUll3dCDDWNZxc5VpZQGxirCfPvdP3rxtiymOda+xvubiuh1zVemKVIXkiKLPCkCZjsnRMOhjRjhtuVzEQtJvk6ZhWpwuekxw6dPcRmq0O+w4eIAg9Wq1WDcuVoUBVyi5C4AnwlVdSRLusfRD4SD901NPa7Uy+5wGuvRUcVsBai9UpRWFcPV655JbwPUCiPAc9tdbi+aHD2peJQSEEge/XlNaVOq0Qou6Lb7fbRI0Wvh8ipCLJC3zfp9loE0Qhvh+WbLuq/j5a50jhVF+PP/E4kR/QnJrBD0O8wCeMI9fVVrrEOs1co44QpCUNd55pvDDgmWee4SMf+Stecvc9nD97nvNnz3Hh/HmEkXzko3/GO/729/HhBx+s24Tz3DXrVAk44Sl+/ud/njzP+fVf/3WKosAvqzq+8siKjNgLGRdpXY59Phu6FhlGRXy65bnnPNPmmGzmut5GmG+7nb2KgCbd7hsZtdsutsCTtx4jN1386qFLF76acFvILIytdxuEuSok8DyXzdWFJTeaLMvxw5jxeEy322Vubo6NjT7j8Zjz58+jlKLT6ZSCDhJKY6sgq9V5xYQBVAtPUWT1bmztVlEJWfapWzvpfZScdVZu+c5Abch1srL0GKrvBNQiFxhdc8NUn9UYQzIclMw6lqCEmNZS0xXvQBnbCk/RnZqpk5zVQlNhLMBN2MrjMEDgRxS54+UbjUZOMHJ9g9/93d/ltltuZfeuXXz5S4/w4Y9+iCgMefDBB3n1q1+NwZBmaS2PVYVkWmt+9Vd/lePHj/Pud76LA3v3lZ9Rkhc5cRiTFRmtsLHj3HkuT/FbPW6anT0WW9ed7SG7u+FlnRmBh6wVO2GiZxx7Taker2KwmZi0W2rhE9n4+rOVSTorN91ypVxSbrt6i5UC3w/KVlZLnqcUeU46GOEJSZ5rrBSoOELnBVPTXYwUeH7I7UfuRAU+u3YvsmfPIlEckiWpc8spygnpPktlNK5k5tXGo4377tUks8bULDWVm+/qws44bGn4VoCo4v/y+yIFWsiyxdX14lff1fM8ipK0oi4FliW8VmeKVquD9DySLMf3AjrTM3XMrkrMulR+vUBV6rYCnGJMmuJFHp7cFh6Vte3qexhjMGUGfXV1FWstn/70Q4xHI+ZmZrlw9hynTp0iHyV0Oh3e/8E/JUkSuu0OyXjMKE1QlAhHti6ge/bs4dyFZwH4O+94J8ePH+epp56qKxOe59UacNvzQ9vHtZhv6o1nIua/XqKLyWpA8u3c4not3G+V09DGIjAYIZBV62h5jLamJnrcfr7CAMJlqRVXZ1I3lTs2ny/KGyHtZu9yBTjZHoBJQymu6HDu1YRUSjk5pWYLC4x1wfT0LJ3uFM2pDq2pLl4UOYhpFNLstPF9D2MKDBqhCzQWie/iyDIhh7BIz7niWZHjCcdNV+18FcONKdF+ogL7aFci9JRXewBZrhGAthohJAqF8iW6KBeasrZdxfS6zNBXi442rhQ4HjpAjlA+vh+iIoktcvJcUBQRSZq7erlyrqg2Fo1AeG5X9aMGUbONlUVNtKlzJ0sdhKHzVGRJ7Ckkw8GYEydO8OzZcywtLZEmI5SBS2fPcvbUaT7z8F+ji4z3vOc99IdD/ukv/iK//Mu/zKteeT+ddpuPfeLj+FKijXacBWXpdvnKEgd27+Xi5Ut84AMfQGvNO9/5Th788Idds1KxMypuR+zGNmPdbuiTP7+R46Zz43cydElZWqN8iKvjnWpXNzhbtXbTJTVMMNmURqtxpBRVjD/5cOfZthBsO6Z67upx9fnyPEf6rvYat1rcdtttNFpt0lyz98BBDh0+zPyuBe644w5mZ2droxJCOA13Y9DlLgrlImL1ll2kSpxN7oSWTWMUwvHf+VHoHr5fHu+w8HXSy2yGCZOvrXMSJWKv7qe3tn5uMgEopWtHrVz+KulWFKb2juqGHWPACgQSWXLdu54fs8Xtr7wIrbVbsAxEfgjasm/fPgLP5/zZc/zFn7+fJx57nCuXL3LH7bfiS8Uf/OEf8J53v5tf+uVf5sd/7O/zxS9+kUJrp7dnNIH0HLQVaJauervdpjAunPA8jy988Yu89a1vJdOFk3D+Oo468fcNHDeNG9/Ytu5YtpI8CgS6hLYq61hrZDWp61XSGW/NTEPlVl1dC73mZ2GrAVU/J72FLYk7trpuFVOKVRZTur+B8hj0+jSabcZFzl33voRXveY76Q369AZ9VOBz+OgR9u7fhxCapSuXsNaQp2OKLEfqDKUUcRzj+T5RHG4p/dWhxcRnqj6XNSUVVVmaA9xOVhpNnudXuaGqFFownvtZxe6VnJTWul4k64WlcK54FDVASaKwQdRo0mi1y6x5Cz8IsFYRlMk8jajlpUXpVkWlrLQlcQtvoVHCqzvwkiQhajTxfNey+v4/+TNe9apX8Y9/9mf5wXf/AP/i//hnzHY6NBsR6yurpNkYX3mkeUYC/NiP/Ci/819+j5/+yZ/iN3/zN3nLW97Cpx56iPF4TCOKy+Yb588tzC/Q6/VI86y6OAC8/e1v54/+9E+QQOQHpLlLGGa6qCHazzcm5+wLET95IW78TWvssI1DG0EhJmL2CWOHcudlq7HX59lm8C6hdO1S3XYaq+11+apuf5W6Z3mctgUoiSrRbGjjSlSNNl7gI8OIV77m9ayvr7P3lgNOs3xulrX1Fb7zO19LkY9JxyNWlq5QaMelrpQiiiI835XaqjKUVxqkUpuJt+qzSikx2mmU10kxgAnWnApvX3kNWyoRYVQrxFZJrMrwK066+rpp53l4YYSSPlEjRnohzWYTP4hKnfQYKR1llRFuZ55kogEIytyBIN2S7TeGGh0nhHJkElrz6U/9NRvr67z/fX9MmqZMNRuceuYZ4ijElg0ykrKvIAgYpQmvuv8BxuMxL3/5y/nUpz7FwYMHeehTD2FxXWsYi8bQbU+590wTl+sIApIsxQJ3HTrMiRNP4Xt+zRx0o8ZezcBvlrHfdDF7tVNvp26u+4Rt9ftm0ga2sm1uZ5w1VWZ88qJWCD27iaOvQgjNpkcgy3BgksZK4EA3FYFkeSIApDVYz9VYpS7j4CxHCg/DCJH5vPm7vouhLtCeYm5xkf2338bi4i5HPDnuc/nZVawxtJsxVod1hj0vMiwONltkOVYblwWXEmlUXR6sEYBK4anNhpqK6KLCl1fXE3Adddi64mCxyLKlMyrbbK21hGHZcls2jlQ7v/JjtNaEUQMrFEgPhEIbt/tZIVCek4u2VpCXghdODce4HErpNVhrQY8xTu2CQroqTGIKMqMp0pSlC5c4f/4CX/niF7h06RL/8Kd+kt///d/npffcy6WzZ0E7Zp58ouPMpgkCeOLYMQqt+fJjX+OHf+iHeN/73ldXeTzfB+04+jf6PeI4RhtX6Eqz1M1LITh+4imOHDrM008/jRCCTBeEZd5h+9hpQ32xW+xW7/T6znbTxezAVYa+09iyo5eP53vVTqW2Ldl0cXXTixHlY1scv53DbhNwUXbNmYnadBCQJAlZ4ZJYH/zQh5manmHPvv08eeIpLi8tcf7iBcZZWk8Way06L3c2JUFRu91bssDG1F14k0DhSa9kS/Yatysb4XZ+L/DxgkrDXNbhQfWovAdrbd1fXmMVyjxBGIb4QYTnh/X/bBmry7JyIJSPEZsVkElGHVW3gTniJqvzMtufbZbx0PRHQ5ZX13j00Uf55Cc/xTMnnqbVjNm7Z5FHHnmE17/uO8mTlCRPSoyBW7iVlPWV6cYtesMB3//93w/A008/zate9SqOHj2KQjBKE3Tp+RhKTj/c3uBJ51Vpa/CF5MKFCzQaDeflQCm8cfOOm2Zn3y6JMxmLTxxUX/jNxPlmgO5ctk1pHLntnBW1004LYW3g11gka6PBZf+kdRP66pBBossJ2mhGKOGxvr7BXHeW1nSXvDD0sowrqyt0Oh1e+cCrGYyGNJotp3FucsbjMbJUTwWI42iTsME6SGaWZShhsdqVqCQChIcQBZO31VQJNs+1uQohwN9kvSkKl+mutOCl7zkEmbV4fkSSJHWSTGuNH7p222w0rJVjHaGmQAVOmSYIgnIBaJQtqg46W2hLkY7d5/E9xwVgrMMglD36gSfRZTNJYp1w5SjTpKZglCbM7lrg8a98lbOnz/DsmbM8+vgjREGENQVvfuOb+MhffYRd03Osra1hMDT8iHGeoKSHMIY0SWj4If/1D/8bb37jG/noxz8OwP49e7jt0B2cPn3asfDids8sz2qxj9xopJZ1LihNU2ZmZmg0GlxeuoIn1Y67+PbxrQqcb4qY3WHjnXu8JcP8HB+tjo2E2XLcjpps1fnY6hFA2bgycQ12St5ZazFy830q5J62FoW7eRWEVJRNKI24hVIeG/0e9z/wHdxx+C6M5yGVx4lTp5nbvcs1bgQBi4uLHDl6J1IKBv01BhvLZMmYIhshlcDkEAQBjbh0oY0hjgKEsJhSEqrVamHDZp3lltKh4arM+WRG3XA1KEZNhDXVrk7pYk/CVqvf8zyvm29cGbAMIcrWW8/zCMK49hSqUmTVWeawCpuZfidgoeuEY2oUo9EIbaDX67O0tMTK0jK99XUe+eznePLxx1hfXSMbrjMajUAKxlrzjrf/v9S9WaxkSXrf94uIs+V28y51a+2ururu2XqWnp4ZcjikSIomDFoyadEQbBmGtRiCDT/YhgE/2NaTAUGQAMs2aNgwYMMPsiCboqyRRFDU2JYoihKhmeFw9u5m79XVtd89t7NGhB++OCczb92qruGIVjFmqu+9mSdPZp4TX8S3/L///xfRWvO3/s6XiZWgAzt6KOW6BbvlPPi5n/s5vvKVr/ATP/Yl3n//ffYePMA6Jz0WLJlnZDNq55Hq5pMO134+n1O55geSjjorZm9fv9ae/SHnXOD/cMbsvnNJz3huZad+3HGPPDdP1nSw5tq3CblThq48pEYuX4eeA+qixnswuuS565fZ2j7HwdERvbt3uHLtOps72/wr15/naD7l/PnzjMfjLpYuyxK8lN20Ee0xrTUuUjjXUNeaKJKsfBIbjFE0VaBM1OvYfPnwjpZ0Y3VYa7vFqQMKBVfd1sv+daVjtPKiJReuS2RkAXBC9kocxwEybCQuD7X71SairhYfynOrQKb22nULVEgCZpmQcR4dnXCwt89sMuX73/0uWSKhQpZljEYD9qaH1N6hrThtr7zyCr/7zW+uJcokGebP7JG4f/8+n/3sZ3nttdf44he/yPs3bvDmu+/QQtq7aoP3D80fhaLxIqnlODuMfNI5tzx+Pd/0L3I8NTt72v5+xpfsXPaV31svwKhl4q4dp7+TXnkNLLvg5PeHEVBnfYaW/w3Ee9CnF4Tg6ib9hPHmNr1ej+OTCb3hiNI6Lly8zIsvvcT5i5cYb+8Q9/oMhj0uXLiAxnOw94D5bIItZxTzCU2V4+vyCYwOAAAgAElEQVRawCc+yEaF2DoxESghrBgNhl3PuvVL43NBxRWWj0kiUnbfdoduQT9tXL5WV096a/X7VcNtr9Xy/ZZG0RqIiZKHyoFRAOW0xt024awu3kVRcHB4TJ6X3Lt3j70HB1RlybDX58H9u/yDX/01Xn/t+5Jtx5GYiNI2pGmP4caIOEnY3Nzk9ddfx7sWIefxIcATrIa8l/WeSxcu8OD+fc5tbQvd1ORkCc9daSE+PY80IWzzjtNh6OoMfNQisPz98dn4RwFu2mPyP6w7e7tCPurCrRp8t2KfPuihcz6ciWwPf9L18yxDb4EektxSXVy6v7/P5SvP8NKnP4M2Bp30uHn3NnsHh0yKnM9sbnLl0sWO+GIxn7NYzNAaaudwTR0IKCqMW0oO17Uk/ogdSRr6rY004li70mbrPVq1NMnr31wr1ggb23ZW65V4AXqJm/dauNi90mgjRJaoJUZedn+NC8AaoKvbe68wK7PrNABotTxojDDitLv6nTt3ODme8tZb71BVFd/42te5ceMGF3bOce7cOT7y4vOMBxmHh4e89+7bxHFMkUvp797eA37k819gMBjQOEtsEmpbkZqU2tqHpkmkNXfv3+fy7nkO9vdpvGNzc8zx8Qlaq7XW1tNKAw6hmVIo8I/IM/0BjsdJP581njpjh3WgwWm3afX5Nv+seUSmfeW6t6BapdSKLERA4j10bIjnn/BCemQBUJFhtDHmuWvXOJpMee+Dm2gT4+MYHSeQRHzy5c/wzLXn2NrZxFtHsZhhXUOaRHjXgDH4JMLXFVXdoLXBRDFpnHQ0VFoLbRReYxu5DpFJiJNANd3lIzRJFOFYLk5xHGN1jDqjjVRetIz5vU7XEHAttBbo6u5dRl23Bq27jHt7nlUwjvGOSCEED0ph2p5vL9DXw+MjTJQwHm+xmM4oygWTwwOu7O7yO1//OlW+CJj/Vgrb0zgRv2waKX9dvHiRmzdvyjzxHoOhstVKjLycV41zDJKUq88+SxLHRNrw6R/5HF/+O38X12Ix/LoUWVuJSXREbZdxuqj+fPicOWs5aBeS1eTz6fzS2ed68sXlqSm9tWoZp0db0pJj/MPPhVhwFcbaPa/WF4H24q1pvQWJjtafkFPI76vQ27XzhgfKRgAWbXbZGMN8seDd928G5hdFnCZcfvYZkiDe+JWvfAW0oq4KqrqgriusrfHeopzFRKrretvePkecicBh7Sxpv8dgY0TSy0j7PbJBHx1HqMhgkngtTo7jWOCqIQPf0lq14Jy2JbU9Hm3QUbwE4MQJSZaSZNJmauIIE0fEaUKcJsIYoxBlWqO73u8ojjFRRJwkGK0lx6GUaNcZI8o2atmc0yb5yrKkqEoePHjAO++8x+3bt3n//ff59je/wztvvc3vfP3rAtyxFpDdtpHtNMCH5Z401jIYDPj0pz/d3S8bZtbpKk5bvcmrktdff53z589TVRVvvvkmP/bFH20Bc6hH2K+1dm1DWIYLZ//PrZhm+5jFr817tfp8hw9Rpza6pb38IDw4T93Ovvrh1zjjTv3sLooi8M7JWN21WTlGfllarQ2PraLl2h16DXO/8pvyhOabkKFHVvkoMoHPrUQnGc+ev0hlGzb6A17+3Cts7p7nox//GNM85+KVy8RRivINTVUwOznANTXGO7QS8oMWt67jiP5giI6jzgglxtYcHh5SFAWj/oDRaEQ/zcDozp1uDcCYUD83Ufi3JKRoM+erX1QZIzXzKCJOpI1ztdtvdazG461oxvLauU5JBujAOqFjV3Zcr/AhXt8/PODGjZuS7LLwxvtvsbG1TZIkvP366ywai7MNCovz0Da9rt6rdjP4G7/yN/lLf/Evds9rNC0z/GnjMEbKf3mec+fWbZRSfP/V1/gT/8Yv4DykUfRQ08uZ8t0PPxQ+06N31Ccx1H+RYcFTY+yt0a6Ox12oR2U5H7VYtO8Bjy/pPe7SikaXx7XxJjKZ87KASJP2MvLCMZ/PefFjH+Xc5ctsbm0RxzH7+/uYNOXk8IitnV2MrfC2FkZab6XRxVs2Nja6zrIolo6z+XxKWVWYFbd7Z2cH31hmsxnzxZSslxBHvS7RJaWtYJwrfenAWomseyw8HidZ10e/GtuvNrmc/gdi7OvelWGVWmy9HVjc3XaBtdYyHo85d+4ct2/d5YObN7h56xZvv/km+XS2ZLj1EOmI0jVh4Ygeycn+xhtvSA7lQzjbhdhEFvj5fM7GYEikFN/63W+ioEtO1nX9A7vBjzPmRz3Xegenx2pY+/s1/6fG2OFsY3xIh0UtV9bW4M9aKFZfe6bRr6b4WZbYHoXEa1Mh8lo5onINsYowUczO7nlq29AbZnzqM59lMpty+/ZtFlXNc89f5/7ePp/4xCe4ePEyk9mUk+N7WFtTLObCmhKB0gJImc5nkmnf2qaqKsZJKpLLVYGynl6WdOotw0EvlKISGiVNJl2HmJe6uGmaDhijtSbKBl28XVSSUMt6A+I4pj8cddej9RLW+vhXkpNyP9TaguFWjMsFbHrb966cx1EH4kgDSpbzJEk4f/4i29vnGG9uk/Z6XHruGoPBgHu37nDfGOqtgkEqTTI33n+fo/kE8N0EVgiAqJ0TX/va12icI43Tjqmn8Q8bflsGjLVhms/ppxnWe+7evUsviinzfG3TWZ06K9Pn4fM+4vEPGwL7/rCzn8pRPaH1P1XG/sOMRxk8PN5DWDtHh61/2GuQUL5DngDLpo04S7l7/x7Xnn+eT7/0CoQ68Ctf/CKzfMHdu/e4fPkyW1tbzGYzlBdlzySOyYYDgcR6hzGKo5NjaUVNe/SHI4ZRhPFOesjrGucapifHbPT7DEd9jOpLw0hVYTXU1kFI6iWBYMHE0jjTLgJ1XYuohLUoE4nYw3DYLQBN6GFfzZifduPbY9vnOipvZWgJhVez07E2kCi8j/FOiQqsUiiThPNIvuGll17i2rXrfO+Nt7l18wOyJKWeTzk+2MdagQ9rrXnmmWdRynDngxtrcS5IfuDdd99lNBgwmy9ITUxlH9Zcb1+zCuYaDoekk2MaZ6UyQgvfPmu3PXt8mKG7H2Z7/iHGU2HsknBo1VpCYi3gjdsbqVmWOFh53Hu1VqpTnjWVl9YL0KguISPH+eAyPbquv/YZNXSNNIHttbZCLpjP5py7cJ5Rr894d4frH/koi7KgwtPb2GDY1Gye2wHlqZtCoKbWoZyiPxwSKcgXM5xt6PczFtMTptMT0mbO9uYWerTDqJcFfHpBbDRlWXPv4IT+cIPRaBOfOFJbdeywSZKA0QyzfrhOXjDfClxT0lhPrzegqW0natnUNc57siwjTjRGB1Ub76QqoCNBwtkwYZUKeHfQygY33uKdyD5pOSRMMo/3oKK+5DvahTXIbCscykh3WpomvPDCCwyHIqpR5QX7e3t871vf5OaN93j3136NvVsfkJqUNO11C5huE3bWCrIt65HP51IpsGffV92Cb4zGWdBZQoEgdNq50wqMdnOwm7frFttRpp16o/UGS/9YQ3/yGH01Z/Fkr3kqjB2WX9Jy9i4su/MyO/nYmL1Luj18fnm83aEf8VkecW4pEanuvLGOGI5GJEnCaGMDZTS3b99GxQkXLl/ig5s3+cxnP8unX/4MZVmG2NMHvrQZdV1zcqLo9TI2xkNi3QNfMxz0hZLJNkwWOf20QmcZGlFzGY8jhsMNoiRmMlsIDj9O0Cy70rqGFb1sY6V2uDrIHzvPYjZlMBKuu7IsO8KJJA1cdW59wRVNeI02AmbyweeUWvNyKCdmUAeqKRSh5Kk6MEuLVXAK6XajRjmDjmwnGnl0dMTk6JgbN26wmM9588032bt/j1/8xV9Eu5rvfOc7vPr974Z7tpqog92dnQCEUeR1RaR0KI2tj8Y7IqUpq5JYG2azWedGn2V3/38wyjzJ+P0k7p5EJOJZRK75InId/xfv/S8ppbaBvwlcQ4Qi/m3v/ZESS/ol4I8DC+DPee+/+QN/sseMs9BKZxvnyu8q7O6rx2rVrbqnM6ynzyltnWrtb+stxyfHoDXzqmBR5CQb20ynJ1xUl/n5n//XaQILa5pKKWo+OaEs82UtO+jQAXit0M5QVhUq0URRQr3IO5GGOBA2zOeiP57g2d3dxRjDdL4AIIqEErqDvHqLtQETXjc0VcmiFArqQX/UUVP3UynxJUmEUTrgD1rXPBSYAr5ceWma8WpJCbZMzj3ceafbtVUpPFpi9e7mhFyJ83jf4BqFVQoVe4r5gr29PT71qU+xmM/R3jEeDvjn//Sf8tf/+l8HltUX3X7mcN7hcMjJyQkmimiaQIr5qMU9vKZ2oud+1nx43NyAddddrs2jX/8va3woXFYpdQm45L3/plJqBPwu8IvAnwMOvfd/RSn1XwJb3vv/Qin1x4H/BDH2LwK/5L3/4oe8h1+tV7ZSjaeTIavPL43dd1XI5WPrK3ir734WoeRD537CBbONR3Wc0Ov3ufTMFUajET/2U3+UNMsYb28RBaGEZ567GnDeB9RljvKOjZG413med7RHWmvposoyvG269yjLGq1hc2ODJImITURRCJNLlKTEacpgMMIF+KkLrDH5fIZRntnkhKaumUxOcM7RGw65cOGSQG+zPkmSkYTPmiQJPggQKMdarN62qLastLA0FKGzWm9d7a6zWmbpbdNeO9s93vUW+JajPkEPtlksFiil+K3f+i1wnt/56teYTo751S9/mdnxkYQzhSxyraF7POd2zrGxscHe/j5KKWazGY2zj3Tj22Rk1dQkUUx+qlW1nYePcuMfFaM/SfXn9zv8Co1184Rw2Q/NW3nv77Y7s/d+CryOyDD/CeCvhcP+GrIAEB7/372MrwKbYcH4kA9/xmOPO36tAu67n49zs87irjs9TvepP254bTBRhI4jTiYzPrh1h3/+1a+yubnJeDzmypUr7OzsSC97VZAlUUfWcHx4yGImNMu9Xq/Dps9mM8q6ARURJz2y3pDBaEiSZBRVRVWJHnrbR55lGUkUUZZ56CePWiIajBF10bIsKcsC5S1JJF1aLcBmle1mFbEleH/QZqW85sWolQuxuReAC0j58HSoJJ1mK4bulyQgamUBFhlutfQAvGU2OcEoTZ7nWGu5eUuYYm/fvs25c+fIMpGsOn1PFYrxeMzBwUG3INpHGHr7XUHwDZsbY6qmJqCfJRfBKqZjWbpk+c0fOdrv9gcx1KnP8iTjB4rZlVLXgFeArwEXfBB39N7fVUqdD4ddAT5YeVmr0X6Xx4zVrqI2ble0FdmVz8AKRJYljv5RrjwsEyztDi8uqm+/U/f+7Y1xpy5gS9XkvCMO8j5J3COJIuZlxdbukOdefJ5Ll65w5dnLOAWTyYSiKomThMFggFXCKhNHoLIIGk+eL1gsZgwGI565ejWIJjoWubjZKvDNp70+ZqiJw6Tu9TOqIhyjdEeLNDk5CSUuS14sWExOKIoFtioBqc33ej1M1u88ifZfkiSAhAqzfB5QdwlaCerN2UBbpTUetxTnaDvY/HLydSVMLc83zq0sBKH+7j3KCist3ncUY847SRqahKapuHXzfb7xjW+QJAl/6t/9UyRRxD/7jX/M3/vb/xfFIiYv5lJdqEoiIz33H9y61THeFEWBVlqUWlnfmSHQmIU59XM/93P88t/6leVcWzHUzsAVnXjHWUH9aXz86Q7Kf2HjMQvYo8YTG7tSagj8beA/895PHrOinBk+n3G+Tp/9rCEljw8fq0b+OINvz3l66FW03CPKd07JZ3F4yqbC0DKgxrz82ZfwSnP//h5RnHLxmUtMJsfs7Oyyvb1NFEVMpychA92WfzzaWXq9XheHf/D++2T9PlevPsfOzk6nVBLHKc6K9vqiLMFZjFouTEkSEyvhUzdG4eqG6XxKscipy5Isy/CBhqofaukYg4oMSkVEUbLW725ZZ4kFUGj0GR7RqquuUJJhD0auFB22vOULkXvqQQnttlMO5UUs06NFPlp5QBPHEd7D7u4uf/JP/ps0TcP3v/s9jg8PuXnzJl/4whe4f/cOr73+feGe2xgzmUwYDIdLqerwec9KzLUjCSXA559/nrt374rhn5WYW0XqqZCkfTpydU88nsjYlVIxYuh/w3v/5fDwfRWkm4Ob/iA8/kQa7X5Fn12tQK0eWeM+9fhq7fxJDf6slbWFWRjCqhwCtNWdvkt2WUFZjzbHXLr8DF4rvv/a61x+9hl+7I/8JNevX+fFj16nKIrwbxHQaGCto6pKmUnK0U8TvLeUpQg2DAYj+lnGwYM9ptMpw9GY4cZIvpEyRIkiTRNwDluVNFWJc440rTsGGq9qeknCINvGbi7hqcVCkk79fl86y5RIOxljSGJpY/VadOzyosBEEp6AwbZusBJjVo3vYLuwrMErNJogp+x8177aXnfJ5HvqxuJDXsGHxKHWIbmmNCZOMbHCNRaHJ80ESHN8fIyJI/rDIff2HjBKEuI4ZTwes7u7yxtvv0UWy7G1bbo5YJ1dW+RPJ3cr25AYEYR8/c03Hpm1X6+eBbTgykQ7vUnolcd/kNDwD3I8STZeAf8b8Lr3/r9beepXgT8L/JXw8++tPP4fK6V+GUnQnfillvsPNU4b8qPAMqdr591icZY3Eur5UrLzmBU3rLuZKy+LA7Nprz9kc2ebH/3Sj/HyZz/H5370Ryhb9U9j2N7eJo2ln9ulMd5b+nWC8w1FseB4MiHWhvF4zGiwGYQkCsbjLZI0oSpzphMhmegPRiFhZtFGwUp/+GKxQOuI0WiEagoqW4naqw+BjteMxpvosINL2U2EGtRKG2sUJR1HXWxitImIdBy61WpQqlOH8biQUPdhJwahuvF4Da6xnaGLxx5ILK3E+W0LL22VwIn6jDB2OPAQ4UiihNFoxI33b/LaG7/Hg7v3eO+ddxmNRpwbjVjMJNy4ceMGo4HU5Iui6K5NS0+m2nt/2jMJLrxSitt375AGptjfz1iVFl/lUHyY8uJf3niSnf0ngD8NfE8p9e3w2F9AjPxXlFJ/HrgJ/FvhuV9HMvFvI6W3f//3++EkPlp/7HFIubPGhx36JLdBXHBHpIPYQciG9/pDkjjjW9/6Fouq4sWPfoSrz17sdj2hWvLM5hOU8sSxiB8YY+j3+6KyGidC1WwMSZIwm83YiEIral1TVSW1F4bWNJLApg7AGWttIKWsyecLtsd9GmtxdUPtpOuuNxiSZLKj142lsVYMXEsEK0m0VieuoW1TbVtV2799kDlu21W7XEdIqjgtpqW1pkFAS7iwMHiPb3XendBZ0SrnWFG1Wb0RbXbc+obpdMoLL7xAXpX82ns3eOWVV/j6b/82b731FhvDETs7OxyfnDCbzaiaGqN0KLE+HHK093v1OaOlhz4JzLBnyS8tX7fytzobw/40j6eCqUbc+EBt3D7W9TWtuuirrYrr2cj1ReFhN+xRRt0CdboyiTKdzG4S3ELvPRtRTJxkLMqCpDfimeefZ3N7i/OXLhLFMZ/85CcBGG7vsLOzFXTRG5TylMWMyCi0kVp3USy6MlYUoJpZnGCU4Nd7A5FBHm9vidIoEZFJSLIeXhm8VxiEB26xOMI1DYvZhH4U0e9nOCskkigB1gxGY5Q2gU3GoLN+l9G31pJlWYdCA8jC4mADgWJVVeBc13ln62pZjguG71ByLb3F1zW4hsX0GOU8aUDG2aaipsXbR6GFWKGMJjLSNisNOBGWhGw0AGUgMuwfHXHvwX3O717kd772Db7ya3+f3/xHv8GDD94IAsnLioz8XBqnC11vj2ItPs339jDFxfoxul0I/cO00WeN+gz383SvemuG6sMLZOHzrFB7PaE++1ODoHvceAjgwsPGe5YX8LhzPG5ERspgZS2EB70kxXuJ75599lnuH55w/fp1iqrk+vXrXLx0iQsXLgjWPM1Ig9ST8w1KaaFgNoo4ETRBlmXSx1w3NIGiaVEv8NaRpimLIieKImb5Qtz48Y6U2qwjjlL6ow20d0SxRushVVGQz6fiQhqDd1biazR4eX+PonE+1NRbsMxSyLCqllBbWJbfJHch8FbvLd389q1YhmMZsUtprkvUKYX3jtoRXHglyTmt8SpgI0IPvjIBYx8yX50HEXpioyjijTfe4O6d+9y7d49z587xx/7YH+Pr/1gLGeXxQZdVX5bLWsN1oLSkP8IzjyN0fJyQ4upzHeHEqQz86dEuMmtZ+uVlXPv5pFk/vfIRn7Tp5qkz9lWjPJ2EWzuu3Ynbv71fEXeQcRYqbvU1q+dph/IO56QUlLYZ6qrEJj0+9/kvMJvnfOmTL/Ov/fzP4/Bk/R5lWaKSiNSkRCZmMplQ1WVAkAhgpGwa8lwy6aPRgFFPdk8Csi3SpsvMR1Eku10Ugdb4ukRpTbmYUjCnroquDTWODPFgIHzm+YJFPieNI5JejLVN2IEVcRShrCfSCteIwou3DWWeY+uIvKzkfGG3l1g6/GxqrBM4qzcGnPSUmyUlLdarLjYXF96iVUSjnSTlvMKF/nUTJ7IQaUUcpYHDXsSo2+pIa+yRETMbD4d8/pXP0csGNEWJq2p+883f5O7+PkVZdjp/sVFdFcApweNLgkzYgU/vqGfOwcfUkzwBO8BKLoDHG3zkl4SVy/M96v3P8irO2KhWDntStvqnx9hVW68N30OpjpRihSd05QWt+90yvy5NeRVnz8rrV9k+HrfLx6H0VDYNylpSY5hVJW+8/Q4vvvgi1ju+9Z1vs7m9xed/5EcYAsPRQIzTKzyOsozJZ9OOOaWpS6qqxDUVk8mEuimJWxXVxpLGCeCIIiPMNQ6UCpRKZQ7O0xsMUWbZZy7GpYhCjB8xYFEU1NbhvaKX9ToJJSAowFqU8wKJdVbaZlVPpKlXyBXl2rb3RDjdvW9wvi2XNaJCgwgzKiM7sLcWH4zNegdeSxysAmZCm46JVikjFLV4UEF1NoQ1PvytlaIJO/x4PObo8IRPfvKTHB+ecO3aNXT5oxwdHfF7v/ca5SKnasq1XQ9k0f9BMuJn5YRWY3blVx58ggSSCkngJ9mzP6xu/cOk+p4eY2/HGVflUX3psA7Gaf9+ktEd1XoIsmCjlfCS4RwbAdm2s7NDrjN+6o/+DBcuXeTS5WcgNly7do17Dx6QpBGN8iTG4EM820tibBoRW5gvptRlhdaKpOWX91BVFdoLkKWsK4qiYDAYsLW1hVKKoihoqppUK6pihrcWZQxVXNDv90l6GXHcw5iQXNOG/nBEXVY0riHOemRKM59PyfOcLE2FKooGbyVRJgktH8Tr1+GrKvR/N1WBrZsg56xIjEY5R2EthN0cFQwfj3OS546SWFRZ4xh0hPEeE0fBjQ9wz+DOoyO5j36JlVdKeOWsoHOwVc1bb7zOxYuXybKMwWDA89df5FvH32I+mZNEhhhNCyHsvMKQiJdFoAuOV+bQ8ndBzT0itj8DLPMkLLDxqb/PChM6j+OsfNMptJxcn+Xz8w8h6GjHU2jsj+bLPBPH49d+rB33MCgnxF1+5VxKqKMb50i1FsWVOJYyTlVh85zh5iaf/sIX2Dx/nt1Ll7n07FUm8xl5WUus7iRrXtQ15WIibrCVnnCUo5dmnNveFuhnXQrlU6S7hJgHRoNhh2Q7mc5EY8wKS2vbkeYtEts6hUsTbF3RxBEgOYKqqlHasDHexONorMMqT5xmRJEg0qq6Jmo9H2vph+SctUHz3VlAmneSWFxpX1e4ppGEI7KT+8AWY60lUpqolwrQCA1KEp1RkmFMLNl2H+JnrdbusVtlxVVtYs2HBp62IuDx2pAlKS+//DKLRUGvlzIY9Hhu92NkWY+jvX3mJ8fsH9wh0xHOWYw2QvPsPSaO5Tu0WfmVfEW34IdJYfw6C8+jjoNTm4taJixXj2kXsNMKu6uvX/YRrLRnr7D4PjSTnepyKo9i6zk9njpjP+0VKaXOdOMfpeMOK0mPRzy/Ohor02uQJNRVJQSEQaI3i1MKu2C4MebW3Tts7u4wzxck/QHnL16g10uxtVzoNIopXU4SGYqmZry5ySKfsVjkjAaDjp9deXHX8zxfihoawyIohe72d7t4vMWseyGTFrdXK6yrA95ePmfa63Wls+FwiFYa58BrT1NbIi3N2VpHaO26ydsaawsg8l5EIrSOcLYRMkv1sBhHi8/3zokhmlbqUrD2KBOIKOWne0wpDNQyzGjfRyu0b9NtAd2HwrpGvJaqZjAQ7r1vfP13OT464PLly7yfL9gZblHOZygdSa+6lkXGNQ6jzDLR1kGnTyXnlCI6C3euzdoxMpZg7iU2Qz/0Wm/UwwuAao21/bt9jVkz7hZ0dHr4kI/wnicO2p8aY18ln4Bg5KvXGmF+fVRhYvWWnT5GrdTmO5c/JGt6ocPLeOgNhhzPZ3g0P/6ln+SD+/dxhwd89ks/yXj3PDvbu2zvnmN3dxfb1JS5p58lOGeY53N8U+F8w/HJIQ/27mGUYnt7O3C+18IWE/rSvW04npwAMN4cc39/j/F4jDYxNZZFUZFEEZFZiiNGqsWxZyilsN6HJpeSvCwp85xe8hyNlu4xj8JpTeUsyuuOp15r18k+tViALMsAhy0rLLJD5T4OCTpRc0VJKU6ZZMkdb0T/XccJyoieXOdEt1l3pbqymrcOZ5cKOu29c85h3bJKYFWMDbLQUZSgjWfY6zMabnByckJRFHzsYx8jMRF7Dx5w58YNoijia7/5D0lRpFFEmsadsdjgrbTnX33/0+PM7PnKAtD+O63YugYfXtm52zLrh2XtZbjO6xQMBKizwgMlHtQPUjp/iox9GUsZVr7oipE+ZMScnbA4/VibhVVn3DznBDUnckiaz37qFW7cus2rr/0ef+mv/je8G9BbR/MFg5Hgry9evMjGhiC2ZrMTtBJBwqqReHc0GOB6PSaTCZPJhCRJuvgTwCQJd+7cYWNjg6ppOJnO6feGaBOzKCoG/UGImaWURYCjNk4411rK6NiIm1rXsjuPBgMwbeAAACAASURBVENUoIcRwQVhoi3qGuU8UZRKBUorWoYp50RZVvkWzdZIdj3w0zfB9W254621qCQOXoJAYhvrSdIEtMF2O5aTerRvHXPW8gGPMiQdymPLCt9SDdfEsVQvnGNnZ4fxeMx0tmD/5Ii9o0OOphN+7Es/QX5yyGIy5eTkCO89Rbno2nJPu+BnoSrPTNCddZz2p74HQHvO9vFWIaf9/UMqAV5h7bpm/Vmz3PuWGegPobG3oyMjgO56tSvt6RVOsWQ8WR2a9cRdtxqHmBxWJphvcM5jq5o0jXjjjbf4xGc+y6df+RyHswUq6/Nn/8P/iN3NEXfu3AmrucPWNfl8irM1ZVFycrSHUp7+IGWRz0iSpGtDreu6420vy5KLFy+yv39AbR0bm5tkWUYVhBLRYtT5bMGg35cEVyodaUkkxBLdhXEu1LPFw0jjhNlkSn8wxJmYKLyfNjFoj3UetEY7MImU2GJiQPD5WkGslGThV2LPuq5xCI1VEieivw4YE0Eti0UdSmuRimgZ0a11YcLKgtF4Dza4u63bS8DHhyy8WIUKcf+6+9v2/bcLcxzHnHvuCi9vjpjNJnzik5/gV//P/wO3mFIvCpJYMej3UIUKfHvLOFeZU8m1FaOxZyxE62GIX/73zFLe+ndUbhmzd805K17M2vVYqfs9zhv4/YDhnhpj72riK7t5u9K3Y9WuH0VE0WKdu5u6MmF0mERrr3PSYJKlPaIo4eXPv0yN5tbdOzzz0Y/x7HNXub+/Tzk74cqVK9R1TV3XzOdziXcb26mhVFVJWS3Y2NigLEsaW5GqmCzLuhh8Op1SVRUbGxv0ej2KqqQoCoajkZy7qtjcGIuBeY9vGubTEo1iNOgTm4iqqboW1STLUCYQMGhN0UjPu0W01sQoNM5amroCovD/CO8bTEsZ7VoKao1iWfZcNbSW6kommgrqpiIZZb3U1rQXII3U1RsiQK/EY62IZXsDvW3ls+T59r45ljrw7eORXhJNHB2dcO/BfR4UC8YbG3zk4x/jg7ffoT8YYH1DlqTk02OOJxOiSEg1a9t053qSevtpl7wdncGu7tJnuJjdlA6L3uNCh9Vzd7p8fn3ROOuz/SDjqTB2A4xCIq79YkvxgdUaiQ0T0jxyxVNKEXnTZXGlMVUyzLpVOsWgQjbT0gCe0WjEj3zpx/lHv/01Pv+lH+cjH/04OM+td27w0Y9+lM3dS+S1YLmrIqean+C9ZTY9olzkWNfQ7/XY3z9henzCSy+9xFtvvUUxn6GclKfG4zHDfsrd2+9jm4aTw5pzuxfo9/s0TUk/7VEZI91uJmI+nwMQO4GiNk0loB/fYBtBu0WIRFPVOLytRC+uKkVQIalFtgmDUWCNwVlLlPYoraOsBK+P6YXrAL1UaKutloqCSVIMiirPWVQlvizIMtFdd95R1dJ44r2V1k+XdguCgo4NFqSkaZT0rHfOiQ8IPKXxK5102tV453BeYW2NUoaiLEWaGse5c9vs7G5zbu+Eg4MD3vm9dzHAF3/yp7nx5ptMDg9oGguqJEHhaykJij6bFi/D6+Vm4v2St6Cl2wrFeddZsixEVjmEaUcAS6tG6b0nCjFSSwcmJ1mn62rncdsM1M5nqyGOxANVITlpIkOUJhSuweGZ5QvG6YgoiiTXcvPdJ7Kzp8LYFZC0Oundaro09PZCGKLOHVoVMVw7l1JoWk5z2YHUivskiT8jqC3nGWxs44xBxxFf/91v8O/9mT/N3vEErxUXLlwgSmKeu36NWVXSOOnYsramrkvqqqApK/JAjeScI8symqbh9u3bvPD8NV577TXwnl6W8d6777K7u8vGxpCyabDW0ssSkiimnM+ovabX74tXUJf0s8BgU1c0tsbWFRhxC8umosgriZeTTGCnkSHSEjbUtsGWbin7pCO5Dm6dOup0kmlVfrq9xi1bbV4UKKVIkqzL4ndKL+KQBzmnZG3nXG1CacLO3p67nfhtE85y1/VB6klw9CIHHUv5EsVwMOLdd9/lN37rn/P888/zUz/90ygc/+1f/svcevstXFUSefFGqrbyoF2Az6rgLjuakBFvw4hIqS653ba6Lj1EuvKaNPicEQacKqXJ97dL79ILFVYcciANHltbWmo1HcUijeWFflt4/nuYJGaQROgoYrOusLkIZ2Tp6Sr+o8dTY+xxyC6usYOw6oIDynUXvfUMlV5P2ymlsG3kr1Yw4CvHaBNLX5eKIO1x6fIldi8/SzYaYY3m5c9/gc2dc6RpSr/fZzKfgfbSQ95U1Pmc6ewIWzco35AkUWCLUdSlAFWmk2OO9h7w4osvcvPmTQA2NobksymRgp2dXeq65vdefQ2tNcMNobIC8NailcG6iroqGA36RFbTaEUaxxRFQxZnknFXRj6Hc8xL15XtAMqiovBlp+7S6/XW3HJrhUZaaw2hxNaqsMrnaLqJHkURzVQqCsUip9KlEHa2tfbwe1PVuNiuqceslreMicFDXVady6q1xga2HclbGDBe6uIOmlAujBMoy5rBcIPJ9IThcMhLL73EbDbjn33tqxwc7rF74TxbGwPu3rzJ26+/hq0KEjQOS6PEFh3CY+AA8T90kNxKSaOEvFlIYrIJoY3SKCyxUeIdavBeC0df8Aa0Nh1deRL07PFe+pGU9NAbbWTeaU0/k3tReqibFdJRa0O1xqHTiMgYenEEWrG1vcVz167hULz6ze9weHiIipMntrOnwthluLU4XCaZoLsUCqXXc++rbZanhw+YbVmtow7DLS5lhFemi+M+9rkv0BsMufL88+g4BpNy7tIlNjc3AWlTbZoG73LKfE6xmFMsZpSLBRpHns+xTcNgMKCuCjRw//59+v0+aZqy/2APWzf0+31pPa1rCm14497rDAYDkiSm3+9zuL9HPp2xs3uu445LE9Fv33+wh1Gefr9PZRtiI+QRrrE0Af+exn2skl22zfonScLJyUlXz28N13nbAXpMkmCbBtssY2QVWmldiDXLRUGv1wu7oaYs805jvaWutn7pbbX1f7kHyzg0jmVnbimw2jGdTun1ejSNeAt17dFWY0MyT2lHE0pnadYnX8yIk4wsidnd3SFNY05mU6IkYe/okDdffZWD+/eIXEOsI0pXYwgLVtDAs87LOQNks7IeQ0PupYW4aSRpaLQJOnyS99A2VESUGHI7jNMCQW4sZVCgwUoAEEe6WzRkgkIxE2+w6/sPvyvvqSvxcqyuwFkW3lNWFTdu3ODdt94m6WVMHhzgvaef/CHb2dvhQwZ2LcsZMrSe1cxo+4KHJR7ami0AZslAo4IbaJEurqzfZzQakdeOK+cvkPb7nL94mWw4om4a6qYhMkKtvFjMKOZHeNtgXY1WnkjDbDZj0O+zWCy6uvVoOOK5554jjmOmJxO01mxtbmCtpSxLec/ZnOGoj3OWg70T8n4f6x0bGzGL2RRjpCFmMpngfNOVvwY9qa/3UtFji5KYQV/aQG1dMysWDAKAx3uP0a4D7ay2pJZ5ITLHYcdvmkbANy0vXZssC9examriJu6+o/e201OPogjlPU1QY5W6fNOV8dodS4gtNUYpolCyk358UbrRvZ7U2b0H54R22ltU4MCLvKZ2LiT0NDbAfdtqx8/+7M9ibc0nP/EJvn7xIocP9ti7/QF7d+9gi5zd3XN88MEHgmbzS0VV4S/Usstb6b+vQ7mwJQmxwcX2ToWGG6GadF1pUWalaymywyLVek1drqhLCq9IjZ3KuPuVGL7d0GxVdQvp0dERehZhbEOSJDSPULo5azwVxu6B9iMrCE0XAt+U8mQoXZzayVdjvzaG1EqSWS3owQJNoC9uQmOGSTTHsznTvMBunmfj6IQXPvUyRDG7Fy/JpMVSLHJsXRFpxfbWiPt3blOVJU0tTLHDwYDpVEgeB4MttNZMJhPKsiSOYwa9PmWZY7SmLEsipWlKaSWdnUyEvCKLGQwFAZcvRONNITd3mGXQNJT5jDzPKRcpVVUyHo5Ig2rroD9GR4Ys7ZOlMa6ppZ6sNfliTr+XMehL7F8HgYj9/fsoJdLNrhFSCRuopOq6Zuf8LkVR0ZRFKN0pmqqmqirquiZLIo4ODtBas7Ex7Gr6s9mM7e1tDg72+MTHPs69e/fCpG1IkwRsQ5oNKfNCvAjAO08vzVjMhDhyMBjQVDW1nQcEoaFpwkLloXQeZWKiJEYpw2jQI0siXn31HQaDEf/kn/wT3nvrTebTGapc8OM/+Uf4+3/3y8wXizA3REY6Rkq6lRKcufe6q4FrLwuA8mHj8SKI4bzD+HbGCkJxGXWGBB/LzcgH9F5Vn0rOnZrD7fsK+s51m1ttHY3zxElCbRuyfi8QeEKcxiRpShIUf55kPDXGbkOmE78sw6167kq3SZUl7xmACrzfOiT41tVCWxfShLhQEWcJm1vb9DfGXL16lXMvvER/NCSvaorJlCjNuHhJiHKVl0y+rUvyYhbKZ4Z8WmLLgn4/IzERFvsQ/FRWatntFLKz1YWowlRlwXQ2Zzweo5Tiwrld3nznba5cuSIyzKOR7MBVTl7MaVyDbRqIInxjO7iq1gatok6XzdbiumeZADOMUZ0LXwUaaoBisSDt9VZaWV1oWTWBO68S3vmWuLFx3Y7T7t5Saqzw1jEYjjiZztjc3GR/f5+mqjg8PKRpKmmthQ5r4EMIkWVZYNuRm22MCgbngzvbemgOrxxKGUGNIaQcrrEo41mUlmvXpCHp6OiEn/nZf5UyL7h8fpcXn7nC//MPfh0fXHhnl7QUYnQC8rVavAmUCRH8OtS6k+j2IHt7m05bTw4/RFPeVRyW2nddHmqF1XI1mbfqAXiW7bmgMXEsgLOmJokSojhmKWj24eOpMHYAgkyEUYooFi71lhtchA8lmaKUTOC2vNN6AO0kbJqmA2rYugFvu6xmf5jSGwzY2j3P5rldNra2yMuay89ss729zWA0ZGdnh/l8Sl0WNFUuJRkXlF8ArcQ9PTo+plxkeC+Z5Pl8D9Ck/ZTR5pimrNg/PEAjnW5FUTCfTFEKhoMBV69fI01TDg4OuPvgPhsbI/J8wag/4OqVK0ynU46PjynqmtG4R5UXLPIJsYmwTYF3TVeCdE0cusMs0XiLgz2B4aZpyoIJ83kuiaMg9xzHsQhB1jX94YaAfzJhuk16/Y6r3XuRMe71etSNZX/vAYPBgMnxIZFW9Ho93nvvPdI05SMf+zgnJydsb46x1gpne2C/yUKnX2IMrrGCc68bcLJ4NE0j3XdRHCilFA4xaO9VwPU70BFN7aFxWAqMjun1Nzg+POD6c89x/armZDZlY2OD3/qnv823UsPk4AAIOAElfDZNMFmnBJLt0cKl572w9+I6DTvnnXiEBGZcAuArtAu3wxJAXqiOirzVs2uTyJ1AptJ4s0QHLmv1AVcS4M7pUNqmr157jiROmcxnFEXBdDrFVjWFtSymkyc2safC2LVS9JKs+9JxHJNECb1Udp/GLLt6Vl33dic3xnRkiCXQOElwxCZC4YTyScfEWQpaU1bCQHMymXHtuY9xcHTIcGvMYDQURtjIgDdgDU1ZMJtNpFllNqduSpI45uLuefJ8jjExg8FAFFmcQwIHh3Oafl/on8pc2GbjWBJri8UCM53y3nvvcfnyJfb2HnDx4kWstSwWC1599VUARkFHzjeWssxF/SRNiY1CBehqWQZlGAVxkjA9OcZ70XyrS3G7iyBpFAXvp3JLbHzWHy5rvNZ2ghV5nnM4OcEYw71797DWsjGSzrzhcBi642BjY4PNTSHMTJKEPF/w7LPPcvvWTVmQm4amrSl7i4nSJfElbUy7XLjbxd0tZRIlUau1tB7jUDqw8BjHfD5ne3OTew/2KauKX//1X2c0GvMLv/AL/PZv/KMOq6CUogkYTOtdB4jxAcbaloJEr11EN50RBQPXlgOV6hYMwQo8jPPomHa6Spx4q0q1bbvi/us2F6XUclFQCuudeLRaoU0EUUR/OEJHBjsTLYK8LHDNsuz5pOOpMPYoitjZ3u6SWJE2xCYiiWLJlrYujw9GblvKJLA4vAZbuS4TXamaSGuyJCWOIvq9PpFJMFmK8x6nDJub2yRZytbWFv3hgMuXL5OmKSfTYxazKfl8ii1ztJDJUVV5kFaqqMoc39jO3c7znNlsQRRFLOo5g17geAtkhm032nwicfrW1hZOQRxfpK5rLl682K3wjXb004zx5ojJZMJsNmM4zMjznDRNaJoa0+/jmoooyciyBKUNdVEyz+dBw23I0dERZVmSpmnIX3gmk0lYICOyEMdHIQwqA4rPe8/m5ibnzp3jmUsXKYqio3K+f+8u4+GIrLfbldqE8LLp3M84jrl18wOGowFpFOOcDWU1caVra7uEYWvc7e+rSSmjdKDCkkYc58DEaaiMyHdQjcV7zXw6YzAYkGaeC+cu8L3vfpt333qTvb09jo+P6WcZZZFTB4ltixPoMJYWoN0JhADau9DPFuGVzC/vHNaIgdbeoZ3Ibq+NlT8tK3X69j+hno8V+G3n0nc4hACpdcIvWE6OUUrxre99F+fgJOzi3nuU12sb3xPZ2RMf+Qc6Wiir1JE1DqMcuJpIaepQb1VKyAlNHBFnPSyevG7Iaxt0wQxxklJ7mDcN86ISuqZUkxhFTwDRjDfH3L79AdY7hpeeJe0l3Lt3i3PnzlHkU1y9wLsSZ0uKMgfr6CUGV1dEHpSOsEaxmM27JGKWJdR1TewUxXSO1ppZSNTFccz9Bw945plnGI/H3Lp1i0FfdNU3NjYYjUbs338gmG/r2Nzc5J133mFzc5PtrXN415BElsjE5IspVSMxeRwpbFUDNfliwWC0CQ4yY0hHI46aRggjtMa5ApTBaM0gTTFIjT3SDVhPrz/AO0tkEqqmpLGWhhh0ShQ5RptjatcACpX2sN5ztPeA3fEQXEM9nxM5RxwZrPKkUYQNdfooTtA6Ii8svZ4R7yhJUFrRWAHkJEkCXurMcZKEOncDAVwlYVmJ8q1ghcU2FmJL4xumkxKPZrDZ5/mPfoT7oz5NNeeZZy/yvW9+nQKwKgIj0tRLFp4GPBgVSWyO7N6AbDLeS5yMCHs632C9RxuNtUssCCxj75awtE3FtedbBXatou5aI1dKoV3deQJU4tHOy2Itlpf8kF577ycZT4WxO2cDQUOIx418oWmINU1gB3VNjfGGKDJkaYw2BpUv0N5RuBrvmwCkETop56G0DYcLyf72+32yQR+U4tq1a5w7v0sSC+3Tpcu71OWCxeSYplrgXUOkhBe99g3zedGVncq86BpdrK1BK6m3WyuJo5WyirU2oOakNfPk5IStrS3qShBQ8/mcsizZ3Nnm/p27nD+/K7tRv8/GxoaQK+5uCxV0XdPr9ej3+9R1zfF0wu72Dmmacnh4iElyhsMh+/v75HlOXhQdpHI4HHbusvDe94ljMSptwo6Cw7WihsaHEpelrAs0nksXLhAlKWXVcHx0xHg8pihyRoNRF4K0bmVRtLX5NkG67NNuwTMygYNhaYNqS00BZdcmF9vSo3ctBJoO/GNrTZIK1XZt4ejgmP/xl/4HJoeHKOrAU5C3ZodrWqirvK1q+yXCbq6RTkK1AoMFAca0MboLFNgGSSy3JeBV7wBWmIv9spYufy/nvuSgg9vvlwvGaWRoy6vX2rbwDIS+gyekun8qjF1WKJkEcdAGFyCVkD8aLeimnmrIq5LJbErRFIHuSIG19JKAhDIKVBoEAyqUcwwGA+I05Zmrz2HimCRLQcWUtWcz0gxHPY4O92iaivn0mMX0BKM8caTR3nVJkyqosKBFOSYvF6SpYMGn0ykA/azXGaZSijzP+fa3v8358+dp8dCDwYC7d+5w+fLlzgDu3bvHufO7zGYzFosF169fZ39/n+eff569/fvkZdGVyg4OhKxhf38fp+S14/GY/aN9yjLvjGU4kOYe54PmWchvDIajLlY2OiyaoSTkrQMtxuB9jXc1ylo8kqiy1uJ1yjPPPsdicsL0YJ+DkyNUAPf0+z2Uoku8iWcjDTkQJJ/USqNSaBBpF4mWsKN17aumkQQ84JX0vNeVLEhaayKtqRY5Ju0T6Yif+amf5tnLz7IxHPHt3/0avq74X//n/4l8vgjzzKOjGI/tFmQd0t4tEi5SD++Yqz0Z7bWLQoINVtqOwwLVfj5AcgxKPSSg2T7W4RWUQrfiG96v7f7t8d21Ucu/v/rd7z2Rnf0w+uz/NfAfAHvh0L/gvf/18Jr/CvjzSOjyn3rv/+/HvodWREm8Bv4A0KaVGRLXPUsTyZLOZ6GrzHa4WR0WjCjSKBWFxVoaKZxraBrDYrHgmatXuXhFOOTG43HH6Oq9pSoW1FUhYoMhphIsM6CiJY2UViJfhCKvhPwBtUJ/HG5mu/tcuXKF2UxKU6PRiFu3bnHhwgUB2OQ5eZ4zGAy4f/8+aRTxwgsv4L0059y7dw+P7a5L21iyWCy6ls/JfIZSiiSKsFZ2/8lkwnw+J0kShsONMBllspahoURFRr57KNmhDcZIllp50MZIzF0L5FZHUr83AZJ77vxF7t36gNFwzPHB/WDcBmubLnxpy34qoBaVXk70Num16ta2EOCqLomihDiWPvSWdktrRRSF7HrdgBIvwbBEo21ubjI9mRBHKbfu3uPjL30KYwyHJ3uB7CPHhSSuSFnJjt61Ukfrhr6aCG6NzRjTleTaz62UkgahU8be8m+3x6wae+e+txl71EPG3n6GVaN3rC8ETzKeZGdvgP/cr+izK6X+3/Dcf++9/6urByulXgL+HeCTwGXgHyqlPupX6xSnhuwImXygAP9rgRpFVZPnBWVtqSsBp5QOysbi62btHJGWCxynEcqooFfmibJUYsQ05qSYk+YzMt0nbmouDvoCQJnPaZqGsqrQwbDKqpTscF3jmxqMJo5TjJLUjHWO/iATPHhAnxXBxW+N3ntJjG1ublJVFbduSW6gqmvqAGu9evUqd+/eDS20Ffv7+0ynU3Z3d0U3vG663WS4MeLw8JC9vT1efPFF7t27x2g0wivF7OSEXq/PvKkY9PocVScopTg5OWEwGFDkJb1ej43NsUBqQ9eabTHgxohARGxplMKrQurBvkZHERqHdz6U1oQeKssy3r95k3E/6dpgq6oKPeer1ZOAG48lt2GipFscdRQvJ7gyRIFBtyiKkEgDMMSJCV5ATqyF9ip3NWhNUZaoKJY+fSyVr3nxpY9z7SPXGW4OWczmFMd70g9goAqLtHhhTdgsTFiYlobUeSAqsOEu5zlKrRvaaujWHhOeWTvfaRzI6r+Wf3/ViNexG+3rlgjIJx0fmsrzj9Znf9T4E8Ave+9L7/17iAzUj37Y+5R1hQuJmjS0WbZkD7PZjKJccG//gL2jY8nQa5EZWlQVVilmiwWNg8p5irqgthaLo3GWpJdhsoTheCj861WFxdMfDahry2w26zLDUhGQnvWibqgDU6pJYtK0FwAshijNwERUZYP1brnqx1GXRGyc6IePxhtY71gUOeOtTaqm7tB2g+GQyXTKdDrl7t27DAYDZvmCrXM7zGYC5On1egBdX7wxhuFwyOHhYaezPp/P6ff7RJF4MEWxAOdoyoq6LpnP54w3N0B5yrIQHfhFzsnJSVcXb7PqHf20dTRWcgtGs1bqaWG29+7d4/DwkCiKOi34JEm6Ba81qDZf0C4EbSPOahmudWlbjyhJM7Iso2wseVWyKKWdthXOrKqKvCyo6ppFPkMpj/MNvV4a5LlSvv/aq+wfHsr8cgLIaZoG11iSKKaXCk2YfA4bPoeh18vQWpEkcddAZG2N95YkWQKZ2u+VZRl1IPIAMfS6lhZjZ62g9gKwJ44ibIBjtwjRKGgI5Hne5SiMMZSlbDhJSFq27n67AOShrPok44fRZ/8JRMDxzwDfQHb/I2Qh+OrKy1p99keOOI64ePGiSO+ORzRNw8/+7M/wxhtvcHi4z3x6yPEJYIT5RXtH46GoatKsh4kippMJs6oiNTFRLE0jx8cTRqMRxf3bTGYzKmcZb23ywsdfRCcxe3t32dgocXVDmmjqumRyfERjBa6apSLkYLIMXy0YDIcConGgjJacgpcFIomM6L8rqX+3N76dzIvFgmvXrrG3t8fGxgZb402cc9y6dYvxeEySpnzypZf4zndETu/4+Fhqy15wAltbW7z//vudEuvWzjbvvPMOvTTj5EQ6wOqqIm8akigJk0CLEWDoJSlH+3vM8gW7u7sBUZjQ1wM0jmF/0Blb27rpvQfn5TtqSKIEtFC9GwW9fp+rV6+SJQkbG0N6vf+PujeJsSw77/x+dx7ePMSLOSOHyqwsVhVHSdRASmqj0W5KbcBLGTZgyBtv7a29oaBu2Oh2ewAaMOCVjbZbRhu2vKAksEmJIotkVbGGrCErKyuHyJinN7/77jwcL859N4stt1RGS0bpAoFARkRGvOGec77v//0HhySR2MCKCbhi2mmaUS7cmPRTopuskOxHVJ1cgMiyKhXHMuVCd9w6RVHIRZ5kKKqOVrYauqpi2gYkinTuNSwsQ+VLr97l+9/7Hl946QV+8Suvcnj0jKMHD4jjiCj0sSwT05RBGjrSJERkGZamU69ZTMYzacOVpfK+0zTJzS8K0pLuC0LScFUVQ1FQiwJ/uaw25yxJGM1mWJbkFiyXS9nGXR7R7/eZjScVKzGOJR6U5rIyGgwGnJ2d8dJLL/HkyZOK+1BtxsVqY/rsS/jfJp/9fwR+H1mj/D7wT4H/hE9L055ff6GxUD6Vz15zHH75136VMPR58skjdEPlwcf3efH2HV55+SXeeONnXF5ecj6aMRwOKYpM3hhCziPX1tYwDIPRaESSpUQ5qHqMaRtkWUIhFNZ6PRzLoFF3OT05QtN1ut0uIg6Jo4jIi0EROKbKwktJCwnMaYaFrcoebTaTc+pWbw0UBdvRKPKMPNNQkSMcXdeqN2610OM4ptfrMRwOcV2Xg4MD3LsvMRwOpVtNatfCxwAAIABJREFUiVy/e+8eL919kcPDQzY3N7m6ukJy2UeVkm1V9czn84oRt6Ld2rpUlaVpimVZWI6N5/ksl0u8pdTaO6bFeHhJt9OXN06alvJc2RtTcsRVVQ6cKARpGsoNUDMwNAm8pcmSRs2h1+kS+T5Zllbx1Fkm3W/r9XpVZq56VUXVf66UXZ2Oufj5/lRVNPKy0lv6IQJV2mGTkPkZtm0iigzigiJJCJc+jWYbHaleG1+NWes0efLwY06OD2k0GtQcm+lkTKteY3h1gdnpkGcJ9VqNxWJBFEmH392d2/jeEs/z0HUTURKhRLHqt6Wnf7fbochy0jxhPp1RFIXES2bzin/hui6e5+H7PpZlVSfxSva88vVb0YmTUjH3+PFjdF3n4OCA6XQqN/M0JY5j+TlNuH79OpPJ5LMu4c8W7KjIfPbvAN8VPx/bvPr+deA7QohXSnAOIcR/VX7vu8C3hRCv/5t+/+ZgIH7nH3yLVqtBzbGYTSZoukKn1SbypRTQNh00t8brr7+O6dhcXA45OD5iPvdkWVtrcPv2bbrtNo/3HzKfzzk7u+Da9g5ra2sIIdjb2wMkwLazu8dkMiGJfSzDoChSsjQmFxm6bqKbJvVWB9etk+ay/Gx3eyiaRloSPMIwxDKkbspQpJwxT2JAlpqLxQLDMOj1enK81m7j+z53797l6eMnVam7WmxbW1scHjyj0+kwmUzodDosl0sGg35VAvOp9mbVegRBgKkbpOGyApNGoxG9Xk+y4mruzwFJzXqDKIpI05z+xibdbp/++haaZmDYDrpmIVRFqsDzDH9REnQcG6dWx3JbMotu4TG9usAxDRRVLszR6IpWq0lUjv0sy8I0LRSlpPaqz0driqKgm6UzjlhhBjZ5iQuohkleSDprlhUyEDPPiZZzFpMRge9h6hJgNS2n3BAVjo6O2N7eoijbjjd/+hMajQZX4xGqgJPjQ37xq1/jydNHdFpdRuMxWVYQxBGNeovNjQ71ep1Hjx6RxBlvvfUWrlsnjmNcV1Yw4/FYTkDKiYnvS63DrVu3uHHjBs+ePcNxHH784x8znE7pdrscHByUijroNBo/Byhubm6Spin9fp/XX3+dvb09LMui0WhwdHREr9fDNE1OTk7Y29vj6eERm5ubNJtNvvNnf/qZgh3/ysWuyK3sfwEmQoj/7FNf3xRl7rqiKP858HUhxO8oivIy8C+QffoW8KfA7b8MoGu4rvgP//1/jyJPKbKE3Z1NLMPEtU18b8nNmzdJo5jxMiCOY1AU6s0m4+mUME5wnTo/+9nPODu74Ctf+jK717dKGmWHq6srsjjhzp07+MtlRU1M8owkSRheHMveWMlRVYXlcoFu2eR5QSpUBhvb1OpNwjTDtB2cWh3VsHHrTWzbRFcpo5AyRJERlZMCkKcwUAFRlmVJk0fDIA4jtre3efz4Mbu7u4xGIzqdDlEcIvKC69evs7+/z927d3n48AEAg8GAw8NDNgaDakG32+0Kc9CzXCLwtoWmKVX5mOcSOwgCyXO3SmtqRdW4fuM2bqOJU2+hqjqmU0Mz5OI0LVXaJhUxRZbjhwFCUak1O2iKTpZEjM5O8Razyt9N11Xa7VZJnQ1RVZVutycZcJpGkMRV6ZmV3HjDMMp02nIUVgZrRqnUr8dJRpzJ2Ko0iVhMhuRxSOB7eOMp5+fnpbWXBAin0yk3ru2RZQnDqytu3Nhjf3+f9tqGTOEpctJEnuKLxYJOp8f2zg5BEHF0esJaryk5GbbD6ekpWSpHjsPhmGazyePHT6WWwnE5OjrC8zx++7d/GyEEV1dXtNttHMdhb28P0zQZTqfVxn55eckHH3xAo1zss9mM+XxeHQyLpce3vvUtZrNZtdnfuHGDxWLB2dnZc4xDs0iShPPzc/6v7/7RX9ti/wbwGvAhz/kC/wXwHwBfRpboB8B/+qnF/18iS/oMWfb/yV/2N2q2Lb76hbsSfELQ73W4ef0aGgXLxYRf+NqXUQpBUMgn2m42OD0+pN1uc3p6Wt440vf9ydNn3Lm9VxkltFutSnsdBAH9fh9T0zk8PJSyUd8jyTOWy0DOcNOUOEsrqmmWZWxvbCIUkzjLqbU6rO/dxG11SfOsHIUBQmqzbS2reit/GVR8cyGee8j7fkh/rVWVb6ZpopaloWWYuK7NyckJ13Z2mM1mNJvNynp6uVyytibn8UUO/X6/kpKqGtWmESx9hBB0Oh10RcWy5ShsNpuhihRF02m3OtQbHda3tjEtF0UzsGy3SlStaK2qQlbkkjlW9tq2aZJEMaPLKwBe/94fYRgGX/7aL9Bc65NnUrKZ5znNeqsCFv1UmoE4piXxgJUmnXJkqWsUCFTFKDdNuVHGcQxZKtuK0Of44AnddguRJ7zzzj1sq8ZyGTAcDqk3LOo1h26nQc12SEIJQC78CRsbGzx+9IhOu1u9x+12m3kgZcS7166hCWTZHGdMp1NOTi9pNBocn1/S6w9odLpopsHhwRG6aRHFKZub6zRch3g5pohCLFOGZaSFYK3XKvPm5XvsBxHj6RQFjZPzC9YGGxydnDKbzXDqLTbW1whDH8e2GPT6iCJhe2OTH/75n7G21mdjfZ1HBydMF3OeHRzxwZP9v57F/v/HVXcdcXfvWsXLno4vGfR7DHptLEOn3XCBgt2bd8mSWJJVfI96vU4Y+Gi6QbPd5fz8HKdWJ468iuyy6gnTNKXX7lQ98ArwKPK0RH8VhqMR0+mUohxXNUrH1yDwqdXqqIZJGKe4rR4vfOELBFHCtet7qKpaAVNqnhCGYYXQmtpqRpzJntlxSr27RLMdVyr7oiDEtm3Gw5GUzpYngWvblTnG6mudToc4jgl8eTo1Gg2ZJKs+V5GtOOuWZUFeyLm6omBaOoauMFhb5+TkjGt7t3BqdZmsqhkYljTF0HWZCQ+g6KtkFyqE3bVtfG/Jo0eP5ARjeIFuGuxc28NpNBEKWJYEqtJYSms1zUB3TZJIouor37fV3DgXktSQiYI8k1FYqx6VvCCPQxQBF6eHzK4uMUwNb7lkOp3zox/+RPbXqoIiUm7cuMbSm1NzXG7u3eT69Zt8eP9tyfLLcwaDAa1Wi/39fVmGB4GcADg246sR7WaL+dyj1WrhuHUeP37K1WRCGEnn3pu3bhPEER98+IBavY5pGHTbLS6O9um3auRFyt/7+7/FaDylSCWOUm82sSyL6XSOH0R0ej1st06SZDx5+pTZbMGz42ParSb1ep2abbGzvc3F6YlklWbSsEIUBddvv8j55QUf3H/An735zmda7J+dRf83eOVZzmQ6Q1U0Hj9+TLfTp9FoYZg29WYLFJUgiAiWHsul9GRP05Tj42OKomAymXB4eIjjOMxmMzzPYzgcVkaJaRSTJymz2YwwlNnntVqNVqtVjrGa6LrO2tpaJRzRNOlmmqQS8ffLcZYics7PDpmNLqnZKhfnx5yfHZWMPTkSAqncW+Wwx1mKZhp0Op2KKpqmqaQGl73sKirZdh1M06bVkqdBWLrb2Lb0nLMsi4uLC5I4qwBKoALCViwu25Ybhuu6KLrcWLrdLkUOSZyS54I7d19kWqbSGIbxPG6qnOmuymIhhGTWfWrslqYplmPT7/exbZswTSlQpDMuz+fQlSFlKQlNS6xBU8o89n9NtbWy5w6CJcvlgigKiAOfwF8wnYxYzMcoeYrIIpLAYzKZ4HmexCCKnBdu3aG3tsaTJ/s0m21yAdOFx+HJMRvbW5JZ2GjgOA6j0QhVVTk+Pmaw1mNzax1F5KgK+L7PfD5F0zRGo9HPuRGdn5+zWMx49uQpSRKx9Dyurq6YTCZ88YtfRDV04ijl3r33OTw8QlVhOpX58aPRqBobnh4f8/DBfRazEUng0+006DbrOKaBpWtMJyPee+9dBDmGJlufxWJR/q0R4+EVa73OZ15nn4uTvdWoi61ut/JVn8+m3Ny7xr/79/4d2s0GTz55gGUbaKrBzs4OR0dH3Njb5enTp/i+T6PRwLIs9vf32d7dgyIt1WJ1NjY2aLfbKFCNrlZJJ6vPK4TUDwKSJOH47IwoTslFQZYWxFmKY2nVuCVNU5x6jYW35Fd+4+/Q7PaIUlB1jYbrPCeoGBIt11Rpo7SaQVu6UUUIr0wM8qKc/ZomipD9/nwue70oku42zaa0t7J0o2LH+b5Ps9GgXq8TRH6VBOtYdqVyE0KwtrZGo9Go7IvjLMfzPO7cfpGsEHJcpErrKCFpDGiGLef4pXxYlFJTTdNIk6xkn8m+d3J5Xhp0Sh7+arMA0FWtAiINx6mMKVassdWCz4UcO2VFgWU5LJdLqeZbzlBRGJ2dcHlxQrxcEHozKHJe+Nqvcn5+yWs/eoPFfCkfu1aQxhFpHPHqq6/yjV/9BtPpHKH4XNvZ5fLsnHarQd1xubw8r55Db70nF2Jc4Hk+RS6o1RqEUcKDBw8Jyrn+5vYuFxcXfOUrX+EHr/2Ure0dptMpjXqNg0cf8Ytf/TKDwQDNtHn65BmakO/VrVsv0Gq1uP/gIevr66UPQljxDsIwZO/6TfI857333mNzc5Olv6he5zzNKhuulWuNH0T879/94Wc62T8X3Pg0zdA0gygK6fV6CFEwmcsxm64q9AfrhP6SNE05OTlBCMHh4SG9Xo/t7W2KoqDZbJKmKcPxmPVBm3anCUAQLjF0lV6vR6/XIQwlkaRIJYHEbTQrMwxvuWQ8nrD0fYIgwo/lfNi0LNJcULckkSKJY2zDpLm9zfDyigKN9Z09ckVFEUUpR5VqMZHlxHmOqgiazWZFWEmLvDJr1HUd0zIIfSmTjYLVPLWgKBJpNJk8d48R5nPSEUJgGBph6GM7z80+rKb83fV6vSyFpZ+bZUkhzXy2wDQsTs8vJGJu2Ci6pCYjcpmuqpeuMSVhCKgYckUuKuluv99nNhqRiwKzzIRf9dmrTaooDSONIi/NHJ7bWUPJNhNSpKIIaXFVZAmz6Zj5bIQO/Pi1H6AjqFkaIonRNIUgiDg7PQdF4Z0PP+SLX/gCIktoNOoYnRaPHj3ilVdela1cXlCweh9KPb9t0+t3ODrKSaOYZs0ld6XZyKP9p2xt7TBfLBGqBDwPj4948aWXmc/nnJ+fY2oqo+EViqpxdHREEAScn5+TZRmDzW3WNtYhmRBHKVEUkqYJilqm5Jboet2V9mW2bTO8OqNer7O7s0EUhdRdp6zepH24qsp2KIlC6Yvw/8Fd9nNxstumKbpujSiOabcazOczmo0G169t0eu0uX5tlzSJWF9fr8CvbrfL4eEhUeBzdXXFSy+9RJEl1Go1KOezkvklT++rq6sKyby+e41+X1pFf/Dgo8qFdT6f8+CTT7gajkmzArveRDdtclFgmxZpHKIqYOkaiipIsoJ6p0er02f75h0sx0UVKc3W85NNQ1R/V9ckwaTRkKVuHEtkOk+keWK9Xicr890VgXwzNQ3dMOn3+5yenuKU1N/ZeEKz2QSRV4+/2+1WnPl6U+rhb954ASEEJycn3LhxQ+aaN6RtdZrmfPzxx9i2JOZsb2zSX+uWVUiE02rTbLYrtxpUqQ9QNLXy3pf2UibnJ8d0u10mwwts05AblioFOG6tUakOk+y5om31GqwuRUgTzxWK/+TRJziWhTcdc3Z6jGtqXF6cI9ISkc9zPKPDzs4ODx8dgFAYj8coaka33aTmOvziV79GnubcvXuXx08/RFc1Bv0u56dnXNveQlXkSNOyDJQy5z6M5deeHRzj+yHPjs4wTYvzywvyTLDwF3zzm99ERTCazNm7fpPTi3M0FG7f3OXBh++RJAkv3H4JPwx54doanU6HDz78CNu2mXkeqqKztbXF0cG+3AR1rXru169fZ7GQhinzxYLNTTldKoqC8XiMXXOxdR0UhTjL+Jd/+tbfnpPdMHTiOCSIYtYHfSmy6Pd58uQJyq2b1F2HbrlLr0ZJzWaTb33rW7zxxhvUGk0eP35cAVe9XrciKXieR55lrK+vc3FxwebmJj/88Wvsbm1j2zYbO9v4vk8Yyp1yc3OTy9FYIvKez9y7pBDSTEHXFAxVw3UkhXKwsc7Sj5l6J/iJwub2Dq2WwXQqTw1LN8gVBV0vvdVd2YuHodzFoyiSbjaqQp5Kma+pGyiK5OUXRVEKWeoVlXY6nbLW71XldODLcaRd5qxD6XeXpiBW/nzSXSbLclRVQygqSZbjuA6NRotZyfLyPA9FFayt9Sq5cZ6nVS+/9D0Mw0JFpqJKeagGikKzI91qkiRByXOyPCHwgrJHLt1oFBVVfe5QA8+VXytCy6oi0FWFWzdu8OTJI1zXpttt890/+g6uY7M56DGcLrBNg2UWcO/9DxkOp0wnM/r9vqzs2m1qlsFiMSMOE+IoJIoiNEWV+AdyQfvLBbu7uxwdHfDSy1/g9PSUMJQ+CJkoiEpnVz8MKFCxXBMtCjk4OmSj38f3PeIkJPCWfPzwIzoNk831NdnaTEekSc777x5x+/ZtbMuSLY6iyxalxFviOCZNpHDK0jUUReC6NrVaowSYFXRdwzBka7OYzjB7bXTdxNT+Bhh0f5NXlmb83d/8dUzLlmOQ2ZS33/4ZGxsbBEGA53mkSURvsCmJMEmGH6X8qz/9c2q1GkEY88oXv8Tl5SWqbvLGG2+ysbHB+vp6dXo+ebqPrus8fvSEmzduMZ1Omc4XhCWwduPGdQzDYDybYZkOYVIw9jyCWEYkBYl0v4GMmR/j1jL2T68QiuSptzsDvOmEyMvZ2NjAMAyiRHqk65pBFIbMZrNK+eZ5Po5TQwjJzlrvr0l+eiHLyRWmoOoaqm6QFYLZbMbGxgbTyQTTNPE8D1FkFcFmuVwghILrurS7HZI4k9p1x8VxXFRDl0k4uoZhmRRC4ctf+Qqz2Yw333iDSAR4ixmL2ZTd3V1EkpQbR1x60xtygZfSVE2Tp7O0pdYZj8fMpzPMUhWo6zqmLduJrBAYqOhloGQupEw4QyEMI2zTIg8jFp5PEmeohsKDR58QhiFv/vS1kj/gEqUZr715j3a7zXDh4RU+k8mMycxne2OTOI7xfY/NjR43b+yyvbXFybNj7r/3Dr/6zV8iyzI++vA+l2fnCCE4PjrC90PGkwntbo/Dk1N0wyHKcoIwRtEtJoslaSb74wwFq+bi+yFFJ6XuOuRpgreY0Kg7WKZKHqR0m3VOzi9w3BooCqenp7R7AyaTKcsolVhQUpAVKmmuoKuwmHtYts7FxYV8fsNLOV0p04fyLME0VDbW++iKimFbJOm/kb7yF67PRRnfbjbEr7z6KgfHR3z961/n3r13uHFtj3arJkGK+YQ0TbmaepimyVvv3GO5XNJsNrl16xajqwtc26LXaaNQ0G23ODs7w7IsFotFNfL64iuvVKIPtbR3hqIsmeSOf3pxQabojCdz3vnoEwqhEudSQikTRGSijKIo5Jm0LtQAy1SxDA1TVdm7cZ2NjQ0Gg4HsVQvp1VZvNqTXfK0m+03fp1arlSKKGNeSYorQD6RKLpWne6PTIwkjDMMgjSMQAteyZE+cJdWGBkU1WdAMHX8p6ZqtbleWh3s3ZS+5syMrD8uBvMC1Hb7/ve8RByGddhNvNsVxHO585WVJUS1DMIoCTMOWYRq6IRe8oQMqwWyCt1hwcXSIa2pkJb9bN22MWk1WR58ah1YAZSnqCLwlcRQxvhpyenzC04cfMB2PqddrfPLxQwncehJwlMi/LsdoKnLhRDIHbXdznV/62is8e/oJv/sf/Q4//MGf86u/+HUJjH3yMevr6yRhJPXtitxwLi4u+MKrr2CaJmvrA8bTOc8ODtENh6OTcw5Ozrm4HFZVyGQ6YtDvoSRLvvHN32A0nZXPYYGWh2x029TqjlT2aQbL+YLZYs7O9VtkOZxejKi3u0ynU2aTkQQscykM2lhvUHPcKoRD0zTW19e5vLwkCAKgkOYlUYxu2WS54J9/9/W/PaO3LE0xDI0b13Y5PT2m3WgymUxQVZX5dMqTJ094+vSpHKmNp5KBlmSkmRRHXA7H+L7P6ekp0+mU+w8+QijP7YuTshSbzGacnp/TbrcrBZHjOFy/fr0aMb344os4jkO326XX65PkMrtstdBRpGtInhcohlnZ/RqaPM2CMOb89IwH9z/iJz/5Ce++/Q7Dy8uSvnsmvcTmcmSk6Xo1mhKlEcLqTRZCUOQre22VMAwrQclK8bRKVpEBDHK+HsdxxY9vt9sIIaoYq5V6Kkricnwm++WiKNjY2KiAtTzPmU6nlZX0akRWCWU+BdaBHMV5noe/8NBQ6LbbNGqf4sWjoRrPM91/zrNA16qRYpbnHB0dMR6PaZd2XUdHRxwcHTKZTUnTjPF0RpLBeDYnSnOCKEE1ZEx03a1xcn5JnufsbG0zHU+4tr3F8dEBT8o27/TomFdeeQUhBJPJpOIpHB2doKo6RU4Z/qiQ5FKpqKiyHVS057qHyWyOruuSoZllnJ+csphP2drawg88sjgmiUOSOKyMRzVVp9PpsCjHx6AShQneMiAII/xlgK5JnkOay/SgxWJRcuztSr24Kt3/dbnsX3Vp3/72t/9t1ulfy/V7v/d73z44OmY897gYDcnTiKuLExbTId1Oi5t37rB38xZZEGIZGkEQ49YbzOZLJouAhR8xns24Gs1Z+AG7G2vEUcxiNsE2DO6+cAu9BNa2N9bJk5RWu8NiPme5nBOGAQown8/Zf7qP49Q4PT4hjxMGnSbL2YIU5GxYgKFrFEWGpuRl2ibkhSAtoKVrxFFEEqcEUYofxPhxxms//imm4/Deu/ekxZYCURLxwq1bLH2f6XRGIcC0bYpCIYgTcqEQJzlxtJB5comsRLI0w9AlYuxYLvVagyLN0QyTJImZTCbUazVa7SaiAM0w6K9vUKgabqtJx6mjKTq6biBUlbTI5QxaVbk6P6fTalKvOVydHjLothCqhmY7aHYDxbBIUkGRyrhmTdPQNZ2jg2fUHJvT4wMGgzWiJMZuNRC6hkDFMk1s3cA2JBBWKAaGZZPmCpmQ5tHkOf/sf/gnkAXsX4z52Xvv8/a9+8S5IFd1zkZzklxQqApJmpEVBZmi4kcp9bqDyFPUvMAUKe1GHUtT2d7a5Pj4GXESoKYZ62s9JrMxL75ylxu3byB0ldOLS+7cvsvjx/vkGQzW+9z/8D6iUDk6OSFKcmqNBgcnp6iGRpTnKIaOgkWr2+f46BjH1EjjGEc3aLfbBEGIpkpDz2arSV5kjOcetVaHw4srohxGsznHZ+dEacJkNkUzNIpC+ttRQByHlcGJ47h4y5AwSnnp1a8wm8/RdMmnePvj/fNvf/vb/9Nftc4+Fyc7IB1Xi4KN9S1u3brFYG0dy7J4//33KQp499179Ho90jTl4uIC3/exbVsSKpKSkaWrZa+c0mp1cJwamqGzDMKKjXV4eFjN13d2dsjznNFoJDXzYczm5iZBENCsN6jVJEmn0XCwbLt6rGp5MIlcoGtg6Io0LVQVVEOlKGRVsRpTPd1/RrPZ5MmTJ5KhV56Wvd4aH3x0n8VigW4YLMrsdqdWk17t5em8+nnDMlEVjU6nU3Hs8zyXji6Wg1oafjRKl9iikCVfHMcVAr5C0z8N5tVqNfI858UX77BcylSaoOQcLObLnzvNV//H9/0qrMLzvIrI02p2KsCwcmXRnpsxfFqrvSIA1Wo1PvzoPs8OD7h9+zaOU+Pq6or9/WcIFXqDtdLlBjIBSZKS5ZIinZVy2RWZaXd3g+VyyXA4ZDgclhbe8nnrpsHx8XHly7f0PA73n9HvdnEch16vB8CDBw8qWXIQBFyWlZlsv7TKhUdRFDkiLEFFy7Lorw/wfb96TEIIzs/PSwA1Z7GYUWQZR0dHz3+uNNIIY0nK0jXZOq109N5sXlVXQgiOjo5YLpdMp9OytP9s1+fiZP9H//D3v71cLknimNlsRhzF/PZvfYu33nwDTdOZzReAwsXpKesb6zi1Gkcnpyiawrz0Bc8KOW8eDNYZj0eMJlP6awMUTaPX7+MHPoZhkqQpf/aDHzAcXjGbTemvrdFoNtENg7XBgCRJyPMC3dAZbGximaZkrCnScEAgcEwTxzZoNxs0azXqNZssDjE0FV0Bt14jL2SbIeQ5jm07TCdzkiTj5PgY3TI5PDquuAEFEKcZaZ4zmoxBlR7iQoFup41uGCRliW9YtsTCNY16o4luGliOTbfTLku+GkmS4jiSPWeaFmuDgUxGMQxUNFRNxlQbplX+Xhnx9PFHH1DkKc1GkzRLieKYjWvXEIqKbtgYhomm6tTdmmxDCsnvV5GWz6Ym5/SOI91/szzHdlw0VUM3dNm3awZJlpGm0lbs7Oycd996i2fPnnF9b48//MP/m48fPal04aPxjGUQriLUSIUoPdhUNMOU6TeKQl7keLMlu9sDdra2WMwmLOYzaqUL0tnFkJ1rezx69AmWbpDFCc16kyhOePTJI1RNJUkj+mtrTMYzFp6PYdh86atf4/TsDF03mExGMgPQ0BkPx1xenBOFAaIoMA2NTqvJ8PIc05AtS5bGtJoNTk5OuXXnLp1Oj4OTM5ZBxHLpS+uvcsNSNYMoCtA1jeXSZ3trk5OTY1zHwdB03FodFLgcXpGVhqWLxYKnF9O/PSe7punYukqtVkMoKicXV/zgh6/xwot38YIQ07I5OD4iTkKOjw7wvUXl4mHosozUNZVCKBydnRFlgkanz3v37/PwyVPeeudd/DBmMptSbzbY3t7GsiyWyyXHp+eyLzw44ODggMvLy2rnT+OIdruNitSkC6SjZ57nGLqGY5rYlkGj5tLtNHBsefpUVt6lXbFpmsxms+r5qqocUT179oz79x9wdTVisZAc708ePcFxXT5++BDbcbi8ukKglqirWrm2mLaFUFRanS6ivOlVVavYayvTiJWxxYriWhF0SvNIRYU4iUpnGL1yoPG8BYZpkiM951ey1NXvAYlTrFJuFSFPMrUE/lapMlkmT3+hPveFf/46qBXfzEKjAAAgAElEQVRGcHAgUfH/4//8Q6bzBQKI4pggSitcRDdMao2mtKbW9HIunqJqUvsuAUQqH4B6vV5WPpI2PZ5MeP+DD0iSjGa9wcXZOR8/eIBSEpw0TWU4GXN5ecmtW7c4OjpmMpkwGg65sXedhSenKUkcc356SRwnEjdKJEEmjCPG4zGdTqeqOpUypLPVbkhQNlxy584LMoWnFFplRYGmmxQIln4g7cpUhUajQbPZrEDN1evfabZwHBchnrvOfpbrc7HYRVHQ63QBqeIqgI8fPUE3TLa2d2i329x98QvounSTOT07YW3QqQwT0KQdVZwXxDkIy+Xw4pL2xg4YDikKb967x9Rb8vDxI27cuoGqq7Q6ZSiiW2e+9Dg6OqpMI3xvwWh4ydnJMXVXBi+uXHCTJCWJAgJ/jk6OqQg2Om22+m1piCFkzpptGmgqJGEiDRyRH5qi8vD+I2I/wFIN0jjlpz/+KcfHJ8znC15//U0OD49562c/I0tT3vvwPjNvSZTlGKaNqhnEeUGt1QbTxHBcDMfFtF3Wt7ZotNuohsF4NiWK4srAwjRNkiTBbTVYBj6Kpn4qj12mq3zj13+d88tL0jxD0Uxsx61KZcOUqS6h7zEcXZYgp4ppSqcVRagoSDruymq7VqtRCAn8FSJDNRxyRa24+O/87C2++8d/wo9+9CMOD4556+33SApot7vkucByLISqkhcqcZoxX/okSUaWFWRZgVNvAiqqUqbEaipbW1tSNeY4tNtt4khuTr/0a78GhoFj1/jpaz9l//E+44srPnnwCbPZhNl8QqfTZDEPeO21n0iFpGnyx9/9HrPZlDSImAxHNFwXU1MohJQdrwBHIRRu3bpFo9nEdmsMBgMajUYlLQ6XPlmcQCFYLjz6/T5+FOJHIWGWMpotKFQD220QhDGPHj8uGZAhURRgGAbNZlO2BpdD/DBmsfzsZfznYs4OguXSk4YIQYAAarUa731wn7t3XuDo6IiXX/0SF4ePKYpCikRMkyzP5BsvAEWWvXmWS3Sz7Dkd28J1pBV1EAQkccjVlZRlGoZBza3L+CJdnjrvv/8+jXaL8XhMb21AOhpVXmJpnqOusgXyQgZbCGmqoJoGuqYQ5AmqIjeFNElkzgDgmApSSyKI05R2TZaWlxeXcHHJ2mCNs5NThBD0+33yNMHSDe69/Q671/fY3d0lDEP6/b7siVVpVaUoCo2W3P3VLEFk8rULw7DqK2eLOa1Om36zTRDGmGZSfV/GNEv2Wr3uoqsK46n0trvZaGJZcqPTdSnDXTH9RJndXmRF5SKraTIuq8gyBt0BCQWGZZLkxfPEmyKvTluKgsuLM64uz+l1ujx+/JiXXrrLyckJQbggyQu0OEMGegpUzaDIcxnyWVYISRhhmjpp6KNq0Gg3KLKU0WjEWruOYThMRtIRqLBkDvxwPKLf6aKqOlHk067VWVtb48n+U77Q6VTBHV4gWZtfeTmj1+1Sr9cqiq3Wg9nco9NtkWcSF6jXHMaTCYYCYRqiKgqzxZBWTbIegyBAswpqjS5uzcYwJFnJMp0K4zENG8MyMU15f3Q6HVRRPO/fPQ+3USeOU7Js+ReERH/Z9bno2f/b/+Yff1vLY5I0J4hims2mjAeOYoIwYGtzgzgKadZdiqKg1mgQJdJOaOZ5q+AOABRFl77iihSthKG0LDJ0BcvQUBSZOpJlGZeXl3z8ySMO9p9JLrUqR0CHR0dEUcxsPi993COsZpc0jVEVga2rFLnA0iEMfPI0ptNqksQxqiJvYk0BFPm5XbcxdA1VVXBsG10VtAwDihwlz7FME3/pEwUxhmYQBj7RMsCbLZhP52xtb3L47KBKRDUNi8HGBucXF2xsbrL0fQbr61AUlfGDphskaUKtXidJJad+sL6Bt1yyXC6rCiqJY3RdKvzyXGrLt7Z3SQsBhYJh27T7a6BArd5AKQooBHkuwyUote7RfEkUBUzHV3S7TSzbJkoTFEPDsiWxxLJcIqFiGDqHz57yJ9/5Dt/94++QRyGmptBrt3j85BHzuUdR5HLcKYR8LxWZlQYFuqYgihxLU8hy6fxbt3RMTaHdcDGQyjVDkwCaU5bUuWowHU9RVBWRw+b2DueXV2SFYOH71JpN7j94QL874PbtF3nn3XexTIMn+0+Zzyb8g9/6+5weH5LEIdd2tgkjqcTc3d7EtU36/S7bO9ucn1+wNlhHKBpr65tMxpdc27vBaDbHqtXIhcpwPGUymZJmGXGSIoTEIExDZzGb0m42IE9YLj3azSaqquP5Icsw4Go4Ze6HzJc+QZwwj7PP1LN/Lhb7f/2P/uG3NzsNFl5AripkWU671UQjx9QV4igkiWOiYIlpOZxfXPDKy68SRQn+MiCOZV+naVoZj6sABa1ajV63yc5mX5JQQh9R5Ozu7IAQ9Hs9FE2l3WxwcX5WmTlmRQGKSrPVJi0EURxxOVvimCZFkUMhaNYsbMuUWWsIgkCq70ajCb/x69+QjzkIaTUcTEPHNnUocmo1izSJsTVp+2QYOqoCUSJNMMIkJU8yQODaLrqmEQYe3tzDNk0ODg5pNZsslx6WaTAaT0pGnidzxnMJCeqGDkJBlP7vYZyW8lOlEmCYpZrN9wPZAyJHfetbuzRaPXxvQaPZot3rYRgmiqqiouCYJlmRyVBJVVAUOcOzU06Pj2k6NmEYMJ5MaLRbKLpBlkOj3kIRGprrYOo6//J/+1/52pe+yOzqnCxccnKwT7BY0Ki7uJZJUuTEpVW4qGwNcxQEuijoNWwGnSb+MkRFVk7rvRaaSGjWbAyVCjX3Fh61Wp04yQj9EMt2eXp0wunlFf2NTQabWwg0ZjOPIi8QAj755CHXrl9nOLxi7/o14jjE8+Z87atfYToZ027UcRybZt0lSWLmsynb21ucnp7Q7fUQikKU5kRJishj5vMl08USy3H40ld/gbfffZfpfEaWF2iqgkAab+qqhiJy+t0OgTfDdR3SJJU57xnMlwHnV2NOr6aEcUaaQ5QXf3sAOlVV0DWFXqeOrj4P/FsRRFZEBseRQQqtVofXXnuNw8NDkjTCtjRqtoVpaGWEjrw9oihAKQTjq6EUwFzb4/bt29TKcVSj0cDSdTRNgoOe59Hr9SoVnF2Thg9JmmMbsi/Vy9QTANXQURQNTTXo9dbodPs06zUuzs6xDBPb1Ok0mzRdm3ZLAnirpBnp+y2fu8wf/5TPuKKQ5kJaVBsGs8mULInxvQVJFBEFAeenZ8RxzGR0RRLJ2KV2u41q6KXxo2TRCYWKELPSZdumhaaoJElCFEVVCk0cJZiOSy40dNPGcWtYlo1WSiz90ltfVVUZkFB8qjzPMoJAlpW1Wo1ut8vTp88YjUaVFDcXckT1ve99jzxL+Vff/RMMBV66fZsvvfwye7tbKHnGZHRVIvzy0jRFthrIG9bUQckTNHIMRX5t0GvTbtRJ4qiyhI7L6c5isSRN8ypoczgekxU5XhDy9r33+PHrb5DlgiCMyVZtCYL5fEqWJRIh11XiOJTga57heXNcx0YVBRtrfelMA3R7PQqhsPB8JtM5s7mH54folo1hWzSaTd5//31pC45CUeRomoqqKDI/XhTUag4glZwrdaOiaCRlwGUQxWQKZArExWeny34uFnuepWyv9zE0lX63jcw/n7KxMWAwGCCylMhfMhpPsSyHi9MzXFueEDf2rrO7tU0UxSRRgmloaEJG1OtIfzgKQVZGG6sCJpMJ7UYTbzan122jKoL1QR/T0JjP53jlrLkoChy3RpJBkmYyZUYIFsuQRqtJkUOtXsepN1j6IecX0n9sMZtxcnRAr9vBW8xQFUEeh9QdE1XkrPdbqEaB6RrkakYmcjAU4lySbVIEiRD4ccQi8NFR0FG4ODtHLQo++vADHj64z1tv/oxep8tisSCJAt7/4ENpQ6VLa+5Op1MKYGQ/PRyOieOYhw8fsrGxwXw6I4kki07XDApFJc1AqDr9wXblqHJ6esp86UmaaRhWM+3VLDqKIsLIQ1UhL9KKsfjiiy+yPtgkjhJs262SXa6uLrBNnZdffIFuq8bZ8VP6LRdbK9CKhG7DwdDlhMVQFRBSIlx3TBxdYavf4cbOBntba3zzV77Gb/7yl3EMlen4ku31PnkaI4qMMPQJggDHdcnynCSIsHSD0WRKmOUEeUasqHhxyjJKePXVL7O9dY08z7l96wUsU2dt0IMi43d/9z/m+rUd7t55gU67Sa/bptVwaNRdjBKk9H0P23Z57/0PuRqO0W2HpIBcwMnpObfvvMjjp/s8efIEUzcksBn6+AsPw9Cp12tSIFTI6c9gbQ1dkaKjvCiYzT3mvk+r0yNKBVEmPz7r9blY7EJI7vjLL7+MV/bgigInJ5KK2G630XW9YhNJxFugaQqL2ZzLizO56yuQpnnlWy1dbOa0Wi2Zr3Z6zHw+r0ZHK4LH5eUll5eXjMdjrq6uuHPnDpqhs7+/z/7+PpomvdILAXkuAZGVNfCKguo2ZBWQJVJm++rLr1CkmRyTmAZFIcczmvY88kfTS1umMlQCTf6N1TQljmMWiwVZyX9fAW5hGHJwcMBiNmE2m5HGSRXUMJvNcGqNyrBwpVhbEXxW4Ysrl5tVWAPIEaGmGiDUimYchiHn5+eV8UaWZZ9C8Kn+XWR5RbLJslRuCklcOrK6mKaNbbkVPfj73/8+f/AHfwBFwVqvjyIKXrh5i93d3coAQ9efK+MMw0DXNBpNKR5qNGo4js1iNpW2X1HMchmXFmFJ9SE1AFY5TvSq4I2F73N+MWMy8bkYznn06BHPDg4YDsdEUYTnzT81Mi348P17FEVW2UUDWMbzVBuQbcPZ2Rm6rjOcTHny5Kk0m0hy0mKVNKwwnc04Ppb3onTfNav7tdNqk+c5/V6PZrNZjToNw2B3d1fGgkehzBvk/8Wj/S+5PhdovBAFJwdPUE2HmgnN5kCSQ2wbx66Rhh6tpovjgLneJE0LAi/FDyLiXKFAPvFcgNAN8kygKZDmGYYGZ2eX1B2Nve0NsjShYbuEwQLTNJlMQ3r9gXQr8XysGjw7OCETkGaCMJYvaEKJ+UmyHEGYIvIl7YaNSGJcUyUPfRLLJM8MvGVMvVU6y5g2yygjKxRmU5/emoOtmqiZgiE00iInjXNEjuRnFwWKCkEs001c00LXVGJvwQIJLq51O/hzj+PDQyzLIkpSbrzQRDFVckWh3hqgmw57N9aYefcYja6IYh9VbZNoGfvHz8oEV50oDFnvb6IoBY2mzmQ+4v79fZbejJ3tAbqfooU+XhpTbzbJTRV0A1VXiYJQ6rCzkFzNWCQRvc0tVFXHMuugmuTYLLMC35/x0+/8Ifd+/CPu7mwzWOsQelOi0JchlUWBiWDQaXGjNWA4uuT0+AjHMjB0jV6vQ61WQ9UMpos5lmkyvjhmaVnkRU69bnFyOaXRaDLxQ0zTIppHtDZchsMhLQvQFTTdJM9jUpGwuSWdjPMi5dnFOa++dJfx6JK9jS3uv3ePdr3NcjFjejlG11WeffIJf/c3f4P3PnwfVcT0Ww6z2YJBd41Go0Nzrc6Dw3O8hc9ar8/B0QU1PcdbLnn73gNOzs4Rms50tsB1a8RhiKoqtNy6FNmMxyh5xGQyYdBtYtdcwighSVJ0x8H3AlS05yi8HFR8puvzcbJTniqaxlq/h7+Y0et0aDXrBOGSJEm4uhpxfn6O7/s/l6YphCBJ81J+CpSnVC5WIguZNJIXPNeAhzF5UTAvgwEWi0UZGS0jmaIwZD6Zlh5uFpkAy9IQQv5609DJS9cTx3ZRS3OKKInZ2NiofOxW8Tyffqz1er0CAlco8+pnFVZ9u0S8VUBk8vSVp6aMcyqKgiSMmM7GLJcLHj54gKkbLErijq7rxGmKYVuYzvNT8uLigjRJKjPKlX1xmmUsAx/N0MmRpfnZ6SmdTquar+e5xBiSNKoqglV1sFgscNwGcVqUjDaBomiEQUwQLImTkCLN0JDmEmu9DrP5hDj0ubi4YDweM51OKwroih4s/6aO67o0mi1sp4ZbaxAmCXEi8+IMQ1p0rcxK9bIvr9ddwtBHVWE8HqJpShnlpZCW4z9VfR4lZZomeZ5zenrK6ekpjx49qtxkVirJwWBAEAScnJ9WnAXLcmQ+nqZh6FblzrNSXF5dXXF5NUJVZRyYjOEeViaoqqpUppqS9GTTqLlYlqQst1otDFOTctayUpH36srd57Ovs88NGt91dTrdHjXHxTJUmjWH2XhIzbJQVCkO6PfatJtNdFVHN6SB43zuYdsWyyRFVxVyoUBJciiKXKKdGrRaDZI0LlNSlgRhwHQ+Z77wKo52kiQUgOf5uG6tkjT+8td/ieHMI4ljRCGwXZs8zxCFQOQZuqFT5AkCqNdc/KWPruu4bk2GOCZpyYSDPCuI4pgwClFUlSDJSXMQagnAiAIpGgVLAwpBzZLmjVL77qMIgapp+EsfVdE4OjrGtk285RLXcRlsbhGnCd1+j6zI0MpR1Q++/13eefNNFEWl3eowGk+Zegu6/R5xlpFmGe1Wi3/6j/8JWRJz9/Z1DFPDrbnopo7tOKRZgaLpaKaFgkoQJei6wf/8z/8FDx5+wmBtk5PDYzrdLqenx6z1ejLuOYt59+23uNh/iK4I8iTi6PCAo4NngKjMPNI0w7EspouAq6vLUjevIZB8gjjOKIAgTDg8OgYKev0+QRCwWCxQVGTab5GjqTqmYaIqCmenZximQ5TmjGdL5stIrpPSHQdRMJtJpV+z7lCkKcPhJfPFjPWNAYv5rFRLpiiqytOnT3nhhdtygUcJOzt7fPjRR+QCDo5OiaKIjfVNFBTG0zGKpjP3lnjLgDhO2NreJo4jup0WosjJs5TBYI2ma7G3u4FCQafTlFOdNGXpByyCGKfmkuUFV5O5zJCXR8LfHjS+KAqyXBrsD/pdus0ahiqIA484CipPN6N0qYnjmDgMZZhhzZZ9fd1ld3cXeSqWgd6q1E0XQJIVFKhkhaDZ6UjFlm4Sx1Lfnuc5rutiqBp5mpAmEoxKopj9x0/olKaVAPVakzwTJEmG5/ssFgtQNJIkZTKZEMcxSZIwm83QdZ1+v8+NGzeYz+dVvy6ALM9Jy55ZSiCVkmEnP+uqjAVGKSqQavWhKAq2ZVa89JOjYz766KOfm2CkeY5QFD76+D4np8ccPjugyHPOjk/4+KMHJFEkxTN5To7Adh0m0ykX56eYhiG9+MIQ07KwnRpoOoqmk+bSjCIt5GsQpxkXVyMODk/xPI+PP/6YyXjIP/vv/zt+9MM/I5zPOHu2z/nRPr63wLFN3v/gnqyGooiLy2Gl117FW8WJlNdS5BXmomtm5f8/n88Jgpia28A0TVneqyqRn5AnKSLL5eumrKYIAqFqFKrGdB5KW2whBUtpnrO+JoM37t69y2wyIvl/qHuzH0uy/L7vc2KPuHH33Jfau3qb6p6eaZIeElykkWzPk21AgPRgw4AfDMMvBvwPmIL1J9iwYUA2CdoGZQu2ZROGTBvUiJzhcJbu6bV6qa4195t519iXE8cPJ/L2EBLFJqCH5gUandV1u7IyMyLOOd/f9/v5Vrq+KmuhI+OtTa7mU9JC7wT39/dJsoyLyYTbt29T1zUv379PURQ6M9/+/JumYXN7l7JuuLhcoAS8/PLLeI5LN9TWZdd26HY7CKHohR2krDBNgySJgBb95dhrXNfk6rK90f9qLIqv0s/uAX8MuO37/7FS6r8QQtwGfh8YAe8C/4FSqhRCuOg+928DU+DvKqWe/as/iUHTKOq64OT5U/0kLwpu39in3xtwOpnieC5RpJNHr732Os+fv+Ds7IJhv8+NW0N++t7HLOIXXx5hhOB6KlFJmEcxnXADqQSLaIXp2PzSt97iR3/yA5xW/LlGBR3s7XE5neI5LovFkstJTmY43L5xk0Wke8+FaVDVEksqyqomLwu6vQGua+ot9WrFzs4en332GcPBiPHmhoYQnF/SHw6wHJesKBiMx5SV5MXZTI/fAMcAyzTY29qgrktcS1LkOa7rEPguCoPZbIoyTJarp5R1wzLSZNXVKmY6naIw+L3/8Xd5+PAh/+Zv/Sa//z//TxiNxDVNjp4+14lA02T/5g1KWXP2+BEXF2dMzy64dXiIqCXKdmksh9pwyOsGW1iYrocyLdJKH0EePTvm0aNH0DTIquSf/sEf8M1XXyJdXPEf/ft/h88++4T/6gf/H6ZpMhiO8R2bzz55SNM0HB0dkWS6ALI73FgjwpeLBYHjMAh9fN/HoKHX7VG0FlhZ5Ax6ATcPd6lKiaz1g1qWFbVVsbe3y4sXR/SGA2bTBYZl0ul2eHI8Iclruh2fVZIx7Ha00GrC8ckLsqTg4Ucf8Z1f/jY3btzg//6D/4uNjRHzxQLTslCNJAzDNadfNgOWyxU/+dk73Lt7H8/TC4/Zzvinl1cAPHjzAfPFihs39njttdf40Y9+hFANN27coBcGLBYLPM/TNt/VDNMw6Pi61SdOEmzX1x0GjckqSrEMwSDssIxjDFODNL/K66vWP3WUUnHb+fYD4D8D/nPgf1NK/b4Q4r8F3ldK/TdCiP8UeEMp9Z8IIf4e8O8ppf7uv+pzdFxbffP2JsvZnMCxuHG4T57nnJ5dEAQhUgh6vQGDgTaPFHnOxsYGWV4wnS9J8orasJjNIybLCNluWAwDDKFAqvW2eHMUYqiKUV+zw+u8aIGLc/IkxQ+72I7LaLTBxeWUspJMrq6Y5mqtfrquXmE6gUfXd5FFiiwydrY3EU3BxmhMkiTYtq4OHg3HKAFX8xkCkyRLmUcxcZwjBWQ5uB2PV199lc8/+ZRBt4NtCvJ4xebGCCH1TkG3oFY6yy0FcZajDI2sapTexTx4403uv/oK//s/+T948803efzoEW89+AbL6RXjXshsckEpAdukrCTf+c3fZJXolpmTkxNE3fDJ++/zy2+/zd0332Jvb48bN2/rwkHDohP2mEyn9HtD3vn5u/yjf/S/sre3x8c//WNu7u8xefGYbDGlyjN+/TtvE0URp23E0/Z8ajR4UhgW5+cTlqsVUkp2d3fbyuKEIssZ9sM1YWg+n+s+N8fBaXvuZKk1Fmlq7FWaxaha4nkuhmHqnvbGYBUlSAVREnOV6Kf/7cN9nh+dIICXbh8Sxys8x2G1WvDqq69ClXHv3j2EafCHf/hP2draYm9vj/l0RpqmulNwOOTi6pI6r7l/7x4vnh8TdEN++JOfEY5GPHz4OZvjDdIkoz/qMhqNyPOUPM3wXZtnz4559ZW7WJbFq6++ysOHD3nw4AHD0OWHP/wBr9y/x9nZiRYtvQDXD1jGBVlWECc581XK2cU5pVSUin89pBqlX3H7S7v9RwF/E/jH7X//XeDfbT/+d9pf0/7+d8VfgtNQLWbYsiyEZZPlJXmhS+7CMMS1HZTUgpLrOCglubi4QLVLd5pERMslZZX/QoVss55hrf1XUs+GHUtfMMulLkio63JdABn6AR0/WAcpbMtAKIVvW1zzVb5MhkVkWabHgcMhVaFHT9dMuGvyS57nuK67Htl02ry6lNpUEwRaL3j//ff51V/9DnVdEnguo2GfsswZjQfsH+icvSUMgiDE64Q4rkdR1cgGiromb8W73/md32ExW/DeT9/BMUw6rsP+3g4misGgh+1YRIslo2Gf42dPefjB+zz74hF5HPHiyWN81+b0+Iif/OQnfPrpp+sqLTDWGfs//MM/5Kc//gnRcsEnH3/Eo88e8ejTT6nLisBzcEwtjE0vJ+RZQhKvWEx1KcMqSkjSHNsPMBwPJwiJsoLTySWrKMOwHfKq4mo+52wyIS0KkjynKErSLF9n1DudzjoNVqTZGrUtLJO6apjOdDS2kA1ZIXFdB8swOD09xbdNwsCmyBI2x0Nc28K1HS7Oz9YU1wcPHvAbv/FbFEXFu+++R1HV2LbLbLZgc2uHfr9Po7RQaTsm3W6Xjc0x3/u3/m2apgV/1iVpGrNYzFgt52yOhwwHfRwbOh2fui6pqgLfd+l0OlxOpiilmM+WuK7/55KCbZs7RZHhOga+5/yVBLqvNHoTQpjAO8A94L8GHgMLpVTdvuUXO9j3gSMApVQthFgCY+DqL/zz0QZ/gDyuqWXD7u6uFkW6AWkaUzc1vf428/lc45EQFEWGKRTDfo/Tyzl5WmkjDU27nVcICaHvYJmCQehT5BlBN6SpKga9LlIqDKXPddeFg8v5jN5gSFOXBK7HS/fu8PGj53T6XdIsA6EwDQgCHRxxgwAh9Kq0e+sQGl2jvFis1sy72bM5nV6XIAi4uJyAhJ2dMacXUyxHYChtkPh//t8/4uVbO3i+y7g/xHNdmiwl7PdYzlcMRyOOzycIy8XxAtJFTKMqqgaUgD/98U8IHBvRKOokpbIsZqengGI86rFaZoS+h+9sMur29OzasHj+8FNe/8ZrqLBDVVlMLo4wVxnR1ZQ/+8EP+eZb3+aNb32b73//j6laNHdZlpwf6dy57zgsFksqS9H3DLqDPmdnFxiGwebWHkmeEXS65C1O7GRyxjJKGG/tskpTJsendFxLo7HTnI3NEZPLhZ5kKINeLyRLCgylf54YNvNVhOn4unijnYBIKZnOFiBMzmcZEhCmpJRgtOUUpil0x7xq2Bj0iOMVg8GAQTdACMHVZILnBXz80SfIWtEfjpkvIwb9EScnJzqT/uwF4bCjve11wWq1YDjsE3gWcbLk137t27zz0/fwPI8yjZG2yf07t1jO5gzGQ167f5s8XnLr5i3yNObmwSEXF2cUScr+3g2WqzndbojnGiR5RppOQZi4pqVNY0bDzb0tnrw4YpZ/tZv9Kwl0SimplPomcIBuZ331X/a29b37F//e+iWE+I+FED8TQvysbsv9TMNGqoZlknM5nZPlBVUjGQ2G2O3oaLVatXZQbZ6oqpJG1a2IozHMtgmWCb5r4brWOjOtlMIxrba1VdENtChyPRbzfZ/lfNgebVUAACAASURBVEGn0yEvdGHF7t42pmmyu7MDUqefkA1ZXpFGMUII7t69g+M4+I7eUl5XN43HY3Z2dhiNRoxGo+uve927JoTAFBAlugvt3q0bWCbrssiLiwut+itFvFxx7969FkxRkqYZ0/kM2QCmpcNArTb5ykv3KcqafieAWhtONkZjZrMZcbxiNB5Q5DlJvCJeReRRhCMEvqkjuZ5r6z5zzyJdLJicnHJ1fsrv/Q//PdH8iocfvccXnz8kT1eoumAxneD7HdI0ZxUXeEHIdLbEcFzcTgiWTdDpkZcadDEYbxAlGcqwWcQps/lSR4ebhrxUJFlNkknK2qCoBEkpKRqT2TIlygqSvOJyviCKk9aToNbmIW2gabRGa+qjXCEbhGFr4IVhUktFVUnCwCdJIrI0JfBdLi8vGAx00s80TY6OjnAcnS4cDAZ4HR3EWsxXBH7IojXHXF1drfPrOzs7PH36lMV8TlFI5suEQb/LarlkNOhxeLDH5uaYl+/fR9Y1vuusq5mDIGA03KBuJCiD2WyGsMz1glEV2iikZEPgOViWwXjQ/yq3MPBXVOOVUgvg+8C/AQyEENc7gwPgtP34GDhsL2wL6AP/QmO8Uuq/U0q9rZR620RvjZdRRC0VRVlzcTWnaWA+W1BXBd2OT6ejabPHz19Q1yX9sLN2dTmuzcHeHhujIf2wq5tLLVvf0EpHOF3b4ZVXXiH0A3a3tpFSsjnSDPY4jjHQ5RNZlrE5GqNkw+npKY5pEbgO33j9dXqdQHv5gaJqqOqChx9/zGqxXPeqe56ne95at9i1m0tHRa31/Pbq4opOp0PgGNy5cwfXdbh35xZNo6t8u71QY6p7fYriy063Xm9ArRqipMZ0dFmA4dg4ns3f/tvf5YMPP2Z7MODG7j6B66CqiuVixsbGBpZjkyQRUbQkiVZcHB3RDwK6jkMRx5RpQhB4ZElMvlpSxEs8E9798Z9y/uI577/zU1azK5oy5YuHH+AZDa6Q1FIhTH1DLaIYy/FAWOR1Q1JURHnF46MTzi8uefLkCRgm33jzTSbTGYWExrCxHI27VqbgxWRKIiGuFbXhcDZdEpUNi6Ti/CpisUyplMFiOWM6nWrgZZLQHw2xbJtKNtQSlGnRYNIIE9UILEuLnN2Oy8bmGMc2uXPrJoKG7c0t0jjhe9/7Hr2wyyeffMKf/dlPKNpuvNPTc1zX5/DwkM8++wyEoNsLeffddxACzs5OWS7nHB+/wHFsvvnN1xgPO7z15gNef/UlXVLa76IaiWOa3Lt3lziOEQpOTnS8+ejkGDBYrJZMJpecnp5RltW6LyGJYgQNQjV4jsHB/va/vptdCLEphBi0H/vA3wI+Af4Z8Hfat/2HwD9pP/4/21/T/v4fqb9MBTQEjelRYZKUFlFuUCsX0+mA5SKFRNgGvinwDOh2QhZRytnFDM/z6Xc6HIxD9ns2u17B2C456Jn0RUbYRByMXbZ6FoEtmJwd0x+O2Ti4iRF0KepqXb44j1e4YcD23jalLEFU9Hs+UiagYvo9i1u39mhkhevamAIupymyhm63r1N4s4giLXBMbfgIgoDZYoYfdgi7fV2VPNxkmoC0e6yyCtnA/OoCQ+bc2hmzPeqTzOcY0uD46TFeP+Tg1k2WcYYbDMhKhW119FO9ltRFRccUOEXF5fPHBBbIJmeVLelu9OgM+6RFDhh0OwNePD1jtUzZ2j3g2fSceZ1B32MlMyzPZTGZYZYK264wzZymXOCZFQ4J465NOpsgk4SObUPRENoBlirwLHBMkyTKqMsSW4EjFY6wEcomy2Ba2ryY5cyzkh+/8y7dXogQEsvS9c4NBnmtNLmlASUMirpGmiZYFtIykEBWQZwUlJViOkvwwj7hYExRQN2YJEWN5bmUjSaCNkIhmwJTVDiWpMoK4sWc5y8mOH7AchUTdDyqIuXp82d0en1mq4xVXvPkxQWVcnl6NOFsGvH9P/0Rs9WKo5MrbH9ALgWO7+s6r6JkK/CIL8/Y6Pp84/69NTlna3MTwxAc7GyTpTG+bTG9ulq33Kpa8fzolFVSUzYus6jhxdmKTNr0NvYIBiM6wyG257YgRIPgF9iIf9nrq5zZd4Hfbc/tBvC/KKX+QAjxEPh9IcQ/AH4O/MP2/f8Q+D0hxBfoFf3v/WWfQAjRtqOETKYrqkYhk5qtzQF7W5ucPn8EY8XmeItOp0OS5ixWEW7X5+T4iL29PU5OTuh4PqPxmJe293j+/DlVVeunfdhlMBhwenG+7hw/OTpmsVhgGxLP8+h2uxqqYLutH71erxY7Ozt0e0Pe++AjsrwCYZLkFQLwbAO3E9LpDdje3CTf2OS9Dz7k1q1b2K5Dv9/HDQJtqlEmjuPQ7Q0oigylJE1dU9YNlgFlkZMXGnb53b/xm3z+6WdrkGG00lvWqi7wPIeyyjANRa/T0QjpuuJwf4ubh4cIJYlXEZ1Q88WFUBzs7zOZnHNxcUFRVOzt77BczlESFnP99c7FgmS54MbePsONMY+efEIQ9tjc3MSybK6mc2RZMRz0+eLpOXdv77bJPYOqEQhgPByiyhzP0p5/y7K4uJoyixKkgCTLWrKLRm0VZYxlmZRtDZQlDE30UQ2SBhoQpoGSFXWjsNCIKmFo4KSuxrbXfv2L1QVFWVOWktps9JtRIMF1LMqiZnNnm9XsiizP2doacHx8iu9ZzGYzVF3z4YcfMhpvMh6PSPOS1SpiFUfaJWea9Loh+4c3ePeD9xmPBgxHGzz85DMMoTjY3cW2Hba3NTA16HT46OPnbI43+OijDznY3ePy8rK98ltij2PjOwFJrsGoT589w/M8fu03fp3PP/+c2WLVeicyHMumUZLQDlBScT75C6Wwf/E++zqURPQCT71xe5+r2ZLT6YK0bbnoeSY7owFvv36fq8kZZqva7x8ecHY+AVNbKReLBVK2fWl+wMXVdF2PPBqNmFxqe+Lh4SHT6QzTNHlxcszu7i5Fogk5/X5fc+fqhufHx0ipz5e11Oq6FBZFrShrSZyXrFJ9cdptPNdoGoLAoSMMXn31Fa2oLqaMRiNs28S0LR5+/CmHh4cslxHTqODiYkK342jjSDdga3NMNwwI/QDRfq2+47JMYobDIR9/8rnGFJUNRycnKCEwLE2bjeOYl++/hGc7xLFWfz3XZbGY8dLdewwGA957/12Nn/Z9bt2+y+ePHuN3etiuw2K21FVPosG2QJYVL7/8MvO5ri3uj4acn5+jGsHx6Qm7eweUZbmuproOdNR1jWgkSlaMhgOKvKQUNqU09Na8KNtGmC+Z89rtpqu1nLa3XpaS/khXZVm2S1YWFEWJoSvc6Xougoa+b+r5dlVhCIsoiSlrRVw0yDa8VGm8LY5o2BiEDDouSK0tN0LvKAbdENFItjaGXM4jVlGCsF0up3NKqciKigawBbpsdGOD0caYq8szLFPg2yZC1myMNT1oMBggGwiCkMvJKUWRc//uPaLVgrt37zIej7m8mvHTn7/PchVz/5XXCcIO//xPfkSaZ1SlJEoybFNoNmPbFdjxWyu2oWPgUkqeTaK/PiURTdPouGVZgmnQUsTZ3NphGcWcXUzo90ZrfPJ1V/Z1xtwwDG7cuIWUktlywdaW7uBSSnJ+fqrpMKbJ5eUl4/GIk5NjDnb3WM7mbAwHgO69fvToEZalQf6dsEeW65RYmhWUVUOSZMRJRtze6KZlUStFjaBSIIXBweFNnh8dEacpm1s7IARJmvPk8TMcx2G1WunaaNUQBjbj4YCDvV0G/S6DXh9DKZ4/e0oYanJOHMe4rstoNFo302Z5wisv32NzPODBqy/T7/g8+MarmGjC7v7eDjcOD1ku58yuligkfuBSFyVplNHtBjRNzbQtSRDKwDC0STfLS3Z2drBcm8vLy/WI8upigmgUCkm/F1JXGb7nUOQplm2gZIGscyxT4Ho2da1rtZSAINTHpShJqOovE3NA61H/kmFQ1zW2YTIehMi8oOfrCKmpGkT7vyml651l82XqDmEiVYNtO2AIDKHVd6Vor6YGQ4Fjafz2dbefEELnJJZLyrpapwuviyuGw+H6Rge9Uej2+8znc54fHxElKXlR4bo+rhfQ6XTWAqzZtvp2OoEGfVbaQDSdXnJ2dobv++zu7hIEoablrGL8TsB0HlNUJbZtooRgtorpdELKsiYva4q8QmDqQouvHmf/eqTeLMPgpdu3+OTxE6SsaQzw/YDnJyd0fZ8orcizKYd7GxqC0ECn12dydcXjxxdsbXdolGI42sCyLM5OjwF9A1/HAweDAf3BCM/z2N3dJUliHMfmbHK5/gFXdcPjZ8+opSIvKhrDpmxqilqCqlkmBcYvzBpkXSMMKCuJbYGwXT7+7Ascx+LF6ZR7d/apZcWNGxqa6TgON27c4OMPH+JaituvvcKzp4+pM8XB3g6Ba7G3vYeqSt5752eEYQjAvVde5enTZ9w6vMHZ2Rn9boc8z7lzuEeapmwOuswuztg92OfiQlf+np6eUqYJu9tDVvM5s8tLqrJgY9zHtARplnCwt0MURcxmK0zD5vzyiu1xyGQ6A2GQ5jV5nuJfR0RX2pfQ63YoyhqLBmUofMeizhPMRugu97aX3XFdJtMpKpcsY53uoqHF76p1vLcuqxY1pZClxLKhYxs4honjWZxPrrAtk5Ivmf2llKi6gRrssmbY65HnBaZt4zo+gpJCNlDVWKaetmx0fJLlHH/UYxmXmCZIK0fWCtc2SNKcsOPTH2yQpCmO4xCnOY5lkNcN4+EQ3/eZXU301wKa71+UCMMk6HQwLYfbt27w7rvvrtuGbx4ccnJ6RLyK6Pe7NJUiFprP8MUXzwgHQ07OzkjTnKOzcwxLUNQawR0nKaHvk6S5ZiuUBUWeQ5atxd6v+vparOymaeL7Prdv36KudU49z3Nsz0cIg+UqpkFwcTkjCPs8fXHEvXv3ODg8ZDR2ubpK1mO5OI7Z29vD8zwcx9FzzrLk2bNn/PjHP+bnP//5esy2sbHxpcqZauPLfD7Hsl1dpSx1BjmKdVbbs02k+gViimmgGr2tbBr0nLnWbjAhIMv0GSxZaQ/B+dkZH7z/Pr1+yN07t1guZty7c5vd7U0aWZNGK6IoWj8UqqrC8T2qqsZ1Xc7Pz3jw4Bt0OgGj0ZDBoI9tmYSdAM91MBSMB0P2tneQZcVg0Gc4HLTcOpsw1Or+dc59MjlHSqlDQmVJGLjUTUNVSpIsb7vwDLrdLpZl0ev1EEIn3UwD8iJdVyg5pv5+qbrWwRL0w3Y4HK5HU0ppZJZq5Hp1V0phWCZSqjWc07IN6rKEpsE2DRxLcwO7gY3nmCChqRoahXYDAnlVgmG0cMo2b1HXCIGe3dsmdVXguvqYEwQmwoAkzUmKjKKusT2fTthjNrtaB2vqVlMxBLieTV6kdDoBWV6xWGo9Z2NjxMHBAd1ud10nNRgM6IddXMteQzrDMFyTfK6bg03TXC9KAHle0Cjt1tRGJr17iTPd1lsUJVlZk2QlUZSuWQRf6T77OqTe/sHf/+3fvrs3pq4lpuOwWiWaz15W9Hp9LNvGtG2EafLZF0/Y2N7h6OSEqpLsHezTCT08XxcnYBgMBj0GwwHCEAxHI7KswPcDtre3UYYu82sUms+tQNgOrq8bVRbLiPl8QdWodWXyYNBFAIHvcmNvm37o60SarPEdE1U12AYICf3AxjAEv/6rv8zl5ILN8Yj33/scpXLGwyFZmoJSHL14jmkqyjxlNBxAVSFrCUiyXIdZDm/dZjQa8yc/+AH379/n+bOn2LZJJ/AJOwFhJyBNEw4O9jl+foRUbeVxnpPEMcPBgKo9V1uWtd6eWo5DmmRUdY3j+HTDkIvLC3q9LmVdU9Y1tFQZwzCxHQuBYDa9QjQNeZrheC6TixmB79FIiefoVtg4Lej2uxjCwHFsfeEKUzvfqhpbgO+79AZ9lIKiLNek2LY8BlMYqKrUUQ/VMBwNCTztojQNk6qqMdDpNteS+L5LVdZkWUXeagKW5dC0sWHfsUFJXGEQ+B6e57PKcnIJWa2oashyyXKVYVmCg709TNMkCEOSNOX1b3wD17G5OD/Hs036YcDN/R22t8dEiwWDQY94tSRNYzzHYXp1yd72Do5jk+cZYb/HbL6g2w0RCLr9LtPpDBDsHx6yWkYIwyRaLZnHKUoJqgYM09C7nabh7bffIlouaRqJrGowdQNSWTdI+OuTehMCqrrEdW08S7PJPdNeX5y6vcThyYtjFnHG+WSKaTkoYawNNrbtEAT6bHR2ekFVSizTIVoluIFPbzhYt6PYtk5JdTodlGFo1pcSeuuuxLpgAViXKmxvamyW7zrc2N9hc9Rnb2tMU9b4NnQDl81Rj2G/y2wy5dmTJzSyot8LcWzIs5R7L92h1w9J45jxxlA/saOI06NjTNNc70I8z8MwDLI814rxxiYff/Qhb7zxxtojvlgsmEwmOI6DECZ+2EEAlvkl2OC67xz0CiqbhkYpiqJitYpwXRfLFNR11a4Q2nwURak2vbRMAMtxcdoRj0ZPG23po2boF0VJraCWDVUl23JE3YrbDTqEvofnWDiGNv6YQLRYkiSJPgSrLy/DBu0EDMMARIPr2tRFvl75jFah16h7hWEKZKMwbQvbtZCyDT0KQcdzCVynTc8pUJKm1rpAXiiKCqSAxtAJJAmsopjpbIbrupydnXHr1i0+/OA9fM/Btgxm85jv/MovUeQJo16Xy4tLhr0ejqNdnVJKut0ueZ5rdLRh4Hkax71c6B2eYVhkWaZFN8vi5s1DfNchSSJqCa+/8QBDGPheB9ex2d3Z5OjoiP6gi+frMtFr66xpfvVix6+FGt/1LPUrL+0SdvvMVwVfPD8jyXIk+sYzHB2eMITFKlrQ9XxMSmzLpNvR8MTNzTEHBwfYts18NmWxWLC7u0fTNBgt2ufi4qKFLeja5DzPMS0HIYQWaFoAo+nomGWv1yNNU8IwxGikjlGiqEvdJ56mqe6Rsz0d/zQMXrv/EheTM1arFTduHHB5OeHWrVvr/jHVNNy6eZvPnnyBqmtcx6GpSvrdHo5ta4OMZdLp9clbuON8coWi4cGD1zk6OuKtt97k5z9/v+2v04JXtIrptKht3/d59vw5YRiS5/m6I88wTPI8p6gUSumLfrQxZDqfMZ0vCYKQOCm5nEYgIHD1Kuu7FpYAZInv2G2Pm0lR1SRZjmXp9tO0yFGYdLshrmPhWYKqLhmMtyhlw+PnR5qEm2RMlhmGbaJBumuQGI4h6AU+e8MOy+W85QIE64YZ1e4elBIYwsIPr0Va7aCrqwYhzHZ1N1puoEaHWQikrMAyOVtkKOtaqRcga0yg71ts9XwM28LzQ1ZxuuYR0tR6FyZr7t29zbJNQH7yyefsbm9ycHDA4cEen378EM/z2BjrWuYbL90niWKuLs852N1jejUhjmPG400Wccz+wQ0uJjOOTk/48PEZGAZFWeCYFkpJOoHHzuYmncABJUmjiIup/n7kVUneNF9Jjf9aCHRC0DZeuC1sQKGkpAKMa+RU0zAcbtIok6wo8B0D19Rz0+tc8Hw+JwgCwrBLnhdMLi/pdrtrblu3q8krlWywHBurkaRJtqaLxHGOMvTKY9u6JfX6AdDveDiWiW0KSiSWoRj3+/iOqwF1QhNTFvMpYeDT74ZcTi64ffsmp6fH3Lx5k+Oj59y+fZej4xeEYYeriwmyrinSBM9xsS0L13VZRjGlbMhbpXmjP0QYMBjoxpnrM7DOducsF9pC7Pu+fmi1M2fDMHBdlzTN1nl9HayQmKaFELLl5lWEYYjnBURptc56W9aXVuOGhp7vYbXClGk7UNWEnR6lrFFC0WQ5pZT4bcxzulwxHOliyu2tLU7OJlRtWg20Eo8y/hxaqWkUpayZr5bYbeDo+lyq/+4aOWaaNsCa3lKWpQ6fmA5C6Nm/0kv8urHFMAykpKXH6hKHoNPFMAyS+RyUoizq9Z+b5XO8ToCsNUPhW998wIcffICFZOOXv4Xr2vzxw0/YGA3aAsgzPNdme3sbwxBrx+RkMmE4HFLXegqQJIlGos2uKKRae/s929EaT5q0X68iDLuopiLNYmgshsMBZTsxquua44vzr36ffR1W9p5vqzcOBvri7HQxLI/zyyseH12AaVIqi0YJqHM2xiMNeqxLdre2qPIE21BsDns0smJ/e5Pz+QLTNMmyjOl0jqw1sqhojRtxplFJjuMQTyOE0DeSbdu6ULHFIRXthWQYBmZT8uDBG1xeXnJ+fq770xybxWJBFOkt8a2bd1BFQqfTWQd2qkayt3fA4ydP2L9xg0ePHvHWt97m6vKcNNVbuaaqieOUKIq4eeM2/dGQk5MTLi4nCCFwAp9oOed7f+u3GA8HHD19wt7BPh9/8hmd/pizySWX0yWD0CUpc7xQC251UWIhONjdoalqFtGS8eaIR58/4+6dW5RZTrRaksQpnW4P0wl4dHTKvNCuPlspbAG9jkNTluxtDpjPF4wGPXqjMYtVRCfs8/T5c5Z5je8H2vAioKn0Q7guC3qeXpG+9eBN3v/kYwzDotsfcHRyynyZIpVm/OktvIHle4xkyng8pijzdUddmqYUZUlRwGjUI89KyrLWD/OqolLQGPoY49omgWtgAZZoQDbasGOamK7L5XJFUupnjAB812U8GGqxrMxopGJzrI1W927dIEkiDGq63ZAwcEjjhK29Q/b393n27Bkvjo/WDa+j0Yg8zZGlbqbZOdhkMBjgByHz+ZLZfEWUXKf0Cm7fvs2dmzd47733eD5LODm7ZHt3jzQptOJe5dR5wuaog4Vi0A/odjqgDI6Pj3l0lX6llf3rIdD9l3//t/u2IkmytSDRDXv0hwMCr8NiPscyDSxLlwcWbe/V6ckJSbRiuVwy6HaQdUW3EzBbLCiLkqATsLW1rRX9OEIqqKSkkjV5oQ0zgefjei6yaYjimNUqame+NWWRU1clqpGYLfur1+uta3qns9m6xvf+/ft8+ukn3L51E8My+Zvf/S5/+mc/1r1wVcWdO3dZrlZsbe7w4vkLZKPderVssN2A5SphFadM50sqBcs443IWUUpJkma88cabPP7iEY1UeK6DbTs4rk9e1aRpgTBNppdTllFCtz8gWqU0VY1jOSTLFb4fUOY5s9mcYX9ELSuSKEZWEsMwCLq6H+58OqMWljaEtOdhxxI4lsnO1gaGofBcl26/Ty0lda2/b6VUlGVFWlRtaYckSXK9QysLVANHxycMhgMcxyZuH5B+W1Wl6Up6R61QbHY8kjReT0XWqztQ16rFdEFeVUilaJSBFArZLl5N02AZmmNgCIVtWlBLZNMgTBPb80BVa6iRqiWrJGZrY4O4SKmlwrJshGWyXC6opeT1b7xGWRQMhiNWyxgaxUcPP2ZnZwdhaP2okYqiKGmkZGdnn/lsjlACGkEtFWdnE8bjLS7OJpjCJPB8lGzwXZ9u2OOzp8/Ii5IsSRiOhkwuz3FtE9U07G1vcDm5YHdnh0G/p9OdBpxMo78+Al0jG8Kw1xoMAkxhUJU5g7DD5kiXLFCX1FWFarSxZtTv0+v18DyPu3fvrPuz54sVW1tb+IFHlujON+2QG5JnBUmakWcVRSXJiqqtK7ZoGpBS4Tgu0Jo90A0dVrta5Hm+zq9fd4xblq5bUkoxHo+1Pz7JePr0OfsHB4w3Nri8vOJnP3+XbtinaRrNWyt1T/p0tuDq6oqyrlBCIIXB1SJiuowplVaLDcumrGpcL2A+X7CIYvywS5rm1HWzxj0LtA21KGriOAFlamxUXtFUqhXVNAqqzL4ELTqeSyVr6lpSVjq8o3c4Wtwb9Po4jk1d12sss+M4KKU4PT+nruvW5dWOJQ2opf5Yl12gSyd3d1m15/DBYMDGaITnWPR7Ibahz9cChapqXQFd6ZGeYYBlGeujixCst+aNAqkUtdJNLopfaNGVGuSp6mZdlLGeTSuJUGCK65CUiQHkaYJh2rieR5QkLFcxfkcDTT795HOCsEstG4q6ZnNzE8/zODk5IfA7dIKwdW7aKAyWyyXdfp/pdMZyuQJlYNsuz58fYVk2aZqt0d6z2UzjzZqarY0hqIYyT3EtkzLPoKm5urqi1+sRRRFlXqwx5l/19bXYxvu2oV7f7q0bMK6Zc5bj6tFCA0cnx1SWT9SSRwLPRtDw6sv3ydOYXuBp+2QvpKpKze6+vKJRAoXFfLkiLfXZq2zqL9tXSoltw8Z4pOtzi5xGVtSlwrHAtrWTSljazaahg7Um4Y7GXFxcMBgM9Nm3FdQ8z2MRrZC1IooiRhs6WRdFGm5Z1zVhT2sJ10TSJ89OsG2bZVohDBPTcYnaMgYHbfPc2xqzOepjNBXb29tYloXfHfDee+/x6WdfYNo+Xtjh6PwKQ4CpoBe4dGyz7UyTusvdNNbiXa/XI05zhGVTSMWnz04oAYRJR0mkgvs3dzBQ9AOXui4p84Kg1ycrSibTBUmS4faGJEnCKisQpq55dkwDKRtu7GxRFgWB67C31W0Zctprvrm1Q1lLjs7OWUYJZaM97wOg07EwLLH2RRi2VtbTLCdJMi0KoqEkkmscWduWq8AzwLFNAkv/rG3VUDUKYZk0hkGclQS+h+u6FEVFkWoS8Kxq2uYZAVKP77Y2+vRDn/nVlLe//RZZErOcT/mVX/kV/uiffx+EyUsv32cxX7Usebg4O+fOnTs0jeLFixds72gTVJzlTCYTpKzaWuYQyxD0+33mUcTx6TleoJHZk8kltm3hOhaqyonShtfu77M5HOjrbLHgn73/+K+TXVaxWC1ZLpfYji62m0+v6HdDRr0O2xsDXnnpzrreyQQG/S5lKTk+Pqbb7VLXNdvb20ynU+oyJ2m342ZbbB/HKWVZU1aSvGzIS0la6PJAYdraotsikIUwMbWdmrqWCAFRFK0JtNfjuOsm1CRJiKJIlyUIyMoC07CZLeakecHp2YVeMZUgihLCsIcpoC4Ls5g9HAAAEwBJREFUTANcx2E87BEEHq5j4AY+nW6IZVoajGlAXlScnV0wnS1IsoKiqLBdX9dYhX3NrzMsslwTcmsFNTrLHWclZS3J2q/fENaappPmJUVV0hgGRVUhFetRmBBgtfVRmrRrkCXpGn6YxFnrc9eeBGHp1VZKhSH0n6GA6XxOVeu6pdlsxmAwwHVt/JazpmRNx3fxXRvHAN8Gz9PaidcCKMuy9dRfjxUxUIZA0k7vYE1SUG3JiFIgGoUhLJRskG3zrKxqUI2GYDQ1Bg1NXYJQBL5Lr+Pheza2YeD5FpYBSbTEd/SosshSDg8P6ff7OoB0cBPX9Xny+BmLKCYrK5IkpawrPv/iEas0IxyM+eDjh8xWEZfzBUmhHX5JlhIlKfPlitliSVnkyKrAMgTDQY97d29pu7CA27dvc7g7WNOQmqZZi51f5fX1UOOBMNS+7yLNeOnuHb744jHnxy+o65rbd+8ROhbf+aVvUVUVP/zhDzm/mLE5ChkNejqwUpZ8/ugxo2Ef37eII02lzYuYJK+o6oa0LKkR1EqXBQrTwAk9HNvhahHpCxqJo6Nc7bnPxDBMPDfQltp23nsNF9RWW6s9W9Y4wuNyMgVDEHT6Oj5bVkxmC+I4Q1ZguhFbfZd+6HN6csRoNMJzwLRcwm6HrFTESYaSJZ5hMRyMuZpN2drXWYGyMPG8gDQvOT494cbhLe7cucNlXJLXFSpeIWsQlkFcVnimQZXWbG0NidOYernStB/TxGr06qskXM1XgN4yy3aFdS29ZTYN7ZxzPe1rqNvZdtjr43ckLyZT/T1ybYqyRqoGoTQb37AcirJG0JAkBZPJhMODPd3QspqhMPBtkzpwaWrdaOr7frvdNvC8AGUI8qIgLQqKssEQFpKG+vrs8C95maax3nGpBkzLXENDPNuhaiSB12nJxRZFlmGaBjuDAENoelCWZXQ3tHHIMWHcD3n66HNuHe7xyiuv8OL4FNN2OL+4wnZdbLfh6PRSHzeUZLHKWZatG840OZuvyIocz3OJ4gxpNsSLBa5tssgzdvt9Dva1A1TRsDEesLc1ZrVasZhrcvGgG2LbNnt7e3zw0Ydf+T77WqzstPU++kM98x4OB8RxvC4ktG2boxfPWS0XdAIfz4TpTNNgfc9d2xXzPF9v+5qmoao0eCBqeeNCCIRhooSBlIrFMuFiOifNSoqyJk41GhmhnWZSCQzLwQv8dU97v9//knzTnl1932c8GqHa8cpikZJXJZdXcyqpMCx9Nm6M1rhxeUVTy5YjJukGAYHvUhc5pmi7zRwLo6m1mcI2EYZitVoRdrRWMZ9rzvnz58+p65q9vV22d7b0KmdAVTdIBbl2WVEjyKqavJTESUGUFWRFSaXgYnJFluU6QILAtgxsQ59pDdQaY3xdHQUaw3xdnlhLrXM0kvXI6ZoTB9ry6Xketq3n9F988QVPn2oSTCfwkHVJFsf4nkO3E6xFuaIoKKVm+ZVSImul9RXV3ujXFT3XL3UNGtVakBb2DI2tEQKjVcxd16UbdPBd/fkWs7meIjSSpiwpMv132R6PaGRFVabsbm/SCXxcz2E5m3J0ckZR1XSCLlIpoiQFw8IwbcpKtq05EGU5aVnp772skALyWmK5NoWsqWTDKq0o62b9MLJtW9eZlQXdsMPtWzfp9Xrs7+zqtGZVYlimFgq/4utrsbKDoqkrDFPgujaXlwsdXhkNUYbJ8dkps9WK3YN9bt06ILDh5PScoNNFKcXk7JSLRqOpHNvkiydPAINagmXb1HmGZwvSWiv9wrIw0BeuME3tCCsKrEoSOBZlozCFCcpAyoK8UvS62t1mWharpe44d/oDQN/wZVHoFs6mwTYFaa6wTJuyhmyZoBQ6t92A3Vj0+mH7QLERpoHemMLb33rAO+++z/7mEOdwD8fxmEcrxoOQ1WLOztYmXzx+zMbmt/E7Hp2iQxzHbG5tkSUrpGhQNVi2Fq9c22wVbMn5ZEYDVKArjZXBah5jOiZSNlRttMtUFWZtUjX6ZgetXZRlsY6kul5AXkjOr64IwoDNMMDzPI5OzmhUs248qYocuxXBbMtke2eL2WzG5tYWruvqDH+ekWcJtiUI/Q7z5RLbtBCmSV6W2uEGRJl+eEl0Zdh1A9L1Sx81BEI2mEJTgB3Toqn0g1wpA0PqME0cJ+tWlyzLNOMuy+h0OjgmVA0kywW+r6O0o9GIZ8+e0u122T844J333ueVB2/y+MkT4rQg6A1ZLCNenJxhWA5hr8MiiqgU1EWu03VFBQKEYSCV1PXfSuPTLKPBclyiJGUz2GxbYgr6/S4fffAuB/s32BwNUU1DXVVIZfLTd97XIa2v+PparOyGYSAM1sridbxQWDbbOzvYvk8lFavZFacvnoHUK59qara3Ntjf21mbYAC2tnbYO9jnzr273Lt3T4/HbIs/p0WKBoVENqzHOrXS/WpVrWmkWV6iDIdKKubz5XpVuw4oLBYLHYFNNFgiTWM8z2FjY4Pd7T6O57K1tcFoNEKYFrXeWpDLmmfHM6QwUcKiqKROT1XVuiwyTSKauiKNtUK7vbnBoNdlPp/iOGJ9ZhNC0e12tfW0rmiKgjDQ2oZtgO95VKVENWBbWq83DTA9F2yLUtHqCfqb45oCs9Hv8S3wfYu61oLnarWiLMv2CGCvf16zRUoUJSyXEbLRozzDMLBN0TrY9Aw9Xi6YLpYsFgtms9k6RNPtamOLazukWYwp9JhKYKKEiVKCsm6o1ZdCXKOEXq0xELRKvtI20mtTzfW0xDAsDCHIq4a8qhGYyFqtE5FNo9Y25GsjlQZT6s89GI519bTv0whBlKY0hsHzF8eYjkuaF5qYm2WUDWR1qUeibX+6oN1RCg1XvW5qDYMQ17bIc33MiOMEYVp4gc94Y0Oz4itdKRZ2AwyhSBJNPUYIbMfBanfEX+X1tVjZhWBNbqnrUjO/EXiex9VsSZyW3Lx5E69JiBZzFlHMW996mziO+fTR57onTQjyqiKPVwhHN75OLl+AYTFfpRTlOlkJQiHbp2qjJI2itVsqGim1Ilzqs6NV1ORVTWBW69YRA+3Ksg0Ty9JKnh4PWazmM5RhYzsm8yRB2B6LVUxR1zqoA9AYCNfgo8+ecevmPh3PJclzRlvbfPrpp+RZQlUn7O0eoJRiO/Q5Oz/BsQXd7U1832U+n66PE1GyxLZtQtVg1BJPKILABWVRlxWDIEApRZIXGCgKZZC2mXzDMCibpgVNWrjCpOv70CiSfInv+y2UouTg4IDlQhuWrhtbi1r3pSd5Tl6WetWSEiUbTMvAcUw8Q6BqSa/boZYNbtChqAqdKFwZrf1WOyVNYaBMQZaUWjkXWoRTwkAYesUu9H8AREsTVgiuAZ6G/pnWDas6wyLDacd6tAGotCiRUkEGYUcfFzY2xu2/N5jP59qAJWzSrCLNrhhv7iINkxcvTvjmN7/JxWxJkmd6u24YCNNAKh3xrZv/v72ziY3kqOL473V193T3zng89q4dk10lWYQUoghBhCCRECeQUM45hAs5cIILHBMhIXGEAwckpHCAG0IBgUQuKIogXDhs+Ep2F6JlNwfkJWuvxx/jmfFMf1VxqOpeJ1rlQ2vvOJ7+S62uri5r6r326656nyX74wOMwMrqObY3tkBrWr5CC3iikMBnMhoh2NzscRBy4cHziMDG7T6ffOQh1tbW2Nh4h1agOBgNUSqg1+vSv73FwEXMpR8hxPVECLvylCuokNJuu4iscsxkYxs/aNHrnWVzYwfPs0v1VPv85dLfbTFI3waNTKdTRLXI8pxu2GW0n9JbOEuUxPQWc6ZZzs2NTQoE7fmUmYcxJaKw/5ieDSooSihctFQJpIAW+3VohxHawGQypNPp0N/eJWzFVjuvSyht7fYziSH0Qjq+RvyStBPhHcA4swka0rJgL4VWGIDvM8lyijRH9g7Y2hmyunaBwd4OYsYUecp4d8TDn1jjjStXiMJz5Lmh1B63NvrW96AE42n2J1OMLliMrNUgUJrM5BSFFcJW22c4zNDeHXfTMi/IgbYfUBY5rZYQh9bVthvFJEmIiEacnd33fdLpFAkTzj7wAPyvz35WYlyJKl/5KGOX2aYoUQJxDFE7IA5LAinIlEYFCVNPkeclYdQlMLC3v0+UJPQ3t2i3OzYZowhBEFo3XS+nNBqDTVklaGtXd4cyhhiNp5y7rFif9iy3Oousyn6hjc0hqBRjrWm1QvZGB4Bm49/X8aOA0PesG7AxFOmULJ3Q6XS4sPYAV9+8bN2TozMU5YQg0ORaE7RCDtIUX4Qw8FnsnoE8ZbWX0N89IAl94jhhPEltDAaQhD5BqNDllM3N/xJ7inYnosyGdDvLZNNFWmGbnZ0d9nYGPP74eQbDPcZbE/AUm1uDDy1nJ0LYcYnw3+Up5dICG6yyJ449hsNtxuMxjz76qMsxZ7OhVGmEKxv29vZ2nWp6a7uPEY+gFVGWmqyEUkq0rqrGOG20U/Z4nq34enjFb23/KboTYURqpRNQa7VNWeA5231lDhHnl51ltphfeeg3q9xtVTGJaktgPFvLLEkSlpaWGI+HJFGHnd1d6z46TSkmU8TRepjuKD6D8ux2porTj+PYzt3FeQcB5HiugIUhUIrAbU8C3wZegA0eqZRs4qn6uVS16ip/CP2ejXNVjfYOf7Dabl+R5xlh2KrnayuXlgwGA+I4qZ9jUdq/qWrwVf4LlfOS/abbAhtaGzwRW+yy8ol3TjR45k7cPOZd27jC0aPzHFNqTOgR+l7tkFOZbZVSxGHAeGRNq1mW1c+/WvJnWUYuHkWh8X1FWaVVW1hwLyvFyorH7S2bBbe3fK7ecloe+6iWzekQGggCq+8Yj8dMp1PGIxumPBoNWV9frwuOjCdTPoqfzMkQdqjTLlemrXppPk3Z3d214X7Oe2t9fZ12u81wOLTx0uaOtlhESJKEwWDAaDRBBcLCYo+sKPE8wRQ2SYK4p+r7PmVeUJYGpUztKVYlUxCENEtZOmOTL2htUKFv02K5RJVKKcpcE7jQz6qUcRC0UEFAt9slN0J+kNdhp5mGIk/ti0nrOlV03O6wsrLC5GDExsYGw+GAhy5ctDXJosjZ7wfkrtpMZYcuipy8GLK8tFiHy+ZphiiPOI4xeNZrT7AC7luvwOXFno2emoxZWGgzHOwiaPIsp9OJrYC5F/HILTttMJGtypMXZW0GA1eEQTyrkPOozZK+ryh0XgtRnmU2Lt2zAtXv9ym0DVLpLkS8s9Wn2o0aNMrzbPkt98+tlOe2VM4HwI1RyiNNtbWz68IGtOGqDh2SC53bL7718isocihaXm3Gq55TFEWEyiOJW/XLaHV1lVu3btW0aK2Z5o4DHiRxhOfhXmIxrZbNHBSECSsr5yi00Ov1SCdjzrRt1ZcwEJaXl0mUT6ntFmd7e5uyNPS3BnUyjcFgl+WzXZvjcHfXfliOqtbb/YCIbAFj3qdqzCnHWeaXdmjov1f6HzLGnPugQSdC2AFE5G8fxuXvNGKeaYeG/vtF/4kwvTVo0OD40Qh7gwZzgpMk7B8Yj3uKMc+0Q0P/faH/xOzZGzRocLw4SV/2Bg0aHCNmLuwi8jURuSYiN0Tk+VnP5zggIr8QkdsicvVQ35KIvCoi19255/pFRH7i+HFZRJ6Y3cyPBiJyQUReE5G3RORfIvId1z8XPBCRSEReF5E3Hf0/cP2PiMglR/9LIhK6/pa7vuHuP3wkE6m8kmZxYPNQvA1cBELgTeCxWc7pmOj8MvAEcPVQ34+A5137eeCHrv008Aese8eTwKVZz/8I6F8DnnDtDvAf4LF54YGjo+3aAXDJ0fVr4FnX/yLwLdf+NvCiaz8LvHQk85gxE54CXjl0/QLwwqwfzjHR+vB7hP0asObaa8A11/4Z8PW7jTstB/B74KvzyAMgAf4BfBHrSOO7/loWgFeAp1zbd+PkXn971sv4B4H1Q9c3Xd88YNUYcwvAnVdc/6nmiVuSfg77dZsbHoiIEpE3gNvAq9gV7Z4xpgpbO0xjTb+7PwCW73UOsxb2uyUUmnfzwKnliYi0gd8C3zXG7L/f0Lv0fax5YIwpjTGfBc4DXwA+fbdh7nws9M9a2G8CFw5dnwfemdFc7jc2RWQNwJ1vu/5TyRMRCbCC/ktjzO9c91zxAMAYswf8GbtnXxSRKhjtMI01/e5+F9i519+etbD/FfiU00qGWGXEyzOe0/3Cy8Bzrv0cdh9b9X/DaaSfBAbVUvfjCrF5u38OvGWM+fGhW3PBAxE5JyKLrh0DXwHeAl4DnnHD3kt/xZdngD8Zt4G/J5wAhcXTWO3s28D3Zj2fY6LxV8AtbPq3m8A3sXuwPwLX3XnJjRXgp44fV4DPz3r+R0D/l7DL0MvAG+54el54AHwG+Kej/yrwfdd/EXgduAH8Bmi5/shd33D3Lx7FPBoPugYN5gSzXsY3aNDgPqER9gYN5gSNsDdoMCdohL1BgzlBI+wNGswJGmFv0GBO0Ah7gwZzgkbYGzSYE/wfGWS+ojolQXwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb17acdcc0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img_tinted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import pdist, squareform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]]\n"
     ]
    }
   ],
   "source": [
    "X = np.array([[1,2],[3,4],[5,6]])\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = squareform(pdist(X, 'euclidean'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         2.82842712 5.65685425]\n",
      " [2.82842712 0.         2.82842712]\n",
      " [5.65685425 2.82842712 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{50: 5, 32: 5, 43: 7}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def getSum(num):\n",
    "    sumed = 0\n",
    "    for n in str(num):\n",
    "        sumed += int(n)\n",
    "    return sumed\n",
    "\n",
    "def findPair(arr):\n",
    "    dic = {}\n",
    "    count = defaultdict(list)\n",
    "    output = []\n",
    "    \n",
    "    for num in arr:\n",
    "        if num not in dic:\n",
    "            dic[num] = getSum(num)\n",
    "        else:\n",
    "            \n",
    "    print(dic)\n",
    "    \n",
    "    for k,v in dic.items():\n",
    "        if v not in count:\n",
    "            count[v] = [k]\n",
    "        else:\n",
    "            count[v] += [k]\n",
    "    for k, v in count.items():\n",
    "        if len(v) <= 1:\n",
    "            output.append(-1)\n",
    "        elif len(v) == 2:\n",
    "            output.append(sum(v))\n",
    "        else:\n",
    "            firstMax = max(v)\n",
    "            v.remove(firstMax)\n",
    "            secondMax = max(v)\n",
    "            output.append(firstMax + secondMax)\n",
    "    return max(output)\n",
    "            \n",
    "\n",
    "    \n",
    "arr = [50, 50, 32, 43]\n",
    "findPair(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if (str.charAt(start % str.length()) == ' ')\n",
    "{\n",
    "    start++;\n",
    "} \n",
    "else\n",
    "{\n",
    "    while (start > 0 && str.charAt((start-1) % str.length())  != ' ')\n",
    "    {\n",
    "        start--;\n",
    "    }\n",
    "}\n",
    "\n",
    "return start / str.length();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len 18\n",
      "8 r\n",
      "14 o\n",
      "20 l\n",
      "26 r\n",
      "32 o\n",
      "38 l\n",
      "36\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def getCount(rows, cols, sentence):\n",
    "    s = ''\n",
    "    for word in sentence:\n",
    "        s += word+' '\n",
    "    n = len(s)\n",
    "    print('len', n)\n",
    "\n",
    "    start = 0\n",
    "    for i in range(rows):\n",
    "        start = start + cols\n",
    "        print(start, s[start%n])\n",
    "        if s[start%n] == ' ':\n",
    "            start += 1\n",
    "        else:\n",
    "            while start > 0 and s[(start-1)%n] != ' ':\n",
    "                start -= 1\n",
    "    print(start)\n",
    "    return start//n\n",
    "        \n",
    "\n",
    "rows = 6\n",
    "cols = 8\n",
    "sentence = [\"hello\", \"world\", 'Ilove']\n",
    "getCount(rows, cols, sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ccd'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def removeConsecutive(s):\n",
    "    out = ''\n",
    "    i = 0\n",
    "    while i < len(s):\n",
    "        if i<len(s)-2 and s[i]*3 == s[i:i+3]:\n",
    "            i = i+3\n",
    "        else:\n",
    "            out += s[i]\n",
    "            i += 1\n",
    "    if len(out) == len(s):\n",
    "        return out\n",
    "    else:\n",
    "        return removeConsecutive(out)\n",
    "\n",
    "s = 'aabbbaccddddccc'\n",
    "removeConsecutive(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4, 0, 1], [3, 0, 2], [2, 2, 1], [2, 1, 1], [1, 2, 2], [1, 2, 0], [1, 1, 2], [1, 1, 0], [1, 0, 0]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0, 2, 1]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ret = [-1] * len(workers)\n",
    "# workers_acquired = [False] * len(workers)\n",
    "# bikes_acquired = [False] * len(bikes)\n",
    "# while distances:\n",
    "#     dist, worker_index, bike_index = distances.pop()\n",
    "#     if workers_acquired[worker_index]:\n",
    "#         continue\n",
    "#     if bikes_acquired[bike_index]:\n",
    "#         continue\n",
    "#     ret[worker_index] = bike_index\n",
    "#     workers_acquired[worker_index] = True\n",
    "#     bikes_acquired[bike_index] = True\n",
    "\n",
    "# return ret\n",
    "def getDistance(p1, p2):\n",
    "    distance = abs(p1[0] - p2[0]) + abs(p1[1] - p2[1])\n",
    "    return distance\n",
    "    \n",
    "def assignBikes(workers, bikes):\n",
    "    bike_assigned = []\n",
    "    store = {}\n",
    "    distance = []\n",
    "    for worker_id, worker in enumerate(workers):\n",
    "        for bike_id, bike in enumerate(bikes):\n",
    "            distance.append([getDistance(worker, bike), worker_id, bike_id])\n",
    "    distance.sort(reverse=True)\n",
    "    print(distance)\n",
    "    \n",
    "    out = [-1]*len(workers)\n",
    "    workers_acquired = [False]*len(workers)\n",
    "    bikes_acquired = [False]*len(bikes)\n",
    "    \n",
    "    while distance:\n",
    "        dist, worker_ids, bike_ids = distance.pop()\n",
    "        if workers_acquired[worker_ids]:\n",
    "            continue\n",
    "        if bikes_acquired[bike_ids]:\n",
    "            continue\n",
    "        out[worker_ids] = bike_ids\n",
    "        workers_acquired[worker_ids] = True\n",
    "        bikes_acquired[bike_ids] = True\n",
    "    return out\n",
    "    \n",
    "workers = [[0,0],[1,1],[2,0]]\n",
    "bikes = [[1,0],[2,2],[2,1]]\n",
    "assignBikes(workers, bikes)\n",
    "# ans 0,2,1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isBST(node, MIN = float('-inf'), MAX = float('inf')):\n",
    "    if not node:\n",
    "        return True\n",
    "    if node.val <= MIN or node.val >= MAX:\n",
    "        return False\n",
    "    return isBST(node.left, MIN, node.val) and isBST(node.right, node.val, MAX)\n",
    "\n",
    "return isBST(root)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(4, 4), (5, 3), (7, 1), (9, -1), (10, -2)}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def countPair(nums, target):\n",
    "    pairs = set()\n",
    "    for num in nums:\n",
    "        complement = target-num\n",
    "        if complement in nums:\n",
    "            if num > complement:\n",
    "                pair = (num, complement)\n",
    "            else:\n",
    "                pair = (complement, num)\n",
    "            pairs.add(pair)\n",
    "    return pairs\n",
    "\n",
    "def countPair2(nums, target):\n",
    "    store = {}\n",
    "    pairs = set()\n",
    "    for num in nums:\n",
    "        if num not in store:\n",
    "            store[num] = 0\n",
    "        store[num] += 1\n",
    "    for num in store:\n",
    "        complement = target - num\n",
    "        if complement in store:\n",
    "            if num > complement:\n",
    "                pair = (num, complement)\n",
    "            else:\n",
    "                pair = (complement, num)\n",
    "            pairs.add(pair)\n",
    "    return pairs\n",
    "        \n",
    "    \n",
    "nums = [-2,10,6,4,9,7,5,3,1,7,0,4,-1]\n",
    "target = 8\n",
    "countPair2(nums, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def threeSum(nums):\n",
    "    \n",
    "    def twoSum(array, target, index):\n",
    "        pairs = set()\n",
    "        store = {}\n",
    "        \n",
    "        \n",
    "        for i in range(index, )\n",
    "        \n",
    "        \n",
    "    used = set()\n",
    "    for i, num1 in enumerate(nums):\n",
    "        target = -nums[i]\n",
    "        if num1 not in used:\n",
    "            used.add(num1)\n",
    "            pair = twoSum(nums, target, i)  \n",
    "nums = [-1, 0, 1, 2, -1, -4]\n",
    "threeSum2(nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{(-2, -1, 3)}\n"
     ]
    }
   ],
   "source": [
    "def threeSum2(nums):\n",
    "    i = 0\n",
    "    output = set()\n",
    "    nums.sort()\n",
    "    while i < len(nums)-2:\n",
    "        j = i+1\n",
    "        k = len(nums)-1\n",
    "        while j < k:\n",
    "            target = nums[i] + nums[j] + nums[k] \n",
    "            if target == 0:\n",
    "                output.add(tuple([nums[i], nums[j], nums[k]]))\n",
    "                j += 1\n",
    "                k -= 1\n",
    "            elif target < 0:\n",
    "                j += 1\n",
    "            else:\n",
    "                k -= 1\n",
    "        i += 1\n",
    "    print(output)\n",
    "\n",
    "nums = [-1, 3, -2]\n",
    "threeSum2(nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[None, None, 2, None, None, None, None, None, None]\n",
      "[None, None, 2, 3, None, None, None, None, None]\n",
      "[None, None, 2, 3, 5, None, None, None, None]\n",
      "[None, None, 2, 3, 5, 8, None, None, None]\n",
      "[None, None, 2, 3, 5, 8, 13, None, None]\n",
      "[None, None, 2, 3, 5, 8, 13, 21, None]\n",
      "[None, None, 2, 3, 5, 8, 13, 21, 34]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recursive\n",
    "def fibonnaci(n):\n",
    "    if n == 0:\n",
    "        return 0\n",
    "    elif n == 1:\n",
    "        return 1\n",
    "    else:\n",
    "        return fibonnaci(n-1) + fibonnaci(n-2)\n",
    "\n",
    "# Iterative\n",
    "def fibonnaci2(n): \n",
    "    a = 0\n",
    "    b = 1\n",
    "    \n",
    "    if n == 0:\n",
    "        return a\n",
    "    for i in range(2, n+1):\n",
    "        c = a + b\n",
    "        a = b\n",
    "        b = c\n",
    "    return c\n",
    "# Dynamic\n",
    "\n",
    "def fibonnaci3(n, store):    \n",
    "    if store[n]:\n",
    "        return store[n]\n",
    "    if n < 2:\n",
    "        return 1\n",
    "    else:\n",
    "        store[n] = fibonnaci3(n-1, store) + fibonnaci3(n-2, store)\n",
    "    print(store)\n",
    "    return store[n]\n",
    "a = 8\n",
    "fibonnaci3(a, [None]*(a+1))\n",
    "# [0,1,2,3,5,8,13,21,34,55,89]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 FIZZBUZZ\n",
      "1\n",
      "2\n",
      "3 FIZZ\n",
      "4\n",
      "5 BUZZ\n",
      "6 FIZZ\n",
      "7\n",
      "8\n",
      "9 FIZZ\n"
     ]
    }
   ],
   "source": [
    "#FizzBuzz\n",
    "\n",
    "def fizzbuzz(n):\n",
    "    for i in range(n):\n",
    "        if i%3 == 0 and i%5 == 0:\n",
    "            print(i, 'FIZZBUZZ')\n",
    "        elif i%3 == 0:\n",
    "            print(i, 'FIZZ')\n",
    "        elif i%5 == 0:\n",
    "            print(i, 'BUZZ')\n",
    "        else:\n",
    "            print(i)\n",
    "fizzbuzz(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Binary Search\n",
    "\n",
    "def binarySearch(nums, value):\n",
    "    \n",
    "    l, r = 0, len(nums)-1\n",
    "    \n",
    "    while l <= r:\n",
    "        middle = (l + r)//2\n",
    "        if value < nums[middle]:\n",
    "            r = middle\n",
    "        elif value > nums[middle]:\n",
    "            l = middle + 1\n",
    "        else:\n",
    "            return middle\n",
    "    \n",
    "binarySearch([0,2,4,7,9,11,23,59,60,65], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeZeroes(i, j, grid):\n",
    "    if i < 0 or j < 0 or i == len(grid) or j == len(grid[0]) or grid[i][j] == '0':\n",
    "        return\n",
    "    else:\n",
    "        grid[i][j] = '0'\n",
    "    makeZeroes(i+1, j, grid)\n",
    "    makeZeroes(i-1, j, grid)\n",
    "    makeZeroes(i, j+1, grid)\n",
    "    makeZeroes(i, j-1, grid)\n",
    "count = 0\n",
    "for i in range(len(grid)):\n",
    "    for j in range(len(grid[0])):\n",
    "        if grid[i][j] == '1':\n",
    "            count += 1\n",
    "            makeZeroes(i, j, grid)\n",
    "return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def countWords(s):\n",
    "    i = 0\n",
    "    word_count = 0\n",
    "    inword = False\n",
    "    \n",
    "    while s[i] == ' ' and i < len(s)-1:\n",
    "        i += 1\n",
    "    while i < len(s):\n",
    "        if s[i] == ' ':\n",
    "            while s[i] == ' ' and  i < len(s)-1:\n",
    "                i += 1  \n",
    "            inword = False\n",
    "            word_count += 1        \n",
    "        else:\n",
    "            inword = True\n",
    "        i += 1\n",
    "    if inword:\n",
    "        word_count += 1\n",
    "    return word_count\n",
    "\n",
    "s = ' s a cat a god  dog lion  ioioio da'\n",
    "countWords(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 3 6 9 8 7 4 5 "
     ]
    }
   ],
   "source": [
    "def spiralPrint(m, n, a) : \n",
    "    ''' k - starting row index \n",
    "        m - ending row index \n",
    "        l - starting column index \n",
    "        n - ending column index \n",
    "        i - iterator '''\n",
    "    k = 0\n",
    "    l = 0\n",
    "    while (k < m and l < n):     \n",
    "        # Print the first row from \n",
    "        # the remaining rows  \n",
    "        for i in range(l, n): \n",
    "            print(a[k][i], end = \" \") \n",
    "        k += 1\n",
    "  \n",
    "        # Print the last column from \n",
    "        # the remaining columns  \n",
    "        for i in range(k, m) : \n",
    "            print(a[i][n - 1], end = \" \") \n",
    "        n -= 1\n",
    "        # Print the last row from \n",
    "        # the remaining rows  \n",
    "        if ( k < m) : \n",
    "              \n",
    "            for i in range(n - 1, (l - 1), -1) : \n",
    "                print(a[m - 1][i], end = \" \") \n",
    "              \n",
    "            m -= 1\n",
    "        # Print the first column from \n",
    "        # the remaining columns  \n",
    "        if (l < n) : \n",
    "            for i in range(m - 1, k - 1, -1) : \n",
    "                print(a[i][l], end = \" \") \n",
    "              \n",
    "            l += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 0, 0, 0, 9, 8, 7, 4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "''' k - starting row index \n",
    "    m - ending row index \n",
    "    l - starting column index \n",
    "    n - ending column index \n",
    "    i - iterator '''\n",
    "def spiral(mat):\n",
    "    k, m = 0, len(mat) # ROWS\n",
    "    l, n = 0, len(mat[0]) #COLS\n",
    "    out = []\n",
    "    while k < m and l < n:\n",
    "        for i in range(l, n):\n",
    "            out.append(mat[k][i])\n",
    "        k += 1\n",
    "        \n",
    "        for i in range(k, m):\n",
    "            out.append(mat[i][n-1])\n",
    "        n -=1\n",
    "        \n",
    "        if k < m:\n",
    "            for i in range(n-1, l-1, -1):\n",
    "                out.append(mat[m-1][i])\n",
    "            m -= 1\n",
    "        if l < n:\n",
    "            for i in range(m-1, k-1, -1):\n",
    "                out.append(mat[i][l])\n",
    "            l += 1\n",
    "    print(out)\n",
    "\n",
    "mat = [[1,2,3,0],\n",
    "       [4,5,6,0],\n",
    "       [7,8,9,0]]\n",
    "spiral(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def search(nums, target):\n",
    "    l, r = 0, len(nums) - 1\n",
    "        \n",
    "    while l <= r:\n",
    "        mid = l + (r-l) // 2\n",
    "        if nums[mid] == target:\n",
    "            return mid\n",
    "        elif nums[mid] >= nums[l]:\n",
    "            if target >= nums[l] and target <= nums[mid]:\n",
    "                r = mid - 1\n",
    "            else:\n",
    "                l = mid + 1\n",
    "        else:\n",
    "            if target >= nums[mid] and target <= nums[r]:\n",
    "                l = mid + 1\n",
    "            else:\n",
    "                r = mid - 1\n",
    "    return -1\n",
    "                \n",
    "\n",
    "nums = [4,5,6,7,0,1,2,3]\n",
    "target = 5\n",
    "search(nums, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 4, 4, 4, 6, 6, 6, 6, 6]\n"
     ]
    }
   ],
   "source": [
    "def getArray(nums):\n",
    "    output = []\n",
    "    for i in range(1, len(nums),2):\n",
    "        output.extend([nums[i]]*nums[i-1])\n",
    "    print(output)\n",
    "\n",
    "nums = [1,2,3,4,5,6]\n",
    "getArray(nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-15-a5ccecd9b7e1>, line 16)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-15-a5ccecd9b7e1>\"\u001b[0;36m, line \u001b[0;32m16\u001b[0m\n\u001b[0;31m    n = 4\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "# Generate non overlapping sets of up to k pairs of indices\n",
    "# return\n",
    "# k = 1 -> {(0,1), (0,2), (0,3), (1,2), (1,3), (2,3)}\n",
    "# k = 2 -> {(0,1)(1,2), (0,1)(1,3), (0,2)(2,3), (0,1)(2,3), (1,2)(2,3)}\n",
    "# k = 3 -> {(0,1)(1,2)(2,3)}\n",
    "\n",
    "def getPairs(n, k):\n",
    "    \n",
    "    nums = [i for i in range(n)]\n",
    "\n",
    "    for i in range(1, k+1):\n",
    "#         print(i, sep=' ')\n",
    "        for j in range(n):\n",
    "            for l in range(j+1, n):\n",
    "#                 print(nums[j], nums[l])\n",
    "n = 4\n",
    "k = 3\n",
    "getPairs(n, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "def getPrime(n):\n",
    "    \n",
    "    if n < 2:\n",
    "        return False\n",
    "    \n",
    "    sqrt = int(math.sqrt(n))\n",
    "    print(sqrt)\n",
    "    for i in range(2, sqrt+1):\n",
    "        if n%i == 0:\n",
    "            return False\n",
    "    return True\n",
    "\n",
    "getPrime(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-19-bcea6d780d3c>, line 23)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-19-bcea6d780d3c>\"\u001b[0;36m, line \u001b[0;32m23\u001b[0m\n\u001b[0;31m    return flags\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "#Sieve of Eratosthenes\n",
    "import math\n",
    "def crossOff(flags, prime):\n",
    "    for i in range(prime*prime, len(flags), prime):\n",
    "        flags[i] = False\n",
    "def nextPrime(flags, prime):\n",
    "    nxt = prime + 1\n",
    "    while nxt < len(flags) and not flags[nxt]:\n",
    "        nxt += 1\n",
    "    return nxt\n",
    "def getPrimes(n):\n",
    "    \n",
    "    flags = [True]*(n+1)\n",
    "    prime = 2\n",
    "    \n",
    "    while prime <= math.sqrt(n):\n",
    "        crossOff(flags, prime)\n",
    "        prime = nextPrime(flags, prime)\n",
    "    \n",
    "    for i in range(len(flags)):\n",
    "        if flags[i]:\n",
    "            print(i)\n",
    "    return flags\n",
    "getPrimes(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]\n"
     ]
    }
   ],
   "source": [
    "primes = []\n",
    "for num in range(2,101):\n",
    "    prime = True\n",
    "    for i in range(2,num):\n",
    "        if (num%i==0):\n",
    "            prime = False\n",
    "    if prime:\n",
    "        primes.append(num)\n",
    "print(primes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "2 7 15\n",
      "4 7 15\n",
      "6 7 15\n",
      "8 7 15\n",
      "9 9 15\n",
      "11 16 15\n",
      "13 16 15\n",
      "15 16 15\n",
      "17 16 15\n",
      "17 16 15\n",
      "17 16 15\n",
      "17 18 15\n",
      "17 18 15\n",
      "17 20 15\n",
      "17 22 15\n",
      "17 22 17\n",
      "19 22 17\n",
      "19 22 17\n",
      "19 22 19\n",
      "21 26 26\n",
      "[0, 2, 2, 2, 4, 4, 6, 7, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 11, 11, 13, 15, 15, 15, 15, 15, 15, 15, 15, 17, 17, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 21]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def minCost(days, costs, minimum):\n",
    "    pass\n",
    "\n",
    "\n",
    "def getMinimumCost(days, costs):\n",
    "    dp = [0]*(days[-1]+1)\n",
    "    \n",
    "    ptr = 0\n",
    "    print(dp)\n",
    "    for i in range(1, days[-1]+1):\n",
    "        if ptr < len(days) and i == days[ptr]:\n",
    "            prev_7 = dp[i-7] if i >= 7 else 0\n",
    "            prev_30 = dp[i-30] if i >= 30 else 0 \n",
    "            print(dp[i-1] + costs[0], prev_7 + costs[1], prev_30 + costs[2])\n",
    "            dp[i] = min(dp[i-1] + costs[0], prev_7 + costs[1], prev_30 + costs[2]) \n",
    "            ptr += 1\n",
    "        else:\n",
    "            dp[i] = dp[i-1]\n",
    "\n",
    "    print(dp)\n",
    "    return dp[-1]\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "days = [1,4,6,7,8,20,22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,50] \n",
    "costs = [2,7,15]\n",
    "getMinimumCost(days, costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 2 7\n",
      "100 2 8\n",
      "100 3 7\n",
      "100 3 8\n",
      "100 4 7\n",
      "100 4 8\n",
      "200 2 7\n",
      "200 2 8\n",
      "200 3 7\n",
      "200 3 8\n",
      "200 4 7\n",
      "200 4 8\n"
     ]
    }
   ],
   "source": [
    "from itertools import product\n",
    "\n",
    "param = [\n",
    "    (100, 200),\n",
    "    (2, 3, 4),\n",
    "    (7, 8)\n",
    "]\n",
    "\n",
    "for i, j, k in product(*param):\n",
    "    print(i, j, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given a directed, acyclic graph of N nodes.  Find all possible paths from node 0 to node N-1, and return them in any order.\n",
    "\n",
    "The graph is given as follows:  the nodes are 0, 1, ..., graph.length - 1.  graph[i] is a list of all nodes j for which the edge (i, j) exists.\n",
    "\n",
    "Example:\n",
    "Input: [[1,2], [3], [3], []] \n",
    "Output: [[0,1,3],[0,2,3]] \n",
    "Explanation: The graph looks like this:\n",
    "0--->1\n",
    "|    |\n",
    "v    v\n",
    "2--->3\n",
    "There are two paths: 0 -> 1 -> 3 and 0 -> 2 -> 3.\n",
    "Note:\n",
    "\n",
    "The number of nodes in the graph will be in the range [2, 15].\n",
    "You can print different paths in any order, but you should keep the order of nodes inside one path.\n",
    "[[4,3,1],[3,2,4],[3],[4],[]]\n",
    "\n",
    "ans = [[0,4],[0,3,4],[0,1,3,4],[0,1,2,3,4],[0,1,4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'List' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-45-e8c9cc7420d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mallPathsSourceTarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m         \u001b[0mdic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m             \u001b[0mdic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'List' is not defined"
     ]
    }
   ],
   "source": [
    "def allPathsSourceTarget(self, graph: List[List[int]]) -> List[List[int]]:\n",
    "        dic = {}\n",
    "        for i, node in enumerate(graph):\n",
    "            dic[i] = node\n",
    "        print(dic)\n",
    "        out = []\n",
    "        edges = []\n",
    "        for i, node in dic.items():\n",
    "            for j in node:\n",
    "                edges.append((i,j))\n",
    "        print(edges)\n",
    "        \n",
    "        \n",
    "        initial = dic[0]\n",
    "        print('start', initial)\n",
    "        \n",
    "        res = []\n",
    "        for i in dic[0]:\n",
    "            temp = [0]\n",
    "            if i in dic:\n",
    "                temp.append([i,dic[i]])\n",
    "                res.append(temp)\n",
    "        print(res)\n",
    "        \n",
    "        for path in res:\n",
    "            temp = []\n",
    "            \n",
    "        \n",
    "#         for i in range(len(edges)):\n",
    "#             temp = []\n",
    "#             a, b = edges[i]\n",
    "#             temp.append(edges[i])\n",
    "#             for j in range(len(edges)):\n",
    "#                 c, d = edges[j]\n",
    "#                 if temp[-1] == c:\n",
    "#                     temp.appenn(edges[j])\n",
    "#             out.append(temp)\n",
    "#         res = []\n",
    "#         print(out)\n",
    "#         for path in out:\n",
    "#             if path[0] == 0 and path[-1] == len(graph)-1:\n",
    "#                 res.append(list(set(path)))\n",
    "#         return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 3], [0, 1]]\n",
      "[[0, 4]]\n",
      "[[0, 3], [0, 1, 3], [0, 1, 2]]\n",
      "[[0, 3], [0, 1, 3], [0, 1, 2, 3]]\n",
      "[[0, 3], [0, 1, 3]]\n",
      "[[0, 3]]\n",
      "[]\n",
      "[]\n",
      "[[0, 4], [0, 1, 4], [0, 1, 2, 3, 4], [0, 1, 3, 4], [0, 3, 4]]\n"
     ]
    }
   ],
   "source": [
    "def allPathsSourceTarget(graph):\n",
    "    target = len(graph)-1\n",
    "    output = []\n",
    "    paths = []\n",
    "    \n",
    "    for node in graph[0]:\n",
    "        if node != target:\n",
    "            paths.append([0, node])\n",
    "        else:\n",
    "            output.append([0, node])\n",
    "    print(paths)\n",
    "    print(output)\n",
    "    i = 0\n",
    "    while paths:\n",
    "        path = paths.pop()\n",
    "        next_nodes = graph[path[-1]]\n",
    "        \n",
    "        if not next_nodes:\n",
    "            continue\n",
    "            \n",
    "        for node in next_nodes:\n",
    "            new_path = path+[node]\n",
    "            if node != target:\n",
    "                paths.append(new_path)\n",
    "            else:\n",
    "                output.append(new_path)\n",
    "        print(paths)\n",
    "    \n",
    "    print(paths)\n",
    "    print(output)     \n",
    "    \n",
    "graph = [[4,3,1],[3,2,4],[3],[4],[]]\n",
    "allPathsSourceTarget(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0]\n",
      "\n",
      "[0, 0, 1, 0, 0, 0]\n",
      "[0, 0, 0, 1, 0, 0]\n",
      "[0, 0, 0, 1, 1, 0]\n",
      "[0, 0, 0, 0, 0, 1]\n",
      "[0, 0, 0, 0, 0, 1]\n",
      "[0, 0, 0, 0, 0, 0]\n",
      "{0: [2], 1: [3], 2: [3, 4], 3: [5], 4: [5]}\n",
      "[[2], [3], [3, 4], [5], [5]]\n"
     ]
    }
   ],
   "source": [
    "def printMatrix(matrix):\n",
    "    for row in matrix:\n",
    "        print(row)\n",
    "def adjList(edges):\n",
    "    dic = {}\n",
    "    for edge in edges:\n",
    "        if edge[0] not in dic:\n",
    "            dic[edge[0]] = [edge[1]]\n",
    "        else:\n",
    "            dic[edge[0]] += [edge[1]]\n",
    "    print(dic)\n",
    "    \n",
    "    return dic.values()\n",
    "        \n",
    "\n",
    "def adjMatrix(edges):\n",
    "    \n",
    "    graph = [[0 for i in range(len(edges))] for j in range(len(edges))]\n",
    "    \n",
    "    printMatrix(graph)\n",
    "    for edge in edges:\n",
    "        i, j = edge\n",
    "        graph[i][j] = 1\n",
    "        \n",
    "    print()\n",
    "    printMatrix(graph)\n",
    "    \n",
    "\n",
    "edgeList = [[0, 2], [1, 3], [2, 3], [2, 4], [3, 5], [4, 5]]\n",
    "adj_matrix = adjMatrix(edgeList)\n",
    "adj_list = adjList(edgeList)\n",
    "print(list(adj_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def canFinish(numCourses: int, prerequisites: List[List[int]]) -> bool:\n",
    "    inDegrees = dict()\n",
    "    stack = []\n",
    "    count = 0\n",
    "\n",
    "    for i in range(numCourses):\n",
    "        inDegrees[i] = 0\n",
    "\n",
    "    for i in range(len(prerequisites)):\n",
    "        inDegrees[prerequisites[i][0]] += 1\n",
    "\n",
    "    for key, val in inDegrees.items():\n",
    "        if val == 0:\n",
    "            stack.append(key)\n",
    "\n",
    "    while len(stack) > 0:\n",
    "        curr = stack.pop()\n",
    "        count += 1\n",
    "\n",
    "        for i in range(len(prerequisites)):\n",
    "            if prerequisites[i][1] == curr:\n",
    "                inDegrees[prerequisites[i][0]] -= 1\n",
    "                if inDegrees[prerequisites[i][0]] == 0:\n",
    "                    stack.append(prerequisites[i][0])\n",
    "\n",
    "    return count == numCourses\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hell world\n",
      "[0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0]\n",
      "hi bye\n",
      "[0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]\n",
      "asd fgh\n",
      "[1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "sqw qwe\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0]\n",
      "[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['YES', 'NO', 'NO', 'YES']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def commonSubstring(A, B):\n",
    "    output = []\n",
    "    for a, b in zip(A, B):\n",
    "        counterA = [0]*26\n",
    "        counterB = [0]*26\n",
    "        isCommon = False\n",
    "        \n",
    "        for l in a:\n",
    "            counterA[ord(l)-ord('a')] += 1\n",
    "        for l in b:\n",
    "            counterB[ord(l)-ord('a')] += 1\n",
    "\n",
    "        for l1, l2 in zip(counterA, counterB):\n",
    "            if l1 >=1 and l2 >= 1:\n",
    "                isCommon = True\n",
    "                break\n",
    "        if isCommon:\n",
    "            output.append('YES')\n",
    "        else:\n",
    "            output.append('NO')\n",
    "    return output\n",
    "\n",
    "A = ['hell', 'hi', 'asd', 'sqw']\n",
    "B = ['world', 'bye', 'fgh', 'qwe']\n",
    "commonSubstring(A, B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def optimalPoint(magic, distance):\n",
    "    optimal_point, current_point = 0, 0\n",
    "    index = 0\n",
    "    for i in range(len(magic)):\n",
    "        current_point += magic[i] - distance[i]\n",
    "        optimal_point += magic[i] - distance[i]\n",
    "        \n",
    "        if current_point < 0:\n",
    "            index = i+1\n",
    "            current_point = 0\n",
    "    if optimal_point < 0:\n",
    "        return -1\n",
    "    else:\n",
    "        return index\n",
    "    \n",
    "magic = [2,4,5,2]\n",
    "distance = [4,3,1,3]\n",
    "optimalPoint(magic, distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>LeadCreated</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>Email</th>\n",
       "      <th>VendorLeadID</th>\n",
       "      <th>CallStatus</th>\n",
       "      <th>WidgetName</th>\n",
       "      <th>PublisherZoneName</th>\n",
       "      <th>PublisherCampaignName</th>\n",
       "      <th>AddressScore</th>\n",
       "      <th>PhoneScore</th>\n",
       "      <th>...</th>\n",
       "      <th>Partner</th>\n",
       "      <th>ReferralDomain</th>\n",
       "      <th>MarketingCampaign</th>\n",
       "      <th>AdGroup</th>\n",
       "      <th>Keyword</th>\n",
       "      <th>SearchQuery</th>\n",
       "      <th>ReferralURL</th>\n",
       "      <th>ReferralURL Parameters</th>\n",
       "      <th>LandingPageURL</th>\n",
       "      <th>Landing Page URL Parameters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2009-07-01 01:24:29.563</td>\n",
       "      <td>Dorinda</td>\n",
       "      <td>kanani@sandwichisles.net</td>\n",
       "      <td>FDF81FDA-A649-437B-B99C-FDDE74F7971B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>w-302252-DebtReduction1-1DC-CreditSolutions</td>\n",
       "      <td>TopLeft-302252</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>google</td>\n",
       "      <td>www.google.com</td>\n",
       "      <td>Debt Holding Tank</td>\n",
       "      <td>Holding Tank - Debt</td>\n",
       "      <td>Debt specialists</td>\n",
       "      <td>debt specialists</td>\n",
       "      <td>http://www.google.com/search</td>\n",
       "      <td>sourceid=navclient&amp;aq=1&amp;oq=debt sp&amp;ie=UTF-8&amp;rl...</td>\n",
       "      <td>http://www.debtreductioninc.com/index8.html</td>\n",
       "      <td>utm_source=google&amp;utm_medium=CPC&amp;utm_content=H...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2009-04-13 15:27:35.990</td>\n",
       "      <td>Presetta</td>\n",
       "      <td>clerk2@ustconline.net</td>\n",
       "      <td>4190ACB7-5026-416C-B987-ED8AD427D5E6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>w-300250-DebtReduction1-1DC-Head2</td>\n",
       "      <td>TopLeft-302252</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>AdKnowledge</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Consolidate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.debtreductioninc.com/index8.html</td>\n",
       "      <td>utm_source=AdKnowledge&amp;utm_medium=CPC&amp;utm_cont...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2009-04-21 07:22:59.000</td>\n",
       "      <td>Gina</td>\n",
       "      <td>wagoner_gina@yahoo.com</td>\n",
       "      <td>hFg80jf_R0CRN55hdhWILw</td>\n",
       "      <td>Unable to contact - Bad Contact Information</td>\n",
       "      <td>w-300250-DebtReduction1-1DC-Head2</td>\n",
       "      <td>TopLeft-302252</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>AdKnowledge</td>\n",
       "      <td>us.mc582.mail.yahoo.com</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Consolidate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.mc582.mail.yahoo.com/mc/showMessage</td>\n",
       "      <td>&amp;fid=Inbox&amp;sort=date&amp;order=down&amp;startMid=0&amp;.ra...</td>\n",
       "      <td>http://www.debtreductioninc.com/index8.html</td>\n",
       "      <td>utm_source=AdKnowledge&amp;utm_medium=CPC&amp;utm_cont...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2009-08-03 19:39:02.000</td>\n",
       "      <td>Kari</td>\n",
       "      <td>usa4ley@yahoo.com</td>\n",
       "      <td>jB01QgYZxkWArI9jWxuufw</td>\n",
       "      <td>Contacted - Doesn't Qualify</td>\n",
       "      <td>w-302252-DebtReduction1-1DC-white</td>\n",
       "      <td>TopLeft-302252</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Google</td>\n",
       "      <td>norwich.kijiji.com</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>Lower Payments</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://norwich.kijiji.com/c-Cars-vehicles-Cars...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.debtreductioninc.com/index12.html</td>\n",
       "      <td>utm_source=Google&amp;utm_medium=cpc&amp;utm_campaign=...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2009-04-13 10:18:37.490</td>\n",
       "      <td>Stephanie</td>\n",
       "      <td>sr1lambert@embarqmail.com</td>\n",
       "      <td>D5B32074-458E-40EC-B185-1FEF20AC626D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>w-300250-DebtReduction1-1DC-BlueMeter</td>\n",
       "      <td>TopLeft-302252</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Google</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>Debt Reduction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.debtreductioninc.com/index8.html</td>\n",
       "      <td>utm_source=Google&amp;utm_medium=cpc&amp;utm_campaign=...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3016</td>\n",
       "      <td>2009-06-30 18:14:33.043</td>\n",
       "      <td>amy</td>\n",
       "      <td>ahokett@sjc.edu</td>\n",
       "      <td>F42BBDFB-D44B-4890-A1B6-D1F0A3838214</td>\n",
       "      <td>NaN</td>\n",
       "      <td>w-302252-DebtReduction1-1DC-white</td>\n",
       "      <td>TopLeft-302252</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>yahoo</td>\n",
       "      <td>www.att.net</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.att.net/s/s.dll</td>\n",
       "      <td>spage=search/error.htm&amp;searchtype=epa&amp;source=a...</td>\n",
       "      <td>http://www.debtreductioninc.com/index8.html</td>\n",
       "      <td>utm_source=yahoo&amp;utm_medium=cpc&amp;utm_campaign=D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3017</td>\n",
       "      <td>2009-04-25 16:56:53.670</td>\n",
       "      <td>brandy</td>\n",
       "      <td>brandy75137@yahoo.com</td>\n",
       "      <td>EA3703A3-61C9-40CE-92F7-61F0E02B1365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>w-300250-DebtReduction1-1DC-Head2</td>\n",
       "      <td>TopLeft-302252</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Google</td>\n",
       "      <td>googleads.g.doubleclick.net</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>Student Debt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://googleads.g.doubleclick.net/pagead/ads</td>\n",
       "      <td>client=ca-pub-7025449865608971&amp;dt=124069479896...</td>\n",
       "      <td>http://www.debtreductioninc.com/index8.html</td>\n",
       "      <td>utm_source=Google&amp;utm_medium=cpc&amp;utm_campaign=...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3018</td>\n",
       "      <td>2009-04-12 18:38:43.067</td>\n",
       "      <td>jennifer</td>\n",
       "      <td>jennifer_woods48375@yahoo.com</td>\n",
       "      <td>64EB2632-E29E-4EB4-B361-45F1F0C735B5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>w-300250-DebtReduction1-1DC-CreditSolutions</td>\n",
       "      <td>TopLeft-302252</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>yahoo</td>\n",
       "      <td>search.yahoo.com</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>debt consolidation or settlement which is better</td>\n",
       "      <td>http://search.yahoo.com/search</td>\n",
       "      <td>ei=UTF-8&amp;fr=yfp-t-501&amp;SpellState=n-1665662351_...</td>\n",
       "      <td>http://www.debtreductioninc.com/index8.html</td>\n",
       "      <td>utm_source=yahoo&amp;utm_medium=cpc&amp;utm_campaign=D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3019</td>\n",
       "      <td>2009-09-23 21:41:08.193</td>\n",
       "      <td>debra</td>\n",
       "      <td>debraroque@att.net</td>\n",
       "      <td>9126C7DB-F5BB-4CE1-BDCF-5E6F1841ADAD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>w-302252-DebtReduction1-1DC-yellowarrow-dark</td>\n",
       "      <td>TopLeft-302252</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Google</td>\n",
       "      <td>NaN</td>\n",
       "      <td>state</td>\n",
       "      <td>Debt Negotiation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.debtreductioninc.com/index8.html</td>\n",
       "      <td>utm_source=Google&amp;utm_medium=cpc&amp;utm_campaign=...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3020</td>\n",
       "      <td>2009-04-27 10:31:42.573</td>\n",
       "      <td>Ricard</td>\n",
       "      <td>silva3131@sbcglobal.net</td>\n",
       "      <td>AF369BF3-15C7-4B47-8E2D-FE218A5DBCC3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>w-300250-DebtReduction1-1DC-Head2</td>\n",
       "      <td>TopLeft-302252</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Google</td>\n",
       "      <td>www.ehow.com</td>\n",
       "      <td>DebtReductionInc</td>\n",
       "      <td>Lower Payments</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.ehow.com/ehow_radlinks_ads.html</td>\n",
       "      <td>term=Lower Monthly Car Payments&amp;channel=fin_mo...</td>\n",
       "      <td>http://www.debtreductioninc.com/index8.html</td>\n",
       "      <td>utm_source=Google&amp;utm_medium=cpc&amp;utm_campaign=...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3021 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 LeadCreated  FirstName                          Email  \\\n",
       "0    2009-07-01 01:24:29.563    Dorinda       kanani@sandwichisles.net   \n",
       "1    2009-04-13 15:27:35.990   Presetta          clerk2@ustconline.net   \n",
       "2    2009-04-21 07:22:59.000       Gina         wagoner_gina@yahoo.com   \n",
       "3    2009-08-03 19:39:02.000       Kari              usa4ley@yahoo.com   \n",
       "4    2009-04-13 10:18:37.490  Stephanie      sr1lambert@embarqmail.com   \n",
       "...                      ...        ...                            ...   \n",
       "3016 2009-06-30 18:14:33.043        amy                ahokett@sjc.edu   \n",
       "3017 2009-04-25 16:56:53.670     brandy          brandy75137@yahoo.com   \n",
       "3018 2009-04-12 18:38:43.067   jennifer  jennifer_woods48375@yahoo.com   \n",
       "3019 2009-09-23 21:41:08.193      debra             debraroque@att.net   \n",
       "3020 2009-04-27 10:31:42.573     Ricard        silva3131@sbcglobal.net   \n",
       "\n",
       "                              VendorLeadID  \\\n",
       "0     FDF81FDA-A649-437B-B99C-FDDE74F7971B   \n",
       "1     4190ACB7-5026-416C-B987-ED8AD427D5E6   \n",
       "2                   hFg80jf_R0CRN55hdhWILw   \n",
       "3                   jB01QgYZxkWArI9jWxuufw   \n",
       "4     D5B32074-458E-40EC-B185-1FEF20AC626D   \n",
       "...                                    ...   \n",
       "3016  F42BBDFB-D44B-4890-A1B6-D1F0A3838214   \n",
       "3017  EA3703A3-61C9-40CE-92F7-61F0E02B1365   \n",
       "3018  64EB2632-E29E-4EB4-B361-45F1F0C735B5   \n",
       "3019  9126C7DB-F5BB-4CE1-BDCF-5E6F1841ADAD   \n",
       "3020  AF369BF3-15C7-4B47-8E2D-FE218A5DBCC3   \n",
       "\n",
       "                                       CallStatus  \\\n",
       "0                                             NaN   \n",
       "1                                             NaN   \n",
       "2     Unable to contact - Bad Contact Information   \n",
       "3                     Contacted - Doesn't Qualify   \n",
       "4                                             NaN   \n",
       "...                                           ...   \n",
       "3016                                          NaN   \n",
       "3017                                          NaN   \n",
       "3018                                          NaN   \n",
       "3019                                          NaN   \n",
       "3020                                          NaN   \n",
       "\n",
       "                                        WidgetName PublisherZoneName  \\\n",
       "0      w-302252-DebtReduction1-1DC-CreditSolutions    TopLeft-302252   \n",
       "1                w-300250-DebtReduction1-1DC-Head2    TopLeft-302252   \n",
       "2                w-300250-DebtReduction1-1DC-Head2    TopLeft-302252   \n",
       "3                w-302252-DebtReduction1-1DC-white    TopLeft-302252   \n",
       "4            w-300250-DebtReduction1-1DC-BlueMeter    TopLeft-302252   \n",
       "...                                            ...               ...   \n",
       "3016             w-302252-DebtReduction1-1DC-white    TopLeft-302252   \n",
       "3017             w-300250-DebtReduction1-1DC-Head2    TopLeft-302252   \n",
       "3018   w-300250-DebtReduction1-1DC-CreditSolutions    TopLeft-302252   \n",
       "3019  w-302252-DebtReduction1-1DC-yellowarrow-dark    TopLeft-302252   \n",
       "3020             w-300250-DebtReduction1-1DC-Head2    TopLeft-302252   \n",
       "\n",
       "     PublisherCampaignName  AddressScore  PhoneScore  ...      Partner  \\\n",
       "0         DebtReductionInc           NaN         5.0  ...       google   \n",
       "1         DebtReductionInc           NaN         NaN  ...  AdKnowledge   \n",
       "2         DebtReductionInc           NaN         NaN  ...  AdKnowledge   \n",
       "3         DebtReductionInc           5.0         3.0  ...       Google   \n",
       "4         DebtReductionInc           NaN         NaN  ...       Google   \n",
       "...                    ...           ...         ...  ...          ...   \n",
       "3016      DebtReductionInc           NaN         NaN  ...        yahoo   \n",
       "3017      DebtReductionInc           NaN         NaN  ...       Google   \n",
       "3018      DebtReductionInc           NaN         NaN  ...        yahoo   \n",
       "3019      DebtReductionInc           5.0         5.0  ...       Google   \n",
       "3020      DebtReductionInc           NaN         NaN  ...       Google   \n",
       "\n",
       "                   ReferralDomain   MarketingCampaign              AdGroup  \\\n",
       "0                  www.google.com   Debt Holding Tank  Holding Tank - Debt   \n",
       "1                             NaN  Financial Services          Consolidate   \n",
       "2         us.mc582.mail.yahoo.com  Financial Services          Consolidate   \n",
       "3              norwich.kijiji.com    DebtReductionInc       Lower Payments   \n",
       "4                             NaN    DebtReductionInc       Debt Reduction   \n",
       "...                           ...                 ...                  ...   \n",
       "3016                  www.att.net    DebtReductionInc   Debt Consolidation   \n",
       "3017  googleads.g.doubleclick.net    DebtReductionInc         Student Debt   \n",
       "3018             search.yahoo.com    DebtReductionInc   Debt Consolidation   \n",
       "3019                          NaN               state     Debt Negotiation   \n",
       "3020                 www.ehow.com    DebtReductionInc       Lower Payments   \n",
       "\n",
       "               Keyword                                       SearchQuery  \\\n",
       "0     Debt specialists                                  debt specialists   \n",
       "1                  NaN                                               NaN   \n",
       "2                  NaN                                               NaN   \n",
       "3                  NaN                                               NaN   \n",
       "4                  NaN                                               NaN   \n",
       "...                ...                                               ...   \n",
       "3016               NaN                                               NaN   \n",
       "3017               NaN                                               NaN   \n",
       "3018               NaN  debt consolidation or settlement which is better   \n",
       "3019               NaN                                               NaN   \n",
       "3020               NaN                                               NaN   \n",
       "\n",
       "                                            ReferralURL  \\\n",
       "0                          http://www.google.com/search   \n",
       "1                                                   NaN   \n",
       "2         http://us.mc582.mail.yahoo.com/mc/showMessage   \n",
       "3     http://norwich.kijiji.com/c-Cars-vehicles-Cars...   \n",
       "4                                                   NaN   \n",
       "...                                                 ...   \n",
       "3016                         http://www.att.net/s/s.dll   \n",
       "3017      http://googleads.g.doubleclick.net/pagead/ads   \n",
       "3018                     http://search.yahoo.com/search   \n",
       "3019                                                NaN   \n",
       "3020         http://www.ehow.com/ehow_radlinks_ads.html   \n",
       "\n",
       "                                 ReferralURL Parameters  \\\n",
       "0     sourceid=navclient&aq=1&oq=debt sp&ie=UTF-8&rl...   \n",
       "1                                                   NaN   \n",
       "2     &fid=Inbox&sort=date&order=down&startMid=0&.ra...   \n",
       "3                                                   NaN   \n",
       "4                                                   NaN   \n",
       "...                                                 ...   \n",
       "3016  spage=search/error.htm&searchtype=epa&source=a...   \n",
       "3017  client=ca-pub-7025449865608971&dt=124069479896...   \n",
       "3018  ei=UTF-8&fr=yfp-t-501&SpellState=n-1665662351_...   \n",
       "3019                                                NaN   \n",
       "3020  term=Lower Monthly Car Payments&channel=fin_mo...   \n",
       "\n",
       "                                    LandingPageURL  \\\n",
       "0      http://www.debtreductioninc.com/index8.html   \n",
       "1      http://www.debtreductioninc.com/index8.html   \n",
       "2      http://www.debtreductioninc.com/index8.html   \n",
       "3     http://www.debtreductioninc.com/index12.html   \n",
       "4      http://www.debtreductioninc.com/index8.html   \n",
       "...                                            ...   \n",
       "3016   http://www.debtreductioninc.com/index8.html   \n",
       "3017   http://www.debtreductioninc.com/index8.html   \n",
       "3018   http://www.debtreductioninc.com/index8.html   \n",
       "3019   http://www.debtreductioninc.com/index8.html   \n",
       "3020   http://www.debtreductioninc.com/index8.html   \n",
       "\n",
       "                            Landing Page URL Parameters  \n",
       "0     utm_source=google&utm_medium=CPC&utm_content=H...  \n",
       "1     utm_source=AdKnowledge&utm_medium=CPC&utm_cont...  \n",
       "2     utm_source=AdKnowledge&utm_medium=CPC&utm_cont...  \n",
       "3     utm_source=Google&utm_medium=cpc&utm_campaign=...  \n",
       "4     utm_source=Google&utm_medium=cpc&utm_campaign=...  \n",
       "...                                                 ...  \n",
       "3016  utm_source=yahoo&utm_medium=cpc&utm_campaign=D...  \n",
       "3017  utm_source=Google&utm_medium=cpc&utm_campaign=...  \n",
       "3018  utm_source=yahoo&utm_medium=cpc&utm_campaign=D...  \n",
       "3019  utm_source=Google&utm_medium=cpc&utm_campaign=...  \n",
       "3020  utm_source=Google&utm_medium=cpc&utm_campaign=...  \n",
       "\n",
       "[3021 rows x 24 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('/Users/lomesh/Downloads/dataset_for_CaseStudy.xls')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xlrd in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 20.0.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip3 install xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.random(size=(3,5))\n",
    "y = np.random.randint(10, size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 5)\n",
      "(8,)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 1)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:, np.newaxis].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 3, 2])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(5,size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "[[0 1 2]\n",
      " [1 2 3]\n",
      " [2 3 4]\n",
      " [3 4 5]\n",
      " [4 5 6]\n",
      " [5 6 7]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4., 5., 6.])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sliding_window(arr, n):\n",
    "    \n",
    "    rows = arr.size - n + 1\n",
    "    stride = arr.strides[0]\n",
    "    print(stride)\n",
    "    x = np.lib.stride_tricks.as_strided(arr,\n",
    "                                          shape=(rows, n),\n",
    "                                          strides = (stride, stride))  \n",
    "    print(x)\n",
    "    return np.mean(x, axis=1)\n",
    "\n",
    "arr = np.arange(8)\n",
    "\n",
    "sliding_window(arr, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-0e5520644890>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnums\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m34\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m45\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m44\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'ds'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'dsd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'dscs'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fds'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnums\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"
     ]
    }
   ],
   "source": [
    "nums = [34,45,32,44]\n",
    "name = ['ds', 'dsd', 'dscs', 'fds']\n",
    "for i, j in (nums, name):\n",
    "    print(i, j)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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