personal_site/experiments/HappyBirthdaySophie/compute_distance_field.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "im = Image.open(\"birthday.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=500x500 at 0x7FCC1124CF50>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('PNG', (500, 500), 'RGBA')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(im.format, im.size, im.mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(im).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fcc143b6050>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOsAAADrCAYAAACICmHVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydd3gU1drAfzOzLbvpvXcgdKSDCgoioFIs2PFauYB67V2vVz+v195FLIgdxV5ABVFA6b33mpBCetvsZndmvj8WEiazCQkkCDq/58kDO2fOmTPlPeU973lfQVVVDAwMTn7EP7sCBgYGzcMQVgODUwRDWA0MThEMYTUwOEUwhNXA4BTBEFYDg1ME09FOEARhAjABwGEXemVlWtq8UgYGf1f2ZnsoKpEFf2lCS9ZZe3e3qct/Tmq1ihkYGGjpOzybletcfoXVGAYbGJwiGMJqYHCKYAirgcEpgiGsBganCIawGhicIhjCamBwimAIq4HBKYIhrAYGpwiGsBoYnCIYwmpgcIpgCKuBwSmCIawGBqcIhrAaGJwiGMJqYHCKYAirgcEpwlE3n58oPKpc938RAUkQ/aYdxixIJ6RepyLG8/pr8qcKa463inuzR7F8cRaps911x3dfbGb3RW8CUCRXc/6DdxOU49bkjX9yJx+kLDyh9T2ZKZKrmVHRiRdXDiXlExGpVqlLUwWB3VcKfDF0Cr2shqePU5U/RVhLZSfjd12M965wxB3ZZFQs0aTb+w2s+79HVYlYXYK8aZvmnP2VqSeiqic9HlXmrfJUPntwJEHL9tMub7Xf89r/JvBQj+uIe2M/05N/16V/V23njlnXIHhBdij8MPJlOlsC2rr6zKuRqFasDLQVEik5Wq3ccqWGBTUR2AQPQwPcmpHaqcoJF9YiuZpBU+8h5cW1qM489AM2cEUqfo7WIzocJAaWtU0FTyFkVeG0ZdeQMrmIgPzleAEpMw1XanjdOQE7C/Hu3Q+qirpmEwevSOGGGWcwLfmPunPyvFU8d/cEMr9d6jsgCFx9812sfOC1Vv/IParMLQfOYMmnpyG5IH52DmpZOe9+a+Obdj8fV9m7PFWMWjERy4JgbCUK4bO2oKbEk/Hd22SYA1vpDv48Tqiwlis1DJp6D0n/W4aiHBJTUQKlXmQFs4V/jphb9/vmvRei7snWlKNmpfJq0tuA/URU+6REVhW6LR1Pyg05eMvKMaUksWNSIneP+ZYsay73bbsYu9nDefFrePOdUcS9uARUFe+efex8oi85U34i0eT7gMsUkaD1BXgPF66qRG6oQUHlWGa6HlWmXHGx02Pj7u3jcJhrmdnhc0LEAPLkGrL/kUjclsUAvmsKAgrH34s/kD2GpHGb4JBfMRkwhYch49el0SnHCRXW63aPJvX1TciKDIJAybX9kWpVQj5e2mievWXhRDkLNcfkQAui8Nd4AceCR5XpsfQaUm7IQS4rRzCZ2HxfHI59Al9fcRZCTS3B23ZiSojn9D92Mub2Z7hh8WRYuh4A+2+bWFCTwlVBxQB8WdETtcrZKnWbVyNx29uTSfqpDKGmFseheuz8Q6KX9dBJsnbkJHbvyIupbwGt0Ps1cABY1iuG8FN/BAycwKWbJ4s64L4mALmsHADXBX249b7PCd5TozlP6d2R4YGb6n6XlernMTsvNxMitv186mRlxJYLSbkxt+5Z5t3clz7ddpHw9GKUtZuRt+3UnB8jBSDbGm+XzwnchGC3tUrdvi7pTcJT/usB8H95w+FgkeaYYjMRIx1/v7GrNEJ3rLCX2Kpz4T+TEyasn7431Dd3AhAlhFsPEio5MW3eqzmvKjmAbhbfhyOrCsmf+hmIWZqe0/6VKZWdeF6LRS4tBUCKCOei6+cTYnbpzi04L4VM89FdzYaIbpC0z1kVj23kovgZchaMTCHT7JvqLM5JrWtkDlORZm+VpSXTzAbCKkp4493+Tz4FOSHCOrUsgcT3t9b9dp3Xiy86fkyZbAel6Y9JaJAu2u10SMs77jodlKvZUuvU/OV4q44pn1v1HHd9mst1uy/E8dP6ut8HxmdxT8RalnzdXXdudZxAoHj0HjPTbGXzIxGYEuKRYqIxpafiuq/smARowVc9dceccULdSMi7JViXfvA8N1bB3OJrNUT3rTjsPNX/y+Mu92ThhMxZi7xByKX1rWnFTRVESg4e/fUi2leu0JybX79qw/fOYAJ2FWs0xmJYKI+nfQ0c33rhgM/vIutVrdBnX5TA+rumNJnvzPfvJnNabt1vVRI546vNPBi5rYlcrUd+dRAhrgLAp4w795ol2EULtuLGG70ZlTFY9xbVKZC8vdvT0zqXwwo6syCx9dyprBwsISNiQaaPVeBY2nJbSeP18KgySfO0PZ1gMhHgqG3xdRqy1CUTurmSv3Jo8BO+dCM6HPSMyQFAcoo6hUCXHnvr/v9LWWfknXuOWuZ7FdE89dklOHJ9ZakClHVRCNug/diqkmHjta9hFiTMlSLePfs06eFbo+ny8mSsZYe0iRaBXletZ2rSgrpexlIhaPMJAu9/N4Qvs4fUHSrpLjN9+DucFeAbri93e5jw4m1I7vp7VSSB/905jRF2N2mzbiJ8Zf2rUEwCL945tS5/Y0gxUfRyLKZIrsZWqj1XMFtIOctXzydnjiNlb/1a9oFBAXS0+AT1yaIOfPn6EE1eV7jAb5OfJVpycMH2keTNSNWkV6RBTI8Car6OAUAVodP4Laz9thPJ8w/qluNSvi+jozSZj/7xEqbKWo1AiRHhCIJKz/+bhHDoFpyxAndf/hU3hOT7ve8HC7rx/cdnYKn0leQNEHAPrCR1wxbNec6zOjLQ9iNHKq4OytVcuGk8rkN1B6hMAUu5UPfeD9PvxjVMSVjKllonl798NyZnfXp5e5WdV0xlfa2L8S/eiclVn6aK8NAdH3NxYIXf+h8rJ1xY1axUXkh8B6ciEbRX33JX1lrJOzQcLam1A9qWOHdMKh0PjZhKZSf9PrqLdq/vJyVnsea8KD/XDj/zNJRrFcqVWoL26dtg66wVJMzSHsudZqfDlAnsPncaRXI1QfsbCJCqkvqw1qgjEnjirGtxvfMxI+xusj0RxL+3Ebmi/uUJZgs7bo5lhH0fEctMRLxTX4Zos7HrlmjOCvD/sR6m5MwkLg8q5YOKFIJ+3MiRNRMkkQti1zN2x3Ay3txX16sqZ/Tg+xuf4fAHvLw0lcg3tfWX2qVTOVElWoJNe+Jp3yA9JjQEJAlH8e66Yzl7+5Cyfi/eA7macwWTCWF3DkF7Q3g2dwTqyo2adLngIPEXHtTd25efDCT2x9mcb6+fi8uqQs8VV5FwXT7xpdr3zUvoetXKBKlueQrgscJO/Hb/6QT+vJpApb7ukbqr+1h6fgdIWEqhYifhox3IhfWrEoEX9IUrINcbQsJHW5GLS+ozihJbJsVDKwvrCZmzBkouRIevJZcKyviooj0Fci1xcwp059pvUrlh5A3cMPIGyseH6NLdEWAXfUPg05dNIP2RFXhzDjSrHp4gEyIi+7wC0XOzj54BUJxO0t9XKVdq2FgbROi8Xc3KJ81fzS3fXgdArieMhjGFREcAdtGNW/VoWmUAIdCBQ/SvGAmyuhFMvjY2ZEcVy93+58uq18uUD0fhudpcJ0BSZARRT+87qoGAEhiAuQn9klxWrv04AfuK3RSMSNGdWzWmFzesXMuMx56lorb5Gmd55x5u/fkfmmMvl2aScFNRnXLtaJT2rFs5JsdbxcI7BmD9cYVmXb8xBJOJQKtveJ7rCQO1+UpN0WEnUNIr/I6XEyKsN4fuovCyLgB4cw7wxYGebPNEILj1cxXv3v3Im7Yhb9qGd/debaIg4I7wPbS3yuNJvb8a1Vv/QgSzheyHB7JnRnek9hm6srOv8DapNJGiolAHdK8ThsOY/tjIEwcHstaVDEdcr+66vTpD/24+A48jSJrjpUiu5qU5I1EqKzVp5ed25JrgA3xdFU34LO18t2REOy4O1C5vHGZ6uxl4Bh9SJq3bzh/VHfjvuvNQ3VrhVr1eEv+3GG92Tv1BWWHlgiyNIm3LwnTdNXaPCybZFIhb9RD5ezN1A5KEJ1Av4R67yMWBFWSYAyn4VC/MjaKqSJX1n2eOt4pvHj5H07uJQUHsfKk/26f1RgrVN+yjeq6t+//QD+7BNH+tJl3s3pFdH59G9kMDG2ZFbJfG9KwPAXhg4SXIRcWa9JxzfHV7aMsYlKpqTVr1kI7cHNq8Rr0lnBBhNQsSLzz4Bvm3DaRs/AB6hmdz17pL8O7T9m6CyYRgtfr+zPqPRLBYuH3YjwC89PFY3XzWM6grs256hg2D3sETHaSvh9UnaP/cchXyQa2hhSkuFmaa+WTmFIqu7aNJUz21uBUTLy88V9eqK4NP48Uv32bSh18hhWg1ndZf1zOtrAeSW/8Re60CZsFnF9tQ0BST0GijkmgKZOwrv1B84wBKrupFuvUg7HBoGi3A7/OTS0tJe3AJw969t+5YxCb9dECx+o65VC8RG/Qacvnsnuz9rBtSZlp9npIyEr/Tj3Aqx9Q3UqIfPZI6sDvSb/HUDu/t527ruWjDddh/XKc5VnhZF7aOe53PhryBENBg3V2UEAXffXxQEUnm1P1aSzmTiV0PmNl59nSiB2uH7gCqWcJ+6LUJbr2YxGb5hu6lu8Nb9P6OhxO2zjrIBuvum8Kyp9/g+bjVqKr+A975dG/Grslh7JocAn8NRrRrzQnFABtBUg1TyxJIm7ZbkyaFhXHGC0tJMwdSqdQierXDFlNSIi/1nAlAQWGI7gFv/m8iszvMJlJy4IxpZAzoR9W4b5JMR4sdm+BnOKoq7HNFkPaV/oNXLvO11P+dNwalRmsYUtytaZ3mrWH7WPn4Gyx/8g1G2Sv8NgZ7P+7Aruf7k3/7QN1zTH97L+9VRPstW3Q4uH7Yb41e25Seyr+nvcvSgVPxRvsaJykiHHlgZ9Ri/fC0R5xPgLfUOgndrR0aCiYTOXfKzO4wm8qkxpduDsrVOF4N1bwzKSyMvv9c06hQKKd3476o+ciqwv8+vVQ3VZL7d+Hr/m82es3dl4URLdkplZ2kf9mypbmi7m0jVn+KIZasKpiW6Nfb1Bg3E0MPMDH0AJdEr9It1JcN78gVQQd4/rvRePO0ypfc8R15MNI3zLnnwAiElZu1hZtNtDMfGsqU6z8MR2iN7tiRVHutRC3TfxjRoU2vzbplE1KJ/pyUEN+cz1ym14ifPmCz7vzG2OWtIfWLQt3x3onZ7LxiKsvveZmCf2jXYL0Hcnli1kUsdEHIRu2GCEGS6BTg+7CnlnZDytYqf1SrmXRTFWGSncyXt7F9Wm8Gz89m9JRfEYK1oxnRbifS6rv3xTXpmJZptbVSVCRPdv8GgJDLG/TKgoA31NcTTt47BusvazTJBy/J4pm4+QBsdceherXzUK/DRLRkZ6kb0qc30E8IAvtuUep2FRWU60dhnhAFSRDxoGIu1ppimhITuDJ5BeVKDdHLdVlJOj1Hf7AV+FOEVUElerW2lZWiori5x4K63w+vHItSrX1IskUg1+sm5Se98qXbFRvrFtYrPVbdsNCdHI5VgCrFRean2vymxATu6DivyTrXyGYiVml7DlN6KndnzGky39ayaIQa7fWk0BAyAotwKrUEHNT3ipLQ/NVCWRV05Ytdsrgl1nc/VsFM+pU79NdwCax0pqNu045QSIghSvJpMX85mIU3X6sEzDkviijJZ+Q7JWEpe0a+w30RO3h1/Vm63svbsz1PxS4CYEFZe52S7eCINEbZfdfKW5CovYfAQJ4422fQsHVWe937LO2k1hl8PLporGYuC1CZZEISRG7ZcCVybgNFpiByaVa98Ed8qh15CCYTlmjft7faHY5Ypf1WlcgQLgvaSqUiE75aO5cFENtotfdPEVa36kHwNtCAOgI0NsHifptOa1faEXJlO9YdDZY0+nfjsfjZdT93fNVed819I6x1anyhgSG56ghgmENvx3ok6/LjEcu1PaQSbGewTb/soKnzH7G65Qxv5zQej17BHq9MwnfaVljo05WH435ssswjmVneG7WBgsMTaa83mgcs4tG1n4fJGxLF6bbGP4uqZMWvtZHiZ1qDICAd2nCx4ucueiWYCJIgst1TTcJC/cjGLHjxqDJh27T1F4OCuG7o/CMurr+2OtonROV7Q1E92smyPKg7N4cfsVQmN9DUh4bweq9PAPj3ttF6RWcTSJ078GT6V80+vyX8KcL6YP6ZSMsbH+oVydVErtO3Tr0Hb+X6FdfizdO2lF6HmWRTfesYcLCBml0QkAN9xz6qyMCUo20NiwZEEXJIk+tRZcwNNqCIdjuebcFazSqQPzAUu+j7cPfWRurU+6LdjuRHqVIb4suT7Q2FhsO3QDPJpuZvUvhwYz+dprK5vLX5dFRZe33PESPCvSsSaQ4eVca+Sr9dsTbEjIiIR5Ux+dnU4wnyCdnPVZ2Qlmq/BzE4CIfo5q3yVILmaYfPgsnEafa9gK/hD11j1qUH29wUydVkztQvoVQlWIg71HDPrxEJ3KG1VRaCAut0EOVV+ndRMCCEINHClOKBUKSdRih2S50ddGvzpwhrmSdA18oWDE0g8dCKSaEsELbMv/2vp8bc5DrZHKdZNzQRAwP59zlfA/BNfg/dcK24u1pnu7q+VibxK+0cxz2wIwGd9Jvdy3p46nqZZ369QGegXj04i5jl+o9l/+UyVsHMrcuv0PW61bEWxOPcf7n/XGuztJHSmiDtsxQEBl1c72kiZkWDxsfhYHC/TTTEo8rELtVL44ErPJgFiS0eD8lf6DWuncf5hNDfftOC81IYEeCk3GtHcTa+fc+peIhZon3uYvt03m3/MQogleufv3xF/Rrxb1UdkbdoR1U5YxPof2hkEjNDvzZc2sOLVTDzw77OuuG3Mz4A8zHtAj46f4qwrvq+i+5YZRp1AvNjVRdUp3ZYJPboxP0J/oeHuy+R6jwarKlJRdm5T3eO/ZCRgUfRP0j1iKfwdXkv1ArtcLc0y4r5h1D9haX63l/0o5EtyTIhyPoRgmTyCYFcK+n3X15U3WzvDG7VQ/R3Vt1xqd3RNySggr/RsUPyPSdZVerM/w4j2KxcFulHo9IIksl3Aadihlq9RtVh8g07Xvl1OGptg6GqVWj0OZSc14GBVp/AeVARGjxDVRRxNLZrSBBwWOqv5ZT1S1yKyTc896gykqvx+acs6+uXPVauM9ppbf4UYT1sw3sksq3+2JT1g5ALtHNBb5CVDmb/LVZgbP3H+c6sc/RzlG4ZZFl8Q+fyzxI0aVJwMP865yfAt0Qw650zdWupwrnFBB7QKjikqCieOMPXW2+qrSHjM+28EaC6qwvLAW1ZUmgIZ6TvwqnUkjxT//iFFimXVAKzG8z1RIkAq/b+l2zM1OW1lggkfat9xlJmGmcG+Qw0ppanEPTbVk26EBDgd4nqu+oYzLn6+xyU6jMMuH71P5AbKKpMqckMDfUNfQPy9I1W0jif4uugR6+prYkUCZN8w+6Je8aibtEaIBT3CsPeyMhC6tiOV9p/Wvf7h28H6EYXzm6+Z/pscSdsi7TPQHQ46Nj+ALKqEPqZvm5t6RPhhAtruVKDpUo/t7tl5E9N5ssZEoCpkeFFVanvxblVD4E5+qd1YLCdbhYbpbKTwJwGFkiSRDurT2E1ZMUEoqcs0ySbEuKJDqzCsVX7YQsmiQyz79jo3ydDg6UiU2oykknRKSeEsFAeiPuJqWVZOFbv16aZLYQHNt9jQ47sQXJqhceUmsS7XT+o+12u1JD4s/Y1C2YLrj7VUKa1XXW2j2C0w4lTqeWFOecjl2vTc8ekcKZNb8E1s6C3blMEkeHcH/szbtWD/ecgnTbXlRHFuMBi9niqiFmpbVyk4GD6h+3BqdQy//2+uryWChWn4suzMTdON6Uq6qsQKNp4tvAMhPwGy1qFpcws8xm9zKwKIWmutpEVJIkbuvvsjv8ozkCp0o5ShNREnkz9mo8rowldpp1OCSYT9uDWNzM8zAkX1i8q0wj6UWvMjSAQLvkeikeVifhRP6lXOlU1Oizq8Iabhw92JeuHycS+o/fuZ+1fjEeVuXz7pdjmrtGle1QTW2qdxL1s0c2Hq05LJM5eUb9x/ghqkVjlrqXdK15dvrK+8YzNWocUFqbN5HLzXskAPnl5uG5ZRExN5P2OH9Bc7t17EcpabSOhFBQy+f7beLKoA7KqcMOeUThmNTCzS0viqk7arYkAjtXZvFCSTpdvbqXdfWt0vZ1iwe87yAoq0N8nsKk2ms4LbiL688aViQVyAAGbGsxnA2wsLMqky7e3EveW/n1GfbmJidnn0GvVpbS7p0SXnjhHZX6NyO/P65VvcmEhM349nWdLMnhz0iUIi7VWUaqi8lthe/K8VZRNTdY9g8K+4cyt7sQLUy7VWeBJsTG8e9p7jd7r8fLn+A1uoIFU26eSZZ0PmPGoMiG7GwztBAHzIQ2baFZ8Y40jHqK6YgOrh0aRVb0BxaVv2eImV9F39K3Ef70bb4NWWi6v4ImnxhOxqRpxqVaQBasV7y3+bXTlwiLueHoSUSsrUFdt0KSJDgdhk/fxRMxyLugyAfH3+iGiNy+fdRckEnFgScMiQRBa1Hr688qgZqUS/M0aZstn8VGHoaS9vw/Vrb2HbZOieCjoc5ah1fZ68/KZe2Vf2m9bq+utACqy/FvyPBy1krGdJiAsqr9PZd8B3rhkDBnr1vr1YFnUzTfX7maRyR2bSvTr9QIrFxxEujSCDlXr/L5PuaKC4gtjiDy4C68fZaP9p3U8vedSQtb79+2VeedS5idmYcpZpU9UZCw3iow++x7CZ+rzR3y4gt9mZxBTsFifF7DQdl5MTnjP+uKWobphTVGvYPpafVrVjypTMe/XflymlCTe6eHrcd7p/z5Sln4OJheX+H2x4Ns8ED1lsc7qCQBFJmLakjpnYkdSM6w7Mzp9wMovuupaWNXrJfLNJair9NrR8gu68m7GF37rAug0wIcpGhhNuNR8TeKWZWm6Y7mDQxADHTi+WEbSfxfrNN9S+wxeGfVeo2Uq67f6FVREiWv6+/9A/aF6alHWbWk0XRlUhiSI2EULtXobfOSi4kbfJ+AblTSyKqC63SjrfXNNKSxMt8ECqHsuUqTeb5N3zz7C312ie+fge+8N9SmHKRuYRLxJP01oLY4qrIIgTBAEYaUgCCsLi49//UheH6IT1iP5qbCzbj1TNUlESb7e9qwAha2Tw/3O5AWrlT1PDkA+S+9apKWYYmMIujubZFMg5qrmK31MSYmk37aVaMmBCYl95/nfFmZK0+9AKemitsgRXNSqllnKiHY7Wx4I5Xy7iw7mGkqG6XfcHAtWwcS+kcfuwC767AO6nU6HqR3em6px/RrNW/qPART8S79rBnzPuHxGGGKXdv4z9+1K6o/Vvh1TfnCN6uvbUdVMSjqJRLehc7ajCquqqm+pqtpbVdXeURHHv34kW1TEoKC6Pyk4mLIO9ekWSdaki0FBVHaNJugIVfzysS+Qc/8A7XndO7JtSlfWXvMygY8dwJSWUn+NsDB2vNKP0n8M0Feob1fd9WqH90b4VOSH9j8yv0Ykdl5DczUBoY8+n/v8PoR8Ws1HqfMB3/zulXHv4j6vj+8cW9P7OdUWTkpkq6B9lhHhVHaqxdMxWXeucFpndrzTnu3nvgVApORgyF2LEbtkad/F+AEc/DYLU2KCtuysDLoF+N8DLAkiL1w+HffIPtrnMbIPhd910D0rU0I8p8XWN8ifZX3CgTv6aq8XFsbByQO5+/WPeP2ZV8i9dyBSTHR9GXGxZD80kI8ef477b56BfHZPTX7XqL7EzihmUbev6PLeNqTOHbT3ec0ARr83nykJS6n9v3JMSYl16UKfrux4rxcvvPIalf+tqX8W9qb9VCumtnUqIzS012yK3t1t6vKfk47rgutrXWytjdEcGxaQV6eKPyhXM78mXpPe2ZKvC+VQJFfz6xHnJZtK6G+rb0zmOM2UKb4yLYJMB/NBJt18G9ZZRyhWRIn4xXZGhmuHwH2sB0g7tEF7ZlUI03t10+xHFaxWshbLDAzS2tz2tx0g2aTf2L2ptoZFNRlMfWUMUW/4maviM6EbvjSb28P2+k33xyKXwgFvvWLHIsicG1DCBxVpPP/96PpdQgLcNeo7Jobqt7AdWYaEypCAfOyimR+dYdSq9c8z1lTOoKPsHd9UW8Om2ti634efx5HvAsAhuhkR4NQoqxq+z8P3cnjN0qPK/OgMwqWa/Zax1u1mu6d+J9FA2wGNl4iFLsj3htTd5zn2As0o5ienlQrFd4MZ5sK6mECyqjC3JoB8bwgvvH0Jcc/7nwoIVis9l9bwZIx+OtUS+g7PZuU6l98FoBMurH8W82tEnul3tkY7KPTqzH+/mN5ksKaHD3Zl1enBKNX1Kn7lzNN45cPX6/wYHY0qxcVpH91BxsMrQBAROmXo5nNSaAgTV6xgtKN1nG0btB5OpZYuX91K+3vWIkVGUHBeCpHvrdKs54s2G5es2duo36jm0pSw/kV8lR+dI3uJwzgTHUeNqvbZnDM0ggpQmWxttqACdP9tEhkPr0BVVHY+2Yuyp2p1Sg81JZ5YU3kjJRj8mfRdfp1PUONiSPmmhPR/bIcGFlJCcgJJ5mOz0W4ufxthnbT0auSSlgWzklWF1nClI+baUL1epPBQnhv7IZXzY3SazLxBYXUacYOTi+oCB6rbzcEhCbwav5iNP3fQG2IMjOZce9v6j/7bCKtarjd4yD2jaYXZfq+TtJl67wcFZx7bWlr52Zn0tuZjrtSnVQ8whr8nOzXRAo8Wdif1U/1Qt/D0tluyOcxJE/m8zfEzNY/v1vT8QgFEp0u3zN2ny7E5wwr6bi1XeO8i9ue1mjKlmGju6jG30XwGJwcJzyxj1UsOFJfepdC1fRe1+fX/Fj1rqewk9ZuWt3xfVJyGWt16PZ7qdmP/epluy1f2PzL9amoNTjIU2a+hRt6VHXk4cqOfDK3L30JYPahY87VKIlNCPGMT1jWSw8e0TQN01iqm1GQuiGw6X0MSe+b6tZQBkDpkct01TW9iMPhz6dVlN6bYGL9ppvRUzr1x8QmJrP63ENbDiEFBeM7pxWLoZzcAACAASURBVPZpvamebuHWML1vIr/5umRRO7w3O97vSe00lauCmnbl0pBfOn1N/ruRiN2y6g8KAvLZPcn8eB93hu9uPLPBn85n6XOo/iBAZ82knt6DsI/KeDpmbSM5W5e/xTqrR5V5sKA3VtHLo1Frm+3TdaEL3j94BjdFL6BPE5uhm8tSl8zbBwcDIAoqj8T97NeIwuDkZK3bzasFQ+t+Pxj301GjG7QUwyjCwOAUwTCKMDD4C2AIq4HBKYIhrAYGpwiGsBoYnCL8fSyYTiBViosnC/viVOo3CSRbS4wlmiaYWpbA1po4RoWuYWhA2zjJPtUxhLUVKVdq6L1wEsnTJMx/bNT4wt0ensr7V4/gouvn82hU8wNPnUy4VQ/lh7wKhom24w5reMmuczjwRibWUhnHmv14Cw7y7ZQb2DPmrdao7l8OQ1hbiT2eKi5/5B4yv1yPUl2tM0WWi0uIfXkxS3/rxtJvNmg2yp9IttQ6KZADeS13CKs2pBOVUsry0z4/ar5S2Un/9+4i/TPfxoattweydeQbfuPeNJfCZ9IJ/t7nlMwLPlc9NqNXbQxDWFsBjypzzsJbaffZapRDW6dEu52d/+6OkOLEU2mh0+O5eHMOoGzYxhVzJ7Jn1NsnvJ5nbxqD6bEwzNsOoDqdtK9eTuXl/eG0o+e9cMuVpD22AuWQ/6yOd4fwWK9ex+0Z4UikiHBePmNGq5X3V8NQMLUCOd4aOjxZpdnjWD66G+uufpntg99nzwVvkzfqkF8kVSXzIy9utW33Pvoje2084h9rkQsLdRvqj0b1jDiNozu5rJwvfjy9VeunpMWT0cYbuE9lDGFtBa7ffpUuvk7+CI8m5ol6hDdGQWlbx1qN4S+iXVG35sV78BfVo2EsnJaw1CVjz9Z6uz9wdpDO15ZBPYawtgJFVQ5dfJ0jcSq1WFrgzrQt2O+tIv1zvduYTqc3T0PtDm0g1IKAJ/jYpfW78tO0fqgEAaWf4damKQxhbSOkfAvyoXitjxT0J+IbvTPwE4msglB97D5qzv/HH0gR4XW/pYxUXhr5YWtUDfDFmLmmffMj1P0dMRRMbUS7d/KZdXEgox1OvlnQl8yKQ6EYBIHC7nbEY2wnv6kO5JGNozXHIhxOfu78eZOa2a8ruyFUauepUkw0fZvp+vTJmPWc/vFFVM/2bfPrc9W64/LEWCVbOaQDBkBMS6ZLwOzGMxgYwtoaBNrcCCaTVgGzcw93fvMPRl/1Bq+Pns5k6TpEL8h2hW9HvoBZ0DrhzfNWcc2OK9i9oT4kZfeeu3gr7Rsij/Dy/tSOEcRf2CBKeI9OLPjCjnJEA9DPWlrnixng7S2nk5yvjcnjzYhjoOMXfnLWx3gdYCvTRQXYUutknzeMRzJnwb98x2KlCkAfG3at283FiyZCUX1aXNZBfuv6uWZddt5XfUhU633wlveI4nx720Vg+ytgCGsr8H6nD7g9/Rrk7VrfTO3fzOfhYV15InoDu8dNPSJFK6j/zBnA5qe6ETh7HZmueq/3NXY7Z0++h3m3PdtkWAYhO58XLxmHsK8+WnzxBVlcdO8v3BfR+AZ70648nr7wUoTsQxEHRIGHx3Zgwt3fMiGkPh7PmBl3kfmMNk5p3lUdWfPglLrfbtVD5/kTyHzJS+aq9RrndFJoCOd9OZa5Hb+vO2ZuEO8595y2C+j0V8GYs7YCaSYbe8fp3X7IO/fwy7Onk+dtPBL5DfvPIOfyGOxfLdP591GcTuJfWMZFm8bXHSvYG96wCF9QrrWbkUtL6/5CP1zCvAkD2eWpwqPKBH+n3yQtFxxEWb+1Pl9xCRHTlvD61LF18U8BJLegKVsuLdVEBJdVhaxZk2l3wxbUFRt0XiTlsnJcr8VTpTTec3Zsb/igOhqGsLYCZkHi5qu+9+unJ2TGCgb+eIfffEtdMrn/TNIEXBbMFgonDkDKPBQhTpGp/iEWj+oTgJRZLQiStWUvzxQMAyAo209kuEZImJ1PnuwT1iK5mrCt+l6vpHv9sVfL0un0VKGmsTElxFM4cUBdADHH/irkQ3ZdHlVGcquac8+NPjVNME8khrC2EhNC9rL3+gx9giLT6Yk87snXmwldMXdiXWjCwzjP78G8h55ny51Rdcfiv9nHEnfj5onCaZ3Z/k5vlMHaa8hl5czZ1InpFUlYd+n9Rol2O1J7P3U+ghyvibA/9AGphvXzWS6VKzXMfHyEtsGxWimfZuOiyb8h+Alh+VuNjdjv9tT99iZFcmPIVt15BloMYW0lzILEzAnPo5zRQ5fmzc7h9+f7sd1Tr41d6pLJer1SEwNUtNsJuDUXq2AibkH9q1FraqhUbLxQko6lRLueK5gt5DwCe857h/zb/fee80s66MJoApSN6YZ7StOWVPOdHVDdDdaQjzDwuG73aIK/0kYnrxrVg686f4is+v+8XKoZtaY+YPbeUQ4ChKbDmBgYwtqqdLYEEPDffL9uR0M+XsoFiyfX/b5iziRdr0p6Mm+3+xQPMsE76932y6XlvHL1pfwyNBNhidYNqhgeyps9fOudoj8zI6DKq9famhLiueOxGYyI3aQbvgtVTmZX+Tz5vbx8KHJhoSZd7d+Nh2LnclCuJv/1DJ1BiGPSASLEAGZ8O7hOQ67YGllWEgTUdOcJceV5qmM8oeNkqUvmy6rgur8paV+w76YOfqNtp7wusKm2hirFRfIsfVm7LwsjQfIT8EqRYel6/xG3gwOxCU07MM/+TB80uWRQMkMCcrkseB1yYpQ20WalT0Djlk1eh4kEyc6vzkRCf9VqwEWHgzOjdvJpVRQZb9cPn3debq1bEnpu93CUGt/81pQQzxt9P2qy/gY+jKWb4+Dp4nb8dm1fxB31H+X9D17FpxNe4qE516Gu0lotmVZv5+3iM0m1FWGfu14blkOU8CS7kQQROxb2jwghadWhfEmJlA1IxFrmxTxnpabM7LGxR42E58/UsaSLQKTk4NLdQ3X1PBxp3q1aif5VX/b+EWYkQeSBPy6mfaG2Pq4zOnJ7+Gv0mn4Hqdm+WLRS5w5cP3hB3Tm5G2PIcB+as0oisVIVYNgEHw2jZz1GXihJZ/74PqirNiFXVNT9tZ96gHw5mLjX9+sinStOJwvf6cMBd5jGgAJACg7khYEzAd/8tya9fmiZPS6Z+S++Tlm6figp60e4GoRKE47cBnNOUUJO880Za2WTZt4MkH1hLIkmKy7VS9hW/bKTPfOQDa9b//lYi2ro/vntpP9v3aH7CiZmWi4PR/pXIOWMTSLN9Ofs7T3VMIT1GPCoMu9NH4GyVrvcIEVFodptVMoBvJo4l8rzu+vyxv2Uy08zBuiEtbZHBh3M9cNcoeaID1jwaVDjZmm1soLJxJAxq+rLWBtGQwLyJKx/aHtOMcDGY718Bgrr16VqyzRbcJx1EKtg5rPKDKT8BlH0RAmHtZZypYaMz/XDb3XVJjLvWIridCJ17kDCXJlXExsPulWdpGh2Jxk0jjEMPgbcqoe4RdoeR+yWRdf3t3JD+DekmWyYBRuVSRL+7I4sFfphaVFXW12A5nKlhswZWs2uU7WilldoMwki6QE+5Y+sKsQt1u/8sZWoqHLj1kGxi7W7acQAG/e388Xe+Ty3F2K2toGQMlN5t+OHeFQVc6k+wh74Gq2c8e246rq5hyyotCOMy4cu4rNPeiFKCp/0fRUw4tI2B0NYW4kd14TyY8xaOEI80y7cRc1LzctflVwvwA/knY1pw24O2wFVJyn8WNpVP3ROjCPd6uvdN3lqsRTXaNzJSJERBBQrOm1t9bDODLPP4ZvqGMKW5nJkqWpyPLHSb4DEruxo2tFgjVUSCRIVQL8P1pSWgvcdmcHR27g7/OdGNbxPRG/giejDdsqGoDYXYxjcCghmC2FZJbrje77SGxyoAVbUht+5IDB0cH1wo58X9UCu8PWiot3OxHPm8uv8HrpQkSX94xjr8PXwF/4+SacoIjSYqjj9fNAZJREtOQgVnagOrWInf3A4/W0SbtVD0pf6vGXdIwgSJSoVFbzaflUNsDIl81Pui9hRJ6hViktjbvlkUQcy5l1HxrzrmFkVoivfoHEMYW0FREcA/8n6Tnc8oFg/SNwzLhJPUANpVVUWfemzPnq1NIUOb9a7NnEO6cLNoVtoirVuN5mv6x2N7bs0jtDdeqOH4oG+nnZ1TSrK9j26dIA7cs/E/qtW+AWrFe/4YkLEAMasuQl1605NurJ9N6NX/rPu91KXTK/pdzBoxj0AfFwZwe9Xnkbm+DVkjl/DmxMvYVVDgwuDRjGEtRVQqqq5e804zbE8bxW2Eq0AiTYb3c/dij/DnqSfSum7Zhyzrh2EvKV+p8z+sQpVqoeE+XplTnF3gX/l9uGm/7sdljdYJkpN5oJxi3Gs1RvIj+zS+Eb42N9LyPh0IntuTNP5aVLdbpwLo2j30SSSbq/WDctVr5fUOyto9+Ek0mbdxH8uvoa0x1YgB/oarcc/vxRlY71W2DR/LZf+8U8MmocxZz0GAgQLO66w026ZAKqK6vWS8pTCk9M7cFfERnZ7PFz26r3E/bhEk2/vvT1ZnPwcPTMzfCZ7RyyZKOu2EHY+mjln5WX9+facl6hUVOw7imnYd2Y+t50d/7URXqm9DqLE9okJ3BfyHRuVWE2SYDJhEhp396ms30rmnfhVHAEkPO3bg9qYGYZ3Xzbp9/nmuSo+I4n7h/g0z8G7GpysyJj22jBoHkftWQVBmCAIwkpBEFYWFhs+XQEkQeTtC95GysqsO6au2sSikRn0euU2br98InEvLtMIo9CnK0+Pf48wyc5HQ99E7N6xyWuYEhPocMcmulls5Mt2BK/+2ctFxSiVlbrjtcNOY9FVz3HTimvwFmhNBeUBXfl3zHwAAiUXoqNxYwRTYgKCufFlFffIPoiOxvfZAhRd2o2rg/Y2mu51/Lm+qU4ljiqsqqq+papqb1VVe0dFGIvXhxkaICO+UYUpIb7umPdArq/nWardfC306cqI936vc4Nyuk1k572WRj90oU9X5A9gevLvAExcdzXevfubVS+xRyeGPvsH0ZKDWqdZv7c0QCLskNnfdcHZ5F3V2V8xeIf0YtSctbiG6deKpeBgCicN4MFX3yPvhu4aw/6GZdz/wMd166iu0eVIwcF16aakRB4//+gOxg18GMPg4+CH9j8y9pPhcHUi8sFCn99gUUK0+JYjxPhY8s+JY/xtP3J7A19HWwe/y8Rfz2Tl+wMxO+t7l+KhLr47c4rGJafHI+msjMQendh3fiiBB+qPl3WAJy7+hEsDfRZG7ZMLKL12gCaf58LSOk2tWZB45+6XuDbgdgKK6ssp6q3w7si3OStAwfbCt7yScjGmQ5tkaoMEzrp2Od/GvoZZkOh09zOMOOuf2L8L1lznyDIOs7bvR/R4/2oCvvVpgeOv281VQYaf4OZiRD5vBb6rtrPKmcanPwyiNr6Wh/v7rPQTzKWMsDd/07c/ZFWh69RbSPq/xZrj29/oa8SE+QvSVORzo2dtBUY7nIx2bOKx61rf3aiCSsLCGt1xKcRY8vi7YSzdnOQofvSyos1GYmTZn1Abgz8TQ1hPch4u6Itptdb4QIyL4Y32n/xJNTL4szCE9STnQE2obnlGCbZjOZ5AMwanJIawnuRsKdJ7TNxzcSgZZr1rUYO/NoawnuRYvtTvUT3ahnODvyaGNvgUQoqKgshQJpw/56jnlis12ATTcUUmNzi5MIT1JKcyWSDo7J7sulLipn4LuSTEt7kdmrYm6/v+ndRGetl6wRRDYP8iGMJ6krNu0qsok5QjBK5pW9zDRK9SCF6Rx5ZzFXoYw+a/BIawnuT4Iq+1zCZ7lbsWx/5qUAyN8V8JQ8H0F+Tl/HNQV2+hbGAS8aamfQofL4tcCu0+mMRb5fFHP9nguDCE9S/Imq+6gKpQkSpxxkd3ayLCtTYv5w4j4+EVvPzh2Da7hoEPQ1j/YsyvEUn6oRAxMBDFDBlPrOfWnCFtdr0Nczqger0YNhptjyGsfzGu++165K07KRrXBVenGpQaFwsXdm2z6w0fs7zJDeoGrYchrCeQ+wp60On1ycyraZtN/OVKDfFzTIiBgfSZtIbpA6djiokiZlnbdHtLXTLLDqaAqhC71KWJkmfQ+hjCeoKQVYVf3hhAyotreXbfiDa5RqUiE7KxBG/PTF6M/51gwQ1i816xW/W0aG5bpbi4985JhF1TgRQbg2VTNns9ocdadYNmYAjrCeLe/N7EfLoJIS2J1zM+a9NrFXeyYULinaJBqNXVeG3+3a4cRlYVOn51C8P/dSuLXM3rhT+qyCBo5QHcXZPZOSkZubiEycuubo3qGzSCIawniLkf90euqMAbYuOcWXfiUVvufG5ejdSsIXTsr4WM2zWc5a/3RHW5Cb+xaf9NNWotYRtE7F8v5+ns85pVlw/398Obc4DdVwqcNWwtos1K+BwbVYqrWfkNWo4hrCeApS6ZhF98HvuFJevp+MhOnixqmdJnkUvh2Usv57mLL2NhI/JgEwScqSHI23biHFpO2PtLqbygOzPafdlk2YGijY7XbkG0WimcmkqR3Py5p2iTeT7+N+Tu7YhYU4rzGBohg+ZhCOsJ4JmcEbB9L+VX9yf74QHIxSVsqGiZEcHVcyairtlKeYdgulv0bl4AIiUHN734FcU3DUD1epCio7jxia/rghg3xb/i5qJ2zCBs1mZ+qE5rdr2UWgmrYKY0y08QaINWxRDWNkZWFbbMa4fichE/YReuTBcIAnnVwUfPfIi78nrS6al8UGQcB1zkNGGUdFVQMaNuWQCCiGAy0cOa3fjJ+MJXps+9nmmFg8gdEoJcWcnj8y48ap1CrC4Es4WMDxSWuCWC9xk+odoaQ1jbmL1eJ2kzCxF7dOL+pNmEhlUjmMx4Zug3lftju6eaP17uV+c32LRyK28Xn9lknjhLGVJEOACyn2hvR/JscSey7s1hzuqujB7/O1JoKPZs6ahz6vcyP8c9tDvSgjU8Mf5aTL+txhUXiPko1zM4dgxhPRF4vKiCgEeVKNsXiuqppWqU3pO+Px7cP4bQD5YgtUsn786BKC4Xs+f08Xvup5Vh9PrPJCQUtj2QgSsrjlse/Bc/ORvfdrOwKBO54CCIKo9FrcPVJ4PkL/PJ8fofah8mWnJQ869SRLsdYdFaRLudilsqCJOM4XBbYQhrGxMiCpT1ikFds4mHbvknWVN9Xgk7xeQ3K//Oz9uDILBtUjRDrloOgkD4JpVS2ak7939bRhD51hKe2ziMXZdPZffVEDxjKc/vO7fR8g/OTEaKjODRQd8iCSIFvXzWSM1ZwFnQfUadR/6tL3Zicc+Pm3VPBseGIaxtTKTkIPlf2zElxGOdvQJ50zbU03twTezio+Zd6IK4BaVI4WH874IZXB62DLFbFhGL8shvJaWrrUwFSSLdchAAS399nNnGsApmPA4QTGYmD/zV2OTexhjCegL4NO1X8kalgChRcUV/rpw2uy7uTVNsdiXAtj2UjGjPsIA8+tskahLbzlGarCpIP+h9PvljkUth7I7hOHJVBIuZBUXtuWH/Gce0fmzQPAxhPUGoooDosHPbfz7j2uCDzcpjFmQEiwVLtcJmjw2nUovgbXnUtaraoxjaKyrzKjvT7ovJRH26EdVqOeqHce/2S6gZXEBJFxi2LI9NuxPIvySEaeXJLa6fQfMwhPUk5urgbAqu6EzAtyt4YvSVDHj2dmwLNiKHBjbqN1gwmQhxaJVDyifRjV4j/3QVuaiIlSOSaHfbUgR7ABXP1ZJsap6iSI1zcWf4biSbF2/OAd7dM7D5N2jQIgxhPQHI6iHBkmVe2jW02fa3VsHMkH8uRezeEWXjVmJfXozq9bJtot2v3+CKgkDE1CSmd/oAgLCIKgSzBbOz8euNHrgKKSQYb14+UmQEFe8HsrDrF3WR5gxOHow30sYM3TyaYddNoGJgDdue6UrIeTu5dun1zc7/bOwabpz5A6akRNQB3em6XGb1eS/rzpNVhbQvVRAEbId63be7fogUHdlk+c/HLid9rgtTXCzOvun80e2rZglq3s6oZt+DQetgCGsbk10UinnOSkKCnUSmNV/TCuBUaplXI5FhLkS1WfAGmXkqZlWz1zIlmp7fOpVaui65hkW5aWCz0hJ7hqSfVBAlrDZP8zMZHBeGsLYxnhIbpoR4JmT+0eK89+adyQuDhnPxN7e1Qc1gekUGKdfsRpzt0wCbK7xkfjKRaeWxzcpvSojj3Z7vt0ndDPQYwtqGuFUPmZ95UEODGBO4rcX5533fC++BXIJ3iRw4PxZLiYtur91CnreqReUEFLj9enFwK2ZUjxfFLJAzOoHcMwPIuGcZ//vh6LbBAEgiQaLPJjgtphjRblgvtSWGsLY1x+FRRVABUaLzlZt54dY3kYoqSH5lHQtqWhZ9Xly8gRll/k0UATyBsO6+KQgyoKq+67aQVzI+Q4yJwuk2/DG1FYawngJkBeZTqQSglrRtAGVr6TFIaQNCPzGi27UVhkf+Uwylawbplvl4VF87+1uNjY8KB6CoIpZSd6P5Pt7ch12p9ZrhYJObCIt2OF2dIBAJHEUv1SSmJpaJDI4PQ1hPQpxKLQVyLaI/ReuyDTx85Y0oNp97F0tuBfK2w5HRy6Bduj6PIpN2xToKjjh00GxhZ7ceqJ5NdceGnLeaXY9C5idltEu6FrPFt3E2LNDJ9I4fYkbFLECBbCEgz/BkeKIxhPUkoFR28rsrkhf3DiN7XRyOHJGE2QUkF27hsKVtJ0sB7t7tMP26CmHJOqxxsaihQSAKSB3bASBUVINXJle2k3aETb1otyOkJOgvvDsXf5a8yrotpF9Z/1swW7g9dTwIAkpwAHlnBBO7ajGkGqaFJxJDWE8ArrhAzMIRi5j7A7ivoAffbO9GwB+BBBQrhM3egrUmnwz3PoB6IRJ9PWh7s4OSjlZiFlnZ9lo3bh0wj0uD12uuc87SSaRctpGJ665mQ79P6jaeV5/bhU9eeUFXr5Gv3Ev8c4txRWqHrqXXDsA1toyqfSGkfu/r3i3ltagrNgAQu7LxezVXeNjvrSLZZMxdWxtDWNuQZW4z5uJqdk0KI1KqD9WY/sBS1j4gkKquB0FADAgAi5myS07DGyCgjikm0u5EuqwGuYFSSTCZ+N+gL7g8qBTQCkTvxGxKgoII/DwYd18PEzZeTdTBvShSEol+hEcx+3rNf46Yqzle2gl29J0BfUG+xCfI2z0uXjo4FIBf5/eg/cv7/N6z+MdabrjqVrKH2ehy9g6mpn5LmBhgmC+2AkcVVkEQJgATAJITDNluLm7Vw33bLid483YQ+mnSBJMZKTGO/ZckUJ0kc/+w7wkWaxjtmItd9C19rHLX8m/rWKSsDAY4mvZOeJhnE3/gug6TCP1mPVmDJxOyyYzq2U5hz2MXlMNC1tFi583EJb6DVy9h8MIJBG6s30BfqNhBUZCiohCWbybldzfVNhtXd57AjvFBvHHBNM61G9ZOx8NRpU9V1beAtwB6d7cdv27/L86XVcE8vvl8xF/CiPt0m985YfY9vZkx4QU6ms2H4q8eRr9GWdExjKEBzdsjGmcKZNtNNtpPdNPhlnWosowpNZl/XzLz2G6mBfxz1XiS929k32ddCHaEUpgTSvtpLli+gczVAi+9MYY7L4ym39j1vJr4a12jZNB8jLFJKyCrCh9URNJh2iTeHTqI2Iu2E/3aYuTiQ7bADVYzars46WaxNRDU1mHZyJfY+3hfpJgoxAAbmx+M4fLAwla/DgCqioyAR5Xx7g5EDAykd2I2y0/7nD2j3uaFmW8h/JpA8fX9UXPySHhqMblDFM56+DZmVoW0TZ3+whjCepx8WRVM53du4bPhA0h9ZAne7BxMCXHk3zGQnc/3QzCZSP/a0yLH2cdDtORg2/Vv8I9f/+CCFfvZev6UNpkvFnU1IecWMG7ZBC7bNYLMx9ahZqXyUtKsunM6WwL4KWsWix5/hXYLaim+aQCCyUTYe0t474JzyJg5keVuY2jcXAxhPUY8qszTxe2YMmkcKY8uxrsvG/p2ZfvUvlw2dymr736NJeOeR+3dCcuBcvZ5W+afKMnkoXxAEianQrnStKdBf1waWM7Nodlt5hcpbfgeADJuPoD7UhHF5WbbjTaNIu0wVsHMK/ErmPfoCyT94qHkugHIO3aTeftSbnvoVj6ujGiTOv7VMIT1GJBVhc4Lr2fB2cmY5q3ClJTIvscGctUHP7Jn9FtcE1yEJIiEiTYqU+3IO3Yz7vtbUb6KRLBaiQmvOOo1oiUHJR0lbL+uZ2pptxNwVy3jvYwvcA3rjlxUjPdgEXl39OP3kS82mSdEDODNxCXM/M+z7PywB0KfrgR/spRPzhnA8C0XnKCan7oYwtpCZFWh48LryJiwG7m4BOeF/Uj7qpCtN03hmuAizblmQSJ28i6k8DDa/WsZEdOWIKYk8m7HD5t9PdXjpdwbQI63iqAcL4LZhNhGYcZr5Ob3wpGSg/+8Oo3dn/Sg4wqBn297xu/ykD/SzIHsGjKdx2e+S9Wl/ZHzDyJdWcvQzaNbFHby74YhrC3k7fIkMiftBUVh13P9+OCl53ktYVmj53+e8TPZ78QidWyH1CGTff+zkWayNetaXocKiszX357BSncsgb9tpeSCjlxgb0WFkapwwB2GW/Ww9qOuCFYrkV2b59DtrACFHWe9x0txK4k7BiOIvlYzHzz7HNundUF11mAdV0nn2Te3uJy/C8bCaQt5eslI2lesoeDmfiy99FkipaY/UkkQWdv3I/b/5HM9mmyyIzVTC3zD+b/w26PhpHxfzv/ljSeqZhWVyWKrLnuoXi+bbuvBgC59iP1gLWJCHP9r/3WrlX80MsyBbBv6Nh2n3ki7CdvJeqWSReconG4z+pGGGE+khQRusSCYTVw7cbZfZYo/JEEkzRxImjmwRZrZa0LWUHNud9RVm4h8cwmq18PgC1cfnhmHQAAAE5dJREFUa9V1tBuxC8FsQfxjLVFTl6DU1LB9YhxnBZzYnTNmQWLbWdPY/n9dUTZu5dZnjN7VH4awtoDlbg9J3x9E6JBGz4C9AFQpLvquGcccZ+trXeNMgfT+z0rKr+qPKSGe3LsH8J/Yea1W/vT0r9n2WneyHxlI9iMD2fFaXxZd8Vyrld8Qp1LLgwXduCn7dF2aJIi8MXoaUrt0Yv4obTQG7d8ZYxjcApyKFUrKKT03g0GHpp0PF5xB+EX7eSllNJMmRfHGqNY1q3s+bjVFT/3OPq+ZdJOXsGb25s0hTLKzZ9TbDY62XvmH+bIqmIfXjcH2WxBxn21FCAnmhe/SuTN8t+a8oQFubrsyluTHl/BVaW8GxTWxY+BviNGzHifjwxdTNL4n6oF8Mu9cxkvnjyH984n85LTW+ws+TiIlB72sllMmQpusKnxTHciAdRfT56FJvDt4IClXbCF6ymKE4CC23hrLhNDNunySICIbFq2NYvSsx0kvq4Ulj71Gl2HXkfKSgLJiE+1u28krU0bxyMAovGNLmd7tfXpYGw+7+FegSnGxrtbCpPVXYf88hPBlBQTv3AXsQrZacQ0/jQODTDx14cdcHFgBNE8jblCPIawtYL8nHBS9Ub1ZkNh25ges7+fiwj8mkTFFgS37CZ++E94TuL/n9ey6LJhxwxZxVtAWhga4T/ktYwflala4I/j3ltGUbw8neIdA3M+5xO3dCqoKkREog09j9xgr0R0L+azzi8Ye1+PEENYW8OiCC2lfvKLR9G4WG7uGTCdvUBX/LRjK7GX9yHqjDHnVJtJXwZqgENYFD+P+0SnYxxQwIn4zt4evJVA8+XuZPZ4q3i4ZyP6acNZ/2YngvTLB87YSVb2XSM92ANT2GXiH9GT3OIlL+q7gnqjviK6bYxuCerwYwtoSZJ/nhcqLmo5aHmcK9BlKXLSM+SNFnto3ksLPkomdk4uSV0DUG7kwVWCRNZR5Z92KYhaQLQKlV1YR5qihW0Qu/439ta48q2Bqky1lsqpQodSrXWVU/rX/AvZX+px+V38fS/B+nx8mS2kt4qJ1QDlx6mIQJVSbFc+ZXSnPsGC/JJ/7MmYxIsB5xKih9ZVVf2cMYT0GOjczajn4rHzOypoFj8JP91hZV5PMO7PPIWgvxM3OIWDxNuQKn63w4T3me0NDuDL1xroySrqGcPBMb5PXubTPCi4MWVX3uxaJG5dfg6ei8bmyqdRExswKBPkIpc72vTicPi2tA9+/gsmEFBcLyYmU9UugqKuAN9XFo32+p3/AXNqbjxTKU3t4fzJjCOsJZITdzQj7Du4bv4MqxcW++1T+e+A8lu0+DQDHmgDiFlchA6aCcrx7fK5TQtfy/+3deXQUVaLH8W9Vd6ez7ztJyEISMAQQUMKqDjAGEXdkUx8IEhRldXTGjXGevqe4IKPgBg8EZGAckBGRUR7CIJKQsAghbAESCAkQgiSQTifdqar5IxjMQuiEpSlzP+dwOF1Vndzu9K/vraq74HuZ7sTZvj7scb2rzraY4pxGz7F/TbvwTzKb0TonQKd2ABQM8KQqsWa0j4dXJQs6f4arpBBiUOt1BhG15/UiwtoMkiph8PMjwdOxvrNN8ZRdSXKBpTEbIKZmm/13ClXTau7R/tPShs+LLk4Hk5vVluCs5t4Kim50q2qUqBxeSpj3xdE//uYK3o/8GNOFJqyb5FLvIpiY2cHZRFgddFapIObLauxJbXk56Dvg6vdYMkmG2tkjRnmdYVTiNxd3JgKPXPVfWY8+7uO2VuIEw0F2NFxOiYmtBecRNesVUjRV9/dMnWW3rZJ5JX0BSAvcRJKLm5NLdGMTn7Ir8PCR/vQfn0byrKc4bG/eMoytXUalwsRpkznQ3c6B7namDU/jo9JGVg0QaomwttBBu4XzTwVhXpNF+NvpDPjXVGcXSTcO28uZ/vxTuK/cisHXB4O3N2TsZtaKe5xdtBuaCGsLPZv/IBwuqHmgabgVmLBrjs3v29o99NM4vFbtxBgWin2FNycWhyMZjbicly7/5FZMhNVBP1jDkMsrah/vORqOarFw+J0UpG5JRK84zQml4SyEFaqNOaWRpO4fzD25qU5vLldpdp4qTCF1/2BS9w++puNGFU2lSqs7XHDRuUAC33JFq7Zz6KkY1nVYzePt0sFw9edQ/q0RF5gcNDuvP27HjkGEP3ZNwX1vTX/eoJtOY/f1xXy+7qf+RHU5Q/c+hmFOIJ47C9GOF1IlSUzo/TSx7x5gVvi/r/us9NurbKS9Ppmg5XvQzhcC8EbSMKa+oZHZdVmTF8qOVZdTqhoxodLB5fK3eI5Xl9Nv9XSC0yUi0g7xSuTX/FARz6q0Acibd1I8sRcb/2smos+w40RYHTSv/RKmJI5GBSo1hTYbyzFGtGFk1DZWMaDOsRutMi/+cTreq3eh2o6idoxHCknCcNZC9eafKLjTjy7PTSbr0Xfxka/PFdDtVTYmvjyJgCXpSH5+SN2SanbkFhAyxkT3+SPZ1n1pg8DaNYWumY8SMtsV8+FiNG8P9qf58ungeU0u6/HI/kdInL4LtbISyz+9+JPvQ2g2G/KpnRhuSmD4hHUtmmStNRNhdZCvDFq9ppoa6MMw7701Yb2wpKOiqYz/+5PEfJGOHBpC/vg4vh47E39Z5ntrKC/8fRQxr24n9uUsbq2exrsjFjDY3fG2aIVqY0ZxD77c0AO/nIvneJYwidXjZxJnajwAQ795hvglGRgCAyhb4sPXSTUzRPTbNo7IMYWEjT1NysLhZHW9uC6OXVNIXP8EiRNzUc+fp1o2QCHET4G35w1l0+JsXg3KafC77JpC8aZwIivz0Xp3QcrJo7rwBAC21Fvo+vp2ng/IbfC8NuvL+DnZ2+H3orURYW0m0+ly/lCYiqHkPIq/J1sqQ3A5ZSH/oWDCDG6ssPgR/0kRWlAQhuUS++Ln8ktT70HPc9w5ehbJIc/QYVYZ0S+nM2fxYCanBTH73oX0cT3baE1bodrYbTMwYv0E4pYqmLbuJ64io84x/sAQ+TnS095p8DPWWw20n3MWQoIp+8yTHzut5JfeStk9lpK8YCQRjxwl4GUT21fa6GauaZ5PKLiNxCcPILm5kvdCTx4etBmAL77uQ8yfs8gclcxLS2ReC86u8/sWnIskZuFR7D07M+vzD1ln6UCJ3QuAif6zGtSoieYi1sbeirI9B7/t1H7xCXWJsDpIBhQfV6Qff+LUID+Us3kcmpXC6jNdUPfsxzYmBZNk4L9zBhOet5dTk3qR1e59oG5t7Cm7kjf4U166JZnMJ7uibN1D3PTDzH3/9/ylXxtKbq4ZAaO6qniGllOR7433IZnwtUUk5NXMSSTHtKU63Je8+13RZAjaBn5f5RA9ew9dIiY1mFfpibXjiN+XSe57PchNnkv964obun/KPUOm47V8K28VpbIspmZ43jGLH7KlgOIxncl+bHbtUhx/GJ3JLfI0YmdsZ/tjScxYrNbWsGeVCmYvuo/Ik5nkzQwiycWNJJf8X78DDd7b37vbmTAhgPjJuRiCgigcGc9Un/kt+0P9homwOijQ4EHMrAMUjIpFyT2CwdeHIf22Yam+OARtRbk3kS/YIcCfu8ZsbnKVuNeCszm4LIPBP04kcqERQ2YuvouO4bvowgGShGQ0odlrZqhXXV05N6IHlofL+LDT58QaK2prqLKhVnpHTKfNm1touwo2DpBrpxPdaJVp/2Ep+Pvx1qCG56S/vLbAJ/OxfenCvi/aw3MXx9Ia/PwY8Hh6nTVzfGQ3sh57lz4/Tyf87S1kjUgiZlJvRqWks2JVX6Le3Iqa0pE1vefg6Kic7t1yKQMOvBhH9kPviSUhGyHC2gwfR6Tz9NIe5A2Ngiobw/2+Z/7pfrX7/7RiFDH70lHu6MoT/l9xuSudCSYPcm9fiP02hf8pSWZheh8CMxsG/GwHuL9/Bq8G//XCh1iu87N9ZDfSHlvDvz5rD2uzeGJFGukj3ua8qjFx/nNE7E3n59EpDHBfAzR+QWtk6FY+M96EXG9iRrXcwv8XJELozjrbfWQ3poxdyfL0VJT0bBImKGTJLkSpW5DMZvKeod44V8eoZlUE9RJEWJvpgzZb6X1LGr6bj9bZHrFewWNXIaqXFycmW4m5xIWexpgkAzOC9jLjnr3QZCeeS3+In/E7ytzxg4l6rYR2f97FI38bj6QoROzZitS9I6OeXdvkleePjt6Gq62owXbNbqO0pPHXMtbnJEmL5zPi+zQSP7RiKC4DWWLfs+Hs6ft+k+UVmk+E9QoND8zgnQ4PYP4mC9XDg4MfJ7D/lnnUP1e9Hr4fN5PecZOIm6dh2nUYycMda2pX7ntrHVP88pt8btG2MGLs+Vj6XBxZNDpiC0uje2MoNV5ywEKKq4G8u+ZxcKCFSq3mNSeaDJil5gU1rzQAf7kUg7dYr/VSRFivUH83hSdnuBG0ogfF91dx8Lb5Dq9lc7WFGT05MvD/qOhv44VTvYgyH2eC71eONysliXsTd9c+vNvjOIuDfGi3zEL5sCp8pEvXzC1p8v6avDwAQ8BZPk5ZdPmDWykR1qvgYL9FUHvq6vwenO6yC+/VzmbvWFDVC9ePVh/qyFv1zk9Rr/3aN5JacxXcJIn+1Zfi/E+WcENIG/Qdspsb3t941q6RekpRkWxNT9QmXD8irAIAw7x3Yb0jiYBlO+n9v1MYe6wPd343BXX3fqzhHhgQHRWcTTSDBQCijJ5EvXSAMznBBM/ZwvG5EglaFgZvb6RninUxEflvnahZhVqL2m7C5/PzSDcnYYxpiyE+loMfxbIuaYWziyYgalahnqUxG8hbVY79QrM3xujaZE8s4fq5bFglSRoPjAeIaiOy3Ro0p0OHcP1cthmsadonmqZ11zSte1CA+IYVBGcR56yCoBMirIKgEyKsgqATIqyCoBMirIKgEyKsgqATIqyCoBMirIKgEyKsgqATIqwtcKajAc1qZdGZ3s4uitCKiLC2QGy/fJTSMtbuTHZ2UYRWRIS1BaI9f0b28iJ0g4Fy9Rouw9ZKfFdhwn9bibOLccMTYW2BmWEbUTrF4ftVNqPz7nZ2cXTvzfxBqIfzOT2kHV1dxJffpYiwtoCn7EruGCOqtZKiD9qJ2vUKnKguxzYnDE1RONu/UsxI0QQR1hb6R/+5SJ3b471yB33fmMZumwhscymaSv/MCXh+mw09klnZ+0NnF+mGJsLaQt3MLiTN24ccG0XIR5kM/XwqaypEreCoHJuVdt+OJ3pcAZK7G2WvVNDJRbx/TRFhvQLvhO2gw9IjyHHRRL+YzntjR3BHzr0UK5bLP7mVKlOtjD3Wh3EvTCXh8W1gcqHkM38yuvzD2UW74YmwXqF3wnbQZdlBzjzRE3nTT7jee5oHJ03j9j33sd4qZtb4RblaSdrxngx4ZRonUg14L83A0CGe0wv8SO+y3NnF0wVJ0zSHD+7e2VXL/DbyGhZHv45VlzPgx4lE/xWkjN1IRhNydAQnBoYSOewIKX55PB+wr9H1Yn6LFE2lUKng9ZMDWbevA6FrXfBZnY1qsSB7eHDw9WTmD/mEvq7VreY9ccStdxawbVdlo5M0i7BeZTk2K++eHMjOhcmEfnEA5WwZqAoGXx+sKQkcHakQ1+Y08+P/ho9saHJlNz0pVizYNI0/Hr+bHUUR+C/zwL2wEiljN2gaSBKGDvEcTw2kz4gdzA7/Ucya2AgRVieoUG18Z/Vn6g/DCfneSMAPhVQfLQBAMpuRY6Mo7RzAyZ6Q2usn7vHbSbzpDHE6mFlwt62SomofTlb78OqG+5ArZWJXVWEqLofCUyjnzgEXXmdUG8puDqZooMKC382vXeRZaJwIq5OVqVY2WIN4fscDBC9zw72wAjKza/fLHh5IrmYqu8VyJsmF89Eqj97+AwCjfDOveIW2llI0lbmlMZRUe6JoMl+s6YNriURIhgXjwQJQFJTSsjrPkTu2pyrck7wRGm3bnGF+4hL8ZRk/g7tTXoPeiLDeQOyawvFqK385MYiN+xII+rcLgVtL0PIKUCt/da9Wrmkiaj2TsQabAbCEyvg8UHfBYzejnUHBOQzz3ovZwXO/Kk1l+bmbWFuchLXaVGdf2cpwPE7W1H6SquG5KRelrKamRL24wptkNiNJEvaUmzgXbebnJOjVJ4e0kI3catZEE7eFRFhvcOutBrIrI/lgbSqmchmPQo2QtccAUEvO1A1xfbIBY1gISrCf49f2VTAUn6X6xKk6AWyMMTQEjDWTu1ckhXGyZ80Skimp2fTxyaWX2xE6uIha82oRYdWZcrWSQ/aav9fTB0ZQdNKvzn7NaqDdUjuGyqu3HGNRXy/KO1XV3ShrvJGyggRTMQD+BjtRxhv/nFrPmgqrWA/jBuQpu9KlpuXL5k4roVPDY8qHVKLg+Bft5bhLLk00Xc31/hecQYRVp0SH99ZH3I0WBJ0QYRUEnRBhFQSdEGEVBJ0QYRUEnRBhFQSdEGEVBJ0QYRUEnRBhFQSdEGEVBJ0QYRUEnRBhFQSdEGEVBJ0QYRUEnWjW4HNJks4DB65dca6qQEBPS5Ppqbx6Kivoq7xtNU0LamxHc8ezHtA0rftVKNA1J0nSNr2UFfRVXj2VFfRX3ksRzWBB0AkRVkHQieaG9ZNrUoprQ09lBX2VV09lBf2Vt1HNusAkCILziGawIOiECKsg6IQIqyDohAirIOiECKsg6MR/AFeu80pUTxfEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "src = np.array(im.convert('L')) > 240\n",
    "plt.imshow(src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%%time\n",
    "src = np.array(im.convert('L')) > 240\n",
    "dist = np.ones(shape = (500,500)) * 1000\n",
    "i, j = np.array(np.where(1 - src))[:, ::1000].reshape(2, 1, 1, -1)\n",
    "I = np.arange(500).reshape(-1,1,1)\n",
    "J = np.arange(500).reshape(1,-1,1)\n",
    "\n",
    "for i,j in pixels:\n",
    "    d = np.sqrt((I - i)**2 + (J - j)**2)\n",
    "    dist = np.minimum(dist, d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "CPU times: user 1min 8s, sys: 3min 11s, total: 4min 20s\n",
      "Wall time: 8min 35s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pixels = np.array(np.where(1 - src))\n",
    "I = np.arange(500).reshape(-1,1,1)\n",
    "J = np.arange(500).reshape(1,-1,1)\n",
    "\n",
    "dist = np.ones((500,500)) * 1000\n",
    "for k in range(10):\n",
    "    print(k)\n",
    "    i, j = pixels[:, k::10].reshape(2, 1, 1, -1)\n",
    "    dist = np.minimum(dist, np.min(np.sqrt((I - i)**2 + (J - j)**2), axis = -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(k)\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pixels = np.array(np.where(1 - src))[:, :]\n",
    "I = np.arange(500).reshape(-1,1,1)\n",
    "J = np.arange(500).reshape(1,-1,1)\n",
    "\n",
    "groups = 50\n",
    "\n",
    "def compute(k):\n",
    "    i, j = pixels[:, k::groups].reshape(2, 1, 1, -1)\n",
    "    dist = np.min(np.sqrt((I - i)**2 + (J - j)**2), axis = -1, initial = 1000)\n",
    "    return dist\n",
    "\n",
    "dist = np.min([compute(k) for k in range(groups)], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfQAAAH0CAYAAADL1t+KAAC22ElEQVR4nO29WbLcOs8sSjs8/xmcqXrdh/9yfzCcABINJdWyMkJBig0IdkhCpar68f/+3//7+vHjx4qutRadZ93LdJmm871Ql38yvr6+4D0T6ji615eX//v377/0yegu09DYT86H1za61+PHgln3/+Laz67bjD7WHKH5tNqM4lqnaL16cxLJmpiLaLxRmu6v1OXT+xPFMzZRl70DjO2y4tY4oLSvr6/1izVqkXFD6TJtxzuhjntpLCpGiZEnZco2vr6+wlCWlbLQotzp0aL9+fNnamHL8bDG6BSxeG2j8ZHjV2nrX137EtV1m4Wug+bXmlsrjT1oevkRwTOyvHQJi4it9czYCGbdfFp/LFso0yr27DSY8dj3nXUv03Y8TehZ4ybvZWe6Bm1qgqYn2iIjltSt8t0FjfTzEBn4qtyptr3NkMG/vPb1OGxk120WmcMD0tPaL0iujnv5zHh7pMhC15k41Hjr7BP7Ezk2d3rcEZjx2PcT617Gy4T+BMOG7p8AlnzYSZK4akFbBt4qc1Xb0x76rv+u/dq6rSCaX2t/RPtFy9b3SF9vnKuyGEwearx19on9kWmIzLO4gyM6Tklm3etx+o/Qf/78mTZeGeOG4pnQiz8NLPlkyBwt8KpeDDwj2ZVdbRttABmvjs279v+U+yQPnd0vUpYXR/cyLTv21bnQ61qmZY27p/8n9idr+2S+vn7//l3qTxfReOz7zrpH4/TLM2ZZA4fyZBqKZ0IdR/dPQPYktstGC/pKoAXplbmi7Wi8OsboXfv3e+iscbPKI5288dPp3tgzsjLjMXmoYfvxKf1hbJ+cc882/vz5k+7DJE566NaYfX01HrlvQUyevEdxJvTiT4O1oGXcS0MTZbXDnkgr44WMxpTsStueV25tfAbv2v9TbmbdVlE1bpZeWh9vvDzyisY+ymOh1/VO6xxqmPCT+oNsX2QTnwJrPPR999Cj7V7rM3TPuOl0eY/iXmilofsnwDJWrKHUZSTkgkb5P3/+HHvMhBYlyjuBrIHo6vSu/fy67aBi3KxySC66RzpH48zELdkRpg41+z6zlp7cH2teK0R+Fz8gD32nZ8eHHacfPxpvuXeNm0zXaXogooX5NFjGCk2A9/hEy2QXtCb16lhZi1LjxFygtqMN0MG79mvrtoqs8Zft6riUg+4ZssqM99RcWOMt48w4SHlWPz6pP2itPd0j15DjaDlqzPgw4yTHaIzQJwybTvdCNHBPgefpsBO2UV3QE566tSh13gmgtqMNMNHmv7z2s+u2g4zxR2nWAThLVt74s3FLdoTJQw1aU5/cH3Sfxd3cgNakNSa7XJXMN0JCRy/NbUEd4yY7mg11XIKZxGiRZBeCRXbV05e87y7ozqL+lzz03c53X/sS3XU7sbYi44b0kPpaa9QjKKR3ZryZuWBgjbeMe2m678w6+4T+RLZP5u/rrrfZPcixzI6JlsMegFxCZ96A34LYPJkmO10xbOg+O9AT0PIyns4uN3ky3ci+4el54RkPvTK+TNveZpia0+++9r36T/DQrb2A5p4hc5bI5P2ELAZThxor/NT+INsn5xzlT33MOImOU2LtO28sv76cR+4nv84m72VnMwYNTVhmEr3TXwXW5O37zKOUCTKvwDLwKM+rd6rt0x76buu7r32J6rrttKnb2PdWe9Z+kXroQ4ZHUJbecl5kuYosBtF4y3LRocZaU5/aH12W2d+TLwRPQI4l65Sw69/aD5DQswaNMW46Xd7LzmcMG7qvDPYErMnb95FxskhJTqC+osVb6aNlsFkPfcLIW21Hm2EC1XX/SWvfkpFZt5EsDWv9sMZNlpEy9l6wSMkjK6SvN+ZZWRGmDjWVNfXk/si07EF9/2fFE1BxSirrX97/RegdMs8aNxRnQh1H99kBr8pA8lhjhR6lyPLRgj5xItVj0DHi021bC5whmwja0/nua9/qu7631m223czcVoyZbsMbo2gte2OflSVx6lCjw8xaemJ/KkQe6RrBciaqMrScjodu1UPj9gehT5H5XYYti259T150EtvlrZNppU0mPUK0SU+1y7Qtx3Cn67Qq9Lr67mvf6ju7bj0ZFiyPZd9XjZlun4lH/YjGnJFlyd6YPNRk19TT+3M1JvaTNR4yL+uh77rsAYj6LfdTBk6myU5XDFplQk556FtW5ElOnEwnDTuS90ke+sT8eWT8nda+7reUxa7bbLuyPGP8LX08uWi8rDKWHG/sGVneuJw41Fjr6VP700FnL0zZEKRLxUO3ykv5esx+/fiRJ/Pqn1mgPJkmB8EzZNbCzKJb35OnJ0aXm1rM2Q3IwNukJ9uN2kYLfrJtvda+89q3+s2sW48QIiAi17JlqONaF4vYkG6M3swcsLIQLCO/77NGfadZ6+kT+yPT0XXqxbdpG7JWzSmJxsnijB8/Cr8UlyH/3UiULu/1oDDGDt0zuMJDZyesiknDjmRe6aFHbTPj2YEmq++89nW/paxonDtt6vnNGH+tI5LLjFG0ljuymHGZPtTsuLWePq0/kYPjvTvU2QvTHrrUB42JbDNL5rqNPWYpQq88lt8NRnmy89mwim59T16VzPWijk6k08Z9667lPdFD15ujCzSW33XtW/2uHJoyuniEFO0RRAKyfTQ2WbKqyMrOBRpvGc8eatBak/ef1p9sm1W9p+sjGVkPHaVl9gNN6N3P2LcC04YNDWIGU94dkscuaDkxaFGfOpFa0BtU6qjzp9tn2j5J6kif77r2JbLrttouMv7ewVbGPYPvjZG+j8ggM97RPcL0oSZaU5/WHwYVPa9CxinR8MbMGmcJitAnX5jbykwbtipOEZLn6WQMlaenJrLJvlgnS6utq9q+kszXWt967Utk1y2qywAZJSuUZRjdI8Ji9Ubz4dXPzoUsP3GoYdbUJ/Un2+bToMdDpnnrnVn/0bj9sgzNvk69/a7blPdyABiD1pncOzx0mc8C6XdyUcsFuO+v8NCZtq/20L/r2pe420NnjJmUoZ9oeWOk7708nZaRa8nSmDzUTK2pp/SHxWkb1EHGQ8+ME7MfRv9tbeLPLOQgXGHQphcDmhCU1n3EpNOm+/F66Os/Hb7r2tf9ZB+VdnXIHh7kPdo77BixemfJLzsGsvzEocZaUyj+Cf3ZV+bdoSdBj8dOY0ndKo/WvU4fI/TOn1nodDkopw3bCQ99y5o8mWr9riCy3c6+f6KHfkoHrc93XPsS7LqtGn0J1I4OMwdgb+yQrtE9khORoCXHwuShxltTKP70/mww7w6d3vtVVJwSz95JeHviF5r47DX1ZxYyXQ7GSYM2vSAyEyPTvdOoJm9E5tP9eKKHHh0uTuG7rn3dx2jdSuPT0YEx/rptT+8dMmPEEoE3F1EbDBjjzh5q2PX0Kf1BciVOOzMTkOOx77OHHms/eOPW9tCrn7FvBbOGDaV1JveUh+4ZJjkxOz3zJnt0P4FoQd7RtjWOV2zu77j2JaJ1i8a62rZn3GQa+zTLGhNrjJj9U5GVGY+pQ423rqz7J/cH9c9Ku2LfV5H10HfZiMwj/PIWQHRNvDCH2pRpskNeqMsxg22Vzy4Ui2SyhsoidakPWhgVnSM81UNH4RX4LmtfIrtuUTuV8beMG2qLQYWwIr09mZ7cjM5Th5poXX1afyI9rtrzHcj1ve+Z8dHjFI2bzP/6arwUd/qPXOSgsGF2sPWAV6HlMUaRkSXloXxt+D05EbRu0YKM2sm0z7bthVfgO6x93Z8N1muw6mfbZbwSXd477DJjh/T2+oBksvEIk4eazJr6hP7ovqF2rtrzVXhOyU5nn0Bq7HRE9H8R+q5g3VevbQQjeTJfDgizWbMLENWpLpSIfJgFjcgJGd0d//nzZ1tvq/6VHjrb9p1kvvWpkPnT1r4Eu26Rsam2yxgvZLTQEyx27Lw4QoYEGXlI/uShJrOmnt6fDb2/r97vHUjbte+9dY/GDDk6EZeMfm0tMmjeYpsybJ0B70DKsyaPlbGBTr06jvTuGncpo+KhV4181La3Ka7Ad1n7uk9rxR46Q+aeLp4RY4zZhiR1NI5IP09PT2c0H568zFycONRYl+7D0/szqdddyDol3gEo2hMSv3aFU9fEV3qYUMezA16RYcmrkLluOyLzKb299mXaSQ+dbRst/kgWA8uDsGR/+tpHfY+MCzLOmXbR2q4+mtWkbpGWbje7Zpm5yMg+cajZ5aOnQJ/UH9SmZfcsmRUuqNZHMqTt2vdZMo/GDYHy0Lfw7DX571RMmAEa8A6kPG9irMdMFmFaxK7LWfdZIDKVfdBtTLYfta3D6bljy3762kd975I5owc7nwx+/vz5h5HX4+jpl9Hbmwstg5G10T3USFnsU6BP6Y+UZ+VH6RmckJHx0Hc57+kGC/jTr/qeLVM1aGzbXogG1cNpD13GrdOblSYXs14QqPzEgoz0uKL9qG3GQ+/OHyMjWsNPX/uo35ZxQYbG0iFCNJ9ZeETFjBFLWN5cIFme3IlDjbYPmXX19P6gNi3vXOvA6Ovp0KlvyYjGR+bJeHVPHPkM/cRXeuQg6cVWmQA04B1YE+iV3bCI01oA3stw3X5Yp0QdP9F+1DajTxVZYvrktY/63SVzRg9vPrNgvHNmnUZ6M3NgtWHJk/pXxgGtgck1dWd/pCwdZ+tXMC2D8dB3fsQZOx1d+mnvyC/FTRs0b6Na93pAI1inuurEZh6X6IlHi9d6RIN0nViMnm7RWE21z7S9y0Wn+goY4yHb+tS1r/ssZTFeAyIFBmht67YtQ4VkReMVrVN2/XgyUV1vTCYONajt6pp6Wn8sexits4nDfWb/s3owTzD0/pLlI29dv39g/rCMjDOLo3JpA2jJ03rJAasaNDmwWmYF7IK22mTI/Ovrf19VY41UFmhReWM12X7UtqXLFLLk5K3lJ6993Y+M1xDp5CHziNF7+3nno3HTukXrNNLbk+nFI3nVQ411kM1cT+4PsocMJuzBtAzmaSvii4hDvDbdt9x1nlc2UyYygEiGVDwKGaCBzcrw5FlAE94l84mFyOhnjdVU+1Hb0fh29agYkWgtP3Htoz6zjwAtUmDHa7dpyZewSH3rHJFUNEaISDy9kcysrMyhBslBe6RyPbk/mfaRHlVMeuhbF+8Jhl7/1rhFkDr/9VKcjGcXxoRB6xq2zCDowexMJpoobYwyJzYUyn5WFz4DTxfd1nT70ThMtYNQWUOTZH7V2td9YMlc65XVwTJ2kX5WWjRuln7ZNYvkd2StNfeujWeDo6dAT+yPjGcIdsImTMvwnBC017JEjtqEn6Fb8Ymr+3Ue2YGOYTvpoSPiYb1wCb2oTxh0BE/P0+0zY8QajCyqHvqnrX3UZ71uvTG2dGLGi32SJesgfZEunv1CsuR91AdLNpLnycocaiL7IXXJXE/uD3oiyeDJHrpn6zN7wYLU+Y9H7jKeMTRsmc5LQ0hPFGYG4ZSHvlb+ZTeG3CMj1V2MUnfZJxnqtqbb/zQPnb2etPZ1HyxPYd/rHzGx9IuQJXMtF63B7Jh58UiHaP8x8rw9LmHtt6yXXl1Td/anQqwTNmFaRpXM5T7cl/c+iWzTfOSO0pjFcfrS+slOZSYkIswskLydbi1YNrT6mTVMLNCi02mn2mfbtsb7ajBrdurtd9nPztqX8B6373v9y2zohTR2rLzDAtLNIwF2vNCY6fuoDxl5niz2UCNlIKJDXrqMT47Plf1BNu/TwDolyJu3nkCyP5Hrfm1NpllxZrFkL+ZnDLU+umNZIPlVRN52Vi8dtzbh1CawNpps51T7Xts6/gSwa/npaz/yGrZBQf1h20eP91lDhQw+Gt9dz7JbWi47bp4dtHS25ESHmsgJkO1EayVjp+/uz8Qj7yfA66te//qekSuhxww+ckdpXpxZLFmDFslBndR5FvSjcU8+A/SovfpoXQJ9hsQYqS4sA3+KUNi2tWHQ9e5CZi3fvfYl0BhG69Hb6xEs+YjUo0exGduj41onGSLoQ4QlLyNLG3Wm3+zhOmtzn9qfTwXjoct8Ft763YA//RptiGhRMAsnawB1Xdkhy8h5sAayuqC6XjhLUNYG9upkES2+k+2zCx/pcxeya/nuta91zxoVT08PXjtZEqgQlbVOI929eajIQg4A2290uEF982xu9B3+J/Tnk6Ftk/U0IssZaHz0vvnrkfsOmYWB0qpX5es8Xkc/GdHbnRNGSgMRKFqMJ9pn27aMfra9E/jEta/lsI/9PB0ryJJApIc1brrP8t7Tn/XQvXQpC7VfIT/mgM2M11P6c/cenoLV97XyB2gJPT6ondIj9yg/Y5hYg+YZte+EqF/RnEy0yxDqZPts29ETgzvBEswT135GHqMnAuOdZ0iganNQf7N7DsnMyNvIfPxm6WXtwaydvrs/T9jDU7DsljUmOz3zgqjl0KQfuUf5qGxknDKXVP67gf3epbeRdTyD6LMfmT/dPtM2s6DvRHYtZx/L7zZO6t/tZxZVEqjaDa0jM6563UfzYY0FejSd9cY93b2Qsbt39SfzffNPAPOxobRrO519QVTfS1v4S2Z6C4DN9zaSt8EyG/M7Qv6/MwNrXjrY9SMv2TICnfbZtmV5y0PP6uEZy0x9Zt3vq/JVtisQEVu0pzNyqyTA2I8TnxNHNoyRWX3cKvVY638kmFkXlh2/sz8WMbGw2q/s32p9LSNau5LENSxSt8ZJx3+hSd4hO/GWcZ++TuEqYxm1n92clpGq9sfyki1dJ9vPtL3LT/S5W1fXZ9bxU8kc9WfDMrYZ/aThqQL9nwFjczxdoz5oY+2t/SvA7L1MeFd/dDuVdid0nZbBOiWsPp6tk3vqx48ftoeuKzBE7m2gyKihMicMGjodddrxTlsMOo+bpjeiXhzakJ1sn23b+mzK05NB1NdsfXbdT5F5xVAwZfUYd9Z6F3qvdG0SkuXBmuOroZ8+yLCKq/sz+ZjdO/hXZHRtiCbzHTJ7Bx2c0WFBxv945B6d3Ji8Kul7G+4EmctB8Yi9K48NuwRoGampBSnj3lhNtM+2rRdzp02JycPQvmfXfYfM5ZqSj4Wtctm+osPUHbB0ztoZSxYzbnf2f0M+oZgm9aswreeEvEkZzPsPrB7e43VN5l9fXzUPfYfWBrLyo3q63dMLdHqDeqQuMdU3j1Sr8taKvWDrc/TuAYVt28vvHmaqMixdsoTDknl0wEL4/ft3yvjrOdHtXIWMN2fZKWatWv16ApFvndF7DDsuyz4VJ16Ae7qHngGys15cp/1ihLOkjtKqBo1F5UWK00CkPrFgNJCh6shHL3ZY7U63n237Ezz0HWaJPEPmWb0n1mFln1aROXhEdoo59JxC9aM5qa/39EeW1XWfglP6TMidlGF9bCif5KIX3yybxnrpX1/BS3FMaOUxcZlWIfUnnJwzOGEEJw2VXhwyXUIblon2K20/1UPXj76n131VR61nF6dJfdqb6+rb7Wvmozm9ptFTErS+tJ7W2usie0A5+dW0p3roMs7oKNM8L1zGdQg9dIbEmbJe3EtjyXzCO+/Uzyzqk6fTKVJlHxMhg9FtP9v29GPgqfqWXpPr/imYPBxonDwoVJE9dFmIPprbX2HdbaLDotRHxy3ddXjVAeX0XE4dTqdkoLmL2st44VK2DuFn6KziTGjFdRq6tyDJPDMR1kmps7iZRX3ydJrZ1Iys6GS51t/GZ6L9Stt67ei0DKY89F3fejyq09C918YTPmI68fGRxNN/aGSCDNeKiQ/tCd2+tb4svXV4+oAyNVZR+xIT+7erR+ScyDYyZB7luZ+hIwUqIeulZFA1bJqAZf86C89a1KeNkzWmFcjFES3G6cfu2ba1DtV2kdxufaTX3et+Cqe9rq7cibVwB6Teljcmy0aknm371AHlinlAe64qY8qGeDYMfaSi0y3Cpjx0rRQKu5j2Uro49egSkfnpRY0IoyqHOe2ixdVtv9q2bN+qw0A/yqzWt/pw57qf2jOnMXX4tcY6i6vGTfY7IvOdNk3q07hSH4sgqzImHCIPUn7G+2bk/0foE4NuHQa8sOqlPB0TBFtpq9MmO64emVfbr7Yt06dO1536Mm49Qeis+87cPnnfTO+T7lhvyCd6J8Zey4zIfKdHpH4SzFO0K/WxbEJFxpRDtOP6bfaIuLUu2fDXxIB3ZVzhpVwN9GtOp7Anc/oQYcnxvOSp9pm20aO97um6KgPpxZzUs+v+KiPJImNIrf17xWfmEwe+qYPBBuq3tb4lOfz4kfut+hPQ/ZHh1fo80UNH+yLzSF32JeOp05+hPwXdk5hER4Z3uEC/5nQSaGNXvD8Z9+ojMu+0X21bLu4neehrzX/W/ZSDrDRIVh6CJADL4H0Cuuss6jda3/t+/3EH+vngrG5T465Jfeol2Qye6KGvlX/ZLfNoHen99QU+Q38qNIFOLJbpU7d1Or3ihDoxNtJwMOWsRdc5UFTanng60PWA0ckapVfkTpL5lKe6weqGyPzpb7OfQHdvef8FsOtlSH2S2K2fpb3C/m109++EDZG67PQKmWfJfa0P89C3MahOmDUoE6duSeZa5id5IRljwD5aPtn2vp/w0qWsDrKbkJE36fGzfbQOiqguc/B48kcHHibHnS2HxlI7DB6pZ3Sa6J93sLhynqcPrFVUCTnSLZL7UYTehfYQpmFtrIwBrXq2E2A8plNj1227Q+osaTH1T2zkKVTHRh9Yu2MsdanKiepNPdLf67JqN3a9ib2l7UnXSz/x5Odu+9fdvxPr+5T3bUHK+KcIfeOEkY02VuZ0nsXdHvKEDlNtV0/YXRmnibvTr6nxyRpoJEOi4w2d3E8TdbWcKe9cypvy0jOwDjZPsn9Th/quHhlMEftHEPq0V33is7uJjXVqE062fULHO9t+Mqbfjch6LU96uiDB9OPE05HK+E/sf+25MoQ+rZvWB3188sn277vgIwh9Lfw5dQUTi8b7bLH6yGYCV7aL2rmq/X9p4099fp5Zl/oFticR+78w97uP+iAWEejVY8MQ+otr8TGELlFdMJOeuffZ4t2e9tWkPuFFTrT9AqOyLp9K6BlMrcPplxJZyPYYL/0uPEmXfx0fQ+gTHsppvIv5enx3Yp9YT5l1qQn8LjKbwsSLVneMgbWu9VzebXOepMuLDyH0Jxvt91R632NQuS4m5uBp8zix7jte+Sd+xWwK+ic7n9D/uz3h6KPGu/EUPe7Er4mTZ1fG1FdM7sCTNtgdeNJhqzIH3RfPOnOQWfd3vhuhP1bKYurlvquBPiO+G08h9Sd81Ij2XkePqfVdwYT9/PoSvxQ3SeyV79+xbT9hU2l0FoKWU5HxBDJd615PvTMG3Ueq2qNF4cS6Pz2+7MHiXyL1J6H7eHvC3lh6far9m/wmSWQDvLADKeOvR+4W+U40ioya9vCitu42BFOPIvUC6L6w1x2XCslM6dDZ2E96QmBt6Il1fyWepMuL/2HiKcm0vXmK/etg8h0Jj9QnZEdy6VGsnjx0B9FJBqU/GdOkPoXuZv/9+/flcyDb1p9bfiqsPfBJ6/7ug7OHzHh1P4LpYmp+u9651GUST7J/T0LEg5WQwa9dIeONW168543oePYx6dMm/rssZEQqd30W1mm3q/NJArP2BVv3TjztCcgOT65RuR+6T732dadH/C8Q+ZMPoBLTJK7Lw7fcI6Jmy66FyTxSePqE7KFLIlUPoLuoJzbF3R7iZLuduejIyLQlQ6/MZHsTh6S716mUc+Whs/tYea35g9BdpI7q30nq0/o8AWg8soT/31vu1XALz5B51kvZqE6W9Si3c2qeOLlvOXeedKe8kW77d6HTd1b3zrrvrg8kL4vuupj2eFkZU2t64rB4hw7Tesjxf4J3frftmNJB18968TJ0v4fOkPpa+DG6R+BZL0X+B3AV6DRXkdEhYmvyn3Cy7Bq/u/W/C9pzRGHVO0f/L13RbWPLyMjSh/YOJjzeDctTv9vAS0gCnHpi0lkHk4f2CVK/y4lAmOrPvs+Ek4CfoUfed/QYPfLGf//+Db1jNMHyvmsMfv/+ffsjtKdtrik8QYc74W3oHa+u+65eU3Kn5vjkHpzwIqcxQebVdvWB7s5x+RR9JuRNkzZ7OHAfua/1t/cdeeGsNy6Nm9yAsvzpf0XryMhCL57p0+mTDNi/DM9DX+ueda/bXOuzn6gw9uXOx+QTRNF9GqjlSHvTkTdFelP6dHHiSQHzlO6UF1/y0NfiXnZD3rmE9pj1JHfhbdA7FvWTHjVNGa9PbV/iCoOiSd1a9xNkjsZW78tPAjoMe5jysiaIVOpUJfUJJ+RpTwSfps8VmCZxVI/20GVFhtgziu2yJ05skkSfgq+vr9Z7AdOexF1jM3nA6Xpjp42CNcaIzKcMpvYM7n7kOyHLevKxMf1041PI4iSeaD+1Pp8+T2h8K4RveuhrxY/UPTKPGtbl5IScIva7cOJJwbSxvBN3PbHoHCYq44/2hyRxHU4S+92PNKd08OQ90aDfRTTTJNydw1P63IkJHSzOrIZ/eOgyI+OlI8WsNASLzGXexKaYeBQ38VnWd8QTjelpIK+RCTeYMft0z+MU5Lj/+PHjvydeT8Rdn1lPH6ZOfH7+6ajagFM88J+HvlbOC9ePK5GC1lu9EuzEThi27uFge1TdRT2JJxx2PrntCXgbWpfb+LQ+3733ENCTve94AJog0omv/k7oMq3PBCafhO24DLvIHBJ+Vcg881k5InXthV+NuxfSdNvZ/qAnBVcaQ93+nYb4xMc6MrQOwJ+GyYPj6f4/hbieiKf1q6vP1EeWU+PC2ICTXnz6kXv2s3J5UNj3OrTyvis6BDb12N76CKByOOi2j9ZCRk6lzonN7LUn29Jtfsd1/2kfL03sBUvOhC534Wn9mZonKesqJyYTVuWlHrmjBq3H7dlB8oi+gidtCgt3k/qWdSfu+jztKZ/lfed1jw5tVRlXYPqwPCHj7vU5pYvlQHRkfRd4H89lCf+vR+66kvYuIu/cm3TPS7fC7mfeT9oYT8U7NjlUDcod6/4JmCaDp/QrwtMebz8BnzJ3EaYObei+E/60nvXrOLq0Utajw7s/z9oG5Q6PGD3efRq6BNL9DOwTYe2D7qM0ie/irU++o6DH8+nr5w5Sf7rNeZo+VVRtwMk9CR+5S4V1ByLvQuKJE9ch9Sr0k4InvWAk61/1+blu/445kW134G3op+BuT3Fyza/1N5lP9+/u8ZrAKZszgSl9niLjpA3IHhJ+Zr3vCNnH6v8S9HjeQero9H7lZre8xrsMzlS7aIN19tE07nwKo3WYxNTHFEjune92TMrbeMLTAomnEPKVNuC0F//TMjwZVD4b3+FL9DymNruUIw3hHaTebTdb9+rH0NZHUnet+yvm+c5H/ZX+ndhXE3Lu9qqn+yOf8D5Bn6swTeJe/V9r9Qh5Ah7RV/CUzw41pk6B032723Bc3f5TPn89ve7vPCRPHNqu3MdTJDpNxlcfeDUm+qPrdWRNkjrS7WqgvnQI/9egbi6q3nt34p9wKpTtP2Wjd2VMbIiJ9ju48iBx5bqX8royp95T6JL6le8nPGGPThL5nf3Z5aefCD5hjibRfTS/w19d7zzy2u8knbX+/1f5mz8z2DkYyAXdWczoYHE3qU/grvbv9hAiVMdF/+/5hMwKpg4oG1eQ+t17wfooLIspmyNlTZLnnWSsx7iCCRtwag2nPHSWxK20u3Hnhv358+fYo7inyPjU9ief/uz7KLyir7oN2e6VY33CO9y4gszvXJdIn468Tp8m9Zmyf1NzpP/lMIuKDZBhB2OfoUeoevHfHXpjTMnoyOrI6H6UMdW+lJdpu9qupwezYa9e9919NznPV6Hqfd19wJV6yPBuOVvGBAl39Zk4YExzUNYGZGVn5bY/Q8+SuH4Uh/K+K56wSafGuvsRxET7VTnT6wxtPJ129bqfMqLdeT6FrZcMq22f0rVzuJjAFJlPYVKfqYPBFBgbcIUX7/+3qcApI+QRfQUd7+0TcPcmnXjRcHJ+nwzGW59c96iN7iPFu8C0j4znXdA6PEGn74pPsunTJB7VDwn9xKmK9d4nPt/oyEKb8t2of75sc1f7nzQHV637adw9xt7nlKdfkKtA6/vihQe0TrqEbxL63Z7grj/ltXRJ/d2s9xPpv9L+3aR+9zhrPJXALXyCji9qmNgbyEmcCulH7v863sdp/4e7+3332Hteoxd+Gu4eZ4RPH9PvhKk5+MS5rNqAK/r6EnoC0176E42mhbt1tdq/Sy9vQ38yvsPHTE/Sd0qXJ9mcaV2eok+l3VM2oHpYeAk9gUkv/UlGJ4LeeHd8Fcm7OnIndNPhpxL8iTE+CWvsn6DvpC6TxDclY8r+fSqZIx08G3CVF3/ZT79+F6ABr36/V8rKyLiSWPUGvvrFLY9gtg7V8TjZB7lBn/KSmwdrnLtjfGqtbt1kKNu5W9+Jsdt1O7ZiUo5Fwl05lfFBBPi0fTZN4oycb0Xok6dzJMuTzSymyJNgZVRIIltn2uuZaF8TzcTh6jsAHXIydaUMy9hKZNd61eBn2th6PUnfzvpk9t+VNucKXRg5WkZFjyfAWy9Vwl/L8dAnPIuujCyJIIM0KWuC1KIFycjotJ8pq3Wd+jGYavvVw8XEJp88LDJtZctXPBW0vicPcFUZ3t6zSNwi87v1ndBH65bZi1M2pyvjlC4snmIDLF6aCkMPfZLYM6HucKYta9JPkjor21vQrIz9ZzNZ/P79O1V+mtC77TNkIw399EcDLMmg8Ip1P9GfzBhndJrce0ju0/Tt2glZ1pJ3pc25QhdWjmeXZP2n2YArQD9yt4xQF55Rq8qbOElpWdMydzyLu7zkqU1RbZ+dB7mJTz1+i0iGlTG97lGcqWeNcxcdGWjvWXtnYvxO6Duhj3U47MqZ0OUuOZGMT7EBnuxq2P4M3SJ6Jlzrz81YPT0inaZleZu0Yswr+u1/CMpCt5fxlPd8Tf7iXrZ9RDbo19Wu2MhaNx1aBviKdV+BRZZTa71ah9GNMaR36zuhCyLzq2xOJOMuOVKG3OusDagcjCw5OuwQche/tiCGhJnOVckcPXKskocMp2R5i7D7diYjY8IzR4stqqPb7ZJ6tn1UV5K51IndyLLuCXikvtueXve67Wwd9uBaWetTe8/S8ffv3/Cwe7e+HV2QTEnmV9ic07pU5UgZ8m9QGRswQZpZHZlwSl7KQ88QfobMtWJ7QqqDr43/lCy0ISqyM/r9+PGj/Jm517ZnuHX7k8i2j8bf0okh8y5hVnF63Vc/BvOIUxvbqm5Te+/379/m2pGk/hR9p3SZkDVhE0/oUpVjrfWIzDuHtimgfnYJf+NXRNLVcCvDkPla/7ch5WRUDa9Hvh1ZaENUZGf0k+0w3mcGDKFWH/FPta/Hf636RpQkVSG9qpHf9dc6v+6nSEOTenetn/bQN7pjOa1vd161zNdDr+ku2/v6+io5SF0bsGWg+8mw9Bk6Q+pr2V8pYQx516ueloUmtCqb0c8i00lSXwt/nr2fCpxoj2lfQo5/V4+ODE0k2VDHPf0qa3XaQ5/UbWrvRTo+Td9JXfQ+OGFzrtalI6dz6KpiwgacRuozdHSiishcdyiaONRWBrrNSVnWpuiclpGMqx8H6fmRTwVk2im9rPWAxupueBvaq3PFup8gDXlZez0rd2rvsYT+FH07ukzKmrCJp3SpyOnYgQkbUrEBGdmdsPTIfa2/vW+LzDPeuZ7UziDpul1ZiMw7si39Tj7m9mC9WCRxktQ9T71KcrL+9CnZIpwoz5LVXU+TpKH7Mr3Ws3UtUrfKP0nfCXumD1hVWRM2cVKXipyKfZRzMmnDsjaACbv445E7S+pW2bV8MvcU17J3/Sys9rqy9KKoyvb0O0GWJ8jsVPuTc4dQXQOZ8ctu2Kl1r9uv1EPEObHWJ/eeR+hP07c7r8gRmrY5d+qSlVPdF/qJYxbTNiBL4hl5fzxyl3EvXAs/Ume8cbS4JJmhjVCBRb4dWbpvHdmayK1H3RPQRvDU6bDbvrVxuuPQ/Tgjo78XaplT617vyww84tS6VnSr1tF1rTmQ+k3ZkElbMWHPpmRN2MRpXVg53SeXXRsyZQM8Tpywy389ct+CKo/UpQLW53nyTVTtlaJ2skAHhglZyIBUZGv9dF1E6tPELvXQfZg+TLDt63Ibd3rnGqz+CCfX/TRp7Mva+1mZU3tP6qYPxLLsk/SdPGxN2ZyujCldWDknbFEVHRug5ZwI/3opbi38dnpE5ox3HnUQHSyqmDiNallofDqy5ZvkUThN7Dq0nhCcInYdIqN91/sEG1Ivna7Dysl8p3fX0yRp7BCRekW3ah2kk9YNlX2SvlN2QsusypqwidO6RHK+ow2o2goGf3joa2Eyz3jpusMM9OKvGifU7pSsyRNqlShPEeyWbYUn25WYWNDT0ATihdmNOrXup0lDh0/yeHd4YixP6Pt66HU5T/HMT9gAZp9WDgK/ZEKFzJFyiOAthTdZTJ5odZ+mZKExqch+ykLN4CpSfwo0aXibVdfzwh2fXPcnPHSpZ3dPTu49NC/TNmRK39dDr8u52zNf66wNYMtmw18scVsEXvXO9USi9rd8Ft6AdWV5J8uM7H+JFD8d6GRupe14FJ5a91d56Kxu6JCf1cXTDZFTZyxP6VudVyRzol86rSJjShdLzpNs5LQNYMpkQwn4PfS1ep+Ve99p3u14G1FOaNdATcpCh5+M7KtPnXKh3YGr20dzXpWj17hHKuyGtg6F3XU/SRo6nNqTJw4assy0DZnQd1qXrqwJmziti5aj9cviU2xAhqQZXXfovhSn46hBSwHrzxL0IkD3kyQwJcsyICzueoSETpMovLv9jKG2DqES1Q0t9UUb1tvEbJ8m1/0J0tDh9J6s6rbjaI3o+zv1Xav2bRFvT072qytjShctR9rIzN7/RBvQLWOFv3SCR+DIE/AmVf9Zgh407aXreAeyP1OydNrTHrN7hy1vsX1S+8xG3qh8nmxdWu/shtb5si86ntX5hKH3SPNOIKdAhjt+hb6atCNP07KDMl/Pp1wz255WMWET9UFvSk7lI6ZPtQFRWCXzr6+vv99ytwgbDVzViCBD5rXTwZQsLYfV87Rn7hGGTvMWaLftTvvWAo0+CmJ1jza1pV+0sTMbGqV11/3kIRit70rdTB+0Ia/0ATkgaB12IXXVcRnK8igdyd2wiMoi+Com6k966Gv9vdc9L/w72QAUMmWsfsO33HXcEpCd0D0xiMy/o4eu87t9s/Tw5ski8KxhYNquto/yd5z9KMjSmRlvpEt3E3v1tqfVXfcTh4Ed7xArmhfL8Oy2LEKsjIH15LBLfkjXCqmjPN0HqbcO2b0XYcImynmdsmVVMv9kGzBF5jrtr7fcdUHr3kqzoAfWmrisXKbdaVmMnp5nzixe65FSNA8WyXoLbKrtavsovlb+gGmBKcts5kofrHyL1DPIHAYQEcl0VI4FWivai4rI0dMFtafb8Yw/mjcLUk996XxL9+zhyHJw2HXL9EvXmUBXjuXwRPNZaf+JNiAq54VW3l+foa/Ff1bODhLyyqOJ6+KELFZ29+Ra3dRIX2ZRTRgUnZZpX8c16WiZa8X/DseQnNUPa/P+/v2b3tBsfmfd67mTyBBoRKRIryypZnXyEDkGul2kuzde0cXqzhK71pnde1GfvHaq0PPeBTrUsvP56TZgx2WI0lBo5f31GboWju43rAGVRhltPhnXoddeBSdkeXp6h5Uo3PXRJEd9iRZItNgm2u60r43+jsuQ3dDVteTpiTYyuvQYMPHqurfI0vMmWRKKDGGFVD1y1DKj9i0S3CEzlrtchsyj8cz2Q5ZBtlj2Z69D1A8pw/sr4l1mAh05yE7udIaTNj7dBnj5Xmjl/dKF0EnIGxD0pxNoEesB9kht61GF1ndSlk7zNq/Xz8gAogVt9YOddG8BTrVdbV8aVjmW1vjuetbXIyO9vX5ovaKNbPVPplvx7rqvEA9L6pE+lhG2SDXSMdN/z5ZkUSH0iNT1fWYcZZyxZXpfoLoTNhGNbdW2RvYv4iSJT7YBbL4eEyvvlyykK3lpGchBjchtqk3d/qQcpKfc1GgRef2W9bRcuUC9frCTj66ptqvtf319/fFHNdoQRsZQfz2S1RvJYnSNLkuWJTe77jvEwx6YGCOYJVVGR7Z9vW+qZC7bQnr9/PmTGsupcZR98spuINsqZVne+rRNzODnz5+u/bPskYdPtgFsPiqnwz8I3ep8dfLloMp7q5zWpQqt76QsnaY3LzJukeFD424tUE/HiEx3aC2Wibaz7cs1EhlIxshbBi4CS75TG5rpD4JHLhlil+1rPVhCypIqS47sOGwdkO2QcxE9gs4ekKyxRLoz/WJssJYn+27Fd7i9S0sOC6RXVk5k/1D/s/zzSTYgm++lraU8dGvgqgMaLT5v4LNtRvpMykH98sg8MnyRfoyx9Eh936NFyvSZbTvb/lr856pZw2HpwdaZ2NDe5u78PkGFgCJS9+KeHrtfEal6emudmPY9MtBz/vPnT5fUrzwgWe2z+wyNkWdbZZixOSwycpB9Z2xhdg9765Kpv8OTNsAj8g6pjxO6XnQ6LSK7jcoJ0tJ3UpZO05/dMP1Dhs9rXx9+LH0iYrUWznTbTPtr+YZ0p6EwA7a/0YbxLuvzNV1X97mKDqFHpK7vPV0zpBrprXVixsDSAcEidTQvJ8YyGkeGeDQh6r3p2SB9wOrsJUsnFllbGL3NLvWzxoLBFTaAJfKorJVmfg89M6B70PSGlOkoridg6yHvJzAhCx0SkCGLyJw5kco8dKr1yluyLYKZbNtqU+dHhtEj9Sw8skHjwG6yaCOjTdnti+6XR0BP++zX0rM63wwRWPoinSz9MnpHpO7ppcdQHz4i4pZ1o3At+7N1BshuR5BjU7GFma+oZW0XknXCBrBELnXO5sPvoSNYb7NroyAHBQ2wtxi1HlOnyGlZSG6FzL02vMOOpyeacJS3L2busm1b7W85VQNZnUNN5MiQMJtY37MbeZLIN5hxZMfWIrpI5yypZvRj+q/3GlNH6+8dKDrjqtuMxhGtUZ2vy3pjEJF6heSse3ZdV23hBiJ1OYfyXspj9UM2QcY7NiBL6lXSpwmdRTTAaIHJ9jsnq0ivqfrRadMjjkgvZoFGOkZkvsOTbet4h3xkmAWaI30vvRWp807Pblh5ybf3pU4TQGRSJXWkV6QnS6qaOCdIPUvmWqZc2zrfGiN57ScgOg/pzo4j0o0lc3YuZJvoZTkGeuwiGXtsPFvI6o0cS6RXxnZpMERbJXCW4K32kSyZNkroaKLZk6U1+BnjxyysSVmIuBGx63QJZoGiw46la4XUp9tGfbcuyzBqffQ9C2+O9j36fFX2v7qJT5K5lDdJ6lpPT1+WQCJd0Bpg+q3n03uMrG2TtjeIzLU+mfFE/fd00+W1zhkyz5J7VAblZ+10ZAu7/GONFaMb0mGa1LMkz5A6ShshdEkKOk3GrVC2rwd/4qAxIcs6eDDEXmnbG6NIxyyZn2h7Ley5IEMelZPyKvDmBclGa1f30dqAls4nSF23GY1jldQtMKQqbYNH5kivqO+7TXmAskgdza9F8gypyzhL6gwqhBwhIno5hhkddZrVNmML2RffIn0qtgvJ02GH1JnH8lY7Ufs6/z9CrwwoInKZLuNeuJbtoT8Rlv4eaVQ2jDVGqLyOs6R+om1ZPksyVj2ZxkL3zzKQHoGjOFqrll5ThxJLto4z48uQEEOsiFQRcUqZEZmzc2zNpfe0Rce9NIbUvfFE+qJ0vX8iWzlJ9lpG5kU5pm3Lplt1sxyk+Sdru5A8HUdEqu+7ZF4heCv/j6+tZb8eYBG5d1qy0rODfzVk3zwy32Ul2M1ntcEedjpkPtX2Wn//gIhHIBVSz4AZ+wyZo7GJdLJIogu9Z5jxvfOzX6RPRI5R3y29WJ2ypGiNtbW+Lb3YvjD6TBJ7xk5V9Iv07XxFrWq7kGwdevEqmWsZnkyUh/T566dfs3+4ggZY3rMbqjr4V8Ja+NGCzW4SPXZaviebIXUUn2h7rQV/wjVjxCNCz0AfBJC+7Dq1xqZL5tm+WWstQ+oZAvL0QwfXnY6MbUW3LKrziPqRCVEfPxWMp84ckqu20CN1uZ7kvYwztivqD0PqHglPe+ZMmbXAb7nvAWU3mDXAlpH0Nhkz8HdBG08UXwsv2MoCtUjD2xBZMp9se62/XwJjDDeT10W0dmWZDAlEnoQe34k+6fnIkrqMs6TOICLJSIcMqXvrsDOP3bXWOYg8DZGdYfppHaAZWH+4gta/jFdtF9KRJXWdXiXzLHmjOCT0TMejAZZxZIC0MerocwqyjxGBW/pnF6i8ryxGGY8Ivtv2jx8/IJnvMCIVfX/KMFqHR+awyZK5HK+IzKt91PsHycwcqryDhjUXei0hY4oOTNaYZAjdG5epeXzhe+rsoSpTD7XvrT80x/K+y20yzpL66SvSp0TokpB0mhVnNttaz/XQN7TB9u41KgtUL1ZWtyqZZ9v2SCtDKkjeKVjrNrt+WdkeqXf0zx66dGgdqqy6Ghnb4a0BlMaOsdRlch5f/B8sUmbsQpfQPZ0ie9WZX8uOXkXqWq7XjtYnReiIyGW6FUfhWp/hoWuyQbpvVHW3xsxqJ5Kl41aadcBi2kYGGYW6jg47HlkF1gHSWqOofqSnlo3GSccz0PODDpZsaM3jKXiHPXQfyTk5j6chDXFHxoQeHrzfwNeI7PjE19Mirtl6dMaGJfUOsU98zq4vitAjIs+SuVV+Y2Kj6Y3ehV4gaNyyC8gaP2uMGHk6zpB6tu0sYTBlKqQeGRUPsky0VtHYMLLlGpwkTcuIVQ3YaXLLrIGq7LXm5/EKIEOdCbttZ8uydtqT3fmKNLrv2k3Ulo5Pk3qWzL32ZNwkdEk2aGAyAxyR+lp/b8wpdGR5OnUJ3Ro/b4wyslGo06L5Qm17n5VboZUnUSFzuaClHKsNXSY7rqwR0mva80YrkPJ1fHLvXI3sGjg9j7KN0/BIfUL2hFz0/X6JjA2vfkXaup+wm6htK+wQ++RLc7rdX96frjADLNO8AUbhWnhTTmwidFCoQuqL7nWbkV7WAo3GiJGt4wyps213xjIitwosAjthhDtEow81kx76vrcOES/+RPUpAHMonYBFHlYeE07qJUm96qFvZL8ijXhmwm56ukfz4ZE4Su+QuZaH5Jc+Q9f33qMPVF9PzFrP89Cz3vlaMwvUGyMWGTJn22ZO0xmvfYcVgtNEvo3MqTeWK08Pdj00911SYA6XWu+X6GuHKeRoTBzOrMNopMcUiXfkoTw9Dqwe7Fekkb2S8Qm7idq0QpbUr7zWIl+K08RtDSoaUC9tLfuxWcfwTMpCBtMbs84CRSHbB0tHj9SZthmjNeFxVkl98vCHUNHN8tARwVZ1yqyRSSL6VFT7fOpQuhb2tjqhls2WrWAfoq1+T+7LLJln7Gak99NJXbfpEjoicmaArcHeA6zLoYGfXhAZRE8LurqxizLSw5OPQhSP2n664d8L+ZSelXFABmWS1JHhsnCSiBiwep5Gdcwz/Zzw1jvEO3UwyITW3uu8zS77I8f1tN1E7UchIlV9n728x/KRbEjoEZFXyPwTPXTLGFUXiTd+qBwiBk+2dW8tyOjJwdPJfOOUx1n9BTF0kK38RWgkX8ar6/SUd7llPwGdX4K78kASEcZEeEJfC1VSR/xjcQ2q11mDrB2NSL1K7JXP2GU7f/x9qndq14OYJXOvPBr8yQWYkcXokNWNXaDeos20yZC51kO39xRjzKLjcUYyd5yFZVhOeOjeuriCjBDp7LbRweYOdMb8hP7RvKHymXBSVrfN6j+oWWkn7SbSRcdPkvrE2++/vF8vkwM0Qea7g8hDR4Nf9RwQJmVlNyOzQL0x2vcZXaPFaM3Pp/0Uph6XKVJHfzKT0cla6xMHD0QwrMdyCrqPlfaRTYjKR7I787jrnRg/ZPQ7oZZ9IrT6ESEidckxiGdYrlkrbzej/mRJ3SNxK32KzL++yM/QqwPc8dDR4HbAyNIeqlVnYoGiOBoXNE4erAUZkbru/9NxisilrAkP3SLzjr7sWugatyzQnCBDaxnjio4eMU48FUFzOQmP1LNyJsNKGx68r0d7h1EZP2k3rb7qe4/MrfgV126P/gzdIncZZ8I92HrgO4M/gexhYmKByjgaI62HZYysOdTx70bmut9TpN79vDvyGLqEjtpDsAjzxFx7X9fUZIj2vGWQLWiZ2rAhIq/0W4+ftoNTiAhjIuzqMyU7ajdyGmW8YzdR215altRPEHvkyf9B6IjIrYGskLk1MdmBvwJTC5VZoNYYrZU/8Ogy3kY8ZdyvQvcUjuD9nzsLROb68FQl9Qy5WN7vVQcKdLDRZOvNoXdQ0ZdMt+auO95atyv2ziSxTh8Qpkldr+cMmXftJtIF3d9J6sxj+V9oE+gB9QbVGlhr4PdgTw38BCyPugtmgXpjhPRAc2S1reMvma//6lgE4pFBVr9It+ocbLnM/7F7ulQJTsqKPnpC7WtCrxxaLVLX8uX9jnfRORhZay8Dj0jvIOuJ/qB1kCHzrN1k+xDZ0R1GpN4ldvYz9v9eirMGmR1gqy4iex1nB/1KdBYpu0CtULbPEriVb5H5dwEiA69/FhHIOUMEwBruqw6l7BzqpwQTYMYZta/TPULMEt8ubx0WKl46236V1KfI9Qryjup0/nCF4Q8ZP2E3kW7ovkvqFWLPvDD312foLBmxg63z9mAjIr/KGEpYG/H0AvVCqVd1TGQ9i7Q+HXKM2HGyNoJHBFnvziKxqfXN/qnFKUy1H5E6K1/ur8l5ZNruysmQalZ2VV7lILCJp/KHK1b6HXYT6arjGVLvEnv27Xf4PfTMADOD7dXVg/Ykojm1QFH/2ZMmowu6//r6Gv1xE91GR1a1rxt6zKLNjDYWIoIKAVyxfjsHiwlDN32wqa5FS87EPFrlTxyUImKohEwbE6FuI/t/Fjof3V9hN5E+6F4TfJfUvfzKV9n++AxddyDjpXvhHmAU3/fWQJ5GRNjTC5QZx7Xmv3bheS2Tnku0FtDa6LSN1i2jJ0PmnfE4RQYZMj2Bbvsn9/fJeZwgCrnm2PKZcFoeu5d0Wfb/LHY95PR4ZH7Sblr9s+IeqVeIvXpt8oefoU+SuVVvrd5XDKbATPzUAvXGTqZJvbLjgfqDPPPo0v3pwjvodaHHjDGcJ8nc0uvqtW21OdW/TPunx+DkPG75Gx2yQMa9E2rZbNmszlWdIrkRz1xlN5Fu1r1F4Ez8JKl/fQVfW7Pu0WB74R5gi8inNksW2kvsonMQ0vp0T5pfX/YjdhS3LinD0j0z/3ozSrmy3xUgmZ7h2307RQKWXlcSqmxfH5zv0EHrMd3GSTKfthUTxDt1MJgIWf01r8g0Jn7SbiJdvXiV1CeJXT6a/+szdDTAaDCtAbYMt46v9TkeOiMju0CzY1TVJUPeTJksuW+ZUZlKXyVQXcvYRP2dhGVgribUO3HFPpfyPmlsI2KYCK+E1yaykzKdiZ+ym1E/IjLfYZbUu8SuP2eHv+WOBqtK5tqoy8FGg3/VItxtdv7mr7pAK2OU7ZdH6lZ+heStMdEHQqv/qK/VzRjJkR8vaf0/iQSqeFL/Jg9QJw9kXpsnMUna0weErC6RnayS+aTdtICIXMazpD5J7OilOeqRezTAaBAypyg9+BObJUMO2cnvLFArROU32PHIkLaVj8pGl350rccDbTyUlukrMw4Scm5QPyvIrLFpQ5PF3e1bqIy/NIBSRnU+KwfJbFta5wo84ryKnDM6Mgd/dH+13bT6YKUxY1cldSbfe/udeuQekXmUtgfXInJEjFNAsizPrfKHKzof3UeL0jvsWH3Q/UFkJfO8tA6pe59DW/Oq+y51qxhXPRYaem60blOkEun0FDKdODTdDWnE0PrNyumstyypT5HrFeRdkeWNp7SjOs2Kn7KbDLSM7DhYcZbYsxf9yB0NsDXoe2A9ObsMGjSZV4GWx8rK/uHKbosZPyaUumbJIkPgKM0jcis9InNLll78sv9Rfxkwh4NoXhl0Dx+dNjvtdvv9JHTmER0KroBH6hOyq/JOHxD0OGvbeaXdZMbBSuuQOnPfInStKDvA3qB75eVAe4M/tcCjdrpymQVqGQxmjLT+EvKPRHRopbFEjtKmri072pRTxO6lV8hAbsRs3e4BQhPQ1cQ8MTcndO6Q+l2ICKASMm1MhEy/5L2eH8QVV9nNDFBfdHo0f4i4dd4UsaceuTNkbpVZK/8y3OQGPUHkzALNjKPUU/dD6++Rua4TEX4mv3J5v1An+z21Bqx6sq0qAViyMnpNkPqW9YmYGH8tKws5jqeRbWuSXCvyMqTthchO7jxt/2T8pN1k++WlW2Qu+52NTxH7X78UpyehQubaSFtELgfGM8AT0AeWjozsApXx7BjJNnZ6l8x3aBG1lY/KsmSO5KP+SXTnKxrTChl0iEDP21XtTkHbii6eQOq7/mkgg90JtWy2bFbnaojWicUtTLxrN7tjoO+nSH2a2P/4pTg9CVUyZ09RyFM7iVNfT6ssyspJ0yNEXZYJrTyWyK10j8wj48ts0iwmiFyiQmwTpHEnqUujUe2LtbYremx5E4ejqYNFtu0O8U4dDE6FKI64hYl37WYG1lx4/bLCCVLPkjt85C6JqkLm3gTJQdab6jQy3zmXY4B0yy7Q7BithYlXgyXviLi9PCu/SuaMAe1uTF3POiD9a+geZGRYOdBMHEjkwaJD6lKvDKYOZzo8Qap3QfPKFJln7Wb38OilMfOQIfUMccuyf/1S3MQAo3APrkXkUwPPYLcl32Tf5K6Ng2ckKgu0MkayfvSIvRpGZSwCj/LZt98Z6INTFROEIjfSp6J7kKkS4VPHjj1casJgxzHb70nSnj4gZEP9PyHadp60mxIZW8PkTZI6c5+5zEfuzADLTmVOUdGp6iqgr+x5sPrdOQChehvVjwhYMMTPEL2MZ68IcpFX14ge5y6hPZGYTkLPQXX8JseuO49bBgtpNNH6Z+vu+06oZZ8Iu0Byp5ygyG7qOan2yRtvq49WmCX1KrG7j9xlXE9IVE4aAIvIvVPVJLobHxEBs0CZcOunx0TrzJDvidDKmyJ4BhOkLjFBBlfiCYcISWbd8Zsa/4qc7uEE2cFK2x0CvYK8p2TqQ5C2pdN2M+pXFYjIZbxC6lFehdipR+7MoEflZefQqUqmPwWIyGU6KpNZlGiM1sKe8SQYEmfKdoic7dcuh/7mNwtpAKaQPWhUCUFu2qftkwq6ZNxBZgxPHKYiYz8RdvWZCjX5nbKbsp2p/YHGskrmO2SIXN9niD185M6QuVVmLd9Dl2nWAHZRNZ4ekXvjZY2LLGONUdVziQg6ezjIyLPiMq1K5lqnLHmiPlXXGDJOWrZXdwrfhdCrmDjUsOvQMqqnMEnap0i6Aj1n03Zzx3XfJ2HtfTQ+moBRWpXUo3z3kXuGzBE5Ibm7DBqcOw2VXFhID2uReXGvrE7LfF6eJeiKzA6pe2kdMv79+/dfaVVZFejNk5G1dT/9XsQTcQUZZlAl82wfJvrtEeodpF2Rqclu0m4+yUNHaRkil3GW2HWZP37LXZJalsyf6qF3/nBFl9FpTJw5HEULcJLAq157Ra6UXSVzjSyRIh0ndLir/a6MbP0pMp4k9c5aytRDxrM6flPkegV5nzooTNrNtf7+CA3xywS0PETgup86zBC5vmeJ/eur8FJchtRRfC2fxKe99Mofrki9EZFXFyUq4+lwwgOfkJH13qfnFK2jDO4i1spYaCI8NaaMDhPtTul+agwQ4U0cRjxS76Ij74oDgs6fsJs6rtvoro3oQGXF7yb11EtxTLgH04rrgdADP3m6qk6qXigorTNma1336PUpXuG0d3wHoUlcSaxyw1pPQK5Ctl1v7J4Gy3ju+NQTJh1Ok+g0SWdsMiOrYzcZDz2rM9sndH81qUfETr8Ux4aWHNmxaODv2uxb15Nk/lRj9jR4m+gO6A21ceV8TpHK1UDjdocMRr5nXCvyMuUz4bQ8Rm73QNDlGlRvLc5RzAKNB0PsXVLPEDvKo16KswgNhWv1PHRrMLNgJ1SSrEXkelyscXrJfAaWIb2b1LcO7xzeg70mJsffM5g6PfM00ZLVCS29o7IZTOkaEVrWbjJ88mkeOkqrkLrMNx+5IzK3FnXWQ1/Lf4HhqseYHsFahxh9z4zd11fuLfZ/GWihyjxvE1+FKx+363bvqv/dDqORQdWG1yKLLqlXdX5iyGB76azd/O4eukzLrEnrgo/cGeKWg2mloTgajMzJl0FE0gyRR4/Y2UXp6fMCwyN1ibvHNUNyE7p2DhJoTzN19D791EMBMsaesZV7V6bpJ3RRm9bl6dAJ7wSro8c5XrjW53voO2TjWWL/65E7Q1JeGZmG4hvM4HcMl3Ux9ay0qI/IaN5NOp8Mi1CuJAakQ6V+RWfrQHNH/6t7Uet/J6nL3zFY628D++PHj7+epFnrz0OWyCdJe/qAcCL0OEf3peqhy/QsrHFmiN1KO0HqKB1+D30Xyj4GkQNsxdFgeN7yaVjGVi+yDJl/fb2P2CeB/vb2DlLTh8VM/Y5xkfvnzgMi2sdMnSv2MQttcPXYen1jDpReX68ahyrZnm5Xgnn0rtPW4j30E/1C91F8ktQZYk+9FMeQehTfOPV4hDU2EZG/ZH49nmb8N9Cauotc7yT1TtudulOHGrlHt7cu5Wmy1mux84SItZlMyPSzIq96EGD3LCIkr/+6L2jMdhlLj8o8sXne2KG0Cqkz9/KiX4qLFgQaeJm+EZH7aVhELvOsshGZr/U+Yu8CLdxJ2Vmg9Zw5NE7p3yXEJ2CC1KuEqudRHrotQpflWTKP7EqXzLPkzuCEzA3mEGD1D+XJugyXdPuD6lvEzh6OJkjdSnNfimNDXXct+3GIvrcGrQJvozFEbnnl+v4l82vxlHGteGgTa1sTTkcGiycdRiqHKU8XT7+IrJn2Pds3TZ4niP6ELAR5SJvgFpmmdWAQ9dUiciveJfUoD6WZL8VlT4nsKQqdqmT6JCRRW/I1Kes0TfZowT2FcL4DrEPUhDHvoKPHBAnd4Z1PkDpLll59He8eahA5TBC51d5a9SefVZzw4E/IiEhd1q146Do/C0tndH8FqUfE/scjd++UtCuc8NCtgctCe1BZj90ib52P6r7ow1sjFe9YQq7zql46zrS361Q95P1o+BPX2uSBd2q/aRmszGz7luOSwdOI/QQih1LnrXXN015PV3SfJfMdWmlZkv/PQ9dvG7Ie6yd46AgWGVvkretII/viDCwCnSD1rneXba+KCTLsHGKmcIKEJxDJnHg68YJDhtRledZDn9DNS6uS+Q4rcSsNeui7EHNiqnroMs0auAyYDeoReUTmbDtPw4RB78qwnsogeGWqZH61cZ5qb2Kt3U3on4wnHIj+FViOpXYYd3yt7+Gh7zBD5N79Xx76zowewVdPUd7gn9g4FpFrfVHZDpk/gUhP6ZMJNyqPK9EmrpK53kSnjLQ+Qd9FBne3/+JFFt7T4k/30Hf8FKnvtNLX1tbCb0J2PHRr8Bh4ZM3mWeRuyWdxgkgnZU0ZfW+d7Hx5H0GXm3j0PCGr0t7VkJv858+fj3+i8TT86/2/E4jU1/psDx2lWeTeJfXS19ZQWZkmlbzSQ5dEbclCBBaR+SSRTmCajJHMTLjWnxsOpe37LCaJXKZ3ZTPtVT4emNSlW18f9p+IU/q9pH4P5JrLcsvGpE1k0itkvsMsqUf35a+t6Tpr3euhR8ZTkr2VFh0IJol9kpQrczclb62/5x3lb7ny/iT0yfXUprfak+1cSYjTROQZ0o7MSTmTmBq/CTlTenwaNKmv5XOLTNPpkzp5aVH8KlKnvrYWGfIpD13nTwARudbVK+fJnCBjS+aJNiptVch819NpEidJDpH5ycMFausOr/aUDt4hvCJr6hA7qZeUicawsl6Qwc2E3b5M4Q590PizXPJJHvoOGVKP8r6+nK+tMUY/Mu5SQfbxSHbircmLiFwvhs5X0a4g48qhayK02l7LJnNUFuEE6Vlk7ulT1cPaVHcQOjq8TMqeOAgh/bJykFGryrJ0i8YxakfX9Ui9i5OHgTsPFlqH7+yh77AS1/dlD906OTFEftJLt4hc6yfvf/78+QgyvhqMXmtxL0CiOLOxJvvtkfmkLswG6yCzHqxDReeQEhmvjY6OWTnWmCNkvWitGzOXniOBZHhpWo9MOIGTMjtA73dJ2R6PTDwBYvMmyHyHXVKHX1vTylYeuXYej7CLAZGz14eKV34lCbPEPxVGba4VP0Zn4hG6BzgZrxB6di4tQ4zWehXs2FkbHB2kM/IY4szK1PGODKRXZU96pNvR0xtHz0AzIaPDpLxKWxMy11p//CuexS0yDcmYAJKnbY8Xz4SV+L7/pRXNGH2Zh+Jr1R+PsKf2TdQske/0qV97myDVu9pf62/vOyJolsA7GyqzObXhtQwqo09GZ2tzyfVYmd8KmXjkoONZHTxdujpm5XgyqnohucwcMGvSW4deHIWWvlHZLq5oK5K5xyfjoev8ij5svrXXorHzQjau7+nfcs8Y/R2XSmY8dDRoCN6EIcL8+vrze7ks+Z0Eq0NGt0zd7LxG+Ru/f/+mD03ResgY18iQorod4pVxLadL6lVdusgQW1bmjk/IQOt4Sm6n3976Y+MyRGlXhRPotoFsj65v2Y0pIFlofqy4Nafe2LBxeU+/FLcrXOWhR4jIDOV3NnyHdK8OWZ33mKAN48VZbzxD6nq8mTSZhzZKZJzRes3qKcdu+vC3HzdmddHpFTBjV5XZkWMZwc48IrlWv9k2rDWIjLAV99JQeDWu1Et/nr7hzfsJDz3aCyjupUXzy8b3feqluC1g0kPXeVVsvSyvXOp3lzfewTSpr1V/pK43lLcBIlJHm9Mqx2w0tNC/vr7+0EOvlcrc63ZOrR2WPJAuVb0iYuuOV1UO0qs7j5ZcrWtGZ28NsqTOEL3WkSXYqXASTFvy83RdT6dP6mnJQWNvxdl5itK8tfr1lfza2lrxd8+zHro3YBb0hHqPNiuG5wletg6zOu6+6zSdp+NRPuudS1ikbvXNmxsv3Vr425Duta7Xa3Qo8fSJ1t8EGE9d97nTLyRvQq6UWZVzQi8kd1/oEMi0Ya3B6NL9iow9k8eEXVzVvuzrWnPvY7G6oDKWPUR9ZMaDWQdW3mUeupUm01nIzYXqyo1YJc1JsO1X54El80kvXfaNgeUho7atMfTaszaB3gCy/ISHvnXf4R2euu7zCY+16wlbxq3joWsZU/2VcesQmJEXXVZZSyfdBmrXCyOdK+FJoP7rvLV672Mx7XvpmTgTVuPpH5bZArIeupXmDZgFb+Iqf0Yh65/2rifa3mPA5kVpOo7SZLrWm4VnHLVu1hh56dbG0IYg264HeXDcYZXYkcGS8Dx11Peux4p06sidkKP1mppHjzQrOltrkLmQHt6aRjpbIVPmaaGG3AeMhz6JyAZG8UxYiad/WGYt/Nhdpu+47pTlocu8CrYu3g/EPD2U/aiW1XOD5sWaK6+sTpfovPh22kPfoV741hhm4dXpknrGyKEynX55bVflWuuv6qFP6WXJ0+OcacNbg+yl9bDWRJQvQy+PWWdPgmWjTrblpVn3nbFn14KM//U9dNk4QyZrrb/SpVLWCarqpaN6U98pnwJLzCxRT3vhDJkz3vlGltSZNWXV89K1vsigorXqybZwes15Bjzas7rOlA4T3lBkCO/SC8mV6ZU2rDVYuZCMqA2pq5WGQt2Hp4aTc88iWr+Wzez2k42bj9x3wRMeOhqIyon/qV450jNT1yujx59J0/EoHxFrtFlYUrc2YddD33l6E6F61tplccoTQPA2uNZJPhVAY8yMoc7XhrMyXqjNipwTelly5X22DW8Nost6Yc6qY8mVbWfSkM6T4SmgvTC9N60+RPZxelzZuPvIfRd8iocu9TjlIXlkegW5635GeTJtt6HTMvWljI3Ic95gvqIWGccM2aA0azN465hpd+sq39FA5HkV5PsIHpnvez3nmiAQ0FhmxsuT2ZFzQi8tj7FtWXnonn37PWvcs2lMHgqz4zAZIj06888gas/SLeoD02cm7r4Ut1bsDcpGOx66zkfYbV7lXUd6VGR7ZdB4V7zwjJeO5qizSRCpy3mT93o8dvvWmEf6RJuh0u4GIkkddohdbkwGiMwtUtd9jdq2DFFmvFAb6D4j54RenlyZlmnDkjdxSVlW3Mv30qwxmCh7MtT2axKRrYnumbWlw0ravo576Dpfp3mDonGazNlQ68zW3X3IjLdVT6bpeJRvxa05yH5GjrxHNF66X1b7Eig/Y2C0Dky76E12K+wQOzLGVijb0k8N0OFDjzd6Y95rPztPXh+3HlU52o5E6zcrt7NWPHlyfNkrqifzUVtsvpfmjU0lPIEr2/DSrLWYHb9MmrwPv7a2C1ppsgHrFOulaVjG8MTJa4rcp8ruflppctwigq4SuDUv1bfZvTHXaVs/q26kI2uErDFCqKy7DqlvHb0+IDLXhwnrELXl/Pz50/wanEUazHh5fUL3GTlbB9RHJCcyqEy5zFph5OmxrVyMHFkGxbNpTL+iOtW6TCj1nuIKy86gPO++On5RGoqX/pxlLfvxOjrFWifbaBPqchMELHW0UJHFlPX6gPLQODP5chw9Atd5+l6jQ+pMn7V+lhwvLdoo3vxp3P3tCWtDawJHl4Q1r4jU0Zigv1iO5inqU0aO10dvL1u2CxlCnS7Tqn1nDHJ0MZ+xW7KjNlG+l8bkWePD1qnIttqYRmR79D2ztqKQWUMyTv2wzFrY8OoFjghCd0J7DgjIICF4OnfJfrqOHsMoT49nRNZRWa9MNB8S1a+oZQ6KlpxIT8YIWOOv244OfXdDk5pH6t686nLWWpZ2AtX1EBlCS05E5NF8eXtDrwlEdLI823drT3lG2LoyL8wh/avE4KUxeV7fmZCBVVf+iUsXnj6R7fTGAaVlx9qKj3vo3r3uFLMpvK+lMYj6xshm6sgxqebpMfPiDJl7BI4WK7uhKl9RY8aF0YHdSJ6RsOZ6l73aM5ebMgJD5JrsvHb1/Mi4DrWxzBhgXceTE/XLInUNZIcYI62NpLdWmL5aBhnd7ysic1aOlYf6yZIJ27+ojDWO2bo6zPxLYQaMzbRsLDtuVhil7fgvmcgSkiwvG4nuZZo1QLJchYitDRj1sULGVTLXY+SV6xK/NdbM4vTgkfruQ0QQOm8DzRujJ5LhGXxt4Na67zE7MrYo3ND9s4gv2mNbdjRXO5wylkivHz/wOwERqcvQaiMaCyRj9xWtURZ6D2ojjOZ92jNnyqDyKN9LQ/1lwkzZjKwOIhmRTfL6htKYMEr7+hKP3Hcia3h1XDZk3aOOsyfsu8idKWuVkeOUIW5rjNn6epwRYaIFm90I1h+u6PlG96gvjB5oc0TG3ZKNxvEKRJvfM1Zr2f21SM/Sodr/zlghm7DWcl/uY0ldI9s/a53uyxtPr68y7hHo9GW1F8Ur+Zk8b2zYOtGYT3vpVtvRXvbilTBK++OR+06MSAvF18p76NZASe8i2kSWvlGYqRuV3Tp30zL5euz0GFt51pivVXvpTf/him6HWTsVQkFGf8dlqGEZ7dOkrucDzYnepCiNIZWI7Cp7xpKR7beOWz/Uk7mYfjJ6ShnadqE5Y2AZcmuuT15W20iPqKyXxuShMWHCap0MonrWfrDuO7qz47rjf70UtwtkiIUldtRxtBGzxCxlnyB1TyeUV0nTcWbMozi6R3MgMfH1tN2GRdreQZFBZNSRYda6aT1kmNXHkq/TrE0u873NWoXej1UZcozYNYLmHdkFi9RlfF/R9+69ti3PzTvo7bTs+EWGHBFm98rI1v2y0iw5Xv1unh4/bxyZ8e3CkuPZ1iiO+tkZu68v46W4XcAzvDq+1oyH3vltdqR3t87Wmc2rpFnxCoF7C8xLk5j4ehqzjqw+ICASZ0kdyUK6oU2SXYdyHNDYyDiaK89oZvs5BYvMmbbR2KB7K7RI3Zp3q+3dHvNVPW//VUiCJR00/9Y18Tk7UwbFrXymXCYvUwaFOp6Zq2wZz+6iuKcrOwYoDr+2tlaeVGRj1j3qODql64E5Seqor6fIXI9JFI/y9XhGhtOaA4QKqcs5kPfeHKF6EgyRe8bdWlM7T89Vtq+Z+nLjST1k3DOkDPFNwJsri0CtPR4dfLw+6b55BO/1A6VLXRm7oZE1/Cwh6nt0ZcncayMb9/Kzad7YZMt4czTxebo13zo92t8ynu0fs4Yu99Blui47QdCZOrvdrBxUL0rL5Ou4tTA8ckf3UbpG9utpeszYcUc6eaStX6DS5fe9BTS3Mk9vlGje2HGXedbcWgaUJb4KsWf2OTPGiDCjdc/0TfczInXLyHt9Y+2GnKsIrJHWF0qveubeZemC4pX8bJqeu6gMU3byJTlrXVn3nn4ojV0vKI3++9S16h66TEOd94y/3qjsxmPKRmVkP6fIXI/TRBzdo3GO0hEsUt991caUGVudt/XfyHx1ySJ2OS5W/9F46o2WmVfdf6vdaFOji/2XtwypswQu46htZox1iGwA0zcdeoSe7XMlZPcYa6RPXVq+116kl5Xv9SubxuTpcc6UzcCr59ncTDwTemnmD8uslTu57/LoXndCb77MJpJteyEj1yuD8qI03Xcv3iVwbyGh8hITf7ZiGXFmjvQ47PLWy05dUtdA69QaK2+upIxdzpOly8tQp2kDqnVn+4qA9p/sgxdHOnhtWPO/IWV7fYvK6HFAcxDpUkXk/WWN9B0X07alK4pPpDF52TGemHNrbVn3np6s7jq00m710KUBnCb1qIzswxSZZ8iaGVNrUeg8y1B6mHqbXbZXnZuIyGU8Q+oy1LpuMARszclaf39tT/bRapfd1Ho/MaTG9EHnWXOD8tG4emPMEmiF1HV974CRtTFZwx/tP2uuLcKR96cur91O3MvPpnXzrLIZWHVQumWzo3glRGn036dG8bVqHjpLAnqjdcpk82Sa7l8lH5X14h65o3srTWPyH9QiQkPjudaff03KErmVbpG6RtZwo7nyxsIrZ21QmS/zMgcVj9R2nl5HWh7a47pcNL5IHlrP2vh78Ig+ar+KLLF7/16n41lCZC/vM/asPEu/KF7J99K8sYvqITndz9MZe8vYdVbnTL/dn35di38cLIWizaTJXRqYLKlHZaV+02Q+lV+Jo3s9zl4aQpXU5RzqNBlHaRFJo7hMYy9ZF+nOjJG1GT2ZFtEwRmrHv778Jxc6LYKeJ5lm7QVd12rXGuOdp+PWOFqHFi/MjMEVQPOp7xny89Ksq/LCnJbvtWfpGumN4hNpzLhGZSJEe95Ls9ZARmcmlHH3p19lWhRfK+ehV4iaIeXISF1B5noMqnE9bujeIiZ2wa5V/wc1K82aF4tsI1Jn80/+4Ei0sa21bpW35ljvHXY8vEOEztO6snvJmgcEZAu8cfEOYBGuIvXILskDi0UckUG2CNJL3+vXyutcXptR+ahPKD+bxuR5emRh1fHsdBRHa6Xa/1/yJiI5KUSX0XlaQX2iZwxItQzTFyvP6yObXy2Lxsy71+UlKi++eeX3eGnjKdNRfOtdIeodj/KtS9ZHfZFrn/nBETTW1t6wxlDH0WZG9b3xQPcWrANKtM9kG0gXr68RmWvZVxE0C6R/ROpr/floN2OsLQK86tJtMzpm4pV8L43JQ2MfrUldl8mzbDmjj5WW6T/toWtj5RkyZJyk4Yw2ArNZumSu9WbqZvJ13JpoPVbMgojSNqbeZrcMLFonMo4MfpWombJWPQ2LYNBBRca1gZZtoP3AQOqt14JFnBbBVmDtfd2HzIFJ9ochcynfaus0wUc2J9MfWXZ7zhussbbuT1zZNix9mXgn30ur5mX2qqwbpTP23uMGJkRp7p+zrNXz0HUas1m0DqfIPJum41F+Nq4nFhkOtJiiBTn9Nvtu0yKztTgP28vLkr6MM0RgjaNFztpAa7lozaCx0HlanwypS2T6ifRg9qQ3tlbbLJlrG+G1F41fhIrNqZC9TovIZoeI4PR99co8ls/oYOmO6kRlK/kTeQjR2o3ssWW/URkvLdOfP762thYmcIu8LSLXRkmishmuJHPdHy8+SeYeuaN8qxzCJKnLedf3URiVsYy5lY/iVv0IDJnrOUdlIh2jtr18Vl6mr2z5fZ16T4GdZz2v2vYwfbbmkgFD6l478j/WrZAhw+w1/Rl7VherbyheyWfzUFrmrXfWBlu23NPD6ksUynj496kyTQqySF7ey7qMkYzKWHlSny6ZZ8g6yvdIWsYjctflmXSNLqnredyYIvMdMkRvxaO03Q+rb3revbHYof7anUU8aDyQTmgteGNlEZnc6Fa7LNi+6fZ3X7Ye0XsKLJFHfde6I52q4ZbZKSPJdeulQ480rXTrmiJzr11L72y8k++NIVNegrGrli2x8lGel5YJdzz8+9S1bPK2iFwbkqnNgPKm0nQ8ykdlvThL9OjeSvPSETp/uGIRuYxniRqFTH32AIDurf5lQi2buXQf9Vhq+ey8RqTutZuBR7JWedQHRLBemzrMkDqyRx6Y+Zd6M2vEA0M+HpFa6fLqkHkk39OzGq/mM+XYsY7mi0lnOAHFO+HXF/hhmbWWuYClAIvkN+QmZDeDLh/lVdK8PrD1O3E0TnpBRAtEYvptdtkeIkcZorQsUWfKeHErTSNr5BG89iqkrtdaF4xxYsH2E+mA0iKbwqwnltC9PmkbI3Wq2qhKqAlXjodHnhaRyev0H7kweqFyVpwte1WaB8ZGe/eoLaRTNjzioe80/Ua7lHUXmWfIOsqfIHA96ZZh9xYZswAlMn+4whKsFzJlKm16+eh+AnL9RFf38+bsvEpsAzXRf2+sM2Ns2Q5rztA6QuUy/ZBtajsgy6Ey3bIeGELLEOhdl6d7tS6Sg/In06w5YtK9+yjeDY956E8mc68fFVkdAo/ukQyNyqN07w9X7iDqqjyku2fso02r4e0FTxf0j3G6r1Kn3c7WDX3efAfYsZb9QuNr7W2mXdmWBzk3SJalhzXHTBiBlSPnPyI0fd+9up68p4/Vj2q8kl9N82CtcfZet4vSojyUdsRDz2wUq7yVF6Vpfbx4hvjZuEf00b2W5aVpdF96O0XmbFm2jFdWy4pIHc2FR+AyzpA6uhAscogILAJDgqwcHbJjvVaORK32ZDuZPmkbJdNZGzVF7qy+LGmitCuuqF0vX+Zl42zZiTQNb34jG57hEI93GN3hn7PsAhF5y7guVyXnLplXyJopm43rSWHuLcPAknWW1DOE3KnLGuqpMozB12XYPbBDRORWPCJ1y4BUiEJucq1PBZWxrpBoVierT1Ku1x/L5nQwTfQsUVrpzBV55x3ZjAyZF8WZstn6Vhr7l7hemneP4jr08izd4d+nruWT1q6MSF6WP0Xmus1KfrbPUVxPhM6L7nV9DdYwMKSeNfAseVdJONMOc+iIiHN/HCTTtTy9vtFazRJ5hlw7ZBdBGoEumD5Z4yjTuth9smTJdGs+vbCDpxL75Etzum3vPqsnI2fHK/lempwDb37Y+yjupXnhjo966DL9JJlP5Xv9ieJ68L085t5KY/I0LFLPEOxUOFnGK8uQgi6j16+1HpEcRNLW+Hqkbq0DvR8zQOOK2vCMGLvekA1Abcu+eHalA2u8Jtp4GrHr+4hg5ZUhcqts1H50yfpTcbYsmybv0VxEad49intpTPj19YX/PnWtvIe+DZUlJ0vmUtkryDoabB33iD6617K8tI3KP6LttmV4NRjinyyT6SciHDbUbXrE7RG6hrUvsoiIHN1be8Hbr1ZbmkSzh6YqdHvfEZPEOnlFunm6yrxqnCnbqe/NB3sfxb00Jvy1PyvQxsqLy/I7XiVzT2ZGXqb+ZFxPAnNvGbLIuJ14nI7qXh2yZb36Ug4zBno9s2DIGuls6VbVo4PIyO406xAi9wG6l8gcmqb69p3JfIMlyidfnt4yj4mzZZl8FPfsvZXu3aO4l8aGIx56h8wrxM3oWClbIXDkrXj3uj6TLuE9To/AEOsUMm11y15pwCPi9gj/BPS+jMpahgoZOO/gIutY7VuHFYvMmfX/4k+wBFm5Jj5jj3SU9x39LTkIEZ/J9J1nyURteGUy8UrY9tCZ75tPpWXyq2V1XA+2l8fcW2leuoYk9QxBdhCRbratjDy2jNW23rwV6PXBXrI+at8zPLocWs9aR69+ZGxlmkXknQOKtideX17kEM1r9qqSebdtWV/H970EImcd3/W8fE3gFtFrXXUb7L1H4CiNDcseujcIKE1v4qjNSI8MKUcD6cUz5I6MU4bQu4/UJ73AycOAJXOC1LX8rr6aaLwTvEVyWfJDBk2HGQL0DjaWAZX5sg9SntcnZB+QPqgvU6S+5Xi6fHdMkOvpf2ZD9xIW36B7lIa4yOOqaJ9b5SrEnomzRC/j/31tzeuYZ8z0vWWEmXwUl22iuAWvL1tGJo7uI/K2DJVnvKy800Q+SeAM+U7JZckyKmcdPNGcRmtc30d6RoS302Vo9ZX9nX7P8DL7VvcpWrcThO1Bz9+/SOYSEameJHMEj1Cj8lF6RPosSXt5Hh/qv8SV9a37KTLX4S85CR6xWWGUlslnDgOV8pk8D+hkJ/sWTWiUvtbfXvpVRD6Bk4eBaA16bXsGJCJTRK7smtJlvfatw6AMkRx94KwAGSpv36L7Spu6fxP414lcIyJeTeIZVMea+Vlja61HZI3qThO4ZTOsQ3eGyGU8S+ZrLc5DzxA0SsuSepaQGbKP5LHyM9eGlf5dMEnknfoRqet2LDLNtKnD7lzr/ed5zN02JDzDaO0XJMOSr2VGT0I8WO1ofV/4Y7HHcb8HVSH1E3pZ+UwacrqyBK7LZUhfPw3RurHxCpmbHnqXwFFahnStfCbOkv0pEtcX+pOOTPkXdei13R1Pa53rsEK4yGgwZJ7tk1VeGiZGBpIz8SSEyWf68a+DGYc9pleRuETkpSP9mTSLfNG9Fa946rq+jFvkLe+Pe+jyLeqIrDtpmfxuPDKOmbKVclkyf4m9j2lj5ZG6VY6RuZavK7N29QFGQ//k7YZFoJYcD1NPQryPO6x2PFnddTCxjq4gzmg8JInfQeRaFwTrYKjTqt64vu88ao/isvylHrpnqL4bqXfIOFt+gsxfYq9DkoFGx6h5a9aD58WiNryPvtC6sPoTGU+PzDNrL9M/q/5uUxssPZeZdjSJZcMOThCnNWde2SkS1/LklfnLX0tnlI766N13ybhShyF2PT4egaN8ykOPvHEvb5LUO8Q8Rco6b6pN78o+lu8YzH8JzDhlDL23N3bIzA9jgGU5j6SRAdLlmTKWDtm+TUGTuBwH3QdPJ+QZnSBtJO8qD9jq/x6zrB67niQfKcuTt58CMcTu6R2ldbxzJp4l/QzJ6/EZ9dBZ8vXyTqZZpMqUjWRcSdrW1f2M/SoD+6nQ68sCY+i9vWGVQ20whiwic7Yvmd//Z4D6NuX5SXm7rchLl3ohOfpC6boPU1781vVk6LXLzIksq8ek87Jc9U12K12nefeTnraub8lCZSL5+9sFUp4MUVrooUsFkFJs3sk0i3yn4l4bzH31miBzrdcdiDzOK2R43lpGLtoolseL1hQj39Nx0lu8ck3IwwrjOXuw9utuB5W39PFIXZZDcU8/hnBPA5EaQ76y3Ek9Ky++oXS9lqP7LIHrOtn6VpzNl8SeIXOUNuKhe3lWmkeybNqpeIW0s+WzV4b8M5g0/JPEngk3pvqB4K37HXrjf9p4PgVdMt/w9tVuJ2oDEbmUnzHQ6LBy2vv22tYvOOq9YI3p1evQmyOUh+bVu/fInSXzDmmz64bJR78HkCHzr68v7KHrzdTNY8tbBBnVQfUrlydH53XbzBB05TN2byMhoAVWhWWQuvAM3M6X95GsE4bNG3u9ASfG5LuB3V8VmYjU18qRuSXnKk/dInKrHZl/x4HS89LRPOq0qjeu85g6d5K5Xi9yvjLeO/TQdcdRmM3rlJ/M9+LIaFSIdPKqkjnqRyaUCwdtvAomPBNEishQeTprz83aHDKcHINIv0+GXHNdOTrUazojRyNrdDuEXVnrSAbrle/0aRLfcjNvsu96bLpO8+5Zcve882qckc+sKy9tLfyzsp6dKnnoTJnp8oigsvnZuCeXue9eE2QudaugU9dDh8xRPqszIvPoNDzR1zu8o7swteYsm8DKly8EaoOYMbqT5M5Ay2S9ciu9owPaI/Jlucm32TXZRvdWHkvgTJ0KWcv4RBrrdKzV8NCZMmxatnw1vxuvEmmlXoXMox+muTr04JVhyHyXy8jcIXPJshN4OqlrA1KVwcx9F2w7uoxcQ/t+ot+6zSm5e09HXrk2+ox+7B6IMPm77Jn7qwm8QuzTafozdhQe89D1gLL1KmkWgUZl2cuTE91Xrum331F/rkB1DaH4hkXyDOTjK5bId7w7bleOewbIkFTloP1/FVgyk+Wl4WTCDrIy0K/6aRn7PpIry504VE78jGvGO/dIm6kzHWfTpH5VgkeOyb5f6wYPvZrXIQNEaJm4V//uq/NYXiKa61MhswZQf6tgvZNJ4yf78hQvXY7hBHmhNXU1JKno799v3TrjPzF/kQz5ONurx+iyy1yx5ry5R3mW/UH3Xa9b18nEK2Qe1e+QuV7HmtTT30O3jG6lTDavkpbJZ+OIGDP3U9cTPmNHiNYFW5YZuwqQgZsideupQXR/JbRRmJJn4SpS2W3dObZV7PVsEbl+MmWNp8y/8vDojbnOy3jjun6FwCc9727+FJnrvH2lv4euB6tbJptXSdMEMB2vEihKZ0l6ksyRXhLsetD1LGTksXOagfU4c62/N+aGtcGQV+vpVNG3U28a1oHFK48OSFb4r0GuaeSBM165LHvXOFqP3dEBK3N/JYHreCf/SjKX66LsoTNlEElky5xMswgiS35ITnTfuU6RudRVz3UXzDqwQmb8snpIeOSMYJWXm85qy5PpeV079GROGvTqgQXpw5L6vwS9zzzi3veR997Vx5or9mtqaF3otIx37pE2U2ci3vHCUVq1vLQpXpm/PHQZZ42sF3bLZNqvpGXyI2Lx8tgrkjH19jujK7sW2DnuwJonqWuE/blq1vO2NpWUJdOknhlYhGf125MxAevAwtZlr11etjG9fp4Ebdv0GvLI3SrntWURtM6zwPzhiiZf1Nfo3iNwhoR1nSjOyPTyJ8mcIWxP5g7/8NBlnDHQTNlumWxeJc0iumzck1XJn7iYA0DUh6vArj1v7iLZG1nSQ8RtbUgtP9JNG1RNdKg+Oz8VMtb1rbGzZGaJHBGWDq9eiyegbafsu1xDuoxVx2vHI2rrxbuM/kweWjvyfprAmTosWcv4FWQe5TGE//X1/78UpwcnCqfLRmWyeVNp3XiGOJnriq+zRXmToZ77KqSOGxlCzbaBxgS1i4yHVY4hugwm+q/H1dIHyWdJXV7MXp+YyzugbZ020BFxyzpeG91142H6Z1z12tr3pwicJeEo/w4yZ8uYX1vzjPB02W6ZTFpkmL189urUnbq6X2ez0iMwa0KXPYXfv3+P/H1odk7X4ghV/ycyIvUqkHfEglkfW89ofL0+VPqXWYtPgXwJ88cP/NHNhk7b995YMWWmYI09s7/lPUvaTJ1O3Mu/isxZ75spY/6wDAozZbXx68qttMWWt8gsk4/KWsbQu5+4pl+as3S25slDZv08Bd58yzSZx0J75MhrvQPR2mSBvlGw1t8G9DtijxVD5vue8cpl2auIXLbNpOu1O0naXn0Uv8ILR2nT3jdD6rSHzpSp1GHlVeVkZVbz2XhkKJl877riDXg55gyy64aZv6vhzY2VthZHVtYv2N0Fax6y/fPmaufdeWg5BTluEZnvOEvuU2tDy/v6qv8uO5pDvUas+wqBZ+pfQeYR0Xp5mTJe+zt0PXQ0QWzIlp2SN5lXzWfjDCFUrtNkbvUrmp8uvDm4GpEuFd3kpt6420NHyPQNfaNgwzJoVdx9AJKQ64Ilc12fSavq5h0Kqr/LrtO6ZFypY8W9/Ku8cC/vmIfuTV42PFG2Wyabp+MWmXllWVKcuCZfmGN10/3voLLWrLanDXxHF7kRPdls+klEumZlIUTGqoo7SF3rvz9e0OlVMtf1Iz3Q9fv3b1rOWjO/yy7vPaK24h0CP0XcKI0l492/CQ89KvvfW+4Vg4omM1tnsmxUJpsXpVnklylr5Xll9XXy7XdGD2sdMKjWk/WRjDs8NksXC9Hny1f1ARmlCXla9ncBGiOLzGV5i8yt/Ogg6HnbWq8MMm+za8Ke9LR1fWtdeXHWcz9F5myZKVJPvRTnGe9K3emy3TKVNIv8snHmypZHV+WxPNOu7tcLH9Y4IWNwhQ6TnvJ3WAOsYd1rPjK6Mu4RNzMHE/MUAc0hmlvvvkvaTP1OfpfMp73vqC1GbuqlOA22LpKVqTMpt9JWJa0bZ4k0c019xm7ppfvyHTDVF220dfxqD3ay3QkZE18vlJAGz0PXsDLGVsfRvZUm0xmPfApo3HSa552zZJ6tk/HCJzzt3e8rvG+mTFTnjx+W8YAILBN2ZE2V0cSTkVNJ88gvS5bd6/SfuXj3n4wu0UgjnP3r17vGL9vufjxb/WvbyX5aByerTctIRtjr2yLpKB6RuSx39WFvtx+lyXvW67bqPJHMo7wT3jcrz1qvrUfuUSjRkcGUzcidzIvSLLKLyrKEyVxXvP3u6fmpQPNbQcUgT7RbwbSnnG2vS16aWDRkXsZoynDHGe/biuuyEeFXIGXpK/s2u+dZ6/Ksdx7FGTle/mkyZ8tkiTpTVofpX4rLGJoJmZk6U2WzeZU0Ns5cXvmJF+ZYfXQ53adPgiSaO/S/Y9ye0J40aqfaq8rfc4LI1zPKOi7lWQeBSA+LoHWeBe/FN3Qo8u4rBN4laxk/QeYssTJlqmXluLGkTnnoHqK62fB0HbZsNq+SVomj+6mr+0cubD+eDm+tXNm+HLfTuNsz37CM2p2Q8+GRdZQv5WW9clkGlav84Yq1tnS6551PEvi0F47SPtH71nPjeujGXMMJng4n2srWmZI7lVYhyRPX5B+5sH19Irx1YN1PQhPdFeN09VxE+x95iHdBrgeGzHfolc145ajOdN+idHnvkbZVbiI+SebT3nfUVlVuRO5y3P/z0BHhTIQMJto4UadTppLGEKFXLiobXd3P2K22rb7o+JOg9bLW9ZTu2vhH+kyD9cynCKXzZy5XQ859hsxlnuWlW2m67snxQOvK88Z1nYqnjuLTZM6QplUvK+cKrzsja+x76BpVmZk2K3WZst0yVppHbijNi09dV/2RC5N3N9Cc6Dgq223TasNrfwLTcqODCYu714OekyqZy3hE5rLcVYcaZp7kfYXAmTpeftULr+adJH6vrhy7DrnTX1tDQIQ0EZ5o80TZiTyG/Ji4JdO7Tr79zuqp43fBewnO0m9K57v7ziDSkSFzVkZVhwlIXRHhyrhn1JEMT9YEiUtZ+8r8jKvnnU8S+ARxozSWhOVYRWG2rJ6LbOiBkXHskXsURopPt5GpM1U2mxcRIbqYMuiqkLn8njFTXpdj+nkHdLtIF2tN3aXzp4EdJ6vcNlgnx9v6+VZpbLXhtfKQgd5pEeFr7HKInKVMJCvzM67y3iNtq9wnkDmao8mynTqMzAij30PPYKpNT4eKjEw7XXlRGpOvL53nlc1c1gGAkR/1DcWvgtWmTmPLPQVP1cvDXTrvuc2Q+daVIfN9n/HKZZldTpIzI0PK0vC8cV2n4qlH5U+QeYZIIzlVuREBZ2Vkw9Yjd6TsyfCkDlkZGblZORZRWvmo7OQ18XU2RncdvwJWeyi9q5cmgxO4cuyuxKl+7XnOknnGS7fKWvp4RD319TRvbVcI3CrnyakQ95bZJV+mzEQdJCMzb9k2bnvkboVRB0+1eapOVU6Uxsa71+Q/s2V012N0AqhtlD8Blswrm17HGRnZdnRb3wHoP9stYxkZdJmn41q2hYrRZxCt7UkCZ0layst4z15e1vveOlQJOVu30xaLUQ99AgxJVsLJNifLZvO8OEOemWvipTndNtOPaBwncMf3vS3IzZvxvro/BhP9/OdUW6g/d443alsSgUxD3rZHDDqOZGl58joB3V+GtHV6hsB13MufJvMsUWfKorremJ8keys8+lvuV4fRAE+1MV02m4eI0kqz8ryyP36c/TpbJo7G05tvxjAy6+VqSHJlDLvXB7T+LHTaypCQ7F+k08n50WvKO2x4ZI3SdFzKQweDib58fflvsnvEvGWgPLaOHCsvPkHYss9RmC2rx7Qb3oX/PHSWpO4KGVzRdqZuRn5WTpSm45nrin9my8TRWMkyGtKTOglkDCbkRQbf85alYdz3kV4eITA/BMMaMP0kAsHTdWo+tRw9ZjovIogsmXd11x79xJvsjNdulbuCzJl5qMhD8vVYf1L432foT4dl4LvhybanylgEF6V5cevq/plL1AarWzRWuu5pIEOsjeoEtmFmCE8aOFQuQx6epzoJRqdIl0m9tCxJBjpdpiGCQfcsmes6mqB1GxoWqev1oslOxyt1vPxp7zuSg8a5SpAMqrJPhb+0ct8xjCbkVFtMnYxctj4iSHQxZbzLI39PtqenzrPuTyIib50+rY8mg409DppYNKr6SEI49WctHilFen99fYV6deZC10WGfZdBRL3j0WEA5el89BW1rP4eUVtxhsArxC37gUKrXlbOVJ2M7Cfhj8/QvyumSbtL4rouUzabF6VViJgl8yypezrqMUJ5XeiN6ZF3Vm5100fEEPXfW6OeXicOKKiNTJ7XF1R2Yn3o9WbpFXngnuGPSCHzFTU0bx2ilvW7ZM4QthyPEwTtYYrcnxCGb7kzZPOdQwYd2VNlK2k6zl6Vr7JZvzIX6SX7jsaiCrnJkdG0CB15UlE7nZP89tLkr5ghPdF4eGO09dr1r/Y2rEfKls7Z+dbrZS3+q4NWuzvuHbR0miagrNdd0XPrKPWN4lqGzPPqTpC5HIuoTKUsI+u7IPwe+hX4jmTdJehKWSuNJU/mmnphLtJFp+nx6ECT2U5DZVDdaX0YeIYoowfq912krvVABnb60b/lRUfQZTwyQKSz7ys/DMPAIuMKgWeJG6VVyDeS4xHxFLl/arhR+h56lcQ8wroD0/3I9CcjM9MuK0fHLbKV1xVfZbPSpuERm0xD919f8We5k/CMpNSLHSt9oJH3V+9J7bGitTwJPc+V9nQdLU+TWAdbxr7Qi2/WuHUIXMY7ZI7Go1MmIjRmLK8Or8Buq/Q99FMKfWJ4RV+myiAi9dI6F/tHLp5+V8D6/rckO4k7SA/pYxkLTzfvEHOV4UGQh4mtR/XAJNeQ156+13WYOY5sgDemO9/7uhk6ZKI2ZZrsi5Wu83a8QtxSVy+06nvk94nEinBlP8Z+Ke4EMX4CrjgoZIk6kssQvYxXyRwRt5du9T+LrAyL2J5C5rJtdmNrPaWXZ3nnd/dvt1/RAa0xJH/j9+/fsIyUpeNM28y8SF3Q1808OUhnj5BRvUkv3Mvret9oTD4tPAGvzbGX4qYV/s7hiTFgy7J5Hgl7V+WrbNmx2fA8q+xmQt4rknNivbNgvGmLlBGhM/KuAiJTFppYLBnWYcYaL7S3Ih12HI2plZ75ippshyXzqFyVzLNEnSl7BTFegasPBJe8FHeC8K7W6U6yniJzpixDvBUy92RkcHpzez/B+gQyZzc6KmORuj7MPBke8bOP6WWf9QFQy+0Qu073xph5Wc7SxdORIf0qmV9N6p8STiLb5sgj9ysJ+krivVO/jN7TxG+ldckcyWOhvSp2bCpA3uuVL8EhMN605W3KfIvIo7pXgTFaSE+m3yhdtql1QHox7TH5FSAdI90jMp8g30hOhfA+gWyndJ9skyL0K8jxDiI+Tb4nDhsd2V3itwg++/Y721e52BHJenK6myVLdJaHJw1eF19f//cSFTpcaOPqybBI/SnwyKUjE62h/Vm6R1JItw1PJ7nWs0SmP1e39LLWm1UuS8JSpy7xP4F4n6jfZFt/WIY7CfuKNk4eDiZx8qDQLWORevaKsI3v79+/1+/fv1MElGmH0WHHrdDSaUIHpI8cDz0+aKyivMzfqJ6EN95WuYxM1IYcF2sc0bjJNA/sfvj586d5MJZyrDhTToYoLdrvXqj7/MRwEle2mWlj/KdfT3TsSYP3tHC6b9kyyLB0iXyHnjFmZJ0g1B8/Zj3Hig5r/c+z3G1vnaReVn05xpuYruxDBMZbYdcSOghaabItPa46jtDJ0+ucaV/Gozo69PK63vdTcUc/rm6j/Bn6lcQ9gTtOcVUdMrqdOCBUSL1L5trYyrhl7GQ9Ld9qL+PdoTaQYbwSlsfKkI5HaE9DdHiKDlPWUwr0BMAjO4ZYPaKVQOnIsHfaZAlbtn2C1O8OJ3BFW9MyaUK/gmRPyLzjUHBKh2nytgxMp2yVzD1jq9P1os+Or9cean9i/phxYKA3u2XAo/pyDO44mFhAc2zNu4TO8wgdzb9Fnh6xI92jPc/sBetwYcnx6kySOurL3aRr4QnEf4dMk9AnyegEwV0p84TBO0XOFd0zstgyDJl7xG3laWMk5VSJXesyDbkpJ0ndM+BRPRlOYLJvHuFY5XecJXVd3htTGdd99MY7SkeHELRvvTY7RG3Vv4oMn3AguFL2FTLN76FPNT4t66mHgUj2ycPBpF4dUtftRWTuEbeVt9/wthY00yZK1+HkBpSyWZ087DqIeLy2UJvTa3/ywCLnmOmTLucRunV5ZI7ajMrsfnhp6IBqETpLxrtet8x0OImTbX2qzKPfQ5+U+VRZdx4KpsOujoyciMA9kt9p0UawjK/VhhVOGyALGZ1kuOP60GDNg67nlWURraHsOCIyt+YbkShaL+jSb67r9RuNYZbwvXG2CNtrJyJPpsxV61viSuK/gkAn9ZyAS+gniCDC0wl8AneMJ9PWxMGAIfb9NamI1HW6Jq4MLMMu5XU3lUeSzA/TZHRCY8KOzykyj0g9e1BBcZlm9dc6nCEyl+uVXd+oLQbenrPmpELm+iB0dTiBk218Z1nuv61NIEsKE21N4MRh4KTMalsn5scidY+8kU67HDJkHiISjMhde2EZI+YZbRYWIaE8rafXXqRnBRk57IFFp8kf09EkLNcHkqvb3GS+da+QuUXqaE40rPGy9otn5DOkdwUJX9nW04hUy5pARVb7kftJgr6yracfLO48ZHlGjdXLI1EpA3kY+r6KDJF7hwJkACrz47Vjldeh1kfmezixPzWs/jEHFo3M9+4t+XK81uqtfwm5TnUbCDqPJXM0z9kDp6fTpLxKWxMyJ/DJ+rT/bW0C7IaaxMl+TMg4IWvaK4sMnQYyepa34xG7LIfayOjAEDzSRRtXpD8D1C4byjhzuLDGnNUz0y9Zj+1fROpSpiY1racVypcqq6E157ofHixS13UnyHySOC29JmU+Ra+nyMjgP0K/k7CvaOOKw8HdMiZlVQ0dS+xeeUSgMq5JQsv12t2hvKyflkVEyRjKKmFaJORB/xY5U69KAJl+oQMY07+IEC1S02VkqNdaxVag+h68PjD1PeLu4gTRP41IJ/GUPjEyfmU2KdMgCk/IfMKh4MThoIMnHCwYUpcGVpa3vBMrvu/l41jGu5MG3/vt810GeWTIgOl+RIcLi9B0GHmdktTl2GlE8zLRL6kzGksrTbav9xeaC+S1ah0nSVyXYQ5PaD1rHTV0X6qHrwomifQp+jztcHAapc/QryTuCTyBmD/lUHACmrwsQt95WWLXBO/pIfWxiAcRiqUDStNABwKL3KwQpVlrqEPcmX558pk+6jgiTxln9sopErdke2Mgw8yBq0NALwn+2yj9feoEriTuk7KfcFj4BEjDjjydfS/jDLEjYkIEIvXQZaQ+lgFmDhSoz2j+WcJD9fRYeUThrT2PzJl+RfAOTdEcWPpXykySuNee7rs8ZOqx3WWs+i9eVAEJ/eme95UyT2y47uHgkw8NFslJZIhd3iOPXRM9I9vSUxOQR766HkrLEJ7ur6cnux48Mmf6Fclm+qX7g/rhka1OQ+nTJG7paY1Xxkt/8aKDPwj9hEc7Keuph4FI9icS7xOQIV9UDhH9Lu/lMfpEj6Sjg0uGyGVbyMOreOhaFy2r451HffQ8c+9w0vHQd3yKxFE/vXzkpb94MY2x/0M/SbpPlXXnoeA7ePesgYuIXZbxyDybZ+lrec6yHHNAiAjP8+YmPHSkD4rLtOwhAfUp8sj1fdVD3/ETJL7hHcp26K2pFy8mMfbDMhN4OoFP4EpizbR1pbGxSC5L7JYs5LVmPHTv0KANsyZB7UVbfbIIT751v9v0DhcTHjqSa/06W8VD1/oiTHnoO+0kia8Vv2Apy7x4cRXov0+9wiu9W8akrBPjNe113+XFa0MniY9py/NW2fSqh26R2v7qmCwXEaA2/vpnST2c8NC1btavs7H1ZcjMb9ZD3/fRAXWKxNfy39K3rtczf3EVLvn71JNE8TRP/OT4nZCZCafmj/FcsocSee89ktftdz10XQd57AiIGCxy8uojTK+b6FE8apf1plFdlC7vmQPMCRLfcfRY3fu45MWLq9B65H6VR3dVGx089WAxPV4e0WegP2OU6VVDjOpFj/c97x3pjMjeqyPrWvpUxi9q866DZaRPxkPfaRWSZ/X1gA5dVvpL6i+egNbfp07gCi8+anNC1t0yJmVVvfdM29HjyCli1/cZ7x2Vizz0bN0qoicJMpxAhsyrhwyWmFkvPQv0URA6iCEil/H3cfuLu3D736de0cZTvfpJPOFg0X1k7JGlbiejk5UWee9IP6s846FHpJ/Bp3noO48hdabe5MFIxhGpozyLyPe9fKfixYur0H7LfeMEeV5J0BWv9EUP1ufNFrGvFX/GGiHrvUf1M/W+u4e+y2UfqzP1J/vikTjjpXtE/nrnL+5EmtCvJO4JPIGY30OBDe9Nau+z7LXwZ+YZZIgjIu2o/enPU6/00Le8DKl35Ezr7pG0zre8dIvMvfiLF1cj/X/oE7iSuE/KfsJh4emIDJx+Oc4i9rVs73jqcWyFaDLG+5SXabV1gtTZch0vfQLso3SUz7wAh9L0Wn7x4mr8RehP97zveLQ/ie7h4BMPDejRpAyt73DLeylrrb/7PfVI3kKHoE55bFd7ulNe+glkvHAvn3kJToc6/gl78sX3xH+EfsfnbRlZTz0MRLI/iXhPg/FyELHLz4s1ubOP5Dfu9FqnwB4Q7vTSTyMiaF0meuENhTqNJfWnjNGLfw+/ThDlvyDriY/2P8W7twyi9bgdkXrmkfzGFQSPcPJJzx1tXw10gIkIHJVhPHKW1PX9+7n5iydg5IdlJvB0Ap/Akx7to7J3wvPSIzJnHsnrfKvME8YiwifoWIFFitZ7GBkvPSLqTBmdL5+EfNe5efEZKBH6U4l8Ak99tD/tdT/Ri0efpUdh9Eh+o0rwEq+x7iHyYi3ituoyj9lR6OVZHjfyxvf6O/HRxosXFYx9Dz2LpxD5CdKdwNMe6Z8metkGMqaRh+6VkXJkewgWobxkH6ND2JEMxkvPEHc2T+qAiPz10F88ASlCfwrpPW3TPPVgMW1kPKLvQP5Np4T+bH2nZbz4rWeV4K3yEt/58+zMZ8MdwmZkRI/TrXiWsD1Zmszfx+0vngRI6P/CqfMpB4tP+bgg8t5R24yBl/UQGWs52UfyuqxuR+sb6Wgh089PAjOHunxXnueJZx6xozTvUbpX3iLyl9RfPAnwt9wn8C8cCp6CJxwsIlJHBnV75rKsJnMtR5ZD9RgPnSV43b7VbxYZYnwSrvTSLeJFaVkPnU3T68y6fv78Of606sWLDsqfoUee2yTeQ8HnQxt6NJeIZH///v2XHB33yF2nSdlWPtKpQvZeXz8Jk166R9he/aqHvu8rj+UZMn899BdPgknoVxA2+1h3Eu+Gux/aM9+QxLoN68+fPyGp63+zssh9p1U99C7Za10+Faz+VcJm6jMeupXuEbc1txkyfz30F0/Ar5Mk+qRDwbvZngGLzNdaJnFapG4RAyJ3nT7hoXv1mPqfAoakUfmqrIyHbuWxn4vv0HpilCFzhtQ//WD34tkY+draCfK8kqCzh4JPNs53IjJ6ETHKcmgusnKv8NAZ0v8EPMFD1+mVz87X+pPAvbmrkPlrG17cidLX1q4g7gk8gZjfQ8H/UDV6yJvWMrPyEBG/HjrG1R46yvcekTMeul57et14T3MqF9OnFy+m4RL6FSR7QuYTvPdPNuAnEBk9xjuPPPPM485NtPLRfeRpI71eDx2XyxI2So/KMN669Kz3vUXqEtahsUPou773cdGLFx38QehP97yvlHmCkLuHg08+NFQfTUpClMRYGQtLzjaw+nN61J6lI9PmJ4AhuA1JrJ5HbaVHT1s8r9kjc+2Re965tX60/l3vXLfxEvqLE/jje+gTeKo3/wQv/l9GZPQswtAk7BljxkNHZI7K6cfyyMAzZC9lPg0RyXnwDkb6yYfXduZwp+fCm8uIcKP+RofHLJlbfXnxYhLtl+JOku5TZd15KJgOrwTrwUTkx3hYVj2PzJHnt7125MFF+mZJ/yowJMfIiA5Ga/3pvUeEyhzIovZ1vErq6BCJ6r5k/uJJaP+wzASeTuATuJJYM219gnHRhnWnVQhhy7I8S6te5XE8S/oozoD5vFnKlhf6XDmzJr2x1GOFdJD3bJtyHXS99OgQgw4GqHyWzF+8OIk0oT+VyCdw4jBwUma1rWl5THsVWZIMkbeU1RUZf/ZRuC6v/+rVq+Pp2iEE1L7uEyIy71fOMmvIGkv0uwFIF91m5kBm6aDLVg8xqC2rrqcv068XL6Zwy9+nPoXIT5DuBKYJFcnMhBNEnyWLDWlYvd/OzhpY7631yBvXxt7Sn9W1Oq7WI+idx5J5hdSjsUSk3m2TJfOoTaZtLRORuKc3Oli9eHEaNKE/hfSetjGeerCYPhREJDWFLRN9vq3btYgxkm99do48TI/MvfYYUkdhBtHhIkPm6ODGtm9Bk7rUQevHtOs95rf0k+3oNr21jOYc1bX01Dq+eHEFbvHQ78ZTDhaTG/3kYcALO8SeMXiWAc54TJZcy8P0iIMhEkvXKGTBPCnIkDl7WMu8sS9JvfuoHx2uvM/vPSJnDhO6vQqpv2T+4kr8k4T+nfCEg0WH1LNtsJ6vJYMlZavels966FrHKTJHelmElr0YfdixW+t/v9/fOUSs5X9dzfr83lszVvvR06Ho4Jg58Lx4MYmX0F8cx9SBgfHSp3VA5MHW0+FJD50lNJbQmb5l0T1E7DLZz++99WO1j2SzZP6S+ou78BL6i+PoevAdAkCydugZXU2U+16GmbZkP6y8Sl8qhNYhc9S/TPmql26992Bhj4E3B1abOt/K0/rpq3uQffEii5fQPxwThqMr4+TLPxPEg2RG+eiRriZ1pg2GzHU+i+hA4ukxNaaZesxhwpPHjLuEfNSvQ6bPlfl4Cf3FnfgnCf0JJDgl44Q+mXCjavwYozpJ5lo2SvM+q53y0K37Ceyv+KG3s2W7Vx2QZLnqgUKPU/aRtjXXE2OuH7G/j9tf3AWa0J9CYE87+Z4g0klZU+NlkXnXs8+SQSTPquvJtdI9Mq946KgfVS89etyMDlvRgYJtwyJHT06HzNl2vLZ33WnbgR61v3hxF27x0J9C7CeIdALTZIxkZr3wE4cEBqwRtgwq4wFLgx99Vlvx0HU/rvDS1/L/B5w5HHnjyZL6JJlHbXlls/1ngMg88+TpJf8X00gT+lMfEz9Nnyu87mpb0/JOwzPA2yh6HpL20qw2ZNwisowRtsh6gsTZA0XFO2c9To/U0ROcKTL32vfKMv1nnjyhNVfx0J+wt158L5Q99KcT6VNkWTJPkmamrdOk7cm2DKo0qpFhleX1PUPeXp7lRWWJRMajg0UGkjytec16px5JsV65TkeHI4YwmbbYg41sNzrIRLDWHOudv3hxCu1H7k8l0is95Al0Hol3wpNgvGKP1D0DjAgHeUlePzPEvmVlMeGNR9Cfmcs5tnRgxpT1OrWs6njpeUQys6SO6kWkLkNWV4/QGa//xYsJ/Frred62lnWSlE/oeQd5PhURYXfgkfqOW+2znrJFVpY+VS+yCtYTZr3TLKGzHjJL7vv789k2rTR9qGG8846Xvu91n//V/f/iWvzhoT+dQK+UeYKEP9HLPoVI98yjUYvU2bGZLDcxHxahVPRkvfPdLkvo2guPSJbxeC2CzBC4BUnobP8jWAdJfZDY+Z6OmcNEpFMXEzJe3AP3kfsV3uwJmXd433eS7eTBplN/rTlPJGN89b1nkLx+dnWv1tf6oj4wj+299OoBydIl8j4rj90zTwaidpGO1hhY/Y901TrrPGac0IEAPS1gww4mifwp+vxrh5PUZ+if4nVHsq8k3St1ODkfXqjh6cB6PxXjGxGBNT46/YqDWEQgqA9fX1///fqZBtuv7gGp4n1G8A4UESJPnZ1Xq/+s3jsuf9CHHRf9MQPSRYZdnDwMfEcSfkqfGBkj30O/kujvJP6TunzCIYkh843IeHqIdI3IRxpXqTcjV5eZJneLMBCha0KISEITrP6XM1Y/74Ak29dEP43uOmF0ig6IkW6ofmXPMWva0i8bTuCkzAkZn0TCk/h1J1Fe0caJNiNdJmU95eCy1p9GnFmoVb1ZArKM2772n3NEhwudb5FmxwP1dNbpqF5GD6Q3+8RDt60PFmhNdKEPIkgPr/zEAdgjU6+eLG89PenqEcWZkGl7Ul6lrQmZE/hkfUwP/UlkPIkr2rxSZrWtDplH3rnE5Pwx5OOVQ/UkOViEt1avH1ofS08rvsEQmfTmUbqnn2fE0R+dnD4gS/02rLVX0QWNP0s2k2Rk6eUdMBhS9/Q9rf/ptk4eMCZkTaAiq/3DMk8i4ZMEekU/JpFpK0PmuzyDjodugSEfrePuj/UIXhO7pUNns7LEgfTe+mhis8COO0to+pCgSf2KJ1+7vWgOsn1nyFP+Fa2n2zSQjiypo7SrwgmcbOM7y/rje+hXktdJMrN0mJT1hDGbCq2218JG9Pfv3+7jxc54WHVZ8kF1tkz9CN4jdilvYn6z+mvy1rp0dcsQWoRTpC77Hemj5449FFpjoNtEa2vj58+f5qFnAp6+rN4nyTeDJxwKJmVOYFIW9NDvIOEr2nwCGV8NRq+18Oejuz4CIvWTBzOWfGR8E7glExG7NV8njAFzKJFploeaHfcsMVy9btEhU+rlgdGXIUj2gIMOEfoJxhQY/TKk7uWdCidxsq1Pldl6y50lsaeT8hMPLlNh1OZa9stuFoHIvuxy1bGI6mfJB8WRTOSVn163nr5a192mddBCYHRkyUzqVOm7RYA7D+mrD1Cy79Fjby0rGium3zquZUrC9sLTxB7pveM6rJLyBBF12p1q/2rZV8g033K/ktROyLxC9lVj12l/rb+9b4vMWe98qk+RrAr5oLiWWSHziU2ZMapr8d8qQOvMerpQGcsqkEykn7yP+onS0HxmdMuMx77PvtGOSL07xtl9gOahQ55s3ZOk2dXtZFt3yKQ99JOkdYoIGb0n2+rqkNEtU9ciJ4/Mdz2JE+Mk29fpFfKxCGTfV8n8hIeu9dXj4c2N10ZEHFky60LK6oyj7oMkST2fGb309fv3b3ednfK2pe5ZQmL2R7RPvDaq+t0VTuCKtqZljvywzFpnSPkKQ5tt6ykhq/Na2NNjvPEJIxyNuZdXuWRdGfeMv/Zmr/TQtb5ePSsPzQ8y+DLuXZLQJuZ+cv1kX2y0YJEdInO9vk5Ar1kZZuVIeczeQG0i23AFwZ3EHf24ug34lvuVRHlFGyfaPIFpUl/L9vTkgkKEdcU4MQeKDPkgY4WIzfPqPHKf6CsiVp2v27K+VRDpr8sypC7Hsws9lhPQfUDEzralx/8uMtc66TBLRAx5o33SaTvqwxPCSVzZZqaN1J+zTBo4to3JtrJtXh1mdVwLv52u87y4LL/zrkK06D3jyhhgbYgRAei41OsUIWmdGCMgSX3rG+mP2tahN56dtaAPGpPQ+nlz6cmQstjrLoegQ166ryieTdNtZfXL6D+JJ+o32dbRt9wncYI0n6gfMk4oXMv+KU4rTcd1+TugCc7Kj8hHX1p2RADWOpHj1u2njiM9PXiPm6N1HhlqNJ5sn5kx2n9aMgE9lmguM3K8dVQdkyuB+uuRRETWbBqTh8JMn6bDK3CHTlvWrb/lfqLNaZ0mdc/IXIt7Oz1D5luHnf4EeIvaMrisZ44MsUUAzOFqsr+WYcyuJasfbNvWZcm12rH01unyQNKBNa/sGGbGQo7Jk5EhkmjerTpIXqbtrJ6fGE4i22b5t9wzxiej/AmyfUqYHVfLgEbEjTbPUwgcIfqOsexXhcz3hbxbJlzrvIeuy0Sy2CcMTJvWWKE2M+Oy8xD5T4+nlJcxrHp9RevnO0L3H8WzaShkylT3xFNR7W+13+233CdI7hNxmtTX6j1S/7SxRQuYNbTMJWVuQsnO2WQ/T5K5JStD6mvZ68oi/F1OQt6fcADQHGU8dClHxq31811xmtS9PGYvdInxrvAEvDbdt9y9DTyp3BO852zY6UNUZ61/i8w3kKceGVrr0p78lvF0D937wRKkv86z5t8ysNb4RmSuy+xyFqLxk7pkYM1tpb41HtF/F3xHWOtCpqFyOo3Jk6Glw0R4F67sR8pDP0mKV+JkPzIEwebJtN2GLvddEBFfhcwlSW05T/XQEXlsPRCZy7WAxhC1r8fYMrzM2pZtI10yQMQRhXJuM+1b6wxdL/6HiKSjubLykPxMmNX9yvAK7LaOvhTHKHG3dz2tW1R3rd7Lbv8CtKeOSMe6vM/Yd33Ps7XmZa3+b9bre2vjy7fZJZFr/VFfLD0jo6nJK3uo1MiMlW7bM/5e/a6HLmXJ61/ZdxVkiNsqH9Vj1sJdZP2UcKP9Utx3DatjsZb/iNKSZ9X7VxEZeXQxL8xZZCjTrDnIEIXXLxSydaNDYyQzMqhfX1//fcXMInPrwKPBesr6QukyDekt0zoeOmr7X96HGWTnCe2BDIGxZRlZ3wVjP/36REyS+1TZtV4vnIVnaPU9Q+ZSbuYQd6pfum/e3GfXF9umFUZkrmVaiPrkzZVFBlb+pIeelfPib2RJ/SSZZ/X81DD1UtynhdEkniD1tXKP1F8C94GMfPXa9bPzPd0fHfeMztaBXYOMPC9ci/uFwYnDTnberLhO2/pFY6Dvkax3f87DW4PM+swSXaVORvaT8HgP/STZT9dZK37c/iKPSVJn53utGdKy+oPiqJxed6zeTJsolL/o5o0FOy5RuQ6RMwac1Q+F7569HhGpV4j2JFmfOihUQ/r/0O8K2UHtys4aedaQvpgDMuiV64keumxn3yM9WL2j9qMwOthMfZUrM2+yvBXPGGOpg9ZnrXc/3wlvfTJruEqIrA5PDb/FI3c9CVkZa3HftX2972dgf16+Fk8Iu07licwJoM24SRyRu4xH63Qt/2tslkGwZFljEJE6O3bRnGmyRnFdhgXq57unnw+GmO8k8bvwmEfud5I7U/bFsxAZdY8Ysge9ab113FpjaH3qPGudWoYlY9TWij92YP7a1YM1h5l/0kMyMtBz8u73z0eGeE+Q9tTBIBs+5pF7NDhVmRPtv3guIm/d+8U4dv2cQGQcLDJH61UfQti2Ufusdy5h/bVrRh85Hpl/0rPSWaC+vvbgs3GSzCvtnDgwWBjx0K8k+5fUXyBIEljLJoYNZk1sTK0NvVEtIyEfuzPrWJdjddAHHETCrHGx/tqV1ccj8OiS9XWcxUvm/x4qBHslSVfaGPkMPatkt62T+r34bDAEwB4SZf4JPWXbOm7lWQePCQ/dOmBkgQ5FjD7TpJ5pe62XzF/8DxNkzsidDi9/5G51skvqL15IMKQekfvGaQ99t6nXNnuAlWUyOug+IkOUfZs9uy+RYZq4tu4ZvHbkhYUKmU+TNYPSI/cTJD9xMHjxQsPz5lhyzGyojF5WOiJ2dg9l20cHGA2W1DvjpufGu6zP2FHdqM21Zv6f/cULiROkzcgoPXKPGj392P7Fiyy0oc88vj7toaODRPawy+qJPAikmwbzFTV9sKjoE5E6+/b7lsN46a/9eXECFRKvHgR2OP7IXSv3kvmLJyEi9V1mrb//FnRSB5RWJfOOh74W//l79a9dM/p4HneGzLUMC6/9eXE3uiQu4T5ynyT3Fy+eBIvU18JE3l3P3mncI0R2vzF6ah1Q/QjMX7t2nxhkSds7BOyDAMLEL929eHESzKFUlnEfuVuVX9J+8emQmyDy0GXaZNso3focX5ex9iGrpyw38TOuU08MJkid8dJf2/XiExF56+lH7i9efBdsb5Px0GV6FlkC97x0VL/qoUt0SD160hHV1fEOqVuP5aWX/nrmL74rHvPTry9e3AHtqcs0RKCTbcp2K4/cdfmMjuiAUP2K2uRn+prMZbxK5lLW65S8+M54Cf3FPwv5V6HS8J96GU4DEbMkRfbN9gkPfaPyFTVLt6w+HVJnyPzFi++Ol9Bf/JPYZB55lyfIABFZx0NfK/dGfvTxmUfq1hMF66OBzBMDK4yInX37/fXOX3x3vIT+4p+CNOqM5+sRvOVhZlAlc4/IrZfpMrD+cAW91a7vq4cgPbYeie/0zFfZXkJ/8d3xEvqLfwbboDPeJUPwyEuOSN4iuyyZX/FGvv7DlUhfHc8AHZQyj9yjx+0vmb/4F/AS+ot/AvoR+1p5D9367Doiee9eQnqSHQ9dpkd5FbDjUpGr4x1S3/rI8MWL74yX0F98a1iP2Nc666GzxL6BPPmqh47kTUA+epf3Jz30HWaJXc+7DF+8+K54Cf3Ft4U05BbpPsVDjwh6Q5Nd5mU42Vb2u9iaJJlDRgWIzHfIkPpa//ueOSJ1HX/x4jvhJfQXHw2LVNBb7GvZL249xUOvkmK2fPbrabrfzMcAVf0tMpdxlCf7E5H5S+ovviNeQn/xkbBIGZXJeuW63FrP8dA1qgeBr68vk9QlaSOvXMav9tB1fK2/f/ktInAdf8n9xXfBS+gvHgv25TBkkJnPxrOkfaWHruE9WWDqW5CfOSMS1+1rXdDBKKuH7pNH6lo+OsTpdIbgX1J/8R3wEvqL25El3wqZZzz0jleeIXJEUJZ3axFY5zG3BUSw7JxYMrJteySu7610q4yV/5L6i0/HS+gvboNHElG9LJl3PHQrX5ZB/dlxnaf7j8p5yBwEWHhPBmTc896nPHStl0XG3n3WQ9dtveT+4hPxEvqLy6GJrPJo/efPn3/9z3VU3yuj87RuKB+VkeWsvkp4hM+On07rIJIjH9FbTyekXtn2PEI97aHv8PXaX3wqXkL/prC8qStlILLJepSRN67LoXoMqW+dqh66RdwW0aM+ZMa7chDwEHm+mbpZ2Zl7Ni8TZ0ndCl+8eApeQhd4AglOyTihTybcYNuOSNaqs8ujz7mRbKs/UTmUj8poHVgP3dLdGy9PbgUVGexBgE1nPfQOyXv5msgjMvfKvHhxNdqE/hQCmyTBCZwg0klZU+PlkSMDi2RlmgX596eS2D2ylvFMOa0PQ/KynJat+8/0V6J6ELDwVA99omzWW/eIHbWD2tJ1Xry4Ao/y0J9C7CeIdALTZIxkZr3w6iHBIjKWzLWs7ON0XTfyylFc6+mVs/r0euic1x3lZ0hbxiMPXca9fCt8if3Flfi11nOIdFLWU/W5wuuutjUtzwIiQNQ+KyvjeXt5upzWydLdI3mrrB4LVM/Dp3roEVFny2dJniH4CVLXeryk/uIK/OGh/wtE+hRZlswTbVTamtLDewRvkZkMv76+/nqbXdaPCNs6LGS9cpbIdZ7OR+PgEb6F6kGAlVmp49W38lhCrpZlCByldUn9JfAXdwA+cn8a+X2KrKc9Eu+EE3pseGSOSHF/Nh4dBhgyz3rvuq+oLVlG9wHpHBF2RPgWKgcBCywxR3WrsiPi9dpD5GzJYQmeJXV06XIIcn1X0a0/JePFc+B+hv40b1vLOknKJ/S8wguv6DUtzyNiDU188kU3KVvXYckckTRL4FY+0kHrGpXV46fLMageBCJ51ToZYmfqe+WjslkCR2lZUtfl0L3EJvU959mwg0kif4o+7+GEfCnu6QR6pcwTJPyJ3ndEqlr+LiehyU+TuSyzZWvPHREnq2PWQ9eHD4bkZVnZV93HzCFIykXjUEGGmKP6FdkMIWfLsgSO8hkiZ9IYeKTexcnDwHck4af0qSqj9Jb7Fd7sCZl3eN+ndDjV7gSZW2RnwfNg9ON3WUeToEXuXr90XlTXkm+Vs3TV/fQI30L1IBDJrJaP6kZk7hFy1FZE1Fbcy2dI3SNxi9TRet7pOrQImQ0ncFLmhIxPJ+FJ/JogmBOk9QQyPom7DgQZud2ya/1NtpYh1kQoyVwDka+Wxx48dF5UF/VX6+WNDUKVmKsHAUsHFK/U9/IjUmfKM4Rt5WUJvkrq0XhssmaIeZrErzwUnJQ5ge+mz38e+pXEeULmncT/XUN2DNb6k5QistXQRLmW/fhdGjhPHy3bI16W4FHfEMlH7WlEhG+hehCIZFbrsKReua+WzRA4SrcIW9+zpC7Xr773SC9DuicJ+o62Th4wJmRNYEof85H7nUR5RRsn2vwkZImaJXMrf7cp7xE80tdtsAcSLZslcKt/sozuF+ojWmMZwrdQPQhYYInZq2vdZ9I9Is+UrRA4SrNI28vzxnCvXUkuVtqOeyFT5lQ4gZNt/Euy1ip8hv4kMp5E9gDw6SHT56jMWvbjb5Qm4RlqXYc9PDAHEtQeS/BWv1C5aAwiwtdAfTixFxDhsW1EZTJEHZVny2YIHOVHRC7j1qFhQ5K0vreInCHWK8k3gyccCiZlTuC0rLFfipONfBoJn8Lpdjv9XismRI/UZX0rbpH5BmPUdd6E7pqcddtRP2UZKQcRri4rER16kG7Zg0AFEXlGdaI5zNSP1ghbliVwlOYRuUzz+r2JV14oXaahOAq9vEyZ6XASJ9v6VJkI8JfiThLfU8i2o9OU7ifkZ+pWiV+meXFmEUcGHpEdIs7soUXnIX1Z8raIOyLsiPAtZA4CWbDEHNWtyPYOEswhA5GzJYcleIbILX02EJlbRJ0ldZQWya2EXdzd/tWy75KZeuR+BwmfItvvGmbniSXzXVanMfEIEalb8hARs/3TeVZdS6eI5K2yElVirh4EsvKrdbz67Fyz5SOizxI4ileJfC2fzC1yz5B6hhA75PkEQu7qdrKtp8j872trk8ZhgpRe/A+nSH0tzoNF5ax8qXMGLAlY5GeRMNv3XdfLt/plEWxE2FVirh4EGLDEHNWtyPbSmPIMyUcEvu+tOiyRr8WTuSzLxBmil6GX1yn7lHACV7R1hf5/eehPIN/ugeC7hdmxWcsmMob4ZX0dj/I3fv/+vX7+/Jme+wyxS32scjI/S+A6H5WxdNA6on554+ehehDIyq/WYefQyvMIOVu2QuCofIbI18p55gxRIzKI8pk8GTJlTpDRCTzxUHCyjY32/6GfJK/vgBPjM1GmQtaovqynkSF1qXu0DizjyxCrTs8QPCpjlfP0QXpl1n71IMCAJeaoblTGKssQcrZslsBRmQyR75C9fv/+TZN6lvSRXlZYIb8scV4VTuKTDgV/vOX+BLKdJsC7w+k+r9XzvjNkHRG/TkeoeupbdraMJl+vvM6PCB6VscpZZSWqhqB6EMjKr9bLHsii+h6Zs2UnSN4DQ8LoQmSOrqgN1B7K98KpMneSr8QT9Tvd1pGX4t5w3uvOll0Lk7TO0/EoXxMXsxAjUtfEpO9Zw+oZfkvPDMFL3WRZi7hR2Ug+g9OGkCVmr25VtkWq3tzqNKZsl8AlIuK1rojMLRkyHcXZfBl6eRk5FYLKypxsu4s7dPLa+OuluIqBefE/PIHU1/I97oisWTLfejCwSN1bbygvsza9uhWC3/kMyVtlJSLCt3DFPu2QOUvqbH2vfIfou+OHSFfe67wMmVeIPKMH0l+noZApUyn7qeEkJtpM//TrG8ZkWx1Dts5a9mN3lBel6ThKsxZUZmFLUt+6WGOG2pDpWWMcGX6rH5Yuukw0/5ZsVC9CJLcLlpi9ulXZLBlXyk8cgPR+QITp5X191cg8ki11y+Z7aajPTJgpi+p+Iqr9new3/Az9X8Xd5L5W7/NxVF+m7Xo6LSqr0yWyn5H//v3bNLiof14ZqVsWVYJHdauEXSXmK/ZqZ0xZUrfyMmR8krwlEPGh9IjkO2QeEXyH9L00Jk+GTBmPzLrEeFd4AlkdoIc+TWyfErIDXJU9UWZPXIXM2XwdlwsGofPim+wzInOpj66D0quGnCEFbwysw0fUZmbtSbmVgwALlpi9ulGZiNQzdU4dbPT6t4hLk568rHTmssifaSvSzeqTlabHxAtZ8kFjPRXehbv7YX6G/oYzXrdHqBVSR3KiNB2P8hFZRAssS+pbV0TkiNjRvdZtity9uhmSj3SobtrqQSDbRrUeS+pZGSf7u9bf8yHJSeajdIs4vXTvynjylg4oXsn30lA4VcarG6EquxteAU+H9vfQPxGT5D5ddq365+O7DZ2WqS9lbLDeIEvqiIzQOHnjuPVCslHeBBlkPER2g1f0usJ4sMRs1e3KP03ea8UErstEpOYROEqbIHOWyDNls2lonKKwQ5RXkfNTQw+p76F/l9BDRdZU2bV6ZF7x0nW84p1LZL+iptO8sUL1Zd9QW1beFGFUSSkzplnZU7jTSz8BtK6jta7LMEQnryhfX9nvpTP5ka6s3l4ak6fH90RZRtZ3xO7fx3ro0yRfIehsnbX8x+6WHFRPpul4lJ/xzteqvfimy2/dq2Qu66Nx0/3WkP06SfAZeZk29dxfAT3e2bp3IyLnbJksUbLkua/uC3Oe7EiXqC9M/5k8b0y7ZZm9kZX1KeHGx36GzmBCNlNnrWs+H5cTOOGlowVhbYrO2+yWx4bGxRujaPxYctf9vILgJ+TvOleS5R1tVoDWrSQXq1xUpkNm8tJp+j5L5kx5S6eMrl6frXGwxoYZv4ycqToZ2U/G19fX937krjublbHWDFEz8lCeTMvkb310mkxHefpeo/s2u9RBkjwaR11WxrPzr/to6eSVuZvQ7iTWu/uugdaoJAqvLCrnkQFKi0iJIcrOVfXkLf0i/aO+ZMbGG9OsHG++syGDquyrwrU+4JF71ohHxj0jc7LsWjNkvtvx8lFZr4wclwgdUt/9iwhb91HmTc3/RpXgmbwXPVjrURr7qDwq6xFLlI8IR9+zRFi5sj8X67UX6YfilfxMXmb8s/KQ/Ew7nxA+/pG7h47M6bJr5Qi7S9YMmXsE7i3qCJ2vqKF0L+7dZ8K17K97yX4zBB+VjfJe/B+i9SaNNVPXKu8RRCbfIxN9v+PMPXtNe+ZMGVQ+KuulMXnMuHfKZutkZN6Nox56xfCe9rpl3QoRZMugvN22zLPKTXjpsrx1b6VZ6HxFTaczcyL1q5C7LrvlWGtpgzlUalTJ/JMPARXjJg17Vq5V11rn1l6o5rNkZRGhVca6Jn9hztIzijNlmfrdvGiOJsqiuhaqMk+ERz9Dz2CirRN1OmXW8kn6lJcuZaA8fY/qMPBIfetnHbwqZK6JfPdFy2bmQpdH+d64VIjewyeT+VocOVv1qnJ1umWEPYPO5CM9MoSkL6aMvKZ/LpbVB+kWlWXrZ8ewU8aaW6ZOtW6nrQ4e88gdoSIjU2eta95Oz5Axm68Xg4x75I7uNyqfj1t/uILmFI1nhsytNSPHRrdlydnlM/moPxodYp7e3Fejor80yhm5lkG07q39EMmJCIElG33pdKvcFJmz7UV9Y/rH1o/G0BrXE2Um6lhriUG3rR2OPnK3DG81rMjM1GHLrlV/oa1L1kxZK47upWyEKqlbJL7bQ+St87pzKftmpVmymfyNCtF76BwCngRpnLP1ojxUxlrX3nqPyqCyKJ8hpJ2H7q00mV4h84nP2ZkyXt8z+dm0zLywcqpydR2NrIyJcPSRu4cJmZk6U2XXup7MK2X1IrLurTSJU19Pk2lWvDKH1pqRY4T6nyV43Qc9juxesMbqOyDbj4xhZOpZ90y6Z9BRWoaQdNxLq15ZMtdtM3pn+sOOTTaNycvMXbVspw4js4L/PPTISHZDiY4Mpuxa3KP0qGw2L0qTE8YQdJSvF4DOQ6Sj61iY/nqaTLfiOpxad2g+0HhE9TayRO+hcwh4GqpGySvvydRpTFlr72TzK4TE3leuE5+zW33I9Jctm01j8jJzVymbrZPZH1WZ/32GnoE0XFeTO1OHlbuWT/yWHFQvSsvk6zhacLqMzkP3UbpGhdSt9YTGFJXPzmF2jrXcna91RfW0jnocK+RsyfpUZPvBGDnGiFrlo3glv0M68tJpqAxzdcg8o0OmP5Wy2bRuXrUsK8+q25XhhaVH7ghZGVJWps5aMy+yMWVQW5U0HY/ys3F0L9tBc8Ui+/U0i/wiMtdrq7qeojleq07wG1mi91A5BDwVnvFi6lVkIiOLyntGuZLvEYK+R3ErDZXxrivfgGfKRPFKvpfGzA8zd+w8s2tB16nWze6psbfctdKnSJ0tu9aZ749HxI3SqsTvEbieYH3vEcvEn63sNjdZI0LSawPNoYxba6qzNuVYTBG81EuiQs66rU9Hth+swfLWO5vnGeVMfib04vLS6VY5eZ0ic0+XTpwtO5GWnbvKfE7KtepqRHXgW+7SyFxF7tNlozJrzZN5hqyjfI/AdR4iBG9RrDXzZyse0cl8617GvflDsqtrRY5XJn+jQvQeKoeAp4IxSFFdLx2VsfKt/RCVZ/N1HksC1r2VZqVP/pmL1S5TPhNnymbrZ8e/UyZaC52y3trOhPCRO4Iuw4aZumzZtea97wwxo7QqWaPJ13GP3NG9laYx9Ta7bFMTPZrbDJl7a7OyVnQ9Nt/qzwZD9NHYfRdk+uKta08mux+i+FR+hlSseyute1U8eUZXlOf1lylbyc+OfzS/nTLVstk6Vkg/cteNniD1tWY+H/fkZPNk2mR+JY7uo4URYYLUd3+tdWLFJw6Auq1I3lpnPHSrvIfuIeBpQMasUjcj08q39khUnpFXJQ6dz953rqsey2fKW/FOPjv+lbxMGWaNVMuiuhrll+JO1GHlrjX3dvopso7yWQLXk6fvEcFHk65RJfU9FoiM0FxY8c66knV122jeqvlWvzYq5GzJ+nRk+uKtdU8ms0eYOpYctixr+L24lYbKZK5pMvf0m4ifyJ/KY+adlZOVp+t4dcsvxW0BbJ21nvd2epWMo/xqWT1xOg+RN5roteZefLMg5wKlZ8lc1quuSaattV4P/RSsdZytm5FpGTzr3pLnGdkonzXqXjybFl0TZJ5tl+lnpv+VsfTSorzKnFbksGUqdf54Ke7r6xyps2XXuu7tdJ0X1c3k6ziaFB33yB3dW2kb06S++y0JG+XpNCveWU+Z9eG175XVc4PkyHwpj4El69OR6Yu33tly1r1XzjOSmbIdMtHxbJp1Tb4wF+nh6VXtc7Wsl8/MUTS3zPxPrJGO3D8euUvstCicLhuVWWv27fQqWUf52biePJ2n8700iam32RGJSx2s9ePFLTndUMtaq0fwu4zM24iI3kPlEPBkWGs5W9dK88pEZT3DyMQnDbaOo3srzUv/+jpL5szl6RaVj+LVfKZcdt46ZTLrKFvWfOS+C02S+lr3vZ2eIW6UViV+lsw9ckf5VjmE6bfZd7uI6GW6vLfip0KvvbVyBL9RIXpmDKWc7wC2L7qcVw8ZOUZONc7mZ0IvbqWhMlPXqX9ys/I6cbbsqTQmj10jmXWTLeu+FOflyXCtaz4ft/JQ+5W0TL4cSJa0rXhE7ro8k64xSepyPqz0Hc+QuSWvEq4189jd0kfCI3oPlUPAU8Gu16i+Vw/lMfuLKVfJr4RePJvWuSZ/LjbKZ/oexavj59XPpnXzMmU2smXDl+J2wW6Z3XCnDMqbStPxKB+V9eIs0aN7K81LR+iS+h47i+g84pRjz64h1AYb6jpr1Qh+o0L0HqqHgE8A2heZelFetF+YstGeZfOtPNagW/dsmcp1+o9cMrpaYxKNhzfOmfxsWnYdVMtEa8iSF35tba17304/QdworUr82bieCNYbR2lrnX+bfbeNiFzmofsonl07uo3MWtL1vHyrX7LOhkf0zJha8/rpyPbL2h+RTK+elecZ1m5+lWx0PJvGXqd/+926on5W6kZjw87DRBqTlynDrC9UZsRD3wKnve+TXvi0l14lcBlH+VY5Ng+h+4crVh5aM1Zch1nvW7cZrdu1agS/kSV6D9VDwKfAW99svaxMK1+X9QxiJh+VrRCCjjP3Ubp1Xf3CnKcfMxaZODu2mfrZNCaPXTvV9UX9sMxEmbWeQ+ZyACaIv0Pg0T2SoVHxurt/uGIRKxOvrC+vrtYBzWE13+rfRoWcrXXznZDtl7dHPLnM3mLqWHLYsh2jj4hH31tpXrq8rvDMIz28fGZcvPFiylbys2ndvEyZDZlG/bDMWj3v2zPaXj2UF6XJDk6QNRo4L+4RfXSvZXlpGlVSl+RtkfjWwSPyO8jck6XlrtX30GU53X9d3kPlEPBJsPZGtm5GJtqv3n1mX7P5HcOu41YaKhPlV8g8qhPpwVyeDGaMuuPJysqmdfPY9YXSbvXQUdqUF54h66wXzsT1wDP3lsfGkvWJr6etZROPTkdrQcYr6yoTrjXzZrucA0uOzJfysuOJ5vu7INM3b9+w5ax7r5xnHDP5E4bdIiGUbpWbuCa9+Uhvpu/ZdrxxZ9pk62fnN7N2quvqEg8d5XXKy05c4aVXCDzyxhF563s2T+LE19NYr5whcyRnKtQy1+oR/C4j8zYiovdQOQR8Eqw9ka1rpXllorKeQe3mVw00IhZ9z5bpXiyZW+W6Ollj0omzZSv52fllyrPrC6WNeOhrXUvmT/DS9cB2vXMrjcnTOPX1NJmH7nW97Do66aFH+ajsLm/1W8rT5ZnxlXK+I9i+MXsD5WX2WCZeyc+EDImg+0xa5brqn9mYfqK8apwp28n30ibLM2XaHno2jy0vFb2CrHfIxj2ir3rjKG3jKV9Pi8hcl0XpOtTyqqGWsVaP4Pe9LLcRlfdQOQR8Cph1nqnPyszuX3kflWfzM6GVVrmP0pnrrq+zZfuBxs+Ls2VP5LNznikfrcHW99CzeVaazsvUnfDSO3E9wMw9InhUTqP6u+zVP1zR+Sg9G9ehLqflZ8K16gRuze1pD13K+m5g+xXtFyvPq8fmecYxk2/lsQbbi6N7K81Lt647fv896p83FtV2IplR2Up9r261vJfX+h66lbdWnpAzZIzSJspWCHzKO/fSJSbfZrdIfOviEfk0maM83WZ2ra5VI/idru/1vOp+yvIeKoeATwG71tn6jEzv3orL+6h8Jb9ilLOkYZVh80+8/V7RI1t/Ot4ty85ZVl4lb9xDP5mWya+W1XE5WFEec2+leekap95ml3p43jq6j+I69PJQWd0msx51eTZ/p7P3en1kx5ed908D2y9mzzDl2H0ZxafyK0Y5QxBR2vR1+p/ZonsrrxOfKnsqLVpbOs310Ne6h8ylkpNeeiXe8c51eS9trRxRn/4HtSh9pzFxHXp5UYj0suSuNeuhZ+49vB46V5dN8+6tuLyPymdshheyeYhI9D1bZup6wmfsXv+YcfTmYaLslWlWnumhr3UfmZ/w0rtxdC/j6B4RPCrH5mlMkfoey4xXfgeZR7LWusdDl/cyzRtvq+53AdunzP6I9hu7N6N4t2zGALOkgu4zaZ0rQ+bTPxfr9WWqn+z4V/NPpaE86KGv9Xwyl506ReAyHpE765176Wtd8za71CMi8sgrv4vMkUwtf63rPXSZZqFyCPgEoH5HY+GVZfePd2/F5T1Tni3LhGweQxpWGS89c532zLM6en0/GT+RP5Gm8/7y0Nc6T+ZSmQmyzpStEriMM/dWmpe+cZLU95xkiFymW3GLsJGMyXAtm7R1m15Zmb/TK/cbHkHruqj+p4PpT3Z/ZPYgmxfFK/lMyOax91aalx5dd7z9Xu07ypuKs2Ur+RNpMu8PD32ta8h80ktHxF6Jd7xzXd5L89Ilql9R+/Hjf19T22OoL0snj8g9stTlUPqpULexVp/gd3rlXo6bhcoh4OnorHWrLCvTu7fiVl5Ul83PhFZalmSsMpn8fd1F5pXL65M1fkx8sqyXP5G2w18y4UlkjvQ6FZdtRXnoHhE8Khela1Qfp0tyZ7DnI0qX95m4Dq12KuFa9c/NrbI7vXLvoXMI+DREeyZTl5XJ7tlMvFK2Ek4RjpdmXbtslZTv+Cc3r3+ZvrNtRvET+dm0tdb/eehrzZK5bKBL1leQ+Qnv3CJ5q7yFk19R2+MbeeUyLRvXIcpDbbOhrrNWn+B3mcp9NN6o7ndGpo/MvmLKWXuZKcfU9cpGeVa5DImge7bM9HXVz8V2ymfHtzIfTNlKfjYt/bW1KG0yXyo76YHLeIbc0X0mbePKF98Q9thnvPJTZI7K6LaZOlYba/EEv+O7TOXeQ+UQ8Klg9g1bl5XJ7ttqvJLPGmKUF5GIvs+kTVynPjNny3n1vLLTcXa+qvmZtP8Px89tPV8tLa0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=500x500 at 0x7FAE4A5B8E50>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantised = 255 - (dist / np.max(dist) * 255).astype(np.uint8)\n",
    "#quantised = (quantised % 2) * 255\n",
    "im2 = Image.fromarray(quantised, mode = 'L')\n",
    "im2 = im2.convert(\"RGBA\")\n",
    "im2.save('distfield.png')\n",
    "im2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on ufunc object:\n",
      "\n",
      "log = class ufunc(builtins.object)\n",
      " |  Functions that operate element by element on whole arrays.\n",
      " |  \n",
      " |  To see the documentation for a specific ufunc, use `info`.  For\n",
      " |  example, ``np.info(np.sin)``.  Because ufuncs are written in C\n",
      " |  (for speed) and linked into Python with NumPy's ufunc facility,\n",
      " |  Python's help() function finds this page whenever help() is called\n",
      " |  on a ufunc.\n",
      " |  \n",
      " |  A detailed explanation of ufuncs can be found in the docs for :ref:`ufuncs`.\n",
      " |  \n",
      " |  **Calling ufuncs:** ``op(*x[, out], where=True, **kwargs)``\n",
      " |  \n",
      " |  Apply `op` to the arguments `*x` elementwise, broadcasting the arguments.\n",
      " |  \n",
      " |  The broadcasting rules are:\n",
      " |  \n",
      " |  * Dimensions of length 1 may be prepended to either array.\n",
      " |  * Arrays may be repeated along dimensions of length 1.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  *x : array_like\n",
      " |      Input arrays.\n",
      " |  out : ndarray, None, or tuple of ndarray and None, optional\n",
      " |      Alternate array object(s) in which to put the result; if provided, it\n",
      " |      must have a shape that the inputs broadcast to. A tuple of arrays\n",
      " |      (possible only as a keyword argument) must have length equal to the\n",
      " |      number of outputs; use None for uninitialized outputs to be\n",
      " |      allocated by the ufunc.\n",
      " |  where : array_like, optional\n",
      " |      This condition is broadcast over the input. At locations where the\n",
      " |      condition is True, the `out` array will be set to the ufunc result.\n",
      " |      Elsewhere, the `out` array will retain its original value.\n",
      " |      Note that if an uninitialized `out` array is created via the default\n",
      " |      ``out=None``, locations within it where the condition is False will\n",
      " |      remain uninitialized.\n",
      " |  **kwargs\n",
      " |      For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.\n",
      " |  \n",
      " |  Returns\n",
      " |  -------\n",
      " |  r : ndarray or tuple of ndarray\n",
      " |      `r` will have the shape that the arrays in `x` broadcast to; if `out` is\n",
      " |      provided, it will be returned. If not, `r` will be allocated and\n",
      " |      may contain uninitialized values. If the function has more than one\n",
      " |      output, then the result will be a tuple of arrays.\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __call__(self, /, *args, **kwargs)\n",
      " |      Call self as a function.\n",
      " |  \n",
      " |  __repr__(self, /)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __str__(self, /)\n",
      " |      Return str(self).\n",
      " |  \n",
      " |  accumulate(...)\n",
      " |      accumulate(array, axis=0, dtype=None, out=None)\n",
      " |      \n",
      " |      Accumulate the result of applying the operator to all elements.\n",
      " |      \n",
      " |      For a one-dimensional array, accumulate produces results equivalent to::\n",
      " |      \n",
      " |        r = np.empty(len(A))\n",
      " |        t = op.identity        # op = the ufunc being applied to A's  elements\n",
      " |        for i in range(len(A)):\n",
      " |            t = op(t, A[i])\n",
      " |            r[i] = t\n",
      " |        return r\n",
      " |      \n",
      " |      For example, add.accumulate() is equivalent to np.cumsum().\n",
      " |      \n",
      " |      For a multi-dimensional array, accumulate is applied along only one\n",
      " |      axis (axis zero by default; see Examples below) so repeated use is\n",
      " |      necessary if one wants to accumulate over multiple axes.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      array : array_like\n",
      " |          The array to act on.\n",
      " |      axis : int, optional\n",
      " |          The axis along which to apply the accumulation; default is zero.\n",
      " |      dtype : data-type code, optional\n",
      " |          The data-type used to represent the intermediate results. Defaults\n",
      " |          to the data-type of the output array if such is provided, or the\n",
      " |          the data-type of the input array if no output array is provided.\n",
      " |      out : ndarray, None, or tuple of ndarray and None, optional\n",
      " |          A location into which the result is stored. If not provided or None,\n",
      " |          a freshly-allocated array is returned. For consistency with\n",
      " |          ``ufunc.__call__``, if given as a keyword, this may be wrapped in a\n",
      " |          1-element tuple.\n",
      " |      \n",
      " |          .. versionchanged:: 1.13.0\n",
      " |             Tuples are allowed for keyword argument.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      r : ndarray\n",
      " |          The accumulated values. If `out` was supplied, `r` is a reference to\n",
      " |          `out`.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      1-D array examples:\n",
      " |      \n",
      " |      >>> np.add.accumulate([2, 3, 5])\n",
      " |      array([ 2,  5, 10])\n",
      " |      >>> np.multiply.accumulate([2, 3, 5])\n",
      " |      array([ 2,  6, 30])\n",
      " |      \n",
      " |      2-D array examples:\n",
      " |      \n",
      " |      >>> I = np.eye(2)\n",
      " |      >>> I\n",
      " |      array([[1.,  0.],\n",
      " |             [0.,  1.]])\n",
      " |      \n",
      " |      Accumulate along axis 0 (rows), down columns:\n",
      " |      \n",
      " |      >>> np.add.accumulate(I, 0)\n",
      " |      array([[1.,  0.],\n",
      " |             [1.,  1.]])\n",
      " |      >>> np.add.accumulate(I) # no axis specified = axis zero\n",
      " |      array([[1.,  0.],\n",
      " |             [1.,  1.]])\n",
      " |      \n",
      " |      Accumulate along axis 1 (columns), through rows:\n",
      " |      \n",
      " |      >>> np.add.accumulate(I, 1)\n",
      " |      array([[1.,  1.],\n",
      " |             [0.,  1.]])\n",
      " |  \n",
      " |  at(...)\n",
      " |      at(a, indices, b=None)\n",
      " |      \n",
      " |      Performs unbuffered in place operation on operand 'a' for elements\n",
      " |      specified by 'indices'. For addition ufunc, this method is equivalent to\n",
      " |      ``a[indices] += b``, except that results are accumulated for elements that\n",
      " |      are indexed more than once. For example, ``a[[0,0]] += 1`` will only\n",
      " |      increment the first element once because of buffering, whereas\n",
      " |      ``add.at(a, [0,0], 1)`` will increment the first element twice.\n",
      " |      \n",
      " |      .. versionadded:: 1.8.0\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      a : array_like\n",
      " |          The array to perform in place operation on.\n",
      " |      indices : array_like or tuple\n",
      " |          Array like index object or slice object for indexing into first\n",
      " |          operand. If first operand has multiple dimensions, indices can be a\n",
      " |          tuple of array like index objects or slice objects.\n",
      " |      b : array_like\n",
      " |          Second operand for ufuncs requiring two operands. Operand must be\n",
      " |          broadcastable over first operand after indexing or slicing.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      Set items 0 and 1 to their negative values:\n",
      " |      \n",
      " |      >>> a = np.array([1, 2, 3, 4])\n",
      " |      >>> np.negative.at(a, [0, 1])\n",
      " |      >>> a\n",
      " |      array([-1, -2,  3,  4])\n",
      " |      \n",
      " |      Increment items 0 and 1, and increment item 2 twice:\n",
      " |      \n",
      " |      >>> a = np.array([1, 2, 3, 4])\n",
      " |      >>> np.add.at(a, [0, 1, 2, 2], 1)\n",
      " |      >>> a\n",
      " |      array([2, 3, 5, 4])\n",
      " |      \n",
      " |      Add items 0 and 1 in first array to second array,\n",
      " |      and store results in first array:\n",
      " |      \n",
      " |      >>> a = np.array([1, 2, 3, 4])\n",
      " |      >>> b = np.array([1, 2])\n",
      " |      >>> np.add.at(a, [0, 1], b)\n",
      " |      >>> a\n",
      " |      array([2, 4, 3, 4])\n",
      " |  \n",
      " |  outer(...)\n",
      " |      outer(A, B, **kwargs)\n",
      " |      \n",
      " |      Apply the ufunc `op` to all pairs (a, b) with a in `A` and b in `B`.\n",
      " |      \n",
      " |      Let ``M = A.ndim``, ``N = B.ndim``. Then the result, `C`, of\n",
      " |      ``op.outer(A, B)`` is an array of dimension M + N such that:\n",
      " |      \n",
      " |      .. math:: C[i_0, ..., i_{M-1}, j_0, ..., j_{N-1}] =\n",
      " |         op(A[i_0, ..., i_{M-1}], B[j_0, ..., j_{N-1}])\n",
      " |      \n",
      " |      For `A` and `B` one-dimensional, this is equivalent to::\n",
      " |      \n",
      " |        r = empty(len(A),len(B))\n",
      " |        for i in range(len(A)):\n",
      " |            for j in range(len(B)):\n",
      " |                r[i,j] = op(A[i], B[j]) # op = ufunc in question\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      A : array_like\n",
      " |          First array\n",
      " |      B : array_like\n",
      " |          Second array\n",
      " |      kwargs : any\n",
      " |          Arguments to pass on to the ufunc. Typically `dtype` or `out`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      r : ndarray\n",
      " |          Output array\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.outer : A less powerful version of ``np.multiply.outer``\n",
      " |                    that `ravel`\\ s all inputs to 1D. This exists\n",
      " |                    primarily for compatibility with old code.\n",
      " |      \n",
      " |      tensordot : ``np.tensordot(a, b, axes=((), ()))`` and\n",
      " |                  ``np.multiply.outer(a, b)`` behave same for all\n",
      " |                  dimensions of a and b.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> np.multiply.outer([1, 2, 3], [4, 5, 6])\n",
      " |      array([[ 4,  5,  6],\n",
      " |             [ 8, 10, 12],\n",
      " |             [12, 15, 18]])\n",
      " |      \n",
      " |      A multi-dimensional example:\n",
      " |      \n",
      " |      >>> A = np.array([[1, 2, 3], [4, 5, 6]])\n",
      " |      >>> A.shape\n",
      " |      (2, 3)\n",
      " |      >>> B = np.array([[1, 2, 3, 4]])\n",
      " |      >>> B.shape\n",
      " |      (1, 4)\n",
      " |      >>> C = np.multiply.outer(A, B)\n",
      " |      >>> C.shape; C\n",
      " |      (2, 3, 1, 4)\n",
      " |      array([[[[ 1,  2,  3,  4]],\n",
      " |              [[ 2,  4,  6,  8]],\n",
      " |              [[ 3,  6,  9, 12]]],\n",
      " |             [[[ 4,  8, 12, 16]],\n",
      " |              [[ 5, 10, 15, 20]],\n",
      " |              [[ 6, 12, 18, 24]]]])\n",
      " |  \n",
      " |  reduce(...)\n",
      " |      reduce(a, axis=0, dtype=None, out=None, keepdims=False, initial=<no value>, where=True)\n",
      " |      \n",
      " |      Reduces `a`'s dimension by one, by applying ufunc along one axis.\n",
      " |      \n",
      " |      Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`.  Then\n",
      " |      :math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =\n",
      " |      the result of iterating `j` over :math:`range(N_i)`, cumulatively applying\n",
      " |      ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.\n",
      " |      For a one-dimensional array, reduce produces results equivalent to:\n",
      " |      ::\n",
      " |      \n",
      " |       r = op.identity # op = ufunc\n",
      " |       for i in range(len(A)):\n",
      " |         r = op(r, A[i])\n",
      " |       return r\n",
      " |      \n",
      " |      For example, add.reduce() is equivalent to sum().\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      a : array_like\n",
      " |          The array to act on.\n",
      " |      axis : None or int or tuple of ints, optional\n",
      " |          Axis or axes along which a reduction is performed.\n",
      " |          The default (`axis` = 0) is perform a reduction over the first\n",
      " |          dimension of the input array. `axis` may be negative, in\n",
      " |          which case it counts from the last to the first axis.\n",
      " |      \n",
      " |          .. versionadded:: 1.7.0\n",
      " |      \n",
      " |          If this is None, a reduction is performed over all the axes.\n",
      " |          If this is a tuple of ints, a reduction is performed on multiple\n",
      " |          axes, instead of a single axis or all the axes as before.\n",
      " |      \n",
      " |          For operations which are either not commutative or not associative,\n",
      " |          doing a reduction over multiple axes is not well-defined. The\n",
      " |          ufuncs do not currently raise an exception in this case, but will\n",
      " |          likely do so in the future.\n",
      " |      dtype : data-type code, optional\n",
      " |          The type used to represent the intermediate results. Defaults\n",
      " |          to the data-type of the output array if this is provided, or\n",
      " |          the data-type of the input array if no output array is provided.\n",
      " |      out : ndarray, None, or tuple of ndarray and None, optional\n",
      " |          A location into which the result is stored. If not provided or None,\n",
      " |          a freshly-allocated array is returned. For consistency with\n",
      " |          ``ufunc.__call__``, if given as a keyword, this may be wrapped in a\n",
      " |          1-element tuple.\n",
      " |      \n",
      " |          .. versionchanged:: 1.13.0\n",
      " |             Tuples are allowed for keyword argument.\n",
      " |      keepdims : bool, optional\n",
      " |          If this is set to True, the axes which are reduced are left\n",
      " |          in the result as dimensions with size one. With this option,\n",
      " |          the result will broadcast correctly against the original `arr`.\n",
      " |      \n",
      " |          .. versionadded:: 1.7.0\n",
      " |      initial : scalar, optional\n",
      " |          The value with which to start the reduction.\n",
      " |          If the ufunc has no identity or the dtype is object, this defaults\n",
      " |          to None - otherwise it defaults to ufunc.identity.\n",
      " |          If ``None`` is given, the first element of the reduction is used,\n",
      " |          and an error is thrown if the reduction is empty.\n",
      " |      \n",
      " |          .. versionadded:: 1.15.0\n",
      " |      \n",
      " |      where : array_like of bool, optional\n",
      " |          A boolean array which is broadcasted to match the dimensions\n",
      " |          of `a`, and selects elements to include in the reduction. Note\n",
      " |          that for ufuncs like ``minimum`` that do not have an identity\n",
      " |          defined, one has to pass in also ``initial``.\n",
      " |      \n",
      " |          .. versionadded:: 1.17.0\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      r : ndarray\n",
      " |          The reduced array. If `out` was supplied, `r` is a reference to it.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> np.multiply.reduce([2,3,5])\n",
      " |      30\n",
      " |      \n",
      " |      A multi-dimensional array example:\n",
      " |      \n",
      " |      >>> X = np.arange(8).reshape((2,2,2))\n",
      " |      >>> X\n",
      " |      array([[[0, 1],\n",
      " |              [2, 3]],\n",
      " |             [[4, 5],\n",
      " |              [6, 7]]])\n",
      " |      >>> np.add.reduce(X, 0)\n",
      " |      array([[ 4,  6],\n",
      " |             [ 8, 10]])\n",
      " |      >>> np.add.reduce(X) # confirm: default axis value is 0\n",
      " |      array([[ 4,  6],\n",
      " |             [ 8, 10]])\n",
      " |      >>> np.add.reduce(X, 1)\n",
      " |      array([[ 2,  4],\n",
      " |             [10, 12]])\n",
      " |      >>> np.add.reduce(X, 2)\n",
      " |      array([[ 1,  5],\n",
      " |             [ 9, 13]])\n",
      " |      \n",
      " |      You can use the ``initial`` keyword argument to initialize the reduction\n",
      " |      with a different value, and ``where`` to select specific elements to include:\n",
      " |      \n",
      " |      >>> np.add.reduce([10], initial=5)\n",
      " |      15\n",
      " |      >>> np.add.reduce(np.ones((2, 2, 2)), axis=(0, 2), initial=10)\n",
      " |      array([14., 14.])\n",
      " |      >>> a = np.array([10., np.nan, 10])\n",
      " |      >>> np.add.reduce(a, where=~np.isnan(a))\n",
      " |      20.0\n",
      " |      \n",
      " |      Allows reductions of empty arrays where they would normally fail, i.e.\n",
      " |      for ufuncs without an identity.\n",
      " |      \n",
      " |      >>> np.minimum.reduce([], initial=np.inf)\n",
      " |      inf\n",
      " |      >>> np.minimum.reduce([[1., 2.], [3., 4.]], initial=10., where=[True, False])\n",
      " |      array([ 1., 10.])\n",
      " |      >>> np.minimum.reduce([])\n",
      " |      Traceback (most recent call last):\n",
      " |          ...\n",
      " |      ValueError: zero-size array to reduction operation minimum which has no identity\n",
      " |  \n",
      " |  reduceat(...)\n",
      " |      reduceat(a, indices, axis=0, dtype=None, out=None)\n",
      " |      \n",
      " |      Performs a (local) reduce with specified slices over a single axis.\n",
      " |      \n",
      " |      For i in ``range(len(indices))``, `reduceat` computes\n",
      " |      ``ufunc.reduce(a[indices[i]:indices[i+1]])``, which becomes the i-th\n",
      " |      generalized \"row\" parallel to `axis` in the final result (i.e., in a\n",
      " |      2-D array, for example, if `axis = 0`, it becomes the i-th row, but if\n",
      " |      `axis = 1`, it becomes the i-th column).  There are three exceptions to this:\n",
      " |      \n",
      " |      * when ``i = len(indices) - 1`` (so for the last index),\n",
      " |        ``indices[i+1] = a.shape[axis]``.\n",
      " |      * if ``indices[i] >= indices[i + 1]``, the i-th generalized \"row\" is\n",
      " |        simply ``a[indices[i]]``.\n",
      " |      * if ``indices[i] >= len(a)`` or ``indices[i] < 0``, an error is raised.\n",
      " |      \n",
      " |      The shape of the output depends on the size of `indices`, and may be\n",
      " |      larger than `a` (this happens if ``len(indices) > a.shape[axis]``).\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      a : array_like\n",
      " |          The array to act on.\n",
      " |      indices : array_like\n",
      " |          Paired indices, comma separated (not colon), specifying slices to\n",
      " |          reduce.\n",
      " |      axis : int, optional\n",
      " |          The axis along which to apply the reduceat.\n",
      " |      dtype : data-type code, optional\n",
      " |          The type used to represent the intermediate results. Defaults\n",
      " |          to the data type of the output array if this is provided, or\n",
      " |          the data type of the input array if no output array is provided.\n",
      " |      out : ndarray, None, or tuple of ndarray and None, optional\n",
      " |          A location into which the result is stored. If not provided or None,\n",
      " |          a freshly-allocated array is returned. For consistency with\n",
      " |          ``ufunc.__call__``, if given as a keyword, this may be wrapped in a\n",
      " |          1-element tuple.\n",
      " |      \n",
      " |          .. versionchanged:: 1.13.0\n",
      " |             Tuples are allowed for keyword argument.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      r : ndarray\n",
      " |          The reduced values. If `out` was supplied, `r` is a reference to\n",
      " |          `out`.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      A descriptive example:\n",
      " |      \n",
      " |      If `a` is 1-D, the function `ufunc.accumulate(a)` is the same as\n",
      " |      ``ufunc.reduceat(a, indices)[::2]`` where `indices` is\n",
      " |      ``range(len(array) - 1)`` with a zero placed\n",
      " |      in every other element:\n",
      " |      ``indices = zeros(2 * len(a) - 1)``, ``indices[1::2] = range(1, len(a))``.\n",
      " |      \n",
      " |      Don't be fooled by this attribute's name: `reduceat(a)` is not\n",
      " |      necessarily smaller than `a`.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      To take the running sum of four successive values:\n",
      " |      \n",
      " |      >>> np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2]\n",
      " |      array([ 6, 10, 14, 18])\n",
      " |      \n",
      " |      A 2-D example:\n",
      " |      \n",
      " |      >>> x = np.linspace(0, 15, 16).reshape(4,4)\n",
      " |      >>> x\n",
      " |      array([[ 0.,   1.,   2.,   3.],\n",
      " |             [ 4.,   5.,   6.,   7.],\n",
      " |             [ 8.,   9.,  10.,  11.],\n",
      " |             [12.,  13.,  14.,  15.]])\n",
      " |      \n",
      " |      ::\n",
      " |      \n",
      " |       # reduce such that the result has the following five rows:\n",
      " |       # [row1 + row2 + row3]\n",
      " |       # [row4]\n",
      " |       # [row2]\n",
      " |       # [row3]\n",
      " |       # [row1 + row2 + row3 + row4]\n",
      " |      \n",
      " |      >>> np.add.reduceat(x, [0, 3, 1, 2, 0])\n",
      " |      array([[12.,  15.,  18.,  21.],\n",
      " |             [12.,  13.,  14.,  15.],\n",
      " |             [ 4.,   5.,   6.,   7.],\n",
      " |             [ 8.,   9.,  10.,  11.],\n",
      " |             [24.,  28.,  32.,  36.]])\n",
      " |      \n",
      " |      ::\n",
      " |      \n",
      " |       # reduce such that result has the following two columns:\n",
      " |       # [col1 * col2 * col3, col4]\n",
      " |      \n",
      " |      >>> np.multiply.reduceat(x, [0, 3], 1)\n",
      " |      array([[   0.,     3.],\n",
      " |             [ 120.,     7.],\n",
      " |             [ 720.,    11.],\n",
      " |             [2184.,    15.]])\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  identity\n",
      " |      The identity value.\n",
      " |      \n",
      " |      Data attribute containing the identity element for the ufunc, if it has one.\n",
      " |      If it does not, the attribute value is None.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> np.add.identity\n",
      " |      0\n",
      " |      >>> np.multiply.identity\n",
      " |      1\n",
      " |      >>> np.power.identity\n",
      " |      1\n",
      " |      >>> print(np.exp.identity)\n",
      " |      None\n",
      " |  \n",
      " |  nargs\n",
      " |      The number of arguments.\n",
      " |      \n",
      " |      Data attribute containing the number of arguments the ufunc takes, including\n",
      " |      optional ones.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Typically this value will be one more than what you might expect because all\n",
      " |      ufuncs take  the optional \"out\" argument.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> np.add.nargs\n",
      " |      3\n",
      " |      >>> np.multiply.nargs\n",
      " |      3\n",
      " |      >>> np.power.nargs\n",
      " |      3\n",
      " |      >>> np.exp.nargs\n",
      " |      2\n",
      " |  \n",
      " |  nin\n",
      " |      The number of inputs.\n",
      " |      \n",
      " |      Data attribute containing the number of arguments the ufunc treats as input.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> np.add.nin\n",
      " |      2\n",
      " |      >>> np.multiply.nin\n",
      " |      2\n",
      " |      >>> np.power.nin\n",
      " |      2\n",
      " |      >>> np.exp.nin\n",
      " |      1\n",
      " |  \n",
      " |  nout\n",
      " |      The number of outputs.\n",
      " |      \n",
      " |      Data attribute containing the number of arguments the ufunc treats as output.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Since all ufuncs can take output arguments, this will always be (at least) 1.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> np.add.nout\n",
      " |      1\n",
      " |      >>> np.multiply.nout\n",
      " |      1\n",
      " |      >>> np.power.nout\n",
      " |      1\n",
      " |      >>> np.exp.nout\n",
      " |      1\n",
      " |  \n",
      " |  ntypes\n",
      " |      The number of types.\n",
      " |      \n",
      " |      The number of numerical NumPy types - of which there are 18 total - on which\n",
      " |      the ufunc can operate.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.ufunc.types\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> np.add.ntypes\n",
      " |      18\n",
      " |      >>> np.multiply.ntypes\n",
      " |      18\n",
      " |      >>> np.power.ntypes\n",
      " |      17\n",
      " |      >>> np.exp.ntypes\n",
      " |      7\n",
      " |      >>> np.remainder.ntypes\n",
      " |      14\n",
      " |  \n",
      " |  signature\n",
      " |      Definition of the core elements a generalized ufunc operates on.\n",
      " |      \n",
      " |      The signature determines how the dimensions of each input/output array\n",
      " |      are split into core and loop dimensions:\n",
      " |      \n",
      " |      1. Each dimension in the signature is matched to a dimension of the\n",
      " |         corresponding passed-in array, starting from the end of the shape tuple.\n",
      " |      2. Core dimensions assigned to the same label in the signature must have\n",
      " |         exactly matching sizes, no broadcasting is performed.\n",
      " |      3. The core dimensions are removed from all inputs and the remaining\n",
      " |         dimensions are broadcast together, defining the loop dimensions.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Generalized ufuncs are used internally in many linalg functions, and in\n",
      " |      the testing suite; the examples below are taken from these.\n",
      " |      For ufuncs that operate on scalars, the signature is None, which is\n",
      " |      equivalent to '()' for every argument.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> np.core.umath_tests.matrix_multiply.signature\n",
      " |      '(m,n),(n,p)->(m,p)'\n",
      " |      >>> np.linalg._umath_linalg.det.signature\n",
      " |      '(m,m)->()'\n",
      " |      >>> np.add.signature is None\n",
      " |      True  # equivalent to '(),()->()'\n",
      " |  \n",
      " |  types\n",
      " |      Returns a list with types grouped input->output.\n",
      " |      \n",
      " |      Data attribute listing the data-type \"Domain-Range\" groupings the ufunc can\n",
      " |      deliver. The data-types are given using the character codes.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.ufunc.ntypes\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> np.add.types\n",
      " |      ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',\n",
      " |      'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',\n",
      " |      'GG->G', 'OO->O']\n",
      " |      \n",
      " |      >>> np.multiply.types\n",
      " |      ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',\n",
      " |      'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',\n",
      " |      'GG->G', 'OO->O']\n",
      " |      \n",
      " |      >>> np.power.types\n",
      " |      ['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',\n",
      " |      'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', 'GG->G',\n",
      " |      'OO->O']\n",
      " |      \n",
      " |      >>> np.exp.types\n",
      " |      ['f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O']\n",
      " |      \n",
      " |      >>> np.remainder.types\n",
      " |      ['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',\n",
      " |      'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'OO->O']\n",
      "\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}