{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncase=1.6;\n",
    "size=490;\n",
    "time=12;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2, imutils\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from skimage.measure import compare_ssim \n",
    "from glob import glob\n",
    "from scipy.spatial import distance\n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.patches as patches\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_if_close(cnt1,cnt2):\n",
    "    row1,row2 = cnt1.shape[0],cnt2.shape[0]\n",
    "    for i in range(row1):\n",
    "        for j in range(row2):\n",
    "            dist = np.linalg.norm(cnt1[i]-cnt2[j])\n",
    "            if abs(dist) < 50 :\n",
    "                return True\n",
    "            elif i==row1-1 and j==row2-1:\n",
    "                return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7D6f2eCEfx8hvqf18BI0Bac81sExeSyvi5rZnLmsEqh2Z0iy6GtLJzpGMwpDxvXp1aLCCjhY6Q5LSLAXs3ZHDupchKmtiaRH1paWlY1pY5riEB8tNYF2bl3zlAmvrl4vk5ZD9yH8t0vJ9WiYwmeuyfAnp0+UkReOmPQLLZYyCfVbQaTwe4+DgYEa69Ax3xn/G65OfI5NR871HriNrA9DcQ/NoZbUEqZGjdt8jzKwM2XXbGZLUoJkEfIJmfTBamd5u7XVeZtLUkkckL9VrEZw3EbUFaMmi+bi8vBakH1COC//5Cmqb9miitxHJIzkyL+WJHi2UkAEg0hS5til/wwfAzMyWGn/mHanRPPe0QcutNK+2JeVogyyByXnWxiSX861GhgidJkkZJIm0HIsgZT7PLyV/dMpCdpe1zrABJye8Rp7ShxhFr626I83V87nKNFa7PFl4VJJeesHbppnbvI+9+qTZTGNYypNzl14Zsl2eD1P6fPv9/szKoDOu1E469pRZqJ7vkOSiOj2iBOyzoDIPQdsMa6yWiKDbao1eXoK3Zqyyakm40yTJIYmSfD7W0xqaNmSRimaKZjoys8PJBShJSCNKKR8fdO5v9PJYZVppNJk9n1QULJH5Oeidi/QETsZnl6nPe3qnjX9N20Dlfd6XdLyJ5k+v15uZ29YGr7l1OLhZ7FkF2tzNEJ112iLjB81oq5b7REOWvOR1zzKLrLaM2d4pktT8Jd7TBKSByMi2lnYRO2Pk5/HScHgTXiM/bfJk6vSuW34y3taMmRhBm9C9Xg+rq6sz/x2NoaVNAvoGo7VL+hczP4KmRdA1v6VcUNzfSeck6fd8SimzyLb3y36RaWiNh2c18c/RfMyeHpF1S1mzZm3WDZDdyDStMaNF8vkf+Yw7RZKEaHCl6SwnUmRqWEQqr1l5a6JsUlbts+Z7s47u8PzexLSCGpl8pPlY5VgELa97E49MV6npW9qgZXpzTUGOo3X0RtuMgeOvO+MgsuWEQRs0yTMajY5pkvRTDtZBcqtPCNoc1WTWoJ2v1RBpWdYYRmtM5vHqmIdcM6dTNGustqzOkaQVWOGwfIxcw7Sc/tbARyq5lIkvGK+DM2SjacLWQGpE65mrnsa5iCBUxnTS6u31elhfX8ejR49mpMiJUoP1+9uy/zSXhCartqHJ/tACKvJ3iiaTiUqSq6urxyydGlOUB6bk/Yy/XH72fOMcWddN1idfQ5CZMr32aNCsJrpGZUQvWOkcSWqTUvPhRZPFcnhbA2/t4jKgoZkfViAoe0yItyUyB6MdkbdJq0e7Lw+RR6Z81j8lx0qmodeo7ezsHCubBx5438oos9y4JHHKccycKojeSsTnA4FrkuTaoZ9x4Avfq1+Sg0eWnv/Nk7tG87LK9uZq5F9dBGQgk9fLyS8T8PG+c3SOJDVYu0dmV5XpJTJRcymHVr9GXJbJm5WNypATnD7ziej5jCyNRZqqNXJZafhYyckr+4YCG+PxGI8fP569AINQ+wo12QfSv5jZVMiMpjKAk2Y7aar0Pkn6ve39/f3ZCz2GwyHOnTtnHgHKaGAyUCkXf+16yBKlJw99zqS3oFljXMZsHVq6NuQcrf1OkWTNINSY4oTsBMk4jTU/mGbOSTnbTlQrX+2kILLiZq00R2rk80xJzXVA1+mPAhsHBwcYjUZYWVk5QUpSI9Q0d9nXnCC993N6EXMpv+UTPjw8nL0/kh65pF9M1PyC3vzyxtMKUEgts0ZxqEGmbG091LQpghYI5HVbyGiXFjpFktwskdeBk4/oWbCCJ22JIIKmjWVcAZocns8l6xD30klZ2zjms7Ait7JvhsMhxuMxdnZ2MJlMZr48ebBbRq4luKkuD3xb/SpJljYMbtZzYibXBLVHvh6N+yPPnTt3YgPy+krbZEkztVw6tchuFFloRGj5fqXLKoPatmb7W+ItEbghIS2TYlG7Y3ZCWDuPRji1pM3zWAMaBbAic9qTI7to5aLUvmtyZII9vE30+N6dO3cwGo2wtHT09hxOlF5wqmka1XqoWZDczObX+KLjhEn3xuMxDg8PZ1okkeT6+vox90FbE1XOsczYLYJMLbR1y2i+XA9tLCRCVmuULg0LnSFJvii9RtaSZVtyzWhj2veaiKoWlZUyWFF1TUZZh/Q7ygWrmSqWqyLzvQa8HcvLy7h48SJeeeUVvP7663j8+DGee+45rK+vn3gJMYHIUwtqWT5KC5YJRxoj10pJHtLy9vb28PDhQ+zu7s5cGKurq9jc3Dz2tI21oWY2FPrP83ouJY+YtfWgadk11kRbLdfzw5NPWW5UVhmZdUGbqVzL2gbJ0RmSBGxTM2tmR2h7VEdLB+gTySvH81lapEwTxpsEHhFzgvTSaTJ57fLaGfmgtD7b2NjAxsYG7t69izt37gA4IkIKfmiQv5tD/9sE4fihcJJpaWlJ/VlYbmrv7e1hf39/tqBXVlawsbGBtbW12SOLsq21slmnOyRoY5ALPuuzzGyClrvCC2rWgjR0eQJDppGf22ip2rqQ6BRJEqJdI9v5UlPj1zyzwdrtNaKytJWM05rX56WPzH7tu+YPjCaRthhl2W0Wvaxbk215eRlvf/vbsbu7iy9/+cuzAMgLL7xwjCilJs6jvjyirUWkLZ+mfLabzGkZFeda5GQymT2rTT8HXMrR79lsbm7OSHIRJq/n2436n2vU1hncGjKrIaJMGk1+6ntNI/bm3zzBGQ+dJEmCd1wmGgQrquyZxrx8774FK9oYwZsEVjovrRWMqYE1eQnW2Fimm1cH/d/c3MSLL76I119/Hffu3Zv1/5UrV7CxsXEsAEP55A9T8eM7PC1fdKQtyuskqxYNl3VzNwC9Im0ymWA4HOLChQszn6qFaO5ZeSzCpO/aBt82cFmjDUozv037OCwT2LNkonWkkW7G8ugsSVqDWHP2UEJOkpodVab1fDBcu+FpMxPH81nVmmqaSWGVr2kcVrnyu+VP9aBp58PhENevX8fNmzfx4MEDfOUrX8Hu7i4ePXqEGzdu4MKFC8e0OyJF6m+KBJOLgvugZPCHHz/i/SJJk/ustMVFx3/29/cxmUywsbGBy5cvnzjzyfPJdmehadG8TJ7GIo+MG0iLVEdEwttqEXWbNteAn06oSR+hUyQ5jx+DI+uTmvdwOmCTZY3mlhmsGr+LTJvVZjMRVK2PtHq0HV872yjLOHfuHG7evInt7W1sbW3hjTfewGg0mpnP/X7/mOnNo5NWsIHuyfdOcoIkU137kS/uhyQzezKZYH9/H3t7e9jd3cXBwcHM1F5fXz8WQNL6KoI2ryzfopZGq9sL9sj2StSsDSt/BtnNdVGgjdYrNyTJUsqLAH4BwFUAEwAvN03zU6WUSwB+CcA7AHwFwN9smmarHLXypwB8D4BdAD/QNM0fBHWkGx8FDGrAF1XGfK8lG8B3mntlZvySUdraye75Y6NrURoZdPDa/cILL+CDH/wg9vf38Ud/9Ef4+te/PsvPj9fIcui3ZrR2SNKRGiN/7ySBPvOyyLQejUbY2trCvXv38ODBA4zHY1y6dAlXr17F+vq6qilnIc1qLk+kNbZB1gy3fNWRn78N5i1LO9WhlbsoTXIE4H9smuYPSikbAD5TSvk3AH4AwG83TfNjpZRPAvgkgB8B8N0A3jP9+zCAn57+N6GF5iMsyl/CfZXZ4xnZ3U7TbiRZZJDRCK00kfllye7dy/pyMwSr9WWv18OVK1fw4Q9/GJPJBF/4whewtbU10yAvXbqEy5cvz36ulWuF/X5/dtaSZCEylS/TpTT8ZyXoHpnrBArQPH78GHt7e3j06BHu3r2L27dvY3d3F+fOncO1a9fwwgsvYHl5ORzbtsRWYwFFpGaVT+Abi+cmqCU0y61Ta/nU3Le+LyS63TTN6wBen35+WEr5YwDXAXwUwHdOk/08gN/DEUl+FMAvNEc98G9LKZullGvTclzURK1r/Imev4Tucx+l57ep0ei4GQi0ix7XmPGe9pL1e3LUOMUz5UVt4/d7vR5eeOEFfOADH8B4PMaf/umf4vbt2xiNRtjZ2cHh4SE2Nzexubk5ex0ZBVe0gE10hIT7JDVMJpPZT8Y+evQIDx48wO3bt3H//n0AR8GlGzduYHV1tbW/0dpcrCCklt/yu2c1QMskt1wZEbz5Kk8UZJBRXKQbx5q72TqrfJKllHcA+CCATwN4gYivaZrXSylXpsmuA3iFZbs1vRaSpAbLr0L/PR9MDTiReZNcO6wbBXy474i+y5coRMgQU0TkWl0acVlaQ025MnhVCwqs3Lx5E4PBAIeHh/jyl7+MW7duYWtrCw8fPsSVK1cwGo2wsbGB1dXVWf/2+/3U45602Wov2F1aevIzshQQIk2StMj79+9jPB7jypUreN/73ocbN24ce3+kdzi7xhUS+TYzJw1qLApr3CKibDPm3kbJv1vKjZbPqj9jiWlIk2QpZR3AvwbwD5qm2XYK1W6c6NlSyksAXgKOzKcMLD8TYRE+mjY7pmbCcxk91T8ja0aLjWSi/NYmoPnigNwjbrLPKO082gHlXV5extve9jZ867d+K5aWlvAnf/In2N7eBoDZ+cT9/X1sbm5iOBxiMBjM8md8aNxHyfPJtk0mExwcHODRo0e4f/8+tre30TQNzp8/j/e85z148cUXsba2dmJT5KSRmVtaX/DglNaOGtSa3NY964hX202RyyY/e3JY5FirzXvjkiLJUsoARwT5i03T/Or08htkRpdSrgG4Pb1+C8CLLPsNAK8pQr0M4GUAeMc73tHwSWBFKDMNAuyJlkVGY/J8HJmyZL5aEz/jY/E0EIv8JIFn/ERaoGEeP5Usazgc4r3vfe+MCL/whS/g7t27M9/g5uYmLl68OIssr66uYjgczrRE6hN+XCjjizo8PDzmh6Sngd58880ZMb/rXe/CN37jN2JzczNlYdSQSBR15vV4R9pknZ5rKUvG85ChlD9z3VsL2TVWk4cjE90uAH4OwB83TfO/slu/DuDjAH5s+v/X2PUfLqV8CkcBmwcZfyRgR1czPpls+YQ2u3DUsTWOZ5LB0+5kmYs+a5Y1daL+zviJIkifmixzOBzi2rVr+NCHPoT19XV85jOfwZtvvonXX38dDx48wNbWFjY2NnDx4kVcuHAB6+vrs9eV0XlIepGGfJJG0yzH4zFGoxH29/exvb2NR48e4Y033sDW1hb29vawsrKC69ev413vehcuXbqEwWCAyWRyog3WmHlmr9REMxqxpkREpGkRa43Jm4Vce5HfnNCGjCONtFbjzGiSfwnA3wbwH0op/3567R/jiBx/uZTyQwC+BuD7pvd+E0fHf76IoyNAP5io49RRQy5t/SrkF8yQGU+jadAaWVgkH2mcNf4cq5zT1By0+xoxrKys4MaNGzh//jzW1tbw2c9+dqZV3r9/H8PhcOafPH/+PIbDIdbW1mb/19bWcP78+WM/q6D5cg8PD/H48WM8fPgQOzs7s/Lv3r2L3d1drK6u4vr16/jmb/5mvO1tb5uZ+N6C9IIlVj9om6hFljIPwTsyVKtsZH3wGXCZs3msuSzXqxxXDsud4iET3f5/ofsZAeAjSvoGwN+LyvWQEbytNhkhE9yQclCamkGXZg1fEHJhaeRYQ/rW5JLl1JjMnnugRq7a+3Rg+wMf+AA2NjbQ7/fx1a9+Fffu3Zu9jYd+snYwGODcuXOzt/JsbGxgc3MTFy5cwMrKCgaDwSwNf8Tw4cOHePToER4+fIiHDx9ia2sLDx48wP7+Pvr9Pq5du4b3vve9uHr16uzFupr2ltHKMhp6FGyxyNIr1yJKy5rjZc5r1Xjz0cujyZKFt/FHCkAnnrjJnJOs6dhMBC+jYWn5tfuaWep1vFxQ3DFvEWTGLM/2X40JUtN/87gCajTVXq+Hy5cvY2NjA88//zy+9rWv4XOf+xxeffXVmb+Qfjen1+thZWVlRpbr6+szU5y0y4sXL2JlZWVmXj969Ag7Ozt48OABdnZ2sLe3BwBYXV3FtWvX8C3f8i0nAjWa9pbpD/Inen0gSU1CO+EhN16CpplKP7VnsXhzKCKwKL8FTfOr8WfW3NfQCZIkaLurNUjWDpwhyCw8LUsrn8tkRf/oe0ZjlWagd8SC+9M0GcPofiwAACAASURBVDLt4WkyJnjUv1Yk1OsbWa63qOhZ74sXL+Lq1au4desWvvSlL+GNN96YBXb29/exu7s78y2Sltnv92fEubGxMfMpElHSb9bQUaSNjQ3cvHkT73rXu44RJMlmnW6Q7ff6yUNWg8pYKFLzzQR4svJF99poglKBqukv69pCAzdPC9ri52ci5xksbbFaaa28VvpMGZGPM2POy+/agMtyMgsquu6VbWnd3pk++T2781tjSMGYjY0NfMM3fAPe//73Y3t7G1//+tfx5ptv4rXXXsPdu3dn/sTd3V3s7OyglCcvrZC/qU31njt3DhcuXMD169fx9re/He95z3vw/PPPYzgcmnJG1okkJ6v9knQ9Lc9zPVlaoUyTORKUJU3N/PdQ68PX6vKuRWsvWiedIUnAfzUa/6zt2vP4Jy1Ht7aYo0mX1SIiubNEovlESbZ5TJroXlZzqEFmd7d8S/SC3uFwiOeeew7Xr1/Hzs4Otra28Oabb+LWrVu4ffs2bt++jUePHs1+4ZB+foHk7vf7swDQ2972Nly/fh3vfve7cfXqVWxubmIwGJjzRWt71jcY3dOI0MqvzQMtuBP5IufFPKavNReyCkeNthm1vTMkWetvtEzYGlMnG8XN+j8yAyP9T5wsLXKKypUE6cnMoRGpR4JcDs/0nxfzuknouM/a2houX76Mmzdv4v3vf/+MNO/du4ft7W3cv38fOzs72N3dnT2/vbq6isuXL+PSpUu4ceMGLl26hPPnz58gR0LGxeGZ4hlNT8srNdIooq35v3mZWgTdGttFBE1rCDK7/mrXaTbC3hmSXAS83SebliMTwJD1eKYlfW+a5kTamsiydS8TLNLK8fJ5Gm9W5ihaquWdl3xp0ZM5vby8jAsXLuD5558/9jvZ9EfjsbKygrW1tZm/0iJHC1p/ZUxxacpGkWrNBNeIT8LyQXpyyDQW2pBnWzeUl7bGnZOxuDpBkrVntbJaZ8aUzdbplbGoiZExayOCzBJVtrxs+VY+mcdajG0CQxyaH1SSSCllZk7zPG03UQ8eWWryerDmvJyDGlnKzdjzK9ZEuvl8qAlaRcFHr41WuqwGKcvLzq9OkKQFrRHcXNCOTpwmMbYtq7Z8mSarZUXap6W51ZZXu/trmmSb40OaZpUJOFjXvR/MqjklkQ16cFiEZsHTKmV+6Xu00ml1ZA6KS+3LI3/NneQR1bybZTTmbcrsBElKLSVSga2JtYjD5ZldigZcqy/jp7M0K36NEyR9j86YReZ2W2LMTLBa98A8fkfqC75RErS5VFPnvOf4ZJ1SLoJHaN5z2BFZSrkssz1SQLT5kTn/mTXV59Xy5vVb16ATJAk8IYPIHOCw/HqZA91ZmWTZGqJItVWmLEO2hxMkz9vGjxNFQTV5tHSnpWHXBoH4piEXP++zWtSQUOY0BofnW5S+wigApMlp5ZNlWBu8JFWtfIlI+83OuyxBZsc1u3Yz6AxJatFeDdKPlAmWeMhorxmC5J+zA+n5/TwzZFFmsJdHI8YaDbkWteb3ZDI59hZyTjBcXk0j88Y8q0la2pYF6ykXkjN6CsaC5RPU8rUhvIyZbyFzrrLNWqV8kRVhlV87TztDkvOg7VGUrIZUU34tQUaaGWlGPK1HbNYTSrUEqk20TNva7tq9Xq8qgEJt6/f7mEwmx97so21cwMlFmzGTLdTmlT7DtucnJTI+ykWgRqZ5Zcie8c0QoGadRXkkOkmSmY6VDmHrnGFtVDe6p8mqaSyR6R/5PrlmnSUsjQCyzniJiBjbBI8yqNEo6U0+GuQcivxsvK6M/y9j/kr5yYfq5dPOP0aRZg3RY681Pk1LVlmmBt6XPF+keEQa/aLO6GbmaydJ0jtGYJkX0aLNTooMLPNOkyPa7Tz/jEWcGYLXdk9t4mV24xqT5bQc6toCl24J7qP0NEieXjsZ4W1glqbuySvT1/rM54lI10Db7OctX/OJUl0clunsIaMlLgKdJEkLmmMZyL/1ZxH1e+VFJq2nZbQZ7IyWxwnS+zGsWn9j5CawkPGxeeV6fi45P7QFmX0CS3s81aqbj21Gc9d+bMybH1KjXBQpatDIS9tktaCZRNRXst7MEai29y1kTPvOkGR2x7ImU9aP4ZXBYZnwtRE+XnaNaesFdbyytHpLKa7Prw0ptzV1PId7Nr8G7UiNtplmzVetHi967NUrId0EkpCtyLYXlbbaMQ9qItdeoCjSTtvOB57O6ne+btr2TSdI0jIjtXNwPF3tGThe7jxyAfbhWEtW61o2ImeVFZnC/A03vN18UmXM60iOGrTxE0dlSR+wV5a2QLNuGU3TsojXK0+anvwYUGSKZkgxE+WO2pwNvHANV7oSpJbt1Rv5KOfZpLnmWzvPOkGSBCvwYj3alDFtoklfo8bXHMvIXJPHVWrlqw2uWO6CGp9jWw3SCpDMq51KwvA0fW5xWORqlc3r4JAbZdtHbDVXgub/9MzuttF5a51YTyBZ+WRQiq7RPNc2ZmvMrL7PrK3aY0URcXaGJDlhyKCI3KFqfDPZnZ3LUXNd1mFNJq08TpDerk73JaFIgtV8R/y+FzCo8W+2gdYuz0faplxrHCyfokwTHUGKzv1ZASNrEUaELTVLSZSer5CXIxH187xHh3ikfjJ5ckwrcnNZ5rnWf22i2zXEy9EZktS0HJoQvHO9SLdWVvaep3Vl/JJW+VY5lgYZab98wWiapCRTWV/ku9FgaaBa3szCrHH2R5OY91fkfuFzyJtv0XPWEpaPsybQZBG2RYSS+DUTOqPdZk1vC1yJiTZ7K6/8rvWJNw9kG9qca/bSd4YkNciFXUuIfLLVquAarLOYteXU1K9pkUSQHnlZhEwBnEX4UeV1y3TiqHkKw0objYHl14s0bW2BZ8xSjYw0MtOQIUqeznqEUY6DliaL2vSUh88tTnhcHuu/hEWyEVnWrLls2k6TJCHqUA+WaZkhXMu/ZWlwiwQnQWmWRyZwpLHy6Gqk5VhlSDmoXA7v0dLMWEZELs1UWqSadqn1ieZf5IEuKbOXl/JbvtdaX6mUWQuIaGVLEz8iSk3D1w7ie5D9RfNL81FGBJlZR5pv04L0q3oKkIVOkGRGTecdq6XLmHNZUqv1S2YGtvZYg9QeI81Ry+9d88hRfq7xS3rEIE1Ii4wsM8yTVdbJ65BmW+TD1fx6VlqZ3tNgZXppVsv5LftScwtYGqhFlCRT1syuUUo0rbs2hnDaqNU2gY6QJEdEdtok0MqI/FNUVht52pCipaVaZMY1tYypm7keyV6zCUSR9YwJzBERaEbz1AiBlyXnizWeGtFnjhbJcqUcWvnW9ayZnzGLZZvlhjsveVkbmKxDO0TP82f6VptXngXY5sSEROdIkkMzm+R9L+9pyBMhGwDySJMI0tMGNS2PrtdEr63yvTbxMrM7c0RyltZZC23xe347nk7WX2suazLw8nl51hG26Gib14feeUrKa5m4kQWXgdTYM+nbruFFkF8WnSZJjmiwrA7XFl+tBuld0+rxdrZoUlgkWEOOGe3OSltLsG02o0wwTTPTauvySM4rS6bPmOVWedq8pLnsmcuZNs0DS0OWJjKXV4N1PdrkqH6rf6J8XtrsGs9u8J0kyWiieMc0MmVbqBksrbw2ZqlXpkaQ1iSxfJk18kSyzqvtcT9Vbb4aZBa5F6HXzFNNDouE+SYgHwfVTHFJfDw6LNugaa+yX63AS43JrpGoFoTRkAmW8D7KkJU1Z6yNSj7qaSFTd6dIUvoSPNNDTsrswssuOEtj88rytDBqjzYpaVFoZjYnvUxQpkbuzK4cpbEioosA77M2eQE9gsyR1Rr5NSrX21Qsv6Qmh6ahynkg0/K14a2TyWSiBqI02bT2Z/2eWtkeOVNdVlBHtj2SO5KLp68N3nSKJCUyQRprQlmRQiDvS7MmKr8v4RGN/C/z8fo4cfI8tWa4JV8UFLLyemMALJ4srb6yTHBLY7TOXFqkYPnuKK22oWtppVxa0ISXq53ntOrXnkKz+sFC28coozK8dNzUpnI5kWXcGFlNMYOorzpNkoDudCd4RCdJUX7WTNcambSy+T3NXJKy8+teRE5GBflCk6RaI79FmNmTAdqCrtE8rDq9dNr3iAij8q3HFbP9mdkkNIKV/WfNGY1gLe0wexZSS6u1QSP/aKyi+5Z1UGM1LIIYLY6Q6AxJemq/5u+RaSSyGmC2jIzWaGl3ETLppcxStozp75WbmTDRdb6YObwx80wx677VNo+gLaKQ5XEt0iI+zSTPbhIRWXIZI5dTF2D5Wz1YSkY0Z+dxv2j5xuNxSrnoDEm2WYDWQV8PsqNoR6k1q3leKW9GE6N73MzOmsrRxKoNFlkkHWlfWQ1OI81sXVH/yfxWAMWrW8vvkSrBekdn1G+8fI0sLa1SupZ4Oml6a8Qa+Y8lkTwN10kEvsYWKU/TNDg8PAQALC8vd1+TLKWEg6MNugyGZHyMWXky0MhKTkTLJSDNZG1H48Qp/ZNW4MAiyDYTNLtxZPx4HNpilaRk9ZvVDmt+ZCHrt0ximV4LukRaq/foouxHSzPViFJqwDXKgwXN9VCjOXuo0SIz6aI4Q+bomYZOkKRETRDAC4ZEzt2ISGpJ13IRaJC+UUmCPI3mr6wJvHiyRH3QNqrokaO3eL3NLtunsr6oDHnqQCO5bODH0i55mkUGuSRBUv0aWZIM3hhHpwE0RJvkvK6Cmmi0N8fkWhkMBieua+gUSXq7Od3POvjlZKjdVbOamKb9ed9leVKD5N9lgCYixnlJ35KzdgfO+DS1iW/5AbPk6Gmx3rjzsbACFZ4MVuAnCsxQfdGclHJZGqeUm+r1LC0ZKa9B1nJ4Fj7VrOstwwudIUmLXOQgZnc2jTCsJxyisjz5vLQRSUkN0rvHy67RImtBWlWNVsxR6welOqxrUX5pBkrUbIqSpD2z1SJFqyzL18jTa+2z5I8IXZNFI3OLwGoCUV5w1Wsjb4tWf5tNPYPa4E9ailJKr5Tyh6WU35h+v1lK+XQp5QullF8qpSxPr69Mv39xev8dlW04LiAjieyflp6uRXV5Zi9PY9XL7/Hyer3eifRaWRpk2Vo+XtciJ5e2OWh1ZSad5jbQyuT1avVpBJmV30sTbTpW/8o5apWVkVvrlyykFcJBGmuGqObx7WrpvXpJNg08XxR0qp3zPGYQyVejSX4CwB8DOD/9/uMAfqJpmk+VUn4GwA8B+Onp/62mad5dSvnYNN3figrn2sQizESrjlKK+ZOeGjRNRdNU6TqQ+1kCTniW9sLT8HqjRacFD7ILTmublc/TtCxfpkaU1hEbWW4kh1W3paFq/dYWluXDZbQsGct09g6WUzrZNrrmaYd8PkTkI+W02t6m72TkOiJjSt/2ILkc+2w5KfYppdwA8F8A+Nnp9wLguwD8yjTJzwP4G9PPH51+x/T+R8qctmDb7JGGpmkGWh5PY/K0Oe+PEx7XNjVtiZdvaY1Si9G0X0tu2YY2/az1U9T/UhbrujdGXj4OSxv10sj2WchoI7IcS9OM5PIgx1yzQKxzjZZsTwveGtU+R2kXkY6Q1SR/EsA/ArAx/X4ZwP2maUbT77cAXJ9+vg7gFQBommZUSnkwTf8mL7CU8hKAlwDg8uXLM+Hn4dOMOb0IWJqWRkQa5BGQzIKUBGnVw3d8r1yL9D1ZLbkImei1p3VE7gavbg4rmGEFWCxI7ZDKoLKjgKKmqXJNT8vnaXZ8E4sCUVI7rWm31iZvHvA6FnE0SJadWbfZNklk+CYkyVLKXwdwu2maz5RSvpMuK0mbxL0nF5rmZQAvA8A73/nOhpOAd3RDm6T8vtMO9bpWRtSZNHCZRW0tlIx80ty28mgLxyovI6OUV5YhF55c9HyRaogmfbSwvU2iJoqbITe+SOXYZc1iWZ9GlFlS8QjQC8RwGWSatuayhFVGtvy25xgjRJwSIaNJ/iUA/2Up5XsADHHkk/xJAJullP5Um7wB4LVp+lsAXgRwq5TSB3ABwD2vAukn1IiFp+Wfa3cruQA0sog0O88E8IjWK1tGlSWy5ohGwhlyrDFVLJOQw9KILD8k3YvOI2plaWVY+aV2VGNdeIvNevorExmWhBdFi/kcm0xO/i59Rn5+nlLey5SXIT6vX7T2ENo+emhh3kh5mLNpmh9tmuZG0zTvAPAxAL/TNM1/A+B3AXzvNNnHAfza9POvT79jev93mkSvR74iC5r/MJOP3498UdZ9YD5nvwfNRxj1Qw2icrW2axF6zafJ/yz/oZbXu67d4/LzseH+QW9+1PZTm3xR/XwOer5Jb31oyM4HzwqZh1iA+GUbMp0ls4xw1yJqR9M07sYwzznJHwHwqVLKPwXwhwB+bnr95wD8q1LKF3GkQX5sjjpmaONLsq5bE5V2Pm2SRBpl5rosi096zYzj9yzZa2SrMcHl96xfKLPAInNQpvM0YwtWdJnya37GSCbPnM2WpckTycnl8DRZ7b7mAuFpJpMJxuMxJhP7N2g0eWvkkrCIMkPsVoS6Zn3W1llFkk3T/B6A35t+/hKADylp9gF8X025FiytLbvDZZzpkqQ4IdUszkjL046lcO2BrmUIUiMibrrxemvkzKTTTCMpA6XL+upknVpbamXVFoD0IbYNMngbtkd+sm5JXPJ6VKZWL+9vWX4b91QW1jhrc9CSoe0YePe0NeFtoBo688QNB5FF9rybV45FrHzSRppjjcnrmROyXFmmzK/JEJFotDFkNEyvjW2i3TW+P09j5mVa9/nvZkuNiaD5PrM+RFmW1ueeZufly6aT87qW7DlpWgoBTwvoL/1oa/5yOedxE7WF5cu20DmSlGZn5oR9xnyzyGcegtQ61ovC8uu0EWhkp/nbvDSy7Oh6Gw0z0pC8OrVFHqGmfZrGKLUqKYtsjzTBgVzQxZM/QyaeHLL+GlKKtEdLgaghXNmn1tyfJ7LMn4zJWBjaPZ5HszCi+dgZktSIKTspPI3RqycrR1tZpUxEcrUEaZmNcgPwtJZM+zxTmt/3fDlSs9Hq0eS0NAuN3GphaV5eOqrLIorM3Kwxbz25rDI0Iqa5JM33yJTPaoaZ/pDjPY/WzOW1CDKzUXuIiLczJAn4kTZtV7W0vKw2oOXVrmll10wELqtGkJrcWvmUxpItu3tnNGLtetYEjkjR2u29OrxJXOPvlOQB1PnI2piYWZNYIzfakDTN2DP7PTPd83lK8zrrv4u0vGhz0j5rG6OmeNSCb/KcgC3Mb+AvAJYvT6bh4J1Ef1Y+Kw03ZbU6tHJkngw4MUpS1LRFjVB5/TKd1j4qN9uvUdronlef1s/yT7oVvHHLyBbly2jXmbqzyGy0HrIbmbwmycQipEger1+szdErg8Mjz7YyZpEZ405pkgTLfFyEqezlsWSRE0zLYwWZNCLUyFLKLAlSm4jWAql5EkmazlIjaHPcytIko+ueJhRpfZamYvn8onTWvUhb92BpafS5bZCHl6W1ldcr67S0S+26545oY1JnCa8N+Vmo9UcCHSVJgreoMqq7hOdHawNuCln19fv9Exqg5Y/Udn3vPr+mEUqmfVqftNU2LFjjFZWdWXg1R4Vkudair9kgtHJOE5OJ/iINGsdoMwHyftkMUbZBVEbWurPyaIjmuYfOkGQUNAD0xZuJnHlqvudDkd8157S1c0uCtLRRzbTm7ZBmqCWjpY1ZiLTuNmTY1ifllceRPZhcg0g7Ijm0zTBqT+2ZRF6fRlBaf1gbppfPgmxjRmvUNFCONsrMvIisqdq6O0OSEh5p8t1STqw2JreERlRkqkRHETQzOTLXeRqLPLNt8mTLTJaaPpNaS1Y26/u8qClP05ieVt0yH3DyTGlW0+L5eB7NfLeCMNa41LbJmk+1gTU+LtYJC6teuY4Wgc6QZLTAPI2RBtpyJmd3M/mdyjs8PMTh4SH29/extLSEwWCAfr9/wpTmBEeHmrX7Uj4CPwitkWsWmfRa/Ro8/2vNI3iU3urztlqZdsrBO0Zi1S21I61Ob1OoIRWuJWrXtHo1wuB1c1jaoJSB7kfR6qhNXn9ZDxJk/N48jefWqkXGJ8rRCZLk2loW2m6q3aPvWaLUSOHw8BD379/H1tYWer0eLl26hLW1tRkZ8jZomqQs1yJCaVpredtMlIwGma1jnonq+ctqFmMbOdqYy5ampbVDm4/ewvbaK90uEXlLQvNMYy0/Tyc3NFmuvCfl1tpiySw/R66irBYZgWvZfM1Z6ARJZuA1xJtwmYABoEeYuSa5tbWFP/uzP8PS0hJu3ryJXq+H5eXlWTqpPfKyNBOa57MIVmufpWkAuZ9/1SanN7kWtXtrctUc1OZ9yBes9hOuloai9Z2lTUpEJKhpTVJbtMgt872Ucuz1ZtbTT1p7NJLXSJTnlRuENgezVgDl0fpA9oeFzJMy1kMO1rhm53bnSLLGtKSGZ4MVka9Okhd1btM02N3dxd27dwEcvUn94sWLszyWec3L1urUCJI00xptOKpLlpcxsTMHqWvdABEiE09uMpHGFpneGfktYrWI3CIWDd49j4j4vJTXLBm8PrPyamVRGRbRRpAbC+A/UeTdz0DOGeBIfu+NRxKdIUlpXsqOBOZ/AsIiSE3j42nJH/ngwQOMRiM8fPgQo9FolocIkne8FWzRzHHKy2WRsnqmGWBHOr321zi3tfqzpBktAp5OLshayPmi9aNHfJ6Zy9siNVoLNdoWLzd73Utj9WXGHZCpx/M183okojUt+zSyiLJptOuZeXZ6cfhKEGnw6FTbCBVFyHikjNfjla9NqKZpMB6PT5S/tLSEfr9/wjy2yNYyp0kWiyAlKD2fpCRnxsfCZfTaztv6NCD7oUYmbTxlP/HyMnNL68toI4rGnsulla/5Pq36IxlkvZpckWvIAi+H93PNmm2rIdZs9tlTAh46o0lq0Pw7Wf+TVoZ3X3Y0ldc0DXq9HtbW1vDcc89hMpngwoULGA6H6Pf7xzRAbQHJCSlNcppstRqkVna27V7arOmkRQjn0QDlOFqblQZJrDWmH2BHq7NyRyY9b9N4PJ5p/dKc5e2reUpI1uGZw95ZRy1v5H/05kutFh3NYc9XrSGaj9n52imSlIOt3Yuczvy6VkakVWj5hsMhnn/+eTx+/BhN0+DKlSvY2NjAYDBQic8qX5rY3AeptZnLpPWV9d0b+EgT4ulOI2ijQRs74ImGPJlMZuTC5dPya2VEiPyOGSLk0OYpv+7JEckl5dOIMsrP14dHlJFZ7fWPVp/W3po5VqNB1pQTldUZkpQ7ViYtYO9W/X7/RB6eXl7nkAtsOBziypUrOH/+PABgbW0Nq6ur6PV6xwItkflFE08GeWq0R60N2XZFZWiyexpE27ojOWSd5O4gbUuatHyhZ9qlLXTPP0p5ZNpM4EE7JqSZ1HSPNgZeR+0xIu26RaoeUXp1yHta/1hHpHgftCW6tmhj6XSGJAG9czVHtJbWWqjeQHkDxPP1ej2srq5idXUVpZTZQXJpMlvtkYQoNVoOT2u0tGOrXqucGlibl6XxzwuNFEiTJKKk/huPx7PP5PqocRVEcgB632c1Nuuep/WRdUF5pFanlUX9otWRIVlP05NlEdpEtrW6vfsWaud3NEcjou4USUpEZqY2CTSThefL7Fwyss6JTftvka9mXkvTvIZkIpM6Y0pri0ZOfkkGni9YKyOSwSJYT3uaTCY4PDzEeDxW+580MHlWlcqKSF27bpmzlkZoEYd2L9LQeSCO5Oj3+yeCVpY5zdsgkSF16SKomRNeGkvOCHwTqSlnXoIEOk6S3m6T3YX4hIw6RCNbjyA1eGa9R6y8TZ5fVqtLIiLQyC8W+dK8TSmSwXOp8EVgEb7UfDgBUloiS0sebwPh7ZMalCQBjRR4fgtSO9XyW6aspiREbgaN6KWWKMdamwMaeWp+yrbapSW79pkwj2WVRSdJ0tuVCdqAa9AIUitTW/jk15SPHkb+TU2D9HyXXBYrECXr0Npo3a9FRrPU6tbKkVq55vuT9fKFSyQ4GAxm6WR/cTLjBCkXeLbN0fyLyJJfo+ucAC1y0fqBt5GOoWl9LjcL76C4JavsS1mu1i+a31WW0yYAmJ1j1v3sOshos50hSW9XzphI1s4lO4ubuTJAI9PJhaOZyJyE5WfNtJblWv7XTFtk/fKaVq61wK30mXGolTlzny+ypaUnB/Wbppkd5Pd8bdYGpj2kYMmluSUsH7dHDNqGntE6NVdSFK0mWeR1SXwW+UtNkG8wpLVmiJL3izXftfbWIKscWOshO587QZK0EGRgoqaDrd0k6ghtIfBFppnalinNP2vPcWsEaWkf/JpF9Lx+K79XrvVdW0CLMJ+sMdbksNqyvLyMXq93LFghNyGtvjbRVMs/x+EdA9LaZfWjZTFpa8PTYDXZtT6QROltONwPbJGttk61cc6uZ5m/5l6E2rydIEmCtRMS5I7l7QhSg7MWvUZAGjFahBsRpLbos+aHJEjPrNC0W95Orwyrbl5GRALZ8jRy9ohDu0b9IvuRDvZHrpUaWP457T6vz5q7kQZHyARcLLeA1PAsks2mlWd5PbK1oKWp8WdLaHOybVkROkWSBD5A2mST6bxFUduRWYL0/I/cPJT5+NGOSBZPE85OsCh/FOnmZWTM9tq+j9JRv9IfaZH9fl/VxOVY1MjhBWQyZ/689lh+wciElt/5nODfpVktNTxrTUlt0AsaaYQctUmWxWU4bWTriKyLTpKkRG2HRjuyl08jRm7GRYtSvjg3UyefNJmFHZnfXtqa+1EAR6tb04pqYG1G1K+k2RBBSstCavXyukTUFkrjBZwsUrW0O8rvKQOyDzQNUivfI1MrnUWUsn9k31jxgwxRav3dVuOvCfJ4eS10iiRrFn8Er8O1hUFaipSBvk8mk9minEwmszNrPPJNC9la6FKLlGQqTXcOK+CwqB25ZsJG5tUiNEhA3zD4gXGuSUptR1s4msxau+Umq+XXNHGLlHg5GrHQniOixgAAIABJREFU90g71zQ4/lkjQJmPf4+I0uo/y+foEaxFjtqmofVBBrVWV5SP0AmSJILRwDuan6PzJr4ViSWik4jeBTmZTDAajbC7u4vxeIzhcIjhcIjBYDCbMJ7ZKu9bJMpNRInayB+vt80k0bQjKs/KMy9xSs2VZJfEpY2RRdwaYWn3qE5LM/bya4eqLa3SmrdWNJy3U/P/ecSmpbO+ZzRK/tnyq1tanVzHXj5erzV3vVMKfK7OS5BAR0gSsH1k3u4sP2tmi1UeQRIkl4PyjsdjPHz4EHfu3MFoNML6+vrsTUClFCwvL2NlZeWYfDVHTShPLRFa5mFUb2SGasEKwiI0yEi+tvC0Qs1k1jQhIH6DuLxuaZWW306r0xoLr17tu/WZYG12lkZJab1yLe3Ryl/jArI2lYxFlVkDGXk6Q5JtNEOrDM9fwq9RWq5B8olOzws/fPgQt2/fxle+8hXs7OxgbW0N58+fn2mT586dw4ULF3D58mWsra3NyJPL4wVaPE0ng6hvrPveRMkGhjgs7ai2LI3QLMgFXSNrdhFn5l+klXqkwtN5m5Pm54t8lfI6L8sj5Ug54Wk9n6CX30LGLWKhRnPMzrFOkGQpJUUUmmlD1wm1ZoCsp2mO3jgzGo2wv7+Pvb093L17F3fu3MHdu3exs7ODR48eYXt7G8PhECsrKxgOh9ja2sLDhw9x8eJFXLp0CefOncNgMDgR5dYIOmo33bd8qbJvaielhLW4pfye1p+VQyvPaqsnr7awrOCBlNXS9Ph9Lp9Wt0ZMvF2aZsnTRmazp91JuSw/rSzT04AtYvXOQFrtlv3IEfVpDbyNkvcJV4jecoEbwD53xu9rsCaZTEOgyTUajWYa48HBAR4/foy9vT08fvwY+/v7ODg4wOHhIQDg6tWrsx8AGwwGMzN7NBphb28Pr776Kl577TVcuHABzz33HDY3N7G5uYmVlZXZW4M00z6rCVrmNZXTxoSNzJYazTHKE2l7FjF6/l2vzkwQQtNEPbNZq19b1JnNXdbvzWHPR0lvQ8paHlQvlcvrljJRezXZIjLjJB2RqucWs8af6q6d97VE2TmS1OBNADlA2tEQALMAABHj3t4e9vf3jxHh48ePcXBwgIODAwBHT3cMh0NcvnwZw+EQq6urWF5exvLyMpaWlmbvkxyPx9je3sarr76Ku3fv4pVXXsHdu3exubmJF154AZcuXcL58+ePkSXJbvlftIknr9X6XKIJUbtza9pvtFi9OjytI9JIgOPPRVMeT1P0NEurbsA3xzVzlZcTBU+s/pOaJ/2XbeZlcBkskpbtjMbOIspserpOcvE2RVYiv0YvYZ5M9Gf1CXI8MuVLdI4kox3XIgq+u4zH45n2R/cODw9nP+i1v7+PnZ0dHBwczDqZCGt5eRmrq6s4d+4cNjc3sbGxgfX1daysrMzIUfobAeD555/H5uYm7ty5g6997Wu4c+cO7t+/j9deew2bm5uz++fPn8e5c+ewvr6Ofr8/i5BzrdJaYFq7s6as/N7WFPdgabweaJPRyvHqIGimaOT45ycS+CKz0nuaoLyv5Y/KkFqWZ0Zb84DPSblZyfIy7oNIdsufm9VmLfdIFp5LRmuD13cRUiRZStkE8LMAvhlAA+DvAPg8gF8C8A4AXwHwN5um2SpHNf8UgO8BsAvgB5qm+YOUNMhNJm0nJ5OZzOSdnR0cHh7O7pGWeHh4eIIQ19bWZiZ0KUfv7eP+RopccwLjcpBWeeXKFayvr2NjYwOvvfYa3nzzTdy7dw+3b9/G/fv3sbq6is3NTayvr+P555/H+fPnZ1Fy+cJYiywzRFcbxDgNEPFIApT3M2k9RO3yghZy4UhiIXjaSGSKa/JK7dYKVEhis9pqkaynlWYCHBnC42tgHoKMoG2O3s/CelZDLbKa5E8B+H+apvneUsoygFUA/xjAbzdN82OllE8C+CSAHwHw3QDeM/37MICfnv53oZk7WnRUTm7yJT58+BA7OzvY2dnB7u7u7Eyj1NLW1tawsbGBzc1NnDt3bnbmkT8pwwlR/tSr5s+gARgMBrhw4QLW19dx9erVWVT8zp07uHPnDnZ2dnDv3j30er2ZDNeuXZtpmRQt1xYEr9fz11j9Og8yv1E8Ho9VjczSrLR7UT0eiVo+L8B+IoRev2adYfV84V6A0DoXqRG2dryG7mllSBn5dVmvRZTaeuLw8nqasjfWWRNdQ8b1VFtG5IvnCEmylHIewF8G8AMA0DTNAYCDUspHAXznNNnPA/g9HJHkRwH8QnPUI/+2lLJZSrnWNM3r2cZovh+54wNHg7G7u4vt7W3cuXMHjx49mg0u/zXDwWAw+8mFtbW12R9FnzVSIm2zDfr9Ps6fP4+1tbUZEd69exdbW1u4c+cOtre3Zz7RnZ0dbG1t4erVqzh//jw2NjZmASGuXVparNWHi0L2R9yjHzTTtJ0M+FhnNU6LRDTfZCZoo5XnaYLaPcuFohGlrDvqr5q8FjlJxUSWod2X5ZXy5KkzzS+YcUvwNjwtRHVlNMl3ArgD4F+WUr4FwGcAfALAC0R8TdO8Xkq5Mk1/HcArLP+t6bVjJFlKeQnASwDwwgsvmP4UaoTszKZpsL+/PyOfhw8fotfr4fLly9jY2MDGxsaMGDkR0iDKRxClpsYj0DUDJrXQ5eVlnD9/HleuXJlpvNvb27h79y62t7fx4MED3Lt3D9vb2zh37txMdoqKr6yszCLp/IfHSM7TQpYc28igbUiZPJrGmTHR5cbrBREkYUayedpaJqBjESWXxyNrWSfJLduuBTekDLJ/PG1Y9p8ms9Y/FtlayLoxsmnb+OUzJNkH8G0A/n7TNJ8upfwUjkxrUw7l2gmpm6Z5GcDLAPC+971vdt/b/XjjDw4OZub1ZDLBpUuXsL6+PjujyJ+AoXKPCWmo2xZBRh0a+YvIrF9bW8Ply5dx9erVmfm9vb2N7e1tPH78eNYePqmJIIl822q486B2oyBkJreXn5ejaR9t/Jma39ta7FIGSisJxiNKmUfe14gyUhQ8f6ZWPrVLC/hpRKZpfFKr5H1iwVrP3gkFkkFri9dWK+28PvoMSd4CcKtpmk9Pv/8KjkjyDTKjSynXANxm6V9k+W8AeM2roJTj7wCUvxYnd9imaWYBmsFggI2NDVy8eHF2RIc6IEsm3JT1CNLylcq8HpENBgMMBgOsrq7i4sWLuHr1Kvb39/Hw4UPs7+/PAktEqsPhUNUiTxPe78N4sI5rZOEFFKRfkWD5M7WIuedj5DJ45jql0cq2TFCPXC3TXJKSTKP5PnmgR+bVIPtRrjtLY5R9orUvG0HPjousy9rA6HqWGC0/L0dIkk3TfL2U8kop5T9pmubzAD4C4HPTv48D+LHp/1+bZvl1AD9cSvkUjgI2DzL+SG0QaBKMRqNj5x+533E4HM7MVP47KNqOSeXKOi2C5HnlTiXLkASZNSGXl5dnvlL5tu1FEGOtafq0iFhDhlAjc5iTpnW0KNK+qGwvX42JHRGlV6ZWdkZ71FDjdpCkK8uITHSrXlmf/KyRVoZE22qLmTTZ6PbfB/CL08j2lwD8IIAlAL9cSvkhAF8D8H3TtL+Jo+M/X8TREaAfzFSgmb/8B+nlLkkESVFK/soySqNBGxxOjnTdIkitLE+DlN+1SUCfPVJsczxGlqURJicTre55zftogQC53VxL75nN3GUBHO8/rx7LN6nB0wylLDXRXc0PGWmFnnyeTPIaz2NtKhZRUl9FpMfriq7x+jRY1seiCBJIkmTTNP8ewLcrtz6ipG0A/L1U7Qx8QIgU+R9w3CHsaW4ZgqQy6LpFkNM2mf5NTeucBx5hETQtSbvmQTunyFHjsrB2fa6pWwtA5l8U5MJqExm3TNusZihl8YjSykf3IkKO6vE0R8t010hU1iX7S1N2+HUvYGO1V6b3NhivTK2sDDrxxA11ICdIOhwuJ7rU2ORitvwO2qKPyJGDiNKrj5dNdVo+FI80PNBiJ1KUZMdJQN7nyNRtbQxtJpyWrtZfqcEK8sg6Nd9lljAlkfAyI/M6W76mOcp0sk6NnDVSktcI3plNj0Qtt4Ilh6ZxSmgE7m06XDatL2W/ad89eTg6QZLAk8cJgeNvnJaaIz/0TdcjWBqnFpypQU0eiywtaAQXfefXKL82kSWiaGBmsklEaWpNeMtkjNJq8rQhTK1cvoi1KLKl5VF6eU+zhizNUNanfabv1skALRBp+SCzPlFeryxHu27JIuvX6vW00TZz1kInSLJpnjxWCJw8byWJUn72INPLncfSPLUy6B5piJE2qJXjRXA9eCaxBU/jlumsyZudXNQXtRoqryPyO1kmm5bP0zA1eO4KTXOh8jztygoyWSaubItGhJpGxn/zR5urGpHzNFp7IyL0otda2uwGp0Fbq7UknK1DQ6dIUpoYUvvLEJyE5XfMIKuxZsx0TZ4aWJoChzdxLdPZuybzewtC66uM9hzVa7XF0ug8WGYiIfJbynoiX6VWBy/HMxU9rZNg+SopjyYvQVoOMq387vW3JQNHFE2PIC0hWedpohMkCRzvBCI0aVp72pC1mCzN0+rYaNCs8qKjCRFJZx/W9+qU9zTfXEaLlX1JebRF5pUX1WWZRNbCtsrWNCQJzV9oaVXemUtrcXPis0hPS0/1TSbHzylymSSJaeZ5W8LWZOIyZDTKrBnumfOyzRlzmeet0SBrLbnOkOTS0nF/o2Zq87Rep2Sj1pYcNUGVDPFKmTiy5Gj5DWV5UUAp4x7ITCIrTa37IiojqyECJ7UjS/vx/F+a7NaZS8tfqGmssuzDw0McHByglCdH2PhRNz4PJVFariapWWm+So+stbJqIGXW4GnHbevUPrctQ0MnSJImSkSM2bIscrTMDYmIeGq1J0t2+Ry2Rc6aJhfJ0+ZQuGbKzYPaDYdD+jileaell/0Q+TAz86FWQ5J1WFrueDzGzs4OHj9+jNXVVZw/f/7YyyEsf6U01bX+kBqnRbrWpsIJ2Wq/telo+SwLwetzno5Qs2YzyObpBEkCULVIiRrtkfsu+H0AqjlhdZhFkFmzV4P12F9kFnsEGtWtTVpAj4Zm2uCRh7a7WwtNgqdbBFFb8kl5rI0vWpiWdhqRK7Vrf38f4/EYg8Fg9so+Ks8K3MiytSCQd19et9rkEWXWV2yVbfUJN/e1Ta/mML2GNkHTTpCk5n/0dhAtQizJka5FZdWYyFr5mmkvUfNyUCmvJ2MtOWv3tYmcxaLTagEEnp/7n2rqzvrCrLyAHf2OiEeOEd1bXl7G5uYmlpeXj53qkEE+Sbqadi5NfI1QtbJkGzQy1TZQks3abLjrwMM841KDNsTI0RmS5CTn+UgskpOmOi+bULMLeQTpaZWyfk1rzGjEVnlaGivdIlHTjxYxyDSaxiDNyEW1ax4tJKsB1dS1tLQ0e3mJfGafoJnFsj7rGJIXqeawAj4WuUrZIhKV9zzLgsvLkVkvNQ8baHV46ARJArqJTQ2XBMnTen5MraxIBkJk+lqBEw+aWa3JmfV5euVEC2Me1Oa30nvlyPHl0V/PdI8WVJuAgTY3M28ZkqYyfZYyUF45zzWi8qLavHytPq0dHjHy75aLhpffRgvMbihWuV7eRRAk0CGSBE4epJXXNL+jZQrzvLUdme3EKJ180WmESIusDWTVTgZNAyFIP1VUZ9YHyaH5I/khaS+yy/No5RGi6KqXn7c986y83Ei5/1Kb3zyfRpSWXFrgRDsHyfNqayUiSt4PlEerT2tHFpbFIt0tHN460ZD1uxM6RZKATo4EapQM8vB7hIggF7XL8PJ43gxBZjQpmTbjV6xB1iepuRS8ydpGJsvfpmlpgP0Im/WdQ1uM0VhF5wGtftHyRuVZZrYGjXw17c8jQU52FqFqstP1TP/ItsiglwXNAjitoJ6GTpGkJEiNCDlREuSikTubZqJwWJ2t7Z6W2aFBu5cxVS1ZF0GQsv7aXXWeumvKlJqa5b/0iMSKzEpYGo9HDmT+Z4I5VpnW4udy8eueD1Crn7sqJAl6RGm1XyP5KN08sNxc8lhVW39zlmifDhVXgsiwlDIbaP5bNfSf/gCfIKO6MvJ4aOvj46QflduGkGQfLVp7Pm3Icda0aX7dAvVzW61eS6dt1hJN06QWr9TAvc2wjT+Yb/BaQJLXq23QPG1W2dDktSyPzPjIMrW57c2FbB0aOqVJykGhQY0miHfcoFbTk/VoGqSWXzO1rTK9vDJ9rfbId1UeBPIW19M0XbKINFxLy/PSWNcImhkv01nnAK1ADhGlpe3UmKlZDZCnld89jVK2yTKjpUbJ+9Qy26NxtNoK5H7y1xpXa/1oGruFTq0M0hiXlp78dAERpTxHSciq2ZE2wtNweWRardy20HY/TUa+C9bIMo9sbxVktMp53BRZ7cN7q7u2UfFyNXeS1S4tj3Y9SquVlynT27w5pBxEptE81u7VaIfZtVKjVXZGk+QmtjaIHJb/aDweYzQaATj67esaktDqy2iaXrqa9LVlevnnNdMj1L6HsQ1q5LY0A8/fZ12j/FZQKBNQsX4qgkeJvWM+nKR4RDfSOj1tNArU1AZboj6hOnlbNR8stc2qT7Z9kcjyQyc0Se5ntEhqMjn+tnL6IxweHmJ3dxePHj3CwcGBa65pWqSVVso5r3bWxvzXoqUeKWZ20iye5q80LhrRWGmm9DwLkfrb6rOsdqjl0bRCS5GI5vS88yLjA9auSc1a3rPmrNc/bWXNasRAxzRJQHf0cp+jdth6Mpng8PAQOzs72N3dxcbGBobDYcpczsIzYyS8n02wYMlqEbol02nhrUyUGvFZR8S8hZjRuLj2o72fUtOeSKMjArGIWqvf0hClZupF07MPWmSOQLU59xlp5tqxsAy0ExFaPdHa6YQmCZzUjmhX57+Y6A3m0tISBoMBVlZWTpyj9DRAb0IuEt5OKtPIz5asi9QYJbIa5KLTnSY8V4rlG47mjwa+iKO3nWfKlWPs+Sf5X3YT1YKjGoFktFiLeDxZajVTb43UlJtFZzRJDus4jwUiyPX1daytrc0WZDaQ4Wlv86SVyPoNLe2RR0pPU3PMvpCDk4H3BEr2lwrnQcYfZ92btx7rdEXmh8a49mf56DJPs2jaGl23ypD1SY0zkovXqdWf9XVqcsp5L+edt+Fpsmrg5XjrqTMkGZ1tpEHQJjUN7GAwOLGDRsiQXsZ3xKE9EeSVL69HfpRFofZnaCXk4pHlPY0Aj5RnEYeYLXgmZxtzkJcrF70VmJHlS0L0jvJw87rtJsv7QMqiySplsJ5ukqSu1SnLtsYgczCeX4v6ojMk6WkB/LMkSq3RGtrci/yQMp98FFGaJ9Hu5znu28D6GQJOZhqReT7V6EyllW9eQs4iIso2RCrPm1qP5EnUtNnyF3JomhyllWm8c5ReXZY2qd2T+aPIv9dX0Tz3Ng3tujZPtfSZ9dUZkvScrJIY5XfNd9cmwheZ456ZK8nBIsiMxujJmIX3c7MEbwFzopSEWnv4nCbt0zC7F6lJRia6dyD7NDRabfFbx5o0kuPapKUV8zS8TAlJnLxcQuZwP0+ruQa8jSAKctVc99AZkiREPsis+SrvZx28GXObYL1hXHNwa/f59cyTAhpOMxgiyawtucm2PS2tclHw3B8WURIil4NlLmv1yPTZKHPG98jv803digvwMvgmaLnNLPOat1H7rPWBlS+7OdUe8+oMSXqaogbZ6TWmapZoLfKUv02j5eEEubTkv9LNk8HCs44Ueyb5PP65p4mahZspyyMewA5gRX5FeVyI//cWvEYsHrFbJKOtA2nGS03SOlqlBWEy4MFcDVn/Ii8jS5adIUkOb3JmyFFiMjk6eC6fwskGYjSClHmlj0YzsRcRlc5EnmkSWEEU/n0ejS46D5o5CqPhtDXNtoERiaw5TrACXLJuS2uKCDLyxcryJcnxcjxz3CLAyEy3IufZ/tXaHWmgXnlZnN45kgWDa2PZ9MATc0Ee07ACMXSd8mQIkj5z534tmT9rzbBLWETEXYLGR2r38k/e499roVksVE70VE5GUbDK53O4rZz8utd26WP35Of3IoWBa470x+vRlBOvvVITrSHMzmiSmondZmLyPFIzsF6SYeXj8F6im51EFrIEWfuDYpl8T8s/+LSPA3nwggtZ1BKQlT77m94aqUSmqhVt1jTlKLouNVHL7JZpCZoLgcqR+bPHinjabKCpzXh3hiQlInKJjhtIU7etNgD4GqQMzliySvnmgTYJ5X0eTV4Uasp7lr5Iz+cVPaoWlesFWTzTsSb6bUWs5TVKS1ZSzfzS5NEi25LkNd+rjG5nDr9nfLhW261+IFjRcy5Hzfh3hiSjCHMNZP6IyDzNUd6XhJeR2xsU7huMSEhrF99J5SaRJapIm6wl2y4FaywtxdPKrDSyPO8IikQNoWZQSyZaWu0oE7+fKdMK9ETPeXvlctdYVrnRNkZrs9SO63nj2BmSBGwNyfLvZSPa1pECC1xz1HbVDEHWEr0XMfdgpa1dgBZR1hKkdkj9WUIz+eR1K30GkTk9L7QFrEXhrSCRFS23yD4TWNEI1ZLbum9dz7gQgJziIa95R4g8PugMSWa1IKsxUaDEc3ZzSNOa59M0Sq2MjAkeye+hRoOpwTzEpp2p9J6+ifIvAtIPFUWH5ZhpJmxkTsu5a42T1Gzkm4KyxGT5/6TvT96zfImeKa2Zy54f0zKloz7x1pp2Lbt2tHHJmN6dIUmJiPS0NFFZ2sFTbSHI9N5iyhJkRlbvWAqH3PWfNSyCs4jyaWqbGkFaZ+60eRJpTLIeL62Vv80GEbletOtt5krGveBplppJ72mZWcso8mFa5remhUdIkWQp5R8C+LsAGgD/AcAPArgG4FMALgH4AwB/u2mag1LKCoBfAPAXANwF8LeapvlKVIdGVlmt0dt1PNPY0iJ5+sjvp5WTBa9vMpmEr9XS4E1cKjcqcx7fmCQ87ykdqTEtUnu0FoZMI4+CyfuAPp/a9JEk45r2auQkfYm8Dik3r9fTTC3i8gItsl5eNx8HS6u0iFL2u9Y2iyy1dWnJJvswQri6SynXAfz3AL69aZpvBtAD8DEAPw7gJ5qmeQ+ALQA/NM3yQwC2mqZ5N4CfmKaLBUkSpJfPOvPYNigkB4BriVY5WS1SmvW0gEi7tcrmZ8RknVbdbUmcQ1vgGuFZ/qDT0hp5f1nmtNenHJaGSWdm22hitPllxve0EbmaIvLN+PuzQRZpcdF3Pr+1+c4Jr00/Si07JW+y7D6Ac6WUPoBVAK8D+C4AvzK9//MA/sb080en3zG9/5GSZDyLBLJ5ObKmK3B8EWvmcinHf14iS5CZQdCiqhyW9lFDlJEs2ckSaUIaUS6SHGlhaGal9sCABc+FMR6P3WNC3lM2cu5y64Dk5nLOS5YZ0rbmCYfWH5PJBKPRyJWRk11kqXmy8vT8j9/jdcj5qilDWnuidpj3zTtTNE3zKoB/BuBrOCLHBwA+A+B+0zSjabJbAK5PP18H8Mo072ia/rIst5TyUinl90spv7+1tRWJUY2s9mRFlWv9nW3rp3R00F0bsFrzgC/YGuKWk5Sn4xsJ14wkFmVCc0LUCMXyLXGHPIfnouH5+SZZQ2geCXBy5Omb5smb92s2dSmzBTmfPTKR9/jvSfH7mkwWkWnl1sDSXElZoV9WJcjTC16fRvPh2L1I0FLKRRxphzcBvA3AGoDvVpKShFpvnJhBTdO83DTNtzdN8+0XL16ka5E4x5AhotrBqdEePF+otaik1kr/l5aWTmhdmq+mDebR0DVE2qHnd2yrWWbMXWqjddRHsxK0/Dwv1yytMY2IWdO2tPwasuTuQdPyPLcRzUX5hJrmduL3rLI9omwzJ73oOA/GZhWYaH1lVt9/BuDLTdPcaZrmEMCvAvhPAWxOzW8AuAHgtennWwBeBIDp/QsA7iXqqUKt6Rh2hHM/64ds4//jBMYnJ5UnNV1P21uU/5Ej46J4mmciPbKU9zztk/sttTJrTWFuTgNPiMR6WktqyRFRWv52Xia/Py96vd4JTU2WbVkqXCZPA43Q1iWh9Y1Xf1RHRvKvAfiLpZTVqW/xIwA+B+B3AXzvNM3HAfza9POvT79jev93mgWeU8k4j6PrBO9N4l693q4p71v1We2g3XswGBz7OQrXHDhlsvTKWjRBekErDs189cqxTN5Mmd48InNcms8EzULIysxhaW+Eea0EGewiudtYIDT/+ObPr0n5NSvM64sav/siqCc8AtQ0zadLKb+Co2M+IwB/COBlAP8XgE+VUv7p9NrPTbP8HIB/VUr5Io40yI/NLeUU2YVfFd53yC7juyFoR5g4vBdkaOWfFqQ5ugjM44fMHKQmSL9jdpwt35SlwWnkSf1GZz8zxEky8jHVzhBymQD/MVE5fl5/efnkNaqPz2/rSJCco1Y/UjrpNoo0Zw4vUObdl2mtc5kErw9T5ySbpvknAP6JuPwlAB9S0u4D+L5MuRKSqOSOrH2e1mlqW565aNUblVG7s2YJ8mmATyreDr6wvX7UJlPtm4R4+jYEK+X2EBGItdhkX2hPZkgylOVwgpFEGc2pGqKMrsv7FllQe9pYBx5p8fokUWoknEHbtFKWSLkBOvTEjRX4yDhZtcmbITJtR4zM2lpoE86bUJlBi8rKahYSNGnakOUif78mqx1J1BIir0e7Z8mmzTcLvL8y4xq9Du+0zlhqzzZL4uJ9ZM0DawPjmrumYVoHzOmadYjcg9XnnsKl4dmrNnOijS/mNAINng/Sur+oaLMsKwpCRYt8XvOZ/luHyDPlW8GpGrQhFN43PMiSrctKy33Lg8FA7R/r7fGyHO+aFjDJ+ti1iLZWpxZE0sbLimZrfspoztYSW026CJ3QJLVOihrokYJFPhppRUEfPtkIXFNN7URJB7tVluVP8tLVaB2yfPm45OHhIcbj8SyYFJXvbUJtCdjTXCyN3ELkX+NjS/Jqc5T044DHAAARuElEQVQCbePx+Ng88QILJBf1kfZkSyS/bIMsu8YHx9Nq9WrznPeRlZdrh1b7uPZKWp9lxdRaWIvEW0aTtHaoaJezyqq5LpEhyKy2Oq+2pMmW1SYsefj/8XiMnZ0d3L17F7u7u61kehqTu1ZrjCKkJLOlXUmC4ke5LJk0GblWJeXoKrLrR1ubcm7y9vO+5v1Jaeh+m4j7POiEJhk9HcExL0FayPgiMwPT5gW1GVm55pCRQ2oIhIODAwDAYDBwiULmuXv3LiaTCVZXV49pk7WIfJd0hMb6qY2MhszvW/7pzMF0Xie/xg+Pe8FG7XsWXOOMHk3NWg81UXF+3zuqJH2QslyvHq5t0mf6z9eRPMxv9bXWzkWgEyQJnHx7SGRCeyaszFv7QtsaU9qqp6a+GqL0yl6UU5/X0+v1MBwOMRwOF1J2BHrKpXaz8RZj7ZEhmY42nMxxoQwWvYizWNTxr4iktPSSWCWxS6KU4ARqKQCUrub0QwadIUmgvd+h1keYLS8THSNkiHiRJoInl4zY8vvLy8uqdqVNLJq4a2truHHjBvr9/kyLjOAtyOinIrwfbJML0ztyop13rMGiyaxNec/6R9M8aPPNaqOmmWr+T+sN4pRe83XyMiUWsTF0giS5U1xbHPOSSxvNhCNzUHyeOp6W/4n3s2e6yAjv8vLyjBy940scbSfmPH1BCyizaD2chqanmazZjaCmXMKitEYqi9e1iLJr2ijnptRMIzfCvPJ2giSBOr9fLWrJKzJrLRBR8gWQ9ftZdbcZXC9KapkqkQmlnZmrka0m2m6hrXlnlbOIzUmWla1byyvzZbTI7Dhk+9+KMEvtTau39mSF5gbJnve1rIXs/K1BZ0gSaE+Q8xJrTbT7NLU+61hPFm2P/CxS6zgttCVY2bZF+G1lmXTY2ZobmnnJ372pyZQ1s61xqyGszPjXbnLZ9NH8ztZZW1fNfO8USbZBhkQ0Uzg6H6ld93yRNUd+MvXVgnZV+k/yaia2Vq91Ho7AI7pPE14who6CZCLVbc6PUv2WC8giytrARo0s89wH7L6wzo5mI9UavH6wgo7ReFr+TC8tQWrCWhoNnTmMpZm48vyYdm3eujwtkv5qiSwrX6ZcKzChgROlfBtNFtpZtqysTxO18mhn67j/Urs+zwFmbUPiZc3jI6dIe3bDWuTGHPWHd4ZRuqBq1uEiYD3vHp277AxJEqxOmaejnsb7DjUCrzm2pJXH01nly/ueT1OWoW1AUj6v3+WBXwlJQlRWzW/eZDbGNpvYIjZcrq2TDBrxemTmveW9BjU+WOspH60fLWVFppH5JQFqc67Wp23JKOvQZOB11lpDnSNJgrXLcyxCw4t2/DbQSCNbpjahMrs38OQnILwjNFIebcJ7i6Cmzxe92WXyRgRY2wYCn49Sk/N8kdkFycmyza9mLgo1fRNpgov2py9iPNuMfad8kpYPI7s4NJzma8raaqjeQEkTr9Z0p8e2sm9Csurg/if5fG6NA7xNv1tPmfBzclJO6WPKbojasRbuX5R+Rg3aAm/ru20b0a7xF8rx4+3MjK/3BI1Ml/GBRuVpJzRke+WpDeussHXNQ2c1yQgZf0aGxBZBnhmTxbvfFnxRS82G/7BYW2ga5CI3mzZP1UgZImKIfNq8baUcPUBweHiIg4MDPH78uJUpbBGLlDXbfu4j9dJ49cs0Eta4an5ceT/j1/Pqausz9fItcp52SpMkLKqBT/uAd1sTW8I6p6aV6WksizhzqZUntUqv3IjEtGe55/HRZY/8WERLGsr+/j6Aoz7T3Bde1D2Ltu3Mng2U9zRtU9PoNG2sdu5oWp4nr6UJR+cePQ26ZjP10AlN0jpew+9bO1YtEdUe/bGQId/a4xJSy8n4ZT1oRyAWoclKssxENKNASVut8jRAwRir770xyfbvs3rc0AtwaAGamn62zOfIrPbmeSZg18ZXXdO2zmmScteRhDjP0xKZCZwtV9NSa1+koKFmN32WsLRKIH6rU43/LELke81qgIRSjh7DpLTaxhXJI0F52hBjFCmviaR7aKMtRvVq2mOt3zR7BlbzX2Yfo4zmUOdIEvA76GlE+CzMc24OWMzTBfPKUIO2T1jw8ctM9kWQBwdfFNnFxrG0tDQjymydWh3aEzYashu/VX5NkM5DhiijDY7L5CF63pqnkcpR5FvVgjY1wUaJzpDkIqJQEpqmpzmipabAo30cp0lO2YGviXZHu3sbWbR6qFwvsmhFVIGT45T9eQcZiZVyWZssb5+1qL3jS4uKZstyavpdyl1LlF7kuK3/kOD1hXUaQYPmVtNOOGh1WLJTudJa9dZVZ0gSWJyjtW2di0gH+C96zZiG1sSshbXxZI/t1JKlPH7T1ume/fXFaL5EC9ELLsg0tS6CKO1kMjk2H7IkG0WxZTuiPpLtigjOkskbU7mBakeBLMXESue12WqbzMPb5K5L884zRuRkJWdv28fvqA5CRpOwkI0uUj3RorWcyjXtfNrPWGvwNHErWEB4Gk9JSXjHXbxo9jx+QA1RwMirMyI5b17Pow1bxAzoGiGXxQvA1FhObSzPTL7OaJK1pDQajXB4eIjJZILBYIDl5eVjZViLrNbR76XlwRu5C/J6sqZyZN7Pa+7PQ5xtTazMbq5pEpPJpOr3vCMth8uY9atFWNRGZB0Mr9FeowcUqNy2aOPblfk9bW9Rx9W8Y3htLbTOapIemqbB/v4+7t+/jzt37uD+/fuz326peS6YIzo0m0GkNXpmzWn4O/kh83kXdCYqPG8ZtGB4unk1Sk17lVi01i3b2fa4j7e58APwWQKzjtp4JqfUPqXWlzFXuWzeUR9rrLQ6pEJitV0+CPHn4ghQBqUUHB4eYnt7Gw8fPsTGxgZWV1exvLx8amaaFVWuPbDOy1lUVDJbL4eneUXIOv2t9BnHPuE0CCYT0GpLnLUEOQ9BL+K0RFSm5/eUWprWt5aWrF3jxGWVJ/NR2my7tOOFb8kjQBGWlpZw7tw5rK2tYTKZYGVlxX2pQ6RNWDuo951jHqK0MK95I+vjZdFB6Xm1V8+Jrk3w2uDHomWzsMi+JmSO/FDdmY2lBm3yzBM0lXm5+0TrW2tsNLeANY801JBldI3jLUmSvV4PGxsb6Pf7uHjxIgaDAYbD4bFH3Nq82EKaEMB82t68B3StMmr8N20mvBekkASrLWZuClqaiCa/djC4xi9Z0xaNFGQEtWbs5iF/b55kyD4bkc7K6Gl7sj5LqwQw+1EvTSv0NgHPJaCNi9XGeTYajrckSQJPtEkysWkQM+aNtkj5tbaObk1jzWiplpyUvy3ZHh4eAsCx/iH/jSS7aGJyuWo2jVrZF7GxcFgkbmk21qa0SI1+3vSZI02ngax2Lk1mrlWe5qmLjCXZSstuLdEzBD/2wwmAvkewnMjeWTurczOBojbOYl53W4zHYxwcHByL6lpHHjz3AsntHZew2jVvRB1oH4zTyuIyaW0EnhwulqZi7ZGdeWH1aWZeyLmWlVEGd2rNU9mnlJ5/1taUtz5kfVRGTbCVWz61yk9nNMla7U0ev+FYxDsktbxS89ImRKYcoB0BZvNQOtpAuDbKNxQZNfRk9Q5/A3WmnMwXLTq5AZ6G+Z1JM68WtCjz71megZW+RiDnLuBECfhPzliuEOue/GyVZ63dCJ0hSa7dtSFKDVpZ3jEQLaDBzUtNG9F+RjaCJoOX33uCx0svfys7Ijsv8hsRqQVvAWUj0G0CPhnflOUPIz9aja+X//phG8g2W2NVczLAkrUNvDKsY24aeDslcWbzU53chaSNsbb2pf80g7ekuV0D62yYdr/Wd5atPwuZVp7/sj5r8M6N8fqyAYpFnLXkaNsvtVrkvBH8WkS/WeMtYo7avpbznL5n+jlLgLXlaGtLujo8F4Ynl9RkLcVDpm9jwXVGk2y7A2s7uLera8dTuAZJWgTwZCA1TY5P8Eib9LTTKC3Pw/9TWimTZ77wOsfjMfb397G0tDQ7GVATJIqCPhzeMY5sIKCtNqRtPFa9dC1anJ423ua5cw8ZLVKTu7bP2vRvjQbp1Ud9mul/uX5lO0/D598ZkpwXRFTyDdcWUWqQBNUmyu0RJS/LCwZYkyRyxHv+QtoAqL7xeIydnR0sLS1hMBjMjmvUyLMIM05bzHJ8+P15jgRp5WXAF6U2d9q+6k1z38g0Vt5M+fPC21A8gqyt23MbASfPsc5DzjX1E8qzdALPhCjlIYDPP2s5KvAcgDeftRAVeKvJC7z1ZD6T93TxNOR9e9M0z8uLXdEkP980zbc/ayGyKKX8/pm8p4u3msxn8p4unqW8f+4DN2c4wxnOMA/OSPIMZzjDGRx0hSRfftYCVOJM3tPHW03mM3lPF89M3k4Ebs5whjOcoavoiiZ5hjOc4QydxBlJnuEMZziDg2dOkqWUv1ZK+Xwp5YullE8+a3kAoJTyYinld0spf1xK+Y+llE9Mr18qpfybUsoXpv8vTq+XUsr/Nm3DH5VSvu0Zyd0rpfxhKeU3pt9vllI+PZX3l0opy9PrK9PvX5zef8czkHWzlPIrpZQ/mfbzd3S5f0sp/3A6Fz5bSvk/SinDLvVvKeVflFJul1I+y65V92cp5ePT9F8opXz8Gcj8v0znxB+VUv7PUsomu/ejU5k/X0r5q+z66XIIPZP7LP4A9AD8GYB3AlgG8P8B+KZnKdNUrmsAvm36eQPAnwL4JgD/M4BPTq9/EsCPTz9/D4D/G0AB8BcBfPoZyf0/APjfAfzG9PsvA/jY9PPPAPhvp5//OwA/M/38MQC/9Axk/XkAf3f6eRnAZlf7F8B1AF8GcI716w90qX8B/GUA3wbgs+xaVX8CuATgS9P/F6efLz5lmf9zAP3p5x9nMn/TlB9WANyc8kbvaXDIU10YSid9B4DfYt9/FMCPPkuZDDl/DcBfwdFTQdem167h6BA8APxzAN/P0s/SPUUZbwD4bQDfBeA3pgvgTTbhZn0N4LcAfMf0c3+arjxFWc9PSaeI653s3ylJvjIlj/60f/9q1/oXwDsE4VT1J4DvB/DP2fVj6Z6GzOLefwXgF6efj3ED9fHT4JBnbW7T5CPcml7rDKam0gcBfBrAC03TvA4A0/9Xpsm60I6fBPCPANDDrJcB3G+aZqTINJN3ev/BNP3TwjsB3AHwL6fugZ8tpayho/3bNM2rAP4ZgK8BeB1H/fUZdLd/CbX92YV5zPF3cKTxAs9Q5mdNktprODpzJqmUsg7gXwP4B03TbHtJlWtPrR2llL8O4HbTNJ/hl5WkTeLe00AfR2bWTzdN80EAOzgyBy086/69COCjODLz3gZgDcB3OzI96/6NYMnXGblLKf8TgBGAX6RLSrKnIvOzJslbAF5k328AeO0ZyXIMpZQBjgjyF5um+dXp5TdKKdem968B/3/7Zs8aRRSF4ec0JthoUqeIC8E2hUVILAKGoIuk2kJYiAR/RJBU+QMhjZAmVQoFRWQ7C7X2oxATNMGVCKYICQhp0qQ4Ke4ZnSaDEXbuFO8DwzLn3uK9L7OHe8+Z4SjiudcxAyyY2U/gGenIvQ5cN7Pi+/yypj96Y/wa8LtGvQfAgbu/j/sXpKTZVH/ngH13P3b3M+AlME1z/S24rJ+5fQZS8wi4D3Q9ztAV2gauOXeS/AhMRJfwCqnI3cusCTMzYBP45u5rpaEeUHT8HpJqlUV8MbqGU8BJccypA3d/7O5j7j5O8vCtu3eBd0DnAr3FOjoxv7Ydg7sfAr/M7GaE7gBfaai/pGP2lJldjWej0NtIf0tc1s/XwLyZjcTueT5itWFmd4FlYMHdT0tDPeBBvDlwA5gAPlBHDhl0MfkfCrdtUvf4B7CSW09ouk3asn8BPsfVJtWV3gDf43c05hvwJNawDdzKqH2Wv93tVjxIfeA5MBTx4bjvx3grg85J4FN4/IrUTW2sv8AqsAvsAFukLmtj/AWekuqlZ6Td1aP/8ZNUB+zHtZRBc59UYyz+dxul+SuheQ+4V4oPNIfos0QhhKgg93FbCCEajZKkEEJUoCQphBAVKEkKIUQFSpJCCFGBkqQQQlSgJCmEEBWcA+tNJ5EDAsl4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(985, 1312)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting initial point\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from PIL import Image\n",
    "\n",
    "k0=0\n",
    "in_fns = glob(\"./pictures/*.jpg\") # Names of files\n",
    "in_fns = sorted(in_fns) # Order them\n",
    "fig,ax = plt.subplots(1)\n",
    "(xi,yi,xf,yf)=(0,0,3280,2464)\n",
    "#(xi,yi,xf,yf)=(0,800,1200,1700)\n",
    "img0A = cv2.imread(in_fns[k0])[yi:yf,xi:xf]\n",
    "img0B = cv2.imread(in_fns[k0+1])[yi:yf,xi:xf]\n",
    "img0C = cv2.imread(in_fns[k0+2])[yi:yf,xi:xf]\n",
    "img0D = cv2.imread(in_fns[k0+3])[yi:yf,xi:xf]\n",
    "#img0 = cv2.imread(in_fns[0])[yi:yf,xi:xf]\n",
    "#ax.imshow(img0)\n",
    "#plt.show()\n",
    "scale_percent=40\n",
    "width = int(img0A.shape[1] * scale_percent / 100)\n",
    "height = int(img0A.shape[0] * scale_percent / 100)\n",
    "dim = (width, height)\n",
    "img00A = cv2.GaussianBlur(cv2.cvtColor(cv2.resize(img0A, dim, interpolation = cv2.INTER_AREA), cv2.COLOR_BGR2GRAY), (21, 21), 0)\n",
    "img00B = cv2.GaussianBlur(cv2.cvtColor(cv2.resize(img0B, dim, interpolation = cv2.INTER_AREA), cv2.COLOR_BGR2GRAY), (21, 21), 0)\n",
    "img00C = cv2.GaussianBlur(cv2.cvtColor(cv2.resize(img0C, dim, interpolation = cv2.INTER_AREA), cv2.COLOR_BGR2GRAY), (21, 21), 0)\n",
    "img00D = cv2.GaussianBlur(cv2.cvtColor(cv2.resize(img0D, dim, interpolation = cv2.INTER_AREA), cv2.COLOR_BGR2GRAY), (21, 21), 0)\n",
    "ax.imshow(img00A,cmap='gray')\n",
    "plt.show()\n",
    "img00A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Susana/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: UserWarning: DEPRECATED: skimage.measure.compare_ssim has been moved to skimage.metrics.structural_similarity. It will be removed from skimage.measure in version 0.18.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/Users/Susana/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: DEPRECATED: skimage.measure.compare_ssim has been moved to skimage.metrics.structural_similarity. It will be removed from skimage.measure in version 0.18.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Users/Susana/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: UserWarning: DEPRECATED: skimage.measure.compare_ssim has been moved to skimage.metrics.structural_similarity. It will be removed from skimage.measure in version 0.18.\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "(score, diff) = compare_ssim(img00A, img00B, full = True)\n",
    "diff = (diff * 255).astype(\"uint8\")\n",
    "(score2, diff2) = compare_ssim(img00B, img00C, full = True)\n",
    "diff2 = (diff2 * 255).astype(\"uint8\")\n",
    "(score3, diff3) = compare_ssim(img00C, img00D, full = True)\n",
    "diff3 = (diff3 * 255).astype(\"uint8\")\n",
    "thresh = cv2.threshold(diff, 0, 255, # Threshold to clean\n",
    "        cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] \n",
    "thresh2 = cv2.threshold(diff2, 0, 255, # Threshold to clean\n",
    "        cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] \n",
    "thresh3 = cv2.threshold(diff3, 0, 255, # Threshold to clean\n",
    "        cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Susana/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:24: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n"
     ]
    }
   ],
   "source": [
    "img=thresh\n",
    "contours,hier = cv2.findContours(img.copy(),cv2.RETR_EXTERNAL,2)\n",
    "#plt.imshow(cv2.drawContours(img, contours, -1, (0,255,0), 3),cmap='gray')\n",
    "LENGTH=len(contours)\n",
    "status = np.zeros((LENGTH,1))\n",
    "for i,cnt1 in enumerate(contours):\n",
    "    x = i    \n",
    "    if i != LENGTH-1:\n",
    "        for j,cnt2 in enumerate(contours[i+1:]):\n",
    "            x = x+1\n",
    "            dist = find_if_close(cnt1,cnt2)\n",
    "            if dist == True:\n",
    "                val = min(status[i],status[x])\n",
    "                status[x] = status[i] = val\n",
    "            else:\n",
    "                if status[x]==status[i]:\n",
    "                    status[x] = i+1\n",
    "\n",
    "unified = []\n",
    "maximum = int(status.max())+1\n",
    "for i in range(maximum):\n",
    "    pos = np.where(status==i)[0]\n",
    "    if pos.size != 0:\n",
    "        cont = np.vstack(contours[i] for i in pos)\n",
    "        hull = cv2.convexHull(cont)\n",
    "        unified.append(hull)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Susana/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:24: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n"
     ]
    }
   ],
   "source": [
    "img=thresh2\n",
    "contours,hier = cv2.findContours(img.copy(),cv2.RETR_EXTERNAL,2)\n",
    "#plt.imshow(cv2.drawContours(img, contours, -1, (0,255,0), 3),cmap='gray')\n",
    "LENGTH=len(contours)\n",
    "status = np.zeros((LENGTH,1))\n",
    "for i,cnt1 in enumerate(contours):\n",
    "    x = i    \n",
    "    if i != LENGTH-1:\n",
    "        for j,cnt2 in enumerate(contours[i+1:]):\n",
    "            x = x+1\n",
    "            dist = find_if_close(cnt1,cnt2)\n",
    "            if dist == True:\n",
    "                val = min(status[i],status[x])\n",
    "                status[x] = status[i] = val\n",
    "            else:\n",
    "                if status[x]==status[i]:\n",
    "                    status[x] = i+1\n",
    "\n",
    "unified2 = []\n",
    "maximum = int(status.max())+1\n",
    "for i in range(maximum):\n",
    "    pos = np.where(status==i)[0]\n",
    "    if pos.size != 0:\n",
    "        cont = np.vstack(contours[i] for i in pos)\n",
    "        hull = cv2.convexHull(cont)\n",
    "        unified2.append(hull)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Susana/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:24: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n"
     ]
    }
   ],
   "source": [
    "img=thresh3\n",
    "contours,hier = cv2.findContours(img.copy(),cv2.RETR_EXTERNAL,2)\n",
    "#plt.imshow(cv2.drawContours(img, contours, -1, (0,255,0), 3),cmap='gray')\n",
    "LENGTH=len(contours)\n",
    "status = np.zeros((LENGTH,1))\n",
    "for i,cnt1 in enumerate(contours):\n",
    "    x = i    \n",
    "    if i != LENGTH-1:\n",
    "        for j,cnt2 in enumerate(contours[i+1:]):\n",
    "            x = x+1\n",
    "            dist = find_if_close(cnt1,cnt2)\n",
    "            if dist == True:\n",
    "                val = min(status[i],status[x])\n",
    "                status[x] = status[i] = val\n",
    "            else:\n",
    "                if status[x]==status[i]:\n",
    "                    status[x] = i+1\n",
    "\n",
    "unified3 = []\n",
    "maximum = int(status.max())+1\n",
    "for i in range(maximum):\n",
    "    pos = np.where(status==i)[0]\n",
    "    if pos.size != 0:\n",
    "        cont = np.vstack(contours[i] for i in pos)\n",
    "        hull = cv2.convexHull(cont)\n",
    "        unified3.append(hull)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "areamin=20000;\n",
    "positions0=[];\n",
    "areas0=[];\n",
    "ejes0=[];\n",
    "for i in range(0,len(unified)):\n",
    "    if len(unified[i])>5:\n",
    "        area=np.pi*cv2.fitEllipse(unified[i])[1][0]*cv2.fitEllipse(unified[i])[1][1]\n",
    "        if area>areamin:\n",
    "            positions0.append(cv2.fitEllipse(unified[i])[0])\n",
    "            ejes0.append(cv2.fitEllipse(unified[i])[1])\n",
    "            areas0.append(np.pi*cv2.fitEllipse(unified[i])[1][0]*cv2.fitEllipse(unified[i])[1][1])\n",
    "positions1=[];\n",
    "areas1=[];\n",
    "ejes1=[];\n",
    "for i in range(0,len(unified2)):\n",
    "    if len(unified2[i])>5:\n",
    "        area=np.pi*cv2.fitEllipse(unified2[i])[1][0]*cv2.fitEllipse(unified2[i])[1][1]\n",
    "        if area>areamin:\n",
    "            positions1.append(cv2.fitEllipse(unified2[i])[0])\n",
    "            ejes1.append(cv2.fitEllipse(unified2[i])[1])\n",
    "            areas1.append(np.pi*cv2.fitEllipse(unified2[i])[1][0]*cv2.fitEllipse(unified2[i])[1][1])\n",
    "positions2=[];\n",
    "areas2=[];\n",
    "ejes2=[];\n",
    "for i in range(0,len(unified3)):\n",
    "    if len(unified3[i])>5:\n",
    "        area=np.pi*cv2.fitEllipse(unified3[i])[1][0]*cv2.fitEllipse(unified3[i])[1][1]\n",
    "        if area>areamin:\n",
    "            positions2.append(cv2.fitEllipse(unified3[i])[0])\n",
    "            ejes2.append(cv2.fitEllipse(unified3[i])[1])\n",
    "            areas2.append(np.pi*cv2.fitEllipse(unified3[i])[1][0]*cv2.fitEllipse(unified3[i])[1][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c1dbc4510>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.transpose(positions0)[0],-np.transpose(positions0)[1],'bo')\n",
    "plt.plot(np.transpose(positions1)[0],-np.transpose(positions1)[1],'ro')\n",
    "plt.plot(np.transpose(positions2)[0],-np.transpose(positions2)[1],'go')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(727.0625, 122.45658111572266)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmin=20;\n",
    "clst0=[];\n",
    "for i in range(0,len(positions0)):\n",
    "    distmin0=ejes0[i][1]/2;\n",
    "    for  j in range(0,len(positions1)):\n",
    "        distmin1=ejes1[j][1]/2;\n",
    "        dist=distance.euclidean(positions0[i],positions1[j])\n",
    "        if dmin<dist<distmin0 or dmin<dist<distmin1:\n",
    "            clst0.append([i,j])\n",
    "\n",
    "clst1=[];\n",
    "size1=[];\n",
    "for i in range(0,len(positions2)):\n",
    "    distmin2=ejes2[i][1]/2;\n",
    "    for j in range(0,len(clst0)):\n",
    "        distmin1=ejes1[clst0[j][1]][1]/2;\n",
    "        dist1=distance.euclidean(positions2[i],positions0[clst0[j][0]])\n",
    "        dist2=distance.euclidean(positions2[i],positions1[clst0[j][1]])\n",
    "        if dmin<dist1<distmin2 and dmin<dist2<distmin2:\n",
    "            clst1.append([positions0[clst0[j][0]],positions1[clst0[j][1]],positions2[i]])\n",
    "            size1.append(ejes0[clst0[j][1]][0])\n",
    "        elif dmin<dist2<distmin2 and dmin<dist2<distmin1:\n",
    "            clst1.append([positions0[clst0[j][0]],positions1[clst0[j][1]],positions2[i]])\n",
    "            size1.append(ejes0[clst0[j][1]][0])\n",
    "error=[];\n",
    "for i in range(0,len(clst1)):\n",
    "    model=np.polyfit(np.transpose(clst1[i])[0],np.transpose(clst1[i])[1],1)\n",
    "    predict = np.poly1d(model)\n",
    "    error.append(r2_score(np.transpose(clst1[i])[1], predict(np.transpose(clst1[i])[0])))\n",
    "j=[];\n",
    "for i in range(0,len(clst1)):\n",
    "    #select only lines\n",
    "    if error[i]>0.9:\n",
    "        j.append(i);\n",
    "clst=[];\n",
    "size=[];\n",
    "for i in range(0,len(j)):\n",
    "    clst.append(clst1[j[i]])\n",
    "    size.append(size1[j[i]])\n",
    "#initial points DDD:\n",
    "heads=[i[0] for i in clst]\n",
    "heads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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StiKTSYN84SrJbP1oF69Dew+n9nghH8fIb6n9fASNAWnPNbBMXksr4ua2Zy5rBKodmdIsuhrSyc6RjMKQ8b16dWiwgo4WZoYkpdkF2Lsjh3UvQ1TWxNIi6nNzc6e0sMxxCQ+Wm8C6Nin5ygXW1i8Xycsh+5H/WqTl+7RMYDLXZfkS0qfLSYrGTXsElssYBfusoNNgMMDh4eGYdOkZ7mygwdr8IpNR871HriNrA9DcH5NoZbUEqZGjdt8jzKwM2XU7MySpQTMJ+ATN+mC0Mr3d2uu8zKSpJY9IXqrXIjhvImpOeksWzcfl5bUg/YByXPjPV1DbpNzyuqY9URrvEU/vcL4GGQAiTZFrm/I3fACMzWyp8bd9R6o27rzdvI2aW2lSbUvK0QZZApPzrI1JLudbjQwRZpokZZAk0nK8Jwd4Ps8vJX90ykJ2l7XOsAFnJ7xGntKHGEWvrbojzdXzuco0Vrs8WXhUkl56IdsmwfvYq0+azTSGpZRTLxGJzpFSuzwfpvT5drvdsZVBZ1ypnXTsKbNQvT4guahOjygB+yyozEPQNsMaqyUi6LZao5eX4K0Zq6xaEp5pkuSQREk+H+tpDU0bskhFM0UzHZnZ4eQClCSkEaWUjw869zd6eawyrTSazJ5PKgqWyPwc9M5FegIn47PL1Oc9vdPGv6ZtoPI+70s63kTzp9PpjM1ta4OX7hRLY46sAm3uZojOOm2R8YNmtFXLfaIhS17yumeZRVZbxmyfKZLU/CXe0wRk+sjItpZ2Gjtj5Ofx0nB4E14jP23yZOr0rlt+Mt7WiExqNwkqt9PpYGlpaey/4z8xa52h1DYYrV3Sv5j5ETQtgq75LeWC4v5OOidJv+dTShlHtr1f9pObn4Q1Hp7VxD9H8zF7ekTWLWXNmrVZN0B2I9O0xowWyed/5DOeKZIkRIMrTWc5kSJTwyJSec3KWxNlk7JqnzXfm3V0h+f3JqYV1MjkI83HKsciaHndm3hkusr+sbRBy/TmmoIcR+vojbYZA28EYqTcRLacMEhWkuf4+PiUJkk/5WAdJLf6hKDNUU1mDdr5Wg2RlmWNYbTGZB6vjknINXM6RbPGasuaOZK0Aisclo+Ra5iW098a+Egl1w6h83sWMmSjacLWQGpE65mrnsY5jSBUxnTS6u10OlhZWcHjx4/HpEj9a7XH+v1t2X+aS0KTVdvQZH9oQSX5O0XD4VAlyaWlpVOWTo0pygNT8n7GXy4/e75xjqzrJuuTryHITJleezRoVhNdozKiF6zMHElqk1Lz4UWTxXJ4WwNv7eIyoKGZH1YgKHtMiLclMgejHZG3SatHuy8PkUemfNY/JcdKpqHXqO3s7JwqmwceeN/KKLPcuCRxynHMnCqI3krE5wOBa5Lk2qGfceAL36tfkoNHlp7/zZO7RvOyyvbmqpw3WS2xBlqwT/pjM+TtxQIkZo4kNVi7R2ZXleklMlFzKYdWv0ZclsmblY3KkBOcPvOJ6PmMLI1Fmqo1cllp+FjJySv7hgIb/AXEXNbaV6jJPpD+xcymQmY0lQGcNdtJU+W/L35wcID9/f3xCz36/T4WFxfNI0AZDUwGKuXir10PWaL05KHPmfQWNGuMy5itQ0vXhpyjtT9TJFkzCDWmOCE7QTJOY80PpplzUs62E9XKVzspiKy4WSvNkRr5PFNScx3QdfrjgY2dnR0sLCycISWpEWqau+xrTpDe+zm9iLmU3/IJHx0djd8fSY9c0i8man5Bb35542kFKKSWWaM41CBTtrYeatoUQQsE8rotZLRLCzNFktwskdeBs4/oWbCCJ22JIIKmjWVcAZocns8l6xD30klZ2zjms9BMIuBs3/T7fQDA9vY2hsPh2JcnD3bLyLUEN9XlgW+rXyXJ0obBzXpOzOSaoPbI16Nxf+Ti4uKZDcjrK22TJc3UcunUIrtRZKERoeX7lS6rDGrbmu1viTdF4IaEtEyKae2O2Qlh7Twa4dSSNs9jDWgUwIrMaU+O7KKVi1L7rsmRCfbwNlEEeHt7G8fHx5ibO3l7DidKLzjVNI1qPdQsSG5m82t80XHCpHuDwWD8A2CHh4djklxZWRm/PzPbJ1oaOccyYzcNMrXQ1i2j+XI9tLGQCFmtUbo0LMwMSfJF6TWylizbkmtGG9O+10RUtaislMGKqmsyyjqk31EuWM1UsVwVme814O0gkhwMBnjttddwcHCAZ555BisrK2deQkwg8tSCWpaP0oJlwhFxca2U5CEtb29vD9vb29jd3R27MJaWlrC+vn7qaRtrQ836hOVC9lxKHjFr60HTsmusibZarueHJ5+y3KisMjLrgjZTuZa1DZJjZkgSsE3NrJkdoe1RHS0doE8krxzPZ2mRMk0YbxJ4RMwJ0kunyeS1y2tn5IOy+mx1dRUbGxu4f/8+gBMipOCHBvm7OfS/TRCOHwonmebm5s48hihN7b29Pezv748X9MLCAlZXV7G8vDx+ZFFra41s1ukOCdoY5ILP+iwzm6DlrvCCmrUgDV2ewJBp5Oc2Wqq2LiRmiiQJ0a6R7XypqfFr3s6d2e21yaEd04jMBqntaekjs1/7rvkDo0mkLUZZdptFL+u2fJVve9vbsLu7iy996UvjAMj169dPEaXUxHnUl0e0tYi05dOUz3aTOS2j4lyLHA6H42e16eeASzn5PZv19fUxSWYXrpfO8+1G/c81ausMbg2Z1RBRJo0mP/W9phF782+S4IyHmSRJgndcJhoEK6rsmca8fO++BSvaGMGbBFY6L60VjKmBNXkJ1thYpptXB/1fX1/Hc889h9deew0PHjwY9/+1a9ewurp6KgBD+eQPU/HjOzwtX3SkLcrrJKsWDZd1czcAvSJtOByi3+/j0qVLY5+qhWjuWXkswqTv2gbfNnBZow1KM79N+zgsE9izZKJ1pJFuxvKYWZK0BrHm7KGEnCQ1O6pM6/lguHbD02YmjufTrDXVNJPCKl/TOKxy5XfLn+pB0877/T5u3bqFO3fu4NGjR/jyl7+M3d1dPH78GLdv38alS5dOaXdEitTfFAkmFwX3Qcngjzx+RP0iSZP7rLTFRcd/9vf3MRwOsbq6iqtXr54K2nBY2lztZmqNV0QeGTeQFqmOiIS31SLqLEG2BT+dUJM+wkyR5CR+DI6sT2rSw+mATZY1mltmsGr8LjJtdgFmIqhaH2n1aDu+drZRYnFxEXfu3MHW1hY2Nzfxta99DcfHx2PzudvtnjK9eXTSCjbQPfneSU6QZKprP/LF/ZBkZg+HQ+zv72Nvbw+7u7s4PDwcm9orKyunAkhaX0lYfkjPneNpiFrdXrBHtleiZm1Y+TPIbq7TAm20XrkhSZZSngPwcwBuABgCeLFpmp8opVwB8IsA3g7gywD+ZtM0m+WklT8B4DsB7AL43qZp/iCoI934KGBQA76oMuZ7LdkAvtPcKzPjl4zS1k52zx8bXYvSyKCD1+7r16/j/e9/P/b39/FHf/RH+OpXvzrOz4/XyHLot2a0dkjSkRojf+8kgT7zssi0Pj4+xubmJh48eIBHjx5hMBjgypUruHHjBlZWVlRNOdtf0qzm8kRaYxtkzXDLVx35+dtg0rK0Ux1audPSJI8B/I9N0/xBKWUVwKdLKf8WwPcC+K2maX6klPJxAB8H8EMAvgPAu0Z/HwTwU6P/JrTQfIRp+Uu4rzJDEJQnkg3QHymUZJFBRiO00kTmlyW7dy/ry80QrNaXnU4H165dwwc/+EEMh0N8/vOfx+bm5liDvHLlCq5evTr+uVauFXa73fFZS5KFyFS+TJfS8J+VoHtkrhMoQHNwcIC9vT08fvwYGxsbuHfvHnZ3d7G4uIibN2/i+vXrmJ+fD8e2LbHVWEARqVnlE/jGYrkJrGseLLdOreVTc9/6PpXodtM0rwF4bfR5u5TyxwBuAfgwgG8bJftZAL+LE5L8MICfa0564PdKKeullJujclzURK1r/Imev4Tucx+l57ep0ei4GQi0ix7XmPGe9pL1e3LUOMUz5UVt4/c7nQ6uX7+O973vfRgMBvjTP/1T3Lt3D8fHx9jZ2cHR0RHW19exvr4+PmdJwRUtYBMdIeE+SQ3D4XD8k7GPHz/Go0ePcO/ePTx8+BDASXDp9u3bWFpaaqUFeZunFYTU8lt+96wGaJnklisjQhS1rz1fmVFceBpJgvJeBlU+yVLK2wG8H8CnAFwn4mua5rVSyrVRslsAXmbZ7o6uhSSpwfKr0H/PB1MDTmTeJNcO60YBH+47ou/yJQoRMsQUEblWl0ZcltZQU64MXtWCAit37txBr9fD0dERvvSlL+Hu3bvY3NzE9vY2rl27huPjY6yurmJpaWncv91uN/W4J2222gt25+be+BlZCgiRJkla5MOHDzEYDHDt2jW85z3vwe3bt0+9P9I7nF3jCol8m5mTBjUWhTVuEVG2GXNvo+TfLeVGy2fVn7HENKRJspSyAuDfAPgHTdNsOYVqN870bCnlBQAvACfmUwaWn4kwDR9Nmx1TM+G5jJ7qn5E1o8VGMlF+axPQfHFA7hE32WeUdhLtgPLOz8/jLW95C77pm74Jc3Nz+JM/+RNsbW0BwPh84v7+PtbX19Hv99Hr9cb5Mz407qPk+WTbhsMhDg8P8fjxYzx8+BBbW1tomgZra2t417veheeeew7Ly8tnNkVOGpm5pfUFD05p7ahBrclt3bOOeLXdFLls8rMnh0WOtdq8Ny4pkiyl9HBCkD/fNM2vjC5/jczoUspNAPdG1+8CeI5lvw3gVUWoFwG8CABvf/vbGz4JrAhlpkGAPdGyyGhMno8jU5bMV2viZ3wsngZikZ8VQPAmnRZoaGNyWppIv9/Hu9/97jERfv7zn8fGxsbYN7i+vo7Lly+PI8tLS0vo9/tjLZH6hB8Xyviijo6OTvkh6Wmg119/fUzMzz//PL7u674O6+vrKQvDmg/a9SjqzOvxjrTJsj3XUpaMJyFDKX/murcWsmusJg9HJrpdAHwCwB83TfO/sVu/BuCjAH5k9P9X2fUfKKV8EicBm0cZfyRgR1czPpls+YQ2u3DUsTWOZ5LB0+5kmdM+a5Y1daL+zviJamWSZfb7fdy8eRMf+MAHsLKygk9/+tN4/fXX8dprr+HRo0fY3NzE6uoqLl++jEuXLmFlZWX8ujI6D0m/gy2fpNE0y8FggOPjY+zv72NrawuPHz/G1772NWxubmJvbw8LCwu4desWnn/+eVy5cgW9Xg/D4fDMxmKNmecXlppoRiPWlIiINC1irTF5s5Dti/zmhDZzKNJIazXOjCb5lwD8bQD/oZTy70fX/jFOyPGXSinfD+AlAN89uvcbODn+8wWcHAH6vkQd544acmnrVyG/YIbMeBpNg9bIwiL5SOOs8edY5Zyn5qDd14hhYWEBt2/fxtraGpaXl/GZz3xmrFU+fPgQ/X5/7J9cW1tDv9/H8vLy+P/y8jLW1tZO/ayC5ss9OjrCwcEBtre3sbOzMy5/Y2MDu7u7WFpawq1bt/AN3/ANeMtb3jI28b0F6QVLrH7QNlGLLGUegndkqFbZyPrgM+AyZ/NYc1muVzmuHJY7xUMmuv3/QvczAsCHlPQNgL8XleshI3hbbTJCJrgh5aA0NYMuzRq+IOTC0sixhvStySXLqTGZPfdAjVw13wGMD2y/733vw+rqKrrdLr7yla/gwYMH47fx0E/W9no9LC4ujt/Ks7q6ivX1dVy6dAkLCwvo9XrjNPwRw+3tbTx+/Bjb29vY3t7G5uYmHj16hP39fXS7Xdy8eRPvfve7cePGjfGLdTXtLaOVZTT0KNhikaVXrkWUljXHy5zUqvHmo5dHkyULb+OPFICZeOImc06ypmMzEbyMhqXl1+5rZqnX8XJBcce8RZAZszzbfzUmSE3/TeoKyGqrnU4HV69exerqKp599lm89NJL+OxnP4tXXnll7C+k383pdDpYWFgYk+XKysrYFCft8vLly1hYWBib148fP8bOzg4ePXqEnZ0d7O3tAQCWlpZw8+ZNfOM3fuOZQI2mvWX6wwr4cUhSk9BOeMiNl9chNVPpp/YslswZWG9DbrvJW/J7advc1zATJEnQdldrkKwdOEOQWXhallY+l8mK/tH3jMYqzUDviAX3p2kyZNrD0/F9OuIAACAASURBVGRM8Kh/rUio1zeyXG9R0bPely9fxo0bN3D37l188YtfxNe+9rVxYGd/fx+7u7tj3yJpmd1ud0ycq6urY58iESX9Zg0dRVpdXcWdO3fw/PPPnyJIks0ju4zFM0mAT6aLLBSp+WYCPBEy7oOadnBIBSqzmUY+z6kGbp4UtMXPz0ROMljaYrXSWnmt9JkyIh9nxpyX37UBl+VkFlR03Svb0rq9M33ye6S1yo1CjiEFY1ZXV/HWt74V733ve7G1tYWvfvWreP311/Hqq69iY2Nj7E/c3d3Fzs4OSnnjpRXyN7Wp3sXFRVy6dAm3bt3C2972NrzrXe/Cs88+O/65iUzbZXslOVmbkiRdT8vzXE+WVijTZI4EZUlTM/891Prwtbq8a9Hai9bJzJAk4L8ajX/Wdu1J/JOWo1tbzNGky2oRkdzZCJzmEyXZJjFpontZzaEGmd3d8i3RC3r7/T6eeeYZ3Lp1Czs7O9jc3MTrr7+Ou3fv4t69e7h37x4eP348/oVD+vkFkrvb7Y4DQG95y1tw69YtvPOd78SNGzewvr6OXq9nzhet7VnfYHRPI0IrvzYPtOBO5IucFJOYvtZcyCocNdpm1PaZIclaf6NlwtaYOtkobtb/kRkY6bPkZGmRU8YUs0x1DxqReiTI5fBM/0kxqZuEjvssLy/j6tWruHPnDt773veOSfPBgwfY2trCw4cPsbOzg93d3fHz20tLS7h69SquXLmC27dv48qVK1hbWztDjoSMi8MzxbMLVeaVGmkU0db837xMLYJuje00gqY1BJldf7XrNBthnxmSnAa83SebliMyBbV6PNOSvjejlydI0rHq9uTy6pKyWeV4+TyNNytzFC3V8k9KvrToyZyen5/HpUuX8Oyzz576nWz6o/FYWFjA8vLy2F9pkaMFrb+yprhlCVj5tACgJQOvQyNBy6TOBJY0mbJo64by0matMCBncc0ESdae1cpqnRlTNlunV8a0JkbGrI0IMktU2fKy5Vv5ZB7Pv1WzUUhoflBJIqWUsTnN87TdRD14ZKnJ68Ga83IOamQpN2PPr1gT6ebzoSZoFQUfvTZa6bIapCwvO79mgiQtaI3g5oJ23OY8ibFtWbXlyzRZsz7SPq3If215tbu/pjG2OWepaVaZgIN13fvBrJpTEtmgB4dFaBY8rVLml75HK51WR+aguNS+PPLX3EkeUdW4uzREY96mzJkgSamlRCqwNbGmcbg8s0vRgGv1Zfx0lmbFr3GCpO/RGbPI3G5LjJkJVuseqC1DphsOh6c2SimrRipZP22NLFramoBeVsuWcmU0US1gY8lM9zUfpyezVqeUGcjNoRotb1K/dQ1mgiSBN8ggMgc4LL9e5kB3ViZZtoYoUm2VKcuQ7eEEyfO28ePUam7WhD1PDbsmP9805OLnfVaLGhLKnMbg8MhT+gqjAJAmp5VPlmFt8JJUtfIlIu03O++yBFmzkWplt8HMkKQW7dUg/UiZYImHjPaaIUj+OTuQnt/PM0OmZQZ7eTRirNGQJ0XGxOVvIecEw+XVNDJvzLOapKVtWbCecuHQ7kcbr+UT1PK1IbyMmW8hc66yzVqlfJEVYZVfO09nhiQnQdujKFkNqab8WoKMNDPSjHhaj9isJ5RqCVSbaJm2TbJrZx33lJaIcjgcnnqzj7ZxAWcXbcZMzsqaOb6jPeVilV17JM6rbxqokWlSGbJnfDMEqFlnUR6JmSTJmse4uH9Q83vURnWje5qsmsYSmf6R75Nr1lnC0ggg64yXiIixTfAoAr2ZJ1uelp4g51DkZ+N1Zfx/GfNXyk8+VC2fFlWWprFlBmuIHnut8WnK/G2CWrKsSPGINPppndHNzNeZJEnvGIFlXkSLNjspMrDMO02OaLfztCeLODMEr+2e2sTL7MY1JsskJFkbEJBuCe6j9DRInl47GeFtYJamnmkLpa/1mU8Ska6BttlPWr7mE6W6OCzT2UNGS5wGZpIkLWiOZSD/1p9p1O+VF5m02cPTbZzTlhycIL0fw6r1N0ZuAgsZH5tXrufnkvNDW5DZJ7C0x1OtuvnYZjR37cfGvPkhNcppkaIGjby0TVYLmklEfSXrzRyBanvfwpvmMDmQ37GsyZT1Y3hlcFgmfG2Ej5ddY9p6QR2vLK3eUk6ebbYOTbch5bamjudwz5RppdHOCGqbadZ81erxosdevRKem8CLbHtRaasdk6Amcu0FiiLttI0WKdNZ/a6dHqjFTJCkZUZq5+B4utozcLzcSeQC7MOxlqzWtWxEziorMoX5G254u/mkypjXkRw1aOMnjsrSfHqZ+q1H+yxompZFvF553mYfmaIZUsxEuaM2ZwMvXMOVrgSpZXv1Rj7KSTZprvnWzrOZIEmCFXixHm3KmDbRpK9R42uOZWSuyeMqtfLVBlcsd0GNz7GtBmkFSCbVTiVheJo+tzgscrXK5nVwyI2y7SO2mitB8396Znfb6Ly1TqwnkKx8MihF12ieaxtzdByKwyK42k1ea4O3zmaGJDlhyKCI3KFqfDPZnZ3LUXNd1mFNJq08TpDerk73JaFIgtV8R/y+FzCo8W+2gdYuz0faplxrHCyfokwTPcMdnfuzAkbWIrQ2CS3CLb9HvkJejkTUz5MeHSIZiDTpmFbk5rLMc63/2kS3a4iXY2ZIUtNyaELwzvUi3VpZ2Xue1pXxS1rlW+VYGmSk/fIFo2mSkkxlfZHvRoOlgWp5MwuzxtkfTWLeX5H7hc8hb75Fz1lLWD7ObKBJQjPfJRFK4tdM6Ix2mzW9LXAlJtrsrbzyu0ao3jyQbWhzrtlLPzMkqUEu7FpC5JOtVgXXYJ3FrC2npn5NiySC9MjLImQK4EzDjyqvW6YTR0QavHwrbTQGll8v0rS1BZ4xSzUy0shMAzc/Pc2Wk1HGTNfSZFGbnvLwucUJj8tj/ZewSDYiy5o1l0070yRJiDrUg2VaZgjX8m9ZGtw0wUlQmuWRCRxprDy6mtFytDKkHFQuh/doqTYGtRq79CvSItW0S61PtPp5oEvK7OWl/Jbv1fOVam2SMmsBEa1saeJHRKlp+NpBfA+yv2h+aT7KiCAz60jzbVqQflVPAbIwEySZUdN5x2rpMuZcltRq/ZKZga091iC1x0hz1PJ71yJtjn+u8Ut6xCBNSFmWtXiym5r032l5tTQeAXpmLy+T4GmwMr00q+X8ln2puQUsDdQiSpIpa2bXKCWa1l0bQzhv1GqbwIyQJEdEdtok0MqI/FNUVht52pCipaVaZMY1tYypm7keyV6zCUSR9YwJ7OWzCDTKG2mOmfHUiD5ztEiWK+XQyreuZ838jFks2yw33EnJy9rAZB3aIXqeP9O32rzyLMDa4I6GmSNJDs1skve9vOchT4RsAMgjTSJITxvUtDy6XhO9tsr32sTLzO7MGZLLBIAy9RAyfjuejkP6MD1zOdqUNa1N8y3yettodt55SsprmbiRBZeB1Ngz6duu4WmQXxYzTZIc0WBZHa6ZP7UapHdNq8fb2aJJYZFgDTlmtDsrbS3BttmMtGDa3vXr+M++/dury/rzgJ1nnx1/jrTDyDzOwtKQpYlM92pJO9rkqH5t3daQo5Y2u8azG/xMkmQ0UbxjGpmyLdQMllZeG7PUK1MjSGuSWL7MGnkiWS0fYxbcTyXxe7/wC2eutdUmtfmR1cC0QEMkj8wjNwGtfFnuHE5rvpZfVtNeZb9aba8x2TUS9fpGaz+lsYiM6smQlbVuLQ2ey+Ct+UzdM0WS0pfgmR7SNMuq31mysjQ2ryxPC6P2aJOSFoVmZnPSywRlauTO7MpRGisiOg3wPmuTF9CftuGQpC01Nct3Z51y0NLK8iPfqOY3lGn52vDWyXA4dIneGt8oKq6hhpypLiuoI9seyR3JxdPXBm9miiQlMkEaa0JZkUIg70uzJiq/L+ERjfwv8/H6OHHyPLVmuCVfFBSy8npjAEyfLK2+spz9crFJX59WFs/nbc6WjzJj2UgtkV/XfJVaWyyTuFZz5mj7GGVUhpeOm9pULieyTNA1qylmEPXVTJMkoDvdCR7RSVKUnzXTtUYmrWx+TzOXpOz8uheRk1FBvtAkqdbIbxFm9mSAtqBrNA+rTi+d9j0iwqh863HFbH9mNgmNYGX/WXNGI1hLO8yehdTSam2IznJqiO5b1kGN1TANYrQ4QmJmSNJT+62op7fAshpgtoyM1mhpdxEy6aXMUraM6e+Vm5kw0XXt+A3gj5lniln3rbZ5BG0RhSyPa5EW8WkmeXaTiMiSyxi5nGYBnj/XgqVkRHN2EveLlm8wGKSUi5khyTYL0Dro60F2FO0otWY1zyvlzWhidI+b2VlTOZpYtcEii6Qj7SurwWmkma0r6j+ZX5sfGglqcvK0HqkSrHd0ZoJEHllaWqV0LfF00vTWiDXyH0sieRKukwh8jU1TnqZpcHR0BACYn5+ffU2ylBIOjjboMhiS8TFm5clAIys5ES2XgDSTtR2NE6f0T1qBA4sg20zQ7MaR8eNxaItVkpLVb1Y7rPmRhazfMollei3oEmmtlmat9aOlmWpEKTXgGuXBguZ6qNGcPdRokZl0UZxB2zQzmAmSlKgJAnjBkMi5GxFJLelaLgIN0jcqSZCn0fyVNYEXT5aoD9pGFT1y9Bavt9ll+1TWF5UhTx1oJJcN/FjaJU8zzSCXJEiqXyNLksEb4+g0gIZok5zUVVATjfbmmFwrvV7vzHUNM0WS3m5O97MOfjkZanfVrCamaX/ed1me1CD5dxmgiYhxUtK35KzdgTM+TW3iW37ALDl6Wqw37nwsrECFJ4MV+IkCM1RfNCelXJbGKeWmej1LS0bKa5C1HJ6GTzXresvwwsyQpEUuchCzO5tGGNpul3EWe/J5aSOSkhqkd4+XXaNF1oK0qhqtmKPWD0p1WNei/NIMlKjZFCVJe2arRYpWWZavkafX2mfJHxG6JotG5haB1QSivOCq10beFq3+Npt6BrXBn7QUpZROKeUPSym/Pvp+p5TyqVLK50spv1hKmR9dXxh9/8Lo/tsr23BaQEYS2T8tPV2L6vLMXp7Gqpff4+V1Op0z6bWyNMiytXy8rmlOLm1z0OrKTDrNbaCVyevV6tMIMiu/lybadKz+lXPUKisjt9YvWUgrhIM01gxRTeLb1dJ79ZJsGni+KOhUO+d5zCCSr0aT/BiAPwawNvr+owB+rGmaT5ZSfhrA9wP4qdH/zaZp3llK+cgo3d+KCufaxDTMRKuOUor5k54aNE1F01TpOpD7WQJOeJb2wtPweqNFpwUPsgtOa5uVz9O0LF+mRpTWERtZbiSHVbeloWr91haW5cNltCwZy3T2DpZTOtk2uuZph3w+ROQj5bTa3qbvZOQ6ImNK3/YguRz7bDkp9iml3AbwXwL4mdH3AuDbAfzyKMnPAvgbo88fHn3H6P6HyoS2YNvskYamaQZaHk9j8rQ5748THtc2NW2Jl29pjVKL0bRfS27Zhjb9rPVT1P9SFuu6N0ZePg5LG/XSyPZZyGgjshxL04zk8iDHXLNArHONlmxPCt4a1T5HaaeRjpDVJH8cwD8CsDr6fhXAw6Zpjkff7wK4Nfp8C8DLANA0zXEp5dEo/eu8wFLKCwBeAICrV6+OhZ+ETzPm9DRgaVoaEWmQR0AyC1ISpFUP3/G9ci3S92S15CJkotee1hG5G7y6OaxghhVgsSC1QyqDyo4CipqmyjU9LZ+n2fFNLApESe20pt1am7x5wOuYxtEgWXZm3WbbJJHhm5AkSyl/HcC9pmk+XUr5NrqsJG0S99640DQvAngRAN7xjnc0nAS8oxvaJOX3nXao17Uyos6kgcssamuhZOST5raVR1s4VnkZGaW8sgy58OSi54tUQzTpo4XtbRI1UdwMufFFKscuaxbL+jSizJKKR4BeIIbLINO0NZclrDKy5bc9xxgh4pQIGU3yLwH4r0op3wmgjxOf5I8DWC+ldEfa5G0Ar47S3wXwHIC7pZQugEsAHngVSD+hRiw8Lf9cu1vJBaCRRaTZeSaAR7Re2TKqLJE1RzQSzpBjjalimYQclkZk+SHpXnQeUStLK8PKL7WjGuvCW2zW01+ZyLAkvChazOfYcHj2d+kz8vPzlPJeprwM8Xn9orWH0PbRQwuTRsrDnE3T/HDTNLebpnk7gI8A+O2maf5bAL8D4LtGyT4K4FdHn39t9B2j+7/dJHo98hVZ0PyHmXz8fuSLsu4Dkzn7PWg+wqgfahCVq7Vdi9BrPk3+Z/kPtbzede0el5+PDfcPevOjtp/a5Ivq53PQ801660NDdj54VsgkxALEL9uQ6SyZZYS7FlE7mqZxN4ZJzkn+EIBPllL+GYA/BPCJ0fVPAPjXpZQv4ESD/MgEdYzRxpdkXbcmKu182iSJNMrMdVkWn/SaGcfvWbLXyFZjgsvvWb9QZoFF5qBM52nGFqzoMuXX/IyRTJ45my1LkyeSk8vhabLafc0FwtMMh0MMBgMMh/Zv0Gjy1sglYRFlhtitCHXN+qyts4okm6b5XQC/O/r8RQAfUNLsA/jumnItWFpbdofLONMlSXFCqlmckZanHUvh2gNdyxCkRkTcdOP11siZSaeZRlIGSpf11ck6tbbUyqotAOlDbBtk8DZsj/xk3ZK45PWoTK1e3t+y/DbuqSyscdbmoCVD2zHw7mlrwttANczMEzccRBbZ825eORax8kkbaY41Jq9nTshyZZkyvyZDRKLRxpDRML02tol21/j+PI2Zl2nd57+bLTUmgub7zPoQZVlan3uanZcvm07O61qy56RpKQQ8LaC/9KOt+cvlnMRN1BaWL9vCzJGkNDszJ+wz5ptFPpMQpNaxXhSWX6eNQCM7zd/mpZFlR9fbaJiRhuTVqS3yCDXt0zRGqVVJWWR7pAkO5IIunvwZMvHkkPXXkFKkPVoKRA3hyj615v4kkWX+ZEzGwtDu8TyahRHNx5khSY2YspPC0xi9erJytJVVykQkV0uQltkoNwBPa8m0zzOl+X3PlyM1G60eTU5Ls9DIrRaW5uWlo7ososjMzRrz1pPLKkMjYppL0nyPTPmsZpjpDznek2jNXF6LIDMbtYeIeGeGJAE/0qbtqpaWl9UGtLzaNa3smonAZdUIUpNbK5/SWLJld++MRqxdz5rAESlau71XhzeJa/ydkjyAOh9ZGxMzaxJr5EYbkqYZe2a/Z6Z7Pk9pXmf9d5GWF21O2mdtY9QUj1rwTZ4TsIXJDfwpwPLlyTQcvJPoz8pnpeGmrFaHVo7MkwEnRkmKmraoESqvX6bT2kflZvs1Shvd8+rT+ln+SbeCN24Z2aJ8Ge06U3cWmY3WQ3Yjk9ckmViEFMnj9Yu1OXplcHjk2VbGLDJjPFOaJMEyH6dhKnt5LFnkBNPyWEEmjQg1spQyS4LUJqK1QGqeRJKms9QI2hy3sjTJ6LqnCUVan6WpWD6/KJ11L9LWPVhaGn1uG+ThZWlt5fXKOi3tUrvuuSPamNRZwmtDfhZq/ZHAjJIkwVtUGdVdwvOjtQE3haz6ut3uGQ3Q8kdqu753n1/TCCXTPq1P2mobFqzxisrOLLyao0KyXGvR12wQWjnnieFQf5EGjWO0mQB5v2yGKNsgKiNr3Vl5NETz3MPMkGQUNAD0xZuJnHlqvudDkd8157S1c0uCtLRRzbTm7ZBmqCWjpY1ZiLTuNmTY1ifllceRPZhcg0g7Ijm0zTBqT+2ZRF6fRlBaf1gbppfPgmxjRmvUNFCONsrMpIisqdq6Z4YkJTzS5LulnFhtTG4JjajIVImOImhmcmSu8zQWeWbb5MmWmSw1fSa1lqxs1vdJUVOepjE9qbplPuDsmdKspsXz8Tya+W4FYaxxqW2TNZ9qA2t8XKwTFla9ch1NAzNDktEC8zRGGmjLmZzdzeR3Ku/o6AhHR0fY39/H3Nwcer0eut3uGVOaExwdatbuS/kI/CC0Rq5ZZNJr9Wvw/K81j+BReqvP22pl2ikH7xiJVbfUjrQ6vU2hhlS4lqhd0+rVCIPXzWFpg1IGuh9Fq6M2ef1lPUiQ8XvzNJ5bqxYZnyjHTJAk19ay0HZT7R59zxKlRgpHR0d4+PAhNjc30el0cOXKFSwvL4/JkLdB0yRluRYRStNay9tmomQ0yGwdk0xUz19WsxjbyNHGXLY0La0d2nz0FrbXXul2ichbEppnGmv5eTq5ocly5T0pt9YWS2b5OXIVZbXICFzL5mvOwkyQZAZeQ7wJlwkYAHqEmWuSm5ub+LM/+zPMzc3hzp076HQ6mJ+fH6eT2iMvSzOheT6LYLX2WZoGkPv5V21yepNrWru3JlfNQW3eh3zBaj/hamkoWt9Z2qRERIKa1iS1RYvcMt9LKadeb2Y9/aS1RyN5jUR5XrlBaHMwawVQHq0PZH9YyDwpYz3kYI1rdm7PHEnWmJbU8GywIvLVSfKizm2aBru7u9jY2ABw8ib1y5cvj/NY5jUvW6tTI0jSTGu04aguWV7GxM4cpK51A0SITDy5yUQaW2R6Z+S3iNUicotYNHj3PCLi81Jes2Tw+szKq5VFZVhEG0FuLID/RJF3PwM5Z4AT+b03HknMDElK81J2JDD5ExAWQWoaH09L/shHjx7h+PgY29vbOD4+HuchguQdbwVbNHOc8nJZpKyeaQbYkU6v/TXOba3+LGlGi4CnkwuyFnK+aP3oEZ9n5vK2SI3WQo22xcvNXvfSWH2ZcQdk6vF8zbweiWhNyz6NLKJsGu16Zp6dXxy+EkQaPDrVNkJFETIeKeP1eOVrE6ppGgwGgzPlz83NodvtnjGPLbK1zGmSxSJICUrPJynJmfGxcBm9tvO2PgnIfqiRSRtP2U+8vMzc0voy2oiisedyaeVrvk+r/kgGWa8mV+QassDL4f1cs2bbaog1m332lICHmdEkNWj+naz/SSvDuy87msprmgadTgfLy8t45plnMBwOcenSJfT7fXS73VMaoLaA5ISUJjlNtloNUis723YvbdZ00iKEk2iAchytzUqDJNYa0w+wo9VZuSOTnrdpMBiMtX5pzvL21TwlJOvwzGHvrKOWN/I/evOlVouO5rDnq9YQzcfsfJ0pkpSDrd2LnM78ulZGpFVo+fr9Pp599lkcHBygaRpcu3YNq6ur6PV6KvFZ5UsTm/sgtTZzmbS+sr57Ax9pQjzdeQRtNGhjB7yhIQ+HwzG5cPm0/FoZESK/Y4YIObR5yq97ckRySfk0oozy8/XhEWVkVnv9o9WntbdmjtVokDXlRGXNDEnKHSuTFrB3q263eyYPTy+vc8gF1u/3ce3aNaytrQEAlpeXsbS0hE6ncyrQEplfNPFkkKdGe9TakG1XVIYmu6dBtK07kkPWSe4O0rakScsXeqZd2kL3/KOUR6bNBB60Y0KaSU33aGPgddQeI9KuW6TqEaVXh7yn9Y91RIr3QVuia4s2ls7MkCSgd67miNbSWgvVGyhvgHi+TqeDpaUlLC0toZQyPkguTWarPZIQpUbL4WmNlnZs1WuVUwNr87I0/kmhkQJpkkSU1H+DwWD8mVwfNa6CSA5A7/usxmbd87Q+si4oj9TqtLKoX7Q6MiTraXqyLEKbyLZWt3ffQu38juZoRNQzRZISkZmpTQLNZOH5MjuXjKxzYtP+W+SrmdfSNK8hmcikzpjS2qKRk1+SgecL1sqIZLAI1tOehsMhjo6OMBgM1P4nDUyeVaWyIlLXrlvmrKURWsSh3Ys0dB6IIzm63e6ZoJVlTvM2SGRIXboIauaEl8aSMwLfRGrKmZQggRknSW+3ye5CfEJGHaKRrUeQGjyz3iNW3ibPL6vVJRERaOQXi3xp3qYUyeC5VPgisAhfaj6cACktkaUlj7eB8PZJDUqSgEYKPL8FqZ1q+S1TVlMSIjeDRvRSS5Rjrc0BjTw1P2Vb7dKSXftMmMSyymImSdLblQnagGvQCFIrU1v45NeUjx5G/k1Ng/R8l1wWKxAl69DaaN2vRUaz1OrWypFaueb7k/XyhUsk2Ov1xulkf3Ey4wQpF3i2zdH8i8iSX6PrnAAtctH6gbeRjqFpfS43C++guCWr7EtZrtYvmt9VltMmAJidY9b97DrIaLMzQ5LerpwxkaydS3YWN3NlgEamkwtHM5E5CcvPmmkty7X8r5m2yPrlNa1ca4Fb6TPjUCtz5j5fZHNzbxzUb5pmfJDf87VZG5j2kIIll+aWsHzcHjFoG3pG69RcSVG0mmSR1yXxWeQvNUG+wZDWmiFK3i/WfNfaW4OscmCth+x8ngmSpIUgAxM1HWztJlFHaAuBLzLN1LZMaf5Ze45bI0hL++DXLKLn9Vv5vXKt79oCmob5ZI2xJofVlvn5eXQ6nVPBCrkJafW1iaZa/jkO7xiQ1i6rHy2LSVsbngarya71gSRKb8PhfmCLbLV1qo1zdj3L/DX3ItTmnQmSJFg7IUHuWN6OIDU4a9FrBKQRo0W4EUFqiz5rfkiC9MwKTbvl7fTKsOrmZUQkkC1PI2ePOLRr1C+yH+lgf+RaqYHln9Pu8/qsuRtpcIRMwMVyC0gNzyLZbFp5ltcjWwtamhp/toQ2J9uWFWGmSJLAB0ibbDKdtyhqOzJLkJ7/kZuHMh8/2hHJ4mnC2QkW5Y8i3byMjNle2/dROupX+iMtstvtqpq4HIsaObyATObMn9ceyy8YmdDyO58T/Ls0q6WGZ60pqQ16QSONkKM2ybK4DOeNbB2RdTGTJClR26HRjuzl04iRm3HRopQvzs3UySdNZmFH5reXtuZ+FMDR6ta0ohpYmxH1K2k2RJDSspBavbwuEbWF0ngBJ4tULe2O8nvKgOwDTYPUyvfI1EpnEaXsH9k3VvwgQ5Raf7fV+GuCPF5eCzNFkjWLP4LX4drCIC1FykDfh8PheFEOh8PxmTUe+aaFbC10qUVKMpWmO4cVcJjWjlwzYSPzahoaJKBvGPzAONckpbajLRxNZq3dcpPV8muauEVKPGtEDgAAIABJREFUvByNWOh7pJ1rGhz/rBGgzMe/R0Rp9Z/lc/QI1iJHbdPQ+iCDWqsrykeYCZIkgtHAO5qfo/MmvhWJJaKTiN4FORwOcXx8jN3dXQwGA/T7ffT7ffR6vfGE8cxWed8iUW4iStRG/ni9bSaJph1ReVaeSYlTaq4kuyQubYws4tYIS7tHdVqasZdfO1RtaZXWvLWi4bydmv/PIzYtnfU9o1Hyz5Zf3dLq5Dr28vF6rbnrnVLgc3VSggRmhCQB20fm7c7ys2a2WOURJEFyOSjvYDDA9vY27t+/j+PjY6ysrIzfBFRKwfz8PBYWFk7JV3PUhPLUEqFlHkb1RmaoFqwgTEODjORrC08r1ExmTRMC4jeIy+uWVmn57bQ6rbHw6tW+W58J1mZnaZSU1ivX0h6t/DUuIGtTyVhUmTWQkWdmSLKNZmiV4flL+DVKyzVIPtHpeeHt7W3cu3cPX/7yl7Gzs4Pl5WWsra2NtcnFxUVcunQJV69exfLy8pg8uTxeoMXTdDKI+sa6702UbGCIw9KOasvSCM2CXNA1smYXcWb+RVqpRyo8nbc5aX6+yFcpr/OyPFKOlBOe1vMJevktZNwiFmo0x+wcmwmSLKWkiEIzbeg6odYMkPU0zckbZ46Pj7G/v4+9vT1sbGzg/v372NjYwM7ODh4/foytrS30+30sLCyg3+9jc3MT29vbuHz5Mq5cuYLFxUX0er0zUW6NoKN2033Llyr7pnZSSliLW8rvaf1ZObTyrLZ68moLywoeSFktTY/f5/JpdWvExNulaZY8bWQ2e9qdlMvy08oyPQ3YIlbvDKTVbtmPHFGf1sDbKHmfcIXoTRe4AexzZ/y+BmuSyTQEmlzHx8djjfHw8BAHBwfY29vDwcEB9vf3cXh4iKOjIwDAjRs3xj8A1uv1xmb28fEx9vb28Morr+DVV1/FpUuX8Mwzz2B9fR3r6+tYWFgYvzVIM+2zmqBlXlM5bUzYyGyp0RyjPJG2ZxGj59/16swEITRN1DObtfq1RZ3Z3GX93hz2fJT0NqSs5UH1Urm8bikTtVeTLSIzTtIRqXpuMWv8qe7aeV9LlDNHkhq8CSAHSDsaAmAcACBi3Nvbw/7+/ikiPDg4wOHhIQ4PDwGcPN3R7/dx9epV9Pt9LC0tYX5+HvPz85ibmxu/T3IwGGBrawuvvPIKNjY28PLLL2NjYwPr6+u4fv06rly5grW1tVNkSbJb/hdt4slrtT6XaELU7tya9hstVq8OT+uINBLg9HPRlMfTFD3N0qob8M1xzVzl5UTBE6v/pOZJ/2WbeRlcBoukZTujsbOIMpuerpNcvE2Rlciv0UuYh0P9WX2CHI9M+RIzR5LRjmsRBd9dBoPBWPuje0dHR+Mf9Nrf38fOzg4ODw/HnUyENT8/j6WlJSwuLmJ9fR2rq6tYWVnBwsLCmBylvxEAnn32Wayvr+P+/ft46aWXcP/+fTx8+BCvvvoq1tfXx/fX1tawuLiIlZUVdLvdcYSca5XWAtPanTVl5fe2prgHS+P1QJuMVo5XB0EzRSPHPz+RwBeZld7TBOV9LX9UhtSyPDPamgd8TsrNSpaXcR9Eslv+3Kw2a7lHsvBcMlobvL6LkCLJUso6gJ8B8A0AGgB/B8DnAPwigLcD+DKAv9k0zWY5qfknAHwngF0A39s0zR+kpEFuMmk7OZnMZCbv7Ozg6OhofI+0xKOjozOEuLy8PDahSzl5bx/3N1LkmhMYl4O0ymvXrmFlZQWrq6t49dVX8frrr+PBgwe4d+8eHj58iKWlJayvr2NlZQXPPvss1tbWxlFy+cJYiywzRFcbxDgPEPFIApT3M2k9RO3yghZy4UhiIXjaSGSKa/JK7dYKVEhis9pqkaynlWYCHBnC42tgEoKMoG2O3s/CelZDLbKa5E8A+H+apvmuUso8gCUA/xjAbzVN8yOllI8D+DiAHwLwHQDeNfr7IICfGv13oZk7WnRUTm7yJW5vb2NnZwc7OzvY3d0dn2mUWtry8jJWV1exvr6OxcXF8ZlH/qQMJ0T5U6+aP4MGoNfr4dKlS1hZWcGNGzfGUfH79+/j/v372NnZwYMHD9DpdMYy3Lx5c6xlUrRcWxC8Xs9fY/XrJMj8RvFgMFA1Mkuz0u5F9Xgkavm8APuJEHr9mnWG1fOFewFC61ykRtja8Rq6p5UhZeTXZb0WUWrricPL62nK3lhnTXQNGddTbRmRL54jJMlSyhqAvwzgewGgaZpDAIellA8D+LZRsp8F8Ls4IckPA/i55qRHfq+Usl5Kudk0zWvZxmi+H7njAyeDsbu7i62tLdy/fx+PHz8eDy7/NcNerzf+yYXl5eXxH0WfNVIibbMNut0u1tbWsLy8PCbCjY0NbG5u4v79+9ja2hr7RHd2drC5uYkbN25gbW0Nq6ur44AQ1y4tLdbqw2kh+yPu0Q+aadpOBnyssxqnRSKabzITtNHK8zRB7Z7lQtGIUtYd9VdNXoucpGIiy9Duy/JKeeOpM80vmHFL8DY8KUR1ZTTJdwC4D+BflVK+EcCnAXwMwHUivqZpXiulXBulvwXgZZb/7ujaKZIspbwA4AUAuH79uulPoUbIzmyaBvv7+2Py2d7eRqfTwdWrV7G6uorV1dUxMXIipEGUjyBKTY1HoGsGTGqh8/PzWFtbw7Vr18Ya79bWFjY2NrC1tYVHjx7hwYMH2NrawuLi4lh2ioovLCyMI+n8h8dIzvNClhzbyKBtSJk8msaZMdHlxusFESRhRrJ52lomoGMRJZfHI2tZJ8kt264FN6QMsn88bVj2nyaz1j8W2VrIujGyadv45TMk2QXwzQD+ftM0nyql/AROTGtTDuXaGambpnkRwIsA8J73vGd839v9eOMPDw/H5vVwOMSVK1ewsrIyPqPIn4Chck8JaajbFkFGHRr5i8isX15extWrV3Hjxo2x+b21tYWtrS0cHByM28MnNREkkW9bDXcS1G4UhMzk9vLzcjTto40/U/N7W4tdykBpJcF4RCnzyPsaUUaKgufP1MqndmkBP43INI1PapW8TyxY69k7oUAyaG3x2mqlndRHnyHJuwDuNk3zqdH3X8YJSX6NzOhSyk0A91j651j+2wBe9Soo5fQ7AOWvxckdtmmacYCm1+thdXUVly9fHh/RoQ7Ikgk3ZT2CtHylMq9HZL1eD71eD0tLS7h8+TJu3LiB/f19bG9vY39/fxxYIlLt9/uqFnme8H4fxoN1XCMLL6Ag/YoEy5+pRcw9HyOXwTPXKY1WtmWCeuRqmeaSlGQazffJAz0yrwbZj3LdWRqj7BOtfdkIenZcZF3WBkbXs8Ro+Xk5QpJsmuarpZSXSyn/SdM0nwPwIQCfHf19FMCPjP7/6ijLrwH4gVLKJ3ESsHmU8Udqg0CT4Pj4+NT5R+537Pf7YzOV/w6KtmNSubJOiyB5XrlTyTIkQWZNyPn5+bGvVL5texrEWGuaPiki1pAh1Mgc5qRpHS2KtC8q28tXY2JHROmVqZWd0R411LgdJOnKMiIT3apX1ic/a6SVIdG22mImTTa6/fcB/Pwosv1FAN8HYA7AL5VSvh/ASwC+e5T2N3By/OcLODkC9H2ZCjTzl/8gvdwliSApSslfWUZpNGiDw8mRrlsEqZXlaZDyuzYJ6LNHim2Ox8iyNMLkZKLVPal5Hy0QILeba+k9s5m7LIDT/efVY/kmNXiaoZSlJrqr+SEjrdCTz5NJXuN5rE3FIkrqq4j0eF3RNV6fBsv6mBZBAkmSbJrm3wP4FuXWh5S0DYC/l6qdgQ8IkSL/A047hD3NLUOQVAZdtwhy1CbTv6lpnZPAIyyCpiVp1zxo5xQ5alwW1q7PNXVrAcj804JcWG0i45Zpm9UMpSweUVr56F5EyFE9nuZome4aicq6ZH9pyg6/7gVsrPbK9N4G45WplZXBTDxxQx3ICZIOh8uJLjU2uZgtv4O26CNy5CCi9OrjZVOdlg/FIw0PtNiJFCXZcRKQ9zkydVsbQ5sJp6Wr9VdqsII8sk7Nd5klTEkkvMzIvM6Wr2mOMp2sUyNnjZTkNYJ3ZtMjUcutYMmhaZwSGoF7mw6XTetL2W/ad08ejpkgSeCNxwmB02+clpojP/RN1yNYGqcWnKlBTR6LLC1oBBd959covzaRJaJoYGaySURpak14y2SM0mrytCFMrVy+iLUosqXlUXp5T7OGLM1Q1qd9pu/WyQAtEGn5ILM+UV6vLEe7bski69fq9bTRNnPWwkyQZNO88VghcPa8lSRK+dmDTC93Hkvz1Mqge6QhRtqgVo4XwfXgmcQWPI1bprMmb3ZyUV/Uaqi8jsjvZJlsWj5Pw9TguSs0zYXK87QrK8hkmbiyLRoRahoZ/80fba5qRM7TaO2NiNCLXmtpsxucBm2t1pJwtg4NM0WS0sSQ2l+G4CQsv2MGWY01Y6Zr8tTA0hQ4vIlrmc7eNZnfWxBaX2W056heqy2WRufBMhMJkd9S1hP5KrU6eDmeqehpnQTLV0l5NHkJ0nKQaeV3r78tGTiiaHoEaQnJOs8TM0GSwOlOIEKTprWnDVmLydI8rY6NBs0qLzqaEJF09mF9r055T/PNZbRY2ZeUR1tkXnlRXZZJZC1sq2xNQ5LQ/IWWVuWdubQWNyc+i/S09FTfcHj6nCKXSZKYZp63JWxNJi5DRqPMmuGeOS/bnDGXed4aDbLWkpsZkpybO+1v1ExtntbrlGzU2pKjJqiSIV4pE0eWHC2/oSwvCihl3AOZSWSlqXVfRGVkNUTgrHZkaT+e/0uT3TpzafkLNY1Vln10dITDw0OU8sYRNn7Ujc9DSZSWq0lqVpqv0iNrrawaSJk1eNpx2zq1z23L0DATJEkTJSLGbFkWOVrmhkREPLXakyW7fA7bImdNk4vkaXMoXDPlJkHthsMhfZzSvNPSy36IfJiZ+VCrIck6LC13MBhgZ2cHBwcHWFpawtra2qmXQ1j+Smmqa/0hNU6LdK1NhROy1X5r09HyWRaC1+c8HaFmzWaQzTMTJAlA1SIlarRH7rvg9wGo5oTVYRZBZs1eDdZjf5FZ7BFoVLc2aQE9Gpppg0ce2u5uLTQJnm4aRG3JJ+WxNr5oYVraaUSu1K79/X0MBgP0er3xK/uoPCtwI8vWgkDefXndapNHlFlfsVW21Sfc3Nc2vZrD9BraBE1ngiQ1/6O3g2gRYkmOdC0qq8ZE1srXTHuJmpeDSnk9GWvJWbuvTeQspp1WCyDw/Nz/VFN31hdm5QXs6HdEPHKM6N78/DzW19cxPz9/6lSHDPJJ0tW0c2nia4SqlSXboJGptoGSbNZmw10HHiYZlxq0IUaOmSFJTnKej8QiOWmq87IJNbuQR5CeVinr17TGjEZslaelsdJNEzX9aBGDTKNpDNKMnFa7JtFCshpQTV1zc3Pjl5fIZ/YJmlks67OOIXmRag4r4GORq5QtIlF5z7MsuLwcmfVS87CBVoeHmSBJQDexqeGSIHlaz4+plRXJQIhMXytw4kEzqzU5sz5Pr5xoYUyC2vxWeq8cOb48+uuZ7tGCahMw0OZm5i1D0lSmz1IGyivnuUZUXlSbl6/Vp7XDI0b+3XLR8PLbaIHZDcUq18s7DYIEZogkgbMHaeU1ze9omcI8b21HZjsxSidfdBoh0iJrA1m1k0HTQAjSTxXVmfVBcmj+SH5I2ovs8jxaeYQouurl523PPCsvN1Luv9TmN8+nEaUllxY40c5B8rzaWomIkvcD5dHq09qRhWWxSHcLh7dONGT97oSZIklAJ0cCNUoGefg9QkSQ09pleHk8b4YgM5qUTJvxK9Yg65PUXAreZG0jk+Vv07Q0wH6EzfrOoS3GaKyi84BWv2h5o/IsM1uDRr6a9ueRICc7i1A12el6pn9kW2TQy4JmAZxXUE/DTJGkJEiNCDlREuSikTubZqJwWJ2t7Z6W2aFBu5cxVS1Zp0GQsv7aXXWSumvKlJqa5b/0iMSKzEpYGo9HDmT+Z4I5VpnW4udy8eueD1Crn7sqJAl6RGm1XyP5KN0ksNxc8lhVW39zlmifDBVXgsiwlDIeaP5bNfSf/gCfIKO6MvJ4aOvj46QflduGkGQfTVt7Pm/Icda0aX7dAvVzW61eS6dt1hJN06QWr9TAvc2wjT+Yb/BaQJLXq23QPG1W2dDktSyPzPjIMrW57c2FbB0aZkqTlINCgxpNEO+4Qa2mJ+vRNEgtv2ZqW2V6eWX6Wu2R76o8COQtridpumQRabiWluelsa4RNDNeprPOAVqBHCJKS9upMVOzGiBPK797GqVsk2VGS42S96lltkfjaLUVyP3krzWu1vrRNHYLM7UySGOcm3vjpwuIKOU5SkJWzY60EZ6GyyPTauW2hbb7aTLyXbBGlklke7Mgo1VO4qbIah/eW921jYqXq7mTrHZpebTrUVqtvEyZ3ubNIeUgMo3msXavRjvMrpUarXJmNEluYmuDyGH5jwaDAY6PjwGc/PZ1DUlo9WU0TS9dTfraMr38k5rpEWrfw9gGNXJbmoHn77OuUX4rKJQJqFg/FcGjxN4xH05SPKIbaZ2eNhoFamqDLVGfUJ28rZoPltpm1SfbPk1k+WEmNEnuZ7RIajg8/bZy+iMcHR1hd3cXjx8/xuHhoWuuaVqklVbKOal21sb816KlHilmdtIsnuSvNE4b0VhppvQkC5H62+qzrHao5dG0QkuRiOb0pPMi4wPWrknNWt6z5qzXP21lzWrEwIxpkoDu6OU+R+2w9XA4xNHREXZ2drC7u4vV1VX0+/2UuZyFZ8ZIeD+bYMGS1SJ0S6bzwpuZKDXis46IeQsxo3Fx7Ud7P6WmPZFGRwRiEbVWv6UhSs3Ui6ZnH7TIHIFqc+4z0sy1Y2EZaCcitHqitTMTmiRwVjuiXZ3/YqI3mHNzc+j1elhYWDhzjtLTAL0JOU14O6lMIz9bsk5TY5TIapDTTnee8Fwplm84mj8a+CKO3naeKVeOseef5H/ZTVQLjmoEktFiLeLxZKnVTL01UlNuFjOjSXJYx3ksEEGurKxgeXl5vCCzgQxPe5skrUTWb2hpjzxSep6aY/aFHJwMvCdQsr9UOAky/jjr3qT1WKcrMj80xrU/y0eXeZpF09boulWGrE9qnJFcvE6t/qyvU5NTzns577wNT5NVAy/HW08zQ5LR2UYaBG1S08D2er0zO2iEDOllfEcc2hNBXvnyeuRHmRZqf4ZWQi4eWd6TCPBIeaZxiNmCZ3K2MQd5uXLRW4EZWb4kRO8oDzev226yvA+kLJqsUgbr6SZJ6lqdsmxrDDIH4/m1qC9mhiQ9LYB/lkSpNVpDm3uRH1Lmk48iSvMk2v08x30bWD9DwMlMIzLPpxqdqbTyTUrIWURE2YZI5XlT65E8iZo2W/5CDk2To7QyjXeO0qvL0ia1ezJ/FPn3+iqa596moV3X5qmWPrO+ZoYkPSerJEb5XfPdtYnwRea4Z+ZKcrAIMqMxejJm4f3cLMFbwJwoJaHWHj6nSfskzO5papKRie4dyD4PjVZb/NaxJo3kuDZpacU8DS9TQhInL5eQOdzP02quAW8jiIJcNdc9zAxJEiIfZNZ8lfezDt6MuU2w3jCuObi1+/x65kkBDecZDJFk1pbcZNuelFY5LXjuD4soCZHLwTKXtXpk+myUOeN75Pf5pm7FBXgZfBO03GaWec3bqH3W+sDKl92cao95zQxJepqiBtnpNaZqlmgt8pS/TaPl4QQ5N+e/0s2TwcLTjhR7Jvkk/rkniZqFmynLIx7ADmBFfkV5XIj/9xa8RiwesVsko60DacZLTdI6WqUFYTLgwVwNWf8iLyNLljNDkhze5MyQo8RweHLwXD6Fkw3EaAQp80ofjWZiTyMqnYk80ySwgij8+yQaXXQeNHMURsN5a5ptAyMSWXOcYAW4ZN2W1hQRZOSLleVLkuPleOa4RYCRmW5FzrP9q7U70kC98rI4v3MkUwbXxrLpgTfMBXlMwwrE0HXKkyFI+syd+7Vk/rQ1w1nCNCLuEjQ+UruXf/Ie/14LzWKhcqKncjKKglU+n8Nt5eTXvbZLH7snP78XKQxcc6Q/Xo+mnHjtlZpoDWHOjCapmdhtJibPIzUD6yUZVj4O7yW62UlkIUuQtT8olsn3pPyDT/o4kAcvuJBFLQFZ6bO/6a2RSmSqWtFmTVOOoutSE7XMbpmWoLkQqByZP3usiKfNBprajPfMkKRERC7RcQNp6rbVBgBfg5TBGUtWKd8k0CahvM+jydNCTXlP0xfp+byiR9Wicr0gi2c61kS/rYi1vEZpyUqqmV+aPFpkW5K85nuV0e3M4feMD9dqu9UPBCt6zuWoGf+ZIckowlwDmT8iMk9zlPcl4WXk9gaF+wYjEtLaxXdSuUlkiSrSJmvJdpaCNZaW4mllVhpZnncERaKGUDOoJRMtrXaUid/PlGkFeqLnvL1yuWssq9xoG6O1WWrH9bxxnBmSBGwNyfLvZSPa1pECC1xz1HbVDEHWEr0XMfdgpa1dgBZR1hKkdkj9aUIz+eR1K30GkTk9KbQFrEXhrSCRFS23yD4TWNEI1ZLbum9dz7gQgJziIa95R4g8PpgZksxqQVZjokCJ5+zmkKY1z6dplFoZGRM8kt9DjQZTg0mITTtT6T19E+WfBqQfKooOyzHTTNjInJZz1xonqdnINwVlicny/0nfn7xn+RI9U1ozlz0/pmVKR33irTXtWnbtaOOSMb1nhiQlItLT0kRlaQdPtYUg03uLKUuQGVm9Yykcctd/2rAIziLKJ6ltagRpnbnT5kmkMcl6vLRW/jYbROR60a63mSsZ94KnWWomvadlZi2jyIdpmd+aFh4hRZKllH8I4O8CaAD8BwDfB+AmgE8CuALgDwD87aZpDkspCwB+DsBfALAB4G81TfPlqA6NrLJao7freKaxpUXy9JHfTysnC17fcDgMX6ulwZu4VG5U5iS+MUl43lM6UmOapvZoLQyZRh4Fk/cBfT616SNJxjXt1chJ+hJ5HVJuXq+nmVrE5QVaZL28bj4OllZpEaXsd61tFllq69KSTfZhhHB1l1JuAfgfAHxL0zTfAKAD4CMAfhTAjzVN8y4AmwC+f5Tl+wFsNk3zTgA/NkoXC5IkSC+fdeaxbVBIDgDXEq1yslqkNOtpAZF2a5XNz4jJOq2625I4h7bANcKz/EHnpTXy/rLMaa9POSwNk87MttHEaPPLjO95I3I1ReSb8fdngyzS4qLvfH5r850TXpt+lFp2St5k2V0Ai6WULoAlAK8B+HYAvzy6/7MA/sbo84dH3zG6/6GSZDyLBLJ5ObKmK3B6EWvmcimnf14iS5CZQdCiqhyW9lFDlJEs2ckSaUIaUU6THGlhaGal9sCABc+FMRgM3GNC3lM2cu5y64Dk5nJOSpYZ0rbmCYfWH8PhEMfHx66MnOwiS82Tlafnf/wer0POV00Z0toTtcO8b94ZoWmaVwD8cwAv4YQcHwH4NICHTdMcj5LdBXBr9PkWgJdHeY9H6a/KckspL5RSfr+U8vubm5uRGNXIak9WVLnW39m2fkpHB921Aas1D/iCrSFuOUl5Or6RcM1IYlomNCdEjVAs3xJ3yHN4Lhqen2+SNYTmkQAnR56+ad54837Npi5ltiDns0cm8h7/PSl+X5PJIjKt3BpYmispK/TLqgR5esHr02g+nLoXCVpKuYwT7fAOgLcAWAbwHUpSklDrjTMzqGmaF5um+Zamab7l8uXLdC0S5xQyRFQ7ODXag+cLtRaV1Frp/9zc3BmtS/PVtMEkGrqGSDv0/I5tNcuMuUtttI76aFaClp/n5ZqlNaYRMWvalpZfQ5bcPWhanuc2orkon1DT3E78nlW2R5Rt5qQXHefB2KwCE62vzOr7zwF8qWma+03THAH4FQD/KYD1kfkNALcBvDr6fBfAcwAwun8JwINEPVWoNR3DjnDuZ/2Qbfx/nMD45KTypKbraXvT8j9yZFwUT/JMpEeW8p6nfXK/pVZmrSnMzWngDSKxntaSWnJElJa/nZfJ70+KTqdzRlOTZVuWCpfJ00AjtHVJaH3j1R/VkZH8JQB/sZSyNPItfgjAZwH8DoDvGqX5KIBfHX3+tdF3jO7/djPFcyoZ53F0neC9Sdyr19s15X2rPqsdtHv3er1TP0fhmgPnTJZeWdMmSC9oxaGZr145lsmbKdObR2SOS/OZoFkIWZk5LO2NMKmVIINdJHcbC4TmH9/8+TUpv2aFeX1R43efBvWER4CapvlUKeWXcXLM5xjAHwJ4EcD/BeCTpZR/Nrr2iVGWTwD416WUL+BEg/zIxFKOkF34VeF9h+wyvhuCdoSJw3tBhlb+eUGao9PAJH7IzEFqgvQ7ZsfZ8k1ZGpxGntRvdPYzQ5wkIx9T7QwhlwnwHxOV4+f1l5dPXqP6+Py2jgTJOWr1I6WTbqNIc+bwAmXefZnWOpdJ8PowdU6yaZp/CuCfistfBPABJe0+gO/OlCshiUruyNrnUZ2mtuWZi1a9URm1O2uWIJ8E+KTi7eAL2+tHbTLVvkmIp29DsFJuDxGBWItN9oX2ZIYkQ1kOJxhJlNGcqiHK6Lq8b5EFtaeNdeCRFq9PEqVGwhm0TStliZQbYIaeuLECHxknqzZ5M0Sm7YiRWVsLbcJ5EyozaFFZWc1CgiZNG7Kc5u/XZLUjiVpC5PVo9yzZtPlmgfdXZlyj1+Gd1xlL7dlmSVy8j6x5YG1gXHPXNEzrgDldsw6Re7D63FO4NDx91WZCtPHFnEegwfNBWvenFW2WZUVBqGiRT2o+03/rEHmmfCs4VYM2hML7hgdZsnVZablvudfrqf1jvT1eluNd0wImWR+7FtHW6tSCSNp4WdFszU8ZzdlaYqtJF2EmNEmtk6IGeqRgkY9GWlHQh082AtdUUztR0sFulWX5k7x0NVqHLF8+Lnl0dITBYDAOJkXle5tQWwL2NBdLI7cQ+df42JK82hx2/Fy9AAARuklEQVSlQNtgMDg1T7zAAslFfaQ92RLJL9sgy67xwfG0Wr3aPOd9ZOXl2qHVPq69ktZnWTG1FtY08abRJK0dKtrlrLJqrktkCDKrrU6qLWmyZbUJSx7+fzAYYGdnBxsbG9jd3W0l05OY3LVaYxQhJZkt7UoSFD/KZcmkyci1KinHrCK7frS1Kecmbz/va96flIbut4m4T4KZ0CSjpyM4JiVICxlfZGZg2rygNiMr1xwyckgNgXB4eAgA6PV6LlHIPBsbGxgOh1haWjqlTdYi8l3SERrrpzYyGjK/b/mnMwfTeZ38Gj887gUbte9ZcI0zejQ1az3URMX5fe+okvRBynK9eri2SZ/pP19H8jC/1ddaO6eBmSBJ4OzbQyIT2jNhZd7aF9rWmNJWPTX11RClV/a0nPq8nk6ng36/j36/P5WyI9BTLrWbjbcYa48MyXS04WSOC2Uw7UWcxbSOf0UkpaWXxCqJXRKlBCdQSwGgdDWnHzKYGZIE2vsdan2E2fIy0TFChoinaSJ4csmILb8/Pz+valfaxKKJu7y8jNu3b6Pb7Y61yAjegox+KsL7wTa5ML0jJ9p5xxpMm8zalPe0fzTNgzbfrDZqmqnm/7TeIE7pNV8nL1NiGhvDTJAkd4pri2NScmmjmXBkDopPUseT8j/xfvZMFxnhnZ+fH5Ojd3yJo+3EnKQvaAFlFq2H89D0NJM1uxHUlEuYltZIZfG6plF2TRvl3JSaaeRGmFTemSBJoM7vV4ta8orMWgtElHwBZP1+Vt1tBteLklqmSmRCaWfmamSribZbaGveWeVMY3OSZWXr1vLKfBktMjsO2f63IsxSe9PqrT1ZoblBsud9LWshO39rMDMkCbQnyEmJtSbafZ5an3WsJ4u2R36mqXWcF9oSrGzbNPy2skw67GzNDc285O/e1GTKmtnWuNUQVmb8aze5bPpofmfrrK2rZr7PFEm2QYZENFM4Oh+pXfd8kTVHfjL11YJ2VfpP8momtlavdR6OwCO6TxJeMIaOgmQi1W3Oj1L9lgvIIsrawEaNLJPcB+y+sM6OZiPVGrx+sIKO0Xha/kwvLUFqwloaDTNzGEszceX5Me3apHV5WiT91RJZVr5MuVZgQgMnSvk2miy0s2xZWZ8kauXRztZx/6V2fZIDzNqGxMuaxEdOkfbshjXNjTnqD+8Mo3RB1azDacB63j06dzkzJEmwOmWSjnoS7zvUCLzm2JJWHk9nlS/vez5NWYa2AUn5vH6XB34lJAlRWTW/eZPZGNtsYtPYcLm2TjJoxOuRmfeW9xrU+GCtp3y0frSUFZlG5pcEqM25Wp+2JaOsQ5OB11lrDc0cSRKsXZ5jGhpetOO3gUYa2TK1CZXZvYE3fgLCO0Ij5dEmvLcIavp82ptdJm9EgLVtIPD5KDU5zxeZXZCcLNv8aua0UNM3kSY4bX/6NMazzdjPlE/S8mFkF4eG83xNWVsN1RsoaeLVmu702Fb2TUhWHdz/JJ/PrXGAt+l36ykTfk5Oyil9TNkNUTvWwv2L0s+oQVvgbX23bSPaNf5COX68nZnx9Z6gkekyPtCoPO2EhmyvPLVhnRW2rnmYWU0yQsafkSGxaZBnxmTx7rcFX9RSs+E/LNYWmgY5zc2mzVM1UoaIGCKfNm9bKScPEBwdHeHw8BAHBwetTGGLWKSs2fZzH6mXxqtfppGwxlXz48r7Gb+eV1dbn6mXb5rzdKY0ScK0GvikD3i3NbElrHNqWpmexjKNM5daeVKr9MqNSEx7lnsSH132yI9FtKSh7O/vAzjpM8194UXds2jbzuzZQHlP0zY1jU7TxmrnjqblefJamnB07tHToGs2Uw8zoUlax2v4fWvHqiWi2qM/FjLkW3tcQmo5Gb+sB+0IxDQ0WUmWmYhmFChpq1WeBygYY/W9NybZ/n1ajxt6AQ4tQFPTz5b5HJnV3jzPBOza+Kpr2jZzmqTcdSQhTvK0RGYCZ8vVtNTaFyloqNlNnyYsrRKI3+pU4z+LEPlesxogoZSTxzAprbZxRfJIUJ42xBhFymsi6R7aaItRvZr2WOs3zZ6B1fyX2ccoozk0cyQJ+B30JCJ8FiY5NwdM5+mCSWWoQdsnLPj4ZSb7NMiDgy+K7GLjmJubGxNltk6tDu0JGw3Zjd8qvyZI5yFDlNEGx2XyED1vzdNI5SjyrWpBm5pgo8TMkOQ0olASmqanOaKlpsCjfRznSU7Zga+Jdke7extZtHqoXC+yaEVUgbPjlP15BxmJlXJZmyxvn7WoveNL04pmy3Jq+l3KXUuUXuS4rf+Q4PWFdRpBg+ZW0044aHVYslO50lr11tXMkCQwPUdr2zqnkQ7wX/SaMQ2tiVkLa+PJHtupJUt5/Kat0z3764vRfIkWohdckGlqXQRR2uFweGo+ZEk2imLLdkR9JNsVEZwlkzemcgPVjgJZiomVzmuz1TaZh7fJXZfmnaeMyMlKzt62j99RHYSMJmEhG12keqJFazmVa9r5pJ+x1uBp4lawgPAknpKS8I67eNHsSfyAGqKAkVdnRHLevJ5EG7aIGdA1Qi6LF4CpsZzaWJ6ZfDOjSdaS0vHxMY6OjjAcDtHr9TA/P3+qDGuR1Tr6vbQ8eCN3QV5P1lSOzPtJzf1JiLOtiZXZzTVNYjgcVv2ed6TlcBmzfrUI09qIrIPhNdpr9IACldsWbXy7Mr+n7U3ruJp3DK+thTazmqSHpmmwv7+Phw8f4v79+3j48OH4t1tqngvmiA7NZhBpjZ5Zcx7+Tn7IfNIFnYkKT1oGLRieblKNUtNeJaatdct2tj3u420u/AB8lsCsozaeySm1T6n1ZcxVLpt31McaK60OqZBYbZcPQvy5OAKUQSkFR0dH2Nrawvb2NlZXV7G0tIT5+flzM9OsqHLtgXVezrSiktl6OTzNK0LW6W+lzzj2CedBMJmAVlvirCXISQh6GqclojI9v6fU0rS+tbRk7RonLqs8mY/SZtulHS98Ux4BijA3N4fFxUUsLy9jOBxiYWHBfalDpE1YO6j3nWMSorQwqXkj6+Nl0UHpSbVXz4muTfDa4Me0ZbMwzb4mZI78UN2ZjaUGbfJMEjSVebn7ROtba2w0t4A1jzTUkGV0jeNNSZKdTgerq6vodru4fPkyer0e+v3+qUfc2rzYQpoQwGTa3qQHdK0yavw3bSa8F6SQBKstZm4KWpqIJr92MLjGL1nTFo0UZAS1ZuwmIX9vnmTIPhuRzsroaXuyPkurBDD+US9NK/Q2Ac8loI2L1cZJNhqONyVJAm9ok2Ri0yBmzBttkfJrbR3dmsaa0VItOSl/W7I9OjoCgFP9Q/4bSXbRxORy1WwatbJPY2PhsEjc0mysTWmaGv2k6TNHms4DWe1cmsxcqzzPUxcZS7KVlt1aoqcIfuyHEwB9j2A5kb2zdlbnZgJFbZzFvO62GAwGODw8PBXVtY48eO4Fkts7LmG1a9KIOtA+GKeVxWXS2gi8cbhYmoq1R3YmhdWnmXkh51pWRhncqTVPZZ9Sev5ZW1Pe+pD1URk1wVZu+dQqPzOjSdZqb/L4Dcc03iGp5ZWalzYhMuUA7Qgwm4fS0QbCtVG+ociooSerd/gbqDPlZL5o0ckN8DzM70yaSbWgaZl/T/MMrPQ1Ajl3ASdKwH9yxnKFWPfkZ6s8a+1GmBmS5NpdG6LUoJXlHQPRAhrcvNS0Ee1nZCNoMnj5vSd4vPTyt7IjsvMivxGRWvAWUDYC3Sbgk/FNWf4w8qPV+Hr5rx+2gWyzNVY1JwMsWdvAK8M65qaBt1MSZzY/1cldSNoYa2tf+k8zeFOa2zWwzoZp92t9Z9n6s5Bp5fkv67MG79wYry8boJjGWUuOtv1Sq0VOGsGvRfSbNd4i5qjtaznP6Xumn7MEWFuOtrakq8NzYXhySU3WUjxk+jYW3Mxokm13YG0H93Z17XgK1yBJiwDeGEhNk+MTPNImPe00Ssvz8P+UVsrkmS+8zsFggP39fczNzY1PBtQEiaKgD4d3jCMbCGirDWkbj1UvXYsWp6eNt3nu3ENGi9Tkru2zNv1bo0F69VGfZvpfrl/ZzvPw+c8MSU4KIir5hmuLKDVIgmoT5faIkpflBQOsSRI54j1/IW0AVN9gMMDOzg7m5ubQ6/XGxzVq5JmGGactZjk+/P4kR4K08jLgi1KbO21f9aa5b2QaK2+m/EnhbSgeQdbW7bmNgLPnWCch55r6CeVpOoHHQpSyDeBzT1uOCjwD4PWnLUQF3mzyAm8+mS/kPV88CXnf1jTNs/LirGiSn2ua5luethBZlFJ+/0Le88WbTeYLec8XT1PeP/eBmwtc4AIXmAQXJHmBC1zgAg5mhSRffNoCVOJC3vPHm03mC3nPF09N3pkI3FzgAhe4wKxiVjTJC1zgAheYSVyQ5AUucIELOHjqJFlK+WullM+VUr5QSvn405YHAEopz5VSfqeU8sellP9YSvnY6PqVUsq/LaV8fvT/8uh6KaX876M2/FEp5ZufktydUsofllJ+ffT9TinlUyN5f7GUMj+6vjD6/oXR/bc/BVnXSym/XEr5k1E/f+ss928p5R+O5sJnSim/UErpz1L/llL+ZSnlXinlM+xadX+WUj46Sv/5UspHn4LM/+toTvxRKeX/LKWss3s/PJL5c6WUv8quny+H0DO5T+MPQAfAnwF4B4B5AP8fgK9/mjKN5LoJ4JtHn1cB/CmArwfwvwD4+Oj6xwH86OjzdwL4vwEUAH8RwKeektw/COD/APDro++/BOAjo88/DeC/G33+7wH89OjzRwD84lOQ9WcB/N3R53kA67PavwBuAfgSgEXWr987S/0L4C8D+GYAn2HXqvoTwBUAXxz9vzz6fPkJy/xfAOiOPv8ok/nrR/ywAODOiDc6T4JDnujCUDrpWwH8Jvv+wwB++GnKZMj5qwD+Ck6eCro5unYTJ4fgAeBfAPgeln6c7gnKeBvAbwH4dgC/PloAr7MJN+5rAL8J4FtHn7ujdOUJyro2Ip0irs9k/45I8uUReXRH/ftXZ61/AbxdEE5VfwL4HgD/gl0/le5JyCzu/dcAfn70+RQ3UB8/CQ552uY2TT7C3dG1mcHIVHo/gE8BuN40zWsAMPp/bZRsFtrx4wD+EQB6mPUqgIdN0xwrMo3lHd1/NEr/pPAOAPcB/KuRe+BnSinLmNH+bZrmFQD/HMBLAF7DSX99GrPbv4Ta/pyFeczxd3Ci8QJPUeanTZLaazhm5kxSKWUFwL8B8A+aptnykirXnlg7Sil/HcC9pmk+zS8rSZvEvSeBLk7MrJ9qmub9AHZwYg5aeNr9exnAh3Fi5r0FwDKA73Bketr9G8GSb2bkLqX8TwCOAfw8XVKSPRGZnzZJ3gXwHPt+G8CrT0mWUyil9HBCkD/fNM2vjC7//+2bMWsUURSFv9sYsTGxW7DQQEibwmJRi4ASNEiqLQILhuCPkLCVf0BsBBurFAqKyHYWaq2mEBOSiCsK2SIxINjYbHEt3h2dxsEVdt4U54Nhmftecd5h9vLevTNHZtaK8RbwLeK513EJWDGzr8Bj0pH7HjBtZsX3+WVNv/XG+Gnge416h8DQ3d/E/VNS0myqv1eBL+5+7O4j4Blwkeb6WzCun7l9BlLzCLgBdD3O0BXaJq45d5J8B8xFl/AEqcjdz6wJMzPgIbDn7ndLQ32g6PitkWqVRfxmdA3bwI/imFMH7r7h7mfd/RzJw1fu3gVeA52/6C3W0Yn5te0Y3P0QODCz+QhdAXZpqL+kY3bbzE7Fs1HobaS/Jcb18wWwZGYzsXteilhtmNk14Daw4u4/S0N9YDXeHDgPzAFvqSOHTLqY/A+F22VS9/gz0MutJzRdJm3ZPwDv41om1ZVeAp/i90zMN+B+rGEbuJBR+yJ/utuz8SANgCfAVMRPxv0gxmcz6FwAtsLj56RuamP9Be4A+8AOsEnqsjbGX+ARqV46Iu2ubv2Pn6Q64CCu9QyaB6QaY/G/e1Ca3wvNH4HrpfhEc4g+SxRCiApyH7eFEKLRKEkKIUQFSpJCCFGBkqQQQlSgJCmEEBUoSQohRAVKkkIIUcEvggO7JiaQ1JAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now we start with a the nhead head\n",
    "nhead=0;\n",
    "fig,ax = plt.subplots(1)\n",
    "h,w=1*size[nhead],1*size[nhead]\n",
    "xmin=heads[nhead][0]-(w/2)\n",
    "ymin=heads[nhead][1]-(h/4)\n",
    "rect = patches.Rectangle((xmin,ymin),w,h,linewidth=1,edgecolor='r',facecolor='none')\n",
    "# Add the patch to the Axes\n",
    "ax.imshow(img00A,cmap='gray')\n",
    "ax.add_patch(rect)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_fns = glob(\"./pictures/*.jpg\") # Names of files\n",
    "in_fns = sorted(in_fns) # Order them\n",
    "imgAll=[];\n",
    "img0 = cv2.imread(in_fns[0])[yi:yf,xi:xf]\n",
    "scale_percent=40\n",
    "width = int(img0.shape[1] * scale_percent / 100)\n",
    "height = int(img0.shape[0] * scale_percent / 100)\n",
    "dim = (width, height)\n",
    "img00 = cv2.resize(img0, dim, interpolation = cv2.INTER_AREA)\n",
    "imgAll.append(img00)\n",
    "for i in range(1,len(in_fns)):\n",
    "    img0 = cv2.imread(in_fns[i])[yi:yf,xi:xf]\n",
    "    img00 = cv2.resize(img0, dim, interpolation = cv2.INTER_AREA)\n",
    "    imgAll.append(img00)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 (583, 50) (870, 337)\n",
      "1 (706, 171) (920, 385)\n",
      "2 (727, 235) (941, 449)\n",
      "3 (748, 320) (962, 534)\n",
      "4 (770, 385) (984, 599)\n",
      "5 (813, 449) (1027, 663)\n",
      "6 (855, 513) (1069, 727)\n",
      "7 (877, 599) (1091, 813)\n",
      "8 (898, 641) (1112, 855)\n",
      "9 (962, 706) (1176, 920)\n",
      "10 (937, 739) (1151, 953)\n",
      "Tracking failure detected\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Susana/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3334: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__' :\n",
    " \n",
    "    tracker_types = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE', 'CSRT']\n",
    "    tracker_type = tracker_types[3]\n",
    " \n",
    "    if tracker_type == 'BOOSTING':\n",
    "        tracker = cv2.TrackerBoosting_create()\n",
    "    if tracker_type == 'MIL':\n",
    "        tracker = cv2.TrackerMIL_create()\n",
    "    if tracker_type == 'KCF':\n",
    "        tracker = cv2.TrackerKCF_create()\n",
    "    if tracker_type == 'TLD':\n",
    "        tracker = cv2.TrackerTLD_create()\n",
    "    if tracker_type == 'MEDIANFLOW':\n",
    "        tracker = cv2.TrackerMedianFlow_create()\n",
    "    if tracker_type == 'GOTURN':\n",
    "        tracker = cv2.TrackerGOTURN_create()\n",
    "    if tracker_type == 'MOSSE':\n",
    "        tracker = cv2.TrackerMOSSE_create()\n",
    "    if tracker_type == \"CSRT\":\n",
    "        tracker = cv2.TrackerCSRT_create()\n",
    "    \n",
    "    height , width , layers = imgAll[0].shape\n",
    "    Data = np.zeros([len(imgAll),2])\n",
    "    video = cv2.VideoWriter('output.avi', cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 20, (width, height))\n",
    "\n",
    "    \n",
    "    img = cv2.cvtColor(imgAll[0], cv2.COLOR_BGR2GRAY)\n",
    "    frame = cv2.GaussianBlur(img, (21, 21), 0)\n",
    "    bbox = (xmin, ymin, w, h)\n",
    "    #bbox = cv2.selectROI(frame, False)\n",
    "    \n",
    "    # Initialize tracker with first frame and bounding box\n",
    "    ok = tracker.init(frame, bbox)\n",
    "    Data[0]=bbox[0]+bbox[2]/2,bbox[1]+bbox[3]/2\n",
    "    p1 = (int(bbox[0]), int(bbox[1]))\n",
    "    p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))\n",
    "    cv2.rectangle(imgAll[0], p1, p2, (0,255,0), 2, 1)\n",
    "    print(0,p1,p2) \n",
    "    video.write(imgAll[0])\n",
    "    \n",
    "    for m in range(1,len(imgAll)):\n",
    "        img = cv2.cvtColor(imgAll[m], cv2.COLOR_BGR2GRAY)\n",
    "        frame = cv2.GaussianBlur(img, (21, 21), 0)\n",
    "        ok, bbox = tracker.update(frame)\n",
    "        if ok:\n",
    "            # Tracking success\n",
    "            p1 = (int(bbox[0]), int(bbox[1]))\n",
    "            p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))\n",
    "            #cv2.rectangle(frame, p1, p2, (255,0,0), 2, 1)\n",
    "        else :\n",
    "             #Tracking failure\n",
    "            print('Tracking failure detected')\n",
    "            np.savez('data', Data)\n",
    "            video.release()\n",
    "            sys.exit()\n",
    "        #assign final value\n",
    "        Data[m] = np.array([(p1[0]+p2[0])/2,(p1[1]+p2[1])/2])\n",
    "        cv2.rectangle(imgAll[m], p1, p2, (0,255,0), 2, 1)\n",
    "        print(m,p1,p2) \n",
    "        video.write(imgAll[m])\n",
    "\n",
    "    np.savez('data', Data)\n",
    "    video.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c1f25eb90>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Data2=Data[0:10]\n",
    "plt.title('Tracking')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.plot(np.transpose(Data2)[0],-np.transpose(Data2)[1],'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(67.42587911848597, 12.227907080891292)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vel=[]\n",
    "scale=1400\n",
    "for i in range(1,len(Data2)-1):\n",
    "    dist=distance.euclidean((Data2[i][0],Data2[i][1]),(Data2[i+1][0],Data2[i+1][1]))\n",
    "    dist2=dist*(100000/(2*scale*scale_percent))\n",
    "    vel.append(dist2)\n",
    "plt.ylim(0, 1000)\n",
    "plt.plot(vel)\n",
    "plt.title('Velocity vs. time')\n",
    "plt.xlabel('time (min)')\n",
    "plt.ylabel('v (µm/min)')\n",
    "plt.show()\n",
    "np.mean(vel),np.std(vel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Mismatch between array dtype ('object') and format specifier ('%.18e')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36msavetxt\u001b[0;34m(fname, X, fmt, delimiter, newline, header, footer, comments, encoding)\u001b[0m\n\u001b[1;32m   1433\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1434\u001b[0;31m                     \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnewline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1435\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: must be real number, not list",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-e2bb39d98f3d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#ncase,nData,Size,Time,meanV,meanStd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mncase\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mData2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavetxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mncase\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'.dat'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36msavetxt\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36msavetxt\u001b[0;34m(fname, X, fmt, delimiter, newline, header, footer, comments, encoding)\u001b[0m\n\u001b[1;32m   1436\u001b[0m                     raise TypeError(\"Mismatch between array dtype ('%s') and \"\n\u001b[1;32m   1437\u001b[0m                                     \u001b[0;34m\"format specifier ('%s')\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1438\u001b[0;31m                                     % (str(X.dtype), format))\n\u001b[0m\u001b[1;32m   1439\u001b[0m                 \u001b[0mfh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Mismatch between array dtype ('object') and format specifier ('%.18e')"
     ]
    }
   ],
   "source": [
    "#ncase,nData,Size,Time,meanV,meanStd\n",
    "data=[ncase,len(Data2),size,time,np.mean(vel),np.std(vel)]\n",
    "np.savetxt('data'+str(ncase)+'.dat', data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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