Pyvist'INSA_v1.ipynb

{
 "cells": [
  {
   "attachments": {
    "PYVIST'INSA.png": {
     "image/png": 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DVLf1Ozt287uX/3jO11W73SM1Ke6xhz+ofOzbW3dyvLL6gn+flJhgagxUpQUXHxfLIw+o93pEwoHDpRfvhZiPLfRJrcUchfHzbbvMdbfPxv2daMmimc9LuGHjzFrpC4sKcDmdUx7X0NRCX//AlMfNx+fXrHVXrGTdFSunPK6to3PKY6zY5W7ZdehmxsB6evs4fLQMAE3XiYnykpaaotSyOJ9777qDp3/+64seMzjko7yqWmk2t82ms7SkmH0Hj0x5bEpSIn/x8Y8oX2tvXz///M3vnPfvVFvokRjDXbF0sdIvKoyNyX3/qSla5ybuf0tru9KHlw/e8z7Ts4GDwSCvb97K29t2UlldS//AIB63i9iYGNJTk1m8aCGLixewdtWK835ou1jrZnyG74naeuXrcTqdSgE7zsy5AQyMc+omqPwebdtlrrt9Nu7vuLycLOWCRhdzw8arTa+rn0h1pYdqd/t8e36nQ6V1PjIyyqtvbuaR+++56HFW7HK3bKCbGQN7c8s2vvat/znn60mJCdx1+6189KH7TP0iZGWks2hBERXVJy563J79h5R/aZcvXqQU6F/83OOmAvZr3/ouXd3nX6J2oa9PFhWBdegff0S9df765q1U1dRd9BgzLdfJLcoLefctaoV2xlXX1vHFr3yd2vqzJ0D29kFLWzsV1SfYsmM3MLYi4eq1V3Dz9Zu45fpNOJ0OKqtrLrouOxgK8cCjf2Hqmm7YdDX/8c9fVjq2ubWNez/ySVPnP5+pZrj7hoc5WHrM1Dln4/6Oi1SX9KrlS4mPi512YRXV61CdEDffnl+zrrvmKqXhx9fe3kJf39Q9FlZsoVu2y93MGNjRC4w/dXX38OSzv+CBR/+CTsVwG7dm1dRjwbsjPDHuug3rTc2uffXNzbyxedsF/76799J0uS8tKWbDujVKx4bDYX7w1LNTHmdmQmRp2dRv+CULi8hMT1M+Z0NTCx//3JfOeTO8EL8/wJYdu/ny177Ju+59mO/+308uOoN/usz8XCJV/S874+KBvnvfIYLBoKlzRvr+TrQ0QpNpbTad6zZcNe3Xqwf6n8bz+/jHHlY67jcvvqr0PMkY+mXC7XZRkKu+dnuqX4iGpmb+/l/+3dQ1lBRPPSHvcGkZfr/autupAt3r8fClz39K6VwAHZ1d/Ou3/veix3T3qHW5u5xOUxPwJjPTOn/ljc1KM2bNrM0+enzqMT6zrbZvfvcH026Z9fb18eSzv+CdU62fSDLTc2VmKdTFTNVC32ZydjtE/v5OFMn6FTdsunpar/N6POTnZk95XDAUoqLq4j2BcPk/v++66XoWFk5dRKiqpo7SsnKCoakD3YrV4qz1rzllyaJi5YITQz4ftfVTB8Se/YcoV/jFGafyaXjU7+fwsTKl8yUlJlx01uynH/swaSnJytf31W/8FwODgxc9pkexhQ7TX4u+aEERm65W2zwnGArxxE+mbp2bWZsdDIUor5z6vhYXFSidD8bmJUSyqzGSlixSKyMK5meGn4/b5ZpyvbPZn9Vs3N9xY3XJC6c8TnV/hfVrV5ua0DpuaUmxUthU19QpLS+9nJ9fm03nkx99SOnYZ5//LYDSplJWrBZnyUA38wm7rKJKeWek3fsPKp83MT5e6bgL1SA/nxUXaKUvLSnmvvfdoXyeX73wMjv27J/yuK7u2a8WZ6Z1/ofX3qShqWXK48zc/xO1dYyMjk55XKKJIhz1DY2Ew2orGC6lgtwc5fsUCoU5XnXhVQSqsjIvPiGu8kQt7Qozkieajfs7rrioAKfTMeVxTz37S6UgdTmdShurTKY6t0Z1WORyfn7fe/ut5GRlTnlcR1c3L7/+NoDyPbdat7slA93MDHcz3Ypm1smqLpnbc8BEXffzFNKw2XS+/NefVe46amxu4Vvf+6HSsapj6DC9QF9QkM/1G9XGGIPBIP/3tNq2mmbuv+r4qpkeiEDA3HjwpWKmstqJunpGRtSD8EKmquE+nZbgbNzf0+dWrPlQVlnFrr0HlI69YaP5bnfVNfbHFN+/Ltfn1+FwKJeCfu5Xvz89dj7kUyszK4F+GTAzTmimW3F0VK1yGqDc6j9WXqlc43j50nPfbB7+wJ8pd6eFw2H+8V//k+HhEaXje8yUf53GxLjHHnlAuXzj7156TfkDlZndx1TfEA0TLZaZFhSZLWbGUSPR3Q5Tj59vN7lcDWbn/o5T+dAz6vfT2tbBm+9sVzrnpqvWmd5ERHUXuVLF+QGX6/N73/vuUKo3MOTz8asXzmyPO6T4nnqpyt9eKpYLdLObKpgpOJGYqNaNDjAyohaa4XCY/YfVdpgqWVB01uSzrIx0HjPRZf3ML39ranlQd2+v8gcTs2PoBbk53HTtNUrH+v0BfvjML5SOdTmdLFCYPDNO9Q1xYHBI+ZxZGenk5ajX0L9UzAT6hVZ+mJV1kRnug4NDynNIxs3W/R2nMnu+vqEJwzDYsn230mzq6OgorlSssQBjIZOqMB/GNzysNP8HLs/n1+Nx89EHP6B07G9efPWshpFqI0la6POcme64js4uOjq7lI9XGccZp1qUBWCvYre70+k4q5zt3/3lp3G71Lr2T9TW8z8/elr5mmBsHLV/4OIT58aZ3RP9sUceUB4m+PWLLyuPs5YUL1BuDalOiARoaJ567H6ijz10v6njZ5vD4VCaJTwuUi30ixWx2bnvgHK1xHGzdX9hbNhIJcjqG8Z2bRsYHGTfoalrQ4C52u6qPYxl5erzfy7H5/fBe95HgsKktVG/n5/+4uxCXqofYKy2Ft1ygT5by3I0TWPTVVcqH3+ysVn5WDMT48bH+O649UauWnuF0muCwSBf/to3TW1NOU59Lbp6oOdmZ3HL9WpbH46MjvLks79UPreZD3THTLwhHq9Um9U87s7bbjK1D/1sW7SgEIdj6sleMFZpq9pkhbgLae/oZM/+Q+f938t/fMv0+Wbr/sLYUjiVIaCJOyW+uUWt2/16E+PoyhPiTBTMudye35joaB7+gFqZ2t/+4bVz6oRMtYJnnNWqxVmuUpy5gjLqvxA3X7eRJBPjLVNVMpuouraO7p5eEhOm7tJfvmQRL78ew19+6jHl8//f08+ZWnI3UU9PLwW5OVMeZ6bL/dEP3a+8rPCXv/2DcsU6mL0NO/bsHyt+Yma9/T/+zefwuN388nd/UH7NbDHze1FedSJis5wvVFZ4umZ1QxbFgjITA/3tbTv5289/asrepuTEBFYsKeGIQl1z5R3WTDRILrfn98MP3KM0LycQCPCT584tL9vXP0A4HJ7yvkiX+zxnZlOFybtCXUiU18vnH3/U1HXsO6g+ex3Uu92XLynhC48/Rnyc2vrJY+WVPPms2vjz+agWl1EN9KyMdN518/VKx/qGh/nxc79SOnacmXFiM/Mn+gcG2bVPfdkigK7rfOlzj/MPf/1ZpaVQs8lM9TOzpVIvpdm6v2PnVnvvqJlQPa27p5fDx44rve6Ga9W63VXfw8xU8rucnt/EhHjuv/u9Sse+8Oob592IxTAMpW536XKfx8xsqmAY524icT5ul4vv/NtXTU20a25tM9VCB/UysFkZ6cpdYSOjo3z5a980PU45kWqXu+qe6B976APKY6DP/foFevvU5yIkxMeZKm9ptgX3zC9/Y+r4ce+74zae/cF35mQ733Fmap9Havw80mb7/qoM1wWDQeobms76mmq3u0pZ5txstfewzu4epR3FJrpcnt9HP3S/UjGeYCjEUxcZjuvrn7rSndWqxVnnX4K5T+91JxumXKtYVJDHk9/9JquXLzV1HS+88rqp48HcOLqq/37ix9SdbJzROVSLy6i00DPSUrnztpuUzjc4ODTljnWTmdk3vq2jk46ublPn33PgsNIGOedTVJDH09/7Fo89rL5UL1Kio6NMTeiMVMnXSJvN+5uWkqw0nlrX0HjOzPa3FJev5WRlUlSQd9FjlHdYm8aHrsvh+U1PTeHP3nO70rGvvP42za1tF/z7XoUtZW02nSSFoc7LhaUC3VS34kXetIoXFPL3f/UZfvaE+U+lvuFhfvm7l6Y+cJLm1jaaWlpNv+5C9h08wnO//v2Mz6Na/lWlsMxHHrxPeQzvmed/qzyxZZzqmyHA0Wnu0fzP3/z2tAuu2O12PvnRD/HEt76utCwpUpaVLFJ+E+7t64vocxhJs3l/VRsDVSdqz/laa3uHcinYqVrpqt3+0904Z74/v3/+kQeVJm+Gw2F+NMVS1l7FlUZW6na3VKCbmfjjdrt43x23cfed7+JDH7ibTz36CP/+1f+PV59/mp//8Lvcfee7lGcFT/T0z39tqpt4oki10od8Pv7x6/8ZkXOp7iMdM8UElpTkJO66/Ralc/UPDPLs8+b3kTbTrWx2fHVcQ1MLX//2xTe1mcqaVct55gffvmRdmGZ+L46Vm5sNfSnN5v1VraJXdeL8Q2mqRWammu2uPsN9er0o8/n5zc3O4s5b1YYT//j2O5xsbLroMX0KLXSw1sQ4ywS66qYK4265fhP/8Nef5e//6jN8/pOP8rGHPsBN122c0SfPyuoaU0usJttjYjvVi/nmfz9BS5t6mdqLUR5Dn2Id+kc+eJ/yB6Sf/PxXyqUbJzLVgptBt/ILr7zOEz9RK0N7IcmJCfzwO9/gytXqBUemy9xSzvk5fg6ze39VZ7hfaLWI6jj64uIFZKSdvxKbzaZTvGDq9zDDMDg2g8I/8/X5ffxjH1Ja/WIYBj/86c+nPE5lDB2sVS3OMoGuuqnCbOnrH+CLX/1X0/s6T7TnwGFT62bPZ8uO3fz+lT/O6BwTKY+hX6SFnpSYwPvvvE3pPD29ffz8Ny8oHTtRXk4WMdFqEyJDoTBlFTMbJ/7+U8+YHuOfzOvx8K2v/YOpHdCmw1wQzs9An837q2kaixctUDr2QoF+srGJE4pr9y/USi8qyFcqFHWysZlBE5Xfzme+Pb/FCwq55fpNSse+9c72s5YOXkiP4vav0kKfh8xMmIk03/Awn/+7r54z+9Wsnt4+TtRNv6BHb18f//zv357RNUwWiTH0Rx64B5fTqXSep372vHKt+YmWn2fjmguJ1MYj//X9H/Ht7z85o3N4PR6+/a9fUapBMB3pqSmmWiAzafnNptm8v4X5uVP2MMFYkZyLDae9tXVms91Vg9FsOdsLmU/P76c+9rDyPI//U2idA/RJoF++zOwkFUndPb18/HNf4lCpuZrUFzKTcfR/+c//UR7zVjU45MPvn7rCnNfjOe8vZEJ8HPe8591K36uzu4fnp1nEYjYLjlzMT37+K77w9/88oxZTUmICX/3SFyJ2TROZ6W5vammlV/FN8FKbDwVlppr4ptrtvmr5UuLjYs/5umqgm91w5mLmw/O7YuliNl29TunYLTt2U1ldo3Ss6hi6dLnPQ6q/lJG058BhHnj008ozXFXPOR2vvPE2b27ZFrHrmKhHYZKfpmlEeT3nfP1DH7hbeSvZJ5/5udIe0+czmwVHprJ5204e/PPPUHmeGdCqrlm/lhs2md9mcyqmKifO0+VqMLv3V7UxMNXPp/JELQ1NU9dMt9l0rt2w/pyvqwZ6pIdF5vr5/fSjjygf+8Onn1M+9k+xy90SpV9VN1WIlO6eXv7nR0/zu5dem/GY92T7D5USDIVMbbfY0dnF1//rexG9jom6e3pJU5gsGB0VddYuR3GxMdz3vjuVvkdbRye/fvHVaV2fw+FQ3kIW1CsEmtHQ1MIjn/w8X/zc47zv3bdO6xyPf/Rh3t66M6LXZeaD7nwtKDPb91f1w4JKBb23tm7nkfvvmfK4GzZtOKtehd1mU9o8JxAIUKHYQjVjrp7f9WtXs3b1CqVjG5qaSUyIV27NZ2WoFSGSQJ9nVDdVmKmW1nae+/Xv+c0fXsU3PDwr32PI56OsoooVJuYEfOXfvmV6zbYZqt34Y8VlOk7/+cF734/Xc26r/Xx++PRz09o8BmDxwiLl9e3DwyNnle6MpFG/n3/6xn9x5GgZX/rcp0xP0iwqyFOu963CzGQvmL8lX2fz/jqdDqXtWA3DoExhfsFb7+xQCvSr1q7G43Gfni9SWJCntAqk8kTtjCbeXsxcPL+f+tjDysfmZGXy7X/9iqlrUpEYH4eu6xHbv2AuWaLLfTa72xuaWnj+9y/x2Oe+yJ0PfIRnnv/trIX5uDITa4FffXMzO/cemMWrUQ/0iSUrvR4P97//PUqva25t4/cvT39mvplx4rKKqln/xf3dy3/k45//0rTqEdx203URu47C/FzlD1ShUJjyyult4DPbZvP+lixU2461vqFJaY/t0rJypa1+XU4nG9atnXAdamu6L8UqhEv1/N6w6WpTQymzRdetUy3OEi10M+OEvuHhCZO8DAKBIKN+Pz7fMF09vXR2dVPf0ERN/UmOlVea2i89Ugpys5WPNbvhwnSorkX3ThhDf+/tNyvtlgTwxE9+RjAUmta1gdmCI5Fp/U7lyLHjPPrZL/LUd7+pvNwKYOWyJRG7BjMfdE/U1jEyOvOZ/7NhNu9vJLvbx721dSf33z31h9kbN119et6L6pBCJCfEXcxsP7+apvHJj35oupcXcSnJSaZLQc9Hlgh0M5/gP/u3X2H/odJZvJqZW1Kivq6zdBbGgydTbaE7HGcep3vee4fSa042NvHSH9+c1nWNM/Mp/1K9IcLYNpv/9I1v8+//9HfKr1lQkI/Nps9oQ51xZlZ+TLfy2KUwm/dXdX91M+d9a+t2pUDfdNU67DYbwVBIuSjWpRwWmc3n9/abr2dBwdRDHZeKVcq/XvZd7qqbKsBY/d/jFdWzfEUzk5+brfyJeMjno+5kwyxfkXqg2051Xa5ctoTC/Fyl1/zgxz+bUXjFxcaQnZmhfHykZ7hP5c13tnNEcXtNGBvTTVDcGncqZoJwvhaUme37q/qhx8z6/AOHj9KjUEc8OjqKtacqrS1UaKEPDg7NeLMls2bj+bXZdP78ww/N9NIiSjVD5rvLPtDN7bDWOOvj3zNl5t9TVlEV8Vn256Ma6BpjExPvUpwlW1t/klff3Dzt6wJzP6+Ozi6l8c1Ie+UNc/9GM12cF+JyOpUme42brzPcZ/P+qn5YMDuzPBwOs2X7LqVjb9x0NSlJicTFxkx5bCSXx5oR6ef3fe++jZws9Q9pl4K00OcJU92K8/RNayJTrapL0N0O0N2jNjkmGAridrm45fqNSsd/76lnZvyBxEyFwLlaZ3280lyv0MShi+kqKVab7AVjM8NPKJTSnAuzeX+Vd1irqTO9AkN1s5brNl6t1DqHuVuFEMnn1+l08NjDD8z0kiLOKkvXLvtAN7XOdh6PE44zFeiX6BdcdVKc3x/ghk0blMpoVp6o5Y3NMy+EY65O+aWZEDfZgMkqXMMj5kvfTmZmoujxqup5u2RnNu+v8s5m02gI7N5/SKn6WkpSIncr7v89Vx9II/n83nfXnZd062BV0uU+D5hdZzvfA93pnPsCKefT09Or1JL2+wPcceuNSuf83pM/nellAXNbIU5VUqK5JTGd3T0z/p7mCsrM39+L2by/yjPcp/HcBINBtu7ao3TsVHukj5urYZFIPb+pMnWVAAAgAElEQVQej5uPPHhfJC4p4qzS5X5Zz3JX3VQBxsJmJqUNL4WSBeoFNNo7Oi/ZMotgKMTA4NBZ68zPx+v1sH7N6inPV1ZRpTzGeDE5WZlKY48wNq6psr7/zttu4uXX345oi/VaxcpWAB1d3dPanGYyKwxFzcb9nUi1F2O6H3je3LKd22++YVqvnaxN8fd9Pj+/D917NwnxahM+W1rb+bt/+Yby972Qh+67W+kDk1W63C/rQDfTCqk8UTNrFZYiZZmJ8cJL3drs7umdMtA3XnWl0n7Gc9E6r61vUJoQ+U9/+5c89vAD/N/Tz0XkjbEgL5d77lJbwgeYmlF8IWZnhs/XCnGzcX/HZWWkE6+wmmBwyEftNFeS7Nizn5GRUeW9DC5G9UPXfH1+Y2Oi+dB971c+z5PP/iIiG15tvOpKpeOsUi3usu5yN9cKmb/diuNU18TCpe8mVRlHf/8dU+95fuTYcbbv3heJSzJZcEQ9tHKyMvmnv/1LXvzZk3ziIw+ZCseJVixdzA++9a9Ke1yP2xWBqn9mgrCnt4+W1vYZf8/ZMFv3F9Rb5zOZWT4yOsqOvfun/fqJzPy+z8fn98MfvFe50FRrewe/n1Dnfib6B9RKYlulWtzl3UI3E4DztBUykZkPKJeq4tm4HoWla7o+9efD//3R05G4HMBswRHz9z8jPZWPP/JBPv7IBzlUWsbOvfs5VFpG6fHyC+63bbfZuPKKlbz39lu59YZNpvYY8PsD/PHtraavczJzE8nm7+/FbN5f1WJUMx23fnPLduUx8ouZTi/KfHl+kxITuP/971U+z1PP/jJivan9iluogjWqxV22ga66qcK4+T4hLj4uVvmTdDgcvuRrUrsisM/6gcOl094edjK73a5cXQtmPkSxavkSVi0fK2sZDIVobWunt6+fnt4+/H4/Xq+XlORE8nOyledBTPbCK3+MyCY75grKzM/fi9m+v6rDdTN939i6czeBQEBp45ULmc78gMnm8vl99EP3Kw87tHd08rsZ7OswWd+AeqBbYWLcZRvoqpsqwNxUWDLLTKuqtr4hIhOnzFAtLnMx//ujyIydAyxaUKi8G9TIyCgnausi9r3tNhvZmRnT7so8n8EhH/9nYq/nizHzLM3XnqvZvL82m66+GcoMA31wyMeeA4e5Zv3aqQ++gEgXxLqUz29Geip33/ku5fP8+Lnnp73r4vmodrmDNZauXbZj6GYrqs13ZgpozMXyq26FUpYXs2vfAQ4cORqhqzE3vnq8qjoitdFn0ze/+4OIdPdlpqcpzySG+dtzNZv3t6ggX6nF2NHVHZHKgqpFZi5kPg+LjLvQ8/uJDz+k3DvR0dXNb/7wakSvy2yX++Xusg10UxPI5umb1kSzPR48Uypj6BfzvSefidCVjFlqYoXDpVqvP10//82LvBChSUBmnqOGphb6TLzhXUqzeX/VN2SJzHOzedvOGX2gnM8b58CFn9/83GzefYtaXQqAH//s+Qk7YUaGmRa6FbrcL9tAN7eT1Px+Qwdz3aRz0ULv6pl+sZOtO/dQWhbZSXzzccvU6fjtH17l3//7+xE7n5kKcfO1ux1m9/6qToiL1PyC3r5+Ds6gd2q+1tmHiz+/j3/0YaVlrABd3T385sVXInlpgLkxdCt0uV+WY+im19nO04k/48wU0BgZjex4sCrVeu7nE6l15+NioqPJzc5SPn4+VkILhkJ878mf8tSzv4zoec1sJTxffy9m+/6qfuiJZEPgra3bWbt6henXjfr9VM3DglhTPb+LFhRx03XXKJ/vJ8/9ilG/P1KXd9rIyKjypETpcp8jpnZg6uqmo7NrFq9m5kyNF1bOzXiwaj33yd7auoPyqhMRvRYzrdCu7h5a2tTXWW/btXc6l2RKZXUNH/uLv454mOu6zuKF6qWQ5+vY7GzeX7fbRUHu1Fv7GoZBWQS7ut98Z8e0NiKqqK4hGAopHz9fnt9PP/aI8pK37p5efvXCy5G6vHOodrtbocv9smyhm5rFO0/ftCaa7+PnMLZSwOzyG8Mw+H6Ex84Bli02swOXuZ/XZ770j1yxcjmf+fOPsMLEREUVNXUn+fFzz0e8NOe4BQV5ysuDgqEQFRH+oBUps3l/lywqVuoGrm9oYnDIZ+rcF9PR2cXR4xWmJr+C+d/3+fD8rlq+xNSs/qd/8WtGRs+/Lj4S+gcHSUpMmPI4K1SLuywD3QrrbCe6XP493b19pJnYKen1zVupnoXhATM9GtNphR44XMqHH/8CBbk53HLDJm6+fiNF+XmmimyMq29oYufe/bz21jscPjrzUpYXY6a7vbqmbla6OCNhNu/vXHS3j3vzne2mA306v+9z/fx++tFHlM/f29fHL3/3B9PXZYbqTHdd10lKTJj3PboXo62+7vaZbUgtxJ8Ar8fDgsJ8iosKyM7MIDo6iiiPh6goL7quEwgE8A0P09XdQ1tHJ7X1DVRU19AVgZ3ThJgpeX7/NEigCyGEEBZwWU6KE0IIIcTZJNCFEEIIC5BAF0IIISxAAl0IIYSwAAl0IYQQwgIk0IUQQggLkEAXQgghLEACXQghhLAACXQhhBDCAiTQhRBCCAuQQBdCCCEsQAJdCCGEsAAJdCGEEMICJNCFEEIIC5BAF0IIISxAAl0IIYSwAAl0IYQQwgIk0IUQQggLkEAXQgghLEACXQghhLAACXQhhBDCAiTQhRBCCAuQQBdCCCEsQAJdCCGEsAAJdCGEEMICJNCFEEIIC5BAF0IIISxAAl0IIYSwAAl0IYQQwgIk0IUQQggLkEAXQgghLEACXQghhLAACXQhhBDCAiTQhRBCCAuQQBdCCCEsQAJdCCGEsAAJdCGEEMICJNCFEEIIC5BAF0IIISxAAl0IIYSwAAl0IYQQwgIk0IUQQggLkEAXQgghLEACXQghhLAACXQhhBDCAiTQhRBCCAuQQBdCCCEsQAJdCCGEsAD7XF/An7r4uFje+v3P5/oyhBBiRn78s+f5zhNPzfVl/EmTFroQQghhARLoQgghhAVIoAshhBAWIIEuhBBCWIAEuhBCCGEBEuhCCCGEBUigCyGEEBYggS6EEEJYgAS6EEIIYQES6EIIIYQFSKALIYQQFiCBLoQQQliABLoQQghhARLoQgghhAVIoAshhBAWIIEuhBBCWIAEuhBCCGEB9rm+ABE5hmGgadqpP4VPfQ3AAMa+rmn6qa+HAH3C8Zz1ujO0068VQggxf0mgzwvj4WsATAhZ/bzHaJqBYZwdsmdeM/nrE7+mn+f4id/XOM+1nfkwIIQQYv6SQJ9zBuNhfXb4nvpbIzx2jDb+/2BgjB98+vixTLadDucz52bCccbZ4a0ZYHDWhwNNA4PwqWvSwNBPBb2EuhBCzGcS6HPNGA9jOLtFPhbAp/97PHQnt6INbdLxZ14zFtRnWv1nwj6MgTG5LY90rwshxOVLAn3OaZwJ8vOF6aSu9LO62s83p3Fyt7k26WtnegQmnlY763ufCfZzx9iFEELMRxLoc00703o2jPGu9VNBapwdzmfa3rbzfHW8e/7Mic+EsXFqEtyp78dYt/pYi318YpxxOtTH/t921rmEEELMbxLoc8wwDEKnxsmNCS1n3Rj7OwMDXdMIG0HG+uc1DEPHAGya7UzD3QhjGGOz2DVNJxgKY7PZ0IyxznVNgzAhMMJjAR4eGxfXxhNem9hdz6kx+vGAH5+MB5PD/dyJfEIIIeaCBPqc0zDQQYOwoWEYYIRD6LpOMOhDt9nBCKPrGsGAgc3mRNM1wmEDDRs27dR/6zY0IBw2MNDRbRr+YHAsqzUbYSMEuoHd5iAUMiAcxqaHCYeD6Db72IeDSdc1nu/GOd34Qggh5hsJ9DlnMDwSwG6zYbPrY+1fzUDTdJx2N8FQCHQ7/gDoRFFX10N7WxuaphMIBBnvMrfbbdh0G7FxXsL4iY+PIibGSxgDu01Dt9ux6RrB4Fjr32EbC+kwYIR1/MEADocNTQsSNgx0nKeGAs6MpY+30o1JQwHSOhdCiLkngT7HDAOCgRDhkIEesqHrGppmoOsQDtnQDB1dsxEiTHefj+rqk+zcUcrAwCA23Y6mO9HQ8QdCjPi7cbnsuN02nC4XNruT5OQM4uJiSEj0kpIaRWyMh8T4WGI8Thx2G7oGNlv4VFe9BoYD/VTX+oW62YUQQsw/EuhzLBgMs2dPLaFQCM2m4XY7cDh0bDYNu+4nJjaaxJQEomOiiIqyk1uQTGrmdYwMD+MfCRAfH8vwsMHQ0DCDg8P09/fjH/XT3TNAW1svJ6rK8PkG0G0BYuOjyEhLJjEhmvS0BHKzkynIyyA1LQGbnVML2WxMnmina/rp+XlnV6O7kMnV5kCWxAkhxOySQJ9jwVCIg0dbsdvt2O12omM8xES7cbudhAkx2tDMyEglui2A0+kkOzOXvLxkYqLTcNp1AkE/XpfnzOx1I3yqOszYWHr3wBDNDT20tnRRWlpNR3sPJ+s7aTjZyJHDTpKTUkhOjmPZ8kUULczAGzX2Wl3TCYeCaLqBQ4NwKISu205NvNPQ9QttAzCxEM5Yd/7kDwDSRS+EEJEngT7nDILGEKMjYfr7h/D5fGCECYaCuF3RxMZ5SEyIx+OJJjU5kba2UU7WHSPK60bTgiQmxOJyuImL9RIX78brdeGwcapBbCM+yktiiZflizPZdM0SfENBqqsbaTjZxtBQAE130dXVw8sv78LjtZOTl8baKxcTH+/F49KxhQ1CNp1gMIDLoXOm9W3nzDp443QLXjs9Y36cdrqe/ORysxLsQggRORLoc8ztdvLpT91OOBTEP+JneCRAwB/GNzxCd5eP5uYumls6qGlqpOzICG6vB6/XTU52HnGxbkYDXoIhG939HYycGCQcDBIf6yU3O4X09GQcLp1QIIzTYcNlM/Am2khel8/K5VkMDAxzoq6Rhnof/oAHHTsnquooPXyMrIxMrt24ntz8JOxROprmIBQOotkMdD0MhAAHYMcIa6dCfHzpHJwpTDP275w4j27ypDohhBAzJ4E+xzTAboxgd7iJdrgIRY2FZijkwbEgjXBoIYbmZyQA+/Z1UHa8l4HBfo6UduNy6vT1HSIvP42cvFiyM9PJzEhmsK+Hw0fqee21nRSX5LF6zUJCYTtejxfD8BMOhbHbbMTHxbL+yuVcsSpEZXUT1dUnWbq4mKBf583X91K6/ySbblzL2qsWUZAfTSgcxGbohAijnRpbD4c1bJr9wkvbtNBYD/ykDWekdS6EEJElgT7nNJx4MIJBbPYAuh4iHA6jawYYfsLBUaqqK9izv5H2zgRiU7JJi4smPaOQUDhI/0AGTc0nqao7hsN+iIT4JDLSM1i6pIA1BUX4fP388dUDOOxQVJBJRnoycXExuF1ODCPMyOgQuk1jaUkWeTmZlB8/SWtrHw8+fAdVlTVs2bqL0vIyrrt+HcuX55IQCy7dhYaOYWjYdAODETTsGIQ4s1nMqcI1WvhU1Ovjf8P4DPrTPwEJdyGEmDEJ9DlnoNvGJrKNjgZwuWzoGGh6iFDIhtPlprh4Gf2DsVRUH6BzsB2PK5YRn47d4WB4dIiYmFRcriR0h5/6+iZONjWwa28dHg8sKclnUUEeiQk2fKOjHD/eRjDQRkqKh8KiTDyeGMBg1O8nKsrJypUFZOf0UlHVSHySiz/74E0cKa3ixZe2UF1VwMoV+axcuRCXA5wODX/ATyjsx+30Ej69tSunu97HCt1oY8VxNA1N0wmHz5S3vfDkOiGEEGbYMvIXfmWuL+JPmYZGVFQCLk8UyclJhEIa4MIIufEH/dhtTmx2jcSUKKJjbQwNtNHV0YZ/JITL6WVgwEd//yg1tY20twQYGBwhKT2Frt5enPZo6uo72LO7jIqqFnwjBpk5eTjd0Zxs6ufI0Rqc7mjiU7zYHDYMPYxut+H2uMnMSiIlI4X9+6pZtGwhy1bk8/IfNtN4coCBQR/ZOTnoOqDbMNDOzIBHJ2xAKDy2BM4fGtueNRw+VS1e19A4t1ystNKFuLwdKi1j9/5Dc30Zf9Ik0OeYzeYiI+la9u2tYv++CtCceKPcOF0aNpuHYGiUUDiA0wHZWUksW1rIsuWFRHm89PT00tndi25z4HJ7cLrB7bETFR2FTbfR09uLy+EiMTmV4ZEgdfUdvPTSTsqON+DzGzS0tPDSS6/T1jFCV/codfWd1NU3MTA0zPDIKCHDQXpOKgcPlBEcdXL7u29gz55qBgeHOXjgMB6PB5tdIxQMEvQbhIJhAoEwwaAOjK1dD6NjszvQ0E/NjBsrnKNpOuMbyGiadmr23Olas6edG/QT1shPqDUvhJhbEuhzT1t93e0y5XgO6bqT/Py7iI5yUpifh26ECfi7yMtLY0lJOosWZhDtcRIMjBIM2tHtNjweHX84TO+gQXtnL8fK6jl8oInmdh9R0R68Xic9PQNgODEIkpCYxKBvgPbWFpKT4omOcbF3zwFyc/NZvnwZaeleak/UsXvXLgry01lUvISYaCejIzruKDfeKAetTYO0tdeQlFRAfe0Jqioryc1Lwe3SyMlKJj0lnth4D709w/iG/MQnxpKckoonSiM2Op6EeA/p6bHohgFGCA0bum1s05jxcDdOddmftQ/8+H+d3ixmfJxek0AXYh758c+e5ztPPDXXl/EnTVroc8zhdHLb++7FHeVlx449+EYHiIvLoK/Pz/5Dx6ioqsUTFUNyciI1lc0c2lPKqH8Ur9eL2+0iKUVnQVE6q6/IJiPDRn93N8GRIEtKiggGfYT8o2RmxlFcWEJhYQY2xwgZmSlk5+SRlppCd1cfb72+h/i4WLIzi2lq7KC9rY/cvDwCQR8drT2MDvvJzMghOTmJ5uaTbNi4jlVrF9PU2EN2TiHe2BiWr1yKPxjEN6jR2tpBa2MHQ0PDVFfUUXm8nh3b9lFzohqnCxITknA4nBiE0LTwWO16NDQthGGMte5hPMSNU38eC3qNU+E/XkkHCXQh5gNpoc89CfQ5ZrPZycwuZHi4n9VrV9LTO8CO7Ufo6zdYvPJKBoYNtu86RGNLA0WFBeTlpnOs6ji79xxkYDBIVFQy+3fV0989zLIlmWy4agkul43Dh/aTlhbD4iWFdHR0cKzsAOmpaeRkZ+J1ReFxuvG4wxQVFLByRQF9XX0sW5bJXXfdQH/PIO0t9cRERZOSlEV/bwjfsI9lK1PJzI7l8KFjJCWn0NDcwNGyStDiqKk5QVFREekZbm6+cT1Xb1hEbnYC3R0dJCbHsfGaG/ANjbB3304aG1qJjU3A5fKi6zbCxihGeHhsEp0RImwET+3fbgD2U1keHht7N2xju8gaYU731DNp61eJeCEuOQn0uSeBPsfcbjcfefjD2PRBXC5IjM8hMTmdxuYOdu89AHYH+QUraO8Ypqq8HqfbQcmKRWTk5dLV383R0gZ0oujt7aa2spmR0SHyC9JZvnIpg8N91NW3s3BhCQWF2ZSWHqa/M0BMVAxuR4D+nn58gz04dJ2FxYm0t9VTeqiCK1YvYvGSDIZ9PRw/VkVuXiJO7wj79h6kuvwkiUnJ1NbWc9f7byYtPZmmxhZWrliG121jzRVFxMZoRLltJMZFs2LVEjTCtLW3ULwkk1tvuxGvN56dO/ZSV9uG3e4lJiYKh9NGMKDj9wfQNLDZbKf2hx8Ldk0bW/eOoWEQBAJwap/3MZNDXEJdiEtJAn3uSaDPNUMjMBTD0ECQ3OxFeFyx+P0D6HYfdoeN5sYujh9rJDBqo7c7zIEDtRw4VktFdQOtHX10dnVTW1NNfW0tLmcsFRUt7N1fS119P7X13fhGYPPm3fT29bFy1Vqqq9sxDA82R4jElGh6ettpaK4lLSODqBgvxUvSOHr8KMEQvO/PricvN4uBwS4yMmK57bZrWLIsG4wQR4+U0drSzs03XUVGZjT791bidHioq6ljwYJsdOyEw34MdJJT4khMjKX0cAXtHd0sX17I4qWL6Ont4u3NW+jp7Sc3rxCbw0YwpGNgB11HI4xmMxgL77F93ceq1ARAD6LpY2PpE9e3j5GNYIS41CTQ554E+hxzuVy868472fzWXrZuLqNkYQkZadnk5WQSGBkmLSUBp8NDb9cwo8M2RgJhguEYnC4HBfkLuOm2ZVx9VTFrVi+ncFEG7a0aNnsCC5csoPpEI60d3axYvoKODh/bd+ynf7ifI8eO0trZRVNLO+s2XElL6xC1da0ULCihtPQk7/6z6zlQeow339rOTbetZklJIbu2HeJY6VGu3rCOvII0Vq0q5u03tvHaH7ay6ZqrCOgGleX1rFq5gj27DpGfl43DaUfDQNd1omPcFBcXsHv7UdqauylZmk9mZirFixbx6su7OHCgkuKShUTFeHC6beiaRihkYIRBxzG2Xt0AjTC6HgAtdKqcrG3CTHgJdCHmigT63JNAn2NOp4P33nYbixflUVSQwoH9B9m6dSvRUbEUFCaRl5uC027HHxqgf7Sf0TAEwgbtnT20dXTQ2eHj0L4aDu6tpqZ6ELczicGBQXzDPvxBH0UFmbhcNmJjoihemMOorxeb4cbp9tLW0cvR0pP4RkOM+EOUHq2ku3eE0tJGlq8oIeBzsuWNPcTHRXP9DSvQ7V527TyC1+MmIyOJksUL6ezuY/euw2TlLKGqqgy7M0haWgZ19fWkp6eiaRrhUBjCIdAgMzed8spqevpGCIQ1evoGiEuMY9+Bo2zffpj+ngAdnT76B/px6A48bhchw8bY3nEGhhE89V+Osep04fHwDp4qaKOf+rNs/iLEpSSBPvck0OeY0+nkjtuuJybGQWJyDHZ7kBHfKHX1FQwNDJGXl8zC4hTSMqMYHukCNOy6k+6uXjTslCxazGC/j3DQidcbRyAwTF5eIjn5CfR1D2DXQ0THjGKEdYKhAW65bT1xsYnEJyRStKCQjvZ+/KNBYmLicbujqKyoIjExjX37yhkaGGDEF6LsSCNNJ4cJBjUCARtHDldTcbyJsKETG+PGNzzMwOAIXk8Ur72yBUIeBgeHqaqoY9Sv097WRXd3D30DfTS3dmCEdXbvPERv9zCx0Ulk56ax6doVgIPdu0opWrAAl8fO1s2lVJRXkZAUR3RMFIYGoWAA0DHCDsIhjXDIQNM1gsEAwUAYXbeh6+OBLlXohLhUJNDnngT6HHM6nGxacwfDvlESEpwULsjk3XdeTWyMl4rjh8nJTuOajUtZtiyL9VcvQjNGqatrIC01g5TkDF577XUGB0dx2KLp6hwiNi6K1tZyhga6uOnGjaxdm0N7cy/VlbUkJ6dRc6KK9s42Ojq7waZTuCCXqhNVDPt93PquG4lN8FLfUENMbDSDg324HUnk5eWydds+du8uIzMrn8XLihn2BUlISOCKtUvZeF0Jx442sumGxRQU5bN92yFuvWM93d0+oj0pVJZVsWf3Ljo7OqmsqmHlinXceutGjh5uZM2VOaRnRON1e1hQmEtLUz/PPPN7Rvz9pKUn09rcQXVlMwcOHqMgfyFOVxQ221gXvN2ugwaBQACbzYnNZkfX9VOFa6TbXYhLSQJ97kmgzzGHzcH6VWvIzIqjoCiD1JQEjJCfpSUFZGdms/nNt6isqCItJZvkpFgWl2STmpZE6ZFDxMfHkp2Tw0D/AANDw/hGu7DZB8jPzSbanUpDYy0Vx5vwuD0kpaQwODxE8aJC0EIkJKVgEKayqpykpGT8oyNs3bqFFcuX4vV6CAVDbNiwnpTkVNxeNxs2rSc1K56K47WcPNmIKzrMzl070XWITYwiOTGBN97Yz9r1a+jsDPD227tYUJzL0dK9XLdpDXe//3bWrV9JXl4+J6qq6O8LUFCQyqF9dUR5XRw8cJiKsnry8xNZvWo5Bw+Ws2J5EQ89chu5uZnU1jbyzE9fpL83SGtbB929ndRW1+Gyu3E4XTgcLsJhQANdZ8I2rucWqRFCRJ4E+tyTQJ9jToeDW6+9kZamLlqbe4mL8xATFQ2GQVJKLIWFBXS2d7Nz+x7SUzJJSoghMzOBuLhoKirLCAahsGAhPT19JCYnsm5dMQ31DVRX1+ByJRMOuunr1Whq6WV4VGPPnmOkpaURwommO4nxxBEbk0JBXjEeRwwH9x8jPjaJQCAIYRvoozQ2tlFdU09MnAeP10NcopvU9BQ6OvvYv/8oZccrqCpvp6Gpi9JjJ3jP+zcxONTJ6EiIBx64noaT1RQWZIAG3ig3bpeb3dvLKDtaTl9/F6MjIyxZUsSqVQspLEincEE6Lk80O3fux+OxMzSo4bB78LidNDc30d3dR1RUHDZdo6WliZqaOlxON1FR0djtYwVnxkrKTvxJS6ALMZsk0OeeBPocc7mc3LDhWkZ8YXq7h6ipbiQ9PZWOtm78oQBpqUkU5hcx0DvCm6/sxe1wk5DkJSExhpIli9mx4yAtzT0sX7GaivI6HI4A73nPJhobuzlSWkdTcwcDQ0P4g9DW1YnHG0t5eSNdPf20tXUQG5XI3t2H6e4aIBTUSYxPp7mxg9HhAOGQjbBtgMzsJDq6xibgLSgqYPv2vbS1dfCBB9/Fbe9eS1JiGgf31ZGalsO+A4eIT4hmwzVXsu2dHaSmx2K3OamsOMHQ0DD79h+htbWLlSsLSUpMw+Vw09HexYYNa3B7NJzOMOAkPtXNiZp2fvTEr3B5vGy4Zhnr1y+mcEEmzU3d+Edc2DWD1asX43Z7OHLkOL19PaSmJaPrOrpuk0AX4hKSQJ97EuhzzOPx8Hd//TipaakMjfhpbOxi2/aDHCytYf+eSqpr26lraCYYcjE4ZPDCK1uoqhykp2eI1rZecrIWUnb0OCdPVpGRnERNdRtb3qlFt0XhtBt4PU4SktwUFKUQCg0TGDFIz8jgmo3L8QeCjAbC2N0u/CE/wVAIXTMI+YeJj40iOyubltZuuvraufcDt9PT46NvoI+Vq5czMNRJe+sgdt1LUVEOCZ5k8guTuO/e9/KzZ/7Irt3l6I54XhcV9b0AACAASURBVH19P9l5BezYeYiM9Bw2XrWKNcsXkJ2bSFZWMi2tjbi9bjo6O8nJz2F4yKCsvJat2/aTn5XLrTfdxNubt5OWkU5mRhpJibG4HDrd7T04XV6OllWxYnUJK9ct5FhFHccqK8jKScVudxMIjBAKGwSDEAobhEJhgsEQobBx3nyXWfFCTJ8E+tyTQJ9jbpeLD957N3ZXkIzsGEZGApQsLUDXobCokCHfIMtWLGDV6gJWXbGExLh8ak5UERMdQ1JiFLHxQdZftRiXzUFDQxP33HcngyPNOJ1BHvnoTfj9ITIyY+nvG6Czowvf4BAYOjUnGunr6aazs5cobwxJyfF0dnTjdkWRmpqCw+Giu7+H+voGAqMaR0trGOjrp6enj7RMD0V5KzhWepxXX9pCd2eA1uZaDh4so2/Qh2HE0N7Vy/pr1hD0++lq62PxohV0dfVy5dWF2Ow6gZAGmk5CYjylR2ro7TRobu6ko7MLA40r1i5j6fJc0jPjiY5xs23bTpITo0lNiyU5KYaU9BhO1rficsRSU1dNSkoKi5cUEwz62bv7CCM+g8FBH00tbQSDQWx2G06HHTAIh0PoNv1U+djxDV40CXQhZkACfe5JoM8xXddZtWIVCckJaLqTUFinvLKGK9auZNjfT0pqMo0NHWx58wCBEQ1D6yNvgYdDB4+RmBjDjTetoKAgkdUrcxgc1Nm85W3WXrmCwweOcaKyhdSUdHwDfSQnRXHtdcuJiYliwaJE7rlvPVlZidTVNNHR3k5KmpcFRYWUVVQSE+fF7hhbFhYbG0MgECY3I5vGhlriY71Ulpezf+8evO4oEuKj6Ohop72lF6crlsrqE8TFZxIiSF+/jxUrC9m39ygOj0F5VT39QwOkZmbiinYwNOzn+LFGTtZ209jQiM0O1920kgUL84lyuXDYQhiGj6ysdAL+EV584TXysnNIS08hyhtFZlYiTS0NxMelcvz4SbxRHpJTUqitPsnWrdsYHBwkKyuL2roaTjY0EBMTS1R0DEY4hN1uw2Ybqzw3Nm1OlrkJMRMS6HNPAn2OaZpOTye0tAyRmZ1JYoqXsKHx4yd/h9MezfZtR3E63CwqXkB7Rw23vnsNxUsKMQwbO7YfJtqbTkpiLOXHK3G7obe/k6rqKm6+5QaOHKogPtHFA/ffRE1FA8WLk1m9Jo/D+8vYvW03CbEppKdF0dneSlVZM3X1tYQNP+Xl9QQCOgnJMSxdtgiHXaN4UQpXrs+hZEEWD37wvRzeX05+diabrl1KfEw8K1ZeSXRcDP6RKKJi3fT293Oyvo0Rv4HDHk17dxtrr17LK69tY9/+UpoauzlwoJTMjHSWLVtBVFQsQdsImdkZxHo9uJxh9PEd1rQgRUUFVJXX8cJvt7B8+VKSUmJxOB1kZCWybfs+li2+jl079lFRfpzU1EQe+fDd9PX3oWtwzTXrCYfhlVdeIxgMkZWZgc1uO1U0Vhvb9AVdWuhCzIAE+tyTQJ9jHo+b9999H2++sZn9Bw5i02MJhwMsXrSCkdFB1ly5ArvuJCnZQX5RLGXHj9N4souMtEzamoLs3naC/r4+UpKjyS3wcv2NV9I/0MvgQB/r1y+m7HAz9TXNFBVn8dYfj9Dd4SMjM5bcrGTqa2tJTY3mww/fxpKlhUAAu9PBtddupKu3lebmNnxDI/R2t1F29BDZWVn4BvuJ9hqsu7IIwgYJCR6iXGlU1JSzal0OGdlJBAM2MjIySUqMJ2wEyc1Np6a2ko6ObuJikujtCnLyxBDhoJf8gjSKF8UTtPuoaWjF0KGwIANf/zAjvhA+Xxi7zYHdDmvXrGVkyMaeXWVkZSUTn+jGYXeSl1fMa69upqmphZjoKK66agUxMW6ysjKprKiitPQ4S5cuYfHixRwvL6emppaMtHRcLgdh41QNeA10CXQhpk0Cfe5JoM8xj8fNF//6E6xaVUxZ2X7sNj833XwVBYVJ5OQmUlfbTFZWGqMjXfiG/DhsSRzad4L62jYKC3MpKshmdHiUrOw4Shbl4/F6yMsrZO/uUmprqkmIT+f4sRraWka54ea1GIaPTdespGRhNkuWFNHX28XO7bspWpDD2vUl+EMhDh85wsZrr6aurpnAqIO05HRamto5sKeSgYER9uwqw+uM52R9Cz7fCC63ne6Bfja/WY7TaaOzY4CK8jLsNp2Ojk6uuSaf5UsXUVXewkMPvRenHiY+Lo3u7gZ2bj1MbU07UQkOmtp9VFX30tnZRmNDNSf/f/be68mtK8/z/FwDbxImEwlkIr03TCY9KYoUZUipSqpSl+ku12Z6dnr3ZWP3af+JeVnzsBOzuzGzMzvRNV1VXd1dUleVRFGU6E1apvcOQCaQcAnv7r37gEyRVVJ3T3R0V0ZL+ESAIBKXQQbPDXxwzvmd728ryMbmPltb2yTjB7icdfQNtLC2tsn42CoDA4NolJiamkOWBDweN2aTkY31bXSyCV9zA+1t7WTSFaYmZ+jv66Kzow0QWFldxe1yI4kSIKBpIEm1JfcaNf6x1IR+/NSEfswYDUb+8PvfRZY1Xrn6Mrdv3SWTKtLb24so6DDo9fzql78mHo0yN7eOXqrjR9+7wYkTfnaDAWTJxdrmKuFIFq/fQmi7wKOHExj1dYR29hjs76PJ62d+foKOTj8Wi8rDT2c4e6YHTVVpb2vB3+rj8aMxMimB12+c5uCgzCef3OPNN95hc2ubcxf6yRdKiJKEzW7kh3/4Lf7ixx/yzW+/xtzCEnuhPKpcQq/XEd4LUe92sbG2Rnhvj8YmH9FknI6eAW7feoJO1GG36Yjvb9LdXo+o5ZibX+bxkzDRWAHZ0EBLWx2vX79ES2sHbd0+/G1NhAJhlpbnGB7pwt/i5dZH48RjZRZXl7G7TFx/8zTtfg/zz9bp6DjJ/YfTOBxGPA0O6j11FAsVfvE3N3n1tYs4bG5i8Rjjk5MMDJ2golQbq8uf+Vw9PPImfPZa01SgWgmvVbvC1Jboa9R4gZrQj5+a0I8Zo0HPn3z/WwgqWEwmhgcHWV1aYWp8lmAwSnR/j1Z/I5WSjpMnRsmmc7T1OrDYTPQPd5AtRtmPltkJhLh/dwVBSHPu1DDXrg4wcqKbjz6+w/lLQzS427l16x7+5lbqnHr2QmE6OnwUC2Uczjo6u9vY219nanKNtvYmUskKd+89xFFfx/T0Ev0D7ZgNdYSCIcrlHB0dfnZDO/i89cQTm5waPU2D20Rwc5e2Vjvfefcdzp/tZX5mlpXFNVrb/Fy+0s7jBxO8884FcrksVy538YMfXOfaa1dw1LlIxjKsrm2RTh3gdFpo8Fqx2nSYZCNdHc2sbyywurpBb283VquHn/30E+xOOzduXMJkFNGJOnxNDTx4+BCft5XNrS1sdgsCEutreyQSMR4/nqJ/qIOenk4ymQzT07M0NXvR6yVk8Te7tVXFfbS//nyP/UWR16Reo0aVmtCPn5rQjxmjQc8ffedNjHoDqqJgNunp6+5heyMAKLx67SW6u/10d7cQ3Img01dYWFyjqbmBUkUlHNtBkMq4XG4SiTyCovHShU5MOgN6k4Yminzy6UMuX71ENi0wM7vM2XOn2AvvYTTpcTkdSEK12r6hwYcsmXj6aBa9aGZ7O8FBuojVXsfYkzl0BoH2tn6WFkOUlRQNDU68vjpOnupgc2uLixd76eluZe7ZPAYDGIwGbBYXK2vrJFJx/G29zC8sUu+p4xvvnOTpozna/U001Iv0djdyYrAVFB3r6yHu3Z9lfTuCzW6hwe0AQcPjbebpozkioTyCXOHVGxeYmVlAUwQ6OptRlQoWq56O7hYWFpeQZDuzs5usr+1h0Fl56+2X2N7a497d+7S2tzA02E8hX2FjbZsmbyNGw1GntiNxg6YpnzV7ef54zueFrr5wXU32Nb461IR+/NSEfswYDXr++AdfRxDLCKKKJOnRgOETPezsbFEqZfA3NyFLOpqaHAS2I2xu7rKxtk9kL47NVsfQcCvnzg5gN5uZGl8ildJwelzYnTba2jzML64xNxNi9Fw/qXSWxw+fcfbCOQLBHdq7XJQLMhurm9z7dIzIXpILF4YZOtGCvc7B1mYMl8tDT/cQoqDH5qjD62tndXWLVPaAg1SB4ZMnqOSzGPUyp4a7EIUiyViMa9dO0N7diF628fjxAr4mDwgqn348RiWvo1LM43IZ8HjMGA167DYzuUwRf0snosnCo6ez3Lo1Tjpbos7tYXJ6g+mJCMOj3Vy+2oPbVYfJqGNmao18Brp6G0EqIAgCnV0dzM4HSEQV9vfjhHY3OXW2m5Onu5iZ3mJ7Z5ue3i6afB5i0TjPphYZHOxE0zQ0DVS1Km5RFFC1MqCCUBW1pn7+iNtzsb8o/JrQa3x1qAn9+KkJ/ZgxGQz86Y9+D01QkSUDICPJMvliBr+vhampedZWt7DZrBwcZNBUHYHABlvbW/QNtHLm9BBWsw21otLeWk+5bGZlI86DscdoqDTU13Hi5Cg/+clN6pwWLNY6NrbD7MfTCKKOqbEQd+8+YH5mjWZfIz6fG0VNUy4rtHe6GTnZzcL8NHUOke3tXXx+Nwgi5YrM1s4egmDnw5v3efPaCCuLC/R3d9LUWMfq4jo2mx5vowuny8TDx3exWdz8d396g0gwyfTkClpFZfbZCi53PSaLGZ1BxGAUWFpfpqndwaXLowQ2C9y9s8hHH87S3uXn+jtnyGQTtLU2Y9IJuJ11BAMx1tYimG0GnPU2BFlHKp3hIJVlZ2eTFn8HolRGli309TXj9jSwsb7DfmSfRp+d1tZWJidXiEZCNDf5KVdKqKqKqoGiCiCqKKqKqioIh9Xwz8WvomlH3d3Uw1E92mOHmtRrfFWoCf34qQn9mDEYDPzxD34PQTAioEMQQKmooMkIkoDf7yWXz7G/nyCTzmO3m7l05QynTp/k/v1x1jdCFAtlEvEoiXgYSQfhyAF6vUAgEGR1OcD8wg7+tiYePVzAYLKQyhSZW5xHlGR0OpnOdh9fu3EJT4MdiwVApqIUyWYyFHJZ3C4XT5/MUiimCWxHQBAJh6PEk0msVid9g13EEilsDjeh3Tie5iY0nY4Pbj2iq6cf0WDC2dDKj//i57garfQPtbO1muRHf/h1kskUT8c22NwOEj/I4nZ3sLoeJbiTJLa3xze/eZ6efg+RSICpiTmKJT2qUKGr24dSKWHQy3i89aytbXKQySPJVkK7EWbnFkAVGRntRRQFdHqRhYUl3O5G/G0uDg5S5HMaiVgJSTLT2dnJXmiHdDpFo9eDJIuUShUy2TyKIqGT9QAoioIkHi3JS4fPRylzWlXu4otFczWh1/hqUBP68VMT+jFjMhr4kx98FxAPg04O41Q0DVEUMZnNtLQ00dzcRGurjwaPHZPZjMlsxNPoYmpqnDOnRxBQOTgogyBiNsNeOIjbVc/Z84PUWW2E95JYHEYWFzY5feoMSlnB5XBw+dIohVycvl4vfr+H+kY39Q0Ompoa8Dd7qXfb6e5t4cypQZaXFjk1eolEPEYgsIVSkcmkcxRLJdYDEfYiWXZ2wwR2o6AzsLKxzdSzVcrIxFJJoskk088CDI6MsLaaQjYonDo3xNZ2Bl+ri/f/5ib3Hj4kuK3R1tJDs8/CG28M0NvTyNlTvezt7HHrgzFiiThDQz00+5yUyxUMJjM+fyOzz3a49+kzLBYLqGV6u3wM9nfgb3YzP7NOKBgjGovjctkZ6O9hajyI1drA/ftP2IuGuHLlIvMLGxiMNjSxxM2P7rG6GuDWzWlisT2a/U0IohFZJ4GmomkCoggICtUV+OoyvIAIqghCLU62xleHmtCPn5rQjxmj0cC/+sF3X6igFtE0DVVVkaRq9rgggKpW0FCoVEpogKzT4airI5NOkc0eMDp6gpY2Ly0tXnp6W1AVgXLJSEXReOlSN2a7gbHxVeKJIP2DbQwO9hLciVHMKzS4LUTCQbo6m0EoI8syaDoEQQFRoVIpk8uXiUQSzM3PMdDfg1Ffx+WrQxwcxFlZXqdYMqIoAplsDlEGTZXp6+/i6ePHNDX58dS7kQU7U+ObzM3sIotG5qZ30IBUNkZHl4cf/vBruF1Wcrk8W+v7lCsRvA0OXA47dXVGXn75HDabkSdj43z66TO8jY34Wi0IkkahIDM5sYwkmJgcH+PKy6OcPd2HTiciChV6ettZW1vl0kuXWF7awutz0tPdz+TUJJIsUCpJSKJK31AXN2/dZ2xiCo+njUK+gs2lkj7IMTG2idvtxWKTkCRQVeFwjFTQqjN2QRBAOxxLsSbzGl8dakI/fmpCP2aMRuPhDP0whvSF4ipBEBFFAUEEQdCQZAFZJyLLMpIooKHS3NTEk6djeD2NGIwysq56rdlSx+pqkHxOIRQ6YC8S5Nu//yqtrc383//uz/G4B9gPRzGZBGRZoFyuYLUbsdfZ0dBQlAJbW7usrwVZXNggFIjS1dlFsZAnGY/T0+MnENjA7bZjtzhQNY1ivkKhFCOTzHFqtJmB3mZam53srK8x3N9BZ4ePUqVAOBKlzmZFr9ORzx9gq4NMPE1vl4eh/i56ehvZj6UI7+2xPL+Et8GNw2ZCJ+vo7m/GbPUyt7DD48fzVMoGgrsHrK/vYbM2IKAyPNRFpZTH63Fg0MvodHpknYCqqXx08w59/cNsbOzS2GRidSlGo7cOQSozNjVPfYOHwHYGSdJjs5kZGuqkf3AAl8ONqhj48MN7OJz1uN1uBKm6b64o5WqRnFY9r66qanU0xReX3I+K5WqSr/HlpCb046cm9GPG+NmSu/BZ/25BEBBF8fBZQBCq7T7F6ipu9TqtWoyl1+tAFUgkDvC3+FFVlYpSxmQ0o6oid+7cpVhK8/vffxOrxUKr30MqkeH+/Sf09vUxMTGB0+XGYLKTSmXp7vGxsR5k7MkEmxtBmnwtuF31VJQikWiIOpuFcGgXQSzS39fNytIq19+4DGUJp93AubP9TD2dwGExcXqklf7eLiKhEP1drfR0e7BY9QQCIVpbXbS1tFEsZXnr6ydYmlvnyeNJihlwua00tfoo5lUie0mUooW93X1cbgc6o4SlzsjOThmlIvD40SKRcIrOrnb6hxoYGm4htB1CUPVkUgkaffVIooSqCTgcTubnVwEzgqbjr//qQ166Okgmm2foRC+Px+cJ7mTo6vGxvXHAqdOdZLJZpqdXUMo6AlthdnfjPBmboqmlBbvDQqVSRkNFVSqHzV6qKxqCICKI4mfjqWnVL1o1odf4slIT+vFTE/oxUxX67/NF55ur++iHgheEw3AT+I0qag1MZjOTk89oae3AYtGjqSKZTJFP7tzB6XRjt7sJh8P0dLaglzV6uvq4/ckEvQNd1DnqWFzZRNFUsmmRiacr5AsHjI720+L3MD+3QHgvhtWs4+LFIS6cG6KsZNncDNDe4eLkaC//+//2v3LjtVeIhlNcutzKjbfO8Iuf3edv3/+AxEGFSkVjPxJmZLSPOrudcChOJGDEaBWIRGMMD/Zx5nQvkiDT2dHJxPRTbn3yBFHS8+63v86TJ/PUNzZy69YkmWya1bU0kUiQ/t4Rwnu7bG3s0dLeyJUrA7jcMnrJwM7WLplsElFSaPJ70VQNvUHPxuYaXm8P9+5OMHxiEFVIc/J0L//+//yACy9dZGJqmpOnhnjrG6f565/dI7AZY205hs1qorvHSyqTZGZuiZWNNa6+fBJB1KPX6RAEAVmSqChlKuXqtoUoyry46lLr5lbjy0xN6MdPTejHjNGo509+8G1enLkdHYM6qpI+ih3VNA31s5VbjaMJnyTpSKez7IVjeJu87EcT3Ln7gMHhXto7/WRzccLBBD6PC5tZj9UmkUynmZxe5OJLV1lc2aGi6Njc2qBQSHHuzEk2VgNsbuzR09XGpUsj9PW2YzFZKBbztLd2EI7uEgwkGezvwufzcvPWFC3tHn75/lPaut2MnDtBJFmmwdtCoQyPH8+SSGZJJBJIoonxiXVsLheywcz4xDhXrg4TCIfx+Wz0D/ehCgYePJxhZmaHiqRne3efXLHA8uIeCGUMegMN9QL/5s/eoVIu8+GvPiYcStDf10JnZyO5fJZyWSMQiGAwyNjsZkRBxmKy8asPbuP1uXG63JTKEpPPlrDV2SkUJbq6m9HpNfZ3S0xPTdHXewK7Q0dPVz0XL/dy+twwB6kyE5MLSLKOlhYP+5F9DhIZUDVMJhOoKrIsIxzmxH9RwlyNGl82akI/fmpCP2aqM/Rv82K62Oc/+J/vrx/N0o9af6qHOeQOp5O798col2FiYoae3h76+rqwWQxsb+/R4m9mPxylrc2Noij4mt3cuT9OtqCCKJLNwumzw8SjCVYWt9GUEm+9dZneXh+SpKEo1b+zWtWt4XJ42NwMEY9VOHlqmFi8wMLyGjqznadTq8TTAt4WP/ceTmK0m+jt7+OTjx9R73KRipfYCAdZXU8QCu+xFdxmP6qwsxPiyeMl1lYiRPcTWK31bGyHOcikcbm8SLKA1Wanr7uL7EEGp0PHufOtDA+1oKkmbn/8gINkEX+rm45OL7t7+zgdTTx7No7b5SB9oLG0EKBcVLl8dZTllSi74RjJgwJdPR1Icgmbxcbssw0mJxc4dfIciUSAP/rjt9lYn0cSZOocFvoG2lheWeXBg8doqobNaiIWiTH+ZA6Xqw5HnaN6Xl2SDsdO/XvGtkaNLwc1oR8/NaEfM1Whf4fnkaOf/8B/LoGj/t2HvwoiGtWNdYPBgK+pmbnZeYaG+hka6kZAw2Q0UMyXWF1dwGgwYzKY8HhcaJKBaDTHzQ/uc+HCSQ6SWQ5iSZoaG7hwfoRTJ3t48OATtjYi1Ne7MZhkEEDVdGiSQFmVmZnd4cOPHhKPafT3dmK26CkqJcqaxtT0AiMjo2iqBVWV2N8vYDWL3HhrmK+9fYKe4XZikQINXjvpuICEgTffuAqqgbe/do3Tox1cermLUs7M6OkmSqUCb71zjvh+gJnJed79xqs8e/aQepeN5iYvQyNtVEoS7//iQ/I5GBntpLW1gYf3J3n56nk+/PU0S0ubnDpzmr7hej65PYff383aehBZNOJyuDl9vp1f/OWnlIplVleW+OY3X6anx8mjRx8zNDTAwsIyvf0dTD+bo1jQs74WolKx8ObbVzgx0k5TczN37jymo6MVTa2gN+jQtMMaCKDWc73Gl5ma0I+fmtCPmd+ucn/Oi7niRzPz6kODz5qFiKKEJIpIooDdYmJwsIuGBgeiqCHLEpom4HbbmRhbweE0kc3k8fo9bG/vMTe3zsZKFK/HTyGXwGGTGeztJJ/J42nU88abZynmFR4+mqSsVpB0JgJ7KW7deszP//Ih+xENnc5FKnOAw2LEZJDJpmNk0yUaPa3cuztFKafR6KtnP7pLJhslEs7gaW6rLlVHdzBIemxGJzvrAaxGjUx6B2edyolBP3aLHpPtgODWHhaLxNZ6gLOjg7S22hl7+gS71cv87A4Wi4zXV0//gJdiocyv358in7bQ2taMs1HHT356B0Q9vf0dtHdaeTqxyU5gA03RGD3lx15nJJlMMTO+AwokEyFevT5Ek9fKYF8Hk1PL9PX2EgzsM/ZoGYvFyOvXL6BpOn71609I58qE98OUSxJUrKCVafQ6kOXDwjhBPRzHmtBrfHmpCf34qQn9mHleFCfw983SfxPhsMDqt67TFFRVq1bGH7YA1VQRnSRTKpVIZ7IkExlsNjuPHk1z6vQw+3t5Zp8t8sorF0ilYngavJRLkMpE6en34W1y4nC7+eDDp3zyyRwrS2HAjM1uo7e/C6NJhyjIoJQYGuygvdWL02mkVFBwu+rJZuMsLS8zMjpIPl8iFDogEIqhqSqqCkrJjNvVQCaVwGzRc/W1QR49mKGYy2K1WujubmFlKYBBNtHf1050N47H7aa7109gO44gyog6iVR6H5/PR29/KwepMg8ejRMJJ9CZTDS2OJH1Rra3gszNLmMyOPE1etnfS3Lt9RMsLcTY2gkR3A5jNho5MdpCb28rG+shWlvqaWrq4vZHj6iUK8RjKX7woxvksjkOUnk2d3aIxlO8/c4VChmF+H4cVJWu7mYEUX2+nnL4ZaxGjS8rNaEfPzWhHzPPj639t/N3z/KqM3b1s2xxrVodj4DDZWd1dQuD3sr9u4/5vW+/zu5ulFh0n1KpTGt7G+WKxHYgiMVmRako5AsF9sMp/q9//xNWVgJ0dbXjchvR6TS+8a1LtLW5qHNYiUbSlNUi9Q1WGtxODMY8kiCgqRm++wfXyWQqPBmbwu3sRlH0bG7tkjxIEIkUWVoKYLPVs7G9yVZglyuvn64W/ylFZp+tojPqsFgtZLM5gtsh3n7nIgvzS+j0Bq6+NsL84homq5ViqcLyQopSWSSTNqEKRSZmptGbLHR29zA7s4FOLxLZy9PcoufGWxcIhfZ5eH8Vs8XNw0f3aW31cOZcF6+9foq7txdxuvRsb0bI5QtEIrtcunSGVLKMrCtz/84EXV3deLwOnj6dpq2lmzde72VlcZnMQZG+/g70+ufpf//w2NWo8S+bmtCPn5rQj5kvFvo/FELy+fc1AEGgoqgIooggaAiCClQjSgVZY2tzj9WVJWxWK16Pg8X5JX70R99kcTFAMpWls6uH9c0dujp7SCSjfPzRp3x8cwyft5nv//AG1988z8BgOxvrEX71/mP2QnkqZTOBnV32IhWeTa8x/mSW8afThHb2KaaLBEM5REkilkixG4qTzwGanq7eTvwtnaiqifB+GFtdA0urm6yvh3jjjYtEQntceGmYWzfHMBo8VMqQz2jU2W0MnWjn/v0ZSooJZB2/+uAO2byZaDLK6mqIt94don+gh9WNGGOPV0mn4/R2DRDY2eU733+VUCBDYCeDyWJhbn6RbCrO19+6TL3XgywJ2J0SLc1dLC8FuHvnCa+9NkpX+yDxaA6LTeS9v7nJO+++gb/dgdfXzPjTVVbmg7z9nHZR5gAAIABJREFU9gWKBZVQMInFasXjsaNp1WYu1eGpzdBrfHmpCf34qQn9mPm7Z+h/XwjJ0Xu//X51Zq6iIQoaAhoaZTRBQdXAZDLirPNSLqhkslEuX75IQ72F0G6c+bkNhoZHCIcT7MeihCMhspk03//eu/zBD65hMuqYndlhdTWAXq/H4bSyuLhCqVRgaGiY/UQU2aBx7ryfyxcG6enw0dPTitlmJhINYbW5iCUTmMwmrDYj6XSMeDRLLl8ivBeno6OXbDFFIhEnk6qgVXSkEyWa/V4WFtZYWVmjmC+zs7NPLqNgqzMx/myWaEyhvaOVYlmhoig0eJx8+OsJ4skkbrePbDpHPFqgv7+NixfP8sGv7pMrCmyH1inkNFpa6mj1u7h+Y5RMIUcimmFhPgAYiET2aW/rJhFRqG/Uc//+GGajmfoGG929bZhtZoxGmdSBwuryFoqi0j/Yym4wS7lcoae3sdpqVRQpl4vVc+mCBtrz5i2adtiWVfvtceSFLm5fdAu8cH8Iz/9cLbimxnFRE/rxUxP6MfPFQv+H9tE///6LPxEFEA6PwQmChooAAjgcDvzNjTTUu2hrb8bhtCNKIgh6JibXsdo8ZPIHLK0sYjBY+MbbX0PRUmxuBNkLFdAbzMTjWRYW58jlEpw7N4TVKiPJeXqGPGiU8DVaeeu1M3R2+PC1m+nv93HyVA/uBjtzc8sYDEZGTvawNLeByQprG0vkMgq5rICsl4lGExgMDtpaPZitEqfP99HR0YSjroFIJEad005Hpx+n20JZkwlGwmxuRmhwtxLY2CewE+Ds+R4yqQQ9PT5ev36Bx49mmZ5exeNtRFF1LC/O4HK4aGv38M1vXiSdiZNMZairs/Ho4STpdI7unibOnO0hm67w+Mlj0ukCPm8LngYDbe1eEskMzc0eJEmkXNRYXd1jfX2Nrp4uJNFEMpakp6cRURQRRdBQEEUJBOWz0Tr0+ucS5DTt+QrM379E/9tC/9yLGjV+Z9SEfvzUhH7M/GP20P9uhN96Pjr7LCIiwmHTF5vNjMGgA61MRSkj6Qw8HdtgeWUHSRZJZ/Z55dVzxMIZpiZWaG72IeosBILbbG6u8/W3X+Fb37pGZ5ePjg4/mUyGmbkNJFEklUxzYrALWRYQZT1mPYiSQktLPfv7+xSyFQq5AwLbAfr72rly5VUy6QzLS/PIujI2q5OF2XWCoTgb63HmZoKMPd4hsnfA4lKQBw+n2d1NsTQfZWd7H1m2EIlGMJsMfOd7FykUkqDJfO3GWT782znWVqP0D/WzuLrA3MICg8MDnBrxc+7SAAszSzT76jEZNf7jf/ivaJqV7t5GPA31JBMZfB4fn97+BL3ejMms48abZ3k2scLJkR7W1zdp8XuoqBoWm4m5hXXS6RLlgkxzUwuKksVmkXC67NWua6KGomgcRr5XR+kwPKgaC/tbX9BeeH30e+3FmX01jOCwgv4wZOjwi1uNGsdBTejHT03ox8w/rdBf5Pl5dVWlKhVURFFDUyvVWFlBQhCMlDWNp0+32doJYLG46OjykUgkSB2UcDoa0ChjshZI54JcuzaC1WRkZTHIxsoey4ubyKKOvdABK4sbSKqFhekdDpIH7IZibG9EKeY1Dg4OaG3uJBHPce7cKU6f9jIxNkM2l2Govweb1UyppOB0usjlM5S0CnklS53LzNBwJ75mL+3t3Rgtdsx2HVffuMTQSDeDQz4segsL87O4XU3kszqePHnE+MQ6fYONJFJBFEWh0dPI3l4Ah9PM5Zf7eTq2THt7Ox/+8pcYJAtXLp8lk88xNDzA5kaEteUwO4F13nnnDQRUFhdX8DTW4aizEQzsoddLWG1WrFYzerOe0O4+glrPfiSN1Wag0Wcgl0ng9/sAEUE8mqEf/b+DpikvSP2oq161ba6qVq3/PDHwhZEVQNAOx1fQfmuCXjN6jeOhJvTjRz7uf0CNfzqeL9UeUS3CEgUVRVEQRRENFVkWUVUol8rIBoFIJEdwdxdBlLny2ghzs9OUKgVMBhmjCeKxOKhOnHV+IsEcjjqFhgYbVqsJs9GIqgqcOt/NoweTzEzuUucy0jfQgdkkkM5lqVTKbO8EyeQgGdvh4f19/uzfvE4mXWR7J0GlEkYU0lQKOfrOjJLKxsnmFTq6Wnk2NUG5WMHtcFPKwt5eiIN0jKXlIJJQxGzWYbEZKZWKfPTrDxno6+bqhTOsrW6iVzT+h3/9LpIeZpfirG6v8tGdm5QKBfoGW5iZW+Py1YtUshWGR3p5Nr/Hz396i0avG7fbSmuHDUe9xMhID3ML8zx6OMkPv/8N7n78jOZ2C+vrO/ia6ylWNEaHe3h85zatbZ1sbO9w5sJp1uZDVJQKOp0BFQVJEAERTat2yhMF7TDWV3peFSEIqKqKeJgz8KKtj04ugMJnOQWacLj9rqChIWhyrZK+Ro2vKDWhf6mpfrCrqoIkSYdV1goaKpoAst4AgsbDB0ukM3nsdW4Ghpp4OvYUg+xCFEzsRzfJHMR47cp3qfeYaW6yYjCKyGI1gvbgIMfebpT9rQxmcz1zCw8Yn5rBWW9joNdPS4cf1Dx9Pa0Uy+ByO/mvf/GAn/6Xh1idBkQy/Ks/+iaBUJL/+P98yvzUOHUmI4n9BHaTg/7uUexmPT19fs6e9RGPJPnrv/qYvv4BkrFNrt84j8mmw2Ky8Dc/+ZjXXx1gZKSZ7c1h/tN/vs3ORoKDXJT1rQDvfv0q7/+yxOzUMqdOnWJo2E80vkcuE+M//L938Dc2oJRyvHXjLA/uzZFJqIR345w40UFrSxu3P35I8VsCbp+ZYlllJ7TD2fOj6CVob27EqM9iMKhEEkU8zfUszchkcwXsdj2KVgZVQMCAplaPF2qiSrmsoJMlsrkCi/NLdHa243Y7KJcrSKL0GznwgqCBqFbX7AUNTasmBaoaqIIKVNAh//3FdDVq1PjSUltyP2b+KZfcj5Znf/shikdnoQ/lgICiVCiXBCLRPD/72R1c9X5yhQKlUglJMqNqeQwGFaWo0dPVxLVX+nA7deSyWXZ2AkxMjDM19YytzQDRWJxCpYxOZ0DRFJzOJlKpPKWSyvTkBEvzy4R306TSKRxuO9lsAZfbxpVXzrMwt8rkxAr9Ax68Hh/B7RhOZx2JZIL19Q0MOj2h0BqxWIzUQYjTp/wUsgWy6TTd3U7isU262/0oJYXgToh79++RL0isbgcpCQYePJjjxLCfKy8NcWLAz9mTw9z9ZIGxJytksjmW1+aJxcvkikW+840zOB12YpE4/f2NBEN7lMo5fN5W6twGZqa3CAXTvHxtkEAwTLGUp621HaNBAEUgcZAgk1MplgXcbgulQhm7zUSd04ZKHtDxbHKV8F4MT6MLVdMQkZifW+XWx3cwGUzMzy8QiURpbm4CoXpOQVW15yvpgopW/SkqEqp6tJdeQaWCjO7wXvjCO+Sf5D6rUeOLqC25Hz81oR8z/3x76C9ydP5ZA6rL7apaQZZFAjv7PHg0S2NTE2WlyFtfe4nZuTkS8TwHiQPefucSFpOOSiXNwtwq2zubCIJAY6OHwcF+BgcH6O/vo6OzieYWD/X1DRQKRfL5NP42O9dfv0xLSyPWOh3ZbIa9SIrQXpixsTnOnO/DZLGwvRWhwWMntJ0mHkvQ1dNJY7ODZKyETi+j18uIepVIZI/H9xbo7+9nemqBzrZWZAlmn23T2GimpdlDMLCPbBC4evUluvucJKMZMokUJtnK2voG0VgGZ72NjfVtOjta+e4fXMdo0Dh7potfv/chZ872MjG2SF/vIIlUip3dEA2uenzNdkrFMh9//JDRUyNoaOhlK7H9OO3tHvQ6iXJFqzaTSZdYW99m9FQfhUKeRo8LBJHUQZEf/3+/xGLR09vvR9M0pidn2d7Y4erVK3T3dNLe3srm9hY2hxWjVUaVNVQ0dLKIolYQBA1N0BBUAcoSQkVA1DREBCRBjyhqh1XzVe2jHcn9vyWBsEaNfzw1oR8/NaEfM78LoT/fWj/6QFcBBVGEctHIoyfjIOho9LlRVI2VpR0a6hu4cOY8VqtKcGeTtZUl+nr6OHf+BB0drXga3BiNBmRZQpJE1MMjciaLjplnM9htdhYWZ2nzu2luasJms+Pxuujs9NPd1cnt20+IRjJomsr0szHeffdNToz42d874NGDCQaGemjyNhPa2UInm3DVO/F5Pfgam1iY2cDpcLGztYXP52V5aQWDZCSXS7MXCvJscpVEPE0+n8PlqGNlcR5J0Dg1OkxbRzMjJ1vIZmLcuz1Gq9+Lx6tHJ+qwmg1k0jmGhnr58OZ9zl7sIbC9z/rGDqdOD1HvbmR8fJxstkyTvxERPQeJFB0djegkgXqPm7HxZ2iikWy2iMVkwGQ00+hzgiKxtLzOxOQ8f/bffwdFgWymwJPH97l86SXq7BZknYjOoCOWTKKKUN/goaSV0VBB1JBEENHQyhVQBVDF6vNhlbyGiChWZ/AvHmw/asNbE3qNf05qQj9+akI/Zn43M/TPH4OqKCWKpQJOlx2zzUylrMdiNbKysI3JYCKXS2K32simksSiAU6e6uKlS6NoKFWxSIdnr9BQ1DKIKgIiJoOeVDJDNp1ncKCPza0tBEGhzmFBJxpQNRGTWaaiSiQSca69dpp0Ksb9TxYYGGilva2ZfEFmfmEJg0FHOlWmodHBQSqOzepEFhRsNjs6wYzb2U5HVyOXX+7nzsdjnD3Tz8WLIyglPWgCL185x5lzXZwcGmJzbZ2h4WYcDjuCUKGxwcPU5CqhYIhLFy4TCq1hMduZnJjlpSujhHYjGAwW0tkUsh5sNgtNzW40TWPs6RJDg6eJ7IcBcNh1GI0CFrOJtfVd8gWZV66P8PThMvWuVrw+GUGQ+fT2Myw2KxcvDaIoGpsb25TKWUZOjKLXSyiqilLRCAR3cdgd2G1WRE1AlCTUakQQiiCiSBJlUUWTNDQdqGIFJNAEBeEwXEhAOIoPPBr12r56jX9WakI/fmpZlF8Jfrv6XUCWdRgNVtAELpwf5BvfPEM8ssPa4hqnT/VyEIuxuT6Hz2dhZKQXvaxDQ0MSqj3RNU1DFEGSBGRJRBaAioqoavgafYiiyH4kyvnz53gyNs3y8joVtYQsgSwJXL16iv1wDKVc4kc//H0qJYXZmRVWVtZJJoKcOdvFQSKN02lHEIucPdvL2JOHBHd3ARmDyUw4Gubuw6d4fHb+8E/fZi8cwVVv48orIyhljeWFFZRyEbfbQGdXK08eziEJGpKm0tVZz/d/9CrJZJTxsWlGTg4yPjGDq97L7EyAy1fOsL0dxNPgQyvrmJlaoVzM8fLL5/B4nKysrmC2mCkWS4SCsWr9gKLh9zeTTqfw+RqwO6xMP1sjkyvx3i9u83RsHIfDAagoChykcrS0tiDpJRQAQUJVNErZCnUmK2JJhIJGOpyhktHQKwaEsoBOk5EVCRENVS2iCiq5coF0qYCmCmiqgKJU995V9SiL4Hd8y9WoUeN3Tk3oXyGOll5VFdCqR9hEqYzRIGHQF2nyOnn3914hsruF3abn9NkuLr7UhyCUEAU91Vz46pEqtaIcpphWZ4SiIKGTZUDF5TBjt1qJRdOoqsDrr73O1laAn/zlT3j09BHbgT1sdUb6B1vYWA9S32DhxGgbTqedkye6aG62EQ6GMOrzXLjox2oWECjw1tcukcsnyRViiKKA22tnaTXAnXtLtHY70BltTE+EaevwMnyqlQf3pglsJdEEjeGRdjY3t5h5toqAHlGCy5dHePmV8zx+OEMiKjM6ehazxUosnuTgQMFgNGExG8nnskiaiXSiiLPOyKuvniEYCFBnd5DPKeyFUhTzCuVymdY2H6mDFJPjq7x0rZdAaJt/+2//A6HwHq/fOI8sy2iaiKBBOpPDVd+IqoEgSegMMnvhfRRVxWq1sxsO8/4vb/Lp7Qfc+/AB2XAWsQBSWUPUKqAWkQUFrVJGp8rIZRlNk1AUDoVO9YicdnSkUf2Cx29/2atRo8a/VGpC/4ohCNVZtSBIh2eeFdSyQCZZwWhU+PbvD3Hlygg9fR6+8c3zCFSvtdvMIIAkVY9F6XS6auORo1uoIqEqFQQULBY9qqrgcjSwF4zg9bq58dZrnD93kXQ6z8e3PuLHP/4xpWKRmzc/5taHTzHqrCwtrWAwwve/dxWrwYJSKPPhLx7x+vVeHt8fp5CBSxevoqplMtkkhUoaX3Mv//k/3Sa4m+TcSwOEoxH2oxnOXRhg8EQLf/U371EqV5D1AtdefYn3f3GLcqVMuaJSrpT5+jvX8Pl9/Pl/uUmTv4GtzRh9A728996v6OhoZje0y9kzFwkFd9je2qJYzDHQ30qd3cL62jatrV3EY1kK+XI1hc9hqebcz2+RzajshDapczj4n/7nH2KxWImGo6hK9figoIHV5qBULlFSSuTLJZ4tzNPS0Uwwtssn9z/l9IVzvPPu1zCYjMwszVHRq2SEArtaiEBui83YKoqaR1IV9BWR6P4BhXwFUZRRVYFyufJZaI2mabwYVXAk+qNH9WfPC+o0tMNr+I33X7weql8Jnr/UvuBRo0aN3wW1PfRj5nexh/7Fe6fP42FFSUBRS2xtbCBqJp5NzdLsb6S1pZFsNs362irDwwPVuFgUJFGCQ9EjSAhICIqCKIiABIJAJBxFqUjEEgH6+7vQ1Ao+bwOd7e0MD51gaLgff7Ofvd0DWlpbsFglFue2CIXCVEoKuVyert4eCkqaUkHAZjdTUJIUCnnOne8itBtlaSNIMBIjXyqxsrjD8EAPDfVGPrz1CT197fj9zdy8fRdB0NHb1UmDx83+fozJ6Rl6e7uRRANGk0wskWVhaQ2tZMNd70bTUrS1+UinEpSLIo2NVvZCSTRNpqPLj84osr93wOb6Hs2+VmLRGN4GBw63hYqosh5I8Gxqka3NDbp627Ea9Vy8MMzBQZ7pqUVOn+5HRiS0HaWp2Y/epKJUFG7ee4DBbKart4M7c/c5f/kkBovGbiFAWo7yYO4OgcoGN2d+zvjmbT64+1PGp+6QzqZx1vmYn95iYz3E8vIyoiDidNahl3VUm7wdybUaRFPNJDiMneVIu0fxNi+K+MXai7+7C6D2G8v6n9/iqfHlp7aHfvzUhH7M/K6K4r6YakW0KAgYDEbcLhfB4C6yJOJt9OBtdDA7M4tBr6O9vQ1BUBFFqSruw1agVTkczvSpoCGjCSKJZIpsTiMajzA01F+tvtZUqkv+KooK9W4j4b0UVquTC5f6yKQryHoY6G8hFIwQDAW4+up1fvnrj0GssLWzw43rV5mfXeLSy0MsLuyj0xtxOhwszoWIJaK8fG2AkpplYWGZk6P99HR38/CTObyN9TjqTTR4m7l96yEuVwP1XgfIAjqzyPTMMhtbu7g9DpByNNQ3cpBM4fM2s7SwSldXL+urAZq8HupdFjRFZeLxKm0tnWSyCQqlDGaLnZ/+7CPGJxaoc9Zz6coJrl49zfTEHH1dHbjrzDybmUXVKrS2NrG5HqS9qxlFLjE5P8lGYIXW7gbGFx/wZOEuO5Elfv34J9yd/VsebH1MsLBINraIOblNcyaMV8zhQoU8BDYjOOrrOXfxFFarhZWVACsr6zT6nJhMBtCOQmo0NE05dKzw2fhph86tdn87vD0Ot2WEw8p5QRA+y6GHF8+6P8834Dcq6muV9V8lakI/fmpCP2aOS+jCYWiJKAhoKKBpWCwm2tr8SKLI/Xt32d7a5CAZ59SpUWxWM4KgIcnCYR75YVK8UG3+omkVFK2IIBpQgUwhTzxRxmK14HSaqXOZEGURnaRHlmWMeh2qIiLpDCwvbzIy2sluJMydT6bxNfqw6F08ejTBylKBvf00p89eIBSIIagmUnHY3g7xP/4v13l0f4xrr7zKViDI7OI2wVCeb337BuFwlFw5R39fJ80N9fzy5/fo7e7B4zFSKMHE2BoXL/WjUsLncRMKrhLYTKPT5fne99/kg7+9z4nBE+QLSZSKDUUtkc3lSefjdPf7qXM4eDAxy24iiWxs4Nadad771afUN/p49ep5lHIZvaTS0+kgXcywHdjkxFA30dg+weAmLW0+Iqkdnq7d5aOl9/iLX/87Vrc/ZXLm56zvfIykbOCU9+k2Vhh1ivTry5yrs9OUriAF0yRTEbaDSYpiK0tr0NF1jutv3sBkBKvVQX9/F4lYjo8+eEhnVzc6uRovW6pUkHQGCgWJdEYjGEwyMbHE/NwWOztxZJ0JVdPQG2REUUNVNFS1uu+vKCqaoCGJIs/334+6+knPXx12j3uu8t+89jePTx7F3/7m+7WK/H951IR+/NSEfswc5wxdO/xAPUqT0zSNSqWCzWrD6/VQqVQ4f/4cHk8DmqYgH4abVA8+Hy7BHi7DVpQSgqShqtWK7VjigO2tGKUSCJJCMpkgFIiwtbHLfjhOMpEjk00jShLvvf9LMmkdkXCSJ0+f4vO20dlej8VShyolafQ2sbg8w9VXznD/k2X8rU2YzHYCWwtcuzrK1OQUZ869SSCYYj9cZOzJHNH4BslwAa/LhdtpIrC7y/3HUxisOtr7Wrh39xEGg4XOtma0skqL18fk+BaFTIXe7hbMVoV0NkEssY+rwU46m0VncBCJFrA6rayuR5hbiDI5uUJs/wAo8a//5B1++L2LuN0SN3/1l5zqaceklhno9PHpe3/OgN+ClNumklgkE5tkc/VjHi9+yMzuE1q9Mlc7PQzbJTrrdHjNIuVimnQiiKOQpjGVYeXeNCZzK20nrtM7+jrXbvwZfv81mupHePnSy0hyhVKxgNFgQNM0GjwuLFYz42PPkGQdFquVze1dHj6Y4aObT7l/f56V5TC5vIAoGykrKsG9MJvbIZaWtsmk84g60Oml6oqMBiCiatUjiqAdNpERQDyanKugPQ+3ed4Jjs9WBV5ckv98/4HqNTWh/8ujJvTjp5bl/hVG4GgfVUF94UNW1ok0N3vx+/2HmeFqVfjwPF/86MNZqy7TFksVJFlClATKRYVUqsjKyjqaKqIzNONvdiAhYzXbKFcUYrEiY+PjOFz1ZLMVdgJbvPON60T206SzCXqHXqLRX8fP/irAyxd7WVkxsbK8iKwr8undx4yePMmpoRFS4SDnTg/y539xE1EEVVbx+vpw1cUwKEXe/6tP6B7oIINGWlIYXwggL4cZOX+Bn/31bQZO9GKzSDT43PScGuKX793l08fr/PAPL3Lzo7uY6uq5P72IUpbJ5zS2N7cZn5ukp6uT7u5WhEoGt1VGK2co7D2CaBpDZp9h8wr6pRTJGRXZYedVKcreX/4flNNRuiwSWryElohhkUWm4klclSKucA4xnqQkCqT1Op6Gdyk2m6kbbcOvGtCbUqjGRs5e+yEGh4vl1V2WFpd46/qbmKwyJV0Fu1mHWilTVsuIOoH+Ex3oDE7e+8V9MrnbIFTwt/lo9LlxlitUygKKUmR7Z590OoW9zsiFiydxuewkk/H/n703C5LzOs80n3P+NfelsvZ9A6qwgwB3UlxEUaYlWy0vbYXdbUfPzEXfTEdMzEzM3Vz1VU90xIQjpt3j6Onpnm7bsmhLaskiLVEkRRIkCJBYiK2qgAJqX7Mqs3L/9zMXWQApW2rbMR6RsuqJQEQhE1VIIP/83/Nt78eFCx+BEoxPjDE1NUwQORgy3pbqCCIlkGI/Gm/n79uJfPXJaHz/2lLyExn4g2a5Aw74++ZA0H/BCVWEJgVCCdAkQeDv17gjTENHk4JIKTRNw/d9pCaQ++s720MS7fSrZSZQSlKp1njz7fMsLOwyNDxO3E7Qam5w9+4CTt3Hb7WXxexWdshk8zzz/BlyHQVe+/4l3njtEppMsLNdZXFpnclDgwz0p6nt7FDIpHnyn3yFf/3738ZMdDM3u8Qjx4cYGexjdXebf/bfvcC/+tf/iYG+I1x4/wr/0//8G6zfvc327jyHT03R4w7x/b98nxs3N3HciNB3WVnZ4n/9l/+RocEBElaCj7bXWPd3+fbbrzC7eofN1U1U4JGNGzQbdaYmD3NqMsPh0T6ee/YMcU3n25VzNHcXiYk6zaurzJWTBM0NntZC3NmPkKGDBYz6Eil9/LqPkhJp6/SHPkHcYCyWod4KSTRr5OwcYSzFltC5QZNzKC7KgKmBPmI34tyZn6NUKZExejn33gzPPvc8yUwKFTURXsjcxgy7WzWmDh0nESvwzlvzvPIX72KacU6cOEWpvEEmlaFS22ZsfJSOfJpMOomUGkEQsri4yDtv/ZDevh4eeewMpx56jrWlTd584z1e+8FbnD17gsOHRyl05tA0jcDzCcMAW7Non+4+NrJpp9Q/Hm1s/177sevvIBI/4IC/P8TpZ146OCp/imQzad74L1//mf+991OdHzdKRQ92cAP7M8z7rmPi43Tqg+1fCJSS+39W4boed+8tcv79q0xMTWFYKV757vtkUhkmJ7s5+9AYpgnb603m5u4yMj5CSIulxXbK991zH/Lbv/urXL20xL17m3zhpaO8+MJxtle3+fDKLGFk8OiTx9ncafJv/907rCw1iNxN/tv/5gXC0GNwdIilpSrv/OgG2Y48TW+X3/unX+BHP3iNRC7OV7/yLKEb8d3vXeSLLz1GqAwuXLjGyy+/wpFjxxgeHkTYNlfOv8/C3Bzjw738i3/2j1n66H0eOz7B+Q/eoCuno/a2MOol4sLHAoTvsre5hHSqJEWEpQKCepnA9RFakjACTRf4voOhSWzLIIoCJBGWYdAQEc2UQahB3MywpRssOQFbusHFyjZXDYfxyQ7+6UiGwm6T771XIj/96+zspvFCk3hKMn1ogHp9j1qtRDqbQ4g42dQoH11ax2kKjh2dprMnS62xhVIBG2ubPPf840yM95KIW0ghCCOFChWmqeE4Puvr61y/fpemIxkZ72JsrB/HCbh66SqVah3Lsukf6GNwsINcJk0iZqNr7e55oQRK7Qu4DPcb8sL9K+s360l/AAAgAElEQVTjFbLtUbqPZ+HFgzD/YAXsz5L7Y4hCiB/b1PfTHpPyJ087/4c/fpnf/8P/+2f2ug/46xzU0D9lPt0u978eIcn9tahCgJQCTdPa29rExxu/1L5hSRRFBEGElJKlxS2Wl5eZmp6iXG6wvrZDIpliYnyC4eE0Pd15qntllpYXOHP2GBOH+ujqKjB5aJhEymL21gbbWzWkMHnyc2e5fu0WtmVw5Mgos7Ob6LpFs+kzONhBrV7n5o17RJGLFYszOjrF3MwdpqYyFDe36S504wd7hAT82m88z8LtVW5cvkVH2iYfs9kr7nJ8qosTU934O2vktAYTvdBV2yFfX2ZE7HC2y6erNUdy9yr2zmWypRmstY+Il+5glO4htu9CcZGwuIxdLWGWKyQaAWLXwXJjxPU8vgOWbqErQcKI4/suuqaQqm3qEvotDFPgBS0iXaeoIt4tbnCpVWdWhCxriqL00WybY0owEkuzUvOYXV+gs6+HI1MDTI/209xr4HkxnnruV8ln+1lbaXJ7pkgu18OxY1Nk8zaJlM7x48OcfXia4aE+Zm8ssrywjdPyaTZ91tc2WVpaY2u7TLPRIpVOc3hqHE1X3LmzwPzcJpVyg6npUaaOTZGIx6k3WqyubLC0sMXmRolqtQVSEUvoIKK2dU0kUUoRhtq+sO/Pt0ftaXepSaJIEUbhfqPm/qHgQNB/pnzSX6A9tRIRBAFhGP6Y78An1/n+VQ5q6J8+Byn3X1j2m5m4360OoH3czLQfLQk+Mae0P7cchiGaZjwYQ1taWmP21iIDIx384Ac/4vNfeJGevojNjZCtjS2GhnuRQvC9732PX/3KL9HbF8cP/LZtrKnT1ZXmyJFBlMpQ2vG5ce0mKI2rV5ZYWtpkdGyQi+/PcvnSLNtbkwx153npxdN889tvMn+vxPD4Hn2dh1mbv8NXv/Q4f/D7L/Po0ye4c+0qLz0zyW9+8Syv/eVf4u7OsTEzg62b1HP30MIKJ6xb1Oqb9JfjaMt1BsM6pOqocotwZQ8raBIYFpbvEDgNNEMHKYkCHyH09miXH4LQ0XSbyFIoqeMJhWlpaJGHU93Dsm06LAvX8dlVEa2URqBbGHEd37bZTlq8s77BppDsGUl2TZ2qphMzFEP5PIMdee5cuUkmkeXpycMMjo7yyJmX0EQHCJ1v/8VbfPPld9GCDnp7Jzg8IRH6HkOjJuPj/STi7dE1r+WzWyziNFtsb5W5evUqpqXz5BOPE4vFaNQb7O6UuXTpGlIaHDk6zZe/9AJ7ezWuXb3FN77xHRQmp04d59HHThOL+dRrAdvbaywubHL1o1k0qdPRkWdicpD+wQK6HiClJEIShe3oO1IuhmbhB+3ndN3AD4L2QVL7q9fqAT8L2p/r/dKalBiGged5GIZBFEV4nodpmgeHrc8wB4L+C8l+mvNBJ/L9XemfrL7sn8pF1HaLYz/1tv+s77dvxI16ne/9xSt84YWXOHfuIl/+8ucZHhvkRz+6jGGmiYIW3V0dfP8vX+XRRx9lYKCfCBfDiogigUBiGDqmJYlC6CjEOH3mEb7znT9jYHiSSqXKt755jiCArq48Tz51mq4uixdeVAwM5fiDf/tdFu71Mfx0kpnrRfpqNv/L//gC3/iPf8Jkp+Tiy/+KF586Q696h1hJMhjtUrx5h9srcZJSkPBdxO4W1esOcc8lYVjokSB0wXciYjGboFZB6SGaIUkaJqrpQSQwzIBIgKsCMGxCWjgiRAKuF1Ks18h3pAmyUIsJKn4TZRtUhEaQSSBjJoFULNfLOHaS7MNnSZlZ3rx5k0oMtESME93dfC4/wPvfeZ+tW2s4/hq5To/Eixrr+XFE7hTn3lvArdtkjTQbmy2cdJUTxycZGhmiuyeDCqBUKnNvfoGFxUUS8Qyjo+N84ZeeJp6QXL92jbv3rjM1dYjTRyaJx5OEQci9O+tcvHiNWzPXOHZimieeepgnnjrDjRt3OPfOFd58/SJDwz08/sRpDk0OMT4+DJHJbrHJ4sIqF96/Su2HZQqdBSYmBujIJ8jn85iGjabp+H6IpumgFEGo0KR+397ggJ8h7bJaOxt3X6zDMGR7exulFDs7O8TjcQYGBgiCAMMwDkT9M8qBoP+iIu5H6Pc7kT/5ARX8dYFvo1BomkRKSRgq7t1b5OjRY1RqFY4eO0Z3byd7lRLl8h6dXXE6u+LslfcIQ3jk0dOEYQTKAAUSizASxOIGmq7IFySXPrzDl4aP87V/8hz//v96BzToH+xnt9gim+ljd2eLzlwPKI8v//I0e7Uyf/T1b1MYbhJ2rPDR2o+IiSHGO++Rb7nEK0Vu/tF3MUMNIXTibpmxho9RjWNHBkGzSp9hQZgmpIoMIHAVpoxjGTqhr4gZEg8XEYT4rouFQIUhXiQJogilSQLlgCnQYxLNiNESBru2x1ZcIBNpRNKiVK9gmQLfCTH9Fl6tRjNhsuJ7bN/bRG008ft6Wc3qRBmTrNAYSSVQM0sUb21hOjadBuyt3GFnVnJZS+PkWuxW+jB8k6OHhnnsEYud0jpEe6QSXawuFZm9NYvntcjlMjz3zNP09HaAhDAURGHI6dPHmJgY5PKlKywvrDLYP0IqnWBgoI+pI1/m3sIy83e2mZ8tkcrE6OnJ8Tu//as0Wx6Li4t88P4c596+gWUbdHcnGRvrZexQN6ceOUSjUWdrY4tyucHqSgnJPTQdCh0F8h0pMpksqVSqvcrXD9E0gdT1nzLOdtBE9/83URSh6zrLy8ssLi7S2dnJvXv3CMOQ3t5eYrHYwXvwGeZA0H+hUW3Lzv30+8edyAD7tfS/8h0CQRi1G+hUBMVikZMnH+L8+cucfeQ4Qmisr5UwLZvV1Q2efvIIa6vrHDt+jFC1ECKOlBphFCKlj6J9MIiET2dXnkLXFomEhu+nSKZ10rlBuroEQyMDXHj3HtPTHRiawsCnGbi8+IVTzK9fZ37nBgs7P+RQn0PavUinDOjy6yQqFUxfoAU60o8wNZPQBS0K0PFR0kGELlGg4wkPJSSGpmNqiih0CAMXL9AwNIUixNUVeyLAzsZQRKSSGexEHF9FSEOCBmWnwVa9xa4tcVouuVZIZq9FjxuiSUHd9xC6j5CSktDwU3ECBGUvYLO1R6tgEQ8CxhNZ+pXE2C6T8CKSdpyg5RLLJNhcKJIfa1F3V+ko9HH82AQDvUk6CxZOs4tvfOMHvPrKezz22EMcO3mU7s4Usbje3pQHuGEdTTcxdIMoCMikkzz7uWeoVR1Wlna5M7PB1ct3SKUNEqkk3V09aIbF3l6R23MrVPYaBEFIImnw+JMnSaRsqs06WxtFLl9dZK98DcPUGB0b4PDkMIcOTSCEh++4VPYaFIsl5mbvUqtVSSRjHDp0iMGhfkzN/LEmrAN+Nggh8H0fTdOIouhB2n10dJS+vj4uXLiA53kAB+/PZ5gDQf+FRCIwf8pz7QKmePB19GPPKgWapqFUQKPVYnu7iOM4RKFBKp1me6PBhXO3GBzqIt0dZ6A7z93Z2/QdHUMFcXSz3RClSx2ERANank/guzSbLVZXlgk8WF9fo1mvMjlmcOvqbeKJGLO3ZkimGvQOvUiuwyYWs4jHSzzyxACvXZ9jB5ettS2uaXUeR5E20yRiHkYYEtcSOJFERBqGihGFLn7gYWoSRIgfVRBaHKUkxUodTzXo7kyhpIOKaQSaixmz6O7pIggdTD2BF0QETkC9UsdtBTSbDoET4AYhhh5jVM9S2tkhoblkUya1pkdDaXiJBLsioJETbOsuLS9FLRC4cR1bghX4qJQiktv0On0sbe9hJyUqqODoIYGewqg2cOaXGHhshJd+5wQiSFGPFFcuznP1xhUGBgf43PNnee/yOVp6gg2vwt7uGgsrS8yv3EJI0KXG2aPPcHzyFHE9jiEFmUySztM5zjw8Qnmvyt3FdfZqDrN3b+HUXDw3IJZKY9uSaqXF3MwK16/fIZVLYZoSqZnEkzlGJ46yt9fkww9X+OY3r5BKxnj0kaMcPzHA+HgXfUM9eCrAczRmZuZ45+IF/PdchoeHODQ+Tm9PJ6amEYWgSYMoCpFobYMjs+1ap2ntTI8iRMp2s92+q3zbIkGJB14L4iCX/19F7Y+mhmG7ObFQKPD222+zurpKo9FgYGCAVCpFFEVo2kGTw2eVA0E/4O/E/dO7YRgkEnGOHz/GtWtXKe76/OWrF1lfX6K7uxfH8XjyqUdoNB1cz0HXdYQM2zdmIfdvvu2fV600UZFFvR6SSfXx6vc+ZLtYpL+/i/fOv8Vvfe1XuHZ9gcnpMa7P3CT/RoZEQmd0ZAgv2uXCxXe4t/MRpD2ITHwyFFt1il5Av4hh6BA4ETEh8Zo1RBSgGYJW5NAQilCTaDkLFYGdsOkd7WTmzjxOVtJT6Mdw2l3pTt1nb8WlGdVp+LvgRkStCF2ZuF6ApwQukkhJGvUQXyg8P4kTtCgGiqovqUofmfWgN0PFcin7dap+lShKoTQFMsLQJYYmyFo2ZiDQ3QBbaHgEYAiiyEWTip2NdQpBndWVq1QbcWYXyvhBk86pFGauwbm7f8Fc8R5//r99AxmLCFUNzazjhbskEwaWtPjoxiuMDUzx4hNf4/Th5zAN8PEg0klnM5w4ld4vz6j9dbkChY4UgBJEkcDzQ1qew165xvr6Nnfm11m6NwfKZqivl/7eIcp728zM3OKDD94jmbQYGxtk+sgAfQOdnDx1iFMPHaJcLnPv7hKXPpzBMG/T1ZGlv7eb3p5upIzwAwfLirWb55QGWoQSAfK+1azY3wyHagu5+tir/oCfzv0u9vtiHYYhsViM559/nps3b7K8vMz09PSDdPxBhP7Z5UDQD/g7IaXcH20TSAmHpybRNJ1krsXayjbZfILJqR4mxsZJJWM0GmFbEARAgJCqbR0q2p2zQujsFMusre5ClEHXk7z11ru89OXHOXP2CN/+c4e+vixjk0/zp1//IWubklg8wdmHD7FXrnDnxjKrG2W0WBIVetiahek4SCtJ0QnwIpMmIa2oRdLwcWIumUQSH4WupUklUqAZuH5A6HtEYVu0dGWzu9yAkiRqOoS+g44F0sQBWsogCEKCEHyl0fQVgYJQSJxQ0YxcapFHzdWoeyGRHYKtiOcsekcylJRLKwqQmo7f8CEmwRQEIkBDogeQTcWQNRdZd4iHGqEQCC1ACwIwJK3mJs3tO/zglT9hN0jSUnFK4V3coEa5tg2aRDcy6N0xKo0Khi4o9Kcw7YhYrIyFjh5C4C3w3Tf+T4TwODX9DFGoYxhxwkCACtF1gPZCHSkMZFs12+OMMsQyIizbJp8yGezLceahI4SBYnenSrFYolL1Ke9laDkW/QMPAyGtVp35u2u89tq79PT0cfLEMY4cG+Hhhwu4JwJ2S3usLW7y/vuz6OIOhyZHGB7pIxYzkCpEiQDYXxRERKTC/dbNtqd82+DmYDnM3xZN03Bd90Fne7Va5eWXX+b06dN0d3czOTmJlPJBU9wBn00OBP2AvzVi3+5V1/X91JxGLGZz7Pg0Ld9Ff/IkYQhCBmhSEXoBQkiiKCAIQpQwiKJ2d71EokTbnOb8+x+yurKGFBafe+4sK2sz9HR2k00mCfwytUqR7t4YX/1Hn+PaRxcprhdpVMeZPtLLwFCW83fPc2Nxjdz0GDFjD1PusdsqsakUYTJOPCOJd6fIxnS8KEAPBFGg8FsB9b0WTr1Ko9pEixu4bkizWSIMTColB+mCbaWpOxCGGk0vROg2SJuWcKgLRTkIKbUiyo0GXqTR8HSsvEWUDchMdtHZ00suayOMFsXKIjW9htMMAJtGKwDNQJkagYwQUiGjkKRmYyPRPQdThUQRSKEhMNB9n0AL0RRUiov4Zo3ra/cwc0nMnCLSFb2dWYJQATXspM+wJtla26FR8Rka6AerCG6TXKwDv+Ehkrt8cOOPGB0ZJWWPE4U6hAZxy0RFPgiFrum4ftiepUdHKR9JiBKK0FcIaRMzDRzXxTBhZCTHQH8SgcT3FDu7Td6/eJ2V5UV6B7o489Bxnn/uSS6+f5Nvvvw6/+4Pi7zwhc/x8OPH6Ontpqeni6NHPUo7NT547xLnzp3j6WfOMjU9htQgjAJ03QBk269QqfZ2OHE/zd6+Xg/4r9M+WAus/R0A8XgcXdf50pe+xPXr11lcXGR1dZVcLncg5p9xDgT9gL8192fQgQcRehD4CAGGIWg266SSGVzHx/M94paFEu3Z9mq1TiabfNCG5wUhQmrMzt7F0CVnz0wTsxPUyi6WTJDNWFhWxJmzJyhuV5iYNOjtNpmanKRaKfP9V16lt+vL7Fa2MGMhnlNjZa5FxtApmGlkyqYcaxIm4gSlIpryKW4VqbUc9JqP70s8T+C7IGW7Ecirt3AjQRNBFNMoxgKKaofWXkQkJb5KUgmh5TsEeDj4NKVEZPOI3h7ivVnyQ93EdYGd6yDQdEJcdr0dSs0tolaJ0HbQZYjQbMrVkKIfYeUyhIZOoPnomsD1AyxhEnmCVssj0qK2kxuA1FGBwlcaZirJ4uI9xo+YHOmReLEGgadjxWw8bwclFPGkRkQdW9r0Z5NsbbiU1uYZGevBSoPwQ1LZkKQJ5fVV3nvvP/Hi5/4HNFLousL3XKT0kVpIEAaYegwV2W1TGKmhaC/rMXSdKGwR+GBb5n4aN8DU2114Rlyjx4zxa199DNd5jFs3F5mfvYnnKXKdKf75f/8iN66t8vYbM3z3u28xMtbHo48eZ3p6hEJHgl/5jadZXlxl5tZt5m7fZXx8nOHhQbIZe9/0qL0spt3IqQhVsB+x35/m0B5ctwcp4x/n/uz5/dT7/QzcoUOHmJ6eplQqsby8jOPsl84O/u8+sxwI+gF/BxRS3l+X2o7YTdNACAiigHQigRQBugFSxPebx2zGRseZuTlPriOLabXXsIahYn1ljTvz99ANnSNTE0wdHubajVVyHQLbMpBC0JErcGtuBt8PMYyI8YlREDrV6g5/8sevcvj4EANDBbKLdZJ9ozQ2NEpFRVNYyFada+UifmkLnCp2CE0PNC+GFxo0PI1QN2l6DgE+ZjWkFSrqSIpRi6bQwVDsNASpnIWWlOgDCWQhhozbZDr6SSY78eJdODJL2bWZqXg4NZ/a4i5ec4eUVSVnFxnKt7B8BxOTKNTxfJOqs4cWM3EJEGYcw5JEwkNE4DQ87GQndXePloBYykJ3FFHQ3mXuOAK35dEzkMcSCTIZi6blorkpdCvAt2rYttmue8oMprSQCjomulhfq3HncsCxUzlymRYxPYGtQ7LPo7Zzk7989Q8Y6n6GqckzxGJxIi1Epz3hEIVhO9WuxP7mvfCBR4Gm7TdaqrYBkRQCz/OQmkLK9l4AKQUxO+KhMwOcPj1GuVLjzp0VblxdwbRNfvkrp0EzuPjBVf7sm99hcuwop04eI5M26OlO8eIXf4m9vSLzd+aZmZ0lncpw+vQpuns6UAp838G07jvNKSLVrq9L0T64/ZRpuF94dP1jKbgv6hsbG+zs7DA9Pc3IyAiWZR0ciD7jHAj6AX8DPz6Pfv9G+fHn+f4NM2pv2hIKQ2svdInQUGHIoYkJvJbiP/z773Dq4RNIIVlYWEXTA3q6e/A9nxOnDuE6AQ8/Ms7CnRt4rQZCZchmU7itCBG6aCSwDRsp4YtffY6v/8nb9PccIsju8q23q1h6iXhPgT1NI8Cg4SS5vFunVytgVmDLc/E0SaTZNFyPZktQbVbwQpcQhd5S+EJgFRJ4SQ2tYJEbHKSn08QpdOKmshS9GntunnI1xK1I/LU483NbxC1BqbhCKtaJCAVnT0yhtHkyMZ18rInpbWI4NSQ6jdDADzQi30CaJtKOI7NxZELhOiEyjNgrVdiNJelNaNQyEuEahKGPHYQ0dR0tLTE6B/FiWZY2EuzUDTL9BSIiPLeCYfbgqxRR5KPZGjKykNQxpcZI/yRDnQnmZ27Qd2KcTHyH7rxJ5MYoaS6r83MUNwpUihVe+OLjOEGV4s4OvT09RFGcSAUI7HZkF7RHHVVoPLALbm/jiwgB3YihooAg9ImiAKUUhmEjpUUkIjIZmzOPHCIMdNbXStz46Ba3767zxBNn+eKLz/Ld77zJm2/9kGefe4yV1du89aOQyYlhjh87yZkzgttzC3zrz15jbHyYRx8/RS5v76fd24cKP3L3JyrEvhOaIIraJjb3L+8HArXfUPdJb3mlPm64a9Pulv/rj//88kmBvr/PQUqJbdu8/vrrXLt2jYmJCU6ePPlpvswD/hYcCPoBf2t+8qn8/rKW/UtJRUAIRKAiHE/DNiUj44PIRIYrV5ao1eqMjXZz6vQolz64xpmHTxIFGnasPQtbrzURKBQe6XQKtxHhuhHJlCTEo7jZQmgGjz55jGs3btA73YFBAiKHQKzTOdrN5madMBI0EoP8cGaDQa2XINjF8Yo4XoQTgJWRRFmTdKGPRM7C6zOwc334sSx+IktDaKxUDTZrFZqrCTZ3G+QzI7iVCoIs2zsQBibTA8dp1R1+53dPMzaSprXXwIi2sY0m5997FxXugNzDN12CyKQWhFQCkyYakW2gxQWaLtDNCE0HpRns7ta4uvARPR0FZLdNc6tJpkOST1o4hoHT0cO9WoySSrO3rFNSYK01KQwramUTp2bRbEp8X5CIedjJEobwScYsYladhC1RTorzr69z4nicotohbuSwzDSdiQTpmAStyNzCy1gxya1bt3jjnYDnnv49BvrOEIU6jYbffp+FD6qxPyIWgRRoUsMwjLbHgDSIlEA3THQpCEIfpXx0aRFFiijysSyNsdEOxkY+x8r6Jufe+4ggjPjqrz/L5Q9v8qM3zvPc80/w6KODzM3M8uqrS6STGYaH+/jqr7/E7Oxdvv/qW3R2Fjg8NUyhK4uhg6Eb7UtRKTSpQaRQoUDJ8BO+5O1LN3ogbPfF7X4qX3zC6OaTu9w/6d/w88v9qPv+1/cxTZPR0VHS6TSGYTzwdv9kNH/AZ4uDd+aAvyd0VKQQsn1TF/s3uihyef/CDXbLdaTWSaMGTiti/s4Ka6vbbG2vI4TNUnKdrkInA4P9pDMdWDELAMMw8IN2p3wYKWqVEp6jE3gho6MdrKxalDe2GewaYae1iJUOqUdV0h0pElqc9T2fokxRS1j0DGWJxbvo67KQVoLASuJbNpvVKjuaYDuwqLZ0lpdKtPwQz5EM909xYupFZi8v0mva9BoJfuUfH6XZVHw4u8Pc/DJjIxPM3LjG7somiXCdjdWb7JU/pLtzEyu2g+M38GS7e0Dh4woIlERqCTTbJjJCQt1B0xWppAUhZHLDRGtr7Io6g8fyHHk4RUe1RbDncnO1QaqQYXNrm5Iy0EUBXekETRevEhKXGRy/iR7qaEJhSQ89cNClRA91UmYHGiZOoGjV4lx6x2F65CFKfh1Th0a1he/exA99ZKKFnVCk0klu3VliYf7PeehEi+7uScrVMrValVhMb4ukiAjDkDCKsMwYyUQKgFgyST6fIZdNE4sZmGa7nu37PgAaJpEvEKodWfd2dfHllz7H2sYOMzOr5NKdfOnLA3xw8SoXL17gH/3qSzz+WBfl3SLFzSZXPrxOJCCTyVPabfCjNy/R29NJd3ee3r4C+UJs/4DYrvcjFWCglNyPTuGTWaf75aT7lqj3m8baz31yA+HPt5Df55Nb1T75WLFYxPd9giBga2uL0dHRgxn0zzgHgn7A38BfvWn95CJkFAVIoaFChdhPcTbrTf785e9x+qGjDA2P8ad/+kN6ewfp7xsllepmY3udXiPJ4aPjLNxd553zH7D4x39OJtZBq6lQWPiBIAhDUBHlUhUNnc58HJ2Q0FccPzrCn79yDj0QxKwErWCXUEbEzTiRWebkFx+juqgoFbfpOtXHRnObD+sbNBpVist7pPMjJJJjTB96jGN+nuWFKs+cHGKnuEu52GJzfYfYjoftSqp7TcrLu8zcvMvhQ6P0FLrI6AlqxTqWZnD10lWOHn4GtzeOL6Du1tGkhhcZ6LqGEBpCQRQIIk+gCwtNWIS6i7QVZiwE5ZCy4ugyID/cTUpVEYaibyxL565FY9EnWg2I690YpiLbMUZv5zha6KOnHPp7lhkayrG7E9FwXaTQMUSGpN1LzLCoVXcpFvcoFdMsLS7QlekmbMW5fmmHoF6is5DFtjOU9+q4Xpy1TYVlRSRTLoX+aW7M17hy8Q0efULxwi89ytghi1xGIpWP42kgaEflkcJxPJqNJpvr69ybn6dSqZGIJ+gb6Gd4eJC+niS2bRP4qj1+pvbT3WFIzDQYH+llsL+H5eUdrt24ztPPnMVtwh/+4X/mxeef46VfepKxcYXreDRbHsVijcV7KzQ3q9y7u8itm7dBKLp7M0xOjdLX300uk8GyDKKo3dR5/3qOopBIhaDkftlA2xe4+0L/43a0UaRoZyfEJ6L6H/+s/LzJ/ScPLUopBgcHmZ+f5+233+ZrX/sayWTyJ0byB3x2OFif+inzaa9P/dsh/sZfkWp3u0f75iOuG/Ktb32Pk8ePMzo+zL/5P/4fRocLDA3lsWOShrfD1uYO6xt7tFoWkVbmkceO0d2d587tReZv77CwUKa4WyeIPKaOTnDn9gZuyyMeg97ePOlkgnhSJxRN3rnyNqEVggkBEWEIvnQJkiYrWz4rG4LZBYM783karUOMDb3IWM8x0lEH05kTnOk7wbNHHyLZjPPsiVFeemqas0fHcCou/+L3nmdseBxbz1JvVOjpH+XJp4b4/AtjuIFLvd5ieGSYcr3EzL0LzC9cxfMqZBIaUoSIKESPdHwHgkijFYS0AkVoSURKRyQEZgLshEITTWK6wsQlY3rELIXUHZKWSYfRxcpdn+WtBBt7ae4UIxa3XEJls1HexhFVejpKFAop8vkUu7tLGEaLyA8p7zgU1yvsbZXRI4/OXMTzzwwxNNjg2HGbsX6N4a48kRNg2QIjEYEtOHL0BIfGxpq6GRcAACAASURBVAidDVIpnyjQ2dkVXL+2yY2bi8zOLtNstMjmcmTTFjFLw7Y14jGdbCZOoZBhbHyAqekJjp+cpqMrR7G4xeWrV5mZucPOTgXbsjAtiaYLpBZhmBBFTXRdwzZ18oU4fT09nD9/noiQxx57iAvnL7GyUqK/v5d4PE48YZPOGEwdGeL4scMUCt2kEmnCCO7ML3Llyix357dZX20we3OTre0dilsliltlPLftJ6AiCMOIMFSEIWiaJFIRURQCgjAK8X2fMAz3a80CRfQTx933dxj+7D6i/x8QQhAEwQNzmTAMkVKytrbG3Nwcx44dY2dnh6GhIUzT/Kn70A/Wp376HEToB/y90L55fWzysb6+RqNZ4/ipcS5+eIeTD53iiy88RjwhUUISIbh7b5u7y3eZmu5jZSHi1rVVrl5a5NSph+nrT7Kz2+DVV99A1/PoxiyLC0v88i+fpbS1jkLiBy5K0zhx6mFOXD/BOzNvIq0QgY+uaygEWhTy21/6CiOFs7z8rXNcuLjKI5PPUl5b57d/92vM37jEaE+et3/4Q7wTezQqDt/65hWOnpjE1zx2i5v8y//9zzhx6iyxfJ0jT9i8/aMLaFc7+d5rZSzZS9zqIqUixkbH2KqvUW21CJXH0lqLpB6j0JFGBHvgByipiIiIDBctrlCGwDBiWFJAoLBiSXRpY2OgKQ8VgednuXJNZ25zm60bS7iRzYK7icp1EOw1Wd6t0FRx3LUGi/MOg/0h+UyCWgW0KOLwRJpUPKR3UKOQLpBOWhhWW5huLPtsrCygGgK9lSYIJMJ0iEQcU+XZKa2RNTT68xI7U0Uqn3oljqYEhhaxcG+Hrc0y5969hhWPkc/nqDdq6LpOT083hc4CyWQMKX26OzNoWkBPTy+TExM0HcnG6gqXLs8QhT6Fjg46Ozvp7MgRj8cxYxCJAKlZpBKK3/y1L3Ph4mXKu3s89/wXuPD+Zb7+9df4/AtnGBvrwrJ1iEDTQ8Ymc4yMZnHdcUrlU2xslVhZ2aS8u4dQAivWiec4OG6VrW0Dzw9oNl10Q8cwTEzToNCRJZNLkskkSWeS7Rls2Xaf0/V2FiqK/E80lf18CPhP4pPZB13XqVarLCwsADAwMMDMzEy7nLIv9gd8NjkQ9AP+ntBBCaSUqAh2dkscPX6cMAhZXF7h+KmTxFKCyANpgPIU6biGU6uQS+l0nZ5kYX4HtxZw/Pgo8/MLNKoeQSA5enyYmbl7+E6Tb37zTVIJjSPHJogLmzAKUUKnq9CFiFwMZSCEJHRciDRypskLZx4mp42hvxjn8uv/hsZSHRnEeeVP3+WXXzqKKV2effY5yqUKv/JrT/LKd94kEUvQNdCP29L4429cY7N0iVL0ESMnNLL9Hhevr9JXGCGQJUJCRKnEE088wfkrF2lFEbYZYFgCVZWsr3jk0iZ23KLSqBMEOpHSMDUL3YjIZSXJDo2qV8E0YmiRjREVqO4Klje2kH6DpK+jlXbpG8piyCTmnsdKc5P0UArdrtCV0Kk3YmiNLpZLVXZbu7hVB9lI0duRIG1EzF6fp7a1Rzql0zHgYaVsbtxscfeWQ8ZOkzHA3UujJcvE85s4wTwyjOM0utomPOwiEil2vBJ2l0X3QJlfP/t5Kg0b7Dy1Vo2N9S0qdY943Ob1ty6TyXbQ3ZWjr7cLhYWhh+ih5NbGLJFuowmBaSbp6IijiFha2mLh7iaOWyOT7mBwqI90Lk4iaRKP2bzw/FPMzK0yO1Pkmede4J1z73L+wiyNls/Zs5MEKmxbwUYCJSRWTCOv23T1DDM9NUS10mSnuMPiwgbVWp2e3gLHjk1imIIgdAhDDdcJqNZaNBse9+6usrNTRBEwPj7O1NQhkqkYCgi8AE1j362uXYOWP4ee8fd93F3XRQjxYP95KpWi2Wzy9ttv09/f/2Bs7YDPLgcp90+Zn4+U+99MFEREqr1a1fND1tY3icVSpFNJfvD6u5w+c5hmvcX5t25x5842cze3yeZz1KoB25tNCl0JPvzgAk89c5KB4TSj4/1cv3mLi5dusbmzRd9wD48/8xT5rjF+9O6HvHHuA2puRKGvgIgp1krzXJ+/TMvbwzRiWFoMU1MELZeHx56iJzlAd2cPN68ucPf2CplYDztbVQYHu6lVqzz59FGWVhdRSmP6yCgX373GY4+fJJUpsLXlUhiyubt1ja3yIoHcJJmC7e0VHnn8BGcfOollS779re/SaDhEoUtXPoVBC0uXEOlUdh0gjhlL40Yh6BJhQWTt0tUfMTSWp1kPMY0kbt3g3q0yd2c2yWeG8FsOXn2dh86Mkuzs4N1bt1nyfbqm+8gNWuQ6mvTlHQ4NpnD2qgz358gmIz73xGG0qMr1K9fQ/DzPP/Min3/hKR596iStIGJ04gxDY4fxwgbxpKRaqYLKMzQywsi4hW5Viaf62S7rNEILleqld/IwHYNZirU9Gl6TnsFBcl0DJFJ5wmaLXDLLxPAIPYUCEyOTdKSz1HbrbKxuMzd7i0RSMj7ZwzPPn2L66BBdnQWk0Lh3d4H5+bvohkUu297XLjS4fvMWly9fY2lxnaWFTVqtFiPjvfQNdPLqq+d55NFnaLRcLl3+iFKpzOHDYyg83KCJYSoi1cQ2U/h+iG3rxOM2hUKeqSN9dHQUuPj+R8zN3kPTDfr6ekgm08TiMXp6Ouju7mR8Ypjjx6bp6upjdnaW1994nUa9SW9PH/G4jet5CCSa1jZciVTUNk8SPy8J9x832zFNE9/3SSQSxOPx/TFDg0qlwsTEBLFY7Kf+nIOU+6fPgaB/yvxDEXT2zSg8z8e0TFw/5MatOUYnhrh2Yx2hJZm7tcLIaA/JZIJYUrG2ukFlr8HVyzd4441L6EYaw+xme8fnjbcv8eG1D/jyb36eL/7aI3QOJlnanOHdy98n12tTcRzePn+VH7zzOq+9+wq3Fm9QdWv4UYtmM8RzfWxTETQFjTWdR44/BUrHsmwuX7pBobOXUqnBpasXWNvcQlomExNTLC6uMjbeQ8IucOf2MsOjBdZ2qwxPJ7l5dw5paJx8OGRgWNGRTzE/f4WtjU3yyX5SiQS3btzBqYWsL1XRQokmPGJxE1SGSjlJuWziRhpoJoZlUejuB5XEd7JUdpNsLBvM32piqDyFXB/b25vUSzUeOTnK4MgA3z53kVbaJn2oj3hnSDJZZygfMN1pcaony/PHp6G4RaIVYraaTE/0UshluHZ1jo3tNYr1Da7dvcW5yzd48/xFAiG5NjvD+OQUjz3xBK5bZGu5jlPS2VmDeqOTRKaXwYlh+gY78EUR09Yp77q03BSdPeMkU2kCr0VC90nGTZJxhfKbGEJgmzA2PERnIU9HIU3LaTJz6y63Zu+hEVAopBgZ7eDkyTEeOnOEeMyg2QpZWl6j3ioxMjLIsZNH6OntRNMky6vr3J5bYGOzyMBAHxc+eIdCVwcdHXnKlQqNpksylSaTThAGEVJIhHARwkcgiUKFbkjCQJDOWDx05jB9fd0s3F3n3Xdu0WhWSaWTmJaO1EL8wEMISKXiHDo0zujoCLVajY8++ohKpUYmnXpQV257yPMg8/7zEs3er53f7+jXNO3Bay+XyyQSCYQQDA4OPrB+/Un/tgNB//Q5EPRPmX8ogq5Uu+P3fu06nkyytLTK5pZDvRnx4QezvPD55znxUA+5QoJcJoGuhTitGqmUyaGjk6xslNiuNPjg2k1+8OZ7jB4ZohascvH6f+Hi1e9ye+ldNku32dxbpFxfw40aNFyXQIIfBGi6Tl9vP4mUIgobOI4iZnTRlzxCPtFDoauTzq4ObszepqMnz7NfPMVes4UbeRT3WtyZWUfTNS6cv8L4yBiri3u0PI+mGyNRMKm1HK7fuMnhowrTLtPVKSh0NtGEz0j/CL/1tcdZWLiMkkWSMYvKtkKESfZqFey4iRUXBMrFjUz8II3rZPCdPirbHWytmizdq+O5JrlsDt/x2FqtoivJ+FCOif4sd5e3WW4JzJ4BEj0JcpmA/rTBQ72DnOoo0BXUGaBCt3Qwqy3Wb9VZX6iRzcbJ9ttcnVnhwpUa12+3qPrgaiHL6zW2tmKsLAZUd23G+gcoL5epLofk7W5M0+X4sZMMDBxnePwoIXHS2W4ef/Jp8qkMXdkEUtSJZzRGJvvR7ZDi7ia9/Z20vCbxRAot1p777urN43gunR2DRL7Nwt0tbt68x95eHdO0sOMWXb1ZegZzDI12kUynaTotbt+eY2N9hd7eTh57/CSDg30kExmk3r7uVpZXaDYdNje2sawkW1tF0pkktmXvL5HRkUqiVAAyxPNdTMNCCo0ogmQywdBIH/0DGSrVBnNzt6ns1UkkksTiFooQFARhQDKZYHhoiK6uTjY3t7h27TrNZpPu7h4CP0BKieM66Lr2c5N+/2SEfr/DHaBWq/Huu+8yPz9PJpNhcnISIcRPHV07EPRPnwNB/5T5ByPo3DfZoL35SkjGxoa5N7/Nndu3mJ/dYHt3i2TG5t7dNWauzrO6tMWZh6Z44umH6B7so6U1+PZrf8p7H72FL5tsV++wWrwO+gaYO8TzHqG1R7ZX0D+pkenyaLSqEOi4TUV3Z5ZKucjkZIp4UuEHcbY3XX77K/+c9YVtBgb7SaVjVFu7vP7Wq/zW734BXY8zOtHPzOwMC3Pb7O1ENKtJzr97hZWVCleuFjl3bo6FlUUqlRqrS3vUyg16e7pJxBV9PRmkgqWVy+R711neuEy1tUA8GdDT0cX21h5dhSGWl4rE43E89rCzEblOm2y+A4VOqVzBtmPEEgnWlppsLNrUSxqHD/XS2bXJkSNJzhw+yeUbO5yf3UBPdGLGQjptl8mUxbGOAvmgjty9R2npBh0JyGWaTB3NsV1qsXA7TmSMkO7K0KBGkziR7EAz4yQzOn1DKRyviPJjSD/kd37zMH5lFi2s8eKvjJDry9ExMs1W2KSuhaz9v+y9129leXbv9/ntHE+OPMxk5a7q7unuyRp5Zq4s2DcJuA++D37wo/8WP/lF1zAM29eycSHJumGUPDM90sQOU9WpQheLLLKY0yFPDjvv7QdWVbcGsgFJM+i+rfoCBMgDEiAJrPPda63v+n47W4T4XL30Mr/1ja+jFzW+/4sf8edvfo8rL62SL1b5o3//H1i8dJmt3ROOB/uUqi1++cv7rC6/hMhsZHSqjRK243B8fMr6+i5HR126/Qm5fBnVlCmV89QaOS5fWiFfLPDg3gY//8kdZueaLC5VKJVcrl9b4MqlBe7cvo3IJPr9MZ1Oj+9970/5+je+jm1ZREGCLKkXynVFQ5Y0LtbeCUJc2BnLMuiGRmuuyWxrlt3dI77//R9hmhbNZpPJdIwsX5y0gcAwdJaWFigWi9y+/R57e7ssLS2RpimSJD8l9P88OvSL31l66qT3iUfA0dER+/v7zM7OIssyxeKFWFGIv91M5wWhf/Z4QeifMb4whP70qT4jRhIgCwiCkGvXZrn50lVWVue588EdfvbTjwgDGVuv02qusPukx49/9h7f+/Gf8v13/xijfsbL38hRnvNAPyaIepBGyEKhUMjh5CeYuQ66MSTvxrSaJpoIkVIJQ5Fo1E1GwwG66lKsuHROx/z2a78Lnowky5QrJRQSHm9+xO7OI1rNPI6h8C//6bfY3d3BzTtcurSMYScsXWkh2x696X0OjtfoD7vIqc75acrWxpDDJwrHe6BK85y1PQ72A3qDCZNgSppMGQ/PyLk52qd9WjMVJtMOnh/jBRmjacjE74DaY+lyHtVMGU8jFNWgWFRwcyN8fxtD9VlaKrDUKrN7doBUlXEKEYWsSwOPXBBxunnM3nof/HkKtTmwFXINF7lkcumVK8g5g73jCY83O9RnF7l8Y5YkCwiDlMjXSKKQSq2AogkIYL6RMdcMOD7cZ31zjJ6/gV2+wnF/SKoM6A4fc97Z43/9P/8dP3jnB/zpj/6A9e07jCZn/OQnP6aQd3njtdd58GCNlIRM2Jye9Al8j9CfMJ0M+Xjtfc7bEzTFoHs+pHs2JUtNuudT7j/8mL3dE0I/RhYqGQo5N8fNl5bIFxzuvPs+3U4P1zYxdR1LV3j5xhU6Zx0Wl1cYjkesrq7y/vv3CXywLRVFvlCvJ+nFw6csxRfEJMEzQVuaXpj/qKrGbGuGldVlHtx/wObmE5rNBqqiPu1kMyRxkRuez+e5du0yBweHPHjwgNnZGXTdQJHl50Y1/zmN3j9tMBOGIeVymaOjIwzD4NKlSyiK8qJD/xzjBaF/xvjCEPpTz3fpqetWmsVIUkaSpmi6TL1p86XXXmI4DgjDDEuVOTk9Q8gZxabJ4s0ie4PbSMUTFPeIcnPK3JxBq5nDMUAhZNzrI0ugSiolp0TRTdCVAfWyS7NWZdTvMR2NadaXmEw9ZA1sU2H/0SnffP3bbKxtcGl1BV2W2d66S86WKOUMHj98RNFxSIhZuapSXTjkfPxzjjp3mSRP+J1/1kTWhqxcLtOYs0mR6XcFYShztB+yud6lfRyy+VAwnbiEXoVoWiQYp2SJgpzmaJ9OcZw6spxH0SycokWlmaNQLDIa+px3jwmzNsVaRr4c4+YiHFMiDkbUiyUkWbBx/ATJjbm8WuFWyeHmTI5LDYvFpSpWoUBnlPHwyZjELKIUq4SGzFgMMcsC2dbodMbsbg8QksrSpTqWGSFlEq6TwwtHJHSxtYy19z/g1rVVTg8OiQYm7dMEtBpXrl8jTY9Ye/LvieKH1BoCSesgKX0q+Tw3ri/RrBfYefwxk8GAYOpx995t4nCMrECvu4+shtRnqjRbVRQpo1ypEoYhfuQjSYJcwcbJ5djf73LWHrK326XXnTD1uwgkWq06KysLTEceDx88QAKKhSK6qtGaneX4pE1rtslw1OXVV2/y85/eYe3+AaEPo8kIxzUxLAUJ6cLqVbpIactISJIYRVXIshhZudibz7RmSLOMD967z3gUUClX0EwFsvBp0IsMQrC8tESSJrzz9jtIkkS1Wn6eWpZxccf+LBfhb411zT69fL+ooYuP9FPf9Omfyfh1nsmlafp8l64oCkdHRxweHpKmKYeHhxiGwcLCApIk/X9av74g9M8eLwj9M8YXhdDh6S7ueYQlZGmCImsIkSFLEo7rsLIyRxR7qFnMyy8vs3y5weVXFtju3+P9zR+h5MaYTkAhB6YcU81DxY1pVlXKJZlC3sUfRYw7PjkjR7WQR1MSysUEJx/SPpoyHig0GmX6gz7V0gw7T3a5snwdJYMsnNJq5uh37/HwwZ/y+itV5mcEb/4/f8DRyT7FSkBjeYBdGlCu5tg5eEQQTwlDmfZ5n0k0ZRLFSKqOYWcYhouhqaSRgj8qIiUtBucKwVjH0otYqgMiRkgyg2FClKRMwlPQBYNJxuZGh143RDNkCmVQbR9ZTsjiKYWcQqWk06oWePR4l/ubxxi6w3y9RkMRlE0fQzkh0wYoNhTqNuXZObYO2jzaPWcUZaRaRkCPVO/g5hTaJxNOT2OCyGNhSaFaEXTOD6jXc1iGTOf0ANvMcX7mcevmFSTRpbm4RD+TedJ9TNffoDI74dVbOl+7VuKblyu8Plelv/mEXKBQVB3ah8c8/PgDnuxsMfUipqMR208ecXa6zfbRJrcf3KF9uoeblznt7JMrGtx65VXq5RaaJFhYrlEttbj30QMO9k5QdYVOx+PBvS1Gk4g4DVlanWVhfp4PP/iY4+NTGs0mVl7CsBTW1rbI5wr0B+f81n/xOidHI4SUESURd+/eY39vj0qljqbpSFJGmiZkZMiyipAyhPS0Y88yDN2gVqszNzfD/t4Zv/zlh9TqVVzXuiA3VSFJYiRJ0Gw0aTZb3Lv3gG53yvxCi5QESUikaXphRpM926unZFn8PEgmzTIycXHjngiexgwnPHtUvsCv7uR/PYT+6QnCs/H7/v4+vu+zubnJG2+8wXg8plqtYprmiw79c4wXhP4Z44tG6BdhbAJJSBdvkCjI4uI+XRKg6TKLi01c02Hv8RYbG1vcP3jMn9/+c6ZqB7eQoEhjCrZMXlcoaRk5KcRWInRtTNGVWGzNoSYyB1vHROOMek1HyOcUSibFUon22TmdM4/Z2hInx33snMzaw9u8fLnBu3/9h5jKAzT5Ia71hLx2TN1pc3M15vBkwnDQ4fHWHu3ziHt3j0nSAh/c7fD4kcTxscX6kx6d0YQgC/G9c1Q1QVcTeudtdFXGNFPC8BzHSRF4KGqEZoRomoJt5ShWCySygmw2Gfk27bMRSCkJIf3BkE63T0qKqad403Oq1ZhbL5epNSrs7g7oHE8RIxUl8cibLoZhMAnHDH2PceAzTTvU58pYuRLv/vIBZ90RppMjDEBg4QcJw3FAvxvQ7Z6wslqlWpYZd/ZYnKliWRIDz+Oo43N87rN+sM3Hx20+2L3LTz/6K3aP76FFElXdZiaB3PmAg9sb6EOFzvGYj+6tczr2UYtFVKeEl+p0gw6qGWFbBqMoIrElDnpHfPTobQ5O2ny8vc1fvvMTsHUUXefxgx2ybMrv/M6XuLRa5+HHh/QGY3KlHO3OmCdbXR49OqI/GPO1r7/Mzu4Ja+tbrKwuYzkWshLQPm1TKTZJ45ivf2OVh/c3uHF9mSvXlzg86PDBB3cJowTdtMmQEZJCkmak8bMzNA1JqAghI0kSliWxtDKHIqv86K9+Rhym1Gp1ksRHli9iYhVZwdRtlpcWeP/DDe4+eMTS4iKykgEXa6MkeuYDn5CJi3CbZ8lwiRCk4llvnl0I+nje1z8Ninlebb/W2n2Wh/5M5e55Hm+99RaapjE3N8fe3h7Xr1/HMIzntf6reEHonz1eEPpnjC8KoX9S4E9NL4V8EX5BfOFhLi7Gj3EaoyiCQsHm0sICaZqwOzxge/SYSXKGpYeUCwJbD8hpGlXLxcjAMVQsA1QpRpOhmBfMzrjIWczR/jm2WUVTDEw3otowaJ90GbYVakUbJkOuz2rM5k549fIIV35A0dpnJi9RkCO8sxPow9bBMW7O5uHmEfsnA/xU4eg0YeSDH7rEwiLTBdVWgUpVx1JsiCH0fGoVjcVlQaE8QpZ8NNkhiQQ5N0camRzvJkRhnuFEpj8NmYoUDAmn5ODmc2RCgHDQ1ApkGmHgo0gJrZrE3KwNWcjJySGrC3XmqgZZIjg/COmeJUyiDFSbOHMRis9wek4iJiytLpLEOnv7YzzPAilHZ9BD0gJMS2fUzzM4V2g15lmYrdI+2adWarIyew2RKrTbEqcnOY7Oe6CBbklUSjqNmoM3VnnnJ3usr8NmV+KhN6Z8c4Y9f8RYLTCIbEZZRGT1kQsSgRbjpwLFsvCMAKVm4tSbSIZOZsr4WsTjncdsr28RJymPH+/RPckg1bhy9RqFSoEgyUgSGSEMcrky45HP5sY+tpsjSiQ2t3bJuSaXV5cZD0JODnt0zofkXJM3vnyDD99/TK875utff5XlSwt0znt8dHeNvb1DVEXGcRwMXUcSytMJeIokcaGOB9I0olorc2nlEvt7JzzZ2iWXc8i7BSRJvshcl0FIMLs4w8npgP29I5rNEpIkkDIFWRFADE/38GRPE98kQfos/Q2BnF1k14nsk7ISvyHV/LP8c+AiEjeKcByH+fl5Xn75ZUqlEouLi8/P816I4j6/eEHonzG+KIQOnwjj/kaxi2exlE/dtBBEKUjIKKnE6VmbB/vrnAdn+MGIomMjiSm2EaFlEa2SQ97MMLUUXY4xVEHOklHlMYYypZhXKBfq3H/vEMcs0WrlCbxzLi/P0z48wzuJKCk2NxbyLFQ76Ol9GPfwB6eE/YBwMiINYvyRRX+o8GSnh5dKHHY6TGKdIJXJFJs0c0F2sewiqqSjywJbn1CtQrkIlpEhMsHpcQfDUFlanGE46JNECaVymSy1OOt4xHLKNIuYJBBiEwYKYajhOjVqtQoIA28qkCVBmk1olnUW5+ukccLBzgmqPKZaG9CaN2jO2DiuwcFRl/2DgE5XQ5CQz9XxvAg/9DFzgkrd4Lzf5+GjI4IgR5Ik5Ioxiqoy7Ar299rkSyqLy1X2NrfJhhMuLTTwvYA0K+DmXKYDHYUy8USl22lzdHZAezjkyFN44ivsJSpersbedErqqEwVn8SRGImYiaQTmwpqxSS3aqPXEnTNpxRJlE2dvCNTaqa4tYRETRj1I9rnPVRVxXaKBGHCwWGHwWjM0uIV5Myg3xkzGo7J5x3qzRK6YfFka5833/wh1WqF11+7SbffRdc1Hq1vUq2UefnlVXq9Lj/72S+Ybc1w9fol6o0yjm2x/WSHtY8fk6Yp5XL5+fpIklIQF/fosiyTximaajA3NwNSwi/fuY3necy0ZsiyhDSLyLIY3dRZWW0xHg7YXN9jfn4OVbm4bRdSBshkmYwQyvNMdgmBJAQiuzCluSBz8ZT0pd+4uC5JLiJlJUmi1+tx+/Ztdnd3URQFwzDQNO35jfoLQv984gWhf8b4IhH6s0L/NLFnXIzfBZCmEMUXpzGanCJERGOuSjc659/+p/8N2RRMRx6z9Tkc3cKQIiq2j6sG5A2ZvGVBNMZWQ2xVhjAkZ0mUyxKzMxXuv7cNoaBRskj9Hq2aBWOd9n7EyXEHQQ8pHRENQ7wR6IqFhMZwGpDKGWlUIIwMIjnlm9/5LopVJZQSEgwqtSKqBmTg9T2UJKZzdMa459Mo1dCES78tI1NnOixyfiShUiBvl9jc3KA3HCDrFn6cEYqYVE/xkilpIsjCjOl4iO+NEQjiMCWNPEb9Hv44ol6usrI6w9lpj15/QmuhQpYNsCyQlZBqrUS1XmIwOmFrY5fJWCafn8F0DUI6BLSxClCsVHi81qO9Jyjal1BlG83IMR1l7O/6+J5JszrP1toB50dTdCtPe9RjEoKbr9Ad9phEU6aRQSLnCVQT34Sx6pGZMkKvcNaLkVQLoyAo1FUa8yVKtYhKJUXER4STM+punm9cvcTlmQm62CWnjCgpNrakOhhl0wAAIABJREFUUXBVAk9mtnmJUrHFeDLG83QOjtqsPXrCj//65xiqhaOb5As5+pMJ9+8/4tr1Wb797dco5Kr8+Ee3OTo656Vbt1hfO+SNN27yozffZPVSkytXlnFsnTd/8As0TWFhvknOdVhdXkHXNG7fuc3u7j7lchXLMpHERcSqomhIQiEKQwxTIiOkUi4xP7fAnTt3ODtrMzs7g6IqCAkkFLLUZ7ZVo33c4913PuTqtdWnuawyz4bpzwRyAumpoFQ8zZV/6jT3jMwl8akp+6fa9l9j7SZJ8vxz0zSp1+sEQcCf/MmfsLe3x0svvfT/a//6gtA/e7wg9M8YXyRC/1X8amCFIEOWn74xxQmylJGIhF7U4+HhBkEWEHhjDvePMRWXnCaYr+qUDBVTknAUHZ0UOfEoWDYGKookiKMAw4hpNh0OnnTwhoJGyeHayjy5nMqDtQl9T2bqT2jW5yiWHUqlGkgREy8kxmXsCSbDMWf9IZNY56O1U+qzVylUZ/nw7nsEfg/d9CiXNXKmgaPpVEompiYwVEHnrMP2VpvQNzD0ElmSoushQdglikPckkOiTIhFRHlmBsVWyZU0Ll9pUK0puHbE8tI89UaBhCHFUoHWzArDSca99/ZwzByqYVyMzJUIJ6egYJHFERl9UjoUqxmzs1cJY4/Do0N6w96FeM8okWUZYQRIMn5fZfdhShoLJuERiqowHBicHAUcHh2SobK902YwChmEKf2xyjSK0HM6aBlJEjH1x0x8jySRyMIJK7MGc40SWdBByUbYkkdO8mlacL1q88Zinn+yVOafLbe47gris4cUg4RX5pZ5qblAK2eTU0JMLcOLNMqlBW7euEopV6NWL1MoOhiWia6qpHFEv9cl8EMMy8V1ynxw5xF7e8fcenmFl15e5M/+7K85OQowLJW33/op3/jGl9nd7jHTqlCuFFlcWOLB/fvs7e6jqRqWbdJs1Ll+4yppmvLw4zW2nxwgyyqGYaHIGmkKqpqRpj5CpCSJjGkYLC0vsru3x/b2Hpadw3ZskjhBkXUkBK3ZGnGUsvbwMfV6HYFKliUgRaRZxtPbuYukQri4X8+ypy16ihDZ05H7MyLPyLK/vUv+++LZ7vxZl35ycsIf/uEfIoTgq1/9Kl/72tdwXfdvjOd/FS8I/bPHC0L/jPFFJPS/OZLLPvn66UuZEChSClKCAPa7u7y98Q6GneJoPo4DmqIxPpviCIfFehVHSyEco0QSUiCwVAlFSMhpim3ZxOEUVZ1SKVbYuNeGwGJ5oUCtoeKlGpv7JySxyaDjYbkx00mPzmmHqS8zGIX0+wnH51OUXIPll36XtSceZ0OPiR/z8pcqvHJzgZmmoFSaYmgTknBEMD4n54CuxaTxlJmZKo5tMxz2iJJzqg0ZWY7RNIkgHpCIKWghHkPcoo3pCrqTbTBHyHbEQXsDxUrxU5+z3oCjkwFZauCPJdbu9ogih5gpphuTLwmkVEERProyRBIhUgYhHrkClEp5olDj7GzKoJdiqQ3CyGYwaXPt6ixKrDMedShWVcx8gpEDLw5QdZVas0y5VqQ38Aj9jDgbEwOnvR6pGpNKIW5RxtYirLSIGbloUYKtpbRqGbP1kJWawdV8hZovKB4PKXU6zE8SauMh1aTPomFSjWwmm8eEB0cEgwNkxrSWlomVKhNPYKoSrmYQhhG+51PI5VhdWaTVrNJqNsgXCiiKzPr6GkKGSq3O4VGPIIz5r/7r73Bw2GZpuUHeKXD33iNOTvvEKSwuNjBMlaXlBfJugSdPDvj4wTpn3RNkWWdutsXy0gKmYbC1dcD62g6mblMoOKRJQpykyLJ+0UkLGU1XabZmmXgpP/7p25RrFRw3d0GQaYyQI6qNInEqeLjxmEazjqJKZCJkEiZkQiOKQzIk0gxEdhFnmiYBQo7JkgtSF0Lik1C0Xy+hZ1n2PEktyzI0TaNcLpMkCR988AGdToeVlRVkWX5B6J9jvCD0zxhfREL/VfzNnbp4/lqWJaRCcDDY5xd3f4xQRkjJGZo1xXZsbq68wvovHxEPRyw1S6RjFUs2IRgjRzEii1CEhpSBZWj4/hQ3p1KwS7z39ha6puCWBhimzr37D3HMKp2TAK+bkcYTpqOYbgc6/Zij0xG+NsdX/8v/hsxtsXD9CgfnD3FKY9on95lMPmJ5WUJXPc5On6DJXVzLYtpTONga441DBAGtVhXXqdI7zzg+CIjDhFwegmSAH3oIWZDLF/DDkAgPxYbT/ojdo1N0y2Bzvcv5CViGRTGvE5xP+e1v3UJTTUJfZTKdYBQsig5IIqToqiTxFEU2iDyTVBmQpj6yIuPkFcoVG0lSeHi/zXQik8UWjUoVXRkhSzIn7Q6yLmO5KgIJbyKwbYGsDylVLUzXY/mqAUoEiopmQeCN0GObgpqnZhVxVQc9KxH1I6RQsNqsU9cjWsqQS05AVZbwts7YemvAwSPByc6YoAPTaZtmQaGUqqgTm/7RgJOzMUfjhK+88duszM4SjD2SGGQhkWYx/d45OcdC12RMUyHnWty8dZVut833f/BDcoU8/VHAn/zHN7l241Xa7QH/9Pe+hK6XWXu0z5OdfUaTKUuLC9iujq4bzM43MG2V4cDn3v33+ejDh+xsHzIYjKjVaqSpzB/90ffo9npUKy16vRH9XkD7bMjYizg66zGY+DiFEs3ZOX72zjs0GnUs12QcnrO+fY/7j+/xeG+Ln777Cx5uPuCkt8vAP6dUK6KrKpkcIyQJVVbxgwhVvfh7EU/P6YSMEDKfDl//dRH6swfuZ25xz8JYNjY26HQ6fPOb32RhYQHXdV9Yv37O8SI+9QV+o/jbMqckIchSEMhAhm4ayGpMsSFjKQ5BnDAYDfHEDl/79hz6IObOWx0uLRoIZ0zcD9GtIsKMmaZDhKqjOTYFs06QZQTJJi+9qrJ2d51K5TL5Uo+FGYnd7T0Wll/jg7cfcT6Q0aQA25EIUxmrtkpaeoWtkcVOZ5vSjAVWh1g+olRJuXZ1lsOdfR7cO0bXVFQlQhN7SIZDc7GAyGz8acTOzhbTsY5QoNZyGPUD9nbHxEofuyhhOQUmkwzTlomziOl4Qq1qcmOpSf+4x2//ziVENGTUbnN5YZbCyybba08oyTYbJ0NSUeLECCjIEqsrOkEc4E80VEWgyAZSVEXTPKJkQJJ1yDKNSrnC9ZsFNjc8Nh9EVAyXwWSPUdjDsm0m3Ygs8ck7OhoW/f6I1asar7xeZtxVufvLNsutWQylzZXrN8iiAX/xpz9gMl7FKnYxzcHTSFGPcBrx8OcnzJXKZI7Al8/JpxHzMy0qlRxnnsLmYZd3HndI5CnLdY9ruTy5aMLNpk0yr9I/GjM8fkJJMlhqztPph5x3e+i6IIsVAm9EpigEgGlaDPp9Lq2u8tLLt3j79i853H7M3lGPP/v+W5QKOXq9mO/+7iqvfOUyrZkS//GP3mRn64DF+WW+/JUFNFVhaWmRS5dWmUxvMhx4dM97DAYDznttyCR+6ztfptcZ8j/9L/87ZCYz83VMRwEywshHqCAUQX88YuvJFj/4yZ9jOAHj4JDBZIAXRKCk5AplfvzxAOMXCoWCxvLSLHMzi8yUl1lq3qRRmqHkVFDlHGQacZiQyhGqIj1V3F88E2dZciGo+zXg2bnapzv0wWDA7du3abVa3Llzh+vXr9NoNJBl+VP57y/wecOLDv0zxj+GDh3xiYwHuLivzSSQIE1gnAx588M/o9KMmW1IqJpG+7yH43oUnT5X5hqoYcrDd7cRIwdLmGSZR5xNiSIfpBRVc4kzgaSpaKZGp3tEztLY34pwczphnHDWSej2UxavrjBNJqxen6c8Z2JUcqwfnHNvcM6j9hbn0Sl77Yf4yQGFcsZ8I8fRzjmnBz6a5CJLAk0ykDJwXFhY0mnOSiwu14nijPEooNcfIisGaaqQZgp+CCJ1CDwVRbrIAQefMJqQM10KssYrqy0WXJl8NKYeJ0S7bUb32tSiItOph5dqhEGO85OQ4VBDl+fJOS1kNcOPB0iqBOmUNPOf5lobiKe3z1mq4pgV2qfnbDzeYm93gqob5HMyxbyEpWXoksSXX7tMo+Gy++SYvGPRmtGYb84SjSJMYWNELr39KbFvMvWnTKcRcRAznZ5hyBlFS6FkymjAsBcT+HkOenl2zhV+trXJqfARJZOgnBDnNKZxQtUt0Nk9xetGTDIdbfY6v/cv/zts3SZ49r8UErV6iVKxQBTGxFGIokgkSUaSRnh+iudlNFsNCsU8l69coj8YcnR6TrczYn39iDCK2Nka8q1vv4Ss2CwuV7n7wQb7+0eMR0McR8d1C9i2SaNZpDnTYGlpgaXlWZYXZ7h16xI3b97AtGwUDZav1Fm+1mIUdjjqbvBw+23Wdt5l72iNTnDCYe+AQI4xSg5mRcOqCLCmlGZsgjQkV3OZph0ebX5Mt98nDBMkLJ5sHLKzvUuhkCeXyz3PW5ck8XSP/myN9es7Y3tG6M/26KZpPt+ZP7O4rVQqaJp2UdIvVO6fS7wg9M8YX3hC/xSZZzw9bcsuOvdAZMjI+MmIH370F9SrIc3ChEpO52D/FNu1MfSUoh6wnFdY0HRO7p3z6P1j4kylUllGxAbeNERRdFIxJKWPpmj4k4wkTqhVZth41EMxLcLMoz+JmF92WVrOs3bvCaurlxgFPc5GY279k0vMragE4TH1isK1lTLj8x7dvSH9kynpVEGKbaTQRkoksmRCvWYxP+fSmrWZnSsznUwolQvEqcfhSZvROCaONVQ1jySbGIZEmk0ZDXtYmoQhC8JhQCVfYNweEPYz4lHG3voh2dTGUZY4Oh6y009RcnXCIKI/HTPoFNnZ9Dg9ntCYqaPoMmgRqTjAMEr4vk6UpMRZxHF7jJAcxoMpne4+lilRKTaYjjosztf5ra/e4PWXl6jmbbonR/jDlNnyLTbunXGw3eF4b4g3kDk7GPPk0S6dNpy3E5IUkjBPFOaYjFXSSCdNQDNldMMmjfJMhg694QhdNdnrSHSiGj46qW3gNnWKZYebSwsYiYTmujz0h0TmAhtbfUzT4Pr1qyyt1oCM/cNNzs5OaLVm0VSJo+N9giCmXK4xHo8J/BBZkrCMAsHU49XXFrhy6SqHB+vs7Z9xeDhgME6ZTCRCEprzOb76jVu0WmWOjs95651fcLB3SrPRIvAzTEMDAbISIcSFk5th25jliLubv+QP/9Mf8wf/4f/gZ3d+xL3Nt+gE2/jijHLLwawkWCWToZ9iF200N8bMhdh50IwIw1SZBD75ksnMnEuUnDMcH1Epu3zjq1+m3+/y3p27yLLFTKOGJPPU0+GZ49zF7fonFrGf/vi7dc9CCKIoQlXV5126qqo4jsNkMmFvbw9ZllleXgZ4Yf36OcYLQv+M8YUn9KfIgOSC0SG9EPgEIkNCwg8H/OjeX1ItTGg6Q1xT5aw9ZRLGNBp1igo0ojHJzjHXKgsUnTl2DhLef2+dcr6GoibIqo/pKITxAMdyGXUi2idTnEKMblbY3/cp18v4UReFgKtXGvQOZQbtFNNVGYxHrFydwe/3qJoWr1+5ihjFdHbPmKmW6Z8EyJEJiUwaxySxRxBMWFiYw7EdfA8OD/rcvv0hvcERvVEHO5fHdk00MyMRPrLqI8kBsiRhaAaJr5AFCkoGw3ZEOHLYP/AYTC0ko844TdgeneG7OU7IGARj4tjDyLsEoYws6Zyejnj4oE0SlpHlMip5wtRA1V0kOc/hyQDbdShXKhhGiGvJNBs6t15x+eprt/AGUzYe7NI9GjPpe/ROIj54Z4ut9QO8kYSprLK14XH/3i6kEkKWiTIVVc6ThnmypEAQ2qAaTAKbwVim09M4O8sgEZSLGi9dmaFRlNg99ej6ZXw/QUga/UmElkRcLTXpPTlCWAksFZi78l0uX/kK7bMuaw8f0R+MmV1o8PKta1iWzuPNdZIk5Or1a8iKzNl5B13Lo2sGSRIwmY5o1ooMeyM0JeS7332NQqFKp++h2gYnZz6d7oTNx2cgheRyOVZXWsy2rjMdBbz//kfoukaSZhiWThCmnA6Pee/hbf7Nv/sf+Df/1//ITz/8a879NpGQkSyJ+nwFI6+gOhJBFiCbProjUSpX6A16lGsmjhuS4VEt5jAMmTAIMUxozcsYdhfdmrK/f5/hYI9vff3rLMze4MdvfkS1ViCft7kwpLnQEzw7Cf2kuj7tEf93I/Qsy5BlmTAMAQiCgHa7ze///u9zfHzM1atXuXHjBvl8/kUe+uccL3boL/Abx/M+QggyJCQpAZGgphJCgGsXcUsVAv8AXdaQswg3ZzMdDPCikECVSKIEXU7xzw7JUeWV5SLHw5Tz3Q6T7Sk3v9wCAbEe4akphVIe82DCsD+kVLeYacoMJiEvXa4hhGDnYZvXX7vK22/dpfc4ww/hYGPAa69+h7nGJUpukbvDX/Ag2EJNbUhOSUjxs4hJ5hEwJUoj3t/cR95SmQx9FFkn8GV0JUUWKZrWQ5hTokhFDwyiUCZFAjTINLIM4iBGkg3i1OV0kAAG+XyZ7b19TF0jFAXCaYBuKxB7yKrOcByj2X1sYxY9qDEZH/PwYUivc7HX1fMd6i2Yn2tyehhhmiFpGNLIL1BdWSbw+4yGIdvbe3SP+5xu+6yfxoRTQapMUKwATfMolQsYlk99xiRNS8SBSRyoyFqGrCXYWkKcZIhQZRIYwBQ5LhBFKZ4YEUUj/GTCwBuRxQM6HDN32UIkKiMREgmdgtzi0YMR7eNz6maDl5Z/mze+9XsgNF69eZVut8/W1iEPPtomTaHRLPDdb7/OZDplf/+cLIuYbVWIY4nJ2CMLBXnX4vBol0Khgm2bbKxtU87l+OZXbnDv0Q5REJIhMB2bP/iDH1LIGdQrDi9dX+HGtWtUWiVOj9sct336d+5xMrjPB/ffZre9RqgMkC2dcqmIomkILQaR0u23sW2b+myDlCFROsCyQcp8TNPl8GCXK5fmKRYM4nhK2XUpWHn2d7tEU4WcI5B0geJo9IL7/PWdf8u3v/yv+dp3lljb2KHVqqJpCmkqyAQIYkDneVcunvXmf//d9rNxu2EYOI7DV77yFeI45vj4GNM0aTab//A3gxf4jeIFob/AbxZP5+1CfGJjeZFvdZEbfaF0h7yTJw5BNVQCP8DOaYSdlCi58LmWJRURR6SjDheGsh2aeYtYKHSHM7z5fx/y6u8s0rp+ja372xyfP6FYMjg67qEbgka9wP77OyjKPMsrTdYOBGHQZ2bOwIvLnD44x2svcan2r5hr3EDKEpolhTR8D9N0QTpgMB0yCVKmWQqqiiRKDEc6aeojIUhFjKpLxFmM6ehI6ZQ0HWMYJrIdI8UOk6lEfzrGkDWsnE0cy3TCiCgdo6gmaTLgbGLR8QRqLEg1DUkzGY77FK0C/jShXtWQJZXQG6DZCbVajdEwYhz1KOZLSGKG999q89PRGE1q0WxoBL0JHS0i9QacHp3Q6/RQZQ0p1UgjmXw5hBJEqcLUs0gTg5PjLnY4pt4qEcTQPogRWCRZgj+NkSQNofmozgDHlUkTicDz8X2FlBrxpMrUS9k+OCJv60i5OieDHVpzRRqVEpOJTllTOD3cZvmrtzgZjtjenTD84Y9oNBe4fu0G1WKF8hsVAj+k1++yvrbHx3c3cPMW3/jW6yRZzMf3DzjtdygWm2QiYTQKsO0KCRHdno9rO6RpgG3mWV2qs3fQ4/jkjMi3KRaaNKo18q7N5uaI3d1fUq3aGGZEKAb88Gd/wdbuBoqjUGzazMxVGXpTEtlDSDGG5aHIEvmqztlxxNHOgNVrBrJu4PkjHCcjUy1yK1fZfvyIL73WQDdlvEmb2cYSIixxsLXJt35riSQZoIiEJJ5wNnyPn98Lef2l/5Zo38EPE2TpmXWsRIaCEMnzAvtEo/IPE6s9E71Np1OCIODg4IBvfetbHB8fMxqNyOVyz7v0F/j84cXI/TPGP46R+0UHkSK48Mh4lsgmkWQpiRSyfvIRndEaSw0FKYNE1niy38Mt5KgqGVVfoB5NsLoZjFJ0SSWYjHAMA0s3KFWafPhBh9OjHBubT/jO776MZk+Zjqf4/hRDM2jWl9h+dAaxYHG+yTvv/py5hatsbO5Tri2wtPwtzk9CRr0U180RJxM+vP8u46TLyVmX4WSKrOhYVg5VNZBFgqpkGLqCaeooqkaSQSIpoKtMRcRUielLIQM5ZiR5ZI6CXskzTIYMolOyXIo755CZZwh9giLD7GwOy0xJY5MQyOUtXD2PrafUygFS0kcXCdVyipxkBF7MwpKDHx3y8f0eZ0cxWWQzHSQkcUrvxOJkW2Ht/gFnx0M6pxFpYBGO8yg0EZKDpMVkyhSnqCOpArQMoSS0uz2OD2OEMLCKIZl2QhiPUHUbIeskWUaUyISxjiTZmA5odkKQRoSpgqS6xJqBFxgMR4CU42wUMApHfOXmHGU1YzyAD9an/It//d9TnimysvIyu9snvHf7EdNJimmY2LaG6xpcurTI0vICg96Yd9+5w2jQ5WvfvEWjUeTwsMNoPKHWsEmETxRepKhJmYxhqPhTn0a9QaVc4PHGFhtre1y59AqD4RjHtVlYmWcaTFhb3+XB+sf8yV/+zyTWOZV5FykX0Zn6DMZglUzyNRU3n6KaCRDhuCmtloZtZZwcDigWCszNlRDZEMuEkmtSdE0GZ+fUi2XqFYU47FDNFxmdj1AR1GoKhpIhST66GXJ8vsOTrSnV4nUuLS+jPFW3X8zWJRDpRYgLXDjKCelph/53J/Usy8iyjDiO0TSN7e3t51avtVqN0WhEpVIhn8+/uEP/HOMFoX/G+MdB6HwijhMZEunzVLaMi9Z9v7vB1uFt5moyjqwQZSk7xyOErjDn2LSEDft9tNOYbJQRT5OLXXyWkcYRcQLtI5XDXZnly3PUF1LSbES33yNKII5M0iil7DY4Oehg50HRLfaPBqhmnv2Dcy5df5U3vvIq777/U773/T/m0c4a6092ebR1TBTbuLkWipJHUQxUSUNTk6fTBkGQQJAIUmEQZBGBFDJSYzxLwjMFqQmym4HlozgRTiXDrSigJkTJiOXLJvOLI+aaPrP1IWV3StnO0EWGmchUXJtyUZC3fUp2TE4zUNIQx9AxlAnj4T6mVGK+1iLx+0Ren2JBQTf6yHKAN8ogLSNnM+TdKoWSgZAFk9BnOJ0QpTpeaDP2NbwoAzlBNhIM20FScoRRwNgfEKcmhlYmVSbE0hlxnJIlBWQpRxx7JEkGSKimhqLHCH2En4aoSh4VgyAx6U4tJhOVztoJvbaHbBX57j//52SKzjiSmGtd5Utfeo1KtcJg0OXBxw84Pu4hCx0hJPIFlYXFOlevXKLf7/Pg/jpRlHH1eoNyOc/O7hGKquI47vNo8X63j6bKTIY+OUfl+o3L5Ao6d+7codVaYuPxEzq9LvMrTTa23+Peo9vobh4jn0OYMVoBCjWHVJ0yDSakkcC2VBwnxjYziq6FIcuU8xaGojPuRti6im1F5O0AQYeym2faU9CFiWOlWGaIIifMN1fZWdulWnSQswRbd8jElEK+xN6WRBIrXF1ZRkJCklTCKL6wVJaeWiw/b8+f2cn+w8buWZahqio7OzuMRiMAbNtmaWnpqQ3uC1Hc5xUvCP0zxhee0D/VMGRPzeKEyC5G7ijEZATEnI32uLv3NqstFwufOE057oaMooDFQok6BvLREOU0RkxVpFgjiy/ewuRMIvATHNPCtYqsre8SpFCpN/DTiHEQEUQJkT9GlyM0zWFv75zKbJ29o3OSpEL7bMxOe593P/grPt56l83DB2wf7ZGqJopeRXfyeFHC2PcIEhhOQ/zEQRglUs1lCvhKRGpGRFaCXNDJChoip+EUdYpFmUIpplaNqBQ8igWfuabJtbkSTSOF7ilfv1njy1dMbs5pfPlSidXaiBltSj7OSHopBc2lZJpUTI2CI1GwSph6AtkZS7NNTCHRPdikWQXHigjDU9IkIF8UaAZ4XoYXqkRxzHA6IFYEWk5BslNiNSKWBEMvYeonJJlMEEkgK0jqBMXwEYpJlhn4oYcXjhGSjGXlUJSUmA5kKarkIDKbNEtImKAoEZppIykRWeiRaRqBYZAFOmKq0uuFdLwpneCQ6kyOIFE52B9zdHrGyqUFFhYbzLRm0BSV7e0D1tY2ODw8wslZFIs2zZkmM806E2/K+vo6nj/m+o0lCgWXTnuAJCTyeZk0lkijFMd1CcIOWQr5XJ7W3Ax7h1u05uvsHxyyvb/JzsEjVCPGyClkaso4mKC6DqoTo7s+bi5H7CdMBgMMIVPJlbAkCV1KMeWEgqNRcvJsfHyfgqlSLeqYaoIlW1RyJbYerTNTqZK3dQw1xdZN8BR0YTNTc5FTgSJipMwiCowLS1kzYXlhnigRxKFAETKSIj8vr4vO/O9P6J/2ck/T9DmB37p1C13XWVlZeZ629sJY5vOLF4T+GeMLT+ifwrODGvEs8Tm5kPakkmCanvP2+l+x3LCxxRhV1TjoeJwOhywUKtSFRmmiEmyNYGpCoJNEghQVkWjESYDtehQqCpGwODuX/l/23qzHsuw803vWWnseznxOjJkRmRmZVVkTqSqyRLYkoiVKLdgyDDcacHcbBnzj32T0f7B80XJbargtkeJYxWINrCHHyMiIjOnM854nX0RWsSixDd9VgcwX2Dfn4tzt9ez1De9LmBg4TY/z8Ripp5hKw1AxSIfhCDItwW/s0L/UyHODBxcPCIsJmRpRGivMuqK1VaPW8bDqBfWuwmppaK6LMussSsUkjBiFAUbDpbnbxN90MeoldgM2Nhw2WwY7NUnPidhyIq6ZKTe9its9k4OWxre3Dd7e0PjuwS0e/ug+39je5aZvoi0f8s2bkoM9jf1dE13LeXh4xHqVUHc2sHQfTRkUZcKt/TtEqzU7mxmvXn+Dy/Njmh1Bs9VgvXSZTCM0Z41Zz4jLCZZQ+kcZAAAgAElEQVTpY+k3iVLJNOyjeSWZWFOoHNt3MSwNqTRWC8E6CjGtCN3UQQqEOcd0UoTYJI86xNEMZY6oNyVSWITBmrwMcXyFkg7xyqHIBVYtol2vs04h0Ff4rsLW6kilWEbw9HLJz3/5HqW24rWXv0eRa9y7f8j27i61mk+t4fDKK/vs7G7z9OiCn/74Ay4vxhzc3sevuWxtbbC/d5PpdMmPfvxzan6df/FHrzLoL/ng/V/w9pvfZbWeEqyndFs7TKZDtrY2mM2XOE6d+WKI14SPPvmIIFhRyQi3EdPYlNRaJkcXE4KwpNu9hW4U1Os57VqN+ZnF9HJBq1bQaSTUXJ0yD9nsKV65c5d7Hz3Et3zabgvHELhOyku3bvDTf/iAOwd3sK2KMg7p1DY4O5xx62aNaD3HMDRcu00Ur4jCgjjuU68btBoblKXC0HSQ+vMW1lX7SvyzzPT/f/r8AyDLsi+MZfr9PoeHh7zzzjv84Ac/II5jNjY2sG37RTjL11gvgP4V63cd6J9P3go+T2HLERTIQlAIKCtBKCqKYs0HT39Mw1ixazVIS5iECUeDBTd3O2xKjWZUED8ZUaWSvMghr1DCwNATDDmnqhS65ZErOBvmrMM6y6yO9BV5miHSijwvEBUYdZ1ffRIitDpJHpAmPrMgxrYaJFmBU7NpdWtoVoxSAaVaU4kAqadQS9FaKbVeQW/bordZQ2opWRYgKdlqt7nWqLNlCnbtght2yjfrFm832nyr2eMbfpdXNIeDUrIZJrTiHDuM2fBbfPreY2QesdG1WC0iqCqc9pK9OzZvvL5Dngo+/mhNqksqVVCru0wmAY7tEQVTFrMlza7LaBSSlBrXblvojkcc17DqOZ0tnziriPJzhNVHOQbjiSKON9GMFpqWUOQhEvDcCstQrNca86AkKQoqociL8mqC21yi9IokEaxWKYZVYbopml5SZBVFEeLWYzQ7YLUsWK4NbM9EGSlxmSHKJqXSMbycSoQow6ASJf/l//l7lmFOJW0On0xotixcz6coBZpR8erLN3nt1VeI0oIf/uinDMdTEODWbPb2drh9+4B7nz3i0YMjDu5s8Prrf8CDRw/Rdcn2RofhxQWdeo/pbEStbuN4Jv3BkMl8xWB8iGGmtLtQEhBnMQUZvU0fW+9ydrwmTSt8F9qNgt3rFrbtMD7PWS+g5lt4tYoynVHlS27uvM7Z0RhT92h1NSwDlEjodrY4ORqzs+1jGAGWbpIHKTI12N71MVSEqQOlR7qO8Tfh08e/Ym/7VXxni1KaSDQE5ZVz2/PbOdVvWyn7fD8dflt//XOnuLIsv+iPz+dzDg8PSdOUv/qrv2I0GrG1tYXjOC+A/jXWC6B/xfpdBzr8+ggpeZ4edXXqcOWPIckBIWM+PnuPKDjj7vVN1uGKSSJ5OFhwveOzqUk6uSB7OkQlGiQVpmFQljlamUIhKSqoVEVWmTw4ShjMTVaFw3AW0GjolPkMKK6msJWOZtqcXlxiOg2miwRpCaIswKrpuE0DzUlAD7DcCPQYzSxRFmALHEdh2xm1Rkm7U7C9KdhpwKato8I12WDIK40a393p8Wajxk6QsDkv0J8uyR6NiR8PCZ8OyIZL8nlMXqSYruDawRbDWcbhRcDeG6+zNgSFoxFWc4yaZPdOi+5NneEsYtgvqKomypRIMyMvKppNk/FsjrINMAVn/QlWTaOxIZkt5ygdtq5pNFqSNC+Rmsn2tS5FlTEZxSwm4Fh1pMpAC8iqENMx0A2FEDp54hBFBlGSYDkK3ayQeoXtmoRRQhKXSE1iegW6lROEAXFu4Zs1TD8jyAbkcYXSXQx/jTRzlJVS6yicek6r6xEtahw/HXJ2cUheZAwGfdI0o7fZQ6ChSYWuKzY2O1y/vk0UhHzy8WecnJygNMlGr8PtOzdoNn2Onpzy8NFDXn1tD991uDwb0qi7aLpgFQQ4dh1NE+zd2ub08gkP7n9Au2eTkaLbFoZjAzaT+RopJb1tHd0smA4SwmWO7wk6GxWdbo6k5OxoQRzobG00sMwSXeVsdbcZXV4yH87oNTYo84B20yGcx5Bn3LjeQYmEuuPw2Xuf8eob17HsFE2U2HqP4WhMd9ciDJesp4K9rdcwZevKLQ6BFOrqw1lAJT7fR/+yycyXn9+uL9u5CiFwHIfBYMC7777L6ekpr732Gpubm8ALY5mvs14A/SvW7zrQvzhbgOKLHbYKwZXblZKKvCioVMaT6UOOzu9ze69LUa2ZpPDhYZ+DzTodTbGlGWRHl8i1wKpM0jillDpCN1BmTi4rDMskU+B2t7mIFFt3XqN3bZtPP3lCs71DkJ0jFazCDGXorFeSVWQSlTlInSSHyk6wWiAM0N0YISpKTUcZOsoxUZ7AsnJ6lsI1Qhx9QNvos6kGvLVt8me3d/nzmy9jPAsYvnNMepiSPKs4uz8hCksKZaI1u7jXb1Ju7sD2LnPVoJ/VOJ5LVvI6Hx3DP/zqnMvK5ZOLOScreNSfM82nuFsFd+5sMLwI+PTDECFM7FpCu7vB6GKC39hkESyZLkdc33+Ns8tLMpmyfb3OdDllOFqzsbWF1zSJ0xWVTHnplU2aHY00lkwnGVWpgxJoVkpWxggtI89zarUWUiuI4pD1UqDpBlLLkEqgrALd0lGGYrFaYHsGpicRuiBaQRiUeDWF52cg58SZTVVKdEujNJaYzQU3bm+SBwaLeUgpYs4vxxyfLPnZTx/i+w1ef+MmSuaUZUieJdimze72Ji/dPcDQdP7h73/A6ek5tw9u02nX2dvfwbIM3v/Fp/iexvf+5E0GF2sm0wlvffsOP/7xP9LubKAZBX/9H/8PvLpGLkNqvQamX2M0X6I0l+3tHabTKbPpFM/32d7aQIqS02dH5FlJo1bHc322t7Y4Pztj+Czj4OYdpHGBrs3Z2XRI5zrj04I3XrqLrmY0fJeHn16wu7uH46W0LIuTe5d4NcXmpkNVzjCUxWio2L5moxk5l6cTnj2dsLFxE8fwEQjKsqAscxDFr2NWq89h/tud437bLfvzaNQkSQjDkE8++YRXXnkFpRRZlnFwcIBhGC9u6F9jvQD6V6zfdaADX6zWVM8PGEFJSYomNaiepzzJnFF4yS8ff0S7YyLUilGc8uhiyq12g4ZU3PRriMsxag16pZNmMcozwCqQVkhR5RhKJy0LMt3jaFDy5GJGHC6hLFiuE9BipAFJmRBFGnleZzDKCEOfKDFJuSrHmw2DtIwoKagoKZRGoevkliQ3c0yZ4ckEUwa0jJSXmxZv9zZ52WnQCiPsWQLDDC3UePpgxGwi8Xs3wO+wqBQPz9Z89jTh8ajk55+e8v6DSz45vOCzwws+/PQp41DjwUnMw/OS87XNJ6cJizyjkDFBuMBSUw5ubTHvR5weZpiqx3Ie0ek0mcxH2K5PWbV5dhLR3dhjPE+ZTBM6nTtQtnl6NMHxWrTabYIgot8f02w2uHajjdQTNN2nKE0qKbGcOko3qLSYIDlHMwpsx0I3BWmSk8SCSqiryXg7oFIrNN1mvYRgVWKbDRodH0PGZElEgcLSJApBFpeAQVolKHdNa0tjdHlBlSlct0YpTOIiJQhD3n//l1yczQnnJXWvieOY6NpV1ChAr7fBa6++Rhxn/PCHP2IyXVCr19ja2uLu3X0efPaE46fnvPraPo5n8s47H/MHb71KGK84OR/w8cNfEVVrvJbHKl8QlRm1eo9VsGA+ntJs+OhKMR5OCYOAra0utZpPsEpYLtZIUWLogt3dDdI04tnpCZYh2Gi3qDsObb9HOA3JVwV7u008LyJNS97/8CF3bl/Hkwm9WofB5ZyNbgfPSsnClMGpZO+6i+9L1kHEZFZxdJSwtbmLbdlIKVCqosgTlFCI31gr+6/fzD8H8+e38zzPv7B+XS6XBEHAW2+9xfb2NpeXl3S7XUzT/CfxyL/WC6B/9XoB9K9Yv+tAr37LL6IqqUSBwrhyGpMaWZmxSOe88+k7aGaO6cAgyHl6uWSz5tDQNPY8G20yQSagVwph5EivRFn5VelWEziOiXQ9+iuNcVhjFIBnC/71//gmAQH3Hw/ILZd1ERKnDkmiEQUlZbzBulDEBFiNOrpnUaglIBF6TCYhlRBrIEyJLSSmKHB1wd2tDt+otdgNNcrjkOXxisGjFedPhkSrJWVesVyveHZxwfHJmOE0I1nrZKEkW+eIvGKjZXP3VhffKDFVQWezg2ZaoPuM5iXSdjANiVkZ9Jo9DAWalvLyy9sEKxgPJAiNLDOo8JnPDebTFnm2yfHxAt2qMRxmzOYZVJK8KBkOIqLAoNFoU6QOs7GkrEq62zaVCigF+LUeJSD1ks62jbIDShJKSpSRoTQNw7II4zklVxPQUsvQzRLTrGGoLaIoJi1GeF6IphmUhU+R6FhegmkZlFpOlEVUqmBru0bdqNN/NqUsK0qpMPwcyy0oK3jy+JSLZzPiqGS5XOF6HpbtohsmWV6i6Yper8P1vWsMhwMOHx+TZQW1hsft23vISvLJrx6hG4pvvPkyh48vQNMZry/56S/fobFVAyNHcyRCKpIkxzQktmUSruekcUK7tU2RV0wnA5SZs3t9B1EJlrMVq8UCz9FpdVPaTY/xeYxIbHr1XZqexu62zunjQ4qkoNc12dpqMh6tWM8Cduo2Xd/is1+dUXMdPCVwqibnp2M2W9BqKEpK5oHNcFBn58Yt6vUWZVEhZYmSEiHkr/vpX4D8twP4yyX2z0EOV1PuSimePn3Kz3/+c+7du8dLL73EjRs3vii3vwD611MvgP4V63cd6PDrvGU+X1t7fiMQhaJCUQpJKQSJTPjgwS+YzC/wPJdJWHE2CujUTXquzTVLo5FHFKsEXSgqPUG4Es0xwcopVYVTs0kcnZlySNw2kaXT6OQU7hN237jLxVTn/aOnlJpLlptEQYLMDbIgJ1AWuVZQWhYYYNgJQqYIJcmlTiIEsaoQusCqKmxZ0HIkb3Tr9CYLyk/PCe8vKZcG50cJp4+XlCkUxZUHuqlVeHqD5UIhEfQ2NJxaQLsrsbSAPJ6jlQEUIVIHt2aB51PhEaUBZdhidKoxHF6g8g6m5uF7Bnfu3iIvJcuV5Bc/P0FW11kHOmf9MXEukaqH5bqsonP2D7ZZLAJMS9DdaLNeKh58uiRcGoxHC0aDBCHamLaiVm8x7Of4DZ2MmHWU0Nvept3zMNySSkIlQ3Q7xbCsqyn3dUGRWbhOnYIYqS2xPCBfEoQFrl/DsUoqTRBGGkmxRvPWSAvCNZiaDcsm4SKi2dJZZxHLeEqj3kAIHd2MCZNLhv0pUWCxXucE0ZJmu4ZlGxRljlQSwzDZ3d2lt9Hh6OkRP/nxO/R6G9y6ucfOTo/LwZwPPvyEv/jLP8L0FH/3w39gFIwxPA1pRDQbddarGNs00DWd5XyBbeqYosd4tEJoMxr1OstlyXS6ZqPbodP20Cg4ffqMhucjKo297euMLk4YPJtw96UNNH3C9maLwfGK9SSk12qyu32NX713n65dp2ZlhEubYHWBJwUOG1xePMMUK7ptA81VjJcGP/jhJW9+919Sq7fQNEVZFVctLGU8L7d/+d3755Pv/xTIn1c5siyjLEvCMOTy8pL9/X0ajQZhGLKzs4OmaS+MZb7GegH0r1i/60D/8rFRlc9DJKRCoKgkV8vpZUWmcoSmc3xyzP3jR6yrlMtFTIhB3S2p2yV1C240TZIkRRcl1B1io0Jv2OAqtLoO3ZJyq8bSEqS+RVG3WHkxK2HSDwqihuByqvP0acFykVBkgihdkhU5/cQC5VGpAtf3ETJCVgXCCCkMRagkpaZQVYESCZat8EXKHdeheRliHwWsLyVRqPP+wzHP4opMr8izAltvQWVSmRF2Z82ckmdzgdNwMc0lSoJjCKoyJwkFQnRYhQ5n/YpFDKVmEkcR13ab9DbbDA5LfvLDZ5wfaQzOMpKoYrme8e0/epXD4/vYNejsCKzaCrc5I5GX7N3qMFses3u9xWIZUcmca7c1/KYgl3M29wWWX/H06ZjRQBIEAtfvMp2lNFpNPL/G0WEfv9ai0TPRbQ2vYZOUAWg6ppNiOCVlabBeWHhuDc1coVtTLFdAqVhMMoQUuI0Qw5mjaQbrVUUlShzfwLU3yaYVk1GENFL8bojSLabTOW6tolJLkiJmsYoYjfsEQUiWaTx+dEacFLSa/pU1aVGiSwNdmty+vc/O7hY/+sefsFqFbG5tcPPOLpoq+ev/829pNbcYhHPeuf8eta6FUy+ZzcfU6y5xHJPFFjVXEUc5mirY7LUY9zPiEmobAt0UPDu9wLFrbHS7dJp1zo8H1BwPGPHtb72EBnz004/ZqNWwWLHV8Bgch0RhjixW2Gj0DwdseT3IbC7u9bm5ucE6HlFMU6y8pNWpoSzFaAlPBnN+8O67XE51Oq1tGqaJkAIlBXmpEFJQUiCEeu4C97y3/vyt/DLQPw9nKYoCTdNQSrFcLhkMBrzxxhu0221OT0/Z3t7GcZyrf3hxQ/9a6gXQv2L9rgP9n+o3EqEqgagUVXm1wlYpSVJE/ODdH5LKmFWWkwqNjifx9Zy2qThoOlR5gU6J8nUKp8JqOSRmgahD2dJYOzpJrc5FnlHU6izIWORNzqcao+WYqhD88dvfZ9yfgRCs4ohlUhDEgkqYNLoeQbLCq1kg12RVgjBMSk2jQKIriSnBkyV1kXPH9WnNMuR5QDSrWOY+v3wSsMzqkPpYoompLESRoEkNoRwarQaWZzBbJqwDG9vqkmc+s5nLeGQznMC6BKOX4W3B9kGDesMljxWi7GAaDo7ZJssygnXIciYIFi6n/XNe/eZNknLFOk4pREVaFmBmLNchumkxnI3o7fSYrsYE0RrDDXDqJXFSUG953L67SVGFTEYJSWJQZA6zaYZQGZtbbcbTEet1hGW6GKZOq+1gWAVltcQ0PAzdR8iKdTBF1w10QyIosR0Nx3VYByFpVmLbNZQGml6RZDlRFLK91eTVgy79/gPyXJGkFl5Dw6s5BKsUiU2j0cA0c7I8ZDobYFqCjc0WSRZzfjZjOl5hWjpeTUeaJWVV4Xo+e/vXGAwv+fjjXyEqwcHtWxzcvcvDR/f5ux//HbEZ4zYlRZnSavokYQilQJMVyyDE0HWqUmc5j3BqFXG2ZLZc4tgu21sd5pMxURjg+4pOryQKE86P17iOwe2bDTp+m/sfPKNu1TBFiaULPv3gIflSsdnwiUcF01PFbsdkejmiysA2EpIFzC5SanWPvILHx0NuvvIqD06e8IufzTn8ZMGkv0TXdGzbQtM1yqqiLCRVeQV5IfLPXz5+G9B//X5eld8Nw+Do6IgHDx4wnU7Z2tpid3cXIcQLY5mvsV4A/SvW7xvQq899rYREVAJRXYWvVAAKNEPjJx++QyBiCt1gnWZsNnRcG1yt4HrbRDMUmpaTGTmRFqHaBnMRUXUk1YbN2rKomh0enI5Iyhr3j0cs0oo4z5iPhth6xvf/9HVWywv6/SVRJjGbFg27RlHoSKsiI0XTLaSxppI5UtcphCSrQNMUWlVgGeCLnFu1OhuFRnK+YB1IUs3ng8dLRkGLSniklSQqDcLcIMwdoqxBHBhUqYUUHUYTxfAyYbmoiHNJSYbTKmntGaT6BL0ZsYwL5ouExbxgMa4hzRl7t02++a0et270GI3O8D2J0BucnoyxzD1mU8linaNbXfLMwDDaJEWJZmtMF2t0s4Zh2kymU/y6i9QUR8fH6JZBd6OO7QAqxW8IDEvy+MGEKDSpORsIbMbTKZblkOUxmpPQ7Fho1hqhTzHtAtepMxqGlJmJbbtIlVKpFY2GT5pIVnMNQYVlF/h1hagyVtMFRTCj0VC4nkcUGKyDAs3I6fQssjQjDNYobU27Z1BWOZf9c+I0Rtc9mvU2hq5x//4DxqM1rVYP3ZJQlWjKZO/6NTZ6HR48POTe/Qfs3drn2rVtfvrpuzwaH9HoulhWThIt0IWOLk2ieAnCo6xKkiSkXndJ0jmVAMOwmM9mVEXF7s4Wmp4yHA0wDcn2dg/fczl+fIZvNnn19i6eWXD08ZRyLWjXC5pWnYe/uMAvNuk0PY4fHWLlFo6qc/L0gu3eFtkqp1i49Do+65Vgslpx8M0OSaXju28Qr00++fARlxdjhsMhjmPQajfIs4oiz68+qEQJXyq9/9Mb9udQF+LKL2I+nzMYDJhOp7z11lssFguazSa6rr8A+tdYL4D+Fev3DejA8156RVkVV1/8Qjw/awosw+Z0esm903vork1apjTrYOoJlszYazsYpqLMA8I8IdUSCluQmjqZC7lnskg15ouc2SAgGRvUVBO9jEjnl/zb/+YvITuhWbvk5p7H4GLBZJyQ5ik1rULTDXJR4TV80qyg1oAoW6M0kDoUSqJpOooCg4KGFOw4FrW0JO0viENJUjmMEo2n04BcpQgzQJg5pVAkhaISElVVVGmE0iNqDZPt3h7TyYzetodVWxFyTqovsLxd+qOUp+cJzfYWhmMwnpyxsWWxf6Dj2hXvv/ePvPV2j5deUXgtg7OzCY8f9YmyFNdtM5/n1Bu3mE1jTMdivpxTa7aJ4jVxknJ99zWePDlDN+Ha7k36gzFllVBvKVBrsnJGb9Nh//odhsOI/vmcYFVhyC7jQUGz0yCrRoRRjO2YdDeubuh5ntNpt4jXiiwxcJ0GSTYBtcT1wTRMgjmYWoNGs8L3MlzLYX6Z0r8csLVTx69rJAkspjl5HtBuG1hWRRiNWSzOqdV9HNfn0eEh4+GURtOnqmL+7M++x3gy552fv0+Zg+NYeK5BHMb4fotbBzcoqPjf/sN/YGtrh59/9ksG+YK8WFHzYbvbZXBxgULhmFucjs6pN5qYliJN5rRaGygpWS7X1H2XLC0ZDEc0Ow26mzs8ObzAsiy6mzntlsEHPzmh6frc2nfZ7bR474f3MQqXjl3nVvt1HnxwTKdbp9twuPfxiK32Hk+PLtEdBxPJyaOU9oZPFldMVjMOXttiHbkEkcu//5/+V/7vv32HLFW4tsHh4TOKvGJ7p4vl6Fc3dBRQfvEC/rpS9vyX53MuZVkihGA2m5GmKW+99RZ/8zd/Q5Zl3Lp1C8/zfmNn/ct6AfSvXi+A/hXr9xHoV9noz/vpXO3MVpSUZY6lDDKr5Bf33qUsc4QGDS/HMnMcWbJT01CaQqQJcVySVSWV6bKMNTJVkJQQLCBbCGpZi/mjJXcaXYxFwCs7PRaX9/jmyy9x/uQZq0lGGucUsUnT26Rp5xi2JEwlQndBq9BsQVrOEWRolqRUOkLT0ESFp2vUdeiaGg0k4eWE9TRFaB6x4fF0MkHT11zbzGgYa2oqoWWm1KwVneaCzuaCdjvEcwJMI6DhO8yXcybTBVJrYjh7XA40lqs2nd19omqBtOZ849sWi9WQ2WTBk0dP+MY3ruE4c4RaoLsR1240wZjjtRRRHjJfTYgzRVEZjKYLvHqHwXCOaduAwWV/yPbOPpNxDELQ2fCYL8akacFmr02eVgTBDNMdsLMvEURUpUaZNlhMNBbLFMdtoEmfIlNkWYhppjSaGlIl+J5OnETM5yGO62HZJZWY4XpgW4J4lRKtwNAUlhlS93UszWM0CEDmtDoWpnLJI0UQzDG0gnaji6m7LJcjEGt6Gy5JsuTo8BFVoTg9HbK/f41XXt1nPLnk8eNDptMVrudgWRqVkrS7bV795je598kn/PCjnxE4BTf22kTrGVkQc3DjGqv1mjDUqXVsgjABBJahUWYlfs3GtxwW8wBNU9SbLeaLglzmvHRnl9nkgvU8pFGrsXfNZjYaMr+MaPsm1ze73PvFkGgEvkypmRqPD09xzB5W1ePs9ISNazscHc/p1reZT1MsJyENFat1yLWDBqOpYL5y2dx6nU5zh163jWM4eF6NxXJOVaVsbXWRz+EtxK/NZq6A/Jtra59Puud5juu6pGnKzs4ON27cQCnF1tbWixv611wvgP4V6/cL6ILq84S1qkLKK6vKKs+RAoQUyALwND49/pjZfIwyBK6XomkRRpXRcySaUKg0JwkK1mFGicdknpGTkeU52QpkrLCSEjGvKC5iespD5iXRMuLR/QuGzySffTgjy0AUBvEyxjcL0FJWsc0iKJF2gVASr1GSZitKClKpITUTDbA0gavleKqkLiVyFUKmX/VbN2qUTZ/LwQyt1HGkjllJtCpGlwGolELkGLJEy9cYekheLHDqDlmhc3FZIK1dZusCv73N4cUxnT2beneFbq2wzDazUcKN/SadXkpV5lBa5HJGxppCJBRqxbVbNTATKj3nYnSO6ZmkZUacaFR5mygwWCxywqBASIPJbE4UFWiay2IekoQF9bpFGoeMhyGu3aDVvIKEYRX0tjzWQcqzpwFJVNFsuuhaRhLHKCExdDDNmHZPR6qK6TjG1B0MUyDEAqUF1OqSJM5JghJd6FhmiVQljquxWK4oM0WtZuJ5Alm6zIYlVWbhezUsS1KWCcv5Et93cDzJxcVT1quU87M5WQrf/aM/YnO7x2K14sHDR5ye96nVfCzHwnJttnptfnbvF9wfH+Loks1uDa1MuDwfUZU663jO6cWSRrOOEBCuFjQaJtE6I40Smo02q2VMlBa0NjyCPCBajLl1s4ula4wvx8gy5LU7d1hcZJw+GrC/3caWFScPzxFRTNMvSDPFw/tT9lo7BEFGpjRWU0WZGhSFJGeCKjWytGBrr83Reco8SxnNQm7u/QGuY6PLgp2da2R5wmqxoChyNjfbCMovVdw/b3v9+pb9eTCLEFe+EFJKWq0W8/kc27bZ39//IpzlhbHM11cvgP4V6/cL6FdzOVKAfF7iE1zt+yIVUl6FgmhSUVHyk4/ewWxLcj1CmiWWA7bSsKIKFZYsJgFZYrBcpqRRThJAsTZYTVNkZSCERHkuHz645Gwh+OjxkP7c4MnZkrS0MesOi0WI51voWskijJG2Q0md+VJQYFEZGlZLsEhPSbdy6bYAACAASURBVEoFmFBIdM0AmWGaGqZrUBmKyjWJiMisjEU44daBh65bHJ+uaLctdCUpn/vPV1QoXaKJCkto8HzPPcw0hNFkGMHTQczWzh7DxZTNOwaNzRihQmRVUrdsPLPJeDDD99skqUFe2hQo0DWchsu9w0+Jy5Ra10G6MFunlDgs4zlxIrA8F8P2kJqiu9VhvF5Q6zkkZUm/v8Br9FivHCbPYnRRJ14JloOSdK3hdzboh0MsJ+Hl220qLWbwzCRZGTh6g5rZpMpz0mSCUiVOLaTWLGnWPJ4dj2jUdymL/Cp5TwTUGhJNaIwGGa5tY9olmplR81pMhwl5llGvQ1XN6bbbjPohcVCyveOgjDmO5XF2HGPZGp3dhEUw5MH9c56ejfns5Ixrt25z9+4eN25dI9c0/q//8vdohoMyJPf77/KjT/4zi9WKJF0TL2dsdmp0NtpcjKdcTtZYbo1sreEZJq2mwXw6xTRtuh2TNF5j6AaTxZJRsKB7sE1WpQyerrjb3eB6R/LZ40MQJm//4V3iYczJr0641nOwU5vhswQR5+zUapSrnOnphFu7Ozx+fIbubRGtYnSnxqx/SbvRYh4t2Nnqcn6aYG5Z3Hv/lL/8i3/LeDalKub02h1GowteunOHv/1P/5lvvfnm8z56RZomKKlRllcRSVQlVVVSFBlKXrnOaUqSpSk//tGP+eDDD3n8+DG+79NsNgFerK19jfUC6F+xfp+ALsSXn+eRLV9ynRIIyqqiEgXSkDy5OGa0GiGMjIoCUyrq0sGKdYpFxXKQEq001quSy8s1YagxnZYsFpLZQnI2ijl8NmAZugzGoHke/cUF7a3rLMIAZekIXSNOUmxHJ05j0tTGsJtM5wvywicrE4z6iqIqKUqdvCrI8qsVvDhNyQtI4oIoKtCtOk69idmqYXV1Mjy2Dzp0t2E1CtCrFKMSKDI8ShwBmahIbYtYWgTSI8RiGisGQUHl6pwPQrxeA1kTLIIFYZAzvFxw/GjC+dmYRw/6DC9jPv7kkE/vn/DRh2eMF1PCSOC5O1z0J6BylF7R7jpIVVGve+R5gm6ELKcL0jSiECuEaBIlFc36Lra9gW64bO3YaNoKS6u4tb+P1AX99YR1skKJnOlgyWpd0dps8d3vvsTRoz6XT3OKRKEkdNpdFqsLKmJAoTDodTc4OZqz072JEAmaHqHrFZpUOI7GeDBC03wQOYYd0mqZxHHMapFi2w1MQ9FsOSRRzsX5HMto4NUUrY2K5TJgOVXU6zUa3ZLx4oLRLObp02cs5zHnw3OOLz7gpP+Av/3B/84//Ow/8eFn79PbrHFw0GG/0yBapEwWgtNpzCKvSFSFpbdpNXzC1ZiySOm225T5nCyL2d/f57I/pBSCQtcZLUKavQathkf/aR/fbbL/8h4PHnyGKTTeunuDeDHn6H6fju3RqzlcHE3JY0HT0lkFCfNYsNHdYt3vo6wm0TwjywSuWWO1mlDfbnC+cvFbOZdLyd0/+HOWgxm7Oz3ufXqPuy/fZrWa8u03v8Uv33+P/f1d8rQAKoqyQGna88CkAimuBj2vAA9lWfHs5ITT01P+5HvfQ0rJ0dERu7u76Lr+winua6wXQP+K9fsE9P8vfX5IXE3bVijNoLO5yfsff8hsNcMxHSxMrNyimEqW/YThacJ4UNLv5ywChzCuodn7OPWX2dn/Drff+DM2r73JeF6RAoUOfqfJZBXh1m1WUY7j1wjjgKJQ2I5FlutUlUTTXbLEJs1LSitB6AZxWRCVOVkuKUpBnOss1iXrRGOeKC6DlIs0ZSIkqdOkaAINA6fbYWO/Ra3VALNkFa0QFFSaZGlbDJTBs0XJ6VxwNAk4HE5ZVCWJVTKLK/rLBYP5hMvBkGF/wnqRUWQVWZFgGA2m0xKpORiOS1qWhEnJ4eMFyyUYtsHefo/NjRq+t0CwwHd1drY9BAs2mh5Crqk1bYbjC/K8TpAuma9SppOMNFljGB7rcUYeFdQ3GqRmRYbJbvc6qmpyMYxpd3oMBx9jaS2iuY4mTdbrBatlhW35SClJ0xxNSHS9oNdzeXL4gJpvYmgaWRRh6BWWkeK7bdbLmDTWsCyNUg7wPYGuGgwuQpSSGPYaz9cw9QbTUUJVFThuheuZCGC1miG1jEbXApVwcXHORx/c55333qUyQt54e483vnMHt+MxmC14eHTCyemAwfCSPIcsFqxXAqlcWq0OhZiyjM+4vtfh9rUdmr7BzYNbnD4bkYQZe3u7zJYjZuEaYTSYxxmaU9Bpu5xfDohFRm+7y8nDCXpccXOnQTQpmBytcaSk7hhcHK/QcxvfcZiOA5Sw6bRt+pcLqsqE2CYOIQGUWWdcxGy/5PPRx1P++E//HWcnl7x06zr9fp/r13s8fXLGzrUOSipOnp7Q2+hiGBZCVleVkee2xkJc2TPneYGUiqqsCMOYo6fHRFHIbDZD13W2t7exLOu/WnZ/AfSvXi+A/hXrBdD/uWSloJS0mh22dna5/9lD5v010TBldZ6STky0vIOl7VKr3eHVN/6cf/kX/zN//t/+L7z9x/+aN7/7P/Dy699n78Yfsnfzu2xsb3Fy8YjL8QjNbOE2LBarFbptMpstqNfbLBcJtuMj9JT1KsY3r1ECcVSyym2km1NYMZUyWcUJGRoVLnFpsCw0pqlgWkC/LDhJE86LNc+SBcfJmn4Zs7ah6hrUD3bp3r2Bf7DH0DT5YLzg48GSo3nCONeZ5ArZsajqisEqwOs5ZFLh+h38hkPNN7EME9MuqTVdXN/AsD3itMKsKbo7LkpXOG6NIjOYjiIcq2R/28dzlvh2xfD0iIP9DbJ4Rq+l49gZml7S3axYrQq6uw6TcMTuzj5latA/SzGqOtPZimfDAWFZMZjH9M8KpmO4fXAHspg0VqRxhqYEaRzgeTV8d4fhhaCqLEy7oCpCijxCN1fc2Nvn8nSCZzZp+BZ5uoJSYeoCy6qocpf5COo1HyUzNA02NnpMJwPKMqDRMClEQLvdZHCxZrlIqTd13FqJZTrMpxFCJDTqEilyyiJH03Rm85Cj8z4f/Oo+v/jwM85mZ1SeyeVCkZsOjucjSXHMAq2IyZZz3npll2+9uokMI3a729x7+BnvfXCMpTmcHj9BipD9m5vMw5DpqmDj5k1GiwlFtWDzWof+NGSyCNnf20cFBucPn/GN2zdZDZacnYyoOTZm7jE6j/Ati6Zv8exZnzQ32ahbpFlGp7HBZT8ikQ0mU5PGjsvS0tC1TTS9hmbVubw44+0/fI333n3Av/iTN/iPf/0jbhz0KAvBj378c65dv4btmkglyMsCpeRVmV0av1kpK+Ho6RFlWXB2dsbLL7/M3t7eFz32F0D/euoF0L9ivQD6b6osS6ocNKGhkGx2N3n14Bu8tH2XN/a/yXfu/jHf/85/x7/6/r/hz77/b/j29/57br/xJ3R3X8X2N9DsDkqvU+JSlhZCanQ3OvR6Wzw9veRseI5Ts8kLHWXmpGVBWdnojmKxijCsFEPaBDMdtAyUwXLlgxOQGityDIQhSIEoK8glJLIkFZBqkoUQjMuMpRRMi4JRAXMhuQhXnAUrLuOY8yDhs/mc90YTHkxDctfD6DWh7qFaDmWtQtQUfreD7mc4vs18scKxdPy6QlMltmNRUuDXTTzfpShA6QrNivB8D8NMaLUVju4zG67Y7CjqboUtNQyhc/L4mD/45g1Ojx9z6/pLrFZn9Nou82WfwXKB1/NZZ31u3fKwrAFObcH+a1uERYRm2miOIsl0HL0JecCg/4DVasH2jsf+tU2yREfX6pyeX2C7TarKZTad4No6hnQIw4Qsm9Cst1jOFniugecIynyJ1FdIVeJ7LmWhuDxJca0eupGAGuL7GlEAk/GS3oaDkHM2um3ClWA2Sqg5HXRd4LmSYLEmXi/Y2TLx/IIkLRlcRJyf5yxmBpbTRXkFmbakvmFTxjnrZYjrG7z6xgbfeG2Dmzt1ymFAK5WUE/jJzz7j40fHBEFKshLs77W4+3KLy8tjhGoxmRnEIuf23X3CZc5qWdDY7JIUOWcXJ7z5+m1MFM8Oj7l57Rrz6ZrlZUbPaGHrIcfHA0yvQ931CYdzaq5GEgTEVYzttAkCnXGoQLeZRDqN7Q6/evCIb735pxyfnqEKi1anxsP7F/yrv3qb9955QLvdZXNzi5/87GfkRUmt1sA0bUAgpaIoyueQrkjSGN93uXPnDvP5nO985zscHByQZRmmaQIvnOK+rnoB9K9YL4D+mxJCQl6hCYWuNDSh0fa73Nl9hdf2XueVa2+w0znAcdpUqk4pPQp0kAYIDSk1eD6lq+SVzWxZQrt9jf3bL3E+OuHktI/bcBiNFzg1jzBboQwdqSBPwNJNskSwjjJM1yEIBMpVZPqaXAiUWYEOBRmVlpGJiEKmxHlCKqD4f9l7sx/J0vNO7zn7vsQekWvt1V3dTZFsiRRFkcOxhYFtGb4wjPk7bQNzMRiMLHOGM6JI9sbq6tqyKtfIjH05++6L6G6SEi98YbEGcD6Jk5GZCORFAuc8+X3f+/5eWSWvoJZlSkmhFBTqUsLQPC6vVrx4NWYR1zS2hzUYIjkWjS2htkRkt0Y0VSpJwPJFRAUMp8R3XKhkZFmk3XERJRnL1qiaDMOUsG2T7bKkN3TQNBHXFaHZYBs6eVxyc3lBt21hm2AqGtfnKZZb49o2X/32nI8+OmK1uKA3OORiEZJJFbIhMb1+y8jzCZOcqJYwnBbLxZbVKuAHf/UYz45puTl2t0BTFa5PS8J1gW2LbIOEorCpGpmiyun1fNIoJ9rWmFoPgRJBCLAtidU0x1BaOK5MVq53Y3XLBMfZ9a8vZgVirWNaNY2wxXF1RElgOUtRpRaSUuG3QRQU5jc1AhKWm+K0UqqsJFxqSIqG3c6wvYI0NllvFbaJiCK0sRWD+4c133sy4jsPRgwcg8XFmGQ1o4inLIOUN+cBKA2OJTG5TBkMH/Oz//4jfL+k7cpYpsfZ1Q2ZFJILMVGcsTcYIYow32zxWi5RXHBzfcp3vnuEhMHFiyseHz0imoVEqw0tdYAut5gvljiGiKcKrBcBpuMhiAl5UmN7PfIiZRlXVJLOKlqjeMcIjcewv89kMqHIwXE9Tl5f0u35ROEuUXA42mOziXn95oIoTDANC03Vd8dddQ1Cg6xIVHWBoqgcHh5hWRaiKKIoyrctbrdC/2+TW6G/Y26F/scQdgNcAAUZBQlVkJFFBQUFUZBBUCiRKRFpBPH3qu1AaGoEoUIUdn21TS0gygq+1+H7f/aXLGcL3p6/od3ZY7ZeYDjKtz3KeSGCWGFaCkkCVZOSNwrIJV7LQFQq0CpURQWhRpQqGiFFkEpURUFCRCoE6kIgR6BCpYzAU3yWVytuLqaokoVttZBVA8N30Fs6GBmVEqF7NaomYegaDTGWA4YZoWgJLafPerGl15MR1QDXURHIEaUYz1OQG5F4U9DvGruzaFNGEhMczWJyOSaKI/yOiu1I1GXBcjVl/6BDlW6Jlmvu3HF5+dUEyeyyKRJkCfa6JteT3WjV7aJmGqxJEWn5RxwNZSx1wfV4DLLO0b6IYzfM55fYjkcUlWzWMUcPj6mJCIIVbb+DJMrM50vafpe6rqDJMDWXcCWRRgL37h2SpRGKUlNVEZKS4Hs2m0VCuM7ptFo0wgrbrZFFi82qIs/B8zU0TcB2JE7fXlGV4Ho6rt1C1wxuxhv8lo2q5vhdAaE0CdYm/dZjvvfgIVYwQxcrguASUd7gt0xOXl0TbiQcy8NwDOpM5frtmqK2qRUJzRD4qx88wtMFzt6+5fDeIy6nWxp8Rn2X2fQVo94BophycXnO3tExUSny7MvP+NkPPkYTSj7/zy/Y9/fQNZHTZzd0nA4A61WEa7WQmpLr1ZyR71LVObUk45kKiZxTyxmF2GIRWbi9fbKgRhAFDNNjuVpjGA6ioJNlJa7rUJawXmfYVpdtUPCb33zJcrGh3+1hGAoARZEjiTK7TjYRWZa+rW+RJOlbqe9eoWl2gTWfP70V+rvmVujvmFuh/xOEXehMIzQg/l41PAJiDeK3KVd8O71N/F2gLBIgCuJu8nojfD2BvaEsCyRRRNcsnnz4AadnMxbBlEaWKGqFRi4o6wpZk0kT4euRoSVVo6NaBXGaYFoaplWQNxWqouA5LqqkYugmhuog1RK6oKCrGoreIEoZYp0hVw2+4rG4WqNWBh2vhaqqCFqNZJSgxBhuiaJVqCpYZo1hNWiqgCStcR0FQ68RxQxdU7kZT3j8vkNTb7GtDFVMsFWFUU9lOg5peRqykOC5AqaRoMoSZa6j6w7T2ZLe0MB0BDabEN3KORx5TC6mdNs+pqXy4vQGxekSRUssy0WQVYJNQssdcHI6o3dwn0ePPK5enSDlBYOuy/hqjCiA5e9S4Mqq4e6DfQw/483FS1RVxzb2uRkH2I6F67o8//IcRfKQJQlVjdA02K5qmsLG9QxUPaauasTGQFICHA/iOCTYZvi+Q1EtkbUa13GYTW9I4wrPUVC0nOFei/UiIViDbfrIeoJpmVyczvDMPpZZ02vXlGFOGYvEYczbsy1JqtMIOn7LZn51hZIrNJFGnIjIck1HszkeHHA2vmGbb9g/1Ig3Mz58eEwchDw7OcHsDJgspwgoHB8ecvn2OY/uHGNoOtfTK0YHXeSm4ebtmJ/+6x+hCQIvP3nJfa+L0Ri8Pl/gtVwcQ+bqfMye26XKdRbhlnbXIwm25GZFp6uTiTnewYirICXDRRZ0FMnkajxhf3RMUzcs5itc10UA1uuAXn/Aar0hyzPu3LlDEof8+tefEmwSDMPANm1EQUGSBKCirktEUdyNs/16tOrvp8XtprkJfPb0S/7xVujvlFuhv2Nuhf6HNLscOWqhAfFreTfsct/5+iEi1ECDSP2tzEUExObrWdDC7iffmL+qSiRRhKZBFhVEVefBww/5/MsvWQUxRSNgexZhNKdBo2kUqjrFcCHNYTg0oVHJ4wzdqCiahiIvqUoNahVVcVElG8ew8C0TT5fxNWhrAn1DZ6i1qNYFwXSNZ2hoBshWhWJXKGaGZlaYhoomyxiyhqE2WHqDqSkIzQZL11GlAtMoafkOTaUSLZeMhiYtt0AVC4Qixzd1KFXKNGDQN7DsBoENml5QZh5BKFM3OhURw6FDGhtkeYhrVDiqzfVpyId/dofzyZZ5JGN4BoubJYPeHuFW4Gq6wu8dYLs6uhqgNRnhfEHLr3BcgcUyxnYFTCcnjJfE2ZreSMbyZKbjCCqbIMhZr2IkSWF/eEgc5TQ1VFWMbjTIksx8MqfIStotH1WTaEiRlRCEFS3fRBJM5tMMx+6iahIIAZ2OhlirbIMliiKgqhWm6ZLEKqtlhumlOH6Kb3vMrmKaMsc0tvQ7GY4usX/0AfPQYrxaESciyVbEVVp0LY+uqyHVOtvFHDmvUMQaZ9DibHqFbjb85Id/weT8LYauc3JxRVKLpHVGlkNVSrx3d8js8gLLUnBaFueXr+i7Q9qGzeWzr/irjx6hpnD1+RmDVo9SdNmsltgymKZLNF/T6u5RNCWbVcqg0yHOF9T1kv7dAYVdkxo2ojqiTCV01caxW6yWGxRFxTAstmEINdiOS5KmVE2JLEvkeYaARLvdZbHYcn4+YbOJKIoaWRKQ1V3P+W7GOt+mxH0j890Ut908hs+fPrsV+jvmVujvmFuh/1Ma6qamooFvAmjYrbRFQQBhJ/ud+NnJnt2Ql29WCiDQCAIN4jfhlkCNAFRVQ9M0OI7Oe+99n8+/+JLlck6RSXQ6FsGmRNWgbLYoikYtRdSFiue7SELFsN9CsStMS0PTTfIioSoamhzSaAVZgEWJJwl0VZOu5GJVOuNXlygVmKaMqJYIxm4CmqQkmCboooSlCLgqWAqIVYLnNqi1itJUtB0bVVQxFR3PsVleN7i2hqo0OJa6mz5Xq+wdtFhOA9qejqoVmIaIojRYVpur8wLD9JlMLtnf7yE0LbbLkrYrYckQLjQsSyNqGt7cRNSiS9txCBc14VaiaCQePt6n24KvPnnJfs9AU0Pmy3P8joKu2szGIY6rYzsi6/WWLCtwfRFFNcnLkv6gRZ5mUEsIjUhDjqpYLKYlkixgOgWOUxNHAqupSLdr47Yi8myNJMiIaBiagSIbzCcZdQ2GJe7koyWIksBstsCyPUxHwnUV6qbm4myCYcjoas6gZxAFJesgZv9QAyYEUUhaNSyTiigriCKQ6BFsG5abhOPDPXqdI0zR5frqFYIJQZnx8vUZ+8Mj7hzc5fTsLVFi09k75HK8wHJcFEOgLObcO95F0EqKyOGwx+nTEx7udXjYG/D21/+ILegIucv0aoOlCRiyx/w6xrV1ZFlmvglouSKN7LEMcu6PHOJ6QyhFPPzBB5zMltx7+Of4Ro80jcnzBEWVKYsKWVYQEBEFFUSJIAiJ4xjbciiLhm0QYVsuaVxT1zKCqLCYb7i6umE+myNJGqZpIcsiVbWbN79Lfdzdq6KwG8362a3Q3zm3Qn/H3Ar9DxEQaITfFd6ISDtZiwKI4m6b/eszcwERQRC/Tp2TfhdDJ3wj993KQhJAEkUkSUKWRSSJr9uA2hzuH7NehEwu51DJqJpKQ0lZFpRNie3LRLFAa6CQ5AtUXULvRditBtOa0OkG9DoNXU/kYCDSMSuaRCbaCASLmmitsp0KzG8yOt4eltlCNSwaUUPVTTRNQpEa9FrGlkU8dVeJ3nU9NCnDUitMNcaUayxZRpFyHLtGlVw2i4RRb4AqF6iSQFUmdPsCZA5JWDDoWiAkGLKNKAk0hQtCQZYv2CxX3L872q2ci5SWX1MUAlfXAfc+eI/xIma9NAizhPE4oWp0/vbf/BtW4+fMLs8wMYhXK+7cMwi2CZPJkv3BHk1VM78JGfZHiIrI1dUMUS/QbVisVohSzcMHPWbjKfFWwbE6JJHInYP3GF9dAhm6IeB6JiIKXz09odvqcDA8QqKhSFNEOUfVYvy2y+XFFEVRMC0JQZli2Tqu0+fk+RoEEacVYfsRnvGYs1clhiFi+msczyTOZF6/mnJ03EJSNtR1zDqd0h5YoJhMlzlpqbGKK85WJquw4MnDfb7/3SEXswmTlUJURFyerpEah/c/eMjrlzmz1QW2b3B9s2Ew3KORGoKo4M6dNunmhmqx5YcfvMf49JS//OAxf/7B+/zi558hNTo9p8Xl83Ncy0XTTK7OF/iej2lpXI03mB0L3Wv46sUFR4/3KS14u7rk+z/9GacXa+7vP6YzaHN2/oZBv8vkZoKiKKiqTlU1TGcTHMfB89tEYUpd7wbXxHFJ1YDf8ZlPN+RFjW5ZSIrJJ5885c3pBUVVMdwfUFUNWVnSCAI0NVVdIAgNn37xJb/65It3+wD5/zm3Qn/H3Ar9D2maBhEJ8euP341P//q8rvlmDS4CX6/IhT9yfTP4BYGm3r2tqqvfbcOXUJUF3VaHphR5cPchV9cTkjQhL0va3R6bcI3lt8iqCFQBw3QIsi3egUnS3KAYCbW4omhSBKlBEiIMVcB3HDodB6dlIMoiZ2eXNGVDkTbEcUMcl9S1gSQ4yJKGKkh0bB1bU9EEAUuT0WQZSgNT8lAqHbVW0aQasSnRJRvPVMnDlHC9ZX/YwTIkdGW3a+HZFttZTMftIxIg1qBrCppqs95uGPQdXn15w2CgIgoSwSqn27dRrZrra4m31wEvz5bcTEpm85J+u8feaMheX2F6dsZ2tuJg2ODZJZv5hoPhIXKtkyQ53Z5JnhXMlhu8rkspJGyiGkEV8Xo622CKUDcc7Y9Yz6ZslgWq6BNsQ9qtNklcUKQWkqwiySHDXo/ZZUS8Leh0LAwzp643SGpFLQR0u22CVUEagq2NEISMmiX9kcZyuUWoXVQ93m3BWwoX52uq2sd0SzyvRpfaLGcJmrUrqlOFkChK8bt7OJ7HbB6QJhabKCYT4OXpmJt1Sib7+L19BHa59S2vxeXlGevNitHQYzpJkPGJsiX+qIWgl6ynNzw6GGHmDbM3r/ngzkNWb9+yOrnhcLTH+fMzpFCn0+lxMx6jiCLtkc9ss6ZQJVy3Q75d4+kq/l6Lm+UNznBA536HX7084eMf/S9EccF6teHocI8wDHFdh7ppdnHLYoPj2ORlSdOAJEoIAtQ1lHWKZZqEYYyiKSiaRhilJEmOoluYhsNkMueTT74gySoQJWRZQhSE3apdgE+/eMqvP3v6p35k3PJ73Ar9HXMr9H/ON1vsO4//YYuM8O3n36+M++PDIn737t2rKIog7DbrBUGkrGpkWcV1XV68eMmPfvITbiZTrm+mCJKAYmhswprRscZ8XWK6HebbCa2RT2VE5E1I3jSUAHJJTYYkSjR1hSzLIEpUTcVyvmI06KPpDaJc0Yi70bFlmZFGMXlUkawz6khBKgyyuEGRNGRJxdBFDLUiDTeYukrLs1EFH4kUx9QIlrvz4F5bQa5Fgm2KZzfUmcT0IubgUEOXZSxDR5Zkpjdr2p5PmpRs4oDDgwe8PZnht9uodo7tP+I3T2ckjcs2BbFu8/HHj7h31+T//ru/Y9Ae0nYLhCZkOJDZzgrisKTMRa6uC2bzLVEaMJmnBLGG5bWpJJXFusRwRVQ1I1hEOKbGXt8mWKckYYkiixR5ja7pKDisVxG6oiKJW3RFpixjNvMGx/EwbZmGCoQSSSxxXZ08q1ncNGiaimGVqGqDZTpsVlvCTY7niVhOjue2mE92OfAtX8M0S2g01qtduE63XSI0DVdnIXtHI3oDiWC+pKkFMrFhK8bEIoRRhVhX+HqLYLWkbiq8dofr2RjH6yKbLebrANO3icoEVRa5M/JIlue8vz+iLaosz8ccdlokiy1XL2f03T3WmzGrTYjj+CyjgHUTo3k2SbSmKFQ83yaKVhR5zeC4w1W6IDLh/nd/yMVlhuPucf/+HS4vr7FtE9/zWC5naIaMrqvEcYgkAs3XHSANlGWGJIoYULiSoAAAIABJREFUpo4sC+R5jixL6JpBXpSIooyqqMiKgaJYbLcp223Mq9cnrJchiiJhmibPXrzmHz/5/P/z58Et/++5Ffo75lbof4jwdevZHzj797/9/fa03335z65v3iAIAkLzh7+kqRvSLKURdolXhmngeR6//NVnfPwXP+TiYszN5AbLddmECZorkFUapSiQVRmbYEtrYCIZBY2ggihTNBki0JQKdS0giCpZUpAkW+JogyTnFNUKUcnQrAbTbmi3FQY9g37XYdhqo8sim+WSYB3w5uScYBNiaTptx8NQFFRFRBVbNHWJoYCmFPiuzunrt9zdP8a1VDS1QlckPKPHF598wocfjlBFAU3R0JQcXYE0lPFbPs9eXbF/vIeoaKS5gSQbXC9jnp6MObmeobjw4UcPULQtNefYhs50MmPv2GUbxrx4dU2aKVxcLwnyBk3T2QQZmtlGd1osNhFhItEZtAnjiuUs43BvD0kICRdrei0ZXZcoy5S6FCnyBklVKatrOm2fxSRCFgzaLR1ZzhEElYuzS+KoZji4hyrL5NUGQYwY7XUp85rpJETXXBzXoqpDPNemTHUm02s8z0DTM7pdk3CpML2quf/gAEFcoYgal6+XGIbG4XEXiYLTF5/z4GGH1sjhxXgKpkMh1EhKjaeCL9W0VJs8EpmvanLJJmpkjLZHGKfEWcx0ueT4/jF1tSBeXPDB0TFnX37Bhwf3aRkOXz37lDt3D1Flj4urGR//8AfUUs04SOk9PmArbLhazTjojSjKhk22ZP94RJSkjOMz7n/vI7aUnF0u+e/+5t9S1Cq/+E+/4cc/+S43N2sWyxu+9/3vsVjOiOOcg8MhYbTBtk0URWW7DdA1g1bbZTK9oq5KDo/2iKKUPK/o9/s0TcNkMkFTdTrtHttNSJrmKLKFgMxnn+9a37569YYXr1/9iZ4Ut/wxboX+jrkV+r8su/a13/sGYdfWJkgoigKNgChAr9MiqyAICg4O74AgMp4ukHSZq0mAYlks4xWuM2J2k6ApIus4piod0tSAxqQqNZqsRZGrxGFOEiRMr6eIgoimqGiaTC2U1E1CkacIdYmqFGhaiu+ldFoNo5GF6xgM+iNUyWRzVVLGOr5nUZUlEKPKOYpUI4orLLNg4N3j8k3I8VGLMp9jqRJ930ZrCoqgZNCVMSWRrich1AlvvlpxfH+fybRklc7AEPjs8xlffDnj7c0pquuSFjaPHh/ywROLeL0kWUU4ZkmZZ2SpgGmZLKY6uukjGyaCUqK4ApbXZhMpKJaG4cvMFylpCkgF0VYiD2CvJ6LUMZvpiv5Qo6jW2I5PUZQUhYShwnYT0u8M2MwlkmiLpslIyorhqEtVOEyvY3RNx9BVFFkjTmf43Qrb8hhfxhR5RqujUZYBg5GNUGnMbkI6HQdRjvEdh/WiYHwZ0OsZeHaOZ6gsZgmNlDLqpQxtOH1zTf/uMfMkIM8UytqlzFU8VcApM5o44mD/PjfLNffef8Q6nGG3GnTJYrmYkVRbsmLLvTs+vgxiEPPDxw+5ev4CV9UZDXt88duXWK0+jdrw/KsxVr9H2RZY1BsOnzxCP2hxFS3RHR9NKNnMx+wd7iF4CqfTK97/4UPM/pDzqcDo8BFHx0N++8Ub7j0YMegf8OzZM7rdDq22wnK5RFMtihyCcI3jqiiaQLCN8L0WoiQzn24xbQ1dVxlfX6NpKr7foiwrwjBClmUs2yLcltTUtPw9alRevHzN6cXJu7rVb+FW6O+cW6H/y/BPFvjfnqvT7PrTJVH6tlBO3KVj4Ld8xldzHMfFtl0sx+d8fMZmK1GJNdsoJA9N4pVOUxQst0vCQGA+z1nMY1azjM2sYny2Yj1Zc/32BrGW8Z0emm5TViJV2SA0IlQSQi1haCK2JaOKAiIldZ1QNSlFFeD7MveOuqTplIuzS3Tdpu21MXUBVV1hmwpSLdAy99guEwxFZdR3EMsQ16zpuS1uThL2RwK6KOBoEqais16kNJJIIUp88uUbruchZePRGzzk+HGLWTCnqjx6nQGGseHFszesZzKrYEGSx0zGFZt1QVHJZE2O5RjUxGyymkoUqBqZ5WoDAhimRrQucByVUa+FVFZYYoVnFhQRu7Y532WxWOO4FtQKVSIjYdOUGYZeoUgF4aZBllXyMsb1TDTNZLNJSJIGWdFQNYGyjtHMAtNWiAOF7RK6XY20OMUxulBau5nuloFuJvSGNutVwnaR0/J1HCdE0WrWyyWuoeFYIkUh8vYkxPI65NmWLFvimDZCFtG3NQxJZBttQGpYLle0PIUyLDB1iMIVaR7Q77kYgsjIEjjwILge86MPvs/yYkGV1jx5/B3GZ5dQ1nhel/OLCaJaIKk651dLar3EPGgz3RZoco1rCMSrGFUv6dztcJYvWakedz74V8wWJYvZlrv399msE+bzOXt7I6qqYL5Y0Wp7SFJNXuz+IdqdpStE8QZBaBBFkOQGSdSQZRFdV0iSEFkWMQx9FzojQVPXuzRGWSBLSyTJZLK45PWb5+/mhr8FuBX6O+dW6H9adufxXxfNfS1yUfg68QqBdsvn888/Y9AbYpoOaZZxcbahrGuqWqRMbfJYZ76ask1nrFYR22VMuErJw4JwvqDchohZyHceHuFYKqoGglQiSl/H3FQV1A1NUyAKGYYpoOvsHpRUVHUOFKTJEt/PGA5lBn2L189mCBXcveugyXMMoYtU2MjE7A0c3jyb850PDiDPkeqEvu8TLlLyIMBWJTQpQ0PCd1t88tkV3aMO55MFD97/kNHhY9aBxKuTV7y9mCBqNjfrNSfnl8RlQ5DppI0JukEhGWxCHasP23rKartEVy2EyiKJQnzf5M7+QyQ0fvyD79D2FWzd5DtP+igs2U7W+LaD48FyGSNJLWy7Tbhd41g28abEMCokaY0ghBSJxGDgs5gWbFcSsqIgagn+QELRXc7eFFC3cVyXWgjRjArTEqnKgsVNwWg4oC5rWm0FTdW4vpyi6RqymtEfQp6JXF+l7B/tI2oTLFXl+dMl/f0hjp9gVDo30xzftdksppiVjV42aEpEmuj43S7TzRLdNInLV3R7BkW0xDclvvvBEV2n4G9/8jHlzYye7HCnNeKz//pfeHg4Qs5rnv7ytzw5fIIYV9xcXvLw+ID1ZcRmFtJtuQSzJZtFyIOPPmQtp4yDJQcHR6hyw9n2iu4HT6g7d3jxdsuwfY8HD4/4/NMTZEXh8Xv3GI/HbLch739wH4CLixtcz6A3aPP2zTmGqfLxx0+YzzfEUc7R8SGCVLFcLmiakv2DPbIsZb1Zsbc/JMsT1psNrqthGCZllRFsA66uTzi9ePMO7+5bboX+jrkV+p8S4dvt910d3S7ERhAE6qqirhpsR8O3PZ49fY0i69hOj25nj8l8TZY3NLVKI8lITonq1tSU2LaJJkp0NJWWGvPkyOB/+9vv8NO/vMd6eUlRblHUhqauyPOcKhcRBQWahrqusG0XTRURpYJaCBDUlLLZIEgJpgyaVCNXKt/94BFvX76m53VoWyaGYKPRoMsxulSzGK+oy4iOPUSXJKLljJ7vcPblio5vo4kFdZ7huhY3s5SkSVknMr/8xzN+++KU8WzFNk7pDo6o5JKcmrJ2sHwbzRQINvmup91JkSmJkxc8vDdgYHscuDYfP3lE1zHpt23u3RmSBAvSaIvfyoiXM9aTl/Q9EbmSCdcNspZgaF2CoMC2dQRq8myJ6xYUVYgm2ciCgudaTG6uGA3uIosK4+spZS1RCQKyKXF075CrqzHzWYrn9RCECsfWMHWJqqyYXkCv64C0RDcKup0eF6dzfLuDYSS4bZFtUPLi9YqDYR/TbOi0fJ4+fcaga3PYNynTiO1ywZNHbSwx4P1HxxQSZGKXi8kCzdSJoy0//tEDyiTmL79zl7aa82f3DjnyPDanL/nhvSOC81OEKObjD37AP/78Uw5bIw5aLuevXtB19xk4Pm+fv6Hj7KMLIourNwzcPeq05OVXbzCP2hQDj9+Mryh9l/5793l+eYro7vHjn/xbbq4Cnn52yg//6iOSJONXv/wtDx7cp9V2+dU/fI5lm7z/5Ii3J5dcnk/4s+9+BNT8l198yv5Bl8HQ49WrC/I8YzTcoywLFosFoigyGPSZzxdkWcGDB/dZr1dEUYyuSxwe9Xh18pKT09st93fJrdDfMbdC/5el+fYA/Q/5XXzsrr1NlBrquqAqM1quR7vVY7naIgouTsen3R5QVwLbLAdLoDFDdD/FcEo0tcCWG/bcgr/914f8j//qLgddmSLZcPb2EknRqBqFNIeiEHbxnLJOXpakaY6uaWiqgKJCI1W71r1GJotLdEHCkmxMQcSWM/ZGDhcvZoihyajnIzVrsu0STVDodR1++YsXtG2fJgswRBmxrJhfFlR5TpmVGLJA3Wzo7I348tUMjBaXkxBB9VAdmUa0yHILRQfLqRGrAqXOGbQqHh06OGLM+0ONH3/U535H547t8XiwRzS+JJhfUaUJr796SV5skZWC1XJBFMzoeBrRaoFYlHTcDtE6IS1lDMsiTlek6Zput0UULDF0CdfxSIIUsdFJ4xTf7bBaXZGVAf1enzQTuVnERHkGcsLx/S6GrTG9jhEbHcuUEKWQdkekqXQmNzMsw0IzGhB3LVrnJwtUsYfl5nSGEnUtc3FR4vkag36BKdksr2p0XWTUhyIREWqR0bDHb756wTgRuU4iFEPHNjLeu2MzsHMe7bdQsyXfe++I4OYMtxFoiQXz16fsuR7JasnkfM7x3l2mV9eUaUavNeDmZkGZ1LjeHovNjCJNGbb3WW4DMrHBNVpswg2xDM7RHc4IeBMFHL3/EboyIgkMFNtjf2/A5eWcsmzY3x+w3a6J44T+sIMsi1yP17Q7Xbp9j+vrK2QE+nsdgiAhWqf0ui1kVSKKQ3TN2hUYFjVxnKDrJrpmMF9MKAsR29ERGoEw2HB1fc3r09uiuHfJrdDfMbdC/9Pzh9vu9beH7U2dIgkqkiTjeDqGYXB9tSCvS6RCpt8aMV+tWQQrakpocmxDRhVzPnrc4X/9H+7z5LjCqELEoiZN4OxyQyl2SAqLsjF2LUN1Qi2kSFpNkedIlLi2gaA0FE1BTY0sgCyKCHWBrev0PR1bLGi3HPZ8h/GbLcE64WjUI1uvKKMISy8Q6HJ1MsVWZLS6Ilsn6IrG88+njDoDHF0AYY7f91CVA16dLghLkagG085pyxoDYcRHdzy+99jkLz98hCvZ/PDDexz1cu51LJjP+PM7be45PtuLOb7sYsom528WbNYhimyyWG0oixLXsZkvMlTFod/xWa8CJFHC8QTW690f3u+o5EXEerriYL/PchrSFDKea5OGIYf7eyxm11iWjm2aLFdXGI6K4XQJkoTZco6oCPh9Fb9tcnF+RZ1lDPs2ohxi2RK2scf0JkXTZCynQlEj+t1D3r5cIEoCppkzGlkUpcvl2RW9to5n6Eh1h8l1Qm9ooMgCm1XJ2eWGQtVYZjGWq7Bnq7y3r7HnxzzujhDyOXsdj9/84u95794DgsmUYj3l/aNHnD27wNc9Wlaf8dmYltOCWuNmvMK0fJpaY3pzjiSbKGqb65sZkqZgtjyC7RyxlFAam3AdkDYpYrfH0/MbbLvHd578BabS4tmLr5AkjXanwzZcEERr+v0hsmJw8vYZmrYLaVoup1RFTNu20aw2q3WCREWnrZJkNXlWoSgysqRQ1SVZVqIoMoZpEAYRDQWWbSJJIptVzmQ+5uRW6O+UW6G/Y26F/i/LN33sf/zaBdTsrm+K5BQEUUQQGjzPRtNFPvvl5/S9PWRUiixjs4jR8JGrFnVao8sJP/3BHe70BZQ8RitkVEElrwWen00ptS6NbqDo4DsVrY6A0xIRlZo4KMmTAtsykWQo62xX0S00CHVF0xRYBjiKQFuzkaoaNQ0YjA749T+8RhFcfE2nCiOMuqLlWEwuMppEw5Yy1KLAlESK0OXy9RVHwzbUBTUxjmMxnQu8vYg4ftTjJ98f8jfvHXNUm5TjU/pGzPjkOdtJymq6YNiz8W2VIovI4ohHjz8Eyefzp29YLBKmq4xNlHBw9y6m5TKfr6CU0E2T2TjANHRcp8X1dIrh2JhKa5dkJin4rkES7qr3h719Xr98QbflQJMwu57w4H6P2WRCU4uYtsNys0JSNAy7zSaomC5iJE3FcHU+fPI+lycnROsNXb+NIKUoqki32+fFszdoUouWpyCKAcf3ZMZnEyxlhCo19DsKVZpyfrJkb8/HtlWKXOHtyTUH+zauYzFbBIhKScffMFRV/ue/foiZnjNQDN5+ccOT9x5x+uIFf/PTv+bv//3P+eD+A4pA4stPfsufPf4Lzl9dEa5Djvfv8/blmCKt6bZHvD2ZEGQbOn6Lq8stUVbiD3bje7fBik7PQUxF1lcreq6P15isFxGO1+Ymi3hxc40im/zsp3+NJMm8PnnB6NBF1eH/+D//d66mJ9xMXvJ3f//v+L/+/t9zffOa/T0PKS+YzlZ0+w/pdX1evv6SmobDw2M2mxVfPj1htNeh0/WYTq+ZTqfcv/8QRYXVMiMKQ95/8j7PXn7B67e3Qn+X3Ar9HXMr9D81/6Qp/feuhgqar5vbm91YSM+zGQ6HbDcBIgKaatPxDtksI9I0oNNWUeWIJk/JVjV1LJNuE8RGJ8h0Xo83VIZK1kT0OgqeU2JqMo7TpqoMKG2qvEGSwXIsZFWiqhs0RaMua6pUQpdc+m0HWw1w7RoxysjXIUeDPb785CmeaaM0JVLVoEQxkqBy8mpDx+qjFWuEbYSmNCxuatJcptWSUYQNjWjgDg/51RfPqWuBJ3d05i8/o84WbKOUvCw4Gjl0rRTyiPkiRLcc2sMBv3mz4uefzviHT095/uack8sNrb5Ed9BmsYxx3Q5lVhCsUlqmiCGVrMY5uqgjIzG5ynA7KW5bZrNO0NU+imoynwpomkSvP2K12NLvOUiCwWZV0euPmC4XLDcZfnuf5TpitQ1p93pkNZxebFF1DdNa8N69IdXGYnkV4zg6upUhyVu6fpfVdU0eWTiugCQoeE6H9SIkWNV4Tk63p1FVBhdvV1iuQLunUOQFq0mIZZr0Ry1mkzEfP7nLX39/xHxyzcP2PtnNjP6Bws3FBb6i8/K3T3l4eMzpV5e09BZdZ8Dp60t8t09dy5y9vcZyOiw3Kct1jGMb5GLOeBvS6/UxzJqgWeG3WnR6PbLcINyEdFyT5XLLNjfQLZkqCajiHAqRV799yt//3c9Zbd7y+u0n/Mf/8B94/uwLNttzVqsLFrMbDEXk7nEfxxIIVwv2XItO/4C4aNgsttimjeVYTKdLJFHj+F6HzSYiTVM0VcOxHaJoSxTWeL6Kpppsgjknb05v29beMbdCf8fcCv1d8cfS5Wr4enJUI+wiY4u8wmmZjPZ6rDdLqrzGt3u4ls1yEbBehIwGHT78aMTxHYFGvqSqE4JAZDyBV+chQSqxWRVojUmTiNShy/pGYbPUSAKd1TwlimWyTCJKBaKooaoUhMpAoUHTBDxLx1Md5FLDKlWiiws6loKlKnz12QSVDqIYoEUxmiKRrH2WY4lRR6PaBIhFgaUOOLlc4LR11KakUUTEtkIiOfzq2ZiDu3v81V/co22ZjHpttvOAgdfjzkihZYps5iIvX6642sT851+/4fVZTZZZHAwM3jts05DiuS6m0WI2nSDLDUWWsZjGGJqKpmisVwmKJqIbIpfXE3SzhaLanF9OcXwTxdS5vDilPxRRVYnpTY5p+Sw2a54/X7GJNG7mBeNxRBQ2hLHEcl0i6yaqabKYBBiCzp2Bhm9G6KLKm+c36EIX37aQpBjfUwi2IZNxyN6Bh6hMabU1gu2WYFnj+S6mXSMLXa7HGwy7wnEU6kwhDkpcS6fjyZw9u+HHP3yEXpSE4wn3hn2mJ9cMHIfNZM7x8IDVeIYr94jWa2bXIa7rMp3HzOZgei1W24LJYkVv3ycuBYI04eiojWZLRHXI3p0WmiGzWKZAn6KsyPKUotKxJZE6b6iLGvIC8pS8SCjjlDw9Q6i3KJWKTImur9GkEiEvMaQGoV4j1Ck938amoZZBdVoUkYxjtBBljSSL0XWVsmxQVZkkzZGk3YyEsiypagFVFdB1kygMOb+8uBX6O+ZW6O+YW6H/qflnHeq/oxFoqL9ZoCNKEjWQ5xGaaXJ41MfSBW4uJ9hGC986RG26zG8iptdzHLNFyx3QH/TYPzjE8dtMNisqrcR1JXptFUNOcNUSTc1pdw0cz6JsItImQVQLRA1EFaJ4y2oxZTkLmNwkmFKbfXdI324jbyvCmxukpsazLNS6z1efXuHrNi3DIFlGtKV9Xjy9oM5ELN1EijfouUMQCSziiLt394nDBNWVSRWJX7y85mQac7h3gIuAyzVHXYunT1/hD/aQHYfXNwm/ebnkly/nyIZCf6Tjqgr/048ec69XsbkKycKce/eGzBcL1usU3TIpK5swrlANkGWJ1WaF4Qj43pCTVxd4LRev5XN+McPveTiGxqsXb4g2Im9eTzg7P+FqvKKRZFbxFtUycV2dulnQ6lhoRpvpJKLdtjCMjOuTMWZl07IUem2JfqvP268WBIuG0dBHM0M8r0aoHX75n15z/+49ZHWL7+msFwqLRcrxcQ9FzAnWDZNxzJ3jQzRd5uZigaOoeFaO0Eh8/l9f87Pv/YDl5RKhKHlvf8jpVyccDO5y/uKSvnfAxdsLdEVHkx3Ozq9RTQfVsriYXNMe+vhdidn6inb/gCfvPWYxvcR0O1h+n9fPX3F9naLIHRbTBfPZHMNo4Zgt0nRKFhVYbgtbV8mWS2xTwzIr6nRLHoYYioLcZKTRBLmu0cQaodqyXU1wDYu2I5Nv12iuitd6j5bVIdikbNIt9x7cpyhS1qsUWRXp9/oEQch2GzAcjDBMgevxijAKGAz3mK9u+OrFl3/Ce/mWf8qt0N8xt0L/b4j6m+jZBoSGqq6/jpcVaKoGiYJR32V/OGKzjFBl6HVcRv0R49M1Tz+ZcvY8Yj6pUMUhUVTxdnyJ4sq0+jDaq+l1Ymxzg+0nNEqIqNcgx2yzKaYnMjhoYzkaLd9iOOrhex1kLKREJLzZsL2a0xINFKGhqXOqpEJBQq80Vuc1hushRgJqnONZOi9erHDsDuKmxqhjNF3hOgyYrhLazj6qaVLaKZ+eLUhqn8s3Ae8fd9HqKUKdMrj7Hj//dMF//O2cf/fF/8PenfxYvp/3fX9/f/N45qnm6qqe+w68vANpKqIkWrLkJI4DBwkC2AECGFlnn1VW2SV/QRYJEiOCDQSRZCuiTFMDSVEcLu/U3be7q7prrjp15nN+85xFS4oTZKm4CbJem8LZHRTw4IPz4Ps8zwkzxUI3thn0Db75gczXtmsUwyt2u13CeUiWFHjhgkoyWCxlkhxqrgaVTBgmFPgowqQsdAQSmmowHF1TCRldbzBbTqkbJnLZYD5evF7XakpoSovWAOpdAyEk1voWW1sypTRjY6fF9m6XML6m265omjrT4wXZssI1ZVrtgl6rTeyXXF4MX689lXNqrkbd2eDVwRWOZaNrgk6nz3IRM76cMlhXadUdVnOVs+NnDPo1uq0Gr549x9Yt2h2HxTBkdP2M+/t7vHh8hu0UkKuML5f0uzscPD6m320wvJqTlxm61mQVzyn1Fe1Bi4X/ik5XYX//HoE3IVjNUKwBJ6dLLg+vsZQeWdpiOgqQq5C628BfxRSZh25Z1E2ThX9NEPrUnQbLyCPwLrAUG6V8/d6hyDwMScMQTfzlClNXqJsdIl+izCNMKUTWtjFrOwTzEN3Ksdw2l1dD0iijv1YnSyuWyzmNWoda3WE+9wmiCc1GD8c28XyPZ8+/5ORmDv2Nugn0N+wm0H9+COmvz65KCASSEMhShaIoKLJAVSQQBaqWsL5u4NiCxewaXdVY6w9wbZu6PWA+clhNOgyHCsfnIV4sSLIMuSypAkHqq2Spgx9ZLPySotRJMo0wBN1qUIoC5AikAElaYmsh+5sm79zaQ09Uzk4uWE1WKIpJleVUwQqnslldlhxdLulZa8hZii4qiHUmVwF1ZUCRxeiEmPUai1Lh5cWcem8H0XX47GxKFFW8/5Vtnn9+iK50KLU6P/jikm9/ds3HQxUGd6nv1rl3u82WKvjausEHexoNzeFnP7pimQ1xui0ODjKyQqDbKlkqqMICWRVk5PhxQVFJJEmFvwqQFAtdb7JcJkiSShzBaLRgc3NAp9smzwVbt1q0Wy5pumRze8DahkVRzNha3+T+/V0W3iF7+3U+fPc2pAlGKTByifA6ZXblY1gG9YaL67x+bHh6PCOPLBpuG6eeUq9ZXJ6tSCOdRt2m1Y6Jw5DxZUKtptMfqER+zmJc0WzadHp1hhdLdCWj09G5vJ4iFRX9ep0vn5xQd22iVcrockqtZjGeLDDcGqqjsohHDLY2kXUHSNjeHFBmJkevhuRpzGKpML6YUkYxiuLiBQbkPnZZUuomaV6hljGZ8IiERJgnKFqBbgiSHCgEciVQSweRF68v9BUKVa5TJTmyKkMhU2UyQgHHruiodSSjR3PzPoaomCcemt4gL3J0XUZIBUmSIssqcRygqK/PEIOCJL3e4xD4HlfXFzeLZd6wm0B/w24C/eeP+Jvzq69fwhdVjiJJSAIQJUIIDM2g061xZ2+TKq+I/QRddmg31+h3d1FFnevrgPGqIC5dNKlJ192GpEYeGUzmFX5icHWdMRzFpLlM4FeYtolTk9D0GMeQEHKJSBMIVuxvtNjbUmi2JoxOSk6eyPRr20hhgVGU2ErO7CplOotQlAYiSNDSJXJoMBzGOI06UpKglSXthoPVbfHDgwvKjW1e+BlXo4I7ex06XY1Pnob8L98+5i8OSqZKk7V7X0EWLvuDJu/0U+7W63jHCe22S6NlsLP+iE8+OyOqKqJKws8gqzQqIbGKI+IiphQlkgRR6qNxa+XAAAAgAElEQVQaGZIksfBGWJbF9s4tjk5PkBQDZJvp1Md0TGqNJscnZzi1nIdvrXM5PGBzR+NrH91jfDVi0K/z7jv7nL58ilqkvH/nI86ejDn98iUt18JU65yeTJhNA7Z312h0VCxbMB0FvDyYsLvXQlbBMtqMRkNWiwmdjoFp6ih0uDqdUa/BxlaPo1dDisyi3amj6YKzVwHrGwaG7nL0ck6vN0CIkIuDOR13g9nMJ5cq6msNJp5HJlVs3m3z7OUR9VqHbtPl+acXzK4Fmioxm4SEMxVTr9AUicUqpJIzTDVHlmTiIqZSEzRHBl3BK1PMGqBoeEFMXobIkoReOcS+Spr4mEZOFmtUmYMhFyhChdImihOMWka7bqMXLqvI5tZb72BoMqeXEypZptPvoKoS81mCpkm02i3KKufk5Ipa3aLTaROGPp4fsbG5znByyrMXz99w9f5yuwn0N+wm0H9+VFX1N6da/+2xNgmJsiwRVAghUeYVZVmiyCqaJrG73WO932U590jinHrDwHY03E6Li8WEeRSh6QaapFGVAllXcLoOZtvErKuUVQ64jMcp03HBclGR+DbBzKWIemReC9ds0G0L1MzHiE06qkY0zTj46QK7GlBlBWkQ0tQtFkuZ1TKkppkQVrRMmzhaMVoESJKFpVfoeoRcl+l9cIs/fnrEJxcpc897vSnseM53fjzkuuySNh7RvX2HrU2JBy2Z8vKcmihIojnLyOaHPztnOk9ZLBfMPJ2PvxwT6YJVFbNKK8Iqx65rSJKFKhQGfZdux0JXNb7ybp/9O7co1QKz7TD2U8a+R6ZWKLb5er5cy9nYarGYzpAo+Yf/0e8QBxGUKe++s8Phly+JFil9e40XP7ri4+88Z/xqjlFaeLMMSWh01ixUXeHl4SW6btHsmQzWZAzd4IffnWHrG9RbObfvNbkeLrg4Kum2urR7EmkkGJ4VGHbF9k6L46Nr0qSg1RXIlc2rlwv21/cIs4yTyyv6Sgstt/D8gN6myyrLiWWFnXe6nC9mBJHJ/u11Ls8PGB5NaTodAm/BbLKibjdxVI3xKiAS0Kw1MDIZrypAh5YhiEXImATFNGk0CzKpROguW7d2WG81CIYLPF9CUyVUtSQvcyShI0s6RRGRZCUVYDcMMiLS0Kdb72G6bVK5SRQW3Lq/geU0GI8WiErQaNSBnPOzCxzHodNukyYpr14dU691aLVrzOdjXhy+4Pj0+E2W8C+9m0B/w24C/edJCYh/a/EMvF4X+/pCuyRJVKWgLCVUVUNQkGchipyimSn3H25Sq5n4gUcYxszDBZNwQlAFGKbANBXyPCOtcmZ+gJeUCGFSZBoyTbJMptFp0+43aLZVZCVGlCvSLGI0mxN6KVaiok4l7NDEKCu6Vp/paMF8lqDKDkpc0XRN/EWMN5eRhYRcRjQMiQSVmQdeBG67jtmXsG9nGLsGX17GnA4jGmv3eXaecBXJNHYe0F9roWsXbHVC+nZAp5lzdLbk1fmYTBGsMnhxuuKnX54zDCtEs8UsjtDtLr1+B9PJadjw1oN1mo6CpZgM2gPSaMX5lcfVeMnTw1O+PDpnHicoto3sFqR5hWGb+NGUJIjptessZnPiMGR7fYNXz0959vkxhQ9Xh2MOP7vASFvIiUURVuRRRqfVIs0iJrMYRZfY2esQRj6X53PyTKLT7rO732I8OWZ4dY1tNOn2GxiaxcvDY3RDod2x0XTB4Zfn2JbL+maTyXhMuCxYW9PQZIPT00vWNjuURcrwaM5ar0MqfBaRz+DWFsPFiOF0ztbOHRYzj+vTESY6ZDLTyRJJGDSabRb+lDQv0SwLSYYoSUAyEWaOZEMqp8iuTme7S2fgUsoCp9Gm3a0TBXOuzueEoYyi5qBE5LmCKBuUJJTVgqJ63WZXtIRMignzElt/vQMgTBO273yI66xTybDyIpI8xtRNsmxJllYISiTJAJFRZAWu46BrGlGYQFVyenHC0cnRG6zfGzeB/obdBPrPk9cz6uL/3gvL3wS69PqzEPJf3TuvkGUFWckpiBBCQpEN+v0at3Z7DHoNJDXh6Pop0/ASxcxoNjQkShTVIkp0sqyBXHTQyhZhkDFf+KyChFUYkhNi2iXtRp1Gs4auadzf26ItFyyPTlkchVS+hqO51Nw6i0nIcuyhZirkOe1aj3BZMLmeYqgaqrDIy4p2q8YqyXhxNcPaaNN66HISHTMvdFYxBGnB9TBie3PAvT34xoOMr+1LXD49wNb7lHKK1XWIc5PJ2KfTblJICkp9i0wy6O30efSVe1i6Tb/bYWfjLqG35Go4Ze5l/OyLA54eXTIJCmaJyjJKcRtd7j14QKft0Os0+OoH7zCbjvGWC9YGHVyzS+BP6TfXGV1M+ewnX2AqaxhFl/PDcyzJoWV3WY5HiFxgGQ6irPD9FfX6Gu3eLpPFNcPRFc3GBm+/9Q3Gkxkvnh1j2RK7ux0c2+TJZ0ckkaC/rjAY1Dl8dkGWFPT7FmtrFudHKYoKG1sW8+uQyKtY6zoUSs7kesre5gaZFjFZ+nQ3NphFHtPFjJ2dfapE4elnn1EzFURasRqFSJKJ22gQJwXz5QKzrqE4TaJ0RZKsaPbaVJZAVkISUbD5YJ9mv00wn5KEIXnZoAgTrl4dshoFaHKDQpKp8hWVyJCFTVXqhPEcRcnQ5RoyBVniExc5br+JZlVEqxVC6Hzl679Jnmscnx6jaTrbexvkScr1xRDHMdjd2SDJYq6HMzqtOv1+l8l4iLfy2dnZZTi65Onzp2+odm/ATaC/cTeB/vPjr1+0/7/H2l7ne/nXn5BlCUkSIEqqUkVRXGTJQRIKEhKKLKg5Nv31JkejL5jEpxTSmJpbQplQFRVlaVBVBmmQ4y88prM5kmSSFjZubQtVc/FWEfPZEn/uEwcBda3kvb017gw6VEuDg8cXXJ+NUYVOr9miCjTODs+hUhGFSs000ITM5HpBDmi6jCGluC0bfdBhJCKOiglTTWVVmCz9iJ21PpuuhRuN+dXbGl/fkakXPrcGG/z0x48Zj2c8O3pBa62Fa3WYnI1xrQaGWWN/dxtv5VFkEdOJz5Nnhzx5ccjR6ZJ5ojH0czp3brF+7y6Z6XDn0Rr3Hz2gEj6i9LFllfNXz3j8xQtkQjRFYj5d0Gl12V7bI5gLvvLw6/Tdhxy/GCNJGf1Gm3CR480XdNstyiInDEJqjottO8wWM8bLCY1Wl5q7yenJBdPFOQ/fus+t3S2++OSEyfWIZsvmwYO3GJ6sOD26pNttsLOxx2JccH4yotNqUavbvHp5ghAF+/trDM/nhMGK9cEO3sonSJZs3dpk6uUMFwu2dveJ/Zyr43O0UsJVFcaXZzimjak5+L6HF8UYjo5TV/C8ED+d47RdnLZLlnnYNjx696u017b58V/8kCT00RWX3NNZnk7JVyssqYaSNgnnAWWyRFZs8rQiy2NUvcS2HCh0Ii9EpqDptChklQv/HMMS7G/sU+YqldZCyCYP375PmuV8/sVzLLPi/v37pHHA4y9e0O122NwYsFoueP78C3Z3b9EfdDk/P+XJl084uTj5d1u0N/4fxHu/9vf/v69X3Ph3olGv8d3f+903/TVu/C2qqoqyTAnKJb/7J/+M7x98m8qaYRuCeB6gFQ2iaJ2ZZ7KYh8R+iObKSLrJ9SpGrVnU2jm1RkxNKTCkkjSZsi4ZfNDu0J6c0c8zqpVgdQH+tUDJKnqOTLnMWQY5ZSbTskxsStLIZ5YppKVHpylRa6oUmzn1bzQYdWR+cJ3wctLgxWWA04Lf+NoGg67CyeMD0suU/kYf0a9Y5AUvnq84PA3o1Fvsbe8RnCf87Omcq0hhsKZycTUiTAwUQ6PeNtBNk7Cs2NywEWWKP01oOTZxOGF4sUKSBKVI8FYReebgui41t0GWrWg3XQadDRpmB1dT+NbXvkU4KSiCCH91ybMnn3J+/gTHrDBKA3+4xFYLKEPIFIpUxmla5Cr4SUytJVCMGNVQkIXO9o7G5rZgOYqYXMUYFuz2u4xXEVfXQ5q2Sr+1yXKpcnF5TWdNwdIbnB1P0fWIRltjOTvDslxUtcnLwxXIBp1Bh5OzMcEyo243Wc0CkiCm4dqoQmI+L5FtG90IKYuSmZdi1i1aDRvLlPH8lFa3x8Z6C1vXef70Ei+MiNMYb+JRRBWapiGxJPZ1ytxCVgqElFMUCqJskRchklqi6CphkSEMBVkvSUiR0grJ1ClaFa4asKk1kfUt3v/1/xy5ts4yzEGRqbkWoqyYTqcIIWg1msRhShBG1GoOiioTRhFxnGDaOn/y/T/j//zut990+f1SU970F7hx4xdNVQkkIaMqAscRpPkljrWg29dQuzn5qsJbqORCZzIN6Kxt4rYtojzlfDmniC30yMWXFXQrQqgledEkzAWl7KIYa6wuYsJRTDgLqLsaclwwm2XMFrDhGNhqyWS+4rLQURWHngm6JVgmS66HJVVVcm8WcOvODlXPpXqZ8ux8ynwa4Tq7tCyV5oOv8s++98fMgjZiWlLfcqnUBEnkHL+Y4kgGD3fbeHabn/zpiNlKYuPWA/ZadYpohiElPLy7z9nLSy5evUBVI0zJ5MXjCC8KaWz18KIZVSGx9+gudVPHn+Xc7u/zwXvvc3m6xFCa/Ae/89ucvXrJ2cEl927fpr5l8cknAR998+9xd/U2/9v//D/RsXMazRJRSvhTBddyUbWKLM1I8tcX7SgTQj9BSU06fQ3FkDk9nxMtUzbWe+haweHFKSQGdwb75EXCwcEFjWaXe3ctzo5jVvmUO3tbjC4XXL644PbdTbJijqJ47G4KlkuYnJ3QcweceFMuhle4mkvdbrBYFAhFxq1rlGXFeKGj1mDrdp26pXI9T6gyl1rLhCTmyQ8PKJKEJIvIy4I0rFOELoYVUhUTskxCzl10WaNQVizTnLiU0SQf1chJkxRSE1X7q/WwqKArCC1H0mLKMoekQhgCVYspxZgkskmTgn6zBVXB0fGIet2l2bJJkpw4y9A0C1UzKKqEIEiQZQ1Ds1G1mzh5025a7m/YTcv9F8/r1n1JXsQM5xc8P/uMMLmkXk9x9ICGXVBzc2qtilyEGI6En14RlxlRWpGUGYajIEQORQWlSRBK1M2czZagnEZMnyvIWYcqUcnjmPl4iaN0adf6HF3MWCQSbqOHa1okfsx45ZPIJo3eJptrLcJgxOnVkmll031wj1jE+EFAlek0pA7HX1yT+TqPX4yYxoJPn13wB99+zuErg9Dv0XfXubPb4tWLJyi1Fkbb5P7ugCo+4929Gl9/y6KYPmF+9JLf/uABX3uwQbn0WW+Y/Na3HjBoSbA65Ve/8h5v395nfnnKb3z91/mv/sk/pZiUlEuDf/yf/BeQZfwf/+Jf8qsf/Hs8uL3DX3z3J8yGEb/5d7/FdHrB1cU5w4sjJlfHRHMfOa9Y63ZYjEdkSQaSwHI00jwjSVRM18ZpJVxcnbBYFty5+wHbuwOeP/+E8XDIo4fvIpcpn/3kAFPZ4v79B0zHp7w6OOXW7n2ElPDxj3/M7voj2o0WH//4c9rOBqKUMY0cQUKz1uPweI4umxjCwA9KUkmhUbcQUsZwPqHesdjctnAdlcvRAtVp03AsWK04fX7GdLJCUzXypMCbB8SrEl1+PaGQZymLeUlc1tBsFT9d4AcFslJD0zWybEqYpOQolGrOLJ6SCYlVkpNmErKoiP0IU+2y29+k5TgcHJywtvEBdx99RL3e4OXj5+RpyTvvP8R1axw8P8MPlrz31a8gSQoff/wJrtNm//Yemq7w4tkJr46POL28abm/STct9zfspuX+i6eqSiAnqyI+Pvox/+O//O+pnGPW1mOa9gql9KGCqHA4HRlo5j6VojGeZLw4zpksFRrtNo4lY0o5rmkjMND8Yz4cKOyEKs6Vw/I0JLgKqKkmclJSrjLKPCRHRdc1Sn+BXhWoikGpGJRSTpot6XUttEGO/cBk1Ux4Wi6Imj2+OClYLAxudwccfvEj8iRi6ucUhk73Vo/2+g5J4hBPEjY1nw8+MLDUBU+eTciqAXv314GEVwdPcPQ6d/Z28f1zjg7OWNsa0B50ePHiEF2x2Fq7TeFlnFxc4jR7tHsDTs6uqNe2ePvh+8yufU5Pr3jr3XdotNb49KdfUq/ZPLhzm+HVK85OX7K3+QhR5nznj/45s+El680Wk6szVvMFDbtHleuUogQ9Q9JVECaFFpHIVzRqHUpUKtnn3r0O2/0mWRBxdXyGZZvU9A6Li5DYj6k3XSTFYDg5RTd0dM1mMZ2hSTo1bYvhcIjphtSaGYoZkhUqUe5ycrJiNirQ9CZ+uERSQpy6TqvdoyxydMui2dnh6eMjsiiCIsFARxQmnj8ljQOkQsewWiRJQZKv/motsUshGZSixI99hGIjSwpZEiBVEoVUkFcKcZ5QKDElUKQKVCVREqEbBkJk2HbG/qDBfmcLTR6w896vUa3XiTWd3d4D4jBgMr/GceoYhooQMrOphyRBt9slSWMW8xW6ZmOYgj//yx/wv/+r33vD1ffL7eYX+ht28wv9F1RVUVUZfh7yF599DwwPTYkxtRRRBSiyTFnK5JXG1XhEki2QlBQUmawSNBoairJEV2ZIwkcAepaxU3OxvBDhLZGygtIvIJNQShWlUogLKJIEQxJIskYpdJIMsjRFUQtUQ0YYNufzKTEL7JrE3Q8fYA7W+PIk5vNPJ4TTgM12j197/1eovJz50Gdna4ftNZe7WwZvbee0qiHLy2OcquDh7hoOMZPDFxhJzvt3H1AsQ65eHmLLFbd3Wkwurxgen7E32MVC4ezgCVWasNV3WA4P8a+OGDgyerLg80/+iHB5gK0mnBx+zpPP/zU1M2A+esaTz/8N12c/RSk8XnzyhOc/+UvahqBtGCiZj06Kq1skPkSrFAEYqo6uliTplAoVQ90kywrSfIailkiVSiUEC2+MZlgYShtvviBNZ7hWjSJRWS4v0VQDWdQo8gqKEqmsE68qVEUhSxOKQmax9NB0DcMqaLUdwjjCW01wLEFvrUe320GRcgb9PbK0zsuDE8LZDDmTyRNBlEnMZwuUSkWX6ohSw0tToionVyEoKxKhEhY5eZmSVApBLvCzmEyGIAWvUFjlEbHICNOCsqhQlQJHBUuT2dneZmuni1MPaHcdDLdFoQi++7M/4Nn4nGkGp5cvGV68Yn0woNttUZYl8/kCSc6p1ZpAznA4ol6rYZo6VSF48fI5B0c3x1nepJtAf8NuAv0XjxACqoJKKklFwY+//EuCfIplZDRcyPMIqTQpCpMUibk/x3VtqHTCTODHU5rdnK1dGVsPMA0Z31siJzNuNTRu12t450tyr0TOJWxdQBaQ+CuE0SXLCkRRUcQJWVZSliCjYbh1oiwhjaa0mgaq0+R8mfP51XPKWkgp2VRxn9/5xj+iZ3VYnR9TLa+ZnI2wNIfNbp2TT3/AoFrwzppDQ1hMn+VcHA1xOxHb/Vt4o4JP//LPuX1LZ73Z4vqFx/XxGe/eegs7c3nx6RF6Jbi9u8Vi4XN8cMjuWpO+63D0+Rm553P/zn2iecrJ80M2em16jTZPP/0hBCPe2d0mvPY5+eIVLU3iTr/F4uyS6HpI26iwJYdoBlIp0XA1kihncr1ESHOaTQdN6jOf5ShmhNuokKmxmMUoesSHv/KQVLM4+/KENXeD/qDH9eyM2XjM1lobSp3r0RWKLOFqO3gTE00NsS2FKCxe/69zk9UqIS0SFC3BdUvcuk6nv46m2Ay620Sxwk9/esz4eIGapZiWy/UkIw8rGqrANEyWkcwsEMSljCynFGqFnwlSBHGZEOaQVA4pAUJZsVjFRJFOKUsUckRS+siWQrvXRTNL7jzocO/dHXY2t7CUnP39Ljv33+KZf8XTxUvaty2enJywKlW+eP6Yg08/pu3UuLg8Z3y9YOV7fPjhV7GtOi+eP8PzIj766D0cx+XxF0+ZjMdkVcGTm7G1N+om0N+wm0D/BVQWgESe56DA0/MDjkefY1k+tVqOUiYgNHJ0NKOBbenU6waFKFksQ5K4wDRUVKXA0gLqVkavr9LQKxp5QD1MyWdLmkqLwC9YjEYoVY6jWWSZRFWUKIpOWRQYmoQqZBIKsjREVRSsmo2fgR/6NPsGt+6ukwidJy8DpqMm/+l/+F/y0bv3GR8+4/TzU4JUptNV2F7T2Gs18U9mXL08xlIFa4MWkjB59niGlinUmytubQ149qMVUtqkP3CRC5eTgzGWodPrGvjDBdErwW3LpS3XuXoxpwgkdje3WS0lLo5X2FrEWqfO6Ysxi9GMvbU1Kl/n8IsTHFmw2TEIRhNGZzP6zTqmnBDOYgpPouVKWGqAyArkSqJuC8qiYLFSyEpBbyBTihxv4aIZKYoVEaYSnzw9ptdwuf9Wl+vRkLOja+q6w2bPZbJUSeOQdlOjiixWUwXLXiCVGYvpCtNUQGh4no6prxN5EnLRpMpV1vt9NvvrJH7Fxz864OooxJFk0mrOIsxJfQldZOi2xdW8YhFUKKoAJSeqMlbCIkkM8qokzgPCuMDS2niRTxzPoAhQpRLJEMj1nI2tOvv9Pveadd7ZqvPuu/sUisFcs4gGOif1kH8zWfLto4AzStSmwkAb0Futs2Hc5ze++ff5R//+f0ynNaBAZbA24K1H7/Dl0wN83+f2ndsYusXjx5/j+yt293bZ3dvkRz/5CS9vFsu8UTfPEm/c+NsmJIq8QhIaurDY6e/zl89UokIiyFNctUQQQ1FRlQJJZERRTJnK1ByL8TwgL1WKUoNCIMkSSTDD1VI2N5u8W3M5XUX4h1PUQqJpmGiVRebriBxEDogUWREUpYosZ9iKSVaFlFVJHmpIVR1dskhmM5bHK7j9Nj1bx9M8hscHDD/2CIY5UVYRVyvi1OL4aEXHirjV19DSHa4uE4bKnEZN4+39dcJ4yfmnAetrbXY3XZbLKWejgEZzg3azxfnxJYZi0WnssypDPjmYUHMsGu0NFlHI5dMhdbdBXcrwh7CsPDqmSp6lXL24wNLqbHdbzCcLopGKqdWpNWE2HlElJTXTRaIkWYQkWYVpyJiuzDTNIXTQ0Ym8BSMhozh1dCtDrhTihYTSAKdbxwsXXFw4WHaD1t06qX/NYipQcdBtQeAFiFKl6ZbkWYio6tStFnGyQFEk1EwlX1VYpoM3DimEjr8Q+OERUexRkiOrMWGgkKZ1FEVGkgVpAmkUo6oaaQXzOCMTglRAEfuvj64IlbKqkyQrkvSCuiGhWg5lLrO7c4t62yDMfBTVYLC1gVmv48UFK91ma7PHtx68TVjAx4cvyYpDYvmMULU4CWTW9S6/8uFd3nnrXWaFhCgNbu3tsP9AJgwirq+v2VhfI88r5rMVcVSwu3sbKJhPl0yLFE3V33Tl/dK7CfQbN/6WVUK8DtMUVFljs72JKCyCLGKZZphaiVbkKOQkpMiqQh5UiELGkDREBZKsIEkqVZVTVTnNmkm+WJEUHooRMGh6qDstzrOE4XyJyNrIhaBhKoQiJIlTbKdJmqakaYwqlRh6SVHmJGmOZnZQSoV0ZVCGFrKwcbWMaHyNkRY4lcTlaMLw6oqo65I6m7TbfYzZl4ynFyhKk3b3LkW2ZDY8Jk4kOh2Xvf7bnJ5c4WcTOp02XXeH85NzUnIGgw2SoOTZlyeotkaz3WU+XXF2dYlsmDhum8uLS7RC4DY2ybKc89E5uqJgWB1iL2V0co3rdLEMB8+bM4k8bL2GoWsEiU/iRVhKE9c0WXkTorRANmt0bYNFEDCd+8hFA7nIkaM5WaBTa/UwrRJvdc25b9K4vcn2PYvzwxdcTRfsdO4iV3D8cohMk6bjEvhzvFWIrZjUrDZF6KMg6JsuSR6SLHxs1SCWbeaTgnmoEyUlqlpSFSsoUhTFIM4jwigHYSMZDsvAJyskskKilCTCPKPUNNIyhHRBxzYYOBWqENza69La7JApOst4gdNps7f2NRr9fSbLMX4Y8Fu/89usdXe4PB8xOjrFcRr809/6x4yjBX/26Y/4+OAZ+rqDkwo0t8XR1SUPv/oNmq7D1dkVLx+/YGNji/39uyyXCy4vL6nVamzvbJImOacnp8hywe07W3z6pfumS++X3k3L/Q27abn/IiqBElHlKLKgkAu+/+RPCVUfWctp6ApaDlVREKOQyQoi15EKnShTGS89JF1B11VUNUMWIVIVIRWCuqGyV1OxRnOkWYocpdQkBVe1kYqM6fVLGo6BpZnMp2MUpaLudsirmCxLkCWZetMgiAJWvofd65FpJbPgDNk00LQtWrnCwff/FdOLY+a5wlSyuI5XmGrGpqnTUVwCP+DsdEKRVPRaG0hSm8nVgvPrKc1OG03WOD0eMVkm2G6bPKo4O7omykGzXXzPY3R+jSRbqLpJuFyyHHq4To8EifF4RBzlWO4aflwxHU/ICxlZb7LwQ8bLEYWkkmPhrRKuJwtyYVPKdRZexGiyQAgXWXWI/RWxFyHrJqrjIiQdfzWhVB1UwySLQ8LVkkG3jWX1ODn7krl3ycN37tHtbvH0y89ZDQ/ZXXuEo7Y4fvUUKS/Y6GxRenPml+e0jBrFKiHxRugio2VYhJ6HWa8zCwtGi5xCMohiQVko5FVKIQUUckZalcRlSZRXpKlCjkJa5qR5hL9aYcoS7ZpCwypouzIffvCIO49ukZAjNRrQXGf/o7+D1NlhKbXpd7f5u9/6B+ysP+T7f/Ix5+dX3Llzn3v332Y0CfjzP/8+rmXzzfe/zq8+eod6pLJRb/PeBx9ya+cer56/4PEXz7h16w5ffe8R48mYT3/2BZ3WGm+99YAo8vnhD39CvWHy8NEWiqzyve/9gPOrM47Pz9508f1Suxlbe8NuxtZ+8VSUlHmBJFWUlISlz3/7v/43fDr/c9o9j91uRUsuqcqYWDEJsorMA13UmUcKn52codZter0mNSWipoY4coica2y5Gv9wq4HzsUd5rBNdF62SvwkAACAASURBVAyfzWi7t0mCjLxQGF2H5JWEYtgEsUxeKSCVaLZJlheIKqKSVKSmy1JbUBukmFstrs0Oj59OWS91ysmMySLnvJB5kSyw9+rs3Opjjq7YKhIaco6gxsqrSJIQIVdYNZ0oL4mimKrMMCybtKgIowJbUzF0gzDNSJIUQ5Uw9Tp+EpIXCY5uoKk2Sy8mkVJcS6fICoIoQ5ENdKWEXMb3IiSlRBJQJAqiLKEqAJ2yUKjKjCqPMYSFJDSSbIGulMiqTioZ+FWKagsUzWSWpmTMsVWwTBvNBbut09kWKLZOmsU0dWipCuq4YHEmI0UCR4MqrIhWEWpeoAqDNFQocxnU8vXDw1JhUaTIgwGHs5STSYiiyYiipMgEeRWCHJHlKWlegKgIgwKhOiRCkFYJFDHb/RauFFLvuqxv3UIx6/hygdxScJq3KbIaNXcd12ywf3sTq2ZxeX7JarWi3WzS73So0oqr8yFZodBoWTh1m8XCY3Q9olGzXh+xKTOG0wV5WnJrc42izAmCkuXCp1azcWs6RZZxfbkEobK+1aEoPEbDBbJkMlh3+cPvfId//vt/8KbL75faTcv9xo2/ZVX1etd7WRXkRYGmmOxt3eWz0Q9JY1hlCpICUlUgVQZpmlCWAsN2kaKMoixRgLISCCRkFChyFEmhqiqoNAopIysqZF1BVhIoFuhKjlG1SOwIRaux8ELKJMF0XITWIE9jpLRAszUKw8JLA1RpyKazji9qZOMZa8GEcvl6/CnJMkxTQ61SRvMSyYBeWuBoJv40QoRjGrZGq24zSSKu5z6KEtHqNIlSF88LsQyJwY5LHCvMVwtcq2AwaBN4KpPV5PURm6pFGEt46Yx6D7zYYryoMGsqjYaGNy8YezmOW2L2dcrCJI8BJURXBEUmiPwUwYx6TaXKXbygohQz3JZOBIRhia75UOVkZZcwzRlYEpZbZ5ZXzKcR9rJA01POjzTkVsX6LZPumkMxEpyMjmhadZqmSjFLWF37OLqMK9cIFhm5H2AYFlECaSwRGSnrt+9xkVZMggMKJaNIaohCQciQ45KmBnmWUpBRkLDIRxTlDN116XfXaBstzCpm/913ydwWo7Ck19viN7/5m8iKycXJNaLQeP8r75OGMccXQ06OT1nf3OTBvdvMxlOeP31FFES899VHOLUaL14ccPD8gla7xUcfvc1iGfL8+RlJmLC3u0b/bpvheMHhsyNsy+Cttx8R+AnXlxPSOGb/ziayCtdXHqfHF/T6HW7tbTGbXzOb+m+48m7ctNzfsJuW+y+mSpTkSUQpKaSiotIyfvbiY8I8QBgSCJm0kkGRKRIJQ7ZAaIyXAZOVh2LpWDUDSynQpQxbLdFkFTnPuOUYmPMS4clkixzhxzQUDSmJKeIlNUujiCJ6PRdTrwi8lDxUaXQ0TLNktqqYVyVr3YhGW/DxxYSB0WMryTh9dcrZNEZRHFquTljpTGcGqmvTvC3w9QBvPmdNr7NltwnShOtgilbX2Nl3EJJCFEuYjsKdB1vkVcnMC+j2XbSuwxeLlKXbwtox8WSN49Bnpio0bq2xlBJeLAJmpsDeHjAtC76crSh7fbS1OteF4CKtUHpNrPUmiSY4nC9YGqBvWtCsMZcd8q6Nvq3g6yXXuUatP8DpCJKsxF9IuDUD04zwVyFBpNHpWzy83aSu2pwcZlRygd2UuT4fMR/F7Ozf5aNff4+Ts0vOT8dUoqDTs5lNVgxPPEzLQjEFw/mCRQSN9XXcnXXOlksOh9d4RU5WSqSFAliEcUJclviZSZ6qVJLCPCtRbIe6o/LRTpMPH7nc+XCLxv1HnKcD3O5D/rN/8l/jOF1+8r1PaWhbfPWdhwih8C9+//dJtZIH795m/9Yej396wPnpBf1uh/c/eIiq2vzhH36XIEx4+Ogu77x3jyzN+ONvfw/Hdvnwa/d59PY+Xzx+wcHBKev9Ld597wFCCP70T/4M161z7/4eG1s9fv/3/jXjyZR7d7f56gePGI1m/PTHn7O7s8HMH/LZ4y/fdOn9Urtpub9hNy33X0AV5FUCeU4u6eRSyan/jP/hd/87rpLnxNqIftvG0ECmRM1l5EwwGa2o9BqHoyFq06Lbr9PSUupKTEdPkXIJp0z5B7vr9A9T8sOS+DLGP76mpTQQSU5ZKKQpVJJKkmsUokaOwsz3icqSQjaxay0KtWKWT9F7TQa31snGI57+7BXL3EU22sRVwkpKSOttzpdwXqWs+gm9PYvtPGZrmNGPaxgdGb0r4XsRXhTQaa+hmxVX18dkuUqzOSD2Qy7nC1LZpdYd4K2mzK5HWJ0ulmYymVySVhWu1YdCsAjmVLKErLpUaUUSLlENG0erkcchQbQEQ0EzLURVUCYRKDqq3aQqA7LAw1AsdN1EIiFbeaiSTavRI1pNCMZX6IqGUqsjuTFKlWAUMnsba5S5xfc/+5jKkehsdLAbr/ekb26t82j/Fvl8RHy+YHIYopYKdUMmDlK8ZYlhtTAbDS5HU/xE5mwy4cL3SVSDpFCRhEaZCcK0IqggqSTiSkGpSnqWxHotY7urYw1sjP6A229/wFsf/h3cRp/R0Ofo6AxdN3HsOov5grmX0uq5rO+scXl1jjfyaBh1tjZaUMkcHh4xnsy5fXeHrZ0+s+mC66sFQqisrddptpocvTrm5GREp9Nn7/aAPCs4Pj4miQUbGz0Ga20uLo+4PJ/gOF22trrkGcznU3zfp94wabcaDC/H/NF3/4zv/ODP3nT1/VK7abnfuPH/A6kCJEElCfKiomP22evdZ/jyjEJxGS0zXEuBuKAmaZiySpIoqJpGlksoSJQI8qqkKCsqJIqiREgSQojXf6XydeteEaBUiFJGklNEKSOEQFchKz0ECq12hlZvMV+EeMEh8zSnvncbt+MyW52RFzKFXMMJZdJ4QdW0Uet1hFGQz5bIehMlr1BjiHLB1rtfoRnHjCevGP9f7L3Zj+TZdef3ub8tfrFHZETkvkTuWZlZmbX2TlLiIsmgLAmagW14E+CH8ZsE8EWAMXoY6C+QrDHsB3koWRI4sDngjLWYpEgO2d3VXXtl5b7vW2Vm7BG//Xf9kF2tYouSxjRHxWHXJ5FALL/lRGQefO85595zjxvksn1oQZKVzTXCUJCIFmjU65wcbBIIiGlppFtl7+gAPRIjY6RxDy6o+iFR3SAqQ0T9GNsJMIIMhq7jB2Vc1yGlxVAaLkF4ROBJYoqBXwmRRoOWcFB0FREK6vt7qLpPPJGgabnUgxaa6aEpkobboHJxgRo4xPU0uhlBelXccotYNk8+l6DZeoZT8UmFSXCi1LebnBtlEh0xPHHGwfk+bWaUq50TjHWl2d09YP+oROfAEAnPpdY4Z2H3Ec/OQwInTs12cbTLTIzjhyjSJXChYUuMeALPr5OM6XS15ehIaKTbY8Q6+9CzWbpGZvCMFAf7klElykhvnsGuAlsHz1jY2OdZ9Zzh4iSpuM7u/Dq6jDAzNIWUFuen51SqLXr7+pi9Mc7h0Qn3784Rj8e4OjNNEPisrmzy+NEiw8NDfO7nbmHZDfZ3DnE9m/ZCJx2dbZydVllc2KBaKTN7fYJYPMLezgknx2UKhTwTV8Y5OztjbfWAdCpBW67tZbvdp55XKfeXzKuU+88gMgTlsqd7qGiEoUJEVai3qnw4dwe9EKPhOFiWj9PwcGo+dt0nYqSwQ8FFs4GRiKBHL3u5RxQfU3jgS5IRjSttSeJlm6AU4tsQ1F0iWoIwVJECAiExTBNFCwnVFkJzsaSFMD0yHQbZ9gTkNDYbu5idCrPv3GRjt8z+yhldRoxoOkpJ86nZDeIpk8CAk1adRtCi2JkjFUmwu7FIuXqAryhULI3VrRLlRgU0FduKUL7wCIOQWNSkWdcIWiACj0Q6gWWHVCo1dE1BN5PYNjRqFVQ9RI1EaXohvueiqSGoKo1mCNIlFC4iEiNwBUoQInwfTTFp1n2k1SJlKAjHwGleDqhMRSewFOyWQ+C0iKoKESWCIiI4fo1cJkoy0ka9Vse2ntHTUSCTzfPs7JxauYWp5cjnOmm2Lqg1SiQTWUonHg/urPPw0Sq7p002d1ssr+9yVD2if2yYD+5v0bI1fASBbmKHBq4USAS+LWg1LBJRjVrjlP6+NnrzKoIqQzMTpAamyA6+w/T0O4Qt0EUSIWM8XTqgXD6lkM9RKLTTN9BDZ2839997j4u9Y968eoPe9jz3732Aa3tMTd2gvSPH8vISP3j3IdNTs1y7PkHgwve/9x6aZjA1M8zU1VG2tvZ59HCeVCrN9OwgUTPLo4cL3Lv7mPErvVydGSIe6+D737uPY/tcnbnC4HAvmxt7fHjnMUPDQ1ybHScM4Fvf/T6Hp0cv2/s+1bxKub9kXqXcfxa5nHkduDa+YSKlRuA22avt8C/+1f/EcfQEaQY4Vo2g1qJxWCIiNMZHxql4DidWCbM9QltHjKzWIKu5tOkOWgCdUZVfGWinbb2KvR5gn4U0D+uk9Tyho6BKBcctoakaYaiC5iN0H6EoqKqGrXjoKR0ZUSnlkywrAs8xiB/FOFlwqB97mGkVuqI0BZRrdWpuyF4YUsnFkKbGYFrHLJ2jNkzOGy1S7Rlcy6dVaxJVMqTiIRG1hdsAv2WQEjp6NsZJuYIiwIwLQhnScEOUICCZjGJ5klYDDFWgxqrYvsBxophKhIjuY7sBShjDt2uAhRlL4ds6miMxVQXLd2kGCoo0iEbAsVqEiomqC6IiwA9t3BASMYN8RgNcGk6ImYjRntVIGAFeq4nQVaoNl4jRjwyTKDGXSMaiXrPZOyjhBwFChpTPJa6XQDU9Cl0hqVSatuQQW2ubWK0Kng9uaGKFOr4CnuMgHUHU0FH0Jl3DnfiGztDwED3DowRmgc78FKlomlK9QTaRpdlqUmo2iMQTmJpD2LTo6yowPNJJJGIQiahsbBzz6MkSyWyEgd5eWhWL05MGybTJwFAn1WaN5aV1DD3O+GiRfCHN6uoGx4dlcu0Frkz1g/SZn1vD9wQ9fe10d+dpNFpsb50SBDrF4TSpdIqTwwrr6zuk01FGxvrJZLI8uDdHvVqnqzvN46V5/vhff+NlO9+nmlcp91e84ieMRICUKIqC9H1UVSEMJfl0jtH+cTbWtjHzJoowkSJE0+Iko3FU1cSqXXb6UjUdgYIUl53iVFVFkSFh6BHiIYSPqoNqCtSoQImohJrAc21QDRRdYGgaUqgEIgDTR1UssvEItgFuHFJ9Njf729k6TjC/U8OlRnuviRM6HNYa+GZIqj2G4uVY3zsjGeqYUqVx4VErR8D16ehtx5MtFBSSsQTpWARThNTPmghVkM7G8FsVWvUm8YSOoglczyMMJElToKpxWi2XwHfpyGcIA4WWH2IqAe0dKUwzSq1WJ2J6JKIxImaeVsuC0CcZi6JJlVKlRNj0aYsmCKRPy7bxjZCEKcAJCW0PQyiomiRiqJTrFqapkElGEbhUSy1kTKczl0OPSpq2g24miWd8jk+32ZlTSLgdJEUEy6+DkSGqhkQ0CBUdu6ZiKiG94xq1Z7B7FiCVGL5QqDdrhIqKBkQjKtlslGx3F3pWoWdsnKErb9FsavQV2oknYpRLFVRh0nCbJLJRpJDIwMZAR0nEOK+UqT+uE42odPd0MjzSTVexwMHRGUf7z5BuSEd3Ek03WFrZIAgDxkZHSSbiHB0ccnJyQiqV4o23+ilVaywubKDrGlemrqDpEQ4Pjvjw7jzZTJrxK4MIJWBv95SF+TWy2RS3X5vEatkc7p6xsrhDT08bk5MDWA2f0+M7L9v1PvW8Srm/ZF6l3H8WuRR0qYCqaqioqEK7nKUuNe7du4dnOjimhaIJMrEM6XgCXI+m4+DqEiWto8UgpjmkFUkipuHbLQpSMJBpR6v7aA0f0fRwWgGGriPVANPwcEMLNR5BiZuIpIobcwhTTWhX8bMCkdfxOiPIoTb2PZejhoPQ4zSrTU4OjtDMOGYqjRWGXNQdRCRGiEPl/JSoGsfUI6RzMTwlIFRdCr0J+sY7CTQDrU0wOBljvDvDwfomTekQplLE0z7NVhVPU0m0qcSiweUe5dKipzdPf08niqYRy6S4fm2K8bFxhBKhs7+Ptz//JgODA5yWm4xduc07n32btkKGs3MYv3ad2bemSOfznNctxq5f5c0vvkn/xCiBZmImk9x88yr5fIpU0iSQNooWYgibRr2Kr0gyuQgR4XF2eoH6URvd0skh2xtbWGUFI4zhhQotx8FyAFQ0XeIFEo0ohqqSTujY9SaNis/FszMs10FoOo5jo+k+qaxKPKeT6sjQPzHD27/6n0MySzTWw9WJm8SiJg8fz9PelaZY7OBgf49qpUp/MYfreFi2S0d3O5XaZVtgI9rO06UVdvcvyBUy9Pa209vVTndXltPTC/YPdigWi4yNDbG+usPO9h6zszcYHOzn/KzO06cLdHS0cXV2nGQyzfvvPaZSKTNQ7GZmdgxBnCdP5rk4rzM20cfk5Agt2+HR/VWEAlMzRUbG+tnbK/P++/dJZmO0XIsn8/Mv2/k+1bwS9JfMK0H/GUQCQiIUEAiQCnC5C1s8GefB4n0u3DNEXCI9Hy1UCFwP33XwFUkY01BjKnpEklY8EgREDJCuQ17TGS7kUcsWas2FZoDnBEQiUaQq0WI+nuqjxg2MlIFIhHimjd6TRGYjaO0JnFSMZjJLXclxdAHHuza13RaRKmSUBJbtU7VaJDJtGNEkpUoVz9NRRBwjHqFhlbFdyBXyaGZItXlBvdmks9hLrK1JubpOu5YmrrbhEOBSIR5JMjLaj2aGGGqGwZ4J3nzzLYo9gzi2oHtomMmZCRqlUxq1FslUBtd1WF9aYnfrgKOzMudnZ6wuP+H9O3c5OD7j/HyHJ/cfs/ToiLOTOq1qjd2NA3ZWnyEtaFXPqTfOqdYcjGQaI56gWqlTK7dQtQSJVBQtbF0OiOJ5enoKaDbs7BxgtyC0BGEgcDwFH3CBQFdohSqWY6AIQUAdoQT4QmdkagZPC1je3cNXIlTqZeJJnWwhTTSV4rUv/CKFiQmUdJpkoovP3Po8Rqjx5OkTUFSuX7/K/u4zdnf3GRsfIpky2N06o60tQ6Ejy9bmPvF4Ek0zuChdkM93EODy4P4SpfI56UyCmJlioNhBoVBgd3eXtZU9JqdG6O3t5OmTNc7OSnR15xga6eH46IxHjxZRVJ3br40jA50nj1ZZXdkmV9CZmp5E0xS++50POT8r09/fy83b4zSbDg/uLXJ+1qB3IMPU1Bilixof3L3H7uHBS3S8V7yqob9kXtXQf/aQEp63fwWBDAUCcdlHXbX5xp2v8wd/8T8TFhxCaRMJVWg56KHA0wVOQkfPKqSSCu1Kgw7NJ2GGeM0as+k0nx0qoq6fEDluEh47OLUQM5LCExJPr2J7AUYsimoaEAVHs4i0maixNGqqk6OaYOfM5/DcYWezxMV+HfugiVnxKMSiRDMJwqhJ3fORRoRIIsNxqcl50yaVb4NohJbn4oQ12juz5NozOLJFxS7TOxBlsthO6lRn7d1tUu2dxNpMzveaNJs1zIyG9E1qpy7tnSlisTQnJxXOzksoakggXQJLUK+VUQ2Bh4LjGYS+QI9CKFsEXoTQi6BpAY4VQhBDUwWe20RXNDwbVCHQjJBA87EsH01XiBoKUdUgFU2jhQG61sQ0BaYZQUWCtOnp6WFn55TdvRroMXwR4gURvCBEagphRMP2QsJAYigqgdIgCHykDGlra0NVDNbX90glTHIdWRwNxq/PkOroINQjFEeucP3abS5O61wc1UinTDr6urgo1Tk6OsI0VdoLfTi2R71RIRZX0BSDZjMglBKhXP4vhb6K4zmYkRhSuDh2Hc/xSSXjDA500Nffie9LSqUS+/tHNOoW2UyGVDLJ2bMKSI140iSWNDg5fsbp6Rm5fIpicZCWVeXo6BlChBTyHfT0dnN29oy9nVPCMKA42Ec2m+H8/IzT0zNUJUJXdzvffe8HfPXP/uzlOt+nnFcR+kvmVYT+s4cQHz964blACIETOOQ6csytz1GqnyMVnyD0MFQVlABfhyCqoEdBV0KSwiOpQMRQCWyLvkSagXQKcVFHtXzchgOhgtA1PDWkpft4qkBNmgSRgDAJblygZbM0zTbeXbzgm9/d4t2/3mN17oTzzTJByUb14gSBScX2qdoWtm+RysZBKFSrDRQ9QFF1mnVwAxUR8Yi3RbB9QaVZJ5ZRKHQXqNVbbK7u8MbEFLoVUr9wOdovEVo+oRVld/2cZvUM3y7TrLisLK7TqJ0hXBu/FaFc9hCOj6K4BGg4LYOIEsFQIgSeSrPqIjyNwDVoNaJIJYorG7TcMlKJ4osoNg1cxcfxTcIwgSI0ZACeLfEciaYIkmZATE/TaKiI0CWfUGlLJLho6Zyc1Gn4IWU/oEkMxzcIDQ9HBtguhChILaTueDSsGF4ILbfG8fEzFDWBbgryXUmuvvk2/ddvc+a5TM7M8Ou//F/RprXz/nfuIlSFa69Noxo6jx7ME/g+09OTpOM5FhYeQxgwOjqCrmlsbqzR3tFGR2eeRr2Jbdnk2iNE9TjNRoNoREMROopQiBpJ1tZ22drc+2gTlQ5yuQJtuSznpVOWVzZJZzL0F7txHIeFuRW6e/qYvTYOhDx8+BhVU5i9NkVHezsnxxXmniyQSqW5cWuUzs521tf3WViYR6ger79+nWQyzcb6AXc+/JCDk8OX43SvAF4J+kvnlaD/LHKZ9JJSIoQEIZFSEIQhKCFSA18GPF18gtAFkYiGlAGo4EcEgQmGqRBRJQkREickFY/itSz6Ekn6kgk4byLsAKvhIISOlAKXEEtCiIoaMSAi8c0QV5eQaWOjFPCX39tid7WJ2ZQkfYWIlEhX4voCGY2gpkwsr0UkolJv1lBNiWFEsRsmlqUTjQm0iKBhG/hArjeKMH2OLyo4DvSNJMjmNGoHZ9hHVVrnFcrlgNJpk8DxMHSBEijUzjxsBxJRg9CPU6pKhB6guRKbNL6dAM8jxKJpg+eFeG6NiGHih3F8EcUnIAyaKIGCFrYR+j6Wd4phmEhh4NOk4ZdpCQtbAUdKPK9By61RtV2UmEYqEYIaULUcfN9jcKCTZqCwc/QMVYvh+R417xQpIlhujZZ9Qig9XCdKEGpISoShTSoWx4w2GB7TeO3WFNrYFbasMp/9zFv8xq/+99SOPe4/WCTf2cmt16c5L1VYeXpCNpHg9dfHKZ1XePBgjmjc4fXXX6dWs1hfW0cAr79+nc2NPdaWNxgcbCcW19jZOMfQVbo682yuH6CIkGQiSa1q0dHZiaoYzM0tcnRYJZuLkUzFGSh20t/fzdrqHo8eLNPTW2B0rIu5p4usru3Q1pbl9bemubg449H9DcJAYWp6gMHBIRYX1rj7wTKhaHH9xgQDAwOsLh8wP7dLNKZx7cYwJ2cXPH769KV63qedV4L+knkl6D+LXAq6EAKQSORlLV0IFKEgpSQai7GxvUG1UUKKECkCUMPLCN0Q6BFIRDSiMiChCGKmjtNqMpjO0K4byPMqgeXRbFhoSoQwADcMcIMQI2KCEiKMAFd1CEyFIBrBFyahGsX2fFQpMaSKIqO4bkg0qeEqFrZsYsYVgsDHD7icgR4GGBENoQgsx0YxQuJtOhY+dbtJLGNS6MrjuBbntSqZfJZro324R2UOl+pYgURTNWzLwnVAJYaiCGxfYDvisie9oiF9HceTePg40seVMWSYAqkSyAiOH8MNTGzHxvWqaKpC4Oi4nsSjiRJR8AOFequBEzZQDYVAhEihIqVENxQ8KXECiWW3qDTL1Jwmjhdg6hGk71FvqDRdn/2TCxpegOP5IHRajTOMSBSUBJ6vIfBIRD1ymTiJBAxf6eXK7Rske0ZpmW1M336bL/9n/5Tzi4Cjkwv6+jqYnLjC7tY+e/sXtHfn6C/m2N8/4O4H81yZGmF4pMjpcZnVlR062rOMjA3iBz4b63t0drYxONTPztYBjuUxNlZECIWDg1PyhezlMsC6j6JpBIGNlAq5fB7Pd9je3qbeqKEQIZGMM36lj0JHgr29I44Pzxkc7iGdiVOrOKwuHZHLZZmeHqfZarK+vke5XGJ4tIehoSLHhyW2N08IArh5e5K+gS7OnlVYX9tndXOT9a2Nl+t6n3JeCfpL5icp6JcRofiHD/xHRMq/maLxH2rbj3POTx/P7Q4BiRAKIJDicr90U48S1aPce3IXPaaAIvBVQQMXPWGgGwJVDUmEkoxhEDE0vEad0fYCCceBSgu34dKou0gRwfcFARpu4KBqKi4uLg4138LVJLbmc24FhGYGEnHKoU+gqQCk0zECp4FGgK6CIkKEACkgVMEPQ8LQR9XAjESwHYEtm8RyIYqp0nIkjWaDzu4CajzCs9oJaWz6jDRnOw0uzhRCoaNqEiWEatPBDuKoioEjAlxp4vsxgsAjwMYSBiFJPE9ghy3s0MZB4rkWrtNAN3TqfouarCATDqEOoaqiRwKSCY24GUNF0NWRoj2jkctEMPWQfFuU9nycVDyGrquYhke+zcDyqlQqNayGSt2u8KxcpWb5eMIjkdTRhEI6nsEgpK8rwcBgjGSHIF/s4soXZ0mP9tOMRggzed7+xX/C1PV3qF+4eA2F8fEp2gpp5hZX2N5/xvDYMH19GbbXNjjcPmRyfJKZ66Ns7eywv3NGsThA30Duch345hHJRJpisYt644LF+XW6Orvp7slxdHTM3v4BxaEiiYRJtVxHU3VyHWl8W6HeLJNIRdBNDc/10HWDnd1DdvcOicbi9PZ0UhzqJptJUSk32N3eJZGIUhzuxmr5rCzvkk5mGB0ZRNEkS/NbXFxcMDoyzECxEz/wuHf3KdL3KRb7KQ72sLy6zMLKystyuFfwStBfOj9JQX9R/J6L4t8liP/Q+z8phBAf//445/6nyQ/bffk5BIpQCcOQMABN6PT0drN9uMXG7hqJhIktXURCwcaVWwAAIABJREFUI9AlWlRDypCEDjHjss1r2Ggw3dODWW2i1F3ceoBjgQwieFaI76m4rTogCAW0ApeWGhBEI7TUFBeuyX4jpKqkOGhoWKFKOm0Sek1iqoLuC5QQQukTECLVkFCBQEpcx0NTFPSIhtA9RBQqjYBYMkkqFyESi/LsuESht5v2/jb8i33s3SrHqy08vxNpRmi5Nq6nE5oaUpfYjo1UQgJFwXJ9PBUcxcCRCk23iR+EeL6NpIrvlFFUSSA0DF2STdi0mT6G5jE60kG+IBFmneL4EJ/5hTcZne5CTcCtL77FjS+8RX68k1ZEMPvZNxl/c4LO0S7M9iRDb87y2i9+iYGpcS5EFXNggNkvvs3gzUm6JgZQ0hpf+idfouf1EaJFHaNP541f/gJm7wC7tRbn5yp9A1P81//tb5LPDbM0v0yxd5jPvvEGlVKND95/QiqR5nOfvYmqxbh75xFuy+MXfuEzpONZ7ty5z8L8Np/9uVt0drXxwZ0HVEo2/cV2rt0YZ2lxjUcP5xkfneEzn7vG3JNlPvxgjps3Z5mZHeP+3XlqZZ9bt0Ywoi737q2TahOMT0ywsLiF6wWMjPWzs3NCKpMiGo3x6MFTTo6fUSgU6Ohoo729g2vXxtlc3+Xe3XnaOwq89c4sJyc73L8/RyqV4zOfu4qmB/z5n3+TZt1naKSft96e4fjohO/9++9wfn6I5XrMLSy9FI97xSWvZrm/ZP5jzXL/aRH0TyNSyo++38v6+WXnOBUZCgI/RNMU/CDAFTYl9YJ/8S9/h9VnTwnbwM2E2JqFGYG0qdEuPNpNHdWq0KkI3u4rkj0pI87PqB/XLrulyQR+zcf3Ja5sgqaCodFUHLykBvkkNTPJqSc4dSKclJNkU1d5Z/Ya5b05lj/4LtFqDcNyseoWddvDU8EDAlQMoYMXIAjQDBUtpRNGE7SULC01JNphoCRMYmqKY+uMfJ/Ozw/l6T+L8Z3/7QnlZoy6EsENFHzfB72CIiV1NMLAQMHG91xCL0OoxAhlGduxAQ1d0zBUi1jUJZnIYMYNlIhDri1Fe1snrt1Eibq09/fjK1k2jg9RkhqZQi+1ps5xqYk0JBgKZizN8ck58XgEjCZmpMDe9gnZjE4iHSFQojgNcKwqfd1ZfCfAt6BRf0YylSSSyGCYKc5OKvS29fFzr/8chUiMw/1TgsBhamIYoWosL2xiWy4zV0eIxk1Wl3c4Py8xeWWIjq48+ztnrC4fMTiUY3Col/PzMsuLO/T0tjM01EO5ZLO8vI4eUZi+Oohjh6wsHiDxmbk2ShB4rKyuoispBoYKhIFkZ/uIZDpNqi3L0VGJaq1JT28XLatBrVKlLZfCdUIIJZGITrNeo9mo09HeztBwNx2dOQxD5eysxNLyBrVqnd7ePlKpFKfPjihdVBkZHaRQKFC6qLG1uYvvS6YnB4klomxt7fF//rt/y7t3P3y5zvcp55Wgv2T+Yy5b+2Tq+m+E5oeF/Pnj5++9mLr/xxb8n8aywf9XpAw/+i4va+iIAKSClJf1cwIJUuIrPpZu8XR/nv/l6/+SzdoGzayNmgVVd0ibKp2GQkaTiNIRM/kso/EEiaMq8riCc+4SeCaBaxA0JF4gwJQ4ATS9EEtIvEQcmU5RT8bRs10UBq6SzU1xY/gLJDQd2zrlw+//OXPvfYOgfkTgNGjWm9huQMuXyFBFR0cNQzThgyLAiBFGdIK4DrE26lKjJVw6+6PEO9NcVE7pVQJ+qWuG5a/Pc2++iqN1EkYsLNdCygiqr2ALH8v1EFqEMATHbREqAbm4jpA20bikt68HhTgBAYk2hes3J9g7POK45pLq6aOt0I3rCg73Swz2j3F9Zoaz8ya7h+cMDw9S7O2iWm/ydH6dVDbJ1NUxmvUWd59uoEcMxgfymKbJ/cVd1vdPuD5TpJDJUDqusbL0lNduXaGzq5vd0wueHZzy1s1bFHs6OD46ZWPzlM58jKnJEU4On7G1doiqqUzPjuG7LtubhzhOi+npEWKxKFuru1xUqvQN9NHZ08Xq6gEX56d0d2fp7e7k/KLG1uYBbW1JBobaqVVa7G6fk0hG6OvrB+myvXOEokBffw+RiGRn+5Rmw6Oru41oIsLJWZmToxq9/TkURaVec/D9kGjUpGW1CIOQbDqD7Tg4loUQCkFogYCR4UGKQx1omqBSbnF4cMLR8QnxqEmhUMC2Qg6PjkmkIhSLvQgBO5tH1Bp1uns6+PDhY776Z3/yst3vU82rlPtL5ic9Ke5FEX8u4M9FUkpJGIZ/p2i+KOjA35sqf/E+z4/9h475pE0/qlb+s5E5kC+UGi6bywihfPQ3CFF1FUUVqKqGkNCR7GBieJK1jW02D/bQEjHUiIYX+Cimgo9LHJ9uM0IkkDhnDVqnNn5LxbU0KpWQSj3kvBlydORwXlFoyAyxjit0jbzB5O1f5kuf+x/4udv/DVPFzzDeNUOGFDERJRZJMzI0Rb6ryPLODq2gSSwrEbqD1EK8MEAIFUOPoqkmfigJlRaRlCBQbVADEpko6Uycs3ObQKswOpqjkEpxvlph78Ez6iJP2W0h1Sh2kKBi1/GlgwhcmpYLag3Xu6C/vZ9CNsrroymmhjPk22LEUikm37lOcrDAge+zVK7T99prvPmFX4dWL4XkNL/yi/8Fn3/nS1TLPkcnLT7zzud467Wb7Kwesrm2w61rs7x27SqNsxbrT9cYHejhF1+/gX9e52DzlI5Uml968xbFQoH60TndqQJfeGeGW5PjbK8coXjwq597i/G+IRYfrbC6sMSN6XEmh3pYW9vl4YMlRkeL3Lw9SbPp8PD+U5LJJG+/M4sMQz58/ym1aoPX3pwml2/j6dN1llcWuXl7giuToxwflHj33XsUiwO8/sYU9XqVux8+RtdNPv+F2yDgvXc/5PS0zOd+/jbZbJp3v/+Eg/0DZq5dYXC0mwf3HrK5uc/s9Ummpwf54L15ZCC4dXMSgc/G+jKdHTl6urvZ2TkgEUtTaC9Qqp7j+wqFzgILSyusrezT3t5JWy5OLp9ksNiB3fSZn1tBKA7vfOY2pVKDD+48pFwuc/3mBD3d/Xx4Z5H7Dx9ycnb8sp3vU82rCP0l85OO0MMw/KHnnxTvHyXEf98kNEVRfuR9Pin+Lx734vWe2/PJ6/6HlAL+0xX1EClftD8ElMvd18LLHu8IkGF4mY2XkqZw2Wru8b/+xf/O95e/j0jYpLM6ZlxCq8p4BK5ETKK+RB7U8UtNjJaG29Jo2TrRZJ5IW572XJH+sXE6i8Nku3owMjlCcbmMS4RRfCkQikALdKQukdJHE5KWVWJx6Q7f/uYf02ys4ztnOLZNywrwmiAtBUXogATVwTckWiqJ1HUwVdKFAi4mZbeMZqpcHR3gVmGEB1/9Pt9+v4YXQihLoGbATGC3Tki0qXS3F1Bd6M9l6G6PEfgh+7ZCV7EbIx7ltNRA6EmmZt5g6sYNShWLnY0jRotFRgZHWNvcZ2VpnatTI4yODbG+vsPTJ/Ncm51maKifnb1jFheW6enKMTt7ldPTMo8fL9HRmefq1AAYMb7//Q+IqHDrxlWi8Th3P3iCbQdMTo2SycZ5OrfEwUmF6elBxkeKbK7v8nRxjf7BfibH+ymVqyzMr6FrEa7fnERKyYN7CziuzWu3ZomaGodHx2xtHTM0NEhxqJ3SeYOVtVVSyTxjE73YjsPC03V0Pcr42DCqpvP06VMajTI3btwklYmyu3PIxvoWo2PDjI4OsLt1xPzSGl3dbcxMjXNxXuPxk2XicZVbt29zfl7n7ocPGB0dore7l5PTY8rlKj19/VRqDVwvwDANfN+jUqmQSETxfY+zZxcMDvZzZXSAVCJBLKbQbJbZ3Dhkc+uAkdEhsvkMm+u7nJ6UybenmJgY5N/8+bf5P772r1+i373ilaC/ZH5cQf9R0e8nX/+4jou43CjkE9H6j0qth2H48bHwdwv6J+/3SUH/u+x7kU8ONJ7b9clywIuvhWH4t2x+cRDw0zFDXiLli/f/KP3+kWlShpep61AiQwh9aCkeLdXl0Drhuw++zfcffBsUC9evocuAqXSKdhQSRpQsMeJ6nJSRJpPuJp3rI53vxkimMM0MoaoiNRUhFEIUQEOoAgL1MlsgBQo6UgUv9FCFQMHDDcocHc7z19/6GrvbcwjRpGXVsJoOgR0QegLpq/iEhBGBloyAFqLFFMyEiW+2E0lHadgV1FDhi2PXad7d4Gv/1xx+EAVVEo/H6OxMo4eSSCJKd18HZizC2fkJyVySoaFJwsIEzy4kW9tH/PKv/hJdHb0sz2+h+hq3b13F83zuP5jHkx63bk4ig5D795+SiGWYvDqMqsPSygZhIJgYH8TQJevLRzQbLmOT/eTyKdZWdznYOWJkpI++Yhd7+6fsbe7R2dnL6EQ35UqNucdrpFIpJib6kYQsLW4iQygOdpErZFldOqRaKdHV00HfQBc72wdsbx3T19tJX7GAbbmsr+6gG4LBwSHMaITl5Q0cy6Y41Es+l2N7Z5f9/Wf09vRRHG6nXm+yML9JMhlleGQQVZVsbx1RrVQZHuknmUqytbmH7Vikk1k6u3Ocn19wcVahLZshVyjQbJQ4PiqTSKTIZlPU6mUadYt0Kkk8kaFSq1KpWqi6jhlV8XyJ7wmECHC9FkEQ4HouEVUlbkYYGR6gt7cDKQOqtQZbW/vUGxZREzLZLJblsbW9yfLmBv/3//Ptl+Rzr4BXgv7S+XEE/e8Tyxcj9CAI0DSVMJSoqvpxyv3ydQ1FUXBdF13Xf+g8IQS+71/u8PWCUH9SXD3PAy7F/Pn1hRB/S3SfC7zv+2iahpR/Y8+L13v+2Pd9dF3/oc/7o8T/+eDj7+KnN8K/HGhJKQh9SRherlT3RICtOFhhk4p1TrVxQdOzMTSVHBopRSMZS2GqOrqqX4qy1AmFiVBNVE1DUVRc3yOQHzW0+WgNvK6oKOJyEEEoQbtcRgfK5VI66eF6FXSlRb1+xF/95b9hceUOql7DdetYzSpW3SGwNdxQQ0QNXDxE1EdvUzEyBorWScwM0YwW2UyB9IWGc+eUzQ2LnkKBZmghYjH6eguk0nlOyxZVp8mV2zfJD1yhpWTZPDjkS69/ia6OLtY29lhd2eXq1XGK/V1srp3y7g8ecP3GFBOTfTw7fsb8/CKDg91cuTLM4VGJ+fk5+gd6uH7tGocHB9x/sEBbJsvbb96kUq0w93iZSq3GF37+cxgG3Llzl0q5yec//zniSZ3lxW02t9aZmb3C0NAgW5sHzD9dojjUx9TkBBcXFzx5vIKhR3n9zWmE0Ll37y6tVou33nqTZMpkY+2AxYVNZq4VGR0rcnhwzsOHT4lGTd544xZ+4HHnvYcYhsaNW1dJp5Jsbx/z6NEKYxO9jF/p52C/zOrKOvlcJ1em+y5T9x88JfDhjbeuEU/A8uIBOzu7zM5ep6e3jf29I1aW9kgmDG6/dpVGs8n7731AIpbj2vUrtJoBq5srpFJJBof7OTursrK2QW9fL50dXayv7mOaETI5la2tHXQRI5POUK1WEMLn9u1ZentyOI5Ls2lTLZd4+GAe3TC5efsm/+5bf8FX//RVG+uXyasa+kvmx62hfzI6ff7axcUF9XqdVsvCMAxs26bRaBCNRnFdj2q1Dkgsy0IIQa1WwzAMWq0WYRhiGAblcpnj42M8z8M0TTRN+/heL9bifd/Htm1UVf0hG16MlJ/b6bouFxcXNBoNPM8jEonQaDQIggBd17FtG8/zcF33b13rxeu8+N7zQceLtv2oCP6njcvo/aN16QBCoiiXE+KNUCUqDQpmjs54J93ZfjqTPXSl+kjHO9GNNlCTSC2J0NMIPYXUokihEUgF76NOcagaQtVBaB/t+KbwXMIFIISKRBB8lCVACEIfNM1EEGF0/CqW47G+vY4WFRjmZS/1y/XtAs+/nIRnBS6u5mNmTKKBSxSLuBmgxmxmJ3sxHcH2Xo3Z0S4mprvxzCwlN0lm6ipv/9P/koHpz1CqdjBcfIvZySkykQ7e+94c9WqJqekhigODfPj+E1qtOmNT/UzPTjI3N8/J0QlXp4cYGR1kcW6bw/1nTM8UGejvZ3PjmEcPF+nt7eL6zCRHB4esrazTXmjj2uwUdsvh4YN5Mm0Jbt6cJZnIcOfOXRzH5dqNK7Rl86yuHLCysszMtTEmr1xldWmbpaVlRkeLzF67QqVa5/7deRKpCDduXkVXo/z7792lXKoyPTNCX3+Whw9X2Np8xuBQD7duz1C+aPD44SKpdJxbt6+iKCZ3P3jC1vYWN25N0z/YwfzcBhtrx0xOj3Dt+jgHh/s8frhMMpnktdevoOlw/94cpycVrt8cpaOzgwf3nnB4eMrwSC9jEwOUzs+Ye7xGIhnjs597Dctq8PjRMqlkjmu3Bmk06zx6sE4ikeDatWG2Nw7Y3thgaLiArsH2xgkDvX3oEZ2TswuSqQzxeIxHD59weFimUMiTzkTIZlOMjg1RrVi8/94861s77B3uvkTPesUrQX/J/DiC/mL6+cX0tuM4fOUrX+Hx4zm+/e2/ZmVllbOzc/75P/8dvvzlX+bevfv89m//NtFojK9+9V/R19fHb/zGb3Dt2jW+9rWvIaUklUrxO7/zO6yvr/P1r3+djo4OMpkMz549wzCMj6PyVqvFwsICf/RHf8TMzAyu6yKE4Pz8nFarRTQaxbIsGo0GqqryrW99i6985SuUSiWWlpZIJBL87u/+LrZtMzExwePHj1lYWLjsalWvk0gk8H0fRVGwLAvbttE0jSAIcBwH13VRVfXjAcBzu1RV/ZGp+582ntsWfLTNqqJcDl40VUMVKoQKhmago6BKFQUVwaVIS1UnECrh817xSBRFoojLVrOKIlEVUIRE/SjpfvkefDRLD5TLM5/X8xWhomkGEpWIkUDX4/T1D2LGTc7LJzTdJvGUSaB4eFIi0fA9Ab5AsUKink40kUJPmAjNJC4S9McidNJCSRW4UMcJu17jrV/575i+9fP4TpzWsyjDg2P09PWyuLLN1sYBxYEOJqbGuKjU2do+JJNK8NprV6mVKmxtbBCPKFybGcdpWTx88BTfD7l5cwpVV5l7tInr+szMjNHWluLhg6fYtsPV2VHyuTyL81ts7+4yNTNCsdjH+vouc0+WKA72Mj4xQrlUYe7pCm25AteuTxKEkpXlPWr1KrdfHyOTybC8uM/uzinjV/ooDnaxs33C9tYJ+fY0N29P0LJsVpYOCEN4661bRKMRVla2ODo8obevnbHxIba39pl/ukp3T57Z69MEPmxu7ON7ITduTdHekWV+4Sl7O2eMjgwyPFrk8OCEtdVDUqkEM7OT+J7Pk4c7SBlw/eY42WyalaVDjg7OGR4apK/YwcnxBesrp3R25hkZ66dUO+HJoy1yuQwjoz04tsP6+i75QpqBgQHOnlUJw5D29jY8z8dyAjQ9ikBFUw0SiSSeZ3N4cMLpcQmhqCQTSfqLl/uyr2yssLy2/pI86hXwStBfOj+OoH9yedmLgv5Hf/THFIuD1KoNLMuirS3PX/7VXzA6Ms7W9jbf/ta3+Gf/4z/jm9/8JkII3n33XYaHh9nc3OSdd94hHo/zh3/4h1y5coXr168zOjrKD37wA77xjW+wsLDA6uoq8Xicv/qrv+Lg4ID333+fvb097ty5g2ma/MEf/AErKys0Gg3+9E//lDt37jAyMsL29jbf/e53+bVf+zXGx8cxDIOFhQWSySS3bt26XBZj26TTaYaHh3ny5AmlUolEIkGpVGJ5eRnLsrAsi6WlJVZWViiXy2xubpJOp0kkEoRhiKqqH6f2f/r4xAx+AaG4XMKmSIkqBCKUlwKtKgRhiBAKilAuI2ohIBRIoaAKBZVLXVaQCCkRMkAVHkL6CEJUJMpHvwL5N/1unt/+eQZEgqJeli4UoSBDEEInYqTpKHSRb29jY3Md26mjGQJFGETMGE4AihrFkHFkQ6XVVHAMMJKSjKIzkszBiUOh+CU+/+u/hWMlOV1v0tU7SPFKN64T8uTxArlslJs3x3BcnwePF1AMhRs3ZzEUlZXFdVq1OhPjg3Rk8zz88CnH+6dcuzZOcbCHjfU9lpc3uTozzuBQF4eHx8zPrTFY7GVycpDj0wuWljZp72xnYnIYzw94//0HxGMJpqaHyOXT3P1wDt/zuXb9Cplcgo31PRYWlxmfLDI03MfJSZkf/OABvb29TEwO0rIaPJ1bx3F9br8xQzwZYWV5i82NQ67fmGVgsIOTk0Pu33tKOpXm2o0rKCo8uD9HECiMTxTp7+9kZWmPtfUNpq4OMzjUw7NnZT54f45kMs709BVi8QjLyzucHD9jcnKE/oFOtjZ32Nk+oLOzg9nrg7SaNvNPN3Aci+s3J0imojx9vEy91mJ0dJjBkQIHBycsL+7Q3tHLtRujNOoWq6u7JGImYyODNOotlpfXaO/I0t3dRa3apFqtk8rEKLS3cfasQuB7ZNuSnJ6cIxSVTDbGwuIKh8enxGJR2gptbGzu8ODJ3D+qR73ih3kl6C+Z/z/L1l5MM1/WpXW+8Y1/S2dHJ62WRa1Wo7u7G8OIcHR0RKVSQdcNvvilL7Cxsc6jR4/4zd/8Tb72ta8Rj8f58pe/TD6fZ2hoiPX1dX7v936Pzs5OdF3n4uKCu3fv4jgOxWKRxcVFjo6O8DyP3//93+dP/uRPeO+99z5Oxy8vL/Po0SN+67d+i5GREVZXV/nOd76DYRikUim++MUvUqvVuLi44LXXXkPTNE5OTshms2xvb9NsNjk9PcUwDBRFoVKpcHR0RD6fx/d9fN+nUCgAkE6nSaVSHwv5T2vKXQIf5bc/+gEhQhCXEfXHR6kC/t/27qy5sfvM7/gXK0lwJ8AVILHv4N5qtlotS2NLs3gyqUoluZnyO0jVvKDEV8lNMhXbGnk0tiXLknrnTgIgQQIkFq7gTnAnsZxcgGyxW63MjKWZllHPpwrVjeY5OAfow/rh/JfnTwm1WgWqEhqVioKqhIpy6BcpoadcsEatUlCpv14ERqNW3+r2uF6H/cUt+UuN7i+eq1RqSqUCahXlLxdqNSoVlIoAejrazXR3u1lb32bv4BBDbT1arQaVRqGkqDg+ypM7yJPa3WdpZZvVxAFXGycY8xo0Gyosng9o6wlg9zjQ6RTGR8dpqW3D7XXSamrh+aNJDrbPGB4KYbN1EpleJB6NMzTgJ+R3EZldZHp6DqfbysBQL4dHpzx8NIqlp4PeAS+FPHz2u4fU1TUwNBxAo4Vf/d9PqK8zMTISotXUyVdfPid3tE9/nw+/108kPE8mk8HjdhEMuYnHl5majNHR2UWwz4pS0vLpb55hqK1i+I4fh93NkyejZLMb3B0ZpLu7m0xmjWdPpjBbTAwMBjg+PuXx46eUSkUGB/ro7ukkPBtlK3uI1+ckGHSTTmcYH52jzWRl6K4TFDW//ofPKeRLDN8JEei1MTO9yOJCGputi75+OycnVzz88jm6KoWRkTsYqhuYmplgffUQr8+Ox+1ma2uXx4+f0dPt5N49N5cXWqKRBdbXdhgZGaK1o5HJiTni8QX8ASf9vW42N3eZmJihq6uTH713j431HeaiCby+HhzOHjKpLVYzq9x/e4Crq0sikRh9/UG0ejWxWAan04dWryUcjbK+vs3W3j7huei/2++T+CYJ9Dfsu85DvxkYplKpOD8/5+OPP8bn82I0GllcXMRms+F0OpmdnaWpqYm6ujoCAT9nZ6dEoxH+7u/+jl/96le43W4+/PBDstksP//5zxkYGCCXy5HP51leXsZms7G+vk5XV3nZxYmJ8h3L/v4+DQ0NzM/PY7PZOD4+xuPxMDw8TDKZ5Gc/+xmNjY1MT0+Ty+X4+c9/jtfrRavVMjY2BsDdu3cB2N3dJZ/PYzKZ2NnZwWq1YjabWVhYoK6ujvPzc5qamtDpdFxeXr5olq+urqa+vv7F5/BDDXQAVKBC/dJzUCiiXAf89diI60HxatSglONfjfp6ifXySHWUchgr16PZS6ryPbuCFtCAqvxQXT9ugvym/x5u+vOVm2eoVUr5S4eqhEoNxVIRpaSlqb4Tu8PLweEJW3vr1NfqKVyecrC7z8nhGWq1jqZGAz2tBgI9Fkz1BjScUrrMo7U66bQFuFRpMTQ1YrHYmBiLsr6VpaOzA18gSGZtk4VYAmOziYE+P1cXF4RnYqjVGu7c7cNgqGN0Ikzu5Ay3z05HZxdT40vsbB/i8/fg9ZVXBFteztDT08k7744QX1xmMZaksbmat+4G2Nk+YHx8lhqDhsHBAFeXKp48nqJYLDI03Ed9fS2RyCIXpyW6zC3ceStIJrXJXHQZY6uBYMjN8dE54Zk4pVKe/gE/ba1GlpfWyG7u4fH04PbY2N48JpFYoaamhmCvh5PTHLHYMvt7R/gCbnpsncTjcZaWVrDaO3j3RyMc5c6YmYmhUnQEgnbq6mpYWlphJbNNd3c7AwM+NtcPCc8sUlevZ2hwAFARno1ycX6C02XD2mMnk86QiG/SYtLi9dpRq7VMTYaBIgNDQZqbjSSXU6yv7WCzduH1udjKbjI5HsFht+H2WFhf22Q7u4fF0kFnVwfxhQQnJ2fYrFb2D445Pc3T1m7i+PSCq0sNzc2tFPMlovPzpFfT/y6/RuL1ZJT7G/ZdR7nfjFIvFovk83m++uoh5+fl/uZAIMDVZYEaQzXn52eo0HB2dkZ3j5mzs2M2NjYYGhpmbGyMtrY23G43iqIwNjbG4uIiJpOJoaEh5ubm0Ov1qFQqGhsbWV5exmg00tzczMnJCWtraxiNRoLBIKOjoxQKBQYGBlhfX2d4eJjq6mrS6TTZbJZ79+5RKpXQ6XQkk0nOz8/x+XzludinpxSLRerq6shkMmi1WlpbW9nb2+Pq6orGxsZyP7NWy+npKS0tLS8G/BkMhpfC/J+bbvdDUh4HUbzuy1aV12dTrufvczPgUMXtyQ3w+nNJAAAbY0lEQVSKoqBSlwO6pIISCiVFQa1obt17l8P6xuvrEZRehPmLeXXXY+MLxQKKUkCl0qKUFFSaK/ZzGZ5NfsIfvvgFWzvLlIpQW9NIe5uZrlozNnMPbV0dpLfT7B0maWlUodL4+Omf/zf0OjNoNBRLCsVigdXMNnPRKKGAH4/bwcJimuVUio7WdkJ9Lvb2DpidjqPXaxga9lNSwexsjN3dHA8e3KG+vopweJHN9X08PjuBoJP5uQWmpxbw+3z4gg62slkmJqZpamrh3sgdzs6PmZ6KUq2r460RP4VCgdhcioODY/r6ArR3ltf2jkbiOF0WvD4HO9uHLC4l0Kg1DA71oVbD3Fyck+MrXM527DYbiwtpUqlV2tpM+AJ2Ts/OmZ1ZQFFUDA8FqanXk1xeIZVco8faidfnYnN9h0QizeXVGSMjA+h1OtKZVVbSW/j8Hmz2TrKbO8xH4jQ01DIw2M/Z2QlzcwsUCkVsVjum1ma2sruk0ytUV+sYHOrl4vKChfkkR0fHDA4FMJqamYsssb17REeniR5LK1f5IrHoMvn8Fb6AjeqaKtLpVS7O8jS1NGC1mllcTLG9naOjzYSpvYVMepPD3CUtRhNanZbT41MMtVVotSr2dg95Mv6EL59+/v3/goh/MQn0N+y7BvrteeM3ze9wM13sulKZCkql8p+KoqBW3wwaA1CRz+fR6XTfGMV+ez468OL5q03bxWLxG2Fxs+1NsCqK8mK63Le9p9uD2YrF4ksj2L9+Ty9PWXt1HMHtc/1T8fJMhfJUsxfPuXlv3/J+rr8AwHX5GuVmoNyLV7+16esLDKle7H1bCeX6gaIpl62lhKJccn61y3ziOb/86H+wuZXE0m7B7x7gJw/+lrY2BxpdNSelM3Zz6+zvZ7E099Dc6ECnq6OkKOWWBXUJFRpWMut89umnuN1uHjx4h8PcCb///STF0hl/9dfvUWuoYmJsnuTyMu++/w5dZiOJRJrp6Qhen53+vhCbm0f89p8+p66+iv/yX/+a3OEpn3zyB/R6NX/+l+9RV1vNl19Ms7a6zn/4jx/Q2FDNXHiJiYkZfvKT97Ha21hd2WH0WRiDQcePP3ybUhF+97s/oNOpeffdd6lt1DE2OkdyaYX7DwZxOi2k02t8/tlnmLscvP/+AxSKfPnFE3K5Q9778Tu0t7cyP59gciyC2+Pj3v0Qudwpn//+Efn8BX/5V+9jMFQTCaeIzScJ9loZGAyQ3Tzg6ZMJVCqFD//8PbRqmIskWFpOYrN1Mzzcx+HhKY8ejlNVpeP9nwyg0+p4/DBKPJ7ggw8f4HJbWV/d4emTGaqq1fz4gxHUOpgYWyCT3mRkJIjLbSUeW2d8bAaTycRf/vRt9g92GX8e5/j4hLd/1Iuhto7IZJJ0ZoWf/s0Dzq+KPHs8i0qt4sc/fo+leIrt7CF2t5nPH/8T/+cX//CvufTF90wC/Q37roH+apgVi8XXhumrc73L86Bv6o2X3Q7wmwFm31Z57vZxb455+3ivnts3j//1fq8e+9Vj3bg97/zVYjM389pv+0E2uX/PvlEq9+Yj+KPf+tf/34qiUCzl0ajLTfjlQxUplk4pqc7JXawxO/2Y0rnC3aEHGJq6UBfrKJb0qHVFFCWPVqWhpOhRoUVRrr/QKRpQFSiVytfHyckZT5885+zsnAcP3qGhpYnJ8QU2NrIEQnYcTivppTUi4Tg+nwe7s52Li3O++mKcuvoaht/yoa8xMPE8xtb2Lvfe7qPL3MHk2ByZ9A7BkItuq5G9vT3Gx8JYrVZ6gw5yR0eMj87Q1GzC73dQXaNmamKZg8MdRkb6aDGaiC+skE5nMPdY8Hh72N/PMT4Wpr6ujv5BN411zczMzLC1tUNfX5Aus4nNjQPGxqJYrB0EA06KBYXR0RkuLwsEQx66re3MRRZZSqzjdnlwuFrIFy8Zez6PSqkh2Gujta2OZHKN2ekF3K4eAkE35+cXPH86gVqjIRT002KqZ34+zvrKIT1WM25PF5dXRaanohTyRUK9bsyWNiKRGHORFE6PHX/AwenZBeGZOBfnp9y71091TT0LizE2N/ZwOrx0W5s4OT1jcmyOmpo6eoMuzi/PefpsApvVTY+1k73dHaYm5/D5AphaDWxmN/j9w6f84fEXf+yFJ74HEuhv2PcZ6N9WgAW+GYY3d+kvSpG+5hi374j/f8Vdbs7j1XN6XXGZ29vc/Hl77virx3v157ff/7fVj/9B96F/z77/QL95YW61FHzdYlMolJvh86VL1LpLlOIZSr5Ijb6OglKDihrQ6K5HBeTL66hThVqlRqMp352DDlTlpvzLyysURcXZ6TlrayukUivYXS7cXgdrq1lmZ+LU1Oh4+/4Q+YtLYvMpDg5y9Pb5aGtvYn5ukZXVTXwBN3aHjbXVLAvzKRobG+jtc3OYO2Z2Nkwhf8XIvTvUGgxMT0U5PzsmGAxhMhlJZ5IsJZKYzd309nlZSWeJL6aoratlYNCPQoHRsVlKpQKhXj8mk5GFhWU21nYwd7biCzjY398llcyyv5ujb8BJY4uRhYU029ldXE4L/pCTdHqV5eVVri4vCPZ6qa+rJx5PspLZwO/3YXeY2dnZYyGWQKOF3lCQqioD6WSKzc1tOjtbcbut7O7vshhLoVHX4Pe7aGg0sLCwxN5ujrY2EzaHmZPjM+KLKTQaFW6PHb1ez+b2NivpLGZLO+buVo6Pzllf3eEqf4XF3ElrWyMrmU2SyXVs9g66eyzkcjkSsSQ6nQ6v302xVGJjfRelWKK9o5F84ZK93XOKJTUTkTF+9Yncob9JMijuDftj56G/LrReDcWbIHy1KfvrAH99YN5+rVf3v/36t3/+ani/emf9um1vXut20/nryrq+7i791fN9df9XP5sfttv91/+6c371Pb8YwP59nJWilAfdKeXuGrVadf1FsHwdVenqKOV16PV1lFCRL+lQabWgVl8P4lNQShfodHqK1wu1qdSq6yq45W6Em+tRo9FiMploazfx9NkMmcwq/oANr8/K0cE5z54+o9PchS/opKpGy8NHz6iuqSbY68NoMjEzHSOTWScYdOH29LCa2WZychaX28LAgI9iScuTRxPo9Bru3R9Ar9fz/OkM2c0dhoZ7sVrNxGJLzExHCYQc+ANuLi9KjI09o76ugbdGQtTVNxOeiZNKJxm5N0h3t4W9/V3+8ePf4fF48Acc6Ku1PHr4nGKpwL37g3S0NRMJLzA6GmFgoA+vz4pWq+HxozGuLksMDgawO+zMzS8SjcTx+V34Aw4KeS2PHk5SKFzS2+fAbO4klVxnLpLCZnPQP+BFq61mfHyag/1Dht/qx2LpYHNzj7FnERoaDLw1EsJoaubJ49HygLygA7e7h5WVLNNTMWrr9AwOB6jSV5NKpkmmUoR6PQSCLjY39hgfn6GmuoYfvXcHnU5FNJwgd3SCy91OW5uJWHSFnd1jQv0Omo2NPB+fIZle+n4uPvFHkUB/w77LKPdvu3O+CezbfdC3m+FfDeeb7W/3Tb8uqG/cDtSb/V8X1jf7vS6Abw9e+7bqbrfP7faxb7/G6wbBve48/nS83A3yrVvd7tb4lm2+yyegqIovytaUP8oS5WVhSygU0et0KAUtOk0NJUWLWqtFRzUatZpiUUGr0pSn2WlAKSlo1FXl19Jcl6S9HgCoUqsplRT0ei0lShhqqgn2utnZzjExtkB7qwmvtwODoZ7R52GqqmrpsXVgs/UwPhZlY30Lh9NCX5+P3OEFs9OL1Nbp6R9y0djQxPMnMfJXejzeTuzOLmZn5llJ7eJyWQj1eri6OmVqcgajqYXh4T5KpSuePZmjxlCD02XEZDLy9PEsu/tH9Fi7sDvNbK5vMT42Q4uxFrfbQmtrO9OTixQK0GVpIRQMkU7tMD42SWtbM/39AWrrahl9Pk3u8Ay73UIo5CeTWScWS9HYWE8o6KJar+OLz59yfnKB29vF8LCf1fQG4dkl6uqq6R/wY6itZnJykrXVTZzObkK9brayO4yPz6DXawkEbHR3W4gtJMik1zCamunrC3B4cMx8NMXZ2Tn9A146O9uJL6ySSa9isbQSDHkpFVVEIzGOjnOEgm6sVgtrqxssxlZoazPRP+imWIBIOMVV/pzhu370VTpGR2e5zCucXR4TkWlrb5Q0ub9h/5broQvxXZRH2pcABVS3+9avp8yhvf4yUQS1Ai9G5Ctf78f1j66flafMwTebEr4exX+VL1AqKaTTK8Ricdrb2+jrC3B8cs7UdJhCvsDQcD/NLQ3MRRJsbOzi9TlwuXpIpzeYjyaor62hf8CLAjx6PE2hkGdwKIDF3EE0vMj6xjbd3SaGh3tJpTZILGWoqqohFLBxflEgtrDE6ekRwaCXNlMr84tx1te2cTjK/dlrq9skl1IUCuf09vmpr69nciLG3u4BTpcFt6ebne0DFmKr1NRU4fXZaGpuYGpqjr29Iyw9HfT0tHOYO2IxlkarqcLnt9HcUs9cZJmd3X2Mxkb8QQe5wzMWF5IAOB0W2tpMLC8l2coeYajVEgw5KOQV4vFVcgfndFtbsDt6yG7ukFzaRF+lweO1YKg3kIhn2Nw4wOHooqvLSO7wiES8vMa6w2mmuaWF5HKatdU1LN1duN1uTi/OWYgtc3Z2jtvZQ4vRSHplje2tXZqbG2nvbGVv74RffPRrvnwmfehvktyhv2Hf93roQnw/rkP5uvDN10vFgep6XjyK+roavXI9CF/hdvfB61r/v245+WagvziySkGjUVPb0EB3j5nE0jLxRAKb3Yrf5+Ls/ILZmRj1tc34/FZqDNVEwgskkylc7m5crh7298+Ynp6ltb2JgUE/9Q21jD6f5ej4mL5eN52dbUTCccbHwvj8Lhyubg4Oj4hGYjQ219Db68dgaGZ2JsrFRY5gX5D2dhOZzCbjY1O4nJ0EAm4KRS2PHk1wlS9xd6SXji4jq6vbzIVX8HiduN1mjo7OCM8uc3Cwx9CdIO2dLSwtpUjEM9htLkK9Dq7yRWZn4mzvbDL8VoC21nZ2d04YfT5Fi7GegaEg1dV6Zmfn2NzawOmx4HZbOTw85dmTGXQ6HQNDXizdzSynthgfn8HrcxPqc3CZvyQ8myGZXCPU66TT3MDqyirRcJKGxhb6+30YagzE42m2t7dxu+24XDbWVnLMzyVQqUsEgk5Mra2MPYuwvp7Fam8nFHJzcHDG1MQsDU0G8qUzZsJyh/4mSaC/YRLo4k+D6puPl/6qelGg5kVUK69u/6I+Hi8vL3tT8OZ6TIdSIF8soVZrURTwelycnJzy/PkkjU2NeD0OTKYWPvmn33J4cIrLZScQcLKV3Scym6K5pYVQr5XqmkaePo6g0YLV1oHd3k1yeZVIOInN2kVfX4Aag4Hno7PU1tbi9XRhNJp48mSCnd19vF4bDoeV1ZVdnjyK0NVlpHfASZVWx+OHE5wcX5TLtjrtzM/FSGfWsNl6cLrM5PPn/PrXv0GrrSbU5yEQcrIQSxBfSNNpbicY8KAoaj779CvU6vIdtM3RSSa1zuz0Em0dJrz+HqxWC2Ojs6yvbREIuPD57BwdnfPwiwhVeg2BgAurrZvFxSSLCymamkz09tmoMdQwOjpFNruP22PH67eQO9omEomj1zUwPBSix9rF+Og0S4kVgr1OXO5O8nkVv//0GScnx4zcC+Bw9bC8vMnszCK1tdW8934/1QYt4dlltrfO8PltBIIOlpc3GJ+IsLqx8u97WYqXSKC/YRLo4ofp1QC/Lh2rfNv8ftVLRWzKN+m3y81eb6X6uuzN1xXrXr2PVygVy038ao2aYrGEuasDnU7LciLD7k6ObksHb93tY21li3Rqjbp6A16fB41GTSJenkdtt3dic5iZmVogubRGY1MDvX0B9Hod0XCMy/wVTo+N5uY6ksk0ezsnGE0mgkE3m2sbRCNL1NXV4/HbMBqbmJ6d5PToHKfDgc3eQzKVIrmUwWRqpL/fj05TxdMn4xwfH+HxuQn1BsmsrpJKbVIsqegf9KNWl4hFl8hu7OHxWAmG7KRTKyQWV9FptQQDPkymZsKzYXZ3D6itNeD3O7m6KDA1OUuhmMftduLxeFhJrxOPx2lsqqW310+NwUB8cZmN9R3M5jZCATeH+0ckFpdRlCIupxNzh5nsxi5LySTVBh19AyG0Wj2JxeXySPquVu6O9LO9vUU8nqZYKOLxW2lrbyGRWCKdXKG9vR2v38Xl1Qlz0XkODnYIhdzsHx0xPSu13N8kCfQ3TAJd/HC92jx++6G8sp3qVlgDLy8dw82Xgtsh/m0DFxVFQavVoiglNGo1Oq0WFDC2NNPV1cbe3i7RSBSTqQ2/34lSKhKZiXF8ckIg6KCjq4ON9V1mZsJYLO30D7rRoGZxIcNV/gq3twejqYF4IkU4EsPpdGC1dZE72icSXkKv1zA4GKKpuZHo3Dy7OwfYnR04HN1kN3Z59nQGk8lI/4APjUZLfDHDwcER/qCD7u52drdPCc8u0NFVPj+VGmKxRbLZbfwBF1abhaOjM+bmFqmrrWf4Toi6uhpWMlnCMzHc7h6CQRcXpwXCszHOz08ZHHLRZTaytXFIJDxPQ4OOvn4/dXX1xGJxkqkENlsXbo+VfL7EXDTB/sEhD94dorW1hb2dc6YmYmh0JQaHfBhqa0klN1hZzdDR0UR/f5CrqyIz0wtsb+0zOBzCbG5nfz/H02dT1NbWMTzch6G6ltXVDVKpFcxd3QR8Di6vCkSjcSJzEdKra9/zNSj+NSTQ3zAJdPFDVc5b5bra4HX4qkClVqNCjUp9M6OgHNQqbh7XYV3e4Prv19v+M2FO+ZAvChXdLCuLUkKtAZ1eR7e5Eyjx6OEfUGtV9PX56e6xMDUeI764htvdjdNtpqa6ic8/HaXbbMbS3YrF3M7z0WmSSxsEAjZ8AS8nJ2f8+pPfYO7qItTrpq29jSdfTXJ4cFJe+MTjYDO7x2effonVYmdw0IOlu5OPfvklFxdF3rrrxWqzsrqS5asvntJj7aZvwEazsZFff/wp+7unDA4F8AfsHByc8tWXz2hra6Kv342ptYlff/wbMuk1+nqD+AM2zs5P+OrLcvnkoTs+/EEny4k0n382TjDQjz9oxdTSxqe/mWApGcMfcJYXhjm65OnjSUpFLUNvubDbbezs7PPRR/9IW7uR/kEP3d09zM8vEJtfwGzpYnAoQKmk49njKTbWtugfCNI/4OUwt8vjx0/R6TQMDIZwup1EwvPEYnEsllaGh3spKvDRLz/h7KREKOhhcDhENJYgGov921+Y4ltJoL9hEujih6wcvC/3cZd/AC/fsb+0V3n7l7a4CfN/6TFvXunrvdTXS8mWSiVMJhMdHZ3Mzsyws7OL0dRGqNdPoXRJJBpGp9PR3dNFe3srz589Z28vi9FoJBAMcH5xyvjkJFV6FR6vD6fbxcz0JMcHJzQ1teD22dnaWWFqehaNTovH5cMfcPH8+Vcc5o5pNRkZHAiyvpEkPBujuqoGv99JraGG2dkxNtb26erq5O17d9ne2mV2Jkzh6orekIe2dhMTU5Ps7u5jbGrlnQf3KJYuGR+b5PT0Eq/Xjj/gJZ1eI76QwlBTjcNpxWa38fmXf2B9Y4eW1nruvz0IKg3Ts1Ocnl9gc5hxO92sr60Si6yi1qhwurpx2O0sLKRILmeob9ASDAVRKRoWF1Osr2cxW8ozCI7O9onFEuztn+ENuHC57ayvbRCbi1OtryEYdNPaYmR+PkEqlaGrs40/+7O3uboqEY4kuMqfspZdJRxd+GMvNfE9kEB/wyTQhXjZqyV8b/5NpVJRKBRerDtQVVWFzWZld3eXqekwzc0m7A4rdbV1RCJhdrZ3sNm68bg95A4PScSX0VVXEQh4qG+oJTYfY3UlW14yNeBmeyvLYiJNQ30Tff0hamtrmJ9fZCu7R0dHF16vnf29PeaicVCqGB7uo9bQwNJSnI3NDfy+AB6vg+PcKdH5Bc4vzhgY6qe+oZb1jQ3W1jZpa2vH7/dwcXlJNLxALnfC3btDGI1GNjZXmZ9LU19f7pOvqa5iIbZMdjNLR2cbfQN+8nmFmelpTo6P8fp8dPeYyWZ3mYvOodNWEQoGMBrrWFvNMh+N02ysY2AwhEajZzm+xvp6FrvTjMdnLS/iEktysJ/jzp07dHS0srW9zXIywcVFnt5QEFNrC+sbm0TnIjQ3G+nr70WtKRGbX2J7a58eawcOdxdnp5eMjs+ynJHV1t4kmYf+hjU21PPL//Xf3/RpCPGD8Y36/6UShcL1egEaNYV8gUKhQFVVFcVinlJJ4eTkio8//pT+wT48XgdqjYrJiVk21rf465/+hFqDms3NPT76h8+5f/8+3oAFRSmRSGzwfHycDz94B2tXG9s7p3z++4eYu1u5f3+EfL7A0ydTRCMx/vZnf4OxpZ6LE/jsd6MY23Tcu9+HCh1z4RSLC2s8eC9AZ5eR/dwJkxNzgIoH776FXq8ildrh+dMJ+gfd+AMeDvcuGH0+RqFwwY9+9A4NTTWcHF3wySe/p7Ojk/v3h1GpiywlVhkfn2HknSGsVguUSkyNx1lIxPlP//kvqDHoKOTzPH04Tf6qxLsPhqhvqGNn74Df/vYP2G09jNwbQK/VEZ1LMT8/R6DXS6jXy+HhGUuJVZ48fcQHH7yH3+/i4uKK8fFZcrkDBgf76ehoZ39/jyePJ9Dr9Xzw4QO0GthcO+GLLyZwek3cHQnw97/4Lf/zf//9G7xyhAS6EEIIUQH+dNaYFEIIIcS3kkAXQgghKoAEuhBCCFEBJNCFEEKICiCBLoQQQlQACXQhhBCiAkigCyGEEBVAAl0IIYSoABLoQgghRAWQQBdCCCEqgAS6EEIIUQEk0IUQQogKIIEuhBBCVAAJdCGEEKICSKALIYQQFUACXQghhKgAEuhCCCFEBZBAF0IIISqABLoQQghRASTQhRBCiAoggS6EEEJUAAl0IYQQogJIoAshhBAVQAJdCCGEqAAS6EIIIUQFkEAXQgghKoAEuhBCCFEBJNCFEEKICiCBLoQQQlQACXQhhBCiAkigCyGEEBVAAl0IIYSoABLoQgghRAWQQBdCCCEqgAS6EEIIUQEk0IUQQogKIIEuhBBCVAAJdCGEEKICSKALIYQQFUACXQghhKgAEuhCCCFEBZBAF0IIISqABLoQQghRASTQhRBCiAoggS6EEEJUAAl0IYQQogJIoAshhBAVQAJdCCGEqAAS6EIIIUQFkEAXQgghKoAEuhBCCFEBJNCFEEKICiCBLoQQQlQACXQhhBCiAkigCyGEEBVAAl0IIYSoABLoQgghRAWQQBdCCCEqgAS6EEIIUQEk0IUQQogKIIEuhBBCVAAJdCGEEKICSKALIYQQFeD/ATK+3l5LhgjCAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "id": "b022eeac",
   "metadata": {},
   "source": [
    "![PYVIST'INSA.png](attachment:PYVIST'INSA.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "57db690b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ebf05567",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyvista as pv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import vtk as vtk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce343366",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "78e7f4c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style='border: 3px solid #ddd;'>\n",
       "  <tr>\n",
       "     <td style='text-align: center; font-weight: bold; font-size: 1.2em; border: 2px solid #fff;' colspan='6'>Fri Mar 11 20:13:31 2022 Paris, Madrid</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>OS</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Windows</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>CPU(s)</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>8</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Machine</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>AMD64</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Architecture</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>64bit</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>RAM</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>7.8 GiB</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Environment</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Jupyter</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>GPU Vendor</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Intel</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>GPU Renderer</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Intel(R) UHD Graphics</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>GPU Version</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>4.5.0 - Build 26.20.100.7584</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; border: 2px solid #fff;' colspan='6'>Python 3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>pyvista</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>0.33.dev0</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>vtk</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>9.1.0</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>numpy</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>1.20.1</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>imageio</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>2.9.0</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>appdirs</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>1.4.4</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>scooby</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>0.5.7</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>matplotlib</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>3.3.4</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>PyQt5</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>5.9.2</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>IPython</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>7.22.0</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>ipyvtklink</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>0.2.1</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>scipy</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>1.6.2</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>tqdm</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>4.59.0</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>meshio</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>4.4.6</td>\n",
       "    <td style= border: 2px solid #fff;'></td>\n",
       "    <td style= border: 2px solid #fff;'></td>\n",
       "    <td style= border: 2px solid #fff;'></td>\n",
       "    <td style= border: 2px solid #fff;'></td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; background-color: #ddd;border: 2px solid #fff;' colspan='6'>Intel(R) oneAPI Math Kernel Library Version 2021.2-Product Build 20210312 for Intel(R) 64 architecture applications</td>\n",
       "  </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "\n",
       "--------------------------------------------------------------------------------\n",
       "  Date: Fri Mar 11 20:13:31 2022 Paris, Madrid\n",
       "\n",
       "                OS : Windows\n",
       "            CPU(s) : 8\n",
       "           Machine : AMD64\n",
       "      Architecture : 64bit\n",
       "               RAM : 7.8 GiB\n",
       "       Environment : Jupyter\n",
       "        GPU Vendor : Intel\n",
       "      GPU Renderer : Intel(R) UHD Graphics\n",
       "       GPU Version : 4.5.0 - Build 26.20.100.7584\n",
       "\n",
       "  Python 3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)]\n",
       "\n",
       "           pyvista : 0.33.dev0\n",
       "               vtk : 9.1.0\n",
       "             numpy : 1.20.1\n",
       "           imageio : 2.9.0\n",
       "           appdirs : 1.4.4\n",
       "            scooby : 0.5.7\n",
       "        matplotlib : 3.3.4\n",
       "             PyQt5 : 5.9.2\n",
       "           IPython : 7.22.0\n",
       "        ipyvtklink : 0.2.1\n",
       "             scipy : 1.6.2\n",
       "              tqdm : 4.59.0\n",
       "            meshio : 4.4.6\n",
       "\n",
       "  Intel(R) oneAPI Math Kernel Library Version 2021.2-Product Build 20210312\n",
       "  for Intel(R) 64 architecture applications\n",
       "--------------------------------------------------------------------------------"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv.Report()\n",
    "# please make sure that your specs match those in the tutorial, particularly pyvista and vtk as some of the\n",
    "# functions used in this code require that they be at least at the updates shown in the tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "55229a53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# separates and names all 9 components of the gradient tensor\n",
    "def gradients_to_dict(arr):\n",
    "    keys = np.array([\"dux/dx\", \"dux/dy\", \"dux/dz\", \"duy/dx\", \"duy/dy\", \"duy/dz\", \"duz/dx\", \"duz/dy\", \"duz/dz\"])\n",
    "    keys = keys.reshape((3,3))[:,:arr.shape[1]].ravel()\n",
    "    return dict(zip(keys, arr.T))\n",
    "\n",
    "# separates and names all 3 components of the gradient vector calculated from the velocity magnitude (U-normed)\n",
    "def gradients_to_dict2(arr):\n",
    "    keys = np.array([\"dU/dx\", \"dU/dy\", \"dU/dz\"])\n",
    "    keys = keys.reshape((3,1))[:,:arr.shape[1]].ravel()\n",
    "    return dict(zip(keys, arr.T))\n",
    "\n",
    "# separates and names all 3 components of the vorticity vector \n",
    "def vorticity_to_dict(arr):\n",
    "    keys = np.array([\"vort_x\", \"vort_y\", \"vort_z\"])\n",
    "    keys = keys.reshape((3,1))[:,:arr.shape[1]].ravel()\n",
    "    return dict(zip(keys, arr.T))\n",
    "\n",
    "# plots a scalar field of choice from the internalMesh\n",
    "def plot_internalMesh(field, edges):\n",
    "    p = pv.Plotter()\n",
    "    p.add_mesh(internalMesh ,scalars=field, show_edges=edges)\n",
    "    # if show_edges is set to True, the mesh will be surimposed on the domain\n",
    "    p.view_xy()\n",
    "    p.add_axes()\n",
    "    p.show()\n",
    "    \n",
    "# plots a scalar field of choice from mesh_cell   \n",
    "def plot_cell(field, edges):\n",
    "    p = pv.Plotter()\n",
    "    p.add_mesh(mesh_cell ,scalars=field, show_edges=edges)\n",
    "    # if show_edges is set to True, the mesh will be surimposed on the domain\n",
    "    p.view_xy()\n",
    "    p.add_axes()\n",
    "    p.show()\n",
    "    \n",
    "# plots a scalar field of choice from mesh_point\n",
    "def plot_point(field, edges):\n",
    "    p = pv.Plotter()\n",
    "    p.add_mesh(mesh_point ,scalars=field, show_edges=edges)\n",
    "    # if show_edges is set to True, the mesh will be surimposed on the domain\n",
    "    p.view_xy()\n",
    "    p.add_axes()\n",
    "    p.show()\n",
    "    \n",
    "# plots the velocity vector field from the data from the internalMesh    \n",
    "def plot_vectors(scale, factor):\n",
    "    geom = pv.Arrow()\n",
    "    glyphs = internalMesh.glyph(orient='U', scale=scale, factor=factor, geom=geom)\n",
    "    # we can scale our vectors with the scale function.\n",
    "    # when set to True, it uses the active scalars to scale the glyphs. The scaling vectors can also be specified by the user\n",
    "    # when set to False, the glyphs won't be scaled\n",
    "    p = pv.Plotter()\n",
    "    p.add_mesh(glyphs)\n",
    "    p.view_xy()\n",
    "    p.add_axes()\n",
    "    p.show()\n",
    "\n",
    "# plots the velocity vector field from the data from mesh_cell\n",
    "def plot_vectors_cell(scale, factor):\n",
    "    geom = pv.Arrow()\n",
    "    glyphs = mesh_cell.glyph(orient='U', scale=scale, factor=factor, geom=geom)\n",
    "    # we can scale our vectors with the scale function.\n",
    "    # when set to True, it uses the active scalars to scale the glyphs. The scaling vectors can also be specified by the user\n",
    "    # when set to False, the glyphs won't be scaled\n",
    "    p = pv.Plotter()\n",
    "    p.add_mesh(glyphs)\n",
    "    p.view_xy()\n",
    "    p.add_axes()\n",
    "    p.show()\n",
    "    \n",
    "# plots the velocity vector field from the data from mesh_point\n",
    "def plot_vectors_point(scale, factor):\n",
    "    geom = pv.Arrow()\n",
    "    glyphs = mesh_point.glyph(orient='U', scale=scale, factor=factor, geom=geom)\n",
    "    # we can scale our vectors with the scale function.\n",
    "    # when set to True, it uses the active scalars to scale the glyphs. The scaling vectors can also be specified by the user\n",
    "    # when set to False, the glyphs won't be scaled\n",
    "    p = pv.Plotter()\n",
    "    p.add_mesh(glyphs)\n",
    "    p.view_xy()\n",
    "    p.add_axes()\n",
    "    p.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526d7797",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"home\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c316275a",
   "metadata": {},
   "source": [
    "<h1 style =\"font-size:300%;text-align:center\">Table of Contents</h1>\n",
    "\n",
    "* [Load the case with OpenFOAMReader](#chapter1)\n",
    "* [Two datasets : cell data and point data](#chapter2)\n",
    "* [Show the mesh](#chapter3)\n",
    "* [Let's plot our fields](#chapter4)\n",
    "* [Plot the velocity field using glyphs](#chapter5)   \n",
    "* [How to access the vector components](#chapter6)\n",
    "* [Let's plot the streamlines](#chapter7)\n",
    "* [Let's calculate the gradients and vorticity](#chapter8)\n",
    "* [Let's plot the gradient components](#chapter9)\n",
    "* [Let's plot the vorticity components](#chapter10)\n",
    "* [Vectors normalization compared to the maximal component](#chapter11)\n",
    "* [Let's plot all of our fields](#chapter12)\n",
    "* [How to plot over a line](#chapter13)\n",
    "* [Plot a scalar over a circular arc](#chapter14)\n",
    "* [Wall shear stress](#chapter15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc99cc7",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3587de99",
   "metadata": {},
   "source": [
    "# Load the case with `OpenFOAMReader`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bad08538",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 500.0, 1000.0]\n"
     ]
    }
   ],
   "source": [
    "reader = pv.OpenFOAMReader(\"C:/Users/pret/Desktop/INSA HDF 5A/PLP 5A/OpenFOAM_example_case/case.foam\")\n",
    "# pv.OpenFOAMReader takes the path of the empty .foam file of the simulation as argument (replace all \\ with /)\n",
    "reader.cell_to_point_creation = False\n",
    "# When set to True, copies cell data and creates point data from it\n",
    "# When set to False, only cell data is read\n",
    "time = reader.time_values # this list will contain all the time values of the simulation\n",
    "print(time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a93770f3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['internalMesh', 'boundary']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table><tr><th>Information</th><th>Blocks</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>MultiBlock</th><th>Values</th></tr>\n",
       "<tr><td>N Blocks</td><td>2</td></tr>\n",
       "<tr><td>X Bounds</td><td>-0.021, 0.290</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-0.025, 0.025</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-0.001, 0.001</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Index</th><th>Name</th><th>Type</th></tr>\n",
       "<tr><th>0</th><th>internalMesh</th><th>UnstructuredGrid</th></tr>\n",
       "<tr><th>1</th><th>boundary</th><th>MultiBlock</th></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "MultiBlock (0x1e09a051dc0)\n",
       "  N Blocks:\t2\n",
       "  X Bounds:\t-0.021, 0.290\n",
       "  Y Bounds:\t-0.025, 0.025\n",
       "  Z Bounds:\t-0.001, 0.001"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader.enable_all_patch_arrays() # enables all the patches, including the boundaries\n",
    "reader.set_active_time_value(time[2]) # set time at whichever value we want to visualise\n",
    "multi_block = reader.read() # multi_block contains all the relevant data (internalMesh and Boundary)\n",
    "print(multi_block.keys())\n",
    "multi_block "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d98722ad",
   "metadata": {},
   "source": [
    "This `MultiBlock` contains all the relevant data from the simulation within two blocks:<br/> \n",
    "* One that contains all the data from the `internalMesh`\n",
    "* One that contains all the data from the boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0c70acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>12225</td></tr>\n",
       "<tr><td>N Points</td><td>25012</td></tr>\n",
       "<tr><td>X Bounds</td><td>-2.060e-02, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-2.540e-02, 2.540e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.000e-04, 5.000e-04</td></tr>\n",
       "<tr><td>N Arrays</td><td>5</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Cells</td><td>float32</td><td>3</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>epsilon</td><td>Cells</td><td>float32</td><td>1</td><td>1.277e-01</td><td>5.788e+03</td></tr>\n",
       "<tr><td>k</td><td>Cells</td><td>float32</td><td>1</td><td>4.460e-04</td><td>5.498e+00</td></tr>\n",
       "<tr><td>nut</td><td>Cells</td><td>float32</td><td>1</td><td>1.402e-07</td><td>5.140e-03</td></tr>\n",
       "<tr><td><b>p</b></td><td>Cells</td><td>float32</td><td>1</td><td>-8.014e+00</td><td>1.504e+01</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x1e09a051d00)\n",
       "  N Cells:\t12225\n",
       "  N Points:\t25012\n",
       "  X Bounds:\t-2.060e-02, 2.900e-01\n",
       "  Y Bounds:\t-2.540e-02, 2.540e-02\n",
       "  Z Bounds:\t-5.000e-04, 5.000e-04\n",
       "  N Arrays:\t5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internalMesh = multi_block[0] # this contains all the data from the internalMesh\n",
    "internalMesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7406c6a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Information</th><th>Blocks</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>MultiBlock</th><th>Values</th></tr>\n",
       "<tr><td>N Blocks</td><td>5</td></tr>\n",
       "<tr><td>X Bounds</td><td>-0.021, 0.290</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-0.025, 0.025</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-0.001, 0.001</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Index</th><th>Name</th><th>Type</th></tr>\n",
       "<tr><th>0</th><th>inlet</th><th>PolyData</th></tr>\n",
       "<tr><th>1</th><th>outlet</th><th>PolyData</th></tr>\n",
       "<tr><th>2</th><th>upperWall</th><th>PolyData</th></tr>\n",
       "<tr><th>3</th><th>lowerWall</th><th>PolyData</th></tr>\n",
       "<tr><th>4</th><th>frontAndBack</th><th>PolyData</th></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "MultiBlock (0x1e09a051220)\n",
       "  N Blocks:\t5\n",
       "  X Bounds:\t-0.021, 0.290\n",
       "  Y Bounds:\t-0.025, 0.025\n",
       "  Z Bounds:\t-0.001, 0.001"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boundary = multi_block[1] # this contains all the data from the boundaries\n",
    "boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "31031975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>37235</td></tr>\n",
       "<tr><td>N Points</td><td>25012</td></tr>\n",
       "<tr><td>X Bounds</td><td>-2.060e-02, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-2.540e-02, 2.540e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.000e-04, 5.000e-04</td></tr>\n",
       "<tr><td>N Arrays</td><td>5</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Cells</td><td>float32</td><td>3</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>epsilon</td><td>Cells</td><td>float32</td><td>1</td><td>1.277e-01</td><td>5.788e+03</td></tr>\n",
       "<tr><td>k</td><td>Cells</td><td>float32</td><td>1</td><td>4.460e-04</td><td>5.498e+00</td></tr>\n",
       "<tr><td>nut</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>5.140e-03</td></tr>\n",
       "<tr><td><b>p</b></td><td>Cells</td><td>float32</td><td>1</td><td>-8.014e+00</td><td>1.504e+01</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x1e09a02e160)\n",
       "  N Cells:\t37235\n",
       "  N Points:\t25012\n",
       "  X Bounds:\t-2.060e-02, 2.900e-01\n",
       "  Y Bounds:\t-2.540e-02, 2.540e-02\n",
       "  Z Bounds:\t-5.000e-04, 5.000e-04\n",
       "  N Arrays:\t5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = boundary.combine(merge_points=False, tolerance=0.0)\n",
    "# this allows us to combine all the boundaries together in one UnstructuredGrid\n",
    "fullMesh = internalMesh.merge(merged, merge_points=True, inplace=False, main_has_priority=False, progress_bar=False)\n",
    "# this allows us to get one complete mesh that contains both the internalMesh and the boundaries\n",
    "fullMesh"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cc4b288",
   "metadata": {},
   "source": [
    "What we call `fullMesh` contains the data from the entire domain in the form of cell data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7484ad22",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aba84490",
   "metadata": {},
   "source": [
    "# Two datasets : cell data and point data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c21d7c",
   "metadata": {},
   "source": [
    "Let's create two datasets:<br/> \n",
    "* One that only contains the cell data\n",
    "* One that only contains the point data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bf23eb1",
   "metadata": {},
   "source": [
    "The datasets will be derived from the `fullMesh`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "82f6fc59",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>37235</td></tr>\n",
       "<tr><td>N Points</td><td>25012</td></tr>\n",
       "<tr><td>X Bounds</td><td>-2.060e-02, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-2.540e-02, 2.540e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.000e-04, 5.000e-04</td></tr>\n",
       "<tr><td>N Arrays</td><td>5</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Cells</td><td>float32</td><td>3</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>epsilon</td><td>Cells</td><td>float32</td><td>1</td><td>1.277e-01</td><td>5.788e+03</td></tr>\n",
       "<tr><td>k</td><td>Cells</td><td>float32</td><td>1</td><td>4.460e-04</td><td>5.498e+00</td></tr>\n",
       "<tr><td>nut</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>5.140e-03</td></tr>\n",
       "<tr><td><b>p</b></td><td>Cells</td><td>float32</td><td>1</td><td>-8.014e+00</td><td>1.504e+01</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x1e09a02e160)\n",
       "  N Cells:\t37235\n",
       "  N Points:\t25012\n",
       "  X Bounds:\t-2.060e-02, 2.900e-01\n",
       "  Y Bounds:\t-2.540e-02, 2.540e-02\n",
       "  Z Bounds:\t-5.000e-04, 5.000e-04\n",
       "  N Arrays:\t5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mesh_cell = fullMesh\n",
    "mesh_point = mesh_cell.cell_data_to_point_data() # converts cell data into point data \n",
    "mesh_cell # contains all the cell data from the fullMesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "321b960b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>37235</td></tr>\n",
       "<tr><td>N Points</td><td>25012</td></tr>\n",
       "<tr><td>X Bounds</td><td>-2.060e-02, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-2.540e-02, 2.540e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.000e-04, 5.000e-04</td></tr>\n",
       "<tr><td>N Arrays</td><td>5</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Points</td><td>float32</td><td>3</td><td>-2.507e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>epsilon</td><td>Points</td><td>float32</td><td>1</td><td>1.277e-01</td><td>5.175e+03</td></tr>\n",
       "<tr><td>k</td><td>Points</td><td>float32</td><td>1</td><td>4.460e-04</td><td>5.495e+00</td></tr>\n",
       "<tr><td>nut</td><td>Points</td><td>float32</td><td>1</td><td>7.009e-08</td><td>5.136e-03</td></tr>\n",
       "<tr><td><b>p</b></td><td>Points</td><td>float32</td><td>1</td><td>-8.012e+00</td><td>1.453e+01</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x1e09a02efa0)\n",
       "  N Cells:\t37235\n",
       "  N Points:\t25012\n",
       "  X Bounds:\t-2.060e-02, 2.900e-01\n",
       "  Y Bounds:\t-2.540e-02, 2.540e-02\n",
       "  Z Bounds:\t-5.000e-04, 5.000e-04\n",
       "  N Arrays:\t5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mesh_point # contains all the point data from the fullMesh"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71d28789",
   "metadata": {},
   "source": [
    "From now on, we will plot the data from `internalMesh`, `mesh_cell` and `mesh_point`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9476c25",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81daecef",
   "metadata": {},
   "source": [
    "# Show the mesh "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0edf9bbe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "498bbe0b94ed41e2a0917b5802c90ca6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plotting object to display vtk meshes or numpy arrays\n",
    "p = pv.Plotter()\n",
    "# Show the mesh in wireframe style\n",
    "p.add_mesh(fullMesh, style=\"wireframe\", color=\"w\")\n",
    "# View in the xy plane\n",
    "p.view_xy()\n",
    "# Show the axes\n",
    "p.add_axes()\n",
    "# Display the plotting window\n",
    "p.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52ffa9b4",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbefbc76",
   "metadata": {},
   "source": [
    "# Let's plot our fields "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "483f5fac",
   "metadata": {},
   "source": [
    "Our code calculates `U-normed`, the velocity magnitude, automatically when plotting the velocity scalar field.<br/>\n",
    "In order to plot `U-normed` later on, we start by plotting the `U` field without displaying it.<br/>\n",
    "We'll do this for `internalMesh`, `mesh_cell` and `mesh_point`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1101948d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<vtkmodules.vtkRenderingOpenGL2.vtkOpenGLActor(0x000001E08C3BC890) at 0x000001E0A21C8340>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pv.Plotter()\n",
    "p.add_mesh(internalMesh ,scalars='U', show_edges=True)\n",
    "# if show_edges is set to True, the mesh will be surimposed on the domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0bac27d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<vtkmodules.vtkRenderingOpenGL2.vtkOpenGLActor(0x000001E08C3BF090) at 0x000001E0A21DC3A0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pv.Plotter()\n",
    "p.add_mesh(mesh_cell ,scalars='U', show_edges=True)\n",
    "# if show_edges is set to True, the mesh will be surimposed on the domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0ccb0b38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<vtkmodules.vtkRenderingOpenGL2.vtkOpenGLActor(0x000001E08C3BF290) at 0x000001E0A21E5100>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = pv.Plotter()\n",
    "p.add_mesh(mesh_point ,scalars='U', show_edges=True)\n",
    "# if show_edges is set to True, the mesh will be surimposed on the domain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d51a723",
   "metadata": {},
   "source": [
    "Now, we can plot all of the scalar fields that have been calculated during the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bf4bb5dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14eac9885e244d3b8abfb71413729af6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='field', options=('U-normed', 'U', 'epsilon', 'k', 'nut', 'p'), val…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_internalMesh(field, edges)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widgets.interact(plot_internalMesh, field=internalMesh.array_names, edges=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bb8b7d2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f7208e8bab24136adb476cae669f648",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='field', options=('U-normed', 'U', 'epsilon', 'k', 'nut', 'p'), val…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_cell(field, edges)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widgets.interact(plot_cell, field=mesh_cell.array_names, edges=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2819ef64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e534a7fdc08848dba8d0940a4c7cceb6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='field', options=('U-normed', 'U', 'epsilon', 'k', 'nut', 'p'), val…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_point(field, edges)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widgets.interact(plot_point, field=mesh_point.array_names, edges=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54008f26",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter5\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abbc7a84",
   "metadata": {},
   "source": [
    "# Plot the velocity field using glyphs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff97b53",
   "metadata": {},
   "source": [
    "We can do the same, but with the velocity vector field as well.<br/>\n",
    "We can scale the vectors with any of the scalar fields available.<br/>\n",
    "To plot the actual velocity vector field, we need to scale the vectors using `U-normed`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4abfdce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "72c528bbaaec4f6ca18356d6aaaca03a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='scale', options=('U-normed', 'U', 'epsilon', 'k', 'nut', 'p'), val…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_vectors(scale, factor)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widgets.interact(plot_vectors, scale=internalMesh.array_names, factor=widgets.fixed(0.0005))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cc06d3f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "201aef7ac6e944d580ec5009e5a62e5a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='scale', options=('U-normed', 'U', 'epsilon', 'k', 'nut', 'p'), val…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_vectors_cell(scale, factor)>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widgets.interact(plot_vectors_cell, scale=mesh_cell.array_names, factor=widgets.fixed(0.0005))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "55d3b41a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71e50ab710094eb98012b67976e5a66c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='scale', options=('U-normed', 'U', 'epsilon', 'k', 'nut', 'p'), val…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_vectors_point(scale, factor)>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widgets.interact(plot_vectors_point, scale=mesh_point.array_names, factor=widgets.fixed(0.0005))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78e8766e",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter6\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb7cf3fa",
   "metadata": {},
   "source": [
    "# How to access the vector components"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad2f70d9",
   "metadata": {},
   "source": [
    "We can access every component of the velocity vector field separately.<br/>\n",
    "This can be particularly useful if we need to plot each component.<br/>\n",
    "We'll do this for `internalMesh`, `mesh_cell` and `mesh_point`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "46a235d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>12225</td></tr>\n",
       "<tr><td>N Points</td><td>25012</td></tr>\n",
       "<tr><td>X Bounds</td><td>-2.060e-02, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-2.540e-02, 2.540e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.000e-04, 5.000e-04</td></tr>\n",
       "<tr><td>N Arrays</td><td>9</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Cells</td><td>float32</td><td>3</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>epsilon</td><td>Cells</td><td>float32</td><td>1</td><td>1.277e-01</td><td>5.788e+03</td></tr>\n",
       "<tr><td>k</td><td>Cells</td><td>float32</td><td>1</td><td>4.460e-04</td><td>5.498e+00</td></tr>\n",
       "<tr><td>nut</td><td>Cells</td><td>float32</td><td>1</td><td>1.402e-07</td><td>5.140e-03</td></tr>\n",
       "<tr><td>p</td><td>Cells</td><td>float32</td><td>1</td><td>-8.014e+00</td><td>1.504e+01</td></tr>\n",
       "<tr><td>U-normed</td><td>Cells</td><td>float32</td><td>1</td><td>5.320e-03</td><td>1.018e+01</td></tr>\n",
       "<tr><td>ux</td><td>Cells</td><td>float32</td><td>1</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>uy</td><td>Cells</td><td>float32</td><td>1</td><td>-9.007e-01</td><td>7.526e-01</td></tr>\n",
       "<tr><td><b>uz</b></td><td>Cells</td><td>float32</td><td>1</td><td>-3.396e-16</td><td>2.769e-16</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x1e09a051d00)\n",
       "  N Cells:\t12225\n",
       "  N Points:\t25012\n",
       "  X Bounds:\t-2.060e-02, 2.900e-01\n",
       "  Y Bounds:\t-2.540e-02, 2.540e-02\n",
       "  Z Bounds:\t-5.000e-04, 5.000e-04\n",
       "  N Arrays:\t9"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ux = internalMesh['U'][:,0]\n",
    "uy = internalMesh['U'][:,1]\n",
    "uz = internalMesh['U'][:,2]\n",
    "\n",
    "internalMesh.cell_data[\"ux\"] = ux\n",
    "internalMesh.cell_data[\"uy\"] = uy\n",
    "internalMesh.cell_data[\"uz\"] = uz\n",
    "\n",
    "internalMesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "674c3677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>37235</td></tr>\n",
       "<tr><td>N Points</td><td>25012</td></tr>\n",
       "<tr><td>X Bounds</td><td>-2.060e-02, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-2.540e-02, 2.540e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.000e-04, 5.000e-04</td></tr>\n",
       "<tr><td>N Arrays</td><td>9</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Cells</td><td>float32</td><td>3</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>epsilon</td><td>Cells</td><td>float32</td><td>1</td><td>1.277e-01</td><td>5.788e+03</td></tr>\n",
       "<tr><td>k</td><td>Cells</td><td>float32</td><td>1</td><td>4.460e-04</td><td>5.498e+00</td></tr>\n",
       "<tr><td>nut</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>5.140e-03</td></tr>\n",
       "<tr><td>p</td><td>Cells</td><td>float32</td><td>1</td><td>-8.014e+00</td><td>1.504e+01</td></tr>\n",
       "<tr><td>U-normed</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>ux</td><td>Cells</td><td>float32</td><td>1</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>uy</td><td>Cells</td><td>float32</td><td>1</td><td>-9.007e-01</td><td>7.526e-01</td></tr>\n",
       "<tr><td><b>uz</b></td><td>Cells</td><td>float32</td><td>1</td><td>-3.396e-16</td><td>2.769e-16</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x1e09a02e160)\n",
       "  N Cells:\t37235\n",
       "  N Points:\t25012\n",
       "  X Bounds:\t-2.060e-02, 2.900e-01\n",
       "  Y Bounds:\t-2.540e-02, 2.540e-02\n",
       "  Z Bounds:\t-5.000e-04, 5.000e-04\n",
       "  N Arrays:\t9"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ux = mesh_cell['U'][:,0]\n",
    "uy = mesh_cell['U'][:,1]\n",
    "uz = mesh_cell['U'][:,2]\n",
    "\n",
    "mesh_cell.cell_data[\"ux\"] = ux\n",
    "mesh_cell.cell_data[\"uy\"] = uy\n",
    "mesh_cell.cell_data[\"uz\"] = uz\n",
    "\n",
    "mesh_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "11b7708b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>37235</td></tr>\n",
       "<tr><td>N Points</td><td>25012</td></tr>\n",
       "<tr><td>X Bounds</td><td>-2.060e-02, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-2.540e-02, 2.540e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.000e-04, 5.000e-04</td></tr>\n",
       "<tr><td>N Arrays</td><td>9</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Points</td><td>float32</td><td>3</td><td>-2.507e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>epsilon</td><td>Points</td><td>float32</td><td>1</td><td>1.277e-01</td><td>5.175e+03</td></tr>\n",
       "<tr><td>k</td><td>Points</td><td>float32</td><td>1</td><td>4.460e-04</td><td>5.495e+00</td></tr>\n",
       "<tr><td>nut</td><td>Points</td><td>float32</td><td>1</td><td>7.009e-08</td><td>5.136e-03</td></tr>\n",
       "<tr><td>p</td><td>Points</td><td>float32</td><td>1</td><td>-8.012e+00</td><td>1.453e+01</td></tr>\n",
       "<tr><td>U-normed</td><td>Points</td><td>float32</td><td>1</td><td>2.528e-03</td><td>1.018e+01</td></tr>\n",
       "<tr><td>ux</td><td>Points</td><td>float32</td><td>1</td><td>-2.507e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>uy</td><td>Points</td><td>float32</td><td>1</td><td>-6.844e-01</td><td>6.314e-01</td></tr>\n",
       "<tr><td><b>uz</b></td><td>Points</td><td>float32</td><td>1</td><td>-1.640e-16</td><td>1.477e-16</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x1e09a02efa0)\n",
       "  N Cells:\t37235\n",
       "  N Points:\t25012\n",
       "  X Bounds:\t-2.060e-02, 2.900e-01\n",
       "  Y Bounds:\t-2.540e-02, 2.540e-02\n",
       "  Z Bounds:\t-5.000e-04, 5.000e-04\n",
       "  N Arrays:\t9"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ux = mesh_point['U'][:,0]\n",
    "uy = mesh_point['U'][:,1]\n",
    "uz = mesh_point['U'][:,2]\n",
    "\n",
    "mesh_point.point_data[\"ux\"] = ux\n",
    "mesh_point.point_data[\"uy\"] = uy\n",
    "mesh_point.point_data[\"uz\"] = uz\n",
    "\n",
    "mesh_point"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f0702d",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter7\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5fe5669",
   "metadata": {},
   "source": [
    "# Let's plot the streamlines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73474763",
   "metadata": {},
   "source": [
    "Now, we'll plot the streamlines of our flow.<br/>\n",
    "This can only be done using point data, so we'll only plot the streamlines using the data from `mesh_point`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cec079ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "face = mesh_point.slice(normal='x') # \"normal\" should be set as the direction of the flow\n",
    "str = mesh_point.streamlines_from_source(face, vectors='U') # creates streamlines from a singular surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5611dd07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abbee13e87c1412fa9d21416ce15a2fc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = pv.Plotter()\n",
    "p.add_mesh(mesh_point, color=\"w\")\n",
    "p.add_mesh(str.tube(radius=0.0004))\n",
    "# View in the xy plane\n",
    "p.view_xy()\n",
    "# Show the axes\n",
    "p.add_axes()\n",
    "# Display the plotting window\n",
    "p.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd3e3b2",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter8\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c28ac4",
   "metadata": {},
   "source": [
    "# Let's calculate the gradients and vorticity "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfa0f65a",
   "metadata": {},
   "source": [
    "Let us first create a list of labels so that we can order our gradient and vorticity components:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8172a6d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Labels = [\"dU/dx\", \"dU/dy\", \"dU/dz\", \"dux/dx\", \"dux/dy\", \"dux/dz\", \"duy/dx\", \"duy/dy\", \"duy/dz\", \"duz/dx\", \"duz/dy\", \"duz/dz\",\n",
    "         \"vort_x\", \"vort_y\", \"vort_z\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41886b6c",
   "metadata": {},
   "source": [
    "After that, we calculate the gradient and vorticity components.<br/>\n",
    "We'll do this for `internalMesh`, `mesh_cell` and `mesh_point`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1de05b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# deriv will contain the data from the gradient of U (9 components)\n",
    "deriv = internalMesh.compute_derivative(scalars='U', gradient=True, divergence=None, vorticity=True, \n",
    "                                          qcriterion=None, faster=False, preference='cell', progress_bar=False)\n",
    "# deriv2 will contain the data from the gradient of U-normed (3 components)\n",
    "deriv2 = internalMesh.compute_derivative(scalars=\"U-normed\", gradient=True, divergence=None, vorticity=False, \n",
    "                                          qcriterion=None, faster=False, preference='cell', progress_bar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7973d33e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>12225</td></tr>\n",
       "<tr><td>N Points</td><td>25012</td></tr>\n",
       "<tr><td>X Bounds</td><td>-2.060e-02, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-2.540e-02, 2.540e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.000e-04, 5.000e-04</td></tr>\n",
       "<tr><td>N Arrays</td><td>24</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Cells</td><td>float32</td><td>3</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>epsilon</td><td>Cells</td><td>float32</td><td>1</td><td>1.277e-01</td><td>5.788e+03</td></tr>\n",
       "<tr><td>k</td><td>Cells</td><td>float32</td><td>1</td><td>4.460e-04</td><td>5.498e+00</td></tr>\n",
       "<tr><td>nut</td><td>Cells</td><td>float32</td><td>1</td><td>1.402e-07</td><td>5.140e-03</td></tr>\n",
       "<tr><td>p</td><td>Cells</td><td>float32</td><td>1</td><td>-8.014e+00</td><td>1.504e+01</td></tr>\n",
       "<tr><td>U-normed</td><td>Cells</td><td>float32</td><td>1</td><td>5.320e-03</td><td>1.018e+01</td></tr>\n",
       "<tr><td>ux</td><td>Cells</td><td>float32</td><td>1</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>uy</td><td>Cells</td><td>float32</td><td>1</td><td>-9.007e-01</td><td>7.526e-01</td></tr>\n",
       "<tr><td>uz</td><td>Cells</td><td>float32</td><td>1</td><td>-3.396e-16</td><td>2.769e-16</td></tr>\n",
       "<tr><td>dU/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-1.452e+03</td><td>4.706e+02</td></tr>\n",
       "<tr><td>dU/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-4.161e+03</td><td>1.187e+04</td></tr>\n",
       "<tr><td>dU/dz</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>0.000e+00</td></tr>\n",
       "<tr><td>dux/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-1.469e+03</td><td>6.284e+02</td></tr>\n",
       "<tr><td>dux/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-4.141e+03</td><td>1.191e+04</td></tr>\n",
       "<tr><td>dux/dz</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>0.000e+00</td></tr>\n",
       "<tr><td>duy/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-2.269e+02</td><td>1.228e+02</td></tr>\n",
       "<tr><td>duy/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-1.107e+03</td><td>1.797e+03</td></tr>\n",
       "<tr><td>duy/dz</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>0.000e+00</td></tr>\n",
       "<tr><td>duz/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-7.221e-14</td><td>7.256e-14</td></tr>\n",
       "<tr><td>duz/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-5.777e-13</td><td>6.588e-13</td></tr>\n",
       "<tr><td>duz/dz</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>0.000e+00</td></tr>\n",
       "<tr><td>vort_x</td><td>Cells</td><td>float32</td><td>1</td><td>-5.777e-13</td><td>6.588e-13</td></tr>\n",
       "<tr><td>vort_y</td><td>Cells</td><td>float32</td><td>1</td><td>-7.256e-14</td><td>7.221e-14</td></tr>\n",
       "<tr><td><b>vort_z</b></td><td>Cells</td><td>float32</td><td>1</td><td>-1.200e+04</td><td>4.182e+03</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x1e09a051d00)\n",
       "  N Cells:\t12225\n",
       "  N Points:\t25012\n",
       "  X Bounds:\t-2.060e-02, 2.900e-01\n",
       "  Y Bounds:\t-2.540e-02, 2.540e-02\n",
       "  Z Bounds:\t-5.000e-04, 5.000e-04\n",
       "  N Arrays:\t24"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grad = gradients_to_dict(deriv[\"gradient\"])\n",
    "grad2 = gradients_to_dict2(deriv2[\"gradient\"])\n",
    "vort = vorticity_to_dict(deriv[\"vorticity\"])\n",
    "\n",
    "for i in range(0, 3):\n",
    "    internalMesh.cell_data[Labels[i]] = grad2[Labels[i]]\n",
    "for i in range(3, 12):\n",
    "    internalMesh.cell_data[Labels[i]] = grad[Labels[i]]\n",
    "for i in range(12, 15):\n",
    "    internalMesh.cell_data[Labels[i]] = vort[Labels[i]]\n",
    "\n",
    "internalMesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c8c5ca3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# deriv will contain the data from the gradient of U (9 components)\n",
    "deriv_cell = mesh_cell.compute_derivative(scalars='U', gradient=True, divergence=None, vorticity=True, \n",
    "                                          qcriterion=None, faster=False, preference='cell', progress_bar=False)\n",
    "# deriv2 will contain the data from the gradient of U-normed (3 components)\n",
    "deriv2_cell = mesh_cell.compute_derivative(scalars=\"U-normed\", gradient=True, divergence=None, vorticity=False, \n",
    "                                          qcriterion=None, faster=False, preference='cell', progress_bar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2c9496e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>37235</td></tr>\n",
       "<tr><td>N Points</td><td>25012</td></tr>\n",
       "<tr><td>X Bounds</td><td>-2.060e-02, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-2.540e-02, 2.540e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.000e-04, 5.000e-04</td></tr>\n",
       "<tr><td>N Arrays</td><td>24</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Cells</td><td>float32</td><td>3</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>epsilon</td><td>Cells</td><td>float32</td><td>1</td><td>1.277e-01</td><td>5.788e+03</td></tr>\n",
       "<tr><td>k</td><td>Cells</td><td>float32</td><td>1</td><td>4.460e-04</td><td>5.498e+00</td></tr>\n",
       "<tr><td>nut</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>5.140e-03</td></tr>\n",
       "<tr><td>p</td><td>Cells</td><td>float32</td><td>1</td><td>-8.014e+00</td><td>1.504e+01</td></tr>\n",
       "<tr><td>U-normed</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>ux</td><td>Cells</td><td>float32</td><td>1</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>uy</td><td>Cells</td><td>float32</td><td>1</td><td>-9.007e-01</td><td>7.526e-01</td></tr>\n",
       "<tr><td>uz</td><td>Cells</td><td>float32</td><td>1</td><td>-3.396e-16</td><td>2.769e-16</td></tr>\n",
       "<tr><td>dU/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-1.308e+03</td><td>7.577e+02</td></tr>\n",
       "<tr><td>dU/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-9.266e+03</td><td>1.314e+04</td></tr>\n",
       "<tr><td>dU/dz</td><td>Cells</td><td>float32</td><td>1</td><td>-1.747e-14</td><td>5.616e-14</td></tr>\n",
       "<tr><td>dux/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-1.334e+03</td><td>6.447e+02</td></tr>\n",
       "<tr><td>dux/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-9.217e+03</td><td>1.321e+04</td></tr>\n",
       "<tr><td>dux/dz</td><td>Cells</td><td>float32</td><td>1</td><td>-1.739e-14</td><td>5.604e-14</td></tr>\n",
       "<tr><td>duy/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-2.269e+02</td><td>3.752e+02</td></tr>\n",
       "<tr><td>duy/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-8.930e+02</td><td>1.102e+03</td></tr>\n",
       "<tr><td>duy/dz</td><td>Cells</td><td>float32</td><td>1</td><td>-1.650e-15</td><td>3.851e-15</td></tr>\n",
       "<tr><td>duz/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-4.886e-14</td><td>4.739e-14</td></tr>\n",
       "<tr><td>duz/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-4.525e-13</td><td>4.561e-13</td></tr>\n",
       "<tr><td>duz/dz</td><td>Cells</td><td>float32</td><td>1</td><td>-9.714e-30</td><td>3.674e-30</td></tr>\n",
       "<tr><td>vort_x</td><td>Cells</td><td>float32</td><td>1</td><td>-4.525e-13</td><td>4.561e-13</td></tr>\n",
       "<tr><td>vort_y</td><td>Cells</td><td>float32</td><td>1</td><td>-4.739e-14</td><td>6.299e-14</td></tr>\n",
       "<tr><td><b>vort_z</b></td><td>Cells</td><td>float32</td><td>1</td><td>-1.321e+04</td><td>9.315e+03</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x1e09a02e160)\n",
       "  N Cells:\t37235\n",
       "  N Points:\t25012\n",
       "  X Bounds:\t-2.060e-02, 2.900e-01\n",
       "  Y Bounds:\t-2.540e-02, 2.540e-02\n",
       "  Z Bounds:\t-5.000e-04, 5.000e-04\n",
       "  N Arrays:\t24"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grad_cell = gradients_to_dict(deriv_cell[\"gradient\"])\n",
    "grad2_cell = gradients_to_dict2(deriv2_cell[\"gradient\"])\n",
    "vort_cell = vorticity_to_dict(deriv_cell[\"vorticity\"])\n",
    "\n",
    "for i in range(0, 3):\n",
    "    mesh_cell.cell_data[Labels[i]] = grad2_cell[Labels[i]]\n",
    "for i in range(3, 12):\n",
    "    mesh_cell.cell_data[Labels[i]] = grad_cell[Labels[i]]\n",
    "for i in range(12, 15):\n",
    "    mesh_cell.cell_data[Labels[i]] = vort_cell[Labels[i]]\n",
    "\n",
    "mesh_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4b694176",
   "metadata": {},
   "outputs": [],
   "source": [
    "# deriv will contain the data from the gradient of U (9 components)\n",
    "deriv_point = mesh_point.compute_derivative(scalars='U', gradient=True, divergence=None, vorticity=True, \n",
    "                                          qcriterion=None, faster=False, preference='point', progress_bar=False)\n",
    "# deriv2 will contain the data from the gradient of U-normed (3 components)\n",
    "deriv2_point = mesh_point.compute_derivative(scalars=\"U-normed\", gradient=True, divergence=None, vorticity=False, \n",
    "                                          qcriterion=None, faster=False, preference='point', progress_bar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d06fe8af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>37235</td></tr>\n",
       "<tr><td>N Points</td><td>25012</td></tr>\n",
       "<tr><td>X Bounds</td><td>-2.060e-02, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-2.540e-02, 2.540e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.000e-04, 5.000e-04</td></tr>\n",
       "<tr><td>N Arrays</td><td>24</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Points</td><td>float32</td><td>3</td><td>-2.507e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>epsilon</td><td>Points</td><td>float32</td><td>1</td><td>1.277e-01</td><td>5.175e+03</td></tr>\n",
       "<tr><td>k</td><td>Points</td><td>float32</td><td>1</td><td>4.460e-04</td><td>5.495e+00</td></tr>\n",
       "<tr><td>nut</td><td>Points</td><td>float32</td><td>1</td><td>7.009e-08</td><td>5.136e-03</td></tr>\n",
       "<tr><td>p</td><td>Points</td><td>float32</td><td>1</td><td>-8.012e+00</td><td>1.453e+01</td></tr>\n",
       "<tr><td>U-normed</td><td>Points</td><td>float32</td><td>1</td><td>2.528e-03</td><td>1.018e+01</td></tr>\n",
       "<tr><td>ux</td><td>Points</td><td>float32</td><td>1</td><td>-2.507e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>uy</td><td>Points</td><td>float32</td><td>1</td><td>-6.844e-01</td><td>6.314e-01</td></tr>\n",
       "<tr><td>uz</td><td>Points</td><td>float32</td><td>1</td><td>-1.640e-16</td><td>1.477e-16</td></tr>\n",
       "<tr><td>dU/dx</td><td>Points</td><td>float32</td><td>1</td><td>-6.806e+02</td><td>7.585e+02</td></tr>\n",
       "<tr><td>dU/dy</td><td>Points</td><td>float32</td><td>1</td><td>-6.797e+03</td><td>1.108e+04</td></tr>\n",
       "<tr><td>dU/dz</td><td>Points</td><td>float32</td><td>1</td><td>-1.754e-12</td><td>1.836e-12</td></tr>\n",
       "<tr><td>dux/dx</td><td>Points</td><td>float32</td><td>1</td><td>-6.788e+02</td><td>8.058e+02</td></tr>\n",
       "<tr><td>dux/dy</td><td>Points</td><td>float32</td><td>1</td><td>-6.762e+03</td><td>1.131e+04</td></tr>\n",
       "<tr><td>dux/dz</td><td>Points</td><td>float32</td><td>1</td><td>-1.754e-12</td><td>1.836e-12</td></tr>\n",
       "<tr><td>duy/dx</td><td>Points</td><td>float32</td><td>1</td><td>-2.319e+02</td><td>2.571e+02</td></tr>\n",
       "<tr><td>duy/dy</td><td>Points</td><td>float32</td><td>1</td><td>-7.466e+02</td><td>1.089e+03</td></tr>\n",
       "<tr><td>duy/dz</td><td>Points</td><td>float32</td><td>1</td><td>-1.068e-13</td><td>1.442e-13</td></tr>\n",
       "<tr><td>duz/dx</td><td>Points</td><td>float32</td><td>1</td><td>-4.513e-14</td><td>4.731e-14</td></tr>\n",
       "<tr><td>duz/dy</td><td>Points</td><td>float32</td><td>1</td><td>-4.435e-13</td><td>4.248e-13</td></tr>\n",
       "<tr><td>duz/dz</td><td>Points</td><td>float32</td><td>1</td><td>-4.088e-29</td><td>4.016e-29</td></tr>\n",
       "<tr><td>vort_x</td><td>Points</td><td>float32</td><td>1</td><td>-4.435e-13</td><td>4.248e-13</td></tr>\n",
       "<tr><td>vort_y</td><td>Points</td><td>float32</td><td>1</td><td>-1.754e-12</td><td>1.836e-12</td></tr>\n",
       "<tr><td><b>vort_z</b></td><td>Points</td><td>float32</td><td>1</td><td>-1.137e+04</td><td>6.832e+03</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x1e09a02efa0)\n",
       "  N Cells:\t37235\n",
       "  N Points:\t25012\n",
       "  X Bounds:\t-2.060e-02, 2.900e-01\n",
       "  Y Bounds:\t-2.540e-02, 2.540e-02\n",
       "  Z Bounds:\t-5.000e-04, 5.000e-04\n",
       "  N Arrays:\t24"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grad_point = gradients_to_dict(deriv_point[\"gradient\"])\n",
    "grad2_point = gradients_to_dict2(deriv2_point[\"gradient\"])\n",
    "vort_point = vorticity_to_dict(deriv_point[\"vorticity\"])\n",
    "\n",
    "for i in range(0, 3):\n",
    "    mesh_point.point_data[Labels[i]] = grad2_point[Labels[i]]\n",
    "for i in range(3, 12):\n",
    "    mesh_point.point_data[Labels[i]] = grad_point[Labels[i]]\n",
    "for i in range(12, 15):\n",
    "    mesh_point.point_data[Labels[i]] = vort_point[Labels[i]]\n",
    "\n",
    "mesh_point"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d5404a",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter9\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b97e20",
   "metadata": {},
   "source": [
    "# Let's plot the gradient components "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ed455565",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4e2f97bc8b04c4d8262083422b38e0c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### multiplot, plotting all the gradient components \n",
    "### {du/dx, du/dy, du/dz, dv/dx, dv/dy, dv/dz, dw/dx, dw/dy, dw/dz}\n",
    "\n",
    "plotter = pv.Plotter(shape=(3, 3))\n",
    "\n",
    "i = 3\n",
    "for j in range (0, 3):\n",
    "    for k in range (0, 3):\n",
    "        plotter.subplot(j, k)\n",
    "        plotter.add_text(Labels[i], font_size=20)\n",
    "        plotter.add_mesh(internalMesh.copy(),scalars=Labels[i])\n",
    "        plotter.view_xy()\n",
    "        plotter.add_axes()\n",
    "        \n",
    "        i = i + 1\n",
    "\n",
    "plotter.show()        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ea0872bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87ab4d3bea494a7985c0fa89e508039d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### multiplot, plotting all the gradient components \n",
    "### {du/dx, du/dy, du/dz, dv/dx, dv/dy, dv/dz, dw/dx, dw/dy, dw/dz}\n",
    "\n",
    "plotter = pv.Plotter(shape=(3, 3))\n",
    "\n",
    "i = 3\n",
    "for j in range (0, 3):\n",
    "    for k in range (0, 3):\n",
    "        plotter.subplot(j, k)\n",
    "        plotter.add_text(Labels[i], font_size=20)\n",
    "        plotter.add_mesh(mesh_cell.copy(),scalars=Labels[i])\n",
    "        plotter.view_xy()\n",
    "        plotter.add_axes()\n",
    "        \n",
    "        i = i + 1\n",
    "\n",
    "plotter.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9e65f434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08583ed5160b4d2d94ddfdfdca78191c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### multiplot, plotting all the gradient components \n",
    "### {du/dx, du/dy, du/dz, dv/dx, dv/dy, dv/dz, dw/dx, dw/dy, dw/dz}\n",
    "\n",
    "plotter = pv.Plotter(shape=(3, 3))\n",
    "\n",
    "i = 3\n",
    "for j in range (0, 3):\n",
    "    for k in range (0, 3):\n",
    "        plotter.subplot(j, k)\n",
    "        plotter.add_text(Labels[i], font_size=20)\n",
    "        plotter.add_mesh(mesh_point.copy(),scalars=Labels[i])\n",
    "        plotter.view_xy()\n",
    "        plotter.add_axes()\n",
    "        \n",
    "        i = i + 1\n",
    "\n",
    "plotter.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68a716f",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter10\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e809a924",
   "metadata": {},
   "source": [
    "# Let's plot the vorticity components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "94e0b09e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c616ea022d084f3da4953f87325226fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pv.set_plot_theme(\"document\")\n",
    "\n",
    "### multiplot, plotting of vorticity components (internalMesh)\n",
    "\n",
    "plotter = pv.Plotter(shape=(1, 3))\n",
    "\n",
    "plotter.subplot(0, 0)\n",
    "plotter.add_text(\"vort_x\", font_size=20)\n",
    "plotter.add_mesh(internalMesh,scalars=\"vort_x\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 1)\n",
    "plotter.add_text(\"vort_y\", font_size=20)\n",
    "plotter.add_mesh(internalMesh.copy(),scalars=\"vort_y\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 2)\n",
    "plotter.add_text(\"vort_z\", font_size=20)\n",
    "plotter.add_mesh(internalMesh.copy(),scalars=\"vort_z\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "17ed8823",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1cb9af37e813430595195082e964d30f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### multiplot, plotting of vorticity components (cell data)\n",
    "\n",
    "plotter = pv.Plotter(shape=(1, 3))\n",
    "\n",
    "plotter.subplot(0, 0)\n",
    "plotter.add_text(\"vort_x\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell,scalars=\"vort_x\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 1)\n",
    "plotter.add_text(\"vort_y\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell.copy(),scalars=\"vort_y\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 2)\n",
    "plotter.add_text(\"vort_z\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell.copy(),scalars=\"vort_z\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0209fd52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8dbe9ad6807e437cbafa09011769c528",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### multiplot, plotting of vorticity components (point data)\n",
    "\n",
    "plotter = pv.Plotter(shape=(1, 3))\n",
    "\n",
    "plotter.subplot(0, 0)\n",
    "plotter.add_text(\"vort_x\", font_size=20)\n",
    "plotter.add_mesh(mesh_point,scalars=\"vort_x\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 1)\n",
    "plotter.add_text(\"vort_y\", font_size=20)\n",
    "plotter.add_mesh(mesh_point.copy(),scalars=\"vort_y\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 2)\n",
    "plotter.add_text(\"vort_z\", font_size=20)\n",
    "plotter.add_mesh(mesh_point.copy(),scalars=\"vort_z\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff015935",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter11\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c65acb3",
   "metadata": {},
   "source": [
    "# Vectors normalization compared to the maximal component "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "204bc083",
   "metadata": {},
   "source": [
    "As shown earlier, we can plot all the gradient components and vorticity components together in two multiplots: one for the gradient components and one for the vorticity components.<br/>\n",
    "In order to compare these plots with each other more efficiently, what we can do is normalize these fields so as to have the same variation scale for all the components.<br/>\n",
    "We'll do this for the data in `internalMesh`, `mesh_cell` and `mesh_point`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "388c70b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "duxdx_norm=(internalMesh[\"dux/dx\"]-min(internalMesh[\"dux/dx\"]))/(max(internalMesh[\"dux/dx\"])-min(internalMesh[\"dux/dx\"]))\n",
    "duydx_norm=(internalMesh[\"duy/dx\"]-min(internalMesh[\"duy/dx\"]))/(max(internalMesh[\"duy/dx\"])-min(internalMesh[\"duy/dx\"]))\n",
    "duzdx_norm=(internalMesh[\"duz/dx\"]-min(internalMesh[\"duz/dx\"]))/(max(internalMesh[\"duz/dx\"])-min(internalMesh[\"duz/dx\"]))\n",
    "duxdy_norm=(internalMesh[\"dux/dy\"]-min(internalMesh[\"dux/dy\"]))/(max(internalMesh[\"dux/dy\"])-min(internalMesh[\"dux/dy\"]))\n",
    "duydy_norm=(internalMesh[\"duy/dy\"]-min(internalMesh[\"duy/dy\"]))/(max(internalMesh[\"duy/dy\"])-min(internalMesh[\"duy/dy\"]))\n",
    "duzdy_norm=(internalMesh[\"duz/dy\"]-min(internalMesh[\"duz/dy\"]))/(max(internalMesh[\"duz/dy\"])-min(internalMesh[\"duz/dy\"]))\n",
    "#duxdz_norm=(internalMesh[\"dux/dz\"]-min(internalMesh[\"dux/dz\"]))/(max(internalMesh[\"dux/dz\"])-min(internalMesh[\"dux/dz\"]))\n",
    "#duydz_norm=(internalMesh[\"duy/dz\"]-min(internalMesh[\"duy/dz\"]))/(max(internalMesh[\"duy/dz\"])-min(internalMesh[\"duy/dz\"]))\n",
    "#duzdz_norm=(internalMesh[\"duz/dz\"]-min(internalMesh[\"duz/dz\"]))/(max(internalMesh[\"duz/dz\"])-min(internalMesh[\"duz/dz\"]))\n",
    "vort_x_norm=(internalMesh[\"vort_x\"]-min(internalMesh[\"vort_x\"]))/(max(internalMesh[\"vort_x\"])-min(internalMesh[\"vort_x\"]))\n",
    "vort_y_norm=(internalMesh[\"vort_y\"]-min(internalMesh[\"vort_y\"]))/(max(internalMesh[\"vort_y\"])-min(internalMesh[\"vort_y\"]))\n",
    "vort_z_norm=(internalMesh[\"vort_z\"]-min(internalMesh[\"vort_z\"]))/(max(internalMesh[\"vort_z\"])-min(internalMesh[\"vort_z\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1b3d63c",
   "metadata": {},
   "source": [
    "In the case of a 2D simulation, the velocity gradient in the direction perpendicular to the plane of the flow is null. This means that the normalization method used here won't work, as it would require us to divide by zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "92998275",
   "metadata": {},
   "outputs": [],
   "source": [
    "internalMesh.cell_data[\"dux/dx n\"] = duxdx_norm\n",
    "internalMesh.cell_data[\"duy/dx n\"] = duydx_norm\n",
    "internalMesh.cell_data[\"duz/dx n\"] = duzdx_norm\n",
    "internalMesh.cell_data[\"dux/dy n\"] = duxdy_norm\n",
    "internalMesh.cell_data[\"duy/dy n\"] = duydy_norm\n",
    "internalMesh.cell_data[\"duz/dy n\"] = duzdy_norm\n",
    "#internalMesh.cell_data[\"dux/dz n\"] = duxdz_norm\n",
    "#internalMesh.cell_data[\"duy/dz n\"] = duydz_norm\n",
    "#internalMesh.cell_data[\"duz/dz n\"] = duzdz_norm\n",
    "internalMesh.cell_data[\"vort_x n\"] = vort_x_norm\n",
    "internalMesh.cell_data[\"vort_y n\"] = vort_y_norm\n",
    "internalMesh.cell_data[\"vort_z n\"] = vort_z_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "19c42a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>12225</td></tr>\n",
       "<tr><td>N Points</td><td>25012</td></tr>\n",
       "<tr><td>X Bounds</td><td>-2.060e-02, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-2.540e-02, 2.540e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.000e-04, 5.000e-04</td></tr>\n",
       "<tr><td>N Arrays</td><td>33</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Cells</td><td>float32</td><td>3</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>epsilon</td><td>Cells</td><td>float32</td><td>1</td><td>1.277e-01</td><td>5.788e+03</td></tr>\n",
       "<tr><td>k</td><td>Cells</td><td>float32</td><td>1</td><td>4.460e-04</td><td>5.498e+00</td></tr>\n",
       "<tr><td>nut</td><td>Cells</td><td>float32</td><td>1</td><td>1.402e-07</td><td>5.140e-03</td></tr>\n",
       "<tr><td>p</td><td>Cells</td><td>float32</td><td>1</td><td>-8.014e+00</td><td>1.504e+01</td></tr>\n",
       "<tr><td>U-normed</td><td>Cells</td><td>float32</td><td>1</td><td>5.320e-03</td><td>1.018e+01</td></tr>\n",
       "<tr><td>ux</td><td>Cells</td><td>float32</td><td>1</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>uy</td><td>Cells</td><td>float32</td><td>1</td><td>-9.007e-01</td><td>7.526e-01</td></tr>\n",
       "<tr><td>uz</td><td>Cells</td><td>float32</td><td>1</td><td>-3.396e-16</td><td>2.769e-16</td></tr>\n",
       "<tr><td>dU/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-1.452e+03</td><td>4.706e+02</td></tr>\n",
       "<tr><td>dU/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-4.161e+03</td><td>1.187e+04</td></tr>\n",
       "<tr><td>dU/dz</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>0.000e+00</td></tr>\n",
       "<tr><td>dux/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-1.469e+03</td><td>6.284e+02</td></tr>\n",
       "<tr><td>dux/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-4.141e+03</td><td>1.191e+04</td></tr>\n",
       "<tr><td>dux/dz</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>0.000e+00</td></tr>\n",
       "<tr><td>duy/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-2.269e+02</td><td>1.228e+02</td></tr>\n",
       "<tr><td>duy/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-1.107e+03</td><td>1.797e+03</td></tr>\n",
       "<tr><td>duy/dz</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>0.000e+00</td></tr>\n",
       "<tr><td>duz/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-7.221e-14</td><td>7.256e-14</td></tr>\n",
       "<tr><td>duz/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-5.777e-13</td><td>6.588e-13</td></tr>\n",
       "<tr><td>duz/dz</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>0.000e+00</td></tr>\n",
       "<tr><td>vort_x</td><td>Cells</td><td>float32</td><td>1</td><td>-5.777e-13</td><td>6.588e-13</td></tr>\n",
       "<tr><td>vort_y</td><td>Cells</td><td>float32</td><td>1</td><td>-7.256e-14</td><td>7.221e-14</td></tr>\n",
       "<tr><td>vort_z</td><td>Cells</td><td>float32</td><td>1</td><td>-1.200e+04</td><td>4.182e+03</td></tr>\n",
       "<tr><td>dux/dx n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>duy/dx n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>duz/dx n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>dux/dy n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>duy/dy n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>duz/dy n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>vort_x n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>vort_y n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td><b>vort_z n</b></td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x1e09a051d00)\n",
       "  N Cells:\t12225\n",
       "  N Points:\t25012\n",
       "  X Bounds:\t-2.060e-02, 2.900e-01\n",
       "  Y Bounds:\t-2.540e-02, 2.540e-02\n",
       "  Z Bounds:\t-5.000e-04, 5.000e-04\n",
       "  N Arrays:\t33"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "internalMesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3d8ecbf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "804f5647b3d44395b2855da236ade396",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plotter = pv.Plotter(shape=(3, 3))\n",
    "\n",
    "plotter.subplot(0, 0)\n",
    "plotter.add_text(\"dux/dx n\", font_size=20)\n",
    "plotter.add_mesh(internalMesh,scalars='dux/dx n')\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 1)\n",
    "plotter.add_text(\"dux/dy n\", font_size=20)\n",
    "plotter.add_mesh(internalMesh.copy(),scalars=\"dux/dy n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 2)\n",
    "plotter.add_text(\"dux/dz n\", font_size=20)\n",
    "plotter.add_mesh(internalMesh.copy(),scalars=\"dux/dz\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(1, 0)\n",
    "plotter.add_text(\"duy/dx n\", font_size=20)\n",
    "plotter.add_mesh(internalMesh.copy(),scalars=\"duy/dx n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(1, 1)\n",
    "plotter.add_text(\"duy/dy n\", font_size=20)\n",
    "plotter.add_mesh(internalMesh.copy(),scalars=\"duy/dy n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(1, 2)\n",
    "plotter.add_text(\"duy/dz n\", font_size=20)\n",
    "plotter.add_mesh(internalMesh.copy(),scalars=\"duy/dz\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(2, 0)\n",
    "plotter.add_text(\"duz/dx n\", font_size=20)\n",
    "plotter.add_mesh(internalMesh.copy(),scalars=\"duz/dx n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(2, 1)\n",
    "plotter.add_text(\"duz/dy n\", font_size=20)\n",
    "plotter.add_mesh(internalMesh.copy(),scalars=\"duz/dy n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(2, 2)\n",
    "plotter.add_text(\"duz/dz n\", font_size=20)\n",
    "plotter.add_mesh(internalMesh.copy(),scalars=\"duz/dz\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "28f84868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85823a7e6e244d6288fc3c60f24ec65b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### multiplot\n",
    "\n",
    "plotter = pv.Plotter(shape=(1, 3))\n",
    "\n",
    "plotter.subplot(0, 0)\n",
    "plotter.add_text(\"vort_x n\", font_size=20)\n",
    "plotter.add_mesh(internalMesh, scalars=\"vort_x n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 1)\n",
    "plotter.add_text(\"vort_y n\", font_size=20)\n",
    "plotter.add_mesh(internalMesh.copy(),scalars=\"vort_y n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 2)\n",
    "plotter.add_text(\"vort_z n\", font_size=20)\n",
    "plotter.add_mesh(internalMesh.copy(),scalars=\"vort_z n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b668494a",
   "metadata": {},
   "outputs": [],
   "source": [
    "duxdx_norm=(mesh_cell[\"dux/dx\"]-min(mesh_cell[\"dux/dx\"]))/(max(mesh_cell[\"dux/dx\"])-min(mesh_cell[\"dux/dx\"]))\n",
    "duydx_norm=(mesh_cell[\"duy/dx\"]-min(mesh_cell[\"duy/dx\"]))/(max(mesh_cell[\"duy/dx\"])-min(mesh_cell[\"duy/dx\"]))\n",
    "duzdx_norm=(mesh_cell[\"duz/dx\"]-min(mesh_cell[\"duz/dx\"]))/(max(mesh_cell[\"duz/dx\"])-min(mesh_cell[\"duz/dx\"]))\n",
    "duxdy_norm=(mesh_cell[\"dux/dy\"]-min(mesh_cell[\"dux/dy\"]))/(max(mesh_cell[\"dux/dy\"])-min(mesh_cell[\"dux/dy\"]))\n",
    "duydy_norm=(mesh_cell[\"duy/dy\"]-min(mesh_cell[\"duy/dy\"]))/(max(mesh_cell[\"duy/dy\"])-min(mesh_cell[\"duy/dy\"]))\n",
    "duzdy_norm=(mesh_cell[\"duz/dy\"]-min(mesh_cell[\"duz/dy\"]))/(max(mesh_cell[\"duz/dy\"])-min(mesh_cell[\"duz/dy\"]))\n",
    "#duxdz_norm=(mesh_cell[\"dux/dz\"]-min(mesh_cell[\"dux/dz\"]))/(max(mesh_cell[\"dux/dz\"])-min(mesh_cell[\"dux/dz\"]))\n",
    "#duydz_norm=(mesh_cell[\"duy/dz\"]-min(mesh_cell[\"duy/dz\"]))/(max(mesh_cell[\"duy/dz\"])-min(mesh_cell[\"duy/dz\"]))\n",
    "#duzdz_norm=(mesh_cell[\"duz/dz\"]-min(mesh_cell[\"duz/dz\"]))/(max(mesh_cell[\"duz/dz\"])-min(mesh_cell[\"duz/dz\"]))\n",
    "vort_x_norm=(mesh_cell[\"vort_x\"]-min(mesh_cell[\"vort_x\"]))/(max(mesh_cell[\"vort_x\"])-min(mesh_cell[\"vort_x\"]))\n",
    "vort_y_norm=(mesh_cell[\"vort_y\"]-min(mesh_cell[\"vort_y\"]))/(max(mesh_cell[\"vort_y\"])-min(mesh_cell[\"vort_y\"]))\n",
    "vort_z_norm=(mesh_cell[\"vort_z\"]-min(mesh_cell[\"vort_z\"]))/(max(mesh_cell[\"vort_z\"])-min(mesh_cell[\"vort_z\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8e2b8517",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_cell.cell_data[\"dux/dx n\"] = duxdx_norm\n",
    "mesh_cell.cell_data[\"duy/dx n\"] = duydx_norm\n",
    "mesh_cell.cell_data[\"duz/dx n\"] = duzdx_norm\n",
    "mesh_cell.cell_data[\"dux/dy n\"] = duxdy_norm\n",
    "mesh_cell.cell_data[\"duy/dy n\"] = duydy_norm\n",
    "mesh_cell.cell_data[\"duz/dy n\"] = duzdy_norm\n",
    "#mesh_cell.cell_data[\"dux/dz n\"] = duxdz_norm\n",
    "#mesh_cell.cell_data[\"duy/dz n\"] = duydz_norm\n",
    "#mesh_cell.cell_data[\"duz/dz n\"] = duzdz_norm\n",
    "mesh_cell.cell_data[\"vort_x n\"] = vort_x_norm\n",
    "mesh_cell.cell_data[\"vort_y n\"] = vort_y_norm\n",
    "mesh_cell.cell_data[\"vort_z n\"] = vort_z_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1c483a7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>37235</td></tr>\n",
       "<tr><td>N Points</td><td>25012</td></tr>\n",
       "<tr><td>X Bounds</td><td>-2.060e-02, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-2.540e-02, 2.540e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.000e-04, 5.000e-04</td></tr>\n",
       "<tr><td>N Arrays</td><td>33</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Cells</td><td>float32</td><td>3</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>epsilon</td><td>Cells</td><td>float32</td><td>1</td><td>1.277e-01</td><td>5.788e+03</td></tr>\n",
       "<tr><td>k</td><td>Cells</td><td>float32</td><td>1</td><td>4.460e-04</td><td>5.498e+00</td></tr>\n",
       "<tr><td>nut</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>5.140e-03</td></tr>\n",
       "<tr><td>p</td><td>Cells</td><td>float32</td><td>1</td><td>-8.014e+00</td><td>1.504e+01</td></tr>\n",
       "<tr><td>U-normed</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>ux</td><td>Cells</td><td>float32</td><td>1</td><td>-2.553e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>uy</td><td>Cells</td><td>float32</td><td>1</td><td>-9.007e-01</td><td>7.526e-01</td></tr>\n",
       "<tr><td>uz</td><td>Cells</td><td>float32</td><td>1</td><td>-3.396e-16</td><td>2.769e-16</td></tr>\n",
       "<tr><td>dU/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-1.308e+03</td><td>7.577e+02</td></tr>\n",
       "<tr><td>dU/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-9.266e+03</td><td>1.314e+04</td></tr>\n",
       "<tr><td>dU/dz</td><td>Cells</td><td>float32</td><td>1</td><td>-1.747e-14</td><td>5.616e-14</td></tr>\n",
       "<tr><td>dux/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-1.334e+03</td><td>6.447e+02</td></tr>\n",
       "<tr><td>dux/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-9.217e+03</td><td>1.321e+04</td></tr>\n",
       "<tr><td>dux/dz</td><td>Cells</td><td>float32</td><td>1</td><td>-1.739e-14</td><td>5.604e-14</td></tr>\n",
       "<tr><td>duy/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-2.269e+02</td><td>3.752e+02</td></tr>\n",
       "<tr><td>duy/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-8.930e+02</td><td>1.102e+03</td></tr>\n",
       "<tr><td>duy/dz</td><td>Cells</td><td>float32</td><td>1</td><td>-1.650e-15</td><td>3.851e-15</td></tr>\n",
       "<tr><td>duz/dx</td><td>Cells</td><td>float32</td><td>1</td><td>-4.886e-14</td><td>4.739e-14</td></tr>\n",
       "<tr><td>duz/dy</td><td>Cells</td><td>float32</td><td>1</td><td>-4.525e-13</td><td>4.561e-13</td></tr>\n",
       "<tr><td>duz/dz</td><td>Cells</td><td>float32</td><td>1</td><td>-9.714e-30</td><td>3.674e-30</td></tr>\n",
       "<tr><td>vort_x</td><td>Cells</td><td>float32</td><td>1</td><td>-4.525e-13</td><td>4.561e-13</td></tr>\n",
       "<tr><td>vort_y</td><td>Cells</td><td>float32</td><td>1</td><td>-4.739e-14</td><td>6.299e-14</td></tr>\n",
       "<tr><td>vort_z</td><td>Cells</td><td>float32</td><td>1</td><td>-1.321e+04</td><td>9.315e+03</td></tr>\n",
       "<tr><td>dux/dx n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>duy/dx n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>duz/dx n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>dux/dy n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>duy/dy n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>duz/dy n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>vort_x n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>vort_y n</td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td><b>vort_z n</b></td><td>Cells</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x1e09a02e160)\n",
       "  N Cells:\t37235\n",
       "  N Points:\t25012\n",
       "  X Bounds:\t-2.060e-02, 2.900e-01\n",
       "  Y Bounds:\t-2.540e-02, 2.540e-02\n",
       "  Z Bounds:\t-5.000e-04, 5.000e-04\n",
       "  N Arrays:\t33"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mesh_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9fe1bdcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "faf4ffb130ae4110bfeb2d7f3993144f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plotter = pv.Plotter(shape=(3, 3))\n",
    "\n",
    "plotter.subplot(0, 0)\n",
    "plotter.add_text(\"dux/dx n\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell,scalars='dux/dx n')\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 1)\n",
    "plotter.add_text(\"dux/dy n\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell.copy(),scalars=\"dux/dy n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 2)\n",
    "plotter.add_text(\"dux/dz n\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell.copy(),scalars=\"dux/dz\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(1, 0)\n",
    "plotter.add_text(\"duy/dx n\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell.copy(),scalars=\"duy/dx n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(1, 1)\n",
    "plotter.add_text(\"duy/dy n\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell.copy(),scalars=\"duy/dy n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(1, 2)\n",
    "plotter.add_text(\"duy/dz n\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell.copy(),scalars=\"duy/dz\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(2, 0)\n",
    "plotter.add_text(\"duz/dx n\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell.copy(),scalars=\"duz/dx n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(2, 1)\n",
    "plotter.add_text(\"duz/dy n\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell.copy(),scalars=\"duz/dy n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(2, 2)\n",
    "plotter.add_text(\"duz/dz n\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell.copy(),scalars=\"duz/dz\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e4d2f9c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d87c3945e9046fbaa031842a823726c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### multiplot\n",
    "\n",
    "plotter = pv.Plotter(shape=(1, 3))\n",
    "\n",
    "plotter.subplot(0, 0)\n",
    "plotter.add_text(\"vort_x n\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell,scalars=\"vort_x n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 1)\n",
    "plotter.add_text(\"vort_y n\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell.copy(),scalars=\"vort_y n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 2)\n",
    "plotter.add_text(\"vort_z n\", font_size=20)\n",
    "plotter.add_mesh(mesh_cell.copy(),scalars=\"vort_z n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5bdc3486",
   "metadata": {},
   "outputs": [],
   "source": [
    "duxdx_norm=(mesh_point[\"dux/dx\"]-min(mesh_point[\"dux/dx\"]))/(max(mesh_point[\"dux/dx\"])-min(mesh_point[\"dux/dx\"]))\n",
    "duydx_norm=(mesh_point[\"duy/dx\"]-min(mesh_point[\"duy/dx\"]))/(max(mesh_point[\"duy/dx\"])-min(mesh_point[\"duy/dx\"]))\n",
    "duzdx_norm=(mesh_point[\"duz/dx\"]-min(mesh_point[\"duz/dx\"]))/(max(mesh_point[\"duz/dx\"])-min(mesh_point[\"duz/dx\"]))\n",
    "duxdy_norm=(mesh_point[\"dux/dy\"]-min(mesh_point[\"dux/dy\"]))/(max(mesh_point[\"dux/dy\"])-min(mesh_point[\"dux/dy\"]))\n",
    "duydy_norm=(mesh_point[\"duy/dy\"]-min(mesh_point[\"duy/dy\"]))/(max(mesh_point[\"duy/dy\"])-min(mesh_point[\"duy/dy\"]))\n",
    "duzdy_norm=(mesh_point[\"duz/dy\"]-min(mesh_point[\"duz/dy\"]))/(max(mesh_point[\"duz/dy\"])-min(mesh_point[\"duz/dy\"]))\n",
    "#duxdz_norm=(mesh_point[\"dux/dz\"]-min(mesh_point[\"dux/dz\"]))/(max(mesh_point[\"dux/dz\"])-min(mesh_point[\"dux/dz\"]))\n",
    "#duydz_norm=(mesh_point[\"duy/dz\"]-min(mesh_point[\"duy/dz\"]))/(max(mesh_point[\"duy/dz\"])-min(mesh_point[\"duy/dz\"]))\n",
    "#duzdz_norm=(mesh_point[\"duz/dz\"]-min(mesh_point[\"duz/dz\"]))/(max(mesh_point[\"duz/dz\"])-min(mesh_point[\"duz/dz\"]))\n",
    "vort_x_norm=(mesh_point[\"vort_x\"]-min(mesh_point[\"vort_x\"]))/(max(mesh_point[\"vort_x\"])-min(mesh_point[\"vort_x\"]))\n",
    "vort_y_norm=(mesh_point[\"vort_y\"]-min(mesh_point[\"vort_y\"]))/(max(mesh_point[\"vort_y\"])-min(mesh_point[\"vort_y\"]))\n",
    "vort_z_norm=(mesh_point[\"vort_z\"]-min(mesh_point[\"vort_z\"]))/(max(mesh_point[\"vort_z\"])-min(mesh_point[\"vort_z\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c001e1ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_point.point_data[\"dux/dx n\"] = duxdx_norm\n",
    "mesh_point.point_data[\"duy/dx n\"] = duydx_norm\n",
    "mesh_point.point_data[\"duz/dx n\"] = duzdx_norm\n",
    "mesh_point.point_data[\"dux/dy n\"] = duxdy_norm\n",
    "mesh_point.point_data[\"duy/dy n\"] = duydy_norm\n",
    "mesh_point.point_data[\"duz/dy n\"] = duzdy_norm\n",
    "#mesh_point.point_data[\"dux/dz n\"] = duxdz_norm\n",
    "#mesh_point.point_data[\"duy/dz n\"] = duydz_norm\n",
    "#mesh_point.point_data[\"duz/dz n\"] = duzdz_norm\n",
    "mesh_point.point_data[\"vort_x n\"] = vort_x_norm\n",
    "mesh_point.point_data[\"vort_y n\"] = vort_y_norm\n",
    "mesh_point.point_data[\"vort_z n\"] = vort_z_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e8bbafaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>37235</td></tr>\n",
       "<tr><td>N Points</td><td>25012</td></tr>\n",
       "<tr><td>X Bounds</td><td>-2.060e-02, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-2.540e-02, 2.540e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>-5.000e-04, 5.000e-04</td></tr>\n",
       "<tr><td>N Arrays</td><td>33</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Points</td><td>float32</td><td>3</td><td>-2.507e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>epsilon</td><td>Points</td><td>float32</td><td>1</td><td>1.277e-01</td><td>5.175e+03</td></tr>\n",
       "<tr><td>k</td><td>Points</td><td>float32</td><td>1</td><td>4.460e-04</td><td>5.495e+00</td></tr>\n",
       "<tr><td>nut</td><td>Points</td><td>float32</td><td>1</td><td>7.009e-08</td><td>5.136e-03</td></tr>\n",
       "<tr><td>p</td><td>Points</td><td>float32</td><td>1</td><td>-8.012e+00</td><td>1.453e+01</td></tr>\n",
       "<tr><td>U-normed</td><td>Points</td><td>float32</td><td>1</td><td>2.528e-03</td><td>1.018e+01</td></tr>\n",
       "<tr><td>ux</td><td>Points</td><td>float32</td><td>1</td><td>-2.507e+00</td><td>1.018e+01</td></tr>\n",
       "<tr><td>uy</td><td>Points</td><td>float32</td><td>1</td><td>-6.844e-01</td><td>6.314e-01</td></tr>\n",
       "<tr><td>uz</td><td>Points</td><td>float32</td><td>1</td><td>-1.640e-16</td><td>1.477e-16</td></tr>\n",
       "<tr><td>dU/dx</td><td>Points</td><td>float32</td><td>1</td><td>-6.806e+02</td><td>7.585e+02</td></tr>\n",
       "<tr><td>dU/dy</td><td>Points</td><td>float32</td><td>1</td><td>-6.797e+03</td><td>1.108e+04</td></tr>\n",
       "<tr><td>dU/dz</td><td>Points</td><td>float32</td><td>1</td><td>-1.754e-12</td><td>1.836e-12</td></tr>\n",
       "<tr><td>dux/dx</td><td>Points</td><td>float32</td><td>1</td><td>-6.788e+02</td><td>8.058e+02</td></tr>\n",
       "<tr><td>dux/dy</td><td>Points</td><td>float32</td><td>1</td><td>-6.762e+03</td><td>1.131e+04</td></tr>\n",
       "<tr><td>dux/dz</td><td>Points</td><td>float32</td><td>1</td><td>-1.754e-12</td><td>1.836e-12</td></tr>\n",
       "<tr><td>duy/dx</td><td>Points</td><td>float32</td><td>1</td><td>-2.319e+02</td><td>2.571e+02</td></tr>\n",
       "<tr><td>duy/dy</td><td>Points</td><td>float32</td><td>1</td><td>-7.466e+02</td><td>1.089e+03</td></tr>\n",
       "<tr><td>duy/dz</td><td>Points</td><td>float32</td><td>1</td><td>-1.068e-13</td><td>1.442e-13</td></tr>\n",
       "<tr><td>duz/dx</td><td>Points</td><td>float32</td><td>1</td><td>-4.513e-14</td><td>4.731e-14</td></tr>\n",
       "<tr><td>duz/dy</td><td>Points</td><td>float32</td><td>1</td><td>-4.435e-13</td><td>4.248e-13</td></tr>\n",
       "<tr><td>duz/dz</td><td>Points</td><td>float32</td><td>1</td><td>-4.088e-29</td><td>4.016e-29</td></tr>\n",
       "<tr><td>vort_x</td><td>Points</td><td>float32</td><td>1</td><td>-4.435e-13</td><td>4.248e-13</td></tr>\n",
       "<tr><td>vort_y</td><td>Points</td><td>float32</td><td>1</td><td>-1.754e-12</td><td>1.836e-12</td></tr>\n",
       "<tr><td>vort_z</td><td>Points</td><td>float32</td><td>1</td><td>-1.137e+04</td><td>6.832e+03</td></tr>\n",
       "<tr><td>dux/dx n</td><td>Points</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>duy/dx n</td><td>Points</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>duz/dx n</td><td>Points</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>dux/dy n</td><td>Points</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>duy/dy n</td><td>Points</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>duz/dy n</td><td>Points</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>vort_x n</td><td>Points</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>vort_y n</td><td>Points</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td><b>vort_z n</b></td><td>Points</td><td>float32</td><td>1</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x1e09a02efa0)\n",
       "  N Cells:\t37235\n",
       "  N Points:\t25012\n",
       "  X Bounds:\t-2.060e-02, 2.900e-01\n",
       "  Y Bounds:\t-2.540e-02, 2.540e-02\n",
       "  Z Bounds:\t-5.000e-04, 5.000e-04\n",
       "  N Arrays:\t33"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mesh_point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "224fac9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6819132287524a1092bde1f70d0b7eab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plotter = pv.Plotter(shape=(3, 3))\n",
    "\n",
    "plotter.subplot(0, 0)\n",
    "plotter.add_text(\"dux/dx n\", font_size=20)\n",
    "plotter.add_mesh(mesh_point,scalars='dux/dx n')\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 1)\n",
    "plotter.add_text(\"dux/dy n\", font_size=20)\n",
    "plotter.add_mesh(mesh_point.copy(),scalars=\"dux/dy n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 2)\n",
    "plotter.add_text(\"dux/dz n\", font_size=20)\n",
    "plotter.add_mesh(mesh_point.copy(),scalars=\"dux/dz\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(1, 0)\n",
    "plotter.add_text(\"duy/dx n\", font_size=20)\n",
    "plotter.add_mesh(mesh_point.copy(),scalars=\"duy/dx n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(1, 1)\n",
    "plotter.add_text(\"duy/dy n\", font_size=20)\n",
    "plotter.add_mesh(mesh_point.copy(),scalars=\"duy/dy n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(1, 2)\n",
    "plotter.add_text(\"duy/dz n\", font_size=20)\n",
    "plotter.add_mesh(mesh_point.copy(),scalars=\"duy/dz\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(2, 0)\n",
    "plotter.add_text(\"duz/dx n\", font_size=20)\n",
    "plotter.add_mesh(mesh_point.copy(),scalars=\"duz/dx n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(2, 1)\n",
    "plotter.add_text(\"duz/dy n\", font_size=20)\n",
    "plotter.add_mesh(mesh_point.copy(),scalars=\"duz/dy n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(2, 2)\n",
    "plotter.add_text(\"duz/dz n\", font_size=20)\n",
    "plotter.add_mesh(mesh_point.copy(),scalars=\"duz/dz\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a06c30",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter5\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d56104cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8139487c8c2e414fa358e25f4349cacc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### multiplot\n",
    "\n",
    "plotter = pv.Plotter(shape=(1, 3))\n",
    "\n",
    "plotter.subplot(0, 0)\n",
    "plotter.add_text(\"vort_x n\", font_size=20)\n",
    "plotter.add_mesh(mesh_point,scalars=\"vort_x n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 1)\n",
    "plotter.add_text(\"vort_y n\", font_size=20)\n",
    "plotter.add_mesh(mesh_point.copy(),scalars=\"vort_y n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.subplot(0, 2)\n",
    "plotter.add_text(\"vort_z n\", font_size=20)\n",
    "plotter.add_mesh(mesh_point.copy(),scalars=\"vort_z n\")\n",
    "plotter.view_xy()\n",
    "plotter.add_axes()\n",
    "\n",
    "plotter.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f2d843e",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter12\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ad5a3bf",
   "metadata": {},
   "source": [
    "# Let's plot all of our fields"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e65ebbc9",
   "metadata": {},
   "source": [
    "At this point, we should have all of the cell data and point data we want. Let's try to plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bfcf39c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3cc000d53795446d9f008fe3c7af3eef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='field', options=('vort_x n', 'U', 'epsilon', 'k', 'nut', 'p', 'U-n…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_internalMesh(field, edges)>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widgets.interact(plot_internalMesh, field=internalMesh.array_names, edges=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8394cb85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "517777c1b56040739c5a2daa3e271202",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='field', options=('vort_x n', 'U', 'epsilon', 'k', 'nut', 'p', 'U-n…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_cell(field, edges)>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widgets.interact(plot_cell, field=mesh_cell.array_names, edges=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "bc994a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8cd8159a1d6d42b693b36cfe92351b06",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='field', options=('vort_x n', 'U', 'epsilon', 'k', 'nut', 'p', 'U-n…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_point(field, edges)>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "widgets.interact(plot_point, field=mesh_point.array_names, edges=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88512362",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter13\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4edf673c",
   "metadata": {},
   "source": [
    "# How to plot over a line"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f665e6a",
   "metadata": {},
   "source": [
    "We can plot data over a straight line if needed.<br/>\n",
    "This can be done for all the data we have, whether it's point data or cell data.<br/>\n",
    "We can do this for `internalMesh`, `mesh_cell` and `mesh_point`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "77cb86b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the 2 points defining the line\n",
    "A = [0, -0.015, 0]\n",
    "B = [fullMesh.bounds[1], -0.015, 0]\n",
    "\n",
    "# Create a Line object\n",
    "line = pv.Line(A, B)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea42b567",
   "metadata": {},
   "source": [
    "Let's show the line in the computational domain:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "54fadb24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4151269fd2334c7abfd86e6b2cc73d3d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = pv.Plotter()\n",
    "p.add_mesh(mesh_point, style=\"wireframe\", color='white')\n",
    "# Show the line in red in the domain\n",
    "p.add_mesh(line, color=\"r\", line_width=5)\n",
    "p.view_xy()\n",
    "p.add_axes()\n",
    "p.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b8d648",
   "metadata": {},
   "source": [
    "Let's make a plot overline.<br/>\n",
    "First, we sample the data on the line.<br/>\n",
    "We do this for `internalMesh`, `mesh_cell` and `mesh_point`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8c7e8b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_line_internalMesh = pv.DataSetFilters.sample_over_line(internalMesh, A, B, resolution=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a60fa0",
   "metadata": {},
   "source": [
    "Then, we plot the data we want to visualize.<br/>\n",
    "We can change the title, labels, grid style, etc. to match what we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3d09099d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1516e30bcca24c05bd400557a15838c9",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(sample_line_internalMesh['Distance'],sample_line_internalMesh['p'],label='$p$')\n",
    "# note that we can show different variables on the same plot\n",
    "plt.xlabel('Distance') \n",
    "plt.ylabel('Pressure')\n",
    "plt.title('Distribution of the pressure along the line')\n",
    "plt.grid(linestyle='dotted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7da021c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_line_cell = pv.DataSetFilters.sample_over_line(mesh_cell, A, B, resolution=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9936e555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88289b53d8b94345918e0227ee0ec604",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(sample_line_cell['Distance'],sample_line_cell['p'],label='$p$')\n",
    "# note that we can show different variables on the same plot\n",
    "plt.xlabel('Distance') \n",
    "plt.ylabel('Pressure')\n",
    "plt.title('Distribution of the pressure along the line')\n",
    "plt.grid(linestyle='dotted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1b1da48c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_line_point = pv.DataSetFilters.sample_over_line(mesh_point, A, B, resolution=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a141f39e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb3378fd13264d50becb7cd86df65bed",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(sample_line_point['Distance'],sample_line_point['p'],label='$p$')\n",
    "# note that we can show different variables on the same plot\n",
    "plt.xlabel('Distance') \n",
    "plt.ylabel('Pressure')\n",
    "plt.title('Distribution of the pressure along the line')\n",
    "plt.grid(linestyle='dotted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3a98e58",
   "metadata": {},
   "source": [
    "As you can see, the plots look much smoother when using point data over cell data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43045236",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter14\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6175389",
   "metadata": {},
   "source": [
    "# Plot a scalar over a circular arc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2a4035",
   "metadata": {},
   "source": [
    "We can plot data from a circular arc too.<br/>\n",
    "Similar to what we did for the straight line, this can be done for all the data we have, whether it's point data or cell data.<br/>\n",
    "We'll do this for `internalMesh`, `mesh_cell` and `mesh_point`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4649d799",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make two points at the bounds of the mesh and one at the center to\n",
    "# construct a circular arc.\n",
    "normal = [0, 0, 1]\n",
    "polar = [0.02, 0, 0]\n",
    "center = [0, 0, 0]\n",
    "angle = 100.0\n",
    "\n",
    "# Preview how this circular arc intersects this mesh\n",
    "arc = pv.CircularArcFromNormal(center, 100, normal, polar, angle)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bef4abd9",
   "metadata": {},
   "source": [
    "Let's see what's in the arc object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d87786f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>PolyData</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>1</td></tr>\n",
       "<tr><td>N Points</td><td>101</td></tr>\n",
       "<tr><td>X Bounds</td><td>-3.473e-03, 2.000e-02</td></tr>\n",
       "<tr><td>Y Bounds</td><td>0.000e+00, 2.000e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>0.000e+00, 0.000e+00</td></tr>\n",
       "<tr><td>N Arrays</td><td>2</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>Texture Coordinates</td><td>Points</td><td>float32</td><td>2</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td><b>Distance</b></td><td>Points</td><td>float64</td><td>1</td><td>0.000e+00</td><td>3.491e-02</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "PolyData (0x1e191e8c0a0)\n",
       "  N Cells:\t1\n",
       "  N Points:\t101\n",
       "  X Bounds:\t-3.473e-03, 2.000e-02\n",
       "  Y Bounds:\t0.000e+00, 2.000e-02\n",
       "  Z Bounds:\t0.000e+00, 0.000e+00\n",
       "  N Arrays:\t2"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a476a24d",
   "metadata": {},
   "source": [
    "Let's show the arc in the computational domain:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fbd127a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54bbb31e6a8c4a9289b50bbff004e6eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = pv.Plotter()\n",
    "p.add_mesh(mesh_cell, style=\"wireframe\", color=\"w\")\n",
    "p.add_mesh(arc, color=\"r\")\n",
    "a = arc.points[0]\n",
    "b = arc.points[-1]\n",
    "p.add_point_labels(\n",
    "    [a, b], [\"A\", \"B\"], font_size=48, point_color=\"red\", text_color=\"red\"\n",
    ")\n",
    "p.view_xy()\n",
    "p.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b579624b",
   "metadata": {},
   "source": [
    "Let's make a plot overline.<br/>\n",
    "First, we sample the data on the arc.<br/>\n",
    "We do this for `internalMesh`, `mesh_cell` and `mesh_point`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e49f7cdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_arc_internal = pv.DataSetFilters.sample_over_circular_arc_normal(internalMesh,center,100,normal,polar,angle)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "659603a6",
   "metadata": {},
   "source": [
    "Then, we plot the data we want to visualize.<br/>\n",
    "We can change the title, labels, grid style, etc. to match what we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "337f2e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba430f706bfb4e6da7b456d9e0659bb0",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(sample_arc_internal['Distance'],sample_arc_internal['p'],label='$p$')\n",
    "# note that we can show different variables on the same plot\n",
    "plt.xlabel('Distance') \n",
    "plt.ylabel('Pressure')\n",
    "plt.title('Distribution of the pressure along the arc')\n",
    "plt.grid(linestyle='dotted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "88fa8afa",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_arc_cell = pv.DataSetFilters.sample_over_circular_arc_normal(mesh_cell,center,100,normal,polar,angle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b7977782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c20c2a5c99254c7c9537f476f4955e5e",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(sample_arc_cell['Distance'],sample_arc_cell['p'],label='$p$')\n",
    "# note that we can show different variables on the same plot\n",
    "plt.xlabel('Distance') \n",
    "plt.ylabel('Pressure')\n",
    "plt.title('Distribution of the pressure along the arc')\n",
    "plt.grid(linestyle='dotted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9643114c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_arc_point = pv.DataSetFilters.sample_over_circular_arc_normal(mesh_point,center,100,normal,polar,angle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ee75cdb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5770643b3e2b4b2d96a96c3f23d3ff49",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(sample_arc_point['Distance'],sample_arc_point['p'],label='$p$')\n",
    "# note that we can show different variables on the same plot\n",
    "plt.xlabel('Distance') \n",
    "plt.ylabel('Pressure')\n",
    "plt.title('Distribution of the pressure along the arc')\n",
    "plt.grid(linestyle='dotted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd00fc2",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter15\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efae81ef",
   "metadata": {},
   "source": [
    "# Wall shear stress"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03c3e1d5",
   "metadata": {},
   "source": [
    "### This part of the code is still a work in progress!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "06563c15",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pdt_scal=np.dot(lower_wall_point.point_data[\"dux/dy\"],lower_wall_point.point_data[\"Normals\"])\n",
    "#lower_wall_point.point_data[\"dux/dy\"].shape\n",
    "#pdt_scal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "92d9965e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#visc=15.6e-6\n",
    "#wss=visc*pdt_scal\n",
    "#lower_wall_point.add_field_data(wss, 'wss')\n",
    "#lower_wall_point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "032b7e17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table>\n",
       "<tr><th>PolyData</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>1</td></tr>\n",
       "<tr><td>N Points</td><td>37236</td></tr>\n",
       "<tr><td>X Bounds</td><td>0.000e+00, 2.900e-01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-1.500e-02, -1.500e-02</td></tr>\n",
       "<tr><td>Z Bounds</td><td>0.000e+00, 0.000e+00</td></tr>\n",
       "<tr><td>N Arrays</td><td>38</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>U</td><td>Points</td><td>float32</td><td>3</td><td>-9.404e-01</td><td>6.108e+00</td></tr>\n",
       "<tr><td>U-normed</td><td>Points</td><td>float32</td><td>1</td><td>8.851e-02</td><td>6.125e+00</td></tr>\n",
       "<tr><td>dU/dx</td><td>Points</td><td>float32</td><td>1</td><td>-1.782e+01</td><td>1.011e+02</td></tr>\n",
       "<tr><td>dU/dy</td><td>Points</td><td>float32</td><td>1</td><td>-2.554e+02</td><td>2.385e+02</td></tr>\n",
       "<tr><td>dU/dz</td><td>Points</td><td>float32</td><td>1</td><td>-4.287e-14</td><td>0.000e+00</td></tr>\n",
       "<tr><td>dux/dx</td><td>Points</td><td>float32</td><td>1</td><td>-3.174e+01</td><td>5.706e+01</td></tr>\n",
       "<tr><td>dux/dx n</td><td>Points</td><td>float32</td><td>1</td><td>4.359e-01</td><td>4.957e-01</td></tr>\n",
       "<tr><td>dux/dy</td><td>Points</td><td>float32</td><td>1</td><td>2.643e-01</td><td>2.757e+02</td></tr>\n",
       "<tr><td>dux/dy n</td><td>Points</td><td>float32</td><td>1</td><td>3.741e-01</td><td>3.893e-01</td></tr>\n",
       "<tr><td>dux/dz</td><td>Points</td><td>float32</td><td>1</td><td>-4.338e-14</td><td>0.000e+00</td></tr>\n",
       "<tr><td>duy/dx</td><td>Points</td><td>float32</td><td>1</td><td>-1.047e+01</td><td>9.855e+01</td></tr>\n",
       "<tr><td>duy/dx n</td><td>Points</td><td>float32</td><td>1</td><td>4.528e-01</td><td>6.757e-01</td></tr>\n",
       "<tr><td>duy/dy</td><td>Points</td><td>float32</td><td>1</td><td>-6.321e+01</td><td>3.304e+01</td></tr>\n",
       "<tr><td>duy/dy n</td><td>Points</td><td>float32</td><td>1</td><td>3.723e-01</td><td>4.247e-01</td></tr>\n",
       "<tr><td>duy/dz</td><td>Points</td><td>float32</td><td>1</td><td>-7.428e-15</td><td>0.000e+00</td></tr>\n",
       "<tr><td>duz/dx</td><td>Points</td><td>float32</td><td>1</td><td>-1.920e-15</td><td>5.752e-16</td></tr>\n",
       "<tr><td>duz/dx n</td><td>Points</td><td>float32</td><td>1</td><td>4.674e-01</td><td>4.944e-01</td></tr>\n",
       "<tr><td>duz/dy</td><td>Points</td><td>float32</td><td>1</td><td>-6.166e-15</td><td>8.239e-15</td></tr>\n",
       "<tr><td>duz/dy n</td><td>Points</td><td>float32</td><td>1</td><td>5.037e-01</td><td>5.203e-01</td></tr>\n",
       "<tr><td>duz/dz</td><td>Points</td><td>float32</td><td>1</td><td>-6.431e-31</td><td>0.000e+00</td></tr>\n",
       "<tr><td>epsilon</td><td>Points</td><td>float32</td><td>1</td><td>2.286e+00</td><td>3.742e+02</td></tr>\n",
       "<tr><td>k</td><td>Points</td><td>float32</td><td>1</td><td>2.361e-02</td><td>4.476e+00</td></tr>\n",
       "<tr><td>nut</td><td>Points</td><td>float32</td><td>1</td><td>4.943e-06</td><td>4.962e-03</td></tr>\n",
       "<tr><td>p</td><td>Points</td><td>float32</td><td>1</td><td>-7.978e+00</td><td>1.187e+01</td></tr>\n",
       "<tr><td>ux</td><td>Points</td><td>float32</td><td>1</td><td>-9.404e-01</td><td>6.108e+00</td></tr>\n",
       "<tr><td>uy</td><td>Points</td><td>float32</td><td>1</td><td>-3.449e-01</td><td>4.517e-01</td></tr>\n",
       "<tr><td>uz</td><td>Points</td><td>float32</td><td>1</td><td>-1.037e-17</td><td>1.726e-18</td></tr>\n",
       "<tr><td>vort_x</td><td>Points</td><td>float32</td><td>1</td><td>-6.166e-15</td><td>8.756e-15</td></tr>\n",
       "<tr><td><b>vort_x n</b></td><td>Points</td><td>float32</td><td>1</td><td>5.037e-01</td><td>5.209e-01</td></tr>\n",
       "<tr><td>vort_y</td><td>Points</td><td>float32</td><td>1</td><td>-4.361e-14</td><td>1.920e-15</td></tr>\n",
       "<tr><td>vort_y n</td><td>Points</td><td>float32</td><td>1</td><td>4.764e-01</td><td>4.891e-01</td></tr>\n",
       "<tr><td>vort_z</td><td>Points</td><td>float32</td><td>1</td><td>-2.813e+02</td><td>9.800e+01</td></tr>\n",
       "<tr><td>vort_z n</td><td>Points</td><td>float32</td><td>1</td><td>6.091e-01</td><td>6.300e-01</td></tr>\n",
       "<tr><td>vtkValidPointMask</td><td>Points</td><td>int8</td><td>1</td><td>1.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>Texture Coordinates</td><td>Points</td><td>float32</td><td>2</td><td>0.000e+00</td><td>1.000e+00</td></tr>\n",
       "<tr><td>Distance</td><td>Points</td><td>float64</td><td>1</td><td>3.353e-10</td><td>2.900e-01</td></tr>\n",
       "<tr><td>vtkGhostType</td><td>Points</td><td>uint8</td><td>1</td><td>0.000e+00</td><td>0.000e+00</td></tr>\n",
       "<tr><td>vtkGhostType</td><td>Cells</td><td>uint8</td><td>1</td><td>0.000e+00</td><td>0.000e+00</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "PolyData (0x1e197d217c0)\n",
       "  N Cells:\t1\n",
       "  N Points:\t37236\n",
       "  X Bounds:\t0.000e+00, 2.900e-01\n",
       "  Y Bounds:\t-1.500e-02, -1.500e-02\n",
       "  Z Bounds:\t0.000e+00, 0.000e+00\n",
       "  N Arrays:\t38"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_line = mesh_point.sample_over_line(A, B)\n",
    "data_line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "947a86a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_line.save('data_line.vtp')"
   ]
  }
 ],
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