Fig_Creator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d7f95e13-40cc-41bf-b032-68a10b3e9c5e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7AAhQM9wczcXJPyNJUCSXcbzsyP69nvXv1V4K8TIjKyBLFXEUF1dZAVZVVZGxaJHI37hRqFZrLUYmNVRVVb+ty6ZMmSLWrl0rEhMTxcaNG8WAAQNEYGCgSEtLE0IIMWvWLGGxWMR3330n4uPjxbPPPitMJpM4dOiQ6xyDBw8WXbp0EVu3bhUbNmwQLVu2FCNHjnTtz87OFo0aNRL33HOP2Lt3r/j666+Fh4eHmDNnjqvN+++/L3x9fUVSUpIQQoiRI0eKLl26uComr169Wnh4eIinn35apKSkuL4yMjJc59i4caPQ6XTijTfeEAcOHBDTp08Xer1e7Nmzp9L3X1H144CAAHHLLbeInTt3iri4OPHEE08IvV4vdu7cWel55OoG1Uv+zKS6ZsGmRLHu2d5CTLeIt155yt3hXJQRH21yrW7w/d/H3B1OvVBf+3YhZP9e3/t3mSS4RPJGQqpI8lNPi7g+fUT2jz+6OxTpMlFfbyTuuOMOERoaKgwGg2jcuLG44447yt0gCCHEK6+8Ipo0aSI8PDxEz549xZ9//lluf0ZGhhg5cqTw8vISFotFjB07VuTl5ZVrs2vXLtGnTx9hNBpF48aNxauvvurad+DAAWE2m8WXX37p2paVlSXCw8PFk08+KYQQYsyYMQI466t///7lrvPtt9+KVq1aCYPBINq3by+WL19e5fv/902EEELExsaKgQMHCn9/f+Ht7S2uvPJK8csvv1R5HpkkqF7yZybVNZ+sTxD7nusgxHSLePzlN90dzkUZ+v4GV5Jg0Zaj7g6nXqivfbsQsn+v7/27IoQQtT16oSHJzc3Fx8eHnJwcWbhQcjk8dBglBw/S5IPZeJfOu5KkmlRcXExiYiJRUVGYTCZ3hyPVsqp+/7KfunDyZybVNbPXHOKGNTcQpTnJa7oHmPbMa1BJgbS6avDb6/knNQ+AZ29sy319m7k5orpP9u2Su/r3+lf1QpLqgcgvF2E9fBhDs8o7QEdeHsX79iEcDrx6967F6CRJkiRJqk+sdhV/xfkBe5p9DtifB73ZzVFdmDOXcSy2ycKFklSXySSBJNUArZcX5o4dq2xTuG0bxyc+jKldO5kkkCRJkiSpUlaHiuPMeuMl+fUuSVByRpJArm4gSXWbXN2gmnTv3p127doxe/Zsd4ci1RPG5s3RR0ZgaNrU3aFIkiRJklSHWe0qD1kfO72hJNd9wVykM5MERXIkgSTVaXIkQTWJjY2V8xYlAPLWrqUk7iCevXph7hBNakEqc3bPQRUqY9uPpalPUwAMTZvSonRZFEmSJEmSpMpY7SpbRVtOCl8aKdlgzXd3SBesxH46MVAkRxJIUp0mkwSSVM3yfv2NnB9+QDjslLRswsjlI0kvSgdgY/JGFg9ZjK/J171BSpIkSefUvXt3tFotEydOZOLEie4OR7qMlc3nzxdmGinZqEW59W44sBxJIEn1h0wSSFI18+jRA+Fw4NGlCzP/fpv0onT8Tf7YVTsnC0/y/s73efbKZ90dpiRJknQOcpSgVFeotmJGaVfRXJMCgN1WiMHNMV0IIUS5woWyJoEk1W31LQkpSXWe7y0303jm6xR1bsmPCT8C8NZVb/FG/zcA+DnhZwpsBQBkfrGIw0OGkv7JJ26LV5IkSZKkuk1vz+cl/TzXa4e1xI3RXDirQy33Wq5uIEl1m0wSSFINWXJwCXbVTqegTnRr1I0rQ6+kqaUphfZCfj/yOwBqfj4l8fFYDye6OVpJkiRJkuoqjaPI9f08+2BsXuFujObC2Ryi3Gs5kkCS6jaZJJCkaqQWFKAWOEcJrEhcAcCIViMAUBSFG6JuAODP5D8BsAweRPinnxIo57pKkiRJklQJxW4FIEd48IJ9NEUBbd0c0YWx/2skgSxcKEl1m0wSSFI1yv7xR+K6XUHclEdIyElAp9FxdcTVrv29GvcCYMuJLdhVO4amTfHq0xtDk8buClmS3OqVV16he/fueHt7ExwczPDhw4mLiyvXJjU1lXvuuYeQkBA8PT3p2rUrS5YsKdcmMzOTUaNGYbFY8PX1Zfz48eTnl6/+vXv3bvr27YvJZCI8PJzXX3/9gmItLi7m3nvvpUOHDuh0OoYPH15hu7Vr19K1a1eMRiMtWrRg/vz5F/yeywghuP7661EUhR9++OGC4pUkqeHQOooBKC6tRHDm/P764N8jCYrtMknQ0Mn+vX737zJJIEnVyJacDMBxTTYAMaExWAyni15FB0RjMVjIs+WxN32vO0KUpDpl3bp1TJw4kS1btrBy5UpsNhsDBw6koHREDsDo0aOJi4vjp59+Ys+ePdxyyy3cfvvt7Nixw9Vm1KhR7Nu3j5UrV7Js2TLWr1/PhAkTXPtzc3MZOHAgkZGRbN++nZkzZzJjxgw+/vjj847V4XBgNpuZNGkSAwYMqLBNYmIiN954I1dffTU7d+5k8uTJ3Hffffx2xnKn5/Oey7z99tsoinLeMUqS1DBpVGcNAh0OGpGJvSjHzRFdGLtaPqlR35Ic0oWT/Xs979+FdElycnIEIFq1aiXatm0r3n//fXeHJLmZPStLPLp0nIieHy0W7lt41v5H/nhERM+PFgv2LhBCCJG/ebPIWrxY2HNzaztUqQEpKioS+/fvF0VFRe4O5ZKkpaUJQKxbt861zdPTU3z++efl2vn7+4tPPvlECCHE/v37BSBiY2Nd+1esWCEURRHJyclCCCE++OAD4efnJ0pKSlxtpk2bJlq3bi2EcP782rVrJ+6//37X/kOHDgkvLy8xd+7cs+IcM2aMGDZs2Fnbn3zySdG+ffty2+644w4xaNCgC3rPQgixY8cO0bhxY5GSkiIAsXTp0krPUdXvv6yfysnJqfR4qTz5M5Pqmhff+1CI6RbX18nf3nR3SBckKaNARE5b5vq64sWV7g6pXmgofbsQsn8/U33o3+VIgmoSGxvL/v375TrKEqrFk80FzlEC3UO6n7W/Q2AHAPak7wHgxLSnSHnmWaxHjtRajNJlxFpQ+Zet+ALaFp1f20uUk+N8Oubv7+/a1qtXL7755hsyMzNRVZWvv/6a4uJirrrqKgA2b96Mr68vV1xxheuYAQMGoNFo2Lp1q6tNv379MBhOLxo2aNAg4uLiyMrKwmQysWjRIhYsWMCPP/6Iw+Hg7rvv5rrrrmPcuHHnHf/mzZvPegoxaNAgNm/efEHvubCwkLvuuovZs2cTEhJy3teXJKlhUhy2cq+FvbiSlnWTzSFHElQ72b+72sj+vfrp3B2AJDU0BzIOUGgvxMfoQ0u/lmftjw6MBk4nCTx69MCRlQUambOTasDLYZXvazkQRn13+vXMFmArrLhtZB8Yu/z067c7QGHG2e1mXPwQWFVVmTx5Mr179yY6Otq1/dtvv+WOO+4gICAAnU6Hh4cHS5cupUWLFoBzTmNwcHC5c+l0Ovz9/UlNTXW1iYqKKtemUaNGrn1+fn507tyZF198kfvuu48777yTo0ePsmzZsgt6D6mpqa7znnmd3NxcioqKMJvN5/WeH3vsMXr16sWwYcMu6PqSJDVMCdpmPGB9jKd0XxKlOYmwWd0d0gX5d00CmSSoBrJ/d7WR/Xv1a3CfSkpKSpg2bRphYWGYzWZiYmJYuXLleR//zTff0LNnTzw9PfH19aVXr16sXr26BiOWGgp7RgYpz/2X5E8+AOCKRlegUc7+Xyw6MBoFheT8ZDKLM2k883UiPv0Ec/v2tR2yJNUpEydOZO/evXz99dfltj/33HNkZ2ezatUq/vrrLx5//HFuv/129uzZU+0xTJkyhVatWvH+++8zb948AgICqv0aZ6roPf/000+sXr2at99+u0avfTmJj4/nzjvvpEmTJnh4eNCmTRteeOEFCgsruWmWpDomQ/HlN7U7a9QuAAhHiZsjujD/HklQIgsXXlZk/+5Un/r3BjeS4N5772Xx4sVMnjyZli1bMn/+fG644QbWrFlDnz59qjx2xowZvPDCC9x2223ce++92Gw29u7dS3JpMTpJqoo1MZHs777DL8AEE5xJgop4G7xp4t2EY3nHiM+KJyY0ppYjlS4r/zlR+T5FW/711ENVtP1Xwmty9XbgDz/8sKsgUZMmTVzbExISeP/999m7dy/tSxNpnTp14s8//2T27Nl89NFHhISEkJaWVu58drudzMxM11C+kJAQTp48Wa5N2eszh/ulpaVx8OBBtFot8fHxDB48+ILeR2XXsVgsZz1lqOw9r169moSEBHx9fcu1v/XWW+nbty9r1669oJgud8eOHaNHjx74+Pjw8MMP4+/vz+bNm5k+fTrbt2/nxx9/dHeIknRODtX5JN6KHgBhr19JAntp/F5GHfkldlThXBZRp21wzytrj+zfAdm/15QGlSTYtm0bX3/9NTNnzuSJJ54AnFUzo6OjefLJJ9m0aVOlx27ZsoUXXniBN998k8cee6y2QpYaEF1QEH4PPcB3cfOBiusRlGnp25Jjecc4lH1IJgmkmmXwdH/bKggheOSRR1i6dClr1649a8hg2ZNezb+m42i1WtTSatk9e/YkOzub7du3061bN8DZEauqSkxMjKvNM888g81mQ6933mSvXLmS1q1b4+fn5zrvuHHj6NChA+PHj+f+++9nwIABtG17/uuR9+zZk19++aXctpUrV9KzZ8/zfs9PPfUU9913X7ltHTp0YNasWQwZMuS8Y5GcFi5cSHZ2Nhs2bHDdiE6YMAFVVfn888/Jysoq929AkuqiAOsJhml20FlT+mHPXr+mG9hLRxJ4GrXkl9gBsMokwaWR/burjezfa0C1l0J0o6lTpwqtVntWhceXX35ZACIpKanSY++44w4RGhoqHA6HUFVV5OXlndc1ZQVk6Uz70veJ6PnRoueXPYVDdVTa7t2/3xXR86PF9I3TReGOHSLhpiHiyJh7azFSqaGprxWQH3roIeHj4yPWrl0rUlJSXF+FhYVCCCGsVqto0aKF6Nu3r9i6das4dOiQeOONN4SiKGL58uWu8wwePFh06dJFbN26VWzYsEG0bNlSjBw50rU/OztbNGrUSNxzzz1i79694uuvvxYeHh5izpw5rjbvv/++8PX1dfUVI0eOFF26dClXMXnfvn1ix44dYsiQIeKqq64SO3bsEDt27HDtP3z4sPDw8BBTp04VBw4cELNnzxZarVb8+uuv5/2eK0IdrX5cH0ybNk0A4tSpU2dt12g0Ij8//6xjLvefmVT3vPbKc+VWN0j+/D53h3RBNh1KF5HTlomrZ65xrXCQmV9y7gMvc/W1bxdC9u/1vX9vUEmCAQMGiLZt2561fdWqVQIQP/30U6XHBgYGiqFDh4pZs2aJgIAAAYiQkBDx3nvvVXlNeSMhnembf74R0fOjxf2/3V9luxWHV4jo+dFi1PJRomjfPrG/dRsR17tPLUUpNUT19UYCqPDrs88+c7U5ePCguOWWW0RwcLDw8PAQHTt2PGvJpIyMDDFy5Ejh5eUlLBaLGDt27FnJ3l27dok+ffoIo9EoGjduLF599VXXvgMHDgiz2Sy+/PJL17asrCwRHh4unnzySde2yMjICuM905o1a0Tnzp2FwWAQzZo1K/dezvc9V/Rzqos3EfXBihUrBCCGDh0qduzYIZKSksTXX38tLBaLmDx5coXHlP3Mjh07JnJyclxfxcXFtRy9JDnNfPlpIaZbhPW/fuKrZ4aKPcs/cHdIF2T9wTQROW2ZGDRrnWj29HIROW2ZSM2pX/2VO9TXvl0I2b/X9/69QU03SElJITQ09KztZdtOnKh47k5WVhbp6els3LiR1atXM336dCIiIvjss8945JFH0Ov1PPDAA1VeOzc3t9xro9GI0Wi8yHci1UfW48nEpewGTq9gUJmyVQ8OZR9C37Qp4Z9+ij7s7H+7ktTQOfvHqrVs2ZIlS5ZU2cbf358vv/yyyjYdO3bkzz//rHBfmzZtzipi5+vrS1JSUrltR85jqdKrrrqKHTt2VLr/fN5zdRwjOQ0ePJj//e9/vPzyy/z000+u7c888wwvvvhilceGh4eXez19+nRmzJhRE2FKUpUU1TlE/w/RjafsE/gwvCtV32nULfbS1Q30Wg0GrYYi1SFXOGjgZP9+fupq/96gkgRFRUUVfjA3mUyu/RXJz88HICMjg6+//po77rgDgNtuu40OHTrw4osvnjNJIG8kpCO3385tmZlsHq+lfUB7KMoGs69zp+qAVdOhaT9oNZAI7wg0ioYCWwFZSiGBfXq7M3RJkqQGrWnTpvTr149bb72VgIAAli9fzssvv0xISAgPP/xwpccdO3YMi8Xiei2T/5K7KMJe+o2zIJ1NrZsfLCpjLa1JoNMqeGhVimxQIpMEklRnNagkgdlspqTk7GqvxcXFrv2VHQeg1+u57bbbXNs1Gg133HEH06dPJykpiYiIiEqvLW8kLm9qURGq1VlEKN0H2vu1gbkDoWlvGPwq7PgCNr0Hcb9C82vQa/WEeYZxPP84R3KOEGgOdPM7kCRJapi+/vprJkyYwMGDB11Vpm+55RZUVWXatGmMHDmy0qWwLBZLub5dktxFUzqSQKPRYKEApTgbCHNrTBfC7hAoqNxb8Bn9+I0hygtyGURJqsMaVEnR0NBQUlJSztpeti0srOI/pv7+/phMJgICAtBqyy8ZEhwcDDinJFSl7Eai7EsmCS4vGrOZ4uUfM+5RLZ6+QTQ6uBLS42DfD2AtgA4jwOwPGfGw/wcAIi2RACTlJVH49w6yl3xPSWKi+96EJElSA/TBBx/QpUuXcstQAQwdOpTCwsIqh45KUl2hKR1J0F/Estt0Pz22PermiC6MXVURaGhpi8OPXPpq9sjpBpJUhzWoJEHnzp05ePDgWfUBtm7d6tpfEY1GQ+fOnTl16hRWa/klZcrqGAQFBVV/wFKDsi9zP/keCtEB7VE2v+/c2O8J8PAHkwW6j3du2/MdcDpJcCT3CBmffELKM89QuC3WHaFLkiQ1WCdPnsThOPuJpc1mA5xrbktSXacRzn/DxYrzIZSi2twZzgWzOQRBZFGk9QYgkByZJJCkOqxBJQluu+02HA4HH3/8sWtbSUkJn332GTExMa66AUlJSfzzzz/ljr3jjjtwOBwsWLDAta24uJhFixbRrl27SkchSFKZvel7AWiv9XaOGDB4QdfRpxt0GOH876FVUJhJhMU5fSUpNwlzp4549u2LLrDiIa+SJEnSxWnVqhU7duzg4MGD5bZ/9dVXaDQaOnbs6KbIJOn8rVS7Mdn6f/xquh4AxVG/kgR2h8p0/UK6FW0EIEDJlTUJJKkOa1A1CWJiYhgxYgRPP/00aWlptGjRggULFnDkyBHmzp3rajd69GjWrVtXrprkAw88wKeffsrEiRM5ePAgERERLFy4kKNHj/Lzzz+74+1I9Uj290tp/dNajjZVaWd0JqDyWt3M99szCPbO57p2jdAFtYagtnDqABxeS6SfcyTB0dyjBD74thujlyRJarimTp3KihUr6Nu3Lw8//DABAQEsW7aMFStWcN9998mHAFK98I8azi61MQGmw1D0bf0bSaAKGimZrtcBSq4cSSBJdViDShIAfP755zz33HMsXLiQrKwsOnbsyLJly+jXr1+Vx5nNZlavXs2TTz7JvHnzKCgooHPnzixfvpxBgwbVUvRSfZX35zpitmTzj6eGVobNANy9oy27/toHQI8ofz67tzueza92JgkS19E4fDIAKQUpCCFQFMVd4UuSJDVY/fr1Y9OmTcyYMYMPPviAjIwMoqKieOmll3jyySfdHZ4knRdH6WoGGp1zuoGmniUJ7A6VYLJdrwPIJdshkwSSVFc1uCSByWRi5syZzJw5s9I2a9eurXB7cHAw8+fPr5nApAat4JorWJz7O8ciTTQqLCJRDWGXI5KOTXxISMtnW2ImLy7fzyvt+8Nf80C1E+oZ6jzWVkCuNRcfo4+b34UkSVLD1KNHD3755Rd3hyFJF0UIQUtxhHBNGmHCWWC7viUJbA4VL+X0UuQBSi4n5eoGklRnNbgkgSS5Q3w7H77N1NLFvxUf7OrFiSIdt3Rtwhu3dWJLYgZ3fbKVr7Yd496YK2n9VBLojJgAf5M/mcWZnNi7jfTp76DoDTRb+r27344kSZIkSXWEQxXcqV3NGN1K9hXGAKAR9S1JIPCk2PU6Fw853UCS6rAGVbjQnbp37067du2YPXu2u0OR3OBglrMgls0Wwcz8waz2uokXh0ej0Sj0ah7IjR2cowbmbDwGutPLY5aNJkhzZGM9lIA1IaFcrQxJkiRJki5vdlWgx/nUvcAQwE+OnsT7VT2Ntq6x26x84WvmNX9fxkXNZIR1hkwSSFIdJpME1SQ2Npb9+/czceJEd4ci1TK1qIhT/+xCbxPsO+IJwNRBrfEwnB6oM65PFAAr9qSSX1K63JbqIMzLWTDruLmIiPmfEfX9ktoNXpLqgPXr1zNkyBDCwsJQFIUffvih0rYPPvggiqLw9ttvl9uemZnJqFGjsFgs+Pr6Mn78ePLz88u12b17N3379sVkMhEeHs7rr79+QXEeOXIERVHO+tqyZYurzb59+7j11ltp2rRphXECvPLKK3Tv3h1vb2+Cg4MZPnw4cXFxFxSLJEmXD4cq0OL8QJ1jDmeS7RF+i3jczVFdmJziQ7zj78sXPhb+4gPQFMvVDRo42bfX775dJgkk6RIV7dnDmBdjmTnXwRX5ybT3szGsc+NybbpG+BIV6EmRzcHfa36A97vDl7e7RhKkFKfheeWVGFu0kAUMpctOQUEBnTp1OudIrKVLl7Jly5YKq9GPGjWKffv2sXLlSpYtW8b69euZMGGCa39ubi4DBw4kMjKS7du3M3PmTGbMmFFuydzztWrVKlJSUlxf3bp1c+0rLCykWbNmvPrqq4SEhFR4/Lp165g4cSJbtmxh5cqV2Gw2Bg4cSEFBwQXHIklSw2dXBTrF+YBBozMAzjn+9UmuPdv1vcCBos2XSYIGTvbt9btvlzUJJOkS5WakUGSAdB+F98U3/Nm6N1pN+Q/6iqIwpFMY7/4Rz+ZkK/3SD0JhBqFdbwbgRMEJd4QuSXXC9ddfz/XXX19lm+TkZB555BF+++03brzxxnL7Dhw4wK+//kpsbCxXXHEFAO+99x433HADb7zxBmFhYSxatAir1cq8efMwGAy0b9+enTt38tZbbzFhwgSKi4vp1q0bvXv3dt1cJCQk0LlzZ9555x3GjRvnul5AQEClNwndu3ene/fuADz11FMVtvn111/LvZ4/fz7BwcFs3779nCvxSJJ0+bE7VNd0A41WhxYHiq3oHEfVLXn/qqHwtulN0vMjgBbuCUiqcbJvr999uxxJIEmX6FjnUMY8rmXRMBWdqqHnNUMrbHdV6yAAlhzzQqA4kwRa5/SElPwUinbtInvJEooPHqy12KWGSwhBoa3QLV/VXVdDVVXuuecepk6dSvv27c/av3nzZnx9fV03EQADBgxAo9GwdetWV5t+/fphMBhcbQYNGkRcXBxZWVmYTCYWLVrEggUL+PHHH3E4HNx9991cd9115W4iAIYOHUpwcDB9+vThp59+uuT3l5OTA4C/v/8ln0uSpIbHoQp0pUkCT0ceCaZ7eHb3AKhHNYyK1PJDxIO1qehKst0TTD3XUPp32bfXbXIkgSRdooNZB0FRiBI2jnp2oKXFt8J2nZr44uuhJ60QSoLCMeUlEWZ1PglIKUgh8/OF5C5fTvC0aZhatarFdyA1REX2ImK+jHHLtbfetRUPvUe1ne+1115Dp9MxadKkCvenpqYSHBxcbptOp8Pf35/U1FRXm6ioqHJtGjVq5Nrn5+dH586defHFF7nvvvu48847OXr0KMuWLXO19/Ly4s0336R3795oNBqWLFnC8OHD+eGHHxg6tOLk4LmoqsrkyZPp3bs30dHRF3UOqeZ0794drVbLxIkTZc0hyW3OLFyo6swAaBCgOkBbP27lS+xZ5V7naTQo1vxKWktVaSj9u+zb67b68ZdFkuqwfzKdRUlaWW1oW15baTutRqFnswBW7E0l1RBJU5IIy88AIKM4A127Nnjm5aIPaVQrcUtSfbB9+3beeecd/v7771qp1zFlyhR++OEH3n//fVasWEFAQIBrX2BgII8/frpYWPfu3Tlx4gQzZ8686BuJiRMnsnfvXjZs2HDJsUvVLzY2FovF4u4wpMucQxV87riOjUoXOgfGQOJ7zh2qrd4kCUwlh+CMP+H5Gg0am0wSXK5k31731Y+/LJJUhzX7aBX3ORy0bWkn4tYbq2zbNcKPFXtTOWBvTFPAkpGIWWemyF5E0W0DaDr+vlqJWWr4zDozW+/a6rZrV5c///yTtLQ0IiIiXNscDgdTpkzh7bff5siRI4SEhJCWllbuOLvdTmZmpmt+YUhICCdPnizXpuz1mXMQ09LSOHjwIFqtlvj4eAYPHlxlfDExMaxcufKi3tvDDz/sKsTUpEmTizqHJEkNn82h8qfakZ0aHc0sZzw1dVhBX31/b2tSiVoA2tOv8zQaDDJJcFEaQv8u+/a6TyYJJOkS2B12uuzKxmQDmhrRNe5cZfuukb4AbM4N5HpAST9ImG8YCTkJnCg4QVOfpjUcsXS5UBSlWof8u8s999zDgAEDym0bNGgQ99xzD2PHjgWgZ8+eZGdns337dlc14tWrV6OqKjExMa42zzzzDDabDb1eD8DKlStp3bo1fn5+rnOPGzeODh06MH78eO6//34GDBhA27ZtK41v586dhIaGXtB7EkLwyCOPsHTpUtauXXvWUElJkqQzOVTnPHCdRnGtbuDcYavkiLqnmEIA9AJsCuRqNITY6mfVd3drCP277NvrPpkkqCZy3uLl6Wj2EeYP0BCSozK2WQ/QaKts3z7MB4NWw1/FYVgbt8cQ3JZQQz4JOQmkFqTWUtSSVLfk5+dz6NAh1+vExER27tyJv78/ERER5YYFAuj1ekJCQmjdujUAbdu2ZfDgwdx///189NFH2Gw2Hn74Ye68807Xkkp33XUXzz//POPHj2fatGns3buXd955h1mzZrnOO3v2bDZv3szu3bsJDw9n+fLljBo1ii1btmAwGFiwYAEGg4EuXboA8P333zNv3jw+/fRT1zmsViv79+93fZ+cnMzOnTvx8vKiRQtnFe+JEyfy5Zdf8uOPP+Lt7e2aW+nj44PZXD+eCkqSVHvsqqCLEk+Y4sDLEYxVaDEoDudIgnqiBGcNphBh4JhiJV+jQWeXIwkaMtm31/O+XUiXJCcnRwAiJyfH3aFIbvDBtiUien606DRnoLCeOnxexwyfvUFETlsmlv59XAghxAubXhDR86PFp8v/Jw5df4NIuGlITYYsNVBFRUVi//79oqioyN2hXLA1a9YI4KyvMWPGVNg+MjJSzJo1q9y2jIwMMXLkSOHl5SUsFosYO3asyMvLK9dm165dok+fPsJoNIrGjRuLV1991bXvwIEDwmw2iy+//NK1LSsrS4SHh4snn3xSCCHE/PnzRdu2bYWHh4ewWCyiR48e4rvvvit3jcTExArfS//+/V1tKtoPiM8+++zCf3ilqvr9y37qwsmfmVSX7DmeLf5+rqsQ0y1i47IFouC/QUJMtwiRmeju0M5b/7k9RPT8aDF2Xi8RPT9aPPZeK/H121PdHVadJ/v2y7tvF8J9/bscSSBJl2D9kT0ANDK3RB94fsOKOjT2YUdSNgdSchnepTGNPJ2FCtMcWVgPHwa9HiFErRRykS6NPT2d4n378Orf392h1GtXXXXVBS2rdOTIkbO2+fv78+WXX1Z5XMeOHfnzzz8r3NemTRsKCwvLbfP19SUpKcn1esyYMYwZM6bKazRt2vSc7+VC3qskSZLNoaItXd1A0elZpXalkaeWGK3RzZFVzJ6VRfHefZg7d0Lr7Q1AkeIc9RCseAK5/EInTnndyh1ujFOqWbJvr9807g5AkuorIQTpSXsJzRB0Dzj/JQvbhDgrZR9IzQNVJcjgC8AxQx4RCxbQ7PslNRGuVA2yv19KSXy867U9I4NjDzxIyvQZCFv9mRsqSZIk1R+OM5ZA1Gr1TLI9wpu+z4LlwuZM14b8jRuJ79mLE088QdaXXwGgCpUixQ5AiMYLAEVTTIlddVuckiRVTSYJJOkiJZwq4Ia/43jnYwe3/FpxBrMibUKdWfX+x+fAS40IOrwegJMlGXjG9MDYsqUcRVAHZS9eTMp//sOxBx7EkZ0NgLBaQVGcr3VyYJYkSZJU/eyqQFc2kkDrLM5mU+vmB2xzx46g1xPwwAMEPjABgDxrHqL0tsbi3QYARVuMVSYJJKnOkne1knSRVscl4VDsFBkgOMDnvI9r3cgbRYFTJTrQWwkqyAIgvSi9pkKVqoHXtddi+ORTLDfegMbH+fs2d+hAxGfzMEVHy8SOJEmSVCMcqkCH80m8VmcAVOx2FYSAOtb3aL29abn6D3RBQa5tOSU5AAiHAXvYQMj9mWBtKvdkfgL0dlOkkiRVRSYJJOkirTu8g93XafnlKjt/3Dj5vI/zNOqI9PcgKSsYgKCcVNBDZnEmubFbUY8ew9ytG8Z6vnRKQ6Pz8yPq+yVoPD3Lbfe88spyr0vi4zG2bFmboUmSJEkNmF0V6JXTNQlWGCbTOvM4HF0OTeveh+wzEwQA2SXZAAiHB15653QDq8ZBpD2xtkOTJOk8yekGknQRHKogM30DAC0doIS0v6Dj24RYOCqcBQt9s46iU5z5urSP55Dy7HMU/vVX9QYsXTRHXp7r+38nCM4khODkK69weMhQ8lavro3QJEmSpMuA3aGixTk0X6MzOP+LALXu1MIp/ucfDg+/mZQZMwBIffElksaNw3o82ZUkwGHGuzRJkK9R0Ili9wQrSdI5ySSBJF2EfSdy8NQ5C9i18gy94OF+zYM9SSpNEmgKThFodq4VW9w6As9+fdEFBFZvwNJFKYmPJ77/VaS9+SbiHPM/FUUBjRaA4r37aiM8SZIk6TJgVwWz7Lcy3+t+hHcotrKBwI66kyQoiY+n5J9/KDl0CICCzZsp2LQZ27Ekcq25APQkkebHfgVAKAoCmSSQpLpKTjeoJt27d0er1TJx4kQmTpzo7nCkGrYpIQPvklM89a2DZuEGuPvCjm8W6EUeHuQp3niLPIL0XqRykpO39yU64tqaCVq6YDnLlyMKC7EeOYqiOXdONeixyXj174/nlTG1EJ0kSZJ0OXCogm8dV9PD25/OnkFnJAms7g3sDJ69e9Pkww9QdM7CioEPPohw2DE0b05h1jFnG1XFZPRCr+iwCTs2pe7EL0lSeTJJUE1iY2OxWCzuDkOqJVsOpaItsNE1QUBJ0QUf3yzIOWw9iUa0J49AxTl8ML1QFi+sS4IefRRzp04YmzU7r/Yag0EmCCRJkqRqZVed66/rNAo6jUIJzlFrdSlJoPP3x/vqq12vfYbc5Pq+IK0AAE9VoOhMeGrNZNvzcMgkgSTVWXK6gSRdIKtd5eDxfcQHafj4eoWgBy985EizQOecvLW2dtjbDCXYw1nk51TRqWqNVbo0iqLgffXVGCIjz9pXaCskKTcJWyVzQh05OWR/v7SmQ5QkSZIaOLtD5QrlH1rbDmDAhlXUvekGVSmwOZMEHkJFozfhpXc+KCnR2t0ZliRJVZAjCSTpAu08ls1JTR4e3gqJ/VsROPz2Cz6Hj4eeAE8DMwvupH/fPgSmfwUn1qPu/YeE/9yALjiYyAXzqz946bwIVQVFqXBZw8ScRF7d9irbUrZhF3Z0Gh2Dmg5iUpdJhHmFAaAWFJBw/Q04MjMxREbg0a1bbb8FSZKqgZxKKNUFdlXwteFFdGkqR2x969x0A2G3k/PjjxiimmHu3AlFo0EtKcF69CiisJBCeyHgnG6g6E146JxJghxFhxBCLiEsSXVQgxtJUFJSwrRp0wgLC8NsNhMTE8PKlSsv+DzXXXcdiqLw8MMP10CUUn22KSEdrSkVgFb+rS76PFGBzk7ycHoBwR7O5RAzrdlYExOxHjlyyXFKF69gwwYOX38DmQu/KLd9ddJqRvw8gk0nNmEXdvQaPXbVzvLDy7nlp1vYmrIVcK6C4H3ttRhbtnCuYy1VqmnTpiilCZkzv8o+kBUXFzNx4kQCAgLw8vLi1ltv5eTJk+XOkZSUxI033oiHhwfBwcFMnToVu/38n1Dt2rWLkSNHEh4ejtlspm3btrzzzjvl2qxdu7bCOFNTU8u1S05O5u677yYgIACz2UyHDh34S65WUm/Fxsayf/9+mSCQ3MrhUNEpzuK5Op2BPSKKP0Un8Apxc2ROtuPHSXnmWZLGjXNtK969m8Shw0h+chqFtrIkgUCjN2MyegMwxXE/NofsIxsq2b/Xbw1uJMG9997L4sWLmTx5Mi1btmT+/PnccMMNrFmzhj59+pzXOb7//ns2b95cw5FK9dWm+HTMxiQiUwSdGgUirFYUg+GCz9MsyJO/jmZxOC2Xrs1MABzyKSLi8wXoAuXqBu6Uu3w51iNHsB496tq2/eR2pq6bilW1cmXolTwT8wyRlkj2pu/l1dhX2X1qNw+teoj3r3mfXo17ETxtGhqzCUWrdeM7qftiY2NxOByu13v37uW6665jxIgRADz22GMsX76c7777Dh8fHx5++GFuueUWNm7cCIDD4eDGG28kJCSETZs2kZKSwujRo9Hr9bz88svnFcP27dsJDg7miy++IDw8nE2bNjFhwgS0Wu1ZieK4uLhy9WeCg4Nd32dlZdG7d2+uvvpqVqxYQVBQEPHx8fj5+V30z0eSJEk9Y1qBVmdgln0EeqEQ33KAG6M6TbVa8ezVC3RaV5FfXWgYGh8ftH6+FFjzAfAUKlqDCbPOec+DYsfqUDHoGtwzSwnZv9d7ogHZunWrAMTMmTNd24qKikTz5s1Fz549z+scRUVFomnTpuKFF14QgJg4cWKV7XNycgQgcnJyLil2qX4oKLGJgf/5WAz/uJVYdWUbsb91G5G/detFneuDNYfENU/NEbYZAWL/m81F9Pxo0f/r/tUbsHRR7Hn5ImvpUlEcHy+EECK7OFv0/7q/iJ4fLR7+42Fhc9jKtS+2F4vJqyeL6PnRoueiniIxO7HWYy4qKhL79+8XRUVFtX7t6vToo4+K5s2bC1VVRXZ2ttDr9eK7775z7T9w4IAAxObNm4UQQvzyyy9Co9GI1NRUV5sPP/xQWCwWUVJSIlRVFddee60YOHCgUFVVCCFERkaGaNy4sXjuuecqjeP//u//xNVXX+16vWbNGgGIrKysSo+ZNm2a6NOnz8W+9UtS1e9f9lMXTv7MpLrk83X7hJhuEWK6RaSlp4vIactE5LRlrr9pdd1DKx8S0fOjxQuv9RIpRw6Ih1c9LKLnR4sWr/xXZOSXuDu8Oq2h9O1CyP79Yrmrf29QqbvFixej1WqZMGGCa5vJZGL8+PFs3ryZY8eOnfMcr7/+Oqqq8sQTT9RkqFI99deRLDoq+zmi11NsBMwm9GFhF3WuqEAPTglfdMJGUH4GAJnFmdhVWcjH3bRenvgOH46xRQsA3vjrDTKKM4jyiWJmv5noNOUHYRm1Rl7r9xqdgzqTZ8vj8XWPYyt98iMcDnJ//53iuIO1/j4A1MJC1MJCxBnTHoTV6txutVbcVlVPt7XZnNtLSs6r7aWwWq188cUXjBs3DkVR2L59OzabjQEDTj8ta9OmDREREa7RXps3b6ZDhw40atTI1WbQoEHk5uayb98+FEVhwYIFxMbG8u677wLw4IMP0rhxY/773/9WGktOTg7+/v5nbe/cuTOhoaFcd911rqcdZX766SeuuOIKRowYQXBwMF26dOGTTz65pJ+JJEmSsJ/xt1WrYI78EFPjhTjU+jFUv6xw4QLbTWj8m2LWmQEYrVuOPfuEO0Or12T/Lvv3mtSgkgQ7duygVatWZy1F2KNHDwB27txZ5fFJSUm8+uqrvPbaa5jN5gu6dm5ubrmvkn/9Dyc1DJsSMmhq3IddUXhpnJnW27ejb9z4os7VxM+DXDwowIS/qqJVNAgEqWt/I+u777D9a16W5B57Tu3hh0M/oKDwfK/nMZUNk/wXg9bArKtn4W/yJz4rnjm75wCQ9sabJE96lPSPPqzNsF3iunYjrms3HFlZrm0Z8+YR17UbJ//3v3JtD/buQ1zXbthOpLi2ZX35JXFdu5HyzLPl2h66dgBxXbthTUhwbcteemmrOfzwww9kZ2dz7733ApCamorBYMDX17dcu0aNGrnmCqamppa7gSjbX7YPoHHjxsyZM4ennnqKp59+ml9++YUvvvgCna7iGXebNm3im2++KZdwDg0N5aOPPmLJkiUsWbKE8PBwrrrqKv7++29Xm8OHD/Phhx/SsmVLfvvtNx566CEmTZrEggULLunnIknS5e3M6QY7s3aj8ziK3rKPorWvujGq08Q5au+UJQmEakSv0WDUGQEI1Z7EXphV1aFSFWT/Lvv3mtSgkgQpKSmEhoaetb1s24kTVWcrp0yZQpcuXbjzzjsv+Nrh4eH4+Pi4vl555ZULPodU921KSMfD6Jyn3tI7HI1Gc9FVecP9PQCFZDUADRCgdya3ct/9kNTn/kvxvv3VFLV0vtSiIpKfmEr2kiWI0nl0H+5yfrgf0nwIXYK7VHl8oDmQZ2KeAeDTPZ8SnxWPz/DhaHx8MEY1O+eN1OVu7ty5XH/99YRd5OicqowYMYKbb76ZV199lTfeeIOWLVtW2G7v3r0MGzaM6dOnM3DgQNf21q1b88ADD9CtWzd69erFvHnz6NWrF7NmzXK1UVWVrl278vLLL9OlSxcmTJjA/fffz0cffVTt70eSpMuHKE0SqGg4lBPn2p5WcvrDoe3kSbK++gpHdnZth8eha68l8bYR2P5V6C3r229JGjeOdltKH3qoRnRaBaPWmSQoURQc1qLaDldyA9m/1z8NqnBhUVERRqPxrO0mk8m1vzJr1qxhyZIlbN269aKufezYsXIjGCqKQ6rfcgptpCUfISO0GDDQ+hwfGM/Fx6zHYtJxwhFIK5IJ0ppIAwrbRRLcqDFab69qiVs6f4V//UXusmUU/r0dn1tuYc+pPfyZ/CdaRcsDHR84r3MMbDqQaxOv5Y+kP3g99nU+vu5jWq5fh8ZNfxNa/70dAOWM0VEB48bhP3o0/CvT3mrjBmdb0+nREn533YXviBHwrwKMLf5YdVZb35tvvug4jx49yqpVq/j+++9d20JCQrBarWRnZ5d72nDy5ElCQkJcbbZt21buXGXVkcvaABQWFrJ9+3a0Wi3x8fEVxrB//36uvfZaJkyYwLPPPlthmzP16NGDDRs2uF6HhobSrl27cm3atm3LkiVLznkuSZKkyhRi4jXbnXQJt7Dr1A7X9lPWPJqXfp/z00+cevMt0ud8TKMnp2K54YZaiU0tLsZ+IgX7iRQ0np7l9tmOHaNg02Ya9TBDa/hZ9zx6+yhMWme/UaIo2K1y5O3Fkv27k+zfa0aDGklgNpsrHOZfXFzs2l8Ru93OpEmTuOeee+jevftFXdtisZT7kkmChmdrYgbdlDjiDHr67lUZ8OEOshcvvqRzhvt7cEI450UFlP7vePTea4iYMwePi/y3KF08Q0QEgf/3EH4jR6IoCgv3LwTgxmY3EmGJOO/zTLliCnqNni0pW1h7bK3bEgQAGg8PNB4e5Ua8KAaDc/u/VuVwtdWc7hoUvd65/V/vobK2F+uzzz4jODiYG2+80bWtW7du6PV6/vjjD9e2uLg4kpKS6NmzJwA9e/Zkz549pKWludqsXLkSi8VSrkOfMmUKGo2GFStW8O6777J69epy19+3bx9XX301Y8aM4aWXXjqvmHfu3Flu9Frv3r2Ji4sr1+bgwYNERkae1/kkSZIqUqzx4EPHUDaE3MPOUztd20/Z813f60NC0IeFYU9NJfnxKdjT02slNkWvJ2rp94R/8gkar/IPN7wHDSb01VdY1dn52iLs6Axm13SDYjmS4JLI/t1J9u81o0GNJAgNDSU5Ofms7Skpzvk3lQ1x+fzzz4mLi2POnDkc+df69Hl5eRw5coTg4GA8PDyqPWap/tiUkEE3zT8sMBgYkiLw/usg1s5HLumc4X4enDjpXO7Q3+EsEJNZnHmpoUoXyRAZSdCkSQBkFGWwMmklAHe3vfuCzhPuHc7odqOZu3cu7+18j/7h/dEoGkri47GlpODVr1+1x16fqarKZ599xpgxY8rNI/Tx8WH8+PE8/vjj+Pv7Y7FYeOSRR+jZsydXXnklAAMHDqRdu3bcc889vP7666SmpvLss88yceJEV7J2+fLlzJs3j82bN9O1a1emTp3KmDFj2L17N35+fuzdu5drrrmGQYMG8fjjj7vmOmq1WoKCggB4++23iYqKon379hQXF/Ppp5+yevVqfv/9d1e8jz32GL169eLll1/m9ttvZ9u2bXz88cd8/PHHtfWjlCSpAbKXFih0aApc8/sBTtlPf+8zZAg+Q4aQeOttKGYTjqysWllOWdFqMbVtW+E+c3R7DO3acGjhcwCYHKDV6U+PJNAoOKzFNR6j5D6yf6+/GtRIgs6dO3Pw4EFyc3PLbS+bQtC5c+cKj0tKSsJms9G7d2+ioqJcX+BMIERFRZX7hyJdnjYlpLNKaUOmVsv6jloCZjyH93XXXdI5w/3N7BVR7Pe9Gn9LE0AmCeqKHw79gF210yGwA20DKr4BQlUhNwWKc8/aNTZ6LF56L+Kz4vkj6Q/yN2zk8JChpDz73CVXCW5oVq1aRVJSEuPGjTtr36xZs7jpppu49dZb6devHyEhIeWGLGq1WpYtW4ZWq6Vnz57cfffdjB49mhdeeAGAU6dOMX78eGbMmEHXrl0BeP7552nUqBEPPvgg4FwZ59SpU3zxxReEhoa6vs4cWWa1WpkyZQodOnSgf//+7Nq1i1WrVnHttde62nTv3p2lS5fy1VdfER0dzf/+9z/efvttRo0aVSM/N0mSLg+KrYho5TC+JYfKbU93nP0Bu+ni72j6xRcYK5mXXdsK7YWu73WqDkWjcdUkKFYUVJscSdCQyf69/lJEA6qktXXrVq688kpmzpzpWsKwpKSE6OhoAgIC2LJlC+BMChQWFtKmTRsA/vnnH/7555+zznfzzTdzww03cP/99xMTE1NhUcTc3Fx8fHzIyck5a1UFqeE4lVdC95dWofM6iDl8Hk0tTfn55p8v+byfbz7Cf3/cx3XtGtG3235m/jWTB7I7c/2KdMydOhL22mvVEL10Pkri41GLSzC1awsaDTctvYmkvCRe6PUCN7f811y8knxY+wrsXARFWdD/Kbj66bPO+f6O95mzew6t/FrxzaBFJF43CHOnToTMmI4uIKBa4y8uLiYxMZGoqChXHRbp8lHV71/2UxdO/sykuuSjb5by4IF72eERxOhGp6fODtYEMPOetTjyC9CYTSj/mlteG4oPHqTkwAEMzVtgjm5fbp9QVZL//pOpy/+Pg+Hwx9FsfJ9P5tu4b/nflv9xdUER97d4lg4D7631uOsL2bdL7urfG9R0g5iYGEaMGMHTTz9NWloaLVq0YMGCBRw5coS5c+e62o0ePZp169a5Ko23adPGlTD4t6ioKIYPH14b4Ut12KYE59y+0MBMsoHW/q2r5bzhfs4pLMcyC/E3O2sTFBTnYT1yBG0Fa7hKNSdj3mfkLF1KwIMPcOqe60jKS8KkNTGo6aDyDU/uh69HQtYR52tFA75n1CsoyAB7Efg04Z5297DowCIOZh1kbeoGrvl1BRo5bUmSJEm6EA47APma8rftaVrn66Oj76HkwD+Ef/IJXn16A7hW6KnpxEH+mrWcmjULn5tvxvzKy+V3CkHe3Q/yX2Dyw1CiOOe1ly0lvFJ04oawQXSo0QglSboYDWq6ATinB0yePJmFCxcyadIkbDYby5Yto5+cAyxdgs0JGVyh/EOIZzw6uyDmmImSxMRLXtIu3N/5ROB4Zj7+wvm/455wlciFnxP2785WqlGK0YDGywuP7t35LfE3APo26YuH/owP9RkJsHC4M0HgEw4jv4FnT0Hnu063WfEkzB0Ipw7iY/ThrrbOfR/u+hDM8imAJEmSdIFU5xS1wn994D9pcN5DqNk5IIRrVaQTzz7LwZgrKbzIFbsuhD40BM9evSqsS6BotYjIxiQFgY8NrDinGZRNN0CxUWJXazxGSZIuXIMaSQDO5Q5nzpzJzJkzK22zdu3a8zpXA5qJIV2iTQkZvKZbzOvFmQTl6Yn+eAlHPH+l1V+xl3TeJqUjCTYp40n5xgaNQzmmzZErG7hB6IwZhDz7LEIIfvvROd/t+qjrTzewFcFXd0L+SWgUDWN+Bo9/jfawFkLqbshNhgVD4P4/GN1utGs0wZqkNVwbeS2O3FwKt2/H++qra/EdSpIkSfWSw5kkKNBoAYGieiI0BWSVOEc5Nv91BY7cXLSlw43VnFzU/HxKEg7j2atXjYbmM3QoPkOHVro/b/5LPPH7fYSUaNme15xwcBUuVDQ2rDJJIEl1UoMbSeAu3bt3p127dsyePdvdoUjV7FhmISmZubTTHOKIXo/JBppmkRhbtCi37MzFMOm1+HnoyRDe+JcODcwuycahOqojdOkCKTodB3IOcqLgBGadmb6N+57embIbck+AVwjc/b0rQXAyt5hNCelsS8wk266DsSsgqC3kp8KiEfig4a42ztEEc/fOxXYyjUNXXc3xRyZhK62yK0lS3ST7dqkuEKXTDQpLl6TTOPwAKLIXoAoVxWBAFxiIUrrsXdCkR4j6fgk+Nw93S7xnKluNIdnRmDeMEwFcSyA21qQQemy522KTJKlyDW4kgbvExsbK4kYN1OaEDNorRzhhFNgVhYwIC62Wr7jkBEGZUB8zaRl+NHGcBEBVHZz89Wc8Cu1YbroJjSxUU+OEEK7f57rj6wDoHdbbNW8SgIgYeHCDcySBdyP+OpLJ67/Gse3I6dUotBqFq1oFMe26ebT66WZI2w+/TGXU4Jf4fP/n7Enfw05xlOB27XDkZGNPS0MfElLt70W6/Mjfe82QfbtUJ5RNN9BoAAeBDjipB4GgyF6Ep96zXPO6srIBnE4SCNWIQetMcpSNJNBrivHKS3BbbPWJ/Bt/+XLX716OJJCkc9iUkE53zT/ElWboW/u3rrYEAUCYr4mTwg894KMxgqKQ858ZpDz7HPZTp6rtOlLlEoffzJGRd1Fy+DDrj68HoF+TCuqY+EchwmP4YO0hbp+zmW1HMlEUaBbkSRM/Mw5V8Mc/aQz+LJEvwmcgFA3s+oqAg6sY3mI4AHP3zKXxe+8S9dNPmDt2rLb3oNc7C0IVFhaeo6XUEFmtVsC5ZJQkSQ1M6UiCIsV5224WBnSlHxxyjh0m7Y03yFy0yC2hJdx0E4eHDMV67FiF+z2/+Z1nv3LQ/VAR+tIkQVlNghJFAXtJrcVaH8m+XSr73Zf9W6gtciSBJFVBCMHGhAxe0sSx0+D8n7OVX6tqvUaoj5k04QuAv6InhxJsXdvhp7dANSYjpIo5srMpiYsDINuksi9jH+AsWghA0lZQ7dDUWTH63T8OMWvVQQBu6dqYJwe1IcTH+VQk4VQ+s1YeZNnuFJ7dYcEcPIpbcxfCL1O5975fWXxwMRtPbCReTaWt4let70Or1eLr60taWhoAHh4e1ZrMkuouVVU5deoUHh4e6HSyW5ekhiZVF8p79uGc8LGCfS86jHiqKjlaLQVHErB/OhdDVBT+pWu2O/LyyF+7DrWgAL8776ixuITDgTXhMAhR6ahH7dEUOh4R2MIO0bToVaCfa7pBsaKgyCRBlWTffvkSQlBYWEhaWhq+vr61/hBA3k1IUhUSTuWTnldEd+NBvi2tItx/4V6S5j5A0MSJ1fIkOLR0JAFAgFBIBE48N5qOUYMv+dzSuWl8fGi24hdKDsbzR8EeANoHtCfQHOhs8MfzcHQjDHmHX42DXAmCZ29sy319m5U7V/MgL96/qyvXR6cwdfEupqYNwtsnjZ4jHqNJUDsGNR3EL4m/MG/vPGb2n4lQVQq3bMGjZ89q6fRDSqculN1MSJcPjUZDRESEvHmUpAYoWRfBAvvtXGPZCJl70WLESxXkaKHQx0jImDFofE5Pi3Hk5HBi6lQUoxHfO26vub8LikLU0u+xZ2RUumxz0lUt+cZygNbedporztEPZdMNihUNikMmCc5F9u2XN19fX9e/gdokkwSSVIVNCRm0VJLxVfKIM/gA4LPvGAUn0gicMKFarhHmY+af0iSBv8MBCmQUZ1TLuaVzUxQFY1QUxqgoNq9/EoA+jfs4dyb/7UwQaHScCOzD1Hm7Abi/b9RZCYIz3dgxlAh/D0bP28qEnDG0/8XOVxNsjIsexy+Jv/D70d95JPth7OMeo2T/ASI+m4dnz57V8l5CQ0MJDg7GZrNd8vmk+sNgMKDRyBmEktQQ2RzOD9c2tRgAPSa8VOeqAAUhPjR6+qly7XXBwXjExKBrFIywWlGMxhqJS9FoMLVpU2WbE819+dOqoVW2A3uRM46yej9WjYJwFNdIbA2J7NsvX3q93m3TCGWSQJKqsPFQOodFKJ91fIfsvDfRKBrC/vci4kQqxubNq+UaoT4mEkRj1mh74e/jBbn/kFEkkwS1TQjBtpRtAMSExjg3bvnA+d/oW/nv2izySux0jfDlycFV3xQBdGjiwzcP9OSuT7aw70Qu9y/4i8+H+tKnUXc2nIxlwYHPub9zF2zHk7GlnqzW96LVauXcdEmSpAbCaM+luZKMas8GQKuYMAhnkiDfln9We43BQOSC+bUYYeXKChd6qgKHxlnbqWwkAYBdJgnOm+zbpdokHztIUiUcqmDL4Uzs6NBENQKgqaUpvr374jdiBFpf32q5TpivmX2iKQ+WTMI/6hoAgpZuImHQYDLmzq2Wa0gVE0KQ9tYscles4FDaP2QUZ2DSmugU1AkKM2H/jwDEhtzBqgNp6DQKr9/W0VV86VxaNfJm/tgeeBt1ND76A5o5fRmX6VwN4YdDP6C5byQt16zGtw4sUyVJkiTVTVcUrOcP41SMOc6aOTrFAy/VObqgID8L4XDPssklCQlk//ADRbt3V9rGmptNVIog4BSuJEFZ4UIAhyqTBJJUF8kkgSRV4kBKLjlFNryMOko0zqq9rf1aV/t1gi2lVX7tKkaNc06hIy8X69Gj2JJPVPv1pNNsx46R8fHHnHhyGrEnnaMIugR3waA1wJ7vwGFFhHTgma3OQVfj+kTRItj7gq4R3diHT8dcwSFNFAiVKw6uoaNnOFbVypcpy9B4ep77JJIkSdJlS1GdqxsUls7pR+ON3eGskxTwwff8E92BjHmfVXhsTS6fVrBhAylPPU3m/PmVtgnYf4LX5jtousGEozQ5oNVo0eB8Ir4w7Mkai0+SpIsnkwTVpHv37rRr147Zs2e7OxSpmmxKSKepksL73p8Td3Q1AF3sYeRv3IjtRPV9eDfqtAR6GVFQ0RQ6hw9u7eJB5MLPCbj/vmq7jlQBjQa/u0ZiGTKErel/A9AjtIdz344vADgQMpSDJ/PxNuqYeHWLi7pMTLMAHh45nDmOISjA3UedxQ+/ifuGPGse4HwiYy8dZSBJUvX7+++/GTp0KP7+/nh4eBAdHc27777r7rAk6ZwU1TkPvag0SVBoaMxau3NanJKb71xdwMOj3DHpH83hYO8+pL/3fo3FpQsOxrNXL0zt21faJsvoIMMbMKmoZ4wg0CmlKxzIwoWSVCfJmgTVJDY2FovFcu6GUr2x8VAGfTR7uSp/Ga9mOYvUtd2VxbGP78Ny0000fmNmtV0rzNfEl9ZJ5K9Jh7BGJHgX4tG9e7WdX6qYoUkTQv77X4QQ/P1NPwC6h3SH/FNQcAqhNfB8ovPmZ2zvpviYL36N2oHtQ8i84Vnif41lUF4y7/q35Lgtn+8OfsdNv+eQ8fHHBDz4AMGTJ1fHW5Mk6Qy///47Q4YMoUuXLjz33HN4eXmRkJDA8ePH3R2aJJ2TIpwjCYpxPkgwaDwQDue8/q0T+zG55QIUk/lfRwkcGRnYUlNrLC7L9ddjuf76KtvERWj4+WEdjyZ74Wls4tqu0xiwOgqxqjJJIEl1kUwSSFIFrHaV2COZjNAcIEejIRlnBx0cGElRi+YYmkVV6/VCfUxknPQhzOEsYJdZLJ8o16bE3ESyS7IxaU20828HWj08tp/dO7ex9dsMzHot4/pc+u/8zl4t+Sr1fzTbNZ4HM07wbFAAC/cvZHj7J0FRsJ86VQ3vRpKkM+Xm5jJ69GhuvPFGFi9eLFeBkOqd09MNnEkCo8YEqvNJfJ4oRBcYeNYxPsOH49W/P/qwsNoLtAJlhQtfto3ltoDejCzdbkRDIdAn8ytguJuikySpMrKnlOqkzEWLyF+/3m3X3308m0KrnZ7aAxwwOJ8eh3uHEzZ6HM2XLSPo//6vWq8X6mPmJL7OJRABNT+fjF9/IWf58mq9jnSasNuxnTyJEIIdJ3cAEB0YjV5bOlpAo+Hjf5xFloZ3aYyvh6FarnvnzbewMegObsgvIMiukl6Uzh8RuTRf8QthL71ULdeQJOm0L7/8kpMnT/LSSy+h0WgoKChALV0+TpLqA82/RhL4YucRxXl/kG89e3UDAH1ICKa2bdH6+NROkJUotBU6v1GN5Yr+GhVnTYLGJXvdEZYkSecgkwRSnVC0d1+570++/ArHHniQgq3b3BLPxkMZNFdOEEAO+83OwnJt/dvW2PVCfEycFH54CYEOBd98SJs8hdT/Tq+xa17uShISONT/KhIGDWZHmjNJ0CW4C1gLQHWQllvMb3udwzTvvjKi2q6rKAq97nuTk/ooIjKd01g+3jsfbUR4tV1DkqTTVq1ahcViITk5mdatW+Pl5YXFYuGhhx6iuFhWVpfqPo1qR+V0ksCk9SSCXBCCzt/tJv2TT1CLimo9rsRbbuXwkCGUJCRU2qbQVsBjSx28uGYJnsUFru0GjXMkhKN0pKYkSXWLTBJIbpf7628cue02Ul9+GSEExpYtsNx4AwHjx2Hu0tktMW1KSOdKzQEADvgEA9A2oOaSBMHeRtKFDwrgp+jI9QDRvhUe3bsj5BOvGmE7dgy0WnTBQew8tRMoTRJsng1vtuafZe9gVwVdI3xpH1a9T2J0Ji8Cpmwlx+sxhN2D1MLjfHvA+VTIkZtbLmkmSdKliY+Px263M2zYMAYNGsSSJUsYN24cH330EWPHjq3y2Nzc3HJfJSVy/rRU+3ZrWvGxY5DrtU7ngZeqYi6BK/44zqk33wJFKXeMEIKcn3+usQSCEIKShARK4g+hGE0VtrGrdkpUK50SBV3SEumQtsq1z1C6HKJdcc/yjZIkVU0mCSS3K96/HxQFjdGIUvrfsNdeI/iJJ9AYqmeI94UosjrYkZTNlZr9ABzQO4fEtdeFc3jIEI49+FC1f3BvZDGRIZyFL32FQoFZIfvdaYR/9CGKnD9bI7wHDKD139vxfOk5juYeBaBTcCeI/x0KTrHjuHPVgVu7NanqNBfNbDLy2Zg+eFr7AzBzyyxSNm/l0DXXkjxpEsJmq5HrStLlJj8/n8LCQkaPHs27777LLbfcwrvvvssDDzzA119/TXx8fKXHhoeH4+Pj4/p65ZVXajFySXLaRFdeVW9zvdZqvfAqvQ/Z0NsXn9tuRWMq/0FdURRSn3+BU2++hS0lpUbiavrtN0TMm4s+OKjC/WX1CD4boKHRlVngfXoFBoPWGa8cSSBJdZMsXCi5XfDjj2EZPAhD8+aubcq/MuK16a+jmVgdKuGGHPJQOGp3flhsXuhNRvwhHNk51f7BPdjbSDrOp9V+DgdoIaskq1qvIZ1NYzRyQOssFtnMpxkWux2O/wXAV5mt0GkUbogOrbHr+3oY+PLqYdyz6RfydOnc89dvzDYa0Xh6YEtNxRAupyBI0qUym51V30eOHFlu+1133cWcOXPYvHkzLVu2rPDYY8eOlVu5yGg0VthOkmqSTRUopU/ctYoWvd6ElyooMil8M8jM/be/WOFx3gMHgsOOoqv+231FUTC1bl1lm7J6BJs6aPA/UoTqdXpUXlmSQI4kkKS6SSYJpDrB1K5dhduL4+LImDMHU8eOBNx7b63EsikhA4CFbedwZ7cTsOFJQj1DCWjdEY+5nyJqYNhesMXEUdGInx1X4uOlgaLjZJdkV/t1pLPtTXcWTYoOjIaE1YDglEdzUosDuLZVEH6eNTuapXlAIA/mFjDTz4tcjx94YdCTvPvYcAzeFQ/flCTpwoSFhbFv3z4aNWpUbntwsHMqWVZW5QlZi8UilzeW3M7LkYtGOUUhYNAa0Ok0GFTnw5Syp/UVCXvZvcVwy2LzKBt8qTvdrxl1zuSdQ1FBiLOmS0iS5F5yHHM16d69O+3atWP27NnuDqXeKNq3D0dubtVtdu8m95cVZH/7HUKIWomrLEnQq3kAB4qdS9K1C2iH1ssLr9698R4woNqvaTHpSNWG8YhtEobgGAAavfc9CYMGk79uXbVf73Jnz8zk+COTOPXBB+w5tRsoTRLErwRgpbUDAMO6NK75YIJac2ff6YTZ7BTpbKR6/MKoudtIz5dznyWpOnTr1g2A5OTkcttPnDgBQFBQxUOlJamu+D91EV+ZngdKkwQaDQZVg9YhKLIW1dr90ZlKDh8me+kPFO3eXWmbArszSRCSp1KUqUdXYHXtM+mdSYIiRQMOa4XHS5LkPjJJUE1iY2PZv38/EydOdHco9YJQVZIff5xDV11N4d9/V9rOcv31+N1zD6EvVTyUrrrlFNnYczwbEPRqEcD+TGddgppc2QCcw/YaWZwZdq1wrqagycjGevQo9lOnavTal6OSf/4hb+VKcn78ib0ZziKBHfzbQ8IfAPxc2B6jTsOAtsG1Eo+h6xge9m4DQKH/dvJP7eH2DzdyePFPqFZ58yRJl+L2228HYO7cueW2f/rpp+h0Oq666io3RCVJ508r7JSUPmk3aozotQqpjkYM/Fuw6DUbx59+qsrjayKJULBpMylPP03Gp3Mrb1M6kuDmdSpHfg/Csuv0cocGvRcAH2luBW3t15+SJKlqcrqB5Bb2U+koOj0oCqY2bSptp/XyIuSZ/9RaXNsSM1GFYJ15GqE/t2Wvybn+cHRgNPl/bkDRajC1b18j6w4Hexs5npmPtvQB8pYbopg89TUMzZpV+7Uud/qISIKnTSNbzSe7ZA56jZ5W1mIoOIVV68Ffamv6tQrEw1BLfyIVhRtv/oIFi3oTp4XBIR/SaXFzSk4e5MCJVNpPmlA7cUhSA9SlSxfGjRvHvHnzsNvt9O/fn7Vr1/Ldd9/x9NNPExYW5u4QJalKZyYJDFoDOq2GO0peZFzxNDSAQ1fxM7/cX34h9aWX8ejWjSbvvlOtMemCgvDs0wdTdHSlbcpqEhR7gM7sAKPZtc+kc9b3KEFONZCkukgmCSS30DcKptmyn7ElJ6Px8Dj3AbVkU0I6UUoqkeI4OUdPcbSJcw5rdGA0aQ/eQ0l8POGffIJX3z7Vfu1GFhO/GJ4mYU8GS4MDSQgReFxxRbVfRwJDk8YEjL2X2MRfYT208W+DwSMIek3ilx3HsBXouLZto3OfqBppDJ5M6/cq4zZM5VeLQp+WmRRmGPlq8xGGDD5F/1ZySLQkXayPPvqIiIgIPvvsM5YuXUpkZCSzZs1i8uTJ7g5Nks5JIxzYzkwSaBRAw4+9zPzWtYSvbrqrwuMUgwFHRgb2kyerPSbLoIFYBg2ssk3ZSILV/Tzo1kwl57qhrn3m0iSBQ8jRcpJUFzW46QYlJSVMmzaNsLAwzGYzMTExrFy58pzHff/999xxxx00a9YMDw8PWrduzZQpU8jOzq75oC9TiqJgaHJ+y8sVxx0k49NPsR4/XqMxbTqUQW+Nczjc3sbOeemRlkh8jD4YWjTH0KI5+iY1M089yNtIpvDGr3RZI7m6Qc07kHkAKJ1O4h9FWs9nmJw1AoBra2mqwZm6Nx/MsLC+AMwf5MOHE15hcWRvxs+P5attSbUejyQ1FHq9nunTp3PkyBGsVivx8fEyQSDVC0IItDhOTzfQGtFrnbfvQmsm11OhyKvi4foePXoQ9cNSmnz0Ya3Fe6ayJMFRfQeGW1/E7nX6/smsd06xjNHsROQkV3i8JEnu0+CSBPfeey9vvfUWo0aN4p133kGr1XLDDTewYcOGKo+bMGECBw4c4O677+bdd99l8ODBvP/++/Ts2ZOiGqhmf1nKOwmpe3Ckp17w/Li0114l7Y03yV+9uoaCg1N5JcSdzKOXxjlHfY+fcwhqdKBzKF2TWbNovmwZxqioGrl+I4uJdHzwdTiXAxJpGeT+/jv5GzfWyPUuV8Jmo2DbNhw5OcRlxQHQ2t+5jNOaf9IA6NjEx1UjorY90fdl/Ix+HCo8yhX9jjO8cxh2VfD093t44ef92B3quU8iSZIkNQh2VaA/I0ngnG6gMEf/FkG2HAAK7YUVHqu1WDC1aYPOz6/W4j1TWVyK6hw1UJbcADCXrnTQRJOKPT+99oOTJKlKDWq6wbZt2/j666+ZOXMmTzzxBACjR48mOjqaJ598kk2bNlV67OLFi88qXtStWzfGjBnDokWLuO+++2oy9AZrb3IOa/9cT/+EmXSw7QLgxIZASop8CXl5Jl79rzmv83gNGICiN6BvUnPrxm85nIGCSh/dARCwR+v8MNYhsEONXfNMwd5G0oUP3Us/BDZKyCL5jUcxX9ENr969ayWGy0HJ4USSRo9B4+VF3FRnkcjWel84tIqN+503Mte0qf1RBGV8Tb5M7T6V/2z4D5/uncPiqDxGHf2Hr7f5MA9IOJXPe3d1wWLSuy1GSZIkqXbYHQId5WsS6DUaIpQ0+u+2Ulyio6h1AgR3qdW4Dg8ZCgiavPcehqZNK2xTNpIgPLWEe7Z9TpBtM7zpLERt1jlHP5QoCvaSImSPJkl1S4MaSbB48WK0Wi0TJpwu8mUymRg/fjybN2/m2LFjlR5bUXXjm2++GYADBw5Ue6wNXU6RjSnf7uLt2e9w34GxdLDtQhUKWXZPCtN02DIK0W16Hhy28zqf/113ET7nI7yvubrGYt6UkEE75SgWkYcweLM33/nvpbaSBI0sJtKFD76l0w3SvFUMnTpgatW6Vq5/uXBkZ6MPC0PTshnpxRkoKLRM3gNf3MotidMB3D7//6ZmN3Fl6JWUOEr435Y5eC1Yz337ltE1+zDrDp7ilg82cTSj8rWxJUmSpIbBrqqsVTuzVnWuslQ2ksCKjh67FUauU7EnHq30+Nzffif9k0+wpaRUW0zC4aDk8GFK4g+hmCuvK1WWJLg2ZxN9T+zGvCvWtc+sdyblrYqC3SpH7EpSXdOgRhLs2LGDVq1aYbFYym3v0aMHADt37iQ8/PyfRKempgIQGBh4zra5ubnlXhuNRoxG43lfqyE5llnI2PmxHErL5wFDAt/6mIj1aUa2b0dSM5vS5PoS/pexkG1RQ+inrTu5400J6QwqrUeQHNmdzJJD6DQ6Wvu3Juurr8j68kssQ4cSeP/9NXL9YIuRU/hgFgIzCvGNwfDIm4R419zoicuRZ0wPWqz+g01H1sO6iURaIvE45rxx2WhrjbdJR4fG1b96xYVQFIX/9f4fI34ewbbAbBLbFNMx18aH3fYyPK0Nh9LyGTZ7Ix/d3Y0rmwW4NVZJkiSp5jhUwQLHIPTCFxNLMWicqxvY0HGkmUpcsI6owMo/qKd/9BElBw5gat0afWho9QSlKEQt/R5Hejq6AP9Km5UlCfSWIoK6Ojg86PT9U1lNghJFwW4trp64JEmqNg1qJEFKSgqhFfwBLNt24sSJCzrfa6+9hlar5bbbbjtn2/DwcHx8fFxfr7zyygVdq6E4nlXIiI82cygtj4DQWL5vdYCZAX6s1RWxM38rqYZv2Be9joFNHmfMrrauOeDnSy0qwpZc/QVujmcVcjSjkBQlCHtkX/Y2aglAG782GLVGSg4nUhJ/CDUnp9qvXaaRt4kM4Uxw+arOYYXZxdk1dr3LXVx+AgCt/FpBknMq0ja1DVc2C0Cndf+fxhDPEF7q8xIA/7nJyIGBxTRK/prVrZbQpbEX2YU2Rn26lU//PFwja2BLkiRJ7mdzlP59V+xAaeFCjYJN6DjQzc5HN2rJamKp9Hivfv3wGTYUbTXWJVA0GkytWuHZqxeKrvLnjWVLIBrMKh4t7dDv9BRTo/aMkQQlciSBJNU17r8TrkZFRUUVPr03mUyu/efryy+/ZO7cuUyZMoWWLVues/2xY8fIyclxfT399NPnH3gDkVVgZfS8bZzMyyUoaglW3yUUO4po7deaR7s+yqNdH6WxV2OK1CwMUQtQjMd5cslucvIKwV5yzvPnrlxJXI8YTvznmWqPfXNCBgDHwwajG7uM3T7O0SNlRQsDxo8j/NNP8Rk+vNqvXcZi1nFCE8pPjp54G5xPsuUKBzWnrGhhG4MfFOdQrJjYK6Lo0+LcI4dqS78m/RjbfiwOrcL00CYc1Rsw7/uK73ze4fYOPjhUwYvLD/DwlzvIL7G7O1xJkiSpmjlUgQ/5eGpLP3Bry0YSaPEsTRAX2Su/vw1+bDJhr72GuUPtTJ08U9lIAk+hUowevVZx7TNonTUJrIqCwyaTBJJU1zSoJIHZbKak5OwPm8XFxa795+PPP/9k/PjxDBo0iJdeeum8jrFYLOW+LrepBqoqmPT1Dg6nZ9Oy6SyKTX+hU7Q81eMpvh3yLfd1uI8RSaF8+EtjRp1qiQMr3hGLaFu8Bvt7V8DWOee8hrFZM7DZsKemItTqrfC+qTRJ0Lu58wPi3nTntIMOQc5OVR8Sglef3hhbtKjW655JURQKLM2YZHsEg7fzOp6PvcahQYOwXeAoGKlianExibfcSvITU0k46aw10rrIeeP1l6MlDrT0rkNJAoBHuj5Cp6BO5KklPBLRlqP7/ElZ8DevRifz/ND26LUKy/ekMOz9DcSfzHN3uJIkSVI1sqsqiwwv86BuCXC6JoENHR6lowzKPozXluK4g2R/v5SiPXurbFdgd8bloQoK843oEw6iWq3A6ZEEJYqCKqcbSFKd06CSBKGhoaRUUJilbFtYWNg5z7Fr1y6GDh1KdHQ0ixcvRlfFMCrptA/XJfBnfBoBTT4nxZSFp6ryQcQwRrUdhUZx/jPLXbac4o2buFvbm0hLJKo2i1NBfxNgTca+8T2wW6u8hqFZM5qv/J1mv65A0VTfP10hBJsS0mmlHKN/Y4HNYWN/xn6g9ooWlgn2do560eHl/G9yGrajSdgz5YiC6mA9coTi/fvJX7+e+IIjALTOck5fiXW0opHFSPMgTzdGeDa9Rs/bV79NY6/GlJzKJOeAmfxkM4V5TRjTqylfT+hJiMVEwqkChs3eyE+7ZEJJkqpL9+7dadeuHbNnz3Z3KNJlyrm6gQNr6eoGzukGGk6pFobMMbPgTTuOUxnnPE91PlzJX7eOlP/8h6xFi6psVzbdwFNVyfvdgPahe11TRg0a50iCfwgjvelN1RabJEnVo0ElCTp37szBgwfPKiK4detW1/6qJCQkMHjwYIKDg/nll1/w8vKqqVAblN3Hs3lrZRzGRj9j9YpHLwTv0Yie/aaXaxc8dSpBj04iaNgtvNjbuQTOcZ/jrDP4oytMgwM/VXkdRVEwhIejKEqV7S7U4fQCTuaW8IphHt0Xx7A/9gNKHCX4Gn1pammKWlBA9uLFFGzeXK3XrUiglwENKga7FoDdD11N5KIvMEY1rfFrXw70jRvTZPb72CeNxoGKr9GX4BO7AdgpWnBls4Bq//dVHQLNgXw04CMKw/z4dKDCinHtMfZyFmTtFmBn5TXH6dXMn0Krg0lf7eCZpXsotjncHLUk1X+xsbHs37+fiRMnujsU6TJlV8snCcpGEjxnHYdGBbMV8pTKp2wWbN3GwV69OXLHndUWkz40FM++fTG1a1dlO9d0A1WAWYGgYETpSIKy6QYORaVELoAoSXVOg0oS3HbbbTgcDj7++GPXtpKSEj777DNiYmJcKxskJSXxzz//lDs2NTWVgQMHotFo+O233wgKcu8SaPWFzaHy1JI9aP3WYPDfjCIEL5/KoPvgWfCvp/3GZlEEPvQQxmZRdA7uzNDmQwF4xbcxANZNH9V6/ACbDqXjQTGdlEMA7NQ6P1x1DuqMoihYk5JIefY5kqc8UeOxBHoZWWmYSvekxQAkNPfAo1s3NJ516+l2faX19sb72ms5GOMcVdTarzXKiAXM85vEDrUFVzStvEqzuzX1acr7177P5q4efNYojilrp1BiK4Ql4/H+dRJfBC3g0X7O/5cWbU1i2Psb5fQDSZKkes5RmiQoKUsSaAzotQo2jY5J9w7jkQe15OgqH4mp8fTEkZmJ/eTJaovJZ8hNRHzyMf6j76myXVmSIEUNJWFQON4/rcDU2rmsc9l0AxQ7Vnv1TiGVJOnSNaix9DExMYwYMYKnn36atLQ0WrRowYIFCzhy5Ahz5851tRs9ejTr1q0rVxF88ODBHD58mCeffJINGzawYcMG175GjRpx3XXX1ep7qS/mbUjkYOEazGG/AfBkZjaDo66H0E7nPPbBTg+y/PBykj1z2K030jElFjISIKB5pcfYMzJInz0bW/IJwudUT1JhU0IGPTQH0OEA3wh25h8DoHNwZ2cDRcGzb180XjX/QT3Qy0gGFvwczg932SXZNX7Ny1FZ0cLW/q2xB0fzZsYJCnBwRWT1VX+uCZ2COvHWVW8xec1k1hxbw+O/PsyMtDCCFA2aXV/xWPAu+t42iwd/zSfuZB5D3t/AjCHtuaN79Y/AkSRJkmqezaHiqZSfbqDTaEBRyDb5UOynUOiovPCfsUVzon78AV1wcG2FDDincpZNN/g/61PYbT5sPWPloLIkgYcmH++jq6BF9Y10kCTp0jWoJAHA559/znPPPcfChQvJysqiY8eOLFu2jH79+lV53K5duwB4/fXXz9rXv39/mSSowPGsQt7e9DOmUGcxnbHZudydXwRXl199wJGXR/r77+M9aDDmLp1dH1bCvcO5Pup6lh1exps+TViQnoBj17dor6l8ZQjFaCLrq69BCGxpaegvsdNTVcHmwxlM1OwDQDTtx460HQB0Ce4CgKlNGyI++bjSc1SnQG8j6cIHX0eSc0PSCXJ//x1948aY27evlRgasrxVq9AFBRGf5qw50dq/Nf+k5lFgdeBt0tGqkbebIzy3vk368sGAD3j8t4e5aeZm0tPA8eSThGR+Bmn7ueLXYazt+yz/F38F6w9l8tT3e/jzUDqv3NIBi0kO6ZQkSapPHKpAjx2r4pyGWDbdYIL2Z0JtW3gLKLQXVnq8xmRyPb2vTVbVil04V92x250JAf0ZSYKy6QaKYsfz1A5AJgkkqS5pUNMNwLnc4cyZM0lJSaG4uJht27YxaNCgcm3Wrl171rriQohKv9auXVuL76D+eG7FCrQhC1EUlRs1fkzOyoauY84aCZC/ejWZCz4n5bnnznqaOab9GAB2eNnJ0Ggo3vENVLHmu9bLk6DHH6PxrLfQVsMQ/P0puWQX2uirdSYJjjfpREZxBnqNnvaBtf+hPMjLQLrwwa+0wFCrzckkT3qUnKU/1HosDY1wOEh+fApH7riTU0mlIwmO7STvzzkEkkPXCD+0mvrxtD0mNIbZN3zCgRZGsj3hxZw/SLzrS2h+DdiL8VrzLAt0L/H8tUHoNArLd6dw47t/8neSLIApSZJUn9j/Pd1Aa0Cv1RBRfJKOO7Pov1t1PbGvLYeuHUDCTTdhq6BYeJlyKy6oBgYd2UruU1PJ/e13oPwSiOI8lsGWJKl2NbgkgbtcbhWQf4/bx7aiN1A0Vjr4d+d/I5ajGfgS9H/yrLaGpk2xDB2C7y03n7WvjX8bOgR2QCiCFzw7MN/noXNeO/D++7Fcf321zNPfnJCBP7m0UY4CsMPsAUC7gHan58vVokCvspEEziRBkq8Nc+fO6M9jZQ6pampeHuauXdE0DuWIuQCtoqXZ1s/o+c9LNFZO0b1p3Z5q8G+dgztzw2tf8sYjYazzP8motZPYcPXjcONboPdAyUthTP9ovnuwJ038zBzLLOK2Dzfxxm9xcv6nJElSPWF3qCx3XEmyJgAom26goM134LHdg5s3q+dcAjF//XrSP/mE4riDlxyPWlSELTkZ66EENFUU+C6LyaToWGd4nBG5ayha+TslcXGu9wHgUBQcdrkEoiTVNTJJUE0upwrI6YXpPLXpETS6fLyVSD4Z/B56kzf0ehi8Q85qb+7Uicavv07A+PEVnm9EqxEArPLW8t7RcPKttVeVfVNCOj01zqHnBLdjR24CcHqqAUDirbdxeMhQiv9V7LImuGoSqM6fwYp2JUR8tYiAcWNr/NoNndbXl8j5n5G58CWERiHcoxH6okys6DggIukWWXeLFlamTVA7Pr7jW7oEdyHPlseM7yfykSMf+wPrYcR8MHjQJcKPXx6+koltClAFvL/mEMNmb+RASu45zy9JkiS5l0MV/Nc+lgRdJOBcFlev1VBsNKA0Lya2pVLldAOArK+/4dSbb1G04+9LjkcxGGj2809EfDavyiRB2egGs6IjUpNGcRMTwc88g9fVVwGnRxIAWKuoqSBJknvIJIF0QQpthdyz/AFsmlMImx9zr/wPnlrTJZ1zYNOBGLVGNMZ0rJrj/HGg6gq8QghKDieS8/PPZ00buRA2h8q2xEw2qe04ftVb0HsyO9N2As6VDcC5rnBJfDwl8fFVdobVJci7/EgCVajkWWWF+up0OOcwAM10FgD2q5GoGgOdw33dGNXFCzAHMHfgXMYbr+F/C2xYXpjDfZuf54RXgKuNZddcph6ZwPq2P9LMXMiBlFyGvr+B2WsOYXPIUQWSJEl1lU0tvc9RnPP7jVojeq2GHIs33j1zWHSN9pzTDTyvjMFn2DAMERGXHI+i1WJs2RLPnj2rLIhbNpLAo7SWQnYjXwLuuRtzhw6Ac5WGMnY5kkCS6hyZJJDOm0218fjaKRwvPIhq9+DWRv+h7bL74cNekH6owmNyV67EnpFR5Xk99Z70a+IsLNnYZy2m9S9CXmrlB9jtJA4fzompT2I7evSi38+uY9kUWB0onoGE9RtHTpvBHMp2vo9OwaWrMygKzX76kfCP56APOXuURHXzNOpI0Yax3NELT8VZZC6rWM4jr06Hs0uTBHbnaI3dajPahVkwG7TuDOuS6LV6JrS+F4tdT0CBwsHjO7nt59v4KeEnZyItMxEQRCR+wyr9ZN4O+Q29o4iZv8Vxwzt/sikh3d1vQZIkSaqAQ1UxUwyKDThduNAqdHiUJhCKHcU41MpHYfqPHk3Ya6/i2atXrcQMZyQJhPOjhk1TfgqnVqNFI5xJBpsqkwSSVNfIJIF0XoQQPL/peTae2IBQ9Rgz7uc/vgch9ziU5IFPk7OOsaWlkTzpUeL7X3XORMHgpoMB0Fn2MDDzS+wHfqm0raLXY+7aFXOXLjjyq56HV5UNh5wfjHo2D0CjUVyrGkRaIgk0BzqvpSgYmjbFq18/FF3tLAaS5d2CybaH8TAG4ZcnKBj7CIm33For127Iku67n8Tb76Bw924AmhU4ky/7RFM6NfF1Y2TVw6NLF6I+m0/brxYTFdGJPGsez2x4hof+eIiUq6bCvcshtBMaaz7Dsxfwt2Uq/2deRVJaJnd9spVJX+3gZK68UZMkSapL7HYHB0zjiLTHA6WFCzUabOjwEKdHgp1rykF1Kdq7j+wl31O0b1+V7criMZd+1FBVHdakJKxnPNzR4UzO2x3WGopWkqSLJZME0nn5cNeH/JjwIwgNRcl3MfXKGIybZjl3XvU06M+ecuBIT8cUHY05OhpdQMBZ+8/Ut0lfPHQepOtht9FAzu7lVbaP+GweTb/6EnP0xa9AsOlQBgM1sdyn/AwZCcSmxgLQPaT7RZ+zOgR6ObPtJo0FuxY0+w9RvH8/wm53a1z1mRCCol27KN69myMlJwBoduoIAHvVpnRo4uPG6KqPR9euRIS1Zf7183m066O0T9ayJWkDw38czoKCBGzjV8Jt88C/GSZrBk+KeSxs8gMaBX7adYKr31jLm7/HkVtsc/dbkSRJkgCHw/n32Fo6tN+oNaLTKoTHnyRxcSj3/eZMFJzPCgdqyaWvIpD326+kPPPMOVddKovHo3S0gCWtkISBg0ieerrAtaI47x1XR0y45LgkSapeMkkgndOyw8v4cNeHABSnDqexoRu3W3+AokwIbAWdRlZ4nKldO6K++5aIz+ad8xpmnZmrwq8C4DdPDzxTtoCj8g/FVc2DOx+FVjs7jmUxUruaLnFvwT/L+OvkXwBc0egKV7v8DRvJXryYksTES7rehQj0MqLFgUkYyDfB8WfuJnLRF3CJ7/ly1/TLRfjOfIl/PHMAiCrIwia0xIsmDWIkwZn0Gj13Zrdh+pcOXvrJE1tRAW/89Qa3Lbudzf5hMHEb3Pgm+ITTY8ST/PRwH7pE+OJjTePr1bH0fW0NH61LoKBEJqYkSZLcSbWXTxIYNM7pBgesEQibggHnKMcCe+UjKx3Z2Rzs1Zu4rt0QtktLAusjIvDs0wdT+6of0pRNNzCXDnawGs1oPDzQGE7XItCUTkHIwXDW8ZIkuZdMEkhV2pG2g/9u/K/zRfZV2LJ78J9+fmi3fuDcdu1/QVv1MHyN2Xxe1xoQOQCANR6eGB35cGLHOY8RQlxU8cJtiZloHCX01B4AIDfiSv7JdK5ecGaSIHvxYlKefY78desu+BoXK9DLyGbjI7RN24TQKBzrEopHt24o2vo7Z97dFEXB2LIlKT2aYtcphHmGkTpuL7dZp6PVm2gRXPNFKWudRkHR6+kU1Ztn+8zA3+TP4ZzDTFg5gYlrJxPXvC88ugsatSe6sQ/fP9SL79qsYbNpEi/YZ/H7rz/R65U/ePP3ONLz5RrWkiRJ7iBKh+KXlCUJSqcb/NSsD/cNmMbKa5wr8xTZKl8hQGOxoObng8OB7WTVxaHPxW/ECCI+/QTfm4dX2a4sSWDUmklUG3G8SXNa/72dyC8WutroSmsvldhlHyNJdU2NTLLOycnh119/5cSJE4SFhTF48GB8fBrGcN7LybG8Yzy6+lFsqo0mhu4cSBlIu1ALg07OBVshNOkObW6q8NiiPXsxtWmNotef9/V6hfVCr9FzXA9H9Dr89v2Ob3jlQ/+PPzqZwi1biFgwH1ObNhf03jYeSucKTRwmrOAVwg6KUIVKpCWSRp6NXO3M0e1R8/Mv+PyXIsjLQKbwxld1rmqQXZxda9du6BJynEtcRvlGsSMNdokWdG9sQatpeKM0vHr3punXX2GMiuIWg4EBzQbywc4P+Pqfr1l/fD3rj6/n+qjrmdh5IpGWSBQhaKLJBBwM025imHYTu9UoFqwdxNXrezHsimaM6x1Fs6AGmFCR6ix5PyFd7hy2s6cbaDQKJSYPkg1mfP29IS+9ypoEikZD1JLF6IKD0VgstRJ32ciGoqBeXL3vUTp5+p7VxlA6FaFlxgrgllqJS5Kk81PtIwk2bdpEs2bNePfdd9m2bRvvvvsuzZo1Y9OmTdV9qTqle/futGvXjtmzZ7s7lGpRZC/i0TWPklWSRQufNiTsHwZomDawOUpW6dD7gS9VOATekZ3N0bvv5tA112I7mXbe1/TUe7rqAawzmyk5uLrK9o7sbBw5ORSfo3hORTYeyqCfxlnAjubX8NfJ7UD5UQQAAffdR8Snn+B55ZUXfI2LFehtJFN441e6NJ2ISyD399+xJSfXWgwNTf7GjeT8vIyU+F0ANPNpxp5k57SDjg1sqsGZTK1bo5QO7bQYLIxbr2OxdTyDIwcBsCJxBcN+GMaMTTM4lp8Mo3+EB9ZD57sRWiMdNYm8afiINdqHMcZ+xDVvruPez7ax7uCpS1p+VJLOR124n2hofbtU/4h/1SQwaJ1/03vo4vlK/yJeBc4izGVP7itjbNkSrY/PJU3XvJDRm2U1CXSKczSpUXf2R46y9Q4i87ZcdEySJNWMah9J8Oijj/Lhhx9y++23u7Z99913TJo0ib/++qu6L1dnxMbGYqml7GxteHXbq8RnxRNgCiDc+n/ssBUSE+VPvzah0OZnSN4OTa6o8NiSw4fReHmhDQhAFxx0Qdft16Qfm05sYp2HmVtOJYDDBtqKRyMEP/4YaHUYW7W8oGtkFljZn5LLm4Y9zg0triX26GIArgip+D3VpkAvI5lY8C1dzqj94p0kH1hN6Cuv4HtzYzdHVz9lffUV+av+QHdbc2gJzePXoTu2nx8ZRMcGUrTwXAq2biPj07kA/O/7JYzveB/v/v0ufyb/yZL4JSw9tJQBEQMYGz2W6OGzUa57Af5egIidS2DucaKDdCgnYW3cKdbFnaRZoBf39mnGrV0b42GonZU/pMtLXbifaGh9u1T/OBx2BFCiKZ8k6JIaT+uCIzTxDmJ/o/MrXHipbElJJN5yK4bmzYn69psq25YlLbSlqQCjFlKeew5HXj6hL76I1ssTvcYAKjiErH8jSXVNtY8kOHToELfeWn65tltuuYWEhITqvpRUQ35O+Jnv479HQWFi++ks2+HseKZd38aZgVaUShME4Kyw3nLNapq89+4FZ6z7N+kPwF8mM1crb6EqlX/4MHfqhDm6fbkiOOdjc0IGQWTRVpMEKOSFd+dAprM2wZkjCYTN5panpYFeRjLOGEmQHKLD3KULWm85zPtimVq3wXxFN/b45ALQ7Og2hhT+gBV9gx5JcCaPHt1p9PRTBEyYgKldO9r4t+GDAR+wYPACejfujSpUfj/6OyOXj2Tcb+NYn7UPR+9HUR7dBbcv5Ob7nmXNlKu4t1dThhl38GHuROJ+nsU1Ly/jpeX7OZZZO8tvSZcPeT8hSWBFz0+O01Mvy5IE7RPiObndl2bHnPcKVRUuBCg+eJD0OR+T8+OPFx2L7cQJ1IIC1Ly8c7YtSxK0PvIDvxieppWaSM6PP5H366+oOdkA6DTOh0B2ZJJAkuqaak8SdOjQgY8++qjctjlz5tD+HFVQpbrhcPZh/rflfwA82OlBvt9kRhVwc3sfusbNgqLs8zqPYjBgCA+/4Os38W5Cc5/moAhyNAc4kJp7wec4lw2H0mmtOY5NMUJYZ3bkH0UVKhHeEYR4hrja5fz4I3HdriDl+eerPYaqBHkbyRQWfFRnx790oDdNv/oS7wEDajWOhiTokYcJ+mwOG4KzAGhms3NENEJj8ibS38PN0dUORVHwHzPGOQKnlD0zk6D/zeOdyCksHrKYIc2GoFN0xKbGMvGPidzw/Q18vG8ep5r2BK8gmgZ6MmNoe95osYtWmmRe1H/GSvEgjTdPZ+wbX/DAwr/YEJ+OqsqpCNKlk/cTkgQFOj8edTzkem3UOp/Mn2jUGO8mRZT4OP/enmskQfH+/ZyaNYvsH3646FjM3brR7JflhL3++jnblsUTUHyKdpqjmLQqQY89RqNnnkHj5XzooS9d3cCO46JjkiSpZlT7GNGPPvqIIUOG8OabbxIeHs6xY8cA+Omnn6r7UlIZIeCveWArAt9waNoXPPwv+DSFtkKmrJtCibWQK0NjiNQM5fXDuzDqNLzg/T1snAtJW2D87xUerxYXU7RjB549e17S2+kX3o+EnAR0nnFsPJRO+7DKh4Pnb9xI0Y6d+AwdgiEi4rzOvykhnaNqBzbc+hdXhznYfOhbAFc9hDIliYmIwkKUc6zeUN0CvQxkYMHP4ew0s4qzavX6DVVijrOWRoDWjI+qskFE0LGJD5oGWLTwfJ16913yV/2B/WQarb79hpf7vsykrpNYuH8hSw8t5UTBCd7b8R4f7vyQq8Kv4tZWt3Jl6JXoRsyDnV8hYj/BO+MQ9+p+515+Z8PB9iw8MJBnffsxMiaS27o1IcDLeO5AJKkC8n5CksCuChTl9JN2g8Y5kiC2cx/uK1xCbiNnovtcSQJT69b4DBt2zqULq6IxGDA2a3ZebctGEniW1lRQ9CYCRt9Wro2+NOHhUNSLjkmSpJrx/+zdd3gV1dbA4d/M6SUnvRdCgNCLQChSFBEUEBVFsAtexYJiv5ZPxXbVay8X7GJvYEEFFCwUAQEFBOktEBJISC+nn5nvj0kCkQAJJDkJ7Pd5eDRz9sysg8jMrFl77QZ/+unUqRObN29mxYoV1d2I+/bti6EeXe6FepIkWPwslO0DQJV0FCcP5Y/oMWyz9yYx3MbgdtGE2w4vy/fn51PwzruEX3YpT2W9xfbi7fQ9EMqdr61nQeKTyO3O5emuuYSs1eYyc8a/jxhG4YwZHHj5FcLGjyf+0UeO++sMTBjIjL9nEGb/i/MXj4Tey4+Y9Ch48y2cK1ZgiI+rU5Jgb5GT3QVOdLJE77bxYDawPGc5oK2ucKiY228n7OKxSMam/bNrN+nZJaWw3NcL2EeZtwy/4kcvi3nfx0NVFCRZZmfJTgDSKtdj3qKk0CXx1OhHcCSR111HoKCQiIkTqqcGxRoiuKP1RG497Vbm757PzC0zWXtgLT/t+Ymf9vxEhDmCc1PPZVSbUXTNuB4yF8HKt1G3zmOgbgMhspcLCjN4at5mnp+/lXO7xHFpn2T6tY48pRMyQv2J+wlBAL8/AJL2oG2QDdV/V6t6MwBWJQDojrq6AYC5Y0cS/vt0o8Z6qHJfOQAhlcsbSobDl8M26LTvICoJBKH5qddTx+LFi+nYsSPR0UdvRmcwGEhMTGTnzp0MHDjwhAIUauH3wrrP4bQrD64u0P0y/IW7Kc1cQ4RzJ+F75jNsz3zaKrG8ERjNXepQLumdzJ3D0okO0TK3qqqy57rr8WzezEZ7Cd/Yv0WWZCbHXQKlbxBp2kvPjAouzNSmH9BnErQ9Ssm7JIMsY+19Ys3/Tos5DbPOQgUuCuQCojOXous0utaxIWcNwRAXhyGpblMblm0vQEahe1IEIWYDuRW57CjZgYRE3/i+Nb+OwYAprfUJfZfjIUkSmfYePFjclhD+j067A+waNx5bm3ZNeoE/WRR99hn5r/4P/ekp0A3SvG4AtqhJjIw/tRuSGZOSSHr1lRrbir/5htwnnyLqhkmcf9NNnN/mfLYVbWPW1lnM2zWPQnchn2z+hE82f0JKSArnpJ7DWcMeoPO5T8GfM2gf052n3V35ZOUeMvdmM2jDdJ5Zdza5IZ05v0ciF56WSMdT/Pf9VDdy5EhGjBjBrbfeesyxBoNB3EcIp7SokvUsNN/GeSRUTzUAUPXav2tv6o3HXN2gIRR/+RWgYhs0CENMzFHHViUJwgLaNVcyWAiUlhIoKUEXGorO4cBYlSSQgIAfmrhyUxCEI6tXT4IhQ4awYMGCOo1dtmwZEydOPK6ghKPwueDji+HbW2D5/6o3r+9wO8P3XE3Pwic42/MMH3MuTslKazmXHiGl+BWVT1fu4ZKnv2XBH1qTPkmSiJw4Aalze96o+AGAm7vfTP5pl/B/p1/P7E4D+dD0FJKrEDWmOyW+01EDR872Rt14A62/+RrHeaNO6CsadAb6J2hTFn6zWije8NMRx0Zccw0J/30aW98+dTr2b9vzuU3/JW+WTYZ1X7B8n1ZF0CWqC6Gm5vNWOSrEBOgw62wY/OD/eyPurVuDHVaL5Nu9m0BREWVubcnDVqUFAGxRk+kkHlYPU7FsOarbjWw72CizbWgb7ut5Nz+P+5lpQ6cxsvVILHoLe8r28Nb6t7hszmWc/dO1PGHXszoylot7xfPtLQP54Yw9XKJfzDemh3nDfTe+pdOY+PI3DH9xES/M38Lf2SViKcVT0A8//MAdd9zBunXrgh2KIDR7asB32PKHANd8+gpbZ8cSXqQDOGYlQRXF7UbxeI4rlvzXXmPf/z2Ib8+eY44t92pJgtDK6Qay0ULOffezY9hwSudp95wGg3ad+dJynvaiSRCEZqNeKbvabuZKSkooKysjKSmpwYISjiDgg8+vhF2LwRgC0R0AWLLtAJM++BOXL0Csw8QNw0dyQY/rMSouWD+LS9sMoU2xncUPP8vZq7+nVd4BtvzaieS+Y5AzBnNPwE9mmZf+8f3pH3kJl725kojYML63P42lLBfCUiiUxpJ37wOUzJlHyptvVofk/PNPDMnJ1Rllc3p6g3zVgYkD+DXrF36zWLgk87cGOaaiqCzdns8N8mqiXLtB8bM8508A+sX3qzHWl5tL0cefYGqfTuioE0t6HI9ouxE9fqySjR3x5XifvIvUdn2PvaNwmOgpUwi98EKe+/0eAFIUGbdqYL8untZRtiBH1/wkvvgCzssuw9y5U/W2imXLyfn3vwm/4nIGT57M4KTBOH1Ofs36lZ/3/Mxv2b+R58zj8y2f8/mWz7Eb7AxKHMSgmFYYu44lcuN3dGMX3eRdPGz4kD+K0pmzqC+X/nIGjtAIhneOY3jnWPqkRqDXiRvFU4GiKEyZMoWFCxfWeZ+SkhKsVquYbiCcWo6QJLCXlxLw6didPhG8H9ZpCcSsybdQ/vPPJDz/3HHd29jPOAPv7t0Yko8+vdMX8OGurCAIqWzCLBut6ELsSFYrql9LHJgqqyGKMIMs/u4XhObkhOp6tm3bxujRo8nLy+N///sfl19+eUPF1eJkZGSg0+mYPHkykydPbpyT/PwYbP8JDFa4Yia06s/8Dfu55ZM1eAMKg9OjeeXSHoRZtYuI4jUg9bwaSZLICIOkrpEUr/TjzDLRPvFP1F/+5K6/o8i0WYmVzVxR3pbL31pJhTdARus0HE4D6NvBFTOR569EtttxnHNOdTgl388h5+67sfbrR8o7byPpdA32VQclDgJgncmIzrUDyvPAfuTStkBxMegN6OxHfujbkFOKoWIfnc27UZFQ257N73PeAKiuXKji3riRgjffxNShQ3CSBDY9W0zXcFV5DOutJgp6p2FJ6drkcZwMZJsNY4f2/PXnfgjAnkGfcfuX22mXGCoeSGshSdJhlTllP/5IoLCQQOHBJppWg5VzY85gVNooPAEPK/at4Jc9v7AwayEF7gLmZc5jXuXY9K6nc7rOQf8DWZyWtZbe8lZ6yDuZIw8hp8TNe8sy+WrZ30jmMAa2i+aM9GgGp0cTF2puui8uNKlevXqxZMkSpk2bdsxrps/nY8yYMcybNw+LxcI111zD888/j9ks/nwIJz814MVTmSQ4dLrBxxMeYc22/QwIM0DesRsXAtX3SP68A8cVS9xDD9ZpXJnv4BKJHl0ceQEXBpOZ+KefJuG/B3vTVCUJAqrvuOIRBKHxHHeSYMGCBVx66aXY7Xbat2/PVVddxezZs3n99dcJDw9vyBhbhFWrVuFwNGLp8pZ5sKxy7vCYN6BVf2avzeauz9dg8Ti5MM3B41f3wqTXHtRz/u//KPtxPinvv4elspNt1NVXYe/Viz1RDr6f+yG7jEtYYPOiV1We35tJV/eTeD3v0bd1LK9c0xup4AOISANLOOGXtiZk+DB0YWHVIRkSEpAMBozJSageD5K14ZaSi7fHk2BNJceZyXKLhXN2LUHqenGtY7PvvIvSuXOJ/89/CLv4oiMec/G2A5ylWwuAlJTBFm8hhe5CLHoLPaJ71Birj4kh7LJLjznnrrFEhlgpxUZYZQa+xFMSlDhOFnnOPDwBD3pJT26hjSIcDIsPCXZYLUbcww9hP2sIxlap1du8e/awY+QobP37k/zG6wxOGszgpMEoqsK6A+tYtHcRy3KWsalgE1tLdrAVeE8HprQ0eppjydA7eKVPK4qLYvllcyHX/D0Ru1LGgk29+PLv3tyvptMuLozB6VrSoFercMyGhktECsE1ZcoUXnvtNe6++2769OlDRkbGEcf+8ssvzJ07l/vuu4+oqCgerVyWdtq0aU0VriAET8CLt/K52iAfrKIpj01i+wE9A/RaL4K6TDeIvvMuYu+/v8a9XGMo82pJApvBxn/avMbstTk8ZDBWN12sYq5MeqQHNkJFAdgiGzUuQRDq7riSBC+//DJ33303ffv25auvviIqKoonnniCJ554gqVLl/LOO+80dJwnNU/AQ2ZJJu6AG7POTKI9EatfJlBerj2kFu2Gr2+kJNOCS98ThyeFL5bu4tHvN9KuMIuXF72CPi4O0y3nVh9TKStHKS+n/Jdfq5MEhthYDLGxdAbW6Lfy/aqfAUjJ7cmaCheF+mLuPSOWK4f10ZINib1qxKmPqLnCgKVLZ9otX4bObqcxnNVqMB9tyuQ3i5n+m34h9AhJAn3lg7wve+9Rj7doywEmyau1H9LPYVnOMkBb+tCgq1m+auncufr3LRii7EYK1RDCAl4A/GvWUbrThn3QQOQGTMac7Hx5eZR8+SV7I7VkS1JIEpv3azdSonle3UkGAyFDhtTYVr54Cfj9ULl6RPX2H36kQ1oa3U+bwm09b6PIXcSKfStYlrOMZTnLyHXmstyZxXKAnyZilI10i+zErxHF9HKWc7nnB67Tz6NItfNLQQ8W/NabSYu74tPZ6JoUSu/UcDJaRdCrVXitK7YILYNOp2PmzJlkZGQwatQofvrpJ7p161br2MzMTEwmE08++SSgNTP88MMPmzJcQQgaNeCvnm5waCWBUS/zuuFFlK1evrJTp8aFhtjjf/GhBgJ1rhit6kdgN9jxuLXrr0l/eOWepbKSIJVdUHFAJAkEoRmpd5Lg8ccfZ+vWrUyYMIHXX3+9em7gww8/zIgRI7jqqqsYOXIkXbuK0uhjWbFvBR9u/JDMtUvItwWosGgXgdb7VP77XoCyMCO/TbuG9qWFtFecGAsTqdi6i9nWuTxl7Q7A0H7psEi7iBwq8oZJRF5/3WHr4aqqylvr3+LVNa8CcFP3mxh/8b9w+xXiHGaG1GOJMsloRGdsvJv0wUkD+WjTByyy2LjIG07PI4yLvP46om6+Cd1RKjlK3T427tnPQMPf2ob0c1m89jng8KUPm4NIu4kCHIQpuQCkP/M12eWfkfbdt5jatQtydC2HZ/NmDrz8CrpWMXA5tCrN49Ld97FNuoyO/+hDIdRPxJVXYB88CMV58O2V4naT88D/obpcpM35HlObNoSbwxmeeBbnpGpTlXaV7GL5vuWszl3Nn7l/UuAu4I8Da/kj1AahNnRAW1+Ari4nXT1ruNOzggv8fbjJNZk/dxfx5+4i3kBbzrJdjJ3eqRH0bhVOj5QwWkfaxDKLLUhiYiLz58/n7LPPZtCgQbz66qtcffXVh42bP38+yckHV7C59dZb67QygiCcFALew5IEgfJyuq7+hR55WyiwuMEe1+irGxS88y4Fb75J+OWXE3PnHUcdW+otBSDEGIKnXGt4bdLLOFevoeTrrzC2TiPy2omYDdqUIa8kQeD4mikKgtA46p0k2L59O88//zy33377YZ9lZGSwZs0a7r77bl577bXDyoqagsfj4eGHH+bDDz+kqKiIbt268cQTTzBs2LBj7pudnc0dd9zB/PnzURSFIUOG8OKLL5KWltagMZZ5y3h0+aP8mPkjk78LMPlvlU9HWFk1MIYKXwVl1kIAdC4vM9a/qy1zmBjHwA4BksJCWGlfiTmikAs6ncb4/qeTeNlqDOaa68/W9hZ8Y8FGnv/jeVbuXwnAhM4TuKn7TUH571QXvWJ7oZfMFOvdvCL15r0jjNNHHjvzvGx7ARlswCz5wJFESVgSa/PWAnBG0hk1xqqKgv9APvqY6KD93kTZTRSqDsIC+wDITw2njSE+KLG0ZLrwcELHjGFbYAdQSEpJHgOVzbjVa+gYJyoJTpQxpWbzqkBxMbb+/fFs3YrxkL838557jtK584iecitp48aRFpbGFR2vQFVVdpfu5s/cP6t/5VTksMWgY4shhFmV+1vk/fQP+xyH3AZTAYzb+wWbnZ1Zmt+N2Xkd+XSldqMZYtbTLSmU7klhdEsKo0dymOhr0Ax9+OGH1S8SunTpwooVK7jsssuYMGECL7zwAldeeSU9evRAkiS++uorvvnmG+6+++4gRy0IwVEsh5OjpgIV1VWP/rw8Bnz7NsVGG7a2WqK2Lj0J1ECAwhkz8O7eTez999erMtGXk41SXg516OVTtfxhCDIP5dzMxYZIFMMMfHv2UjxzFtZ+/Yi8diIWvfaiySNJ4BdJAkFoTuqVJJgxYwYdOnSgb98jd1m3WCxMmzaNyy+/nO3bt59wgPU1YcIEZs2axe233067du147733GDlyJL/++utR11ouLy9nyJAhlJSU8MADD2AwGHjxxRc544wzWLt2LZF1eBA9oj2/w57lbC008OPiPXzQ+Q/c8gFkSYetczfYvI6bEi5jRI+JLNySx/L8LMZfsgmX9QBycS460z5k0z5+6+yFzi5gBwZ2MPfAj8z9VjtFqCmUcFM4EeYIws3h2Aw2LHoLellPkbuIzYWb2VmivX0z68zck3EP49qPO/7v1ASMOiOdwnuyrnAZ6wp+R1UvOO6H9sXbDpCnhrEmchSnde7E0pxlBNQAbcPakhRSc2UOX84+dpx9NrroKNotWlSjlLqpRNmN7FJDqnsSzL65K6+c9cox9hL+ydK1K5anujL/51tgL6T6vJSoVvShCYRaRYf0hmaIiyN5+jStLPWQ/1fdf28gkJ+PbDl4Q+rNyiJr0g1Ye/fm4scf4+J0bTrR/or9/J3/N+vz17M+fz0b8jfg9Dv5u3ANsAaAXxNlTMoG2vnWMs7jJyoQgsuZSLYrnUXb+7J0+8EEUEyIqTJhEEq3pDC6J4WJ//ZBdsUVV9T4uVWrVixdupQPP/yQadOmce+99wIHV1Q655xzmDp1apPHKQjNwUZzD35UzsXCl9WVBJLRSHbnPrQqW4698j6h3FeOqqpHvU+SdDoK3nqbQEkJ4VdcgblDhzrHEXv//URcdVWdEgtVPQlCZANpvm34JBeZehlzx45E3zYFY6tWAFgPrSQQSQJBaFbqlSS45ppr6jx2wIABDBgwoN4BnYiVK1fy2Wef8eyzz1a/dbj66qvp0qUL//73v1m2bNkR950+fTrbtm1j5cqV1Q2URowYQZcuXXj++eer50LW29Yf4ZNxqCr4vovlbKeOFVaZNSkRVOy9gqfKo3GMHktJqQXXW79X75Yq2XjB/TFvWu7Ea2lDmEWPzV5OalwpGPezo3gH24u3s7NkJ56AhxJPCSWeEjJLM48Yil7SMzx1OLf0uIVkR/IRxzUn56adwbrCZXj0G8jZsZ7EtrXPWS39cT5lP/+E49xzCTnrrBqfqarKoi0HyFZbUzxsHHSIYdFi7SZ0cNLgw47l25sFsow+IjIoCQLQKgkKcNA+oJXpFXuKgxLHyWJ36W4AUnw+Nqut6Zggqgga0z/nraa8+w7ujZswtk6t3ub6ax3eXbuQQ2o2kPTf/xRt8/I4/Z67sZ5zBwElwM78rfyd/zd/l2xmS8EGthZtw4WXv00m/jaZAAXCs4As7PLvtCIFtyuS6JJy+vgOENgZy44tsSwngv1qBKbwRNqlJFQnDzonhIqmiE3sueee47zzzqND5UOKJElcffXVXH311RQUFLBp0yYqKipITU2lffv2QYuzSVYuEoSj8AcUJEm7FzDK2pt3Y1ISf1x3P62XX4i9MpmmouL0O7EZjr60b/gVlwPSUado1kY2mTC1aVOnsVVJAntlvG6MmPQyprZtMbVtWz3OatCSHh5JIuB1If4WFoTmo15Jgn9e1JubWbNmodPpmDRpUvU2s9nMv/71Lx544AGysrJqzGv8574ZGRk1Oix36NCBoUOH8sUXXxxXkkBVFKS2w9gVMZAvnCUYOxbSebdKJ4+P4epZfOjoyE5vBfsVFXwBImxGhrSPYXCajRG//wdj/hZej5oFV8464jkCSoASbwlF7iIK3YUUuYsochdR4a/A7XfjDXgJN4eTHJJMr9hehJpC6/09gmlIymCe+eMpDNZd6L8aB/ds0qZf/INrzRpKv/0OXYjjsCTBzvwKsotdGPUyfdMi8Ct+fsv+DTh8qgGArV8/2q9ZTaCgoHG+VB2EWgxsUNPw+TsBB0SS4DgpLheKycDeMq2pZarPzwIlSTQtbGKy2Yy152k1ttkHDyL5jder3xZXca5ZTeBAPpJeuzzpZB1x67Lx3zqV3meeSfJrn6GoClllWeRMf5XC/EwWd/bwpz6XXMWFrrSc+IINFNskdsdIaOmhbAyKQlwgQGt/gAS/n+h9Ku9kjiHXl4Ss2rnIlsNw0x6iwhOIiU0gOjoGvSUUzA4whUBkW6hssoWiACpQ+XeRqoAaACWg/bvBAnLl7a7PDYoPZD3IBm17M53i1ZSeeeYZ7r33Xlq1asWoUaMYNWoUQ4YMwWQyERkZedTKv6bU6CsXCcIx+AIqyNoSgUbdwT5QRr2MFwMmVUUvyfhVhXJv+TGTBNFTpjRqvHBIJYGkVW1pSYLDUwCWQyoJ/D63SBIIQjNSryTB0S7qzcGaNWtIT08/7ILep4+25vfatWtrTRIoisK6deu49tprD/usT58+zJ8/n7KyMkJCjrxk2tdXDuD30ZHEmdsx4exHsP70C6UffczKGx7k4YremOK/xhglM9QSzZPbVmPIfomrh9jxnH4Huwtd6GSJ1EgbOjUAX1wN+RvAGgXnH73EXCfriDBHEGGOoA11y/C2JEkhSYTq4ikJ7GMdxQwryoSI1oeNCxl6FrIjBFu//od9tmjLAc6U15KSmILVoOOP3D8p9ZYSagqlW3TtlQmyyYSckNDQX6fOZFlitXUA8z1J2HiJrr/tY9cn43Gcey6R104MWlwtier3s6VPXwixYbvSh9smExMIsEVNpr/oRxB0OocD+xmHJ+lS3nkHz7ZtmNLTq7d5s/aCqiJVXmtkSaaVoxXe+asJy9nHWdd8gaVrVyp8FWTO/BD51Zcp6JzIlxNj2VWSyW5/Gf+ZoRBVKvHMWBMrkrUb07bZC5j0h8LuaIlZg2TmAiavwvDvFRwu2NxOweVQMasqenM3kopk/CEGcm37UYuzUIDwQpD9EgVhUGGWCABySCq2cnDrFAqNZejdxehUSMlVCXFCXjS4bBI6wB9IpeMWPy6Djk0dVWK8OaBKdP9LISofNnbUsS9eh4pESVkSFy08QLnFwNfnRJOo5KAi0Wu1m/j9fv7ubGJ3qhEJKPMkcM6SA8wekkapzk9zk5uby4oVK5g3bx5z5sxh+vTpWCwWhgwZwqhRoxg5ciQp/+h5IQinoqHFX9DbMJPXCKmxuoFJL+NBjwTYdGZK/M5Ga17oy8uj6NNPMbVpS+h5o445vrongaQ9ZnhUA3aDjKooBEpKUCoqMCYlYTs0SeBx0TyeJgRBgHomCY52UT/vvPMYMWJEUC/q+/btIz7+8OZuVdtycnJq3a+wsBCPx3PMfY9W8thmm4fnjAWgFLBi9gge/NpE+O5y9n3wOOYx2nnP7zSWh/o+iG7hk7DkedjxC6YBt5EeW5l8KD8AX98AO34GnQnGfQCO4D2oNhc9Y07n131f8pvVwrBdi2tNElgzMrAeYZ3tXzbl8qR+Bim5B2BLJIvKNwEwKHEQevm4VgFtElF2EwcOaHP/jKUu3OvWYQ5i2W1L49ufCz4falk5pRZo61eQga1KEhPiGmfZTuHEmdPTMR+SIACImHANoaPPQ62cflMlfNw4/PkF6GNiAW1N7uSoNA60aUNaxx68eN4TAPgVP9tnDEV153F9z5vYFaeQU7KL0Jyd9N2yhTCvgW+Q8BPAI8sM/lOh1QFYF69nY7Q25ajnts1cOkthawI8eI0eLNrN7RM/+UnPgf+OldnUThvbZete7v1UYVcs3HutHipXAZqwyE+nLHjhQpm/O2pj2+/dy7gfAuwLhzld9OysXOXmzCyV7jtUlraC321atUVq2U567ApQaIdXzEVkVf4+nH4gQPttKktaB/jd4gYg3llC378DfHJ6PvusSoP992kokiTRr18/+vXrx6OPPkpubi5z5sxh3rx53HfffUyePJlOnToxatQozjvvPE4//XTkIE3/EoRg0gc8oNP+H66qJCj94QdOf+xJpNAAgb467LKJEpzVD+fHorjd+PPzMSYlHXsw4NmylYLXXsfUrm5JgupKgspqKxcmIvUygZIStvXXVpTqsH4dlsrpBtuJwZl8JkevgRAEoSnV6wnpaBf1e++9l5tvvjmoF3WXy1VrVYPZbK7+/Ej7Ace1b5VNA2M5z2vhN91esiw6ZmaUEdXGwFc9swGJqztdzd2979Yaygx9GKLSIf1cqOzsypLnYfFz4HOC3gKXzIDUpu3p0FxdkH6WliSwmCnf8gv2XnXvjVHq9lGW+ScphgMoegtS6zNY8L1WnXFm8pmHjVf9frLvvgdTmzZEXn8dsjl4ndEj7Ub0+7Q/H8s6wI0XP0NY205Bi6elMSYlkr5yBd8sfxd1/zskKUbKVAuZchKtIsWtSEsiSRL6qKjDtkfdeONh2xzDh+MYPrzGNr2sp+3MWQTKymmfEF/9/7W3dSblrZaRGBXFBcOG4fK7KHQXUprzBhV7shnWbTAJRhN7ikrQ+zaRGb6SfcZQ/Nn9MBBAUqFUXkqBtRhv0ekE9iShqjrceQdw6n/A7w3HtWs8esmH3ahSZF1ITlQ+MVJvegcSMesVbOEKW7ssxxVm57ywXtiUIgyygpSxm62ty+nWJorU0BAkAgTCYll51X58eomLo0Ixl+8BRUHtVcxfqR5at7Jzqd6CioorPoVlo/fTNbk9rQNuNvF44/zHaSCxsbFce+21XHvttfj9fpYsWcLcuXP57rvveOaZZwgLC2P48OHccccdR22eLAgnG0nx4am8W69KEgSKijAXHmCpqQtvtJuB3fYUeIso9x47SeDevJldYy5CFxlJ+m9L6hSDPiqSsPHj0UdG1Gl8dU+Cytlk5Zgx6XXo7NqLD8liQXE6qysjvJKEW3/kal1BEJreCb1GbW4XdYvFgsdzeHdUt9td/fmR9gOOa98q45+fg8PhYP73T/Nk7gf81E57cyRj4OF+DzA2fWzNjrPdL615gLzNWoIg4TQY/QrE114GfyoamNwXnaonTw+7spfSVVVrndOrer24N29GMpmq37gv2nKA4ZLWEFJOH86Gskyyy7Ox6C0MShx02DE8O3ZQ9sMPVNjtRE2+uXG/2DG0MZXxofla+ilJ5ETKuPt3w+RoFdSYWhqdw8Hm0ArYD3LyRXTd1p0OcQ4MdVjCSTi56KOj0UdH19hmTE0lIjW1+merwYrVYIX7tQfq3v84RkBROVDmIaPYSW6pB5c3gPvs69juVzhPr+MivYxRL+Mw65HvvZ+uViOrLAZCzHr0Ohm4HYCh/wyu8q+aGleFC47wRY60vTaXaf8oLS3lzWaUJBg5ciQjRozg1ltvrfVzvV7PkCFDGDJkCM8++yyZmZnMmTOHuXPnsmTJEpEkEE4pkuLXuv9D9UN1yLnnsphI3v9tL+39SnUfgrpUEhgSE0FVUT0eFJcL+Rj3twDmjh2Jf/SROsdclSRw6C0UqA6K1RDMBhnJYKDD+nVIldVVpsLKl3OyH48/cKTDCYIQBA1Wa13bRX3u3LnMmTOnyS7q8fHxZGdnH7Z93z5trfmEI8wxj4iIwGQyVY+rz77/NPy8+wisyODrrN9IiYtkQvcLD1tir1a9r4X+N0N8D9HU6h9MOhPxpi7s9a5lld5N1/xtEJ1+2Lj8t94i/9X/4Th/NInPPAPAgg37uV1eqQ3odAE/ZP4AaKsaWA2HL+Ojj4gg9v77UFyuoK1sUMUcqr05DQsoVMgyRe4iWokkQb3tKdsDgOKNAiTaxYq3FcLx0ckScaFm4kKDV2F0Mvjhhx+YP38+Z5xxBt26HTshnpqaKlYXEE5ZsuKrThJUrW6gDw8n0L4TWX/5SPUrOIzaFLq69CTQhYTQbvkydGFhx72s9LFUJStsnS6m1y9nAzDGqD1yVCUI4GBlhFlyImetgJizGyUeQRDqr15JgsWLF9OxY0ei//E25kjsdjtz5sw5rsCOR48ePfj1118pLS2t0bxwxYoV1Z/XRpZlunbtyh9//HHYZytWrCAtLe2oTQv/aUTfoYzoe9i7oqNrdXjDPeGgAYmD+HzXWpZaLEzctQipliSBpXsPdKGhyGYtK+4LKORtXU6avB9FZ0ZqO4wfvx8LwLmp59Z6Hn10NBH1WOqzMYU7QihVLYQpAQ74dDhXrKTMXkTIkCHBDq1FKPrsMwIlpTjV7WCBsrIwANJjRD8CQQg2RVGYMmUKCxcurPM+JSUlWK1WDIc8ZAjCyU5W/XiqkgT/WN3gSt0Czs/bwqwYbRpAXXsS6MPD6xVDoKwM2W6vc1KhqpLAIFmBEgAstSwzW1UZIUk+THuXQi+RJBCE5qJer0qHDBnCggUL6jR26dKlTJzYtF3Yx44dSyAQ4M0336ze5vF4mDFjBn379q1e2WDPnj1s3rz5sH1XrVpVI1GwZcsWfvnlFy655JKm+QLCEY3vMgyAP8wWtji61jrG1r8f7X5fTvxjjwKwalchI/y/ah92HM36skz2VezDorcwMLF5LK91NJF2E4Wqg7CAQmgFhN/zItm33xHssFqM4i9mcuDFF9Fn5wFwx87HGCb/ISoJBKEZ6NWrF0uWLGHatGnHHOvz+TjvvPOIiIggPDycyZMnV08FFISTXY1KgsokgXP1GsKW/UrPsq308SwjxKf9/1DXJEF9qIrCtoGD2NqnL779++u0T1WSQC9V9iCQwGzQHjkKP/iQfQ89jGvDhurvU7W6gSAIzUe9kgT/XM8atMz+3r17GyygE9G3b18uueQS7r//fv7973/z5ptvctZZZ5GZmckzleXnAFdffTUdO3asse/NN99MmzZtGDVqFM8++ywvvfQSw4YNIzY2lrvuuqupv4rwD+0iWmNUYlAklU+yMmsdI+l0NbLcc9fn0EfWkkFyj8v4MfNHQGtYaNYfXi6s+nxU/P47gdLShv8CxyHKbqSQEMIUhTILuBIjMXfujOr1Bju0FsExahTyiLPYE6liVaGrby8+dKTHikoCQQi2KVOm0LdvX+6++25WrVp11LG//PILc+fO5d577+Wxxx7jo48+OqHr8n/+8x8kSaJLly7HfQxBaCpZxLEfrTq26s178axZRL70BLYcJwA2Vbv3qUvjQgDX3xvY98gj5L/22jHH+vPztf4FTif6yMhjjldUpTpZEfH7dD41PMFQw9/V92dlv/xC8cyZeHfuqv4+qiTh84vEnyA0Jyc06Xrbtm307duXbt268cknnzRUTCfkgw8+4Pbbb+fDDz9kypQp+Hw+vv/+ewYPHnzU/UJCQli4cCGDBw/miSee4KGHHqJ79+4sWrSoztMrhMaV7tCWOFy6b+Exx3p9fub+ncso71OsG/IevtQBzNmpTX0ZkTqi1n0827ezZ8JEtp89rNaEWFOLspsoqKwkcJskFj8zltRPPkYyGo+9s0Dkv64l967x5IVLJPt8SMAeOVmsbCAIzYBOp2PmzJmEh4czatQo1q1bd8SxmZmZmEwmnnzySe68806eeOKJYyYWjmTv3r08+eST2Gzi7wGhZfhQHs0q2gIHKwlMbdLwde9FmV37c2yvXGqwLj0JAPx5uRR/9jmlP84/5lhDTAzt164h7bvvavQTOJJyXzmKqi3ZGJ6/hf66jUTrD1YJhF5wAVFTbsWUnl5j+oTX56xT7IIgNI3jThIsWLCAfv364XK5aN++PVdddRXjx4+nqKioIeOrN7PZzLPPPsu+fftwu92sXLmSc845p8aYhQsX1voQmJSUxMyZMykpKaGsrIzvvvuOtm3bNlXowjFc0E57uC8MrMC54s1ax7g3bybzssvZdMllFFZ4CbeZ6TTwApbkLKPAXUCkOZKBSbVPNQgUF2NISsLcqVOjNfOpjyi7iSI1hDBF6/hb7CkObkAtUFaZtpJ8ks+HWzVgiWqFTg7+f1tBECAxMZH58+cjyzKDBg3igw8+qHXc/Pnzq6cLAtx6662sXLnyuM559913069fP3r3/ufaFYLQPPkDKkh+4GDjwsh//QvfM6+SnaS9xLJVLTVYx+kG5s5diLz+eqJumFSn8bLZjCmtdZ3GlrgrexDoLRi92oO/33AwKRc25kKib74Zc/v06u8D4AmISgJBaE6OK0nw8ssvM3LkSDp27MiqVatYunQpU6dO5euvv6Zr1678+OOPDR1ns5eRkUGnTp3qNL9SOD4XdRqAyW/DL/tZvPw5UA5fLkcXGoprzRr0WzYS6itnZNd49DqZr7d9DcD5bc7HINeeCbf170/bnxaQ8uYbjfo96irCZmSV2p5Mn3ZhFkmCuguUl6M4newt06ZCJfv87FQTaBsXGuTIBEH48MMPq1c16NKlCytWrKBz585MmDCBHj168Nxzz/HTTz/x888/M3nyZL755hvGjBlzwuddvHgxs2bN4qWXXjrhYwlCU/EFFKTKJEFVeT6ASS/jUbX7GXvli6+6JgkMsTHE3HUnjhG1V1aeiBKvliQINYUiVU5/UPSHryYFIEkSOlV7FPH6RU8CQWhO6r0E4uOPP87WrVuZMGECr7/+enWX4YcffpgRI0Zw1VVXMXLkSLp2rb253Mlq1apVNVZUEBqeUa8n3jSYzMA85hsDnLtrEbQ5q8YYQ3w8Uc88yy+L3mWB7R5KHY+zvyKKxdmLAbiw3YXHPE9zKec36mXmG4dR4Y3Awiec9vlf7Jo+jugpU7APav6NF4Op6KOPOPDSy6SekQynQ5Lfzw41nnTRtFAQgu6KK66o8XOrVq1YunQpH374IdOmTePee+8FDvZBOuecc5g6deoJnTMQCHDrrbdy3XXXnXL3J0LL9pTyEu/qMtmKHoPukOUD9TIeKpMESmWSoI49Ceqj8KOPCRQW4hg5AlMdqmtLPJVJAmMosm83AIrhYC8g1e9HKdfi1IWFoUcmgIJXEf2WBKE5qXclwfbt23n++ed55513DluGKCMjgzVr1nDjjTcedX6hIByvMe0uAOBXq4X8VbVPOVgalcpI+zJi5BLSkhL5ZNMnKKpCRlwGaaFpte7THHoQ1CbKbkQNaBl4e24Z7vXr8e3LCXJUzZ8vNxeAXKP2ZiLZ72e7kiiSBILQTEmSxNVXX82KFSvIy8tj0aJFzJs3j02bNjFv3jys1trfRNbV66+/zu7du3n88cfrtV9paWmNXx6P54TiEIT6clCOImlz/KsqCfZcPwllwqXYC7VyfntAqzSoa08C0B7WPTt24Nm27ajjSr76ivzp0/Hu2VOn41YlCcJMYeiq4jEeTBIUvv8+W/v1J/eppwGQJG3Z6j8SxtY5dkEQGl+9kgQzZszgt99+4/bbbz/iGIvFwrRp01iyZAnvvvvuicYnCDWM69EXoysOvyTx9f5lUHL4yhqBJS8RIrkosLXFmdKHmVtnAnBNp2uOeNyyH35gx4iR5L9Re+IhWCLtJnQB7aZgbn8jSdOnYx80KMhRNX/xU6fS7vflfNlNSxJ4vDFsVlPEygaC0Ezk5uayv5bl1MrLy7Hb7QwcOJBzzjmH9u3bn/C5CgoKePjhh3nooYfq3Yg4OTmZ0NDQ6l9PPfXUCccjCHWlqio61Y+nsk9SVZLAm5mJumsnPwd60tn3PvZh/wEOLj1YF0Uff8zOUedx4JVXjjou7JKxhI4Zgyk9vU7HrZpu4DA50Pm1JIZkPNiTQLZp12GlMuEmSdp3ytMde+UEQRCaTr2mG1xzzZEfsv5pwIABDBgwoN4BCcLR2E16WllHs019i1l2GxMXPoX+goN9IDZu+IuRztmUZZspN3dl3qr3KfeVk+pIZVDSkR+unatW4d21C39BflN8jTrrqc/kFf0DDCOR1XEu7EPObBZNFVuCIqOPQqMHGZmJFY9jNhh4LfzE3kYKgtAwJk3SGqbNnj0bgJUrVzJhwgS2bNkCQPv27bnkkkuYNGkSiYmJJ3SuBx98kIiICG699dZ675uVlVVjKqHJZDrKaEFoWH5FxSD58VZe96umGyS++CLFeQXsnJOHM2DAbtT+jJZ6676Es6ldOySrFfRHfxQIv+wywi+7rM7HrZ5uYLDjNTiQvBVI5oMJ+rCLxhB20ZjqqZ16yQAquH2icaEgNCf1qiQ4WuZflOAJTeWG3mPQ+c3kGPR8u3027FmhfRDwof92MhbJy95NMTh/WsGaOe8BcG2Xa5GlI/9xj77tNpKmTyfs4oub4BvUnTEkijBFKzP0q/46NyUSDq5sEGaMAXS0ibYji5UNBKFZWLFiBUOHDgW0e4gLL7wQp9PJTTfdxPXXX4/D4eCJJ56ge/fufPfdd8d9nm3btvHmm28yZcoUcnJyyMzMJDMzE7fbjc/nIzMzk8LCwiPu73A4avwSSQKhKfkCCgYC1UkCk6z9+bN06Yz99P44DVqpvlmnPYS7/C58iq9Ox7b27Uv7P1aR9OKLDRpzdZLAHME7p/9Me8/7yKaDU/0ko7FG7ydj5fKNcUVLGzQOQRBOTL0qCZoy8y8IRzK0fSL6n88h4JjN9PBIRjviMQB5M28n3bOeCtWE4ZJr2LJzGVvDt9IuPJ3z25x/1GPqQkMJOWtI03yBejCHxWJWVSyKgt4tkf/bQnRhSVh7nhbs0JotX3Y2+a+/QVm4GyLBKmnlxW1jxFQDQWguiouLCQ8PB+D7778nOjqaZcuWYbMdLEvesWMHN9xwA2PHjmXJkiX06dOn3ufJzs5GURSmTJnClClTDvu8devW3HbbbWLFA6FZ8gVUDBysJDDqDj5cmw06Wkn7uU3/FSE//VS9vdRTSqTl2KX7kk53zDHevdno7DZ0YWF1jrk6SWAKpcCrrUJlNR75XBa0FyHtin+p8zkEQWh89aokaKrMvyAcjV4nc3Ovq1D9dnJ1Ci9t+YYKj59Zu0x4VD2fpz5G0aVn8EDvHWTGSdzV6y508rEvhs1RWGgYLtVImKLQebeK++Z/k/fC88EOq1lzb9tG8cyZWH5cDkBG/goe08+gTbRIEghCcxEVFUVxcTEA8+bN4/rrr6+RIABo06YNc+fOpUuXLvVuOFilS5cufP3114f96ty5MykpKXz99df861//OtGvIwiNQqskqNmTQPX7KZkzB/eihYRLFVyk+w3T9nmEGLS39fWZcnAsuU88wdZ+/SmeNavO+1T1JAgzheGsTBJYjAffSQaKi8l99ln2P6H1UTDIWuLDp/obKmxBEBpAvSoJmirzLwjHckWftry3ZhxF9nf5YNPbzPvDRn5JX5bZ2vPgeSOY/OtEFFXhwrYXMiDx6L0x8l58CUN8PI6RI9A1s2UsI+1GCnAQFlAosuvwp8RjFFU6R2VMTibq5ptZWbAYKKaVz4MPvUgSCEIz0r9/fz766CN69+7N7NmzOe+882odZzQaufLKK3nssceO6zxRUVFceOGFh22vqhyo7TNBaC78AZXNaiJ+SZvqa9KZCJSVkXPX3QCol9ylDfQ5cZhSKfOV1StJUDp/PsWffY61Tx+ibrzhsM8DZVojRGNqap2PWVVJ4HAWccXmyaTpwyk0/rf6c9Xvp/Cdd0GSiH3g/uokQQCRJBCE5qRelQRNlfkXhGMx6XW8e8l1yBW9QFLJs06H2LWcdraJyQsnkufKo3Voa+6Ou5LS+fOPeJxASQkF77zD/kceIXCUeanBEmU3UaiGEKYE2JIskfnaHST897/H3vEUZmrThugpt7KgrzZ3M8nnZ5caR1q07Rh7CoLQVB566CE2bdrEwIED8Xg8vPbaa2zcuLHWscXFxXi9Yg114dTjCyhM8d9c/XPVdANr375YevbEX7m0oOxzEmKsrCTw1D1JECgupmLZMiqWLav189SPPyJ91Uos3brV+ZjV0w18PtpUrOY0eTsWw8FqTp3DQcTEiURPuRUCAfTVSYJAnc8hCELjq1clQVNl/luijIwMdDodkydPZvLkycEO55TQNiaEby99kRsX3Mpe91+oEd/z3tbvAWgd2prprf5NzqgLkSwW7AMHIh9hne3oW2/Fs3VrvTLlTSXKbmSX6iAsoF30iz3FwQ2oBalqXJjs95OpxtE6SiQJBKG56NatG1u3buX777+nb9++BAIBzjzzTC688EJGjx5Nhw4dMBqN/PTTT7z44ov06tWrQc+/cOHCBj2eIDQGX0AB+WAjQqPOiD7cSqv33wNA/e9scIGk+HAYtIRBfSoJ7AMGEPfIVKwZGUccowsJOeJntak6f2jlzxWYsRzSk0AyGom999/VPxsrl3X0SyJJIAjNSb2SBA899BADBw5k4MCBGI1GXnvtNTp37kynTp0OG3uqZf5XrVpVY5kkoWm0igjn+0ve56vtX7EgcwE+xceAxAFc3uFyLHoLO1qlYEptTaCsrNYkgS40lKgbJgUh8rqJspv4SOlMgd8HlFPkLgp2SM2ed/duvBEhFLq1ypAkvx+3IxWzoWX2pRCEk1VcXBzXXXdd9c8LFy7k2muv5e23365e6lVVVUJDQ3n+edGLRTj1+BUVSdLK8PWSHr1c87ZdMtjApf27Q68lwuuTJDAkJhJ+6aUNEyza/6/VlQSKCkCFaj5q40KjzgyAX1QSCEKzUq8kQbAz/4JQG52s45L0S7gk/ZLDPms9a1a9s+DNidWo40P5fBSfCRM/0+nZ79jlW0LStP9hiI0NdnjNjqoo7Dz/AlSPh+ibdHhDFEwBPfbo1GCHJgjCMXTq1Inff/+dVatWsWLFCgoKCkhISGDMmDFERUUFOzxBaHJev8IHxie4GR1G2XDY5zqDCb8qo5cUHHptOcT6TDc4mgPTpuHN3E34peOx1vF+vtxXTkDVHvZDA9o/Kzg8SaAGAihlZUgWCyZ9ZZJAUhskbkEQGka9kgQgMv9Cy3K0BEH+m29hPa0Hlt69q//sNjeSJBFlN5Eb0N4QhO08gLtkL4GCApEkqEWguBjJYEDx+SgMgQ5+P5lqLGkxospHEFqKjIwMMo5S/iwIpwq/ohIuFwFRmHRaksC5ahX7H38Cc6dOmFufjxMTDlw4Ksv267u6gVJRQcXKlQSKigm7aEz19vJfF+L++29CKlc1q4uqKZEWvQWT363Fq5qJ+Ucl3+7Lr8D1118kTfsfRqN2ff5BP5ibVBWa6f2YIJxq6p0k+CeR+RdaAsXtJu/5F4icOAFDQgKlP/zAgRdeAFkmbc73mFq3DnaIRxRpN5Ffol38F4xN5ZZet2FISgpyVM2TPiKC9FUr+XjVmwQ2/Q+bL5wlSmvaxIh+BIIgCELL4gsoyJVz9auaFvoLi/Bs3YocEoIlXcdgz0tMHdMLh+VXoP5JAs+OHey96Wbk0FBCL7wASdZ6mkffNgX3hg1YenSv87GqpkRGmCPAUw5olQQh5pqPG3Ll9FylvByzQ5sKmiWFiwSBIDQjJ5wkqCIy/0Jztv+RRyn55huU0lIS/vs09kGDCL3gAgytUpp1ggBgiLyWe+Vp3EAMq9rLhJw1JNghNWuSJJGpHABgXWAAP/uH8mmUWP5QEARBaFl8Pj96WSvDN1VWClh79yL5nbeRLVYsGxSKCaFcNeIwaq0C6zvdwNyxI6aOHbF06YzidKGza0l1+6BB2AcNqtexqnoBRZgjQA3gQ0cpVkLMNadKJL74IrLRgGQ0Yl6yHYCA6jvseIIgBE+DJQkEoTmLuuUWyhcvJvSC8wGQbTYS/vs0qtr858AZ7BGEFWlvEkrcJUGOpmXYW7YXgJJS7W2FqCQQBEEQWhqvz4Nc+Xa9ahUAfWQk9gEDADBvXQOAxxcgyqRd7+pbSSAZDKR9/VWDxHtokkA961E6LeyLEghwianm40ZVIgLAatC+Vyt1l1Z9YBJJfUFoDkSSQDglGJMSSZvzPbqwsBrbm2svgkMZHDGEBxQA5LwCyn//HUNMDKa0tCBH1vzkv/kWvr17kaK2QygEvOGEmPVE203BDk0QBEEQ6sXv9eCtvE+pqiQ4lMWo41+6OQxY/wmFjrOB+icJ/klVVQrffRfbgAGY2rev133SoUkCj1/BFwDQHTbd4FCWysaFneWt4MwXSQJBaCbkYAcgCE1FHx7eIpIC/2QJiyVU0ZIEQ//wkjVhIsWffxHkqJqnsp9/oviLLwjk5gGwgKfoHhlokf/dBUEIvoyMDDp16sS0adOCHYpwCvL7vHiqkgSVD9Oe7dspW7gQz85dmA06zpDX0THve0IrtAf0qiUIj0fF7yso/X4Oec8+R+blV6DWcynzAlcBAOHmcMrc2tKNkgQ2Y80kgXPNGnKffZbir77GZqz8XpKEz+M87tgFQWhYopKggWRkZKDT6Zg8eTKTJ08OdjjCSSQ0NBxJ0WNSFPIdEnLrZHThYcEOq1mKnDCB/L9Xs934KXoVIgMqMTHxwQ5LEIQWatWqVTgcYnUUITh8foUstCbgVdMNSr75hoK33yHimmuwnHYhTrSH7HBVSyYUuYtQVbXeyfGCd2eQ98wz6MLCsJ91FroQO7KpflV4h1YSmL+/iTcMu/mffCWyXDMWz5atFL7zLvazh2LrdKb2XQGf28nhCz0KghAMIknQQMSNhNBYokJMFOIgVFH4sZeeiVNfICqyc7DDapYcI0awqUcEB+Z/RqrXR5YaR5uYIy+DKQiCIAjNVbnOwTOBsZj5snq6gT4mBnOXLhiSkrAYdDjRtodXtljyKl6cfic2Q/168YScPZQDr76KY+QIYu+7Dwz1f1yvkSTYvYhzdPl8aLj8sHHmTh2JmDgRc4f22CqXR/TIEl53BdZ6n1UQhMYgkgSC0MxF2U0UqiGEB9zk6aHYXRzskJq1qqaFiX4/u9RWtIkWTQsFQRCElsfrV0DWyvarlkCMuPpqIq6+GgDz4p241MpVD/w+zDoz7oCbQndhvZMExpQU2v78E/qIiOOOtypJEGmOQOfVpj2o5tDDxlm6dcPSrZt23sz5QOV0A3fFcZ9bEISGdVL1JCguLmbSpElER0djs9kYMmQIq1evPuZ+iqLw3nvvcf7555OcnIzNZqNLly488cQTuN3uJohcEI4sym7iV6UH3oD2RrzYUxzcgJop/4EDeHbsILtgFwDJfj+71DjaRIsmSIIgCELL4/UrIGlLA9bWuNBsPFhJgK9CW3qQgw/r9XUiCYJDzxuhtyIrWtyqKeyo+1R9L68k4fe4Tuj8giA0nJMmSaAoCqNGjeKTTz7hlltu4ZlnniEvL48zzzyTbdu2HXVfp9PJxIkTOXDgADfeeCMvvfQSffr0YerUqYwYMaJFLJMnnLzCLAZeVsaz1d+e8DKVyP+bzu6rrwl2WM1OyezZ7Bx1Hq1fmwdAks/PbhJIiRTFi4IgCELLYy/dyr/03wFHWN3gkOkGeJ2Em8MBrS9BU1NUpfq8Eao2hcCvyhgstU/5UwMBAqWl1RUSHknCLxoXCkKzcdJMN5g1axbLli1j5syZjB07FoBx48aRnp7O1KlT+eSTT464r9FoZOnSpZx++unV266//npSU1OZOnUqP//8M2effXajfwdBqI0sS0TYjJQGrPhNELpmJ052ovr9SPqT5n/hE6Z4vcg2G9kOrTQz2e9nhb0VJr0uyJEJgiAIwnHwlGGTy4HQ6iTBvoen4tm+nejbbsNiSa6eboDPSbg9eEmCUk8pATUAQHjly7USbNgth/c28BcUsG3AQJAkTL98AEAWERRF9Sa56UIWBOEoTppKglmzZhEbG8tFF11UvS06Oppx48Yxe/ZsPB7PEfc1Go01EgRVxowZA8CmTZsaPmBBqIcouwmd30S5GVZd15+k118LdkjNTvTNN5P+xyo+6aeVOGZ50tFHtwtyVIIgCIJwfFSfB2/VEoiVSQL35s24Vq9GqSjHYpT5JDCUq8I/hFEvVE83KHAXNHmsVVMNHEYHBk85ACWqDYf58JcZsr1yGqCqYvJoSzw7JR3lxuimCVYQhGM6aZIEa9asoWfPnshyza/Up08fnE4nW7durfcx9+/fD0BUVFSDxCgIx+sCaREPyN+gyhLrMyIJOfNMUUVQi1JvKUWBMgCmOu8gLj4hyBEJgiAIwvFR/R48lUmCqrL82PvuJfGVlzF36YLNqKcUG9k+BxjM1UmCYFQSVCUmIswR4HMSkHSUYsNuqiVJYDLRbtlSOqz7C3NYpLZR8uH2B5oyZEEQjuKkSRLs27eP+PjD10Ov2paTk1PvYz7zzDM4HA5GjBhxzLGlpaU1fh2tckEQ6ktvDScsoF08izxNf/FvKfaWaysb6FUHqEaxsoEgCILQcvndeOWalQTWnj1xDB+OISYGW+UDeLlHm2YXzJ4Eec48AKKt0dBuGA92+ZXx3oewm2pfSlEfEYFkNB5c2lH2YMhd12TxCoJwdM0ySaAoCm63u06/qpoKulwuTKZaOr+azdWf18eTTz7JTz/9xNNPP01YWNgxxycnJxMaGlr966mnnqrX+QThaHT2aMIUrSRPl51Hxe+/46usdBHAuzebPdf+i/Ln/weAzhcKqKSJlQ0EQRCElirgPayS4FB2k54E8pnifQvmP3hwdQPP8a1ucCIOOA8AEG3RpgyUeQJ4MBJSy3SDQ5n12n26IgeI2j23cYMUBKHOmmW98uLFixkyZEidxm7atIkOHTpgsVhqfXtftYShxWKp8/k///xzHnzwQf71r39x00031WmfrKwsHA5H9c+1JSwE4XiZwmII36klCc6ancWe/04k7tFHCR8/LsiRNQ/eXbuoWLYMXXIktIdh3s2E62aTFjUs2KEJgiAIwvE5ZLpB1Rv38qVLkU0mzN26YTfrsUsurpR+QF27iohOwwEodDV9kiDXmQtArDUWgBKX1h/IUUvjQoDib77Bs20b+nPOqt7m9Vc0cpSCINRVs0wSdOjQgRkzZtRpbNV0gvj4ePbt23fY51XbEhLqNjd5wYIFXH311YwaNYrXX3+9jhGDw+GokSQQhIZkCY0lVNGmG+SE+undtg2yWSSiqpjS2xH/nyf4OvM74E+SfX52GxOItIvfI0EQBKFlcqlGiiuXODTpTKiqStZ114Oq0m7JYuxhEZSqlcv8ukuJMGnTDYLRuLBqukGMNQZWvMHN+7/CKvcjwta71vGlc+ZSsWQJMW1aV2/z+sQSiILQXDTLJEFcXBwTJkyo1z49evRgyZIlKIpSo3nhihUrsFqtpKenH/MYK1asYMyYMfTu3ZsvvvgCvWgMJzQTYeERWP3aUn7vngU3XjETi77u1TEnO0NsLGEXX8xvP86F/ZDk97MjrPWxdxQEQRCEZup3y2CWly9Bz3ZtuoHPh6ljB1SnC8lixaSXccpa7x1J8RFtCAGgwFWAoirIUtPNKj7gqpxuYI2GzYvp7/udX6Q0wq2HT5MACBk6FFObNljbtENeI6FIqqgkEIRmpFn2JDgeY8eOJTc3l6+++qp6W35+PjNnzmT06NE1yv937NjBjh07auy/adMmRo0aRWpqKt9//329picIQmOLDjHjUu3oq9Ye9pQEOaLmaW/pHgCSfT6MMWL5Q0EQBKHl8gYUkLWmhCadCcloJO2rr2jzwzx0dhuSJIHRRkDVpiREokdCIqAGqpckbCpVlQSx1lhwaY0TS7AdMUkQful4Yu+7F0v37ujRXoJ4A+6mCVYQhGM6aV6Vjx07ln79+jFx4kQ2btxIVFQU06dPJxAI8Oijj9YYO3ToUAAyMzMBKCsr45xzzqGoqIh77rmHOXPm1Bjfpk0b+vfv3yTfQxBqE2k38mOgN+bANsr1AYrcRcTZ4oIdVrPhXLUKNTyUvPL9IIPVZyUpLjbYYQmC0MJlZGSg0+mYPHkykydPDnY4winG6w8gSdrc/toaFwLYTQZK3TbCKUfvLSfSEkm+K588Zx5RlqZZwltV1RrTDZSyPGSgQHUQbqs97kMZJD1e/PiV+jUZFwSh8Zw0SQKdTsfcuXO55557eOWVV3C5XGRkZPDee+/Rvn37o+5bUFBAVlYWAPfdd99hn19zzTUiSSAEVaTNxCP+CVj9L9Imfz++2x4iJ7EtCf99OtihBZ2qquyZdAOqy0XUDTqKw1SK/TG0jhLLHwqCcGJWrVol+g0JQXNW6WxKQrPZy8HGhf9kN+spc1kIl8rBU0qMNYZ8V7622kBk08RZ5CnCp2jJjGhLNGq5ljAolMJwHGV1AzUQQPV6MUoGKlQ3fsXbJPEKgnBsJ02SACA8PJy3336bt99++6jjqioIqqSmplYvpSgIzZFRL+Mw6/EFbBj8YPhjA879YsoBgFJejjElBdfePRwI9dLa7ydTSaCbSBIIgiAILViEP6+yksCASWfCs2MH+6ZOxZiYVP2SwGbSU4oNOADuEmIsMWxkI3muvCaLs2r5wwhzBAZZj+LMB8BnjtKmRNSieNYs9j34EPazh6LrbwN/Gb+GDGdEk0UtCMLRnDQ9CQThZBcVYkLyW8mJhF23jibu0UeCHVKzoAsJIW32N6z/6B4COgnFG8lCpTupkSJJIAiCILRcsuKtXgLRqDMSKCzE9cefuNatqx5jN+mZ5L2TOcMXQtqZWuNADvYIaApVyx/GWGPAXYxcWREQqIylNlJl7y+ltAy9Xrteb9UlN3KkgiDU1UlVSRBMYt6i0Ngm8h1ZLOdTSwjbuyYxsueAYIfUrOwtzwZgs6cb+Y4zsRh1QY5IEARBEI6fTvHhrUwSmHQmjG3iSHzpRSTjwXn+NqOeHKIokMJBZ6hOElS93W8KVQmJaEs0uIoIyEYqAjpCbPYj7hNy1lm0W7YUnd2O6durAHD7ReNCQWguRJKggYh5i0JjkyxhhHsDAE3etbglyCrT+ooovkjRj0AQBEFo8XT/qCTQh0XgOPfcGmNsJu1WvtyjrYIQa9Wa9jZlJUF2ZZI+wZ4AEWl8MmwVT8/+gwFWwxH3kS0W5MpqArOsfYdkz/rGD1YQhDoRSQJBaCEkWxSRLkX7YVcWFdJyzJ06oQsNDW5gQZb7zLN4tm7F1H43xIDNaxZJAkEQBKHF06veGpUEtQkx6zlTXkufzd9A1AiiwyorCVxNV0mQXaYlCZJDtOkCRU4fFViIqMPKBgBWSZv9PMTzQ+MEKAhCvYmeBILQQuhDYgkPaJUEZ721mj0Tr8W9cWOQowo+5x9/UPHbb5QXa29NXpfeIy1aJAkEQRCEFk71EDgkSeDbvx/n6tV4K1fkAnBYDPSQt9N7/xeQuYRYm1ZJsK9iX5OFubd8LwCJ9kQACiu0ngRHW/5QcbvJf/0Ncp99FrNsBiAgK40cqSAIdSWSBILQQpjDYogIaBfQ/ZE6TO3awhG6Bp9KYu+7j5CH7+PveD+SquL2iOUPBUEQhJavSD1YPWDUGSmdM5fdl1/BgVdfrd4ebjVQrFbO/XcWVj+ol3hKKPeWN0mcVdMNkkKS4M/3uHDLPZwvLyM2pPbqBwAkiQMvvUThO+8SqmrJBK+kQMDfFCELgnAMIkkgCC2ENTyOcEWrJHjpYgNp332HrV+/IEcVfNaep5F/dneKQiRiAwGy1XjSoo7cLEkQBEEQmjtFUXnAf031z0bZiGyzYmiVgiE2tnp7uNVIgVrZE6siH5vBRpgpDDj48N6YnD5ndZ+kRHsiZK+mR8VSUqX9xDrMR9xPNpkIu+QSIq69FrNRS+x7JBn8rkaPWRCEYxM9CQShhYgID8fu1/J6Zb5yfAEfBt2RmwKdSqpKHZN8fvZICSSGW4IckSAIgiAcP49fAUl7q66X9ehkHeGXXkr4pZfWGBdmNZBPZW+iCq0PQYI9gWJPMTnlObSPaN+ocVZdf0NNoYQYQ6Bcm/qXTygxR0kSAMQ//hgAxmVPwwFwSxJ+dwV6U0ijxiwIwrGJSgJBaCEi7WYW+fsgq9rPRZ6i4AbUDHh376Z8yRIO7NJ6MyT7/TjtrdDJYhqGIAiC0HK5fQGQfMCRmxYChFmN5KtVSQLtAb1qykFTVBLsLavZj0CtjCFfdRDrOMp0g0PYjVpi3yNJuF0VjRClIAj1JZIEgtBCRIWYuMd3M/5ACN12KhTceBt5zz8f7LCCqnT+fLKun0TsBz8BWiWBFNkmyFEJgiAIwolx+wP82/gRAKaj3K6HWw3kV003cBVBwNekSYLqfgT2JACUUq1hYp4aTkzI0SsJAFS/H1tlYbNblvC4mqaPgiAIRyeSBA0kIyODTp06MW3atGCHIpykbEYdJr2M6rdhdwMr1+Ja+1ewwwoq2WbD1K4duyO1ksxEnx9bfNsgRyUIgiAIJ8btU0iTtbf0Rll7iM576SX23HADFcuWVY8Lsxopxo5frbylr8hv0iTB7tLdQGXTQr8XuUxLEjgt8Rj1R3/MyHnwQTZ36UrKT1o14DI1nQpDVOMGLAhCnYieBA1k1apVOByOYIchnMQkSSLKbqI4YGVrokTenePJ6HlesMMKqojLLyfi8suZ9cUQcMEyz0C6x4QHOyxBEARBOCFuXwBVCgB6TLLW/d/11184l/9O6Hmjq8eFmPRIksw53v/y6W0jiLHHkmBPAJomSbCrZBcAbcLaQGk2Eioe1YDeEXuMPUE2aZUGlnJtWkUW4ZTLoh+BIDQHopJAEFqQO6WPGKWuJz9UYs/ANlh79w52SEHn9rvJc+UD8LH7ElpHi+UPBUFoGKJKUAgWty+AImtVclU9CaImTSL+icex9OhePU6WJcKsRnaoiRQRBrJMSkgKAFllWSiq0qhx7ijeAUCb0DZQkY9PZ2GvGkW049gNhKNumUy7pb9RfNW5AEiyD5dPLIEoCM2BqCQQhBZEMUcQHtAu+FVLDp3qqt6UqAETBKy0jhJJAkEQGoaoEhSCxe1TKisJwKTTKgls/fvXOjbMaqCwwkthhReAxJBE9LIel99FnjOPOFtco8RY7C6mwF0AQOvQ1hBl5ZU+C3n317+5sA6rDOnDtco/U5kVgHC5EH9xNrSKaJR4BUGoO1FJIAgtiGKNIULRbhqU7bsoX7oUxekMclTB4d29mx0jRlL674cBMPnsxJgDRNqMQY5MEARBEE6M2x9AkbSXAsajrG4AEBNiYpj8B7G/PQhb5mGQDdXVBDtLdjZajFXHjrfFYzVoD/qZhS4qsNAq0lrn45h12rSDVrpszDkrGj5QQRDqTSQJBKEFkUJiqisJBj/3K1n/ug7vnj1Bjio4vLt34921C2W31tjpjEAWoxy7kCSx/KEgCILQsnl8B5MEJr32EO366y/cW7ai+nw1xsaHWugrbyJt1yeweylQ+Wafgz0DGkNVkiAtLK16254CbQnDVpHHrurz5eaR/8abOGb+AoBbklA8YglEQWgORJJAEFoQY2gckQGtkiAv2oipXTvUU3T+nqVHD1Lem8HaS7oB2vKH+mixsoEgCILQ8rm9fsokAwBGvRlVVcm87HJ2XXAB/sKiGmPjQs0cUMO0H8pygaZJEtToRwDw6eXclv8IraV9pNYhSRAoLODAiy9injUfAI8kobjFEoiC0ByIJIEgtCDm8LjqSoJXr40m7btvsXTtEuSogkPncGDr1481Sdp0i3hfAEd8myBHJQiCIAgnzu1XeTRwBQAmox3V68WQlIQuMhLZVvMBPD7UTI4aqf1QolXXNUWSYGvRVgDahrWFgB91+wLOYhV+ZFIijj3dQB8bS+iYMUgjhwJakkD1iiSBIDQHonGhILQgjoh4wiqTBEWVzYJOdVml2nQLk89O65jQIEcjCIIgCCfO7QuAdHB1A9lkou38H2sdG+cwk61GaT+UZAHQ2qElCare9jc0RVXYWLARgE6RnSB/K1LAS5lqwWdPwmLUHfMY+ogIEp56EnfJTvjmW1ySjCSSBILQLIhKggYilkkSmkKkw8pKXy8Ayv1OvAFvkCMKnpLv51D22xLyS/MA8HujxMoGgiAIwknB41eQJK33gFF39Ia88aEWstRo7YfSbAj4aBPWBgmJAncB+ZXLBDekrLIsyn3lmHQm2oS1gf3rAdikptA6un4rglQ1LvRIEnhFTwJBaA5EkqCBrFq1io0bNzJ58uRghyKcxKLsRu7z3IqqyvTaprD72n9x4JVXgx1Wk1MVhX3/93/svW4SlrIAOlWlxJdQpzmQgiAIgtDc6ctzuFC/ENAqCY4mLtRMPqF4VAOoCpTmYDVYSQ1NBWBz4eYGj29D/gYA2ke0Ry/rYf86ADYqregYX78kgUnVIysqXlkCn6gkEITmQCQJBKEFCbMakSUJ1W/H4QT/ij9wbfg72GE1OcXpwnb66Sitk8gPhXi/n2JTCjaTmEElCEJNq1at4pZbbqFz587YbDZSUlIYN24cW7duDXZognBEkruYGJ02rdCoM+LevJmsG24k97/PHDY2ym7EajSQXd2XQJty0CG8A9BISYICLUnQObKztqEqSaC2omN8SJ2Ps/vKq8jLOIPuO1UAFoec3bCBCoJwXE6qJEFxcTGTJk0iOjoam83GkCFDWL16db2P4/P56NSpE5Ik8dxzzzVCpIJwfHSyRITNBH47G1MkSv89gagbbwx2WE1OZ7eR/Np0tr06mYBOwuuNJi8yI9hhCYLQDP33v//lyy+/ZOjQobz88stMmjSJxYsX07NnT/7++9RLsgotg+Jz4a5c0tekM+Hbt4/yRYtwrlp12FhJkmgVaWOC714WXrgKWg0AoENk4yUJ/s7X/t/pFNkJVBW1crrBRqUVnRLqXkkgGbWpFCEu7ee/dGKVIkFoDk6a126KojBq1Cj++usv7rnnHqKiopg+fTpnnnkmf/75J+3atavzsV599VX2nKJrzwvN3126z1mq7mBRuIWs/m3om35asEMKmr1lWhfnvd729GvVKcjRCILQHN1555188sknGI0H53WPHz+erl278vTTT/PRRx8FMTpBqJ3qdeE9JElgbt+euMcfQxdS+wN46ygbc/bFsr1Mz5mV+1VVEmwq2NSgsbn9btbna0mB02JOA08pvpAUvE4XO6Vk2sbY63ys+KeeQjLo+f27YYCXMo/oSSAIzcFJU0kwa9Ysli1bxnvvvcfUqVOZPHkyCxcuRKfTMXXq1DofJy8vj8cee4x77723EaMVhOOnmMOICgQAGqUZUUuSVaaVVKreCNG0UBCEWp1++uk1EgQA7dq1o3Pnzmza1LAPT4LQYLxOrZEfYJSNGBISCL/kEhznnlPr8Kpr4K78gw/ZnaO0qQB7yvZQ6C5ssNDWHViHT/ERY4khJSQFzKH8MvhzunnepnVcJCb9sVc2qGKIjUEfEYFJbwEgzrW2weIUBOH4nVRJgtjYWC666KLqbdHR0YwbN47Zs2fj8XjqdJz77ruP9u3bc+WVVzZWqIJwQgLWGKL8AVBVfFu2Uf7bUpQ6/vk+WeTcdz87Ro7CtkSbThTrV2kTXfc3F4IgnNpUVSU3N5eoqKhghyIItfPXrCQ4ltQoG0lSHoN2PAdz7gYg1BRK2zCtfH9N3poGC+2P3D8A6B3XG6kyxnV7S1CQ6ZZ0fEsRWyStuPlCl6jsEYTm4KRJEqxZs4aePXsiyzW/Up8+fXA6nXVqULRy5Uref/99Xnrppeq/9AShuZFCYrRKAkli8H9+JOu66/Dv2xfssJqUe+sWvDt3st+tVVL8K7BCVBIIglBnH3/8MdnZ2YwfP/6o40pLS2v8qusLB0E4UZLfdbCSQGfEt38/7q1b8RcV1Tq+dZQVEz7OLf8G1n4CqtYI8LQYbUrimtyGSxKs2q/1RciIq+wFpCiszy4BoGtiWL2O5dm2jfzX32DQeq1CUpFO3aWdBaE5OWmSBPv27SM+Pv6w7VXbcnJyjrq/qqrceuutjB8/nv79+9f7/OJGQmgqhtD46ukGB2LNmNLTUbyn1kU1+X//I+L1l1mToK0h7fQlkhRuCXJUgiC0BJs3b2by5Mn079+fa6655qhjk5OTCQ0Nrf711FNPNVGUwqku4PfXaFxY+P4H7Dr/AgreervW8a2j7OxWY/GqOvBVVK9wUJ0kaKBKggpfBWsPrAUOJgnUaRk8mHU9baTselcSeLZt48BLL9F3rROAgORrkDgFQTgxzbJxoaIoeOv40GMymZAkCZfLhcl0eDmW2WwGwOVyHfU47733HuvXr2fWrFn1DxjtRuJQU6dO5ZFHHjmuYwnC0ZjDDyYJnpsUyQ9jZwc5oqZnSEgg15BP+W6JGL8flyUVve6kyXkKgtBI9u/fz6hRowgNDWXWrFnodEefO52VlYXDcbBRXG33GYLQGH40ns1mdRU6dmPSmZCMRnQREejCwmodH2EzEh5iY5cnnvbSXjiwBcJS6BXbC9CWLCz1luIw1n3lgdqs2LcCv+InOSSZVo5W4CpGKthOe6BUDic9tu7LHwIY09IIvegidvn/BLLxyQp+nxe9wXjMfQVBaDzNMkmwePFihgwZUqexmzZtokOHDlgsllrf3rvdbgAsliO/ZSwtLeX+++/nnnvuOexhv67EjYTQVEIjognza/+e78pHVdVTcnrM7tLdALTy+VHC04IcjSAIzV1JSQkjRoyguLiYJUuWkJCQcMx9HA5HjWu7IDQVt08Bk3axN+qMxNxxOzF33H7UfTrGO9i2K4n27IUDm6HdMBLsCaSFprGzZCfLc5ZzTmrtjQ/rakn2EgAGJg7UNuRqSyHuVaNIiI/HqK9fwt7coQMJT/6HrT/fBnuzcUoSFeVlhIZHnlCcgiCcmGaZJOjQoQMzZsyo09iq6QTx8fHsq2VedtW2o90MPPfcc3i9XsaPH09mZiYAe/dqS6sVFRWRmZlJQkLCYd2RDyVuJISmEhdq5Xdfb2AvHsVLua+cEGP9MvctmXPNGjxbtlJg2gBAis+PNTk9yFEJgtCcud1uRo8ezdatW/npp5/o1EksmSo0b25/ACxa6X1dGhcCdIwPYduORNABuRuqtw9KHMTOkp0s3rv4hJIEqqryW/ZvwCFJgmytgfB6pTVdj7NpIYDdpN3HOGUJZ1mRSBIIQpA1yyRBXFwcEyZMqNc+PXr0YMmSJSiKUqN54YoVK7BaraSnH/khYs+ePRQVFdG5c+fDPnvyySd58sknWbNmDT169KhXTILQGOIcZkZ4b8EeeIQ+O5xkXzeJyD4Dib5lcrBDaxJlP86n8L33sA1Jgn6Q4FOJSGod7LAEQWimAoEA48ePZ/ny5cyePfu4+g4JQlM7x/UDc0LzKQLMenOd9ukU7+AbtbKyLvvP6u2Dkgbx/sb3+S37NwJKAJ1c9yUKD7WxcCP7K/Zj0VsONi2sPM9fShu61bNp4aFskhlJVamQZdwVZcd9HEEQGkazTBIcj7FjxzJr1iy++uorxo4dC0B+fj4zZ85k9OjRNcr/d+zYAUCbNm0AmDJlChdeeGGN4+Xl5XHDDTcwYcIELrjgAlq3Fg8hQvMQZjVg1Mso/hBCnE7UlWtxW8OCHVaTMbVtg/2MM9gUo70lMXodtI0VVTyCINTurrvu4ttvv2X06NEUFhby0Uc1l1gTSx4LzVFr/w4k2QfosOgt5D71FP6CQiInXY/5CC++OsQ5+EvR7m1VTzmSzw0GMz1jeuIwOih0F7I6b/XBB/x6WpC5ANCqCCx6bRqvmv0nEvCX2oYLjrOSYNvgM7gwL4+fb9TxC50ZLJ861ZGC0FydVEmCfv36MXHiRDZu3EhUVBTTp08nEAjw6KOP1hg7dOhQgOqpBT179qRnz541xlR91rlz58MSCIIQTJIkEecwU+C3sSFF4sBdl5Ha54Jgh9VkwsaOJfTii5nzST/ww4/u4VwULZY/FAShdmvXrgXgu+++47vvvjvsc5EkEJojveLGVdlvyKw3U7ZwIb7dewi/9MjLdqZF2yjXhdHHPY1Zt15EikGrQDDoDAxNGcrX27/mx8wfjytJoKoqC3ZrSYLhrYZrG8sPIJVkoagS23RtaBdjr/dxAdBrlQ12F2yyxlGMSPwLQrCdNO3AdTodc+fOZfz48bzyyivcc889REVF8csvv9C+fftghycIDeom+RtGqRvIjZDIPD0VS/fuwQ6pSRW4C3D6naiqxA7jUBxmQ7BDEgShmVq4cCGqqh7xlyA0NwFFxaC6q5dAtOgtRN9yCzH33oshJeWI+xl0MulxdvII5++ckhqfDU/VHuwX7F6AT6n/MoMbCzeyp2wPJp2JQUmDtI0+J5nxI1isdKNtcsJxrzLU6oMP+ev9O9kZD5LspdzjP67jCILQcE6aSgKA8PBw3n77bd5+u/Y1ZKtUVQkcTWpqqrh5EJovWxSRPm0ZxHx3fpCDaTqq3w+yzJ7SPdrPvjDaxYQHOSpBEARBaDhOrx+d5EU9JElgGz26Tvt2Twrj7+xS/tpbzMjOsVDZp6tvfF8izBEUugtZsncJZ6WcVa+YvtuhVeEMSR6CzVBZvRfeilfD7+fLXXu5NTWiXsc7lDEpEZMrClWSCJMLCJTkAMdefUQQhMZz0lQSCMKpRHLEEx3QkgT+7TspX7KEQHlFkKNqfOWLl7DltJ74HngKgAifgfaRYi1lQRAE4eTh8gbQSQeX9Tbr6ta4EKB7chgW3Jz1113wXDvwlANgkA2c3+Z8AL7e9nW94vEpPubunAvA6DY1kxV/7C4EoPcJJAkArAYrAB10O4jIXnhCxxIE4cSJJEEDycjIoFOnTkybNi3YoQinAGN4IlEBBYAzXvqNrOsn4d21s0nO/eHyTM7/32/kl3uOPbiBeTMzUT0eKnxa5+Ph/h20Ef0IBEEQhJOI0xtA1nkBMEp6ZEXFs20bvpycY1a5dk8Kw4WJBNc2cOZD5m/Vn41pNwaAxdmL2Vu2t87xLM5aTJGniEhzJKcnnK5tdBVRuOMP9hSUI0lwWkpY/b7kIVzr1xP5xUIytig4ZRmlMrEhCELwiCRBA1m1ahUbN25k8uRTYxk6Ibhs0SlEVVYS7IvRY+rQQSvFbwIPzd7Aur0lvPTT1iY536Eirr6KNvN/ZPFZWlOjUJ+F1nFiLWVBEATh5FHh9aNI2jXdrDPiLyhg5+jz2T78nGPu2zbGjtWoZ1Ggq7Zh+4Lqz9JC0zg94XQUVeH9De/XOZ6Z22YCcEHbC9DLlTOVN88h4sOhvGd4ho5xjhPqDeRc9Qe2d7+h32YVpyyhiiSBIASdSBIIQgsUGRVHaGXfoRcuNZH2zddYTzutSWPIKXY36fkAJL0eY0oKf5nytJ+94bQ93m7KgiAIgtAMubwBbgpoL50sxhBUjwddWBi68DCkyj4FR6KTJbomhvKzUrlq1+Y5oCjVn1/b5VoAvtn+DYXuwmPGkl2ezbLsZQCMbTf24AcbZwOwRm3L4PToOn+32pg7d4ZRZ/F3qoRTksB78k+fFITmTiQJBKEFig21oPq1dYSLvKV4A94mj8EXUI49qBEoqkKWt6gyhkSiQ0xBiUMQBEEQGoPTG0CStDcBFr0FY0oK6b8vJ33Jkjrt3yM5jKVKF9yyDcr2wd5V1Z/1ietDp8hOuANuPt386TGP9eXWL1FR6Rffj2RHsraxIh91+88AfBs4nTNOMElg69sH29R7+bW7jFOW0flEJYEgBJtIEghCCxTrMPObPwO9or1RyK3IbZLzuryB6n+Xj/E2o6EFysvJ/e8zZH32Ph41gE5V0VnaH/OtiiAIgiC0JE6vH2Qt+W/W171pYZUeyWF4MfCbLkPbUPnWH0CSpOpqgk83f0qF78hv7X2Kj6+3a00OL0m/5OAHG75GUgOsU1pzwJhCr1YnvspQVeNCpySh85We8PEEQTgxIkkgCC2QUS/ztvlavL5IOmSpFN94O/sentro5y1yHqxYCChNu0Sod/t2CmfMoHzamyBJJPn96KI6NGkMgiCcWkRTYiEYnN4A1xm+AcAi1X+uf7+0SGQJvqionIa4cTYc0vDw7JSzSXWkUuIp4cONHx7xOIuyFpHvyifCHMGQ5CHaRlWFP7V+BrMDAxjeOQ6j/sQfJ6x6KwafCioQKDvh4wmCcGJEkkAQWqhYhxnFF4bRp6JfvRHX2rWNfs7CioNJglK3r9HPdyjZ4SD8qqvIG5AOQBuvD1tixyaNQRCEU4toSiwEg9Pjo4tuG6A1LqxYuZLse/5N4QdHfqA/VLjNSPfkMBYp3cmL6AkZ/4JDpiXqZB0397gZgPc3vE+Jp6TW48zaOguAMW3HYNBVJiv2/A6563FjZFZgMKO7xx/v16whZ+SFfPxcgIRCmK0b0CDHFATh+IkkgSC0UHEOM7I/hMxYic2TzyHu4Yca/ZyHVhKUuJo2SWBKSyPu/x5g8QWtAdjs7klyQlKTxiAIgiAIjc3nKsMla7foFqMd744dlH73Hc5VK+t8jDPSo/Fg5OHw52DQnaCv2b/nnNRzSA9Pp9xXzoy/Zxy2/96yvSzL0RoWXtzu4oMfbJkDwNf+AehsEQxoG1Xfr1cryWQEILxcZUFAvAAQhGATSQJBaKGGs4zJ6kJKbRLreodj7d270c9ZWOFlmPwHN+m+pcTZ9M0SAbYX7wRgh7sH7eNCghKDIAiCIDQWv6sMd2W/HbPBhuW004j5979xnH9+nY9xdsdYABZuzaPcc/gSybIkc+tptwLw8aaPKXAV1Pj8m+3fHN6wEGDY4zwb/SRvB0YytlcSBl3DPEqkvPEGd94bzd+tJMq8TlS1aac0CoJQk0gSNBAxb1FoaubwROL92oV/X8W+JjlnUYWXt4wvcK/hM/p5lzfpRdy3fz+KorC9aDsAZjWBpHBLk51fEARBEJqC4i7DJWtJAovegrlDByKvnYhj2LA6H6NzgoPWUTbcPoWf12fCmo9g6/waY85IOoMukV1wB9x8vuXz6u2qqjJnp1YxcGHbC2vss6vAybSsVHaSyJV9Wx3fF6yFITERyWEHSaK9tIWKQxolC4LQ9ESSoIGIeYtCU7PHphFXmSQI7MykfPFi/AcONOo5S8sONhPqJW2p9e1EYwiUlbH9zCFszcjA5yxFUmFAqFWsbCAIgiCcdAKeg5UEFv3xJcMlSWJ09wQA3L+9DrMnw/wHIeCvMeaaLtcA8Nnmz3D73QCsy1/H3vK9WPSWgw0L3SXgKeP9ZZkADGkfQ0qk9bhiOxK7Qfuu/zZ9QEmZWAZREIJJJAkEoYWKjG9FVOW1/ryZe8madAPOP/5o1HMeqPCzINALgDZSDgXlTTPlwJeVBXo9AYsJj1Eixe+jd6i7Sc4tCIIgCE3KU4ZL0m7RzXoz3r3Z+PbvR/XW75p7fmWS4Km8vijmcMjfAn99WmPM2Slnk2BLoMhTxPzdWqXBp5u1MUNThlYvTchvL6K80BnvH9rKBhMHpB7vt6uVZ8cOhi0qZ+hahRJZpqyk4Ng7CYLQaESSQBBaqMQIO6rPAcDuKAVDh3TQ6xv1nFmlfj4IaOWOCVIBewqdjXq+KuZOneiwZjUbHtPWaU7z+nCkdG+ScwuCIAhCk/JW4D5kukHOvfey/cwhlP3yS70O0zbGzoC2kRQrVuZHXqlt/PVJ8B68dutlPZe0166ts7bOYv2B9dVTDa7sVLlPRT6sfBvZU0Ke30735DAGNlDDwiqeHTsYOGcPQ/5SKNXJOEsLG/T4giDUj0gSCEILFWU3UqRGEx4I8Na5OgLvPlOv+YrHY0+hk31qBADxUgG7Cyoa9XyHkgwGNgd2ABDn1dE6JaXJzi0IgiAITeV3fR++UE4HwKwzaxt1OnRh4fU+1uQz2wJw9+4++EOSoCwHltfsn3Vh2wvRS3rW5K3h8rmXA3B+m/PpHNlZG7D4WfCWsVFN5WflNG4Z0rbBp/uZ2rZl56DWLO8oUyrLuEWSQBCCSiQJBKGFkiSJImMccX6tuU+uM7dRz1fq9pFY8DuD5PUAmPGRlde0F/GtRdq60UZPBO1jxcoGgiAIwsnH5QugyNp8QrPeTOrHH9Hh7/VY+2TU+1j920TSPy2Scr+Od0xa/wF+ewGKs6rHRFmiGJk2svrnCHMEd/S6Q/shbxOsegeA//guo31cKEM7xBznNzsyU1oa224czpw+WpLAW17U4OcQBKHuRJJAEFqwzNDe+H1hAOwrb7wVDlRV5a3FOxmn+5Wphg/Z3OZa2nveY/Gu8iZZ4SDngf8j78UXyS2oTISoyYRaDY1+XkEQBEFoak5vAEnyAQcbF0qShCTX/7ZdkiQev7AzBp3EU3s7URTZE3xO+P6OGuOm9p/K6LTROIwOnhr0FFGWKFAU+O42UHz8ovZmqdKVyUPaIsuN0zTYYdKmUJbIOnxOkSQQhGASSQJBaMF2J57PBm8PHBUqCQ++xc4LLmzwh/b9JW6ue/8PXv1lO+0l7c1D8mnDMep0bN5fxtz1+xv0fP8UKC2l5KuvKHjjTUpkP7KqYrF1a9RzCoIgCEKw9Kn4lda63cDxr25wqLYxIVw/KA2QmFT2LwLJ/eGc/9QYY9QZeXLQkywev5jTE7SpDvz5LmStwC1b+D/PNXRPDmNU1/gTjudIQo2hGHwq5cgEnMWNdh5BEI5NJAkEoQVLCreg+EJxmSDq72w8W7YQKC5usON/91cOw15cxM+b8+ioy6adnAOALaUHEwemAiq3f76Gb//KabBzHkaSiH3gATzjzsVllkjy+zEliKaFgiAIwsmprXczZp22BKC9xEv23feQ++yzJ3TMW89qR2KYhVWl4TwW9RxEt691nE7WHfzBWYQqyTztuYR9RDJ1dKdGqyIAaPPge3z8XAA1K5xtunaNdh5BEI5NJAkaSEZGBp06dWLatGnHHiwIDSQp3ILBZ8Onl5h9VRtS3n8f2WY74eMGFJWHZ//NrZ+uocztY1L0Rr4NewEJFdqPBFTuLX2KP+13kqLsZcqna3h7yc4T/0K10IWEEHH1VWy4rDcAB9ztiG8jkgSCIAjCyckYqKC8cmqBrchD6fffUzp33gkd02LU8cSYLgC8v3w3s9dmax9s/REKdtS6T36vKVyhe5b3A8O5vG8KPVPq3zixPnTWyvsXp44NautGPZcgCEfXuOulnUJWrVqFw+EIdhjCKSYlVM/H0jSuJpaf27m4r2+fEz6mL6Bwx+dr+X7dPmRJYV7Cu7QvqFx2KSKNVRlXERlwkVa+n0h/Lt9ZH+da1xSemAM5xW4eHNWxUd40bCvcDkCxJ5WuydENfnxBEARBCDaPP4BFdVEuaUkCa0IiYffei9QASxwPaR/DzWe2YfrCHdz1xV9EZf3IgNV3QWgSXPM9hLfSBgZ8uAIykz74g9Xl8bSLsfPQqE4nfP5j0T8whQm/TqJcJ9GpwtPo5xME4chEJYEgtGCtY8Mx+ewA5LoO4Amc2EVVVVXu/XId36/bh0En8b/Le9O+fWeQ9TDwTrKu/Jxrl97LBd9eSNnFb0Fib6yBUj4xPcVE3TzeXbqT+75ah6I0XF8E55o1+AsLWZu3AQCzkkRyxInP0RQEQTgWUSUoNLVytx8bLioqk+0hialETpxAxFVXNsjx7xrengt6JOBXVG5baqbIlAjFe+C9UbD/b9j4LeprA3j0gzms3lOMw6xn+hU9sRh1xz74CQpLaI3TLCHrnJhLa69uEAShaYhKAkFowcKsRraosdiVUnROiT0/fk1iTFusvXsf1/Fe+mkbX63ORifD61f2YmjHWOjwf9D9MojtzIZdP1SP/Wj3XG6a8D18dxvyus+ZaviQLnIm9/5xPVajnqmjO53wOsqq38+eCRNRPR48NxggAgaHOBp8fWZBEITaiCpBoamVuf0YZBdK5XXObrA36PF1ssSL43qQHG7lf79u55zi+/jO8V9iS/bA6wMAkIA0/2eY9FfxzoQM2jXRksNVqxsgK0xwvQ1c1STnFQThcCdVJUFxcTGTJk0iOjoam83GkCFDWL16dZ33VxSF1157jR49emCxWIiMjOSss87ir7/+asSoBeHEFJtbkezzk7FNxX/noxS88+5xHWfBxlxe/nkbZ8mrWZT8FkPbVN4UGCwQ2xmA7cXbq8d/tPEjAjojjHkDzn0aJB0X65bwkmE67y3L5H+/bK/tNPXiLyjAEB8PNivZ4Qo2RaFPROQJH1cQBEEQmqNyjx9J1qoCZSQMxRX49u9H8Xob7ByyLHH3Oe15+qKuFMgRjCy9n1+lvgD4kfnQfzYvqZfxv8t7kpEa0WDnPRZDfikXL1UZs0xBpQy3L9Bk5xYEoaaTJkmgKAqjRo3ik08+4ZZbbuGZZ54hLy+PM888k23bttXpGNdeey1TpkyhV69evPrqqzz88MOkpKSQl5fXyNELwvFzO1rTyudjXwRUtI7BmJJc72PsK3Fxz6y/6CFt503TKyTl/gorXj9s3KFJglJvKTtKdoAkQb+b4LLPwGDD3vMSAJ5fsJU56/Yd/xcDDLGxtPlhHtvfuBFVkkj3+IhJP/G+C4IgCILQHJW6fSC7AbDpLeRPn872M4dQ8Prh1+QTdWmfFD64tg+2iHgmum6jn/tVertf4zXbzcz41+kM6xTb4Oc8GqW0lPGLA5y3QkHWOSmsaLjEiCAI9XPSTDeYNWsWy5YtY+bMmYwdOxaAcePGkZ6eztSpU/nkk0+Ouv8XX3zB+++/z1dffcWYMWOaImRBaBByVDuS9/n5oZXMvGFn81D/++u1v6Ko3PH5WvTOfN62voxe8UL6uXD6rYeN3VFcc47gugPrSA9P135IHw63r+dMWyQT9RuYsTSTu2auJTnCQreksOP9egCsz1sJQKzHROeUmBM6liAIgiA0V+VuP7cFbkXPW4SYQsEfAL0eXVjjrCwwoG0UC+4czMItB9hT4CQ5wsIZ6TFN0oPgn4zJyazubmWbw82ZUin55R4SwkQPIkEIhpOmkmDWrFnExsZy0UUXVW+Ljo5m3LhxzJ49G4/n6A3dXnjhBfr06cOYMWNQFIWKiorGDlkQGoQtoT2tfH4Adpfurvf+X/yRxYqd+bxqmkaUUgBR7eHit0FnqDHO7Xezp2wPAGPaaom0dQfW/SMYbSrA/43syJg2ME6Zx00frabYeWJvA9YXbAHA4osjKVzcMAiCIAgnpzK3H6+sAGAz2ol//DE6rF9H+OWXNdo5TXod53SO4/rBaZzbJT4oCQIA2Wpl7pVt+XKgTMDgo7BU3IsLQrCcNEmCNWvW0LNnT2S55lfq06cPTqeTrVu3HnHf0tJSVq5cSUZGBg888AChoaHY7XbS0tL44osv6nT+0tLSGr+OlZQQhIYS06o9O9za2se7SnbWa98DZR6enLuJa3Tz6S/9DQYrXPoxmGo2KQooAT7c+CGKqhBqCmVI8hCgliRBJb2vjOdL7+Yxw/sMKJvL3TP/QlXrt+KBqijsuuhisu68g33lhQA4bL1E00JBEAThpFXu8SPptOkGVU0LJUlqkCUQW4JQi9YDoVSWKS0U030FIVhOmiTBvn37iI+PP2x71bacnJwj7rtjxw5UVeWzzz7j3Xff5ZlnnuHjjz8mOjqaSy+9lB9++OGI+1ZJTk4mNDS0+tdTTz11/F9GEOohLTaMV103ADBs7n62nn02pfPm1Wnf/8zZSKQni/sMn2sbhj0GUe2qP1dUhW93fMuFsy/klTWvADC+/Xi6RXcDYEfJDj7b/NnhBzaHIve6BoAn9DPI37yMt5bUL4Hh27MH98aNlP30M0VmCAkoJKaNrNcxBEEQBKEl8ZfmcplBu4bbDLYgR9P0HKZQTF4Vp1uHqzg32OEIwimrWaYlFUXBW8curiaTCUmScLlcmEymwz43m80AuFyuIx6jvLwcgIKCAn7//Xf69tU6vJ5//vm0bt2aJ554gnPPPfeocWRlZdVYJqm2WAShMViNelLDI8nzhWJ3FRLYm41n23YYcfT9/txdxDdrc8iQS9BZwyCuI/T+V/XnWWVZPLT0If7M/ROAUFMoU06bwiXplyBJEhO7TGTG3zP4z4r/sL14O//O+DdGnfHgCc64F/avx7j5e14zvsQFP0TTq1U4vVrVrVOyPiaG5LfeYsmKj1DkpXRwBujcqWt9f3sEQRAEoeUo2097/Q4gnBBDCDn33odstxN92xR0p8BynN2XH+CadwNsSo0ms6OYXigIwdIsKwkWL16MxWKp068tWyrnKlsstZb4u93u6s+PpOqz1q1bVycIAOx2O6NHj2blypX4/f6jxuxwOGr8EkkCoSl1jLVi9oTyQy+ZXU9MIPyqK486XlVVnpy7CYC0nsMw3LpCW8qwcrrOz7t/5uJvL+bP3D+x6C3c1vM2frz4R8a1H1dd7n9Hzzu4ucfNSEh8vuVzrp53Ndnl2QdPIklw4WuoUenES4W8qn+Z2z9eRVEduxXLViv2QQOZ3dUKwGr3cDolhNb3t0YQBEEQWgx/RRHlsnaddWCiZPZsij7+WLumngKMCQnav7h1bHfbgxuMIJzCmmUlQYcOHZgxY0adxlZNJ4iPj2ffvsOXW6vallD1l04tqj6LjT18qZeYmBh8Ph8VFRWEhooHFKF5GiMtJi3wN+/HOvgrWWFk+NG7IP+4YT9/7i7CYtBx5/B0sJirP/tq21c8uvxRFFWhV2wvHh/wOMkhhy+rKEkSN3W/ia5RXblvyX1sKNjAuO/G8cpZr9Artpc2yOxAGv8x6ltD6OvdzDXOGdwzK4K3ru5d594C6/L/AqB16Gnodc0yrykIgiAIDUJ1FlJembC3GezE3HcvgeJiZPup8cBs6nUaE2//mhIlifbF7mCHIwinrGaZJIiLi2PChAn12qdHjx4sWbIERVFqNC9csWIFVquV9PT0I+6bkJBAXFwc2dnZh32Wk5OD2WwmJCSklj0FoXmwp55G2/0+ALYVbTvqWK9f4el5m7lSt4CBHVKItQ+v/uyLLV/w+O+PA3BRu4t4uN/D6OSjdzkemDiQmefN5K5Fd7E+fz03LLiBF898kUFJg7QB0elIY16Hz69ksG49z2/awzu/RXLdoLQjHlP1+yn6+GPK28VR7stFRce57TLq8lshCIIgCC2W5CmmXK/dx1psoUTW8364pYsKS6DCImH2FGA4sBMYEOyQBOGUdNK8lhs7diy5ubl89dVX1dvy8/OZOXMmo0ePrlH+v2PHDnbsqLne+/jx48nKymLBggU19p89ezZnnXXWYasmCEJzkpR+Gu082pSYwJ9rKfjgA3x5tXcF/mTFbpwF2Txg+JRztz0K234EYPHexfxnxX8AmNB5Ao/0f+SYCYIq8fZ43j3nXQYnDcYT8DDllyn8vPvngwM6joax7/LnsFm4MPP0vM2s2VN0xOO5N20m96mnKZl8D6iQ4DZzTsfUOsUiCIIgCC2V3lNMReV0gxDjqfeCKtoSDYBFX8B49xf4A0qQIxKEU9NJ8+Q7duxY+vXrx8SJE3nssceYPn06Z555JoFAgEcffbTG2KFDhzJ06NAa2+6//37i4+O5+OKLeeSRR3jxxRcZMGAAPp+PJ598sim/iiDUW2JUODpvDBZFYfx8F3lPPoX7778PG1fi8vHyz9u4R/85VtyQlAHp57KpYBN3L7obRVUY03YMd/a6s95LDZr1Zl4a8hIjUkfgV/3cs/geVuxbcXBAl4u5dEAHRnWNx6+o3PLxakqcviMez37mmWxPk1BliTRvOKlRp16XZ0EQBOHUYvCWUFb5YirEBb7sbNRj9MU6mURbo+m1TeHyH1XisveTWyaWFBeEYDhpkgQ6nY65c+cyfvx4XnnlFe655x6ioqL45ZdfaN++/TH3j42N5bfffuPss8/mxRdf5P/+7/9ISkpi0aJFdO/evQm+gSAcP1mWyLel09HrZV1rifJ+ndDVMkXmtYU7SHJt4RL9Ym3Duf+l3FfBXYvuwuV30T++Pw/1f6jeCYIqBtnAk4Oe5OyUs/EpPqb8MoUN+RuqP5ckiacu6sK9IT9wn/MZ7pm5FlVVDzuOpWsXEl54kudGaTdGiVHDDxsjCIIgCCcTVVUx+Uspquy/E7dkC9uHnk32PfcEObKmE2IIoWOOzNC/VCwHKthfcuTVyQRBaDwnTZIAIDw8nLfffpv8/HwqKipYuHAhvXv3PmxcZmYmmZmZh21PS0vjq6++oqSkBKfTyc8//0xGhpgHLbQMvriedPF4+XiIjh9vPA3rP/7s7i1y8u7SnTxs+EDb0O1S1MSePP7742SVZRFvi+fZM57FIBtOKA69rOfpwU/TJ64PTr+Tm366iV0lu6o/d5Tv4obAp4zW/U7q1neYsTSz1uP8sexlSnUy1oDK+QOvPqGYBEEQjkdGRgadOnVi2rRpwQ5FOAW4fQoPea9ho6yV3Fu9EhgMGOKP3Hz7ZCNJEns6hDJzgIQ71UWOaF4oCEFxUiUJgkncSAjBFtbxDLq5tbK8P3P/POzz5+dvZZiynAx5K6rBCkMfZvaO2czdNRedpOOZwc8QamqYFTxMOhMvD3mZjhEdKfIUceOCGzngPKB9GN0e+dynALhP/xlr5r3Dz5tyq/dVXC4Ul4sftn0NQKI3gW7JkQ0SlyAIQn2sWrWKjRs3Mnny5GCHIpwCipxevBhQddrb89Abr6PDX2uJnnJrkCNrWmWdkpk5WEd5sovcguJghyMIpySRJGgg4kZCCLb0Ln1YVzEMVNhevJ3ckpzqUv71e0v4bs1u7tN/CoA04HZ24eXJFVq/jck9JtMjpkeDxmM32nnt7Ndo5WhFTkUON/98M+Xecu3DjOtQM65HllSe10/n80/e4Y/MQgBKvvuOLb0ziFnsBaBX6yuPe/qDIAiCILQUxU4fSF4kWevXE2GOQJJlZLP5GHueXKLs2vLm+XodpXmZwQ1GEE5RIkkgCCcJm8XE6sTJKK5EnnvbT8Hpw/Dn5qKqKk/M2YgfPV+lPgwdzsPTdxL3LLoHl99F37i+XNvl2kaJKdISyWtnv0aEOYLNhZu5c+Gd+AI+kCSkEf9F6XghRinAdPlZFrzzMLNW7sK1cRMEAhRYJEwBHbefOb5RYhMEQRBODorLRcns2TW2lf36K4WffIKqtJzu+IUVXu4yvQdoPX6semtwAwqSGGsMBr+Ks1iPundzsMMRhFOSSBIIwklkeOdYfM50VEAKKLg3b+aLP7JYsasQk17m4jFj4dKPee6v6Wwp2kK4KZwnBz1Z56UOj0dySDLTVsXE+wAAQvBJREFUh07HorewfN9yHln+iFbhIOuQL34Lf5dx6CWFu+VPmP71Twxw9+LWq9owv6dMRtw52A5ZvlQQBEEQDuXLziZz3Dhy7rsfX3Z29fbCd2eQ+9jj5E9/LYjR1c+BMieDDL8DECOFkD3lNnKfegrVd+SVgE5GUZYo/u+zAAO/sOLenHvsHQRBaHAiSSAIJ5GRXePpWBHC9PN0TL7VxK70dP4zZwNpUg53DU8nKdzKnJ1z+GzLZwA8MfAJYqwxjR5X56jOPH/G8+gkHd/u+JZHlz+KoiqgN6K/+E0Co15kddoNHDClIJlyyU3aTXGIzH0Db2702ARBEITmJbfUzas/b+M/czayfEfBUcfqo6Mxpqaii4okUFoKaKsEhAw7G0OrFMIvv6wpQm4QZUX5lOm1f0/1OChbsIDimbNArw9uYE0s1hrLtgSJCqNMXqmExx8IdkiCcMo5tf7WEYSTXKzDzDOW33ko1Mcuk8SEmdO50pfHHaYvkY1PsqMYHl3+KADXd72ewUmDmyy2QUmDeHzA4zy49EG+3PYl3oCXxwY8hl7Wo8u4lr4Z8Kffz8QfrmNdAQxrNYxWjlZNFp8gCIIQfH9lFTPxvVUUVngJpZzPl6xnRO+OPDGmCwbd4e+2JKORxBde4P/bu+/wKKq2j+Pf2b6bTe8x9N5BBQSRKkUBRaodRAUFBAvCg48FBV8FVFSwACooFlCKWBAQEUUEHpoFCJ2QQAKE9LZJdve8fwSiERBCymbD/bkuLsjZM7O/WXbmnpxprsxMDEFBhW2aRtC99xJ4xx1oxtI9saciOVITSdUXntlnDggm/JnbUY68K+6+PNV8q/F8Bx2fdvAn49ANjE3NpU6o3dOxhLiiyCCBEFVMaPdHGfjDQ0wxB2H0/ZoR6XEY3W6OuXN4aO1DRfchGN2y4m+y2bdOX4w6I//Z8B++Pvw1p3JOMeX6KUTaI3ErNz+NHsjV2ftJbWdj7NVjKzyfEEIIz8nOczL60x1cm7uRJ23LOGw5xU82G3tP2+m4sBYdajWgfVQ7ukZ0QLd9N76dOwOgGY1FAwR/9/cBguwt/yN740bCHn+sohanxFwZJ0jVFQ6EWELDCep4l4cTeUZNv5rkmTQggxv02zmafK0MEghRwWSQQIgqJqhFH3rtuZHd2zcTfFzPUy1DadagFZ/GLSMtL41a/rWY3ml6ud6H4N/0qtULk97Efzb8hy0ntnDz8ptpG9GWrNRTTNywj2g3NHhghJxFIIQQV5gZq/Zyd+b7NPJfy7jgQOKNoWdeUcBhVsUeZlXsdxxfo9FtewF+Yx7iqjHjLjrfgoQE4h54AAoKsDRqiN9NN5Xrcly2rBOknjlbItAS6OEwnhNgCcDPaCejIIuJtrfYlDiYrg3DPR1LiCuK3JNAiKpG0/Ab8j5DDkXS/TeF6YSRtzP2kJaXRuPgxszrPo8gy7lHXCpS1+pdWdxnMW0i2uB0O9mYsJHdmft5s7+J0wNuoE/nER7NJ4SoWvLy8pg4cSJRUVFYrVbatm3L999/7+lY4m+2xqYQufVF0sI2MiYijHijkSBzIPc1uJ2H6vyHgoR7yEvqht0dSo7eiVuDKemfsfzA8qLH/V6IMSqKkJEjCRg0EJ8bKu4yu5Iy5CQVXW4QlVx4U0bldHo4lWfU8K/J1QfcOFf7ETH/VU/HEeKKI2cSlJHWrVuj1+sZPXo0o0dX/GncQhSjNxB1zyOcjvmdunUycYe7aBfVjv71+mPWV46nBdTyr8X7Pd8nJjmG3cm7MevNXHfHdYTaQi8+sRBClMCwYcNYsmQJjz76KPXq1WPBggXcfPPN/Pjjj3To0MHT8a54jgIXixd9gIrcwhq7LwD3Nr6X0S1HYzMWPgawRfhpRn/4C+9l/MCpJqm80roW2/wy2fjrs3x9+Gsmt5tMdb/qF3yPkNGjKv21/Za805z0LxwkaPzBzxz880OiZszAv28fDyereDX8apLk+gPzKQMhBbs8HUeIK44MEpSRrVu34ufn5+kYQhQJGDCAAAYwxtNBLqJRcCMaBTfydAwhRBX1v//9j0WLFjFjxgzGjx8PwL333kvTpk2ZMGECv/76q4cTile+38W3vpsw2H0woGNapxn0qNmjWJ/r6wbz1h2tOLS4GrfkxtEhdzcLg9vzjjuJrSe20v+r/jzc4mGGNhmKQXfu7u0/BwgKTpzAGBFRrstVEo4CF8/lDsGuPwYkY9KbwWjEGBXp6WgeUd2vOuura2zuWkCBqSatClxYjJ65TFJUbY4CF3sS0vnjWOGf34+lEZmzj8Yt2vJk7+bnvWHqlUAGCYQQHpe7ezeOP//E3qUrxvDyfySjEOLKsWTJEvR6PSNG/HUZk8Vi4f777+epp54iPj6eatWqXXQ+zuxU3FmnMIXVh0p+RNqb/HrkGJ8efRqDPRaTzsKsrm/Q/qr25/RLef99au2JwXXnm0xZ8Sb/0X3M8P2/0i20AVNrtmDz6d95fcfrrI5dzfPtn7/g4LM7P5+TU18k/auvqLVsGebatcp7ES9JQloubjTcxkw0wG/OTGr4VoeLXEpRVdXwrUGWTWN9C41Bx5LZdyKTFtUCPB1LeDFHvoPDR//kWNzvnDy5l6y0WJyORCzuZE65A5nv6gVo4DbztH4u9XY4+DRhDPeM+A863ZW3zZdBAiGqMKUUefv3g9uNpVHlPVqf+tFC0leswH/XLqKmTvV0HCFEFbJz507q169/ztl+bdq0AeC333674CDB5mVzCAwNw9igPvFLH2KNbxaWE75gb4Jv48Y0b9CceoG1qelfEx+jD+RlQdJeOLkLTsVAq3sgomnhzFJjIeE3uOoa8I+WgQbgWOoxnl47CL0tCwM23u85h5ZhLc/p50xJIWn2WyiHg5adOuK++2mGfVybmbqZ1EjaxzvpCXzTbigzTqwnJiWGO769g6FNhvJwi4exGCzF5qUZDBQcO4ZyOMj+9ddKM0hwLDUXdLlounwAInwi0HRX5hFMgBr+hTcvjjUaaajFsT42RQYJvIw7P5+C+HiU04WlQf2i9pytW8mLjcXaogWW+oXtztRUkufOA51G+JNPAoX7sEmfLCR740YMvbqiurTD4XKQl5oCT8/A7XYT98w95OSkkpl9Gr/vtxG6M56YJj78r6mBdFXAaWVhxLLjFOhdvNUH8o2F293mh900ParYG62xo54RyMLKEtrsc1OghyerW1EGK9fnvkfevJ+4664F+NjPPfNIKUVOvovsPCc5+S6cLhdOZz75uZnk5meQobdQ4HZiOHYcnSMFd7AV9DqUcqPLzESXlo7basYZ5IvDtwZK0zDHxWHIPo07xIoyGdEAXWYOhpQMlM2MMyyQPP+aaJoR8sqvjsgggRBVWMr8BZyaPh17t25Ue2u2p+NckK1NGxx79hA4aJCnowghqpjExEQiI889ZftsW0JCwgWnDXxxHgnV9Dw91AARADam/pJH/YQdvNbvN75IXgRArROKR75xkR7sZk+3PGoWOAl1uTjy9WGysu3srFubq4Li6ZzzNW6HDt0JO8n2IGIaVCfFFEaG0Z/IY6exOXLJqOGHO8yC2egGhwO/42k49G5ORJopcBdQ4CrAnOnA6XaSY9Hh0oOGhr/ZnwBzAGHWCAKN1TG5olG5YbiUGbNRT3SglaY+GVxlykazBYF/NfjHU27yDh7EsXcfPte3xxBYeHd9Z3Iymd+vxdKkCZamTcrsuv749DjuW96fJFMefi6NN3q+R8uwZuftawgKosaHC8hY+R1+t9xCF00j/OHhPPBhNE/nTqdN/j6u+2MzXz6wjJe3TmfN0TV8sOsD1h5dy+T2k2kd0bpoXppOR+SLU8mPi8enbZsyWZaycDw1m6esbzELCDIHnjO4caWp418Hg6YnVVOYTqVgf3s6qsM7lf6+EpWFKijAlZEBgCE4uKjdsW8/rtRUzPXqFrUXnDpF1vr16Ky2Yve/SP38c/IOHMT/lr5YmzXDrdxkHdxH0v+9jLLb0E+dQJ4rD4fTgfb8Gxh/2U7Kw/1J7tqMvIIc3LEnaPHEB+T5Wlg+pQuOgmwczlw6f7qfhn+ms7pHIBtaW3AAxkw9L80/Rp4B+oZ+ghM3BZriwe9cdP9Nscj1C8vSCgfNfHMU7+9wAfDU1meLBlyHxrlodlixKTyD7ehBA3OB4toDhX3f6lO4vfNxK649qtFrs5vNbXwwXVMLg9WO0hsZ99LPAEx6IpRDulR+9LFh2RrPH5268HszK5s7hhOrq0l2bhbjVuzD35nNZ7e6ybCAQ6fReYui76+wrrnGBz3/2r5+PMOJyQmjH9aTFFCYt/f/3Az9wc2Gxhqzbv2r73uvO/HLhcce1HM8pLBvt51uRq5ys7WexoyBf/V1nGxc2q/KBckggRBVmE/7dmhmMzqzCaVUpS2uAQP649//Nk/HEEJUQbm5uZjN596w1WKxFL1+Iaf9IcsHjEpRoGm0yDTjrtaQk3lHSTPUxC8nCYP5JEGZGtFJ4DDo+MLPt2j6F1bvouEx2HfV/1ht17HaHkLjo24m/+DGHJzGK42zwLUHXNDvexctYhWz+urY0LRwZ7h2ouLlBS5O+8Go0X/tsj2x1EXb/YoPuutYdW1h3+AMxW1fuTjtpzG7rw40DaNS+J0Ow+GoTgp1GObcxSRW4crXyDrtg9sWQWDX9lDzBqjZgeOPP0He/v1Ev/M2vl26AJB34AAnJk/GEB5O3fU/lv4/BNh9ejcjvhlKhj6PCKeTiTXGce1V5x8gOMvaogXWFi2Kfm4c5ccHj/Tlic8i+Tl2Pl+faEfNxUeZ2m8Kvat15cXtrxKXGcfw1cMZUG8Aj13zGP5mfwCMkZEYzzNw5EnpJ+Oob94LhNJzn5X4h0fh17s3/n16ezqaR1gMFuoF1udowh5SNwTTwvUT6du2E9D6Wk9HqzBKKVCq6IyS3D/+IG3JUoyREYQ8/HBRv6P33Yfjz11cNXMm9hsKb8SavXkL8Q8+iLlRI6KXLCKrIIus/CyynpuE9tseUp4eTnK7BmTlZ6H/cz/Nn11MZpidz3XLyczLJKsgm4ELj9PwYB4zUxbzQzONPM1FjZOKGZtcpPrAyC9/KcowLsHF9XmKr3Z9wXfWpQCEpiletkCm0cGyk389TcYa5iaznsYf9nT2uzML2/SKFddp5OshV3MV9f2liY7DkYrYcLC53RiURrYWwGs3uwA99R1p+JGH0a0jp7ae9YEGLIEW7sryxWAKJyW8L7vv2k+o2cLHXW6iTnQ9TGYrmbXWk9NgMwNbX8t9N95Y+Hnn5xN37f24c3NZdu8nHMiO5bXvXqdG+noCsiHL5WC7/hhwDKyKVkdd6BTE6/SkmQr3r3P1bkxON5aCs/+JOkBPlsWFyamwKjd2l4YGaHpFhg1cJkWgy006/ig0Uu3pOPUu/N1u8pyF8zXpFal2KLAoQpxuUjV/3Oiw6f6qN2VNBgmEqMLMDRpQf9Ov6Gw2T0e5qMo6gCGE8G5Wq5W8vLxz2h0OR9HrF9Lsy3X4+fkRvieG//36Lb1uHcXVYwpPOe2kFAmH/iR751KO1ihgx7BcTppMtA20k+FMxOHOYG+To6REFKCLCqWGjx2UHn/fXA7XOEq2L3TJN+CXn4qPK4/8kAYcy8tEMzXi+pRj3KB24ZuhI99uw8fi5pWTqRgpHLA4nNsCiCc1swv94+PpY9hEboqR0Hg/HD4urs/NZ4/ZRKpezz0bT9A+JpF3bt7KZy10rHRF0+NIPoN+NuC0Z5IW+inVdnyEAbA0uBedzYam/+tIlc5mw+eGGzBeFVVsO33ihSlYGjfC/5Zb0EymS/q/UErxyZ6PeXXrKzg1Nw3y8hlhH8CNNz503v4pH3+Cb/cbMYaHn/f1ELuZ+fe3Z8GvUSSu2svR/Unc+NpPfBG9lGWOJN6ofy1fJG1j6YGl/BD3A2NajmFA/QHFbmzoSksjef4CQkePuuTlKA+OpMMkGs482eCoi6xff8TSqKHH8lQGzUObE5MSw/fNriI/swbXO0x08nSoUnC5XexO3s22k9s4mnGU45nHSchOINeZW3SWkEFnwGKw8ODidOofyuWzB+pyuk4QRp2RujHp9P38D5Kq+/Nd0zgMOgNu5ebGpP1EZGXx7qYZ7E2bTabLQeiBDEYDB07v5daPrynK8LDLRd0Q+HjfAna6CgcfIpMVd9fTSLFns/r0/4r6BjZwsydMY2+oi7wz636SP8zqoyPHpMPucmNVbsxK8XNHxbb2CpfZyfU5Cofbh81ac+65z4CmjPRL24KvcqJHj6GemZR6RlpipHWaCYcxjD+D+3L8FitWg5Wn0n/CT1OYjHYszexYTL6YrX4YzT4YbAFoUS2wmfRYjXqsBoXZZPr3fchbzm3y7dwZ386di7VpJhM1Pl5Y9HMDcwPm3PUO66pt43/13sXPEMfgPB1JUZ0J8w/i4MgjWA0uJl9TD7/AUGxmXyxt9VjG6Lk9JJoHgqIwGs8MUA8r/Ovb88RrCzz894bhhX9df56+7YH7//ZzRkYG/k++f+FlLwVNXezhsuJfZWRk4O/vT3p6ujzdQIgSytmxE3dmBj433HBFX3spRHm60utU9+7dOX78OHv27CnW/sMPP3DjjTfy1Vdf0bdv32KvVehnphSkHIbgOn81rZyAe9dSyMtE78rDhZ58zUy2ZiMNO08ZJnBchaIDOlkP0MpwBH+LH+HJWQT5+hJ1Sw+wBHCMfE5OfB7b5j18M6gan9U7hdPtJChDMWGJi8QgjTdv0aHTNKILXMQVtKCaby2ujqxPv7QNNMxNxadeD6h7Y2G+Mzvijj17ONJ/AOh01PluJaYaNS66mDHJMbz46//xe8pvAHTOzuE2Sz+6Dn/1vP1TP/+cE88+hz40hDrffov+Iv8PB09l8eyKXew4lMBa85NEa6cB2BrVmBf9bRxynAKgbkBdJraZyHWR16GU4ki/28jbt4/gB+4n7MzTLzzh1WnP4rZ+yEf+foy23czg3CZYr74Ga9MmHsvkaSsOruDpjU8TbGhA7J/3cUuLKN68o5WnY5VYcm4yH8d8zFcHv+JU7qlir/nmKDr/qfDLUXzS5a/BuacWuWh5RDGvp47vry7cPwrKUHT5Q5HkDz83+2ufKTxFoVOQ4gt5Z45oa2d+vVN/++XZ5nZjd7vxdbvxcSvsbje5bl+2OFuC24xymRmurcOqNIyYMGLBoLNi1Hww6u3kWGpxKLwfPiYLvmYTdTO3YTKZMFjtmMw2TFY7ZqsNq80Hi80Xm9WK1aTHpNfJgaByUp61Ss4kEOIKUZCQgD4oCJ2lclznqJTi1Kuvkrt9O6GPjiPkofMfSRJCiNJo2bIlP/74IxkZGcV2orZs2VL0ukdpWrEBAgDt5unob55e+IPbhV6nxwpYgRDg82K9iz8q8O+qAdXmL0G5XDRyuRinV+xN2csfSX/wc4td7E85hCEjlgKVx1GTHky7iHPvIu44fHlmHuG7Y6j12yvUUBZqBzekbo2O1KzenbCJE3CeOFlsgCDv4EFMtWv/dXq0M5dNCZtY8Ocidp7eDChMbsVjKWnUjx5NmyGTLpjd3qkTptq18b+l70UHCADqhtn55IG2fPNHInd/8ya35ixlhOEbWifsYUkCfB5Zh7d9DBxMO8iDax6kS7UujL92PCFjRnPqlVfwu/nmi75HeXG63Jiz4tnlZwQgqPk1BDUY7LE8lUWzkMJLUDJdh3nc8Bnzdw8gPbcp/lajh5Ndmvysk7y3fhLzk7fjwA2Ar8tNa4eDhvn5+AVdTUHIbbRdNw2XXs8d1RNxmRW5mkZ+EyMZzTT6BrrpcUrjf6oOH7lu5JM6LjTNyVPJC3FqGjoUJgUmpdDSdBRg4pCzFgvyb8Vu8iXQ4stI1zfYjL7obEHobYEYfILQ+wRisgdj8Q9lgn8o/lYjfhYjdosB/SXfyb9e+X14wuPkTIJSOjuCU79+ffR6PaNHj2b06NGejiVEMUlvzuL03LmET5hA0L33eDoOAMrp5PTcuaR++BG1vlxe6a4PFaKquNLPJNiyZQvXXXcdM2bMYPyZI8V5eXk0bdqU4OBgNm/efM40V9Jn5lZuTmSf4GDqITbGxbA9YR9HM46QzzHchgvfr0GPlQBDFEGmaCI1PUEuHwb939ekB9n5dGhTDusTOOk6hktzF01jy6jLO5n7CO45kxrX9DxnnsrtLnZWmTs7G81mK/FRyNx8Fws3x/LZjzu5reBrhulX46flkq7TMatpD5Zk7cOlXBh0Bu5pdA8PNhyGrz2oRO9Rlg6eyuLPWYN5q+YRThoMfHTTR7QK874j5mXNrdx0WNSBzPxMFiac4IvEO2lnCaf3tEmV/sj0vpR9PLn+cY5kxgHQzJHHAwmZNNxpQG8Fd0MDK11tmeK8h8d2LOJgYDRDaq+nwGCkAAP5GCjAQIEq/DuGGqyy9CbIx0igzUQHtROTjx8WexBWvyB8AkIJ9PMjyNdMkI+JIJsJg17O0Kzq5EwCL7B169YqvyMhvJchLAycThy7d3k6ShHNYCB01CiChw3zinsmCCG8U9u2bRk0aBCTJk3i1KlT1K1blw8//JDY2Fjef798ruX0JjpNR5Q9iih7FB2r3VDUnpKdz5q9h9kSv5dDp2Nw5ezEoI8j15TJCaMOl5ZLsvMQyc5DHACaHXFTgJsMdxob2YRya6BBYK6eJEc7Wgf15r4ebWhVNwhNf+7uZ8727Zx4YQqhjz1adK2wzsfnspbJatIzomMd7mxbg/m/tKD3L/3om/8dg/TrWf37QG5s5U+S6Qt2JG1m/u75rDi0grGtxtKvbj/cJ09hCA6u0PsT7EnMIFJ/jJMGA7VOKKL3puCyZFzSGRRVmU7T0eGqDnx35DvWm2zcue57dE5FcuerCbm5l6fjnSv1KI6YVUxz2FgWPxM3Bfg49Vx7qiaOzJasOZpG5P7VJFv8GFP/OQL8rVxjN7Or8TiCfUxs9BlKoK1wECDIx0TUmV/2A31M3GkxMLXYwEg7jy2muDLImQSldCUdbRDey52fT96ePVg9fVrtGZX5SQtCVDVSpwpvUvjMM8/w8ccfk5qaSvPmzZkyZQo9e557NBvkMzsfpRTJ2fkcS83laFISu5OPEZsRS272buqnf02uVoApF4JyFDp/F5FuI9FaJH4fZ2Fu0IRqM2diDA8DwJWVjTuz8JfgswMByR/M59T06Zhq16b2118Vu3liaTkKXCzbcZz3Nxzi0OkcADRN8WLoFD7zTSfWVHj6et+TUdz9RTKBvW4i4oUXKqxOTf7yDzru6ce4q3x4/Dsj1/2WS8iohwkdO7ZC3r8yWx27mvE/jad6QQHzVubyy6kmZI/9D/cN7ujpaMUkbFlOwJoxfOqj443gAAD8TtQi6PTNRDS7lpbVAqgbZKHG3BmEDexPYCe5F5MoPTmTQAhRKjqTqdIMEKR+9hmZ69cT8cwzmKKjPR1HCHEFsFgszJgxgxkzZng6itfSNI0Qu5kQu5mW1QK4tdj1yK8U3oCxIAc0PRjMoGnk/vknsXMGU3DoEIbgv07nP/H882R8/TWRL79EQL9+QOGjcAsSEwkZ9XCZDhAAWIx67mxbndtbV+OnA0l8+GssG/adwJUSzcfpR/jK38Q7Af4cyzyGynJzaOeP+GelYfMNLNMcF7I1Lp1PdHdh4UsMISEYq2tYmjatkPeu7G646gZMOhNxRshqdZqtuY355s8cunfPITrQ82chxqfksG3x/9HnxGymhQSw+MwjUAfGXs2gpb9hbbiOmtNH/jXg9NYbHkwrxKWrUkNYaWlpjBgxgtDQUHx8fOjSpQs7duy45Ok///xzrrvuOgICAggODqZTp058++35HlYhhPdyZWVx+t05KJfr4p3/Jmf7do4/MR7H/v0opUhfsYKEif+hJCcjubKySHr7bbJ/+pn05V+WMLkQQohKS9PA5ANGS9FTEKzNmlFn7fdc9eqraIa/jksphwOMRpwnThS16f39ifjvUxgCy+8Xc51Oo0uDMBbc14a147txuO0L3MS7xCbfwpy4PGqH5fDyIB1j+6Vx1zf92Jeyr9yynJWSnU9MYgZ6a+G166nD+1B3zWrsXbqU+3t7A5vRRvuo9gAs87fzqPU78goKmLDkDwry8j2Wy+VWvP/zAda9Ppxep2bzRHgwi/180dB48toneerhGeiNRjSDEVdamsdyCnG5qszlBm63mxtuuIHff/+dJ598kpCQEN5++23i4+PZvn079er9+x04Z82axdixY+nduzd9+vTB4XCwYMECfv/9d5YuXUr//v3PO52ckii8iXK7iR04CMeePYRP+g9BQ4de8rRxDzxI9i+/EDB4MMEjHuTQTTdDQQHRb83Gt1u3S55P3uHDZHz3HSGjRsklB0JUAKlTJSefWflSbjdoWqWoAdl5TpbtPM7HGw9RLfkXOvgtZ0FoHskGPXrNyJPVhnFH5zHotPI5rrZk+zHGf7GTgAbTcenSmNN9TtEvxaLQr8d/ZeTakVjdijXxx5maPwoVk8+dxzbReME8rI0aVWie2NPZPPP5Ju5NfJGWpt8YFxZKUo6JZsf09HpkBj1qFj5xJP/YMYxXXVUpvueiairPWlVlBgk+//xzhgwZwhdffMHAgQMBSEpKon79+tx00018+umn/zp9/fr1CQgIYMuWLUUrc0ZGBldddRVdu3ZlxYoV551OdiSEt0n/+htOTJ5MrRVfluh0f8eePZyeO4+wJx7HVK0aye+9h3K6CL5/OJrx/I8jyjt8hLSlSzDXrUfAbf3KaAmEECUhdark5DO78iil2HQomQUbj5Bx8Ft+D99Nl9g9PLzSzZe3hHH/kJFEtbyj6CyJsjL8/Y08lDCU+6tbCc/U8/VDm7AarGX6Ht5OKcWQb4YQkxLDAzku2lZ/hsSpC2iYGkdMzyH0m/kcukt+bN/lc7kVH22KZdqqvfRx/8h9Ph8wNjwMMnXMnOfCgI7ay5ZjaVC/3LMIAeVbq6rM5QZLliwhPDy82BH/0NBQBg8ezIoVK8jLy/vX6TMyMggLCys22ufn54fdbsdqlY21qDr8+/ah1rKlxQYITrzwAienTceZmlrUlvH99yRM/A8Zq1YBYGncmOjXZ2KqVg2A4AceIOShkUUDBInPPse+Nm1JW7a8aB4Fx4+T8v4HnHz55WLzFkIIISoTTdNoXzeEuUNb88rjE7i39gs0218Dgxusx5O4Y+dUlszuQmLs3jJ7z0NJWdgOfcchWy5+2Yo33s4jceCduLKyy+w9qgJN0xjZfCQAC3xM+F7XnIznX+Wt5rfxuOVa7nxvMzGJGeWaYWdcKne+s57nv96Do8DF7trhDL0qmlMGPfaadbB0vB57++vR2eR3BlE1VJkbF+7cuZOrr74a3T/uFNqmTRvmzp3L/v37adas2QWn79y5M0uWLGHWrFn07dsXh8PBrFmzSE9PZ9y4cRd9/4yM4hsns9mM2Wy+vIURopyZatQo+rczJYXURYvB7SZ4xINF7Xl795G+YgUYDfj1uvijhvJjY3FnZID7r3sd2Nq0xr9/f+wdb0Bvt5ftQgghhBDlIDrQxqSbG5Hd9Uu+mTmDlcFfkqJ38LxfMnevuJnq2gCq93qC9vXCL/tUcpdb8cKXv/OMYSnP+PpQL06BTodmNKK3X96jH6uyrtW70qNGD9YcXcP49eOZ32s+zTJDOPF7HJsPa9zy2o88mbyZ0PuG0eWaWgTYLv8Rlo4CF/EpORxKyubP+NOk7P6R5mk/ME+/hb62p+nk/p4Gy/Yzs5+OG2p1ZFrHafj0MqKZzXJpgagyqszlBna7nSFDhpzzzOOVK1fSu3dvVq1adcFHHQGcOnWKO++8kx9++KGoLSQkhK+++op27S78LNKzp3n803PPPcfkyZNLviBCVDBXVhYZK1eSf/gIYROeLHokT86OneRs24alSWPs119/0fk4k5JwZWZhCAm+4p/tLERlIqfOl5x8ZuLvsvNzefK7J/kldT2Df3YT28zJkEwrS6yPcn2HLvS/Ohq7+dKPu6Vm5zNx6R803/8mPawrGRAdiUHTs6b7MnwznHK6+gWkOdK4/dvbOZ51nKts4bx6aDdNHDkcM9Vm///sRO49zZHgCF7qeA8+wdFEBAcQYjdj1OvQaaDXaYUP4XC5yXe5KXApnE4XeQVOknNdpGTn4chKp1bBIRro4mmpO0gn3R+EaBkcMBr5xM+Xr3x9mTavgOhkODq4Pd0nz0WvK9uncQhxqa64RyC63W7y8y/tjqXmM6N2ubm55z1yb7FYAMjNzf3X+dhsNho0aEB0dDR9+vQhMzOTmTNn0r9/fzZs2EDdunX/dfr4+Phi/zlyFoHwFnq7ncDBg89pt13dCtvVrS55PobQUAyhoWUZTQghhPA4H5OVt2+dzZYZ/8Hv1xWc2q3nqQcLmJjyNNNWvMj0VZEMvCaa3s0jqRdmx89iRKfTUEpR4FLk5DtJSHMQl5LDpkOn+XbHER50fcZIw7c8EhgCQJfqXQmNrA2RHl7YSizAEsD8nvMZvno4x7KOcVdkCP0ys7kjI562URonjgfQvm4MG8yP8WZaP94+cSu9YrdwMtyfKcEL0OPGqDkxUYARFyacGHHymnMgs123AdBSi2OReQrZmsYRnZHff/ejICWKiffoces0QPFnn4ZU17fkxgcekQECUWVVykGCn3/+mS6X+OiXmJgYGjZsiNVqPe99BxwOB8BF7yswaNAgDAYDX3/9dVHbrbfeSr169fjvf//L4sWL/3V6Pz8/OdoghBBCVCGtW7dGr9czevRoRo8e7ek4wsNaDhhB7Po/2Ha1kwxjIv8ND8Lsv4HsuO4s+jWbvZtXEu8OxYEJnd6IchUU/kKqOUlVdjIovOxumuE9hhjW86Xdh+NpVhql6Xik3yMeXjrvEGmPZFGfRUzZPIXVsatZ6uvDUl8fAqJN1GmtUS3PjMWZS3ZoHsOPrKLvNxtIOm3ny9scuNFwa1BnmwV9vsaeFi5O+utI9DlI3xNv0uHXUxwJV7RqXx23BijF+0td+DqgyXE9Ydd3YWiTobQKu/QDKEJ4q0o5SNCwYUPmz59/SX0jIyOL/k5MTDzn9bNtUVFRF5zH4cOHWbVqFXPnzi3WHhQURIcOHdi4ceOlRhdCCCFEFbF161Y5ACCKmGvXpt7yLxmr17D+OY85f8whOmULXXK2c7RxU8Ynr6FxXj62s1fy/u3BPzO0+/gpaABNo/wwhd7N9JjD/JADL3/mxORyE9L2MHSr5ZkF8zL+Zn9e6fQKdzW6iw93f8gvx38hzZXHdh1st2qADRy7aeZwU6OmRkJQNh8E/HVp8Ft7nYRmwActLBzy0YBkrk9203yfG0MOuK8v/PUo0BLE9n5B1KveirdvG0FAoJzmIa4clXKQICIigmHDhpVompYtW7Jhwwbcbnexmxdu2bIFm81G/foXvr7r5MmTALhcrnNeKygowOl0XnDas2cvXOzpCZVRXl4eL730EpMmTfK6yyO8OTt4d35vzg7enV+ye4435/fmOuUp3vyZefN3FSp/fp3JhA4Y1XIU10W05fSwkVSLzebzzJ3cd0M4AIEuF3a3G5tbYVNgBRyBMViMr3HoaALfZuWABTArkptXo57xKmxt23pysYDK/9n/U6uwVrQKa0WuM5f9p/fz5sI3ubbbtTg1JwXuAgoaFXCwlxOdpuNuTYde06PTdKT0/oPsDAe3tW2DOSoKP5Mf/o1yKah3mBa167Gu3fX4mf0w6yvmM/C2z/2fvDm/N2eH8q1VVebGhYsXL+b222/niy++YODAgQCcPn2aevXq0bNnTxYtWlTU99ChQwDUqVMHgKSkJCIiIujYsSPr1q0rujPpsWPHaNSoER06dOC777477/seO3aMatWqER8fT3QJnjlfGXjzjZm8OTt4d35vzg7enV+ye4435/fmOuUp3vyZefN3Fbwrv3K5SPn0UxLnvM1XT17HdznbSMtP44Zdbu74yc2vjTQ+7lp4zbrepfjoVRd6F4wa70OD6ldzd+O76RB0LZrFUnTTYE/yps/+nyS753hzfm/ODuVbqzy/RSojAwcO5LrrruO+++7jhRde4O2336Zz5864XC6ef/75Yn27detGt27din4ODQ1l+PDhrF+/nm7dujF79mxeeukl2rVrR25uLpMmTSrTrG+99Va59L2c/iVV3nnKM783Z7+c+Zfn96ykrqTP3puzV0T/8px3Zcp+OfOvTOusKDlv/r56c/bLmX9ZrWuaXk/wPffQ5KdfeOrWmXzb+1v2jNrDwwH9CMmAngHtea3za7zY4UWmd30NfUAAOl8fvmk9j7k95tIxuiPvzJ9fogECb/7svTl7RfQvz3lXpuyXM//KVB+9/bO/ZKoKSUlJUffff78KDg5WNptNderUSW3duvWcfjVq1FA1atQo1lZQUKBmzZqlWrZsqex2u7Lb7apLly5q3bp1//qe8fHxClDx8fGXnLNRo0bl0rek/dPT0xWg0tPTK0WekvT35uxKVb783py9pP3LO783Zy/P/t6cXanKl78kfS+nTl3pyru2l2f/yvZdLWn/ypb/crKnxsWpnN9+U45Dh4q9XpCSotwu12XPv7z7S330TH9vzq5U5cvvzdlL2r8863ulvCfB5QoMDOS9997jvffe+9d+sbGx57QZDAbGjBnDmDFjSvSe6szVGpmZmWRkZFzSNC6Xq1z6lrT/2X7lNf/y7O/N2aHy5ffm7CXtX975vTl7efb35uxQ+fKXpG9mZibwV70SF1fetb08+1e272pJ+1e2/JeTPUvT0NUqvAlh3t+n1eshK+uy51/e/aU+eqa/N2eHypffm7OXtH951vcqc08CTzl8+HDRvQ2EEEKIyurQoUPUrl3b0zG8gtR2IYQQ3qI86rsMEpSS2+0mISEBX1/fohseCiGEEJWFUorMzEyioqKKPf1HXJjUdiGEEJVdedZ3GSQQQgghhBBCCCEEUIWebiCEEEIIIYQQQojSkUECIYQQQgghhBBCADJIIIQQQgghhBBCiDNkkEAIIYQQQgghhBCADBJcUF5eHhMnTiQqKgqr1Urbtm35/vvvLzrdsmXLGDJkCLVr18Zms9GgQQOeeOIJ0tLSyj/0GZeb/Z+6d++OpmmMGTOmHFKeX2mzL168mHbt2uHj40NAQADt27dn3bp15Zi4uNLkX7t2LV26dCEkJISAgADatGnDwoULyznxX7Kysnjuuefo1asXQUFBaJrGggULLnn6tLQ0RowYQWhoKD4+PnTp0oUdO3aUX+C/KU32H374geHDh1O/fn1sNhu1a9fmgQceIDExsXxD/01pP/u/e/DBB9E0jT59+pRtyAsoi+xr166la9eu+Pv74+vryzXXXMPixYvLJ/A/lDb/9u3b6dOnDxEREdjtdpo3b86bb76Jy+Uqv9DA1q1bGTNmDE2aNMHHx4fq1aszePBg9u/ff0nTe3J99SSp7Z6p7eDd9V1qu+e2Fd5c3725toN313dvre1Qieq7Eud1++23K4PBoMaPH6/mzJmj2rVrpwwGg9qwYcO/ThccHKyaNWumnnnmGTVv3jw1duxYZTKZVMOGDVVOTk6lzv53S5cuVT4+PgpQo0ePLse0xZUm+3PPPac0TVODBg1S7777rpo1a5YaOXKk+uijjyogeaHLzb9ixQqlaZpq3769mjVrlpo9e7bq2LGjAtRrr71WIdmPHDmiAFW9enXVuXNnBaj58+df0rQul0u1b99e+fj4qMmTJ6vZs2erxo0bK19fX7V///7yDa5Kl/2aa65RtWrVUhMmTFDz5s1TkyZNUr6+vio8PFwlJiaWb/AzSpP/77Zu3aoMBoOyWCyqd+/eZR/0PEqb/YMPPlCapqkePXqo2bNnq3feeUc9+uijasaMGeUX+m9Kk3/btm3KZDKpJk2aqNdee029++676tZbb1WAGjt2bLnmHjBggIqIiFCPPPKImjdvnpoyZYoKDw9XPj4+6s8///zXaT29vnqS1HbP1HalvLu+S2333LbCm+u7N9d2pby7vntrbVeq8tR3GSQ4jy1btiig2Jc4NzdX1alTR7Vr1+5fp/3xxx/Pafvwww8VoObNm1fWUc9Rmux/71+zZk31wgsvVOiORGmyb9q0SWmaVmFF93xKk7979+4qKipKORyOoraCggJVp04d1bx583LL/HcOh6OoaG7durVEG9TFixcrQH3xxRdFbadOnVIBAQHqjjvuKI+4xZQm+08//aRcLtc5bYD673//W9ZRz6s0+c9yu92qXbt2avjw4apGjRoVtiNRmuxHjhxRVqu1QoruhZQm/4MPPqhMJpNKTk4u1t6xY0fl5+dX1lGL2bhxo8rLyyvWtn//fmU2m9Vdd931r9N6en31FKntnqntSnl3fZfa7tlthTfXd2+u7Up5d3331tquVOWp73K5wXksWbIEvV7PiBEjitosFgv3338/mzZtIj4+/oLTdu7c+Zy22267DYCYmJgyz/pPpcl+1vTp03G73YwfP748o56jNNlff/11IiIiGDduHEopsrKyKiJyMaXJn5GRQWBgIGazuajNYDAQEhKC1Wot19xnmc1mIiIiLmvaJUuWEB4eTv/+/YvaQkNDGTx4MCtWrCAvL6+sYp5XabJ37NgRnU53TltQUFCFrLNQuvxnLVy4kF27dvHiiy+WUapLU5rs7777Li6XixdeeAEoPD1QKVWW8S6qNPkzMjKwWCwEBAQUa4+MjCz39bZ9+/aYTKZibfXq1aNJkyYX/d56en31FKntnqnt4N31XWq7Z7cV3lzfvbm2g3fXd2+t7VB56rsMEpzHzp07qV+/Pn5+fsXa27RpA8Bvv/1WovmdOHECgJCQkDLJ929Kmz0uLo6XX36ZadOmVVgBO6s02X/44Qdat27Nm2++SWhoKL6+vkRGRjJ79uzyjFxMafJ37tyZ3bt388wzz3Dw4EEOHTrElClT2LZtGxMmTCjP2GVi586dXH311ecU4zZt2pCTk3PJ11FVFllZWWRlZVXIOlsWMjMzmThxIk899VSpd0gq0tq1a2nYsCErV64kOjoaX19fgoODeeaZZ3C73Z6Od1GdO3cmIyODkSNHEhMTw9GjR3n33XdZtmwZkyZNqvA8SilOnjx50e9tVVtfL5XUds/UdvDu+i61vWptK7ypvntrbQfvru+VrbaDZ+q74bKSVnGJiYlERkae0362LSEhoUTzmzZtGnq9noEDB5ZJvn9T2uxPPPEErVq14vbbby+XfP/mcrOnpqZy+vRpNm7cyLp163juueeoXr068+fP55FHHsFoNDJy5MhyzQ6l++yfeeYZjhw5wosvvsjUqVMBsNlsLF26lFtvvbV8ApehxMREOnbseE7735e9WbNmFR3rsr3++uvk5+czZMgQT0e5JC+88AJWq5XHHnvM01FK5MCBA+j1eu677z4mTJhAixYtWLZsGVOnTsXpdPLSSy95OuK/evDBB9m9ezdz5szhvffeA0Cv1zN79mweeuihCs/zySefcPz48aIjNxdS1dbXSyW13TO1Hby7vkttr1rbCm+q795a28G763tlq+3gmfougwTnkZubW+zUsLMsFkvR65fq008/5f3332fChAnUq1evzDJeSGmy//jjjyxdupQtW7aUW75/c7nZz556mJyczKJFi4o2/AMHDqRZs2ZMnTq1QgYJSvPZm81m6tevz8CBA+nfvz8ul4u5c+dy99138/3333PdddeVW+6yUJbrjKf9/PPPPP/88wwePJiuXbt6Os5F7d+/nzfeeIPPPvvsvP8HlVlWVhZut5uXX36ZiRMnAjBgwABSUlJ44403eOqpp/D19fVwygvT6/XUqVOHnj17MmjQICwWC5999hmPPPIIERER9OvXr8Ky7N27l9GjR9OuXTuGDh36r32r0vpaElLbPVPbwbvru9T2qrOt8Kb67s21Hby7vlem2g6eq+8ySHAeVqv1vNdsOByOotcvxYYNG7j//vvp2bNnhV1LdLnZnU4nY8eO5Z577qF169blmvFCLjf72Xaj0VjsiI5Op2PIkCE899xzxMXFUb169XJIXTzH5X5vxowZw+bNm9mxY0fRKUKDBw+mSZMmjBs3zqM7d5eirNYZT9u7dy+33XYbTZs2LRo9ruzGjRtH+/btGTBggKejlJjVaiU7O5s77rijWPsdd9zBqlWr2Llz53lHxCuLl19+mTfeeIMDBw5gt9uBwvW2S5cujB49mj59+mAwlH+ZPXHiBL1798bf37/o+ul/U1XW15KS2u6Z2g7eXd+ltleNbYW31Xdvru3g3fW9stR28Gx9l3sSnEdkZOR5n6F6ti0qKuqi8/j999+55ZZbaNq0KUuWLKmwL9PlZv/oo4/Yt28fI0eOJDY2tugPFF4TFRsbS05OTrnlhsvPHhQUhMViITg4+JyVJywsDCg8ZbG8XW7+/Px83n//fXr37l3sGiKj0chNN93Etm3byM/PL5/QZaQs1hlPi4+Pp0ePHvj7+7Ny5cpKO8L9d+vWrWPVqlWMGzeu2HrrdDrJzc0lNjaWjIwMT8e8oLPfi/Dw8GLtFbnelsbbb79N165di3YizrrllltISEgo2oaWp/T0dG666SbS0tJYtWrVJa1rVWF9vRxS2z1T28G767vUdu/fVnhbfff22g7eXd8rQ20Hz9d3GSQ4j5YtW7J///5zVsCzI74tW7b81+kPHTpEr169CAsLY+XKled8ycrT5WaPi4ujoKCA66+/nlq1ahX9gcKdjFq1arFmzZpKmV2n09GyZUuSkpLOKbhnrxUMDQ0t+8D/cLn5k5OTcTqduFyuc14rKCjA7Xaf97XKpGXLluzYseOcm9Fs2bIFm81G/fr1PZTs0iQnJ9OjRw/y8vJYvXr1ea8/rYzi4uIA6N+/f7H19vjx46xbt45atWrxwQcfeDjlhV1zzTUAHD9+vFh7Ra63pXHy5MkLrrdQeBS3PDkcDvr27cv+/fv55ptvaNy48SVN5+3r6+WS2u6Z2n42n7fWd6nt3r2t8Mb67u21Hby7vnu6tkMlqe8lemDiFWLz5s3nPBPX4XCounXrqrZt2xa1HT16VMXExBSbNjExUdWuXVtFRUWpI0eOVFTkIpebPSYmRi1fvvycP4C6+eab1fLly1VCQkKlzK6UUjNnzlSAmjt3blFbbm6uql27tmrcuHG55j7rcvM7nU4VEBCg6tevX+y5qJmZmSo6Olo1bNiwQvL/3b89UzYhIUHFxMSo/Pz8orZFixad81zWpKQkFRAQoIYMGVIRkYuUNHtWVpZq06aN8vX1Vdu2bavApOdXkvxHjx4973obGhqqrr32WrV8+XJ18ODBSpldKVW0jXnqqaeK2lwul+rQoYMKCgoq9mzxilDS/E2bNlVBQUHq9OnTRW1Op1Ndc801ytfXt1jfsuZ0OtUtt9yiDAaD+vbbby/Yr7KvrxVJartnantp8ivl+foutb3ybCu8ub57c20vaX6lKld996bafva9KkN9l0GCCxg0aJAyGAzqySefVHPmzFHt27dXBoNB/fTTT0V9OnXqpP45ztKiRQsFqAkTJqiFCxcW+7NmzZpKnf18ADV69OjyjFvM5WbPyclRTZo0UUajUY0fP169+eabqnXr1kqv16uVK1dW+vxTp05VgGrVqpWaOXOmeuWVV1SjRo0UoD7++OMKyz9r1iw1ZcoU9fDDDytA9e/fX02ZMkVNmTJFpaWlKaWUGjp0qAKK7Sg7nU513XXXKbvdrp5//nn11ltvqSZNmihfX1+1d+/eSp391ltvVYAaPnz4Oevs8uXLKyR7afKfT40aNVTv3r0rIHWhy83udrtVt27dlKZpasSIEeqtt95S3bt3V4CaM2dOpc//8ccfK0DVqVNHTZs2Tb355puqXbt2ClBTp04t18zjxo1TgOrbt+8539uFCxcW9aus66unSG0vVNG1XSnvru9S2z27rfDm+u7NtV0p767v3ljblao89V0GCS4gNzdXjR8/XkVERCiz2axat26tVq1aVazP+QoCcME/nTp1qtTZz6eidyRKk/3kyZNq6NChKigoSJnNZtW2bdtzpi1vpcn/ySefqDZt2qiAgABltVpV27Zt1ZIlSyoqulKqsABd6Pt7diN0oWKWkpKi7r//fhUcHKxsNpvq1KmT2rp1a6XP/m/T1ahRo9Lnv9C8KnJHojTZMzMz1bhx41RERIQymUyqWbNmFbrzXNr8q1atUp06dVIhISFF+d99991yz3x2O3KhP2dV1vXVU6S2F/LEIIE313ep7Z7dVnhzfffm2n72Pb21vntjbVeq8tR3TSmlEEIIIYQQQgghxBVPblwohBBCCCGEEEIIQAYJhBBCCCGEEEIIcYYMEgghhBBCCCGEEAKQQQIhhBBCCCGEEEKcIYMEQgghhBBCCCGEAGSQQAghhBBCCCGEEGfIIIEQQgghhBBCCCEAGSQQQgghhBBCCCHEGTJIIIQQQgghhBBCCEAGCYQQQgghhBBCCHGGDBIIIcrM5MmT0TSt6OcFCxagaRqxsbEVlmHYsGFomoamaTRt2rTM5vv6668XzVfTNE6fPl1m8xZCCCEqM6nvQlxZDJ4OIIQQZS0kJISZM2cSEBBQZvPs1asXISEhLFu2jOXLl5fZfIUQQghxaaS+C1ExZJBACFFu7rnnHm6//XbMZnOFvq+Pjw933313mc6zYcOGNGzYkIMHD8pOhBBCiCua1Hchqja53EAIQXZ2drnMV6/XY7FYip2iWNmU17ILIYQQnib1XQhxOWSQQIgrzNnrCvfs2cOdd95JYGAgHTp04OjRo4waNYoGDRpgtVoJDg5m0KBBF7ze8JdffqF169ZYLBbq1KnDnDlzzulzvmsWhw0bRs2aNS+Y66zMzEweffRRatasidlsJiwsjO7du7Njx44yX3YhhBDC20l9l/ouRFmRyw2EuEINGjSIevXq8X//938opdi6dSu//vort99+O9HR0cTGxvLOO+/QuXNn9uzZg81mK5r2zz//pEePHoSGhjJ58mScTifPPfcc4eHhZZbvoYceYsmSJYwZM4bGjRuTnJzML7/8QkxMDFdffXWp5v3PZRdCCCGqCqnvUt+FKC0ZJBDiCtWiRQs+/fTTop9zc3MZOHBgsT59+/alXbt2LF26lHvuuaeo/dlnn0UpxYYNG6hevToAAwYMoFmzZmWW79tvv+XBBx/k1VdfLWqbMGFCmcz7n8suhBBCVBVS36W+C1FacrmBEFeohx56qNjPVqu16N8FBQUkJydTt25dAgICip0C6HK5WL16Nf369SvagQBo1KgRPXv2LLN8AQEBbNmyhYSEhDKb51n/XHYhhBCiqpD6LoQoLRkkEOIKVatWrWI/5+bm8uyzz1KtWjXMZjMhISGEhoaSlpZGenp6Ub+kpCRyc3OpV6/eOfNs0KBBmeWbPn06u3btolq1arRp04bJkydz+PDhMpn3P5ddCCGEqCqkvgshSksGCYS4Qv39yALAI488wosvvsjgwYP5/PPPWbNmDd9//z3BwcG43e4ye98L3QnZ5XIV+3nw4MEcPnyYWbNmERUVxYwZM2jSpAnfffddqTP8c9mFEEKIqkLquxCitGSQQAgBwJIlSxg6dCivvvoqAwcOpHv37nTo0IG0tLRi/UJDQ7FarRw4cOCceezbt++i7xMYGHjOPAGOHj16TltkZCSjRo3iyy+/5MiRIwQHB/Piiy9e8jKVhKZpzJo1i+rVqxMREcGMGTPK5X2EEEKIiiT1Xeq7ECUlgwRCCKDwmcf/vBPwrFmzzjkCoNfr6dmzJ19++SVxcXFF7TExMaxevfqi71OnTh3S09P5448/itoSExNZvnx50c8ul6vYKZAAYWFhREVFkZeXV6LlKolvvvmGXbt2sX79el577TV++OGHcnsvIYQQoiJIfZf6LkRJydMNhBAA9OnTh4ULF+Lv70/jxo3ZtGkTa9euJTg4+Jy+zz//PKtWreKGG25g1KhROJ1OZs2aRZMmTYrtHJzP7bffzsSJE7ntttsYO3YsOTk5vPPOO9SvX7/oBkqZmZlER0czcOBAWrRogd1uZ+3atWzdurXY3ZDL2qRJk/Dz88PPz4/777+fxYsX061bt3J7PyGEEKK8SX2X+i5ESckggRACgDfeeAO9Xs8nn3yCw+Hg+uuvZ+3atee9o3Hz5s1ZvXo1jz/+OM8++yzR0dE8//zzJCYmXnQnIjg4mOXLl/P4448zYcIEatWqxUsvvcSBAweKdiJsNhujRo1izZo1LFu2DLfbTd26dXn77bd5+OGHy2X5AapVq1bs37///nu5vZcQQghREaS+S30XoqQ09c/zj4QQwosNGzaMdevWsWPHDgwGAwEBAZc0naZp/Pjjj3Tu3BmAp59+mlOnTjF37lwAHA4HWVlZTJ8+nRkzZpCUlERISEg5LYUQQggh/k7quxAVR+5JIISocuLj4wkNDaVDhw4lmm7atGlkZGSwb98+PvjgAwYPHlz02rvvvktoaKjc8EgIIYTwEKnvQlQMudxACFGlTJgwgbvvvhsAu91eomlvvvlmmjZtSl5eHo899hg33nhj0WsDBgygadOmRT/7+/uXTWAhhBBCXJTUdyEqjlxuIIQQFJ6OGB8fT3R0tKejCCGEEKKMSH0XouTkcgMhhBBCCCGEEEIAMkgghBBCCCGEEEKIM+RyAyGEEEIIIYQQQgByJoEQQgghhBBCCCHOkEECIYQQQgghhBBCADJIIIQQQgghhBBCiDNkkEAIIYQQQgghhBCADBIIIYQQQgghhBDiDBkkEEIIIYQQQgghBCCDBEIIIYQQQgghhDhDBgmEEEIIIYQQQggByCCBEEIIIYQQQgghzpBBAiGEEEIIIYQQQgAySCCEEEIIIYQQQogz/h/RvwiUCog0kwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Side by side zone comparison for PV and Surface density\n",
    "\n",
    "import readfargo3d as fg\n",
    "import numpy as pl\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,\n",
    "                               AutoMinorLocator)\n",
    "path4 = './bae2017_01jup700x256/'\n",
    "path3 = './bae2017_01jup1400x512/'\n",
    "path2 = './bae2017_01jup2800x1024/'\n",
    "path  = './bae2017_01jup5600x2048/'\n",
    "itime = 100\n",
    "r0    = 1.0\n",
    "fargo           = fg.frame(itime,rho=True,vphi=True,vr=True,vtheta=False,dir=path)\n",
    "fargo_init      = fg.frame(0,rho=True,vphi=True,vr=True,vtheta=False,dir=path)\n",
    "fargo_half      = fg.frame(itime,rho=True,vphi=True,vr=True,vtheta=False,dir=path2)\n",
    "fargo_halfinit  = fg.frame(0,rho=True,vphi=True,vr=True,vtheta=False,dir=path2)\n",
    "fargo_quart     = fg.frame(itime,rho=True,vphi=True,vr=True,vtheta=False,dir=path3)\n",
    "fargo_quartinit = fg.frame(0,rho=True,vphi=True,vr=True,vtheta=False,dir=path3)\n",
    "fargo_eight     = fg.frame(itime,rho=True,vphi=True,vr=True,vtheta=False,dir=path4)\n",
    "fargo_eightinit = fg.frame(0,rho=True,vphi=True,vr=True,vtheta=False,dir=path4)\n",
    "\n",
    "nf = itime\n",
    "phi = np.loadtxt(path +'domain_x.dat')\n",
    "rad  = np.loadtxt(path +'domain_y.dat')\n",
    "phi   = 0.5*(phi[:-1] + phi[1:])                                                                              \n",
    "rad   = 0.5*(rad[3:-4] + rad[4:-3])\n",
    "phi2d, rad2d = np.meshgrid(phi, rad)\n",
    "nx = len(phi)\n",
    "ny = len(rad)\n",
    "rho_init   = pl.fromfile(path +'gasdens0.dat').reshape(ny,nx)\n",
    "rho   = pl.fromfile(path+'gasdens' + str(nf) + '.dat').reshape(ny,nx)\n",
    "vphi_init   = pl.fromfile(path + 'gasvx0.dat').reshape(ny,nx)\n",
    "vphi   = pl.fromfile(path+'gasvx' + str(nf) + '.dat').reshape(ny,nx)\n",
    "vrad     = pl.fromfile(path+'gasvy' + str(nf) + '.dat').reshape(ny,nx)\n",
    "vrad_init = pl.fromfile(path+'gasvy0.dat').reshape(ny,nx)\n",
    "omegap = np.loadtxt(path+'planet0.dat')[itime][-1]\n",
    "vphi += omegap*rad2d\n",
    "omegapinit = np.loadtxt(path+'planet0.dat')[0][-1]\n",
    "vphi_init += omegapinit*rad2d\n",
    "\n",
    "dphi = np.abs(fargo.phi[0]-fargo.phi[1])\n",
    "\n",
    "pv_2d = ((rad2d*vphi)[1:,:]-(rad2d*vphi)[:-1,:])/(rad2d[1:]-rad2d[:-1])\n",
    "pv_2d = pv_2d[:,:-1]\n",
    "pv_2d -= ((vrad[:,1:]-vrad[:,:-1])/dphi)[1:,:]\n",
    "pv_2d /= rad2d[:-1,:-1]\n",
    "pv_2d /= rho[:-1,:-1]\n",
    "pv_original = pv_2d\n",
    "\n",
    "pv_2di = ((rad2d*vphi_init)[1:,:]-(rad2d*vphi_init)[:-1,:])/(rad2d[1:]-rad2d[:-1]) \n",
    "pv_2di = pv_2di[:,:-1]\n",
    "pv_2di -= ((vrad_init[:,1:]-vrad_init[:,:-1])/dphi)[1:,:]\n",
    "pv_2di /= rad2d[:-1,:-1]\n",
    "pv_2di /= rho_init[:-1,:-1]\n",
    "pv_original_init = pv_2di\n",
    "\n",
    "#PV calculations for all other zones (3) --------\n",
    "nf = itime\n",
    "phi = np.loadtxt(path2 +'domain_x.dat')\n",
    "rad  = np.loadtxt(path2 +'domain_y.dat')[3:-3]\n",
    "phi   = 0.5*(phi[:-1] + phi[1:])                                                                              \n",
    "rad   = 0.5*(rad[:-1] + rad[1:])\n",
    "phi2d, rad2d = np.meshgrid(phi, rad)\n",
    "nx = len(phi)\n",
    "ny = len(rad)\n",
    "rho_init2   = pl.fromfile(path2 +'gasdens0.dat').reshape(ny,nx)\n",
    "rho2   = pl.fromfile(path2+'gasdens' + str(nf) + '.dat').reshape(ny,nx)\n",
    "vphi_init2   = pl.fromfile(path2 + 'gasvx0.dat').reshape(ny,nx)\n",
    "vphi2   = pl.fromfile(path2+'gasvx' + str(nf) + '.dat').reshape(ny,nx)\n",
    "vrad2     = pl.fromfile(path2+'gasvy' + str(nf) + '.dat').reshape(ny,nx)\n",
    "vrad_init2 = pl.fromfile(path2+'gasvy0.dat').reshape(ny,nx)\n",
    "omegap2 = np.loadtxt(path2+'planet0.dat')[itime][-1]\n",
    "vphi2 += omegap2*rad2d\n",
    "omegapinit2 = np.loadtxt(path2+'planet0.dat')[0][-1]\n",
    "vphi_init2 += omegapinit2*rad2d\n",
    "\n",
    "dphi = np.abs(fargo.phi[0]-fargo.phi[1])\n",
    "\n",
    "pv2 = ((rad2d*vphi2)[1:,:]-(rad2d*vphi2)[:-1,:])/(rad2d[1:]-rad2d[:-1])\n",
    "pv2 = pv2[:,:-1]\n",
    "pv2 -= ((vrad2[:,1:]-vrad2[:,:-1])/dphi)[1:,:]\n",
    "pv2 /= rad2d[:-1,:-1]\n",
    "pv2 /= rho2[:-1,:-1]\n",
    "pv_half = pv2\n",
    "\n",
    "pv_init2 = ((rad2d*vphi_init2)[1:,:]-(rad2d*vphi_init2)[:-1,:])/(rad2d[1:]-rad2d[:-1]) \n",
    "pv_init2 = pv_init2[:,:-1]\n",
    "pv_init2 -= ((vrad_init2[:,1:]-vrad_init2[:,:-1])/dphi)[1:,:]\n",
    "pv_init2 /= rad2d[:-1,:-1]\n",
    "pv_init2 /= rho_init2[:-1,:-1]\n",
    "pv_half_init = pv_init2\n",
    "\n",
    "#----------------\n",
    "phi = np.loadtxt(path3 +'domain_x.dat')\n",
    "rad  = np.loadtxt(path3 +'domain_y.dat')[3:-3]\n",
    "phi   = 0.5*(phi[:-1] + phi[1:])                                                                              \n",
    "rad   = 0.5*(rad[:-1] + rad[1:])\n",
    "phi2d, rad2d = np.meshgrid(phi, rad)\n",
    "nx = len(phi)\n",
    "ny = len(rad)\n",
    "rho_init3   = pl.fromfile(path3 +'gasdens0.dat').reshape(ny,nx)\n",
    "rho3   = pl.fromfile(path3+'gasdens' + str(nf) + '.dat').reshape(ny,nx)\n",
    "vphi_init3   = pl.fromfile(path3 + 'gasvx0.dat').reshape(ny,nx)\n",
    "vphi3   = pl.fromfile(path3+'gasvx' + str(nf) + '.dat').reshape(ny,nx)\n",
    "vrad3     = pl.fromfile(path3+'gasvy' + str(nf) + '.dat').reshape(ny,nx)\n",
    "vrad_init3 = pl.fromfile(path3+'gasvy0.dat').reshape(ny,nx)\n",
    "omegap3 = np.loadtxt(path3+'planet0.dat')[itime][-1]\n",
    "vphi3 += omegap3*rad2d\n",
    "omegapinit3 = np.loadtxt(path3+'planet0.dat')[0][-1]\n",
    "vphi_init3 += omegapinit3*rad2d\n",
    "\n",
    "dphi = np.abs(fargo.phi[0]-fargo.phi[1])\n",
    "\n",
    "pv3 = ((rad2d*vphi3)[1:,:]-(rad2d*vphi3)[:-1,:])/(rad2d[1:]-rad2d[:-1])\n",
    "pv3 = pv3[:,:-1]\n",
    "pv3 -= ((vrad3[:,1:]-vrad3[:,:-1])/dphi)[1:,:]\n",
    "pv3 /= rad2d[:-1,:-1]\n",
    "pv3 /= rho3[:-1,:-1]\n",
    "pv_quarter = pv3\n",
    "\n",
    "pv_init3 = ((rad2d*vphi_init3)[1:,:]-(rad2d*vphi_init3)[:-1,:])/(rad2d[1:]-rad2d[:-1]) \n",
    "pv_init3 = pv_init3[:,:-1]\n",
    "pv_init3 -= ((vrad_init3[:,1:]-vrad_init3[:,:-1])/dphi)[1:,:]\n",
    "pv_init3 /= rad2d[:-1,:-1]\n",
    "pv_init3 /= rho_init3[:-1,:-1]\n",
    "pv_quarter_init = pv_init3\n",
    "#---------------\n",
    "phi = np.loadtxt(path4 +'domain_x.dat')\n",
    "rad  = np.loadtxt(path4 +'domain_y.dat')[3:-3]\n",
    "phi   = 0.5*(phi[:-1] + phi[1:])                                                                              \n",
    "rad   = 0.5*(rad[:-1] + rad[1:])\n",
    "phi2d, rad2d = np.meshgrid(phi, rad)\n",
    "nx = len(phi)\n",
    "ny = len(rad)\n",
    "rho_init4   = pl.fromfile(path4 +'gasdens0.dat').reshape(ny,nx)\n",
    "rho4   = pl.fromfile(path4+'gasdens' + str(nf) + '.dat').reshape(ny,nx)\n",
    "vphi_init4   = pl.fromfile(path4 + 'gasvx0.dat').reshape(ny,nx)\n",
    "vphi4   = pl.fromfile(path4+'gasvx' + str(nf) + '.dat').reshape(ny,nx)\n",
    "vrad4     = pl.fromfile(path4+'gasvy' + str(nf) + '.dat').reshape(ny,nx)\n",
    "vrad_init4 = pl.fromfile(path4+'gasvy0.dat').reshape(ny,nx)\n",
    "omegap4 = np.loadtxt(path4+'planet0.dat')[itime][-1]\n",
    "vphi4 += omegap4*rad2d\n",
    "omegapinit4 = np.loadtxt(path4+'planet0.dat')[0][-1]\n",
    "vphi_init4 += omegapinit4*rad2d\n",
    "\n",
    "dphi = np.abs(fargo.phi[0]-fargo.phi[1])\n",
    "\n",
    "pv4 = ((rad2d*vphi4)[1:,:]-(rad2d*vphi4)[:-1,:])/(rad2d[1:]-rad2d[:-1])\n",
    "pv4 = pv4[:,:-1]\n",
    "pv4 -= ((vrad4[:,1:]-vrad4[:,:-1])/dphi)[1:,:]\n",
    "pv4 /= rad2d[:-1,:-1]\n",
    "pv4 /= rho4[:-1,:-1]\n",
    "pv_eighth = pv4\n",
    "\n",
    "pv_init4 = ((rad2d*vphi_init4)[1:,:]-(rad2d*vphi_init4)[:-1,:])/(rad2d[1:]-rad2d[:-1]) \n",
    "pv_init4 = pv_init4[:,:-1]\n",
    "pv_init4 -= ((vrad_init4[:,1:]-vrad_init4[:,:-1])/dphi)[1:,:]\n",
    "pv_init4 /= rad2d[:-1,:-1]\n",
    "pv_init4 /= rho_init4[:-1,:-1]\n",
    "pv_eighth_init = pv_init4\n",
    "\n",
    "pv2dorig = pv_original.mean(axis=1)\n",
    "pv_init2dorig = pv_original_init.mean(axis=1)\n",
    "pv2dhalf = pv_half.mean(axis=1)\n",
    "pv_init2dhalf = pv_half_init.mean(axis=1)\n",
    "pv2dquart = pv_quarter.mean(axis=1)\n",
    "pv_init2dquart = pv_quarter_init.mean(axis=1)\n",
    "pv2deight = pv_eighth.mean(axis=1)\n",
    "pv_init2deight = pv_eighth_init.mean(axis=1)\n",
    "#-----------------------------------------------------\n",
    "\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(1,2,figsize=(12, 4))\n",
    "figsize=(12,6)\n",
    "fig.tight_layout\n",
    "\n",
    "ax1.plot(fargo.r,((fargo.rho-fargo_init.rho)/fargo_init.rho).mean(axis=0), label='5600x2048')\n",
    "ax1.plot(fargo_half.r,((fargo_half.rho-fargo_halfinit.rho)/fargo_halfinit.rho).mean(axis=0), label='2800x1024',linestyle='dashed')\n",
    "ax1.plot(fargo_quart.r,((fargo_quart.rho-fargo_quartinit.rho)/fargo_quartinit.rho).mean(axis=0), label='1400x512')\n",
    "ax1.plot(fargo_eight.r,((fargo_eight.rho-fargo_eightinit.rho)/fargo_eightinit.rho).mean(axis=0), label='700x256',linestyle='dotted')\n",
    "ax1.legend()\n",
    "ax1.set_xlim(0.2,2)\n",
    "ax1.set_ylim(-0.85,0.85)\n",
    "#ax1.ylim(-2,4)\n",
    "ax1.set_xticks([0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4,1.6,1.8,2.0])  # Adjust these values as needed\n",
    "ax1.set_xticklabels(['0.2', '0.4', '0.6', '0.8', '1.0', '1.2', '1.4','1.6','1.8','2.0'],fontsize=12)  # Labels corresponding to the tick positions\n",
    "ax1.set_xlabel(r'radius [r$\\rm_p$]',fontsize=12)\n",
    "ax1.set_ylabel(r'$\\delta$$\\Sigma$/$\\Sigma$$_{\\rm o}$',fontsize=12)\n",
    "ax1.tick_params(axis='both', which='both', direction='in', labelsize='12')\n",
    "ax1.minorticks_on()\n",
    "\n",
    "ax2.plot(fargo.r[1:],(pv2dorig-pv_init2dorig)/pv_init2dorig, label = '5600x2048')\n",
    "ax2.plot(fargo_half.r[1:],(pv2dhalf-pv_init2dhalf)/pv_init2dhalf, label = '2800x1024',linestyle='dashed')\n",
    "ax2.plot(fargo_quart.r[1:],(pv2dquart-pv_init2dquart)/pv_init2dquart, label = '1400x512')\n",
    "ax2.plot(fargo_eight.r[1:],(pv2deight-pv_init2deight)/pv_init2deight, label = '700x256',linestyle='dotted')\n",
    "ax2.set_xlabel(r'radius [r$\\rm_p$]',fontsize=12)\n",
    "ax2.set_ylabel(r'$\\delta$$\\zeta$/$\\zeta$$_{\\rm o}$',fontsize=12)\n",
    "ax2.set_xlim(0.2, 2)\n",
    "ax2.set_ylim(-0.75,9.5)\n",
    "ax2.set_xticks([0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4,1.6,1.8,2.0])  # Adjust these values as needed\n",
    "ax2.set_xticklabels(['0.2', '0.4', '0.6', '0.8', '1.0', '1.2', '1.4','1.6','1.8','2.0'],fontsize=12)  # Labels corresponding to the tick positions\n",
    "ax2.tick_params(axis='both', which='both', direction='in',labelsize='12')\n",
    "ax2.minorticks_on()\n",
    "#ax2.text(0.45,4,'orbit 1000',fontsize=14,color='black',alpha=0.8)\n",
    "#ax2.set_title(r't$_{\\rm p}$ = '+str(itime*10))\n",
    "ax2.legend(loc='upper right')\n",
    "#plt.savefig('PV1mjup_2D'+str(itime*10)+'_allzones',dpi=300,bbox_inches='tight')\n",
    "\n",
    "#ax2.set_title('orbit '+str(itime*10))\n",
    "#plt.savefig('density_plot_allzones01mjup'+str(itime*10)+'.png',dpi=300,bbox_inches='tight')\n",
    "plt.subplots_adjust(hspace=0.14, wspace=0.18)\n",
    "plt.savefig('PaperImages/Combined_Fig_1Mjup_allzones_1000orbits.png',dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01b2084c-ef0b-47ff-9399-9f0c4ba60126",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "import readfargo3d as fg\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "dir1 = './bae2017_01jup1400x512/' #2D\n",
    "dir2 = './p3disof_01jup/' #3D\n",
    "for itime1 in np.arange(0,101):\n",
    "    itime1 = itime1\n",
    "\n",
    "    r0 = 1.0\n",
    "    fargo1    = fg.frame(itime1,rho=True,vphi=True,vr=True,vtheta=False,dir=dir1) \n",
    "    fargo2    = fg.frame(itime1,rho=True,vphi=True,vr=True,vtheta=True,dir=dir2) \n",
    "    fargo1i    = fg.frame(0,rho=True,vphi=True,vr=True,vtheta=False,dir=dir1) \n",
    "    fargo2i    = fg.frame(0,rho=True,vphi=True,vr=True,vtheta=True,dir=dir2) \n",
    "\n",
    "    #3D Density Image\n",
    "    phi3d = fargo2.phi\n",
    "    rad3d = fargo2.r\n",
    "\n",
    "    rad3d, phi3d = np.meshgrid(rad3d, phi3d)                                                                                                                       \n",
    "\n",
    "    X3d = rad3d*np.cos(phi3d)\n",
    "    Y3d = rad3d*np.sin(phi3d)\n",
    "\n",
    "    #integration\n",
    "    rho3d = fargo2.rho.sum(axis=2)*fargo2.r\n",
    "    rho3di = fargo2i.rho.sum(axis=2)*fargo2i.r\n",
    "\n",
    "    #slice\n",
    "    #rho3d = fargo2.rho[:,:,-1]\n",
    "    #rho3di = fargo2i.rho[:,:,-1]\n",
    "\n",
    "    vmin = -1.2\n",
    "    vmax = 1.5\n",
    "    levels = np.linspace(vmin,vmax,256)\n",
    "\n",
    "    fig = plt.figure(figsize=(10, 8))\n",
    "    gs = gridspec.GridSpec(2, 2, width_ratios=[1, 1], height_ratios=[2, 1], hspace=0.3, wspace=0.3)\n",
    "\n",
    "    # First Image Plot (Upper Left)\n",
    "    ax1 = plt.subplot(gs[0, 0])\n",
    "\n",
    "    # Second Image Plot (Upper Right)\n",
    "    ax2 = plt.subplot(gs[0, 1])\n",
    "\n",
    "    # ... (rest of the code for image plots)\n",
    "\n",
    "    # Line Plot (Centered in the Second Row)\n",
    "    ax3 = plt.subplot(gs[1, :]) \n",
    "\n",
    "    #2D\n",
    "    phi2d = fargo1.phi\n",
    "    rad2d  = fargo1.r\n",
    "\n",
    "    rad2d, phi2d = np.meshgrid(rad2d, phi2d) #explain meshgrid?                                                                                                                         \n",
    "\n",
    "    X2d = rad2d*np.cos(phi2d)\n",
    "    Y2d = rad2d*np.sin(phi2d)\n",
    "\n",
    "    rho_2di   = fargo1i.rho\n",
    "    rho_2d   = fargo1.rho\n",
    "\n",
    "    ax1.contourf(X2d, Y2d,((rho_2d-rho_2di)/rho_2di), levels=levels)\n",
    "    ax1.set_title(r'2D, $\\delta$$\\Sigma$/$\\Sigma$$_{\\rm o}$, time = '+ str(itime1*10)+ ' orbits', fontsize=14)\n",
    "    ax1.set_xlabel('X [au]', fontsize=12)\n",
    "    ax1.set_ylabel('Y [au]', fontsize=12)\n",
    "    ax1.set_xlim(-2.0,2.0)\n",
    "    ax1.set_ylim(-2.0,2.0)\n",
    "    #3D\n",
    "\n",
    "    cp = ax2.contourf(X3d, Y3d,((rho3d - rho3di)/rho3di), levels=levels)\n",
    "    cax2 = fig.add_axes([0.2, 0.9, 0.6, 0.05])\n",
    "    cd = fig.colorbar(cp, cax=cax2,orientation='horizontal',fraction=0.046, pad=0.04)\n",
    "    cax2.xaxis.set_major_locator(ticker.MaxNLocator(symmetric=True))\n",
    "    #cd.set_label(r'$\\delta$$\\Sigma$/$\\Sigma$$_{\\rm o}$', labelpad=-37, y=0.2, x = -.05, fontsize = 12)\n",
    "\n",
    "\n",
    "    ax2.set_xlabel('X [au]', fontsize=12)\n",
    "    ax2.set_ylabel('Y [au]', fontsize=12)\n",
    "    ax2.set_title(r'3D, $\\delta$$\\Sigma$/$\\Sigma$$_{\\rm o}$, time = '+ str(itime1*10)+ ' orbits', fontsize=14)\n",
    "    ax2.set_xlim(-2.0,2.0)\n",
    "    ax2.set_ylim(-2.0,2.0)\n",
    "\n",
    "\n",
    "    #----------2D azimuth avg-----------\n",
    "\n",
    "    ax3.plot(fargo2.r,((rho_2d-rho_2di)/rho_2di).mean(axis=0), label = '2D')\n",
    "    ax3.plot(fargo2.r,((rho3d-rho3di)/rho3di).mean(axis=0), label = '3D')\n",
    "    ax3.legend()\n",
    "    ax3.set_xlabel('r [au]', fontsize=12)\n",
    "    ax3.set_ylabel(r'$\\delta$$\\Sigma$/$\\Sigma$$_{\\rm o}$', fontsize=12)\n",
    "    ax3.set_title(r'$\\delta$$\\Sigma$/$\\Sigma$$_{\\rm o}$, azimuthally averaged', fontsize=14)\n",
    "    ax3.set_xlim(0.2,2.0)\n",
    "    #ax3.set_ylim(-2.0,2.0)\n",
    "    fig.subplots_adjust(top=0.82)\n",
    "\n",
    "    plt.savefig('./2D3Dsdens_movie/01MjupOrbit'+str(itime1*10)+'.png',dpi=300,bbox_inches='tight')\n",
    "    plt.close()\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f1be6b07-aa7b-415f-9243-72e6372162db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/blue/jbae/grobbins1/fargo3d/outputs'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a527d3b3-873b-4bad-917d-f781691abdf1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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