diff --git a/flasc/energy_ratio/__init__.py b/flasc/energy_ratio/__init__.py index d30c1e6f..30ebde4a 100644 --- a/flasc/energy_ratio/__init__.py +++ b/flasc/energy_ratio/__init__.py @@ -13,5 +13,6 @@ energy_ratio_gain, energy_ratio_suite, energy_ratio_visualization, - energy_ratio_wd_bias_estimation + energy_ratio_wd_bias_estimation, + energy_ratio_polars ) \ No newline at end of file diff --git a/flasc/energy_ratio/demo_capabilities.ipynb b/flasc/energy_ratio/demo_capabilities.ipynb new file mode 100644 index 00000000..1b74d29f --- /dev/null +++ b/flasc/energy_ratio/demo_capabilities.ipynb @@ -0,0 +1,1064 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Demo polars capabilities and interactively develop it" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pathlib import Path\n", + "import pandas as pd\n", + "import polars as pl\n", + "import seaborn as sns\n", + "\n", + "from floris import tools as wfct\n", + "from floris.utilities import wrap_360\n", + "\n", + "\n", + "from flasc.energy_ratio import energy_ratio_suite\n", + "from flasc.energy_ratio import energy_ratio_polars as erp\n", + "\n", + "from flasc.visualization import plot_layout_with_waking_directions, plot_binned_mean_and_ci\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "N = 2" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use FLORIS to generate a wake steering data set" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/pfleming/Projects/FLORIS/flasc/flasc/energy_ratio/../examples/floris_input_artificial/gch.yaml\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "file_path = Path.cwd()\n", + "fi_path = file_path / \"../examples/floris_input_artificial/gch.yaml\"\n", + "print(fi_path)\n", + "fi = wfct.floris_interface.FlorisInterface(fi_path)\n", + "fi.reinitialize(layout_x = [0, 0, 5*126], layout_y = [5*126, 0, 0])\n", + "\n", + "# # Show the wind farm\n", + "plot_layout_with_waking_directions(fi)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num Points 16000\n" + ] + }, + { + "data": { + "image/png": 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DrjjUf989daDmAs9Gws9Qn0wthP/pEpKUlISkpCScO3cOS5YswQsvvEAm22w2B10ewGKxwG63Iz4+Pmo7cLS3MdrbxzDfVeySp54riEc/UWHsLFmyBIqiYPDgwdi/fz/mz5+PIUOGYPbs2eFWjWEYhumCyFEJXEE8+okKf3xNTQ3mzJmDIUOG4NZbb8XEiROxZMkSfhNnGIZhOoQ9O98tosKzc9111+G6664LtxoMwzBMhKBmOTYhKg9XMN5EhbHDMAzDRCeVdS34/IgZ336xm/RwQEOrK0DZGoUHDpj2sLHDMAzDdFn+ueEolp40AyePGSI/NYHDHb4LsLHDMAzDdFnqW1wemPN7p+Pi/pnk8i8ZlEUuk+l6sLHDMAzDdFkc7kDii/t1x/0zBodZGyZS4c1KhmEYpsvicHJRTSZ02NhhGIZhuiwO9wlxKxs7TAiwscMwDMN0WcQ2FufCYUKBjR2GYRimyyI8O2zsMKHAAcoMwzBMyBTtLMf249XkcktO1gJgY4cJDTZ2GIZhmJCoabRh7qItcMcSG0JKHC9XjH649zAMwzAhUd9qh1NxeV/umNCHVLbT6cSpYwcxc3g2qVzmuwUbOwzDMExIOBwul0681YxHZg0jlW2z2VBUdACJsbxcMfrhAGWGYRgmJNSimhxXw3RR2NhhGIZhQkIcD+dcOExXhY0dhmEYJiTs7shkPjHFdFXY2GEYhmFCwsHGDtPF4YgvhmGY7winapvx0ZbjaLU7ieW2AACsZn5/ZrombOwwDMN8R/jTsn1YtOGoYfKT4iyGyWaYUGBjh2EY5jtCTaMNAHBhnwwMykkmlW2CCd8flUsqk2GoYGOHYRjmO4LdfWrqqrF5uOmi3mHWhmE6D95gZRiG+Y6gFtU0cSAx890i4o0dh8OBRx99FH379kVCQgL69++Pp59+GopiYJEWhmGYCETkw+FTU8x3jYjfxvr973+P119/He+++y6GDx+Ob7/9FrNnz0ZaWhruueeecKvHMAzTZXBXdWBjh/nOEfHGztq1a3HVVVdh1qxZAIA+ffrgX//6FzZu3BhmzRiGYboW7NlhvqtEvLFz8cUX44033sDevXsxaNAgbN++HWvWrMHLL78cbtUYhmF0UVx6CgdO12u61uFwoOyECce/PgSLxf/R72NVTQDY2GG+e0S8sfPQQw+htrYWQ4YMgcVigcPhwLPPPoubbrrJ59+0tLSgpaVF/bm2thaAq7quzWYj003IopTZ1Yj2NnL7Ip9Ia+PRqkbc9Y9vg/wrC3B0n+ar4yyRcz8i7fnpIdrbaGT7tMo0KREeyfv+++9j/vz5ePHFFzF8+HBs27YN8+bNw8svv4zbbrutw7954okn8OSTT7b7fNGiRUhMTDRaZYZhGJ8cqQNeLrEi1qxgTHf66TktFri8lxPWiD+ewjBAY2MjbrzxRtTU1CA1NdXndRFv7OTn5+Ohhx7CnDlz1M+eeeYZ/POf/8SePXs6/JuOPDv5+fk4c+aM35sVLDabDcXFxZg+fTpiYmLI5HYlor2N3L7IJ9LauOVoNa7/20YUZCRg2X2XBLw+0toXLNHePiD622hk+2pra5GZmRnQ2In4bazGxkaY29RjsVgscDp9136Ji4tDXFxcu89jYmIM6WhGye1KRHsbuX2RT6S00WR2xd1YLeag9I2U9ukl2tsHRH8bjWifVnkRb+z84Ac/wLPPPouCggIMHz4cW7duxcsvv4w77rgj3KoxDMMEjchybOUgYoYhI+KNnT//+c949NFH8ctf/hKVlZXIy8vDz3/+czz22GPhVo1hGCZohFPazFmOGYaMiDd2UlJS8Morr+CVV14JtyoMwzAho3p2LGzsMAwVEW/sMAzDhIPK2mZ8VXoKDiftGY+9p+oAcP0qhqGEjR2GYRgdPPH5LhTtrDBMfnyM/wSBDMNoh40dhmEYHZypawUAnNe7G3LS4kllW0wm3HRRAalMhvkuw8YOwzCMDhzuFGU/m9QPM4fnhFkbhmH8wTk0GYZhdGB3x+rwEXGG6fqE1dg5duwYjh8/rv68ceNGzJs3D2+88UYYtWIYhgmMqCBuZmOHYbo8YTV2brzxRqxYsQIAUFFRgenTp2Pjxo145JFH8NRTT4VTNYZhGL843Plw2LPDMF2fsBo7JSUluPDCCwEAH374IUaMGIG1a9fivffewzvvvBNO1RiGYfwiPDsWNnYYpssT1gBlm82m1qhaunQprrzySgDAkCFDUF5eHk7VGIaJAhRFwZr9Z7G+0oSGzSdgtdAd5z7XaAPA+XAYJhIIq7EzfPhwLFy4ELNmzUJxcTGefvppAMDJkyfRvXv3cKrGMEwUsPNEDWa/uxmABf86sMuQ7+B8OAzT9QmrsfP73/8e11xzDV588UXcdtttGD16NADgv//9r7q9xTAMo5dTtS0AgASLgu8NyILZRLtz37t7Ekb0TCOVyTAMPWExdhobG5GYmIgpU6bgzJkzqK2tRbdu3dTf/+xnP0NiYmI4VGMYJooQpRxyEoE3bh6HmJiYMGvEMEw4CEuAcmZmJr7//e/jjTfewJkzZ7wMHQDo06cPsrOzw6EawzBRhDB2uKYmw3y3CYuxs2fPHsycORMffvghevfujYsuugjPPvssdu7cGQ51GIaJUkSWYxNoi3UyDBNZhMXYKSgowK9+9SssXboUp06dwrx587Bz505ccskl6NevH+bNm4fly5fD4XCEQz2GYaIE9Xg4e3YY5jtN2MtFpKWl4YYbbsD777+P06dP469//SscDgdmz56NrKwsvPfee+FWkWGYCMXucHt22NhhmO80YS8E2tzcjB07dqCyshJO91vY9OnTMX36dOTn58Nut4dZQ4ZhjOZsfQvWHTwLJ/Fu05aj1QDYs8Mw33XCauwsXrwYt956K86cOdPudyaTibexGOY7wtxFW7Hu4FnD5FvZ2GGY7zRhNXZ+9atf4cc//jEee+wx9OjRI5yqMAwTRipqmwEAI3qmIjWe9ni41QyMiT1FKpNhmMgirMbOqVOncP/997OhwzDfccQR8aeuGoFxBd0CXB0cNpsNRUVFpDIZhokswhqg/KMf/QgrV64MpwoMw3QBhLHDFcQZhjGCsHp2XnvtNfz4xz/G119/jZEjR7bLbnrPPfeESTOGYToTYeyY+dgUwzAGEFZj51//+he++uorxMfHY+XKlTBJE53JZNJk7PTp0wdHjhxp9/kvf/lL/OUvfyHVl2EYY7ALzw4fm2IYxgDCauw88sgjePLJJ/HQQw/BbNa3o7Zp0yavU1slJSWYPn06fvzjH1OpyTCMwTgVUdaBjR2GYegJq7HT2tqK66+/XrehAwBZWVlePz///PPo378/Jk+eHKp6DMNIKIqCrceqcba+lVx2s831wmLhmB2GYQwgrMbObbfdhg8++AC//e1vSeS1trbin//8J+6//36vLbG2tLS0oKWlRf25trYWgOvUhs1mI9FFyJP/H41Eexu5fR6+3ncGd/xji6H6mBQn+b3mZxjZRHv7gOhvo5Ht0yrTpChK2Crk3XPPPfjHP/6B0aNHY9SoUe0ClF9++eWg5H344Ye48cYbcfToUeTl5fm87oknnsCTTz7Z7vNFixYhMTExqO9kmO8Ka0+Z8MFBCxItCrIT6OXnJyn4YV8nl3ZgGEYzjY2NuPHGG1FTU4PU1FSf14XV2Jk6darP35lMJixfvjwoeTNnzkRsbCw+//xzv9d15NnJz8/HmTNn/N6sYLHZbCguLsb06dPbGXLRQrS3kdvn4b2Nx/DE57sxc1g2XrthTOcoSAA/w8gm2tsHRH8bjWxfbW0tMjMzAxo7Yd3GWrFiBZmsI0eOYOnSpfj4448DXhsXF4e4uLh2n8fExBjS0YyS25WI9jZy+wDA5XKxWi0ReS/4GUY20d4+IPrbaET7tMoLe9VzKt5++21kZ2dj1qxZ4VaFYaISdwFxTvzHMEzEERXGjtPpxNtvv43bbrsNVmvYC7kzTFTicDoB8IkphmEij6gwdpYuXYqjR4/ijjvuCLcqDBO1OFy2DufCYRgm4ogKN8iMGTMQxjhrhulS1DTZsON4taZr7XYH9lSbkLr/LKxWi99rD5yuB8BZjhmGiTyiwthhGMbDDW+sR2l5bRB/YcHruzdrvtoaQhJQhmGYcMDGDsNEGceqGgEA/bKSEGsJYJgoCmrr6pCakgItCW4SYi24ZlxPCjUZhmE6DTZ2GCbKEEU13519IfIz/CfJtNlsKCoqQmHhxVF95JVhmO827I9mmCjDIYpq8qkphmEYAGzsMEzU4XB7djgfDsMwjAs2dhgmilAURTV2zGzsMAzDAGBjh2GiCqeUgYE9OwzDMC44QJlhwsTeU3WobbKRyrQ5PNYOx+wwDMO4YGOHYcLA59tP4lf/2mrod3A+HIZhGBds7DBMGDh0pgEAkBJnRWZKHLn8iQMykRDrPyMywzDMdwU2dhgmDIhcONeM64mnrhoRZm0YhmGiG/ZzM0wYcIoTU1xUk2EYxnDY2GGYMGDnXDgMwzCdBhs7DBMGnJzlmGEYptNgY4dhwoDdwcYOwzBMZ8EBygzjh8ZWO443ALtO1sJqpRsulXXNANjYYRiG6QzY2GEYHzidCn7wl3U4WmXFizvWG/IdbOwwDMMYDxs7DOODVocTR6uaAADZKXHkJ6dS4q2YNrQHqUyGYRimPWzsMIwP7FKhqWX3TURKYnwYtWEYhmH0wgHKDOMDh5PrTDEMw0QDbOwwjA+8jB1O/scwDBOxRIWxc+LECdx8883o3r07EhISMHLkSHz77bfhVouJcISxY4ICM3t2GIZhIpaIj9k5d+4cJkyYgKlTp+LLL79EVlYW9u3bh27duoVbNSbCcaglHcKsCMMwDBMSEW/s/P73v0d+fj7efvtt9bO+ffuGUSMmHJTXNKGx1UEq81StKxdOVLg/GYZhvsNEvLHz3//+FzNnzsSPf/xjrFq1Cj179sQvf/lL3HXXXT7/pqWlBS0tLerPtbW1AACbzQabzUamm5BFKbOr0RXa+OG3x/HIZ6WGyTebovcZdoXnZzTR3kZuX+QT7W00sn1aZZoURVECX9Z1iY93HQe+//778eMf/xibNm3Cvffei4ULF+K2227r8G+eeOIJPPnkk+0+X7RoERITEw3Vl6Hn3wfNWHPKjBiTghgD3DDnZyn4YV8nvWCGYRgmJBobG3HjjTeipqYGqampPq+LeGMnNjYW559/PtauXat+ds8992DTpk1Yt25dh3/TkWcnPz8fZ86c8XuzgsVms6G4uBjTp09HTEwMmdyuRFdo4+8+K8UH3x7HvZf2x9yp/Ulld4X2GUm0tw+I/jZy+yKfaG+jke2rra1FZmZmQGMn4rexcnNzMWzYMK/Phg4dio8++sjn38TFxSEuLq7d5zExMYZ0NKPkdiXC20ZXBHFsjNUwHaL9GUZ7+4DobyO3L/KJ9jYa0T6t8iI+9nLChAkoKyvz+mzv3r3o3bt3mDRiOhuR6ZgT/zEMwzAdEfHGzn333Yf169fjueeew/79+7Fo0SK88cYbmDNnTrhVYzoJp3sn1srGDsMwDNMBEW/sXHDBBfjkk0/wr3/9CyNGjMDTTz+NV155BTfddFO4VWM6CfbsMAzDMP6I+JgdAPj+97+P73//++FWgwlAi92Bs/Wt5HIbWuwA2NhhGIZhOiYqjB2m69Nid2DqiytxsqbZsO8wc/0qhmEYpgPY2GE6hcraFtXQibXS7552T4rF9/p1J5fLMAzDRD5s7DCdgqgzlRJnxc4nZ4ZZG4ZhGOa7RMQHKDORgRpEbOGtJoZhGKZzYWOH6RSEZ8fCcTUMwzBMJ8PGDtMpOPh4OMMwDBMm2NhhOgU2dhiGYZhwwQHKTDtqm22wO7TVh7XZbKi3AVUNrYiJ8f035xpd+XXY2GEYhmE6GzZ2GC/eWnMIT31RGuRfWfHItys1XcnGDsMwDNPZ8DYW48XGQ1WGyp8+tIeh8hmGYRimLezZYbxwuItqPnvNCNxwQUHA6202G4q+/BKFV1yBmJiYgNeb2bPDMAzDdDJs7DBeiEDiGItZk2FiNptgNrn/z4YMwzAM0wXhbSzGC86HwzAMw0QbbOwwXghjx8qZjhmGYZgogY0dxgu70wmAK4gzDMMw0QPH7EQoDqeCVruTXK7Ir2Pl+BuGYRgmSmBjJwKpbbbh8j+uxsmaZsO+g4ONGYZhmGiBt7EikH2n6g01dLolxmBEzzTD5DMMwzBMZ8KenQjE6c6F06d7IoruvYRcfqzFDKuF7WCGYRgmOmBjJwJR42osZiTG8iNkGIZhGH/w63sEIjw7HETMMAzDMIGJeGPniSeegMlk8vpvyJAh4VbLUOwi8R8bOwzDMAwTkKjYAxk+fDiWLl2q/my1RkWzfOJkY4dhGIZhNBMVVoHVakVOTk641eg02LPDMAzDMNqJCmNn3759yMvLQ3x8PMaPH48FCxagoMB3xe6Wlha0tLSoP9fW1gJwVfC22Wxkes14ZQ0OnbXi3nVfkcmUMQOk+upBfH+49TAKbl/kE+1t5PZFPtHeRiPbp1WmSVHc0a4Rypdffon6+noMHjwY5eXlePLJJ3HixAmUlJQgJSWlw7954okn8OSTT7b7fNGiRUhMTCTT7dmtFlQ2G+d9mdbTiR8U0GdRZhiGYZhIoLGxETfeeCNqamqQmprq87qIN3baUl1djd69e+Pll1/GnXfe2eE1HXl28vPzcebMGb83K1hO1zZixYqVmDx5MqwxMWRyAVdV8vREWpl6sNlsKC4uxvTp0xFD3MauALcv8on2NnL7Ip9ob6OR7autrUVmZmZAYycqtrFk0tPTMWjQIOzfv9/nNXFxcYiLi2v3eUxMDOmDyEpNRHIM0CM9KSo7sAz1vetqcPsin2hvI7cv8on2NhrRPq3yIv7oeVvq6+tx4MAB5ObmhlsVhmEYhmG6ABFv7PzmN7/BqlWrcPjwYaxduxbXXHMNLBYLbrjhhnCrxjAMwzBMFyDit7GOHz+OG264AWfPnkVWVhYmTpyI9evXIysrK9yqMQzDMAzTBYh4Y+f9998PtwoMwzAMw3RhIt7YoUAcSBP5dqiw2WxobGxEbW1t1AadRXsbuX2RT7S3kdsX+UR7G41sn1i3Ax0sZ2MHQF1dHQAgPz8/zJowDMMwDBMsdXV1SEtL8/n7qMuzowen04mTJ08iJSUFJhNdEkCRv+fYsWOk+Xu6EtHeRm5f5BPtbeT2RT7R3kYj26coCurq6pCXlwez2feZK/bsADCbzejVq5dh8lNTU6OyA8tEexu5fZFPtLeR2xf5RHsbjWqfP4+OIOKPnjMMwzAMw/iDjR2GYRiGYaIaNnYMJC4uDo8//niHpSmihWhvI7cv8on2NnL7Ip9ob2NXaB8HKDMMwzAME9WwZ4dhGIZhmKiGjR2GYRiGYaIaNnYYhmEYholq2NhhGIZhGCaqYWOHYRiGYZioho0dhmEYhmGiGjZ2GIZhGIaJatjYYRiGYRgmqmFjh2EYhmGYqIaNHYZhGIZhoho2dhiGYRiGiWrY2GEYhmEYJqphY4dhGIZhmKiGjR2GYRiGYaIaNnYYhmEYholq2NhhGIZhGCaqYWOHYRiGYZioho0dhmEYhmGiGjZ2GIZhGIaJatjYYRiGYRgmqmFjh2EYhmGYqIaNHYZhGIZhoho2dhiGYRiGiWrY2GEYhmEYJqphY4dhGIZhmKjGGm4FugJOpxMnT55ESkoKTCZTuNVhGIZhGEYDiqKgrq4OeXl5MJt9+2/Y2AFw8uRJ5Ofnh1sNhmEYhmF0cOzYMfTq1cvn79nYAZCSkgLAdbNSU1PJ5NpsNnz11VeYMWMGYmJiyOR2JaK9jdy+yCfa28jti3yivY1Gtq+2thb5+fnqOu4LNnYAdesqNTWV3NhJTExEampqVHZgIPrbyO2LfKK9jdy+yCfa29gZ7QsUghLWAOUFCxbgggsuQEpKCrKzs3H11VejrKzM65oDBw7gmmuuQVZWFlJTU3Hdddfh1KlTXtdUVVXhpptuQmpqKtLT03HnnXeivr6+M5vCMAzDMEwXJazGzqpVqzBnzhysX78excXFsNlsmDFjBhoaGgAADQ0NmDFjBkwmE5YvX45vvvkGra2t+MEPfgCn06nKuemmm7Br1y4UFxfjiy++wOrVq/Gzn/0sXM1iGIZhGKYLEdZtrMWLF3v9/M477yA7OxubN2/GpEmT8M033+Dw4cPYunWrur307rvvolu3bli+fDmmTZuG3bt3Y/Hixdi0aRPOP/98AMCf//xnFBYW4qWXXkJeXl6nt4thGIZhmK5Dl4rZqampAQBkZGQAAFpaWmAymRAXF6deEx8fD7PZjDVr1mDatGlYt24d0tPTVUMHAKZNmwaz2YwNGzbgmmuuafc9LS0taGlpUX+ura0F4NpXtNlsHermdDphs9mgKIrm9tjtdlitVtTX18Nqpb3VTa0OxMeYyY/KK4qCZpsTCbEWTdcH08ZmmwNx1vDrHAz1za0wW+ifoclkQqsTSEmIC3xxkLTYnTABiLUGdtyK/u6r37elocWOpDj6acPucMLhVBAXQ/8MqxuaAGhvo1acTgXNdgcSY+nvR0OLHYmxFk1jJZhnqCgKGlsdhjzDxlY7EmK06RwMNpsNLQ765we45qQYixkWM33KkWDGSjDPMJjxHSxGje+mlhacbQYam1uQSCxba7/oMsaO0+nEvHnzMGHCBIwYMQIA8L3vfQ9JSUl48MEH8dxzz0FRFDz00ENwOBwoLy8HAFRUVCA7O9tLltVqRUZGBioqKjr8rgULFuDJJ59s9/lXX32FxMT2j8JisSAzM1NXYFVOTg4OHjwY9N/5w6EANa0mxFsUJBI/wQY70OowITVWgUXj+NfSRqdb51izgiTi+LQmO9DsMCElRgHl+FcAVLeakJJJ/wybHUBNi4Ka2lok2uvI5NqdwFNbLEiwAg+OdkDrHF5cXBzwmm9Pm/B/+y34ST8HxvfQbvRr4fltFjTYgUfHOkBps5ZVm/D/dlswK98EaGhjMPy/UjMO15vw2FgHkgn79IkG4IUdVkzKceKHfZ2B/8CNlmf4z/1mbDljwoOjHeiREIqW3pxrAZ7easHIDAWzB2nXWQufHzFj6UkrjjcUoz/d+RE02YEnt1iQlwjcM8JBJxjA6nITPjpswR2DHBjdXftYCfQMHU7gqa0WxJqBh8doH99a2HrGhHf2WXBdPwcmEI/vF3dYcLzBCoeyDNmE/Q4AGhsbNV3XZYydOXPmoKSkBGvWrFE/y8rKwr///W/cfffdePXVV2E2m3HDDTdg3LhxfpMHBeLhhx/G/fffr/4sjq7NmDGj3WksRVFw4sQJ2O125ObmBvW9iqKgoaEBSUlJpG87h882Is7hmlAGZieTyQWAfZWuwO7uyXHISAw8g2ttY3lNM2Jb7ACM0TkFQEqcFTlp8WRyq5tsiKlzeQAHZNE+w32n6pBkb0VqfAz65A5Cjx49SORuOVqNmg0bUWMDLp0+I6DXwWazobi4GNOnTw9ozN/76FcAgC/L4/D07EtJ9AWAc42tKF+3EgAw7MJLMKiH/yOkwSB0/t8xC16cPZXsJIjDqeDeda6FKbHvWBSOyiWRCwCz/rwWQD1WV5jx5pzLA16v5xnWpA/C7MsGUKgLAHjokxI4lJPYdtaEwsLAOgeD0LlMycWvCseSyV208RiaHLtxoA4oLCwkkwt4dF5bm46Hbxkf8Hqtz3DXyVpUb1gPAJh82XSkxNNZ2ULnL47H4tnZl5HJrWu24d51KwAAYy/4HkYXZJDJBjw7M4HoEsbO3Llz1cDitkmBZsyYgQMHDuDMmTOwWq1IT09HTk4O+vXrB8DlVaisrPT6G7vdjqqqKuTk5HT4fXFxcV5bY4KYmJh2Hc1ms6G5uRl5eXlITg5ukRZbXwkJCSEZZ22xV7XC5H5yHXmi9OJwKjBZW11yE+KRmBh4i0VrG201dpjcbhdKnRXFo3NsfCyp7DPNjTBZXW841M/QFNMKU0wculnMqKurRW5uLiyW0F0aZZUN6r9jY2IRo3FbqKO+74uaJjvp8dEDZzyTVUpCvGFHU4NpYyDKz3reJvO6JZHqvLfSc5I0GLmB2tds83gvslJo7/OO455nSClXDhuIj7GSyi4t13efg6HZ5iB9hnsrGzVfq5fG1uB0DsTBEx7P9eiCDHKdtcoL62ksRVEwd+5cfPLJJ1i+fDn69u3r89rMzEykp6dj+fLlqKysxJVXXgkAGD9+PKqrq7F582b12uXLl8PpdOKiiy4KWUeHwzVBxMbGhiyrqyNPhlbChR0AWh20rm2B3SFPhrTxHk2tdlJ5Aqc0gScmuny6VPEI245Wk8jxR7+sJFJ5O45Xk8rrDErLa9R/W7Xu94aZ/ZIR1SOVzgMKeDzC1JxtaFX/PagHrUd4u0H9zi7NdcPz0khlb+uEsZJH6B0HgB3HXWPFaqLdGguWsHp25syZg0WLFuGzzz5DSkqKGmOTlpaGhATXIvD2229j6NChyMrKwrp163Dvvffivvvuw+DBgwEAQ4cOxeWXX4677roLCxcuhM1mw9y5c/GTn/yE9CTWd6FmVpONdt+6M5B1pn5CLXZjDLRWSW6M1YJmQtk7TtQEvkgHtc0eY2xoLmHgBIxbdJxOz+SaEUc70W7tBKOSGvk+R8p0trvc4zFKIj6AsKeCLlZO5miVx/vSNzMyXgwapRe7YdQG2rFqAEAudWRykITV2Hn99dcBAFOmTPH6/O2338btt98OACgrK8PDDz+Mqqoq9OnTB4888gjuu+8+r+vfe+89zJ07F5dddhnMZjN++MMf4tVXX+2MJnQ68gQeRxyN39RqjLETzAm2YGmMRAPNoPsMeL+9U1ImLQzdNMRyBUPpSW177sFSXusxI3MSaPugeFulpsXu6Rs9UmlP6nWG14+aSNS5ROrP1Ie89lYYP76zUmh3MXaddI2V3MTvsGdHyyL4/PPP4/nnn/d7TUZGBhYtWkSlVpem2W7cVtPKlStx6w9n4euSw1C6638juf3221FdXY1PP/0UAGCXDLQYS3A6t5XVFiMNtEfv+yXqamvwypvvkcpuNNDYkVFAN7nIiw617XpYin+h1HmX5OWi3mnae8qzOFDej4OnPTFXeem0x1ZKJS8Jpc7VjZ6tpnRiQ3i7ZFRSdjv5pZGarUfPqf+m/hY5HIBS9na39wWgH98H3H26Z1J4jZ2wxuwwwaNlcV+4cCFSUlJgt3tck/X19YiJiWnnRVu5ciVMJhMOHDiA4WMuwLLNe5BCWB8M8I4FMps832kymWA2m5GWloaxY8figQceUFMKCP70pz/hnXfe0SRbL4cPH4bJZMK2bdvUz1rtTjzw5AI89fL/I98eM2q7sKHFmBgjwLitJiO9flulCZwaOZaEku0G6mxUXI1sRCUT52iRjUpKTlQ3qf+2ErtfdhnkqaSY63yx3SBPpUwBGztMMGjxCkydOhX19fX49ttv1c++/vpr5OTkYMOGDWhu9rj3V6xYgYKCAvTv3x/W2BhkZvcgj0/ypXNZWRlOnjyJTZs24cEHH8TSpUsxYsQI7Ny5U70mLS0N6enpvmU3UUa8SHJtDqSkpiE1LY0+FsigSavMoIUBMC6+4XRdS+CLdFJiUPySzaBge8AT32AErQbFoBkZvyTHv1Aib0NSJxTcZ9A4NGqLGvCOjaKkSnop6EkbvhQ0bOxEGFqs+8GDByM3NxcrV65UP1u5ciWuuuoq9O3bF+vXr/f6fOrUqbA7nNi0bg1G53dDrTuT9TvvvIP09HQsWbIEQ4cORXJyMi6//HIv74vD4cAjjzyCjIwMdO/eHQ888EC7t3Vf3qjs7Gzk5ORg0KBB+MlPfoJvvvkGWVlZuPvuu9Vrbr/9dlx99dXqz1OmTMHcuXMxb948ZGZm4hc3/xAAsG9PKa6/9kokJyejR48euOWWW3DmzBn175xOJ1544QUMGDAAcXFxKCgowLPPPgsA6inAsWPHwmQyYcqUKWhqdeDR+36JeXfepMpoaWnBPffcg+zsbMTHx2PixInYtGmT1700mUxYtmwZzj//fCQmJuLiiy9uV9zWYZA3w8hFx6iJdqdBBgkA7DtljM6HzzQEvkgnuw0yKuua6bMPC4wyKg31+klbTdScazTmXhup84HTxowVuW/E0SdHDwo2doLElW7drvm/plZHUNf7+09RFDRrfDubOnUqVqxYof68YsUKTJkyBZMnT1Y/b2pqwoYNGzB16lSfWyuNjY146aWX8H//939YvXo1jh49it/85jfq719++WUsWrQIf//737FmzRpUVVXhk08+8ZIhxxn5IyEhAb/4xS/wzTfftMudJPPuu+8iNjYWK1atxqMLXkZtTQ3u+slVGDlqNL799lssXrwYp06dwnXXXaf+zcMPP4znn38ejz76KEpLS7Fo0SI1kd/GjRsBAEuXLkV5eTk+/vjjDu/HAw88gI8++gjvvvsutmzZggEDBmDmzJmoqqryuu6RRx7BH/7wB3z77bewWq2444471N8ZGSuwMwKPcBtpoFXUGuP1M1Jno7wCRgWBA8Z5FMtrjHl+ALC7wpj70aJxrtODkVtNNocx89IWt4GWFG5LB10kqWAk0WRzYNhjS8Ly3aVPzdT8tjN16lTMmzcPdrsdTU1N2Lp1KyZPngybzYaFCxcCANatW4eWlhaXsePD+yKu79+/PwBXAsinnnpK/f2f/vQn3Hfffbj22mthNpuxcOFCLFnifX9k93kg7YcMGQLAFUfTtgyIYODAgXjhhRdQ3dgKJa0Rb/zpJQwZPgq/e+JpdE92nWB56623kJ+fj7179yI3Nxd/+tOf8Nprr+G2224DAPTv3x8TJ04E4MrUDQDdu3dXE1FWnPQOjGxoaMDrr7+Od955B1dccQUA4G9/+xuKi4vx5ptvYv78+er1zz77LCZPngwAeOihhzBr1iw0NzcjPj5es+Gnh7I2ngyqF+Oz9d5bTZTTYmkb9zmVzg4DjcodJ6q9fqb8JnnLl9Kx0TZ+iVJnOaiaUue28UuUsvdKY4XyXhyo9Pb6Uepc1sbrRyW7po0nilJnYWS7suYbt2WtBTZ2IgiHU/ue+5QpU9DQ0IBNmzbh3LlzGDRoELKysjB58mTMnj0bzc3NWLlyJfr164eCggIcOduxaz4xMVE1dAAgNzdX9brU1NSgvLwc5513nvp7q9WK888/XzXKgnVFi+v9xQ2J7xMLw97dJdi07mv0zune7toDBw6guroaLS0tuOwy7SnQ7W0WywMHDsBms2HChAnqZzExMbjwwguxe/dur2tHjRql/js311VGoLKyEgUFBYaexDLKK2DUMWug/QROha/+TMGecmN0bjQoiSVgnGfHyK0mIz0ZRsWKbTFwq2lfpTH9zsitZBFgPrhHMoCzhn2PFtjYCZKEGAtKn5qp6Vqn04m62jqkpKaQlBpwBBEYOWDAAPTq1QsrVqzAuXPnVE9DXl4e8vPzsXbtWqxYsQKXXuqqcdRs61h221TcJpMpqAku2GBOYTj06dPH5zVJSa5INxG/1NhQj8nTLseCBc+jW5J3jojc3Nygi3g6Q5zA5XsmjDan21A1MsdOWwONCqNOYgHep2Io2WLgVpNRJ4QM3WoyyKisNDDA3KigWbuBAeZGncQCfM/RoWLk+BZpJUb1TAN8RyZ0ChyzEyQmkwmJsVbN/yXEWoK63t9/TUGeppg6dSpWrlyJlStXeh05nzRpEr788kts3LgRU6dOBaBvrzktLQ25ublepTrsdrvXz8F4MpqamvDGG29g0qRJ6taSP0T80tARo3Fg7x4U9O6NAQMGeP2XlJSEgQMHIiEhAcuWLetQjigFIkqDdBQE3r9/f8TGxuKbb75RP7PZbNi0aROGDRumuY1GHR89Z9BRaMC4RdjI+KVdJ417W61tNsYDY+RJrL0GeQUM1dmoU00GBeMCwB6DYoFqDQww7wyv3+hetFmZ9cDGTgTR3Bq8sbNmzRps27ZN9ewAwOTJk/HXv/4Vra2tmDp1akiu6HvuuQevvPIKPv30U+zZswe//OUvUV1drf7eX06ZyspKVFRUYN++fXj//fcxYcIEnDlzRs2sHQjxhnb9bT9FTfU5/Gz2rdi0aRMOHDiAJUuWYPbs2XA4HIiPj8eDDz6IBx54AP/4xz9w4MABrF+/Hm+++SYA16mwhIQENbD51Omqdt+VlJSEu+++G/Pnz8fixYtRWlqKu+66C42Njbjzzjs13y+jSlAYVSYCMG7ROSIdK46PoZ2KjNpqkj1zKcQ5ZdrGL1Eihjj1fd5lYL8zKkDZ0FxGBp0ANOrEG2BcgPnxcx6vbR/ishl6YGMnghDBrRb31kggE2Xq1KloamrCgAED1JNHgMvYqaurU4+ohxKJf//99+P666/H7NmzMX78eKSkpOCaa67x6Ox2vXYUgzN48GDk5eXhvPPOw/PPP49p06ahpKREk6dE3mrqnd8L736yGA6HAzNmzMDIkSMxb948pKenq9uHjz76KH7961/jsccew9ChQ3H99dersUdWqxWvvvoq/vrXvyIvLw83Xv+jDr/z+eefxw9/+EPccsstGDduHPbv348lS5agW7dumu+X0FsULaXybXQ0GVLJFq7oggxXcRuqMA25zk+M+zlR6SziGzLdAetUcuVTPFkpbtlEwsVWk0jMR6VzZZ3HaBjUI8Ulm0h4qduojCFOUS0HmA/Pc+tMJLudJ4PQwVjvTuwpipZSZQTv0Ngh0luklejVLcEtll5n6tJGeuCYnQiixW04JMRa1EHljz59+nTotendu7fX58L7csH4idh/qg4N7kDJ22+/Xa1RJrj66qu9/tZqtWLBggX4y1/+0mFckkiglxhjUeVOmTJFszepbfZkkTtIfsOOtZrRu29/vLvoA/U0VlvMZjMeeeQRPPLIIx3+/qc//Sl++tOfAnAN/sZWO1567a9enqn4+Hi8+uqrPuuuddSuMWPGqJ/JsQLxMRbSLS0R3zAwO5k0U67cnlG90kiTvAlPRvekWK80+BScqXdt640tSEdx6SkyuV6eDOJsk+K5jclPx5r9ZwJcrR05jiQhhvYIsPD6jeyZRhondVDaauqVnoBdJ+m8DyJB5rDcVFJvmjwnD81N9TrxFSq73UblgOxk0pxXXltN+ele3phQEf0uLYG2hIhewm9uMZpQFEW1uBOJq/+KRddCnDkZ8NRySSDW2cgK7SJ+if4+exZ06qytwiswknhvXJ78+hG7osUEPiyPtjyJHH82jLhCu1gcqe8F4EnRMIr4GQoDjdrQATwZjscWaPduakE20KhrAAoDbXR+OqncPZLhlJFEW0xTGGgje9L2DTkXVf+sZFLZ4gVsaG4KqVy9sLETIcgnbeKJJy3hJYknXtzltwZqY0cYaLFBFhbVgnChJ8TSOj6FgWYiL0Dh7RWgRF50gi3iGoi97gmcenGX4yZy0uJJZQsDbQTxoiOn1R+cQ7s4lBq06Di9tppojUoRYJ6TSvv8AE+GY+qgWSNPYu13b8tSj295S486nkvEAo3ulU4qVy9s7EQI8pYHde0qEQuUSGxEycG41EaJMByojSiHl1FJq7N4htRyZaPSqEWH+k0V8LxVjuyZTipXPolFbVaKXEbUBtpuA4tp7lGNynRSufKWZu/uxnj9RvQ0rijxEGKvn+h3Q4iNVUVR1LhK6vFdcsLV71Li6SNahFeY2tusFzZ2IgThfTFkq8ne0VZT6EFqRlbpFbFA1K55WWfqasgdGmgEsYCyK3pAtmeipUj4Jt78vCfZ0OXK+ZfkrSYKncUbthFbTQ3ucSh7dih0FsGcXoYwURSxyHAsP0OKIFRvr5/70ASRzsIrMDyP9j7L+YZ6pIrgdRqdhVHp3TdClyvizwBgkGRIUehdWu7qd159g0Bn+aWReitZL2zsRAhioTRyq8mo7bFYi5n8DduubjUZEwtkxFaTGmBOfJ9lrwD5Ee4KT1wNpZ0tlxjo1S2B9G6L+0Gtc02TJ9fJkJwUUp3FVtOwXFqd5a2mET3TSGWXSFtN1ONFZDge2TON1D1X4uX1o9VZjJXheamksuXxnUS8tV5W4TEqKXU+LGUwp/b66YWNHY0YmRZdC80GLZRy3Srq44GqgUass9PA+KXmVuO2msSbmPwMFSX0U0jCFd3dgK0mkeF4RB51fINn0TETe9BEjp3hxDrLXoH0RNp77QnmpH0LPlnjCTCPlABUOcB8KPG2jfBUUm81AZ65lDqeSxhoqQZsNYm0EtTbhbLXj/owhl746HkAYmJiYDKZcPr0aWRlZQUVL+N0OtHa2orm5uaQy0U0N7smLbPTjNYWOxR7KxwmB5qbQ3uEdU2tUOwuN2lLSwscthYodgdaW8xoNgVeiP21sbGpGYrTCStMaGlRoNhb4VTMaG4OLVlYU6td1dne2gJ7awsUu92lszU0o7S+sQmK3QGrNQbNzc3q94T6DG0OpypLcdhga22Gs6UJ52pqEW81q1mc9SAMB+rAVvmo/PC8VNJ6U2Iy7GvAVlOd+wjwiJ6pOElYjqLDrSYixDFlagNtt5RcMZb4ZUYYDtQGmlxMM484wFx40KiNnepG7wDzL3dWkMn2GGi099k7wDwNFTV09RzEnCRy93QF2NgJgMViQa9evXD8+HEcPnw4qL9VFAVNTU1ISEgIKahYUYBK96Rtqo+D3angbH0r4qxmOGs7ziujlZomG+qa7bCaTTjUdBan61rQYnfCUROraYvIXxvL3QFqjuRYWEwmVNa1wGo2wVQf2gTW0GLHuUYbTABimxJwtqEVTa0OtCbGoDrE4M4T55qgAGhNiEF9rBmVNS0wAbA2xof0DJttDnXvPbYpAdWNrahpssFhjsX084eEZEgJzw71onPEwABUUXxwcA/aRaehTa4TUmPnpDE6y17jkT3TcLqeLnOwuM/ZKaHNEx1RKW81EeIVYE4coyiMbOp0B7JRmRpPm1dGGNnUHrS2aSU6LqajD6GzER40vbCxo4Hk5GQMHDgQNltw9UlsNhtWr16NSZMmtSuoGQwVNU144pMNAIAv752EjYfO4okVuzAsNxV/vnGIbrkAMP/f27Hl6Dlc2DcDC64dilfe34qSEzV44gfDcEnf7IB/76uNrXYHfvrx1wCARXd9D2frW/DEf7ciJy0e7/10aEg6v/xVGf63sxJ9M5Pw99uG4R+f78Lqvadxz6UDcdWQniHJ/unHKwEAf7x+DFJS4vCLTzfAalbwxdyJIT3DRRuP4M2vjyM5zorP5k7Eq8v24p8bKnH7xH6YFYJXB/BsNVGf1Cg10BW926BFR059T72t55nAaXWWi2kO7JFMa+y4s1RTe/28AszzUlHbRFcvTBho/bOMy2VEvS0r+kYScQwh4Nlqovb6ieBkALASn5bdZZDXLxTY2NGIxWKBxRJcR7ZYLLDb7YiPjw9podx7oBon6lz72KnJiXCaa3GizoHsbgri40PzknxzuBZVDQ7kZqQiPj4eVc0KTtQ5YDfFaJLtq40HT9aqOhdkpaGq2dUGk9VJovOJOgfO75+M+Ph41LQCJ+ocaIElJNnnGlpVnYf26o6aJhtO1DkQY1JCfoYbjtThRJ0DF3R33ecGuxm1rQqUEIMCHW0CUGVCjTITNYTauqIpwtfkrSYv2SHKFYtOjMXk5RWgiLgTW01tDbRQZZd6BZhL8VwhygWAEh+LTqjP8PAZz1ZTQUai6l2k0Hn78Y6NylBly8U0h+amqqkxKPrzNrdROaiNURmqaHmrqV2/C1G4uM9tcxlRPMNqdy4j6vilUOAA5Qhgu3sgCVc0pWtXJDMTnZIqIn/niWr13yaTiVTnMindO0Cns1z3KCU+hlTntltNVLKPn/NsNfXPSiY9UbHjuLcrmkpnudSHeFulkr3NXbJgoPsIPtX9kLea1LFCrLPIZUT5DMWpJuH1oxvf3ltNlLtNZRXeXj8q0XKAebekWFKdd7Xx+lHJLq/1rmtG6V8VLzOD1fFNI1cOMKf2NocCGzsRgFh0BhHHCsgnsajdutuOGeeKFsfOqd8ath8zzhUttpqoc04YeepBbAlRu6Ll2j49iDPk7jLotM1ZKcMxdeyEKIg6MJv2tJR3gDn1+K4GAPTunkgqF/CcPKVeKMXibkRRSrHVRL0tK28lUweYC+NvCHF/ltNK9EzvOgHKbOxEAGqnJJ7A5dM1+Rm0ndKoE0KyK5raQFMXHWKjUnZFU2cTFYuOEWn1RV4Z6kVHuPwNyI+J/ac73moKFTnXSaJBuU6ojcpjBtY1E9uF1C9gcoA5deDzdoNeGtsGmFOy9eg5AECmjwLHoSAMeKPmUYA+wDwU2NiJAEQAI7UnQyyUAH2nFMX2qD0Ze6RTD2mJtKcejMob0tYVTclOMYETG5WyK5q6zIB4w6b2ZACeGCbqsbLVvdVkRFr9kzWu/kFtoMmLDnUuI1F/jNpA2ysHmBMv8GJ8U7+AnZYCzKlfSHeqRiXtWJEDzKlrV4l1xYgM5qHAxk4XR3ZFt63SG2qAmuiUBRntXdGhBqkJV3S7oNkQBYs3nY5cuiEHXQpXdNtgztDEqidigPbFNEPVWSwOQzuYZEORLec6yW2T6yRUnYVrviPDLxTZcq6Ttt6oUHUWhgO1zrLXbyzx+BYGWkdbCaH2aRFg3taTEarOW9w6d7QlG6pssX3aLlg7NLFe8Uttk5yGqrPHq9/eqAxFtBxgTn0Awd/4Dids7HRxvIrtuY0Sqnc08abj1SkJhMuuaPHWQKXzjo7edAiEy67oMfndqMQC8Cw6mcn0GY7Ptg0wJ1J667Fz6r+pvX4dLToUXyHHL6WIXCdEqov4JdkrQCH6hJQHSE2wSKSzZ9HxjBWK+yzXjxudT+tB29lB/BL1ToioHE49vtMlTzOVbI9Xnzbweavk1RdePyqd9/kwKsNNWI2dBQsW4IILLkBKSgqys7Nx9dVXo6yszOuaiooK3HLLLcjJyUFSUhLGjRuHjz76yOuaqqoq3HTTTUhNTUV6ejruvPNO1NfXIxoQAwkwzhU9jHjbZk+bUw+UiDcd6reG021ynVAijhYPzKbVWXZFj2njFQiVHe5gbSMyHLe69W7ryQgV4fWjLqkCAMeqjCmbIS861LlO9lWKAFTjtpqyiLeaxNxBvdV0tt4zvqm3qcX4HkQ8vuW0EmMLupHKFp5K6lhNAGh0n7akNoRDJazGzqpVqzBnzhysX78excXFsNlsmDFjBhoaPC62W2+9FWVlZfjvf/+LnTt34tprr8V1112HrVu3qtfcdNNN2LVrF4qLi/HFF19g9erV+NnPfhaOJpEjOiV12nTA44oeU5BOKlcsOkaURBFeAepFRwQvAvT1toRRSX3q4dAZ40497DkljEpaw69OCjAfTp55121UEusse/3G9U4nlb3DbexkGZDh+Jw71wl1TIb8AmaU1486KF7eajIqwJzaQJMPkHQUahAKIvaR2kBrbPV49alj/UIlrEkFFy9e7PXzO++8g+zsbGzevBmTJk0CAKxduxavv/46LrzwQgDA7373O/zxj3/E5s2bMXbsWOzevRuLFy/Gpk2bcP755wMA/vznP6OwsBAvvfQS8vLyOrdRxOx2DyTqE0JyrpORPdNJZauLDvFAkhnXm/ZNZ5t72yYtgTboGQAq3AHK1N6XLUc8W03UXr/9p4wppinyDQH091rdaiIeKyKAGAD6ZRIX06xov9VEgRxgTt3vxEmsfgamlaD2ZIj4RCPTSowiPmkpG5XUaSXUAyQGnlrMMKAwcSh0qQzKNTWuQZSRkaF+dvHFF+ODDz7ArFmzkJ6ejg8//BDNzc2YMmUKAGDdunVIT09XDR0AmDZtGsxmMzZs2IBrrrmm3fe0tLSgpcXj1qytdT0gm80WdEkIfwhZocjcpwagJqty7A6X9exUnLpll0hBs2lxJlWOeIu12+2aZHfURtHhB2QneXS221X5enWWXdH9MuI9Ojtd2yJ2h0O37FL3UfkBWR3oDP3PUHZFD8/1PEOnW2dHCDrvOO4ydnqme+6FvLVls9lgCzDCffXRBrcxPKpnikdnh8PdJv39bsuRswBcGY69ZCiSzjpli/wew/M8OjscnkVfr9zNh86o/zYpDtjcMSvBjpWO2CsCUHt4+obDHtz47nAMSvFLGQlm3eO7I/YIAy07mXR8i6y7ADAgM0EaKy6dQxkrIunfAElnG4HOXmklpH4X7Pju6BmK8d0jNa7d8wOAVpsNNps+I6i22dV2eXw7nA637vrv8+bDVQBcsUWyDIq10BdaZXYZY8fpdGLevHmYMGECRowYoX7+4Ycf4vrrr0f37t1htVqRmJiITz75BAMGDADgiunJzvau4WS1WpGRkYGKio4rzy5YsABPPvlku8+/+uorJCbSJ8kqLi7W/bfnGl2PyHFqP4qK9gEASs6ZAFhQU12DoqIiXXJXl7tkAMCXX37p+b4qCwATtmzdCuWo9rB8uY37TrlkxNSeQFHRcQDA0XoAsKKpqUm3zrvOeXResXSJ+nl5uRmAGaW7dqGoqkSX7O2HXTontlap+lW3uHQG9D/DU00eGTvXrcQu99x05LBL5/0HDqDItk+X7PW7XTqnmxpVnR2Kt86JGke43L4Wh0fGyZINKHKH0ZWdcN3/Y8eOoajoiC6dl+11tTs7zunVD2w2V1tWr16NvTqGoCK1u+HIThSd2QkA2Fnp6TN6n+HnR1w6J1kVL53r6106b9iwAVV79B1hOV3v0tl5+iCKig4AAHaL8V1TG9RYkdu3pqLj8X32rKst27Ztg+X4VuihrNzV7ti6kygqOgEAON4AAFY0NzfrHt97qj06r172lfp5RYVL57KyMhTV7dEle+shl85JredU/WpbXToriqJb5zPNLhkAULpxFcrc4/uQe3wfPHgQRUX7NcuTn+E3u1wyupnazpmu71u2dCmSdThHW6XxXVG6CUK9spOu+3/8xAkUFR0LXjCA4v0unXvEd3xPQ1kLfdHY2Bj4InQhY2fOnDkoKSnBmjVrvD5/9NFHUV1djaVLlyIzMxOffvoprrvuOnz99dcYOXKkru96+OGHcf/996s/19bWIj8/HzNmzEBqKp1bz2azobi4GNOnT9dVV6nF7gTWLQUA3HblVDXbbHzZafxtz1akpaehsPB7unT7+pNdwOET6JeZiMLCiern75VvAmrPYdzYsbhiRE5AOR218d51ronqxhnjMdYdD7TzRA3+sHMDEhISUFg4SZfOB5YfAPYcQFKsBYWFM9TPv6rbga1nKzBs+HAUfq9Al2yh81UTRqFwjGvrs7ymGY9vWQ0Aup/hx1tPANt2AQC+P6tQ/XxL0R6sqjiKAf37o3D6QF06P7ZtOQA7LhszEIWX9gfgSlVw//qlqs6Btoo6en6bDp8DNm4CAPz4Ko/Ox1YfwhdH9yE/Px+FhcN16fyXA2sB1OPCQXkoLPSM3ye2r0CD3YZJkyZhgI78OxW1zcB617O67eqZiHPHXTVtOYFFB1z3X/cz/McWAGcwvFcGCgsvUD9/7cA3qGhqwEUXXYTv9cvwLcAHdoc0vn8wRY27St53Bgv3bEFaWioKC8cHlNPRM1z331Lg0HEUZCSgsPAS9doPTn2LvTVVGDNmDApH5QatM+AZK9dPvwgX9nG1u7S8Fi/uWI/4+HgUFk7WJffoqoPA7v2Is5pRWOjpd8UfbgfOnMLgwYNReEm/kHT+/sUjUXieq1jw6boWPLp5FUwmk9f3BcPnO8qBrS7D+gfS+N6xuAwryo+gX79+KJw5KKCcjp7hkztWALBh6mjvOUK05bJp03QVu912rBrYuBEAcP1VV6hxVyfXHMZnR/aiV8+eXmMzGP76/9YBqMP5A3JRWDjKb/uoEDszgegSxs7cuXPVwOJevXqpnx84cACvvfYaSkpKMHy4a3IdPXo0vv76a/zlL3/BwoULkZOTg8rKSi95drsdVVVVyMnpeLGOi4tDXFz7gMCYmBjyBxGK3N2nqtV/98xIVjul1V2Q1GQy69Z3rzgemJfmJUN8h8ViDUq2aGOVlFZ/WK9uiIlxdTGr1arK16tzmVvngT1SvHV272dbzPruh1eukz7dVRkxMZ5gO73PcM8p19ZKTqp3IVGz2XU2wGzR/wxr3JWmz+vr0dlk9mxjxVi16yy3b/epBq/PVZ3dp4XMZv3PUGQ4Htc7o0MZMTHB9TtBacVZ9d/JiZ5gfrl4r95nKI7SjuqV3vFYsVp0yd1/xjNJF3RPUeOuLOr4Du4+y+3bKyX963CsWPTpLLJqA8CIXp5nKMY3TNDdN/a4czsN7JHs3e9M5pB0lrd+zpPGtzXGs8Wpe05yj5XM5FiS8S0/w6oGWzudva616hsr8viOjfUYSxb3+DbpnEcBYL97K3msz/FNv8ZqlRfW01iKomDu3Ln45JNPsHz5cvTt29fr98I9JTqOwGKxqHui48ePR3V1NTZv3qz+fvny5XA6nbjooosMboGxtC22R4kIUBtDHDEv65wcZ8ypB+qU7Ic74dQD9UksOcCc+iitKPVhRIZjYVdSn9QQMRlGZjimPrW4UzoBaFRaCfL7fNKjM3mAuXt8U5+0PH6ug1xGRJQalJXZq5hmT2PqbRmSVsJdb3FMFzt2DoTZszNnzhwsWrQIn332GVJSUtQYm7S0NCQkJGDIkCEYMGAAfv7zn+Oll15C9+7d8emnn6pHzAFg6NChuPzyy3HXXXdh4cKFsNlsmDt3Ln7yk59E/EkscaqJegEGPBmO22ZlDhUxGVIXrQM8GY6pdS6RgjnbZjgOFZHrZBSxgSZXaKfOdaIuOsQ6V0plM+hzGRlzEksOMKdehMVCaWhaCerx7T5NR306CPAcOzfq9BhAPy+Jl0Zqo3JvhSdPHHXdO3HCl/p4v+zV72rZk4Ewe3Zef/111NTUYMqUKcjNzVX/++CDDwC43FNFRUXIysrCD37wA4waNQr/+Mc/8O6773rtsb733nsYMmQILrvsMhQWFmLixIl44403wtUsMgJ6MnTm9a6RTj34eiNRdCYjF28NHZUvALxdysGgpdie3iznwkAzotjemXrXBODLK6A3NbucLdiX10/vMxTbNqN9HKXVrbOGYpp6ZYtFx5chrLdvHDztWXTyfb106BQuTi36yjek917USxnMqWs1qfWlfCxmenWWx7cvI1vv3CHGSrqPWnqhVEc4Ves6IerTQNM9vgN79fXqLdJKUOtc2lEG8y5EWD07WjrvwIED22VMbktGRgYWLVpEpVaXwfPW4D34Q93Rkr0CbV3RocoWOret7m0KMRm5P1d0qFt8Yqup7ZtOqDrLafXbpk4PVbZ6VL7NVlOo90JRFNUVTf0MxVZTR7lOQtVblFVpP1ZCvM+SgdbWmxHq/djnw5MRqs5l0vhuW0wzVJ2FV6DdfQ5RboXk9Wvfp0MS7TEqice3nOahbT09qn7Xpzu9V1+klWhrVIY8vg306lPQNbViAAB17lwI1NsJwq0bY6F3RYvASOq4GtmTQT2YynxM4KEi3PKAAa7ocmPil85JXj9qV7TQmTqRmRxgTu2aF2PFiK0m4fan19k1VqhrSgGevF/Uc5LsFaDOYF5mkM4irxNAn8FcGGjUOssB5tReP2GgtTX8ugps7HRRjHRFi4mFulPKnjrqzLtGbjWJt0qjdAaMCzCnXijlDKjUrmjhUaS+z3Kx3D7daYMuhYFGXdrCO8Cc9hmKfje0g0rZoaAoiprh2CgDzddWUygIrzB1zJUcC0QdYF6mxtXQ6lwm1S1MT6TNcOzLQ95VYGOniyK7oqnTbgtXNPUbtj9XdKioBhqxzv5c0aEivFHU9wLwFNujL+fge6spVDwZjolPl0gGGnUxTZEtmLpvyMU0s4nrYhnlQTstZTCnPn1UWu7qd9T32WGg10+MbyO2mmpVr74xNcKM2GoqM6jEDBVs7HRRxECKtZjJA9TE2/swP51STyyg7BXw5YoOVWd/x6z16Cy7ont169gVrVdnYaD588zpkS27ov0tDnruhzAc/FXK1qOzvNXkzzWvR7aRW00iwNzfdqEencX4Npn8BKDq7Hh7KjqOUQlVtnhzB/wEmAcvFoDHQPPn5dIjW04r0duH10/vfRYGGrXODZJX369sPeM7wAESQJ/OWg6QhBs2drooYgIflNPeKxBKIJnXqYcOJsNQZAtXdGZye09UqLs4ItdJR67oUESLCQto74oOVWd1D7uj+xyCbNkVndbG7R+qM32XHwMtFJ1P1ngCzPtnddSn9SN07sjbEIpcOdfJUOJnWOLeauroVFOoz9DmqhnSoVFJoXNH+XVCHStiK7IjAy2k8S3FArULMA91fAvDoQODJBTRstevowzJoegttjiHdLDFGYrcyjqP1486rQQVbOx0UXaeEIsOrRtTvKkC9K5o4SKlDmy1S1tN1K7oHe7EbkYk2BJbTdRvOsIQTiAO5AQ8QdXUrujdkleA2oWuxqgQb4EcqPR4Bai9RuIZUo/B6kbjxreqs5G5jAwaK/kZtAHEgGeriXx8u42oGIuJPNZvj4gFIt4e0+LVDzds7HRRdhsU2b5HigXy5YrWi8gIS73oHDEwAFUYO9QTuOyKpn6G290V6wcRv0EZ6Yre4daZOj4F0LbVpIcSAwPMfR2HDl2ux6hMJQ4w36GOb9qxckJKK9GP+KVj27FqAMDgHrT3WU4rQR0btd2t88Bs2vts5AGS7cdcfYM6vpQSNna6ONRvOtuOVgOgT/UOyKeajDn+C9CfetjjZ6spFMpkVzTxCTLhQaP2+okEaQC9K1pddIi9DSInEED/DIXO/bPovX7qVhP1ouM2KqlLtQCeU03U91k+tUgdYK4l1k8PohwHALVAMxXipZH6FO5ZKcMx9f3YYdALGCVs7HRBzmnslHoC1LYfF1tN/jtlsKK1uqL16CwWHV8BxKrs4EWrCbb8Gmg6BO9w6xwol1EoQdWBJqxgRWt1ResKbnW7zwNN4MHKPqKxrpkenbVuNQUru7ZZCjD3F0QcnFgAnkUn0AnAYLNre+cyMmZ8B8pFpUe22Gqi9mTsOFGt6To9WZ9FiZlARmWwz3C3hgzmgD6d1YMNxC9glLCx0wUJmOskBOfG7gCdUq+3/ni1/wDUUAgUK6BXZznXSUdbIKH4kMQEPsCHK1qvbK+tpg7q8YSy27L16DkAQDcfuU5CuR+n3QGMvhYdvXpvP+5/qymU+yECRam3IQPlOglFZ0/QLO2be7mUVqLDQPAQdBbbY4N8GJV6ZcsB5m2zgQMhjm+3h9znS2MIwoVd6eulUa9oobMRxXLLa4zx6lPCxk4XZKt7oTQi18mJaoNc0Sd8n3oIFZGVmToWSLxBAUC2QRmOjXRFUy/COzohwJw6S/W2Yy4DrbcBuU5EsVzqrWRhVBpZLJe6b8hbydTFcoVRSW2gyWklqAPMxQlA6rFSLWUwpzYcPF59Wp1lrx91QVRK2NjpgghX9EADOyV1ZeFt7oFEXRYB8OSVoU6wJbwvRqC6og1KVAgACcTG8N6KwLlO9GBkhuNSgxYdOcCcegLXupUcLLLXz1dBVL1sdXsFOkorESrCgKeOX5LHN3WA+X53gVjql8Y9ktePOoO5MCqpX8BOeHn16ePbqGBjpwsiPBnUnbLcwAzHYnvMlytaL/KphzH53UhlixMEAw3IcCzsSvpFx+UVMMIVLXIZkZ8ucRvvAH2AuSimSW2gyQHm1CdMxDYW9Qmh01JaCWrjTwQRU8uVA8ypX8DEqSbqE16AR+/RxIaw8OonGuDV9+QyMiYoHqAPMKek62r2HebQGW1p9YMNUNshvekEckUHG6S2z52TxF9mTiB4nQ+clk89+D/VFKzO4hh+IAMt2HA9OdcJdZChSNyoZdEJ5n7IXr9xBen+5Qaps4gV0LKVEKxsUSx3NPH22JYjLqNSy5ZssDp7chkF6BtB9ued0lZToFwnwcagerwCgQy04ATLGY4DFdMMOiDXbVRSj5U6KcA80BZnsPdZ5NjR9AIWhGy5fWMDju/gEF4/I9JKUMLGThfDu1N27MnQHaDmNnb8uaL1enuFK9qXy1+v3C3ugeSS0bEQvfdjX6BFR6fgEil+ydcRYL33Qyw6vk4I6XXXywHmvraa9OqsLjp+jcrghcteP+p+J2JU/C06oW6N+Fp09GYxF55Kf8U09eos0hL4irnSq7PwVAJ+vH46dd5/yv+2rN57IWdl9pXCQ+/9EIawvxOAevQWAcSA7wMkeu+HeGmkTitBDRs7XQw57Tb5VpN70aFOViXFn2J0PnHl8BPGZTgWruixxNtjYgI3IsOxcEWPos6/dMy4XCf7Aiw6epFPNVHHkpS5t5Kpt23OSsU0qY/pesY37bzhMDDWT3ijAqWV0INIKxHIkxEsWzWmldDDwTOu8U2dIFOOX6IOjDcq7IIaNna6GFslTwZ12m2jAlBPeZwCyEujnbSMmsC15jrRg8g5QZ2YT4vXTy8i8DnLAFf0OfcJk7HEC+UWyStAHYAqDDTqRUeOb6AOMBeeSmojSt5qyveTy0gPorgotVHZ2OoJMKd+hmKs+EoroRd5y4t6fIvj/UZkOBZpJahjKqlhY6eLIdznRmY4HkP8pnOk3rPQUAegClc09ZFl+Sgt9b0u64RTD9Serj2nxKJDa6DJuU6oTzWJ7YR+BpwAsbu9GeN6p5PKFYuOMWkl3OPboJNYgBFpJVz9jvp4v3xqsRvxAr9XY4LMYKn1vH8ZdoCE+qXRJrn1qb361LCx08UwqlPKrmjqYM7jDa4J0FhXtDFvOoa4ot35PYw6PQbQu6JF+nvqRWdvhfYA82AxyqiUM5gPpt5qco/vAdRpJSSvAPXLjDiJZURaCZHhOFBQfLBs74S0EtRGpfzSSO7Vd48V6pNY8gESaq8+NWzsdDHKgnjTCSbS/6DUKXt1o3VFn2x0DVIti04wOtcbWExTXXQMLLanaTIM4n6I+AatruhgTlWIY8ua4peCECxv22jZagqmf4gJXMt9DuZe7JC8flpqTAWjs4gzGkG8dXrGE3/qt2yGIBidxVaTlgDUYOTKGcy1LMLByC4N8qUxGNnCsKTud8fdxo7WtBLByBYBylril4K5F7JRSe3Vp4aNnS6GKLbnr1PqiU/YIaXV9+eK1nOKoMKdM87fNoUeuaUaXdF67oeYwP0d/9Wjs3zqwd/2ii6dxVF54q0mOcDcX+JGXc/QvegEWoD1hNyIDMf+PGh6dBZbnIG8Z3qmdpHh2N9CqedeHGvw/JG/tBJ6dN6rYStZj867Kzzj2+8J0eBFq0alv3g8PXIr6zzj21+ckZ77caIxsFwgeL1lr76/+CU990PM0dQZqo2AjZ0uhFNjMU09iD1s6q0EAGh0uIaJUSc1jEC4oqmT/pUEkeskWESsAHXsS4WBAeZqVXliz1yNlFaf+sirmiDTwABz6mcotpK7GxCAKtJKUI/vXdJYIQ8wdwdr0+tsXAbzcreHnDqgWvbqUweY79ZgVHYV2NjpQhySTj30pj714H6Lot6zlU89UJ/yMnLRUTMcUwc+u43KFA3bH8EiMhxTT+CyV4A8w7E7FojaqNxVbmCAeYU4iZVOKvdYlXEB5ifdUwf1oiPnMjLq1KKRGY6pjUoRv2REWomzLa6xR31UXg7Wpg4w33fKmBcwI9A1I3fr1s1HhWET4uPjMWDAANx+++2YPXt2yAp+l5C3bahznWhxReuT63lroM51Igw06kXntJTLiDrnkOrJIF4Y5GKa1F6SEwYGmNe1GJPhWIwVqwFxAsIrQH1UvlQy0KgDzMubXPeBunyBSHIH0Acoi2K5HVUkD4WqBrlsBu2LUqlB41v26lMXADUywFyklaB+mTECXcbOY489hmeffRZXXHEFLrzwQgDAxo0bsXjxYsyZMweHDh3C3XffDbvdjrvuuotU4WimxN0ptW41BRNIdqZeZDjWNrFolb2r3JPYTVMAqjaxADyGlNYjjVp1FhMWoM0VrQSxm10WpFGp9X6IEiKAdle01vtxMkj3uVad5QDzIRoNNM3P0G3saPUmatVZ3mrSuqBplS0ya2vdagpmfNe0up8hcb8TCyVAP76DzWWk9X7slsa31mKaWvXeU2GMzkfPeYrl9tZYLFf7/QjueL/WeyEHmAcqE9QV0GXsrFmzBs888wx+8YtfeH3+17/+FV999RU++ugjjBo1Cq+++iobO0EggmYDVf8N9l1WdkUHWhyC3ToXE8vgAG9QwcpVFEV1RQd60wn2fgi3bqBTD3rCCI64A1ADTSzBipYNtECuaJMpuIWy3D3PBtQ5SKXLpADUQCfIgr0fIlaAWueKIIrl6h0rgYyoYO+FLQivX7A6i7ESqJq1Hv+aSCtB/QxF3FygrSY941uklaAe37ull8ZAdQuDHofqCV/avrFXKpZrRDJSanT5UpcsWYJp06a1+/yyyy7DkiVLAACFhYU4ePBgaNp9xxDbNtQuUiNd0XvceVSoLftzUgAqdXZV4RWg3g5yGOiKFhN4foYBuYzsrlmOXmfjtprKDBorsleAOsB8j0HBnLLXL1AxzWDxGGi0W001TZ7xTZ0nSRhokbXV5OobOQYcIBHb9oFeooOlJEivX7jRZexkZGTg888/b/f5559/joyMDABAQ0MDUlK6vmurKyGK7VEHEQfrig6GMrXukTGxL4B2V7RWxAROHVB9rMrjivZVTFMv4s2PuhSA7IqmNv5EvxtC3DfkAHPqfDU7jwe31RQMIgM29aJTKnkFqAPMRb+j7htyXbP0RGNi/aiNqJM1ngBzX8U09SIMYeqThXIG86HkBpox99kodBk7jz76KObPn48rr7wSzzzzDJ555hlcddVVeOCBB/D4448DAIqLizF58mS/chYsWIALLrgAKSkpyM7OxtVXX42ysjL194cPH4bJZOrwv3//+9/qdUePHsWsWbOQmJiI7OxszJ8/H3a7vaOv7LKILRvAiEXHmKzMgCfXCfWbjjh2bsSppoPuN2FqneWtJuoAc2E4UPeNfZLXj9oV7ZkMaXU+LRXTpF4cRBAxtVyvAHODFp0+3WlPcAKeuKtAWyDBIjyV8TH0B4JFrB/1S6N8gIQ6wFzMHdQectmrT50LpzTCjB1dK8ldd92FYcOG4bXXXsPHH38MABg8eDBWrVqFiy++GADw61//OqCcVatWYc6cObjgggtgt9vx29/+FjNmzEBpaSmSkpKQn5+P8vJyr79544038OKLL+KKK64AADgcDsyaNQs5OTlYu3YtysvLceuttyImJgbPPfecnuaFBdkVrfVUjNawDGE4BDOBKxqky67owRq3mrTGkogj3IOIdfZ2RdNOhuI+BzOpKBpviNjWCyb/kpb7IbwCZpN2r59WnXfryLGjRWfZK5AYS2sMi623YAw0LffjiA6vn5Z7AUhjJYiTR1p0bmgJPq2E1r4hjB2t84ZW5O8PxkBz/Z3//i/uc3YQLwVan6HIYD4sCC+opvEtGWjU47vUoO1Co9A9U0yYMAETJkwI6csXL17s9fM777yD7OxsbN68GZMmTYLFYkFOTo7XNZ988gmuu+46JCe7BvZXX32F0tJSLF26FD169MCYMWPw9NNP48EHH8QTTzyB2Fh6d7QRyFtNgVzRwe5EGdUpvV3R/rea9AYZanprCEK27IoOVJU82A2BnceDMCqDEC67orU8QxOCOCHk7ncDiN3ygCetvhYDLZj+IbKBG1EsV91q0rBQBqNzaTC5ToINfHaPQy1egWBEywGogbb1gh3fO9XxraU/axdeKaWVCJyJODild7hLn2gZ38HcDznAXIu32RTECBdjJVCAuUtucLS69aZOgGsUuo2dAwcO4O2338bBgwfxyiuvIDs7G19++SUKCgowfPhwXTJraty1f9xxP23ZvHkztm3bhr/85S/qZ+vWrcPIkSPRo0cP9bOZM2fi7rvvxq5duzB27Nh2clpaWtDS4hkUtbWuichms8Fms7W7Xi9ClhaZW49UAXC5ogNdb3cvfoqiaJLd4t4iG9ojOeD1wqp32B0Br9121KVzjDmwHnabeEvUprPwdA3RorPT1T6Hwxnw2pJj5zw/OB2wOR0+r7VJW6FadBYG62ANOjvdE4XTGVjnMikmIyvRormP2m32gNeKyXBgdlJgnZ1C58DP8FyjJ9fJgMyEwDq75267PbDO246e06yzw+F5vgGvlbx+QzTIVseKI/BYEeO7V3p8YD2CHN8NLQ63zoHnDmcQOqvj22IKGBZgswU3VsT26eAe2vudFp13HqtS/22BEzab0+e1NrtHls1mg+L0vzUlMj4P0jJWghjf+6RcZTnJ1sD9zj1YbBrG93b3fDcoO/Cc5HDfZ0XD+K5r9vx+YGbgfhfMWhgsWmXqMnZWrVqFK664AhMmTMDq1avxzDPPIDs7G9u3b8ebb76J//znP0HLdDqdmDdvHiZMmIARI0Z0eM2bb76JoUOHqltlAFBRUeFl6ABQf66oqOhQzoIFC/Dkk0+2+/yrr75CYiL9vndxcXHAa1bvsgAwIVWpR1FRkd9r91SbAFhQV1sb8NpmByAe88ldG1C0178eZ06bAZixbft2xJzc5vfaxXtd1+YmBG6jK8OrFS0tLQF1ds3HLp2rD+1A0ekdfq8/fsKlx549u1FUW+r32s+Puq5NjVEC6lFv8+ih5Rmea3Rd21KxH0VF+/xee/CIS4+DBw+hqOiA32vXnXI9bwD48ssvA+qhKK6+tGzZMqQGcGyWnXJda6k9iaKiE36vLT3p0uPEyRMoKjrm99p9NR6dv17+VUCdm5tdeqxZswaHA7yEbj7oujahuSrgM9x2xqNHoGfoKqbpeoZl367GvgCvujU1Lj02bdyE+n3+37RXlbiuTTc1BtRZ3Lu6usBzQas0vk/v24qiI1v9Xn+60tXvduzYgYSK7X6vLdrnujY7zhlQD1d9PCtaW1uDGt+1R0pQVFXi9/oT7vG9b/8+FDX5n8C+POa6NskaeHw32j16fLl4MSwBnvfpete1racOBhyz+93zzOFDh1FU5P9U8qbTnj66dOlS/0oAcDpdfWnF8uVID7Cjtrvcda1Zy/gud+lxsvwkioqO+732QC0g7t3alYHnRoGWeTRYGhsbA18EncbOQw89hGeeeQb333+/14mrSy+9FK+99poekZgzZw5KSkqwZs2aDn/f1NSERYsW4dFHH9UlX+bhhx/G/fffr/5cW1uL/Px8zJgxA6mpdFs9NpsNxcXFmD59OmJi/Lvcf7t5GQAHpowZiMKp/f1em7r/LF7fvRkpqakoLBzv99qtx6qBjRsBANddVRhQ53+f3gzUnMWY0aNROCbP77V/2rcGQCNyE5WAbSyrqMPvd6xDXFwcCgun+JVbUdsMrF8NAJh97eWICxAMuPKjndh0uhxDhgxF4cQ+fq/98J3NAM5iZEEmCgvP83vt2YZWPPLtSgAI2L5WuxNY55qobvn+ZOQHqCxfsmQvlp08jH79+qLw8sF+r1372S7g4An0y0xCYWHgreP71n8FRXGlgggUdHzvOpchcvXk83DJoGy/155aewSfHilDz7yeKCwc6ffav64+BJTuQ1KsBYWFMwLq/FzJKtTYWjBx4sSAMT5C5yvGj0Dheb38XuvYUY5/7NsJIPAz/LKkAtjqMqy/PyvwWHnjyDocb6jDBRdegEkDM/1e++jW5QDsmDRqAAqnDfB77fqDVXit9FukpCQHfN4lJ2qBjesBAD+aFXie+fjsFqD6DEaNGoXCcT39Xvvan78B0IALBuYFfN77K+uxYPtaxMbGorBwqt9rz9a3AOtXAQDuuGZmwMSeqz7aCZwux8ABA1E41f+9+/j/tgA4gxH5GSgsvMDvtTVNNjy8aQUA4IrLL/d7qMDu8Izvmwsn+S3yCwB7iveh+MQh9OnbB4WFQ/xeu+mL3cD+Y8iMDzyPAsBvNhbD4VAw9dJLkRsgPlCMlWunBu6jZ9YfxUeH9yAvNw+FhaP8XvvWN4eBXXsRZzWjsDDwWAlmLQwWsTMTCF3Gzs6dO7Fo0aJ2n2dnZ+PMmTNBy5s7dy6++OILrF69Gr16dTx5/ec//0FjYyNuvfVWr89zcnKw0b2YC06dOqX+riPi4uIQF9d+EYiJiSF/EFrligRbo/O7BbzWavVMDoGuLTnpcpFazSZNbRNBbGaLJeD1B8+4LOpeSUrANlpjPF0tkNy9lR5XdHJC4GBAk9k1SZnNZg2yXfdjWF5qwGtjrB4XuNVq9Xv9oSrPVlPfrNSAwYBm98RqMgXWubTcpfOQ3JSg+mcgnWslV/QoDf3O7L7PJg19qcSdN2RgD406u2+XxeJfZznAfExB94CyLRbXWFGUwONwp5rrJJ58rNQ2u7Z5RuWnB9ZZHd8a7rOcjE7DPGMOQucDIoFer8A6x7jHt4LA47vsdLX679SkwMH85mDGt3tLaGhuWmCdpd0Pa0yM32R+x6o9W00Dc9ICxlUGM75FFvrchMDzKOCJ2Qk0vuUA8zEFGYH7nfs+Q8P43uEeK4O0jm83RqyxWuXpOj+Xnp7e7pQUAGzduhU9e/p/W5BRFAVz587FJ598guXLl6Nv374+r33zzTdx5ZVXIisry+vz8ePHY+fOnaisrFQ/Ky4uRmpqKoYNG6ZZl3Ai5zrRUlAtmMC6bceqAbgWHS3oidgvSA4cLBeMzluPVgMInHVXj2yRYEvLSaxgchKJ+6z174LRWVRo13qqSaveu6WgWS3BvsHcD5HrRGv+Ja33o1zKcBwowBwITmcRNKv1BKBWneUM5lpqCAU3vt11jxK0BawGcz+EXamtNEIQOrvHd6AM5qrkICJny93FcjUdxtARFA9oy2UU1Ph2G2h5WlNzaRRdJgeYJ2t4aQxmfIvM+RFy7BzQaez85Cc/wYMPPoiKigqYTCY4nU588803+M1vftPO8+KPOXPm4J///CcWLVqElJQUVFRUoKKiAk1NTV7X7d+/H6tXr8ZPf/rTdjJmzJiBYcOG4ZZbbsH27duxZMkS/O53v8OcOXM69N50RYxMuy1OTFHnQjgrFdvLIw5zEosOdV4gOdcJdeVwYewUEFerBzy5jEYSF3nc6tbZagqmopE2DrvLZlDnBdrpPhEDBE6rHyxi0aHOdSJKDABGFNN0LTq5ibTPsFoKMB9OfNpm54lqAPSZ0WWvH3URVzG+qTNUA55cRgVJtM9QvDRSVzoHPF4/6vFtJLpmi+eeew5DhgxBfn4+6uvrMWzYMFxyySW4+OKL8bvf/U6znNdffx01NTWYMmUKcnNz1f8++OADr+veeust9OrVCzNmtN/7t1gs+OKLL2CxWDB+/HjcfPPNuPXWW/HUU0/paVpY2Hr0XOCLdBKsV0ArwiABAA21NINin5qVmVZnOdcJtVFi1JuO7IoeRb7ouL0CxPaZ7PUbW9CNVLaYwDOT6VNKCAOeugr3VukEIHUG8/2nXQZaT+KFcpfk9UsmTuwpPA7UY+X4Oc9Lct9M2gzmIukfdRV12eunxUMeDOLFwIhksoIxBemGyaZGVy+OjY3F3/72Nzz22GPYuXMn6uvrMXbsWAwcODAoOVqTFz333HN+EwT27t07YOR9V2aHQZ4MwOOKHkvcKcWi45oIabNVn3S7oqkzHAudAfq0+kZ5BfZIxTS7EZcwEF6/PGKvgJzrJFAxzWARi87AbNr7LGcwp/b67XBvNVEvwIBH7z7EU8eWIy4DLVAxTT0cqzKmbMY2yetHncFcvIANIX4Bk736KcThokaVoDgrZTCPlOzJQBDGjnx6qSPWr1+v/vvll1/Wr9F3ELHoBJMtWAvnpK0mai9JqVqCIglAs/+Lg0B2RY/rTesVEG861GnTAaDO7YGh9mRsOVINwBhXtEgl34vYKyDHL1EX09yrLjq0Y0XOYJ6XRrtVseeUCOaktUjkAHNqz45qVBLrLL/gjuudTip7u7vfBZPhWCsigzm1ISyMSkBfFXZ/iPFN/dIoxy9RZzA3Es2abt3qnb9hy5YtsNvtGDzYdWx27969sFgsOO88/8d5mfaIBFvUbzryVlNSkK7oQE63vV6u6LNkco9KW029g9xqCjTd7w7BqPSntxxgHuwWSCCdxaKjJ8Ox1uWvT0pwC2WgZygyzRqR4VgUy6VedOSt5KC9fgHux373WAm2PEmgp7LrhMfrlxjsmhNAuNhqCvbNPVDfEF5bAOiXGVyfDnQ/hBdUTyyQP73lDOajiePmRAmKfplJAGr8X9yGQPfD7n5xHBfsC1gAweJlhnp702g0a7tixQr13y+//DJSUlLw7rvvols31408d+4cZs+ejUsuuYReyyhHuKLHadxq0voGIN50gim2p3WaF0bJqJ5pwCkNcjUKlr0CWl3RWmUH+6aj9V7IW01aq2Vr1VmPK1qL6DOSKzpXoyND8/1wH6UNZltWy/2QMxxrNXa06ixiVIIJQNX6DEVaCa2Ljubx7TYqYwJlw5Nla7xOBFVrOR0KBDG+pa1krcU0teosjp1rLYujVWc5w3GPVG1eI62y93p5/bQZO1pEywHmWrfetM9J7hcwA2OBjEDXxuYf/vAHLFiwQDV0AKBbt2545pln8Ic//IFMue8CcjHNocTuxt0hvOn4Q3ZFjyEO5hQ1sahPpQFAlXtbb2w+7VbT9iCPnQfDfneAuZYjy8Eg60wdYF6mejJo+/OhM55Fp1eApI3BIiZw6hgEOcCc+n6IreT+xHXNvAPM00lli1psWtNKBINIK2HUSSyAfnwLY2ckcVV5eauJPMDc/QIWKQVABbqMndraWpw+fbrd56dPn0ZdXV0Hf8H4okTaakqNp3X7C68A9Uks+dRD7+7Ui47bk0FsoMmuaOrTNsIrECirqh5sDtfCQ71tI7Y4k6gtHXj6B3X8ksgpA9DHMIkJ3Ki+ARgXYD40h9bYqZByGVEbUrvVAHNauXIxTepnKLaSqec6wJNWYky+MSEMgbLP60GklaCek4xG15245pprMHv2bHz88cc4fvw4jh8/jo8++gh33nknrr32WmodoxrRKYNxRWtFuKLJT5dIbw1aXdFaEV4B6glLrtBOHcAodKY2KqukAHNqj4NYdKhd0XKAOb3h4D4qT5yrBvBkOKY20MT4pg4+BTxpJUYT32d5fJMHmLvH4ShineWtJvIA83JjvH6yV38I8ctdZwSYU8cvGY0u/9bChQvxm9/8BjfeeKNacdRqteLOO+/Eiy++SKpgtCNc0QN0HKX1F1Qnd0qt++5ef+/nd8IV3S0xeE9UoKC6UFzR/u6HHKytxxXtT2/hitZjVGrVOdgA80CyhfE3Ii8VwQSYA/7vxeGznlNNwQaYA/51FrFAek5i+dNZDjDXY7AqfqQLA01XgHmAaF9hV47qlYZjwT1C/zq7+53WDMdecgPoLAKUx+jYSvYnWvaQ60kr4e9+CENKz1ayv/sh+gYApOoI5vcnW/VU6sjN5e9eGJnLyGh0GTuJiYn4f//v/+HFF1/EgQOu6q/9+/dHUlJkNb4rIBbKYBLGaRnK8qmH/tnan4sWO0B4BYI5zq5FZznXSTD7wVpki+2EYFzRWm0i4YoOxqjUprM+V7QWvYUrenSvNKB95RfdckukbZtgcp1ouR8eT0a6drla+rMUYB5MskItOutZdLTIrZS2mgZkJcN/DXpJtgbhpeXBn8TSorMcYB5M/JI2nXUEmGu8TqSVGEM9vt2n6YLdktVyP8RhjKDGioZrZKOS2qtvNCFFLiUlJWHUKP/VURn/CGOHOgB1l9Qp46xGuaLTSeWKAQoY54oeQZyFWD71QJ28S3j9qN3n8hvh8LwU7Ndo7GhBGGiZGmrxBMuZete9pt6W3RWi188fIq0E+fgu9xhogaqGB4v6AkY8vg+e9ozvfOIM5uIFjDoIvE7KZUSdUFAYaNTxifL4pp7vxEujHq9+uIks0yzKUBRFdUWPII7GF53SiFwnwmtEve8uu3XJMxy7Fx3qkgu7parT1PfaqKBZ2RXdpzutN1bcD+r+LKfVp04oWGpQ0KyiKKq3knzRMTDAXE0rYWCwNnWAuVEvjXKGY+oTZHsMil8ql7z61DF5HqOSVufOgI2dMCKn1adOf1+qejJoFx25mCZ1QK6erSat1LkDUPXsYftDGGixxOnpAY+BZpTOgAEB5u4tIWqdZa8fdYCy2Lah1lkOMCf3+hm06Dh1bjVpQfQ7I4ppigzH1DrvdAdrG5DAXDWkqA3h3ZLXjzrAXBho1Dp3BmzshJHdIbqi/QWSlYV47NxX8JucVl+PK9pfUJ24H3ozSfu6HxSuaF96i60mvTmSfOnsvdVEez+EUak3l5G/ZygyHOudDH3rHOpWk++/EXWP9OYN8XU79kgnAPXkOvEX6rsnxFwnvnSWi+Xq8fr501l4/XTr7OPzUAPMAd/3o1RHfKKXXJ/fp6gxTLrHig/hJSdC22ryFwh+otpd14z4JbozYGMnjIhFJz3YTqlhrheu6GAHUiDRpeX6XNFa1ie9E3gg2Xpd0SYNN3q3TqMykM5yrpNgXdGB9NYTYO6S6x+vAPOg74d/6btO6ttq0vIMG92LZdCLTgCdRTBnsIaOFmNOpJUIfqHU1jeAIAPMNegsEjcGb5D4ly2P72AMeG0663xpDCD7tJTBPNiaaYH6dGm5q98FbVQG0NlIr35nwMZOGBFeAeoANb2nHrQgFp0C4gBDwJN3glpn8aZjxOkBsThQZxM10hUtnuFQA4tpUm9VqMHaxJOsVwbzHIMCUIm3sIzcahIGmhFbTSLAnH5b1nWfzSb6AHPR76jH9x4p1o+6mGapmheIVmfZ69ebONavM2BjJ4yIPCrUFcmPheiK9oeYDKkncApXtC+EztRVp71OPRhkoAVzFForIoCRuvCskQHmYkGj7htyssk04hMmot9Rn6Y7WeMJMKfOcCxSB1CPFTmDOXVZHJH3i7osDuAppkkdoyJ0NuIAybEqV/8wqjwJQB9g3hmwsRNGxFaTkV6BYFzRWvB4BWh1FjlUAPq6WMKoHNyDVmfxpgrQG38iiy31BC67oqknQ6GzEcnGmmxiq8kYQziB2HsGAAfcW03UQcTyCUBqb+Uug17ADlR6vH55abQB5iKImNqoPGdggLnQ2UivPrWBJubRXt3ovX6dARs7YUJ2ResOyPURSCYK14UyqfiKURNuf72eDF9yxUAKxRUdKFA0lMW9I9GyUanXFe07yFBM4CHo3IHsUANQAd/PcIe7CncoE3hHOsvFNHUHivpQWlQODyWtfkeyvXOd6HyGvnR2j+9Qyp74eoZn3Qu87oXSV38OOcDcd2C8iAWiNtDkAHO9dQt99Tsx31GnUTghpZXop/Olw+dYcfc7aqOys2BjJ0zIruhgJ9pAAWpiAh+ko1P6m4jkXCfUgZHbjlYD0OfJ0BKECugJQPX/azH49biiA+ksApR1LZR+RIeSVj/QIhXqCSFflEkBqMEmKwy0rqpbyTqMSn+ixak0IPg+HUhnMb71eBv8yZYDzIN9AQvUk8QLWH8dxXID3Q9RLDfYWKBAOov7rOckXSDZIteVnhcwf/dD3koO1qsfSGe9Bxu6CmzshAk5wVYM8VZTqMfOfSFOgABALrErWsQKULuLZVc09SK8vRNc0dTBnFvdRmV+Br0r2nOqifg+uxdKI4vlUvcNcSIGoA8w362jnIMW5Lpm1P3Ds9VEe59rpbQS1NuF4gWMupim08Ctpq3usUI9PwOeYrmRmFAQYGMnbIhFh7oCNyAn2KLtlOJNB6A/9bC/0hgDTd5q0vOGpkU2tYF2/Jxnq6kfdQDqCWMMNDnAnHoCF14B6sSbRqbVF+ObOusuAJypDy2XkS+EUQkYUTbDGK+ffKqJOsBc1EyjNtDKpbQS1DF5YiuZWq4cYE6d8bmzYGMnTOw8UQ2A/q1BdkVTd0rxptNPhys6EHpd0YEQbzpGpNUXCbaMCvQFjEurTz2BywHm2Sm0b5VGGZVygDm1kS2eIXUJCjnAnLp2lTAqjUgrIYrlUo+VbcfOATAmrcQRd7HcYcRxNTul8U3t1RcV2qljgeQAcyO8Rp0BGzthYq+7U1Ln9pBznVBHzRtVuE7OdTKC2EATb6sDiHWWXdHU9XiEV6BHKr3XT7iijfJkGIEoFUFfnsSz6BhVTJM6vsEr1wmxUWJUXiA5wDyYKtxa2H7MGKNS9vqRj2+3gWZEWgk1wJx6S89Ar19nwcZOmDjtrosVSpHHjoLmxZsOEGKn7ED4PoKtpo4i/eWtJr2nHnwhFp1Q387a6i0HmFMX2xPxHoNDNITb3mo5wDykyuEdPEM1KD5ET2VHfVrYlWMK0kOS3ZYtbgMtJT607c2OdBa5jKhPAMpbTaHkMupoHO4XL2ChjO8OPpNPNXULYVuvI9llRJ7KtvdDrlsYivHXUfmTUjWXEe2cJHv1QxnfHeks+p3eE15dATZ2woDsitbTKf3ZMCJoVm8xTX/Tp3BF61l0/Om85ajLQIvT6Yr2J/uwcEXreNPxJ3dHiK5of7KFJ2OozknWl+gDpz3FNPV4jfzpLILi9U7gvmRXN4a21eSvP4e66PjSWfb6jS3oFrxcP78Ti47eDMf+ZNe5PTBj8mnHylb3+LbqNM78/ZUYK9SnmuTxHWcN3uvnT3aoW8m+RMsB5nq8+v503nMqtPHdFWBjJwyIBRgA8rvRuqL3lNO8NbRFdkUble6dOn5JdkWPI/YKiEXHCFe0iCWhdp9vkbaa6ANQ3VtN1FWnpaPyScQB5mLRod62OXZOzmVEO753q0Yl7ViRA8xH9kwnlS2C4qk9oDLjegdvVPpDeMiDrluoAZGWYLQOo9IfwqgEDDhAEmKx3K4AGzthQO6U1Gn19xG4ojtCLgCanki7wIfqFfCFnOuEOq2+uB/UJ4RkVzS1sVNiYIZjofc4HZ4Mf4igeCMyHIsM5qOpA/mlrSbqDOb7DIoF8s5lRDu+9xg0vs9IxTSp4wjF8X4jA8xD2kruAOGNMiLDcYPbGB5L/NLYmbCxEwbEmw512nTA44qm7pTCQDOiJIpwRY8iP7LsMSqpc50YFYAqbzXlptL2D+GKpp7A5VwnRhnZRnr9xuTTGmhi0aEuewJ40kpQL5SGegXE+Dbo8AFAH2AuXsCoTy3KXv1exF59o14aZa9+pObYAcJs7CxYsAAXXHABUlJSkJ2djauvvhplZWXtrlu3bh0uvfRSJCUlITU1FZMmTUJTkydAtKqqCjfddBNSU1ORnp6OO++8E/X19e3kdBWEK3pgyAFq3oFk3q7o0Dpl2yA1z5sOrc4yobqi28oWWyAUxfbaai28RqEalW113kYUgNqRbOEVCHXRads3SqT4hlDvdVudPQGoIfa7Nj8fl9Lqh+rpaquzKF8QcrB2G7neuU7SQ5Pd5o6I+KVQ00p0NL5FMc2QX8DaiBZGJUVaibb3Q6SVCH18e/8sj+9Q00q01Vm8gIV60rKtzrJX34i8UZ1FWI2dVatWYc6cOVi/fj2Ki4ths9kwY8YMNDR4Aq3WrVuHyy+/HDNmzMDGjRuxadMmzJ07F2azR/WbbroJu3btQnFxMb744gusXr0aP/vZz8LRJE2Euuj4GiJyp+yus1P6eqkTrmi9+Rt86VxZ50mwpdeQ8qlziK5oXzrLGY71HqX1+Qzdi04ormhf90NkONa71eRL5x1uozKUDMe+dBYZjvV6X3zJlRNk6s3R4qu1Iq2E3kXHl857K0ILMPcnWxiVelNh+Cp/4l1MU6dsn3OSa6zoTSvhS2eKtBK+ZIt0BzkheG19ed5EWgm98Ym+dBYetAg9ca5CG/EXJIsXL/b6+Z133kF2djY2b96MSZMmAQDuu+8+3HPPPXjooYfU6wYPHqz+e/fu3Vi8eDE2bdqE888/HwDw5z//GYWFhXjppZeQl5fXCS0JDuGKpt5q2tkJGY6p82TICbbIXdFEbzptOShtNfUk3h8XEzh1DEI9QTFNX4jUAdRxUd65TowJiu9mQACqSCsxlnp7zJ2IFKAf38IrQH2f5QBz6gzmqlFJHDQr5zKiTrAoXsD01C30h5xWgnqrSXj1qcd3ZxNWY6ctNTWugZGRkQEAqKysxIYNG3DTTTfh4osvxoEDBzBkyBA8++yzmDhxIgCX5yc9PV01dABg2rRpMJvN2LBhA6655pp239PS0oKWFk9wW22ta+Kz2Wyw2WztrteLkCXLbJE65cCsRF3fZ3e4Fi5F8ZYtYoH6dtcnF/C81TgcDi8ZIsPxiLxk7887aGOHOts9i6187XZ3XE1irCUEnZ1unZ1eMsRWxci8FF2ybTaPznabDTa3B2DrkSrPdzvscDra/WlAHG6dnU5vncW++widOsvY7XZVxvajHp2TY03tnpuW73I4HG6dFa/rd7sNh2G5+nUWdo2ss8hVAwC90+P0jRVp60f++9ITnmR0+nVuP1ZsUgDqkB46x7db57bjW7zM5HdL0DUGAd/jW6SVGKHzGdrs7fUBPP0u1moOfXw7vXUWCVRH9dQ7vr37hs3kujdbj5yVvtwBm44B7nCKsdJmfJ9yjZW2c1Iwz1BsX9mksbJD2h5LizPpuh9ifCtKm/Htzvs1LEf/+A6mfXplB6LLGDtOpxPz5s3DhAkTMGLECADAwYMHAQBPPPEEXnrpJYwZMwb/+Mc/cNlll6GkpAQDBw5ERUUFsrOzvWRZrVZkZGSgoqKiw+9asGABnnzyyXaff/XVV0hMpE+VXlxcrP77SD0gbvvmr5frcg3ur3XJaGioR1FRkfr5xjILABO6wfvzYKisNAMwY8eOnUg6tQMAUGfz6Hx421qU72z/d3IbO5Tb5JJhs9m8dFtZ5vq+7Fi7bp2PHnXJ2Lu3DEWNewCIRHQunc/u34qiE1uDluvyCrtkLFu2DGK343+HXd+XFqvo1nm/W+fDR46gqOiQ+nlts+v7Wsv3oqioffyaFhwOVz9YsWIFMty7HSvLTQBcnrOOdA70/ACgpMIlo6K8HEVFJ9TP91W6vs9SfQxFRUd16dzU6JKxdu1anHS/9O6s8ui8rHiJLrnbz3pkyG3cfsT1fcmtZ3U/w3PnXDI2b96M1kOuBehEAyD6zNZvVmC7jvF9qM4lo6GxwUu3DXtc35dhatD9DE+dcvW7kpISFJ12DeRGqZ8f2bEOp0uD1/lMs0uG3e49jpfvdX1fjziH7vt87JhLxoEDB1DUuh+AMI5dOlcd2I6i8u1By3Xt6rpkLFnyFeLcjuUvjri+LzlG//jed8zV744ePYqiosPq51UNru+zVexDUdHedn+n5Rna7a5+sGrlSmS6d8O+rvD08y+//FKXziWn3OO7osKr3WUVru+z1h5HUdExXbIFWtoXLI2NjYEvQhcydubMmYOSkhKsWbNG/UxY9D//+c8xe/ZsAMDYsWOxbNkyvPXWW1iwYIGu73r44Ydx//33qz/X1tYiPz8fM2bMQGoqnUvUZrOhuLgY06dPR0yMy13+/qbjwE7XbDJrVqEuuRsPV+HPu75FUlISCgsnqp8/9O1SAE5cfuFQFF7cW5fsz6q2AudOY+TIkSg8vxcA4Ov9Z4BvtwAArvmBt84dtbEjDp1pwLPbvoE1JgaFhTPVz/+4dw2ARlwyojcKC4fo0nntZ7uwrvIEBg4ajMIp/QC4Yz3WfwMAuOXqy3UdAa5rtuPBTcsBAJdedimSE1wzy/tvfwugCuP6ZqGwcJwunfcu248lJw6id+/eKCwcCgBobLUD61zfd+uVlyIzWV9cxoPfLoXN6cSUKVPV2J+VH5cAh09icI9kFBZerF6r9fkBwLkNR/GfQ3vQIycHhYVjALjeAu9d55rAbph5se7A+Bf3fA20NGH8xRdjrDtOYt+y/UDZQaTGW1FYOEOXXMuuU3hr73YogFcb7133FQDgmkljcfnwHrpkv3N8Aw7X12DcuPMwfZjrhes/W04AO3YBAL6vc3xvOVqNV0o2IjExEYWFl6ifP7JlOQA7Zl4wGIUT+6qfB/MMv6jeBlRVYvjwESi8MB8AsP5gFbDpWwDAj67Up/PRqkY8vXUNLFar1/h+df83ABowcVg+CguH6ZK97rMS4NRJ9OvXH4XTB6nfh/WuteLWqy/XFXfV1OrA/I3LAAAzZsxQ8zj9+93NAM5ibO9MFBaep0vnAysO4MvjB5BfUKC2u8XmANa5vu/m70/1qjEVzDP87ZZlaHE4MHnyFDV57Def7gIOnUC/zCQUFk7QpXPtpuP44GApevTogcLCsernYqz8ZPp43aEXwbQvWMTOTCC6hLEzd+5cNbC4V69e6ue5ubkAgGHDvAfJ0KFDcfSo6w0yJycHlZWVXr+32+2oqqpCTk5Oh98XFxeHuLj2C0lMTAz5g2grd497n7lfZpLu74qxuv/OZPKS0eR2RY/tnaFbtgj8tlgsqoyyUy7LOT7G7FNuoHsn/07+tziKOa53txB0dr3RWMwe/fae9lj7CfH6jIZYp+e1PMbqaZ9IoDe2QP99tlhcOpslnQ+We2KBctKTdMdliEBDq9XaTudRvdI71FlL37dYre10rpQqOA/N64YYnUf8zab2Ope5iw8Oy0vVfZ+tVs8UJ9oo5zoZ2SuUfuceK1Z5rLh0zs9ICFlnU5vxLeKuxvjod5qeYUfj232fYywm3TrH+hjfB9wB5iGNFTG+LZ5+t08a30kJ+sa3Qzqf47p3rvu+330/xhTo7xvWDsZ3WaWUTLZ7cofjW8szNHcwvve6dR7VKy2EfufS2STpfFbKZTSsVzf1HunFiDVWq7ywnsZSFAVz587FJ598guXLl6Nv375ev+/Tpw/y8vLaHUffu3cvevd2eS7Gjx+P6upqbN68Wf398uXL4XQ6cdFFFxnfiCARp5qog2bltPrUeRbECQLqwDf51MOwXFrZJQSnHnwhMhyP6EkbGFly0vOGQh+A6jJ2QqnF1hG7pBOARuU6oa7uLRfLzScOQBXB2tRZxuukXEbUuV9EsDZ14LocYE6debfkhEtnIzKYV7gNeOo5ukQK1jYqwJxaZ7muGXWAeWcTVu3nzJmDRYsW4bPPPkNKSooaY5OWloaEhASYTCbMnz8fjz/+OEaPHo0xY8bg3XffxZ49e/Cf//wHgMvLc/nll+Ouu+7CwoULYbPZMHfuXPzkJz/pkiex9ro7TygFAjtCRMwDNHllZMoM0lnksgBCz+/RFnE/qHWWTz2QJ9BzG2ih5pRpi6IoaoZj6vuxyz2Bh1pMsyNEhmNqneUUDaHmOmmLOAFI/WKwV8pwTJ3rZI9B41sOMKcuFSGMSj017/whB5iHUvC4I4RRaUTZDJFWgtrIFqfpEolfZMJBWI2d119/HQAwZcoUr8/ffvtt3H777QCAefPmobm5Gffddx+qqqowevRoFBcXo3///ur17733HubOnYvLLrsMZrMZP/zhD/Hqq692VjOCQmQ4pq8v5eqURqTV3xdCsT1/7JI8GXqKafqjrEJMhrQ6i2ywAL3XSBho1JNsFUGuE18Iw4FaZzmXEXW/E2/Y1MeKAaDanVaCXmfXfQ4ll5EvxFihP7LsGd/UGcyFgUbd7+QM5nqLrfpCpJWgfkmqaTLO61dqkNcvHITV2PGXTVfmoYce8sqz05aMjAwsWrSISi3DkF3R1MUHRaeklis/I+rJUBhoehOk+UNkODZKZ4DeFe15W6WdWIx0RQsDjXoyPCrlOunTndbrJww06rEiZzCn9wq4+p0R41vYlUYZaEZsNQmvML2n0mOgUdctNOplZo9kVKYR540Sc9JQnclkuxJcG6sTIXdFS7aiZ9+dplMK0eSu6A50pvI2CNFyMU2qyVDIViu0E7miZXu/xa23UbECVFtNss4i/oXsPrtly14B6mKau07SeqOEzvL4pqiLpXQwVqje3IXo03IxTQJDyltnV7+jiiFUczE5jB/f/YiK5cr3o57Yq99WZyqvvqyzx6sfuTWxBGzsdCLiTSfWYg7JK9DRn4pYgVD3sNvKFq5XIDRXdEetFfvBoRpobXWWA1BDKrvQwWeiHk+oC0Nb2d6uaNr7Ie5zqItOW52dTjqvny+dQy2W29FYEVtNoRqVbUWLRcdsCs3r19Gf7ibaLmw3vqVYv8RYWq9fiTq+aXWWi2n2Jvb67XRnqaYeK3IxTb0ld3wJ94zv0F7A2urs7dWP/G0sNnY6EVGPZ1COcWn1qVOni8XdCFe0SKs/grea1CBwAEhPpL3XYtGhDnw+WeMJMKcOutzhHivU2zZyMU3qZyh0pj4NCUDdaqI+TUdZLLctJ91e4UgKMFe3mqjHt+T105s/yxc71fFNq7MIBQCM6dOdDRs7nUiJQZ1SdkVTyxZF4Kg7u80AV7RAVBYOtZp1R4itJuoAc7FQGhFgLt6EqV3RpVKAud5imoFkU8cCHaj0eP1C9Rq1hcrr1xa5mCa1wSrGN3UtNjnAnHpbVuicn0EbQAx4tpqo00qIGoCxxFuygOfQBLWBZmSAeThgY6cTEblO6Asxet4aqHOdiLcoaiPKyABUseiE6tZti1xMkzrIcJtqVBro9SOewIXO2QTxKW05p55qMsaoBIzLdULdN3ZLW8kp8bQeGDUWiDgA9fg5z/imLiCpev2IDTQ5wJza2yzGCrUH1MjxvdWtM3Wqg3AR2VmCIpRRRK5o0c23Ha0GQOuKFmPIc6qJNhBQrnZOdepB6LxPXXToJixF8eRIAoDuRK5oUdTPiBNCiuLtiqaSLXQ2xpOheAWYk40Vd98QW8n9SfM6uYTb3d4MugBUlzyxUCZRvsi4bwj1qSahc8kJ+q0mMXeILV/Kl0ZFUbD/tMfrl02WVsKl9S4DcmgpioKzktePbr5z6bxT3ZaN7GrnAvbsdBJVXp0yxIC9Nj9vJ3zTkWXLrujR+bQBqFuPuqqdhxJArMpu83ODSLDVizYwUiw6FLlO2so+eFqcagp9wpJFy1lb46yhLZbtg9ddiw7FBC6LPnyWJsC8rVzAswhTeCrl+yEHmA8nDnxWt5Ip7rMkXA4wH9kznUwu4BnfuQRbhW3vR62rQi95UPy2Y+c6/JxC9j7CrSZZtJyrLFSvfludxY4BtVc/XLCx00nI8Q1JxLlOxJsOdayA7Irum0lr3avHzg10RVOXGdiuuqKNy2VE5ckQbHVP4Ea4os/UG5PLSHgqAeO2mqjzhnjlOiEO9qU0KmXKpbpmA4nf3kXQ7EDi8S1nMA/1BawtYtuGKq2EjBji1HOSMCqNyGAuymZEw0ksgI2dTmOLu1OSuqLdGJVgS3gyAPpTD55Fh1bnvQaeehDH8KkXHVFrC6C/H8KTQT2By7lO9FZC9oVYdIzIcKwGmBMvOlvcBloccaA2ABxxB5hTxwLtkMY3dQZzETQ7lHisyBmOqTOYi2P41Icx5LqF9DXCaHMZCWSvPvX4Dhds7HQSolMOIO6UTq9O2Y1UtjB2jMhwLFzR1EdphVFpBOqpB3KDxLPVRH3qQcQvURtocq6T/G60RokwKqkncDnAfDRxvytRPRnGBaBSj29hVBqRVkLEklCP760Gev1UA82gFw6APoO5ONJupFef+gBJuGBjp5NQPRnEAWpGFdNUoKhHDykXHUVRvFzRY/LT6WRD8bjPqU89wJNWf1zvdDq5iuSKJp4IFShqrhNKT4aieHv9KNPqKwqw/5QxR2mNymWkKNKi04MyaNY7wJzyJI8Cz9HigYTbsorincGcdHwrnoMN1GklFACtbm/lOEKjUh7f1GklFADHqlzzP2UqjLbjmzqDebiIjlZEAOJNmOJNR36h2S4dpaVwRcuy952i82SYpLA6uZgmxbFlWWexoJEEc0o6i6y7AFFwqyS71O0+p/IKiDdeyemHcQSuaFlncVKDKleN/JYuiuVSuM9ludvcCyXVlmxHfXokwfFfWWd5fFN4/WSdKbeSZZ3lDOYkxTQl2XtOia2m0MeKfC/qmj1ev+HEz7BUfWmkHd+K1/gO3UCT74c4aWlEWolwwcZOJ+Dlis6ndUXvNDDDsXBFk8dkSFtNRrmiqZP+7TxhXIC5WHSGGFhMkzr+ZQ+hUSnTJHn9qJ+hOF1iRACqYFxv2vEtcsoYkeFYeI3GRNL4do8V6jw48lZyKnEuI6O2mk5KXn1qT5dRW8nhhI2dTqBCckVTbjUBwO4KMYETZziWXNHUwZziJJaRGY4pXdGAx9gxIsOxMEooXf6A96JD7Yrea9CiI9dq6k58gsyoU4tnpAzm1ONwdzmdJ0PG7pDSSlDHL7lzylCklWiLSCtBbVRuJ0wr0RaRVmI08fiWt5qoM5iXVYgCoNFxEgtgY6dT2CEl0KMOQBWJ7qg75T5pqymX+NSDMNCoJ3CvYprER4tFFlvyAFTp39TGjjAqKSpwt0VkOKb2+m0zMMOxSBpHffx32zHP+KbOYF6mjm9ag8S7WC6x169cxC/RjsGGVuMymIutJvq0Ep5/U49v4Y0yMq1EtJzEAtjY6RR2uQcStStagScXAvVJjR0GZDgGXDoLVzT1orNL2mqidkXvJYxfkjlmYNkMEYBKvei0SF4/6rpHu90GGrUHVIEnwzG9UekaK9RpJRTFk1aCWme5bAZlWgkFnhcl6vFdKnn9uhEv8HvVrMzG5TKiLpuhvoARb8vaDKxrFk7Y2OkE9pB7MlyTk+yKpopvEEFqYgInCTCEdxCxcEVTvTUInXecqAZA54qWdRZbkVSLjpC9U4oVoHJFC7WPn3MtlFSJCoXOe6RaTVQBjELng26PA5VRKeTWe5x+dMfw1fshAsxp5Aqd5Vg/siPcbuElbqOSKleNPOKEh5UqFkjIluNqSORKSotTi2OJjcqdxAHmgEdvcRKLLBGpW+4+KVdZXhr9VmS4YGOnExCnmkJNyd4W+dh5T+L9cWHcUx//bZQyHA8lTkPebHN5HKjjJmSo31bFKa9uifQBqAJqr4B8HJp6q0lArfPZFo+e1AHmAurs12IBBugDzEXSOGpPhnzsnNoL2qSOb+MCzKnjasR2rxEZjgVjiA+9lEv9jtKrH27Y2OkEjle7Og/1qQcZ6gzHAupFR4baFS2gPsUj0z/bmARb1EZlZ8ju050+w7GAetERGJHhWGDkWKHOcCww6j4DxpwQBegTFcoY9aJE/WInQ13tXEDl1e8qsLFjMHKuE6Mi26lynXQE9duqwCCHAABjJ/BQi2n6gtpjJGOUK9rI/XyjjrxSH++XoQ4iFhgRYC4YbWC/ixSvnwx1gLnAqHkUoA8wFxhlRIULNnYM5rTHI4jeBtT6AUKvsuwPoyreRuKbjhG5TgSjDHyGRrmijZzAjbrX1MesZYxIpQAAIwz0+lGXRhAYudVklFFJXcpBxkhvlFFefSNfwMIBGzsGc6LB0xGNSrtt5LaNUa5oI98ajHJFG6mzUVtNRi3AAH2OHQF1zhAZo3QGjNPbSA+aEXXvAGPnJOo8SQIjt5KNMtCM3GqKppNYABs7hnPcbexQdsq23mHKRViWbTIZ54qmHPxtVTTKFU2qc5ufSV3RknDKING2OpNuCRmlcxulKRc0WTRlWv3249u4fkc1vg29z21kU3lg2rbcyH5HGd8mi6YMjWh7P6gr1ocbNnYM5oQ7dxd11WkZo1zRRm1hAcZ5SYw81WRkNlGjXNFGvq0akcwMMK4/A7TFNGWM1Jn6VJOAOqmnjFFeAepiuTJGejKM8uobOb6NjBULB2zsGMyJRtciZmSnpMqV0RYjLfvBBhlSRtZyMcrY6W3gqSajdDZyq8lIo5I6g7nASGPHqK0Ko8YgYFxMHnUtNhmj+p2hW00GbY9ZzCbDvPrhgo0dg6mzuTqMUXu2gHFbTUYaaEYFAxq56FBnOBZQG2jNUjHNiPEKdMKpRepaWzJGGmhGBZgb1TcAIM0gDyt1XiAZ6gzHAuoYIzmD+VCD+p2RAebhgo0dA5ETbBk1GUbiqQcjXdFGLjqGuaKJFx2blFnbKFc09RanXEzTqABUagPNIeWVMKrfGRpgbtBWshHFcgXDciMvwJw6hEFOzGpU2hEjwy7CRViNnQULFuCCCy5ASkoKsrOzcfXVV6OsrMzrmilTpsBkMnn994tf/MLrmqNHj2LWrFlITExEdnY25s+fD7vdjnBz+KxcbI8wQFn6N/XCUNdsTLE92flE7YpWvLwCxgQ+pxJnQJVlGxkrQOn1MxkURAwAtVK/S4ylu9eyztQT+FGD6pqZpBFOXddMXihpg9c9OhsbC0Q5J3l0pjZ05PsROePbI8tIr364MO4VWwOrVq3CnDlzcMEFF8But+O3v/0tZsyYgdLSUiQleSaPu+66C0899ZT6c2KiJ8bB4XBg1qxZyMnJwdq1a1FeXo5bb70VMTExeO655zq1PW0R9WeAyNlqkuseGeWKpjbQyms8ZTOoq5Krcok9aC2d4PUz9Ah3BB5LpY5Bq6zzeKOM2mqi3pbdK9U96p4cGV6/ylrjvX7URqXNIRXLNTCEwSgiUedAhNXYWbx4sdfP77zzDrKzs7F582ZMmjRJ/TwxMRE5OTkdyvjqq69QWlqKpUuXokePHhgzZgyefvppPPjgg3jiiScQG2vcPn0gtrsrhxuZVp+6U56pbyWV1xHUi3uJVO3cqLT61F6BA6fr1X/nRogrWtT5ASLnzU9acwxNzmcU1FtNojiskVD3jd0VHgPNqAzm1GPl8BmPVz8/w5gAZWoPWl2zZ3wbGWMaLrpUzE5Njcs4yMjI8Pr8vffeQ2ZmJkaMGIGHH34YjY0e9/G6deswcuRI9OjRQ/1s5syZqK2txa5duzpHcR8s2ngcAL1XoKHF44o2MnkXJfJW0yjigqgVtc2BLwqR4Xm0k+Hucs8EbpTXj/pETJm06BgVYE79hn38nGeu6JdlXPwLJU1SgHmkZLFVpAhz6mzBh882Br4oRKhfwErLjffqG+n1M8qrH07C6tmRcTqdmDdvHiZMmIARI0aon994443o3bs38vLysGPHDjz44IMoKyvDxx9/DACoqKjwMnQAqD9XVFR0+F0tLS1oafG4RmtrXR3TZrPBZrN1+Dd6sJpNsDsVjMxLJpULxTMZJseaSGWPyU/DtmM1OK8gXZNccU2gax0OT0xGv+7xpDpPH5qN4t2VyE6JI5XrkNwCY3qmkMq+uF8GDrnf/kj7hsSkgRkBZWt9fgDQr7vnDZVa56RYCxpaHbh8eDap7JQ4jyfA6bDDZqNbeHpnJOJIVSN+MCqHVGenNFbS482kz/D83un49kg1RvVMJdbZMycNzEwglT1zWBaWlJ5Gt8QYWp2lAPPzC9JIZV/Utxv2uF8OKOdRmckDu5PqXNDNuPGtp33Byg6ESVHkd+7wcffdd+PLL7/EmjVr0KtXL5/XLV++HJdddhn279+P/v3742c/+xmOHDmCJUuWqNc0NjYiKSkJRUVFuOKKK9rJeOKJJ/Dkk0+2+3zRokVe8UCh8p+DZjgAXN3biThC76tTAb44akZ+koKxmbSP71g9sK7SjMt7OZFKvAP41XETkmOAi3vQ6lzZBKwsN+PSPCcyiXeE1lSY0OIALutJq3NNK7DkuBkTejjRk9jh8O1pEyqaTJiV7yQtuNpoB4qOmjE204n+xDtCu86ZUFZjwlUFTlDuRNqdwH+PmjEkTcGwbrTP8EAtsPWMGd8vcIIyfl1RgC+OmdEzUcE44vF9vAFYe8qMmb2cSCMe30tPmBBvASbm0Op8uglYUW7G1Fwnsoh3hNaeMqHBDkw3aHxf3MOJXsTje8sZE040mPD9Atrx3WQH/nfMjDHdnRgQQTu+jY2NuPHGG1FTU4PUVN+KdwljZ+7cufjss8+wevVq9O3b1++1DQ0NSE5OxuLFizFz5kw89thj+O9//4tt27ap1xw6dAj9+vXDli1bMHbs2HYyOvLs5Ofn48yZM35vVrDYbDYUFxdj+vTpiImJPrcgEP1t5PZFPtHeRm5f5BPtbTSyfbW1tcjMzAxo7IR1G0tRFPzqV7/CJ598gpUrVwY0dACoRk1ubi4AYPz48Xj22WdRWVmJ7OxsAEBxcTFSU1MxbNiwDmXExcUhLq79SYSYmBhDOppRcrsS0d5Gbl/kE+1t5PZFPtHeRiPap1VeWI2dOXPmYNGiRfjss8+QkpKixtikpaUhISEBBw4cwKJFi1BYWIju3btjx44duO+++zBp0iSMGjUKADBjxgwMGzYMt9xyC1544QVUVFTgd7/7HebMmdOhQcMwDMMwzHeLsJ7Gev3111FTU4MpU6YgNzdX/e+DDz4AAMTGxmLp0qWYMWMGhgwZgl//+tf44Q9/iM8//1yVYbFY8MUXX8BisWD8+PG4+eabceutt3rl5WEYhmEY5rtL2Lex/JGfn49Vq1YFlNO7d28UFRVRqcUwDMMwTBTRZY6ehxNhdIkj6FTYbDY0NjaitrY2avdho72N3L7IJ9rbyO2LfKK9jUa2T6zbgZwnbOwAqKtz5UPIz88PsyYMwzAMwwRLXV0d0tJ8J7TsEkfPw43T6cTJkyeRkpJCmu1SHGk/duwY6ZH2rkS0t5HbF/lEexu5fZFPtLfRyPYpioK6ujrk5eXBbPYdhsyeHQBms9lvIsNQSU1NjcoOLBPtbeT2RT7R3kZuX+QT7W00qn3+PDqCLlUbi2EYhmEYhho2dhiGYRiGiWrY2DGQuLg4PP7441Gd3DDa28jti3yivY3cvsgn2tvYFdrHAcoMwzAMw0Q17NlhGIZhGCaqYWOHYRiGYZioho0dhmEYhmGiGjZ2GIZhGIaJatjYMZC//OUv6NOnD+Lj43HRRRdh48aN4VapHQsWLMAFF1yAlJQUZGdn4+qrr0ZZWZnXNc3NzZgzZw66d++O5ORk/PCHP8SpU6e8rjl69ChmzZqFxMREZGdnY/78+bDb7V7XrFy5EuPGjUNcXBwGDBiAd955x+jmteP555+HyWTCvHnz1M+ioX0nTpzAzTffjO7duyMhIQEjR47Et99+q/5eURQ89thjyM3NRUJCAqZNm4Z9+/Z5yaiqqsJNN92E1NRUpKen484770R9fb3XNTt27MAll1yC+Ph45Ofn44UXXjC8bQ6HA48++ij69u2LhIQE9O/fH08//bRXLZxIa9/q1avxgx/8AHl5eTCZTPj000+9ft+Z7fn3v/+NIUOGID4+HiNHjiQpquyvfTabDQ8++CBGjhyJpKQk5OXl4dZbb8XJkyejon1t+cUvfgGTyYRXXnklYtoHaGvj7t27ceWVVyItLQ1JSUm44IILcPToUfX3XWpuVRhDeP/995XY2FjlrbfeUnbt2qXcddddSnp6unLq1Klwq+bFzJkzlbffflspKSlRtm3bphQWFioFBQVKfX29es0vfvELJT8/X1m2bJny7bffKt/73veUiy++WP293W5XRowYoUybNk3ZunWrUlRUpGRmZioPP/ywes3BgweVxMRE5f7771dKS0uVP//5z4rFYlEWL17caW3duHGj0qdPH2XUqFHKvffeGzXtq6qqUnr37q3cfvvtyoYNG5SDBw8qS5YsUfbv369e8/zzzytpaWnKp59+qmzfvl258sorlb59+ypNTU3qNZdffrkyevRoZf369crXX3+tDBgwQLnhhhvU39fU1Cg9evRQbrrpJqWkpET517/+pSQkJCh//etfDW3fs88+q3Tv3l354osvlEOHDin//ve/leTkZOVPf/pTxLavqKhIeeSRR5SPP/5YAaB88sknXr/vrPZ88803isViUV544QWltLRU+d3vfqfExMQoO3fuNKx91dXVyrRp05QPPvhA2bNnj7Ju3TrlwgsvVM477zwvGZHaPpmPP/5YGT16tJKXl6f88Y9/jJj2aWnj/v37lYyMDGX+/PnKli1blP379yufffaZ1xrXleZWNnYM4sILL1TmzJmj/uxwOJS8vDxlwYIFYdQqMJWVlQoAZdWqVYqiuCammJgY5d///rd6ze7duxUAyrp16xRFcQ0Ks9msVFRUqNe8/vrrSmpqqtLS0qIoiqI88MADyvDhw72+6/rrr1dmzpxpdJMURVGUuro6ZeDAgUpxcbEyefJk1diJhvY9+OCDysSJE33+3ul0Kjk5OcqLL76oflZdXa3ExcUp//rXvxRFUZTS0lIFgLJp0yb1mi+//FIxmUzKiRMnFEVRlP/3//6f0q1bN7XN4rsHDx5M3SQvZs2apdxxxx1en1177bXKTTfdpChK5Lev7ULSme257rrrlFmzZnnpc9FFFyk///nPDWtfR2zcuFEBoBw5ckRRlOho3/Hjx5WePXsqJSUlSu/evb2MnUhqn6J03Mbrr79eufnmm33+TVebW3kbywBaW1uxefNmTJs2Tf3MbDZj2rRpWLduXRg1C0xNTQ0AICMjAwCwefNm2Gw2r7YMGTIEBQUFalvWrVuHkSNHokePHuo1M2fORG1tLXbt2qVeI8sQ13TW/ZgzZw5mzZrVTodoaN9///tfnH/++fjxj3+M7OxsjB07Fn/729/U3x86dAgVFRVe+qWlpeGiiy7yamN6ejrOP/989Zpp06bBbDZjw4YN6jWTJk1CbGyses3MmTNRVlaGc+fOGda+iy++GMuWLcPevXsBANu3b8eaNWtwxRVXREX72tKZ7Qn3uBTU1NTAZDIhPT1d1SuS2+d0OnHLLbdg/vz5GD58eLvfR0P7/ve//2HQoEGYOXMmsrOzcdFFF3ltdXW1uZWNHQM4c+YMHA6H1wMEgB49eqCioiJMWgXG6XRi3rx5mDBhAkaMGAEAqKioQGxsrDoJCeS2VFRUdNhW8Tt/19TW1qKpqcmI5qi8//772LJlCxYsWNDud9HQvoMHD+L111/HwIEDsWTJEtx9992455578O6773rp6K8/VlRUIDs72+v3VqsVGRkZQd0HI3jooYfwk5/8BEOGDEFMTAzGjh2LefPm4aabbvL67khtX1s6sz2+runM9jY3N+PBBx/EDTfcoBaJjPT2/f73v4fVasU999zT4e8jvX2VlZWor6/H888/j8svvxxfffUVrrnmGlx77bVYtWqVqltXmlu56jmjMmfOHJSUlGDNmjXhVoWMY8eO4d5770VxcTHi4+PDrY4hOJ1OnH/++XjuuecAAGPHjkVJSQkWLlyI2267Lczahc6HH36I9957D4sWLcLw4cOxbds2zJs3D3l5eVHRvu8yNpsN1113HRRFweuvvx5udUjYvHkz/vSnP2HLli0wmUzhVscQnE4nAOCqq67CfffdBwAYM2YM1q5di4ULF2Ly5MnhVK9D2LNjAJmZmbBYLO2izk+dOoWcnJwwaeWfuXPn4osvvsCKFSvQq1cv9fOcnBy0traiurra63q5LTk5OR22VfzO3zWpqalISEigbo7K5s2bUVlZiXHjxsFqtcJqtWLVqlV49dVXYbVa0aNHj4huHwDk5uZi2LBhXp8NHTpUPRUhdPTXH3NyclBZWen1e7vdjqqqqqDugxHMnz9f9e6MHDkSt9xyC+677z7VUxfp7WtLZ7bH1zWd0V5h6Bw5cgTFxcWqV0foFant+/rrr1FZWYmCggJ1zjly5Ah+/etfo0+fPqpekdo+wLXGWa3WgPNOV5pb2dgxgNjYWJx33nlYtmyZ+pnT6cSyZcswfvz4MGrWHkVRMHfuXHzyySdYvnw5+vbt6/X78847DzExMV5tKSsrw9GjR9W2jB8/Hjt37vQavGLyEoNh/PjxXjLENUbfj8suuww7d+7Etm3b1P/OP/983HTTTeq/I7l9ADBhwoR26QL27t2L3r17AwD69u2LnJwcL/1qa2uxYcMGrzZWV1dj8+bN6jXLly+H0+nERRddpF6zevVq2Gw29Zri4mIMHjwY3bp1M6x9jY2NMJu9pyqLxaK+XUZ6+9rSme0JV78Vhs6+ffuwdOlSdO/e3ev3kdy+W265BTt27PCac/Ly8jB//nwsWbIk4tsHuNa4Cy64wO+80+XWjqDCmRnNvP/++0pcXJzyzjvvKKWlpcrPfvYzJT093SvqvCtw9913K2lpacrKlSuV8vJy9b/Gxkb1ml/84hdKQUGBsnz5cuXbb79Vxo8fr4wfP179vTg+OGPGDGXbtm3K4sWLlaysrA6PD86fP1/ZvXu38pe//KXTj54L5NNYihL57du4caNitVqVZ599Vtm3b5/y3nvvKYmJico///lP9Zrnn39eSU9PVz777DNlx44dylVXXdXhUeaxY8cqGzZsUNasWaMMHDjQ6yhsdXW10qNHD+WWW25RSkpKlPfff19JTEw0/Oj5bbfdpvTs2VM9ev7xxx8rmZmZygMPPBCx7aurq1O2bt2qbN26VQGgvPzyy8rWrVvV00id1Z5vvvlGsVqtyksvvaTs3r1befzxx0mOLvtrX2trq3LllVcqvXr1UrZt2+Y178gnjyK1fR3R9jRWV2+fljZ+/PHHSkxMjPLGG28o+/btU4+Ef/3116qMrjS3srFjIH/+85+VgoICJTY2VrnwwguV9evXh1uldgDo8L+3335bvaapqUn55S9/qXTr1k1JTExUrrnmGqW8vNxLzuHDh5UrrrhCSUhIUDIzM5Vf//rXis1m87pmxYoVypgxY5TY2FilX79+Xt/RmbQ1dqKhfZ9//rkyYsQIJS4uThkyZIjyxhtveP3e6XQqjz76qNKjRw8lLi5Oueyyy5SysjKva86ePavccMMNSnJyspKamqrMnj1bqaur87pm+/btysSJE5W4uDilZ8+eyvPPP29422pra5V7771XKSgoUOLj45V+/fopjzzyiNfCGGntW7FiRYfj7rbbbuv09nz44YfKoEGDlNjYWGX48OHK//73P0Pbd+jQIZ/zzooVKyK+fR3RkbHTlduntY1vvvmmMmDAACU+Pl4ZPXq08umnn3rJ6Epzq0lRpDSkDMMwDMMwUQbH7DAMwzAME9WwscMwDMMwTFTDxg7DMAzDMFENGzsMwzAMw0Q1bOwwDMMwDBPVsLHDMAzDMExUw8YOwzAMwzBRDRs7DMMwDMNENWzsMAzDMAwT1bCxwzAMwzBMVMPGDsMwDMMwUQ0bOwzDMAzDRDX/HzUr0JTXzLFSAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a time history of points where the wind speed and wind direction step different combinations\n", + "ws_points = np.arange(5.0,10.0,0.25)\n", + "wd_points = np.arange(250.0, 290.0, 0.25,)\n", + "num_points_per_combination = 5 # How many \"seconds\" per combination\n", + "\n", + "# I know this is dumb but will come back, can't quite work out the numpy version\n", + "ws_array = []\n", + "wd_array = []\n", + "for ws in ws_points:\n", + " for wd in wd_points:\n", + " for i in range(num_points_per_combination):\n", + " ws_array.append(ws)\n", + " wd_array.append(wd)\n", + "t = np.arange(len(ws_array))\n", + "\n", + "print(f'Num Points {len(t)}')\n", + "\n", + "fig, axarr = plt.subplots(2,1,sharex=True)\n", + "axarr[0].plot(t, ws_array,label='Wind Speed')\n", + "axarr[0].set_ylabel('m/s')\n", + "axarr[0].legend()\n", + "axarr[0].grid(True)\n", + "axarr[1].plot(t, wd_array,label='Wind Direction')\n", + "axarr[1].set_ylabel('deg')\n", + "axarr[1].legend()\n", + "axarr[1].grid(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the power of the second turbine for two cases\n", + "# Baseline: The front turbine is aligned to the wind\n", + "# WakeSteering: The front turbine is yawed 25 deg\n", + "fi.reinitialize(wind_speeds=ws_array, wind_directions=wd_array, time_series=True)\n", + "fi.calculate_wake()\n", + "power_baseline_ref = fi.get_turbine_powers().squeeze()[:,0].flatten() / 1000.\n", + "power_baseline_control = fi.get_turbine_powers().squeeze()[:,1].flatten() / 1000.\n", + "power_baseline_downstream = fi.get_turbine_powers().squeeze()[:,2].flatten() / 1000.\n", + "\n", + "yaw_angles = np.zeros([len(t),1,3]) * 25\n", + "yaw_angles[:,:,1] = 25 # Set control turbine yaw angles to 25 deg\n", + "fi.calculate_wake(yaw_angles=yaw_angles)\n", + "power_wakesteering_ref = fi.get_turbine_powers().squeeze()[:,0].flatten() / 1000.\n", + "power_wakesteering_control = fi.get_turbine_powers().squeeze()[:,1].flatten() /1000.\n", + "power_wakesteering_downstream = fi.get_turbine_powers().squeeze()[:,2].flatten() /1000." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas version (for time)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Build up the data frames needed for energy ratio suite\n", + "df_baseline_pd = pd.DataFrame({\n", + " 'wd':wd_array,\n", + " 'ws':ws_array,\n", + " 'pow_ref':power_baseline_ref,\n", + " 'pow_000':power_baseline_ref, \n", + " 'pow_001':power_baseline_control,\n", + " 'pow_002':power_baseline_downstream\n", + "})\n", + "\n", + "df_wakesteering_pd = pd.DataFrame({\n", + " 'wd':wd_array,\n", + " 'ws':ws_array,\n", + " 'pow_ref':power_wakesteering_ref,\n", + " 'pow_000':power_wakesteering_ref, \n", + " 'pow_001':power_wakesteering_control,\n", + " 'pow_002':power_wakesteering_downstream\n", + "})\n", + "\n", + "# Create alternative versions of each of the above dataframes where the wd/ws are perturbed by noise\n", + "df_baseline_noisy_pd = pd.DataFrame({\n", + " 'wd':wd_array + np.random.randn(len(wd_array))*5,\n", + " 'ws':ws_array + np.random.randn(len(ws_array)),\n", + " 'pow_ref':power_baseline_ref,\n", + " 'pow_000':power_baseline_ref, \n", + " 'pow_001':power_baseline_control,\n", + " 'pow_002':power_baseline_downstream\n", + "})\n", + "\n", + "df_wakesteering_noisy_pd = pd.DataFrame({\n", + " 'wd':wd_array + np.random.randn(len(wd_array))*5,\n", + " 'ws':ws_array + np.random.randn(len(ws_array)),\n", + " 'pow_ref':power_wakesteering_ref,\n", + " 'pow_000':power_wakesteering_ref, \n", + " 'pow_001':power_wakesteering_control,\n", + " 'pow_002':power_wakesteering_downstream\n", + "})\n", + "\n", + "color_palette = sns.color_palette(\"Paired\",4)[::-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the energy ratio suite object and add each dataframe\n", + "# separately. \n", + "fsc = energy_ratio_suite.energy_ratio_suite()\n", + "# fsc.add_df(df_baseline_pd, 'Baseline', color_palette[0])\n", + "# fsc.add_df(df_wakesteering_pd, 'WakeSteering', color_palette[1])\n", + "fsc.add_df(df_baseline_noisy_pd, 'Baseline (Noisy)', color_palette[2])\n", + "fsc.add_df(df_wakesteering_noisy_pd, 'WakeSteering (Noisy)', color_palette[3])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataframes differ in wd and ws. Rebalancing.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", + " r, k = function_base._ureduce(a,\n", + "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", + " r, k = function_base._ureduce(a,\n", + "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", + " r, k = function_base._ureduce(a,\n", + "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", + " r, k = function_base._ureduce(a,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "done\n", + "Dataframes differ in wd and ws. Rebalancing.\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'name': 'WakeSteering (Noisy)/Baseline (Noisy)',\n", + " 'color': (0.12156862745098039, 0.47058823529411764, 0.7058823529411765),\n", + " 'er_results': baseline baseline_lb baseline_ub wd_bin bin_count\n", + " 0 0.993021 0.991214 0.992926 237.0 4\n", + " 1 0.994481 0.994541 0.995625 239.0 11\n", + " 2 0.988364 0.988472 0.990429 241.0 34\n", + " 3 0.990310 0.990412 0.992252 243.0 59\n", + " 4 0.986453 0.981036 0.986168 245.0 127\n", + " 5 0.981644 0.981765 0.983944 247.0 220\n", + " 6 0.969089 0.969207 0.971333 249.0 306\n", + " 7 0.974457 0.974508 0.975429 251.0 443\n", + " 8 0.954166 0.953161 0.954113 253.0 593\n", + " 9 0.929688 0.929786 0.931557 255.0 641\n", + " 10 0.923411 0.924082 0.936161 257.0 761\n", + " 11 0.916194 0.892904 0.914968 259.0 769\n", + " 12 0.908919 0.910919 0.946925 261.0 793\n", + " 13 0.962032 0.962271 0.966579 263.0 760\n", + " 14 1.007537 0.964714 1.005283 265.0 835\n", + " 15 1.149424 1.150844 1.176417 267.0 748\n", + " 16 1.295031 1.297871 1.348999 269.0 762\n", + " 17 1.344629 1.274221 1.340923 271.0 763\n", + " 18 1.457927 1.458811 1.474730 273.0 800\n", + " 19 1.363313 1.364282 1.381737 275.0 751\n", + " 20 1.311677 1.314399 1.363382 277.0 776\n", + " 21 1.201737 1.162489 1.199671 279.0 789\n", + " 22 1.129018 1.121481 1.128621 281.0 763\n", + " 23 1.108013 1.107674 1.107995 283.0 710\n", + " 24 1.058602 1.049012 1.058098 285.0 659\n", + " 25 1.038914 1.034224 1.038668 287.0 551\n", + " 26 1.023737 1.023991 1.028571 289.0 460\n", + " 27 1.010827 1.010936 1.012893 291.0 325\n", + " 28 1.008223 1.008413 1.011834 293.0 206\n", + " 29 1.005245 1.005332 1.006900 295.0 121\n", + " 30 1.011489 1.011803 1.017442 297.0 53\n", + " 31 1.012907 1.003173 1.012395 299.0 20\n", + " 32 1.001096 1.000699 1.001075 301.0 5\n", + " 33 1.000172 1.000101 1.000168 303.0 3,\n", + " 'df_freq': ws_bin wd_bin ws_bin_edges wd_bin_edges freq\n", + " 0 6.5 233.0 [6.0, 7.0) [232.0, 234.0) 0\n", + " 1 9.5 233.0 [9.0, 10.0) [232.0, 234.0) 0\n", + " 2 10.5 235.0 [10.0, 11.0) [234.0, 236.0) 0\n", + " 3 5.5 237.0 [5.0, 6.0) [236.0, 238.0) 1\n", + " 4 7.5 237.0 [7.0, 8.0) [236.0, 238.0) 0\n", + " .. ... ... ... ... ...\n", + " 335 2.5 297.0 [2.0, 3.0) [296.0, 298.0) 0\n", + " 336 8.5 299.0 [8.0, 9.0) [298.0, 300.0) 0\n", + " 337 9.5 301.0 [9.0, 10.0) [300.0, 302.0) 0\n", + " 338 10.5 301.0 [10.0, 11.0) [300.0, 302.0) 0\n", + " 339 5.5 303.0 [5.0, 6.0) [302.0, 304.0) 0\n", + " \n", + " [340 rows x 5 columns],\n", + " 'er_test_turbines': [2],\n", + " 'er_wd_step': 2.0,\n", + " 'er_ws_step': 1.0,\n", + " 'er_wd_bin_width': 2.0,\n", + " 'er_bootstrap_N': 2}]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "# Print out the energy ratio\n", + "# fsc.print_dfs()\n", + "\n", + "\n", + "# Calculate and plot the energy ratio for the downstream turbine [2]\n", + "# With respect to reference turbine [0]\n", + "# datasets with uncertainty quantification using 50 bootstrap samples\n", + "fsc.get_energy_ratios(\n", + " test_turbines=[2],\n", + " wd_step=2.0,\n", + " ws_step=1.0,\n", + " N=N,\n", + " percentiles=[5., 95.],\n", + " verbose=False,\n", + " num_blocks=10\n", + ")\n", + "print('done')\n", + "# fsc.plot_energy_ratios(superimpose=True)\n", + "\n", + "fsc.get_energy_ratios_gain(\n", + " test_turbines=[2],\n", + " wd_step=2.0,\n", + " ws_step=1.0,\n", + " N=N,\n", + " percentiles=[5., 95.],\n", + " verbose=False,\n", + " num_blocks=10\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Polars implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# # Build the dataframes\n", + "# # Build up the data frames needed for energy ratio suite\n", + "# df_baseline = pd.DataFrame({\n", + "# 'wd_000':wd_array,\n", + "# 'ws_000':ws_array,\n", + "# # 'pow_ref':power_baseline_ref,\n", + "# 'pow_000':power_baseline_ref, \n", + "# 'pow_001':power_baseline_control,\n", + "# 'pow_002':power_baseline_downstream\n", + "# })\n", + "\n", + "# df_wakesteering = pd.DataFrame({\n", + "# 'wd_000':wd_array,\n", + "# 'ws_000':ws_array,\n", + "# # 'pow_ref':power_wakesteering_ref,\n", + "# 'pow_000':power_wakesteering_ref, \n", + "# 'pow_001':power_wakesteering_control,\n", + "# 'pow_002':power_wakesteering_downstream\n", + "# })\n", + "\n", + "# # Create alternative versions of each of the above dataframes where the wd/ws are perturbed by noise\n", + "# df_baseline_noisy = pd.DataFrame({\n", + "# 'wd_000':df_baseline_noisy_pd.wd.values,# wd_array + np.random.randn(len(wd_array))*5,\n", + "# 'ws_000':df_baseline_noisy_pd.ws.values,# ws_array + np.random.randn(len(ws_array)),\n", + "# # 'pow_ref':power_baseline_ref,\n", + "# 'pow_000':power_baseline_ref, \n", + "# 'pow_001':power_baseline_control,\n", + "# 'pow_002':power_baseline_downstream\n", + "# })\n", + "\n", + "# df_wakesteering_noisy = pl.DataFrame({\n", + "# 'wd_000':df_wakesteering_noisy_pd.wd.values,# wd_array + np.random.randn(len(wd_array))*5,\n", + "# 'ws_000':df_wakesteering_noisy_pd.ws.values,#ws_array + np.random.randn(len(ws_array)),\n", + "# # 'pow_ref':power_wakesteering_ref,\n", + "# 'pow_000':power_wakesteering_ref, \n", + "# 'pow_001':power_wakesteering_control,\n", + "# 'pow_002':power_wakesteering_downstream\n", + "# })" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " wd_bin ws_bin df_name count\n", + "0 255.0 5.5 baseline 111\n", + "1 243.0 5.5 baseline 11\n", + "2 243.0 4.5 baseline 4\n", + "3 247.0 6.5 baseline 40\n", + "4 251.0 6.5 baseline 86\n", + ".. ... ... ... ...\n", + "625 299.0 9.5 wakesteering 1\n", + "626 279.0 11.5 wakesteering 12\n", + "627 243.0 10.5 wakesteering 5\n", + "628 249.0 11.5 wakesteering 5\n", + "629 243.0 11.5 wakesteering 1\n", + "\n", + "[630 rows x 4 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_freq_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pfleming/Projects/FLORIS/flasc/flasc/energy_ratio/energy_ratio_polars.py:1160: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " ax.scatter(df_unbinned[\"wd_bin\"], df_unbinned[\"ws_bin\"], c=color_dict[label],alpha=0.25, s=1)\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ero.plot_uplift()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ero.plot(polar_plot=True, show_wind_speed_distrubution=False)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare the energy ratio plots" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare the mean values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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gLqM2FrOKRVWwqDJmRS6PC5354p+taJK07zVm/9MPcGZax+pn6wruHHLBIevngDEPct06ePBgVq1aRZMmTQ777+eOO+445PN7aJpGmzZtKhSzovGOx79xt9tNYmJipV4bmM1mEhMTK/XzrbJztIrGrOgxVKmk2+v1sn79+vK/N23axOLFi0lMTKRWrVrcf//97Nixg48//hiAm266iTfeeIN7772Xa6+9lmnTpvHll1/y888/n6hDOKUNalcrNgtkoZ86SfZDzgK5546Vqsj/TqQGYFJwWs1kJsXRpE4WNbPrMm/rv4ld+6x4aifa0fXYpBi6YaAZsf+ORKPkbt5AQs066IaEQexxwwB9d9KeXhZkU/G/d4DrJNpIdVgxMHbPISKh6zpRwyCqGQSDAX75ZAdnDrwYQzERjGiEoxoR3UDTDKIGbMjzUivFUf6hvSXfR51kOyW7dpK7bSM52zaRs2UTOzdvZMfWjeTt2EbQ72PT6uVsWr1/F/S4xGTSa9WhMLsucUkpWGx2bDY7XxX9jdPpxO5w4nA4sNvtuFwu7DYratADLi82uw1Fid2EkHZ3tpIkiQSnmYf7NcNsUcoTeTSDQW2zkCUIRXWCvRsQjOgEIxr+UBi9eAe75ESCUQhrOuFobNxZOGqwvtmnvD91KVrQix70YoT86OEA9eIkTBYbTRqnEohoBMIaofSaeIwwkYAPLbjXHXgtiq5F8ASjeIKxLmmBjQsI56zd53y0/mgk7qyGZDduwdA776dNdiJN0t04LSqHvu8pCIIgnAj+QJBfp/3JT7/9HzNnTGfDisXomnb4F1aQJMvY7E7sLjeJqekkpKaTkZFJZo1MMjNrULNGDWrXyqJurZq4HVa2bVxP3QYNMauxVmr5AK1mHWolMH9r8Z5b/LSvlVDhWbfDkShzl61j2tyFLFiyjPVrVrNz8zrKcreQ3vtqGp15CYXeMAWbVpMzfuRB40QT65BQP3aTXrbExrBKignF5kKxOjDMdsx2F3anmyW+OORNRfRqlEKmK4NtaW/ijk8gISGB5MQkEpPicdss1DOVkkMidqsZp0XBYVGxDO6ERZWxmBR+XLKTl39fQ7zDTIkvwqizW3FZ+1r/Nobo9WLXWrqBtnsI345N6+mSVRckGU03dl+PEdtGN5AtVsZM2UiCw0yxL8yN3epyQasamBUZiymWUKu7r/00TWNV8Q4y422HTGyO5Bqzoo5HTEE4mCqVdP/zzz/06tWr/O+RI2MfTFdffTXjx48nJyeHrVu3lj9fp04dfv75Z0aMGMGrr75KzZo1ee+998RyYSdQnSRnpX5o1UtykOGy7L/sxAGGYmuaTC6Q7rIiyzK6sVdivjtJT3GYyU6w4wlFsZti3Z8VOZakysQS1PKEVYJI2MQvQMua8VitlvLHNd0gFI0lot3rJbOp0Mu24gDZ8TZ61k1GlkFqmIphtIwl6HrsC0mSDMLBAMsX/M3OQg8b1q9n44b1bNm4nu2bN1Kcv4vSogJKiwpYs3j+EZ8vSZax2uxY7Q6sNtte/21Htdiw2B1kZNehduPTqNvkNDYWJuK0qFhVGZMik2BWUGULGFZWFe+gR8NUooZEMKKVJ+SBSJQmGS5kdwqfzN2y+7hgYOuadKiTiMHuiWQkCQyDQR3GIyEhY9DY6mGF10GZz0tJSSneYIi41CzCmkahN8yHqy8iWpqH5sknnLuO8K6N6CEfJesX4SspZPSUSwCwqDLBmeNJdjuIsyq0+HsFXVs3xWkxYTXJJ8Usl4IgCNVFNBrlj9nz+Om335k54w+W/TN3v1bpxNR0HO54rHZH7Maxw4nNbsfucGB3OHA4nLt/OzBJBompGThdLlxOBy6nE5fLRZzLicVuZ2FuYN/ZrYG+jVJxmlUUWSofugWxpA7Aoir73uT/H/WSHMgYey0ftf/ETYFQhL8Wr2RdbhnLomnc/NliVqxYzvzXb8MIH7gre1nORjblx8Yky4544uu3we5yY3O6sTvdONxxOF1u3O54WjXIwlW/PhZVwaQ2Rrl8daxrLVDiC/Psr6vZe0yoJEGvxqmkuSzYWwzEZVVxmFUsplhrsUk22Lm5lJ6NUjGZDnzJP6RTNmc0SWVzoZ/aSfbypVQlSUKRQJH3/T7VNJkdQLLz4K1/d5zRkEvbZe0X81hV9jXm8YopCAdSpZLunj17HnJCg/Hjxx/wNYsWLTqOpRJONLv5yNd4LP+yOEBbqNtqIqOCcWQ99oViUmTUvSZdkxUp1t15d4+S9DgrBxvUoOkGEU0nohuEohoBCRrXrU2rTrWQFAVFAlWWUGWJUo+H5avWMmPBMnK2bqSspJiQ30cw4EcPB3d3s/PHxsz7fYQCsf+O7J6jwNB1Aj4vAV/FJt9LTE2nYfOWNDmtJS1atqZdu7bUqlmDPSul+MMaFrOKy2oi3r7vuWxfO5GhXeqwLq+MzHgbqa7dPQZi/ysfX7V7zhR0TaM0x0Pn+ikgp6HrRnmCvmf7rMShjJ6yrnxM92VtMzF5dvLP/PmUBDWsKQ62FwcIhiNsm/k9W6Ox4/7jy/dQnYkk1m5C3SYt6NqtK5dfcA41423YLSql/jBbivxibXZBEIRKEIlqLFy+mh9//Y0/pvzO0n/m4i0t2WebuKQUWnfqSsO2XWjWoSspmTWBWIJ8duM07GZlz3zgsZm0d79O1zXWrF5N48aNy5d7+l8mi2+/VulE+7GNfZ84fyv3f7ssdiMZnUsbmtELt7N42XI2rltN3tYN+HZtxYiGcLXrT+KZw2BtAbrmjCXcsoItuSYJNeqQnl2ftFr1WOJ1oibW+Lfc7iRe/+QbEh2W2M1+KbZMqirLKJJOYqQAvz3WjTd2XRBrEd5zEyGsG7zy+5ry78iHzm3KwDY1YzcU5P2vdzRNYyfs1U37wDLibJX+3Xg8YgpCdValkm5BOBkpsoQiK1gBl0UlYlEo3QlpLgtRQyIQ1YhoOqGIgWp10KZNG2o1bs76gn9n62yc6iTTfeDJNHRdJ3fjWiyJafj8fsrKvHh9Pnw+H2VlZfj8Pko9ZWzNLyHgLWPr+tVsXrWcnC0bKMrLZe60XOZO+608XnxyKg2btaBRo0a06tCZdm3bkl0rC7MqY1WV3TcgYjcdaic7qJ186GUc9tA0jdIcqJFw8C5kt/VuyMC2/94dT3Za8Aab4b2gF6WBCPllIUqDETbkFDG5aCQbVy4lZ90yggXbiXqLyFs+i7zls1iyaCFf7Ywj3m7CYVZY9ftEzBkNsNVoxIPnN+eqTrUxq4eeuV4QBOFU5Q9Hy3uYWU0KUd1g3eat/PTrFP6YNpUFs/+kIHfnPq+xOZyc1r4znbr15IzevenQpiVOi4kcT4AF20v3SZDjbaaD79yIJYiH6rF00F5wRyEQivDx5Fk8/sMyzGl1AYj6y3jh2sEHfoGiYpc16siFNGjYiLT4bIxWv9KkYQPcDitWVcZtNRFnMzFtTR4v/d+/SfLj/ZoxsG0WkgSKFOvmvich1jSNVasKaJt98LGzw1Prc3GbGpXegiwIwvEnkm5B+I/tGUtmN6soikL87rFS4d0t4sGIRppswWVWCUQ1rIqMWVXwhaPl3dvlvbrAYxiYTCrxcW6SEhMOut+dniCr8/5tAc+06mxbt4qFCxawfMliVi9fwvZN6ykpyGPejCnMmzGFT94ZC8TGmTds1oLGp7WkRas2tGvbhjq1s9ExiOrg3n3Rs3e59v7vPWuAVsT/3h1PcJhJcJjJItYqHohoeOsmcWG7B9hV7OX3z98mu/tFzF+8nOWLF7B5zXKUjCYYQIk/QkFuDsVT342de3scd/7Snt/O78ewyy6gbmYSLquK22rCaqrcyREFQRCqo/UFXuZvK8Ff5mHVwrmsWzCbRXP+YtuGfefbUFQTTVu3o237DvTsew6dO3Ug3m7Hblawqv+O2W2Q4qJGnK1SEuS9HU0vOMMwKPD4+eq3P/np9z9YPH8ueWuXoAU82Oq1J/WS2Kzeij0OxZ2CxRlPcs26pNeuT+16DWncpAlNG9bHpsos+mk8fbv0JdntwGqui92kYDMrWFWlPJFunOFmQOvKTZJFC7IgVE8i6RaEE0ySpNiyJApgUoizmsq7pEf3maBER9tropLYJHGxZTMAAmEd5N1jqXe3KUgY5Ylvot1E+5rxhKIaDouCzaTSuGYqfXr1KC+Lx1PGP4sWs2DBApYs/Id1q1aybeNaSosKmP/XNOb/Na18W1d8IrUbN6d2o2Y44xJIineTnBCHw+HA5XLj3DMOzx0bh+d0xMZM5XiCsaVTdnerkyViy6zEJqMnGNXxR6LEWc24reo+E95IklR+oZUK1HSb+B04p1VtzmhVD0/gXAq8IbzBKMW+MHM3FfLtnzuxNzqd4JYl6P5SfMum8PWyKXzz4r2kNG5H74FDufDcvmQl2ki2m3afyygOqxgbLgjCyc8wDKK6QSAcZcrs+Xz+zbcsnvkHG1cu2WfyM0mSqNukOR1O78Hp3Xty+uldiXPZ8eVuIatuA+wW0wFXmYCjS5ArQziqk+cJsCQ3wKdrlvPBY7eQv2YhRmTf8eaSaokt6bmXmrd8wDMDTqNGvI14mwmn1YTNpGAzKchGlEU/QZtaCYeduVgkyYIggEi6BaFK2tMl/VD2JOPRaJT1uZDutiDtNYGcYUDUMND1f2cdlVUDkyoR1cEbisbGkimx8eSyJOF2u+jdoxs9u51O4baNJGXVxecPsGDxEhYuWMDSJYtZvWwx2zaspaykiGVz/2TZ3D8rflyqCZvDgdW+ZxIdB7bdv+1OB2arA8ViIy45hWbtutC7czvqpbjKu7XvWdf7f8XbzbELn4TYBWQwolMWitAgzcXPy3NJGXA/hhYluG0FgXVzCW6YR6RkF3nLZzGlXlfmhtKpk+wg2xqie3KQLXoCTquJNJcVt82Ey6rGZksXSbggCCcBTTcIazr5JV5+/L8p/PbLz8ye9huF/9NlPC2rNs07dqVnz170PbM3iUnJqLKERVVwmhVUGTbkgtOiHnKSsuMhpzRQPulZRpwNXTfwRzSWb9zOxJ+mMGPGX+zMycF+1h3ouztalZZ6MCJBZKuT1AYtqdeiHa3adaJj+7aszg/y+byt5V3BHz6/GQPbZB1wKFIoVLGeW4IgCHuIpFsQqqk9rcTS7gsdq0k57NqEe5b9iGgGET3WlT0U1QlGYsuBSbsndds7isvpoGfXLvTs2qX8sR0Fpfwyaz6bVy1n+8a1BHxlBHw+IkE/QZ+XgN8fm9DNH1tjNRKOTXimRSN4S0v2m3DnYNwJSbTp0oPuvc/gzD59qJNVA6tJKU/C9QN0WZckCZs51s0v1WXluQGnxSbHUVQcdVpy6+ALaJ7hYta8hfw5ZTLmVl3Z4YeN+T4Wzvqc8X99ii01m0Yde9HtzHPo2LEDLqsJl8VEqsuC227CbTXhsqiHnZxGEAShKjAMg3BUxzBg3urN/PjLZKb//iuLZk0n6P93/hCz1UrzDl1p070Pp3XqXj75WesaccTtnhk79vkbW7dZq8RlwI7E3pOead5CGmjb2Ll6EVtXLiCwa/M+29bsdi2pifE0SI/DMvx+MpPcNGvaDKfVRJzNTJrbQqLDwiV2E8N71RfjpQVBOC5E0i0IpxBZknbPvA6wb1f2iG7sTsJjPwC+sIaqGOWzq+9p6U2Kd1KvWSvqNWu1T/wutROwqvsm/oZhEApHKC0tZdvGtchWFz6fD4+3DG+Zl7KyMrw+HyWlpeQVeQj4veRu2cTKBXPwFBcy/edJTP95Ek8AtRs2pWO3nnQ/40x6du9Wvi9vKAqK6YAt4Ze1r0X3hinlF1JpLiulgQgtss7g/N5dKPGH2eUJsi7Pxzez/JTKCoG8LSz+cTyLfxzPOFciddv2oEvvs+jd+0zM5tj472BEI6zpNM+Mo1G664R0nRQEQTiYqB5LtEsCIeYsXMrkn39m5tRfWbt0IYaul2+XmJJG1zP6cva553FG794URxU2FvnLnz8tw02dRAdmRaoSvX025nkZNWlZ+dJZxdM/4o/l0/bZxppck6wmbWjeriN9zmlG13RYF47DZq5NsstCqstCvN28381T0RVcEITjRVwl7kXTtEPetd3zXGXe2dU0DVVVD7vvI4259++qFq+6xTwV6sckg0mWsKsquqEQDkfZBMRZZMI6RDSNYAQwjPIu6Q2S7awr+PfCrFGyA7Msoe91MbeH2aSSlJiAHEgjoUZtZFkun1jN2P1/oajO39tLy18TjYRZt3QBGxfM4u8//2D9yqVsXruSzWtXMvH9NzFZLJzWrjPZ2bWZOvcf2rRqhUVVsJhkrIqCuld39FSnmVRnbDkZw9BxWxXcVjvZCTZKgxEKy0I0TnXSfcwY7J4RvPXjLObN+J2dy+YQLSti7fRvWTfnN2YHP6dVdjIWVebP1btAUZGAKzpkcf5pGaS5rcTbVVwW0z4z0h5r/fyv6hSzMv/9VKfjruoxq8Nn26kc80jqxx/W8Iaj2M0KJlkiFNXYVuRj2oy/mPZ/k5n3x/+xa/uWfV5Tr0lzevc9m/POP58WLVujGxIGBqoiUVNRqBVvIaIZxNtMOMwqEBuqdLyP+2Axdd1g4bptPDHmHf748UsSLhiFKSETAGutFkTyt1CrWRtOP70rXbp0JjklHc0wsKoycVYZKVpEpzqJxDvMWPa6MWwYOkdTdPHvp2pdwxwo5qlYP9WhjHtinUz1U9F9SkZFpxQ+CY0dO5axY8eiaRpr165lzpw5OJ3OE10sQag2IoZEyFCwSBom6dg/Sop0Mzs0G7Ep1QxqKAES5TAAeYXF/Dl3HrNmz2HBnJkU5+/a57UJKWm069yVLl260KNzB1IS44+5nL5AiC//bxa/TplKflDC3vMGINZ6v/O9m1FscVizW2DLbsF7w3qRnVSx5dMEQRAqw96fmb6yElbOmsbff05j8azp+Mv+vYmpmsy07tCJHj160KdHNzIzM05coY9AOBLl81//4quvJ7FlyUzQogC4u1xGQvcry7eTJfjgwhok20VbkiAI/y2v10vnzp0pLS3F7XYfdLtTOunew+PxEBcXR1FR0SFP1p7kvGHDhocdO1tRoVCI0aNHM2LEiMPOgFlRlV3O43Hc1SWmqJ8Dx9R0g+juLukhTUPTKZ99XCqflTz2gMTuedR1je2bN5JVpy6KrLL76d3b/LttIKrhC2nYVBlFkfCFdSKaRlQzkCQJRQYFWLZyJb9O/o1p/zeZtcsXEQmFyssnSRL1mpxG5x69aXN6D5IatEA1xVq6T0tz0jDFiXKA8dh7jrte/QZ4QhpF3jA7SwKUBiNEdYMCX5iZ6/KZ8c9ydr5z4z6vlVQzSfVa0KRNJ7r17kPLli1RZQmHRaYGJbjSapHgsOCwHPtF4an676e6HHd1iFkdPttO5ZgVqR9PMMIPy7bzx7dfMH/aZNYsnocWjZY/H5eQRPcz+3LOeefRvUdvLDY7wfxt2FOzsJhV7GYFiyxjUuUDTlBZEcfjXEYiUX6b+TcvvD2B+b9/T6Q0r/w5Z40GdD//Ehp2OZvvVnswdi/YcW3nbC5tW5M4h5k4q2mfyc+qw2cbVI/35fGIKeqnap9LUT+Hj+nxeEhMTDxs0i1uCe5FUQ4/EdWRbFfRfUaj0UqNuXfsyoxZHcpY2TFF/Rw4pqKA2XSAFxzCnu43TqvlkOW0mk0k2P/9O94OEU0nrOkEozrBSJRw1KBRk+bUq9+AJIvBpZMmMWvu30ydMoW5M/5g89qVrF+5lPUrl/LJuDFY7Q5O69SdbudfTPT03tjMKkl2M3azesCZac0mlTSrhbQ4Ow3S3RT5w+SXhdhZEsCsyMzZWEzmTe8R3LKE4JalseXIfCUUrPmHv9b8w+J1W8k672aaZLipHW+idmADyU3sOGxWkhxm0txWEuxm4mymAyb/FVXV35fH699PVT/u6hCzOn22nYoxD1U/mm5Q5A/x3lc/89pj95C7dVP5czXqNuTMs87hwgv6cVqb9iDFlj5UZQmrCkEgM96OxVS5qzFUxnEXeYN8u2gn3yzazvKt+Wz/8VOMkB/Z6qRx13MZNORqWrZqhT8cxW5S6N0KdB2aZrqpn+I87PFUh8+2PbFPxZiifqrmuTweMU+2+qno/kTSLQhClRdbLkzGYQbdMMWS8KhOqXf3OBqThV69z+TMM/tgUiR27sjh199/548pU1gwazqe4kLmT5vM/GmTcSckcUb/i7l88BDat22Dw7x7Rl7TgZe7URWZVJeVVJeVhmkuin1hPMEob/wBpvh03K3O4tK2NYgP5TNj+h8snTcLpWEHfCGNfzYXM3PLUnZ9/gCKzUVG47Y0adeZLl2706J5M+JsZtLjrCQ7LcTZYhO0CYIgHIhuGPjDGvPXbubxB+5nxk9fAxCfnMr5V91E2x59SMuqTasMNzazgkWVsZkULKqCWZHB0Mkj9nlaFSZEg9hY7fE//sHosW+zYdVyUgY/jyRJyCYrTfvdQIv6WQwYcCG6ZEaRJVwWlaYZblJcFtxWsYyjIAjVh0i6BUGoVmQptkasRVUwS7FJ29JdVpBVAlGNcFQnMTWNwUOGMOiKIczbWsSWNSuY/ev3zPxlEqWF+Xz70Tt8+9E71GnUlH4DL2fQFVeQXSMDu+nQF3AmRSbVbWVkn0YMbFOTFTs9uKwqUcPA40+hboNGKDfegs2skFcWZFVOGf+3fQ55ZhtaoIzti6azfdF0fn/3WUzORDKbtqXPoBvp0qEN6W4riiwRjGi0qhVPrUQxPlwQhNgcEsGozi5PkNffeY93Xngcb0kxkiQxYMhQzr3+LuyuWJfGJqlOshLsmFUZk7zvbOMnaHUvYP81tVdt2cmDL73F75M+x7tzffl21qINnHdmd86rJVFqGknUkHBZVbIS7KS6rSQ6zMfUO0gQBOFEEUm3IAjVnt2kYLGYiTcMIruXyQlGNQJhjfrJTmT5NOo0OY1Bt41ix5LZ/PLNRGZP/ZVNa1by2tMPM/b5x2nf/QwuuvRyzmjfgiJ/CKfVglU9eItQrSQHtXZPnGYYBp5glNJAhPyyEHllQVwWlba14ul2982k3j6Ab//ZyJ8zZrB64RxKNi0n4i1iy7zf+anpefyeZyXBbiJn7VIkRcGa2ZC7+jTi+q51ROu3IJzCwlGd0nCI6fOX8Ph9I1j5zxwA6jduxotjXue0th2QZQNFkkiwWXBZq95l3d5rakfyNqEv/p5dS2ZgaJHYBrJKVuvunH/JYHr2OgNJgjRbGXEOO5kJDpKc+844LgiCUB1VvU9nQRCEoyRJEmZFwqzIOC0qut0g2WmhdqKdQn8YCYnT617AVZcOoKCgkAmff8G3Ez9j3bJFzJ32G3On/YYrPoEz+8W6n5/eqQNuqwmbSTlk64okScTZTMTZTNRKtBOKapT6IxT7w+SU+HEoFtq260C7jp1xWlSi4SC/T5/F7Fl/YWnUlO2lYYr9EbxLfsO3bAr2pj14vuRqkhwm2tRKIDPehst6hIPoBUGotqJarBfPutwiXh49hi/feY1oJIzZauXOex/kupuHYzKbiLeacFuPbW6I4ymnNFCecANE/aXkLZwCgD29Dl3OvYRLBl1OXHwiFlUm2WmhRpwVX14ZrWslVPrYTEEQhBNFJN2CIJy0ZEnCZlKwmRRSnBYCEQ1PMEIgouOMS+DO225lxG23snjZCj755GMmT/qS4vxdfPvJe3z7yXtkN2hMv4GDuPLKITTIzopNvKZIhx1HaFEVUt0KqW4rdZPsrFlTQIc6SeR7YxOyBXWF07t154zevXBaVJZuL2HM1HUoNhcg4V85A/+a2Tyx8TJuH3kPWenJZCXYyEp0kGAXybcgnKx0w8AXipLnCVAQluh3Zg92bt4AQOceZ/DCmFfJqFkbu1km3mbGVoV7whiGwUeT5+BdNx97/fYAWLNb4O40kAEDBnBWzy6YZIUEh5msBBspLivxdhO6rrMq7zDBBUEQqhmRdAuCcEqQJQmHWcVmUvZJviWg5WnNaP3iCzzz1JN8/dVX/PjTz8yeMpkt61bzxrOP8eYLT9Kuay8GXj6YSwdcSHK8E8MAXziKy6JiNx/8o1Te3QJVM8FGdrKzvBW8yBcmpzSIJxjBZlKQgITe1+Fo2pPiP94nuGUpq3+bwG1//UDfIcO54ppr2VTgo0aCjZrxtv/mpAmC8J8wDAP/7s+l9Vtzefzh+5n63UQAEpJTeOzZFznvwotRZIm4Kt66bRgGO4q9XHXnI0z//E1QVDKvfxPVlYwkyST2uoa+PZvTPDOeNLeFRIcZVTnwRJaCIAgnC5F0C4JwSjlQ8u2PaMiShFlVOatbJ6644goKikv49PMv+G7iZ6xZsoB5M6Ywb8YUnrh3BD3OvYCO515MvWatkCSJDrUSqJdUsYnP9m4Fb5jmoiwUpcQfpjgQ5p0/N2JOr0fa5U9TP7iWWZ++SjB/K5PfeYY1O4q4c+RIIprBtkIfDS2wsyRAerwdk7hgFYRqKxjRYnNCBMOM/+gTXn/6YcpKigC4aPDVPPbk09hccdjNSpVv3S72hfngx+k8evdwfDvWARBX5zQkYv3LZQnuPasRF7WuKearEAThlCKSbkEQTkl7J9/+sEZZKIIvGJvYRzcMUpMSGTH8Fu689WaWrFjFhE8+YfKkiRTuyuHnLz7i5y8+ok6T07j4xpEY3c4gyW4m3nZkXb9l+d+x4KPObsKgdrVYuLWYUFRHogW3XzWQcW+/xx/ffUqk0Rm8/PtaWtaMY0DzFLDA35uKSHT6qJPsJCPOKi5iBaEaiWg6nmCEslCUpSvX8PDdd7D075kAZNdvRN8zezPy4aewWi3EW024qnDrdlkowvKtudx23yMs/ukj0DVkq5MBtzzAJYOuQNPBaVVpl51AZrz9RBdXEAThPyeaRwRBOKXJkoTTopLmspLqtAAQCGsEIhq6YSBJEq2aN+Wl559l0cq1jPn4a04/ZwAmi4VNq5bx0p1Defiqfnw4cRL53iChqIZhGEdVltrJDi5qU5P+LTNpXiMOJIWh11/Pl7/+yblt6qBIEou3FXPj5f0ZcP0ISvJ34A1F+XtTIdPX5LMm10PZ7hsHgiBUTZpuUBIIk+MJklPs5cknn2LgGV1Y+vdMzBYLd9z/KJOnzqBGeho2k0y6y0q8vWouleUPRQF4f8Za+vToyuIfPgBdo2abXrz3w59cfNlgMuLsnHtaBv1b1hAJtyAIpyyRdAuCILC75dsS6/yT6rRgVmT8eyXfAGaTyoXnn82tT7/G67/8zflX3YTFamPjiiWMHDqI7l278uFXP5DnDRGIHH3y7bCoNK8RR9cGKdROchDWdPo2TeP5gadR38ghnLOW9X9PYVi/Hjz56EPYjBCaYbBwawkz1uazbHspxb7wUe9fEITKF9VjLdu5ZQEKfRGmTp/BOT0688GY54iEQ7Tr2pMps+dz+133oJpjvWZSXVWzB0sgrLE218OU1bt4a34Rb/y5DVN2S1RnItc++jrvfvwZjoRkGqW76FAnkXi7+UQXWRAE4YQS3cv3omkamqYd8vm9f1fWPlVVPey+jzTm3r+rWrzqFlPUT9WOWZn1syeGVZWwmU0EwjJloSi+YARFljCrMmZZolGygzXAFXc+yHlX3cj/TXiLn7/4iNVLFnDzFRfxWuv23HnP/Zx/Tl8cJmmf2EcizqrQumYcNeKsbMgvY5cnxMjB59Gt4Xe89uyjFG9Ywj/fj+fi37/lnKuGM3L4TUSA1TklbCooIyPOQlaCgySHuXxCt8Mde3Won+rwvqzqMavDZ9vJEjOs6fjDUbwhDV9YIzc/nxeffIxfv/4UgLjEZB548lkuvfQyorqBVZGwm82oqoqh61WqfkJRjZ0lQTbme5nwzY/8U6Tit6cD0P/aEQxo8QyqzU0oHKFVDTe1kx0o8pHts7p8R4p/P1X7uE/F+qkOZdwT62Sqn4ruUzIqoSkkEomQm5uL3+8nJSWFxMTEYw35nxg7dixjx45F0zTWrl3LnDlzcDqdJ7pYgiBUAxFDImQoWCQNk2SwM7+Qce+P5+evPycSCgHQtHV7br3lFrp3alfp+9d1nbcn/c4Hb75KMH8bALaUmjz5ypv0bVn7sMuaCYLw38oLK3z98298OuZpPEUFAJxz0WXcP+I2EuLjTnDpKm7brkJue+gZNsz9Pyw1m9Ji2Evc3imZVhliVQVBEE49Xq+Xzp07U1paitvtPuh2R510l5WVMWHCBL744gvmzZtHOBzryihJEjVr1qRv374MGzaM9u3bH/VB/Fc8Hg9xcXEUFRUd8mTtSc4bNmyIolROd69QKMTo0aMZMWIEFoulUmJWdjmPx3FXl5iifqp2zMqun0OVUdMNAhGNslCUYEQrb/mW/ye53bJtBy+++AI/fvEJkXAs+W7etiN33PsAZ/fpjdMSm7xNlY9udI8/FGVLoRdKc1gbdOIwybw27h1+++QNFHcK6Ve/QvMa8QzukIXbZmJboR9VlqiVZKdJupuMOOsBW72re/2ImEemOny2VceY9Rs0IKpLeMNR/BEN3QAjGmb8x5/w/thX2bVtMwA16jbkugefZfB5Z5QPbYmzmrCosc+FqlI/UU1nV1mIjflecksDfDtpEpPGPkXUVwJItDhrEOOeGEnImUG+L0qNeCvNasThshzZpJLHWs7/Mh5Unfo5GWKK+qna51LUz+FjejweEhMTD5t0H1X38ldeeYWnn36aevXq0a9fPx544AEyMzOx2WwUFRWxfPly/vrrL/r27UvHjh15/fXXadCgwdHs6j+lKEqFKqqi21V0n9FotFJj7h27MmNWhzJWdkxRP1U75vGqnwPFU5TYmG6n1bx7Pd0wwYiOJBmYFRlVlpAkiTrZWbz5xuvcfc+9PP/8c/w8cQLLF/zNDZddQMtOXbnz3gfp3bM7bouKw6Ie8XJfLrtCE4vKqtIcaiY62Foc5Lobb+LaoVfx3ayVzC1SWLHTw6gv/6F4+njiOl6MKT6VQe1qURbSqJ3koEmGu3z8ekWO/Wj9l/UjYh55nOry2VYdYmp6rP2iMBAltLunYSQU4N33PmD8uNco3JUDgDMunvOuvJHzrhyGajIT0gxqJdhwWU373MA70fWj6wa5niAb8r3klAbZuXMnLzx8DzlL/wLAlprN3U+N5pzeXXEFdrHGr9Eo3U3jDDcWtfI+P6rq58aJrp+TMaaon6p5Lo9HzJOtfiq6v6NKuufPn8+ff/5Js2bNDvh8hw4duPbaa3nrrbf48MMP+euvv6pF0i0IgnA4iizhsqjYd6/zHfuJ4gvrSBLlCXjd7CzefnMsa+++m+eeeorfvvuKJXNnMvSic2jdpQd33vcAPbudjsOs4rT828J1JFpnJVAzMcK6vDJyozqXndmBi3WDCX9vYconn+Jd+DO+pb8Td/ogvtAuol3ttqzP91LkC9Mk003NeJvohi4IxyCq6/jDGp5ArFdLKKLj95Xx5ptv8ul74/AUx9bbTkhJ5dwrb+SMiwZjtTvKX5+V4CDuCJcaPN6KfGHW7ipje7EfgOlz5vH2PVejh3wgq3S66DqefOwhbFYru0oDuMzQulYCtZOdh507QhAE4VR1VEn3559/XqHtLBYLN91009HsQhAEoUpT5NhSY06LSlQzEYzq+CMawf9JwOtl1+L5R+/nvgce4MXnn2fyN5+zaPYMrr5gBm279uLO+x6ga5fOOC0qDrOKVZUrnAjLskRmvI0Ul4VtRX7W7vLiDYbp3iCFWQ06Ety2nNDWZZTM+BjfqplMq/E8V5zXk4KyEH9vLKQgxUmjdBd2s5hTUxCORFjT8YWilIWiRDQDBYO8ggI+evM9vvr4fQI+LwBpNbO59tY7uOHaoRSFDNYW+ACQgHZZ8cRXoYQ7GNHYkO9lQ76X3NIgBd4Q09fms2anihqfjsVs4uEXXqNrhzaEozrbivykOkygQ3aSXSTcgiAIh1CpV1qff/45/fv3x+FwHH5jQRCEk4SqyDgV+YAJeDAS62taJzubd995m7X33sfzzz3Lr5O+YMHMP7hy5h+073EGI0Y9ROvWbQFId1uP6GLcpMjUTXGSHmdlY76PBVuLsKbXJ+3yZ/Ct+IPiKe8SydvIm3dexrypV/HMU4+jWK2syfVQ6I21eqc5q87FvyBURYZhEIrqeMNRfOFYsm1WZPJ2buWVl1/h+4mflE+imFWvEcPuGMnVgy/HpKqEojrxisHptRNRJIkEu6nK3OzSdIOdJQHW5JZR4A2xZFsh77z/Ic5mvZFUE6pq4qZn3uXi05tiMql4Q1EKvSFqJzlolu5ky8bCE30IgiAIVV6lrtN94403smvXrsoMKQiCUK2ou5PvVKeFTLeNVGdskhDNMPCFNWpmZ/P2O+8wde4izr74cmRFYf6MqVxxTi+uuOQifvxrHpNX72JZTmn5ONGKsptj63v3a1GDG7vXRZYlnM17U2PYOFJa9gJD558fxnPlDbeyoyRAVpIDbzjKnA2FLN9ZejxOhyBUe7ph4A9r5HvD5HiClAajKJLE1g1ruXnY9XRr24KvP36PSChEg9Na88r7nzLnnwXccM1VyIqKL6whyxKpTitZ8TZqxNuqTMJd6A0xf3MRczcVUhYM8/PPP/LyTRdSNPl1Sud8CYCOQZ92jTCZVAq9ITyBMKfViKNtdgL2g8wNIQiCIOyrUj8tK2H1MUEQhJOGqsjlE5ZluKxEDAn/7jHgWbXrMPbtd9hwz7288OwzTP3xGxb9NYWlc6Zz3pU3MuD6O3BaFNJcNmxH0OUcINFh5t6zGnN2s3TmbipEQqLOjWcw8etv+fSN51DbXswjPyznvNMyGNi2JppusCbXSzM75HqCZMbbxVhv4ZRn7L5RVhaKEIjoAFhUicWLFvHSCy8w49cfy697TuvYlWuvu45LBg6Mra9txFY70A1wW03E20xHPGni8RQIa2ws8LIhz0tI01mycAFvPv8YxRuWACBbXahxqQAYBuSUBgmENexmlfa1k8hKiM0HUZlr4gqCIJzMqs43gCAIwknsf1vA01xWnGYTDRs24IU33ualb/6gbY8+aNEoP3w4lnsvPZNJ3//MLk+QQn+YiKYf0f5kWaJVrQSGnl6Hrg2S8YWinN+vP5OmzqZnmyYYBvy0NIcrbrid77//gZoJsTV2520qYtmO0vJu8YJwKjIMg5JAhDxviGBEx6rKLPx7Nhf378f5vboyffIPGIZBh159mfDj7/z666+c2bkdsiwT1XW8YQ1Vlkh1Wkh2mKtUwr29OMDM9fks3V7Kug0buOumoTw9bADFG5YgqWbcnQZS46Z3cbboA4Asga7rJLssdK6XRK1EcVNOEAThSFXqt8DkyZOpUaNGZYYUBEE46Zj2JOCuWAJeM95ORnZd7hr9ASNefpfEtAzyd2zl7msHMfTKK1ixYQu5ZUHKQlH0I+xRZFEVmma46VQ3CZdFpSyocV3XOtzTtxGmXSvI+XMir48axpVDBrMtNx+3VWXFTg9zNhSSWxoUPZiEU45uGBQFIuSWBfGFIkz9/TfO6dObS87ry99/TkOSZXqcN4BJ02bz/XffcWaPruVJaCCsEY4axNtMpLusOC1qlUlQi3yx8ebzNxeRVxZi+to8Hn3kYbb9MxWQqHf6ebz/80zue/gJVJsTiCXcA1rXoE12Ip3qJJHoMJ/AIxAEQai+KrV7edeuXSsznCAIwknPpMikOC10qJXA/K3FtO91Ns07dOXH90bz/SfvMWPyD5zz1x/ccu9D3HDDMOIdVlzmI7tfKkkSGXE24mwmVu70sKnQR3aSnTG3XcKoXctZPvlTNs2bQr/+fzPkzoe4fuhQ8spCzNlYQINUF/VTnVhNlbvupSBURbphUOQPsz7fy4+//s5nrz7N5tXLAVBNZvoMuIw7Ro6kZZNG+yTT0d09UcyqTILDgt2kVJlkOxCOzUq+Kb+MhICPP1YVMG1zkEBEI67bEJxylBH3P0L3Tu0AaAycVtPNmtwykhxmutRLpn6qC0XMTi4IgnDUjqql++yzz2bu3LmH3a6srIznn3+esWPHHs1uBEEQThn1khz0b5ZOr3rJnNsim9GvvMIXk/+g4WmtCXjLePmR++jXpxcz5sxjV1msxepoJlprUyuB9tmJSEj4NIU3x7zMg+98iz2jHlqgjI+evY/LLuqPvygXp9XEsh2lzNlYSJ4neDwOWxCqDE03KPCFyS8L89W33/H8bVeyefVyLFYb5wy+nslzF/Phe+/Qqmnj8oR6zyRrES32bzHFacFhrhqt25pusLXIz1/r8lm8tZB33/+A3medx0ejnyQQ0aiT7OCxwWfw7fc/lCfchmHgCUTwhTSaZ8bRt1kGjdLdIuEWBEE4RkfV0n3JJZdw8cUXExcXR79+/WjXrh2ZmZlYrVaKi4tZuXIlM2fO5JdffuG8887jxRdfrOxyC4IgnHTsZrV8VuNAROP0ju354f+m8cF77/H680+wbvliBp3Ti4FX38CIG68l3xsi3sERtarJskTtZAfx9lir99ZiP926dKDHb3/w6ONPMue78excPpfrLuvP819Mo12dZHZ5gszeUEDDNBf1Up1YVNHqLZxcorpOoS9CWSjCb5N/5tV7b0aLRunU53yuGfU07oREama693lNWNMJRw1sZhm32cTmXaBWkeS0wBti7a4ythb6mDx5Ml+Pex7/rs0ASDtWcUOnDHo2r4W85+aBHhvDXhaM4LKoNMuIo1aSnbgqtI64IAhCdXZUSfd1113HkCFD+Oqrr5g4cSLvvPMOpaWx5WYkSaJp06acddZZzJ8/nyZNmlRqgY8nTdMOORPnnucqc7ZOTdNQVfWw+z7SmHv/rmrxqltMUT9VO2Zl1k9VOm6zDEk2FQWDG4YN44IL+vPgqHuZ/sv3fPXh20z75QdGPfEcAwYMwGlRcFtNmI9gsiaXRaFNrTjibSrr8srAMHjnsdv56eJLeOaBu7C0Oo8x0zbQuW4x13TORpZg+fYSCsoCNEp3k7x7KbTDHfvJWj/VPWZ1+Gz7r2JGNJ0ifxhfWGPGtCncc+PVaNEInfqcz61Pv46ixi6VLKqMruvohkEwoiNLEG9VcVlMYOiVWs6jrZ9AOMrmQj8bC3xMnzWXj8Y8TdH6xQDIVid9Lh3K3XePxGF3AAbRqEaxP0IwohFvM9G6hpuMeFv5qguH23d1qPPq8N2zJ+bev0+VmKJ+qv65PB4xT6b6qeg+JaOSZskpLS0lEAiQlJSEyVQ97oyOHTuWsWPHomkaa9euZc6cOTidzhNdLEEQhIP6/a85PPf0k+Tt2AZAxx5n8vAD95GdmV5p+whGND5fVsq3q8vQDdDX/cVpliKevOsmzGYxkZJwcpoxey4jbhtOJByi4xnncOszb6CazIBBDSVAohw+0UWskJyyCE+8/y0z338SAEkx0fbsgTx9181kpiSc4NIJgiCcXLxeL507d6a0tBS3233Q7Sot6a7OPB4PcXFxFBUVHfJk7UnOGzZsiKJUTvfKUCjE6NGjGTFiBBbL4VuOKqKyy3k8jru6xBT1U7VjVnb9VOXj9oejlAQihDSDaMDHE088wZcfvYcWjWCx2blhxH3ccsutuB1W4q2mI574LBAKs3njBtaHXCiqSpLDxKZCP2N/W8rfz12JHvDgzqzLXU++jL1GY2omWDirWQZu68Fvsp5K9VPdYlaHz7bjHbN23XqUhHSCUZ3Zf07n+sGXEAmF6Nj7LCZ8+hmKyUwgomEzKZgViWBER5HBbVVxmk37jHM+kfWzPr+MP9cW4AtFWby9lL/WFxIJ+Njx9jBqt+rMgw8/SpMGdcHQcQXzWRt0IskKaW4LtRIdpDjNqEe5pFl1qPPq8N0D1eNcHo+Yon6q9rkU9XP4mB6Ph8TExMMm3ZU6e3l1pyhKhSqqottVdJ/RaLRSY+4duzJjVocyVnZMUT9VO+bxqp+qeNwum4LFbKLIH8ZrOHjgzlu48pqh3HPnbaxY8DdvPPMoP38zkSdeeo3Tu3TCbTGIs5lQ5YpdTNsssRbsNtlJrMwtY1txiJoJDp4b1IkXix/gl7efwbNzI49efzHuDheS0PMa1uUHuKdvo4Mm+KdS/VS3mNXps+14xSwOakR0iXmzZzJsyGVEQiHa9ziTzz7/AofNCsTmWQhFNYJRA4fVRILNdMg5Df7r+pkwdwsPfrMAz/wfCG5bTuoljyFJEq3qZvDoT3/RNDujfM3xskCYpjbITnZRK8lJksOMXElj0KtDnVeH7549sU/FmKJ+qua5PB4xT7b6qej+KnWdbkEQBOH4MSsyKQ4LcbbYB3yTxo35vylTeeSl13HGxbNpzUqu6t+HO28bzsadu9hS5GdzkQ9fKFrhfdRIsNGlfjLZSXZyPQF8YY0HbrueF7+YgqNZLzB0PH9PouSvz/hg1ib+WpePfoSzqAvCiRQI7xnLbTB/7iyuu3wgoWCAtt168/nEL8sTbt0w8O7eNtlhJtVpqVKTCO4o9jPqw9/Y8c5NlMz4iODGBQQ3/sOtPetx/7lNaJSVTqE3xJZCP4YBTXdPBNcqK54Ul6XSEm5BEATh8ETSLQiCUI0oskSCLdYqLUkSQQ1uuuE6/vpnMWcNuAzDMPjhs/H07dKeMe99xOzNRfywMpf1Bd4K78NtNdEuO4E2WQlENJ0dxX6c8Ykk97uLxHNuB6B09hf41sxh/pZiNuZXPLYgnEjeUJQCf2zJvQXz/+aayy4iGPDTqnN3vvjyK5x2GwBR3cAX1nCYFNJdNuJs5vKZvquK35duJe/bZ9E8+SjuFJLOvwtr3ba4bSZ2eYLsKPFjUWXaZyfQo2EKTdIP3u1REARBOL5E0i0IglDN7FkeLMVpxmaS8YU1UlJS+Oij8Xz0zU/UqF2PkoI83rh/OM8Pv5LSogLmbyuhLFjxFm9VkWmQ5qJLvWRSXBYMAyQJXC374mrXH4DA5sUk2c2syPGwS6zjLVRhhmFQFoqS7wuBAQuWLo8l3H4fLTp25ctvJuFy2IHYUmB7ZvNOcVowq1XvUimnNMBjD9xDJH8zsj2e9Ctfwtm8F7Iso0iQYDfRuW4y3RumUD/NVT4buSAIgnBiVNo3yYIFC5gwYQITJkxg4cKFlRVWEARBOAiLqpDqtBJvMxGKGoSiOmf1OYOfZsxh4M13YTJbWDpnBs/cdDne0mJ2lgaIHmFX8GSnhY51kuhSL5mBbWoiSZDQ61qSL7iPxD438c2i7YSiGku2leAJRo7TkQrC0TMMg9JghAJvCEWCJUsWccuNN+L3ltG8XWe+mvQtbqcDwzAIRDSimkGSw0yi3bzPZGlVRWkgwi2PjWbL7J8AiZT+d6O6kpAkuKFrXc5rkUnX+inUSrRXqe7wgiAIp7JjvvWZl5fHoEGDmD59OvHx8QCUlJTQq1cvvvjiC1JSUo51F4IgCMJBKLJEkt2MWZEpDoTxhzXinTYuuuFOOp5xHk/fNIht61fz3K1DaDbpR+JsJpIcZkxHMFux1aTQvIabO89sSLtaCWwo8JJ0fnNenbqOtbu8TF6WQ9+m6SzfXkq72olVsmVQODXtmUSsKBDBLEssWbyYIRf1x+/10LRNB7769jviXE4Mw8Af1lEViSSnGYe5arYMByMa387fwI/vvABA50tv5OqbLsOiKrSvk0jTDHd5TxhBEASh6jjmK6PbbruNsrIyVqxYQVFREUVFRSxfvhyPx8Ptt99eGWUUBEEQDkGSJNxWE6lOKxZVJqpDw2QHNeo24IG3PscVn8jGlUu5/vKB5BQWU+ALEdb0I95HjXgb57XMpFejVOJsJm7tWR8j6GPiM7fz9pjn2FrkZ3WuB7ESpVAV6IZBkT9MkT+MRZFYtnwZlw84D6+nlEYt2vDlpO9IiHOj7R6/bTXJpLksVTbh1nWDhVuLeXPmdlKveJbsbgO44677qJ/q4tL2WTTLjBMJtyAIQhV1zEn3r7/+yptvvkmTJk3KH2vatCljx45l8uTJRxVz7Nix1K5dG6vVSseOHZk3b94htx8zZgyNGjXCZrORlZXFiBEjCAbF+EJBEE4tNpNCqtOC22oi3maifVY8/bq248OvvsfhimPVovlcc/klFJR4yfeGCEWPLPEGcFpUWmbFk+a2kuq20EreQmD9PGZ88RYzpvzC2l1lbCn0H4ejE4SK0/RYwl0SiGBVZVasWM7lF5yLt7SEhi1a8+5bb5IYH0dU0/FHNFwWU5Wbnfx/bcgr46X/W0NeWYjUWvV4afSrmE0qzTLjsFfRGwWCIAhCzDEn3bquYzKZ9nvcZDKh60d+QTdx4kRGjhzJo48+ysKFC2nZsiVnnXUWeXl5B9z+s88+Y9SoUTz66KOsWrWK999/n4kTJ/LAAw8c8b4FQRCqO1WRSXaYSXLExqNaVIXTO7Rl/FffYnM4Wfr3LK4dchklXj/53iDBiHbE+7CbVVrVjMdpVhk65HLq9xoIwDtP3MWm9WtZtrOU/LJQZR+aIFSIphsU+sOUBqJYTQqrVq3isv7n4Skuon6zlnz17Q8kuByEozpBzSDBZiLZaUY9giEX/7Xc0iB3PT+O2bNmIUtwa896RDWDhmku0uOsJ7p4giAIwmEc8zdM7969ueOOO9i5c2f5Yzt27GDEiBGcccYZRxzvlVde4YYbbmDo0KE0bdqUt956C7vdzgcffHDA7WfPns3pp5/OFVdcQe3atenbty+XX375YVvHBUEQTlayJBFvM5PmtGJSJPwRna6dO/HuZ19jsdr4568/GHbNlXgDIfK8IfxHkXgnOMy0zIoH4JnnXsBdpwV6yM9jtw0lv7CIZTtKjmh9cEGoDFFdp8AXwhOMYDPJrF69mkv6n0tpUQF1mzRn0o8/k5yYAMS6n6fsnjCtqi0HtrfSQIRx30zhp9cfZtdn99PVXUy83UyNBBsNUl0nuniCIAhCBRxzf6Q33niD/v37U7t2bbKysgDYtm0bzZs3Z8KECUcUKxwOs2DBAu6///7yx2RZ5swzz2TOnDkHfE2XLl2YMGEC8+bNo0OHDmzcuJFffvmFK6+88qD7CYVChEL/tsJ4PJ4DPv6/NE0jEokQCoVQlMrpgrZnf4fa75Gq7HIej+OuLjFF/VTtmJVdP9XluCsaUwHcJonCcIiSsEG3zh15/YMJ3HrNFcyeMpmbr7+WN956m1AoSIJVPeJyJtlkGiVbWbIjxDOvv8OdV5yPP38bj464madffw85EhvmI+qn6sWsDp9tRxozoukU+mI3kewmhZUr13DZBedRUpBH7YZNmPjNd7idDkp9ASKRCG4VLJJOOBz+T8tZEXvqxePz8+eqnTx/z40YWoS0Zp0ZcGYXJDQaJFkxtAihCt4zO9H1c6JiVofvHqge5/J4xBT1U7XPpaifw8es6HFIRiXMeGMYBlOmTGH16tUANGnShDPPPPOI4+zcuZMaNWowe/ZsOnfuXP74vffey4wZM/j7778P+LrXXnuNu+++G8MwiEaj3HTTTYwbN+6g+3nsscd4/PHH93t81KhRWK2im5YgCCe3rbuK+OjdcWjRCJ17n0Wfrh2R5WPvWrt4WynfjX8DtAjNu5/HwN7tK6G0gnDkPP4g48ePpygvl5p1GjDkisuxmqpu9/GD0XWDcV9OJn/1PFR3MrfeeAMJDsuJLpYgCIKwWzAY5LnnnqO0tBS3233Q7Spl5g1JkujTpw99+vSpjHBHZPr06TzzzDO8+eabdOzYkfXr13PHHXfw5JNP8vDDDx/wNffffz8jR44s/9vj8ZCVlcXIkSMPebI0TWPNmjU0atSoUu/2jB49mhEjRmCxVM4XaWWX83gcd3WJKeqnases7PqpLsd9NDEDEY0CbwjNMLCbFJq0aMV9w29gzrTfSElL54UXX8Cft4OM7DrE261HNAtyRNNZvLWE+kU+QpY4/m/CWIrrnom7wxl45k3i0qE3Ujct4WgPt1xVOZcnQ8zq8NlW0ZiBiEaRL0RYN7CrMhs3b+GSC86jKC+XrHoN+eq7H0lJSSGk6bgsJtwWhQ3r1laL+pm+2Uv+6nkgK9z73Js0a9ma7CQHrWslHPEa4tXhfXk8YlaH7x6oHufyeMQU9VO1z6Won8PH9Hg8PPfcc4eNd1RJ92uvvcawYcOwWq289tprh9z2SJYNS05ORlEUdu3atc/ju3btIj09/YCvefjhh7nyyiu5/vrrATjttNPw+XwMGzaMBx988ICtNxaL5YCVfLDH99A0DZPJhMViqbQKrei+j0Rll/N4HHd1ibmHqJ+qGXOPyqqf6nLcRxPTYgGzxUK+N4hmwJWDLyccjfDQ7Tfxw+cfYbXbefD2m/FpClZdJs5mqnDibQHa1DURkRSuGToUR5NuzN8R4IN5OznbMLMuP0RakkSiw3wMR111zuXJEhOq9mdbRWL6wlFKI1Ek1Uy8SWbjli1cNqAfBTk7qFG7Ht/8NJnU1DR0A9JdJuJsJozdE8BW5frJ8wRZvq2QPyfEeu31ueYuOnbpgkVVaJGdhN26/wS2/3UZq0vM6vDdA9XjXB6PmKJ+qva5FPVz+JgVPYajSrpHjx7N4MGDsVqtjB49+qDbSZJ0REm32Wymbdu2TJ06lQsvvBCIzY4+depUhg8ffsDX+P3+/RLrPSdGrBUrCILwL7tJIdlhId8bIhjRuO7qqwgFQzxxz+18+f44TLLEs889T2EgjAYk2EwVnmBqz4zmczcVckmnBuRNX8+WQj8/5Sk0KChi2Q4bHWonYTNX3SWZhOrDMAzKQlEK/WEkQJZh2dqNXHXReeTt2EZGdh2++XEyySlpIEGKw4LDrCBJEkc+beB/yxOM8PemIqYt3QR6lMxW3bnnrjvxBKO0rpWA6ygSbkEQBOHEOqqke9OmTQf878owcuRIrr76atq1a0eHDh0YM2YMPp+PoUOHAnDVVVdRo0YNnn32WQD69evHK6+8QuvWrcu7lz/88MP069ev0u/ICIIgVHcOs4rhgHxfiFBU45YbbyAQCPDiI/fx6btv4oxL4KEHH6DEH8YwjCOa2TnBYaZlzXjmbSrkhq51eOCV91jz7Ys8seAnnh/3EQ6zelTdYgVhb7phUBqIUByIoMoShf4wc1as58kbLiV32xbSambz9Y+TSU7PwKzKJNrN2EzV43ogFNVYuq2Y92dvxdn7Rlw1GvDKvTdS6IvQON1FzXjbiS6iIAiCcBSOeVaRJ554Ar/fv9/jgUCAJ5544ojjXXbZZbz00ks88sgjtGrVisWLF/Prr7+SlpYGwNatW8nJySnf/qGHHuKuu+7ioYceomnTplx33XWcddZZvP3220d/UIIgCCcxp0Ul2WFB0yEc1bnrztsZfv+jALz90tO8MnoMVpNMSSBCoS+Mple811BmvI3mNeKwmBQu6dECwzDYseQv3nrtRdbne1mfV3a8Dks4BeiGQZE/TJE/jFmRMDD49c+5PHL1BeRu3URKZhYPvj2R+JQMHBaVVKel2iTcum6wKsfDZ/O2sWqXF0UyeOLuW8HiIM1lpXG6+4jmWhAEQRCqjmNOuh9//HG8Xu9+j/v9/gPOEF4Rw4cPZ8uWLYRCIf7++286duxY/tz06dMZP358+d+qqvLoo4+yfv16AoEAW7duZezYscTHxx/VvgVBEE4FTrNCot1ERDcIazoP3HcvV910GwCjn3iQN8e9jd2kUBqMJd7RI0i86yY7aZzmpk2rNrQ/dxAAM78Yx9TfJrMyx8POksBxOSbh5BbVDQq8YUoDUawmGZMi8+kXX/H4tRdRmLuTjNr1eOidiSRn1EBVJFIcFkxK9ZmxfHOhj3e//pWPn7kLPeSng7qNJIcJRZZolunGWk1uHgiCIAj7O+bZyw3DOOCd1yVLlpCYmHis4QVBEITjQJIk3FYThgFFgQhIBnfdfAOaAZ++/TrP3D8Si9XK9UOvoSwUQTcMkhzmCiUxsizROMNFqdfPuW3qUeq9mLV/fMMHT42kdt162EzNcVhU4mxibKpQMRFNpyQYwReOYjMpSBg8/NgTvPtKbKhZi849uO25sThccQDUcNuq1TCGXZ4gvy9cx7uP3U7EU0CNjHQad21CiT9MmzqppLrFcqaCIAjV2VHfAk5ISCAxMRFJkmjYsCGJiYnlP3FxcfTp04dLL720MssqCIIgVCJJkoizmUiwmQhFdWRZ4vlnn+Hiq2KrQTx213A+/XwidrOCLxylwBcirOkVim1SZJrVcCNJMOrRJ4ir2wI95OfpEdexbVcBS7eXEIpW9SmthKqi0BfGF45iNysEAgEGDx5cnnBfcOX13PPqeByuOCSgQ60EHJZKWRH1P1EWjLBwSyEPjbiZiKcAa3JNnnvyUSQJshLs1E91nugiCoIgCMfoqL+VxowZg2EYXHvttTz++OPExcWVP2c2m6lduzadO3eulEIKgiAIx4ckScTbTES1KEHAQOK1V8cQDAb5+csJ3H/bDVgsZi65eAD+iI7uDZHssGBRD3/P1m6OfcWkuB08/uq73D34PAJ5W3l97Fjuv28ULotKi5rxyNWoRVL4bwUjsRszwaiOw6KybfsOrrh0IOtXLEFRVUY9/SJDr70eSQKTLJPkMJe/76qDcFRn2fZSHnvyWQpW/Y2kmnns1feQzbEJ0xqlu6pVF3lBEAThwI76m+nqq68GoE6dOnTp0gWTSXQTFARBqI5iibeZXUAgouGwmHj7rXFcEwwy5YevuevGoVitVvqfdw7+iE6+N0iyw1LhMaan1YhDk1Ruf3YcH3w2idKG57JwWwmKIuO0qtRPdR3fAxSqJW8o1rsCwGaSmf33PG4YPIii/Fxc8Qm8/v4Eunbvjt2skOSwYK5myWls4rRSPvzmZ+Z9HVuP+8JbHqZD29bkFsUmHIy3H9va9oIgCELVcMzfUD169ChPuIPBIB6PZ58fQRAEoerbsyyY26oSiOhIssIHH35At7POJxoJM3zoFfzflGnYTTIRzSDfFyIQqVj38PQ4G80y3XTq0JFb774fSZL5esF2thb5WLnTQ54neDwPTahmDMOgNBAm3xfC2D1/34TPv2BQv7Moys8lq15Dvvt9Oqd3747LaiLFaa12CTfEJk6btngd458eCYZO3S7nMmL4MHZ5gtROcpzo4gmCIAiV6Ji/pfx+P8OHDyc1NRWHw0FCQsI+P4IgCEL1kWAz47KoBCIaqqryyYQJdOzZh0goxLAhlzJlxkyCUQ1fSCPfG8IfjlYobt1kJ43T3TTLdNOjYQp6NMwzD97D4hWrWLK9lLJg5DgfmVAd6IYRW6rOH0GRJEwyvPD629x3y/VEQiE69DiDn6ZMJ7tOXRJsJpIdZtRqODwhzxNk6fYSvvxzKYYBttRsXn/jDQp9EZIcZhqli94fgiAIJ5NjHvh0zz338McffzBu3DiuvPJKxo4dy44dO3j77bd57rnnKqOM/xlN09C0g7fc7HnuUNsczT5VVT3svo805t6/q1q86hZT1E/VjlmZ9VOdjvt4xcTQibcqaLqGNxjBblaZ8NnnXHrxRSya8yfDrriYB9/6grpNW1A3wYauayTZDzyOdu/6UVWdBql2/KEwZzdJYep7T1P8z088N3IpT7w3ic0FZZzROJXsQ7TwVbdzWZVjVsXPNk03KAmG8QSiWFSZgM/Hddddy8z/+xmAy667haeeegpZUYm3KrgtCoauc6R7O9H14w1FWLatmMnLc8hRM8i+4Q3u7FYDi8VKKBilaXo8VlWqcvVTXWNWh++ePTH3/n2qxBT1U/XP5fGIeTLVT0X3KRmGUfHFVw+gVq1afPzxx/Ts2RO3283ChQupX78+n3zyCZ9//jm//PLLsYQ/rsaOHcvYsWPRNI21a9cyZ84cnE4xS6ggCML/KvEGuOGW4axeNA9nXDwPvfMltRo0prHqwSQd+dfI2m25DLpsEJGyQmwNOpFy0QPIksxtHRPpW1+08p3qtuXs4tbb72DT6hUoqom7H36cIRf1O9HFqjT/bCvj8T+LMIC7uiTRq4649hAEQaiOvF4vnTt3prS0FLfbfdDtjjnpdjqdrFy5klq1alGzZk0mTZpEhw4d2LRpE6eddhper/dYwv8nPB4PcXFxFBUVHfRk6bpOMBhk06ZN1K5dG0Wp2ARChxMOh/noo4+4+uqrMZsrZ8IUTdPYvHlzpZWzsuNVp5iifqp2zMqun8OV0WQyHXHZ99zUa9iwYaWey/8iZkTTKfSHCYQ1QprO3xtyefbmK1i/fBHuxGQefvcrzu3SGrtJRtch0WHCaVaRdo8PD4VCjB49mhEjRmCxWMr3VRoIM39zMZN+m8E7910FWpS4bkOIP30QsgTTR/agRoLthB33qRDzYHVzLI62jHu/z2wmhVlz/+bGqy6npCAPd0ISL45+lV5nn4/VpJBoN1d4Ar/KLuexxAyGNbYV+9lY4GPl+s08cN1A3F0H0//iS7mhWx1ySgIkO820q52IRVWqVP1U95jHo4yifkT9VMWY1aGMcPLVj8fjITEx8bBJ9zF3L69bty6bNm2iVq1aNG7cmC+//JIOHTrw448/Eh8ff6zh/1OKohzwpIbDYTZt2oSmaRiGwfbt28svKo+VYRh07NiRnJycSo1ZmeWs7HjVLaaon6odszLrpyJljI+PJz09/Yj3d7DPl2NxvGMqikKqopDvDREJRrE5nNz7xsc8feMgtqxZwTM3DaLFT7/RskkjQlGd4oAGkkyc1YQkSSiKQjQa3a+ciU4bLbJgWfPWJPa9haLJr1H616eY0+pir9+BmRsKGdS+1kGXEquO57KqxTxY3VSGI4kZimoUBaIEowYOq4kPP/6Ex+66nUg4RHaDJnz0+USSLBJ2i0qK04a5AkvVHY9yHm3MUFRjR3GAdXlein1hguEwT959M5GSXIILvmPwcyMpCURRVYWmNRKwW8zlcapC/ZxMMSsznqgfUT9VOWZVL+PJVj8V3d8xJ91Dhw5lyZIl9OjRg1GjRtGvXz/eeOMNIpEIr7zyyrGGP+EMwyAnJwdFUahZsyaRSASLxVJpSYOu6xQUFJCcnIwsV87FhGEYhEKhSitnZcerTjFF/VTtmJVdP4cqo2EY+P1+8vLyAMjIyDjm/VUHJkUm2WHBAOok2tkE3P/mZzw17BK2b1jL4AvP47vJv1O/bh3CUZ0ifwTDgHjboZeRzIiz0a1BCmNb9iWcux7vol8o+PElMq5+GW+oMWt2ldE43VVp7xWh6glENAp8ISKagVWB++5/gE/eHANA595n8e6HH2Gx2gnmbSHZbq7UhPt4i2o6O0uDrNtVRoEvjMOssC6vjJeeegTPpmVIZhuXP/AKqslEQWmANrUSSHZWTouPIAiCUPUcc9I9YsSI8v8+88wzWb16NQsWLKB+/fq0aNHiWMOfcNFoFL/fT2ZmJna7nWAwiNVqrdSkQVVVrFZrpSZ1QKWVs7LjVaeYon6qdszKrp/DldFmi3V5zsvLIzU1tdLvplZVZlUm2WHGMAzirSaMTDdffj+Zgef3YefmjQzsdy7fT/4/smtlIWk6RYEwBmCTDz16qUOdRIb3qs/r+g1E8rcQKdiKNewlxWVhxc5STLJEvVSnSLxPQr5wbA1uXYdIwMeVQ69h9pTJAAy56Q6eeupJooaEyyITBNRqsiSYrhvsKguwPs9LjieIVZWpEW9j9sLlPHPfSIKbFwGQdM7tTN2p0D7HQ4useOokiyXCBEEQTmbHlHRHIhHOPvts3nrrLRo0aABAdnY22dnZlVK4qmDPjHSVNZ5XEITqzW63A7HPv1Ml6QawqArJDgu6EULTDerWyuSbHyZz4Tl9yN22mYv7ncsPv/5OZkY6AMX+MCFZP2RMSZK4/YwGNM1w8XvTN/h18Rb81mQ+mLWZm3rUY8mOUlRFprZISE4q3lAs4QbYsX0rV146kM1rV2IyW3jspVcZMmQIUR0S7SacZpm8E1zeithzw+6fLUXsKA0hSxKZcVYAHnvuZaZ99ApGNAyKiYQeV+No0g3diLX2N8lwV5ubCoIgCMLROaZPeZPJxNKlSyurLFWaaGkRBAFO7c8Cq0khxWlBkSUCEY06tWvx9U+TSU7PZPum9Qw4/1zy8vMxKTJWk0zJ7rW39UPM12lSZHo2SqNfx6bc3K8LFlVm2Y5SJs1ZjdWksHhbMduL/f/VIQrH2Z6EOxzVmTr9T87r1Y3Na1cSn5TCJ9/+zOWDh6AbkOK0EG8zIVeDf29FvjCLt5UAkFMaJMVlJSPexvo8L6MmLWPxrghGNIy1disyrx+Lu8OFAEgSnN4gmbjDDMUQBEEQqr9jvrU6ZMgQ3n///cooiyAIglDF2UwKyQ4zkhRrpWtQry4Tv/+FhJQ0tqxbxYB+51FYVIQqy9jUWE+AQl8YTT944m0zKzSr4SY70c7VXWoT2DCf8bf34/OJXyHLMou2lZBbGvyvDlE4TryhKPm+EHllIUa/9R43DboQT3EhdRo146epM2jfoROKJJHqtOCyqFX+BpcnGGHJtmJmrstnc2HsxlBGvI2CgkKe/vB7Hv9pJTtKAmS068P1T7/Lg29MwJKYCcQS7pt71KNNVsKJPARBEAThP3LMY7qj0SgffPABU6ZMoW3btjgc+3YDPBkmUxOOXu3atbnzzju58847gVgr4bfffssFF1xw3PfdvXt3brrpJq644opKjbt582bq1KnDokWLaNWq1THH69SpE/fccw8XX3zxsRdOEP4DdrNKMpDvDRGMajRr0ojPvv2Jy/qdzYaVy7j4gv5899Mv2K2xiaHKQhHMvjCJDjPqQWYkT3VZaZzuIhDRqOHbQF7Ix2cv3EfTxg1p2vw0Fm4tpm2tuP/wKIXKVLa7hTui6YwZ/Qqfv/o0AO17n8PNT44hMS0dq0kmyWHGolbtYRv+cJTNBT42FvjwhqIkOSykuMzovhJefesDvn7zWZBkMm8Yx5mt6nJ5+1o4re0AaJWVwMocD3WSHJzfIvOgM/QLgiAIJ5djbulevnw5bdq0weVysXbtWhYtWlT+s3jx4kooonA0hg0bhizLSJKEJEkkJSVx9tlnn/DhADk5OZxzzjnHfT8//PADu3btYtCgQeWP1a5dG0mSmDt37j7b3nnnnfTs2bPCsbOyssjJyaF58+aVUtaHHnqIUaNGoeuHHv8qCFWJw6zuHuMdWxapdYvmTJj0Iw53HKuXLGDggAvwen0A2FSFslCEQl+I6CHe53VTnGQnObjrocdJadQOIxLk8duvJegtJRjRWLK7C69QvexJuBUJXn/11fKEu//QW7njhbew2uwgxbqUV+WEOxjRWL+rjBlr81m6e76BWol2nFaVBUtXcMbAa5j40ig0fykWZxw3d0jmhm51cVpj7Ru6bhDRDBqluejWIAWbueoeqyAIglC5jjnp/uOPPw76M23atMooo3CUzj77bHJycsjJyWHq1Kmoqsr5559/QsuUnp6OxXL8l0V57bXXGDp06H4zWlutVu67775jiq0oCunp6ajqMXcUAeCcc86hrKyMyZMnV0o8QfivOC0qyXYzmg7hqE6Htq356OvvsTldLP9nLoMHX05U01BkCbtZoSwUja35rR048VYVmaaZbpJcdp594z0siRmEi3O45bqribPIlAWjQKxbr1A9lAUjFHhjCffYsW/yxtMPA3DRsBEMum1U+Wd0zTg7pio6mVhE09lc4GPmugL+2VKMbkBWop04mwl/MMjd9z/M7Zf0pWDdIiTVTPfBt/Pj1Jn07NiqPEZZMMK2Ij9xNpX2tRNJ3z3JmiAIgnBqqJrfcCepnNIAszcUkFMa+E/2Z7FYSE9PJz09nVatWjFq1Ci2bdtGfn5++Tb33XcfDRs2xG63U7duXR5++GEikX8vaJcsWULv3r1JTU0lLi6Otm3b8s8//5Q/P3PmTLp164bNZiMrK4vbb78dn8930DJJksR3330HwJYtW5BlmUmTJtGrVy/sdjstW7Zkzpw5+7zmSPeRn5/PtGnT6Nev337PDRs2jLlz5/LLL78c9PW6rvPEE09Qs2ZNbDYbffr04ddffy1/fvPmzUiSVN6To7i4mMGDB5OSkoLNZqNBgwZ8+OGHAPTu3Zvhw4fvV764uDimTp0KxJL4c889ly+++OKgZRKEqsppUUm0m4joBmFNp2unjrz/xSQsNjuLZv/J979OIRiKzebsMCv4w7G1mcMHSbzdVhPNariJT0zk8dc+QDJZKF77D7fddS9pcbEbdku2leANRf/LwxSOkGEYeIIRCnxhFBneefc9Xnj4XgAuG3Y7F9/473Kj7bPicVkr5yZmZdtZEmDWhgLmbirEH9HISrST6DAjSxIL1++kf6/Tmf3lOAwtQmrjdoz9ZgrPPfEINlssqY5qOtuL/ATCGqfVjKNLvWQy420n+KgEQRCE/1rV/JY7QTRNK18ibO/HDMPY58cXihzxBC/fLNzOYz+uRDdAluCxfk25uE1NdF0nGNXxhSKHXWfYZlIqtN89S5fsKS+A1+vlk08+oX79+iQmJpY/7nQ6+fDDD8nMzGTZsmUMGzYMp9PJvffGLo4GDx5Mq1atGD16NDabjSVLlqCqKoZhsGHDBs4++2yefPJJ3n//ffLz87ntttsYPnw4H3zwwT7lMfaavfh//37wwQd58cUXadCgAQ899BCXX34569atQ1XVCu9j7+P+66+/sNvtNG7ceJ/9QKyL+Y033sj999/PWWedhSzL+5wvgDFjxvDyyy/z1ltv0bJlS8aOHcuFF17I8uXLadCgwX7n96GHHmLlypX88ssvJCcns379egKBAIZhcN1113Hbbbfx0ksvlbfwT5gwgczMTHr16lUeq3379jz//PP7lbci/rf8laE6xZQkab/31LHE2/v3wbYxDOOAnxcHsmebimxbUVUtpsMko1llivxR0HV6du3CuE8mcuPggSybP4drr72WTz/9FJPJhE2V8IWi6JpOot2MWd3/cy/DZaZBioNotAnXPfgi7z12O6t++5Q3PurEQ4N6UlgWZNGWQtpkJWA9xi66Ve1c/lcxNU1DVdUKv48rGhNic70ENIMiXwRFlvjgw494ZlQsyb7sult4/ukn8UUMzGps0jSHWT1oGU7kuSz2h1m4tYioplMj3oIqy4CBJxDms7+3MWNdAXJafdRAGYNue5B7BvXBZ08DQ8cwDEoCEXzBKBnxVhqkukh2Wip8LMezfqry+/J4xDxeZRT1U7WP+1Ssn+pQxj2xTqb6qeg+JaMyr4CrmbFjxzJ27Fg0TWPt2rXMmTMHp9O5zza6HvvyzM7OxmKx4A9rtH1uxgkp74JRPbBX8AJz2LBhfP7551itsbvtPp+P9PR0vvnmG1q3bn3Q140ZM4avvvqKWbNmAZCWlsbLL7/MkCFD9tv25ptvRlEU3njjjfLHZs+eTd++fSkoKMBqtdK4cWOGDx9e3tprt9v54osv6N+/P1u2bKFJkya8+eabXHPNNQCsWrWKtm3bsmjRIho1alShffyvN954g3HjxrFixYp9Ht9Tlssuu4zmzZszevRorrjiCu655x6WLl3Kb7/9BkC9evW48cYby288AHTr1o22bdsyZsyY8nLPmTOHli1bMnDgQJKSknj77bf3K0swGKRevXq89tpr5ROldezYkf79+/Pggw+Wb/fTTz8xaNAgPB7PYW++CCdWKBRiy5YtSJIk6uowfp/5N/fefgvRSJie5/Rn9DNPoB7FmN3hj7zIgk15JPe9kcfPrEGbDNFSWF1M/PFXnn7wXgzD4ILLr+KJUXdX28nDdF3nufe/YqFcn6A1GQnomaFzRcsEMpLiT3TxBEEQhBPA6/XSuXNnSktLcbvdB93ulG7pvvXWW7n11lvxeDzExcXRqFGj/U5WMBhky5YtWCyW3Un3iVsv1mq1YDUfvsr23Efp2bMn48aNA2JdoMeNG8eAAQP4+++/yc7OBmDixIm8/vrrbNiwAa/XSzQaxe12lyezI0aM4JZbbuHzzz+nT58+XHLJJdSrVw+AFStWsHTpUiZOnLjPvnVdJycnhyZNmiBJEqqq7pMcm83mfcZ1t23btvz52rVrA1BaWorVaq3QPvZ+PBQKEYlEsNls+yXke8qSlZXFXXfdxVNPPcWQIUNQFAVZlrFarXg8HnJycujRowdWqxVd18nLy+P0009n2bJlWK3W8rJbLBasViu33norAwcOZOnSpfTp04cLL7yQLl267K4zK0OGDGHChAkMHjyYhQsXsmLFCr766issFkt5z4W4uDh0XUeSpAPeSDhcfYdCoX3iHavqEnNP/aSmplZKAlzRMppMJrKzsytUV3tu6jVs2BBFqZyJk6pqTMMwKA5GKPVHsJpkLr6oBitWrGD8W68zffIPPOhw8vbbb6EoCoZhEAjrmFWJRLsZq2n/fRb5Qvy9uZh7HnuKLxfsZMbaAp6fWcCT/ZuS7LKSUxKkboqd5pnxB2wx/6+OuzrGDIVCjB49mhEjRlTaPBvRaJR169ZhS8nCpCpM/Pprnn7oPgzDoP/lV/P6G68TjBrE2VQSbOYKrcF9os7ljpIA8zcVkewyY1YV5i1exhOj7qJo/WJs9TvQ9oZnuKFbXRqkxm7Ulxk6rmA+m6JxRDSDrEQb9VNduK1Htwb38aif6vC+PB4xj0cZRf2I+qmKMatDGeHkqx+Px1OheMecdPt8vv2WCauuFEXZ76QqilI+A7gkSdhMMise63tESUNuaZAzR89g72VqZQmmjOhBqsvMrl27SEtLq7Tu5Xs4nU4aNGhQ/nfbtm2Ji4vjvffe46mnnmLOnDkMGTKExx9/nLPOOou4uDi++OILXn755fL9PP7441xxxRV8//33TJkyhccee4wvvviCAQMG4PV6ufHGG7n99tv323etWrXKY+w5d3v8799ms7n87z3nYE+34YruY2/JyckUFxcf8Lk9+77rrrsYN24c48aNO2A5967zPWU52DbnnnsuW7Zs4ZdffuH333/nzDPP5NZbb+Wll14C4IYbbqBVq1bs2LGD8ePH07t37/Ky74lVXFyMw+HAbrcfoCYr5n/Pa2Wo6jH3rp/KLOeh4u157kCfF4dypNtX15hJDgVZkikJRJCRyEqO4+lX3+L+24bxy9efcZvFwptj30BRFBxWmUBEpzAQJVlRsP9P4p3ittM4XWPh1hKGdMpmZ0mQtbml3P3ky4x78l7S4+1syA+gKCotasShHsNEXFXxXB7PmIqiEI1GKy2eYRj4orFx+iZV4dvvv2PUrddj6DrnDLyCsWPfIKSB22YiyWFBOcLW7v/yXAYjGuvyfJSFNXZuKWL8uDEs/PFD0KJIqoVOnbvw6IXNMe81maYvFMUF2EwqrbLjqBFnO6YW/cqun/+NfSrGrMx4on5E/VTlmFW9jCdb/VR0f8ecdKelpXHppZdy7bXX0rVr12MNV+VJkoTNrB7RBX7dFCfPDjiNB75djmYYKJLEMwOaUzfFia7r2EwKdrN63Luq7ukOGwjEJnKbPXs22dnZ+3Rz3rJly36va9iwIbfddhv33HMPV1xxBR9++CEDBgygTZs2rFy5kvr16x+3Mh/NPlq3bk1ubi7FxcUkJCQccBun08nDDz/MY489Rv/+/csfd7vdZGZmMmvWLHr06FH++OzZs+nQocNB95mSksLVV1/N1VdfTbdu3bjnnnvKk+7TTjuNdu3a8e677/LZZ5/x+uuv7/f65cuXH7LbvyBUF7IkkWA3YwD5pSEArrh0IJFolEfuvJnvPv0Qq9XK6FdeLr+RGYjo5HuDJDti43v3VifZSbE/zIZ8H3ecUY8rL7+ckpUzuaVkBx+//zZpcVbW7ipDlSWaZ8ZV267L1ZlhGJQGIxT7Y5Pb/fDTT9x147XomkafCy/hnbffIqRL2M0ySXbzESfc/7XNBT5+WZ7DZz9MoeDn0USLdwKQ1qwzT77wCs0b/3szO6rr5HlCqJIBJuhcNwmHzXyiii4IgiBUUcec5U2YMIGioiJ69+5Nw4YNee6559i5c2dllO2kcln7Wsy8rxef39CJmff14rL2tY77PkOhELm5ueTm5rJq1Spuu+02vF5v+azeDRo0YOvWrXzxxRds2LCB1157jW+//bb89YFAgOHDhzN9+nS2bt3KrFmzmD9/fnmX7vvuu4/Zs2czfPhwFi9ezLp16/j+++/3m637WBzNPlq3bk1ycnL5uPSDGTZsGHFxcXz22Wf7PH7PPffw/PPPM3HiRNasWcMzzzzD4sWLueOOOw4Y55FHHuH7779n/fr1rFixgp9++mmfbu8A119/Pc899xyGYTBgwID9Yvz111/07dv3kOUVhOpClmJdxt2WWNdaTTe4YejVPPjcaAC+eH8c9z/4EBC7GWg3KxgG5HtD+81KrsgSTTPiSHJY0HS4achFgMTGGZN49IXXsZoUUlwWVueWsTrXU6kT9QmHtyfhLvJHMCkSv/81lxE3XI0WjdDrvAv58IMPiBgyFkUiyW45pt4I/4USf5h5W4r4auZK8r5+nGjxThRnIikXjuKdT7/cJ+Eu8YfZURwgxWmhY50kgGOe2E8QBEE4OR3zt9+FF17Id999x44dO7jpppv47LPPyM7O5vzzz2fSpElEo2JZlz0y4mx0rptERtx/MwnQr7/+SkZGBhkZGXTs2JH58+fz1Vdf0bNnTwD69+/PiBEjGD58OK1atWL27Nk8/PDD5a9XFIXCwkKuvvpqWrRowWWXXcY555zD448/DkCLFi2YMWMGa9eupVu3brRu3ZpHHnmEzMzMSjuGo9mHoigMHTqUTz/99JCxTSYTTz75JMFgcJ/Hb7/9dkaOHMldd91Fy5Yt+eOPP/juu+/26aq/N7PZzP3330+LFi3o3r07iqLst/zX5ZdfjqqqXH755fuNA96xYwezZ89m6NChhyyvIFQne1q8AQJRDd0wGH7zjdzzxPMAjH/jFR59/Iny7WPDZ2KJtycY2Sd5dlhUmtdwg2EwuF8f+l51GwBT33+Gz3/8HbtZJcFuZsVOD+vzvCLx/o8YhkFp4N+Ee8q0adw3YjjRSJiufc9j/EcfETEkFFkiyWE56nH3/xVdN1iXV8b2ogBa0IuakIEppTaZ14/D3rgr+WVhILYm/bYiP5pu0CYrnk51k0hxVc64REEQBOHkVGkTqaWkpDBy5EhGjhzJ66+/zj333FO+hNJNN93EqFGjjmm8qnBk3nnnHT7++OPDdoN/4YUXeOGFF/Z57M477wRiyeTnn3+OYRgEg0GsVut+8dq3b8///d//HTT+5s2b9/l77yWZsrOzyycP2yM+Pn6/C+bD7eNARowYQbNmzdiyZUv5pHH/WxaIJcOXX375Po/Jssyjjz7Ko48+iq7r5Obmkp6eXv587dq19ynjQw89xEMPPXTI8hQUFBAMBrnuuuv2e+61117jmmuuoWbNmkdyiIJQ5e3pRuw0m/CHNexmhbvuvJ1AMMgbzzzK2y8/i81mY9S99wBgVRVCUZ0CXxjdMIizmso/H9LdVuqlOqG4lAfuH8X6VcvZOH8abz50K43q/0qbJnXRDYOlO0pRFZk6ySfHXCNV1Z4lsYoCEcyKxPQZf3LLlYOIhEJ06tWXCZ9+CnJseckkhxnbASbKq2pyPUG2FPpxWBRMiTVIv+pldF8pstWBLEGK20J+WYhgRKNWop3G6S7id99YqsxlagRBEISTT6Xddt61axcvvPACTZs2ZdSoUQwcOJCpU6fy8ssvM2nSJC688MLK2pUgHFZ6ejrvv/8+W7duPaHliEQi5Obm8tBDD9GpUyfatGmz3zapqak8+eSTJ6B0gvDfSHSYcVhU/OFYi/dDo+7l+hGjABjz1CO8NOY1iv1hglENiypjUiQK/RFKAhH03Te4JEmiXnJspuh8b5hx77yHK6MumreIe2+6ivxSL26bCZtZYfG2YrYVnbiVJk52exLu4kAEiyIxa9Zsbrj8YkLBAG269ODTzz9HUU1EdYMku3m/cfpVUSiqsSa3DEmCn5flACBJMoozAVmCqzpnEwjF3p+d6ibRvnZiecItCIIgCIdzzN+EkyZN4sMPP+S3336jadOm3HLLLQwZMoT4+Pjybbp06bLfGFdBON6qwo2eWbNm0atXLxo2bMjXX399wG3uuuuu/7hUgvDfUmWJZLsJwzDwhzUcZoUnHn2YUDDIJ+PG8NIj91EcMjjj4sE0TnWS6bYioVPkj7V4J9hjy0vt6Z5sN6uEkXjjw8+4dkAfArs28fTHk3nplotJsJsp9IZYtK0YVZH+s+E8p4q9E26zIjH377+5dtBFhAJ+WnfuzrgxL2E2WwhFdRLtZlyWqp9wA2wt9LOrLMjPP3zP31NnktrtMh69qC3+sIYqQZzdTL1kBw3SXDiqyTEJgiAIVccxf3MMHTqUQYMGMWvWLNq3b3/AbTIzM/eZIVsQThU9e/YU40sFAVAVmWSHhQJC+CI6DpPM408+QYHHy+RP3+P9p0exec1yrrzrURIbZWBVFSSgJBDBABL3alVsku7mn20lZNWuw0Oj3+OjhYXssmYxfvZmru9WlySnhV2eIIu3lmCpq5DoEC2SleHfLuVhLIrMggULueaSAQS8ZbTo0IWJX31NqHgXwYhOvMNMnM1U6UsOHg+eYIS1u7wYkRATX3+KSGkeretlUDupO9uK/KTHWWmS7ibNbakWxyMIgiBUPcecdOfk5Bx2rLbNZuPRRx891l0JgiAI1ZhJkUlyWDC8scQ7HDUYMvIRrHYH3777KlO/nsD6ZYt4/f2P6dCiKaoiY5UkSgNRdAPiLbFxwTUTbBQHo6zNLaPPGb1JbVTCi7+tYerqPGol2ujbLINUl4UdxQGWbCuhQ51E0Tp5jMoTbn8YiyqzZMlShlzcD19ZKU3btOerSd9hd9gJFYPTopBgi/VOqOoMw2B9nhdvKMKY0S8TKc3DHJfCA/eMoMAbIt1tpXPdJKzVYEy6IAiCUHUd85juaDSKx+PZ76esrIxwOFwZZRQEQRBOEubdLd5WVQJDQpIkLrn5bu574xNc8YlsWbOCy87qwYTPJwKxruk2k4wnGKHQH/tOkWVpd8ujlV2eIK1rJTCofRbB7at45vr+/LVoFZIkkRlvY1dZkGU7Solo+ok87GpNNwyK9kq4V6xcyRUDzsdbWkKjFm345rsfcLucBCKxc5xgN1X5tbj32OUJsanAx66dO5j/3XgAhtzxEBarnYimUz/NKRJuQRAE4Zgdc9IdHx9PQkLCfj/x8fHYbDays7PLZ4EWBEEQBLMaS7xdVoW6ibGeUi279OSZz3+lSesOBHxe7r7xGm659VYCwSCKLOEwK+VreEc0HZtZoVmmG0WW8AQinN8ig+jsjwjnbeLR24aybMsuVuV6sKgKmwt8rMrxoOtiqMeRimix2eRLdifcq1evZlD/c/EUF1K/WQu++eFH4uPi8Ef02I0UQJWr9tJge0Q0nTW7Ymu7v/DkwxjREEn1W3HdVVeQXxakRpyNTDEngCAIglAJjrm/3fjx43nwwQe55ppr6NChAwDz5s3jo48+4qGHHiI/P5+XXnoJi8XCAw88cMwFPp40Tdtv2Q9N0zAMo/wHqNQxuoZhIEnSPvErI+bev6tavOoWU9RP1Y5ZmfVTkTLu2deBPi8OZM82lbmkUHWKqarqAc+VKkGiVUXXzbjNCoYEtlrx9Pu//+PBhx/h07deY9InH7BkwXzeHf8JjRs2wKZKhOD/2bvvuKrKP4Djnzu4Ay57CspwIA5cuVBx5aw0Ncu9SjPTzFmm5Sy1cuVILWdlajbMpqaJSVqONAcKpiKiIMq+wN339wdxfyJD1GuCPu/XixfcM77nOee593C/5zznebih1eGpAU8nB2r6OHHiSiZqBxXLP17H4O4d0SX9w/gxL+PVbRJSqYQhzQNxkIFaLqGqt+Y/2e/yHrO0uoH893me0UxGnhG9yYLaQcbZuDj6dH+CjNTrhNSszVfffoeHmxt5BhMyiQQ3pYwMO5axoJw3/7ZnzMupOaRk5rE/ej9Xju4BiZQ3Zs9FZzAil0BVL0ewWijLpv/r+rnbmDf/flRi/tfntnuJefPvRyWmqJ/yfyzvR8yHqX7Kuk2J9R6/qT7++OOMHDmS5557rtD0L774gtWrV7Nnzx4+/fRT3nnnHc6ePXsvm7K7FStWsGLFCsxmM3FxcRw8eBCNpvAXMovFYhtTWqlUPqCSCoJQXuj1ei5duoREIkFaQe7oVUQ/7/ud2W++QXZGOmqNM9Nmvc3Tndrfdr2vdv/OzAkvg9WCW9uhuDbvjVQC63oE4OUonuu+G0arhH8uJzPqhcGkJl+lSrVQNqxdg4+n+4Mu2j0zmK206d4XbUIM9dp1Z9PSdx50kQRBEIQKRKvVEhERQWZmJi4uLiUud89Jt1qt5sSJE9SoUaPQ9HPnzlG/fn1yc3O5ePEiderUITe3fI6bmpWVhaurK2lpaUUOlk6n49KlS4SEhKBUKtHr9SiV9uvB1GKxkJKSgo+Pj92+wFutVruWsyDewYMHefzxx0lLSys0JFx5KOPdxoyPj6dq1ar89ddfNGjQoMh8e9fPW2+9xbVr11i6dKnd66dWrVq8+uqrjBs37p5jTpkyhaysLFasWPFA6+d27F0/ZSmjTqfj4sWLBAUFoVKpbhuz4KJeaGgoMpl9ng2tKDH1ej2LFy9m/PjxpV60zDOYSc01YDRbUDlIkf1blxfiLzFi6GDOHD8CwDODhzNl7CgqhdRAZwaFTIKnowKDxcKfF1IxmOFalo7X5rxP+i+rAfDsOhZN/U689UQYXs5KVHIpTYLdcVUr7tt+V4SYJdWN3mgmXWck12BGJZdyPcfIgVPnmD28N9evXiYgpDrbf9hJgL8fRrMFo9mKl5MCjVJeIfYb8vuiOXfuHGfzNBxOyGDTnmPk/rGFNcsWonH1wGCx0rKaJy4qhwdWzrJ+du5ERakfe8d8kOe2O1ERjuX9iCnqp3wfS1E/t4+ZlZWFh4fHbZPue77sX6VKFdauXcv8+fMLTV+7di1VqlQBIDU1FXf38n9FXCaTFTmoMpkMiURi+wEK/X2vCprG2jPmqlWreO2110hLS8PBIf9Lg1arxd3dnZYtWxIVFWVbNioqinbt2vHPP/9QrVq125a14PfdlnXfvn3MmjWL48ePo9PpCAgIoEWLFnz88ccoFAo2bNjAuHHjyMjIuKv4d1q+wMBAkpKS8PLyKnYde9ZPcnIyS5cu5cSJE7bYw4YNY+PGjcybN48pU6bYlt2+fTs9e/a8oybThw4dQqPR2OV9NGnSJKpVq2b7bU/l/fNTELekeAXzijtflOZOl38YYspkMkwm021jatQyFAo5WToj2ToTRosVlYOU6lVD+Hn3HqZMncbmj5bz1Sdr+PvoIdZs/IyaNaqTZ7SQqjPh5aSkdoA7hy6m4uqowOWxpzBnXSfrz69J/Xk5cldffFwb4Omk5EpGHievZtM02BO1QlaorOX5WNo75q11Y7FaydIZydQZMVtAo3LAYLbww95olk8bw/Wrl/GtEszUVZvx9vPFAhgt4OGkxOWWocHK834DXNfqAbAiYfvxJOSuPkyZ9wG+fl4kpOZSr7Ir7k63v6B2P8tZ1s/O3cZ+FGM+iHPb3cZ+FGOK+imfx/J+xHzY6qes27vnW0MLFixg8eLF1K9fn+HDhzN8+HAaNGjAkiVLWLhwIQCHDx+mT58+97opoYzatWuHVqvlyJEjtmn79+/Hz8+PP//8E51OZ5u+d+9eAgMD7Z5YFScmJoYuXbrQuHFj9u3bx+HDh1m6dCkKhcKuz2CUlcFgQCaT4efnh1x+/5udrlmzhhYtWhAUFFRoukql4t133yU9Pf2e4nt7e992+L6y8vLyokOHDqxcudIu8QThdhQyKZ6OCnxdVKgcpOQazOiMZhQODixe8D7LP9mCk4sr/5w+wZPtItn65deoHaSYzFaua/W4OzpQ3VuDyWxleKuqeLQbhqZ+Z9TVm6KsXIvf/0lFIpFQyVVNcqaOk1cyMIkezQEwmC3c0BpIzTEgJb/TuqysbMa9+iozhj5NSmIC3gGBvLl6K+7efuTozeQZLbiqHXCrIGNxFzCZLZxPyQbgs1+PojdZCPXV0Kq6F+k5BtwdFYR4OT3gUgqCIAgPm3tOurt3705sbCxPPPEEaWlppKWl0bVrV86ePctTTz0FwKhRo1i0aNE9F1Yom5o1a+Ln51fkjvbTTz9NSEgIf/zxR6Hp7dq1A+DTTz+lcePGODs74+fnR//+/UlJSSlxO7m5uXTt2pWWLVva7kyvWbOGWrVqoVKpCAsL48MPP7Qtv2vXLvz8/HjvvfeoW7cuVatWpUuXLnz88ceo1WqioqIYNmwYmZmZtruJM2fOBPKbokyaNImAgACcnJxo1qxZof0DiI6OpkOHDjg6OlKlShXGjh1LTk6ObX5wcDBz5sxh8ODBuLi48OKLLxIfH49EIuH48eO24yGRSNizZw+NGzdGo9HY3uM3e/vtt/Hx8cHZ2Znhw4czZcqUYpun32zLli1069atyPQOHTrg5+fHvHnzSl3/q6++ok6dOiiVSoKDg20XtQqEhISwZMkSIL+Z9MyZMwkMDESpVOLv78/YsWMBmD17NnXr1i0Sv0GDBrz11lu210888QRbt24ttUyCYE8SiQRHBxm+zip8NEqkUgk5BjNGs4XePZ7m530HCA1vSE52JuNeGMir4ycgtZqwWK3cyDFQ2cMRfzcVYX7OLOvXiEUfLGPEzKVI5Aq2HL7MnrPXkEkl+LmquZiay9nkbLt27FcRafUmrmXryNYbUTvIUMilfLV9B62aNGT7p2uxWq1EPtWbOZ98h6efPwASwEXpgIejokIl3ACJ6XlczdDx86EYdkx9jhvfvsfAJgFYrJBjMFPDV4OjQjz7LwiCINjXPSXdRqORxx9/HKPRyLx58/j666/5+uuvmTdvHsHBwXYq4sMj15D/5SbXYLrv22rTpk2hpHTv3r20bduWNm3asHfvXgDy8vL4888/bUm30Whkzpw5/P3332zfvp34+HiGDh1abPyMjAw6duyIxWLhl19+wc3NjU2bNjF9+nTeeecdzpw5w9y5c3nrrbfYuHEjAH5+fiQlJfHbb78VG7NFixYsWbIEFxcXkpKSSEpKYtKkSQCMGTOGgwcPsmXLFk6cOMGzzz5Lly5dOHfuHADnz5+na9eu9OjRg7///putW7cSHR3NmDFjCm1jwYIF1K9fn2PHjhVKMG81bdo0Fi5cyKFDh5DL5QwfPtw2b9OmTbzzzju8++67HD16lMDAwNveEU5LSyMmJobGjRsXmSeTyZg7dy7Lli0jMTGx2PWPHj3Kc889R9++fTl58iQzZ87krbfeYsOGDcUu/9VXX7F48WJWr17NuXPn2L59O+Hh4QA8//zznDlzhsOHD9uWP3bsGCdOnGDYsGG2aY0bNyYxMZH4+PhS900Q7E0qkeCscsDPWYWb2gGTxUqOwUxIUBCbN6ylzwujAPhi3So6P96exMsJSIAsvYlgLyccZBJkUgl1q3jQt3lVnm7gj9VqZcHcOXy9KxqFXIqnk4KzyVkkpOU92J19QEz/Dp92PUeP2WLFSSHjatJV+vTpw+jBfbiRfBXfykEs3LiNUbMX4+LuAUCIuxo3Rwc8nBRIK1jCnWswEXstG4Vcytz574HFjKcKalRy54ZWj49GSRV3+7QWEgRBEISb3dPlXAcHB9vzqY8Kq9WKyWy546v7F9NyOJKYaXvduLIrIR5OWCwWzP/GlN7mhotMWvbnVlu3bs1rr72GyWQiLy+PY8eO0aZNG4xGI6tWrQLg4MGD6PV6W9L9/PPP29avWrUqS5cupUmTJmi12kLNr5OTk+nTpw81atTg888/R6HI75BoxowZLFy4kF69egH5d15jYmJYvXo1Q4YM4dlnn2Xnzp20adMGPz8/mjRpQseOHRkyZAguLi4oFApcXV2RSCT4+fnZtpeQkMD69etJSEjA3z//TsukSZP4+eefWb9+PXPnzmXevHn079+fMWPGoFKpCA0NZenSpbRp04aVK1faOrxq3749EydOtMUuKaF85513aNOmDRaLhdGjRzN48GB0Oh0qlYply5bxwgsv2BLU6dOns2vXLrRabYn1kZCQgNVqtZX/Vj179qRBgwbMmDGDtWvXFpm/aNEiHn/8cduFgtDQUGJiYnj//fcZMmRIsdvz8/OjQ4cOODg4EBgYaBvSr3LlynTu3Jn169fTpEkTANavX0+bNm2oWrWqLUalSpUAuHTpkriIJjwQDjIpnk5KnBRyMnVGtDojCoUDixa8T8tWrXhj7MvEnviLLq0jmLd0FU9374bZAiGeTsQkZ+OokKOQS+nTuAoHv9lIwu9bWHzsZ5w/2kbHiIboTRZOJ2USYt/Hv8q9PKOZ69n5jxkpZRIUUvhw1UcsnDOdXG0WUpmMvi+8zKyZM3DWOKEzmck1mMEqwUUtx8tJiVxasRJugPMpOaTnGPh2x7eknzuKRCZn5tx3MZgsmMwWavhqUMjFiASCIAiC/d1zG6qBAwcW25Haw8pshW9PJt1znCOJmYWScFKSb7vOs/X8kcvKnnTn5ORw+PBh0tPTCQ0NxdvbmzZt2jBs2DB0Oh1RUVFUrVqVwMBAIP9u6syZM/n7779JT0/HYsl/3jEhIaFQMtaxY0eaNm3K1q1bbZ0H5OTkcP78eV544QVGjBhhW9ZkMuHq6grk39Fdv349b7/9Nnv27OHAgQPMmzeP9957j0OHDtmSvFudPHkSs9lMaGhooel6vR5PT08A/v77b06cOMHnn39um2+1WrFYLFy8eJFatWoBFHunuTj16tWz/e3r6wtASkoKgYGBxMbG8vLLLxdavmnTpvz6668lxsvLy7+bVlpv1++++y7t27e33d2/2ZkzZ3j66acLTWvZsiVLliwp9nn4Z599liVLltia8D/xxBN069bNdvFkxIgRPP/88yxatAipVMrnn3/O4sWLC8VQq9UA5XbUAeHRofq32bNaJiGB/GbAPZ7uQcMGDXhh8EDiTh5j7NB+HBg+mjlvv42j2oEANxWJGXlUcXdEJpUw5/Wx9D3wExmX45jzcn9cPtlOs/CaXMvMBRlk6Yy4Oz3c2bfFaiUrz0iGzoj+3+fZY2PjeG3COE4eyX/sqHqd+ixYuoLmTR6zraeSy7BYQC6T4OWkwEFW8RLTG1o9F25okVpNfP1h/qM8LXsMISy0BlfSc/F3U1PJVf2ASykIgiA8rO456TaZTKxbt47du3fz2GOP4eRUuAMS8Sz3g1GtWjUqV67M3r17SU9Pp02bNgD4+/tTpUoVDhw4wN69e2nfPn/c25ycHDp37kznzp3ZtGkT3t7eJCQk0LlzZwwGQ6HYTz75JF999RUxMTG2JssFd3k//vhjmjVrVmj5W3v1CwgIYNCgQTz77LPMnTuXmjVrsmrVKmbNmlXsvmi1WmQyGUePHi0Sq2Bcda1Wy4svvsjIkSOLDPdUcFEBKPL+LElBr+83K7gIcTe8vLwASE9Pt/19q9atW9O5c2feeOONEpv1l1WVKlWIjY1l9+7d/PLLL7z88su8//777Nu3DwcHB7p164ZSqeSbb75BoVBgNBrp3bt3oRhpaWlAfgdtgvCgSSUSnJT5/7I8HB3QGi1UqhLEj7t/ZeqUKXyxfjVb1qzg+JE/Wb3+E1x9KuGqN3E5PZcANxUurq5s+OIbBjzdlZyUBF5/oQ/LP99BnaoBoINTVzJpEuKAyuHhTLwNJgvpeQa0ehMKuRSZxcSfJ87w9jtzMRkNKNWOjJo0jYnjx+JwU8sms8WKzmhBJgNPRyVKecU7PmaLlbhr2RjMFt5ftBhDehIKZw/emjaVXIMJmVRCdR8Nsgp4914QBEGoGO456T516hSNGjUCIC4urtC8u+1gZcWKFbz//vskJydTv359li1bZmsaW5yMjAymTZvG119/TVpaGkFBQSxZsoQnnnjirrZfGpkEeodXuqN9yzWY+eHstULTJMATYb6o5BKupVzD18f3tuMM3+kXgnbt2hEVFUV6ejqTJ0+2TW/dujU//fQThw4dYtSo/Gcjz549S2pqKvPnz7cN9XZz7+c3mz9/PhqNhscff5yoqChq166Nr68v/v7+XLhwgQEDBpS5jO7u7lSqVMnW4VlxPZk3bNgQs9lMSkoKkZGRxcZp1KgRZ86coVq1aqhUqvvauU/NmjU5fPgwgwcPtk27+fno4lSrVg0XFxdiYmKKjGl/s/nz59OgQQNq1qxZaHqtWrX4/fffC037/fffbWMHGo3GIrHUajXdunWjW7dujB49mrCwME6ePEmjRo2Qy+UMGTKE9evXo1Ao6Nu3r+3OdoGYmBgcHByoU6dOqfsmCP81N7UCjUqS3+RcD/PfX0iLVq2Y+upozh4/wpNtWzBq9hIaRj4OcilXMnX4Oavw8vZh7dbtDO3VFV1qIq8OeZY1X3xLPXe4mqHj1JVMGlRxQ14B7+SWxGq1ojWYSc81YLRYcVTI2B/9O5PGjuby+fz/2Y0j27N46XJqVAuxrWexWskzmgEJTkoZLioH1BX0gsSVjDwS0/NITkri6Pb8x3deePlVnF2cuZyuo7qPBm+NfcaKFQRBEITi3HPSXdApl71s3bqVCRMmsGrVKpo1a8aSJUvo3LkzsbGx+Pj4FFneYDDQsWNHfHx8+PLLLwkICODSpUu4ubnZtVwFJBIJcpn0jpI6F7WUpoHuHE5Ix0p+wt0k0B0XtQMWiwXZvzFvl3TfqbZt2zJmzBiMRqPtTjfkd7I2ZswYDAaD7XnuwMBAFAoFy5Yt46WXXuLUqVPMmTOnxNgLFizAbDbTvn17oqKiCAsLY9asWYwdOxZXV1e6dOmCXq/nyJEjpKenM2HCBFavXs3x48fp2bMnVatWJTMzk61bt3L69GmWLVsG5PcwrtVq2bNnD/Xr18fR0ZHQ0FAGDBjA4MGDWbhwIQ0bNuT69evs2bOHevXq8eSTT/L666/TvHlzxo8fz8iRI9FoNMTExPDLL7+wfPlyux7XV155hREjRtC4cWNatGjB1q1bOXHiRKEm+LeSSqV06NCB6OjoIs3EbxYeHs6AAQNYunRpoekTJ06kSZMmzJkzhz59+nDw4EGWL19eqHf4m23YsAGz2UyzZs1wdHTks88+Q61WFxqubPjw4bZm97cm9AXTIiMjiyTjglAeKOVSvJ0UOClkZOqMPNm9B+H16jNy6CD+Of037786lDbdn6Pb0JfxD65GcpYOdycFPv4BLP3kS0b360Ze8nleGtyP77ZsxM9NyfnrWhwVMmpVcqlwvXIXx2S2kKEzkqUzIZOAQZvF5GnT2P75BqxWKy7unrw2ay7PDx5o+/9jsVrRm/L7GnF0yE+2HR1kFfZ45BnMxCZnoZRL2LT7MFKFGreAqrzUvwfJeUbUChnVvDUVdv8EQRCEisFuWd4///zDzp07bc+u3u0wLIsWLWLEiBEMGzaM2rVrs2rVKhwdHVm3bl2xy69bt460tDS2b99Oy5YtCQ4Opk2bNtSvX/+u9+V+qObpRPc6frSv7kX3On5U87z/44C2a9eOvLw8qlevbnsuGfKT7uzsbGrWrGl7jtrb25sNGzawbds2ateuzfz581mwYEGp8RcvXsxzzz1H+/btiYuLY/jw4axZs4b169cTHh5OmzZt2LBhAyEh+XdPmjZtilar5aWXXqJu3bp07tyZP//8k+3bt9suCrRo0YKXXnqJPn364O3tzXvvvQfkd/Q1ePBgJk6cSM2aNenRoweHDx+2NR2vV68eUVFR/PPPP7Ru3ZqGDRsyffr0EjsuuxcDBgzgjTfeYNKkSTRq1IiLFy8ydOjQUp/Xhvwkd8uWLbdtpj579uwiyzRq1IgvvviCLVu2ULduXaZPn87s2bNLbIbu5ubGxx9/TMuWLalXrx67d+/mu+++sz0DD1CjRg1atGhBWFhYkUcCAL788stCvbYLQnkjkUhwUsjx1ajwdlJQvVo1Ptn+M5375ndyuG/HF0x+ph2LJ44gMe4E6TkGdCYLQdVr8M7HW5E7uaKs14VZ+65jsljxcFJyJjmby+kVv0fzXIOZa1o9GXlGlHIpO77dTqumjfhm03qsViude/Vj9JgxDO7XB6lUitVqRWfM7zBNIZPiq1Hh66zCSSGv0AnphRtabmgNxKVoueYYTMjLHzN/+UdIpVIy8oxU83LCVV30cSJBEARBsKd7vtOdmprKc889x969e5FIJJw7d46qVavywgsv4O7uXmQs4dIYDAaOHj3KG2+8YZtWcIfw4MGDxa6zY8cOIiIiGD16NN9++y3e3t7079+f119/vcjzvwX0ej16vd72Oisrq9jpBWUq6JDLYrHY/r6bLyEquRSVPL+n74KkquDiREFce7BarVitVgIDA21NtW+OXaVKlWKn9+nThz59+hSKZTabsVqt6PV6WrduXWS9JUuW2MaGtlgs9O3bl759+xYpk8VioX79+rbhwwpiFjx/fXM5VqxYwYoVKwqtK5PJmDFjBjNmzCg2NsBjjz3Gjh07ijzTXTD/woULRfb51mN06z5arVbq1q2LyWQqVM5p06Yxbdo0W5xOnTpRrVq1UuuwU6dO+Pv7s2XLFnr27InFYrFdTLq1TAUXr26e3rNnT3r27Flk3wvq+/z587Yydu/ene7du5d4rCC/Dq5evcqoUaOKlPvHH39EKpXSq1cvu78v7/bzU1LMgt/2KGdZylhwzA0GQ5n2w2w2YzQa0ev1JZ6T7lRFiVlwPr31vHq3SiujSgoypQSzi4ohr80movPTfLdhBUf3/cLhvT9zeO/P1H6sOU8MGEHrto9Tt04tPvgmipW/J3Mh3ch7P8cyuUN15FYTxy6mILN44OV8d02OH1T9WK1WDGYruQYTmbr8R05SryXx+muvEf3LjwD4BQYz+93FtG/dgs8+Xoler0NvMmO05Pdk7qpywFEhQYYZo6FoJ433WsY7dS8x03MMxF1NQyGFz/9IAODpx4KpGeKLUZeCk9xKJWe5Xd6f9t53e392oPzVz38VsyKc26BiHMv7EVPUT/k+lqJ+bh+zrPshsd7tLel/DR48mJSUFNasWUOtWrX4+++/qVq1Kjt37mTChAmcPn26zLGuXr1KQEAABw4cICIiwjb9tddeY9++ffz5559F1gkLCyM+Pp4BAwbw8ssv888///Dyyy8zduzYYhM0gJkzZxbbadeUKVOK3K10cnKiZcuWBAQEFBo2S3h05eXl8cknn9C2bVtkMhnbt29n8eLFbN68mdatW5e67qlTpzh79myRTsv+a6mpqXz77bfMmzePw4cPF3kc4/vvv8ff39/WX4PwfyaTiStXrvD777/b+iIQyheXwFB867VEIpVy+fxZvv14CX/+uguzKT8RrVItlNbtO1C1kjcZOPKToSZ52RnITnzHkHbhVORHuuUqRxycXNFnp3P8VAw/ffs1eTlaZHI5Hbo/S9PwmpRxEIyHxncnkonXq/ELj6Cn8gxyyT197REEQRAEG51Ox/z588nMzMTFxaXE5e456fbz82Pnzp3Ur18fZ2dnW9J94cIF6tWrV+rYxbe6m6Q7NDQUnU7HxYsXbVchFi1axPvvv09SUvFDexV3p7tKlSqkpKQUOVh6vZ7ExESCg4NRKpWF7s7ag9Vq5dq1a/j6+to1pj3Lae94FS3mrfWTl5fH008/zbFjx9DpdNSsWZOpU6faxif/r8t5N/FkMhleXl4sXryY/v373/cy3s+Y9vz8lKWMOp2O+Ph4KleujFJ5+zuhZrOZ2NhYatasaderrxUhpl6vZ/HixYwfP75Mx+p27qSMOQYTqblGzP+2TEi6cpXVq1awY8tn6HLzL5Z4VapMj8HDeaxJc8aOGIYxPYnQjv14/725ZOWaqeSqpFGgO46KO7vger/rRyKVojdZ0BvN5BrNGMwWLFYr6TozlzINJF6IY82c14n7O78zzBp1GzJv0RKaNsx/7MpotqDN0/HNxo8YNnI0Hi5OdhkGrDy9L6+k53LoYhraXC0vPd0Oc046/Sa8zagRz3M9O48QeSYh1WqgVtqnabm9993enx0oX/XzX8asCOc2qBjH8n7EFPVTvo+lqJ/bx8zKysLHx+e2Sfc937rNycnB0dGxyPS0tLQ7PpBeXl7IZDKuXSvc0/e1a9fw8/Mrdp1KlSrh4OBQ6GDUqlWL5ORkDAYDCoWiyDpKpbLYshU33Wq1IpFIkErzOzor+NteSUNBk9iCuPZwc5ntlYjYe78rSszi6sfJyYndu3eXm3LeTbzbXWuryPVzL8pSxoJ5CoWizEm3g4MDSqXSrv8IKkLMAiWdc+/UnZRRqVTi4Zw/XFSOwYSzo5q57y5kytQ3Wb1yJZvXreZGUiJr3p3JVjd3atd9jL+jk4j7ZTOz33Fjzoy3SMk1ciI5h9qVXNEo5ajkZXvv3o9jaTSZcHBwIM8iQWe0YDBZsVglyOUOOCulZGZns+PHnRyL/pU9X23CbDKicnRi5KRpTHz1FRQOckwWCzqTFZkDeP/bE7mvu7Ndv/SUh/elzmjmYroBlVrF9BnTMeek4+RdmRdfGIpFKscqkePg4ICzk7rcf37s9dmB8lM//3XMinBug4pxLO9HTFE/5ftYivq5fcyy7sM9f0uNjIzkk08+sb0ueKb0vffes/WMXVYKhYLHHnuMPXv22KZZLBb27NlT6M73zVq2bMk///xT6HnOuLg4KlWqVGzCLQiCIDw6ZFIJLioH/FzUeDo54OnhweQpb3Dg7zOMfms+PgGBZGek83f0bmTy/LueR79cyfuLl+Lm6MDVTB1xKdkkZeaRnK0jM8+IVm/Kv8NssmC2WO+649DbMVus5BrMpOYaSM7SAZCeZ8JksaKQQcyJ4yx4/z2e7NKRelWr8O4rg9m1dQNmk5GGkR14/8tfeenl0chkMnL0ZgwmKy5KOZVcVHg4PbxDZF1KzeG6Vs+xk2c4t+cLAF6ZOgelSkVKlo5KbqV3eikIgiAI9nbPd7rfe+89Hn/8cY4cOYLBYOC1117j9OnTpKWlFTsM0e1MmDCBIUOG0LhxY5o2bcqSJUvIyclh2LD83mgHDx5MQEAA8+bNA2DUqFEsX76cV199lVdeeYVz584xd+5cxo4de6+7JgiCIDwk5FIJbmoFTgo5Wr2JLJmEsa+8TK9+A4n+fhsb1q/j4pmTtuX3rpuPzJzHq5NeIylDh0YpRyqVkGs0UDD2o1QCMokEqUSCQi7FQSpBLpUgseZfBDaZLXfcssNssaIzmcn7tydxoyU/oZdjJeFKEn/+9Cv7o37lyIHfyM5IL7Sul18AdZtH0qR9Vxq0bPfvdiXoTBY0SjnOqv/frdebTfd8TMujzDwj51K0OCnlLJ37FlhMBNZvQbduT5FrMCGXSajqreHG5ZQHXVRBEAThEXLPSXfdunWJi4tj+fLlODs7o9Vq6dWrF6NHj7YNR3Un+vTpw/Xr15k+fTrJyck0aNCAn3/+2TbkVUJCQqFmpFWqVGHnzp2MHz+eevXqERAQwKuvvsrrr79+r7smCIIgPGQcZFLcHfOT7yy9EamnM726dqBG2x78c/wPtq39kJN/7gdg98YPOPfnr7wwcTrSRs0I8tbgpVGgksvye7m35o9rbbFayNFbsFrBKrFiNecn3UnZOmRSGQ4yKQ4yCQ4yKTKpBJlEkv9bmp+wmywW9CbLTYm2BYlVQnZ2Jvui9hL166/8uT+Kq5cuFNoXtZOGek1b0rJtOzp06IhPlWDibuTa5od4OOKqluOqUqB2sN9jHeWVxWIl7loWuXozX+34noyzf4BUxsy57yKRSLiRraeGrzNeTgpuPOjCCoIgCI8Uu3TH7erqWmj4pHs1ZswYxowZU+y8qKioItMiIiL4448/7LZ9QRAE4eGmkEvxkitxlEu4kAIBripo0IL5a1pzLuYEs8YOJ/3aFS6dPcn0Ec9SPbwRTw56kcpVQ6kd6EN1fx+cNcV3QGY2m9GRfxfcCuhMZnKNVgpaoUskEqQSMJqtGCwWFP8m30ajkcOHDhH1624O/hZF7MljWMz/H7ZLKpMRGt6QiNbtaPd4ByJbNEOtVGK1WjFbrZjMVhwVDuiMJtwcHfDRqFA7yJA+5Ml2gaQsHQlpuUgkFravzG8N1+rpgYSF1SIrz4ijUk41b81Df/FBEARBKH/sknRnZGRw6NAhUlJSioyVO3jwYHtsQhAEQRDsTinP7xylQWV3HOTZxKfmUq12PT7f/QcrvtrD5QM/EBO1g39O/sUHr71UaF2JVIraUYOjszNOGmccNc5o/v1bpVLh5uGFs4sLzs7OOLu44OrqgouLCy7OLpgc1KSbHcjNzuLUoWjOHNrPycMHycspPOKHf1BVmka2pXXb9jxWqxo16tRDIpHYkmytwQxWKzKpBIVMip/KAYVc/Ugl21arlcT0PE5eyUQmlfLNsau4d30F05EvmfbWW/k9u+caqBfgiqvaAbP5zsYfFwRBEIR7dc9J93fffceAAQPQarW4uLgUuoIskUhE0i0IgiCUe05KOU2CPFDKZMSmZKNROTDq2U58VbU2L01+g7Vz3+DimZPo83LJ1WZjMZuxWizkarPI1WbZrbmys5s7jSIiiWzXns4dO1G9ajAAJpOZjKvx5BjMSP5tnq6QSXFxkKGQS1HIpPnPkz8iiXYBo9lCXHI2Z69lo5BLSc8xEP3PDdSVazNnzBe4umpI1epxUysI9nJ60MUVBEEQHlH3nHRPnDiR559/nrlz5xY7dJggCIIgVARymZT6VdxQyCScTs7GSSWnTU1vju7bydGoXbh6ejPnk+/w8g/EkJtLdTcZxrwcMjIzyMzKIjsri8zMLLKyMklNuYbOYCJHm012ViY52uz8n+z837laLXk52chkcmo2aEJ480ie7NyJdi2bIpNKMVusGC1WcoyW/DvZkvy26e5qOSqFwyObZN8sW2fk1JVMLqXl4qlR4ugg491vjwJS2tb0oZq3BpPFglZvommwxx2Pty4IgiAI9nLP/4GuXLnC2LFjRcItPHKioqJo164d6enpuLm53fH6bdq04aWXXqJ///5AfsuQb775hh49eti3oHcgODiYcePG8eqrrwL5Y1Lbu0wRERFMnjyZZ555xm4xBcFeZFIJtf1dkUolnErKIi3bwEV5MAFVQ0k8H8usF56hyejFtGnWEC9XR0KDfKh7y/BbFouF1MsX8KxStdjx43UmMwfi07FarVgtFqT/jv/ZKMAVnRkwmZHL8ntEV8vz72TLsPJPEripFXYfK7UiSs7UcfJKBmk5Bvzd1GTlGfnoq90cmDMcz4hn6DPgfQBuZBvwdVFR2V18RxEEQRAenHsep7tz584cOXLEHmUR7OjFF1+0DVWjUCioXr06s2fPxmT6/zAxZrOZxYsXEx4ejkqlwt3dna5duxYZ6m3Dhg1l7ok+LCwMpVJJcnJykXlt27ZFIsm/M6NWq6lXrx7z5s0rMsbtN998Q/PmzXF1dcXZ2Zk6deowbty4Oz8I/7ENGzaUOfn+/vvvuXbtGn379r2/hbpHV69epWvXrnaNOW3aNKZMmVKk/wdBKC+kUglhfi6E+7vi5CDjZIoJTc+ZOPmFkHEjhT3vjSLlQgx5ZitnkrUkZuShNZjQ6vN/cvT559kcgxltwY++4Cd/nO0QD3V+h2r/JtDVPB1xVsnxUDvg56LC30VNJWeVraf14jpsexSZLVbOXcvmz4upZOtNVPZwJPrcDcZ8/hffrZ6L1ajDSZeCq6MCg8mCyWyhho8GhVwcP0EQBOHBuec73U8++SSTJ08mJiaG8PBwHBwcCs3v3r37vW5CuEtdunRh/fr16PV6fvzxR0aPHo2DgwNvvPEGVquVvn37snv3bt5//30ef/xxsrKyWLFiBW3btmXbtm13fHczOjqavLw8evfuzcaNG4sdtm3EiBHMnj0bnU7Hzp07GTNmDO7u7owaNQqAPXv20KdPH9555x26d++ORCIhJiaGX3755Y7KYjQai7wXy5OVK1cydOjQYu+ClSd+fn52b77atWtXRowYwU8//cSTTz5p19iCYC/5ibczUgnEpWj56q9E3PvMxfjFdAxJ51g+cTDKheuJaNGCy6k5uCrd8HdTA1bMZjOXUsBXo0AiLXxXuuASo4+TkhAPJ3INZpxVclxVDo98c/Hb0RnNnLqSyfnrWlwdFbiqHUjV6vl4/wWyj/+EPjEGiVyJpUl/UrV68gxmKrurqeSqftBFFwRBEB5x9/yNf8SIEVy+fJnZs2fz7LPP0qNHD9tPz5497VHG/4zZbC72x2q12n6AQq/t8SORSOweE0ChUODr60tgYCAvvfQSHTp0YMeOHVitVrZu3cqXX37Jxo0beeGFFwgODqZevXqsXr2a7t27M3z4cLRa7R3t99q1a+nXrx8DBw5k3bp1xZZJrVbbyjR48GDq1avHL7/8Yltmx44dtGzZkkmTJhEaGkqNGjV4+umnWb58eYnbvXjxIhKJhC1bttCpUyfUajWfffYZVquVjz/+mFq1aqFSqQgLC2PFihW29fR6vW08eZVKRVBQEHPnzi0U89ixY7b6SU9PRyKRsHfv3iLHZe/evQwbNozMzEzb3fwZM2YUW96UlBSioqJ46qmnihyfgjvLarWaqlWrsm3btkLLvPbaa4SGhuLo6EjVqlV58803MRgMtnIcP36cdu3a4ezsjIuLC4899hiHDx+2rb9//34iIyNRq9VUqVKFV155pcR6hv83L7/5mHz11Ve0a9cOR0dH6tevz4EDBwqVsbRtFMTs2rUrW7ZsKZefn7J+xks6XxT3U9r55W5/KkpMuVxe7stYUkyLxUI1L0dGtAzmrSfCmNStAWs2b8ejegMs+lwWjBvMiZhY5FIJp6+kk5yRi0IKSll+4qyUSVDLC/84Fvw4SPFydCDQTYW7So4UKxaL5T/dd3vXzf2sn1Stjj8vXOdCSja+zgpcVTKwWtgfk8CNn5aRtvNDAFwinkXq7E1CqhaFDEK8HMFa9LhWhM9PRaqf8h6zIpzbKsqxFPVTvmNWhDI+jPVTFvd8p7siNxFdsWIFK1assB2s2NhYNBpNoWUsFostQSug1+vJyckpMa5MJkOlUtlel7asVCrFzc3NljiVtKyT0533umqxWNDpdLbXCoUCnU6HTqfjs88+o0aNGnTs2LHQMpA/TvrXX3/NDz/8QPfu3W1N0m8+BrfKzs5m27Zt7Nu3j5o1a5KRkcGePXto2bJlofKYzWZ0Oh1Wq5UDBw5w9uxZqlataiuDl5cXp0+f5ujRo9SpU6dM+1lQrjfeeIN58+bRoEEDlEolGzZsYPr06SxatIgGDRpw/PhxxowZg0KhYODAgSxZsoQdO3bw6aefUqVKFRITE0lMTESn09liGo1GW/0UlLHg74I60+l0NGrUiPfff585c+Zw/PhxADQaTZFjC7B3715b0nzr/OnTpzN79mzeffddPv/8c/r160f16tUJCwsD8i9arF69mkqVKnHq1ClGjx6NWq1mwoQJ6PV6BgwYQP369dm/fz8ymYwTJ07Y3gcXLlyga9euzJgxgw8//JAbN24wfvx4Ro0axUcffQTkJ5smk6lQXRfsb8G0adOmMXfuXD744ANmzpxJv379OHXqFHK5vEzb0Ov1NGzYkIULFxZ7fO7UzZ8feyntva7X6zEajZw/f/6OWirExcXZo2gVLubTTz/NhQsX7BrzQex3E7f///3dZx/xzPNjyNVUZuOpPLxdE2kd7ERuSiZnUx5sOe/E/agbuD/7nXL5Ip6ApyNgysZssLL8p6NsfP8tjKmJgASX5s/gGvEsUgnUdsrFy8HAjcuZJfYsL+rn0YpZEc5tUDGO5f2IKeqn/Ma7HzEfpvrRarWlzi/wSHflOXr0aEaPHk1WVhaurq7UrFkTFxeXQsvodDouXbqEUqlEqVSi1+tRKpWldhz3xBNP8P3339tee3l5kZubW+yyrVu3ZsuWLfj4+CCVSgkMDOTGjaJfEe7k4sbNdxRVKhVWq5U9e/awe/duxowZg0ql4vz589SuXbvQxYEC9evXByA+Ph6VSoVcnv82USqVJTZ9/PTTT6lRowaNGjUCoG/fvnz22Wc8/vjjtmWkUikfffQRGzZswGAwYDQaUalUjBs3zlaO8ePH88cff9CkSROCgoJo3rw5HTt2ZMCAASiVymK3XTB93Lhx9OjRw1bOTp06sXDhQttz02FhYfzzzz+sX7+e4cOHc/XqVUJDQ2nfvj0SiYTQ0NAiMR0cHMjIyMDHx8dWRoVCgUqlQqFQAKBSqXBxccHT0xOJREJwcHCp9ZOUlISPjw9qtbrI8ezdu7etqf28efOIiorio48+4sMP8+/izJw507ZszZo1uXjxIlu3bmXChAkolUouX77M5MmTbXVYt25d2/KLFi2if//+TJo0yTZt2bJltG3blo8++giVSoVEIkEul9ve6zfvb8ExmTRpkq0Vy5w5c6hbty6JiYmEhYWVuo3Vq1cjkUhQKpUEBQWRmJiIQqG4pyb2FouFlJQU2+fnXhVcYCvtvQ7574ugoKBiPz+3MpvNxMXFERoaarcOsCpKTL1ez+LFixk/fnyJn9878SD322q1Ep+ay8krmaid3Pl821d8/PtlDlxI4/3fb5BqdaRZiCfZOhM1/ZwgI6lc14+96wbsX0aDycI/17KQZCWRYHHD/d8O6zJyDazYd4EjJ5Ixpl5B4eKJx5MTUAbVRyqBAU0DyVF5UD/EExdV0ceMKsLnpyLUT0WJWRHObVAxjuX9iCnqp3wfS1E/t4+ZlZVVpnh3nXQ/8cQTbN68GVdXVwDmz5/PSy+9ZOtIKjU1lcjISGJiYu52E/85mUxW5KDKZDJbc+GCL+FleeaurM/lFTSNvTn+vcS72Q8//ICzszNGoxGLxUL//v2ZNWuWLVbBdkvaVnH7XVI51q9fz8CBA23zBw0aRJs2bVi2bBnOzs625QYMGMC0adNIS0vjrbfeolWrVoXuhms0Gn744QfOnz/P3r17+eOPP5g0aRJLly7l4MGDxV7sKNhm48aNba9zc3M5f/48w4cP58UXX7QtazKZcHV1RSKRMGzYMDp27EhYWBhdunThqaeeolOnTkWOwa31U9JxKev7Iy8vz5bg3rpsixYtCk2LiIjg+PHjtmlbt25l6dKlnD9/Hq1Wi8lksl0okkgkTJgwgREjRvDZZ5/RoUMHnn32WapVqwbAiRMnOHHiBJ9//rktvtWa36Q1Pj6eWrVqFdqfm4/vzdPq169v+9vf3x+A69evU6tWrdtuIyQkBIlEgqOjIxaLBYPBgFp9989blvXzczdxS4pXMK+480Vp7nT5hyGmTCbDZDLZvZwPar+r+TgjkUo5fjkDq0LG6PY1cFRe5JeTicwdO4Q2nZ5g4quvcDY5hzAVWJGU2/q5X3VTEPteY2bpjJxKzCQxLYfajuQn3BIpJxLS+PC3i2TmGXGtWp92r7/P4N7dMCs0XMvS4e2iJEdnJsRLg7tT6RfFyuu+F8Qpz/VTEWNWhHNbQexHMaaon/J5LO9HzIetfsq6vbtOunfu3FmoCebcuXN57rnnbEm3yWQiNjb2bsOXe6U1Jbj14KekpJSwZL7MzEzb3/Hx8fdUrpu1a9eOlStXolAo8Pf3t92xBggNDeXMmTPFrlcw/eY7v6WJiYnhjz/+4NChQ4U6TzObzWzZsoURI0bYprm6ulK9enWsViufffYZ4eHhRERE0KFDh0Ixq1WrRrVq1Rg+fDjTpk0jNDSUrVu3MmzYsBLLcXMT/IL6+fjjj2nWrFmh5Qrqp1GjRly8eJGffvqJ3bt389xzz9GhQwe+/PJL213TglYDkN/U3B68vLxIT0+/4/UOHjzIgAEDmDVrFp07d8bV1ZUtW7awcOFC2zIzZ86kf//+/PDDD/z000/MmDGDLVu20LNnT7RaLSNHjmTs2LFFYgcGBpa5HDd3UFeQmBa0xChtG1WqVLEtl5aWhpOT0z0l3ILwX5NIJFT1ckICHLucAcDzLUO4GP0dCfHH2PnRMXK02cyc9jpY4HRSFnUD3EXP2XcoKTOPk4mZpOca8XdTgSEbs8XKkk++5NsVs/HpPZ2q1Wvw6uOhBLg3ta3nqVGSqtXj4aQgxEtTyhYEQRAE4b9110n3zclIca8fdnfyjHVpy1oslkJJ9908u13adqtXr17svL59+9K/f3++++47unXrVmjewoUL8fT0pGPHjmXaztq1a2ndujUrVqwoNH39+vWsXbu2UNJ9M41Gw9ixY5k0aZKtw7LiBAcH4+joWOqz8bfy9fXF39+fCxcuMGDAgBKXc3FxoU+fPvTp04fevXvTpUsX0tLS8Pb2BvKbghcMl1bwrHZJFApFmTpTaNiwIdeuXSM9PR0PD49C8/744w8GDx5c6HXDhg0BOHDgAEFBQUybNs02/9KlS0Xih4aGEhoayvjx4+nXrx/r16+nZ8+eNGrUiJiYmBLfE/ZQ2jasVqvtGe5Tp07Z9ksQKhKJREKIV/55uiDxnvP6WPLSkjn41cdEf76Uydps1s8Zxz/XtJitEB7ghspBjK19O2aLlfMp2ZxOykIikVDFQ40EK8lZebw4fQIX93wOWFGe+Jq3J31W5GKGyWIhR2+iSbAHaoU43oIgCEL58Ug/0/0o69u3L9u2bWPIkCFFhgzbsWMH27ZtK3QBwGw2F2rmDPnPPVevXp1PP/2U2bNnF3p+GGD48OEsWrSI06dPl9gp2siRI3n77bf56quv6N27NzNnziQ3N5cnnniCoKAgMjIyWLp0KUajscwXAQrMmjWLsWPH4urqSpcuXdDr9Rw5coT09HQmTJjAokWLqFSpEg0bNkQqlbJt2zb8/Pxwc3NDKpXSvHlz3nvvPebMmUNsbCxvvvlmqdsLDg5Gq9WyZ88e6tevj6OjY7HN4Rs2bIiXlxe///57kQse27Zto3HjxrRq1YpNmzZx6NAh1q5dC0CNGjVISEhgy5YtNGnShB9++IFvvvnGtm5eXh6vvfYavXv3JiQkhMTERA4fPswzzzwDwOuvv07z5s0ZM2YMw4cPx8nJyTYc2/Lly+/o2JaktG0sW7bMttz+/fttTfkFoaIpLvFe+N48pmuc2b1xEcd2rKd/dgbLli3nn5QcTGYrDaq4i0SwFLkGE6evZnL+eg7ujgpc1Pktanb/8TfzXnuZvCv5Hdk0e+I55r23oEjCbbFauZalx8dFRWX3kvtcEQRBEIQH4a7bvBX3zKMYX7TikEgkfPHFF0ydOpXFixdTs2ZNIiMjuXTpElFRUUXG6NZqtTRq1IiGDRvafrp168aOHTtITU0tdni4WrVqUatWLVvSWBwPDw8GDx7MzJkzsVgstGnThgsXLjB48GDCwsLo2rUrycnJ7Nq1i5o1a97RPg4fPpw1a9awfv16wsPDadOmDRs2bCAkJAQAZ2dn3nvvPRo3bkyTJk2Ij4/nxx9/tDUtX7duHSaTiS5dujBhwgTefvvtUrfXokULXnrpJfr06YO3tzfvvfdescvJZDIGDRpU6LnnArNmzWLLli3Uq1ePTz75hM2bN1O7dm0gf8z78ePHM2bMGBo0aMCBAwd46623CsVNTU1l8ODBhIaG8txzz9G1a1dmzZoFQL169di3bx9xcXFERkbSsGFDpk+fbnsu2x7Kso0rV65w4MCBUh8VEITyriDxbljFjVyDmfRcA7OnT6XHmOkAxOz9hhdGjMDLUUZCWi5HLqWh1ZsecKnLpxtaPX9eSOPC9Rz8XFW4qB0wWSzMXLyaGcO6kXclDrlaw6T3V7N42Yeo1IWTaq3exOXUXJyVcmpXchHN+QVBEIRy556alw8dOtTW65xOp+Oll16y3R0tbcgd4f4r6I26NHK5nEmTJhXqabo4Q4cOpW/fvrbOv25VWpPqmzvSi4qKKnaZVatW2f5u164d7dq1K7U8twoODraNnXzrEFT9+/enf//+xa43YsSIEpu+Q/5Fg+joaJKTk/Hz80MqlRZ6jKJt27ZFHqtYuXIlK1euvG2Zx4wZQ+PGjbl06RJBQUHA/x/RePnll0tc77333iuSzL/66qvodDoUCgWbN28udbtNmjRh165dJc4v6FOgoCwWi8VW5wXH+WZubm5FppW0jYLlli5dytChQ6lcuXKpZRWE8q7oHW8Dr40fi8bJic/ee4OEv/Yx/8v9vNG3LVcz8jBZrDQKdMdVXbRH7UeRxWIlIS2XU1cz0RstVHZ3RCqVcEOr581FH3Ni40wAKtUIZ8nq9VQJCi60vsFkISVLh4NMQm1/F6p5a3BSigZ8giAIQvlz1/+dhgwZUuj1wIEDiyxz87OpgiD8n5+fH2vWrCEhIcGWdD8qfHx8mDhx4oMuhiDYRXGJ98sjhuGhhG8S5JzXO7NgZxwTO4aSkq3j8MU0GgW54+GkeKDlftD0JjNnk7KIu6bFUSnH3z2/U8Ujl9JYte88Wp8GqKvUpnW79swf/wJ5zgG2dS1WK6laA3lGM1Xc1NTwdcbb2T7DzgiCIAjC/XDXSff69evtWQ5BeOT06NHjkXwkY+LEiY/kfgsPr5sT7+OXM8jAwohnn6BhpiPv7YrjbHI2b6z5jjf6tCE9V8Khi2k0CnLDx/n247w/jNJzDJy6mklieh4+LkocFXIMRhOzFq3ivGt9JDIHqvm6sujrH/F3d0Sed822brbOSFqOAQ9HBfUru1LZ3RGZVJxPBEEQhPJNPPgkCIIgCPeoIPFuUMUNvSF/aLyafs68+WRtHNIu8teH4xjauzvG3ExyDCYOX0wnKTPvAZf6v2WxWIm/kcOB8zdIztJR2UONo0JO7IUEenV/ir0fzSTjt0/pWtePWd3r4O/x/2G/jCYLiem56Axm6vq7ElnDmyBPJ5FwC4IgCBWCSLoFQRAEwQ4kEglVvTWEB7gCkJFnIMTLiRGtqyGVydEmxjL8uW5kpqZgtFg4HJ/G5fTcB1zq/4bOaOZ4YgZHLqVhBdQOMmKTs1n12ZcM79GetLgjSORKurZowOCIYOSy/K8nFkt+XxDXtXoqu6lpWd2LugGuoid4QRAEoUIRPY4IgiAIgh0Fezlx5jroDRbSMRAZ0ZQln37LhGG9ybsWz6i+3Vi04UsCg4I4Ep9GZq6RKh6OD20Ha9ez9Zy6kklylg5fFxV/XEjlo6hY0qI2kn14OwCaSlWZt3wtjzUIt62XpTOSqdXj6ghNgjwI8BB3tgVBEISKSSTdNzGbzUV64jabzbaesQt6X761t+Z7YbVakUgkheLbI+bNv8tbvIoWU9RP+Y5pz/opSxkLtlXc+aI4BcuUZdmyqkgx5XJ5mY9VWeLd/Nse7mfMupWcOZWUTQYWHqtfh1VbdvDygGfQp11l/MCnmbLsU7wCgrmepaOSm4pAd/W/yXfRTtbsXU57101BzEK//21OHpuSjclsJdBdRXqekQ93RHN9+7sYrp0HwLnRU6z9cBGVvdzAasFgMnM9W4/SQUZtfw1kZOPnogCrBXsUtSK8j/6L+nlUYlaEc1tBzJt/PyoxRf2U/2N5P2I+TPVT1m1KrPb8BlzBrFixghUrVmA2m4mLi+PgwYNoNJpCy1gsFqxWK0FBQbbh0QRBeHTp9XouXbqERCKxjekuCGV14fJV+g8dTk7KZaSObvgNfBeFRwCvNPOgU3XnB128+8pqtbLur3S+OBhH0vqxSGQOeD7xKo41mjG3gy/1fB/NjuUEQRCEikur1RIREUFmZiYuLi4lLvdIJ90FsrKycHV1JS0trcjB0ul0XLp0iZCQEJRKJXq9HqVSabfely0WCykpKfj4+NjtC7zVarVrOe0dryLFFPVTvmPau37KUkadTsfFixcJCgpCpbp9klBwUS80NBSZzD7PoVaUmHq9nsWLFzN+/Hi7XLSsKPt9a8xLqbmcSMxAqZDiplYQcyGRlwc9i1Ttgk/v6UjkDkgksKxvA1RyKRk5RhzkEvzd1AR6OOLppMBisdi1nPauG/j/frtVCuJscjbpuUZ8XVQ4yKWkZeWw8dBVDsWnA5B34SgO3kHInb2QSmBujzpIJRK8nBRU93WmkosKqVRSYev8Xt3P+inP+30/YlaEcxtUjGN5P2KK+infx1LUz+1jZmVl4eHhcdukWzQvv4lMJityUGUyGRKJxPYDFPr7XhU0jbVnzJtj2zNmRSijvWOWVj9RUVG0a9eO9PR03Nzc7jh227Zteemll+jfv7/dynpzGSUSCd988w09evSwW0x7sEfM+Ph4QkJCOHr0KH5+fnYr55QpU8jKyuLDDz8sMV7Btoo7X5TmTpd/GGLKZDJMJpPdy1ne9/vWmFV9nJFIpRxLSMeKCavaFd9+74BUjkSe/xy3IS2Jl0au5KXRY2kf0Yg8o5n4tDwup+sIcFMT6KG2aznvR90Yzfm9th9NyMCCFH8PJ6QSCV9+9xPLpo/H/YlxaEIa8liwO0ckj2GxglQCvRpWxkmloLq3hhAvJ1QORctT0ercHnHux2enIPajGLMinNsKYj+KMUX9lM9jeT9iPmz1U9btibaRD6kXX3wRqVSKRCJBoVBQvXp1Zs+ejclksi1jNptZvHgx4eHhqFQq3N3d6dq1K7///nuhWBs2bKBSpUpl2m5YWBhKpZLk5OQi89q2bWtLWNRqNfXq1WPevHlFnp/95ptvaN68Oa6urjg7O1OnTh3GjRt35wfhP7Zhw4YyJ9/ff/89165do2/fvve3UP+RO9l3e6hSpQpJSUnUrVvXrnEnTZrEpk2buHDhgl3jCkKIlxMNA93RG82oHGTI1Bqkiv+3lMg+soMrf/zAW4M60+2pJ/li+w/4uahwd1KQkJbLwfOpACRn6TBbyl8Dtcw8I38l5N/FdlTI8HNVodfpGTNuIovGDcaYdQPd4a+Y/XQdxncIZXGfBoxuW42pXcN4rnFlIqt7UauSS7EJtyAIgiBUdCLpfoh16dKFpKQkzp07x8SJE5k5cybvv/8+kN+Mtm/fvsyePZtXX32VM2fOEBUVRZUqVWjbti3bt2+/4+1FR0eTl5dH79692bhxY7HLjBgxgqSkJM6ePcukSZOYMWMGq1atss3fs2cPffr04ZlnnuHQoUMcPXqUd955B6PReEdludPl/2srV65k6NCh4pngu2AwGJDJZPj5+SGX27exjpeXFx06dGDlypV2jSsIwL/jeLvjpJDRv2kgBR1xSyXQrVdvgh5rB0hIO3uIj954gc6RzVi0YjXOCvDU5N8R//NCKgfO3+BKRh6mf+8sP0hWq5XLabkcOH+Dq+k6ADQqB44cP8XTXdrz13cbASuhbXqyedtXVPXWoDeZydWbaRzkQddwfxoHeeDmWLTzOEEQBEF4WIhv/A8xpVKJn58fQUFBjBo1ig4dOrBjxw4AvvjiC7788ks++eQThg8fTkhICPXr1+ejjz6ie/fuDB8+nJycnDva3tq1a+nfvz+DBg1i3bp1xS7j6OhoK9PgwYOpV68ev/zyi23+d999R8uWLZk8eTI1a9YkNDSUHj16sGLFihK3Gx8fj0QiYevWrXTq1Am1Ws2mTZsAWLNmDbVq1UKlUhEWFsaHH35oW89gMDBmzBgqVaqESqUiKCiIefPmFYp5/Phx2/IZGRlIJBKioqKKlCEqKophw4aRmZlpu5s/c+bMYst7/fp1oqKi6Natm23apEmTeOqpp2yvlyxZgkQi4eeff7ZNq169OmvWrAHg8OHDdOzYES8vL9zc3OjUqRN//fVXiccIYMaMGVSqVIkTJ04A+RdJIiMjUavVVKlShbFjx5Za53///Tft2rXD2dkZFxcXHnvsMY4cOVLqvuv1eiZNmkRAQAAajYbWrVsXOX63K0dwcDBz5sxh8ODBuLi48OKLLxapn6ioKCQSCXv27KFx48Y4OjrSokULYmNjC23r7bffxsfHB2dnZ4YPH86UKVNo0KBBoWWeeOIJtm7dWuqxFIS7VZB4Nw5yZ/bTdXnryVos7duQMX2fZPMX21j34wEadu2H1EFF3rWLfL9sOk+3b8kXhy9zPceEl0bJ9Ww9B87f4PfzN7iclmtr1v1f0xnNnLySyaH4VIxmK/7uKiwWC+8u+ZBX+3ZGmxiHTO3M8zOXs2Hdx7i7upCtM3ItU0cNHw0R1TwJcFMjFcOACYIgCA85kXTfIavVSk5Ojl1/cnNzy7TcvfZ5p1arMRgMAHz++eeEhoYWSvwKTJw4kdTU1ELJ8O1kZ2ezbds2Bg4cSMeOHcnMzGT//v0lLm+1Wvn99985e/YsCsX/73D4+flx+vRpTp06dQd7lu+NN97g5ZdfJiYmhs6dO7Np0yamT5/OO++8w5kzZ5g7dy5vvfWW7S780qVL2bFjB1988QWxsbFs2rSJ4ODgO94uQIsWLViyZAkuLi4kJSWRlJTEpEmTil02OjoaR0dHatWqZZvWpk0boqOjbcMO7Nu3Dy8vL1uCeuXKFc6fP0/btm2B/OM9ZMgQoqOjOXjwINWqVePJJ58kOzu7yPasViuvvPIKn3zyCfv376devXqcP3+eLl268Mwzz3DixAm2bt1KdHQ0Y8aMKXEfBwwYQOXKlTl8+DBHjx5lypQpODg4lLrvY8aM4eDBg2zZsoW///6bXr160bVrV86dOwdQ5nIsWLCA+vXrc+zYMd56660Syzht2jQWLlzIkSNHkMvlPP/887Z5mzZt4p133uHdd9/l6NGjBAYGFntHu3HjxiQmJhIfH1/idgThXtx8x9vPVY2n5v8dyYTVrMGK5cv49sBJOg+biMLVG1X1pvxwOoXh315h1W8XiL9wHl8XFem5Rg5eSCX63A0upeZgMP13yXdajoFDF9M4fTULN0cF3s5KcvRmxq39hW+Xz8Rq1OMZ2oi13+5l+KD8x2huaPVk5ZkID3ClQRU30ZRcEARBeGSIjtTuUG5uLt7e3g9k21qtFicnpztez2q1smfPHnbu3Mkrr7wCQFxcXKGk72YF0+Pi4sq8jS1btlCjRg3q1KkDQN++fVm7di2RkZGFlvvwww9Zs2YNBoMBo9GISqVi7NixtvmvvPIK+/fvJzw8nKCgIJo3b06nTp0YMGDAbXs4fPXVV+nRowcqlQqJRMKMGTNYuHAhvXr1AiAkJISYmBhWr17NkCFDSEhIoEaNGrRq1QqJREJQUFCZ9/dWCoUCV1dXJBIJfn5+pS576dKlIr1tR0ZGkp2dzbFjx3jsscf47bffmDx5sq2Zf1RUFAEBAVSvXh2A9u3b29a1Wq2sWLGCSpUqsW/fvkJ3zE0mEwMHDuTYsWNER0cTEBAAwLx58xgwYIDtWfkaNWqwdOlS2rRpw8qVK4s91gkJCUyePJmwsDDbOgWK2/eEhATWr19PQkIC/v7+WK1Wxo0bx549e1i/fj1z5869bTkKegdv3749EydOtMUuKSF+5513aNOmDZDfKdqTTz6JTqdDpVKxbNkyXnjhBYYNGwbA9OnT2bVrF1qttlCMgv4LLl26dNcXYQThdkK88s/lxy+ncyUjD3dHBxwV//+X7Onhzow33+CN1yZy5Pw19pzPIiYpmz2//srmLW/iXbs5fZ8fRa9uncnMNfLHhVQ8nZRU9XbC30193xJai8XKpbRcTl/NJM9gprKHGrlUyqkrmayM+odUdU004Y/TpGE9Zr05BblMhsVqJTlDh8pBSrMQDyq7q+3eQaMgCIIglGci6X6Iff/992g0GoxGIxaLhf79+xdq8mzP0eLWrVvHwIEDba8HDhxImzZtWLZsGc7O/x97dsCAAUybNo20tDTeeustWrVqRYsWLWzznZyc+OGHHzh//jx79+7ljz/+YOLEiXzwwQccPHgQR0fHEsvQuHFj2985OTmcP3+eF154gREjRtimm0wmXF1dARg6dCgdO3akZs2adOnShaeeeopOnTrZ5XiUJi8vr8hQU25ubtSvX5+oqCgUCgUKhYIXX3yRGTNmoNVq2bdvny2ZBLh27RpvvvkmUVFRpKSkYDabyc3NJSEhoVDcguEY/vjjD7y8vGzT//77b06cOGFrhg/57weLxcLFixdtifXNJkyYwPDhw/n000/p0KEDzz77LNWqVStxP0+ePInZbCY0NLTQdL1ej6enZ5nKUXAB6Oa6LU29evVsfxckzykpKQQGBhIbG8vLL79caPmmTZvy66+/FpqmVuf3Ep2bm1umbQrC3crvqVtKQmou17J1XM/W46iQ4+bogIMs/6KcQqGgRa0qtAizcC0xgRlLLpOChOsxf7Bs0h+sXVCVLv2GM2LoQHKNZn6JuUae0UywpyMB7o44OshQOkhxkEmRy6Q4yCQoZFIs/3aqeSf/B/IMZs4kZ/FPihYnpZzKHo7k5emYPPNtrga0Rqp2obKLA+989DHVfPKHTTGaLVzNyMPHWUm9ym54aewzPIwgCIIgVCQi6b5Djo6OZGdn23Wc4WvXruHr63vbTrVKSziL065dO1auXIlCocDf379Qp1OhoaGcOXOm2PUKpt+aLJUkJiaGP/74g0OHDvH666/bppvNZrZs2VIo6XV1daV69epYrVY+++wzwsPDiYiIoEOHDoViVqtWjWrVqjF8+HCmTZtGaGgoW7dutd2lLM7NrQAK7l5+/PHHNGvWrNByBV37N2rUiIsXL/LTTz+xe/dunnvuOTp06MCXX35pq4ubv5Daq3M2Ly8v0tPTi0xv27YtUVFRKJVK2rRpg4eHB7Vq1SI6Opp9+/YVutM7ZMgQUlNT+eCDDwgMDATy67vg8YECHTt2ZPPmzezcuZMBAwbYpmu1WkaOHFmolUGBgni3mjlzJv379+eHH37gp59+YsaMGWzZsoWePXsWu7xWq0Umk3H06FFkMlmhMbALLsSUtRxlbeHh4OBg+7vgM2qx3FmT27S0NIAH1qJFeLRUclXj56IiS2fieraey2m5pGTpsFituKgVOKvkSP99L1f3VLJqwRxOvDCIFcuWcWrvdnKTL/D14qns+HgBoe17kxPaFeQOSIDnGlehcbA7VgtYsYIEpEiQyyRgzj+f7Y1NQeOYf2fcUSFDKZfhIJPgIMtP1BXy/ERdqzNx+moW17Ly8HXNX/7w8VNMe/VFtIlxqGscp88bSxjdUI3RWQNArsHE9Sw9QZ6OhFd2Q6MUXzkEQRCER5P4D3iHJBIJTk5Odk26HR0dcXJysntP1k5OTrbmyLfq27cv/fv357vvvivyXPfChQvx9PSkY8eOZdrO2rVrad26dZHOztavX8/atWsLJd0302g0jB07lkmTJnHs2LESj2lwcDCOjo531LGbr68v/v7+XLhwoVCyeSsXFxf69OlDnz596N27N126dCEtLc2WcCUlJdnumN7cqVpxFAqF7Zns0jRs2JBr166Rnp6Oh4eHbXqbNm1Yt24dcrmcLl26APmJ+ObNm4mLi7M9zw3w+++/8+GHH/LEE09gtVr5559/uHHjRpFtde/enW7dutG/f39kMpltiLJGjRoRExNT4vujpLtfoaGhhIaGMn78ePr168f69evp2bNnsfvesGFDzGYzKSkpREZGYrVabU29C+r6duWwp5o1a3L48GEGDx5sm3b48OEiy8XExODg4GB7VEIQ7jeJRIKr2gFXtQMhXk6kavUkZeZxJUNHYlouDjIp7mo5BW2G6tUOY/XKFVy7PoulK1YRvf1TjNmpnPv9JyrVegoJYAU+2f4TNQZ3pU61ANu2LFYrJrMVnT7/M240W0nPNWKy6DGZ//+5t1qtyCQSZDIJcpkUk9mC1QpVPJwAK+998CHfrnwHq1GPTO1MvwEDGd66Kqq8axiBjFwDWr2JWpWcqVXJFYVcdCEjCIIgPLpE0v2I6tu3L9u2bWPIkCG8//77PP7442RlZbFixQp27NjBtm3bCt1dNJvNHD9+vFBirFQqqV69Op9++imzZ88uMmby8OHDWbRoEadPny4xgRk5ciRvv/02X331Fb1792bmzJnk5ubyxBNPEBQUREZGBkuXLsVoNJb5IkCBWbNmMXbsWFxdXenSpQt6vZ4jR46Qnp7OhAkTWLRoEZUqVaJhw4ZIpVK2bduGn58fbm5uSKVSmjdvznvvvcecOXOIjY3lzTffLHV7wcHBaLVa9uzZQ/369XF0dCy2dULDhg3x8vLi999/L3TBo3Xr1mRnZ/P9998zf/58ID/p7t27N5UqVSrU8qBGjRp8+umnNG7cmMzMTCZNmmRrFn2rnj178umnnzJo0CDkcjm9e/fm9ddfp3nz5owZM4bhw4fj5ORETEwMv/zyC8uXLy8SIy8vj8mTJ9O7d29CQkJITEzk8OHDPPPMMyXue2hoKAMGDGDw4MEsXLiQBg0akJiYSHR0NPXr1+fJJ5+843Lci1deeYURI0bQuHFjWrRowdatWzlx4gRVq1YttNzvv/9u601dEP5rMqkEHxcVPi4qQn3NXNfqSUzP5Ua2Dm95fgdmLmoFcpkUX28v3pn5JnlTJvH+yg0cvapDIslPbs06LcmfTWHEZ1NwcPXGMzCUoNDa1K5bj6aNG1EzJL8libujArmi+OG6LBYrJosVk8WCBDlqhYwrydcYN2Y0V45FAeAZ2oiFKz4mtGoQWPNblaRk6wEJDau4UdVLI3onFwRBEB55Ium+idlsLnK3zmw2Y7VabT9g32ehrVYrEomkUHx7xCz4XVrMrVu3smTJEhYvXszLL7+MSqUiIiKCvXv30rJly0JxtFotjRo1KrR+tWrVmD9/PqmpqfTo0aPItsLCwqhVqxZr1qxh0aJFhcpUsKy7uzuDBg1i5syZ9OzZk9atW/Phhx8yePBgrl27hru7Ow0bNmTnzp2EhoYWuz+31kvB7xdeeAG1Ws2CBQuYPHkyTk5OhIeH8+qrr2K1WtFoNLz33nucO3cOmUxGkyZN+OGHH2z1sXbtWl544QW6dOlCWFgY7777Lp07dy72/WC1WomIiGDkyJH06dOH1NRUpk+fXuywYVKplEGDBrFp06ZCnZ65ubkRHh7OtWvXqFmzJlarlcjISCwWC23atCm072vWrGHkyJE0atSIKlWqMHPmTKZOnVqkzgteP/PMM5jNZgYNGoREIqFXr15ERUXx5ptv2u5CV6tWjeeee67Y97pUKiU1NdVWL15eXvTs2ZOZM2eWuu/r1q3j7bffZuLEiVy5cgVPT0+aN2/OU089hdVqJTw8vNRy3LofxdV5QX3dutyt0/r378/58+eZNGkSOp2OZ599liFDhnD48OFCy3755ZfMmDGjxM9OQbzizhfFKVimLMuWVUWKKZfLy3ysyhLv5t/2UJ5jKmQQ4KrE30VBRq6e5IQM1FK4npWHFXBWydEo5aiVCkaPGMorW45T8LY1Z6cid/PDlJGMMfM6ySevk3zyd/78CtYD7k26U6NjfxL/SKCKuxJpxmWaNqyHs9P/LxRKJfllyNaZSM7UcebUSZZNGYEx6wZI5bQb+Aqzpr2GXCYDq8W2v45yCXUC3PF1UWK1WriXw1Ce6+d+xrT3Z6cg5s2/H5WYFeHcVhDz5t+PSkxRP+X/WN6PmA9T/ZR1mxKrPTPICmbFihWsWLECs9lMXFwcBw8eRKPRFFrGYrFgtVoJCgq6be/ZglBWycnJNG7cmAMHDpT4DLVw/z311FP4+vqydu1aAHbu3Mkbb7zBoUOHCvWBcDO9Xs+lS5eQSCR2fyREEO7Frn+yWX4oDYs1P2Ee09SD+u4W9h+L4ejJGOJiY0mK/4fs5It4dBiJc8OuAOiT4kjeOAEkUhx9AvELqk710FAa1q2N1CuYzf/kN1e36LRcXTcWB4UDb899l84R9R/sDguCIAjCA6bVaomIiCAzMxMXF5cSl3ukk+4CWVlZuLq6kpaWVuRg6XQ6Ll26REhICEql0tYRlD2f6U5JSSkyhNS9uLnDKnuU097xKlLM+1k/P/30E15eXkWGVbvbeOX9WD7o+snNzWXVqlV07twZmUzG5s2bmTNnDrt27bJ15Ldt2zZ8fX2JjIwssYw6nY6LFy8SFBRUpBf64hRc1AsNDbV14nevKkpMvV7P4sWLbb3o36uKst//Vcw8g4nr2QYSM3JJyzFgMFkwWCzk6M34u6nxdCq+2bhOr+fStQyi9v2GulpjjvwexbFP3sGcl1Xs8h4dX8L5sfxWOcYbl3lvSFuqVvK0zc/RmUjPNRDiqUaVm1Ihj2V5i2nvzw5UjP2+HzErwrkNKsaxvB8xRf2U72Mp6uf2MbOysvDw8Lht0i2al99EJpMVOagymQyJRGL7AQr9fa8KmsbaM+bNse0ZsyKU0d4x72f99OzZU9SPHWKVtX6kUik//fQTc+fORafTUbNmTb766qtCfQU8++yz6HS6UuMVzCvufFGaO13+YYgpk8kwmUx2L2d53+//KqZGLUOjVhLkpSEjz8i1LB2X03JRyI3kGiyAESelvMiY3SqVmmqVZFySXKd9kyoMbvkClknD+Cf+Mn8cPsqpkyeIjz3N9Utx6FOvIPesbFvXwasKOhTw77PjqVo9OqOFOgFuVPNy5FxcSoU8luUt5v367BTEfhRjVoRzW0HsRzGmqJ/yeSzvR8yHrX7Kuj2RdAuC8EhQq9Xs3r37QRdDEOxOKpXg4aTAw0lBNW8NqTl60nIMXMvSkZlrIMVkQSaTolHKcVLKkBfTKkQqlRJaNSi/Q7Q+vYD8hHr0xt9B9v+h+KQS8HVRYbFaSc7QoXKQ0izEg8ru6jsenk8QBEEQHhUi6RYEQRCEh4RCLqWSq5pKrmpq+bmQrTeRkWsgJVvPDa2eaxn5Y4DLye/4pbQnzDw1SkY+Xpc10Rdsz4kPb1UVF7UDiWm5eGmU1K/ihpdG9HciCIIgCKURSbcgCIIgPISk0v+P/x3k6YTeZCYzz0hGjoHEtGwAEjNycXAw46jM7wn91vG024X5UK+yK9eydPi6qFArZFxNzyPI05Hwym5olOJrhCAIgiDcjvhvWUaivzlBEECcC4SKSymX4eMsw8dZRaCbgv1A8xBPso1wLUtPqlaP0WxBIZehUcpxVMiQSiV4apR4apRk5BpIyzFQq5IztSq5FknQBUEQBEEonki6b6Pg4XiDwVCmnooFQXi45ebmAuDg4HCbJQWh/CroKDDA3RGlUonJbMm/C55nJCVLT1qOnow8A1YrOCpkGM1WpBJoWMWNql4apFL7drAoCIIgCA8zkXTfhlwux9HRkevXryOXyzEajQB2633ZYrFgMpnQ6XR2H5IK7FNOe8erSDFF/ZTvmPaun9LKaLVayc3NJSUlBTc3N7v3jikID5JcJrXd0a7mrSHPYCYjz2DrkM1ssVI3wJVKruoHXVRBEARBqHDKZdK9YsUK3n//fZKTk6lfvz7Lli2jadOmt11vy5Yt9OvXj6effprt27fbpSwSiYRKlSpx8eJFLl26hNFoxMHBwa6JSGZmJlqt1q4x7VlOe8eraDFF/ZTvmPasn7KU0c3NDT8/v3veliCUZ2qFDLXi/x2ymSxW0ZxcEARBEO5SuUu6t27dyoQJE1i1ahXNmjVjyZIldO7cmdjYWHx8fEpcLz4+nkmTJhEZGWn3MikUCmrUqIFOp+Off/4hKCjIbne5DAYDP/74Iy+++CIKhcIuMc1mM+fPn7dbOe0dryLFFPVTvmPau35uV0YHBwdxh1t45EilEhSiObkgCIIg3LVyl3QvWrSIESNGMGzYMABWrVrFDz/8wLp165gyZUqx65jNZgYMGMCsWbPYv38/GRkZdi+XVCpFqVQilUpRqVR2++ItkUjIyclBqVSiVNpn2BWz2WzXcto7XkWKKeqnfMe0d/3cjzIKgiAIgiAIj7Zy1VbMYDBw9OhROnToYJsmlUrp0KEDBw8eLHG92bNn4+PjwwsvvPBfFFMQBEEQBEEQBEEQyqRc3em+ceMGZrMZX1/fQtN9fX05e/ZssetER0ezdu1ajh8/Xubt6PV6W2dJAFlZWcVOv5XZbMZoNKLX6+12F6xge6Vt907Zu5z3Y78rSkxRP+U7pr3rp6Lsd0WJKeqn/MasCOe2RzmmqJ/y/T9S1I+on/IYsyKUER6++inrfkis5WjQ2atXrxIQEMCBAweIiIiwTX/ttdfYt28ff/75Z6Hls7OzqVevHh9++CFdu3YFYOjQoWRkZJTakdrMmTOZNWtWkelTpkwRw4IJgiAIgiAIgiAIt6XT6Zg/fz6ZmZm4uLiUuFy5SroNBgOOjo58+eWX9OjRwzZ9yJAhZGRk8O233xZa/vjx4zRs2LDQ1QeLxQLkN0uPjY2lWrVqRbZz6x3tzMxMAgMDOX/+PM7OziWWz2w2c+7cOWrUqGHXqz0rVqxg9OjRdn1m2J7lvB/7XVFiivop3zHtXT8VZb8rSkxRP+U3ZkU4tz3KMUX9lO//kaJ+RP2Ux5gVoYzw8NVPdnY21apVIyMjA1dX1xKXK1dJN0CzZs1o2rQpy5YtA/KT6MDAQMaMGVOkI7WC3sRv9uabb5Kdnc0HH3xAaGhomXo0TkxMpEqVKvbbCUEQBEEQBEEQBOGRcPnyZSpXrlzi/HL1TDfAhAkTGDJkCI0bN6Zp06YsWbKEnJwcW2/mgwcPJiAggHnz5qFSqahbt26h9d3c3ACKTC+Nv78/ly9fxtnZ+bZj/TZp0oTDhw/f2U6VIisriypVqnD58uVSmyTcKXuX097xKkpMUT/lO+b9qJ+KsN8VJaaon/Ibs6Kc2x7VmKJ+yvf/SFE/on7Ka8yKUMaHrX6sVivZ2dn4+/uXuly5S7r79OnD9evXmT59OsnJyTRo0ICff/7Z1rlaQkICUql9O12XSqWlXpm4mUwms+sbpICLi4td49q7nPdjvytKTBD1U55jgn3rp6Lsd0WJCaJ+ynPM8n5ue9Rjivopn/EKiPopn/EKPIr1UxHKWOBhqp/SmpUXKHdJN8CYMWMYM2ZMsfOioqJKXXfDhg32L9BNRo8efV/j24u9y3k/9ruixLwfRP2UXxVlvytKTHurKPtdUWLaW0XZ74oS094qyn5XhP+R90NFOJb3I6aon/Ib737FvB/K+76Xu2e6HzVZWVm4urretsc74cEQ9VO+ifop30T9lF+ibso3UT/lm6if8k3UT/n2qNaPfdtpC3dMqVQyY8YMu/XeJ9iXqJ/yTdRP+Sbqp/wSdVO+ifop30T9lG+ifsq3R7V+xJ1uQRAEQRAEQRAEQbhPxJ1uQRAEQRAEQRAEQbhPRNItCIIgCIIgCIIgCPeJSLoFQRAEQRAEQRAE4T4RSbcgCIIgCIIgCIIg3Cci6RYEQRAEQRAEQRCE+0Qk3YIgCIIgCIIgCIJwn4ikWxAEQRAEQRAEQRDuE5F0C4IgCIIgCIIgCMJ9IpJuQRAEQRAEQRAEQbhPRNItCIIgCIIgCIIgCPeJSLoFQRAEQRAEQRAE4T6RP+gCCIIgCIJw/5jNZoxG44MuhiAIgiBUWA4ODshksrteXyTdgiAIgvCQ0mq1JCYmYrVaH3RRBEEQBKHCkkgkVK5cGY1Gc3frW8V/YkEQBEF46JjNZs6dO4ejoyPe3t5IJJIHXSRBEARBqHCsVivXr18nNzeXGjVq3NUdb3GnWxAEQRAeQkajEavVire3N2q1+kEXRxAEQRAqLG9vb+Lj4zEajXeVdIuO1ARBEAThISbucAuCIAjCvbnX/6Ui6RYEQRAEQRAEQRCE+0Qk3YIgCIIgPNSGDh3K22+/DcD+/fupX7/+fdnOV199xYgRI+wSa9OmTXTr1u2u14+Li6N58+Z3tI5EIsHJyYlFixbd9XYftEGDBvH9998/6GI8MuLj45HLK9bTqnPnzmXMmDH3JbY4Bzx45fUcIJJuQRAEQRD+U8HBwTg6OqLRaPD09OS5554jPT39P9l2ZGQkf//9932JPWvWLCZOnAjkJyMSiYR+/foVWiYsLIyoqKjbxhowYADffffdXZclNDQUPz+/O44RGxvLhAkTADCZTPTu3ZsqVaogkUiIj48vdd3z58/TsmVLHB0dadSoUZmPc2xsLN26dcPb2xsvLy969erF1atX72o7EydOZMaMGWXa7qNs9uzZ9OnTx/Zap9OhVCoLHbuYmBjUajUGg8Hu209PT2fIkCH4+vri4uJCnTp12LBhA3D/E/mpU6eyfPny+xJbnAPEOaAkFevSlCAIgiAId23g2j+5kpF3R+uYzVay9UZkUgkapbzE59oC3NR89kKzMsfdtWsXrVq1IicnhwEDBjBnzpwKfXfl0KFDyOVywsLCbNOkUim7du3i7Nmzhab/V/r168eaNWvu6W5ZZGQkEydOpF27dmXaXteuXdmzZw/r16+nZ8+exMXF3TaByszMpFevXnzyySc4OjoyefJkhg4dyq5du+54Ow0aNCA3N5djx47RsGHDu9rn++3Xf66TazDZXpstVkwWK3KpBJn0zp8btVrBaLFitVpxkEnRKOW0r+5d6jqtWrXio48+sr0+dOgQwcHBREdH26ZFR0fTtGlTFArFHZfpdsaPH4/FYiEuLg6NRsOpU6dKTbLsxWQy3beEXpwDxDmgNOJOtyAIgiA8Iq5k5HHxRs4d/SSk55Kea+SG1kB8am6Jy91pMl/AycmJ7t27c+bMGdu0uXPnEhQUhIuLCxEREZw4ccI275133qFSpUq4uLgQHh5OTEwMAGlpafTv3x8fHx+qVq3Kxo0bi91eVFQU1atXt72WSCSsXLmSkJAQvLy8mDdvnm2e2WxmxowZBAUF4evry8SJEzGZTMWF5eeffyYyMrLQNIlEwpgxY5g9e3ax6+h0OkaPHo2fnx+BgYHMnj0bi8UCwIYNG+jQoQMAeXl59OvXDw8PDzw8PGzbefHFF5k5c6YtntVqJSQkhAMHDgDQunVr9uzZU2KZb0cul/Pqq68SERFx22VjY2OJiYlh6tSpqFQqRo0ahcViYf/+/bddt2nTpgwbNgx3d3eUSiVjxozh4MGDd72d1q1b8/PPP5d9R/9juQYT2Xqz7SfXaMFgtpJrtBSaXtYfrcGM3pQfI8dgLpTQl6R58+akpKRw8eJFID/BHjFiBGfPnrW9X6Kjo23vtV69euHj44OHhwfPPvssaWlpxcZdvnw59evXJyUlhby8PMaMGYO/vz+VK1dm/vz5tuUOHz7MgAEDcHV1RSaTUb9+fbp27QpAp06dMJvNaDQaNBoNCQkJt/0srlixgho1auDl5cWQIUPIyckB8j9H7dq1Y+TIkbi6urJ+/XpmzpzJ8OHDgf+fD2bPno2HhwfBwcHs3LnTFvfcuXNERETg7OzMM888Q58+fWyPqtxKnAPEOaA0IukWBEEQBOGBycrKYvv27TRr9v+75GFhYRw5coTU1FQ6duzI4MGDATh79iyrVq3i2LFjZGZmsm3bNjw8PID85/j8/f25fPkyP/74I2+88UahZL00v/76KydPniQqKopZs2Zx/vx5ABYtWsT+/fs5cuQIsbGx/PXXX6xatarYGCdPnqRGjRpFpo8bN46ff/6Zs2fPFpk3Z84cTp8+zZkzZ4iOjuazzz7jk08+KbLcxo0bycnJ4cqVK6SkpDB37lwABg4cyObNm23LFXxJbdGiBQCVKlVCJpPZ9ufzzz+nXr16ZTomdyomJobQ0FCUSqVtWnh4OKdPn77jWL/99ht16tS56+2EhYWVue4fVY6OjjRs2NB2Z7sgwQ4PD+fYsWO2aa1atQLyk+6LFy9y8eJFsrOzi00iFyxYwLp16/j111/x8fFh0qRJpKWlERcXx6FDh/j0009tz9o2a9aMKVOm8Omnn9oS/wK7du1CJpOh1WrRarUEBgaW+lnctm0bq1atYvfu3Vy+fBmj0VioefH+/fuJiIggPT2dgQMHFil3fHw8SqWSlJQUpk6dyosvvmib169fPzp16kRaWhpDhw7lm2++KfGYinOAOAeURiTdgiAIgiD857p27Yqbmxvu7u7ExcUxatQo27xevXrh7e2Ng4MDU6dO5cSJE2i1WuRyOXq9njNnzmA2mwkLC8PPz4/k5GSioqKYN28eSqWSsLAw+vfvz9dff12mskyZMgWNRkPdunWpV68eJ0+eBGDt2rW8/fbbeHt74+bmxsSJE/nyyy+LjZGRkYFGoyky3d3dndGjRzNnzpwi87Zs2cKMGTNwd3cnMDCQiRMnFvoCXcDBwYHU1FQuXLiAXC633eWKjIxEr9fz119/AbB58+Yiz486OzuTmZkJQP/+/e/bF1GtVouLi0uhaS4uLmi12juK888//zB16lRbUnE327l5n4WSRUZGEh0djcVi4fjx4zRq1IiWLVsSHR3N1atXSUhIsCVvAwcOxMnJCVdXV8aPH1+oGTrA22+/zRdffMGePXvw9PTEarWyfv16FixYgEajwd/fn1GjRtk+P8uXL6dXr14sWLCA6tWrEx4ezh9//FFiWUv7LK5du5Y33niDoKAg1Go1U6dOLfQ5rVatGkOHDkUqlaJWq4vEdnJyYvLkycjlcgYOHEhCQgIZGRnEx8dz+vRppk6dioODA926dSt0cfBW4hwgzgGlEUm3IAiCIAj/uZ9++omMjAxyc3Pp0aMHTz75pG3exx9/TJ06dXB1dcXPzw+r1UpqairVq1dn4cKFTJ06FV9fX4YPH05WVhYJCQnodDrbF3I3NzdWr15NcnJymcri6+tr+9vR0dH25S0hIcF2ccDNzY0BAwaQkpJSbAxXV9cSv1yOHz+eH3/8kdjY2ELTr169SmBgoO11UFBQsc+1Dho0iMcff5yePXsSFBRkawIvkUjo378/mzdvxmw2s23bNvr3719o3ezsbFxdXctwFO6NRqMhKyur0LSsrKxik5CSXL16lU6dOjFnzhzat29/19v5r/b5bpkt1gddBCD/ue7o6GhOnTpFjRo1cHBwsCXd0dHRhIeH4+LigslkYty4cbZHPnr37k1qaqotjsViYcmSJUyYMAF3d3cArl+/Tl5eHrVr17Z9fqZOncq1a9eA/M/Z9OnT+fvvv0lJSaFx48b06tXL1rT6VqV9FhMSEhg5cqRtXqtWrbh+/bpt3cqVK5d6HLy9vZFKpbZyQX5il5ycjLe3d6E7qqXFEucAcQ4ojUi6BUEQBEF4YJRKJYMGDeLo0aPcuHGD+Ph4xo0bx8aNG0lPTycpKQmJRILVmp+oDBo0iIMHDxIbG0t8fDyLFi0iICAAjUZDeno6GRkZZGRkkJ2dXWJT8LIKCAhg7969tpiZmZm2Z8hvFR4ezrlz54qd5+Hhwcsvv1zkTpe/vz8JCQm21wkJCfj7+xdZX6FQMHv2bOLi4ti5cycffPCBrffjgQMHsnXrVnbv3o2vry9169a1rZecnIzJZKJatWp3uut3rHbt2pw7dw69Xm+bdurUqRKbiN7qxo0bdOjQgRdffJGRI0fe03ZiY2MJDw+/i734b5jKUdJ99uxZduzYQcuWLYH8Zt9//vlnoee5N23aRFRUFAcOHCArK4svv/zS9nmE/M7Cfv75Z8aNG8dvv/0GgJeXF0qlkgsXLtg+P1lZWfz0009FyuHp6cnEiRNJSkoiLS2t2M4aS/ssBgQEsHHjRtu8jIwM2zPdQImdP96On58f169fL9R7e2JiYonLi3OAOAeURiTdgiAIgiA8MEajkc8//xwfHx88PT3RarVIpVK8vb0xmUyFns2MjY0lKioKg8GAo6MjSqUSmUxGQEAAERERvPnmm+Tm5mIymfjrr79KTJDL6vnnn+fNN98kKSkJq9VKfHw8+/btK3bZLl26FGlye7MJEybw/fffk5SUZJvWp08f5syZQ3p6OpcvX2bRokX07du3yLp79+7l9OnTWCwWXFxckMvlyGQyIP8LqJeXFxMnTixyh+u3336jffv299Rbs16vR6fTFfn7VjVr1qRWrVrMnz8fvV7PRx99hEQisSVuGzZsIDg4uNh1s7Ky6Ny5M0899RRTpkwptTy32w7k73fnzp3vYm//G/K76KH8fvDy8iI0NJRly5bZkm4nJyf8/PzYunWr7Xnu7OxsVCoV7u7u3LhxgwULFhSJ1bhxY7744guee+45jhw5glQqZciQIUycOJGMjAwsFgtnzpzh0KFDQH5z9L/++guj0YhWq2X16tVUrVoVLy8vvLy8sFgshRLc0j6Lzz//PHPnzrU9t5yUlGSXTrSCg4Nt7zWj0cgPP/zAn3/+WeLy4hwgzgGlEUm3IAiCIAj3TGc039HynTp1QqPR4OXlxd69e9m+fTsSiYS6desycuRI6tWrR3BwMCEhIbYhi/R6PZMnT8bT05PAwEDb86WQfzcuMTGRqlWr4uPjw7hx48jLu7se1QtMnjyZiIgIWrZsiaurK926dePy5cvFLtusWTMMBkOR5qMFPD09GTVqVKFmkW+99RY1a9YkLCyMiIgI+vbty5AhQ4qsm5SURI8ePXBxcaFJkyaMHDmy0BfMgQMHEhMTU+RZzs2bNzNixAjb602bNpX5rlOBmjVrolar0ev1hIWFFXom9qWXXuKll16yvf7888/ZtWsXbm5urFy5kq+//tr2ZT8xMdGW2N3qm2++4a+//uLDDz+09Vh9c1PRO9nO33//jUql4rHHHruj/fwv3c2wYPdLZGQk169fL9Q7dcuWLUlJSbEl3YMHD8bd3R1fX18iIyPp0qVLsbFat27NunXr6N69OzExMSxevBhXV1fCw8Px8PBg8ODBpKenA/m9bA8cONDWY/i5c+fYvn07kJ/4T5kyhQYNGuDm5kZCQkKpn8V+/frxwgsv8OSTT+Li4kKbNm3u+YJbgc2bN/PDDz/g4eHBunXreOqppwo1N7+ZOAeIc0BpJNab24cIgiAIgvBQ0Ol0XLx4kZCQEFQqFVD8ON1lHYe71G0ZzXhpFHw3JvL2Cz/EvvzyS3bt2lVo/OP/arsffPBBoSFzzp07Z2uKX9Y6ValUqFQqZs6cybhx4+xaxq5du7Jo0SJq1apl17i3GjRoEH369OGpp566r9u5F7eO0307t47Dfbuc3VFx+3G6hbsTERHBK6+8UuSOcgFxDihZRT8HFPc/9U6IpFsQBEEQHkL3+gVBqBj0ej1PPPEEgwYNYujQoQ+6OILwUPnzzz+pVKkSAQEBbNmyhRdffJH4+Hi8vcvPRQ1xDvhv3Ov/VNG8XBAEQRAEoQI6fvw4Hh4eKBQKBgwY8KCLIwgPncTERCIiInBxcWHevHls27atXCXc4hxQcYg73YIgCILwEBJ3ugVBEATBPsSdbkEQBEEQBEEQBEEop0TSLQiCIAiCIAiCIAj3iUi6BUEQBEEQBEEQBOE+EUm3IAiCIAiCIAiCINwnIukWBEEQBOGhNnToUN5++20A9u/fT/369e/Ldr766itGjBhhl1ibNm2iW7dud71+XFwczZs3v6N1JBIJTk5OLFq06K63+6C1adOGkydPPuhiPDLi4+ORy+UPuhh3ZO7cuYwZM+a+xBbngAevvJ4DRNItCIIgCMJ/Kjg4GEdHRzQaDZ6enjz33HOkp6f/J9uOjIzk77//vi+xZ82axcSJE4H8ZEQikdCvX79Cy4SFhREVFXXbWAMGDOC7776767KEhobi5+d3xzFiY2OZMGECAH/88QePP/447u7u+Pr6MmzYMLKyskpc9/Dhw9SrVw9HR0fatGnDpUuXyrTNCxcu0KRJE9zd3fHw8KBHjx4kJSXd1XbGjx/PzJkzy7azj7DZs2fTp08f22udTodSqWTGjBm2aTExMajVagwGg923n56ezpAhQ/D19cXFxYU6deqwYcMG4P4n8lOnTmX58uX3JbY4B4hzQEkq1qUpQRAEQRDu2qB1f5KUkYfJYiUzz4ir2gG5VFLsshl5RhQyKY4KWbHzcw1mDGYLbmoHACq5qfn0+WZlLsuuXbto1aoVOTk5DBgwgDlz5lTouyuHDh1CLpcTFhZmmyaVStm1axdnz54tNP2/0q9fP9asWXPXd8syMjIYPXo0nTp1wmq1MmzYMCZNmsRHH31UZFm9Xk+vXr2YMWMGAwcOZM6cOQwcOJD9+/ffdjve3t588cUXBAcHYzQamT59OmPHjmXbtm13vJ0nnniCESNGkJKSgo+Pz13t9/2295/raPUmDGYLCpkUqaT4z6DebEEmkZT4GTVZrJitVpSywvfQHBUy2lUvfSzpVq1aFarHQ4cOERwcTHR0tG1adHQ0TZs2RaFQlHXXymz8+PFYLBbi4uLQaDScOnWKq1ev2n07tzKZTPctoRfnAHEOKI240y0IgiAIj4jzKVoup+eRmJ5Heq6RxPQ8Lpfwc0Nr4GqmrsT5VzN13NAabK/Pp2jvqkxOTk50796dM2fO2KbNnTuXoKAgXFxciIiI4MSJE7Z577zzDpUqVcLFxYXw8HBiYmIASEtLo3///vj4+FC1alU2btxY7PaioqKoXr267bVEImHlypWEhITg5eXFvHnzbPPMZjMzZswgKCgIX19fJk6ciMlkKjbuzz//TGRkZKFpEomEMWPGMHv27GLX0el0jB49Gj8/PwIDA5k9ezYWiwWADRs20KFDBwDy8vLo168fHh4eeHh42Lbz4osvFrqjY7VaCQkJ4cCBAwC0bt2aPXv2lFjm2+nSpQu9evVCo9Hg7OzMiy++yMGDB4tdNioqCqVSyfDhw1GpVEybNo2jR49y8eLF227H2dmZkJAQJP8mn1KplPPnz9/VdhQKBY0aNWLPnj13tc//hSydiRyjGYPZSo7RjNZgKvZHb7KQW8r8XKMZvclSZHqW7vb13bx5c1JSUmzHLTo6mhEjRnD27Fnb+yU6Otr2XuvVqxc+Pj54eHjw7LPPkpaWVmzc5cuXU79+fVJSUsjLy2PMmDH4+/tTuXJl5s+fb1vu8OHDDBgwAFdXV2QyGfXr16dr164AdOrUCbPZjEajQaPRkJCQcNvP4ooVK6hRowZeXl4MGTKEnJwcIP9z1K5dO0aOHImrqyvr169n5syZDB8+HPj/+WD27Nl4eHgQHBzMzp07bXHPnTtHREQEzs7OPPPMM/Tp08f2qMqtxDlAnANKI5JuQRAEQXhEGMwWjGYLJosVyL9TZvx32q0/Bco633DTtDuRlZXF9u3badbs/3fJw8LCOHLkCKmpqXTs2JHBgwcDcPbsWVatWsWxY8fIzMxk27ZteHh4ADBo0CD8/f25fPkyP/74I2+88UahZL00v/76KydPniQqKopZs2bZvuwtWrSI/fv3c+TIEWJjY/nrr79YtWpVsTFOnjxJjRo1ikwfN24cP//8M2fPni0yb86cOZw+fZozZ84QHR3NZ599xieffFJkuY0bN5KTk8OVK1dISUlh7ty5AAwcOJDNmzfbliv4MtyiRQsAKlWqhEwms+3P559/Tr169cp0TIrz22+/UadOnWLnxcTEFIrt6OhItWrVOH36dJnju7m5oVarWbBgga15691sJywsrMx1/yCYrVas+R9BrFawlPBT4E7nm61WbsfR0ZGGDRva7mwXJNjh4eEcO3bMNq1Vq1ZAftJ98eJFLl68SHZ2drFJ5IIFC1i3bh2//vorPj4+TJo0ibS0NOLi4jh06BCffvop33//PQDNmjVjypQpfPrpp0WSsl27diGTydBqtWi1WgIDA0v9LG7bto1Vq1axe/duLl++jNFoLNRMfv/+/URERJCens7AgQOLlDs+Ph6lUklKSgpTp07lxRdftM3r168fnTp1Ii0tjaFDh/LNN9+UeEzFOUCcA0ojkm5BEARBeES4qR1wUTugUeU3r9So5Lj8O+3WH6kElHJpifOVcilSCbbXBc3My6pr1664ubnh7u5OXFwco0aNss3r1asX3t7eODg4MHXqVE6cOIFWq0Uul6PX6zlz5gxms5mwsDD8/PxITk4mKiqKefPmoVQqCQsLo3///nz99ddlKsuUKVPQaDTUrVuXevXq2TrhWbt2LW+//Tbe3t64ubkxceJEvvzyy2JjZGRkoNFoikx3d3dn9OjRzJkzp8i8LVu2MGPGDNzd3QkMDGTixImFvkAXcHBwIDU1lQsXLiCXy213uSIjI9Hr9fz1118AbN68ucjzo87OzmRmZgLQv3//u/4ievDgQT788EOmT59e7HytVouLi0uhaS4uLmi1ZW8BkZGRQXp6Ou+++y6hoaF3vZ2b97k8UsqkyGX5d/TkMgkKubTYHwCphBLnF7Q6v3X6rc3NSxIZGUl0dDQWi4Xjx4/TqFEjWrZsSXR0NFevXiUhIcGWvA0cOBAnJydcXV0ZP358oWboAG+//TZffPEFe/bswdPTE6vVyvr161mwYAEajQZ/f39GjRpl+/wsX76cXr16sWDBAqpXr054eDh//PFHiWUt7bO4du1a3njjDYKCglCr1UydOrXQ57RatWoMHToUqVSKWq0uEtvJyYnJkycjl8sZOHAgCQkJZGRkEB8fz+nTp5k6dSoODg5069at0MXBW4lzgDgHlEYk3YIgCIIg/Od++uknMjIyyM3NpUePHjz55JO2eR9//DF16tTB1dUVPz8/rFYrqampVK9enYULFzJ16lR8fX0ZPnw4WVlZJCQkoNPpbF/I3dzcWL16NcnJyWUqi6+vr+1vR0dH25e3hIQE28UBNzc3BgwYQEpKSrExXF1dS/xyOX78eH788UdiY2MLTb969SqBgYG210FBQcU+1zpo0CAef/xxevbsSVBQkK0JvEQioX///mzevBmz2cy2bdvo379/oXWzs7NxdXUtw1Eo2enTp+nZsyeffvoptWvXLnYZjUZTpIOlrKysYpOQ0ri4uDB48GCefvppWzPbO92OPfb5UdCqVSuio6M5deoUNWrUwMHBwZZ0R0dHEx4ejouLCyaTiXHjxtke+ejduzepqam2OBaLhSVLljBhwgTc3d0BuH79Onl5edSuXdv2+Zk6dSrXrl0D8j9n06dP5++//yYlJYXGjRvTq1evYuscSv8sJiQkMHLkSNu8Vq1acf36ddu6lStXLvU4eHt7I5VKbeWC/MQuOTkZb29vlEplmWKJc4A4B5RGJN2CIAiCIDwwSqWSQYMGcfToUW7cuEF8fDzjxo1j48aNpKenk5SUhEQiwfpvk9lBgwZx8OBBYmNjiY+PZ9GiRQQEBKDRaEhPTycjI4OMjAyys7NLbApeVgEBAezdu9cWMzMz0/YM+a3Cw8M5d+5csfM8PDx4+eWXi9zp8vf3JyEhwfY6ISEBf3//IusrFApmz55NXFwcO3fu5IMPPrD1fjxw4EC2bt3K7t278fX1pW7durb1kpOTMZlMVKtW7U533eb8+fN07tyZRYsWFbowcqvatWsXGqYnLy+P8+fPl9gUtTQmk4nk5ORiE5iybCc2Npbw8PA73u6jplWrVpw9e5YdO3bQsmVLIL/Z959//lnoee5NmzYRFRXFgQMHyMrK4ssvv7R9HiH/+duff/6ZcePG8dtvvwHg5eWFUqnkwoULts9PVlYWP/30U5FyeHp6MnHiRJKSkkhLS7M913uz0j6LAQEBbNy40TYvIyPD9kw3UGy8svDz8+P69euFem9PTEwscXlxDhDngNKIpFsQBEEQhAfGaDTy+eef4+Pjg6enJ1qtFqlUire3NyaTqdCzmbGxsURFRWEwGHB0dESpVCKTyQgICCAiIoI333yT3NxcTCYTf/31V4kJclk9//zzvPnmmyQlJWG1WomPj2ffvn3FLtulS5ciTW5vNmHCBL7//vtCw+D06dOHOXPmkJ6ezuXLl1m0aBF9+/Ytsu7evXs5ffo0FosFFxcX5HI5Mll+r/K1a9fGy8uLiRMnFrnD9dtvv9G+ffu77q05MTGRDh06MHXq1CKxb9W2bVvy8vJYt24der2ed955h8cee4yQkBAAZs6cSdu2bYtdNyoqir/++guz2Ux6ejoTJ07kscceK9KEtCzbMRgMHD16lMcff/yu9vlR4uXlRWhoKMuWLbMl3U5OTvj5+bF161bb89zZ2dmoVCrc3d25ceMGCxYsKBKrcePGfPHFFzz33HMcOXIEqVTKkCFDmDhxIhkZGVgsFs6cOcOhQ4eA/Obof/31F0ajEa1Wy+rVq6latSpeXl54eXlhsVgKJbilfRaff/555s6da3tuOSkpiZ9//vmej09wcDC1atVi/vz5GI1GfvjhB/78888SlxfnAHEOKI1IugVBEARB+M916tQJjUaDl5cXe/fuZfv27UgkEurWrcvIkSOpV68ewcHBhISE2IYs0uv1TJ48GU9PTwIDA23Pl0L+3bjExESqVq2Kj48P48aNIy8v757KOHnyZCIiImjZsiWurq5069aNy5cvF7tss2bNMBgMRZqPFvD09GTUqFGFmkW+9dZb1KxZk7CwMCIiIujbty9Dhgwpsm5SUhI9evTAxcWFJk2aMHLkyEK9JA8cOJCYmJgiz3Ju3ryZESNG2F5v2rTpju46rV27lvj4eF577TVbT9I3r9+1a1dbh05KpZJvvvmGJUuW4Obmxm+//cZnn31mWzYxMdGW2N0qOzvb1pN1zZo10ev1fPXVV3e1nZ9++onIyMhCjwwIJYuMjOT69etERETYprVs2ZKUlBRb0j148GDbOM2RkZF06dKl2FitW7dm3bp1dO/enZiYGBYvXoyrqyvh4eF4eHgwePBg0tPTgfxetgcO/B975x0fRbUF4G+2J9lsegdCJ5RQBYwBxIJKsQEqJRRBQKwUUUAFpKuIYK8oIlIFn4Ig0kERBESBQOgkQEKA9LZt5v2x2SGb3TQMijrf7+3DnXJu2zmZc++55yTIEcOPHz/ON998AzgM//Hjx9OyZUv8/f1JTk4u91ns27cvQ4cOpXv37phMJm699dY/PeHmZMmSJaxdu5bAwEAWLFhArHSUkAABAABJREFUjx49XNzNS6LoAEUHlIcgSZUIcaigoKCgoKDwj6KoqIjTp09Tp04dDAYDcGPl6f43snLlSjZs2OAxh+31Lnf+/Pku+XCPHz8uu+JX1r3WYDBgMBiYMmUKo0aNqtY6tmnThg0bNhAUFFStckvTuXNn3nrrrT8Vofl6cyPk6Va4NuLi4nj66afLXPVVdEDZ/NN1gKe/qVVBMboVFBQUFBT+hfzZFwSFfwZms5lu3boxYMAABg8e/HdXR0HhX8Xu3buJiIggKiqKpUuXMnz4cM6cOUNIyI0zqaHogL+GP/s3VXEvV1BQUFBQUFD4B3LgwAECAwPR6XT079//766OgsK/jnPnzhEXF4fJZGLWrFmsWLHihjK4FR3wz0FZ6VZQUFBQUPgXoqx0KygoKCgoVA/KSreCgoKCgoKCgoKCgoKCwg2KYnQrKCgoKCgoKCgoKCgoKFwnFKNbQUFBQUFBQUFBQUFBQeE6oRjdCgoKCgoKCgoKCgoKCgrXCcXoVlBQUFBQUFBQUFBQUFC4TihGt4KCgoKCgsK/msGDBzN9+nQAduzYQYsWLa5LOV9//TXDhg2rFlmLFy/m3nvvveb7jx07xs0331ylewRBwMfHh7lz515zuX83t956KwcPHvy7q/Gf4cyZM2g0mr+7GlVi5syZPPXUU9dFtqID/n5uVB2gGN0KCgoKCgoKfym1a9fG29sbo9FIUFAQDz/8MJmZmX9J2R07duT333+/LrJfeeUVxo4dCziMEUEQ6Nu3r8s1MTExbN26tUJZ/fv357vvvrvmujRs2JDw8PAqy0hKSmLMmDEAnDp1irZt2xIQEEBgYCAPPPAAqampZd7766+/0rx5c7y9vbn11ls5e/Zslev9+OOPIwgC586du6ZyRo8ezZQpU6pc7n+NqVOn8sgjj8jfi4qK0Ov1TJ48WT6WmJiIl5cXFoul2svPzMxk0KBBhIWFYTKZaNq0KZ9//jlw/Q35iRMn8s4771wX2YoOUHRAWfyzpqYUFBQUFBQUrplBn+0hNbsQSYKcIit2UcJk0KJRC/I15Z0ric0ukVNkRa0SMBm0RPp7sfDRdpWuy4YNG+jQoQP5+fn079+fadOm/aNXV/bs2YNGoyEmJkY+plKp2LBhA0ePHnU5/lfRt29fPvnkk2teLQsJCWH58uXUrl0bq9XKpEmTeOaZZ1ixYoXbtWazmZ49ezJ58mQSEhKYNm0aCQkJ7Nixo9Ll7d+/n8TExHKvqaicbt26MWzYMNLT0wkNDa1ag/8iNh9PJ6vIhgrQqlUInh8xbKKEzS6hUQtoVEKlz3lr1XSuH1JuHTp06MBHH30kf9+zZw+1a9dm586d8rGdO3fSrl07dDpd1RpYCUaPHo0oihw7dgyj0cihQ4e4cOFCtZdTGpvNdt0MekUHKDqgPJSVbgUFBQUFhf8IqdmFWO0SNlHCoFGTW2Tj9JV88opsWO0SZqtIckYB6blmdGoVEmC1Sx4/EqBTq0jPNZOcUcCFrMJrqpOPjw/33XcfR44ckY/NnDmT6OhoTCYTcXFx/PHHH/K5GTNmEBERgclkIjY2Vn5By8jIoF+/foSGhlK3bl0WLlzosbytW7dSv359+bsgCLz//vvUqVOH4OBgZs2aJZ+z2+1MnjyZ6OhowsLCGDt2LDabzaPc9evX07FjR5djgiDw1FNPMXXqVI/3FBUV8eSTTxIeHk6tWrWYOnUqoigC8Pnnn3PnnXcCUFhYSN++fQkMDCQwMFAuZ/jw4S4rOpIkUadOHX7++WcAOnXqxKZNm8qsc0X4+vpSp04dhGKrUKVScfLkSY/Xbt26Fb1ez2OPPYbBYODFF19k3759nD59ulJlSZLEM888w5tvvlnudRWVo9PpaN26NZs2bapCS/9asopsWO0iapWABIiS549KcJzPt9gptIry8UKrSL7FjoTjmtL3FVjtFdbh5ptvJj09Xe63nTt3MmzYMI4ePSr/Xnbu3Cn/1nr27EloaCiBgYE89NBDZGRkeJT7zjvv0KJFC9LT0yksLOSpp54iMjKSGjVqMHv2bPm6X3/9lf79++Pn54daraZFixZ07doVgLvuugu73Y7RaMRoNJKcnFzhs/juu+/SoEEDgoODGTRoEPn5+YDjObrtttsYMWIEfn5+fPbZZ0yZMoXHHnsMuKoPpk6dSmBgILVr1+aHH36Q5R4/fpy4uDh8fX3p1asXjzzyiLxVpTSKDlB0QHkoRreCgoKCgsJ/EJVKINxkQKdWkZpTRKHFTlpOERa7SITJgF6rrlCGXqsmwmTAYhfJKbJeUz1ycnL45ptvaN++vXwsJiaGvXv3cuXKFbp06cLAgQMBOHr0KB988AG//fYb2dnZrFixgsDAQAAGDBhAZGQkKSkpfP/990yYMMHFWC+PzZs3c/DgQbZu3corr7wiv1TOnTuXHTt2sHfvXpKSkti/fz8ffPCBRxkHDx6kQYMGbsdHjRrF+vXrOXr0qNu5adOmcfjwYY4cOcLOnTv58ssv+eKLL9yuW7hwIfn5+Zw/f5709HRmzpwJQEJCAkuWLJGv27VrFwC33HILABEREajVark9X331Fc2bN69Un5TE398fLy8v5syZI7udliYxMdFFtre3N/Xq1ePw4cOVKmPhwoXExMTQpk2bcq+rTDkxMTGVHvu/AxWO1WihrCXuEug1KnRqFRa7iNnm+FjsIjq1Cr3m2l/jvb29adWqlbyy7TSwY2Nj+e233+RjHTp0ABxG9+nTpzl9+jS5ubkejcg5c+awYMECNm/eTGhoKM899xwZGRkcO3aMPXv2sGjRItasWQNA+/btGT9+PIsWLXIzyjZs2IBarSYvL4+8vDxq1apV7rO4YsUKPvjgAzZu3EhKSgpWq9XFTX7Hjh3ExcWRmZlJQkKCW73PnDmDXq8nPT2diRMnMnz4cPlc3759ueuuu8jIyGDw4MGsXr26zD5VdICiA8pDMboVFBQUFBT+ozgNb61KRVpOEWZb5Q1uJ07D22aXqlR2165d8ff3JyAggGPHjjFy5Ej5XM+ePQkJCUGr1TJx4kT++OMP8vLy0Gg0mM1mjhw5gt1uJyYmhvDwcNLS0ti6dSuzZs1Cr9cTExNDv379WLVqVaXqMn78eIxGI82aNaN58+ZyEJ5PP/2U6dOnExISgr+/P2PHjmXlypUeZWRlZWE0Gt2OBwQE8OSTTzJt2jS3c0uXLmXy5MkEBARQq1Ytxo4d6/IC7USr1XLlyhVOnTqFRqORV7k6duyI2Wxm//79ACxZssRt/6ivry/Z2dkA9OvX75peRLOyssjMzOTVV1+lYcOGHq/Jy8vDZDK5HDOZTOTl5VUoPzs7m5kzZ8qGRHlUppySbb4RcbiUV2xwOylpeFeHwe2kY8eO7Ny5E1EUOXDgAK1btyY+Pp6dO3dy4cIFkpOTZeMtISEBHx8f/Pz8GD16tIsbOsD06dNZvnw5mzZtIigoCEmS+Oyzz5gzZw5Go5HIyEhGjhwpPz/vvPMOPXv2ZM6cOdSvX5/Y2Fh++eWXMuta3rP46aefMmHCBKKjo/Hy8mLixIkuz2m9evUYPHgwKpUKLy8vN9k+Pj6MGzcOjUZDQkICycnJZGVlcebMGQ4fPszEiRPRarXce++9LpODpVF0gKIDykMxuhUUFBQUFBT+ctatW0dWVhYFBQU88MADdO/eXT738ccf07RpU/z8/AgPD0eSJK5cuUL9+vV54403mDhxImFhYTz22GPk5OSQnJxMUVGR/ELu7+/Phx9+SFpaWqXqEhYWJv+3t7e3/PKWnJwsTw74+/vTv39/0tPTPcrw8/Mr8+Vy9OjRfP/99yQlJbkcv3DhArVq1ZK/R0dHe9zXOmDAAO644w4efPBBoqOjZRd4QRDo168fS5YswW63s2LFCvr16+dyb25uLn5+fpXohfIxmUwMHDiQ+++/X3Z/LYnRaCQnJ8flWE5OjkcjpDRTpkxhxIgRldp/WZlyqqvN/3Y6dOjAzp07OXToEA0aNECr1cpG986dO4mNjcVkMmGz2Rg1apS85aN3795cuXJFliOKIvPmzWPMmDEEBAQAcOnSJQoLC2nSpIn8/EycOJGLFy8Cjuds0qRJ/P7776Snp3PTTTfRs2dPj78tKP9ZTE5OZsSIEfK5Dh06cOnSJfneGjVqlNsPISEhqFQquV7gMOzS0tIICQlBr9dXSpaiAxQdUB6K0a2goKCgoPAfRRQl0nKKsIoi4SYDeo3D1dxciT2hTsxWO6k5RWUGXKsIvV7PgAED2LdvH5cvX+bMmTOMGjWKhQsXkpmZSWpqKoIgIEmOlfQBAwawa9cukpKSOHPmDHPnziUqKgqj0UhmZiZZWVlkZWWRm5tbpit4ZYmKimLLli2yzOzs7DKD/MTGxnL8+HGP5wIDA3niiSfcVroiIyNJTk6WvycnJxMZGel2v06nY+rUqRw7dowffviB+fPny9GPExISWLZsGRs3biQsLIxmzZrJ96WlpWGz2ahXr15Vm+4Rm81GWlqaR8OiSZMmLml6CgsLOXnyJE2bNq1Q7pYtW5g9ezbh4eGEh4cD0Lp1a9avX39N5SQlJREbG1ultv2VWO2i/HuuDCVdyku6mv9ZOnTowNGjR/n222+Jj48HHG7fu3fvdtnPvXjxYrZu3crPP/9MTk4OK1eudKm/SqVi/fr1jBo1iu3btwMQHByMXq/n1KlT8vOTk5PDunXr3OoRFBTE2LFjSU1NJSMjw6MXQHnPYlRUFAsXLpTPZWVlyXu6gSp5FZQkPDycS5cuuURvLy+itqIDFB1QHorRraCgoKCg8B/EaXA793B76dQue7wrY3g7DW6dWoXJoL2melitVr766itCQ0MJCgoiLy8PlUpFSEgINpvNZW9mUlISW7duxWKx4O3tjV6vR61WExUVRVxcHC+99BIFBQXYbLZKRcGtiCFDhvDSSy+RmpqKJEmcOXOGbdu2ebz2nnvucXO5LcmYMWNYs2aNS7qdRx55hGnTppGZmUlKSgpz586lT58+bvdu2bKFw4cPI4oiJpMJjUaDWu3YAtCkSROCg4MZO3as2wrX9u3buf322685WvPWrVvZv38/drudzMxMxo4dS5s2bdxcOwE6d+5MYWEhCxYswGw2M2PGDNq0aUOdOnUAx0pW586dPZazadMmDh48yIEDBzhw4AAAP/zwA7fddluVy7FYLOzbt4877rjjmtr8VyDiCHZWGcO79B7u0nu8/wzBwcE0bNiQt99+Wza6fXx8CA8PZ9myZfJ+7tzcXAwGAwEBAVy+fJk5c+a4ybrppptYvnw5Dz/8MHv37kWlUjFo0CDGjh1LVlYWoihy5MgR9uzZAzjc0ffv34/VaiUvL48PP/yQunXrEhwcTHBwMKIouhi45T2LQ4YMYebMmfK+5dTUVI/GWlWpXbs2jRs3Zvbs2VitVtauXcvu3bvLvF7RAYoOKA/F6FZQUFBQUPiPUdrgdu7hLh1crTzDu6TBHW4ylJn2qCzuuusujEYjwcHBbNmyhW+++QZBEGjWrBkjRoygefPm1K5dmzp16sgpi8xmM+PGjSMoKIhatWrJ+0vBsRp37tw56tatS2hoKKNGjaKw8NoiqjsZN24ccXFxxMfH4+fnx7333ktKSorHa9u3b4/FYnFzH3USFBTEyJEjXdwiX375ZRo1akRMTAxxcXH06dOHQYMGud2bmprKAw88gMlkom3btowYMcIlSnJCQgKJiYluezmXLFnCsGHD5O+LFy+u1KqTk9zcXDnCdKNGjTCbzXz99dfy+a5du8p7MPV6PatXr2bevHn4+/uzfft2vvzyS/nac+fOyYadp75xrnA5V7lKuvVWpZx169bRsWNHly0DNxo6tSBHGS/P8C4raFp1Gt4dO3bk0qVLxMXFycfi4+NJT0+Xje6BAwcSEBBAWFgYHTt25J577vEoq1OnTixYsID77ruPxMRE3nzzTfz8/IiNjSUwMJCBAweSmZkJOCJVJyQkyBHDjx8/zjfffAM4DP/x48fTsmVL/P39SU5OLvdZ7Nu3L0OHDqV79+6YTCZuvfXWPz3h5mTJkiWsXbuWwMBAFixYQI8ePVzczUui6ABFB5SHIFXFv0VBQUFBQUHhH0FRURGnT5+mTp06GAwGwJGn+0JWYYV5uCvK1V06R7cgQIRf1fJ0/xtZuXIlGzZscMl//FeVO3/+fJd8uMePH5dd8SvrXmswGDAYDEyZMoVRo0ZVax3btGnDhg0bCAoKqla5pencuTNvvfXWNUVo/qvYeuISeRYbFrtUZq7u8vJwV3RNZfJ0K1wbcXFxPP30024ryk4UHVA2/3Qd4OlvalVQjG4FBQUFBYV/IX/2BUHhn4HZbKZbt24MGDCAwYMH/93VUVD4V7F7924iIiKIiopi6dKlDB8+nDNnzhAScuNMaig64K/hz/5NVdzLFRQUFBQUFBT+gRw4cIDAwEB0Oh39+/f/u6ujoPCv49y5c8TFxWEymZg1axYrVqy4oQxuRQf8c1BWuhUUFBQUFP6FKCvdCgoKCgoK1YOy0q2goKCgoKCgoKCgoKCgcIOiGN0KCgoKCgoKCgoKCgoKCtcJxehWUFBQUFBQUFBQUFBQULhOKEa3goKCgoKCgoKCgoKCgsJ1QjG6FRQUFBQUFBQUFBQUFBSuE5q/uwI3AqIocuHCBXx9fSudPF5BQUFBQeFGxmKxIIoidrsdu93+d1fnb2XIkCHUq1ePF198kR07dvDss8+yf//+ai9n1apV/PDDD3z44Yd/WtZXX33FsmXL+N///ndN9x87dozBgwfz888/V/oejUaDt7c3r7zyCqNHj76mcv9ubrvtNt566y1iY2P/7qr8Jzhz5gyNGjXCbDb/3VWpNLNmzSI1NZW33nqr2mUrOuDv53rpALvdjiiK5OXlYbFY5OOSJJGbm0tkZCQqVdnr2VVOGXb69Gl27NjB2bNnKSgoICQkhFatWhEXF/ePTUly7tw5atas+XdXQ0FBQUFBodqIjo7mgw8+IDg4+O+uihv33XcfGRkZqFQqdDodbdu2ZcKECZhMputS3pQpU6hZsyZDhw69LvKd9O3bl1mzZlG7dm0uXLjA/fffz1133cWMGTPka3r37s2ECRNo06bNda0LwHPPPcd9991Hp06dKnV927ZtWbNmDWFhYW7nZs2axapVq8o8D3D48GGmT59OSkoKTZo04ZVXXiEiIqLCcp195eXlJR+bMGECXbt2rXI5W7du5fvvv+e1116rTJP/s3z88cecOnWKWbNmAWA2m7ntttsYNGgQI0aMAODUqVMMHDiQLVu2oNVqPcq5cOECPXv25JdffqlS+Tk5OcydO5ddu3ZhNpsJDQ1lwIAB3Hvvvdcs80ZA0QH/bh1w+fJlHn/8cc6ePet2LiUlhRo1apR5b6VXuhcvXsz8+fPZu3cvYWFhREZG4uXlRUZGBidPnsRgMNC/f39eeOEFoqOjr60lfxO+vr6Ao7PK+4Nvt9tJSkqiUaNGqNXqainbbDYzd+5cxowZg16vrxaZ1V3P69Huf4pMZXxubJnVPT7/lHb/U2Qq4/P3yrRYLFy8eJHatWvLk+I9P9jFlXwLRr0au82OWqMmz2ynyOK6Eh7se3W87KJEZr7F5XyAjw616qpn2OVcM1EBXiwe1BKDwVCh15hOp+OHH36gQ4cO5OfnM2DAAL799lveeOMNl+skSaKoqKhSMssjKCiIiIgIWrVqVW0yS3L2Sj7JifvRGLy5/Z4eCAJkizpUKhW//roXi6ChdWxTAPR6PXXq1qNZ8xYAFNns+OpdDRpRFLl48SJhYWHlrpxUxIgRI1iyZAnPPvtspdvdrFkztxfH/fv3k56e7na+pEyLxcKDDz7Iyy+/TP/+/Zk+fTqvvvoq27Ztq7CeAQEBqNVqcnNzK6yn2Wwut5ymTZvy6quvEhUVRWho6HUZ7z87PltOXsFiF+XvVruEXRSRAEcNBQzaq3IlCcw20UWGXqOiZHOKrFfP++jU3NkguNy29+7dm0cffZRWrVoBsH37durUqcPJkyflY3v37qVdu3a0a9euRF1cZQYEBADI91SWIUOG4O/vz7Fjx9BoNJw4cYLU1FRatWp1zTKdlDc+NpsNjabqjr6V+R3t2bMHX19fHnzwQcDxu1apVOzduxcvLy9iYmIAMBgM1K9fn5YtW1brb9NTHUvqgMri6RlPTEz0qANKUtGz6cTT+JTUARVRVjnr16/HYDC46YBrpXR/FhUVcebMGfbu3YtOp5Ovy8nJoWbNmrI9WRaV+tW1atUKnU7H4MGD+frrr91Whc1mM7t27WLp0qXcdNNNvPfeezz00EPX0Ly/B+cP02QyVWh0G41GTCZTtb6UGgwGTCZTtRp11VnP69Huf4pMZXxubJnVPT7/lHb/U2Qq4/P3yiwqKuLSpUuo1Wr5nvNZhVzOs9Cqpj9oHa8AR9PyAGhZw48D57IBqBHgJf9tTM0u5HK+hZoBXghAcmYhapVApL9jRUKSJC7nW0BALqsyL5DOa00mE/fffz8rVqyQ6zlz5kw+/PBDMjMzady4MR999BEtWjiM1BkzZvDOO++Qn59PdHQ0y5Yto0mTJmRkZPDUU0+xceNGjEYjkydPZtCgQYDj77xKpUKtVrNlyxYee+wxTpw4gSAICILAe++9x2uvvUZubi5jx45lwoQJcr9PnTqVzz//nKKiIhISEnj11VfdXtptImzatImb2t/CpTwLdhFEyVHu0BGPM/fV2Xyy8AtHfwEWUaLI7nihe2n886z53zfodDoee+wxXnrpJdRqNStWrGDt2rVs3LiRwsJChgwZwg8//AA4DMsdO3YwfPhwIiMjmTJlijwWdevWZfHixdxyyy107tyZYcOGIUmS3N8VjU/J34tT5ujRo5k/fz433XSTy/mScnfs2IFer2f48OEAvPzyywQHB5OcnEydOnUq/C04/y0p01M9KyrHy8uL1q1bs3XrVvr27VuhvGuh5O/pWozuHLMNo/7qbyi7yDGpFeitJaPACkj46K/Wt8Biw2wX8dE5+infYkelAm+dQ4YkSZiLjfhAby35FnuFfRkfH096errcb7t27WLYsGG8+eabSJKERqPh559/plOnTqjVanr27MnOnTux2Wx07tyZjz/+mKCgIJexA3jnnXf4+OOP+fHHH/H19WXcuHGsWrUKlUrFU089xfjx4wGHQf/GG28QGBhIUVERrVu3luvYtWtX7HY7fn5+ACQmJhIVFVXus/juu+8yb948MjMz6datG5MmTUKtVvPFF1+wcOFCGjZsyNKlS5kzZw7nz5/n3LlzfPLJJ2zdupXHHnuMgQMHMm/ePEwmEx9++CF33303AMePH2fgwIEcOnSIO+64A71eT2xsLC+99JLbuP7444907NjRpU8EQeCpp55ixowZfPXVV/K1znGxWq08//zzrFq1ykUHqFQqPv/8c7788stK6wBJklCpVDRo0MCjDqjsZEPpZ1ylUjFmzBiPOqAkldUBnp6f0r+j8iirnJSUFGJiYtBoNC464Fop/fw462s0Gj16d1ekXyqlKWbPns3u3bt54oknPLph6/V6OnfuzAcffMDRo0epW7duJZujoKCgoKCg8FeiUQkevzsNbnB9eXC+2KdkFpKcWQiA0aDxeO21kJOTwzfffEP79u3lYzExMezdu5fLly9z++23y8bz0aNH+eCDD/jtt9/Izs5mxYoVBAYGAjBgwAAiIyNJSUnh+++/Z8KECfzxxx+VqsPmzZs5ePAgW7du5ZVXXuHkyZMAzJ07lx07drB3716SkpLYv38/H3zwgbsAQeDw4cPENmlEmMlApL+BMJPjpezxJ59m08YNJB09ipfW8fKm06jw0qp587VZJB05wpEjR9i5cydffvklX3zxhZv4hQsXkp+fz/nz50lPT2fmzJkAJCQksGTJEvm6Xbt2AXDLLbcAEBERgVqtltuzbNkyefKisixcuJCYmJgKXWETExNp3ry5/N3b25t69epx+PDhSpVjt9uJjIykdu3ajBs3joKCgmsuJyYmptJj/3dQ+olxPkIOg9t57OpVmmLDJN9iJ7/YK0WjVnm8tqSM8vD29qZVq1bs3LkTgJ07d9KxY0diY2P57bff5GMdOnQAoGfPnpw+fZpTp06Rm5vL1KlT3WTOmTOHBQsWsHnzZkJDQ3nuuefIyMjg2LFj7Nmzh0WLFrFmzRoA2rdvz/jx41m0aBFnzpxxkbNhwwbUajV5eXnk5eVRq1atcp/FFStW8MEHH7Bx40ZSUlKwWq3MmTNHlrdjxw7i4uLIzMwkISHBrd5nzpxBr9eTnp7OxIkTZWMOHO7id911F1euXCEhIYHVq1eX2acHDx6kQYMGbsdHjRrF+vXrOXr0qNu5WbNmkZiYWG06YPfu3UDZOuCrr75yeX4qw5dffkmjRo3+Mh0QHR3NqFGjqqwDEhMT5WM3og6olNHtnO2pDEFBQX/JHgUFBQUFBQWFqtMkwlTu97rBPi7f/by0LqtyvgYNJoO23HsqQ9euXfH39ycgIIBjx44xcuRI+VzPnj0JCQlBq9Xy/PPP88cff5CXl4dGo8FsNnPkyBHsdjsxMTGEh4eTlpbG1q1bmTVrFnq9npiYGPr168eqVasqVZfx48djNBpp1qwZzZs35+DBgwB8+umnTJ8+nZCQEPz9/Rk7diwrV670KCM7Oxujj9HtuH9AAMNGPM6c2bPczq1YvpwXXnyJgIAAatWqxdixY11eoJ1otVquXLnCqVOn0Gg0dOzYEYCOHTtiNpvloHBLlixxW9nx9fUlO9sxofLII4/w+++/V6pPnG2aOXOm/IJfHnl5eW7egiaTiby8vArvDQ4OZt++faSkpLBt2zb++OMPXnjhhWsup2Sbb0T0GtfXb38v1+fJV++6IqnTqFwmy7RqAZ1aVe49laFjx47s3LkTURQ5cOAArVu3Jj4+np07d3LhwgWSk5Nl4y0hIQEfHx/8/Px4+umn+emnn1xkTZ8+neXLl7Np0yaCgoKQJInPPvuMOXPmYDQaiYyMZOTIkfLz884779CzZ0/eeOMN+bkrbw93ec/ip59+yoQJE4iOjsbLy4sJEyawdu1a+d569eoxePBgVCqVy55hJz4+PowbNw6NRkNCQgLJyclkZWVx5swZDh8+zMSJE9FqtXTv3t1lcrA0WVlZGI3uOiAgIIAnn3ySadOmuZ1buXIlkyZNqjYdsHz5cvr06eNyb8nnoV+/flUyRrOzs3n99df/Uh2wfft2Dhw4UGUdkJ+fL3+/EXXAn4pe3r17dz755JNKbZBXUFBQUFD4L9Dvk194Od6PDq9tpsjmHqv0iTsaEGjUIUoO97WS+OjUdG9y/f6mhhj1LnuxAdQqgVY1/ZEkqcxV6wahRrmunq7x89K6GRIVsW7dOjp06IDZbGby5Ml0796dvXv3Ao4gT/PmzePcuXMIgoAkSVy5coX69evzxhtvMHHiRI4dO8aDDz7I3LlzSU5OpqioiJCQEFm+3W6nf//+lapLyaBA3t7e8kticnIyXbt2ldssSRJRUVEeZZhMJvLyPb9cPvnMs7RoEsOxpCSX46kXLlCjhAdhdHQ0Fy5ccLt/wIABnD17lgcffBCz2czjjz/OhAkTEASBfv36sWTJElq0aMGKFSvYuHGjy725ubmym25VmTJlCiNGjKjUvkij0UhOTo7LsZycHI9GiKd7W7duDTj6YNq0afTr14933nnnmsr5M23+Kyj9BKkEgSAfXbnPoJ+XttxnUKdREaRxyBDFysVI7tChAy+++CKHDh2iQYMGaLVa4uPjeffdd4mKiiI2NhaTyYTNZuO5555j9erVZGZmIkmSS4BGURSZN28e77zzjrwf+9KlSxQWFtKkSROX6+Lj4wHHczZp0iRefvllzp8/z8svv0zPnj05d+6cx7qW9ywmJyczYsQInnjiCfn6ktGlywtuBRASEiK7OXt7ewMOwy4tLY2QkBD0er3c9+XJ8vPzK9PAHD16NPXq1SOptA5ITaVWrVry9z+jA5o3b86qVauqXQcMHTr0L9cBs2fPplevXrz99tuVLsfH5+rk742oA/6U0b19+3YKCwurqy4KCgoKCgr/eLyK912+1qsFCO6GaHJ2AfF1ggDILLCQdCmPQqtIoLcWdRkv3KUNebNNRJIcL9pPdWmAX/FKmUoQZFdVrWQjChu5ZmulsotU5CZ+vVJq6vV6BgwYwKuvvsrly5fJy8tj1KhRbNu2jVatWpGdnU1wcLD80jtgwAAGDBjA5cuX6dOnD3PnzmXYsGEYjUYyMzOrtZ5RUVEsW7ZMfhksj6ZNm3LqxAmP5wIDAxk6bAQzS0QwBoiIjORcSgotmzgCLCUnJxMZGel2v06nY+rUqUydOpWjR4/SuXNn4uLi6Ny5MwkJCdxzzz3ceeedhIWF0axZM/m+tLQ0bDYb9erVq0qzZbZs2cL58+d5/fXX5WOtW7fmiy++4J577nG5tkmTJrz33nvy98LCQk6ePEnTpk2rXK5zosUTlSknKSmJgQMHVrncv5vqeAYd11Te6D569CjffvutbAy3b9+egQMHEhUVJa+mLl68mK1bt/Lzzz8TGRnJd999xzPPPCPLUalUrF+/nh49ehAZGUmnTp0IDg5Gr9dz6tQpeQtIWQQFBTFmzBg+//xzMjIyPLazvGcxKiqK6dOn07NnT8Bh3KelpZXqk6oTHh7OpUuXsFgscvT2c+fOlfmbjo2N5fjx4x7PBQYG8sQTT7itdkdERJCcnEz9+vWBP6cD7rjjjmrXAVu3buX8+fPMmzdPPvZX6ACVSlVlHVBygudG1AHXHhJTQUFBQUFBwY0LWUUAzFybyKT/HXT7qFQCe5Iz2ZuSyckr+WhUAr56NVa7SJHNcz5tpyE/9b5mBBv1SBK8cE8M7/RtjbdWTad6wXSqF0yHukHE13F8WtXwR69Ru62q3WhYrVa++uorQkNDCQoKIi8vD5VKRUhICMfSspk+fToAxy/msHb7Xj5fuYYDZy5xJtuKoNZiEcE7IISb2rXnuRcmcP5yFpdyCti2a4/LHj8nF7Id43M0LZfECw73w2NpOSReyCbxQjZWu0i+2UZmgYW+CYN4YcJEjpw8S0a+mT+SjpcZjbtLly78sqvsfLhPPP0s675fS1pqqnys90MP8dqsmWRmZpKSksLcuXPdXEPBYfwePnwYURQxmUxoNBo54FCTJk0IDg5m7Nix9OvXz+W+7du3c/vtt19TtGZwBIc7ePAgBw4c4MCBAwD88MMP3HbbbW7Xdu7cmcLCQhYsWIDZbGbGjBm0adNGDqA0ZcoUOnfu7LGcPXv2cPz4cSRJ4sKFC0yePJl7773X47UVlWOxWNi3bx933HHHNbX5v0RwcDANGzbk7bfflo1uHx8fwsPDWbZsmbyfOzc3F4PBQEBAAJcvX2b+/Plusm666SaWL1/Oww8/zN69e1GpVAwaNIixY8eSlZWFKIocOXKEPXv2AA539P3792O1WsnLy+PDDz+kbt26BAcHExwcjCiKLqveQ4YM4aWXXiI1NRVJkjhz5oz8LA4ZMoSZM2fK+5ZTU1PZsmXLn+6f2rVr07hxY2bPno3VamXdunXynmlP3HPPPfIeeU+MGTOGNWvWkFpCB/Tq1Yvp06dXiw547rnnePjhh13u+7M6YOPGjezZs4fffvvtL9UBEyZM4L777vN4bVnl1K5dG7hxdcCfMrqjo6PLzNunoKCgoPDPod8njr10HV7bTKtpG+RPk8nraTxpPR//fJqv/zjv8fND0sW/ufY3Fk4j98nbGzCmSyO3jyM9kERshJ/bx0vrOXJrerYZgFe+O8zlPDMB3loW/HSaSf87iCAI7E3JdPn8mpzJwQvZWOwioofVgnyzjQtZhRRYbB7Ls4kiqdmFZORbPK42SJLElTwzaTlF2ETRg4SKueuuuzAajQQHB7Nlyxa++eYbBEGgWbNmjBgxgubNm9O5TVOio6PR6XRE+HsR6CXw1qwpdIitS5e2zTD5mXhuzGiMeg2LFy8mPe0CbZrF0Kh2DSaOG+vRG08qdr1VqwTZ1V5V/N+O74LjfwI8M2YM7drfTNc7O1M7IoQ+vR4kJSXFY3vatWuHxWLh+LEkj+cDg4IYNmKEi1vkxBdfokHDhsTExBAXF0efPn3koHElSU1N5YEHHsBkMtG2bVtGjBghr0KCY79tYmKi237uJUuWMGzYMPn70qVLXVbBKiIoKIjw8HD5A8jutgDdunWTc+Hq9XpWr17NvHnz8Pf3Z/v27Xz55ZeyrHPnzsmGXWlOnDhBly5dMBqNtG/fnpiYGJdAWF27dpX3lFZUzrp16+jYsWOZeYRvFGx2kXyLDZvd8/MjSpIjarnNXuYzWGS1U2Cxe3zGK0vHjh25dOkScXFx8jFnZHOn0T1w4EACAgIICwujU6dOdOnSxaOsTp06sWDBAu677z4SExN588038fPzIzY2lsDAQAYOHEhmZqZc/4SEBIKCgmjcuDEnTpzgm2++ARyG//jx42nZsiX+/v4kJyczbtw44uLiiI+Px8/Pj3vvvVd+Fvv27cvQoUPp3r07JpOJ2267jWPHjl1zn5RkyZIlrF27lqCgIL744gt69OhRZjaO9u3bY7FY3FzInQQFBTFy5EgXHTBhwgQaVqMOKG10l9YBixcvrtLKc0U6oCrPZmV1QLt27WjSpEmVdMCiRYvka29UHSBIZa3d/4fIycnBz8+P7OzsClOGHTlyhMaNG1drSp3Zs2czfvz4ak1JVZ31vB7t/qfIVMbnxpZZ3ePzT2n39ZA5+LNfeK6tkUxdKAgqCq12Xv/hKOcyC3nhnhi0OhV3NPK8pysxLYfbG7if+6+Oz91vbuWNLsGcsQcgeVhnzrTY8DVoaF3D3+3cict53OGhLwd+uovn2/vKMl9Zc5hWNQO4r0UkWRYbneoHu1x/NrOA7LwCaqtziahRi2A/R/7QRz/fw8lL+RSYbYiSiISAl1aNoYSxL0oSOUU2l5oH+ehcNqJmlMrbHeSjI8LPi/f7xFZrPuRjF3OpadKQkmOTDQ8JivesCoT5GVCXs3zg76VzO3b6Uh7hRjVmydHmrAIrBVY7kX4OF3y7JLkEjhMlKLDa8dY6vBFKB5ErWc/lq/7Hlk0/Mu+d9+VzdklCp1Fhs0t469x/Y2abu0yne2x4eHilUlKtXLmS+fPns2PHDvnY8ePHGTBgALt27ZLdtSvKCWwwGDAYDEyZMoVRo0ZVWG5VcmC3adOGDRs2EBQUVG0yPdG5c2feeustObrx9crTXZXxKc2m4+lkF12d8NKqBDQqAVGSUAkCIu55uQ0alfwISkBROee9dGo61wu5Lm2vbpk34vh4wlnP22+/naefftrNq8TJypUr2bBhAx999FGlZVZH2506YMOGDbK80jqgMpTWAdVZR6cOCAgIuG7jYzAYuO2221x0QHXIdObpPn36NHXq1HHZslVZO7JSvgbJyckum/wr4vz582UGGlFQUFBQuPE4n1kIGJm5NpFCu0RekQ27JBFs1LHw59MMv70+vyZnUk3vRAp/E58NbgdcfSnNVXkOcBPs627MSsX/JwiejVnnC0p14oz5FurredLGLknYJQFvrZpS8eHcjJa/gvsf7Mn9D/b8S8s0m828//77DB061OV4gwYNyo0G7YnqHr+S7Nu377rJLsnWrVv/knL+DJ4m1q6H8anw59m9ezcRERFERkaydOlS/vjjjzJX+gF69+5N7969/8IaXtUBQ4YMcTl+o+oA8Rq9oyrLjaoDKmV0t23blgceeIDHHnuMtm3berwmOzub5cuXM3/+fIYPH+4SYEFBQUFB4cbGGez2ydsblLk66+elJTrA2+3cicueo7X2/fgXWgAdXt2ETXD9c+OM4F0W3jo1XWPCK98AhWsiwFuLTuO++mqXJCRJQOMhNdHfYcwqeObAgQPEx8fTqVOnSkdqV1BQqDznzp2jZ8+eZGVlUbt2bZYvX+6SJeHvprQOsNs9xwVR+PuplNGdmJjIjBkz6NKlCwaDgTZt2hAZGYnBYCAzM5PExEQOHz5M69atee211+jWrdv1rreCgoKCwg2O05X2td4t0WhdDeySEbxLcyajgPQ8c5lyK0rF5QmjXsPdjW6s/V0KCn+Wli1buuSmVVBQqF569epFr169XDwRbiRK6gBJkhSj+wamUkZ3UFAQc+fOZcaMGaxdu5adO3dy9uxZCgsLCQ4Opn///tx9991VCs6hoKCgoPDv5mJOEQ2AGWsPYxM02EQJjUpAAIbdVp+9KZku14uSY6+uXZLQlrNRtzKpuEqTmJbj8bhC9XM0Lddj4KcQkwFNaR/wYlQqAT8Pe6X/KTgDWV3MKXJL1hTia8BmdxwttCovxAoKCgr/RaoUP97Ly+tv2augoKCgoPDPI8Soh3x46rYGXMi18dqGJJ6/qxE1A73JstiIjfBzuX7/uSyCfHREB3iX6bKucONTI8DL43GLXXQJUFaSf7ox6nTBD/DRIUnIUdHtooSEhJe2ZGA2R7AsJ+YyolcrKCgoKPx7uLakbQoKCgoKCn8TM9cmUmR3X0kddlt99iRnyjvSbaKEKEkICGjV5QcmGr5oLxYPdt+tTUIJNRkw6tRuwY10alWZK+vVzcUcM+DLW5uPU2RzpBz7/VwWSRdzGXF7PX4vzjftxC5KqCUJi136U+mEroXU7CKXSONC8b8hvgbyzI6Izf/WOFFqQUASKE4D5dgTXxqnwW0XJdTFnh8KCgoKCv9uFKNbQUFBQeEfRVnB3rIsVvItdppHmNxWVCtaOU/NKiLfJiLgWKW0ixKSBEaDFqtdxJkhSxCuGkkF/HWrs9bi1dCBcbURS7XdLkrkF88Y3FTT/+pxqxnLlTyXVdW/AmekcYtNJC2niHCTAZ1GhV2S8Na5jkuBxY5GLfBXzgtY7CKgJj3XjCg5xhlJIi3HEbU3xNfgtvIuAGZb5cc7PdcRkyDS3/Oqf6HNjiiBl4dtEgoKCgoK/z4Uo1tBQUFB4T9PZqEFqx28dRrMNjuFxUasKErodGpECWzFaU40KhUq4e9ZrT18IRuTl55Aow6rTeRynpk6YVfTfqXmFOGjU2PQqEGUKLYn/7HYi8Pqp+cUIXK1LULx/4X46qtsEDvRa1Ro1SpHnAFBwCaK2OwOd3C7JKARHCvVAsh5rq0ePCxKYraJWEpEd88rsmH00sj7vCVJQpSutqMieQoKCgoK/w6UKVYFBQUFhf88L3ZrwuR7m9Io3BerXWTa/c2Ydn8z7KJE80g/Wkb5cVPNAGJCfSmw2qkb5IOX1j3VFlw1AOf+mMSsdUfcPhqVY1/vgfPZbp88c/nG4ze/neeTnac4czmfLUnpfP7zGRe39/PZRRy7lM+FnCLyzTasdhHJLbTXPwfnKr3VLmGxiVjtjo/F7jBuJcCgUSFJYC/1KcngwYOZPn06AHt/+Zn27duTW2Tjcp6ZnCIbZpudy7kWMgss8j02SaLIJmK2iUiS5Pi3jL50uvAvW76Csc884TDUgZwia3H9RUSxWJ5dlKXYJKlMmatWreL2u7tx+EI2icWfQ+cdn0t5ZjILLPIno/iTWWAhu7jMY8eOcfPNN1epvwVBwMfHh7lz51bpvhuJW2+9lYMHD/7d1fjPcObMGTSaf9Ya3syZM3nqqaeui+yvv/6aYcOGVYusxYsXc++9917z/YoOuLFQjG4FBQUFBYVqJKo4kFikvxcXsotcPjUCvbEVG/KRfg435oISnzKCe8uoBYG+7WrROMLEvS0iaVc70O2aEKOO6AAvTF5aNKob8898w/r1CDD5UjM0kOjIcAb170tmZqbbdc6AZAE+OkrufhYATXHwMpUgYNCqK/1Cc9PNt7B7924AfPQa/L21GLRqAo2uZYDjJcmgVctllIVzcuDDea8xcPiTSMC5lGRa1Axg+OAB6NQq1CoBvUbFza1i2bl9W4X17NmzJ4uWfU2tQG9qFn9ESSoOxOZIg+f8SDj2zfvoNXILGjZsSHh4ON99910le8ZBUlISY8aMARwGlSAIGI1G+bN48eIy7/31119p3rw53t7e3HrrrZw9e7bS5aalpdG7d2/8/f2pUaMGEyZMuKZyRo8ezZQpUypd7n+VqVOn8sgjj8jfi4qK0Ov1TJ48WT6WmJiIl5cXFovFk4g/RWZmJoMGDSI8PJywsDCaNWvG559/Dlx/Q37ixIm8884710X2K6+8wtixY4Grz0/fvn1dromJiWHr1q0Vyurfv3+Vn9+SVJcO8Pb2xtfX9y/TAX5+fgQFBf3rdECVf9ELFy4kODiY7t27A/D888/z0Ucf0aRJE5YsWUJ0dHS1V1JBQUFB4fpiL3adnrfpGAWWEtGUBYc79Yjb63El30JWodXNXbkiQ/G/Stvagew8mSEbQRJwc4nAa/5eWvQaFUUl3JHVZXSmczHb30dHwzBfwGHo3VzXPZBbmFEvr36XFjfk81+5mFsEEtjsNsQSpqpNFMkptKFWCQR4a8vdC67yUM8wXz3vPhJb5j2lWfP9Olq1i8NcVMBjgwYybdq0MldX9BoVarWAzS7Jgdm8SxjBAqBRCVjEqq3q+5TY++8soySaEoHOHKvXZffJr7/+ik6rpU79hvIxlUrF1s2bSDp6lEYxMagFdwnlySyw2CmwFAKONl/9fQjkW2xXyyk+X1DiGEDfvn355JNP/tRqmVqtJi+v4mwCZrOZnj17MnnyZBISEpg2bRoJCQns2LGjUuXcd9999OnTh4ULF2I2mzl37tw1ldOtWzeGDRtGeno6oaGhlW/oX8jm4+lYPGwtEEURVTkTZRISFpuEJEnoNKoyn1EvrYpb64WUW4cOHTrw0Ucfyd/37NlD7dq12blzp3xs586dtGvXDp1OV1GTqszo0aMRRZGkpCQ0Gg0nTpwgNTW12sspjc1mu24G/Z49e9BoNMTExMjHVCoVGzZs4OjRoy7H/yqqSwfk5ua6BRMtTXXpgC+++AJBEDhx4kSlyxkwYAAbNmwAblwdUOUp8JkzZ+Ll5ZjF37VrF++++y6vvfYawcHBjB49utorqKCgoKBw/XGuzvoZNKRmFxFo1JNVaOVijplH4+tgFyWaRZgI99VTYLUjFO9pLrDay82p/V/mUp4jmFZUgJccUCs9t0g+Lxa7LGtUAgFe2uJjnmXpNQ4DM6vAIu9zBhwGNOCjU+Ojc1xTXvqti7lFhPoaCDUZiAwwUiPAW/7UDjJSO8iHK/kWUrPNBPvqCffz8vgJ9TW4fS4WBw+rKj4+PnTtcS9HjhyRj82cOZPo6Gha1I2kc+fOHDp0ELvdkWrrs3fnckebGJrViaBTuzYcSUwE4MqVDIYPHkhM7Rq0adqIJV8u8ljenp930qxZM8ARZTzE18Bnn3xEm2aNiG9ah3lzXkPjdGu32Zn2yhQa1q9LdI1IXhz/PDabzaPcH3/8kbY3xwGg06jQqR17xYcOf5yZM6bL1zkjumsEgaKiIsaOeobw8HBq1arF1KlTEYsnwJYtW8aIvg8QZjJg0kqMeXwI8U3r0KFZHXp0uQ0vrZqxTz/JnFnT8dap0aoFRFGiReNG/PzzzwB06tSJTZs2lVnn6mTr1q3o9Xoee+wxDAYDL774Ivv27eP06dMV3rtu3Tr0ej1jxozB29sbLy8vYmM9T+BUVI5Op6N169Zs2rSpWttXnWQX2bCJEl5atcvHR691O1by463VYDJosEkSOUWOCTJP1xVaK05Dd/PNN5Oeni73286dOxk2bBhHjx6Vfy87d+6kY8eOgMPzIjQ0lKCgIPr3709GRoZHue+88w4tWrQgPT2dwsJCnnrqKSIjI6lRowazZ8+Wr/v111/p378/fn5+qNVqWrRoQdeuXQG46667sNvt8spqcnIydrudyZMnEx0dTVhYGGPHjnX5Xb/77rs0aNCA4OBgBg8eTEFBAQCff/45t912GyNGjMDPz4/PPvuMKVOm8NhjjwGO31P9+vWZOnUqgYGB1K5dmx9++EGWe/z4ceLi4jCZTPTt25c+ffrIW1VKs379erm/nAiCwFNPPcXUqVM93lNUVMSTTz7pUQd8/vnn3HnnnQAUFhbSt29fAgMDCQwMlMsZPny4y6quJEnUrVv3P6kDzpw5A9y4OqDKb0opKSnUr18fgG+++YZevXoxfPhwZs2aVemZDAUFBQWFa6ffJ78A0OG1zbSatkH+NJ2ynphJ6/hk12mWHzjH8gPnWPH7eb7+4zzLfz/P8gPnWH/0YrmyQ3wNPHNHAx6+qSZjujTitkah5BbvEQWHwVA/2IeYUCMxoUbqB/tcz6b+o5Ek6N++FsM61mV4p7r0b1/LZfeu1S4R6WcgNsJEvWAfmoX7luk1EODjMMo7NQhxHQ/JsfpVcjxKluEw8CrvimDy0tKyhj/5Fhu/p2TLweOuJzk5Oaz97n+0b99ePhYTE8PevXvZm3SW22+/nSeHD8XXoCEn7SzLFi1g885fOHjqAp8u+oqAQIeL/YjHHqVGVCQnTp3hm2/XMHXSy/zxxx9u5V11WdfKx3Zs28qWn/ey9H/rmDNrBufPnsZLo+K9t+bx0087+WnXbn4/lMjvB37jgw8+8NiOw4cPU6defYJ8dAQb9QT4OFYHRzz5ND9u+IGko0dlg1uvUaHXqJj/2iyOHknkyJEj7Ny5ky+//JIvvvjCTfaSxYsoLCjglz+OcfzMOV6c4niB79u/H8uXLZOv27PboRtuueUWACIiIlCr1Zw8eRJwGPItWrSo1Lg4sdvtREZGEh0dzahRo2RjpjSJiYk0b95c/u7t7U29evU4fPhwhWU4V1m7du1KSEgI3bp1c5mEqWo5MTExHsf+RkGrEsg128g1V90QUgkCQd46NGqBK/mW4mj8Vcfb25tWrVrJK9tOAzs2NpbffvtNPtahQwfAYXSfPn2aU6dOkZub69GInDNnDgsWLGDz5s2Ehoby3HPPkZGRwbFjx9izZw+LFi1izZo1ALRv357x48ezaNEi2VhysmHDBtnDIi8vj1q1ajF37lx27NjB3r17SUpKYv/+/fKzuGLFCj744AM2btxISkoKVquVOXPmyPJ27NhBXFwcmZmZJCQkuNX7zJkz6PV60tPTmThxIsOHD5fP9e3bl7vuuosrV66QkJDA6tWry+zTgwcP0qBBA7fjo0aNYv369Rw9etTt3KxZs0hMrFgHLFy4kPz8fM6fP096ejozZ84EICEhgSVLlsjXObfOlKUDvvrqK5fnpzLY7XaioqL+Mh0QHBzMHXfcUWUdkFg8AQs3pg6ostFtNBq5cuUK4HgounTpAoDBYKCwsLB6a6egoKCg4IZX8Yrma71a8Faf1vLng/43Me/hVug1ahqFGomrHUhc7QB5L2rLKD90ZeWrLrbUOjQIIcjoSPmk06jo1DAEf2+H8SBJEoHeOvy9tI7UWYKAv5cWTRmWonMVdv7m4yzecxaTQcPiPWd5fUMSKhX8fiHb5SNKElfyLfx+IZsCT0mzi3lr83Fe35Dk9lGpHPm4T1zOd5Ndnrxqp7gvG0eYaBRukvuqUbiJppF+jkskCb1GRYTJIBuBBq0aXQVeA7fUD8bfW4ckOVxMm0X5ydG1neMRKJ8vdomuovv/X2V4339vD2pHhlIrPJQTx48zcuRI+VzPnj0JCQlBq9Xy/PPPk3joINiK0Gg0WMwWTh5Pwksr0KBRI8LDw0lLS2Pn9m1MnzETvV5PTEwMvR/pw6pVq8os36BRy8/Gs2Oew2TypU2rFjRpFsuhQ4dQCQKLFn7O5FemEhISgr+/P08/O4qVK1d6lJednY2/yRd9qb3f/gEBjHh8JLNmzpBd1J1DsnLFcsa/+BIBAQHUqlWLsWPHurxAO9FqtWRlZpB89jQajYa4eIch1KFDR8xmM7/t3w/AqhXL6P3wwy73+vr6kp3tyOP+yCOP8Pvvv5c9KKUIDg5m3759pKSksH37dg4cOMALL7zg8dq8vDxMJpPLMZPJVCnX9PPnz7N06VKeeeYZzp8/T5cuXXjwwQex292f28qUU7LNNyIatQpfveZvN7w7duzIzp07EUWRAwcO0Lp1a+Lj49m5cycXLlwgOTlZNt4SEhLw8fHBz8+Pp59+mp9++slF1vTp01m+fDmbNm0iKCgISZL47LPPmDNnDkajkcjISEaOHCk/P++88w49e/bkjTfeoFmzZjRv3pxffvmlzLp++umnTJ8+XX4Wx44dK8v69NNPmTBhAtHR0Xh5eTFhwgTWrl0r31uvXj0GDx6MSqWSvXVL4uPjw7hx49BoNCQkJJCcnExWVhZnzpzh8OHDTJw4Ea1WS/fu3V0mB0uTlZWF0Wh0Ox4QEMCTTz7JtGnT3M6tXLmSSZMmVUoHXLlyhVOnTqHRaOSV7o4dHTpgf7EOWL58OX369HG5t+Tz0K9fvyoZo8HBwfz0008kJyf/ZTrgwoUL3HPPPTzwwANV0gH5+fny9xtRB1R5U0OXLl147LHHaNWqFceOHaNbt26AY4a3du3a1V0/BQUFBYVSpGebASMz1yZSZJewiRKCIOC0p4fdVp+UrEKSMwuxig7jTK8WOHap7D98qdlFgMnjObsooVarOJyWS7MIz9d4ItiohwIYdEttjl0u4qvdydzbIpJG4SbsokTj4r3J4DBC953LxqARaBzmy9kMzzPpAP3bRyN42PdoEyVUdonoAAP+XlqXc+XJq27K60sn+85l06aGX4V75DxhFyVe+e4wwUY9z9zhvqoCcDgtF7vVQsNr3IrpNLwPnMvi95RsWtT0q/agbP/7bg2t2sVht1mYMfUVunfvzt69ewH4+OOPmTdvHskp51CrHOm6MjOuULdePV6ZOZsZUyZz8sRxut13H9Nnv87Z5LMUFRVRMzJclm+32+nfv3+l6iIaro6Xl5cX+cUviSkpyTxwbw8o3ostSRJRUVFu91vtottLX0mGPfE0rWMbcywpyeV46oUL1KhZU/4eHR3NhQsX3O5/pG9/Eo+dYsSgftisFgYPfYyXXpyIIAg80qcPy5ctZVKzWP63ahX/+36dy725ubn4+flVqh9KYzQaad26tVy32bNn06tXL95++22P1+bk5Lgcy8nJ8WiElMbLy4sOHTrQtWtXJEli1KhRvPrqq5w4cYJGjRpVuZw/0+a/Ct/ieAJOo9tXX7VXcqfhfaXAwpV8C0E+ugon7UrToUMHXnzxRQ4dOkSDBg3QarXEx8fz7rvvEhUVRWxsLCaTCZvNxnPPPcfq1avJzMxEkiSCg4NlOaIoMm/ePN555x0CAgIAuHTpEoWFhTRp0sTluvj4eMCxOjlp0iRefvllzp8/z8svv0zPnj3L3MufnJxM165dZZ1Z8llMTk5mxIgRPPHEE/L1JYO/1ahRo9x+CAkJkffSe3t7Aw7DLi0tjZCQEPR6PVJxMJPyZPn5+ZVpYI4ePZp69eqRVFoHpKZSq1Yt+XtZOmDAgAGcPXuWBx98ELPZzOOPP86ECRMQBIF+/fqxZMkSmjdvzqpVq9i4caPLvX9WB7Rq1Qq1Wv2X6QCA5557junTp1dJB/j4XPW8uxF1QJX/gr777rvExcVx6dIlvv76a4KCHEFc9u3b5xadT0FBQUGh+gn2dVhST97egDFdGlFotdMozJcxXRoxpksj7KJEbIQfzSP9aFPDnxCjHlGC2Ai/MtNcORFLRUmTJInTlx2GRJFNdNlPfPWa8usrShKHzjtmnA+ez/ZYRk7xi2dZZZTk7JV8+QXIU70zCixu5ysKr/XW5uO8/kMS+5MzsdhEXvvhKK//4L4if/xyHhqVwPHLeRWunls87K02W+0UZ5kmx2yrcj1LjsflPHOxPFfsxampoOw94pXhWle803PNpOUUcSXfglrlWIlLy3F4PRRa7RRa7UgScuostUbHw337s2/fPi5fvsyZM2cYNWoUCxcuZM/Rs5w6dUrOkw3wcN/+rNu8jV37/yDl7Fnee3s+oeGRGI1GUtMvk3bpCmmXrnD+UkaZruBOSna/2ebevsioKL5d/yOnLqRzNu0SKRcvu7gwlqRp06acPHHc4zm/wECGDBvBzJmue0EjIiM5l5Iif09OTiYyMtLtfp1Ox9PPv8iW3Qf4+n/f8dF777Jt21YA+vXrz4oVK9iyeRMhoaE0adpMvi8tLQ2bzUa9evXK7IOqoFKp3H6zTpo0aeKSpqewsJCTJ0/StGnTCuU2a9as0hNQlSknKSmpzP2gNxK+es3fuuLdoUMHjh49yrfffisbw+3bt2f37t0u+7kXL17M1q1b+fnnn8nOzmbx4sUuvwOVSsX69esZNWoU27dvBxwrpHq9nlOnTpGVlUVWVhY5OTmsW7fOrR5BQUGMGTOG1NRUMjIyPP4WoqKi2LJliywrOztbfhajoqJYuHChfC4jI8MlCNe1TG4ChIeHc+nSJRcDvqxJAYDY2FiOH/esAwIDA3niiSfcVrsjIiJITk6Wv5enA6ZOncqxY8f44YcfmD9/vhwBPSEhgWXLlrFx40Y5EryT/5IOKDnBcyPqgCob3Tk5Obz11lv873//45577pGPT5kyhQEDBlRr5RQUFBQU/lpeXXeEtzYd53h6Lgt/PsOMtUf4YtcZ+fxv57P5/Xy2nALr9/PZFJQRuKuoOJjPq+uO8sc5h9H9x7lspn7n2N+VXWjl2KU8fjufzfFLV1cIfytHJsCSPclyvY6n5/LWpmNM/e6w7LSbUWBl/7lsDqXmyGVUZCBn5VtIzizgSr6FnCIbKZmFpOUUYRclIk0G7KJEgcVOoVXEJkoUWsUKDdrXNyTx+vqjbE1KZ0tSOq+vP8qM76/uUTt+KZ/fzmdz7FIe2YVWDqXmlFlPc3FfzvkhyWU8nPJKjsdv56+61NlE0W2Soypci+EtihLW4pzadlFy5NYuzqntCLrnGCdRckwymK1WVi5bKgdpysvLQ6VSERISgs1mk4MWpeea+fX3w2zZupVzV3LJtgro9HrUajWhEZHc1K49L730Ern5+eQVWfht/36PBrLzhTE9p4jU7Kvb4q7kmYu9FEBEotBqp2/CIKZPmURaaipWu8iZM6fZts1zuq8uXbqw86efuJhTRF6RjfxSOddHPv0s677/ntS0VCx2Cbsk8UDP3rw2ayaZmZmkpKQwd+5cN9dQcOw5P5F0BFEU8TWZUGs0qNWOCbTGTZoQHBTMiy88T++HXe/dvn07t99++zVHa96zZw/Hjx9HkiQuXLjAhAkTuO+++zxe27lzZwoLC1mwYAFms5kZM2bQpk0b6tSpAzjeEzt37uzx3p49e/L777+zceNG7HY777zzDuHh4XIMoaqUY7FY2LdvH3fcccc1tfmv5u80vIODg2nYsCFvv/22bHT7+PgQHh7OsmXL5P3cubm5GAwGAgICuHz5MvPnz3eTddNNN7F8+XIefvhh9u7di0qlYtCgQYwdO5asrCxEUeTIkSPs2bMHcLij79+/H6vVSl5eHh9++CF169YlODiY4OBgRFF0MXCHDBnCSy+9RGpqKpIkcebMGflZHDJkCDNnzpT3LaemprJly5Yq92VpateuTePGjZk9ezZWq5V169bJe6Y9cc8997hEfy/NmDFjWLNmjUuU9l69ejF9+vQKdcCWLVs4fPgwoujwqtGU0AFNmjQhODiY5557jodLbS+pDh1w4sSJv1wHzJs3r8o6wOlxfaPqgCob3XXq1OHy5ctuxzMyMuROVVBQUFD4ZyJK0K52IA1Cfbm7aRhqlXsYLoNWTZivnjBffbm5iw1alSyzdBkS4OelpYafl8fZ7fLSkAk4gmHd1SScBqG+tK0d6FaGBIQY9SXKKFseIO9jL12Gs54hRn2Fq9CeCPHVc0u9YOLrBRPsq3c7LwgCNfy85DLKQl/cl57GA6jUeIDDDbqqVNXw9tap3frK+V0tCHJcgYfu6050aCANa0WyY/s2vvnmGwRBoFmzZowYMYLmzZtzR7tYoqOj0ep0jgBk2Jk5+UU6NK3D3e0drq+PP/UMAB8sWEjqhfPExjSkYXQUE55/zmOsGefvTe3BFdfZtyoENGoVT40ew03tb6b7nZ2pExFCn949SSmxMl2Sdu3aYbVYST51Eh+9Bm+d61gEBgXx6GPDyc3JQaMSUAsC4198ifoNGhATE0NcXBx9+vRh0KBBbrLT0tJ4ekg/YutEcEfHeAYNGUqHDlejJPft14+jR47Qq9QL95IlSxg2bJj8fenSpS6rYBVx4sQJunTpgtFopF27djRp0sQlQFW3bt147bXXANDr9axevZp58+bh7+/P9u3b+fLLL+Vrz507Jxt2pQkKCmLlypU8/fTTBAQE8O2337Jq1SrZqOjatascOKqictatW0fHjh0JCwurdDv/bqrb8K7KRFvHjh25dOkScXFx8rH4+HjS09Nlo3vgwIEEBAQQFhZGp06d5HhOpenUqRMLFizgvvvuIzExkTfffBM/Pz9iY2MJDAxk4MCBZGZmAo7Jr4SEBIKCgmjcuDEnTpzgm2++ARyG//jx42nZsiX+/v4kJyczbtw44uLiiI+Px8/Pj3vvvVd+Fvv27cvQoUPp3r07JpOJ2267jWPHjlW5Hz2xZMkS1q5dS1BQEF988QU9evRAr/esq9u3b4/FYnFzIXcSFBTEyJEjXVyjJ0yYQMOGDSvUAampqTzwwAOYTCbatm3LiBEjXCKlJyQkkJiY6GZ0l9YBixcvrtTKs5MTJ07Qo0cPfH19PeqAqjybldUB/v7+rF69mtWrV1daByxadDVbxY2qAwSpLB+BMlCpVKSlpbnlPTt79ixNmjQpcz/TjUxOTg5+fn5kZ2e7bcwvid1u58iRIzRu3Fj+EfxZzGYzs2fPZvz48WU+xFWluut5Pdr9T5GpjM+NLbO6x+ef0u6Bn+7i+fa+nLEHICHwyprDtKoZwH0tHC5pWRYbnepf3W93tngFt3UNf05czuOOBu55K2+fs5m37gll0vZMHmh9dX/ZofPZLN+bwtSesWg1KgSgVQ0/OT+sKEocTc/j/mYRbjIfeX87TdK3sDegE4dSC+TIzU0iTTxycy3iinNWZxRYOHXl6p5rAQjy0dGloWs9nX350LJk7m1Zg2ZRV/drLfs1mUduroW2OLVWgJeWeiUiq5+8nM89Me5/gJ0y92Z7M+37oy6u7Q/fVJNmtfzkep68nE9m4dXI4b56DT2ahLvJdPZlnxUpjL4rRk6pZrWLzPz+CJMfaIZW4zhWN8ibQO+rG6+T0nPp0cS9L/t/8jMTbjax5LjI4j1XjT61SmDyA03lOoqixG/nsx2rypKN2upcoqNrE+Lv2D/f8/2fyDPb0KpU2Ow27JIKQXDNDy5JrsHXnMHYrHaJ7EKrnMe79GSJM093gV1FRr7FJXWZl1ZNgFGHUedYbbGJEuYSEwBqAfy93DegJ6XlUMtPy8kMC/7eWpdJhcx8CwFGnVwPjSCg11w1pM02Eb9Se/sBTl3KI8KoJt+uIjW7yMVVMtBHh0GnlutptonYSpxXFwerK6ueH3yxgt9+2cbct99z9GVx35XsK71a5RJ8sMhmd2u7KIqkpaXh5Rck33shuwhvrRp/by12ScJYYg/wqq+/5u233mLtj5tRqwRMBi3Hjx9nwIAB7Nq1S3bPLyoqwmAwlOnGaTAYMBgMTJkyhVGjRnm8piSVkemkTZs2bNiwQd6WWB0yPdG5c2feeustObrxn5XnCef4hIeHl5tXuyy2nbzkMa2XxWbHLjmim2uuIRWjM4+3l1bFXY3Crkvbq1vmjTg+nnDW8/bbb+fpp5+mX79+Hq9buXIlGzZscMmBXpHM6mj7ypUrmT9/Phs2bJDlldYBlaG0DqjOOjp1QEBAwHUbH4PBwG233eaiA6pDplCc4vH06dPUqVMHg8EgX1dZO7LSvgZjxowBHH84Jk2aJAcaAMdLy+7du2nZsuU1NElBQUFB4e/G+cc0t8h1lSWnRHoqcBgRNlGSVyttlZi3zSm0FrsWC1jtEjmFrjKdq6+C4EyBRYUbxXNL1Su70AoIqATHSnppF8uK5pcLrXbZ4NaoBGyi5FaGU6azjLJkOvvSLkoUlchjXmi5WoazrVZ76f3t5VZTHg9nHe2i5HKPTZLkVWVP70erRjpWGZwvpVdEb7QalSPoXTH5Zhs+Bg0qQZBleWvV8iqwxS5iMrgbns4XFAC75Gynw9izl2qYs5YCFe9jd1J6r79cRrEMqYq+CGJxBHiXeooV1bP8Mrr0uJ/+/a6uNEmihFD8rFytZ/ViNpv56MMPGDj4UZfjDRo0KDcatCec43c92Ldv33WTXRLnPtcbmVvrhbgdux7Gp8KfZ/fu3URERBAZGcnSpUv5448/ylzpB+jduze9e/f+C2vo0AHvv/8+Q4YMcTl+o+oA8TqnorxRdUCljW5nzj5Jkjh48CA63dVZWZ1OR4sWLXjuueeqv4YKCgoKCtedcJPD6LIVRzt3vvRl5luIrx8MkkTjMCPns4sotNrlKLmFVjsVLcjYRYmeraNoFuXHoXPZ7Dp1xeW82SYS5qsnwleP2S5yPruo3P3SN9cLIrOgZK5qp/Ep0TLKj4wCK+m5Zpd2VPQn/lKumfqhRu5sHIa/t5Ydxy+7lSFJUCfQmwBvLRkFVi7nmT3KCit2I68bYuRijhnfYgM1PbeI+qFGJEmiRYSJ1FwzZpvdpYyKyCpwjEfHBsFkFVjZeOQiJf3NC612TAYNUX4GtKINS0ZeuUaev5eWvFL7yG3Fne+lVSNJEha7hChJqOWowRVWE0mCAG8dXjo1hRY7eaVcZqXi1TytWlVchucRcq4I++jVcr1KygDHhIBNlNzc3iuqps0uodeqMRk0qFUCeUU2tzLg6sq0pzKcOCdW9BoVVruEXuOot1V0ZA7w0qgQBAGrXSwe58oZVY7f8dUGF1jtFGXbCTHpKbTaOfj779xzR2fi4jvQs3g/95/Zw6+goODKuXPn6NmzJ1lZWdSuXZvly5cTEuI+afJ3ceDAAeLj4+nUqRP9+/f3mGJL4cag0ka3MyDBo48+yvz588tdPldQUFBQ+IdRbAMMuqU2JU2Cu5qGOwwKAXx0GhqGGF1e6n31GrI05bvND46vjU7ncMVqWSuAZjVc03jU8PeS3dU1ahUNQ4ycKSfF1x0xYZhLrBBLwLBOdREEx97GYB8dgd6uK7EVpdKpFejNwLirKczubhrutv85Jswo1zPYR+dmSDoRiovq266WSz2jg3yoH+oLgqOdNf293AykivZk3xEThqp4m4K3TsPAuNou5331GvyKjXy71Y5aJZS7P95Lp8arVKoiU7H7tDP/t0HjKqCiRTgJx152lzJK7W8uOR6CIMj9WhrnpInJoHWbOClZhkYloFG5llFeu8GRhz5Ic3UBweSldTPUDSV+2xqVgCiVLzTQR+dST12xu7uzfZ5+h2W1HRzbLCTBfR+/VFy3tm1acyXLNXVORYG0jqTmeDweYjK4uL271FElyL+rkpy6nE+kUc3RtFyPk0aeZJbc3+/J/V9B4UaiV69e9OrVy8UT4UaiZcuW8tZeSZIUo/sGpsqO9J999plicCsoKCj8S9GoVNjsEtPXJHLqUp68glcSlSBw+ko+p6/kl2swOCNBl87v7Py+/1wWOUVWjzIqWgfUqlWcupTH9DWJ2OySWxkqQUCUrpZRkTxP+ycdq7AOGaLkbhxVZq1Sq1axav85Vu0/V2Zf5hRZ5TLKkpmWbS6znkClxqM8JMkxXhabWGYdzHbRZR922cLKPlVgtbu5mlcWAbDYxOJ92GUXXVEZsot/GWU463mt68UCkFVgJaug7N+dvXjVuqIyqmU8SlEjwIuagd5uH5UARr0Go16Dl1aNCPKnLC8Mpz3tSabJS+vYisFVTxPnz9Nbq65w8kZBQUHh30SV48fn5+cze/ZsNm3aRHp6uptf/qlTp6qtcgoKCgoK7pzPKgJ8eWvzcYpsDtfq389lkXQxFyQYeWd9DpzPll9q7aLDPfj3C9mVMhRPX87DYhdZ/dt5xt7VyO28KElcKXa9jg4s22woz11awrEv+kxGAc0j/Tyer4hV+89hsYucuZJPwzBft/O5ZptchqdVupLY7KK8glwSoXj/dq7Z5jGAVkXY7CIHUrIAuK9FpEej+XRGgVxGWVTkel5yPK7F8LbY7EiSRGaBhTCT55Ucp+u1toIlZItdlFd4SyPhMCQryhdfFpnFOdgtdtElaJoTsXhPu8UmugSHK32Nsy5lIRVfp75Gy7DA4hhLf2/PvxlzcQq1ilzBswqthJbRV87x0FexK9NyzB5/T8G+BvKL6y1JlVuVsRV7cpQMSOd8tiVJwkundpEjSQ5DvcimrMYpKCj8t6iy0f3YY4+xbds2BgwYQEREhBLsQUFBQeEvxvly661TE2rSUTfEh4x8CxdzzKRmF2K1O1bRfHRqvLVqvLRq9FoVuUU2+aW6NFabYwL12wPnOXoxD3AEK1uyJ9kRSbx5OOm5Zgqtdpcc2knpeRUaJv/77QIBJm/qBPtw+nI+V/Is9GpXAwCLXeLE5XwMGhW+Bg1mq0ih1U5ROXm6Nx+9SGqORXbv/vbAeeoEG+nZroYj5U6RDbPNTk7xeYtdqvAl/8vdZwnwMRBm0hPoo+PUpXyu5FvoG+eI5H42s4DMAg16jRpfg6a4DM+rjM6c2usOpXI242rKqgU/nSbCz4serSPJLrKSW2SjyCbKwdTOZhbgVYGr/sp9KRgNrmN+b+tI+XxSet7VMRdsqIsnXMoiz2zDJiLvLRdFiYx8C4FGHfbifeyi5CqjIkMxu9CCVq1GoxLQaVRYbI7c5v4+DgNUxGF0OqOmO8rwLMtZVm6R1bG3vPjCrAILeo0afx9HJG9RlBC5uootAkJFExV5ZjRqFVqVgFqtwmy1YxMlAo0Ol3NzseGuwuFeLYpSmfV0bEVQcynXjF2SZIP/Yk4R4X4Gl0juToQS/VAWzvHQqAT0WjV2u4hVlPArYcwX2eyoil30nQH6yiPAW+vR68Ix3gIateDmBl/Wb93pOh5a7OpvsYmk5RQRbjKg06iwS5Jb6rQCix2NWqhUbAAFBQWFfwtVNrrXrVvH2rVry8yzpqCgoKDgzk0zfry62beYQqud57rGEODjnioJHK6edzcqO89kUlou3notcfWCsIkS6w6mubjV5lvsFFjs1Av2wUurJjmzsEwD2fkSfvB8NoW2YsNFcuz/DPLR0SU2HJ1GRUpWocsKYb7FjncFq5aHLmTTQqulRoA3B1Ky+P1cFr3a1ZTPZxVa0WtUhPrqkSRIySosdyU0KS2X89lXg5jlFNn4/VwWPdvVwKhTcyXfIq/8OqnIC/d8ZiGnLxfSp11NQn0NrP0jlSv5FpwOx1a7Y3U/2EeQy/AUdAscUdoBfk/OIq9EWqBzmYWkZhXRo3WkPB4ljRmrXUKr8izTed2FzELMYiEnL+Uhio7JlQdUUbLxY7aJWGyiIy2YZCNckso1buyiREGJQGoSyBMeKkFARMJmr1pccLsIVrsNH50GnUZFgcVOgcUmG93giLKukhy/O7GcmN7OX2uRVXTZq+yot0OmWhCwI5U5HmVhtUtYbHYCfbRoVQLZVtElUJqEYzVZW5xT215OPZ3PT6CPjgKLXY4ybzI49omX3KsvSRKFxeOpFsrf0+0cD41KhdGgQZIECgptLka3vXhiRK8RiiPOX9/IwAoKCgoKVafKRndAQACBgYHXoy4KCgoK/1qKrCIfDbzJ5diABbvRa9XcUjvQo9dQYprngEdOjHoNj3aoi9GgISZcy8Nta7JkT7LLNXWDfWS36JhQIycu53sWVlx8sygTv57NdiljaAdHkDJ/Ly11g304WUqGJzffkjSNNHFvi0gEQeDeFpFuAcq0KoGYUCNatQqdl4q6wT7yfnBPDIyrzdtbTroEMmtew08O/BUd4AhQllHC8K6MU9bDbWvSKNwRs2Roh7q8t/WEy/lAby3RAV5yGccvee5LVbEB/GCbGiz6xXU8Hm5bEwFHQK2YUCOJablYSxiLZQWycrpr92oThZfedZLGapdQFRvrkSYDQcWTOKLVgpSdK9fHE35ejgBlhSUMb+cqrYAj2JVeo6KojJXOsvDSqWXD0N9b65bOS8BhiDrLsJWx8cH5XAQZdaTnml1SepUMzqZTq5AQq2x4B/ho0RcbxMG+Oi7lukak16iurvrq1FXvh+pArRII9tWhEgQMWjUBPu7X6DUqeULtWvf1KygoKChcP6ocSG3atGlMmjSJgoKyI8tWhfPnz5OQkEBQUBBeXl7Exsayd+9e+bwkSUyaNImIiAi8vLy48847OX78uIuMjIwM+vfvj8lkwt/fn6FDh5KXl1ct9VNQUFCoDvy9tIxf9QeT/neQSf87yPhVf+Dv5Vil+zUli70pmW6fgnJcrMFhSBoNV+dOfQ3u86i6EgaXJ5fS0viUimRdugxP+3kresf3NWhl40klCBhLlYHgWreK9gz76DVuJppviT3bgiBUqq2lMZVop9HgXoZWrZLbIRS78paHr4fNtiXL8FTHsmT66DTF9fK8RzjSZCDSZHCL2g4VB3yraHvAtZhwpWV6KkMo84s7ngzJ0jLLEzF48GCmT58OwN5ffqZ9+/YOGSV+a5UJ6FdRX6z532peGvs0JoMWk0HrFrUdHL8dfbFbe3lR9VetWsVTj/Zxq5unveqe6nXs2DFuvvnmCmrsSqCPnvBAf+a/+WaV7ruRGDBgAGvWrPm7q/Gf4cyZM2g0VV7D+1uZOXMmTz311HWR/fXXXzNs2LBqkbV48WLuvffea77/WnSAIAj4+Pgwd+7cay737+ZG1QFVfit54403+OGHHwgLCyM2NpbWrVu7fKpCZmYm8fHxaLVa1q1bR2JiIm+88QYBAQHyNa+99hpvvfUWH3zwAbt378bHx4e7777bJXl7//79OXz4MD/++CNr1qxh+/btDB8+vKpNU1BQULhuvNi9CePviWFMl0aM6dKI8ffEMO3+ZthERyC02Ag/t09ZLtYBxYZVvtnmsvc5PceMt86RW7mmvxcaleCyl9SRy7r8el7KtRDp70WftjVpXSvArQyHq6tATX8vuYyygnw53eYv5bquWqfnmgGJekHeBPvosNkllxXMIqu9XIPWbLWTb7HRJjqAPm1rEunvRXqOaxmFVofbu7OMitrtrVNzMefqKmdRcRnOenpr1W77civa23wp14K3Tk3XZuF0bRbuVoZddLhEB/vo5DLKkmnyMKFSkiAfHUE+umuKs2IVJbRqFYE+Orx1GnnftBNnVHWdWlVh6jVwuNeXXnF2ruYbio3Nlo0bEujnS3CAH1HhoQzu35fMzMwyZYrF+7a9dRoCfXRo1Sq3MkTJ4a7tLKMs2t8Sz+7du1EJyPvpwbEXWixRT7Xgvte8Ikf7Oa/O5PGnn8VLp+ZS6jnC/bwYPniAyzUtmzXlpx3bXCZxPNGzZ08WLF6BWOq5ddbZOR4CnvfEN2zYkPDwcL777rty61yafX8c4tnRo+XvaWlpDOj7CH5+fgQFBTFhwoQy7/1t3156d4mndngg3e++k5Tks5Uqc/HixRiNRoxGI76+vgQGBqJWq7l8+bLH60+ePEl8fDze3t60bt2a33//XT43duxYJk+eXMnW/neZOnUqjzzyiPy9qKgIvV7v0neJiYl4eXlhsViqvfzMzEwGDRpEeHg4YWFhNGvWjM8//xy4/ob8xIkTeeedd66L7FdeeYWxY8cCjnYIgkDfvn1dromJiWHr1q0Vyurfv3+Vn9+SXKsOSEpKYsyYMfL3tLQ0HnrooUrpgF9//ZXmzZvj7e3NrbfeytmzVdcBRqMRLy8vVCrVv0oHVPkX/cADD1Rb4a+++io1a9bks88+k4/VqVNH/m9Jkpg3bx4vvfQS999/PwBffPEFYWFhfPPNN/Tp04cjR46wfv16fv31V266yeG6+fbbb9OtWzfmzJlDZGQkCgoKCv8mDMWrZ8M71XMxViP9DYy+syGCAGG+eoJ9dC57hkVJwkvr2Wi6lOt4qco3O9Ic7Tju+EMX6mvg859O88SdDUhMy0VCQqdWFe93drz4W+yeDRHnpEF8gxCX47c2dHwP8NYR4K0j3FfviBRdvF7nrdPgZS57ld8uSTx1WwOCi4M3NY4wkZJZ6HJNhMmAUadGEAQCvHWcvlKGW30xT3auz+US7uh20VGGs57+XlryLa51qmg1PcJPz+g7G8ruy61rBZBZcPXlVZQkmob7yvt9/b20nC4nP3l10O+TX0jNdvSV3S4iCCqXvOzg2EcsCOWv6nqyFSP8vPi0fwuCjXq3VFa+xd4NapWAGoeXwHffryM+vgP5+fkMHJDAtGnT3FZXnIZ1Rr4FjVqFzS6SZ3ek0bKLEgJXI2FLkqNeVrF8F3DnqnGor97FcJeQCCmO3K5WCahV7pMg5RnJv/76Kxq1hgYNr0b8V6lUbN28iaSjR2kUE1NuvTzh56XFx+Bwz3dm69YVxwxwTixoVOoyJ7769u3LJ5988qdWy3o/+AAP9n6YJYu/RBAETpy4uu3COZmSnmOmyFzE4P59eHzMCzzQ6xHen/caI4c+yrpNW9xk2uySy2+of//+9O/fH3C8+82YMYONGzcSHBxcZru6du3Kpk2b+Oyzz3jwwQc5duwYGo2Gli1bUlBQwG+//UarVq2uud3Xk83H0909mSSuPot/coeAt1bN7Q1Cy72mQ4cOfPTRR/L3PXv2ULt2bXbu3Ckf27lzJ+3atUOn8xx35M8wevRoRFEkKSkJjUbDiRMnSE1NrfZySmOz2a6bQb9nzx40Gg0xJZ51lUrFhg0bOHr0qMvxv4rq0AEPPfQQffv25YsvvnDTASUxm8307NmTyZMnk5Dg0OkJCQns2LGjwjJK6gBw2Ijr1q2rtA7o2bOnbHjfqDqgyr+66pw5+Pbbb7n77rt56KGH2LZtG1FRUTzxxBOyW8bp06dJS0vjzjvvlO/x8/Ojffv27Nq1iz59+rBr1y78/f1lgxvgzjvvRKVSsXv3bh588EG3cs1mM2ZziSA8OTkej5fGbrdjtVoxm82oPaSWuRac5ZVXblWp7npej3b/U2Qq43Njy6zu8bme7bbbLUge3qREqw27WsTioQ02q8Vj21SiDavVSoBBh4jIqbRMAn30hPpoADui1Y7V4rhPC+QXWCiy2TEZtIg2q0eZoV4qrFYrvVqGI0ru9bSYzdQK9fLYxrQcz7pTJTr2XEf5arBbzRw6n0NsDT9q+mkRrRa5jmoAO1zJt2LQqB1Rj23ubXf2pUGlQ+clINquGrA1TBpEq4WLmXkE+mgxCGAr8VJbVrudMjWCjlAfNVm5BWTkm6kd7IOXl+BST70AFrONzAIbgT5apDJk2swWrFYrwd46ROyIxUahVoBQHzV2i4UrOXmYDFrUgLVERPmyZDrHXLQJSKXGR7Qi17Ekks2KWpKQJElO73n6cj45hVa3a6uDIquIVFyeTuUIo11oteOlVTsMxeJzVysoYRNFvLy86Nq9B2u+WSXXc9asWXz00UdkZGTSpEljZr/5Nk2axgIwb86rLPj4AwoKCqhZsxaLFi+mceMmZGRkMGbUs2zZshmjj5HnJ77IiKGPFhd1tR9+2rGNKeOeYd1PvyFKEo0j/Zk06w0+fW8++Xl5PPnMKMY+/zzg+H28NmsGS75chLnIzEN9+vLmnNfcX9oliR9//JH2t9xyNXy4JCEIAkOHP87M6dP4/ItFzouRRAlJFCkqKuKF58fx3f++QafTMXToUF588UUEQWDZsmV88+13fP3t9xQWFvL0yOFs27wJgEaNG7Nl6zaeHPk4EZGRvPTyJERRxC5JxMY0ZPGXX3LLLbfQoUMHhg4disViQa12GOeZ+Z51UXBx1HarXZRTnv34ww9odDpGPv0MhuKtDU2bNpXHSaNy9G2gj5bNu7ZiMOgZMmQIXlo1EydOJKZODVKTz1K7xIKKXZJQ4TDYS6eddY7VV199JRtlpUlKSiIxMZFt27ah0+kYMWIEr776Ktu2beO2224DoGPHjqxbt44WLVq4jH11ZdyR06NJnttQEblmW5mTldWBvbhvy2t7u3btSE9P5+TJk9SpU4cdO3bw2GOPMW/ePCwWCxqNhh07dtChQwdEUaRXr1789NNP2Gw2OnfuzIcffkhQUJDcfue/7777Lp988gk//PADvr6+PP/886xevRqVSsWTTz7JCy+8ADgmqV5//XV8fX0xm83ExsbSvHlzRFHkrrvuwm63YzQaATh06BBRUVFMmzaNhQsXUlRURP/+/Zk9e7b8LL733nvMnz+fzMxMunXrxqRJk5AkiQULFrBo0SIaNGjAsmXLeO2117hw4QLnzp3j448/ZuvWrQwfPpwBAwYwf/58TCYT77//PnfffTcAx48fZ/DgwRw6dIg77rgDnU5HbGwsL774olu/r1u3Tu4vZ58IgsCTTz7JK6+8wuLFi+VrRVFEFEUKCwsZO3Ysq1atctEBKpWKzz//nK+++ooNGzZQWFjI0KFD2bBhA+B4Drdt28aIESOIjIxk8uTJ8ljXrVuXRYsWuemAyk42OOsG8P3336PX6xk1apT8GyqpA0qyefNm9HqHDgCYMGECb775pvwbc1KZ52fRokU8++yzVdIBO3fu5K677kIQBBcdcK2Ufn6cz5TFYnF5nir7Dvq3bsI4deoU77//PmPGjGHixIn8+uuvPPPMM+h0OgYNGkRaWhoAYWGu0XvDwsLkc2lpaYSGus7maTQaAgMD5WtKM2vWLF555RW343PnzsVg8Jyf9Hrz5j94/9R/AWV8bmz+DeNzuIzjv3g4Vh/49tur3wUgs/jjpDyHrt0ejjUoJbM0WUBKOTL3eDjmnNNP2bYCABNwNqlydQTP9ayIqrbbE0IpOVWV2YTy+xIguZxzVR2fLDyPjY+PD/Hx8RTmZJFW4IhzEmBQc3PdoPIrd40kXcgiKyvL5ZgasJTw/HdOk4h2O7nZWeRcuURubi7frlpJbGysy9/9NWvWYDKZmDdvHk8Ne5Qff/yREydOsPDTj/hh/XpCQkI4efIkaiSyrlxi0IABNGjQgD27d5OSksLDDz9MgzrRNGnShMLCQnJzc0lLS8NLLEQURYzi1dgv+3ZuZsumjaSkpNCtWze63XUHtWvX5v333+enbVv5fu1aNBoNQ4cO5fXXX+fRRx91aacB+O2334iLi8Oe73gSxUJHQMJHB/QnPj6evbt3Ub9+fex2O3k5WWRducTs2bM5fPAPNm/eTH5+Po888ggmk0l2+xVtViy5GSz+4gsKcrPZu/dXtFot+/btI/NyOj26d2PcuHE8OfJxwGHISMUv3WlpaQiCgEql4pdffqF+/fqsXr2ad999l40bN7qNnznH+W8mhVmOCbY9P20nMjycXj26cuDAAZo0acKMGTNo0KCB3G7nkCf+vo/GMY1QF2VjKXK8YEZHR7Pnl5/x8/UQ/Q3w5H9y6NAhTp8+TadOnTy+x/3888/UqVPHZTtCgwYN+OWXX2jcuDEAkZGR7Nmzp8z3wOri4sWL13SfRpAI9fc8iVkdZOQVVqrtzZo1Y+3atfTu3ZvNmzczevRoGjZsyMaNG2nZsiXbt29n5syZpKWlcfvtt/P6669js9kYOXIkEyZMYOrUqVy6dAlwvI9/8MEHrF69miVLliCKIk8++STZ2dls27aN3Nxc+vbtS2RkJF26dCE2NpZx48Zx4sQJ2rVrR61ateR6LVq0iPj4eI4dOyYfmzp1Kps3b2bNmjVuz+J3333Hu+++y+LFiwkKCuK5555jzpw5TJo0iezsbHbs2MF9993H4cOHMZvNvPfeexQWOvooIyODM2fOYLFYOHDgAEuXLmXYsGHs2eP4q/bwww9zxx13sHTpUrZs2cLw4cOpXbu2x/799ddfiYuLk885+6ZPnz7Ex8ezc+dO6tevj81mIyMjg4sXLzJ79mwOHDjgUQdkZ2djNptJS0vjiy++ICMjg19/vaoD0tLS6Nq1K+PGjWPEiBFyHex2+zXpACfp6emygb5161bCw8Pp0qWLRx1Qkt27d9OwYUOXvomOjmbnzp14ebn/3st6fg4dOsTJkyerrAP2798vG9nXQwfYbDays7P5/vvvyc+/qr1Kbnkuj0oZ3YGBgRw7dozg4GACAgLKnSnMyMioVMHgmEm56aabmDlzJgCtWrXi0KFDfPDBBwwaNKjScqrKhAkTXPYq5OTkULNmTcaMGYPJZCrzPrvdTlJSEo0aNarWlbo333yT0aNHo9frq0VmddfzerT7nyJTGZ8bW2Z1j8/1bHey3d/j6lKWxYbRoKFFpJ/buVMZ+XSuF+J2fOjnuxnbzpdvz8KR1Dz2JTv+8HSPjSDIV0/NQC9a1fLHbBMxW0XOZjnciYONOux2iTsbursdDvxkF+Nv8WPnZZ3HlW5fHy2xUSY0KneX6rQcM7c3cK/nwI9/osmVHRQ0upufTmdzMcdMmElPfP1gYqJM3FQrAJsoUmQVyTbbuJznMMmi/b3Isdi4s5R7pLMvn11/EbsIgsrhcOvMJf347fXRa9WO/M+CAILg0uPdm4S71dEpc/15FRdzLKw96HBvbBMdQIMwI/VCjbSJDnD0pU3kYq6ZguL94jqNii4e+nLQp7t4Ia78vmwSYfIY9b2svhz62W7GtveVZWYVWtCr1Xjp1Pj6aGlV09/lepsoYrda8SrKwsvkT5DJsWKk1px0k11dqDVq/P39ybE63IfzzDZHGjS1I3ieQad25GyWQKVSM2DAAFRqNXm5udSrX5+FCxfKk+zOlZIzl/N5+eWXmT9/PkWSFp1vIBarjZMpaYTUrEeT1u0waNSkX0zjl19+YdX/vkOr1RIWWYOH+vRlx44d3H777Xh5eeHr60t4eDgWtTcqlQq1MUDOAPbUcxMwhdagaWgNmjRrRsr5C7Rs05bly5fz3ocfU6dBDIIAz44dx/tvz3fb03j6cj4FBQUYA0NQ+zji0qi8HFZsaK26DH98JO++9z6fLfwCtVqN0eSPf1AIa9asZf5778tup+PGjWP16tU888wzAAhqDZKXP5Lel0uZ2fxxJp02LZvTsUtXvHVq7u7eg9Gjx3DybApNmrdkzbof6P1IH8LDr/7WTSYTOp2OsLAwHn30Ue7slYCo06BWqdCoHPvBbaKEvnjrid4UgG9gMCpBICMzi2+//ZavVnzNvffcxVtvvcXw4cM5dOgQarWaM5fzCTeqybFCnsWO3tefK6K3POa+fv6gVuMfFIIoOVa5nS79asGRUq00r732Gj169KB+/foe3zc1Gg1BQUEubQwJCUGlUsnHIiMj2b17N+Hh4UiShNlsRq/XV+tK98WLFwkLC7smmeqcS9VSjzLlqzWEh4dU2PbbbruNQ4cO8cQTT3DkyBG6dOnC/v37OXr0KM2bN+f8+fN0794dk8nEk08+CTjaPmrUKKZNm0Z4eLhscHz66aesX7+erVu3EhAQgCRJLF++nGPHjslbPZ966ik2b97MgAED+OSTT3jjjTf49NNPGT16NE2aNOHDDz/k5ptvlmWWHOMVK1bwySef0LRpUwDGjx/P3LlzmTBhAqtWreLFF1+UvV6nTJlCjx49ePfdd/Hz86NevXo8++yzsiznnuHw8HACAwPx8fHhlVdeQaVS8cQTT/DCCy9gMBjIysri2LFj/PLLL+h0Oh5++GE++eQTWZeUpqioiIiICPmcsx0xMTE8+eSTfPDBB3z55Zfy4qBzcvGjjz5y0wHPPvssfn5+6PV6wsPDCQoKIj/foWeaNm1KjRo1ALj//vsZM2YMFy5coFWrVqxfv55+/fp51AHh4eGMHDmSkSNHlvv7CQ0Nle/Pzs7m22+/ZfXq1dx5551uOqAkKpWKkJAQl7IDAwPRarUuxyp6fl5//XXuu+8+j4Y9lK0D7Ha7LLOkDrhWSj8/RUVF5OXlMXz4cJd3zpycHGbPnl2hvEoZ3W+++Sa+vr4AzJs379pq7oGIiAiaNGnicqxx48Z8/fXXwNWH7eLFi0RERMjXXLx4kZYtW8rXpKenu8hwziCV1dF6vd7jC3pZx53Y7Xa0Wi16vb7aXsgrW3ZVqO56Xo92/1NkOlHG58aU6aS6xud6tlut0nk0ulWSCrVWg85D/TVaq8d2WSQVWq2WpfuSKbJJgMPQ/OagQxe+8mAzcqwC57IdbsRqrcNtNNMM3lqNR5nphSJarRaVRuvmvvx7ShadmoRyKstK/WCjm7Go0opl1hNg1e/p2FAjoeZ8jo1l+9OYWjsEm6DhfF4RucWpv5z1PJdv91hPZ182rRFE4yh/t/IktRZRrcZHp6ZukOvKWmp2kcc6OmV+sTuZQpuESlAjSbD7bA6/nM1has9mWAUNp7Pzi40FNWqtGjOgFtQeZV4scPRlrkXC6OW6FzKrwIJ/gI7T2VaX9F5ONFrJo0xRpZHH50ByNpuOpqNVCwy4ORo/fx1a3dV7zDaRUxl5qEUb9TTIKx0Awp/dLFouAoIgkFEqh7nF7kjfFqnXAAIW0RGObOnqb7n5lnjMZjOvzZjKvffeK2cv+fjjj5k3bx7JKedQFwfry8jMoE69+kyZMZtZ0yZz6sQJut13H9Nnv87ps8kUFRURXeNqHBe73U7//v1RqVTF0eaL+8EZgR4BW7Gbo9rHn0KriJdOjZeXN/n5BQgqFSkpKTx4/71XXwwliaioKLk/ndhECT8/P/Jy865ueC/x7/Ann6FNbGOOJiU5SlYJCCoVqakXqFmrliyvTp06pKamyuXZRIkr+Rbuuv8hzpw9y+hhA7BYzAwZNpwXxo8HBHo+/AjLli1jUrPmfLt6FavXrnOpX25uLgEBAXI/WO2QUWBFJQgEGnXkFtow2+xEBnjJ9bWIABJ6Ly/ibomny933YDDoGDduHDNmzODUqVM0atQIm4Q85oLWi5ycXKQSY56Xm4vRxxeL6DC4XfsFt3602+0sW7aM9957T65vaUwmEzk5OW5t9PX1lY/l5+fj7++PSqVCKnbzL0veteB0eS35bFWF653VTSju24ra3rFjR1588UUSExNp0KABer2eDh068O6771KjRg1iY2Px9/fHZrPx3HPPsXr1ajIzM5EkieDgYFQqFSqVClEUmT9/Pu+88w5BQQ5PmvT0dAoLC2nWrJlcniiKxMfHo1KpMBqNTJ48mUmTJnH+/Hlefvllevfuzblz5+Q+Ldm3ycnJdO/eXW6HVOJZTElJYeTIkS4RyZ0uwCqViho1arjIKqkPnIaic2XX6dJeUFBAeno6ISEheHl5yX1Zs2bNMsfd39+fgoICt/qrVCrGjBlDvXr15AxMzrJTU1OpXbu2mw5wnndeO2jQIFJSUujVqxdms5nHH39cnvzr168fy5Yto2XLlqxatYqNGzeWqQMqQ8myvby8uOWWW+S+L60DSuLr60tubq5LOTk5OZhMJpdj5T0/drudpUuX8umnn5ZZ37J0gNFolH/rJXXAtVL6+XH+q9PpXP5OV/b9s1JGd8lV5+pcgY6PjycpKcnl2LFjx4iOjgYcP7zw8HA2bdokG9k5OTns3r1bnqWJi4sjKyuLffv20aZNG8Cxp0AURTkliIKCgsLfzdwfk7CIV4M4OaMxP9WlIXZJ4sB5hytqyfeisoJja4uN3jrBPhxJy0MqcW332AgEAcJNBgRBICXLNcBYRcGnHYGjXF/OUjILis85onqXNrpLR7t24swtbdCqyLddDXRl0KiRkDDqNTQI9uH45XzZ8JbvVZf9Vnouq9DN6BYlSa52vsUu/7GsLHc1Ded/v6fKUaAFoF6Iw3D31WtoGGIk6VKeS+C6isRfyrNg9HL9Y5yeayY6zFhcT5ub0V1RdGy4Oh5Wu0R6rplaoUaX80VWuyOSN1RCWvUTZNRxKdfi2A8Hcv50cPz+DRrXjtPr9TzStz/z35jD5cuXycvLY9SoUWzbtg2fyPqEGUSCg4Pl3/hDffrxUJ9+XLlymZFDBvLe2/MZ+OgQjEYjF9Ivy2VZ7aLHldSS2EoEfLPYRbxwnWyLjIzio4Vf0qI4GI9GJeBXhsymTZty8qTnAEP+gYEMGTaCV2fNdDkeERlJSkoKLZs4VrmSk5NdAsBq1Cr0GjWg48nnJvLkcxM5feIYwx65j07xt9Dp1s7069+fB+/twa233U5IaChNilcCweHya7PZqFevnku5KkEg2KhHo3YY3hl57tGp9WoVsc2aceTwoTIna5x55YOMOuo3jGH5F58CjjE3FxVx5vQpmjZtiqE417u9LKVWzI8//ogkSS6xfErTpEkTjh8/Lq9AgcMdtaQHY1JSErGxseWWpeAIpnb06FG+/fZb4uPjAWjfvj0DBw4kKiqKjh07Ao7I0lu3buXnn38mMjKS7777TvbGAIeRtn79enr06EFkZCSdOnUiODgYvV7PqVOnCAwMLLceQUFBjBkzhs8//5yMjAyPujsqKoply5Z5zJQUFRXF9OnT6dmzJ+Aw6kq6FV/rZEt4eDiXLl3CYrGg1Tqe+3Pnzsmr7aWJjY11S2vsJDAwkCeeeIJp06a5HI+IiCA5OZn69esD7jrAiU6nY+rUqUydOpWjR4/SuXNn4uLi6Ny5MwkJCdxzzz3ccccdciR4J2XpgMrSrFkz/vjjj0pd26RJE9577z35e2FhISdPniyzvzzh1AF33XVXueV40gFObwy4MXXANZn/drudr7/+munTpzN9+nRWr16N3V5+PllPjB49ml9++YWZM2dy4sQJvvrqKz766CO50wRBYNSoUUyfPp1vv/2WgwcPMnDgQCIjI+Uo6o0bN+aee+6R91/89NNPPPXUU/Tp00eJXK6goHDDkJ5jZmiHujzW0fEZ2qEuF3PM2EQJq12iwGonwqSncZiv/PGUj7ckbaIDXCJPqwWBm2pffbkJMercXpU9uYc7jl81ikrTtlimSa/xmAu8rHo6UoOBl8bhVuyjd/zrpVWjFgQS03I5mp6HzS6iUQnyR6sS5KjInmhdK8DtmEoQkCRHNPUI36q7kbauFSDnfRZwGKttS/Slt06NX6m2e+oruNqX0UHebufqBPsgSWDQqAgxus+OV2Ylum3tQIKNOuqF+FAn2H2vrK9Bg0mvQa9RlVnH60lW8SqqSlXiU+zqX2Szy9HGheKPzWpl5bKlhIaGEhQURF5enrz6ZLfb5fzaACeOH+OnHduxWCx4eXmj0+vRqNVE16hBu5tvZsqklykoKMBms3Hgt99ITEwst6664sjxapUKHw85tQcOHszsaVNIT01FK0Dy2TNs27bN7ToB6NKlC/t27/JYjloQePLpZ/lh3fekpV2N0Nyr90O8PmsmmZmZpKSkMHfuXPr06eMi10evZs/POziRdMSxF91oQqPRoCr2xolt2pSgoCAmTXiBXg/3cSl3+/bt3H777W4BlPRaFRq14FJG6fZoVAL3P/AgB//4g61bNmG325k3bx7h4eGygeBEo1LRoVMnioqKWL30S8xmMwvefYPmLVvJQdRemzGN++7p4rF/nCxatIg+ffqUG/CpUaNGNG7cmNmzZ2M2m/noo4/kwEkl2+0MhKVQNsHBwTRs2JC3335bNrp9fHwIDw9n2bJldOjQAXCsIhoMBgICArh8+TLz5893k3XTTTexfPlyHn74Yfbu3Suvzo4dO5asrCxEUeTIkSPyXunp06ezf/9+rFYreXl5fPjhh9StW5fg4GCCg4MRRZFz587J8ocMGcJLL71EamoqkiRx5szVZ3HIkCHMnDmTkycd22dSU1PZssU9an5VqV27tvxbs1qtrFu3jt27y44Ocs8997hEfy/NmDFjWLNmjUuU9l69ejF9+vQydYCTLVu2cPjwYURRxGRy6ACnR16TJk0IDg7mueee4+GHH3a5rywdUFl69uzJwYMH2bhxY7k6AKBz584UFhayYMECzGYzM2bMoE2bNnIQtSlTptC5c+dyy1u0aBF9+/a9Jh3g/A07232j6YAqG90nTpygcePGDBw4kFWrVrFq1SoSEhKKZ3irtlesbdu2csCFZs2aMW3aNObNm+cSMv7555/n6aefZvjw4bRt25a8vDzWr1/vEvBs8eLFxMTEcMcdd9CtWze3NAgKCgoKfzd2SSIj/+pqUka+uXjVR5KNuUKra5TOstIAOXEatQ3CfAnw1rqVYbaJSIBeo5LLKCsPtCcD0EmYyYAkQXSgt0cjrixDMdjHIbP/zdEM71SPEZ3qMbxTPQbH18YmStQL9qFesA/1Q4wukw0xYb54acp27w81lV3XRqFGgstpS1l89tNph2HtpcXfW4uvXsPGIxdRFU8OJKblkme2oVUJ6IvzQNvKiLrq7EtPKcWcxxqEGMvMw14RYSYDA+Nqc3/LKI9lqASB6EBvogO8r7sbqycCvHUEGXUEG/WEGPUEG/UEGXXFv0U1eo1jO0Tv+7pTKzSQhrUi2bl9G9988w2CINCsWTNGjBhB8+bNuaNtM6Kjo+WURRazmakvT6BxnShuatoQk8nE6NGj0ahVfLZwERfOn6dJowZER0UwYdxYCgsLy62rs3sCvLVoPPTl6LHPcfPNN9Oty23UCAumT68HSUlxD1unUato164dFquVE8ePuZ3Xa1REhocybPgIOWMKwIQXX6JBw4bExMQQFxdHnz593DwKrXaJyxfTGDMsgVsa16Jvj9sZNGQoHTo4jExRkujdpy9JR4/wUIm8ywBLliyRM8IALF26lAduvxlbqcjZJfOVOyedREkiKCiIr5Yu4/kxo/D392f16tWsXr1aftl/tE9PXnvtNQBUGh3zPvmSJQs+oEPT2uzZ9TPvf3o1Jez58+dof3McGkHw+PKZl5fHN998w4ABA9zOPf744zz++OPyd2dEZ39/f95//31WrVolv6T//vvvGAwG2ftRoXw6duzIpUuXiIuLk4/Fx8eTnp4uG90DBw4kICCAsLAwOnXqRJcunidPOnXqxIIFC7jvvvtITEzkzTffxM/Pj9jYWAIDAxk4cKAc/EqSJBISEggKCqJx48acOHGCb775BnAY/uPHj6dly5b4+/uTnJzMuHHjiIuLIz4+Hj8/P+699175Wezbty9Dhw6V95/fdtttLkHY/gxLlixh7dq1BAUF8cUXX9CjR48y3Ynbt2+PxWJx8+J1EhQUxMiRI111wIQJNKxAB4BjIuGBBx7AZDLRtm1bRowY4TLRlJCQQGJiopvRXVoHLF68uEorz0FBQSxevJhnnnnGow7o2rWrHJtLr9ezevVq5s2bh7+/P9u3b+fLL7+UZZ07d87FMC7Nn9EBX3/99Q2vAwSpore6UnTr1g1Jkli8eLHsLnLlyhUSEhJQqVSsXbv2ulT0epKTk4Ofnx/Z2dkVBlI7cuQIjRs3rtZAULNnz2b8+PHVume4Out5Pdr9T5GpjM+NLbO6x+d6tnvhERt+PnpqBTpWP89eyedKnoXWdQOIqxNEntlGgcVOaHHuaUmSOHE5n26N3WNT9P/kZybcbOKTQxZC/byoEeCNXZT4PSWLYF89tUK8iasTRK7ZhtlqJ8hHhyAI5JltpOUW0TXGXWa/j35m4i0mtl3SYfcQ/Mtk1HGThxVmcOyX7tLIPaDYQ+9up9nlLfjHPYSgcXXJLU9eWTKdfVmddbweMq9HX5Ynsyx5otWClH2RqJq1CPZzxGUZ/NkeLuY4gvvY7HYEoYy5d6HslXxJAk/ODWEmA+/3iSXXKnh0a1erBXx0nlcvynIFP3Upjwij+h8j86sVq9m6eRNz3nq3UvLKkul0j9X5BpJvsaPTqOQJlgKLHW+9GmOxTLsksfrrr3n/3bfZtGUbZruIn0HL8ePHGTBgALt27UIQHPvii4qKyLMJ5BbZMHldLTPfbMPHoKFGkB8Gg4EJL73M0888K/8GnDLLane+XUVOoRWjQYNKEBAlibwiGyZvrVzPuPZt+XbtOkKK8+6abSJ+Xu4ynfU0GAzX5BY8YMAAHnnkEXr06FEt8jzhHJ/w8PBr2jO69cQlCkvn6cYx0VEd3ileWjWd64dcl7ZXt8wbcXw84azn7bffztNPP02/fv08Xrdy5Uo2bNhQqcW/6mz7ypUrmT9/Phs2bJDlldYBlcFgMGAwGJgyZQqjRo2q1jq2adOGDRs2EBAQcN3Gx2AwMHDgQBcdUB0ynYHUTp8+TZ06dVwWfytrR1bZ12Dbtm388ssvLvszgoKCmD17drmzFwoKCgr/ZVrU9EdEkFevo4N8iC4R7Muo12DUa+TzgiCgK2sDdrGlcFPtQMTitTq1SqB19FXDS5IkfPUafPVX1bxRr0GbXz1/4BT+mXz+aDvg6kup2ifA4+b0azE8nS8o/2XufaAnPR7oWa0yffSu4+Bdyg3eZrHw6ccfMvhRR8R352g602iVRiUILgZ3yTKycj0l8Sod5cHz+ZIyPZWxa/evrvdcJy+MRYsWVXzR30zn+u4ZCq6H8anw59m9ezcRERFERkaydOlS/vjjjzJX+gF69+5N7969/8IaOhYg3n//fTnrg5OydEB5XE8dvm/fPoAyc3NXFzeqDqjy25deryc3N9fteF5enuz+paCgoKDgmcnfHmbyt2Vl5YZzWUXsO5eNxVb2H6XU7Ir+KArsO5d9jTVUUFC4UbiUa+ayh48AFFjt7N67n8iwENQaLQ889AhFNnuZW0hKYxclLmQVklNoLfOafKudfA8rsiVlAOUGSMu32rHYr+9LtoLC9eLcuXPE/Z+9846Pqsz+//ve6SW9kgAJJfQmKIiABcvKAhZQ6UUUsAui/sSKiOjuouKuunZRVEQQe8MC0pSOAoHQSYBAeplk+r2/P+7MzUxmJgkIivudz+s1kLnlPO3eZ57znHM+p29f4uLimDdvHh9++CEpKaGbJn8Wtm3bRmJiInq9Pig8N4qzDydt6R4yZAhTpkzhjTfeoHdvZcd8/fr13HLLLVx11VWnvYJRRBFFFH80zn3yO+weGZ1GVEmxAG67NIdEa+jmoleSseg1DO7ULORcIIJjul0k1mOulmSZ0lrlmnK7u1HmaY8kIYrBVq+yGheJvphnp8frYz6uw5/BZh1FFFGcGhIt+rAmYRkw6zT0PrcnJRVVQefcTVRwHT5l2u72hlillTLkgL/Dw++Z43B5MRsiLyk9kkwYnrooojjrMXz4cIYPHx7kiXA2oUePHtTUKB4qsiyfErF1FH8MTlrp/ve//82ECRPo27evSp/v8Xi46qqrwrIZRhFFFFH81ZAaY+REtUtNa+VXvC0+Rmg/ZBlfiikpKC9xODy7PI8ye11arPnf78Gk0/DA0I6U1Lg4VukIsgYVVNgxN0K09c+vd6PXaxnYIZWDJTXsLqzG7vYye5iSJmN7YTUaUSDepBCDHat0BG0iRMK+Ihur9xaTlWThkvYpIa6Obq9EfrlCUNXSn9s3DIptDVvk84psxBi0NIsNZRuXGtkekGWZFXnFHC6tYUBOCm3rpc0CqHK4Kaxy+lzsG5Znc7gxGeql7/IpFHlFNprFGhpNPxUO1Q43X21XUtf8vWs6MfVkyLJMYZWTaqeHZg0QxBXbnAF1qjeGMuwvURZdLRNMIeRqTdlocXokbA43eq0mLEO9LMvq89mY66vTI6np4urD4fYi+hjqT8WFttrhweXxYjXqQlLXgbIB5vZKiA08554A62yk+FmH24tOIzaaQSAcJFmmslaxHseZdWjqjZcsy7glJWVgOCK8QFQ7PSHPjCJE2VQDhbH/ZPqyxObE5ZXV59tv8c5IMOGRZLyyjFcKTl4Xif7Hf7TK4aHS4VFSqpl0VNrdeCSZZvFG9boatxcRhUzQK8sRZUYRRRRR/C/ipJXu+Ph4Pv30U/bu3cuuXbsQBIGOHTuGpY6PIoooovgr4s6BOcgIfPbrMbYWlPPYEIXps8LloWuzOPU6m9PDb4VVdGsWy/HqhhVMk14D9uBc1Ga9BmRIMuuocXoorgnNldsQZCAnNYZuzeNpk2Ilv7Q2hJxHKwo0jzOiFQVqnB5qXI3vgq/eW0x5rZvy2gq6N48jqR4beFmti1pfOWW1kV1TG9qIEAQlL3JprYtEsw5jvQ2G+uzK9VFW42JbQYVa33BKd2GVM6iMhpBbWE2v7KSQMuJjDbi8EsernKekdG8/WslRX670HUer6NsmuAynR1K9G46rGwSh8PdleY2bOEs95VwgaDzSYk6eVNDmUJQkj8uDSa9BUy9PuqKMKX83ZrC0OT0kasOHm0koed21YlOSowXDI8nUujxqfQ1hWOrdXimojHBQrbNuL+Z6ceseSUajEZB8sjTiyZtna1117tR2lxedNnQTxD+eShmRe8Lu8mLSa0PbIqCOh0eS0TWQ174+rEZt2LzcoMwXGgQckvfkvWJkmVijHr1WJNaoDfLs8UPjSwuoQVA3DaKIIooo/i/g1JK2oQTn+xXtKOFDFFFEEUXDuK5Xc57/MTit4qjeLREEZQ5tmWCi2unBERDLHWkh7fRdE2fScayiltdWHfBdLxJj0KoLW1DyA+8triNHaop1KSvJQnltBbFGbVgrm0Kw5fL9raHa4Qm5pjH4q6HTCGGtfY1ZGGOMOmKNWqocniBCuqBrDFpKa13oNI0reBlxoRb7wLZbG3CdbQjN480IQhkAmfGhZeg0IjqNgNsrN6kMaxgrdCDC5ZpuCvRaDR6XB40oqPnKA6FYhH0kf43Kath668/PfbLQCAIaUcAryegjpJQTRQFJkpskP+xzF9D2hqzlDUGvEfG/ceHK8LdfbkIZGpGw4xGIk2W7Nmo1WI06qh11G2aB6dIElBRn9gZ4JcLBatRh0ClyDDqljKB6gkoOKRBdO0YRRRT/t3BKq4g33niD5557jr179wKKAj5t2jRuvvnm01q5KKKIIor/FfgVU/8aW5KVY4HJofzWMVFQzkfSjzPiFJfNcX2zwqakcnskOqXHhF3wN07CBpe0T6F78zhijLqwCpTVoKWDz7Ks04gRle5ki8Gvm4dFTrIlogtvY4qGXisyvm821Q53SGy8H81iDSSadeg0oprTPBKaJ5qob1zXa0VkWalnOFfmpqBlkpmb+7cCCLuBoREFcpKtuL0SBq3I8arw9Uz2tVGvFUPqCQSNR300RbWJMWoVC7cghGWV1ogCJl96scZCKWIM2ohWUqNWPGWFSxAgyWLAK0e2Yut8G05CA/X036vXiCH19FfLX89TgV4rkuLzNgib114QMGqVshuqJ0CixRCR5dvkeyZPpS/9BGj+VGJSvTr4v52M5Poy1DJ88kJaGfUujyKKKP4P4aRXEY8++ih33303Q4cOZcmSJSxZsoShQ4cyffp0Hn300TNRxyiiiCKKvzxKbS56tIjn7kvbcfel7ejRIl6N0wVwe2WMWg05yRa6Z8TRPM6IHGFV2lgcKEITrmnodkEgyWpo0GKpWGgbLqOx80ad5pRiZv3Qa0WSrKHx4H4IgvC7ywClnr/HKhdj1IWPy/VBIwqNltGUvv49Yw6KMtpQMwXh1OKwAyH+ThmCQIO8BIIghC1j4sSJzJkzB4BN69fRp0+fM1LPLz79mBl33oboq8fJ1jMQy5YtY/yI4Q3KaOj+PXv2cP7554c9J8kysSYd6XFGkqwG9R0x6bUkxcfy7/nPoRUFTDoRk04kkvrtH4tYozaEwdzPom7WaTBqRUThj9GzL7roIrZv3/4HlBQFwKFDh9BqT9lx9k/B3LlzueOOO86I7I8++ojJkyefFlnvvfceQ4cOPeX7G5oDIkEQBCwWC88+++wpl/tn42ydA076F/q///0vr732Gk899RRXXXUVV111FU899RSvvvoqL7300pmoYxRRRBHFXx7ntIxnWM/mJFj0JFj0DOvZnF4BebW1GoGOaVbiTDo0okB6rDGEeTyKKP5X0CGnDUlxMaQkxNE8PZUJY0ZRXl7+h5R93vn9WL9+/RmRPe8fc7nlzrsByD98mPQ4E1Mmjgu6pkeXzqz6aWWjsoYNG8bCxR+dcl3atWtHeno6n3/+eci5BIseq0GrupKnBPAA/Lojl+nT78GgEfng/UWkJMTTPCUBq9WKyWRCFEVKSkqAurhyu9uLV5LZsn0XV1xyES1SErjmsv7s3P4bDo9XZVR3erw4fJ/6m4qLFy+mXbt2xMXF0a9fP9atWxexbRs3bqRbt26YzWYuuugiDh8+rJ6bPn06s2bNOsVe+7+D2bNnM2LECPW7w+HAYDDw2GOPqcdyc3MxmUy4XCfHN9IUlJeXM2HCBNLT00lLS6NLly4sWLAAOPOK/IMPPsgLL7xwRmQ//vjjzJgxA1DaIQgCo0aNCrqmQ4cOrFy5slFZY8aMCfv+NhUNzQENIS8vj3vuuQdQFP+UlBRiYmLCzgH1sX//fvr164fZbKZnz578+uuvTS7XPwfExsZy7rnn/s/NASf9RLvdbs4999yQ47169cLjOfm4viiiiCKKsw3//nEvDo9C2uT2Svzr2zwQYOrANvx6rC7/texzc91XYms0rlKrEakfIanTKO7Lucer6ZhmDbFaRSMeozjdmPzOJk5UOahyuHG4POh1WmIMWsSALXhZVpSoWpcXjSgQa9QGWdFlWWH+rqh1qwz/Zp2GlBgD/76+S5Pr8vlXX3NBv/7U1NQwcdxYnnjiib+0dWXjxo1oNVpy2rVXj4miyMoffyBv927ad+jwh9dp1KhRvP7660HWshKbM2zoSoqPPd+vFANce8MIrr1BUcziTXr+8Y9/8PXXX5OcnAzUWboTzHpkYPQdNzPw8r/x8Zff8MG77zBh1A1sz90dVoEKTG12/PhxJkyYwJdffskll1zCSy+9xPXXX8+xY8dC7nM6nQwbNozHHnuMsWOV52bs2LGsXr0agL///e9MnjyZoqIiUlNTQ+4/G/DT/mKqHB4kGTQ+zyRBULwDRKFKIdvzyngkGUEAvSbUc0KS6/g9dBqF78B/hVGr4cI2yQ3WoX///rz66qvq9w0bNpCdnc2aNWvUY2vWrKF3797o9eFDeH4Ppk+fjiRJ5OXlodVq2bdvH4WFhae9nPrweDxnTKHfsGEDWq2WDgHvuiiKLF++nN27dwcd/6MQbg44GYwZM4bhw4djNBoRBCFkDghX3qBBg/jhhx946623uPbaa9mzZ0+jfR44BwwcOJDXXnuN6667rslzwLhx41i+fDlw9s4BJ23pHjduHP/9739Djr/66qvRpOxRRBHF/wTG983m5gGtKbG5qLR7uP68Ftw8oDVeSaZjWoz6sRq0eCQZq0GHqZH0XpEgCAK1bi/uRti6o4jidOBYpZ3fjlZyqLSW49Uu8stq2VlYRetkK+dlJ9GzZSI7jlWxv7iGwkoHR8rt5BZW45FkemUl0isrEa8ss/NYFUcr7ByrcLDnhI1tRyobjZuPBIvFwqAhQ9m1a5d6bO7cuWRlZdG9dQYXX3wxO3fUuQrO/9c/6NauFW2bp3Jh717sys0FoKysjInjxpKV2YxO7XN4/913wpb3y9rVdOlStzmQHmdiweuvcl7XDnRq1Zz58/6pnvN6vTzx+Czat21NdvMMHvp/90c0MHz33Xf0ueCCoGOCIHDTlFt46sk5Ye9xOBzMmHYX6enptGzZktmzZyNJiiK1ePFirr96MAB2u51bJo2nQ1YGHbIyGHL5QABuv/UW5sx+XJUnyzJdO7RTLUQXXnghP/zwQ1CdE8x61eMm8OOfgQxaTcjHr/AtXLiQceOCLfd+7Nu7hz15u7l7xv0YjUYm3jwFSZJYu2Z12OsDcfToUZKSkrj00ktVy2BhYWFY74eVK1diMBi4+eabMRqNPPTQQ2zevJmDBw8CoNfr6dmzJz/88EOj5f5ZKK91Y3N5qXV7qXZ5KbO7sRq0pFqNJJr1lNW6qXJ6qHV7qXF5KbcrCnqyxUCyxYAkQ7ndTa1bkVHp8FBW6ybRrCfZYlCzGTSE888/n6KiIrXf1qxZw+TJk9m9e7f6vKxZs4YBAwYAiudFamoqSUlJjBkzhrKysrByX3jhBbp3705RURF2u5077riDjIwMmjdvztNPP61et3HjRsaMGUNcXBwajYbu3bszaNAgAK644gq8Xi9WqxWr1Up+fj5er5fHHnuMrKws0tLSmDFjRtBz/eKLL5KTk0NycjITJ06ktrYWgAULFnDJJZcwdepU4uLieOutt5g1a5bKQ7Vy5Uratm3L7NmzSUxMJDs7m2+//VaVu3fvXvr27UtsbCyjRo1i5MiRaqhKfXzzzTdqf/khCAJ33HEHs2fPDnuPw+Hg9ttvDzsHLFiwgMsuuwxQ5oBRo0aRmJhIYmKiWs6UKVOCrLqyLNO6desG54Dfg4bmgLy8PHJzc3nwwQcxGo3ceuutSJKkbog1hPpzwNixY096Djh06BBw9s4BpxQA9sYbb9ClSxduvvlmbr75Zrp27cprr72GKIrcc8896ieKKKKI4q+I73KP887Ph9Tvb687xCdbjyLLSt7co5V29pfUUOpLiVNa4wxiHQ+HRRvy+Xp7IduPVLD9SAVfby8MKmNPsY1DZbUUVjnUMhqTCeDySGw4WMbu41URr6mwuymyOVVio3CoaoCB3OWrR4U9cnqwxlBc7WTd/hKKIyhmXkmmyOZsUhmuBvrlRLUjJG3ayWD38So2HCyLWIbd7W20jGpn44ubxsajITR1zMtqQ11CS308AudmJTCqd0vO84U4fPqrYk345UApACadhpHntmBotwwAdh6rUlLOOT3sPKaUO7RbBjec20JJfQchhHoeScbm9EQkCpNlJad2WUUlX37+aVCcdYcOHdi0aRMbdh9i4MCB3HnLTQDs3ZPHO2+9xverf2ZvwQneWPg+MXHxSLLMTTdOoFlGM/YcOMTHn37O4488wm+//dZIbyr4aeUKVqzbxEdffsu8p57kwH4l08C/5z/H2rVrWLnmZ7Zs38mv27by8ssvh9zvlWR27txJ6zah6VOn3n4ny5d/y87c3JDsAU89OYfdubns2rWLNWvW8O677/LOO6GbBYvff5dqm41ftu9hx/58HpqlLOBHjR7NksWLAaW/165VFtoX+JT/Zs2aodFo2O9rz+LFi7m473kN9kWk53Lr1q3s37+f66+/Puz5Pbt30bpNW9xo1DHv2LkLub5NEagb8/rPRI8ePWjbti3ffvstXq+XhQsXcu6555KQkEB95Obm0q1bN/W72WymTZs27Ny5Uz3WoUOHJo/9n4Vki542SRZSfCSJh8uV1IJFvndUIwq0STTT0pf1oNzuxu2VcHslyn3zZMt4E60TzarHgT/tpKMJc6DZbOacc85RLdt+Bbtr165s3bpVPda/f39AUboPHjzIgQMHqK6uDqtEzps3jzfffJMff/yR1NRU7r33XsrKytizZw8bNmxg4cKFfPHFFwD06dOHBx54gIULF6rKkh/Lly9Ho9Fgs9mw2Wy0bNmSZ599ltWrV7Np0yby8vLYsmWL+i4uWbKEl19+me+//56CggLcbjfz5s1T5a1evZq+fftSXl7O2LFjQ+p96NAhDAYDRUVFPPjgg0yZMkU9N2rUKK644gpKS0sZO3YsH3/8ccQ+3b59Ozk5OSHHp02bxjfffMPu3btDzj311FPkNmEOePvtt6mpqeHo0aMUFRUxd+5cAMaOHcuiRYvU6/yhM5HmgPfffz/o/TkZNDYH5Obm0q5dOwyGunCVrl27Br2bkVB/DliwYMFJzwGBc83ZOAectNK9Y8cOevbsSUpKCvv372f//v0kJyfTs2dPduzYwdatW9m6dSvbtm07A9WNIoooojjz2H6kkn1FNvV7ic3Jlnxlt9Ws02Bzeim3u9V4Rq8ceaHqx4FiG9sKKkiJMZAcY2BbQUVQGQ6PREmNC1GoK6MpStnqvcWs2VfCV9uPU1BWG3Le5vRQUGHnRLWzwVziNb78x+FSiq3eW4wgQEGFHVsYhbI+gVJ9yLLMks0F/HKgjCWbC8KWcbzawYlqZ8QyAvHz/tBYMllW3DCLbC4OltY2KTVafeSX1fLV9uOs2VfC6r3FYcs4WFpbV0YEWih//Y/6FtH1yxAEIo5HJJmBaOqYl9S4Ij5D3ZrHY9Jp6No8Xj3mdHvJO1ENwIhzW2A2aEmJMXBhjuJGuONYparoD8hJJiXGgNWg5bpezQHUHNp+lNe4qHF6KI+Qf/7aq4bSIj2Flukp7Nu7l1tvvVU9N2zYMFJSUhA1Wu6//35yd+ygxmZDq9HidLrYk5eH1+slp317ElPTyD9yjNU//cTsOXMxGAy079CB60aMZNmyZQ32pX9zZfwtdyPrDHTs1JlOXbqyY8cOAN5+6y1mPjKLuKRkTNZYbr97GkuXLg2RI8kylZWVGEzmkHPxCQncNOUWnp47F3e98Vi65EPuf+hhEhISaNmyJTNmzAhaQKvyESktLWVn3l4kRPr2UxShfv0H4HQ52bx5My6vxIcffsDw628IujcmJobKSiUsZsSIESz9bk2I/EC4JSnsc7Nw4UKuvvpqYmNjw95XU1OD0WwNGvOYmBhqbHXznFuScUsyLq8U5OKu0WgYM2YMw4YNw2g0MmfOHF577bWw5dhstpA6xMbGYgsoJ7DNZyuSzHq0okCiuc512yvJVPo2r9okmtFqREw6Dem+mPtyu5sKX6q39BgDJp0GnUakVaLy3FXY3Se1mTdgwADWrFmDJEls27aNnj170q9fP9asWcOxY8fIz89XlbexY8disViIi4vjzjvvZO3atUGy5syZw4cffsgPP/xAUlISsizz1ltvMW/ePKxWKxkZGdx6663q+/PCCy8wbNgwnnnmGbp06UK3bt345ZdfItb1jTfeYM6cOaSkpBAfH8+MGTNUWW+88QYzZ84kKysLk8nEzJkz+fLLL9V727Rpw8SJExFFEZMpNHWjxWLhvvvuQ6vVMnbsWPLz86moqODQoUPs3LmTBx98EJ1Ox+DBgxskYayoqMBqtYYcT0hI4Pbbb+eJJ54IObd06VIeffTRRucAnU5HaWkpBw4cQKvVqpbuAQMG4HQ62bJlCwAffvghI0eODLo38H0YPXr0KSujjc0BTXk3IyFwDjAYDDz66KO8/vrrJ1VOTU1detSzcQ446aCGFStWnIl6RBFFFFGcNRjXN4v/rNhP4Nrl0o6pCII/xZOFvGIbNa46a0JjBNkaQWBC/1akxSrpvib1b8V/V+4LuiYzzkhajHI+J9nCnuLGf6iiOH34I2Lo/ypx+hIy/qRZ/tdAK4p4fG6PgZnPG8+CHh5LPvmc8/pegNPp5F9Pzmbw4MFs2rQJgNdee4358+eTX3AEjaiktSorK6VVmzbMevJpnpr9KAf27ePvV13FnKf/xZEjBTgcDlpmpKvyvV5vk8PeklJS1L9NJpOqKBYU5HPDtVfV8S3IMpmZmWFlxMbGUhuw6AvELXfcxbldO7J3T17Q8cJjx2jRooX6PSsrK2wM49XXj+TQ4cNMnzwOr9vFxJsn89CDDyIIAjeMGMmSDxfzUOeufLZsGR9/+XXQvdXV1cTFxTXeCQ3A6/WyaNEi3njjjYjXWCwWamzVIWVbwigh9bF8+XIeffRR1q9fT4cOHVi0aBFDhw4lLy8Pszl4I8NqtVJVFezlUVVVFaTsnI42/9nwp5QLhD+dZH2c6rzSv39/HnroIXbs2EFOTg46nY5+/frx4osvkpmZSdeuXYmNjcXj8XDvvffy8ccfU15ejizLQTG9kiQxf/58XnjhBdUyWVxcjN1up1OnTkHX9evXD1Csk48++iiPPPIIR48e5ZFHHmHYsGEcOXIkbF3z8/MZNGiQ+i7KAe9ifn4+U6dO5bbbblOvDyR/a968eYP9kJKSgugjtvA/bzabjePHj5OSkoLBYFA3chuSFRcXF1HBnD59Om3atCEvr94cUFhIy5Yt1e+R5oBx48Zx+PBhrr32WpxOJ7fccgszZ85EEARGjx7NokWL6NatG8uWLeP7778PuvePmgOa8m5GQuAc0LFjR5YuXcqQIUNOag6wWCzq97NxDvh9+UWiiCKKKP4HEWfUhSxsYgNSPomiEJKGqrHsQma9ho+3HOGVn/bzyk/7+XjLESx6hcBKKwroRIHSGhe5x6vJPV7N7iJboxZkgAE5KfRvm8zfu6bTIjHUymY1aGkRbyItxkC6T6EPB4te62tHaEMG5KQgy9Ai3oTVELpX21hObUEQuL5XC85vncj1vVqELSM9xkhajCFiGYHoG4YgSMk3DKlWPa2SzKeU7qlFopm/d02nf9tkBuSkhJwXBIFWSea6MiIsdf31z0wItai0SDQjy0Qcj6YosE0d82SLPmK6tN+OVGB3e/n1SIV6zKDT0KmZYj1YuvkINqeHomoHq/cqngUd02PokqEsYlbtLaa42kmN08OHmwoAMOuDxy3Bosdi0JIQIY+6VlTyaseYTYwcPYbNmzdTUlLCoUOHmDZtGm+//Tab9+Rz4MABdXwBrh85mi+//4k1m3+l4PBhXnnhebJbNMdqtXKsqITC4lIKi0s5WlwW1hU8EP60eGa9FqshNK1bRmYmXy//joLjxRwrKqHgREmQC6MfoiDQuXNnDh/cH7actJRkbp4ylXlPzw063iwjgyMFBer3/Px8MjIyQu5PjLHw/x56lFUbf+WDjz/n1ZdeVBnQR40ew7KlS1j70wpS01LpHBCvfvz4cTweD23atFGPxZsbJsXSiWLIc7Pih++RZZkrrrgi4n3tOnQk/9ABtHjVMd+duzNI6dL55jq9jzjMj19//ZVLLrmELl26oNFouO6667Db7SEKCkCnTp2C0gHZ7Xb2799P586d1WN5eXl07dq1wXb+2SitdeGRZEoDwkA0okC8SXkOD5bV4vZK2N1ejvvCcuKNOhJ8549XO7G7FWb4/T6Plzij9qRSJPbv35/du3fz2Wefqcpwnz59WL9+fVA893vvvcfKlStZt24dlZWVvPfee0HeRKIo8s033zBt2jRWrVoFQHJyMgaDgQMHDlBRUUFFRQVVVVV8/fXXIfVISkrinnvuobCwkLKysrDzd2ZmJitWrFBlVVZWqu9iZmYmb7/9tnqurKyMffvqNrVPNU1heno6xcXFQQp8pE0BUFyp9+7dG/ZcYmIit912W4i1u1mzZuTn56vfI80Ber2e2bNns2fPHr799luef/55lQF97NixLF68mO+//15lgvcj3BxwKvjuu+8anQM6derE3r17cTrrwsh27NgR9G5GQv05YMSIESc9BwTONWfjHBBVuqOIIooo6qHY5kSnEbgwJ4VhPTNJtOg5URXsCuxwe4kzammbbCHOqA1rfQjEhH7ZjOzdkhHntWDEeS0Y2bslky9sjcerkLN1SIshJ8VKm2SL+rHoGndG0mtFerdKpEN6eHcvgHiTjtSAXLzhEGuMXJZfMfEvBk8FKTEGLmiTHJSaKBAaUSDVamhSGQ3lD0+LMZ4yqR1Ah/RYerdKjFiGSadptIyYRjYNgEbHoyE0dcwTwyhXSVal/zcdLmfRhnw2H1bCJq7urizyzstOBKDW5eXDTQV88ZvCJtw5IxazQYvFoKVzhlLu578dY/GmAjW+PabeM6QVBawGbcS82oIgKAz+Xg9LFn+gkjTZbDZEUSQlJQXJ6w0iLdq3dw9rV6/C5XJhMpnRGwzotFqaN29O7/PPZ9ajj1BbW4vH42Hb1q1hFeRwMOs1YTfOxk+YyOxZj1FSdAIBOHz4ED/99FPIdRpR4PLLL2fDzz+Hla8VBaZNv4dvvvqK48frGJqHX3c9/3xqLuXl5RQUFPDss8+GuIYCrF39EwUH9qDXCMTExKDVatFolGewY6dOJCUl8/D/u58RI0cFbdusWrWKgQMHBjEHN5Tn3N+W+vjg/fcYNWpUgwzEbXPakdOuPW+9NB+v28XCt95AEAT69VcUt4XvvE3Hdm3RacSQOvTq1YuVK1eye/du9hfb+OSTT6i1O3CZksg9Vql+im1Ouve+gJpaO//576scL6/mkVmz6X5OT+LTMql0uHG5XGzevJlLL720wXb+2SipcbG/tIYSnyt+lm+Tzh/j7ZFkDpTVkl+hhKkkmHRoNSI6jagq3vkVdg6U1aou5am+99vYxDkwOTmZdu3a8Z///EdVui0WC+np6SxevFiN566ursZoNJKQkEBJSQnPP/98iKxzzz2XDz/8kBtuuIFNmzYhiiITJkxgxowZVFRUIEkSu3btYsOGDYDijr5lyxbcbjc2m41XXnmF1q1bk5ycTHJyMpIkBSm4kyZN4uGHH6awsBBZljl0qO5dnDRpEnPnzlXjlgsLC0+LZ252djYdO3bk6aefxu128/XXXzeYbvDKK68MYn+vj3vuuYcvvvgiiKV9+PDhzJkzp9E5YMWKFezcuRNJkoiNjQ2aAzp16kRycjL33nsvN9wQHF4Sbg44Fbz77ruNzgHt27dX+8vpdPLqq68iCIK6ebNgwQKys7PD3hs4B8iyzEcffYTD4aB169Yh11588cXY7XbefPNNnE4nTz75JL169VJln61zQFTpjiKKKKKoB5New7TL2nFZpzR6tEjgzoE5dM6oc1OSZZnWSRZyUqzEm3TkpFgxNKAIRhHF2QKdRmR075a0S7Vi1Iq0SrYwundLVRnXiAITL8imX5skLHoN8SYdV3XPoE+rJFVGn1ZJDO2WQbLVgFEncn7rJCZekH3S1qShfx9ESkIcLZqlsfqnn/jkk08QBIEuXbowdepUunXrxkW9OpOVlaWmLHI5ncx+ZCYdW2Vybmcln+udd08D4K23F3Ls6FE6tc8hK7MZM++bgd0eGld/Mpg+41769DmfSy++kPTkREYOv5aCAMt0IHr37o3L7WLf3j1hzyclJTF5ytQgt8iZDz1MTrt2dOjQgb59+zJy5EgmTJgQcm/RiUJuHH0DbZuncuUl/Zkw6SZVmQWFUG3XrlxuGBG8WF+0aBGTJ09Wv3/wwQdc2KfnSfWBzWbjy88/C8tY/PC9d3PnnXeq3//7+tv89OP3tG+ZzttvvMZb7y9WF+lHjxyhb98LQmQADBw4kHvvvZcrr7yS7q0zmDt3LvNfeYv4+Hg0osAtY6/jtf88gygIGI0G3v3gQ15+6T+0ykhl3do1vPLmWwiCMjd//fXXDBgwgLS0tJNq5x8Jk06jWKUFZZOubZIFo1ZRoERBoF2yhTSrAa3PKyAr3qQq1KAo1y3jTRi1IhpBINWqp12yRWWZP5n9vAEDBlBcXEzfvn3VY/369aOoqEhVusePH09CQgJpaWlceOGFXH755WFlXXjhhbz55ptcddVV5Obm8txzzxEXF0fXrl1JTExk/PjxKhu1LMuMHTuWpKQkOnbsyL59+/jkk08ARfF/4IEH6NGjB/Hx8eTn53PffffRt29f+vXrR1xcHEOHDlXfxVGjRnHTTTcxePBgYmNjueSSS9izJ/x7eLJYtGgRX375JUlJSbzzzjsMGTIkiCgsEH369MHlcoW1zoIyB9x6663Bc8DMmbRrwhxQWFjINddcQ2xsLOeddx5Tp04NYkofO3Ysubm5IUp3/Tngvffea5LlORA2m7IRFm4OuOWWW7jlllvU7++//z7Lly8nPj6e//73vyxbtkydA44cOaJu7tRH4BwQGxvL448/zuLFi1UX8UGDBqnkcQaDgY8//pj58+cTHx/PqlWrWLhwoSrrbJ0DBPlU2Gb+x1BVVUVcXByVlZURyQFAiWfYtWsXHTt2VHeXfi+cTidPP/00DzzwQMSX+GRxuut5Jtr9V5EZHZ+zW+bpHh9/HbfZrEjh3Hw10CEthoQwFsTDZbVc1i40H6Rf5k/FerxyqMxYq55zW4aycwIUVjq4vH2ozNGvruPBC2LPepnXv7iKLiUriO97PYI22ILdkLxIMs9EX/4vjE8keZLbhVx5gswWLUmOiwHg5rc3KpY1Wcbj9SL44hglSckJLAoCWo0ACEELd1muy6us1YS6HwMkmHW8NKIr1W4hLB2cRiOoYQz14fZKQSEcfhwottHMqvnLyHx/yces/PEH5v37xSbJiyRTkiSOHz+OxpIQNnalvsyPP/qIF1/4N9+v+EmVt3fvXsaNG8fPP//sc8+XcTgcDba7eVKckoLnkUe54667m9zupvTl1UMH849/zqNDx44NytxXVE1mjBanHPo74ZVlzHottW4vZp0mRLl0eiSuuvJy/v3vf6vsxv52+/MMnw74xyc9PV2NBT4ZrNpfouZC90OWwStJiKIYVmmWUd5TUEKcwrVEkpX2mvUaLmqTckbafrplno3jEw7+eg4cOJA777yT0aNHh71u6dKlLF++PCgHemMyT0fbly5dyvPPP8/y5ctVefXngKbAaDRiNBqZNWsW06ZNO611HDRoEM8++yzt27c/Y+NjNBq55JJLguaA0yFTEAQcDgcHDx6kVatWGI114WFN1SPPTHb4KKKIIor/UewvraWjVmxwER1FFKcbRyvtQCyLNuSjEG7XkQnd9bd27A0g3RN8/2glD81kOYjN+PUJSrqok1XqAhFJWfIvUP4vY+g1wxhyzbA/tEyn08lrr77MhBsnBR3PyclpkA06HMqrwxPBnQ58+vmXjV90GuCPcz2bcWEYXoozoXxG8fuxfv16mjVrRkZGBh988AG//fZbREs/wHXXXcd11133B9ZQmQP++9//MmnS758DzuQc7o/n9+chP1M4W+eAU1415ubmkp+fH0QuAHDVVVf97kpFEUUUUfyZWLevhNQ4E8lWAzqtSJnNRWmNk26+nMaFVU6SLTIGrYheI+IKk3c2Eqp96V5iwigtoKSMcXulBt3V/RbHiPBZJXWaU9tBlmWZshoXMUZdxNhmfx0aKqOxejrcXnQRrKZNgcsjUe1wk2jRh12kyrKM03Pq/RBYT4NWPOWF8OkYc398f6nNhd0jM6lfK6ocbpZuPoJHktUYWZuPUb91ohmt7EE+iZQpHklGIwgRSQFPh2OcJMsINJ3YSHmGNJTVuAj3NKXFGhSL4UlUrSntONl6hrsfUN19w9UhHDt1yHUNXCPLMr/+uo3LLr6IfgMGMHJUeMtbQ/i9Y96Ubv+9fRlFFH8mjhw5wrBhw6ioqCA7O5sPP/yQlJRQos0/C9u2baNfv35ceOGFjBkzBq+38RztUfw5OGml+8CBA1x77bVs375ddVeCusk0OthRRBHFXx0r8oqwe2R6ZSXQLi2GDzcW4JVlumXFA0ou1Aq7W4mJTTJzsLQ24uI6EPmltXy0VSGGGX5Oc1omBTNPeyWZvSU23F6ZJLM+4mLbT7zj8kghrvr5pbV0jdGzu8hGq0RzCBN4U/JAr8grZltBBbFGLeP7Zoco3janh4M+ttxWYdiz6+rpjHgOYG9JDTqNQE6yNUTxboy53eWReOfnQ1Q5PPRoEc/ADqEu2YVVTkprXeg0ArGNkJsdKbPTLCG4LS6PhCAo9Uwy68mIi8z+Hgmna8xjTEr9DVoRu8dLea2LjYfKAMXlNDNOIWGqdno4VuWgsNpBmklER+PM+gDVDg+1Lg8aUSDJEhqq4ZVknH738kYEVjs9ERnoHR4lcMPYxE0M/xX+Ta1EH8mUDJTanMiAXiPi8OXbNgY8q5E2ffyyXGE2pvyPncMj+eJpT2FzQK57zwQE0uIMONxepTE++f6nuzHpZTVOEi3GsGNo90i069yNE2WVp7RxdTrG3ONr9+kc8yiiOJswfPhwhg8fHuSJcDahR48ean5qWZajethZjJPe/r/77rtp1aoVRUVFmM1mdu7cyapVqzj33HPPWnN+FFFEEcXJonerRIZ2z6Bjs1hG9WnpS4tVt2A0akXap1qx6LW0T7U2ibjmSEUtsqws7I9WhBI8ub0Sbq+yHLc5PY3KszlCrzlSUav+HZhH3I+mWKYOlyo/4FUOj2qlDUSNyxPw96n9wPvX3m6vHFY58jbiOVDtcFPla7+/viHX+PrQ7W18q+FYZeh4BLa9KePhxwlfep9FG/L5YfcJLHoNFr2GH3af4J2fD6HVCOwrtrG32Mb+0hpkWWGUrnK4g/o2HLKTlTykdpeXkurQTQ2TTvlZd3nkRvswEC5ffKlXksNueEgBxxqT6vI07OEgN0FGuLICPQFOh+oW9rkLaKd0Ev3nV3o9kkysSUuCWU+CWU+sSau2VysKSCjWb4NWVDxlGiFg9EqNb0BJp+iBcLaOeRRRRBHF/yJO2tL9888/8+OPP5KcnKyQPYgi/fv356mnnuKuu+5i69atZ6KeUUQRRRR/KM5vlaRar9ulxYTkGk4061UrWVNdpLtmxlFQpih3XTJDyTYMWpEksx6b00N6rIEaZ3iF1u9KnGDRhbjcds2MAxnMOg2J5lB35qYoKwNyUli9t5isJItqWQxEolmPzVe3RLOOEpsr5JrAeoaDLINBI2I1aMO6VWsbsTAmWvT0aBHP4dKasDm1AZrFGjhe5fRZ4Bpe8u8vtrGzsBpRUAjE/NbQaRmx6EQBBNS4aYUlOXJf+vu91OYiLc7MwA6pyLLMyj3FHCypweORyUgxsd+3WZDjYx22u70U2xr2Dsg7Xg0oObDbplrZcaxKtR7KskyRbyziTVr0PieIpuhkVqMOm8ONXqsJO25aUVCVsMaMlQ3lWReJTAIV9vqAwuxuL2aDFgHweMM3Spbr6hep3f7+CpdWyd92kYZDJ0JkBvxd4/Sq6dNqnF6Meg0aUUD0mbo9soxWjclvWK5JH348kMFfvcZSgEXC6Rhz//iczjH3bxYVVTtDNo5SY404PV4EwOmJWvSiiCKKvw5OWun2er3ExCgsqMnJyRw7doz27duTlZUVkSI/iiiiiOKvhBiDlg825quLbhkZp8eLKCiKoiTLlNa4qLDXWUKb4rYdY9Qx4rwWEc8LghDkwhxJ6U7xpY4RBCFEl4wx6pCBNj6LaEgZTVj6tk210jbVGvG8TiNGlB9cTyM0wMnSroEyxEbqKQhCWJfyQMQadSrhV2Flw+Qw/ljp8ednodWIfLW9kONVDryyjFuS8Sdiz4w1YjVoqXF5KK0Jv9kgBigwh0preHPtwaDzMkoMtkmnwe72srekzlJvimD5VPLEK6lTUmIMmHQaerZM8CndkBdApAbKxojgddNUm7BBK2KwRs4AIAgCBl9Ko8Zi9Q1aMeLb0NT8wX74u1KnEXB6ZErrb0r4CtJrBFzeOndoiOzK51cwNUJ4xu1TqWegVur0eHHagt9dVZkXlEfJ4Wm8ntBAzncBdTxOFadjzP2bjadzzP3zbqJFT2GFA0EAg07E7vIiI2MMyBPs9EjKJoYg4JXlk0qVFUUUUUTxR+Kkle4uXbrw66+/0qpVK/r06cM///lP9Ho9r776atgE5lFEEUUUfzVM6JcdNtWTR5IjKoqNKXVnO4p8ysyHGwuocQcoBD7L7+2Xt2NfiaLY1bfO/S8sdC0GDdf0zEDrW/AP6pLO2v0lQdc0jzOqjN4WvbZRl/Przm3Bh5uO4PQpWBaDhqu6Z6rnW8QZOWFzUhkQJtBYvveOzWLo3iIRUFJ3jerdMmjjRSsKZCWYEX0KpVaMTJL1l4Cv8rFGHdVOjxIb7UOcqc6TQxQE9CK4Aiyjf0a7Eyw6ymvcdURqYvDGmF4j4vEq1m4/msIH8X8RkiQTY9RiMWgRBIg1hqr1ogAmjag+7425uUcRRRRR/Fk4aaX74YcfVgP2Z8+ezZAhQxgwYABJSUl88MEHp72CUUQRRRRRnHkk+dzI/9Y5HU+4DQevRMv48KRpjRGm/RUw7JzmQe0WBIH+bVOQJJn2KeE3WhoLKTBpRcb0yYp4XhAE0mOMpMfUHYvUl2mxigfEuVmJQfU06TRIcuQ6+suJhPJaF3IYa3iqnxU8nLyI0s4srAZtw27MooAxYEwiZRTwk56V17oId8nvabuIEDYkIxBajaC6lzdUz//r0IgCVqM26Ht9BIYAKAzpf0TNoogiiihOHidNpPa3v/2NYcOUHJRt27Zl9+7dlJSUUFRUxKWXXnraKxhFFFFEEcWZh+K+DLmFVUHHK2pdvLn2IKIokFds43B5bRDBkuRLyxUORTZF5ocb83nn50Pq591fDqMRBfaV2NTP3uLgj90d2Yq8aEM+768/zPvr89XPe78cRqcRgmQEyq9tQB4ois+3O4/z5tqD6qe8Nth9vMLuJq/Ypn7cEeKK/X35zc7jeALykXokiW93Hlf7Mq/YFhKX2pgCVml3B9Xxi9+OBZ2XZJmCCjt5xTYOltXilqQGw9ndXhm9ViTOpFM/VoNC/qV61cuKhdb/aYyBurzWRVmNi9KAT1mNCwFweLx1H3fdJxJZmMfn1lxa4wqJ7/Uzgjs8Eg6PFEocF/B14sSJzJkzB4CtG3+mT58+xBh1CPgJDJWP1aBFDmivRhDUflDEN8TiDUuWLuXeu28LrkJAHR0eKTSOO8L4LFu2jHEjhoc/2QTs2bOH888//6TuMeu1JMfH8u/5z51yub8X/ncg0mPrlWVqGnhmLrroIrZv336GahdFfRw6dAit9pQzEP8pmDt3LnfccccZkf3RRx8xefLk0yLrvffeY+jQoad8/6nMAYIgYLFYePbZZ0+53D8bZ+sccNJK96RJk6iurg46lpiYSG1tbUhS9iiiiCKKKP5a2Hy4nG0FFQBU2d0s23o06LzDI7G3pEbJMyzL7C2piciS7U9DVGRzURLwKap24vVZztNjjNjdUpBS4vBIYS2QfpTaXByvcnJhuxT+3rUZ6bFGTlQ78UhykAyLTquW0Rhh1aIN+SGM8h8HtL2kxsmJevHEjcU2F1c7eefnw2pfvfPz4ZAyDpXbVTklNc5G3WM/+zVYyS6qdiKKQtB41LqDFfnG2K1rnB5qXXVM1vU3GySCY5AbY/XWiiJur4Qn4OP2SqoyqxNFundoR2ZyPC1SE2nTohk3jh1NeXl5g3IVy7RSdq3LG+Le75bqGNtlOXK7z+tzAevXr6esxoWr3hgGtt0j+eL5A9BY3uoXnnmaETfeCkBB/mGaJ5iZMnFc0DXndOvMqpU/qWVEquewYcNYuPijBstrCO3atSM9PZ3PP//8pO7btiOXu6ZNV7+/9MJ/6NKuLXFxcVx00UXs2rUr4r0/fPct5/foTKtmSUwYdT0VjYypH7IsM2vWLFq0aMG57Vpw2223YasNH7LjkWS2bN5E717nkBgXw+WXXsLhw4fV89OnT2fWrFlNa+z/YcyePZsRI0ao3x0OBwaDgccee0w9lpubi8lkwuUKz1/xe1BeXs6ECRNIT08nLS2NLl26sGDBAuDMK/IPPvggL7zwwhmR/fjjjzNjxgxAaYcgCIwaNSromg4dOjQp49OYMWNO+v0NxKnOAXl5edxzzz3q95deeons7OwmzQFff/01bdu2xWKxcPXVVzc6r/sROAfEx8czefLkBp+7jRs30q1bN8xmMxdddNFfYg446Sf67bff5umnn1bJ1Pyw2+288847vPnmm6etclFEEUUUfxbcXomvthdSaXfzt87pqntvIEpsTkpr3SSFYQkPJ08UNSHHQGHFbh5vwnSy5E0oVtVvdx4nzqTj712bhTAuS7JMQbkdl1eiebwpopzA9m3JL6db8ziW554AoH/bZCRJpl2yhT0+0q9at7eOlC2S4TNAmRh5bgvMBi21Lg8fbCxA8CmKR3yputKsBuJNOmRZVsuIhD6tE1m5p5SvdxQyvGdzth2pUNrqcwX3eCX2l9VSUusi0azjSKW90dzSoBBrjeubhSgI7D5exbr9pQiCUs/SWoU0r02iGa1GxOOVOFYVXinw96W/xGMVdjSiqJbhr2eF3c0Jm5OCCjutEs2U1rojEqkF9mX/tsm0S4tBlmUWrDsEBI+HgMKIjteNWBVMsDZ14SZKbC5sTjfVDjdmvVYlutKIgqq0SrJMlcODVhSIM+kQhDoKPhnFjbei1oXHKxNv1qPXiCRZ9Tx/XWfsPqU/0aJHFBQG7LIal9ohfkX348+/ov+A/tTU1HDj+HE88cQTIdYVPwGZxaCh2uGh0u4mwayn1p9aTQajTgQZHF4JtySjEQVcXinyYxnwt4BAklVxB3e4fYq870a/xdWoEdUc2/WVdLWeGpGNGzei1Wpp1badajkXRZGVP/7A4X17aN+hg9q/bp8HhEeSG7R+lNW4kAPY6UGxRKXFGoLi2wPbFviojxo1itdffz3IWlbr8mLSR55rXB4JWZYRBIHNmzfx+GOP8u2PK+l9Tncee+wxJk2axM8//xxyX3FxEbdMmsA/XniNiy66mFkPTOfB++7h7XcWqtdIsozLIynPU0A9FyxYwJIlS/jll184bodH7prM3CfncOf9D6PXKlkOKu1uUmMN1NgdTBw1gvsefIgbRo7mmafncuPE8Xz13QpA5u9//zuTJ0+mqKiI1NSGyRb/LKw6UILN6cEjKaSKOt/8IMkSoqAYtryyjMsrIQJ6rRhCgimj9KUEqleGHwadyIWtkxusQ//+/Xn11VfV7xs2bCA7O5s1a9aox9asWUPv3r3R6xsOmTgVTJ8+HUmSyMvLQ6vVsm/fPgoLC097OfXh8XjOmEK/YcMGtFotHTp0UI+Josjy5cvZvXt30PE/CuHmgJPBpk2bePzxx1mzZg2dO3ducA4oKipi1KhRvP/++1x88cXceuut3HXXXSxcuDCM5GAEzgGxsbGMHj2a2bNnqx5KgXA6nQwbNozHHnuMsWPH8sQTTzBu3DiWL18OcNbOAU22dFdVVVFZWYksy1RXV1NVVaV+ysvL+eqrr86qhkURRRRR/B4cLKlhf3ENJTYXGw+VhZyXZZnCaicur0RhtbNR9vLDpbUhxw6W1CD4XE/DpYpqCiP6xkNllNhc7C+u4WAYZbXa4aHK6YlYRn2YfYtxh9tLlS9PdY6PPE4QBFr42NVtTq+aB9sQIbWS3/vaqBMx+2JxzXqtWoZXllUX7Thf7GZgGZHQLjVGbZtf8QjcsNBqRFVZCyyjMVzWKU0lteqQHuurT10KI40gqERr2ia4WV/WKQ2Aw2W1HPSR0PmPAcT7iMACLbSRZAYaXAPH48ou6UDweGTGGVU59Um6SmwuBrRLZs8JG8cqnewvruGyTmlc0TmdSzsq/1/ROR2PJHOk3M6h0lpaJCppzy7xfQZ2SCUz3sTBkloKyu04PV4uap8SxCyuEPAJIX8j1ym9Pj0Di8XCoMFDg6wnc+fOJSsri+5tMrn44os5sDsXUMZi/rx/cGmvDlzQsSUX9unFrl25IEBFWRlTJo4nu3kGPTu3Z9G74Rd6v6xZTZcuXQCINWnJTDDz9huvcvG5XbioWxvmz/unWknJ62XOE4/TMacNrVpm8sjM+/F4wocqfPfdd5x/QT9AYdX2K5c3TbmFp+c+CdTFJcu+fxwOB/dNv4v09HRatmzJ7NmzkXwK+eLFi7lxxNV4vBIGPDx052T6d27FBZ2yGXz5QERBYPodt/HUk0+g1QjotSJ6jUCPju1Zt24dABdeeCE//PBDUJ0bIwCUkNXnreBwPp27dKFzl65oNBpGjx5Nbm5u2Pu+/vwzOnXrQb9LLscj6rj3gYf57ONl2O113h0er4yEL/d4wDP95ZdfMnXqVDIzM7FaY7j33nv59MP3EYA4ow6DVsTimzfWrvoJg0HPuImTMBiN3D/zQbZt2cKx/EMA6PV6evbsyQ8//NBgO/9MONxeKh0ealzK/+mxBprFGcmMN9MszkizOCOSLFPj8lLt8mLRa9Xj/o9Fr6Xa5aXG5UWS5aBzTnfjhHLnn38+RUVFHDyoZFdYs2YNkydPZvfu3erzsmbNGgYMGAAonhepqakkJSUxZswYyspCfxcBXnjhBbp3705RURF2u5077riDjIwMmjdvztNPP61et3HjRsaMGUNcXBwajYbu3bszaNAgAK644gq8Xi9WqxWr1Up+fj5er5fHHnuMrKws0tLSmDFjRtBz/eKLL5KTk0NycjITJ06ktlb5zV2wYAGXXHIJU6dOJS4ujrfeeotZs2Zx8803A7By5Uratm3L7NmzSUxMJDs7m2+//VaVu3fvXvr27UtsbCyjRo1i5MiRYRVBgG+++UbtLz8EQeCOO+5g9uzZYe9xOBzcfvvtYeeABQsWcNlllwGKcXPUqFEkJiaSmJioljNlypQgq64sy7Ru3brBOeBkcPjwYTp37kzXro3PAR9//DHnnnsuf//73zGbzcyaNYslS5YEzQGREDgHxMTE8MADD6ieD/WxcuVKDAYDN998M0ajkYceeojNmzdz6NAh4OydA5qsdMfHx5OYmIggCLRr146EhAT1k5yczKRJk7j99tvPZF2jiCKKKP4wpMYY0PlyRbdICCUQEwRBXQRa9JpGU3GlWEMtBakxdel6/KzYQWU0gbLKXzedRgiS54dRp1HZxcOVUR9+N2N9gCIduEiv8ivaWlHtn0ju5f5yHW5JtdLJsqyWEagQBrrxVjWiFNS46ix8/nraA6x+siyrVsqTYYY+XFq3aRHYZjFAgQ9sR2Ouxod8myBJFgOxPgU7sIxA93R/GZFkBjYjsG4HihV5geNRHXA+nDRREMhMULweMhNMYfuoseeqsfejvsu0+j2gKP+hqqoqvvz8U/r06aOe69ChA5s2bWJT3mEGDhzIXbcqC+SD+/bw7luvs/jrn1ibe5g33nmfxASFzf2Wm28kvVkz8vYf5INln/LEY4/w22+/hemBOvj5CNau/onv1mzg9Q8/Z95TT3LgwH4AXvz3fNatXcOqdb+wdftOft22jZdffjmsrJ07d9I8S8niohUFVcGeetudfL/8W/J27w4OcxDgmafnsnvXLnbt2sWaNWt49913eeedd0JkL37/XWpra9iUu491O/bz0KzZCAKMGD2aZUsW46cOWP+LYn264IILAGjWrBkajYb9+5X2LF68mOGX92uwT6DueRt42WU4nS5+3bYVt9vNwoULueKKK9Tr/Jb/shoXv+3YSfuOndH42m5NyUCr07Frz141dl+SFdo+AUW5D0Tgsy/LMkXHjyF6atFq/N4OyvyVt3sXnbp0VfpZEIizWmjdug27d+Wqc2aHDh0aHfs/EwL1fj/CvIP++VoUwqddO9m5vT7MZjPnnHOOatn2K9hdu3Zl69at6rH+/fsDitJ98OBBDhw4QHV1dVglct68ebz55pv8+OOPpKamcu+991JWVsaePXvYsGEDCxcu5IsvvgCgT58+PPDAAyxcuFBVlvxYvnw5Go0Gm82GzWajZcuWPPvss6xevZpNmzaRl5fHli1b1HdxyZIlvPzyy3z//fcUFBTgdruZN2+eKm/16tX07duX8vJyxo4dG1LvQ4cOYTAYKCoq4sEHH2TKlCnquVGjRnHFFVdQWlrK2LFj+fjjjyP26fbt28nJyQk5Pm3aNL755ht2794dcu6pp54iNze30Tng7bffpqamhqNHj1JUVMTcuXMBGDt2LIsWLVKvW79+PRB5Dnj//ffp1q1bxDbUx+WXX47T6WTr1vBzQCByc3ODZLdq1QqdTqeW3RjqzwFHjx6lsrKy0XLMZjNt2rQJ2gw4G+eAJr+lK1asQJZlBg4cyEcffURiYqJ6Tq/Xk5WVRUZGxhmpZBRRRBHFH414s56b+7fG4fGSYA7vWpedaMblkdBrRU5UNWxFjjPrqW9wjTfrkWVol2JtNFVUJHRvEU/LJDNGrSasy6hBK9I+1YpXUv6OlNqs2l6nqJn1GrQakcs6pvH9rhMs2XyEWS3jOVReqyopsT7LdHGNKyL5V+BC8q11h+icEcvOYwpRmyz7lL9YI0erHBwsqyXWoMXplXB6pMhu1sCyLUcAuLh9ClqNSKxRS5XDgygKFNmclPvyp2tFQS2jrLbxmMTdx6spsblIMOvYW6RYpiVJRhQEdBoBt1dxfU8w6Si3R3YFr/K5ovtltE21IiOz/mAZu49XIwgCx6sdaqqwZjEGtYxICOzLJZuPkJNqVcnKIHg8Kh0enJ5azKKXJCm8xWt4z+aU17oiPtuNPVcNvR8aESSvEn7hz0UeCL1PUxh29VBlYV1dTZu2bVnw5hvqNX7C1vLjVdx///384x//oLbGRqzZiMvl4vD+PcQnJpHToT06UeTwkWOsW72KdxcvxWAw0LljR4bfMIJly5aFLDAD9Ru/p8Sk26bhFPTkdOhEpy5d2bljB5lZrXjvnQX8+7+vkpCYjEeWuf2uu3n5P8+HkDB5JZnKykr0JmUDwqDTYPApSvGJCUzyWbv/++bbAOh8ffDx0g95/sWXVSPGjBkzWLRoEePHjwfq3OtdskBJaSk7d+8lO6c9ffv1RyMKDBgwAJfTxeYtWzjnnHNY/MFihl9/Q1DdYmJi1IXriBEjGDRsZMh4BsLoS8EFYLVaGTxkCJf0VxbwLVq0YPXq1eq1WtVyL2Oz2UjPyESvEX1jLhMTE4vLXoNWFNR0bhpfyEagzn3llVfyzDPPcM0111DlEPjXv/4FgK26htSkuvUmQE1NDTExsWq5ADGxMdhsdRtaMTExES2xZwsCfz/CIcmix2rQohEVnoT6qD+3nwoGDBjAmjVrGDNmDNu2baNnz57069ePNWvWkJmZSX5+vqq8+ZVVWZa58847Q5TuOXPm8Nlnn/HDDz+QkJCALMu89dZb7Nu3T7VY33rrrSxdupQhQ4bwwgsvMG/ePJ555hl27NhBp06deO211yISf73xxhu8+eabpKSkADBjxgzmzZvHHXfcwRtvvMHMmTPJylKyRcycOZPBgwer97Zp04aJEycCYDKFhllZLBbuu+8+RFFk7NixTJ06lYqKCioqKti5cydr165Fp9MxePDgoM3B+qioqMBqDc0kkZCQwO23384TTzzBe++9F3Ru6dKlvP766yFzgL++fuh0OkpLSzlw4ACdO3dWLd0DBgzA6XSyxTcHfPjhh4wcGfyOB84Bo0ePZvTo0RHbUB9Wq5XBgwfTu3dvIHQOCITNZqNFixZBx2JjY7HZbGGvD0TgHBAXF8dTTz0FKO97XFxcSDmxsbEh5fiza8HZOQc0Wem+6KKLADh48CAtWrRADDMBRBFFFFH8L8GkD69w+CEKQlgLxMniZBdM/pzaizcWUFvPjVAjCtxxCjm1/ezeWlHg2p7NAWiZaKZLRhw7jlUiCHVWwaz4OutoVrxJrU9j8CvcWl88NyhpoPxKbKCFu7F+7ZoZR+tkZXFzTY9M3vlFIVHxK9wC0CrRrJZR5XQ3KO/vXZvx0dZjlNiclPja0zWz7oc+O8HMXp/l2l9GJEcEewAj+dXdM3wWT4Grumfw2a/HEARUhTvRpCPWqFPLOFLRsBtep2axbC6oVBV6qNsYAGU8DlfYFTZv2UuSJnwaJY0okGwNtWAHIpJC7kek9yPOpFf70K9wC0Jdvmq/VX/xx59x/gX9cDqd/HPObAYPHsymTZsAeO2115g/fz75BUfQ+J4Xh62S9u1yePSJp3jhX3PYv3cvQ66+mjlP/4ujRwpwOBy0z6rLg+71ehkzZkxoxX0dEmfSqcRpMQlJdW0ymaipsWHQihwtKGDEtVepmx6yLNM8MzNEpCzLxMbGUltTQ7xZr1pz/bjljrs4t2tH9u7J86W2Us4eLyykecAiNSsri2PH6sjyBEEJnRg6bASFRwq486YxuF1OJt08hZkPzkQQBG4YMZJlSxbTpVs3Pl32Ecu++DqobtXV1UGLVl94emQEbpa9+QYfLVnC1p276NA6m+eff56rr76azZs3+y7196Ues8WCrbo6aMyrq6uwWK2IooBWBo8sq6zjgbPepEmTKCgo4KKLLsLhcnPPtLv58ccfiU9OCaleXIyVWls1Bo2oZgeorqrGarWo7arf5rMRTfn9aOy3QSuKnKK+DShx3Q899BA7duwgJycHnU5Hv379ePHFF8nMzKRr167Exsbi8Xi49957+fjjjykvL0eWZZKT62LGJUli/vz5vPDCCyQkJABQXFyM3W6nU6dOQdf166d4WpjNZh599FEeeeQRjh49yiOPPMKwYcM4cuRI2Lrm5+czaNCgoHcx0/cu5ufnM3XqVG67rS5zQCAJV/PmzRvsh5SUFFWvMZuV3w2bzcbx48dJSUnBYDCov1kNyYqLi4uoYE6fPp02bdqQl5cXdLywsJCWLVuq3+vPAX6MGzeOw4cPc+211+J0OrnllluYOVOZA0aPHs2iRYvo1q0by5Yt4/vvvw+69/e8D6+//jofffQRe/fupXnz5iFzQCCsVitVVcEZUKqqqsJuRNRH4Bzg8XiYMWMG3333HWlpaSHXRirHYrGo38/GOeCk/VH8u0i1tbXk5+eHMMudjMtCIJ5++mlmzpzJ3Xffzfz58wElzmHGjBl88MEHOJ1O/va3v/HSSy8FDUB+fj633norK1aswGq1MmHCBJ566qm/XPqCKKKI4uQx6rVf6A70/8cPeITgd/62S3NIDOPS7YdVr+VvHUIn878C/Dm1r+ycjt0jU1ztVImsBEEIyqkt+1J6eWUZk04T0eIb7yODG9W7ZVAe6N6tEjmnZTySJJMVb8KgDY5lNuoa3pgAGHt+FjY3VDk8xBm1WI3BxHOpVgPJFj12txeNIGDQipQ2YJkeeV5LhICNX61GZFK/VkiSTPM4o8KQXS/OXB8h7tyPJIueGy/IptSmMFqnxBiCZIiCQPsUK26vhMsrodeIVDrCK/IJvr4c26cl3gDVItlq4MYLstV6mnSaINduURDUePdI6JWVQKfM+KAxD4RRp6FdsgWnR0LyuNA6ahqNPT/dEFDa6vUxc2sEQVW01WsE0GsE9BoBg9nEyDFjeP7ZeZSUlGCz2Zg2bRo//fQTsc1zSNZ7SU5OVuP2rxs5mutGjqastITbb57AKy88z42TbsZqtXLkRLHaXo8kE2eMTHKoDdh4iDHqSLIaQvZRMjIyWfDue3Q/5xwEBLxyeJkaUaBz584cP3JItf6qkCE9JZnJU6Yy/59PEaiON2uWwdGjR+jZpSOgrGnqew1aDFrMhhgefOQxHnpkFof27+G6oVfSr98FXHjRRYwcM5prhw7hsssuIzUtlS5du6j3Hj9+HI/HQ5s2bSL2Q0PY/ttvDB4yhKysbHQ6Hbfccgv33Xcf5eXlqnKFr0XdunTmi08/Js6kQyMIHD2Sj8ftpnVrpWx/fnLFxVwISvkliiKPP/44jz/+OAeKbez8ZQXdup9DgjWU36Fzp068/uoraEUBrajBbrdz4MB+OnXqrG4q5uXlqd4CfuQWVgVwI8gq61x6nDFsDnAZ0AgQbzr9JGJnC/r378/u3bv57LPPVGW4T58+jB8/nszMTNWa+t5777Fy5UrWrVtHRkYGn3/+OXfddZcqRxRFvvnmG4YMGUJGRgYXXnghycnJGAwGDhw4EOQdGw5JSUncc889LFiwgLKysrBzVmZmJosXL6Znz55hz82ZM0f1kJEkiePHj6vnT3UOTE9Pp7i4GJfLhU6nvPdHjhyhc+fOYa/v2rUre/fuDXsuMTGR2267jSeeeCLoeLNmzcjPz6dt27ZA+DkAFK/i2bNnM3v2bHbv3s3FF19M3759ufjiixk7dixXXnkll156qcoE78fvnQN+/fVXBg8eTHZ2NoIgRJwDADp16sTSpUvV74cOHcLtdjep7MA5AJQQg549e6LRhP4mdurUiZdeekn9brfb2b9/f9AGT7g54M/GSe+PFRcXM2TIEGJiYujcuTPnnHNO0OdUsHHjRl555ZUQhX369Ol8/vnnLFmyhJ9++oljx46pLxQou9iDBw/G5XKxbt063n77bRYsWMCjjz56SvWIIooo/lrwW6kUd0XUj0ErYjFo0fly7Rq0wR+9RsRRLz/yXwnF1YoVsdrpQacRyYg3keyLu80vVZSsA2U1auyvUafBotf6mKTDyzRolR82j1dmw8FS3l9/mOW5x6lyuFXl02+VsTk97C+tCSojfD0VV3avVyLGqCMz3oTVqMPjlRBQ8nQfqVSY1UVBwBLApB0pThyUOONqh5svtx/j/Q2H2Xy4XM2TbNEr4y7JMkU2p1pGI1muOFKuWJiTYwxkxJvQaUSqHW4EQWBfSQ0lNU4kWUanEdUyIonUa+sWCfmlNSzemM/SzUfIL6sj0/OPh9srkV9Rq5YRSWZDYw6EjIdRp8Gk0yjqXdN45E4bal1eRWERBXQaUUlp5jvn9Cis3qrVWxDweNwsXfyBStJks9kQRZGUlBS8Xq9KWuSVZLZtz+Xb736gwmbHaDKj1xvQarVkNs+kd5/zmfXII5RX2bA7XWzbsjUs4Y/XZx0NZCLXiko0sMou7lV4AcZNnMgTsx6j6PhxQObw4UP89NNPITIFQeDyyy9nwy/rcHkkympcVNpdAefhzmnT+earrzhxvBCPpIRRXHvddfzrqbmUl5dTUFDAs88+G+Qa6vEqJHvrVv/EgT27EQUZa0wMWq0WCQGnR6J9+44kJSXx4P+7nxtGBqcmWrVqFQMHDjxlQ0TPXr34+quvOHb0KF6vl9dee43MzEx1se319WGty8uVQ67it61bWLPie5wOO8/+40muunYYos6A2ysx5aZJTL15EmI95nKAkpISDhw4gCzL7MrNZebMmUyddj+l1U6Kq52cqHJQXO1EAHr3G4C91s7rb7xBZU0tTz75JN3P6Ula85YKO7rLxebNm7n00kuDysiMN9EiQfnEGHRoNCItEkyIghJXHfjxb/D80RtWfzSSk5Np164d//nPf1Sl22KxkJ6ezuLFi9V47urqaoxGIwkJCZSUlPD888+HyDr33HP58MMPueGGG9i0aROiKDJhwgRmzJhBRUUFkiSxa9cuNmzYACju6Fu2bMHtdmOz2XjllVdo3bo1ycnJJCcnI0lSkNV70qRJPPzwwxQWFiLLMocO1b2LkyZNYu7cuWrscGFhIStWrPjd/ZOdnU3Hjh15+umncbvdfP3112rMdDhceeWVQezv9XHPPffwxRdfBLG0Dx8+nDlz5kScA/xYsWIFO3fuRJIkYmNj0Wq1qkLaqVMnkpOTuffee7nhhuDwkt87B5x77rl8/fXXHI0wBwTi2muvZePGjXzzzTfU1tby+OOPc/3116su/RMnTgxxm/cjcA7YuXMnM2bMCEpfF4iLL74Yu93Om2++idPp5Mknn6RXr15kZ2cDRJwD/myctNI9bdo0KioqWL9+PSaTiW+++Ya3336bnJwcPvvss5OugM1mY8yYMbz22mtBA1hZWckbb7zBs88+y8CBA+nVqxdvvfUW69at45dffgGUXZDc3FzeffddevTowaBBg3jiiSd48cUXz0hOwSiiiOLsgn899MCgjjxxdRf189DfO+GVJGpcXlonWejaLC7o0y0jDqvhr+sN4yel+mTrUfYX+93IZT7ZdpTvdxchCIrScLTKweFyu+oWV+Vwh00zFIhFG/PZcawKh0fiSLmdpZuPBJVxqNzO0SqHksPYV4YzQm5pf7mLNhaoLqd2t5d3fjmMICrs5jUuLwfLalWmdH8ZkWSCwvq+ZPMRTlQ5cbglfj1SobqXg2Lh3FtSQ7ndrZbRWLtX5BXxybZjap33F9tYsvmIwl7uSxm2t6RGjV/3SDJ2V8Myv9heyPe7i6hxKUzw3+86wSfb6twGqxxuDpTVYndLahmRZDY05qIoBI1H4JhLcuQ80GcKdreXUptTLVeSZYXZ3Odh7vUZGa8e/HfSEuPJykhn1U8/8cknnyAIAl26dGHq1Kl069aNi3t1JisrC51eT6XdTa3DzjNzHuW8Dlmc16UdsXGx3HHX3SDDS28s4MjRo/Tq3IGcrObMvH9Gg6y51Q4PFbV13gpOj6Ru5MkoY3zLXdPp3ed8Lrv4IjJSkhg57FoKCgrCyuvduzcOp4vfduYiyTKqTu+bp5KSkrhp8hSqqhSLqwxM/38P0jYnhw4dOtC3b19GjhzJhAkTVJkyyubi0WOFTBo7gvYt0xg0cAATJt3E+f36IwMuSea6EaPYvSuX628YEbTJsmjRIiZPnqx+/+CDD7iwT6ilMBLGjhvPFX/7G5f0v4DExEQWL14cZMkafFEfPvjgA+xuL4Ipjv+89iYP3TedLm1bcKKohLn/elYd8yNHjtDn/Avq2hbwXBYXF3PFFVdgsVi4beIo7rrrLq4eOpgEi55Ei547J9zAwv/ORwZiLWY+WLqUV158gexmqfyybg0L3n7HRywm8PXXXzNgwICwbqlRhGLAgAEUFxfTt29f9Vi/fv0oKipSle7x48eTkJBAWloaF154IZdffnlYWRdeeCFvvvkmV111Fbm5uTz33HPExcXRtWtXEhMTGT9+vJq3WZZlxo4dS1JSEh07dmTfvn188skngKL4P/DAA/To0YP4+Hjy8/O577776Nu3L/369SMuLo6hQ4eq7+KoUaO46aabGDx4MLGxsVxyySXs2bPntPTPokWL+PLLL0lKSuKdd95hyJAhGAzhQ3P69OmDy+UKcSH3IykpiVtvvTXINXrmzJm0a9cu4hzgR2FhIddccw2xsbGcd955TJ06NYgpfezYseTm5oYo3fXngPfeey+ipT4cJkyYwBVXXEHv3r3DzgGdO3dW49RTU1N5//33uf3220lOTqa0tJR///vf6rVHjhxRN3fqI3AOuPrqq5kxY4bKZg8waNAglTzOYDDw8ccfM3/+fOLj41m1alVQWrKzdQ4Q5MboV+uhWbNmfPrpp/Tu3ZvY2Fg2bdpEu3bt+Oyzz/jnP//Z4A5POEyYMIHExESee+45Lr74Ynr06MH8+fP58ccfufTSSykvLyc+Pl69Pisri2nTpjF9+nQeffRRPvvsM7Zt26aeP3jwIK1bt1YJBcLB6XTidNbFIFZVVdGiRQuKiopCAvMD4fV6ycvLo3379mHdHU4FTqeT5557junTp0d8iU8Wp7ueZ6LdfxWZ0fE5u2UOef5H+ts30OWKUWh0wS6AlS43Gq2GLukxYZldD5TVcHGb4HhBfx1XF+mQhdA9yRiLjswEEwkmXYj78vEqJwNzQuMP/TLXlOjxeGFHoUJm0qVZHKIoEGPRcU6LePX6GqeXGreHBJOO0hp3WJnjX/+ZBy6IY/yyIzg8MmN6t+RopZ2VecUAPDCkA+1TYjlUoVhWM2KMmPUi+0prMWlFhnRuFiJz4pu/cP/5sYxfdoTUWBN9WiVSVO3gpz0lqsxWiVaO+azX2T739UMVkWWOenk13SvWstjRDZ1Bx7BzWvDhpgJcHomZQzrSPi0Gh1ui0CezbZKZWpfEsWpHWJn+vvS3+6J2ySRbDWw6XM7h0lpmDulI52ax7C+tQZIhxqAl2aLH4ZaosLvC1rG+zIvbJZMZb+a9DflquzukxVJS46LaqeStbpVoZn9pDQZNhL5842fu71s3Pld1V1wFP/v1mCqzc3os+3xp5JrFGDHqREpqXHi8UliZDY35g0M7kpMco46HOuYaL5raClq2zCIlXompm7Jwsxpi4PV48fo0QkmWcbgldBqFBV0rCkqOcgBkZLkuPtus04S4i4MSI/3iDV04Wu3B4ZYQRSUuvKzGhSxDs3gjRq0GSZZVtnqDzyvBK8nEmULdtg8U28iI0XKw3KWMqVGLVhSocXlxeSRFpk6jbtJoBCXOtSGZB0tsNLMGy9RrRJWULj3eiEmrwSNJeGVFZ9ZrRZweJWdyOJmHSmpIt2p4ZeES1q5awXP/eRGAilo3zeKVkAdREHD6NHGdj+TPI0nIcqhMWZY5ceIELl0M1U6l35MselxeiWqHh2bxRtU7xeWV+OzjZbz23xdZ/sMK3F6JOJOOvXv3MmHCBNauXavmm3c6nVR7lN0PryzjcHtVjyCNRqB5cjxGo5EHH36E2++8S7X8yyjvU6TxaWzMXW43fc/rxU+/bMRqNDQ4Pv6+9NfT5ct5btRp0GtFzPXioP0bJP6c8FcPuoL58+er3pP+drtkjbofUWlXNrjS44x4ZTlkE1aSZOweCa0ohG23f3zS0tJOyRq+5mApjnobizLg9khoNULErAv+Tb+QEAZ/vX2bPVaDhgGtktS2GwyG02a1P90yz1Qdf8/4RJLpdDoZOHAgd9xxR0QysqVLl/Ldd9/xyiuvNFnm6Wj70qVL+fe//83y5ctVefXngKbAbDZjNBp57LHHuPvuu09bHT0eDz169GDr1q1otdozNj4Gg4FLL700aA44HTIFQcDhcHDo0CGaN28epBNUVVWRmppKZWVlg3rkSSvdsbGx/Pbbb2RnZ5OVlcX7779Pv379OHjwIJ07d1bz4jUFH3zwAU8++SQbN27EaDQGKd3vv/8+N954Y5ByDMpO8iWXXMI//vEPpkyZwuHDh4Py6dXW1mKxWPjqq6+CdkgCMWvWLDVmIBAPPPAARmPD+WGjiCKKKKKI4q8Ai8VCv379yMzMjPKc/A/D6XQybtw4hg8fzogRI/7s6kQRxf8UtmzZQlpaGunp6Xz66afcf//9rF+/nqSkpD+7aiqic8AfA4/Hw9GjR1m7dm0QU7rD4eDpp59uVOk+6V/h9u3bk5eXR3Z2Nt27d+eVV14hOzubl19+mWbNQnfnI6GgoIC7776b77777g9XdGfOnMk999yjfvdbuu+5556opfsPkPdXkhkdn7Nb5pmydL/6m50OzeKDzlU73WQmmdFpNZh1GlolBecmbszSPe3bE9jdMi6vPw+2kmP1tstysOi1yPhcb32WDEFQctA2ZJV+cEUp+4pruaJTGmv2FVPrkriqewadWsTSpVkcbo/MoYpa9BqBVIuRI1V2rHoNgzqmR5Q5ftkRruvVQvXdX5lXxJFyOw8M6YDFoMMjyWTHm9FplfNuj8wJmyNsPce8soau5WtY7OiGGw1jz8/iXZ8b+ANDOtClmcIsWljlwOby0jzWxHGb4roeb9JxebvUsH05ftkRUmKMXNS+7vy7vxzmwaEd6ZQeq7KMt00yq7vohVWOsO32y1y8x8NH2wqJNWo5v3USy3NP0CLBxJh+WWo9/XJzki3sLamJaOEf/epaHu6fwPhlR7iiczM1R3dVrYvPfitk5pCOtE6yquPRLLbuN/BIhf2kxzywLwPHPN2kQeuqxBATT0pcMHus3xJUJpmQERRyPp/BodrhIcmqx6ARcUtKUia9RlTDOTxeWU1RVl+m0+mk0gUlNsVqnGTVU2oLsCD7rJRur4QkK+nD3JKSuzmc1XNfkY3msYpVWqsRiQkot9TmCrF0GzSi2g63VwpLeua3pJY5ZMpr3WhEAYtBQ5Xdg14rkmjVq/VU5TZi6Q60yMeadCoxl9crYzIobs86X9osUSDIU8btkSJaujHFUe1UrPqxJi02hwdJruvL337dxuWXXEzffv0ZfeNkzAa9QiAXpo71Ld3ltS7cvjyGSVZ9iBXZI9V5JGgEVJb9cH15Osc80NLtlWRVlk4jkBxjCLF0O31yQeERiK1nmfa3+0iVW71ORuH/0AgCyTH6sJ4bsqxMg+GeoTNpST2brdJnQubZbum22+1cc801VFRUkJWVxeLFi0/KPbuxev7etm/bto0BAwYwYMAAbr31ViRJOqvH2y/zr/j+OBwObDYbU6ZMCbF0P/30043KO2ml++6771YJAB577DGuvPJK3nvvPfR6PQsWLGiynM2bN1NUVBTEQuj1elm1ahUvvPAC3377LS6Xi4qKiiD38hMnTpCeriyc0tPTVUKGwPP+c5FgMBjCKlCRjgfWT6fTYTAYTpvS0NSyTwanu55not1/FZl+RMfnLJXpYyzX6PQhSrcoC2i0GnR6A/ow7oFanTtkTP117NTcHOJeHqvVIWj1NEswk2gOdS/X6uSwz4hfZlGNhN0jM/wcJd3IR1uPqm3IToljj0+hy0gwYtKJFNkUV+NwMj1o0Ol0HCp34UZDSpyV81qLfL+7iI9+PUGXVsnIoo6Cmlo0Oj3psUYseg0auxc0YliZkqhFp9PhQcPK/RWKe3mVk4PlLkCDqNWTkWDlaJWDghoPrRKUTYeCmlpMOn3EegK40SBqdQgaHXq9nhqXF43OgKDVYXdL2GUNGp2GGIsRUadTYsYJrae/Lz1oOFDuIrvCRVqMkZ8PlPrK0KPTGzAYPHgkmRInpFqVMtDoGhyfT3cU4kbDOa1SSYkz4aaEA+UuRK0eQaujyOZSnjMBtQyNKISVqdXp1Xp+9OsJhp+jpLX56LciQIOg1RFjMaKxe7HL4JA16phrGujLSGOu1RuQRZ06HuqYi15kX8hy/TSfko9MTPCR61U7vVgMGjxehe0eQUDUiOgEGZekfAy+Z15GDps2VJZlJU2U04MMPhdZwVeGck7wsVdLCCCgluGRwssUVTd3hTTM4JHRagRqnApZG4Jy3u8K75FB5ysPhLAyJV+7K+xuZMCsEuN5cHoUkj8BRTFEENQUX/6FXDiZdXUQKK91q+nWyu1uTEatSiiHLCFRl6feX0ak8XH5xwPQaTRYDAJVPoI/EOjW/Rzyi5UYWb0oqGU0ND4CArIAcWY9dpfiXq4RRaWtgVkBBBlBkNU+a6gvT+eYq2OKgEYUiDfrcXslJUtCvToCGAQhyL28vkx/u11exf26fmYCWUCpmw86UVCzHbi84fsy8P05XSl0/fUURfG0Kg1nu8wzUcfTOT7XX389119/vZK20OHAaDSeVX3Zs2dP1erqr+PZPN7w131//P/r9cG/003VD05a6R47dqz6d69evTh8+DC7d++mZcuWQTn7GsOll17K9u3bg47deOONdOjQgf/3//4fLVq0QKfT8cMPPzB8+HBAoX/Pz89XyR769u3Lk08+SVFREampisXju+++IzY2Nog2PoooovjfhMu3GH3muzwkUYcg+Ml5BG67rC06jcI87YdyXvk7Ur5qUBb63giBN2kxp7b5Em/WY69yqso21OVBFgSBZjEGCqudHKtyqOdNERKwFgfkxb64XQqiKNAi0UyCWU95rQtRFDhYroT6GDQiFr1GLaPC3nC+alCIyg4G9NvF7RQLvkWvwaARcXolVT4Q1koEUGKra8swX+7vq3pksmhDPoIA+0vrZDSLUXaS/WU0hAFtk1m+u5gfdxcFHfcTd2UlKDHX1U4P1T4270h9GYgEs54WCSYEQeDidims3FOMKApB9cxOtKhlHKuMQNLl+8H/o8YcCBoP/5jjldA0sviIM+koq3Xj9HhxBjL6B+TUFiUZCdR45MZ60v9e+tPQxZt1lPnipR0BjOF60a/ECggRIt38tbcatFQ53CrhXv16GjQiDq9CSOf1huaBDkQgM75WFNH7+jzGqKPa4QYhuJ7+59GgEYMYz4MrqtRUIwpIXlklZPPD/47ofdZul+T3bWm4P6t9+dxjfNZWvVZE61ukOgPqIgaUESke2I/yWlfQ/ObwSOCEtFhDXVaHJgYe+vvydI55fei1dWMUDoIgoNP4uAkiTdxArFFLlcOD2yvjN5b7rdk6UUAjCLilOqt5FFFEEcXpxO8O8jKbzWFz5jWGmJiYoDxyoMSfJSUlqcdvuukm7rnnHhITE4mNjeXOO++kb9++nH/++QBcccUVdOrUiXHjxvHPf/6T48eP8/DDD3P77befNqtkFFFEcfYiM94ERXBNj+akJCjus7IsU1BuxyvJdM9QXG6rnR6sPsUTwOH2crzKGVHumcCQrukcLHOw8VAZHq9E3zbJtEgwqedjjTqMOg1FNoWRO9aoDUmr44d/UT3snEwMemWRKwgC1/TIoKDcjiwrRDtpVoOqcPvLqE/eUx+jzmvJloJKdp+oIs6k46KcFDWvtiAIZCWYqHF5Ka5xIsmQZjVgj5B+zV/uqPNaoPWtck06DePPz0KWFNdOvVagWYxRtTz5yyiyRR6f7GQL1/cy8POBUkpsTtqmxNCzZbx6XisK5CRbKKt1UWH3oNcKGLUNe1dc0j6VtDiTWufWKVZSYgyqC2qsUSFl8/e9VhQazU/+R425JMnoNYI6Hv4x91seG1LCRFEgyWrA7mN414QhjtJrRSRJsUzWWTMjw6gTMem1qsIsCkoZ+BQcRckRg9oa0TLhO27QiiSY9dS4PHi8MgatiDmwngIYtSIeSSHoEgQQQ7JuByPGZ4H2w6AV0fo3RVDGXasJrGTkDSY/4k06nB6JGpcHWVY2CwIVWFEUfJZZRbnTCAINVVOnEbAadEHlxpt1ICsbhzI+UjYxuJ4NIdaoQ4KQMZfl4Hz2gWMeafPGP26ndczPECwGLSa9Vk0ZB4rXhkCdZVsjav5wtv8oooji/waapHQHxj83hmefffaUK1Mfzz33HKIoMnz4cJxOJ3/729+CkqFrNBq++OILbr31Vvr27YvFYmHChAnMnj37tNUhiiiiOHvhdCuLp6SYOtdyQRBomai4PpfWuEiy6EOUCKNOE1G5OZNomWhW6wZ1KXNqXB5MOg16jUjzuDqlrKQmvOKZ4rO2Wwxa7B6J4monOo1IslVPy0QzsizTJsmilmF3e5FkGZNOE9HC77dyajUCPbMS6JmVoJ5z+yxVdrcXo1bEatAGsf1GUrpTYoxQChpfzusqh4dYo5YYow4ZmbbJFvVayVdPURAwakU1JjYc3F6ZGKOOKzqFhhHVuDzoNaLSHxYDyRalryL1pR/NE0y4JSUfttsrkRJjUOopB9fT7ZVweSUlxjmCLFdAf/wRYw7QKrGujv4xlz1erNCgEuaVZDSigFmvwVxvE0GSFYVE8CmbfiulpxFToJ+jwCsp6cpEQVDH01DPYukvIxL8/eXP+x0urjiwnlpRUJmdI9VT61Ow9L5c6x6vjIyMVuN77mQw6ALcj+U65ayxeoLfMquvd97nli8o9QxU9hvqT4UPwOc678tv798IqG/99ZfRGLy+MTkdY+4f19M55mcSgqCMv39DIZwF3b+p1NhzHkUUUURxMmiS0r1169ag71u2bMHj8dC+fXsA9uzZg0ajoVevXr+rMitXrgz6bjQaefHFF3nxxRcj3pOVlcVXX331u8qNIooo/poobUCRkoGDZbW4vFIQUVXg+T8S767P5/LOzUi2+pRAm5PPfj3G48O6cKRScS9OMOlI9Z2XZJnaRnJLb8kvZ1N+ZdCxq7pn0CJFUfIcbi+HK4JdoGMM4a2z/nzFC385zPXntlQVk42Hyth+tJLHh3Uh3ycrK96E0We5dri9jearXrQhH4cUvLh97Jo6Ippim5Oyem7vCWEIlvz4YGM+bVJj6d0qEVCUkfc25PPoNZ3VvgSF8My/gI7oFuxDaY2Lj7YeCzrWJSNO7UtJltlXUhP03ERyBS/39eUfNeaBfRk45jrZQ7ZGClII66OiVolrTrIaVDWoxuVBr9Pj8ta5QBsCiNQae3lklP6sX25GfN3mgscr41HPy426gpfVuDBoRSy+zR7Zd6xZvDGonkZtoLLccEU9UqgbuEmnCVKYnR4poLmN17O0RiFSC1T89UKgK7iMtp4FvTHLao3LE/KOBfalLIe6mjeEilo3kiyftWP+R+C4L6SjWZwp7Aas2yvhkpSxiiKKKKI4HWjSnLdixQr1M3ToUC666CKOHDnCli1b2LJlCwUFBVxyySUMHjz4TNc3iiiiiCIE+4ttlNW4cLq9HK90sPNYpbqYLK91U17rotblxSvJOD1eKu1uNQaxIdhd3pBFeSAkX57bprojfvbrMbySjEeS1JzNsozKBF1ud2PzxSAfKKttdKG781gVAO3SYkjxKW5+uZIsq8qXUSuqZXgixDyadXV7sJ9sU2QcKLax/aii4MlSnUJzuMKuECMFlNEQ/ApJl4y67BB+19Iqh1tVuOOMWrUMRyPK545jleSXKXHMSzYfUcsIVNYP+eKcqxzuiO3246vtCkFostVATqpVLcOPA2W16nA0tCEAYAroyz9izEVRCBkPxSNBo5bXGCp8z7nTI6kKnlYQ1EVCoFLX2PNeUVunfJkCmaZ9L6Uk1SlfgWWEQ6DKY3d71fe2PEDBC1SM/KRjkiRHbrfvRKXvudNqRHUTyR7w3AUq3I0pX4Gu0hW1LjUbgb9fRepctD2yjOR7Xp0eqcExd7rrxsOo06ibYYEdExhr31gMfyBOx5j7x+B0jnljj6vse9ZPMuMtkizj8krUBmxg+DkCZJ+PgD9/uFf1sIhau6OIIorTg5PeaHzmmWd46qmnSEiocz1MSEhgzpw5PPPMM6e1clFEEUUUDcK3oPtgQwGfbTvK8SoHC9YdZPHGAvWSWreX/aW17Cm2YXd7OVRmZ29JTaOugxW1Ll5fc4C31h7i14KKsNccKqtlb0kNh8pqw54PhF+R2328iv1FCklZh/QYZFmmWYyRVj4X5KNVDnXhFwmBi80bzm1B/7bJDO2eQfu0GPV4lY+AKc6oJSvBHFRGOMSY6hRFRUmVWLmnWC1DRiYrwUycT1mstLvVMrQRXMED63njBdn0bpXEjRdkA4qbpyTLFFYr3gqtE82kxxiDyoiEa31s4N/vOoHHK6mKkiTJpFoNtPO5g7u9clAZjaFDegxXdc9gQE4K1/dSiN/8Cq1/PNolW4LKCIdYX1/+kWMeOB7+MU+xGFTSrUhIsihWXa8vdrfaT1QmK6EGeq2oKpxeqeE6+uG/JNlqwGLQqpZ+P1xSnSXVX0YkXVHjUzL9BF1VDreqHAXW079Z46+dq149J06cyJw5cwBY//M6+vTpAyiKbLxJh9WgVUnp/POKX4JRKwaVEbaevndg5Tef8/j9d+Nwe3G660jJ9FoRnUZQSdn89WuoN5ctW8bE0dcBkGjRYzVoiTfp1A2CwPHQCoJaht+7Y8+ePSoHTiAaGvN4s57mKQm8/J/nQ8oIB/+50znmnoBwlnBweSUcHqlR75X6sLu8bPp1J0Muu6jOwu/b8HJ6lLnC4ZF8hHwnJfr/NA4dOoRW+7spov5QzJ07lzvuuOOMyP7oo4+YPHnyaZH13nvvMXTo0FO+P9Ic0BAEQcBisZzWcOGzCafSJ6cLJ610V1VVUVxcHHK8uLiY6urq01KpKKKIIoomwbcwapVsZsz5WWQlWbixX6tgSwvKgrh9ihWrQUtOsiUkxjsciqqdag7bgvJQpVqWZWp8FpMal7dRi0hLX4x1lcOjusW3ClDcdAGKq98KFoloKNDAExhbne2TJ/jSKwFBOWt1jZBAQZ2VKnBBG1iGv+8CUxlFir/2L8RN+roUHoIgqGUEWs8CY1wbGx9LQByqv56BsamCIKgWv5MhRQocj8A2+8dDKwpB7WiMCOqPHPPA8Qgc80ZHXKiLYY1kOfTr7bLcNKs5hJKtqd9995/TsR2pCbGkJcbTslkaN44dTXl5eUR5gRZcfz3rl6F+a6SO551/AevXrweC442DnmO5nkx/GY106IvPPs34KbfjlWQOHTpE9xYJTLlxXND955/TlTWrfqorI4LMYcOG8cJbHwDBbfXXOXA8gvZWfJe2a9eO9PR0Pv/88xDZDY351u07uWvaNLWMN159lR6d2hETE8N1111HRUVF2PqKgsChgwe4+m8DaZORxMhBF7Fj+29B4xFEpFavd2+55Rbatm1Lh2ZxrFq1KsgbSZIkHnngXtpkptEuqwX//c/zeOXIz+zTTz9NSkoKiYmJ3H///ciyjMWgpXvnjiSnpLLiu6+DrjdqRTQ+Pon/Kw7ls2fPZsSIEep3h8OBwWDgscceU4/l5uZiMplwuSJ7fJ0qysvLmTBhAunp6aSlpdGlSxc19fCZVuQffPBBXnjhhTMi+/HHH2fGjBmA0g5BEBg1alTQNR06dAgJqQ2HMWPGhH1/m4qG5oCGkJeXF8Tn9dprr9G6detG5wCA/fv3069fP5Vo+9dff21yuf45QBCEkP6RJIlp06YRHx9PWloazz33XIOyws0BcOp9cjpw0k/0tddey4033sgzzzxD7969AVi/fj333Xcfw4YNO+0VjCKKKKKICN/qqGfLBFVpS4s10jbVSuByOc6oVZmmRVEg1aqnsAH28kUb8nF4ZOJNOiRZpqzGxTs/H2Lale3ZV2JTF7oGH2O0KBDkshgOK33prVolW/B4JXYfr+abncfp3TYJgEqflVKvqSMgiqgEBawKdx+vokN6LJIs833uCeU+ZKwGDRUONwWVDtolWxAEQS2jIfitS0adhhijlmqHh93Hq2iRohC0HfXFIscYtciyTIXDHeSGGgh/2KrdJVHr8mDWa6lxetQyNIKATlRyFVfY3cSbdGoZDVkV9xUrVuO4AKtfYP+7famjAstoCr7ZeZyJfbMRBIE9J2yAonRoAuJzPV4JrUbE7W04Vhr+uDG/oH1y0Hj4xxxCNx1ueXcze4uqsbu8Sq5UQUCvVay9Gp9V3+72UmX3qOzYAM3i6nLTyrLMiSqnUlcBkBVLbPMEE88N76yW6yfU8v8NQABZ98eff0X/Af2pqanhxvHjeOKJJ0KtK777/KmsNGKdJTewbQqJGCFlhEOg4ldpd6tW2aCQhsA9Aln53timw8aNGxFEDa3atsPgS3MliiIrf/yBvN27ad+hg2oZFgPLaECmRgSPpNTN/6xX2T1YjFofY7kMXmXTxaitZ6IHRo0axeuvvx5kLQscDz/hWX1rtn/Dcc2qn3h67hN8+tW3nNO5A3fddRd33HEH7777bkhdJVnmtpsnMPCyK1j0yZcseOstJo66gV937kJAVInW/M+6VG93pEePHowcOZLxE28EwBzAP/H2G6+xbs1qftm6HUdNNYOvuJzu3bpx2WWXhdTjq6++4sUXX+SXX37BYrFw+eWX07p1a24YNwmLQcugq6/j40ULufjyQSH3+vvjTJOo/bi3WE1nCMpzbdCKvrzA1b5QqOB51azXqM+1TOhvjkErql5HBq3IgNYNp/Ht378/r776qvp9w4YNZGdns2bNGvXYmjVr6N27N3q9PpyI34Xp06cjSRJ5eXlotVr27dtHYWHhaS+nPjwezxlT6Dds2IBWq6VDhw7qMVEUWb58Obt37w46/kch3BxwMlixYgVPPvkkK1asoE2bNg3OAf7yBg0axA8//MBbb73Ftddey549e5rU5/454Kabbgo59/LLL7Ny5Ur27NlDZWUlF198Md26dePSSy8NuTbcHNC+fXtV7u/tk1PFSVu6X375ZQYNGsTo0aPJysoiKyuL0aNHc+WVVwYxi0cRRRRRnGn400AV10svdcJHkpNo1iEKoW6KdncjpFo2FyU2FxU+l90S33e3R6JlvBmrXovDI+H0yrglGWcTfBH9SmBajEFJdYay2BZFgbxiGyd8bcj0pa1KNjewyAnQGtbtL+XNtQdZsO6QWgaAOcDav6ekJqiMcDgRkCu6R/N4REFgUOd0tQxRFNgTQCRm0op1ZURqfkA9P9hYwJtrD7J4k+L6L0uKVbeFry9O2JzkFduCyoiEXw6UAnB5xzREQaBnSyXcyd+XB3zu/klmXVAZjUGW4a11h3hz7UHW7i/xHZODxmN/WW1QGeHg78s/csyDxgNlzA+W1QY9E6CQD1Y7PBTbnKTGmSitceFwe7nu3BZc27M5w3u1IDPBzPEqB1ajDofbS2Glg35tU7i6RyZX98ikX9tkjlbYcXgkrAYdxyodpMYaKfXlZfZ7HZTVuCixOdV8zf6B9adn8vjceTUGE1f+fQi7du1S6zl37lyysrLo3iaTiy++mC1bFWtJrFHH8/P+wWW9OnBBx5Zc2KcXv27fidMrUV5Wxi03jie7eQa9Ondg0bsLw3bl+rWrg1KWZhLeEFUAAN8mSURBVCaYeemllxjQqzMXdWvD/H/9E1A8EbxeL7Men0WHtm1o3SKThx+4H48ndPPK45X47rvv6Nm7r3Kvj0FfEARumnILT86Zg8Mj4Va9JpQ+8Lqc3Df9LtLT02nZsiWzZ89WNkOAxYsXM3XUtQCUVFRz04SxdMzOoH+XbIZcPhBRgLtuu5V/PPkEoOTbtru9dOuYw7p16wC48MIL+eGHH4Lq7Cf6M+uVRXCcnxHe94g5vXXq8PJvvuKGESNp36EjBoOBRx99lCVLllBbqzz/fgXarNdwaP9e9uzezegpd1PrFblh/E1IksTaNWvUMXdLPhfuMLHst9xyCxdffDE6nc7X/3VL1KWL3+fWO6eRkppKh/btufGmm1j8/nthx3fhwoVMnTqVNm3akJ6ezj333MP777+vjJMk07NPX9avXYVWCD/TSJKMQOSwmdOBGpcHh8eLUSfi8HjxSBLZiWZaJVnITjRj0mmodXvRaZQNkVq3l7QYA1mJZrISzaTFGKh1e/HKMjqNQK3bi0mnUc/XV9jD4fzzz6eoqIiDBw8CioI9efJkdu/erT4va9asYcCAAYDieZGamkpSUhJjxoyhrKwsrNwXXniB7t27U1RUhN1u54477iAjI4PmzZvz9NNPq9dt3LiRMWPGEBcXh0ajoXv37gwapGyEXHHFFXi9XqxWK1arlfz8fLxeL4899hhZWVmkpaUxY8aMoOf6xRdfJCcnh+TkZCZOnKg+owsWLOCSSy5h6tSpxMXF8dZbbzFr1ixuvvlmQCFxbtu2LbNnzyYxMZHs7Gy+/fZbVe7evXvp27cvsbGxjBo1ipEjR6qhKvXxzTffqP3lhyAI3HHHHREzKzkcDm6//fawc8CCBQvUjSW73c6oUaNITEwkMTFRLWfKlCnMmjVLlSfLMq1bt25wDjgZfPnll4wYMYKOHcPPAYHIy8sjNzeXBx98EKPRyK233ookSaxevbpJZdWfAwKxcOFC7r33XlJTU8nJyWHy5Mm88847YeXUnwNmzJgRdO3v7ZNTxUkr3Waz8uNUWlrK1q1b2bp1K2VlZbz00ktYLJFj3KKIIoooTjcSfbGJgcqGJMu0S4sBWaZ1koWuzWKJ86V+8kMrChh1kae/Ub1bkhoTHJM4zBdHDEpMZIolWEEKl3omEBnxJsb3zVLdksf3zQopIzvBrC5Okyz6IJfrcBjaPSPoe6avDFB+6HOSLSEM28Z6rvf10ad1opouzGrUqfHTflh0GnJ8lnN/GY3lLu7VMiHo+wVtklR3fJ1GJDshWCm26DSN5sAedk6mL50S9GgRT+/sxKDzada6dGG6AKKsSPgrj7kkyUHjUX/MA12TZVkhYZt+eXuu69WC6Ze3p11aTJDV2KzTMOXC1ow9P4u7Lm3HNT0ygzaual1erumRyZ0Dcxh7fhZTLmwd5N5v0mlUlnE/Al3i66emrq6q4qsvPlPjrEFxv9y0aRO/5B5k4MCBPDTtFhLMeg7u28PCt15n+aqf2bb/KG8sfJ94H8fM7ZMnkZGRQd7+g3z06Wc88dgj/Pbbb6Gd6OuPxID+3rBuNR99t5Yln3/DvKef5MD+/WhEgVf+8zy/rF3L96vX8cu27fz26zZefvnlsGOzc+dOWrVpq+SnDsDU2+/kx++Xszdvt1J8gCn+X0/PZfeuXezatYs1a9bw7rvvBi8kBUgw6/ls6SLstbV8tzGXtdv389BjygJ+5OgxfLLkQ3Uxt3H9LwBccMEFADRr1gyNRsP+/fsBRZEfdtkFWA1adcxEUaiLZ/dBI9QRJ8qyrL7jsizjcrnYu3evryuV4yadhqMH95LVug16g9J+q0FLx05d2JW7E0FAjWevNwxNwp683XTqXLdR0qVLV3Jzc8Nem5ubS7du3dTvXbt2VTd0JEkmp1VLtFoNVSeOqKn4gtouKiEwBo3YaE76U4VGFOjaLLbB36gOqVZyUqx0aRZDdoIpyBvBI8lkJ5jokh5DToqVDqnWoE2CptjpzWYz55xzjmrZ9ivYXbt2VbMWrVmzhv79+wOK0n3w4EEOHDhAdXV1WCVy3rx5vPnmm/z444+kpqZy7733UlZWxp49e9iwYQMLFy7kiy++AKBPnz488MADLFy4kEOHDgXJWb58ORqNBpvNhs1mo2XLljz77LOsXr2aTZs2kZeXx5YtW9R3ccmSJbz88st8//33FBQU4Ha7mTdvnipv9erV9O3bl/LycsaOHRtS70OHDmEwGCgqKuLBBx9kypQp6rlRo0ZxxRVXUFpaytixY/n4448j9un27dvJyckJOT5t2jS++eYbdu/eHXLuqaeeIjc3N/Ic4MPbb79NTU0NR48epaioiLlz5wIwduxYFi1apF7nD52JNAe8//77Qe9HUyAHeRUFzwGByM3NpV27dhgMde9V165d2blz50mVFw7h3utIchu7tn6f/FE4Zf8Ki8Vy0oMWRRRRRHE6UVTtpBPw65FKthy1+Y4qCw9RFMg9XsczUeWwBd3b0FpKKwoM6ZYR+QIg0awnMWCh2lge6Es7pOKR6wrViiJDumUgSTLtU6xh79FpGl7wxZt0TOrXKuJ5URBomRBMnhapnmm+tGrtUmPwBKzYEsx6JvVrFbGe/rzaDaFjRiydWiRFPG/QakJkN9Sf487PCupLgC6ZcQ32ZWNGq9M55v6+/LPHXPa40FbXBGm4ggBXdK7Lb241aNXvfnfwPq3rxkojCuomjH/hlZVkISupbpO9eYKZ5glmPt92VD1m0mlCuBWCIMDIa69Co9FQXV1Nm7ZtWfDmG+ppf7haVbGN+++/n3/84x84amvQaLXKgm9PHn36JpPTrj0mvYYTx4+zZtVPLF76ETqdjvYdOnDdiJEsW7Ys4lpFQAlRALjn3vtomZYEaUl06tKVnTt20LpNGxa+vYD/vvoqzZulAXDH3dP477/nh5AwaTUilZWVJMXHhri2x8cnMOWWW3nun0/x5tvB1vePPvyQ5//7MgkJCSQkJDBjxgwWLVrE+PHjg8YgwWqipqqCmpJjNO/YifR+iiLUr39/nC4nudu30eOcnny69EOuu2FEUBkxMTFUVlYCMGLECP52zYgQpUwTEEtg8FnpAS6/4m9MmjieMRMm0qNTB2bNmoUgCNTU1IT0p9thJyEuLohELSY2Rr1WEIJTup2MC3eNzYY1pi77QUxsDDU2W9hrbTYbsbF118bGxmLzXavXiugBqzWGqqrKsJtc+kY2vk4H9AF9rNOINFc9YZQNtMDNOUEQ1D71v4MxBm0Q94XVoMVq0Krnm7pVMGDAANasWcOYMWPYtm0bPXv2pF+/fqxZs4bMzEzy8/NV5c2vrMqyzJ133hmidM+ZM4fPPvuMH374gYSEBGRZ5q233mLfvn2qxfrWW29l6dKlDBkyhBdeeIF58+bxzDPPsGPHDjp16sRrr70WkeTqjTfe4M033yQlJQWAGTNmMG/ePO644w7eeOMNZs6cSVaWsuk8c+bMoIxKbdq0YeLEiQCYTKGeTxaLhfvuuw9RFBk7dixTp06loqKCiooKdu7cydq1a9HpdAwePDhoc7A+KioqsFpD5/aEhARuv/12nnjiCd57L9hDY+nSpbz++ushc4C/vn7odDpKS0s5cOAAnTt3Vi3dAwYMwOl0smXLFs455xw+/PBDRo4cGXRv4BwwevRoRo8eHbEN9XHllVcyduxYpkyZQtu2bRucA+q/exD8/v0eNPRen8q1gX3yR+GvRTcYRRRRRBGAZIsB7DC8Z3MEbbA7kkeSadMAw3RhQD7n+vhwUwGDumWqSoMsy2w4VEZWmoW8YmXizow1BlnvGmN1tnsklm05qpKvGbQiw3o2V8/LskxxjYvygHzV8RFYvBX35Th+PlDKudlJqrXJ7vby2baj3D+kI3nFNkQBWiVaVAuI7EuZ0xCOVTr4Yvtx9Xu7NCv92tTFBnokmcPlteqCWRQg3hg+hVZRlZK+6mh5Lc2T49TjRyvsQX0ZZ9SSZjWo7fBIcoMhALIss/5gmZo+CxRXc39ObVDcNwNzdsc0Yjn/K4+5IAhqHeuPub+cxupcaXfxzPI9XNAmiSu7NAt7zWOfKZaC2Vd3CXu+0seCXVrjwmrQBnkCuL0Sep2ouBWjWNyXffY5F/Trj9Pp5InHZzF48GA2bdoEKMQ98+fPJ7/gCBpRUGL9jxeRmN6c6Q/PZs6sR8k/uJ/BQ6/iX888Q0FBPg6Hg6zMurpLXi9jxowJqafX57pZVutSle7klFRAYbM2mUyUV1Xj8EgcKchn2FVDg+LZMzMzQ2RC3cKurMalckH4ccddd9OtY3v25OUBssrAXVh4jBbNW6jXZWVlcexYXb54t1fG5vQwfMRojhTkc9O4kbicTibePJnp9/0/AIZdP4IlixfTtVt3Pl72EZ98GUwSVl1dTVxcXNAxt1dSU6aB4gGTGJCfHFlxM+970SXc+8CDjB1xPbbqaqZNm0ZMTAzNmyvPscI0rqG0xoXBZKa6uiqojOrqagwmMw6PFJKf/GSycVmsVmwBsqurqrGEUW4ArFYrVVV111ZVVWG1WimqcqjjWF1djVdjoqTaSUqsAbvbi0zjm3NnEi6PxG+FVaTFGCKGxGw+oigJ57aID3v+SIWDEzYnSeaG0xr60b9/fx566CF27NhBTk4OOp2Ofv368eKLL5KZmUnXrl2JjY3F4/Fw77338vHHH1NeXo4syyQn1/0uSJLE/PnzeeGFF9TsRsXFxdjtdjp16hR0Xb9+/QDF0v7oo4/yyCOPcPToUR555BGGDRvGkSNHwtY1Pz+fQYMGhX0X8/PzmTp1Krfddpt6fSD5m/95jYSUlBREXziD2az8jthsNo4fP05KSgoGg0GdRxuSFRcXF1ERnD59Om3atCEvLy/oeGFhIS1btlS/158D/Bg3bhyHDx/m2muvxel0cssttzBz5kwEQWD06NEsWrSIbt26sWzZMr7//vuge8PNAU3FZZddxgMPPMCwYcOoqqoKmQMCUf/dg7r37/ci0nt9qtf+nj45VZz57bwooogiir8YnB6JRRvy1dQ1X2wvDFLwQEnzVONS4oFsTk+jMXRLNxWoyldgGX7kV9iDlC//NQ1hX5GNr3coCrLHq8gLLEOSYX9pjeo6nF9hbzRf9Q+7TgR933OirgxJltlfGpxuTZIjp/bxY0VeMUd9+aPzy2r5dufxoPOVDg8FPgXZX0ZDJGXf5J4IGY/vAuptcwYr3NC4VU0jCny67Sif/3qUz387xtLNRzhYUuMjVlIIrY7bHBwqq+FweS0Hy2oibgyU+FKUffXbMWSUeFezXqOWoRVFn4xaqpxuVb5GbJxvINyYB3pt1B9zT7181XZXaF55SZbV/tx+pBKPFFqHQAUy8G8/PFJdrmdQUnu5fe+PyxOs4NXd40sjZTBw/cgxbN68mZKSEg4dOsS0adN4++232bInnwMHDiAIgvqcDR0+koWfLueTlRsoyD/Mf56fT3p6Bharlf1HT3DgWBEHjhWRf6I0ois4KIp/YFsq7O6QZzk9I4Mvvv2Oo0UlFBwv5lBhcVi3Zo9XonPnzuTt2QMoHgd+BU4QwBobz6TJU5n75JN+XjYEIL1ZM/LzA+aB/HwyMoI9LhxuL3avwH0PPsrqTb/x4sKlvPrSiwoDOnD9yNEsXbKEFT/8QGpqKh07dVbvPX78OB6PhzZt2qjHXN7Q8XC4vUHmUUdAXPdNU29l02+5nDhxguHDh2O1WsMuuJtlt+Xg/v04nU51zHfn7qR9R0Xp8tTb9DuZzALt2ndgV+4O9fvOnTuClLlAdOrUie3bt6vfd+zYQceOHUmyGkiw6HHbyvB6PXTr3J4Eix4ZZdPBpNNg0AZ/zpR7ef2Wy7JMuV15Fv2bNvXh9HjD/u2HJMuU+nKvNyW1HyhK9+7du/nss89UZbhPnz6sX78+KJ77vffeY+XKlaxbt47Kykree++9oDlaFEW++eYbpk2bxqpVqwBITk7GYDBw4MAB1WpcVVXF119/HVKPpKQk7rnnHgoLCykrKwubxSEzM5MVK1aosiorK9V3MTMzk7fffls9V1ZWxr59+9R7G8s0EQnp6ekUFxcHKfCRNgVAcWMO53YNkJiYyG233cYTTzwRdLxZE+YAAL1ez+zZs9mzZw/ffvstzz//vMrwPXbsWBYvXsz333+vMsH7EW4OOFnccsst7Nmzp9E5oFOnTuzduxens84LbMeOHXTu3Dnk2pNFuPc6ktzGrj0dfXIqiCrdUUQRxV8esizz4+4i3lp7kH1F4XeZqxxu8opsHKt0NMo6nZWoWMi35Fdgd3sp9ilRfrfglj4rhF+xO1oV2WoeiFbJFm68IJtJ/VqRnVSX3svjIxcCyEm2BJURDn73ZYDjVQ7sbi+/+iwgrZItaj397oclNa6gMhrD0G4ZTOrXivHnZ6llgGIZBMW1sZ2vng2l90qNrWuDX9H+3qcc++uY4/NGsLu9eCRZLSNSGjKoU2rHnZ/FpH6tGOpzC/fHnfrHo2W8KaiMhnBpxzT+1jmd/jkp9G+bzGWdlO9uj0SLeLP6aRZnIj3WSEacCYshvPXczzXgl1n/4/ZKpMcayYw3BcluEW+OKNOfMsus11DlcPPZr0f57NdjmPUaRJ/brtGXp1kjwuHyWo5WOUJS2VXa3cz5IpeXV+7jYImNl1fuY84XuXzjU+SrnR6e+DyXZ7/LY1tBOR9vPcJTX+1i/vd7VBnzv9/DU1/t4uOtR9iWX86z3+XxxOe56kLfbz32K3ZVAXmg/fUEX0o5GexOFx99+IFK0mSz2RBFkZSUFOwut0paJApQcHA/W9avRfJ4MJvNSvywIJLSLIPzevfhqccfw1Fbi+Tx8OvWrWEVZH9fBlriZbkuPzQojPIGjciYcROZ/dijHC8sxOmVyD98iJ9++ilEplYUuPzyy/l103riTDriTDpifB4gsqy4EN81bTrfffMVRcePoxNF9BqR4dfdwLx/PEV5eTkFBQU8++yzQa6h/nCDtat/YlfuTmocbizWGLRaLSa9DqNWpFOnTiQmJTHz/93HDSNGBW2yrFq1ioEDBwYxB1f7WPPjTXqSrYaQGHT/ZohGEJDdTg7s2QXIHD58mMmTJ/Pwww+rVkG/R0WcSUd2mxyy27bjxfnzKKm0sfS9BQiCwCUXXYhRK7Jo4Tt079AO5PCbYC6XC4fDocaM+v+udnj4/+ydd3wU5dbHv7N90zshlIQeOqICIYCAFUGvFKmhCCIiRZpeQFEELqIvKipeO6BIFZF7RUUUpYlSpBN6CyWQENKTrTPvH7M7yWZ30wiI3v19PgPZKWfOPM/Os895zjm/89Bjj/PeOwu4lp7O6VOnWLzoUwY6wp3tooTJasfiWChISkriww8/5MyZM1y9epW33nqLgQMHklkgG7O//bqdDp06u7SJxS5istrLbazeKAosdpKv5JJjspJ8JZe9F7O5kCWPW1ZRYu/FbA5eziEj38LZ6wXsu5TNodSidKlDqbnsu5TN2esFXMu3cPByDnsvZivtailnofGIiAgaNmzIu+++qxjd/v7+REdHs2rVKiWfOzc3F4PBQGhoKNeuXePtt992k3XXXXexevVq+vbty549e1CpVAwdOpTJkyeTlZWFKIocPXqUXbt2AXI4+t69e7FareTl5fHhhx9St25dIiIiiIiIQBRFFwN3+PDhvPjii6SmpiJJckk+57s4fPhw5s6dq+Topqam8ssvv1SoTzwhLi6Oxo0bM2/ePKxWK99//72SM+0JDz30kAv7e0lMmjSJ9evXu7C09+7dmzlz5ngdA5z45ZdfOHLkCKIoEhQUhEajQa2Wfy+aNGlCREQEU6ZMoW/fvi7XeRoDKoLCwkKSk5ORJM9jQHE0atRIaS+z2cxHH32EIAjK4s2SJUuIi4vzei9vYwDI7/X8+fNJT0/n1KlTfPzxxy5pOMVRcgx48803Xc690TapLMpldLdu3Vqpnzlr1iyPjHU++OCDD38WMgss7L+QRWaBlW0n0z2ek5pjxmIXySiwlOlBvruuTMp1Kj1X8YQ1qV6UH1RqrmopaFsnTFlxT6gn580KQpHXIsSoVTwr5blH4+hAADLzLRy/mqPcw4kox2Q6x2RT7qHxkjPsNGT9dGp+P5vBNwcv8/2RKwQbtYqXNttkQ62SJ6gpWYWczyzAYhe5kXmqShAIdRhpZpudrEJnGa3Sf56aVA9SciI9kSFBURuqSoa1/gVR0pDv2CCSLvFRjoUBieggg2zIB8uGfEywkUh/HRqVypXoTpA9Yl3iq1EnIoAu8VGIkuSSAyoBDaICaVEzhPsaV3MhSXPCT6fmvsbVaFErhPpRAS6mfXlZnx9/tDvVwkOoW7M627ZuYd26dQiCQLNmzRg1ahQtWrTg/jbNiY2NRafTEWjQosXGW3NeokOzOjzYVg59Hf/sBHRqFYs+W0rq5Uu0bhZPo7iaTP/nFAoLC91v7HjHii9wKCzwxd47QYCxEydxV9t23Nf5HupUj2RAn15cuHDB4/O0adMGi8XC6ZMnPB4PDw9nxMhRLmGP/5z+Ag0aNiQ+Pp6EhAT69+/P0KFDXa4zatVcu3qFJ5P606peDAN73MvQ4SNIdBhEWpXA4/0HcOzoUXqXmHCvWLGCkSNHKp9XrlzJY13lnFnnOyGUeG5nW2hVAoWFhQwdNIiakWF06NCBhx9+mNGjRyvnznhuAuPGjVP6fN67H7Pl5010aBrH6qWLWLxslTKxvXL5Em3aJSAieTRuH3jgAYxGI2dPn+LRRx8ltlooh46fwmyz02/ICFq3aU/bVs3o0qkjo8eNJ6FjZ0xWO2fOnaNWVBjnz59HlCS6d+/O6NGjadOmDfHx8TzwwAMMHToUq12i0GJn3VerSRr6hMu9baKECEp0xq1ATLCBIIOWmGCDx+PBBg1hflpqBBs8vlMalUDNYAPhflqCvaSllAcdO3YkPT2dhIQEZV9iYiJpaWmK0T1kyBBCQ0OpVq0anTp14v777/coq1OnTixatIhHH32U5ORk3nrrLYKDg2nevDlhYWEMGTJEsSckSSIpKYnw8HAaN27MqVOnWLduHSAb/lOnTqVVq1aEhISQkpLCc889R0JCAomJiQQHB/PII48o7+KAAQMYMWIE3bt3JygoiC5dunDihOf3sKJYsWIF3377LeHh4Xz++ef06NHDhSisONq2bYvFYnELIXciPDyc0aNHu4wB06ZNo2EZYwDICwmPPfYYQUFB3H333YwaNcqFKT0pKYnk5GQ3o7vkGLBs2bIKeZ4LCwsZMmQIgYGBHseAp59+mqefflr5vHz5cjZu3EhISAjvv/8+a9euVcaAixcvKos7nuAcA06cOMGDDz6I0Wjk/PnzAIwePZp77rmHBg0a0L59eyZNmqSUC0tJSSEwMFD5PpQcAx588EGGDx/utU1uFQSpLJcPMunAyZMnqVmzJmq1mtTUVKKiom6FfrcEOTk5BAcHk52d7UYAUBx2u52jR4/SuHFjZXXpRmE2m5k3bx5Tp071+hJXFFWt58147r+KTF//3N4yH39vK82u/YLf3b1ZuvsSOSYbrWqF0DU+iqAAHXcVY82+nG0io8CCVi3QICKAtFwz9zdyHcecOi7cV8iG5DRqhhq5Oy6Mr/ddQq9RMbVHY1rWCEGUJE5ek0lEGkUGcDw9D6NGxWPN3UPCnDIfX5VC+/qRCgHVuYx8fj6WxsyezWgUFci5zALUAtSPkPOOREniYlahR5n9PvyVGYnBJH11kSyTnZ531GDH6WtczTFzb3wUHeIjaVkjhFyzjcs5Jvx1aiL99ZzLLMBPq+YfHnJ2H39/Oy93DOHzYzY8RTg3rx1C4+qex8cck5UHG1Vz299r4RZaZmzmC9MdWFEzPLEOi36Vy9O8/FhTWtYIAeDktTxESWbyvppnotAqEmLQ0K1xtIu84m0pIjCoreyJt9lFPv/9PK/0akbz6sFKfnODCH9lEeNCVqHH53bKrMrnvhltWZpMr/JsFowFGdSoXZvIYHmB5vEPfyPHZCVIr8Fmt2GXVOSZbRRYXGsGVwty1OWWZEOkZFm+akGGItI3Ca7mmqkWpOfzIXeQaYZrjvMjAvTK3zEhRoWV3myTvZJ6tQqrKJePKsmgDXA6LZeYQA0ZhSV99jKMOrVXZnq7JBWVwyouMz2PmAA1aQVyCLROo8JfpyGzwIJKEIgONih6OqNDDBo5H11F6Xp+vmotW37ZxGtvLSyXjt70FEWRK1eugF8I1/OtiJJEqJ+OPLMNq110aUtRlFjz1Vd89O+F/PjzZqx2kVA/HSdPnmTw4MH89ttvCIKcF28ymTh9Xfb4hgfolcWW6/kWokMM1AwPRm8w8Pz0Fxk//lllwcZml5QIBk9tWZ4+/0eP7sye9zrNmzYttc9PXM2lVpCG09flBU/ngpME6LVyGTZnfzj7xgmrXSTE6Cqz+HNfv3yeic88ybc/bVEWQNVqufa7hGzIFl/ws9pFgjx8h5z9Ex0d7dHjVxY2n0p38fZb7CJWu6QQqakEOeTd2T+i5M5zYdQWsatLyCkCTpH+OjX3NYxSnt1gMFQ6xLokqlrmzdDxRvvHE5x6du3alXHjxnklI1uzZg0bN250qYFelsyqePY1a9bw9ttvs3HjRkVeyTGgPDAYDBgMBmbOnMmECROqVMdu3brx5ptv0qhRo5vWP2XpWZE2KSnTZDJx9uxZ6tSpg8FQtFhWXjuyXEtjrVq14oknnqBDhw5IksT8+fO9Jq+/9NJL5RHpgw8+/I+h2ztbefP+SO6YvZECq6QwJTvHvH92b0JAsdV6Z94jyCWMHvVC7gRymOiQhDhyTVZlglYS1YP0hPlp0apVpYYuA2w5IXvLE+qGKyGiZpuIIAhczTOT5QibdXoXQo1yPeOysOlYGrXDZJKWFEedZ0mS0DsmjXZJNkCDDFqyCq1upZ+cyHV4g50e+1A/HZ0aRPLlHxfZdCyNjo2juJRdSJ4jxzYqQK9MJMuxzlpluJotexmNOhV+ag3fHLxMsFGL1S6iUas4cz0fSZI9imoBruSakChfbrPZJrJs53nqRQaQnCp7DSTHJaFGLZmFVk5eyyfEoCXHbHUrV/S/ii9Hyd4s56Q0Xx2gtLVBq5aNYUkiJtSIUatGchBqgdxHIIdLezKWnBMUZ3SI09A0atVyrrQgG292qciAFgS5HvOt9DA6vazOMGt/nUYZE0RJUvS0Od4VjWOQ0ghCmXnI3R/tycOP9qwyXeX8XvlvtUogQC8vDiAUeWULTGYWf/whQ4c94ZLf36BBA37//Xc3mQatigKLnYw8s0ufA2Tk5Cl9bhEl1I793h7bSaRWnj5fte4boOw+Lx6U4iSk02tUFFrtxIQaXfrDWetdIwjYS0RslER4gI7IRg35btNW9zbRqFx+c242OtePdNt3M4xPH24cO3fupHr16sTExLBy5UoOHjzo1dMP0KdPH/r06XMLNZQdRO+//76LNxe8jwGlwWQqX7pcZeDM5xc98IbcKlSmTaoK5TK6lyxZwssvv8z69esRBIHvv//eYxy8IAg+o9sHH3zwiGhHHvLcni1IuV7I6xuPM+X+htRyGKFZFhsJxWot772YRbi/jthQP05dK7vchE6jcstNLA5BEMqs1VwcnRtGKgZ3/7trsXL3BQQBxeA2atVUc9wv0l9XZl53h/oR/HgsXTG2nfdwol64H6czChClont4q3/tzJH106nxN6j45qDMdBrqp8VsE1GrBAptdpmcSxBIdeim16huKBS8oogMNEAm3BtfDdSu3iKrTaS+l7JZUCwP2AP63FmTpTsvYLaJisEdHWRQ8pcj/XUUWu2YbCJZpcjxQTaIZHZwmfBMQH5XBOTPIOehSaB8d8pjDmjVKoXp3V+vweq42FbMejM4FkIEgZtGWOUJxReeAg1axeAO89cpxqNTT5VQFIatUQtYy5krW3W6ougGsuHtHJfsEhw6cIDu93WmfYeO9Hd43spqS3+dBotdwubIYy66mfyfIIBOJWARJZyPW54lqyrrc8f+UD8tGQ5SseIEd8X7Q7TJYeHOe5T25GqHN9vzLYVbZnD78NfCxYsX6dWrF1lZWcTFxbF69WqlbNntgP3795OYmEinTp0YNGgQdnvZDgAf/hyUy+hu1KgRK1euBGSGwk0OhkwffPDBh78jBrWpjSgUTTP9dBqlVnXNYAN6jdolx84Z7lga6kT4MyzBnzRH7nRUoN7FqNaoVDSKDMBsE7GJInqNmqxCd5ZoKCKAEsCFMdq5zy5KOKeQouTKEuwtVzrDYWz8fPQqhTb3qWnLuFAuZnnIjy1VpvysPx+7iq3Ez01p8kqTCfKCx/DEOmQWWCi02An115XIxRWIDfXDJkqYbXY0KhW55tKN76p97pvRlt5lepOnlWzUFMRSoxtEScKTf9DBceYRZdnHof5aSpowIUYtSKDTCAgI7jK8yHSG4OaYrB4XjIx6tdcyeN4CWpzNoVYJFFrsLu+QRqVCoJiRKRUtPpSG0vQsTcfS9AS53dQlvhN6jcrRlirubH0HaZklas2Ww3oMMWoRJTm3Wmbod71IpRIwqATlWbx5+J3XVWWfKzo46lPbRTkyqngtcSd0jrYQkcc8exmRCFWNY1dyPe6PDDK4tKlAkepqQfAYqu/D7YnevXvTu3dvl0iE2wmtWrVSamZLkuQzum9jVJh54c8MCfDBBx98uBVQqQTScsxsPZmOxS7SqlYIDavJObH+OnnYtIsSV/PMFFhs+Os1SsijN1zLsxDirye6GGnOtVwztaP8OZ2RT7ifjmCDBr1GhR5nKLhnWc5w9OohRswePG+SJOHvpS61t8nzTZGpVYFFzuu0C64/N6XJK00mwIm0XOIiAgn10xHqKM1tctSrPnUtD3+9hkh/PRqVgMbRXzlmr+KAv0BbliLTmzy1KKKyl/691GpUeLR+JO8M8p6zq4uQW2hDlOTznOkDAgLVgvWOknVF1yth5l5kOcNstWqV57tWQk/nYpdG5dnzKUneo0y8ySxVz1J0LE0myOXvJASlLUE20qsFG1zY1h23kXXxKq1IplEnl8NSlSAYNDuiHbRqlcMbLe/3FiGjhOpXYZ87Q9ZNNjt6jUziqC52ttVelJ6kVQkggMp5/Nba3F771enFlyNHUP6Gst8fH3zw4e+JStEdnj59mgULFnD06FFApqp/9tlnb3m9Mx988OGvg7QcMxDIOz+fJMdkJ0Cv5vPfzyte21Fd63HgcpHHxi5JXMuzkFVoveVhf3vOX2fvhSJ20R2nM9hxOoOXH5MZP002O+czizyLOSab1/xrJ74/nErdqEDa1pFZy3eezeDI5RzurB+GzWHAX80zK+RfJpvda/1rZwhn4+pBHom6vOXcyrp69vjeFJk6LeRDfHSQW3h5afJKkwmw88x1Np/IYGhCHGqVQEaemf8cuMwrvZphl+T+yDHZiA01YtCo5fBUS+mr/7d9W5Yi06s8G6gKhFLzQ40atVfXtTeDoqwI6wC9xqsxq/XynnjzUDptQqNW7dVUqaief4bM0ozu0tozQK/x2D+VaUsnzDaRQqudED+dsvCQkWcmJtSoRDiY7XKetDOU21u0hDPipyr73Il8s51ck00hfLOJEjqh6Dq7JGG3S+gdCwQgk8rdSug1KkI8vHt2SUKrVmGzS26VKMqqnuGDDz78PVFhZpkffviBJk2asGvXLlq0aEGLFi3YuXMnTZs25ccff7wZOvrggw9/AwQaZaMh0CATAeWZ7WQWWAgyaqkT4Y/NLhFi1GLUqrGLEla7hNku5yd7m7DmWUoPGb6aa/JquJbmbTjqqIf6SIsY+t5VSymZ5AyfdBrcEf466oX7E+mFvK0kjlzOId9sI99s48hl2agXRYm6YX7K5DXdEUZc3Kj3wRX+OjV+OjXr9l/im4OX+fHoVaVetV6jQueoVX0xWy5rdi6z4Jbmsvvgw+0M53iWVSCPNfnmIuZ6g1qlkA7aJNlr7dxuFZzjvXPYL3Do59RXqxIwqFWyl5sioj9bBfLtTVY7+Wab1+cSJQmrXbylz+2DDz78vVFhT/fUqVOZOHEi8+bNc9v/z3/+s1RGPx988OF/F85c1eTLOZgdOalmm8jhS9nUjfSXwxEFuJbvmsecY7J5DcV1sg+XhFx2BdLyLGTkW2lcLcDN22cpY4LWsUGEUv+5z501+fy38wgqXOrLhjs8HGGOUj6lIbFeOD8dv8bhy9mKgd2xQQQgh3LWCfPj5LV8sgqtRJTiAfYBujau5tGDbLVJVI/0nG9XmufcBx/+l2DUqpXxSpKkIjI1B323gGzYWkVHPe1bHGrkHKtD/HRcyzNTaLW71Ip3GuVqQVDI2pDkRYLyeJIsNrlUHMjGdYjGvcSYs6SduoxnL7TYKbSaHHo7r4eoIAM2u7y0623h1wcffPjfQoU93UePHmXEiBFu+4cPH05ycnKVKOWDDz78DeGYkNQINSpzOJUANUONSr3l6EADNYINbpeVRVJ2M1CcFKgqeG2dprpGpVJIl1zv4YMPPvhwm6E4A5gPbrCJEja7XEM9IkBPRICeUD8dEjKruoScPqBWCRg0agwa9S1l6vfBBx9uH1R4JhsZGcn+/fvd9u/fv9/HaO6DDz6UCb2mKOdRlGSvi7YYO6+2RCi5BF5zUgMNnoN1BEEugRQVoKNOuJ/H63VluDC2nkwnPddMvtnG6j0XZF1E19zMjAILNlEio8Azy3hx7DidAUDj6ECaxQQr9wC53u5pRymxYIOm1PxPH3zwoeIYNmwYc+bMAWD37zto27btTbnPd/9dx/MTxlSJrLVr1/LEwMcrff2JEydo166d2/7inldBEIpyjgUI0GuJCg3m7QVvAXIt7Fs9Hjnzx53h5Aat2mUMt8uMbS5RRwiUSWbphE6jItioJUCvIUDvyjUxYthQvv/uW/QaOXy9PAuuEpJLG3lqr79zQbJz5855LCN8O2Pu3LmMHTv2psj+6quvGDlyZJXIWrZsGY888kilr/c2BpQGQRDw9/fnzTffrPR9/8oYPHgw69evvymyK2x0jxw5kqeeeorXXnuNbdu2sW3bNubNm8eoUaOq7Evmgw8+/H2RkWemYbVABrapTYNqAUoJLScKrSJ6jRxuHR2oRyV4Z3QO0JVedqVaoMGNxMaJ0iZBjavLTOXfHLzMqj0XlEmqMw88NtQIyKHwpzPy3ULiPcFPpybYqGXT8TR+Pp5GsFGLn06NRq3ifFYBIKFWySH35zML0KtvbU1tH3y4lWjasD5RoUFEh4dQO6YawwYNIDMz85bc++527dm5c+dNkf3W/83lqWfGA3Ah5Ty1w/156onBLue0btGMbVu2lCmrV69eLF7+ZaV1adiwIdHR0XzzzTcu+wscpIJOAjA/vavB9Pv+QzwzfgIaQcBmt5E0oB/NGtZDEATOnTvncq6psJDhw4cTX7sa7Vo04j9frfaqjyiK/HPKJGpWi6Bu7Rq8987bHs9zGtPO8c9fr2H9f75m2GMPUDM8mFFPjsBkF5XQcrUkMqh/P5o2rEuYv95Nx5I4d+YMj3e/j2axUTzQKYHDhw4qx8ZPmMicWbNkZvRiBG3eEKDXIAgCtmKVfZx/a1UCerUKFaVXY/izMGvWLPr166d8NplM6PV6Xn75ZWVfcnIyRqMRi6Xs37iKIjMzk6FDhxIdHU21atVo1qwZS5YsAW6+IT99+nQWLlx4U2S/8sorTJ48GZCfQxAEBgwY4HJOfHw8mzdvLlPWoEGD3N7fisDbGFAWjh8/zqRJkwCw2WwMHDiQ2rVrexwDCgsLSUpKIjAwkNq1a7NixQqvckVRZMKECYSFhdGyZUsWLFhQbp3WrFlDu3btMBgMDBs2zOWYzWbj8ccfp0GDBqhUqjLHgNOnT5OYmIifnx+tW7fmwIEDyrHJkye7vANViQp/o2fMmEFgYCBvvPEG06ZNAyAmJoaZM2cyfvz4KlfQBx98+HvhsTtqEBPqD0B89SBSrhfI5V8cRnCon5YaIQYlBK9aoL7Ues43A3fFhlEjNIDfzmSQZ7bSqlYo8dGBynGDRk39cH/S883kmW346TRuHvqSiPFSPspmFwn3QsTmbaLmzMc8mprjUWaL2BAyvXjfb6lMB9HdsSs5biXDSpNXmsyq1vFmyLzV/eNNnlq0opckF+bp0cv+IDPfgsUukppVSESg3iXSRLlWELiaa8aoVRNkdJ8qqAWB7EIrBVY71YPklJBQfx1v9mpCodXuMSLZqFMrBpUErP3mOxLaJ5Kfn8/IJ4Ywe/ZsN++K81HLI7MkvJEl3iyZu3fvRqXWUKNOfQqtdkxWOyqVis2bNnH06FEaNopXpNgdNbJLkwlQaLPjKfmkvDoOGDCATz75xMVbptOoHMSDRWWtIgL0Shi5AAojuM0G7RMTGTN+Ao885M7Z89brc7l27Rrb9h/j5IljjBzYh/pNWlC3fgM3PT/58EO2btnCngNHyMnJpseD99P2zju49957XWQ6n9Zfr0avkRdNQ0JDeXrcBPbt3kl2dqZcS91RMsxmF2mfmMj4CRN4+IGyeYWeHjGErvc/yJf//Z6VX3zOsAF9OZR8DI1GQ8tWrSgsKGD/vn20uuOOMmUFGjT4GzQuhGvOdnXymGhUarf3evuZDAqsNgqtIkatCrVKcBDWSQhCrmLsF1jsaFSCUuWjJCw2EZsoueS8A+jVajrUDS9V9w4dOvDRRx8pn3ft2kVcXBzbt28v0nP7dtq0aYNOV/U8IxMnTkQURY4fP45Go+HUqVOkpqZW+X1Kwmaz3TSDfteuXWg0GuLj45V9KpWKjRs3cuzYMZf9twqexoCKIjExkeeee46uXbu6HXv55Ze5du0aly5dIjk5mW7dutG6dWsaNWrkdu4HH3zA5s2bOXbsGKdOnaJfv360bNnSbQzwhLCwMKZMmcKOHTu4fv262/EOHTowduxYHnrooTJlDRgwgG7durFp0yYWL15Mz549OXHiBBqNhlatWlFQUMC+ffto1apVmbIqggp7ugVBYOLEiVy8eJHs7Gyys7O5ePEizz77bKllSXzwwYf/bZgc3pWazsLKDtQO80PlCAcXJYkAvcYl502rVnnNgUvLkwlsvtxzgeU7z7Ns53mW7zzP8p0pqFVyzWaXLb3o79LIbURRIjJQz6MtYxjYJpYm1YMUHfItNmyiHE4YHWigfkQAMUGGMj0iqVmFXMp03wRBIN9i97jZvVSWcZacue1lOtjOLnuQWZq80mRWtY5/h/7xJq/QKrpN9jPzLQxqG8t98dVIzTHTrWl1nmhfx20bkhBHgcVG7TA/hrSLc9sGtY2lZqgf+WYbg9rGMqhtLNcdER9Wu4jF5r7hKPfkLPkkOv42+PnRrXsPpQwpyOGfsbGxtK5fg86dO3P40EFFzsI3XyexRQNa169Bp7Z3ciQ5WS4xmJHBiKGDqV+7Bi2bNGTFF0s9tuXvv26lWbNmip6NooNYuuhjut7djLaN41jwxuuKnhabjX/NfoVmjerTILYmL059HpvNnTRRkiR+/PFHWrdpp+hptYsIgsCIUU/z2tx/KTKlYs+eX1jIlAnjiY6Opnbt2syaNQvR4SldtWoVQ/s8isUmkpObz4RRT3B3o9rc3ag2PR7oil2SGD/maebOmaXItokiLRs3ZMeOHQB06tSJTZs2uegc6BhjnQzdJY13XTEPr0qtYfTY8bTxEqL63zUrmTp1Knq/AJq0vIvOD3TjP2tWeezzlSuWMebZiYRFRhJXrz5DnhjO559/7iZT7TBWDQ6D2y5KtE3sxMOPPkZEZKRSpUCrllnfNGoNo8eOo03bBI86Fsepkyc4cfwYz05+XvaYPfkUoijy6/ZtyjmJHTvy48YfypTlhMoRgm+zi9jsksfwcufvh7OtzXY7NYKNFFjl/+tHBNAgMoCGUYE0iAygfoS82UQJf71G+Vxy89drsImS236zvWzytnbt2pGWlsbZs2cB2cAeOXIkx44dU74v27dvp2PHjoAceREVFUV4eDiDBg3yaPgALFy4kJYtW5KWlkZhYSFjx44lJiaGmjVrupAw7969m0GDBhEcHIxaraZly5Z069YNgAceeAC73U5AQAABAQGkpKRgt9t5+eWXiY2NpVq1akyePNnle/3ee+/RoEEDIiIiGDZsGAUFcsrWkiVL6NKlC6NGjSI4OJjFixczc+ZMnnzySQA2b95M/fr1mTVrFmFhYcTFxfHDD0X9f/LkSRISEggKCmLAgAH0799fSVUpiQ0bNijt5YQgCIwdO5ZZs2Z5vMZkMjFmzBiPY8CSJUu47777ANmjPGDAAMLCwggLC1Pu89RTTzFz5kxFniRJ1K1bt9QxoCLQaDSMGTOGhATP79fSpUt58cUXCQoKol27dvzjH/9g+fLlXs+dMmUKUVFR1K1blyeffNLjGOAJXbt2pU+fPh5TmTUaDc8++2y50oaOHz9OcnIy06dPx2AwMHr0aERRZNu2ojGgU6dObNiwoVx6VQQ3tNQTGBhY9kk++OCDD0BmgXf26MtZhcSE+3H0ah5No8s/roT768EEV3PNWCla6Y8M1GOzi9QO8UOSJC5ku5cOCzN6D01ftiuFtnUjqBsZAECBxcbK3Rd4pVczLmbLhr5Bo6J2iNGRPy5hKqP2qjfGbVGUqBli9HiNN8Ztp2f89pepBwt0ja/mVqe7NHmlyaxqHW+GzFvdP17l2SxoClS3fEE8yKD17LuVUDx2AnKNZ51aRU5ODt+u/6/LhCk+Pp49e/aQYVHz2cL/48WJo9mw5XdOnTjOmi8Ws2HzDiKjqnHp3GmiIsLQqVU8M3I4jeLjOXb6LOfOnaXHgw+Q2PZuWrRo4aKG2tEexfXc+/t2ftq+m4sXztPj3o70fbwvdevVY8Hbb7Fzx69s3fE7Wq2WAX378MEHH7jlg2pUAkeOHKF12w4EO8aWHIP8/6hnxtGmZRPOnjpBo0bxCAhoVSp0ahWvvvYqx44e5ejRo+Tm5nLfffdRu3ZthgwZIuuqEgg2avnvys+wmgvZc+QUGq2Ww/t2o1OrGDAwiXHPPM2Ml+RwyN9/2wEItG/fHoDq1aujVqs5ffo0DRs2ZNWqVbz6+nzW/vSrWzms6iFyxILFsRhR/LCnb1BmZibpaVflBQyt3JYtmjfnj927lDYo3ucnjh2lVcsWige4SbNm/PTD956+KYC8MJFRMnWnhCIWu1gsDaf0aJbr+Rb+OHCI2Dr1yLcJ5Ntk2Y2bNuPgocO0TewEQN36Ddj7xx5MNnuFMrGdqVIxXt7tQpsdUSqqbf5nw8/PjzvuuIPt27dTp04dtm/fzssvv8xPP/3Evn37uPvuu9m+fTv//ve/AdnoXrp0KVarlccff5xZs2bx9tuuKQLz589n+fLl/Pzzz4SHhzNmzBgyMzM5ceIEOTk53H///TRr1owePXrQtm1bpk6dSlpaGnfffbeLF3jjxo3Ur1+fvLw8Zd///d//sW3bNvbs2YNWq6Vnz57Ku/jll1/ywQcf8NNPPxEVFcXw4cOZP3++ovu2bdsYOnQo77//Pmazmddee81F73PnzqHX60lLS2PRokU89dRTnD9/HpC9ot27d2fLli3897//ZeDAgTRv3txjmx46dIh77rnHbf+ECROoV6+eR2/3q6++SnJystsYUDKE+rPPPiM/P59Lly6h1Wr57bffAEhKSmLkyJGK4e1MnfE0BjRq1Ijly5czb948Dh48yI0iMzOTK1euuIyxzZs3V3QrieTkZJdzmzVrxrfffnvDelQEycnJNGzYEL1er+xr3rw5R44coUuXLoD8+7Nr164qv/etpwT2wQcf/qdx4mou6blmzDY76blmjl3JYfPxNADMNjsZ+RaHN1me+OWYrFjLclMiez6aVA8CID3XrORsX3WUnAEINWqVEjBllXHZfCKdPIcBtHK3TKSGBCGOyaTJJio1ta/mmRF9Cdg++FAh9P7HI9SsFkGt6EhOnzzJ6NGjlWO9evUiMjISrVbL888/z9Ejh8nPy0Oj0WCxmDl14jh2u50GjRpRLTqaq1eusH3rFl6Z/S/0ej2NGsXTp19/1q5dWy5dnnl2Mv4BATRq3JQmzZpz5MhhAD5fspgZM18hMjKSkJAQxj47gTVr1niUkZ2djZ+/v9v+kNBQRo4azetz/+V27KsvV/PPF14kNDSU2rVrM3nyZI85kVqtlszr1zl//iwajYaExA4AJHbogMVsZv++vQCsWb2KPn37ulwbGBhIdnY2AP369WPT9l0EGbQEG103pxGuVanQquVFAeem8mAoOg2i4g6YgMAgCvLz3M51nh8YGKR8DgoKcjGqFDgUcS7UqoqTvVGU622zS8rfGqFsqjKVSiA3Nxf/gEAC9RqCjVqsdpHAwEDy8vJl7z/gHxhIbnaOsjhQGuRoCJG8YuUrnX9Ljk2UJGyipLSvVHK1409Ex44d2b59O6Iosn//flq3bk1iYiLbt2/n8uXLpKSkKMZbUlIS/v7+BAcHM27cOH799VcXWXPmzGH16tVs2rSJ8PBwJEli8eLFzJ8/n4CAAGJiYhg9erTy/ixcuJBevXrxxhtv0KxZM1q0aMHvv//uVddPP/2UOXPmKO/i5MmTFVmffvop06ZNIzY2FqPRyLRp01yMuXr16jFs2DBUKhVGo/uiiL+/P8899xwajYakpCRSUlLIysri3LlzHDlyhOnTp6PVaunevXup3tSsrCwCAgLc9oeGhjJmzBhmz57tdmzNmjW89NJL5RoDMjIyOHPmDBqNRvF0d+zYEbPZzN698hiwevVq+vfv73Jt8TFg4MCBVWJwg+cxwOt77Tg/KKgcY8BNREkdPOlRvL2qEn8tukEffPDhLwtneOyGQ6mYRTBqVXL4qygp7K8qQVDytzUqAdERcu6NPddJWKPXqOjrKDt2Z2woS3+Xa2oDZDsmQA0i/FEJAlEBek5eK32Qv6dhJBuS09hyMp329eRa2gaNClGSqBagJ9Jfx8lr+WQWWokK0JNtsmH8E8qa+eDDXxlf/ecb2id2wGw2M2fWTLp3786ePXsA+Pjjj1mwYAEpFy468l0lMjOvE1e3Hi/OepXX58zkzOlTdH/kUV6f/wYXLqRgMpmoU7O6It9utzNo0KBy6RIZWRSyaDQayXdMwC5eSKH3Px5RogUkSaJGjRoeZQQFBVGQn+/x2Jjxz9KySSNOnDjusj819TK1atVSPsfGxnL58mW363v1HcClCyk8NWQAFrOZYSNGMnXaNARB4PF+/fly9Sqat2jJ12u/Yt23rt7j3NxcgoODy9EKFYPTuMjNzQWtPOnOy83Bz9/d6HCen5ubo3zOycnxaKAUD3dXCQJh/q6Eb06b1eb4w1lSUlOG2R2o1xAREkxeXi65ZhsBDnm5ubkEBwWgUQtoEMjLzSXAMSkvyz6+nmfBuSRcPAUp0KhR6p+XjBi4jWxuOnTowAsvvMDhw4dp0KABWq2WxMRE3nvvPWrUqEHz5s0JCgrCZrMxZcoUvv76azIzM5EkiYiICEWOKIosWLCAhQsXEhoaCkB6ejqFhYU0adLE5bzExERA9rS/9NJLzJgxg0uXLjFjxgx69erFxYsXPeqakpJCt27dPL6LKSkpjBo1imeeeUY5vzj5W82aNUtth8jISFQqlaIXyMbZlStXiIyMRK/XK4slpckKDg72akROnDiRevXqcfx4yTEgldq1ayufvY0BgwcP5vz58/Ts2ROz2czTTz/NNMcYMHDgQFasWEGLFi1Yu3YtP/30k8u1t2IMcBqy3t5r5/k5OWWPATcTJXXwpMfNai/fLNEHH3y4JXCGGA5NjGNEhzoMbBvLiA51GNmpLlq1CpsoE8LEhfkRXy2Q+pEBNIwKIL5aoFePgzNftU5kkXfJEykU4JIXHqQvnfU8MlAOO8outCpMv/WiigZkX51VH3yoOuj1evoNHMQff/zBtWvXOHfuHBMmTOCzzz5j74kUzpw5o6RxAPTuN5B1P/zC5p37uJBynoVvLyAmpgYBAQFcuJLOxavX5C39Oh988MEN6RYTU4Nvf/hRkZly9RrJyckez23atClnz5zyeCwsLIwnn3razdtdvXoMFy5cUD6npKQQExPjdr1Op2PytBls2XWApV/+h4/ef09hQO8/cCBrv/ySX37eRFRUFE2aNlOuu3LlCjabjXr16lX42ctCaGgokVHVOHLkiLLv+NFkGsY39nh+o8aNOXL4sPL5aPIRmjZt6nZecZtUX2wxsypG3fjGjUk5e4bCQpPy+3Es+QiNmxTpcerkCZo0a+ZNhAvC/OX63AatTJYmShJGrVwW06BVY9CqMWrV6DUqxfN9O/18dOjQgWPHjvHf//5XMYbbtm3Lzp07XfK5ly1bxubNm9mxYwfZ2dksW7bMxWOvUqnYsGEDEyZMYOvWrQBERESg1+s5c+YMWVlZZGVlkZOTw/ffu6cUhIeHM2nSJFJTU7l+/brHlJgaNWrwyy+/KLKys7OVd7FGjRp89tlnyrHr169z6lTRu1jZFJvo6GjS09NdDHhviwIghymfPHnS47GwsDCeeeYZN2939erVSUlJUT6XNgbMmjWLEydO8MMPP/D2228rDOhJSUmsWrWKn376SWGCd+JmjwHR0dEcOnRI2Xf48GGP7zVAkyZNXM49csTzGHAz0aRJE06ePInZXFQ5p6TOx48f95pCcCOokNFttVq59957vX6hfPDBBx+8obQ8to4NIgEI99O5TLKUa73U1NY43NnHUnOxOULQ00uUIHPCGU4uShJZZeT3bjxyBT+dGoNGzR/nr+OnU3P2Wj4alYrzmQWcu16AWgVqFZzPlP/2RZf74EPlYLVaWbNqpULSlJeXh0qlIjIyEpvN5kJadPrkCX7bvhWLxYLR6IdOp0etVhNTowZt2rZj1ssvUVBQgM1mY/++fV4N5PIiaegwZs98mSupqUiSxPnz59jipdzX/fffz+6dnnMZAcY+O4EN33/H1StFDM29+jzO/706l8zMTC5cuMCbb77pFhoKsGPbFo4fS0YURQICg9BoNKjUcsh1fOMmhEeEM/2fz/N4P9fSRFu3bqVr1643xNZsNpsxmUxufwP8o08/5s2bR15uLvv/2MPG77/lsd59PcrpP2Ag7yx4k/T0dE6fPsXnixYpuesgG0abN292WdR0Mszb7Xby8gux2WzY7XZMJpNCDCVJpetYHNG161GnfkM+++BtbFYLa5YtQRAEEjt0UGT99ut2utxXNgt6cfjp1EoovlHnXqbSyWGg4vaq2R0REUHDhg159913FaPb39+f6OhoVq1aRQdHu+Tm5mIwGAgNDeXatWtuudwAd911F6tXr6Zv377s2bMHlUrF0KFDmTx5MllZWYiiyNGjR5Vc2Tlz5rB3716sVit5eXl8+OGH1K1bl4iICCIiIhBF0cXAHT58OC+++CKpjnfx3Lmid3H48OHMnTuX06dPA7L3+Jdffrnh9omLi6Nx48bMmzcPq9XK999/X2q5wYceesiF/b0kJk2axPr1611Y2nv37s2cOXPKHAN++eUXjhw5giiKBAXJY4DaMQY0adKEiIgIpkyZQt8S6SU3ewxISkpizpw55ObmsmvXLv7zn/8wcOBAj3KSkpKYP38+6enpnD17lk8++cTjGOAJxd/7kmNAWToWR6NGjZQ+NZvNfPTRRwiC4EKAt3XrVh588MHyNU4FUCGjW6vVVlkegA8++PC/hcgAvddj9aMCkCSICTZ4XJFWeZmkFM8z/Pz38yz69SzfHJTDspw51jWDZXKglKxCjqfncfJavtu1JXFPoygebBpNl/goOjaI5MGm0TzYNBqrXSQ6yED1YAO1QvyoFeJHdJD8t7/ecz1wH3zwwTMe6/Ew0eEhxNWIZtuWLaxbtw5BEGjWrBmjRo2iRYsWdL6rGbGxsUrJIovFzL9efoGW9WvRrmU8QcFBjBn/LACfLPmcS5cu0rxxQ+rWimHac5MpLLyxcoMTJk2mTdt23N/lHmpEhTOgd08Xz3RxtGnTBovFwumTJzweDw8PZ8TIUS6hjf+c/gINGjYkPj6ehIQE+vfvz9ChQ92uTbt6hZGD+9MkLppH7uvI0OEjFEMRoN+AgRw7mszjffu5XLdixQpGjhypfF65ciX3tr+rQm3QunlTokODMJvNxMfHu+TETnj+BcLCwrizaT1GDRvI7NfeoF6DhgDs/O1XYqOLylY9+dTTdOjYiTuaNea+zp14ZvyzSqmgixcvEhgYSPPmzXEOzVrHYmtGnpkln31Oo5rhzJv9Cl+uXE5EcCALXn8VALNdpFWzpkQEB3rU8emnn2bcuHFk5Fsw2+zMe/djdmz5mWZ1Yvhy6SIWL1+FDRUmm8gf+/aj1xtoc+edQPmjmlSCQKi/jlB/ncdrBEEmDTRo1beVpxvknOD09HQXdurExETS0tIUo3vIkCGEhoZSrVo1OnXqxP33e16U6NSpE4sWLeLRRx8lOTmZt956i+DgYJo3b05YWBhDhgwhMzMTkMPDk5KSCA8Pp3Hjxpw6dYp169YBsuE/depUWrVqRUhICCkpKTz33HMkJCSQmJhIcHAwjzzyiPIuDhgwgBEjRtC9e3eCgoLo0qULJ054fg8rihUrVvDtt98SHh7O559/To8ePVxIuIqjbdu2WCwWtxByJ8LDwxk9erTLGDBt2jQalmMMSE1N5bHHHiMoKIi7776bUaNGuRiKSUlJJCcnuxndJceAZcuWVdi73LJlS/z8/Dy+X07W9+rVq9O7d28WLlyolAvbtm2bS9j26NGjueeee2jUqBH/+Mc/mDhxoscxwBOWLl2K0WhkxowZfPHFFxiNRpcF2fj4eMLCwryOAU8//bTyefny5WzcuJGQkBDef/991q5dqyxKHDhwAIPBwJ2OMaAqIUgVZHSYOHEier3ehfb/r46cnByCg4PJzs52S64vDrvdztGjR2ncuLGyunSjMJvNzJs3Ty654eUlriiqWs+b8dx/FZm+/qk6mQM/2sH09kFsSddhl9xnHUEBOu6qHerx2tRsE/c3ci8T0WvhFlpmbOb3oA4cTSta1XywaTTtGoTTskYIINc4vpRTdDzUqEUQ4MFG1VzkOZ/73b0FWDxwt3WMj1IYzUui0GKjW+Not/03Q+ZjC7cyp3PYbS+zx4KfuTt7G+dr3Yekcg3pL02eN5m+/vEs05s8ld1KoCmDmrVjiQyRc25L1umODNSj9fCOqwSUOt3BJep0S5RepzstX8QT9WGgQeMxkgXkSJFgDxUFTqXlUiNQ85eRuWTFV2zdvIm5by4slzxvMkVR5MqVK1h0QUgeFh1Lyly39ivef28hP2z6RZF38uRJBg8ezG+//VZUZcFkKvW5a0YEYzAYmPrCDMaMG1/u577RtlyxYgUHDx7k1Vdf5XR6HjEBarLMkGu2KbnRIDODF/cki6KEpViYkVqAEKNrTWnnc5++bgFBJsMsbhjrtEUM/6NHPEGfvo/TvXsPAGyiRLDB/bmd/aP2D/UYL65WC/jrPHsWrXaRg5dzSqnTLVRZnW7nsxsMnhe0K4OqlnkzdHT2T3R0tJKrfaNw6tm1a1fGjRvn1Zu7Zs0aNm7c6FIDvSyZVfHsa9as4e2332bjxo2KvJJjQHlgMBgwGAzMnDmTCRMm3LL+KT4GVAZVpefgwYPp168fPXr0cJNpMpk4e/YsderUwWAwKNeU146scKyBzWZj0aJF/PTTT9x55534l2DqfPPNNysq0gcffPDhhtCubgQJDbznaQfoNTQqYZRcy/cchg6w9eQ1Cm3u65H3NKlGao7nkCVDGURqVSnzuoPV97aX6WB333Yyw6WkW1nySpNZ1TreDJm3un+8ydNKNvzUkkut7vcHyav3zklPhujnsdBSTKjR68RFBYT66dz2OycouWabW31wgECjxsVAKinTE2yO8/8qMjs++AiJD/Qgt1gKS2nySpMJkGuyeeyf4jLNZjMff/QhA4cMwyJKirwGDRp4ZIMu7bkvXS/ywBXX+Wa35YABAxgwYIDbOQF6jUJ45lGGSsBQLGrJVko7h/vrvJayM2hlbRZ/9pnX66sSHeqGu+27GcaNDzeOnTt3Ur16dWJiYli5ciUHDx706ukH6NOnD3369LmFGspjwPvvv8/w4cNd9nsbA0qDt7Dsmw1vY8CtxtKlS2+a7Aob3YcPH6Z169YAbqEbFR0kXn31VdauXcuxY8cwGo20b9+e1157TQlLALnzJ0+ezMqVKzGbzTz44IP8+9//plq1Ig9VSkoKo0eP5pdffiEgIIChQ4fy6quv3lD+gg8++HD7Iy1HDh3NKrAQGlhkdB+7kkNsNX+Op8ssopH+OsKKGQlmm6gQpHlCpwYRHj2UoigpXr2SKLTYPO6/GTLD/LR/DZn+OsiGjg3C3TzdpckrTWZV63gzZN7q/vEmT2W3ojbllRoiG2jQePSkIoHOC5dCWfwFgXqNR69nZWQ6uSD+0jJLkVeaTCi7fw4e2M8DXTvTvkNHBg4ciNZR9aE03O5tKfNzqMnItxBs1LpVrzDZiu6kVQmux8t4dgnILbRiKVaGsmRdbbsoYXUo52Mb9uHixYv06tWLrKws4uLiWL16NZGRkX+2Wgr2799PYmIinTp1YtCgQdjtpZdD9eHPQ4Wt0qogJnBiy5YtjBkzhrvvvhubzcb06dN54IEHSE5OVjzoEydO5Ntvv+XLL78kODiYsWPH0qtXL6U+oN1up3v37kRHR7Njxw5SU1MZMmQIWq2WuXPnVpmuPvjgw+2L9QdT6XlXHAF6DXvPZ7L/YhYPtiwqHZSeb0GSZE+HxS5yLrOg1BJfwX46rF5Kg3sLlTTbSl90rEqZTob2212mxhE2FmzUgdo9EqG0kNvS2tPXP+WVJ8iL4aU8uloQvNIpe19IL92yUTsIo6pCpvPsv7rM0p0S3tuzrP5p2eoOrl4vWU/2r90/Toe5SpArSDj5NyRJIjrY4PJ1tomSS4mxspCR5yHCSZDvKQiyvNK85T7876F379707t3bJRLhdkKrVq3Id5QqlCTJZ3Tfxqi0K/jUqVOcPn2aTp06YTQalTyUimDDhg0un5csWUJUVBR//PEHnTp1Ijs7m08//ZTly5fTtWtXABYvXkzjxo35/fffadeuHRs3biQ5OVmhyW/VqhWzZ8/mn//8JzNnzlTIV3zwwYfbB3+cz+TAxSxa1gzhzlj3PO5Cq52LWYXo1CpqhRo9SJARFWSEDPnv7w+n0rt1TfZfzAJk71+jyABsdpHT1wu4VmAhzE/LxWzZO+4tR84HH3zwwYc/D04CtSCD1s0sl6TSF+vspdAUWR3ebQGB8AB5bujMF7fYRfQalWJwG9QqEMBmL90Azy60EuSn87iuZbaJSJKETqPylZn0wQcfKh45k5GRwb333kvDhg15+OGHFdr7ESNGMHny5BtSJjtbXq0NCwsD4I8//sBqtXLfffcp58THx1O7dm1++00uy/Hbb7/RvHlzl3DzBx98kJycHJfakT744MPtAVGS2HoinawCK1tPpHvMA0zPM2OyieSYbeSaSg8LdiLXVES4Y9QW5Q9r1ColBNIuSVgdk6iSIYs++OCDDz78feEMKQ8qRgxocPxWSMo/Du+98+ehjJ8Js03EYvXsWbRLEiJlG+4++ODD/wYq7OmeOHEiWq2WlJQUGjdurOzv168fkyZN4o033qiUIqIoMmHCBBITE5Wi7leuXEGn0xESEuJybrVq1bhy5YpyTnGD23ncecwTzGazS1F0J3V/yf0lYbfbsVqtmM3mKmXHLv5/VaCq9bwZz/1Xkfm/3j/3vLYRswciqCc71yPUX/YU2CVAkhQj1l+v4b4G7kzjKtGG1WoFu0DtEC2Xs0zEhBgQ7DJRkGgFq0VuZz0idqsFlQBqSYvN8XyeZAJokSc9OuxosWOz2hFtFqwWM5IkKdfarRrsVpncy2qR3GQ6nxu7DU8JiqLNgs1i8dhm3nS8GTKL2vI2lyk5Fkzs7nXRS5PnTaavfzzL9CZPsFuRJAlJkhBF14uKCpdIHqN8ndd5gid5rtdUncwiXf+6MkuT502mr3+qti0lSULtyLSw2OyKN10UJZCKwteRJCRAEiVH2Ln3tgRQCRIatVAUE6+cUPSnIIBUTEZZ/SOKYpUyjd/uMm+Wjs7/vX3HKiPzf7Utff0jyxRFOXrFYrG43KO8NkKFS4ZFR0fzww8/0LJlSwIDAzlw4AB169blzJkztGjRgry8vIo9kQOjR4/m+++/Z/v27dSsWROQ66g98cQTbg/Tpk0bunTpwmuvvcZTTz3F+fPn+eGHH5TjBQUF+Pv7891339GtWze3e82cOZNXXnnFbf/UqVNvu1wNH3zwwQcffKgM/P39SUxMpEaNGj5iUR988MEHH3y4AdhsNi5dusSvv/6q5NGDTPo9b968qi8Zlp+fj5+fn9v+69evV7qO8dixY1m/fj1bt25VDG6QDXyLxUJWVpaLt/vq1atER0cr5+zatctF3tWrV5VjnjBt2jQmTZqkfM7JyaFWrVpMmjSpzDrdx48fp1GjRlXqSX3rrbeU+udVgarW82Y8919F5v96/6TYQzwy52ZZbNQO86NWiHu+9Znr+XSu587sOWLxTia3DWT7NR2ihzrdgf5a7qgV4lGfKzlmujZwl/nA/B/pavuD/SGJtIqLdOgusmL3BaY/0hitRm4PAagb7qfk1aXnWzBb7fRoWt1FnvO5V5yweSSralormEbVPI8ROWarRw//zZA55JPfmNo++LaXOeCDbbTM+pWseve7EamVJs+bTF//eJbpTZ5gt2KwZGMICiEiyLVkniRJXL16Fbs+CE8xtAadWgm9LQm7JBHkoYyTM6rkuklyc/xVVubZa/lUD1D/pWWWJs+bTF//VG1bms1msi1gtUtkF7pG3lQPMRTJlMBcjNlchef65M7+uS4akRAINmpljzdyOLmfXu3iCVMLQtFxsfT+0ev1Veqpu91l3iwdr169SrVq1W7b574ZMv8KOjpl/hX7x2QykZeXx1NPPeViE+Tk5DBv3rwy5VXY6O7YsSOff/45s2fPBlDc7a+//jpdunSpkCxJkhg3bhxff/01mzdvpk6dOi7H77zzTrRaLZs2baJ3794AsiGQkkJCQgIACQkJ/Otf/yItLY2oKHmi8uOPPxIUFESTJk083lev13s0oLztd8Jut6PVatHr9VVm3JT33hVBVet5M577ryLTif/V/nnvl/OYbFLRnE8CBBjVtR7ZVsjLcK/nKKD22FZXCmSZX+2/iskmOkUp/465vwHnc4pNhopOQOVFZlCAH2RBq7hIBIdRp1HD4MT6oNYSGxGATq1SmJ+diNHpSc83u8l0PvfPJ9I81lduWS+KKwWe8/d0ao1HHW+GzPRC8a8h03H+zyevYyvxc1OaPG8yff3jWaY3eVpJxCA4wmBVru+AM6Qvx2TzuLBmNGiUskkloRLc5QEKoWqOyeqxHFRlZNrEqpE5bNgw6tevz4svvsjvO37ltZeeZ+WGbVWu55qv1rD9l03M/L93yiXPm0xRFFm7di1r/rOehUtWVUrHEydOMGTIEKVOb3n6J8iow8/Pj2kzXuaZ8RPK/dy3qs8r05bFiX41aoHwAD02u4SEhEatQmH5B54cPpTeffry0MPdkZAQRc86Ot+fIKPOIaMIsnEtoNeokZDcSNSEMt6fE2n5HkPoI4MMCj+J23OrBIINnhcHBEFApVJVqSFSXOa5c+eoX78+Nlv5+FduhY7O/nHKLYm5c+dy+fJlFi5cWOV6fvXVV2zYsIGPP/74hmUuW7aMlStX8s0331RKx5JjQHkgCAJ+fn7Mnj2bSZMm/Sn9UxlUlZ6DBw+mX79+9OjRw02m83+dTufyO11e+6DCRvfrr7/Ovffey549e7BYLDz//PMcOXKE69evK2W8yosxY8awfPly/vOf/xAYGKjkYAcHB2M0GgkODmbEiBFMmjSJsLAwgoKCGDduHAkJCbRr1w6ABx54gCZNmjB48GBef/11rly5wosvvsiYMWOqzEjywYf/VQxpH4fJJvH6hmMMbFubupGyt8wuSjSrXuRZO5shh9nUCffn/PUCj7JEx0Spa6MoKDbI2uwie1MyEUV5chIVoEdXbBIjSRJpnsq8AFrHIkOeycrWM+lkF1ppVC2IljWDAfDXyUOcKElcy7eQY7Ji0KqJCtCXyo9TPcSI2QP5jSRJ+Os8L2x4IoS7WTKd7L23vUytCixyHVy74O598iavNJlVrePNkHmr+8ebPLUoorK7ftPHLN9LZoEFJLDZbYioyDPbyCnm/dOoVVQL0svlqrxAVWzyL0oS6blmIgP0LB7cCq1a5bkYlFREYNg8vgHX0tNQqVTo9Hru6dyFRZ98TGioazUD5+SpPDLdD3luy7sTEtm5cycXc2xVJtOp5wdvvc5bH32GTqPiYsp57m3TnJ59Hmfx58uK7t+qGW++8x4dO91TqsxevXrxwOND8MjmVQ4dGzZsSHR0NN988w2PPPKIcry0tgTYvf8wNRxRhzabjeFDBrFn924uX7rI2bNniYuLUy75Yf1/+OKjdzlw8CDd/9Gbee98UKqeb81/nYXvLMButzN42BO8/cZ8twmypz7f/duvvP3aHI4c2s8dre/k2x9+Us4/eeI4L077J3t270KSJO7p1ImFCxcSExPjIjer0IpRq+byhXM8+/STHD50gPoNGvLuBx/TslVLJAmeGT+B8aNH0a17dwQERKH0ccPpvbbYRPLMNgRB/t3RaVTI1fqc+eJgFUUkCVSCwC+n0h3nCxi1KqWHRUnC5iE6RhQlTl7Pd9ln0Khd3sPi5dB1GjUd6oSXqvusWbM4cuQIq1bJizomk4ng4GCmTp2qpGAmJydz5513kp2dXeXVgDIzM5kwYQIbNmygsLCQWrVq8dxzzzFs2LAbNuTLwvTp02+KXIBXXnmF1atXA3Du3Dnq1KlD//79WbFihXJOfHw8H3zwAffcc0+psgYNGsSgQYMqrYu3MaAsHD9+XIk8ttlsDBw4kD179nDxovsYsGbNGubPn8/+/fvp378/S5YsKVX2vHnzeOONN7DZbDz55JO8/vrr5TKSt27dyowZM/jjjz+466672Lx5s4u+U6ZM4ffff0eSJDp5GQOcOH36NEOGDGHfvn3Ex8ezePFiWrZsCcDkyZMZMWIEPXr0KFOniqLCRnezZs04ceIECxcuJDAwkLy8PHr16sWYMWOoXr162QKK4f333wegc+fOLvsXL17MsGHDAHjrrbdQqVT07t0bs9nMgw8+yL///W/lXLVazfr16xk9ejQJCQn4+/szdOhQZs2aVdFH88EHHzzg7LU8LHaRr/ddYvIDjdyOi5JERoE8YY8N8z5BcRoTK3an8FjrWhi1agqtdlbsSgHgcQHyLXbOXi+geqBeLhcjSZzLLHSZTHjCuv2XsSIbHQcuZnHgYhYvP9YUkL0mpzOKJivOewQbvA9/jasHeQwLliQI9fM88cgxuZOF3SyZAY7QxNtepk4L+RAfHeQWXl6avNJkVrWON0OmySEoI98iR4qUgFolUGgVcVo5QjGjytuif2n941VHG6gKBJcJTWaBhdH31Fc+f7j1NHvOZ7pdmnK9gEVD73aLEikJq11k+Ge7AZnJGeTqAd5GAqcBJgDr1n9H+8QO5OfnM3zoYGbPns2bb77per5D9fLILAlvpNE3S+bu3bvRajU0ayKTzBq0alQqFZs3beLUyeM0ahTvOFtALQjKPUojtzZq1F7rdJdHxwEDBvDJJ5+4TLhLe26nXKdsSSWQ2KEDY5+dyCMP3e92blhoKBMmTOCX7b+RlZnpUjmipJ4/bPieTz78gJ+3bMff359Huz/EokVNGDFihOv5HvonODCApGHDyUi7ws8//uDy7Hm5OfzjsZ58vGgJeqOROTOmM2zYMDZu3OjaLqJEjsnKqOFDuP+BB1n1n+9YtvQzhg3sy84DR9BoNDRp3pL8ggIO7NtHyzvu8EgO5wIJsgotRbW9JXlcCCjGkC6KEpZi3nlJksg128i32OlYNwxNGZ6+bJOVvRez3fYXWEXuqhlCoIffsuQrOWUoDh06dOCjjz5SPu/atYu4uDi2b9+u7Nu+fTtt2rS5KeV3J06ciCiKHD9+HI1Gw6lTp5SKSDcTNpvtpnFc7Nq1C41GQ3x8vLJPpVKxceNGjh075rL/VsHTGFBRJCYm8txzzynlm4sjLCyMKVOmsGPHDq5fv16qnO+++4733nuPHTt2kJ+fz+DBg4mPj3cbAzzBz8+Pp556ikuXLvHdd9+5HMvOzqZnz558+OGHhIWF8fzzz3scA5wYMGAA3bp1Y9OmTSxevJiePXty4sQJNBoNrVq1oqCggH379tGqVasy9aoIKuXTDw4O5oUXXmD16tV89913zJkzp8IGNxSxwpXcnAY3gMFg4L333uP69evk5+ezdu1at1zt2NhYvvvuOwoKCkhPT2f+/Pk+0hgffKgC/Hf/Jb45cBmAXJOVFbtS2HjkChKQlmvm/PUCjqcVkSceT8tTJt4lERlQFHny3wOXAFi79yIAceH+iKJETJBMZJiaK7OO51vsSpkXjyjmFbw3Por+d9eiToQ/UOQtOevwDgTqNdQL91fuYfGi5/8qruXJ7NfbT11jy4l0t02jFkjNNnE5u1DZUrNNpGabKPRSMuevhLKe29NWYPH83KH+8gJD85hgWtYMcdssdokIfx1BBi1Wu4SIRKiflnB/HQZN1abGlIZ8s43tp64B8G7/O1g6vC2fP9GGmqEyV8MPRzxXACmOjckyh0qdcH9qhbrzvZQH/v7+dOvxCEePHlX2zZ07l9jYWFrWq0Hnzp05euSQcuzdN17nziZ1aRIbTae2d3LsaDIgc8sMHzKYOrViaB7fkOVffO7xfr//uk2pkgJQO9yfpYs+JvGOJrRsUJsF819Xjtntdv416xWaNKhH3do1eOGfz3v0vtlEiR9//JFWdyeQY7KSa7KS7/Bkjhj1NK/OmYPVLjpqRUvYRPnv3PwCJj87jujoaGrXrs2sWbOU0MtVq1YxoJfsbTEVFjJ25FCa16tJ83o16fGAPPkd98zTzJ1d5GSQJIkW8Q3ZsWMHAJ06dWLTpk2V9hhqNBqeGTueNo7owpJI6HgPPXv2JCLCnXOjJFYuW8bwJ0dSt149qkVHM+bZiXz+uec+KomWd7Sm5+P9qVmrltuxu+5uw+ChwwgNDUWv1zN27FilrGxxhPhpOXf6JKdPHGfU+CkYDAb+MWAYoiiy57dfMahVqAWBhMQOfL9BJuf1FoXghMUuKgZ3qJ/OZfHLGd3lNLh1KgGDWuWyhlIeKmOnwd00OpAu9SPoUj+CptGBAOy5mFW2AC9o164daWlpnD17FpAN7JEjR3Ls2DHl+7J9+3Y6duwIyJEXUVFRhIeHM2jQIK8G1sKFC2nZsiVpaWkUFhYyduxYYmJiqFmzpkve6+7duxk0aBDBwcGo1WpatmypkB8/8MAD2O12AgICCAgIICUlBbvdzssvv0xsbCzVqlVj8uTJLt/r9957jwYNGhAREcGwYcMoKJCj7pYsWUKXLl0YNWoUwcHBLF68mJkzZ/Lkk08CsHnzZurXr8+sWbMICwsjLi7OhZz55MmTJCQkEBQUxIABA+jfvz9z5szx+OwbNmxQ2ssJQRAYO3asV2egyWRizJgxHseAJUuWKGWTCwsLGTBgAGFhYYSFhSn3eeqpp5g5c6YiT5Ik6tatW6VjwJgxY5S03pLo2rUrffr0UVJ8S8PSpUsZNWoU9erVIyoqiokTyz8G3HXXXQwaNIjatWu7HWvTpg1PPPFEmWMAyF7x5ORkpk+fjsFgYPTo0YiiyLZt25RzOnXqxIYNG8qlV0VQKaM7MzOT+fPnM2LECEaMGMEbb7xR5uqGDz748NfDoUvZ5DjqZIsSHE3N4WhqDkig06i4lm8hv5jhkW+xF634l0DxMLh8s3yN00DvGi8P1oF6DQGOMNlCq0havhxW7o04x+r4YaoZaiQ23B8/nYbODeXJn3Ni41QnxpEPV/wePhShPIZiuL+OCH89eo0ai11Cp1GVaihec6QF/OrBoNVqVA7j3bNBe6sN+bKe29Nm0N5YLppOLbefzS6RUWApNQS+tEURjVrlthCSmm0iPc+CXZS8yj10SZ7Md29enRCHsSAIAtO7yZ7aVXsulPkMq/fIkSqT7m9Y6YWsnJwcvv3mP7Rt21bZFx8fz549e/g9+Sxdu3Zl3NMjyTXZOHA4maWLP+HLDdv4/dgFPl26nICgEKx2kSefGEpUdDSHjp9m5dp1zJoxg4MHD5ZLh1+3bWHjtl2s/u/3zJ/3L86cPg3Au28vYMev29my43f2HjzCgf37+OCDD9yuV6sEjhw5QoP69dGoVNhESRnznnpmHJt+2siZk8eVnFy1SkCjEnjztbkcO3qUo0ePsn37dr744guPk9AvVy6joKCA3YdPsu/4OV54WZ7A9xswiC9XF+V87/xdnmi2b98egOrVq6NWqznteJ5Vq1Zxf8c25WqTqsaxY8k0bd5c+dy0aTOOHDlS5ffZunUrTZs2ddufZ7Zx7tQJ4urWw4qarAIrKkGgsUMPi10uB9SwYSOSDx/CYhPLNIqdC2+hfjolQsBpeFtFSfn9UQuO30BBfse0ju/B9QLvZROheOk4iCq2cF387woWIVLg5+fHHXfcoXi2nQZ28+bN2bdvn7KvQ4cOgGx0nz17ljNnzpCbm+vRiJw/fz6LFi3i559/JioqiilTpnD9+nVOnDjBrl27WLp0KevXrwegbdu2TJ06laVLl3Lu3DkXORs3bkStVpOXl0deXh61a9fmzTffZNu2bezZs4fjx4+zd+9e5V388ssv+eCDD/jpp5+4cOECVquV+fPnK/K2bdtGQkICmZmZJCUluel97tw59Ho9aWlpTJ8+naeeeko5NmDAAB544AEyMjJISkri66+/9tqmhw4dokGDBm77nWH0x44dczv26quvkpycXOYY8Nlnn5Gfn8+lS5dIS0tj7ty5ACQlJbmEru/cuRPwPgYsX76cFi1aeH2Gm4nk5GSXezdv3vyWjgFOHRo2bOiSglxSj/j4+HL/dlQEFZ4xbN26lbi4ON555x0yMzPJzMzknXfeoU6dOmzdurXKFfTBBx/+PDSr4cqIHKDXMKJDXQQBQoxa6jq8ysXhzD2tCLzXcS39usx8ecKSXWjhm4OX+ebgZdYfSsVPp0YlCJzPLECtArUKzmcWKJvZJnok/fGhfAjUawjUa8g128g1e189Dw2QJ5/NargbtGarTKZntYsEGjTuBu0t9PiCHE5dUQilMgOUDyUNb2+etdIWRSx20W0hJNxfR4hRKxNLedHT7njBSpIz2R0vR2k53U4436OU6wWkOxZZcs02ck3uGwKKt1cCev/jEWpUi6BWdCSnT55k9OjRitxevXoRGRmJQa/j+eef58TRw4iWQvwNOqxWM5fOnUSnhvoNG1EjpjoZaVf5ddtWXpnzL3R6PQ0axdOnX3/Wrl3r9bmdegIMG/0sosZATJ1GNGnWnIOHDmK1i3y+ZBHTXppJSFg4/oFBjBn/LGvWrPHYFtnZ2fj5B6DTqOT+cCxChISGMnLU07z26ly3a9Z8+SX/nP4ioaGh1K5dm8mTJ7tMoO2iRK7Jih0V165lcPTEKQptkNChA1a7SJuE9pjNZvbs+QOrXWT1qpX0fryvyz0CAwPJzpYXWPr168eP21wrvtwq5OflExRY9JsSGBRY6TKz3nDm9CmmT5+uGCTFEaDTIFoKCQ4OJtioJdCgkf8PDKSwoACtg3QzOCiIvNwctGqVy2KxJ5Rm8JZ6pVCus246OnbsyPbt2xFFkf3799O6dWsSExPZvn07ly9fJiUlRTHekpKS8Pf3Jzg4mHHjxrnxOM2ZM4fVq1ezadMmwsPDkSSJxYsXM3/+fAICAoiJiWH06NHK+7Nw4UJ69erFG2+8QbNmzWjRokWphF+ffvopc+bMITIykpCQECZPnqzI+vTTT5k2bRqxsbEYjUamTZvGt99+q1xbr149hg0bhkqlwmh0r7ri7+/Pc889h0ajISkpiZSUFLKysjh37hxHjhxh+vTpaLVaunfv7rI4WBJZWVkEBAS47Q8NDWXMmDEKCXVxrFmzhpdeesnrGOCEVqslIyODM2fOoNFoFE93x44dMZvN7N27F4DVq1fTv39/l2uLjwEDBw68KQZleZCXl+dSJSooKKjKx4BTp7yPAZ508KRH8faqSlQ4BnvMmDH069eP999/X2FKttvtPPPMM4wZM4ZDhw6VIcEHH3z4q+BylolAvQYJCQEBQYBlO88z+t76JF/JRZIktCpBMRME8BoOLhazcp2Gub9OTb7FzuYT6QyJ8ifXbCPP4TkwalVUC9BzKceEyYvXMyLAAJlwT8Mot5xhq00kOtLg9dnKyvG9neH0IG89kY7ZLrmRkNxRJ5TUbHdmefCeM1xRBDpyjHPNNgIrEzkgQLifjowCCxn5FsL9dS4Eerca5zLyMdkl4sLdF5JuNpyGd0a+BYuHHPDyoHh/FP9cGlrVDAHgPwcuc3+TaIKNWkRR4o0fjwPQ7273UN6S6HtnLVb/cYH/23icepFy24miBAIYdWqXhQlJKiKeEoCv1n1DQmIiZrOZf81+he7du7Nnzx4APv74YxYsWEDKhYuoVQKSJJGVeZ24uvWY/sqrzP/XK5w9fYrujzzKa/83nwsXLmAymahXSybOkQDRbvdIQuRcTPDTqnGOVjViqmN0fI+NRiOFBflIwMULF+jX81HlHZMkiRo1anhsi6CgIAry5YmbTqNC5xjnBGDMuGdp1TSekyeOu1xzJfUytYqFTMfGxnL58mWXc+wS9O43kPTUizz7ZBIWi5knRozkn1OnAgJ9+/Xj6zWradWqJf/5ei3r1n/vcn1ubi7BwcEedb5ROCObzDYRq11SFjGcMOrVjpB68PP3JzM7S/mcnZ3j0UCpLFIvX6Znj+7Mnj3bY+4pgL9/AHm5rvnOuTm5BAQUvfd5ebkEBZWvvfz1GnJMNjILLIqHO7PAglFvRKMScNrsdgnUDrJQSZKwOhLvw/zcGceLo/jYnpZnVjzcxclFb4SpuUOHDrzwwgscPnyYBg0aoNVqSUxM5L333qNGjRo0b96coKAgbDYbU6ZM4euvvyYzMxNJkoiIiFDkiKLIggULWLhwoUKGmJ6eTmFhoUslIVEUSUxMBGRP+0svvcSMGTO4dOkSM2bMoFevXly8eNGjrikpKXTr1s3ju5iSksKoUaN45plnlPMtlqIoguLliD0hMjJSYdF2lkXOy8vjypUrREZGotfrlQWW0mQFBwd7NSInTpxIvXr1OH7cdQxITU11CZn2NAaAzKp9/vx5evbsidls5umnn2batGkIgsDAgQNZsWIFLVq0YO3atfz0008u197MMaAiCAgIICen6P3LyanaMeDy5cs8+OCDpY4BJXXwpMfNaq8KG92nTp1izZo1LqWJ1Go1kyZNKndcvg8++PDXwMC2tbF7qKltEyXqefByA16NvfRik4RereUfrUdb1WDFrhTOXstHpRK4nCNfWz1Qronor1Oj/xMNsdsVof7y5E6S5JJPrWqGEFSslqwzJNoTvIVtVyZE0WnYWUvLuy8FKkGolOG9/dQ1F4IySZLDOFvXDav0YkNcuD9Hr+Ypf98IrKUxY3mB0/DOLyVyoCyUNLyDyviF99dr6NQgkq0n0xm7Yq/b8fsaVyvzng2rFU1UFCI1x2JaocXuZnh7gl6vp9+AQSx4Yz7Xrl0jLy+PCRMmsGXLFgJqNCBKbyciIkL5jvbsO4CefQdwPeMaE59+goXvLGDYEyPk3M/UNARBXggUJYkQY+UJoDQqgeoxMXzy2TLuvLM1ArJn31PdZoCmTZty9sxp5bPyXRYgODSMJ58a5ebtjq4ew4ULF2jVVCZYSklJcWHcVQmy4WZHzfjnX2Ti1BmcPnmcgY89TPv2CXTsdA/9Bgyk16OP0LlrV6KiomjStChf/cqVK9hsNurVq1fpdigNTkIzrVqFRiUoCxdOOBdaJKBRfGOOHDlCj0ceRQCOJR/xGgJaUVy7do1HHn6IYcNHMGrUKK/nNWgUz9nTp5XauwDHjh7h2QkTlXNOnjhBk2I5/6VBp1Y50glEuSpAMTi95DqVgEUhU5OUENPoQH2ZRIUAd9UMYc/FLI5cyeUIuS7HWte8McOgQ4cOHDt2jP/+97+KMdy2bVuGDBlCjRo1FG/qsmXL2Lx5Mzt27CAmJoZvvvmG8ePHF3tWFRs2bKBHjx7ExMTQqVMnIiIi0Ov1nDlzhrCwsFL1CA8PZ9KkSSxZsoTr1697XEioUaMGq1atonXr1h6PzZkzh169egGyce+siASVX5iIjo4mPT0di8WCViu/9xcvXvT6vW3evDknT570eCwsLIxnnnnGzdtdvXp1UlJSqF9fJrgsOQY4odPpmDVrFrNmzeLYsWN07tyZhIQEOnfuTFJSEg899BD33nsv1apVc+GsuNljQEXQpEkTDh06pDCDHz58uErHgB49ejBy5MhSx4AmTZpw8uRJlzHg8OHDTJo0STnn+PHjNC+WClNVqPBstnXr1i5kJ04cPXpUoVv3wQcffCgJZ63SAXfXVhhujVo1Q9rFyh43SfZu1w3zI8hRb1QQBGJDjV5D1vMssrf62JUcjlzOdtkEQfY4eNtKMxSPprrLK0tmWYbnzZDZrEYQ/joN+y9muZR8qgyyCytn7AXqNUp+YmXgNLw1akH29JbDgC8ZYt2qVij1IwOwia6kZBUJV68d5kedCH/OXsvnXEZ+qeeWhexK9oVOXeQdrSyKh/7nW8ru0yc71OGxVq6e24ZRAXw0+K4yDYLT6Xm88eMJaoUaiQgoMm7VgoBRq0aUoNBiL5OMymq1smbVSoWkKS8vD5VKRWRkJBaL1YW06MypE/z+61YsFgtGox96nR61Wk1MjRrc3bYds2e+REFBAXabjQP79pOcnFxmG3iDACQNHcbcWS9z8dJlREni/PlzbNmyxeP5999/P3t2eiDvkeSQ9tHjJvDD999xtZgx0LtPH/5v3lwyMzO5cOECb775pktoqOCIGNizYyuHDh/GarMTEBiERqNBrZK/0/GNmxAeEc4LU5/n8b6uYaVbt26la9euLuSyhVa7x80Juyi5bAWFJgoLCwEwm82YTEULW3a7HZPJhN1mwy7aMZtMHgmbBKD/wIF89uknnDp9mqtXr/Leu28zZMgQ5Zy4uDiWLFmisK8X1y3fbCUrNx+r1YpdFMkvKMRkljkLMrOyeazHwzzYrRsTpjznsW+c8mLi6lG3QUPeefP/yM4rYMmiTxAEgbbtE5Xn/XX7Nrreez92USp7MVKQCdqCDFrUKgGVIMi/X8UuU6kE9GoVajmlG0GQ3/X4qPJ5+AINGo/G9V01QzzW5q4IIiIiaNiwIe+++65idPv7+xMdHc2qVauUfO7c3FwMBgOhoaFcu3aNt99+212fu+5i9erV9O3blz179qBSqRg6dCiTJ08mKysLURQ5evQou3bJ6Q1z5sxh7969WK1W8vLy+PDDD6lbty4RERFEREQgiqKL13v48OG8+OKLpKamypVNzhW9i8OHD2fu3LlK3nJqaiq//PLLDbUNyN/Jxo0bM2/ePKxWK99//72SM+0JDz30kAv7e0lMmjSJ9evXu7C09+7dmzlz5ngdA5z45ZdfOHLkCKIoEhTkGAMcDtAmTZoQERHBlClT6NvXNb3E0xhQURR/772NATabzeVvT0hKSuLDDz/kzJkzpKens2DBAo9jgCeIoojJZMJqtbr8DbKn+qGHHqJbt25MnTq11Gdp1KiR0qdms5mPPvoIQRBcCPC2bt3Kgw8+WK62qQjK9et+8OBBZRs/fjzPPvss8+fPZ/v27Wzfvp358+czceJEJk6cWLYwH3zw4X8SkYHyiqJGLZBrsnIpq5BckxWNWkXr2FBESaJ2iB9atQpRksi32JSJoLfSOGZH7aTLWYVcynTdBEEg32L3upVm26V6kFeWzLJsxZshU6NS0bJWcJUY3s4wUZtYca+15gajESpjeJdEkFGLXZTKRUrmDXHh/lVieDtzpHMrkcKguoFQUSechne+xV5mxSNBEOjduiZLh7dlybA2fP5EG2b0aOqx9FNxnE7P47UNx6gZamRGj6a81fcOahdjL1eryja8H3vkYapHhFKnZnW2bd3CunXrEASBZs2aMWrUKFq0aMF9bZsTGxuL1lGyyGy28OrMF7mzYW063BFPUHAQY8Y9C8Aniz/j0qVLtGjSiLq1azDt+cmKsVhZTJg4mbbt2vHwfZ2pWS2CAX16cuGCZ4K5Nm3aYLVaOHPqhMt+CXkhIjgsjCdGPuUS2vj8tBdo0KAh8fHxJCQk0L9/f4YOHepyvYBA9vV0JowYRKt6MTx2fyeGDB9Be4dBBNCv/0COHT1Kn779XK5dsWIFI0eOVD6vXLmSbp3aYLGJbpszIMGOhF0q2u5q2YzqYcGYzWbi4+NdcmLXfbmSsLAw3pg3m3VfrqRxrQgWvvkaAJcuXiAuOpwLKTLZ3kPdHmbEU09x/z0duKtVc+69736GDx8OyAsvGRkZtGvXTjF0rfYi3X7bvo0WcVGMeWoEv/26neiwIMY98zR2SeK//1nHgf37+PSjD6kZGaYwXjvx9NNPM27cOEXevHc/Ydvmn7mrYS2WL/mExctXIajV2CWJAwcOoNcbaH7HHUruf2mwOVYIdBoVoX46wvx1ysKZJBWVyhQEORpAr1EhCAI6tVAh72uwQUuX+hF0rhdO53rhdKkf4bFUWGXQsWNH0tPTXdipExMTSUtLU4zuIUOGEBoaSrVq1ejUqRP33+9ePg5k1udFixbx6KOPkpyczFtvvUVwcDDNmzcnLCyMIUOGkJkplyiUJImkpCTCw8Np3Lgxp06dYt26dYBs+E+dOpVWrVoREhJCSkoKzz33HAkJCSQmJhIcHMwjjzyivIsDBgxgxIgRdO/enaCgILp06cKJEyc86lhRrFixgm+//Zbw8HA+//xzevTo4ULCVRxt27bFYrG4hZA7ER4ezujRo13GgGnTptGwYeljAMgLCY899hhBQUHcfffdjBo1ysVQTEpKIjk52c3oLjkGLFu2rMLe5ZYtW+Ln5+dxDFi6dClGo5EZM2bwxRdfYDQalYXSlJQUhX0eoHv37owePZp27dpxzz338MADD3gcAzxh69atGI1GhgwZwrZt2zAajcpzff311+zdu5ePPvqIwMBAj2PA008/rXxevnw5GzduJCQkhPfff5+1a9cqixIHDhzAYDBw5513VqiNygNBKkdMoUolDxJlnSoIAnb7X690TE5ODsHBwWRnZ7sl1xeH3W7n6NGjNG7c2CW8/kZgNpuZN28eU6dO9foSVxRVrefNeO6/isz/9f7Zkq7zGF4eFKDjrtqhHq9NzTZxfyP30hH9PvyVGYnB9P/yAnkWV6NqSLtY6kQH0LJGCOl5Zq6XMB5DjVoeincPdX3snc3ckbmFrIYPu+V0N68dQuPq3t/nHJOVBxu5ynQ+9+fHbB5rNpcm05O84jKfWn/FY83mCQ82xL9Y/m3Jms2PNXMPM+v1723M6hTKu3sLsYiyoXzgQjb5FjnUvNsdMTSI9OxFKbTaPbblI+9u5dUuYYz4TyoNqwe51Y5NjI+suMx3fuGuzK2cr3U/ksq1fzzJk2u+W7DZJYIMGh5u7Foe0tmWzucuicT4SGLD/MjIt6BRy4a804j1pqMnmecy8jl7LZ86Ef4MSoyr8HP3fG8rs+8Jo/+XF2gUHewS+u/t2cuSWbLPyyOvoLCQSHsWtWNjiQqRv7djlu+Vw2AlsNltSILnBROVIHM4OH/2i9sHFpvItTwLWrVAZKBeaeNQo475vRqTli8qudJ2UaLQakclQFSQAYMXT763sO1TabnUCNRw+roFrYdIgACDptIyi+tZlkwJeWFKJeAxZN0pc8mKr9i6eRNz31zoJs/u8HirBUGpRe1NT2d4rEUXhOQYEyQkCi12RAmig13bct3ar3j/vYX8sOkXRd7JkycZPHgwv/32mzKHM5lMZBR6jj0w6tTUjAjGYDAw7cUZymIGyHp78qreSFsKFNWX/+2333jnnXdYsWIFp9NyiQnUeNTTqFN7rWbhTc/yPLdT5sgnhtLr8b50e7h7qc/t7J8M0Q8JCPHTKYttNlHCT692Mao1gqDwGdhEiUOpOVhs7vNlUZIQPfzmQpGX3BPkhZ2izzq1ig51I5RnNxgMN5T77XKvKpZ5M3R09k90dLSSq32jcOrZtWtXxo0bx8CBAz2et2bNGjZu3OhSA70smVXx7GvWrOHtt99m48aNirySY0B5YDAYMBgMzJw5kwkTJtyy/ik+BlQGVaXn4MGD6devHz169HCTaTKZOHv2LHXq1MFgKOIMKq8dWa5lMmcNPx988MEHJ2yi6GaYlQdOZuRmMUEcviyv9q7bf5mJDzUkx2RVDO5ggwazTcRkE70Sqd0slMwZdsIbQZmE98mQE81jgj0aiha7RKy/XmECD9Srldzc8pbNcnq8D1zIZv/FLLrd4W6olwWtY8aWb7Fx4EI2LWsFV6p/bwTFc7wrW36qOClZRoHFxfAuL5w53Wev5VeqrqbzB98ZgVAy574yqEzOvb9Og8okuFQBeG+gnA/pyagrDqex5DSQQPbgl5bX7JygFIfT411otSPgeFcq/CSyJ9E5DtxoCH5lKiwJyM/vHL+84eFHe/LQoz09HpNfMcHhQXU1vMung5wzXWixywsiDr3MZjOffvwRQ4Y94XJ+gwYNSmWD9oRr2RVjEq5MFQhnWxaPRklISPBaB/hW4uPFn1XofI1KwCpKZBVYCHeQnGUVWPDTG1HhcEZJEjZJQiXKoeaSJNGhTribLOf7k2stIie1ixKCII+NarWAv07jdo3kOC/oBkPNffCMnTt3Ur16dWJiYli5ciUHDx706ukH6NOnD3369LmFGspjwPvvv694jZ2ozBhQcgy/VbhdxoClS5feNNnlMrpjY2NvmgI++ODDXwsHLmTRqUkUx9PyqB8R4JZvLXoJaC1uNDzRPg5BELg7LozFO84pTOKpuTLZWt0wPyWf9EquqdIGWGVRmoHsjaCsPPmzpaEy7NPFUdzwVqsELHaxUmzgLWoEs/N8VpUY3oWVIAVzGt45psq3pyfDu6JwGt4qleBYDKl4KGezGkHsPp9dJYZ3ZXPuS/OOlfd6jUrAJkrYRMmtvFh54DS8EVBkVFSKXq1CksBsu3HDuzxhw54gcOPh/1VleCPJbZl86AAPdu1M+w4d6dt/QLlk5JptirHmlAmuTOMebuwRYmWsboe4qii596dDwMGuD9kOIjWVUPRkkiQTp0nIdbuFCrRXocVOjsmKIAiE++tQl/iyiJKE2Sb/4mqqyAPpgzsuXrxIr169yMrKIi4ujtWrVxMZGflnq6Vg//79JCYm0qlTJwYNGvSXjDj+X0GlEkIuX77M9u3bSUtLQyyR/1eczdAHH3z4a0OU3P1SFzILHMfAZLW7G91eJhXO3X66orA7QSjyghVHcQKnQL2GDJsrK+zNRq7Jil5XMQOpqnJxocjwrqiB4zS8kaRKl+EKNGhpVTOE/Rdv3PCuLBO3ShDQaW6sPUsa3v6VKGsWF+6PKEpVshByo4a3a859xfrjRo2bkoZ3eep3l4TTMLkRw9tpaN+o4e28r11yL7d3K1DS8K6oBgJFntAmzVuSmpFVIRnOUmlFof8SRp3apaRbSXizFZ0M3Wa7WC4m7r8bAnQaj6takgRaL9/P8i76OLktJEnCZhfRaUuE6UtFfG1lkRX6UHn07t2b3r17u4Qa305o1aoV+fkyB4kkST6j+zZGhY3uJUuWMGrUKHQ6HeHh4S4/WIIg+IxuH3z4G0ElCJSsfnR3nFz6I0iv8Ujk4o30zDmXK7DYKbDY8NNpyDfbXAxurTNUr9BKiFGLJElcyjZ5zdm8WTh4KdtjLu6twI3Wv9aoVNhFFFKyyhjeQUZ3w7sy8NdroABSrhdQK6JiMqrCC1YV9a9FUNjAoWoM78rAGc59+FKOx5z7m43ihndlSOqcKOk1v1HDuzJwjlGFFjt6rdrrmHUzUdzwrqi324kbbcviof+FFjuBlQhPdjadxSYiSTce+g9FC0yyR971WKne+L8Z/HVqZZFL5yGPXSXIBH2SJP09ogZ88OFvjgob3TNmzOCll15i2rRpVUZO4IMPPtyeWLErBYvd6cWVlMlQk1rBWOwip6/Jq6vOtTdJKiVfs9gC3crdruy/TkOkVoiRM9cLuJpn5mqxut63GlWZi1sZ3Gj9awQqVf+6OEoa3veUo2ZzSRgdRvf5jALsguaG619XBjej/vWNGt5/1Zx7T7m4lZVRVYb3jZgaKpVAodWO8U82vCuTry9fWbWG9+2Sc++MGnJ65IujNG+8M7f57wKNY+zyBkEQ0Dsigv5XFiJ88OGvjAob3QUFBfTv399ncPvgw22GO2ZvpNAmuRCvAPyzexMCSniknRMrP62aR5tV9yqzflQAdSOLmBgzCyx8ve8SNlHCZBPx16qJCTYo9xIlics5pZNw3BkbyvbT15XP7euFEx8t30OrVhEXauRcZlGZH3+tGr3W83jjLG+VXWhBUrlPtsyl5IKXlotYlbm4lUWgXlOpklNOFCclqwrDW62S+7cyYfSx4X6ccizQ/FmGt7UK6l/D3yvnvjKoilzcqsgTVww7AewSlfIWG7Uq8i32KjG8K2vsqQW4kWBQX859EcrKubeLkkeGdSiFqLAMVeyS5JWYr7IyffDBh78nKmx0jxgxgi+//LLM4uM++ODDrUV0sBGTTSLXZEWrViklUIw6FX7FwpTtjvBQrVpV5sSqYVQgxaNyQ/10DE+sgyhKNPJQokglCF5DwWXW72DOpOcTVozY6lhqLsdSc5nUrRFnr8v54sWNEasoYbd4npZez5dzvbedzMCKa/jdPU2qkVrKAkBpIetVmYvrRGVycauq/nVxw7uicBreCEKl2cBrh/lhFzScrSLD25dzf+Oh/06YbSI6TdWUGqwIqiJPXKdRgUSVsIHfqOHtmltbMRm+nHtX3Kyc+1yzzWOURqBRg8XLoklZT5Brsnm0oW9EZmaBxWMOfVSQHpOXtApfcLkPPtz+qLDR/eqrr9KjRw82bNhA8+bN0WpdJz5vvvlmlSnngw8+lB9DEuIQPfz02kXIt9i5q1aI27HzDiPXGy5nm1h/6IryuWG1ABLrRSifbaLE+cwCJexcJUCIl7zAMD95f/MaQZ6ZwW0iNUM9G4WFXpjBw/x1kA0dG4S71YEWRYnqQd4JT7zJdKKqcnGd+LNycUsa3kEe8vDLQpBRi12UsNmlKinDBZBI5dlffTn3Nx7674RcGUCoklzciqK4sVhZIignc3NZhvewYcOoX78+L774Irt/30GvF5/jm59/82h4VwZOQ/c/675mx5ZNzHvrvUrJKY61a9fy9Tff8smyNWWe6ynn/sSJEwwZMqRCJYPC/fX4+fkxbcbLjJ8w8W+Vcx+o13j2dEugc3xxut1/L/PfWkDTZs2BskuiBRo0HkvuFZdZEmXJtNoldBoVAcWiaUQH27zzWgE5usB5a5sXoceu5BIXouXI5RyPCw4xIUYEL+2roqiOemk4d+4c9evXx2a7sQoetxJz587l8uXLLFy4sMplf/XVV2zYsIGPP/74hmUtW7aMlStX8s0331Tq+sqMAYIg4Ofnx+zZs5k0aVKl7vtXxj333MPChQtp3rx5lcuu8K/sq6++yg8//MDVq1c5dOgQ+/btU7b9+/dXuYI++OBD+WAT3acTTk8weJ4ElTXN3XT0qsvnE1fz+P6wbISLksTpjHyXH3tR8l5b2slsG+ynI8TDBqDXqDxuKi+TAqcBG2ysmLzSZJaU37JWsJLjfSNw5uJ66qebDafhrVELN1R+LdxfpxjelcnrjQv3p06Ef6XrXzvh7I+cwsqH398IAvUaAvUarJXNH3Xk3DsNb0sl8jGdhne+xaaE/lcGOo0Ks81+y8vyNWvUgGphwcREhFK/VnWGJQ0kMzOzUrLUDkIpuyS5ET96wt3t2rNz507ls9PwVgnex6+y4FyDeu+NeSSNHIOExMWU89SNDGDUE4Ndzr2zZTO2bd1SpsxevXrx6bIvy68DjlrqDl0aNmxIdHR0hSfsew4c5pnxE7CJElabjcED+tG0QV0EQeDcuXMu57768nSaNm1K07hout3Tlk0bv0enUaHXqN1y7kVR5J9TJlMrOpJ6sTV57923S9XDmXNvFyW+++/X9HqoC7Uignl65AiX886ePcM9iQnUrh5FXI1qPPbYY6SmprrJU6sENCqBI/v38vA9bWlSO5IB/3iICynnEQQBQRAYM/5ZXv3XHOVzWasOakGWWXIDFBklt/KsZJisdrId45uE62+5c5+p2LjhLZTdWV2kerCBmBCjQsioVgks/+BNnhyahL9Wjb9WjdpuJSY0kHlzZgFy+ycnJ2M0GrFYqr6CSGZmJkOHDiU6Oppq1arRrFkzlixZAsiGvEZTqQJL5cL06dNvisEN8MorrzB58mRAfg5BEBgwwLWcX3x8PJs3by5T1qBBgyptcEPlx4Djx48rBrfNZmPgwIHUrl3b4xgwefJk6tWrR2BgIC1atGD9+vVe5YqiyIQJEwgLC6Nly5YsWLCg3DqtWbOGdu3aYTAYGDZsmMuxM2fO0KZNG2JiYggPD/c6Bjixe/duWrRogZ+fH/fccw/nz59Xjk2cOJGZM2eWW6+KoMLf6DfeeINFixa5PbAPPvjw5+L174+h02noGh/F2Wv5HEvNpdBqZ1YvebXuUGouapVAiFFLoF7D5WxTucJjH2kRQ2SgHptd5PPfz3PFEbJ93VGTNFCvoXqgHkEQuJxjwvY3I3S5HXNxC80VNwxuVv3rynq8b5f615XF7ZRzX5HQ/7HL95JdaJVJp2w2JEFFdqGVbJOVYINWaUuVILhVQrrmIDeMCNB7DGEO9tPyf481Lrf+6775joTERPLz8xkxbAizZ8+udLRcVdW/LrTYEYTKEYrt3r0bnVZLXL2GFFrsSEioVCo2b9rEiePHaNgovoISK46SOfcDBgzgk08+4ZFHHqmQjOLh6gmJHRjz7EQeeeh+t3P9AwJYt24dgdF1+G3HNp4eOpD1P/9Krdg4RZgz5/7Tjz9i+7Yt7D14hOycbLo/cD9t77yDe++916MexXPug4JDefKZ8Rzau5vcbNfFmYiISD5btpzY2DjMFitvvDqb8ePH8+WX7gsWZrOZp4YOYMLz0+n5eH/emT+P0U8+wU+/yIsgDz7UjXHPPE16WhqRUVHlbrOK4tezGW6LbZIkkWe2yalfjvdLnWNCRMJulzibXYhaAEOJdBBBkAnm1CoBq13EZBMJ99ORWCdcWRS/lmfGYpcU49wuStRrfhefL16ETZSwSxLbf/udWrGx7Nzxq6LP9u3badOmDTpdxVOTysLEiRMRRZHjx4+j0Wg4depUqYZSVcFms900g37Xrl1oNBri44vedZVKxcaNGzl27JjL/luFyowBJZGYmMhzzz1H165d3Y4FBgby/fffU79+fbZs2ULPnj3Zt28fderUcTv3gw8+YPPmzRw7doxTp07Rr18/WrZs6XUMKI6wsDCmTJnCjh07uH79usuxyMhIVq1aRXR0NGq1mpdffrnUMaBXr168/PLLJCUlMXv2bJKSkti2bRsADz/8MCNHjiQtLa3K67FXeNan1+tJTEysUiV88MGHG4cENIgKpEXNEO5rXM0lj9sJjUqgZrCBcD8tweUMM44M1MvXqlU0rV5EqpZVKBtvUQE6ZYIQFaC/wae4PVEyF7cynklnLm5VeLzzygiN94aqrH99ox5vZ/3r3EqwipeMQKgKj3dl+qOqcu5v1ONdPPS/rP7ILLTw/EPx/POheKb3aMYL3Zvwep+WdG0UxdEruTSICuDF7k2Y/nBjpnVz3UKMOkKMOqZ1a8zzD8W7bVkFcj9UlAjL39+fbt0f4ejRo8q+uXPnEhsbS6t6NejcuTNHjxxWjr335uu0bVqPFnWqc0/bOzl2NBmA7MzrjHpiCA1q16B540Ys/+Jzj/fb+es2mjVrpnyuGxnAF4s/5p47m9GxeV0W/N/rjpB3sNvt/Gv2KzRtWJ96sTV5YerzXsNof/zxR+5u1x6jVo0oyd5KQRAYPupp5s39l8drTCYTUyaMJzo6mtq1azNr1ixEx3dx1apVDOzVQz6vsJDxTw3jjga1uKNBLR55QJ78jh8zmrkOzyTIhlKLxg3ZsWMHAJ06dWLTpk0VDv11Gt4ajYaRz4ylTdt2Hs8b/9x0GjRogEqlIqHDPTRoFM/hg/sB15x7uwQrly9j3ISJREZFUb9+A4Y8MZzPP/fcR7IORREIrdp14MEej3mcBAcGBhIXV0f5HVKpVJw+fdrtvFyzjZ9/+QWNTkePx5OwomHYmEkc3L+PU6dPY7WLCGoNLVq2YtOmTVjt4g0x9ZcGi12kZUywy9aiejD5VpFaIX60qR1Km9qh3FkrhLtrhdIuLozm0UEUWkWsdokWMUG0rhlC65oh3FFD/r9lTDAxQUbyLXYlss25+BRg0FCS8a1F67u4lp7GxfPn0KlV7PxtB4OHDefkiRPK92X79u107NgRkCMvoqKiCA8PZ9CgQW6GjxMLFy6kZcuWpKWlUVhYyNixY4mJiaFmzZrMmzdPOW/37t0MGjSI4OBg1Go1LVu2pFu3bgA88MAD2O12AgICCAgIICUlBbvdzssvv0xsbCzVqlVj8uTJLt/r9957jwYNGhAREcGwYcMoKJDT55YsWUKXLl0YNWoUwcHBLF68mJkzZ/Lkk08CsHnzZurXr8+sWbMICwsjLi6OH374QZF78uRJEhISCAoKYsCAAfTv3585c+Z4fPYNGzYo7eWEIAiMHTuWWbNmebzGZDIxZswYj2PAkiVLuO+++wAoLCxkwIABhIWFERYWptznqaeecvHMSpJE3bp1b3gMcEKj0TBmzBgSEhI8Hp85cyYNGzZEpVLRpUsXmjRpwt69ez2eu3TpUqZMmUJUVBR169blySefLHUMKI6uXbvSp08fojwshgUGBlKnTtljAMj9rdfrefLJJzEYDLzwwgv88ccfnD17FgCdTkfr1q3ZtGlTufSqCCo8Y3j22Wd59913q1wRH3zw4cYQbNRyOauAj7eeYfnOFLRqFYF6jRJSp1EJqAWBk+n5HL2aR77FXmapGj+dmm8OXOKbg5f55uBlzmXk46eTc+xUAqhVcCGrkPOZBZzPLOBCVkGZ+Wp/VZTMxb3RkOAbMbwDdPKCSUoZOfmeUJX1r52GXmXycYvXv74dDO/Dl3L+9ND/jPzKLWLAjYf+97u7Nklta/PFzhRW7U6plA5OFFrsFWLzzsnJ4dtv/kPbtm2VffHx8ezZs4fdx87RtWtXJo0ZCcDpk8dZ/tmnrP95BwfOXOaTz5cTGhoGwFMjniCmegyHT55h5dp1zHppBgcPHiyXDr9t28L3W3ayfN33zJ/3L86eOY1NlHj37QX89uuvbPn1N/44cJgD+/fxwQcfeJRx5MgR6tStp7CBO5tg1DPj+OnHjZw4fsztmtdf/RdHjyZz9OhRtm/fzhdffOEyCRUlOe/+q1XLKCwo4LeDJ9h99CwvvCxP4PsNGMia1auU83c5cjfbt28PQPXq1VGr1coEdNWqVTx0T1E7lwan4Q2U6x3PzsrkxNFkGhTz6EsUhf4fO3aUZs2K8iSbNGvGkSNHytCh/KH/taIjiQ4LYv78+R5zUUVR4vSJYzRu2gyjTo1RpyYsOJC4OnU5ceyo8jvZqFE8R48cQqMSKJtu9NYhxKjlzpoh5Fls/FHO3w9nukWeyYZarXL8dstbgL8/zVu2YvO2bZhtdn7f8SsJiR1o0rQphw7sR5Jko7tDhw6AbHSfPXuWM2fOkJub69GInD9/PosWLeLnn38mKiqKKVOmcP36dU6cOMGuXbtYunSpEn7ctm1bpk6dytKlS91Cljdu3IharSYvL4+8vDxq167Nm2++ybZt29izZw/Hjx9n7969yrv45Zdf8sEHH/DTTz9x4cIFrFYr8+fPV+Rt27aNhIQEMjMzSUpKctP73Llz6PV60tLSmD59Ok899ZRybMCAATzwwANkZGSQlJTE119/7bW9Dx06RIMGDdz2T5gwgQ0bNnDsmPsY8Oqrr5Kc7H0McOKzzz4jPz+fS5cukZaWxty5cwFISkpixYoVynnO1BlvY8Dy5ctp0aKF12e4EWRmZnL48GGaNGni8XhycrLLvZuVYwyoCKpXr46fn5/XMcCTDn5+ftSrV89Fj/j4+HL/dlQEFY6v2LVrFz///DPr16+nadOmbkRqa9eurTLlfPDBh/JjcEIsdsl9gmC1iTSJDlRyqotDZhT3jnsbV8Pq4XfdZpeoEWL0eE3ODYTcVhTOcNdfT13DJrgOZ3fUCfX4fM7JY2UYfm+f+tdyFMO5jHxMdulPrX+dkW/BYqucoXg71L924s+sf1081LyyudWeQv8rSgnW7+7aAHyxM8Xlc0VR3vrXvR97BLVaTW5uLvXq12fJok+VY7169QIgOy2X559/ntdee438vDzUag0Wi5lTJ48RGh5Og0aNMGhUXL1yhe1bt7Bi9Rq0Wi3x8Y3p3bcfa9euLdcE8+lnJ+MfEECjxk1p0qw5x5NlA/rzzxbz7w8+JsLhYR07fgLvv7uAsWPHusnIzs7Gz1+u6qBWCUoYcGhoKCNHPc1rr87l0yWuk+k1X37JO//+gNDQUEJDQ5k8eTIrVqxgyJAhclsKjnrkKg1Z16+Tcv4sDeOb0C5RNoTaJ3bAYrawf98+Wt1xB1+uXkWfx/u63CMwMJDs7GwA+vXrR5cej3stnVUSTsO7rEUUURR5fvxoHuzxD+o3dA2hdYb+5+fl4R9YFC0VFBhEXl5eOXQoCv0vzQa+cCWdrOwc1iz/nIYNG7odN+rU5OfnY/QLQEJSFiADAgPJL6ZHQGBApfkFbjachvcfF7P440I2d5YxXjkXTUL9dMqyiZPhXhCgXftEdv/+G4OTkjh86CDt7r6LxMRE/vh9B7Vq1CAlJUUx3pzGqiRJjBs3zs3onjNnDv/973/ZtGkToaGhSJLE4sWLOXXqlOKxHj16NGvWrKFHjx4sXLiQ+fPn88YbbyiG2scff0y7dp6jKj799FMWLVqkRDtMnjyZ+fPnM3bsWD799FOmTZtGbGwsANOmTaN79+7KtfXq1VPSYo1G97mLv78/zz33HCqViqSkJEaNGkVWVhZZWVkcOXKEX3/9Fa1WS/fu3V0WB0siKyuLgAD3yi6hoaGMGTOG2bNns2zZMpdja9as4ZNPPnEbA0qm8Wq1WjIyMjhz5gxNmzZVPN0dO3bEbDazd+9e7rjjDlavXk3//v1dri0+BgwcOJCBAwd6fYbKQhRFnnjiCXr37k3jxp7TjPLy8ggKKjYGBJVvDCgvUlNTsVgsLFq0yOMY4EkHT3oEBgZ6jeS4EVTY6A4JCVF+DH3wwYe/AAQ8Gtx/B4T6ayELmtUIdmMvt9glryWycs02rJXMPb+d6l/Hhftz9Gqe8veNwFoeBqoScBp6+ZXwVDvxd6x/fbvk3EfqK/6dqgrDu7z1r79a9w0JiYmYzWb+NfsVunfvzp49ewD4+OOPWbBgASkXLjrKWUlkZV4nrm49pr/yKvP/9QpnT5+i+yOP8tr/zefChQuYTCbq1ipaeLHb7QwaNKhcOkdEFoUsGo1G8vPz0KgELl24QO/HHlHeT0mSqFGjhkcZQUFBFOQXTdyU5xZg1Jjx3Nm8MSdPHHe55krqZWrVqqV8jo2N5fLly8pnlSCg16h56LHHuXwxhaeHDsRiMfPEiJH8c+pUBEHg8X79WLN6Fc1btGDd11+xbv33LvfIzc0lOLjypeXKqn8N8OLzE8jNyeadjz/zeFwtyPnfWdk5So53Tm6ORwPFsw6y4Y0ke/+95dwHBQUxZMgQmjVrxqVLl1AVe4/VgkBwYCDHcnMptNgx6tQICOTl5uJfTI+83DyCS0zKbydU1PAuieJcLm0T2jNv1kyOHD5M/fr10Wq1JLRvz4fvv0+16jE0b96coKAgbDYbU6ZM4euvvyYzMxNJkoiIKKpkIooiCxYsYOHChYSGhgKQnp5OYWGhi9dTFEUlRdXPz4+XXnqJGTNmcOnSJWbMmEGvXr24ePGiR71TUlLo1q2bEkJc/F1MSUlh1KhRPPPMM8r5xcnfatasWWqbREZGKt8VPz8/QDbOrly5QmRkJHq9XokMLE1WcHCwVyNy4sSJ1KtXj+PHXceA1NRUatcuGmdLjgFODB48mPPnz9OzZ0/MZjNPP/0006ZNQxAEBg4cyIoVK2jRogVr167lp59+crn2RseA8uCZZ54hOzubVatWeT0nICCAnJwc5XNOTvnHgPKitDHAkw6e9LhZ7VVho3vx4sVVroQPPvhwe+Lno1cp9ODFbBkXysWsQo/XeDN4Mhzsq1Up05lDWlHcDBKsiqKq6l+b7FKV1L/OrmR4tk6twnqD5aaqyvCu6vrXlTW8b4ec+4x8C1kmG0GVEHejhndF61/r9Xr6DRjEgjfmc+3aNfLy8pgwYQJbtmzBL6Y+0QaRiIgIZdLbs+8AevYdwPWMa0x8+gkWvrOAYU+MkHM/U9OUSbkoobA1VwYCUD2mBp989gUt77gDjaOWtTeZTZs25ewZ9zxCSYLQsDCGjxzFa6/OdTkWXT2GCxcu0Kqp7B1OSUkhJsY1YkOnURHgZ2TUpGmMf/5FLpw9ycDHHqZ9+wQ6drqHfgMG0uvRR+jctStRUVE0aVqUr37lyhVsNhv16tVT9lV1/esXXniBwwf288XX36LXe+f1iI9vzLHkwzRt3hwQOHrkCE2bNi33fYqnxpRWS9xms3HlyhWPHq2G8Y1ZtuQTudKGxY5gt3Du7BkaFzMMT548wYCB5VusyTFZPZYMM+rVXlOQqqIC2o0a3k60TWjP8ePH+Hb9N7RLkD3ad7dpy5PDnyC6eoziTV22bBmbN29mx44dxMTE8M033zB+/PiiZ1Kp2LBhAz169CAmJoZOnToRERGBXq/nzJkzhIWFlapHeHg4kyZNYsmSJVy/ft3j97NGjRqsWrWK1q1bezw2Z84cxSkoiiJXrhSVO63s9z06Opr09HQsFosS2Xvx4kWv39vmzZtz8uRJj8fCwsJ45plnmD17tsv+6tWrk5KSQv369QHPYwDIucazZs1i1qxZHDt2jM6dO5OQkEDnzp1JSkrioYce4t5771WY4J3wNAZUNZ5//nn++OMPfv7551LHgCZNmnDo0CFFvyMVHAPKi9LGgCZNmvDvf/9b+VxYWMjp06dd9Dh+/LgSbVSV+Hu6v3zwwYcqQfUQIzVC3TdJkvDXqT1u3mwdpXRJFcp0hjx6q1FaGqqaBKsyebQVJcHyhOJluM5l5Ff4eiec3o/KLEZUxktfEs4yXDeS4/13zrl3LlodvJjFvpRMl02rFriWb1a2HJMVjUpAdLAS3+oc7wKrXQ4FRjZcTVY7BRYbgoDCiC0hG382UaLQbOHLVSsVkqa8vDxUKhWRkZFIdrtCWiQhcebUCX7/dSsWiwWj0Q+9To9arSamRg3ubtuO2TNfoqCgAJvNxv59+0hOTq7wsxfH4KFDeXX2TK6kpmK1i5w/f44tW9zLfdlEifvvv59dv++gwGKjwGKj0Cp/j52vxzPjnmXD999x5coV+b0XJXr27sP/zZtLZmYmFy5c4M0333QLDQX44/dtpJw+QaHFis4YgEajQa2Sw9fjGzchPCKcF6Y+z+N9Xa/dunUrXbt2dWFrrmjOvdlsxmQyuf0NMqnd999/z2erviYgILBUOf0GDOS9t98i89o1Tp46yeeLF7lMbAVB8FpGyW63YzaZsNltSKIdk8mEySKz8G/buoX9+/Zht9vJysxk8uTJ3HnnnW6TbYC2iR0xmwpZ/+UyTCYzb89/nRat7iAuTmZatlgs7N+3l85d3FmavUEQ3LfSUFXcbJXJ8S6J8IgIGjRoyAf/fo8ERxi5v78/1apFs/arL5V87tzcXAwGA6GhoVy7do2333Yv93bXXXexevVq+vbty549e1CpVAwdOpTJkyeTlZWFKIocPXqUXbt2AXI4+t69e7FareTl5fHhhx9St25dIiIiiIiIQBRFF6/38OHDefHFF0lNTUWSJM6dK3oXhw8fzty5c5W85dTUVH755ZcKt0dJxMXF0bhxY+bNm4fVauX77793KTdYEg899BDbt2/3enzSpEmsX7/ehaW9d+/ezJkzp8wx4JdffuHIkSOIokhQUJA8BqjlMaBJkyZEREQwZcoU+vZ1TS/xNAZUFKWNAXPmzGH9+vVs2LCBwMDSx4CkpCTmz59Peno6Z8+e5ZNPPqnQGGAymbDZbC5/g0yOtnfvXux2O5lljAGdO3emsLCQRYsWyVFW//oXd955p8K2brFY+OOPP8rFqF5RVHjWWadOHerWret188EHH/4crNiVwue/nXPZvvj9PGqVwKlreZxMd9/yy/DINa4eRNOYYLdNkuQcMU+bt1D2AIf3siplBvvJK8+HL/1v1b8uboDtTckkI8+Mv07N5axCdp/NcDPC0h3btXwzJg/12gGCjXL/HLyU/afXv84120otPXfAg+G5LyUTrUau9aQSILPAQnpeURt4e+6SKGl4V4IjTvF0p2bL/eHJSE4v1j/FN296hjq8q7HhftQKM2LQqgg2aokL98NiEwk0aJUtQK9Bp1GhVasU0qTKoLKGt7NOs9ZB3OT8LElyvrfKscDT+9Hu1IoKo37tGLZv3cK6desQBIFmzZoxatQoWrRoQde7mxEbG4tWp8NktWM2m3l15ovc2bA2He6IJyg4iDHjngXgk8WfcenSJVo0aUTd2jWY9vxkCgs9R8+UF89OnEybtm15+L4uxFWPpH/vnly4cMHtPJUg0KZNG2xWK5fOnUavUaNzTIglx3cyPCKcESOfIjcnB8HRDs9Pf4EGDRoSHx9PQkIC/fv3Z+jQoW7y065eZewTA0lsXJs+D3Vm6PARtHcYRAD9+g/k2NGj9Onbz+W6FStWMHLkSOXzypUr+UfXdkr96/LgzhbNiA4Lwmw2Ex8f75ITu+C1OZw5c4bEO5rQLLYazWKrsW6NHF6667dfiYsOV8598qlRJHboxF0tmvDwvZ0ZPW68MrG9ePEigYGBNG/eHE/4evUKGteKYN7sV1i1Yjk1woN547VXsYkSubm5PPnEEGpWi+CuVs0wm8189dVXyrUPP/wwr7/+OiBHVbz/2Qo++/jfJDaNZc/OX3n/k8XKa/7jDxto3z6RqGrl49rQqFXYRQmNSoWfTqNskgRaleBxq4pFSifKMrydi4/X8+WorOvFtox8CwLQtn170tPTaXVnG0xWOyabnbvbtSM9LU0xuocMGUJoaCjVqlWjU6dO3H+/e/k4kJmyFy1axKOPPkpycjJvvfUWwcHBNG/enLCwMIYMGaLky0uSRFJSEuHh4TRu3JhTp06xbt06QDb8p06dSqtWrQgJCSElJYXnnnuOhIQEEhMTCQ4O5pFHHlHexQEDBjBixAi6d+9OUFAQXbp04cSJE1XSxitWrODbb78lPDyczz//nB49enj15rZt2xaLxeIWQu5EeHg4o0ePdglvnjZtGg0blj0GpKam8thj/9/emYdHUWX9/1vVazpJJ2RPCCACArKLitFXWQUZRwGdF3+Iy6ivK4zO4AajCDrugzOKCyoO4L6+A6/ODCDDDjIISAQVESLIlpUk3em9q+r+/qiuSlfvSbpDAufzPHmga7l16p66Vffce+45k2G1WnHBBRfgzjvv1ERKv+GGG/DDDz+EGd2h74D333+/xbPLQ4YMgcViifgOmDt3LioqKtCjRw917b6ybn3z5s0at+27774bI0eORN++fTFp0iT84Q9/SPgd8O677yItLQ1z587Fe++9h7S0NHVAtqmpCTfccAOKiorQr1+/sHfAxIkT1cBzJpMJy5cvx4svvojs7Gxs2rQJ7733nnrsypUrcemll6IwwXdAS+BYvPDFIYSObvn9fuzevRurVq3Cgw8+iNmzZydVwPbAbrcjKysLNpst4qiIgiiK2LdvH/r376+OLrUVr9eLZ599FrNnz47pktESki1nKu67s5SZCv1c98ZWzL0kC7d/UQlPiJv1PWP7ICcjuqtyhkmPCX21LwLlvv/74yMAx2PSkBI4fSK+2COvCZo3eQDOLbLi58CsW/fsNOh5Dkca3TDwHCYPCndjUsp850chYiC1Qd2z0b84cluxe/xhMgLA6D+vw8sTC3DHP6o09y0FZr3un9hPNcxD4Xlg8sBwOa9auB7nN2zCh77zYDSaNC7Bl/TLR5/86GuF3H4RV/SLXJe3f16JJp/2xnU8h1kT+6oyMmhnLHgOuHZw+FpPpcyXv3HDF6EuFTl9ooSTTh/0Ok51NY8kIwBMfmUTnhyVg8c2NuCbY9q1SUNLs3DleV3RIzcdjDHUh7jgZ5n1+FX/orAyL35mDd68qhg3/v0Y3H4Go1422Bhkj4KHr+wPcyANXah7JAfgmgj3fslTqzBe2I7/w/nwsWbd8hxw/6+0+g7u/zPGoOO5sPoMftZ7FWiDA1bZPLhjbG+YDbKMFoMcnVjBL0q4MsJ9R9OP3e1H+bFGPHr1uTgrNz1iRzmafv7ruX/j9SuL8N8fH0HfIqtmDWL5MRvmTRmoytklzaAaoQDg9Yu4akBxWJmxdP7I1eeiR2BpQbDODUzAWbom9OhxFvKz5RmI0DzdopLZOeT2dBynLpq1uf3qQIzZqEN+nDzdJxwifIKkiZtg0PHIyzTJaaQAgAFCUAPiIQ+6hbKv0o6zsg041OBTn5HgSxdlmTWuo6FSRSrzx0o7emQbcLjRH3GQqzDLrN5f6F4uTpmL3vkUX29ZjydfULK8MGSnGzUy6oPqFpB1lp2mLVNxj/UZrWHuyz5BQm6mEXqeV1NCrfj7/2LRq69g9dr1qlv9gQMHcOONN2Lbtm3gOHldvMfjiVmXXXOzYDKb8fAfH8XdM5vdh8EBXdKi6yeRupRrI3Jdfvjhh9izZw+eeeYZHKxpQtdMPWqcUljAtwyzHubAM8TQ7OWkuJpHWlKg3Hek8kSJITvdAJ6TI5dfOX4cnn/hLxg4SA6+F22JQrB+vAKDVxDlgZaAbMFyhp3LgO+q7DHzdOsiuIrzMWbRRYnBK0hq/Sp5ug9UN6HUqkelQ4Qj4EGUl2GCwyvA4xdR0iUNaXodGABvQB5T4L3qF6WIz7pSn2azOSnLFJJdHtCsn6KiorA1va1FkXPMmDH43e9+FzUY2WeffYYvv/wSb775ZsJlJuPeP/vsM7z00kv48ssv1fJC3wGJYDabYTabMX/+fPz+979vN/0EvwNaQ7LkHDVqFBYuXIjBgweHlenxeHDo0CH07NkTZrNZPSdRO7LFvgb33XdfxO2vvvqqGgCFIIjoVNk9ALJgNujA8fInUumrZKYZYq7Lipcy5brzu4HnOVhMelzWJw+bDtSBA4fGgMtwUaYJaYHOfs8cS9Q11KmgS7rccRlUkqUxbgRJwrdHbRAZQ6ZZH3Etbrz7HlSShW9OOJIWfbrIakLl8SYAQGGmCdVNXliMOnlGMUu+D2W2OcOkg8Mrqu7z0fjmSAN8UnNfWzFmxwwoRK3TC8Zkw16SGGodXuiirFcMRnFfVmQEAG/QjHuwIZtm4OH2S1HdrpVOv8cvoX9xcwCRn6qb4PSJ8IkS9BIfONagrtEVJRZVP13SjYANaPKI8APokWPBLwGZg+vS5vZDkBjMBh6ewChPuj72wNnPdU70LWx2Zau0y+5uij5cfjHM6I7Ft8ca4Qg8mDwn151Zz4PnOVUfwYYKY805cEOxGJo/rV5BUg3sYG8IRR9NXkHTqffHmX2MpPNggk83B/IkS0GmzivXy+shlU7PSckCBsBibJbZ4xdRlG3W3HOwociY/AyEonRQgOb6Nur5MAMcaM7lrdR1NJQqVo5R6hIAXD4hkJKqOTVS8NrxaF4j+oDiDDoOroDODToeflGSB5oYwAeOUYw65RrR2qQyKDP+15Pw68lT1O2hA3NSIG+1xhBtoTdFcP5rgIPg8+Jvi9/ETb+9RXNcnz598J9ACrFgJCbXTajOGYDKBrta13yIziNhCNSTUc+rARUVnSvnKXUpBgZYlXoIrstp06Zh2rRpLaoHDrKxrSxZ0IeOBiaALrBOH4GlD//88t8tThKmGNpKbmxjAjEuLumZG7ZNaT8n3ZETtKUZdZrnPxiRMWSZw9ukMqCXZtCpRjdjDB7lnR24dw7yzLxfYrIHRIxKOFDThG5WA36obH5WwBjAcSjKMkeM36AEvuM5hA0wdXS2b9+O4uJilJSU4KOPPsKePXuizvQDwG9+8xv85je/aUcJ5QmiRYsW4dZbb9Vsj/YOiEWw63h70pp3QCqI5t6eDJK2pnvixImaqXyCICKjdLauHlKCu0b2xl0je+Oa80pRbffAJ0hw+WWDoYvFgHOLMnFuUSb6FWTIa8XidAeCO9fBH+1IHabkObi1jdAgWK1Zi5uZpk/KWlyFSB2etg7wOrwCumaZ0TNPXoNdkmWGwytACMzqKp37bIsR2RYjMs2GuBeNKGcLjk2EJC0/TIiWVHESPTQByAMtDq8Ah1do7qBysqGs6EPp3Jv0vKyvBIQIPoJF+X9LaMl57am7dqE9XlpJuEY0nbcGl09U14gH/ymG0rfl5ehRUgi9wYApU/8f/FLr1vB3VhTDG2hdbA+F4DJaU4qyhMIriG1aagTIASWbPH40eQTNHzh5MCvSX1J1ziHOgxsYtOJ5GAK5zXnlX44DzyHsj0PH6XO0lGPHjqGsrAxZWVlYsGABPvnkEzVtWUegvLwcOTk5MBqNCWdrIE4NrV9VH8Jnn30WNzohQRDNM0BffHsCSmYhOYiYHjzf7CZ30ulDQ5BrsCCxuFFPP9t1DL8eXAKXT8DmA3Vy2WDokmZAg9uPqiYvjDoeep7DL41uGJMRRjUJhAbBOlX5rxWCZ62V2cSzcrSRwS0GHVx+EY6AEg0J1GXwDEjwNZTZ3VD3+nhlnpVrwUmXXTPjGXyN4NOVa0RbH2/3Brwhssya7WfnpeO7E3aNC3RwuYk8QkVWE47aBXWWuzjkGpkmPRrcfriD1jLEs2dD9VESKFPRR1rIjFC85ymZOnf5m2MltIfOgwk+3StIgC7+YF3oM6HIqbFhuIj/jVmmX5RUAyT0GjqOg8BYzFnuUEwhZShlKrPcoVXHx5E0eK8/xLW2WU65fOUa0Z7LYKOHi9I+lHsNdc2P9awb9TwYC/f2YQzQ6TgMHTYUJ042qganMqMYj46scyV9oTPgyRBMZpo+zBtEMegYons3xCPSrHlbZrzb+mUVmewNEzxrzFizl0Yo0epUfq51aHT7VW8Mm0dQZ/g5rtkDR3l2pDgKUgYocjOMqHP64As8myIYGBjMQQG7BImpbusMQHIW/rUv1157La699lqNq3FHYujQoXA65SCqjDGIYstTVhLtQ4uN7mHDhml85RljqKqqQm1trSYEO0EQkenWRc4Bee3wUohM+wEVRIahXaNHf6y0xXb7cflEfLKzOcjPgBJ5bYlBx6uG95Fgl/I4HfwtB+vC1p0DwLCeXSLKwtCGVDQcOkz+a47jMLQ0Cw0uP7yChPzA+slg0ow6GHTNeaHjXWVwVyt8UvNRyjVESUKawQizgQ+TNZ67pEHHY1BXK+ocPhh0HHIs2rWjHMchx2KAT5QgSoDZwINF6VApLtGFmSYEp5lWriFKcnR5k54Pu0a0tYsKBZlmWNLkFG9ZaQakhxiaPM+hi8UAj18Cz8kGQKyO34CSTIhMe81CqxmCKCHNYpAHlkKenXj6SabO0wNuu+2l89D7UHTOCRL4OCa3Sc+HrXnkAi7WOj5g0LSiDSlB1ERJ9uQIa4ec7LIuMaasKo9YTrN7LB+2ttmg41U5eWXqtyVwHCxGnTy7GTBoQuXgOA56yF5EsVp5dDnlugx4hEd8H8Uz7zhOTsEmiAwcF/kZ0QdymTPEH2wIbcPKNTqKzhVD06QP17m6DEfRR2gRbZjwjWR4txR1kC0wWBNtCUosEs1znwjKIF6mSR+xahgDDFHe32KUAQxlgKfO4W0eoAjEDOAATTBIxrQutWeQEwZBhNFio3vy5Mma30paj1GjRqFfv37JkosgTltkY9WKT3ce0xi0DAy/G38ODtQ6AESe/Yj16c1JN8LhE+EX5Q+fUc+jyuaBXsfjcIM8w2jUKWs0EzNGQ9dfK/hEFjU3dbLzX7fW8G5L/msgYLzEyb+tDzLuxDhu8ZE6sUpH12Js/fi/nudRZI0+8s5xHExB66PFKL1SizF6Pet5HoyxqOsJE6ldJapvNPiAAdRM9N6ZnucRbTA/1jXikSydpxl4tbxI12gvnfOcBMRpjnyMDn1boyzzHKeu5417jSjqVsXjuBjHtEVOLr5xxQUZsq2Uk+NaPnOqFYFT109HPUa5RhzDhlfWMUfa1xl0zsUfWIiEyy9GnAnPTNNHdE0XpEgZuOMTuua+pYZ3S/Pcx0Lx0HD7I7/504y6qFHsoznaK+2li8UYdgQDNN+bUOLF1iCI05kW907mzZuXCjkI4oyhwCpHQR/XrwAn7HIwroJME3ieg1+Q0KPQoh7rFSQIkgSTXgc9L6eBiobXL8ozIYHfisuYqARG0SAb3/E6m/La6JYZvcnKfx1seLeU0PzXrTW8CaJTo9g2NL1EEKqrfijBwd7Cd7buWgzyrH4yDe9WyRG4YXndd8QLRZ3R7iCrzwiiw9DWb2nS1nQTBNEy3v/6CNxBM92jzslHt3zZ4BYkCRUnXZrjzXo+5uzYmP6FEdN7SRJDaXZa+A7I6b1i8d1xO84ptrY5GnhLCTW8rebWvapy04046fQl1fBmrA0u9Gh+accqI941EnntJ0POeDICce6jjddIhETkTPU1EjkfSK3OI11D4nQQGWBrqEem2aDdJ0kQBAEi80Usz++VADG6IeLhwl0NGGPwer0Q/WJE46Y1ZYp+H7xesVOXGbO8KGWSfpJXl+p9+yLP9rZGRlU/nDfMBR4A/JwIXs9BZIAvEK1eMbylOPoR/M0hUY0cg0eQ4BIAi04CH0XOaGVyoh9erwQLH3ne2u/xwmiI/H33MwYPF96p8Hg88Br0qLMLkKD1dMrJMMLuC5kc4LT/NSKyfoDWLWuIhKIfj8eT1JRhyZYz2WV2BhmBzqkfAKitrQXHcTAYwjMFJELCPVmeD18DFArHcRAEIeYxBNGZKHv235gEYOD81RCCQoAwAPOnDIQpxuizgQd+M6Q0bHttIACSxaiDkllIkBi+PlyPsQOL8EuDC35JCqytk/PnSozBL0lwRe5/pQwlGngy0nC1lGDDO1o02HqHXJflRxs0eaB1PIdRAwpg9/ihD6ThqnF4VaM72rpIhYpaJ0pDgmjZ3H7odbyaA7mLxaAx4hMxhA/WOuQotJADaPUp0OYR9wlyCimFrCiDDUq6MrtbgCnEnfpgrUOVU89zYTlmo81qqPslht1Hbervs/PSw8qwe/xqsCM9zyEjymDQyYB+RImBC3p8RIlp6jLTpA9Ls+MTY8tpc/vxc51T/T24a5Zmv8SYJhBhpin2TFFn1rmO41QZNTrneDQZs8DsjRC82vSAjDHYbDb4dWYwAL6gAUCDjoPNpNe0EWW9MCC/lyINADLG4Pf7UecSAwGfgtsYg92oV5fNRBoAizSbV9PkgcfEw+aVo3L7g54Lo56DzaiVM9h9mOe4iGVW2TzwpPE46RbD8iEzxmA3tU3O4FlFnlNSQWrLiSenoh8nM0KvC+9/2Y26kIBt2v2RZFT0Y/PKAa+SoXOlLttL51mW1tWl8lwC4d5YNqMuJDhkYnVps9nghilwp1qsaQa1zHA/s8jLYBQ5T7rlwQZFCuV8b5pBrcsIC1ci6qfa5oE7jUe9W2xO0xZ0cqbZEHMwOlqZ/jQeJ13NgxjKmm5XQEYu5L6DF1eElqnct8FgSKqxZLPZ4HA4klpmKuRMZpmdQUalzM6oH47jUFpaCp2udZ4nCRvdy5cvj7pv27ZtWLhwIaQ2pulpC6+++ir+/Oc/o6qqCkOGDMHLL7+MCy+88JTJQ5weGHgekOTcv5zeCD3PyVFkmRw0Rvn4c5ANOSWIDGPR10oq7tJjI8xM+0Up5lrNWDPTkYKeSRLDeWfn4ITNE3FtWjw7enDXLGz/pTEphndloxx4bXcgX7WCkrt1zIBC1ASMNF7bb4MQxVDsYtHmgVbQ85wmD3S9ywfGtJ2z9BgDJg6vAJvbrzE2gw08AGh0+zVBrLxx0sTYlbQvIdcIJtj4SqTMQyed6BeUU9seSDOjIEgMPkHSGLTRjFnFqPupxgFjkC/ksQYXapt0MOh42N1+SJCfK+WIWHm6FfZXN6Ff1+bsFj9U2jElaH+TV0CuvrkufYIUd3AgVB8/VNpxFbqqvxtD6jaeEd+Zdc4FNZhQnQs6E47zXTCxZ56mHJ/Ph3/961/4KftC/FjrDqvvO0f3RknAQ8YvSnD6mnVs1PMY37MgTDZRFFFRUYGnNtVCkIBzgvKo/1TdhLvG9oYxaL1ncK5vvyhidIQyF7y3A/ddmIWP9jnx7XG7Zp+O4/A/o3qpcto8fs3MaDQ5H1n8FeaPzMOTG2uRb01DZlBgv31VdswY10eVk+OgyX/s9ou4PEKZjy7+CvNG5uG5LbVw+JpnaDkOuGtMb42RFVrXep7D+J7abAuKfr7wnQsBPPoXWdV9Dq+Aq4d31dRlulGnRguXJAmX9QxPaaTo56N9Tnx3oikpOlfqsr10PmNsHxQEfSMT0Xnwc+kVGUq7WDQ6HzOwUCNjsM6j1WVw+7H7gaMNWs+0u0b3QXF2s5zB76N47efJjbKcLdX5gAhy/uWDnfjd+VasPCyi0S1gX5UdaQYd+hVnQsfzOK9nFnrkWMLOA+Qo7ANC3hsA8OKHuzBjeCa21nDQvq44nFNswdn56WHnKDh9QliZyn3P/OIovBHe0zPH9YEhyjpxAw9M6FcUtl3Rzx133AGjMTl5wRU5e/To0WqjK9VldgYZgc6rH4PB0KayEza6J02aFLZt//79mD17Nr744gtMnz4dTzzxRKsFaQsff/wxZs2ahddffx0jRozAiy++iAkTJmD//v0oKAh/sRFEoih9WZOeh8jJH2MltYrqzglogpzwyjBvOxMp6JkgSRACeTwjBSWLZywdqHHArOfhE0R8fageBh0HHc9j1IAC1Dm9YICadgSB/+ujjDAqa9mbvILGaNh9tBGAHPFUH6jbHEvzSKXTJyQc8XRoaRbKj9nCguIo5+dYDOqMYLwiXT5BNcCC1/EoZYTKFK88Z8C46t4lDQzA0Qa3nG9XlTHCNaKUJQY6JkYdhwM1TeosgiAxGHWc+kwyxuDyCfD4OVXGaI9mdsCYLMlKgydCx8cnSDBn6ALXkdT0XmkGPmqwu9wME2ALz5+r/NboI8i92h9nADe4rlqi83gkS+fKAEaVzQOjjlNn1gRRQqXNow5gMMjvGMbkfyUWPV2PordIOue55ijJjCFM5zy4sDQ3HMfB6XRih6MJToEDgzzI6BPlCPKSzoDGgGeNxDgwXt+sH4aIaXNEUQTP86hzS/AIDA1HmtR9XkGEn9PDoNOrg18Nvub74jhdxDJ/OukFz/PYcbQJNo/8XBj1vOr9EiynyOnVOpUYABZ+3wDgFuSI7ZUOES7Jh+IsXaDuGI43ifBCj9x0s/rcSLogQ1GIXKYPevA8D4k3wMs4VNnlQcYiqxlNAo/MDJN6bGiXjefC61PRzwmPAD90yM+S1Po/0eiDF3pkmOQ26/SJ8IKHTid36QRIMfVT72E4YpffPUr7AQAvdOq9ekQBfo5DulFp8yximTUuSa1Lt8CQm9X8/B6xC2F1KQY/RxIfscyTHgae53HSLeF4k6iR08vpNfrwQZ6+Vdooz4WXqdy3zmDE8UYXRE6rcy/04Did5l2hXEOMUpeKfk6aGY7Z/ahuEjXvdi/XXJeMMfi55nee0yfG1I9Sl8nQuVKXNh/g5/QoyM5E+bFGVDnlgXQvdGC6yK6yYhT97Kv1gOez8K8fqjWZLgDg96U5qHRGf3/zfHT9cHojBFGUA95JynIZQOQNsBibZRSkZk8SMULbkc+T9WMymWAymcL2twZFTrPZnFSjLplldgYZgTNXP61aKHnixAnMmzcPb7/9NiZMmIDy8nIMHDiwzcK0lr/85S+4/fbbccsttwAAXn/9dfzzn//EkiVLMHv27FMmF9G+XPrndXjz18UYOH+1Zq00IHfs5k4egLQokY1NOg7XDO4att2g4wFRjnrK63TQQ07LBQQMzEAUWMVIMPC8+v9ohkidQ+4hRpqZjpSKK3glVksjmLY1/7XJwKPW2WyweAVgWLdMdRZZlFjYjKLFHMXVOGCIhAZGy8swos7h07j8BbsGmfW6uIMDCkrnMS/kGmY9D48gaYyvWDVp1HFweAUcrHGo2+QBBw4NLj84yB1le9C9x/NmcnoF6HkOlYHOuJ7n0OQRVAMMaB7kkXOqRg/aYTYqs3Acci2mMK8KnyAhLyOyS2S0SLWJIs9sS6qsbr8U1VW/KvAsmw28pi7NetlLRKlLQJ4VDnanjPWUHqpzwaCT2973lXboeTkCdXBd6ji5HLUuE7jv4Mjlwc+g8tyYQlzgo923MoBRaDUjK8YABmMMrkBUYcU9M1r6HnVQJNuiGRTx+kXVWwQAJAAWvS5kIDDy/QLywAhzS2h0+WHQybOwOp7XDLKEEu815BcZLCa97IkShNcvITc9Wsclsn6U+87LMCMjLfZgkFeQ5FRpgbqMpp+GwDOSinbuFURUN3nU92x1U+w0j/HQ8/Job0Vts9cFgzzQorwXeU6uc/U9EkdBP9c6kWbgIUoMP1TaVc+W4PbDAiuVle9dtG+PEDQACI5rUTuPJqbyrYjUzkP10ZJ2fqxBXmLR0nYej9wMI6qbvDjS0LyEI/jdHXqNRMsPPi83o9nYVgiOEp5ozyA4rea3R20Y2b8w/kkh5AVkaXT5UZRtwVm5zTPbsTKbALEH+kVJ9nAy6eVnUx5E5tUldpw6QBlYdie1LiUoQZwqWmR022w2PP3003j55ZcxdOhQrF27FpdeemmqZEsIn8+HXbt2Yc6cOeo2nucxbtw4bNu27RRKRrQ3ipuVxagDxzNNvlCzgYfZoINZz0NkDBJrnmGK1RVXZmQMPA8pMHuipE1iYOrMl55TZr+b/x+NLunyiG2kmeloH6wmr4Amr4DMGAbzt8caw4x4xhjGDChUZ9IaXD71gwXI7pmx6JplhsfPUOf0Qc9zOLfYqtmv4zlkmvWqC63VrI8b67xrtllz36XZaaoSOE7rfqhcwxgn9OuAYiu+q3ZBkBjyM4zoGhI4zmLUgUHulCvXiNU5CzZsJImhyu6BIDF4fCJyso1q517Hc2oHKp4h4vKL0PGcunygyu6Byy+2yrjJCLhG+kQJVXYPiqzmuB1txcAztCG6vCjJZfAcYAm0A5dfjOq6XWA1AXbZ+BQ5WeYGlw9NXglev4QuFqNapsQYLAad2tGMdTs+UYIgMRRbzTAZdGhw+dDo8kMQo6c1i6efNAOPwydd4Lnm9YcZJnmtJx9wXhElFmSURC8zEc8MRR8Sk+tSx3NytoI4bvDBeP0iKu0eOQVZkD5cflEtMx6NLh8a3QzZFgO6WIxqmYqnRGvWxmVbDKgMZGYINbwjIUos6nICMcH6UNqkUcernfZoZeZnyjMrqWjnVXbZ4A5u53pd87eopeRlGHGsSYAvsPRIaefyAEb4DFEiA2u5GSakxxgMitTOo5UZqS6BxNp5tOpQBloitXNJYoGUeJHfY7GqWBkYCG3nXNB5oe08nlH3c50DAvRIN+rUdFgGHa/xZgGaBweA+BGQ9TyHwV2131qTXie3hcDn0WrWJ/Q8KQMYe441wi3Ia+QlBtWDzaDXZkVRvNcUOWJxVm469lU71P+3lfHnFuHzPVWql4iCIDHkWozIMOnh9Ak4FjQxEU/G/3puLQSu2dRhTPaqe2BiP3ndfSDmi4KO55Bh0mNC35YPRhBEPBI2up9//nk899xzKCoqwocffhjR3fxUUFdXB1EUUViobSCFhYX48ccfI57j9Xo10ehsNptaVvD2UERRRENDA2pra5PmuuD1euHxeFBbW5tUF4tkypmK+1bKHDTnM3iF8A/Qg7/qGxbgJpjJg0rCtnntjWhoMIJz2yEJDIpdp9cBfg8HR8NJ+AIpQ4I7Y3zg419bG945lNx2eAQP/JINfqaDUcep+UEdjfWQYkQT1/EcamvD3bbqamvR0AB8e6Am7N4v6KrHIa897BxANvi9HFBbq222Sl1KThskSQ6Qo3xDdBwHe8NJCAGjE2h2MVZckCPJqJRZcaQKPgZYAUACDh13QGLA5X0sOOZtChtd99gCZeZFqBe3DQ0NJlQcqYI7sCY4uEPjaqwHjDq4G5vlVGSV5Qx/XdXV1sEjeXCksgqWgNOm1+HCz45G+Pqk45i3SXO/zdeSO1mhcqp16baBiSwQgM0HQZRQlGGE01aPJiZ3pr2CBIcowRDo5MvPUPh9K2Ua/A5AAmrrZJdbHQCjxCA4G1HltUecqdBHqUvRbUNDA4dCgxdHGr2o9jahIMOodsiD5QSaDTzGZEMyks6rq6rR0MChproG7kg5a51ZqPHwavwCR9A+nov8HEluGzweDxhvA+P0sLnkNeddzHo0NZ5EuiTLKEoMDr+IpkAnn1PbZGT9+F02FJkNMAoCmABkcwDH+eFpqkedzxCxQx5PP7l6L446vPCKEgoyjGqQxPq6urA4C15BXrJhMeoivjfsjXVoaODU5ygUp60e1V49GJODNLlCAoFFbJMBnStlev0iahw+mHQ87A0nYRFlGRVdOwJl6/jIdal8e1z+Rph5PXwOINB/hpkBnqYGVLl4NShSMFGfy0BdWuGGh/PB3tAEzq1HlsWg3nfwcwk0xwQw6/mI921rrENDAx+zLuv8RvgDbRF6Hs3xJiPXZV1dreZZV936OcDMcfA5GlEdeA8zAI7AZXku+r3XVNegoYGL2M7dtgbUOCPXZbT6VPRj5B0oMnCocfg07TxSXQYPrMV6r8eqy0bRBLdfBBdoiw4l+CSQ0HMJIOF2Lr/Xw+tScMllRmrn9oZ6CE6d+t4NJdZ7o8jkh9svocbh07TzhpN1MEWIpeIVpKj3regn2+wBC7h4I+iwhpN1MGaa1PducK5tIUp/Q5EzTXDi8HG5MQZ/z8f1tkDnle/NLXdb1e9btOfSylxoaGhAqdkf5gquyJneRbumW3lG/aIUo/3I52YyEZXVTtTVyhMOsfowQOBbnqvVm3Lfn/9H7hNlMAnB6bw9tnr87LKpv4Pjs3iifHvU9qN3Qsc1T5R4/BIMYIDHDodX2xoNgUCATZKE2pzwZyuVfeHO0F8/02yflpTZ1CS/7+MNqHEswaRjPM8jLS0N48aNi3nhv//974kUlzROnDiBrl274quvvkJZWZm6/aGHHsLGjRuxffv2sHPmz5+Pxx9/vD3FJAiCIAiCIAiCIE5Djh49itLS8KxFCgnPdN90001JC8GeTPLy8qDT6VBdXa3ZXl1djaKi8IiGADBnzhzMmjVL/S1JEurr65Gbmxv3Hi+44ALs2LGj7YIHsNvt6NatG44ePQqr1Rr/hARJtpzJLq+zlEn66dhlpkI/neG+O0uZpJ+OW2ZnebedqWWSfjr2N5L0Q/rpqGV2BhlPN/0wxtDU1ISSknAv3GASNrqXLVuW6KHtitFoxPDhw7F27VpMnjwZgGxEr127FjNnzox4TqRoednZ2QldT6fTJfUBUbBarUktN9lypuK+O0uZAOmnI5cJJFc/neW+O0uZAOmnI5fZ0d9tZ3qZpJ+OWZ4C6adjlqdwJuqnM8iocDrpJysrK+4xrYpe3tGYNWsWbr75Zpx//vm48MIL8eKLL8LpdKrRzJPJjBkzkl5mKki2nKm4785SZiog/XRcOst9d5Yyk01nue/OUmay6Sz33VnKTDad5b47wzcyFXSGukxFmaSfjlteqspMBR393hNe093ReeWVV/DnP/8ZVVVVGDp0KBYuXIgRI0acarHiYrfbkZWVBZvNlpJRJKJtkH46NqSfjg3pp+NCuunYkH46NqSfjg3pp2NzpurntJjpBoCZM2dGdSfvyJhMJsybNy9p0fuI5EL66diQfjo2pJ+OC+mmY0P66diQfjo2pJ+OzZmqn9NmppsgCIIgCIIgCIIgOhrREyETBEEQBEEQBEEQBNEmyOgmCIIgCIIgCIIgiBRBRjdBEARBEARBEARBpAgyulPAM888gwsuuACZmZkoKCjA5MmTsX//fs0xd955J3r16oW0tDTk5+dj0qRJ+PHHHzXHHDlyBFdeeSUsFgsKCgrw4IMPQhCE9ryV05JE9KPAGMPEiRPBcRxWrFih2Uf6SQ2J6GfUqFHgOE7zd9ddd2mOIf0kn0TbzrZt2zBmzBikp6fDarXisssug9vtVvfX19dj+vTpsFqtyM7Oxm233QaHw9Get3JaEk8/hw8fDms3yt+nn36qHkdtJzUk0n6qqqpw4403oqioCOnp6TjvvPPwv//7v5pjqP2khkT0U1FRgSlTpiA/Px9WqxVTp05FdXW15hjST2pYtGgRBg8erOZ2Lisrw8qVK9X9Ho8HM2bMQG5uLjIyMnDttdeG6Ybebakjnn7efPNNjBo1ClarFRzHobGxMayM073tkNGdAjZu3IgZM2bgP//5D9asWQO/34/x48fD6XSqxwwfPhxLly7Fvn37sHr1ajDGMH78eIiiCAAQRRFXXnklfD4fvvrqK7z99ttYtmwZHnvssVN1W6cNiehH4cUXXwTHcWHbST+pI1H93H777aisrFT/nn/+eXUf6Sc1JKKbbdu24YorrsD48ePx9ddfY8eOHZg5cyZ4vvlzM336dHz//fdYs2YN/vGPf2DTpk244447TsUtnVbE00+3bt00baayshKPP/44MjIyMHHiRADUdlJJIu3npptuwv79+/H5559j7969uOaaazB16lTs3r1bPYbaT2qIpx+n04nx48eD4zisW7cOW7duhc/nw1VXXQVJktRySD+pobS0FM8++yx27dqFnTt3YsyYMZg0aRK+//57AMAf/vAHfPHFF/j000+xceNGnDhxAtdcc416Pr3bUks8/bhcLlxxxRX44x//GLWM077tMCLl1NTUMABs48aNUY/59ttvGQB28OBBxhhj//rXvxjP86yqqko9ZtGiRcxqtTKv15tymc8kouln9+7drGvXrqyyspIBYMuXL1f3kX7aj0j6GTlyJLvvvvuinkP6aR8i6WbEiBHs0UcfjXrODz/8wACwHTt2qNtWrlzJOI5jx48fT6m8ZxqJfHuGDh3Kbr31VvU3tZ32I5J+0tPT2TvvvKM5Licnhy1evJgxRu2nPQnVz+rVqxnP88xms6nHNDY2Mo7j2Jo1axhjpJ/2pkuXLuytt95ijY2NzGAwsE8//VTdt2/fPgaAbdu2jTFG77ZTgaKfYNavX88AsIaGBs32M6Ht0Ex3O2Cz2QAAOTk5Efc7nU4sXboUPXv2RLdu3QDIs0WDBg1CYWGhetyECRNgt9vVUSMiOUTSj8vlwvXXX49XX30VRUVFYeeQftqPaO3n/fffR15eHgYOHIg5c+bA5XKp+0g/7UOobmpqarB9+3YUFBTg4osvRmFhIUaOHIktW7ao52zbtg3Z2dk4//zz1W3jxo0Dz/PYvn17+97AaU68b8+uXbtQXl6O2267Td1Gbaf9iKSfiy++GB9//DHq6+shSRI++ugjeDwejBo1CgC1n/YkVD9erxccx2lyC5vNZvA8r77jSD/tgyiK+Oijj+B0OlFWVoZdu3bB7/dj3Lhx6jH9+vVD9+7dsW3bNgD0bmtPQvWTCGdC2yGjO8VIkoTf//73uOSSSzBw4EDNvtdeew0ZGRnIyMjAypUrsWbNGhiNRgDyuq7gFwMA9XdVVVX7CH8GEE0/f/jDH3DxxRdj0qRJEc8j/bQP0fRz/fXX47333sP69esxZ84cvPvuu7jhhhvU/aSf1BNJNz///DMAYP78+bj99tuxatUqnHfeeRg7diwOHDgAQK7/goICTVl6vR45OTmkmyQS69uj8Le//Q39+/fHxRdfrG6jttM+RNPPJ598Ar/fj9zcXJhMJtx5551Yvnw5evfuDYDaT3sRST8XXXQR0tPT8fDDD8PlcsHpdOKBBx6AKIqorKwEQPpJNXv37kVGRgZMJhPuuusuLF++HOeeey6qqqpgNBqRnZ2tOb6wsFCtd3q3pZ5o+kmEM6Ht6E+1AKc7M2bMwHfffaeZ6VGYPn06Lr/8clRWVmLBggWYOnUqtm7dCrPZfAokPTOJpJ/PP/8c69at06yhI04N0dpP8BqfQYMGobi4GGPHjkVFRQV69erV3mKekUTSjbKu8c4778Qtt9wCABg2bBjWrl2LJUuW4Jlnnjklsp6JxPr2AIDb7cYHH3yAuXPntrNkBBBdP3PnzkVjYyP+/e9/Iy8vDytWrMDUqVOxefNmDBo06BRJe+YRST/5+fn49NNPcffdd2PhwoXgeR7Tpk3Deeedp4lZQaSOvn37ory8HDabDZ999hluvvlmbNy48VSLRQSIpp9EDe/THTK6U8jMmTPVQAClpaVh+7OyspCVlYU+ffrgoosuQpcuXbB8+XJMmzYNRUVF+PrrrzXHK1EYI7k7Ey0nmn7WrVuHioqKsBHTa6+9Fpdeeik2bNhA+mkH4rWfYEaMGAEAOHjwIHr16kX6STHRdFNcXAwAYR/Y/v3748iRIwDk+q+pqdHsFwQB9fX1pJskkUjb+eyzz+ByuXDTTTdptlPbST3R9FNRUYFXXnkF3333HQYMGAAAGDJkCDZv3oxXX30Vr7/+OrWfdiBW+xk/fjwqKipQV1cHvV6P7OxsFBUV4eyzzwZA77dUYzQaVa+P4cOHY8eOHXjppZdw3XXXwefzobGxUdN3q66uVuud3m2pJ5p+3njjjbjnnglth4bmUgBjDDNnzsTy5cuxbt069OzZM6FzGGPwer0AgLKyMuzdu1fzAK5ZswZWq5VGjNpIPP3Mnj0be/bsQXl5ufoHAH/961+xdOlSAKSfVNKa9qPoSDH6SD+pIZ5uzjrrLJSUlISl2fnpp5/Qo0cPALJuGhsbsWvXLnX/unXrIEmSOnhCtI6WtJ2//e1vuPrqq5Gfn6/ZTm0ndcTTjxKXInTWVKfTqV4k1H5SR0vaT15eHrKzs7Fu3TrU1NTg6quvBkD6aW8kSYLX68Xw4cNhMBiwdu1add/+/ftx5MgRdU0xvdvaH0U/iXBGtJ1TFsLtNObuu+9mWVlZbMOGDayyslL9c7lcjDHGKioq2NNPP8127tzJfvnlF7Z161Z21VVXsZycHFZdXc0YY0wQBDZw4EA2fvx4Vl5ezlatWsXy8/PZnDlzTuWtnRbE008kEBK9nPSTOuLp5+DBg+yJJ55gO3fuZIcOHWL/93//x84++2x22WWXqWWQflJDIm3nr3/9K7NarezTTz9lBw4cYI8++igzm81qZgbGGLviiivYsGHD2Pbt29mWLVtYnz592LRp007FLZ1WJPpuO3DgAOM4jq1cuTKsDGo7qSOefnw+H+vduze79NJL2fbt29nBgwfZggULGMdx7J///KdaDrWf1JBI+1myZAnbtm0bO3jwIHv33XdZTk4OmzVrlqYc0k9qmD17Ntu4cSM7dOgQ27NnD5s9ezbjOI59+eWXjDHG7rrrLta9e3e2bt06tnPnTlZWVsbKysrU8+ndllri6aeyspLt3r2bLV68mAFgmzZtYrt372YnT55Uyzjd2w4Z3SkAQMS/pUuXMsYYO378OJs4cSIrKChgBoOBlZaWsuuvv579+OOPmnIOHz7MJk6cyNLS0lheXh67//77md/vPwV3dHoRTz/Rzgk2uhkj/aSKePo5cuQIu+yyy1hOTg4zmUysd+/e7MEHH9SkcWGM9JMKEm07zzzzDCstLWUWi4WVlZWxzZs3a/afPHmSTZs2jWVkZDCr1cpuueUW1tTU1I53cnqSqH7mzJnDunXrxkRRjFgOtZ3UkIh+fvrpJ3bNNdewgoICZrFY2ODBg8NSiFH7SQ2J6Ofhhx9mhYWFzGAwsD59+rAXXniBSZKkKYf0kxpuvfVW1qNHD2Y0Gll+fj4bO3asatAxxpjb7Wb33HMP69KlC7NYLGzKlCmssrJSUwa921JHPP3Mmzcvbvs63dsOxxhjyZ49JwiCIAiCIAiCIAiC1nQTBEEQBEEQBEEQRMogo5sgCIIgCIIgCIIgUgQZ3QRBEARBEARBEASRIsjoJgiCIAiCIAiCIIgUQUY3QRAEQRAEQRAEQaQIMroJgiAIgiAIgiAIIkWQ0U0QBEEQBEEQBEEQKYKMboIgCIIgCIIgCIJIEWR0EwRBEEQb2LBhAziOQ2NjY5vK+e1vf4vJkye36Jxly5YhOztb/T1//nwMHTq0TXK0lva89tq1a9G/f3+Iotiu8vzwww8oLS2F0+lMarkEQRDE6Q0Z3QRBEAQB4PXXX0dmZiYEQVC3ORwOGAwGjBo1SnOsYmhXVFTg4osvRmVlJbKystpZ4nAeeOABrF27NuXX4TgOK1asOCXXBoCHHnoIjz76KHQ6XbtcT+Hcc8/FRRddhL/85S/tel2CIAiic0NGN0EQBEEAGD16NBwOB3bu3Klu27x5M4qKirB9+3Z4PB51+/r169G9e3f06tULRqMRRUVF4DjuVIitISMjA7m5uVH3+3y+U3btZLFlyxZUVFTg2muvTfm1InHLLbdg0aJFmsEZgiAIgogFGd0EQRAEAaBv374oLi7Ghg0b1G0bNmzApEmT0LNnT/znP//RbB89erT6/2D3csXle/Xq1ejfvz8yMjJwxRVXoLKyUj1fFEXMmjUL2dnZyM3NxUMPPQTGWFwZly1bhu7du8NisWDKlCk4efKkZn+oS7Xisv7UU0+hpKQEffv2BQAcPXoUU6dORXZ2NnJycjBp0iQcPnxYU9aSJUswYMAAmEwmFBcXY+bMmQCAs846CwAwZcoUcByn/g69tiRJeOKJJ1BaWgqTyYShQ4di1apV6v7Dhw+D4zj8/e9/x+jRo2GxWDBkyBBs27YtZh189NFHuPzyy2E2mzXbn332WRQWFiIzMxO33XabZpBE4a233kL//v1hNpvRr18/vPbaa5r9X331FYYOHQqz2Yzzzz8fK1asAMdxKC8vV4+5/PLLUV9fj40bN8aUkyAIgiAUyOgmCIIgiACjR4/G+vXr1d/r16/HqFGjMHLkSHW72+3G9u3bVaM7Ei6XCwsWLMC7776LTZs24ciRI3jggQfU/S+88AKWLVuGJUuWYMuWLaivr8fy5ctjyrZ9+3bcdtttmDlzJsrLyzF69Gg8+eSTce9p7dq12L9/P9asWYN//OMf8Pv9mDBhAjIzM7F582Zs3bpVHRhQZsIXLVqEGTNm4I477sDevXvx+eefo3fv3gCAHTt2AACWLl2KyspK9XcoL730El544QUsWLAAe/bswYQJE3D11VfjwIEDmuMeeeQRPPDAAygvL8c555yDadOmxZxF3rx5M84//3zNtk8++QTz58/H008/jZ07d6K4uDjMoH7//ffx2GOP4amnnsK+ffvw9NNPY+7cuXj77bcBAHa7HVdddRUGDRqEb775Bn/605/w8MMPh13faDRi6NCh2Lx5c6xqJwiCIIhmGEEQBEEQjDHGFi9ezNLT05nf72d2u53p9XpWU1PDPvjgA3bZZZcxxhhbu3YtA8B++eUXxhhj69evZwBYQ0MDY4yxpUuXMgDs4MGDarmvvvoqKywsVH8XFxez559/Xv3t9/tZaWkpmzRpUlTZpk2bxn71q19ptl133XUsKytL/T1v3jw2ZMgQ9ffNN9/MCgsLmdfrVbe9++67rG/fvkySJHWb1+tlaWlpbPXq1YwxxkpKStgjjzwSVRYAbPny5ZptodcuKSlhTz31lOaYCy64gN1zzz2MMcYOHTrEALC33npL3f/9998zAGzfvn1Rr52VlcXeeecdzbaysjK1XIURI0Zo5OnVqxf74IMPNMf86U9/YmVlZYwxxhYtWsRyc3OZ2+1W9y9evJgBYLt379acN2XKFPbb3/42qowEQRAEEQzNdBMEQRBEgFGjRsHpdGLHjh3YvHkzzjnnHOTn52PkyJHquu4NGzbg7LPPRvfu3aOWY7FY0KtXL/V3cXExampqAAA2mw2VlZUYMWKEul+v14fN3oayb98+zTkAUFZWFveeBg0aBKPRqP7+9ttvcfDgQWRmZiIjIwMZGRnIycmBx+NBRUUFampqcOLECYwdOzZu2dGw2+04ceIELrnkEs32Sy65BPv27dNsGzx4sPr/4uJiAFDrKhJutzvMtTxe3TidTlRUVOC2225T7zkjIwNPPvkkKioqAAD79+/H4MGDNWVfeOGFEWVIS0uDy+WKKiNBEARBBKM/1QIQBEEQREehd+/eKC0txfr169HQ0ICRI0cCAEpKStCtWzd89dVXWL9+PcaMGROzHIPBoPnNcVxCa7ZTQXp6uua3w+HA8OHD8f7774cdm5+fD55v3/H44LpSgtFJkhT1+Ly8PDQ0NLToGg6HAwCwePHiMOO8NRHQ6+vrNYMqBEEQBBELmukmCIIgiCBGjx6NDRs2YMOGDZpUYZdddhlWrlyJr7/+OuZ67nhkZWWhuLgY27dvV7cJgoBdu3bFPK9///6acwBogrslynnnnYcDBw6goKAAvXv31vxlZWUhMzMTZ511Vsz0XwaDIWaObKvVipKSEmzdulWzfevWrTj33HNbLHMww4YNww8//KDZFq9uCgsLUVJSgp9//jnsnnv27AlADqS3d+9eeL1e9bxo69W/++47DBs2rE33QRAEQZw5kNFNEARBEEGMHj0aW7ZsQXl5uTrTDQAjR47EG2+8AZ/P1yajGwDuu+8+PPvss1ixYgV+/PFH3HPPPWr082jce++9WLVqFRYsWIADBw7glVde0UQDT5Tp06cjLy8PkyZNwubNm3Ho0CFs2LAB9957L44dOwZAjkT+wgsvYOHChThw4AC++eYbvPzyy2oZilFeVVUVddb5wQcfxHPPPYePP/4Y+/fvx+zZs1FeXo777ruvxTIHM2HCBGzZskWz7b777sOSJUuwdOlS/PTTT5g3bx6+//57zTGPP/44nnnmGSxcuBA//fQT9u7di6VLl6o5t6+//npIkoQ77rgD+/btw+rVq7FgwQIA0KSDO3z4MI4fP45x48a16T4IgiCIMwcyugmCIAgiiNGjR8PtdqN3794oLCxUt48cORJNTU1qarG2cP/99+PGG2/EzTffjLKyMmRmZmLKlCkxz7nooouwePFivPTSSxgyZAi+/PJLPProoy2+tsViwaZNm9C9e3dcc8016N+/v5piy2q1AgBuvvlmvPjii3jttdcwYMAA/PrXv9ZEHX/hhRewZs0adOvWLeqM77333otZs2bh/vvvx6BBg7Bq1Sp8/vnn6NOnT4tlDmb69On4/vvvsX//fnXbddddh7lz5+Khhx7C8OHD8csvv+Duu+/WnPc///M/eOutt7B06VIMGjQII0eOxLJly9SZbqvVii+++ALl5eUYOnQoHnnkETz22GMAoFnn/eGHH2L8+PHo0aNHm+6DIAiCOHPg2KlaZEYQBEEQBNEKHnzwQdjtdrzxxhspvc7777+PW265BTabDWlpafD5fOjTpw8++OCDsCBxBEEQBBENmukmCIIgCKJT8cgjj6BHjx4xA661hnfeeQdbtmzBoUOHsGLFCjz88MOYOnUq0tLSAABHjhzBH//4RzK4CYIgiBZBM90EQRAEQRAAnn/+ebz22muoqqpCcXExJk+ejKeeegoWi+VUi0YQBEF0YsjoJgiCIAiCIAiCIIgUQe7lBEEQBEEQBEEQBJEiyOgmCIIgCIIgCIIgiBRBRjdBEARBEARBEARBpAgyugmCIAiCIAiCIAgiRZDRTRAEQRAEQRAEQRApgoxugiAIgiAIgiAIgkgRZHQTBEEQBEEQBEEQRIogo5sgCIIgCIIgCIIgUgQZ3QRBEARBEARBEASRIv4/ZIC6NQ98PfQAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_erb = ero.df_result\n", + "\n", + "axarr = fsc.plot_energy_ratios(superimpose=True)\n", + "\n", + "ax = axarr[0]\n", + "ax.plot(df_erb['wd_bin'], df_erb['baseline'], color='k',label='POLARS result (baseline)',ls='--')\n", + "ax.plot(df_erb['wd_bin'], df_erb['wakesteering'], color='k',label='POLARS result (wake steering)',ls='-')\n", + "\n", + "ax.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare uncertainty bounds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig ,ax = plt.subplots()\n", + "\n", + "# Plot pandas results with uncertainty\n", + "df_pandas_base = fsc.df_list[0]['er_results']\n", + "ax.plot(df_pandas_base['wd_bin'], df_pandas_base['baseline_lb'],'-', color='k', label='PANDAS lower bound')\n", + "ax.plot(df_pandas_base['wd_bin'], df_pandas_base['baseline_ub'],'--', color='k', label='PANDAS upper bound')\n", + "\n", + "ax.plot(df_erb['wd_bin'], df_erb['baseline_lb'],':', color='r', label='POLARS lower bound')\n", + "ax.plot(df_erb['wd_bin'], df_erb['baseline_ub'],':', color='orange', label='POLARS upper bound')\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare the gain values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig ,ax = plt.subplots()\n", + "\n", + "# NOTE THAT PANDAS VERSION IS RATIO WHILE POLARS IS PERCENT CHANGE\n", + "\n", + "# Plot pandas results with uncertainty\n", + "df_pandas_base = fsc.df_list_gains[0]['er_results']\n", + "ax.plot(df_pandas_base['wd_bin'], df_pandas_base['baseline'],'-', color='k', label='PANDAS')\n", + "ax.fill_between(df_pandas_base['wd_bin'], df_pandas_base['baseline_lb'], df_pandas_base['baseline_ub'], color='k', alpha=0.2)\n", + "\n", + "ax.plot(df_erb['wd_bin'], df_erb['uplift'] * .01 + 1.0,'--', color='r', label='POLARS')\n", + "\n", + "ax.plot(df_erb['wd_bin'], df_erb['uplift_lb'] * .01 + 1.0,':', color='r', label='POLARS lower bound')\n", + "ax.plot(df_erb['wd_bin'], df_erb['uplift_ub'] * .01 + 1.0,':', color='orange', label='POLARS upper bound')\n", + "\n", + "ax.grid(True)\n", + "ax.axhline(1, color='k')\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test out simley approach to calculating energy ratio uplift in regions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (1, 3)
delta_energybase_test_energyuplift
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732234.6340549.6320e67.602095
" + ], + "text/plain": [ + "shape: (1, 3)\n", + "┌───────────────┬──────────────────┬──────────┐\n", + "│ delta_energy ┆ base_test_energy ┆ uplift │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ f64 ┆ f64 ┆ f64 │\n", + "╞═══════════════╪══════════════════╪══════════╡\n", + "│ 732234.634054 ┆ 9.6320e6 ┆ 7.602095 │\n", + "└───────────────┴──────────────────┴──────────┘" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "ero = erp.compute_uplift_in_region(df_energy,\n", + " ['baseline', 'wakesteering'],\n", + " test_turbines=[2],\n", + " use_predefined_ref=True,\n", + " use_predefined_wd=True,\n", + " use_predefined_ws=True)\n", + "\n", + "ero.df_result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (1, 9)
delta_energy_expdelta_energy_ubdelta_energy_lbbase_test_energy_expbase_test_energy_ubbase_test_energy_lbuplift_expuplift_ubuplift_lb
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" + ], + "text/plain": [ + "shape: (1, 9)\n", + "┌────────────┬────────────┬────────────┬────────────┬───┬────────────┬──────────┬─────────┬─────────┐\n", + "│ delta_ener ┆ delta_ener ┆ delta_ener ┆ base_test_ ┆ … ┆ base_test_ ┆ uplift_e ┆ uplift_ ┆ uplift_ │\n", + "│ gy_exp ┆ gy_ub ┆ gy_lb ┆ energy_exp ┆ ┆ energy_lb ┆ xp ┆ ub ┆ lb │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞════════════╪════════════╪════════════╪════════════╪═══╪════════════╪══════════╪═════════╪═════════╡\n", + "│ 732234.634 ┆ 931394.533 ┆ 581022.737 ┆ 9.6320e6 ┆ … ┆ 7.5244e6 ┆ 7.602095 ┆ 8.10439 ┆ 7.34586 │\n", + "│ 054 ┆ 294 ┆ 135 ┆ ┆ ┆ ┆ ┆ 3 ┆ │\n", + "└────────────┴────────────┴────────────┴────────────┴───┴────────────┴──────────┴─────────┴─────────┘" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "ero = erp.compute_uplift_in_region(df_energy,\n", + " ['baseline', 'wakesteering'],\n", + " test_turbines=[2],\n", + " use_predefined_ref=True,\n", + " use_predefined_wd=True,\n", + " use_predefined_ws=True,\n", + " N=20)\n", + "\n", + "ero.df_result" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env_scada", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/flasc/energy_ratio/energy_ratio_polars.py b/flasc/energy_ratio/energy_ratio_polars.py new file mode 100644 index 00000000..fc81e02a --- /dev/null +++ b/flasc/energy_ratio/energy_ratio_polars.py @@ -0,0 +1,1176 @@ +# This is a work in progress as we try to synthesize ideas from the +# table based methods and energy ratios back into one thing, +# some ideas we're incorporating: + +# Conversion from polars to pandas +# Constructing tables (but now including tables of ratios) +# Keeping track of frequencies is matching sized tables + +import warnings + +import numpy as np +import pandas as pd +import polars as pl +import seaborn as sns +import matplotlib.pyplot as plt + + +# def get_mid_bins(bin_edges): +# """_summary_ + +# Args: +# bin_edges (NDArray): a set of bin edges +# """ + +# print(bin_edges[:-1] + np.diff(bin_edges)/2.0) + +# def convert_to_polars(df_): +# """_summary_ + +# Args: +# df_ (Pandas DataFrame): a pandas dataframe + +# Returns: +# Polars DataFrame: a polars dataframe +# """ +# return pl.from_pandas(df_) + +def cut(col_name, edges): + """ + Bins the values in the specified column according to the given edges. + + Parameters: + col_name (str): The name of the column to bin. + edges (array-like): The edges of the bins. Values will be placed into the bin + whose left edge is the largest edge less than or equal to + the value, and whose right edge is the smallest edge + greater than the value. + + Returns: + expression: An expression object that can be used to bin the column. + """ + c = pl.col(col_name) + labels = edges[:-1] + np.diff(edges)/2.0 + expr = pl.when(c < edges[0]).then(None) + for edge, label in zip(edges[1:], labels): + expr = expr.when(c < edge).then(label) + expr = expr.otherwise(None) + + return expr + + +def bin_column(df_, col_name, bin_col_name, edges): + """ + Bins the values in the specified column of a Polars DataFrame according to the given edges. + + Parameters: + df_ (pl.DataFrame): The Polars DataFrame containing the column to bin. + col_name (str): The name of the column to bin. + bin_col_name (str): The name to give the new column containing the bin labels. + edges (array-like): The edges of the bins. Values will be placed into the bin + whose left edge is the largest edge less than or equal to + the value, and whose right edge is the smallest edge + greater than the value. + + Returns: + pl.DataFrame: A new Polars DataFrame with an additional column containing the bin labels. + """ + return df_.with_columns( + cut( + col_name=col_name, + edges = edges + ).alias(bin_col_name) + ) + +def add_ws_bin(df_, ws_cols, ws_step=1.0, ws_min=-0.5, ws_max=50.0): + """ + Add the ws_bin column to a dataframe, given which columns to average over + and the step sizes to use + + Parameters: + df_ (pl.DataFrame): The Polars DataFrame containing the column to bin. + ws_cols (str): The name of the columns to average across. + ws_step (float): Step size for binning + ws_min (float): Minimum wind speed + ws_max (float): Maximum wind speed + + Returns: + pl.DataFrame: A new Polars DataFrame with an additional ws_bin column + """ + + edges = np.arange(ws_min, ws_max+ws_step,ws_step) + + df_with_mean_ws = ( + # df_.select(pl.exclude('ws_bin')) # In case ws_bin already exists + df_.with_columns( + # df_.select(ws_cols).mean(axis=1).alias('ws_bin') + ws_bin = pl.concat_list(ws_cols).list.mean() # Initially ws_bin is just the mean + ) + .filter( + pl.all(pl.col(ws_cols).is_not_null()) # Select for all bin cols present + ) + + .filter( + (pl.col('ws_bin') > ws_min) & # Filter the mean wind speed + (pl.col('ws_bin') < ws_max) & + (pl.col('ws_bin').is_not_null()) + ) + ) + + return bin_column(df_with_mean_ws, 'ws_bin', 'ws_bin', edges) + +def add_wd_bin(df_, wd_cols, wd_step=2.0, wd_min=0.0, wd_max=360.0): + """ + Add the wd_bin column to a dataframe, given which columns to average over + and the step sizes to use + + Parameters: + df_ (pl.DataFrame): The Polars DataFrame containing the column to bin. + wd_cols (str): The name of the columns to average across. + wd_step (float): Step size for binning + wd_min (float): Minimum wind direction + wd_max (float): Maximum wind direction + + Returns: + pl.DataFrame: A new Polars DataFrame with an additional ws_bin column + """ + + edges = np.arange(wd_min, wd_max + wd_step, wd_step) + + # Gather up intermediate column names and final column names + wd_cols_cos = [c + '_cos' for c in wd_cols] + wd_cols_sin = [c + '_sin' for c in wd_cols] + cols_to_return = df_.columns + if 'wd_bin' not in cols_to_return: + cols_to_return = cols_to_return + ['wd_bin'] + + + df_with_mean_wd = ( + # df_.select(pl.exclude('wd_bin')) # In case wd_bin already exists + df_.filter( + pl.all(pl.col(wd_cols).is_not_null()) # Select for all bin cols present + ) + # Add the cosine columns + .with_columns( + [ + pl.col(wd_cols).mul(np.pi/180).cos().suffix('_cos'), + pl.col(wd_cols).mul(np.pi/180).sin().suffix('_sin'), + ] + ) + ) + df_with_mean_wd = ( + df_with_mean_wd + .with_columns( + [ + # df_with_mean_wd.select(wd_cols_cos).mean(axis=1).alias('cos_mean'), + # df_with_mean_wd.select(wd_cols_sin).mean(axis=1).alias('sin_mean'), + pl.concat_list(wd_cols_cos).list.mean().alias('cos_mean'), + pl.concat_list(wd_cols_sin).list.mean().alias('sin_mean'), + ] + ) + .with_columns( + wd_bin = np.mod(pl.reduce(np.arctan2, [pl.col('sin_mean'), pl.col('cos_mean')]) + .mul(180/np.pi), 360.0) + ) + .filter( + (pl.col('wd_bin') > wd_min) & # Filter the mean wind speed + (pl.col('wd_bin') < wd_max) & + (pl.col('wd_bin').is_not_null()) + ) + .select(cols_to_return) # Select for just the columns we want to return + ) + + return bin_column(df_with_mean_wd, 'wd_bin', 'wd_bin', edges) + + +def add_power_test(df_, test_cols): + + return df_.with_columns( + pow_test = pl.concat_list(test_cols).list.mean() + #df_.select(test_cols).mean(axis=1).alias('pow_test') + ) + + +def add_power_ref(df_, ref_cols): + + return df_.with_columns( + pow_ref = pl.concat_list(ref_cols).list.mean() + # df_.select(ref_cols).mean(axis=1).alias('pow_ref') + ) + +def generate_block_list(N, num_blocks=10): + """Generate an np.array of length N where each element is an integer between 0 and num_blocks-1 + with each value repeating N/num_blocks times. + + Args: + N (int): Length of the array to generate + num_blocks (int): Number of blocks to generate + + """ + + # Test than N and num_blocks are integers greater than 0 + if not isinstance(N, int) or not isinstance(num_blocks, int): + raise ValueError('N and num_blocks must be integers') + if N <= 0 or num_blocks <= 0: + raise ValueError('N and num_blocks must be greater than 0') + + # Num blocks must be less than or equal to N + if num_blocks > N: + raise ValueError('num_blocks must be less than or equal to N') + + + block_list = np.zeros(N) + for i in range(num_blocks): + block_list[i*N//num_blocks:(i+1)*N//num_blocks] = i + return block_list.astype(int) + +def get_energy_table( + df_list_in, + df_names=None, + num_blocks=10,): + """ + Given a list of PANDAS dataframes, return a single + POLARS dataframe with a column + indicating which dataframe the row came from as well as a block + list to use in bootstrapping. + + Parameters: + df_list_in (list): A list of PANDAS dataframes to combine. + df_names (list): A list of names to give to the dataframes. If None, + the dataframes will be named df_0, df_1, etc. + n_blocks (int): The number of blocks to add to the block column for later bootstrapping. + + Returns: + pl.DataFrame: A new Polars DataFrame with an additional column containing the df_names + """ + + # Convert to polars + df_list = [pl.from_pandas(df) for df in df_list_in] + + if df_names is None: + df_names = ['df_'+str(i) for i in range(len(df_list))] + + # Add a name column to each dataframe + for i in range(len(df_list)): + df_list[i] = df_list[i].with_columns([ + pl.lit(df_names[i]).alias('df_name') + ]) + + # Add a block column to each dataframe + for i in range(len(df_list)): + df_list[i] = df_list[i].with_columns([ + pl.Series(generate_block_list(df_list[i].shape[0], num_blocks=num_blocks)).alias('block') + ]) + + return pl.concat(df_list) + +def resample_energy_table(df_e_, i): + """Use the block column of an energy table to resample the data. + + Args: + df_e_ (pl.DataFrame): An energy table with a block column + + Returns: + pl.DataFrame: A new energy table with (approximately) + the same number of rows as the original + """ + + if i == 0: #code to return as is + return df_e_ + + else: + + num_blocks = df_e_['block'].max() + 1 + + # Generate a random np.array, num_blocks long, where each element is + # an integer between 0 and num_blocks-1 + block_list = np.random.randint(0, num_blocks, num_blocks) + + return pl.DataFrame( + { + 'block':block_list + } + ).join(df_e_, how='inner', on='block') + + +# Internal version, returns a polars dataframe +def _compute_energy_ratio_single(df_, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step = 2.0, + wd_min = 0.0, + wd_max = 360.0, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, + bin_cols_in = ['wd_bin','ws_bin'], + ): + + """ + Compute the energy ratio between two sets of turbines. + + Args: + df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. + df_names (list): A list of names to give to the dataframes. + ref_cols (list[str]): A list of columns to use as the reference turbines + test_cols (list[str]): A list of columns to use as the test turbines + wd_cols (list[str]): A list of columns to derive the wind directions from + ws_cols (list[str]): A list of columns to derive the wind speeds from + wd_step (float): The width of the wind direction bins. + wd_min (float): The minimum wind direction to use. + wd_max (float): The maximum wind direction to use. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. + bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. + + Returns: + pl.DataFrame: A dataframe containing the energy ratio for each wind direction bin + """ + + # Identify the number of dataframes + num_df = len(df_names) + + # Filter df_ that all the columns are not null + df_ = df_.filter(pl.all(pl.col(ref_cols + test_cols + ws_cols + wd_cols).is_not_null())) + + # Assign the wd/ws bins + df_ = add_ws_bin(df_, ws_cols, ws_step, ws_min, ws_max) + df_ = add_wd_bin(df_, wd_cols, wd_step, wd_min, wd_max) + + # Assign the reference and test power columns + df_ = add_power_ref(df_, ref_cols) + df_ = add_power_test(df_, test_cols) + + bin_cols_without_df_name = [c for c in bin_cols_in if c != 'df_name'] + bin_cols_with_df_name = bin_cols_without_df_name + ['df_name'] + + df_ = (df_ + .filter(pl.all(pl.col(bin_cols_with_df_name).is_not_null())) # Select for all bin cols present + .groupby(bin_cols_with_df_name, maintain_order=True) + .agg([pl.mean("pow_ref"), pl.mean("pow_test"),pl.count()]) + .with_columns( + [ + pl.col('count').min().over(bin_cols_without_df_name).alias('count_min')#, # Find the min across df_name + ] + ) + .with_columns( + [ + pl.col('pow_ref').mul(pl.col('count_min')).alias('ref_energy'), # Compute the reference energy + pl.col('pow_test').mul(pl.col('count_min')).alias('test_energy'), # Compute the test energy + ] + ) + .groupby(['wd_bin','df_name'], maintain_order=True) + .agg([pl.sum("ref_energy"), pl.sum("test_energy"),pl.sum("count")]) + .with_columns( + energy_ratio = pl.col('test_energy') / pl.col('ref_energy') + ) + .pivot(values=['energy_ratio','count'], columns='df_name', index='wd_bin',aggregate_function='first') + .rename({f'energy_ratio_df_name_{n}' : n for n in df_names}) + .rename({f'count_df_name_{n}' : f'count_{n}' for n in df_names}) + .sort('wd_bin') + ) + + # In the case of two turbines, compute an uplift column + if num_df == 2: + df_ = df_.with_columns( + uplift = 100 * (pl.col(df_names[1]) - pl.col(df_names[0])) / pl.col(df_names[0]) + ) + + # Enforce a column order + df_ = df_.select(['wd_bin'] + df_names + ['uplift'] + [f'count_{n}' for n in df_names]) + + else: + # Enforce a column order + df_ = df_.select(['wd_bin'] + df_names + [f'count_{n}' for n in df_names]) + + + return(df_) + +# Bootstrap function wraps the _compute_energy_ratio function +def _compute_energy_ratio_bootstrap(df_, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step = 2.0, + wd_min = 0.0, + wd_max = 360.0, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, + bin_cols_in = ['wd_bin','ws_bin'], + N = 1, + ): + + """ + Compute the energy ratio between two sets of turbines with bootstrapping + + Args: + df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. + df_names (list): A list of names to give to the dataframes. + ref_cols (list[str]): A list of columns to use as the reference turbines + test_cols (list[str]): A list of columns to use as the test turbines + wd_cols (list[str]): A list of columns to derive the wind directions from + ws_cols (list[str]): A list of columns to derive the wind speeds from + wd_step (float): The width of the wind direction bins. + wd_min (float): The minimum wind direction to use. + wd_max (float): The maximum wind direction to use. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. + bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. + N (int): The number of bootstrap samples to use. + + Returns: + pl.DataFrame: A dataframe containing the energy ratio between the two sets of turbines. + + """ + + # Otherwise run the function N times and concatenate the results to compute statistics + df_concat = pl.concat([_compute_energy_ratio_single(resample_energy_table(df_, i), + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in, + ) for i in range(N)]) + + if 'uplift' in df_concat.columns: + df_names_with_uplift = df_names + ['uplift'] + else: + df_names_with_uplift = df_names + + return (df_concat + .groupby(['wd_bin'], maintain_order=True) + .agg([pl.first(n) for n in df_names_with_uplift] + + [pl.quantile(n, 0.95).alias(n + "_ub") for n in df_names_with_uplift] + + [pl.quantile(n, 0.05).alias(n + "_lb") for n in df_names_with_uplift] + + [pl.first(f'count_{n}') for n in df_names] + ) + .sort('wd_bin') + ) + + +def compute_energy_ratio(df_, + df_names, + ref_turbines=None, + test_turbines= None, + wd_turbines=None, + ws_turbines=None, + use_predefined_ref = False, + use_predefined_wd = False, + use_predefined_ws = False, + wd_step = 2.0, + wd_min = 0.0, + wd_max = 360.0, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, + bin_cols_in = ['wd_bin','ws_bin'], + N = 1, + ): + + """ + Compute the energy ratio between two sets of turbines with bootstrapping + + Args: + df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. + df_names (list): A list of names to give to the dataframes. + ref_turbines (list[int]): A list of turbine numbers to use as the reference. + test_turbines (list[int]): A list of turbine numbers to use as the test. + ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds + wd_turbines (list[int]): A list of turbine numbers to use for the wind directions + use_predefined_ref (bool): If True, use the pow_ref column of df_ as the reference power. + use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. + use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. + wd_step (float): The width of the wind direction bins. + wd_min (float): The minimum wind direction to use. + wd_max (float): The maximum wind direction to use. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. + bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. + N (int): The number of bootstrap samples to use. + + Returns: + pl.DataFrame: A dataframe containing the energy ratio between the two sets of turbines. + + """ + + # Check that the inputs are valid + # If use_predefined_ref is True, df_ must have a column named 'pow_ref' + if use_predefined_ref: + if 'pow_ref' not in df_.columns: + raise ValueError('df_ must have a column named pow_ref when use_predefined_ref is True') + # If ref_turbines supplied, warn user that it will be ignored + if ref_turbines is not None: + warnings.warn('ref_turbines will be ignored when use_predefined_ref is True') + else: + # ref_turbine must be supplied + if ref_turbines is None: + raise ValueError('ref_turbines must be supplied when use_predefined_ref is False') + + # If use_predefined_ws is True, df_ must have a column named 'ws' + if use_predefined_ws: + if 'ws' not in df_.columns: + raise ValueError('df_ must have a column named ws when use_predefined_ws is True') + # If ws_turbines supplied, warn user that it will be ignored + if ws_turbines is not None: + warnings.warn('ws_turbines will be ignored when use_predefined_ws is True') + else: + # ws_turbine must be supplied + if ws_turbines is None: + raise ValueError('ws_turbines must be supplied when use_predefined_ws is False') + + # If use_predefined_wd is True, df_ must have a column named 'wd' + if use_predefined_wd: + if 'wd' not in df_.columns: + raise ValueError('df_ must have a column named wd when use_predefined_wd is True') + # If wd_turbines supplied, warn user that it will be ignored + if wd_turbines is not None: + warnings.warn('wd_turbines will be ignored when use_predefined_wd is True') + else: + # wd_turbine must be supplied + if wd_turbines is None: + raise ValueError('wd_turbines must be supplied when use_predefined_wd is False') + + + # Confirm that test_turbines is a list of ints or a numpy array of ints + if not isinstance(test_turbines, list) and not isinstance(test_turbines, np.ndarray): + raise ValueError('test_turbines must be a list or numpy array of ints') + + # Confirm that test_turbines is not empty + if len(test_turbines) == 0: + raise ValueError('test_turbines cannot be empty') + + # Set up the column names for the reference and test power + if not use_predefined_ref: + ref_cols = [f'pow_{i:03d}' for i in ref_turbines] + else: + ref_cols = ['pow_ref'] + + if not use_predefined_ws: + ws_cols = [f'ws_{i:03d}' for i in ws_turbines] + else: + ws_cols = ['ws'] + + if not use_predefined_wd: + wd_cols = [f'wd_{i:03d}' for i in wd_turbines] + else: + wd_cols = ['wd'] + + # Convert the numbered arrays to appropriate column names + test_cols = [f'pow_{i:03d}' for i in test_turbines] + + # If N=1, don't use bootstrapping + if N == 1: + # Compute the energy ratio + df_res = _compute_energy_ratio_single(df_, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in) + else: + df_res = _compute_energy_ratio_bootstrap(df_, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in, + N) + + # Return the results as an EnergyRatioResult object + return EnergyRatioResult(df_res, + df_names, + df_, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in, + N) + + + + + +# Use method of Eric Simley's slide 2 +def _compute_uplift_in_region_single(df_, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step = 2.0, + wd_min = 0.0, + wd_max = 360.0, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, + bin_cols_in = ['wd_bin','ws_bin'] + ): + + """ + Compute the energy uplift between two dataframes using method of Eric Simley's slide 2 + Args: + df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. + df_names (list): A list of names to give to the dataframes. + ref_cols (list[str]): A list of columns to use as the reference turbines + test_cols (list[str]): A list of columns to use as the test turbines + wd_cols (list[str]): A list of columns to derive the wind directions from + ws_cols (list[str]): A list of columns to derive the wind speeds from + wd_step (float): The width of the wind direction bins. + wd_min (float): The minimum wind direction to use. + wd_max (float): The maximum wind direction to use. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. + bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. + + Returns: + pl.DataFrame: A dataframe containing the energy uplift + """ + + # Filter df_ that all the columns are not null + df_ = df_.filter(pl.all(pl.col(ref_cols + test_cols + ws_cols + wd_cols).is_not_null())) + + # Assign the wd/ws bins + df_ = add_ws_bin(df_, ws_cols, ws_step, ws_min, ws_max) + df_ = add_wd_bin(df_, wd_cols, wd_step, wd_min, wd_max) + + # Assign the reference and test power columns + df_ = add_power_ref(df_, ref_cols) + df_ = add_power_test(df_, test_cols) + + bin_cols_without_df_name = [c for c in bin_cols_in if c != 'df_name'] + bin_cols_with_df_name = bin_cols_without_df_name + ['df_name'] + + df_ = (df_.with_columns( + power_ratio = pl.col('pow_test') / pl.col('pow_ref')) + .filter(pl.all(pl.col(bin_cols_with_df_name).is_not_null())) # Select for all bin cols present + .groupby(bin_cols_with_df_name, maintain_order=True) + .agg([pl.mean("pow_ref"), pl.mean("power_ratio"),pl.count()]) + .with_columns( + [ + pl.col('count').min().over(bin_cols_without_df_name).alias('count_min'), # Find the min across df_name + pl.col('pow_ref').mul(pl.col('power_ratio')).alias('pow_test'), # Compute the test power + ] + ) + + .pivot(values=['power_ratio','pow_test','pow_ref','count_min'], columns='df_name', index=['wd_bin','ws_bin'],aggregate_function='first') + .drop_nulls() + .with_columns( + f_norm = pl.col(f'count_min_df_name_{df_names[0]}') / pl.col(f'count_min_df_name_{df_names[0]}').sum() + ) + .with_columns( + delta_power_ratio = pl.col(f'power_ratio_df_name_{df_names[1]}') - pl.col(f'power_ratio_df_name_{df_names[0]}'), + pow_ref_both_cases = pl.concat_list([f'pow_ref_df_name_{n}' for n in df_names]).list.mean() + ) + .with_columns( + delta_energy = pl.col('delta_power_ratio') * pl.col('f_norm') * pl.col('pow_ref_both_cases'), # pl.col(f'pow_ref_df_name_{df_names[0]}'), + base_test_energy = pl.col(f'pow_test_df_name_{df_names[0]}') * pl.col('f_norm') + ) + + ) + + return pl.DataFrame({'delta_energy':8760 * df_['delta_energy'].sum(), + 'base_test_energy':8760 * df_['base_test_energy'].sum(), + 'uplift':100 * df_['delta_energy'].sum() / df_['base_test_energy'].sum()}) + + +def _compute_uplift_in_region_bootstrap(df_, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step = 2.0, + wd_min = 0.0, + wd_max = 360.0, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, + bin_cols_in = ['wd_bin','ws_bin'], + N = 20, + ): + + """ + Compute the uplift in a region using bootstrap resampling + + Args: + df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. + df_names (list): A list of names to give to the dataframes. + ref_cols (list[str]): A list of columns to use as the reference turbines + test_cols (list[str]): A list of columns to use as the test turbines + wd_cols (list[str]): A list of columns to derive the wind directions from + ws_cols (list[str]): A list of columns to derive the wind speeds from + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. + wd_step (float): The width of the wind direction bins. + wd_min (float): The minimum wind direction to use. + wd_max (float): The maximum wind direction to use. + bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. + N (int): The number of bootstrap samples to use. + + Returns: + pl.DataFrame: A dataframe containing the energy uplift + """ + + df_concat = pl.concat([_compute_uplift_in_region_single(resample_energy_table(df_, i), + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in, + ) for i in range(N)]) + + return pl.DataFrame({ + 'delta_energy_exp':df_concat['delta_energy'][0], + 'delta_energy_ub':df_concat['delta_energy'].quantile(0.95), + 'delta_energy_lb':df_concat['delta_energy'].quantile(0.05), + 'base_test_energy_exp':df_concat['base_test_energy'][0], + 'base_test_energy_ub':df_concat['base_test_energy'].quantile(0.95), + 'base_test_energy_lb':df_concat['base_test_energy'].quantile(0.05), + 'uplift_exp':df_concat['uplift'][0], + 'uplift_ub':df_concat['uplift'].quantile(0.95), + 'uplift_lb':df_concat['uplift'].quantile(0.05), + }) + + +def compute_uplift_in_region(df_, + df_names, + ref_turbines=None, + test_turbines= None, + wd_turbines=None, + ws_turbines=None, + use_predefined_ref = False, + use_predefined_wd = False, + use_predefined_ws = False, + wd_step = 2.0, + wd_min = 0.0, + wd_max = 360.0, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, + bin_cols_in = ['wd_bin','ws_bin'], + N = 1, + ): + + """ + Compute the energy ratio between two sets of turbines with bootstrapping + + Args: + df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. + df_names (list): A list of names to give to the dataframes. + ref_turbines (list[int]): A list of turbine numbers to use as the reference. + test_turbines (list[int]): A list of turbine numbers to use as the test. + ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds + wd_turbines (list[int]): A list of turbine numbers to use for the wind directions + use_predefined_ref (bool): If True, use the pow_ref column of df_ as the reference power. + use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. + use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. + wd_step (float): The width of the wind direction bins. + wd_min (float): The minimum wind direction to use. + wd_max (float): The maximum wind direction to use. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. + bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. + N (int): The number of bootstrap samples to use. + + Returns: + pl.DataFrame: A dataframe containing the energy ratio between the two sets of turbines. + + """ + + # Check if inputs are valid + # If use_predefined_ref is True, df_ must have a column named 'pow_ref' + if use_predefined_ref: + if 'pow_ref' not in df_.columns: + raise ValueError('df_ must have a column named pow_ref when use_predefined_ref is True') + # If ref_turbines supplied, warn user that it will be ignored + if ref_turbines is not None: + warnings.warn('ref_turbines will be ignored when use_predefined_ref is True') + else: + # ref_turbine must be supplied + if ref_turbines is None: + raise ValueError('ref_turbines must be supplied when use_predefined_ref is False') + + # If use_predefined_ws is True, df_ must have a column named 'ws' + if use_predefined_ws: + if 'ws' not in df_.columns: + raise ValueError('df_ must have a column named ws when use_predefined_ws is True') + # If ws_turbines supplied, warn user that it will be ignored + if ws_turbines is not None: + warnings.warn('ws_turbines will be ignored when use_predefined_ws is True') + else: + # ws_turbine must be supplied + if ws_turbines is None: + raise ValueError('ws_turbines must be supplied when use_predefined_ws is False') + + # If use_predefined_wd is True, df_ must have a column named 'wd' + if use_predefined_wd: + if 'wd' not in df_.columns: + raise ValueError('df_ must have a column named wd when use_predefined_wd is True') + # If wd_turbines supplied, warn user that it will be ignored + if wd_turbines is not None: + warnings.warn('wd_turbines will be ignored when use_predefined_wd is True') + else: + # wd_turbine must be supplied + if wd_turbines is None: + raise ValueError('wd_turbines must be supplied when use_predefined_wd is False') + + # Confirm that test_turbines is a list of ints or a numpy array of ints + if not isinstance(test_turbines, list) and not isinstance(test_turbines, np.ndarray): + raise ValueError('test_turbines must be a list or numpy array of ints') + + # Confirm that test_turbines is not empty + if len(test_turbines) == 0: + raise ValueError('test_turbines cannot be empty') + + num_df = len(df_names) + + # Confirm num_df == 2 + if num_df != 2: + raise ValueError('Number of dataframes must be 2') + + # Set up the column names for the reference and test power + if not use_predefined_ref: + ref_cols = [f'pow_{i:03d}' for i in ref_turbines] + else: + ref_cols = ['pow_ref'] + + if not use_predefined_ws: + ws_cols = [f'ws_{i:03d}' for i in ws_turbines] + else: + ws_cols = ['ws'] + + if not use_predefined_wd: + wd_cols = [f'wd_{i:03d}' for i in wd_turbines] + else: + wd_cols = ['wd'] + + # Convert the numbered arrays to appropriate column names + test_cols = [f'pow_{i:03d}' for i in test_turbines] + + # If N=1, don't use bootstrapping + if N == 1: + # Compute the energy ratio + df_res = _compute_uplift_in_region_single(df_, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in) + else: + df_res = _compute_uplift_in_region_bootstrap(df_, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in, + N) + + # Return the results as an EnergyRatioResult object + return EnergyRatioResult(df_res, + df_names, + df_, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in, + N) + + + +class EnergyRatioResult: + """ This class is used to store the results of the energy ratio calculations + and provide convenient methods for plotting and saving the results. + """ + def __init__(self, + df_result, + df_names, + energy_table, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in, + N + ): + + self.df_result = df_result + self.df_names = df_names + self.energy_table = energy_table + self.num_df = len(df_names) + self.ref_cols = ref_cols + self.test_cols = test_cols + self.wd_cols = wd_cols + self.ws_cols = ws_cols + self.wd_step = wd_step + self.wd_min = wd_min + self.wd_max = wd_max + self.ws_step = ws_step + self.ws_min = ws_min + self.ws_max = ws_max + self.bin_cols_in = bin_cols_in + self.N = N + + # self.df_freq = self._compute_df_freq() + + def _compute_df_freq(self): + """ Compute the of ws/wd as previously computed but not presently + computed with the energy calculation. """ + + # Temporary copy of energy table + df_ = self.energy_table.clone() + + # Filter df_ that all the columns are not null + df_ = df_.filter(pl.all(pl.col(self.ref_cols + self.test_cols + self.ws_cols + self.wd_cols).is_not_null())) + + # Assign the wd/ws bins + df_ = add_ws_bin(df_, self.ws_cols, self.ws_step, self.ws_min, self.ws_max) + df_ = add_wd_bin(df_, self.wd_cols, self.wd_step, self.wd_min, self.wd_max) + + # Get the bin count by wd, ws and df_name + df_group = df_.groupby(['wd_bin','ws_bin','df_name']).count() + + return df_.to_pandas(), df_group.to_pandas() + + def plot(self, + df_names_subset = None, + labels = None, + color_dict = None, + axarr = None, + polar_plot=False, + show_wind_speed_distrubution=True, + ): + + # Only allow showing the wind speed distribution if polar_plot is False + if polar_plot and show_wind_speed_distrubution: + raise ValueError('show_wind_speed_distrubution cannot be True if polar_plot is True') + + # If df_names_subset is None, plot all the dataframes + if df_names_subset is None: + df_names_subset = self.df_names + + # If df_names_subset is not a list, convert it to a list + if not isinstance(df_names_subset, list): + df_names_subset = [df_names_subset] + + # Total number of energy ratios to plot + N = len(df_names_subset) + + # If labels is None, use the dataframe names + if labels is None: + labels = df_names_subset + + # If labels is not a list, convert it to a list + if not isinstance(labels, list): + labels = [labels] + + # Confirm that the length of labels is the same as the length of df_names_subset + if len(labels) != N: + raise ValueError('Length of labels must be the same as the length of df_names_subset') + + # Generate the default colors using the seaborn color palette + default_colors = sns.color_palette('colorblind', N) + + # If color_dict is None, use the default colors + if color_dict is None: + color_dict = {labels[i]: default_colors[i] for i in range(N)} + + # If color_dict is not a dictionary, raise an error + if not isinstance(color_dict, dict): + raise ValueError('color_dict must be a dictionary') + + # Make sure the keys of color_dict are in df_names_subset + if not all([label in df_names_subset for label in color_dict.keys()]): + raise ValueError('color_dict keys must be in df_names_subset') + + if axarr is None: + if polar_plot: + _, axarr = plt.subplots(nrows=1, ncols=2, figsize=(10, 5), subplot_kw={'projection': 'polar'}) + else: + if show_wind_speed_distrubution: + num_rows = 3 # Add rows to show wind speed distribution + else: + num_rows = 2 + _, axarr = plt.subplots(nrows=num_rows, ncols=1, sharex=True, figsize=(10, 5)) + + # Set the bar width using self.wd_step + bar_width = (0.7 / N) * self.wd_step + if polar_plot: + bar_width = bar_width * np.pi / 180.0 + + # For plotting, get a pandas dataframe + df = self.df_result.to_pandas() + + # Get x-axis values + x = np.array(df["wd_bin"], dtype=float) + + # Add NaNs to avoid connecting plots over gaps + dwd = np.min(x[1::] - x[0:-1]) + jumps = np.where(np.diff(x) > dwd * 1.50)[0] + if len(jumps) > 0: + df = pd.concat( + [ + df, + pd.DataFrame( + { + "wd_bin": x[jumps] + dwd / 2.0, + "N_bin": [0] * len(jumps), + } + ) + ], + axis=0, + ignore_index=False, + ) + df = df.iloc[np.argsort(df["wd_bin"])].reset_index(drop=True) + x = np.array(df["wd_bin"], dtype=float) + + # Plot horizontal black line at 1. + xlims = np.linspace(np.min(x) - 4.0, np.max(x) + 4.0, 1000) + + if polar_plot: + x = (90.0 - x) * np.pi / 180.0 # Convert to radians + xlims = (90.0 - xlims) * np.pi / 180.0 # Convert to radians + + # Plot the horizontal line at 1 + axarr[0].plot(xlims, np.ones_like(xlims), color="black") + + # Plot the energy ratios + for df_name, label in zip(df_names_subset, labels): + + axarr[0].plot(x, df[df_name], "-o", markersize=3.0, label=label, color=color_dict[label]) + + # If data includes upper and lower bounds plot them + if df_name + "_ub" in df.columns: + + axarr[0].fill_between( + x, + df[df_name + "_lb"], + df[df_name + "_ub"], + alpha=0.25, + color=color_dict[label], + ) + + # Format the energy ratio plot + axarr[0].legend() + axarr[0].grid(visible=True, which="major", axis="both", color="gray") + axarr[0].grid(visible=True, which="minor", axis="both", color="lightgray") + axarr[0].minorticks_on() + # axarr[0].set_grid(True) + + # Plot the bin counts + df_unbinned, df_freq = self._compute_df_freq() + df_freq_sum_all_ws = df_freq.groupby(["wd_bin","df_name"]).sum().reset_index() + + + for i, (df_name, label) in enumerate(zip(df_names_subset, labels)): + df_sub = df_freq_sum_all_ws[df_freq_sum_all_ws["df_name"] == df_name] + + x = np.array(df_sub["wd_bin"], dtype=float) + if polar_plot: + x = (90.0 - x) * np.pi / 180.0 # Convert to radians + axarr[1].bar(x - (i - N / 2) * bar_width, df_sub["count"], width=bar_width, label = label, color=color_dict[label]) + + axarr[1].legend() + + # Get the bins + wd_bins = np.array(df_freq["wd_bin"].unique(), dtype=float) + ws_bins = np.array(df_freq["ws_bin"].unique(), dtype=float) + num_wd_bins = len(wd_bins) + num_ws_bins = len(ws_bins) + + if show_wind_speed_distrubution: + # Plot the wind speed distribution in df_freq as a heat map with wd on the x-axis and ws on the y-axis + + ax = axarr[2] + for df_name, label in zip(df_names_subset, labels): + df_sub = df_freq[df_freq["df_name"] == df_name] + ax.scatter(df_unbinned["wd_bin"], df_unbinned["ws_bin"], c=color_dict[label],alpha=0.25, s=1) + + + def plot_uplift(self, + axarr = None, + polar_plot=False, + show_wind_speed_distrubution=True, + ): + self.plot( + df_names_subset = 'uplift', + labels = ['uplift'], + color_dict = {'uplift':'k'}, + axarr = axarr, + polar_plot=polar_plot, + show_wind_speed_distrubution=show_wind_speed_distrubution, + ) + diff --git a/flasc/energy_ratio/energy_ratio_visualization.py b/flasc/energy_ratio/energy_ratio_visualization.py index 9bb4c747..2887430e 100644 --- a/flasc/energy_ratio/energy_ratio_visualization.py +++ b/flasc/energy_ratio/energy_ratio_visualization.py @@ -265,402 +265,3 @@ def plot( plt.tight_layout() return axarr - -# def table_analysis( -# df_list, -# fout_xlsx, -# hide_bin_count_columns=False, -# hide_ws_ti_columns=False, -# hide_pow_columns=False, -# hide_unbalanced_cols=True, -# fi=None -# ): -# # Save some useful info -# header_row = 2 -# first_data_row = header_row + 1 -# first_data_col = 1 - -# # Extract variables -# ws_step = df_list[0]["er_ws_step"] -# wd_bin_width = df_list[0]["er_wd_bin_width"] - -# # Extract relevant details -# name_list = [df["name"] for df in df_list] -# df_per_wd_bin_list = [ -# d["er_results_info_dict"]["df_per_wd_bin"]for d in df_list -# ] -# df_per_ws_bin_list = [ -# d["er_results_info_dict"]["df_per_ws_bin"] for d in df_list -# ] -# test_turbines = np.array(df_list[0]["er_test_turbines"], dtype=int) - -# # Append column names with data label for df_per_wd_bin -# for ii, df in enumerate(df_per_wd_bin_list): -# name = name_list[ii] -# df.columns = ["{:s}_{:s}".format(c, name) for c in df.columns] - -# # Append column names with data label for df_per_ws_bin -# for ii, df in enumerate(df_per_ws_bin_list): -# name = name_list[ii] -# df = df.reset_index(drop=False).set_index(["wd_bin", "ws_bin"]) -# df.columns = ["{:s}_{:s}".format(c, name) for c in df.columns] -# df_per_ws_bin_list[ii] = df - -# # Concatenate information from different dataframes -# df_per_wd_bin = pd.concat(df_per_wd_bin_list, axis=1) -# df_per_ws_bin = pd.concat(df_per_ws_bin_list, axis=1) - -# # Merge df_per_wd_bin information into df_per_ws_bin -# df_per_ws_bin = df_per_ws_bin.reset_index(drop=False).set_index("wd_bin") -# df_per_wd_bin["ws_bin"] = 999999.9 # very high number -# df_merged = pd.concat([df_per_ws_bin, df_per_wd_bin]) -# df_merged = df_merged.sort_values(by=["wd_bin", "ws_bin"]) -# df_merged = df_merged.reset_index(drop=False) - -# wd_intervals = [pd.Interval(a, b, "left") for a, b in zip( -# df_merged["wd_bin"] - wd_bin_width / 2.0, -# df_merged["wd_bin"] + wd_bin_width / 2.0 -# ) -# ] -# ws_intervals = [pd.Interval(a, b, "left") for a, b in zip( -# df_merged["ws_bin"] - ws_step / 2.0, -# df_merged["ws_bin"] + ws_step / 2.0 -# ) -# ] - -# df_table = pd.DataFrame( -# { -# "wd_bin": wd_intervals, -# "ws_bin": ws_intervals, -# } -# ) - -# # Overwrite placeholder large numbers with new interval -# total_ids = [i.right >= 999999.9 for i in ws_intervals] -# df_table.loc[total_ids, "ws_bin"] = "TOTALS" -# df_merged.loc[total_ids, "ws_bin"] = "TOTALS" - -# # Add bin counts for the dataframes -# cols = ["bin_count_{:s}".format(n) for n in name_list] -# for c in cols: -# df_table[c] = df_merged[c].fillna(0).astype(int) - -# # Add balanced bin count per ws and in total -# df_table["bin_count_balanced"] = df_table[cols].min(axis=1).astype(int) -# df_table.loc[total_ids, "bin_count_balanced"] = int(0) -# Ntot = df_table.groupby(["wd_bin"])["bin_count_balanced"].sum() -# df_table.loc[total_ids, "bin_count_balanced"] = np.array(Ntot, dtype=int) - -# df_merged["bin_count_balanced_tot"] = df_table.loc[total_ids, "bin_count_balanced"] -# df_merged["bin_count_balanced_tot"] = df_merged["bin_count_balanced_tot"].bfill().astype(int) - -# # add ws_mean and ti_mean for all dataframes -# for col in ["ws_mean", "ti_mean"]: -# for n in name_list: -# c = "{:s}_{:s}".format(col, n) -# if c in df_merged.columns: -# df_table[c] = df_merged[c] - -# # Add reference power and energy -# bin_totals = np.array(df_merged["bin_count_balanced_tot"]) -# for n in name_list: -# pow_mean = df_merged["pow_ref_mean_{:s}".format(n)] -# energy_unbal = df_merged["energy_ref_unbalanced_{:s}".format(n)] -# energy_bal_norm = df_merged["energy_ref_balanced_norm_{:s}".format(n)] -# energy_bal = bin_totals * energy_bal_norm - -# df_table["ref_pow_{:s}".format(n)] = pow_mean -# df_table["ref_energy_unbalanced_{:s}".format(n)] = energy_unbal -# df_table["ref_energy_balanced_{:s}".format(n)] = energy_bal - -# # Fill empty entries with 0.0 for energy -# df_table["ref_energy_unbalanced_{:s}".format(n)] = ( -# df_table["ref_energy_unbalanced_{:s}".format(n)].fillna(0.0) -# ) -# df_table["ref_energy_balanced_{:s}".format(n)] = ( -# df_table["ref_energy_balanced_{:s}".format(n)].fillna(0.0) -# ) - -# # Add empty column/spacer -# df_table["___"] = None - -# # Add test power and energy -# bin_totals = np.array(df_merged["bin_count_balanced_tot"]) -# for n in name_list: -# pow_mean = df_merged["pow_test_mean_{:s}".format(n)] -# energy_unbal = df_merged["energy_test_unbalanced_{:s}".format(n)] -# energy_bal_norm = df_merged["energy_test_balanced_norm_{:s}".format(n)] -# energy_bal = bin_totals * energy_bal_norm -# energy_ratio = df_merged["energy_ratio_unbalanced_{:s}".format(n)] -# energy_ratio_bal = df_merged["energy_ratio_balanced_{:s}".format(n)] - -# df_table["test_pow_{:s}".format(n)] = pow_mean -# df_table["test_energy_unbalanced_{:s}".format(n)] = energy_unbal -# df_table["test_energy_balanced_{:s}".format(n)] = energy_bal -# df_table["energy_ratio_unbalanced_{:s}".format(n)] = energy_ratio -# df_table["energy_ratio_balanced_{:s}".format(n)] = energy_ratio_bal - -# # Fill empty entries with 0.0 for energy -# df_table["test_energy_unbalanced_{:s}".format(n)] = ( -# df_table["test_energy_unbalanced_{:s}".format(n)].fillna(0.0) -# ) -# df_table["test_energy_balanced_{:s}".format(n)] = ( -# df_table["test_energy_balanced_{:s}".format(n)].fillna(0.0) -# ) - -# # Define change in unbalanced and balanced energy ratios -# bl = df_table["energy_ratio_unbalanced_{:s}".format(name_list[0])] -# bl_bal = df_table["energy_ratio_balanced_{:s}".format(name_list[0])] - -# for n in name_list[1::]: -# df_table["change_energy_ratio_unbalanced_{:s}".format(n)] = ( -# (df_table["energy_ratio_unbalanced_{:s}".format(n)] - bl) / bl -# ) -# df_table["change_energy_ratio_balanced_{:s}".format(n)] = ( -# (df_table["energy_ratio_balanced_{:s}".format(n)] - bl_bal) -# / bl_bal -# ) - -# # Add empty column/spacer -# df_table["___0"] = None - -# # Add empty rows in df_table after each wd_bin -# df_empty = pd.DataFrame([None]) -# df_array = [] -# splits = np.where(total_ids)[0] -# splits = np.hstack([0, splits + 1]) # Add zero - -# for ii in range(len(splits) - 1): -# lb = splits[ii] -# ub = splits[ii+1] -# df_array.append(df_table[lb:ub]) -# df_array.append(df_empty) - -# df_table_spaced = pd.concat(df_array, axis=0, ignore_index=True) -# df_table_spaced = df_table_spaced[df_table.columns] -# df_table = df_table_spaced - -# # Write out the dataframe with xslxwriter -# writer = pd.ExcelWriter(fout_xlsx, engine="xlsxwriter") -# df_table.to_excel( -# writer, -# index=False, -# sheet_name="results", -# startcol=first_data_col, -# startrow=header_row, -# ) -# workbook = writer.book -# worksheet = writer.sheets["results"] - -# # FORMATTING - -# # Format large numbers to 2 decimal -# fmt_rate = workbook.add_format({"num_format": "0.00", "bold": False}) -# cols = df_table.columns -# change_list = [ -# i -# for i in range(len(cols)) -# if ("_mean_" in cols[i]) or ("_pow_" in cols[i]) or ( -# ("_energy_" in cols[i]) and not ("energy_ratio_" in cols[i]) -# ) -# ] -# for c in change_list: -# worksheet.set_column( -# c + first_data_col, c + first_data_col, 10, fmt_rate -# ) - -# # Format energy ratios to 3 decimal -# fmt_rate = workbook.add_format({"num_format": "0.000", "bold": False}) -# cols = df_table.columns -# change_list = [i for i in range(len(cols)) if "energy_ratio" in cols[i]] -# for c in change_list: -# worksheet.set_column( -# c + first_data_col, c + first_data_col, 10, fmt_rate -# ) - -# # Format change and TI into a percentage -# fmt_rate = workbook.add_format({"num_format": "%0.0", "bold": False}) -# cols = df_table.columns -# change_list = [ -# i -# for i in range(len(cols)) -# if ("change" in cols[i]) or ("ti_" in cols[i]) -# ] -# for c in change_list: -# worksheet.set_column( -# c + first_data_col, c + first_data_col, 10, fmt_rate -# ) - -# # # Make "totals" rows bold -# # bold_format = workbook.add_format({'bold': True}) -# # total_ids = np.where(df_table["ws_bin"] == "TOTALS")[0] -# # for ri in total_ids: -# # worksheet.set_row(first_data_row + ri, 15, bold_format) - -# # Make the seperator columns very narrow and black -# fmt_black = workbook.add_format({"fg_color": "#000000"}) -# change_list = [i for i in range(len(cols)) if "___" in cols[i]] -# for c in change_list: -# worksheet.set_column( -# c + first_data_col, c + first_data_col, 1, fmt_black -# ) - -# # Add data bars to the bins counts -# change_list = [i for i in range(len(cols)) if "bin" in cols[i]] -# for c in change_list: -# worksheet.conditional_format( -# first_data_row, -# c + first_data_col, -# df_table.shape[0] + first_data_row, -# c + first_data_col, -# {"type": "data_bar", "max_value": 100}, -# ) - -# # Add color to the change columns -# change_list = [i for i in range(len(cols)) if "change" in cols[i]] - -# for c in change_list: -# worksheet.conditional_format( -# first_data_row, -# c + first_data_col, -# df_table.shape[0] + first_data_row, -# c + first_data_col, -# { -# "type": "3_color_scale", -# "min_value": -1.0, -# "min_type": "num", -# "max_value": 1.0, -# "mid_value": 0.0, -# "mid_type": "num", -# "min_color": "#FF0000", -# "mid_color": "#FFFFFF", -# "max_color": "#00FF00", -# "max_type": "num", -# }, -# ) - -# # Add color to energy ratios -# change_list = [ -# i -# for i in range(len(cols)) -# if ("er_" in cols[i]) and not ("change" in cols[i]) -# ] -# for c in change_list: -# worksheet.conditional_format( -# first_data_row, -# c + first_data_col, -# df_table.shape[0] + first_data_row, -# c + first_data_col, -# { -# "type": "3_color_scale", -# "min_value": 0.25, -# "min_type": "num", -# "max_value": 2.0, -# "mid_value": 1.0, -# "mid_type": "num", -# "min_color": "#0000FF", -# "mid_color": "#FFFFFF", -# "max_color": "#00FF00", -# "max_type": "num", -# }, -# ) - -# # Header -# # Adding formats for header row. -# fmt_header = workbook.add_format( -# { -# "bold": True, -# "text_wrap": True, -# "valign": "top", -# "fg_color": "#5DADE2", -# "font_color": "#FFFFFF", -# "border": 1, -# } -# ) -# for col, value in enumerate(df_table.columns.values): -# worksheet.write(header_row, col + first_data_col, value, fmt_header) - -# # If a floris model is provided, use it to make layout images -# if fi is not None: -# # Make that first colum wide -# worksheet.set_column("A:A", 30) - -# # Create image folder -# img_folder = os.path.join(os.path.dirname(fout_xlsx), "xlsx_images") -# if not os.path.exists(img_folder): -# os.makedirs(img_folder, exist_ok=True) - -# # For each bin were checking, make image of wake scenarios -# sort_df = df_table[["wd_bin", "ws_bin"]].copy() -# sort_df = sort_df.sort_values(["wd_bin", "ws_bin"]).dropna() -# for wdb in sort_df.wd_bin.unique(): -# row_top_ws_bin = sort_df.index[wdb == sort_df["wd_bin"]][0] -# wd_arrow = wdb.mid # Put arrow in middle of bin -# fig, ax = plt.subplots(figsize=(2, 2)) -# fi.reinitialize_flow_field( -# wind_direction=wd_arrow, wind_speed=8.0 -# ) -# fi.calculate_wake() -# hor_plane = fi.get_hor_plane() -# hor_plane = wfct.cut_plane.change_resolution( -# hor_plane, -# resolution=(200, 200), -# ) -# wfct.visualization.visualize_cut_plane(hor_plane, ax=ax) -# im_name = os.path.join(img_folder, "wd_%03d.png" % wd_arrow) -# fig.savefig(im_name, bbox_inches="tight") - -# # Insert the figure -# worksheet.insert_image(first_data_row + row_top_ws_bin, 0, im_name) - -# # Make the first row bigger -# worksheet.set_row(0, 120) - -# # Get a list of blank columns indicating turbine starts -# blank_cols = [i for i in range(len(cols)) if "___" in cols[i]] - -# # Plot the layout on top -# fig, ax = plt.subplots(figsize=(3, 2)) -# fi.vis_layout(ax=ax) -# xt = np.array(fi.layout_x) -# yt = np.array(fi.layout_y) -# ax.plot(xt[test_turbines], yt[test_turbines], "mo", ms=25) -# im_name = os.path.join(img_folder, "layout.png") -# fig.savefig(im_name, bbox_inches="tight") -# worksheet.insert_image(0, blank_cols[0] + 1, im_name) - -# # Hide columns if necessary -# if hide_bin_count_columns: -# cols = [i for i, c in enumerate(df_table.columns) if "bin_count" in c] -# for ii in cols: -# worksheet.set_column(ii + 1, ii + 1, None, None, {'hidden': 1}) - -# if hide_ws_ti_columns: -# cols = [ -# i for i, c in enumerate(df_table.columns) if ( -# ("ws_mean_" in c) or ("ws_std_" in c) or -# ("ti_mean_" in c) or ("ti_std_" in c) -# ) -# ] -# for ii in cols: -# worksheet.set_column(ii + 1, ii + 1, None, None, {'hidden': 1}) - -# if hide_pow_columns: -# cols = [ -# i for i, c in enumerate(df_table.columns) if ( -# ("ref_pow_" in c) or ("test_pow_" in c) -# ) -# ] -# for ii in cols: -# worksheet.set_column(ii + 1, ii + 1, None, None, {'hidden': 1}) - -# if hide_unbalanced_cols: -# cols = [i for i, c in enumerate(df_table.columns) if "unbalance" in c] -# for ii in cols: -# worksheet.set_column(ii + 1, ii + 1, None, None, {'hidden': 1}) - -# # Freeze the panes -# worksheet.freeze_panes(first_data_row, first_data_col) - -# writer.save() -# print("File successfully written to {:s}.".format(fout_xlsx)) diff --git a/flasc/raw_data_handling/sqldatabase_management.py b/flasc/raw_data_handling/sqldatabase_management.py index b017f96f..bd468260 100644 --- a/flasc/raw_data_handling/sqldatabase_management.py +++ b/flasc/raw_data_handling/sqldatabase_management.py @@ -11,9 +11,10 @@ # the License. -import os import numpy as np import pandas as pd +import polars as pl +from pathlib import Path from time import perf_counter as timerpc import datetime @@ -27,6 +28,7 @@ import sqlalchemy as sqlalch + class sql_database_manager: # Private methods @@ -44,45 +46,88 @@ def _create_sql_engine(self, password): name = self.db_name usn = self.username address = "%s:%d" % (self.host, self.port) + self.url = "%s://%s:%s@%s/%s" % (dr, usn, password, address, name) self.engine = sqlalch.create_engine( - url="%s://%s:%s@%s/%s" % (dr, usn, password, address, name) + url= self.url ) + self.inspector = sqlalch.inspect(self.engine) self.print_properties() def _get_table_names(self): - return self.engine.table_names() + return self.inspector.get_table_names() + + def _does_table_exist(self, table_name): + return table_name in self._get_table_names() def _get_column_names(self, table_name): - df = pd.read_sql_query( - "SELECT * FROM " + table_name + " WHERE false;", self.engine + columns = self.inspector.get_columns(table_name) + return [c['name'] for c in columns] + + def _create_table_from_df(self, table_name, df): + + print(f'Creating Table: {table_name} with {df.shape[1]} columns') + + # Convert to pandas for upload + df_pandas = df.to_pandas() + df_pandas = df_pandas.iloc[:10] + + df_pandas.to_sql( + table_name, + self.engine, + index=False, + method="multi" + ) + + # Make time unique and an index to speed queries + query = 'CREATE UNIQUE INDEX idx_time_%s ON %s (time);' % (table_name, table_name) + print('Setting time to unique index') + with self.engine.connect() as con: + rs = con.execute(sqlalch.text(query)) + print(f'...RESULT: {rs}') + con.commit() # commit the transaction + + def _remove_duplicated_time(self, table_name, df): + + start_time = df.select(pl.min("time"))[0, 0] + end_time = df.select(pl.max("time"))[0, 0] + original_size = df.shape[0] + + print(f'Checking for time entries already in {table_name} between {start_time} and {end_time}') + time_in_db = self.get_data(table_name, + ['time'], + start_time=start_time, + end_time=end_time, + end_inclusive=True ) - return list(df.columns) + + df = df.join(time_in_db, on='time',how="anti") + new_size = df.shape[0] + if new_size < original_size: + print(f'...Dataframe size reduced from {original_size} to {new_size} by time values already in {table_name}') + return df + def _get_first_time_entry(self, table_name): - tn = table_name - column_names = self._get_column_names(tn) - if 'time' in column_names: - df_time = pd.read_sql_query( - sql="SELECT time FROM %s ORDER BY time asc LIMIT 1" % tn, - con=self.engine - ) - if df_time.shape[0] > 0: - return df_time["time"][0] - return None + # Get the table corresponding to the table name + table = sqlalch.Table(table_name, sqlalch.MetaData(), autoload_with=self.engine) + + stmt = sqlalch.select(table.c.time).order_by(table.c.time.asc()).limit(1) + with self.engine.begin() as conn: + result = conn.execute(stmt) + for row in result: + return row[0] def _get_last_time_entry(self, table_name): - tn = table_name - column_names = self._get_column_names(tn) - if 'time' in column_names: - df_time = pd.read_sql_query( - sql="SELECT time FROM %s ORDER BY time desc LIMIT 1" % tn, - con=self.engine - ) - if df_time.shape[0] > 0: - return df_time["time"][0] + # Get the table corresponding to the table name + table = sqlalch.Table(table_name, sqlalch.MetaData(), autoload_with=self.engine) - return None + stmt = sqlalch.select(table.c.time).order_by(table.c.time.desc()).limit(1) + print(stmt) + with self.engine.begin() as conn: + result = conn.execute(stmt) + for row in result: + return row[0] # General info functions from data def print_properties(self): @@ -103,7 +148,11 @@ def print_properties(self): def launch_gui(self, turbine_names=None, sort_columns=False): root = tk.Tk() - sql_db_explorer_gui(master=root, dbc=self, turbine_names=turbine_names, sort_columns=sort_columns) + sql_db_explorer_gui(master=root, + dbc=self, + turbine_names=turbine_names, + sort_columns=sort_columns + ) root.mainloop() def get_column_names(self, table_name): @@ -113,8 +162,8 @@ def batch_get_data(self, table_name, columns=None, start_time=None, end_time=None, fn_out=None, no_rows_per_file=10000): if fn_out is None: fn_out = table_name + ".ftr" - if not (fn_out[-4::] == ".ftr"): - fn_out = fn_out + ".ftr" + if not (fn_out.suffix == ".ftr"): + fn_out = fn_out.with_suffix(".ftr") # Ensure 'time' in database column_names = self._get_column_names(table_name=table_name) @@ -122,13 +171,16 @@ def batch_get_data(self, table_name, columns=None, start_time=None, raise KeyError("Cannot find 'time' column in database table.") # Get time column from database + print("Getting time column from database...") time_in_db = self.get_data(table_name=table_name, columns=['time'], start_time=start_time, end_time=end_time) - time_in_db = list(time_in_db['time']) + time_in_db = list(time_in_db.select("time").to_numpy().flatten()) + print("...finished, N.o. entries: %d." % len(time_in_db)) splits = np.arange(0, len(time_in_db) - 1, no_rows_per_file, dtype=int) splits = np.append(splits, len(time_in_db) - 1) splits = np.unique(splits) + print(f"Splitting {len(time_in_db)} entries data into {len(splits)} subsets of {no_rows_per_file}.") for ii in range(len(splits) - 1): print("Downloading subset %d out of %d." % (ii, len(splits) - 1)) @@ -138,12 +190,17 @@ def batch_get_data(self, table_name, columns=None, start_time=None, start_time=time_in_db[splits[ii]], end_time=time_in_db[splits[ii+1]] ) - fn_out_ii = fn_out + '.%d' % ii - print("Saving file to %s." % fn_out_ii) - df.to_feather(fn_out_ii) + fn_out_ii = fn_out.with_suffix(".ftr.%03d" % ii) + print("Saving file to %s" % fn_out_ii) + df.write_ipc(fn_out_ii) def get_data( - self, table_name, columns=None, start_time=None, end_time=None + self, + table_name, + columns=None, + start_time=None, + end_time=None, + end_inclusive=False, ): # Get the data from tables if columns is None: @@ -156,22 +213,33 @@ def get_data( query_string += " WHERE time >= '" + str(start_time) + "'" if (start_time is not None) and (end_time is not None): - query_string += " AND time < '" + str(end_time) + "'" + if end_inclusive: + query_string += " AND time <= '" + str(end_time) + "'" + else: + query_string += " AND time < '" + str(end_time) + "'" elif (start_time is None) and (end_time is not None): - query_string += " WHERE time < '" + str(end_time) + "'" + if end_inclusive: + query_string += " WHERE time <= '" + str(end_time) + "'" + else: + query_string += " WHERE time < '" + str(end_time) + "'" - query_string += " ORDER BY time;" - df = pd.read_sql_query(query_string, self.engine) + query_string += " ORDER BY time" + + df = pl.read_database(query_string,self.url) # Drop a column called index if "index" in df.columns: - df = df.drop(["index"], axis=1) + df = df.drop("index") - # Make sure time column is in datetime format - df["time"] = pd.to_datetime(df.time) + # Confirm that the time column is in datetime format + if "time" in df.columns: + if not (df.schema["time"] == pl.Datetime): + df = df.with_columns(pl.col("time").cast(pl.Datetime)) return df + + #TODO: This is a fresh redo check it works def send_data( self, table_name, @@ -181,81 +249,135 @@ def send_data( df_chunk_size=2000, sql_chunk_size=50 ): - table_name = table_name.lower() - table_names = [t.lower() for t in self._get_table_names()] - - if (if_exists == "append"): - print("Warning: risk of adding duplicate rows using 'append'.") - print("You are suggested to use 'append_new' instead.") - - if (if_exists == "append_new") and (table_name in table_names): - if len(unique_cols) > 1: - raise NotImplementedError("Not yet implemented.") - - col = unique_cols[0] - idx_in_db = self.get_data(table_name=table_name, columns=[col])[ - col - ] - - # Check if values in SQL database are unique - if not idx_in_db.is_unique: - raise IndexError( - "Column '%s' is not unique in the SQL database." % col - ) + + # Make a local copy + df_ = df.clone() + + # Check if table exists + if not self._does_table_exist(table_name): + + print(f'{table_name} does not yet exist') + + # Create the table + self._create_table_from_df(table_name, df_) + + # Check for times already in database + df_ = self._remove_duplicated_time(table_name, df_) + + # Check if df_ is now + if df_.shape[0] == 0: + print('Dataframe is empty') + return + + # Write to database + print(f'Inserting {df_.shape[0]} rows into {table_name} in chunks of {df_chunk_size}') + time_start_total = timerpc() + + # Parition into chunks + df_list = (df_.with_row_count('id') + .with_columns(pl.col('id').apply(lambda i: int(i/df_chunk_size))) + .partition_by('id') + ) - idx_in_df = set(df[col]) - idx_in_db = set(idx_in_db) - idx_to_add = np.sort(list(idx_in_df - idx_in_db)) - print( - "{:d} entries already exist in SQL database.".format( - len(idx_in_df) - len(idx_to_add) - ) + num_par = len(df_list) + for df_par_idx, df_par in enumerate(df_list): + print(f'...inserting chunk {df_par_idx} of {num_par}') + + df_par.drop('id').write_database( + table_name, + self.url, + if_exists='append' ) - - print("Adding {:d} new entries...".format(len(idx_to_add))) - df_subset = df.set_index('time').loc[idx_to_add].reset_index( - drop=False) - - else: - df_subset = df - - if (if_exists == "append_new"): - if_exists = "append" - - # Upload data - N = df_subset.shape[0] - if N < 1: - print("Skipping data upload. Dataframe is empty.") - else: - print("Attempting to insert %d rows into table '%s'." - % (df_subset.shape[0], table_name)) - df_chunks_id = np.arange(0, df_subset.shape[0], df_chunk_size) - df_chunks_id = np.append(df_chunks_id, df_subset.shape[0]) - df_chunks_id = np.unique(df_chunks_id) - - time_start_total = timerpc() - for i in range(len(df_chunks_id)-1): - Nl = df_chunks_id[i] - Nu = df_chunks_id[i+1] - print("Inserting rows %d to %d." % (Nl, Nu)) - time_start_i = timerpc() - df_sub = df_subset[Nl:Nu] - df_sub.to_sql( - table_name, - self.engine, - if_exists=if_exists, - index=False, - method="multi", - chunksize=sql_chunk_size, - ) - time_i = timerpc() - time_start_i - total_time = timerpc() - time_start_total - est_time_left = (total_time / Nu) * (N - Nu) - eta = datetime.datetime.now() + td(seconds=est_time_left) - eta = eta.strftime("%a, %d %b %Y %H:%M:%S") - print("Data insertion took %.1f s. ETA: %s." % (time_i, eta)) - - + total_time = timerpc() - time_start_total + print(f'...Finished in {total_time}') + + + # #TODO: UPDATE TO POLARS + # #TODO: Paul note (may 31 2023), POLARS API not up to PANDAS so using PANDAS here + # def send_data( + # self, + # table_name, + # df, + # if_exists="append_new", + # unique_cols=["time"], + # df_chunk_size=2000, + # sql_chunk_size=50 + # ): + # table_name = table_name.lower() + # table_names = [t.lower() for t in self._get_table_names()] + + # if (if_exists == "append"): + # print("Warning: risk of adding duplicate rows using 'append'.") + # print("You are suggested to use 'append_new' instead.") + + # if (if_exists == "append_new") and (table_name in table_names): + # if len(unique_cols) > 1: + # raise NotImplementedError("Not yet implemented.") + + # col = unique_cols[0] + # idx_in_db = self.get_data(table_name=table_name, columns=[col])[ + # col + # ] + + # # Check if values in SQL database are unique + # if not idx_in_db.is_unique: + # raise IndexError( + # "Column '%s' is not unique in the SQL database." % col + # ) + + # idx_in_df = set(df[col]) + # idx_in_db = set(idx_in_db) + # idx_to_add = np.sort(list(idx_in_df - idx_in_db)) + # print( + # "{:d} entries already exist in SQL database.".format( + # len(idx_in_df) - len(idx_to_add) + # ) + # ) + + # print("Adding {:d} new entries...".format(len(idx_to_add))) + # df_subset = df.set_index('time').loc[idx_to_add].reset_index( + # drop=False) + + # else: + # df_subset = df + + # if (if_exists == "append_new"): + # if_exists = "append" + + # # Upload data + # N = df_subset.shape[0] + # if N < 1: + # print("Skipping data upload. Dataframe is empty.") + # else: + # print("Attempting to insert %d rows into table '%s'." + # % (df_subset.shape[0], table_name)) + # df_chunks_id = np.arange(0, df_subset.shape[0], df_chunk_size) + # df_chunks_id = np.append(df_chunks_id, df_subset.shape[0]) + # df_chunks_id = np.unique(df_chunks_id) + + # time_start_total = timerpc() + # for i in range(len(df_chunks_id)-1): + # Nl = df_chunks_id[i] + # Nu = df_chunks_id[i+1] + # print("Inserting rows %d to %d." % (Nl, Nu)) + # time_start_i = timerpc() + # df_sub = df_subset[Nl:Nu] + # df_sub.to_sql( + # table_name, + # self.engine, + # if_exists=if_exists, + # index=False, + # method="multi", + # chunksize=sql_chunk_size, + # ) + # time_i = timerpc() - time_start_i + # total_time = timerpc() - time_start_total + # est_time_left = (total_time / Nu) * (N - Nu) + # eta = datetime.datetime.now() + td(seconds=est_time_left) + # eta = eta.strftime("%a, %d %b %Y %H:%M:%S") + # print("Data insertion took %.1f s. ETA: %s." % (time_i, eta)) + +#TODO: UPDATE TO POLARS class sql_db_explorer_gui: def __init__(self, master, dbc, turbine_names = None, sort_columns=False): @@ -264,7 +386,7 @@ def __init__(self, master, dbc, turbine_names = None, sort_columns=False): self.master = master # Get basic database properties - self.df = pd.DataFrame() + self.df = pl.DataFrame() table_names = dbc._get_table_names() min_table_dates = [ dbc._get_first_time_entry(table_name=t) for t in table_names @@ -453,7 +575,7 @@ def load_data(self): start_time=start_time, end_time=end_time, ) - df = df.set_index("time", drop=True) + # df = df.set_index("time", drop=True) if df.shape[0] <= 0: print( @@ -463,29 +585,40 @@ def load_data(self): else: print("...Imported data successfully.") - old_col_names = list(df.columns) + old_col_names = [c for c in list(df.columns) if not c=='time'] new_col_names = [ chr(97 + tables_selected[ii]).upper() + "_%s" % c - for c in df.columns + for c in old_col_names ] col_mapping = dict(zip(old_col_names, new_col_names)) - df = df.rename(columns=col_mapping) + df = df.rename(col_mapping) # If specific turbine names are supplied apply them here if self.turbine_names is not None: columns = df.columns for t in range(len(self.turbine_names)): columns = [c.replace('%03d' % t,self.turbine_names[t]) for c in columns] - df.columns = columns + # df.columns = columns + df = df.rename(dict(zip(df.columns,columns))) df_array.append(df) # Merge dataframes - self.df = pd.concat(df_array, axis=1).reset_index(drop=False) + # self.df = pl.concat(df_array, axis=1)# .reset_index(drop=False) + df_merge = df_array[0] + + if len(df_array) > 1: + for df_ in df_array[1:]: + df_merge = df_merge.join(df_, on='time',how='outer') + + #Save it now + self.df = df_merge # If sorting the columns do it now if self.sort_columns: - self.df = self.df[sorted(self.df.columns)] + # self.df = self.df[sorted(self.df.columns)] + self.df = self.df.select(sorted(self.df.columns)) + self.update_channel_cols() self.create_figures() @@ -533,7 +666,7 @@ def update_plot(self, channel_no): ax = self.axes[channel_no] ax.clear() for c in self.channel_selection[channel_no]: - ax.plot(self.df.time, np.array(self.df[c].values), label=c) + ax.plot(self.df['time'], np.array(self.df[c]), label=c) ax.legend() ax.grid(True) @@ -572,186 +705,3 @@ def ci_select(self, channel_no, evt=None): channels = [self.df.columns[idx] for idx in indices] self.channel_selection[channel_no] = channels self.update_plot(channel_no=channel_no) - - -# def get_timestamp_of_last_downloaded_datafile(filelist): -# time_latest = None -# for fi in filelist: -# df = pd.read_feather(fi) -# time_array = df['time'] -# if not all(np.isnan(time_array)): -# tmp_time_max = np.max(df['time']) -# if time_latest is None: -# time_latest = tmp_time_max -# else: -# if tmp_time_max > time_latest: -# time_latest = tmp_time_max - -# return time_latest - - -# # # # # # # # # # # # # # # # # # # # # # # # # # -# # # # RAW DATA READING FUNCTIONS -# # # # # # # # # # # # # # # # # # # # # # # # # # -# def find_files_to_add_to_sqldb(sqldb_engine, files_paths, filenames_table): -# """This function is used to figure out which files are already -# uploaded to the SQL database, and which files still need to be -# uploaded. - -# Args: -# sqldb_engine ([SQL engine]): SQL Engine from sqlalchemy.create_engine -# used to access the SQL database of interest. This is used to call -# which files have previously been uploaded. -# files_paths ([list, str]): List of strings or a single string containing -# the path to the raw data files. One example is: -# files_paths = ['/home/user/data/windfarm1/year1/*.csv', -# '/home/user/data/windfarm1/year2/*.csv', -# '/home/user/data/windfarm1/year3/*.csv',] -# filenames_table ([str]): SQL table name containing the filenames of -# the previously uploaded data files. - -# Returns: -# files ([list]): List of files that are not yet in the SQL database -# """ -# # Convert files_paths to a list -# if isinstance(files_paths, str): -# files_paths = [files_paths] - -# # Figure out which files exists on local system -# files = [] -# for fpath in files_paths: -# fl = glob.glob(fpath) -# if len(fl) <= 0: -# print('No files found in directory %s.' % fpath) -# else: -# files.extend(fl) - -# # Figure out which files have already been uploaded to sql db -# query_string = "select * from " + filenames_table + ";" -# df = pd.read_sql_query(query_string, sqldb_engine) - -# # # Check for the files not in the database -# files = [f for f in files -# if os.path.basename(f) not in df['filename'].values] - -# # Sort the file list according to ascending name -# files = sorted(files, reverse=False) - -# return files - - -# # # # # # # # # # # # # # # # # # # # # # # # # # -# # # # DATA UPLOAD FUNCTIONS -# # # # # # # # # # # # # # # # # # # # # # # # # # - - -# def omit_last_rows_by_buffer(df, omit_buffer): -# if not 'time' in df.columns: -# df = df.reset_index(drop=False) - -# num_rows = df.shape[0] -# df = df[df['time'] < max(df['time']) - omit_buffer] -# print('Omitting last %d rows (%s s) as a buffer for future files.' -# % (num_rows-df.shape[0], omit_buffer)) -# return df - - -# def remove_duplicates_with_sqldb(df, sql_engine, table_name): -# min_time = df.time.min() -# max_time = df.time.max() - -# time_query = ( -# "select time from "+ table_name + -# " where time BETWEEN '%s' and '%s';" % (min_time, max_time) -# ) - -# df_time = pd.read_sql_query(time_query, sql_engine) -# df_time["time"] = pd.to_datetime(df_time.time) -# print("......Before duplicate removal there are %d rows" % df.shape[0]) -# df = df[~df.time.isin(df_time.time)] -# print("......After duplicate removal there are %d rows" % df.shape[0]) - -# # Check for self duplicates in to-be-uploaded dataset -# print("......Before SELF duplicate removal there are %d rows" % df.shape[0]) -# if "turbid" in df.columns: -# df = df.drop_duplicates(subset=["time", "turbid"], keep="first") -# else: -# df = df.drop_duplicates(subset=["time"], keep="first") -# print("......After SELF duplicate removal there are %d rows" % df.shape[0]) - -# # Drop null times in to-be-uploaded dataset -# print( -# "......Before null time/turbid duplicate removal there are %d rows" -# % df.shape[0] -# ) -# df = df.dropna(subset=["time"]) -# print("......After null time duplicate removal there are %d rows" % df.shape[0]) - -# return df - - -# def batch_download_data_from_sql(dbc, destination_path, table_name): -# print("Batch downloading data from table %s." % table_name) - -# # Check if output directory exists, if not, create -# if not os.path.exists(destination_path): -# os.makedirs(destination_path) - -# # Check current start and end time of database -# db_end_time = get_last_time_entry_sqldb(dbc.engine, table_name) -# db_end_time = db_end_time + datetime.timedelta(minutes=10) - -# # Check for past files and continue download or start a fresh download -# files_result = fsio.browse_downloaded_datafiles(destination_path, -# table_name=table_name) -# print('A total of %d existing files found.' % len(files_result)) - -# # Next timestamp is going to be next first of the month -# latest_timestamp = get_timestamp_of_last_downloaded_datafile(files_result) -# if latest_timestamp is None: -# db_start_time = get_first_time_entry_sqldb(dbc.engine, table_name) -# db_start_time = db_start_time - datetime.timedelta(minutes=10) -# current_timestamp = db_start_time -# elif latest_timestamp.month == 12: -# current_timestamp = pd.to_datetime('%s-01-01' % -# str(latest_timestamp.year+1)) -# else: -# current_timestamp = pd.to_datetime("%s-%s-01" % ( -# str(latest_timestamp.year), str(latest_timestamp.month+1))) - -# print('Continuing import from timestep: ', current_timestamp) -# while current_timestamp <= db_end_time: -# print('Importing data for ' + -# str(current_timestamp.strftime("%B")) + -# ', ' + str(current_timestamp.year) + '.') -# if current_timestamp.month == 12: -# next_timestamp = current_timestamp.replace( -# year=current_timestamp.year+1, month=1, -# day=1, hour=0, minute=0, second=0) -# else: -# next_timestamp = current_timestamp.replace( -# month=current_timestamp.month+1, -# day=1, hour=0, minute=0, second=0) - -# df = dbc.get_table_data_from_db_wide( -# table_name=table_name, -# start_time=current_timestamp, -# end_time=next_timestamp -# ) - -# # Drop NaN rows -# df = dfm.df_drop_nan_rows(df) - -# # Save dataset as a .ftr file -# fout = os.path.join(destination_path, "%s_%s.ftr" % -# (current_timestamp.strftime("%Y-%m"), table_name)) - -# df = df.reset_index(drop=('time' in df.columns)) -# df.to_feather(fout) - -# print('Data for ' + table_name + -# ' saved to .ftr files for ' + -# str(current_timestamp.strftime("%B")) + -# ', ' + str(current_timestamp.year) + '.') - -# current_timestamp = next_timestamp diff --git a/flasc/timing_tests/energy_ratio_timing.py b/flasc/timing_tests/energy_ratio_timing.py index 00d7a01c..22e2ffd8 100644 --- a/flasc/timing_tests/energy_ratio_timing.py +++ b/flasc/timing_tests/energy_ratio_timing.py @@ -25,15 +25,15 @@ import pandas as pd from flasc.energy_ratio import energy_ratio_suite +from flasc.energy_ratio import energy_ratio_polars as erp N_ITERATIONS = 5 - def load_data_and_prep_data(): # Load dataframe with artificial SCADA data root_dir = os.path.dirname(os.path.abspath(__file__)) ftr_path = os.path.join( - root_dir, '..','examples_artificial_data', 'raw_data_processing', 'postprocessed', 'df_scada_data_600s_filtered_and_northing_calibrated.ftr' + root_dir, '..','..','examples_artificial_data', 'raw_data_processing', 'postprocessed', 'df_scada_data_600s_filtered_and_northing_calibrated.ftr' ) if not os.path.exists(ftr_path): @@ -62,33 +62,41 @@ def time_energy_ratio_with_bootstrapping(): # Load the data df = load_data_and_prep_data() - # Load an energy ratio suite from FLASC - s = energy_ratio_suite.energy_ratio_suite(verbose=False) - - # Add dataframe to energy suite - s.add_df(df, 'data') + # Build the polars energy table object + # Speciy num_blocks = num_rows to implement normal boostrapping + df_energy = erp.get_energy_table([df],['baseline'],num_blocks=df.shape[0]) # For forward consistency, define the bins by the edges ws_edges = np.arange(5,25,1.) wd_edges = np.arange(0,360,2.) - # Create bins - ws_bins = [(ws_edges[i], ws_edges[i+1]) for i in range(len(ws_edges)-1)] - wd_bins = [(wd_edges[i], wd_edges[i+1]) for i in range(len(wd_edges)-1)] + # Get what new polars needs from this + ws_max = np.max(ws_edges) + ws_min = np.min(ws_edges) + ws_step = ws_edges[1] - ws_edges[0] + wd_max = np.max(wd_edges) + wd_min = np.min(wd_edges) + wd_step = wd_edges[1] - wd_edges[0] - # Run this calculation N_ITERATIONS times and take the average time - + # # Run this calculation N_ITERATIONS times and take the average time time_results = np.zeros(N_ITERATIONS) for i in range(N_ITERATIONS): start_time = time.time() - er = s.get_energy_ratios( - test_turbines=1, - ws_bins=ws_bins, - wd_bins=wd_bins, - N=N, - percentiles=[5.0, 95.0], - verbose=False - ) + + df_erb = erp.compute_energy_ratio( + df_energy, + ['baseline'], + test_turbines=[1], + use_predefined_ref=True, + use_predefined_wd=True, + use_predefined_ws=True, + ws_max=ws_max, + ws_min=ws_min, + ws_step=ws_step, + wd_max=wd_max, + wd_min=wd_min, + wd_step=wd_step, + N=N) end_time = time.time() time_results[i] = end_time - start_time @@ -98,8 +106,6 @@ def time_energy_ratio_with_bootstrapping(): - - if __name__=="__main__": warnings.filterwarnings('ignore') diff --git a/requirements.txt b/requirements.txt index d3b67ba6..192b5d05 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,3 +13,4 @@ sqlalchemy streamlit tkcalendar seaborn +polars \ No newline at end of file diff --git a/setup.py b/setup.py index bdb6ecf2..587f7ce5 100644 --- a/setup.py +++ b/setup.py @@ -26,7 +26,8 @@ 'sqlalchemy', 'streamlit', 'tkcalendar', - 'seaborn' + 'seaborn', + 'polars' ] ROOT = Path(__file__).parent diff --git a/tests/energy_ratio_test.py b/tests/energy_ratio_test.py index 02301b1c..f70336dc 100644 --- a/tests/energy_ratio_test.py +++ b/tests/energy_ratio_test.py @@ -9,6 +9,7 @@ from flasc.dataframe_operations import dataframe_manipulations as dfm from flasc import floris_tools as ftools from flasc.examples.models import load_floris_artificial as load_floris +from flasc.energy_ratio import energy_ratio_polars as erp def load_data(): @@ -151,3 +152,44 @@ def test_energy_ratio_regression(self): self.assertEqual(out.loc[3, "bin_count"], 34) self.assertEqual(out.loc[4, "bin_count"], 38) self.assertEqual(out.loc[5, "bin_count"], 6) + + def test_energy_ratio_regression_polars(self): + # Load data and FLORIS model + fi, _ = load_floris() + df = load_data() + df = dfm.set_wd_by_all_turbines(df) + df_upstream = ftools.get_upstream_turbs_floris(fi) + df = dfm.set_ws_by_upstream_turbines(df, df_upstream) + df = dfm.set_pow_ref_by_turbines(df, turbine_numbers=[0, 6]) + + wd_step=2.0 + ws_step=1.0 + # wd_bin_width=3.0, + + df_energy = erp.get_energy_table([df],['baseline']) + + ero = erp.compute_energy_ratio( + df_energy, + ['baseline'], + test_turbines=[1], + use_predefined_ref=True, + use_predefined_wd=True, + use_predefined_ws=True, + wd_max=360.0, + wd_min=wd_step/2.0, + wd_step=wd_step, + ws_max=30.0, + ws_min=ws_step/2.0, + ws_step=ws_step, + ) + + # Get the underlying polars data frame + df_erb = ero.df_result + + self.assertAlmostEqual(df_erb['baseline'].item(1), 0.8087321721301793, places=4) + self.assertAlmostEqual(df_erb['baseline'].item(2), 0.903263, places=4) + self.assertAlmostEqual(df_erb['baseline'].item(3), 0.930883, places=4) + + self.assertEqual(df_erb['count_baseline'].item(1), 25) + self.assertEqual(df_erb['count_baseline'].item(2), 27) + self.assertEqual(df_erb['count_baseline'].item(3), 22) \ No newline at end of file