{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pyAPP7 Examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Content**:\n", "These examples are grouped into three main sections:\n", "\n", "* [Files](#Files)\n", "* [Mission Computation](#Mission-Computation)\n", "* [Performance Charts](#Performance-Charts)\n", "\n", "**Version**: pyAPP7 version 1.0\n", "\n", "**Note**: This example was written as a jupyter notebook (version 4.4.0), and has been tested with Python 2.7.16 |Anaconda (64-bit). The notebook file is available in the *Examples* directory of the pyAPP7 distribution. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports & Constants" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Imports for plotting (matplotlib) and arrays (numpy):" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Jupyter Notebook specific imports:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Constants:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "APP7DIR = r'C:\\Program Files (x86)\\ALR Aerospace\\APP 7 Professional Edition'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Files module is used to open, change and save APP Files. It can be used for:\n", "* [acft](#Aircraft-File-%28*.acft%29) (Aircraft)\n", "* [mis](#Mission-File-%28*.mis%29) (Mission Computation)\n", "* [perf](#Performance-Chart-File-%28*.perf%29) (Performance Charts)\n", "\n", "file types.\n", "\n", "It is recommended to create a new file using the APP GUI and subsequenty modify this file using Python/pyAPP7, instead of creating a file from scratch with pyAPP7.\n", "\n", "Import the pyAPP7 modules" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from pyAPP7 import Files\n", "from pyAPP7 import Database\n", "from pyAPP7 import Units" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Units and Database modules are imported as well for this example. They are useful to convert units and translate APP indices to human-readable text" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aircraft File (\\*.acft)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To load an APP aircraft model, the class *AircraftModel* is used. A new instance can be created directly with the *fromFile* class method:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "aircraftpath = r'data\\\\LWF.acft'\n", "acft = Files.AircraftModel.fromFile(aircraftpath)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have the aircraft file available in the *acft* variable. All data within the aircraft can be accessed through class member variables directly, or by using *get* functions. This examples shows how to access fields in the *General Data* tab of APP's aircraft model GUI:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Aircraft Name:', 'LWF')\n", "('Author:', 'ALR')\n" ] } ], "source": [ "data = acft.getGeneralData()\n", "print ('Aircraft Name:', data.m_sAircraftName)\n", "print ('Author:', data.m_sAuthor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Getter functions exist for all the main datasets. To print lists of the available data sets, use:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Standard']\n", "['Cruise', 'TO Flaps 27\\xb0']\n", "['LWF']\n", "['AIM-9 Wingtip']\n" ] } ], "source": [ "print(acft.getMassLimitsNames())\n", "print(acft.getAeroNames())\n", "print(acft.getPropulsionNames())\n", "print(acft.getStoreNames())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example demonstrates how to loop through an X2Table (in this case the CL/CDi table) and correctly lable the drag polars:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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JUVlZWb06R8C1f3t6eq+Pe76Q63fy5BqevO6uoVKKgqoWV5ahvWW03anwM+qZHB/KRYlhTI4PJeTQltHHqmMYnN5n+jE0mBvYWLGRzPJMMisyKW4qBiDIGESUdxR59XnYnLaO441uRh5Pe5zZsbMPG6cvfg6V3Y5lzx7M2dmYc3Joy87BkpuLsloB0Pn6YkxKwjM5CUdLC42ffoYymzvO1zw9ifj9EwTMmXNa5tsXryGApmnHFUD0iVsYx0PTtBHAq8ClxwoehBCiJ0xWO+t313ZsTLW/oQ2AoZF+zJs8mKmJYYw+smV0H65jOJTJZmJL1RYyyzPZUL6B3LpcFAovvRcpESlcG38tqZGpxAXGodN0PPTtQx23MTx0HqRHp3cKHvoCZbdjKSzEnLPTFTBkZ2POy+sICHTe3hiTkgi86SaMScPwTE7GEB2NdsjKFUdDIy1r1qAsFjQPD3zS009b8HAuOCsCCE3TBgCfADcrpfLP9HyEEGe/oprWjlqGI1tG/+KiWKYkhBLpf0i2wNICe/p+HYPNaSO7JpsN5RvILM9kW/U27E47ep2eUaGjuHfUvaRFppEUkoRB13l77/kT5jP7s9lUtFYQ7BnM0xOePgPv4nDK4cC6d29HVsGcnY05NxfV5gr0dF5eGIcNI/D66zEmJWFMTsJ94MDDgoWuRP3lz+y54krs5eXog4OJ+vOfTsfbOWf0iQBC07T3gHQgRNO0UuApwACglHoZeBIIBl5qr2a2H096RQghDjDbHGzcW8eavCqWbzFRuSIDgCGh3txywUCmJoaREhOIh7595YDDDiUb+/y+Ek7lpKC+oOOWRFZFFia7CQ2NxKBEbh56M2mRaYwOH42n/ti3T7wMXrw07aWOPhBHLuE81ZTTibWoGHOOK6vQlpODeeculMnV4lvz9MQ4bBgBc3+CZ3IyxuRkV7Dg1vMVHzovLwa88nJHHwhZwtkzfSKAUEr99BjP3wl0KpoUQoju7G9o69hjYt3ugy2jEwJ13HdxIunxYQwIbv/S6GP9GLpT2lzakWHYWLGxY1OpGL8YrhxyJamRqYwLH3fCu07GBsby6axPe3PKXVJOJ7Z9+w5mFXJyMO/cibO1FQDNaMSYmEjAnDkYk5PwTErCffDgEwoWjsYjLo4hy5b22njnkz4RQAghRG+wOZxsKq5v3/66mrzKZgD6B3oyN6U/UxPCSBscTOb670i/IMZVx7B9WZ+vY6htq+0ofNxQvoH9La4GDKGeoUyImkBqZCppkWlEeEec0Xl2RymFraTkYFYhOwdzTg7OFldbb83DA4/EBPxnzWq/DZGMx5DBaHr5muqr5E9GCHFWq2p2tYzOyKviu/zDW0bPTRlKekIYQ0LbW0ZbWqBoFUN2vwU7f9dn6xhaba1kVWS5sgwVmRTUFwDga/BlXMQ4bhl2C2mRaQzyH9S5SVUfoJTCtn//IashsjHn7MTZ5OrOqRkMeCQm4nflFXh2BAtD0AydazJE3yUBhBDirOJwKraVNpCRW8WavGp27G8EINzPg8tHHGgZHYyv0XBwX4m1GYfVMfTTDDBoYp+pY7A6rGyr3uaqYyjPZEfNDhzKgYebB6PCRvGrMb8iNSKVocFD0ev61j/bSinsZWWu2xA5B29FOBpdfy4YDBjj4/GbOdN1GyI5GY/YWDR39zM7cXHS+tYnUQghulDfamVtQTVrcl0to+tNNnQajB0YyMMzXC2jh0b6ooGrjmHb593WMXy/18LkaTPO2PtxOB3k1ud2BAybKzdjdpjRaTqSg5P5WfLPSI1MZVTYKDzcPI494GmilMJeUYE5JwfvL75g39vvuIKF+vYtuPV6POLj8L3kYoxJyRiTkvBIiEcnwcI5SQIIIUSfo5Qip6ypffvrarbsq8epIMjbnakJYaQnhjE5LoQAL/f2fgwrIPP46xic+zJO+/spbiruWCmRWZ5Jk9WVzh/iP4Q5cXNIjUwlJSKlz7SFVkphr6rqyCocuA3hqHW14fHW6bDHxeFz0VTXaoikJDwSEtB59J2AR5xaEkAIIfqEJrONdQU1Hc2cqppd+zGM7O/PLy6KY2piGCP6+aOztbr2lVib0af7MVSZqjqKHjPLM6k0VQIQ4R3BRQMuIjUyldSIVEK9Qs/oPA+wdQQLrlsRbTnZOKprXE/qdHgMGYLP5MkdqyEyq6pIv+SSMztpcUZJACGEOCMOtIxe097MKavo8JbRUxNcLaNDvdxcdQyFn8LKvtuPocnaxI8VP3YEDXsb9wLg7+HP+IjxpEWmkRqZygDfAWe88NFeU3NYcaM5Oxt7VZXrSU3DfchgfCZM7FgNYUxM6NwjISPjtM9b9C0SQAghThuT1c4Pe1wto9fkHmwZnRjhy12TBzM1IYwx0f7o6/dA4XJY1kUdw4RfuDIMZ7gfg9lu7mgRnVmeyc66nTiVE0+9J2PCxzAn1nVbIiEooWMDqjPBXlfnyiwcCBiyc7BXVLie1DTcBw3CKy21YzWEMTERnbf3GZuvOHtIACGEOKWKalpdAUNeNRsKa7HanXi5uzEpNoT7L4olPSGUSF2Ta1+JrWvgk4wj6hiucQUMZ7gfg91pZ2ftzo4Mw9aqrVidVvSanuGhw5k3Yh6pEamMDB2Jwe3MLEe019e7MgoH6hZysrGXlXc87x4Tg9fYsRiTk/FMTsJj6DDcfCRYECdGAgghxHHLr2zm/nc388INY4gP9+18QNUunB/dxpbx/+aLikAy8qoorHF1FRwc6s3NaQOZmhDGuH4GPEozofATeKeLOoYhU12/n4I6ht31u/lL2V/oX9+f2MDYox6nlGJPwx4yK1wBQ1ZFFi02V9OjhMAErk+8ntTIVMaGj8XbcHJfwpaCgo52yh5xccd1jqOxEfPOnR1ZBXNODrbS0o7nDQMG4DVqFMYbb3LdikgahptvF39mQpwgCSCEEMfFZLVz+xsbKWs0c/sbP7Ly15Pxcnf9E7K/oY3vcoq4ePVVBNqrCV96C584n2HUkH7cOiGG9LhABprzoHAZfNdVHcN1rizDKa5jMNlM3LvqXipsFdy36j6WzFpy2F4P5S3lHc2bMsszqWlzFRH29+nPjJgZpEWmMS5iHMGewb02J6fJxL6778FeXk7J3fcweNnSTvUGjubm9szCwdUQtn37Op439O+PMTmZwOuvcwULw4bh5u/fa3MUoisSQAghjsvDi7dT02J1bRnRYuGON39kZHQga3KryKts5gXDf/F2a0CnKSLdmsmKeRt9wgzXSolv+0Ydw5Prn6TOXIdCUdtWy++++x2XDb6so45hX7PrSznIGNTRHjo1MpV+Pv1O2ZzKHnvctTRSKey1tex/5FGCbr7JlVVob8pkLS7uON4QFYUxOZmAa67BmOwKFvSBgadsfkIcjQQQQohj+vDHElbvqsJidwJgsTv5obCODYV1XDAkmDcj85i8exs6uxUAN6cFCr9x/eojdQxLCpbwbem3WByu5aEWp4XVJatZXbIab4M3KeEpHbcl4gLiTstKifrFi2lZswZlcc1JWSy0rFxJy8qVAOgjIzEmDcP/6tmuxkzJSRIsiD5DAgghRLcKq1t48vNszDZnp+cCvd159+pgeOUfYG/rfLJnEPxq22mYZdeUUuxt3Mu6snU8u+lZbE5bp2P83P3IuC4Dg+7UFz4qpbAWFWHKzKQ1M5PmFV+5dgE9gs7XlyFfLkcfEnLK5yTEiZIAQgjRicXu4KucSt7NLGZDYR0a4KZpONq/7LwwM9t9I7/2y4QXtgAaaG6gHAcHMXjBJX867XNvtDSSWZ7J+rL1rCtbR0Wra8lisDGYBksDjkPmaHQz8vC4h09p8GDbv5/WDZm0Zm7AlLkRe6WroZQ+LAzjyBFYcnaibAcDG83Tk/DfPSrBg+jzJIAQQnQorG7h/R9LWLyplLpWK9FBnjw8I4G5Kf15+vMcqnet42pWc5XbD3hrZtDFwvSnYeRPYcUjkPcl2M2gN0L8TBh94ymfs8PpILs2m/X7XQHDjpodOJUTH4MPaZFpzBsxjwlRE+jn04+Hvn2IjJIMLA4LHjoP0qPTmR07u1fnY6uqwpS50RUwbMjsWBnhFhSEV+p4vFPT8Eodj3tMDJqmUfrAgx23MTQPD3zS0wmYM6dX5yTEqSABhBDnuSOzDXqdxsXDwvnp+AFMig1B11YL217l+fpF6PR5mJQHa/QTmXbDbzAOnggHagVmvQgvpkJjKXiHwqwXTtmcK1or+KHsB9aVreOHsh9osjahoZEUnMSdw+9kYtREhocO75RZmD9hPrM/m015aznBnsE8PeHpk56Lvb4eU+ZGTBszad2QibWwEACdnx9e48YRdMsteKWl4hHXdV1F1F/+zJ4rrsReXo4+OJioP5/+rI0QJ0ICCCHOU91lG8K8DbBnNXy0yJVVcNrQ9UuhcsrfuXvLQP5x4ySMR/aBcPeGGz+Cj26HuW+4fu4lZruZzZWbWVe2jvVl69ndsBuAUM9QpkZPZWK/iaRFphFo7L7A0MvgxUvTXuLeL+/lxWkvHraE83g5mpsx/ZiFKXMDrZkbseTmAqB5eeGVMpaAa+bglZqGcWgimpvbMcfTeXkx4JWXO/pAdGoZLUQfJQGEEOeRY2YbGovhx2dg67vQtB+8gmH8PBhzM4QNJRz4dGo3LxA2FO7bcNLzVEpR2FjIuv2ugCGrMguLw4JBZ2Bs+FhmDZnFhH4TTmi1RGxgLI9FPdZtE6lDOU0mTJu3uAKGDZmYc3LA6URzd8dzzBhCH/gVXuNT8RyejGY4sVoKj7g4hixbekLnCnGmSAAhxHmg22yDEchdBm8tcrWTRoPYaTDjL5BwGejdT8scGy2NbCjfwPqy9awvW99R/BjjF8NP4n/ChKgJpISnnFDWoCecFgttW7d1rJRo274dbDbQ6/EcMYKQe+7GKzUNz1EjZetqcV6TAEKIc9Qxsw2VO+C738P2D8HcAAEDYOrjMOoG8O9/yud3oPhx3f51rCtbR3ZNNk7lxNfgS2pkKvNGzGNi1ESifKJO6TyUzUZbdvbBgGHzFldfBp0OY1ISwbfegldqGl5jRssmU0IcQgIIIc4x3WYb9GbY8RH87y0o3+ZqJT30StctipjJp3w77IrWCtfyyv3r2FC+oaP4MTkkmbuG38XEfhMZHjIcve7U/dOkHA70+/ZR+9rrtGZuoC1rE06TCQCPhAQCr78Or9RUvFJScPPzO2XzEOJsJwGEEOeAbrMNQ4LQ7VsHX/8ddn3uWmYZPhwu/QcMn3tKO0Oa7WY2VW5yFT/uX8+exj0AhHmGMTV6KpP6TSItMo0AY8Apm4NSCktBwcGllRt/JLipiSrAfdAg/GZd5VpaOX4c+qAzt9unEGcbCSCEOIt1m21QdbD1TfjybagvAg9/GHWjK9sQOerg8stedGAHywOrJTZVbjqs+HF27OwTLn7syRxsxcW0bsh0La3M3OjaawLXplO+F09nn58/Y2+7DUN42CmZgxDnAwkghDjL2JyKz7eVdZ1tGOSHruAr+Px3sPsbUE6IuRDSH4NhV52SjasOLX5ct38dlSZXp8VB/oOYGz/XVfwYkYKn/tRtmmUrK3MFDO11DPYKVwGmPiwM74kT8E5NxSs1Fff+rtqO/IwMCR6EOEkSQAhxljiQbXjvBxPNti2HZxvMxbD5P/Dp+2CqAd9ImPSgK+MQPKRX52F32smuye5oFX1k8ePd/e4+5cWP9upqWjM3dvRiOLC1tVtgIF6pqXintQcM7d0ehRC9TwIIIfqwrmobRoW68cvLxzJpgBHdzk/hw7egJBN0elf76DG3wJBp4NZ7f70rWis6VktsKN9As7X5tBY/2uvrMf34I6YNrgyDdY+rlkLn64vX+PEE3XQjXqlpeMTFop3iQlAhhIsEEEL0QUetbRjbj9I1CxmzaxksXgLWFgiJh4v/CCOvB5/eScsfKH78fv/3rC9bT2Gjqz1zmGcY0wZMY2LUxFNa/OhoaXEFDJkbac3MdHV7VMrV7XHsWALmXI3X+FSMw4YeV7dHIUTvkwBCiNMov7KZ+9/dzAs3jCH+iFbQ1vJszO/eynzjQywu8Tu8tiFSodvxASx6i7CaPDB4Q/LVMPoWiB7fbUHk7vrdPLT2IZ7NefLWAAAgAElEQVSZ/MxRuy8eWfy4p7CUKbk3kJH4LYMH9WNO3BwmRE0gNiD2uG8JWAoKOtoze8TFdXuss60N0+bNrgzDxkzM2TngcLi6PY4eTegvf+Fq3nQS3R6FEL2rTwQQmqa9DlwBVCmlkrt4XgOeAy4DTMBtSqnNp3eWQpwck9XO7W9spKzRzO1v/MjKX0/Gy11PYXULH2/I58ZNcwlXtfym5ffETV/C1eMGE1a5DrY8C+9/CU479B9HbsL9JM55FDx8j/2aNhP3rrqXitYK7lt1H0tmLeno5NhoaeSH8h9Yv9/V+fFA8WOsdxyz8u9H1+bBLcWPc8NtaRg8evZ/+U6TiX1334O9vJySu+9h8LKlh+3x4LRaMW/b1rHNddu2w7s9Bs+7C+/UNDxHj5Juj0L0UX0igADeBF4AFh3l+UuBuPZfqcD/tf8uxFnj4cXbqWmxohTUtFi48dVMPPQ6NhTW8aLhv4Tom3BDEaFr4u7cO2BbMzSXufajSL0HRt8MYYlUZGSQeBzBA8CT65+kzlyHQlHbVssvV/+S0eGjWb9/Pdm1B4sf06LSmBA1gYlRE9n2Xi1F1hocOGlrtrF60S5m3NUpru9W2WOPu5ZOKoW9tpayxx4n+LZbOwofTZu3oMxmV7fHYcPauz2m4jVmjHR7FOIs0ScCCKXUWk3TYro5ZBawSCmlgA2apgVomhaplCo/LRMU4iR9+GMJq3dVYbE7AbDYnWzZ10Cgl57XRuQytXA7OrsVAM1hgZo8CB0Gl/4N4i89of0olhQs4duSb7E4LK7XdFrIrMgksyKTESEjOlpFJ4ckdxQ/7lpXRvGOGhw21zwdNidFO2rYta6MoROPb1VF/ccf0/Ltt6520ICyWGhesYLmFSsA8IiPJ+DauXinpUm3RyHOYprrO/nMaw8glh3lFsYy4G9Kqe/bf14FPKKUyjriuHnAPIDw8PCx77//fq/Ps6WlBR8fn14f93xxvl6/X6xupdna+XFfd8VmwzwMjtZOz1kN/qyf2DkpdzzX0OK08FjpY1hV5xf11nnzt+i/dXle7idOHF3M080DEq8+vtUNob/+Dbr21tCHchqN1PxxPsr3+LInp9L5+jnsTXINT15fvYZTp07dpJRKOdZxfSIDcRy6qtrqFPkopRYACwBSUlJUenp6r08kIyODUzHu+eJ8vH5r86vxMGyl2Xr4N3OKoYiXQj/FUNuK6yN+yEfa4IX7ZX8lfXR6p/G6u4bN1mbey32Pt3a+hVVZ0Wk6nMrZ8bzRzcijaY+SHnv4+c11ZrZ8VYzTtr/TmHp3HZOvi2fohKNnIJTDQcuaNdS9uRBTF8GD5ulJv98/QdKVVx51jNPpfPwc9ja5hifvbL+GZ0sAUQpEH/Jzf6DsDM1FiONSWm/iT8t2sSKngphgLwaFerO9tJFwRzmPGD7kct0P0Bbs2pOiaB0UfOXap0JvdPVzGH3jcb9Wg7mBt3e9zbu73qXZ1szk/pOZN2Ieb+18i4ySDCwOCx46D9Kj05kdO7vjvKaaNjZ/Vcyu9eWgYOjEKFobLJTm1eOwOXEz6IgZHnLU4MHR0krjJ59Q99Zb2EpKMERFEfbII5g2baL1u+9QFguahwc+6ekEzJlz0tdUCNF3nC0BxOfA/ZqmvY+reLJR6h9EX2W2Ofjf2kJezNgNwEOXxHPX5ME4mqv54oUHmWX/CoemxzbxNxgufACMfjD6JngxFRpLwTsUZr1wXK9V21bLwp0L+SD3A0x2E9MHTGfeiHkMDR4KQNyEOGZ/NpuK1gqCPYN5esLTADRWm9j0ZTF5GypAB8MmRjF6xgD8gj2xWRy8+/QGWuosePm6c9EtQzu9rrV0P/Vvv03D4sU4W1rwHDOGsIcewnfaRWh6PYHXXcueK67EXl6OPjiYqD//qZeurhCir+gTAYSmae8B6UCIpmmlwFOAAUAp9TKwHNcSzt24lnHefmZmKkT31uRW8fTSHIpqTVyaHMHjlw+lv7eCdf+Gdc/xE6eJ5R7TSbz+LwwZfEhPBndvuPEj+Oh2mPuG6+duVLZW8mbOmyzOX4zVaWVGzAzuGn4XcYGH91vwMnjx0rSXOvpAWOtg/Zc7yd9Yic5NI2lKP8ZcMgCfQGPHOQYPN664fyRf/y+HS+5K6ljCqZSibctW6hYupHnlStA0/GbOJOjWW/AcMeKw19V5eTHglZc7+kAcuoRTCHFu6BMBhFLqp8d4XgH3nabpCNFjJXUmnl66k292VTI4xJtFPxvP5CGBsOUtyPgbtFRA4hVo057i8tD4rgcJGwr3bej2dfa37OeD2g/I/CQTp3JyxeAruHP4ncT4xxz1nNjAWF4f9w5ZnxTxddYG3PQ6RlzUn9EXD8Dbv+seC8FRPvz0KddKaWWz0fT119QtXIR5+3Z0/v4E3/EzAm+8EUNExFFf1yMujiHLlnb7foQQZ68+EUAIcbYy2xy8/O0e/i9jD246jUdmJnLHxBjcdy+Hl56G2gKIToNrF8GAE29dUtxUzKs7XmXZnmUopbg6/mruSL6D/r79uz2vprSFrOVF7NlShd7djVEXD2DU9AF4+R17WaijsZGGjz6i7u13sFdU4D5wIOFP/p6A2bMloyCEkABCiBP1zc5Knl6WQ0ldG5ePiOSJy4cS2bgNFv3ctblVSDxc/y4kXNZtq+nu7GnYw4LtC1hRtAKDzsC1CdeS2JzI1Rdc3e151fuayVpeROHWagxGN8bOGMjI6dF4+hw7cLAWFVG36C0alixBtbXhlZZGxFNP4jNlimxUJYToIAGEED1UXNvK00t3sjq3itgwH965M5WJ/rWw/A7I+wJ8IuDK52DUTSe8I2ZuXS4Lti/gm+JvMOqN3DLsFm5NupUQzxAyMjKOel5lURNZy4so2l6Du6eecZfHMOKiaIze3e8foZTClJlJ3cJFtGRkoOn1+F1xBUG33oIxMfGE3oMQ4twmAYQQx6nN6uD/Mnbz8tpCDDqNxy5L5LbhRty/m++qdTB4w0VPQNq9xyyCPJod1TtYsH0BGaUZ+Bh8uHP4ndw87GYCjYHdnldR2MiPXxSxL6cWDy89qVcNYvjUaDw8u/8r7rRaaVr2BXWLFmHJzcUtKIiQe+8l8KfXow8JOaH3IIQ4P0gAIcQxKKX4emcl85fuZH9DG1eNjOLxaf0I3/EyvPiSa5Or8fNg8sPgfWJfupsqN7Fg+wLWl63H38Of+0bdxw1Db8DPvfs2z2UFDfz4xV5Kc+sx+hhImz2Y4VP6436MwMFeV0f9e+9R/977OGpq8IiLI/LPf8Lviitk8yohxHGRAEKIbuytaeUPn+fwbX418eE+vP+z0aTVfQZv/APa6iD5J66sQ9CgHo+tlCKzIpNXtr1CVmUWQcYgHhz7INclXIe34egZDKUUpXn1ZH2xl/35DXj6GpgwJ5akyVG4G7v/K23Oz6du0SKaPl+KslrxnjKZ4FtvxeuCC457m24hhAAJIITokslq58U1u/nf2r2463U8cVkCt/lvRr/8F9BQDIOmwMVPQ9ToHo+tlOK7/d/xyvZX2F69nTDPMB4Z9wjXxF+Dp96z2/NKd9VTtEqxs2YLXv7uTJobx7ALozC4H327beV00vr999S9uZDW9evRjEb851xN0C234DF4cI/nL4QQIAGEEIdRSrEiu4I/LttJWaOZq0f348mkKgLX3QHlWyF8ONz0MQyZ1uOVFU7lZPW+1SzYvoBddbuI8o7i92m/Z1bsLDzcjn7bQClFcXYtWcuLqNzbhN4TJl8fz9CJkegNRw8cnG1tNH72OXWLFmEtLEQfFkbogw8ScO1c9IHd11QIIcSxSAAhRLs91S384fMcviuoITHCl1cu8WD4zvmweBX4R8PVr8Dwa6GHSxkdTgdfF3/Ngu0L2N2wm2jfaOZPmM8VQ67AoDv66gilFEXba/jxiyKq9zXjG2Rkyg0JVNnyGZ5+9P4PtspK6t95l4YPPsDR2IgxKYmof/4Dvxkz0Nx7vi24EEJ0RQIIcd5rtdh5fvVuXvu+EKPejX9OD+CaxjfRLf0IjP5wyZ9g3F1gMB57sEPYnDaWFy7n1R2vUtRUxGD/wfz1wr8yM2Ymet3R/+opp6JwWzVZy4uoKWnBL8TI1JsTSUiLwM1NR0ZGQZfntWXnULdwIU1ffgkOB77TpxF06614jh0r9Q1CiF4nAYQ4byml+GJHOX9atouKJjO3jPTlUZ8v8PrhdUCDib+ESQ+CZ8/S/VaHlc/2fMZrO15jf8t+EgIT+NeUfzF94HR02tGzF06nYs/mKrKWF1FX1op/mCfTbhtK/LhwdG5dn6ccDppXr6Zu4ULasjah8/Ym6MYbCLzpJtyjo7s8RwgheoMEEOK8VFDZzFOf57B+Ty2jIjxYPHwb/bNfBksTjLoRpv4O/LtvE30ks93MxwUf80b2G1SaKkkOTubR8Y8ypf+UbjMAToeTgqwqNn1ZRH2FicAILy7+2TBiU8LR6bo+z7WN9sfUvfW2axvtfv0Ie/QRAq65Bjdf3x7NWwghToQEEOK80mKx89w3+byxrggfd413Uwq4YN8CtE37IW4GTP8DhA/r0Zgmm4kP8z7kzZw3qTXXMiZsDPMnzOeCqO6XRjodTvI3VpL1ZRGNVW0ERXlzyZ1JDBkTdtTAwVq6H5/Fi9n90MNdbqMthBCni/yLI84LSik+31bGn7/YRVWzmafiS7i59U302bnQbyzMWQAxk3o0ZrO1mfdz32fRzkU0WBpIjUzlnyP+ybiIcd2e57A7ycusYNOXRTTVmAmJ9uHSu4czaGQIWheBg2sb7S3ULVxE88qVeAE+l15K0G234jl8eI/mLIQQvUUCCHHOy6to5snPssncW8c1YeX8IfgDfPdthKDBMPdNGDa7R0syGy2NvL3rbd7Z9Q7N1mYu7Hch80bMY1TYqG7Pc9ic7PqhnE0rimipsxA20JdJ18YTMzy4y0yFstlo+upr6hYuxLxjR8c22rmDBzPs6u430xJCiFNNAghxzmoy2/jPygIW/lBEkkc138YsZWDFSnCEwmXPwNjbwK37TaYOVdtWy8KdC/kg9wNMdhPTBkxj3oh5DAvu/paH3eZg5/flbP6qmNYGC+GD/Ei/IZEBSUFdBg6OxkbqP/yQ+nfedW2jHRNz2DbaO7vZTEsIIU4XCSDEOUcpxZIt+/nL8ly01irejvqatPqlaLUeMOVRmHA/eBx/oWFlayVv5rzJ4vzFWBwWZsbM5M4RdxIfGN/teTarg53flbH562JMjVYiY/2ZdutQ+icGdhk4WPbupf6tt2hY8qlrG+0L0oj4w1P4TJ4s22gLIfocCSDEOWVnWRNPfZ5NTlE5TwSt4jrtU9zqzK5sw5RHwDf8uMcqaynj9ezX+aTgE5zKyeWDL+fO4XcyyL/7fS+sZjvZa/ezdeU+2ppt9EsI4JKfJREVH9ApcOjYRvvNha5ttA0G/K680rWNdkLCiVwCIYQ4LSSAEOeExjYbz67M590fdnObcS1v+X2C0VQHQ6+CaU9BSOxxj7WvaR+v7niVpXuWggazY2fzs+SfEe3bfV8Fa5udHd+WsnVlCeZWG9FDA0m5bBBRcQGdjnVarTQtXebaRjsvz7WN9n33yTbaQoizhgQQ4qzmdCo+3lzK37/cxXjz96z3+5gQSylETICL50N09ysiDlXYUMiCHQv4cu+XGHQGrk24ltuTbyfCO6Lb8ywmG9vXlLJtVQkWk50BScGMuzyGiMH+nY6119ZS/9771L/3Ho7aWtlGWwhx1pIAQpy1svc38uRn2ehLfuAd7w9JMOSB31CY/gHEzzjulRV5dXm8sv0Vvin+BqPeyC3DbuHWpFsJ8ew+E2ButbFtdQnbV5dibbMTMyKElMtiCI/x63xsfr6rzfTSZbKNthDinCABhDjrNJpsPPN1Hj9u/J7H3D9ksscmlGcUTH0BRt0AuqPvUHmo7JpsXtn+ChklGXgbvLlz+J3cPOxmAo3dt65ua7Gy7ZsStmeUYjM7GDw6lJRLYwgdcHhhpnI6af3uO+oWLqR1/Q+yjbYQ4pwiAYQ4azidio82lfDGl+u5w/YuT7t/h+bhA5OeQku9B9y9jmuczZWbWbB9AevK1uHn7se9o+7lhsQb8PfofMvhUKYmK1tX7mPH2v3YrQ5ix4SRclkMwf18Dp9nWxuNn31G3cJFWPfulW20hRDnJAkgxFlhe2kDf1uSyYWVb/G5/iv0BtCl3gsX/ga8go55vlKKzIpMXtn2ClmVWQQZg3hgzANcn3g93gbvbs9tbbSw5et95Kzdj8PuJG5cOGMvjSEo8vDzOm2jnZxM1D//id/MGWiG4+83IYQQZwMJIESfVt9q5d8rduCx5TVe0n9GgL4FNfxatIuegMCBxzxfKcV3+79jwfYFbKveRqhnKL8d91uuibsGL0P3GYuWejObv97Hzu/KcDoVCeNdgUNA+OHnte3Ipm7RItc22k4nvtOmudpMjxkj9Q1CiHOWBBCiT3I4FR9sLCb7q1e51/ke/fU12AdNhUueRosceczzncrJmn1reGX7K+yq20WkdyRPpD7B7LjZeLh1v9qhqbaNLV/tY+f6MnBCwgURjJ05EP/Qg4GDcjhoXrWKuoWLaNsk22gLIc4/EkCIPmdrSQOffLSI6xpe5QZdMebQZLj0VfRDph7zXIfTwcrilbyy/RV2N+wm2jea+RPmc8XgKzAco211Y3Ubm1cUkftDBWgwdEIkY2YMxC/E8+D4LS00fty+jXZp6cFttH/yE9x8fLoZXQghzi0SQIg+o7bFwjuffs6Y/P8wX5dNq08/1Mz/YUz+CRyjlbPdaWf53uX8b/v/KGoqYrD/YP564V+ZGTMTva77j3lDpYlNK4rIy6xEp9NIujCK0TMG4htk7DjGWrrf1WZ68WKcra2ubbQffli20RZCnLfkXz5xxjmcis/XrMPju7/wS9ZhcvfHPOVPeF8wD/Td326wOWx8tuczXt3xKvtb9pMQmMC/pvyL6QOno9O6DzrqK1rJ+rKIgo2V6PQ6RqT3Z/QlA/AOcL1mxzbaby6k+ZtvQKfDb8YM2UZbCCHoIwGEpmkzgecAN+BVpdTfjnh+ALAQCGg/5lGl1PLTPlFx0lqqiiiaPxzmvkHM0BS25u2h6JM/cLn5C5ROT93oXxB0ycNgPHxJ5e763Ty09iGemfwMsYGxmO1mPin4hNezX6fSVElScBKPjHuE9Oj0ToWLtWUtfP2/HC65K4ngKB9q97eQ9WURuzdVoTfoSB7rS8jnzzDkl3/CI8CjYxvtooVvsMHSyNg6EwPu+BmBN96IIaL7rpRCCHG+OOMBhKZpbsCLwMVAKfCjpmmfK6V2HnLYE8CHSqn/0zRtGLAciDntkxUnxdTSyNidfyRM1VL9wXV8GXw5E2s+ZLhmoTRmDgPmzMfDv1/n82wm7l11LxWtFfx81c+5Nv5a3s19l5q2GkaHjebpCU8zIWpClysebBYHy17YRku9hc+f20r4QD/2bq/B4OHGmEsGMmJiCGXXz8FeXs6+u+YRMHcuDR9+iLm6inVJg2jz9GDziP6MvO8+DEZjp/GFEOJ8dcYDCGA8sFspVQigadr7wCzg0ABCAQf6A/sDZad1hqJX7Hr5FpJUI26aIkzVcGntQvIDL6T/3L8xsF/yUc97cv2T1JprUSgqWiv475b/khqRyj8m/4OU8JRul0quWrQLU5MVFJgarRRl15ByWQwjL4rG6GOg9IEHcdTUgFLYKyqoef55vC5II3viWKz79oLNiqmpga9efo4rHnjkVFwWIYQ4K3V/k/j06AeUHPJzaftjh/oDcJOmaaW4sg+/OD1TE71l4yf/ZWjzDxg1GwA6DczKQMOAS/DqJnhYUrCEjJIMrA5rx2Pubu5cPvhyxkWM6zZ4yPluP3u3VuO0q47H3PQ6/IKNGH0M1H3wIc3ffIOyHhxb8/CgIime4n17cdhcjztsNvZs3kj2mpUn/P6FEOJcoymljn3UqZyAps0FZiil7mz/+WZgvFLqF4cc82tcc/2XpmkXAK8ByUop5xFjzQPmAYSHh499//33e32+LS0t+MhyvR4bvuZmgrWmTo/XKj92TH2ry3OUUjxc8jAWZen0nI/Oh79G//Wor2czKfKXKlfu6ghuHpA8poSgv/8DzXnYRwinBl8nD8LZxaoPvacXI2+796ivebrIZ/DkyTU8eXINT15fvYZTp07dpJRKOdZxfeEWRilwaOed/nS+RXEHMBNAKfWDpmlGIASoOvQgpdQCYAFASkqKSk9P7/XJZmRkcCrGPZdZzCZKvw0kWB0eQJiUB3tGPdzl9WyxtvDU+qewKAs6TYfzkFjR6GbkkbRHSI/tfB7AvpxaVr6xE01nR9M4LAOhd9cxKrAYv3/8E83bGyyWjgxEk9Gd7QMjcOp0aDod6pDgQu/hwbRb7yK5D/zZy2fw5Mk1PHlyDU/e2X4N+8ItjB+BOE3TBmma5g5cD3x+xDH7gGkAmqYNBYxA9WmdpTgh5rZWcp+bxRBVTIHbEMzK1czJrAzs9J3A+Kt/2emcvLo8rv/ielbtW8UDYx5g+oDpHd0jPXQepEenMzt2dqfznA4nGz7dw9Lnt+Hl585Pfz+eQSNDcTO4PuZubhDSsAu/9/+G/1VXEffNSnwuugjl4UFBWCDr4vtj9TIy66EniEudgJvB3XWewcCQMeNJnnrxqbpMQghx1jnjAYRSyg7cD3wF7MK12iJH07T5mqZd1X7Yb4C7NE3bBrwH3KbO9L0XcUzmtlbyn5vFyLaNZCY9Sb8H11Cv+eNUUK8FMOyehZ3OWVKwhBuX34jJZuLVS17ljuF38MeJfyTIGISGRrBnME9PeLrTeS31Fj59dgubVhQzbGIkP3k0hcAIb6bdMhRPHwOgMLTWMqz4Y6JfeZmov/4FN39/3O+7h/VDoiiIDCLK4uC25xYQOy6Nmfc8gJe/P6Dh7R/IjHt+deovmBBCnEX6wi0M2ns6LD/isScP+e+dwMTTPS9x4symFgqeu5Jk8xY2jnia1GseAGD1sN+TnPsvmPsGXj4Hez202dv484Y/89mez0iNSOVvk/9GiGcIAF4GL16a9lJHH4gjN8Eqzqnlmzd2Yrc5mX77MBJSD/ZqsOfvZOSuBWz1vZi0yH3E/fcT3Pz8cDodZC1dwvoP38bg78u4ulZSn3kej7BwAAxGI3Me/QPL/vN3rnjgEVnCKYQQR+gTAYQ4t7S1NrP7v1eSZN5K1qj5h92m8AmLIebaHYcdX9RYxK+//TW763dz94i7+fnIn+OmczvsmNjAWD6d9elhjzkdTjKX7mXzimKC+3kz465kAiNcW2w7LRZqXniR2tdewzs0lJ/8egA+k28CoK6slBUvPUt5QR5x4ycw/c578fIP6PQ+QqIHctu/XuqVa3I62SpbqX03l+AbEjGEd79VuRBCnCgJIESvMrU0Uvj8VSSZt7Fp9J8YP/v+bo9fUbSCp9Y9hbubOy9Nf4lJ/SYd1+u01Jv5+rUcync3MmxSFBdeG4fe3RV0tG3fTtljj2HdvQf/a+YQ/uijuPn6opxONn/5Od+/twi9uzuX/eIhEidOOae23HZaHdS8kYOj0ULNGzmE/3osOne3Y58ohBA9JAGE6DWmlkb2/vcKhlp2sHnsXxl31c+PeqzVYeWZrGd4L/c9RoaO5JkpzxDhfXxtoouzXbcsHHYnF/9sGPHjXec5LRZqnn+e2tffQB8WRvT/FuBz4YUANFSUs+L//sP+3BwGjxnHxfN+gU9g0Mm/6T6mfnE+jhZX4yxHi5X6xfkE3zD0TE9LCHEOkgBC9IrW5gaK/3s5idYctqT8nZQr7z7qsWUtZfwm4zdk12Zz87CbeXDMg8fcahvab1l8vpfNXxUT3M+HGXclddyyaNu6lbLHHsdaWEjA3J8Q9tvfdmQdtq5cztp33sDNTc/Mex9k2OSLzqmswwEtG8tp21kLB5at2hXmXXW0/FiBzzjZw0MI0bskgBAnraWpnpLnLyfeuout458h5fI7j3pstimbx5c+jlM5eTb9WaYPnH58r3HoLYsLo7hwruuWhdNspvr556l740304eFEv/oqPpNc9baNVZV8/cpz7MvezsARo5lxz6/wDQ7plffcl9hr22jdVEnzmpJOjbOUzUnTiiIJIIQQvU4CCHFSmhvr2P/C5cRZc9mW+m/GXnZ7l8fZnXae3/I8r1e/TmJQIv+a8i8G+A04rtc47JbFHcOIb/8yNG3ZQvljj2Pdu5eAa68l7LcP4+bjg1KKHau/ImPRawBcPO9+hl8045zKOjitDtqyazBlVWIpbAQN3MI8cdSYwXEwitAMOvwujTlzExVCnLMkgBAnrKmhlvIXLmOIrYDtFzzL2Jm3dXlctamah9c+zKbKTUz0mch/Lv0PRv2xl0U6HE42fl7I5q/2Edzfh5l3JRMQ7uXKOjz3X+refBN9ZATRr72Kz0RX1qG5toavX/kvRds2E500ghn3/Ar/9qWZZzulFNaSZkKzNcrXZKIsDtyCjfhdMhCvMeHoAzyofXfXwdsYeg3j0CB8UiT7IITofRJAiBPSWF9D5YuXMti2hx0TnmPMjJu7PC6zPJPfrv0tbfY2/jLpL/iW+B5X8NBcZ2blazmU72kk6cIoJrXfsjBt3kL5Y49hLSoi4LrrCHv4oY6sw861q1nz5gIcDjsX/eweRl18GVoXe1qcbRzNVkybq2jNqsBe3cb/s3fnYVVV6wPHv/sMHOAwjyqDiojijKKWVmpaqTmUaZo0aHX7VTZcG2+TNpu3rvfeyuayUsuu5liaqYmWOSEiKqigoCAyc4BzDoczrd8fKEqgqICgrs/z9Ch7r7322huM96y99vt6qhXcevqjj22FS3uvGjMrvuOjsM7ZhcNQidrDBd/xUc04ckmSrmQygJAu2KngoZ3tMPsGvk/vm++u1cYpnHye/Dkf7fmItjm8B3QAACAASURBVF5t+fLmL4n0jSQ+K77e/jP3FrLh69QajyycFRXkvfM+xd98g7Z1a8K/+hL9gAEAGEuKWff5hxzZtYOQzl245ZG/49uqTWNf9iUlHE4sB4oxJeRhOVgMTnBp64XvHaEklB5g0LBOdR6nclETMLVrdR4I+QqnJElNRQYQ0gUpLcoj/6ORtLNnknL9XGKG3VWrTYmlhBf+eIEtx7cwsv1IZl47s1b2yLqc7ZGFOTGREy+8iPXoUXwmTSTomWdRe+gRQnDgz8389uXH2K1WBt/7IDEjRqNSXb6/NG15Jkw78zAn5eM02lB5avG8PhT3PsFog6ruoYg/cM4+tMF6Wk3vcymGK0nSVUwGENJ5MxTmUvjxSNraj5I66GN63XhnrTZJ+Uk8s+kZii3FvHLNK0yImnBeixfLiy38+sV+co+U0vWGEK6bEInKbiVv1jsUf/tt1azDvK/QX3stAOZSA+u//Ii07X/SOrITw6dNx69NaKNf86XgrLBj3lOAaVcetqxyUCm4RfvhHhuMa5QfivrKWfwpSdKVQwYQ0nkpKThB8ScjCLNnc2Dwp/QcMr7GfiEEC1IXMCdhDsH6YOaPnE9X/67n1Xfm3kLWf52C0y64+YGudOwbjDkhgZyXXsJ29Bi+k+8i8KmnUXtU5Xw4tO0P1n/xEdYKM9dPnkLsqNtRqS+vWQfhFFQeMWBOyMO8rwjsTjTB7njfGoF7TCBqD5fmHqIkSdI5yQBCqldx/nEMn4wkxHGcg0M+pcfgO2rsL7eWM2PLDNYfW8+QsCG8MfANvHXeZ+ntNIfDyfYVR9j96+lHFl6ekPv225TMX4A2JITwr79Gf01/ACrKy9jw1Scc/HMzwRGRDH90OgFhbZvkmpuKvdiCOTEPU0IeDkMliqsGfWww+thgtCEeV9SrppIkXdlkACGdU1FeNmWfjqSNI4e0oV/S44axNfYfKD7AU/FPVWWX7PM093W974IfWXS7IYSBEyKxJiVy5KWXsR07hm9cHEFPTUelr5p1SE/YzrrPPsBiLGfAnXH0GzsBteby+PEVNgcV+4swJeRRmW4ABXSRPngPb4dbV38U7eU1eyJJkgQygJDOoTA3C+NnI2jlyCP9pq/oft2Y6n1CCH5M+5FZ22fh4+rDvOHziAmKOa9+M5MLWf9NCk6H4OYHu9Khiyf5s9+hZMECtGFhhH/zDfr+/QCwmIxs/PozUjb/RmB4O+548XWC2kU0yfU2JiEEtmwjpoRczHsKEBYHaj9XvIaF494nGI2vLA8uSdLlTQYQUp0Kc49h+mwkQY58jtz8Nd0G3lq9z2wz8+a2N1l1ZBXXtr6Wd254Bz/X+gtTORxOcpOc7D+QTECYB7c82A3t0X0cGfsytqwsfO++u2rWwb3qbYOMpF38+un7mAwlXHPHJK4ZNxG1pv6aGc3JYbRi3p2PKSEPe54ZRavCrVsA7rHB6Np7o6jkIwpJkq4MMoCQainMOYrpi5EEOgrIuOVrug4YWb3viOEIT296msOGwzza81Ee6vEQ6vN4bbLqkcU+io5At0EhXDuiDcUfzOHEwoVVsw7ffoO+X9WsQ6XZzKb5X7D3t1/xDw1n7DMv06pDxya73oYSDoHl4MmcDQeKwSlwCfPE5/ZI3HsGonKV/8wkSbryyP+zSTXkH8+g8ouRBDiLyRzxLV2vGV69b/WR1by69VVc1a58ctMnDGgz4Lz6PPORRegAhdiIUo6Nfwxbdja+99xD0PS/V886HN2bxNpP/ouxqIi+Y8czYPxkNC4t840EW74ZU0Ie5sS8qpwNHlo8rmuDvk8w2mB9cw9PkiSpSckAQqqWl30Y25e34us0kDVyPl363wxApaOSd3e+yw8HfyAmKIZ3b3iXYH399SUcDifblh8had0xAsI8uPnuDhx5+yWOvb0JbXg4bed/i3vfvgBYLRVsXvg1e379Gd/WIUx6fTZtoqKb8nIvitNix5xcgDkhD+uxclCBa2d/9LHBuHbyRVFf/qmzJUmSzocMICQAcrPSsX91Kz7OUrJHLaBz36oy21nlWTwd/zSpxalM6TqFJ3o/gVZV/zqEU48sco+U0W1QCL3CSii4fyJuOTn43XcvgX//Oyo3NwCyU/bxyyf/oTQ/jz63jmXgxHvQ6lrOIkPhFFRmlGJOyKNiXyHC5kQT5I73yPa4xwSh9myZMySSJElNSQYQErnH0nDMuxVvZxnZoxfSOXYoAL8d+42X/3gZgP8O+S83ht94Xv1lJBey4esUnE7BTfdE4rl+HjmvLULbNpySp5+iy4MPAmCrtPDHovkkrlmJd1AwE2fMIrRLt6a5yItgN1gw78rHtCsPR7EFRafGvXcQ+thWaENlzgZJkq5uMoC4yuVkHoRvRuEpjJwYu4jOvQdjc9p4P/F9vt7/NV38u/DeoPcI8wyrt6+/PrK4IdaG6ZWpGE6cwO+++wj8+5Nkb99edd5Dqfzy0X8oOXGcXrfcyg2Tp6J1bf5ZB2FzUpFSeDpngwBdB2+8b2qLa1d/WZxKkiTpJBlAXMVyMg6gfDMKPWbyxv5AVMwN5JnyeG7zcyTmJzKx00Se7fssOrWu3r7Kiir49Yv95GWU0XVAMJHpSzA8uQiXtm1pu3AB7r17A+C029m8cB4Jq5bh4e/P+JffpG33Xk19qeckhMB23IhpVx7m3QUIix21jw7PG8PR9wlG49f8gY0kSVJLIwOIq9TxI6movx2NG2byb/8fHXtex585f/KPzf/A4rDwzvXvcGvErfV3xOlHFsIpGDxIi8vnj1N+Ihe/qVMJfPIJVCdnFnIPp5G6ZD6WkiK6D72FQXc/gM69/iqdTcVhsmHenY85IQ9brgk0Cm7dAtD3CUbXwUfmbJAkSToHGUBchbLT96FdMAYdlRTcvpj23a/h46SP+XjPx0R4RzBn8BwifOrP9uhwONm27DBJ67MICHGnd8VG7K99i6pdO9ouXIh776rMlA67jW0/LmL78sVo3Ny544XXaNerecpNC4fAklaCOSGXitRicAi0oR743NYB9x6BqNxbdqIqSZKklkIGEFeZrPS96BaMQYuNojt+xK9jFI+sf4StJ7YyOmI0L1/zMu7a+mcFznxk0bmTitCfXsF+Ige/++8n8InHq2cd8jOP8MvcORQcy6TroKFoOkQ3S/BgKzBj3pWHaVc+znIrKr0Wj2vbVBWxaiVzNkiSJF0oGUBcRY4dSsL1u9vRYscw4UdKA7Q8tGoCBouBmdfO5I6Od5zXmwUZewrY8E0qwino77Uf/acfoWnfntYLF+Aec2rWwc6OFYvZ9uMi3Dy9GPvsK0TG9ic+Pr6Jr/I0Z6WdiuSqBZHWo2VVORs6+aHvE4xrZz8UjczZIEmSdLFkAHGVOHowCf33Y1HhpGTCj2wWKfxn7X9o49GGBSMXEO1ff9Imh93J1uWH2bM+C38/heiED3HJPoD/gw8Q8Nhj1bMOhccy+eXj/5B3JJ3OAwdx49T/w83Tq6kvEahaEGnNLMOUkEfF3gKE1Ykm0A3vEe1wjwlG7SVzNkiSJDUGGUBcBY6m7kL/wzgATtyxkE/zFrAxayNDw4fyxsA38HTxrLePMx9ZRLhmEb7sPVzbh9Pm++9w69kTAKfDwc5VS9m6eCEu7npGP/UCUf0HNum1nWIvrcScmIc5IQ970cmcDT2DcI8NxiXcU+ZskCRJamQtIoBQFGU48F9ADXwhhHinjjZ3Aq8CAtgjhJh8SQd5mclMTYDFU0nucz+f53zODK2KsuFzePfQ21WvavZ9jruj7z7rL9aiHCNr5ybSZf9XuP/fU2xeZ8Bps9Pz+GL8D2/G/8H74bYx/PDRvxn19+dRqdX8MvffnEg/SMf+Axj2wKO4e/s0yrXY8kwUfXcA/8mda9SYEHYnFSlFVTkb0kpAgEt7b3xvDMete0CtnA35+fksXryYCRMmEBQU1ChjkyRJulo1ewChKIoamAvcBGQDOxVFWSmESDmjTUfgBWCgEKJEURT5f/9zMBtL0f0wCU+K+PDEZ+Rq1TzZLpiK/a/j7+bPvOHz6BV09twLtkoHP32QhLHYyraACThWFOKjKiX6z3/hHeJDm0Xfo4mKYt7Tj1BeVMiimc9jr6xE6+rKrU88S6cBNzTaJ36n1UHhvP04SispnLef4Kf6YC+swJyQhzkpH6fZjtrbBc8hYVU5G/zd6uzHarWycOFCSktLWbhwIdOmTcOlhRbpkiRJuhw0ewAB9APShRBHABRFWQSMBVLOaPM3YK4QogRACJF/yUd5GUn95F66iRJeCvKlSK1CKArldhMBrgEsHr0YX1ffcx6/4dtUzCUVoKhxaNxwM+fRa+fbBD44lYDHpqHS6Vj1n3cwGwwgBJUmI3pfP+5557/ofc7d94UqWXIIh9EKAhxlVk68vR1hcYBawa2rP/rYVugi68/ZsGLFCkwmEwAmk4kVK1YwYcKERh2rJEnS1aQlLEMPAbLO+Dr75LYzRQFRiqJsURRl28lHHlIddix9ny7lf7LaU8cmdzesqtPfYqPNSHxW/DmPT92SQ+buPJycnv6v1PmQF3It2nZtUel07Nu4jsMJ23HYbafbmE1k7E5o1Gsx7szFkloMdlG1wSkQlQ7cegXS5qX++E+OxjXKt97gITExkYMHD2K32wGw2+0cOnSIxMTERh2vJEnS1UQRQjTvABRlAnCLEOLBk1/fA/QTQjx+RpufABtwJxAK/A50E0IY/tLXQ8BDAMHBwX0WLVrU6OM1Go14eHg0er+NpfvGe/BXyhgUHkKxunbdBg+VB7PCZp31+ANLnTistbdrrWUM2Ps2he/+k6QvP8BhrazVRuPmTs8pj55zfBdy/9qvV6G21w4O7C6CzBud59VHaWkpSUlJ1PVzrtVqGTjw0izybEwt/WfwciDvYcPJe9hwLfUeDhkyZJcQIra+di3hEUY2cGalplAgp44224QQNiBDUZSDQEdg55mNhBCfAZ8BxMbGisGDBzf6YOPj42mKfhvL1r03cW3Rj9xRZuRzHy84Yy2Cq9qV5695nsGRg+s81mKycWT1NiqsVuD0cSpHJZFZqwl94R94+HqRaLdV9XvGL2WNTsfQ+/5Gt3ruzfncP3tpJaUrD1NhL6q1T9GqCBjTgXaxrc7ZR25uLhs2bCAtLQ0XFxccDgcOh6N6v1arZeTIkcSczFtxOWnpP4OXA3kPG07ew4a73O9hSwggdgIdFUVpDxwHJgF/fcNiOXAX8LWiKAFUPdI4cklHeRkoLy2mQ9FGCnDnV70enRBVeRFUKnQqHYPDBnNb5G11Hmu3Olj9UTKVRis+JYco847AqdKiclgJKEkhqpsHhsj2rPjnGwSEtcUrMJjMPYk4bFbUWi0devej25CbGjR+4RAY/8yhbN1REAKv4e2wZpdjOXDyMYZGwTXaD49zBA9FRUVs3LiRffv24erqytChQ+nfvz8rVqyofoyh0WiIioq6LIMHSZKklqLZAwghhF1RlMeAtVS9xvmVEGK/oiivAwlCiJUn992sKEoK4ACeFULU/nh6lUuZ/zR9RQkv9J7MUcPvvJ+bx5v+gRSoFPzd/HltwGt1Hud0CtZ9lcKJw6V0O/A1IW1UbPHrhrHEioutnO4l67A9/yor//kGvq3bMP7lN9G66KrewigsRO/tyy0PP9mgsVuzyilZmobthAnXTr74jI1E4+eK0+ogb84uHIZK1B4u+I6PqvP40tJSNm3axO7du9FoNFx//fUMGDAAN7eqtzLGjh3L3LlzKS0tRa/XM3bs2AaNV5Ik6WrX7AEEgBBiNbD6L9tmnPF3ATx18j+pDgd2rqdvwTIWtrqFNYY/GBE0hPZHNzIj5k3+nT2P9254r84aF0IIfv/hEEeSCojKXE6Yj5G2n3yDr1F1Mg/Ez2iefZyV//1nVfDwylu4e3kDMO4fr/LTf2Yz6u/Po3W9uJLXzgo7pWszMW0/gcrTBb+4aNy6+Ve/BqpyURMwtWt1Hoi/5nYwmUz88ccf7NixAyEEffv25frrr8fTs2ZyLBcXF+Li4qrzQMhXOCVJkhqmRQQQUsNYKy3o1jzFMZUf3wWW0kZpw6vD3sF9hDvtgEF9zv5pO3HtUfZtOk7bvE1EiEOEf7EAtZcX/l4w+a0bOLbPm2WzX8fnL8EDQEBYW6b866OLGrMQgoo9BRh+OoLTZMNjQBu8bm6LSlf7R1IbrKfV9JoFuCwWC9u2bePPP//EZrPRo0cPBg8ejK/v2V8jDQoKYtq0aRc1XkmSJKmmBgUQiqJsEUJcfsvYrzCJ37/ONc6jPNVtHNmmBL665avzqqh5cNsJti0/QitDMlHFmwj/biGagIDq/cf27akKHoJbMeEvwUND2AorMCxPpzLdgDbUg4Cp3XAJOb+VyDabjZ07d/L7779TUVFBdHQ0Q4YMkZklJUmSLrGGzkC0aZRRSBctK20PMRmfsdCvH+tMCcRFx9G3Vd/6j0spZsO3qfiZMuh67Efazv8abcjp9BvH9iWzbPbreAcFN1rwIGxOfNMV8tbvQlGr8BnbAX3/1vXmcQBwOBwkJSURHx9PeXk5ERERDB06lJCQv6YMkSRJki6FegMIRVE+BJKBvcA+IUT5GbubN4nEVU44nZQtfgytSsvXrQTh2nCeiHmi3uMKjpWz5pNkPCwF9Dg4j7ZffYquQ4fq/Vn7k1k2+zW8g4K5c8bbjVLTwpJWgmHFYfwLVbj1DMBnVARqz/rXITidTvbv38/GjRspLi4mNDSUcePG0b59+waPSZIkSbp45zMDkQz0AOKAboqilHEymADqL+MoNZmdKz6knzWZxzveRJ7lEF8P/rreRxdlhRX89EES6opSeu75kHYfz8GtW9fq/Vkpe1naiMGDo9yK4ecjVCQVoPF35Xisg/7jO9d7nBCCtLQ0NmzYQF5eHkFBQUyaNIlOnTrJypqSJEktwPkEEK2ADCHEYwCKooRSFVB0p+r1SqkZFOVl02nPOyzy6Ey8/SD3drmX3sG9z3lMhdHKyvd3Yysz0nv3+0S8+zr6fv2q92el7GXpO6/iHXjysUUDggfhFJh25FL6SwbC5sRzaDheg8M4sGVzvcdmZmayYcMGsrKy8PX1Zdy4cXTr1g2VqiVkXpckSZLg/AKIu4Hq0o1CiGwgW1GUNsD+phqYdG4ZC58kAiufhbjSzjWIx2MeP2d7m9XBz3P3UJ5volfSXDrO/DueNw6p3p+dso+l77yKV0AQE155q0FFsaw5RgzL0rFmlaPr4I3PbZFoA+tf1JmTk8Nvv/1Geno6np6ejBo1ipiYGNR1pOSWJEmSmtf5BBAVQghzHdu/BXYDZy+sIDWJPRsXE1u2nkfaDqDIlsO/h72Pq+bseRicTsG6L/aRl1FGt31f0umJu/AePbp6f3bqyeDBP5A7Z7x90cGDs9JO2bpjGLccR6XX4jexE269Aut95FBYWMhvv/1GSkoKbm5u3HTTTfTr1w+tVntR45AkSZKa3nkFEIqitBZCnDhzoxDCqiiK42wHSU3DbCwlaNMLLHUP5Q9VNvd3vZ+egT3P2l4IweZFB8lILiIqbTFd4wbhFxdXvT87dR9LZ72Kp38Ad86cdVHBgxACy/4iDKsO4yizou/XCu9b2qFyP3cAYDAY2LRpE0lJSWg0Gm644QYGDBiA60UmpZIkSZIunfMJIP4FrFAUZYIQ4uipjYqiBFGVVlq6hJLnP08XVSH/DelGpEcg03qdOzHSrl+Osn9zDuHHfqXHTe3xf/jh6n3ZB/azdNarePgHMOEiZx7sxRYMKw9jOVCMtrUev7hodOFe5zzGaDTy+++/k5BQVf67f//+XHfddS2yKp0kSZJUt3oDCCHEYkVR3IFdiqJsA5IAFTABeLVphyedKX3PH/TNXcSjoT0odZTx0cBPcVGf/VXI1D9PsH3FEYJzd9C7p5qg55+rfpxw/EBKdfBw54y38fD1O+e5bXmm6nTS2mA9wuGk/PfjlG84Bgp439oejwEhKOrTjyvy8/OrU0cHBQVhsVjIyMhgy5Yt2O12evXqxaBBg/DxafhropIkSdKldV6JpIQQ3yiKshS4HegKmIC7hBAJTTk46TS7zQorn+Bnd3/+dCnhoe4P0TWg61nbH9tfxMb5KfgWH6Bf6AnavPGv08HDwVR+nDUTD1+/8woenFYHhfP24yitpHDefnzv6IjhpyPY88y4dvXHZ3QHND66GsdYrVYWLlxIaWkpCxcupHfv3mzduhWLxULXrl0ZMmQIAWdkvZQkSZIuL+edifJkAqlvm3As0jkk/PA20SKDf7bpRJR3CA/3ePisbfOPlrHmoyT05dn090gmdM4HKJqqb3XOoVSWzppx3sEDQMmSQziMVhBUBRFf7kPto8P/3i64dfGv85gVK1ZgMpmAqkqZGzduJDIyEm9vb0afsYBTkiRJujzJYlqXgZyMA/RI+4jprTtiEpW8dd1baNV1L1AsLahg1b8T0JhK6G/fRPtPP0B1svJkzqFUfnx7Bnof36rgwa/uX/5nMu7MxZJaDPaTSUcFoFLwGBR61uAhMTGRgwcPYrfbq7dpNBq6dOlCWVnZhV28JEmS1CLJzDwtnHA6KfzhMTa66/jTrYKHej5EZ7+6MzlWGK2sfG87dqOJvqWr6PjJHFR6PQA5hw7w49szcPf2YcJ5Bg8AZb9kImzOmhudgvL1x+psb7VaWb16dY3gAcBut7Nhw4bzOqckSZLU8skZiBZu1+oviLDu4uG2HYj2i+DB7g/W2c5mdbDqvR0YSyqJzVtK9Jfvoj65OPFE2sGq4MHLhztnzMLT7/zWHjitDlS+LjhNthrbFa0KrxHtarU/evQoy5cvx263o1KpcDpPBx5arZZhw4ZRWlp6nlcuSZIktWRyBqIFKy3KIyLhDV4IDKdC5eDN695Eq6r96MLpcLL2gwQKTlTSPftHen78BtqT5a1PpB1kyVuv4O7lzZ0zZ+Hpf37Bg73YQsFHe7AfN6Fp5Q6ak29XaBRco/3wiG1V3dZms7F27VrmzZuHEIIpU6YQHR2N5uS6C41GQ1RUFDExMQ28I5IkSVJLIWcgWrCD8/9OgbuTP92dPNnrcaJ8o2q1EUKwcd4ejqaZ6Jy9ir7/fRaX0FAATqRXBQ9uXl5MmPH2eQcPlnQDxd+lIpwQMKUrLu29yZuzC4ehErWHC77jT48jOzubZcuWUVRURGxsLDfddBM6nY42bdowd+5cSktL0ev1jB07tnFuiiRJktQiyACihdr3x0oiyn7hifB2dPPvzJSuU+pst3PpAQ4klNA2ZwMD3p6KrmNHAHLTD/HjWzNw8/Lizhmz8AoIrPecQgiMW3IoXX0ETYA7Afd2QRPgBkDA1K7VeSBULmrsdjvx8fFs2bIFLy8v7rnnHjqcURLcxcWFuLi46jwQLi71l+6WJEmSLh8ygGiBLGYj3hue46XANtjUKt667i00qtrfqpSNmexcd4JW+TsY/I9bcetZldI6N/0QS956BVcPD+6c8fb5BQ82ByXL0jEn5uPaxR+/iVGodKfPqQ3W02p6H6Cq6NWyZcsoKCigd+/e3HzzzXWmnw4KCmLatHNnypQkSZIuTzKAaIF2L3yZYvdS/tQH8HTM40T4RNRqk5GUS/yidHwNBxn6cCweAwYAkHs47XTwMHMWXgFB9Z7PXlpJ0fwUbNlGvIaF43ljOIqqdgEsu93O77//zubNm/Hw8CAuLo6OJ2c8JEmSpKuLDCBamIyUnbQ9sYDpYSH0DOzOPV3uqdUm73AJaz9JRm/M4abxIfjcPKxq+5F0lrz1Mjq9x8nHFvUHD5WZpRQtSEVYnfjf0wW3rnW/3pmbm8vy5cvJzc2lZ8+eDB8+HDc3t4ZdrCRJknTZkgFEC+Kw26lcOo13AgOwaTS8OfBN1Cp1jTaGPBOr3tuOtqKMYUM0BE64DagKHha/+RI6dw8mzpyFV2D9wYNx+wkMKw+j8XXF/2/RaIP1tcfkcLBlyxbi4+Nxc3Nj0qRJdO5cdx4KSZIk6eohA4gWJOHH98h1zeZPd3+e6/N32nm3q7HfXFbJijc24bDaGdbdQMiDDwEnZx7efBmdu75qzUM9wYOwOzGsPIxpRy6unXzxm9QZlVvtH4X8/HyWL19OTk4O3bp1Y8SIEej1tYMMSZIk6eojA4gWIi/7MIEH3+eJsAB6B8UQFx1XY7+t0sGKV9djrlRzQ5sMIp5+ouq4k8GDi7s7d86YhXdQ8DnP4yi3UrQgFevRMjwHh+J1c7ta6x2cTidbt27lt99+Q6fTMWHCBLp2PXvhLkmSJOnqIwOIFuL4d4/xSaAHDo0Lbw58E5VyOseX0+Hkp1fXUmzS0d8ziS6vPYWiKORlHGbJmy+jdXM7r+DBmlVO0fwUnBV2/CZ3xr1H7bczCgsLWb58OdnZ2XTu3JlRo0bh4eHR6NcrSZIkXd5kANECJK6dT4ZqD1vd/Xmx79OEeYVV7xNCsO6ddeSUuNJDlUif2U+iqFTkZx6pCh5c3Zg4s/7gwbQrj5Jlaag9XAh8pCcubWoGBU6nk+3bt7NhwwY0Gg3jxo2je/fu1SXAJUmSJOlMMoBoZmWGIrQ7ZzA7xJ9+wX2Z2Glijf1bPtxIepaWSFsyAz96HEWrJT/zCIvfeAmtzpU7Z87CO6jVWXoH4XBSujoD45YcdBHe+MVFo9bXTIddXFzMihUrOHr0KFFRUYwePRpPT88muV5JkiTpyiADiGaWMn86X/hrEBodr1/3Ro1HF7vnb2HPfgixHGToBw+i0umqgoc3X0aj03HnjLfxCT578OAw2ShemErlkVI8BrbBe2QEivr0jILT6SQhIYF169ahUqkYO3YsvXr1krMOkiRJUr1kANEMMlMTOLp8Km+H+jLakcl2L29m9H+eEI8QcremsPaLfUT39iYhWYV3xX5K/FIxGEYiiguqggcXFybOmIVPq9Y1+rXlmarTTQuHXES6rgAAIABJREFUoGh+Co5yK74TojCFKXz0yUdMmDCBoKAgDAYDK1asICMjgw4dOjBmzBi8vb2b6Y5IkiRJl5sWEUAoijIc+C+gBr4QQrxzlnbjgcVAXyFEwiUcYqMxG0tx/m8Sb4VoyXUU86mPF7GBvRnfcTyVJeX8/MUBLBo/diaD3pKDyS0RY24JS956BYfNhsbFpWrm4S/Bg9PqoHDefhyllRR8mozT6kDtriXo/3pCsI6FJwtbLVy4kAEDBrBhwwYARo0aRZ8+feSsgyRJknRBmj2AUBRFDcwFbgKygZ2KoqwUQqT8pZ0n8ASw/dKPsvGkfnIvCwKgWK1CKAoI0Os8URSF1S8tp1IdBIoKhKDC8SeVJiMIgamkGLWLC5Pf+he+rdrU6rdkySEcRisIcJrtKO4agh6PQe3pwuLFizGZTACUlZWxZs0a2rVrx9ixY/H19b3Ut0CSJEm6Aqjqb9Lk+gHpQogjQggrsAioq/bzG8A/AculHFxj2rH0fdLFbra466hUnbz1CmzN3sKPb35Iri0Qoapa4Giv3I/ZlovDZq0+XgGOp+6v1a9xZy6W1GKwi9MbbU4qDhSTmJjIoUOHsNvtQNVbHWq1mh49esjgQZIkSbpozT4DAYQAWWd8nQ30P7OBoigxQJgQ4idFUZ45W0eKojwEPAQQHBxMfHx8ow/WaDRedL/d97zLs209qVDVjNus2CnMCMWpPV3y2m75A7DXaGe3WtnwzecUKjXfomj3mwqNreYjCGFzUrjqEGt0v2Oz2WrsczgcrFmzhrKysou6joZoyP2Tqsh72HDyHjacvIcNd7nfw5YQQNT18L36o7SiKCrg38CU+joSQnwGfAYQGxsrBg8e3DgjPEN8fDwX2++O4md59PB7zA7wwnZG9kcXNPh7HiWvohucXIugcb0Oe8V6wFndTqPTMfS+v9HtjPMLh5O85CTsOaYa51K0KvxHR9D24DHS09Nr7NNqtYwcOZKYmJiLuo6GaMj9k6rIe9hw8h423MXeQ5vNRnZ2NhbLZTuZ3Gi8vb1xdXVttvO7uroSGhqKVqutv3EdWkIAkQ2EnfF1KJBzxteeQDcg/uRCv1bASkVRxlxuCyn7jXsC39c/R40TuwChKGiFwrCQIdg2eqO4OEAIhEqLWnHDjhNFpUI4nai1Wjr07ke3ITdV9yecguL/HcKeY0LTWo+9wFz1GEOjoO3sw/rsbaSnp+Pj44PRaMRut6PRaIiKimqW4EGSJCk7OxtPT0/atWt31S/eLi8vb7acO0IIioqKyM7Opn379hfVR0tYA7ET6KgoSntFUVyAScDKUzuFEKVCiAAhRDshRDtgG3DZBQ8A1koL29wrsKhU+DicKELgrw/mjq3dKHYNp3cvFTqHCeEwYa34FZ+gVnj4+gEKem9fbnn4yeq+hFNQ8mMaFXsK8BrejqBHeqL2qHoE4tCr+MW+iz179jBkyBAeeeSR6iJYer2esWPrWmIiSZLU9CwWC/7+/ld98NDcFEXB39+/QTNBzR5ACCHswGPAWiAV+J8QYr+iKK8rijKmeUfXuJJ++Yr/eWsIU3x5I19NmHso74e/QHKGN96qMvo9PJSRD3RCmH5GUVkZ/fSLjHvhNfxDw7j9HzPRnpzqEkJgWHkY8648PIeG4zU4DJWLmoCpXbEGqljjuYeMzAzGjBnDoEGD0Ol0xMXFERgYSFxcHC4uLvWMVJIkqenI4KFlaOj3odkDCAAhxGohRJQQooMQ4q2T22YIIVbW0Xbw5Tj7IJxOUtM+J9NFyxPXvcCgF/by0/jVHP04iUqdL4Mf7I1KpZBXnonVkc2gu+8nqF0EAWFtmfKvjwgIa1vVjxCU/pyBadsJPAaF4jUsvPocpeoKVqp3UlRazF133UXv3r2r9wUFBTFt2jSCgs5d6luSJOlKpygK99xzT/XXdrudwMBARo0adVH9tWvXjsLCwsYaHgAZGRn079+fjh07MnHiRKxWa53tZs2aRWRkJJ06dWLt2rWNOob6tIgA4mqQsn0tq7zMtFK8uKndzQAcm7eEIy7daN/GSmjvcAqPZbJp/pe079WHmBF1T76U/XoU4x/H8RjQBu/hp58hZmVl8eWXX2K1WpkyZQpRUVGX7NokSZKa0qG8cm7+9yYO5ZU3Sn96vZ59+/ZRUVEBwLp16wgJCWmUvhvL888/z/Tp00lLS8PX15cvv/yyVpuUlBQWLVrE/v37+eWXX3j00UdxOByXbIwygLhEtu94l4M6Fx7q8zhqlRprbi5b1xWiVpwMemIwNmslP7//Li7u7gx/dHqdU0tlG45RvjELfb9WeI+OqG5z4MABvvnmG1xdXXnggQda3D8ESZKki2W22pk6bwdp+UamztuJ2Wqv/6DzMGLECH7++WcAvv/+e+66667qfTt27GDAgAHExMQwYMAADh48CFS9Av/MM8/QvXt3evTowQcffFB9zAcffEDv3r3p3r07Bw4caNDYhBD89ttvjB8/HoD77ruP5cuX12q3YsUKJk2ahE6no3379kRGRrJjx44GnftCyADiEjh+JJUNrtn4Cx23Rd8BwJ43vqTIpzOxw1qj93Fl84KvKMw6yvBHp+Pu7VOrj/LN2ZStO4p7TBA+t0VWBw8JCQn88MMPBAUF8cADD+Dv739Jr02SJKkpPbskmUKjFSGg0FjJc0uSG6XfSZMmsWjRIiwWC8nJyfTvfzr9UOfOndm8eTO7d+/m9ddf58UXXwTgs88+IyMjg927d5OcnExcXFz1MQEBASQmJvLII4/w3nvv1TrfwYMH6dWrV43/Bg4cSK9evTAYDDXaFhUV4ePjg0ZT9aJkaGgox48fr9Xn8ePHCQs7/RLj2do1lZbwGucVb/2vr5LspuOJTlPRqrSU/LKOZGNHvHyt9BrXncO7tpO09mf63DqW9r361DreuDWH0tUZuHUPwHd8FIpKQQjBxo0b2bx5M5GRkUyYMAGdTtcMVydJknRxXlu1n5Scsye0yy+zcLTYjPNkZqBKu5PVe08w5N2NBHnVnT+hSxsvZo7uWu+5e/ToQWZmJt9//z0jR46ssa+0tJT77ruPtLQ0FEWpTsa3fv16Hn744epf7H5+ftXHjBs3DoA+ffqwdOnSWufr1KkTSUlJNbad7TVOIUStbXXNSp9vu6YiA4gmVl5azCaRhJfTjXtiH8BRXs72zzZhCbqRmx/sTkVpCb98/F8C20Vw3V1Tah1v2pmLYcVhXKP98JvUCUWt4HA4WLVqFUlJScTExDBq1CjUavWlvzhJkqQmlFVSUR08nOIUVdvPFkBciDFjxvDMM88QHx9PUVFR9fZXXnmFIUOGsGzZMjIzM6sTZgkhzvoL+tQHOLVaXV064EwHDx5k4sSJNa/F6USlUhEfH4+Pz+mZ54CAAAwGQ3XunuzsbNq0qV0DKTQ0lKys04mcz9auqcgAoon9tOp1drq7cG+r0bhqXEmf/W8y/K+jfaSO0C7+LHnrFezWSm594lk0f8kGZt6dT8nSNHRRvvjHRaOoVVRWVrJ48WLS09O54YYbGDJkiHwlSpKky1J9MwX/25nFzJX7qbCdXhjoplXz+tiuTIgNO8eR5+f+++/H29ub7t2710gpXVpaWr2W7Ouvv67efvPNN/PJJ58wePBgNBoNxcXFNWYhzuVCZiAURWHIkCEsWbKESZMm8c0339SZv2fMmDFMnjyZp556ipycHNLS0ujXr995jacxyDUQTchht7Ox/Ff0Tnj0xn9g3rWLxFQXFI2a6+/vQ8JPyzi2bw9D7nsI/5Ca/xjMewspXnwQXXtv/O+ORtGoMBqNfPPNNxw+fJhRo0Zx4403yuBBkqQr1p19w7gxOgidpupXlU6jYmh0UKMED1D1Cf7JJ5+stf25557jhRdeYODAgTXeanjwwQcJDw+nR48e9OzZk++++65RxlGX2bNnM2fOHCIjIykqKuKBBx4AYOXKlcyYMQOArl27cuedd9KlSxeGDx/O3LlzL+lstFLXM5QrQWxsrEhIaPx0EReS/33lT+/xUtE33Ka/ltfGfMjOSdNJCLqDfiNCCeum8P0rz9Ahtj+jp79QIxCoSC2iaH4qLmGeBNzfDZVOTVFREQsWLKC8vJzx48fTuXPnRr+2S0HWIGg4eQ8bTt7DhrvYe5iamkp0dPR5tzdb7dw0ZxM5BgttfNxY99QNuLtcGZPnzZnK+pS6vh+KouwSQsTWd6ycgWhCq7MW4uYUTB8xi/xPPifFYyCeHtDtxhB+fv+f6H38uPmhJ2oED5a0EooWpKJtoydgaldUOjXZ2dl8+eWXWCwW7rvvvss2eJAkSbpQ7i4a5k3tR8dgD+ZN7XvFBA9XAvmdaAKbdq3g7d0vojbC20tCWZ26B6/UExhbBeJq+YG1n/yJIS+XiTNmoTYp5H6+C//JnXGabBR9m0K5n53fxBYmlIVgOGZg8eLF6PV67r77bgICApr78iRJki6pqGBPfp0+qLmHIf2FDCAaWZEhl9d3v0ipE977UUN6579RWerE0HogzooFlJWVUlZwnL5j7qBNZDR5c3bhKK2k4PO9OCsdCB81vyjbKS0qY968eVRUVNC6dWsmT57c7FNdkiRJknSKfITRyJ794TYManhktSA35G5sWg9QVNjM63DYjFWNFIXS/DxKlhzCYbSCAKfRBgpsbZ2JyWwGoKKiAr1ez5QpU2TwIEmSJLUoMoBoRP9aNI29LuUM2CsIKe9PiV83nGoX7JZ9OG1HEOLkal4hcBwwYd5fAPbTi1gPcpy0w+k13iG2Wq3s37//Ul+KJEmSJJ2TDCAa0XJzPBaVisnxgmNtx+JUVyUWsVv+AGomFunmNRDFUfMVzJ1KGjZHzXY2m40NGzY06bglSZIk6ULJAKIR3eY+GBen4LvBCuFHV6ByVJVfVbsOrNV2X9kWhFLzFdrOonYRLK1Wy7Bhw5pmwJIkSVehK6mcN8CxY8fw8PCoswZHU5IBRCN6etJcAhwK8T1UlKu3obOUgBColJNrVZWqBB9qrRb3jgEoKHByEsKssZKmzUOr1VbnWddoNERFRRETE9MclyNJktQy5KfC3Guq/mwEV0o571OmT5/OiBEjLuHoqsgAohEJIbApoBNQ4qGjUueJ4rBgt2xFpQ3A088PUNB7+9JTPwhcVKg8XXDiJN5lP5WKjXvvvRe9Xg9U/ZDXlb5UkiTpqmE1wcIJUHCg6k+rqVG6vRLKeQMsX76ciIgIunatv4BYY5MBRCPaemADBRq41RRKeFlfnBp3WpUuRzgNDL5nCuNeeBX/0DBGj3oK29FyfEd1IPCBbuzyOkaOs5jRo0cTFhZGXFwcgYGBxMXF4eLi0tyXJUmS1HxWTANTASCq/lzxWKN0eyWU8zaZTMyePZuZM2c2yj25UDIPRCP6OXEeihCMsw5kd+v2+PkrFGsFQfoO9Lp5EIqicPcrc8ibk4gu0gf32GBSU1PZYz1M37596dmzJwBBQUFMmzatma9GkiSpia35B+TuPfv+8lwoOQLCWfW13QIpy+H9ZPBsVfcxrbrDiHfqPfWVUM575syZTJ8+HQ8Pj/out0nIAKIR7alIIdoBtl3FmDwH0rptATkbcrn9+ZkoioIQgpKl6SAEvuM6UlhYyPLlywkJCeGWW25p7uFLkiS1LIajp4OHU4SzavvZAogLcLmX896+fTtLlizhueeew2AwoFKpcHV15bHHGmeWpj4ygGgkh3L2c1RrZ2pRKEcsoWh9rBzd8wutIqNoH1NVk8ScmE/loRK8R0fg0Cv88PkPaDQa7rzzzuqIVpIk6apR30xB4gJY8yzYzKe3ad1h5HsQE3f2487T5V7O+/fff6/++6uvvoqHh8clCx5AroFoND/++QkA/bI7UBDQk4A22ZQX5jNwQhyKouAot2L46Qgubb3QX9OalStXUlRUxPjx4/H29m7m0UuSJLVAve+GqFtA41r1tcYVooY3SvAAl3857+YmP/Y2kkTDdjrY7ZQeD0QEOsg/9jttoqJp27M3AIYV6QibA987OrJj5w7279/P0KFDiYiIaOaRS5IktWBj58Lc/lCaDfpAGPthg7s0Go21tg0ePLj6UcW1117LoUOHqve98cYbQNWr9XPmzGHOnDk1js3MzKz+e2xsbI3ZjIsVERHBjh07am0fM2YMY8aMqbX91VdfbfA5L5ScgWgEBeV5HNKYuSk9gOM+MXi57cNkKGLgxLtRFAXz3kIq9hXhNawtORUF/Prrr3Tq1InrrruuuYcuSZLUsrnoIW4xBHau+tNF39wjkk6SMxCN4H9/fIJTUWh3oh8ZHq7YyncT2qUbYV174DTbMKxIRxviAb28WfzFZ/j4+HD77befdTGOJEmSdIagaJi2rblHIf2FnIFoBNtyNtDBYKeI7mjsu7AYDQw4ufbB8NMRnGY7XrdHsGTpEiwWCxMnTsTV1bW5hy1JkiRJF00GEA1ktppIVRUzam8UZR6tsdr2EN6tJ2FdulNxsBhzYj6eg0PZvH8bx44dY8yYMQQHBzf3sCVJkiSpQWQA0UArdsynUgFP4/UIyy5slSYGTIjDabFjWJqGJsidrFblbN26lb59+9KjR4/mHrIkSZIkNZhcA9EAm3atYE38/5h26FGOK1tRrCaiu12Pek0F6d47WVPxBzdeP5SVq1YSGhoqk0VJkiRJV4wWMQOhKMpwRVEOKoqSrijKP+rY/5SiKCmKoiQrirJBUZS2zTHOMxUZcnl360wGpj+Iyfo7wlmCIgTdGUhFXjkrj26iRGVi2W8/odFomDBhgkwWJUmS1AJcKeW8Fy5cWKO2hkqlqpWsqik1ewChKIoamAuMALoAdymK0uUvzXYDsUKIHsAS4J+XdpS1PfvDbdy6Mw6zbQeIqixp/QJGIEx2NmtTqKDqm+10OgkODpbJoiRJki5Sekk6t624jfSS9Ebp70op5x0XF0dSUhJJSUnMnz+fdu3a0atXr0s2xmYPIIB+QLoQ4ogQwgosAmrk7BRCbBRCnMplug0IvcRjrOFfi6YRtjcaG2qc9kzAQTuPbrRx70CaOo8sVSEO5XT+9uPHj5OYmNhs45UkSbpcmW1mHt3wKEcMR5i2YRrmM9NaN8CVUs77lL9ew6XQEubUQ4CsM77OBvqfpS3AA8CaunYoivIQ8BBAcHBwo2QD+yuj0chyUzx3V7yFsfI7oKpoSk+/wWhULuzUpGNXahZ/sdlsrFmzhrKyskYfz+XGaDQ2yfflaiLvYcPJe9hwF3sPvb29KS8vP+/2L29/maKKIgSCwopCXtz0Im/0f+OCz/tXo0ePZvbs2QwaNIikpCQmTZrExo0bKS8vJyQkhJ9//hmNRsPGjRt57rnnWLBgAV988QVpaWls3ry5uhZGeXk5Qgg8PDzYtGkTn3/+ObNmzeLDD2tmzExLS2PKlCk1tp0qzvXzzz/XKKZVVFSEl5dX9QyJj48PWVlZ57xv33//PYsWLbqgewtgsVgu+t9CSwgg6sqmVLuWKaAoyt1ALDCorv1CiM+AzwBiY2PFqbSkjSk+Pp7bjIPJcVuBv/0abJbNgJ09xfH08b+ZvvZI/tQcrDEDodVqGTlyJDExMY0+nstNfHw8TfF9uZrIe9hw8h423MXew9TU1OoCUrN3zOZA8dk/rReYC8gyZuE8WZHT6rSy8fhG7lp3F4HugXUe09mvM8/3e77ecVx77bVkZ2fz008/MWrUKNzd3dFoNHh6emIwGLj//vtrlPP29PTkjz/+4LHHHsPX1xeg+joURWHy5Ml4enoycOBAVq9eXatIVu/evUlOTq6x7WzFtCwWCyqVqnqfh4cHarW6zrZQVZXTw8OD/v3P9dm7bq6urhf9u6klPMLIBsLO+DoUyPlrI0VRhgEvAWOEEJWXaGx1enrSXI52T0GLA0VT9dws07iPclsJnRxt8BCu1SGQRqMhKipKBg+SJEkX6LjxeHXwcIoTJ8eNxxul/1PlvP869X+qnPe+fftYtWoVFosFaHg57zMXPPbq1YuBAwfSq1cvDAZDjbZnlvMGzlrO+5RFixZd8scX0DJmIHYCHRVFaQ8cByYBk89soChKDPApMFwIkX/ph1jb47f8l5fLn2b4/knY7UcBsKkqQVU1paJWVDhwotfr6yzDKkmSdLWrb6ZgWdoyZu2YRYW9onqbq9qVl655idsib2vw+S/3ct5QtVB/8eLFbN68+bzG0ZiafQZCCGEHHgPWAqnA/4QQ+xVFeV1RlFMlx94FPIDFiqIkKYqyspmGWy23JItWZX4IZ1Xk6OUXTJA+HFtHVwwqM3179SEwMJC4uDhcXFyaebSSJEmXn9s73s4NoTegU1d9utepdAwOG9wowQNcGeW8N2/eTGhoaLNUdlaEqHO5wWUvNjZWJCQkNHq/p577zV3zGjlrMgko9sbDs5T7Xv6A/A92k9G3kg17/+Dxxx/H39+/0c9/uZPPnhtO3sOGk/ew4RqyBiI6Ovq825ttZm5bcRu5plxa61uzbOwy3LXuF3zeluhsMxCXUl3fD0VRdgkhYus7ttlnIC5XJ0oz8TMHIRyFBEZEUJlRCkBG2XH8/f1l8CBJktQI3LXufDT0IyJ8Ipg7dO4VEzxcCWQAcZEKLbl4WP0QzlIC27an8kgpDl8NR7OPERUV1dzDkyRJumJE+kayfOxyIn0jm3so0hlkAHGRSuwGEK6AICC0LdbMUvICzDgcDhlASJIkSVc8GUBcJIMwY1VX/d3XvRVOs51j5OPq6kp4eHjzDk6SJEmSmpgMIC6CEAJdqQcOYUBRFFyNrggERwqziIyMRK1WN/cQJUmSJKlJyQDiIhSZC2lTHIRwFOHh5Y/tqJEizwrMFWb5+EKSJEm6KsgA4iIcPLaHVmVVAURAaFsqM0rJ9ipFURQ6duzY3MOTJEmSzuFKKedttVqZOnUq3bt3p2fPnjWSYQ0ePJhOnTpVZ73Mz2/8HIwygLgI6SeS8TFXvYER2r4rTqONo9Y8wsPDcXNza+7hSZIkXVEq09I4PGo0lWlpjdLflVLO+/PPPwdg7969rFu3jqeffhqn83Tq74ULF1aX+w4KCmr0McoA4iIcLz6Mi63qXeQAbRvKqaCgvEg+vpAkSWpkTrOZY//3MNbDh8n6v4dxmmU571NSUlIYOnQoAEFBQfj4+NAUCRTPRgYQF6HAdBxx8ta5VbiT7V6VzrpTp07NOSxJkqQrTs6LL+EoKgIhsBcVkfPSy43S76RJk1i0aBEWi4Xk5OQalSw7d+7M5s2b2b17N6+//jovvvgiAJ999hkZGRns3r2b5ORk4uLiqo8JCAggMTGRRx55hPfee6/W+S6kmFZRURE+Pj5oNFXlqkJDQzl+vHYBsZ49e7JixQrsdjsZGRns2rWLrKys6v1Tp06lV69evPHGGzRF1umWUEzrslNmLsGmVKIoKsQJG1muxfi5+snsk5IkSRcg9+23qUw9+6d1W0EBtmPH4OS0vKispHztWtKHj0AbWHc5b110Z1qd/IV/Lj169CAzM5Pvv/+ekSNH1thXWlrKfffdV6OcN8D69et5+OGHq3+xn1lIa9y4cQD06dOHpUuX1jrfhRTTquuXfV1VQO+//35SU1OJjY2lbdu2DBgwoHpsCxcuJCQkhPLycu644w7mz5/Pvffee857cqFkAHERdIUuCEcxgZ7hVJaZOe6WT98e/c5a5lWSJEm6cLbs7OrgoZrTiS07+6wBxIU4Vc47Pj6eoqKi6u2nynkvW7aMzMzM6pofDS3nPXHixL9cihOVSkV8fDw+Pj7V288s563RaM5azluj0fDvf/+7+usBAwZUL+Q/tabD09OTyZMns2PHDhlAtASepX44HUWE+V/HcVUJDuGUjy8kSZIuUH0zBSU//kjem28hKk6X81bc3Gj1ysv4nPzE3xCXezlvs9mMEAK9Xs+6devQaDR06dIFu92OwWAgICAAm83GTz/9xLBhw85rnBdCroG4QFZHJV7GABDlBHu3I0tXhE6nk9knJUmSGpnvHXfgMWgQyslP94pOh8fgwY0SPMDlX847Pz+f3r17Ex0dzezZs5k/fz4AlZWV3HLLLfTo0YNevXoREhLC3/72t0YfoyznfYEWrPyKoiUHsFemML7Lc/yg+pP2nTswYcKERj/XlUiWUW44eQ8bTt7DhrtU5bydZjOHR43GfuIE2tatifhpFSr3K6MipyznfZUpMGaidqhxV3tSZCnD7LDI1zel/2/vzqPjKs88j3+fqtJiLZa120bygpETGxDYYJbJGXAObQymY4c0a5OAGZYODacnEw/dDAkk7iYnzBA63RzgEGbS2E7oBEInjQnYTgNxQ2ZswBBwYtyWMd43yZItqWSVSiW984fKQpYtq26VVJt/n3PquOrWrXsfHkpVT733vfcRkVHiKyhg0o+eIXfaNGp/9EzWFA/ZQHMgPDrcuZcKypkwZgq7/Yd09UkRkVGWV1fHtF+/kuowZBCNQHjU2d5CD0HGF0xlV6CZ2tpaClQRi4jIaUYFhEdjmo3enmYKCqpopl2HL0RE5LSkAsKjMYcLyffBoUDfaUU6fVNERE5HmgPhUX5HIZX549nlO8S44hIqKipSHZKIiEjSaQTCA+cc/u5cyvNr2Oc7zOdmfF5XnxQRyTCZ0M77ySef5KyzzsLMTrnt5cuXU1dXR11dHcuXLx/RGIajAsKDvc07oBd6i0rosV4+93kdvhARGW3N+4L8bOk7NO8Ljsj2MqGd9xe+8AVef/11Jk+ePOQ6LS0tLF26lHfeeYd3332XpUuXcvjw4aTFqALCg4+3vYff5ziUGybXn6OrT4qIjLLurh5+/eRHtBzo4NUnN9Ld1TP8i2KQzu28AWbNmsWUKVNOuc6aNWuYN28eZWVllJaWMm/ePFavXp3wvmOlAsKDvQ2bKM8tZLf/EGfWTu3veiYiIqPjjRWb6WzvBgdH28O8uWLziGw3ndt5x2qL/QE4AAAVd0lEQVTv3r3U1tb2Px6q7fdo0TegBx0NjZQVnslR6+Dz9TNTHY6ISEZ7+8UGDu0e+rBER2sXbU2dHOu40NPdyycfNNL08DoKS/JO+pqK2iL+8w3Dn16fzu28YxVr2+/RogLCA9cYJlRagNFB3ed09UkRkdHU3hzihO9I17d8qALCi3Rt5x2rmpqa47qI7tmzJ6k9XlRAeJDTOYbG6hDVheUUFhamOhwRkYw23EjB5v+7j7deaCAS7u1fFsj1cdlN05nxnyYmvP90becdq/nz5/Pggw/2T5z8zW9+w/e///24t+eV5kB4UFxUQ7OvnZyu7lSHIiKS9WZ8YSKTz63An9P3VeXP8THl3IoRKR4gvdt5P/HEE9TU1LBnzx7q6+u58847AdiwYUP//bKyMh566CHmzJnDnDlzePjhh2MuaEZCWrTzNrOrgH8E/MD/cc49Ouj5PGAFcAHQDNzonNtxqm2OdDvv7Rs/4mf/8gphizCmN4cbrvtTptafN2LbP12ojXLilMPEKYeJS1Y77+6uHv556XqCLV0Ul+Vz83cuJifP73m/6UjtvBNkZn7gKeBqYCZws5kNnqF4B3DYOXcW8EPgfyY3Slj9szWE6Tuu1WUR1vxsTbJDEBE57eTk+fnT+86jbEIh19xXnzXFQzZIeQEBXAR84pz71DkXBn4OLBq0ziLg2CW2XgKusCRONX1uySM054YgusdecxzKDfHckkeSFYKIyGmrfGIRN3/nYsonFqU6FBkgHSZRngHsHvB4D3DxUOs45yJm1gqUA8dd39PM7gbuBqiurj5uUkwiDhYaEes9blnEejlY6B+xfZwugsGgcpYg5TBxymHi4s1hSUkJ7e3tIx9QBurp6Ul5LkKhUNx/C+lQQJxsJGHwxIxY1sE59yzwLPTNgRipY5zbX/kde4t8xxURAedjfAc6juqRjj0nTjlMnHKYuETmQKT6uH+6SIc5EPn5+cyaNSuu16bDIYw9QO2AxzXAvqHWMbMAUAK0JCU64PbHv015OL+/ZPE5oyKcz+LHv5WsEERERNJKOhQQ7wF1ZjbVzHKBm4CVg9ZZCdwWvX8d8KZL8ukjV908n7zogE2uCzD/5vnJ3L2IiEhaSXkB4ZyLAPcBa4DNwIvOuU1m9rdmtjC62o+BcjP7BPgm8ECy45xafx6TmnzgoDJ3jE7hFBHJUNnSzvvDDz/k0ksv5eyzz6a+vp4XXnhhRGMYTsoLCADn3GvOuenOuWnOue9Flz3snFsZvR9yzl3vnDvLOXeRc+7TVMTZ1rGHqt6xhHLSIm0iIqeFQ7t3smzJX3Jo984R2V62tPMuKChgxYoVbNq0idWrV/ONb3wj7sZc8dA3oQeRgjAV3Xk0dR6hra0t1eGIiGS97lCIXz76XZr37uZXjy6lOxQake1mQzvv6dOnU1fX15dp4sSJVFVV0dTUlPC+Y6UCwgNfVQ55HR0ANPxHQ4qjERHJfquf+QeOtraCc3S0HmbNM/84ItvNhnbeA7377ruEw2GmTZuW8LZilQ6ncWaMws9PoOM3zRSVlbJl48dceNGwV/oUEZEh/HbZszTuHPqIdPBwC60H9ve3re7p7qZh/e/48Te2UVR68p4PVZPP5IuL7x5239nQzvuY/fv387WvfY3ly5fj8yVvXEAFhAeTPlfP5l+/zqSeOhr27aS7u5ucnJxUhyUikpXaGg8y+IQ75xxtjQeHLCC8yPR23gBtbW1cc801PPLII1xyySVxbSNeKiA8mDl1Dpvcm5R2+YkEeti+fTvTp5+6Ha2IiJzccCMFf/ztv/Hmc8/Q3dXVvyyQl8cVt3+dc744L+H9Z3o773A4zLXXXsutt97K9ddfH/d24qU5EB5Uj5uI84EFDxNwfrb8x5ZUhyQikrXO+eI8ps6egz8nFwB/Tg7TZl80IsUDZH477xdffJG33nqLZcuW9c+rGFykjKa0aOc9Gka6nfcxf3/7XzCRQjprptFS3Mk3718y5JCWnEiXEE6ccpg45TBxSWvnHQrx3JJ7aD90iLEVlSx+/Gly8vM97zcdpcOlrDO6nXemCRV30BTazeTeCtqPBjlw4ECqQxIRyVo5+fl85YHvUl5Ty7UPfCdriodsoALCo85xHYRdgLLuvgkzDQ06nVNEZDRV1E5m8eNPU1E79EWVJPlUQHgUqnT4/OW0H91PlSuhYYsKCBEROf2ogPCosLASP4XsP/optZFy9u7bm/J+7iIiIsmmAsKj0vwz8OOjKbSLSb0VAGzdujXFUYmIiCSXCgiPKsdOpTsQIdTTwbiCIor8YzQPQkRETjsqIDyqKjyD4Ji+QxYd/lYm9VSwbdu2/kudiohIesuWdt7HtLW1ccYZZ3DfffeNaAzDUQHhUcACtBU3Y74S9h/5lNpwGd3d3ezYsSPVoYmIZKXugx0c+OH7dB/sGJHtZUs772MeeughLr/88iREdTwVEHFoL23B/OXsat7MhN5SAr6ADmOIiIyC3nAPh57bRKTxKIee20RvuGf4F8UgG9p5A7z//vscPHiQK6+8MuF9eqUCIg5d5RF8vlJagnvJKy2gNr+KLVu2nND0RUREEnP4pQZ6gmFw0BMMc/ilkfmxlg3tvHt7e1myZAmPPfZYXK9PlJppxaF0TDk5vblEcFAdoHZnKdvdPg4ePMj48eNTHZ6ISEY48so2wvuGPizR09ZFT0sIjv02izg6/3CI/Y+9h39s3klfkzuxkHFfmjbsvrOhnffTTz/NggULqK2tjXsbiVABEYeqwhqwXgCOjumgprMU8vuuSqkCQkRkZPQc7vqseDjG9S0fqoDwItPbea9bt463336bp59+mmAwSDgcpqioiEcffdTztuKhAiIONeV1HMzZC2GjKbSbGmoZP7aShoYGLrvsslSHJyKSEYYbKQi+d4DWldtw3b39yyzHR8miaRRdmPiPtUxv5/3888/331+2bBkbNmxIWvEAmgMRl2ln1HOksBnzjWPPtk34S3KZnFPNnj17CAaDqQ5PRCQrFM0ZT/6MMghEf/UHjPwZZSNSPEDmt/NONbXz9mjt2rWcf/F53P+925m5Ywpj8lq4Yd6D7Nm6i1/2/D8WLVrErFmzRny/2UJtlBOnHCZOOUxcstp594Z7OPj379NzpAv/uDyqv3kBvly/5/2mI7XzPg2V5I9jX9lBzF9OR/AwgUlFlHbkU1xUrNM5RURGkC/XT8XtZxOoLqDi9rOzpnjIBpoDEQczI1J0lICNpQdHZ0EHhnFmaS2bt31CJBLpn6UrIiKJyakuZPx/uyDVYcggGoGIU6mvkJzoobGWtn34inKY5CoIh8O6KqWIiGQ9FRBxKguU4qwTMA7t3UXemSVUNeYTCOiqlCIikv1UQMSpcswE2vMOYb5SmnZsJ29qCdbWw9TaKboqpYiIZD0VEHGaWHomzYVNmL+Cpu3byTuzBICpRRNpbW2lsbExxRGKiIiMHhUQcZoyfib7xjXh85cTbG3GjfXhKwxQEyoF0GEMEZE0lU3tvHft2sWVV17JjBkzmDlzZlLn4KW0gDCzMjP7NzPbGv239CTrnG9m68xsk5ltNLMbT7atZJsxaRafVjZh/nIAnvuvfwHVOQR2hqn0l/CHDzfy1FNPaSRCRCRBjY2NI/p5mk3tvG+99Vbuv/9+Nm/ezLvvvktVVVWSIkz9CMQDwBvOuTrgjejjwY4CtzrnzgauAv7BzLxfNHyE5fb6iPgiYH0XAQm2trBp07/T2xZmYmgcjc1NNDU18fzzzxMOh1McrYhIZgqHwzz//PMj/nmaDe28P/74YyKRCPPmzQOgqKiIgoKChPcdq1QXEIuA5dH7y4EvD17BOdfgnNsavb8PaAQqkxbhEB78xfXctv7P6Ql9drXLUtcXVpOvrX9ZR0cHL7/8ctLjExHJBi+//DIdHX0dO0fy8zQb2nk3NDQwbtw4vvKVrzBr1izuv//+4y69PdpSfbWjaufcfgDn3H4zO+XYi5ldBOQC25IR3FAe//m91P5hBt346Y1sB2BK0TlU5J/BFv8+Gn2t/etGIhEaGhr44IMPmD17dqpCFhFJO6tWreLAgQNDPt/e3s7hw4f7z2qLRCJ8/PHHPPHEE0NeAnr8+PFcffXVw+47G9p5RyIR3n77bX7/+98zadIkbrzxRpYtW8Ydd9wR9za9GPUCwsxeB07W+eRbHrczAfgJcJtzrneIde4G7gaorq4+rrvaSAkGg/xrx1q+2vk9gl3/DPS1bT2vbC4BXy7vBT4hYseH193dzapVq2hrazvJFk8vwWBwVP6/nE6Uw8Qph4mLN4clJSW0t7cDfZ+Np/rFfOTIkRNOiXfOceTIkSGH6ru7u/u3fyrt7e3Mnz+fJUuW8Nprr9HS0kIkEqG9vZ0HHniASy+9lBUrVrBz506uueYa2tvb6e7uprOz84TtO+f69xsKhejq6jphna1bt7J48eITXmdmvPrqq0O283bOEQwG+9uFD1RaWkp9fT2VlZV0dnYyf/581q9fzw033DDsf/8xoVAo7r+FUS8gnHN/MtRzZnbQzCZERx8m0Hd44mTrjQVeBb7tnFt/in09CzwLfc20RqNZztq1a/lycC77xrxMeeQSukNvARE+alnLBeXzmBM5i3WBLccVETk5OSxYsEBNtlATo5GgHCZOOUxcIs20jv3qXrhw4SnX/eCDD1i1alX/CACM3OdpcXEx99xzD1VVVVxyySWsXbuWQCBAcXExR48eZdq0aRQXF/PSSy9hZhQXF7NgwQJWrFjBggULjmvnbWYUFRVRXFxMYWEhfr//hJGF2bNns3HjxuOWxTICMXDbg82dO5e2tjZCoRCVlZWsW7eOCy+80NOoRn5+fty5TPUciJXAbdH7twEnHNwys1zgV8AK59wvkhjbkJbc9BS7z91MLj34AlMAPzuCf2Tf0U+p66mmtrcCv+tLbSAQYPr06SoeREQ8mj17NtOnT+8/ZDDSn6eZ3s7b7/fzgx/8gCuuuIJzzz0X5xx33XXXqMU0WErbeZtZOfAiMAnYBVzvnGsxswuBrzvn7jSzrwLPAZsGvHSxc+7DE7f4mdFs5z137lyajxzgtp9exZc2/DXB0Cvg2hlXNp6rJ91FV2sn/5K/niAhSkpKuPfee8nNzR3xWDKRfvklTjlMnHKYuGS18w6Hwzz11FO0trZm3eep2nknwDnX7Jy7wjlXF/23Jbp8g3Puzuj9nzrncpxz5w+4nbJ4SIbyceO5/9KlfDT1nxibdzklZdUsevAhKv/LOYypLubPb7yZyspKbrnllqx5s4uIJFtubi633HKLPk/TUKrPwshol1+wiMsvWBR99PX+5cfazt47494URCUikl2qqqq49159nqabVM+BEBERkQykAkJERJJK3YrTQ6L/H1RAiIhI0uTn59Pc3KwiIsWcczQ3N5Ofnx/3NjQHQkREkubYqYlNTU2pDiXlQqFQQl/gicrPz6empibu16uAEBGRpMnJyWHq1KmpDiMtrF27NqOvEaRDGCIiIuKZCggRERHxTAWEiIiIeJbSS1mPJjNrAnaOwqYrgEOjsN3ThfKXOOUwccph4pTDxKVrDic75yqHWylrC4jRYmYbYrlGuJyc8pc45TBxymHilMPEZXoOdQhDREREPFMBISIiIp6pgPDu2VQHkOGUv8Qph4lTDhOnHCYuo3OoORAiIiLimUYgRERExDMVECdhZleZ2RYz+8TMHjjJ83lm9kL0+XfMbEryo0xvMeTwMjP7wMwiZnZdKmJMdzHk8Jtm9rGZbTSzN8xsciriTGcx5PDrZvYHM/vQzH5nZjNTEWc6Gy6HA9a7zsycmWXsWQWjJYb34WIza4q+Dz80sztTEadnzjndBtwAP7ANOBPIBT4CZg5a5y+BZ6L3bwJeSHXc6XSLMYdTgHpgBXBdqmNOt1uMOfwiUBC9f4/eh3HlcOyA+wuB1amOO51useQwul4x8BawHrgw1XGn0y3G9+Fi4MlUx+r1phGIE10EfOKc+9Q5FwZ+DiwatM4iYHn0/kvAFWZmSYwx3Q2bQ+fcDufcRqA3FQFmgFhy+Fvn3NHow/VA/G31slMsOWwb8LAQ0KSw48XyeQjwd8D/AkLJDC5DxJrDjKMC4kRnALsHPN4TXXbSdZxzEaAVKE9KdJkhlhzKqXnN4R3AqlGNKPPElEMzu9fMttH3BfhXSYotUwybQzObBdQ6536dzMAySKx/y38WPRz5kpnVJie0xKiAONHJRhIG/yqJZZ3TmfKTuJhzaGZfBS4EHhvViDJPTDl0zj3lnJsG/A3w7VGPKrOcModm5gN+CCxJWkSZJ5b34SvAFOdcPfA6n41wpzUVECfaAwys/mqAfUOtY2YBoARoSUp0mSGWHMqpxZRDM/sT4FvAQudcV5JiyxRe34c/B748qhFlnuFyWAycA6w1sx3AJcBKTaQ8zrDvQ+dc84C/3/8NXJCk2BKiAuJE7wF1ZjbVzHLpmyS5ctA6K4HbovevA9500ZkwAsSWQzm1YXMYHTr+EX3FQ2MKYkx3seSwbsDDa4CtSYwvE5wyh865VudchXNuinNuCn1zcRY65zakJty0FMv7cMKAhwuBzUmML26BVAeQbpxzETO7D1hD3+zZf3LObTKzvwU2OOdWAj8GfmJmn9A38nBT6iJOP7Hk0MzmAL8CSoEvmdlS59zZKQw7rcT4PnwMKAJ+EZ3Du8s5tzBlQaeZGHN4X3QUpxs4zGc/DISYcyinEGMO/8rMFgIR+r5TFqcsYA90JUoRERHxTIcwRERExDMVECIiIuKZCggRERHxTAWEiIiIeKYCQkRERDxTASEiIiKeqYAQERERz3QhKREZNWZ2PfDfgTFAAfAT4ADwXeAgfX0CGoC/ds5tT1GYIhIHjUCIyKgws9voa1D1Z9EmQecDR4F64GHn3PnOufOAN4BfWvRymiKSGXQlShEZcWY2FtgOzHHOfTroubeAv3HOrRuwbD9wsXNuV3IjFZF4aQRCREbDtcA7g4uHqHOATYOWddLXF0VEMoQKCBEZDWcDHw5eaGa1QLtzrm3AshxgAvBp9PHfJStIEYmfCggRGQ0dnPzzpZ4TRx9uB950zrWb2Xg0uVskI6iAEJHR8BpwvZlVA5hZnpndBZwL/PHYSmZ2JfA/6DtTA2AWJxm5EJH0o0pfREacc+49M/susMbM/PR91vwUmAnMNbMr6DuFczNwlXNuS/Sl5wO/TEHIIuKRzsIQkbRhZj8G7nLO9aY6FhE5NRUQIiIi4pnmQIiIiIhnKiBERETEMxUQIiIi4pkKCBEREfFMBYSIiIh4pgJCREREPFMBISIiIp6pgBARERHPVECIiIiIZ/8fqwkJzsflVN0AAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "i = 0\n", "aero = acft.getAero(i) #get the first aerodanymic dataset, in this case 'Cruise'\n", "\n", "fig = plt.figure(figsize=(8.3, 5.8)) #A5 landscape figure, size is in inches\n", "ax = plt.subplot(1,1,1)\n", "\n", "for val, table in zip(aero.cdITable.value,aero.cdITable.table):\n", " ax.plot(table[:,1], table[:,0], 'd-', label='Mach = '+str(val))\n", " \n", "# adjust Axis properties\n", "ax.set_title(acft.getAeroName(i))\n", "ax.legend(loc='best')\n", "ax.set_xlabel('$CD_i$')\n", "ax.set_ylabel('$CL$')\n", "ax.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For an detailed explaination of the XTables classes, consult the pyAPP user guide.\n", "\n", "A more involved example would be to compare lift curves of all available aero datasets:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8.3, 5.8)) #A5 landscape figure, size is in inches\n", "ax = plt.subplot(1, 1, 1)\n", "for aero, aeroName in zip(acft.getAeroList(), acft.getAeroNames()):\n", " ax.plot(aero.clTable.table[0][:,0]*Units.DEG, aero.clTable.table[0][:,1], label=aeroName.decode('cp1252'))\n", "\n", "ax.set_xlabel(u'$AoA$ [°]')\n", "ax.set_ylabel(u'$CL$')\n", "leg = ax.legend(loc=2)\n", "#fig.savefig('CL_comparison.png',dpi=200)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Additionally, this example demonstrates the use of the *Units* module to convert from radians to degrees.\n", "\n", "**Note**: In oder for the legend label for the *TO Flaps 27°* setting to be printed correctly, the *aeroName* string has to be converted to unicode with the enconding of the original text file, in this case *cp1252*. In addition, to print the ° sign in the x-axis label, the string has to be unicode and is typed with the prefix 'u'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mission File (\\*.mis)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The mission file is loaded using the classmethod *fromFile* in the MissionComputationFile class:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "missionpath = r'data\\\\LWF Air Combat Mission RoA.mis'\n", "missionFile = Files.MissionComputationFile.fromFile(missionpath)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have now the mission file as a python variable *missionFile* in the memory ready to be be examined and changed.\n", "\n", "For example, *getInitialCondition()* can be used to access the initial conditions. The return value is of type *Files.FlightData*" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0\n", "1.0\n" ] } ], "source": [ "initFd = missionFile.getInitialCondition()\n", "print(initFd.alt.xx) #altitude in meters\n", "print(initFd.fuel.xx) #initial fuel as a factor [0...1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To loop through the segments, use *getSegmentList()* to access the list of segments. The following code prints the segment index (identifier) of each segment:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SEG_GROUNDOP\n", "SEG_TAKEOFF\n", "SEG_CLIMB\n", "SEG_BESTCLIMBRATE\n", "SEG_ACCELERATION\n", "SEG_TARGETMACHCRUISE\n", "SEG_MANEUVRE\n", "SEG_STOREDROP\n", "SEG_MANEUVRE\n", "SEG_STOREDROP\n", "SEG_LOITER\n", "SEG_SPECIFICRANGE\n", "SEG_DECELERATION\n", "SEG_CASDESCENT\n", "SEG_LANDINGROLL\n" ] } ], "source": [ "for segment in missionFile.getSegmentList():\n", " print(segment.segmentIndex)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to display the label of each segment instead of the index string, we can use the Database class:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "db = Database.Database()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ground Operation\n", "Takeoff\n", "Climb\n", "Climb at Best Rate\n", "Acceleration\n", "Cruise at Mach\n", "Maneuver at Max. LF\n", "Store Drop\n", "Maneuver at Max. LF\n", "Store Drop\n", "Loiter\n", "Cruise at Best SR\n", "Deceleration\n", "Descent at CAS\n", "Landing Roll\n" ] } ], "source": [ "for segment in missionFile.getSegmentList():\n", " print(db.GetTextFromID(segment.segmentIndex))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, the type and value of the segment end condition can shown:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Seg. Time', ':', 600.0)\n", "('Velocity', ':', 75.4455900943)\n", "('Altitude', ':', 500.0)\n", "('Altitude', ':', 9500.0)\n", "('Mach', ':', 0.9)\n", "('Seg. Dist.', ':', 320053.202172)\n", "('Turns', ':', 12.5663706144)\n", "('Seg. Time', ':', 100.0)\n", "('Turns', ':', 6.28318530718)\n", "('Seg. Dist.', ':', 100.0)\n", "('Seg. Time', ':', 600.0)\n", "('Seg. Dist.', ':', 402135.694779)\n", "('CAS', ':', 102.888888976)\n", "('Altitude', ':', 500.0)\n", "('Velocity', ':', 0.01)\n" ] } ], "source": [ "for segment in missionFile.getSegmentList():\n", " print(db.GetTextFromID(segment.endValue1.realIdx),':', segment.endValue1.xx, )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following code examples we show how to make changes to the mission and save it to a new file.\n", "\n", "The frist example shows how to change the initial fuel mass to 80% and the initial altitude to 1000 m:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "initFd = missionFile.getInitialCondition()\n", "initFd.fuel.xx = 0.8\n", "initFd.alt.xx = 1000.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we change parameters of a segment, in this example the altitude (stop condition) of the segment \"Climb at Best Rate\" (segment index 3) from 9500m to 7000m:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9500.0\n", "7000.0\n" ] } ], "source": [ "print(missionFile.getSegment(3).endValue1.xx)\n", "missionFile.getSegment(3).endValue1.xx = 7000.0\n", "print(missionFile.getSegment(3).endValue1.xx)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition, we change the altitude of the initial climb after takeoff (Segment index 2) to 500m above the starting altitude." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "500.0\n", "1500.0\n" ] } ], "source": [ "print(missionFile.getSegment(2).endValue1.xx)\n", "missionFile.getSegment(2).endValue1.xx = initFd.alt.xx + 500.0\n", "print(missionFile.getSegment(2).endValue1.xx)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we save the changed mission to a new file." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "missionpath_mod = r'data\\\\LWF Air Combat Mission RoA_mod.mis'\n", "missionFile.saveToFile(missionpath_mod,overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performance Chart File (\\*.perf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A PerformanceChartFile is instantiated via the fromFile classmethod:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "chartpath = r'data\\\\LWF Climb Rate Chart 50% Fuel.perf'\n", "chart = Files.PerformanceChartFile.fromFile(chartpath)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example shows how to change the flight state (initial condition). The function *getInitialCondition* returns an instance of type FlightData:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "fd = chart.getInitialCondition()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0\n", "0.0 Mach\n", "0.5 Fuel Percent\n" ] } ], "source": [ "print fd.alt.xx\n", "print fd.speed.xx, db.GetTextFromID(fd.speed.realIdx) #Mach Number\n", "print fd.fuel.xx, db.GetTextFromID(fd.fuel.realIdx)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** the speed variable can be either **Mach** or **TAS**. Check the corresponding *realIdx* string. Similarly, the variables *payload*, *climb*, *thrust* and *pull* can be of different type" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Change the fuel from the current state (50%) to 100%" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5\n" ] } ], "source": [ "print(fd.fuel.xx)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "fd.fuel.xx = 1.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To change the aircraft **Configuration**, for example from Dry (configuration index 0) to Reheat (configuration index 1), access the *ProjectAircraftSetting* class. To see what configurations are available, open the aircraft model." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Configurations in the aircraft model:\n", "['Cruise, Dry', 'Cruise, Reheat', 'TOL, Reheat', 'TOL, Dry'] \n", "\n", "0 Cruise, Dry\n", "1 Cruise, Reheat\n" ] } ], "source": [ "configNames = acft.getConfigurationNames()\n", "print 'Configurations in the aircraft model:\\n', configNames, '\\n'\n", "\n", "cfg = chart.getAircraftConfiguration()\n", "print cfg.activeSetting, configNames[cfg.activeSetting]\n", "cfg.activeSetting = 1\n", "print cfg.activeSetting, configNames[cfg.activeSetting]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, *External Store Configurations* can be changed:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Store configurations in the aircraft model:\n", "['Air-to-Air'] \n", "\n", "0 Air-to-Air\n" ] } ], "source": [ "storeConfigNames = acft.getStoreConfigurationNames()\n", "print 'Store configurations in the aircraft model:\\n',storeConfigNames,'\\n'\n", "\n", "cfg = chart.getAircraftConfiguration()\n", "print cfg.activeStoreSetting, storeConfigNames[cfg.activeStoreSetting]\n", "cfg.activeStoreSetting = -1 #use -1 for no external stores (clean)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To access the computation, use the *getComputation* method. The type of performance chart can be checked with the *CompType* variable. In the case of a **Point Performance Computation**, the type of equation solved is stored in *resData.CmpType*." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point Performance Computation\n", "Climb\n" ] } ], "source": [ "comp = chart.getComputation()\n", "print db.GetTextFromID(comp.CompType)\n", "print db.GetTextFromID(comp.resData.CmpType)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The *resData* attribute also holds the data ranges for the chart in two *X0Tables*, one for the **X-Range** the other for the **Parameter**:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "REAL_MACH\n", "[ 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75\n", " 0.8 0.85 0.9 0.95]\n", "REAL_ALT\n", "[ 0. 2500. 5000. 7500. 10000.]\n" ] } ], "source": [ "print comp.resData.X1Range.X0Typ\n", "print comp.resData.X1Range.table\n", "\n", "print comp.resData.X2Range.X0Typ\n", "print comp.resData.X2Range.table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, to change the computed altitudes, replace the *table* with a new numpy array:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 5000. 10000.]\n" ] } ], "source": [ "comp.resData.X2Range.table = np.linspace(0.0, 10000.0, 3)\n", "print comp.resData.X2Range.table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or, add values manually (as *floats*):" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 10000.]\n" ] } ], "source": [ "comp.resData.X2Range.table = np.array([0.0, 10000.0])\n", "print comp.resData.X2Range.table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save your modified file:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "chartpath_mod = r'data\\\\LWF Climb Rate Chart 100% Fuel.perf'\n", "chart.saveToFile(chartpath_mod, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mission Computation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the *Mission* module from pyAPP7:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "from pyAPP7 import Mission" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to run APP mission computations, create an instance of the *MissionComputation* class. The path to the directory where the APP executable can be found has to be provided" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "misCmp = Mission.MissionComputation(APP7Directory = APP7DIR)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "misCmp.run(missionpath)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "res = misCmp.result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Access data by looping through the segments. To get a specific variable, find the index of the variable by using the function *getVariableIndex*. To access the data of the segment, use *getData*. *getData* returns a 2D numpy array, with the first dimension being the datapoint and the second dimension the variable. For example, the variable *Fuel Mass* at the end of each segment can be obtained by using:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fuel Mass [kg] : 1896.218\n", "Fuel Mass [kg] : 1859.49218997\n", "Fuel Mass [kg] : 1779.27456082\n", "Fuel Mass [kg] : 1532.93143492\n", "Fuel Mass [kg] : 1520.88752268\n", "Fuel Mass [kg] : 1123.86312629\n", "Fuel Mass [kg] : 914.583951541\n", "Fuel Mass [kg] : 914.583951541\n", "Fuel Mass [kg] : 815.505620848\n", "Fuel Mass [kg] : 815.505620848\n", "Fuel Mass [kg] : 619.613596679\n", "Fuel Mass [kg] : 225.152121272\n", "Fuel Mass [kg] : 222.695756009\n", "Fuel Mass [kg] : 104.648393277\n", "Fuel Mass [kg] : 100.120415794\n" ] } ], "source": [ "idx_fuel = res.getVariableIndex('Fuel Mass')\n", "\n", "for seg in res.getSegmentList():\n", " print res.getVariableName(idx_fuel),':',seg.getData()[-1,idx_fuel]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of using getData to access the raw output, we can call *getVariableData* and get a list of numpy arrays for the output of a specific variable:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fuel Mass [kg]\n", "15\n", "[ 2000. 1998.2703 1996.5406 1994.8109 1993.0812 1991.3515\n", " 1989.6218 1987.8921 1986.1624 1984.4327 1982.703 1980.9733\n", " 1979.2436 1977.5139 1975.7842 1974.0545 1972.3248 1970.5951\n", " 1968.8654 1967.1357 1965.406 1963.6763 1961.9466 1960.2169\n", " 1958.4872 1956.7575 1955.0278 1953.2981 1951.5684 1949.8387\n", " 1948.109 1946.3793 1944.6496 1942.9199 1941.1902 1939.4605\n", " 1937.7308 1936.0011 1934.2714 1932.5417 1930.812 1929.0823\n", " 1927.3526 1925.6229 1923.8932 1922.1635 1920.4338 1918.7041\n", " 1916.9744 1915.2447 1913.515 1911.7853 1910.0556 1908.3259\n", " 1906.5962 1904.8665 1903.1368 1901.4071 1899.6774 1897.9477\n", " 1896.218 1896.218 ]\n" ] } ], "source": [ "var_name, mission_data = res.getVariableData('Fuel Mass')\n", "print var_name\n", "print len(mission_data)\n", "print mission_data[0] #time-dependent data of the first segment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A list of the fuel consumed per segment can be easily ontained using a list comprehension:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[103.782, 36.7258100321, 80.2176291448, 246.34312590799999, 12.043912239799999, 397.02439638800001, 209.279174746, 0.0, 99.0783306923, 0.0, 195.892024169, 394.46147540700002, 2.4563652631499999, 118.047362732, 4.5279774831899999]\n" ] } ], "source": [ "idx_segFuel = res.getVariableIndex('Seg. Fuel')\n", "\n", "segFuelList = [seg.getData()[-1,idx_segFuel] for seg in res.getSegmentList()]\n", "print segFuelList" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Seg. Fuel [kg]')" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8.3, 5.8)) #A5 landscape figure, size is in inches\n", "ax = plt.subplot(1,1,1)\n", "ax.bar(range(len(segFuelList)),\n", " segFuelList,\n", " align='center',\n", " color='#336699')\n", "ax.set_xticks(range(len(segFuelList)))\n", "ax.set_xticklabels(res.getSegmentNameList(), rotation=45, ha='right')\n", "ax.set_ylabel(res.getVariableName(idx_segFuel))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looping through the segments can also be useful to plot the mission profile:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "idx1 = res.getVariableIndex('Time')\n", "idx2 = res.getVariableIndex('Distance')\n", "idx3 = res.getVariableIndex('Altitude')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8.3, 5.8)) #A5 landscape figure, size is in inches\n", "\n", "ax = plt.subplot(2,1,1)\n", "for seg in res.getSegmentList():\n", " ax.plot(seg.getData()[:,idx1],seg.getData()[:,idx3])\n", "\n", "ax.set_xlabel(res.getVariableName(idx1))\n", "ax.set_ylabel(res.getVariableName(idx3))\n", "\n", "ax = plt.subplot(2,1,2)\n", "for seg in res.getSegmentList():\n", " ax.plot(seg.getData()[:,idx2],seg.getData()[:,idx3])\n", "\n", "ax.set_xlabel(res.getVariableName(idx2))\n", "ax.set_ylabel(res.getVariableName(idx3))\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Matplotlib offers a lot of formatting options for legends: http://matplotlib.org/api/legend_api.html#matplotlib.legend.Legend" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "idx1 = res.getVariableIndex('Distance')\n", "idx2 = res.getVariableIndex('Altitude')\n", "idx_segDst = res.getVariableIndex('Seg. Dist')\n", "\n", "fig = plt.figure(figsize=(8.3, 5.8)) #A5 landscape figure, size is in inches\n", "ax = plt.subplot(1,1,1)\n", "\n", "colormap = plt.cm.rainbow\n", "ax.set_prop_cycle('color',[colormap(i) for i in np.linspace(0, 0.9, 7)])\n", "\n", "for i,seg in enumerate(res.getSegmentList()):\n", " if seg.getData()[-1,idx_segDst]>2.0:\n", " ax.plot(seg.getData()[:,idx1], seg.getData()[:,idx2], label=seg.getName(),lw=2.0)\n", "\n", "ax.set_xlabel(res.getVariableName(idx1))\n", "ax.set_ylabel(res.getVariableName(idx2))\n", "plt.subplots_adjust(bottom=0.2)\n", "ax.legend(bbox_to_anchor=(1.05,-0.1), ncol=3, fontsize = 9, handlelength = 2.0)\n", "ax.grid()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "misCmp_mod = Mission.MissionComputation(APP7Directory = APP7DIR)\n", "misCmp_mod.run(missionpath_mod)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "res_mod = misCmp_mod.result\n", "idx_segDst = res.getVariableIndex('Seg. Dist')" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8.3, 5.8)) #A5 landscape figure, size is in inches\n", "ax = plt.subplot(1,1,1)\n", "\n", "colormap = plt.cm.rainbow\n", "ax.set_prop_cycle('color',[colormap(i) for i in np.linspace(0, 0.9, 7)])\n", "\n", "for i,seg in enumerate(res.getSegmentList()):\n", " if seg.getData()[-1,idx_segDst]>2.0:\n", " ax.plot(seg.getData()[:,idx1], seg.getData()[:,idx2], label=seg.getName(),lw=2.0)\n", "\n", "for i,seg in enumerate(res_mod.getSegmentList()):\n", " if seg.getData()[-1,idx_segDst]>2.0:\n", " ax.plot(seg.getData()[:,idx1], seg.getData()[:,idx2],lw=2.0)\n", "\n", "ax.set_xlabel(res.getVariableName(idx1))\n", "ax.set_ylabel(res.getVariableName(idx2))\n", "plt.subplots_adjust(bottom=0.2)\n", "ax.legend(bbox_to_anchor=(1.05,-0.1), ncol=3, fontsize = 9, handlelength = 2.0)\n", "ax.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result of a mission computation can also be loaded from the result text-file after the computation:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "resfile = r'data\\\\LWF Air Combat Mission RoA.mis_output.txt'\n", "res = Mission.MissionResult.fromFile(resfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Complex Mission Loop" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "cap_path = r'data\\\\LWF CAP Loop.mis'\n", "cap_path_mod = r'data\\\\LWF CAP Loop_mod.mis'" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0, 'SEG_GROUNDOP'),\n", " (1, 'SEG_TAKEOFF'),\n", " (2, 'SEG_CLIMB'),\n", " (3, 'SEG_BESTCLIMBRATE'),\n", " (4, 'SEG_ACCELERATION'),\n", " (5, 'SEG_TARGETMACHCRUISE'),\n", " (6, 'SEG_LOITER'),\n", " (7, 'SEG_STOREDROP'),\n", " (8, 'SEG_STOREDROP'),\n", " (9, 'SEG_MANEUVRE'),\n", " (10, 'SEG_SPECIFICRANGE'),\n", " (11, 'SEG_NOCREDIT')]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mis = Files.MissionComputationFile.fromFile(cap_path)\n", "[(i, seg.getName()) for i, seg in enumerate(mis.getSegmentList())]" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "idx_loiter = 6\n", "idx_combat = 9" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "range_combat = np.linspace(0, 10, 6) # minutes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read a CAP mission from an existing file, adjust the end-value of the combat segment and save the mission to another file. Afterwards, run the mission, extract the result and store it to a list (i.e. *loiter_time*)." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "loiter_time = []\n", "for i in range_combat:\n", " misFile = Files.MissionComputationFile.fromFile(cap_path)\n", " combat = misFile.getSegment(idx_combat)\n", " combat.endValue1.xx = i*60.0 # convert minutes to seconds\n", " misFile.saveToFile(cap_path_mod, overwrite=True)\n", " \n", " mis = Mission.MissionComputation(APP7DIR)\n", " mis.run(cap_path_mod)\n", " \n", " res = mis.getResult()\n", " \n", " idx_segTime = res.getVariableIndex('Seg. Time')\n", " loiter_time.append(res.getSegment(idx_loiter).getData()[-1,idx_segTime])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the results as a bar-chart." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Time on Station [min]')" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8.3, 5.8)) #A5 landscape figure, size is in inches\n", "ax = plt.subplot(1,1,1)\n", "width = 1.4\n", "ax.bar(x=range_combat-2.0*width,\n", " height=loiter_time, width=width,\n", " tick_label=[str(c) for c in range_combat],\n", " align='center',\n", " color='#336699')\n", "ax.set_title('Combat Air Patrol (CAP)')\n", "ax.set_xlabel('Combat Time [min]')\n", "ax.set_ylabel('Time on Station [min]')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: input data is always in SI units (e.g. the combat time segment *endValue* is in seconds), but the output values are formatted (e.g. loiter time is in minutes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Performance Charts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the Performance module from pyAPP7" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "from pyAPP7 import Performance" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "perf = Performance.PerformanceChart(APP7Directory=APP7DIR)\n", "perf.run(chartpath)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result is loaded into a *PerformanceChartResult* instance:" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "res = perf.result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A *PerformanceChartResult* contains a list of *ResultLine* objects. The *ResultLine* contains the data as a 2d numpy array, with the first dimension being the datapoints and the second dimension the variable index:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(16L, 88L)\n", "[[ 0. 0. 0. ..., 64.27329108\n", " 64.93182676 20.39259343]\n", " [ 0. 0. 0. ..., 64.27329108 79.2340213\n", " 30.97531047]\n", " [ 0. 0. 0. ..., 64.27329108\n", " 94.60491582 38.3654778 ]\n", " ..., \n", " [ 0. 0. 0. ..., 68.52726956\n", " 288.03991245 26.42931541]\n", " [ 0. 0. 0. ..., 68.23628197\n", " 306.24737031 3.24777545]\n", " [ 0. 0. -0. ..., 69.04558153\n", " 315.66211611 -69.76337364]]\n" ] } ], "source": [ "line = res.getLine(0)\n", "data = line.getData()\n", "print data.shape\n", "print data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To find the index of the desired variable, use the *getVariableIndex* function:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "idx1 = res.getVariableIndex('CAS')\n", "idx2 = res.getVariableIndex('Climb Speed')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The lines can then be plotted using Matploltib:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Climb Speed [m/sec]')" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8.3, 5.8)) #A5 landscape figure, size is in inches\n", "ax = plt.subplot(1,1,1)\n", "\n", "#Plot the lines\n", "for line in res.getLineList():\n", " ax.plot(line.getData()[:,idx1],line.getData()[:,idx2], label=line.getLabel())\n", "\n", "ax.legend(loc=3)\n", "ax.set_xlabel(res.getVariableName(idx1))\n", "ax.set_ylabel(res.getVariableName(idx2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since each data line is a numpy array, data can easily be processed using the powerful functions of numpy. This example extracts the maxima of each line and plots them.\n", "**Note:** the line contains NaNs, therefore the function *np.nanargmax* is used to extract the maxima." ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Climb Speed [m/sec]')" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8.3, 5.8)) #A5 landscape figure, size is in inches\n", "ax = plt.subplot(1,1,1)\n", "\n", "#Plot the lines\n", "for line in res.getLineList():\n", " ax.plot(line.getData()[:,idx1], line.getData()[:,idx2], label=line.getLabel())\n", "\n", "ax.set_prop_cycle(None) #Resets the color cycle\n", "\n", "#Plot the maxima\n", "for line in res.getLineList():\n", " xdata = line.getData()[:,idx1]\n", " ydata = line.getData()[:,idx2]\n", " idx_max = np.nanargmax(ydata) #find the location of the maximum\n", " ax.plot(xdata[idx_max], ydata[idx_max], 'd', label=str(ydata[idx_max]))\n", "\n", "ax.legend(loc=3, numpoints=1, ncol=2)\n", "ax.set_xlabel(res.getVariableName(idx1))\n", "ax.set_ylabel(res.getVariableName(idx2))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.16" } }, "nbformat": 4, "nbformat_minor": 1 }