From b7800c5855bf80d9337214ac77f6e9b29f008575 Mon Sep 17 00:00:00 2001 From: Jimmy-INL <52417034+Jimmy-INL@users.noreply.github.com> Date: Thu, 10 Feb 2022 12:49:26 -0700 Subject: [PATCH 01/52] adding custom basis for quick testing --- examples/basis_comparison-Copy1.ipynb | 885 ++++++++++++++++++++++++++ pysensors/basis/__init__.py | 4 +- pysensors/basis/_custom.py | 82 +++ 3 files changed, 969 insertions(+), 2 deletions(-) create mode 100644 examples/basis_comparison-Copy1.ipynb create mode 100644 pysensors/basis/_custom.py diff --git a/examples/basis_comparison-Copy1.ipynb b/examples/basis_comparison-Copy1.ipynb new file mode 100644 index 0000000..bda136e --- /dev/null +++ b/examples/basis_comparison-Copy1.ipynb @@ -0,0 +1,885 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basis comparison\n", + "Compare reconstruction performance of different choices of basis:\n", + "* Raw input\n", + "* SVD/POD modes\n", + "* Random projections\n", + "\n", + "We'll perform comparisons using Olivetti faces dataset from AT&T." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:21:25.483328Z", + "start_time": "2022-02-04T02:21:25.479647Z" + } + }, + "outputs": [], + "source": [ + "from time import time\n", + "import warnings\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "from sklearn import datasets\n", + "\n", + "import pysensors as ps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data consists of 10 pictures of 40 different people, each 64 x 64." + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:21:27.493726Z", + "start_time": "2022-02-04T02:21:27.471856Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "400 4096\n" + ] + } + ], + "source": [ + "faces = datasets.fetch_olivetti_faces(shuffle=True, random_state=99)\n", + "X = faces.data\n", + "\n", + "n_samples, n_features = X.shape\n", + "print(n_samples, n_features)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:21:28.065729Z", + "start_time": "2022-02-04T02:21:28.059962Z" + } + }, + "outputs": [], + "source": [ + "# Global centering\n", + "X = X - X.mean(axis=0)\n", + "\n", + "# Local centering\n", + "X -= X.mean(axis=1).reshape(n_samples, -1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:21:29.800752Z", + "start_time": "2022-02-04T02:21:29.795662Z" + } + }, + "outputs": [], + "source": [ + "# From https://scikit-learn.org/stable/auto_examples/decomposition/plot_faces_decomposition.html\n", + "n_row, n_col = 2, 3\n", + "n_components = n_row * n_col\n", + "image_shape = (64, 64)\n", + "\n", + "def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):\n", + " plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n", + " plt.suptitle(title, size=16)\n", + " for i, comp in enumerate(images):\n", + " plt.subplot(n_row, n_col, i + 1)\n", + " vmax = max(comp.max(), -comp.min())\n", + " plt.imshow(comp.reshape(image_shape), cmap=cmap,\n", + " interpolation='nearest',\n", + " vmin=-vmax, vmax=vmax)\n", + " plt.xticks(())\n", + " plt.yticks(())\n", + " plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:21:30.899846Z", + "start_time": "2022-02-04T02:21:30.603748Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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7B81E1+4vH7Om/qLvZkdrDIT3Zi11bn0ve529p+rcj1S0a5oVn+O+d/NkbYz3iNe/Xtfahsq6uvfdsSc9I+v7k56FndoadmeTxG23HVRL4hlTf2PQ0jD2559/fnX/TCY3MTExMXFmcSomt9lscvny5SN9fWfsNaPiF9ZSg3Xlbqeea4m0Yz5cDwnxJMmwO8YSb9e+r23JppPgRk4Qa845lvaQXviccSLp8H3tw0hC7ebR53gcb3nLW5IkH/nIR44+e+KJ3fqZ1+rM0Tk2mAWNJM/6udfdbMLr0+2da3E8GoH2zTLqPLgPI4O8v6/tuI+2RdZrjOw0vh6vVbK2XcoMxfNd+wBgzzgtIa3Xe55zbr311iQHe3Q0/+fOncsrXvGK1fUZ3f+dPfUkjO7POsdmOLYtga7PZmPMC32tbVRNR21v5BhUj/e9wHqYFdZ7YqR1sk2we4bwP88iax86LR7n8B3vX/GKVyQ5sNUmx+147uuzzz47mdzExMTExI2J+SM3MTExMXFmcSouv7e3l1tuueWISpq6JlvaDkWG5vJ5F0szUgFeSzzbyKXeaoqqsjNNtzppLYavU0fW663FpJwUr1LVFL4e7+2wg2qgM8KP+mTX3vqd54/3OBu9+c1vPjoH9QfH4jo+wpUrV3ZUjp16k5AE1gy3cFQx3TxhuPb8WCW3porunFMM78GRarBzHrEhfqSer/uRflt15nO6PjvMhWOYR9ag9gP1t69jN/s6z1wHlRPnElvJ5+yX2hfWen9/f9Vp7EUvetHRPuDcGn5i1Rzzz77o1MhWyfmZMVq3ipFavLveSarnbl+MQkWs3nOfu+sw/3bBr30cPd/8XF8Lu2Fv+JnPWlTTCvvbTmcce++99ybZhhTUdnk9yTQ1mdzExMTExJnFqZgcBmB+ZZH6OhdrJA27sXauqJaGad/SSsfSbAAduWl37r/uk/vWsc1rzQiw5nhgx4rOQcRMwcG4do3uQIgH87nmFEJ7dmQxA3rrW996dM4v/dIvJTkIxkwO3HzXQgiuXr26E4DfMWyCfpnzr33ta0dtGKPsO3ZX7gKtR+CcTloeJRtYyxRixuT9PgoPqOeMwk867QZMzc4CZsad8xdSOPMJuD5zU9fNcw6jN/us55iBnzt3bnUv13uRwO/qZGGWwJzD9jpHFDsrOdTGLLobs9n3KPC//j8Ka1jLdHLSunf7w05J1nbwWud9xBg5xsy4S2BRmVqy3Uvdc5W9Ull+7dsrX/nKJFsHlCR55JFHjp3z5JNPrgatTyY3MTExMXFmcSomd9NNN+Wuu+7akSKq9DBibPzqIhnU4D6kAX69redGGqtSA+A6uCI7eJK2OvY3Ck0Y2eqSXanUNgD6WqUjB0BbEu1YDddBKkJ/bjZgabNem+vQR5gd0lGdE9pxn+zWj4482TIuxnXrrbeuSuObzWYYRF1BIKglUd5XCZ7/bUviHD4n7AV7crLLVrxn1mxzowQFXS7Da2lv9P3ofmLcXfA53/me897t7OP01bklLYXXNbANHaZo6brLWeig/Q5oAbw3KxOlv1ybdGHcA9w/dX9yP3zbt31b2xczoLqmtDcK5O7ug1E4S8d0ge8BP7PWfA/Y+5zrMKS1MAvmEy0K4L0TTdQ+8R33HH3mecHcJcnLX/7yJNu96vuK73kmJNvQJe/rESaTm5iYmJg4szi1Te5lL3vZkYTAa03Jwq8q3335y19Osg3m49j6a24W5MzTSGedJxu/5kgpsBQHWN5xxx1H59gWYrtN5wVpuwbSCn21DaCCc83gzHJrkDPXhm2YOfJq21ky1pvznvnElpZkJ1XbqGoE3nBJ8u3f/u1Jks9//vNJTpcR3eOsY7FkiUTI9zVZqxM9M4fMqdenSthORg3WEvbanjryXK3S5UnzYk+1ug8YB3vGDK5jQGaxXJ9xstZd4L/tePZ667xUvQZrdllgL72TsL+/f3SdL37xi0mSj33sY0ff48lJH7jX6C8Mvtp+nJScMcIe6CPv2YfJdt55jtkOxRygYUp2bXK2F3ZptsAoIbyD0auGjOsxbyMv6M5u6LR7vp94vvK8qOd6T/LMcLrBDtYksDb1+f2JT3zi2DlriQSSyeQmJiYmJs4wTh0nd/HixXzlK19JspUiKivjF//xxx9Psis1IOHUX3PHbSGtIJWslTVBonCsjhNEVyCRIcnZM8/2iNpvYN2x7QRVsnCcUOfVlvTs1jp/l8nppCekSped4Vykr2pXIQ4FKf/+++8/1jfGX89529veliR59NFHkxzM48juhD3OqeC6RMaWoPGsYjxd2iPYKXNshmWbTb0O0iIS56i8SW2Hc5x2ybFo9fxRHJbbrtI4GhCYCceYSdS2WV9YBNoN3tvrtmoBbIOjL1y/8462rQm7DbaTLvWY+31Scu+rV68ePVM+/vGPJ9l62SW7sXn0k7F35Xlsg+O+/I3f+I0ku+y/aj7YK1yHROYwjo7hcaz3Dq9mzbUPwFoZ9ihtUK6qjhlbGMewLraD1T76OW3bMG2xJsl2LXmG2CeA/VcZMesF0A7ZY7cyOZ7X7M2rV69OJjcxMTExcWPi1Amar1y5siMBVhvJpz71qSRb6cDSo9lFshuBz7no3m3LqkyIz8x0bFfp4myQKJBa6GMneY8SlDrWxN5vdZ46JpL05U0cE8i59NHSetWNd/Evya6HWWVd2AcopcMrGU46e9EDDzyQZCvVPv7446vlay5fvtyyZGCPMSRQ2x27hLK2VTIeM4+6V2EcjrHy/q7r5fEx16PK1N05owTNTz31VJLjyWjRhJjJwRyYo2r7of94DbI+ZvRd0UxnumHPODNFZz9mXdjPzlBTtQ3e1xcvXlzVAly5cuVY1osk+e7v/u6j/12wlT5hi1vz0PY9a8/BtQKizC1tsIbcj/VZxTG+d13EtoK+8J2vx76wn0SynQvmAObDurMf6rOKeWJd2EujjFXVW5lzuY41M102HXtI4sNx3333HetHLbjLNWGRa+WfksnkJiYmJibOMOaP3MTExMTEmcWpiy1tNpsdFQ0Us35G0DDU0m66VV1ZHS7qMTaYOhg02dJdu0uvBd5atQidtjG89hF1iAMr7VpPn6urMv+jynA6tC7pLaoM2uPVDgedCtShFlwHNUmXmBUDPXNDWABqS9azqgYI0Ky15tbS6yzLsrMuVUWLSoR+nuTsU8+xurBzlEmOq3OcdBb19agmYj3WauvuWGNUGZo+dfvboQKsHet15513HjvO/yfb/TeqBVaPp33vTQcur91fqPlw1CAwuzNRVFXxqM2rV6/mN3/zN4/6wl6sqZ6Ax8a8oZqu96XXjGeJ3ec79b/NIbyiIqat6mTBnHJvMy8Od6p7qDpK1f6PkgRUtTXXZjyoka3yrONy4gg/63n18yfZqitpF7W71ZT1vsZJBTjkh75WNTNOKJ/85CeP+jQdTyYmJiYmbkicmsnt7+8f/fJj2KzSMswNKR83cCd27co7jJIR222+Sh42Dlty75wskD7sEOK2uusYlrQ5F9aWbF2dHTRrSapzqDCTc+kYG4YrnPqLY3ntGPHdd9+dZCsFssZIwlUy5RxCCX7yJ39ypw+1L+fOndtx165zbBd+5slsqRro7WDisiJOslylZPqCtIzkyTFeU/e3YpSCLBmXUulcxuvxya4zDNI4TK5z6WefVRdrjyPpHULsaOJkvuBakpfXqt+Gk7HffPPNwxCC/f39XLp06Ygl0be657kW82NHKc5dKy/l9w7A5h6sx9iJoyZLqONLdoOymVP63qWpoj2+8z3hZ1UXssC9wL27pk3henbh7+69OqYKB8I7CLzuVfaGE4PThlODJVvtRf09WEu4MJncxMTExMSZxamY3LIsuemmm3bK6Lzuda87OsZu6i5w18H6VAf2OmVT96vtdFugK2MC26tSVj22Y4yW6u0e7baq7p/geQfGmx1WKQkp3Cm6HFCM5FalMwfP0waSlRPQ1v4imdkmyJrUtGXMDzry7/zO78xHP/rRdHCpHbP3+hnzAHt0mEh1sXeaK5eBMpOr/XdJHwJT6SNSbU37ZWZjVuFg3don29y877syN7ZhO9ykS1bsz5yk2Gy6K3rsxAEO5+jsU2Y31sB0idzrsWvS+LIsRzY+B+TXazspMWPs1oX1YE+YcdreX93lrfWBcdAGe6eOyc8Q2KVDLqo2inPQDDnxtMdZr+dk8TXJQNIHZ3MM545KCllbUL8DXiezw659M3HGVW2N2GJrUvk1W/hkchMTExMTZxbfEJNDusPulmx/tZE07LnUecjZUwhW0UmpPtelRkZldNbSiJmZIk1UicP2FOvvfZ1qk4ORuAy8PSO7xNOcaw9Ql37v7EYeJ9IZ0nmXmNfM95577jnWfpXcasHLJHnnO9+Zf/gP/2HW4KD9CqdZshaA+al98L5ykuc1+1dX4ijZ9X6t+w2p14yKdbHNpLv26LpOyl3bH5VccvLybhyW4NEoOKlw7aOZEfczbKbanmwfss3JrLPDGpM7d+5cbr311p0UbVVb4jVzAgnb6ms7TgxvbQnn1pRgvv+wE3mNa5IG2Ajj6BJjeFx+FpEgAzs/Wg0YXmcrZa14ZQ8zrqoZcV/dR2uJ6nOV+XEquDW7JXPt5OF+3tQ5IjCcc5588snJ5CYmJiYmbkxcF5Nz7EL9lbU0hpRkdlZZkhmN7SdIWK95zWuSHJc8nJLLRR2dcLaiYzT1fRe341gnS6lrKYCQgs0+OyZn+xR2PaQw5oY2q70DKcySE+fCDuv4HLuDxEj7sPZaRNFM641vfGPLDsDVq1eHHoUVnnfHCtW55X+uyx5hPI5B6zwJYaROS2WPtnos+5c9ZHtDlcZHBUg9BrPCCtt8R4nBk+1eMdtwAUzmptMC+N5wWZi6Vx13Ze/E7n5yeaP9/f1hrJNLfHXlhdgjSP4ci0aliye17Z09xNg5l71fbVfWWHHf+N6qcWCOw2VPMhedh6Rtvk6uzrOQvtbioraJ0a6fN3VcTjnGvrLWBmBXTLZ7gxhb9qG1Q5XdMhfsIft0dJo/e9leunRpxslNTExMTNyYOHWC5v39/R09apWOrC/HY80FVqtEgDREmQh7oz388MNJtvahamOgD+jEKZPhkitd3JKlcrOyqm+2BIvkYO8t2qwxNUge9pRE6huV3qn9Zlzoo5HczFhrH4l5Y+7pM95JZDNJthIgfUQK4zpIdpWBc23W4/bbbx/GExqd5OWYL6Q79kfnDcgx7I13vvOdx9pw3GLt3+tf//okyYMPPphktxAu0n+15zjO0+tum3D9zLY423W7Yr32TPReYe4rS2Mfk1zb3spIy0jw1dZE9iLbtnyP1HPsaeq17SR42+/WbHJ7e3t5yUtecrSmXWwgY7Qdkn6apdcxmqWixcB2yTz+/M///NG5JIfG4xMNAm3ip9Dtc3toM2/VgxD4XuAY+xN07JZnEPe215B9UOeEe4A5cIYTxkV5raqRIXaTdYY5OktLHeeo3Jk9dqv2hmPp07PPPjttchMTExMTNyauK+OJMxzUX1mkIpiAPQs7LyekA6RhzkECQRp67LHHkhzXcyPhwFpgPEhy/OpX6QE7k3Xylo5qX83kbBNx3Ewt8mfp0nk2u4wUtkvZpuRMAHV8SGOMk/n87Gc/e+x9PYc+MRe8R7J3nFQdexeL2GFvb6/VsQMkQNsbuTbz1+X4hJUhUVOGwxkoqn2NdthnluBtN6rHIMHbu5V9UO1q9nJ1HBno8q66eKXZIPdZjVWF1dIH1t0xbm94wxuS5FgJG7fP+iONO16vjq+Lfauv1WvRrGJt7+zt7eVFL3rR0drZc7L2j33L+mOz4hlTPcGrV3Cyzdzznve8J0ny3ve+N0nywQ9+8NjYk+388LyhT6zHj/zIjyQ5bk/metx/3Lv25vT7ZDtfziTlc+q9gcaGZwV94brVFgccr0Z7zCtaNe6Den/xGc9PPEDJNcq92d1Xzkjk/dEVduWzl770pTsexMfaHn4zMTExMTHxLY75IzcxMTExcWbxDTmeQBurCgja7mBsqCUqqUq3+R8V0Gc+85kkW9r7jne8I8nWuaS6U1ttY3URlLyqGjjflcjtEl8dT1ypG6AycUXlqgq8//77k2wdPVChMEed2z1jRw3jkh5Q/67Uil3WMQij9usqunud7PSBCo/r1bHX1EIjV16qO/v7rtSO1VtOf1T7baM6jk52m0ftUvcq6m/WDPUOn6Oqq6pH1pV2WCcHoXepi7xHruV7V4Zmv9tlnHun9pFzWDvuPcb18Y9/PEZ1DqnXZ886nV09xypb1tGu8/XYk1TcXAvnk2S7tjVMg3lhrLixM0/snS7dGs8f2nA6Mr6vJgiXZWJOCSVAxVmrlzuo3InS19zg3WfMM1bddYHWYOSc0YUf2WTjtrqwCtrh/mTPcC4q9Vrle1ROy05MVTXNOdzbb3zjG/ORj3ykHVsymdzExMTExBnGqZnc5cuXd4IKq6GcX20bejmW950bKb/WsBR+qZ2EuEpjSNSWrF0gtLI/MwUkKbvad4VI7YDitGW0XZ1xzKBgFRhqHShfx2gnHwysSGwYk2vIAn2wizLXhdlVBsnYmXMkX0tStY+WIl944YUTmZzH1bFJlyBhLTkWY3/tA/P/xBNPJNmuN/PDWOu+41zmFDYEc0Q7UN32mVvWzKy5SyBgluz7xsHSXckl+gBjYDwdK6dPLvbJMZwD66gBxA7g5jpO6l2ZKve01w90ziWjdHgdSO7tsi61Dw6+fu1rX5tke1/A0rvgZYpvfuADH0iyvS+5T2irslz6awe3t771rUm2bKU+q1w02dqFrrSTi4g6gTrjJfyq3p+eU/rIuB0o342LvnGui6nWZ4g1YMwbc864aqJrnmOMywm2u/I8Lq91zz33HK1hh8nkJiYmJibOLE4dQpCMiwzW/x0UPCrVkOwGctrWY5fhKuk4YTKShsvLVCBRmgXYJlTHZb25x27X6ypFufw8+nTeIzVVGwNjdSAvQAq3TTLZ1WfbXuRUaPXYTtqq53ZM7lptcs8///wOq6nz5ATFThtnJlKxlr4r2S03U8fC2LDn2F279tFFJL0fnEpr9Fmyq1Hoklc7rMJjp21skbVvSMm4cH/iE59IspsEoSYTN7Ni/8HWWIvKiFxodXSP1PvJWoBrgfdWbY9+MU+wcj6H0TmkpPbBoTBe03qPdWEMyW7Chzq3TtDugHsz73qMnwf06b777kuyG8qQbNeMveGE453/wGiv0q6fc7WvPKO4f3mFRXfPYq7jgHxrjupzh+9o79u+7dvasAswmdzExMTExJnFN8TkOn1pl8C1HotU0ennHWBt9mAJJNlKan510dZOYhwVdHUS5voZfXB7ll7rnNC+i5iit7fHXj0HhuLCji4WWvvKZ05K6/Q6VQq1PQXJjb65xEv9v0pbax5i2FbqOXWeHBzrhMZO0ZTsli2yFyLg+65/TpDr5Ni1LaftMiN1QgGPsbbh63U2Of538K899eoehuW7TAk2JhcQ7WyOLq2C9qErtGkm6jnu7JW29dbk3cZms8kLL7ywowWq2iAnSWCv0749ZusYzdxsK7MtrZ7jdFRcn3uum1teK+uq76v/wOgeQOuAxgXbXGX61s5Yk9OxTffRRYJpsytC68QYLiTM3qnPHY4ZMTf7SyTbfU0yg5e//OXryQSG30xMTExMTHyL49Sldqo0xq9wldzM4Hhv76wKS/CjVD9deiDrsV02HQmhk+Cdlso2hS5Bs5PEWlrqkuxayvd4HK9XQXuOpfO4al9drNKvXbLfkTQ7ksqTXmJbs8kRZ1nbrRKabUX2sOrKGCFtO7YJido2pppSyHuT+XHatWrfs83VRTLXbI0jG5UZfoWZFcdYm3Itti3sUd4PdT5tz4Mh0I/OrjIq/moP5Aoz4pM8cy9durQz95UR+JrYMh1TBQOq7Vir4Dg/1r/uHf53Ym5sfy4AnOymqLJmZS1e1vGSLhKLV2e1GzqZePf8rN/X9kfFZv387pgjtmyYFhor1qQyWHuC+3nWpYOsCeGTg+dn59UMJpObmJiYmDizOLVN7iQPKdszLLWaMXTfjV6tS052vR5tZ0AC6LwdzeBsE6l9pD1LMp6DziZn+wPjsFRe55Hr8TryLFsrBmnpxuOqbbr/XhMzlq69rlBsRU3u3WkBYLRmbEieSMn1OpaGbV9zBpIuE4nH6HI23X4DljhpqzvHe8daj7VExt4ztndUSdfjsr3VDKV62dJHx8d5n9e+W8sxKpa65qW6tnf29/dz6dKlHVtmXcuR3d7xq9W70jbX0fysaZBGc90l4fZzAEbj8VQPYNvkzegYL96u1Ssaj8vRelgLUsdl7+TRc73OJ/uI+9QxdZ392IV7Xaqt0zrxHde+cOHCsExTMpncxMTExMQZxvyRm5iYmJg4s7gudaVraHVw0O9a8Oco7ZENwWtGfatFTXure/4oBdNIfZWMVU6jfnQBnXaf93hrwPcoMB7ajtqqM1YDq06syqkqIgcZm/5b5VHHXPt2kgOEHXM6VYnb4JwuoBeHAqvveL+m1vV1Rnu1OseM1t9t1eO837pQmHrdCqtZUUdxPcbVqYB8v4wqlNd+uH7YyAmsmwf2qvfomnt3NV+shZ/s7+8P9wXnJ7vu8g707qquj0KW6JvDVOqxDnfxHFc1m1WyVo+7Cns9x2kLnbKPNa4p2hgrIQL0dZR+rcImDZ/Lc6equp3Ewan71lK48eyzmaYzIXDst3/7tyc5SAO5+hwcfjMxMTExMfEtjusKBrck2AUIW0rtkisblrqADdpdxW5LGmvBsnb3X6tWbVj6tSRnibL7zuPgPUlWk90krk4m7ADLKpXZBdrMxAmbK8xuuxAFH1sltpPKhVgyrGttV3qHPHThFPxvZ5FRIu0KM1w7JNnIX9uzY4HnuHM8GiUQcJ/XjmFucO1nn1QtgBMEjJyIQHWoGIWduD+doX/E/kHdWyfNRcX+/n6effbZnRCL7rnDPPhZ0gUv+5nkBBKjBNrduSPmUc9xsDTz5cQLlfHRDkzOCZvNkruyOWZWDhPqytg46YUdbUB1kiFsp2PNtc36uROdO5SgczwhtRkJoO+99942/AZMJjcxMTExcWZxXUzOzKqzP4xS/HTwsZZ8bcOqNhI+cxoiu6B2BRvNWtZsjban2aXbY6l6dacPs2sy51DiI9kWfSSdE/puX59zaz9GunHbK7vUapbGfb0qgdN+ZbOj9V6WJefPn99Zn1oiyJLnyJbTBaRjG7jWcIbajqXWUdLlZGsPdPiH12MtzKVLGNBdt7bj8TDnSNKdbdO24JNs0PWYteQGHp/PcVKF7r7yOWtalGVZ2kLJXdgM8+E0WKAbK585CbZZS5e+0PMEE2F/135bM1HHl/QJyD2H1lhwPfZl3as8E52U2mE1dVwOERj5GtiOmezah0carC7JhsM5fB9je6994Npf/OIXV+/3yeQmJiYmJs4srovJrSUrtq7dnpP+vLZnu49tf/zaV/uDAywdPI0U00mRo8Knnf650w37mPq+fm47gSXFroAo6XnwjIKpONi5S1fFuti7zfa9Ot+dvtzH1DaSXRvDTTfdNAzKhMkxxi441wzXXqAdIxnZcW3zs82ktm+bzKj0StfHUTLpep01dtthzZ43CvCv6zJKTj7yTryWe8M2zo5tjthYd86IzXTY29vLxYsXV1NMcT73gT0VO49WP8e870ZajWSXlbn9bk5sA7fnrz1DPQfJbiJ6xkni6TpHaIF4dqApcWLq2keX9LK2yXurK3sFRgyu08R1LK9+Xhk59mjm5KmnnlrVBEwmNzExMTFxZnFdcXJrEo5j20ZSbKdDtaRpBseveS0QiVQwSufUScAjW5/1z5UdjYoW8t72ozoWjrFXJRJWZ3dBukOyIrEs0pjLxlcJjnZOUwbEpXtG3m+dZFrtnmsSeT2W/VHn2Cxi5BW21q7tjl0RW19vBKcKq/97f6/FAI08PG2XWksFNUryvJYmz+2PbI4VZiRmci7Xkuzey7azrXlkVjve2t45d+7cqje0nxWOH+vsu5zPmnqeRl6C3Vh8bvf8s73JybdHNrv6HfecC97yHKi2K7RBzAEJk3l2OA6w64P7Yrtrp5Ez6/ezk9fueqME7vWcBx544NhnawVTk8nkJiYmJibOME5dauf8+fPDJMUVo/IbazFpoyTH/Jp/6UtfSpI8/vjjR+cgUSANOeMATKj2pxYJrOfay6pK8MSnce4ojsTHJ9siljBQrvea17zm2DmVWXk8SC14XVIAE2ZXGYQlRtsY8UCrEpDjU0ZJa2upEnttVWnbIKMF7dOHLjmwk8FaSu5spSOPSHuLdYx+dI7tuvX/kS1uDSOPtVEJpnqM35txd3GZZqrXkmnFx5hdd+zX6+P913kzn2RrNK5evbrK9Eaeqs50U4/rYtmSsTbD/Ul2PRUdY1mvZ69D7l2YFK+djwN9whvZWUS4frV3ObaOvsDoaKvuP8cEjuLmvP+TXZ+J0Zp2+8D3j+MPO20hfbn11ltn0dSJiYmJiRsT80duYmJiYuLM4rpCCIzOLfekVEZr6h27jGNMffTRR5Mcr357//33J9k1Hlu1UdWHqPxQCZLUFMrvJKjJlvqjNhy5QLtyb7JVV9JvVHQk2+1UXk7M++pXvzpJ8vGPfzxJ8sQTTyTZzq9Vh8lW9cD17CLdUXw7bABX7a7X5py1JKk+x0HVydh9GXRhDqPkA3Yq6tQrDi/w2FGpdjWzRtWVR8HT9TtwLSnUrN53yrM1NaJVZqPrdWo5q8lG6cvq/6OQHAdoV3Tp3UbwnHahHdznI2eyrh6ix2EV6tqYfZ/YAWYtHKBzNKp9TXbXnevynjAB3tc5Yi4wMXz2s589dr0777wzyfH9jXOaHfg8B11Feu83qxW5Tj3H5qRRuFiXeq7WUlzbP5PJTUxMTEycWZyKyW02m9b1f638yrUY5EfJOHHUgMGRwBgJJNk6b5jJuM36PU4bSAQwELOCzpkDhkYfORcm6aD0em0MvRx7++23HxtnB/pI5d877rgjSfLII48k6aXBkYTozysrY+xI3ZZUnT6rHuOEtiPUvUBA55oENiqF07np+xp2He/CNFxOBkcAJGDYQWWbDrA3w3GZlvqZWYUleh9fx8HY0Sg4MXcXaH1SaZ8OZvkjNtM9B3zuqNxNbbdqXkb929/fz3PPPbfjUNM5TDj0ZuRcVM8BflatlU8asWAzL5hRPdYJHEYhM137vOc+hMl1lcHZx7/8y7+cZPucccVwtES13VESBTtn1X3gZMtOmt1p8ZxajuenSwzV5w6OM5z7xBNPzLReExMTExM3Jk7N5C5fvryTjqpKkaOAxjWXYUuHBH0/9thjSbZMDint9a9//dG5nRtsbcupupItkzPbHKW/qv/zndMGmTlWluQCg7yHNRGWUCXLUZgG5SVs56vjH0nEto100s9ImnVIQb1OTa02CthGC4DkSTu1mO1IanVC3m7vdDaXZJf11eshYSLJspdsT6lr6QBas5XOJjcqSGvJ3faW+hnrgk0YiRem0NlD19aitt2lOrNt2/PbJeoeldZZSx82she6vzX5t7ULyW66PY99TWNgHwDbkroSX7Awh+e4uG33DPGzhFc0PvUcMyoncXZBVzQ9yUEJmiT56Ec/mmSXUWGr6+xdts0B7h8Ha9c+OvTL7KxLx+b15zqcU/c3n/Hse9/73ncsWNyYTG5iYmJi4sziG0rQ3CVfHiWB9ft6HL/sSBqUnHn44YeTbH/V3/rWtyY5XuJ9VDbHEnaV4AFSkO1tXbob26ys83dgaQ0kHyVTdoLWajekL5Z+GNc999yTJPnkJz+ZZOttWduj/5ZEPe7uWM9fJ4VZGn/uuefWvZz29o6YHKhMyzr70fu15AOjwGTWoKY98nz4Ol2ybUvOLmtjm1C9ttu1t2unBejYcrJNyItGoc4rUq+l5GvxbAYjJrSW6uqktGUdqxklQzauXr26Yweq97yDoZljM/sueJk5pf+je7uOj/bRznS2WI/Zn7kIMM+W7p6AqfBsZB9jd6MtNDz1M+aG5yZ9Zq5qcg3sdtjM0W64OHGXGtAsj76ZQXYaJNv6OJfr1vvpve9977G+Pvzww+3zHUwmNzExMTFxZnHqtF7nzp3bkfa7lCujODIfl2wlKaQQbHBIq+iWX/e61yU5zrDMoOyx5oS9ya79xJ+7pEf9jHOq11SyleBdCqN+Byy9wuwqQ0VCR2KznhvvOubkU5/61NG5zCPSvfXnLjDb9cnoClXa5nL58uWhHYiUcPS7s8GMPHNHJXDWzvFaIs1WidBjRhocpXuqn9kz1/abLoGxY89sR3bi3grHE9GWE4bXPjm2sYsVPAkjBtelq3JM4lpibdvKa8o3A3uu7+Wu7JNt/i7yWefJcZFOBmxPXZhWsp1j9jPHYifi/q1raS9uniGsD+9r6jw0NHhTf/rTn06yZTGMk+fE5z73uZ3rveMd7zjWF/pImsR6DteGMcLo8Gi3Jq4+Q8yMvTeZ33pv2GvT8XL4T7z//e8/OgcbI9jb25uldiYmJiYmbkycisnt7+/n0qVLOxJwlaicONTHdkUlOcdSCzYq2EqNcPe59kyzna9ez8yKY/m8y3hiJudMA0hJSHtV6hsl5jWjrOzQpeTtbURbsD9iR5LkoYceSrKVypCGfN0uETCfwS4tua8V2FxL0Ly3t5ebb775SLqsXlmGk7OuscxRGRmzi65gI4zD9oC1BOQju6AzkNS+OqbOthKPu57rvjkDBOfUpOP8b62CmV3HVH2PjTQwdfze36P4s/q5E6mfVHyz2uS6GFxYg9mjGV7nzUu7tMExfr5Vuyf3FPYtJ1L/tV/7tZ0+4nHJdblnYYOsWy0QCmNzJhc+R9vVJYr/0R/90WPX4XmK1zrjrhmk6AMsj+vSJ8bgGMzaB85xOTLfz/UcjuWV/c2erWyT87EX1mdth8nkJiYmJibOLE4dJ3fp0qUdibSLnQKOI+u875AS+LVG+iIeDlZh78cKvrN3lctb1D5wjAstmtHVY/kOicZSRBdPZrvRyLuuzh0SDFIeEjxSpr2J0J3X6zCf3/Ed35FkyxRdfqT2iesy56xNtYN6XGu2JLAsSy5cuLC6LlzD2oC1eart1/ZcvJI2q5Rs6Rv2wjGsLRJjsivlu9RJ5z3MMS5bNGKS1c5BX5COXWyW8VavOqRw9gzjMKOzjSjZ9RYc5U7sPOQcL+f1q3Myskt2wBfAz5BOGzDSmjiOrp7P+mPX4hzucV5hb8l2T5g5cr8yj/gX1DHTfzRWnGM7a7LrTU22EvpuL1/8F5JtzBzjYp/Rd54ZdW4Yu0t7safI0oSNrj7/rFUY5TKt17MNDhAP7NJmyfHsJ8nB3lyLs5xMbmJiYmLizGL+yE1MTExMnFmcOhh8b29v1V3a5WqsYupoJUZUHE9QC/DqQNguDQ2AxmNkdSBu7YupvlFptR1bSIjKe/rmtDvJ7jzxnYPOO1WuK2jbAaVzd0eVgJoCFQcqj2sJch4lc+5S8lRnj5Eb+N7eXm655ZYdg3VVcXINVHP0ycmWK0ZpoewU0zmeAObH7ticgzqp9oH5Z909T3UenCTaKc2saq+qaNSV7A3W1utf9w6f4ZRgh421ZLZcb22dfD0HsY+CzjtnHKuZOyzLkptvvnlHrVv7NApE9/dVXU3CdydpQJ3nlF1VTeZUfOwZVMQ4c9Tk6xyL+pPrOmFy7aPbx5mMfeD0gjUJBapSqzYZh9XW9X+evVwfZxXGRZt33XXXzrk299CGE1wk271uxz2uT2mx+qzy8/OkRAKTyU1MTExMnFmcmsktyzJMpJvssiK7djvBcbIrcWIwdWqpNceGUUHFLvDbiZgd0Nm5KLv/dsQwY10rB2M32bUEti5xgyTHOQ7wrv0m9IKQgt/xO35Hkq2k2hVpNbvEAG2nnNoHvnvmmWdWg8EvXLiwE0zdOR7QLlKxnTrW0kON3Ne78ACujZTMWB0cXKVjB9azLnbL75IdOLCaz12qqO5V3NaR/junodqPeizt4zQAfE+c5L5f++bPk5NTaNnRqjtm1XHgMPzEe71jf94PDiWqgcTsCYcDwS7QKHQaKxdRtts8Dhs1tIPnHOXBHAbSFSLlOpxrhzCeXZxbw55Ii+jE91yfeayOLnZ0chkqWCfzWMd39913J9k6uNEXp/vqSpixB1kLrks/6v5gnar2aY3NTSY3MTExMXFmcWomt7+/v8pazJIcPNoleIWdcCySqBOxWkdev3MqLtvIOpuF7SnuW5UUze5odxR03CUyNgO2VF4lYQezWuK1hF3th3xmhmp7YZeiyyEEtj10oRGVeY0SNLvgbreWo+SvMC3srPWcUfiC2UWXYJr2YEEwHvYj9oHK2rkONln2EG3YBTvZ2mWccsq2YVy6K3PENjFKQO6kz7W/SNSwGObRbXWJyH2Pmxl1hZLtMs7ntN8FAY8SrFegBajzkvT2VbNJWATpsEhonmwZDevte9chEDUJOtch/RXnMMdmz8mW3bnwLfscu1e1AbMnGCtzynXQetHHmrbOiTC4LnuWElP1HObCdmlCVNhnnFNDJABhBn42d+EC3Dfci77nYKPdfqs2wMnkJiYmJiZuSFyXTW4t7ZHtTaOSHZ2HDQyDX3WnekEyrRIdUoLZmfXAXRJpJA08hmjXElZtj3OQOMwgOt24GYj16xyLvrtez+l66BPfd6VdHJBu+6El1NpvM1Ikra4MDO3T3le/+tWhl9yyLFmWZYdNdqyMds2WO5uFGaZZsQs31uuxhvSF9zAvM8h6HSf8dYLZLqCXFGxOJMAc877zzHTgvRlsHZc91bgee6TaUZLjDGtkg7Mdr2pvuqQQFV3hYl9vzeOTa9grtbbH2HiFkXBPweTq3uGanMOa+dnVeebyP0yOvjC3Zj7JLrNxIVzsUdjS6zH00d6U3n913fxsJDkEbfDcq88BvqM9bGJOKwfqM9LX4/nmQPzuGcJe5V5zEdT6DBslNh9hMrmJiYmJiTOLb8i7EkmoSrprsTFJXy6F//k1r+U36veOTUq2UrdjndALIx1117v//vuTbJkV+u83velNxz5PtlIP59IufeVzJLgqLdMXQOwH9hba/uxnP3t0DCl/nHiW6yEldXYq4DQ+TutV57FLnJ3set5VL076QB+feuqpIZMjya4ZQpdujWu6PE6XWmzkZWpGZ5tjPdYerIyLc2usE5/ZI5M5dlqnZLuveHVSX7PartTTqGwN46znOO6SfcA+xF7k9Gm1fdttzRS6uFMXB/axnb32pBgnjjl37txR+50djz0C82CdWGPmvCb69TlOewYD6eK7OMf2VrOZrtCq7WrcA+y7Oi5rbrgu97KZcC0KzDh4jnF9bIOdto1nwygdI9ftShfRV85xsdSO8btgLH1ij9oDNtk+86qmaqb1mpiYmJi4IXHqBM0vvPDCTqn3yiJsI/KvexfrhMRBu5akaB+puTIsWBEM6l3veleSbXYPvOAqYE5kDUBKYDx4LFWGigTj+BQkEXsQ1SKmjkeC5ZEtwBJqHTvjsv3LHpSVYTkhKgyCcZpB1M9s80HCWrOHIWU++eSTQ9uKC+56zTmm9osxeS9VG8IombMlUdt7a/vOBME8dUl9LcG6rA1sqSbzxSbmYqKjLCJrBWXt7djZ0FzQ1eyPY/EWrOO0N7Q9QR1PWT+z5yVzBUOp++1aGBzY39/P888/vxMvW7UlZo+2t/lZUs93X7jHnVWm+gJwDvuJ64yKtdbPzDa5x7iXq7bB+3cUUweDq1oH1p9+Yz+kzy4SXL9zyTLPvT1nO9gjvXvu2GOW6/CM7rynu3bWMJncxMTExMSZxfyRm5iYmJg4szh1ZfCvf/3rOxS1qrCgkE6RBCXvaoJhtEflNwrw7VSBuAZDvQlOdBAlgY9J8uCDDybZGjdRaeK8goqw0mGOJWDTKcFQE3Tpg1CR0B7uzbSF+qKGEDB/rlNFW/TZaXDqmFkXG3e7+l5OoWb1hEMa6v+oFj7ykY/s1LmrqGve1V2zSnakgqqqOTs7uDYgWEuc7MrsdtjoUgrZ5RmVJOqXOi5X2XZwtJMPdPNkFeYo6XOF1XAOHKdN1Fh1zE5I7v1RnwFWpV1LUnb37Vrqynl/VHWl06mNguer+pD/WTurtEng/PjjjydJ3va2tx2dS41Gh0fYUaTeDzY1MAfc27zW2pBW9XLfOyG0938dh5Muj2q4JbuObrTBsTZnVDMJ8+gQKdCFITm9H/uL562d0JLdhBWjBBRgMrmJiYmJiTOLUzG5S5cu5aGHHsp3fdd3JemNq5YSXYW7k8aRoJFOOMYlfZAqanJnWAvH4IhiI+tb3vKWo3OQzGAgsCHYWDXeAqST6vSS7FazdgqtZNf5Bqnv13/915PsVv1OtpIZ7RNIDCOF/SLhdG7ntMv8mhlXVuNkui7LYqegZCtJM9dPP/30iVKVWVKVBB0s60SsLsuRbB09aA9JtGNF9RrJdh+NnFfsUp5s9woSNcc4CXE3t8AONHYq6cqKAEvUXZo8s3HadegCbdXrcU/YuWzEJOt3Dsh3SEjHwEFN3m24qryD3ZMtC2JNR0HGtXI29zL9hBngkEYo0Xvf+94kyfve976jc7//+78/SfLAAw8cuz6gr2htku3eOcnppnMiYxzMEcfQfhcOwjPC7Nuani7dnsNq/DxgrirrdEgCzx/Ooa1u79jJh/u4uwedEnCz2cy0XhMTExMTNyZOxeReeOGFfOELX8h9992XZPtrXu0B/OLzC+ygSNslkrE7vlkg13n9619/dC52LCQaJDmkiNe+9rVJjks49fxkV3IHXaosu2c7jMLj7+bAyXxdcifZslqYyhve8IZj74FDF+p1GLPtdp0unrF6rpH6OpaB3pzg0le+8pVD2wrSuNur8Lywv7AlYMOs62JbBfvKZVJYp2qzYO84TZTTu1VWTnvMO+Emdq2v7M3lSzqbaO37GpPz2tpGkoxtcbZLdgHyJC12iivb2eo53hvuW5fCrQtFGGGz2eTy5cs7vgD1HmPvwF7QxrCWhBRhN6r/8+xAW8LzjXsN+9vP/dzPHZ3LnmBt0WpwPc6tvgCwSbvWOzF0DehmzmjfxY1pswZLAzQ4trMxRzwHGHftL2sH23WoBG106DQEyW5gfrL7jLcmjPnsCmXXsl0zGHxiYmJi4obEqZjc1atX87Wvfe3IBoN0VCVSpBInB7YEWqUwp5uy9O0kvNgEk61k8Yu/+ItJthI20j/H1jLttGfG6ISsXcCrvUUtuXdldSyp2Y7mUhv1mHe84x1JtvYBACuw3SPZzi3SUFfqpPan/m/9OZKc7TnJ1vusFjdd042TpLkbe7Jro+I9a8l1sBslWxbGHuJYey66cGQdM1Ky09R579bzYbGcC+uzHTnZSqNmcLRlu2u1r9qLzTY52zSScTJdJx/oPJ0t7fuetO22wt6DjMtB9/X86h07SrS7v7+fp59+eieZQfXeYx/YRsq+4P6ozIqxsJbcp6y/vRP/4B/8g0fneu87GBvtRt1vtIcmycyd13pPOKm2U/JxLH3Hnphs94TX0jbUym55rrnMleeV49Y8tIH3QRcgz1owf/StK8nlPXnx4sVpk5uYmJiYuDFx6rRem83mKI4MCaArg8AvKxIWx3QJXm3bs43EnlJVgvvBH/zBJFtJBkaHjQZvqjUp0tKxGWSym+7Iko2T1FbJYlRwlXlkTrCHJFtGgvcWUr69ODvmYInQNsGuDL1tcY7dgslVbzGky+oteK3pmuxlWf93om7es6Y1Por+sBdhVBzrBMNd8Vyug4bC7LArz4OE/sY3vjHJdg1hBZVF4YFGH9kjn/jEJ5Ikd999d5LtmtfxIUnTBhI8TIJ9WD1ziRWlD54/GEXnmclYHYvk+av7zXve2prO1uhz1vbO5cuX8+ijjx7NE+fWFG2Mn1RlzBf2KHuY1jHA8py42vdtTdVmj2zuZe5h1qmupYuXoo1xQdq6d1wstfY/2a4t4632PNvkrFEwy+2+w+bM+ruNTjNi26n9Fro4UMZJ/62p6OJAR+XOjMnkJiYmJibOLE7F5M6dO5dbb711JzlttdMgXZktWF/bnYPkh+SBVI704CKXydZ78od/+IePnYuEgdTUeR85FmSUgSLZSv3OBMB4XA6mSiuOgzNzZJywtmQr3TsxLhglbK7nWNqy3aJLtowE55g0ZzdJtutP39bK0C/LckyS75ItO27NZXNAZfLO/MBcYodwyZ0unsw2YZiQM8YkWzaEVI/kTp+w+cAKkt2kzfZMg+HB1pHw67XZv8wNDIJ9WO04tIdHLnYa+s6+5jqVOTBfLkMEunvDGTycQJd7oq6113SNyT311FP52Z/92XzP93zPsTHX/etnhr27HWtJu7Vf9BtW5pIude/wzOAYZ8vBls3ntX3bpZlL+lrnlmPMZHz/OFtU7Qvr4JJbXSyptVuO7eMc5rFqdjyPtqc5A0pth76yJ3nWs471+eZ5HJX3Ojp+9duJiYmJiYlvYZyKySGNOyNF9Qazd6XLtHeF+uw9ya87v/y2jdRfddqF+fze3/t7kyQ/8zM/kyT5lV/5lSTJ7/pdv+voHMea0R7SGeOq+nTbtSx1uZRHlaxdOoaxI1l1Ej4Sqa9jD7Q1+yHzZ+nfsV31Oo6Pcx7JyuToC+eueTm51E5nm2XuuDb7ynaVLv8gawVbYWwum9Od69gv9gGFa/FwTbYMDc9i2kXy5NzKjkY2q2rjSXZjhmo79ijzvVLtIGgE2OfsJccKdveT7Sr2olwrBwScb7CLifN+XrOrPP/88/nkJz955H1sO2iytUnZU5J2YRxdRh4zWtbJe6pjr7THHLMPYNN1Hzi/JPucY9jv1U5tbYbzoLpYa72nbdNmb9oW2+W9Ncs0o0LrUs+lfWd2clme7vfCWhwz8jour+FLXvKSGSc3MTExMXFjYv7ITUxMTEycWZxKXZkcUE87nFQVEOoTq61Qldm1mzaTcZDqWlkRp7UiJQ9t4qZNUuYk+d7v/d5jfcPRYK0EhZ1eoNFONOwkqLUvqA/taNMZZK3WG6mabNzvjumqsSd9mRu79jvZalVP2DnlpPCBvb29nTWtfahpeuo4HPBawbqgjkL1hJrSiW2r2zn72GvoyvDVWcHBqk7V5BCDZFdNyZ7lGAeHV7VYVZnXtrz+1bWcPjhsh3PtENCpcEcqVq+f+1v71KmkgZNSr+HcuXN56Utfmp/+6Z9Osg0Lqios/kfNZVd0+lvvEycu5r2dehyWkOw6q9lxwpXD6zF2QHOquDonfp52oTBJX32ddeYc2rIauT6rfKyfJTwHMCHUfeeyPH7uML7uety/oyriXTB4/W4Gg09MTExM3JA4tePJhQsXjqQkpMwqreCYwC88EoaLaVZDocu4uJyD0yzVX227BiPh4IiCdF4NwHbvdWJerl8Zo4symom4wGaVqCxBuy0kmypR21g/Ko/iQo/JbmkfjqGv9KNeb1SAEOmL69d1tNR1okS1t7djsO/Kbvi92Ut16vH84/6NU4f3Tr0GUn8tVlvHxf6oDNLsjusxT4ynBuVyjNNSAYd61OvVcjJ1HNYs1ONcageYKTsJbh1Hl/Krtlkl65HWwaiMb5REusPe3l4uXLhwVOz4wx/+cJLjDkHMC9fgHseZi+9rGS0zatiJ+2+3+XqOA5L5nLaroxZzxvo6kJzP69y4NBn3rh3OOs2InTlYZzuT1XXhfPYv99pIo1Cd5czcXdCYflQm5wQMdnAxa6vXrpq31f0z/GZiYmJiYuJbHNdlk+NXFnZUXfL5Za4pqpLdMjq1yCBSFlKyA6BdoqFKK9arIxXxHvtKx/4cKAo612pLtpaW12wWdvNF6rBNqytfYinSxQXNCpOtdGlGymvn/jtKxMuxuERXFoXkW4NmT7KxrNlinG5tVMh1zRUdiRMp2ba5Lhkx+4tzfP3KkhirpVS7hdc9yjWxOzghr92ksVsn2z3v/ruwcGV/9IV7rNvPta36+ai0z2gt6vldcdSkX3OXCNrf3x9K48uy5Kabbjq6fwgLqoWQHfjMOrmYbmURTtAOc2dubQOu96e1L75/eA5x3WQb3sC97PdO4ZXsBn07rZdLftXvmQvfN75nuqB6rsuedaHaTjPicJNRuFNdA7QonIsGZrQPazsOyRphMrmJiYmJiTOL6woGH5WdSXaLCY4SvFZ7kIMSzU4s9dVfbtsZHMzsQqX1WAcvulx7VyzTErQl6S7Y1HBJmS51jYOxnU5p5MFUxzEqA9SVS6H/Tn/lwNEqmVr3viZRLctyzCYH1tKRjeyR1VZqL1BLoE5SXDUIMDe+w35i20hljk4mbjtnx1ps43HCWtaf18rk7LGG1O/1qvNozcfI/tWVRhntN++zjhFbmwG8V+s1q2ZkjcldvHjxaG1Zg+rB6mBoxg5bYj90gchO38Y+cDLi2n/6Qh9sE4PJ1eecS0VxjO1dFdxvtINmyh6tfF7tqyeVBWM8dR7pI688h7xXQX3vvej55TqUIUq2+5n70nupS4JBe5WtT5vcxMTExMQNiVPb5Pb29o4kA5ek4PtkK4E4USoSQfVUsi6XWBP0vo7JqMc73Y29j7hOlZZcgNAxaLazJLuxc6NSN53Uag8hM7c1u4qlY3uprqVZoj174HVeqrTruBzm3J6go3GMJKrNZpP9/f0hq6nXoL9O8m09fT3H9lW864htwjZX7Sou/Mh4kLA7Jsf8uBSI2V83D7a9WvvguKZky0C4B9xuJ2HbJuvrj/pV27fGApiB1fO9n61BqLCEfpKH3NWrV4/mxUmyk20cLM8BJ3tnTbuSVB4re94xvdVj1rYwpxNDS1C9uukDfguO8a3PG+DnplMqMrdOulzHwXewPF+33tP0sUsWXd+bFSa7vgceA/GGXCPZtRtbQ9bdgzwHYIZml8ZkchMTExMTZxanZnIVtgsk27IesDHbu5Coq4SDVIIki+TBrzfSGF6cXUJZQF/sZVX76MwDo8wanRenJXjr4p2cNBl7t5mFdPExZgaObWGcHXOkb0hwlsKqLt52B+aGDDKdZGdmVTOadKDobkVXQNHvPdddcm9g71D0/0jl1Sbn9vkO9uSSP8mutG0bVrceo73jPq9ldjGjYo0dj1rbp71RBhL3uX5mFuYxdOtmrYLbqnvHn12+fHlYMmWz2eSFF17Y8T7FDpVs2cNnPvOZJFu2AMPCi6/bOy4vAxOmTdrqvGxHcZMwy8r+7LVp/4Qug5Qz6/i9bcIdkyOWjVfuezRaXUkx+t0VOq2fd+d6H9M3e2rWdqwVGiWkr6i+IdMmNzExMTFxQ2L+yE1MTExMnFmcWl1ZKzw7XUuypaKoK6G9Tq9VVWVW37iGkgOTu5RgNlBaFVVVAF1V4vraOTpYBeO6eA7S7QJfadcpwLoAYmAjvl3lmecaIuG+WpXBmtQK1KiA6Ivnj3WragHWqRuzQQiBx9iFg4wS+3api2ywRrXNeHiPsb9L0Eu7qHP43Elj6xidsMBp1qpzlPeI97vTsHXqHCfVde3Dbu67gN16bKc+HQVyg7VwEYedWMXZmQyqimvNaWmz2RztN8wWVXWMEwrqadSUDteoITDet64uj6ME90RVj9JXnkk4mhAegONT56jlSumuUF7V4+xJPxN9LnNR+3jXXXelwnuFxPRdoLXDC5g/5oa560IyGDPvWS+n50t295vvyW6Pojau380EzRMTExMTNySui8nZUFudSF796lcn2bIH2AKSCBJ1ZR60g9Ti0jOW/rsyJnZjNnuy631tZyRNVLiEjtmYjflVajVDGZ1THQ4sybhd97nOidOWOeVRZ+C3odnlR1jjbm5OSqvDMfv7+20aMjAqsbRWImjkAMR6s8+clDvZdQTAKcqVwjtHEFezt+G87uFRsuNRCZzKNh1m4ABl0K3BSYzYaebq/14f7926Bl1pqorOkcuJoDunpIr9/f2jtTNDqf21MxTPH+5b2Hmy6yDhMTvNW5eEwAkE3I+6Tg474J51mM5tt922cw5p9TjHz1X2e30WO20Z5zAHrFsNr/JzjWNdUopzuuTevJIk3ynWKui/nyt2eKnPO4cLnZSAYzK5iYmJiYkzi1Mxuc1mk+eee24nWWzVOyPtEEpgqYVjK7PCfoJU4IKknIM0XqU1dMTWJVsq62xktGOp0vaCZDeJs1NjWb9fXdVtc3FQe+cua5uf23Jqta4Aqm0kSHn0ser+nXAW3b7tlbWkjwupdkG/hu1OnT1gxFa79s0wbH9kXLRdA1GdmsvFbR2U7v/rdW0nWJPgabcyg/q+C3r3sWt2tVFfjW4N3J7vp05qHrl783mXXMH3wrIsQ7vKZrPJ1atXjwL7sfF068K9zJ506ZvaB4dAmdmYCfP8Sbb2Ou9ZWBgMqEsMb20QDLUrmso5zJMDuXnO0hb9qu2wn7nuY489lopqm+eedvJ4F3jlfbVx0jfuBebCRaJrKIYDx/3c65JI2+a7loQimUxuYmJiYuIM47pscmtpaJAWnI4GSRQJof6C4wkFYEGjJLH1V90Bzk7RVfsNnDJmpM/vyqWMJI2RLaO2O/LI61ibJShgyQc7UrXJufwQkinSnj2l6jlIW2aqnXelmehJZXa68XQY2a66gp2efzMQe7RiE062jMDrPgqArd85FZ2ZfOeROUqvNkpIXc/t0ijVzyu65Mf1veeoS35reD9U+4rT0o2SmdfrOJh6s9kM047RJswAVlPvcTMQgr+d/LgyYtuxrAWwLbFqcyr7qePgWeZyR8lu+i4/I7vSX6wRjMkB937eVa0azJP5cjLzL3zhC8fOrX201sfenDwnqmaHfr/2ta9NstUGOZl1va94fnk+raGp9+jIe3eEyeQmJiYmJs4srovJ+f8qPSAJIMHgocQx/CJXCQfpxzFfJ9kHkq2Ec5Kdo0rHo7I4a3YHH2OJ2iyja+OkgpT1+xGDggX4+nXc2AM4x15InTcn18M+ambK91XiMgM5STfe2VA6e9Aoroy91SUUHvUJ5tulFEKSZ+5GsZaVtTnuynatLmG29+bIq7LzJu3iPLs+VoxiEc1ubd+p544Ka3b73/Yue62yZ6snHnaoOicjm+6FCxdy77335l3vetexcVTGw1o+/PDDSY7bipLdmM5k6xnpe837gfWrzzlrRWjXjKR6O5rhuliv267nOPWbfR265Mgu4Mp7rss90d2DwHZk0KX5w7MeBscaO31Yl5QdRkj/fWzn9zFKA2dMJjcxMTExcWZxau/Kq1ev7ngfdnpnJJpRQtsqrSA98GtNclOyGDjOopM8RglEu4TJo+KcLtNR20QKRWJkfJZ0XE6l9sEswGypSjiOS+s84eq5dQ0eeeSRJNu5f9Ob3nTsHMZZ7Xjoxs2WTrp+HcfFixdX7XKV6XWMb1SYc82DkPGPipZaGu88/Nh3xGna7mT7W7Jd3y4hc+1PNz7bU/x9l6jbmXbWMvr4MycCtp2yK5fivemyLHUe+WzE/rl/a2ws81NtnKO984pXvCJ/9I/+0SMbKpqfeo/96q/+6rFrex+wTp03r/vtGLQuttOJ4GEr3rOVUZq1AJ4p3R7lHuXVfTVjrUzOHpDWBnCdyjZpn3vBvgG8ovHhuZFsy1rB4BzD1yV1pj1nY/Herb8XnUZvTfs2mdzExMTExJnF/JGbmJiYmDizOJW6clmW3HTTTTvpYiq9Jv0M7qnQTFLxOHFzsht0S7u4AUONofk1NZODfp1c2cb9+p1rZkGvHSScbOmzHQ7sSNOlzhqlHAOup1fH4ZALjrG6pFP/OsWV+1HVPU5PxbkO3u+C6q8ldIC0Xg707qqTW63RVaMGDkTvVL91fDVlEnvVqnXmoFNTWmVqg7xV0vUcq1Sthu1c6EfB51ZbdcGyVu+zlr5/6zWYYx9jJ5Wq+nJVdMaJKgvHk5p8wMkVTkqyu7+/v7OGH/rQh46+59mAEwfzhBMEKvwu/R1raKcR96dz1LLK144odZ7sCEKf7eBUn6eoZv1McvV39g7ONBWMjzm49957k2zVlF06QcPJILh3XvOa1xwdg0nH43DiihoMblW6Haoc3F+PdZjBCJPJTUxMTEycWZza8eTSpUutSy3glxcjM9KCgws76dGMDgnDlZoJNkx2U2Y5wLYL7HWKGrvw29heQbswt1Hi2jV3+RED6hIPI/Vg2EYStSNIZY60DwNm7h1sWo3GSGFmTVy/M75b6nr++edPDCFw4HidY0vBZjqdy7DndhQ0jeR5zz33HH0GIyBAHCmYte2YhUuM0DeHjnQB3U5+62Dtbu5G6dscfF73jlO0cV3vgy75rVNwMU4HEte54TvmmL1kyb6bz2tNBffCCy8c7UW0QIQL1GubFdv5ompL+J958X62NqjOMfPDc86OOZ2mAnAv075LIlUtl1MQsjdheDiImHkn23WBfdmxz+nE6vmuQM4cMc9Vm2aMEnGwD7q0XnYQdBhHZXJ20JrB4BMTExMTNyxOHQyOVJX0hfOs5ya1jNPO1ATNllb8q86xXBdbXbKVpBwg6FIrHdMBZhCdNM61GZ8DrDv7pK9nduGksjVYlvEwX/SRYz73uc8da6P2lb44wJLx8XnVvzt5LGth+1R1A6+ux1x3JFXB4kbBzT62juNabH5mQ+7H61//+iTHmZwlQezILoHSpXcblT7qrj9KAWf7R5cezRoJ9rXH27n0uywK62X7Ue2rQwTsmt0FB7OfkNB5XUs4fRqcO3cuL3/5y3cYfZXu7cJPP2HpnIN2I9na4GxvZs/Dkrj3qz3Pqb8cbO6wimR7j9Eu8wTLhMHVtfT9Z+0Z97bZex0Pz2BYmJk9oVrJdr2t2UHr4fCqysrsW8B3sM9qDwcjm/aaH4PZ/1o6uGQyuYmJiYmJM4zlJH3msYOX5UtJHvnt687EGcDrNpvNq/zh3DsT14C5dyauF+3eSU75IzcxMTExMfGthKmunJiYmJg4s5g/chMTExMTZxbzR25iYmJi4sxi/shNTExMTJxZnCpO7ty5c5vz58/vRPPXuB5nHPAx3TnAn60dOzrnpM/X2vW4rsUpx8deyznX4+wzmptvBNeSh9K5EbsciTVzxte+9rU899xzO5172ctetqGoYr1e157nx++7fo+w9v03MoejskDddU/aT2v9+K10DFvro48Z9ela+uPYui77h2NHL1++PNw7t9566+ZVr3rVTt+6veM95L6cZs2v5/6/lrUclYPq2hjFWI6wdj953tbWZfTe89yVu/F3zmDUwdl3Rq9JH0v3zDPP5NKlS+3kn+pH7vz587nrrruOAhMd9JfsJlF2Al3XXavtOPnx6H1NKHpSldiuvtuoIjOBjl3VZScMPekHsQvKdDoxJyVdC3j09RwsWeGbx4mGu9pL/sFyajUCVUkum2yDaAkcvXTpUv7BP/gHO20nB4lyf+zHfmwniLauJcG2rgnmuV1L7+UUWr6hu9RpTp3lue1qWTlxwVq9Pc+/U2Q5fVRXqdt7ZC01l+ttjfaqkx8k27X0fdoFqhu049pntFX7yHcEJD/22GP5W3/rb7XtvvrVr85f/at/def+qQklHMTOd/WY2pfaBz+cR+n96r4bJVtfE0a9n70+Diyv4wFO7OB9V/eOK7Y78XmXAs/PUx/rGnQ1OJ3vfB+TvIOA/TomxuxnPHPg2nTJNkkDQfUvf/nL8+53vzsjTHXlxMTExMSZxXWl9bJ0UX9lkVxgTiOppZMILXWbpaxVf+36WV/ruZasLdE4tU2yW2pnlKqrYwyWrD0Op4aq7Rtuq5PkPW+nUaWM1Aa+fh1HTXG2pua6dOnSzrxVluRUX2ZJLj9U+2OWwh51SaK6d5FazfrMPLpyKXxmybebt1EKMydZNgvtruPxmYVWjNiXy+d0aZGs1QDd+np9rqUfXZL00Z4/d+5cXvrSlx4xhK7fJ6V26ipMW1tBn1wRvLvXnfZslHS7YqQu9J6q6fKcPtBp3ZwYvO7vkbpyVCaog/e1GVd9RrIXR8mVSbRfx0d/3Rcz1C5NXn2GrKlCJ5ObmJiYmDizOHWpnf39/VXjYy1l4XPrayfpuuTEqI2qVx9JUJZsax+7xLTJVprAxthJpmZqvLfeu+v3SOqzRN+NZ3Ruxxz83bU4rYykzNEY6jG1GOdaguYXXnhhhx3V40dFUl1yqUuCPWKQSI1O9l3PoQ9Ipaz/mnQIm8AG7XVfsxsCM/g1NmI7pZlwV/rG7Xt+1/po9uVEuvV6Tto7sjV1a8R3a2WalmXJhQsXdsr/dM+Jk+yPncbDSZWtoej2vm1j1+KAZkZl26+LG1eMbNm2H3b2Y88BcDmi+r+fo9ZydDZo9oGPYR65V0iaXfttDYVtzt2z8VqdiCaTm5iYmJg4s5g/chMTExMTZxanUlcuy5JlWY4oJvS+Vom1WmOk/uriHkY03urFSufXHCPq9x3dHdUl6lz6fa6NqlZ91D7aacUUfc0Zx3PiueqqLlul6nbX1CHAc221SL0Oxz799NNDI7brydX4KGB1Cv2mCjKVoOu6cT4qRqvOqKWFyqSqK1E5uc9UTHc4TAX1/O6///4kW3fma4kFstOFnRQ6IzuwA4WdMeo5jNWqNd+TNbyG2masN23Y0aCuFWN/1asOksDbEaVz+nANx+eff37oWLYsS86fP7+jQuucLE5S0XeOU96LtM/YUXUzNxUj9WTngHaSY12nwnefrE72s6TOsVWKDkfxGCpG8bJWrXfqX68j94/rzSXbkCSrcr3GtU07p5xUXX4yuYmJiYmJM4tTO55cvXp1h63UEIKRkdsG7GqodwVhvhs5olTJ09cZVZat1/NnJ0kCtf+jqs52iqmSrqVWH9NdfySl+HOPt37nOXFblcF0Aa+174y/rjUGZJjPxYsXh45HtE07DtpNtozDDO7jH/94kq3mwBXJa3scAwuD6bBnanVnzmFeqBTN9akifvvttx+d85rXvCbJLkOESa4lErADg6XxLrHAyEkEif2JJ55Ikjz55JNH3zE/9JFxef1Z28pUR6EDXJ/x1vuJY9/2trcl2VaANnPstA3Xkl0Ihzf2igO/6/mj4OwuIN3u6/TXDL+rym0GZccjJ7Ko51iT5Pu0C1kZZXsx8+nCTzqHvYruc/rvpAdrGiRrwKypYl/UZwT3DYzO68U4u6Dz6vSz5oQymdzExMTExJnFqZnclStXjiSdW2+9NclxZjXKfeYUSvWc+n89xtKxAxFr+9YD88tP22s2OdvKrH/uxnWSq32FJRxft2NalpgcZgEs8dR2HEDscXfpdczELalWiYpzsFXcfPPNJ9qkzBirLclpiD760Y8mST75yU8eG2O1jdx9991JtimDvvKVrxwbO+jskDAc2znMnipzhAnSHmENXN8p6hh3cnIOv85uZDd8h1PAph9//PGdccF8bNdYC13wXoTZ0WfbPmt7fPfOd77z2Ljs7l4/6xiBQQIK748uAN4p09bs02YrgHmr6QprW8l2T9Ae98XI3l/74FRW3NPMab2XrTlyKsK1pBcjrRP7mnHX8dO+nyG+N7pQMAeBs07uKzbcZDeYnXvbNuCqyWB/130wg8EnJiYmJm5IXBeTs/63S0LqX3yklE4StJTALz2SAb/mDtLsrjNKXNoFg3MMUhgSjSWtOkZ7UY4YT72ebYtmcpaeanvMlz2K1oLB6b9Zn9vo7HhIsfaMo4+V/Vlie+KJJ9pA7Xo833dMjv5gi/vMZz5z1G49p9odkKhZO5jNyDu0piHi2k6Ma1ZRGQNMjooK2P6QLmkfLUftS5cmrKLzDHRwrAOiOy9Vp35iXCS2ZV/APpnvZHdv2LuyY7fgQx/60LHr3HvvvcfGX9etS503AokEuvWvx9TvPD8dq7HGiDXkvZMCdAnUR2mpOluj95u1WsxFl0zeNj8zLNv767VtT7eGpzIr21zZx7Z5d8zb7M/B9d262fPTzxRQWXX15h715Vi/Vr+dmJiYmJj4FsapEzRfvXp1GFOV7P4iu3QC0kqX1steliMvxCr9OzGp7Wqd9DBKJOpzOq8j65dHSXCr5GXPLns7OSFs/b9jXfV6a2VMbF+znaBK0SNvUXt+dWl8YDdPPfXUarLXvb29YbqgZCuZPfroo0ftJVuJkHWBPXX9ok9mz0ik2PCSLRv7tm/7tiRbuxbMpktsbQndNjgz4NrHa43p7LxescE5roi5wHOtjueOO+44NubXve51x+biYx/7WJLkwQcfPDqX+aMPXM8stO4djoHd4Q3LuGsdQaPa3deSe1++fHlH49OxFt//o2Toya6nosu7sKY8b+pzzp7lnRdg7UdtZxS/2rG/UQmarmSZrwesxbCGB+/o2r5ZH+Ma2ejqZ9be2d5fz4H1cwx9gVWDOie0O9KIGJPJTUxMTEycWVxXgmZY01pMlH/NLSGuSUUj1kLhzs6TZmSb62xEti+4bEXnfWi2Z9ZpxlXnpmNb3Tn1epb6RhljbHOq8Lg8J7VNS3tmXF1Mn6W8tQTNjmXpypd8/vOfT7JlUvT77W9/e5LkrW9967HXJLnvvvuSbD0wP/WpTx3rPxIi8W2vf/3rj8597Wtfe2wcMBsyqzguK9ll37aVdcVgPf+josAdHLvF9X/oh34oSfKmN70pSfL+97//6JwPfOADx8YF22NtYXS8vuUtbzk61/2n4CXjM2tLtszQ3rYjO1X9n/G8+MUvXk3MXuNzfS8m4/hR3+Od9D8q8WR7V+3fK17ximPtwkhhSZ0maeTt7OvW/ea9AvPx510pKY7B5lbnuva92o+B2Z61DFWbBuwpOcpgU88dZX+hT9117Pn7zDPPTO/KiYmJiYkbE/NHbmJiYmLizOK6KoOvOT34MyglFLwLajZldUJZqCgqkkphbWiGilv1WKnzKH2P1XtdujKraE3RHRZQP3OfrGKoahP6NjJWM3+d+sXGadfFQg3XqYxHSbG5bh0X64HjyVp6tGVZctNNN60m2SUNFeuBA8W73vWuJFvVZHWyYCyk4MLZwqqyzlmB/tMXVFDMBePC6SPZquKssmeOu5RTrjlnRysHfNc1pY844bAffuEXfiHJ1mmkpkdjDlD5OF0eyaW7tF6d40yyXX/mnvCOZDtPDsVxcnHMDcnWwYA+3HbbbW0txmQbuuS9WNfSDlIjk0TnJGdHLTuecT9VBw2HJDhpPetW94H3Rq2ll+ze63WsI9Wpn0dVBYp6+pWvfGWSrUrVat/6nPMzlr6y7z2uuldHqdW8JnWvAo+L+8lhXvUz+t+Fs1RMJjcxMTExcWZxaiaXbKUHM4ZkN42W2VLHCJCQHLhtV3skE9habd+pi5xSphqpkWgcNAvssl7HRZ8cNGmmVSUqO6mMUpxVCceByQ4kNXOsfXXl5FHZino9j8tOQMx9XTeCtHFOuO2224ZsjurOwGwp2boNw6hIjAwj6cIpbHhnL5kVdQHJXJvkxg607sp80N4ovKFz7cbBBTitklMa1b3qcdgBxKWGkl1XdJfN4f6xU1GyW0rHDmO0VdmfpfsvfOELx9qirzUd2+/5Pb/nWHtrTI6xrJWkGjkCjZ4HFbRrpw47sXQB/vSfOYC1W5NQ+8Ce4RWGyz6szMRr6UQPTj1W3zMe7gnOMVur7vrsEZcbctmmzknGYUGeo85R0SEP9H8tJGvNyaTDZHITExMTE2cWp2Zy+/v7O7rq+gs9Cqy2DrkGk44Ca23vMNuofbBtznruqhu3xD4KVlwLdESyoS92Wa/jMxO1vcCSZIWT+zp9jwNW6/g8f/TN0m732SiNUO0jTOiktDocc/PNN+8E08OIkq3EyTzB6Mx0sSnV72iX/vHqfVftD1zHe4T3Xaknu5ebadFH2G2SvPnNb06yldQ5584770yyleSdVirZ2iXN3GwnqqmZYMCwCtrwXLGHKpu2ZoDvkP75vq4bdjqH6zAu24KS5Nd//deTbMMYXvKSl6xqAc6dO9cmlAaj9Foj21Wyq8Gx7cj246r5YM9zPebH2qhOo+PQDmvEqg3YabTcRz9T6jjZE1zHzy76XK/npN5eE89Z/d7PjFE6u65wrcdrX4P6fi3kpsNkchMTExMTZxanZnLVtoKE0BUxdfJmM48qASB1j3S4Li9RpRWzPCQQX7fqfq13XpO+wKhcTp2X+nlXLsW2OXtoVQnFQZBOGm0v1cqwbIcyIzFjSbYsArsJkrqT+3ZB55xTi4say7Lk/PnzR+dzTmURtrkyhxzDHNSxMjZsRE5CbIm76vNHNiCnpKssyd6OsErsk4wL5pUk73vf+5Jsg8wJdv/Df/gPH2uL/feqV73q6Nxqx0q2KbLuuuuuJFvPOYLd62fWSLB3zHbqvvN+Yk0Yd1c01eVQbIeHweL1mWyZLmtbGbZBgmYzgWpfHRWg9RpXRsD9yGdOyWVvzi74fBRI3nkPsw7sVRjuGkNlb9COn5GsE23WeeT5zDGsC20y99XrdVRix+nzHPReYZ+DUdL+2idgjVVnN7ZGYhZNnZiYmJi4YXFd3pUuRdEV7DSjsQTYSeOWvm3/4H2VPCiSaY8b67m7mBrHgrncfZeGalTm3p5yXZJdPnOiVNsRk62Eg7RliYrvuX5NWmxJh/e2DXbxJfR/ZGOo9iKv6fnz51clqmrPhTlWCc0epZaksT9hx0m2rIdjKLUDwzJjrPuEPejks8wlY62MwQmKSRPGucSP4WFY+0ZfYTQwHNrqbI62yTJ2GB1jqPcE8YbMBeOzzZn5rGwDRmC7u5l8jRkDtMPaen91dhXsQa94xStWvea6slBVo2PmZBbRFf09KZ2XPcFrH2yDh+17jutzgPlwPJlZU7Xn2yZnNsuxvHZMzn4LHkM9xx7gfiZync7OagYPzOzqs9ie7vSFuepi6kBNBzmZ3MTExMTEDYlTM7m9vb2jeBF+1auEY/uSWVMncfBLjwSIVGrW4lI1yW4MlZPRIilW6cLSHdKEE8t2HlkurWG7WueZ6ewlzoDCeKtHHn1hTpDcGQdSepcw1YlgsZXRBkwC202ylQwdO2gba7VPIZHx2VoZ+itXruSrX/3q0Rra1lTb87rAGrA7VZsVbIiCncyLWWyXDJm+wvKYa6RnStR0SWId40Tf8QitNlv24nd+53cm2V1L+ob0+tM//dNH59JfxgzTou/YAqvE67HSN2wxvPJ53Xecy5ywBtxXrEWN/aNPeFmyJtx7tFXnkT0D873zzjuHBXf39vaOeeZ2diDf085e0rGMUQkqsyTGV9kC7fE8YH34nLWs59DOqFBol7jZz0BrjE6DLil6HUOy+yx0cVuuj2dw3XfsK8bOPTLySK7t8x37zD4H9XkxbXITExMTExOHOBWTw0OOX1sYQ2eTA84e0Nk5kEaRipHosD9w7Hd8x3ckOS55wEYok8Iv/vd93/cl2ZZgqd5u1rkjsZslVf298w9io0CSQcLtSkfYzmUPrEceeeRY35Ot5ITE9IY3vOHYOOjrAw88kCT5yZ/8yaNzKb/y6U9/+lhfyXPIdapty6VCnFmF18r+HJuzVsTw6aefzi/8wi/k/vvvT7Jd8ypV2v7DtZgDJGG8FJPkwx/+8LHrME+Wmru8kDAM1s5ziwcj3pD12u4rx9BmPQfWQ35NYIkahkeOziT5x//4HydJ/sAf+APHxuOYu8qSWEPi49iL2Or4HMm7K59E+7YNw3ZrbBX9Zc5hNbTPPVLt2YwdJve5z31uNQfhsixHe7Rj1mY2zszRtW3W57yk7IvOQ9vZimDwzBPPi2orZT7MsJ1f8yS7dh0f4+qyTzlHr8soOYtNnRPWmWNs14OJV80N57jkknOo1nlkH3Gsix6vZUuxx/kIk8lNTExMTJxZzB+5iYmJiYkzi1OpK/f29nLx4sUd9+HOvddOIk403JUvAainUIM8+uijSbZ0H3Vi/c7ptVCRQJW7oEz6gFoS9VinShuVx3CC1C5Y1mNmvE6zU4GKi+twDOoQVE/MfZ2Td77znUm2DidUfkaF9qu/+qs718UV3utnV+yq7mNuaaemXjKeeeaZvP/97z+ag67sBnOJ2gsVEKqKxx9/PMl2zZPtfKPqow3WkrZQW9eAZPpiNSVtcW5NYEv7hK5wHaqVM653v/vdR+cw/3YdHyXbrkH17Hn6YMcQ9lZ1BGGN6D/3K2pxBwlXxxPac0C+VU31nv3EJz6RZKvC4lzUohxb1XDsa4696667WpUU1/za1752pJpzGr76GfPiZNfMW02yzPlOQjxKbVVd+1kXxsQa2tminkN7qIKtinPavdq+XfsdpsG+rM8dhyY4nZdLPtU+MA6XSuM9ppCqJrbDEaDvXeJ7+suYUe86qUN9FttZZc3hLZlMbmJiYmLiDOPUjicXLlw4Jp0kxyWqUQFSpAZ+7asEb7d/JE9L1o899tjwegDjPtKEk+Emyac+9aljfUGycequKnk6FdaoBA5SS1f6BOnf0jASb5XgXCSR7+xCjnROG/V/rxN9Zb4feuihGEjjjMNG6tpHpOLKBEYS1c0335wHHnjgSAKFrXRSK5KlA7xh2NXxgPXGSYT5csgKbX3+858/Ope5ZD851MKhK8l2bm2IR+sAW2atk93yP6Pk5d5DyW5JIuaLuWe96lo7IJlzcdyij6xFl5aP+8X3rzUa9TPWCQbOK8dyv9U+sQbf933fl5/6qZ9KBxI007cuUBiWYIbr0kBdCAzPihF7NSNKtvc31yWA3yE4dS3NZBzC0KU8dMC4NSpO6l2vZ/bH3NDH7p4G7G+eFXZS6hLfs+cZu51vuH7VBvl+tcNQB6cJu3Tp0mRyExMTExM3Jq7LJmcJof4yOxDViUz5Ve8SpSJBYW9C4rFkWCV5JFozKiQrJI2aRgyph3ORQFxosWOMSEG2PSFJwM5qUUnbt5Acqy3LfcRVnXaZE2xnSNowOQJxky3jQZKirMla6RPbCZzyrAtyR9qriWZHNrkXv/jF+Z7v+Z585CMfSdKX6aE91hAbnCXAKo1bl++xkV4L21llIGatZvJdeiqn9WLvsv9+7dd+7djnyZYpOSE4fTFbWkt7hETNencJs0dJxOkzYRxmRsl2/9ZA23oMc1IlZ7cDW2Y9u5JcYC3spGJvb2/nuVNDfOjXKN1dl5qL9lyKiP3mxPHdPhilv+M6tY+2DxpOKJHsFkl1ei/2g4sd1zFbU8C59V4AzImfq97D7MvO18GhC/Y9WAtkd8iK+1VR/TAmk5uYmJiYuCFxaptclRS60gkOZLTtyra6ZFcStO4a5oNto0p/2GLol3/ROwnOyVrttbWWoNnpfGy36ZItO/jcko3tBnWs9sTDs5A5oc3KAmGxSGMuIWJPsGTrYWhdvO07I6aWHHgY/uIv/mL7Heng8G4kCLgr2cJ6wC5ZQ9hLDUhn/pHkbTt1eaa6P0a2WHvmdTYEryXzh33y7W9/+9E5rK9LILmESydh4zVpTYIDoqu34yiJuEvtOLl4st1HXQqm2ue6D+x5acbgpMnJrtfe008/3abrApvNZqe8TF0Xl2WifduQ0IjU/jiZPPeN2VN99tnz0syD63UJoUfrYU1SPcbPVTNJNBn13nByZ5ch6zymR4Wd2V/ewx0TN1Oz52eX8Nz3AOOxli/ZroPPHWEyuYmJiYmJM4vr8q50+pkurYqLGFp67DyIOBZJil9xzun0zqPYGUsNVaLifydvttTQ2eT8Csx0qmTtcSAtucBmlYqcCsl2PScx7goRch1smTAjp+apc2J2aSm9jttxWHfddVerO68wG+881pCK2Vf0Eym1xmky78yhE+PCSD1vydhz1HNRpUgzBvYqc0ufawovS6mdpqCOpXoNoqmwlsPxWB3bdCkVF0IdFRat17M3Jder96BtVn4+YPOqNjnH/z3zzDOrtpqahLcrnsw6OO7KydYrizDTcWwb82imn+wya7Mxp7iq59hr03bvLrm319kxli5hVcflxNZmZ3XefY6ZvO2q9X6vidrrd/596JLlj8oedXuU3wHuuZP2zmRyExMTExNnFtflXWkW00nv1t3aLlWllY7ddXAJ+Nqu+2Kpskbh0w7nunRDZ2u0J6m9qZAKGcuaXp1jzGqQbivMAv3eLDfZSlTWXdu7qZ5jFmtm3CWNRXomwfXLX/7yoefY/v5+nn322aNrwgJrgVB7vY5i3bpkxLY/mi1Zaq/tOMOF2VInedqDEakS5lgza1jKH+1zXz/ZrqU9Mi1RV4nbNl+XsHJR2K6gsGPRzEJrHCjXrjblChhknRP6wto+9dRTQ9vKZrPJ1atXd7z3Knx/uGwW92DVAjgJMa9oBazNqM8D+mLG6LXuysp4/c1C6n1pr3HHIdvuWTVIfr7Yjttlr6FvziDlwtUd7G9hLZpZb9cX+zx0c89aVm3Aml1uMrmJiYmJiTOL+SM3MTExMXFmcerK4Muy7KgA1urJmV6DLjDU59qltgtyNU21islqhPqdXYhtAO7UEzZSW23gwMtkV4Xp96hxqroSdZFdeR2K0alw7IbrderWZOQE4XXrVMU4trz0pS8dquI2m02ef/75IzUX4QCkWKtjtbrVxukagGoVCPPG/kIl1aldPac26ncqEDvt0C6hA05Rl+zuSe8h2uhUXMDB+a6tVtffe9XOBMyR6xvWvnT19yqq6mt0b7PGVU0JMB+8+c1vTnIwR2vOA6gsK+rxTjNVVX5J76zCWnmvj9a/7p3RXrEppDNbOHSje46CUSo438tdcmffL1Y9OwyhjtGhJA4s75yy6IP3nfvcVT73Pe4EAl3Kw3rvz2DwiYmJiYkbEtfF5Ox8MTquwsdWadUBxyOJl1/z6pLKd3ZTtZG19sfOEU495v506NhePbdiVFIFaR+2USVeXNR5RTJ15W47ldTx2MHE89lJpq727jRFHYvGwP3lL395KPnv7+/n0qVLR9KrEyonW+keZmiG44TDyW4Iid2+XWG47hPvYzMrV3Cu/8NSSBvG9WFYnaOOJXmHAayxTcA5sH7mvo7LqedGmoTOmcDr7eS9nfOXkyM7GTJzUksksW703wkLKkjQbCe2LhzAJXV8v9RK3WYcTsx8LY5oI02V20q295bT4QHmtt4TI4boRAId+/RaOoyG18rk7NhkB66RI2H9zu99P9Xnr9kdc+Jj6z70nlxjcclkchMTExMTZxinDgavTK5L1zOyyTggsYN/1Wl3zY3VQYsje0QXfG49sFM2dbBUAty3Kq24PY5BysSFt0uvZD06QNLqgqod0uGA0Y7JOczBbtVr4R0c8+STT66ub907LnKabItvfu/3fm+SXTtUN1a7PDscZFSIs343ksZpq7PjOHE214NBdKnuvO9Gtpi6tzyfzAEJvN/2trcl6dmmQ0bMFK3BSLaMyMzdtpMuTR6aCafUgq39xE/8xNE5b3zjG48du8bkNptNrly5spOGr8JJERijEwtXLYDXd2TDXEth5eeaA73rPcY9zXe2N3U251FS75HWqbPN2vfAYUd1//l544D1UTKM2q6T83teOw2Z0y86nWFNPtCFYEyb3MTExMTEDYlT2+QqujI2wN6Ho0Sj9ZiR96Gl9fqrbenK1+tSjjlp60jPveY1OvJY7HTVTtDrtsxY6mdIxyNvt85u1LHJ2ld7k9bPvAaWwqoExzFIx7fddtuQ8aEFYDzsHbzrkuRXfuVXkmyTEsP2aLNj0bZFnMSa65htRxvZ4qqnHsdQ4siFb11MtbZjbQNYSxnnfUz7SNwPPvhgkuRNb3rT0Tkej707zSgrHMjrvdt5yHGMS+uA9773vUmOMxRssuCWW24Z7p39/f1jDKzb88D7wUHs3X0CrPUZ7al6ru8Tp9/rUnT5XjNTrP4E3jMjP4EuubPvZfaxU991pX18Xd+Dfr537dn2C7p1HqW681hqe13ijQ6TyU1MTExMnFl8Q0yus5GY4Via7CScUXkee3h1NoyRF6cl4HqO9faW3DqMkpyO+tF5gDqtlwsQVg9Je3jZfmK2Wa9ndjlKmlzXgHaR9my/cYLtCrzmPv3pT++UHAHLsuTmm2/eKXN/3333HR3z8Y9/PEnyvve9L0nyzne+M8mW2a0lyrWtaCQZdrYre5u5jTpPxMNR0BUbHR6FjKtex3M2YlL0tUs87D6/6lWvSrJlclWbgncqx7gYrWPJugLGLsti22ydV86xPYdjPvrRjyZJvv3bv/3oHDPr8+fPD6X4zWaTzWazmlAa+H4fMa1kN2WW7/GRfb/+7/gxa50quM9HybU7r257VY72Dmt8LekEfY9XuKSYiwN7jjr/iNEzqts73l/2snTh1TqOk7wqwWRyExMTExNnFtfF5MxMqiToX2ve29uoSrr25LI3Guh05L6OpXJ7ISa7uvAuK0pts8K2CntKOb6ktmMp07aMzl4EbIewN1WV4DzXlv4678oRg1uzyVlC/OpXv3rN5VI890nylre8JUnywQ9+MMmW2dEn7F5dRhCvB+9tJ+gSg4+8UZEiv/KVrxx999hjjyXZrhnxcjXJbe1Hbd+s3N6u3lv1fyfThUFShJZ4vST58Ic/nCS59957k2xZHszOHnN1H8DGXEAWO46LZybbOeUYWC1zg/2tahu658CIye3t7eWmm27ambe6D8x4zGI6nwDblbzHPT+1/95nfv7ARLr7wbYkM+yOoVhTcVL2lDoOP8d8bGWdJ9n+HXPbab/8TLYWqD4baRd2u2aXdR87D9YOk8lNTExMTJxZzB+5iYmJiYkzi+tSV66l7LIqzp+vhRJYbcmrg0uvJUWXqXE9DpUMVJu0Wj63UwG6xpQDSrtz7WhC/11frNa6cmXmUR2zTr2wZpiv6FRFo+TBdkDpzrnttttOvLZVGXUtWefv/M7vTLINtH7ooYeSJJ/5zGeSHFcNMv+oh1BpklLKqZQ6NZvVRg58feKJJ47Oob3Xve51SbZ7idcunMbOD/TZqjXQOa24Ajp9QgVZE0Kzn1H3OtkBKcFo+/bbbz86FzWoHRmcALn22Qm1GSfXcYXyOsbq/LOWcOD8+fPDcJ1k1+QwCjPoVGVWS1pFvLan7Zxk9WGXoguMHE46dbyTGozUl53Di1NjMU72TN07OI7ZNOTnnOeoG4/VvF0YD30aja9TTdtMMtWVExMTExM3LE7F5DabzWrwXzJ2sugYlWH3VCS/tcrho/RDfu2kB6QWl3foAsn5DknXFaAt4aylADKr5djKAhx6YQeTtQDpUeXhtUTAI0cht9Wlq6rz1gXfd31x0GyyG6zKHMNMcNuvVd45BhbMmn7xi19MslsRvAuncCJru+BXpoMzxaiUD+i0Da5wbscTM8juGNqgErn7nCTveMc7kmyZsF3l2bOw33o9V7w2c1tzjgEOPvf4k13WtyaNL8uS8+fPH7VLn9bK/YxSqHVMzo4nft8lJwejhAvduEZJnM3k6nXMqN2un6udY5XDQjiG9eAeSrb7qgsPS3adVurac++Nkjt34xslunYwfz3Hz8+9vb31VIzDbyYmJiYmJr7FcV2ldrrUPkcNKqB2FMTYpQVykN9ISqnSk3W318LkkL6REmyHcDhAvTbSD27Sloo7huK54boc49IRya6rLlKsS4qALtmymcEoKL1i5JbbJeZ1APlaqR2wxuSYUzNPJHYXuUy29qBXvvKVx/rPMS5nUtcU5kwbsCFYYXc9/re7vCXRel3vhVEJnLV1YQ5gkpSM6ZL6cgzjwl5IG07NVNfaEvyosGuXgJxjff+YKdf26n06ksaXZcne3t5OWakuLd2oTJa/r/97vkdpo7ok72CUTL4yId+Ptt85HVrFSUyO/VbZMvvb4UFcv2NyZve2+a8FYPMstM+BWXunqfA+HgXK12PAlStXZoLmiYmJiYkbE6dmcvv7+zv6+Sq9+xd1lCi5CwZf0y8nfZC427dtrCuTQb+RdGyLc9mO2p49kji2C+gGDki3F9Uao1rzOKt97mxhDgYdBXpXdPru2lY39zCENRa3LEsuXLiwsx6130iUZnkuo4PXXj0HRmV7AOtk22myXX+nOzLzquNyWSS8OZ1cFxbF2Gtfua7tn2vlS2Dw3rPMJ9qJZJdlcmxny6pt1HGMCtZ2Xnz2XB0lNq7zaCZy0003nZjWy96vnaYFcOxJ9089hn6P7te1hO3+vAvsHpVWYi+xtl0SabNAs1BrJZLt3uc771H2CXs42dqy7WXpvdnZ5EYagpEmo7ZjeJydL0iXJLrDZHITExMTE2cWp2ZynU57rcjj0YVW4tbMfkZSEhJIlzJplE7MOvJkK2XBwkaSRhfD5dL12IJ4j0TdSXDW219LMUZLmbzvbDHuP7BtoStg6ziVUeHDGq9Cu7CkO+64Y9WuUPuwlmTVzJr3jomr39k2atZkT7Nkl+173thvNS6Pecfjkja4PuODRXXjqHaTek4ntdI3pG2kb7PcuoewWfGdi5l63BW+f+3x2jGUUTqqUaqrpI8FW8P+/v5O8uO6liNvSrPLzq7WeYzWfjP2urd9vdF4Oq9nJ573PVP3h2NrR8nXOacybOabvQg7455AO1CTgDuJ9OhZZe1H7YtjFUfxiLX9kbf4aZ5VI0wmNzExMTFxZnEqJlcT7PK+OybZzeaw5iFpr0P/mlvSrufaM9M2mS7ew1KYsy907A8pyCwDqciegZ0O2XYC2uqS3o5KyY9i4NZiCEdMpbJA2zSvxTsNYAd4+9vf3mb8SLaxTmv9PSnDRHcuUigSJX3hGCRT7wf6VF+5PgwO2x/xZknymte85li7tOcYuMpa6BtzDMt0UcnOluXYMK8p1612lS996UtJtgwEeI1tE0x2132UKLyz43V21nqd7r4d2amMvb29NpuH4cTla3G5tiGZtYyOq5/5HLOV7hk5in30s6yOw3vUz1fWsLJA9pk9v9nf9ieoxzgJcpcI3u+tBfA+WyvW65hEzmX/V3QlsWac3MTExMTEDYlT2+TOnz+/w47qL/hJ3kxrUe8jXavjOqru3MX87Hlj6TzZSiX2skQaxqZRS6xYp885eCPZU7LaZFyuxPYU2q5eg5aKre8eFTGsY7dEbb1+/d6Me+TVVdcIdkFc2cWLF1djnc6dO7cq6XYxWLVvjmNMtkzOrHyk4+/6xzFkUmG9eK1sxjYw2xj5vkq47BXn0TRr7rwf+Yy4OBfc7ApgWnNAe743ur7aHj3KD1jnZMTOPa4K2+ZrUVQDDZKZXJ0nM0Kv/7V64tVjvUfXCpLSl1GMXYXn1FooPwfr9Zyj0/dl1aR4TzIu9h/H1nm3rW+kEXNGktrOqFSa7aTJrs+BfSo6vw8/o9Y8c5PJ5CYmJiYmzjDmj9zExMTExJnFqdSV58+fz8tf/vKjMh+dERqYbo5S6NTvrF4bpaHq3IBNkVHrdEGhLh9Cu6R1+tjHPpbkeIkVjLhOs2X6Tls4qiTbpKeoNqHrVExec0O28daUfzR39bNRBeR6jtfQx3TOKw5iPSnJbtfHLtTCwb7Mpat9J2O10MihoVOnuwI4qsFOBeR19zicDinZDR2wmtKq4RrYTZ8+//nPJ9mqtK0mq7B60KpB74O6BiPnH6uzO5WT2/O92ZWfqs5e15KkOdmaEyr8fBmVtamfWzXnhAedWh/Ypd6VyTuHCeCxM272RTV1ADueOIE26KqXg1GYVTfvTu9nh54u7GL0zPD1unu+S99V0fWxqt2nunJiYmJi4obEqZjcuXPn8rKXveyIkXTgF96u9sAu/smYjdhA26WJGbE/S2HVTRbG9slPfjLJVjp++OGHk+yWMantMXbawJnExt0qbX72s59Nkjz66KNJtoUvv+u7vivJbtLdZFvmpXMsSXYl7c7xYORE0oU5mKmNEsJ2btQ1mfRJBQwt1XWMwGN0gt81qc79XLsex7Ae7F2k8E6i/tznPpdk19GA97RZk97CRNkzJBCw0wx75jd+4zeOzoXBPfLII8f66oThVWrnf8Ip7LLOfDLO7n4aJQMAXckapwBzUHCXhqs+L9b2TnVM6Zw5PDYnWHAC7XrOaI/7GVLPdSq7UXHZjrUwd4zdTK7eB2ZUvk99z3RarlFqNtAl23afRyVx1hx5HOawtr7ukx3vah99zQsXLqw6PE4mNzExMTFxZnHqYPCLFy+uuoGPflFHBSLr+XZ9dzJYfsG79DqGiw3W4wgVgEE99thjSba2EFI2VWmcvlC4kwBhMy679tbrILnjqs71cFWvth+7nY+CgbtUOV4X6967shmjsiJmQHVcnuOrV6+ul7zY2xuW/anXsKTrPnR2XUuJHjvzVhk2/8OgYVwuQVJZAWwfhkUbnOtA2yR54xvfmCS59957k2zXH6ZIX2mjhq5wvY9+9KPHvqN99jJhHMmWKbI3vYccYNwlZvC8+X1lf2YKXSmU+n39bFQap2Kz2ax+313DbHLN5jNKQ+Vju1Rmtsmz/p3d0GE/1gawH6o9z89NM5vR/VrbHRVY7WBG1THg0edmin5GjQLKax89n9eyXrfccstkchMTExMTNyZOHQx+5cqVnQTHawkybVfrPHpGUqLfdx5+9tix/twMqF77jjvuSLKVeJ3Mt56DdI+dAyZn6diegMmW8TiQG8kNpnrnnXfunOOyMyOPvLWSHpZM6WtnIxmVoe88MmEPzMlaeh2841y4s6YhQvrtvLDq513wqvegJXpSXXWJjJGcad9FU+v1YPkPPfRQkuP2s4oa2A9TxJ52zz33JNkt6cTc1Da5DuxvZL+u+412bSd28oHO49n2qJFdpQvOBZa0u2BxexaeVPiyC1jvvJFHtrnOy9oaD7+6/10KK7fPa/fc8fPM2hHOrZ7Ztsm7rJE9xOv4ulJotW9r3tzekyPNWF2ztZRfdQx1TsyIvX5cv1v/mh5xeldOTExMTNyQOBWT+/rXv55/9s/+2ZEk0hUXHaVlWdOZWnI6if3V6/G/PQlHevUKpxOjjc4z1Cl3YH/MgYu1dnF59p5yefrKajjWHlj2shwxmWR3LuzFtealNkpA20mm9PGf//N/fkwKrdjf38/zzz9/9L2TFNdrWqL3nHbJry1Zmym4UGQ9lnNZD5hcV0T1/vvvT7L1wHVboMa6weo+/vGPH/sOBsz88Xm1ySHpYnuD/eOpyee1XEpXfDPZ7mvmHHbY2WhGdjXHAya79+e1xHL6Xl5jcsuy5KabbjpaQ+ar85R03Jo1LRV+3ozYRLcvR3FrHnOXMNnrgiYB7VD16jUDdQkcx/p1TI57jr45RVxXPssanW7O63H1f+8VM+EuBeGoDdA93+p+nja5iYmJiYkbEqdicleuXMmXv/zlo19QvAbX4pbAWokVpCDbnayP7iTOToKp6DI0+LuRV1X14nS8HddF6rftrBbatK7fiXo7G6BjwkYJk23Hqu0BS1LdnDgubpQ4tcZIPvjgg8fG+vjjj+8wHLDZbHLlypUdZlilSOYFRjPKyNAlyh0l4l3LCMMxrCH7GUm6i9GBuf/O3/k7j53zhS984Vjf6/VYI+YbiX3kTVq9bCntA+jra1/72iRbBlfniL0zijN1gV8YXR2791l3/wDbTUbevWtagL29vVV77k033bSz5zvPPtiJSxR1LHEUvzXaO50tuGMnyZa1VYbNZ7bRM7fYYuuzg3uCz2w3RhvQJep2wVvmjXXnfZ0bl9ahj9bwdJl9RjHRRp0r5tTrtLZ3QE1sPZncxMTExMQNifkjNzExMTFxZnHqYPCbbrrpiP52ruim7aMg3QoHD7pGkilsZ8C2is4G4C7gtaoju2O7oHNUAK5tZQeHqgKw8XhUhbsapJ0yy+oWB1Z2rtHAzil21ql9tLoCcB2CkpOtIwXnXL16dWf93YYN29XBwXX2rsUFGXSJCeo5oO5V2kFFxyvqQqvUKjiWsA8fUwN6T6qYzJygeqrjdh1BVItWedX1v/vuu49dD+cHV/BGfUZog6/d9dGpz+pndtzo1PDAxz777LPDvWN1ZZdmizFZXel93D1/fIxrE9qppfbfpgCHGlXVs53UHOaAGpvUccl2/lEx2knqvvvuS7JNAFDvaVScfDaamy4pxCjFnvduvX9HJhXGvRZA7rnw83vtd+OkVIKTyU1MTExMnFmcmsmdO3dup5xMdZMdBWM6uLmDDdfAjiFdsk5LD6OKxsluFW8bOelrPXdUtdzOHKBzN3Yy3bUSFKNQjJFhtkrBDjsYBet3pU+ArwdDJZ1VsptE9aQEu1euXNlJslsZ0MjIbON3x+SvpeJzcnycrAPOAaOExnVcdsvGeI9Ei5ajjgvGRPsOheD6zGd1PKBd9g7va5iBx0UfcE5wcLYrhtd7kqB5s9k1Rwu7tzuZQ5dk3O2c5Hhy/vz5o/VydfZ6bYcZdG25D6NyNdbKdMnE+c7agC5cYMRKWFvYGKkDu77RZ9bfTmxVw+TrmCU5FVn9ziycPvLKWGr6MjNEjvX93P0G+DnjFIR1XNaEnTt3bgaDT0xMTEzcmLgum5zddTvYXrcWGDpiX6Ngwi4Y2CyQviFN1F/6Ll1TRReq4M+QOBzYabtSspUq+Qypy/PX9edaGVydT7OaUcDlmis+bIDPsdtUd/MueH9NGj937txq4tW1QNN6TpdKyPPh69gGlOwG44/sOLWvzAvu3k7ujGTbFaRlL9IX2mJOuwTUzCdB3y7oSp+rpoEwDsaHPafah5I+wJc++t7zWDrX8ZFmwoka6v9VA7MWDH7zzTfvMMI1BlLPrX2q47HbutPemcl16c9YF54pvDqMo+uDbWIwb+xsyTaRgIPb2Tuwfu5Pr3Gyu/e9hnUenZYQe9oo6UU3Ptpwyi6HXyW7rBlYQ1afK16vWTR1YmJiYuKGxamZ3Pnz53ckwE5CGxXfBF3CTXvIWa/eebs5GNxejl1aMdvc7H3Ueeo5UN1jd5LfKsmPEuZy3U76t/RlG+DIblnPNcv161qia8D7J554Islxicssec3eutlscvXq1WtKKWZ9vL1Pq52CeWduPU+WWjvdPmOC8WBXsWSfbFkSEjavTmBQ7dS2MyBt2yb3qU99KknywAMP7FyPcWDXQ5JnTmq6Muw1fMf+gimw72ABdS0IModdOj2aPQ/rPAE/AzomdJK2oWJvby8vetGLdvZ+HbM9os2W/Fyon5m5+XtQWRLrS3o/p4DrvBQ7+3kFbd51111Hn8EMWSunLcOGyj6p5cFgmbYpuphqnUfbVUl24BSOvNaSUoC5p8/0jf3XeThbA+NUi51HJt/ddNNNk8lNTExMTNyYODWTu3Dhwo4nU4VZhMGvcOcFZEneHpOWQI4NRN5AtN/ZzpyYGbj9rpyIJXaOtd2hY4H2MnKaryr1mcHBRGhrlKi5tjuKHesk6xEDRvrD46ubk8pe19b92Wef3bFVdLFO9Zza386W6IKdZopmCHXPkoqLc5BosW/AsKrmAJsY7Agp1WWn6jzAAGjfzMcFXes8cC6MEUkduwfj7BJPu13G7j5Wb07GCquwbZv1q/eONS6jWNkuoTK4fPnykM3t7e3lxS9+8Q4TqWzZnss1/q72qV7DqanoJ/eY7euw6GQ3RZf3nZ8TFaNYRNqo4xp5I3P/kKibGLuads/erfaqZA9VOzvtsvddDsx2zDqfzBdzw3fsry7FmjVffGe/ia78UGXNk8lNTExMTNyQuC7vSqRLpNgqeVqSstRqPXiyq/e3zcjJQqt0ZOlkJP1XT0b6iGRhSYPX2nfG7NgqSyKdB6hjgpw9YDRXdU5G3m6dja6TsrrrrMX9MB6kPFhPx4grUx1JVFevXs1TTz11NGbHYXUYsVa3WzHKsAObqawDZuP4IY6lqG69Bt/xOmIKnfdhlxA32bJk9lQtmlrZQ+2jbcH1evY0tB3Ha1z7wz2NLYb9hZ2oi5Pznh8VHe2kftbgJCZ3yy237HjrVRsZjMaMZ8Saax9gTvbk4z3XqWvBZ47L8z1W9+PaXq/nVA2Ly3CN7Ls8wyorJ6bV2ZmYR9hR1W7YA9zjsF1xTfvkAq/dveGyZrzC5Fw8tX5WPT0nk5uYmJiYuCFxXd6VZiLYC5Jdz7FR1o2ubIUlT9t8bB+o13H5DY7tWADtI8nYM9K6+WRri+EYJF3r7/2abKUjJHWXKFrzSgSjnIxrsYM+Zy0P3Kg4KtIgEmKVZkelijrs7+/nmWee2YlNq95Z9M82HjO6OrfO7ei9ydrCjpj7ZJsjkH3g0iTMSc1higcm59Ane7fVoqmvfvWrj53jwrL0Ee+zyrDZd+x5X9dajtpvjmHfYbdBku9KofgepA17Edb7apRn0HlKK+hDLWa7lrVms9nseNnWfcA+8nyYcdVruKyQ2Z9ZVN3fHOu96mdXHbttob7nOg2M+zIqQ8Y4q8YKz0tgjVnne2DP9lEpobWMRR4v98+aB63vW+6jmlEFeJ5mxpOJiYmJiRsW80duYmJiYuLM4lTqymSbninpS3XcfvvtSXaD+9YSJ4/SAo0qDXdlbFAxod5BhVpdqwGU3kHYDiCtalFUPq78jZrAxuuqCrL6k/lCjYWaqqpfRkHnwGrFSutdQmjkCFDVC64wjnrn13/914+11YUdgDW1wWazyaVLl47WpXO2YT9ZFWtHoa6SMetsgzkqOr6v/cPdmmM4l7V86KGHkiT33HPP0TkOkmaemD/UStV5xCrVLswk6dPkuawVc8A+79RitG+1KHPvEix1v1jNC1C/2pkgGe9R39f1XqQPfHbp0qVVdWXdW93+HbnLW31ZK3WzLjZteF66JBR2IrJDSDeWUQJyJ4ru1rKWs6rnODylts1etErVbdZnFWN3aSU7DDJ3nfMax9o5xW3XY3llznllnut+ZJ7sUDPCZHITExMTE2cWp2ZyVaLqpHEkNBuonV6rM3auudLX7+uvOu0gaeIogWTTuW07YN0peZwiKtlKxXatt+TRpT0yu6P9z372s0m27JfXrg+jQq+WypLdtDejlGedgRvAbh577LEkuw5FXbsnFS88f/78kTGcfVLXEucGO7Q4QL1L5wSYB9aH67lgZLJ1PKEv7KE3v/nNSbZr3jlJ4egwKpZa55Z27TDhYHPmuK4F547CP5zuqbZDuzjF8J42mbvqnk37aBe4n2D09LkmZHDKNDs2MN7qjMP/NYznpIK7ZkB17VkP5n/kmNElznZo0qjUUx2zExfY8cmOMMluiS07eXT3ch1/7Zudh7gnCHup1+bYUXHYjhHbscYONZ3Dm0NSTirAW88xk7OTUdVUeb/dcsst62nhht9MTExMTEx8i+PUTC5Zl9yRLJEER8mBq9RWi98l47IZIwkh2dpzYANmCh37s7uqUwPV69u2OCrXjkRVpT6nc3LgNWypJldFomEeKajI+JConeKm9nFU+qSzG1iP/slPfjLJlhERbFul3JOCWysIP7FU2aWjYixIpXzesQiXCKL/2D1JVtyFHzA2p/OCYVtqTrbrYnsXUiUMpQsgpx0S58IukPr5vrPNWYPgYzptCoCpORF1Fw5Au/QF7QLnfOYznzn2eXcO+5h57Wz3ZufPP//80CZHcm/2aMeSvB52l+/2Kv2z9sWhLFynzvnIVmWtVGe7tDZrFKRf+2Qm50QS3BtV0+ME5/TZ6czq+vN/Zx9MdpPMV4w0cn4OdVo8pzj086j+1jiV2lrB3WQyuYmJiYmJM4xTM7kuuLFLsouktlZaxRgFL48SN9f/kTBcTgRprXriWGJyaXeCRKvU74Svlsr8fZfuxkzU9px6jiUqp+Jxeqw6JyOG5cTMnT4dr71PfOITO3NQ+1XbsdTcASYHusTCgL3DWjogtErHzCHzAYvBpgh7QPrn83odS+X2xKx9ZL55tc2nSyJtmwvXpXyRg90rc+wSCye7BSjr/rZHmr2h7TnXJS5wUnHm/NFHH01y3G7kPcnY2UskEKhMzgx8La0X4zcTqKzFrNQMCHTJ3d0nM7cuNeDIzmVv56qdMRszg+w8ZbtSYcmWuXktK8zYrD1hH64xVO+RNW2Q2ay1TN1zyXZQsz8z9GQ7p+yrCxcuTCY3MTExMXFj4tRMbm9vb9V2ZgblQpedh80opZQlu66wp71zrK/vygH5OpbGXOK9tosUYZsC14d9VNieZw85p0GqfeMVdsl7pK+uaOEoWXSXHBswJxTuxE5oT8cuQXPHsLv2r1y5siPp1jIfbveRRx5Jsmt/gCEk2/WgX8S+8epCuPV6Zu6vec1rkmzXkLmtewjGhiRN7Jk99Oo8cSzn0r6ldLOo2p7n1ra5ug/oi+cW5sM52Cs7Sd4pn5z6rLbNsZzrUlxOhF7bqbbU0f7ZbDZ54YUXdu7Lyl6ZW6cY6+xp7oNZGcc6gXFtg+cbY6LvzDHr0fke2O40SmNY/+/2VW2LvVzvbfsL+HnQeY3avmrbmPtV953tkPSJOeliiK2J4xinr6tzwjXrPlhLjTiZ3MTExMTEmcV1ZTyxt0z9FbX3DV5ZTszb2fb4zPrZUYaAeowlDXshdd6OZlKwC/pcJQ7ad2JhS0ldEmQXRTT7shRT+++YN6Qjex111xsxuC4RLFL9hz/84WPHOjltXWsn8+3ie8ALL7yQz3/+8zsSNjFcyVYyQ/KDTbr8R5WoLcFZCrcmoe6DO++889gr68K8wbi6AqiwP/YSjM5Fe5OtZyrn0IbtHl0spGPOuB5tdXF59B+bhRNNcyzjq9lZYCasi6Vv2qpJ2VlL3/usjYu2Jtv9ey3Je2Fya4nAR0zHz4W6R63JYc9YC2R7eLK9X2C0zBdzwJrXckAutDoqC9XFrTnG1sVrO/8Ixu7yUE5MX23e/s42TWcFqnvV62LG3Y17VLDWZa/WypDt7+/POLmJiYmJiRsT80duYmJiYuLM4hsKIQCdWg8666SwHWW188golACs1TCy+ypUuXO2QC0JNfextW3GYbUBgNZ3zgNWR9qRoksj5nad3gvVWqdGtIqhG7uPI2SAYF8bi7u+8V11K16rDP7Vr351R71S9w4OGg5mZr3pI8HUya6bN/PitrpK0TiavOENb0iya5BHJdiNifnhOgTrO0yj9skqe6v719ygnRjBx3ZhBw6R8XU793McdqyCdLhIDatwgodReq66D+2IdlKIEQHh9dh6zknOVV1yYI5hLVFP2qW/q3PInPIZ80aIBXun1l/kfxKz0waqTjuv1PHwGW2wr0eu/vWzkUONkxQk22cialiHDKByfe1rX3vs82T3nra5qUvOz7X9nHPF+6qOtOnBKmpjMrmJiYmJiTOL60rrBTqnD0t+/GrDhJw6K9mV6sBaaQ3gBMw2QNvpo4PdpTm3OojYSNu5w9brVeZjKXgU+FhhQ+woRZevW48dSdZ8X4NzP/CBDyTZlW7X2IYdTjabzaob+NWrV4/mj3Eh8Sa7btm0S1+cFDvZSnpIp/fee2+SreHfe6uWWmEvOkVaVxkeODjbISpd4KvTNllTMKr2XOFku7wyn/Ucl32xAwDHIpXXaul2JqKtWnYqOe78w/8OlLeDV0VXnXzNeWB/f38nuLl7Hrii+WjvJ7sB43bDd2qr2j/OhcHjxMN+5t6qziqwMFLz+XpOLFDBnmdfu5wSYTVrc0j7HGuNSbLL5NiTXJe+s2fqM9LOeNxXfkbW/eAQAadLdLmtCtbHe9OYTG5iYmJi4sziuoLBLZlVaRYJh19XpDwXNa2/zC4N4hQyXVJnMJLczJ6q1DfSGZtRdZKbr+vr+LVezy7Qvm5XBqTrf/2+CwcYpRGzfv3Tn/700Tm46/t6bqML/K+plEZ2pb29vVy8ePFor9jmk+yyEq83+6O6rwNL+U74yvVqmiWvKZKnbVcVTnbs/W13/WTXvdv2TWss6v3U2Vzr552LtedtVB6l00bAREh/5jRZSPp1DJayrRkZaWpqX9aCeTebTfb394fJDbrzrycxvLUl7INu7zsMCJbGc48SRY8//vixcdRjHDBu+27tE89IGJSTG9BG3TvsdWtjWENeO3br1Fn33Xffsb51qfy4F2C1I1+DLpTEhYWdyKCyW4chnYTJ5CYmJiYmzixOzeSqRAU6KQwpBWnJaZyqJGTvGyfddWqj+gs+Km3hsvddsmV7XNlm0SUwHnmBuTxPV07CXm1rhWTpo4tzOgi8Y3JmS8Bl45E2a3snlTeqfRwxkxGWZdlhJtW2g42AfrqAYpco17ZC9hf7zbr96u3moGzmxQViu1IkTiTAsdhIqseavfNgfUj0XM/eb7VdJGunojJrqu2axTj4mbWtwcBOW8d3Dljv1sBz4u87b8jqIbdmT6rndCW3fC953e2FmOx6SvsehzV1+4558dzSFoy42ouw09EH9jv34etf//okx7UATp/Fqz2b0ZDV5yr2M8+NU99VJs61GbttcOzVLsm7bc2jZ2Z9Llm7xDg8ns5223lrdphMbmJiYmLizOIb8q7s4snsoYgkg8SGHrh69o28Dp0k1PFl9VgkKHsWdmVtrIMfJU6tErxjZ0bFGLtUZyP2xzFrcR4nJaleK3lhdmtW263bqIR9Jy35u7WSF9hV1jzkvO5d+ZCk1+n7veNvQJWsHbPZFa10P0bjY1/Qt+q55rX60pe+lCS5++67j33e3U9ItKMirZ207JRPI8macXFP1r4SQ0j6KqT+rvxRZR7Jrn3S/fD/dQ5GILVXsluaKNllbqyD4ycrRl6ajhF1WaOKkWd2l2bMmgOn0GKtq83Z13RaL2sW6pzQb/rCnHj96/rB3DgHO6GTIoOuwKufo6B7Do7KM7G/Os9Js9tz587NUjsTExMTEzcmTs3kkMjXvk92vXRcqqNKprC6UUyWbVhrzMeSQRfjwv+W+rsyQGBkhxyVmamShfXnlhw7byHbTyyNmx1UG5BtF5by3J/6mcfjz7s+gsuXL6/ujStXruzYYrrEwgAWgR2tk+DZR6M5PYkh1HGY6ayxV79nHLa71f+RSpGo6TuSO1JzZVaMg6wYXM82n86eO7L90gbzWz1cmVuyynzuc59LspXw8bqsczNKNGz7cWfjrlqFk9bKGok1O/TIM7teo85zbdfetV3pLcAa+nqOM022z0JrkKzdqKWkOJ9nJGvG+jNemHYtCuwk4uyhL3zhC0mSO+64I8nxOMlRRiJr2To/ApiVtXeM1+XJku0etcZiFCfc9eUkW+5kchMTExMTZxbzR25iYmJi4sziVOpK0jZZrVAp60jVSFoYjKyVsqI2sGrMasPOMO8+WCXTqRh8zCj1T1WvjMIAriVVlx1dHADbqTGdrHoULIsKoKPsrnhsNWWnWhypVLsKvR7HKDEvx166dGmoKk52XboZmx1Qqqrb6qlR3T0nfk12005Zlcr76mTBddxXh73U9bfTCKos1JT0iT7W0I4HHnggyVYN5QB82ub+qn2wQ4jTVaH6qveI1dXMo9VIdR6dRNgVp50+q46jOqWsqSv39/d3VPh1vzkl3MjxrO5fjrUjg9WIXR07m0Fc169LQG61Oypg1IasR5eiDVUjakuHB3FOdXj66Ec/euw75pyAbtTjdf0Zh587fiZ36liH3tB3hxvUc7gevwGj8LHOSa6u8ZqZZDK5iYmJiYkzi+sqtWMpac0ZwdKkk3gmW4kPKcXu+bgtO5g52a0k635cC7OyUbpL6jxicDbuO7A9GQdN29GhM8iOXPnN3Crb8fpYMu3CKtxeF2SeHJ9ns4qTgsKvXr26U/6jczxhjEilDtav0qoZpw3VSJMOqk+2TIrv2F9O2FyBtDoKlUE6r/Pk6uTuO32ErVWHCM4hrRLSOaBsS2Vyo3RxhC7AyswGk61kPUoIDGOqLvl8ZqcEj69LCdc5QRlXr17NU089tZNwoc6x7yHvcScGT7bz47JVZgqdFsWltpyEorun6b8Dqnm+4QBSz2H9n3jiiSTbNaxMurZVn7d29GCcfqZ0ITJ2jvHcdGEhtOtEBXYYrPeBK6zTvn83Krs1M7z55ptnCMHExMTExI2JU9vkXnjhhR138vrLbBuYGRB66XqObUc1UDzZ/XWvEqF1xXYvXit9Y6nPTK4LdLQEagmnc2sdJaF1ctJrCV2wTatLZuwSOIA+MkedXcV9M1vrUCXrtVI7+/v7OxJvV8SSV9iRg4treR5cpx346oByuzUnWwaDNIkb8+23356kX0tK+YyKinYMGOb58MMPH3u1VgN3/VoUliKc3/u935tkN4C8cwMfJb11WZYunIe5diFhmBBu8NVO6fa9tp02xeu1VnAXe67tghVOd2W259d6zqgArZ9l9R5wEgqX5enSrfnedTo5+lyTiHMd5ovvaMOJLGofWSP2n+3HfN4l2HdgN9c3g6uM1oVOaddJF7jfkl1m6kTXoI7LdrurV6+uagImk5uYmJiYOLNYTgqkO3bwsnwpySO/fd2ZOAN43WazeZU/nHtn4how987E9aLdO8kpf+QmJiYmJia+lTDVlRMTExMTZxbzR25iYmJi4sxi/shNTExMTJxZzB+5iYmJiYkzi/kjNzExMTFxZjF/5CYmJiYmzizmj9zExMTExJnF/JGbmJiYmDizmD9yExMTExNnFv9/2id0kv7iNvgAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_gallery(\"First few centered faces\", X[:n_components])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll learn the sensors using the first 300 faces and use the rest for testing reconstruction error." + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:51:44.484186Z", + "start_time": "2022-02-04T02:51:44.481801Z" + } + }, + "outputs": [], + "source": [ + "X_train, X_test = X[:300], X[300:]" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:51:45.722543Z", + "start_time": "2022-02-04T02:51:45.349991Z" + } + }, + "outputs": [], + "source": [ + "from pydmd import DMD,HODMD\n", + "dmd = DMD(svd_rank=35,exact=False,opt=False)\n", + "dmd.fit(X_train.T)\n", + "U = dmd.modes\n", + "np.shape(U)\n", + "Hodmd = HODMD(svd_rank=35,d=5,exact=False,opt=False)\n", + "Hodmd.fit(X_train.T)\n", + "UHodmd = Hodmd.modes\n" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T03:10:56.374075Z", + "start_time": "2022-02-04T03:10:56.364961Z" + } + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "object of type 'builtin_function_or_method' has no len()", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/4d/z3xfv_kx21g_zvpk__ybdy5wjqwgnv/T/ipykernel_78842/1947659873.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m 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see how the number of basis modes used affects the reconstruction error." + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:53:41.682801Z", + "start_time": "2022-02-04T02:53:41.678262Z" + } + }, + "outputs": [], + "source": [ + "max_basis_modes = 200\n", + "n_sensors = 2\n", + "\n", + "models = [\n", + " (\n", + " 'Custom',\n", + " ps.SSPOR(\n", + " n_sensors=n_sensors,\n", + " basis=ps.basis.Custom(n_basis_modes=max_basis_modes, U=U)\n", + " )\n", + " ),\n", + " (\n", + " 'Custom',\n", + " ps.SSPOR(\n", + " n_sensors=n_sensors,\n", + " basis=ps.basis.Custom(n_basis_modes=max_basis_modes, U=UHodmd)\n", + " )\n", + " ),\n", + "\n", + " (\n", + " 'Identity',\n", + " ps.SSPOR(\n", + " n_sensors=n_sensors,\n", + " basis=ps.basis.Identity(n_basis_modes=max_basis_modes)\n", + " )\n", + " ),\n", + " (\n", + " 'SVD',\n", + " ps.SSPOR(\n", + " n_sensors=n_sensors,\n", + " basis=ps.basis.SVD(n_basis_modes=max_basis_modes)\n", + " )\n", + " ),\n", + " (\n", + " 'Random Projection',\n", + " ps.SSPOR(\n", + " n_sensors=n_sensors,\n", + " basis=ps.basis.RandomProjection(n_basis_modes=max_basis_modes)\n", + " )\n", + " ),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:53:42.854190Z", + "start_time": "2022-02-04T02:53:42.850441Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "SSPOR(basis=,\n", + " n_sensors=2, optimizer=QR())" + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "name,model1 = models[1]\n", + "model1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:53:44.556728Z", + "start_time": "2022-02-04T02:53:44.552551Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.01081226+0.0041771j , 0.01081226-0.0041771j ,\n", + " 0.01053635+0.01299688j, ..., -0.01331744+0.01571602j,\n", + " -0.01299805+0.j , 0.02183318+0.j ],\n", + " [ 0.01551894+0.00339955j, 0.01551894-0.00339955j,\n", + " 0.00992298+0.01392816j, ..., -0.01557961+0.01544972j,\n", + " -0.01076506+0.j , 0.02271762+0.j ],\n", + " [ 0.01776523+0.00288624j, 0.01776523-0.00288624j,\n", + " 0.01055673+0.0140293j , ..., -0.01877962+0.01605825j,\n", + " -0.00682095+0.j , 0.02672054+0.j ],\n", + " ...,\n", + " [-0.02068003+0.01112751j, -0.02068003-0.01112751j,\n", + " -0.0117961 -0.00428799j, ..., 0.02228742+0.00780084j,\n", + " -0.01379396+0.j , -0.01789464+0.j ],\n", + " [-0.02330952+0.01277846j, -0.02330952-0.01277846j,\n", + " -0.01120195-0.00414563j, ..., 0.02323647+0.00901809j,\n", + " -0.01178332+0.j , -0.01430092+0.j ],\n", + " [-0.02258067+0.01353797j, -0.02258067-0.01353797j,\n", + " -0.01094244-0.00395666j, ..., 0.02169603+0.00869081j,\n", + " -0.01108236+0.j , -0.0114726 +0.j ]],\n", + " dtype=complex64)" + ] + }, + "execution_count": 185, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model1.basis.fit(X_train)\n", + "model1.basis.basis_matrix_" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:53:49.060243Z", + "start_time": "2022-02-04T02:53:45.342220Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train time for Custom basis: 4.0531158447265625e-06\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/abdomg/projects/pysensors/pysensors/reconstruction/_sspor.py:478: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " error[k] = score(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train time for Custom basis: 3.0994415283203125e-06\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/abdomg/projects/pysensors/pysensors/reconstruction/_sspor.py:478: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " error[k] = score(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train time for Identity basis: 0.002521038055419922\n", + "(4096, 200)\n", + "Train time for SVD basis: 0.0885918140411377\n", + "Train time for Random Projection basis: 0.013406753540039062\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", + "ax.set(\n", + " xlabel=\"Number of basis modes\",\n", + " ylabel=\"RMSE\",\n", + " title=f\"Reconstruction error ({n_sensors} sensors)\",\n", + ")\n", + "\n", + "n_basis_modes_range = np.arange(1, max_basis_modes, 5)\n", + "\n", + "# Suppress warning arising from selecting fewer basis modes than\n", + "# the number of examples passed to the Identity basis\n", + "# (results in some examples being thrown away)\n", + "with warnings.catch_warnings():\n", + " warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "\n", + " for name, model in models:\n", + " t0 = -time()\n", + " model.basis.fit(X_train)\n", + " print(f\"Train time for {name} basis: {time() + t0}\")\n", + "\n", + " errors = np.zeros_like(n_basis_modes_range, dtype=np.float64)\n", + " for k, n in enumerate(n_basis_modes_range):\n", + " model.update_n_basis_modes(n, X_test, quiet=True)\n", + " errors[k] = model.reconstruction_error(X_test, [n_sensors])[0]\n", + "\n", + " ax.plot(n_basis_modes_range, errors, \"-o\", label=name)\n", + "\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:50:23.173925Z", + "start_time": "2022-02-04T02:50:22.896146Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.03725206, 0.03725206, -0.04305432, ..., 0.07484102,\n", + " 0.07484102, 0.07520393],\n", + " [ 0.06861386, 0.06861386, -0.075698 , ..., -0.03332578,\n", + " -0.03332578, -0.02807535],\n", + " [-0.0798585 , -0.0798585 , 0.02884919, ..., -0.04854688,\n", + " -0.04854688, -0.05121052],\n", + " ...,\n", + " [-0.00837904, -0.00837904, -0.02156027, ..., -0.07699031,\n", + " -0.07699031, -0.0671259 ],\n", + " [ 0.09092192, 0.09092192, 0.01293033, ..., 0.02918206,\n", + " 0.02918206, 0.04691554],\n", + " [-0.01964494, -0.01964494, 0.04019649, ..., -0.01040314,\n", + " -0.01040314, -0.01088595]], dtype=float32)" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pydmd import DMD\n", + "dmd = DMD(svd_rank=0)\n", + "dmd.fit(X_train)\n", + "U = dmd.modes.real\n", + "model1.basis.basis_matrix_ = U\n", + "# model.fit(X_train)\n", + "model1.basis.basis_matrix_ " + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:50:23.226620Z", + "start_time": "2022-02-04T02:50:23.219199Z" + } + }, + "outputs": [], + "source": [ + "max_basis_modes = 200\n", + "n_sensors = 100\n", + "\n", + "models = [\n", + " (\n", + " 'Identity',\n", + " ps.SSPOR(\n", + " n_sensors=n_sensors, \n", + " basis=ps.basis.Custom(n_basis_modes=max_basis_modes)\n", + " )\n", + " ),\n", + " (\n", + " 'SVD',\n", + " ps.SSPOR(\n", + " n_sensors=n_sensors, \n", + " basis=ps.basis.SVD(n_basis_modes=max_basis_modes)\n", + " )\n", + " ),\n", + " (\n", + " 'Random Projection',\n", + " ps.SSPOR(\n", + " n_sensors=n_sensors, \n", + " basis=ps.basis.RandomProjection(n_basis_modes=max_basis_modes)\n", + " )\n", + " ),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-04T02:50:58.250623Z", + "start_time": "2022-02-04T02:50:58.124897Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train time for Identity basis: 3.0994415283203125e-06\n" + ] + }, + { + "ename": "TypeError", + "evalue": "'NoneType' object is not subscriptable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/4d/z3xfv_kx21g_zvpk__ybdy5wjqwgnv/T/ipykernel_78842/979401524.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_basis_modes_range\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_basis_modes_range\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_n_basis_modes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquiet\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 24\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreconstruction_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mn_sensors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/pysensors/pysensors/reconstruction/_sspor.py\u001b[0m in \u001b[0;36mupdate_n_basis_modes\u001b[0;34m(self, n_basis_modes, x, quiet)\u001b[0m\n\u001b[1;32m 350\u001b[0m ):\n\u001b[1;32m 351\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis_modes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_basis_modes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefit_basis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquiet\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mquiet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/pysensors/pysensors/reconstruction/_sspor.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, quiet, prefit_basis, seed, **optimizer_kws)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0;31m# Get matrix representation of basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 148\u001b[0;31m self.basis_matrix_ = self.basis.matrix_representation(\n\u001b[0m\u001b[1;32m 149\u001b[0m \u001b[0mn_basis_modes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis_modes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m )\n", + "\u001b[0;32m~/projects/pysensors/pysensors/basis/_base.py\u001b[0m in \u001b[0;36mmatrix_representation\u001b[0;34m(self, n_basis_modes, copy)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis_matrix_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mn_basis_modes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis_matrix_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mn_basis_modes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_validate_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_basis_modes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", + "ax.set(\n", + " xlabel=\"Number of basis modes\",\n", + " ylabel=\"RMSE\",\n", + " title=f\"Reconstruction error ({n_sensors} sensors)\",\n", + ")\n", + "\n", + "n_basis_modes_range = np.arange(1, max_basis_modes, 5)\n", + "\n", + "# Suppress warning arising from selecting fewer basis modes than\n", + "# the number of examples passed to the Identity basis\n", + "# (results in some examples being thrown away)\n", + "with warnings.catch_warnings():\n", + " warnings.filterwarnings(\"ignore\", category=UserWarning)\n", + "\n", + " for name, model in models:\n", + " t0 = -time()\n", + " model.basis.fit(X_train)\n", + " print(f\"Train time for {name} basis: {time() + t0}\")\n", + "\n", + " errors = np.zeros_like(n_basis_modes_range, dtype=np.float64)\n", + " for k, n in enumerate(n_basis_modes_range):\n", + " model.update_n_basis_modes(n, X_test, quiet=True)\n", + " errors[k] = model.reconstruction_error(X_test, [n_sensors])[0]\n", + "\n", + " ax.plot(n_basis_modes_range, errors, \"-o\", label=name)\n", + "\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Random projection and Identity bases give similar performance, with Identity winning out for larger numbers of modes. The POD basis performs best for smaller numbers of modes, but its accuracy tapers off as the number of basis modes grows large." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Varying the number of sensors\n", + "Next we'll explore the reconstruction error for a fixed number of basis modes (100) as the number of **sensors** is varied." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2020-12-06T21:29:41.188007Z", + "start_time": "2020-12-06T21:29:41.184139Z" + } + }, + "outputs": [], + "source": [ + "n_basis_modes = 100\n", + "\n", + "models = [\n", + " (\n", + " 'Identity',\n", + " ps.SSPOR(basis=ps.basis.Identity(n_basis_modes=n_basis_modes))\n", + " ),\n", + " (\n", + " 'SVD',\n", + " ps.SSPOR(basis=ps.basis.SVD(n_basis_modes=n_basis_modes))\n", + " ),\n", + " (\n", + " 'Random Projection',\n", + " ps.SSPOR(basis=ps.basis.RandomProjection(n_basis_modes=n_basis_modes))\n", + " ),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2020-12-06T21:29:44.559677Z", + "start_time": "2020-12-06T21:29:41.189444Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train time for Identity basis: 0.015327930450439453\n", + "Train time for SVD basis: 0.037960052490234375\n", + "Train time for Random Projection basis: 0.018201112747192383\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", + "ax.set(\n", + " xlabel=\"Number of sensors\",\n", + " ylabel=\"RMSE\",\n", + " title=f\"Reconstruction error ({n_basis_modes} basis modes)\",\n", + ")\n", + "\n", + "sensor_range = np.arange(1, 200, 5)\n", + "for name, model in models:\n", + " t0 = -time()\n", + " model.fit(X_train, quiet=True)\n", + " print(f\"Train time for {name} basis: {time() + t0}\")\n", + " \n", + " errors = model.reconstruction_error(X_test, sensor_range=sensor_range)\n", + " ax.plot(sensor_range, errors, \"-o\", label=name)\n", + " \n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When the sensor count is small, Identity and Random projection bases produce the best reconstruction error. As the number of sensors grows, the POD basis wins out." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sensor locations\n", + "Let's compare the sensor locations for the three bases." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2020-12-06T21:29:44.766935Z", + "start_time": "2020-12-06T21:29:44.561231Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(15, 4))\n", + "n_sensors = 60\n", + "\n", + "for ax, (name, model) in zip(axs, models):\n", + " img = np.zeros(n_features)\n", + " sensors = model.get_all_sensors()[:n_sensors]\n", + " img[sensors] = 16\n", + " \n", + " ax.imshow(img.reshape(image_shape), cmap=plt.cm.binary)\n", + " ax.set(title=name, xticks=[], yticks=[])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar sensor locations are chosen for this dataset across all three bases considered." + ] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.5" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "nbTranslate": { + "displayLangs": [ + "*" + ], + "hotkey": "alt-t", + "langInMainMenu": true, + "sourceLang": "en", + "targetLang": "fr", + "useGoogleTranslate": true + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "position": { + "height": "306.4px", + "left": "1116px", + "right": "20px", + "top": "120px", + "width": "346.4px" + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pysensors/basis/__init__.py b/pysensors/basis/__init__.py index 0be5d72..3d8a674 100644 --- a/pysensors/basis/__init__.py +++ b/pysensors/basis/__init__.py @@ -1,6 +1,6 @@ from ._identity import Identity from ._random_projection import RandomProjection from ._svd import SVD +from ._custom import Custom - -__all__ = ["Identity", "SVD", "RandomProjection"] +__all__ = ["Identity", "SVD", "RandomProjection","Custom"] diff --git a/pysensors/basis/_custom.py b/pysensors/basis/_custom.py new file mode 100644 index 0000000..43a7471 --- /dev/null +++ b/pysensors/basis/_custom.py @@ -0,0 +1,82 @@ +""" +custom mode basis class. +""" +from ._base import InvertibleBasis +from ._base import MatrixMixin + +class Custom(InvertibleBasis, MatrixMixin): + """ + Generate a custom transformation which maps input features to + custom modes. + + Assumes the data has already been centered (to have mean 0). + + Parameters + ---------- + n_basis_modes : int, optional (default 10) + Number of basis modes to retain. Cannot be larger than + the number of features ``n_features``, or the number of examples + ``n_examples``. + + Attributes + ---------- + basis_matrix_ : numpy ndarray, shape (n_features, n_basis_modes) + The top n_basis_modes left singular vectors of the training data. + + """ + + def __init__(self, n_basis_modes=10,U = None, **kwargs): + if isinstance(n_basis_modes, int) and n_basis_modes > 0: + super(Custom, self).__init__()#n_components=n_basis_modes, **kwargs + self._n_basis_modes = n_basis_modes + self.custom_basis_ = U + else: + raise ValueError("n_basis_modes must be a positive integer.") + + def fit(self, X): + """ + Parameters + ---------- + X : array-like, shape (n_samples, n_features) + The training data. + + Returns + ------- + self : instance + """ + # self.basis_matrix_ = super(Custom, self).fit(X).components_.T + self.basis_matrix_ = self.custom_basis_ + return self + + def matrix_inverse(self, n_basis_modes=None): + """ + Get the inverse matrix mapping from measurement space to + coordinates with respect to the basis. + + Note that this is not the inverse of the matrix returned by + ``self.matrix_representation``. It is the (psuedo) inverse of + the matrix whose columns are the basis modes. + + Parameters + ---------- + n_basis_modes : positive int, optional (default None) + Number of basis modes to be used to compute inverse. + + Returns + ------- + B : numpy ndarray, shape (n_basis_modes, n_features) + The inverse matrix. + """ + n_basis_modes = self._validate_input(n_basis_modes) + + return self.basis_matrix_[:, :n_basis_modes].T + + @property + def n_basis_modes(self): + """Number of basis modes.""" + return self._n_basis_modes + + @n_basis_modes.setter + def n_basis_modes(self, n_basis_modes): + self._n_basis_modes = n_basis_modes + self.n_components = n_basis_modes From 62b79a233a551ae01125e20e06fde0c96b4dffd2 Mon Sep 17 00:00:00 2001 From: Jimmy-INL <52417034+Jimmy-INL@users.noreply.github.com> Date: Thu, 10 Feb 2022 13:23:11 -0700 Subject: [PATCH 02/52] retrieving previous version --- pysensors/basis/__init__.py | 3 +- pysensors/basis/_custom.py | 82 ------------------------------------- 2 files changed, 1 insertion(+), 84 deletions(-) delete mode 100644 pysensors/basis/_custom.py diff --git a/pysensors/basis/__init__.py b/pysensors/basis/__init__.py index 3d8a674..f738c8d 100644 --- a/pysensors/basis/__init__.py +++ b/pysensors/basis/__init__.py @@ -1,6 +1,5 @@ from ._identity import Identity from ._random_projection import RandomProjection from ._svd import SVD -from ._custom import Custom -__all__ = ["Identity", "SVD", "RandomProjection","Custom"] +__all__ = ["Identity", "SVD", "RandomProjection"] diff --git a/pysensors/basis/_custom.py b/pysensors/basis/_custom.py deleted file mode 100644 index 43a7471..0000000 --- a/pysensors/basis/_custom.py +++ /dev/null @@ -1,82 +0,0 @@ -""" -custom mode basis class. -""" -from ._base import InvertibleBasis -from ._base import MatrixMixin - -class Custom(InvertibleBasis, MatrixMixin): - """ - Generate a custom transformation which maps input features to - custom modes. - - Assumes the data has already been centered (to have mean 0). - - Parameters - ---------- - n_basis_modes : int, optional (default 10) - Number of basis modes to retain. Cannot be larger than - the number of features ``n_features``, or the number of examples - ``n_examples``. - - Attributes - ---------- - basis_matrix_ : numpy ndarray, shape (n_features, n_basis_modes) - The top n_basis_modes left singular vectors of the training data. - - """ - - def __init__(self, n_basis_modes=10,U = None, **kwargs): - if isinstance(n_basis_modes, int) and n_basis_modes > 0: - super(Custom, self).__init__()#n_components=n_basis_modes, **kwargs - self._n_basis_modes = n_basis_modes - self.custom_basis_ = U - else: - raise ValueError("n_basis_modes must be a positive integer.") - - def fit(self, X): - """ - Parameters - ---------- - X : array-like, shape (n_samples, n_features) - The training data. - - Returns - ------- - self : instance - """ - # self.basis_matrix_ = super(Custom, self).fit(X).components_.T - self.basis_matrix_ = self.custom_basis_ - return self - - def matrix_inverse(self, n_basis_modes=None): - """ - Get the inverse matrix mapping from measurement space to - coordinates with respect to the basis. - - Note that this is not the inverse of the matrix returned by - ``self.matrix_representation``. It is the (psuedo) inverse of - the matrix whose columns are the basis modes. - - Parameters - ---------- - n_basis_modes : positive int, optional (default None) - Number of basis modes to be used to compute inverse. - - Returns - ------- - B : numpy ndarray, shape (n_basis_modes, n_features) - The inverse matrix. - """ - n_basis_modes = self._validate_input(n_basis_modes) - - return self.basis_matrix_[:, :n_basis_modes].T - - @property - def n_basis_modes(self): - """Number of basis modes.""" - return self._n_basis_modes - - @n_basis_modes.setter - def n_basis_modes(self, n_basis_modes): - self._n_basis_modes = n_basis_modes - self.n_components = n_basis_modes From ceb456a32e61e4634ee4fc64261f06bf1af46df0 Mon Sep 17 00:00:00 2001 From: Jimmy-INL <52417034+Jimmy-INL@users.noreply.github.com> Date: Thu, 10 Feb 2022 13:33:44 -0700 Subject: [PATCH 03/52] adding the custom basis --- pysensors/basis/__init__.py | 3 +- pysensors/basis/_custom.py | 82 +++++++++++++++++++++++++++++++++++++ 2 files changed, 84 insertions(+), 1 deletion(-) create mode 100644 pysensors/basis/_custom.py diff --git a/pysensors/basis/__init__.py b/pysensors/basis/__init__.py index f738c8d..3d8a674 100644 --- a/pysensors/basis/__init__.py +++ b/pysensors/basis/__init__.py @@ -1,5 +1,6 @@ from ._identity import Identity from ._random_projection import RandomProjection from ._svd import SVD +from ._custom import Custom -__all__ = ["Identity", "SVD", "RandomProjection"] +__all__ = ["Identity", "SVD", "RandomProjection","Custom"] diff --git a/pysensors/basis/_custom.py b/pysensors/basis/_custom.py new file mode 100644 index 0000000..43a7471 --- /dev/null +++ b/pysensors/basis/_custom.py @@ -0,0 +1,82 @@ +""" +custom mode basis class. +""" +from ._base import InvertibleBasis +from ._base import MatrixMixin + +class Custom(InvertibleBasis, MatrixMixin): + """ + Generate a custom transformation which maps input features to + custom modes. + + Assumes the data has already been centered (to have mean 0). + + Parameters + ---------- + n_basis_modes : int, optional (default 10) + Number of basis modes to retain. Cannot be larger than + the number of features ``n_features``, or the number of examples + ``n_examples``. + + Attributes + ---------- + basis_matrix_ : numpy ndarray, shape (n_features, n_basis_modes) + The top n_basis_modes left singular vectors of the training data. + + """ + + def __init__(self, n_basis_modes=10,U = None, **kwargs): + if isinstance(n_basis_modes, int) and n_basis_modes > 0: + super(Custom, self).__init__()#n_components=n_basis_modes, **kwargs + self._n_basis_modes = n_basis_modes + self.custom_basis_ = U + else: + raise ValueError("n_basis_modes must be a positive integer.") + + def fit(self, X): + """ + Parameters + ---------- + X : array-like, shape (n_samples, n_features) + The training data. + + Returns + ------- + self : instance + """ + # self.basis_matrix_ = super(Custom, self).fit(X).components_.T + self.basis_matrix_ = self.custom_basis_ + return self + + def matrix_inverse(self, n_basis_modes=None): + """ + Get the inverse matrix mapping from measurement space to + coordinates with respect to the basis. + + Note that this is not the inverse of the matrix returned by + ``self.matrix_representation``. It is the (psuedo) inverse of + the matrix whose columns are the basis modes. + + Parameters + ---------- + n_basis_modes : positive int, optional (default None) + Number of basis modes to be used to compute inverse. + + Returns + ------- + B : numpy ndarray, shape (n_basis_modes, n_features) + The inverse matrix. + """ + n_basis_modes = self._validate_input(n_basis_modes) + + return self.basis_matrix_[:, :n_basis_modes].T + + @property + def n_basis_modes(self): + """Number of basis modes.""" + return self._n_basis_modes + + @n_basis_modes.setter + def n_basis_modes(self, n_basis_modes): + self._n_basis_modes = n_basis_modes + self.n_components = n_basis_modes From 1ccd23fafed0ca39dac6cde204efa228d133df1c Mon Sep 17 00:00:00 2001 From: Jimmy-INL <52417034+Jimmy-INL@users.noreply.github.com> Date: Thu, 17 Feb 2022 10:19:39 -0700 Subject: [PATCH 04/52] adding the tutorial --- examples/basis_comparison-Copy1.ipynb | 173 +++++++++++++++++++------- 1 file changed, 125 insertions(+), 48 deletions(-) diff --git a/examples/basis_comparison-Copy1.ipynb b/examples/basis_comparison-Copy1.ipynb index bda136e..3720615 100644 --- a/examples/basis_comparison-Copy1.ipynb +++ b/examples/basis_comparison-Copy1.ipynb @@ -15,11 +15,11 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2022-02-04T02:21:25.483328Z", - "start_time": "2022-02-04T02:21:25.479647Z" + "end_time": "2022-02-11T03:41:30.417811Z", + "start_time": "2022-02-11T03:41:29.407472Z" } }, "outputs": [], @@ -51,11 +51,11 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2022-02-04T02:21:27.493726Z", - "start_time": "2022-02-04T02:21:27.471856Z" + "end_time": "2022-02-11T03:41:31.828680Z", + "start_time": "2022-02-11T03:41:31.798246Z" } }, "outputs": [ @@ -77,11 +77,11 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2022-02-04T02:21:28.065729Z", - "start_time": "2022-02-04T02:21:28.059962Z" + "end_time": "2022-02-11T03:41:32.539676Z", + "start_time": "2022-02-11T03:41:32.532499Z" } }, "outputs": [], @@ -102,11 +102,11 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2022-02-04T02:21:29.800752Z", - "start_time": "2022-02-04T02:21:29.795662Z" + "end_time": "2022-02-11T03:41:34.795977Z", + "start_time": "2022-02-11T03:41:34.789826Z" } }, "outputs": [], @@ -132,11 +132,11 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2022-02-04T02:21:30.899846Z", - "start_time": "2022-02-04T02:21:30.603748Z" + "end_time": "2022-02-11T03:41:35.856425Z", + "start_time": "2022-02-11T03:41:35.669692Z" } }, "outputs": [ @@ -164,11 +164,11 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2022-02-04T02:51:44.484186Z", - "start_time": "2022-02-04T02:51:44.481801Z" + "end_time": "2022-02-11T03:41:37.498997Z", + "start_time": "2022-02-11T03:41:37.496833Z" } }, "outputs": [], @@ -178,11 +178,89 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 34, "metadata": { "ExecuteTime": { - "end_time": "2022-02-04T02:51:45.722543Z", - "start_time": "2022-02-04T02:51:45.349991Z" + "end_time": "2022-02-11T16:41:13.678134Z", + "start_time": "2022-02-11T16:41:13.000576Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "first mode is : [ 0.02161312 0.02356213 0.02436712 ... -0.01424331 -0.01122156\n", + " -0.00952435]\n", + "first mode is : [ 0.02641718-0.00283139j 0.03057836-0.0035621j 0.03492383-0.00456587j\n", + " ... -0.02310508+0.00338802j -0.02023996+0.0032135j\n", + " -0.01810705+0.00297554j]\n", + "first mode is : [-0.02504727+0.j -0.02960636+0.j -0.03531519+0.j ... 0.03384322+0.j\n", + " 0.03121531+0.j 0.02839542+0.j]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/abdomg/miniconda3/envs/myenv/lib/python3.9/site-packages/numpy/core/_asarray.py:102: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return array(a, dtype, copy=False, order=order)\n", + "/Users/abdomg/miniconda3/envs/myenv/lib/python3.9/site-packages/numpy/core/_asarray.py:102: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return array(a, dtype, copy=False, order=order)\n", + "/Users/abdomg/miniconda3/envs/myenv/lib/python3.9/site-packages/numpy/core/_asarray.py:102: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "first mode is : [-0.02291244+8.036276e-04j -0.02671259+7.084303e-04j\n", + " -0.0313051 +7.145689e-05j ... 0.03113792+9.114330e-03j\n", + " 0.02855065+9.065691e-03j 0.02588998+8.364958e-03j]\n", + "first mode is : [ 0.0092756 -0.01193332j 0.01108589-0.01766157j 0.0138324 -0.02483492j\n", + " ... -0.0234978 +0.03297051j -0.02229962+0.03155063j\n", + " -0.02036817+0.02890316j]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/abdomg/miniconda3/envs/myenv/lib/python3.9/site-packages/numpy/core/_asarray.py:102: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(5):\n", + " hodmd = HODMD(svd_rank=i+1,d=2, exact=False,opt=False)\n", + " hodmd.fit(X_train.T)\n", + " U = hodmd.modes\n", + " print('first mode is : {}'.format(U[:,0]))\n", + " plt.plot(U[:,0])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "ExecuteTime": { + "end_time": "2022-02-11T03:44:38.587708Z", + "start_time": "2022-02-11T03:44:38.265806Z" } }, "outputs": [], @@ -199,11 +277,11 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 23, "metadata": { "ExecuteTime": { - "end_time": "2022-02-04T03:10:56.374075Z", - "start_time": "2022-02-04T03:10:56.364961Z" + "end_time": "2022-02-11T03:44:38.901700Z", + "start_time": "2022-02-11T03:44:38.890149Z" } }, "outputs": [ @@ -214,7 +292,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/4d/z3xfv_kx21g_zvpk__ybdy5wjqwgnv/T/ipykernel_78842/1947659873.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mwidths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m 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has no len()" ] @@ -245,17 +323,17 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 24, "metadata": { "ExecuteTime": { - "end_time": "2022-02-04T02:53:41.682801Z", - "start_time": "2022-02-04T02:53:41.678262Z" + "end_time": "2022-02-11T03:44:42.079095Z", + "start_time": "2022-02-11T03:44:42.074387Z" } }, "outputs": [], "source": [ "max_basis_modes = 200\n", - "n_sensors = 2\n", + "n_sensors = 100\n", "\n", "models = [\n", " (\n", @@ -299,22 +377,22 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 25, "metadata": { "ExecuteTime": { - "end_time": "2022-02-04T02:53:42.854190Z", - "start_time": "2022-02-04T02:53:42.850441Z" + "end_time": "2022-02-11T03:44:42.668037Z", + "start_time": "2022-02-11T03:44:42.664558Z" } }, "outputs": [ { "data": { "text/plain": [ - "SSPOR(basis=,\n", - " n_sensors=2, optimizer=QR())" + "SSPOR(basis=,\n", + " n_sensors=100, optimizer=QR())" ] }, - "execution_count": 184, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -333,11 +411,11 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 26, "metadata": { "ExecuteTime": { - "end_time": "2022-02-04T02:53:44.556728Z", - "start_time": "2022-02-04T02:53:44.552551Z" + "end_time": "2022-02-11T03:44:44.295653Z", + "start_time": "2022-02-11T03:44:44.291318Z" } }, "outputs": [ @@ -366,7 +444,7 @@ " dtype=complex64)" ] }, - "execution_count": 185, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -378,11 +456,11 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 27, "metadata": { "ExecuteTime": { - "end_time": "2022-02-04T02:53:49.060243Z", - "start_time": "2022-02-04T02:53:45.342220Z" + "end_time": "2022-02-11T03:44:48.136086Z", + "start_time": "2022-02-11T03:44:45.209361Z" } }, "outputs": [ @@ -405,7 +483,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train time for Custom basis: 3.0994415283203125e-06\n" + "Train time for Custom basis: 1.9073486328125e-06\n" ] }, { @@ -420,15 +498,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train time for Identity basis: 0.002521038055419922\n", - "(4096, 200)\n", - "Train time for SVD basis: 0.0885918140411377\n", - "Train time for Random Projection basis: 0.013406753540039062\n" + "Train time for Identity basis: 0.0022819042205810547\n", + "Train time for SVD basis: 0.06403183937072754\n", + "Train time for Random Projection basis: 0.008278131484985352\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] From 20c24602c16aa1455f99b366eb54dddb98f6c20a Mon Sep 17 00:00:00 2001 From: Jimmy-INL <52417034+Jimmy-INL@users.noreply.github.com> Date: Thu, 12 May 2022 12:46:53 -0600 Subject: [PATCH 05/52] readding custom basis --- pysensors/basis/__init__.py | 3 +- pysensors/basis/_custom.py | 88 +++++++++++++++++++++++++++++++++++++ 2 files changed, 90 insertions(+), 1 deletion(-) create mode 100644 pysensors/basis/_custom.py diff --git a/pysensors/basis/__init__.py b/pysensors/basis/__init__.py index f738c8d..3d8a674 100644 --- a/pysensors/basis/__init__.py +++ b/pysensors/basis/__init__.py @@ -1,5 +1,6 @@ from ._identity import Identity from ._random_projection import RandomProjection from ._svd import SVD +from ._custom import Custom -__all__ = ["Identity", "SVD", "RandomProjection"] +__all__ = ["Identity", "SVD", "RandomProjection","Custom"] diff --git a/pysensors/basis/_custom.py b/pysensors/basis/_custom.py new file mode 100644 index 0000000..546faa7 --- /dev/null +++ b/pysensors/basis/_custom.py @@ -0,0 +1,88 @@ +""" +custom mode basis class. +""" +from ._base import InvertibleBasis +from ._base import MatrixMixin + +class Custom(InvertibleBasis, MatrixMixin): + """ + Use a custom transformation to maps input features to + custom modes. + + Assumes the data has already been centered (to have mean 0). + + Parameters + ---------- + n_basis_modes : int, optional (default 10) + Number of basis modes to retain. Cannot be larger than + the number of features ``n_features``, or the number of examples + ``n_examples``. + U: The custom basis matrix + + Attributes + ---------- + basis_matrix_ : numpy ndarray, shape (n_features, n_basis_modes) + The top n_basis_modes left singular vectors of the training data. + + """ + + def __init__(self, U, n_basis_modes=10, **kwargs): + if isinstance(n_basis_modes, int) and n_basis_modes > 0: + super(Custom, self).__init__()#n_components=n_basis_modes, **kwargs + self._n_basis_modes = n_basis_modes + self.custom_basis_ = U + else: + raise ValueError("n_basis_modes must be a positive integer.") + + def fit(self, X): + """ + Parameters + ---------- + X : array-like, shape (n_samples, n_features) + The training data. + + Returns + ------- + self : instance + """ + # self.basis_matrix_ = self.custom_basis_[:,: self.n_basis_modes] @ self.custom_basis_[:,: self.n_basis_modes].T @ X[: self.n_basis_modes, :].T.copy() + # self.basis_matrix_ = self.custom_basis_ @ self.custom_basis_.T @ X[: self.n_basis_modes, :].T.copy() + self.basis_matrix_ = self.custom_basis_[:,:self.n_basis_modes] + # self.basis_matrix_ = (X @ self.custom_basis_[:,:self.n_basis_modes] @ self.custom_basis_[:,:self.n_basis_modes].T)[:self.n_basis_modes,:].T + + # self.basis_matrix_ = ((X @ self.custom_basis_).T)[:,:self.n_basis_modes] + # self.basis_matrix_ = ((X @ self.custom_basis_ @ self.custom_basis_.T).T)[:,:self.n_basis_modes] + return self + + def matrix_inverse(self, n_basis_modes=None): + """ + Get the inverse matrix mapping from measurement space to + coordinates with respect to the basis. + + Note that this is not the inverse of the matrix returned by + ``self.matrix_representation``. It is the (pseudo) inverse of + the matrix whose columns are the basis modes. + + Parameters + ---------- + n_basis_modes : positive int, optional (default None) + Number of basis modes to be used to compute inverse. + + Returns + ------- + B : numpy ndarray, shape (n_basis_modes, n_features) + The inverse matrix. + """ + n_basis_modes = self._validate_input(n_basis_modes) + + return self.basis_matrix_[:, :n_basis_modes].T + + @property + def n_basis_modes(self): + """Number of basis modes.""" + return self._n_basis_modes + + @n_basis_modes.setter + def n_basis_modes(self, n_basis_modes): + self._n_basis_modes = n_basis_modes + self.n_components = n_basis_modes From 960d971593cb536d51c3dfffd6982752c7831f65 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Sun, 15 May 2022 10:17:18 -0600 Subject: [PATCH 06/52] trying to have the basis matrix as the identity matrix --- pysensors/basis/_base.py | 4 ++-- pysensors/basis/_identity.py | 10 ++++++---- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/pysensors/basis/_base.py b/pysensors/basis/_base.py index 261cb5b..f91c131 100644 --- a/pysensors/basis/_base.py +++ b/pysensors/basis/_base.py @@ -43,9 +43,9 @@ def matrix_representation(self, n_basis_modes=None, copy=False): n_basis_modes = self._validate_input(n_basis_modes) if copy: - return self.basis_matrix_[:, :n_basis_modes].copy() + return self.basis_matrix_[:, :n_basis_modes].copy()#self.original_data @ else: - return self.basis_matrix_[:, :n_basis_modes] + return self.basis_matrix_[:, :n_basis_modes]#self.original_data @ def _validate_input(self, n_basis_modes): """ diff --git a/pysensors/basis/_identity.py b/pysensors/basis/_identity.py index 4835088..38ef4ac 100644 --- a/pysensors/basis/_identity.py +++ b/pysensors/basis/_identity.py @@ -6,6 +6,7 @@ from warnings import warn from numpy import identity +import numpy as np from sklearn.base import BaseEstimator from sklearn.utils import check_array @@ -52,7 +53,8 @@ def fit(self, X): ------- self : instance """ - + # Store original data + self.original_data = X # Note that we take a transpose here, so columns correspond to examples if self.n_basis_modes is None: self.basis_matrix_ = check_array(X).T.copy() @@ -65,10 +67,10 @@ def fit(self, X): ) ) - self.basis_matrix_ = check_array(X)[: self.n_basis_modes, :].T.copy() + self.basis_matrix_ = np.eye(X.shape[1])[:,:self.n_basis_modes] #check_array(X)[: self.n_basis_modes, :].T.copy() - if self.n_basis_modes < X.shape[0]: - warn(f"Only the first {self.n_basis_modes} examples were retained.") + # if self.n_basis_modes < X.shape[0]: + # warn(f"Only the first {self.n_basis_modes} examples were retained.") return self def matrix_inverse(self, n_basis_modes=None): From db3c6cbc84983793a371db99900374ec84fa89dc Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Fri, 24 Jun 2022 12:15:53 -0600 Subject: [PATCH 07/52] Adding region constraint notebook --- examples/region_qrModified.ipynb | 1334 ++++++++++++++++++++++++++++++ 1 file changed, 1334 insertions(+) create mode 100644 examples/region_qrModified.ipynb diff --git a/examples/region_qrModified.ipynb b/examples/region_qrModified.ipynb new file mode 100644 index 0000000..1ac468b --- /dev/null +++ b/examples/region_qrModified.ipynb @@ -0,0 +1,1334 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 269, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn import datasets\n", + "from sklearn import metrics\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "import pysensors as ps" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of samples: 400\n", + "Number of features (sensors): 4096\n" + ] + } + ], + "source": [ + "faces = datasets.fetch_olivetti_faces(shuffle=True)\n", + "X = faces.data\n", + "\n", + "n_samples, n_features = X.shape\n", + "print('Number of samples:', n_samples)\n", + "print('Number of features (sensors):', n_features)\n", + "\n", + "# Global centering\n", + "X = X - X.mean(axis=0)\n", + "\n", + "# Local centering\n", + "X -= X.mean(axis=1).reshape(n_samples, -1)" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": {}, + "outputs": [], + "source": [ + "n_row, n_col = 2, 3\n", + "n_components = n_row * n_col\n", + "image_shape = (64, 64)\n", + "\n", + "def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):\n", + " '''Function for plotting faces'''\n", + " plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n", + " plt.suptitle(title, size=16)\n", + " for i, comp in enumerate(images):\n", + " plt.subplot(n_row, n_col, i + 1)\n", + " vmax = max(comp.max(), -comp.min())\n", + " plt.imshow(comp.reshape(image_shape), cmap=cmap,\n", + " interpolation='nearest',\n", + " vmin=-vmax, vmax=vmax)\n", + " plt.xticks(())\n", + " plt.yticks(())\n", + " plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_gallery(\"First few centered faces\", X[:n_components])" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4032 384 4092 ... 2165 2561 1802]\n" + ] + } + ], + "source": [ + "#Find all sensor locations using built in QR optimizer\n", + "max_const_sensors = 230\n", + "n_sensors = 100\n", + "n_const_sensors = 5\n", + "optimizer = ps.optimizers.QR()\n", + "model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors)\n", + "model.fit(X)\n", + "\n", + "all_sensors = model.get_all_sensors()\n", + "print(all_sensors)" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([63, 6, 63, ..., 33, 40, 28]), array([ 0, 0, 60, ..., 53, 1, 10]))\n", + "(4096, 2)\n", + "[ 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58\n", + " 59 60 61 62 63 105 106 107 108 109 110 111 112 113 114 115 116 117\n", + " 118 119 120 121 122 123 124 125 126 127 169 170 171 172 173 174 175 176\n", + " 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 233 234 235\n", + " 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253\n", + " 254 255 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312\n", + " 313 314 315 316 317 318 319 361 362 363 364 365 366 367 368 369 370 371\n", + " 372 373 374 375 376 377 378 379 380 381 382 383 425 426 427 428 429 430\n", + " 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 489\n", + " 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507\n", + " 508 509 510 511 553 554 555 556 557 558 559 560 561 562 563 564 565 566\n", + " 567 568 569 570 571 572 573 574 575 617 618 619 620 621 622 623 624 625\n", + " 626 627 628 629 630 631 632 633 634 635 636 637 638 639]\n" + ] + } + ], + "source": [ + "#Define Constrained indices\n", + "a = np.unravel_index(all_sensors, (64,64))\n", + "print(a)\n", + "a_array = np.transpose(a)\n", + "print(a_array.shape)\n", + "#idx = np.ravel_multi_index(a, (64,64))\n", + "#print(idx)\n", + "constrained_sensorsx = []\n", + "constrained_sensorsy = []\n", + "for i in range(n_features):\n", + " if a[0][i] < 10 and a[1][i] > 40: # x<10 and y>40\n", + " constrained_sensorsy.append(a[0][i])\n", + " constrained_sensorsx.append(a[1][i])\n", + "\n", + "constrained_sensorsx = np.array(constrained_sensorsx)\n", + "constrained_sensorsy = np.array(constrained_sensorsy)\n", + "\n", + "constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1)\n", + "constrained_sensors_tuple = np.transpose(constrained_sensors_array)\n", + "\n", + "\n", + "#print(constrained_sensors_tuple)\n", + "#print(len(constrained_sensors_tuple))\n", + "idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (64,64))\n", + "\n", + "#print(len(idx_constrained))\n", + "#print(constrained_sensorsx)\n", + "#print(constrained_sensorsy)\n", + "#print(idx_constrained)\n", + "print(np.sort(idx_constrained[:]))\n", + "all_sorted = np.sort(all_sensors)\n", + "#print(all_sorted)\n", + "idx = np.arange(all_sorted.shape[0])\n", + "#all_sorted[idx_constrained] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", + "\n", + "ax = plt.subplot()\n", + "#Plot constrained space\n", + "img = np.zeros(n_features)\n", + "img[idx_constrained] = 1\n", + "im = plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", + "\n", + "# create an axes on the right side of ax. The width of cax will be 5%\n", + "# of ax and the padding between cax and ax will be fixed at 0.05 inch.\n", + "divider = make_axes_locatable(ax)\n", + "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", + "\n", + "plt.colorbar(im, cax=cax)\n", + "plt.title('Constrained region');" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [], + "source": [ + "#New class for constrained sensor placement\n", + "from pysensors.optimizers._qr import QR\n", + "class GQR(QR):\n", + " \"\"\"\n", + " General QR optimizer for sensor selection.\n", + " Ranks sensors in descending order of \"importance\" based on\n", + " reconstruction performance. \n", + "\n", + " See the following reference for more information\n", + "\n", + " Manohar, Krithika, et al.\n", + " \"Data-driven sparse sensor placement for reconstruction:\n", + " Demonstrating the benefits of exploiting known patterns.\"\n", + " IEEE Control Systems Magazine 38.3 (2018): 63-86.\n", + " \"\"\"\n", + " def __init__(self):\n", + " \"\"\"\n", + " Attributes\n", + " ----------\n", + " pivots_ : np.ndarray, shape [n_features]\n", + " Ranked list of sensor locations.\n", + " \"\"\"\n", + " self.pivots_ = None\n", + " \n", + " def fit(\n", + " self,\n", + " basis_matrix, idx_constrained, const_sensors,\n", + " ):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " basis_matrix: np.ndarray, shape [n_features, n_samples]\n", + " Matrix whose columns are the basis vectors in which to\n", + " represent the measurement data.\n", + " optimizer_kws: dictionary, optional\n", + " Keyword arguments to be passed to the qr method.\n", + "\n", + " Returns\n", + " -------\n", + " self: a fitted :class:`pysensors.optimizers.CCQR` instance\n", + " \"\"\"\n", + "\n", + " n, m = basis_matrix.shape # We transpose basis_matrix below\n", + " \n", + " # Initialize helper variables\n", + " R = basis_matrix.conj().T.copy()\n", + " #print(R.shape)\n", + " p = np.arange(n)\n", + " #print(p)\n", + " k = min(m, n)\n", + " \n", + " \n", + " for j in range(k):\n", + " r = R[j:, j:]\n", + " # Norm of each column\n", + " dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0))\n", + " \n", + " dlens_updated = f_region(idx_constrained,dlens,p,j, const_sensors)\n", + " \n", + " # Choose pivot\n", + " i_piv = np.argmax(dlens_updated)\n", + " #print(i_piv)\n", + " \n", + " \n", + " dlen = dlens_updated[i_piv]\n", + " \n", + " if dlen > 0:\n", + " u = r[:, i_piv] / dlen\n", + " u[0] += np.sign(u[0]) + (u[0] == 0)\n", + " u /= np.sqrt(abs(u[0]))\n", + " else:\n", + " u = r[:, i_piv]\n", + " u[0] = np.sqrt(2)\n", + " \n", + " # Track column pivots\n", + " i_piv += j # true permutation index is i_piv shifted by the iteration counter j\n", + " print(i_piv) # Niharika's debugging line\n", + " p[[j, i_piv]] = p[[i_piv, j]]\n", + " print(p)\n", + "\n", + "\n", + " # Switch columns\n", + " R[:, [j, i_piv]] = R[:, [i_piv, j]]\n", + " \n", + " # Apply reflector\n", + " R[j:, j:] -= np.outer(u, np.dot(u, R[j:, j:]))\n", + " R[j + 1 :, j] = 0\n", + " \n", + "\n", + " self.pivots_ = p\n", + " \n", + "\n", + " return self\n", + "#function for mapping sensor locations with constraints\n", + "def f_region(lin_idx, dlens, piv, j, const_sensors): \n", + " #num_sensors should be fixed for each custom constraint (for now)\n", + " #num_sensors must be <= size of constraint region\n", + " \"\"\"\n", + " Function for mapping constrained sensor locations with the QR procedure.\n", + " \n", + " Parameters\n", + " ----------\n", + " lin_idx: np.ndarray, shape [No. of constrained locations]\n", + " Array which contains the constrained locations mapped on the grid.\n", + " dlens: np.ndarray, shape [Variable based on j]\n", + " Array which contains the norm of columns of basis matrix.\n", + " num_sensors: int, \n", + " Number of sensors to be placed in the constrained area.\n", + " j: int,\n", + " Iterative variable in the QR algorithm.\n", + "\n", + " Returns\n", + " -------\n", + " dlens : np.darray, shape [Variable based on j] with constraints mapped into it. \n", + " \"\"\"\n", + " if j < const_sensors: # force sensors into constraint region\n", + " #idx = np.arange(dlens.shape[0]) \n", + " #dlens[np.delete(idx, lin_idx)] = 0\n", + " \n", + " didx = np.isin(piv[j:],lin_idx,invert=True)\n", + " dlens[didx] = 0\n", + " \n", + " # otherwise don't do anything\n", + " #else: \n", + " #dlens[lin_idx-j] = 0\n", + " return dlens\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "447\n", + "[ 447 1 2 ... 4093 4094 4095]\n", + "493\n", + "[ 447 493 2 ... 4093 4094 4095]\n", + "625\n", + "[ 447 493 625 ... 4093 4094 4095]\n", + "59\n", + "[ 447 493 625 ... 4093 4094 4095]\n", + "635\n", + "[ 447 493 625 ... 4093 4094 4095]\n", + "4032\n", + "[ 447 493 625 ... 4093 4094 4095]\n", + "320\n", + "[ 447 493 625 ... 4093 4094 4095]\n", + "4039\n", + "[ 447 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4093 228 4095]\n", + "3635\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2432\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1412\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "657\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "713\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "892\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1003\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2069\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "4053\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3833\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3168\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3786\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1187\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2306\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1445\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "833\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1156\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2394\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "667\n", + "[ 447 493 625 ... 4093 228 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"1208\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3102\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1508\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1136\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "681\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3339\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2396\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "767\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "980\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1008\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3867\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1369\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1369\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3394\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3918\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3987\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "839\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2080\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "961\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1129\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1436\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2722\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3892\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3963\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3514\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2520\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3648\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3419\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3460\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3303\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "779\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2216\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "996\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2866\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3375\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3159\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2174\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3702\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3282\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3047\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1733\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2726\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "4039\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3706\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3816\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3904\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2594\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "771\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2075\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2946\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1157\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "4072\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "4085\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1424\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3097\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2839\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3675\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "512\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2668\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3327\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "581\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3264\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3580\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1229\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1728\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3901\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "614\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3620\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1152\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3076\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3258\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "974\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "4040\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1234\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "558\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3288\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "733\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3299\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1448\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "651\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3726\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1078\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1861\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3587\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2364\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1309\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "2039\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "3497\n", + "[ 447 493 625 ... 4093 228 4095]\n", + "1639\n", + "[ 447 493 625 ... 4093 228 4095]\n" + ] + }, + { + "data": { + "text/html": [ + "
SSPOR(basis=Identity(n_basis_modes=400), n_sensors=100, optimizer=GQR())
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "SSPOR(basis=Identity(n_basis_modes=400), n_sensors=100, optimizer=GQR())" + ] + }, + "execution_count": 277, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "optimizer1 = GQR()\n", + "model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors)\n", + "model1.fit(X, quiet=True, prefit_basis=False, seed=None, idx_constrained = idx_constrained, const_sensors = n_const_sensors)" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 447 493 625 59 635 4032 320 4039 4092 2204 878 1088 3779 3093\n", + " 4087 2624 2331 2783 4043 3456 2901 3039 1023 1224 1052 3164 2011 4031\n", + " 1188 4037 4 3358 2815 2880 2648 1100 2210 2909 1535 3643 3550 718\n", + " 1728 1212 4089 4084 2399 1155 3654 4049 2457 1178 4034 969 3395 1140\n", + " 2797 2367 2048 2987 3327 768 584 2341 1138 898 2656 1055 1037 3179\n", + " 4066 1035 1217 2022 3890 3075 3220 859 3323 761 3656 2897 2214 1109\n", + " 1603 2470 3744 3652 1818 2264 1406 726 3236 1068 1112 2304 592 3776\n", + " 1210 2137 2981 2525 3107 925 1094 2077 2793 4035 4041 4091 2626 1021\n", + " 63 1434 2493 3583 3031 3026 2089 1344 1126 2197 2705 4045 1250 3586\n", + " 1270 3845 1102 944 1943 749 2 2111 766 3704 2774 3517 3351 1071\n", + " 3729 2333 948 1446 3201 994 3632 806 2028 755 1041 2849 3334 2148\n", + " 773 2905 3129 3225 2345 787 2607 3086 374 3836 0 1043 513 3844\n", + " 2071 1163 1244 3461 976 4076 1665 3306 643 1089 960 3846 508 1016\n", + " 3958 910 1596 2327 862 1956 1010 3905 1895 2778 857 2964 3071 1277\n", + " 3912 3099 1855 698 3113 1859 24 1090 3701 2817 3898 2970 3465 2207\n", + " 2852 3118 2466 4038 1101 955 2202 2651 597 2343 817 2884 2926 1142\n", + " 57 868 1854 2921 4094 1006]\n", + "True\n" + ] + } + ], + "source": [ + "all_sensors1 = model1.get_all_sensors()\n", + "print(all_sensors1[:230])\n", + "\n", + "print(np.array_equal(np.sort(all_sensors),np.sort(all_sensors1)))" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [], + "source": [ + "xmin = 40\n", + "xmax = 64\n", + "ymin = 0\n", + "ymax = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 447 493 625 59 635 4032 320 4039 4092 2204 878 1088 3779 3093\n", + " 4087 2624 2331 2783 4043 3456 2901 3039 1023 1224 1052 3164 2011 4031\n", + " 1188 4037 4 3358 2815 2880 2648 1100 2210 2909 1535 3643 3550 718\n", + " 1728 1212 4089 4084 2399 1155 3654 4049 2457 1178 4034 969 3395 1140\n", + " 2797 2367 2048 2987 3327 768 584 2341 1138 898 2656 1055 1037 3179\n", + " 4066 1035 1217 2022 3890 3075 3220 859 3323 761 3656 2897 2214 1109\n", + " 1603 2470 3744 3652 1818 2264 1406 726 3236 1068 1112 2304 592 3776\n", + " 1210 2137]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "top_sensors = model1.get_selected_sensors()\n", + "print(top_sensors)\n", + "img = np.zeros(n_features)\n", + "img[top_sensors] = 16\n", + "plt.plot([xmin,xmin],[ymin,ymax],'r')\n", + "plt.plot([xmin,xmax],[ymax,ymax],'r')\n", + "plt.plot([xmax,xmax],[ymin,ymax],'r')\n", + "plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", + "plt.title('Sensor locations for cost-constrained selector');\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100\n", + "(230,)\n" + ] + } + ], + "source": [ + "print(n_sensors)\n", + "print(idx_constrained.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('base')", + "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": "3.9.12" + }, + "vscode": { + "interpreter": { + "hash": "3d597f4c481aa0f25dceb95d2a0067e73c0966dcbd003d741d821a7208527ecf" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From dbbb98062021052431a9a341c6dffb9359004d40 Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Fri, 24 Jun 2022 15:23:31 -0600 Subject: [PATCH 08/52] Tried to fix the const_sensors = 0 error --- examples/region_qrModified.ipynb | 938 +++---------------------------- 1 file changed, 72 insertions(+), 866 deletions(-) diff --git a/examples/region_qrModified.ipynb b/examples/region_qrModified.ipynb index 1ac468b..7ea480a 100644 --- a/examples/region_qrModified.ipynb +++ b/examples/region_qrModified.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 269, + "execution_count": 782, "metadata": {}, "outputs": [], "source": [ @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 270, + "execution_count": 783, "metadata": {}, "outputs": [ { @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 271, + "execution_count": 784, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 272, + "execution_count": 785, "metadata": {}, "outputs": [ { @@ -91,22 +91,22 @@ }, { "cell_type": "code", - "execution_count": 273, + "execution_count": 786, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[4032 384 4092 ... 2165 2561 1802]\n" + "[4032 384 4092 ... 3397 1565 2776]\n" ] } ], "source": [ "#Find all sensor locations using built in QR optimizer\n", "max_const_sensors = 230\n", - "n_sensors = 100\n", - "n_const_sensors = 5\n", + "n_sensors = 200\n", + "n_const_sensors = 0\n", "optimizer = ps.optimizers.QR()\n", "model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors)\n", "model.fit(X)\n", @@ -117,14 +117,14 @@ }, { "cell_type": "code", - "execution_count": 274, + "execution_count": 787, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(array([63, 6, 63, ..., 33, 40, 28]), array([ 0, 0, 60, ..., 53, 1, 10]))\n", + "(array([63, 6, 63, ..., 53, 24, 43]), array([ 0, 0, 60, ..., 5, 29, 24]))\n", "(4096, 2)\n", "[ 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58\n", " 59 60 61 62 63 105 106 107 108 109 110 111 112 113 114 115 116 117\n", @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 275, + "execution_count": 788, "metadata": {}, "outputs": [ { @@ -217,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 276, + "execution_count": 789, "metadata": {}, "outputs": [], "source": [ @@ -271,21 +271,23 @@ " p = np.arange(n)\n", " #print(p)\n", " k = min(m, n)\n", - " \n", - " \n", + "\n", " for j in range(k):\n", " r = R[j:, j:]\n", " # Norm of each column\n", " dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0))\n", + " if const_sensors == 0:\n", + " didx = np.isin(p[j:],idx_constrained,invert= False)\n", + " dlens[didx] = 0\n", + " i_piv = np.argmax(dlens)\n", + " dlen = dlens[i_piv]\n", + " else:\n", " \n", - " dlens_updated = f_region(idx_constrained,dlens,p,j, const_sensors)\n", - " \n", - " # Choose pivot\n", - " i_piv = np.argmax(dlens_updated)\n", - " #print(i_piv)\n", - " \n", - " \n", - " dlen = dlens_updated[i_piv]\n", + " dlens_updated = f_region(idx_constrained,dlens,p,j, const_sensors)\n", + " # Choose pivot\n", + " i_piv = np.argmax(dlens_updated)\n", + " #print(i_piv)\n", + " dlen = dlens_updated[i_piv]\n", " \n", " if dlen > 0:\n", " u = r[:, i_piv] / dlen\n", @@ -297,9 +299,9 @@ " \n", " # Track column pivots\n", " i_piv += j # true permutation index is i_piv shifted by the iteration counter j\n", - " print(i_piv) # Niharika's debugging line\n", + " #print(i_piv) # Niharika's debugging line\n", " p[[j, i_piv]] = p[[i_piv, j]]\n", - " print(p)\n", + " #print(p)\n", "\n", "\n", " # Switch columns\n", @@ -336,6 +338,7 @@ " -------\n", " dlens : np.darray, shape [Variable based on j] with constraints mapped into it. \n", " \"\"\"\n", + " \n", " if j < const_sensors: # force sensors into constraint region\n", " #idx = np.arange(dlens.shape[0]) \n", " #dlens[np.delete(idx, lin_idx)] = 0\n", @@ -344,7 +347,7 @@ " dlens[didx] = 0\n", " \n", " # otherwise don't do anything\n", - " #else: \n", + " #else: \n", " #dlens[lin_idx-j] = 0\n", " return dlens\n", "\n" @@ -352,825 +355,19 @@ }, { "cell_type": "code", - "execution_count": 277, + "execution_count": 790, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "447\n", - "[ 447 1 2 ... 4093 4094 4095]\n", - "493\n", - "[ 447 493 2 ... 4093 4094 4095]\n", - "625\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "59\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "635\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4032\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "320\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4039\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4092\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2204\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "878\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1088\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3779\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3093\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4087\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2624\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2331\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2783\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4043\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3456\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2901\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3039\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1023\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1224\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1052\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3164\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2011\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4031\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1188\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4037\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "635\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3358\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2815\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2880\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2648\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1100\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2210\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2909\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1535\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3643\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3550\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "718\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1728\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1212\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4089\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4084\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2399\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1155\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3654\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4049\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2457\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1178\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4034\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "969\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3395\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1140\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2797\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2367\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2048\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2987\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3327\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "768\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "584\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2341\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1138\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "898\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2656\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1055\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1037\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3179\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4066\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1035\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1217\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2022\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3890\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3075\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3220\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "859\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3323\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "761\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3656\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2897\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2214\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1109\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1603\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2470\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3744\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3652\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1818\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2264\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - 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SSPOR(basis=Identity(n_basis_modes=400), n_sensors=100, optimizer=GQR())
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SSPOR(basis=Identity(n_basis_modes=400), n_sensors=200, optimizer=GQR())
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
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}, { "cell_type": "code", - "execution_count": 279, + "execution_count": 792, "metadata": {}, "outputs": [], "source": [ @@ -1235,26 +432,33 @@ }, { "cell_type": "code", - "execution_count": 280, + "execution_count": 793, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ 447 493 625 59 635 4032 320 4039 4092 2204 878 1088 3779 3093\n", - " 4087 2624 2331 2783 4043 3456 2901 3039 1023 1224 1052 3164 2011 4031\n", - " 1188 4037 4 3358 2815 2880 2648 1100 2210 2909 1535 3643 3550 718\n", - " 1728 1212 4089 4084 2399 1155 3654 4049 2457 1178 4034 969 3395 1140\n", - " 2797 2367 2048 2987 3327 768 584 2341 1138 898 2656 1055 1037 3179\n", - " 4066 1035 1217 2022 3890 3075 3220 859 3323 761 3656 2897 2214 1109\n", - " 1603 2470 3744 3652 1818 2264 1406 726 3236 1068 1112 2304 592 3776\n", - " 1210 2137]\n" + "[4032 384 4092 4039 703 2209 529 2331 878 3779 3093 4087 1153 2624\n", + " 2140 2901 657 2783 4044 3039 1212 1224 3456 4 3165 1188 1052 4037\n", + " 4031 3358 1087 2010 2239 2880 1100 2590 3643 2909 3550 969 1178 3654\n", + " 1024 4089 1140 1728 2773 4034 1155 2751 3828 3395 2399 711 2521 1535\n", + " 2048 2987 4050 1037 2278 755 3327 3220 1138 2200 994 1035 834 857\n", + " 3977 2469 4063 2897 768 2022 3011 1603 701 3179 1068 925 3656 1434\n", + " 3236 1210 2327 3845 1344 1158 1112 3323 3745 2981 2154 581 749 1943\n", + " 3776 0 2797 2269 1470 2713 2304 4035 3525 1102 2202 1071 3002 1207\n", + " 3581 944 4085 3026 3107 337 698 4091 1896 3705 1055 3031 4077 3586\n", + " 1061 2562 2854 1919 2657 2705 726 3161 2197 786 795 3071 1021 679\n", + " 2015 2345 2431 3652 1250 2148 2 1043 3351 3335 2459 2849 3519 1077\n", + " 1818 4047 3201 1041 1601 3472 901 2970 2152 3844 1956 1446 3632 1787\n", + " 1277 3306 976 2028 1016 2840 3268 3087 2517 948 2340 3703 3793 1090\n", + " 1006 3846 2858 2607 577 1095 2877 3905 1282 2926 1109 2429 3901 2964\n", + " 3099 1162 868 961]\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1270,24 +474,26 @@ "print(top_sensors)\n", "img = np.zeros(n_features)\n", "img[top_sensors] = 16\n", + "const_loc = top_sensors[:230]\n", "plt.plot([xmin,xmin],[ymin,ymax],'r')\n", "plt.plot([xmin,xmax],[ymax,ymax],'r')\n", "plt.plot([xmax,xmax],[ymin,ymax],'r')\n", "plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", "plt.title('Sensor locations for cost-constrained selector');\n", + "#np.equal(np.sort(const_loc),np.sort(idx_constrained))\n", " " ] }, { "cell_type": "code", - "execution_count": 281, + "execution_count": 794, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "100\n", + "200\n", "(230,)\n" ] } From 4fb1085505847a1010f3efe64342f0fd84536858 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Sat, 25 Jun 2022 11:26:06 -0600 Subject: [PATCH 09/52] Fixing n_const_sensssors = 0, and edding checks for limitations --- examples/region_qrModified.py | 349 ++++++++++++++++++++++++++++++++++ 1 file changed, 349 insertions(+) create mode 100644 examples/region_qrModified.py diff --git a/examples/region_qrModified.py b/examples/region_qrModified.py new file mode 100644 index 0000000..0b4c51e --- /dev/null +++ b/examples/region_qrModified.py @@ -0,0 +1,349 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[1]: + + +import matplotlib.pyplot as plt +import numpy as np +from sklearn import datasets +from sklearn import metrics +from sklearn.model_selection import train_test_split + +import pysensors as ps +# from ..pysensors.optimizers._ccqr import CCQR + + +# In[2]: + + +faces = datasets.fetch_olivetti_faces(shuffle=True) +X = faces.data + +n_samples, n_features = X.shape +print('Number of samples:', n_samples) +print('Number of features (sensors):', n_features) + +# Global centering +X = X - X.mean(axis=0) + +# Local centering +X -= X.mean(axis=1).reshape(n_samples, -1) + + +# In[3]: + + +n_row, n_col = 2, 3 +n_components = n_row * n_col +image_shape = (64, 64) + +def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): + '''Function for plotting faces''' + plt.figure(figsize=(2. * n_col, 2.26 * n_row)) + plt.suptitle(title, size=16) + for i, comp in enumerate(images): + plt.subplot(n_row, n_col, i + 1) + vmax = max(comp.max(), -comp.min()) + plt.imshow(comp.reshape(image_shape), cmap=cmap, + interpolation='nearest', + vmin=-vmax, vmax=vmax) + plt.xticks(()) + plt.yticks(()) + plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.) + plt.show() + + +# In[4]: + + +plot_gallery("First few centered faces", X[:n_components]) + + +# In[5]: +# reduce the X +imageSize = 64 +image_shape = (imageSize, imageSize) + +X = X[:,:imageSize**2] +n_features = X.shape[1] + +#Find all sensor locations using built in QR optimizer +max_const_sensors = 230 +n_const_sensors = 10 +n_sensors = 405 +optimizer = ps.optimizers.QR() +model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors) +model.fit(X) + +all_sensors = model.get_all_sensors() +print(all_sensors) + + +# In[6]: + + +#Define Constrained indices +a = np.unravel_index(all_sensors, (64,64)) +print(a) +a_array = np.transpose(a) +print(a_array.shape) +#idx = np.ravel_multi_index(a, (64,64)) +#print(idx) +xmin = 0 +xmax = 10 +ymin = 40 +ymax = 64 + +constrained_sensorsx = [] +constrained_sensorsy = [] +for i in range(n_features): + if a[0][i] < xmax and a[1][i] > ymin: # x<10 and y>40 + constrained_sensorsx.append(a[0][i]) + constrained_sensorsy.append(a[1][i]) + +constrained_sensorsx = np.array(constrained_sensorsx) +constrained_sensorsy = np.array(constrained_sensorsy) + +constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1) +constrained_sensors_tuple = np.transpose(constrained_sensors_array) + + +#print(constrained_sensors_tuple) +#print(len(constrained_sensors_tuple)) +idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (64,64)) + +#print(len(idx_constrained)) +#print(constrained_sensorsx) +#print(constrained_sensorsy) +#print(idx_constrained) +print(np.sort(idx_constrained[:])) +all_sorted = np.sort(all_sensors) +#print(all_sorted) +idx = np.arange(all_sorted.shape[0]) +#all_sorted[idx_constrained] = 0 + + +# In[7]: + + +from mpl_toolkits.axes_grid1 import make_axes_locatable + +ax = plt.subplot() +#Plot constrained space +img = np.zeros(n_features) +img[idx_constrained] = 1 +im = plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) + +# create an axes on the right side of ax. The width of cax will be 5% +# of ax and the padding between cax and ax will be fixed at 0.05 inch. +divider = make_axes_locatable(ax) +cax = divider.append_axes("right", size="5%", pad=0.05) + +plt.colorbar(im, cax=cax) +plt.title('Constrained region') +plt.show() + + +# In[8]: + + +#New class for constrained sensor placement +from pysensors.optimizers._qr import QR +class GQR(QR): + """ + General QR optimizer for sensor selection. + Ranks sensors in descending order of "importance" based on + reconstruction performance. + + See the following reference for more information + + Manohar, Krithika, et al. + "Data-driven sparse sensor placement for reconstruction: + Demonstrating the benefits of exploiting known patterns." + IEEE Control Systems Magazine 38.3 (2018): 63-86. + """ + def __init__(self): + """ + Attributes + ---------- + pivots_ : np.ndarray, shape [n_features] + Ranked list of sensor locations. + """ + self.pivots_ = None + + def fit( + self, + basis_matrix, idx_constrained, const_sensors, + ): + """ + Parameters + ---------- + basis_matrix: np.ndarray, shape [n_features, n_samples] + Matrix whose columns are the basis vectors in which to + represent the measurement data. + optimizer_kws: dictionary, optional + Keyword arguments to be passed to the qr method. + + Returns + ------- + self: a fitted :class:`pysensors.optimizers.CCQR` instance + """ + + n, m = basis_matrix.shape # We transpose basis_matrix below + + ## Assertions and checks: + if n_sensors > n_features - max_const_sensors + n_const_sensors: ##TODO should be moved to the class + raise IOError ("n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors") + if n_sensors > n_samples + n_const_sensors: + raise IOError ("Currently n_sensors should be less than number of samples + number of constrained sensors,\ + got: n_sensors = {}, n_samples + n_const_sensors = {} + {} = {}".format(n_sensors,n_samples,n_const_sensors,n_samples+n_const_sensors)) + + # Initialize helper variables + R = basis_matrix.conj().T.copy() + #print(R.shape) + p = np.arange(n) + #print(p) + k = min(m, n) + + + for j in range(k): + r = R[j:, j:] + # Norm of each column + dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0)) + + # if j < n_const_sensors: + dlens_updated = f_region(idx_constrained,dlens,p,j, const_sensors) + # else: + # dlens_updated = dlens + # Choose pivot + i_piv = np.argmax(dlens_updated) + #print(i_piv) + + + dlen = dlens_updated[i_piv] + + if dlen > 0: + u = r[:, i_piv] / dlen + u[0] += np.sign(u[0]) + (u[0] == 0) + u /= np.sqrt(abs(u[0])) + else: + u = r[:, i_piv] + u[0] = np.sqrt(2) + + # Track column pivots + i_piv += j # true permutation index is i_piv shifted by the iteration counter j + print(i_piv) # Niharika's debugging line + p[[j, i_piv]] = p[[i_piv, j]] + print(p) + + + # Switch columns + R[:, [j, i_piv]] = R[:, [i_piv, j]] + + # Apply reflector + R[j:, j:] -= np.outer(u, np.dot(u, R[j:, j:])) + R[j + 1 :, j] = 0 + + + self.pivots_ = p + + + return self +#function for mapping sensor locations with constraints +def f_region(lin_idx, dlens, piv, j, const_sensors): + #num_sensors should be fixed for each custom constraint (for now) + #num_sensors must be <= size of constraint region + """ + Function for mapping constrained sensor locations with the QR procedure. + + Parameters + ---------- + lin_idx: np.ndarray, shape [No. of constrained locations] + Array which contains the constrained locations mapped on the grid. + dlens: np.ndarray, shape [Variable based on j] + Array which contains the norm of columns of basis matrix. + num_sensors: int, + Number of sensors to be placed in the constrained area. + j: int, + Iterative variable in the QR algorithm. + + Returns + ------- + dlens : np.darray, shape [Variable based on j] with constraints mapped into it. + """ + if j < const_sensors: # force sensors into constraint region + #idx = np.arange(dlens.shape[0]) + #dlens[np.delete(idx, lin_idx)] = 0 + + didx = np.isin(piv[j:],lin_idx,invert=True) + dlens[didx] = 0 + + # otherwise don't do anything + else: + didx = np.isin(piv[j:],lin_idx,invert=False) + dlens[didx] = 0 + #dlens[lin_idx-j] = 0 + return dlens + + +# In[9]: + + + +optimizer1 = GQR() +model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors) +model1.fit(X, quiet=True, prefit_basis=False, seed=None, idx_constrained = idx_constrained, const_sensors = n_const_sensors) + + +# In[10]: + + +all_sensors1 = model1.get_all_sensors() +print(all_sensors1[:n_const_sensors]) + +print(np.array_equal(np.sort(all_sensors),np.sort(all_sensors1))) + + +# In[11]: + + +# xmin = 40 +# xmax = 64 +# ymin = 0 +# ymax = 10 + + +# In[12]: + + +top_sensors = model1.get_selected_sensors() +imageSize = X.shape[1] +yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(imageSize)) +xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(imageSize)) + +img = np.zeros(n_features) +img[top_sensors[n_const_sensors:]] = 16 +plt.plot(xConstrained,yConstrained,'*r') +plt.plot([xmin,xmin],[ymin,ymax],'r') +plt.plot([xmin,xmax],[ymax,ymax],'r') +plt.plot([xmax,xmax],[ymin,ymax],'r') +plt.plot([xmin,xmax],[ymin,ymin],'r') +plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) +plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) +plt.show() + + + +# In[13]: + + +print(n_sensors) +print(idx_constrained.shape) + + +# In[ ]: + + + + From c011daf0f10c23c4341549488b450dd59b3dad52 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Sat, 25 Jun 2022 12:04:49 -0600 Subject: [PATCH 10/52] pushing the notebook to compare changes --- examples/region_qrModified.ipynb | 271 ++++++++++++++++++++++++------- examples/region_qrModified.py | 32 ++-- 2 files changed, 219 insertions(+), 84 deletions(-) diff --git a/examples/region_qrModified.ipynb b/examples/region_qrModified.ipynb index 1ac468b..b1a713f 100644 --- a/examples/region_qrModified.ipynb +++ b/examples/region_qrModified.ipynb @@ -2,8 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 269, - "metadata": {}, + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-24T18:50:42.959640Z", + "start_time": "2022-06-24T18:50:40.955004Z" + } + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -17,8 +22,13 @@ }, { "cell_type": "code", - "execution_count": 270, - "metadata": {}, + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-24T18:50:47.418572Z", + "start_time": "2022-06-24T18:50:47.374771Z" + } + }, "outputs": [ { "name": "stdout", @@ -46,8 +56,13 @@ }, { "cell_type": "code", - "execution_count": 271, - "metadata": {}, + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-24T18:50:48.199077Z", + "start_time": "2022-06-24T18:50:48.193238Z" + } + }, "outputs": [], "source": [ "n_row, n_col = 2, 3\n", @@ -71,12 +86,17 @@ }, { "cell_type": "code", - "execution_count": 272, - "metadata": {}, + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-24T18:50:49.187561Z", + "start_time": "2022-06-24T18:50:48.993811Z" + } + }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -91,22 +111,34 @@ }, { "cell_type": "code", - "execution_count": 273, - "metadata": {}, + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-24T18:50:49.721611Z", + "start_time": "2022-06-24T18:50:49.641048Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[4032 384 4092 ... 2165 2561 1802]\n" + "[4032 384 4092 ... 1340 945 2928]\n" ] } ], "source": [ + "# reduce the X\n", + "imageSize = 64\n", + "image_shape = (imageSize, imageSize)\n", + "\n", + "X = X[:,:imageSize**2]\n", + "n_features = X.shape[1]\n", + "\n", "#Find all sensor locations using built in QR optimizer\n", "max_const_sensors = 230\n", - "n_sensors = 100\n", - "n_const_sensors = 5\n", + "n_const_sensors = 0\n", + "n_sensors = 399\n", "optimizer = ps.optimizers.QR()\n", "model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors)\n", "model.fit(X)\n", @@ -117,14 +149,19 @@ }, { "cell_type": "code", - "execution_count": 274, - "metadata": {}, + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-24T18:50:50.340729Z", + "start_time": "2022-06-24T18:50:50.331232Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(array([63, 6, 63, ..., 33, 40, 28]), array([ 0, 0, 60, ..., 53, 1, 10]))\n", + "(array([63, 6, 63, ..., 20, 14, 45]), array([ 0, 0, 60, ..., 60, 49, 48]))\n", "(4096, 2)\n", "[ 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58\n", " 59 60 61 62 63 105 106 107 108 109 110 111 112 113 114 115 116 117\n", @@ -144,18 +181,23 @@ ], "source": [ "#Define Constrained indices\n", - "a = np.unravel_index(all_sensors, (64,64))\n", + "a = np.unravel_index(all_sensors, (imageSize,imageSize))\n", "print(a)\n", "a_array = np.transpose(a)\n", "print(a_array.shape)\n", "#idx = np.ravel_multi_index(a, (64,64))\n", "#print(idx)\n", + "xmin = 0\n", + "xmax = 10\n", + "ymin = 40\n", + "ymax = 64\n", + "\n", "constrained_sensorsx = []\n", "constrained_sensorsy = []\n", "for i in range(n_features):\n", - " if a[0][i] < 10 and a[1][i] > 40: # x<10 and y>40\n", - " constrained_sensorsy.append(a[0][i])\n", - " constrained_sensorsx.append(a[1][i])\n", + " if a[0][i] < xmax and a[1][i] > ymin: # x<10 and y>40\n", + " constrained_sensorsx.append(a[0][i])\n", + " constrained_sensorsy.append(a[1][i])\n", "\n", "constrained_sensorsx = np.array(constrained_sensorsx)\n", "constrained_sensorsy = np.array(constrained_sensorsy)\n", @@ -166,7 +208,7 @@ "\n", "#print(constrained_sensors_tuple)\n", "#print(len(constrained_sensors_tuple))\n", - "idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (64,64))\n", + "idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (imageSize,imageSize))\n", "\n", "#print(len(idx_constrained))\n", "#print(constrained_sensorsx)\n", @@ -181,12 +223,17 @@ }, { "cell_type": "code", - "execution_count": 275, - "metadata": {}, + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-24T18:50:51.284415Z", + "start_time": "2022-06-24T18:50:51.107325Z" + } + }, "outputs": [ { "data": { - "image/png": "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", 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", "text/plain": [ "
" ] @@ -217,8 +264,13 @@ }, { "cell_type": "code", - "execution_count": 276, - "metadata": {}, + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-24T18:50:51.861338Z", + "start_time": "2022-06-24T18:50:51.848985Z" + } + }, "outputs": [], "source": [ "#New class for constrained sensor placement\n", @@ -247,7 +299,7 @@ " \n", " def fit(\n", " self,\n", - " basis_matrix, idx_constrained, const_sensors,\n", + " basis_matrix, idx_constrained, const_sensors\n", " ):\n", " \"\"\"\n", " Parameters\n", @@ -264,7 +316,14 @@ " \"\"\"\n", "\n", " n, m = basis_matrix.shape # We transpose basis_matrix below\n", - " \n", + "\n", + " ## Assertions and checks:\n", + " if n_sensors > n_features - max_const_sensors + n_const_sensors: ##TODO should be moved to the class\n", + " raise IOError (\"n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors\")\n", + " if n_sensors > n_samples + n_const_sensors:\n", + " raise IOError (\"Currently n_sensors should be less than number of samples + number of constrained sensors,\\\n", + " got: n_sensors = {}, n_samples + n_const_sensors = {} + {} = {}\".format(n_sensors,n_samples,n_const_sensors,n_samples+n_const_sensors)) \n", + " \n", " # Initialize helper variables\n", " R = basis_matrix.conj().T.copy()\n", " #print(R.shape)\n", @@ -342,18 +401,22 @@ " \n", " didx = np.isin(piv[j:],lin_idx,invert=True)\n", " dlens[didx] = 0\n", - " \n", - " # otherwise don't do anything\n", - " #else: \n", - " #dlens[lin_idx-j] = 0\n", + " else: \n", + " didx = np.isin(piv[j:],lin_idx,invert=False)\n", + " dlens[didx] = 0\n", " return dlens\n", "\n" ] }, { "cell_type": "code", - "execution_count": 277, - "metadata": {}, + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-24T18:50:54.500481Z", + "start_time": "2022-06-24T18:50:52.891827Z" + } + }, "outputs": [ { "name": "stdout", @@ -790,7 +853,13 @@ "1101\n", "[ 447 493 625 ... 4093 4094 4095]\n", "955\n", - "[ 447 493 625 ... 4093 4094 4095]\n", + "[ 447 493 625 ... 4093 4094 4095]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "2202\n", "[ 447 493 625 ... 4093 4094 4095]\n", "2651\n", @@ -1163,14 +1232,11 @@ }, { "data": { - "text/html": [ - "
SSPOR(basis=Identity(n_basis_modes=400), n_sensors=100, optimizer=GQR())
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], "text/plain": [ "SSPOR(basis=Identity(n_basis_modes=400), n_sensors=100, optimizer=GQR())" ] }, - "execution_count": 277, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1184,8 +1250,12 @@ }, { "cell_type": "code", - "execution_count": 278, + "execution_count": 10, "metadata": { + "ExecuteTime": { + "end_time": "2022-06-24T18:50:54.533405Z", + "start_time": "2022-06-24T18:50:54.528869Z" + }, "scrolled": true }, "outputs": [ @@ -1216,27 +1286,20 @@ ], "source": [ "all_sensors1 = model1.get_all_sensors()\n", - "print(all_sensors1[:230])\n", + "print(all_sensors1[:n_const_sensors])\n", "\n", "print(np.array_equal(np.sort(all_sensors),np.sort(all_sensors1)))" ] }, { "cell_type": "code", - "execution_count": 279, - "metadata": {}, - "outputs": [], - "source": [ - "xmin = 40\n", - "xmax = 64\n", - "ymin = 0\n", - "ymax = 10" - ] - }, - { - "cell_type": "code", - "execution_count": 280, - "metadata": {}, + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-24T18:50:56.112724Z", + "start_time": "2022-06-24T18:50:55.989212Z" + } + }, "outputs": [ { "name": "stdout", @@ -1254,7 +1317,7 @@ }, { "data": { - "image/png": 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OkMys4WpLQpLmkhLQ1RHxF3nwk5IW5vELgS11lWdmk+uVSzrqOjom4EpgfUR8sjLqZmBlfr0SuKmO8sysf9R1suJrgXcBP5C0Lg/7MPAJ4DpJq4BHgLfVVJ6Z9YlaklBEfAuYqL13Yh1lmNnM9MppJz5j2syKchIys6IafQGrmdWnqUfI3BIys6KchMysKCchMyvKfUJmA8LPojcza8NJyMyK6tndsUG9P7RZv3FLyMyKchIys6KchMysqJ7tE3IfkFl/cEvIzIpyEjKzotS03RpJOwJqWmxm/ULSaEQMl44D3BIys8KchMysqMYloeXLl/uxzB3WC4+BscHRuCRkZoPFScjMinISMrOievaMaZs997dZk7glZGZF1fUs+n0k3SnpHkn3SfpIHn6opDskbZR0raS96ijPzPpHXS2h54AVEfEK4GjgZEnHAZcAl0bEYcAzwKqayjOzPlFLEork2fx2bv4LYAVwfR6+FjizjvLMrH/U1ickaU9J64AtwK3AQ8DWiNieP7IZWDzBd1dLGpE0MjY2VldIZtYDaktCEfGLiDgaWAIcAxw5g++uiYjhiBgeGhqqKyQz6wG1Hx2LiK3A7cBrgHmSxk8DWAI8Vnd5tnuaeAlHNaYmxWWdUdfRsSFJ8/LrfYGTgPWkZPTW/LGVwE11lGdm/aOukxUXAmsl7UlKbNdFxC2S7geukfQx4G7gyprKM7M+4ZuamQ0g39TMzCxzEjKzohqXhHxTs/r5aJM1WeOSkJkNFichMyvKScjMivJNzQbAdPvXWvuL6uiXq06zZBzWXG4JmVlRTkJmVpR3x/rUbHaDOrHbM5tpevdrsLglZGZFOQmZWVFOQmZWlK+iH3Cz6Tvq5zj6WcupD76K3swMnITMrLDGJSFfRd9d48t6psu77qvyZxuHTV9Tl3HjkpCZDRYnITMrymdMD4BOXBDaySa9L2AdLG4JmVlRTkJmVpSTkJkV5T6hAdBrfSq9Fq/tHreEzKyoWpOQpD0l3S3plvz+UEl3SNoo6VpJe9VZnpn1vrpbQucC6yvvLwEujYjDgGeAVTWXZ2Y9rrYkJGkJ8GbgivxewArg+vyRtcCZdZVnZv2hzpbQZcCHgOfz+xcBWyNie36/GVjc7ouSVksakTQyNjZWY0hm1nS1JCFJpwFbImJ0Nt+PiDURMRwRw0NDQ3WEZGY9oq5D9K8F3iLpVGAf4AXA5cA8SXNya2gJ8NhUExodHd1x2r4P1c5eN28S5huS2e6opSUUERdFxJKIWAa8Hfh6RLwDuB14a/7YSuCmOsozs/7R6fOELgB+T9JGUh/RlR0uz8x6TOPuMT08PBwjIyOlwzDra5J8j2kzM3ASMrPCfAGrzUovHxEb1Jum1XU/8Lq5JWRmRTkJmVlRTkJmVpT7hGxWerkfpZdj3x3V+W5S/5BbQmZWlJOQmRXVuDOmJe0IqGmx1Wm2h7h9YarVwWdMm5llTkJmVpSTkJkV1bgktHz5ciKi7/sgxudxpvM52+/NRjfLqpukXf6suRqXhMxssDgJmVlRPmPaZqXph++bGJO155aQmRXlJGRmRfXs7phvTFV2ngdleVvnuSVkZkU5CZlZUU5CZlZUz/YJDWqfRCfnu5/62fppXvqdW0JmVlRtLSFJm4BtwC+A7RExLOkg4FpgGbAJeFtEPFNXmWbW++puCb0+Io6u3CzpQuC2iDgcuC2/txno5oWY1QtWu7H70sn56va82Ox1enfsDGBtfr0WOLPD5ZlZj6kzCQXwVUmjklbnYQsi4vH8+glgQbsvSlotaUTSyNjYWI0hmVnT1Xl07PiIeEzSrwC3SnqgOjIionr/6JZxa4A1AMPDw247mw2Q2lpCEfFY/r8FuAE4BnhS0kKA/H9LXeUNin7u2+jX+eq02falNfUmb7UkIUn7STpg/DXwRuBe4GZgZf7YSuCmOsozs/5R1+7YAuCGnGHnAJ+PiL+SdBdwnaRVwCPA22oqz8z6RC1JKCIeBl7RZvjTwIl1lGFl+Qzk5pjtsvdjoM3M2nASMrOinITMrKievYrefRTd5eVrneKWkJkV5SRkZkWpac3s6qUdTYvNrF9IGq3c7aIot4TMrCgnITMrqnFJaPny5b6osYG6eXM1GyyNS0JmNlichMysKCchMyuqZ8+Ytu5yH11zVPvk+qFe3BIys6KchMysqL7ZHZuoiTrZha6+CNZ6Ub+tp24JmVlRTkJmVpSTkJkV1Td9QhPtJ0+2/9xv+9al9NshY+sut4TMrCgnITMrqmdvaubD62az55uamZlltSUhSfMkXS/pAUnrJb1G0kGSbpW0If8/sK7yzKw/1NkSuhz4q4g4kvRI6PXAhcBtEXE4cFt+P6np3tRs/DO+AZqV4Ju81aeWJCTphcBvAFcCRMTPImIrcAawNn9sLXBmHeWZWf+oqyV0KDAG/E9Jd0u6QtJ+wIKIeDx/5glgQbsvS1otaUTSyNjYWE0hmVkvqCsJzQFeBfy3iHgl8BNadr0i7TO13W+KiDURMRwRw0NDQzWFZGa9oK4ktBnYHBF35PfXk5LSk5IWAuT/W2oqz6wo90nWp5YkFBFPAI9KOiIPOhG4H7gZWJmHrQRuqqM8M+sfdV479q+AqyXtBTwMvIeU5K6TtAp4BHhbjeWZWR+oLQlFxDqg3RmYJ9ZVhpnNXlNPJfAZ02ZWlJOQmRXlJGRmRfXUVfTTvXnWZJ+bzQ24OnHF/qDcCGxQ5rNquutLHQ9hmO266avozcwyJyEzK6qJu2NjpHOK5gNPFQ4HHEcrx7GrXo3jkIhoxDVSjUtC4ySNNGGf1XE4DsfRWd4dM7OinITMrKgmJ6E1pQPIHMeuHMeuHMduamyfkJkNhia3hMxsADgJmVlRjUtCkk6W9KCkjZKmfDpHzWV/VtIWSfdWhnX1sUWSDpZ0u6T7Jd0n6dxCcewj6U5J9+Q4PpKHHyrpjlw/1+b7R3WcpD3z/ctvKRWHpE2SfiBpnaSRPKzrj7Xqt8drNSoJSdoT+BRwCnAUcLako7oYwlXAyS3DZvzYot20HTg/Io4CjgM+mJdBt+N4DlgREa8AjgZOlnQccAlwaUQcBjwDrOpwHOPOJT1GalypOF4fEUdXzsnpdr1ATY/XaozWe+WW/ANeA3yl8v4i4KIux7AMuLfy/kFgYX69EHiwy/HcBJxUMg7gnwDfA44lnZU7p119dbD8JaQNawVwC6BCcWwC5rcM62q9AC8E/i/5oFKpOOr8a1RLCFgMPFp5vzkPK2lajy3qBEnLgFcCd5SII+8CrSM9oOBW4CFga0Rszx/pVv1cBnwIeD6/f1GhOAL4qqRRSavzsG7Xy249XquJmpaEGi3Sz0xXzmmQtD/wJeC8iPhxiTgi4hcRcTSpJXIMcGSny2wl6TRgS0SMdrvsNo6PiFeRugs+KOk3qiO7VC+79XitJmpaEnoMOLjyfkkeVlLXH1skaS4pAV0dEX9RKo5xkZ6meztpt2eepPF7k3ejfl4LvEXSJuAa0i7Z5QXiICIey/+3ADeQEnO366XvHq/VtCR0F3B4PvKxF/B20mODSurqY4uU7lJ1JbA+Ij5ZMI4hSfPy631J/VLrScnord2KIyIuioglEbGMtD58PSLe0e04JO0n6YDx18AbgXvpcr1EPz5eq3SnVJuOt1OBvyP1P/xhl8v+AvA48HPSL84qUv/DbcAG4GvAQR2O4XhSU/r7wLr8d2qBOH4NuDvHcS/wx3n4i4E7gY3AF4G9u1g/JwC3lIgjl3dP/rtvfN3sdr3kMo8GRnLd3AgcWCKOuv582YaZFdW03TEzGzBOQmZWlJOQmRXlJGRmRTkJmVlRTkJmVpSTkJkV9f8B5VYrVgZnmX4AAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -1268,20 +1331,33 @@ "source": [ "top_sensors = model1.get_selected_sensors()\n", "print(top_sensors)\n", + "## TODO: this can be done using ravel and unravel more elegantly\n", + "yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features))\n", + "xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features))\n", + "\n", "img = np.zeros(n_features)\n", "img[top_sensors] = 16\n", + "img[top_sensors[n_const_sensors:]] = 16\n", + "plt.plot(xConstrained,yConstrained,'*r')\n", "plt.plot([xmin,xmin],[ymin,ymax],'r')\n", "plt.plot([xmin,xmax],[ymax,ymax],'r')\n", "plt.plot([xmax,xmax],[ymin,ymax],'r')\n", + "plt.plot([xmin,xmax],[ymin,ymin],'r')\n", "plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", - "plt.title('Sensor locations for cost-constrained selector');\n", + "plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors))\n", + "plt.show()\n", " " ] }, { "cell_type": "code", - "execution_count": 281, - "metadata": {}, + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2022-06-24T18:51:00.889636Z", + "start_time": "2022-06-24T18:51:00.885673Z" + } + }, "outputs": [ { "name": "stdout", @@ -1306,8 +1382,9 @@ } ], "metadata": { + "hide_input": false, "kernelspec": { - "display_name": "Python 3.9.12 ('base')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1321,7 +1398,77 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.5" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "nbTranslate": { + "displayLangs": [ + "*" + ], + "hotkey": "alt-t", + "langInMainMenu": true, + "sourceLang": "en", + "targetLang": "fr", + "useGoogleTranslate": true + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false }, "vscode": { "interpreter": { diff --git a/examples/region_qrModified.py b/examples/region_qrModified.py index 0b4c51e..15b7ae7 100644 --- a/examples/region_qrModified.py +++ b/examples/region_qrModified.py @@ -11,7 +11,7 @@ from sklearn.model_selection import train_test_split import pysensors as ps -# from ..pysensors.optimizers._ccqr import CCQR +# from pysensors.optimizers._ccqr import CCQR # In[2]: @@ -70,8 +70,8 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): #Find all sensor locations using built in QR optimizer max_const_sensors = 230 -n_const_sensors = 10 -n_sensors = 405 +n_const_sensors = 0 +n_sensors = 399 optimizer = ps.optimizers.QR() model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors) model.fit(X) @@ -84,7 +84,7 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): #Define Constrained indices -a = np.unravel_index(all_sensors, (64,64)) +a = np.unravel_index(all_sensors, (imageSize,imageSize)) print(a) a_array = np.transpose(a) print(a_array.shape) @@ -111,7 +111,7 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): #print(constrained_sensors_tuple) #print(len(constrained_sensors_tuple)) -idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (64,64)) +idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (imageSize,imageSize)) #print(len(idx_constrained)) #print(constrained_sensorsx) @@ -174,7 +174,7 @@ def __init__(self): def fit( self, - basis_matrix, idx_constrained, const_sensors, + basis_matrix, idx_constrained, const_sensors ): """ Parameters @@ -278,12 +278,9 @@ def f_region(lin_idx, dlens, piv, j, const_sensors): didx = np.isin(piv[j:],lin_idx,invert=True) dlens[didx] = 0 - - # otherwise don't do anything else: didx = np.isin(piv[j:],lin_idx,invert=False) dlens[didx] = 0 - #dlens[lin_idx-j] = 0 return dlens @@ -304,23 +301,14 @@ def f_region(lin_idx, dlens, piv, j, const_sensors): print(np.array_equal(np.sort(all_sensors),np.sort(all_sensors1))) - -# In[11]: - - -# xmin = 40 -# xmax = 64 -# ymin = 0 -# ymax = 10 - - # In[12]: top_sensors = model1.get_selected_sensors() -imageSize = X.shape[1] -yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(imageSize)) -xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(imageSize)) +print(top_sensors) +## TODO: this can be done using ravel and unravel more elegantly +yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features)) +xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features)) img = np.zeros(n_features) img[top_sensors[n_const_sensors:]] = 16 From fb5ea4ea095e36e6912f554b603e74868a8874a2 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Mon, 27 Jun 2022 09:31:20 -0600 Subject: [PATCH 11/52] starting the conversion from notebook to script, and fixing the zero issue --- examples/region_qrModified.ipynb | 1613 +++++++++++++++--------------- examples/region_qrModified.py | 4 +- pysensors/optimizers/__init__.py | 3 +- pysensors/optimizers/_gqr.py | 174 ++++ 4 files changed, 986 insertions(+), 808 deletions(-) create mode 100644 pysensors/optimizers/_gqr.py diff --git a/examples/region_qrModified.ipynb b/examples/region_qrModified.ipynb index b1a713f..45cf59c 100644 --- a/examples/region_qrModified.ipynb +++ b/examples/region_qrModified.ipynb @@ -123,7 +123,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[4032 384 4092 ... 1340 945 2928]\n" + "[4032 384 4092 ... 1912 3987 2369]\n" ] } ], @@ -161,21 +161,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "(array([63, 6, 63, ..., 20, 14, 45]), array([ 0, 0, 60, ..., 60, 49, 48]))\n", + "(array([63, 6, 63, ..., 29, 62, 37]), array([ 0, 0, 60, ..., 56, 19, 1]))\n", "(4096, 2)\n", - "[ 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58\n", - " 59 60 61 62 63 105 106 107 108 109 110 111 112 113 114 115 116 117\n", - " 118 119 120 121 122 123 124 125 126 127 169 170 171 172 173 174 175 176\n", - " 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 233 234 235\n", - " 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253\n", - " 254 255 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312\n", - " 313 314 315 316 317 318 319 361 362 363 364 365 366 367 368 369 370 371\n", - " 372 373 374 375 376 377 378 379 380 381 382 383 425 426 427 428 429 430\n", - " 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 489\n", - " 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507\n", - " 508 509 510 511 553 554 555 556 557 558 559 560 561 562 563 564 565 566\n", - " 567 568 569 570 571 572 573 574 575 617 618 619 620 621 622 623 624 625\n", - " 626 627 628 629 630 631 632 633 634 635 636 637 638 639]\n" + "[2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2688 2689 2690 2691\n", + " 2692 2693 2694 2695 2696 2697 2752 2753 2754 2755 2756 2757 2758 2759\n", + " 2760 2761 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2880 2881\n", + " 2882 2883 2884 2885 2886 2887 2888 2889 2944 2945 2946 2947 2948 2949\n", + " 2950 2951 2952 2953 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017\n", + " 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3136 3137 3138 3139\n", + " 3140 3141 3142 3143 3144 3145 3200 3201 3202 3203 3204 3205 3206 3207\n", + " 3208 3209 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3328 3329\n", + " 3330 3331 3332 3333 3334 3335 3336 3337 3392 3393 3394 3395 3396 3397\n", + " 3398 3399 3400 3401 3456 3457 3458 3459 3460 3461 3462 3463 3464 3465\n", + " 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3584 3585 3586 3587\n", + " 3588 3589 3590 3591 3592 3593 3648 3649 3650 3651 3652 3653 3654 3655\n", + " 3656 3657 3712 3713 3714 3715 3716 3717 3718 3719 3720 3721 3776 3777\n", + " 3778 3779 3780 3781 3782 3783 3784 3785 3840 3841 3842 3843 3844 3845\n", + " 3846 3847 3848 3849 3904 3905 3906 3907 3908 3909 3910 3911 3912 3913\n", + " 3968 3969 3970 3971 3972 3973 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", 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ZzbSk5YMdGx44cKChss2srlF0RNozam1tRLwYEfMp+uBaIOnNdVcQEasioj8i+vv6+tos08za5RZf1ahiPiKeBu4EFgJPSJoBkN4nbP/6Zr3Kx/haq3NWty89PQlJRwG/CzxM0Qnh0jTbUopOCM1sgnHwVdW5jm8GsFrF4+UmAWsj4g5JdwNrJS0DdgMXdLBOM2tTr4VaHSMGX0Q8ALylxfgnKR4qYmYTmIOvyndumGXMHZG25uAzy5xbfFUOPrPMOfiqHHxmmXPwVTn4zDLn4Kty8JllrBev0avDwWeWOZ/VrXLwmWXOLb4qB59Z5hx8VQ4+s4z5GF9rDj6zzDn4qhx8ZpnzyY0qB59Z5tziq3LwmWXMx/hac/CZZc7BV+XgM8ucg6/KwWeWOQdflYPPLGPuiLQ1/yJmmWvqYUOSFkp6RNIOSSteZr7flPSipD9udEMa5OAzy1wTwZceNnYdcD4wD7hI0rxh5vtbYEMHNqUxDj6zzDXU4lsA7IiInRHxPLAGWNxivg8AX2SCP2fbwWeWuVEE3zRJW0qv5aXFzAT2lIYH0rjyemYC/wFY2eltGiuf3DDL2CgvYD4YEf3DLarFuBgyfC3wkYh4caKfSXbwmWWuobO6A8DJpeFZwN4h8/QDawZbj8AiSYci4stNFNAkB59Z5hpqfW0G5ko6BXgMuBC4uDxDRJxSWudNwB0TMfRgFMf4JE2W9F1Jd6ThEyRtlPRoep/auTLNrF1NnNyIiEPA5RRna7cDayNim6RLJV3ahc1o1GhafFdQbPBxaXgFsCkirknX9KwAPtJwfWY2Bk12UhAR64H1Q8a1PJEREe9tZKUdUqvFJ2kW8PvA9aXRi4HV6fNqYEmjlZlZI5q6gDkndVt81wIfBo4tjZseEfsAImKfpJNafTGdEl8OMHv27PYrNbO2+Ja1qhF/EUnvBPZHxNZ2VhARqyKiPyL6+/r62lmEmbWpbmvPLb6qM4E/lLQIeCVwnKSbgSckzUitvRnUuFJ769atE+IHjhh6+ZFZvibCv7mJZsQWX0RcGRGzImIOxSnsr0fEu4F1wNI021Lg9o5VaWZtc4uvaizX8V0DrJW0DNgNXNBMSWbWpF4LtTpGFXwRcSdwZ/r8JHBO8yWZWZMcfFW+c8MsY3JHpC05+Mwy5xZflYPPLHMOvioHn1nmHHxVDj6zjPXipSp1OPjMMueTG1UOPrPMucVX5eAzy5yDr8rBZ5YxH+NrzcFnljkHX5WDzyxzDr4qB59Z5nxWt8rBZ5YxH+NrzcFnljkHX5WDzyxzDr4qB59Z5hx8VQ4+s8w5+KocfGYZc0ekrTn4zDLnFl+Vg88scw6+KgefWeYcfFUOPrOM+QLm1hx8ZpnzyY0qB59Z5tziq3LwmWXOwVdVK/gk7QKeBV4EDkVEv6QTgH8C5gC7gD+JiKc6U6aZtcPH+Fobzc7/OyJifkT0p+EVwKaImAtsSsNmNsEMht9Ir14ylqOei4HV6fNqYMmYqzGzxjUVfJIWSnpE0g5JlYaOpHdJeiC97pJ0akc2qAF1j/EF8DVJAfxjRKwCpkfEPoCI2CfppFZflLQcWN5ItWY2ak2c1ZU0GbgOOBcYADZLWhcRD5Vm+yHwOxHxlKTzgVXA6WNeeQfUDb4zI2JvCreNkh6uu4IUkqsAUnCaWZc0uBu7ANgRETvTctdQ7PW9FHwRcVdp/nuAWU2suBNq/VcQEXvT+37gSxQ/whOSZgCk9/2dKtLM2jeKXd1pkraUXuU9tZnAntLwQBo3nGXAV5rfmmaM2OKTdDQwKSKeTZ/PA64G1gFLgWvS++2dLNTM2jOKFt/B0snLymJajGu5ByfpHRTB97a6K+62Oru604EvpR9vCvC/IuKrkjYDayUtA3YDF3SuTDNrV0O7ugPAyaXhWcDeFuv6deB64PyIeLKJFXfCiMGX9ukrZ2fSRp3TiaLMrDkNBd9mYK6kU4DHgAuBi4esZzZwG3BJRPygiZV2iu/cMMtYUx2RRsQhSZcDG4DJwA0RsU3SpWn6SuAq4ETgMylsD73MrvO4cvCZZa6pi5MjYj2wfsi4laXP7wfe38jKOszBZ5a5Xrsrow4Hn1nmHHxVDj6zjPXifbh1OPjMMueOSKscfGaZc4uvysFnljkHX5WDzyxjPsbXmoPPLHMOvioHn1nmHHxVDj6zzPmsbpWDzyxjPsbXmoPPLHMOvioHn1nmHHxVDj6zzDn4qhx8Zplz8FU5+Mwy1lRHpLlx8Jllzi2+KgefWeYcfFUOPrPMOfiqHHxmGfMFzK05+Mwy55MbVQ4+s8y5xVfl4DPLnIOvqlYbWNLxkr4g6WFJ2yW9VdIJkjZKejS9T+10sWY2OoPH+Oq8ekndnf9PAV+NiDcCpwLbgRXApoiYC2xKw2Y2wTj4qkYMPknHAW8HPgcQEc9HxNPAYmB1mm01sKQzJZrZWDj4quoc43sdcAC4UdKpwFbgCmB6ROwDiIh9kk5q9WVJy4HlDdVrZqPgW9Zaq/OLTAFOAz4bEW8BfsoodmsjYlVE9EdEf5s1mtkYuMVXVSf4BoCBiLg3DX+BIgifkDQDIL3v70yJZjYWDr6qEYMvIh4H9kh6Qxp1DvAQsA5YmsYtBW7vSIVmNiYOvqq61/F9ALhF0pHATuB9FKG5VtIyYDdwQWdKNLOx6LVQq6NW8EXE/UCrY3TnNFqNmTWqF1tzdfjODbPM+axulYPPLHNu8VU5+Mwy5+CrchvYLGNN3qsraaGkRyTtkFS5lleFT6fpD0g6rSMb1QAHn1nmmgg+SZOB64DzgXnARZLmDZntfGBuei0HPtv81jTDwWeWuUmTJtV6jWABsCMidkbE88Aaivv1yxYDn4/CPcDxgzc5TDTdPsZ3EPgRMC19Hhel/93GtY4S13E419F+Da8tD2zdunWDpGk1v/tKSVtKw6siYlX6PBPYU5o2AJw+5Put5pkJ7Ku5/q7pavBFRB+ApC0T4d5d1+E6JnodY60hIhY2VUqrxbcxz4TgXV0zq2MAOLk0PAvY28Y8E4KDz8zq2AzMlXRKunX1Qor79cvWAe9JZ3fPAH4y2HXdRDNe1/GtGnmWrnAdh3Mdh5sIdUyEGoiIQ5IuBzYAk4EbImKbpEvT9JXAemARsAP4GcU9/ROSIibkLriZWcd4V9fMeo6Dz8x6TleDb6RbXjq43hsk7Zf0YGlc1x+PKelkSd9Q8YjObZKuGI9aJL1S0nckfS/V8bHxqKNUz2RJ35V0x3jVIWmXpO9Lun/wWrZxqsOPcu2CrgVfzVteOuUmYOj1TOPxeMxDwIci4k3AGcBl6Tfodi0/B86OiFOB+cDCdBZuvB4ZegXFI0sHjVcd74iI+aXr5sajDj/KtRsioisv4K3AhtLwlcCVXVz/HODB0vAjwIz0eQbwSLdqKdVwO3DueNYCvAq4j+Iq/K7XQXGt1ybgbOCO8fqzAXYB04aM62odwHHAD0knHcerjl54dXNXd7jbWcbLYY/HBFo+HrNTJM0B3gLcOx61pN3L+ykeErUxiodJjcdvci3wYeAXpXHjUUcAX5O0VcUjUcejjvKjXL8r6XpJR49DHdnrZvD90tzO0mmSjgG+CHwwIp4Zjxoi4sWImE/R4log6c3drkHSO4H9EbG12+tu4cyIOI3iUMxlkt4+DjWM6VGuVl83g2+i3c4yLo/HlHQERejdEhG3jWctABHxNHAnxTHQbtdxJvCHknZR9PZxtqSbx6EOImJvet8PfImiN5Ju1+FHuXZJN4Ovzi0v3dT1x2Oq6Bbmc8D2iPjkeNUiqU/S8enzUcDvAg93u46IuDIiZkXEHIq/D1+PiHd3uw5JR0s6dvAzcB7wYLfrCD/KtXu6eUCR4naWHwD/AvyXLq73VoqucV6g+F91GXAixUH1R9P7CV2o420Uu/cPAPen16Ju1wL8OvDdVMeDwFVpfNd/k1JNZ/H/T250+/d4HfC99No2+HdznP6OzAe2pD+bLwNTx/PPJdeXb1kzs57jOzfMrOc4+Mys5zj4zKznOPjMrOc4+Mys5zj4zKznOPjMrOf8P8w7c03hjFwzAAAAAElFTkSuQmCC", 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" ] @@ -422,818 +426,812 @@ "name": "stdout", "output_type": "stream", "text": [ + "4092\n", + "[4092 1 2 ... 4093 4094 4095]\n", + "320\n", + "[4092 320 2 ... 4093 4094 4095]\n", "447\n", - "[ 447 1 2 ... 4093 4094 4095]\n", + "[4092 320 447 ... 4093 4094 4095]\n", "493\n", - "[ 447 493 2 ... 4093 4094 4095]\n", - "625\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "59\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "635\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4032\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "320\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4039\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4092\n", - "[ 447 493 625 ... 4093 4094 4095]\n", + "[4092 320 447 ... 4093 4094 4095]\n", + "4042\n", + "[4092 320 447 ... 4093 4094 4095]\n", "2204\n", - "[ 447 493 625 ... 4093 4094 4095]\n", + "[4092 320 447 ... 4093 4094 4095]\n", + "657\n", + "[4092 320 447 ... 4093 4094 4095]\n", "878\n", - "[ 447 493 625 ... 4093 4094 4095]\n", + "[4092 320 447 ... 4093 4094 4095]\n", "1088\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3779\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3093\n", - "[ 447 493 625 ... 4093 4094 4095]\n", + "[4092 320 447 ... 4093 4094 4095]\n", + "2560\n", + "[4092 320 447 ... 4093 4094 4095]\n", "4087\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2624\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2331\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2783\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "4043\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3456\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "2901\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "3039\n", - "[ 447 493 625 ... 4093 4094 4095]\n", + "[4092 320 447 ... 4093 4094 4095]\n", + "2837\n", + "[4092 320 447 ... 4093 4094 4095]\n", + "2395\n", + "[4092 320 447 ... 4093 4094 4095]\n", + "3098\n", + "[4092 320 447 ... 4093 4094 4095]\n", "1023\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1224\n", - "[ 447 493 625 ... 4093 4094 4095]\n", - "1052\n", - 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447 ... 182 4094 20]\n", + "2016\n", + "[4092 320 447 ... 182 4094 20]\n", + "1283\n", + "[4092 320 447 ... 182 4094 20]\n", + "1623\n", + "[4092 320 447 ... 182 4094 20]\n", + "2111\n", + "[4092 320 447 ... 182 4094 20]\n", + "881\n", + "[4092 320 447 ... 182 4094 20]\n", + "3377\n", + "[4092 320 447 ... 182 4094 20]\n", "2594\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "771\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "2075\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "2946\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "1157\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "4072\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "4085\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "1424\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "3097\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "2839\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "3675\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "512\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "2668\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "3327\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "581\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "3264\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "3580\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "1229\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "1728\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "3901\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "614\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "3620\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "1152\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "3076\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "3258\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "974\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "4040\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "1234\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "558\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "3288\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "733\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "3299\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "1448\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "651\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "3726\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "1078\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "1861\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "3587\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "2364\n", - "[ 447 493 625 ... 4093 228 4095]\n", - "1309\n", - "[ 447 493 625 ... 4093 228 4095]\n", + "[4092 320 447 ... 182 4094 20]\n", + "3858\n", + "[4092 320 447 ... 182 4094 20]\n", + "705\n", + "[4092 320 447 ... 182 4094 20]\n", + "1127\n", + "[4092 320 447 ... 182 4094 20]\n", + "1270\n", + "[4092 320 447 ... 182 4094 20]\n", + "1413\n", + "[4092 320 447 ... 182 4094 20]\n", + "522\n", + "[4092 320 447 ... 182 4094 20]\n", + "2829\n", + "[4092 320 447 ... 182 4094 20]\n", + "624\n", + "[4092 320 447 ... 182 4094 20]\n", + "3413\n", + "[4092 320 447 ... 182 4094 20]\n", + "1326\n", + "[4092 320 447 ... 182 4094 20]\n", + "1800\n", + "[4092 320 447 ... 182 4094 20]\n", + "1174\n", + 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", 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", 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" ] @@ -1351,7 +1354,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2022-06-24T18:51:00.889636Z", @@ -1363,7 +1366,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "100\n", + "399\n", "(230,)\n" ] } @@ -1384,7 +1387,7 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.9.5 ('myenv')", "language": "python", "name": "python3" }, @@ -1472,7 +1475,7 @@ }, "vscode": { "interpreter": { - "hash": "3d597f4c481aa0f25dceb95d2a0067e73c0966dcbd003d741d821a7208527ecf" + "hash": "be0c9c2c722339dec0978f6a1152ea12dcb8d0ab1123e781ed3c52ac6383bc23" } } }, diff --git a/examples/region_qrModified.py b/examples/region_qrModified.py index 15b7ae7..fdadb31 100644 --- a/examples/region_qrModified.py +++ b/examples/region_qrModified.py @@ -70,8 +70,8 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): #Find all sensor locations using built in QR optimizer max_const_sensors = 230 -n_const_sensors = 0 -n_sensors = 399 +n_const_sensors = 2 +n_sensors = 20 optimizer = ps.optimizers.QR() model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors) model.fit(X) diff --git a/pysensors/optimizers/__init__.py b/pysensors/optimizers/__init__.py index 6415345..8a1dfa9 100644 --- a/pysensors/optimizers/__init__.py +++ b/pysensors/optimizers/__init__.py @@ -1,8 +1,9 @@ from ._ccqr import CCQR from ._qr import QR - +from ._gqr import GQR __all__ = [ "CCQR", "QR", + "GQR" ] diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py new file mode 100644 index 0000000..f2265c7 --- /dev/null +++ b/pysensors/optimizers/_gqr.py @@ -0,0 +1,174 @@ +import numpy as np + +from ._qr import QR + + +class GQR(QR): + """ + General QR optimizer for sensor selection. + Ranks sensors in descending order of "importance" based on + reconstruction performance. This is an extension that requires a more intrusive + access to the QR optimizer to facilitate a more adaptive optimization. This is a generalized version of cost constraints + in the sense that users can allow n consttrained sensors in the constrained area. + if n = 0 this converges to the CCQR results. + @ authors: Niharika Karnik (@nkarnik2999), Mohammad Abdo (@Jimmy-INL), and Krithika Manohar (@kmanohar) + """ + def __init__(self,idx_constrained,n_sensors,const_sensors): + """ + Attributes + ---------- + pivots_ : np.ndarray, shape [n_features] + Ranked list of sensor locations. + """ + self.pivots_ = None + self.constrainedIndices = idx_constrained + self.nSensors = n_sensors + self.nConstrainedSensors = const_sensors + + def fit( + self, + basis_matrix + ): + """ + Parameters + ---------- + basis_matrix: np.ndarray, shape [n_features, n_samples] + Matrix whose columns are the basis vectors in which to + represent the measurement data. + optimizer_kws: dictionary, optional + Keyword arguments to be passed to the qr method. + + Returns + ------- + self: a fitted :class:`pysensors.optimizers.CCQR` instance + """ + + n_features, n_samples = basis_matrix.shape # We transpose basis_matrix below + max_const_sensors = len(self.constrainedIndices) + + ## Assertions and checks: + if self.nSensors > n_features - max_const_sensors + self.nConstrainedSensors: + raise IOError ("n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors") + if self.nSensors > n_samples + self.nConstrainedSensors: + raise IOError ("Currently n_sensors should be less than number of samples + number of constrained sensors,\ + got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}".format(n_sensors,n_samples,const_sensors,n_samples+const_sensors)) + + # Initialize helper variables + R = basis_matrix.conj().T.copy() + #print(R.shape) + p = np.arange(n_features) + #print(p) + k = min(n_samples, n_features) + + + for j in range(k): + r = R[j:, j:] + # Norm of each column + dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0)) + + # if j < const_sensors: + dlens_updated = f_region(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors) + # else: + # dlens_updated = dlens + # Choose pivot + i_piv = np.argmax(dlens_updated) + #print(i_piv) + + + dlen = dlens_updated[i_piv] + + if dlen > 0: + u = r[:, i_piv] / dlen + u[0] += np.sign(u[0]) + (u[0] == 0) + u /= np.sqrt(abs(u[0])) + else: + u = r[:, i_piv] + u[0] = np.sqrt(2) + + # Track column pivots + i_piv += j # true permutation index is i_piv shifted by the iteration counter j + # print(i_piv) # Niharika's debugging line + p[[j, i_piv]] = p[[i_piv, j]] + # print(p) + + + # Switch columns + R[:, [j, i_piv]] = R[:, [i_piv, j]] + + # Apply reflector + R[j:, j:] -= np.outer(u, np.dot(u, R[j:, j:])) + R[j + 1 :, j] = 0 + + self.pivots_ = p + + + return self + +## TODO: why not a part of the class? +#function for mapping sensor locations with constraints +def f_region(lin_idx, dlens, piv, j, const_sensors): + #num_sensors should be fixed for each custom constraint (for now) + #num_sensors must be <= size of constraint region + """ + Function for mapping constrained sensor locations with the QR procedure. + + Parameters + ---------- + lin_idx: np.ndarray, shape [No. of constrained locations] + Array which contains the constrained locations mapped on the grid. + dlens: np.ndarray, shape [Variable based on j] + Array which contains the norm of columns of basis matrix. + num_sensors: int, + Number of sensors to be placed in the constrained area. + j: int, + Iterative variable in the QR algorithm. + + Returns + ------- + dlens : np.darray, shape [Variable based on j] with constraints mapped into it. + """ + if j < const_sensors: # force sensors into constraint region + #idx = np.arange(dlens.shape[0]) + #dlens[np.delete(idx, lin_idx)] = 0 + + didx = np.isin(piv[j:],lin_idx,invert=True) + dlens[didx] = 0 + else: + didx = np.isin(piv[j:],lin_idx,invert=False) + dlens[didx] = 0 + return dlens + +def getConstraindSensorsIndices(xmin, xmax,ymin,ymax, all_sensors): + n_features = len(all_sensors) + imageSize = int(np.sqrt(n_features)) + a = np.unravel_index(all_sensors, (imageSize,imageSize)) + constrained_sensorsx = [] + constrained_sensorsy = [] + for i in range(n_features): + if (a[0][i] > xmin and a[0][i] < xmax) and (a[1][i] > ymin and a[1][i] < ymax): # x<10 and y>40 + constrained_sensorsx.append(a[0][i]) + constrained_sensorsy.append(a[1][i]) + + constrained_sensorsx = np.array(constrained_sensorsx) + constrained_sensorsy = np.array(constrained_sensorsy) + constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1) + constrained_sensors_tuple = np.transpose(constrained_sensors_array) + if len(constrained_sensorsx) == 0: + idx_constrained = [] + else: + idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (imageSize,imageSize)) + return idx_constrained + +def boxConstraints(position,lowerBound,upperBound,): + for i,xi in enumerate(position): + f1 = position[i] - lowerBound[i] + f2 = upperBound[i] - position [i] + return +1 if (f1 and f2 > 0) else -1 + +def functionalConstraint(position, func_response,func_input, freeTerm): + g = func_response + func_input + freeTerm + return g + + +if __name__ == '__main__': + pass From daee413283234423d6841cebaa2cbbaf473af595 Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Mon, 27 Jun 2022 15:19:13 -0600 Subject: [PATCH 12/52] Adding Script for gqr --- pysensors/optimizers/_gqr.py | 167 ++++++++++++++++++++++++++++++----- 1 file changed, 143 insertions(+), 24 deletions(-) diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index f2265c7..2c90796 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -1,6 +1,13 @@ import numpy as np -from ._qr import QR +from pysensors.optimizers._qr import QR + +import matplotlib.pyplot as plt +from sklearn import datasets +from sklearn import metrics +from mpl_toolkits.axes_grid1 import make_axes_locatable + +import pysensors as ps class GQR(QR): @@ -9,8 +16,15 @@ class GQR(QR): Ranks sensors in descending order of "importance" based on reconstruction performance. This is an extension that requires a more intrusive access to the QR optimizer to facilitate a more adaptive optimization. This is a generalized version of cost constraints - in the sense that users can allow n consttrained sensors in the constrained area. + in the sense that users can allow n constrained sensors in the constrained area. if n = 0 this converges to the CCQR results. + + See the following reference for more information + Manohar, Krithika, et al. + "Data-driven sparse sensor placement for reconstruction: + Demonstrating the benefits of exploiting known patterns." + IEEE Control Systems Magazine 38.3 (2018): 63-86. + @ authors: Niharika Karnik (@nkarnik2999), Mohammad Abdo (@Jimmy-INL), and Krithika Manohar (@kmanohar) """ def __init__(self,idx_constrained,n_sensors,const_sensors): @@ -19,6 +33,12 @@ def __init__(self,idx_constrained,n_sensors,const_sensors): ---------- pivots_ : np.ndarray, shape [n_features] Ranked list of sensor locations. + idx_constrained : np.ndarray, shape [No. of constrained locations] + Column Indices of the sensors in the constrained locations. + n_sensors : integer, + Total number of sensors + const_sensors : integer, + Total number of sensors required by the user in the constrained region. """ self.pivots_ = None self.constrainedIndices = idx_constrained @@ -40,24 +60,22 @@ def fit( Returns ------- - self: a fitted :class:`pysensors.optimizers.CCQR` instance + self: a fitted :class:`pysensors.optimizers.QR` instance """ n_features, n_samples = basis_matrix.shape # We transpose basis_matrix below - max_const_sensors = len(self.constrainedIndices) + max_const_sensors = len(self.constrainedIndices) #Maximum number of sensors allowed in the constrained region ## Assertions and checks: if self.nSensors > n_features - max_const_sensors + self.nConstrainedSensors: raise IOError ("n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors") - if self.nSensors > n_samples + self.nConstrainedSensors: + if self.nSensors > n_samples + self.nConstrainedSensors: ## Handling zero constraint? raise IOError ("Currently n_sensors should be less than number of samples + number of constrained sensors,\ - got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}".format(n_sensors,n_samples,const_sensors,n_samples+const_sensors)) + got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}".format(n_sensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors)) # Initialize helper variables R = basis_matrix.conj().T.copy() - #print(R.shape) p = np.arange(n_features) - #print(p) k = min(n_samples, n_features) @@ -65,16 +83,11 @@ def fit( r = R[j:, j:] # Norm of each column dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0)) - - # if j < const_sensors: - dlens_updated = f_region(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors) - # else: - # dlens_updated = dlens + dlens_updated = f_region(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors) #Handling constrained region sensor placement problem + # Choose pivot i_piv = np.argmax(dlens_updated) - #print(i_piv) - - + dlen = dlens_updated[i_piv] if dlen > 0: @@ -87,10 +100,7 @@ def fit( # Track column pivots i_piv += j # true permutation index is i_piv shifted by the iteration counter j - # print(i_piv) # Niharika's debugging line p[[j, i_piv]] = p[[i_piv, j]] - # print(p) - # Switch columns R[:, [j, i_piv]] = R[:, [i_piv, j]] @@ -105,6 +115,7 @@ def fit( return self ## TODO: why not a part of the class? + #function for mapping sensor locations with constraints def f_region(lin_idx, dlens, piv, j, const_sensors): #num_sensors should be fixed for each custom constraint (for now) @@ -115,10 +126,12 @@ def f_region(lin_idx, dlens, piv, j, const_sensors): Parameters ---------- lin_idx: np.ndarray, shape [No. of constrained locations] - Array which contains the constrained locations mapped on the grid. + Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. dlens: np.ndarray, shape [Variable based on j] Array which contains the norm of columns of basis matrix. - num_sensors: int, + piv: np.ndarray, shape [n_features] + Ranked list of sensor locations. + const_sensors: int, Number of sensors to be placed in the constrained area. j: int, Iterative variable in the QR algorithm. @@ -138,14 +151,35 @@ def f_region(lin_idx, dlens, piv, j, const_sensors): dlens[didx] = 0 return dlens -def getConstraindSensorsIndices(xmin, xmax,ymin,ymax, all_sensors): +def getConstraindSensorsIndices(xmin, xmax, ymin, ymax, all_sensors): + """ + Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix. + + Parameters + ---------- + xmin: int, + "Fill" + xmax : int, + "Fill" + ymin : int, + "Fill" + ymax : int + "Fill" + all_sensors : np.ndarray, shape [n_features] + Ranked list of sensor locations. + + Returns + ------- + idx_constrained : np.darray, shape [No. of constrained locations] + Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. + """ n_features = len(all_sensors) imageSize = int(np.sqrt(n_features)) a = np.unravel_index(all_sensors, (imageSize,imageSize)) constrained_sensorsx = [] constrained_sensorsy = [] for i in range(n_features): - if (a[0][i] > xmin and a[0][i] < xmax) and (a[1][i] > ymin and a[1][i] < ymax): # x<10 and y>40 + if (a[0][i] >= xmin and a[0][i] <= xmax) and (a[1][i] >= ymin and a[1][i] <= ymax): # x<10 and y>40 constrained_sensorsx.append(a[0][i]) constrained_sensorsy.append(a[1][i]) @@ -153,7 +187,7 @@ def getConstraindSensorsIndices(xmin, xmax,ymin,ymax, all_sensors): constrained_sensorsy = np.array(constrained_sensorsy) constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1) constrained_sensors_tuple = np.transpose(constrained_sensors_array) - if len(constrained_sensorsx) == 0: + if len(constrained_sensorsx) == 0: ##Check to handle condition when number of sensors in the constrained region = 0 idx_constrained = [] else: idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (imageSize,imageSize)) @@ -172,3 +206,88 @@ def functionalConstraint(position, func_response,func_input, freeTerm): if __name__ == '__main__': pass + faces = datasets.fetch_olivetti_faces(shuffle=True) + X = faces.data + + n_samples, n_features = X.shape + print('Number of samples:', n_samples) + print('Number of features (sensors):', n_features) + + # Global centering + X = X - X.mean(axis=0) + + # Local centering + X -= X.mean(axis=1).reshape(n_samples, -1) + + n_row, n_col = 2, 3 + n_components = n_row * n_col + image_shape = (64, 64) + + def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): + '''Function for plotting faces''' + plt.figure(figsize=(2. * n_col, 2.26 * n_row)) + plt.suptitle(title, size=16) + for i, comp in enumerate(images): + plt.subplot(n_row, n_col, i + 1) + vmax = max(comp.max(), -comp.min()) + plt.imshow(comp.reshape(image_shape), cmap=cmap, + interpolation='nearest', + vmin=-vmax, vmax=vmax) + plt.xticks(()) + plt.yticks(()) + plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.) + + # plot_gallery("First few centered faces", X[:n_components]) + + #Find all sensor locations using built in QR optimizer + max_const_sensors = 230 + n_const_sensors = 7 + n_sensors = 50 + optimizer = ps.optimizers.QR() + model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors) + model.fit(X) + + all_sensors = model.get_all_sensors() + + ##Constrained sensor location on the grid: + xmin = 20 + xmax = 40 + ymin = 25 + ymax = 45 + sensors_constrained = getConstraindSensorsIndices(xmin,xmax,ymin,ymax,all_sensors) #Constrained column indices + + ##Plotting the constrained region + # ax = plt.subplot() + # #Plot constrained space + # img = np.zeros(n_features) + # img[sensors_constrained] = 1 + # im = plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) + # # create an axes on the right side of ax. The width of cax will be 5% + # # of ax and the padding between cax and ax will be fixed at 0.05 inch. + # divider = make_axes_locatable(ax) + # cax = divider.append_axes("right", size="5%", pad=0.05) + # plt.colorbar(im, cax=cax) + # plt.title('Constrained region'); + + ## Fit the dataset with the optimizer GQR + optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors) + model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors) + model1.fit(X) + all_sensors1 = model1.get_all_sensors() + + top_sensors = model1.get_selected_sensors() + print(top_sensors) + ## TODO: this can be done using ravel and unravel more elegantly + yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features)) + xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features)) + + img = np.zeros(n_features) + img[top_sensors[n_const_sensors:]] = 16 + plt.plot(xConstrained,yConstrained,'*r') + plt.plot([xmin,xmin],[ymin,ymax],'r') + plt.plot([xmin,xmax],[ymax,ymax],'r') + plt.plot([xmax,xmax],[ymin,ymax],'r') + plt.plot([xmin,xmax],[ymin,ymin],'r') + plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) + plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) + plt.show() From bb9a25114eecd1fdce66d2e3f3c5772553794f3b Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Wed, 29 Jun 2022 14:29:36 -0600 Subject: [PATCH 13/52] Script updated with Linear indices function for Twist prototype --- pysensors/optimizers/_gqr.py | 40 +++++++++++++++++++++++++++++------- 1 file changed, 33 insertions(+), 7 deletions(-) diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index 2c90796..692e289 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -84,7 +84,7 @@ def fit( # Norm of each column dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0)) dlens_updated = f_region(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors) #Handling constrained region sensor placement problem - + # Choose pivot i_piv = np.argmax(dlens_updated) @@ -151,7 +151,16 @@ def f_region(lin_idx, dlens, piv, j, const_sensors): dlens[didx] = 0 return dlens -def getConstraindSensorsIndices(xmin, xmax, ymin, ymax, all_sensors): + # a = np.isin(piv[j],lin_idx) + + # if np.count_nonzero(a) < const_sensors: + # dlens = dlens + # else: + # didx = np.isin(piv[j:],lin_idx,invert=False) + # dlens[didx] = 0 + # return dlens + +def getConstraindSensorsIndices(xmin, xmax, ymin, ymax, nx, ny, all_sensors): """ Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix. @@ -175,7 +184,7 @@ def getConstraindSensorsIndices(xmin, xmax, ymin, ymax, all_sensors): """ n_features = len(all_sensors) imageSize = int(np.sqrt(n_features)) - a = np.unravel_index(all_sensors, (imageSize,imageSize)) + a = np.unravel_index(all_sensors, (nx,ny)) constrained_sensorsx = [] constrained_sensorsy = [] for i in range(n_features): @@ -190,7 +199,17 @@ def getConstraindSensorsIndices(xmin, xmax, ymin, ymax, all_sensors): if len(constrained_sensorsx) == 0: ##Check to handle condition when number of sensors in the constrained region = 0 idx_constrained = [] else: - idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (imageSize,imageSize)) + idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny)) + return idx_constrained + +def getConstrainedSensorsIndicesLinear(xmin,xmax,ymin,ymax,df): + x = df['X (m)'].to_numpy() + n_features = x.shape[0] + y = df['Y (m)'].to_numpy() + idx_constrained = [] + for i in range(n_features): + if (x[i] >= xmin and x[i] <= xmax) and (y[i] >= ymin and y[i] <= ymax): + idx_constrained.append(i) return idx_constrained def boxConstraints(position,lowerBound,upperBound,): @@ -222,6 +241,8 @@ def functionalConstraint(position, func_response,func_input, freeTerm): n_row, n_col = 2, 3 n_components = n_row * n_col image_shape = (64, 64) + nx = 64 + ny = 64 def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): '''Function for plotting faces''' @@ -241,8 +262,8 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): #Find all sensor locations using built in QR optimizer max_const_sensors = 230 - n_const_sensors = 7 - n_sensors = 50 + n_const_sensors = 2 + n_sensors = 200 optimizer = ps.optimizers.QR() model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors) model.fit(X) @@ -254,7 +275,12 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): xmax = 40 ymin = 25 ymax = 45 - sensors_constrained = getConstraindSensorsIndices(xmin,xmax,ymin,ymax,all_sensors) #Constrained column indices + sensors_constrained = getConstraindSensorsIndices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices + + # didx = np.isin(all_sensors,sensors_constrained,invert=False) + # const_index = np.nonzero(didx) + # j = + ##Plotting the constrained region # ax = plt.subplot() From f71f77de3a624c358892cf2108394aa2d722c694 Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Wed, 6 Jul 2022 11:10:10 -0600 Subject: [PATCH 14/52] Adding gqr with new function for optimal constraint sensor placement and subsequent .ipynb --- examples/region_optimal.ipynb | 348 ++++++++++++++++++++++++++++++++++ pysensors/optimizers/_gqr.py | 46 +++-- 2 files changed, 375 insertions(+), 19 deletions(-) create mode 100644 examples/region_optimal.ipynb diff --git a/examples/region_optimal.ipynb b/examples/region_optimal.ipynb new file mode 100644 index 0000000..b4cc664 --- /dev/null +++ b/examples/region_optimal.ipynb @@ -0,0 +1,348 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 350, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from pysensors.optimizers._qr import QR\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import datasets\n", + "from sklearn import metrics\n", + "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", + "\n", + "import pysensors as ps" + ] + }, + { + "cell_type": "code", + "execution_count": 351, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of samples: 400\n", + "Number of features (sensors): 4096\n" + ] + } + ], + "source": [ + "faces = datasets.fetch_olivetti_faces(shuffle=True)\n", + "X = faces.data\n", + "\n", + "n_samples, n_features = X.shape\n", + "print('Number of samples:', n_samples)\n", + "print('Number of features (sensors):', n_features)\n", + "\n", + "# Global centering\n", + "X = X - X.mean(axis=0)\n", + "\n", + "# Local centering\n", + "X -= X.mean(axis=1).reshape(n_samples, -1)\n", + "\n", + "n_row, n_col = 2, 3\n", + "n_components = n_row * n_col\n", + "image_shape = (64, 64)\n", + "nx = 64\n", + "ny = 64" + ] + }, + { + "cell_type": "code", + "execution_count": 352, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):\n", + " '''Function for plotting faces'''\n", + " plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n", + " plt.suptitle(title, size=16)\n", + " for i, comp in enumerate(images):\n", + " plt.subplot(n_row, n_col, i + 1)\n", + " vmax = max(comp.max(), -comp.min())\n", + " plt.imshow(comp.reshape(image_shape), cmap=cmap,\n", + " interpolation='nearest',\n", + " vmin=-vmax, vmax=vmax)\n", + " plt.xticks(())\n", + " plt.yticks(())\n", + " plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)" + ] + }, + { + "cell_type": "code", + "execution_count": 353, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_gallery(\"First few centered faces\", X[:n_components])" + ] + }, + { + "cell_type": "code", + "execution_count": 354, + "metadata": {}, + "outputs": [], + "source": [ + "#Find all sensor locations using built in QR optimizer\n", + "max_const_sensors = 230\n", + "n_const_sensors = 6\n", + "n_sensors = 50\n", + "optimizer = ps.optimizers.QR()\n", + "model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors)\n", + "model.fit(X)\n", + "\n", + "all_sensors = model.get_all_sensors()" + ] + }, + { + "cell_type": "code", + "execution_count": 355, + "metadata": {}, + "outputs": [], + "source": [ + "##Constrained sensor location on the grid: \n", + "xmin = 20\n", + "xmax = 40\n", + "ymin = 25\n", + "ymax = 45\n", + "sensors_constrained = ps.optimizers._gqr.getConstraindSensorsIndices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices " + ] + }, + { + "cell_type": "code", + "execution_count": 356, + "metadata": {}, + "outputs": [], + "source": [ + "class AQR(QR):\n", + " \"\"\"\n", + " General QR optimizer for sensor selection.\n", + " Ranks sensors in descending order of \"importance\" based on\n", + " reconstruction performance. This is an extension that requires a more intrusive\n", + " access to the QR optimizer to facilitate a more adaptive optimization. This is a generalized version of cost constraints\n", + " in the sense that users can allow n constrained sensors in the constrained area.\n", + " if n = 0 this converges to the CCQR results.\n", + "\n", + " See the following reference for more information\n", + " Manohar, Krithika, et al.\n", + " \"Data-driven sparse sensor placement for reconstruction:\n", + " Demonstrating the benefits of exploiting known patterns.\"\n", + " IEEE Control Systems Magazine 38.3 (2018): 63-86.\n", + "\n", + " @ authors: Niharika Karnik (@nkarnik2999), Mohammad Abdo (@Jimmy-INL), and Krithika Manohar (@kmanohar)\n", + " \"\"\"\n", + " def __init__(self,idx_constrained,n_sensors,const_sensors,all_sensors):\n", + " \"\"\"\n", + " Attributes\n", + " ----------\n", + " pivots_ : np.ndarray, shape [n_features]\n", + " Ranked list of sensor locations.\n", + " idx_constrained : np.ndarray, shape [No. of constrained locations]\n", + " Column Indices of the sensors in the constrained locations.\n", + " n_sensors : integer, \n", + " Total number of sensors\n", + " const_sensors : integer,\n", + " Total number of sensors required by the user in the constrained region.\n", + " \"\"\"\n", + " self.pivots_ = None\n", + " self.constrainedIndices = idx_constrained\n", + " self.nSensors = n_sensors\n", + " self.nConstrainedSensors = const_sensors\n", + " self.all_sensorloc = all_sensors\n", + "\n", + " def fit(\n", + " self,\n", + " basis_matrix\n", + " ):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " basis_matrix: np.ndarray, shape [n_features, n_samples]\n", + " Matrix whose columns are the basis vectors in which to\n", + " represent the measurement data.\n", + " optimizer_kws: dictionary, optional\n", + " Keyword arguments to be passed to the qr method.\n", + "\n", + " Returns\n", + " -------\n", + " self: a fitted :class:`pysensors.optimizers.QR` instance\n", + " \"\"\"\n", + "\n", + " n_features, n_samples = basis_matrix.shape # We transpose basis_matrix below\n", + " max_const_sensors = len(self.constrainedIndices) #Maximum number of sensors allowed in the constrained region\n", + "\n", + " ## Assertions and checks:\n", + " if self.nSensors > n_features - max_const_sensors + self.nConstrainedSensors:\n", + " raise IOError (\"n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors\")\n", + " if self.nSensors > n_samples + self.nConstrainedSensors: ## Handling zero constraint?\n", + " raise IOError (\"Currently n_sensors should be less than number of samples + number of constrained sensors,\\\n", + " got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}\".format(n_sensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors))\n", + "\n", + " # Initialize helper variables\n", + " R = basis_matrix.conj().T.copy()\n", + " p = np.arange(n_features)\n", + " k = min(n_samples, n_features)\n", + "\n", + "\n", + " for j in range(k):\n", + " r = R[j:, j:]\n", + " # Norm of each column\n", + " dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0))\n", + " dlens_updated = f_region_optimal(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.nSensors) #Handling constrained region sensor placement problem\n", + "\n", + " # Choose pivot\n", + " i_piv = np.argmax(dlens_updated)\n", + " \n", + " dlen = dlens_updated[i_piv]\n", + "\n", + " if dlen > 0:\n", + " u = r[:, i_piv] / dlen\n", + " u[0] += np.sign(u[0]) + (u[0] == 0)\n", + " u /= np.sqrt(abs(u[0]))\n", + " else:\n", + " u = r[:, i_piv]\n", + " u[0] = np.sqrt(2)\n", + "\n", + " # Track column pivots\n", + " i_piv += j # true permutation index is i_piv shifted by the iteration counter j\n", + " p[[j, i_piv]] = p[[i_piv, j]]\n", + "\n", + " # Switch columns\n", + " R[:, [j, i_piv]] = R[:, [i_piv, j]]\n", + "\n", + " # Apply reflector\n", + " R[j:, j:] -= np.outer(u, np.dot(u, R[j:, j:]))\n", + " R[j + 1 :, j] = 0\n", + "\n", + " self.pivots_ = p\n", + "\n", + "\n", + " return self" + ] + }, + { + "cell_type": "code", + "execution_count": 357, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def f_region_optimal(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors):\n", + " counter = 0\n", + " mask = np.isin(all_sensors,lin_idx,invert=False)\n", + " const_idx = all_sensors[mask]\n", + " updated_lin_idx = const_idx[const_sensors:]\n", + " for i in range(n_sensors):\n", + " if np.isin(all_sensors[i],lin_idx,invert=False):\n", + " counter += 1\n", + " if counter < const_sensors:\n", + " dlens = dlens\n", + " else:\n", + " didx = np.isin(piv[j:],updated_lin_idx,invert=False)\n", + " dlens[didx] = 0\n", + " return dlens" + ] + }, + { + "cell_type": "code", + "execution_count": 358, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4032 384 4092 4039 447 493 2204 657 878 2880 1088 4087 2837 3779\n", + " 3093 2395 581 2751 1023 2970 2783 2432 2010 1188 1161 4095 1116 3100\n", + " 4037 4044 3293 3456 1212 1037 3643 1178 2963 59 2336 1535 67 4089\n", + " 1728 3654 3828 1140 1155 4063 4034 755]\n" + ] + } + ], + "source": [ + "optimizer1 = AQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors)\n", + "model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors)\n", + "model1.fit(X)\n", + "all_sensors1 = model1.get_all_sensors()\n", + "\n", + "top_sensors = model1.get_selected_sensors()\n", + "print(top_sensors)" + ] + }, + { + "cell_type": "code", + "execution_count": 359, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "## TODO: this can be done using ravel and unravel more elegantly\n", + "img = np.zeros(n_features)\n", + "img[top_sensors] = 16\n", + "plt.plot([xmin,xmin],[ymin,ymax],'r')\n", + "plt.plot([xmin,xmax],[ymax,ymax],'r')\n", + "plt.plot([xmax,xmax],[ymin,ymax],'r')\n", + "plt.plot([xmin,xmax],[ymin,ymin],'r')\n", + "plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", + "plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors))\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.12 ('base')", + "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": "3.9.12" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "3d597f4c481aa0f25dceb95d2a0067e73c0966dcbd003d741d821a7208527ecf" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index 692e289..3e5844c 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -27,7 +27,7 @@ class GQR(QR): @ authors: Niharika Karnik (@nkarnik2999), Mohammad Abdo (@Jimmy-INL), and Krithika Manohar (@kmanohar) """ - def __init__(self,idx_constrained,n_sensors,const_sensors): + def __init__(self,idx_constrained,n_sensors,const_sensors,all_sensors): """ Attributes ---------- @@ -44,6 +44,7 @@ def __init__(self,idx_constrained,n_sensors,const_sensors): self.constrainedIndices = idx_constrained self.nSensors = n_sensors self.nConstrainedSensors = const_sensors + self.all_sensorloc = all_sensors def fit( self, @@ -83,7 +84,7 @@ def fit( r = R[j:, j:] # Norm of each column dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0)) - dlens_updated = f_region(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors) #Handling constrained region sensor placement problem + dlens_updated = f_region_optimal(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.nSensors) #Handling constrained region sensor placement problem # Choose pivot i_piv = np.argmax(dlens_updated) @@ -151,14 +152,21 @@ def f_region(lin_idx, dlens, piv, j, const_sensors): dlens[didx] = 0 return dlens - # a = np.isin(piv[j],lin_idx) - - # if np.count_nonzero(a) < const_sensors: - # dlens = dlens - # else: - # didx = np.isin(piv[j:],lin_idx,invert=False) - # dlens[didx] = 0 - # return dlens +def f_region_optimal(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors): + counter = 0 + mask = np.isin(all_sensors,lin_idx,invert=False) + const_idx = all_sensors[mask] + updated_lin_idx = const_idx[const_sensors:] + for i in range(n_sensors): + if np.isin(all_sensors[i],lin_idx,invert=False): + counter += 1 + if counter < const_sensors: + dlens = dlens + else: + didx = np.isin(piv[j:],updated_lin_idx,invert=False) + dlens[didx] = 0 + return dlens + def getConstraindSensorsIndices(xmin, xmax, ymin, ymax, nx, ny, all_sensors): """ @@ -167,13 +175,13 @@ def getConstraindSensorsIndices(xmin, xmax, ymin, ymax, nx, ny, all_sensors): Parameters ---------- xmin: int, - "Fill" + Lower bound for the x-axis constraint xmax : int, - "Fill" + Upper bound for the x-axis constraint ymin : int, - "Fill" + Lower bound for the y-axis constraint ymax : int - "Fill" + Upper bound for the y-axis constraint all_sensors : np.ndarray, shape [n_features] Ranked list of sensor locations. @@ -296,7 +304,7 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): # plt.title('Constrained region'); ## Fit the dataset with the optimizer GQR - optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors) + optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors) model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors) model1.fit(X) all_sensors1 = model1.get_all_sensors() @@ -304,12 +312,12 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): top_sensors = model1.get_selected_sensors() print(top_sensors) ## TODO: this can be done using ravel and unravel more elegantly - yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features)) - xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features)) + #yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features)) + #xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features)) img = np.zeros(n_features) - img[top_sensors[n_const_sensors:]] = 16 - plt.plot(xConstrained,yConstrained,'*r') + img[top_sensors] = 16 + #plt.plot(xConstrained,yConstrained,'*r') plt.plot([xmin,xmin],[ymin,ymax],'r') plt.plot([xmin,xmax],[ymax,ymax],'r') plt.plot([xmax,xmax],[ymin,ymax],'r') From 9cb0a4a5813f5c423a1fdd5775995d10d1a398fd Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Mon, 11 Jul 2022 10:52:24 -0600 Subject: [PATCH 15/52] minor changes to the examples/region_optimal.ipynb and _gqr --- examples/cost_constrained_qr.ipynb | 62 +++- examples/region_optimal.ipynb | 437 +++++++++++++++++++++++++---- pysensors/optimizers/_gqr.py | 15 +- 3 files changed, 457 insertions(+), 57 deletions(-) diff --git a/examples/cost_constrained_qr.ipynb b/examples/cost_constrained_qr.ipynb index e1156d1..1695d93 100644 --- a/examples/cost_constrained_qr.ipynb +++ b/examples/cost_constrained_qr.ipynb @@ -334,8 +334,9 @@ } ], "metadata": { + "hide_input": false, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -349,7 +350,35 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.9.5" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "nbTranslate": { + "displayLangs": [ + "*" + ], + "hotkey": "alt-t", + "langInMainMenu": true, + "sourceLang": "en", + "targetLang": "fr", + "useGoogleTranslate": true }, "toc": { "base_numbering": 1, @@ -363,6 +392,35 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, diff --git a/examples/region_optimal.ipynb b/examples/region_optimal.ipynb index b4cc664..bfb3386 100644 --- a/examples/region_optimal.ipynb +++ b/examples/region_optimal.ipynb @@ -2,8 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 350, - "metadata": {}, + "execution_count": 82, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:22:04.386599Z", + "start_time": "2022-07-10T04:22:04.382732Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", @@ -20,8 +25,13 @@ }, { "cell_type": "code", - "execution_count": 351, - "metadata": {}, + "execution_count": 83, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:22:07.391526Z", + "start_time": "2022-07-10T04:22:07.354464Z" + } + }, "outputs": [ { "name": "stdout", @@ -55,8 +65,13 @@ }, { "cell_type": "code", - "execution_count": 352, - "metadata": {}, + "execution_count": 84, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:22:09.785781Z", + "start_time": "2022-07-10T04:22:09.779128Z" + } + }, "outputs": [], "source": [ "def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):\n", @@ -76,12 +91,17 @@ }, { "cell_type": "code", - "execution_count": 353, - "metadata": {}, + "execution_count": 85, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:22:10.835009Z", + "start_time": "2022-07-10T04:22:10.642255Z" + } + }, "outputs": [ { "data": { - "image/png": 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", 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\n", "text/plain": [ "
" ] @@ -96,39 +116,108 @@ }, { "cell_type": "code", - "execution_count": 354, - "metadata": {}, - "outputs": [], + "execution_count": 86, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:22:11.651751Z", + "start_time": "2022-07-10T04:22:11.555299Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unconstrained Optimal sensors, n = 4096, [4032 384 4092 4039 447 493 2204 657 878 2880 1088 4087 2837],...\n", + "Unconstrained Optimal sensors, n = 10, [4032 384 4092 4039 447 493 2204 657 878 2880]\n" + ] + } + ], "source": [ "#Find all sensor locations using built in QR optimizer\n", "max_const_sensors = 230\n", - "n_const_sensors = 6\n", - "n_sensors = 50\n", + "n_const_sensors = 0\n", + "n_sensors = 10\n", "optimizer = ps.optimizers.QR()\n", "model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors)\n", "model.fit(X)\n", "\n", - "all_sensors = model.get_all_sensors()" + "all_sensors = model.get_all_sensors()\n", + "top_sensors0 = model.get_selected_sensors()\n", + "print('Unconstrained Optimal sensors, n = {}, {},...'.format(len(all_sensors),all_sensors[:n_sensors+3]))\n", + "print('Unconstrained Optimal sensors, n = {}, {}'.format(len(top_sensors0),top_sensors0))" ] }, { "cell_type": "code", - "execution_count": 355, - "metadata": {}, - "outputs": [], + "execution_count": 87, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:22:20.877032Z", + "start_time": "2022-07-10T04:22:20.866607Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The constrained sensors are [1826 1765 1695 2084 1704 2529 2404 2210 1888 2406 1892 2402 2721 2022\n", + " 2398 1699 2454 1895 2661 2847 2589 2532 1697 2018 2272 1638 1621 2592\n", + " 2461 2214 1749 2326 1692 2408 1636 2015 2267 2659 2276 2780 1885 2456\n", + " 2464 1760 2004 2536 2394 2270 2088 2516 2083 2517 1832 2710 1767 2906\n", + " 1817 2069 2329 2344 2343 2466 2072 2521 2791 2900 2140 1894 2471 2773\n", + " 2649 2085 2775 1684 2076 2841 1762 2854 2081 2789 1627 2726 2647 2844\n", + " 2850 2010 1694 2198 1821 2265 2459 2082 1624 2792 1879 2838 1886 2660\n", + " 1945 2715 2327 2006 2147 1896 1702 2200 2390 2264 1943 1758 2598 2334\n", + " 1691 2713 1757 2901 2397 2012 2774 1944 1951 2599 2407 1623 2728 2337\n", + " 2148 1950 2269 2910 2275 2650 1893 1689 2655 2528 2324 2914 2338 1698\n", + " 2212 2206 2335 1701 2907 2719 2531 1628 2075 1629 2711 1751 2918 2393\n", + " 2911 1630 2594 1763 1633 2526 2263 2584 2273 2836 2588 1625 2523 2197\n", + " 2150 2071 2718 2086 2913 1830 2280 1881 2845 1635 2009 2452 2068 2074\n", + " 1876 2133 1815 1686 2139 2142 2462 1754 1942 2902 2787 2016 1820 2662\n", + " 2405 2203 1693 2396 2722 2527 2277 2007 1748 2279 1878 2266 2201 1949\n", + " 2211 1882 2401 2582 1759 2580 2591 2709 2720 1890 2278 1620 2458 2472\n", + " 2395 2342 2596 1750 2852 1761 1891 1634 1688 2657 2530 2525 2143 2019\n", + " 2196 1637 2656 2332 1831 1957 2519 2778 2644 1958 2331 2339 2597 2079\n", + " 2654 1825 1948 2202 2648 2851 2021 2209 2849 1766 2839 1884 2149 2135\n", + " 2014 1947 2389 2467 2268 2714 2341 2208 2453 2772 1639 2457 1753 1696\n", + " 2855 2777 2581 2013 2843 2716 2853 2325 2070 1626 2723 1632 1687 2912\n", + " 1880 1814 2585 2651 1822 2534 2271 2144 2005 2020 2790 1823 2727 2524\n", + " 2595 2905 2152 2917 2919 2204 1703 1813 2469 2145 2590 2904 1816 2645\n", + " 1812 2782 2781 2073 2137 2785 2783 2465 2779 2708 1952 2151 1622 1700\n", + " 2468 2077 2132 2460 2388 2087 1959 2455 2840 1953 2340 1752 2399 1941\n", + " 2205 2261 1877 2784 1631 2658 2652 1883 1640 2600 1764 2664 2136 2533\n", + " 2330 2717 2593 2080 2391 2023 2663 2199 2392 2024 1829 2518 1940 2848\n", + " 2274 1827 1955 2786 2646 2583 2008 1768 2846 1956 2842 2400 2712 2141\n", + " 2725 1887 2207 1824 1685 2213 2017 1690 2587 2909 1818 1828 2146 2653\n", + " 2586 2903 2463 1755 2403 2078 2916 2333 2470 2522 1756 1889 1960 1946\n", + " 2776 2856 2336 2328 2788 2134 2260 2915 2520 2908 2262 2920 2215 2535\n", + " 2138 2724 1819 2216 1954 2011 2837]\n", + "The constrained sensors are [2204]\n" + ] + } + ], "source": [ "##Constrained sensor location on the grid: \n", "xmin = 20\n", "xmax = 40\n", "ymin = 25\n", "ymax = 45\n", - "sensors_constrained = ps.optimizers._gqr.getConstraindSensorsIndices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices " + "sensors_constrained = ps.optimizers._gqr.getConstraindSensorsIndices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices\n", + "print('The constrained sensors are {}'.format(sensors_constrained))\n", + "print('The constrained sensors are {}'.format(top_sensors0[np.isin(top_sensors0,sensors_constrained,invert=False)]))" ] }, { "cell_type": "code", - "execution_count": 356, - "metadata": {}, + "execution_count": 88, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:22:22.713344Z", + "start_time": "2022-07-10T04:22:22.699841Z" + } + }, "outputs": [], "source": [ "class AQR(QR):\n", @@ -239,8 +328,13 @@ }, { "cell_type": "code", - "execution_count": 357, - "metadata": {}, + "execution_count": 89, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:22:24.092467Z", + "start_time": "2022-07-10T04:22:24.086984Z" + } + }, "outputs": [], "source": [ "\n", @@ -262,17 +356,19 @@ }, { "cell_type": "code", - "execution_count": 358, - "metadata": {}, + "execution_count": 90, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:22:27.911973Z", + "start_time": "2022-07-10T04:22:26.180620Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[4032 384 4092 4039 447 493 2204 657 878 2880 1088 4087 2837 3779\n", - " 3093 2395 581 2751 1023 2970 2783 2432 2010 1188 1161 4095 1116 3100\n", - " 4037 4044 3293 3456 1212 1037 3643 1178 2963 59 2336 1535 67 4089\n", - " 1728 3654 3828 1140 1155 4063 4034 755]\n" + "[4032 384 4092 4039 447 493 657 878 4087 3779]\n" ] } ], @@ -288,26 +384,19 @@ }, { "cell_type": "code", - "execution_count": 359, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:22:27.951889Z", + "start_time": "2022-07-10T04:22:27.951875Z" } - ], + }, + "outputs": [], "source": [ "## TODO: this can be done using ravel and unravel more elegantly\n", "img = np.zeros(n_features)\n", "img[top_sensors] = 16\n", + "img[top_sensors0] = 5\n", "plt.plot([xmin,xmin],[ymin,ymax],'r')\n", "plt.plot([xmin,xmax],[ymax,ymax],'r')\n", "plt.plot([xmax,xmax],[ymin,ymax],'r')\n", @@ -316,11 +405,196 @@ "plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors))\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-11T16:34:56.472709Z", + "start_time": "2022-07-11T16:34:56.456089Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4032 384 4092 4039 447 493 2204 657 878 2880] [4032 384 4092 4039 447 493 657 878 4087 3779]\n" + ] + } + ], + "source": [ + "print(top_sensors0,top_sensors)" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-11T16:34:57.421237Z", + "start_time": "2022-07-11T16:34:57.413767Z" + } + }, + "outputs": [], + "source": [ + "test_sensors = [4032, 384, 4092, 4039, 447, 493, 657, 878, 2880, 1088]" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-11T16:34:58.310764Z", + "start_time": "2022-07-11T16:34:58.175996Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8899602" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.norm((X - model1.predict(X[:,top_sensors0])))/np.linalg.norm(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-11T16:34:59.404387Z", + "start_time": "2022-07-11T16:34:59.378062Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.80111694" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.norm((X - model1.predict(X[:,top_sensors])))/np.linalg.norm(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-11T16:35:00.194386Z", + "start_time": "2022-07-11T16:35:00.169872Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.84778905" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.norm((X - model1.predict(X[:,test_sensors])))/np.linalg.norm(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:24:41.516465Z", + "start_time": "2022-07-10T04:24:41.495858Z" + } + }, + "outputs": [], + "source": [ + "test_sensors2 = [x for x in all_sensors if x not in sensors_constrained][n_const_sensors:n_sensors]" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:24:42.898448Z", + "start_time": "2022-07-10T04:24:42.876231Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.84778905" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.norm((X - model1.predict(X[:,test_sensors2])))/np.linalg.norm(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:28:43.703593Z", + "start_time": "2022-07-10T04:28:43.681515Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.0730837" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "XX = np.zeros_like(X)\n", + "XX[:,top_sensors] = X[:,top_sensors]\n", + "model1.score(XX)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { + "hide_input": false, "kernelspec": { - "display_name": "Python 3.9.12 ('base')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -334,9 +608,78 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.5" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "nbTranslate": { + "displayLangs": [ + "*" + ], + "hotkey": "alt-t", + "langInMainMenu": true, + "sourceLang": "en", + "targetLang": "fr", + "useGoogleTranslate": true + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false }, - "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "3d597f4c481aa0f25dceb95d2a0067e73c0966dcbd003d741d821a7208527ecf" diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index 3e5844c..1c62db8 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -35,7 +35,7 @@ def __init__(self,idx_constrained,n_sensors,const_sensors,all_sensors): Ranked list of sensor locations. idx_constrained : np.ndarray, shape [No. of constrained locations] Column Indices of the sensors in the constrained locations. - n_sensors : integer, + n_sensors : integer, Total number of sensors const_sensors : integer, Total number of sensors required by the user in the constrained region. @@ -88,7 +88,7 @@ def fit( # Choose pivot i_piv = np.argmax(dlens_updated) - + dlen = dlens_updated[i_piv] if dlen > 0: @@ -174,7 +174,7 @@ def getConstraindSensorsIndices(xmin, xmax, ymin, ymax, nx, ny, all_sensors): Parameters ---------- - xmin: int, + xmin: int, Lower bound for the x-axis constraint xmax : int, Upper bound for the x-axis constraint @@ -187,7 +187,7 @@ def getConstraindSensorsIndices(xmin, xmax, ymin, ymax, nx, ny, all_sensors): Returns ------- - idx_constrained : np.darray, shape [No. of constrained locations] + idx_constrained : np.darray, shape [No. of constrained locations] Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. """ n_features = len(all_sensors) @@ -232,7 +232,6 @@ def functionalConstraint(position, func_response,func_input, freeTerm): if __name__ == '__main__': - pass faces = datasets.fetch_olivetti_faces(shuffle=True) X = faces.data @@ -278,16 +277,16 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): all_sensors = model.get_all_sensors() - ##Constrained sensor location on the grid: + ##Constrained sensor location on the grid: xmin = 20 xmax = 40 ymin = 25 ymax = 45 - sensors_constrained = getConstraindSensorsIndices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices + sensors_constrained = getConstraindSensorsIndices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices # didx = np.isin(all_sensors,sensors_constrained,invert=False) # const_index = np.nonzero(didx) - # j = + # j = ##Plotting the constrained region From 0a3477d7b0fd16729446505190eb70fae04be77b Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Tue, 12 Jul 2022 16:26:25 -0600 Subject: [PATCH 16/52] Adding different cases in f_region --- examples/region_optimal.ipynb | 651 +++++++++++++++++++++++++++++----- pysensors/optimizers/_gqr.py | 5 +- 2 files changed, 571 insertions(+), 85 deletions(-) diff --git a/examples/region_optimal.ipynb b/examples/region_optimal.ipynb index bfb3386..a2e441f 100644 --- a/examples/region_optimal.ipynb +++ b/examples/region_optimal.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 82, + "execution_count": 884, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:04.386599Z", @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 885, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:07.391526Z", @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 886, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:09.785781Z", @@ -91,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 887, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:10.835009Z", @@ -101,7 +101,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 888, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:11.651751Z", @@ -136,7 +136,7 @@ "source": [ "#Find all sensor locations using built in QR optimizer\n", "max_const_sensors = 230\n", - "n_const_sensors = 0\n", + "n_const_sensors = 2\n", "n_sensors = 10\n", "optimizer = ps.optimizers.QR()\n", "model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors)\n", @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 889, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:20.877032Z", @@ -165,35 +165,35 @@ "The constrained sensors are [1826 1765 1695 2084 1704 2529 2404 2210 1888 2406 1892 2402 2721 2022\n", " 2398 1699 2454 1895 2661 2847 2589 2532 1697 2018 2272 1638 1621 2592\n", " 2461 2214 1749 2326 1692 2408 1636 2015 2267 2659 2276 2780 1885 2456\n", - " 2464 1760 2004 2536 2394 2270 2088 2516 2083 2517 1832 2710 1767 2906\n", - " 1817 2069 2329 2344 2343 2466 2072 2521 2791 2900 2140 1894 2471 2773\n", - " 2649 2085 2775 1684 2076 2841 1762 2854 2081 2789 1627 2726 2647 2844\n", - " 2850 2010 1694 2198 1821 2265 2459 2082 1624 2792 1879 2838 1886 2660\n", - " 1945 2715 2327 2006 2147 1896 1702 2200 2390 2264 1943 1758 2598 2334\n", - " 1691 2713 1757 2901 2397 2012 2774 1944 1951 2599 2407 1623 2728 2337\n", - " 2148 1950 2269 2910 2275 2650 1893 1689 2655 2528 2324 2914 2338 1698\n", - " 2212 2206 2335 1701 2907 2719 2531 1628 2075 1629 2711 1751 2918 2393\n", - " 2911 1630 2594 1763 1633 2526 2263 2584 2273 2836 2588 1625 2523 2197\n", - " 2150 2071 2718 2086 2913 1830 2280 1881 2845 1635 2009 2452 2068 2074\n", - " 1876 2133 1815 1686 2139 2142 2462 1754 1942 2902 2787 2016 1820 2662\n", - " 2405 2203 1693 2396 2722 2527 2277 2007 1748 2279 1878 2266 2201 1949\n", - " 2211 1882 2401 2582 1759 2580 2591 2709 2720 1890 2278 1620 2458 2472\n", - " 2395 2342 2596 1750 2852 1761 1891 1634 1688 2657 2530 2525 2143 2019\n", - " 2196 1637 2656 2332 1831 1957 2519 2778 2644 1958 2331 2339 2597 2079\n", - " 2654 1825 1948 2202 2648 2851 2021 2209 2849 1766 2839 1884 2149 2135\n", - " 2014 1947 2389 2467 2268 2714 2341 2208 2453 2772 1639 2457 1753 1696\n", - " 2855 2777 2581 2013 2843 2716 2853 2325 2070 1626 2723 1632 1687 2912\n", - " 1880 1814 2585 2651 1822 2534 2271 2144 2005 2020 2790 1823 2727 2524\n", - " 2595 2905 2152 2917 2919 2204 1703 1813 2469 2145 2590 2904 1816 2645\n", - " 1812 2782 2781 2073 2137 2785 2783 2465 2779 2708 1952 2151 1622 1700\n", - " 2468 2077 2132 2460 2388 2087 1959 2455 2840 1953 2340 1752 2399 1941\n", - " 2205 2261 1877 2784 1631 2658 2652 1883 1640 2600 1764 2664 2136 2533\n", - " 2330 2717 2593 2080 2391 2023 2663 2199 2392 2024 1829 2518 1940 2848\n", - " 2274 1827 1955 2786 2646 2583 2008 1768 2846 1956 2842 2400 2712 2141\n", - " 2725 1887 2207 1824 1685 2213 2017 1690 2587 2909 1818 1828 2146 2653\n", - " 2586 2903 2463 1755 2403 2078 2916 2333 2470 2522 1756 1889 1960 1946\n", - " 2776 2856 2336 2328 2788 2134 2260 2915 2520 2908 2262 2920 2215 2535\n", - " 2138 2724 1819 2216 1954 2011 2837]\n", + " 2464 1760 2004 2536 2394 2270 2088 2516 2388 2405 2716 2727 1635 2087\n", + " 2791 2599 2787 2901 2651 2595 2856 2774 2914 1878 1762 2471 1625 2342\n", + " 2527 2137 2197 1634 2583 1947 1823 1818 2327 2789 1750 2335 2400 1832\n", + " 1684 2584 2073 1624 2273 2660 2334 2708 2139 2085 2080 2339 1828 2790\n", + " 2263 1686 2081 2324 2014 2399 2836 2905 2916 2907 1693 2585 2462 2009\n", + " 2470 2781 2140 2208 2013 2535 2141 2920 2526 2275 2600 1639 2715 2710\n", + " 2330 2206 2778 1627 2919 2521 2784 2524 1946 1822 1629 2076 2020 2663\n", + " 2467 2518 2658 2391 1814 2854 1825 2726 2717 2719 2647 2469 2597 1819\n", + " 2401 2134 2072 1630 2590 2525 2772 2403 2274 1817 2332 2211 2340 1877\n", + " 2393 2530 1755 1830 1951 2010 2213 1876 2850 1831 1880 1890 2343 1754\n", + " 1955 2591 1950 2580 2902 2773 2207 2341 2338 2783 2852 2016 2069 2586\n", + " 2079 2842 2148 1944 2086 1884 2397 1960 2909 1756 2459 2652 2396 2582\n", + " 1956 2199 2724 2846 1767 2908 2005 1632 2792 2017 2075 2083 2728 2204\n", + " 2711 2519 2646 2196 2786 2911 2776 2913 1943 1883 2460 2720 2713 2779\n", + " 2152 2654 1763 1821 1628 1761 1941 2520 2144 1620 2725 2082 2133 2203\n", + " 2209 2657 1690 2596 1887 1815 2458 2271 1640 2068 1820 1688 2455 1626\n", + " 2465 1691 2269 2849 2714 2655 2268 1751 2915 2533 2840 1893 2021 2132\n", + " 2718 1685 2202 2325 2146 1882 2070 2522 1694 2722 1954 2594 2723 2904\n", + " 2392 2336 2466 2463 2151 2265 1886 2788 2656 2145 2019 2071 1816 2337\n", + " 1953 1752 2528 1891 1766 1952 1881 1768 2149 1827 1696 2266 1631 2215\n", + " 1942 1959 2912 1701 2262 2838 2598 2777 2024 1623 2851 2198 2855 1949\n", + " 2212 1764 2910 2205 2077 2389 2918 2917 2390 2588 1703 2523 1945 1622\n", + " 1813 1633 2662 2023 2201 2587 2644 1748 2457 2581 1896 2395 2709 2078\n", + " 2906 2328 1700 2785 2853 1889 2280 2839 2007 2775 2844 2147 2648 1758\n", + " 2453 2261 2843 1948 2900 2645 2331 2653 1637 2012 2200 2279 2333 2136\n", + " 1698 1879 1753 2008 2278 1940 2329 2074 2845 1829 2142 1812 2143 1824\n", + " 2150 2848 1957 1757 2472 2841 2903 1702 2712 2264 2277 2534 2407 2531\n", + " 1894 2135 2138 2468 2011 2664 1689 2216 2344 2517 2593 2452 1958 1687\n", + " 2650 2649 2260 2837 2006 2782 1759]\n", "The constrained sensors are [2204]\n" ] } @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 890, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:22.713344Z", @@ -294,7 +294,7 @@ " r = R[j:, j:]\n", " # Norm of each column\n", " dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0))\n", - " dlens_updated = f_region_optimal(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.nSensors) #Handling constrained region sensor placement problem\n", + " dlens_updated = f_region_updated(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.nSensors) #Handling constrained region sensor placement problem\n", "\n", " # Choose pivot\n", " i_piv = np.argmax(dlens_updated)\n", @@ -328,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 891, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:24.092467Z", @@ -338,25 +338,55 @@ "outputs": [], "source": [ "\n", - "def f_region_optimal(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors):\n", + "def f_region_updated(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors):\n", " counter = 0\n", " mask = np.isin(all_sensors,lin_idx,invert=False)\n", " const_idx = all_sensors[mask]\n", " updated_lin_idx = const_idx[const_sensors:]\n", - " for i in range(n_sensors):\n", - " if np.isin(all_sensors[i],lin_idx,invert=False):\n", - " counter += 1\n", - " if counter < const_sensors:\n", - " dlens = dlens\n", + " var = np.isin(all_sensors[:n_sensors],lin_idx, invert=False)\n", + " n = np.count_nonzero(var)\n", + " print(n)\n", + "\n", + "\n", + " if any(var) == False:\n", + " if j < const_sensors:\n", + " didx = np.isin(piv[j:],lin_idx,invert=True)\n", + " dlens[didx] = 0\n", + " else:\n", + " didx = np.isin(piv[j:],lin_idx,invert=False)\n", + " dlens[didx] = 0\n", + "\n", + " elif n >= const_sensors:\n", + " for i in range(n_sensors):\n", + " if np.isin(all_sensors[i],lin_idx,invert=False):\n", + " counter += 1\n", + " if counter < const_sensors:\n", + " dlens = dlens\n", " else:\n", " didx = np.isin(piv[j:],updated_lin_idx,invert=False)\n", " dlens[didx] = 0\n", + "\n", + " elif n < const_sensors:\n", + " for i in range(n_sensors):\n", + " if np.isin(all_sensors[i],lin_idx,invert=False):\n", + " counter += 1\n", + " if counter <= n:\n", + " dlens = dlens\n", + "\n", + " elif n <= counter and counter <= const_sensors:\n", + " \n", + " didx = np.isin(piv[j:],updated_lin_idx,invert=True)\n", + " dlens[didx] = 0\n", + " else:\n", + " didx = np.isin(piv[j:],updated_lin_idx,invert=False)\n", + " dlens[didx] = 0\n", + "\n", " return dlens" ] }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 892, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:27.911973Z", @@ -368,7 +398,406 @@ "name": "stdout", "output_type": "stream", "text": [ - "[4032 384 4092 4039 447 493 657 878 4087 3779]\n" + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", + "1\n", 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+807,36 @@ "model1.fit(X)\n", "all_sensors1 = model1.get_all_sensors()\n", "\n", - "top_sensors = model1.get_selected_sensors()\n", - "print(top_sensors)" + "top_sensors = model1.get_selected_sensors()\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 893, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:27.951889Z", "start_time": "2022-07-10T04:22:27.951875Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "## TODO: this can be done using ravel and unravel more elegantly\n", "img = np.zeros(n_features)\n", "img[top_sensors] = 16\n", - "img[top_sensors0] = 5\n", "plt.plot([xmin,xmin],[ymin,ymax],'r')\n", "plt.plot([xmin,xmax],[ymax,ymax],'r')\n", "plt.plot([xmax,xmax],[ymin,ymax],'r')\n", @@ -408,29 +848,71 @@ }, { "cell_type": "code", - "execution_count": 110, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-11T16:34:56.472709Z", - "start_time": "2022-07-11T16:34:56.456089Z" + "execution_count": 894, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4032 384 4092 4039 447 493 2204 657 878 2880]\n", + "(10, 4096)\n", + "0.0\n" + ] } - }, + ], + "source": [ + "print(top_sensors0)\n", + "c = np.zeros([len(top_sensors0),n_features])\n", + "print(c.shape)\n", + "for i in range(len(top_sensors0)):\n", + " c[i,top_sensors0[i]] = 1\n", + "phi = model.basis_matrix_\n", + "optimality = np.linalg.det((c@phi).T @ (c@phi))\n", + "print(optimality)" + ] + }, + { + "cell_type": "code", + "execution_count": 895, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[4032 384 4092 4039 447 493 2204 657 878 2880] [4032 384 4092 4039 447 493 657 878 4087 3779]\n" + "[4032 384 4092 4039 447 493 2204 657 878 2880]\n", + "(10, 4096)\n", + "0.0\n" ] } ], "source": [ - "print(top_sensors0,top_sensors)" + "print(top_sensors)\n", + "c1 = np.zeros([len(top_sensors),n_features])\n", + "print(c1.shape)\n", + "for i in range(len(top_sensors)):\n", + " c1[i,top_sensors[i]] = 1\n", + "phi1 = model1.basis_matrix_\n", + "optimality1 = np.linalg.det((c1@phi1).T @ (c1@phi1))\n", + "print(optimality1)" ] }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-11T16:34:56.472709Z", + "start_time": "2022-07-11T16:34:56.456089Z" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 896, "metadata": { "ExecuteTime": { "end_time": "2022-07-11T16:34:57.421237Z", @@ -444,7 +926,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 897, "metadata": { "ExecuteTime": { "end_time": "2022-07-11T16:34:58.310764Z", @@ -455,10 +937,10 @@ { "data": { "text/plain": [ - "0.8899602" + "0.79669476" ] }, - "execution_count": 112, + "execution_count": 897, "metadata": {}, "output_type": "execute_result" } @@ -469,7 +951,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 898, "metadata": { "ExecuteTime": { "end_time": "2022-07-11T16:34:59.404387Z", @@ -480,10 +962,10 @@ { "data": { "text/plain": [ - "0.80111694" + "0.79669476" ] }, - "execution_count": 113, + "execution_count": 898, "metadata": {}, "output_type": "execute_result" } @@ -494,7 +976,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 899, "metadata": { "ExecuteTime": { "end_time": "2022-07-11T16:35:00.194386Z", @@ -505,10 +987,10 @@ { "data": { "text/plain": [ - "0.84778905" + "0.88171" ] }, - "execution_count": 114, + "execution_count": 899, "metadata": {}, "output_type": "execute_result" } @@ -519,7 +1001,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 900, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:24:41.516465Z", @@ -533,7 +1015,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 901, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:24:42.898448Z", @@ -542,14 +1024,17 @@ }, "outputs": [ { - "data": { - "text/plain": [ - "0.84778905" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" + "ename": "ValueError", + "evalue": "x has the wrong number of features: 8.\n Expected 10", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 19'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m np\u001b[39m.\u001b[39mlinalg\u001b[39m.\u001b[39mnorm((X \u001b[39m-\u001b[39m model1\u001b[39m.\u001b[39;49mpredict(X[:,test_sensors2])))\u001b[39m/\u001b[39mnp\u001b[39m.\u001b[39mlinalg\u001b[39m.\u001b[39mnorm(X)\n", + "File \u001b[0;32m~/projects/pysensors/pysensors/reconstruction/_sspor.py:190\u001b[0m, in \u001b[0;36mSSPOR.predict\u001b[0;34m(self, x, **solve_kws)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[39mPredict values at all positions given measurements at sensor locations.\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[39m Predicted values at every location.\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 189\u001b[0m check_is_fitted(\u001b[39mself\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mranked_sensors_\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 190\u001b[0m x \u001b[39m=\u001b[39m validate_input(x, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mranked_sensors_[: \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mn_sensors])\u001b[39m.\u001b[39mT\n\u001b[1;32m 192\u001b[0m \u001b[39m# For efficiency we may want to factor\u001b[39;00m\n\u001b[1;32m 193\u001b[0m \u001b[39m# self.basis_matrix_[self.ranked_sensors_, :]\u001b[39;00m\n\u001b[1;32m 194\u001b[0m \u001b[39m# in case predict is called multiple times.\u001b[39;00m\n\u001b[1;32m 195\u001b[0m \u001b[39m# Although if the user changes the number of sensors between calls\u001b[39;00m\n\u001b[1;32m 196\u001b[0m \u001b[39m# the factorization will be wasted.\u001b[39;00m\n\u001b[1;32m 198\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_sensors \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbasis_matrix_\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]:\n", + "File \u001b[0;32m~/projects/pysensors/pysensors/utils/_base.py:28\u001b[0m, in \u001b[0;36mvalidate_input\u001b[0;34m(x, sensors)\u001b[0m\n\u001b[1;32m 26\u001b[0m n_features \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(x) \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39mndim(x) \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m \u001b[39melse\u001b[39;00m x\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m]\n\u001b[1;32m 27\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(sensors) \u001b[39m!=\u001b[39m n_features:\n\u001b[0;32m---> 28\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 29\u001b[0m \u001b[39m\"\"\"x has the wrong number of features: {}.\u001b[39;00m\n\u001b[1;32m 30\u001b[0m \u001b[39m Expected {}\"\"\"\u001b[39;00m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m 31\u001b[0m n_features, \u001b[39mlen\u001b[39m(sensors)\n\u001b[1;32m 32\u001b[0m )\n\u001b[1;32m 33\u001b[0m )\n\u001b[1;32m 35\u001b[0m \u001b[39mreturn\u001b[39;00m x\n", + "\u001b[0;31mValueError\u001b[0m: x has the wrong number of features: 8.\n Expected 10" + ] } ], "source": [ @@ -558,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:28:43.703593Z", @@ -608,7 +1093,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.5" + "version": "3.9.12" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index 1c62db8..7ae1d53 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -71,8 +71,8 @@ def fit( if self.nSensors > n_features - max_const_sensors + self.nConstrainedSensors: raise IOError ("n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors") if self.nSensors > n_samples + self.nConstrainedSensors: ## Handling zero constraint? - raise IOError ("Currently n_sensors should be less than number of samples + number of constrained sensors,\ - got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}".format(n_sensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors)) + raise IOError ("Currently n_sensors should be less than min(number of samples, number of modes) + number of constrained sensors,\ + got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}".format(self.nSensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors)) # Initialize helper variables R = basis_matrix.conj().T.copy() @@ -85,6 +85,7 @@ def fit( # Norm of each column dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0)) dlens_updated = f_region_optimal(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.nSensors) #Handling constrained region sensor placement problem + #dlens_updated = f_region(self.constrainedIndices,dlens,p,j,self.nConstrainedSensors) # Choose pivot i_piv = np.argmax(dlens_updated) From 876d6fb79c4fcae675093a047d32fd16250d8eca Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Wed, 13 Jul 2022 11:11:32 -0600 Subject: [PATCH 17/52] Adding the Trace(R) calculation --- pysensors/optimizers/_gqr.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index 7ae1d53..cebf2c2 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -41,6 +41,7 @@ def __init__(self,idx_constrained,n_sensors,const_sensors,all_sensors): Total number of sensors required by the user in the constrained region. """ self.pivots_ = None + self.optimality = None self.constrainedIndices = idx_constrained self.nSensors = n_sensors self.nConstrainedSensors = const_sensors @@ -112,7 +113,8 @@ def fit( R[j + 1 :, j] = 0 self.pivots_ = p - + self.optimality = np.trace(np.real(R)) + print("The trace(R) = {}".format(self.optimality)) return self From ea74f8f2ae7eb60600e9b4fde6499d1e1987d29f Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Thu, 28 Jul 2022 14:11:37 -0600 Subject: [PATCH 18/52] Adding updated gqr with constraints moved to utils and region_optimal with radius constraints --- examples/region_optimal.ipynb | 4869 +++++++++++++++++++++++++++++---- pysensors/optimizers/_gqr.py | 286 +- pysensors/utils/__init__.py | 9 +- 3 files changed, 4466 insertions(+), 698 deletions(-) diff --git a/examples/region_optimal.ipynb b/examples/region_optimal.ipynb index a2e441f..dc99f3c 100644 --- a/examples/region_optimal.ipynb +++ b/examples/region_optimal.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 884, + "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:04.386599Z", @@ -20,12 +20,13 @@ "from sklearn import metrics\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "\n", - "import pysensors as ps" + "import pysensors as ps\n", + "from matplotlib.patches import Circle\n" ] }, { "cell_type": "code", - "execution_count": 885, + "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:07.391526Z", @@ -65,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 886, + "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:09.785781Z", @@ -91,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 887, + "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:10.835009Z", @@ -116,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 888, + "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:11.651751Z", @@ -128,90 +129,51 @@ "name": "stdout", "output_type": "stream", "text": [ - "Unconstrained Optimal sensors, n = 4096, [4032 384 4092 4039 447 493 2204 657 878 2880 1088 4087 2837],...\n", - "Unconstrained Optimal sensors, n = 10, [4032 384 4092 4039 447 493 2204 657 878 2880]\n" + "[4032 384 4092 4039 447 493 2204 657 878 2880 1088 4087 2837 3779\n", + " 3093]\n" ] } ], "source": [ "#Find all sensor locations using built in QR optimizer\n", - "max_const_sensors = 230\n", - "n_const_sensors = 2\n", - "n_sensors = 10\n", + "#max_const_sensors = 230\n", + "#n_const_sensors = 2\n", + "n_sensors = 15\n", "optimizer = ps.optimizers.QR()\n", "model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors)\n", "model.fit(X)\n", "\n", "all_sensors = model.get_all_sensors()\n", "top_sensors0 = model.get_selected_sensors()\n", - "print('Unconstrained Optimal sensors, n = {}, {},...'.format(len(all_sensors),all_sensors[:n_sensors+3]))\n", - "print('Unconstrained Optimal sensors, n = {}, {}'.format(len(top_sensors0),top_sensors0))" + "print(top_sensors0)\n", + "#print('Unconstrained Optimal sensors, n = {}, {},...'.format(len(all_sensors),all_sensors[:n_sensors+3]))\n", + "#print('Unconstrained Optimal sensors, n = {}, {}'.format(len(top_sensors0),top_sensors0))" ] }, { "cell_type": "code", - "execution_count": 889, + "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:20.877032Z", "start_time": "2022-07-10T04:22:20.866607Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The constrained sensors are [1826 1765 1695 2084 1704 2529 2404 2210 1888 2406 1892 2402 2721 2022\n", - " 2398 1699 2454 1895 2661 2847 2589 2532 1697 2018 2272 1638 1621 2592\n", - " 2461 2214 1749 2326 1692 2408 1636 2015 2267 2659 2276 2780 1885 2456\n", - " 2464 1760 2004 2536 2394 2270 2088 2516 2388 2405 2716 2727 1635 2087\n", - " 2791 2599 2787 2901 2651 2595 2856 2774 2914 1878 1762 2471 1625 2342\n", - " 2527 2137 2197 1634 2583 1947 1823 1818 2327 2789 1750 2335 2400 1832\n", - " 1684 2584 2073 1624 2273 2660 2334 2708 2139 2085 2080 2339 1828 2790\n", - " 2263 1686 2081 2324 2014 2399 2836 2905 2916 2907 1693 2585 2462 2009\n", - " 2470 2781 2140 2208 2013 2535 2141 2920 2526 2275 2600 1639 2715 2710\n", - " 2330 2206 2778 1627 2919 2521 2784 2524 1946 1822 1629 2076 2020 2663\n", - " 2467 2518 2658 2391 1814 2854 1825 2726 2717 2719 2647 2469 2597 1819\n", - " 2401 2134 2072 1630 2590 2525 2772 2403 2274 1817 2332 2211 2340 1877\n", - " 2393 2530 1755 1830 1951 2010 2213 1876 2850 1831 1880 1890 2343 1754\n", - " 1955 2591 1950 2580 2902 2773 2207 2341 2338 2783 2852 2016 2069 2586\n", - " 2079 2842 2148 1944 2086 1884 2397 1960 2909 1756 2459 2652 2396 2582\n", - " 1956 2199 2724 2846 1767 2908 2005 1632 2792 2017 2075 2083 2728 2204\n", - " 2711 2519 2646 2196 2786 2911 2776 2913 1943 1883 2460 2720 2713 2779\n", - " 2152 2654 1763 1821 1628 1761 1941 2520 2144 1620 2725 2082 2133 2203\n", - " 2209 2657 1690 2596 1887 1815 2458 2271 1640 2068 1820 1688 2455 1626\n", - " 2465 1691 2269 2849 2714 2655 2268 1751 2915 2533 2840 1893 2021 2132\n", - " 2718 1685 2202 2325 2146 1882 2070 2522 1694 2722 1954 2594 2723 2904\n", - " 2392 2336 2466 2463 2151 2265 1886 2788 2656 2145 2019 2071 1816 2337\n", - " 1953 1752 2528 1891 1766 1952 1881 1768 2149 1827 1696 2266 1631 2215\n", - " 1942 1959 2912 1701 2262 2838 2598 2777 2024 1623 2851 2198 2855 1949\n", - " 2212 1764 2910 2205 2077 2389 2918 2917 2390 2588 1703 2523 1945 1622\n", - " 1813 1633 2662 2023 2201 2587 2644 1748 2457 2581 1896 2395 2709 2078\n", - " 2906 2328 1700 2785 2853 1889 2280 2839 2007 2775 2844 2147 2648 1758\n", - " 2453 2261 2843 1948 2900 2645 2331 2653 1637 2012 2200 2279 2333 2136\n", - " 1698 1879 1753 2008 2278 1940 2329 2074 2845 1829 2142 1812 2143 1824\n", - " 2150 2848 1957 1757 2472 2841 2903 1702 2712 2264 2277 2534 2407 2531\n", - " 1894 2135 2138 2468 2011 2664 1689 2216 2344 2517 2593 2452 1958 1687\n", - " 2650 2649 2260 2837 2006 2782 1759]\n", - "The constrained sensors are [2204]\n" - ] - } - ], + "outputs": [], "source": [ "##Constrained sensor location on the grid: \n", - "xmin = 20\n", - "xmax = 40\n", - "ymin = 25\n", - "ymax = 45\n", - "sensors_constrained = ps.optimizers._gqr.getConstraindSensorsIndices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices\n", - "print('The constrained sensors are {}'.format(sensors_constrained))\n", - "print('The constrained sensors are {}'.format(top_sensors0[np.isin(top_sensors0,sensors_constrained,invert=False)]))" + "# xmin = 20\n", + "# xmax = 40\n", + "# ymin = 25\n", + "# ymax = 45\n", + "# sensors_constrained = ps.optimizers._gqr.getConstraindSensorsIndices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices\n", + "# print('The constrained sensors are {}'.format(sensors_constrained))\n", + "# print('The constrained sensors are {}'.format(top_sensors0[np.isin(top_sensors0,sensors_constrained,invert=False)]))" ] }, { "cell_type": "code", - "execution_count": 890, + "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:22.713344Z", @@ -237,7 +199,7 @@ "\n", " @ authors: Niharika Karnik (@nkarnik2999), Mohammad Abdo (@Jimmy-INL), and Krithika Manohar (@kmanohar)\n", " \"\"\"\n", - " def __init__(self,idx_constrained,n_sensors,const_sensors,all_sensors):\n", + " def __init__(self,n_sensors,all_sensors,r,nx,ny):\n", " \"\"\"\n", " Attributes\n", " ----------\n", @@ -251,10 +213,12 @@ " Total number of sensors required by the user in the constrained region.\n", " \"\"\"\n", " self.pivots_ = None\n", - " self.constrainedIndices = idx_constrained\n", " self.nSensors = n_sensors\n", - " self.nConstrainedSensors = const_sensors\n", " self.all_sensorloc = all_sensors\n", + " self.radius = r\n", + " self._nx = nx\n", + " self._ny = ny\n", + "\n", "\n", " def fit(\n", " self,\n", @@ -275,14 +239,14 @@ " \"\"\"\n", "\n", " n_features, n_samples = basis_matrix.shape # We transpose basis_matrix below\n", - " max_const_sensors = len(self.constrainedIndices) #Maximum number of sensors allowed in the constrained region\n", + " #max_const_sensors = len(self.constrainedIndices) #Maximum number of sensors allowed in the constrained region\n", "\n", " ## Assertions and checks:\n", - " if self.nSensors > n_features - max_const_sensors + self.nConstrainedSensors:\n", - " raise IOError (\"n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors\")\n", - " if self.nSensors > n_samples + self.nConstrainedSensors: ## Handling zero constraint?\n", - " raise IOError (\"Currently n_sensors should be less than number of samples + number of constrained sensors,\\\n", - " got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}\".format(n_sensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors))\n", + " #if self.nSensors > n_features - max_const_sensors + self.nConstrainedSensors:\n", + " #raise IOError (\"n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors\")\n", + " #if self.nSensors > n_samples + self.nConstrainedSensors: ## Handling zero constraint?\n", + " #raise IOError (\"Currently n_sensors should be less than number of samples + number of constrained sensors,\\\n", + " #got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}\".format(n_sensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors))\n", "\n", " # Initialize helper variables\n", " R = basis_matrix.conj().T.copy()\n", @@ -294,7 +258,7 @@ " r = R[j:, j:]\n", " # Norm of each column\n", " dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0))\n", - " dlens_updated = f_region_updated(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.nSensors) #Handling constrained region sensor placement problem\n", + " dlens_updated = f_region_distance_constraints(self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j) #Handling constrained region sensor placement problem\n", "\n", " # Choose pivot\n", " i_piv = np.argmax(dlens_updated)\n", @@ -328,7 +292,79 @@ }, { "cell_type": "code", - "execution_count": 891, + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "def f_region_distance_constraints(r,nx,ny,all_sensors, dlens, piv, j):\n", + " \n", + " \n", + " n_features = len(all_sensors)\n", + " a = np.unravel_index(all_sensors, (nx,ny))\n", + " x_cord = a[0][j-1]\n", + " y_cord = a[1][j-1]\n", + " print(x_cord, y_cord)\n", + " constrained_sensorsx = []\n", + " constrained_sensorsy = []\n", + " for i in range(n_features):\n", + " if ((a[0][i]-x_cord)**2 + (a[1][i]-y_cord)**2) < r**2: \n", + " #print(a[0][i],a[1][i])\n", + " constrained_sensorsx.append(a[0][i])\n", + " constrained_sensorsy.append(a[1][i])\n", + " #print(constrained_sensorsx, constrained_sensorsy)\n", + " constrained_sensorsx = np.array(constrained_sensorsx)\n", + " constrained_sensorsy = np.array(constrained_sensorsy)\n", + " constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1)\n", + " constrained_sensors_tuple = np.transpose(constrained_sensors_array)\n", + " idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny))\n", + " print(idx_constrained)\n", + " didx = np.isin(piv[j:],idx_constrained,invert=True)\n", + " dlens[didx] = 0\n", + " return dlens\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "# def distance_constraints_indices(r,nx,ny,all_sensors,n_samples,j):\n", + "\n", + "# n_features = len(all_sensors)\n", + "# a = np.unravel_index(all_sensors, (nx,ny))\n", + "# x_cord = a[0][j]\n", + "# y_cord = a[1][j]\n", + "# #print(x_cord, y_cord)\n", + "# constrained_sensorsx = []\n", + "# constrained_sensorsy = []\n", + "# for i in range(n_features):\n", + "# if ((a[0][i]-x_cord)**2 + (a[1][i]-y_cord)**2) < r**2: \n", + "# #print(a[0][i],a[1][i])\n", + "# constrained_sensorsx.append(a[0][i])\n", + "# constrained_sensorsy.append(a[1][i])\n", + "# #print(constrained_sensorsx, constrained_sensorsy)\n", + "# constrained_sensorsx = np.array(constrained_sensorsx)\n", + "# constrained_sensorsy = np.array(constrained_sensorsy)\n", + "# constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1)\n", + "# constrained_sensors_tuple = np.transpose(constrained_sensors_array)\n", + "# idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny))\n", + "# print(idx_constrained)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "# constrained_indices = distance_constraints_indices(r,nx,ny,all_sensors,n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:24.092467Z", @@ -338,55 +374,55 @@ "outputs": [], "source": [ "\n", - "def f_region_updated(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors):\n", - " counter = 0\n", - " mask = np.isin(all_sensors,lin_idx,invert=False)\n", - " const_idx = all_sensors[mask]\n", - " updated_lin_idx = const_idx[const_sensors:]\n", - " var = np.isin(all_sensors[:n_sensors],lin_idx, invert=False)\n", - " n = np.count_nonzero(var)\n", - " print(n)\n", + "# def f_region_updated(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors):\n", + "# counter = 0\n", + "# mask = np.isin(all_sensors,lin_idx,invert=False)\n", + "# const_idx = all_sensors[mask]\n", + "# updated_lin_idx = const_idx[const_sensors:]\n", + "# var = np.isin(all_sensors[:n_sensors],lin_idx, invert=False)\n", + "# n = np.count_nonzero(var)\n", + "# print(n)\n", "\n", "\n", - " if any(var) == False:\n", - " if j < const_sensors:\n", - " didx = np.isin(piv[j:],lin_idx,invert=True)\n", - " dlens[didx] = 0\n", - " else:\n", - " didx = np.isin(piv[j:],lin_idx,invert=False)\n", - " dlens[didx] = 0\n", + "# if any(var) == False: #f_region\n", + "# if j < const_sensors:\n", + "# didx = np.isin(piv[j:],lin_idx,invert=True)\n", + "# dlens[didx] = 0\n", + "# else:\n", + "# didx = np.isin(piv[j:],lin_idx,invert=False)\n", + "# dlens[didx] = 0\n", "\n", - " elif n >= const_sensors:\n", - " for i in range(n_sensors):\n", - " if np.isin(all_sensors[i],lin_idx,invert=False):\n", - " counter += 1\n", - " if counter < const_sensors:\n", - " dlens = dlens\n", - " else:\n", - " didx = np.isin(piv[j:],updated_lin_idx,invert=False)\n", - " dlens[didx] = 0\n", + "# elif n >= const_sensors: #f_region_optimal\n", + "# for i in range(n_sensors):\n", + "# if np.isin(all_sensors[i],lin_idx,invert=False):\n", + "# counter += 1\n", + "# if counter < const_sensors:\n", + "# dlens = dlens\n", + "# else:\n", + "# didx = np.isin(piv[j:],updated_lin_idx,invert=False)\n", + "# dlens[didx] = 0\n", "\n", - " elif n < const_sensors:\n", - " for i in range(n_sensors):\n", - " if np.isin(all_sensors[i],lin_idx,invert=False):\n", - " counter += 1\n", - " if counter <= n:\n", - " dlens = dlens\n", + "# elif n < const_sensors:\n", + "# for i in range(n_sensors):\n", + "# if np.isin(all_sensors[i],lin_idx,invert=False):\n", + "# counter += 1\n", + "# if counter <= n:\n", + "# dlens = dlens\n", "\n", - " elif n <= counter and counter <= const_sensors:\n", + "# elif n <= counter and counter <= const_sensors:\n", " \n", - " didx = np.isin(piv[j:],updated_lin_idx,invert=True)\n", - " dlens[didx] = 0\n", - " else:\n", - " didx = np.isin(piv[j:],updated_lin_idx,invert=False)\n", - " dlens[didx] = 0\n", + "# didx = np.isin(piv[j:],updated_lin_idx,invert=True)\n", + "# dlens[didx] = 0\n", + "# else:\n", + "# didx = np.isin(piv[j:],updated_lin_idx,invert=False)\n", + "# dlens[didx] = 0\n", "\n", - " return dlens" + "# return dlens" ] }, { "cell_type": "code", - "execution_count": 892, + "execution_count": 59, 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1833 1509 1505\n", + " 1572 1763 2087 1511 1952 1963 2147 2152 2026 2017 1834 1578 1512 1579\n", + " 1768 1829 1771 1640 1957 1769 1954 2149 1832 2020 1641 1894 1576 1571\n", + " 1955 1961 1698 1828 1633 1707 1962 1444 1506 1447 1890 2146 2083 1379\n", + " 2150 1635 1835 1896 1770 1510 2021 1899 1378 1446 2019 1577 1448 1956\n", + " 1959 2025 1767 2151 1827 1825 1887 2081 1703 1762 1705 1632 1960 2086\n", + " 1700 1951 2023 1701 1889 1381 1569 1897 1893 1514 1573 1507 2016 1958\n", + " 2082 1953 1513 1830 1639 1384 1637 1759 1449 1696 1382 1380 1567 1823\n", + " 1643 1702 2024 1706 1831 1766 1898 1824 1574 1891 2148 1443 1634 1631\n", + " 1764 2085 1761 1441 1568]\n", + "9 5\n", + "[ 6 329 459 12 141 269 73 334 716 200 521 133 143 261 526 332 523 653\n", + " 453 524 525 201 260 70 714 139 207 580 582 13 458 711 387 136 393 651\n", + " 325 517 715 75 263 581 390 11 515 396 76 202 74 456 392 518 10 516\n", + " 645 68 588 8 77 461 268 463 140 650 335 138 132 267 455 712 648 649\n", + " 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3795 3731 3725 3666 4053 3793 3852 4044 3664 3916 3789 4045\n", + " 3665 4050 3850 3727 3849 3792 3981 3917 4049 3729 3860 3662 3787 3988\n", + " 3857 3723 3660 3984 3661 3918 4046 3794 3724]\n", + "46 26\n", + "[1388 1392 1710 2027 1840 1907 1704 2093 1713 1836 1519 1642 1524 1581\n", + " 1966 1389 1521 2032 1965 1842 1651 1708 1773 1833 1394 1839 1963 1772\n", + " 1712 1969 2026 1834 1841 1588 1578 1652 1512 1579 1768 2034 1451 1771\n", + " 1648 1640 1769 1450 1327 1452 1832 1641 1326 1576 1455 1715 1328 1905\n", + " 1961 1778 1649 1456 1901 1903 1779 1454 1709 1585 1971 1522 1707 1962\n", + " 1523 1323 1837 1586 1387 1835 1584 1964 1329 1896 1770 1515 1908 2096\n", + " 1393 2030 1390 1899 1902 1520 2095 1714 1776 1904 1577 1970 1516 1457\n", + " 1900 1650 1324 1968 1325 1391 1644 1838 2031 1705 2033 1587 1777 1716\n", + " 1967 2092 1386 1897 1514 1453 1459 1774 1513 1780 1449 1517 1645 1844\n", + " 1646 1643 2094 1706 1775 1898 2097 2091 2029 1583 1647 1582 1843 1711\n", + " 1458 1580 1518 2028 1906]\n", + "43 31\n", + "[1710 2027 1840 1704 2093 2089 2159 2022 1836 1895 1642 1966 2161 2284\n", + " 2214 2408 2032 2222 1965 2349 1708 2088 1773 1833 2344 2413 2414 2411\n", + " 2155 1839 2348 2087 2345 1963 1772 2152 1969 2026 1834 1841 2090 1768\n", + " 1829 2153 1771 1640 1957 1769 2149 1832 1641 1894 2160 1905 2282 1961\n", + " 2346 2409 1901 1903 1709 2218 1707 1962 2225 2224 2343 1837 2150 2278\n", + " 1835 1964 1896 1770 2096 2021 2030 1899 1902 2095 2288 2154 1776 1904\n", + " 1959 2219 2025 1767 2350 2158 1900 2220 2151 2279 2217 1968 2286 1703\n", + " 2351 1644 1838 2031 1705 1960 2086 2033 2347 2156 2215 1967 2216 2023\n", + " 2092 1897 1893 1958 1774 1830 2287 2283 2280 1645 1646 1643 2412 2094\n", + " 2410 2157 2024 1706 1831 1775 1766 1898 2285 2097 2213 2091 2029 2223\n", + " 2281 1711 2085 2221 2028]\n", + "38 0\n", + "[ 38 32 107 168 35 163 41 96 33 224 360 170 421 164 167 234 226 105\n", + " 98 420 423 99 230 97 103 228 165 296 36 232 102 100 425 108 225 362\n", + " 101 424 290 297 289 359 40 356 172 104 294 43 361 39 162 354 355 357\n", + " 34 231 227 358 298 292 169 419 233 229 299 291 44 42 295 171 293 166\n", + " 236 422 106 37 161 160 235]\n", + "31 26\n", + "[1826 1765 1695 1570 1888 1508 1892 1502 1699 1697 1504 2018 1692 1636\n", + " 1442 1311 2015 1885 1760 1376 1436 1509 1505 1503 1572 1763 1952 2011\n", + " 1819 1883 2017 1818 2077 1829 2078 1566 1371 1500 1310 1954 1949 1822\n", + " 2079 1571 1955 1374 2014 1437 1698 1758 1561 1828 1757 1633 1444 1693\n", + " 1506 1754 1562 1499 1313 1890 1379 2080 1635 2012 1628 1950 1948 1821\n", + " 1378 1627 2019 1626 1886 1956 1373 1817 1314 1947 1497 1827 1825 1625\n", + " 1694 1630 1887 2081 1501 1762 1882 1632 1375 1755 1440 1700 1951 1946\n", + " 1498 1701 1889 1569 1820 1893 1573 1438 1507 1434 1753 2016 2082 1953\n", + " 1689 1565 1564 1637 1435 1759 1439 1884 1696 1567 1312 1823 1629 1691\n", + " 1824 2076 1891 1443 1634 1631 1764 1308 1690 1881 1563 1761 1441 1309\n", + " 1372 2013 1568 1377 1756]\n", + "18 36\n", + "[2322 2000 2449 2191 2454 2195 2317 2444 2705 2326 2129 2319 2062 2067\n", + " 2456 2004 2516 2509 2131 2200 2445 2263 2325 2136 2257 2380 2578 2196\n", + " 2006 2318 2327 2453 2188 2387 2254 2582 2255 2391 2125 2451 2583 2709\n", + " 2447 2517 2064 2198 2134 2381 2639 2455 2510 2127 2193 2126 2005 1937\n", + " 2640 2384 2262 2002 2703 2070 2518 2065 2258 2324 2577 2646 2708 2128\n", + " 1939 2385 1941 2390 2579 2133 2515 2003 2580 2383 2192 2253 2643 2264\n", + " 2706 2514 2388 2001 2386 2511 1935 2328 2707 2513 2576 2392 2645 2644\n", + " 2450 2194 2132 1936 2135 2066 2259 2508 1999 2642 2061 1938 2448 2199\n", + " 2512 2389 2320 2641 2321 2575 2071 2581 2197 2574 2316 2452 2382 2252\n", + " 2446 2068 2704 2260 2063 2189 2069 2130 2638 2323 2520 2256 2124 2190\n", + " 2261 1998 1940 2573 2519]\n", + "18 9\n", + "[594 848 210 591 785 401 720 913 845 467 340 334 910 716 850 659 976 526\n", + " 914 404 472 596 724 978 917 402 339 655 213 653 852 524 525 718 471 719\n", + " 535 855 207 341 781 792 780 595 721 664 273 337 784 912 600 847 791 532\n", + " 208 211 212 468 789 662 723 396 849 343 529 722 782 209 846 338 534 599\n", + " 407 790 654 275 469 588 918 726 728 277 342 405 461 463 977 853 783 658\n", + " 725 597 335 464 276 979 398 717 536 854 911 980 400 399 533 465 593 787\n", + " 527 530 336 975 661 660 462 460 589 851 786 657 981 274 788 592 590 271\n", + " 915 403 531 470 916 278 333 408 406 663 397 652 598 270 272 727 656 528\n", + " 466]\n", + "63 63\n", + "[3903 4095 3711 3839 4026 3899 3772 3967 3837 3708 4030 3836 3965 4027\n", + " 3962 3775 3709 4091 3897 3834 4092 4094 3902 4029 3901 3838 4028 4093\n", + " 3961 4090 3771 4025 4031 3966 3900 3898 4089 3773 3835 3774 3963 3964\n", + " 3710]\n", + "17 28\n", + "[1809 1682 1741 2000 1553 1870 2191 1804 2195 1555 1679 1621 1875 1749\n", + " 1997 1549 2129 1489 2062 2067 2004 2131 1806 1808 1683 2196 1680 1932\n", + " 2006 1612 1873 1739 1617 1878 1687 2125 1488 1867 2064 1869 1548 1492\n", + " 1423 1933 1554 1490 2127 2193 1807 1868 2126 1931 2005 1937 1943 1619\n", + " 1675 1427 1622 1811 1813 1491 2002 2070 1676 1742 1558 2065 1803 1550\n", + " 2128 1877 1939 1426 1485 1942 1941 2133 2003 1677 1995 2192 1425 1613\n", + " 1493 2001 1678 2007 1746 1935 1812 1815 1611 1623 1810 2194 1616 1620\n", + " 1879 1934 1618 2132 1615 1557 1936 1487 2066 1999 1814 1744 2061 1938\n", + " 1681 1551 1748 1805 1871 1422 1556 1684 1486 1872 1996 2060 1686 1874\n", + " 1876 1424 1428 1750 1743 1747 2068 1751 2063 1685 2069 2130 2190 1740\n", + " 1614 1998 1940 1745 1552]\n", + "48 28\n", + "[1710 2027 1840 1907 2093 1973 1713 2159 1836 1519 1642 1524 1581 1966\n", + " 2161 1782 1521 2032 2222 1965 1842 1651 1708 1773 2100 1839 1963 1772\n", + " 2164 1712 1909 1969 2026 1834 1841 1588 1652 1579 2034 1771 1648 2160\n", + " 2098 1455 1715 1905 1778 1649 1456 1901 1717 1903 1779 1454 1709 1585\n", + " 1971 1522 1707 1962 2225 1523 2224 1837 2036 1586 1846 1835 1584 2099\n", + " 1964 1770 1908 2096 2038 2030 1899 1902 1520 2095 2162 1714 2037 1776\n", + " 1904 1970 1516 1457 2158 1900 1650 2163 1968 1845 1972 1644 1838 2031\n", + " 1910 2033 1587 1718 1777 1716 2156 1967 2035 2092 1781 1453 1459 2226\n", + " 1774 1780 1517 1645 1844 1974 1646 2101 1643 1653 2094 2157 1654 2227\n", + " 1706 1775 1898 2097 1589 2091 2029 1583 2223 1647 1582 1843 1711 1458\n", + " 1580 1518 2221 2028 1906]\n", + "63 5\n", + "[ 63 511 251 383 441 191 638 569 313 59 255 316 444 125 507 380 447 767\n", + " 319 186 575 253 699 636 189 315 634 446 127 701 445 702 639 126 382 252\n", + " 378 314 249 122 190 764 187 572 379 571 377 254 442 443 765 318 510 766\n", + " 637 508 188 185 506 250 574 317 570 60 62 509 61 505 700 635 573 124\n", + " 703 381 123]\n", + "63 12\n", + "[ 511 831 638 1151 444 1023 507 447 767 575 699 826 636 958\n", + " 829 1018 1147 830 956 634 446 954 1085 894 701 445 1021 702\n", + " 639 827 761 1148 764 828 572 571 633 893 765 1019 510 955\n", + " 766 1020 637 892 1213 508 890 1086 574 570 698 895 1212 1082\n", + " 509 1017 957 762 1022 1215 763 700 697 959 953 635 573 703\n", + " 1087 1214 891 1084 1150 825 889 1083 1149]\n", + "51 29\n", + "[1710 1840 1907 2093 1976 1973 1713 2159 1524 1966 2161 1782 1521 2032\n", + " 1965 1842 1651 1773 1785 1525 2100 1839 2294 2230 2164 2167 1712 1849\n", + " 1909 1848 1969 1841 1588 1652 2034 2166 1648 1912 2228 2105 2160 2098\n", + " 2293 1715 1905 1778 1649 2103 1901 1717 1903 1779 1709 1585 1971 1522\n", + " 2289 2225 1523 2224 1837 1719 2036 2041 1586 1846 1584 2099 1847 1908\n", + " 2096 2038 2030 1902 1520 2039 2095 2288 2162 1714 2037 1776 1904 1970\n", + " 2158 1650 2163 1783 1913 1968 1845 1972 1838 2031 2290 1910 2033 1587\n", + " 1718 1777 1716 1591 1967 1784 2035 1781 2040 2291 2231 2226 1774 2104\n", + " 1780 1655 1720 2292 2229 1975 1721 2165 1844 1911 1974 1646 2101 1977\n", + " 2168 1653 2094 1590 1654 2227 1775 1656 2097 1589 2029 1526 1583 2223\n", + " 1647 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3800 3918 4046\n", + " 3475 4054 3992 3864 3794 3724]\n", + "16 13\n", + "[ 594 848 591 785 720 913 845 467 1104 1232 1039 1035 910 716\n", + " 850 659 976 842 526 1041 914 1170 1167 596 779 724 978 917\n", + " 655 1099 653 852 974 907 524 525 718 719 906 714 1171 781\n", + " 1105 780 595 721 971 982 1230 784 778 912 1165 847 651 532\n", + " 715 970 789 662 723 1046 1103 849 1038 529 722 782 1231 846\n", + " 908 1106 1042 1040 790 654 1166 843 588 918 726 1107 1172 1037\n", + " 461 463 977 853 783 658 725 650 597 464 979 717 854 911\n", + " 1036 980 1233 844 465 1109 1101 593 787 527 530 587 975 661\n", + " 972 660 1100 462 1044 589 851 786 657 1102 973 981 1034 1229\n", + " 1108 788 592 909 590 915 1234 531 916 652 1169 1164 1043 1168\n", + " 656 528 1045 1235 466]\n", + "56 59\n", + "[3832 4083 3827 3959 3705 4086 3577 4026 3899 3772 3644 3699 3766 3511\n", + " 3956 3837 3708 3769 3954 3702 3895 4030 3826 3574 3836 3965 4027 3636\n", + " 3962 3709 4091 3897 3834 3510 4092 3762 3445 3581 4019 3902 4029 3830\n", + " 3572 3901 3890 3838 4028 4018 3698 4093 3579 3513 3828 3960 3700 3763\n", + " 3891 3643 3706 3765 4085 3961 4090 3578 3448 3771 3893 4084 3958 4021\n", + " 4022 4025 3642 3646 3575 4024 3641 3707 3701 3639 3514 3451 3764 3894\n", + " 3966 3450 3447 3896 3900 3515 3957 3898 4088 3768 3637 4089 3773 3640\n", + " 3449 3573 3635 3509 3645 3516 3831 3512 3767 3835 3833 3571 4020 3774\n", + " 3634 3638 3963 3508 3964 3704 3710 3829 4087 3703 3446 3576 3892 3580\n", + " 3770 3955 4023]\n", + "18 26\n", + "[1809 1682 1361 1741 2000 1553 1870 1804 1555 1679 1621 1875 1358 1749\n", + " 1549 1489 2067 2004 1816 1806 1808 1560 1295 1683 1680 1363 2006 1612\n", + " 1873 1617 1878 1687 1488 1362 2064 1869 1548 1492 1423 1933 1494 1554\n", + " 1421 1490 1431 1807 1868 2005 1937 1943 1619 1484 1299 1427 1622 1811\n", + " 1813 1491 2002 1880 1676 1742 1558 2065 1550 1877 1939 1426 1485 1942\n", + " 1941 2003 1677 1296 1495 1425 1365 1613 1493 2001 1678 1746 1935 1812\n", + " 1297 1815 1623 1810 1616 1620 1879 1559 1934 1430 1618 1496 1615 1359\n", + " 1557 1936 1487 2066 1999 1360 1300 1814 1744 1938 1681 1551 1748 1805\n", + " 1871 1422 1556 1684 1486 1752 1366 1872 1624 1298 1686 1874 1876 1424\n", + " 1428 1301 1750 1743 1747 2068 1364 1751 2063 1685 1429 1688 2069 1740\n", + " 1614 1998 1940 1745 1552]\n", + "46 19\n", + "[1388 1392 1262 1330 1130 1136 1132 1519 1581 879 1389 1521 1203 1261\n", + " 1322 940 1002 878 1004 1065 1394 1003 1193 1064 1198 1578 1258 1072\n", + " 1579 1200 1140 1451 1648 1135 1129 1001 876 1450 1067 1139 1327 1452\n", + " 1259 1326 1331 1455 1328 1005 1009 1071 1460 1649 1456 1202 1268 1454\n", + " 1585 1522 1523 1321 1323 1011 1586 1387 1584 877 1329 1515 1393 1390\n", + " 1520 1138 1075 1256 1066 1008 1448 1320 1396 941 1516 1457 1395 1010\n", + " 1324 1204 1128 1385 1325 1391 1644 1264 946 1194 1197 1076 1195 1073\n", + " 1386 1134 1514 1453 1459 881 1332 1192 1196 1513 1265 943 875 1384\n", + " 1266 1449 1199 1517 1645 1646 1007 945 1643 1267 1137 1074 1068 1260\n", + " 1133 938 880 944 1006 1201 942 1131 1583 1257 1070 1263 1647 939\n", + " 1582 1458 1580 1518 1069]\n", + "0 59\n", + "[3776 4032 3648 3909 4036 4037 3392 3714 3524 3906 3587 3460 3456 3843\n", + " 3520 4034 3586 3584 3717 3393 3394 3905 3712 3844 3970 3651 3716 3649\n", + " 3779 3590 3842 3845 3458 4035 4033 3585 3459 3718 3522 3523 3973 3654\n", + " 3781 3777 3910 3778 3972 3525 3907 3588 3521 3780 3904 3395 3589 3846\n", + " 3968 3974 3457 3841 3969 3713 3971 3715 3908 3653 3782 3652 3650 3840]\n", + "36 32\n", + "[1826 1765 2084 2404 2089 2210 1888 2406 1892 2402 2022 1699 1895 1697\n", + " 2018 2272 2214 2408 2015 2276 1760 2270 2088 1833 2344 2468 2342 1763\n", + " 2087 1952 2345 2147 2152 2026 2017 2090 1768 1829 2153 2078 1957 1954\n", + " 2149 1832 2020 2079 2407 2338 1894 1955 2014 2282 1961 2466 1698 1828\n", + " 2218 2206 2277 1962 2337 2335 1890 2271 2343 2146 2083 2150 2080 2278\n", + " 1896 2021 1950 2274 2154 2019 2339 1886 1956 1959 2025 1767 2211 2467\n", + " 2151 2209 2273 2279 2217 1827 1825 1887 2207 2081 2465 1703 1762 2471\n", + " 2405 1960 2086 2403 2144 1700 1951 2215 2400 2275 2216 2023 2212 1701\n", + " 1889 1897 1893 2016 1958 2082 1953 1830 2336 2145 2208 2340 2280 1823\n", + " 2469 2143 1702 2024 1831 2401 1766 1898 1824 1891 2213 2148 1764 2281\n", + " 2085 1761 2341 2142 2470]\n", + "23 63\n", + "[3858 4055 3990 4061 3865 3985 3996 4051 3799 3923 3668 3922 3671 3987\n", + " 3924 3926 3732 3993 3861 3859 3801 4057 3735 3933 4052 3738 4058 3866\n", + " 3802 3986 3995 4060 3863 3869 3921 3989 3798 3739 3672 3796 3927 3867\n", + " 3928 3929 3674 3925 3795 3868 3797 3669 3737 3731 3736 4053 4056 3803\n", + " 3734 3930 3994 4050 3997 3932 3862 3673 3670 4059 4049 3860 3931 3733\n", + " 3991 3988 3857 3800 3804 4054 3992 3864 3794]\n", + "1 3\n", + "[ 6 193 0 576 320 133 261 453 260 70 580 386 128 387 129 258 4 325\n", + " 517 257 2 449 448 263 390 515 577 1 513 516 130 68 66 256 321 65\n", + " 132 194 5 327 326 192 452 451 197 134 324 322 7 578 512 196 199 131\n", + " 323 262 389 195 388 198 454 3 71 450 259 514 135 64 391 67 69 384\n", + " 385 579]\n", + "40 26\n", + "[1826 1388 1765 1710 2027 1704 1570 2089 1508 1892 2022 1836 1699 1895\n", + " 1642 1581 1383 1638 1636 1965 1445 1708 1322 1575 2088 1773 1833 1509\n", + " 1572 1763 2087 1511 1963 1772 2026 1834 1578 2090 1512 1579 1768 1829\n", + " 1317 1451 1771 1640 1957 1769 1450 1452 1832 2020 1641 1894 1576 1571\n", + " 1955 1961 1698 1901 1828 1709 1319 1707 1962 1444 1506 1321 1323 1447\n", + " 1890 1837 1635 1387 1835 1964 1896 1770 1515 1510 2021 1899 1902 1446\n", + " 1577 1448 1956 1320 1959 2025 1767 1516 1900 1385 1827 1703 1644 1762\n", + " 1838 1705 1960 2086 1700 2023 1701 1386 1381 1897 1893 1514 1573 1453\n", + " 1507 1958 1774 1513 1830 1639 1384 1637 1449 1517 1645 1646 1382 1380\n", + " 1643 1318 1702 2024 1706 1831 1766 1898 1574 1891 1443 2091 1634 1764\n", + " 1582 2085 1580 1518 2028]\n", + "27 0\n", + "[ 27 32 153 21 25 149 222 281 89 94 156 350 96 33 224 213 409 214\n", + " 349 285 30 28 345 158 152 97 348 86 151 85 343 414 92 154 287 217\n", + " 347 91 159 150 225 31 279 221 29 220 284 344 411 223 22 90 95 286\n", + " 413 87 288 157 88 215 155 280 282 346 283 278 408 216 26 23 24 93\n", + " 219 351 161 218 160 412 410]\n", + "63 57\n", + "[3903 3583 4095 3711 3387 3839 3391 3705 3577 3517 3899 3772 3967 3644\n", + " 3837 3708 3769 4030 3836 3390 3965 4027 3962 3455 3775 3709 3897 3834\n", + " 4092 3389 3581 4094 3902 4029 3901 3838 4028 4093 3579 3513 3324 3326\n", + " 3643 3706 3578 3771 3642 4031 3646 3641 3707 3452 3514 3451 3966 3450\n", + " 3900 3515 3898 3454 3647 3773 3518 3453 3519 3645 3516 3582 3835 3327\n", + " 3833 3774 3963 3964 3710 3388 3580 3770 3325]\n", + "17 52\n", + "[3345 3275 3217 3730 3024 3086 3538 3215 3599 3342 3408 3025 2959 3148\n", + " 3027 3023 3668 3280 2960 3089 3154 3541 2963 3605 3534 3151 3084 3091\n", + " 3726 3728 3411 3732 3094 3470 3159 3476 3286 3287 3412 3414 3346 3598\n", + " 2964 3472 3155 3351 3415 3604 3663 3158 3536 3479 3223 3090 3413 3085\n", + " 3344 3404 3537 3218 3284 3087 3026 3153 3277 3410 3219 3531 3285 3468\n", + " 3341 3343 3467 3669 3542 3603 3731 3600 3221 3666 3532 3471 3028 3535\n", + " 3282 3406 3409 3029 3474 3021 3157 3664 3601 3149 3339 3606 3283 3533\n", + " 3405 3220 3665 3156 3539 3152 3347 3540 3597 3667 3092 3350 3276 3543\n", + " 3093 3727 3469 3477 3602 3348 3473 3222 2962 3150 3596 3729 3212 3278\n", + " 3662 3216 3478 3214 3349 3088 3213 3340 3403 3281 3661 2961 3211 3407\n", + " 3475 3279 2958 3022 3147]\n", + "59 52\n", + "[3583 3711 3387 3391 3071 3705 3128 3577 3517 3772 3644 3320 3263 3511\n", + " 3708 3769 3574 3390 3004 3189 3258 3191 3455 3129 3003 3196 3709 3384\n", + " 3262 3510 3389 3000 3445 3581 3064 3386 3130 3253 3260 3383 3382 3579\n", + " 3513 3324 3261 3326 3002 3643 3063 3706 3192 3317 3126 3578 3448 3771\n", + " 3257 3642 3646 3195 3256 3575 3006 3641 3707 3321 3193 3452 3639 3514\n", + " 3451 3131 3381 3319 3450 3447 3070 3067 3194 3197 3005 3322 3515 3318\n", + " 3132 3001 3385 3454 3647 3768 3066 3773 3640 3518 3449 3573 3453 3509\n", + " 3519 3198 3199 3645 3516 3133 3512 3582 3134 3327 3135 3774 3259 3638\n", + " 3704 3710 3254 3703 3388 3255 3446 3190 3065 3068 3576 3069 3580 3770\n", + " 3325 3127 3323]\n", + "57 6\n", + "[511 251 383 54 441 245 638 569 313 59 255 316 184 308 444 500 125 376\n", + " 507 380 447 438 631 319 757 186 575 181 57 253 699 247 826 636 189 315\n", + " 563 564 435 634 446 627 630 701 445 702 504 501 639 182 56 827 567 761\n", + " 694 382 252 117 378 314 249 121 122 190 440 566 439 764 187 828 760 632\n", + " 503 499 572 375 379 118 571 120 377 254 824 442 443 695 633 765 373 502\n", + " 307 629 318 510 696 310 183 637 508 188 185 565 180 436 506 692 250 574\n", + " 317 570 698 437 309 60 822 374 372 693 119 371 246 509 762 763 505 700\n", + " 248 697 758 55 312 635 244 573 124 823 381 58 628 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1839 2348 2087 2345 1963 1772 2474 1712 2152 1969 2026 1834 1841\n", + " 2090 2153 2034 1771 1769 1832 2352 2480 2160 2098 1905 2282 1961 2346\n", + " 2409 1901 1903 1709 2218 1971 2289 1707 1962 2225 2224 1837 2478 1835\n", + " 2417 2099 1964 1896 1770 2096 2030 1899 1902 2095 2288 2162 2154 1776\n", + " 1904 1959 2219 2025 2350 1970 2158 1900 2220 2477 2151 2163 2279 2217\n", + " 1968 2286 2351 1838 2031 2479 2290 2415 1960 2033 1777 2347 2156 2353\n", + " 2215 1967 2216 2023 2035 2092 1897 2291 2226 1774 2287 2283 2280 2412\n", + " 2094 2410 2157 2227 2024 1706 1775 1898 2285 2097 2091 2029 2223 2281\n", + " 1711 2476 2221 2028 1906]\n", + "15 9\n", + "[594 848 210 591 459 785 401 720 913 845 269 467 340 334 910 777 716 850\n", + " 521 659 976 842 526 914 404 332 523 596 779 724 978 402 339 655 653 852\n", + " 974 907 524 525 718 719 714 207 781 780 595 458 721 273 337 784 778 912\n", + " 393 847 651 532 715 208 468 789 723 396 849 529 722 782 209 846 338 908\n", + " 654 843 275 469 588 405 461 268 463 977 783 658 725 650 597 335 464 267\n", + " 649 398 717 911 400 206 399 533 331 844 465 522 593 787 527 530 336 587\n", + " 975 661 972 394 660 462 457 460 589 851 786 657 973 274 788 592 909 330\n", + " 590 271 915 403 586 531 204 205 333 397 652 270 272 656 585 528 713 395\n", + " 466]\n", + "11 51\n", + "[2957 3345 3275 3217 3145 3659 2891 3024 3526 3086 3215 3599 3342 3408\n", + " 2959 3148 3529 3023 3280 3089 3464 2955 3398 3527 3209 3206 3402 3534\n", + " 3151 3084 3205 3470 3018 3146 3274 3333 3598 3472 3528 3462 3399 3270\n", + " 3077 2951 3536 3273 3082 3017 3085 3344 3404 3080 3400 3656 2893 3087\n", + " 3141 3337 3463 3153 3277 3207 2889 3531 3079 3468 3595 3210 3341 3272\n", + " 3343 3271 3336 3467 3014 3532 3471 3083 3535 3406 3409 3397 3021 3149\n", + " 2956 3339 3461 3533 3143 3142 3405 3078 3338 3593 3152 3016 3591 3597\n", + " 3144 2888 3594 3657 3658 3401 2953 3276 3469 3015 3473 3150 3596 3212\n", + " 2894 3278 3662 3216 3214 3592 3334 3081 2890 3019 3208 3088 3213 3340\n", + " 3403 2952 3281 3530 3660 3466 2892 3661 3465 3269 3211 3407 2954 3335\n", + " 3279 3020 2958 3022 3147]\n", + "53 3\n", + "[251 54 441 245 239 569 313 59 184 308 500 114 376 370 438 631 177 186\n", + " 181 51 57 247 47 369 315 563 564 435 175 431 627 630 498 504 501 182\n", + " 56 567 111 304 117 378 179 314 249 240 121 122 116 440 368 566 439 187\n", + " 113 241 632 503 499 375 50 379 118 120 306 377 242 52 442 497 443 373\n", + " 502 303 307 432 626 629 562 310 183 53 176 112 185 49 565 180 436 506\n", + " 305 250 437 434 309 374 372 119 371 246 561 505 367 433 248 55 312 48\n", + " 244 496 58 628 115 243 178 311 568 123]\n", + "56 30\n", + "[1907 1976 1973 2235 1782 1842 1785 2100 1789 1914 1978 2294 2230 2164\n", + " 1854 2167 1849 1909 1848 2046 1652 2360 2034 2109 2166 1912 2228 2105\n", + " 2298 2108 2044 2098 2293 1715 2363 2296 2300 1778 2103 1717 1779 2170\n", + " 1658 1979 1918 1971 1594 1592 1595 1916 1719 2036 2041 1846 2099 2169\n", + " 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2721 2398 2661 2847 2589 2532 2272 2592 2461\n", + " 2659 2276 2780 2464 2270 2591 2468 2593 2333 2342 2654 2147 2723 2596\n", + " 2783 2535 2530 2716 2526 2658 2848 2533 2407 2338 2850 2331 2662 2462\n", + " 2715 2466 2911 2534 2268 2788 2206 2277 2594 2337 2722 2335 2524 2271\n", + " 2343 2652 2146 2269 2278 2460 2598 2789 2397 2274 2785 2339 2913 2597\n", + " 2663 2211 2784 2467 2209 2273 2590 2916 2588 2463 2207 2599 2587 2656\n", + " 2465 2334 2471 2787 2405 2403 2849 2144 2717 2523 2782 2915 2726 2400\n", + " 2275 2395 2396 2660 2786 2212 2781 2912 2845 2719 2595 2657 2527 2724\n", + " 2851 2336 2145 2914 2208 2727 2340 2653 2718 2469 2910 2143 2531 2725\n", + " 2459 2852 2528 2790 2401 2853 2205 2332 2213 2148 2651 2399 2525 2720\n", + " 2846 2655 2341 2142 2470]\n", + "53 30\n", + "[1840 1907 1976 1973 1713 2159 2354 2161 1782 2032 1842 1651 1785 2100\n", + " 1914 1978 1839 2294 2230 2164 2167 1712 1849 1909 1848 1969 1841 1588\n", + " 1652 2360 2034 2166 1912 2228 2105 2160 2098 2293 1715 2296 1905 1778\n", + " 1649 2103 1717 1903 1779 2170 1979 1971 2289 2225 2355 1592 2224 1719\n", + " 2036 2041 1586 1846 2099 2169 1851 1847 1908 2096 2038 2171 2039 2095\n", + " 2162 1714 2106 2037 1776 2358 1904 1970 1657 1650 2163 1783 1913 1968\n", + " 1845 2357 1972 2031 1915 2290 1910 2033 1587 1718 1777 2043 1716 1591\n", + " 2295 1967 1784 2035 2234 1781 2040 2291 1786 2231 2226 2104 1780 1655\n", + " 1720 1850 2292 2229 1975 1721 1787 2165 1844 1911 1974 2101 1977 2168\n", + " 1653 1590 1654 2227 1775 2232 1656 2356 2097 1589 1722 2359 1843 2102\n", + " 2107 2233 2042 1906 2297]\n", + "36 37\n", + "[2084 2529 2404 2210 2406 2402 2721 2022 2398 2661 2532 2018 2272 2592\n", + " 2214 2408 2659 2276 2464 2536 2270 2088 2591 2344 2468 2593 2342 2087\n", + " 2345 2602 2147 2723 2596 2474 2535 2538 2152 2530 2017 2153 2526 2658\n", + " 2149 2020 2533 2407 2473 2338 2662 2600 2462 2601 2282 2466 2346 2409\n", + " 2791 2534 2218 2788 2206 2277 2594 2337 2722 2335 2271 2343 2146 2083\n", + " 2150 2080 2278 2598 2472 2021 2789 2274 2019 2785 2339 2597 2663 2211\n", + " 2467 2151 2209 2273 2590 2279 2217 2664 2463 2207 2081 2599 2656 2465\n", + " 2334 2471 2787 2405 2086 2403 2144 2726 2215 2400 2275 2216 2660 2023\n", + " 2786 2212 2595 2657 2527 2724 2082 2336 2145 2208 2727 2340 2280 2728\n", + " 2469 2143 2531 2725 2410 2528 2790 2537 2401 2665 2213 2148 2399 2720\n", + " 2281 2085 2655 2341 2470]\n", + "17 50\n", + "[2957 3345 3275 3217 2832 3024 3086 3538 3215 3599 3342 3408 3025 2959\n", + " 3148 3027 3023 2897 3280 2960 3089 3154 3541 2963 3534 3151 3084 3091\n", + " 2896 3411 3094 3470 3159 3476 3030 2966 3286 3287 3412 3414 3346 3598\n", + " 2964 3472 2900 3155 2835 3351 3415 2898 3604 3158 3536 3223 3090 3413\n", + " 3085 3344 3404 3537 3218 3284 2893 3031 3087 3026 3153 3277 3410 3219\n", + " 2833 2830 3285 2834 3468 3341 3343 3603 3600 3221 3471 3083 3028 3535\n", + " 3282 3406 3409 3029 3474 3021 3157 3601 3149 2956 3339 3283 3533 3405\n", + " 3220 3156 3539 3095 3152 3347 3540 2901 2895 3092 3350 3276 2965 3093\n", + " 3469 3477 2899 3602 3348 3473 3222 2831 2962 3150 3212 2894 3278 3216\n", + " 3478 3214 3019 3349 3088 3213 3340 3403 3281 2836 2961 3211 3407 3475\n", + " 3279 3020 2958 3022 3147]\n", + "50 20\n", + "[1388 1392 1262 1330 1713 1136 1132 1519 1524 1581 1078 1389 1521 1399\n", + " 1203 1651 947 1461 1261 1525 1394 1207 1712 1588 1198 1072 1652 1200\n", + " 1140 1648 1135 1269 1139 1327 1452 1326 1144 1331 1336 1335 1455 1715\n", + " 1328 1009 1071 1460 1649 1456 1014 1717 1202 1268 1454 1585 949 1522\n", + " 1143 1523 1011 1334 1586 1584 1329 1205 1463 1393 1142 1390 1520 1138\n", + " 1714 1075 1008 1396 1527 1271 1141 1516 1457 1395 1010 1650 1324 1204\n", + " 1325 1391 1270 1464 948 1264 946 1197 1587 1076 1716 1591 1073 1013\n", + " 1077 1134 1398 1453 1459 1332 1196 1265 943 1272 1266 1397 1528 1199\n", + " 1517 1646 1007 1400 945 1267 1079 1653 1137 1074 1012 1590 1260 1654\n", + " 1462 1133 944 1006 1589 1201 1526 1583 1070 1263 1647 1582 1206 1711\n", + " 1458 1208 1518 1069 1333]\n", + "51 63\n", + "[3832 4083 3827 3959 4079 4086 3696 3699 3766 4013 3956 3954 3702 3895\n", + " 3823 3826 3886 4081 3887 4078 3822 3952 3760 3897 3885 3762 4019 4016\n", + " 3830 3890 4018 3698 3949 3828 3960 3700 3950 3763 3891 3824 3765 4085\n", + " 3961 4015 3893 4084 3958 4021 4022 4025 4014 3697 4024 3701 3764 3894\n", + " 4082 3951 3896 3759 3825 3957 4088 4017 4089 3831 3767 3888 3889 4020\n", + " 3953 4080 4077 3829 4087 3761 3892 3955 4023]\n", + "63 24\n", + "[1599 1403 1407 1785 1598 1402 1789 1983 1470 1854 1401 1465 1275 1277\n", + " 1406 1533 1405 1597 1404 1469 1658 1918 1338 1532 1594 1595 1276 1916\n", + " 1851 1530 1341 1660 1724 1723 1466 1531 1982 1593 1657 1853 1915 1980\n", + " 1213 1919 1467 1534 1788 1529 1725 1278 1981 1791 1786 1279 1661 1212\n", + " 1663 1850 1726 1339 1721 1596 1787 1215 1342 1790 1852 1727 1468 1535\n", + " 1855 1722 1659 1471 1343 1214 1917 1662 1340]\n", + "32 0\n", + "[ 38 27 32 35 222 163 94 156 350 96 33 224 415 164 349 226 285 98\n", + " 30 28 417 99 230 416 353 158 97 352 348 228 414 165 92 36 102 154\n", + " 287 100 91 159 225 31 101 290 289 221 29 220 284 356 223 90 162 354\n", + " 95 355 286 413 34 288 227 157 292 155 419 229 291 283 418 293 166 26\n", + " 93 219 351 37 161 218 160]\n", + "34 24\n", + "[1826 1765 1695 1704 1570 1888 1508 1892 1502 1699 1315 1383 1697 1504\n", + " 1638 1692 1636 1442 1311 1445 1760 1575 1376 1436 1509 1505 1503 1572\n", + " 1763 1511 1952 1252 1512 1768 1829 1317 1566 1640 1957 1500 1310 1954\n", + " 1186 1822 1894 1576 1571 1955 1374 1437 1698 1316 1758 1249 1828 1757\n", + " 1633 1319 1444 1693 1506 1447 1313 1890 1183 1379 1635 1628 1510 1821\n", + " 1378 1446 1189 1886 1448 1956 1373 1767 1314 1246 1827 1187 1825 1694\n", + " 1184 1630 1887 1501 1703 1762 1632 1375 1185 1440 1700 1951 1248 1701\n", + " 1889 1381 1569 1893 1573 1438 1507 1953 1250 1565 1830 1254 1564 1639\n", + " 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2605 2666]\n", + "17 21\n", + "[1682 1361 1553 1420 1104 1232 1039 1555 1679 1547 1621 1228 1358 1549\n", + " 1110 976 1489 1041 1170 1167 978 1295 974 1683 1680 1363 1612 1171\n", + " 1105 1291 1617 1488 1362 1230 1548 1492 1423 1165 1494 1554 1421 1367\n", + " 1490 1431 1175 1292 1483 1237 1619 1484 1299 1427 1622 1103 1491 1038\n", + " 1742 1558 1231 1550 1106 1042 1357 1227 1040 1426 1166 1485 1303 1238\n", + " 1107 1172 1356 1037 1677 1296 977 1495 1425 1365 1613 1493 1678 979\n", + " 1746 1293 1297 980 1233 1163 1616 1620 1109 1559 1101 1430 1618 1294\n", + " 1173 1615 1359 1557 975 1174 1100 1487 1239 1044 1102 1360 1229 1300\n", + " 1355 1108 1744 1681 1234 1419 1302 1551 1748 1422 1556 1684 1486 1366\n", + " 1298 1236 1169 1424 1428 1301 1743 1747 1364 1164 1043 1685 1429 1168\n", + " 1045 1614 1745 1235 1552]\n", + "19 1\n", + "[ 17 210 83 141 401 153 21 269 467 81 25 15 340 149 334 145 281 89\n", + " 143 404 402 339 213 82 144 214 207 341 13 273 337 80 14 147 208 211\n", + " 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4049 3729 3860 3931 3733 3991 3988\n", + " 3857 3984 3800 3804 4054 3992 3864 3794]\n", + "59 0\n", + "[ 63 251 383 54 441 191 245 313 59 255 316 184 444 125 376 380 319 186\n", + " 181 57 253 247 189 315 446 127 445 182 56 126 382 252 117 378 314 249\n", + " 121 122 190 440 187 375 379 118 120 377 254 442 443 318 310 183 53 188\n", + " 185 250 317 60 62 119 246 61 248 55 312 124 381 58 311 123]\n", + "49 43\n", + "[2542 2861 2801 2739 2671 2416 2927 2420 2930 2732 2678 3187 3182 3053\n", + " 2612 2545 2859 2795 2414 2541 2933 3054 2418 3058 2679 2806 2807 2742\n", + " 3055 2738 2990 2741 2993 2796 2860 2609 3117 2419 3121 2935 3118 2480\n", + " 2740 2864 2997 2805 2674 2928 2544 3125 2869 2668 2550 2798 2934 2996\n", + " 2608 3061 3123 2670 2868 2540 2478 2417 2607 3184 2995 2863 3186 3119\n", + " 2867 2992 2870 2989 2923 2736 3188 2477 2604 2549 2987 2999 3056 2548\n", + " 2803 2667 3122 2479 2802 2483 2415 2676 2482 2737 2669 2924 2611 2865\n", + " 2615 2614 2672 2734 2613 3059 2991 2543 2929 2804 2998 2988 2871 2994\n", + " 2931 2675 2606 3120 2673 3183 2485 2800 2797 3052 3057 2925 2484 2866\n", + " 2731 2610 2481 2733 2603 2546 2547 2932 2926 2862 2799 3062 3060 2743\n", + " 3185 2735 2605 2677 3124]\n", + "38 37\n", + "[2084 2529 2404 2089 2210 2406 2402 2022 2475 2661 2532 2272 2592 2284\n", + " 2214 2408 2730 2659 2276 2464 2536 2088 2344 2468 2593 2411 2155 2342\n", + " 2348 2087 2345 2602 2147 2723 2596 2474 2535 2538 2152 2530 2090 2153\n", + " 2658 2149 2020 2533 2407 2473 2338 2662 2600 2601 2282 2466 2346 2409\n", + " 2791 2534 2218 2788 2277 2594 2337 2722 2540 2343 2146 2083 2150 2278\n", + " 2598 2472 2729 2021 2789 2274 2154 2019 2339 2219 2597 2025 2663 2211\n", + " 2220 2604 2467 2151 2209 2273 2279 2217 2664 2599 2667 2465 2539 2471\n", + " 2787 2405 2086 2403 2347 2726 2215 2400 2275 2216 2660 2023 2212 2792\n", + " 2595 2657 2724 2082 2793 2336 2145 2283 2208 2727 2340 2280 2728 2469\n", + " 2412 2531 2725 2410 2024 2528 2790 2537 2401 2665 2213 2148 2603 2281\n", + " 2085 2341 2476 2470 2666]\n", + "36 29\n", + "[1826 1765 1695 2084 1704 1570 2089 2210 1888 1508 1892 2022 1699 1895\n", + " 1697 2018 1638 2214 1636 2015 2276 1760 1575 2088 1833 1509 1505 1572\n", + " 1763 2087 1511 1952 2147 2152 2026 2017 1834 2090 1768 1829 2153 2078\n", + " 1640 1957 1769 1954 2149 1832 2020 1822 2079 1641 1894 1576 1571 1955\n", + " 2014 1961 1698 1758 1828 1633 2277 1962 1506 1890 2146 2083 2150 2080\n", + " 2278 1635 1896 1770 1510 2021 1950 2274 2019 1886 1956 1959 2025 1767\n", + " 2211 2151 2209 2273 2279 1827 1825 1694 1887 2081 1703 1762 1705 1632\n", + " 1960 2086 2144 1700 1951 2215 2275 2216 2023 2212 1701 1889 1569 1897\n", + " 1893 1573 1507 2016 1958 2082 1953 1830 1639 2145 1637 1759 2208 1696\n", + " 1823 2143 1702 2024 1706 1831 1766 1898 1824 1574 1891 2213 2148 1634\n", + " 1631 1764 2085 1761 1568]\n", + "16 11\n", + "[ 594 848 591 459 785 401 720 913 845 467 1104 1039 334 910\n", + " 716 850 659 976 842 526 1041 914 404 523 596 779 724 978\n", + " 917 402 339 655 653 852 974 907 524 525 718 719 906 714\n", + " 781 1105 780 595 721 337 971 784 778 912 847 651 532 715\n", + " 468 789 662 723 396 1103 849 1038 529 722 782 846 338 908\n", + " 1106 534 1042 1040 790 654 843 469 588 918 726 1107 1037 461\n", + " 463 977 853 783 658 725 650 597 335 464 979 398 717 854\n", + " 911 1036 980 400 399 533 844 465 522 1101 593 787 527 530\n", + " 336 587 975 661 972 660 462 460 1044 589 851 786 657 1102\n", + " 973 981 788 592 909 590 915 403 586 531 916 333 397 652\n", + " 598 1043 656 528 466]\n", + "25 2\n", + "[ 27 83 153 21 25 340 149 222 281 89 94 156 404 350 472 537 339 213\n", + " 471 409 535 214 349 341 540 285 477 30 28 345 147 211 212 158 152 348\n", + " 86 151 85 343 414 92 154 287 217 534 347 91 407 159 150 539 275 469\n", + " 31 277 342 405 473 279 221 29 220 284 344 411 20 276 536 223 22 90\n", + " 148 95 286 413 87 157 88 215 155 280 282 476 346 283 470 278 408 216\n", + " 26 406 23 19 24 93 219 351 538 218 84 474 412 475 410]\n", + "47 33\n", + "[2027 1840 1907 2093 1973 2089 2542 2159 1836 2354 2475 1966 2416 2161\n", + " 2284 2420 2032 2222 1965 1842 2349 2545 1773 2100 2413 2414 2541 2411\n", + " 2155 1839 2418 2348 2345 1963 1772 2164 1969 2026 1841 2090 2153 2034\n", + " 2228 2419 2352 2480 2160 2544 2098 2293 1905 2282 1961 2346 1778 1901\n", + " 1903 2218 1971 2289 1962 2225 2355 2540 2224 1837 2036 2478 1835 2417\n", + " 2099 1964 1908 2096 2030 1899 1902 2095 2288 2162 2154 2037 1776 1904\n", + " 2219 2025 2350 1970 2158 1900 2220 2477 2163 2217 1968 2286 2357 1972\n", + " 2351 1838 2031 2479 2483 2290 2415 2033 2482 1777 2347 2156 2353 1967\n", + " 2035 2092 2543 2291 2226 1774 2292 2287 2229 2283 2165 2101 2412 2094\n", + " 2410 2157 2227 1775 1898 2481 2285 2356 2097 2091 2029 2546 2223 2281\n", + " 1843 2476 2221 2028 1906]\n", + "30 23\n", + "[1826 1695 1570 1888 1508 1502 1699 1315 1697 1504 1692 1636 1442 1311\n", + " 1885 1760 1376 1121 1305 1436 1560 1505 1503 1572 1763 1819 1883 1818\n", + " 1115 1117 1566 1371 1500 1245 1310 1186 1822 1571 1374 1180 1437 1698\n", + " 1316 1758 1249 1561 1307 1757 1241 1633 1433 1306 1444 1693 1506 1754\n", + " 1562 1499 1313 1183 1379 1635 1181 1628 1821 1378 1627 1432 1626 1116\n", + " 1886 1373 1314 1246 1497 1243 1825 1625 1368 1694 1184 1630 1887 1501\n", + " 1762 1632 1375 1178 1185 1369 1755 1440 1700 1498 1248 1889 1569 1496\n", + " 1820 1370 1438 1242 1507 1434 1753 1250 1689 1565 1564 1435 1759 1439\n", + " 1884 1696 1380 1567 1312 1823 1629 1251 1118 1179 1691 1120 1824 1119\n", + " 1624 1443 1634 1631 1308 1690 1563 1761 1688 1244 1182 1441 1304 1309\n", + " 1372 1247 1568 1377 1756]\n", + "63 17\n", + "[ 831 1151 1023 1146 767 1403 1407 1402 958 829 1018 1147 830 956\n", + " 1470 954 1085 894 1021 1275 1277 1406 1533 1405 827 1404 1469 1338\n", + " 1148 1532 1276 764 1273 828 1341 1211 893 765 1019 1081 955 766\n", + " 1020 1337 892 1213 1467 890 1534 1210 1278 1086 1279 895 1212 1082\n", + " 1017 1339 957 1022 1215 1342 1209 1468 959 953 1535 1274 1471 1343\n", + " 1087 1214 891 1145 1084 1150 1340 1083 1149]\n", + "0 63\n", + "[4038 3776 4032 3648 3909 4036 4037 3714 3906 3843 4034 3905 3712 3844\n", + " 3970 3651 3716 3649 3779 3842 3845 4035 4033 3973 3781 3777 3910 3778\n", + " 3972 3907 3780 3904 3846 3968 3974 3841 3969 3713 3971 3715 3908 3650\n", + " 3840]\n", + "11 57\n", + "[3275 3659 3914 3526 3919 3786 4043 3599 3342 3408 3790 3847 3529 3983\n", + " 3980 3853 4041 3464 3851 3979 3398 3527 3402 3534 3726 3913 3728 3982\n", + " 3975 3470 3854 3788 3978 3274 3598 3717 3915 4042 3472 3528 3462 3399\n", + " 3663 3911 3920 3536 3273 3590 3404 3856 3400 3656 3845 3537 3848 3337\n", + " 3463 3277 3977 3791 3718 3855 3531 3654 3468 3595 3781 3341 3272 3343\n", + " 3336 3467 3600 3725 3532 3471 3535 3406 3719 3910 3793 3852 3525 4044\n", + " 3664 3601 3916 3789 3339 3461 3533 3655 4045 3405 3665 3338 3593 3591\n", + " 3597 3976 3589 3594 3657 3846 3658 3850 3401 3722 3276 3721 3727 3849\n", + " 3469 3720 3792 3981 3473 3917 3596 3729 4040 3785 3278 3662 3592 3340\n", + " 3403 3530 3912 3787 3857 3723 3660 3466 3661 3918 3465 3407 3783 4046\n", + " 3784 3653 3335 3782 3724]\n", + "11 45\n", + "[2887 2957 3275 3145 2832 2891 3024 3086 3215 3025 2959 2705 3148 2765\n", + " 3023 2767 2897 2762 2960 3089 2509 2955 3209 3151 3084 2759 2896 2637\n", + " 3018 3146 2569 2631 2758 2766 2694 2825 3274 2630 2633 2760 2827 2695\n", + " 2639 2571 2698 2510 3077 2951 3013 2768 3273 2640 3082 2886 3017 3085\n", + " 2950 3080 2570 2703 2702 2568 2567 2693 2893 3087 2572 3277 2761 3207\n", + " 2757 2833 2830 2889 3079 3210 3272 3014 3083 2826 2636 2635 2700 2824\n", + " 3021 2949 2764 3149 2956 3143 3142 3078 3152 3016 3144 2697 2888 2505\n", + " 2895 2953 2828 3276 2508 2634 2696 3015 2829 2821 2831 3150 2575 3212\n", + " 2894 2632 3278 3214 2769 2822 3081 2890 2507 3019 3208 3088 3213 2952\n", + " 2574 2506 2823 2892 2885 2504 2961 2704 3211 2763 2954 2638 3020 2958\n", + " 3022 2573 2699 2701 3147]\n", + "17 38\n", + "[2322 2832 2449 2191 2454 2195 2317 2444 2705 2326 2129 2765 2319 2062\n", + " 2067 2767 2516 2509 2131 2445 2263 2325 2257 2380 2578 2196 2647 2637\n", + " 2318 2327 2453 2188 2387 2766 2254 2582 2255 2391 2125 2451 2583 2709\n", + " 2447 2517 2064 2198 2770 2381 2639 2835 2455 2571 2443 2510 2127 2193\n", + " 2126 2768 2640 2384 2262 2703 2379 2702 2518 2065 2258 2324 2572 2577\n", + " 2646 2708 2128 2773 2710 2833 2385 2830 2834 2390 2579 2133 2515 2772\n", + " 2580 2383 2192 2253 2643 2706 2315 2514 2251 2388 2636 2386 2511 2635\n", + " 2707 2700 2513 2576 2645 2644 2450 2194 2132 2066 2259 2508 2642 2448\n", + " 2512 2389 2320 2831 2641 2321 2575 2769 2581 2197 2507 2574 2316 2452\n", + " 2382 2836 2252 2446 2068 2704 2260 2063 2189 2130 2638 2323 2256 2190\n", + " 2261 2573 2519 2701 2771]\n", + "34 37\n", + "[2084 2529 2404 2210 2406 2402 2721 2398 2661 2589 2532 2018 2272 2592\n", + " 2461 2214 2408 2015 2659 2276 2464 2536 2270 2591 2344 2468 2593 2333\n", + " 2342 2654 2147 2723 2596 2783 2535 2530 2017 2078 2526 2658 2149 2020\n", + " 2079 2533 2407 2338 2662 2600 2462 2466 2534 2204 2268 2788 2206 2277\n", + " 2594 2337 2722 2335 2524 2271 2343 2146 2083 2269 2150 2080 2278 2460\n", + " 2598 2472 2021 2789 2397 2274 2019 2785 2339 2141 2597 2663 2211 2784\n", + " 2467 2151 2209 2273 2590 2279 2588 2463 2207 2081 2599 2656 2465 2334\n", + " 2471 2787 2405 2086 2403 2144 2726 2215 2400 2275 2396 2216 2660 2786\n", + " 2212 2719 2595 2657 2527 2724 2016 2082 2336 2145 2208 2340 2280 2653\n", + " 2718 2469 2143 2531 2725 2528 2401 2205 2332 2213 2148 2399 2525 2720\n", + " 2085 2655 2341 2142 2470]\n", + "63 3\n", + "[ 63 511 251 383 441 191 638 313 59 255 316 444 125 507 380 447 319 186\n", + " 575 57 253 636 189 315 446 127 445 639 126 382 252 378 314 249 121 122\n", + " 190 187 572 379 571 377 254 442 443 318 510 637 508 188 185 506 250 574\n", + " 317 60 62 509 61 573 124 381 58 123]\n", + "10 61\n", + "[4038 4047 3659 3914 3919 3786 4043 3790 3847 3529 3983 3980 3853 3909\n", + " 4041 3851 3979 3527 4036 4037 3726 3913 3728 3982 3975 3854 4039 3788\n", + " 3978 3598 3717 3915 4042 3528 3844 3663 3716 3911 3920 3590 3856 3656\n", + " 3845 3848 3977 3791 3718 3855 3531 3973 3654 4048 3595 3781 3725 3532\n", + " 3719 3910 3852 3972 4044 3916 3789 3533 3655 4045 3593 3591 3597 3780\n", + " 3976 3594 3657 3846 3658 3850 3722 3721 3727 3849 3720 3974 3792 3981\n", + " 3917 3596 4040 3785 3662 3592 3530 3912 3787 3723 3660 3984 3661 3918\n", + " 3783 3908 4046 3784 3653 3782 3724]\n", + "33 42\n", + "[2529 2404 2402 2721 2398 2661 2847 2589 2532 2592 2461 2659 2780 2464\n", + " 2973 2591 2843 2468 2593 2844 3040 2654 2723 2596 2783 2535 2530 2716\n", + " 3044 2526 2978 2658 2848 3105 2533 2779 2974 2338 2977 2850 2662 2462\n", + " 2715 2466 2911 2791 2534 2919 2788 3045 2854 2594 2337 2722 2335 2524\n", + " 2652 3102 3038 2909 2460 2598 2789 2918 2981 2397 2982 2785 3041 2339\n", + " 2913 2597 2907 3042 2663 2784 2467 2590 2916 2588 2463 2599 2587 2656\n", + " 2465 3037 2334 2917 2787 2405 2403 2855 2849 2717 2523 2782 2915 2726\n", + " 2980 3107 2400 2660 2786 2781 2912 3108 2845 2719 2595 2657 2527 2724\n", + " 2851 2976 2975 2336 2914 2979 2727 2340 2653 3103 2718 2469 3104 2910\n", + " 2531 2725 2852 2528 2790 2401 2853 3039 2651 2908 2399 3043 2525 3106\n", + " 2720 2846 2655 2972 2470]\n", + "12 22\n", + "[1098 1361 1741 1553 1420 1353 1804 1104 1232 1039 1679 1035 1547 1228\n", + " 1358 1549 1489 1737 1167 1806 1350 1295 1099 1674 1680 1612 1351 1478\n", + " 1739 1291 1617 1543 1287 1488 1362 1230 1672 1548 1423 1165 1554 1421\n", + " 1490 1671 1292 1096 1286 1807 1483 1484 1675 1544 1103 1481 1160 1546\n", + " 1038 1222 1802 1224 1542 1676 1742 1231 1803 1480 1223 1550 1545 1414\n", + " 1357 1227 1607 1426 1801 1166 1485 1354 1356 1037 1159 1677 1296 1288\n", + " 1738 1425 1613 1289 1033 1678 1606 1293 1297 1611 1036 1233 1163 1290\n", + " 1616 1101 1618 1294 1482 1162 1615 1359 1479 1100 1487 1226 1102 1034\n", + " 1360 1229 1352 1355 1418 1673 1744 1417 1681 1234 1419 1551 1805 1422\n", + " 1486 1416 1298 1169 1424 1610 1743 1164 1736 1609 1168 1097 1161 1740\n", + " 1614 1225 1608 1552 1415]\n", + "15 34\n", + "[2322 1809 2000 1870 2449 2191 1804 2195 2317 2444 1875 1997 2129 2319\n", + " 2062 2067 2314 2004 2509 2131 1806 1808 2445 2325 2257 2380 1930 2313\n", + " 2578 2196 1932 2318 2188 1873 2387 2254 2255 2125 2378 2451 2447 1867\n", + " 2064 1869 1933 2381 2377 2185 2443 2510 2127 2058 2193 1807 1868 2126\n", + " 1931 2005 1937 2384 2442 2002 2249 2379 2059 2186 2250 2065 2258 2324\n", + " 2572 2577 2128 1939 2122 2385 2121 2133 2515 2003 2383 1995 2192 2253\n", + " 2315 2514 2251 1993 2388 2001 2386 2511 1935 2513 2576 1810 2450 2194\n", + " 1934 2132 1936 2066 2259 2508 1999 2061 1938 2448 2512 2389 2320 2321\n", + " 2575 1805 1871 2197 2057 2507 1872 2187 1996 2574 2316 2452 2382 2060\n", + " 2252 1874 2446 2068 2260 2063 2189 2069 2130 2323 2123 2256 2124 2190\n", + " 2261 1998 1940 2573 1994]\n", + "40 61\n", + "[4074 4068 3944 3877 4013 4071 4066 3884 3874 3810 3886 3757 4004 4073\n", + " 4078 4072 3822 4069 3947 3621 3623 3747 3948 3885 3559 3748 3689 3561\n", + " 4007 3626 3754 3949 3817 3950 3880 3946 3749 3627 4076 3691 3758 3620\n", + " 3687 4075 3557 3684 4014 3751 3625 4002 3882 4006 3879 3558 4067 3875\n", + " 4012 3746 3692 3818 3820 3814 3756 3563 3628 3811 3812 3685 4070 3815\n", + " 4011 3878 3941 3819 4009 3821 3813 3683 3938 4003 3752 4008 3755 3881\n", + " 3816 3624 3943 3876 4010 3688 3753 4077 3622 3883 3942 3939 3940 3693\n", + " 3686 4005 3562 3560 3750 3945 3690]\n", + "63 59\n", + "[3903 3583 4095 3711 3839 3705 4026 3517 3899 3772 3967 3644 3837 3708\n", + " 3769 4030 3836 3965 4027 3962 3455 3775 3709 4091 3897 3834 4092 3581\n", + " 4094 3902 4029 3901 3838 4028 4093 3579 3643 3706 3961 4090 3578 3771\n", + " 4025 3642 4031 3646 3641 3707 3452 3966 3900 3515 3898 3454 3647 3773\n", + " 3518 3453 3519 3645 3516 3582 3835 3833 3774 3963 3964 3710 3580 3770]\n", + "63 52\n", + "[3583 3711 3387 3391 3071 3577 3517 3772 3644 3263 3708 3390 3004 3258\n", + " 3455 3775 3196 3709 3262 3389 3581 3386 3130 3260 3579 3513 3324 3261\n", + " 3326 3643 3578 3007 3257 3642 3646 3195 3006 3707 3321 3193 3452 3514\n", + " 3451 3131 3450 3070 3067 3194 3197 3005 3322 3515 3132 3385 3454 3647\n", + " 3773 3518 3449 3453 3519 3198 3199 3645 3516 3133 3582 3134 3327 3135\n", + " 3774 3259 3710 3388 3068 3069 3580 3325 3323]\n", + "38 31\n", + "[1826 1765 2027 2084 1704 2404 2089 2210 1888 2406 1892 2022 1836 1699\n", + " 1895 2018 1638 2214 2408 1636 2276 2088 1833 2344 2155 2342 1763 2087\n", + " 1952 2345 1963 2147 2152 2026 2017 1834 2090 1768 1829 2153 1771 1640\n", + " 1957 1769 1954 2149 1832 2020 2407 1641 2338 1894 1955 2282 1961 2346\n", + " 2409 1698 1828 2218 2277 1962 1890 2343 2146 2083 2150 2080 2278 1635\n", + " 1835 1964 1896 1770 2021 1899 2274 2154 2019 2339 1956 1959 2219 2025\n", + " 1767 2211 1900 2220 2151 2209 2273 2279 2217 1827 1825 2081 1703 1762\n", + " 1705 2405 1960 2086 2403 2144 1700 2156 2215 2275 2216 2023 2212 1701\n", + " 2092 1889 1897 1893 2016 1958 2082 1953 1830 1639 2145 1637 2283 2208\n", + " 2340 2280 1702 2024 1706 1831 1766 1898 1824 1891 2213 2148 2091 1764\n", + " 2281 2085 1761 2341 2028]\n", + "44 28\n", + "[1710 2027 1840 1704 2093 1713 2089 2159 2022 1836 1895 1519 1642 1581\n", + " 1966 1638 2032 2222 1965 1842 1708 1575 2088 1773 1833 2155 1839 2087\n", + " 1963 1772 1712 2152 1969 2026 1834 1841 1578 2090 1512 1579 1768 2153\n", + " 2034 1451 1771 1648 1640 1769 1450 1452 1832 1641 1894 1576 2160 1455\n", + " 1905 1961 1778 1649 1901 1903 1454 1709 2218 1585 1707 1962 1837 1835\n", + " 1584 1964 1896 1770 1515 2096 2030 1899 1902 1520 2095 1714 2154 1776\n", + " 1904 1577 1959 2219 2025 1767 1970 1516 2158 1900 2220 1650 2217 1968\n", + " 1703 1644 1838 2031 1705 1960 2033 1777 2156 1967 2023 2092 1897 1514\n", + " 1453 1958 1774 1513 1830 1639 1449 1517 1645 1646 1643 2094 1702 2157\n", + " 2024 1706 1831 1775 1766 1898 2097 2091 2029 1583 2223 1647 1582 1711\n", + " 1580 1518 2221 2028 1906]\n", + "63 47\n", + "[3387 3391 3071 2878 3263 3390 3004 3258 3455 3129 3003 3196 2873 2815\n", + " 2811 2943 3262 3389 2879 2814 2877 2940 3130 3260 2749 2685 2748 2750\n", + " 2687 3324 3261 3326 3002 2686 3007 2747 3257 2875 2938 3195 2937 3006\n", + " 3193 3452 2939 2941 3131 2751 3070 3067 3194 2812 3197 3005 3322 3132\n", + " 3001 2684 3454 3066 3453 2876 2813 3198 3199 3133 3134 3327 2942 3135\n", + " 3259 2810 2874 3388 3065 3068 3069 3325 3323]\n", + "55 61\n", + "[3832 4083 3827 3959 3705 4086 3577 4026 3899 3772 3699 3766 3956 3837\n", + " 3708 3769 3954 3702 3895 3826 3574 3836 3965 4027 4081 3636 3962 4091\n", + " 3897 3834 4092 3762 4019 4029 3830 3572 3901 3890 4028 4018 3698 4093\n", + " 3828 3960 3700 3763 3891 3643 3706 3765 4085 3961 4090 3578 3771 3893\n", 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2232 2610 2481 2285 2356 2097 2487 2546 2547 2223 2359 2102 2476 2735\n", + " 2424 2605 2221 2551 2677]\n", + "16 47\n", + "[2957 3345 3275 3217 2832 2891 3024 3086 3215 3342 3408 3025 2959 2705\n", + " 3148 3027 2765 3023 2767 2897 3280 2960 3089 3154 2955 2963 3151 3084\n", + " 3091 2896 3411 2637 3094 3018 3030 2966 3146 2766 3346 2964 2827 2770\n", + " 2900 3155 2639 2835 2898 3158 2768 2640 3082 3090 3085 3344 2703 2702\n", + " 3218 3284 2893 3087 3026 3153 3277 2708 2773 3410 3219 2833 2830 3285\n", + " 2834 3210 3341 3343 2772 2643 3221 2706 3083 3028 2826 3282 3406 3409\n", + " 3029 2707 2700 3021 3157 2764 3149 2956 3283 3405 3220 3156 3152 3347\n", + " 2901 2895 3092 2828 3276 2965 3093 2837 2642 2899 3348 2829 2838 3222\n", + " 2831 2962 3150 2641 3212 2894 3278 3216 3214 2769 2890 2902 3019 3088\n", + " 3213 3340 3281 2836 2892 2961 2704 3211 2763 3407 2954 2638 3279 3020\n", + " 2958 3022 2701 2771 3147]\n", + "0 0\n", + "[ 6 193 0 320 133 261 260 70 386 128 387 129 258 4 257 2 1 130\n", + " 68 66 256 321 65 132 194 5 192 197 134 324 322 196 131 323 195 198\n", + " 3 259 64 67 69 384 385]\n", + "30 37\n", + "[2529 2404 2210 2402 2721 2398 2589 2532 2272 2592 2461 2015 2267 2659\n", + " 2276 2780 2456 2464 2394 2270 2200 2591 2468 2593 2333 2654 2011 2147\n", + " 2596 2783 2530 2017 2265 2077 2521 2078 2716 2522 2526 2658 2079 2779\n", + " 2338 2584 2014 2331 2462 2715 2466 2585 2204 2268 2206 2594 2337 2722\n", + " 2335 2524 2271 2652 2146 2269 2080 2460 2012 2330 2393 2397 2274 2785\n", + " 2339 2141 2203 2649 2329 2075 2211 2202 2784 2467 2209 2273 2590 2588\n", + " 2264 2458 2463 2207 2081 2587 2656 2465 2334 2137 2403 2328 2144 2717\n", + " 2523 2782 2392 2400 2275 2395 2396 2266 2201 2212 2781 2719 2595 2657\n", + " 2457 2527 2016 2082 2336 2145 2208 2340 2653 2718 2143 2531 2459 2528\n", + " 2139 2140 2401 2205 2332 2076 2651 2650 2399 2525 2720 2714 2074 2138\n", + " 2655 2520 2142 2013 2586]\n", + "60 4\n", + "[ 63 511 251 383 441 191 638 569 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779 724 978 917 1295\n", + " 655 1099 653 852 974 907 718 719 855 1171 781 1105 780 595\n", + " 721 971 982 1230 784 912 1165 847 791 532 715 789 662 723\n", + " 1046 1237 1299 1103 849 1038 529 722 782 1231 846 908 1106 1042\n", + " 1040 919 790 983 654 1166 843 918 726 1107 1172 1037 1047 1296\n", + " 977 853 783 658 725 597 979 717 854 911 1297 1036 980 1233\n", + " 1111 844 1109 1101 593 787 527 530 1294 1173 975 661 972 660\n", + " 1174 1100 1044 589 851 786 657 1102 973 981 1229 1300 1108 788\n", + " 592 909 590 915 1234 531 916 1298 1236 652 1169 1164 1043 727\n", + " 1168 656 528 1045 1235]\n", + "56 2\n", + "[251 54 441 245 569 313 59 316 184 308 444 500 114 125 376 370 507 380\n", + " 438 186 181 51 57 253 247 189 315 435 445 504 501 182 56 567 126 382\n", + " 252 117 378 179 314 249 121 122 190 116 440 566 439 187 503 375 50 379\n", + " 118 571 120 306 377 254 242 52 442 443 373 502 307 318 310 183 53 508\n", + " 188 185 565 180 436 506 250 317 570 437 309 60 62 374 372 119 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212 152 86 151 85\n", + " 146 343 209 154 338 217 91 407 150 275 79 277 342 405 279 344 20 16\n", + " 276 22 90 148 18 87 88 215 155 280 282 274 403 278 408 216 26 406\n", + " 23 19 24 219 218 272 84]\n", + "6 55\n", + "[3275 3145 3659 3526 3786 3648 3847 3529 3909 3464 3398 3527 3209 3206\n", + " 3402 3140 3913 3392 3714 3524 3205 3332 3587 3460 3456 3274 3843 3333\n", + " 3520 3586 3584 3717 3393 3394 3712 3528 3844 3462 3399 3204 3266 3651\n", + " 3267 3270 3203 3716 3649 3911 3273 3779 3590 3404 3202 3842 3400 3656\n", + " 3845 3458 3331 3268 3848 3141 3337 3265 3463 3585 3207 3459 3718 3522\n", + " 3531 3523 3654 3468 3595 3781 3210 3272 3271 3336 3467 3777 3532 3719\n", + " 3910 3778 3397 3525 3907 3396 3339 3461 3143 3655 3142 3588 3338 3593\n", + " 3591 3521 3780 3144 3395 3589 3594 3657 3846 3658 3850 3330 3401 3722\n", + " 3329 3139 3721 3849 3720 3457 3596 3785 3592 3334 3713 3208 3340 3403\n", + " 3328 3530 3912 3787 3723 3660 3466 3465 3269 3715 3783 3908 3784 3653\n", + " 3335 3782 3652 3650 3724]\n", + "13 13\n", + "[1098 594 848 591 459 785 720 913 845 1104 1232 1039 1035 1228\n", + " 910 777 716 850 839 521 659 976 842 526 1041 914 1167 523\n", + " 779 978 655 1099 653 974 907 524 525 718 719 906 714 781\n", + " 1105 780 458 721 711 775 971 1230 784 778 912 1165 905 847\n", + " 651 715 970 1096 723 1103 849 1038 529 776 722 782 1231 846\n", + " 841 908 1106 1042 1227 1040 654 1166 843 588 1037 461 463 977\n", + " 783 658 650 903 464 1032 1033 712 648 649 979 717 911 968\n", + " 1036 1163 844 522 1101 593 787 527 587 1162 975 972 1100 462\n", + " 460 589 851 786 657 1226 1102 973 1034 1229 967 904 592 909\n", + " 590 915 586 840 1031 969 652 1169 584 647 1164 1043 1168 656\n", + " 585 528 713 1097 1161]\n", + "57 57\n", + "[3903 3583 3832 3711 3387 3827 3839 3959 3705 4086 3577 4026 3517 3899\n", + " 3772 3644 3320 3699 3766 3511 3956 3837 3708 3769 3702 3895 3574 3836\n", + " 3965 4027 3636 3962 3775 3709 4091 3897 3834 3384 3510 4092 3389 3445\n", + " 3581 3386 3902 4029 3830 3572 3901 3838 4028 3383 3382 3579 3513 3828\n", + " 3960 3700 3324 3763 3891 3643 3706 3765 3961 4090 3578 3448 3771 3893\n", + " 3958 4021 3444 4022 4025 3642 3646 3575 4024 3641 3707 3701 3321 3507\n", + " 3452 3639 3514 3451 3764 3381 3894 3966 3319 3450 3447 3896 3900 3322\n", + " 3515 3957 3898 4088 3318 3385 3454 3647 3768 3637 4089 3773 3640 3518\n", + " 3449 3573 3635 3453 3509 3519 3645 3516 3831 3512 3767 3582 3835 3833\n", + " 3571 3774 3638 3963 3508 3964 3704 3710 3829 4087 3703 3388 3446 3576\n", + " 3892 3580 3770 4023 3323]\n", + "35 26\n", + "[1826 1765 1695 2084 1704 1570 1888 1508 1892 1502 2022 1699 1895 1315\n", + " 1383 1697 1504 2018 1638 1636 1442 2015 1885 1445 1760 1575 1833 1376\n", + " 1509 1505 1503 1572 1763 1511 1952 2017 1512 1768 1829 1317 1566 1640\n", + " 1957 1769 1954 1832 2020 1822 1641 1894 1576 1571 1955 1698 1316 1758\n", + " 1828 1757 1633 1444 1693 1506 1447 1313 1890 2083 1379 2080 1635 1896\n", + " 1510 2021 1950 1821 1378 1446 2019 1886 1577 1448 1956 1959 1767 1314\n", + " 1827 1825 1694 1630 1887 2081 1501 1703 1762 1705 1632 1375 1960 2086\n", + " 1440 1700 1951 2023 1701 1889 1381 1569 1897 1893 1573 1438 1507 2016\n", + " 1958 2082 1953 1513 1565 1830 1639 1637 1759 1439 1696 1382 1380 1567\n", + " 1312 1823 1629 1318 1702 1831 1766 1824 1574 1891 1443 1634 1631 1764\n", + " 2085 1761 1441 1568 1377]\n", + "51 42\n", + "[2542 2861 2801 2354 2739 2671 2416 2927 2420 2930 2678 2612 2545 2617\n", + " 2541 2933 2418 3058 2679 2806 2807 2742 2872 3055 2738 2990 2741 2993\n", + " 2873 2609 2419 3121 2488 2935 2352 3000 2480 2740 2864 2936 2997 2805\n", + " 2674 2553 2928 2544 3125 2869 2550 2798 2934 2552 2996 2422 2608 3061\n", + " 3123 2670 2355 2868 3063 2478 2417 2607 2995 2863 2867 3126 2992 2421\n", + " 2870 2358 2736 2937 2549 2999 2680 3056 2548 2803 2357 3122 2479 2802\n", + " 2483 2415 2423 2676 2482 2737 2669 2353 2486 2611 2865 2615 2614 2672\n", + " 2734 2613 3059 2991 2543 2929 2804 2744 2998 2808 2871 2994 2931 2675\n", + " 2606 3120 2673 2485 2800 2797 3057 2925 2484 2866 2681 2610 2809 2481\n", + " 2356 2487 2733 2546 2547 2745 2932 2926 2862 2799 3062 3060 2743 2735\n", + " 2605 2551 2677 2616 3124]\n", + "43 1\n", + "[ 45 38 239 107 168 41 177 237 47 364 426 302 360 170 175 431 428 167\n", + " 46 234 105 423 111 304 230 103 300 430 240 165 238 368 296 429 232 102\n", + " 113 241 425 365 494 108 110 362 101 174 424 297 303 359 366 40 173 301\n", + " 488 172 489 176 104 112 294 49 43 361 39 305 491 231 358 298 169 233\n", + " 229 493 299 44 42 363 295 171 367 293 166 492 427 48 236 106 37 109\n", + " 235 490]\n", + "48 17\n", + "[1388 1392 1262 1330 1130 1136 1132 1519 1078 879 1389 1521 1203 947\n", + " 1261 1322 940 1002 878 818 1004 1394 1003 882 1198 1258 1072 1200\n", + " 819 1140 1135 1269 876 1067 1139 1327 1452 1259 1326 1331 1455 1328\n", + " 1005 1009 1071 1460 1456 1014 1202 1268 1454 949 1522 754 1523 1323\n", + " 1011 1334 1387 877 1329 885 1205 1393 1142 1390 1520 1138 820 1075\n", + " 815 1066 1008 1396 941 1141 1457 1395 1010 1324 1204 1325 1391 1270\n", + " 948 1264 946 1194 1197 1076 1195 749 750 1073 751 1013 950 753\n", + " 1077 1134 755 1453 1459 881 1332 1196 1265 943 875 1266 1397 813\n", + " 1199 1517 812 1007 945 817 1267 1137 1074 1012 1068 1260 1133 938\n", + " 880 944 1006 884 814 1201 942 1131 1070 1263 939 1206 1458 816\n", + " 752 883 1518 1069 1333]\n", + "29 63\n", + "[4055 4061 3865 4065 3996 4001 4066 3676 4064 3678 3937 3741 3874 3810\n", + " 3993 4062 3801 4057 3675 3933 3738 4058 3866 3742 3802 3806 3995 3679\n", + " 3745 3872 4060 3871 3863 3869 3739 3927 3867 3928 3999 3929 3674 3680\n", + " 4002 3744 3868 4067 3875 3737 4000 3809 3873 3934 4056 3803 3930 3743\n", + " 3994 3677 3997 3935 3938 3998 3932 4003 4059 3808 3807 3936 4063 3805\n", + " 3740 3931 3870 3991 3800 3939 3804 3992 3864]\n", + "52 4\n", + "[ 54 441 245 239 569 313 184 308 500 114 376 370 438 631 177 186 181 51\n", + " 57 247 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2707 2523 2513 2576 2392 2645 2644\n", + " 2450 2395 2073 2194 2396 2266 2201 2132 2457 2135 2259 2837 2642 2648\n", + " 2448 2199 2512 2389 2838 2320 2775 2641 2321 2459 2071 2581 2197 2332\n", + " 2651 2650 2452 2836 2714 2068 2260 2138 2069 2130 2323 2520 2256 2261\n", + " 2841 2519 2774 2771 2586]\n", + "9 21\n", + "[1098 1420 1353 1093 1035 1547 1228 1358 1549 1737 1167 1350 1605 1295\n", + " 1099 1674 1219 1095 1612 1351 1478 1739 1291 1543 1287 1348 971 1158\n", + " 1230 1672 1548 1155 1423 1165 1413 1421 1284 1671 1476 970 1292 1096\n", + " 1286 1483 1484 1675 1544 1481 1029 1160 1546 1222 1224 1349 966 1542\n", + " 1676 1221 1231 1541 1480 1285 1223 1550 1545 1414 1357 1092 1227 1540\n", + " 1539 1607 1166 1485 1354 1347 1670 1356 1735 1037 1734 1159 1677 1412\n", + " 1604 1283 1288 1738 1613 1289 1032 1033 1606 1293 968 1611 1036 1411\n", + " 1163 1290 1101 1294 1482 1162 1359 1479 972 1100 1487 1226 1102 1034\n", + " 1229 1352 1355 1418 1673 1417 967 1419 1030 1551 1031 1422 1486 969\n", + " 1157 1416 1610 1164 1736 1609 1094 1477 1097 1161 1740 1475 1614 1156\n", + " 1220 1225 1608 1669 1415]\n", + "39 29\n", + "[1826 1765 2027 2084 1704 2093 2089 1508 1892 2022 1836 1699 1895 1642\n", + " 1697 2018 1638 2214 1636 1965 2276 1708 1575 2088 1773 1833 1509 1572\n", + " 2155 1763 2087 1511 1963 1772 2147 2152 2026 2017 1834 1578 2090 1512\n", + " 1579 1768 1829 2153 1771 1640 1957 1769 1954 2149 1832 2020 1641 1894\n", + " 1576 1571 1955 2282 1961 1698 1901 1828 1709 2218 2277 1707 1962 1890\n", + " 2146 2083 1837 2150 2278 1635 1835 1964 1896 1770 1510 2021 1899 2154\n", + " 2019 1577 1956 1959 2219 2025 1767 2211 1900 2151 2279 2217 1827 1825\n", + " 2081 1703 1644 1762 1705 1960 2086 1700 2156 2215 2216 2023 2212 1701\n", + " 2092 1889 1897 1893 1514 1573 1958 2082 1953 1513 1830 1639 1637 2280\n", + " 1643 1702 2024 1706 1831 1766 1898 1574 1891 2213 2148 2091 1634 2029\n", + " 1764 2281 2085 1761 2028]\n", + "43 38\n", + "[2093 2089 2542 2861 2406 2159 2475 2661 2671 2416 2284 2214 2408 2730\n", + " 2222 2732 2349 2536 2545 2088 2859 2795 2344 2413 2414 2541 2411 2155\n", + " 2342 2348 2345 2602 2474 2535 2538 2152 2090 2153 2796 2860 2609 2352\n", + " 2480 2533 2407 2473 2544 2662 2600 2668 2601 2282 2346 2409 2798 2791\n", + " 2534 2218 2608 2277 2289 2670 2856 2540 2224 2343 2278 2478 2417 2607\n", + " 2598 2472 2729 2288 2154 2736 2219 2597 2663 2350 2158 2220 2477 2604\n", + " 2151 2279 2217 2664 2794 2286 2599 2667 2351 2539 2857 2471 2479 2415\n", + " 2405 2347 2726 2156 2669 2353 2215 2216 2092 2672 2734 2792 2543 2793\n", + " 2287 2858 2283 2606 2727 2673 2280 2728 2469 2797 2412 2094 2410 2157\n", + " 2537 2731 2481 2285 2665 2733 2091 2603 2223 2281 2862 2799 2341 2476\n", + " 2735 2605 2470 2221 2666]\n", + "13 4\n", + "[ 17 329 210 591 83 459 12 141 401 269 467 81 73 15 334 145 200 521\n", + " 143 526 332 523 402 339 655 653 524 525 201 82 144 139 207 13 458 273\n", + " 337 136 80 393 14 651 147 208 211 75 263 11 146 396 529 76 209 338\n", + " 202 74 456 392 10 654 275 588 8 79 77 461 268 463 140 650 16 335\n", + " 138 464 267 455 398 327 142 400 206 399 18 331 465 522 593 527 530 336\n", + " 587 264 394 462 457 460 589 72 266 274 78 592 199 330 9 590 265 271\n", + " 403 586 204 205 333 397 71 652 135 520 270 391 272 137 656 585 528 203\n", + " 328 395 466]\n", + "12 28\n", + "[1809 1682 1741 2000 1553 1870 1420 2191 1804 1679 1547 1997 1549 1737\n", + " 2062 1806 1808 1930 1674 1866 1680 1932 1863 2056 1612 1799 2188 1873\n", + " 1739 1617 1543 2125 1488 1867 1672 2064 1869 1928 1548 1423 1933 1421\n", + " 1671 2185 1990 1798 2127 2058 1807 1868 2126 1931 1864 1483 1937 1484\n", + " 1675 1544 1481 2002 1546 1802 1676 2059 1742 2186 2065 1803 1480 1550\n", + " 1545 2128 1607 1801 1485 2122 2121 1670 1735 1734 1927 1800 1677 1995\n", + " 2120 1738 1993 1613 2001 1991 1678 1746 1935 1606 1611 1810 1992 1926\n", + " 1929 1616 1934 1618 1482 1615 1936 1487 1862 1999 1418 1673 2055 1744\n", + " 1417 2061 1938 1681 1419 1551 1805 1871 1422 1486 2057 1872 2187 1996\n", + " 2060 1874 1610 1743 1865 1736 1609 2063 2189 2123 2124 2190 1740 1614\n", + " 1998 1608 1745 1994 1552]\n", + "44 17\n", + "[1388 1392 1262 1330 1130 1136 1132 1519 1383 879 1389 1062 1261 1322\n", + " 940 1002 878 1004 1065 871 1003 1193 1064 1198 1258 1072 1200 1451\n", + " 1135 1129 1001 876 1450 1067 1327 1126 1452 1255 1259 1326 1455 1328\n", + " 1005 1009 1071 1456 1202 1454 999 745 1319 936 1321 1323 748 1387\n", + " 877 1329 1515 1393 1390 1138 808 1190 815 1256 1066 1008 1448 1320\n", + " 935 941 810 1516 1010 1324 1128 1385 998 1063 1325 1391 937 1000\n", + " 1264 946 1194 1127 1197 1195 811 749 750 1073 751 1386 1134 1514\n", + " 873 1453 881 1192 1196 1513 1254 1265 943 875 1384 1266 1449 813\n", + " 1191 1199 1517 812 1007 945 934 747 1137 1074 1318 1068 1260 1133\n", + " 938 880 944 1006 872 814 1201 942 1131 1257 1070 1263 746 939\n", + " 816 809 1518 1069 874]\n", + "60 6\n", + "[ 63 511 251 383 831 441 191 638 569 313 59 255 316 184 444 125 376 507\n", + " 380 447 438 631 767 319 186 575 57 253 699 247 826 636 189 829 315 830\n", + " 634 446 127 630 701 445 702 504 639 827 567 761 126 382 252 378 314 249\n", + " 121 122 190 440 566 439 764 187 828 760 632 503 572 375 379 571 120 377\n", + " 254 442 443 695 633 765 502 318 510 766 696 310 183 637 508 188 185 506\n", + " 250 574 317 570 698 60 62 374 246 509 762 763 61 505 700 248 697 312\n", + " 635 573 124 703 381 58 825 311 568 123]\n", + "52 7\n", + "[441 245 239 569 313 184 308 500 114 376 370 438 631 177 757 181 247 302\n", + " 756 369 818 563 564 435 634 882 431 819 627 630 495 498 887 821 504 688\n", + " 501 182 567 886 761 694 304 117 378 179 754 314 430 249 558 689 240 116\n", + " 440 368 560 566 439 113 241 760 632 503 885 625 499 375 118 820 494 306\n", + " 377 242 824 690 442 497 695 633 373 502 303 307 366 432 626 629 562 696\n", + " 310 183 687 176 565 180 436 622 506 305 692 559 751 753 570 698 437 755\n", + " 434 309 881 822 374 372 693 119 371 246 561 505 817 367 433 248 697 758\n", + " 312 884 244 496 623 624 686 823 691 628 115 816 752 759 243 178 883 311\n", + " 568]\n", + "59 34\n", + "[1976 2235 2238 1914 2617 1983 2557 1978 2294 2230 2618 1854 2167 1849\n", + " 2491 1848 2046 2489 2492 2360 2109 2166 2425 2366 1912 2488 2105 2302\n", + " 2298 2108 2044 2431 2553 2293 2363 2296 2111 2300 2427 2103 2552 2170\n", + " 1979 1918 2422 2490 2554 1916 2041 2558 2169 1851 2365 2038 2171 2429\n", + " 2421 2039 2106 2037 2174 2358 2430 2621 1982 2367 1853 1913 2357 2428\n", + " 2236 1915 2239 1980 2556 2237 2423 2555 1919 2043 2486 2295 2559 1981\n", + " 2175 2234 2494 2045 2040 2231 2303 2047 2104 1850 2229 1975 2620 2619\n", + " 2110 2165 2301 1911 2622 1974 2101 1977 2168 2362 2172 1852 2426 2299\n", + " 2232 2495 2487 2173 2493 2359 2364 2102 1917 2361 2107 2424 2551 2616\n", + " 2233 2042 2297]\n", + "15 61\n", + "[4047 3858 3730 3659 3914 3919 3538 3786 4043 3985 3599 4051 3790 3923\n", + " 3983 3668 3980 3853 4041 3851 3979 3922 3534 3726 3913 3728 3987 3924\n", + " 3982 3732 3861 3859 3854 3788 3978 4052 3598 3915 4042 3986 3663 3920\n", + " 3536 3921 3856 3989 3537 3796 3977 3791 3855 4048 3595 3925 3795 3797\n", + " 3603 3731 3600 3725 3666 3532 3535 4053 3793 3852 4044 3664 3601 3916\n", + " 3789 3533 4045 3665 4050 3597 3667 3658 3850 3722 3721 3727 3849 3602\n", + " 3792 3981 3917 4049 3596 3729 3785 3860 3662 3733 3787 3988 3857 3723\n", + " 3660 3984 3661 3918 4046 3794 3724]\n", + "37 41\n", + "[2529 2404 2922 2406 2402 2721 2475 2661 2847 2532 2592 2408 2730 2659\n", + " 2276 2464 2536 2859 2983 2795 2591 2344 2468 2593 2342 2345 2602 2723\n", + " 2596 2783 2474 2535 2538 2530 3044 3046 2978 2658 2848 2533 2407 2473\n", + " 2338 2977 2850 2662 2600 2601 2466 2409 2920 2791 2534 2919 2788 3045\n", + " 2277 3048 2856 2854 2594 2337 2722 2343 2278 3047 2598 2472 2921 2729\n", + " 2789 2918 2981 2274 2982 2785 2339 2913 2597 3042 2663 2984 2784 2467\n", + " 2279 2916 2664 2794 2463 2599 2667 2656 2465 2539 2857 2471 2917 2787\n", + " 2405 2403 2855 2849 2915 2726 2980 2400 2275 2660 2786 2912 2719 2792\n", + " 2595 2657 2527 2724 2851 2793 2914 2858 2979 2727 2340 2280 2728 2469\n", + " 2531 2725 2410 2852 2528 2790 2537 2731 2401 2853 2665 2603 3043 2720\n", + " 2985 2655 2341 2470 2666]\n", + "14 48\n", + "[2957 3345 3275 3217 3145 2832 2891 3024 3086 3215 3342 3408 3025 2959\n", + " 2705 3148 3027 2765 3023 2767 2897 2762 3280 2960 3089 3154 2955 2963\n", + " 3209 3402 3151 3084 3091 2896 3470 3018 3146 2766 2825 3274 3346 2964\n", + " 2827 2770 3472 2900 3155 2835 2898 2768 3273 3082 3090 3017 3085 3344\n", + " 3404 3080 2703 2702 3218 3284 2893 3087 3337 3026 3153 3277 3410 3219\n", + " 2833 2830 2889 2834 3468 3210 3341 3272 3343 3467 3471 3083 3028 2826\n", + " 3282 3406 3409 2700 3021 2764 3149 2956 3339 3283 3405 3220 3156 3338\n", + " 3152 3347 3016 3144 2888 2895 2953 3092 2828 3276 3469 2899 3473 2829\n", + " 2831 2962 3150 3212 2894 3278 3216 3214 2769 3081 2890 3019 3208 3088\n", + " 3213 3340 3403 2952 3281 2892 2961 2704 3211 2763 3407 2954 3279 3020\n", + " 2958 3022 2699 2701 3147]\n", + "35 20\n", + "[1570 1508 1502 1699 1315 1383 1697 1504 1638 1062 1636 1442 1311 1445\n", + " 1575 1376 1121 1509 1122 1505 1503 1572 1511 996 1193 1064 1252 1512\n", + " 1117 1317 1566 1060 1129 928 1245 1310 1126 1186 1255 1057 1576 1571\n", + " 1374 1437 1698 1316 1249 999 993 1633 1319 1444 1506 1321 1447 1313\n", + " 1183 1379 1635 1181 1510 1378 1446 1190 1189 1256 1448 1320 1373 1314\n", + " 1246 1128 1385 998 1187 1184 1063 994 1501 932 1632 1375 1185 1127\n", + " 1123 1440 1700 931 1248 1701 1381 1569 997 1573 1438 1124 1507 1058\n", + " 1192 1250 1513 992 1254 1639 1384 1055 1637 1439 1449 1191 1061 1696\n", + " 1382 1380 1567 1312 929 934 1318 1702 1251 1118 1059 1054 1056 1120\n", + " 1253 1119 1574 1443 930 933 1634 1631 1257 1188 1182 1441 995 1309\n", + " 1125 1247 991 1568 1377]\n", + "54 63\n", + "[3832 4083 3827 3959 3705 4086 4026 3899 3699 3766 3956 3769 3954 3702\n", + " 3895 3826 4027 4081 3962 3952 4091 3897 3834 4092 3762 4019 4016 3830\n", + " 3890 4028 4018 3828 3960 3700 3763 3891 3765 4085 3961 4090 3893 4084\n", + " 3958 4021 4022 4025 4024 3701 3764 3894 4082 3896 3825 3900 3957 3898\n", + " 4088 3768 4017 4089 3831 3767 3835 3833 3888 3889 4020 3953 4080 3963\n", + " 3964 3704 3829 4087 3703 3892 3770 3955 4023]\n", + "50 1\n", + "[ 45 54 245 239 184 308 500 114 370 438 177 181 51 247 237 47 302 369\n", + " 435 175 431 46 495 498 501 182 56 111 304 117 179 300 430 240 116 238\n", + " 368 113 241 365 499 375 50 118 108 110 120 306 242 52 174 497 373 303\n", + " 307 366 432 173 301 310 183 53 172 176 112 49 180 436 305 437 434 309\n", + " 374 372 119 371 246 44 367 433 248 55 312 48 244 496 236 115 109 243\n", + " 178 311]\n", + "19 7\n", + "[594 848 210 591 83 785 401 720 269 467 81 340 149 334 145 281 850 659\n", + " 143 526 404 472 537 596 724 402 339 655 213 653 852 525 718 471 409 719\n", + " 535 82 144 214 207 341 595 665 721 664 273 337 784 80 600 791 532 345\n", + " 147 208 211 212 468 789 662 723 86 151 85 146 849 343 529 722 209 338\n", + " 534 599 407 790 150 654 601 275 469 726 728 277 342 405 461 463 473 853\n", + " 279 783 344 658 725 597 335 464 276 398 536 854 400 148 206 399 533 465\n", + " 593 787 527 530 336 661 660 215 462 589 851 786 657 280 274 788 592 590\n", + " 271 403 531 470 278 333 408 216 406 663 397 598 270 272 727 656 84 528\n", + " 466]\n", + "47 23\n", + "[1388 1392 1710 1840 1262 1330 1713 1836 1136 1132 1519 1642 1524 1581\n", + " 1389 1521 1203 1842 1651 1461 1261 1708 1322 1773 1525 1394 1839 1772\n", + " 1712 1841 1588 1198 1578 1258 1652 1579 1200 1451 1771 1648 1135 1450\n", + " 1327 1452 1259 1641 1326 1331 1455 1715 1328 1905 1778 1460 1649 1456\n", + " 1901 1717 1903 1779 1202 1268 1454 1709 1585 1522 1707 1523 1321 1323\n", + " 1837 1586 1387 1835 1584 1329 1770 1515 1393 1390 1902 1520 1138 1714\n", + " 1776 1904 1577 1396 1516 1457 1900 1395 1650 1324 1385 1325 1391 1644\n", + " 1838 1705 1264 1197 1587 1777 1716 1195 1386 1134 1514 1453 1459 1332\n", + " 1774 1196 1513 1265 1780 1266 1397 1449 1199 1517 1645 1646 1643 1267\n", + " 1653 1137 1260 1133 1706 1775 1589 1201 1583 1263 1647 1582 1843 1711\n", + " 1458 1580 1518 1333 1906]\n", + "56 48\n", + "[3387 3128 3320 2930 3511 3187 3379 3316 2933 3004 3058 2806 3189 2807\n", + " 2742 3258 3191 3129 2872 2741 3003 3196 2873 2811 3384 3262 3510 2935\n", + " 3389 3000 3445 2936 2997 2805 3064 3125 2877 2869 2940 3386 3130 3253\n", + " 2934 3260 3252 2996 3061 3123 3383 3250 2868 3382 3380 3513 3324 3261\n", + " 3326 3002 3063 2995 3192 3186 2867 3317 3126 2870 3448 2747 3444 3257\n", + " 2875 2938 3188 3195 3256 2937 2999 3006 3321 3193 3452 2939 2941 3514\n", + " 3451 3122 3131 3381 3319 3450 3447 3070 3067 3194 2812 3197 3005 3322\n", + " 3515 3318 3132 3001 3385 3059 2804 2744 3066 2998 2746 2808 2871 3449\n", + " 2994 2931 3314 2876 3509 3198 3133 3512 3134 3315 2942 3259 3251 2809\n", + " 3254 2810 2745 2874 2932 3388 3255 3446 3190 3065 3062 3068 3060 3069\n", + " 2743 3325 3124 3127 3323]\n", + "16 17\n", + "[1098 848 1361 785 720 1420 913 845 1104 1232 1039 1035 1228 910\n", + " 1358 850 1110 976 1489 1041 914 1170 1167 978 917 1295 1099 852\n", + " 974 907 718 1363 719 906 1171 781 1105 780 1291 721 1488 971\n", + " 1362 982 1230 784 912 1423 1165 847 1421 1490 970 1292 723 1046\n", + " 1237 1299 1427 1103 849 1491 1038 722 782 1231 846 908 1106 1042\n", + " 1357 1227 1040 1426 1166 1485 843 918 1238 1107 1172 1356 1037 1296\n", + " 977 853 783 1425 1365 979 717 1293 911 1297 1036 980 1233 1163\n", + " 844 1290 1109 1101 787 1294 1173 1162 1359 975 972 1174 1100 1487\n", + " 1044 851 786 1226 1102 973 981 1034 1360 1229 1300 1355 1108 788\n", + " 909 915 1234 1302 916 1422 1486 1298 1236 1169 1424 1428 1301 1364\n", + " 1164 1043 1168 1045 1235]\n", + "16 19\n", + "[1098 848 1361 1553 1420 913 845 1104 1232 1039 1555 1035 1228 910\n", + " 1358 850 1549 1110 976 1489 1041 914 1170 1167 978 1295 1099 974\n", + " 1363 1171 1105 1291 1617 1488 971 1362 1230 912 1548 1492 1423 1165\n", + " 1554 847 1421 1490 1292 1046 1483 1237 1619 1484 1299 1427 1103 849\n", + " 1491 1038 1231 846 1550 908 1106 1042 1357 1227 1040 1426 1166 1485\n", + " 1354 1238 1107 1172 1356 1037 1296 977 1425 1365 1613 1493 979 1293\n", + " 911 1297 1036 980 1233 1163 1290 1616 1109 1101 1430 1618 1294 1173\n", + " 1162 1615 1359 975 972 1174 1100 1487 1044 851 1226 1102 973 981\n", + " 1034 1360 1229 1300 1355 1418 1108 909 915 1234 1419 1302 1551 916\n", + " 1422 1556 1486 1366 1298 1236 1169 1424 1428 1301 1364 1164 1043 1429\n", + " 1168 1045 1614 1235 1552]\n", + "42 25\n", + "[1388 1765 1710 2027 1840 1704 1508 1836 1895 1519 1642 1581 1966 1383\n", + " 1638 1389 1636 1965 1445 1261 1708 1322 1575 1773 1833 1509 1572 1839\n", + " 1511 1963 1772 1712 2026 1834 1578 1258 1512 1579 1768 1829 1451 1771\n", + " 1648 1640 1769 1450 1452 1832 1255 1259 1641 1326 1894 1576 1455 1961\n", + " 1456 1901 1828 1903 1454 1709 1319 1707 1962 1444 1321 1323 1447 1837\n", + " 1387 1835 1584 1964 1896 1770 1515 1510 1390 1899 1902 1520 1446 1776\n", + " 1256 1577 1448 1320 1959 2025 1767 1516 1900 1324 1385 1325 1391 1703\n", + " 1644 1838 1705 1960 1700 2023 1701 1386 1381 1897 1893 1514 1573 1453\n", + " 1958 1774 1513 1830 1639 1384 1637 1449 1517 1645 1646 1382 1643 1318\n", + " 1702 1260 2024 1706 1831 1775 1766 1898 1574 2029 1583 1257 1764 1647\n", + " 1582 1711 1580 1518 2028]\n", + "19 34\n", + "[2322 1809 2000 2449 2191 2454 2195 2317 1875 2326 1997 2129 2319 2062\n", + " 2067 2456 2004 2516 2131 1808 2200 2263 2325 2136 2257 2578 2196 2006\n", + " 2265 2318 2327 2008 2453 1873 2387 2254 2582 1878 2255 2391 2125 2451\n", + " 2447 2517 2064 2198 2134 2381 2455 2127 2072 2193 2126 2005 1937 1943\n", + " 2384 2262 1811 1813 2002 2070 2518 2065 2258 2324 2393 2577 2128 1877\n", + " 1939 2385 1942 1941 2390 2579 2329 2133 2515 2003 2580 2383 2192 2253\n", + " 2264 2514 2388 2001 2386 2007 2137 2511 1935 2328 1812 2513 2576 2392\n", + " 1810 2450 2073 2194 1879 2201 1934 2132 1936 2135 2066 2259 1999 1814\n", + " 2061 1938 2448 2199 2512 2389 2320 2321 1871 2071 2581 2197 1872 2009\n", + " 2452 2382 1874 1876 2446 2068 2260 2063 2189 2069 2130 2323 2256 2190\n", + " 2261 1998 1940 2519 1944]\n", + "40 2\n", + "[ 45 38 107 168 35 163 41 237 364 426 302 360 170 421 164 428 167 46\n", + " 234 226 105 98 550 420 423 485 99 230 103 300 228 165 238 296 429 36\n", + " 232 551 102 100 549 425 365 108 110 486 362 101 174 424 290 552 297 359\n", + " 554 366 555 40 487 356 173 553 301 488 172 489 104 294 43 361 39 162\n", + " 354 355 357 34 491 231 227 358 298 292 169 419 233 229 299 291 44 42\n", + " 363 295 171 293 166 492 427 236 422 484 106 37 109 235 490]\n", + "17 1\n", + "[ 17 210 83 12 141 401 21 269 467 81 15 340 149 334 145 143 404 332\n", + " 402 339 213 82 144 214 139 207 341 13 273 337 80 14 147 208 211 212\n", + " 468 75 11 86 151 85 146 76 209 338 150 275 79 277 342 405 77 268\n", + " 463 279 140 20 16 335 464 267 276 398 142 22 400 148 206 399 18 465\n", + " 87 336 215 462 274 78 271 403 278 204 205 333 23 19 397 270 272 84\n", + " 203 466]\n", + "52 23\n", + "[1392 1710 1840 1907 1330 1713 1519 1524 1782 1521 1399 1203 1842 1651\n", + " 1461 1785 1525 1402 1394 1207 1712 1909 1848 1841 1588 1652 1200 1140\n", + " 1401 1648 1465 1269 1139 1327 1326 1331 1336 1335 1455 1715 1328 1905\n", + " 1778 1460 1649 1456 1717 1779 1658 1202 1268 1454 1338 1585 1522 1594\n", + " 1143 1523 1592 1334 1719 1586 1846 1584 1329 1273 1847 1908 1530 1205\n", + " 1463 1393 1142 1390 1520 1138 1714 1776 1466 1396 1527 1271 1141 1457\n", + " 1395 1593 1657 1650 1204 1783 1845 1391 1270 1464 1337 1264 1910 1587\n", + " 1718 1777 1716 1591 1529 1784 1781 1398 1459 1332 1265 1780 1655 1720\n", + " 1272 1266 1397 1528 1721 1844 1911 1646 1400 1267 1653 1137 1590 1654\n", + " 1462 1775 1656 1589 1722 1201 1526 1583 1263 1647 1582 1843 1206 1711\n", + " 1458 1208 1518 1333 1906]\n", + "47 41\n", + "[2922 2542 2861 2801 2354 2739 2475 2671 2416 2927 2284 2420 2930 2730\n", + " 2732 2349 3053 2612 2545 2859 2795 2413 2414 2541 2411 3054 2418 2348\n", + " 3058 2602 2474 2538 3055 2738 2990 2741 2993 2796 2860 2609 2419 2352\n", + " 2480 2740 2864 2473 2805 2674 2928 2544 2869 2668 2601 2798 2608 2289\n", + " 2670 2355 2868 2540 2478 2417 2607 2995 2863 2729 2867 2992 2288 2989\n", + " 2923 2736 2350 2477 2604 2549 2987 3056 2548 2794 2286 2803 2667 2351\n", + " 2539 2857 2479 2802 2483 2290 2415 2676 2482 2347 2737 2669 2924 2353\n", + " 2611 2865 2672 2734 2613 2991 2543 2929 2804 2988 2793 2994 2931 2287\n", + " 2675 2858 2606 2673 2485 2800 2797 3052 2412 3057 2925 2410 2484 2866\n", + " 2537 2731 2610 2481 2285 2665 2733 2603 2546 2547 2932 2926 2862 2799\n", + " 2476 2735 2605 2677 2666]\n", + "31 44\n", + "[2529 2721 2661 2847 2589 2592 2461 2659 2780 2464 2973 2591 2843 2593\n", + " 2713 2844 3040 2654 2723 2596 2783 2530 3170 2716 3044 2526 2978 2658\n", + " 2848 3105 2779 3168 2974 2977 3230 2850 2462 2905 2715 2466 2911 2842\n", + " 2788 3045 2906 2778 2594 2722 2524 2652 3102 3038 3229 2909 2460 3163\n", + " 3034 2789 2981 2777 2785 3041 3231 2913 3167 2649 3233 2907 3042 3036\n", + " 2784 3171 2590 2916 2588 2463 2587 2656 2465 3037 3234 2917 2787 2849\n", + " 2717 3098 2523 2782 2915 2980 3107 3101 2660 2786 3232 2781 2912 3108\n", + " 2845 2719 2595 2657 2527 2724 2851 3169 3099 2976 2975 3100 2914 3033\n", + " 3035 2979 3228 2653 3164 3103 2718 2969 3104 2910 2531 2725 2852 2528\n", + " 3166 2853 3165 3039 2651 2908 2650 3043 2970 2525 3106 2720 2714 2846\n", + " 2971 2655 2972 2841 2586]\n", + "13 36\n", + "[2322 2000 2449 2191 2195 2317 2444 1997 2129 2319 2062 2314 2509 2441\n", + " 2131 2445 2257 2380 1930 2313 2578 1932 2637 2056 2318 2188 2569 2375\n", + " 2387 2254 2255 2125 2378 2451 2633 2447 2064 2439 1933 2381 2377 2639\n", + " 2571 2698 2185 2443 2510 2127 2058 2193 2126 1931 2640 2384 2442 2249\n", + " 2570 2376 2703 2379 2702 2059 2186 2568 2250 2065 2258 2572 2577 2128\n", + " 2122 2385 2121 2515 2383 1995 2192 2253 2440 2120 2315 2514 2251 1993\n", + " 2247 2636 2001 2386 2511 2635 1935 2700 2119 2513 2503 2576 2450 2194\n", + " 2312 1934 2505 1936 2184 2066 2259 2508 1999 2634 2061 2183 2448 2512\n", + " 2320 2641 2321 2575 2057 2507 2187 1996 2574 2506 2316 2382 2060 2252\n", + " 2446 2504 2704 2311 2063 2189 2130 2638 2323 2123 2256 2124 2190 2248\n", + " 1998 2573 2699 2701 1994]\n", + "29 40\n", + "[2529 2402 2721 2398 2847 2589 2272 2592 2461 2267 2659 2780 2456 2464\n", + " 2394 2270 2973 2591 2843 2593 2713 2333 2844 2654 2723 2840 2783 2647\n", + " 2530 2265 2712 2521 2716 2522 2711 2391 2526 2658 2583 2848 2779 2974\n", + " 2338 2584 2850 2331 2462 2905 2715 2466 2455 2585 2911 2842 2204 2268\n", + " 2206 2906 2778 2594 2337 2722 2335 2524 2271 2652 2269 2909 2460 2776\n", + " 2330 2393 2777 2397 2785 2913 2203 2649 2907 2329 2202 2784 2467 2273\n", + " 2590 2588 2458 2463 2207 2587 2656 2465 2334 2787 2403 2328 2849 2717\n", + " 2523 2782 2392 2400 2395 2396 2266 2786 2781 2912 2845 2719 2595 2657\n", + " 2457 2527 2976 2975 2336 2208 2648 2653 2718 2910 2531 2775 2459 2528\n", + " 2401 2205 2332 2651 2908 2650 2399 2970 2525 2720 2714 2846 2971 2655\n", + " 2520 2972 2841 2519 2586]\n", + "25 0\n", + "[ 27 83 153 21 25 149 222 281 89 94 156 213 409 214 349 341 285 30\n", + " 28 345 147 211 212 158 152 348 86 151 85 343 92 154 217 347 91 407\n", + " 159 150 31 277 342 279 221 29 220 284 344 411 20 276 223 22 90 148\n", + " 95 286 87 157 88 215 155 280 282 346 283 278 408 216 26 406 23 19\n", + " 24 93 219 218 84 412 410]\n", + "9 1\n", + "[ 6 329 459 12 141 269 73 15 334 200 133 143 261 332 201 260 70 139\n", + " 207 13 458 136 393 14 4 325 75 263 390 11 396 76 202 74 456 392\n", + " 10 68 8 79 77 268 140 138 132 267 455 5 327 326 142 206 331 197\n", + " 134 324 264 394 7 457 460 72 266 78 196 199 330 9 265 271 131 262\n", + " 204 205 333 389 195 198 454 397 3 71 259 135 270 391 137 203 67 69\n", + " 328 395]\n", + "57 55\n", + "[3583 3832 3711 3387 3391 3959 3705 3577 3517 3899 3772 3644 3320 3699\n", + " 3766 3511 3837 3708 3769 3702 3379 3895 3574 3836 3316 3390 3443 3636\n", + " 3258 3962 3191 3455 3775 3196 3709 3897 3834 3384 3510 3389 3445 3581\n", + " 3386 3253 3260 3830 3572 3901 3838 3383 3382 3380 3579 3513 3828 3960\n", + " 3700 3324 3763 3261 3326 3643 3706 3765 3192 3961 3317 3578 3448 3771\n", + " 3893 3958 3444 3257 3642 3646 3195 3256 3575 3641 3707 3701 3321 3507\n", + " 3193 3452 3639 3514 3451 3764 3381 3894 3319 3450 3447 3194 3896 3900\n", + " 3322 3515 3898 3318 3385 3454 3647 3768 3637 3773 3640 3518 3449 3573\n", + " 3635 3453 3509 3519 3645 3516 3831 3512 3767 3582 3835 3833 3571 3774\n", + " 3259 3638 3963 3508 3964 3704 3710 3829 3254 3703 3388 3255 3446 3190\n", + " 3576 3580 3770 3325 3323]\n", + "25 60\n", + "[4055 3990 4061 3865 3996 4051 3799 3923 3668 3541 3676 3605 3613 3678\n", + " 3741 3671 3482 3987 3924 3926 3732 3993 3861 3859 4062 3801 4057 3675\n", + " 3735 3483 3549 3933 3607 4052 3738 4058 3866 3742 3802 3806 3995 3604\n", + " 3679 3548 3479 4060 3871 3863 3869 3989 3798 3546 3739 3672 3610 3796\n", + " 3612 3927 3867 3928 3999 3929 3674 3925 3795 3608 3868 3797 3669 3542\n", + " 3737 3731 3736 3614 4053 3934 3481 4056 3803 3547 3606 3734 3930 3743\n", + " 3994 3609 3677 3667 3997 3935 3998 3932 3862 3673 3670 3543 4059 3807\n", + " 3544 3545 4063 3805 3860 3740 3478 3931 3870 3733 3484 3991 3988 3800\n", + " 3611 3804 4054 3992 3864 3480]\n", + "15 0\n", + "[ 17 210 83 12 141 401 21 269 81 73 15 149 334 145 143 332 402 339\n", + " 213 201 82 144 139 207 13 273 337 80 14 147 208 211 212 75 11 85\n", + " 146 396 76 209 338 202 74 10 275 79 77 268 140 20 16 335 138 267\n", + " 276 398 142 400 148 206 399 18 331 336 266 274 78 9 271 204 205 333\n", + " 19 397 270 272 137 84 203]\n", + "45 24\n", + "[1388 1392 1710 1840 1262 1704 1330 1713 1836 1519 1642 1581 1966 1383\n", + " 1389 1521 1965 1842 1651 1261 1708 1322 1575 1773 1833 1394 1839 1511\n", + " 1963 1772 1712 1834 1841 1198 1578 1258 1512 1579 1200 1768 1451 1771\n", + " 1648 1640 1769 1450 1327 1452 1832 1259 1641 1326 1576 1455 1715 1328\n", + " 1905 1778 1649 1456 1901 1903 1779 1454 1709 1585 1522 1707 1962 1523\n", + " 1321 1323 1447 1837 1586 1387 1835 1584 1964 1329 1770 1515 1393 1390\n", + " 1899 1902 1520 1714 1776 1904 1577 1448 1320 1767 1516 1457 1900 1395\n", + " 1650 1324 1385 1968 1325 1391 1703 1644 1838 1705 1264 1194 1197 1587\n", + " 1777 1195 1967 1386 1897 1514 1453 1459 1774 1196 1513 1265 1639 1384\n", + " 1449 1199 1517 1645 1646 1643 1260 1706 1775 1898 1583 1257 1263 1647\n", + " 1582 1711 1458 1580 1518]\n", + "46 30\n", + "[1710 2027 1840 1907 2093 1713 2089 2159 1836 1642 1581 1966 2161 2284\n", + " 2032 2222 1965 1842 2349 1708 2088 1773 1833 2100 2155 1839 2348 1963\n", + " 1772 2164 1712 2152 1969 2026 1834 1841 2090 1579 1768 2153 2034 1771\n", + " 1648 1769 1832 2352 2160 2098 1715 1905 2282 1961 1778 1649 1901 1903\n", + " 1779 1709 2218 1585 1971 2289 1707 1962 2225 2224 1837 2036 1835 1584\n", + " 2099 1964 1896 1770 1908 2096 2030 1899 1902 2095 2288 2162 1714 2154\n", + " 1776 1904 2219 2025 2350 1970 2158 1900 2220 1650 2163 2217 1968 2286\n", + " 1972 2351 1644 1838 2031 1705 2290 1960 2033 1777 2347 2156 2353 1967\n", + " 2035 2092 1897 2226 1774 1780 2287 2283 1645 1844 1646 1643 2094 2157\n", + " 2227 2024 1706 1775 1898 2285 2097 2091 2029 1583 2223 1647 1582 1843\n", + " 1711 1580 2221 2028 1906]\n", + "15 16\n", + "[1098 848 1361 785 720 1420 913 845 1104 1232 1039 1035 1228 910\n", + " 716 1358 850 976 842 1041 914 1170 1167 779 978 917 1295 655\n", + " 1099 653 852 974 907 718 1363 719 906 1171 781 1105 780 1291\n", + " 721 971 1362 1230 784 778 912 1423 1165 905 847 1421 715 970\n", + " 1292 723 1237 1299 1103 849 1038 722 782 1231 846 841 908 1106\n", + " 1042 1357 1227 1040 1426 654 1166 843 1107 1172 1356 1037 1296 977\n", + " 853 783 658 1425 1033 979 717 1293 911 1297 1036 980 1233 1163\n", + " 844 1290 1109 1101 787 1294 1173 1162 1359 975 972 1100 1044 851\n", + " 786 657 1226 1102 973 981 1034 1360 1229 1300 1355 1108 788 909\n", + " 915 1234 916 1422 969 1298 1236 652 1169 1424 1164 1043 1168 656\n", + " 1097 1161 1045 1225 1235]\n", + "20 5\n", + "[ 17 594 210 591 83 401 153 21 467 81 340 149 334 145 281 89 659 143\n", + " 526 404 472 537 596 724 402 339 213 471 409 535 82 144 214 207 341 595\n", + " 721 664 273 337 80 600 532 345 147 208 211 212 468 662 152 723 86 151\n", + " 85 146 343 529 722 209 154 338 217 534 599 407 150 601 275 469 79 726\n", + " 277 342 405 463 473 279 344 658 725 20 16 597 335 464 276 398 536 142\n", + " 22 400 148 206 399 18 533 465 87 593 527 530 336 661 88 660 215 462\n", + " 657 280 282 274 346 592 271 403 531 470 278 408 216 406 23 19 663 598\n", + " 24 538 270 218 272 727 656 84 528 474 410 466]\n", + "58 62\n", + "[3903 4095 3832 3839 3959 3705 4086 4026 3899 3772 3967 3644 3766 3956\n", + " 3837 3708 3769 3702 3895 4030 3836 3965 4027 3962 3775 3709 4091 3897\n", + " 3834 4092 4094 3902 4029 3830 3901 3838 4028 4093 3828 3960 3643 3706\n", + " 3765 4085 3961 4090 3771 3893 4084 3958 4021 4022 4025 3642 4031 4024\n", + " 3641 3707 3639 3894 3966 3896 3900 3957 3898 4088 3768 4089 3773 3640\n", + " 3645 3831 3767 3835 3833 4020 3774 3963 3964 3704 3710 3829 4087 3703\n", + " 3892 3770 4023]\n", + "39 21\n", + "[1388 1765 1704 1570 1508 1130 1699 1132 1315 1642 1581 1383 1638 1389\n", + " 1062 1636 1442 1445 1261 1322 1575 1002 1509 1122 1505 1065 1572 1511\n", + " 996 1193 1064 1252 1578 1258 1512 1579 1768 1317 1060 1451 1640 1769\n", + " 1129 1001 1450 1067 1126 1452 1186 1255 1259 1641 1576 1571 1316 1249\n", + " 999 1319 1707 1444 1506 1321 1323 1447 1313 1379 1635 1387 1770 1515\n", + " 1510 1378 1446 1190 1189 1256 1066 1577 1448 1320 1767 1516 1314 1324\n", + " 1128 1385 998 1187 1063 1325 1703 1644 1705 1000 1185 1194 1127 1197\n", + " 1123 1700 1195 1701 1386 1381 1569 997 1514 1573 1453 1124 1507 1192\n", + " 1250 1196 1513 1254 1639 1384 1637 1449 1191 1061 1517 1382 1380 1643\n", + " 1318 1702 1260 1251 1706 1766 1059 1253 1574 1443 1634 1131 1257 1764\n", + " 1188 1441 1580 1125 1377]\n", + "54 16\n", + "[1330 1136 1078 1146 757 1399 1203 826 947 1461 756 1402 818 1018\n", + " 1147 1394 1207 956 882 954 1072 1200 819 1140 1401 1465 1269 1275\n", + " 1139 887 821 888 827 886 1144 761 1331 1336 694 1335 1009 1460\n", + " 1014 1202 1268 1338 1148 949 754 1143 1276 1011 1334 1329 1273 760\n", + " 885 1205 1463 1142 1138 820 1075 952 1008 824 1396 1211 1271 1141\n", + " 695 1395 1010 1019 1204 1081 955 1270 1464 1020 1337 696 948 1264\n", + " 892 946 1016 890 1076 1210 1073 692 951 1013 950 1077 1398 755\n", + " 1459 1080 1212 881 1332 822 1265 693 1082 1272 1266 1017 1397 1339\n", + " 1400 945 762 817 1267 1079 1137 1074 1012 1209 1462 697 758 953\n", + " 880 944 1274 884 1201 823 691 1015 1206 1208 891 1145 759 1084\n", + " 883 825 889 1083 1333]\n", + "18 55\n", + "[3858 3345 3217 3730 3919 3538 3215 3599 3342 3408 3799 3790 3923 3668\n", + " 3280 3154 3541 3922 3605 3534 3151 3726 3671 3728 3924 3411 3732 3861\n", + " 3859 3470 3476 3854 3286 3287 3735 3412 3414 3346 3607 3598 3472 3155\n", + " 3351 3415 3604 3663 3920 3536 3479 3413 3344 3404 3921 3856 3798 3537\n", + " 3218 3284 3672 3796 3153 3277 3410 3219 3791 3855 3285 3468 3925 3341\n", + " 3343 3795 3416 3608 3797 3669 3542 3603 3731 3600 3725 3736 3221 3666\n", + " 3532 3471 3535 3282 3406 3409 3474 3793 3157 3664 3601 3789 3606 3283\n", + " 3533 3734 3405 3220 3665 3156 3539 3152 3347 3540 3597 3667 3862 3670\n", + " 3350 3543 3727 3469 3477 3544 3602 3792 3348 3473 3222 3596 3729 3860\n", + " 3278 3662 3216 3478 3214 3733 3349 3340 3281 3857 3660 3661 3407 3475\n", + " 3794 3352 3279 3480 3724]\n", + "10 59\n", + "[4038 4047 3659 3914 3526 3919 3786 4043 3599 3790 3847 3529 3983 3980\n", + " 3853 3909 4041 3464 3851 3979 3527 3402 4037 3534 3726 3913 3728 3982\n", + " 3975 3470 3854 4039 3788 3978 3598 3717 3915 4042 3528 3844 3462 3399\n", + " 3663 3716 3911 3920 3590 3404 3856 3400 3656 3845 3848 3463 3977 3791\n", + " 3718 3855 3531 3973 3654 3468 3595 3781 3467 3600 3725 3532 3535 3719\n", + " 3910 3852 3972 3525 4044 3664 3916 3789 3533 3655 4045 3405 3588 3593\n", + " 3591 3597 3780 3976 3589 3594 3657 3846 3658 3850 3401 3722 3721 3727\n", + " 3849 3469 3720 3974 3792 3981 3917 3596 4040 3785 3662 3592 3403 3530\n", + " 3912 3787 3723 3660 3466 3984 3661 3918 3465 3783 3908 4046 3784 3653\n", + " 3782 3652 3724]\n", + "36 39\n", + "[2529 2404 2210 2406 2402 2721 2398 2661 2532 2272 2592 2214 2408 2730\n", + " 2659 2276 2464 2536 2591 2344 2468 2593 2342 2654 2345 2602 2147 2723\n", + " 2596 2783 2474 2535 2538 2530 2526 2658 2149 2848 2533 2407 2473 2338\n", + " 2850 2662 2600 2462 2601 2466 2346 2409 2791 2534 2919 2788 2277 2856\n", + " 2854 2594 2337 2722 2335 2271 2343 2146 2150 2278 2598 2472 2729 2789\n", + " 2918 2274 2785 2339 2913 2597 2663 2211 2784 2467 2151 2209 2273 2590\n", + " 2279 2916 2664 2463 2599 2656 2465 2334 2471 2917 2787 2405 2403 2855\n", + " 2849 2915 2726 2215 2400 2275 2216 2660 2786 2212 2719 2792 2595 2657\n", + " 2527 2724 2851 2793 2336 2145 2914 2208 2727 2340 2280 2718 2728 2469\n", + " 2531 2725 2410 2852 2528 2790 2537 2401 2853 2665 2213 2148 2399 2720\n", + " 2281 2655 2341 2470 2666]\n", + "48 37\n", + "[2093 2542 2801 2159 2354 2739 2475 2671 2416 2161 2284 2420 2032 2222\n", + " 2732 2349 2612 2545 2100 2413 2414 2541 2411 2155 2418 2348 2294 2230\n", + " 2602 2164 2474 2538 2738 2034 2609 2228 2419 2352 2480 2740 2674 2160\n", + " 2544 2098 2293 2668 2282 2550 2346 2798 2422 2218 2608 2289 2670 2225\n", + " 2355 2540 2224 2478 2417 2099 2607 2096 2030 2421 2095 2288 2162 2358\n", + " 2736 2219 2350 2158 2220 2477 2604 2549 2163 2548 2286 2803 2357 2667\n", + " 2351 2539 2031 2479 2802 2483 2290 2415 2033 2676 2482 2347 2737 2156\n", + " 2669 2353 2486 2611 2035 2092 2614 2672 2734 2613 2543 2291 2226 2292\n", + " 2287 2675 2229 2283 2606 2165 2673 2485 2800 2797 2412 2094 2410 2157\n", + " 2227 2484 2610 2481 2285 2356 2097 2733 2603 2029 2546 2547 2223 2799\n", + " 2476 2735 2605 2221 2677]\n", + "33 26\n", + "[1826 1765 1695 2084 1570 1888 1508 1892 1502 1699 1895 1315 1697 1504\n", + " 2018 1638 1692 1636 1442 1311 2015 1885 1445 1760 1575 1376 1436 1509\n", + " 1505 1503 1572 1763 1511 1952 1819 1883 2017 1829 2078 1566 1957 1500\n", + " 1310 1954 1949 2020 1822 2079 1894 1571 1955 1374 2014 1437 1698 1316\n", + " 1758 1828 1757 1633 1444 1693 1506 1499 1313 1890 2083 1379 2080 1635\n", + " 1628 1510 2021 1950 1948 1821 1378 1627 1446 2019 1886 1956 1373 1767\n", + " 1314 1827 1825 1694 1630 1887 2081 1501 1703 1762 1632 1375 1755 1440\n", + " 1700 1951 1701 1889 1381 1569 1820 1893 1573 1438 1507 2016 1958 2082\n", + " 1953 1565 1830 1564 1639 1637 1759 1439 1884 1696 1380 1567 1312 1823\n", + " 1629 1702 1831 1766 1691 1824 1574 1891 1443 1634 1631 1764 1563 1761\n", + " 1441 2013 1568 1377 1756]\n", + "11 63\n", + "[4038 4047 3659 3914 3919 3786 4043 3985 3790 3847 3983 3980 3853 3909\n", + " 4041 3851 3979 4037 3726 3913 3982 3975 3854 4039 3788 3978 3915 4042\n", + " 3911 3920 3921 3856 3656 3845 3848 3977 3791 3855 3973 4048 3725 3719\n", + " 3910 3852 4044 3916 3789 4045 3976 3657 3846 3658 3850 3722 3721 3727\n", + " 3849 3720 3974 3792 3981 3917 4049 4040 3785 3662 3912 3787 3857 3723\n", + " 3660 3984 3661 3918 3783 4046 3784 3782 3724]\n", + "17 62\n", + "[4047 3858 4055 3730 3990 3919 4043 3985 3599 4051 3799 3790 3923 3983\n", + " 3668 3980 3853 3851 3979 3922 3726 3728 3987 3924 3926 3982 3732 3861\n", + " 3859 3854 3788 4052 3598 3915 3986 3604 3663 3920 3863 3921 3856 3989\n", + " 3798 3796 3927 3791 3855 4048 3925 3795 3797 3669 3603 3731 3600 3725\n", + " 3666 4053 3793 3852 4044 3664 3601 3916 3789 4045 3734 3665 4050 3667\n", + " 3862 3727 3602 3792 3981 3917 4049 3729 3860 3662 3733 3991 3787 3988\n", + " 3857 3984 3661 3918 4046 4054 3794 3724]\n", + "62 44\n", + "[3071 2878 3263 2617 2557 3004 2618 2491 3129 2872 2492 3003 3196 2873\n", + " 2815 2811 2943 3262 3000 2879 2936 3064 2814 2877 2940 3130 3260 2749\n", + " 2685 2682 2748 2623 2554 2750 2687 3261 3002 2558 2686 3007 2747 2875\n", + " 2621 2938 3195 2937 2680 3006 2939 2683 2941 3131 2556 2751 2555 3070\n", + " 3067 3194 2812 3197 2559 3005 2494 3132 3001 2684 2744 3066 2746 2808\n", + " 2876 2813 3198 2620 2619 3199 3133 2622 3134 2942 3135 2681 3259 2809\n", + " 2495 2493 2810 2745 2874 3065 3068 3069]\n", + "19 24\n", + "[1809 1682 1361 1741 1553 1232 1555 1679 1621 1875 1358 1749 1549 1489\n", + " 1170 1816 1806 1808 1560 1295 1683 1680 1363 1171 1873 1617 1878 1687\n", + " 1488 1362 1492 1423 1494 1554 1421 1367 1490 1561 1431 1807 1433 1937\n", + " 1237 1619 1299 1427 1622 1811 1813 1491 1742 1558 1231 1550 1357 1877\n", + " 1939 1426 1432 1485 1942 1941 1303 1238 1172 1677 1296 1495 1497 1625\n", + " 1368 1425 1365 1613 1493 1678 1746 1812 1369 1297 1815 1233 1623 1810\n", + " 1616 1620 1879 1559 1430 1618 1294 1496 1173 1615 1359 1557 1936 1174\n", + " 1487 1239 1753 1689 1360 1300 1814 1744 1938 1681 1234 1302 1551 1748\n", + " 1871 1422 1556 1684 1486 1752 1366 1872 1624 1298 1236 1686 1874 1876\n", + " 1169 1424 1428 1301 1750 1743 1747 1364 1751 1685 1429 1168 1688 1304\n", + " 1614 1940 1745 1235 1552]\n", + "32 23\n", + "[1826 1765 1695 1570 1888 1508 1502 1699 1315 1697 1504 1638 1692 1636\n", + " 1442 1311 1885 1445 1760 1376 1121 1436 1509 1122 1505 1503 1572 1763\n", + " 1252 1117 1317 1566 1371 1500 1245 1310 1186 1822 1571 1374 1180 1437\n", + " 1698 1316 1758 1249 1828 1307 1757 1633 1306 1444 1693 1506 1562 1499\n", + " 1313 1890 1183 1379 1635 1181 1628 1510 1821 1378 1627 1446 1626 1886\n", + " 1373 1314 1246 1827 1243 1187 1825 1694 1184 1630 1887 1501 1762 1632\n", + " 1375 1185 1755 1123 1440 1700 1498 1248 1701 1889 1381 1569 1820 1370\n", + " 1573 1438 1507 1434 1250 1565 1564 1637 1435 1759 1439 1696 1382 1380\n", + " 1567 1312 1823 1629 1318 1702 1251 1118 1691 1120 1253 1824 1119 1574\n", + " 1891 1443 1634 1631 1764 1308 1188 1690 1563 1761 1244 1182 1441 1309\n", + " 1372 1247 1568 1377 1756]\n", + "61 1\n", + "[ 63 511 251 383 441 191 313 59 255 316 184 444 125 376 507 380 447 319\n", + " 186 57 253 247 189 315 446 127 445 56 126 382 252 378 314 249 121 122\n", + " 190 187 379 120 377 254 442 443 318 510 183 508 188 185 506 250 317 60\n", + " 62 119 509 61 248 55 312 124 381 58 311 123]\n", + "33 63\n", + "[4068 4061 4065 3996 4001 3877 4071 4066 4064 3678 3937 3741 3874 3810\n", + " 4004 4069 4062 3747 3933 3742 3806 3748 3995 4007 3679 3745 3872 4060\n", + " 3871 3869 3749 3867 3999 3684 3682 3680 4002 4006 3879 3744 3868 4067\n", + " 3875 3746 4000 3809 3814 3811 3812 3873 4070 3934 3878 3941 3743 3813\n", + " 3683 3997 3935 3938 3998 3932 4003 4059 3808 3807 3936 4063 3943 3876\n", + " 3805 3931 3870 3942 3939 3804 3940 4005 3681]\n", + "34 31\n", + "[1826 1765 1695 2084 2404 2210 1888 1892 2402 2022 1699 1895 1697 2018\n", + " 2272 2214 1636 2015 2276 1885 1760 2270 2088 2342 1763 2087 1952 2147\n", + " 2152 2017 2077 1829 2078 1957 1954 1949 2149 1832 2020 1822 2079 2338\n", + " 1894 1955 2014 1698 1758 1828 1757 2204 1633 2206 2277 2337 2335 1890\n", + " 2271 2146 2083 2269 2150 2080 2278 1635 2012 1896 2021 1950 1948 1821\n", + " 2274 2019 2339 2141 1886 1956 1959 1767 2211 2151 2209 2273 2279 1827\n", + " 1825 1694 1887 2207 2081 1762 2334 1632 2405 1960 2086 2403 2144 1700\n", + " 1951 2215 2400 2275 2216 2023 2212 1701 1889 1820 1893 2016 1958 2082\n", + " 1953 1830 2336 2145 1637 1759 1884 2208 2340 1696 1823 2143 1702 2024\n", + " 1831 2140 2401 1766 1824 2205 2076 1891 2213 2148 2399 1634 1631 1764\n", + " 2085 1761 2341 2142 2013]\n", + "15 50\n", + "[2957 3345 3275 3217 3145 2832 2891 3024 3086 3538 3215 3599 3342 3408\n", + " 3025 2959 3148 3027 3023 2897 3280 2960 3089 3154 2955 2963 3209 3402\n", + " 3534 3151 3084 3091 2896 3411 3470 3018 3476 3146 3274 3412 3346 3598\n", + " 2964 3472 3155 2898 3536 3273 3082 3090 3413 3017 3085 3344 3404 3537\n", + " 3218 3284 2893 3087 3337 3026 3153 3277 3410 3219 2833 2830 3531 3285\n", + " 2834 3468 3210 3341 3343 3467 3600 3221 3532 3471 3083 3028 3535 3282\n", + " 3406 3409 3029 3474 3021 3157 3601 3149 2956 3339 3283 3533 3405 3220\n", + " 3156 3539 3338 3152 3347 3597 2895 3401 3092 2828 3276 3093 3469 2899\n", + " 3602 3348 3473 2829 2831 2962 3150 3596 3212 2894 3278 3216 3214 3081\n", + " 3019 3349 3088 3213 3340 3403 3281 3466 2892 2961 3211 3407 2954 3475\n", + " 3279 3020 2958 3022 3147]\n", + "28 62\n", + "[4055 3990 4061 3865 4065 3996 3799 4001 4066 3676 4064 3613 3678 3937\n", + " 3741 3874 3810 3926 3993 4062 3801 4057 3675 3735 3933 3738 4058 3866\n", + " 3742 3802 3806 3995 3679 3745 3872 4060 3871 3863 3869 3798 3739 3672\n", + " 3610 3612 3927 3867 3928 3999 3929 3674 3680 4002 3744 3868 3737 4000\n", + " 3736 3614 3809 3873 3934 4056 3803 3615 3930 3743 3994 3609 3677 3997\n", + " 3935 3938 3998 3932 3862 3673 4059 3808 3807 3936 4063 3805 3740 3931\n", + " 3870 3991 3800 3611 3804 4054 3992 3864]\n", + "15 56\n", + "[3858 3345 3275 3217 3730 3659 3919 3538 3786 3985 3215 3599 3342 3408\n", + " 3790 3923 3529 3983 3668 3980 3853 3280 3541 3851 3922 3605 3402 3534\n", + " 3726 3728 3411 3982 3732 3859 3470 3476 3854 3788 3412 3346 3598 3915\n", + " 3472 3986 3604 3663 3920 3536 3413 3344 3404 3921 3856 3537 3218 3796\n", + " 3277 3410 3791 3855 3531 3468 3595 3341 3343 3795 3467 3797 3669 3603\n", + " 3731 3600 3725 3666 3532 3471 3535 3282 3406 3409 3474 3793 3852 3664\n", + " 3601 3916 3789 3339 3283 3533 3405 3665 3539 3338 3593 3347 3540 3597\n", + " 3667 3594 3657 3658 3850 3401 3722 3276 3721 3727 3469 3477 3602 3792\n", + " 3981 3348 3473 3917 3596 3729 3212 3785 3860 3278 3662 3216 3214 3733\n", + " 3213 3340 3403 3281 3530 3787 3857 3723 3660 3466 3984 3661 3918 3465\n", + " 3407 3475 3794 3279 3724]\n", + "32 35\n", + "[2084 2529 2404 2210 1888 2406 2402 2398 2589 2532 2018 2272 2592 2461\n", + " 2214 2015 2267 2659 2276 1885 2464 2394 2270 2591 2468 2593 2333 2342\n", + " 2654 1952 2011 2147 2596 2530 2017 2077 2078 1954 2526 1949 2658 2149\n", + " 2020 2079 2533 2338 1955 2014 2331 2462 2466 2204 2268 2206 2277 2594\n", + " 2337 2335 2524 1890 2271 2146 2083 2269 2150 2080 2278 2460 2012 2330\n", + " 2021 1950 1948 2397 2274 2019 2339 2141 1886 2203 1956 2075 2211 2202\n", + " 2467 2209 2273 2590 2588 2458 2463 1887 2207 2081 2656 2465 2334 2405\n", + " 2086 2403 2144 2523 1951 2400 2275 2395 2396 2266 2212 1889 2595 2657\n", + " 2527 2016 2082 1953 2336 2145 2208 2340 2653 2469 2143 2531 2459 2528\n", + " 2139 2140 2401 2205 2332 2076 1891 2213 2148 2399 2525 2074 2085 2138\n", + " 2655 2341 2142 2013 2470]\n", + "12 38\n", + "[2322 2449 2191 2317 2444 2705 2765 2319 2062 2314 2767 2762 2509 2441\n", + " 2445 2257 2380 2313 2578 2637 2318 2188 2569 2631 2375 2766 2254 2825\n", + " 2255 2125 2630 2378 2633 2447 2502 2760 2827 2439 2695 2381 2377 2639\n", + " 2571 2698 2185 2443 2510 2127 2058 2193 2126 2768 2640 2384 2442 2249\n", + " 2570 2376 2703 2379 2702 2059 2186 2310 2568 2250 2567 2258 2572 2577\n", + " 2128 2761 2122 2385 2830 2121 2383 2192 2253 2440 2120 2315 2514 2251\n", + " 2247 2826 2636 2386 2511 2635 2700 2764 2513 2503 2576 2450 2374 2312\n", + " 2697 2505 2184 2828 2508 2634 2696 2642 2061 2183 2448 2512 2829 2320\n", + " 2246 2831 2641 2321 2575 2632 2057 2507 2187 2574 2506 2316 2382 2060\n", + " 2252 2446 2504 2704 2763 2438 2311 2063 2189 2638 2123 2256 2124 2190\n", + " 2248 2566 2573 2699 2701]\n", + "38 25\n", + "[1826 1765 1704 1570 1508 1892 2022 1836 1699 1895 1315 1642 1383 1697\n", + " 1504 1638 1636 1442 1445 1760 1708 1322 1575 1833 1509 1505 1572 1763\n", + " 1511 1772 1834 1252 1578 1512 1579 1768 1829 1317 1451 1771 1640 1957\n", + " 1769 1450 1954 1452 1832 2020 1255 1641 1894 1576 1571 1955 1961 1698\n", + " 1316 1828 1633 1319 1707 1962 1444 1506 1321 1447 1890 1379 1635 1387\n", + " 1835 1896 1770 1515 1510 2021 1899 1378 1446 2019 1256 1577 1448 1956\n", + " 1320 1959 2025 1767 1516 1314 1385 1827 1825 1703 1644 1762 1705 1632\n", + " 1960 1440 1700 2023 1701 1386 1889 1381 1569 1897 1893 1514 1573 1507\n", + " 1958 1513 1830 1254 1639 1384 1637 1449 1696 1382 1380 1643 1318 1702\n", + " 1251 2024 1706 1831 1766 1898 1253 1824 1574 1891 1443 1634 1257 1764\n", + " 1761 1441 1580 1568 1377]\n", + "48 57\n", + "[4083 3827 4079 3696 3699 3500 3766 4013 3956 3954 3702 3379 3823 3826\n", + " 3574 3373 3884 3631 3499 3886 3443 4081 3636 3440 3757 3887 4078 3435\n", + " 3822 3439 3952 3947 3376 3760 3377 3948 3885 3510 3762 3445 4019 4016\n", + " 3502 3311 3694 3830 3572 3626 3754 3564 3890 4018 3698 3380 3949 3828\n", + " 3700 3950 3763 3891 3824 3438 3765 3627 3378 3567 3375 4015 3691 3758\n", + " 3566 3498 3893 3309 3444 4014 3882 3505 3697 4012 3692 3818 3504 3820\n", + " 3441 3701 3507 3629 3756 3563 3628 3764 3569 3503 3894 3501 3565 4082\n", + " 3819 3951 3759 3825 3436 3821 3957 3630 3632 3637 4017 3573 3635 3314\n", + " 3509 3755 3437 3374 3633 3570 3315 3506 3312 3571 3695 3310 3888 3889\n", + " 4020 3953 4080 3634 3442 3313 4077 3638 3508 3883 3829 3693 3761 3892\n", + " 3568 3562 3955 3372 3690]\n", + "35 0\n", + "[ 38 32 168 35 222 163 41 94 96 33 224 421 164 167 226 105 98 30\n", + " 420 417 99 230 416 353 158 97 352 103 228 165 296 36 232 102 287 100\n", + " 159 225 31 101 290 289 221 29 359 40 356 223 104 294 39 162 354 95\n", + " 355 357 286 34 288 231 227 157 358 292 169 419 233 229 291 295 418 293\n", + " 166 93 422 351 37 161 160]\n", + "21 2\n", + "[ 17 27 210 83 401 153 21 467 81 25 15 340 149 145 281 89 143 404\n", + " 472 402 339 213 471 409 535 82 144 214 207 341 273 337 80 532 345 147\n", + " 208 211 212 468 152 86 151 85 146 343 209 154 338 217 534 347 91 407\n", + " 150 275 469 79 277 342 405 473 279 344 20 16 335 276 536 22 400 90\n", + " 148 18 533 465 87 530 336 88 215 155 280 282 274 346 283 271 403 531\n", + " 470 278 408 216 26 406 23 19 24 219 218 272 84 410 466]\n", + "47 45\n", + "[2922 2542 2861 2801 2739 2671 2927 3115 2930 2730 2732 3187 3182 3053\n", + " 2545 2859 2795 2541 2933 3054 3116 3058 3180 3049 3249 3055 2738 2990\n", + " 2741 2993 2796 2860 2609 3117 3121 3118 2740 2864 2997 2805 2674 2928\n", + " 2544 3125 2869 2668 3243 3311 2798 3244 2996 2608 3061 3123 2670 3250\n", + " 2868 2540 3050 2607 3184 3181 2995 2921 2863 2729 3186 3119 2867 2992\n", + " 3245 2989 3309 2923 2736 3113 3188 2604 3051 2987 3056 2794 2803 2667\n", + " 2857 3122 2802 2676 2737 2669 2924 2611 2865 2672 2734 3059 2991 2543\n", + " 2929 2804 2986 3179 2988 2793 2994 2931 3314 2675 2858 2606 3120 2673\n", + " 3183 2800 2797 3052 3057 2925 3312 3310 2866 3114 2731 3308 3178 3251\n", + " 2610 3313 2733 3248 3247 2603 2546 2932 2926 2862 2799 2985 3060 3246\n", + " 3185 2735 2605 2666 3124]\n", + "14 52\n", + "[2957 3345 3275 3217 3145 3659 3024 3086 3538 3215 3599 3342 3408 3025\n", + " 2959 3148 3529 3023 3280 2960 3089 3464 3154 2955 3209 3402 3534 3151\n", + " 3084 3091 3726 3728 3411 3470 3018 3476 3146 3274 3412 3346 3598 3472\n", + " 3155 3528 3663 3536 3273 3082 3090 3085 3344 3404 3400 3537 3218 3284\n", + " 3087 3337 3026 3153 3277 3410 3219 3531 3468 3595 3210 3341 3272 3343\n", + " 3336 3467 3603 3600 3725 3666 3532 3471 3083 3535 3282 3406 3409 3474\n", + " 3021 3664 3601 3149 2956 3339 3283 3533 3405 3220 3665 3156 3539 3338\n", + " 3593 3152 3347 3540 3597 3144 3594 3658 3401 3276 3727 3469 3602 3348\n", + " 3473 3150 3596 3729 3212 3278 3662 3216 3214 3081 3019 3208 3088 3213\n", + " 3340 3403 3281 3530 3723 3660 3466 3661 3465 2961 3211 3407 3475 3279\n", + " 3020 2958 3022 3147 3724]\n", + "56 5\n", + "[251 54 441 245 569 313 59 316 184 308 444 500 125 376 370 507 380 438\n", 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1854 2491 2046 2489 2492 2360 2109 2425\n", + " 2366 2105 2302 2298 2108 2044 2431 2363 2296 2111 2300 2427 2170 1979\n", + " 1918 2490 2623 2554 1916 2041 2558 2169 1851 2365 2171 2429 2106 2174\n", + " 2430 2621 1982 2367 1853 2428 2236 1915 2239 1980 2556 2237 2555 1919\n", + " 2043 2559 1981 2175 2234 2494 2045 2040 2303 2047 2104 2620 2619 2110\n", + " 2301 2622 1977 2168 2362 2172 1852 2426 2299 2232 1855 2495 2173 2493\n", + " 2364 1917 2361 2107 2424 2233 2042 2297]\n", + "16 53\n", + "[3345 3275 3217 3730 3659 3024 3086 3538 3215 3599 3342 3408 3025 3790\n", + " 3148 3027 3023 3668 3280 3089 3154 3541 3605 3402 3534 3151 3084 3091\n", + " 3726 3728 3411 3732 3470 3476 3286 3274 3412 3414 3346 3598 3472 3155\n", + " 3604 3663 3536 3090 3413 3085 3344 3404 3537 3218 3284 3087 3026 3153\n", + " 3277 3410 3219 3791 3531 3285 3468 3595 3210 3341 3343 3795 3467 3669\n", + " 3542 3603 3731 3600 3725 3221 3666 3532 3471 3535 3282 3406 3409 3474\n", + " 3793 3021 3157 3664 3601 3149 3789 3339 3606 3283 3533 3405 3220 3665\n", + " 3156 3539 3338 3152 3347 3540 3597 3667 3594 3092 3350 3276 3727 3469\n", + " 3477 3602 3792 3348 3473 3222 3150 3596 3729 3212 3278 3662 3216 3478\n", + " 3214 3349 3088 3213 3340 3403 3281 3530 3660 3466 3661 3211 3407 3475\n", + " 3794 3279 3022 3147 3724]\n", + "47 13\n", + "[1262 1130 1136 1132 879 757 1203 557 947 1261 756 940 1002 878\n", + " 818 1004 1065 563 1003 882 1198 1072 1200 819 1140 627 495 1135\n", + " 684 498 1001 876 1067 1139 821 688 1005 1009 1071 1202 682 745\n", + " 949 754 621 1011 558 689 560 748 877 885 625 618 685 1138\n", + " 820 494 1075 815 1066 1008 690 497 941 810 1010 556 626 555\n", + " 562 687 937 948 1264 946 1197 1076 1195 811 622 749 750 1073\n", + " 692 559 751 1013 620 753 1077 1134 755 873 881 1196 1265 693\n", + " 943 493 875 1266 813 1199 619 812 683 561 1007 945 817 747\n", + " 1137 1074 1012 1068 1260 1133 938 492 880 944 1006 884 814 496\n", + " 623 1201 942 624 686 1131 691 1070 1263 746 628 939 816 752\n", + " 681 809 883 1069 874]\n", + "61 54\n", + "[3903 3583 3711 3387 3839 3391 3705 3577 3517 3899 3772 3644 3320 3263\n", + " 3511 3837 3708 3769 3836 3390 3258 3455 3775 3196 3709 3834 3384 3262\n", + " 3389 3581 3386 3902 3130 3260 3901 3838 3383 3579 3513 3324 3261 3326\n", + " 3643 3706 3578 3448 3771 3257 3642 3646 3195 3256 3575 3641 3707 3321\n", + " 3193 3452 3639 3514 3451 3131 3319 3450 3447 3194 3197 3900 3322 3515\n", + " 3898 3132 3385 3454 3647 3768 3773 3640 3518 3449 3453 3519 3198 3199\n", + " 3645 3516 3133 3512 3582 3134 3835 3327 3833 3135 3774 3259 3704 3710\n", + " 3703 3388 3576 3580 3770 3325 3323]\n", + "5 17\n", + "[1098 1353 1093 1035 777 839 842 773 771 1350 897 1409 1099 907\n", + " 1091 1219 772 906 1095 1024 1216 1351 770 1478 769 1291 1287 1344\n", + " 1348 711 835 1280 832 775 1154 971 1158 1155 961 905 1413 1284\n", + " 1474 1476 970 1089 1096 1286 960 1029 1160 1222 838 1224 1349 776\n", + " 966 1221 1026 1480 1285 1410 1223 841 1414 1092 963 1227 706 1346\n", + " 1025 1217 1354 1345 1347 1159 836 900 1412 1283 1288 1282 903 708\n", + " 1289 1032 1033 712 837 968 1152 1281 1411 1163 964 1088 1290 902\n", + " 1162 962 1479 834 1226 1034 709 1352 1417 707 967 1218 904 1153\n", + " 1027 710 1030 840 901 1031 969 1157 1090 1416 899 896 1094 1477\n", + " 1097 898 1161 833 1475 774 1156 1220 1225 1028 965 1415]\n", + "15 26\n", + "[1809 1682 1361 1741 2000 1553 1870 1420 1804 1555 1679 1547 1621 1875\n", + " 1358 1749 1997 1549 1489 1737 2062 1806 1808 1295 1930 1674 1866 1683\n", + " 1680 1363 1932 1612 1873 1739 1617 1488 1362 1867 2064 1869 1548 1492\n", + " 1423 1933 1554 1421 1490 1292 1807 1868 1931 1483 1937 1619 1484 1675\n", + " 1427 1811 1813 1491 1481 2002 1546 1802 1676 1742 2065 1803 1550 1545\n", + " 1357 1877 1939 1426 1801 1485 1356 2003 1677 1995 1296 1738 1425 1613\n", + " 1493 2001 1678 1746 1935 1293 1812 1297 1611 1810 1616 1620 1934 1618\n", + " 1294 1482 1615 1359 1557 1936 1487 2066 1999 1360 1355 1418 1673 1744\n", + " 2061 1938 1681 1419 1551 1748 1805 1871 1422 1556 1684 1486 1872 1996\n", + " 1298 2060 1874 1876 1424 1610 1428 1743 1747 1865 1609 2063 1685 1740\n", + " 1614 1998 1940 1745 1552]\n", + "11 16\n", + "[1098 848 1420 913 845 1353 1104 1232 1039 1093 1035 1228 910 777\n", + " 716 1358 839 976 842 1041 1167 779 1295 1099 653 974 907 718\n", + " 719 906 714 1095 1351 781 1105 780 1291 1287 711 775 971 1158\n", + " 1230 784 778 912 1165 905 847 1421 651 715 970 1292 1096 1286\n", + " 1103 849 1029 1160 1038 1222 838 1224 776 966 782 1221 1231 846\n", + " 1223 841 908 1357 1227 1040 654 1166 843 1354 1356 1037 1159 1296\n", + " 977 1288 783 650 903 1289 1032 1033 712 648 649 837 717 1293\n", + " 911 968 1036 1233 1163 844 1290 1101 902 1294 1162 1359 975 972\n", + " 1100 1226 1102 973 1034 1229 1352 1355 1418 1417 967 904 909 1419\n", + " 1030 840 901 1031 1422 969 1157 1416 652 1169 1164 1094 1168 713\n", + " 1097 1161 774 1225 965]\n", + "30 3\n", + "[ 27 32 153 25 35 222 163 281 89 94 156 350 96 546 33 224 415 409\n", + " 164 349 541 540 482 226 285 98 477 30 28 542 420 417 481 345 99 416\n", + " 353 158 152 97 352 348 228 607 414 92 36 154 287 100 545 217 605 347\n", + " 91 159 539 225 609 31 290 606 473 289 221 29 220 284 344 411 356 478\n", + " 223 90 162 354 95 355 483 286 413 34 288 227 157 88 292 155 280 419\n", + " 282 480 603 291 476 346 283 604 418 543 408 216 26 24 93 608 219 479\n", + " 351 538 161 218 160 474 412 475 410 544]\n", + "59 60\n", + "[3903 3583 4095 3832 3711 3839 3959 3705 4086 3577 4026 3517 3899 3772\n", + " 3967 3644 3766 3837 3708 3769 3702 3895 4030 3836 3965 4027 3962 3775\n", + " 3709 4091 3897 3834 4092 3581 4094 3902 4029 3830 3901 3838 4028 4093\n", + " 3579 3513 3960 3643 3706 3765 4085 3961 4090 3578 3771 3893 3958 4021\n", + " 4022 4025 3642 4031 3646 3575 4024 3641 3707 3701 3639 3514 3894 3966\n", + " 3896 3900 3515 3957 3898 4088 3647 3768 4089 3773 3640 3518 3645 3516\n", + " 3831 3512 3767 3582 3835 3833 3774 3638 3963 3964 3704 3710 3829 4087\n", + " 3703 3576 3580 3770 4023]\n", + "60 58\n", + "[3903 3583 4095 3832 3711 3387 3839 3391 3959 3705 3577 4026 3517 3899\n", + " 3772 3967 3644 3766 3511 3837 3708 3769 3702 3895 4030 3574 3836 3390\n", + " 3965 4027 3962 3455 3775 3709 4091 3897 3834 4092 3389 3581 4094 3386\n", + " 3902 4029 3830 3901 3838 4028 4093 3579 3513 3960 3643 3706 3961 4090\n", + " 3578 3448 3771 3958 4025 3642 4031 3646 3575 4024 3641 3707 3452 3639\n", + " 3514 3451 3894 3966 3450 3896 3900 3515 3898 4088 3385 3454 3647 3768\n", + " 4089 3773 3640 3518 3449 3453 3519 3645 3516 3831 3512 3767 3582 3835\n", + " 3833 3774 3638 3963 3964 3704 3710 3703 3388 3576 3580 3770 4023]\n", + "58 17\n", + "[1151 1078 1023 1146 1399 1403 826 1407 1402 958 829 1018 1147 830\n", + " 1207 956 1470 954 1085 1140 1401 894 1465 1269 1021 1275 887 1277\n", + " 1406 888 1533 1405 827 886 1144 761 1336 1335 1404 1469 1014 1268\n", + " 1338 1148 1532 949 1143 1276 1334 764 1273 828 1530 760 885 1205\n", + " 1341 1463 1142 952 824 1466 1527 1211 1271 1141 1531 893 765 1019\n", + " 1204 1081 955 1270 1464 1020 1337 948 892 1213 1016 1467 890 1076\n", + " 1529 1210 1278 1086 951 1013 950 1077 1398 1279 1080 895 1212 1332\n", + " 822 1082 1272 1017 1397 1528 1339 1400 957 762 1022 1215 763 1342\n", + " 1079 1012 1209 1462 1468 959 953 1274 823 1343 1087 1015 1206 1214\n", + " 1208 891 1145 759 1084 1150 1340 825 889 1083 1149 1333]\n", + "11 24\n", + "[1361 1741 1553 1870 1420 1353 1804 1679 1547 1228 1358 1549 1489 1737\n", + " 1806 1808 1350 1605 1295 1930 1674 1866 1680 1932 1863 1612 1351 1799\n", + " 1478 1739 1291 1617 1543 1287 1488 1230 1867 1672 1869 1928 1548 1423\n", + " 1165 1933 1413 1421 1671 1798 1292 1286 1807 1868 1931 1864 1483 1484\n", + " 1675 1544 1481 1160 1546 1802 1224 1349 1733 1542 1676 1742 1231 1541\n", + " 1803 1480 1223 1550 1545 1414 1357 1227 1607 1801 1166 1485 1354 1670\n", + " 1356 1735 1734 1800 1677 1296 1288 1738 1425 1613 1289 1678 1606 1293\n", + " 1611 1163 1929 1290 1616 1934 1294 1482 1162 1615 1359 1479 1487 1226\n", + " 1360 1229 1352 1355 1418 1673 1744 1417 1681 1419 1551 1805 1871 1422\n", + " 1486 1416 1424 1610 1743 1164 1865 1736 1609 1477 1161 1740 1614 1225\n", + " 1608 1745 1669 1552 1415]\n", + "49 33\n", + "[2027 1840 1907 2093 1973 2542 2159 2354 1966 2416 2161 2284 2420 2032\n", + " 2222 1965 1842 2349 2545 2100 2413 2414 2155 1839 2418 2348 2294 1963\n", + " 2230 2164 2167 1909 1969 1841 2034 2166 2228 2419 2352 2480 2160 2544\n", + " 2098 2293 1905 1778 2103 1901 1903 1779 2422 1971 2289 2225 2355 2224\n", + " 1837 2036 2478 2417 2099 1964 1908 2096 2038 2030 2421 1902 2039 2095\n", + " 2288 2162 2037 1776 2358 1904 2219 2350 1970 2158 1900 2220 2477 2163\n", + " 2548 1968 1845 2286 2357 1972 2351 1838 2031 2479 2483 2290 2415 1910\n", + " 2033 2482 1777 2347 2156 2353 2295 1967 2035 2092 2543 2291 2231 2226\n", + " 1774 1780 2292 2287 2229 1975 2283 2165 1844 1974 2101 2485 2412 2094\n", + " 2157 2227 2484 1775 2481 2285 2356 2097 2091 2029 2546 2547 2223 2359\n", + " 1843 2102 2221 2028 1906]\n", + "45 21\n", + "[1388 1392 1710 1262 1330 1713 1130 1136 1132 1519 1642 1581 1383 1389\n", + " 1521 1203 1261 1708 1322 1575 1773 1002 1004 1065 1394 1511 1003 1772\n", + " 1712 1193 1198 1578 1258 1072 1512 1579 1200 1451 1771 1648 1640 1135\n", + " 1129 1450 1067 1327 1452 1255 1259 1641 1326 1576 1331 1455 1328 1005\n", + " 1071 1649 1456 1202 1454 1709 1585 1522 1319 1707 1523 1321 1323 1447\n", + " 1586 1387 1584 1329 1770 1515 1393 1390 1520 1138 1776 1256 1066 1577\n", + " 1008 1448 1320 1516 1457 1395 1650 1324 1128 1385 1325 1391 1644 1705\n", + " 1264 1194 1197 1587 1195 1073 1386 1134 1514 1453 1459 1774 1192 1196\n", + " 1513 1265 1384 1266 1449 1191 1199 1517 1645 1646 1007 1643 1267 1137\n", + " 1068 1260 1133 1706 1775 1006 1201 1131 1583 1257 1070 1263 1647 1582\n", + " 1711 1458 1580 1518 1069]\n", + "19 63\n", + "[4047 3858 4055 3730 3990 3919 3865 3985 4051 3799 3790 3923 3983 3668\n", + " 3853 3922 3728 3987 3924 3926 3982 3732 3993 3861 3859 3854 4057 3735\n", + " 4052 3986 3920 3863 3921 3856 3989 3798 3796 3927 3791 3928 3855 3929\n", + " 4048 3925 3795 3797 3669 3731 3666 4053 3793 4056 3664 4045 3734 3665\n", + " 4050 3667 3862 3670 3727 3792 3981 3917 4049 3729 3860 3733 3991 3988\n", + " 3857 3984 3800 3918 4046 4054 3992 3864 3794]\n", + "38 16\n", + "[1130 1132 1315 1383 1062 1445 743 1322 1121 940 1002 865 1122 742\n", + " 1004 1065 871 1003 996 1193 1064 1252 1258 1317 738 1060 1129 1001\n", + " 876 928 1067 1126 1186 1255 803 1259 1057 868 1316 1249 675 744\n", + " 999 993 745 1319 1444 936 1321 1323 1447 1313 870 1379 866 806\n", + " 1378 1446 808 1190 1189 1256 1066 1448 1320 935 810 1314 864 1128\n", + " 1385 677 867 998 1187 1184 1063 679 680 994 807 937 1000 932\n", + " 1185 1194 1127 739 1123 1195 811 676 931 678 1248 1386 1381 997\n", + " 873 1124 1058 1192 1250 1196 992 1254 875 1384 804 1449 1191 1061\n", + " 740 1382 1380 802 929 934 1318 1068 1260 1251 938 1059 1056 1120\n", + " 1253 872 1443 930 933 741 1131 1257 746 1188 939 805 681 809\n", + " 869 995 1125 801 874]\n", + "21 25\n", + "[1809 1682 1361 1553 1555 1679 1621 1875 1749 1489 2004 1816 1808 1305\n", + " 1560 1819 1683 1680 1363 2006 1818 2008 1873 1617 1878 1687 1488 1362\n", + " 1492 1423 1494 1554 1367 1490 1561 1431 1807 1433 2005 1937 1237 1943\n", + " 1619 1754 1562 1299 1427 1622 1499 1811 1813 1491 2002 1880 1558 1877\n", + " 1939 1426 1627 1432 1942 1626 1941 1303 1238 1817 1945 2003 1495 1497\n", + " 1625 1368 1425 1365 1493 1882 2007 1746 1812 1369 1755 1297 1815 1623\n", + " 1810 1616 1498 1620 1879 1559 1430 1618 1496 1370 1615 1557 1487 1239\n", + " 1434 1753 1689 1360 1300 1435 1814 1744 1938 1681 1234 1302 1551 1748\n", + " 1556 1684 1691 1752 1366 1872 1624 1298 1236 1686 1874 1876 1424 1428\n", + " 1301 1750 1743 1747 1690 1364 1881 1751 1563 1685 1429 1688 1304 1940\n", + " 1745 1235 1240 1944 1552]\n", + "50 5\n", + "[ 54 245 239 184 308 500 114 376 370 438 631 177 757 181 51 557 247 237\n", + " 47 364 302 756 369 563 564 435 175 431 428 46 627 630 495 498 504 688\n", + " 501 182 567 694 111 304 117 179 300 754 621 430 558 689 240 116 238 440\n", + " 368 560 566 429 439 113 241 365 503 625 499 375 50 118 494 110 306 242\n", + " 52 690 174 497 373 556 502 303 307 366 432 626 629 562 173 301 310 183\n", + " 687 53 172 176 112 49 565 180 436 622 305 692 559 751 753 437 755 434\n", + " 309 374 372 693 493 119 371 246 561 367 433 248 492 312 48 244 496 623\n", + " 236 624 686 691 628 115 109 752 243 178 311 568]\n", + "0 57\n", + "[3776 4032 3526 3648 3909 3392 3714 3524 3906 3332 3587 3460 3456 3843\n", + " 3520 4034 3586 3584 3717 3393 3394 3905 3712 3844 3462 3266 3970 3651\n", + " 3267 3716 3649 3779 3590 3842 3845 3458 3331 3265 4035 4033 3585 3459\n", + " 3718 3522 3523 3654 3781 3777 3778 3397 3972 3525 3907 3396 3461 3588\n", + " 3521 3780 3904 3395 3589 3846 3330 3968 3329 3457 3841 3969 3713 3328\n", + " 3971 3264 3715 3908 3653 3782 3652 3650 3840]\n", + "23 59\n", + "[3858 4055 3730 3990 3865 3538 3985 3996 4051 3799 3923 3668 3541 3922\n", + " 3676 3605 3613 3741 3671 3482 3987 3924 3926 3732 3993 3861 3859 3476\n", + " 3801 4057 3675 3735 3483 3412 3933 3414 3607 4052 3738 4058 3866 3802\n", + " 3986 3995 3415 3604 3548 3479 4060 3413 3863 3869 3921 3989 3798 3546\n", + " 3739 3672 3610 3796 3612 3927 3867 3928 3929 3674 3925 3795 3416 3608\n", + " 3868 3797 3669 3542 3603 3737 3731 3736 3666 4053 3481 3793 4056 3803\n", + " 3547 3601 3606 3734 3930 3665 3994 3539 3609 4050 3540 3677 3667 3997\n", + " 3932 3862 3673 3670 3543 3418 4059 3477 3544 3602 3545 3805 3729 3860\n", + " 3740 3478 3931 3417 3733 3991 3988 3857 3800 3611 3804 3475 4054 3992\n", + " 3864 3794 3480]\n", + "32 40\n", + "[2529 2404 2210 2406 2402 2721 2398 2661 2847 2589 2532 2272 2592 2461\n", + " 2659 2276 2780 2464 2394 2270 2973 2591 2843 2468 2593 2333 2844 2654\n", + " 2723 2596 2783 2530 2716 2522 2526 2978 2658 2848 2533 2779 2974 2338\n", + " 2977 2850 2331 2662 2462 2715 2466 2911 2534 2268 2788 2206 2778 2594\n", + " 2337 2722 2335 2524 2271 2652 2269 2909 2460 2598 2789 2397 2274 2785\n", + " 2339 2913 2597 2211 2784 2467 2209 2273 2590 2916 2588 2458 2463 2207\n", + " 2587 2656 2465 2334 2787 2405 2403 2849 2717 2523 2782 2915 2726 2400\n", + " 2275 2395 2396 2660 2786 2781 2912 2845 2719 2595 2657 2527 2724 2851\n", + " 2976 2975 2336 2914 2208 2979 2340 2653 2718 2469 2910 2531 2725 2459\n", + " 2852 2528 2790 2401 2853 2205 2332 2651 2908 2650 2399 2525 2720 2714\n", + " 2846 2655 2341 2470 2586]\n", + "35 2\n", + "[ 38 32 168 35 222 163 41 94 350 96 546 33 224 360 421 415 164 349\n", + " 167 482 226 285 105 98 30 550 420 417 423 481 485 99 230 416 353 158\n", + " 547 97 352 103 228 414 165 296 36 232 102 287 100 548 545 549 159 225\n", + " 486 31 101 424 290 297 289 221 29 359 40 487 356 223 104 294 361 39\n", + " 162 354 95 355 483 357 286 34 288 231 227 157 358 292 169 419 233 229\n", + " 480 291 295 418 293 166 93 422 484 479 351 37 161 160 544]\n", + "12 19\n", + "[1098 1361 1420 845 1353 1104 1232 1039 1035 1547 1228 910 1358 1549\n", + " 976 1489 842 1041 1170 1167 1350 1295 1099 974 907 906 1095 1612\n", + " 1351 1105 1291 1287 1488 971 1158 1362 1230 912 1548 1423 1165 905\n", + " 847 1421 970 1292 1096 1286 1483 1484 1544 1103 1481 1160 1546 1038\n", + " 1222 1224 1231 1480 846 1223 841 1550 1545 1414 908 1106 1042 1357\n", + " 1227 1040 1426 1166 1485 843 1354 1356 1037 1159 1296 977 1288 1425\n", + " 1613 1289 1032 1033 1293 911 1297 968 1611 1036 1233 1163 844 1290\n", + " 1101 1294 1482 1162 1615 1359 975 1479 972 1100 1487 1226 1102 973\n", + " 1034 1360 1229 1352 1355 1418 1417 967 904 909 1234 1419 1030 1551\n", + " 1031 1422 1486 969 1416 1298 1169 1424 1610 1164 1609 1094 1168 1097\n", + " 1161 1614 1225 1552 1415]\n", + "43 48\n", + "[2922 2861 2927 3115 3500 2730 2732 3182 3053 2859 2983 2795 3373 3499\n", + " 3054 3173 3116 3366 3180 3112 3049 3435 3249 3055 3439 2990 3176 3376\n", + " 3306 3368 2993 2796 2860 3433 3117 3121 3367 3046 3118 3111 3241 2864\n", + " 2928 3307 3305 3243 3502 3311 2798 2920 2791 3244 2919 3431 3304 3369\n", + " 3045 3048 2856 2854 3434 3050 3301 3109 3438 3047 3184 3181 2921 2863\n", + " 2729 3119 2918 3375 3496 3242 2981 2992 3498 2982 3245 2989 3497 3309\n", + " 2923 3302 3113 2984 3370 3051 2987 3175 3056 2794 2857 2917 2855 3501\n", + " 2924 3436 2734 2792 2991 2929 3238 2986 3174 3179 2988 2793 3432 2858\n", + " 3120 3303 3437 3374 3183 3371 2728 2797 3110 3052 3057 2925 3239 3312\n", + " 3310 3114 2731 3308 3178 3313 2733 3248 3247 3177 3240 2926 2862 2799\n", + " 3237 2985 3246 3185 3372]\n", + "17 47\n", + "[2957 3345 3217 2832 2891 3024 3086 3215 3342 3408 3025 2959 2705 3148\n", + " 3027 2765 3023 2767 2897 3280 2960 3089 3154 2955 2963 3151 3084 3091\n", + " 2896 3411 3094 3159 3030 2966 3286 2766 3412 3346 2839 2709 2964 2827\n", + " 2770 2900 3155 2639 2835 2898 3158 2768 2640 3223 3090 3085 3344 2703\n", + " 2702 3218 3284 2893 3031 3087 3026 3153 3277 2708 2773 3410 3219 2833\n", + " 2830 3285 2834 3341 3343 2772 2643 3221 2706 3083 2967 3028 3282 3406\n", + " 3409 3029 2707 3021 3157 2764 3149 2956 3283 2644 3220 3156 3095 3152\n", + " 3347 2901 2895 3092 2828 3276 2965 3093 2837 2642 2899 3348 2829 2838\n", + " 3222 2831 2962 3150 2641 3212 2894 3278 3216 3214 2769 2902 3019 3349\n", + " 3088 3213 3281 2836 2892 2961 2704 3211 3407 2903 2638 3279 3020 2958\n", + " 3022 2701 2774 2771 3147]\n", + "19 29\n", + "[1809 1682 1741 2000 1553 1870 2191 2195 1555 1679 1621 1875 1749 1997\n", + " 2129 1489 2062 2067 2004 1816 2131 1806 1808 2136 2257 1683 2196 1680\n", + " 2006 2008 1873 1617 1878 1687 1488 2064 2198 2134 1869 1492 1933 1494\n", + " 1554 1490 2127 2072 2193 1807 2126 2005 1937 1943 1619 1622 2262 1811\n", + " 1813 1491 2002 1880 2070 1742 1558 2065 2258 2128 1877 1939 1942 1941\n", + " 2133 1817 1945 2003 1677 2192 1493 2001 1678 2007 1746 1935 1812 1815\n", + " 1623 1810 2073 2194 1616 1620 1879 1559 1934 1618 2132 1615 1557 1936\n", + " 2135 2066 2259 1753 1689 1999 1814 1744 2061 1938 2199 1681 1551 1748\n", + " 1805 1871 1556 2071 1684 2197 1752 1872 1624 2009 1686 1874 1876 1750\n", + " 1743 1747 2068 2260 1881 1751 2063 1685 1688 2069 2130 2256 1614 2261\n", + " 1998 1940 1745 1944 1552]\n", + "41 0\n", + "[ 45 38 239 107 168 35 163 41 237 47 364 426 302 360 170 175 164 428\n", + " 167 46 234 105 423 111 99 230 103 300 228 165 238 296 36 232 102 100\n", + " 425 365 108 110 362 101 174 424 297 359 40 173 301 172 104 294 43 361\n", + " 39 357 231 227 358 298 292 169 233 229 299 44 42 363 295 171 293 166\n", + " 427 236 422 106 37 109 235]\n", + "14 5\n", + "[ 17 329 594 210 591 83 459 12 141 401 720 269 467 81 73 15 340 334\n", + " 716 145 200 521 143 526 404 332 523 402 339 655 653 524 525 718 201 719\n", + " 82 144 139 207 595 13 458 721 273 337 136 80 393 14 651 532 715 147\n", + " 208 211 212 468 75 11 146 396 529 76 209 338 202 74 456 392 10 654\n", + " 275 588 79 77 461 268 463 140 658 650 16 335 138 464 267 276 398 717\n", + " 142 400 148 206 399 18 331 465 522 593 527 530 336 587 264 394 462 457\n", + " 460 589 657 266 274 78 592 330 590 265 271 403 586 531 204 205 333 397\n", + " 652 520 270 272 137 656 585 528 203 328 395 466]\n", + "14 14\n", + "[1098 594 848 591 785 720 913 845 1104 1232 1039 1035 1228 910\n", + " 777 716 850 659 976 842 526 1041 914 1170 1167 523 779 724\n", + " 978 1295 655 1099 653 852 974 907 524 525 718 719 906 714\n", + " 1171 781 1105 780 1291 721 971 1230 784 778 912 1165 905 847\n", + " 651 715 970 1292 1096 723 1103 849 1038 529 776 722 782 1231\n", + " 846 841 908 1106 1042 1227 1040 654 1166 843 588 1107 1037 1296\n", + " 977 783 658 650 1032 1033 712 649 979 717 1293 911 1297 968\n", + " 1036 980 1233 1163 844 1101 593 787 527 1294 587 1162 975 972\n", + " 1100 1044 589 851 786 657 1226 1102 973 1034 1229 1108 788 904\n", + " 592 909 590 915 1234 586 840 916 969 652 1169 1164 1043 1168\n", + " 656 528 713 1097 1161]\n", + "29 38\n", + "[2529 2210 2402 2721 2398 2847 2589 2272 2592 2461 2267 2659 2780 2456\n", + " 2464 2394 2270 2200 2591 2843 2263 2593 2713 2333 2844 2654 2783 2647\n", + " 2530 2265 2327 2712 2077 2521 2078 2716 2522 2391 2526 2658 2583 2848\n", + " 2079 2779 2338 2584 2331 2462 2715 2466 2455 2585 2842 2204 2268 2206\n", + " 2778 2594 2337 2722 2335 2524 2271 2652 2269 2080 2460 2330 2393 2777\n", + " 2397 2274 2785 2339 2141 2203 2649 2329 2075 2202 2784 2467 2209 2273\n", + " 2590 2588 2264 2458 2463 2207 2587 2656 2465 2334 2137 2403 2328 2144\n", + " 2717 2523 2782 2392 2400 2275 2395 2396 2266 2201 2781 2845 2719 2595\n", + " 2657 2457 2527 2336 2145 2208 2648 2653 2718 2143 2531 2459 2528 2139\n", + " 2140 2401 2205 2332 2076 2651 2650 2399 2525 2720 2714 2074 2846 2138\n", + " 2655 2520 2142 2519 2586]\n", + "9 12\n", + "[1098 591 459 845 1093 1035 910 777 716 839 521 842 526 523\n", + " 773 771 779 655 1099 653 453 974 907 524 525 718 772 719\n", + " 906 714 1095 580 781 780 582 458 711 835 775 971 1158 778\n", + " 393 905 847 651 517 715 970 1096 581 390 396 1029 1160 1038\n", + " 838 776 966 782 846 456 841 908 392 963 518 516 654 645\n", + " 843 588 1037 1159 461 836 900 783 650 903 708 1032 455 1033\n", + " 712 648 649 837 717 911 968 1036 1163 583 964 844 522 643\n", + " 1101 902 587 1162 644 975 972 394 519 1100 457 460 589 973\n", + " 1034 709 707 967 904 909 590 710 1030 586 840 901 1031 969\n", + " 454 652 899 584 647 520 1164 391 1094 585 713 1097 1161 774\n", + " 646 1028 395 579 965]\n", + "59 7\n", + "[511 251 383 831 441 191 638 569 313 255 316 184 444 125 376 507 380 447\n", + " 438 631 767 319 186 575 253 699 247 826 636 189 829 315 830 634 446 630\n", + " 894 701 445 702 504 501 888 639 827 567 761 694 126 382 252 378 314 249\n", + " 121 122 190 440 566 439 764 187 828 760 632 503 572 375 379 571 120 377\n", + " 254 824 442 443 695 633 893 765 373 502 629 318 510 766 696 310 183 637\n", + " 892 508 188 185 565 890 506 250 574 317 570 698 437 309 374 693 246 509\n", + " 762 763 505 700 248 697 758 312 635 573 124 703 823 381 891 759 825 311\n", + " 568 889 123]\n", + "0 9\n", + "[193 576 320 773 771 897 453 772 260 580 770 386 582 769 835 832 387 258\n", + " 961 325 517 257 642 449 448 581 390 515 577 960 513 963 518 706 516 645\n", + " 836 900 256 321 704 708 194 837 192 705 452 643 451 324 644 962 322 641\n", + " 834 709 578 707 512 710 323 389 195 388 454 450 899 259 514 640 896 898\n", + " 768 833 774 646 384 385 579]\n", + "15 46\n", + "[2957 3345 3275 3217 3145 2832 2891 3024 3086 3215 3342 3025 2959 2705\n", + " 3148 3027 2765 3023 2767 2897 2762 3280 2960 3089 3154 2955 2963 3151\n", + " 3084 3091 2578 2896 2637 3018 3146 2766 2825 3346 2964 2827 2770 2900\n", + " 3155 2639 2835 2698 2898 2768 2640 3082 3090 3017 3085 3344 2703 2702\n", + " 3218 2893 3087 2572 2577 3026 3153 3277 2708 2761 2773 3219 2833 2830\n", + " 2889 2834 3210 3341 3343 2772 2643 2706 3083 3028 2826 3282 2636 3029\n", + " 2635 2707 2700 3021 3157 2764 2576 3149 2956 3283 3220 3156 3152 2901\n", + " 2895 2953 3092 2828 3276 2965 3093 2837 2642 2899 2829 2831 2962 3150\n", + " 2641 2575 3212 2894 3278 3216 3214 2769 3081 2890 3019 3088 3213 3340\n", + " 2574 3281 2836 2892 2961 2704 3211 2763 2954 2638 3279 3020 2958 3022\n", + " 2573 2699 2701 2771 3147]\n", + "60 5\n", + "[ 63 511 251 383 441 191 638 569 313 59 255 316 184 444 125 376 507 380\n", + " 447 438 631 767 319 186 575 57 253 699 247 636 189 315 634 446 127 701\n", + " 445 702 504 639 182 56 567 761 126 382 252 378 314 249 121 122 190 440\n", + " 566 439 764 187 632 503 572 375 379 571 120 377 254 442 443 633 765 502\n", + " 318 510 766 696 310 183 637 508 188 185 506 250 574 317 570 698 60 62\n", + " 374 119 246 509 762 763 61 505 700 248 697 312 635 573 124 703 381 58\n", + " 311 568 123]\n", + "44 35\n", + "[2027 2093 2089 2542 2406 2159 2354 2475 2671 1966 2416 2161 2284 2214\n", + " 2408 2032 2222 1965 2349 2536 2545 2088 2344 2413 2414 2541 2411 2155\n", + " 2342 2418 2348 2087 2345 1963 2602 2474 2535 2538 2152 2026 2090 2153\n", + " 2352 2480 2407 2473 2160 2544 2098 2600 2668 2601 2282 1961 2346 2409\n", + " 1901 1903 2218 2608 2289 2670 1962 2225 2540 2224 2343 2150 2278 2478\n", + " 2417 1964 2607 2472 2096 2030 1899 1902 2095 2288 2162 2154 2219 2025\n", + " 2350 2158 1900 2220 2477 2604 2151 2279 2217 1968 2286 2667 2351 2539\n", + " 2031 2471 2479 2290 2415 1960 2086 2033 2482 2347 2156 2669 2353 2215\n", + " 1967 2216 2023 2092 1897 2543 2226 2287 2283 2606 2280 2412 2094 2410\n", + " 2157 2024 2537 1898 2481 2285 2097 2665 2091 2603 2029 2223 2281 2476\n", + " 2605 2470 2221 2666 2028]\n", + "63 61\n", + "[3903 3583 4095 3711 3839 4026 3899 3772 3967 3644 3837 3708 3769 4030\n", + " 3836 3965 4027 3962 3775 3709 4091 3897 3834 4092 3581 4094 3902 4029\n", + " 3901 3838 4028 4093 3643 3706 3961 4090 3771 4025 4031 3646 3707 3966\n", + " 3900 3898 3647 4089 3773 3645 3582 3835 3833 3774 3963 3964 3710 3580\n", + " 3770]\n", + "12 11\n", + "[1098 329 594 848 591 459 785 720 913 845 1039 1035 334 910\n", + " 777 716 850 839 521 976 842 526 914 332 523 779 655 1099\n", + " 653 974 907 524 525 718 719 906 714 781 780 582 458 721\n", + " 711 775 971 784 778 912 393 905 847 651 715 970 396 1103\n", + " 849 1038 529 838 776 722 782 846 456 841 908 392 518 1040\n", + " 654 843 588 1037 461 463 977 783 658 650 903 335 464 1032\n", + " 455 1033 712 648 649 398 717 911 968 1036 400 399 331 583\n", + " 844 465 522 1101 593 527 530 902 587 975 972 394 519 1100\n", + " 462 457 460 589 786 657 1102 973 1034 967 904 592 909 330\n", + " 590 710 586 840 333 969 397 652 584 647 520 656 585 528\n", + " 713 1097 774 646 395]\n", + "17 42\n", + "[2957 2322 2832 2891 2449 3024 2454 3086 2444 3025 2959 2705 3027 2765\n", + " 3023 2319 2767 2897 2960 2516 3089 2509 2963 2445 3091 2578 2896 2647\n", + " 2637 2318 2453 2966 2387 2766 2582 2711 2451 2839 2583 2709 2447 2517\n", + " 2964 2827 2770 2900 2381 2639 2835 2571 2510 2898 2768 2640 3090 2384\n", + " 2703 2702 2518 2893 2324 3087 2572 2577 3026 2646 2708 2773 2710 2833\n", + " 2385 2830 2834 2579 2515 2772 2580 2383 2643 2706 2514 2388 3028 2636\n", + " 3029 2386 2511 2635 2707 2700 3021 2764 2513 2576 2956 2645 2644 2450\n", + " 2901 2895 3092 2828 2508 2965 2837 2642 2899 2448 2512 2829 2389 2838\n", + " 2320 2831 2962 2775 2641 2321 2575 2894 2769 2581 2902 2507 3088 2574\n", + " 2452 2382 2836 2446 2892 2961 2704 2763 2903 2638 2323 2958 3022 2573\n", + " 2519 2699 2701 2774 2771]\n", + "63 6\n", + "[ 63 511 251 383 831 441 191 638 569 313 255 316 444 125 507 380 447 767\n", + " 319 186 575 253 699 636 189 829 315 830 634 446 127 701 445 702 639 126\n", + " 382 252 378 314 249 190 764 187 828 572 379 571 377 254 442 443 633 765\n", + " 318 510 766 637 508 188 506 250 574 317 570 698 60 62 509 763 61 505\n", + " 700 635 573 124 703 381 123]\n", + "54 6\n", + "[251 54 441 245 569 313 316 184 308 444 500 114 376 370 507 380 438 631\n", + " 177 757 186 181 51 57 699 247 636 756 369 315 563 564 435 634 819 627\n", + " 630 498 821 504 501 182 56 567 761 694 304 252 117 378 179 754 314 249\n", + " 689 240 121 122 116 440 368 560 566 439 187 241 760 632 503 625 499 572\n", + " 375 379 118 820 571 120 306 377 242 52 824 690 442 497 443 695 633 373\n", + " 502 307 432 626 629 562 696 310 183 53 508 185 565 180 436 506 305 692\n", + " 250 570 698 437 755 434 309 822 374 372 693 119 371 246 561 762 505 433\n", + " 248 697 758 55 312 635 244 496 624 823 691 628 115 759 243 178 825 311\n", + " 568]\n", + "60 56\n", + "[3903 3583 3832 3711 3387 3839 3391 3705 3577 4026 3517 3899 3772 3967\n", + " 3644 3320 3766 3263 3511 3837 3708 3769 3702 3895 4030 3574 3836 3390\n", + " 3965 4027 3258 3962 3455 3775 3709 3897 3834 3384 3262 3510 3389 3581\n", + " 3386 3902 4029 3260 3830 3901 3838 4028 3383 3579 3513 3960 3324 3261\n", + " 3326 3643 3706 3961 3578 3448 3771 3257 4025 3642 4031 3646 3575 3641\n", + " 3707 3321 3452 3639 3514 3451 3966 3450 3447 3896 3900 3322 3515 3898\n", + " 3385 3454 3647 3768 3773 3640 3518 3449 3453 3519 3645 3516 3831 3512\n", + " 3767 3582 3835 3327 3833 3774 3259 3638 3963 3964 3704 3710 3703 3388\n", + " 3446 3576 3580 3770 3325 3323]\n", + "17 2\n", + "[ 17 210 83 12 141 401 21 269 467 81 15 340 149 334 145 143 526 404\n", + " 332 402 339 213 82 144 214 139 207 341 13 273 337 80 14 532 147 208\n", + " 211 212 468 75 11 86 151 85 146 396 343 529 76 209 338 150 275 469\n", + " 79 277 342 405 77 461 268 463 279 140 20 16 335 464 267 276 398 142\n", + " 22 400 148 206 399 18 331 465 87 527 530 336 215 462 274 78 271 403\n", + " 531 278 204 205 333 406 23 19 397 270 272 84 528 203 466]\n", + "38 34\n", + "[2027 2084 2404 2089 2210 2406 1892 2402 2022 1895 2475 2532 2018 2272\n", + " 2284 2214 2408 2276 2536 2088 1833 2344 2468 2411 2155 2342 2348 2087\n", + " 2345 1963 2147 2596 2474 2535 2538 2152 2530 2026 2017 2090 1829 2153\n", + " 1957 1954 2149 1832 2020 2533 2407 2473 2338 1894 1955 2600 2601 2282\n", + " 1961 2466 2346 2409 2534 1828 2218 2277 1962 2337 1890 2343 2146 2083\n", + " 2150 2080 2278 1896 2598 2472 2021 2274 2154 2019 2339 1956 1959 2219\n", + " 2597 2025 2211 2220 2467 2151 2209 2273 2279 2217 1827 2081 2599 2465\n", + " 2471 2405 1960 2086 2403 2144 2347 2156 2215 2400 2275 2216 2023 2212\n", + " 2092 1897 2595 1893 2016 1958 2082 1953 1830 2336 2145 2283 2208 2340\n", + " 2280 2469 2412 2531 2410 2024 1831 2537 2401 1898 1891 2213 2148 2091\n", + " 2281 2085 2341 2470 2028]\n", + "8 3\n", + "[ 6 329 459 12 141 269 73 334 200 521 133 261 332 523 453 524 201 260\n", + " 70 139 386 582 13 458 387 136 393 258 14 4 325 517 2 75 263 581\n", + " 390 11 396 76 202 74 456 392 518 10 516 130 68 8 66 77 461 268\n", + " 140 138 132 267 194 455 398 5 327 326 142 206 452 331 583 522 451 197\n", + " 134 587 324 264 394 519 322 7 457 460 72 266 78 196 199 330 9 265\n", + " 131 586 323 262 204 205 333 389 195 388 198 454 397 3 71 259 135 584\n", + " 520 270 391 137 585 203 67 69 328 395]\n", + "55 9\n", + "[ 441 245 569 313 308 444 500 376 370 507 380 438 631 757\n", + " 699 247 826 947 636 756 818 829 315 1018 563 564 435 634\n", + " 882 954 819 627 630 701 445 498 887 821 504 501 888 827\n", + " 567 886 761 694 1014 378 949 754 314 249 689 440 566 439\n", + " 764 828 760 632 503 885 625 499 572 375 379 820 952 571\n", + " 377 824 690 442 497 443 695 633 765 373 502 307 626 629\n", + " 955 562 696 310 637 948 892 508 1016 565 890 436 506 692\n", + " 250 951 1013 950 753 570 698 437 755 434 309 822 374 372\n", + " 693 371 246 509 1017 561 762 763 505 817 1012 700 433 248\n", + " 697 758 953 312 635 884 244 573 823 691 1015 628 891 759\n", + " 883 825 311 568 889]\n", + "14 59\n", + "[4047 3858 3730 3659 3914 3919 3538 3786 4043 3985 3599 3408 4051 3790\n", + " 3923 3529 3983 3668 3980 3853 4041 3851 3979 3922 3534 3726 3913 3728\n", + " 3987 3924 3982 3732 3859 3470 3854 3788 3978 3598 3915 4042 3472 3986\n", + " 3604 3663 3920 3536 3404 3921 3856 3656 3537 3796 3848 3977 3791 3855\n", + " 3531 4048 3468 3595 3795 3467 3603 3731 3600 3725 3666 3532 3471 3535\n", + " 3406 3409 3474 3793 3852 4044 3664 3601 3916 3789 3533 4045 3405 3665\n", + " 3539 3593 4050 3597 3667 3976 3594 3657 3658 3850 3722 3721 3727 3849\n", + " 3469 3720 3602 3792 3981 3473 3917 4049 3596 3729 3785 3860 3662 3592\n", + " 3403 3530 3912 3787 3988 3857 3723 3660 3466 3984 3661 3918 3407 4046\n", + " 3784 3794 3724]\n", + "52 37\n", + "[2542 2801 2159 2354 2739 2671 2416 2161 2420 2222 2678 2612 2545 2100\n", + " 2617 2414 2418 2294 2679 2806 2230 2618 2807 2164 2742 2167 2738 2489\n", + " 2741 2360 2034 2166 2425 2609 2228 2419 2488 2352 2298 2480 2740 2805\n", + " 2674 2553 2160 2544 2098 2293 2296 2550 2103 2552 2422 2608 2289 2490\n", + " 2225 2355 2554 2224 2036 2478 2417 2099 2607 2169 2096 2038 2421 2039\n", + " 2288 2162 2037 2358 2736 2350 2549 2163 2680 2548 2286 2803 2357 2351\n", + " 2479 2802 2483 2290 2415 2423 2033 2676 2482 2737 2353 2486 2295 2611\n", + " 2615 2035 2234 2614 2672 2613 2543 2291 2231 2804 2744 2226 2104 2292\n", + " 2287 2675 2229 2606 2165 2673 2101 2485 2168 2362 2227 2426 2484 2681\n", + " 2232 2610 2481 2356 2097 2487 2546 2547 2223 2359 2102 2361 2743 2424\n", + " 2551 2677 2616 2233 2297]\n", + "56 51\n", + "[3387 3705 3128 3577 3517 3644 3320 3511 3187 3702 3379 3574 3316 2933\n", + " 3390 3004 3443 3636 3189 3258 3191 3129 3003 3196 3384 3262 3510 2935\n", + " 3389 3000 3445 2936 2997 3581 3064 3125 3386 3130 3253 2934 3260 3252\n", + " 3572 2996 3061 3123 3383 3250 3382 3380 3579 3513 3324 3261 3326 3002\n", + " 3643 3063 3706 3378 3192 3186 3317 3126 3578 3448 3444 3257 2938 3642\n", + " 3188 3195 3256 3575 2937 2999 3641 3707 3701 3321 3507 3193 3452 2939\n", + " 3639 3514 3451 3122 3131 3381 3319 3450 3447 3067 3194 3197 3322 3515\n", + " 3318 3132 3001 3385 3059 3454 3637 3066 2998 3640 3518 3449 3573 3314\n", + " 3453 3509 3198 3516 3133 3512 3134 3315 3506 3571 3259 3251 3442 3638\n", + " 3508 3704 3254 3703 3388 3255 3446 3190 3065 3062 3068 3576 3060 3069\n", + " 3580 3325 3124 3127 3323]\n", + "14 21\n", + "[1098 1682 1361 1741 1553 1420 1353 1104 1232 1039 1555 1679 1035 1547\n", + " 1228 1358 1549 976 1489 1041 1170 1167 1295 1099 974 1674 1680 1363\n", + " 1612 1171 1105 1739 1291 1617 1488 971 1362 1230 1548 1492 1423 1165\n", + " 1554 1421 1490 1292 1483 1619 1484 1675 1299 1427 1544 1103 1491 1481\n", + " 1160 1546 1038 1224 1676 1742 1231 1480 1550 1545 1106 1042 1357 1227\n", + " 1040 1426 1166 1485 1354 1107 1172 1356 1037 1677 1296 977 1288 1425\n", + " 1613 1289 1678 1293 1297 1611 1036 1233 1163 1290 1616 1101 1618 1294\n", + " 1482 1162 1615 1359 975 972 1100 1487 1226 1102 973 1034 1360 1229\n", + " 1300 1352 1355 1418 1744 1417 1681 1234 1419 1551 1422 1556 1486 1416\n", + " 1298 1236 1169 1424 1610 1428 1743 1364 1164 1609 1168 1097 1161 1740\n", + " 1614 1225 1745 1235 1552]\n", + "21 27\n", + "[1809 1682 2000 1553 1555 1679 1621 1875 1749 1489 2067 2004 1816 2131\n", + " 1808 1560 2136 1819 1683 1883 1680 1363 2006 1818 2008 1873 1617 1878\n", + " 1687 1488 1362 2134 1492 1494 1554 1367 1490 1561 1431 2072 1807 1433\n", + " 2005 1937 1943 1619 1754 1562 1427 1622 1811 1813 1491 2002 1880 2070\n", + " 1558 2065 1877 1939 1426 1627 1432 1942 1626 1941 2133 1817 1945 2003\n", + " 1495 1947 1497 1625 1368 1425 1365 1493 2001 1882 2007 1746 1935 1812\n", + " 1755 1815 1623 1810 1946 2073 1616 1498 1620 1879 1559 1430 1618 2132\n", + " 1496 1615 1557 1936 2135 2066 1753 1689 1814 1744 1938 1681 1551 1748\n", + " 1871 1556 2071 1684 1691 2010 1752 1366 1872 1624 2009 1686 1874 1876\n", + " 1428 1750 1743 1747 2068 1690 1364 1881 1751 1563 1685 1429 1688 2069\n", + " 2130 1940 1745 1944 1552]\n", + "51 57\n", + "[3832 4083 3827 3959 3705 4086 3577 3696 3699 3766 3511 3956 3769 3954\n", + " 3702 3379 3895 3823 3826 3574 3316 3631 3886 3443 4081 3636 3440 3757\n", + " 3887 3822 3439 3952 3376 3760 3897 3377 3885 3510 3762 3445 4019 4016\n", + " 3502 3694 3830 3572 3890 3383 4018 3698 3382 3380 3513 3828 3960 3700\n", + " 3950 3763 3891 3824 3438 3765 3378 4085 3567 3375 4015 3758 3317 3566\n", + " 3448 3893 4084 3958 4021 3444 4022 3505 3697 3575 3504 3641 3441 3701\n", + " 3507 3629 3639 3764 3569 3503 3381 3894 3501 3565 4082 3447 3951 3896\n", + " 3759 3825 3821 3957 3318 3630 3632 3768 3637 4017 3640 3573 3635 3314\n", + " 3509 3831 3512 3767 3633 3570 3315 3833 3506 3312 3571 3695 3888 3889\n", + " 4020 3953 4080 3634 3442 3313 3638 3508 3704 3829 3703 3446 3693 3761\n", + " 3576 3892 3568 3955 4023]\n", + "22 36\n", + "[2322 2449 2454 2195 2326 2129 2267 2067 2456 2004 2394 2516 2131 2200\n", + " 2263 2325 2713 2136 2257 2578 2196 2647 2006 2265 2327 2008 2712 2521\n", + " 2453 2387 2582 2522 2711 2391 2451 2583 2709 2517 2198 2134 2584 2331\n", + " 2455 2585 2072 2193 2204 2268 2005 1943 2384 2262 2524 2002 2070 2518\n", + " 2065 2460 2258 2324 2330 2393 2577 2646 2708 2128 1939 2710 2385 1942\n", + " 1941 2203 2390 2649 2579 2329 2133 2075 1945 2515 2003 2580 2202 2192\n", + " 2643 2264 2458 2514 2388 2587 2386 2007 2137 2328 2707 2523 2513 2392\n", + " 2645 2644 2450 2395 2073 2194 2396 2266 2201 2132 2457 2135 2066 2259\n", + " 2642 2648 2448 2199 2512 2389 2320 2321 2459 2139 2140 2071 2581 2197\n", + " 2010 2332 2009 2650 2452 2074 2068 2260 2138 2069 2130 2323 2520 2256\n", + " 2261 1940 2519 1944 2586]\n", + "54 27\n", + "[1840 1907 1976 1973 1713 1524 1782 1521 1399 1842 1651 1461 1785 1525\n", + " 2100 1914 1978 2164 2167 1712 1849 1909 1848 1969 1841 1588 1652 2034\n", + " 2166 1401 1648 1465 1912 2105 2098 1715 1905 1778 1460 1649 2103 1717\n", + " 1779 1658 1979 1585 1971 1522 1594 1523 1592 1595 1916 1719 2036 2041\n", + " 1586 1846 1584 2099 2169 1851 1847 1908 1530 1463 2038 1660 1724 2039\n", + " 1714 2106 2037 1776 1723 1904 1466 1396 1527 1970 1531 1395 1593 1657\n", + " 1650 2163 1783 1913 1968 1845 1972 1464 1915 1980 1910 2033 1587 1718\n", + " 1788 1777 2043 1716 1591 1529 1784 2035 1781 2040 1786 1398 1459 2104\n", + " 1780 1655 1720 1850 1397 1528 1975 1721 1596 1787 2165 1844 1911 1974\n", + " 2101 1977 1400 2168 1653 1852 1590 1654 1462 1656 1589 1722 1659 1526\n", + " 1843 1458 2102 2042 1906]\n", + "28 26\n", + "[1826 1695 1570 1888 1502 1697 1504 1692 1311 2015 1885 1760 1376 1816\n", + " 1305 1436 1560 1505 1503 1952 2011 1819 1883 1818 2008 2077 2078 1566\n", + " 1371 1500 1878 1310 1687 1949 1822 2079 1374 1494 2014 1437 1698 1758\n", + " 1561 1431 1307 1757 1633 1433 1943 1306 1693 1506 1754 1562 1622 1499\n", + " 1890 1880 1558 2012 1628 1950 1948 1821 1627 1432 1626 1886 1373 1817\n", + " 2075 1945 1495 1947 1497 1825 1625 1368 1694 1630 1887 1501 1762 1882\n", + " 1632 1375 1369 1755 1815 1440 1623 1951 1946 2073 1498 1879 1559 1889\n", + " 1569 1496 1820 1370 1438 1434 1753 2016 1953 1689 1565 1564 1435 1814\n", + " 1759 1439 1884 1696 1567 1823 1629 1691 2010 1752 1824 1624 2076 2009\n", + " 1634 1686 1631 1750 1308 2074 1690 1881 1751 1563 1761 1688 1441 1309\n", + " 1372 2013 1568 1756 1944]\n", + "25 4\n", + "[ 27 83 153 21 467 25 340 149 222 281 89 94 156 404 350 472 537 339\n", + " 213 415 471 409 535 214 349 341 541 540 665 285 664 477 30 667 28 542\n", + " 600 532 345 147 211 212 468 158 662 152 348 86 151 85 343 414 92 154\n", + " 287 217 605 534 347 599 91 407 159 150 601 539 275 469 277 342 405 668\n", + " 473 279 221 29 220 284 344 411 20 597 478 276 536 223 22 90 148 95\n", + " 533 286 413 87 157 88 215 155 280 282 603 476 346 283 403 604 470 278\n", + " 408 216 26 406 23 663 598 24 602 93 219 479 351 538 218 84 474 412\n", + " 666 475 410]\n", + "44 54\n", + "[3696 3115 3500 3182 3823 3373 3884 3631 3499 3116 3886 3440 3366 3180\n", + " 3757 3887 3435 3822 3249 3439 3176 3376 3306 3368 3623 3760 3433 3117\n", + " 3377 3367 3885 3430 3118 3241 3559 3307 3305 3689 3243 3502 3561 3311\n", + " 3244 3694 3431 3626 3754 3304 3369 3564 3698 3817 3434 3824 3438 3184\n", + " 3627 3378 3181 3567 3119 3375 3496 3242 3691 3758 3566 3498 3245 3687\n", + " 3497 3309 3302 3494 3751 3625 3113 3882 3505 3370 3697 3558 3692 3818\n", + " 3504 3820 3441 3629 3756 3563 3628 3569 3503 3501 3565 3819 3759 3436\n", + " 3821 3630 3632 3752 3179 3432 3314 3755 3881 3816 3303 3437 3374 3183\n", + " 3371 3633 3624 3570 3688 3506 3239 3312 3695 3310 3753 3114 3308 3178\n", + " 3634 3442 3313 3495 3622 3883 3248 3247 3177 3240 3693 3761 3686 3568\n", + " 3562 3246 3560 3372 3690]\n", + "7 60\n", + "[4038 3659 3914 3526 3786 4043 3847 3529 3980 3853 3909 4041 3464 3851\n", + " 3979 3527 4036 4037 3913 3714 3975 3524 3906 3587 3460 4039 3788 3978\n", + " 3843 4034 3586 3717 3915 4042 3905 3528 3844 3462 3970 3651 3716 3649\n", + " 3911 3779 3590 3842 3656 3845 3848 3463 4035 4033 3977 3718 3531 3523\n", + " 3973 3654 3595 3781 3777 3725 3719 3910 3778 3852 3972 3525 3907 4044\n", + " 3916 3789 3461 3655 4045 3588 3593 3591 3780 3976 3589 3594 3657 3846\n", + " 3658 3850 3722 3721 3849 3720 3974 3981 3917 3841 3596 3969 4040 3785\n", + " 3592 3713 3971 3530 3912 3787 3723 3660 3466 3661 3465 3715 3783 3908\n", + " 3784 3653 3782 3652 3650 3724]\n", + "12 49\n", + "[2887 2957 3345 3275 3217 3145 2832 2891 3024 3086 3215 3342 3408 3025\n", + " 2959 3148 3529 2765 3023 2767 2897 2762 3280 2960 3089 3464 3154 2955\n", + " 3209 3206 3402 3534 3151 3084 2896 3470 3018 3146 2766 2825 3274 3346\n", + " 2827 3472 3399 3270 2951 3273 3082 3090 3017 3085 3344 2950 3404 3080\n", + " 3400 3218 2893 3087 3337 3026 3153 3277 2761 3207 2830 2889 3531 3079\n", + " 3468 3210 3341 3272 3343 3271 3336 3467 3014 3532 3471 3083 3535 2826\n", + " 3282 3406 3409 2824 3021 2764 3149 2956 3339 3533 3143 3142 3405 3078\n", + " 3338 3152 3016 3144 2888 2895 3401 2953 2828 3276 3469 3015 2829 2831\n", + " 2962 3150 3212 2894 3278 3216 3214 3334 3081 2890 3019 3208 3088 3213\n", + " 3340 3403 2952 3281 3530 3466 2892 3465 2961 3211 2763 3407 2954 3335\n", + " 3279 3020 2958 3022 3147]\n", + "40 37\n", + "[2027 2084 2404 2089 2210 2542 2406 2402 2022 2475 2661 2532 2284 2214\n", + " 2408 2730 2222 2732 2659 2276 2349 2536 2088 2795 2344 2413 2468 2414\n", + " 2541 2411 2155 2342 2348 2087 2345 2602 2147 2596 2474 2535 2538 2152\n", + " 2530 2026 2090 2153 2149 2533 2407 2473 2338 2662 2600 2668 2601 2282\n", + " 2466 2346 2409 2791 2534 2218 2277 2594 2540 2343 2150 2278 2478 2598\n", + " 2472 2729 2021 2789 2274 2154 2339 2219 2597 2025 2663 2350 2211 2220\n", + " 2477 2604 2467 2151 2279 2217 2664 2794 2286 2599 2667 2539 2471 2405\n", + " 2086 2403 2347 2726 2156 2669 2215 2275 2216 2660 2023 2212 2092 2792\n", + " 2595 2724 2793 2283 2606 2727 2340 2280 2728 2469 2412 2531 2725 2410\n", + " 2157 2024 2790 2537 2731 2285 2665 2213 2148 2091 2603 2281 2085 2341\n", + " 2476 2605 2470 2221 2666]\n", + "13 31\n", + "[1809 1741 2000 1870 2191 1804 2195 2317 1679 1875 1997 2129 2319 1737\n", + " 2062 2067 2314 2131 1806 1808 2257 2380 1930 2313 1674 1866 1680 1932\n", + " 1863 2056 1612 2318 1799 2188 1873 1739 2254 2255 2125 2378 1867 2064\n", + " 1869 1928 1933 2381 2185 2127 2058 2193 1807 1868 2126 1931 1864 1937\n", + " 1675 2384 1811 2002 1802 2249 2379 1676 2059 1742 2186 2250 2065 1803\n", + " 2258 2128 1939 1801 2122 2121 1927 1800 2003 1677 2383 1995 2192 2253\n", + " 2120 2315 1738 2251 1993 1613 2001 1991 1678 1746 1935 2119 1611 1810\n", + " 1992 1929 2194 1616 1934 1615 1936 2184 2066 1999 1673 2055 1744 2061\n", + " 1938 2183 1681 2320 2321 1805 1871 2057 1872 2187 1996 2316 2382 2060\n", + " 2252 1874 1610 1743 1865 1736 2063 2189 2130 2123 2256 2124 2190 1740\n", + " 1614 2248 1998 1745 1994]\n", + "33 62\n", + "[4068 4061 4065 3996 4001 3877 4071 4066 4064 3678 3937 3741 3874 3810\n", + " 4004 4069 4062 3747 3933 3616 3742 3806 3748 3995 4007 3679 3745 3872\n", + " 4060 3871 3869 3749 3618 3620 3867 3999 3684 3682 3680 4002 4006 3617\n", + " 3879 3744 3868 4067 3875 3746 4000 3614 3809 3814 3811 3812 3685 3873\n", + " 4070 3815 3934 3878 3803 3941 3615 3619 3743 3813 3677 3683 3997 3935\n", + " 3938 3998 3932 4003 4059 3808 3807 3936 4063 3943 3876 3805 3740 3931\n", + " 3870 3942 3939 3804 3940 4005 3750 3681]\n", + "49 2\n", + "[ 45 54 245 239 107 308 500 114 370 438 177 181 51 247 237 47 364 302\n", + " 369 563 564 435 175 431 428 46 495 498 501 182 111 304 117 179 300 430\n", + " 558 240 116 238 368 560 429 113 241 365 499 375 50 118 494 108 110 306\n", + " 242 52 174 497 373 303 307 366 432 562 173 301 310 183 53 172 176 112\n", + " 49 180 43 436 305 559 437 434 309 374 372 493 119 371 246 299 44 363\n", + " 561 171 367 433 55 48 244 496 236 115 109 235 243 178 311]\n", + "19 61\n", + "[4047 3858 4055 3730 3990 3919 3865 3538 3985 3599 4051 3799 3790 3923\n", + " 3983 3668 3853 3541 3922 3605 3726 3671 3728 3987 3924 3926 3982 3732\n", + " 3993 3861 3859 3854 3801 4057 3735 3607 4052 3986 3604 3663 3920 3536\n", + " 3863 3921 3856 3989 3798 3537 3672 3796 3927 3791 3928 3855 3929 4048\n", + " 3925 3795 3797 3669 3542 3603 3737 3731 3600 3725 3736 3666 4053 3793\n", + " 4056 3664 3601 3789 3606 4045 3734 3665 3539 4050 3540 3667 3862 3670\n", + " 3727 3602 3792 3981 3917 4049 3729 3860 3662 3733 3991 3988 3857 3984\n", + " 3800 3918 4046 4054 3992 3864 3794]\n", + "63 11\n", + "[ 511 383 831 638 569 1151 444 1023 507 380 447 767 575 699\n", + " 826 636 958 829 1018 830 956 634 446 954 1085 894 701 445\n", + " 1021 702 639 827 761 382 1148 764 828 572 571 443 633 893\n", + " 765 1019 510 955 766 1020 637 892 508 890 506 1086 574 570\n", + " 698 895 509 957 762 1022 763 700 697 959 953 635 573 703\n", + " 381 1087 891 1084 1150 825 889 1083 1149]\n", + "63 4\n", + "[ 63 511 251 383 441 191 638 313 59 255 316 444 125 507 380 447 319 186\n", + " 575 253 636 189 315 446 127 701 445 702 639 126 382 252 378 314 249 121\n", + " 122 190 187 572 379 571 377 254 442 443 318 510 637 508 188 185 506 250\n", + " 574 317 570 60 62 509 61 505 700 635 573 124 703 381 58 123]\n", + "18 13\n", + "[ 594 848 591 785 720 913 845 467 1104 1232 1039 910 716 850\n", + " 1110 659 976 526 1041 914 1170 1167 596 724 978 917 655 653\n", + " 852 974 718 719 855 1171 781 792 1105 780 595 721 664 982\n", + " 784 912 847 856 791 532 468 789 662 723 1046 1237 1103 849\n", + " 1038 529 722 782 1231 846 908 1106 534 1042 1040 919 599 790\n", + " 983 654 1166 469 918 726 728 1107 1172 1037 1047 463 977 853\n", + " 783 658 725 597 464 979 717 854 911 1036 980 1233 1048 533\n", + " 1111 844 465 1109 1101 593 787 527 530 1173 975 661 972 660\n", + " 1174 1044 589 851 786 657 1102 973 981 1108 788 592 909 590\n", + " 915 1234 531 916 663 1236 652 598 1169 920 1043 727 1168 656\n", + " 528 1045 984 1235 466]\n", + "37 60\n", + "[4074 4068 3944 4065 4001 3877 4071 3490 4066 4064 3937 3874 3810 4004\n", + " 4073 4072 4069 3556 3553 3947 3621 3623 3747 3616 3559 3748 3689 3561\n", + " 4007 3679 3626 3754 3745 3872 3871 3817 3880 3946 3749 3554 3496 3691\n", + " 3491 3618 3620 3687 3999 4075 3557 3684 3682 3493 3680 3494 3751 3625\n", + " 4002 3882 4006 3617 3879 3744 3558 4067 3875 3746 4000 3818 3809 3814\n", + " 3811 3812 3685 3873 4070 3815 4011 3878 3941 3619 3819 3743 4009 3813\n", + " 3683 3555 3935 3938 4003 3752 4008 3755 3881 3808 3807 3816 3624 3936\n", + " 4063 3943 3876 4010 3688 3753 3495 3622 3883 3942 3939 3940 3686 3492\n", + " 4005 3560 3750 3945 3690 3681]\n", + "7 13\n", + "[1098 845 1093 1035 777 716 839 521 842 523 773 771 779 897\n", + " 1099 653 453 907 1091 772 906 714 1095 580 781 770 780 582\n", + " 769 458 711 835 775 971 1158 778 1155 961 905 651 517 715\n", + " 642 970 1096 581 515 1029 1160 1222 838 1224 776 966 1221 1026\n", + " 456 1223 841 908 1092 963 518 706 516 645 1025 843 588 1037\n", + " 1159 836 900 650 903 708 1032 455 1033 712 648 649 837 717\n", + " 968 1036 705 452 1163 583 964 844 522 643 902 587 1162 644\n", + " 962 972 519 1100 641 457 834 1226 973 1034 709 578 707 967\n", 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23\n", + "[1388 1392 1710 1840 1907 1262 1330 1713 1136 1519 1524 1581 1389 1782\n", + " 1521 1399 1203 1842 1651 1461 1261 1708 1773 1525 1394 1839 1772 1712\n", + " 1841 1588 1198 1652 1579 1200 1140 1451 1648 1135 1269 1139 1327 1452\n", + " 1326 1331 1335 1455 1715 1328 1905 1778 1460 1649 1456 1717 1903 1779\n", + " 1202 1268 1454 1709 1585 1522 1707 1523 1323 1334 1837 1719 1586 1387\n", + " 1584 1329 1515 1908 1205 1463 1393 1390 1902 1520 1138 1714 1776 1904\n", + " 1396 1527 1516 1457 1395 1650 1324 1204 1845 1325 1391 1270 1644 1838\n", + " 1264 1197 1587 1718 1777 1716 1591 1781 1134 1398 1453 1459 1332 1774\n", + " 1265 1780 1655 1266 1397 1199 1517 1645 1844 1646 1643 1267 1653 1137\n", + " 1590 1260 1654 1462 1775 1589 1201 1526 1583 1263 1647 1582 1843 1711\n", + " 1458 1580 1518 1333 1906]\n", + "13 24\n", + "[1809 1682 1361 1741 1553 1870 1420 1353 1804 1232 1555 1679 1547 1228\n", + " 1358 1549 1489 1737 1167 1806 1808 1295 1930 1674 1866 1683 1680 1363\n", + " 1932 1612 1351 1873 1739 1291 1617 1543 1488 1362 1230 1867 1672 1869\n", + " 1548 1423 1165 1933 1554 1421 1490 1671 1292 1807 1868 1931 1483 1619\n", + " 1484 1675 1427 1544 1491 1481 1546 1802 1676 1742 1231 1803 1480 1550\n", + " 1545 1357 1227 1607 1426 1801 1166 1485 1354 1356 1735 1800 1677 1296\n", + " 1288 1738 1425 1613 1289 1678 1746 1935 1293 1297 1611 1233 1810 1163\n", + " 1290 1616 1934 1618 1294 1482 1162 1615 1359 1479 1936 1487 1226 1360\n", + " 1229 1352 1355 1418 1673 1744 1417 1681 1419 1551 1805 1871 1422 1486\n", + " 1872 1416 1298 1424 1610 1743 1747 1164 1865 1736 1609 1168 1740 1614\n", + " 1225 1608 1745 1552 1415]\n", + "22 17\n", + "[1361 913 1104 1232 850 1110 976 1041 914 1170 1305 724 978 988\n", + " 917 987 852 1363 855 1115 1171 792 1105 1049 1371 729 1362 982\n", + " 912 857 1492 1494 1180 856 791 924 1367 1112 1431 789 1175 1307\n", + " 1241 723 1046 1433 1237 1306 1299 1427 849 1491 985 1177 1052 921\n", + " 1106 1042 1040 919 922 1426 793 790 983 1432 986 923 1116 1303\n", + " 918 1238 726 728 1107 1172 1113 1047 1296 977 853 1495 1497 858\n", + " 1243 1114 1368 725 1365 1493 979 1178 854 1050 1369 1297 980 1233\n", + " 1048 1111 1109 787 1430 1496 1173 1370 1174 1242 1239 1434 1044 851\n", + " 786 981 1300 1051 1108 788 915 1234 1302 1179 916 1366 1298 1236\n", + " 1169 1428 1301 1308 794 1176 1364 920 1043 727 1429 1168 859 1244\n", + " 1304 1045 984 1235 1240]\n", + "25 1\n", + "[ 27 83 153 21 25 340 149 222 281 89 94 156 350 472 213 471 409 214\n", + " 349 341 285 30 28 345 147 211 212 158 152 348 86 151 85 343 92 154\n", + " 287 217 347 91 407 159 150 275 31 277 342 405 473 279 221 29 220 284\n", + " 344 411 20 276 223 22 90 148 95 286 413 87 157 88 215 155 280 282\n", + " 476 346 283 470 278 408 216 26 406 23 19 24 93 219 218 84 474 412\n", + " 475 410]\n", + "54 58\n", + "[3832 4083 3827 3959 3705 4086 3577 4026 3696 3899 3772 3644 3699 3766\n", + " 3511 3956 3708 3769 3954 3702 3379 3895 3826 3574 3836 4027 3443 3636\n", + " 3962 3952 3760 3897 3834 3384 3510 3762 3445 4019 3830 3572 3890 3383\n", + " 4018 3698 3382 3380 3579 3513 3828 3960 3700 3763 3891 3824 3643 3706\n", + " 3765 4085 3961 4090 3578 3448 3771 3893 4084 3958 4021 3444 4022 4025\n", + " 3642 3505 3697 3575 4024 3641 3707 3701 3507 3639 3514 3764 3569 3381\n", + " 3894 3450 4082 3447 3896 3825 3900 3515 3957 3898 4088 3385 3632 3768\n", + " 3637 4017 4089 3640 3449 3573 3635 3509 3831 3512 3767 3633 3835 3570\n", + " 3833 3506 3571 3888 3889 4020 3953 3634 3442 3638 3963 3508 3964 3704\n", + " 3829 4087 3703 3446 3761 3576 3892 3568 3580 3770 3955 4023]\n", + "48 31\n", + "[1710 2027 1840 1907 2093 1973 1713 2159 1836 2354 1966 2416 2161 2284\n", + " 2032 2222 1965 1842 1651 2349 1708 1773 2100 2413 2414 2155 1839 2418\n", + " 2348 1963 1772 2230 2164 1712 1909 1969 2026 1834 1841 2090 2034 2166\n", + " 1771 1648 2228 2419 2352 2160 2098 2293 1715 1905 1778 1649 1901 1903\n", + " 1779 1709 2218 1971 2289 1962 2225 2355 2224 1837 2036 1846 1835 2417\n", + " 2099 1964 1908 2096 2038 2030 1899 1902 2095 2288 2162 1714 2154 2037\n", + " 1776 1904 2219 2350 1970 2158 1900 2220 1650 2163 1968 1845 2286 1972\n", + " 2351 1838 2031 2290 2415 1910 2033 1777 1716 2156 2353 1967 2035 2092\n", + " 1781 2291 2226 1774 1780 2292 2287 2229 2283 2165 1645 1844 1974 1646\n", + " 2101 2094 2157 2227 1775 1898 2285 2356 2097 2091 2029 2223 1647 1843\n", + " 1711 2102 2221 2028 1906]\n", + "19 47\n", + "[2957 3345 3217 2832 3024 3086 3215 3408 3025 2959 2705 3027 3023 2767\n", + " 2897 3280 2960 3089 3154 2963 3151 3091 2896 3411 2840 3160 3094 3159\n", + " 3030 2966 3286 3287 2766 2711 3412 3414 3346 2839 2709 2964 2770 2968\n", + " 2900 3155 2905 2835 3097 3351 2898 3158 2768 2640 3223 3090 3413 3085\n", + " 3344 2703 3218 3284 2776 3288 2893 3031 3087 3026 2646 3153 2708 2773\n", + " 3410 2710 3219 2833 2830 3285 2834 3161 3343 2772 3096 3032 2643 3221\n", + " 2706 2967 3028 3282 3409 3029 2707 3021 3157 3149 3283 2645 2644 3220\n", + " 3156 3095 3152 2904 3347 2901 2895 3092 3350 3225 2965 3093 2837 3033\n", + " 2642 2899 2969 3348 2829 2838 3222 2831 2962 3150 2775 2641 2894 3278\n", + " 3216 3214 2769 2902 3349 3088 3213 3281 2836 3224 2961 2704 2903 3279\n", + " 2958 3022 2841 2774 2771]\n", + "63 50\n", + "[3583 3387 3391 3071 2878 3517 3644 3263 3390 3004 3258 3455 3129 3003\n", + " 3196 2943 3262 3389 2879 3581 2877 2940 3386 3130 3260 3579 3324 3261\n", + " 3326 3002 3007 3257 3646 3195 3006 3321 3193 3452 2939 2941 3514 3451\n", + " 3131 3450 3070 3067 3194 3197 3005 3322 3515 3132 3385 3454 3647 3066\n", + " 3518 3449 3453 2876 3519 3198 3199 3645 3516 3133 3582 3134 3327 2942\n", + " 3135 3259 3388 3065 3068 3069 3580 3325 3323]\n", + "17 33\n", + "[2322 1809 2000 1870 2449 2191 2195 2317 1875 2326 1997 2129 2319 2062\n", + " 2067 2004 2516 2131 1806 1808 2445 2263 2325 2257 2380 2196 1932 2006\n", + " 2318 2327 2453 2188 1873 2387 2254 1878 2255 2125 2451 2447 2064 2198\n", + " 2134 1869 1933 2381 2510 2127 2193 1807 1868 2126 1931 2005 1937 1943\n", + " 2384 2262 1811 1813 2002 2070 2059 1742 2065 2258 2324 2128 1877 1939\n", + " 2385 1942 1941 2390 2133 2515 2003 2383 1995 2192 2253 2315 2514 2251\n", + " 2388 2001 2386 2007 1746 2511 1935 1812 2513 1810 2450 2194 1934 2132\n", + " 1936 2135 2066 2259 1999 1744 2061 1938 2448 2199 2512 2389 2320 2321\n", + " 1748 1805 1871 2071 2197 1872 2187 1996 2316 2452 2382 2060 2252 1874\n", + " 1876 2446 1743 1747 2068 2260 2063 2189 2069 2130 2323 2123 2256 2124\n", + " 2190 2261 1998 1940 1745]\n", + "53 11\n", + "[ 441 569 500 1078 376 879 370 438 631 757 699 826 947 756\n", + " 818 1018 563 564 435 634 882 954 819 1140 627 630 498 1139\n", + " 887 821 504 688 501 888 827 567 886 1144 761 694 1009 1014\n", + " 949 754 1143 1011 689 440 560 566 439 760 632 503 885 625\n", + " 1142 499 375 1138 820 1075 952 571 815 1008 824 690 497 1141\n", + " 695 633 1010 373 502 1081 626 629 955 562 696 687 948 946\n", + " 1016 565 890 1076 436 506 1073 692 559 951 751 1013 950 753\n", + " 570 698 437 1077 755 434 1080 881 822 374 372 693 943 371\n", + " 1017 561 945 762 763 505 817 1079 1074 1012 433 697 758 953\n", + " 880 944 635 884 496 623 624 823 691 1015 628 891 816 752\n", + " 759 883 825 568 889]\n", + "50 45\n", + "[2861 2801 2739 3128 2671 2927 2930 2732 2678 3187 3182 3053 2612 2545\n", + " 3316 2933 3054 3116 3058 2679 2806 3189 2807 2742 3191 2872 3249 3055\n", + " 2738 2990 2741 2993 2796 2860 2609 3117 3121 2935 3118 3000 2740 2864\n", + " 2936 2997 2805 3064 2674 2928 2544 3125 2869 3311 2798 3253 2934 3252\n", + " 2996 2608 3061 3123 2670 3250 2868 3063 2607 3184 3181 2995 2863 3186\n", + " 3119 2867 3317 3126 2992 2870 2989 2736 3188 2549 2999 3056 2548 2803\n", + " 3122 2802 2676 2737 2669 2924 2611 2865 2614 2672 2734 2613 3059 2991\n", + " 2543 2929 2804 2744 2998 2988 2808 2871 2994 2931 3314 2675 2606 3120\n", + " 2673 3183 2800 2797 3052 3057 3315 2925 3312 2866 3251 2610 3313 2733\n", + " 3248 3247 2546 2547 3254 2932 2926 2862 2799 3190 3062 3060 3246 2743\n", + " 3185 2735 2677 3124 3127]\n", + "55 54\n", + "[3832 3387 3827 3705 3128 3577 3517 3772 3644 3320 3699 3766 3511 3187\n", + " 3708 3769 3702 3379 3895 3574 3316 3443 3636 3189 3258 3191 3129 3709\n", + " 3897 3377 3834 3384 3510 3389 3762 3445 3581 3125 3386 3130 3253 3260\n", + " 3252 3830 3572 3383 3250 3698 3382 3380 3579 3513 3828 3700 3324 3763\n", + " 3643 3706 3765 3378 3192 3317 3126 3578 3448 3771 3893 3444 3257 3642\n", + " 3188 3505 3195 3697 3256 3575 3641 3707 3441 3701 3321 3507 3193 3452\n", + " 3639 3514 3451 3764 3569 3381 3894 3319 3450 3447 3194 3896 3322 3515\n", + " 3898 3318 3385 3768 3637 3640 3449 3573 3635 3314 3453 3509 3645 3516\n", + " 3831 3512 3767 3633 3835 3570 3315 3833 3506 3571 3259 3634 3251 3442\n", + " 3313 3638 3508 3704 3829 3254 3703 3388 3255 3446 3190 3576 3892 3580\n", + " 3770 3325 3124 3127 3323]\n", + "36 25\n", + "[1826 1765 1695 1704 1570 1888 1508 1892 1502 2022 1699 1895 1315 1642\n", + " 1383 1697 1504 2018 1638 1636 1442 1445 1760 1575 1833 1376 1509 1505\n", + " 1503 1572 1763 1511 1952 2017 1834 1252 1578 1512 1768 1829 1317 1566\n", + " 1640 1957 1769 1450 1954 1832 2020 1822 1255 1641 1894 1576 1571 1955\n", + " 1698 1316 1758 1249 1828 1633 1319 1444 1506 1447 1313 1890 1379 1635\n", + " 1896 1770 1510 2021 1378 1446 2019 1577 1448 1956 1320 1959 1767 1314\n", + " 1385 1827 1825 1694 1630 1887 1703 1762 1705 1632 1375 1960 1440 1700\n", + " 2023 1701 1889 1381 1569 1897 1893 1514 1573 1438 1507 1958 1953 1250\n", + " 1513 1830 1254 1639 1384 1637 1759 1439 1449 1696 1382 1380 1567 1312\n", + " 1823 1318 1702 1251 1706 1831 1766 1253 1824 1574 1891 1443 1634 1631\n", + " 1764 1761 1441 1568 1377]\n", + "45 62\n", + "[4074 4083 3827 3944 4079 3696 4013 4071 3954 3823 3826 3884 3631 3886\n", + " 4081 3757 3887 4073 4078 4072 3822 3952 3947 3760 3948 3885 3762 4019\n", + " 3689 4016 3694 4007 3626 3754 3890 4018 3949 3817 3950 3880 3891 3824\n", + " 3946 3627 4076 4015 3691 3758 4075 4014 3882 3879 3697 4012 3692 3818\n", + " 3820 3629 3756 3628 3815 4011 4082 3819 3951 3759 3825 4009 3821 3630\n", + " 3632 3752 4008 4017 3755 3881 3816 3943 4010 3695 3753 3888 3889 3953\n", + " 4080 4077 3883 3693 3761 3955 3945 3690]\n", + "55 21\n", + "[1330 1524 1078 1146 1782 1521 1399 1203 1651 1403 1461 1785 1525 1402\n", + " 1018 1147 1394 1207 1588 1652 1140 1401 1465 1269 1275 1139 1277 1533\n", + " 1405 1144 1331 1336 1335 1715 1597 1404 1469 1460 1014 1717 1658 1202\n", + " 1268 1338 1148 1532 1585 1522 1594 1143 1523 1592 1595 1276 1334 1719\n", + " 1586 1329 1273 1530 1205 1341 1463 1393 1142 1660 1138 1075 1723 1466\n", + " 1396 1527 1211 1271 1141 1531 1457 1395 1593 1657 1650 1204 1081 1783\n", + " 1270 1464 1337 1213 1016 1467 1587 1718 1076 1716 1591 1529 1210 1784\n", + " 1013 1781 1077 1786 1398 1459 1080 1212 1332 1265 1780 1655 1720 1082\n", + " 1272 1266 1017 1397 1528 1339 1721 1596 1400 1267 1079 1653 1012 1590\n", + " 1654 1209 1462 1468 1656 1274 1589 1722 1201 1659 1526 1015 1206 1458\n", + " 1208 1145 1340 1083 1333]\n", + "42 42\n", + "[2922 2542 2861 2406 2475 2661 2671 2532 2927 3115 2408 2730 2732 2349\n", + " 2536 3053 2859 2983 2795 2344 2413 2414 2541 2411 3054 3116 2348 2345\n", + " 2602 3112 2596 3049 2474 2535 2538 2990 2796 2860 3117 3046 3111 2533\n", + " 2407 2864 2473 2928 2544 2662 2600 2668 2601 2346 2409 2798 2920 2791\n", + " 2534 2919 2788 2608 2670 3048 2856 2854 2540 2343 3050 2478 3047 2607\n", + " 2598 2472 2921 2863 2729 2789 2918 2981 2982 2989 2923 2736 2597 3113\n", + " 2663 2984 2477 2604 3051 2987 2916 2664 2794 2599 2667 2539 2857 2471\n", + " 2479 2917 2855 2347 2726 2669 2924 2660 2672 2734 2792 2991 2543 2724\n", + " 2986 2988 2793 2858 2606 2727 2728 2800 2469 2797 3052 2412 2725 2925\n", + " 2410 2852 2790 2537 3114 2731 2853 2665 2733 2603 2926 2862 2799 2985\n", + " 2476 2735 2605 2470 2666]\n", + "9 8\n", + "[329 591 459 845 269 334 777 716 200 839 521 842 261 526 332 523 773 779\n", + " 655 653 453 907 524 525 718 201 772 719 260 906 714 139 580 781 780 582\n", + " 458 711 775 387 136 778 393 905 651 325 517 715 263 581 390 515 396 838\n", + " 776 782 202 456 841 908 392 518 516 654 645 843 588 461 268 463 140 650\n", + " 903 335 138 708 267 455 712 648 649 837 398 717 327 326 399 452 331 583\n", + " 844 522 643 451 197 527 902 134 587 324 644 264 394 519 462 457 460 589\n", + " 266 709 707 904 199 330 590 265 710 586 323 840 262 204 205 333 389 388\n", + " 198 454 397 652 135 584 647 520 270 391 137 585 713 203 774 646 328 395\n", + " 579]\n", + "34 22\n", + "[1826 1765 1695 1570 1508 1502 1699 1315 1383 1697 1504 1638 1636 1442\n", + " 1311 1445 1760 1575 1376 1121 1436 1509 1122 1505 1503 1572 1763 1511\n", + " 1252 1512 1829 1317 1566 1060 1640 1500 1245 1310 1126 1186 1255 1057\n", + " 1576 1571 1374 1437 1698 1316 1758 1249 1828 1633 1319 1444 1693 1506\n", + " 1447 1313 1183 1379 1635 1181 1628 1510 1378 1446 1190 1189 1256 1448\n", + " 1320 1373 1314 1246 1827 1187 1825 1694 1184 1630 1501 1703 1762 1632\n", + " 1375 1185 1123 1440 1700 1248 1701 1381 1569 1573 1438 1124 1507 1058\n", + " 1250 1565 1254 1564 1639 1384 1055 1637 1759 1439 1191 1061 1696 1382\n", + " 1380 1567 1312 1823 1629 1318 1702 1251 1118 1766 1059 1056 1120 1253\n", + " 1824 1119 1574 1443 1634 1631 1764 1308 1188 1761 1244 1182 1441 1309\n", + " 1372 1125 1247 1568 1377]\n", + "52 61\n", + "[3832 4083 3827 3959 4079 3705 4086 4026 3696 3699 3766 3956 3769 3954\n", + " 3702 3895 3823 3826 3574 3886 4081 3636 3887 4078 3962 3822 3952 3760\n", + " 3897 3834 3762 4019 4016 3830 3572 3890 4018 3698 3828 3960 3700 3950\n", + " 3763 3891 3824 3765 4085 3961 4015 4090 3758 3893 4084 3958 4021 4022\n", + " 4025 4014 3697 3575 4024 3701 3639 3764 3569 3894 4082 3951 3896 3759\n", + " 3825 3957 3898 4088 3632 3768 3637 4017 4089 3640 3573 3635 3831 3767\n", + " 3633 3570 3833 3571 3695 3888 3889 4020 3953 4080 3634 3638 3704 3829\n", + " 4087 3703 3761 3892 3770 3955 4023]\n", + "46 34\n", + "[2027 1840 2093 2089 2542 2159 1836 2354 2475 1966 2416 2161 2284 2420\n", + " 2408 2032 2222 1965 2349 2545 2088 2100 2344 2413 2414 2541 2411 2155\n", + " 1839 2418 2348 2345 1963 2164 2474 2538 2152 1969 2026 1841 2090 2153\n", + " 2034 2609 2228 2419 2352 2480 2473 2160 2544 2098 1905 2282 1961 2346\n", + " 2409 1901 1903 2218 2608 1971 2289 1962 2225 2355 2540 2224 1837 2036\n", + " 2478 1835 2417 2099 1964 2607 2096 2030 1899 1902 2095 2288 2162 2154\n", + " 1904 2219 2025 2350 1970 2158 1900 2220 2477 2604 2163 2217 1968 2286\n", + " 2351 2539 1838 2031 2479 2483 2290 2415 2033 2482 2347 2156 2353 1967\n", + " 2216 2035 2092 2543 2291 2226 2292 2287 2283 2606 2280 2412 2094 2410\n", + " 2157 2227 2024 1898 2481 2285 2356 2097 2091 2603 2029 2546 2223 2281\n", + " 2476 2605 2221 2028 1906]\n", + "30 1\n", + "[ 27 32 153 25 35 222 163 281 89 94 156 350 96 33 224 415 164 349\n", + " 226 285 98 477 30 28 417 481 345 99 416 353 158 152 97 352 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657 1102 973 981 1034 1360 1229\n", + " 1300 1108 788 592 909 590 915 1234 916 1298 1236 652 1169 1164\n", + " 1043 1168 656 1045 1235]\n", + "31 20\n", + "[1695 1570 1508 1502 1315 1697 1504 1692 1442 1311 1445 1376 1121 1305\n", + " 1436 1509 1122 1505 1503 988 1572 987 1115 1252 1117 1317 1566 1371\n", + " 1060 1500 928 1245 1310 1186 1057 1571 1374 1180 1437 924 1698 1316\n", + " 1249 1307 993 1241 1633 1433 1306 1444 1693 1506 1562 1499 1313 1177\n", + " 1183 1379 1052 1635 1181 1628 1378 1627 1189 1116 1053 1373 1113 1314\n", + " 989 1246 925 926 1497 1243 1187 1114 1694 1184 927 1630 994 1501\n", + " 1632 1375 990 1178 1050 1185 1369 1123 1440 1498 1248 1381 1569 1370\n", + " 1438 1242 1124 1507 1434 1058 1250 1565 992 1564 1055 1435 1051 1439\n", + " 1696 1380 1567 1312 929 1629 1251 1118 1179 1059 1054 1056 1120 1253\n", + " 1119 1443 930 1634 1631 1308 1188 1563 1244 1182 1441 995 1309 1372\n", + " 1125 1247 991 1568 1377]\n", + "44 42\n", + "[2922 2542 2861 2801 2475 2671 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4028 4093 3579 3513 3960\n", + " 3643 3706 3961 4090 3578 3771 4025 3642 4031 3646 4024 3641 3707 3452\n", + " 3639 3514 3451 3966 3450 3896 3900 3515 3898 4088 3454 3647 3768 4089\n", + " 3773 3640 3518 3453 3519 3645 3516 3831 3767 3582 3835 3833 3774 3963\n", + " 3964 3704 3710 3703 3576 3580 3770 4023]\n", + "31 31\n", + "[1826 1695 2084 2210 1888 1892 2402 2398 1699 1697 2018 2272 1692 2015\n", + " 2267 2276 1885 1760 2270 2333 1763 1952 2011 1819 2147 1883 2017 1818\n", + " 2077 1829 2078 1957 1954 1949 2149 2020 1822 2079 2338 1955 2014 2331\n", + " 1698 1758 1828 1757 2204 1633 2268 2206 2337 1693 1754 2335 1890 2271\n", + " 2146 2083 2269 2080 2012 1628 2021 1950 1948 1821 2397 2274 2019 2339\n", + " 2141 1886 2203 1956 1817 2075 1945 2211 2202 2209 2273 1947 1827 1825\n", + " 1694 1630 1887 2207 2081 1762 2334 1882 1632 2137 1755 2144 1951 1946\n", + " 2400 2275 2073 2396 2266 2201 2212 1889 1820 1893 2016 2082 1953 2336\n", + " 2145 1759 1884 2208 1696 1823 1629 2143 2139 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261 453 260 580 386 128 387 129 258 4 325 517 257\n", + " 642 2 449 448 581 390 515 577 1 513 518 706 516 130 68 66 256 321\n", + " 704 65 132 194 326 192 705 452 643 451 197 134 324 644 322 641 578 707\n", + " 512 196 131 323 262 389 195 388 198 454 3 450 259 514 640 64 67 69\n", + " 384 385 579]\n", + "63 8\n", + "[511 251 383 831 441 191 638 569 255 316 444 507 380 447 767 319 575 253\n", + " 699 826 636 189 958 829 315 830 956 634 446 894 701 445 702 639 827 761\n", + " 382 252 378 314 190 764 828 572 379 571 377 254 442 443 633 893 765 318\n", + " 510 766 637 892 508 188 506 574 317 570 698 895 509 957 762 763 505 700\n", + " 697 959 635 573 703 381 891]\n", + "45 30\n", + "[1710 2027 1840 1907 1704 2093 1713 2089 2159 1836 1895 1642 1581 1966\n", + " 2161 2284 2032 2222 1965 1842 2349 1708 2088 1773 1833 2155 1839 2348\n", + " 2087 1963 1772 1712 2152 1969 2026 1834 1841 1578 2090 1579 1768 2153\n", + " 2034 1771 1648 1769 1832 2352 1641 2160 2098 1905 2282 1961 2346 1778\n", + " 1649 1901 1903 1779 1709 2218 1971 2289 1707 1962 2225 2224 1837 1835\n", + " 1584 2099 1964 1896 1770 2096 2030 1899 1902 2095 2288 2162 1714 2154\n", + " 1776 1904 1959 2219 2025 1767 2350 1970 2158 1900 2220 2151 2163 2217\n", + " 1968 2286 2351 1644 1838 2031 1705 1960 2033 1777 2347 2156 1967 2216\n", + " 2023 2035 2092 1897 2226 1774 2287 2283 1645 1646 1643 2094 2157 2024\n", + " 1706 1831 1775 1898 2285 2097 2091 2029 1583 2223 1647 2281 1582 1843\n", + " 1711 1580 2221 2028 1906]\n", + "39 63\n", + "[4074 4068 3944 4065 4001 3877 4013 4071 4066 3937 3884 3874 3810 4004\n", + " 4073 4072 4069 3947 3747 3948 3885 3748 3689 4007 3754 3949 3817 3880\n", + " 3946 3749 4076 3687 4075 3684 3751 4002 3882 4006 3879 4067 3875 4012\n", + " 3818 3820 3814 3811 3812 3685 3873 4070 3815 4011 3878 3941 3819 4009\n", + " 3813 3938 4003 3752 4008 3755 3881 3816 3943 3876 4010 3688 3753 4077\n", + " 3883 3942 3939 3940 3686 4005 3750 3945 3690]\n", + "53 2\n", + "[251 54 441 245 239 313 59 184 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1485 1303 1238 1107 1172 1356 1677 1296 1495 1425 1365 1613 1493 1678\n", + " 1746 1293 1812 1297 1611 1233 1623 1810 1616 1620 1559 1430 1618 1294\n", + " 1173 1615 1359 1557 1487 1102 1360 1229 1300 1355 1108 1744 1681 1234\n", + " 1419 1302 1551 1748 1805 1871 1422 1556 1684 1486 1366 1872 1298 1236\n", + " 1686 1874 1876 1169 1424 1428 1301 1750 1743 1747 1364 1685 1429 1168\n", + " 1740 1614 1745 1235 1552]\n", + "13 43\n", + "[2887 2957 2832 2891 2449 3024 3086 2444 3025 2959 2705 3148 2765 3023\n", + " 2767 2897 2762 2960 3089 2509 2441 2955 2963 2445 2380 3151 3084 2759\n", + " 2578 2896 2637 3018 3146 2569 2631 2766 2825 2378 2633 2447 2760 2827\n", + " 2770 2695 2381 2639 2835 2571 2698 2443 2510 2898 2951 2768 2640 3082\n", + " 2384 3017 3085 2442 2570 2703 2379 2702 2568 2567 2893 3087 2572 2577\n", + " 3026 2761 2833 2830 2889 2834 2579 2383 2643 2706 2514 3083 2826 2636\n", + " 2511 2635 2707 2700 2824 3021 2764 2513 2576 3149 2956 3152 3016 2697\n", + " 2888 2505 2895 2953 2828 2508 2634 2696 2642 2899 2448 2512 2829 2831\n", + " 2962 3150 2641 2575 2894 2632 2769 3081 2890 2507 3019 3088 2952 2574\n", + " 2506 2382 2823 2446 2892 2504 2961 2704 2763 2954 2638 3020 2958 3022\n", + " 2573 2699 2701 2771 3147]\n", + "27 35\n", + "[2398 2454 2589 2272 2461 2326 2015 2267 1885 2456 2464 2394 2270 2200\n", + " 2591 2263 2325 2136 2333 2654 2011 1883 2006 2265 2327 2008 2077 2521\n", + " 2453 2078 2522 2391 2526 1949 2583 2079 2198 2134 2584 2014 2331 2462\n", + " 2455 2585 2072 2204 2268 2206 1943 2337 2262 2335 2524 2271 2652 1880\n", + " 2269 2070 2080 2518 2460 2012 2330 2393 1950 1948 2397 2141 1886 2203\n", + " 2390 2649 2329 2133 2075 1945 2202 2209 2273 2590 1947 2588 2264 2458\n", + " 2463 2207 2081 2587 2465 2334 1882 2007 2137 2328 2144 2523 2392 1951\n", + " 1946 2400 2395 2073 2396 2266 2201 2457 2527 2135 2016 2336 2145 1884\n", + " 2208 2648 2653 2199 2389 2143 2459 2528 2139 2140 2071 2401 2197 2010\n", + " 2205 2332 2076 2651 2009 2650 2399 2525 2074 1881 2138 2069 2520 2142\n", + " 2261 2013 2519 1944 2586]\n", + "0 48\n", + "[3072 2688 3206 3140 3392 3010 3205 3332 2820 3456 2818 2690 2756 3333\n", + " 3074 3393 3394 3200 3204 3266 3267 3270 3077 3203 3013 2886 2950 3202\n", + " 3458 2883 3331 3268 3141 3265 2947 3012 3459 2884 2945 3073 3014 3138\n", + " 3008 2817 2949 3396 3011 3142 3078 2944 3395 3330 2691 3329 3139 2752\n", + " 3136 3457 3009 3076 3075 2821 2755 2946 2882 2948 2689 3328 3201 3264\n", + " 2885 3137 2753 3269 2881 2880 2816 2819 2754]\n", + "51 18\n", + "[1392 1262 1330 1136 1519 1524 1078 879 1389 1521 1399 1203 947 1461\n", + " 1261 1525 818 1394 1207 882 1588 1198 1072 1200 819 1140 1401 1135\n", + " 1269 1139 887 821 1327 1326 886 1144 1331 1336 1335 1455 1328 1005\n", + " 1009 1071 1460 1456 1014 1202 1268 1454 1585 949 1522 1143 1523 1011\n", + " 1334 1586 1584 1329 1273 885 1205 1463 1393 1142 1390 1520 1138 820\n", + " 1075 952 1008 1396 1527 1271 1141 1457 1395 1010 1204 1081 1325 1391\n", + " 1270 1464 1337 948 1264 946 1016 1197 1587 1076 1073 951 1013 950\n", + " 1077 1134 1398 1459 1080 881 1332 822 1265 943 1272 1266 1017 1397\n", + " 1199 1007 1400 945 817 1267 1079 1137 1074 1012 1590 1209 1462 1133\n", + " 880 944 1006 884 1589 1201 942 1526 1070 1263 1015 1206 1458 1208\n", + " 1145 816 883 1069 1333]\n", + "15 47\n", + "[2957 3345 3275 3217 3145 2832 2891 3024 3086 3215 3342 3408 3025 2959\n", + " 2705 3148 3027 2765 3023 2767 2897 2762 3280 2960 3089 3154 2955 2963\n", + " 3209 3151 3084 3091 2896 2637 3018 3146 2766 2825 3274 3346 2964 2827\n", + " 2770 2900 3155 2639 2835 2898 2768 2640 3082 3090 3017 3085 3344 3404\n", + " 2703 2702 3218 3284 2893 3087 3026 3153 3277 3410 3219 2833 2830 2889\n", + " 2834 3210 3341 3343 2772 3221 2706 3083 3028 2826 3282 3406 2636 3409\n", + " 3029 2707 2700 3021 3157 2764 3149 2956 3339 3283 3405 3220 3156 3152\n", + " 3347 2901 2895 2953 3092 2828 3276 2965 3093 2837 2642 2899 2829 2831\n", + " 2962 3150 2641 3212 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3\n", + "[ 63 511 251 383 441 191 638 569 313 59 255 316 184 444 125 376 507 380\n", + " 447 319 186 575 57 253 247 636 189 315 634 446 127 445 504 639 56 126\n", + " 382 252 378 314 249 121 122 190 440 439 187 572 375 379 571 120 377 254\n", + " 442 443 318 510 183 637 508 188 185 506 250 574 317 570 60 62 119 509\n", + " 61 505 248 55 312 635 573 124 381 58 311 123]\n", + "9 27\n", + "[1741 1870 1420 1353 1804 1679 1547 1997 1549 1737 1806 1350 1605 1989\n", + " 1930 1674 1866 1932 1863 2056 1612 1351 1799 1478 1739 1543 1867 1672\n", + " 1925 1869 1928 1548 1933 2054 1413 1421 1671 1476 1990 1798 1795 2058\n", + " 1807 1868 1931 1864 1483 1484 1675 1544 1481 1546 1802 1733 1542 1676\n", + " 2059 1742 1603 1541 1803 1480 1550 1545 1414 1540 1539 1607 1801 1485\n", + " 2122 2121 1354 1797 1670 1356 1735 1734 1927 1800 1677 1731 1995 1796\n", + " 1604 2120 1988 1738 1732 1993 1613 1991 1678 2053 1860 1935 1606 2119\n", + " 1611 1667 1992 1926 1929 1934 1482 1615 1479 1862 1923 1352 1355 1418\n", + " 1673 2118 2055 1417 2061 1419 1551 1668 1805 1871 1486 2057 1416 1996\n", + " 1924 2060 1610 1743 1859 1865 1736 1609 2123 1477 2124 1740 1614 1998\n", + " 1861 1608 1994 1669 1415]\n", + "35 41\n", + "[2529 2404 2406 2402 2721 2398 2661 2847 2589 2532 2272 2592 2461 2408\n", + " 2659 2276 2464 2536 2983 2591 2468 2593 2342 3040 2654 2723 2596 2783\n", + " 2535 2530 3044 3046 2526 2978 2658 2848 2533 2407 2473 2338 2977 2850\n", + " 2662 2600 2462 2601 2466 2911 2920 2791 2534 2919 2788 3045 2277 2856\n", + " 2854 2594 2337 2722 2335 2343 2278 2598 2472 2729 2789 2918 2981 2274\n", + " 2982 2785 3041 2339 2913 2597 3042 2663 2784 2467 2273 2590 2916 2664\n", + " 2463 2599 2656 2465 2857 2471 2917 2787 2405 2403 2855 2849 2717 2782\n", + " 2915 2726 2980 2400 2275 2660 2786 2781 2912 2845 2719 2792 2595 2657\n", + " 2527 2724 2851 2976 2793 2975 2336 2914 2979 2727 2340 2653 2718 2728\n", + " 2469 2910 2531 2725 2852 2528 2790 2537 2401 2853 2665 2399 3043 2525\n", + " 2720 2846 2655 2341 2470]\n", + "36 35\n", + "[2084 2529 2404 2089 2210 2406 1892 2402 2022 2398 1895 2661 2532 2018\n", + " 2272 2592 2214 2408 2015 2659 2276 2464 2536 2270 2088 2344 2468 2593\n", + " 2342 2087 1952 2345 2147 2596 2474 2535 2152 2530 2017 2090 2153 2078\n", + " 1957 1954 2658 2149 2020 2079 2533 2407 2473 2338 1894 1955 2662 2600\n", + " 2462 2282 2466 2346 2409 2534 2218 2206 2277 2594 2337 2335 1890 2271\n", + " 2343 2146 2083 2150 2080 2278 2598 2472 2021 2274 2154 2019 2339 1956\n", + " 1959 2597 2025 2663 2211 2467 2151 2209 2273 2279 2217 2463 2207 2081\n", + " 2599 2465 2334 2471 2405 1960 2086 2403 2144 2215 2400 2275 2216 2660\n", + " 2023 2212 1889 2595 2657 2527 1893 2016 1958 2082 1953 2336 2145 2208\n", + " 2340 2280 2469 2143 2531 2410 2024 2528 2537 2401 1891 2213 2148 2399\n", + " 2281 2085 2341 2142 2470]\n", + "50 28\n", + "[1710 1840 1907 2093 1976 1973 1713 2159 1836 1519 1524 1581 1966 2161\n", + " 1782 1521 2032 1965 1842 1651 1461 1708 1773 1525 2100 1839 1772 2164\n", + " 1712 1909 1848 1969 1841 1588 1652 2034 2166 1648 1912 2228 2160 2098\n", + " 1455 1715 1905 1778 1460 1649 1456 2103 1901 1717 1903 1779 1709 1585\n", + " 1971 1522 2225 1523 2224 1837 1719 2036 1586 1846 1584 2099 1964 1847\n", + " 1908 2096 2038 2030 1902 1520 2039 2095 2162 1714 2037 1776 1904 1970\n", + " 1457 2158 1900 1650 2163 1783 1968 1845 1972 1644 1838 2031 1910 2033\n", + " 1587 1718 1777 1716 1591 1967 1784 2035 1781 2040 1459 2226 1774 1780\n", + " 1655 1720 2229 1975 2165 1645 1844 1911 1974 1646 2101 1653 2094 1590\n", + " 1654 2227 1775 1656 2097 1589 2029 1526 1583 2223 1647 1582 1843 1711\n", + " 1458 2102 1518 2028 1906]\n", + "59 10\n", + "[ 511 383 831 441 638 569 313 316 444 376 1023 507 380 447\n", + " 438 631 767 757 575 699 826 636 958 829 315 1018 830 956\n", + " 634 446 954 1085 630 894 701 445 1021 887 821 702 504 501\n", + " 888 639 827 567 886 761 694 382 378 314 440 566 439 764\n", + " 828 760 632 503 885 572 375 379 952 571 377 824 442 443\n", + " 695 633 893 765 1019 502 1081 629 318 510 955 766 1020 696\n", + " 637 892 508 1016 565 890 506 1086 951 574 950 317 570 698\n", + " 1080 895 822 693 1082 509 1017 957 762 1022 763 505 700 697\n", + " 758 959 953 312 635 573 703 823 381 1015 891 759 1084 825\n", + " 568 889 1083]\n", + "28 43\n", + "[2529 2721 2398 2847 2589 2592 2461 2780 2456 2464 2394 2973 2591 2843\n", + " 2593 2713 2844 3040 2654 2840 2783 2647 2712 2521 2966 2716 2582 2522\n", + " 2711 2526 2978 2658 2839 2583 2848 2779 2974 2977 2968 2584 2850 2462\n", + " 2905 2715 3097 2585 2911 2842 2906 2778 2594 2722 2524 2652 3102 3038\n", + " 2909 2460 2776 3031 2393 3163 3034 2646 2710 2777 2397 2785 3041 2913\n", + " 3167 2649 2907 3161 3036 3096 2784 3032 2590 2588 2458 2463 2967 2587\n", + " 2656 3037 2849 2717 3098 2523 2782 3101 2395 2396 2786 2904 2781 2912\n", + " 2845 2719 2657 2457 2527 3099 2976 2975 3100 2914 3033 3035 2648 2653\n", + " 3164 3103 2718 2969 3104 2910 2838 2775 2459 2528 3166 2902 3165 3039\n", + " 2651 2908 2650 2399 2970 2525 2720 2714 2846 2971 2903 2655 2520 3162\n", + " 2972 2841 2519 2774 2586]\n", + "45 8\n", + "[239 879 370 557 237 364 743 426 302 940 878 369 818 360 563 170 435 175\n", + " 431 428 234 627 495 684 498 876 688 423 304 744 682 745 300 754 621 430\n", + " 558 689 240 617 238 368 560 296 429 748 877 551 241 425 365 625 499 618\n", + " 685 494 808 815 306 362 690 174 424 497 941 810 552 297 556 303 359 554\n", + " 366 432 626 555 615 679 680 487 562 173 553 301 488 687 172 489 176 361\n", + " 811 622 749 750 305 491 559 751 620 753 755 298 434 873 881 233 943 493\n", + " 875 371 616 299 813 619 812 363 683 561 171 817 747 367 433 938 492 880\n", + " 944 427 814 496 623 236 942 624 686 691 746 939 816 752 681 809 235 874\n", + " 490]\n", + "14 32\n", + "[2322 1809 1741 2000 1870 2449 2191 1804 2195 2317 2444 1679 1875 1997\n", + " 2129 2319 2062 2067 2314 2004 2131 1806 1808 2445 2257 2380 1930 2313\n", + " 1866 2196 1680 1932 2056 2318 2188 1873 1739 2254 2255 2125 2378 2447\n", + " 1867 2064 1869 1928 1933 2381 2185 2443 2127 2058 2193 1807 1868 2126\n", + " 1931 1864 1937 1675 2384 1811 2002 1802 2249 2379 1676 2059 1742 2186\n", + " 2250 2065 1803 2258 2128 1939 1801 2122 2385 2121 2003 1677 2383 1995\n", + " 2192 2253 2120 2315 1738 2251 1993 2001 1678 2386 1746 1935 1810 1992\n", + " 1929 2194 1934 2132 1936 2184 2066 2259 1999 1744 2061 1938 2448 1681\n", + " 2320 2321 1805 1871 2057 1872 2187 1996 2316 2382 2060 2252 1874 1876\n", + " 2446 1743 2068 2260 1865 2063 2189 2130 2323 2123 2256 2124 2190 1740\n", + " 2248 1998 1940 1745 1994]\n", + "29 29\n", + "[1826 1695 1888 1502 1699 1697 1504 2018 2272 1692 2015 2267 1885 1760\n", + " 2270 1816 1503 2136 1763 1952 2011 1819 1883 2017 1818 2008 2077 2078\n", + " 1566 1500 1687 1954 1949 1822 2079 1955 2014 1698 1758 1561 2072 1757\n", + " 2204 1633 2268 2206 1943 1693 1754 1562 1499 1890 2271 2146 2083 1880\n", + " 2269 2080 2012 1628 1950 1948 1821 1627 2019 1626 2141 1886 2203 1817\n", + " 2075 1945 2202 2209 1947 1827 1825 1625 1694 1630 1887 2207 2081 1501\n", + " 1762 1882 2007 1632 2137 1755 2144 1815 1951 1946 2073 1498 1879 2266\n", + " 2201 1889 1569 1820 1753 2016 2082 1953 1689 1565 1564 2145 1759 1884\n", + " 2208 1696 1567 1823 1629 2143 2139 2140 2071 1691 2010 1752 1824 2205\n", + " 1624 2076 1891 2009 1634 1631 2074 1690 1881 1751 1563 1761 2138 1688\n", + " 2142 2013 1568 1756 1944]\n", + "51 25\n", + "[1392 1710 1840 1907 1973 1330 1713 1519 1524 1581 1782 1521 2032 1399\n", + " 1842 1651 1461 1773 1785 1525 1394 1839 1712 1849 1909 1848 1969 1841\n", + " 1588 1652 2034 1648 1465 1912 1269 1327 1331 1335 1455 1715 1328 1905\n", + " 1778 1460 1649 1456 1717 1903 1779 1268 1454 1709 1585 1971 1522 1523\n", + " 1592 1334 1837 1719 2036 1586 1846 1584 1329 1847 1908 1463 1393 2038\n", + " 1390 1902 1520 1714 2037 1776 1904 1396 1527 1970 1457 1395 1593 1657\n", + " 1650 1783 1968 1845 1391 1972 1270 1464 1838 1264 1910 2033 1587 1718\n", + " 1777 1716 1591 1529 1967 1784 2035 1781 1398 1453 1459 1332 1774 1265\n", + " 1780 1655 1720 1266 1397 1528 1975 1721 1517 1645 1844 1911 1974 1646\n", + " 1400 1267 1653 1590 1654 1462 1775 1656 1589 1526 1583 1647 1582 1843\n", + " 1711 1458 1518 1333 1906]\n", + "19 32\n", + "[2322 1809 1682 2000 1870 2449 2191 2454 2195 1875 1749 2326 1997 2129\n", + " 2319 2062 2067 2004 1816 2131 1806 1808 2200 2263 2325 2136 2257 1683\n", + " 2196 1680 2006 2265 2318 2327 2008 2453 1873 2387 2254 1878 2255 2391\n", + " 2125 2451 2064 2198 2134 1869 1933 2127 2072 2193 1807 2126 2005 1937\n", + " 1943 2384 2262 1811 1813 2002 1880 2070 2065 2258 2324 2128 1877 1939\n", + " 2385 1942 1941 2390 2133 1945 2003 2383 2192 2253 2264 2388 2001 2386\n", + " 2007 1746 2137 1935 2328 1812 1815 1810 2450 2073 2194 1879 2201 1934\n", + " 2132 1936 2135 2066 2259 1999 1814 1744 2061 1938 2448 2199 2389 1681\n", + " 2320 2321 1748 1871 2071 1684 2197 1872 2009 2452 1686 1874 1876 1750\n", + " 1743 1747 2068 2260 1881 1751 2063 1685 2189 2069 2130 2323 2256 2190\n", + " 2261 1998 1940 1745 1944]\n", + "37 22\n", + "[1826 1765 1704 1570 1508 1699 1315 1642 1383 1697 1504 1638 1062 1636\n", + " 1442 1311 1445 1322 1575 1376 1121 1509 1122 1505 1503 1572 1763 1511\n", + " 1193 1064 1252 1578 1258 1512 1579 1768 1829 1317 1060 1451 1640 1769\n", + " 1129 1450 1126 1186 1832 1255 1259 1641 1576 1571 1698 1316 1249 1828\n", + " 1633 1319 1444 1506 1321 1323 1447 1313 1379 1635 1387 1515 1510 1378\n", + " 1446 1190 1189 1256 1577 1448 1320 1767 1314 1128 1385 1827 1187 1184\n", + " 1063 1703 1762 1705 1632 1375 1185 1194 1127 1123 1440 1700 1248 1701\n", + " 1386 1381 1569 1514 1573 1124 1507 1058 1192 1250 1513 1830 1254 1639\n", + " 1384 1637 1439 1449 1191 1061 1696 1382 1380 1567 1312 1643 1318 1702\n", + " 1251 1706 1831 1766 1059 1253 1574 1443 1634 1631 1257 1764 1188 1761\n", + " 1441 1125 1247 1568 1377]\n", + "51 49\n", + "[2801 3128 2927 3320 2930 3511 3187 3182 3379 3053 3574 3373 3316 2933\n", + " 3054 3058 3443 2806 3189 3440 3191 3129 3249 3055 3439 2990 3376 2993\n", + " 3117 3121 3377 3384 3510 2935 3118 3000 3445 2864 2936 2997 2805 3064\n", + " 2928 3125 2869 3311 3253 2934 3252 3572 2996 3061 3123 3383 3250 2868\n", + " 3382 3380 3063 3438 3184 3378 3181 2995 3192 2863 3186 3119 3375 2867\n", + " 3317 3126 2992 2870 3245 3448 2989 3309 3444 3257 3188 3505 3256 2999\n", + " 3056 3504 3441 3321 3507 2803 3193 3122 3569 2802 3503 3381 3319 3447\n", + " 2865 3318 3001 3385 3059 2991 2929 2804 2998 2871 2994 3573 2931 3314\n", + " 3509 3120 3374 3183 2800 3570 3057 3315 3506 3312 3571 3310 2866 3251\n", + " 3442 3313 3508 3248 3247 3254 2932 2926 3255 3446 3190 3065 3062 3568\n", + " 3060 3246 3185 3124 3127]\n", + "15 62\n", + "[4047 3858 3730 3659 3914 3919 3786 4043 3985 3599 4051 3790 3923 3983\n", + " 3980 3853 4041 3851 3979 3922 3726 3913 3728 3987 3924 3982 3732 3861\n", + " 3859 3854 3788 3978 4052 3598 3915 4042 3986 3663 3920 3921 3856 3989\n", + " 3796 3977 3791 3855 4048 3925 3795 3797 3731 3600 3725 3666 4053 3793\n", + " 3852 4044 3664 3601 3916 3789 4045 3665 4050 3597 3667 3850 3722 3727\n", + " 3849 3602 3792 3981 3917 4049 3596 3729 3785 3860 3662 3787 3988 3857\n", + " 3723 3660 3984 3661 3918 4046 3794 3724]\n", + "5 2\n", + "[ 6 329 193 0 73 200 320 133 261 453 201 260 70 139 386 128 387 136\n", + " 129 393 258 4 325 517 257 2 449 75 263 390 11 515 202 74 456 1\n", + " 392 518 10 516 130 68 8 66 256 321 65 138 132 267 194 455 5 327\n", + " 326 192 452 331 451 197 134 324 264 394 519 322 7 457 72 266 196 199\n", + " 330 9 265 131 323 262 389 195 388 198 454 3 71 450 259 514 135 64\n", + " 520 391 137 203 67 69 384 385 328]\n", + "59 21\n", + "[1599 1151 1146 1399 1403 1407 1461 1785 1525 1598 1402 1789 1018 1147\n", + " 1207 1470 1085 1401 1465 1269 1021 1275 1277 1406 1533 1405 1144 1336\n", + " 1335 1597 1404 1469 1658 1338 1148 1532 1594 1143 1592 1595 1276 1334\n", + " 1719 1273 1530 1205 1341 1463 1142 1660 1724 1723 1466 1527 1211 1271\n", + " 1531 1593 1657 1019 1081 1270 1464 1020 1337 1213 1016 1467 1534 1788\n", + " 1591 1529 1210 1725 1278 1784 1086 1786 1398 1279 1661 1080 1212 1663\n", + " 1655 1720 1082 1272 1726 1017 1397 1528 1339 1721 1596 1787 1400 1022\n", + " 1215 1342 1079 1790 1590 1654 1209 1462 1727 1468 1535 1656 1274 1589\n", + " 1722 1659 1526 1471 1343 1087 1206 1214 1208 1145 1662 1084 1150 1340\n", + " 1083 1149 1333]\n", + "55 3\n", + "[251 54 441 245 569 313 59 316 184 308 444 500 114 125 376 370 507 380\n", + " 438 631 177 186 181 51 57 253 247 189 369 315 563 564 435 634 630 445\n", + " 498 504 501 182 56 567 252 117 378 179 314 249 121 122 116 440 566 439\n", + " 187 113 241 632 503 499 375 50 379 118 571 120 306 377 242 52 442 443\n", + " 633 373 502 307 629 310 183 53 508 188 185 49 565 180 436 506 305 250\n", + " 317 570 437 434 309 60 374 372 119 371 246 61 505 433 248 55 312 244\n", + " 124 381 58 628 115 243 178 311 568 123]\n", + "15 2\n", + "[ 17 329 210 83 459 12 141 401 21 269 467 81 73 15 340 149 334 145\n", + " 143 526 404 332 402 339 213 524 525 201 82 144 139 207 341 13 273 337\n", + " 80 14 147 208 211 212 75 11 85 146 396 529 76 209 338 202 74 10\n", + " 275 79 277 77 461 268 463 140 20 16 335 138 464 267 276 398 142 400\n", + " 148 206 399 18 331 465 527 530 336 394 462 460 266 274 78 330 9 265\n", + " 271 403 204 205 333 19 397 270 272 137 84 528 203 395 466]\n", + "45 3\n", + "[ 45 239 107 114 168 370 41 177 51 557 237 47 364 426 302 369 360 170\n", + " 435 175 431 428 167 46 234 495 498 105 423 111 304 179 103 300 621 430\n", + " 558 240 238 368 560 296 429 232 113 241 425 365 618 50 494 108 110 306\n", + " 362 242 174 424 497 297 556 303 359 554 307 366 432 555 40 173 553 301\n", + " 488 172 489 176 104 112 49 43 361 39 622 305 491 559 620 231 298 434\n", + " 169 233 493 371 299 44 42 619 363 295 561 171 367 433 492 427 48 496\n", + " 623 236 624 106 115 109 235 243 178 490]\n", + "58 12\n", + "[ 831 441 638 569 444 1078 1023 1146 507 631 767 757 575 699\n", + " 826 636 756 958 829 1018 1147 830 1207 956 634 954 1085 630\n", + " 894 701 445 1021 887 821 702 504 888 639 827 567 886 1144\n", + " 761 694 1014 1148 949 1143 440 566 439 764 828 760 632 503\n", + " 885 1142 572 820 952 571 824 442 443 1211 695 633 893 765\n", + " 1019 502 1081 629 510 955 766 1020 696 637 948 892 1213 508\n", + " 1016 565 890 1210 506 1086 692 951 574 1013 950 570 698 1077\n", + " 1080 895 1212 822 693 1082 509 1017 957 762 1022 763 505 1079\n", + " 1012 1209 700 697 758 959 953 635 884 573 703 823 1087 1015\n", + " 628 1208 891 1145 759 1084 1150 825 568 889 1083 1149]\n", + "51 14\n", + "[1330 1136 1078 879 631 757 1203 947 756 878 818 563 564 1207\n", + " 882 1198 1072 1200 819 1140 627 630 1135 1269 1139 887 821 688\n", + " 888 886 1144 761 1331 694 1328 1005 1009 1071 1014 1202 1268 949\n", + " 754 1143 1011 689 1334 560 566 877 1329 760 885 625 1205 1142\n", + " 1138 820 1075 952 815 1008 824 690 941 1271 1141 695 1010 1204\n", + " 1081 626 629 562 1270 696 687 948 1264 946 1016 565 1076 749\n", + " 750 1073 692 951 751 1013 950 753 1077 1134 755 1080 881 1332\n", + " 822 1265 693 943 1266 1017 813 1199 561 1007 945 817 1267 1079\n", + " 1137 1074 1012 1133 758 953 880 944 1006 884 814 623 1201 942\n", + " 624 686 823 691 1070 1263 1015 628 1206 1208 1145 816 752 759\n", + " 883 1069 825 889 1333]\n", + "63 21\n", + "[1599 1151 1023 1146 1403 1407 1598 1402 1789 1147 1470 1085 1401 1465\n", + " 1021 1275 1277 1406 1533 1405 1597 1404 1469 1658 1338 1148 1532 1594\n", + " 1595 1276 1273 1530 1341 1660 1724 1723 1466 1211 1531 1593 1020 1337\n", + " 1213 1467 1534 1788 1529 1210 1725 1278 1086 1791 1279 1661 1212 1663\n", + " 1726 1339 1596 1022 1215 1342 1790 1209 1727 1468 1535 1274 1659 1471\n", + " 1343 1087 1214 1662 1084 1150 1340 1083 1149]\n", + "60 57\n", + "[3903 3583 4095 3832 3711 3387 3839 3391 3959 3705 3577 4026 3517 3899\n", + " 3772 3967 3644 3766 3511 3837 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837 717 911 968 1036 1163 583 964\n", + " 844 522 1101 902 587 1162 644 975 972 519 1100 457 460 589\n", + " 1226 1102 973 1034 1229 709 967 904 909 590 710 1030 586 840\n", + " 901 1031 969 652 584 647 520 1164 1094 656 585 713 1097 1161\n", + " 774 646 1225 1028 965]\n", + "46 49\n", + "[2922 2861 2801 2927 3115 3500 2930 3187 3182 3379 3053 2859 2795 3373\n", + " 3316 3499 3054 3116 3058 3443 3440 3180 3112 3049 3435 3249 3055 3439\n", + " 2990 3176 3376 3306 3368 2993 2796 2860 3433 3117 3121 3377 3118 3241\n", + " 2864 2928 3307 3305 3243 3502 3311 2798 3244 3252 3304 3369 2996 3564\n", + " 3123 3048 3250 3380 3434 3050 3438 3184 3378 3181 2995 2921 2863 3186\n", + " 3567 3119 3375 3242 3566 2992 3498 3245 2989 3309 2923 3113 3188 2984\n", + " 3505 3370 3051 2987 3056 3504 3441 3563 3122 3569 3503 3501 3565 2924\n", + " 2865 3436 3059 2991 2929 2986 3179 2988 2994 2931 3314 2858 3120 3437\n", + " 3374 3183 3371 2800 2797 3052 3057 3315 3506 2925 3312 3310 2866 3114\n", + " 3308 3178 3251 3442 3313 3248 3247 3177 3240 2926 2862 2799 2985 3568\n", + " 3060 3246 3185 3372 3124]\n", + "24 38\n", + "[2322 2398 2454 2195 2589 2461 2326 2267 2780 2456 2394 2270 2516 2200\n", + " 2843 2263 2325 2713 2136 2333 2654 2578 2196 2840 2647 2265 2327 2712\n", + " 2521 2453 2716 2387 2582 2522 2711 2391 2526 2451 2839 2583 2709 2517\n", + " 2779 2198 2134 2584 2331 2462 2715 2455 2585 2842 2072 2204 2268 2778\n", + " 2262 2524 2652 2269 2070 2518 2460 2258 2776 2324 2330 2393 2646 2708\n", + " 2773 2710 2777 2397 2203 2390 2649 2579 2329 2133 2075 2515 2772 2580\n", + " 2202 2590 2643 2588 2264 2458 2514 2388 2587 2334 2386 2137 2328 2707\n", + " 2717 2523 2392 2645 2644 2450 2395 2073 2396 2266 2201 2132 2457 2135\n", + " 2259 2837 2642 2648 2653 2199 2389 2838 2775 2459 2139 2140 2071 2581\n", + " 2197 2205 2332 2651 2650 2452 2525 2714 2074 2260 2138 2069 2323 2520\n", + " 2261 2841 2519 2774 2586]\n", + "32 38\n", + "[2529 2404 2210 2406 2402 2721 2398 2661 2847 2589 2532 2272 2592 2461\n", + " 2267 2659 2276 2780 2464 2394 2270 2591 2468 2593 2333 2342 2654 2147\n", + " 2723 2596 2783 2530 2077 2078 2716 2522 2526 2658 2848 2079 2533 2338\n", + " 2850 2331 2662 2462 2715 2466 2534 2204 2268 2788 2206 2277 2594 2337\n", + " 2722 2335 2524 2271 2652 2146 2083 2269 2080 2278 2460 2598 2330 2397\n", + " 2274 2785 2339 2141 2203 2597 2211 2784 2467 2209 2273 2590 2588 2458\n", + " 2463 2207 2081 2587 2656 2465 2334 2787 2405 2403 2849 2144 2717 2523\n", + " 2782 2400 2275 2395 2396 2266 2660 2786 2212 2781 2845 2719 2595 2657\n", + " 2527 2724 2851 2082 2336 2145 2208 2340 2653 2718 2469 2143 2531 2725\n", + " 2459 2528 2140 2401 2205 2332 2213 2148 2651 2650 2399 2525 2720 2846\n", + " 2655 2341 2142 2470 2586]\n", + "47 0\n", + "[ 45 245 239 107 308 114 370 41 177 181 51 237 47 364 302 369 170 175\n", + " 431 428 46 234 105 111 304 117 179 300 430 240 116 238 368 429 113 241\n", + " 365 50 108 110 306 242 52 174 303 307 366 432 173 301 53 172 176 112\n", + " 49 180 43 305 298 434 169 233 371 299 44 42 363 171 367 433 48 244\n", + " 236 106 115 109 235 243 178]\n", + "57 58\n", + "[3903 3583 3832 3711 3387 3827 3839 3959 3705 4086 3577 4026 3517 3899\n", + " 3772 3967 3644 3699 3766 3511 3956 3837 3708 3769 3702 3895 4030 3574\n", + " 3836 3965 4027 3636 3962 3775 3709 4091 3897 3834 3384 3510 4092 3445\n", + " 3581 3386 3902 4029 3830 3572 3901 3838 4028 3383 3382 4093 3579 3513\n", + " 3828 3960 3700 3763 3891 3643 3706 3765 4085 3961 4090 3578 3448 3771\n", + " 3893 3958 4021 4022 4025 3642 3646 3575 4024 3641 3707 3701 3452 3639\n", + " 3514 3451 3764 3894 3966 3450 3447 3896 3900 3515 3957 3898 4088 3385\n", + " 3647 3768 3637 4089 3773 3640 3518 3449 3573 3635 3453 3509 3645 3516\n", + " 3831 3512 3767 3582 3835 3833 3571 4020 3774 3638 3963 3508 3964 3704\n", + " 3710 3829 4087 3703 3388 3446 3576 3892 3580 3770 3955 4023]\n", + "10 36\n", + "[2191 2317 2444 1997 2319 2062 2314 2509 2441 2445 2380 1930 2182 2313\n", + " 2437 1932 2637 2116 2056 2318 2188 2180 2569 2631 2375 2436 2254 2244\n", + " 2501 2255 2125 2630 2565 2378 2633 2447 2502 1928 2439 1933 2695 2054\n", + " 2381 2377 2571 2698 2185 1990 2443 2510 2308 2127 2117 2058 2126 1931\n", + " 2384 2442 2249 2570 2376 2373 2379 2059 2186 2310 2568 2250 2567 2181\n", + " 2572 2128 2122 2121 2372 1927 2383 1995 2192 2253 2440 2120 2315 2251\n", + " 1993 2247 2636 1991 2053 2511 2635 2700 2119 2503 2500 1992 1929 2374\n", + " 2312 2245 2697 2505 2184 2508 2634 2696 2118 2055 2061 2183 2448 2512\n", + " 2320 2246 2575 2632 2309 2057 2507 2187 1996 2574 2506 2316 2382 2060\n", + " 2252 2446 2504 2438 2311 2063 2189 2638 2123 2256 2124 2190 2248 1998\n", + " 2566 2573 2699 2701 1994]\n", + "15 43\n", + "[2957 2832 2891 2449 3024 3086 2444 3025 2959 2705 3148 3027 2765 3023\n", + " 2767 2897 2762 2960 2516 3089 2509 3154 2955 2963 2445 2380 3151 3084\n", + " 3091 2578 2896 2637 3018 2569 2766 2825 2451 2633 2709 2447 2964 2827\n", + " 2770 2900 2381 2639 2835 2571 2698 2443 2510 2898 2768 2640 3090 2384\n", + " 3085 2570 2703 2702 2893 3087 2572 2577 3026 3153 2708 2761 2773 2833\n", + " 2385 2830 2889 2834 2579 2515 2772 2580 2383 2643 2706 2514 3083 3028\n", + " 2826 2636 2386 2511 2635 2707 2700 3021 2764 2513 2576 3149 2956 2645\n", + " 2644 2450 3152 2697 2901 2895 2953 2828 2508 2965 2634 2837 2642 2899\n", + " 2448 2512 2829 2831 2962 3150 2641 2575 2894 2769 2581 2890 2507 3019\n", + " 3088 2574 2506 2382 2836 2446 2892 2961 2704 2763 2954 2638 3020 2958\n", + " 3022 2573 2699 2701 2771]\n", + "50 61\n", + "[3832 4083 3827 3959 4079 4086 3696 3699 3766 4013 3956 3954 3702 3895\n", + " 3823 3826 3884 3631 3886 4081 3636 3757 3887 4078 3822 3952 3760 3948\n", + " 3885 3762 4019 4016 3694 3830 3572 3890 4018 3698 3949 3828 3960 3700\n", + " 3950 3763 3891 3824 3765 4085 4076 3567 4015 3758 3893 4084 3958 4021\n", + " 4022 4014 3697 4024 4012 3820 3701 3756 3764 3569 3894 4082 3951 3896\n", + " 3759 3825 3821 3957 4088 3630 3632 3768 3637 4017 3573 3635 3831 3767\n", + " 3633 3570 3571 3695 3888 3889 4020 3953 4080 3634 4077 3638 3829 4087\n", + " 3703 3693 3761 3892 3568 3955 4023]\n", + "54 57\n", + "[3832 4083 3827 3959 3705 4086 3577 4026 3696 3899 3772 3644 3320 3699\n", + " 3766 3511 3956 3708 3769 3954 3702 3379 3895 3826 3574 3836 3316 3443\n", + " 3636 3962 3760 3897 3834 3384 3510 3762 3445 4019 3386 3830 3572 3890\n", + " 3383 4018 3698 3382 3380 3579 3513 3828 3960 3700 3763 3891 3824 3643\n", + " 3706 3765 3378 4085 3961 3317 3578 3448 3771 3893 4084 3958 4021 3444\n", + " 4022 4025 3642 3505 3697 3575 4024 3504 3641 3707 3441 3701 3321 3507\n", + " 3639 3514 3451 3764 3569 3381 3894 3319 3450 3447 3896 3825 3900 3515\n", + " 3957 3898 4088 3318 3385 3632 3768 3637 4089 3640 3449 3573 3635 3509\n", + " 3516 3831 3512 3767 3633 3835 3570 3315 3833 3506 3571 3888 3889 4020\n", + " 3953 3634 3442 3638 3963 3508 3704 3829 4087 3703 3446 3761 3576 3892\n", + " 3568 3580 3770 3955 4023]\n", + "5 4\n", + "[ 6 329 193 459 0 73 200 521 320 133 261 453 201 260 70 139 580 386\n", + " 582 458 128 387 136 129 393 258 4 325 517 257 642 2 449 75 448 263\n", + " 581 390 515 577 202 74 456 1 392 513 518 10 516 130 645 68 8 66\n", + " 256 321 65 138 132 267 194 455 648 5 327 326 192 452 331 583 522 643\n", + " 451 197 134 324 644 264 394 519 322 7 457 72 266 578 512 196 199 330\n", + " 9 265 131 323 262 389 195 388 198 454 3 71 450 259 514 135 64 584\n", + " 647 520 391 137 585 203 67 646 69 384 385 328 395 579]\n", + "14 8\n", + "[329 594 848 210 591 459 785 141 401 720 913 845 269 467 340 334 910 777\n", + " 716 145 850 521 659 143 842 526 404 332 523 596 779 724 402 339 655 653\n", + " 907 524 525 718 719 144 714 139 207 781 780 595 458 721 273 337 784 778\n", + " 912 393 847 651 532 715 208 468 723 396 849 529 722 782 209 846 338 202\n", + " 456 908 392 654 843 275 588 461 268 463 140 783 658 650 335 464 267 712\n", + " 648 649 398 717 911 142 400 206 399 331 844 465 522 593 787 527 530 336\n", + " 587 394 660 462 457 460 589 786 657 266 274 592 909 330 590 265 271 403\n", + " 586 531 204 205 333 397 652 584 520 270 272 656 585 528 713 203 328 395\n", + " 466]\n", + "17 45\n", + "[2957 3217 2832 2891 3024 3086 3215 3025 2959 2705 3148 3027 2765 3023\n", + " 2767 2897 3280 2960 2516 3089 3154 2955 2963 3151 3084 3091 2578 2896\n", + " 2637 3094 3030 2966 2766 2711 2839 2709 2964 2827 2770 2900 3155 2639\n", + " 2835 2510 2898 3158 2768 2640 3090 3085 2703 2702 3218 3284 2893 3031\n", + " 3087 2577 3026 2646 3153 2708 2773 2710 3219 2833 2830 2834 2579 2515\n", + " 2772 2580 2643 3221 2706 2514 3083 2967 3028 3282 2636 3029 2511 2707\n", + " 2700 3021 3157 2764 2513 2576 3149 2956 3283 2645 2644 3220 3156 3095\n", + " 3152 2901 2895 3092 2828 2965 3093 2837 2642 2899 2512 2829 2838 2831\n", + " 2962 3150 2775 2641 2575 2894 3278 3216 3214 2769 2581 2902 3019 3088\n", + " 3213 2574 3281 2836 2892 2961 2704 2763 2903 2638 3279 3020 2958 3022\n", + " 2573 2699 2701 2774 2771]\n", + "44 5\n", + "[ 45 239 107 168 370 41 177 557 237 47 364 426 302 369 360 170 175 431\n", + " 428 167 46 234 495 684 498 105 688 550 423 111 304 230 682 745 103 300\n", + " 621 430 558 240 617 238 368 560 296 429 748 232 551 113 241 425 365 625\n", + " 618 685 494 108 110 486 306 362 242 174 424 497 552 297 556 303 359 554\n", + " 366 432 555 615 40 680 487 562 173 553 301 488 687 172 489 176 104 112\n", + " 294 43 361 622 749 750 305 491 559 751 620 231 358 298 434 169 233 493\n", + " 616 299 44 42 619 363 683 295 561 171 747 367 433 166 492 427 48 496\n", + " 623 236 624 686 422 746 106 109 681 235 178 490]\n", + "17 16\n", + "[ 848 1361 785 720 913 845 1104 1232 1039 1035 1228 910 1358 850\n", + " 1110 659 976 1041 914 1170 1167 724 978 917 1295 655 1099 852\n", + " 974 907 718 1363 719 855 1171 781 1105 780 721 971 1362 982\n", + " 1230 784 912 1423 1165 847 789 1175 1292 723 1046 1237 1299 1427\n", + " 1103 849 1038 722 782 1231 846 908 1106 1042 1357 1227 1040 919\n", + " 1426 790 983 654 1166 843 918 1238 1107 1172 1037 1047 1296 977\n", + " 853 783 658 725 1425 1365 979 717 854 1293 911 1297 1036 980\n", + " 1233 1163 1111 844 1109 1101 787 1294 1173 1359 975 972 660 1174\n", + " 1100 1239 1044 851 786 657 1102 973 981 1360 1229 1300 1108 788\n", + " 909 915 1234 1302 916 1422 1298 1236 1169 1424 1428 1301 1364 1164\n", + " 1043 1168 656 1045 1235]\n", + "32 27\n", + "[1826 1765 1695 2084 1570 1888 1508 1892 1502 1699 1697 1504 2018 1638\n", + " 1692 1636 1442 2015 1885 1760 1376 1436 1509 1505 1503 1572 1763 1952\n", + " 2011 1819 2147 1883 2017 1818 2077 1829 2078 1566 1957 1500 1954 1949\n", + " 2020 1822 2079 1894 1571 1955 1374 2014 1437 1698 1758 1828 1757 1633\n", + " 1444 1693 1506 1754 1562 1499 1890 2146 2083 1379 2080 1635 2012 1628\n", + " 2021 1950 1948 1821 1378 1627 2019 1626 2141 1886 1956 1373 1947 1827\n", + " 1825 1694 1630 1887 2081 1501 1762 1882 1632 1375 1755 2144 1440 1700\n", + " 1951 1946 1701 1889 1569 1820 1893 1573 1438 1507 2016 1958 2082 1953\n", + " 1565 1830 1564 2145 1637 1759 1439 1884 1696 1567 1823 1629 2143 1702\n", + " 1766 1691 1824 2076 1574 1891 1443 1634 1631 1764 1690 1563 1761 2142\n", + " 1441 2013 1568 1377 1756]\n", + "20 31\n", + "[2322 1809 1682 2000 1870 2191 2195 1621 1875 1749 2326 2129 2062 2067\n", + " 2004 1816 2131 1806 1808 2200 2263 2325 2136 2257 1683 2196 1680 2006\n", + " 1818 2265 2327 2008 1873 2387 1617 1878 2255 1687 2391 2064 2198 2134\n", + " 2127 2072 2193 1807 2126 2005 1937 1943 1619 1622 2262 1811 1813 2002\n", + " 1880 2070 2065 2258 2324 2128 1877 1939 2385 1942 1941 2390 2133 1817\n", + " 1945 2003 2202 2192 2264 2388 2001 1882 2386 2007 1746 2137 1935 2328\n", + " 1812 1815 1623 1810 1946 2073 2194 1620 1879 2201 1934 1618 2132 1936\n", + " 2135 2066 2259 1753 1999 1814 1744 1938 2199 2389 1681 2320 2321 1748\n", + " 1871 2071 1684 2197 2010 1752 1872 2009 1686 1874 1876 1750 2074 1743\n", + " 1747 2068 2260 1881 1751 2063 1685 2138 1688 2069 2130 2323 2256 2190\n", + " 2261 1998 1940 1745 1944]\n", + "20 57\n", + "[3858 4055 3345 3730 3990 3919 3865 3538 3985 3599 3408 4051 3799 3790\n", + " 3923 3668 3541 3922 3605 3534 3726 3671 3728 3482 3987 3924 3926 3411\n", + " 3732 3861 3859 3470 3476 3854 3801 3286 3287 3735 3412 3414 3346 3607\n", + " 4052 3738 3598 3866 3472 3802 3986 3351 3415 3604 3663 3920 3536 3479\n", + " 3413 3863 3344 3921 3856 3989 3798 3546 3537 3284 3672 3610 3796 3927\n", + " 3410 3791 3928 3855 3929 3285 3674 3925 3795 3416 3608 3797 3669 3542\n", + " 3603 3737 3731 3600 3736 3666 3471 3535 3282 4053 3409 3474 3481 3793\n", + " 3664 3601 3606 3283 3734 3665 3539 3609 4050 3347 3540 3667 3862 3673\n", + " 3670 3350 3543 3727 3477 3544 3602 3792 3348 3473 3545 4049 3729 3860\n", + " 3662 3478 3417 3733 3349 3281 3991 3988 3857 3984 3800 3407 3475 4054\n", + " 3992 3864 3794 3352 3480]\n", + "53 22\n", + "[1392 1330 1713 1519 1524 1078 1782 1521 1399 1203 1842 1651 1403 1461\n", + " 1785 1525 1402 1394 1207 1712 1848 1588 1652 1200 1140 1401 1648 1465\n", + " 1269 1275 1139 1327 1144 1331 1336 1335 1455 1715 1328 1778 1460 1649\n", + " 1456 1717 1779 1658 1202 1268 1338 1585 1522 1594 1143 1523 1592 1595\n", + " 1334 1719 1586 1846 1584 1329 1273 1847 1530 1205 1463 1393 1142 1520\n", + " 1138 1714 1075 1466 1396 1527 1271 1141 1531 1457 1395 1593 1657 1650\n", + " 1204 1783 1845 1391 1270 1464 1337 1264 1467 1587 1718 1076 1777 1716\n", + " 1591 1529 1210 1784 1781 1077 1398 1459 1080 1332 1265 1780 1655 1720\n", + " 1272 1266 1397 1528 1339 1721 1844 1400 1267 1079 1653 1137 1074 1590\n", + " 1654 1209 1462 1656 1274 1589 1722 1201 1659 1526 1583 1263 1647 1843\n", + " 1206 1458 1208 1145 1333]\n", + "12 62\n", + "[4038 4047 3858 3659 3914 3919 3786 4043 3985 3599 3790 3847 3983 3980\n", + " 3853 4041 3851 3979 3922 3726 3913 3728 3982 3975 3854 4039 3788 3978\n", + " 3598 3915 4042 3986 3663 3911 3920 3921 3856 3656 3848 3977 3791 3855\n", + " 4048 3595 3725 3719 3910 3793 3852 4044 3664 3916 3789 4045 3593 4050\n", + " 3597 3976 3594 3657 3846 3658 3850 3722 3721 3727 3849 3720 3974 3792\n", + " 3981 3917 4049 3596 3729 4040 3785 3662 3912 3787 3857 3723 3660 3984\n", + " 3661 3918 3783 4046 3784 3794 3782 3724]\n", + "45 36\n", + "[2027 2093 2089 2542 2159 2354 2475 2671 1966 2416 2161 2284 2408 2032\n", + " 2730 2222 2732 1965 2349 2536 2545 2088 2344 2413 2414 2541 2411 2155\n", + " 2418 2348 2345 1963 2602 2474 2535 2538 2152 2026 2090 2153 2609 2419\n", + " 2352 2480 2407 2473 2160 2544 2098 2600 2668 2601 2282 2346 2409 2218\n", + " 2608 2289 2670 1962 2225 2355 2540 2224 2343 2478 2417 1964 2607 2472\n", + " 2096 2030 2095 2288 2162 2154 2736 2219 2025 2350 2158 2220 2477 2604\n", + " 2151 2163 2279 2217 1968 2286 2667 2351 2539 2031 2471 2479 2483 2290\n", + " 2415 2033 2482 2347 2156 2669 2353 2215 1967 2216 2092 2672 2734 2543\n", + " 2291 2226 2287 2283 2606 2673 2280 2412 2094 2410 2157 2227 2537 2731\n", + " 2610 2481 2285 2097 2665 2733 2091 2603 2029 2546 2547 2223 2281 2476\n", + " 2735 2605 2221 2666 2028]\n", + "18 14\n", + "[ 594 848 591 785 720 913 845 1104 1232 1039 910 716 850 1110\n", + " 659 976 1041 914 1170 1167 596 724 978 917 1295 655 653 852\n", + " 974 718 719 855 1171 781 792 1105 780 595 721 982 1230 784\n", + " 912 1165 847 856 791 532 1112 789 1175 662 723 1046 1237 1299\n", + " 1103 849 1038 529 722 782 1231 846 908 1106 1042 1040 919 790\n", + " 983 654 1166 918 1238 726 728 1107 1172 1037 1047 1296 977 853\n", + " 783 658 725 597 979 717 854 911 1297 1036 980 1233 1048 533\n", + " 1111 844 1109 1101 593 787 527 530 1173 975 661 972 660 1174\n", + " 1100 1044 851 786 657 1102 973 981 1300 1108 788 592 909 590\n", + " 915 1234 531 916 663 1298 1236 598 1169 1301 920 1043 727 1168\n", + " 656 528 1045 984 1235]\n", + "45 19\n", + "[1388 1392 1262 1330 1130 1136 1132 1519 1642 1581 1383 879 1389 1521\n", + " 1203 1261 1322 940 1002 878 1004 1065 1394 1003 1193 1064 1198 1578\n", + " 1258 1072 1512 1579 1200 1451 1648 1135 1129 1001 876 1450 1067 1139\n", + " 1327 1452 1255 1259 1326 1331 1455 1328 1005 1009 1071 1456 1202 1454\n", + " 1585 1522 1319 1321 1323 1447 1387 1584 877 1329 1515 1393 1390 1520\n", + " 1138 1075 1256 1066 1577 1008 1448 1320 941 1516 1457 1395 1010 1324\n", + " 1128 1385 1063 1325 1391 1644 937 1000 1264 1194 1127 1197 1195 1073\n", + " 1386 1134 1514 1453 1459 1192 1196 1513 1265 943 875 1384 1266 1449\n", + " 1191 1199 1517 1645 1646 1007 945 1643 1267 1137 1074 1068 1260 1133\n", + " 938 880 944 1006 1201 942 1131 1583 1257 1070 1263 1647 939 1582\n", + " 1458 1580 1518 1069 874]\n", + "10 43\n", + "[2887 2957 3145 2832 2891 3086 2444 2959 3148 2765 3023 2767 2762 2960\n", + " 2509 2441 2955 2445 2380 3084 2759 2896 2637 3018 2820 3146 2629 2569\n", + " 2631 2758 2375 2766 2694 2501 2825 2756 2630 2565 2378 2633 2502 2760\n", + " 2827 2439 2695 2381 2377 2639 2571 2698 2443 2510 2951 3013 2768 2640\n", + " 3082 2886 3017 3085 2442 2950 3080 2570 2376 2703 2379 2702 2568 2567\n", + " 2693 2893 2572 2761 2757 2830 2889 2884 2564 3079 3014 2440 3083 2692\n", + " 2826 2636 2511 2635 2700 2824 3021 2949 2764 2503 2576 3149 2956 3143\n", + " 3078 3016 3144 2697 2888 2505 2895 2953 2828 2508 2634 2696 3015 2829\n", + " 2821 2831 2575 2894 2632 2822 3081 2948 2890 2507 3019 2952 2574 2506\n", + " 2823 2446 2892 2885 2504 2704 2763 2954 2438 2638 2628 3020 2958 2566\n", + " 3022 2573 2699 2701 3147]\n", + "13 60\n", + "[4047 3858 3730 3659 3914 3919 3786 4043 3985 3599 4051 3790 3847 3923\n", + " 3529 3983 3980 3853 4041 3851 3979 3922 3534 3726 3913 3728 3987 3982\n", + " 3975 3859 3470 3854 4039 3788 3978 3598 3915 4042 3472 3986 3663 3911\n", + " 3920 3536 3921 3856 3656 3537 3848 3977 3791 3855 3531 4048 3468 3595\n", + " 3795 3467 3731 3600 3725 3666 3532 3471 3535 3719 3793 3852 4044 3664\n", + " 3601 3916 3789 3533 3655 4045 3665 3593 4050 3597 3667 3976 3594 3657\n", + " 3658 3850 3722 3721 3727 3849 3469 3720 3602 3792 3981 3917 4049 3596\n", + " 3729 4040 3785 3662 3592 3530 3912 3787 3857 3723 3660 3466 3984 3661\n", + " 3918 3783 4046 3784 3794 3724]\n", + "51 52\n", + "[3128 3577 3696 3320 3699 3766 3511 3187 3182 3702 3379 3574 3373 3316\n", + " 3631 3058 3443 3636 3189 3440 3191 3249 3055 3439 3376 2993 3760 3121\n", + " 3377 3384 3510 3118 3762 3445 2997 3125 3502 3311 3253 3252 3572 2996\n", + " 3061 3123 3383 3250 3698 3382 3380 3513 3700 3763 3063 3438 3765 3184\n", + " 3378 3181 2995 3192 3186 3567 3119 3375 3317 3566 3126 2992 3245 3448\n", + " 3309 3444 3257 3188 3505 3697 3256 3575 3056 3504 3441 3701 3321 3507\n", + " 3193 3639 3122 3764 3569 3503 3381 3319 3501 3565 3447 3318 3630 3385\n", + " 3059 3632 3637 2998 3640 3449 2994 3573 3635 3314 3509 3120 3437 3512\n", + " 3374 3183 3633 3570 3057 3315 3506 3312 3571 3695 3310 3634 3251 3442\n", + " 3313 3638 3508 3248 3247 3254 3703 3255 3446 3190 3761 3062 3576 3568\n", + " 3060 3246 3185 3124 3127]\n", + "39 11\n", + "[1130 1062 557 743 426 940 1002 865 546 742 360 1004 1065 871\n", + " 421 1003 996 1064 738 1060 482 1129 684 1001 876 1067 1126 550\n", + " 803 868 420 423 485 675 744 547 999 682 745 621 936 870\n", + " 617 866 748 877 806 551 548 545 549 425 618 685 614 808\n", + " 486 1066 609 362 935 424 941 810 611 552 556 359 554 1128\n", + " 677 867 998 555 615 1063 679 680 673 487 356 994 553 807\n", + " 488 937 1000 932 489 1127 739 361 811 749 483 676 931 357\n", + " 678 491 620 997 358 873 1124 419 875 804 610 616 813 1061\n", + " 619 812 740 683 802 929 934 747 613 612 938 1059 492 674\n", + " 872 427 930 933 741 422 484 746 939 805 737 681 809 869\n", + " 995 1125 801 874 490]\n", + "5 61\n", + "[4038 3776 4032 3914 3526 3786 4043 3648 3847 3909 4041 3851 3979 3527\n", + " 4036 4037 3913 3714 3975 3524 3906 3587 4039 3978 3843 4034 3586 3717\n", + " 3915 4042 3905 3712 3528 3844 3970 3651 3716 3649 3911 3779 3590 3842\n", + " 3656 3845 3848 4035 4033 3585 3977 3718 3522 3523 3973 3654 3781 3777\n", + " 3719 3910 3778 3972 3525 3907 3655 3588 3593 3591 3780 3904 3976 3589\n", + " 3657 3846 3658 3850 3722 3968 3721 3849 3720 3974 3841 3969 4040 3785\n", + " 3592 3713 3971 3912 3787 3723 3715 3783 3908 3784 3653 3782 3652 3650\n", + " 3840]\n", + "40 39\n", + "[2404 2922 2542 2406 2402 2475 2661 2532 2284 2214 2408 2730 2732 2659\n", + " 2276 2349 2536 2859 2795 2344 2413 2468 2414 2541 2411 2155 2342 2348\n", + " 2345 2602 2723 2596 2474 2535 2538 2152 2530 2153 2796 2860 2658 2149\n", + " 2533 2407 2473 2338 2662 2600 2668 2601 2282 2466 2346 2409 2920 2791\n", + " 2534 2919 2218 2788 2277 2670 2856 2854 2594 2722 2540 2343 2150 2278\n", + " 2478 2598 2472 2921 2729 2789 2918 2154 2339 2923 2219 2597 2663 2350\n", + " 2220 2477 2604 2467 2151 2279 2217 2664 2794 2599 2667 2539 2857 2471\n", + " 2917 2787 2405 2403 2855 2347 2726 2669 2215 2275 2216 2660 2212 2734\n", + " 2792 2595 2724 2793 2858 2283 2606 2727 2340 2280 2728 2469 2797 2412\n", + " 2531 2725 2410 2852 2790 2537 2731 2853 2285 2665 2213 2733 2603 2281\n", + " 2341 2476 2605 2470 2666]\n", + "26 37\n", + "[2398 2454 2589 2272 2592 2461 2326 2267 2780 2456 2464 2394 2270 2516\n", + " 2200 2591 2263 2325 2713 2136 2333 2654 2011 2196 2647 2265 2327 2008\n", + " 2712 2077 2521 2453 2078 2716 2582 2522 2711 2391 2526 2583 2517 2779\n", + " 2198 2134 2584 2331 2462 2715 2455 2585 2072 2204 2268 2206 2778 2262\n", + " 2335 2524 2271 2652 2269 2070 2518 2460 2012 2776 2324 2330 2393 2646\n", + " 2710 2777 2397 2141 2203 2390 2649 2329 2133 2075 2580 2202 2590 2588\n", + " 2264 2458 2463 2207 2388 2587 2334 2007 2137 2328 2717 2523 2392 2645\n", + " 2400 2395 2073 2396 2266 2201 2781 2457 2527 2135 2336 2208 2648 2653\n", + " 2718 2199 2389 2143 2775 2459 2528 2139 2140 2071 2581 2197 2010 2205\n", + " 2332 2076 2651 2009 2650 2399 2452 2525 2714 2074 2260 2138 2655 2520\n", + " 2142 2261 2013 2519 2586]\n", + "45 47\n", + "[2922 2861 2801 2671 2927 3115 2930 2730 2732 3187 3182 3053 2859 2983\n", + " 2795 3373 3054 3116 3058 3440 3180 3112 3049 3435 3249 3055 3439 2990\n", + " 3176 3376 3306 2993 2796 2860 3117 3121 3377 3118 3111 3241 2864 2928\n", + " 3307 3305 2668 3243 3311 2798 2920 3244 2919 3304 3369 3123 2670 3048\n", + " 2856 3250 3434 3050 3438 3047 3184 3181 2995 2921 2863 2729 3186 3119\n", + " 3375 3242 2867 2992 3245 2989 3309 2923 2736 3113 2984 3370 3051 2987\n", + " 3175 3056 2794 2667 2857 3122 2802 2855 2737 2669 2924 2865 3436 2672\n", + " 2734 2792 3059 2991 2929 2986 3179 2988 2793 2994 2931 3314 2858 3120\n", + " 3437 3374 3183 3371 2800 2797 3052 3057 2925 3239 3312 3310 2866 3114\n", + " 2731 3308 3178 3251 3313 2733 3248 3247 3177 3240 2926 2862 2799 2985\n", + " 3246 3185 2735 3372 2666]\n", + "44 26\n", + "[1388 1392 1710 2027 1840 1704 2093 1713 2089 1836 1895 1519 1642 1581\n", + " 1966 1638 1389 1521 2032 1965 1842 1708 1322 1575 1773 1833 1839 1511\n", + " 1963 1772 1712 1969 2026 1834 1841 1578 2090 1512 1579 1768 1451 1771\n", + " 1648 1640 1769 1450 1327 1452 1832 1641 1326 1894 1576 1455 1905 1961\n", + " 1778 1649 1456 1901 1903 1454 1709 1585 1522 1707 1962 1321 1323 1447\n", + " 1837 1586 1387 1835 1584 1964 1896 1770 1515 1510 2030 1390 1899 1902\n", + " 1520 2095 1714 1776 1904 1577 1448 1959 2025 1767 1516 1457 1900 1650\n", + " 1324 1385 1968 1325 1391 1703 1644 1838 2031 1705 1960 1777 1967 2092\n", + " 1386 1897 1514 1453 1774 1513 1830 1639 1384 1449 1517 1645 1646 1643\n", + " 2094 1702 2024 1706 1831 1775 1766 1898 1574 2091 2029 1583 1647 1582\n", + " 1711 1580 1518 2028 1906]\n", + "52 40\n", + "[2542 2801 2354 2739 2671 2416 2420 2930 2678 2612 2545 2617 2414 2933\n", + " 2418 2294 2679 2806 2230 2618 2807 2742 2872 2738 2489 2741 2360 2993\n", + " 2873 2425 2609 2228 2419 2488 2935 2352 2480 2740 2864 2936 2997 2805\n", + " 2674 2553 2928 2544 2293 2869 2296 2550 2798 2934 2552 2996 2422 2608\n", + " 2289 2490 2670 2225 2682 2355 2554 2868 2478 2417 2607 2995 2863 2867\n", + " 2421 2870 2288 2358 2736 2549 2999 2680 2548 2803 2357 2351 2479 2802\n", + " 2483 2290 2415 2423 2676 2482 2737 2353 2486 2295 2611 2865 2615 2614\n", + " 2672 2734 2613 2543 2291 2929 2231 2804 2744 2226 2998 2746 2808 2871\n", + " 2994 2931 2292 2675 2229 2606 2673 2485 2800 2227 2426 2484 2866 2681\n", + " 2610 2809 2481 2356 2487 2546 2547 2810 2745 2932 2359 2799 2361 2743\n", + " 2735 2424 2551 2677 2616]\n", + "42 20\n", + "[1388 1392 1262 1704 1508 1130 1136 1132 1519 1642 1581 1383 1638 1389\n", + " 1062 1445 1261 1708 1322 1575 940 1002 1509 1004 1065 1511 1003 1193\n", + " 1064 1198 1252 1578 1258 1512 1579 1200 1317 1451 1640 1135 1129 1001\n", + " 1450 1067 1327 1126 1452 1255 1259 1641 1326 1576 1455 1328 1005 1071\n", + " 1316 1456 1454 999 1709 1319 1707 1444 936 1321 1323 1447 1387 1515\n", + " 1510 1390 1520 1446 1190 1189 1256 1066 1577 1448 1320 935 941 1516\n", + " 1324 1128 1385 998 1063 1325 1391 1703 1644 1705 937 1000 1264 1194\n", + " 1127 1197 1195 1386 1381 1134 1514 1573 1453 1124 1192 1196 1513 1254\n", 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2013 2586]\n", + "39 24\n", + "[1826 1388 1765 1704 1570 1508 1892 1836 1699 1895 1315 1642 1581 1383\n", + " 1697 1638 1389 1636 1442 1445 1708 1322 1575 1773 1833 1509 1505 1572\n", + " 1763 1511 1772 1193 1834 1252 1578 1258 1512 1579 1768 1829 1317 1451\n", + " 1771 1640 1957 1769 1450 1452 1832 1255 1259 1641 1894 1576 1571 1961\n", + " 1698 1316 1828 1709 1633 1319 1707 1962 1444 1506 1321 1323 1447 1379\n", + " 1635 1387 1835 1896 1770 1515 1510 1899 1378 1446 1190 1189 1256 1577\n", + " 1448 1956 1320 1959 1767 1516 1314 1324 1385 1827 1703 1644 1762 1705\n", + " 1960 1194 1700 1701 1386 1381 1569 1897 1893 1514 1573 1453 1507 1958\n", + " 1192 1513 1830 1254 1639 1384 1637 1449 1191 1517 1645 1382 1380 1643\n", + " 1318 1702 1251 1706 1831 1766 1898 1253 1574 1891 1443 1634 1257 1764\n", + " 1188 1761 1441 1580 1377]\n", + "55 60\n", + "[3832 4083 3827 3959 3705 4086 3577 4026 3899 3772 3644 3699 3766 3511\n", + " 3956 3837 3708 3769 3954 3702 3895 3826 3574 3836 3965 4027 4081 3636\n", + " 3962 3709 4091 3897 3834 3510 4092 3762 4019 4029 3830 3572 3901 3890\n", + " 4028 4018 3698 4093 3579 3513 3828 3960 3700 3763 3891 3643 3706 3765\n", + " 4085 3961 4090 3578 3771 3893 4084 3958 4021 4022 4025 3642 3697 3575\n", + " 4024 3641 3707 3701 3639 3514 3764 3894 4082 3896 3825 3900 3957 3898\n", + " 4088 3768 3637 4017 4089 3773 3640 3573 3635 3509 3831 3512 3767 3835\n", + " 3833 3571 3889 4020 3953 3634 3638 3963 3508 3964 3704 3829 4087 3703\n", + " 3761 3576 3892 3770 3955 4023]\n", + "49 39\n", + "[2542 2861 2801 2159 2354 2739 2475 2671 2416 2927 2161 2284 2420 2930\n", + " 2222 2732 2678 2349 2612 2545 2413 2414 2541 2411 2418 2348 2294 2679\n", + " 2806 2164 2742 2738 2741 2796 2609 2228 2419 2352 2480 2740 2864 2805\n", + " 2674 2160 2928 2544 2293 2869 2668 2550 2798 2422 2608 2289 2670 2225\n", + " 2355 2868 2540 2224 2478 2417 2607 2863 2867 2421 2288 2162 2358 2736\n", + " 2350 2158 2477 2604 2549 2163 2548 2286 2803 2357 2667 2351 2539 2479\n", + " 2802 2483 2290 2415 2423 2676 2482 2347 2737 2669 2353 2486 2611 2865\n", + " 2615 2614 2672 2734 2613 2543 2291 2929 2804 2226 2931 2292 2287 2675\n", + " 2229 2606 2673 2485 2800 2797 2412 2227 2484 2866 2731 2610 2481 2285\n", + " 2356 2487 2733 2603 2546 2547 2223 2932 2926 2359 2862 2799 2476 2743\n", + " 2735 2605 2221 2551 2677]\n", + "62 62\n", + "[3903 4095 3832 3711 3839 4026 3899 3772 3967 3644 3837 3708 3769 4030\n", + " 3836 3965 4027 3962 3775 3709 4091 3897 3834 4092 4094 3902 4029 3901\n", + " 3838 4028 4093 3960 3643 3706 3961 4090 3771 4025 4031 3646 4024 3707\n", + " 3966 3896 3900 3898 4088 3647 4089 3773 3645 3835 3833 3774 3963 3964\n", + " 3710 3770]\n", + "40 32\n", + "[1765 2027 2084 1704 2093 2404 2089 2210 2406 1892 2022 1836 1895 2475\n", + " 1966 2018 2284 2214 2408 2222 1965 2276 2349 2088 1833 2344 2411 2155\n", + " 2342 2348 2087 2345 1963 1772 2147 2474 2152 2026 1834 2090 1768 1829\n", + " 2153 1771 1957 1769 1954 2149 1832 2020 2407 2473 1894 1955 2282 1961\n", + " 2346 2409 1901 1828 2218 2277 1707 1962 1890 2343 2146 2083 1837 2150\n", + " 2278 1835 1964 1896 1770 2472 2021 2030 1899 1902 2274 2154 2019 2339\n", + " 1956 1959 2219 2025 1767 2211 2158 1900 2220 2151 2279 2217 1827 2286\n", + " 1703 1705 2471 2405 1960 2086 2347 2156 2215 2275 2216 2023 2212 1701\n", + " 2092 1897 1893 1958 2082 1830 2283 2340 2280 2469 2412 2094 1702 2410\n", + " 2157 2024 1706 1831 1766 1898 2285 1891 2213 2148 2091 2029 1764 2281\n", + " 2085 2341 2470 2221 2028]\n", + "45 27\n", + "[1388 1392 1710 2027 1840 1907 1704 2093 1713 2089 2159 1836 1895 1519\n", + " 1642 1581 1966 1389 1521 2032 1965 1842 1651 1708 1575 1773 1833 2155\n", + " 1839 1963 1772 1712 1969 2026 1834 1841 1578 2090 1512 1579 1768 2034\n", + " 1451 1771 1648 1640 1769 1450 1452 1832 1641 1576 2160 1455 1715 1905\n", + " 1961 1778 1649 1456 1901 1903 1779 1454 1709 1585 1971 1522 1707 1962\n", + " 1837 1586 1387 1835 1584 1964 1896 1770 1515 2096 2030 1390 1899 1902\n", + " 1520 2095 1714 2154 1776 1904 1577 1959 2025 1767 1970 1516 1457 2158\n", + " 1900 1650 1968 1391 1703 1644 1838 2031 1705 1960 2033 1587 1777 2156\n", + " 1967 2092 1386 1897 1514 1453 1774 1513 1639 1449 1517 1645 1646 1643\n", + " 2094 2157 2024 1706 1831 1775 1898 2097 2091 2029 1583 1647 1582 1843\n", + " 1711 1580 1518 2028 1906]\n", + "16 51\n", + "[2957 3345 3275 3217 3024 3086 3538 3215 3599 3342 3408 3025 2959 3148\n", + " 3027 3023 2897 3280 2960 3089 3154 3541 2963 3402 3534 3151 3084 3091\n", + " 2896 3411 3094 3470 3476 3146 3286 3274 3412 3414 3346 3598 2964 3472\n", + " 3155 2898 3604 3663 3158 3536 3082 3090 3413 3085 3344 3404 3537 3218\n", + " 3284 2893 3087 3026 3153 3277 3410 3219 3531 3285 3468 3210 3341 3343\n", + " 3467 3603 3600 3221 3666 3532 3471 3083 3028 3535 3282 3406 3409 3029\n", + " 3474 3021 3157 3664 3601 3149 2956 3339 3283 3533 3405 3220 3665 3156\n", + " 3539 3338 3152 3347 3540 3597 3667 2895 3092 3350 3276 3093 3469 3477\n", + " 2899 3602 3348 3473 3222 2962 3150 3596 3212 2894 3278 3662 3216 3478\n", + " 3214 3019 3349 3088 3213 3340 3403 3281 3466 3661 2961 3211 3407 3475\n", + " 3279 3020 2958 3022 3147]\n", + "16 46\n", + "[2957 3345 3217 2832 2891 3024 3086 3215 3342 3025 2959 2705 3148 3027\n", + " 2765 3023 2767 2897 2762 3280 2960 3089 3154 2955 2963 3151 3084 3091\n", + " 2578 2896 2637 3094 3018 3030 2966 3146 2766 3346 2709 2964 2827 2770\n", + " 2900 3155 2639 2835 2898 3158 2768 2640 3082 3090 3085 3344 2703 2702\n", + " 3218 3284 2893 3087 2577 3026 3153 3277 2708 2773 3219 2833 2830 2834\n", + " 2579 3341 3343 2772 2643 3221 2706 3083 3028 2826 3282 2636 3029 2707\n", + " 2700 3021 3157 2764 2576 3149 2956 3283 2644 3220 3156 3152 3347 2901\n", + " 2895 3092 2828 3276 2965 3093 2837 2642 2899 2829 2838 2831 2962 3150\n", + " 2641 2575 3212 2894 3278 3216 3214 2769 2890 2902 3019 3088 3213 2574\n", + " 3281 2836 2892 2961 2704 3211 2763 2954 2638 3279 3020 2958 3022 2573\n", + " 2699 2701 2774 2771 3147]\n", + "43 44\n", + "[2922 2542 2861 2801 2475 2661 2671 2927 3115 2730 2732 3182 2536 3053\n", + " 2859 2983 2795 2541 3054 3116 3180 2602 3112 3049 2474 2535 2538 3055\n", + " 2990 3176 2993 2796 2860 3117 3046 3118 3111 3241 2864 2473 2928 2662\n", + " 2600 2668 2601 3243 2798 2920 2791 3244 2919 2608 3045 2670 3048 2856\n", + " 2854 2540 3050 2478 3047 2607 2598 3181 2472 2921 2863 2729 3119 2789\n", + " 2918 3242 2981 2992 2982 3245 2989 2923 2736 3113 2663 2984 2477 2604\n", + " 3051 2987 3175 3056 2664 2794 2599 2667 2539 2857 2917 2855 2726 2737\n", + " 2669 2924 2865 2672 2734 2792 2991 2543 2929 2986 3179 2988 2793 2858\n", + " 2606 3120 2727 2673 3183 2728 2800 2797 3110 3052 3057 2725 2925 2790\n", + " 2537 3114 2731 3178 2853 2665 2733 2603 3177 3240 2926 2862 2799 2985\n", + " 3246 2476 2735 2605 2666]\n", + "41 28\n", + "[1765 1710 2027 2084 1704 2093 2089 1892 2022 1836 1699 1895 1642 1581\n", + " 1966 1638 2214 1636 1965 1708 1575 2088 1773 1833 1509 1572 2155 1839\n", + " 1763 2087 1511 1963 1772 2152 2026 1834 1578 2090 1512 1579 1768 1829\n", + " 2153 1451 1771 1640 1957 1769 1450 2149 1452 1832 2020 1641 1894 1576\n", + " 1955 1961 1901 1828 1903 1709 2218 1707 1962 1447 1837 2150 1635 1835\n", + " 1964 1896 1770 1515 1510 2021 2030 1899 1902 1446 2154 2019 1577 1448\n", + " 1956 1959 2219 2025 1767 1516 1900 2220 2151 2217 1827 1703 1644 1838\n", + " 2031 1705 1960 2086 1700 2156 2215 1967 2216 2023 1701 2092 1897 1893\n", + " 1514 1573 1958 1774 1513 1830 1639 1637 1449 1517 1645 1646 1643 2094\n", + " 1702 2157 2024 1706 1831 1775 1766 1898 1574 1891 2091 2029 1764 1647\n", + " 1582 1711 2085 1580 2028]\n", + "20 39\n", + "[2322 2832 2449 2454 2195 2705 2326 2129 2319 2456 2767 2897 2394 2516\n", + " 2131 2200 2263 2325 2713 2257 2578 2196 2840 2647 2265 2318 2327 2712\n", + " 2521 2453 2387 2582 2522 2255 2711 2391 2451 2839 2583 2709 2447 2517\n", + " 2198 2134 2770 2900 2584 2639 2835 2455 2585 2510 2898 2193 2768 2640\n", + " 2384 2262 2703 2702 2518 2258 2776 2324 2330 2393 2577 2646 2708 2773\n", + " 2710 2777 2833 2385 2834 2390 2649 2579 2329 2133 2515 2772 2580 2383\n", + " 2192 2643 2264 2458 2706 2514 2388 2386 2511 2328 2707 2513 2576 2392\n", + " 2645 2644 2450 2194 2132 2901 2457 2135 2259 2837 2642 2899 2648 2448\n", + " 2199 2512 2389 2838 2320 2775 2641 2321 2575 2769 2581 2197 2902 2574\n", + " 2650 2452 2382 2836 2446 2714 2704 2260 2903 2130 2638 2323 2520 2256\n", + " 2261 2519 2774 2771 2586]\n", + "60 9\n", + "[ 511 251 383 831 441 638 569 313 255 316 444 376 1023 507\n", + " 380 447 438 631 767 319 575 253 699 826 636 958 829 315\n", + " 1018 830 956 634 446 954 630 894 701 445 1021 887 702 504\n", + " 888 639 827 567 761 694 382 252 378 314 249 440 566 439\n", + " 764 828 760 632 503 572 375 379 952 571 377 254 824 442\n", + " 443 695 633 893 765 1019 502 318 510 955 766 1020 696 637\n", + " 892 508 890 506 250 574 317 570 698 895 822 509 1017 957\n", + " 762 1022 763 505 700 697 758 959 953 312 635 573 703 823\n", + " 381 891 759 825 568 889]\n", + "18 18\n", + "[ 848 1361 785 1553 913 1104 1232 1039 1555 1228 910 1358 850 1110\n", + " 976 1489 1041 914 1170 1167 978 917 1295 852 974 1363 1171 1105\n", + " 1488 1362 982 1230 784 912 1492 1423 1165 1494 1554 847 1421 1367\n", + " 1490 1112 1431 789 1175 1292 1046 1237 1299 1427 1103 849 1491 1038\n", + " 1231 846 1106 1042 1357 1040 919 1426 983 1166 1303 918 1238 1107\n", + " 1172 1356 1037 1047 1296 977 853 783 1368 1425 1365 1493 979 854\n", + " 1293 911 1297 1036 980 1233 1048 1111 1109 1101 787 1430 1294 1173\n", + " 1359 1557 975 972 1174 1100 1487 1239 1044 851 786 1102 973 981\n", + " 1360 1229 1300 1108 788 909 915 1234 1302 1551 916 1422 1556 1486\n", + " 1366 1298 1236 1169 1424 1428 1301 1176 1364 1164 1043 1429 1168 1304\n", + " 1045 984 1235 1240 1552]\n", + "15 18\n", + "[1098 848 1361 785 1553 1420 913 845 1353 1104 1232 1039 1035 1228\n", + " 910 1358 850 1549 976 1489 1041 914 1170 1167 978 1295 1099 974\n", + " 907 1363 906 1171 781 1105 780 1291 1488 971 1362 1230 784 912\n", + " 1548 1423 1165 1554 847 1421 1490 970 1292 1483 1237 1484 1299 1427\n", + " 1103 849 1491 1038 782 1231 846 1550 908 1106 1042 1357 1227 1040\n", + " 1426 1166 1485 843 1354 1107 1172 1356 1037 1296 977 783 1425 1365\n", + " 1289 1033 979 1293 911 1297 1036 980 1233 1163 844 1290 1109 1101\n", + " 1294 1173 1162 1359 975 972 1100 1487 1044 851 786 1226 1102 973\n", + " 981 1034 1360 1229 1300 1355 1418 1108 909 915 1234 1419 1551 916\n", + " 1422 1486 969 1298 1236 1169 1424 1428 1301 1364 1164 1043 1168 1097\n", + " 1161 1045 1225 1235 1552]\n", + "47 59\n", + "[4074 4083 3827 4079 3696 3699 3500 4013 3956 3954 3823 3826 3884 3631\n", + " 3499 3886 4081 3636 3440 3757 3887 4078 3822 3439 3952 3947 3760 3948\n", + " 3885 3762 4019 3689 4016 3502 3694 3572 3626 3754 3564 3890 4018 3698\n", + " 3949 3817 3828 3700 3950 3763 3891 3824 3438 3765 3946 3627 4076 3567\n", + " 4015 3691 3758 3566 3893 4084 4075 4021 4014 3625 3882 3505 3697 4012\n", + " 3692 3818 3504 3820 3441 3701 3507 3629 3756 3563 3628 3764 3569 4011\n", + " 3503 3501 3565 4082 3819 3951 3759 3825 3436 4009 3821 3957 3630 3632\n", + " 3637 4017 3635 3755 3881 3437 3633 3570 4010 3506 3571 3695 3753 3888\n", + " 3889 4020 3953 4080 3634 3442 4077 3883 3829 3693 3761 3892 3568 3562\n", + " 3955 3945 3690]\n", + "57 27\n", + "[1907 1976 1973 1599 1524 1782 1399 1651 1403 1461 1785 1525 1598 1402\n", + " 1789 1914 1983 1978 1854 2167 1849 1909 1848 1588 2046 1652 2109 2166\n", + " 1401 1465 1912 2105 1533 2108 2044 1715 1597 1404 1469 2103 1717 1779\n", + " 2170 1658 1979 1918 1532 1971 1594 1592 1595 1916 1719 2036 2041 1846\n", + " 2169 1851 1847 1908 1530 1463 2038 2171 1660 1724 2039 2106 2037 1723\n", + " 1466 1527 1531 1982 1593 1657 1783 1853 1913 1845 1972 1464 1915 1980\n", + " 1910 1919 1467 1587 1718 1534 1788 2043 1716 1591 1529 1725 1784 1981\n", + " 1781 2045 2040 1791 1786 1398 1661 1663 2104 1780 1655 1720 1850 1726\n", + " 1528 1975 1721 1596 1787 1844 1911 1974 2101 1977 1400 2168 2172 1653\n", + " 1790 1852 1590 1654 1462 1727 1468 1855 1656 1589 1722 1659 1526 1843\n", + " 2102 1917 2107 1662 2042]\n", + "42 6\n", + "[ 45 239 107 168 41 557 237 364 743 426 302 742 360 170 421 175 431 428\n", + " 167 234 495 684 105 550 420 423 485 304 230 744 682 745 103 300 228 621\n", + " 430 558 240 165 617 238 368 560 296 429 748 232 551 102 548 549 425 365\n", + " 618 685 614 494 808 108 110 486 362 174 424 810 552 297 556 303 359 554\n", + " 677 366 432 555 615 679 40 680 487 356 173 553 301 807 488 687 172 489\n", + " 104 294 43 361 39 811 622 749 750 357 678 491 559 620 231 358 298 292\n", + " 169 233 229 493 616 299 813 44 42 619 812 363 683 295 171 747 367 293\n", + " 613 612 166 492 427 496 623 236 624 686 422 484 746 106 109 681 809 235\n", + " 490]\n", + "17 48\n", + "[2957 3345 3275 3217 2832 2891 3024 3086 3215 3342 3408 3025 2959 2705\n", + " 3148 3027 2765 3023 2767 2897 3280 2960 3089 3154 2955 2963 3151 3084\n", + " 3091 2896 3411 3094 3470 3159 3476 3030 2966 3286 3287 2766 3412 3346\n", + " 2964 2770 3472 2900 3155 2835 2898 3158 2768 3223 3090 3413 3085 3344\n", + " 2703 2702 3218 3284 2893 3031 3087 3026 3153 3277 2708 2773 3410 3219\n", + " 2833 2830 3285 2834 3341 3343 2772 3221 2706 3471 3083 2967 3028 3282\n", + " 3406 3409 3029 3474 2707 3021 3157 3149 2956 3283 3405 3220 3156 3095\n", + " 3152 3347 2901 2895 3092 2828 3350 3276 2965 3093 2837 2899 3348 3473\n", + " 2829 2838 3222 2831 2962 3150 3212 2894 3278 3216 3214 2769 2902 3019\n", + " 3349 3088 3213 3340 3281 2836 2892 2961 2704 3211 3407 2903 3475 3279\n", + " 3020 2958 3022 2771 3147]\n", + "32 21\n", + "[1695 1570 1508 1502 1699 1315 1697 1504 1692 1636 1442 1311 1445 1760\n", + " 1376 1121 1436 1509 1122 1505 1503 1572 1763 1115 1252 1117 1317 1566\n", + " 1371 1060 1500 1245 1310 1186 1057 1571 1374 1180 1437 1698 1316 1758\n", + " 1249 1307 1757 993 1633 1306 1444 1693 1506 1562 1499 1313 1183 1379\n", + " 1052 1635 1181 1628 1510 1378 1627 1446 1190 1189 1116 1053 1373 1314\n", + " 989 1246 1243 1187 1694 1184 1630 994 1501 1762 1632 1375 990 1178\n", + " 1185 1123 1440 1700 1498 1248 1381 1569 1370 1573 1438 1242 1124 1507\n", + " 1434 1058 1250 1565 992 1254 1564 1055 1637 1435 1759 1439 1696 1382\n", + " 1380 1567 1312 1629 1318 1251 1118 1179 1059 1054 1056 1120 1253 1119\n", + " 1574 1443 1634 1631 1308 1188 1563 1761 1244 1182 1441 995 1309 1372\n", + " 1125 1247 991 1568 1377]\n", + "46 4\n", + "[ 45 239 107 308 500 114 168 370 41 177 51 557 237 47 364 426 302 369\n", + " 360 563 170 435 175 431 428 46 234 495 684 498 105 688 111 304 179 300\n", + " 621 430 558 689 240 116 238 368 560 296 429 232 113 241 425 365 625 499\n", + " 618 685 50 494 108 110 306 362 242 174 424 497 297 556 303 554 307 366\n", + " 432 626 555 562 173 553 301 488 687 172 489 176 104 112 49 180 43 361\n", + " 436 622 305 491 559 620 298 434 169 233 372 493 371 299 44 42 619 363\n", + " 683 561 171 367 433 492 427 48 244 496 623 236 624 686 106 115 109 235\n", + " 243 178 490]\n", + "13 39\n", + "[2322 2832 2891 2449 2191 2317 2444 2705 2765 2319 2314 2767 2762 2509\n", + " 2441 2445 2257 2380 2313 2578 2896 2637 2318 2188 2569 2631 2375 2387\n", + " 2766 2254 2825 2255 2125 2378 2451 2633 2447 2760 2827 2770 2439 2695\n", + " 2381 2377 2639 2571 2698 2185 2443 2510 2127 2193 2126 2768 2640 2384\n", + " 2442 2249 2570 2376 2703 2379 2702 2186 2568 2250 2567 2258 2893 2572\n", + " 2577 2128 2761 2122 2833 2385 2830 2579 2515 2383 2192 2253 2440 2643\n", + " 2706 2315 2514 2251 2826 2636 2386 2511 2635 2707 2700 2764 2513 2503\n", + " 2576 2450 2312 2697 2505 2895 2828 2508 2634 2696 2642 2448 2512 2829\n", + " 2320 2831 2641 2321 2575 2894 2632 2769 2890 2507 2187 2574 2506 2316\n", + " 2382 2252 2446 2892 2504 2704 2763 2311 2189 2638 2323 2123 2256 2124\n", + " 2190 2248 2573 2699 2701]\n" ] } ], "source": [ - "optimizer1 = AQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors)\n", + "r = 7\n", + "optimizer1 = AQR(n_sensors,all_sensors,r,nx,ny)\n", "model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors)\n", "model1.fit(X)\n", "all_sensors1 = model1.get_all_sensors()\n", @@ -812,7 +4553,7 @@ }, { "cell_type": "code", - "execution_count": 893, + "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:27.951889Z", @@ -820,9 +4561,17 @@ } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2431 319 192 4092 894 4032 3268 2209 969 2963 429 23 3769 1068\n", + " 59]\n" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -837,26 +4586,36 @@ "## TODO: this can be done using ravel and unravel more elegantly\n", "img = np.zeros(n_features)\n", "img[top_sensors] = 16\n", - "plt.plot([xmin,xmin],[ymin,ymax],'r')\n", - "plt.plot([xmin,xmax],[ymax,ymax],'r')\n", - "plt.plot([xmax,xmax],[ymin,ymax],'r')\n", - "plt.plot([xmin,xmax],[ymin,ymin],'r')\n", - "plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", - "plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors))\n", + "fig,ax = plt.subplots(1)\n", + "ax.set_aspect('equal')\n", + "ax.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", + "#plt.plot([xmin,xmin],[ymin,ymax],'r')\n", + "#plt.plot([xmin,xmax],[ymax,ymax],'r')\n", + "#plt.plot([xmax,xmax],[ymin,ymax],'r')\n", + "#plt.plot([xmin,xmax],[ymin,ymin],'r')\n", + "#plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", + "# #plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors))\n", + "print(top_sensors)\n", + "top_sensors_grid = np.unravel_index(top_sensors, (nx,ny))\n", + "# figure, axes = plt.subplots()\n", + "for i in range(len(top_sensors_grid[0])):\n", + " circ = Circle( (top_sensors_grid[1][i], top_sensors_grid[0][i]), r ,color='r',fill = False )\n", + " ax.add_patch(circ)\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 894, + "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[4032 384 4092 4039 447 493 2204 657 878 2880]\n", - "(10, 4096)\n", + "[4032 384 4092 4039 447 493 2204 657 878 2880 1088 4087 2837 3779\n", + " 3093]\n", + "(15, 4096)\n", "0.0\n" ] } @@ -874,15 +4633,16 @@ }, { "cell_type": "code", - "execution_count": 895, + "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[4032 384 4092 4039 447 493 2204 657 878 2880]\n", - "(10, 4096)\n", + "[2431 319 192 4092 894 4032 3268 2209 969 2963 429 23 3769 1068\n", + " 59]\n", + "(15, 4096)\n", "0.0\n" ] } @@ -912,7 +4672,7 @@ }, { "cell_type": "code", - "execution_count": 896, + "execution_count": 63, "metadata": { "ExecuteTime": { "end_time": "2022-07-11T16:34:57.421237Z", @@ -926,7 +4686,7 @@ }, { "cell_type": "code", - "execution_count": 897, + "execution_count": 64, "metadata": { "ExecuteTime": { "end_time": "2022-07-11T16:34:58.310764Z", @@ -937,10 +4697,10 @@ { "data": { "text/plain": [ - "0.79669476" + "1.2498279" ] }, - "execution_count": 897, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -951,7 +4711,46 @@ }, { "cell_type": "code", - "execution_count": 898, + "execution_count": 65, + "metadata": { + "ExecuteTime": { + "end_time": "2022-07-10T04:55:26.961534Z", + "start_time": "2022-07-10T04:55:26.877827Z" + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'XX' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 19'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m optimizer_faces \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39moptimizers\u001b[39m.\u001b[39mQR()\n\u001b[1;32m 7\u001b[0m model \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39mSSPOR(basis\u001b[39m=\u001b[39mbasis1,optimizer\u001b[39m=\u001b[39moptimizer_faces, n_sensors\u001b[39m=\u001b[39mn_sensors0)\n\u001b[0;32m----> 8\u001b[0m model\u001b[39m.\u001b[39mfit(XX)\n\u001b[1;32m 10\u001b[0m all_sensors0 \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mget_all_sensors()\n\u001b[1;32m 11\u001b[0m top_sensors0 \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mget_selected_sensors()\n", + "\u001b[0;31mNameError\u001b[0m: name 'XX' is not defined" + ] + } + ], + "source": [ + "max_const_sensors = 1280\n", + "n_const_sensors0 = 3\n", + "n_sensors0 = 7\n", + "n_modes0 = 10\n", + "basis1 = ps.basis.SVD(n_basis_modes=n_modes0)\n", + "optimizer_faces = ps.optimizers.QR()\n", + "model = ps.SSPOR(basis=basis1,optimizer=optimizer_faces, n_sensors=n_sensors0)\n", + "model.fit(XX)\n", + "\n", + "all_sensors0 = model.get_all_sensors()\n", + "top_sensors0 = model.get_selected_sensors()" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2022-07-11T16:34:59.404387Z", @@ -976,7 +4775,7 @@ }, { "cell_type": "code", - "execution_count": 899, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2022-07-11T16:35:00.194386Z", @@ -1001,7 +4800,7 @@ }, { "cell_type": "code", - "execution_count": 900, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:24:41.516465Z", @@ -1015,7 +4814,7 @@ }, { "cell_type": "code", - "execution_count": 901, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:24:42.898448Z", diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index cebf2c2..aa5c700 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -27,7 +27,7 @@ class GQR(QR): @ authors: Niharika Karnik (@nkarnik2999), Mohammad Abdo (@Jimmy-INL), and Krithika Manohar (@kmanohar) """ - def __init__(self,idx_constrained,n_sensors,const_sensors,all_sensors): + def __init__(self,idx_constrained,n_sensors,n_const_sensors,all_sensors): """ Attributes ---------- @@ -37,14 +37,14 @@ def __init__(self,idx_constrained,n_sensors,const_sensors,all_sensors): Column Indices of the sensors in the constrained locations. n_sensors : integer, Total number of sensors - const_sensors : integer, + n_const_sensors : integer, Total number of sensors required by the user in the constrained region. """ self.pivots_ = None self.optimality = None self.constrainedIndices = idx_constrained self.nSensors = n_sensors - self.nConstrainedSensors = const_sensors + self.nConstrainedSensors = n_const_sensors self.all_sensorloc = all_sensors def fit( @@ -121,7 +121,7 @@ def fit( ## TODO: why not a part of the class? #function for mapping sensor locations with constraints -def f_region(lin_idx, dlens, piv, j, const_sensors): +def f_region(lin_idx, dlens, piv, j, n_const_sensors): ##Will first force sensors into constrained region #num_sensors should be fixed for each custom constraint (for now) #num_sensors must be <= size of constraint region """ @@ -135,7 +135,7 @@ def f_region(lin_idx, dlens, piv, j, const_sensors): Array which contains the norm of columns of basis matrix. piv: np.ndarray, shape [n_features] Ranked list of sensor locations. - const_sensors: int, + n_const_sensors: int, Number of sensors to be placed in the constrained area. j: int, Iterative variable in the QR algorithm. @@ -144,7 +144,7 @@ def f_region(lin_idx, dlens, piv, j, const_sensors): ------- dlens : np.darray, shape [Variable based on j] with constraints mapped into it. """ - if j < const_sensors: # force sensors into constraint region + if j < n_const_sensors: # force sensors into constraint region #idx = np.arange(dlens.shape[0]) #dlens[np.delete(idx, lin_idx)] = 0 @@ -155,7 +155,31 @@ def f_region(lin_idx, dlens, piv, j, const_sensors): dlens[didx] = 0 return dlens -def f_region_optimal(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors): +def f_region_optimal(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors): ##Optimal sensor placement with constraints (will place sensors in the order of QR) + """ + Function for mapping constrained sensor locations with the QR procedure (Optimally). + + Parameters + ---------- + lin_idx: np.ndarray, shape [No. of constrained locations] + Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. + dlens: np.ndarray, shape [Variable based on j] + Array which contains the norm of columns of basis matrix. + piv: np.ndarray, shape [n_features] + Ranked list of sensor locations. + j: int, + Iterative variable in the QR algorithm. + const_sensors: int, + Number of sensors to be placed in the constrained area. + all_sensors: np.ndarray, shape [n_features] + Ranked list of sensor locations. + n_sensors: integer, + Total number of sensors + + Returns + ------- + dlens : np.darray, shape [Variable based on j] with constraints mapped into it. + """ counter = 0 mask = np.isin(all_sensors,lin_idx,invert=False) const_idx = all_sensors[mask] @@ -171,159 +195,97 @@ def f_region_optimal(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors return dlens -def getConstraindSensorsIndices(xmin, xmax, ymin, ymax, nx, ny, all_sensors): - """ - Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix. - - Parameters - ---------- - xmin: int, - Lower bound for the x-axis constraint - xmax : int, - Upper bound for the x-axis constraint - ymin : int, - Lower bound for the y-axis constraint - ymax : int - Upper bound for the y-axis constraint - all_sensors : np.ndarray, shape [n_features] - Ranked list of sensor locations. - Returns - ------- - idx_constrained : np.darray, shape [No. of constrained locations] - Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. - """ - n_features = len(all_sensors) - imageSize = int(np.sqrt(n_features)) - a = np.unravel_index(all_sensors, (nx,ny)) - constrained_sensorsx = [] - constrained_sensorsy = [] - for i in range(n_features): - if (a[0][i] >= xmin and a[0][i] <= xmax) and (a[1][i] >= ymin and a[1][i] <= ymax): # x<10 and y>40 - constrained_sensorsx.append(a[0][i]) - constrained_sensorsy.append(a[1][i]) - - constrained_sensorsx = np.array(constrained_sensorsx) - constrained_sensorsy = np.array(constrained_sensorsy) - constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1) - constrained_sensors_tuple = np.transpose(constrained_sensors_array) - if len(constrained_sensorsx) == 0: ##Check to handle condition when number of sensors in the constrained region = 0 - idx_constrained = [] - else: - idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny)) - return idx_constrained - -def getConstrainedSensorsIndicesLinear(xmin,xmax,ymin,ymax,df): - x = df['X (m)'].to_numpy() - n_features = x.shape[0] - y = df['Y (m)'].to_numpy() - idx_constrained = [] - for i in range(n_features): - if (x[i] >= xmin and x[i] <= xmax) and (y[i] >= ymin and y[i] <= ymax): - idx_constrained.append(i) - return idx_constrained - -def boxConstraints(position,lowerBound,upperBound,): - for i,xi in enumerate(position): - f1 = position[i] - lowerBound[i] - f2 = upperBound[i] - position [i] - return +1 if (f1 and f2 > 0) else -1 - -def functionalConstraint(position, func_response,func_input, freeTerm): - g = func_response + func_input + freeTerm - return g - - -if __name__ == '__main__': - faces = datasets.fetch_olivetti_faces(shuffle=True) - X = faces.data - - n_samples, n_features = X.shape - print('Number of samples:', n_samples) - print('Number of features (sensors):', n_features) - - # Global centering - X = X - X.mean(axis=0) - - # Local centering - X -= X.mean(axis=1).reshape(n_samples, -1) - - n_row, n_col = 2, 3 - n_components = n_row * n_col - image_shape = (64, 64) - nx = 64 - ny = 64 - - def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): - '''Function for plotting faces''' - plt.figure(figsize=(2. * n_col, 2.26 * n_row)) - plt.suptitle(title, size=16) - for i, comp in enumerate(images): - plt.subplot(n_row, n_col, i + 1) - vmax = max(comp.max(), -comp.min()) - plt.imshow(comp.reshape(image_shape), cmap=cmap, - interpolation='nearest', - vmin=-vmax, vmax=vmax) - plt.xticks(()) - plt.yticks(()) - plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.) - - # plot_gallery("First few centered faces", X[:n_components]) - - #Find all sensor locations using built in QR optimizer - max_const_sensors = 230 - n_const_sensors = 2 - n_sensors = 200 - optimizer = ps.optimizers.QR() - model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors) - model.fit(X) - - all_sensors = model.get_all_sensors() - - ##Constrained sensor location on the grid: - xmin = 20 - xmax = 40 - ymin = 25 - ymax = 45 - sensors_constrained = getConstraindSensorsIndices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices - - # didx = np.isin(all_sensors,sensors_constrained,invert=False) - # const_index = np.nonzero(didx) - # j = - - - ##Plotting the constrained region - # ax = plt.subplot() - # #Plot constrained space - # img = np.zeros(n_features) - # img[sensors_constrained] = 1 - # im = plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) - # # create an axes on the right side of ax. The width of cax will be 5% - # # of ax and the padding between cax and ax will be fixed at 0.05 inch. - # divider = make_axes_locatable(ax) - # cax = divider.append_axes("right", size="5%", pad=0.05) - # plt.colorbar(im, cax=cax) - # plt.title('Constrained region'); - - ## Fit the dataset with the optimizer GQR - optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors) - model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors) - model1.fit(X) - all_sensors1 = model1.get_all_sensors() - - top_sensors = model1.get_selected_sensors() - print(top_sensors) - ## TODO: this can be done using ravel and unravel more elegantly - #yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features)) - #xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features)) - - img = np.zeros(n_features) - img[top_sensors] = 16 - #plt.plot(xConstrained,yConstrained,'*r') - plt.plot([xmin,xmin],[ymin,ymax],'r') - plt.plot([xmin,xmax],[ymax,ymax],'r') - plt.plot([xmax,xmax],[ymin,ymax],'r') - plt.plot([xmin,xmax],[ymin,ymin],'r') - plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) - plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) - plt.show() +# if __name__ == '__main__': +# faces = datasets.fetch_olivetti_faces(shuffle=True) +# X = faces.data + +# n_samples, n_features = X.shape +# print('Number of samples:', n_samples) +# print('Number of features (sensors):', n_features) + +# # Global centering +# X = X - X.mean(axis=0) + +# # Local centering +# X -= X.mean(axis=1).reshape(n_samples, -1) + +# n_row, n_col = 2, 3 +# n_components = n_row * n_col +# image_shape = (64, 64) +# nx = 64 +# ny = 64 + +# def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): +# '''Function for plotting faces''' +# plt.figure(figsize=(2. * n_col, 2.26 * n_row)) +# plt.suptitle(title, size=16) +# for i, comp in enumerate(images): +# plt.subplot(n_row, n_col, i + 1) +# vmax = max(comp.max(), -comp.min()) +# plt.imshow(comp.reshape(image_shape), cmap=cmap, +# interpolation='nearest', +# vmin=-vmax, vmax=vmax) +# plt.xticks(()) +# plt.yticks(()) +# plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.) + +# # plot_gallery("First few centered faces", X[:n_components]) + +# #Find all sensor locations using built in QR optimizer +# max_const_sensors = 230 +# n_const_sensors = 2 +# n_sensors = 200 +# optimizer = ps.optimizers.QR() +# model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors) +# model.fit(X) + +# all_sensors = model.get_all_sensors() + +# ##Constrained sensor location on the grid: +# xmin = 20 +# xmax = 40 +# ymin = 25 +# ymax = 45 +# sensors_constrained = getConstraindSensorsIndices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices + +# # didx = np.isin(all_sensors,sensors_constrained,invert=False) +# # const_index = np.nonzero(didx) +# # j = + + +# ##Plotting the constrained region +# # ax = plt.subplot() +# # #Plot constrained space +# # img = np.zeros(n_features) +# # img[sensors_constrained] = 1 +# # im = plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) +# # # create an axes on the right side of ax. The width of cax will be 5% +# # # of ax and the padding between cax and ax will be fixed at 0.05 inch. +# # divider = make_axes_locatable(ax) +# # cax = divider.append_axes("right", size="5%", pad=0.05) +# # plt.colorbar(im, cax=cax) +# # plt.title('Constrained region'); + +# ## Fit the dataset with the optimizer GQR +# optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors) +# model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors) +# model1.fit(X) +# all_sensors1 = model1.get_all_sensors() + +# top_sensors = model1.get_selected_sensors() +# print(top_sensors) +# ## TODO: this can be done using ravel and unravel more elegantly +# #yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features)) +# #xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features)) + +# img = np.zeros(n_features) +# img[top_sensors] = 16 +# #plt.plot(xConstrained,yConstrained,'*r') +# plt.plot([xmin,xmin],[ymin,ymax],'r') +# plt.plot([xmin,xmax],[ymax,ymax],'r') +# plt.plot([xmax,xmax],[ymin,ymax],'r') +# plt.plot([xmin,xmax],[ymin,ymin],'r') +# plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) +# plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) +# plt.show() diff --git a/pysensors/utils/__init__.py b/pysensors/utils/__init__.py index 415fd67..d24d201 100644 --- a/pysensors/utils/__init__.py +++ b/pysensors/utils/__init__.py @@ -1,10 +1,17 @@ from ._base import validate_input from ._optimizers import constrained_binary_solve from ._optimizers import constrained_multiclass_solve - +from ._constraints import get_constraind_sensors_indices +from ._constraints import get_constrained_sensors_indices_linear +from ._constraints import box_constraints +from ._constraints import functional_constraints __all__ = [ "constrained_binary_solve", "constrained_multiclass_solve", "validate_input", + "get_constraind_sensors_indices", + "get_constrained_sensors_indices_linear", + "box_constraints", + "functional_constraints" ] From 772b525e99a23186e55b76a956440fc188004860 Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Thu, 28 Jul 2022 14:34:53 -0600 Subject: [PATCH 19/52] Small corrections in _constraints --- pysensors/utils/_constraints.py | 128 ++++++++++++++++++++++++++++++++ 1 file changed, 128 insertions(+) create mode 100644 pysensors/utils/_constraints.py diff --git a/pysensors/utils/_constraints.py b/pysensors/utils/_constraints.py new file mode 100644 index 0000000..5eb4ae8 --- /dev/null +++ b/pysensors/utils/_constraints.py @@ -0,0 +1,128 @@ + +""" +Various utility functions for mapping constrained sensors locations with the column indices for class GQR. +""" + +import numpy as np + + +def get_constraind_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors): + """ + Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix. + + Parameters + ---------- + x_min: int, + Lower bound for the x-axis constraint + x_max : int, + Upper bound for the x-axis constraint + y_min : int, + Lower bound for the y-axis constraint + y_max : int + Upper bound for the y-axis constraint + nx : int + Image pixel (x dimensions of the grid) + ny : int + Image pixel (y dimensions of the grid) + all_sensors : np.ndarray, shape [n_features] + Ranked list of sensor locations. + + Returns + ------- + idx_constrained : np.darray, shape [No. of constrained locations] + Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. + """ + n_features = len(all_sensors) + image_size = int(np.sqrt(n_features)) + a = np.unravel_index(all_sensors, (nx,ny)) + constrained_sensorsx = [] + constrained_sensorsy = [] + for i in range(n_features): + if (a[0][i] >= x_min and a[0][i] <= x_max) and (a[1][i] >= y_min and a[1][i] <= y_max): + constrained_sensorsx.append(a[0][i]) + constrained_sensorsy.append(a[1][i]) + + constrained_sensorsx = np.array(constrained_sensorsx) + constrained_sensorsy = np.array(constrained_sensorsy) + constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1) + constrained_sensors_tuple = np.transpose(constrained_sensors_array) + if len(constrained_sensorsx) == 0: ##Check to handle condition when number of sensors in the constrained region = 0 + idx_constrained = [] + else: + idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny)) + return idx_constrained + +def get_constrained_sensors_indices_linear(x_min,x_max,y_min,y_max,df): + """ + Function for obtaining constrained column indices from already existing linear sensor locations on the grid. + + Parameters + ---------- + x_min: int, + Lower bound for the x-axis constraint + x_max : int, + Upper bound for the x-axis constraint + y_min : int, + Lower bound for the y-axis constraint + y_max : int + Upper bound for the y-axis constraint + df : pandas.DataFrame + A dataframe containing the features and samples + + Returns + ------- + idx_constrained : np.darray, shape [No. of constrained locations] + Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. + """ + x = df['X (m)'].to_numpy() + n_features = x.shape[0] + y = df['Y (m)'].to_numpy() + idx_constrained = [] + for i in range(n_features): + if (x[i] >= x_min and x[i] <= x_max) and (y[i] >= y_min and y[i] <= y_max): + idx_constrained.append(i) + return idx_constrained + +def box_constraints(position,lower_bound,upper_bound,): + """ + Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix. ##TODO : BETTER DEFINITION + + Parameters + ---------- + position: ##TODO: FILL + + lower_bound : ##TODO: FILL + + upper_bound : ##TODO: FILL + + Returns + ------- + idx_constrained : np.darray, shape [No. of constrained locations] ##TODO: CHECK IF CORRECT + Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. + """ + for i,xi in enumerate(position): + f1 = position[i] - lower_bound[i] + f2 = upper_bound[i] - position [i] + return +1 if (f1 and f2 > 0) else -1 + +def functional_constraints(position, func_response,func_input, free_term): + """ + Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix. ##TODO: BETTER DEFINITION + + Parameters + ---------- + position: ##TODO : FILL + + func_response : ##TODO : FILL + + func_input: ##TODO : FILL + + free_term : ##TODO : FILL + + Returns + ------- + g : ##TODO : FILL + + """ + g = func_response + func_input + free_term + return g \ No newline at end of file From c0ab2510a6fa52fd1bda3f2fae49e9241caa8be7 Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Tue, 2 Aug 2022 13:50:23 -0600 Subject: [PATCH 20/52] Separating constraint processing function from the function for dlens updation for radius based constraints --- examples/region_optimal.ipynb | 4293 +-------------------------------- 1 file changed, 79 insertions(+), 4214 deletions(-) diff --git a/examples/region_optimal.ipynb b/examples/region_optimal.ipynb index dc99f3c..64bf64f 100644 --- a/examples/region_optimal.ipynb +++ b/examples/region_optimal.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 48, + "execution_count": 358, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:04.386599Z", @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 359, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:07.391526Z", @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 360, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:09.785781Z", @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 361, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:10.835009Z", @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 362, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:11.651751Z", @@ -130,7 +130,7 @@ "output_type": "stream", "text": [ "[4032 384 4092 4039 447 493 2204 657 878 2880 1088 4087 2837 3779\n", - " 3093]\n" + " 3093 2395 581 2751 1023 2970]\n" ] } ], @@ -138,7 +138,7 @@ "#Find all sensor locations using built in QR optimizer\n", "#max_const_sensors = 230\n", "#n_const_sensors = 2\n", - "n_sensors = 15\n", + "n_sensors = 20\n", "optimizer = ps.optimizers.QR()\n", "model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors)\n", "model.fit(X)\n", @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 363, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:20.877032Z", @@ -173,7 +173,55 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 364, + "metadata": {}, + "outputs": [], + "source": [ + "def distance_constraints_indices(r,nx,ny,piv,n_sensors,j): ##Need to combine all the idx_constrained \n", + " \n", + " n_features = len(all_sensors)\n", + "\n", + " a = np.unravel_index(piv, (nx,ny))\n", + " x_cord = a[0][j-1]\n", + " y_cord = a[1][j-1]\n", + " #print(x_cord, y_cord)\n", + " constrained_sensorsx = []\n", + " constrained_sensorsy = []\n", + " for i in range(n_features):\n", + " if ((a[0][i]-x_cord)**2 + (a[1][i]-y_cord)**2) < r**2: \n", + " #print(a[0][i],a[1][i])\n", + " constrained_sensorsx.append(a[0][i])\n", + " constrained_sensorsy.append(a[1][i])\n", + " #print(constrained_sensorsx, constrained_sensorsy)\n", + " constrained_sensorsx = np.array(constrained_sensorsx)\n", + " constrained_sensorsy = np.array(constrained_sensorsy)\n", + " constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1)\n", + " constrained_sensors_tuple = np.transpose(constrained_sensors_array)\n", + " idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny))\n", + " #print(idx_constrained)\n", + " return idx_constrained" + ] + }, + { + "cell_type": "code", + "execution_count": 365, + "metadata": {}, + "outputs": [], + "source": [ + "def f_region_distance_constraints(r,nx,ny,all_sensors, dlens, piv, j,n_sensors):\n", + " if j == 0:\n", + " return dlens\n", + " else:\n", + " \n", + " idx_constrained = distance_constraints_indices(r,nx,ny,piv,n_sensors,j)\n", + " didx = np.isin(piv[j:],idx_constrained,invert=False)\n", + " dlens[didx] = 0\n", + " return dlens\n" + ] + }, + { + "cell_type": "code", + "execution_count": 366, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:22.713344Z", @@ -258,8 +306,8 @@ " r = R[j:, j:]\n", " # Norm of each column\n", " dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0))\n", - " dlens_updated = f_region_distance_constraints(self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j) #Handling constrained region sensor placement problem\n", - "\n", + " dlens_updated = f_region_distance_constraints(self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j,self.nSensors) #Handling constrained region sensor placement problem\n", + " #print(dlens_updated)\n", " # Choose pivot\n", " i_piv = np.argmax(dlens_updated)\n", " \n", @@ -292,79 +340,7 @@ }, { "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "def f_region_distance_constraints(r,nx,ny,all_sensors, dlens, piv, j):\n", - " \n", - " \n", - " n_features = len(all_sensors)\n", - " a = np.unravel_index(all_sensors, (nx,ny))\n", - " x_cord = a[0][j-1]\n", - " y_cord = a[1][j-1]\n", - " print(x_cord, y_cord)\n", - " constrained_sensorsx = []\n", - " constrained_sensorsy = []\n", - " for i in range(n_features):\n", - " if ((a[0][i]-x_cord)**2 + (a[1][i]-y_cord)**2) < r**2: \n", - " #print(a[0][i],a[1][i])\n", - " constrained_sensorsx.append(a[0][i])\n", - " constrained_sensorsy.append(a[1][i])\n", - " #print(constrained_sensorsx, constrained_sensorsy)\n", - " constrained_sensorsx = np.array(constrained_sensorsx)\n", - " constrained_sensorsy = np.array(constrained_sensorsy)\n", - " constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1)\n", - " constrained_sensors_tuple = np.transpose(constrained_sensors_array)\n", - " idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny))\n", - " print(idx_constrained)\n", - " didx = np.isin(piv[j:],idx_constrained,invert=True)\n", - " dlens[didx] = 0\n", - " return dlens\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "# def distance_constraints_indices(r,nx,ny,all_sensors,n_samples,j):\n", - "\n", - "# n_features = len(all_sensors)\n", - "# a = np.unravel_index(all_sensors, (nx,ny))\n", - "# x_cord = a[0][j]\n", - "# y_cord = a[1][j]\n", - "# #print(x_cord, y_cord)\n", - "# constrained_sensorsx = []\n", - "# constrained_sensorsy = []\n", - "# for i in range(n_features):\n", - "# if ((a[0][i]-x_cord)**2 + (a[1][i]-y_cord)**2) < r**2: \n", - "# #print(a[0][i],a[1][i])\n", - "# constrained_sensorsx.append(a[0][i])\n", - "# constrained_sensorsy.append(a[1][i])\n", - "# #print(constrained_sensorsx, constrained_sensorsy)\n", - "# constrained_sensorsx = np.array(constrained_sensorsx)\n", - "# constrained_sensorsy = np.array(constrained_sensorsy)\n", - "# constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1)\n", - "# constrained_sensors_tuple = np.transpose(constrained_sensors_array)\n", - "# idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny))\n", - "# print(idx_constrained)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "# constrained_indices = distance_constraints_indices(r,nx,ny,all_sensors,n_samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, + "execution_count": 367, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:24.092467Z", @@ -422,4125 +398,14 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 368, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:27.911973Z", "start_time": "2022-07-10T04:22:26.180620Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "57 35\n", - "[1976 1973 2235 2238 2420 2678 2100 1914 2617 2557 1978 2294 2679 2230\n", - " 2618 2164 2167 2491 2046 2489 2492 2360 2109 2166 2425 2366 1912 2228\n", - " 2419 2488 2105 2302 2298 2108 2044 2431 2553 2293 2363 2296 2111 2550\n", - " 2300 2427 2103 2552 2170 1979 2422 2490 2682 2355 2554 1916 2036 2041\n", - " 2558 2099 2169 2365 2038 2171 2429 2421 2039 2106 2037 2174 2358 2430\n", - " 2621 2549 2367 2163 2680 1913 2548 2357 2428 2683 2236 1915 2239 1980\n", - " 2483 2556 1910 2237 2423 2555 2043 2486 2295 2615 1981 2175 2234 2614\n", - " 2494 2613 2684 2045 2040 2291 2231 2303 2104 2292 2229 1975 2620 2619\n", - " 2110 2165 2301 1911 1974 2101 1977 2485 2168 2362 2172 2227 2426 2484\n", - " 2681 2299 2232 2495 2356 2487 2173 2493 2359 2364 2102 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1759 1449 1696 1382 1380 1567 1823\n", - " 1643 1702 2024 1706 1831 1766 1898 1824 1574 1891 2148 1443 1634 1631\n", - " 1764 2085 1761 1441 1568]\n", - "9 5\n", - "[ 6 329 459 12 141 269 73 334 716 200 521 133 143 261 526 332 523 653\n", - " 453 524 525 201 260 70 714 139 207 580 582 13 458 711 387 136 393 651\n", - " 325 517 715 75 263 581 390 11 515 396 76 202 74 456 392 518 10 516\n", - " 645 68 588 8 77 461 268 463 140 650 335 138 132 267 455 712 648 649\n", - " 398 5 327 326 142 206 399 452 331 583 522 451 197 527 134 587 324 264\n", - " 394 519 462 7 457 460 589 72 266 78 196 199 330 9 590 265 271 710\n", - " 131 586 323 262 204 205 333 389 195 388 198 454 397 71 652 259 135 584\n", - " 647 520 270 391 137 585 713 203 646 69 328 395]\n", - "42 63\n", - "[4074 4068 3944 4079 3877 4013 4071 3823 3884 3886 3757 3887 4004 4073\n", - " 4078 4072 3822 4069 3952 3947 3948 3885 3689 4016 4007 3754 3949 3817\n", - " 3950 3880 3946 4076 4015 3691 3758 3687 4075 4014 3751 3882 4006 3879\n", - " 4012 3692 3818 3820 3814 3756 4070 3815 4011 3878 3941 3819 3951 4009\n", - " 3821 3813 3752 4008 3755 3881 3816 3943 3876 4010 3688 3753 3888 4080\n", - " 4077 3883 3942 3940 3693 4005 3750 3945 3690]\n", - "15 63\n", - "[4047 3858 3730 3914 3919 3786 4043 3985 4051 3790 3923 3983 3980 3853\n", - " 4041 3851 3979 3922 3726 3913 3728 3987 3924 3982 3861 3859 3854 3788\n", - " 3978 4052 3915 4042 3986 3663 3920 3921 3856 3989 3796 3977 3791 3855\n", - " 4048 3925 3795 3731 3725 3666 4053 3793 3852 4044 3664 3916 3789 4045\n", - " 3665 4050 3850 3727 3849 3792 3981 3917 4049 3729 3860 3662 3787 3988\n", - " 3857 3723 3660 3984 3661 3918 4046 3794 3724]\n", - "46 26\n", - "[1388 1392 1710 2027 1840 1907 1704 2093 1713 1836 1519 1642 1524 1581\n", - " 1966 1389 1521 2032 1965 1842 1651 1708 1773 1833 1394 1839 1963 1772\n", - " 1712 1969 2026 1834 1841 1588 1578 1652 1512 1579 1768 2034 1451 1771\n", - " 1648 1640 1769 1450 1327 1452 1832 1641 1326 1576 1455 1715 1328 1905\n", - " 1961 1778 1649 1456 1901 1903 1779 1454 1709 1585 1971 1522 1707 1962\n", - " 1523 1323 1837 1586 1387 1835 1584 1964 1329 1896 1770 1515 1908 2096\n", - " 1393 2030 1390 1899 1902 1520 2095 1714 1776 1904 1577 1970 1516 1457\n", - " 1900 1650 1324 1968 1325 1391 1644 1838 2031 1705 2033 1587 1777 1716\n", - " 1967 2092 1386 1897 1514 1453 1459 1774 1513 1780 1449 1517 1645 1844\n", - " 1646 1643 2094 1706 1775 1898 2097 2091 2029 1583 1647 1582 1843 1711\n", - " 1458 1580 1518 2028 1906]\n", - "43 31\n", - "[1710 2027 1840 1704 2093 2089 2159 2022 1836 1895 1642 1966 2161 2284\n", - " 2214 2408 2032 2222 1965 2349 1708 2088 1773 1833 2344 2413 2414 2411\n", - " 2155 1839 2348 2087 2345 1963 1772 2152 1969 2026 1834 1841 2090 1768\n", - " 1829 2153 1771 1640 1957 1769 2149 1832 1641 1894 2160 1905 2282 1961\n", - " 2346 2409 1901 1903 1709 2218 1707 1962 2225 2224 2343 1837 2150 2278\n", - " 1835 1964 1896 1770 2096 2021 2030 1899 1902 2095 2288 2154 1776 1904\n", - " 1959 2219 2025 1767 2350 2158 1900 2220 2151 2279 2217 1968 2286 1703\n", - " 2351 1644 1838 2031 1705 1960 2086 2033 2347 2156 2215 1967 2216 2023\n", - " 2092 1897 1893 1958 1774 1830 2287 2283 2280 1645 1646 1643 2412 2094\n", - " 2410 2157 2024 1706 1831 1775 1766 1898 2285 2097 2213 2091 2029 2223\n", - " 2281 1711 2085 2221 2028]\n", - "38 0\n", - "[ 38 32 107 168 35 163 41 96 33 224 360 170 421 164 167 234 226 105\n", - " 98 420 423 99 230 97 103 228 165 296 36 232 102 100 425 108 225 362\n", - " 101 424 290 297 289 359 40 356 172 104 294 43 361 39 162 354 355 357\n", - " 34 231 227 358 298 292 169 419 233 229 299 291 44 42 295 171 293 166\n", - " 236 422 106 37 161 160 235]\n", - "31 26\n", - "[1826 1765 1695 1570 1888 1508 1892 1502 1699 1697 1504 2018 1692 1636\n", - " 1442 1311 2015 1885 1760 1376 1436 1509 1505 1503 1572 1763 1952 2011\n", - " 1819 1883 2017 1818 2077 1829 2078 1566 1371 1500 1310 1954 1949 1822\n", - " 2079 1571 1955 1374 2014 1437 1698 1758 1561 1828 1757 1633 1444 1693\n", - " 1506 1754 1562 1499 1313 1890 1379 2080 1635 2012 1628 1950 1948 1821\n", - " 1378 1627 2019 1626 1886 1956 1373 1817 1314 1947 1497 1827 1825 1625\n", - " 1694 1630 1887 2081 1501 1762 1882 1632 1375 1755 1440 1700 1951 1946\n", - " 1498 1701 1889 1569 1820 1893 1573 1438 1507 1434 1753 2016 2082 1953\n", - " 1689 1565 1564 1637 1435 1759 1439 1884 1696 1567 1312 1823 1629 1691\n", - " 1824 2076 1891 1443 1634 1631 1764 1308 1690 1881 1563 1761 1441 1309\n", - " 1372 2013 1568 1377 1756]\n", - "18 36\n", - "[2322 2000 2449 2191 2454 2195 2317 2444 2705 2326 2129 2319 2062 2067\n", - " 2456 2004 2516 2509 2131 2200 2445 2263 2325 2136 2257 2380 2578 2196\n", - " 2006 2318 2327 2453 2188 2387 2254 2582 2255 2391 2125 2451 2583 2709\n", - " 2447 2517 2064 2198 2134 2381 2639 2455 2510 2127 2193 2126 2005 1937\n", - " 2640 2384 2262 2002 2703 2070 2518 2065 2258 2324 2577 2646 2708 2128\n", - " 1939 2385 1941 2390 2579 2133 2515 2003 2580 2383 2192 2253 2643 2264\n", - " 2706 2514 2388 2001 2386 2511 1935 2328 2707 2513 2576 2392 2645 2644\n", - " 2450 2194 2132 1936 2135 2066 2259 2508 1999 2642 2061 1938 2448 2199\n", - " 2512 2389 2320 2641 2321 2575 2071 2581 2197 2574 2316 2452 2382 2252\n", - " 2446 2068 2704 2260 2063 2189 2069 2130 2638 2323 2520 2256 2124 2190\n", - " 2261 1998 1940 2573 2519]\n", - "18 9\n", - "[594 848 210 591 785 401 720 913 845 467 340 334 910 716 850 659 976 526\n", - " 914 404 472 596 724 978 917 402 339 655 213 653 852 524 525 718 471 719\n", - " 535 855 207 341 781 792 780 595 721 664 273 337 784 912 600 847 791 532\n", - " 208 211 212 468 789 662 723 396 849 343 529 722 782 209 846 338 534 599\n", - " 407 790 654 275 469 588 918 726 728 277 342 405 461 463 977 853 783 658\n", - " 725 597 335 464 276 979 398 717 536 854 911 980 400 399 533 465 593 787\n", - " 527 530 336 975 661 660 462 460 589 851 786 657 981 274 788 592 590 271\n", - " 915 403 531 470 916 278 333 408 406 663 397 652 598 270 272 727 656 528\n", - " 466]\n", - "63 63\n", - "[3903 4095 3711 3839 4026 3899 3772 3967 3837 3708 4030 3836 3965 4027\n", - " 3962 3775 3709 4091 3897 3834 4092 4094 3902 4029 3901 3838 4028 4093\n", - " 3961 4090 3771 4025 4031 3966 3900 3898 4089 3773 3835 3774 3963 3964\n", - " 3710]\n", - "17 28\n", - "[1809 1682 1741 2000 1553 1870 2191 1804 2195 1555 1679 1621 1875 1749\n", - " 1997 1549 2129 1489 2062 2067 2004 2131 1806 1808 1683 2196 1680 1932\n", - " 2006 1612 1873 1739 1617 1878 1687 2125 1488 1867 2064 1869 1548 1492\n", - " 1423 1933 1554 1490 2127 2193 1807 1868 2126 1931 2005 1937 1943 1619\n", - " 1675 1427 1622 1811 1813 1491 2002 2070 1676 1742 1558 2065 1803 1550\n", - " 2128 1877 1939 1426 1485 1942 1941 2133 2003 1677 1995 2192 1425 1613\n", - " 1493 2001 1678 2007 1746 1935 1812 1815 1611 1623 1810 2194 1616 1620\n", - " 1879 1934 1618 2132 1615 1557 1936 1487 2066 1999 1814 1744 2061 1938\n", - " 1681 1551 1748 1805 1871 1422 1556 1684 1486 1872 1996 2060 1686 1874\n", - " 1876 1424 1428 1750 1743 1747 2068 1751 2063 1685 2069 2130 2190 1740\n", - " 1614 1998 1940 1745 1552]\n", - "48 28\n", - "[1710 2027 1840 1907 2093 1973 1713 2159 1836 1519 1642 1524 1581 1966\n", - " 2161 1782 1521 2032 2222 1965 1842 1651 1708 1773 2100 1839 1963 1772\n", - " 2164 1712 1909 1969 2026 1834 1841 1588 1652 1579 2034 1771 1648 2160\n", - " 2098 1455 1715 1905 1778 1649 1456 1901 1717 1903 1779 1454 1709 1585\n", - " 1971 1522 1707 1962 2225 1523 2224 1837 2036 1586 1846 1835 1584 2099\n", - " 1964 1770 1908 2096 2038 2030 1899 1902 1520 2095 2162 1714 2037 1776\n", - " 1904 1970 1516 1457 2158 1900 1650 2163 1968 1845 1972 1644 1838 2031\n", - " 1910 2033 1587 1718 1777 1716 2156 1967 2035 2092 1781 1453 1459 2226\n", - " 1774 1780 1517 1645 1844 1974 1646 2101 1643 1653 2094 2157 1654 2227\n", - " 1706 1775 1898 2097 1589 2091 2029 1583 2223 1647 1582 1843 1711 1458\n", - " 1580 1518 2221 2028 1906]\n", - "63 5\n", - "[ 63 511 251 383 441 191 638 569 313 59 255 316 444 125 507 380 447 767\n", - " 319 186 575 253 699 636 189 315 634 446 127 701 445 702 639 126 382 252\n", - " 378 314 249 122 190 764 187 572 379 571 377 254 442 443 765 318 510 766\n", - " 637 508 188 185 506 250 574 317 570 60 62 509 61 505 700 635 573 124\n", - " 703 381 123]\n", - "63 12\n", - "[ 511 831 638 1151 444 1023 507 447 767 575 699 826 636 958\n", - " 829 1018 1147 830 956 634 446 954 1085 894 701 445 1021 702\n", - " 639 827 761 1148 764 828 572 571 633 893 765 1019 510 955\n", - " 766 1020 637 892 1213 508 890 1086 574 570 698 895 1212 1082\n", - " 509 1017 957 762 1022 1215 763 700 697 959 953 635 573 703\n", - " 1087 1214 891 1084 1150 825 889 1083 1149]\n", - "51 29\n", - "[1710 1840 1907 2093 1976 1973 1713 2159 1524 1966 2161 1782 1521 2032\n", - " 1965 1842 1651 1773 1785 1525 2100 1839 2294 2230 2164 2167 1712 1849\n", - " 1909 1848 1969 1841 1588 1652 2034 2166 1648 1912 2228 2105 2160 2098\n", - " 2293 1715 1905 1778 1649 2103 1901 1717 1903 1779 1709 1585 1971 1522\n", - " 2289 2225 1523 2224 1837 1719 2036 2041 1586 1846 1584 2099 1847 1908\n", - " 2096 2038 2030 1902 1520 2039 2095 2288 2162 1714 2037 1776 1904 1970\n", - " 2158 1650 2163 1783 1913 1968 1845 1972 1838 2031 2290 1910 2033 1587\n", - " 1718 1777 1716 1591 1967 1784 2035 1781 2040 2291 2231 2226 1774 2104\n", - " 1780 1655 1720 2292 2229 1975 1721 2165 1844 1911 1974 1646 2101 1977\n", - " 2168 1653 2094 1590 1654 2227 1775 1656 2097 1589 2029 1526 1583 2223\n", - " 1647 1843 1711 2102 1906]\n", - "54 0\n", - "[251 54 441 245 313 59 184 308 114 376 370 438 177 186 181 51 57 247\n", - " 315 435 182 56 252 117 378 179 314 249 240 121 122 116 440 439 187 113\n", - " 241 375 50 118 120 306 377 242 52 373 307 310 183 53 176 188 112 185\n", - " 49 180 436 305 250 437 309 60 374 372 119 371 246 248 55 312 48 244\n", - " 124 58 115 243 178 311 123]\n", - "18 60\n", - "[4047 3858 4055 3730 3990 3919 3538 3985 3599 4051 3799 3790 3923 3983\n", - " 3668 3980 3853 3541 3922 3605 3534 3726 3671 3728 3987 3924 3926 3982\n", - " 3732 3861 3859 3476 3854 3735 3788 3607 4052 3598 3472 3986 3604 3663\n", - " 3920 3536 3863 3921 3856 3989 3798 3537 3672 3796 3927 3791 3928 3855\n", - " 4048 3925 3795 3797 3669 3542 3603 3731 3600 3725 3736 3666 3471 3535\n", - " 4053 3474 3793 4056 3852 4044 3664 3601 3916 3789 3606 4045 3734 3665\n", - " 3539 4050 3540 3597 3667 3862 3670 3727 3477 3602 3792 3981 3473 3917\n", - " 4049 3729 3860 3662 3733 3991 3988 3857 3660 3984 3661 3800 3918 4046\n", - " 3475 4054 3992 3864 3794 3724]\n", - "16 13\n", - "[ 594 848 591 785 720 913 845 467 1104 1232 1039 1035 910 716\n", - " 850 659 976 842 526 1041 914 1170 1167 596 779 724 978 917\n", - " 655 1099 653 852 974 907 524 525 718 719 906 714 1171 781\n", - " 1105 780 595 721 971 982 1230 784 778 912 1165 847 651 532\n", - " 715 970 789 662 723 1046 1103 849 1038 529 722 782 1231 846\n", - " 908 1106 1042 1040 790 654 1166 843 588 918 726 1107 1172 1037\n", - " 461 463 977 853 783 658 725 650 597 464 979 717 854 911\n", - " 1036 980 1233 844 465 1109 1101 593 787 527 530 587 975 661\n", - " 972 660 1100 462 1044 589 851 786 657 1102 973 981 1034 1229\n", - " 1108 788 592 909 590 915 1234 531 916 652 1169 1164 1043 1168\n", - " 656 528 1045 1235 466]\n", - "56 59\n", - "[3832 4083 3827 3959 3705 4086 3577 4026 3899 3772 3644 3699 3766 3511\n", - " 3956 3837 3708 3769 3954 3702 3895 4030 3826 3574 3836 3965 4027 3636\n", - " 3962 3709 4091 3897 3834 3510 4092 3762 3445 3581 4019 3902 4029 3830\n", - " 3572 3901 3890 3838 4028 4018 3698 4093 3579 3513 3828 3960 3700 3763\n", - " 3891 3643 3706 3765 4085 3961 4090 3578 3448 3771 3893 4084 3958 4021\n", - " 4022 4025 3642 3646 3575 4024 3641 3707 3701 3639 3514 3451 3764 3894\n", - " 3966 3450 3447 3896 3900 3515 3957 3898 4088 3768 3637 4089 3773 3640\n", - " 3449 3573 3635 3509 3645 3516 3831 3512 3767 3835 3833 3571 4020 3774\n", - " 3634 3638 3963 3508 3964 3704 3710 3829 4087 3703 3446 3576 3892 3580\n", - " 3770 3955 4023]\n", - "18 26\n", - "[1809 1682 1361 1741 2000 1553 1870 1804 1555 1679 1621 1875 1358 1749\n", - " 1549 1489 2067 2004 1816 1806 1808 1560 1295 1683 1680 1363 2006 1612\n", - " 1873 1617 1878 1687 1488 1362 2064 1869 1548 1492 1423 1933 1494 1554\n", - " 1421 1490 1431 1807 1868 2005 1937 1943 1619 1484 1299 1427 1622 1811\n", - " 1813 1491 2002 1880 1676 1742 1558 2065 1550 1877 1939 1426 1485 1942\n", - " 1941 2003 1677 1296 1495 1425 1365 1613 1493 2001 1678 1746 1935 1812\n", - " 1297 1815 1623 1810 1616 1620 1879 1559 1934 1430 1618 1496 1615 1359\n", - " 1557 1936 1487 2066 1999 1360 1300 1814 1744 1938 1681 1551 1748 1805\n", - " 1871 1422 1556 1684 1486 1752 1366 1872 1624 1298 1686 1874 1876 1424\n", - " 1428 1301 1750 1743 1747 2068 1364 1751 2063 1685 1429 1688 2069 1740\n", - " 1614 1998 1940 1745 1552]\n", - "46 19\n", - "[1388 1392 1262 1330 1130 1136 1132 1519 1581 879 1389 1521 1203 1261\n", - " 1322 940 1002 878 1004 1065 1394 1003 1193 1064 1198 1578 1258 1072\n", - " 1579 1200 1140 1451 1648 1135 1129 1001 876 1450 1067 1139 1327 1452\n", - " 1259 1326 1331 1455 1328 1005 1009 1071 1460 1649 1456 1202 1268 1454\n", - " 1585 1522 1523 1321 1323 1011 1586 1387 1584 877 1329 1515 1393 1390\n", - " 1520 1138 1075 1256 1066 1008 1448 1320 1396 941 1516 1457 1395 1010\n", - " 1324 1204 1128 1385 1325 1391 1644 1264 946 1194 1197 1076 1195 1073\n", - " 1386 1134 1514 1453 1459 881 1332 1192 1196 1513 1265 943 875 1384\n", - " 1266 1449 1199 1517 1645 1646 1007 945 1643 1267 1137 1074 1068 1260\n", - " 1133 938 880 944 1006 1201 942 1131 1583 1257 1070 1263 1647 939\n", - " 1582 1458 1580 1518 1069]\n", - "0 59\n", - "[3776 4032 3648 3909 4036 4037 3392 3714 3524 3906 3587 3460 3456 3843\n", - " 3520 4034 3586 3584 3717 3393 3394 3905 3712 3844 3970 3651 3716 3649\n", - " 3779 3590 3842 3845 3458 4035 4033 3585 3459 3718 3522 3523 3973 3654\n", - " 3781 3777 3910 3778 3972 3525 3907 3588 3521 3780 3904 3395 3589 3846\n", - " 3968 3974 3457 3841 3969 3713 3971 3715 3908 3653 3782 3652 3650 3840]\n", - "36 32\n", - "[1826 1765 2084 2404 2089 2210 1888 2406 1892 2402 2022 1699 1895 1697\n", - " 2018 2272 2214 2408 2015 2276 1760 2270 2088 1833 2344 2468 2342 1763\n", - " 2087 1952 2345 2147 2152 2026 2017 2090 1768 1829 2153 2078 1957 1954\n", - " 2149 1832 2020 2079 2407 2338 1894 1955 2014 2282 1961 2466 1698 1828\n", - " 2218 2206 2277 1962 2337 2335 1890 2271 2343 2146 2083 2150 2080 2278\n", - " 1896 2021 1950 2274 2154 2019 2339 1886 1956 1959 2025 1767 2211 2467\n", - " 2151 2209 2273 2279 2217 1827 1825 1887 2207 2081 2465 1703 1762 2471\n", - " 2405 1960 2086 2403 2144 1700 1951 2215 2400 2275 2216 2023 2212 1701\n", - " 1889 1897 1893 2016 1958 2082 1953 1830 2336 2145 2208 2340 2280 1823\n", - " 2469 2143 1702 2024 1831 2401 1766 1898 1824 1891 2213 2148 1764 2281\n", - " 2085 1761 2341 2142 2470]\n", - "23 63\n", - "[3858 4055 3990 4061 3865 3985 3996 4051 3799 3923 3668 3922 3671 3987\n", - " 3924 3926 3732 3993 3861 3859 3801 4057 3735 3933 4052 3738 4058 3866\n", - " 3802 3986 3995 4060 3863 3869 3921 3989 3798 3739 3672 3796 3927 3867\n", - " 3928 3929 3674 3925 3795 3868 3797 3669 3737 3731 3736 4053 4056 3803\n", - " 3734 3930 3994 4050 3997 3932 3862 3673 3670 4059 4049 3860 3931 3733\n", - " 3991 3988 3857 3800 3804 4054 3992 3864 3794]\n", - "1 3\n", - "[ 6 193 0 576 320 133 261 453 260 70 580 386 128 387 129 258 4 325\n", - " 517 257 2 449 448 263 390 515 577 1 513 516 130 68 66 256 321 65\n", - " 132 194 5 327 326 192 452 451 197 134 324 322 7 578 512 196 199 131\n", - " 323 262 389 195 388 198 454 3 71 450 259 514 135 64 391 67 69 384\n", - " 385 579]\n", - "40 26\n", - "[1826 1388 1765 1710 2027 1704 1570 2089 1508 1892 2022 1836 1699 1895\n", - " 1642 1581 1383 1638 1636 1965 1445 1708 1322 1575 2088 1773 1833 1509\n", - " 1572 1763 2087 1511 1963 1772 2026 1834 1578 2090 1512 1579 1768 1829\n", - " 1317 1451 1771 1640 1957 1769 1450 1452 1832 2020 1641 1894 1576 1571\n", - " 1955 1961 1698 1901 1828 1709 1319 1707 1962 1444 1506 1321 1323 1447\n", - " 1890 1837 1635 1387 1835 1964 1896 1770 1515 1510 2021 1899 1902 1446\n", - " 1577 1448 1956 1320 1959 2025 1767 1516 1900 1385 1827 1703 1644 1762\n", - " 1838 1705 1960 2086 1700 2023 1701 1386 1381 1897 1893 1514 1573 1453\n", - " 1507 1958 1774 1513 1830 1639 1384 1637 1449 1517 1645 1646 1382 1380\n", - " 1643 1318 1702 2024 1706 1831 1766 1898 1574 1891 1443 2091 1634 1764\n", - " 1582 2085 1580 1518 2028]\n", - "27 0\n", - "[ 27 32 153 21 25 149 222 281 89 94 156 350 96 33 224 213 409 214\n", - " 349 285 30 28 345 158 152 97 348 86 151 85 343 414 92 154 287 217\n", - " 347 91 159 150 225 31 279 221 29 220 284 344 411 223 22 90 95 286\n", - " 413 87 288 157 88 215 155 280 282 346 283 278 408 216 26 23 24 93\n", - " 219 351 161 218 160 412 410]\n", - "63 57\n", - "[3903 3583 4095 3711 3387 3839 3391 3705 3577 3517 3899 3772 3967 3644\n", - " 3837 3708 3769 4030 3836 3390 3965 4027 3962 3455 3775 3709 3897 3834\n", - " 4092 3389 3581 4094 3902 4029 3901 3838 4028 4093 3579 3513 3324 3326\n", - " 3643 3706 3578 3771 3642 4031 3646 3641 3707 3452 3514 3451 3966 3450\n", - " 3900 3515 3898 3454 3647 3773 3518 3453 3519 3645 3516 3582 3835 3327\n", - " 3833 3774 3963 3964 3710 3388 3580 3770 3325]\n", - "17 52\n", - "[3345 3275 3217 3730 3024 3086 3538 3215 3599 3342 3408 3025 2959 3148\n", - " 3027 3023 3668 3280 2960 3089 3154 3541 2963 3605 3534 3151 3084 3091\n", - " 3726 3728 3411 3732 3094 3470 3159 3476 3286 3287 3412 3414 3346 3598\n", - " 2964 3472 3155 3351 3415 3604 3663 3158 3536 3479 3223 3090 3413 3085\n", - " 3344 3404 3537 3218 3284 3087 3026 3153 3277 3410 3219 3531 3285 3468\n", - " 3341 3343 3467 3669 3542 3603 3731 3600 3221 3666 3532 3471 3028 3535\n", - " 3282 3406 3409 3029 3474 3021 3157 3664 3601 3149 3339 3606 3283 3533\n", - " 3405 3220 3665 3156 3539 3152 3347 3540 3597 3667 3092 3350 3276 3543\n", - " 3093 3727 3469 3477 3602 3348 3473 3222 2962 3150 3596 3729 3212 3278\n", - " 3662 3216 3478 3214 3349 3088 3213 3340 3403 3281 3661 2961 3211 3407\n", - " 3475 3279 2958 3022 3147]\n", - "59 52\n", - "[3583 3711 3387 3391 3071 3705 3128 3577 3517 3772 3644 3320 3263 3511\n", - " 3708 3769 3574 3390 3004 3189 3258 3191 3455 3129 3003 3196 3709 3384\n", - " 3262 3510 3389 3000 3445 3581 3064 3386 3130 3253 3260 3383 3382 3579\n", - " 3513 3324 3261 3326 3002 3643 3063 3706 3192 3317 3126 3578 3448 3771\n", - " 3257 3642 3646 3195 3256 3575 3006 3641 3707 3321 3193 3452 3639 3514\n", - " 3451 3131 3381 3319 3450 3447 3070 3067 3194 3197 3005 3322 3515 3318\n", - " 3132 3001 3385 3454 3647 3768 3066 3773 3640 3518 3449 3573 3453 3509\n", - " 3519 3198 3199 3645 3516 3133 3512 3582 3134 3327 3135 3774 3259 3638\n", - " 3704 3710 3254 3703 3388 3255 3446 3190 3065 3068 3576 3069 3580 3770\n", - " 3325 3127 3323]\n", - "57 6\n", - "[511 251 383 54 441 245 638 569 313 59 255 316 184 308 444 500 125 376\n", - " 507 380 447 438 631 319 757 186 575 181 57 253 699 247 826 636 189 315\n", - " 563 564 435 634 446 627 630 701 445 702 504 501 639 182 56 827 567 761\n", - " 694 382 252 117 378 314 249 121 122 190 440 566 439 764 187 828 760 632\n", - " 503 499 572 375 379 118 571 120 377 254 824 442 443 695 633 765 373 502\n", - " 307 629 318 510 696 310 183 637 508 188 185 565 180 436 506 692 250 574\n", - " 317 570 698 437 309 60 822 374 372 693 119 371 246 509 762 763 505 700\n", - " 248 697 758 55 312 635 244 573 124 823 381 58 628 759 243 825 311 568\n", - " 123]\n", - "18 3\n", - "[ 17 594 210 591 83 12 141 401 21 269 467 81 15 340 149 334 145 143\n", - " 526 404 332 596 402 339 213 471 82 144 214 207 341 595 13 273 337 80\n", - " 14 532 147 208 211 212 468 152 86 151 85 146 396 343 529 76 209 338\n", - " 534 407 150 275 469 79 277 342 405 77 461 268 463 279 140 344 20 16\n", - " 597 335 464 276 398 142 22 400 148 206 399 18 533 465 87 593 527 530\n", - " 336 88 215 462 280 274 78 592 271 403 531 470 278 204 205 333 408 216\n", - " 406 23 19 397 24 270 272 84 528 466]\n", - "63 2\n", - "[ 63 511 251 383 191 313 59 255 316 444 125 507 380 447 319 186 575 57\n", - " 253 189 315 446 127 445 126 382 252 378 314 249 121 122 190 187 572 379\n", - " 377 254 442 443 318 510 508 188 185 250 574 317 60 62 509 61 573 124\n", - " 381 58 123]\n", - "45 32\n", - "[1710 2027 1840 1907 2093 2089 2159 1836 2354 1895 2475 1966 2416 2161\n", - " 2284 2032 2222 1965 1842 2349 1708 2088 1773 1833 2344 2413 2414 2411\n", - " 2155 1839 2348 2087 2345 1963 1772 2474 1712 2152 1969 2026 1834 1841\n", - " 2090 2153 2034 1771 1769 1832 2352 2480 2160 2098 1905 2282 1961 2346\n", - " 2409 1901 1903 1709 2218 1971 2289 1707 1962 2225 2224 1837 2478 1835\n", - " 2417 2099 1964 1896 1770 2096 2030 1899 1902 2095 2288 2162 2154 1776\n", - " 1904 1959 2219 2025 2350 1970 2158 1900 2220 2477 2151 2163 2279 2217\n", - " 1968 2286 2351 1838 2031 2479 2290 2415 1960 2033 1777 2347 2156 2353\n", - " 2215 1967 2216 2023 2035 2092 1897 2291 2226 1774 2287 2283 2280 2412\n", - " 2094 2410 2157 2227 2024 1706 1775 1898 2285 2097 2091 2029 2223 2281\n", - " 1711 2476 2221 2028 1906]\n", - "15 9\n", - "[594 848 210 591 459 785 401 720 913 845 269 467 340 334 910 777 716 850\n", - " 521 659 976 842 526 914 404 332 523 596 779 724 978 402 339 655 653 852\n", - " 974 907 524 525 718 719 714 207 781 780 595 458 721 273 337 784 778 912\n", - " 393 847 651 532 715 208 468 789 723 396 849 529 722 782 209 846 338 908\n", - " 654 843 275 469 588 405 461 268 463 977 783 658 725 650 597 335 464 267\n", - " 649 398 717 911 400 206 399 533 331 844 465 522 593 787 527 530 336 587\n", - " 975 661 972 394 660 462 457 460 589 851 786 657 973 274 788 592 909 330\n", - " 590 271 915 403 586 531 204 205 333 397 652 270 272 656 585 528 713 395\n", - " 466]\n", - "11 51\n", - "[2957 3345 3275 3217 3145 3659 2891 3024 3526 3086 3215 3599 3342 3408\n", - " 2959 3148 3529 3023 3280 3089 3464 2955 3398 3527 3209 3206 3402 3534\n", - " 3151 3084 3205 3470 3018 3146 3274 3333 3598 3472 3528 3462 3399 3270\n", - " 3077 2951 3536 3273 3082 3017 3085 3344 3404 3080 3400 3656 2893 3087\n", - " 3141 3337 3463 3153 3277 3207 2889 3531 3079 3468 3595 3210 3341 3272\n", - " 3343 3271 3336 3467 3014 3532 3471 3083 3535 3406 3409 3397 3021 3149\n", - " 2956 3339 3461 3533 3143 3142 3405 3078 3338 3593 3152 3016 3591 3597\n", - " 3144 2888 3594 3657 3658 3401 2953 3276 3469 3015 3473 3150 3596 3212\n", - " 2894 3278 3662 3216 3214 3592 3334 3081 2890 3019 3208 3088 3213 3340\n", - " 3403 2952 3281 3530 3660 3466 2892 3661 3465 3269 3211 3407 2954 3335\n", - " 3279 3020 2958 3022 3147]\n", - "53 3\n", - "[251 54 441 245 239 569 313 59 184 308 500 114 376 370 438 631 177 186\n", - " 181 51 57 247 47 369 315 563 564 435 175 431 627 630 498 504 501 182\n", - " 56 567 111 304 117 378 179 314 249 240 121 122 116 440 368 566 439 187\n", - " 113 241 632 503 499 375 50 379 118 120 306 377 242 52 442 497 443 373\n", - " 502 303 307 432 626 629 562 310 183 53 176 112 185 49 565 180 436 506\n", - " 305 250 437 434 309 374 372 119 371 246 561 505 367 433 248 55 312 48\n", - " 244 496 58 628 115 243 178 311 568 123]\n", - "56 30\n", - "[1907 1976 1973 2235 1782 1842 1785 2100 1789 1914 1978 2294 2230 2164\n", - " 1854 2167 1849 1909 1848 2046 1652 2360 2034 2109 2166 1912 2228 2105\n", - " 2298 2108 2044 2098 2293 1715 2363 2296 2300 1778 2103 1717 1779 2170\n", - " 1658 1979 1918 1971 1594 1592 1595 1916 1719 2036 2041 1846 2099 2169\n", - " 1851 1847 1908 2038 2171 1660 1724 2039 2162 2106 2037 1723 2174 2358\n", - " 1970 1982 1593 1657 2163 1783 1853 1913 1845 2357 1972 2236 1915 1980\n", - " 1910 2237 1718 1788 2043 1716 1591 2295 1725 1784 1981 2035 2234 1781\n", - " 2045 2040 1786 2231 2104 1780 1655 1720 1850 2292 2229 1975 1721 1787\n", - " 2110 2165 1844 1911 1974 2101 1977 2168 2362 2172 1653 1790 1852 1590\n", - " 1654 2227 2299 2232 1656 2173 1589 1722 1659 2359 1843 2102 1917 2361\n", - " 2107 2233 2042 1906 2297]\n", - "36 63\n", - "[4074 4068 3944 4065 4001 3877 4071 4066 4064 3937 3874 3810 4004 4073\n", - " 4072 4069 4062 3747 3748 4007 3745 3872 3871 3817 3880 3946 3749 3687\n", - " 3999 3684 3682 3751 4002 3882 4006 3879 3744 4067 3875 3746 4000 3809\n", - " 3814 3811 3812 3685 3873 4070 3815 3934 3878 3941 4009 3813 3683 3935\n", - " 3938 3998 4003 3752 4008 3881 3808 3807 3816 3936 4063 3943 3876 4010\n", - " 3870 3942 3939 3940 3686 4005 3750 3945 3681]\n", - "33 39\n", - "[2529 2404 2210 2406 2402 2721 2398 2661 2847 2589 2532 2272 2592 2461\n", - " 2659 2276 2780 2464 2270 2591 2468 2593 2333 2342 2654 2147 2723 2596\n", - " 2783 2535 2530 2716 2526 2658 2848 2533 2407 2338 2850 2331 2662 2462\n", - " 2715 2466 2911 2534 2268 2788 2206 2277 2594 2337 2722 2335 2524 2271\n", - " 2343 2652 2146 2269 2278 2460 2598 2789 2397 2274 2785 2339 2913 2597\n", - " 2663 2211 2784 2467 2209 2273 2590 2916 2588 2463 2207 2599 2587 2656\n", - " 2465 2334 2471 2787 2405 2403 2849 2144 2717 2523 2782 2915 2726 2400\n", - " 2275 2395 2396 2660 2786 2212 2781 2912 2845 2719 2595 2657 2527 2724\n", - " 2851 2336 2145 2914 2208 2727 2340 2653 2718 2469 2910 2143 2531 2725\n", - " 2459 2852 2528 2790 2401 2853 2205 2332 2213 2148 2651 2399 2525 2720\n", - " 2846 2655 2341 2142 2470]\n", - "53 30\n", - "[1840 1907 1976 1973 1713 2159 2354 2161 1782 2032 1842 1651 1785 2100\n", - " 1914 1978 1839 2294 2230 2164 2167 1712 1849 1909 1848 1969 1841 1588\n", - " 1652 2360 2034 2166 1912 2228 2105 2160 2098 2293 1715 2296 1905 1778\n", - " 1649 2103 1717 1903 1779 2170 1979 1971 2289 2225 2355 1592 2224 1719\n", - " 2036 2041 1586 1846 2099 2169 1851 1847 1908 2096 2038 2171 2039 2095\n", - " 2162 1714 2106 2037 1776 2358 1904 1970 1657 1650 2163 1783 1913 1968\n", - " 1845 2357 1972 2031 1915 2290 1910 2033 1587 1718 1777 2043 1716 1591\n", - " 2295 1967 1784 2035 2234 1781 2040 2291 1786 2231 2226 2104 1780 1655\n", - " 1720 1850 2292 2229 1975 1721 1787 2165 1844 1911 1974 2101 1977 2168\n", - " 1653 1590 1654 2227 1775 2232 1656 2356 2097 1589 1722 2359 1843 2102\n", - " 2107 2233 2042 1906 2297]\n", - "36 37\n", - "[2084 2529 2404 2210 2406 2402 2721 2022 2398 2661 2532 2018 2272 2592\n", - " 2214 2408 2659 2276 2464 2536 2270 2088 2591 2344 2468 2593 2342 2087\n", - " 2345 2602 2147 2723 2596 2474 2535 2538 2152 2530 2017 2153 2526 2658\n", - " 2149 2020 2533 2407 2473 2338 2662 2600 2462 2601 2282 2466 2346 2409\n", - " 2791 2534 2218 2788 2206 2277 2594 2337 2722 2335 2271 2343 2146 2083\n", - " 2150 2080 2278 2598 2472 2021 2789 2274 2019 2785 2339 2597 2663 2211\n", - " 2467 2151 2209 2273 2590 2279 2217 2664 2463 2207 2081 2599 2656 2465\n", - " 2334 2471 2787 2405 2086 2403 2144 2726 2215 2400 2275 2216 2660 2023\n", - " 2786 2212 2595 2657 2527 2724 2082 2336 2145 2208 2727 2340 2280 2728\n", - " 2469 2143 2531 2725 2410 2528 2790 2537 2401 2665 2213 2148 2399 2720\n", - " 2281 2085 2655 2341 2470]\n", - "17 50\n", - "[2957 3345 3275 3217 2832 3024 3086 3538 3215 3599 3342 3408 3025 2959\n", - " 3148 3027 3023 2897 3280 2960 3089 3154 3541 2963 3534 3151 3084 3091\n", - " 2896 3411 3094 3470 3159 3476 3030 2966 3286 3287 3412 3414 3346 3598\n", - " 2964 3472 2900 3155 2835 3351 3415 2898 3604 3158 3536 3223 3090 3413\n", - " 3085 3344 3404 3537 3218 3284 2893 3031 3087 3026 3153 3277 3410 3219\n", - " 2833 2830 3285 2834 3468 3341 3343 3603 3600 3221 3471 3083 3028 3535\n", - " 3282 3406 3409 3029 3474 3021 3157 3601 3149 2956 3339 3283 3533 3405\n", - " 3220 3156 3539 3095 3152 3347 3540 2901 2895 3092 3350 3276 2965 3093\n", - " 3469 3477 2899 3602 3348 3473 3222 2831 2962 3150 3212 2894 3278 3216\n", - " 3478 3214 3019 3349 3088 3213 3340 3403 3281 2836 2961 3211 3407 3475\n", - " 3279 3020 2958 3022 3147]\n", - "50 20\n", - "[1388 1392 1262 1330 1713 1136 1132 1519 1524 1581 1078 1389 1521 1399\n", - " 1203 1651 947 1461 1261 1525 1394 1207 1712 1588 1198 1072 1652 1200\n", - " 1140 1648 1135 1269 1139 1327 1452 1326 1144 1331 1336 1335 1455 1715\n", - " 1328 1009 1071 1460 1649 1456 1014 1717 1202 1268 1454 1585 949 1522\n", - " 1143 1523 1011 1334 1586 1584 1329 1205 1463 1393 1142 1390 1520 1138\n", - " 1714 1075 1008 1396 1527 1271 1141 1516 1457 1395 1010 1650 1324 1204\n", - " 1325 1391 1270 1464 948 1264 946 1197 1587 1076 1716 1591 1073 1013\n", - " 1077 1134 1398 1453 1459 1332 1196 1265 943 1272 1266 1397 1528 1199\n", - " 1517 1646 1007 1400 945 1267 1079 1653 1137 1074 1012 1590 1260 1654\n", - " 1462 1133 944 1006 1589 1201 1526 1583 1070 1263 1647 1582 1206 1711\n", - " 1458 1208 1518 1069 1333]\n", - "51 63\n", - "[3832 4083 3827 3959 4079 4086 3696 3699 3766 4013 3956 3954 3702 3895\n", - " 3823 3826 3886 4081 3887 4078 3822 3952 3760 3897 3885 3762 4019 4016\n", - " 3830 3890 4018 3698 3949 3828 3960 3700 3950 3763 3891 3824 3765 4085\n", - " 3961 4015 3893 4084 3958 4021 4022 4025 4014 3697 4024 3701 3764 3894\n", - " 4082 3951 3896 3759 3825 3957 4088 4017 4089 3831 3767 3888 3889 4020\n", - " 3953 4080 4077 3829 4087 3761 3892 3955 4023]\n", - "63 24\n", - "[1599 1403 1407 1785 1598 1402 1789 1983 1470 1854 1401 1465 1275 1277\n", - " 1406 1533 1405 1597 1404 1469 1658 1918 1338 1532 1594 1595 1276 1916\n", - " 1851 1530 1341 1660 1724 1723 1466 1531 1982 1593 1657 1853 1915 1980\n", - " 1213 1919 1467 1534 1788 1529 1725 1278 1981 1791 1786 1279 1661 1212\n", - " 1663 1850 1726 1339 1721 1596 1787 1215 1342 1790 1852 1727 1468 1535\n", - " 1855 1722 1659 1471 1343 1214 1917 1662 1340]\n", - "32 0\n", - "[ 38 27 32 35 222 163 94 156 350 96 33 224 415 164 349 226 285 98\n", - " 30 28 417 99 230 416 353 158 97 352 348 228 414 165 92 36 102 154\n", - " 287 100 91 159 225 31 101 290 289 221 29 220 284 356 223 90 162 354\n", - " 95 355 286 413 34 288 227 157 292 155 419 229 291 283 418 293 166 26\n", - " 93 219 351 37 161 218 160]\n", - "34 24\n", - "[1826 1765 1695 1704 1570 1888 1508 1892 1502 1699 1315 1383 1697 1504\n", - " 1638 1692 1636 1442 1311 1445 1760 1575 1376 1436 1509 1505 1503 1572\n", - " 1763 1511 1952 1252 1512 1768 1829 1317 1566 1640 1957 1500 1310 1954\n", - " 1186 1822 1894 1576 1571 1955 1374 1437 1698 1316 1758 1249 1828 1757\n", - " 1633 1319 1444 1693 1506 1447 1313 1890 1183 1379 1635 1628 1510 1821\n", - " 1378 1446 1189 1886 1448 1956 1373 1767 1314 1246 1827 1187 1825 1694\n", - " 1184 1630 1887 1501 1703 1762 1632 1375 1185 1440 1700 1951 1248 1701\n", - " 1889 1381 1569 1893 1573 1438 1507 1953 1250 1565 1830 1254 1564 1639\n", - " 1384 1637 1759 1439 1696 1382 1380 1567 1312 1823 1629 1318 1702 1251\n", - " 1831 1766 1253 1824 1574 1891 1443 1634 1631 1764 1188 1761 1441 1309\n", - " 1372 1247 1568 1377 1756]\n", - "42 45\n", - "[2922 2861 2661 2671 2927 3115 2730 2732 3182 2536 3053 2859 2983 2795\n", - " 2541 3054 3173 3116 3180 2602 3112 3049 2535 2538 3055 2990 3176 3306\n", - " 2796 2860 3117 3044 3046 3118 3111 3241 2864 2928 3307 3305 2662 2600\n", - " 2668 2601 3243 2798 2920 2791 3244 2919 3304 2788 3045 2670 3048 2856\n", - " 2854 2540 3050 3109 3047 2598 3181 2921 2863 2729 3119 2789 2918 3242\n", - " 2981 2992 2982 3245 2989 3309 2923 2736 3113 2663 2984 2604 3051 2987\n", - " 2916 3175 3056 2664 2794 2599 2667 2539 2857 2917 2855 2726 2669 2924\n", - " 2980 2734 3108 2792 2991 3238 2724 2986 3174 3179 2988 2793 2858 2606\n", - " 3120 2727 3303 3183 2728 2800 2797 3110 3052 2725 2925 3239 2852 2790\n", - " 2537 3114 2731 3308 3178 2853 2665 2733 2603 3177 3240 2926 2862 2799\n", - " 2985 3246 2735 2605 2666]\n", - "17 21\n", - "[1682 1361 1553 1420 1104 1232 1039 1555 1679 1547 1621 1228 1358 1549\n", - " 1110 976 1489 1041 1170 1167 978 1295 974 1683 1680 1363 1612 1171\n", - " 1105 1291 1617 1488 1362 1230 1548 1492 1423 1165 1494 1554 1421 1367\n", - " 1490 1431 1175 1292 1483 1237 1619 1484 1299 1427 1622 1103 1491 1038\n", - " 1742 1558 1231 1550 1106 1042 1357 1227 1040 1426 1166 1485 1303 1238\n", - " 1107 1172 1356 1037 1677 1296 977 1495 1425 1365 1613 1493 1678 979\n", - " 1746 1293 1297 980 1233 1163 1616 1620 1109 1559 1101 1430 1618 1294\n", - " 1173 1615 1359 1557 975 1174 1100 1487 1239 1044 1102 1360 1229 1300\n", - " 1355 1108 1744 1681 1234 1419 1302 1551 1748 1422 1556 1684 1486 1366\n", - " 1298 1236 1169 1424 1428 1301 1743 1747 1364 1164 1043 1685 1429 1168\n", - " 1045 1614 1745 1235 1552]\n", - "19 1\n", - "[ 17 210 83 141 401 153 21 269 467 81 25 15 340 149 334 145 281 89\n", - " 143 404 402 339 213 82 144 214 207 341 13 273 337 80 14 147 208 211\n", - " 212 468 152 86 151 85 146 343 209 338 217 407 150 275 469 79 277 342\n", - " 405 77 279 344 20 16 335 464 276 142 22 400 148 206 399 18 465 87\n", - " 336 88 215 280 274 78 271 403 470 278 205 216 406 23 19 24 270 272\n", - " 84 466]\n", - "11 7\n", - "[329 591 459 141 401 720 845 269 73 334 777 716 200 521 143 842 261 526\n", - " 332 523 779 655 653 453 524 525 718 201 719 714 139 207 781 780 582 458\n", - " 273 711 337 775 136 778 393 651 325 517 715 208 75 263 581 390 396 529\n", - " 776 76 782 846 202 74 456 841 392 518 654 645 843 588 77 461 268 463\n", - " 140 783 650 335 138 464 267 455 712 648 649 398 717 327 326 142 400 206\n", - " 399 331 583 844 465 522 593 527 336 587 264 394 519 462 457 460 589 72\n", - " 657 266 78 592 199 330 590 265 271 710 586 840 262 204 205 333 389 198\n", - " 454 397 652 135 584 647 520 270 391 272 137 656 585 528 713 203 646 328\n", - " 395]\n", - "47 3\n", - "[ 45 245 239 107 308 500 114 370 41 177 181 51 557 237 47 364 426 302\n", - " 369 563 170 435 175 431 428 46 234 495 498 105 111 304 117 179 300 621\n", - " 430 558 240 116 238 368 560 429 113 241 425 365 625 499 50 494 108 110\n", - " 306 362 242 52 174 497 297 373 556 303 307 366 432 626 555 562 173 301\n", - " 53 172 176 112 49 180 43 361 436 622 305 491 559 620 437 298 434 309\n", - " 169 233 372 493 371 299 44 42 363 561 171 367 433 492 427 48 244 496\n", - " 623 236 624 106 115 109 235 243 178 490]\n", - "13 27\n", - "[1809 1682 1741 2000 1553 1870 1420 1804 1555 1679 1547 1875 1358 1997\n", - " 1549 1489 1737 2062 1806 1808 1930 1674 1866 1683 1680 1932 1863 1612\n", - " 1799 1873 1739 1617 1543 2125 1488 1867 1672 2064 1869 1928 1548 1423\n", - " 1933 1554 1421 1490 1671 2127 2058 1807 1868 2126 1931 1864 1483 1937\n", - " 1619 1484 1675 1544 1811 1481 2002 1546 1802 1676 2059 1742 2065 1803\n", - " 1480 1550 1545 1357 2128 1939 1607 1801 1485 2122 1354 1356 1735 1927\n", - " 1800 1677 1995 1738 1993 1425 1613 2001 1678 1746 1935 1611 1810 1992\n", - " 1929 1616 1934 1618 1482 1615 1359 1936 1487 1999 1360 1355 1418 1673\n", - " 1744 1417 2061 1938 1681 1419 1551 1805 1871 1422 1486 2057 1872 1996\n", - " 2060 1874 1424 1610 1743 1747 1865 1736 1609 2063 2123 2124 1740 1614\n", - " 1998 1608 1745 1994 1552]\n", - "49 26\n", - "[1392 1710 1840 1907 1973 1330 1713 1836 1519 1524 1581 1966 1389 1782\n", - " 1521 2032 1965 1842 1651 1461 1708 1773 1525 2100 1394 1839 1772 1712\n", - " 1909 1969 1841 1588 1652 1579 2034 1771 1648 1327 1452 1326 1331 2098\n", - " 1455 1715 1328 1905 1778 1460 1649 1456 1901 1717 1903 1779 1454 1709\n", - " 1585 1971 1522 1707 1523 1837 1719 2036 1586 1846 1835 1584 2099 1964\n", - " 1329 1515 1847 1908 2096 1393 2030 1390 1899 1902 1520 2095 1714 2037\n", - " 1776 1904 1396 1527 1970 1516 1457 1900 1395 1650 1783 1968 1845 1391\n", - " 1972 1644 1838 2031 1910 2033 1587 1718 1777 1716 1591 1967 2035 1781\n", - " 1453 1459 1332 1774 1780 1655 1397 1517 1645 1844 1911 1974 1646 1643\n", - " 1653 2094 1590 1654 1462 1775 2097 1589 2029 1526 1583 1647 1582 1843\n", - " 1711 1458 1580 1518 1906]\n", - "41 32\n", - "[1765 2027 2084 1704 2093 2089 2406 1892 2159 2022 1836 1895 2475 1966\n", - " 2284 2214 2408 2222 1965 2276 2349 1708 2088 1773 1833 2344 2413 2411\n", - " 2155 2342 2348 2087 2345 1963 1772 2147 2474 2152 2026 1834 2090 1768\n", - " 1829 2153 1771 1957 1769 2149 1832 2020 2407 2473 1894 1955 2282 1961\n", - " 2346 2409 1901 1828 1903 2218 2277 1707 1962 2343 2083 1837 2150 2278\n", - " 1835 1964 1896 1770 2472 2021 2030 1899 1902 2095 2154 2019 1956 1959\n", - " 2219 2025 1767 2350 2211 2158 1900 2220 2151 2279 2217 2286 1703 1838\n", - " 2031 1705 2471 2405 1960 2086 2347 2156 2215 2275 1967 2216 2023 2212\n", - " 2092 1897 1893 1958 1830 2287 2283 2340 2280 2412 2094 1702 2410 2157\n", - " 2024 1706 1831 1766 1898 2285 1891 2213 2148 2091 2029 2223 2281 2085\n", - " 2341 2476 2470 2221 2028]\n", - "16 31\n", - "[2322 1809 1682 1741 2000 1870 2191 1804 2195 2317 1679 1875 1749 1997\n", - " 2129 2319 2062 2067 2004 2131 1806 1808 2257 1930 1866 1683 2196 1680\n", - " 1932 2006 2318 2188 1873 1739 2387 1617 2254 1878 2255 2125 1867 2064\n", - " 2198 2134 1869 1933 2381 2127 2058 2193 1807 1868 2126 1931 2005 1937\n", - " 1619 2384 1811 1813 2002 1802 2070 1676 2059 1742 2186 2065 1803 2258\n", - " 2324 2128 1877 1939 2122 2385 1942 1941 2133 2003 1677 2383 1995 2192\n", - " 2253 2251 1613 2001 1678 2386 1746 1935 1812 1810 2194 1616 1934 1618\n", - " 2132 1615 1936 2066 2259 1999 1814 1744 2061 1938 1681 2320 2321 1748\n", - " 1805 1871 1684 2197 1872 2187 1996 2316 2382 2060 2252 1874 1876 1743\n", - " 1747 2068 2260 2063 2189 2069 2130 2323 2123 2256 2124 2190 1740 1614\n", - " 2261 1998 1940 1745 1994]\n", - "62 9\n", - "[ 511 251 383 831 441 638 569 255 316 444 1023 507 380 447\n", - " 767 319 575 253 699 826 636 958 829 315 830 956 634 446\n", - " 954 894 701 445 1021 702 504 639 827 761 382 252 378 314\n", - " 440 764 828 760 632 572 379 571 377 254 824 442 443 633\n", - " 893 765 1019 318 510 955 766 1020 696 637 892 508 890 506\n", - " 574 317 570 698 895 509 957 762 1022 763 505 700 697 959\n", - " 635 573 703 381 891 825 568 889]\n", - "17 12\n", - "[ 594 848 591 785 401 720 913 845 467 1104 1039 910 716 850\n", - " 659 976 526 1041 914 1170 1167 404 596 779 724 978 917 402\n", - " 655 653 852 974 907 524 525 718 719 855 1171 781 1105 780\n", - " 595 721 971 982 784 912 847 791 651 532 715 468 789 662\n", - " 723 1046 1103 849 1038 529 722 782 846 908 1106 534 1042 1040\n", - " 919 599 790 983 654 1166 843 469 588 918 726 1107 1172 1037\n", - " 461 463 977 853 783 658 725 597 464 979 398 717 854 911\n", - " 1036 980 400 399 533 844 465 1109 1101 593 787 527 530 587\n", - " 975 661 972 660 462 1044 589 851 786 657 1102 973 981 1108\n", - " 788 592 909 590 915 403 531 916 663 652 598 1169 1043 727\n", - " 1168 656 528 1045 466]\n", - "34 34\n", - "[1826 2084 2529 2404 2210 1888 2406 1892 2402 2022 2398 2532 2018 2272\n", - " 2592 2461 2214 2408 2015 2276 2464 2270 2088 2591 2344 2468 2593 2333\n", - " 2342 2087 1952 2147 2596 2152 2530 2017 2077 1829 2078 1957 1954 2526\n", - " 1949 2149 2020 2079 2533 2407 2338 1894 1955 2014 2462 2466 2534 1828\n", - " 2204 2268 2206 2277 2594 2337 2335 1890 2271 2343 2146 2083 2269 2150\n", - " 2080 2278 2012 2021 1950 2397 2274 2019 2339 2141 1886 1956 1959 2597\n", - " 2211 2467 2151 2209 2273 2279 1827 1825 2463 1887 2207 2081 2465 2334\n", - " 2471 2405 2086 2403 2144 1951 2215 2400 2275 2396 2216 2023 2212 1889\n", - " 2595 2527 1893 2016 1958 2082 1953 2336 2145 2208 2340 2280 1823 2469\n", - " 2143 2531 2024 2528 2140 2401 1824 2205 2332 2076 1891 2213 2148 2399\n", - " 2085 2341 2142 2013 2470]\n", - "12 0\n", - "[ 6 17 329 210 12 141 269 81 73 15 334 145 200 143 332 201 82 70\n", - " 144 139 207 13 273 136 80 393 14 208 75 263 11 146 396 76 209 202\n", - " 74 10 8 79 77 268 140 16 335 138 267 398 142 206 399 18 331 336\n", - " 134 264 394 7 72 266 78 199 330 9 265 271 204 205 333 198 397 71\n", - " 135 270 272 137 203 328 395]\n", - "46 39\n", - "[2542 2861 2801 2159 2354 2739 2475 2671 2416 2927 2161 2284 2420 2408\n", - " 2730 2222 2732 2349 2536 2612 2545 2859 2795 2344 2413 2414 2541 2411\n", - " 2155 2418 2348 2345 2602 2474 2538 2738 2796 2860 2609 2419 2352 2480\n", - " 2740 2864 2473 2674 2160 2928 2544 2600 2668 2601 2282 2346 2409 2798\n", - " 2218 2608 2289 2670 2225 2355 2540 2224 2478 2417 2607 2472 2863 2729\n", - " 2288 2923 2736 2219 2350 2158 2220 2477 2604 2548 2664 2794 2286 2803\n", - " 2667 2351 2539 2479 2802 2483 2290 2415 2676 2482 2347 2737 2156 2669\n", - " 2924 2353 2611 2865 2672 2734 2543 2291 2929 2226 2793 2287 2675 2858\n", - " 2283 2606 2673 2728 2800 2797 2412 2925 2410 2157 2484 2866 2537 2731\n", - " 2610 2481 2285 2356 2665 2733 2603 2546 2547 2223 2926 2281 2862 2799\n", - " 2476 2735 2605 2221 2666]\n", - "13 2\n", - "[ 17 329 210 83 459 12 141 401 269 81 73 15 334 145 200 143 526 332\n", - " 523 402 339 524 525 201 82 144 139 207 13 458 273 337 136 80 393 14\n", - " 147 208 211 75 263 11 146 396 76 209 338 202 74 392 10 275 8 79\n", - " 77 461 268 463 140 16 335 138 464 267 398 327 142 400 206 399 18 331\n", - " 465 522 527 336 264 394 462 7 457 460 72 266 274 78 199 330 9 265\n", - " 271 204 205 333 19 397 71 135 270 272 137 528 203 328 395]\n", - "57 8\n", - "[511 251 383 441 245 638 569 313 316 184 308 444 500 376 507 380 447 438\n", - " 631 767 757 186 575 253 699 247 826 636 756 829 315 563 564 830 435 956\n", - " 634 446 954 627 630 701 445 887 821 702 504 501 888 639 182 827 567 886\n", - " 761 694 382 252 378 314 249 440 566 439 764 187 828 760 632 503 885 499\n", - " 572 375 379 820 952 571 377 824 442 443 695 633 893 765 373 502 629 318\n", - " 510 955 766 696 310 183 637 892 508 188 185 565 890 436 506 692 250 951\n", - " 574 950 317 570 698 437 755 309 822 374 372 693 371 246 509 762 763 505\n", - " 700 248 697 758 953 312 635 573 703 823 691 381 628 891 759 825 311 568\n", - " 889]\n", - "17 6\n", - "[ 17 594 210 591 83 459 785 141 401 720 269 467 81 15 340 149 334 145\n", - " 659 143 526 404 332 523 596 724 402 339 655 213 653 524 525 718 471 719\n", - " 535 82 144 214 207 341 595 721 273 337 784 80 14 532 147 208 211 212\n", - " 468 662 723 85 146 396 343 529 722 782 209 338 534 599 407 150 654 275\n", - " 469 588 79 277 342 405 77 461 268 463 279 140 783 658 725 20 16 597\n", - " 335 464 267 276 398 717 142 400 148 206 399 18 533 331 465 593 787 527\n", - " 530 336 587 661 660 215 462 460 589 786 657 274 78 788 592 590 271 403\n", - " 531 470 278 204 205 333 406 19 397 652 598 270 272 656 84 528 203 395\n", - " 466]\n", - "51 59\n", - "[3832 4083 3827 3959 4079 3705 4086 3696 3699 3766 3511 4013 3956 3769\n", - " 3954 3702 3895 3823 3826 3574 3631 3886 3443 4081 3636 3440 3757 3887\n", - " 4078 3822 3952 3760 3897 3885 3510 3762 3445 4019 4016 3694 3830 3572\n", - " 3890 4018 3698 3949 3828 3960 3700 3950 3763 3891 3824 3765 4085 3961\n", - " 3567 4015 3758 3566 3893 4084 3958 4021 3444 4022 4025 4014 3505 3697\n", - " 3575 4024 3504 3641 3441 3701 3507 3629 3639 3764 3569 3503 3894 4082\n", - " 3951 3896 3759 3825 3821 3957 4088 3630 3632 3768 3637 4017 3640 3573\n", - " 3635 3509 3831 3767 3633 3570 3833 3506 3571 3695 3888 3889 4020 3953\n", - " 4080 3634 3442 3638 3508 3704 3829 4087 3703 3446 3693 3761 3576 3892\n", - " 3568 3955 4023]\n", - "32 29\n", - "[1826 1765 1695 2084 1570 2210 1888 1892 1502 2022 1699 1697 1504 2018\n", - " 2272 1692 1636 2015 1885 1760 2270 1505 1503 1572 1763 1952 2011 1819\n", - " 2147 1883 2017 1818 2077 1829 2078 1566 1957 1954 1949 2149 2020 1822\n", - " 2079 1894 1571 1955 2014 1698 1758 1828 1757 2204 1633 2206 1693 1506\n", - " 1754 1890 2271 2146 2083 2269 2080 1635 2012 1628 2021 1950 1948 1821\n", - " 1627 2274 2019 2141 1886 1956 2075 2211 2209 2273 1947 1827 1825 1694\n", - " 1630 1887 2207 2081 1501 1762 1882 1632 2086 1755 2144 1700 1951 1946\n", - " 2275 2212 1701 1889 1569 1820 1893 1507 2016 1958 2082 1953 1565 1830\n", - " 1564 2145 1637 1759 1884 2208 1696 1567 1823 1629 2143 1702 2139 2140\n", - " 1766 1691 2010 1824 2205 2076 1891 2148 1634 1631 1764 2074 1690 2085\n", - " 1761 2142 2013 1568 1756]\n", - "9 49\n", - "[2887 2957 3275 3145 2891 3526 3086 3215 3342 2959 3148 3529 3023 2762\n", - " 3464 2955 3398 3527 3209 3206 3402 3151 3084 2759 3140 3205 3332 3018\n", - " 3146 2758 2825 3274 3333 2760 2827 3528 3462 3399 3204 3267 3270 3077\n", - " 3203 2951 3013 3273 3082 2886 3017 3085 2950 3404 3080 3400 3331 3268\n", - " 2893 3087 3141 3337 3463 2947 3277 3012 2761 3207 2889 3531 2884 3079\n", - " 3468 3210 3341 3272 3343 3271 3336 3467 3014 3532 3083 2826 3406 3397\n", - " 2824 3021 2949 2764 3396 3149 2956 3339 3461 3143 3011 3142 3405 3078\n", - " 3338 3016 3144 2888 3401 2953 2828 3276 3139 3469 3015 2829 3076 3075\n", - " 2821 3150 3212 2894 3278 3214 3334 2822 3081 2948 2890 3019 3208 3213\n", - " 3340 3403 2952 3530 2823 3466 2892 2885 3465 3269 3211 2763 2954 3335\n", - " 3279 3020 2958 3022 3147]\n", - "18 58\n", - "[4047 3858 3345 3730 3990 3919 3538 3985 3599 3408 4051 3799 3790 3923\n", - " 3983 3668 3853 3541 3922 3605 3534 3726 3671 3728 3987 3924 3926 3411\n", - " 3982 3732 3861 3859 3470 3476 3854 3735 3788 3412 3414 3346 3607 4052\n", - " 3598 3472 3986 3604 3663 3920 3536 3479 3413 3863 3344 3921 3856 3989\n", - " 3798 3537 3672 3796 3927 3410 3791 3928 3855 4048 3925 3343 3795 3608\n", - " 3797 3669 3542 3603 3731 3600 3725 3736 3666 3532 3471 3535 3406 4053\n", - " 3409 3474 3793 3852 3664 3601 3916 3789 3606 3533 3734 3665 3539 4050\n", - " 3347 3540 3597 3667 3862 3670 3543 3727 3469 3477 3544 3602 3792 3981\n", - " 3348 3473 3917 4049 3596 3729 3860 3662 3478 3733 3349 3991 3988 3857\n", - " 3660 3984 3661 3800 3918 3407 4046 3475 4054 3864 3794 3724]\n", - "45 44\n", - "[2922 2542 2861 2801 2739 2475 2671 2927 3115 2930 2730 2732 3182 3053\n", - " 2545 2859 2983 2795 2541 3054 3116 3058 3180 2602 3112 3049 2474 2538\n", - " 3055 2738 2990 2993 2796 2860 2609 3117 3121 3118 2480 2864 2674 2928\n", - " 2544 2600 2668 2601 3243 2798 2920 2791 3244 2919 2608 2670 3048 2856\n", - " 2540 3050 2478 3047 2607 3184 3181 2995 2921 2863 2729 3119 3242 2867\n", - " 2992 3245 2989 2923 2736 3113 2663 2984 2477 2604 3051 2987 3056 2664\n", - " 2794 2803 2667 2539 2857 3122 2479 2802 2855 2737 2669 2924 2865 2672\n", - " 2734 2792 3059 2991 2543 2929 2986 3179 2988 2793 2994 2931 2675 2858\n", - " 2606 3120 2727 2673 3183 2728 2800 2797 3052 3057 2925 2866 2537 3114\n", - " 2731 3178 2610 2665 2733 3248 3247 2603 3177 2926 2862 2799 2985 3246\n", - " 2476 3185 2735 2605 2666]\n", - "36 23\n", - "[1826 1765 1695 1704 1570 1508 1892 1502 1699 1895 1315 1642 1383 1697\n", - " 1504 1638 1636 1442 1311 1445 1760 1322 1575 1376 1121 1509 1122 1505\n", - " 1503 1572 1763 1511 1252 1578 1512 1768 1829 1317 1566 1640 1769 1310\n", - " 1450 1126 1186 1832 1255 1641 1894 1576 1571 1374 1698 1316 1249 1828\n", - " 1633 1319 1444 1506 1321 1447 1313 1890 1379 1635 1510 1378 1446 1190\n", - " 1189 1256 1577 1448 1320 1767 1314 1385 1827 1187 1825 1694 1184 1630\n", - " 1703 1762 1705 1632 1375 1185 1127 1123 1440 1700 1248 1701 1386 1889\n", - " 1381 1569 1893 1514 1573 1438 1124 1507 1192 1250 1513 1830 1254 1639\n", - " 1384 1637 1759 1439 1449 1191 1696 1382 1380 1567 1312 1318 1702 1251\n", - " 1706 1831 1766 1253 1824 1574 1891 1443 1634 1631 1257 1764 1188 1761\n", - " 1441 1125 1247 1568 1377]\n", - "17 24\n", - "[1809 1682 1361 1741 1553 1870 1420 1804 1232 1555 1679 1547 1621 1875\n", - " 1358 1749 1549 1489 1170 1167 1806 1808 1295 1683 1680 1363 1612 1171\n", - " 1873 1739 1617 1687 1488 1362 1230 1869 1548 1492 1423 1494 1554 1421\n", - " 1367 1490 1431 1292 1807 1483 1937 1237 1619 1484 1675 1299 1427 1622\n", - " 1811 1813 1491 1676 1742 1558 1231 1550 1357 1877 1939 1426 1166 1485\n", - " 1172 1356 1677 1296 1495 1425 1365 1613 1493 1678 1746 1935 1293 1812\n", - " 1297 1611 1233 1623 1810 1616 1620 1559 1934 1430 1618 1294 1615 1359\n", - " 1557 1936 1487 1360 1229 1300 1814 1355 1744 1938 1681 1234 1419 1302\n", - " 1551 1748 1805 1871 1422 1556 1684 1486 1366 1872 1298 1236 1686 1874\n", - " 1876 1169 1424 1428 1301 1750 1743 1747 1364 1751 1685 1429 1168 1740\n", - " 1614 1940 1745 1235 1552]\n", - "57 4\n", - "[511 251 383 54 441 191 245 569 313 59 255 316 184 308 444 500 125 376\n", - " 507 380 447 438 631 319 186 181 57 253 699 247 636 189 315 564 435 634\n", - " 446 127 630 445 504 501 182 56 567 694 126 382 252 117 378 179 314 249\n", - " 121 122 190 116 440 566 439 187 632 503 499 572 375 379 118 571 120 377\n", - " 254 52 442 443 695 633 373 502 307 629 318 510 696 310 183 53 637 508\n", - " 188 185 565 180 436 506 250 574 317 570 698 437 309 60 62 374 372 119\n", - " 371 246 509 61 505 700 248 697 55 312 635 244 573 124 381 58 115 243\n", - " 311 568 123]\n", - "14 29\n", - "[1809 1682 1741 2000 1553 1870 2191 1804 1679 1547 1875 1997 1549 2129\n", - " 1489 1737 2062 2067 2004 2131 1806 1808 2257 1930 1674 1866 1683 1680\n", - " 1932 2056 1612 2188 1873 1739 1617 2254 2255 2125 1488 1867 1672 2064\n", - " 1869 1928 1548 1933 1554 2127 2058 2193 1807 1868 2126 1931 1864 1483\n", - " 1937 1619 1484 1675 1811 2002 1546 1802 1676 2059 1742 2186 2065 1803\n", - " 1550 2128 1939 1801 1485 2122 2121 1800 2003 1677 1995 2192 2253 1738\n", - " 2251 1993 1613 2001 1678 1746 1935 1812 1611 1810 1992 1929 2194 1616\n", - " 1934 1618 1615 1936 1487 2066 1999 1673 1744 2061 1938 1681 1551 1748\n", - " 1805 1871 1684 1486 2057 1872 2187 1996 2060 2252 1874 1876 1610 1743\n", - " 1747 2068 1865 1736 1609 2063 2189 2130 2123 2256 2124 2190 1740 1614\n", - " 1998 1940 1745 1994 1552]\n", - "16 44\n", - "[2957 3217 2832 2891 2449 3024 3086 3215 3025 2959 2705 3148 3027 2765\n", - " 3023 2767 2897 2762 2960 2516 3089 2509 3154 2955 2963 2445 3151 3084\n", - " 3091 2578 2896 2637 3018 3030 2966 2766 2451 2709 2447 2964 2827 2770\n", - " 2900 3155 2639 2835 2571 2698 2510 2898 2768 2640 3090 3085 2703 2702\n", - " 3218 2893 3087 2572 2577 3026 2646 3153 2708 2773 2710 3219 2833 2830\n", - " 2834 2579 2515 2772 2580 2643 2706 2514 3083 3028 2826 2636 3029 2511\n", - " 2635 2707 2700 3021 2764 2513 2576 3149 2956 2645 2644 2450 3156 3152\n", - " 2901 2895 3092 2828 2508 2965 2634 3093 2837 2642 2899 2448 2512 2829\n", - " 2838 2831 2962 3150 2641 2575 2894 3216 3214 2769 2581 2890 2902 3019\n", - " 3088 3213 2574 2836 2446 2892 2961 2704 2763 2954 2638 3020 2958 3022\n", - " 2573 2699 2701 2774 2771]\n", - "22 62\n", - "[3858 4055 3730 3990 3865 3985 3996 4051 3799 3923 3668 3922 3605 3671\n", - " 3987 3924 3926 3732 3993 3861 3859 3801 4057 3735 3607 4052 3738 4058\n", - " 3866 3802 3986 3995 3604 3920 4060 3863 3921 3856 3989 3798 3739 3672\n", - " 3796 3927 3867 3928 3929 3674 4048 3925 3795 3608 3868 3797 3669 3603\n", - " 3737 3731 3736 3666 4053 3793 4056 3803 3606 3734 3930 3994 3609 4050\n", - " 3667 3932 3862 3673 3670 4059 3792 4049 3729 3860 3931 3733 3991 3988\n", - " 3857 3984 3800 3804 4054 3992 3864 3794]\n", - "59 0\n", - "[ 63 251 383 54 441 191 245 313 59 255 316 184 444 125 376 380 319 186\n", - " 181 57 253 247 189 315 446 127 445 182 56 126 382 252 117 378 314 249\n", - " 121 122 190 440 187 375 379 118 120 377 254 442 443 318 310 183 53 188\n", - " 185 250 317 60 62 119 246 61 248 55 312 124 381 58 311 123]\n", - "49 43\n", - "[2542 2861 2801 2739 2671 2416 2927 2420 2930 2732 2678 3187 3182 3053\n", - " 2612 2545 2859 2795 2414 2541 2933 3054 2418 3058 2679 2806 2807 2742\n", - " 3055 2738 2990 2741 2993 2796 2860 2609 3117 2419 3121 2935 3118 2480\n", - " 2740 2864 2997 2805 2674 2928 2544 3125 2869 2668 2550 2798 2934 2996\n", - " 2608 3061 3123 2670 2868 2540 2478 2417 2607 3184 2995 2863 3186 3119\n", - " 2867 2992 2870 2989 2923 2736 3188 2477 2604 2549 2987 2999 3056 2548\n", - " 2803 2667 3122 2479 2802 2483 2415 2676 2482 2737 2669 2924 2611 2865\n", - " 2615 2614 2672 2734 2613 3059 2991 2543 2929 2804 2998 2988 2871 2994\n", - " 2931 2675 2606 3120 2673 3183 2485 2800 2797 3052 3057 2925 2484 2866\n", - " 2731 2610 2481 2733 2603 2546 2547 2932 2926 2862 2799 3062 3060 2743\n", - " 3185 2735 2605 2677 3124]\n", - "38 37\n", - "[2084 2529 2404 2089 2210 2406 2402 2022 2475 2661 2532 2272 2592 2284\n", - " 2214 2408 2730 2659 2276 2464 2536 2088 2344 2468 2593 2411 2155 2342\n", - " 2348 2087 2345 2602 2147 2723 2596 2474 2535 2538 2152 2530 2090 2153\n", - " 2658 2149 2020 2533 2407 2473 2338 2662 2600 2601 2282 2466 2346 2409\n", - " 2791 2534 2218 2788 2277 2594 2337 2722 2540 2343 2146 2083 2150 2278\n", - " 2598 2472 2729 2021 2789 2274 2154 2019 2339 2219 2597 2025 2663 2211\n", - " 2220 2604 2467 2151 2209 2273 2279 2217 2664 2599 2667 2465 2539 2471\n", - " 2787 2405 2086 2403 2347 2726 2215 2400 2275 2216 2660 2023 2212 2792\n", - " 2595 2657 2724 2082 2793 2336 2145 2283 2208 2727 2340 2280 2728 2469\n", - " 2412 2531 2725 2410 2024 2528 2790 2537 2401 2665 2213 2148 2603 2281\n", - " 2085 2341 2476 2470 2666]\n", - "36 29\n", - "[1826 1765 1695 2084 1704 1570 2089 2210 1888 1508 1892 2022 1699 1895\n", - " 1697 2018 1638 2214 1636 2015 2276 1760 1575 2088 1833 1509 1505 1572\n", - " 1763 2087 1511 1952 2147 2152 2026 2017 1834 2090 1768 1829 2153 2078\n", - " 1640 1957 1769 1954 2149 1832 2020 1822 2079 1641 1894 1576 1571 1955\n", - " 2014 1961 1698 1758 1828 1633 2277 1962 1506 1890 2146 2083 2150 2080\n", - " 2278 1635 1896 1770 1510 2021 1950 2274 2019 1886 1956 1959 2025 1767\n", - " 2211 2151 2209 2273 2279 1827 1825 1694 1887 2081 1703 1762 1705 1632\n", - " 1960 2086 2144 1700 1951 2215 2275 2216 2023 2212 1701 1889 1569 1897\n", - " 1893 1573 1507 2016 1958 2082 1953 1830 1639 2145 1637 1759 2208 1696\n", - " 1823 2143 1702 2024 1706 1831 1766 1898 1824 1574 1891 2213 2148 1634\n", - " 1631 1764 2085 1761 1568]\n", - "16 11\n", - "[ 594 848 591 459 785 401 720 913 845 467 1104 1039 334 910\n", - " 716 850 659 976 842 526 1041 914 404 523 596 779 724 978\n", - " 917 402 339 655 653 852 974 907 524 525 718 719 906 714\n", - " 781 1105 780 595 721 337 971 784 778 912 847 651 532 715\n", - " 468 789 662 723 396 1103 849 1038 529 722 782 846 338 908\n", - " 1106 534 1042 1040 790 654 843 469 588 918 726 1107 1037 461\n", - " 463 977 853 783 658 725 650 597 335 464 979 398 717 854\n", - " 911 1036 980 400 399 533 844 465 522 1101 593 787 527 530\n", - " 336 587 975 661 972 660 462 460 1044 589 851 786 657 1102\n", - " 973 981 788 592 909 590 915 403 586 531 916 333 397 652\n", - " 598 1043 656 528 466]\n", - "25 2\n", - "[ 27 83 153 21 25 340 149 222 281 89 94 156 404 350 472 537 339 213\n", - " 471 409 535 214 349 341 540 285 477 30 28 345 147 211 212 158 152 348\n", - " 86 151 85 343 414 92 154 287 217 534 347 91 407 159 150 539 275 469\n", - " 31 277 342 405 473 279 221 29 220 284 344 411 20 276 536 223 22 90\n", - " 148 95 286 413 87 157 88 215 155 280 282 476 346 283 470 278 408 216\n", - " 26 406 23 19 24 93 219 351 538 218 84 474 412 475 410]\n", - "47 33\n", - "[2027 1840 1907 2093 1973 2089 2542 2159 1836 2354 2475 1966 2416 2161\n", - " 2284 2420 2032 2222 1965 1842 2349 2545 1773 2100 2413 2414 2541 2411\n", - " 2155 1839 2418 2348 2345 1963 1772 2164 1969 2026 1841 2090 2153 2034\n", - " 2228 2419 2352 2480 2160 2544 2098 2293 1905 2282 1961 2346 1778 1901\n", - " 1903 2218 1971 2289 1962 2225 2355 2540 2224 1837 2036 2478 1835 2417\n", - " 2099 1964 1908 2096 2030 1899 1902 2095 2288 2162 2154 2037 1776 1904\n", - " 2219 2025 2350 1970 2158 1900 2220 2477 2163 2217 1968 2286 2357 1972\n", - " 2351 1838 2031 2479 2483 2290 2415 2033 2482 1777 2347 2156 2353 1967\n", - " 2035 2092 2543 2291 2226 1774 2292 2287 2229 2283 2165 2101 2412 2094\n", - " 2410 2157 2227 1775 1898 2481 2285 2356 2097 2091 2029 2546 2223 2281\n", - " 1843 2476 2221 2028 1906]\n", - "30 23\n", - "[1826 1695 1570 1888 1508 1502 1699 1315 1697 1504 1692 1636 1442 1311\n", - " 1885 1760 1376 1121 1305 1436 1560 1505 1503 1572 1763 1819 1883 1818\n", - " 1115 1117 1566 1371 1500 1245 1310 1186 1822 1571 1374 1180 1437 1698\n", - " 1316 1758 1249 1561 1307 1757 1241 1633 1433 1306 1444 1693 1506 1754\n", - " 1562 1499 1313 1183 1379 1635 1181 1628 1821 1378 1627 1432 1626 1116\n", - " 1886 1373 1314 1246 1497 1243 1825 1625 1368 1694 1184 1630 1887 1501\n", - " 1762 1632 1375 1178 1185 1369 1755 1440 1700 1498 1248 1889 1569 1496\n", - " 1820 1370 1438 1242 1507 1434 1753 1250 1689 1565 1564 1435 1759 1439\n", - " 1884 1696 1380 1567 1312 1823 1629 1251 1118 1179 1691 1120 1824 1119\n", - " 1624 1443 1634 1631 1308 1690 1563 1761 1688 1244 1182 1441 1304 1309\n", - " 1372 1247 1568 1377 1756]\n", - "63 17\n", - "[ 831 1151 1023 1146 767 1403 1407 1402 958 829 1018 1147 830 956\n", - " 1470 954 1085 894 1021 1275 1277 1406 1533 1405 827 1404 1469 1338\n", - " 1148 1532 1276 764 1273 828 1341 1211 893 765 1019 1081 955 766\n", - " 1020 1337 892 1213 1467 890 1534 1210 1278 1086 1279 895 1212 1082\n", - " 1017 1339 957 1022 1215 1342 1209 1468 959 953 1535 1274 1471 1343\n", - " 1087 1214 891 1145 1084 1150 1340 1083 1149]\n", - "0 63\n", - "[4038 3776 4032 3648 3909 4036 4037 3714 3906 3843 4034 3905 3712 3844\n", - " 3970 3651 3716 3649 3779 3842 3845 4035 4033 3973 3781 3777 3910 3778\n", - " 3972 3907 3780 3904 3846 3968 3974 3841 3969 3713 3971 3715 3908 3650\n", - " 3840]\n", - "11 57\n", - "[3275 3659 3914 3526 3919 3786 4043 3599 3342 3408 3790 3847 3529 3983\n", - " 3980 3853 4041 3464 3851 3979 3398 3527 3402 3534 3726 3913 3728 3982\n", - " 3975 3470 3854 3788 3978 3274 3598 3717 3915 4042 3472 3528 3462 3399\n", - " 3663 3911 3920 3536 3273 3590 3404 3856 3400 3656 3845 3537 3848 3337\n", - " 3463 3277 3977 3791 3718 3855 3531 3654 3468 3595 3781 3341 3272 3343\n", - " 3336 3467 3600 3725 3532 3471 3535 3406 3719 3910 3793 3852 3525 4044\n", - " 3664 3601 3916 3789 3339 3461 3533 3655 4045 3405 3665 3338 3593 3591\n", - " 3597 3976 3589 3594 3657 3846 3658 3850 3401 3722 3276 3721 3727 3849\n", - " 3469 3720 3792 3981 3473 3917 3596 3729 4040 3785 3278 3662 3592 3340\n", - " 3403 3530 3912 3787 3857 3723 3660 3466 3661 3918 3465 3407 3783 4046\n", - " 3784 3653 3335 3782 3724]\n", - "11 45\n", - "[2887 2957 3275 3145 2832 2891 3024 3086 3215 3025 2959 2705 3148 2765\n", - " 3023 2767 2897 2762 2960 3089 2509 2955 3209 3151 3084 2759 2896 2637\n", - " 3018 3146 2569 2631 2758 2766 2694 2825 3274 2630 2633 2760 2827 2695\n", - " 2639 2571 2698 2510 3077 2951 3013 2768 3273 2640 3082 2886 3017 3085\n", - " 2950 3080 2570 2703 2702 2568 2567 2693 2893 3087 2572 3277 2761 3207\n", - " 2757 2833 2830 2889 3079 3210 3272 3014 3083 2826 2636 2635 2700 2824\n", - " 3021 2949 2764 3149 2956 3143 3142 3078 3152 3016 3144 2697 2888 2505\n", - " 2895 2953 2828 3276 2508 2634 2696 3015 2829 2821 2831 3150 2575 3212\n", - " 2894 2632 3278 3214 2769 2822 3081 2890 2507 3019 3208 3088 3213 2952\n", - " 2574 2506 2823 2892 2885 2504 2961 2704 3211 2763 2954 2638 3020 2958\n", - " 3022 2573 2699 2701 3147]\n", - "17 38\n", - "[2322 2832 2449 2191 2454 2195 2317 2444 2705 2326 2129 2765 2319 2062\n", - " 2067 2767 2516 2509 2131 2445 2263 2325 2257 2380 2578 2196 2647 2637\n", - " 2318 2327 2453 2188 2387 2766 2254 2582 2255 2391 2125 2451 2583 2709\n", - " 2447 2517 2064 2198 2770 2381 2639 2835 2455 2571 2443 2510 2127 2193\n", - " 2126 2768 2640 2384 2262 2703 2379 2702 2518 2065 2258 2324 2572 2577\n", - " 2646 2708 2128 2773 2710 2833 2385 2830 2834 2390 2579 2133 2515 2772\n", - " 2580 2383 2192 2253 2643 2706 2315 2514 2251 2388 2636 2386 2511 2635\n", - " 2707 2700 2513 2576 2645 2644 2450 2194 2132 2066 2259 2508 2642 2448\n", - " 2512 2389 2320 2831 2641 2321 2575 2769 2581 2197 2507 2574 2316 2452\n", - " 2382 2836 2252 2446 2068 2704 2260 2063 2189 2130 2638 2323 2256 2190\n", - " 2261 2573 2519 2701 2771]\n", - "34 37\n", - "[2084 2529 2404 2210 2406 2402 2721 2398 2661 2589 2532 2018 2272 2592\n", - " 2461 2214 2408 2015 2659 2276 2464 2536 2270 2591 2344 2468 2593 2333\n", - " 2342 2654 2147 2723 2596 2783 2535 2530 2017 2078 2526 2658 2149 2020\n", - " 2079 2533 2407 2338 2662 2600 2462 2466 2534 2204 2268 2788 2206 2277\n", - " 2594 2337 2722 2335 2524 2271 2343 2146 2083 2269 2150 2080 2278 2460\n", - " 2598 2472 2021 2789 2397 2274 2019 2785 2339 2141 2597 2663 2211 2784\n", - " 2467 2151 2209 2273 2590 2279 2588 2463 2207 2081 2599 2656 2465 2334\n", - " 2471 2787 2405 2086 2403 2144 2726 2215 2400 2275 2396 2216 2660 2786\n", - " 2212 2719 2595 2657 2527 2724 2016 2082 2336 2145 2208 2340 2280 2653\n", - " 2718 2469 2143 2531 2725 2528 2401 2205 2332 2213 2148 2399 2525 2720\n", - " 2085 2655 2341 2142 2470]\n", - "63 3\n", - "[ 63 511 251 383 441 191 638 313 59 255 316 444 125 507 380 447 319 186\n", - " 575 57 253 636 189 315 446 127 445 639 126 382 252 378 314 249 121 122\n", - " 190 187 572 379 571 377 254 442 443 318 510 637 508 188 185 506 250 574\n", - " 317 60 62 509 61 573 124 381 58 123]\n", - "10 61\n", - "[4038 4047 3659 3914 3919 3786 4043 3790 3847 3529 3983 3980 3853 3909\n", - " 4041 3851 3979 3527 4036 4037 3726 3913 3728 3982 3975 3854 4039 3788\n", - " 3978 3598 3717 3915 4042 3528 3844 3663 3716 3911 3920 3590 3856 3656\n", - " 3845 3848 3977 3791 3718 3855 3531 3973 3654 4048 3595 3781 3725 3532\n", - " 3719 3910 3852 3972 4044 3916 3789 3533 3655 4045 3593 3591 3597 3780\n", - " 3976 3594 3657 3846 3658 3850 3722 3721 3727 3849 3720 3974 3792 3981\n", - " 3917 3596 4040 3785 3662 3592 3530 3912 3787 3723 3660 3984 3661 3918\n", - " 3783 3908 4046 3784 3653 3782 3724]\n", - "33 42\n", - "[2529 2404 2402 2721 2398 2661 2847 2589 2532 2592 2461 2659 2780 2464\n", - " 2973 2591 2843 2468 2593 2844 3040 2654 2723 2596 2783 2535 2530 2716\n", - " 3044 2526 2978 2658 2848 3105 2533 2779 2974 2338 2977 2850 2662 2462\n", - " 2715 2466 2911 2791 2534 2919 2788 3045 2854 2594 2337 2722 2335 2524\n", - " 2652 3102 3038 2909 2460 2598 2789 2918 2981 2397 2982 2785 3041 2339\n", - " 2913 2597 2907 3042 2663 2784 2467 2590 2916 2588 2463 2599 2587 2656\n", - " 2465 3037 2334 2917 2787 2405 2403 2855 2849 2717 2523 2782 2915 2726\n", - " 2980 3107 2400 2660 2786 2781 2912 3108 2845 2719 2595 2657 2527 2724\n", - " 2851 2976 2975 2336 2914 2979 2727 2340 2653 3103 2718 2469 3104 2910\n", - " 2531 2725 2852 2528 2790 2401 2853 3039 2651 2908 2399 3043 2525 3106\n", - " 2720 2846 2655 2972 2470]\n", - "12 22\n", - "[1098 1361 1741 1553 1420 1353 1804 1104 1232 1039 1679 1035 1547 1228\n", - " 1358 1549 1489 1737 1167 1806 1350 1295 1099 1674 1680 1612 1351 1478\n", - " 1739 1291 1617 1543 1287 1488 1362 1230 1672 1548 1423 1165 1554 1421\n", - " 1490 1671 1292 1096 1286 1807 1483 1484 1675 1544 1103 1481 1160 1546\n", - " 1038 1222 1802 1224 1542 1676 1742 1231 1803 1480 1223 1550 1545 1414\n", - " 1357 1227 1607 1426 1801 1166 1485 1354 1356 1037 1159 1677 1296 1288\n", - " 1738 1425 1613 1289 1033 1678 1606 1293 1297 1611 1036 1233 1163 1290\n", - " 1616 1101 1618 1294 1482 1162 1615 1359 1479 1100 1487 1226 1102 1034\n", - " 1360 1229 1352 1355 1418 1673 1744 1417 1681 1234 1419 1551 1805 1422\n", - " 1486 1416 1298 1169 1424 1610 1743 1164 1736 1609 1168 1097 1161 1740\n", - " 1614 1225 1608 1552 1415]\n", - "15 34\n", - "[2322 1809 2000 1870 2449 2191 1804 2195 2317 2444 1875 1997 2129 2319\n", - " 2062 2067 2314 2004 2509 2131 1806 1808 2445 2325 2257 2380 1930 2313\n", - " 2578 2196 1932 2318 2188 1873 2387 2254 2255 2125 2378 2451 2447 1867\n", - " 2064 1869 1933 2381 2377 2185 2443 2510 2127 2058 2193 1807 1868 2126\n", - " 1931 2005 1937 2384 2442 2002 2249 2379 2059 2186 2250 2065 2258 2324\n", - " 2572 2577 2128 1939 2122 2385 2121 2133 2515 2003 2383 1995 2192 2253\n", - " 2315 2514 2251 1993 2388 2001 2386 2511 1935 2513 2576 1810 2450 2194\n", - " 1934 2132 1936 2066 2259 2508 1999 2061 1938 2448 2512 2389 2320 2321\n", - " 2575 1805 1871 2197 2057 2507 1872 2187 1996 2574 2316 2452 2382 2060\n", - " 2252 1874 2446 2068 2260 2063 2189 2069 2130 2323 2123 2256 2124 2190\n", - " 2261 1998 1940 2573 1994]\n", - "40 61\n", - "[4074 4068 3944 3877 4013 4071 4066 3884 3874 3810 3886 3757 4004 4073\n", - " 4078 4072 3822 4069 3947 3621 3623 3747 3948 3885 3559 3748 3689 3561\n", - " 4007 3626 3754 3949 3817 3950 3880 3946 3749 3627 4076 3691 3758 3620\n", - " 3687 4075 3557 3684 4014 3751 3625 4002 3882 4006 3879 3558 4067 3875\n", - " 4012 3746 3692 3818 3820 3814 3756 3563 3628 3811 3812 3685 4070 3815\n", - " 4011 3878 3941 3819 4009 3821 3813 3683 3938 4003 3752 4008 3755 3881\n", - " 3816 3624 3943 3876 4010 3688 3753 4077 3622 3883 3942 3939 3940 3693\n", - " 3686 4005 3562 3560 3750 3945 3690]\n", - "63 59\n", - "[3903 3583 4095 3711 3839 3705 4026 3517 3899 3772 3967 3644 3837 3708\n", - " 3769 4030 3836 3965 4027 3962 3455 3775 3709 4091 3897 3834 4092 3581\n", - " 4094 3902 4029 3901 3838 4028 4093 3579 3643 3706 3961 4090 3578 3771\n", - " 4025 3642 4031 3646 3641 3707 3452 3966 3900 3515 3898 3454 3647 3773\n", - " 3518 3453 3519 3645 3516 3582 3835 3833 3774 3963 3964 3710 3580 3770]\n", - "63 52\n", - "[3583 3711 3387 3391 3071 3577 3517 3772 3644 3263 3708 3390 3004 3258\n", - " 3455 3775 3196 3709 3262 3389 3581 3386 3130 3260 3579 3513 3324 3261\n", - " 3326 3643 3578 3007 3257 3642 3646 3195 3006 3707 3321 3193 3452 3514\n", - " 3451 3131 3450 3070 3067 3194 3197 3005 3322 3515 3132 3385 3454 3647\n", - " 3773 3518 3449 3453 3519 3198 3199 3645 3516 3133 3582 3134 3327 3135\n", - " 3774 3259 3710 3388 3068 3069 3580 3325 3323]\n", - "38 31\n", - "[1826 1765 2027 2084 1704 2404 2089 2210 1888 2406 1892 2022 1836 1699\n", - " 1895 2018 1638 2214 2408 1636 2276 2088 1833 2344 2155 2342 1763 2087\n", - " 1952 2345 1963 2147 2152 2026 2017 1834 2090 1768 1829 2153 1771 1640\n", - " 1957 1769 1954 2149 1832 2020 2407 1641 2338 1894 1955 2282 1961 2346\n", - " 2409 1698 1828 2218 2277 1962 1890 2343 2146 2083 2150 2080 2278 1635\n", - " 1835 1964 1896 1770 2021 1899 2274 2154 2019 2339 1956 1959 2219 2025\n", - " 1767 2211 1900 2220 2151 2209 2273 2279 2217 1827 1825 2081 1703 1762\n", - " 1705 2405 1960 2086 2403 2144 1700 2156 2215 2275 2216 2023 2212 1701\n", - " 2092 1889 1897 1893 2016 1958 2082 1953 1830 1639 2145 1637 2283 2208\n", - " 2340 2280 1702 2024 1706 1831 1766 1898 1824 1891 2213 2148 2091 1764\n", - " 2281 2085 1761 2341 2028]\n", - "44 28\n", - "[1710 2027 1840 1704 2093 1713 2089 2159 2022 1836 1895 1519 1642 1581\n", - " 1966 1638 2032 2222 1965 1842 1708 1575 2088 1773 1833 2155 1839 2087\n", - " 1963 1772 1712 2152 1969 2026 1834 1841 1578 2090 1512 1579 1768 2153\n", - " 2034 1451 1771 1648 1640 1769 1450 1452 1832 1641 1894 1576 2160 1455\n", - " 1905 1961 1778 1649 1901 1903 1454 1709 2218 1585 1707 1962 1837 1835\n", - " 1584 1964 1896 1770 1515 2096 2030 1899 1902 1520 2095 1714 2154 1776\n", - " 1904 1577 1959 2219 2025 1767 1970 1516 2158 1900 2220 1650 2217 1968\n", - " 1703 1644 1838 2031 1705 1960 2033 1777 2156 1967 2023 2092 1897 1514\n", - " 1453 1958 1774 1513 1830 1639 1449 1517 1645 1646 1643 2094 1702 2157\n", - " 2024 1706 1831 1775 1766 1898 2097 2091 2029 1583 2223 1647 1582 1711\n", - " 1580 1518 2221 2028 1906]\n", - "63 47\n", - "[3387 3391 3071 2878 3263 3390 3004 3258 3455 3129 3003 3196 2873 2815\n", - " 2811 2943 3262 3389 2879 2814 2877 2940 3130 3260 2749 2685 2748 2750\n", - " 2687 3324 3261 3326 3002 2686 3007 2747 3257 2875 2938 3195 2937 3006\n", - " 3193 3452 2939 2941 3131 2751 3070 3067 3194 2812 3197 3005 3322 3132\n", - " 3001 2684 3454 3066 3453 2876 2813 3198 3199 3133 3134 3327 2942 3135\n", - " 3259 2810 2874 3388 3065 3068 3069 3325 3323]\n", - "55 61\n", - "[3832 4083 3827 3959 3705 4086 3577 4026 3899 3772 3699 3766 3956 3837\n", - " 3708 3769 3954 3702 3895 3826 3574 3836 3965 4027 4081 3636 3962 4091\n", - " 3897 3834 4092 3762 4019 4029 3830 3572 3901 3890 4028 4018 3698 4093\n", - " 3828 3960 3700 3763 3891 3643 3706 3765 4085 3961 4090 3578 3771 3893\n", - " 4084 3958 4021 4022 4025 3642 3575 4024 3641 3707 3701 3639 3764 3894\n", - " 4082 3896 3825 3900 3957 3898 4088 3768 3637 4017 4089 3773 3640 3573\n", - " 3635 3831 3767 3835 3833 3889 4020 3953 3638 3963 3964 3704 3829 4087\n", - " 3703 3761 3576 3892 3770 3955 4023]\n", - "50 36\n", - "[2093 1973 2542 2159 2354 2739 2671 2416 2161 2284 2420 2032 2222 2678\n", - " 2349 2612 2545 2100 2413 2414 2541 2418 2348 2294 2230 2164 2167 1969\n", - " 2738 2741 2360 2034 2166 2609 2228 2419 2488 2352 2480 2740 2674 2160\n", - " 2544 2098 2293 2296 2550 2103 2552 2422 2608 1971 2289 2670 2225 2355\n", - " 2540 2224 2036 2478 2417 2099 2607 2096 2038 2030 2421 2095 2288 2162\n", - " 2037 2358 2736 2350 1970 2158 2220 2477 2549 2163 2548 1968 2286 2357\n", - " 1972 2351 2031 2479 2483 2290 2415 2423 2033 2676 2482 2737 2156 2353\n", - " 2486 2295 2611 1967 2615 2035 2614 2672 2613 2543 2291 2231 2226 2292\n", - " 2287 2675 2229 2606 2165 2673 2101 2485 2168 2412 2094 2157 2227 2484\n", - " 2232 2610 2481 2285 2356 2097 2487 2546 2547 2223 2359 2102 2476 2735\n", - " 2424 2605 2221 2551 2677]\n", - "16 47\n", - "[2957 3345 3275 3217 2832 2891 3024 3086 3215 3342 3408 3025 2959 2705\n", - " 3148 3027 2765 3023 2767 2897 3280 2960 3089 3154 2955 2963 3151 3084\n", - " 3091 2896 3411 2637 3094 3018 3030 2966 3146 2766 3346 2964 2827 2770\n", - " 2900 3155 2639 2835 2898 3158 2768 2640 3082 3090 3085 3344 2703 2702\n", - " 3218 3284 2893 3087 3026 3153 3277 2708 2773 3410 3219 2833 2830 3285\n", - " 2834 3210 3341 3343 2772 2643 3221 2706 3083 3028 2826 3282 3406 3409\n", - " 3029 2707 2700 3021 3157 2764 3149 2956 3283 3405 3220 3156 3152 3347\n", - " 2901 2895 3092 2828 3276 2965 3093 2837 2642 2899 3348 2829 2838 3222\n", - " 2831 2962 3150 2641 3212 2894 3278 3216 3214 2769 2890 2902 3019 3088\n", - " 3213 3340 3281 2836 2892 2961 2704 3211 2763 3407 2954 2638 3279 3020\n", - " 2958 3022 2701 2771 3147]\n", - "0 0\n", - "[ 6 193 0 320 133 261 260 70 386 128 387 129 258 4 257 2 1 130\n", - " 68 66 256 321 65 132 194 5 192 197 134 324 322 196 131 323 195 198\n", - " 3 259 64 67 69 384 385]\n", - "30 37\n", - "[2529 2404 2210 2402 2721 2398 2589 2532 2272 2592 2461 2015 2267 2659\n", - " 2276 2780 2456 2464 2394 2270 2200 2591 2468 2593 2333 2654 2011 2147\n", - " 2596 2783 2530 2017 2265 2077 2521 2078 2716 2522 2526 2658 2079 2779\n", - " 2338 2584 2014 2331 2462 2715 2466 2585 2204 2268 2206 2594 2337 2722\n", - " 2335 2524 2271 2652 2146 2269 2080 2460 2012 2330 2393 2397 2274 2785\n", - " 2339 2141 2203 2649 2329 2075 2211 2202 2784 2467 2209 2273 2590 2588\n", - " 2264 2458 2463 2207 2081 2587 2656 2465 2334 2137 2403 2328 2144 2717\n", - " 2523 2782 2392 2400 2275 2395 2396 2266 2201 2212 2781 2719 2595 2657\n", - " 2457 2527 2016 2082 2336 2145 2208 2340 2653 2718 2143 2531 2459 2528\n", - " 2139 2140 2401 2205 2332 2076 2651 2650 2399 2525 2720 2714 2074 2138\n", - " 2655 2520 2142 2013 2586]\n", - "60 4\n", - "[ 63 511 251 383 441 191 638 569 313 59 255 316 184 444 125 376 507 380\n", - " 447 438 319 186 575 57 253 699 247 636 189 315 634 446 127 701 445 702\n", - " 504 639 182 56 567 126 382 252 378 314 249 121 122 190 440 439 187 632\n", - " 503 572 375 379 118 571 120 377 254 442 443 633 502 318 510 310 183 637\n", - " 508 188 185 506 250 574 317 570 698 60 62 374 119 246 509 61 505 700\n", - " 248 697 55 312 635 573 124 703 381 58 311 568 123]\n", - "47 63\n", - "[4074 4083 3827 4079 3696 4013 3956 3954 3823 3826 3884 3886 4081 3757\n", - " 3887 4073 4078 3822 3952 3947 3760 3948 3885 3762 4019 4016 3694 3890\n", - " 4018 3698 3949 3828 3950 3763 3891 3824 3946 4085 4076 4015 3758 3893\n", - " 4084 4075 4021 4014 3882 3697 4012 3692 3818 3820 3756 4011 4082 3819\n", - " 3951 3759 3825 4009 3821 3957 4017 3755 3881 4010 3695 3888 3889 4020\n", - " 3953 4080 4077 3883 3693 3761 3892 3955 3945]\n", - "17 14\n", - "[ 594 848 591 785 720 913 845 1104 1232 1039 1035 910 716 850\n", - " 1110 659 976 526 1041 914 1170 1167 596 779 724 978 917 1295\n", - " 655 1099 653 852 974 907 718 719 855 1171 781 1105 780 595\n", - " 721 971 982 1230 784 912 1165 847 791 532 715 789 662 723\n", - " 1046 1237 1299 1103 849 1038 529 722 782 1231 846 908 1106 1042\n", - " 1040 919 790 983 654 1166 843 918 726 1107 1172 1037 1047 1296\n", - " 977 853 783 658 725 597 979 717 854 911 1297 1036 980 1233\n", - " 1111 844 1109 1101 593 787 527 530 1294 1173 975 661 972 660\n", - " 1174 1100 1044 589 851 786 657 1102 973 981 1229 1300 1108 788\n", - " 592 909 590 915 1234 531 916 1298 1236 652 1169 1164 1043 727\n", - " 1168 656 528 1045 1235]\n", - "56 2\n", - "[251 54 441 245 569 313 59 316 184 308 444 500 114 125 376 370 507 380\n", - " 438 186 181 51 57 253 247 189 315 435 445 504 501 182 56 567 126 382\n", - " 252 117 378 179 314 249 121 122 190 116 440 566 439 187 503 375 50 379\n", - " 118 571 120 306 377 254 242 52 442 443 373 502 307 318 310 183 53 508\n", - " 188 185 565 180 436 506 250 317 570 437 309 60 62 374 372 119 371 246\n", - " 61 505 248 55 312 244 124 381 58 115 243 178 311 568 123]\n", - "42 17\n", - "[1388 1262 1130 1136 1132 1383 879 1389 1062 1261 743 1322 940 1002\n", - " 878 1004 1065 871 1511 1003 996 1193 1064 1198 1252 1258 1072 1512\n", - " 1200 1317 1060 1451 1135 1129 1001 876 1450 1067 1327 1126 1452 1255\n", - " 1259 1326 1328 1005 1071 1316 744 1454 999 745 1319 936 1321 1323\n", - " 1447 870 748 1387 877 806 1515 1390 1446 808 1190 1189 1256 1066\n", - " 1008 1448 1320 935 941 810 1516 1324 1128 1385 998 1063 1325 1391\n", - " 807 937 1000 932 1264 1194 1127 1197 1195 811 749 1386 1381 997\n", - " 1134 1514 873 1453 1124 1192 1196 1513 1254 943 875 1384 1449 813\n", - " 1191 1061 1199 1517 812 1382 1007 934 747 1318 1068 1260 1133 938\n", - " 944 1006 1253 872 814 933 942 1131 1257 1070 1263 746 1188 939\n", - " 809 869 1125 1069 874]\n", - "21 0\n", - "[ 17 27 210 83 153 21 81 25 15 340 149 145 281 89 143 404 402 339\n", - " 213 82 144 214 207 341 273 337 80 345 147 208 211 212 152 86 151 85\n", - " 146 343 209 154 338 217 91 407 150 275 79 277 342 405 279 344 20 16\n", - " 276 22 90 148 18 87 88 215 155 280 282 274 403 278 408 216 26 406\n", - " 23 19 24 219 218 272 84]\n", - "6 55\n", - "[3275 3145 3659 3526 3786 3648 3847 3529 3909 3464 3398 3527 3209 3206\n", - " 3402 3140 3913 3392 3714 3524 3205 3332 3587 3460 3456 3274 3843 3333\n", - " 3520 3586 3584 3717 3393 3394 3712 3528 3844 3462 3399 3204 3266 3651\n", - " 3267 3270 3203 3716 3649 3911 3273 3779 3590 3404 3202 3842 3400 3656\n", - " 3845 3458 3331 3268 3848 3141 3337 3265 3463 3585 3207 3459 3718 3522\n", - " 3531 3523 3654 3468 3595 3781 3210 3272 3271 3336 3467 3777 3532 3719\n", - " 3910 3778 3397 3525 3907 3396 3339 3461 3143 3655 3142 3588 3338 3593\n", - " 3591 3521 3780 3144 3395 3589 3594 3657 3846 3658 3850 3330 3401 3722\n", - " 3329 3139 3721 3849 3720 3457 3596 3785 3592 3334 3713 3208 3340 3403\n", - " 3328 3530 3912 3787 3723 3660 3466 3465 3269 3715 3783 3908 3784 3653\n", - " 3335 3782 3652 3650 3724]\n", - "13 13\n", - "[1098 594 848 591 459 785 720 913 845 1104 1232 1039 1035 1228\n", - " 910 777 716 850 839 521 659 976 842 526 1041 914 1167 523\n", - " 779 978 655 1099 653 974 907 524 525 718 719 906 714 781\n", - " 1105 780 458 721 711 775 971 1230 784 778 912 1165 905 847\n", - " 651 715 970 1096 723 1103 849 1038 529 776 722 782 1231 846\n", - " 841 908 1106 1042 1227 1040 654 1166 843 588 1037 461 463 977\n", - " 783 658 650 903 464 1032 1033 712 648 649 979 717 911 968\n", - " 1036 1163 844 522 1101 593 787 527 587 1162 975 972 1100 462\n", - " 460 589 851 786 657 1226 1102 973 1034 1229 967 904 592 909\n", - " 590 915 586 840 1031 969 652 1169 584 647 1164 1043 1168 656\n", - " 585 528 713 1097 1161]\n", - "57 57\n", - "[3903 3583 3832 3711 3387 3827 3839 3959 3705 4086 3577 4026 3517 3899\n", - " 3772 3644 3320 3699 3766 3511 3956 3837 3708 3769 3702 3895 3574 3836\n", - " 3965 4027 3636 3962 3775 3709 4091 3897 3834 3384 3510 4092 3389 3445\n", - " 3581 3386 3902 4029 3830 3572 3901 3838 4028 3383 3382 3579 3513 3828\n", - " 3960 3700 3324 3763 3891 3643 3706 3765 3961 4090 3578 3448 3771 3893\n", - " 3958 4021 3444 4022 4025 3642 3646 3575 4024 3641 3707 3701 3321 3507\n", - " 3452 3639 3514 3451 3764 3381 3894 3966 3319 3450 3447 3896 3900 3322\n", - " 3515 3957 3898 4088 3318 3385 3454 3647 3768 3637 4089 3773 3640 3518\n", - " 3449 3573 3635 3453 3509 3519 3645 3516 3831 3512 3767 3582 3835 3833\n", - " 3571 3774 3638 3963 3508 3964 3704 3710 3829 4087 3703 3388 3446 3576\n", - " 3892 3580 3770 4023 3323]\n", - "35 26\n", - "[1826 1765 1695 2084 1704 1570 1888 1508 1892 1502 2022 1699 1895 1315\n", - " 1383 1697 1504 2018 1638 1636 1442 2015 1885 1445 1760 1575 1833 1376\n", - " 1509 1505 1503 1572 1763 1511 1952 2017 1512 1768 1829 1317 1566 1640\n", - " 1957 1769 1954 1832 2020 1822 1641 1894 1576 1571 1955 1698 1316 1758\n", - " 1828 1757 1633 1444 1693 1506 1447 1313 1890 2083 1379 2080 1635 1896\n", - " 1510 2021 1950 1821 1378 1446 2019 1886 1577 1448 1956 1959 1767 1314\n", - " 1827 1825 1694 1630 1887 2081 1501 1703 1762 1705 1632 1375 1960 2086\n", - " 1440 1700 1951 2023 1701 1889 1381 1569 1897 1893 1573 1438 1507 2016\n", - " 1958 2082 1953 1513 1565 1830 1639 1637 1759 1439 1696 1382 1380 1567\n", - " 1312 1823 1629 1318 1702 1831 1766 1824 1574 1891 1443 1634 1631 1764\n", - " 2085 1761 1441 1568 1377]\n", - "51 42\n", - "[2542 2861 2801 2354 2739 2671 2416 2927 2420 2930 2678 2612 2545 2617\n", - " 2541 2933 2418 3058 2679 2806 2807 2742 2872 3055 2738 2990 2741 2993\n", - " 2873 2609 2419 3121 2488 2935 2352 3000 2480 2740 2864 2936 2997 2805\n", - " 2674 2553 2928 2544 3125 2869 2550 2798 2934 2552 2996 2422 2608 3061\n", - " 3123 2670 2355 2868 3063 2478 2417 2607 2995 2863 2867 3126 2992 2421\n", - " 2870 2358 2736 2937 2549 2999 2680 3056 2548 2803 2357 3122 2479 2802\n", - " 2483 2415 2423 2676 2482 2737 2669 2353 2486 2611 2865 2615 2614 2672\n", - " 2734 2613 3059 2991 2543 2929 2804 2744 2998 2808 2871 2994 2931 2675\n", - " 2606 3120 2673 2485 2800 2797 3057 2925 2484 2866 2681 2610 2809 2481\n", - " 2356 2487 2733 2546 2547 2745 2932 2926 2862 2799 3062 3060 2743 2735\n", - " 2605 2551 2677 2616 3124]\n", - "43 1\n", - "[ 45 38 239 107 168 41 177 237 47 364 426 302 360 170 175 431 428 167\n", - " 46 234 105 423 111 304 230 103 300 430 240 165 238 368 296 429 232 102\n", - " 113 241 425 365 494 108 110 362 101 174 424 297 303 359 366 40 173 301\n", - " 488 172 489 176 104 112 294 49 43 361 39 305 491 231 358 298 169 233\n", - " 229 493 299 44 42 363 295 171 367 293 166 492 427 48 236 106 37 109\n", - " 235 490]\n", - "48 17\n", - "[1388 1392 1262 1330 1130 1136 1132 1519 1078 879 1389 1521 1203 947\n", - " 1261 1322 940 1002 878 818 1004 1394 1003 882 1198 1258 1072 1200\n", - " 819 1140 1135 1269 876 1067 1139 1327 1452 1259 1326 1331 1455 1328\n", - " 1005 1009 1071 1460 1456 1014 1202 1268 1454 949 1522 754 1523 1323\n", - " 1011 1334 1387 877 1329 885 1205 1393 1142 1390 1520 1138 820 1075\n", - " 815 1066 1008 1396 941 1141 1457 1395 1010 1324 1204 1325 1391 1270\n", - " 948 1264 946 1194 1197 1076 1195 749 750 1073 751 1013 950 753\n", - " 1077 1134 755 1453 1459 881 1332 1196 1265 943 875 1266 1397 813\n", - " 1199 1517 812 1007 945 817 1267 1137 1074 1012 1068 1260 1133 938\n", - " 880 944 1006 884 814 1201 942 1131 1070 1263 939 1206 1458 816\n", - " 752 883 1518 1069 1333]\n", - "29 63\n", - "[4055 4061 3865 4065 3996 4001 4066 3676 4064 3678 3937 3741 3874 3810\n", - " 3993 4062 3801 4057 3675 3933 3738 4058 3866 3742 3802 3806 3995 3679\n", - " 3745 3872 4060 3871 3863 3869 3739 3927 3867 3928 3999 3929 3674 3680\n", - " 4002 3744 3868 4067 3875 3737 4000 3809 3873 3934 4056 3803 3930 3743\n", - " 3994 3677 3997 3935 3938 3998 3932 4003 4059 3808 3807 3936 4063 3805\n", - " 3740 3931 3870 3991 3800 3939 3804 3992 3864]\n", - "52 4\n", - "[ 54 441 245 239 569 313 184 308 500 114 376 370 438 631 177 186 181 51\n", - " 57 247 47 302 369 563 564 435 175 431 627 630 495 498 504 501 182 56\n", - " 567 694 111 304 117 378 179 314 430 249 689 240 121 122 116 238 440 368\n", - " 560 566 439 113 241 632 503 625 499 375 50 118 494 110 120 306 377 242\n", - " 52 690 442 174 497 695 373 502 303 307 366 432 626 629 562 310 183 53\n", - " 176 112 185 49 565 180 436 506 305 692 250 559 437 434 309 374 372 693\n", - " 119 371 246 561 505 367 433 248 55 312 48 244 496 624 691 628 115 243\n", - " 178 311 568]\n", - "22 38\n", - "[2322 2449 2454 2195 2705 2326 2267 2067 2456 2394 2516 2131 2200 2263\n", - " 2325 2713 2136 2257 2578 2196 2840 2647 2265 2327 2712 2521 2453 2387\n", - " 2582 2522 2711 2391 2451 2839 2583 2709 2517 2198 2134 2770 2584 2331\n", - " 2835 2715 2455 2585 2072 2193 2268 2778 2640 2384 2262 2524 2652 2070\n", - " 2518 2460 2258 2776 2324 2330 2393 2577 2646 2708 2773 2710 2777 2385\n", - " 2203 2390 2649 2579 2329 2133 2515 2772 2580 2202 2643 2588 2264 2458\n", - " 2706 2514 2388 2587 2386 2137 2328 2707 2523 2513 2576 2392 2645 2644\n", - " 2450 2395 2073 2194 2396 2266 2201 2132 2457 2135 2259 2837 2642 2648\n", - " 2448 2199 2512 2389 2838 2320 2775 2641 2321 2459 2071 2581 2197 2332\n", - " 2651 2650 2452 2836 2714 2068 2260 2138 2069 2130 2323 2520 2256 2261\n", - " 2841 2519 2774 2771 2586]\n", - "9 21\n", - "[1098 1420 1353 1093 1035 1547 1228 1358 1549 1737 1167 1350 1605 1295\n", - " 1099 1674 1219 1095 1612 1351 1478 1739 1291 1543 1287 1348 971 1158\n", - " 1230 1672 1548 1155 1423 1165 1413 1421 1284 1671 1476 970 1292 1096\n", - " 1286 1483 1484 1675 1544 1481 1029 1160 1546 1222 1224 1349 966 1542\n", - " 1676 1221 1231 1541 1480 1285 1223 1550 1545 1414 1357 1092 1227 1540\n", - " 1539 1607 1166 1485 1354 1347 1670 1356 1735 1037 1734 1159 1677 1412\n", - " 1604 1283 1288 1738 1613 1289 1032 1033 1606 1293 968 1611 1036 1411\n", - " 1163 1290 1101 1294 1482 1162 1359 1479 972 1100 1487 1226 1102 1034\n", - " 1229 1352 1355 1418 1673 1417 967 1419 1030 1551 1031 1422 1486 969\n", - " 1157 1416 1610 1164 1736 1609 1094 1477 1097 1161 1740 1475 1614 1156\n", - " 1220 1225 1608 1669 1415]\n", - "39 29\n", - "[1826 1765 2027 2084 1704 2093 2089 1508 1892 2022 1836 1699 1895 1642\n", - " 1697 2018 1638 2214 1636 1965 2276 1708 1575 2088 1773 1833 1509 1572\n", - " 2155 1763 2087 1511 1963 1772 2147 2152 2026 2017 1834 1578 2090 1512\n", - " 1579 1768 1829 2153 1771 1640 1957 1769 1954 2149 1832 2020 1641 1894\n", - " 1576 1571 1955 2282 1961 1698 1901 1828 1709 2218 2277 1707 1962 1890\n", - " 2146 2083 1837 2150 2278 1635 1835 1964 1896 1770 1510 2021 1899 2154\n", - " 2019 1577 1956 1959 2219 2025 1767 2211 1900 2151 2279 2217 1827 1825\n", - " 2081 1703 1644 1762 1705 1960 2086 1700 2156 2215 2216 2023 2212 1701\n", - " 2092 1889 1897 1893 1514 1573 1958 2082 1953 1513 1830 1639 1637 2280\n", - " 1643 1702 2024 1706 1831 1766 1898 1574 1891 2213 2148 2091 1634 2029\n", - " 1764 2281 2085 1761 2028]\n", - "43 38\n", - "[2093 2089 2542 2861 2406 2159 2475 2661 2671 2416 2284 2214 2408 2730\n", - " 2222 2732 2349 2536 2545 2088 2859 2795 2344 2413 2414 2541 2411 2155\n", - " 2342 2348 2345 2602 2474 2535 2538 2152 2090 2153 2796 2860 2609 2352\n", - " 2480 2533 2407 2473 2544 2662 2600 2668 2601 2282 2346 2409 2798 2791\n", - " 2534 2218 2608 2277 2289 2670 2856 2540 2224 2343 2278 2478 2417 2607\n", - " 2598 2472 2729 2288 2154 2736 2219 2597 2663 2350 2158 2220 2477 2604\n", - " 2151 2279 2217 2664 2794 2286 2599 2667 2351 2539 2857 2471 2479 2415\n", - " 2405 2347 2726 2156 2669 2353 2215 2216 2092 2672 2734 2792 2543 2793\n", - " 2287 2858 2283 2606 2727 2673 2280 2728 2469 2797 2412 2094 2410 2157\n", - " 2537 2731 2481 2285 2665 2733 2091 2603 2223 2281 2862 2799 2341 2476\n", - " 2735 2605 2470 2221 2666]\n", - "13 4\n", - "[ 17 329 210 591 83 459 12 141 401 269 467 81 73 15 334 145 200 521\n", - " 143 526 332 523 402 339 655 653 524 525 201 82 144 139 207 13 458 273\n", - " 337 136 80 393 14 651 147 208 211 75 263 11 146 396 529 76 209 338\n", - " 202 74 456 392 10 654 275 588 8 79 77 461 268 463 140 650 16 335\n", - " 138 464 267 455 398 327 142 400 206 399 18 331 465 522 593 527 530 336\n", - " 587 264 394 462 457 460 589 72 266 274 78 592 199 330 9 590 265 271\n", - " 403 586 204 205 333 397 71 652 135 520 270 391 272 137 656 585 528 203\n", - " 328 395 466]\n", - "12 28\n", - "[1809 1682 1741 2000 1553 1870 1420 2191 1804 1679 1547 1997 1549 1737\n", - " 2062 1806 1808 1930 1674 1866 1680 1932 1863 2056 1612 1799 2188 1873\n", - " 1739 1617 1543 2125 1488 1867 1672 2064 1869 1928 1548 1423 1933 1421\n", - " 1671 2185 1990 1798 2127 2058 1807 1868 2126 1931 1864 1483 1937 1484\n", - " 1675 1544 1481 2002 1546 1802 1676 2059 1742 2186 2065 1803 1480 1550\n", - " 1545 2128 1607 1801 1485 2122 2121 1670 1735 1734 1927 1800 1677 1995\n", - " 2120 1738 1993 1613 2001 1991 1678 1746 1935 1606 1611 1810 1992 1926\n", - " 1929 1616 1934 1618 1482 1615 1936 1487 1862 1999 1418 1673 2055 1744\n", - " 1417 2061 1938 1681 1419 1551 1805 1871 1422 1486 2057 1872 2187 1996\n", - " 2060 1874 1610 1743 1865 1736 1609 2063 2189 2123 2124 2190 1740 1614\n", - " 1998 1608 1745 1994 1552]\n", - "44 17\n", - "[1388 1392 1262 1330 1130 1136 1132 1519 1383 879 1389 1062 1261 1322\n", - " 940 1002 878 1004 1065 871 1003 1193 1064 1198 1258 1072 1200 1451\n", - " 1135 1129 1001 876 1450 1067 1327 1126 1452 1255 1259 1326 1455 1328\n", - " 1005 1009 1071 1456 1202 1454 999 745 1319 936 1321 1323 748 1387\n", - " 877 1329 1515 1393 1390 1138 808 1190 815 1256 1066 1008 1448 1320\n", - " 935 941 810 1516 1010 1324 1128 1385 998 1063 1325 1391 937 1000\n", - " 1264 946 1194 1127 1197 1195 811 749 750 1073 751 1386 1134 1514\n", - " 873 1453 881 1192 1196 1513 1254 1265 943 875 1384 1266 1449 813\n", - " 1191 1199 1517 812 1007 945 934 747 1137 1074 1318 1068 1260 1133\n", - " 938 880 944 1006 872 814 1201 942 1131 1257 1070 1263 746 939\n", - " 816 809 1518 1069 874]\n", - "60 6\n", - "[ 63 511 251 383 831 441 191 638 569 313 59 255 316 184 444 125 376 507\n", - " 380 447 438 631 767 319 186 575 57 253 699 247 826 636 189 829 315 830\n", - " 634 446 127 630 701 445 702 504 639 827 567 761 126 382 252 378 314 249\n", - " 121 122 190 440 566 439 764 187 828 760 632 503 572 375 379 571 120 377\n", - " 254 442 443 695 633 765 502 318 510 766 696 310 183 637 508 188 185 506\n", - " 250 574 317 570 698 60 62 374 246 509 762 763 61 505 700 248 697 312\n", - " 635 573 124 703 381 58 825 311 568 123]\n", - "52 7\n", - "[441 245 239 569 313 184 308 500 114 376 370 438 631 177 757 181 247 302\n", - " 756 369 818 563 564 435 634 882 431 819 627 630 495 498 887 821 504 688\n", - " 501 182 567 886 761 694 304 117 378 179 754 314 430 249 558 689 240 116\n", - " 440 368 560 566 439 113 241 760 632 503 885 625 499 375 118 820 494 306\n", - " 377 242 824 690 442 497 695 633 373 502 303 307 366 432 626 629 562 696\n", - " 310 183 687 176 565 180 436 622 506 305 692 559 751 753 570 698 437 755\n", - " 434 309 881 822 374 372 693 119 371 246 561 505 817 367 433 248 697 758\n", - " 312 884 244 496 623 624 686 823 691 628 115 816 752 759 243 178 883 311\n", - " 568]\n", - "59 34\n", - "[1976 2235 2238 1914 2617 1983 2557 1978 2294 2230 2618 1854 2167 1849\n", - " 2491 1848 2046 2489 2492 2360 2109 2166 2425 2366 1912 2488 2105 2302\n", - " 2298 2108 2044 2431 2553 2293 2363 2296 2111 2300 2427 2103 2552 2170\n", - " 1979 1918 2422 2490 2554 1916 2041 2558 2169 1851 2365 2038 2171 2429\n", - " 2421 2039 2106 2037 2174 2358 2430 2621 1982 2367 1853 1913 2357 2428\n", - " 2236 1915 2239 1980 2556 2237 2423 2555 1919 2043 2486 2295 2559 1981\n", - " 2175 2234 2494 2045 2040 2231 2303 2047 2104 1850 2229 1975 2620 2619\n", - " 2110 2165 2301 1911 2622 1974 2101 1977 2168 2362 2172 1852 2426 2299\n", - " 2232 2495 2487 2173 2493 2359 2364 2102 1917 2361 2107 2424 2551 2616\n", - " 2233 2042 2297]\n", - "15 61\n", - "[4047 3858 3730 3659 3914 3919 3538 3786 4043 3985 3599 4051 3790 3923\n", - " 3983 3668 3980 3853 4041 3851 3979 3922 3534 3726 3913 3728 3987 3924\n", - " 3982 3732 3861 3859 3854 3788 3978 4052 3598 3915 4042 3986 3663 3920\n", - " 3536 3921 3856 3989 3537 3796 3977 3791 3855 4048 3595 3925 3795 3797\n", - " 3603 3731 3600 3725 3666 3532 3535 4053 3793 3852 4044 3664 3601 3916\n", - " 3789 3533 4045 3665 4050 3597 3667 3658 3850 3722 3721 3727 3849 3602\n", - " 3792 3981 3917 4049 3596 3729 3785 3860 3662 3733 3787 3988 3857 3723\n", - " 3660 3984 3661 3918 4046 3794 3724]\n", - "37 41\n", - "[2529 2404 2922 2406 2402 2721 2475 2661 2847 2532 2592 2408 2730 2659\n", - " 2276 2464 2536 2859 2983 2795 2591 2344 2468 2593 2342 2345 2602 2723\n", - " 2596 2783 2474 2535 2538 2530 3044 3046 2978 2658 2848 2533 2407 2473\n", - " 2338 2977 2850 2662 2600 2601 2466 2409 2920 2791 2534 2919 2788 3045\n", - " 2277 3048 2856 2854 2594 2337 2722 2343 2278 3047 2598 2472 2921 2729\n", - " 2789 2918 2981 2274 2982 2785 2339 2913 2597 3042 2663 2984 2784 2467\n", - " 2279 2916 2664 2794 2463 2599 2667 2656 2465 2539 2857 2471 2917 2787\n", - " 2405 2403 2855 2849 2915 2726 2980 2400 2275 2660 2786 2912 2719 2792\n", - " 2595 2657 2527 2724 2851 2793 2914 2858 2979 2727 2340 2280 2728 2469\n", - " 2531 2725 2410 2852 2528 2790 2537 2731 2401 2853 2665 2603 3043 2720\n", - " 2985 2655 2341 2470 2666]\n", - "14 48\n", - "[2957 3345 3275 3217 3145 2832 2891 3024 3086 3215 3342 3408 3025 2959\n", - " 2705 3148 3027 2765 3023 2767 2897 2762 3280 2960 3089 3154 2955 2963\n", - " 3209 3402 3151 3084 3091 2896 3470 3018 3146 2766 2825 3274 3346 2964\n", - " 2827 2770 3472 2900 3155 2835 2898 2768 3273 3082 3090 3017 3085 3344\n", - " 3404 3080 2703 2702 3218 3284 2893 3087 3337 3026 3153 3277 3410 3219\n", - " 2833 2830 2889 2834 3468 3210 3341 3272 3343 3467 3471 3083 3028 2826\n", - " 3282 3406 3409 2700 3021 2764 3149 2956 3339 3283 3405 3220 3156 3338\n", - " 3152 3347 3016 3144 2888 2895 2953 3092 2828 3276 3469 2899 3473 2829\n", - " 2831 2962 3150 3212 2894 3278 3216 3214 2769 3081 2890 3019 3208 3088\n", - " 3213 3340 3403 2952 3281 2892 2961 2704 3211 2763 3407 2954 3279 3020\n", - " 2958 3022 2699 2701 3147]\n", - "35 20\n", - "[1570 1508 1502 1699 1315 1383 1697 1504 1638 1062 1636 1442 1311 1445\n", - " 1575 1376 1121 1509 1122 1505 1503 1572 1511 996 1193 1064 1252 1512\n", - " 1117 1317 1566 1060 1129 928 1245 1310 1126 1186 1255 1057 1576 1571\n", - " 1374 1437 1698 1316 1249 999 993 1633 1319 1444 1506 1321 1447 1313\n", - " 1183 1379 1635 1181 1510 1378 1446 1190 1189 1256 1448 1320 1373 1314\n", - " 1246 1128 1385 998 1187 1184 1063 994 1501 932 1632 1375 1185 1127\n", - " 1123 1440 1700 931 1248 1701 1381 1569 997 1573 1438 1124 1507 1058\n", - " 1192 1250 1513 992 1254 1639 1384 1055 1637 1439 1449 1191 1061 1696\n", - " 1382 1380 1567 1312 929 934 1318 1702 1251 1118 1059 1054 1056 1120\n", - " 1253 1119 1574 1443 930 933 1634 1631 1257 1188 1182 1441 995 1309\n", - " 1125 1247 991 1568 1377]\n", - "54 63\n", - "[3832 4083 3827 3959 3705 4086 4026 3899 3699 3766 3956 3769 3954 3702\n", - " 3895 3826 4027 4081 3962 3952 4091 3897 3834 4092 3762 4019 4016 3830\n", - " 3890 4028 4018 3828 3960 3700 3763 3891 3765 4085 3961 4090 3893 4084\n", - " 3958 4021 4022 4025 4024 3701 3764 3894 4082 3896 3825 3900 3957 3898\n", - " 4088 3768 4017 4089 3831 3767 3835 3833 3888 3889 4020 3953 4080 3963\n", - " 3964 3704 3829 4087 3703 3892 3770 3955 4023]\n", - "50 1\n", - "[ 45 54 245 239 184 308 500 114 370 438 177 181 51 247 237 47 302 369\n", - " 435 175 431 46 495 498 501 182 56 111 304 117 179 300 430 240 116 238\n", - " 368 113 241 365 499 375 50 118 108 110 120 306 242 52 174 497 373 303\n", - " 307 366 432 173 301 310 183 53 172 176 112 49 180 436 305 437 434 309\n", - " 374 372 119 371 246 44 367 433 248 55 312 48 244 496 236 115 109 243\n", - " 178 311]\n", - "19 7\n", - "[594 848 210 591 83 785 401 720 269 467 81 340 149 334 145 281 850 659\n", - " 143 526 404 472 537 596 724 402 339 655 213 653 852 525 718 471 409 719\n", - " 535 82 144 214 207 341 595 665 721 664 273 337 784 80 600 791 532 345\n", - " 147 208 211 212 468 789 662 723 86 151 85 146 849 343 529 722 209 338\n", - " 534 599 407 790 150 654 601 275 469 726 728 277 342 405 461 463 473 853\n", - " 279 783 344 658 725 597 335 464 276 398 536 854 400 148 206 399 533 465\n", - " 593 787 527 530 336 661 660 215 462 589 851 786 657 280 274 788 592 590\n", - " 271 403 531 470 278 333 408 216 406 663 397 598 270 272 727 656 84 528\n", - " 466]\n", - "47 23\n", - "[1388 1392 1710 1840 1262 1330 1713 1836 1136 1132 1519 1642 1524 1581\n", - " 1389 1521 1203 1842 1651 1461 1261 1708 1322 1773 1525 1394 1839 1772\n", - " 1712 1841 1588 1198 1578 1258 1652 1579 1200 1451 1771 1648 1135 1450\n", - " 1327 1452 1259 1641 1326 1331 1455 1715 1328 1905 1778 1460 1649 1456\n", - " 1901 1717 1903 1779 1202 1268 1454 1709 1585 1522 1707 1523 1321 1323\n", - " 1837 1586 1387 1835 1584 1329 1770 1515 1393 1390 1902 1520 1138 1714\n", - " 1776 1904 1577 1396 1516 1457 1900 1395 1650 1324 1385 1325 1391 1644\n", - " 1838 1705 1264 1197 1587 1777 1716 1195 1386 1134 1514 1453 1459 1332\n", - " 1774 1196 1513 1265 1780 1266 1397 1449 1199 1517 1645 1646 1643 1267\n", - " 1653 1137 1260 1133 1706 1775 1589 1201 1583 1263 1647 1582 1843 1711\n", - " 1458 1580 1518 1333 1906]\n", - "56 48\n", - "[3387 3128 3320 2930 3511 3187 3379 3316 2933 3004 3058 2806 3189 2807\n", - " 2742 3258 3191 3129 2872 2741 3003 3196 2873 2811 3384 3262 3510 2935\n", - " 3389 3000 3445 2936 2997 2805 3064 3125 2877 2869 2940 3386 3130 3253\n", - " 2934 3260 3252 2996 3061 3123 3383 3250 2868 3382 3380 3513 3324 3261\n", - " 3326 3002 3063 2995 3192 3186 2867 3317 3126 2870 3448 2747 3444 3257\n", - " 2875 2938 3188 3195 3256 2937 2999 3006 3321 3193 3452 2939 2941 3514\n", - " 3451 3122 3131 3381 3319 3450 3447 3070 3067 3194 2812 3197 3005 3322\n", - " 3515 3318 3132 3001 3385 3059 2804 2744 3066 2998 2746 2808 2871 3449\n", - " 2994 2931 3314 2876 3509 3198 3133 3512 3134 3315 2942 3259 3251 2809\n", - " 3254 2810 2745 2874 2932 3388 3255 3446 3190 3065 3062 3068 3060 3069\n", - " 2743 3325 3124 3127 3323]\n", - "16 17\n", - "[1098 848 1361 785 720 1420 913 845 1104 1232 1039 1035 1228 910\n", - " 1358 850 1110 976 1489 1041 914 1170 1167 978 917 1295 1099 852\n", - " 974 907 718 1363 719 906 1171 781 1105 780 1291 721 1488 971\n", - " 1362 982 1230 784 912 1423 1165 847 1421 1490 970 1292 723 1046\n", - " 1237 1299 1427 1103 849 1491 1038 722 782 1231 846 908 1106 1042\n", - " 1357 1227 1040 1426 1166 1485 843 918 1238 1107 1172 1356 1037 1296\n", - " 977 853 783 1425 1365 979 717 1293 911 1297 1036 980 1233 1163\n", - " 844 1290 1109 1101 787 1294 1173 1162 1359 975 972 1174 1100 1487\n", - " 1044 851 786 1226 1102 973 981 1034 1360 1229 1300 1355 1108 788\n", - " 909 915 1234 1302 916 1422 1486 1298 1236 1169 1424 1428 1301 1364\n", - " 1164 1043 1168 1045 1235]\n", - "16 19\n", - "[1098 848 1361 1553 1420 913 845 1104 1232 1039 1555 1035 1228 910\n", - " 1358 850 1549 1110 976 1489 1041 914 1170 1167 978 1295 1099 974\n", - " 1363 1171 1105 1291 1617 1488 971 1362 1230 912 1548 1492 1423 1165\n", - " 1554 847 1421 1490 1292 1046 1483 1237 1619 1484 1299 1427 1103 849\n", - " 1491 1038 1231 846 1550 908 1106 1042 1357 1227 1040 1426 1166 1485\n", - " 1354 1238 1107 1172 1356 1037 1296 977 1425 1365 1613 1493 979 1293\n", - " 911 1297 1036 980 1233 1163 1290 1616 1109 1101 1430 1618 1294 1173\n", - " 1162 1615 1359 975 972 1174 1100 1487 1044 851 1226 1102 973 981\n", - " 1034 1360 1229 1300 1355 1418 1108 909 915 1234 1419 1302 1551 916\n", - " 1422 1556 1486 1366 1298 1236 1169 1424 1428 1301 1364 1164 1043 1429\n", - " 1168 1045 1614 1235 1552]\n", - "42 25\n", - "[1388 1765 1710 2027 1840 1704 1508 1836 1895 1519 1642 1581 1966 1383\n", - " 1638 1389 1636 1965 1445 1261 1708 1322 1575 1773 1833 1509 1572 1839\n", - " 1511 1963 1772 1712 2026 1834 1578 1258 1512 1579 1768 1829 1451 1771\n", - " 1648 1640 1769 1450 1452 1832 1255 1259 1641 1326 1894 1576 1455 1961\n", - " 1456 1901 1828 1903 1454 1709 1319 1707 1962 1444 1321 1323 1447 1837\n", - " 1387 1835 1584 1964 1896 1770 1515 1510 1390 1899 1902 1520 1446 1776\n", - " 1256 1577 1448 1320 1959 2025 1767 1516 1900 1324 1385 1325 1391 1703\n", - " 1644 1838 1705 1960 1700 2023 1701 1386 1381 1897 1893 1514 1573 1453\n", - " 1958 1774 1513 1830 1639 1384 1637 1449 1517 1645 1646 1382 1643 1318\n", - " 1702 1260 2024 1706 1831 1775 1766 1898 1574 2029 1583 1257 1764 1647\n", - " 1582 1711 1580 1518 2028]\n", - "19 34\n", - "[2322 1809 2000 2449 2191 2454 2195 2317 1875 2326 1997 2129 2319 2062\n", - " 2067 2456 2004 2516 2131 1808 2200 2263 2325 2136 2257 2578 2196 2006\n", - " 2265 2318 2327 2008 2453 1873 2387 2254 2582 1878 2255 2391 2125 2451\n", - " 2447 2517 2064 2198 2134 2381 2455 2127 2072 2193 2126 2005 1937 1943\n", - " 2384 2262 1811 1813 2002 2070 2518 2065 2258 2324 2393 2577 2128 1877\n", - " 1939 2385 1942 1941 2390 2579 2329 2133 2515 2003 2580 2383 2192 2253\n", - " 2264 2514 2388 2001 2386 2007 2137 2511 1935 2328 1812 2513 2576 2392\n", - " 1810 2450 2073 2194 1879 2201 1934 2132 1936 2135 2066 2259 1999 1814\n", - " 2061 1938 2448 2199 2512 2389 2320 2321 1871 2071 2581 2197 1872 2009\n", - " 2452 2382 1874 1876 2446 2068 2260 2063 2189 2069 2130 2323 2256 2190\n", - " 2261 1998 1940 2519 1944]\n", - "40 2\n", - "[ 45 38 107 168 35 163 41 237 364 426 302 360 170 421 164 428 167 46\n", - " 234 226 105 98 550 420 423 485 99 230 103 300 228 165 238 296 429 36\n", - " 232 551 102 100 549 425 365 108 110 486 362 101 174 424 290 552 297 359\n", - " 554 366 555 40 487 356 173 553 301 488 172 489 104 294 43 361 39 162\n", - " 354 355 357 34 491 231 227 358 298 292 169 419 233 229 299 291 44 42\n", - " 363 295 171 293 166 492 427 236 422 484 106 37 109 235 490]\n", - "17 1\n", - "[ 17 210 83 12 141 401 21 269 467 81 15 340 149 334 145 143 404 332\n", - " 402 339 213 82 144 214 139 207 341 13 273 337 80 14 147 208 211 212\n", - " 468 75 11 86 151 85 146 76 209 338 150 275 79 277 342 405 77 268\n", - " 463 279 140 20 16 335 464 267 276 398 142 22 400 148 206 399 18 465\n", - " 87 336 215 462 274 78 271 403 278 204 205 333 23 19 397 270 272 84\n", - " 203 466]\n", - "52 23\n", - "[1392 1710 1840 1907 1330 1713 1519 1524 1782 1521 1399 1203 1842 1651\n", - " 1461 1785 1525 1402 1394 1207 1712 1909 1848 1841 1588 1652 1200 1140\n", - " 1401 1648 1465 1269 1139 1327 1326 1331 1336 1335 1455 1715 1328 1905\n", - " 1778 1460 1649 1456 1717 1779 1658 1202 1268 1454 1338 1585 1522 1594\n", - " 1143 1523 1592 1334 1719 1586 1846 1584 1329 1273 1847 1908 1530 1205\n", - " 1463 1393 1142 1390 1520 1138 1714 1776 1466 1396 1527 1271 1141 1457\n", - " 1395 1593 1657 1650 1204 1783 1845 1391 1270 1464 1337 1264 1910 1587\n", - " 1718 1777 1716 1591 1529 1784 1781 1398 1459 1332 1265 1780 1655 1720\n", - " 1272 1266 1397 1528 1721 1844 1911 1646 1400 1267 1653 1137 1590 1654\n", - " 1462 1775 1656 1589 1722 1201 1526 1583 1263 1647 1582 1843 1206 1711\n", - " 1458 1208 1518 1333 1906]\n", - "47 41\n", - "[2922 2542 2861 2801 2354 2739 2475 2671 2416 2927 2284 2420 2930 2730\n", - " 2732 2349 3053 2612 2545 2859 2795 2413 2414 2541 2411 3054 2418 2348\n", - " 3058 2602 2474 2538 3055 2738 2990 2741 2993 2796 2860 2609 2419 2352\n", - " 2480 2740 2864 2473 2805 2674 2928 2544 2869 2668 2601 2798 2608 2289\n", - " 2670 2355 2868 2540 2478 2417 2607 2995 2863 2729 2867 2992 2288 2989\n", - " 2923 2736 2350 2477 2604 2549 2987 3056 2548 2794 2286 2803 2667 2351\n", - " 2539 2857 2479 2802 2483 2290 2415 2676 2482 2347 2737 2669 2924 2353\n", - " 2611 2865 2672 2734 2613 2991 2543 2929 2804 2988 2793 2994 2931 2287\n", - " 2675 2858 2606 2673 2485 2800 2797 3052 2412 3057 2925 2410 2484 2866\n", - " 2537 2731 2610 2481 2285 2665 2733 2603 2546 2547 2932 2926 2862 2799\n", - " 2476 2735 2605 2677 2666]\n", - "31 44\n", - "[2529 2721 2661 2847 2589 2592 2461 2659 2780 2464 2973 2591 2843 2593\n", - " 2713 2844 3040 2654 2723 2596 2783 2530 3170 2716 3044 2526 2978 2658\n", - " 2848 3105 2779 3168 2974 2977 3230 2850 2462 2905 2715 2466 2911 2842\n", - " 2788 3045 2906 2778 2594 2722 2524 2652 3102 3038 3229 2909 2460 3163\n", - " 3034 2789 2981 2777 2785 3041 3231 2913 3167 2649 3233 2907 3042 3036\n", - " 2784 3171 2590 2916 2588 2463 2587 2656 2465 3037 3234 2917 2787 2849\n", - " 2717 3098 2523 2782 2915 2980 3107 3101 2660 2786 3232 2781 2912 3108\n", - " 2845 2719 2595 2657 2527 2724 2851 3169 3099 2976 2975 3100 2914 3033\n", - " 3035 2979 3228 2653 3164 3103 2718 2969 3104 2910 2531 2725 2852 2528\n", - " 3166 2853 3165 3039 2651 2908 2650 3043 2970 2525 3106 2720 2714 2846\n", - " 2971 2655 2972 2841 2586]\n", - "13 36\n", - "[2322 2000 2449 2191 2195 2317 2444 1997 2129 2319 2062 2314 2509 2441\n", - " 2131 2445 2257 2380 1930 2313 2578 1932 2637 2056 2318 2188 2569 2375\n", - " 2387 2254 2255 2125 2378 2451 2633 2447 2064 2439 1933 2381 2377 2639\n", - " 2571 2698 2185 2443 2510 2127 2058 2193 2126 1931 2640 2384 2442 2249\n", - " 2570 2376 2703 2379 2702 2059 2186 2568 2250 2065 2258 2572 2577 2128\n", - " 2122 2385 2121 2515 2383 1995 2192 2253 2440 2120 2315 2514 2251 1993\n", - " 2247 2636 2001 2386 2511 2635 1935 2700 2119 2513 2503 2576 2450 2194\n", - " 2312 1934 2505 1936 2184 2066 2259 2508 1999 2634 2061 2183 2448 2512\n", - " 2320 2641 2321 2575 2057 2507 2187 1996 2574 2506 2316 2382 2060 2252\n", - " 2446 2504 2704 2311 2063 2189 2130 2638 2323 2123 2256 2124 2190 2248\n", - " 1998 2573 2699 2701 1994]\n", - "29 40\n", - "[2529 2402 2721 2398 2847 2589 2272 2592 2461 2267 2659 2780 2456 2464\n", - " 2394 2270 2973 2591 2843 2593 2713 2333 2844 2654 2723 2840 2783 2647\n", - " 2530 2265 2712 2521 2716 2522 2711 2391 2526 2658 2583 2848 2779 2974\n", - " 2338 2584 2850 2331 2462 2905 2715 2466 2455 2585 2911 2842 2204 2268\n", - " 2206 2906 2778 2594 2337 2722 2335 2524 2271 2652 2269 2909 2460 2776\n", - " 2330 2393 2777 2397 2785 2913 2203 2649 2907 2329 2202 2784 2467 2273\n", - " 2590 2588 2458 2463 2207 2587 2656 2465 2334 2787 2403 2328 2849 2717\n", - " 2523 2782 2392 2400 2395 2396 2266 2786 2781 2912 2845 2719 2595 2657\n", - " 2457 2527 2976 2975 2336 2208 2648 2653 2718 2910 2531 2775 2459 2528\n", - " 2401 2205 2332 2651 2908 2650 2399 2970 2525 2720 2714 2846 2971 2655\n", - " 2520 2972 2841 2519 2586]\n", - "25 0\n", - "[ 27 83 153 21 25 149 222 281 89 94 156 213 409 214 349 341 285 30\n", - " 28 345 147 211 212 158 152 348 86 151 85 343 92 154 217 347 91 407\n", - " 159 150 31 277 342 279 221 29 220 284 344 411 20 276 223 22 90 148\n", - " 95 286 87 157 88 215 155 280 282 346 283 278 408 216 26 406 23 19\n", - " 24 93 219 218 84 412 410]\n", - "9 1\n", - "[ 6 329 459 12 141 269 73 15 334 200 133 143 261 332 201 260 70 139\n", - " 207 13 458 136 393 14 4 325 75 263 390 11 396 76 202 74 456 392\n", - " 10 68 8 79 77 268 140 138 132 267 455 5 327 326 142 206 331 197\n", - " 134 324 264 394 7 457 460 72 266 78 196 199 330 9 265 271 131 262\n", - " 204 205 333 389 195 198 454 397 3 71 259 135 270 391 137 203 67 69\n", - " 328 395]\n", - "57 55\n", - "[3583 3832 3711 3387 3391 3959 3705 3577 3517 3899 3772 3644 3320 3699\n", - " 3766 3511 3837 3708 3769 3702 3379 3895 3574 3836 3316 3390 3443 3636\n", - " 3258 3962 3191 3455 3775 3196 3709 3897 3834 3384 3510 3389 3445 3581\n", - " 3386 3253 3260 3830 3572 3901 3838 3383 3382 3380 3579 3513 3828 3960\n", - " 3700 3324 3763 3261 3326 3643 3706 3765 3192 3961 3317 3578 3448 3771\n", - " 3893 3958 3444 3257 3642 3646 3195 3256 3575 3641 3707 3701 3321 3507\n", - " 3193 3452 3639 3514 3451 3764 3381 3894 3319 3450 3447 3194 3896 3900\n", - " 3322 3515 3898 3318 3385 3454 3647 3768 3637 3773 3640 3518 3449 3573\n", - " 3635 3453 3509 3519 3645 3516 3831 3512 3767 3582 3835 3833 3571 3774\n", - " 3259 3638 3963 3508 3964 3704 3710 3829 3254 3703 3388 3255 3446 3190\n", - " 3576 3580 3770 3325 3323]\n", - "25 60\n", - "[4055 3990 4061 3865 3996 4051 3799 3923 3668 3541 3676 3605 3613 3678\n", - " 3741 3671 3482 3987 3924 3926 3732 3993 3861 3859 4062 3801 4057 3675\n", - " 3735 3483 3549 3933 3607 4052 3738 4058 3866 3742 3802 3806 3995 3604\n", - " 3679 3548 3479 4060 3871 3863 3869 3989 3798 3546 3739 3672 3610 3796\n", - " 3612 3927 3867 3928 3999 3929 3674 3925 3795 3608 3868 3797 3669 3542\n", - " 3737 3731 3736 3614 4053 3934 3481 4056 3803 3547 3606 3734 3930 3743\n", - " 3994 3609 3677 3667 3997 3935 3998 3932 3862 3673 3670 3543 4059 3807\n", - " 3544 3545 4063 3805 3860 3740 3478 3931 3870 3733 3484 3991 3988 3800\n", - " 3611 3804 4054 3992 3864 3480]\n", - "15 0\n", - "[ 17 210 83 12 141 401 21 269 81 73 15 149 334 145 143 332 402 339\n", - " 213 201 82 144 139 207 13 273 337 80 14 147 208 211 212 75 11 85\n", - " 146 396 76 209 338 202 74 10 275 79 77 268 140 20 16 335 138 267\n", - " 276 398 142 400 148 206 399 18 331 336 266 274 78 9 271 204 205 333\n", - " 19 397 270 272 137 84 203]\n", - "45 24\n", - "[1388 1392 1710 1840 1262 1704 1330 1713 1836 1519 1642 1581 1966 1383\n", - " 1389 1521 1965 1842 1651 1261 1708 1322 1575 1773 1833 1394 1839 1511\n", - " 1963 1772 1712 1834 1841 1198 1578 1258 1512 1579 1200 1768 1451 1771\n", - " 1648 1640 1769 1450 1327 1452 1832 1259 1641 1326 1576 1455 1715 1328\n", - " 1905 1778 1649 1456 1901 1903 1779 1454 1709 1585 1522 1707 1962 1523\n", - " 1321 1323 1447 1837 1586 1387 1835 1584 1964 1329 1770 1515 1393 1390\n", - " 1899 1902 1520 1714 1776 1904 1577 1448 1320 1767 1516 1457 1900 1395\n", - " 1650 1324 1385 1968 1325 1391 1703 1644 1838 1705 1264 1194 1197 1587\n", - " 1777 1195 1967 1386 1897 1514 1453 1459 1774 1196 1513 1265 1639 1384\n", - " 1449 1199 1517 1645 1646 1643 1260 1706 1775 1898 1583 1257 1263 1647\n", - " 1582 1711 1458 1580 1518]\n", - "46 30\n", - "[1710 2027 1840 1907 2093 1713 2089 2159 1836 1642 1581 1966 2161 2284\n", - " 2032 2222 1965 1842 2349 1708 2088 1773 1833 2100 2155 1839 2348 1963\n", - " 1772 2164 1712 2152 1969 2026 1834 1841 2090 1579 1768 2153 2034 1771\n", - " 1648 1769 1832 2352 2160 2098 1715 1905 2282 1961 1778 1649 1901 1903\n", - " 1779 1709 2218 1585 1971 2289 1707 1962 2225 2224 1837 2036 1835 1584\n", - " 2099 1964 1896 1770 1908 2096 2030 1899 1902 2095 2288 2162 1714 2154\n", - " 1776 1904 2219 2025 2350 1970 2158 1900 2220 1650 2163 2217 1968 2286\n", - " 1972 2351 1644 1838 2031 1705 2290 1960 2033 1777 2347 2156 2353 1967\n", - " 2035 2092 1897 2226 1774 1780 2287 2283 1645 1844 1646 1643 2094 2157\n", - " 2227 2024 1706 1775 1898 2285 2097 2091 2029 1583 2223 1647 1582 1843\n", - " 1711 1580 2221 2028 1906]\n", - "15 16\n", - "[1098 848 1361 785 720 1420 913 845 1104 1232 1039 1035 1228 910\n", - " 716 1358 850 976 842 1041 914 1170 1167 779 978 917 1295 655\n", - " 1099 653 852 974 907 718 1363 719 906 1171 781 1105 780 1291\n", - " 721 971 1362 1230 784 778 912 1423 1165 905 847 1421 715 970\n", - " 1292 723 1237 1299 1103 849 1038 722 782 1231 846 841 908 1106\n", - " 1042 1357 1227 1040 1426 654 1166 843 1107 1172 1356 1037 1296 977\n", - " 853 783 658 1425 1033 979 717 1293 911 1297 1036 980 1233 1163\n", - " 844 1290 1109 1101 787 1294 1173 1162 1359 975 972 1100 1044 851\n", - " 786 657 1226 1102 973 981 1034 1360 1229 1300 1355 1108 788 909\n", - " 915 1234 916 1422 969 1298 1236 652 1169 1424 1164 1043 1168 656\n", - " 1097 1161 1045 1225 1235]\n", - "20 5\n", - "[ 17 594 210 591 83 401 153 21 467 81 340 149 334 145 281 89 659 143\n", - " 526 404 472 537 596 724 402 339 213 471 409 535 82 144 214 207 341 595\n", - " 721 664 273 337 80 600 532 345 147 208 211 212 468 662 152 723 86 151\n", - " 85 146 343 529 722 209 154 338 217 534 599 407 150 601 275 469 79 726\n", - " 277 342 405 463 473 279 344 658 725 20 16 597 335 464 276 398 536 142\n", - " 22 400 148 206 399 18 533 465 87 593 527 530 336 661 88 660 215 462\n", - " 657 280 282 274 346 592 271 403 531 470 278 408 216 406 23 19 663 598\n", - " 24 538 270 218 272 727 656 84 528 474 410 466]\n", - "58 62\n", - "[3903 4095 3832 3839 3959 3705 4086 4026 3899 3772 3967 3644 3766 3956\n", - " 3837 3708 3769 3702 3895 4030 3836 3965 4027 3962 3775 3709 4091 3897\n", - " 3834 4092 4094 3902 4029 3830 3901 3838 4028 4093 3828 3960 3643 3706\n", - " 3765 4085 3961 4090 3771 3893 4084 3958 4021 4022 4025 3642 4031 4024\n", - " 3641 3707 3639 3894 3966 3896 3900 3957 3898 4088 3768 4089 3773 3640\n", - " 3645 3831 3767 3835 3833 4020 3774 3963 3964 3704 3710 3829 4087 3703\n", - " 3892 3770 4023]\n", - "39 21\n", - "[1388 1765 1704 1570 1508 1130 1699 1132 1315 1642 1581 1383 1638 1389\n", - " 1062 1636 1442 1445 1261 1322 1575 1002 1509 1122 1505 1065 1572 1511\n", - " 996 1193 1064 1252 1578 1258 1512 1579 1768 1317 1060 1451 1640 1769\n", - " 1129 1001 1450 1067 1126 1452 1186 1255 1259 1641 1576 1571 1316 1249\n", - " 999 1319 1707 1444 1506 1321 1323 1447 1313 1379 1635 1387 1770 1515\n", - " 1510 1378 1446 1190 1189 1256 1066 1577 1448 1320 1767 1516 1314 1324\n", - " 1128 1385 998 1187 1063 1325 1703 1644 1705 1000 1185 1194 1127 1197\n", - " 1123 1700 1195 1701 1386 1381 1569 997 1514 1573 1453 1124 1507 1192\n", - " 1250 1196 1513 1254 1639 1384 1637 1449 1191 1061 1517 1382 1380 1643\n", - " 1318 1702 1260 1251 1706 1766 1059 1253 1574 1443 1634 1131 1257 1764\n", - " 1188 1441 1580 1125 1377]\n", - "54 16\n", - "[1330 1136 1078 1146 757 1399 1203 826 947 1461 756 1402 818 1018\n", - " 1147 1394 1207 956 882 954 1072 1200 819 1140 1401 1465 1269 1275\n", - " 1139 887 821 888 827 886 1144 761 1331 1336 694 1335 1009 1460\n", - " 1014 1202 1268 1338 1148 949 754 1143 1276 1011 1334 1329 1273 760\n", - " 885 1205 1463 1142 1138 820 1075 952 1008 824 1396 1211 1271 1141\n", - " 695 1395 1010 1019 1204 1081 955 1270 1464 1020 1337 696 948 1264\n", - " 892 946 1016 890 1076 1210 1073 692 951 1013 950 1077 1398 755\n", - " 1459 1080 1212 881 1332 822 1265 693 1082 1272 1266 1017 1397 1339\n", - " 1400 945 762 817 1267 1079 1137 1074 1012 1209 1462 697 758 953\n", - " 880 944 1274 884 1201 823 691 1015 1206 1208 891 1145 759 1084\n", - " 883 825 889 1083 1333]\n", - "18 55\n", - "[3858 3345 3217 3730 3919 3538 3215 3599 3342 3408 3799 3790 3923 3668\n", - " 3280 3154 3541 3922 3605 3534 3151 3726 3671 3728 3924 3411 3732 3861\n", - " 3859 3470 3476 3854 3286 3287 3735 3412 3414 3346 3607 3598 3472 3155\n", - " 3351 3415 3604 3663 3920 3536 3479 3413 3344 3404 3921 3856 3798 3537\n", - " 3218 3284 3672 3796 3153 3277 3410 3219 3791 3855 3285 3468 3925 3341\n", - " 3343 3795 3416 3608 3797 3669 3542 3603 3731 3600 3725 3736 3221 3666\n", - " 3532 3471 3535 3282 3406 3409 3474 3793 3157 3664 3601 3789 3606 3283\n", - " 3533 3734 3405 3220 3665 3156 3539 3152 3347 3540 3597 3667 3862 3670\n", - " 3350 3543 3727 3469 3477 3544 3602 3792 3348 3473 3222 3596 3729 3860\n", - " 3278 3662 3216 3478 3214 3733 3349 3340 3281 3857 3660 3661 3407 3475\n", - " 3794 3352 3279 3480 3724]\n", - "10 59\n", - "[4038 4047 3659 3914 3526 3919 3786 4043 3599 3790 3847 3529 3983 3980\n", - " 3853 3909 4041 3464 3851 3979 3527 3402 4037 3534 3726 3913 3728 3982\n", - " 3975 3470 3854 4039 3788 3978 3598 3717 3915 4042 3528 3844 3462 3399\n", - " 3663 3716 3911 3920 3590 3404 3856 3400 3656 3845 3848 3463 3977 3791\n", - " 3718 3855 3531 3973 3654 3468 3595 3781 3467 3600 3725 3532 3535 3719\n", - " 3910 3852 3972 3525 4044 3664 3916 3789 3533 3655 4045 3405 3588 3593\n", - " 3591 3597 3780 3976 3589 3594 3657 3846 3658 3850 3401 3722 3721 3727\n", - " 3849 3469 3720 3974 3792 3981 3917 3596 4040 3785 3662 3592 3403 3530\n", - " 3912 3787 3723 3660 3466 3984 3661 3918 3465 3783 3908 4046 3784 3653\n", - " 3782 3652 3724]\n", - "36 39\n", - "[2529 2404 2210 2406 2402 2721 2398 2661 2532 2272 2592 2214 2408 2730\n", - " 2659 2276 2464 2536 2591 2344 2468 2593 2342 2654 2345 2602 2147 2723\n", - " 2596 2783 2474 2535 2538 2530 2526 2658 2149 2848 2533 2407 2473 2338\n", - " 2850 2662 2600 2462 2601 2466 2346 2409 2791 2534 2919 2788 2277 2856\n", - " 2854 2594 2337 2722 2335 2271 2343 2146 2150 2278 2598 2472 2729 2789\n", - " 2918 2274 2785 2339 2913 2597 2663 2211 2784 2467 2151 2209 2273 2590\n", - " 2279 2916 2664 2463 2599 2656 2465 2334 2471 2917 2787 2405 2403 2855\n", - " 2849 2915 2726 2215 2400 2275 2216 2660 2786 2212 2719 2792 2595 2657\n", - " 2527 2724 2851 2793 2336 2145 2914 2208 2727 2340 2280 2718 2728 2469\n", - " 2531 2725 2410 2852 2528 2790 2537 2401 2853 2665 2213 2148 2399 2720\n", - " 2281 2655 2341 2470 2666]\n", - "48 37\n", - "[2093 2542 2801 2159 2354 2739 2475 2671 2416 2161 2284 2420 2032 2222\n", - " 2732 2349 2612 2545 2100 2413 2414 2541 2411 2155 2418 2348 2294 2230\n", - " 2602 2164 2474 2538 2738 2034 2609 2228 2419 2352 2480 2740 2674 2160\n", - " 2544 2098 2293 2668 2282 2550 2346 2798 2422 2218 2608 2289 2670 2225\n", - " 2355 2540 2224 2478 2417 2099 2607 2096 2030 2421 2095 2288 2162 2358\n", - " 2736 2219 2350 2158 2220 2477 2604 2549 2163 2548 2286 2803 2357 2667\n", - " 2351 2539 2031 2479 2802 2483 2290 2415 2033 2676 2482 2347 2737 2156\n", - " 2669 2353 2486 2611 2035 2092 2614 2672 2734 2613 2543 2291 2226 2292\n", - " 2287 2675 2229 2283 2606 2165 2673 2485 2800 2797 2412 2094 2410 2157\n", - " 2227 2484 2610 2481 2285 2356 2097 2733 2603 2029 2546 2547 2223 2799\n", - " 2476 2735 2605 2221 2677]\n", - "33 26\n", - "[1826 1765 1695 2084 1570 1888 1508 1892 1502 1699 1895 1315 1697 1504\n", - " 2018 1638 1692 1636 1442 1311 2015 1885 1445 1760 1575 1376 1436 1509\n", - " 1505 1503 1572 1763 1511 1952 1819 1883 2017 1829 2078 1566 1957 1500\n", - " 1310 1954 1949 2020 1822 2079 1894 1571 1955 1374 2014 1437 1698 1316\n", - " 1758 1828 1757 1633 1444 1693 1506 1499 1313 1890 2083 1379 2080 1635\n", - " 1628 1510 2021 1950 1948 1821 1378 1627 1446 2019 1886 1956 1373 1767\n", - " 1314 1827 1825 1694 1630 1887 2081 1501 1703 1762 1632 1375 1755 1440\n", - " 1700 1951 1701 1889 1381 1569 1820 1893 1573 1438 1507 2016 1958 2082\n", - " 1953 1565 1830 1564 1639 1637 1759 1439 1884 1696 1380 1567 1312 1823\n", - " 1629 1702 1831 1766 1691 1824 1574 1891 1443 1634 1631 1764 1563 1761\n", - " 1441 2013 1568 1377 1756]\n", - "11 63\n", - "[4038 4047 3659 3914 3919 3786 4043 3985 3790 3847 3983 3980 3853 3909\n", - " 4041 3851 3979 4037 3726 3913 3982 3975 3854 4039 3788 3978 3915 4042\n", - " 3911 3920 3921 3856 3656 3845 3848 3977 3791 3855 3973 4048 3725 3719\n", - " 3910 3852 4044 3916 3789 4045 3976 3657 3846 3658 3850 3722 3721 3727\n", - " 3849 3720 3974 3792 3981 3917 4049 4040 3785 3662 3912 3787 3857 3723\n", - " 3660 3984 3661 3918 3783 4046 3784 3782 3724]\n", - "17 62\n", - "[4047 3858 4055 3730 3990 3919 4043 3985 3599 4051 3799 3790 3923 3983\n", - " 3668 3980 3853 3851 3979 3922 3726 3728 3987 3924 3926 3982 3732 3861\n", - " 3859 3854 3788 4052 3598 3915 3986 3604 3663 3920 3863 3921 3856 3989\n", - " 3798 3796 3927 3791 3855 4048 3925 3795 3797 3669 3603 3731 3600 3725\n", - " 3666 4053 3793 3852 4044 3664 3601 3916 3789 4045 3734 3665 4050 3667\n", - " 3862 3727 3602 3792 3981 3917 4049 3729 3860 3662 3733 3991 3787 3988\n", - " 3857 3984 3661 3918 4046 4054 3794 3724]\n", - "62 44\n", - "[3071 2878 3263 2617 2557 3004 2618 2491 3129 2872 2492 3003 3196 2873\n", - " 2815 2811 2943 3262 3000 2879 2936 3064 2814 2877 2940 3130 3260 2749\n", - " 2685 2682 2748 2623 2554 2750 2687 3261 3002 2558 2686 3007 2747 2875\n", - " 2621 2938 3195 2937 2680 3006 2939 2683 2941 3131 2556 2751 2555 3070\n", - " 3067 3194 2812 3197 2559 3005 2494 3132 3001 2684 2744 3066 2746 2808\n", - " 2876 2813 3198 2620 2619 3199 3133 2622 3134 2942 3135 2681 3259 2809\n", - " 2495 2493 2810 2745 2874 3065 3068 3069]\n", - "19 24\n", - "[1809 1682 1361 1741 1553 1232 1555 1679 1621 1875 1358 1749 1549 1489\n", - " 1170 1816 1806 1808 1560 1295 1683 1680 1363 1171 1873 1617 1878 1687\n", - " 1488 1362 1492 1423 1494 1554 1421 1367 1490 1561 1431 1807 1433 1937\n", - " 1237 1619 1299 1427 1622 1811 1813 1491 1742 1558 1231 1550 1357 1877\n", - " 1939 1426 1432 1485 1942 1941 1303 1238 1172 1677 1296 1495 1497 1625\n", - " 1368 1425 1365 1613 1493 1678 1746 1812 1369 1297 1815 1233 1623 1810\n", - " 1616 1620 1879 1559 1430 1618 1294 1496 1173 1615 1359 1557 1936 1174\n", - " 1487 1239 1753 1689 1360 1300 1814 1744 1938 1681 1234 1302 1551 1748\n", - " 1871 1422 1556 1684 1486 1752 1366 1872 1624 1298 1236 1686 1874 1876\n", - " 1169 1424 1428 1301 1750 1743 1747 1364 1751 1685 1429 1168 1688 1304\n", - " 1614 1940 1745 1235 1552]\n", - "32 23\n", - "[1826 1765 1695 1570 1888 1508 1502 1699 1315 1697 1504 1638 1692 1636\n", - " 1442 1311 1885 1445 1760 1376 1121 1436 1509 1122 1505 1503 1572 1763\n", - " 1252 1117 1317 1566 1371 1500 1245 1310 1186 1822 1571 1374 1180 1437\n", - " 1698 1316 1758 1249 1828 1307 1757 1633 1306 1444 1693 1506 1562 1499\n", - " 1313 1890 1183 1379 1635 1181 1628 1510 1821 1378 1627 1446 1626 1886\n", - " 1373 1314 1246 1827 1243 1187 1825 1694 1184 1630 1887 1501 1762 1632\n", - " 1375 1185 1755 1123 1440 1700 1498 1248 1701 1889 1381 1569 1820 1370\n", - " 1573 1438 1507 1434 1250 1565 1564 1637 1435 1759 1439 1696 1382 1380\n", - " 1567 1312 1823 1629 1318 1702 1251 1118 1691 1120 1253 1824 1119 1574\n", - " 1891 1443 1634 1631 1764 1308 1188 1690 1563 1761 1244 1182 1441 1309\n", - " 1372 1247 1568 1377 1756]\n", - "61 1\n", - "[ 63 511 251 383 441 191 313 59 255 316 184 444 125 376 507 380 447 319\n", - " 186 57 253 247 189 315 446 127 445 56 126 382 252 378 314 249 121 122\n", - " 190 187 379 120 377 254 442 443 318 510 183 508 188 185 506 250 317 60\n", - " 62 119 509 61 248 55 312 124 381 58 311 123]\n", - "33 63\n", - "[4068 4061 4065 3996 4001 3877 4071 4066 4064 3678 3937 3741 3874 3810\n", - " 4004 4069 4062 3747 3933 3742 3806 3748 3995 4007 3679 3745 3872 4060\n", - " 3871 3869 3749 3867 3999 3684 3682 3680 4002 4006 3879 3744 3868 4067\n", - " 3875 3746 4000 3809 3814 3811 3812 3873 4070 3934 3878 3941 3743 3813\n", - " 3683 3997 3935 3938 3998 3932 4003 4059 3808 3807 3936 4063 3943 3876\n", - " 3805 3931 3870 3942 3939 3804 3940 4005 3681]\n", - "34 31\n", - "[1826 1765 1695 2084 2404 2210 1888 1892 2402 2022 1699 1895 1697 2018\n", - " 2272 2214 1636 2015 2276 1885 1760 2270 2088 2342 1763 2087 1952 2147\n", - " 2152 2017 2077 1829 2078 1957 1954 1949 2149 1832 2020 1822 2079 2338\n", - " 1894 1955 2014 1698 1758 1828 1757 2204 1633 2206 2277 2337 2335 1890\n", - " 2271 2146 2083 2269 2150 2080 2278 1635 2012 1896 2021 1950 1948 1821\n", - " 2274 2019 2339 2141 1886 1956 1959 1767 2211 2151 2209 2273 2279 1827\n", - " 1825 1694 1887 2207 2081 1762 2334 1632 2405 1960 2086 2403 2144 1700\n", - " 1951 2215 2400 2275 2216 2023 2212 1701 1889 1820 1893 2016 1958 2082\n", - " 1953 1830 2336 2145 1637 1759 1884 2208 2340 1696 1823 2143 1702 2024\n", - " 1831 2140 2401 1766 1824 2205 2076 1891 2213 2148 2399 1634 1631 1764\n", - " 2085 1761 2341 2142 2013]\n", - "15 50\n", - "[2957 3345 3275 3217 3145 2832 2891 3024 3086 3538 3215 3599 3342 3408\n", - " 3025 2959 3148 3027 3023 2897 3280 2960 3089 3154 2955 2963 3209 3402\n", - " 3534 3151 3084 3091 2896 3411 3470 3018 3476 3146 3274 3412 3346 3598\n", - " 2964 3472 3155 2898 3536 3273 3082 3090 3413 3017 3085 3344 3404 3537\n", - " 3218 3284 2893 3087 3337 3026 3153 3277 3410 3219 2833 2830 3531 3285\n", - " 2834 3468 3210 3341 3343 3467 3600 3221 3532 3471 3083 3028 3535 3282\n", - " 3406 3409 3029 3474 3021 3157 3601 3149 2956 3339 3283 3533 3405 3220\n", - " 3156 3539 3338 3152 3347 3597 2895 3401 3092 2828 3276 3093 3469 2899\n", - " 3602 3348 3473 2829 2831 2962 3150 3596 3212 2894 3278 3216 3214 3081\n", - " 3019 3349 3088 3213 3340 3403 3281 3466 2892 2961 3211 3407 2954 3475\n", - " 3279 3020 2958 3022 3147]\n", - "28 62\n", - "[4055 3990 4061 3865 4065 3996 3799 4001 4066 3676 4064 3613 3678 3937\n", - " 3741 3874 3810 3926 3993 4062 3801 4057 3675 3735 3933 3738 4058 3866\n", - " 3742 3802 3806 3995 3679 3745 3872 4060 3871 3863 3869 3798 3739 3672\n", - " 3610 3612 3927 3867 3928 3999 3929 3674 3680 4002 3744 3868 3737 4000\n", - " 3736 3614 3809 3873 3934 4056 3803 3615 3930 3743 3994 3609 3677 3997\n", - " 3935 3938 3998 3932 3862 3673 4059 3808 3807 3936 4063 3805 3740 3931\n", - " 3870 3991 3800 3611 3804 4054 3992 3864]\n", - "15 56\n", - "[3858 3345 3275 3217 3730 3659 3919 3538 3786 3985 3215 3599 3342 3408\n", - " 3790 3923 3529 3983 3668 3980 3853 3280 3541 3851 3922 3605 3402 3534\n", - " 3726 3728 3411 3982 3732 3859 3470 3476 3854 3788 3412 3346 3598 3915\n", - " 3472 3986 3604 3663 3920 3536 3413 3344 3404 3921 3856 3537 3218 3796\n", - " 3277 3410 3791 3855 3531 3468 3595 3341 3343 3795 3467 3797 3669 3603\n", - " 3731 3600 3725 3666 3532 3471 3535 3282 3406 3409 3474 3793 3852 3664\n", - " 3601 3916 3789 3339 3283 3533 3405 3665 3539 3338 3593 3347 3540 3597\n", - " 3667 3594 3657 3658 3850 3401 3722 3276 3721 3727 3469 3477 3602 3792\n", - " 3981 3348 3473 3917 3596 3729 3212 3785 3860 3278 3662 3216 3214 3733\n", - " 3213 3340 3403 3281 3530 3787 3857 3723 3660 3466 3984 3661 3918 3465\n", - " 3407 3475 3794 3279 3724]\n", - "32 35\n", - "[2084 2529 2404 2210 1888 2406 2402 2398 2589 2532 2018 2272 2592 2461\n", - " 2214 2015 2267 2659 2276 1885 2464 2394 2270 2591 2468 2593 2333 2342\n", - " 2654 1952 2011 2147 2596 2530 2017 2077 2078 1954 2526 1949 2658 2149\n", - " 2020 2079 2533 2338 1955 2014 2331 2462 2466 2204 2268 2206 2277 2594\n", - " 2337 2335 2524 1890 2271 2146 2083 2269 2150 2080 2278 2460 2012 2330\n", - " 2021 1950 1948 2397 2274 2019 2339 2141 1886 2203 1956 2075 2211 2202\n", - " 2467 2209 2273 2590 2588 2458 2463 1887 2207 2081 2656 2465 2334 2405\n", - " 2086 2403 2144 2523 1951 2400 2275 2395 2396 2266 2212 1889 2595 2657\n", - " 2527 2016 2082 1953 2336 2145 2208 2340 2653 2469 2143 2531 2459 2528\n", - " 2139 2140 2401 2205 2332 2076 1891 2213 2148 2399 2525 2074 2085 2138\n", - " 2655 2341 2142 2013 2470]\n", - "12 38\n", - "[2322 2449 2191 2317 2444 2705 2765 2319 2062 2314 2767 2762 2509 2441\n", - " 2445 2257 2380 2313 2578 2637 2318 2188 2569 2631 2375 2766 2254 2825\n", - " 2255 2125 2630 2378 2633 2447 2502 2760 2827 2439 2695 2381 2377 2639\n", - " 2571 2698 2185 2443 2510 2127 2058 2193 2126 2768 2640 2384 2442 2249\n", - " 2570 2376 2703 2379 2702 2059 2186 2310 2568 2250 2567 2258 2572 2577\n", - " 2128 2761 2122 2385 2830 2121 2383 2192 2253 2440 2120 2315 2514 2251\n", - " 2247 2826 2636 2386 2511 2635 2700 2764 2513 2503 2576 2450 2374 2312\n", - " 2697 2505 2184 2828 2508 2634 2696 2642 2061 2183 2448 2512 2829 2320\n", - " 2246 2831 2641 2321 2575 2632 2057 2507 2187 2574 2506 2316 2382 2060\n", - " 2252 2446 2504 2704 2763 2438 2311 2063 2189 2638 2123 2256 2124 2190\n", - " 2248 2566 2573 2699 2701]\n", - "38 25\n", - "[1826 1765 1704 1570 1508 1892 2022 1836 1699 1895 1315 1642 1383 1697\n", - " 1504 1638 1636 1442 1445 1760 1708 1322 1575 1833 1509 1505 1572 1763\n", - " 1511 1772 1834 1252 1578 1512 1579 1768 1829 1317 1451 1771 1640 1957\n", - " 1769 1450 1954 1452 1832 2020 1255 1641 1894 1576 1571 1955 1961 1698\n", - " 1316 1828 1633 1319 1707 1962 1444 1506 1321 1447 1890 1379 1635 1387\n", - " 1835 1896 1770 1515 1510 2021 1899 1378 1446 2019 1256 1577 1448 1956\n", - " 1320 1959 2025 1767 1516 1314 1385 1827 1825 1703 1644 1762 1705 1632\n", - " 1960 1440 1700 2023 1701 1386 1889 1381 1569 1897 1893 1514 1573 1507\n", - " 1958 1513 1830 1254 1639 1384 1637 1449 1696 1382 1380 1643 1318 1702\n", - " 1251 2024 1706 1831 1766 1898 1253 1824 1574 1891 1443 1634 1257 1764\n", - " 1761 1441 1580 1568 1377]\n", - "48 57\n", - "[4083 3827 4079 3696 3699 3500 3766 4013 3956 3954 3702 3379 3823 3826\n", - " 3574 3373 3884 3631 3499 3886 3443 4081 3636 3440 3757 3887 4078 3435\n", - " 3822 3439 3952 3947 3376 3760 3377 3948 3885 3510 3762 3445 4019 4016\n", - " 3502 3311 3694 3830 3572 3626 3754 3564 3890 4018 3698 3380 3949 3828\n", - " 3700 3950 3763 3891 3824 3438 3765 3627 3378 3567 3375 4015 3691 3758\n", - " 3566 3498 3893 3309 3444 4014 3882 3505 3697 4012 3692 3818 3504 3820\n", - " 3441 3701 3507 3629 3756 3563 3628 3764 3569 3503 3894 3501 3565 4082\n", - " 3819 3951 3759 3825 3436 3821 3957 3630 3632 3637 4017 3573 3635 3314\n", - " 3509 3755 3437 3374 3633 3570 3315 3506 3312 3571 3695 3310 3888 3889\n", - " 4020 3953 4080 3634 3442 3313 4077 3638 3508 3883 3829 3693 3761 3892\n", - " 3568 3562 3955 3372 3690]\n", - "35 0\n", - "[ 38 32 168 35 222 163 41 94 96 33 224 421 164 167 226 105 98 30\n", - " 420 417 99 230 416 353 158 97 352 103 228 165 296 36 232 102 287 100\n", - " 159 225 31 101 290 289 221 29 359 40 356 223 104 294 39 162 354 95\n", - " 355 357 286 34 288 231 227 157 358 292 169 419 233 229 291 295 418 293\n", - " 166 93 422 351 37 161 160]\n", - "21 2\n", - "[ 17 27 210 83 401 153 21 467 81 25 15 340 149 145 281 89 143 404\n", - " 472 402 339 213 471 409 535 82 144 214 207 341 273 337 80 532 345 147\n", - " 208 211 212 468 152 86 151 85 146 343 209 154 338 217 534 347 91 407\n", - " 150 275 469 79 277 342 405 473 279 344 20 16 335 276 536 22 400 90\n", - " 148 18 533 465 87 530 336 88 215 155 280 282 274 346 283 271 403 531\n", - " 470 278 408 216 26 406 23 19 24 219 218 272 84 410 466]\n", - "47 45\n", - "[2922 2542 2861 2801 2739 2671 2927 3115 2930 2730 2732 3187 3182 3053\n", - " 2545 2859 2795 2541 2933 3054 3116 3058 3180 3049 3249 3055 2738 2990\n", - " 2741 2993 2796 2860 2609 3117 3121 3118 2740 2864 2997 2805 2674 2928\n", - " 2544 3125 2869 2668 3243 3311 2798 3244 2996 2608 3061 3123 2670 3250\n", - " 2868 2540 3050 2607 3184 3181 2995 2921 2863 2729 3186 3119 2867 2992\n", - " 3245 2989 3309 2923 2736 3113 3188 2604 3051 2987 3056 2794 2803 2667\n", - " 2857 3122 2802 2676 2737 2669 2924 2611 2865 2672 2734 3059 2991 2543\n", - " 2929 2804 2986 3179 2988 2793 2994 2931 3314 2675 2858 2606 3120 2673\n", - " 3183 2800 2797 3052 3057 2925 3312 3310 2866 3114 2731 3308 3178 3251\n", - " 2610 3313 2733 3248 3247 2603 2546 2932 2926 2862 2799 2985 3060 3246\n", - " 3185 2735 2605 2666 3124]\n", - "14 52\n", - "[2957 3345 3275 3217 3145 3659 3024 3086 3538 3215 3599 3342 3408 3025\n", - " 2959 3148 3529 3023 3280 2960 3089 3464 3154 2955 3209 3402 3534 3151\n", - " 3084 3091 3726 3728 3411 3470 3018 3476 3146 3274 3412 3346 3598 3472\n", - " 3155 3528 3663 3536 3273 3082 3090 3085 3344 3404 3400 3537 3218 3284\n", - " 3087 3337 3026 3153 3277 3410 3219 3531 3468 3595 3210 3341 3272 3343\n", - " 3336 3467 3603 3600 3725 3666 3532 3471 3083 3535 3282 3406 3409 3474\n", - " 3021 3664 3601 3149 2956 3339 3283 3533 3405 3220 3665 3156 3539 3338\n", - " 3593 3152 3347 3540 3597 3144 3594 3658 3401 3276 3727 3469 3602 3348\n", - " 3473 3150 3596 3729 3212 3278 3662 3216 3214 3081 3019 3208 3088 3213\n", - " 3340 3403 3281 3530 3723 3660 3466 3661 3465 2961 3211 3407 3475 3279\n", - " 3020 2958 3022 3147 3724]\n", - "56 5\n", - "[251 54 441 245 569 313 59 316 184 308 444 500 125 376 370 507 380 438\n", - " 631 757 186 181 57 253 699 247 636 189 315 563 564 435 634 446 627 630\n", - " 445 498 504 501 182 56 567 761 694 382 252 117 378 179 314 249 121 122\n", - " 190 116 440 566 439 187 760 632 503 499 572 375 379 118 571 120 306 377\n", - " 254 242 52 442 443 695 633 373 502 307 629 318 510 562 696 310 183 53\n", - " 637 508 188 185 565 180 436 506 692 250 574 317 570 698 437 434 309 60\n", - " 374 372 693 119 371 246 509 762 763 505 700 248 697 758 55 312 635 244\n", - " 573 124 381 58 628 115 759 243 178 311 568 123]\n", - "63 15\n", - "[ 831 638 1151 1023 1146 767 699 826 1407 636 958 829 1018 1147\n", - " 830 956 954 1085 894 701 1021 1275 1277 702 1406 639 1405 827\n", - " 1404 1148 1276 764 828 1341 1211 893 765 1019 1081 955 766 1020\n", - " 637 892 1213 890 1210 1278 1086 1279 895 1212 1082 1017 1339 957\n", - " 762 1022 1215 763 1342 1209 700 959 953 1274 703 1343 1087 1214\n", - " 891 1145 1084 1150 1340 825 889 1083 1149]\n", - "62 34\n", - "[2235 2238 1914 1983 2557 1978 1854 2491 2046 2489 2492 2360 2109 2425\n", - " 2366 2105 2302 2298 2108 2044 2431 2363 2296 2111 2300 2427 2170 1979\n", - " 1918 2490 2623 2554 1916 2041 2558 2169 1851 2365 2171 2429 2106 2174\n", - " 2430 2621 1982 2367 1853 2428 2236 1915 2239 1980 2556 2237 2555 1919\n", - " 2043 2559 1981 2175 2234 2494 2045 2040 2303 2047 2104 2620 2619 2110\n", - " 2301 2622 1977 2168 2362 2172 1852 2426 2299 2232 1855 2495 2173 2493\n", - " 2364 1917 2361 2107 2424 2233 2042 2297]\n", - "16 53\n", - "[3345 3275 3217 3730 3659 3024 3086 3538 3215 3599 3342 3408 3025 3790\n", - " 3148 3027 3023 3668 3280 3089 3154 3541 3605 3402 3534 3151 3084 3091\n", - " 3726 3728 3411 3732 3470 3476 3286 3274 3412 3414 3346 3598 3472 3155\n", - " 3604 3663 3536 3090 3413 3085 3344 3404 3537 3218 3284 3087 3026 3153\n", - " 3277 3410 3219 3791 3531 3285 3468 3595 3210 3341 3343 3795 3467 3669\n", - " 3542 3603 3731 3600 3725 3221 3666 3532 3471 3535 3282 3406 3409 3474\n", - " 3793 3021 3157 3664 3601 3149 3789 3339 3606 3283 3533 3405 3220 3665\n", - " 3156 3539 3338 3152 3347 3540 3597 3667 3594 3092 3350 3276 3727 3469\n", - " 3477 3602 3792 3348 3473 3222 3150 3596 3729 3212 3278 3662 3216 3478\n", - " 3214 3349 3088 3213 3340 3403 3281 3530 3660 3466 3661 3211 3407 3475\n", - " 3794 3279 3022 3147 3724]\n", - "47 13\n", - "[1262 1130 1136 1132 879 757 1203 557 947 1261 756 940 1002 878\n", - " 818 1004 1065 563 1003 882 1198 1072 1200 819 1140 627 495 1135\n", - " 684 498 1001 876 1067 1139 821 688 1005 1009 1071 1202 682 745\n", - " 949 754 621 1011 558 689 560 748 877 885 625 618 685 1138\n", - " 820 494 1075 815 1066 1008 690 497 941 810 1010 556 626 555\n", - " 562 687 937 948 1264 946 1197 1076 1195 811 622 749 750 1073\n", - " 692 559 751 1013 620 753 1077 1134 755 873 881 1196 1265 693\n", - " 943 493 875 1266 813 1199 619 812 683 561 1007 945 817 747\n", - " 1137 1074 1012 1068 1260 1133 938 492 880 944 1006 884 814 496\n", - " 623 1201 942 624 686 1131 691 1070 1263 746 628 939 816 752\n", - " 681 809 883 1069 874]\n", - "61 54\n", - "[3903 3583 3711 3387 3839 3391 3705 3577 3517 3899 3772 3644 3320 3263\n", - " 3511 3837 3708 3769 3836 3390 3258 3455 3775 3196 3709 3834 3384 3262\n", - " 3389 3581 3386 3902 3130 3260 3901 3838 3383 3579 3513 3324 3261 3326\n", - " 3643 3706 3578 3448 3771 3257 3642 3646 3195 3256 3575 3641 3707 3321\n", - " 3193 3452 3639 3514 3451 3131 3319 3450 3447 3194 3197 3900 3322 3515\n", - " 3898 3132 3385 3454 3647 3768 3773 3640 3518 3449 3453 3519 3198 3199\n", - " 3645 3516 3133 3512 3582 3134 3835 3327 3833 3135 3774 3259 3704 3710\n", - " 3703 3388 3576 3580 3770 3325 3323]\n", - "5 17\n", - "[1098 1353 1093 1035 777 839 842 773 771 1350 897 1409 1099 907\n", - " 1091 1219 772 906 1095 1024 1216 1351 770 1478 769 1291 1287 1344\n", - " 1348 711 835 1280 832 775 1154 971 1158 1155 961 905 1413 1284\n", - " 1474 1476 970 1089 1096 1286 960 1029 1160 1222 838 1224 1349 776\n", - " 966 1221 1026 1480 1285 1410 1223 841 1414 1092 963 1227 706 1346\n", - " 1025 1217 1354 1345 1347 1159 836 900 1412 1283 1288 1282 903 708\n", - " 1289 1032 1033 712 837 968 1152 1281 1411 1163 964 1088 1290 902\n", - " 1162 962 1479 834 1226 1034 709 1352 1417 707 967 1218 904 1153\n", - " 1027 710 1030 840 901 1031 969 1157 1090 1416 899 896 1094 1477\n", - " 1097 898 1161 833 1475 774 1156 1220 1225 1028 965 1415]\n", - "15 26\n", - "[1809 1682 1361 1741 2000 1553 1870 1420 1804 1555 1679 1547 1621 1875\n", - " 1358 1749 1997 1549 1489 1737 2062 1806 1808 1295 1930 1674 1866 1683\n", - " 1680 1363 1932 1612 1873 1739 1617 1488 1362 1867 2064 1869 1548 1492\n", - " 1423 1933 1554 1421 1490 1292 1807 1868 1931 1483 1937 1619 1484 1675\n", - " 1427 1811 1813 1491 1481 2002 1546 1802 1676 1742 2065 1803 1550 1545\n", - " 1357 1877 1939 1426 1801 1485 1356 2003 1677 1995 1296 1738 1425 1613\n", - " 1493 2001 1678 1746 1935 1293 1812 1297 1611 1810 1616 1620 1934 1618\n", - " 1294 1482 1615 1359 1557 1936 1487 2066 1999 1360 1355 1418 1673 1744\n", - " 2061 1938 1681 1419 1551 1748 1805 1871 1422 1556 1684 1486 1872 1996\n", - " 1298 2060 1874 1876 1424 1610 1428 1743 1747 1865 1609 2063 1685 1740\n", - " 1614 1998 1940 1745 1552]\n", - "11 16\n", - "[1098 848 1420 913 845 1353 1104 1232 1039 1093 1035 1228 910 777\n", - " 716 1358 839 976 842 1041 1167 779 1295 1099 653 974 907 718\n", - " 719 906 714 1095 1351 781 1105 780 1291 1287 711 775 971 1158\n", - " 1230 784 778 912 1165 905 847 1421 651 715 970 1292 1096 1286\n", - " 1103 849 1029 1160 1038 1222 838 1224 776 966 782 1221 1231 846\n", - " 1223 841 908 1357 1227 1040 654 1166 843 1354 1356 1037 1159 1296\n", - " 977 1288 783 650 903 1289 1032 1033 712 648 649 837 717 1293\n", - " 911 968 1036 1233 1163 844 1290 1101 902 1294 1162 1359 975 972\n", - " 1100 1226 1102 973 1034 1229 1352 1355 1418 1417 967 904 909 1419\n", - " 1030 840 901 1031 1422 969 1157 1416 652 1169 1164 1094 1168 713\n", - " 1097 1161 774 1225 965]\n", - "30 3\n", - "[ 27 32 153 25 35 222 163 281 89 94 156 350 96 546 33 224 415 409\n", - " 164 349 541 540 482 226 285 98 477 30 28 542 420 417 481 345 99 416\n", - " 353 158 152 97 352 348 228 607 414 92 36 154 287 100 545 217 605 347\n", - " 91 159 539 225 609 31 290 606 473 289 221 29 220 284 344 411 356 478\n", - " 223 90 162 354 95 355 483 286 413 34 288 227 157 88 292 155 280 419\n", - " 282 480 603 291 476 346 283 604 418 543 408 216 26 24 93 608 219 479\n", - " 351 538 161 218 160 474 412 475 410 544]\n", - "59 60\n", - "[3903 3583 4095 3832 3711 3839 3959 3705 4086 3577 4026 3517 3899 3772\n", - " 3967 3644 3766 3837 3708 3769 3702 3895 4030 3836 3965 4027 3962 3775\n", - " 3709 4091 3897 3834 4092 3581 4094 3902 4029 3830 3901 3838 4028 4093\n", - " 3579 3513 3960 3643 3706 3765 4085 3961 4090 3578 3771 3893 3958 4021\n", - " 4022 4025 3642 4031 3646 3575 4024 3641 3707 3701 3639 3514 3894 3966\n", - " 3896 3900 3515 3957 3898 4088 3647 3768 4089 3773 3640 3518 3645 3516\n", - " 3831 3512 3767 3582 3835 3833 3774 3638 3963 3964 3704 3710 3829 4087\n", - " 3703 3576 3580 3770 4023]\n", - "60 58\n", - "[3903 3583 4095 3832 3711 3387 3839 3391 3959 3705 3577 4026 3517 3899\n", - " 3772 3967 3644 3766 3511 3837 3708 3769 3702 3895 4030 3574 3836 3390\n", - " 3965 4027 3962 3455 3775 3709 4091 3897 3834 4092 3389 3581 4094 3386\n", - " 3902 4029 3830 3901 3838 4028 4093 3579 3513 3960 3643 3706 3961 4090\n", - " 3578 3448 3771 3958 4025 3642 4031 3646 3575 4024 3641 3707 3452 3639\n", - " 3514 3451 3894 3966 3450 3896 3900 3515 3898 4088 3385 3454 3647 3768\n", - " 4089 3773 3640 3518 3449 3453 3519 3645 3516 3831 3512 3767 3582 3835\n", - " 3833 3774 3638 3963 3964 3704 3710 3703 3388 3576 3580 3770 4023]\n", - "58 17\n", - "[1151 1078 1023 1146 1399 1403 826 1407 1402 958 829 1018 1147 830\n", - " 1207 956 1470 954 1085 1140 1401 894 1465 1269 1021 1275 887 1277\n", - " 1406 888 1533 1405 827 886 1144 761 1336 1335 1404 1469 1014 1268\n", - " 1338 1148 1532 949 1143 1276 1334 764 1273 828 1530 760 885 1205\n", - " 1341 1463 1142 952 824 1466 1527 1211 1271 1141 1531 893 765 1019\n", - " 1204 1081 955 1270 1464 1020 1337 948 892 1213 1016 1467 890 1076\n", - " 1529 1210 1278 1086 951 1013 950 1077 1398 1279 1080 895 1212 1332\n", - " 822 1082 1272 1017 1397 1528 1339 1400 957 762 1022 1215 763 1342\n", - " 1079 1012 1209 1462 1468 959 953 1274 823 1343 1087 1015 1206 1214\n", - " 1208 891 1145 759 1084 1150 1340 825 889 1083 1149 1333]\n", - "11 24\n", - "[1361 1741 1553 1870 1420 1353 1804 1679 1547 1228 1358 1549 1489 1737\n", - " 1806 1808 1350 1605 1295 1930 1674 1866 1680 1932 1863 1612 1351 1799\n", - " 1478 1739 1291 1617 1543 1287 1488 1230 1867 1672 1869 1928 1548 1423\n", - " 1165 1933 1413 1421 1671 1798 1292 1286 1807 1868 1931 1864 1483 1484\n", - " 1675 1544 1481 1160 1546 1802 1224 1349 1733 1542 1676 1742 1231 1541\n", - " 1803 1480 1223 1550 1545 1414 1357 1227 1607 1801 1166 1485 1354 1670\n", - " 1356 1735 1734 1800 1677 1296 1288 1738 1425 1613 1289 1678 1606 1293\n", - " 1611 1163 1929 1290 1616 1934 1294 1482 1162 1615 1359 1479 1487 1226\n", - " 1360 1229 1352 1355 1418 1673 1744 1417 1681 1419 1551 1805 1871 1422\n", - " 1486 1416 1424 1610 1743 1164 1865 1736 1609 1477 1161 1740 1614 1225\n", - " 1608 1745 1669 1552 1415]\n", - "49 33\n", - "[2027 1840 1907 2093 1973 2542 2159 2354 1966 2416 2161 2284 2420 2032\n", - " 2222 1965 1842 2349 2545 2100 2413 2414 2155 1839 2418 2348 2294 1963\n", - " 2230 2164 2167 1909 1969 1841 2034 2166 2228 2419 2352 2480 2160 2544\n", - " 2098 2293 1905 1778 2103 1901 1903 1779 2422 1971 2289 2225 2355 2224\n", - " 1837 2036 2478 2417 2099 1964 1908 2096 2038 2030 2421 1902 2039 2095\n", - " 2288 2162 2037 1776 2358 1904 2219 2350 1970 2158 1900 2220 2477 2163\n", - " 2548 1968 1845 2286 2357 1972 2351 1838 2031 2479 2483 2290 2415 1910\n", - " 2033 2482 1777 2347 2156 2353 2295 1967 2035 2092 2543 2291 2231 2226\n", - " 1774 1780 2292 2287 2229 1975 2283 2165 1844 1974 2101 2485 2412 2094\n", - " 2157 2227 2484 1775 2481 2285 2356 2097 2091 2029 2546 2547 2223 2359\n", - " 1843 2102 2221 2028 1906]\n", - "45 21\n", - "[1388 1392 1710 1262 1330 1713 1130 1136 1132 1519 1642 1581 1383 1389\n", - " 1521 1203 1261 1708 1322 1575 1773 1002 1004 1065 1394 1511 1003 1772\n", - " 1712 1193 1198 1578 1258 1072 1512 1579 1200 1451 1771 1648 1640 1135\n", - " 1129 1450 1067 1327 1452 1255 1259 1641 1326 1576 1331 1455 1328 1005\n", - " 1071 1649 1456 1202 1454 1709 1585 1522 1319 1707 1523 1321 1323 1447\n", - " 1586 1387 1584 1329 1770 1515 1393 1390 1520 1138 1776 1256 1066 1577\n", - " 1008 1448 1320 1516 1457 1395 1650 1324 1128 1385 1325 1391 1644 1705\n", - " 1264 1194 1197 1587 1195 1073 1386 1134 1514 1453 1459 1774 1192 1196\n", - " 1513 1265 1384 1266 1449 1191 1199 1517 1645 1646 1007 1643 1267 1137\n", - " 1068 1260 1133 1706 1775 1006 1201 1131 1583 1257 1070 1263 1647 1582\n", - " 1711 1458 1580 1518 1069]\n", - "19 63\n", - "[4047 3858 4055 3730 3990 3919 3865 3985 4051 3799 3790 3923 3983 3668\n", - " 3853 3922 3728 3987 3924 3926 3982 3732 3993 3861 3859 3854 4057 3735\n", - " 4052 3986 3920 3863 3921 3856 3989 3798 3796 3927 3791 3928 3855 3929\n", - " 4048 3925 3795 3797 3669 3731 3666 4053 3793 4056 3664 4045 3734 3665\n", - " 4050 3667 3862 3670 3727 3792 3981 3917 4049 3729 3860 3733 3991 3988\n", - " 3857 3984 3800 3918 4046 4054 3992 3864 3794]\n", - "38 16\n", - "[1130 1132 1315 1383 1062 1445 743 1322 1121 940 1002 865 1122 742\n", - " 1004 1065 871 1003 996 1193 1064 1252 1258 1317 738 1060 1129 1001\n", - " 876 928 1067 1126 1186 1255 803 1259 1057 868 1316 1249 675 744\n", - " 999 993 745 1319 1444 936 1321 1323 1447 1313 870 1379 866 806\n", - " 1378 1446 808 1190 1189 1256 1066 1448 1320 935 810 1314 864 1128\n", - " 1385 677 867 998 1187 1184 1063 679 680 994 807 937 1000 932\n", - " 1185 1194 1127 739 1123 1195 811 676 931 678 1248 1386 1381 997\n", - " 873 1124 1058 1192 1250 1196 992 1254 875 1384 804 1449 1191 1061\n", - " 740 1382 1380 802 929 934 1318 1068 1260 1251 938 1059 1056 1120\n", - " 1253 872 1443 930 933 741 1131 1257 746 1188 939 805 681 809\n", - " 869 995 1125 801 874]\n", - "21 25\n", - "[1809 1682 1361 1553 1555 1679 1621 1875 1749 1489 2004 1816 1808 1305\n", - " 1560 1819 1683 1680 1363 2006 1818 2008 1873 1617 1878 1687 1488 1362\n", - " 1492 1423 1494 1554 1367 1490 1561 1431 1807 1433 2005 1937 1237 1943\n", - " 1619 1754 1562 1299 1427 1622 1499 1811 1813 1491 2002 1880 1558 1877\n", - " 1939 1426 1627 1432 1942 1626 1941 1303 1238 1817 1945 2003 1495 1497\n", - " 1625 1368 1425 1365 1493 1882 2007 1746 1812 1369 1755 1297 1815 1623\n", - " 1810 1616 1498 1620 1879 1559 1430 1618 1496 1370 1615 1557 1487 1239\n", - " 1434 1753 1689 1360 1300 1435 1814 1744 1938 1681 1234 1302 1551 1748\n", - " 1556 1684 1691 1752 1366 1872 1624 1298 1236 1686 1874 1876 1424 1428\n", - " 1301 1750 1743 1747 1690 1364 1881 1751 1563 1685 1429 1688 1304 1940\n", - " 1745 1235 1240 1944 1552]\n", - "50 5\n", - "[ 54 245 239 184 308 500 114 376 370 438 631 177 757 181 51 557 247 237\n", - " 47 364 302 756 369 563 564 435 175 431 428 46 627 630 495 498 504 688\n", - " 501 182 567 694 111 304 117 179 300 754 621 430 558 689 240 116 238 440\n", - " 368 560 566 429 439 113 241 365 503 625 499 375 50 118 494 110 306 242\n", - " 52 690 174 497 373 556 502 303 307 366 432 626 629 562 173 301 310 183\n", - " 687 53 172 176 112 49 565 180 436 622 305 692 559 751 753 437 755 434\n", - " 309 374 372 693 493 119 371 246 561 367 433 248 492 312 48 244 496 623\n", - " 236 624 686 691 628 115 109 752 243 178 311 568]\n", - "0 57\n", - "[3776 4032 3526 3648 3909 3392 3714 3524 3906 3332 3587 3460 3456 3843\n", - " 3520 4034 3586 3584 3717 3393 3394 3905 3712 3844 3462 3266 3970 3651\n", - " 3267 3716 3649 3779 3590 3842 3845 3458 3331 3265 4035 4033 3585 3459\n", - " 3718 3522 3523 3654 3781 3777 3778 3397 3972 3525 3907 3396 3461 3588\n", - " 3521 3780 3904 3395 3589 3846 3330 3968 3329 3457 3841 3969 3713 3328\n", - " 3971 3264 3715 3908 3653 3782 3652 3650 3840]\n", - "23 59\n", - "[3858 4055 3730 3990 3865 3538 3985 3996 4051 3799 3923 3668 3541 3922\n", - " 3676 3605 3613 3741 3671 3482 3987 3924 3926 3732 3993 3861 3859 3476\n", - " 3801 4057 3675 3735 3483 3412 3933 3414 3607 4052 3738 4058 3866 3802\n", - " 3986 3995 3415 3604 3548 3479 4060 3413 3863 3869 3921 3989 3798 3546\n", - " 3739 3672 3610 3796 3612 3927 3867 3928 3929 3674 3925 3795 3416 3608\n", - " 3868 3797 3669 3542 3603 3737 3731 3736 3666 4053 3481 3793 4056 3803\n", - " 3547 3601 3606 3734 3930 3665 3994 3539 3609 4050 3540 3677 3667 3997\n", - " 3932 3862 3673 3670 3543 3418 4059 3477 3544 3602 3545 3805 3729 3860\n", - " 3740 3478 3931 3417 3733 3991 3988 3857 3800 3611 3804 3475 4054 3992\n", - " 3864 3794 3480]\n", - "32 40\n", - "[2529 2404 2210 2406 2402 2721 2398 2661 2847 2589 2532 2272 2592 2461\n", - " 2659 2276 2780 2464 2394 2270 2973 2591 2843 2468 2593 2333 2844 2654\n", - " 2723 2596 2783 2530 2716 2522 2526 2978 2658 2848 2533 2779 2974 2338\n", - " 2977 2850 2331 2662 2462 2715 2466 2911 2534 2268 2788 2206 2778 2594\n", - " 2337 2722 2335 2524 2271 2652 2269 2909 2460 2598 2789 2397 2274 2785\n", - " 2339 2913 2597 2211 2784 2467 2209 2273 2590 2916 2588 2458 2463 2207\n", - " 2587 2656 2465 2334 2787 2405 2403 2849 2717 2523 2782 2915 2726 2400\n", - " 2275 2395 2396 2660 2786 2781 2912 2845 2719 2595 2657 2527 2724 2851\n", - " 2976 2975 2336 2914 2208 2979 2340 2653 2718 2469 2910 2531 2725 2459\n", - " 2852 2528 2790 2401 2853 2205 2332 2651 2908 2650 2399 2525 2720 2714\n", - " 2846 2655 2341 2470 2586]\n", - "35 2\n", - "[ 38 32 168 35 222 163 41 94 350 96 546 33 224 360 421 415 164 349\n", - " 167 482 226 285 105 98 30 550 420 417 423 481 485 99 230 416 353 158\n", - " 547 97 352 103 228 414 165 296 36 232 102 287 100 548 545 549 159 225\n", - " 486 31 101 424 290 297 289 221 29 359 40 487 356 223 104 294 361 39\n", - " 162 354 95 355 483 357 286 34 288 231 227 157 358 292 169 419 233 229\n", - " 480 291 295 418 293 166 93 422 484 479 351 37 161 160 544]\n", - "12 19\n", - "[1098 1361 1420 845 1353 1104 1232 1039 1035 1547 1228 910 1358 1549\n", - " 976 1489 842 1041 1170 1167 1350 1295 1099 974 907 906 1095 1612\n", - " 1351 1105 1291 1287 1488 971 1158 1362 1230 912 1548 1423 1165 905\n", - " 847 1421 970 1292 1096 1286 1483 1484 1544 1103 1481 1160 1546 1038\n", - " 1222 1224 1231 1480 846 1223 841 1550 1545 1414 908 1106 1042 1357\n", - " 1227 1040 1426 1166 1485 843 1354 1356 1037 1159 1296 977 1288 1425\n", - " 1613 1289 1032 1033 1293 911 1297 968 1611 1036 1233 1163 844 1290\n", - " 1101 1294 1482 1162 1615 1359 975 1479 972 1100 1487 1226 1102 973\n", - " 1034 1360 1229 1352 1355 1418 1417 967 904 909 1234 1419 1030 1551\n", - " 1031 1422 1486 969 1416 1298 1169 1424 1610 1164 1609 1094 1168 1097\n", - " 1161 1614 1225 1552 1415]\n", - "43 48\n", - "[2922 2861 2927 3115 3500 2730 2732 3182 3053 2859 2983 2795 3373 3499\n", - " 3054 3173 3116 3366 3180 3112 3049 3435 3249 3055 3439 2990 3176 3376\n", - " 3306 3368 2993 2796 2860 3433 3117 3121 3367 3046 3118 3111 3241 2864\n", - " 2928 3307 3305 3243 3502 3311 2798 2920 2791 3244 2919 3431 3304 3369\n", - " 3045 3048 2856 2854 3434 3050 3301 3109 3438 3047 3184 3181 2921 2863\n", - " 2729 3119 2918 3375 3496 3242 2981 2992 3498 2982 3245 2989 3497 3309\n", - " 2923 3302 3113 2984 3370 3051 2987 3175 3056 2794 2857 2917 2855 3501\n", - " 2924 3436 2734 2792 2991 2929 3238 2986 3174 3179 2988 2793 3432 2858\n", - " 3120 3303 3437 3374 3183 3371 2728 2797 3110 3052 3057 2925 3239 3312\n", - " 3310 3114 2731 3308 3178 3313 2733 3248 3247 3177 3240 2926 2862 2799\n", - " 3237 2985 3246 3185 3372]\n", - "17 47\n", - "[2957 3345 3217 2832 2891 3024 3086 3215 3342 3408 3025 2959 2705 3148\n", - " 3027 2765 3023 2767 2897 3280 2960 3089 3154 2955 2963 3151 3084 3091\n", - " 2896 3411 3094 3159 3030 2966 3286 2766 3412 3346 2839 2709 2964 2827\n", - " 2770 2900 3155 2639 2835 2898 3158 2768 2640 3223 3090 3085 3344 2703\n", - " 2702 3218 3284 2893 3031 3087 3026 3153 3277 2708 2773 3410 3219 2833\n", - " 2830 3285 2834 3341 3343 2772 2643 3221 2706 3083 2967 3028 3282 3406\n", - " 3409 3029 2707 3021 3157 2764 3149 2956 3283 2644 3220 3156 3095 3152\n", - " 3347 2901 2895 3092 2828 3276 2965 3093 2837 2642 2899 3348 2829 2838\n", - " 3222 2831 2962 3150 2641 3212 2894 3278 3216 3214 2769 2902 3019 3349\n", - " 3088 3213 3281 2836 2892 2961 2704 3211 3407 2903 2638 3279 3020 2958\n", - " 3022 2701 2774 2771 3147]\n", - "19 29\n", - "[1809 1682 1741 2000 1553 1870 2191 2195 1555 1679 1621 1875 1749 1997\n", - " 2129 1489 2062 2067 2004 1816 2131 1806 1808 2136 2257 1683 2196 1680\n", - " 2006 2008 1873 1617 1878 1687 1488 2064 2198 2134 1869 1492 1933 1494\n", - " 1554 1490 2127 2072 2193 1807 2126 2005 1937 1943 1619 1622 2262 1811\n", - " 1813 1491 2002 1880 2070 1742 1558 2065 2258 2128 1877 1939 1942 1941\n", - " 2133 1817 1945 2003 1677 2192 1493 2001 1678 2007 1746 1935 1812 1815\n", - " 1623 1810 2073 2194 1616 1620 1879 1559 1934 1618 2132 1615 1557 1936\n", - " 2135 2066 2259 1753 1689 1999 1814 1744 2061 1938 2199 1681 1551 1748\n", - " 1805 1871 1556 2071 1684 2197 1752 1872 1624 2009 1686 1874 1876 1750\n", - " 1743 1747 2068 2260 1881 1751 2063 1685 1688 2069 2130 2256 1614 2261\n", - " 1998 1940 1745 1944 1552]\n", - "41 0\n", - "[ 45 38 239 107 168 35 163 41 237 47 364 426 302 360 170 175 164 428\n", - " 167 46 234 105 423 111 99 230 103 300 228 165 238 296 36 232 102 100\n", - " 425 365 108 110 362 101 174 424 297 359 40 173 301 172 104 294 43 361\n", - " 39 357 231 227 358 298 292 169 233 229 299 44 42 363 295 171 293 166\n", - " 427 236 422 106 37 109 235]\n", - "14 5\n", - "[ 17 329 594 210 591 83 459 12 141 401 720 269 467 81 73 15 340 334\n", - " 716 145 200 521 143 526 404 332 523 402 339 655 653 524 525 718 201 719\n", - " 82 144 139 207 595 13 458 721 273 337 136 80 393 14 651 532 715 147\n", - " 208 211 212 468 75 11 146 396 529 76 209 338 202 74 456 392 10 654\n", - " 275 588 79 77 461 268 463 140 658 650 16 335 138 464 267 276 398 717\n", - " 142 400 148 206 399 18 331 465 522 593 527 530 336 587 264 394 462 457\n", - " 460 589 657 266 274 78 592 330 590 265 271 403 586 531 204 205 333 397\n", - " 652 520 270 272 137 656 585 528 203 328 395 466]\n", - "14 14\n", - "[1098 594 848 591 785 720 913 845 1104 1232 1039 1035 1228 910\n", - " 777 716 850 659 976 842 526 1041 914 1170 1167 523 779 724\n", - " 978 1295 655 1099 653 852 974 907 524 525 718 719 906 714\n", - " 1171 781 1105 780 1291 721 971 1230 784 778 912 1165 905 847\n", - " 651 715 970 1292 1096 723 1103 849 1038 529 776 722 782 1231\n", - " 846 841 908 1106 1042 1227 1040 654 1166 843 588 1107 1037 1296\n", - " 977 783 658 650 1032 1033 712 649 979 717 1293 911 1297 968\n", - " 1036 980 1233 1163 844 1101 593 787 527 1294 587 1162 975 972\n", - " 1100 1044 589 851 786 657 1226 1102 973 1034 1229 1108 788 904\n", - " 592 909 590 915 1234 586 840 916 969 652 1169 1164 1043 1168\n", - " 656 528 713 1097 1161]\n", - "29 38\n", - "[2529 2210 2402 2721 2398 2847 2589 2272 2592 2461 2267 2659 2780 2456\n", - " 2464 2394 2270 2200 2591 2843 2263 2593 2713 2333 2844 2654 2783 2647\n", - " 2530 2265 2327 2712 2077 2521 2078 2716 2522 2391 2526 2658 2583 2848\n", - " 2079 2779 2338 2584 2331 2462 2715 2466 2455 2585 2842 2204 2268 2206\n", - " 2778 2594 2337 2722 2335 2524 2271 2652 2269 2080 2460 2330 2393 2777\n", - " 2397 2274 2785 2339 2141 2203 2649 2329 2075 2202 2784 2467 2209 2273\n", - " 2590 2588 2264 2458 2463 2207 2587 2656 2465 2334 2137 2403 2328 2144\n", - " 2717 2523 2782 2392 2400 2275 2395 2396 2266 2201 2781 2845 2719 2595\n", - " 2657 2457 2527 2336 2145 2208 2648 2653 2718 2143 2531 2459 2528 2139\n", - " 2140 2401 2205 2332 2076 2651 2650 2399 2525 2720 2714 2074 2846 2138\n", - " 2655 2520 2142 2519 2586]\n", - "9 12\n", - "[1098 591 459 845 1093 1035 910 777 716 839 521 842 526 523\n", - " 773 771 779 655 1099 653 453 974 907 524 525 718 772 719\n", - " 906 714 1095 580 781 780 582 458 711 835 775 971 1158 778\n", - " 393 905 847 651 517 715 970 1096 581 390 396 1029 1160 1038\n", - " 838 776 966 782 846 456 841 908 392 963 518 516 654 645\n", - " 843 588 1037 1159 461 836 900 783 650 903 708 1032 455 1033\n", - " 712 648 649 837 717 911 968 1036 1163 583 964 844 522 643\n", - " 1101 902 587 1162 644 975 972 394 519 1100 457 460 589 973\n", - " 1034 709 707 967 904 909 590 710 1030 586 840 901 1031 969\n", - " 454 652 899 584 647 520 1164 391 1094 585 713 1097 1161 774\n", - " 646 1028 395 579 965]\n", - "59 7\n", - "[511 251 383 831 441 191 638 569 313 255 316 184 444 125 376 507 380 447\n", - " 438 631 767 319 186 575 253 699 247 826 636 189 829 315 830 634 446 630\n", - " 894 701 445 702 504 501 888 639 827 567 761 694 126 382 252 378 314 249\n", - " 121 122 190 440 566 439 764 187 828 760 632 503 572 375 379 571 120 377\n", - " 254 824 442 443 695 633 893 765 373 502 629 318 510 766 696 310 183 637\n", - " 892 508 188 185 565 890 506 250 574 317 570 698 437 309 374 693 246 509\n", - " 762 763 505 700 248 697 758 312 635 573 124 703 823 381 891 759 825 311\n", - " 568 889 123]\n", - "0 9\n", - "[193 576 320 773 771 897 453 772 260 580 770 386 582 769 835 832 387 258\n", - " 961 325 517 257 642 449 448 581 390 515 577 960 513 963 518 706 516 645\n", - " 836 900 256 321 704 708 194 837 192 705 452 643 451 324 644 962 322 641\n", - " 834 709 578 707 512 710 323 389 195 388 454 450 899 259 514 640 896 898\n", - " 768 833 774 646 384 385 579]\n", - "15 46\n", - "[2957 3345 3275 3217 3145 2832 2891 3024 3086 3215 3342 3025 2959 2705\n", - " 3148 3027 2765 3023 2767 2897 2762 3280 2960 3089 3154 2955 2963 3151\n", - " 3084 3091 2578 2896 2637 3018 3146 2766 2825 3346 2964 2827 2770 2900\n", - " 3155 2639 2835 2698 2898 2768 2640 3082 3090 3017 3085 3344 2703 2702\n", - " 3218 2893 3087 2572 2577 3026 3153 3277 2708 2761 2773 3219 2833 2830\n", - " 2889 2834 3210 3341 3343 2772 2643 2706 3083 3028 2826 3282 2636 3029\n", - " 2635 2707 2700 3021 3157 2764 2576 3149 2956 3283 3220 3156 3152 2901\n", - " 2895 2953 3092 2828 3276 2965 3093 2837 2642 2899 2829 2831 2962 3150\n", - " 2641 2575 3212 2894 3278 3216 3214 2769 3081 2890 3019 3088 3213 3340\n", - " 2574 3281 2836 2892 2961 2704 3211 2763 2954 2638 3279 3020 2958 3022\n", - " 2573 2699 2701 2771 3147]\n", - "60 5\n", - "[ 63 511 251 383 441 191 638 569 313 59 255 316 184 444 125 376 507 380\n", - " 447 438 631 767 319 186 575 57 253 699 247 636 189 315 634 446 127 701\n", - " 445 702 504 639 182 56 567 761 126 382 252 378 314 249 121 122 190 440\n", - " 566 439 764 187 632 503 572 375 379 571 120 377 254 442 443 633 765 502\n", - " 318 510 766 696 310 183 637 508 188 185 506 250 574 317 570 698 60 62\n", - " 374 119 246 509 762 763 61 505 700 248 697 312 635 573 124 703 381 58\n", - " 311 568 123]\n", - "44 35\n", - "[2027 2093 2089 2542 2406 2159 2354 2475 2671 1966 2416 2161 2284 2214\n", - " 2408 2032 2222 1965 2349 2536 2545 2088 2344 2413 2414 2541 2411 2155\n", - " 2342 2418 2348 2087 2345 1963 2602 2474 2535 2538 2152 2026 2090 2153\n", - " 2352 2480 2407 2473 2160 2544 2098 2600 2668 2601 2282 1961 2346 2409\n", - " 1901 1903 2218 2608 2289 2670 1962 2225 2540 2224 2343 2150 2278 2478\n", - " 2417 1964 2607 2472 2096 2030 1899 1902 2095 2288 2162 2154 2219 2025\n", - " 2350 2158 1900 2220 2477 2604 2151 2279 2217 1968 2286 2667 2351 2539\n", - " 2031 2471 2479 2290 2415 1960 2086 2033 2482 2347 2156 2669 2353 2215\n", - " 1967 2216 2023 2092 1897 2543 2226 2287 2283 2606 2280 2412 2094 2410\n", - " 2157 2024 2537 1898 2481 2285 2097 2665 2091 2603 2029 2223 2281 2476\n", - " 2605 2470 2221 2666 2028]\n", - "63 61\n", - "[3903 3583 4095 3711 3839 4026 3899 3772 3967 3644 3837 3708 3769 4030\n", - " 3836 3965 4027 3962 3775 3709 4091 3897 3834 4092 3581 4094 3902 4029\n", - " 3901 3838 4028 4093 3643 3706 3961 4090 3771 4025 4031 3646 3707 3966\n", - " 3900 3898 3647 4089 3773 3645 3582 3835 3833 3774 3963 3964 3710 3580\n", - " 3770]\n", - "12 11\n", - "[1098 329 594 848 591 459 785 720 913 845 1039 1035 334 910\n", - " 777 716 850 839 521 976 842 526 914 332 523 779 655 1099\n", - " 653 974 907 524 525 718 719 906 714 781 780 582 458 721\n", - " 711 775 971 784 778 912 393 905 847 651 715 970 396 1103\n", - " 849 1038 529 838 776 722 782 846 456 841 908 392 518 1040\n", - " 654 843 588 1037 461 463 977 783 658 650 903 335 464 1032\n", - " 455 1033 712 648 649 398 717 911 968 1036 400 399 331 583\n", - " 844 465 522 1101 593 527 530 902 587 975 972 394 519 1100\n", - " 462 457 460 589 786 657 1102 973 1034 967 904 592 909 330\n", - " 590 710 586 840 333 969 397 652 584 647 520 656 585 528\n", - " 713 1097 774 646 395]\n", - "17 42\n", - "[2957 2322 2832 2891 2449 3024 2454 3086 2444 3025 2959 2705 3027 2765\n", - " 3023 2319 2767 2897 2960 2516 3089 2509 2963 2445 3091 2578 2896 2647\n", - " 2637 2318 2453 2966 2387 2766 2582 2711 2451 2839 2583 2709 2447 2517\n", - " 2964 2827 2770 2900 2381 2639 2835 2571 2510 2898 2768 2640 3090 2384\n", - " 2703 2702 2518 2893 2324 3087 2572 2577 3026 2646 2708 2773 2710 2833\n", - " 2385 2830 2834 2579 2515 2772 2580 2383 2643 2706 2514 2388 3028 2636\n", - " 3029 2386 2511 2635 2707 2700 3021 2764 2513 2576 2956 2645 2644 2450\n", - " 2901 2895 3092 2828 2508 2965 2837 2642 2899 2448 2512 2829 2389 2838\n", - " 2320 2831 2962 2775 2641 2321 2575 2894 2769 2581 2902 2507 3088 2574\n", - " 2452 2382 2836 2446 2892 2961 2704 2763 2903 2638 2323 2958 3022 2573\n", - " 2519 2699 2701 2774 2771]\n", - "63 6\n", - "[ 63 511 251 383 831 441 191 638 569 313 255 316 444 125 507 380 447 767\n", - " 319 186 575 253 699 636 189 829 315 830 634 446 127 701 445 702 639 126\n", - " 382 252 378 314 249 190 764 187 828 572 379 571 377 254 442 443 633 765\n", - " 318 510 766 637 508 188 506 250 574 317 570 698 60 62 509 763 61 505\n", - " 700 635 573 124 703 381 123]\n", - "54 6\n", - "[251 54 441 245 569 313 316 184 308 444 500 114 376 370 507 380 438 631\n", - " 177 757 186 181 51 57 699 247 636 756 369 315 563 564 435 634 819 627\n", - " 630 498 821 504 501 182 56 567 761 694 304 252 117 378 179 754 314 249\n", - " 689 240 121 122 116 440 368 560 566 439 187 241 760 632 503 625 499 572\n", - " 375 379 118 820 571 120 306 377 242 52 824 690 442 497 443 695 633 373\n", - " 502 307 432 626 629 562 696 310 183 53 508 185 565 180 436 506 305 692\n", - " 250 570 698 437 755 434 309 822 374 372 693 119 371 246 561 762 505 433\n", - " 248 697 758 55 312 635 244 496 624 823 691 628 115 759 243 178 825 311\n", - " 568]\n", - "60 56\n", - "[3903 3583 3832 3711 3387 3839 3391 3705 3577 4026 3517 3899 3772 3967\n", - " 3644 3320 3766 3263 3511 3837 3708 3769 3702 3895 4030 3574 3836 3390\n", - " 3965 4027 3258 3962 3455 3775 3709 3897 3834 3384 3262 3510 3389 3581\n", - " 3386 3902 4029 3260 3830 3901 3838 4028 3383 3579 3513 3960 3324 3261\n", - " 3326 3643 3706 3961 3578 3448 3771 3257 4025 3642 4031 3646 3575 3641\n", - " 3707 3321 3452 3639 3514 3451 3966 3450 3447 3896 3900 3322 3515 3898\n", - " 3385 3454 3647 3768 3773 3640 3518 3449 3453 3519 3645 3516 3831 3512\n", - " 3767 3582 3835 3327 3833 3774 3259 3638 3963 3964 3704 3710 3703 3388\n", - " 3446 3576 3580 3770 3325 3323]\n", - "17 2\n", - "[ 17 210 83 12 141 401 21 269 467 81 15 340 149 334 145 143 526 404\n", - " 332 402 339 213 82 144 214 139 207 341 13 273 337 80 14 532 147 208\n", - " 211 212 468 75 11 86 151 85 146 396 343 529 76 209 338 150 275 469\n", - " 79 277 342 405 77 461 268 463 279 140 20 16 335 464 267 276 398 142\n", - " 22 400 148 206 399 18 331 465 87 527 530 336 215 462 274 78 271 403\n", - " 531 278 204 205 333 406 23 19 397 270 272 84 528 203 466]\n", - "38 34\n", - "[2027 2084 2404 2089 2210 2406 1892 2402 2022 1895 2475 2532 2018 2272\n", - " 2284 2214 2408 2276 2536 2088 1833 2344 2468 2411 2155 2342 2348 2087\n", - " 2345 1963 2147 2596 2474 2535 2538 2152 2530 2026 2017 2090 1829 2153\n", - " 1957 1954 2149 1832 2020 2533 2407 2473 2338 1894 1955 2600 2601 2282\n", - " 1961 2466 2346 2409 2534 1828 2218 2277 1962 2337 1890 2343 2146 2083\n", - " 2150 2080 2278 1896 2598 2472 2021 2274 2154 2019 2339 1956 1959 2219\n", - " 2597 2025 2211 2220 2467 2151 2209 2273 2279 2217 1827 2081 2599 2465\n", - " 2471 2405 1960 2086 2403 2144 2347 2156 2215 2400 2275 2216 2023 2212\n", - " 2092 1897 2595 1893 2016 1958 2082 1953 1830 2336 2145 2283 2208 2340\n", - " 2280 2469 2412 2531 2410 2024 1831 2537 2401 1898 1891 2213 2148 2091\n", - " 2281 2085 2341 2470 2028]\n", - "8 3\n", - "[ 6 329 459 12 141 269 73 334 200 521 133 261 332 523 453 524 201 260\n", - " 70 139 386 582 13 458 387 136 393 258 14 4 325 517 2 75 263 581\n", - " 390 11 396 76 202 74 456 392 518 10 516 130 68 8 66 77 461 268\n", - " 140 138 132 267 194 455 398 5 327 326 142 206 452 331 583 522 451 197\n", - " 134 587 324 264 394 519 322 7 457 460 72 266 78 196 199 330 9 265\n", - " 131 586 323 262 204 205 333 389 195 388 198 454 397 3 71 259 135 584\n", - " 520 270 391 137 585 203 67 69 328 395]\n", - "55 9\n", - "[ 441 245 569 313 308 444 500 376 370 507 380 438 631 757\n", - " 699 247 826 947 636 756 818 829 315 1018 563 564 435 634\n", - " 882 954 819 627 630 701 445 498 887 821 504 501 888 827\n", - " 567 886 761 694 1014 378 949 754 314 249 689 440 566 439\n", - " 764 828 760 632 503 885 625 499 572 375 379 820 952 571\n", - " 377 824 690 442 497 443 695 633 765 373 502 307 626 629\n", - " 955 562 696 310 637 948 892 508 1016 565 890 436 506 692\n", - " 250 951 1013 950 753 570 698 437 755 434 309 822 374 372\n", - " 693 371 246 509 1017 561 762 763 505 817 1012 700 433 248\n", - " 697 758 953 312 635 884 244 573 823 691 1015 628 891 759\n", - " 883 825 311 568 889]\n", - "14 59\n", - "[4047 3858 3730 3659 3914 3919 3538 3786 4043 3985 3599 3408 4051 3790\n", - " 3923 3529 3983 3668 3980 3853 4041 3851 3979 3922 3534 3726 3913 3728\n", - " 3987 3924 3982 3732 3859 3470 3854 3788 3978 3598 3915 4042 3472 3986\n", - " 3604 3663 3920 3536 3404 3921 3856 3656 3537 3796 3848 3977 3791 3855\n", - " 3531 4048 3468 3595 3795 3467 3603 3731 3600 3725 3666 3532 3471 3535\n", - " 3406 3409 3474 3793 3852 4044 3664 3601 3916 3789 3533 4045 3405 3665\n", - " 3539 3593 4050 3597 3667 3976 3594 3657 3658 3850 3722 3721 3727 3849\n", - " 3469 3720 3602 3792 3981 3473 3917 4049 3596 3729 3785 3860 3662 3592\n", - " 3403 3530 3912 3787 3988 3857 3723 3660 3466 3984 3661 3918 3407 4046\n", - " 3784 3794 3724]\n", - "52 37\n", - "[2542 2801 2159 2354 2739 2671 2416 2161 2420 2222 2678 2612 2545 2100\n", - " 2617 2414 2418 2294 2679 2806 2230 2618 2807 2164 2742 2167 2738 2489\n", - " 2741 2360 2034 2166 2425 2609 2228 2419 2488 2352 2298 2480 2740 2805\n", - " 2674 2553 2160 2544 2098 2293 2296 2550 2103 2552 2422 2608 2289 2490\n", - " 2225 2355 2554 2224 2036 2478 2417 2099 2607 2169 2096 2038 2421 2039\n", - " 2288 2162 2037 2358 2736 2350 2549 2163 2680 2548 2286 2803 2357 2351\n", - " 2479 2802 2483 2290 2415 2423 2033 2676 2482 2737 2353 2486 2295 2611\n", - " 2615 2035 2234 2614 2672 2613 2543 2291 2231 2804 2744 2226 2104 2292\n", - " 2287 2675 2229 2606 2165 2673 2101 2485 2168 2362 2227 2426 2484 2681\n", - " 2232 2610 2481 2356 2097 2487 2546 2547 2223 2359 2102 2361 2743 2424\n", - " 2551 2677 2616 2233 2297]\n", - "56 51\n", - "[3387 3705 3128 3577 3517 3644 3320 3511 3187 3702 3379 3574 3316 2933\n", - " 3390 3004 3443 3636 3189 3258 3191 3129 3003 3196 3384 3262 3510 2935\n", - " 3389 3000 3445 2936 2997 3581 3064 3125 3386 3130 3253 2934 3260 3252\n", - " 3572 2996 3061 3123 3383 3250 3382 3380 3579 3513 3324 3261 3326 3002\n", - " 3643 3063 3706 3378 3192 3186 3317 3126 3578 3448 3444 3257 2938 3642\n", - " 3188 3195 3256 3575 2937 2999 3641 3707 3701 3321 3507 3193 3452 2939\n", - " 3639 3514 3451 3122 3131 3381 3319 3450 3447 3067 3194 3197 3322 3515\n", - " 3318 3132 3001 3385 3059 3454 3637 3066 2998 3640 3518 3449 3573 3314\n", - " 3453 3509 3198 3516 3133 3512 3134 3315 3506 3571 3259 3251 3442 3638\n", - " 3508 3704 3254 3703 3388 3255 3446 3190 3065 3062 3068 3576 3060 3069\n", - " 3580 3325 3124 3127 3323]\n", - "14 21\n", - "[1098 1682 1361 1741 1553 1420 1353 1104 1232 1039 1555 1679 1035 1547\n", - " 1228 1358 1549 976 1489 1041 1170 1167 1295 1099 974 1674 1680 1363\n", - " 1612 1171 1105 1739 1291 1617 1488 971 1362 1230 1548 1492 1423 1165\n", - " 1554 1421 1490 1292 1483 1619 1484 1675 1299 1427 1544 1103 1491 1481\n", - " 1160 1546 1038 1224 1676 1742 1231 1480 1550 1545 1106 1042 1357 1227\n", - " 1040 1426 1166 1485 1354 1107 1172 1356 1037 1677 1296 977 1288 1425\n", - " 1613 1289 1678 1293 1297 1611 1036 1233 1163 1290 1616 1101 1618 1294\n", - " 1482 1162 1615 1359 975 972 1100 1487 1226 1102 973 1034 1360 1229\n", - " 1300 1352 1355 1418 1744 1417 1681 1234 1419 1551 1422 1556 1486 1416\n", - " 1298 1236 1169 1424 1610 1428 1743 1364 1164 1609 1168 1097 1161 1740\n", - " 1614 1225 1745 1235 1552]\n", - "21 27\n", - "[1809 1682 2000 1553 1555 1679 1621 1875 1749 1489 2067 2004 1816 2131\n", - " 1808 1560 2136 1819 1683 1883 1680 1363 2006 1818 2008 1873 1617 1878\n", - " 1687 1488 1362 2134 1492 1494 1554 1367 1490 1561 1431 2072 1807 1433\n", - " 2005 1937 1943 1619 1754 1562 1427 1622 1811 1813 1491 2002 1880 2070\n", - " 1558 2065 1877 1939 1426 1627 1432 1942 1626 1941 2133 1817 1945 2003\n", - " 1495 1947 1497 1625 1368 1425 1365 1493 2001 1882 2007 1746 1935 1812\n", - " 1755 1815 1623 1810 1946 2073 1616 1498 1620 1879 1559 1430 1618 2132\n", - " 1496 1615 1557 1936 2135 2066 1753 1689 1814 1744 1938 1681 1551 1748\n", - " 1871 1556 2071 1684 1691 2010 1752 1366 1872 1624 2009 1686 1874 1876\n", - " 1428 1750 1743 1747 2068 1690 1364 1881 1751 1563 1685 1429 1688 2069\n", - " 2130 1940 1745 1944 1552]\n", - "51 57\n", - "[3832 4083 3827 3959 3705 4086 3577 3696 3699 3766 3511 3956 3769 3954\n", - " 3702 3379 3895 3823 3826 3574 3316 3631 3886 3443 4081 3636 3440 3757\n", - " 3887 3822 3439 3952 3376 3760 3897 3377 3885 3510 3762 3445 4019 4016\n", - " 3502 3694 3830 3572 3890 3383 4018 3698 3382 3380 3513 3828 3960 3700\n", - " 3950 3763 3891 3824 3438 3765 3378 4085 3567 3375 4015 3758 3317 3566\n", - " 3448 3893 4084 3958 4021 3444 4022 3505 3697 3575 3504 3641 3441 3701\n", - " 3507 3629 3639 3764 3569 3503 3381 3894 3501 3565 4082 3447 3951 3896\n", - " 3759 3825 3821 3957 3318 3630 3632 3768 3637 4017 3640 3573 3635 3314\n", - " 3509 3831 3512 3767 3633 3570 3315 3833 3506 3312 3571 3695 3888 3889\n", - " 4020 3953 4080 3634 3442 3313 3638 3508 3704 3829 3703 3446 3693 3761\n", - " 3576 3892 3568 3955 4023]\n", - "22 36\n", - "[2322 2449 2454 2195 2326 2129 2267 2067 2456 2004 2394 2516 2131 2200\n", - " 2263 2325 2713 2136 2257 2578 2196 2647 2006 2265 2327 2008 2712 2521\n", - " 2453 2387 2582 2522 2711 2391 2451 2583 2709 2517 2198 2134 2584 2331\n", - " 2455 2585 2072 2193 2204 2268 2005 1943 2384 2262 2524 2002 2070 2518\n", - " 2065 2460 2258 2324 2330 2393 2577 2646 2708 2128 1939 2710 2385 1942\n", - " 1941 2203 2390 2649 2579 2329 2133 2075 1945 2515 2003 2580 2202 2192\n", - " 2643 2264 2458 2514 2388 2587 2386 2007 2137 2328 2707 2523 2513 2392\n", - " 2645 2644 2450 2395 2073 2194 2396 2266 2201 2132 2457 2135 2066 2259\n", - " 2642 2648 2448 2199 2512 2389 2320 2321 2459 2139 2140 2071 2581 2197\n", - " 2010 2332 2009 2650 2452 2074 2068 2260 2138 2069 2130 2323 2520 2256\n", - " 2261 1940 2519 1944 2586]\n", - "54 27\n", - "[1840 1907 1976 1973 1713 1524 1782 1521 1399 1842 1651 1461 1785 1525\n", - " 2100 1914 1978 2164 2167 1712 1849 1909 1848 1969 1841 1588 1652 2034\n", - " 2166 1401 1648 1465 1912 2105 2098 1715 1905 1778 1460 1649 2103 1717\n", - " 1779 1658 1979 1585 1971 1522 1594 1523 1592 1595 1916 1719 2036 2041\n", - " 1586 1846 1584 2099 2169 1851 1847 1908 1530 1463 2038 1660 1724 2039\n", - " 1714 2106 2037 1776 1723 1904 1466 1396 1527 1970 1531 1395 1593 1657\n", - " 1650 2163 1783 1913 1968 1845 1972 1464 1915 1980 1910 2033 1587 1718\n", - " 1788 1777 2043 1716 1591 1529 1784 2035 1781 2040 1786 1398 1459 2104\n", - " 1780 1655 1720 1850 1397 1528 1975 1721 1596 1787 2165 1844 1911 1974\n", - " 2101 1977 1400 2168 1653 1852 1590 1654 1462 1656 1589 1722 1659 1526\n", - " 1843 1458 2102 2042 1906]\n", - "28 26\n", - "[1826 1695 1570 1888 1502 1697 1504 1692 1311 2015 1885 1760 1376 1816\n", - " 1305 1436 1560 1505 1503 1952 2011 1819 1883 1818 2008 2077 2078 1566\n", - " 1371 1500 1878 1310 1687 1949 1822 2079 1374 1494 2014 1437 1698 1758\n", - " 1561 1431 1307 1757 1633 1433 1943 1306 1693 1506 1754 1562 1622 1499\n", - " 1890 1880 1558 2012 1628 1950 1948 1821 1627 1432 1626 1886 1373 1817\n", - " 2075 1945 1495 1947 1497 1825 1625 1368 1694 1630 1887 1501 1762 1882\n", - " 1632 1375 1369 1755 1815 1440 1623 1951 1946 2073 1498 1879 1559 1889\n", - " 1569 1496 1820 1370 1438 1434 1753 2016 1953 1689 1565 1564 1435 1814\n", - " 1759 1439 1884 1696 1567 1823 1629 1691 2010 1752 1824 1624 2076 2009\n", - " 1634 1686 1631 1750 1308 2074 1690 1881 1751 1563 1761 1688 1441 1309\n", - " 1372 2013 1568 1756 1944]\n", - "25 4\n", - "[ 27 83 153 21 467 25 340 149 222 281 89 94 156 404 350 472 537 339\n", - " 213 415 471 409 535 214 349 341 541 540 665 285 664 477 30 667 28 542\n", - " 600 532 345 147 211 212 468 158 662 152 348 86 151 85 343 414 92 154\n", - " 287 217 605 534 347 599 91 407 159 150 601 539 275 469 277 342 405 668\n", - " 473 279 221 29 220 284 344 411 20 597 478 276 536 223 22 90 148 95\n", - " 533 286 413 87 157 88 215 155 280 282 603 476 346 283 403 604 470 278\n", - " 408 216 26 406 23 663 598 24 602 93 219 479 351 538 218 84 474 412\n", - " 666 475 410]\n", - "44 54\n", - "[3696 3115 3500 3182 3823 3373 3884 3631 3499 3116 3886 3440 3366 3180\n", - " 3757 3887 3435 3822 3249 3439 3176 3376 3306 3368 3623 3760 3433 3117\n", - " 3377 3367 3885 3430 3118 3241 3559 3307 3305 3689 3243 3502 3561 3311\n", - " 3244 3694 3431 3626 3754 3304 3369 3564 3698 3817 3434 3824 3438 3184\n", - " 3627 3378 3181 3567 3119 3375 3496 3242 3691 3758 3566 3498 3245 3687\n", - " 3497 3309 3302 3494 3751 3625 3113 3882 3505 3370 3697 3558 3692 3818\n", - " 3504 3820 3441 3629 3756 3563 3628 3569 3503 3501 3565 3819 3759 3436\n", - " 3821 3630 3632 3752 3179 3432 3314 3755 3881 3816 3303 3437 3374 3183\n", - " 3371 3633 3624 3570 3688 3506 3239 3312 3695 3310 3753 3114 3308 3178\n", - " 3634 3442 3313 3495 3622 3883 3248 3247 3177 3240 3693 3761 3686 3568\n", - " 3562 3246 3560 3372 3690]\n", - "7 60\n", - "[4038 3659 3914 3526 3786 4043 3847 3529 3980 3853 3909 4041 3464 3851\n", - " 3979 3527 4036 4037 3913 3714 3975 3524 3906 3587 3460 4039 3788 3978\n", - " 3843 4034 3586 3717 3915 4042 3905 3528 3844 3462 3970 3651 3716 3649\n", - " 3911 3779 3590 3842 3656 3845 3848 3463 4035 4033 3977 3718 3531 3523\n", - " 3973 3654 3595 3781 3777 3725 3719 3910 3778 3852 3972 3525 3907 4044\n", - " 3916 3789 3461 3655 4045 3588 3593 3591 3780 3976 3589 3594 3657 3846\n", - " 3658 3850 3722 3721 3849 3720 3974 3981 3917 3841 3596 3969 4040 3785\n", - " 3592 3713 3971 3530 3912 3787 3723 3660 3466 3661 3465 3715 3783 3908\n", - " 3784 3653 3782 3652 3650 3724]\n", - "12 49\n", - "[2887 2957 3345 3275 3217 3145 2832 2891 3024 3086 3215 3342 3408 3025\n", - " 2959 3148 3529 2765 3023 2767 2897 2762 3280 2960 3089 3464 3154 2955\n", - " 3209 3206 3402 3534 3151 3084 2896 3470 3018 3146 2766 2825 3274 3346\n", - " 2827 3472 3399 3270 2951 3273 3082 3090 3017 3085 3344 2950 3404 3080\n", - " 3400 3218 2893 3087 3337 3026 3153 3277 2761 3207 2830 2889 3531 3079\n", - " 3468 3210 3341 3272 3343 3271 3336 3467 3014 3532 3471 3083 3535 2826\n", - " 3282 3406 3409 2824 3021 2764 3149 2956 3339 3533 3143 3142 3405 3078\n", - " 3338 3152 3016 3144 2888 2895 3401 2953 2828 3276 3469 3015 2829 2831\n", - " 2962 3150 3212 2894 3278 3216 3214 3334 3081 2890 3019 3208 3088 3213\n", - " 3340 3403 2952 3281 3530 3466 2892 3465 2961 3211 2763 3407 2954 3335\n", - " 3279 3020 2958 3022 3147]\n", - "40 37\n", - "[2027 2084 2404 2089 2210 2542 2406 2402 2022 2475 2661 2532 2284 2214\n", - " 2408 2730 2222 2732 2659 2276 2349 2536 2088 2795 2344 2413 2468 2414\n", - " 2541 2411 2155 2342 2348 2087 2345 2602 2147 2596 2474 2535 2538 2152\n", - " 2530 2026 2090 2153 2149 2533 2407 2473 2338 2662 2600 2668 2601 2282\n", - " 2466 2346 2409 2791 2534 2218 2277 2594 2540 2343 2150 2278 2478 2598\n", - " 2472 2729 2021 2789 2274 2154 2339 2219 2597 2025 2663 2350 2211 2220\n", - " 2477 2604 2467 2151 2279 2217 2664 2794 2286 2599 2667 2539 2471 2405\n", - " 2086 2403 2347 2726 2156 2669 2215 2275 2216 2660 2023 2212 2092 2792\n", - " 2595 2724 2793 2283 2606 2727 2340 2280 2728 2469 2412 2531 2725 2410\n", - " 2157 2024 2790 2537 2731 2285 2665 2213 2148 2091 2603 2281 2085 2341\n", - " 2476 2605 2470 2221 2666]\n", - "13 31\n", - "[1809 1741 2000 1870 2191 1804 2195 2317 1679 1875 1997 2129 2319 1737\n", - " 2062 2067 2314 2131 1806 1808 2257 2380 1930 2313 1674 1866 1680 1932\n", - " 1863 2056 1612 2318 1799 2188 1873 1739 2254 2255 2125 2378 1867 2064\n", - " 1869 1928 1933 2381 2185 2127 2058 2193 1807 1868 2126 1931 1864 1937\n", - " 1675 2384 1811 2002 1802 2249 2379 1676 2059 1742 2186 2250 2065 1803\n", - " 2258 2128 1939 1801 2122 2121 1927 1800 2003 1677 2383 1995 2192 2253\n", - " 2120 2315 1738 2251 1993 1613 2001 1991 1678 1746 1935 2119 1611 1810\n", - " 1992 1929 2194 1616 1934 1615 1936 2184 2066 1999 1673 2055 1744 2061\n", - " 1938 2183 1681 2320 2321 1805 1871 2057 1872 2187 1996 2316 2382 2060\n", - " 2252 1874 1610 1743 1865 1736 2063 2189 2130 2123 2256 2124 2190 1740\n", - " 1614 2248 1998 1745 1994]\n", - "33 62\n", - "[4068 4061 4065 3996 4001 3877 4071 4066 4064 3678 3937 3741 3874 3810\n", - " 4004 4069 4062 3747 3933 3616 3742 3806 3748 3995 4007 3679 3745 3872\n", - " 4060 3871 3869 3749 3618 3620 3867 3999 3684 3682 3680 4002 4006 3617\n", - " 3879 3744 3868 4067 3875 3746 4000 3614 3809 3814 3811 3812 3685 3873\n", - " 4070 3815 3934 3878 3803 3941 3615 3619 3743 3813 3677 3683 3997 3935\n", - " 3938 3998 3932 4003 4059 3808 3807 3936 4063 3943 3876 3805 3740 3931\n", - " 3870 3942 3939 3804 3940 4005 3750 3681]\n", - "49 2\n", - "[ 45 54 245 239 107 308 500 114 370 438 177 181 51 247 237 47 364 302\n", - " 369 563 564 435 175 431 428 46 495 498 501 182 111 304 117 179 300 430\n", - " 558 240 116 238 368 560 429 113 241 365 499 375 50 118 494 108 110 306\n", - " 242 52 174 497 373 303 307 366 432 562 173 301 310 183 53 172 176 112\n", - " 49 180 43 436 305 559 437 434 309 374 372 493 119 371 246 299 44 363\n", - " 561 171 367 433 55 48 244 496 236 115 109 235 243 178 311]\n", - "19 61\n", - "[4047 3858 4055 3730 3990 3919 3865 3538 3985 3599 4051 3799 3790 3923\n", - " 3983 3668 3853 3541 3922 3605 3726 3671 3728 3987 3924 3926 3982 3732\n", - " 3993 3861 3859 3854 3801 4057 3735 3607 4052 3986 3604 3663 3920 3536\n", - " 3863 3921 3856 3989 3798 3537 3672 3796 3927 3791 3928 3855 3929 4048\n", - " 3925 3795 3797 3669 3542 3603 3737 3731 3600 3725 3736 3666 4053 3793\n", - " 4056 3664 3601 3789 3606 4045 3734 3665 3539 4050 3540 3667 3862 3670\n", - " 3727 3602 3792 3981 3917 4049 3729 3860 3662 3733 3991 3988 3857 3984\n", - " 3800 3918 4046 4054 3992 3864 3794]\n", - "63 11\n", - "[ 511 383 831 638 569 1151 444 1023 507 380 447 767 575 699\n", - " 826 636 958 829 1018 830 956 634 446 954 1085 894 701 445\n", - " 1021 702 639 827 761 382 1148 764 828 572 571 443 633 893\n", - " 765 1019 510 955 766 1020 637 892 508 890 506 1086 574 570\n", - " 698 895 509 957 762 1022 763 700 697 959 953 635 573 703\n", - " 381 1087 891 1084 1150 825 889 1083 1149]\n", - "63 4\n", - "[ 63 511 251 383 441 191 638 313 59 255 316 444 125 507 380 447 319 186\n", - " 575 253 636 189 315 446 127 701 445 702 639 126 382 252 378 314 249 121\n", - " 122 190 187 572 379 571 377 254 442 443 318 510 637 508 188 185 506 250\n", - " 574 317 570 60 62 509 61 505 700 635 573 124 703 381 58 123]\n", - "18 13\n", - "[ 594 848 591 785 720 913 845 467 1104 1232 1039 910 716 850\n", - " 1110 659 976 526 1041 914 1170 1167 596 724 978 917 655 653\n", - " 852 974 718 719 855 1171 781 792 1105 780 595 721 664 982\n", - " 784 912 847 856 791 532 468 789 662 723 1046 1237 1103 849\n", - " 1038 529 722 782 1231 846 908 1106 534 1042 1040 919 599 790\n", - " 983 654 1166 469 918 726 728 1107 1172 1037 1047 463 977 853\n", - " 783 658 725 597 464 979 717 854 911 1036 980 1233 1048 533\n", - " 1111 844 465 1109 1101 593 787 527 530 1173 975 661 972 660\n", - " 1174 1044 589 851 786 657 1102 973 981 1108 788 592 909 590\n", - " 915 1234 531 916 663 1236 652 598 1169 920 1043 727 1168 656\n", - " 528 1045 984 1235 466]\n", - "37 60\n", - "[4074 4068 3944 4065 4001 3877 4071 3490 4066 4064 3937 3874 3810 4004\n", - " 4073 4072 4069 3556 3553 3947 3621 3623 3747 3616 3559 3748 3689 3561\n", - " 4007 3679 3626 3754 3745 3872 3871 3817 3880 3946 3749 3554 3496 3691\n", - " 3491 3618 3620 3687 3999 4075 3557 3684 3682 3493 3680 3494 3751 3625\n", - " 4002 3882 4006 3617 3879 3744 3558 4067 3875 3746 4000 3818 3809 3814\n", - " 3811 3812 3685 3873 4070 3815 4011 3878 3941 3619 3819 3743 4009 3813\n", - " 3683 3555 3935 3938 4003 3752 4008 3755 3881 3808 3807 3816 3624 3936\n", - " 4063 3943 3876 4010 3688 3753 3495 3622 3883 3942 3939 3940 3686 3492\n", - " 4005 3560 3750 3945 3690 3681]\n", - "7 13\n", - "[1098 845 1093 1035 777 716 839 521 842 523 773 771 779 897\n", - " 1099 653 453 907 1091 772 906 714 1095 580 781 770 780 582\n", - " 769 458 711 835 775 971 1158 778 1155 961 905 651 517 715\n", - " 642 970 1096 581 515 1029 1160 1222 838 1224 776 966 1221 1026\n", - " 456 1223 841 908 1092 963 518 706 516 645 1025 843 588 1037\n", - " 1159 836 900 650 903 708 1032 455 1033 712 648 649 837 717\n", - " 968 1036 705 452 1163 583 964 844 522 643 902 587 1162 644\n", - " 962 972 519 1100 641 457 834 1226 973 1034 709 578 707 967\n", - " 904 909 1027 710 1030 586 840 901 1031 969 1157 1090 454 652\n", - " 899 584 647 520 1094 585 713 1097 898 1161 833 774 1156 1220\n", - " 646 1225 1028 579 965]\n", - "9 55\n", - "[3275 3145 3659 3914 3526 3786 3599 3342 3790 3847 3148 3529 3853 3464\n", - " 3851 3398 3527 3209 3206 3402 3534 3726 3913 3524 3205 3332 3587 3470\n", - " 3460 3146 3788 3274 3333 3598 3717 3915 3528 3462 3399 3651 3663 3270\n", - " 3716 3911 3273 3590 3404 3400 3656 3845 3331 3268 3848 3337 3463 3277\n", - " 3207 3459 3718 3531 3523 3654 3468 3595 3781 3210 3341 3272 3343 3271\n", - " 3336 3467 3725 3532 3471 3535 3406 3719 3910 3397 3852 3525 3396 3916\n", - " 3789 3339 3461 3533 3143 3655 3142 3405 3588 3338 3593 3591 3597 3780\n", - " 3144 3395 3589 3594 3657 3846 3658 3850 3401 3722 3276 3721 3727 3849\n", - " 3469 3720 3596 3212 3785 3278 3662 3592 3334 3208 3213 3340 3403 3530\n", - " 3912 3787 3723 3660 3466 3661 3465 3269 3211 3407 3715 3783 3784 3653\n", - " 3335 3782 3652 3147 3724]\n", - "49 23\n", - "[1388 1392 1710 1840 1907 1262 1330 1713 1136 1519 1524 1581 1389 1782\n", - " 1521 1399 1203 1842 1651 1461 1261 1708 1773 1525 1394 1839 1772 1712\n", - " 1841 1588 1198 1652 1579 1200 1140 1451 1648 1135 1269 1139 1327 1452\n", - " 1326 1331 1335 1455 1715 1328 1905 1778 1460 1649 1456 1717 1903 1779\n", - " 1202 1268 1454 1709 1585 1522 1707 1523 1323 1334 1837 1719 1586 1387\n", - " 1584 1329 1515 1908 1205 1463 1393 1390 1902 1520 1138 1714 1776 1904\n", - " 1396 1527 1516 1457 1395 1650 1324 1204 1845 1325 1391 1270 1644 1838\n", - " 1264 1197 1587 1718 1777 1716 1591 1781 1134 1398 1453 1459 1332 1774\n", - " 1265 1780 1655 1266 1397 1199 1517 1645 1844 1646 1643 1267 1653 1137\n", - " 1590 1260 1654 1462 1775 1589 1201 1526 1583 1263 1647 1582 1843 1711\n", - " 1458 1580 1518 1333 1906]\n", - "13 24\n", - "[1809 1682 1361 1741 1553 1870 1420 1353 1804 1232 1555 1679 1547 1228\n", - " 1358 1549 1489 1737 1167 1806 1808 1295 1930 1674 1866 1683 1680 1363\n", - " 1932 1612 1351 1873 1739 1291 1617 1543 1488 1362 1230 1867 1672 1869\n", - " 1548 1423 1165 1933 1554 1421 1490 1671 1292 1807 1868 1931 1483 1619\n", - " 1484 1675 1427 1544 1491 1481 1546 1802 1676 1742 1231 1803 1480 1550\n", - " 1545 1357 1227 1607 1426 1801 1166 1485 1354 1356 1735 1800 1677 1296\n", - " 1288 1738 1425 1613 1289 1678 1746 1935 1293 1297 1611 1233 1810 1163\n", - " 1290 1616 1934 1618 1294 1482 1162 1615 1359 1479 1936 1487 1226 1360\n", - " 1229 1352 1355 1418 1673 1744 1417 1681 1419 1551 1805 1871 1422 1486\n", - " 1872 1416 1298 1424 1610 1743 1747 1164 1865 1736 1609 1168 1740 1614\n", - " 1225 1608 1745 1552 1415]\n", - "22 17\n", - "[1361 913 1104 1232 850 1110 976 1041 914 1170 1305 724 978 988\n", - " 917 987 852 1363 855 1115 1171 792 1105 1049 1371 729 1362 982\n", - " 912 857 1492 1494 1180 856 791 924 1367 1112 1431 789 1175 1307\n", - " 1241 723 1046 1433 1237 1306 1299 1427 849 1491 985 1177 1052 921\n", - " 1106 1042 1040 919 922 1426 793 790 983 1432 986 923 1116 1303\n", - " 918 1238 726 728 1107 1172 1113 1047 1296 977 853 1495 1497 858\n", - " 1243 1114 1368 725 1365 1493 979 1178 854 1050 1369 1297 980 1233\n", - " 1048 1111 1109 787 1430 1496 1173 1370 1174 1242 1239 1434 1044 851\n", - " 786 981 1300 1051 1108 788 915 1234 1302 1179 916 1366 1298 1236\n", - " 1169 1428 1301 1308 794 1176 1364 920 1043 727 1429 1168 859 1244\n", - " 1304 1045 984 1235 1240]\n", - "25 1\n", - "[ 27 83 153 21 25 340 149 222 281 89 94 156 350 472 213 471 409 214\n", - " 349 341 285 30 28 345 147 211 212 158 152 348 86 151 85 343 92 154\n", - " 287 217 347 91 407 159 150 275 31 277 342 405 473 279 221 29 220 284\n", - " 344 411 20 276 223 22 90 148 95 286 413 87 157 88 215 155 280 282\n", - " 476 346 283 470 278 408 216 26 406 23 19 24 93 219 218 84 474 412\n", - " 475 410]\n", - "54 58\n", - "[3832 4083 3827 3959 3705 4086 3577 4026 3696 3899 3772 3644 3699 3766\n", - " 3511 3956 3708 3769 3954 3702 3379 3895 3826 3574 3836 4027 3443 3636\n", - " 3962 3952 3760 3897 3834 3384 3510 3762 3445 4019 3830 3572 3890 3383\n", - " 4018 3698 3382 3380 3579 3513 3828 3960 3700 3763 3891 3824 3643 3706\n", - " 3765 4085 3961 4090 3578 3448 3771 3893 4084 3958 4021 3444 4022 4025\n", - " 3642 3505 3697 3575 4024 3641 3707 3701 3507 3639 3514 3764 3569 3381\n", - " 3894 3450 4082 3447 3896 3825 3900 3515 3957 3898 4088 3385 3632 3768\n", - " 3637 4017 4089 3640 3449 3573 3635 3509 3831 3512 3767 3633 3835 3570\n", - " 3833 3506 3571 3888 3889 4020 3953 3634 3442 3638 3963 3508 3964 3704\n", - " 3829 4087 3703 3446 3761 3576 3892 3568 3580 3770 3955 4023]\n", - "48 31\n", - "[1710 2027 1840 1907 2093 1973 1713 2159 1836 2354 1966 2416 2161 2284\n", - " 2032 2222 1965 1842 1651 2349 1708 1773 2100 2413 2414 2155 1839 2418\n", - " 2348 1963 1772 2230 2164 1712 1909 1969 2026 1834 1841 2090 2034 2166\n", - " 1771 1648 2228 2419 2352 2160 2098 2293 1715 1905 1778 1649 1901 1903\n", - " 1779 1709 2218 1971 2289 1962 2225 2355 2224 1837 2036 1846 1835 2417\n", - " 2099 1964 1908 2096 2038 2030 1899 1902 2095 2288 2162 1714 2154 2037\n", - " 1776 1904 2219 2350 1970 2158 1900 2220 1650 2163 1968 1845 2286 1972\n", - " 2351 1838 2031 2290 2415 1910 2033 1777 1716 2156 2353 1967 2035 2092\n", - " 1781 2291 2226 1774 1780 2292 2287 2229 2283 2165 1645 1844 1974 1646\n", - " 2101 2094 2157 2227 1775 1898 2285 2356 2097 2091 2029 2223 1647 1843\n", - " 1711 2102 2221 2028 1906]\n", - "19 47\n", - "[2957 3345 3217 2832 3024 3086 3215 3408 3025 2959 2705 3027 3023 2767\n", - " 2897 3280 2960 3089 3154 2963 3151 3091 2896 3411 2840 3160 3094 3159\n", - " 3030 2966 3286 3287 2766 2711 3412 3414 3346 2839 2709 2964 2770 2968\n", - " 2900 3155 2905 2835 3097 3351 2898 3158 2768 2640 3223 3090 3413 3085\n", - " 3344 2703 3218 3284 2776 3288 2893 3031 3087 3026 2646 3153 2708 2773\n", - " 3410 2710 3219 2833 2830 3285 2834 3161 3343 2772 3096 3032 2643 3221\n", - " 2706 2967 3028 3282 3409 3029 2707 3021 3157 3149 3283 2645 2644 3220\n", - " 3156 3095 3152 2904 3347 2901 2895 3092 3350 3225 2965 3093 2837 3033\n", - " 2642 2899 2969 3348 2829 2838 3222 2831 2962 3150 2775 2641 2894 3278\n", - " 3216 3214 2769 2902 3349 3088 3213 3281 2836 3224 2961 2704 2903 3279\n", - " 2958 3022 2841 2774 2771]\n", - "63 50\n", - "[3583 3387 3391 3071 2878 3517 3644 3263 3390 3004 3258 3455 3129 3003\n", - " 3196 2943 3262 3389 2879 3581 2877 2940 3386 3130 3260 3579 3324 3261\n", - " 3326 3002 3007 3257 3646 3195 3006 3321 3193 3452 2939 2941 3514 3451\n", - " 3131 3450 3070 3067 3194 3197 3005 3322 3515 3132 3385 3454 3647 3066\n", - " 3518 3449 3453 2876 3519 3198 3199 3645 3516 3133 3582 3134 3327 2942\n", - " 3135 3259 3388 3065 3068 3069 3580 3325 3323]\n", - "17 33\n", - "[2322 1809 2000 1870 2449 2191 2195 2317 1875 2326 1997 2129 2319 2062\n", - " 2067 2004 2516 2131 1806 1808 2445 2263 2325 2257 2380 2196 1932 2006\n", - " 2318 2327 2453 2188 1873 2387 2254 1878 2255 2125 2451 2447 2064 2198\n", - " 2134 1869 1933 2381 2510 2127 2193 1807 1868 2126 1931 2005 1937 1943\n", - " 2384 2262 1811 1813 2002 2070 2059 1742 2065 2258 2324 2128 1877 1939\n", - " 2385 1942 1941 2390 2133 2515 2003 2383 1995 2192 2253 2315 2514 2251\n", - " 2388 2001 2386 2007 1746 2511 1935 1812 2513 1810 2450 2194 1934 2132\n", - " 1936 2135 2066 2259 1999 1744 2061 1938 2448 2199 2512 2389 2320 2321\n", - " 1748 1805 1871 2071 2197 1872 2187 1996 2316 2452 2382 2060 2252 1874\n", - " 1876 2446 1743 1747 2068 2260 2063 2189 2069 2130 2323 2123 2256 2124\n", - " 2190 2261 1998 1940 1745]\n", - "53 11\n", - "[ 441 569 500 1078 376 879 370 438 631 757 699 826 947 756\n", - " 818 1018 563 564 435 634 882 954 819 1140 627 630 498 1139\n", - " 887 821 504 688 501 888 827 567 886 1144 761 694 1009 1014\n", - " 949 754 1143 1011 689 440 560 566 439 760 632 503 885 625\n", - " 1142 499 375 1138 820 1075 952 571 815 1008 824 690 497 1141\n", - " 695 633 1010 373 502 1081 626 629 955 562 696 687 948 946\n", - " 1016 565 890 1076 436 506 1073 692 559 951 751 1013 950 753\n", - " 570 698 437 1077 755 434 1080 881 822 374 372 693 943 371\n", - " 1017 561 945 762 763 505 817 1079 1074 1012 433 697 758 953\n", - " 880 944 635 884 496 623 624 823 691 1015 628 891 816 752\n", - " 759 883 825 568 889]\n", - "50 45\n", - "[2861 2801 2739 3128 2671 2927 2930 2732 2678 3187 3182 3053 2612 2545\n", - " 3316 2933 3054 3116 3058 2679 2806 3189 2807 2742 3191 2872 3249 3055\n", - " 2738 2990 2741 2993 2796 2860 2609 3117 3121 2935 3118 3000 2740 2864\n", - " 2936 2997 2805 3064 2674 2928 2544 3125 2869 3311 2798 3253 2934 3252\n", - " 2996 2608 3061 3123 2670 3250 2868 3063 2607 3184 3181 2995 2863 3186\n", - " 3119 2867 3317 3126 2992 2870 2989 2736 3188 2549 2999 3056 2548 2803\n", - " 3122 2802 2676 2737 2669 2924 2611 2865 2614 2672 2734 2613 3059 2991\n", - " 2543 2929 2804 2744 2998 2988 2808 2871 2994 2931 3314 2675 2606 3120\n", - " 2673 3183 2800 2797 3052 3057 3315 2925 3312 2866 3251 2610 3313 2733\n", - " 3248 3247 2546 2547 3254 2932 2926 2862 2799 3190 3062 3060 3246 2743\n", - " 3185 2735 2677 3124 3127]\n", - "55 54\n", - "[3832 3387 3827 3705 3128 3577 3517 3772 3644 3320 3699 3766 3511 3187\n", - " 3708 3769 3702 3379 3895 3574 3316 3443 3636 3189 3258 3191 3129 3709\n", - " 3897 3377 3834 3384 3510 3389 3762 3445 3581 3125 3386 3130 3253 3260\n", - " 3252 3830 3572 3383 3250 3698 3382 3380 3579 3513 3828 3700 3324 3763\n", - " 3643 3706 3765 3378 3192 3317 3126 3578 3448 3771 3893 3444 3257 3642\n", - " 3188 3505 3195 3697 3256 3575 3641 3707 3441 3701 3321 3507 3193 3452\n", - " 3639 3514 3451 3764 3569 3381 3894 3319 3450 3447 3194 3896 3322 3515\n", - " 3898 3318 3385 3768 3637 3640 3449 3573 3635 3314 3453 3509 3645 3516\n", - " 3831 3512 3767 3633 3835 3570 3315 3833 3506 3571 3259 3634 3251 3442\n", - " 3313 3638 3508 3704 3829 3254 3703 3388 3255 3446 3190 3576 3892 3580\n", - " 3770 3325 3124 3127 3323]\n", - "36 25\n", - "[1826 1765 1695 1704 1570 1888 1508 1892 1502 2022 1699 1895 1315 1642\n", - " 1383 1697 1504 2018 1638 1636 1442 1445 1760 1575 1833 1376 1509 1505\n", - " 1503 1572 1763 1511 1952 2017 1834 1252 1578 1512 1768 1829 1317 1566\n", - " 1640 1957 1769 1450 1954 1832 2020 1822 1255 1641 1894 1576 1571 1955\n", - " 1698 1316 1758 1249 1828 1633 1319 1444 1506 1447 1313 1890 1379 1635\n", - " 1896 1770 1510 2021 1378 1446 2019 1577 1448 1956 1320 1959 1767 1314\n", - " 1385 1827 1825 1694 1630 1887 1703 1762 1705 1632 1375 1960 1440 1700\n", - " 2023 1701 1889 1381 1569 1897 1893 1514 1573 1438 1507 1958 1953 1250\n", - " 1513 1830 1254 1639 1384 1637 1759 1439 1449 1696 1382 1380 1567 1312\n", - " 1823 1318 1702 1251 1706 1831 1766 1253 1824 1574 1891 1443 1634 1631\n", - " 1764 1761 1441 1568 1377]\n", - "45 62\n", - "[4074 4083 3827 3944 4079 3696 4013 4071 3954 3823 3826 3884 3631 3886\n", - " 4081 3757 3887 4073 4078 4072 3822 3952 3947 3760 3948 3885 3762 4019\n", - " 3689 4016 3694 4007 3626 3754 3890 4018 3949 3817 3950 3880 3891 3824\n", - " 3946 3627 4076 4015 3691 3758 4075 4014 3882 3879 3697 4012 3692 3818\n", - " 3820 3629 3756 3628 3815 4011 4082 3819 3951 3759 3825 4009 3821 3630\n", - " 3632 3752 4008 4017 3755 3881 3816 3943 4010 3695 3753 3888 3889 3953\n", - " 4080 4077 3883 3693 3761 3955 3945 3690]\n", - "55 21\n", - "[1330 1524 1078 1146 1782 1521 1399 1203 1651 1403 1461 1785 1525 1402\n", - " 1018 1147 1394 1207 1588 1652 1140 1401 1465 1269 1275 1139 1277 1533\n", - " 1405 1144 1331 1336 1335 1715 1597 1404 1469 1460 1014 1717 1658 1202\n", - " 1268 1338 1148 1532 1585 1522 1594 1143 1523 1592 1595 1276 1334 1719\n", - " 1586 1329 1273 1530 1205 1341 1463 1393 1142 1660 1138 1075 1723 1466\n", - " 1396 1527 1211 1271 1141 1531 1457 1395 1593 1657 1650 1204 1081 1783\n", - " 1270 1464 1337 1213 1016 1467 1587 1718 1076 1716 1591 1529 1210 1784\n", - " 1013 1781 1077 1786 1398 1459 1080 1212 1332 1265 1780 1655 1720 1082\n", - " 1272 1266 1017 1397 1528 1339 1721 1596 1400 1267 1079 1653 1012 1590\n", - " 1654 1209 1462 1468 1656 1274 1589 1722 1201 1659 1526 1015 1206 1458\n", - " 1208 1145 1340 1083 1333]\n", - "42 42\n", - "[2922 2542 2861 2406 2475 2661 2671 2532 2927 3115 2408 2730 2732 2349\n", - " 2536 3053 2859 2983 2795 2344 2413 2414 2541 2411 3054 3116 2348 2345\n", - " 2602 3112 2596 3049 2474 2535 2538 2990 2796 2860 3117 3046 3111 2533\n", - " 2407 2864 2473 2928 2544 2662 2600 2668 2601 2346 2409 2798 2920 2791\n", - " 2534 2919 2788 2608 2670 3048 2856 2854 2540 2343 3050 2478 3047 2607\n", - " 2598 2472 2921 2863 2729 2789 2918 2981 2982 2989 2923 2736 2597 3113\n", - " 2663 2984 2477 2604 3051 2987 2916 2664 2794 2599 2667 2539 2857 2471\n", - " 2479 2917 2855 2347 2726 2669 2924 2660 2672 2734 2792 2991 2543 2724\n", - " 2986 2988 2793 2858 2606 2727 2728 2800 2469 2797 3052 2412 2725 2925\n", - " 2410 2852 2790 2537 3114 2731 2853 2665 2733 2603 2926 2862 2799 2985\n", - " 2476 2735 2605 2470 2666]\n", - "9 8\n", - "[329 591 459 845 269 334 777 716 200 839 521 842 261 526 332 523 773 779\n", - " 655 653 453 907 524 525 718 201 772 719 260 906 714 139 580 781 780 582\n", - " 458 711 775 387 136 778 393 905 651 325 517 715 263 581 390 515 396 838\n", - " 776 782 202 456 841 908 392 518 516 654 645 843 588 461 268 463 140 650\n", - " 903 335 138 708 267 455 712 648 649 837 398 717 327 326 399 452 331 583\n", - " 844 522 643 451 197 527 902 134 587 324 644 264 394 519 462 457 460 589\n", - " 266 709 707 904 199 330 590 265 710 586 323 840 262 204 205 333 389 388\n", - " 198 454 397 652 135 584 647 520 270 391 137 585 713 203 774 646 328 395\n", - " 579]\n", - "34 22\n", - "[1826 1765 1695 1570 1508 1502 1699 1315 1383 1697 1504 1638 1636 1442\n", - " 1311 1445 1760 1575 1376 1121 1436 1509 1122 1505 1503 1572 1763 1511\n", - " 1252 1512 1829 1317 1566 1060 1640 1500 1245 1310 1126 1186 1255 1057\n", - " 1576 1571 1374 1437 1698 1316 1758 1249 1828 1633 1319 1444 1693 1506\n", - " 1447 1313 1183 1379 1635 1181 1628 1510 1378 1446 1190 1189 1256 1448\n", - " 1320 1373 1314 1246 1827 1187 1825 1694 1184 1630 1501 1703 1762 1632\n", - " 1375 1185 1123 1440 1700 1248 1701 1381 1569 1573 1438 1124 1507 1058\n", - " 1250 1565 1254 1564 1639 1384 1055 1637 1759 1439 1191 1061 1696 1382\n", - " 1380 1567 1312 1823 1629 1318 1702 1251 1118 1766 1059 1056 1120 1253\n", - " 1824 1119 1574 1443 1634 1631 1764 1308 1188 1761 1244 1182 1441 1309\n", - " 1372 1125 1247 1568 1377]\n", - "52 61\n", - "[3832 4083 3827 3959 4079 3705 4086 4026 3696 3699 3766 3956 3769 3954\n", - " 3702 3895 3823 3826 3574 3886 4081 3636 3887 4078 3962 3822 3952 3760\n", - " 3897 3834 3762 4019 4016 3830 3572 3890 4018 3698 3828 3960 3700 3950\n", - " 3763 3891 3824 3765 4085 3961 4015 4090 3758 3893 4084 3958 4021 4022\n", - " 4025 4014 3697 3575 4024 3701 3639 3764 3569 3894 4082 3951 3896 3759\n", - " 3825 3957 3898 4088 3632 3768 3637 4017 4089 3640 3573 3635 3831 3767\n", - " 3633 3570 3833 3571 3695 3888 3889 4020 3953 4080 3634 3638 3704 3829\n", - " 4087 3703 3761 3892 3770 3955 4023]\n", - "46 34\n", - "[2027 1840 2093 2089 2542 2159 1836 2354 2475 1966 2416 2161 2284 2420\n", - " 2408 2032 2222 1965 2349 2545 2088 2100 2344 2413 2414 2541 2411 2155\n", - " 1839 2418 2348 2345 1963 2164 2474 2538 2152 1969 2026 1841 2090 2153\n", - " 2034 2609 2228 2419 2352 2480 2473 2160 2544 2098 1905 2282 1961 2346\n", - " 2409 1901 1903 2218 2608 1971 2289 1962 2225 2355 2540 2224 1837 2036\n", - " 2478 1835 2417 2099 1964 2607 2096 2030 1899 1902 2095 2288 2162 2154\n", - " 1904 2219 2025 2350 1970 2158 1900 2220 2477 2604 2163 2217 1968 2286\n", - " 2351 2539 1838 2031 2479 2483 2290 2415 2033 2482 2347 2156 2353 1967\n", - " 2216 2035 2092 2543 2291 2226 2292 2287 2283 2606 2280 2412 2094 2410\n", - " 2157 2227 2024 1898 2481 2285 2356 2097 2091 2603 2029 2546 2223 2281\n", - " 2476 2605 2221 2028 1906]\n", - "30 1\n", - "[ 27 32 153 25 35 222 163 281 89 94 156 350 96 33 224 415 164 349\n", - " 226 285 98 477 30 28 417 481 345 99 416 353 158 152 97 352 348 228\n", - " 414 92 36 154 287 100 217 347 91 159 225 31 290 289 221 29 220 284\n", - " 411 478 223 90 162 354 95 355 286 413 34 288 227 157 88 292 155 280\n", - " 282 480 291 476 346 283 418 216 26 24 93 219 479 351 161 218 160 412\n", - " 475 410]\n", - "19 10\n", - "[ 594 848 591 785 401 720 913 845 467 340 910 850 659 976\n", - " 526 1041 914 404 472 537 596 724 978 917 402 339 655 653\n", - " 852 525 718 471 719 535 855 341 781 792 595 665 721 664\n", - " 729 273 337 982 784 912 857 600 847 856 791 532 468 789\n", - " 662 723 1046 849 343 529 722 782 846 338 534 1042 1040 919\n", - " 599 407 793 790 983 654 601 275 469 918 726 728 277 342\n", - " 405 461 463 473 977 853 783 658 725 597 335 464 276 979\n", - " 398 717 536 854 911 980 400 399 533 465 593 787 527 530\n", - " 336 975 661 660 462 1044 589 851 786 657 981 274 788 592\n", - " 590 915 403 531 470 916 278 408 406 663 598 920 1043 272\n", - " 727 656 528 1045 466]\n", - "58 2\n", - "[ 63 251 383 54 441 191 245 569 313 59 255 316 184 308 444 125 376 507\n", - " 380 447 438 319 186 181 57 253 247 189 315 446 127 445 504 182 56 567\n", - " 126 382 252 117 378 314 249 121 122 190 116 440 439 187 503 572 375 379\n", - " 118 571 120 377 254 52 442 443 373 502 318 510 310 183 53 508 188 185\n", - " 180 506 250 317 570 437 309 60 62 374 372 119 246 509 61 505 248 55\n", - " 312 244 573 124 381 58 311 568 123]\n", - "16 15\n", - "[1098 594 848 591 1361 785 720 913 845 1104 1232 1039 1035 1228\n", - " 910 716 1358 850 1110 659 976 842 1041 914 1170 1167 779 724\n", - " 978 917 1295 655 1099 653 852 974 907 718 1363 719 906 1171\n", - " 781 1105 780 595 721 971 1362 982 1230 784 778 912 1165 847\n", - " 715 970 789 1292 723 1046 1237 1299 1103 849 1038 722 782 1231\n", - " 846 908 1106 1042 1357 1227 1040 790 654 1166 843 918 1107 1172\n", - " 1037 1296 977 853 783 658 725 979 717 854 1293 911 1297 1036\n", - " 980 1233 1163 844 1109 1101 593 787 1294 1173 1162 1359 975 972\n", - " 660 1174 1100 1044 589 851 786 657 1102 973 981 1034 1360 1229\n", - " 1300 1108 788 592 909 590 915 1234 916 1298 1236 652 1169 1164\n", - " 1043 1168 656 1045 1235]\n", - "31 20\n", - "[1695 1570 1508 1502 1315 1697 1504 1692 1442 1311 1445 1376 1121 1305\n", - " 1436 1509 1122 1505 1503 988 1572 987 1115 1252 1117 1317 1566 1371\n", - " 1060 1500 928 1245 1310 1186 1057 1571 1374 1180 1437 924 1698 1316\n", - " 1249 1307 993 1241 1633 1433 1306 1444 1693 1506 1562 1499 1313 1177\n", - " 1183 1379 1052 1635 1181 1628 1378 1627 1189 1116 1053 1373 1113 1314\n", - " 989 1246 925 926 1497 1243 1187 1114 1694 1184 927 1630 994 1501\n", - " 1632 1375 990 1178 1050 1185 1369 1123 1440 1498 1248 1381 1569 1370\n", - " 1438 1242 1124 1507 1434 1058 1250 1565 992 1564 1055 1435 1051 1439\n", - " 1696 1380 1567 1312 929 1629 1251 1118 1179 1059 1054 1056 1120 1253\n", - " 1119 1443 930 1634 1631 1308 1188 1563 1244 1182 1441 995 1309 1372\n", - " 1125 1247 991 1568 1377]\n", - "44 42\n", - "[2922 2542 2861 2801 2475 2671 2416 2927 3115 2408 2930 2730 2732 2349\n", - " 2536 3053 2545 2859 2983 2795 2413 2414 2541 2411 3054 3116 2348 2345\n", - " 2602 3049 2474 2535 2538 3055 2738 2990 2993 2796 2860 2609 3117 3118\n", - " 2480 2864 2473 2674 2928 2544 2662 2600 2668 2601 2346 2409 2798 2920\n", - " 2791 2534 2919 2608 2670 3048 2856 2854 2540 3050 2478 2607 2598 2472\n", - " 2921 2863 2729 3119 2918 2992 2989 2923 2736 3113 2663 2350 2984 2477\n", - " 2604 3051 2987 3056 2664 2794 2599 2667 2351 2539 2857 2471 2479 2802\n", - " 2415 2855 2347 2726 2737 2669 2924 2865 2672 2734 2792 2991 2543 2929\n", - " 2986 2988 2793 2858 2606 2727 2673 2728 2800 2797 3052 2412 2925 2410\n", - " 2866 2790 2537 3114 2731 2610 2481 2665 2733 2603 2546 2926 2862 2799\n", - " 2985 2476 2735 2605 2666]\n", - "61 59\n", - "[3903 3583 4095 3832 3711 3839 3959 3705 3577 4026 3517 3899 3772 3967\n", - " 3644 3837 3708 3769 3895 4030 3836 3965 4027 3962 3455 3775 3709 4091\n", - " 3897 3834 4092 3581 4094 3902 4029 3901 3838 4028 4093 3579 3513 3960\n", - " 3643 3706 3961 4090 3578 3771 4025 3642 4031 3646 4024 3641 3707 3452\n", - " 3639 3514 3451 3966 3450 3896 3900 3515 3898 4088 3454 3647 3768 4089\n", - " 3773 3640 3518 3453 3519 3645 3516 3831 3767 3582 3835 3833 3774 3963\n", - " 3964 3704 3710 3703 3576 3580 3770 4023]\n", - "31 31\n", - "[1826 1695 2084 2210 1888 1892 2402 2398 1699 1697 2018 2272 1692 2015\n", - " 2267 2276 1885 1760 2270 2333 1763 1952 2011 1819 2147 1883 2017 1818\n", - " 2077 1829 2078 1957 1954 1949 2149 2020 1822 2079 2338 1955 2014 2331\n", - " 1698 1758 1828 1757 2204 1633 2268 2206 2337 1693 1754 2335 1890 2271\n", - " 2146 2083 2269 2080 2012 1628 2021 1950 1948 1821 2397 2274 2019 2339\n", - " 2141 1886 2203 1956 1817 2075 1945 2211 2202 2209 2273 1947 1827 1825\n", - " 1694 1630 1887 2207 2081 1762 2334 1882 1632 2137 1755 2144 1951 1946\n", - " 2400 2275 2073 2396 2266 2201 2212 1889 1820 1893 2016 2082 1953 2336\n", - " 2145 1759 1884 2208 1696 1823 1629 2143 2139 2140 2401 1691 2010 1824\n", - " 2205 2332 2076 1891 2213 2148 2009 2399 1634 1631 1764 2074 1881 2085\n", - " 1761 2138 2142 2013 1756]\n", - "54 41\n", - "[2801 2354 2739 2420 2930 2678 2612 2545 2617 2933 2418 2294 2679 2806\n", - " 2618 2807 2742 2491 2872 2738 2489 2492 2741 2360 2873 2425 2609 2811\n", - " 2419 2488 2935 3000 2480 2740 2864 2936 2997 2805 3064 2674 2553 2544\n", - " 2293 2869 2296 2550 2427 2934 2552 2996 2422 2608 3061 2490 2682 2748\n", - " 2355 2554 2868 3002 3063 2417 2995 2867 2421 2870 2358 2747 2736 2875\n", - " 2938 2937 2549 2999 2680 2548 2803 2357 2939 2683 2802 2483 2556 2423\n", - " 2676 2555 2482 2737 2486 2295 2611 2865 2812 2615 2614 2672 2613 3001\n", - " 2684 3059 2291 2929 2804 2744 2998 2746 2808 2871 2994 2931 2292 2876\n", - " 2675 2620 2619 2673 2485 2800 2362 2426 2484 2866 2681 2610 2809 2481\n", - " 2356 2487 2546 2547 2810 2745 2874 2932 2359 3065 3062 3060 2361 2743\n", - " 2424 2551 2677 2616 2297]\n", - "0 5\n", - "[193 0 576 320 133 261 453 260 580 386 128 387 129 258 4 325 517 257\n", - " 642 2 449 448 581 390 515 577 1 513 518 706 516 130 68 66 256 321\n", - " 704 65 132 194 326 192 705 452 643 451 197 134 324 644 322 641 578 707\n", - " 512 196 131 323 262 389 195 388 198 454 3 450 259 514 640 64 67 69\n", - " 384 385 579]\n", - "63 8\n", - "[511 251 383 831 441 191 638 569 255 316 444 507 380 447 767 319 575 253\n", - " 699 826 636 189 958 829 315 830 956 634 446 894 701 445 702 639 827 761\n", - " 382 252 378 314 190 764 828 572 379 571 377 254 442 443 633 893 765 318\n", - " 510 766 637 892 508 188 506 574 317 570 698 895 509 957 762 763 505 700\n", - " 697 959 635 573 703 381 891]\n", - "45 30\n", - "[1710 2027 1840 1907 1704 2093 1713 2089 2159 1836 1895 1642 1581 1966\n", - " 2161 2284 2032 2222 1965 1842 2349 1708 2088 1773 1833 2155 1839 2348\n", - " 2087 1963 1772 1712 2152 1969 2026 1834 1841 1578 2090 1579 1768 2153\n", - " 2034 1771 1648 1769 1832 2352 1641 2160 2098 1905 2282 1961 2346 1778\n", - " 1649 1901 1903 1779 1709 2218 1971 2289 1707 1962 2225 2224 1837 1835\n", - " 1584 2099 1964 1896 1770 2096 2030 1899 1902 2095 2288 2162 1714 2154\n", - " 1776 1904 1959 2219 2025 1767 2350 1970 2158 1900 2220 2151 2163 2217\n", - " 1968 2286 2351 1644 1838 2031 1705 1960 2033 1777 2347 2156 1967 2216\n", - " 2023 2035 2092 1897 2226 1774 2287 2283 1645 1646 1643 2094 2157 2024\n", - " 1706 1831 1775 1898 2285 2097 2091 2029 1583 2223 1647 2281 1582 1843\n", - " 1711 1580 2221 2028 1906]\n", - "39 63\n", - "[4074 4068 3944 4065 4001 3877 4013 4071 4066 3937 3884 3874 3810 4004\n", - " 4073 4072 4069 3947 3747 3948 3885 3748 3689 4007 3754 3949 3817 3880\n", - " 3946 3749 4076 3687 4075 3684 3751 4002 3882 4006 3879 4067 3875 4012\n", - " 3818 3820 3814 3811 3812 3685 3873 4070 3815 4011 3878 3941 3819 4009\n", - " 3813 3938 4003 3752 4008 3755 3881 3816 3943 3876 4010 3688 3753 4077\n", - " 3883 3942 3939 3940 3686 4005 3750 3945 3690]\n", - "53 2\n", - "[251 54 441 245 239 313 59 184 308 500 114 376 370 438 177 186 181 51\n", - " 57 247 47 369 315 563 564 435 175 498 504 501 182 56 567 111 304 117\n", - " 378 179 314 249 240 121 122 116 440 368 566 439 187 113 241 503 499 375\n", - " 50 379 118 120 306 377 242 52 442 497 373 502 303 307 432 562 310 183\n", - " 53 176 112 185 49 565 180 436 305 250 437 434 309 374 372 119 371 246\n", - " 505 367 433 248 55 312 48 244 58 115 243 178 311 568 123]\n", - "28 2\n", - "[ 27 32 153 25 222 281 89 94 156 350 96 33 472 537 224 415 409 214\n", - " 349 541 540 226 285 98 477 30 28 542 417 345 416 353 158 152 97 352\n", - " 348 86 151 343 414 92 154 287 217 347 91 407 159 150 539 225 31 342\n", - " 290 473 289 279 221 29 220 284 344 411 478 223 22 90 162 354 95 286\n", - " 413 34 87 288 157 88 215 155 280 282 480 476 346 283 543 278 408 216\n", - " 26 23 24 93 219 479 351 538 161 218 160 474 412 475 410]\n", - "51 0\n", - "[ 45 54 245 239 184 308 114 370 438 177 181 51 57 247 237 47 302 369\n", - " 435 175 46 182 56 111 304 117 179 249 240 121 116 238 368 113 241 375\n", - " 50 118 110 120 306 242 52 174 373 303 307 432 173 310 183 53 176 112\n", - " 185 49 180 436 305 437 434 309 374 372 119 371 246 367 433 248 55 312\n", - " 48 244 115 109 243 178 311]\n", - "57 0\n", - "[ 63 251 54 441 191 245 313 59 255 316 184 308 444 125 376 380 438 186\n", - " 181 51 57 253 247 189 315 127 182 56 126 252 117 378 179 314 249 121\n", - " 122 190 116 440 439 187 375 379 118 120 377 254 52 442 443 373 318 310\n", - " 183 53 188 185 180 250 317 309 60 62 374 119 246 61 248 55 312 244\n", - " 124 381 58 115 243 311 123]\n", - "17 23\n", - "[1809 1682 1361 1741 1553 1870 1420 1104 1232 1555 1679 1547 1621 1228\n", - " 1875 1358 1749 1549 1489 1170 1167 1806 1808 1295 1683 1680 1363 1612\n", - " 1171 1105 1873 1291 1617 1687 1488 1362 1230 1548 1492 1423 1165 1494\n", - " 1554 1421 1367 1490 1431 1292 1807 1483 1237 1619 1484 1675 1299 1427\n", - " 1622 1103 1811 1813 1491 1676 1742 1558 1231 1550 1106 1357 1426 1166\n", - " 1485 1303 1238 1107 1172 1356 1677 1296 1495 1425 1365 1613 1493 1678\n", - " 1746 1293 1812 1297 1611 1233 1623 1810 1616 1620 1559 1430 1618 1294\n", - " 1173 1615 1359 1557 1487 1102 1360 1229 1300 1355 1108 1744 1681 1234\n", - " 1419 1302 1551 1748 1805 1871 1422 1556 1684 1486 1366 1872 1298 1236\n", - " 1686 1874 1876 1169 1424 1428 1301 1750 1743 1747 1364 1685 1429 1168\n", - " 1740 1614 1745 1235 1552]\n", - "13 43\n", - "[2887 2957 2832 2891 2449 3024 3086 2444 3025 2959 2705 3148 2765 3023\n", - " 2767 2897 2762 2960 3089 2509 2441 2955 2963 2445 2380 3151 3084 2759\n", - " 2578 2896 2637 3018 3146 2569 2631 2766 2825 2378 2633 2447 2760 2827\n", - " 2770 2695 2381 2639 2835 2571 2698 2443 2510 2898 2951 2768 2640 3082\n", - " 2384 3017 3085 2442 2570 2703 2379 2702 2568 2567 2893 3087 2572 2577\n", - " 3026 2761 2833 2830 2889 2834 2579 2383 2643 2706 2514 3083 2826 2636\n", - " 2511 2635 2707 2700 2824 3021 2764 2513 2576 3149 2956 3152 3016 2697\n", - " 2888 2505 2895 2953 2828 2508 2634 2696 2642 2899 2448 2512 2829 2831\n", - " 2962 3150 2641 2575 2894 2632 2769 3081 2890 2507 3019 3088 2952 2574\n", - " 2506 2382 2823 2446 2892 2504 2961 2704 2763 2954 2638 3020 2958 3022\n", - " 2573 2699 2701 2771 3147]\n", - "27 35\n", - "[2398 2454 2589 2272 2461 2326 2015 2267 1885 2456 2464 2394 2270 2200\n", - " 2591 2263 2325 2136 2333 2654 2011 1883 2006 2265 2327 2008 2077 2521\n", - " 2453 2078 2522 2391 2526 1949 2583 2079 2198 2134 2584 2014 2331 2462\n", - " 2455 2585 2072 2204 2268 2206 1943 2337 2262 2335 2524 2271 2652 1880\n", - " 2269 2070 2080 2518 2460 2012 2330 2393 1950 1948 2397 2141 1886 2203\n", - " 2390 2649 2329 2133 2075 1945 2202 2209 2273 2590 1947 2588 2264 2458\n", - " 2463 2207 2081 2587 2465 2334 1882 2007 2137 2328 2144 2523 2392 1951\n", - " 1946 2400 2395 2073 2396 2266 2201 2457 2527 2135 2016 2336 2145 1884\n", - " 2208 2648 2653 2199 2389 2143 2459 2528 2139 2140 2071 2401 2197 2010\n", - " 2205 2332 2076 2651 2009 2650 2399 2525 2074 1881 2138 2069 2520 2142\n", - " 2261 2013 2519 1944 2586]\n", - "0 48\n", - "[3072 2688 3206 3140 3392 3010 3205 3332 2820 3456 2818 2690 2756 3333\n", - " 3074 3393 3394 3200 3204 3266 3267 3270 3077 3203 3013 2886 2950 3202\n", - " 3458 2883 3331 3268 3141 3265 2947 3012 3459 2884 2945 3073 3014 3138\n", - " 3008 2817 2949 3396 3011 3142 3078 2944 3395 3330 2691 3329 3139 2752\n", - " 3136 3457 3009 3076 3075 2821 2755 2946 2882 2948 2689 3328 3201 3264\n", - " 2885 3137 2753 3269 2881 2880 2816 2819 2754]\n", - "51 18\n", - "[1392 1262 1330 1136 1519 1524 1078 879 1389 1521 1399 1203 947 1461\n", - " 1261 1525 818 1394 1207 882 1588 1198 1072 1200 819 1140 1401 1135\n", - " 1269 1139 887 821 1327 1326 886 1144 1331 1336 1335 1455 1328 1005\n", - " 1009 1071 1460 1456 1014 1202 1268 1454 1585 949 1522 1143 1523 1011\n", - " 1334 1586 1584 1329 1273 885 1205 1463 1393 1142 1390 1520 1138 820\n", - " 1075 952 1008 1396 1527 1271 1141 1457 1395 1010 1204 1081 1325 1391\n", - " 1270 1464 1337 948 1264 946 1016 1197 1587 1076 1073 951 1013 950\n", - " 1077 1134 1398 1459 1080 881 1332 822 1265 943 1272 1266 1017 1397\n", - " 1199 1007 1400 945 817 1267 1079 1137 1074 1012 1590 1209 1462 1133\n", - " 880 944 1006 884 1589 1201 942 1526 1070 1263 1015 1206 1458 1208\n", - " 1145 816 883 1069 1333]\n", - "15 47\n", - "[2957 3345 3275 3217 3145 2832 2891 3024 3086 3215 3342 3408 3025 2959\n", - " 2705 3148 3027 2765 3023 2767 2897 2762 3280 2960 3089 3154 2955 2963\n", - " 3209 3151 3084 3091 2896 2637 3018 3146 2766 2825 3274 3346 2964 2827\n", - " 2770 2900 3155 2639 2835 2898 2768 2640 3082 3090 3017 3085 3344 3404\n", - " 2703 2702 3218 3284 2893 3087 3026 3153 3277 3410 3219 2833 2830 2889\n", - " 2834 3210 3341 3343 2772 3221 2706 3083 3028 2826 3282 3406 2636 3409\n", - " 3029 2707 2700 3021 3157 2764 3149 2956 3339 3283 3405 3220 3156 3152\n", - " 3347 2901 2895 2953 3092 2828 3276 2965 3093 2837 2642 2899 2829 2831\n", - " 2962 3150 2641 3212 2894 3278 3216 3214 2769 3081 2890 3019 3088 3213\n", - " 3340 3281 2836 2892 2961 2704 3211 2763 3407 2954 2638 3279 3020 2958\n", - " 3022 2699 2701 2771 3147]\n", - "15 36\n", - "[2322 2000 2449 2191 2195 2317 2444 2705 1997 2129 2319 2062 2067 2314\n", - " 2516 2509 2441 2131 2445 2325 2257 2380 2313 2578 2196 1932 2637 2318\n", - " 2453 2188 2387 2254 2255 2125 2378 2451 2447 2517 2064 1933 2381 2377\n", - " 2639 2571 2185 2443 2510 2127 2058 2193 2126 1937 2640 2384 2442 2002\n", - " 2249 2570 2703 2379 2702 2059 2186 2250 2065 2258 2324 2572 2577 2128\n", - " 2122 2385 2121 2579 2133 2515 2003 2580 2383 1995 2192 2253 2643 2706\n", - " 2315 2514 2251 2388 2636 2001 2386 2511 2635 1935 2700 2513 2576 2450\n", - " 2194 1934 2132 2505 1936 2066 2259 2508 1999 2642 2061 1938 2448 2512\n", - " 2389 2320 2641 2321 2575 2197 2507 2187 1996 2574 2506 2316 2452 2382\n", - " 2060 2252 2446 2068 2704 2260 2063 2189 2130 2638 2323 2123 2256 2124\n", - " 2190 2261 1998 2573 2701]\n", - "61 3\n", - "[ 63 511 251 383 441 191 638 569 313 59 255 316 184 444 125 376 507 380\n", - " 447 319 186 575 57 253 247 636 189 315 634 446 127 445 504 639 56 126\n", - " 382 252 378 314 249 121 122 190 440 439 187 572 375 379 571 120 377 254\n", - " 442 443 318 510 183 637 508 188 185 506 250 574 317 570 60 62 119 509\n", - " 61 505 248 55 312 635 573 124 381 58 311 123]\n", - "9 27\n", - "[1741 1870 1420 1353 1804 1679 1547 1997 1549 1737 1806 1350 1605 1989\n", - " 1930 1674 1866 1932 1863 2056 1612 1351 1799 1478 1739 1543 1867 1672\n", - " 1925 1869 1928 1548 1933 2054 1413 1421 1671 1476 1990 1798 1795 2058\n", - " 1807 1868 1931 1864 1483 1484 1675 1544 1481 1546 1802 1733 1542 1676\n", - " 2059 1742 1603 1541 1803 1480 1550 1545 1414 1540 1539 1607 1801 1485\n", - " 2122 2121 1354 1797 1670 1356 1735 1734 1927 1800 1677 1731 1995 1796\n", - " 1604 2120 1988 1738 1732 1993 1613 1991 1678 2053 1860 1935 1606 2119\n", - " 1611 1667 1992 1926 1929 1934 1482 1615 1479 1862 1923 1352 1355 1418\n", - " 1673 2118 2055 1417 2061 1419 1551 1668 1805 1871 1486 2057 1416 1996\n", - " 1924 2060 1610 1743 1859 1865 1736 1609 2123 1477 2124 1740 1614 1998\n", - " 1861 1608 1994 1669 1415]\n", - "35 41\n", - "[2529 2404 2406 2402 2721 2398 2661 2847 2589 2532 2272 2592 2461 2408\n", - " 2659 2276 2464 2536 2983 2591 2468 2593 2342 3040 2654 2723 2596 2783\n", - " 2535 2530 3044 3046 2526 2978 2658 2848 2533 2407 2473 2338 2977 2850\n", - " 2662 2600 2462 2601 2466 2911 2920 2791 2534 2919 2788 3045 2277 2856\n", - " 2854 2594 2337 2722 2335 2343 2278 2598 2472 2729 2789 2918 2981 2274\n", - " 2982 2785 3041 2339 2913 2597 3042 2663 2784 2467 2273 2590 2916 2664\n", - " 2463 2599 2656 2465 2857 2471 2917 2787 2405 2403 2855 2849 2717 2782\n", - " 2915 2726 2980 2400 2275 2660 2786 2781 2912 2845 2719 2792 2595 2657\n", - " 2527 2724 2851 2976 2793 2975 2336 2914 2979 2727 2340 2653 2718 2728\n", - " 2469 2910 2531 2725 2852 2528 2790 2537 2401 2853 2665 2399 3043 2525\n", - " 2720 2846 2655 2341 2470]\n", - "36 35\n", - "[2084 2529 2404 2089 2210 2406 1892 2402 2022 2398 1895 2661 2532 2018\n", - " 2272 2592 2214 2408 2015 2659 2276 2464 2536 2270 2088 2344 2468 2593\n", - " 2342 2087 1952 2345 2147 2596 2474 2535 2152 2530 2017 2090 2153 2078\n", - " 1957 1954 2658 2149 2020 2079 2533 2407 2473 2338 1894 1955 2662 2600\n", - " 2462 2282 2466 2346 2409 2534 2218 2206 2277 2594 2337 2335 1890 2271\n", - " 2343 2146 2083 2150 2080 2278 2598 2472 2021 2274 2154 2019 2339 1956\n", - " 1959 2597 2025 2663 2211 2467 2151 2209 2273 2279 2217 2463 2207 2081\n", - " 2599 2465 2334 2471 2405 1960 2086 2403 2144 2215 2400 2275 2216 2660\n", - " 2023 2212 1889 2595 2657 2527 1893 2016 1958 2082 1953 2336 2145 2208\n", - " 2340 2280 2469 2143 2531 2410 2024 2528 2537 2401 1891 2213 2148 2399\n", - " 2281 2085 2341 2142 2470]\n", - "50 28\n", - "[1710 1840 1907 2093 1976 1973 1713 2159 1836 1519 1524 1581 1966 2161\n", - " 1782 1521 2032 1965 1842 1651 1461 1708 1773 1525 2100 1839 1772 2164\n", - " 1712 1909 1848 1969 1841 1588 1652 2034 2166 1648 1912 2228 2160 2098\n", - " 1455 1715 1905 1778 1460 1649 1456 2103 1901 1717 1903 1779 1709 1585\n", - " 1971 1522 2225 1523 2224 1837 1719 2036 1586 1846 1584 2099 1964 1847\n", - " 1908 2096 2038 2030 1902 1520 2039 2095 2162 1714 2037 1776 1904 1970\n", - " 1457 2158 1900 1650 2163 1783 1968 1845 1972 1644 1838 2031 1910 2033\n", - " 1587 1718 1777 1716 1591 1967 1784 2035 1781 2040 1459 2226 1774 1780\n", - " 1655 1720 2229 1975 2165 1645 1844 1911 1974 1646 2101 1653 2094 1590\n", - " 1654 2227 1775 1656 2097 1589 2029 1526 1583 2223 1647 1582 1843 1711\n", - " 1458 2102 1518 2028 1906]\n", - "59 10\n", - "[ 511 383 831 441 638 569 313 316 444 376 1023 507 380 447\n", - " 438 631 767 757 575 699 826 636 958 829 315 1018 830 956\n", - " 634 446 954 1085 630 894 701 445 1021 887 821 702 504 501\n", - " 888 639 827 567 886 761 694 382 378 314 440 566 439 764\n", - " 828 760 632 503 885 572 375 379 952 571 377 824 442 443\n", - " 695 633 893 765 1019 502 1081 629 318 510 955 766 1020 696\n", - " 637 892 508 1016 565 890 506 1086 951 574 950 317 570 698\n", - " 1080 895 822 693 1082 509 1017 957 762 1022 763 505 700 697\n", - " 758 959 953 312 635 573 703 823 381 1015 891 759 1084 825\n", - " 568 889 1083]\n", - "28 43\n", - "[2529 2721 2398 2847 2589 2592 2461 2780 2456 2464 2394 2973 2591 2843\n", - " 2593 2713 2844 3040 2654 2840 2783 2647 2712 2521 2966 2716 2582 2522\n", - " 2711 2526 2978 2658 2839 2583 2848 2779 2974 2977 2968 2584 2850 2462\n", - " 2905 2715 3097 2585 2911 2842 2906 2778 2594 2722 2524 2652 3102 3038\n", - " 2909 2460 2776 3031 2393 3163 3034 2646 2710 2777 2397 2785 3041 2913\n", - " 3167 2649 2907 3161 3036 3096 2784 3032 2590 2588 2458 2463 2967 2587\n", - " 2656 3037 2849 2717 3098 2523 2782 3101 2395 2396 2786 2904 2781 2912\n", - " 2845 2719 2657 2457 2527 3099 2976 2975 3100 2914 3033 3035 2648 2653\n", - " 3164 3103 2718 2969 3104 2910 2838 2775 2459 2528 3166 2902 3165 3039\n", - " 2651 2908 2650 2399 2970 2525 2720 2714 2846 2971 2903 2655 2520 3162\n", - " 2972 2841 2519 2774 2586]\n", - "45 8\n", - "[239 879 370 557 237 364 743 426 302 940 878 369 818 360 563 170 435 175\n", - " 431 428 234 627 495 684 498 876 688 423 304 744 682 745 300 754 621 430\n", - " 558 689 240 617 238 368 560 296 429 748 877 551 241 425 365 625 499 618\n", - " 685 494 808 815 306 362 690 174 424 497 941 810 552 297 556 303 359 554\n", - " 366 432 626 555 615 679 680 487 562 173 553 301 488 687 172 489 176 361\n", - " 811 622 749 750 305 491 559 751 620 753 755 298 434 873 881 233 943 493\n", - " 875 371 616 299 813 619 812 363 683 561 171 817 747 367 433 938 492 880\n", - " 944 427 814 496 623 236 942 624 686 691 746 939 816 752 681 809 235 874\n", - " 490]\n", - "14 32\n", - "[2322 1809 1741 2000 1870 2449 2191 1804 2195 2317 2444 1679 1875 1997\n", - " 2129 2319 2062 2067 2314 2004 2131 1806 1808 2445 2257 2380 1930 2313\n", - " 1866 2196 1680 1932 2056 2318 2188 1873 1739 2254 2255 2125 2378 2447\n", - " 1867 2064 1869 1928 1933 2381 2185 2443 2127 2058 2193 1807 1868 2126\n", - " 1931 1864 1937 1675 2384 1811 2002 1802 2249 2379 1676 2059 1742 2186\n", - " 2250 2065 1803 2258 2128 1939 1801 2122 2385 2121 2003 1677 2383 1995\n", - " 2192 2253 2120 2315 1738 2251 1993 2001 1678 2386 1746 1935 1810 1992\n", - " 1929 2194 1934 2132 1936 2184 2066 2259 1999 1744 2061 1938 2448 1681\n", - " 2320 2321 1805 1871 2057 1872 2187 1996 2316 2382 2060 2252 1874 1876\n", - " 2446 1743 2068 2260 1865 2063 2189 2130 2323 2123 2256 2124 2190 1740\n", - " 2248 1998 1940 1745 1994]\n", - "29 29\n", - "[1826 1695 1888 1502 1699 1697 1504 2018 2272 1692 2015 2267 1885 1760\n", - " 2270 1816 1503 2136 1763 1952 2011 1819 1883 2017 1818 2008 2077 2078\n", - " 1566 1500 1687 1954 1949 1822 2079 1955 2014 1698 1758 1561 2072 1757\n", - " 2204 1633 2268 2206 1943 1693 1754 1562 1499 1890 2271 2146 2083 1880\n", - " 2269 2080 2012 1628 1950 1948 1821 1627 2019 1626 2141 1886 2203 1817\n", - " 2075 1945 2202 2209 1947 1827 1825 1625 1694 1630 1887 2207 2081 1501\n", - " 1762 1882 2007 1632 2137 1755 2144 1815 1951 1946 2073 1498 1879 2266\n", - " 2201 1889 1569 1820 1753 2016 2082 1953 1689 1565 1564 2145 1759 1884\n", - " 2208 1696 1567 1823 1629 2143 2139 2140 2071 1691 2010 1752 1824 2205\n", - " 1624 2076 1891 2009 1634 1631 2074 1690 1881 1751 1563 1761 2138 1688\n", - " 2142 2013 1568 1756 1944]\n", - "51 25\n", - "[1392 1710 1840 1907 1973 1330 1713 1519 1524 1581 1782 1521 2032 1399\n", - " 1842 1651 1461 1773 1785 1525 1394 1839 1712 1849 1909 1848 1969 1841\n", - " 1588 1652 2034 1648 1465 1912 1269 1327 1331 1335 1455 1715 1328 1905\n", - " 1778 1460 1649 1456 1717 1903 1779 1268 1454 1709 1585 1971 1522 1523\n", - " 1592 1334 1837 1719 2036 1586 1846 1584 1329 1847 1908 1463 1393 2038\n", - " 1390 1902 1520 1714 2037 1776 1904 1396 1527 1970 1457 1395 1593 1657\n", - " 1650 1783 1968 1845 1391 1972 1270 1464 1838 1264 1910 2033 1587 1718\n", - " 1777 1716 1591 1529 1967 1784 2035 1781 1398 1453 1459 1332 1774 1265\n", - " 1780 1655 1720 1266 1397 1528 1975 1721 1517 1645 1844 1911 1974 1646\n", - " 1400 1267 1653 1590 1654 1462 1775 1656 1589 1526 1583 1647 1582 1843\n", - " 1711 1458 1518 1333 1906]\n", - "19 32\n", - "[2322 1809 1682 2000 1870 2449 2191 2454 2195 1875 1749 2326 1997 2129\n", - " 2319 2062 2067 2004 1816 2131 1806 1808 2200 2263 2325 2136 2257 1683\n", - " 2196 1680 2006 2265 2318 2327 2008 2453 1873 2387 2254 1878 2255 2391\n", - " 2125 2451 2064 2198 2134 1869 1933 2127 2072 2193 1807 2126 2005 1937\n", - " 1943 2384 2262 1811 1813 2002 1880 2070 2065 2258 2324 2128 1877 1939\n", - " 2385 1942 1941 2390 2133 1945 2003 2383 2192 2253 2264 2388 2001 2386\n", - " 2007 1746 2137 1935 2328 1812 1815 1810 2450 2073 2194 1879 2201 1934\n", - " 2132 1936 2135 2066 2259 1999 1814 1744 2061 1938 2448 2199 2389 1681\n", - " 2320 2321 1748 1871 2071 1684 2197 1872 2009 2452 1686 1874 1876 1750\n", - " 1743 1747 2068 2260 1881 1751 2063 1685 2189 2069 2130 2323 2256 2190\n", - " 2261 1998 1940 1745 1944]\n", - "37 22\n", - "[1826 1765 1704 1570 1508 1699 1315 1642 1383 1697 1504 1638 1062 1636\n", - " 1442 1311 1445 1322 1575 1376 1121 1509 1122 1505 1503 1572 1763 1511\n", - " 1193 1064 1252 1578 1258 1512 1579 1768 1829 1317 1060 1451 1640 1769\n", - " 1129 1450 1126 1186 1832 1255 1259 1641 1576 1571 1698 1316 1249 1828\n", - " 1633 1319 1444 1506 1321 1323 1447 1313 1379 1635 1387 1515 1510 1378\n", - " 1446 1190 1189 1256 1577 1448 1320 1767 1314 1128 1385 1827 1187 1184\n", - " 1063 1703 1762 1705 1632 1375 1185 1194 1127 1123 1440 1700 1248 1701\n", - " 1386 1381 1569 1514 1573 1124 1507 1058 1192 1250 1513 1830 1254 1639\n", - " 1384 1637 1439 1449 1191 1061 1696 1382 1380 1567 1312 1643 1318 1702\n", - " 1251 1706 1831 1766 1059 1253 1574 1443 1634 1631 1257 1764 1188 1761\n", - " 1441 1125 1247 1568 1377]\n", - "51 49\n", - "[2801 3128 2927 3320 2930 3511 3187 3182 3379 3053 3574 3373 3316 2933\n", - " 3054 3058 3443 2806 3189 3440 3191 3129 3249 3055 3439 2990 3376 2993\n", - " 3117 3121 3377 3384 3510 2935 3118 3000 3445 2864 2936 2997 2805 3064\n", - " 2928 3125 2869 3311 3253 2934 3252 3572 2996 3061 3123 3383 3250 2868\n", - " 3382 3380 3063 3438 3184 3378 3181 2995 3192 2863 3186 3119 3375 2867\n", - " 3317 3126 2992 2870 3245 3448 2989 3309 3444 3257 3188 3505 3256 2999\n", - " 3056 3504 3441 3321 3507 2803 3193 3122 3569 2802 3503 3381 3319 3447\n", - " 2865 3318 3001 3385 3059 2991 2929 2804 2998 2871 2994 3573 2931 3314\n", - " 3509 3120 3374 3183 2800 3570 3057 3315 3506 3312 3571 3310 2866 3251\n", - " 3442 3313 3508 3248 3247 3254 2932 2926 3255 3446 3190 3065 3062 3568\n", - " 3060 3246 3185 3124 3127]\n", - "15 62\n", - "[4047 3858 3730 3659 3914 3919 3786 4043 3985 3599 4051 3790 3923 3983\n", - " 3980 3853 4041 3851 3979 3922 3726 3913 3728 3987 3924 3982 3732 3861\n", - " 3859 3854 3788 3978 4052 3598 3915 4042 3986 3663 3920 3921 3856 3989\n", - " 3796 3977 3791 3855 4048 3925 3795 3797 3731 3600 3725 3666 4053 3793\n", - " 3852 4044 3664 3601 3916 3789 4045 3665 4050 3597 3667 3850 3722 3727\n", - " 3849 3602 3792 3981 3917 4049 3596 3729 3785 3860 3662 3787 3988 3857\n", - " 3723 3660 3984 3661 3918 4046 3794 3724]\n", - "5 2\n", - "[ 6 329 193 0 73 200 320 133 261 453 201 260 70 139 386 128 387 136\n", - " 129 393 258 4 325 517 257 2 449 75 263 390 11 515 202 74 456 1\n", - " 392 518 10 516 130 68 8 66 256 321 65 138 132 267 194 455 5 327\n", - " 326 192 452 331 451 197 134 324 264 394 519 322 7 457 72 266 196 199\n", - " 330 9 265 131 323 262 389 195 388 198 454 3 71 450 259 514 135 64\n", - " 520 391 137 203 67 69 384 385 328]\n", - "59 21\n", - "[1599 1151 1146 1399 1403 1407 1461 1785 1525 1598 1402 1789 1018 1147\n", - " 1207 1470 1085 1401 1465 1269 1021 1275 1277 1406 1533 1405 1144 1336\n", - " 1335 1597 1404 1469 1658 1338 1148 1532 1594 1143 1592 1595 1276 1334\n", - " 1719 1273 1530 1205 1341 1463 1142 1660 1724 1723 1466 1527 1211 1271\n", - " 1531 1593 1657 1019 1081 1270 1464 1020 1337 1213 1016 1467 1534 1788\n", - " 1591 1529 1210 1725 1278 1784 1086 1786 1398 1279 1661 1080 1212 1663\n", - " 1655 1720 1082 1272 1726 1017 1397 1528 1339 1721 1596 1787 1400 1022\n", - " 1215 1342 1079 1790 1590 1654 1209 1462 1727 1468 1535 1656 1274 1589\n", - " 1722 1659 1526 1471 1343 1087 1206 1214 1208 1145 1662 1084 1150 1340\n", - " 1083 1149 1333]\n", - "55 3\n", - "[251 54 441 245 569 313 59 316 184 308 444 500 114 125 376 370 507 380\n", - " 438 631 177 186 181 51 57 253 247 189 369 315 563 564 435 634 630 445\n", - " 498 504 501 182 56 567 252 117 378 179 314 249 121 122 116 440 566 439\n", - " 187 113 241 632 503 499 375 50 379 118 571 120 306 377 242 52 442 443\n", - " 633 373 502 307 629 310 183 53 508 188 185 49 565 180 436 506 305 250\n", - " 317 570 437 434 309 60 374 372 119 371 246 61 505 433 248 55 312 244\n", - " 124 381 58 628 115 243 178 311 568 123]\n", - "15 2\n", - "[ 17 329 210 83 459 12 141 401 21 269 467 81 73 15 340 149 334 145\n", - " 143 526 404 332 402 339 213 524 525 201 82 144 139 207 341 13 273 337\n", - " 80 14 147 208 211 212 75 11 85 146 396 529 76 209 338 202 74 10\n", - " 275 79 277 77 461 268 463 140 20 16 335 138 464 267 276 398 142 400\n", - " 148 206 399 18 331 465 527 530 336 394 462 460 266 274 78 330 9 265\n", - " 271 403 204 205 333 19 397 270 272 137 84 528 203 395 466]\n", - "45 3\n", - "[ 45 239 107 114 168 370 41 177 51 557 237 47 364 426 302 369 360 170\n", - " 435 175 431 428 167 46 234 495 498 105 423 111 304 179 103 300 621 430\n", - " 558 240 238 368 560 296 429 232 113 241 425 365 618 50 494 108 110 306\n", - " 362 242 174 424 497 297 556 303 359 554 307 366 432 555 40 173 553 301\n", - " 488 172 489 176 104 112 49 43 361 39 622 305 491 559 620 231 298 434\n", - " 169 233 493 371 299 44 42 619 363 295 561 171 367 433 492 427 48 496\n", - " 623 236 624 106 115 109 235 243 178 490]\n", - "58 12\n", - "[ 831 441 638 569 444 1078 1023 1146 507 631 767 757 575 699\n", - " 826 636 756 958 829 1018 1147 830 1207 956 634 954 1085 630\n", - " 894 701 445 1021 887 821 702 504 888 639 827 567 886 1144\n", - " 761 694 1014 1148 949 1143 440 566 439 764 828 760 632 503\n", - " 885 1142 572 820 952 571 824 442 443 1211 695 633 893 765\n", - " 1019 502 1081 629 510 955 766 1020 696 637 948 892 1213 508\n", - " 1016 565 890 1210 506 1086 692 951 574 1013 950 570 698 1077\n", - " 1080 895 1212 822 693 1082 509 1017 957 762 1022 763 505 1079\n", - " 1012 1209 700 697 758 959 953 635 884 573 703 823 1087 1015\n", - " 628 1208 891 1145 759 1084 1150 825 568 889 1083 1149]\n", - "51 14\n", - "[1330 1136 1078 879 631 757 1203 947 756 878 818 563 564 1207\n", - " 882 1198 1072 1200 819 1140 627 630 1135 1269 1139 887 821 688\n", - " 888 886 1144 761 1331 694 1328 1005 1009 1071 1014 1202 1268 949\n", - " 754 1143 1011 689 1334 560 566 877 1329 760 885 625 1205 1142\n", - " 1138 820 1075 952 815 1008 824 690 941 1271 1141 695 1010 1204\n", - " 1081 626 629 562 1270 696 687 948 1264 946 1016 565 1076 749\n", - " 750 1073 692 951 751 1013 950 753 1077 1134 755 1080 881 1332\n", - " 822 1265 693 943 1266 1017 813 1199 561 1007 945 817 1267 1079\n", - " 1137 1074 1012 1133 758 953 880 944 1006 884 814 623 1201 942\n", - " 624 686 823 691 1070 1263 1015 628 1206 1208 1145 816 752 759\n", - " 883 1069 825 889 1333]\n", - "63 21\n", - "[1599 1151 1023 1146 1403 1407 1598 1402 1789 1147 1470 1085 1401 1465\n", - " 1021 1275 1277 1406 1533 1405 1597 1404 1469 1658 1338 1148 1532 1594\n", - " 1595 1276 1273 1530 1341 1660 1724 1723 1466 1211 1531 1593 1020 1337\n", - " 1213 1467 1534 1788 1529 1210 1725 1278 1086 1791 1279 1661 1212 1663\n", - " 1726 1339 1596 1022 1215 1342 1790 1209 1727 1468 1535 1274 1659 1471\n", - " 1343 1087 1214 1662 1084 1150 1340 1083 1149]\n", - "60 57\n", - "[3903 3583 4095 3832 3711 3387 3839 3391 3959 3705 3577 4026 3517 3899\n", - " 3772 3967 3644 3766 3511 3837 3708 3769 3702 3895 4030 3574 3836 3390\n", - " 3965 4027 3962 3455 3775 3709 4091 3897 3834 3384 3510 4092 3389 3581\n", - " 4094 3386 3902 4029 3830 3901 3838 4028 4093 3579 3513 3960 3324 3326\n", - " 3643 3706 3961 4090 3578 3448 3771 4025 3642 4031 3646 3575 4024 3641\n", - " 3707 3321 3452 3639 3514 3451 3894 3966 3450 3447 3896 3900 3322 3515\n", - " 3898 3385 3454 3647 3768 4089 3773 3640 3518 3449 3453 3519 3645 3516\n", - " 3831 3512 3767 3582 3835 3327 3833 3774 3638 3963 3964 3704 3710 3703\n", - " 3388 3576 3580 3770 3325 3323]\n", - "10 13\n", - "[1098 848 591 459 720 845 1039 1093 1035 1228 910 777 716 839\n", - " 521 976 842 526 523 773 779 655 1099 653 974 907 524 525\n", - " 718 772 719 906 714 1095 781 780 582 458 711 775 971 1158\n", - " 784 778 912 1165 905 847 651 715 970 1096 581 1103 1029 1160\n", - " 1038 838 1224 776 966 782 846 456 1223 841 908 518 1227 1040\n", - " 654 1166 645 843 588 1037 1159 461 836 900 783 650 903 708\n", - " 1032 455 1033 712 648 649 837 717 911 968 1036 1163 583 964\n", - " 844 522 1101 902 587 1162 644 975 972 519 1100 457 460 589\n", - " 1226 1102 973 1034 1229 709 967 904 909 590 710 1030 586 840\n", - " 901 1031 969 652 584 647 520 1164 1094 656 585 713 1097 1161\n", - " 774 646 1225 1028 965]\n", - "46 49\n", - "[2922 2861 2801 2927 3115 3500 2930 3187 3182 3379 3053 2859 2795 3373\n", - " 3316 3499 3054 3116 3058 3443 3440 3180 3112 3049 3435 3249 3055 3439\n", - " 2990 3176 3376 3306 3368 2993 2796 2860 3433 3117 3121 3377 3118 3241\n", - " 2864 2928 3307 3305 3243 3502 3311 2798 3244 3252 3304 3369 2996 3564\n", - " 3123 3048 3250 3380 3434 3050 3438 3184 3378 3181 2995 2921 2863 3186\n", - " 3567 3119 3375 3242 3566 2992 3498 3245 2989 3309 2923 3113 3188 2984\n", - " 3505 3370 3051 2987 3056 3504 3441 3563 3122 3569 3503 3501 3565 2924\n", - " 2865 3436 3059 2991 2929 2986 3179 2988 2994 2931 3314 2858 3120 3437\n", - " 3374 3183 3371 2800 2797 3052 3057 3315 3506 2925 3312 3310 2866 3114\n", - " 3308 3178 3251 3442 3313 3248 3247 3177 3240 2926 2862 2799 2985 3568\n", - " 3060 3246 3185 3372 3124]\n", - "24 38\n", - "[2322 2398 2454 2195 2589 2461 2326 2267 2780 2456 2394 2270 2516 2200\n", - " 2843 2263 2325 2713 2136 2333 2654 2578 2196 2840 2647 2265 2327 2712\n", - " 2521 2453 2716 2387 2582 2522 2711 2391 2526 2451 2839 2583 2709 2517\n", - " 2779 2198 2134 2584 2331 2462 2715 2455 2585 2842 2072 2204 2268 2778\n", - " 2262 2524 2652 2269 2070 2518 2460 2258 2776 2324 2330 2393 2646 2708\n", - " 2773 2710 2777 2397 2203 2390 2649 2579 2329 2133 2075 2515 2772 2580\n", - " 2202 2590 2643 2588 2264 2458 2514 2388 2587 2334 2386 2137 2328 2707\n", - " 2717 2523 2392 2645 2644 2450 2395 2073 2396 2266 2201 2132 2457 2135\n", - " 2259 2837 2642 2648 2653 2199 2389 2838 2775 2459 2139 2140 2071 2581\n", - " 2197 2205 2332 2651 2650 2452 2525 2714 2074 2260 2138 2069 2323 2520\n", - " 2261 2841 2519 2774 2586]\n", - "32 38\n", - "[2529 2404 2210 2406 2402 2721 2398 2661 2847 2589 2532 2272 2592 2461\n", - " 2267 2659 2276 2780 2464 2394 2270 2591 2468 2593 2333 2342 2654 2147\n", - " 2723 2596 2783 2530 2077 2078 2716 2522 2526 2658 2848 2079 2533 2338\n", - " 2850 2331 2662 2462 2715 2466 2534 2204 2268 2788 2206 2277 2594 2337\n", - " 2722 2335 2524 2271 2652 2146 2083 2269 2080 2278 2460 2598 2330 2397\n", - " 2274 2785 2339 2141 2203 2597 2211 2784 2467 2209 2273 2590 2588 2458\n", - " 2463 2207 2081 2587 2656 2465 2334 2787 2405 2403 2849 2144 2717 2523\n", - " 2782 2400 2275 2395 2396 2266 2660 2786 2212 2781 2845 2719 2595 2657\n", - " 2527 2724 2851 2082 2336 2145 2208 2340 2653 2718 2469 2143 2531 2725\n", - " 2459 2528 2140 2401 2205 2332 2213 2148 2651 2650 2399 2525 2720 2846\n", - " 2655 2341 2142 2470 2586]\n", - "47 0\n", - "[ 45 245 239 107 308 114 370 41 177 181 51 237 47 364 302 369 170 175\n", - " 431 428 46 234 105 111 304 117 179 300 430 240 116 238 368 429 113 241\n", - " 365 50 108 110 306 242 52 174 303 307 366 432 173 301 53 172 176 112\n", - " 49 180 43 305 298 434 169 233 371 299 44 42 363 171 367 433 48 244\n", - " 236 106 115 109 235 243 178]\n", - "57 58\n", - "[3903 3583 3832 3711 3387 3827 3839 3959 3705 4086 3577 4026 3517 3899\n", - " 3772 3967 3644 3699 3766 3511 3956 3837 3708 3769 3702 3895 4030 3574\n", - " 3836 3965 4027 3636 3962 3775 3709 4091 3897 3834 3384 3510 4092 3445\n", - " 3581 3386 3902 4029 3830 3572 3901 3838 4028 3383 3382 4093 3579 3513\n", - " 3828 3960 3700 3763 3891 3643 3706 3765 4085 3961 4090 3578 3448 3771\n", - " 3893 3958 4021 4022 4025 3642 3646 3575 4024 3641 3707 3701 3452 3639\n", - " 3514 3451 3764 3894 3966 3450 3447 3896 3900 3515 3957 3898 4088 3385\n", - " 3647 3768 3637 4089 3773 3640 3518 3449 3573 3635 3453 3509 3645 3516\n", - " 3831 3512 3767 3582 3835 3833 3571 4020 3774 3638 3963 3508 3964 3704\n", - " 3710 3829 4087 3703 3388 3446 3576 3892 3580 3770 3955 4023]\n", - "10 36\n", - "[2191 2317 2444 1997 2319 2062 2314 2509 2441 2445 2380 1930 2182 2313\n", - " 2437 1932 2637 2116 2056 2318 2188 2180 2569 2631 2375 2436 2254 2244\n", - " 2501 2255 2125 2630 2565 2378 2633 2447 2502 1928 2439 1933 2695 2054\n", - " 2381 2377 2571 2698 2185 1990 2443 2510 2308 2127 2117 2058 2126 1931\n", - " 2384 2442 2249 2570 2376 2373 2379 2059 2186 2310 2568 2250 2567 2181\n", - " 2572 2128 2122 2121 2372 1927 2383 1995 2192 2253 2440 2120 2315 2251\n", - " 1993 2247 2636 1991 2053 2511 2635 2700 2119 2503 2500 1992 1929 2374\n", - " 2312 2245 2697 2505 2184 2508 2634 2696 2118 2055 2061 2183 2448 2512\n", - " 2320 2246 2575 2632 2309 2057 2507 2187 1996 2574 2506 2316 2382 2060\n", - " 2252 2446 2504 2438 2311 2063 2189 2638 2123 2256 2124 2190 2248 1998\n", - " 2566 2573 2699 2701 1994]\n", - "15 43\n", - "[2957 2832 2891 2449 3024 3086 2444 3025 2959 2705 3148 3027 2765 3023\n", - " 2767 2897 2762 2960 2516 3089 2509 3154 2955 2963 2445 2380 3151 3084\n", - " 3091 2578 2896 2637 3018 2569 2766 2825 2451 2633 2709 2447 2964 2827\n", - " 2770 2900 2381 2639 2835 2571 2698 2443 2510 2898 2768 2640 3090 2384\n", - " 3085 2570 2703 2702 2893 3087 2572 2577 3026 3153 2708 2761 2773 2833\n", - " 2385 2830 2889 2834 2579 2515 2772 2580 2383 2643 2706 2514 3083 3028\n", - " 2826 2636 2386 2511 2635 2707 2700 3021 2764 2513 2576 3149 2956 2645\n", - " 2644 2450 3152 2697 2901 2895 2953 2828 2508 2965 2634 2837 2642 2899\n", - " 2448 2512 2829 2831 2962 3150 2641 2575 2894 2769 2581 2890 2507 3019\n", - " 3088 2574 2506 2382 2836 2446 2892 2961 2704 2763 2954 2638 3020 2958\n", - " 3022 2573 2699 2701 2771]\n", - "50 61\n", - "[3832 4083 3827 3959 4079 4086 3696 3699 3766 4013 3956 3954 3702 3895\n", - " 3823 3826 3884 3631 3886 4081 3636 3757 3887 4078 3822 3952 3760 3948\n", - " 3885 3762 4019 4016 3694 3830 3572 3890 4018 3698 3949 3828 3960 3700\n", - " 3950 3763 3891 3824 3765 4085 4076 3567 4015 3758 3893 4084 3958 4021\n", - " 4022 4014 3697 4024 4012 3820 3701 3756 3764 3569 3894 4082 3951 3896\n", - " 3759 3825 3821 3957 4088 3630 3632 3768 3637 4017 3573 3635 3831 3767\n", - " 3633 3570 3571 3695 3888 3889 4020 3953 4080 3634 4077 3638 3829 4087\n", - " 3703 3693 3761 3892 3568 3955 4023]\n", - "54 57\n", - "[3832 4083 3827 3959 3705 4086 3577 4026 3696 3899 3772 3644 3320 3699\n", - " 3766 3511 3956 3708 3769 3954 3702 3379 3895 3826 3574 3836 3316 3443\n", - " 3636 3962 3760 3897 3834 3384 3510 3762 3445 4019 3386 3830 3572 3890\n", - " 3383 4018 3698 3382 3380 3579 3513 3828 3960 3700 3763 3891 3824 3643\n", - " 3706 3765 3378 4085 3961 3317 3578 3448 3771 3893 4084 3958 4021 3444\n", - " 4022 4025 3642 3505 3697 3575 4024 3504 3641 3707 3441 3701 3321 3507\n", - " 3639 3514 3451 3764 3569 3381 3894 3319 3450 3447 3896 3825 3900 3515\n", - " 3957 3898 4088 3318 3385 3632 3768 3637 4089 3640 3449 3573 3635 3509\n", - " 3516 3831 3512 3767 3633 3835 3570 3315 3833 3506 3571 3888 3889 4020\n", - " 3953 3634 3442 3638 3963 3508 3704 3829 4087 3703 3446 3761 3576 3892\n", - " 3568 3580 3770 3955 4023]\n", - "5 4\n", - "[ 6 329 193 459 0 73 200 521 320 133 261 453 201 260 70 139 580 386\n", - " 582 458 128 387 136 129 393 258 4 325 517 257 642 2 449 75 448 263\n", - " 581 390 515 577 202 74 456 1 392 513 518 10 516 130 645 68 8 66\n", - " 256 321 65 138 132 267 194 455 648 5 327 326 192 452 331 583 522 643\n", - " 451 197 134 324 644 264 394 519 322 7 457 72 266 578 512 196 199 330\n", - " 9 265 131 323 262 389 195 388 198 454 3 71 450 259 514 135 64 584\n", - " 647 520 391 137 585 203 67 646 69 384 385 328 395 579]\n", - "14 8\n", - "[329 594 848 210 591 459 785 141 401 720 913 845 269 467 340 334 910 777\n", - " 716 145 850 521 659 143 842 526 404 332 523 596 779 724 402 339 655 653\n", - " 907 524 525 718 719 144 714 139 207 781 780 595 458 721 273 337 784 778\n", - " 912 393 847 651 532 715 208 468 723 396 849 529 722 782 209 846 338 202\n", - " 456 908 392 654 843 275 588 461 268 463 140 783 658 650 335 464 267 712\n", - " 648 649 398 717 911 142 400 206 399 331 844 465 522 593 787 527 530 336\n", - " 587 394 660 462 457 460 589 786 657 266 274 592 909 330 590 265 271 403\n", - " 586 531 204 205 333 397 652 584 520 270 272 656 585 528 713 203 328 395\n", - " 466]\n", - "17 45\n", - "[2957 3217 2832 2891 3024 3086 3215 3025 2959 2705 3148 3027 2765 3023\n", - " 2767 2897 3280 2960 2516 3089 3154 2955 2963 3151 3084 3091 2578 2896\n", - " 2637 3094 3030 2966 2766 2711 2839 2709 2964 2827 2770 2900 3155 2639\n", - " 2835 2510 2898 3158 2768 2640 3090 3085 2703 2702 3218 3284 2893 3031\n", - " 3087 2577 3026 2646 3153 2708 2773 2710 3219 2833 2830 2834 2579 2515\n", - " 2772 2580 2643 3221 2706 2514 3083 2967 3028 3282 2636 3029 2511 2707\n", - " 2700 3021 3157 2764 2513 2576 3149 2956 3283 2645 2644 3220 3156 3095\n", - " 3152 2901 2895 3092 2828 2965 3093 2837 2642 2899 2512 2829 2838 2831\n", - " 2962 3150 2775 2641 2575 2894 3278 3216 3214 2769 2581 2902 3019 3088\n", - " 3213 2574 3281 2836 2892 2961 2704 2763 2903 2638 3279 3020 2958 3022\n", - " 2573 2699 2701 2774 2771]\n", - "44 5\n", - "[ 45 239 107 168 370 41 177 557 237 47 364 426 302 369 360 170 175 431\n", - " 428 167 46 234 495 684 498 105 688 550 423 111 304 230 682 745 103 300\n", - " 621 430 558 240 617 238 368 560 296 429 748 232 551 113 241 425 365 625\n", - " 618 685 494 108 110 486 306 362 242 174 424 497 552 297 556 303 359 554\n", - " 366 432 555 615 40 680 487 562 173 553 301 488 687 172 489 176 104 112\n", - " 294 43 361 622 749 750 305 491 559 751 620 231 358 298 434 169 233 493\n", - " 616 299 44 42 619 363 683 295 561 171 747 367 433 166 492 427 48 496\n", - " 623 236 624 686 422 746 106 109 681 235 178 490]\n", - "17 16\n", - "[ 848 1361 785 720 913 845 1104 1232 1039 1035 1228 910 1358 850\n", - " 1110 659 976 1041 914 1170 1167 724 978 917 1295 655 1099 852\n", - " 974 907 718 1363 719 855 1171 781 1105 780 721 971 1362 982\n", - " 1230 784 912 1423 1165 847 789 1175 1292 723 1046 1237 1299 1427\n", - " 1103 849 1038 722 782 1231 846 908 1106 1042 1357 1227 1040 919\n", - " 1426 790 983 654 1166 843 918 1238 1107 1172 1037 1047 1296 977\n", - " 853 783 658 725 1425 1365 979 717 854 1293 911 1297 1036 980\n", - " 1233 1163 1111 844 1109 1101 787 1294 1173 1359 975 972 660 1174\n", - " 1100 1239 1044 851 786 657 1102 973 981 1360 1229 1300 1108 788\n", - " 909 915 1234 1302 916 1422 1298 1236 1169 1424 1428 1301 1364 1164\n", - " 1043 1168 656 1045 1235]\n", - "32 27\n", - "[1826 1765 1695 2084 1570 1888 1508 1892 1502 1699 1697 1504 2018 1638\n", - " 1692 1636 1442 2015 1885 1760 1376 1436 1509 1505 1503 1572 1763 1952\n", - " 2011 1819 2147 1883 2017 1818 2077 1829 2078 1566 1957 1500 1954 1949\n", - " 2020 1822 2079 1894 1571 1955 1374 2014 1437 1698 1758 1828 1757 1633\n", - " 1444 1693 1506 1754 1562 1499 1890 2146 2083 1379 2080 1635 2012 1628\n", - " 2021 1950 1948 1821 1378 1627 2019 1626 2141 1886 1956 1373 1947 1827\n", - " 1825 1694 1630 1887 2081 1501 1762 1882 1632 1375 1755 2144 1440 1700\n", - " 1951 1946 1701 1889 1569 1820 1893 1573 1438 1507 2016 1958 2082 1953\n", - " 1565 1830 1564 2145 1637 1759 1439 1884 1696 1567 1823 1629 2143 1702\n", - " 1766 1691 1824 2076 1574 1891 1443 1634 1631 1764 1690 1563 1761 2142\n", - " 1441 2013 1568 1377 1756]\n", - "20 31\n", - "[2322 1809 1682 2000 1870 2191 2195 1621 1875 1749 2326 2129 2062 2067\n", - " 2004 1816 2131 1806 1808 2200 2263 2325 2136 2257 1683 2196 1680 2006\n", - " 1818 2265 2327 2008 1873 2387 1617 1878 2255 1687 2391 2064 2198 2134\n", - " 2127 2072 2193 1807 2126 2005 1937 1943 1619 1622 2262 1811 1813 2002\n", - " 1880 2070 2065 2258 2324 2128 1877 1939 2385 1942 1941 2390 2133 1817\n", - " 1945 2003 2202 2192 2264 2388 2001 1882 2386 2007 1746 2137 1935 2328\n", - " 1812 1815 1623 1810 1946 2073 2194 1620 1879 2201 1934 1618 2132 1936\n", - " 2135 2066 2259 1753 1999 1814 1744 1938 2199 2389 1681 2320 2321 1748\n", - " 1871 2071 1684 2197 2010 1752 1872 2009 1686 1874 1876 1750 2074 1743\n", - " 1747 2068 2260 1881 1751 2063 1685 2138 1688 2069 2130 2323 2256 2190\n", - " 2261 1998 1940 1745 1944]\n", - "20 57\n", - "[3858 4055 3345 3730 3990 3919 3865 3538 3985 3599 3408 4051 3799 3790\n", - " 3923 3668 3541 3922 3605 3534 3726 3671 3728 3482 3987 3924 3926 3411\n", - " 3732 3861 3859 3470 3476 3854 3801 3286 3287 3735 3412 3414 3346 3607\n", - " 4052 3738 3598 3866 3472 3802 3986 3351 3415 3604 3663 3920 3536 3479\n", - " 3413 3863 3344 3921 3856 3989 3798 3546 3537 3284 3672 3610 3796 3927\n", - " 3410 3791 3928 3855 3929 3285 3674 3925 3795 3416 3608 3797 3669 3542\n", - " 3603 3737 3731 3600 3736 3666 3471 3535 3282 4053 3409 3474 3481 3793\n", - " 3664 3601 3606 3283 3734 3665 3539 3609 4050 3347 3540 3667 3862 3673\n", - " 3670 3350 3543 3727 3477 3544 3602 3792 3348 3473 3545 4049 3729 3860\n", - " 3662 3478 3417 3733 3349 3281 3991 3988 3857 3984 3800 3407 3475 4054\n", - " 3992 3864 3794 3352 3480]\n", - "53 22\n", - "[1392 1330 1713 1519 1524 1078 1782 1521 1399 1203 1842 1651 1403 1461\n", - " 1785 1525 1402 1394 1207 1712 1848 1588 1652 1200 1140 1401 1648 1465\n", - " 1269 1275 1139 1327 1144 1331 1336 1335 1455 1715 1328 1778 1460 1649\n", - " 1456 1717 1779 1658 1202 1268 1338 1585 1522 1594 1143 1523 1592 1595\n", - " 1334 1719 1586 1846 1584 1329 1273 1847 1530 1205 1463 1393 1142 1520\n", - " 1138 1714 1075 1466 1396 1527 1271 1141 1531 1457 1395 1593 1657 1650\n", - " 1204 1783 1845 1391 1270 1464 1337 1264 1467 1587 1718 1076 1777 1716\n", - " 1591 1529 1210 1784 1781 1077 1398 1459 1080 1332 1265 1780 1655 1720\n", - " 1272 1266 1397 1528 1339 1721 1844 1400 1267 1079 1653 1137 1074 1590\n", - " 1654 1209 1462 1656 1274 1589 1722 1201 1659 1526 1583 1263 1647 1843\n", - " 1206 1458 1208 1145 1333]\n", - "12 62\n", - "[4038 4047 3858 3659 3914 3919 3786 4043 3985 3599 3790 3847 3983 3980\n", - " 3853 4041 3851 3979 3922 3726 3913 3728 3982 3975 3854 4039 3788 3978\n", - " 3598 3915 4042 3986 3663 3911 3920 3921 3856 3656 3848 3977 3791 3855\n", - " 4048 3595 3725 3719 3910 3793 3852 4044 3664 3916 3789 4045 3593 4050\n", - " 3597 3976 3594 3657 3846 3658 3850 3722 3721 3727 3849 3720 3974 3792\n", - " 3981 3917 4049 3596 3729 4040 3785 3662 3912 3787 3857 3723 3660 3984\n", - " 3661 3918 3783 4046 3784 3794 3782 3724]\n", - "45 36\n", - "[2027 2093 2089 2542 2159 2354 2475 2671 1966 2416 2161 2284 2408 2032\n", - " 2730 2222 2732 1965 2349 2536 2545 2088 2344 2413 2414 2541 2411 2155\n", - " 2418 2348 2345 1963 2602 2474 2535 2538 2152 2026 2090 2153 2609 2419\n", - " 2352 2480 2407 2473 2160 2544 2098 2600 2668 2601 2282 2346 2409 2218\n", - " 2608 2289 2670 1962 2225 2355 2540 2224 2343 2478 2417 1964 2607 2472\n", - " 2096 2030 2095 2288 2162 2154 2736 2219 2025 2350 2158 2220 2477 2604\n", - " 2151 2163 2279 2217 1968 2286 2667 2351 2539 2031 2471 2479 2483 2290\n", - " 2415 2033 2482 2347 2156 2669 2353 2215 1967 2216 2092 2672 2734 2543\n", - " 2291 2226 2287 2283 2606 2673 2280 2412 2094 2410 2157 2227 2537 2731\n", - " 2610 2481 2285 2097 2665 2733 2091 2603 2029 2546 2547 2223 2281 2476\n", - " 2735 2605 2221 2666 2028]\n", - "18 14\n", - "[ 594 848 591 785 720 913 845 1104 1232 1039 910 716 850 1110\n", - " 659 976 1041 914 1170 1167 596 724 978 917 1295 655 653 852\n", - " 974 718 719 855 1171 781 792 1105 780 595 721 982 1230 784\n", - " 912 1165 847 856 791 532 1112 789 1175 662 723 1046 1237 1299\n", - " 1103 849 1038 529 722 782 1231 846 908 1106 1042 1040 919 790\n", - " 983 654 1166 918 1238 726 728 1107 1172 1037 1047 1296 977 853\n", - " 783 658 725 597 979 717 854 911 1297 1036 980 1233 1048 533\n", - " 1111 844 1109 1101 593 787 527 530 1173 975 661 972 660 1174\n", - " 1100 1044 851 786 657 1102 973 981 1300 1108 788 592 909 590\n", - " 915 1234 531 916 663 1298 1236 598 1169 1301 920 1043 727 1168\n", - " 656 528 1045 984 1235]\n", - "45 19\n", - "[1388 1392 1262 1330 1130 1136 1132 1519 1642 1581 1383 879 1389 1521\n", - " 1203 1261 1322 940 1002 878 1004 1065 1394 1003 1193 1064 1198 1578\n", - " 1258 1072 1512 1579 1200 1451 1648 1135 1129 1001 876 1450 1067 1139\n", - " 1327 1452 1255 1259 1326 1331 1455 1328 1005 1009 1071 1456 1202 1454\n", - " 1585 1522 1319 1321 1323 1447 1387 1584 877 1329 1515 1393 1390 1520\n", - " 1138 1075 1256 1066 1577 1008 1448 1320 941 1516 1457 1395 1010 1324\n", - " 1128 1385 1063 1325 1391 1644 937 1000 1264 1194 1127 1197 1195 1073\n", - " 1386 1134 1514 1453 1459 1192 1196 1513 1265 943 875 1384 1266 1449\n", - " 1191 1199 1517 1645 1646 1007 945 1643 1267 1137 1074 1068 1260 1133\n", - " 938 880 944 1006 1201 942 1131 1583 1257 1070 1263 1647 939 1582\n", - " 1458 1580 1518 1069 874]\n", - "10 43\n", - "[2887 2957 3145 2832 2891 3086 2444 2959 3148 2765 3023 2767 2762 2960\n", - " 2509 2441 2955 2445 2380 3084 2759 2896 2637 3018 2820 3146 2629 2569\n", - " 2631 2758 2375 2766 2694 2501 2825 2756 2630 2565 2378 2633 2502 2760\n", - " 2827 2439 2695 2381 2377 2639 2571 2698 2443 2510 2951 3013 2768 2640\n", - " 3082 2886 3017 3085 2442 2950 3080 2570 2376 2703 2379 2702 2568 2567\n", - " 2693 2893 2572 2761 2757 2830 2889 2884 2564 3079 3014 2440 3083 2692\n", - " 2826 2636 2511 2635 2700 2824 3021 2949 2764 2503 2576 3149 2956 3143\n", - " 3078 3016 3144 2697 2888 2505 2895 2953 2828 2508 2634 2696 3015 2829\n", - " 2821 2831 2575 2894 2632 2822 3081 2948 2890 2507 3019 2952 2574 2506\n", - " 2823 2446 2892 2885 2504 2704 2763 2954 2438 2638 2628 3020 2958 2566\n", - " 3022 2573 2699 2701 3147]\n", - "13 60\n", - "[4047 3858 3730 3659 3914 3919 3786 4043 3985 3599 4051 3790 3847 3923\n", - " 3529 3983 3980 3853 4041 3851 3979 3922 3534 3726 3913 3728 3987 3982\n", - " 3975 3859 3470 3854 4039 3788 3978 3598 3915 4042 3472 3986 3663 3911\n", - " 3920 3536 3921 3856 3656 3537 3848 3977 3791 3855 3531 4048 3468 3595\n", - " 3795 3467 3731 3600 3725 3666 3532 3471 3535 3719 3793 3852 4044 3664\n", - " 3601 3916 3789 3533 3655 4045 3665 3593 4050 3597 3667 3976 3594 3657\n", - " 3658 3850 3722 3721 3727 3849 3469 3720 3602 3792 3981 3917 4049 3596\n", - " 3729 4040 3785 3662 3592 3530 3912 3787 3857 3723 3660 3466 3984 3661\n", - " 3918 3783 4046 3784 3794 3724]\n", - "51 52\n", - "[3128 3577 3696 3320 3699 3766 3511 3187 3182 3702 3379 3574 3373 3316\n", - " 3631 3058 3443 3636 3189 3440 3191 3249 3055 3439 3376 2993 3760 3121\n", - " 3377 3384 3510 3118 3762 3445 2997 3125 3502 3311 3253 3252 3572 2996\n", - " 3061 3123 3383 3250 3698 3382 3380 3513 3700 3763 3063 3438 3765 3184\n", - " 3378 3181 2995 3192 3186 3567 3119 3375 3317 3566 3126 2992 3245 3448\n", - " 3309 3444 3257 3188 3505 3697 3256 3575 3056 3504 3441 3701 3321 3507\n", - " 3193 3639 3122 3764 3569 3503 3381 3319 3501 3565 3447 3318 3630 3385\n", - " 3059 3632 3637 2998 3640 3449 2994 3573 3635 3314 3509 3120 3437 3512\n", - " 3374 3183 3633 3570 3057 3315 3506 3312 3571 3695 3310 3634 3251 3442\n", - " 3313 3638 3508 3248 3247 3254 3703 3255 3446 3190 3761 3062 3576 3568\n", - " 3060 3246 3185 3124 3127]\n", - "39 11\n", - "[1130 1062 557 743 426 940 1002 865 546 742 360 1004 1065 871\n", - " 421 1003 996 1064 738 1060 482 1129 684 1001 876 1067 1126 550\n", - " 803 868 420 423 485 675 744 547 999 682 745 621 936 870\n", - " 617 866 748 877 806 551 548 545 549 425 618 685 614 808\n", - " 486 1066 609 362 935 424 941 810 611 552 556 359 554 1128\n", - " 677 867 998 555 615 1063 679 680 673 487 356 994 553 807\n", - " 488 937 1000 932 489 1127 739 361 811 749 483 676 931 357\n", - " 678 491 620 997 358 873 1124 419 875 804 610 616 813 1061\n", - " 619 812 740 683 802 929 934 747 613 612 938 1059 492 674\n", - " 872 427 930 933 741 422 484 746 939 805 737 681 809 869\n", - " 995 1125 801 874 490]\n", - "5 61\n", - "[4038 3776 4032 3914 3526 3786 4043 3648 3847 3909 4041 3851 3979 3527\n", - " 4036 4037 3913 3714 3975 3524 3906 3587 4039 3978 3843 4034 3586 3717\n", - " 3915 4042 3905 3712 3528 3844 3970 3651 3716 3649 3911 3779 3590 3842\n", - " 3656 3845 3848 4035 4033 3585 3977 3718 3522 3523 3973 3654 3781 3777\n", - " 3719 3910 3778 3972 3525 3907 3655 3588 3593 3591 3780 3904 3976 3589\n", - " 3657 3846 3658 3850 3722 3968 3721 3849 3720 3974 3841 3969 4040 3785\n", - " 3592 3713 3971 3912 3787 3723 3715 3783 3908 3784 3653 3782 3652 3650\n", - " 3840]\n", - "40 39\n", - "[2404 2922 2542 2406 2402 2475 2661 2532 2284 2214 2408 2730 2732 2659\n", - " 2276 2349 2536 2859 2795 2344 2413 2468 2414 2541 2411 2155 2342 2348\n", - " 2345 2602 2723 2596 2474 2535 2538 2152 2530 2153 2796 2860 2658 2149\n", - " 2533 2407 2473 2338 2662 2600 2668 2601 2282 2466 2346 2409 2920 2791\n", - " 2534 2919 2218 2788 2277 2670 2856 2854 2594 2722 2540 2343 2150 2278\n", - " 2478 2598 2472 2921 2729 2789 2918 2154 2339 2923 2219 2597 2663 2350\n", - " 2220 2477 2604 2467 2151 2279 2217 2664 2794 2599 2667 2539 2857 2471\n", - " 2917 2787 2405 2403 2855 2347 2726 2669 2215 2275 2216 2660 2212 2734\n", - " 2792 2595 2724 2793 2858 2283 2606 2727 2340 2280 2728 2469 2797 2412\n", - " 2531 2725 2410 2852 2790 2537 2731 2853 2285 2665 2213 2733 2603 2281\n", - " 2341 2476 2605 2470 2666]\n", - "26 37\n", - "[2398 2454 2589 2272 2592 2461 2326 2267 2780 2456 2464 2394 2270 2516\n", - " 2200 2591 2263 2325 2713 2136 2333 2654 2011 2196 2647 2265 2327 2008\n", - " 2712 2077 2521 2453 2078 2716 2582 2522 2711 2391 2526 2583 2517 2779\n", - " 2198 2134 2584 2331 2462 2715 2455 2585 2072 2204 2268 2206 2778 2262\n", - " 2335 2524 2271 2652 2269 2070 2518 2460 2012 2776 2324 2330 2393 2646\n", - " 2710 2777 2397 2141 2203 2390 2649 2329 2133 2075 2580 2202 2590 2588\n", - " 2264 2458 2463 2207 2388 2587 2334 2007 2137 2328 2717 2523 2392 2645\n", - " 2400 2395 2073 2396 2266 2201 2781 2457 2527 2135 2336 2208 2648 2653\n", - " 2718 2199 2389 2143 2775 2459 2528 2139 2140 2071 2581 2197 2010 2205\n", - " 2332 2076 2651 2009 2650 2399 2452 2525 2714 2074 2260 2138 2655 2520\n", - " 2142 2261 2013 2519 2586]\n", - "45 47\n", - "[2922 2861 2801 2671 2927 3115 2930 2730 2732 3187 3182 3053 2859 2983\n", - " 2795 3373 3054 3116 3058 3440 3180 3112 3049 3435 3249 3055 3439 2990\n", - " 3176 3376 3306 2993 2796 2860 3117 3121 3377 3118 3111 3241 2864 2928\n", - " 3307 3305 2668 3243 3311 2798 2920 3244 2919 3304 3369 3123 2670 3048\n", - " 2856 3250 3434 3050 3438 3047 3184 3181 2995 2921 2863 2729 3186 3119\n", - " 3375 3242 2867 2992 3245 2989 3309 2923 2736 3113 2984 3370 3051 2987\n", - " 3175 3056 2794 2667 2857 3122 2802 2855 2737 2669 2924 2865 3436 2672\n", - " 2734 2792 3059 2991 2929 2986 3179 2988 2793 2994 2931 3314 2858 3120\n", - " 3437 3374 3183 3371 2800 2797 3052 3057 2925 3239 3312 3310 2866 3114\n", - " 2731 3308 3178 3251 3313 2733 3248 3247 3177 3240 2926 2862 2799 2985\n", - " 3246 3185 2735 3372 2666]\n", - "44 26\n", - "[1388 1392 1710 2027 1840 1704 2093 1713 2089 1836 1895 1519 1642 1581\n", - " 1966 1638 1389 1521 2032 1965 1842 1708 1322 1575 1773 1833 1839 1511\n", - " 1963 1772 1712 1969 2026 1834 1841 1578 2090 1512 1579 1768 1451 1771\n", - " 1648 1640 1769 1450 1327 1452 1832 1641 1326 1894 1576 1455 1905 1961\n", - " 1778 1649 1456 1901 1903 1454 1709 1585 1522 1707 1962 1321 1323 1447\n", - " 1837 1586 1387 1835 1584 1964 1896 1770 1515 1510 2030 1390 1899 1902\n", - " 1520 2095 1714 1776 1904 1577 1448 1959 2025 1767 1516 1457 1900 1650\n", - " 1324 1385 1968 1325 1391 1703 1644 1838 2031 1705 1960 1777 1967 2092\n", - " 1386 1897 1514 1453 1774 1513 1830 1639 1384 1449 1517 1645 1646 1643\n", - " 2094 1702 2024 1706 1831 1775 1766 1898 1574 2091 2029 1583 1647 1582\n", - " 1711 1580 1518 2028 1906]\n", - "52 40\n", - "[2542 2801 2354 2739 2671 2416 2420 2930 2678 2612 2545 2617 2414 2933\n", - " 2418 2294 2679 2806 2230 2618 2807 2742 2872 2738 2489 2741 2360 2993\n", - " 2873 2425 2609 2228 2419 2488 2935 2352 2480 2740 2864 2936 2997 2805\n", - " 2674 2553 2928 2544 2293 2869 2296 2550 2798 2934 2552 2996 2422 2608\n", - " 2289 2490 2670 2225 2682 2355 2554 2868 2478 2417 2607 2995 2863 2867\n", - " 2421 2870 2288 2358 2736 2549 2999 2680 2548 2803 2357 2351 2479 2802\n", - " 2483 2290 2415 2423 2676 2482 2737 2353 2486 2295 2611 2865 2615 2614\n", - " 2672 2734 2613 2543 2291 2929 2231 2804 2744 2226 2998 2746 2808 2871\n", - " 2994 2931 2292 2675 2229 2606 2673 2485 2800 2227 2426 2484 2866 2681\n", - " 2610 2809 2481 2356 2487 2546 2547 2810 2745 2932 2359 2799 2361 2743\n", - " 2735 2424 2551 2677 2616]\n", - "42 20\n", - "[1388 1392 1262 1704 1508 1130 1136 1132 1519 1642 1581 1383 1638 1389\n", - " 1062 1445 1261 1708 1322 1575 940 1002 1509 1004 1065 1511 1003 1193\n", - " 1064 1198 1252 1578 1258 1512 1579 1200 1317 1451 1640 1135 1129 1001\n", - " 1450 1067 1327 1126 1452 1255 1259 1641 1326 1576 1455 1328 1005 1071\n", - " 1316 1456 1454 999 1709 1319 1707 1444 936 1321 1323 1447 1387 1515\n", - " 1510 1390 1520 1446 1190 1189 1256 1066 1577 1448 1320 935 941 1516\n", - " 1324 1128 1385 998 1063 1325 1391 1703 1644 1705 937 1000 1264 1194\n", - " 1127 1197 1195 1386 1381 1134 1514 1573 1453 1124 1192 1196 1513 1254\n", - " 1639 1384 1449 1191 1061 1199 1517 1645 1646 1382 1380 1643 1318 1068\n", - " 1260 1133 1706 938 1006 1253 1574 1131 1583 1257 1070 1263 1188 939\n", - " 1582 1580 1125 1518 1069]\n", - "30 35\n", - "[2084 2529 2404 2210 1888 2402 2398 2589 2018 2272 2592 2461 2015 2267\n", - " 2276 1885 2456 2464 2394 2270 2200 2591 2468 2593 2136 2333 2654 1952\n", - " 2011 2147 1883 2530 2017 2265 2077 2521 2078 2522 1954 2526 1949 2079\n", - " 2338 2014 2331 2462 2466 2072 2204 2268 2206 2594 2337 2335 2524 2271\n", - " 2652 2146 2083 2269 2080 2460 2012 2330 2393 1950 1948 2397 2274 2019\n", - " 2339 2141 1886 2203 2329 2075 2211 2202 2467 2209 2273 2590 1947 2588\n", - " 2264 2458 2463 1887 2207 2081 2587 2656 2465 2334 2137 2403 2328 2144\n", - " 2523 2392 1951 1946 2400 2275 2395 2073 2396 2266 2201 2212 1889 2657\n", - " 2457 2527 2016 2082 1953 2336 2145 1884 2208 2340 2653 2143 2531 2459\n", - " 2528 2139 2140 2401 2010 2205 2332 2076 2148 2651 2009 2399 2525 2074\n", - " 2138 2655 2142 2013 2586]\n", - "39 24\n", - "[1826 1388 1765 1704 1570 1508 1892 1836 1699 1895 1315 1642 1581 1383\n", - " 1697 1638 1389 1636 1442 1445 1708 1322 1575 1773 1833 1509 1505 1572\n", - " 1763 1511 1772 1193 1834 1252 1578 1258 1512 1579 1768 1829 1317 1451\n", - " 1771 1640 1957 1769 1450 1452 1832 1255 1259 1641 1894 1576 1571 1961\n", - " 1698 1316 1828 1709 1633 1319 1707 1962 1444 1506 1321 1323 1447 1379\n", - " 1635 1387 1835 1896 1770 1515 1510 1899 1378 1446 1190 1189 1256 1577\n", - " 1448 1956 1320 1959 1767 1516 1314 1324 1385 1827 1703 1644 1762 1705\n", - " 1960 1194 1700 1701 1386 1381 1569 1897 1893 1514 1573 1453 1507 1958\n", - " 1192 1513 1830 1254 1639 1384 1637 1449 1191 1517 1645 1382 1380 1643\n", - " 1318 1702 1251 1706 1831 1766 1898 1253 1574 1891 1443 1634 1257 1764\n", - " 1188 1761 1441 1580 1377]\n", - "55 60\n", - "[3832 4083 3827 3959 3705 4086 3577 4026 3899 3772 3644 3699 3766 3511\n", - " 3956 3837 3708 3769 3954 3702 3895 3826 3574 3836 3965 4027 4081 3636\n", - " 3962 3709 4091 3897 3834 3510 4092 3762 4019 4029 3830 3572 3901 3890\n", - " 4028 4018 3698 4093 3579 3513 3828 3960 3700 3763 3891 3643 3706 3765\n", - " 4085 3961 4090 3578 3771 3893 4084 3958 4021 4022 4025 3642 3697 3575\n", - " 4024 3641 3707 3701 3639 3514 3764 3894 4082 3896 3825 3900 3957 3898\n", - " 4088 3768 3637 4017 4089 3773 3640 3573 3635 3509 3831 3512 3767 3835\n", - " 3833 3571 3889 4020 3953 3634 3638 3963 3508 3964 3704 3829 4087 3703\n", - " 3761 3576 3892 3770 3955 4023]\n", - "49 39\n", - "[2542 2861 2801 2159 2354 2739 2475 2671 2416 2927 2161 2284 2420 2930\n", - " 2222 2732 2678 2349 2612 2545 2413 2414 2541 2411 2418 2348 2294 2679\n", - " 2806 2164 2742 2738 2741 2796 2609 2228 2419 2352 2480 2740 2864 2805\n", - " 2674 2160 2928 2544 2293 2869 2668 2550 2798 2422 2608 2289 2670 2225\n", - " 2355 2868 2540 2224 2478 2417 2607 2863 2867 2421 2288 2162 2358 2736\n", - " 2350 2158 2477 2604 2549 2163 2548 2286 2803 2357 2667 2351 2539 2479\n", - " 2802 2483 2290 2415 2423 2676 2482 2347 2737 2669 2353 2486 2611 2865\n", - " 2615 2614 2672 2734 2613 2543 2291 2929 2804 2226 2931 2292 2287 2675\n", - " 2229 2606 2673 2485 2800 2797 2412 2227 2484 2866 2731 2610 2481 2285\n", - " 2356 2487 2733 2603 2546 2547 2223 2932 2926 2359 2862 2799 2476 2743\n", - " 2735 2605 2221 2551 2677]\n", - "62 62\n", - "[3903 4095 3832 3711 3839 4026 3899 3772 3967 3644 3837 3708 3769 4030\n", - " 3836 3965 4027 3962 3775 3709 4091 3897 3834 4092 4094 3902 4029 3901\n", - " 3838 4028 4093 3960 3643 3706 3961 4090 3771 4025 4031 3646 4024 3707\n", - " 3966 3896 3900 3898 4088 3647 4089 3773 3645 3835 3833 3774 3963 3964\n", - " 3710 3770]\n", - "40 32\n", - "[1765 2027 2084 1704 2093 2404 2089 2210 2406 1892 2022 1836 1895 2475\n", - " 1966 2018 2284 2214 2408 2222 1965 2276 2349 2088 1833 2344 2411 2155\n", - " 2342 2348 2087 2345 1963 1772 2147 2474 2152 2026 1834 2090 1768 1829\n", - " 2153 1771 1957 1769 1954 2149 1832 2020 2407 2473 1894 1955 2282 1961\n", - " 2346 2409 1901 1828 2218 2277 1707 1962 1890 2343 2146 2083 1837 2150\n", - " 2278 1835 1964 1896 1770 2472 2021 2030 1899 1902 2274 2154 2019 2339\n", - " 1956 1959 2219 2025 1767 2211 2158 1900 2220 2151 2279 2217 1827 2286\n", - " 1703 1705 2471 2405 1960 2086 2347 2156 2215 2275 2216 2023 2212 1701\n", - " 2092 1897 1893 1958 2082 1830 2283 2340 2280 2469 2412 2094 1702 2410\n", - " 2157 2024 1706 1831 1766 1898 2285 1891 2213 2148 2091 2029 1764 2281\n", - " 2085 2341 2470 2221 2028]\n", - "45 27\n", - "[1388 1392 1710 2027 1840 1907 1704 2093 1713 2089 2159 1836 1895 1519\n", - " 1642 1581 1966 1389 1521 2032 1965 1842 1651 1708 1575 1773 1833 2155\n", - " 1839 1963 1772 1712 1969 2026 1834 1841 1578 2090 1512 1579 1768 2034\n", - " 1451 1771 1648 1640 1769 1450 1452 1832 1641 1576 2160 1455 1715 1905\n", - " 1961 1778 1649 1456 1901 1903 1779 1454 1709 1585 1971 1522 1707 1962\n", - " 1837 1586 1387 1835 1584 1964 1896 1770 1515 2096 2030 1390 1899 1902\n", - " 1520 2095 1714 2154 1776 1904 1577 1959 2025 1767 1970 1516 1457 2158\n", - " 1900 1650 1968 1391 1703 1644 1838 2031 1705 1960 2033 1587 1777 2156\n", - " 1967 2092 1386 1897 1514 1453 1774 1513 1639 1449 1517 1645 1646 1643\n", - " 2094 2157 2024 1706 1831 1775 1898 2097 2091 2029 1583 1647 1582 1843\n", - " 1711 1580 1518 2028 1906]\n", - "16 51\n", - "[2957 3345 3275 3217 3024 3086 3538 3215 3599 3342 3408 3025 2959 3148\n", - " 3027 3023 2897 3280 2960 3089 3154 3541 2963 3402 3534 3151 3084 3091\n", - " 2896 3411 3094 3470 3476 3146 3286 3274 3412 3414 3346 3598 2964 3472\n", - " 3155 2898 3604 3663 3158 3536 3082 3090 3413 3085 3344 3404 3537 3218\n", - " 3284 2893 3087 3026 3153 3277 3410 3219 3531 3285 3468 3210 3341 3343\n", - " 3467 3603 3600 3221 3666 3532 3471 3083 3028 3535 3282 3406 3409 3029\n", - " 3474 3021 3157 3664 3601 3149 2956 3339 3283 3533 3405 3220 3665 3156\n", - " 3539 3338 3152 3347 3540 3597 3667 2895 3092 3350 3276 3093 3469 3477\n", - " 2899 3602 3348 3473 3222 2962 3150 3596 3212 2894 3278 3662 3216 3478\n", - " 3214 3019 3349 3088 3213 3340 3403 3281 3466 3661 2961 3211 3407 3475\n", - " 3279 3020 2958 3022 3147]\n", - "16 46\n", - "[2957 3345 3217 2832 2891 3024 3086 3215 3342 3025 2959 2705 3148 3027\n", - " 2765 3023 2767 2897 2762 3280 2960 3089 3154 2955 2963 3151 3084 3091\n", - " 2578 2896 2637 3094 3018 3030 2966 3146 2766 3346 2709 2964 2827 2770\n", - " 2900 3155 2639 2835 2898 3158 2768 2640 3082 3090 3085 3344 2703 2702\n", - " 3218 3284 2893 3087 2577 3026 3153 3277 2708 2773 3219 2833 2830 2834\n", - " 2579 3341 3343 2772 2643 3221 2706 3083 3028 2826 3282 2636 3029 2707\n", - " 2700 3021 3157 2764 2576 3149 2956 3283 2644 3220 3156 3152 3347 2901\n", - " 2895 3092 2828 3276 2965 3093 2837 2642 2899 2829 2838 2831 2962 3150\n", - " 2641 2575 3212 2894 3278 3216 3214 2769 2890 2902 3019 3088 3213 2574\n", - " 3281 2836 2892 2961 2704 3211 2763 2954 2638 3279 3020 2958 3022 2573\n", - " 2699 2701 2774 2771 3147]\n", - "43 44\n", - "[2922 2542 2861 2801 2475 2661 2671 2927 3115 2730 2732 3182 2536 3053\n", - " 2859 2983 2795 2541 3054 3116 3180 2602 3112 3049 2474 2535 2538 3055\n", - " 2990 3176 2993 2796 2860 3117 3046 3118 3111 3241 2864 2473 2928 2662\n", - " 2600 2668 2601 3243 2798 2920 2791 3244 2919 2608 3045 2670 3048 2856\n", - " 2854 2540 3050 2478 3047 2607 2598 3181 2472 2921 2863 2729 3119 2789\n", - " 2918 3242 2981 2992 2982 3245 2989 2923 2736 3113 2663 2984 2477 2604\n", - " 3051 2987 3175 3056 2664 2794 2599 2667 2539 2857 2917 2855 2726 2737\n", - " 2669 2924 2865 2672 2734 2792 2991 2543 2929 2986 3179 2988 2793 2858\n", - " 2606 3120 2727 2673 3183 2728 2800 2797 3110 3052 3057 2725 2925 2790\n", - " 2537 3114 2731 3178 2853 2665 2733 2603 3177 3240 2926 2862 2799 2985\n", - " 3246 2476 2735 2605 2666]\n", - "41 28\n", - "[1765 1710 2027 2084 1704 2093 2089 1892 2022 1836 1699 1895 1642 1581\n", - " 1966 1638 2214 1636 1965 1708 1575 2088 1773 1833 1509 1572 2155 1839\n", - " 1763 2087 1511 1963 1772 2152 2026 1834 1578 2090 1512 1579 1768 1829\n", - " 2153 1451 1771 1640 1957 1769 1450 2149 1452 1832 2020 1641 1894 1576\n", - " 1955 1961 1901 1828 1903 1709 2218 1707 1962 1447 1837 2150 1635 1835\n", - " 1964 1896 1770 1515 1510 2021 2030 1899 1902 1446 2154 2019 1577 1448\n", - " 1956 1959 2219 2025 1767 1516 1900 2220 2151 2217 1827 1703 1644 1838\n", - " 2031 1705 1960 2086 1700 2156 2215 1967 2216 2023 1701 2092 1897 1893\n", - " 1514 1573 1958 1774 1513 1830 1639 1637 1449 1517 1645 1646 1643 2094\n", - " 1702 2157 2024 1706 1831 1775 1766 1898 1574 1891 2091 2029 1764 1647\n", - " 1582 1711 2085 1580 2028]\n", - "20 39\n", - "[2322 2832 2449 2454 2195 2705 2326 2129 2319 2456 2767 2897 2394 2516\n", - " 2131 2200 2263 2325 2713 2257 2578 2196 2840 2647 2265 2318 2327 2712\n", - " 2521 2453 2387 2582 2522 2255 2711 2391 2451 2839 2583 2709 2447 2517\n", - " 2198 2134 2770 2900 2584 2639 2835 2455 2585 2510 2898 2193 2768 2640\n", - " 2384 2262 2703 2702 2518 2258 2776 2324 2330 2393 2577 2646 2708 2773\n", - " 2710 2777 2833 2385 2834 2390 2649 2579 2329 2133 2515 2772 2580 2383\n", - " 2192 2643 2264 2458 2706 2514 2388 2386 2511 2328 2707 2513 2576 2392\n", - " 2645 2644 2450 2194 2132 2901 2457 2135 2259 2837 2642 2899 2648 2448\n", - " 2199 2512 2389 2838 2320 2775 2641 2321 2575 2769 2581 2197 2902 2574\n", - " 2650 2452 2382 2836 2446 2714 2704 2260 2903 2130 2638 2323 2520 2256\n", - " 2261 2519 2774 2771 2586]\n", - "60 9\n", - "[ 511 251 383 831 441 638 569 313 255 316 444 376 1023 507\n", - " 380 447 438 631 767 319 575 253 699 826 636 958 829 315\n", - " 1018 830 956 634 446 954 630 894 701 445 1021 887 702 504\n", - " 888 639 827 567 761 694 382 252 378 314 249 440 566 439\n", - " 764 828 760 632 503 572 375 379 952 571 377 254 824 442\n", - " 443 695 633 893 765 1019 502 318 510 955 766 1020 696 637\n", - " 892 508 890 506 250 574 317 570 698 895 822 509 1017 957\n", - " 762 1022 763 505 700 697 758 959 953 312 635 573 703 823\n", - " 381 891 759 825 568 889]\n", - "18 18\n", - "[ 848 1361 785 1553 913 1104 1232 1039 1555 1228 910 1358 850 1110\n", - " 976 1489 1041 914 1170 1167 978 917 1295 852 974 1363 1171 1105\n", - " 1488 1362 982 1230 784 912 1492 1423 1165 1494 1554 847 1421 1367\n", - " 1490 1112 1431 789 1175 1292 1046 1237 1299 1427 1103 849 1491 1038\n", - " 1231 846 1106 1042 1357 1040 919 1426 983 1166 1303 918 1238 1107\n", - " 1172 1356 1037 1047 1296 977 853 783 1368 1425 1365 1493 979 854\n", - " 1293 911 1297 1036 980 1233 1048 1111 1109 1101 787 1430 1294 1173\n", - " 1359 1557 975 972 1174 1100 1487 1239 1044 851 786 1102 973 981\n", - " 1360 1229 1300 1108 788 909 915 1234 1302 1551 916 1422 1556 1486\n", - " 1366 1298 1236 1169 1424 1428 1301 1176 1364 1164 1043 1429 1168 1304\n", - " 1045 984 1235 1240 1552]\n", - "15 18\n", - "[1098 848 1361 785 1553 1420 913 845 1353 1104 1232 1039 1035 1228\n", - " 910 1358 850 1549 976 1489 1041 914 1170 1167 978 1295 1099 974\n", - " 907 1363 906 1171 781 1105 780 1291 1488 971 1362 1230 784 912\n", - " 1548 1423 1165 1554 847 1421 1490 970 1292 1483 1237 1484 1299 1427\n", - " 1103 849 1491 1038 782 1231 846 1550 908 1106 1042 1357 1227 1040\n", - " 1426 1166 1485 843 1354 1107 1172 1356 1037 1296 977 783 1425 1365\n", - " 1289 1033 979 1293 911 1297 1036 980 1233 1163 844 1290 1109 1101\n", - " 1294 1173 1162 1359 975 972 1100 1487 1044 851 786 1226 1102 973\n", - " 981 1034 1360 1229 1300 1355 1418 1108 909 915 1234 1419 1551 916\n", - " 1422 1486 969 1298 1236 1169 1424 1428 1301 1364 1164 1043 1168 1097\n", - " 1161 1045 1225 1235 1552]\n", - "47 59\n", - "[4074 4083 3827 4079 3696 3699 3500 4013 3956 3954 3823 3826 3884 3631\n", - " 3499 3886 4081 3636 3440 3757 3887 4078 3822 3439 3952 3947 3760 3948\n", - " 3885 3762 4019 3689 4016 3502 3694 3572 3626 3754 3564 3890 4018 3698\n", - " 3949 3817 3828 3700 3950 3763 3891 3824 3438 3765 3946 3627 4076 3567\n", - " 4015 3691 3758 3566 3893 4084 4075 4021 4014 3625 3882 3505 3697 4012\n", - " 3692 3818 3504 3820 3441 3701 3507 3629 3756 3563 3628 3764 3569 4011\n", - " 3503 3501 3565 4082 3819 3951 3759 3825 3436 4009 3821 3957 3630 3632\n", - " 3637 4017 3635 3755 3881 3437 3633 3570 4010 3506 3571 3695 3753 3888\n", - " 3889 4020 3953 4080 3634 3442 4077 3883 3829 3693 3761 3892 3568 3562\n", - " 3955 3945 3690]\n", - "57 27\n", - "[1907 1976 1973 1599 1524 1782 1399 1651 1403 1461 1785 1525 1598 1402\n", - " 1789 1914 1983 1978 1854 2167 1849 1909 1848 1588 2046 1652 2109 2166\n", - " 1401 1465 1912 2105 1533 2108 2044 1715 1597 1404 1469 2103 1717 1779\n", - " 2170 1658 1979 1918 1532 1971 1594 1592 1595 1916 1719 2036 2041 1846\n", - " 2169 1851 1847 1908 1530 1463 2038 2171 1660 1724 2039 2106 2037 1723\n", - " 1466 1527 1531 1982 1593 1657 1783 1853 1913 1845 1972 1464 1915 1980\n", - " 1910 1919 1467 1587 1718 1534 1788 2043 1716 1591 1529 1725 1784 1981\n", - " 1781 2045 2040 1791 1786 1398 1661 1663 2104 1780 1655 1720 1850 1726\n", - " 1528 1975 1721 1596 1787 1844 1911 1974 2101 1977 1400 2168 2172 1653\n", - " 1790 1852 1590 1654 1462 1727 1468 1855 1656 1589 1722 1659 1526 1843\n", - " 2102 1917 2107 1662 2042]\n", - "42 6\n", - "[ 45 239 107 168 41 557 237 364 743 426 302 742 360 170 421 175 431 428\n", - " 167 234 495 684 105 550 420 423 485 304 230 744 682 745 103 300 228 621\n", - " 430 558 240 165 617 238 368 560 296 429 748 232 551 102 548 549 425 365\n", - " 618 685 614 494 808 108 110 486 362 174 424 810 552 297 556 303 359 554\n", - " 677 366 432 555 615 679 40 680 487 356 173 553 301 807 488 687 172 489\n", - " 104 294 43 361 39 811 622 749 750 357 678 491 559 620 231 358 298 292\n", - " 169 233 229 493 616 299 813 44 42 619 812 363 683 295 171 747 367 293\n", - " 613 612 166 492 427 496 623 236 624 686 422 484 746 106 109 681 809 235\n", - " 490]\n", - "17 48\n", - "[2957 3345 3275 3217 2832 2891 3024 3086 3215 3342 3408 3025 2959 2705\n", - " 3148 3027 2765 3023 2767 2897 3280 2960 3089 3154 2955 2963 3151 3084\n", - " 3091 2896 3411 3094 3470 3159 3476 3030 2966 3286 3287 2766 3412 3346\n", - " 2964 2770 3472 2900 3155 2835 2898 3158 2768 3223 3090 3413 3085 3344\n", - " 2703 2702 3218 3284 2893 3031 3087 3026 3153 3277 2708 2773 3410 3219\n", - " 2833 2830 3285 2834 3341 3343 2772 3221 2706 3471 3083 2967 3028 3282\n", - " 3406 3409 3029 3474 2707 3021 3157 3149 2956 3283 3405 3220 3156 3095\n", - " 3152 3347 2901 2895 3092 2828 3350 3276 2965 3093 2837 2899 3348 3473\n", - " 2829 2838 3222 2831 2962 3150 3212 2894 3278 3216 3214 2769 2902 3019\n", - " 3349 3088 3213 3340 3281 2836 2892 2961 2704 3211 3407 2903 3475 3279\n", - " 3020 2958 3022 2771 3147]\n", - "32 21\n", - "[1695 1570 1508 1502 1699 1315 1697 1504 1692 1636 1442 1311 1445 1760\n", - " 1376 1121 1436 1509 1122 1505 1503 1572 1763 1115 1252 1117 1317 1566\n", - " 1371 1060 1500 1245 1310 1186 1057 1571 1374 1180 1437 1698 1316 1758\n", - " 1249 1307 1757 993 1633 1306 1444 1693 1506 1562 1499 1313 1183 1379\n", - " 1052 1635 1181 1628 1510 1378 1627 1446 1190 1189 1116 1053 1373 1314\n", - " 989 1246 1243 1187 1694 1184 1630 994 1501 1762 1632 1375 990 1178\n", - " 1185 1123 1440 1700 1498 1248 1381 1569 1370 1573 1438 1242 1124 1507\n", - " 1434 1058 1250 1565 992 1254 1564 1055 1637 1435 1759 1439 1696 1382\n", - " 1380 1567 1312 1629 1318 1251 1118 1179 1059 1054 1056 1120 1253 1119\n", - " 1574 1443 1634 1631 1308 1188 1563 1761 1244 1182 1441 995 1309 1372\n", - " 1125 1247 991 1568 1377]\n", - "46 4\n", - "[ 45 239 107 308 500 114 168 370 41 177 51 557 237 47 364 426 302 369\n", - " 360 563 170 435 175 431 428 46 234 495 684 498 105 688 111 304 179 300\n", - " 621 430 558 689 240 116 238 368 560 296 429 232 113 241 425 365 625 499\n", - " 618 685 50 494 108 110 306 362 242 174 424 497 297 556 303 554 307 366\n", - " 432 626 555 562 173 553 301 488 687 172 489 176 104 112 49 180 43 361\n", - " 436 622 305 491 559 620 298 434 169 233 372 493 371 299 44 42 619 363\n", - " 683 561 171 367 433 492 427 48 244 496 623 236 624 686 106 115 109 235\n", - " 243 178 490]\n", - "13 39\n", - "[2322 2832 2891 2449 2191 2317 2444 2705 2765 2319 2314 2767 2762 2509\n", - " 2441 2445 2257 2380 2313 2578 2896 2637 2318 2188 2569 2631 2375 2387\n", - " 2766 2254 2825 2255 2125 2378 2451 2633 2447 2760 2827 2770 2439 2695\n", - " 2381 2377 2639 2571 2698 2185 2443 2510 2127 2193 2126 2768 2640 2384\n", - " 2442 2249 2570 2376 2703 2379 2702 2186 2568 2250 2567 2258 2893 2572\n", - " 2577 2128 2761 2122 2833 2385 2830 2579 2515 2383 2192 2253 2440 2643\n", - " 2706 2315 2514 2251 2826 2636 2386 2511 2635 2707 2700 2764 2513 2503\n", - " 2576 2450 2312 2697 2505 2895 2828 2508 2634 2696 2642 2448 2512 2829\n", - " 2320 2831 2641 2321 2575 2894 2632 2769 2890 2507 2187 2574 2506 2316\n", - " 2382 2252 2446 2892 2504 2704 2763 2311 2189 2638 2323 2123 2256 2124\n", - " 2190 2248 2573 2699 2701]\n" - ] - } - ], + "outputs": [], "source": [ "r = 7\n", "optimizer1 = AQR(n_sensors,all_sensors,r,nx,ny)\n", @@ -4553,7 +418,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 369, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:27.951889Z", @@ -4565,13 +430,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2431 319 192 4092 894 4032 3268 2209 969 2963 429 23 3769 1068\n", - " 59]\n" + "[4032 384 4092 4039 493 575 2204 657 878 2880 1088 4087 2837 3779\n", + " 3093 2395 581 2751 2010 3039]\n" ] }, { "data": { - "image/png": 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kSJGXX6ZJvmWLb/MqO1ukZ0+Rm26iiW8Xubkiw4eLJCWFtsJovDNvHs3zHTv4XTdqJPL77+adb8UKkaZNad7v3+/9fTt2nO1QC4V//uEWYO/ewD+zbx9z4xs14s3m+PGAP+p8M94X/fqJdOlir6LnkZtLk7N9e2fIE0106iRSsqTIU0+Z64vJIyND5JlnuMrPnXvu70+eFGnXLjzzXUTknXdEbrst+M/l5tIncfPNIhUqsHBm3Tq/H/Ol7KHG2b8E0Nvzc28AX4R4HN98/jkTGebNs70NLwB6St94g6WFTmjvFC1Mm8YagAYNrGu5XKIEMGoUOwj93/+dPdQjMxO4+WZ68gONFBSFhDGPXSn2IZg7l1GHihWBq69mRKp7d9aFfPUVsGULE8QOHz67M07R8vhd1WcC2A8gC8AeAH0AVAKwGMAWAN8DqOjvOBLsyl7UXs4p7NvHfVVKit2SRD4TJtBvs3EjnWkuF1excOLZwbJlC3M13nmHK/rVV3M7EW4EZu1a5mwY4XMQ4XF27hT57DORp5+mnPXqiZx/vkilSiI1aoRvxhv1CErZ77qraC+tU5gxg15ibc6HzqxZvFC3bMl/LS1N5LLLGDpLS7NOlh07uLjUry9y//3GhFqnTmW0wULMMOPN5dtv2eFj1Ci7JfHO3XdzdNLo0XZLEpmsXw8MHMikkoL14eXKsUlm2bIs77Si574IsGgRk3P27WMKqxGJOE5rme3tLmDGI+CV/YorRGbODOcGZw2bNtGct8KhFE1kZTFN9P33fb9v0SKRCy8Ueegh81b5XbtoDrtcNLunTGGI9cyZ8I/drp3IkiXhHycIImtl37CBKZG33GK3JP5p2JAVeHPm2C1JZPH662zM4G/WW5cuwJ9/MpW0fn06y7ZtM0aGlBSmryYlAZ06sTClWTP2Bzz/fHaQCYecHMreqpUh4hqB85T9vfc4EDFSuo8OGECPqyYwNmxgRGPSpMDywMuWZavkP/7g+9u1Y5nzvHnBdeYVT+efqVPZyvu221jxuHkz6yDyehsoxbZg774b+Oz1ojh5kvUa5cqFfgwfhNIfX4lYN2U0OTlZUnw1yj95kv221qyJnA4t2dks1PnyS0fdxR3L7bezhv3xx0P7fHo6MHs2O6yuWME+7XlFLxddxGKYhASGz06dyi+cyevpd+mlXEz8FU+NG0d/wRchRpUPHKDVcPBgaJ/3Q2EFz9Pj5ORkpKSkFKn9zmpe8fPP/A+KFEUH6Mjp1Qv45hut7P7Yt49NNSdNCv0YiYns2Xbffex0s21bfhXc6tWM0585wzh6YiJj9/368WZQs2bgVWW9e7Pt2d9/cwEKlpwcx/VGdJayu92R2XG0TRvOc9P4ZuJE4I47aJobQVwclblBAx7XSM47j91gx48PLSpUsiRvPA7CWXv2SB34523YnyafrCzuhfv3t1uSwOnfn8MaQhloUaoUtxEmdT0q7GkPBK3sRlCvHudzHzpktyTO5YcfgAsvZOw8Urj4YqBRo9Bi/YmJ3DZs3my8XCHiHGU/dozDCE2a7mkqSnG/Ho73NtpZtowhrkijUycmeIWCwyw+5yh7Wlp+g3wTMHWML8DimLQ0c44dDUSq1RaOwmpl90JGBp0akYoDHTKOItKVPZQQdXKyVvYisTDebxrR8DeYwcGDnExat67dkgRPXrhuz57gP9u2LUdmmxRrDxbnKHtioqkrY7Cey6DJyODfoDmXXbuY8BKJWzSl6EfatSv4z5YtC/TsSY++A3COspcrRyddpK6Ox44ZFz+ONiL9RhjOQtS/P4dhOGDwiHOUvVIlztHaudNuSYJHhKOmkpLslsSZnDkT2XP1EhL4N4RC69YsrPnmG2NlCgHnKDtAZ4iv3HmnsmMHkyiqV7dbEmdSokToyuIEMjPDa5U1aBDbmGVnGydTCDhP2R3kvQyYSPU0W0VeNplJmO6POX06vG3IHXdw+suYMcbJFALOU/YVK+yWInic1pHEadSrxyGbDti3Bo0IK+caNAj9GHFxLP559VWOLLMJZyn7ZZdx2L2/LplOIicH+PRT1lhriqZ8eaBaNXaQjTS2baPjtUqV8I5Tty7wwgucJWjTTc9Zyl62LE2ecEogrea773ghRGK1npU4LMEkYIzcovXrx6jTwIG2RJ2cpewAQxUTJrBKKhIItS94rBHJ/pjkZGOOFRfHxhsrVwJDh1qu8M5T9hYt8ju/OJ3t21kk0auX3ZI4n0svZQfXSMqjEGGzjfbtjTtm2bLsqLtoEfDYY2zAYRHOU3YAePJJdglxeq75sGE0zSI5YcQqLr2UF/Yvv9gtSeAsX87S5SuuMPa4lSoBS5Zwhe/Zk9NcLMCZyn7TTRy989xzdkvindmz2T10+HC7JYkMlIq85pzjxnFbGWeCmpQvT6uhfn3W+M+ebfw5CuOtx3SBuGUtAEsAbACwHsBjntcrAlgEjoBaBKCCv2MFNRHmwAGO7bVyFnugHDpE2cycNBqNpKVxoqmvyalO4fBhynrkiPnn+v13zqG/7bbgpr0WQbh947MBPCkiTQC0AzBQKdUEwDAAi0WkATj3bZihd6Fq1YC33mIf7/R0Qw8dFiJcoe69l22NNYFTrhy7y775pt2S+Oftt4EePWhym027dky3vugioGlT+oB+/NF4/4a3u4C3BzixtStCGNsc9Mjm3FyR++4T6d7dmAkdRjB8OKeHnD5ttySRyd69nKLjdtstiXfWrOFc9r//tv7caWkib78t0rixSJMmIqNHiyxdyjnv/sjK8rmyB9U3XilVB8BPAJoB+FtEynteVwBS8557w2/f+KLIyuL43NKlgRkz7C2oePll9iv/+Wd2ptGExowZwGuvMfPQacNAsrKASy5hLLxPH/vkEOHqPns2w39r17LFusvFsc0lS9KXkJFBJ+KaNcCffyK5cWOvfeMDVnalVGkAPwIYJSJzlVJpBZVbKZUqIhWK+FxfAH0BoHbt2q5dodQFp6cDt97KHu2ffmp9RxsR4JlnODBg0SI2NNCEjgidsElJzCpzEi+8APz2G4eLmtXCLBSyszlNZ+VK4MgRKnlODnWhdGmOrmrVCsmdO3tV9kBN9+IAFgB4osBr5pvxBcnM5Pjbli1FVq8O/TjBsm+fyA03cBDh4cPWnTfa2bdPpGpVkfnz7ZYkn0WLuMWww3w3iLAcdB4TfTKAjSJSsGznSwC9PT/3Bvfy5pGQAHz0EcsFu3blHdjMLDsR4MMPufq0aMH4sDbdjaNGDc5ru/deZ8Te//gDuOsuDumMpIlEweDtLiD5K3hHAALgTwCrPY/rAFQCvfBbAHwPoKK/Y4W1shdk926Ra68VadVK5Icf6MgzkrVrRW66SaRZM5EVK4w9tuZsFi7kavr99/bJsHQpZfjmG/tkMAhfK3vQ3vhwHoYpuwgVfPp0kaZN6bl8++3wZnhnZorMmiXSqZPI+eeLjBolkpFhnLwa7/z4I5Vt6lTjb9y+yM0VmTGD51682Lrzmohh3vhwCckb7w8Resfzpm5edx0r0JKTOTu9dOmiP3fmDLBuXf6Ezy++YNbewIHAjTdGdhulSGTtWuCee2hCT5jAVk5mcuAA8MgjrLOfMSNqhnJGzhTXUFCKUzs6dQL276cX1e0GZs6kMteuDVStyvz1uDh69tPSWFtdr17+uN/HHqOya+yheXM2Lhk1ijfp117jpFajPeIiwMcfA088wdHNn3wSXsupCCLyV3ZfZGWxM8ixY1Ty3FwqfZkyQJMmnNSpcR6rVgEPPsjQ0oABwN138zsLh5MnqeTjxvE6mDLFuNJVBxHdK7svihenJ10TWbRqxXjy4sVUzmeeoaf8rru46pcqFdhx0tM5s33WLEZWLruMraG6dDGnuMXhRLeyayIXpaiUXboAu3dztvujj9JSq1+fW6/WrZm7npdklZEBHD1Ky8DtBrZs4STW667ja7Vr2/s32Ux0m/Ga6CMzk848t5ur9j//5BdKJSay2CYpiTeD5s0je35gCMSuGa+JPkqU4F47CvfbZhN7GxeNJkbRyq7RxAha2TWaGEEru0YTI2hl12hiBK3sGk2MoJVdo4kRtLJrNDGCVnaNJkbQyq7RxAha2TWaGEHnxmu8c/BgfiefzZtZcJKRwdLhxES21M5r/lGvXkyWjUYSWtk1+YhwuujEiez4evJkvjJ36cJmHyVKsClIejqwcyf7+A8dyuqz5GS2lurVS0+2dSBa2TVs0/XBB2wUER/PyaUvvsjZY4G2hTp8mDeIiROBp54CevfmcerVM1V0TeBouyuWyc3lAMOLLgJ+/52KunYtm27Wqxdc/7cqVTim69tv2YM9Lo4DC/v04aqvsR2t7LHKtm3AlVeyZdMff7BB52WXGdPgsV49tn/avp3DPZo3B777LvzjasJCK3usIQK8+y5X3R49gJ9+Aho2NOdcZcoA770HTJ3Kts19+tAPoLEFreyxRG4uMHgw+7L/+ivw+ONAsWLmn/eqq7g9yMri6K5jx8w/p+YcApn1VlIptVwptUYptV4p9bzn9bpKqWVKqa1KqU+UUg6bvas5i9xcoG9fhtF+/NG81dwbZcoA06cDHTsCV1zBxpAaSwlkZc8E0FlEkgC0BHCtUqodgNEAxopIfQCpAGwcZq3xiQiHImzYwL1z+fL2yKEU9/LXXQd06wacOGGPHDGKX2X3jJDK22gV9zwEQGcAczyvTwfQwwwBNQYwcSLwww/AN994H4dlFUoBL7/M/u+9e/NGpLGEgOLsSqliANwA6gN4F8A2AGkiku15yx4ANU2RUBMeO3cCw4fTdK9QwW5piFIM+blcjAbceafdEkUWIsDevUBKCqMq6elssZ3geycdkLKLSA6Alkqp8gDmAWgUqFxKqb4A+gJA7Rhv0m85IpxnNmQIx105iRIlgGnTgOuvZwiwenW7JXI2e/bw/+u33+h3EWHG4sUXM7MxIYEK74OgMuhEJE0ptQRAewDllVLxntX9AgB7vXxmAoAJAIdEBHM+TZhMmMB98ZNP2i1J0SQn82bUvz8wd67xQxwjndzc/BFYP/5IC6hvX1pEF1xQ9P+Xj3wGv8qulKoCIMuj6IkAuoLOuSUAegKYBaA3gC9C+oM05nDqVL75Hu/grOiRIzmPb+lSrvAaMn8+w6QlS3K45YwZYftbArkKagCY7tm3xwH4VES+VkptADBLKfUigFUAJoclicZYZs4EOnQAmja1WxLflCjBcdnjxmllB1in8MQTLEgaP555CQZZPH6VXUT+BHDOpHoR2Q6grSFSaIwlL0vu5ZftliQw7rmHVsi+fcD559stjX3Mn08z/YYbgD//DH9MdSF0Bl00smwZcPw4cPXVdksSGGXLAnfcwRBhrDJ2LBV9+nRaOQYrOqCVPTqZPBno1y+ymkn07w9MmhSbcffnnwfef58pzJ07m3aaCLoaNAHz66/c60USzZszdLRnj92SWMtrrzHX4OefTZ8fr5U92jh5kok0TnfMFUYphuLcbrslsY5PP6VvZdEioGpV00+nlT3aWL0aaNbMbzaVI3G5YkfZDxwAHn0UmDOHMXML0MoebbjdVJpIJFaUXYQ+ioceojVjEQ7OttCExNatQKOAs5mdRePGlD/amTkT2LKFe3UL0coebaSnA6VKmXJoVSi5Q4z2nJcqRfmjmdOn2TTkm2+YUGQh2oyPNjIzLb+IDCOAYo6IZ+ZMoG1bS833PLSyRxsJCWz/FIlkZUWmYzFQ8jIbBwyw5fTajI82SpY0zRQ23GwvTHo65Y9Wli9n7vs119hyer2yRxu1a7OhQSSybZvpiSW28v777LJrU2ajVvZoI5LDV5EcNgyEpUuBm26y7fRa2aMNlwtYtQrIybFbkuBJSbHFcWUJR4/y0aCBbSJoZY82KlRg6uXmzXZLEjzRvLKvXAm0amVrcZJW9mjkkkvYoSaS2LGD3XWidRCk22271aKVPRrp3ZtdTiKpXHTCBOC++6K3D92GDaxZsBGt7NFI165sNLlsmd2SBEZmJjBlCj3V0cqpU2zSYRJKKSil4PbhnNXKHo3ExbHQYtw4uyUJjDlz2HTy4ovtlsQ8MjJsz2zUyh6t3H8/8NVXHCbgZHJzgbfesi2rzDKKFbM9QqKVPVqpVAkYNIjtqZy8dx83DiheHLjxRrslMZfERK7uJiEiEBG4fEQztLJHM8OHs83TjBl2S1I027YBzz3H/boVo6PtpGpV260srezRTEICRwYNGcI2zU4iNxd48EHg6aeje6+ehwMyG7WyRzstW9JZd999wJkzdkuTz8svs8pt8GC7JbEGl4sZgjailT0WGDGCfcjvvtt2JxEA5gBMmgTMnh395nsejRtzS3X8uG0iBFzi6hn/lAJgr4h0V0rVBee8VQLHOd8rIg5aOmzm1Ck2f3S7+Vi1Cjh2jE4aEZZyli3LkJPLxUfr1uaMVY6PZwukG26gwn/wgX114+PHAy+8wKKQmjE05Ts+HkhKYu6DTW2+g1nZHwOwscDz0QDGikh9AKkA+hgpWESSnQ188QXrlatUoYm6YQNw2WXcO//+O/DXX8CmTaxt/vRToHt37qefe47lnZdcwqkgRteklygBfPkl2yL16AGkphp7fH/k5FDJR49mKm/9+tae3wn06kVnpF3kuex9PcCRzIsBdAbwNQAF4AiAeM/v2wNY4O84LpdLopKDB0VefFGkVi2R9u1FPvhAJD09+ONkZYl8+aXItdeKVK4sMmSIyPbtxsp65ozIoEEiNWuKfPWVscf2xqZNIpdeKtKpk8iePdac04mkpoqULy9y4IBpp/DoWJH6F+jK/iaAfwHI9TyvBCBNOJsdAPYAiCGbzIMITeJmzVjI8fnnwG+/AffeG1rHlfh4mtrz5wN//MHX2rQBXnmFVoMRFC/OJJaPPuL01N69zVvlc3KAMWOASy/lLLclS2LLdC9M+fJAz54cz2UH3u4Ckr+qdwcwzvPzFeDKXhnA1gLvqQVgnZfP9wX3+im1a9c27Y5mOXv3inTvLtKihcjKleadZ8cOkauuEmnTRmT9emOPffKkyP/9n0i1aiL//rfI338bd9wJE/h/06mTyNatxhw3GnC7aQFmZppy+HBX9g4AblRK7QQdcp0BvAWgvFIqz8F3AYAiMwZEZIKIJItIcpUqVUK4HTmQuXMZ0mrdGlixgnXKZlGnDscD9ekDXH45V2WjOO884O23gR9+YG+0pCTg5puBhQuDD9OJAOvW0VqoXZutkl97jat5tJathkLr1vx/tmGctpIgUimVUlcAGCL0xs8G8JmIzFJKvQ/gTxHxWXmRnJwsKTbHGsNm4kQ60774wvr65J07geuvZ2rpSy8ZXw568iTN+4kT6Vhs2pR/o8sFNGnCm0OJEoyPZ2Rw65IXbXC7GV24916OHo7mXnLhsncvF4iFC7loGEhycjJSUlKKvDDCUfaLwJW+IoBVAO4REZ9NvyNe2d9/n/vnRYvsay905AjQrRs9/G+8YV79d17oMCWFirx5MyMEGRnc9ycmcv+dFzZ0uYBq1cyRJRqZPp0z2ZcvNzQMapiyGyBI5Cr7zJnAU08xbGS3WZqaSpO+Z09g5Eh7ZdGEhgidsc2bG2rS+1J2nUEXCBs3cuLm/Pn2KzrAxJuFC5mFtmCB3dJoQkEpeuVnzwbeeceSU+ohEf7IyQEeeAB4/nnehZ1C9epU9oceAtauBcqVs1siTbBUqwZ8/z23ZMWLsxzZRPTK7o8xY7g/7d/fbknO5eqrgWuvZVWbJjKpU4cRi9Gjac6buK3Wyu6Lv/7ilzBliq0tgH3y+us06bU5H7nUrw/8/DPw2We8ef/9tymncegV7BBeeolOubp17ZbEO2XLAm++CTz7rN2SaMKhZk3WTnTqxMjGpEmGr/Ja2b1x5Ah7uD38sN2S+OfGG4EDB2xvjqAJk+LF2V1oyRKGea+6Cvj2Wzb6MACt7N6YPJnZZBUr2i2Jf4oVYxvmSOkmq/FNs2Zc5e+5h70I6tcHXn2VC1AYaGUvipwc3lkjqePpgw8yjdfq0lWNORQvzu80JYW9CNavZ9j3kkt4XU6ZAqxZw577AZr7zgq9nTyZ3/AhJSW/4UN6en7DhzJlGAIrmLlldM79778zlBVJQwarVmVThLlzmUeviQ6UAtq25ePkSeqE2816htdeY9POnBxm4WVl+Uy/tV/Zc3JYNDFuHD2SefnYV1wBPPEEY5ElS/KPzsxkwUbeDeHVV/nvRRcxNHbXXUDp0uHLtGIF0LFj+Mexmo4dKbtW9uikdGnG5C+77OzXs7NZuJSQALRr5/Xj9in74cMsuBg/HqhRg6bJvHmMafuienWgUSPWRwN0Xnz/PW8Ww4ZxnzNwYHgdS1NSgC5dQv+8Xbhczm0brTGP+Hg+/GD9nl2ElVXNmtEEmTuXjRruu8+/ohdFXByTSz7/nCt+2bK88z37bOjdVCN1dHDLltzbOamLrMYxWKvsWVn0cL/yCk33yZONVaratYEXX8w389u25c/BcOIEsHs3SzojjfPOY07A+vV2S6JxINYqe97Y2pQUc51f55/PGPkTT3DVDyYNcf9+fj4As8iR1Klj++QRjTOxVtnr1+fKa8U0S6W4NVi1ipVFgwYFlpyQnh7adiJgsdT/HqaQmGh8Z1pNVGCtsp93nqWnA8A0xCVLaM4/8oj/FT4rizHOSKV4cf4NGk0hYiOpplw5ph2uWQMMHer7vSVLmjpt03QyMkLrbKuJemJD2QEm48yfz/DevHne31eqFJMXTELO7rxrPKdOmboN0UQusaPsAPPcp01jHN5bnnHt2vzdiROWimYY69czD0GjKURsKTsAdOjAhJxHHy369/HxTMddtcpauYzgwAGa8XXq2C2JxoHEnrIDjAi43UzoKQoHzNIOCbebfcnN8vRrIprYVPZSpdjkb+TIor3zycmRq+yRmPmnsYTYVHaAFWLZ2Sy+KUyHDsDixZEXwvruu8gs4NFYQuwqu1Isvimq4UPDhiyk+fxzy8UKmdWrgT172MNMoymC2FV2gBl2CxbQsVWYgQMjq/PLe++xFXGkpvlqTCcgZVdK7VRKrVVKrVZKpXheq6iUWqSU2uL5t4K5oppA+fJAjx5Mpy1Mjx7Apk3M53c6//wDfPqprmPX+CSYlf1KEWkpInkVLMMALBaRBgAWe55HHh06sOFDYYoX54BCG6ZtBs3bb9N8r17dbkk0DiYcM/4mANM9P08H0CNsaezAV5htyBDg119ZjutU1q3jGOdXXrFbEo3DCVTZBcBCpZRbKdXX81o1Ednv+fkAgMgc4dm0KUcPF5UiW7o0a+779XNmI8fsbI6mGjUKuPBCu6XROJxAlb2jiLQG0A3AQKVUp4K/FCZ6F5nsrZTqq5RKUUqlHD58ODxpzSAhgTX23ppcXHklcNNNwOOPWypWQLz6Kv0OkdDbXmM7ASm7iOz1/HsIwDwAbQEcVErVAADPv4e8fHaCiCSLSHIVo7vAGsWFF7JphTdGjwZ++YU985zCokWcBDNpks6Y0wSE3ziNUuo8AHEicsLz89UA/gPgSwC9Abzi+fcLs4Qs2OjBlGoxfw0fSpdmxdwVV7DHXa9exssQDL//zk66c+dq8z2SOHUKOHqU9Qs5OSxFLl0aqFzZkht2IEHZagDmeRQuHsDHIvKdUmoFgE+VUn0A7AJwu3limkx8vP9suQYNqPDXXMMvq3dva2QrzA8/sJBnxoxzWwprnEN2Nm/KKSl0ALvdHNhYsSIXl7g4XkfHj/P6S07On4PQsSNvAAbjV9lFZDuApCJePwrgKsMlsoPMzMAaPrRowa43V18N7NwJPPOMdV1tRDgF5OmnmRdw+eXWnFcTHPv2cWs1YQIHd7RvD3TuzAGhTZqce72IsGdg3k1hwgQ6Xa+/nhme7dsbtupHRLqVaY0e8jh5MvCGD40a8Y7dpw8b8k+bxpJYM9m9mzH/gwe5sjdrZu75NMHjdtO3s2gRLa9vv+Xi4A+lgAsu4KNHD76Wmsrr6v77eV0OGsQbQLhjwwt2TjH74XK5xJHUqyeyfn1wn8nNFZk0SaRyZZEXXhDJzDRerpyc/HP85z8iZ84Yfw5NeKSniwwbJlK1qsh//yvyzz/GHTsnR2ThQpEOHUQ6dhTZssXvRzw6VqT+xXZuPMBxUgcPBj9BRimu7itXcsjFhReyZHbPnvBlOnYMGDOGMk2YwAq8ESMiuxFmNLJ8OffYmzYBf/7Jhihlyxp3/Lg4Vmf++CNw6620JN96K+QRzlrZV64EkpI49jgUatUCvv6aI6iOHaPpdsst7HO3Z0/g/eqPHGGJ6oMPcnbdypXA9Om8kQRiDmqsZcwY4IYbeBP+7DPOJDSLYsWAwYO5fZw9mzeA48eDPkxE7NlNxaiGD02bsiHGyy8DH37IFblfP1oAycnsIFOlCvdgStETm5qaP5UzLY3vueYaYPNmOnc0zkMkX8FTUnizt4oGDbjKDxrEZK/vvgtqgrESs51fBUhOTpaUlBTLzhcQnTvT69mzp/HHFuHqnjd+OjWVSp6bS6UvU4ZWhcvF2dvhOmA05jNiBC25hQuNHxUeKCKMBM2fDyxdyixKD8nJyUhJSSnSfR/bK/vGjSxhvfFGc46vFO/8tWpxxp0msnnzTWDOHOCnn+xTdIDX1UsvAadPM0S3eHFAoePYXkrefx946CHmx2s0vli1igq2YIG9ip6HUsDYsdzuPfdcQB+J3ZX91CnurSOxZbTGWs6cYcz7jTc4V8ApxMVxwUpKouV4ySW+326RWM5j3DigUydnfXkaZzJqFK+Te+6xW5JzqVaN4bj77/c7tiw2V/ZNm5jttGyZ3ZJonM7atezvt3q1c6sLb7+dIbkXXvD5tthT9pwcxrKffZYecI3GF6+/zo5F559vtyTeydu/JyUxR8MLsWfGv/UWq4wGDrRbEo3TOXoU+PJLLg5Op1Ytv8VRsaXsS5eyV9uUKTqmrfHP1KkMy5pQbmoKAwb4/LW1ZnxGBvdACQlMKqle3bqw14oV3Nt8+qk23zX+yc3lXv3jj60/d0YG6zUyMthnoUQJ6kuNGr7Tuq/yXXFurbJv3QrceSdDGenpNJOaNs0v2m/XjuWbRjtCfv6ZhQSTJ7PbjEbjj7VrqVht25p7HhGmSy9fnt/kYvNmWhOJidxynjnDUPGJE0DLlvn60qHD2Xt0P9aqtcpevjz/mBIl+PzUKXo53W4q5Isv8o8cMIA3hVKlwjufCPDBB3SwzJwJdOkS7l+giRXcbsatzfLAp6Xx2nzvPVoRl13GGop+/Vj4VFRGXGoqC6TcbqbsDhnCxXHgQG43/E0D8lb7asbDVb68SNOmIitWeK/f/fZbke7dRSpVEnn8cZEDB4KpAM5nzx6R668XSUoSWb06tGNoYpcBA0TGjjX+uDt2iPTtK1K+vEivXiI//sjeCKGQkSHy8cesda9ZU+Q//3FQPXu9esDw4cznfeaZc5MA4uKAbt2Ar77iHjs3l+GETz4JvFQ0N5eloa1aAW3a0DxKOqerlkbjm5QUY8df5/kA2rRhIszGjcCsWUzsCtV6KFGCFvDPP7Mzzl9/+X6/t7uAGY//darZv1+kZ0+RKlVEhg4V2b7d+91r2TKRxo1FbrnF9yp/7JjImDEiDRqIJCeLrFoVxC1SoylAdrZIYqLI8ePGHG/7dpErrxS55BKRDRuMOaYXnLOy51G9OjN+fv2V3sY2bbjaf/IJp7MUXMXbtuU+pWFDrtC//pr/u0OHWObXpw8dFW43e3ctX05HhkYTCqdP08osUyb8Y335Ja/hbt147TZuHP4xQ8TeDLoGDVhc8MILVPSPPgKeeILmfevWNKOqVqVXsk4dNuTr2pWKvHs3vZOtW7Pb66ZNuuGDxhjS0wNvQOqLDz+kE+3bb7mgeVCFzHaxqKeEM9JlS5Vi98wHHuDz/fu5Sq9aRaVOT89v+HDbbWz59M47LEzQyTEaoxEJ3ws/cyYwdCi7ATdpYoxcYeIMZS9MjRpA9+58FMWDD1LpmzRhuEKjMZKSJf1WkPlkwQL2jPv+e8coOhCp6bKXX86V/e67fY9t0mhCoVQpDg45cyb4zx45wnLT2bO9zhMo7DizioCUXSlVXik1Ryn1l1Jqo1KqvVKqolJqkVJqi+ffCmYLexa3387w2ogRlp5WEwMUL05/0vr1wX/20Uc5h69TJ//vtZhAV/a3AHwnIo3AUVAbAQwDsFhEGgBY7HluLe+8Q6feb79ZfmpNlONyMdYeDHPnMnL04ovmyBQmfpVdKVUOQCcAkwFARM6ISBqAmwBM97xtOoAe5ojog8qVqfAPPOB/MKNGEwwuF53EgZKWxrTVqVON8eSbQCAre10AhwFMVUqtUkpN8oxuriYieUPND4DTXq3n1lsZt//8c1tOr4lSgl3Zp01jW/JLLzVNpHAJRNnjAbQG8J6ItAJwCoVMdqGXoUhPg1Kqr1IqRSmVcvjw4XDlLZqBA9lTTqMxCpcL2LUL2L7d/3tzc3n9ObwhSiDKvgfAHhHJa9g2B1T+g0qpGgDg+fdQUR8WkQkikiwiyVXMasHbowfzgjdsMOf4mtijZEmgd29g/Hj/7/3hB3rw27c3Xy5frF3r89d+lV1EDgDYrZTKm3x4FYANAL4E0NvzWm8AX4QuZZgkJAAPP6xXd42xPPII9+D+Yu7vvsuybLsbUvq5/gP1xj8K4COl1J8AWgJ4CcArALoqpbYA6OJ5bh/33qv37RpjqV+f4d3Zs72/58wZjoK6807r5CqK48dZReeDgDLoRGQ1gKJS1Xz3wbGS+vXZDOPgQXMnampii8GD+ejZs2gv+7p1QN26xhTNhMPo0cB117FGxAuRmUFXFEoFHy7RaPzRrZvv5C2jpgCHQ0oKMGkSi8p8ED3KDmhl15iDr+Qtt9ve+ozMTKbnjh3LELQPok/ZV660WwpNtFEweev06bN/t2oVy6zt4vnnmdobgM8gupS9alU25dNojObWWxlau/POs7M1jx61r4/Chx8CM2aw3VUAkQBLld3tdkMpdU7xvmGULKmr4DTmMWECkJ1Nszk7m68Z1eiiCPJ0pUidmTePjTEWLPBrvucRXSt7XByzmTQaM0hIYBjuyBFWXWZmGtPoIlhmzAD69we++SaoevnoUvaMDMcWIWiihFKl2FeuWDH2VYiLs86aTE/naj58OLP2gowCKCuL55VShwHsAlAZwBHLTlw0TpAB0HIURstxNsHKcaGIFJmXbqmy/++kSqWIiK39pJwgg5ZDy2GlHNFlxms0Gq9oZddoYgS7lH2CTectiBNkALQchdFynI1hctiyZ9doNNajzXiNJkawVNmVUtcqpTYppbYqpSzrRquUmqKUOqSUWlfgNctbYSulaimlliilNiil1iulHrNDFqVUSaXUcqXUGo8cz3ter6uUWub5fj5RSiWYKUcBeYp5+ht+bZccSqmdSqm1SqnVSqkUz2t2XCOmtW23TNmVUsUAvAugG4AmAO5USlk1LmMagGsLvWZHK+xsAE+KSBMA7QAM9PwfWC1LJoDOIpIENiO5VinVDsBoAGNFpD6AVAB9TJYjj8fA9uR52CXHlSLSskCoy45rxLy27d7Guxr9ANAewIICz58G8LSF568DYF2B55sA1PD8XAPAJqtkKSDDFwC62ikLgFIAVgK4BEzeiC/q+zLx/Bd4LuDOAL4GoGySYyeAyoVes/R7AVAOwA54fGlGy2GlGV8TwO4Cz/d4XrMLW1thK6XqAGgFYJkdsnhM59Vgo9BFALYBSBMRT4WHZd/PmwD+BSCvqKGSTXIIgIVKKbdSqq/nNau/F1PbtmsHHXy3wjYDpVRpAJ8BGCwix+2QRURyRKQluLK2BdDI7HMWRinVHcAhEXFCx5GOItIa3GYOVEqdNb/Jou8lrLbt/rBS2fcCqFXg+QWe1+wioFbYRqOUKg4q+kciMtdOWQBAON1nCWgul1dK5fUltOL76QDgRqXUTgCzQFP+LRvkgIjs9fx7CMA88AZo9fcSVtt2f1ip7CsANPB4WhMA3AG2o7YLy1thKxYlTwawUUTG2CWLUqqKUqq85+dE0G+wEVT6nlbJISJPi8gFIlIHvB5+EJG7rZZDKXWeUqpM3s8ArgawDhZ/L2J223azHR+FHA3XAdgM7g+HW3jemQD2A8gC7559wL3hYgBbAHwPoKIFcnQETbA/Aaz2PK6zWhYALQCs8sixDsBIz+sXAVgOYCuA2QBKWPgdXQHgazvk8JxvjeexPu/atOkaaQkgxfPdfA6gglFy6Aw6jSZG0A46jSZG0Mqu0cQIWtk1mhhBK7tGEyNoZddoYgSt7BpNjKCVXaOJEbSyazQxwv8DN4gy30KY2J0AAAAASUVORK5CYII=", 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BTKuO8rCnpoo88IDIsGEeJ+T7l6JF7Sd2tFhY0SWQefttpp/O6yQkMKvukiVMLf7DD0b3yD7vvity773Zjz3wgMiMGZ61s2kTq9PqmCNfj4STPwLol/F/PwA/eD3ahITQM6xXL1rVT55kja1nnmFmkKVLs59vtQJPPMFQ1MmTvb4sihcH/slhikhOpjXYz4kAPeaffwyNntKM8HBqZgMGUKO77z6je5SbPXt4n+UMkBo6lB6YnmhYTZqw7uAvv2jaRXdxZ+ttHoCNAGoqpU4ppQYCGA+gvVLqMIB2Gc+1oXhxfpF79nBd9OyztNDGx1Od6tePA8J33/mWx6tBA7pgZmXrVg48eibF1ILt29n/YKBVK4aXlipluIdZLtLSOBCNHZs7H2H79lySepIjUKnMQcIA3LHG9xKRCiJSUEQqisgsEbkkIm1FpIaItBORnNZ631GKdal37eKavG5dpgK6dIlWUV8F0p4X18yZ/i3R6w1WK4U90O0KnlC8OB2FFi82uifZefddbrU99VTu10JCaKzzNGtNr16MrPQ2240PBIB7kguKFQPatqWKffAgBV4LP+mcwn7xIo0nBqYNcosjR3gDli5tdE+0QYQ+AzEx3HJbv97oHpGYGPpvxMQ43h3wZu+8SBFqrAMG+N19NrCF/eJFjoQjR1IQ9+9nUMSoUb5bo6OjObra2vniC6Br18AXoq1bg2tWP3aMA3rnzsDcudyV+eMPY/s0dy5DaleudB6W6m3QVK9e3IV65x3v++gNjix3ejzsWuPtsW+fyLPPipQqJfL889lLOl28yBpZo0e7baG0i9UqUqcOrf7JybQKb97sW5v+4P77WXMuWPjmG5GuXTOfL1/Oart6VE11h08+EalYUWTPHtfnXrwoUry4dzXVz5wRKVdOZOtWz9/rhMCp9eZM2M+ezaynVb48t5b+/NPxudWri8ya5c33kcknn7DQ5OuvU4i8+dH8yfHjHAC1LoppJJ98wvp2WVm/nvfASy/5XEgT9AERzmtOOH+eNf1q1fKsqmzhwizJ7Q3z54tUrixy6pR377eDM2H3b+zg5cs0rqWk0GPq8GGq0nFxTBbZpAmrZXTv7tzSHhEB/PADcM893KarXNm7/vTtC7z6KrB2LQ2Bgei5lZUZMxjw402BgkDFXlKHFi34ezzzDOMXvvhC38QVCxdy1+fxx5nb3RObUFiY94kpHn2UO0vt2/Me1LkwiX+F/eJFuhja3GWrVGHKoQ8+4P+eCFudOsCLL9J1ceVK7wQ1LIxBL3ffzcyjgUxyMg1Gv/1mdE+0xVFSh7Jl6Yf+7bdcx3fvDjz3nHaupiK0ik+cSN+KRYu8G1AKFPAtZ+Err3Cia9mS93GlSt635QpHU74eD0vZstqqyqmpIk2aiHz2mXfvf/55LhvKlNFUldKFsWNFunQxuhfa8+GHIsOHOz/n/HmRV18ViYgQaddO5Pvv+du7AXKq8dev0+bRsCGXgpMm+bZUcOSJ6SmTJolUqiSyapVPzQTOmj08nK6eWrJ3r0jp0jSWeMK4cTTQXbwo8uabrNceqGv23bs5IJ08aXRPtGfmTJH+/d07NymJbtV33cU1/YMPchBcvlzkwoXc51uttPssWkS7TKdOIjffTIPgihW+u62mpIgUKOD2wOOSpUtpHBw61OsBJHCEvX59kRo1RMaM0Vaw+vYVef9998+fMIE+yn//zefJybTwz5mjXZ+0IjWV/vqe+mHnFTZs4OfzlKNHRebNoxGvVStaxW+6iRbuihVFypblrFuunEjnziJvvCGyeDGt4FqxYwcNeloSH8/Br0oVDkgeykngCLvFInL6tEi9eiIvvMCRUQs2bhSpVs31SJ2aKvLyyyK1a+dW27dv5w3y11/a9EkrxowRad8+cLUOX7lxQyQ8nAOuL6SnU1DOnKEGdPasyJUr+n5vs2aJ9O6tT9tLl3JibNBAZPp0t2d6PQJhvKdCBWbs3LuXda+0SNXTtCldLp0FGOzbR++7bdto5MoZsx4VxfI/nTsHTn63BQvoR/3554G/U+AtRYqwQIM39dOyEhLCIJPy5enHHhHBe0LP703PcOjOnVm/4L33uIMVGcndicWLWQlHPHcqMyZtZ6lS/AAxMdw6GzGCVklv00HZAgymTQM65siglZbGBIaTJjHd0aBBjm+Al1+mj3anTkyq4Es9Ml/56SeWOVq5MncQRrBh82b0JkWzkcTGAj176td+SAi35dq3p4DPns3tV5vXnsUC1KrFAbNQIaZUc9acfj11gVJM/bxtG7PNNGzIQBRPEwLY6NaNe5W2tD8JCdyqsliAVav4wwwe7HykV4oDQ5MmQOvWzKxjBF9/zeCLJUu0qXYa6LRqRb+JvMSpU/QT8dcAVakS8MYbwLJlTNQZG8uJ69ZbuYWcmuo6CtSRfq/Hw6EHndVKi2rXrvQQGz6cLrOeEhkp8vPPIiNGsJ0uXbj28XTdZrXSoBMZKfLLL573w1tu3GDfK1akBT6/cOMGd1Q88VwzmjffzO35FwAEjgedI2zhrPfeyyotM2Yw0k2EM7PFQlXv9tvpjFOoUHYvvLg4jnTnz1OtGjKEx7z1rFOKRQFatKD20akT1056JoxYv55JORo3ZhRYoAfkaEmRIvRemz6dqcoCndRUaqEGJaHwFiVeLPS9JTo6WmLdjeMVoeDHxWUK8/HjTBSZnMz87mFhFOjoaA4ImzZRjZ80SbtOX7lCw93KlWy3a1dtK5ScPs368AsX0hjXrZt2beclDh0C7rqLv7nBNdFcsnAhc+etW2d0T3IRHR2N2NhY+2tVR1O+Hg+3o968ZckSkY4d9Wl7xQqRO++kiv3OO77t11qtjLZ76CE6eQwd6rlTUDDSqZPI5MlG98I5KSkiUVEiCxca3RO7BIwaHxcX928uddFDoyhenIn89aBDBz527KDVv3ZtGpaaN6dW0agRk0rYw2pl0gmblrJsGS2tQ4fSiBgM+eS04KOP+H126cL0YIHI+PHc3uvRw+ieeIxf1Xil1L8X0+W6W7ZQgPyR8ufKFW6P2aL2duxg1FJkZGaATVIS63vt28eBwGZ7aNmSe/7BunfuCx9+yPyC69YFRp23rOzaRVvS9u261lr3BWdqfGAY6LTCavVfxc8SJRix16cPn6enc9155gyFPDWVQl+0KPdCvS2Ekd8YPpzCPmUKo9wChdRUJj6dMCFgBd0VfhV2i8UCtw103mBkwfvQUKr2tWsbc/1gISSEzlYtWjDJaLt2RveIk8jgwdzTDvQchU4IMD3JR86csV+/zSRvUaMG49h79TI+H50I8yYcOMD8h3l46RVcwh4Xl/dcLk3s07Il8OWX3Or89Vdj+pCeThvQ+vUsVpLHMwQFn7AHU+bV/E7Hjly/P/YY1/D+rIB69iyz4+zfT3drRzsteYjgEfZgLJ5gwhl+/Xqq0G3aAEeP6ns9EWDePFbcqVePAVFBsjXqTvmnSkqpNUqpfUqpvUqp5zKOl1JKrVRKHc74a+zQt39/cBVPMMnk9tsZlvzAAwxn/vhjuktrzalTzHc3ZgyDkN59l56aQYI7M3sagBdFpA6AZgCGKaXqABgFYJWI1ACwKuO5ccyeDTz8sKFdMNGR0FDghRdYv33JEpb5fuMNhn76ggiwejXw0EOMMKxbl5GYjRtr0+8AwuXWm4icAXAm4/9rSqn9AG4F0BVAq4zT5gBYC2CkLr10RWIihX3TJkMub+JHatZkAMq+fayZ3qABU4p37UqHpVq1XPtaXL1Kgd60ifdNgQLAsGFMWX3TTX75GEbg0T67UqoygIYANgOIyBgIAOAsgAhtu+YB33zDH7paNcO6YOJn6tShOv/uu1xjr1jB/0+f5gzdoAHX2oUL056TmMit2bg4qusNGtC+M2MGU4nn4S01d3HbXVYpVQzAOgBjReR7pdRlESmZ5fV4Ecm1bldKDQIwCAAiIyMtJ0+e1KTj/yLCddybb9Kn2iR/c+UKDbV79zJOIjGRM3dYGH0wGjWi45O/PC39jM/uskqpggC+AzBXRL7POHxOKVVBRM4opSoAsJvWRURmAJiR0RHtHeJnz6YrY6dOmjdtkgcpUYIBSq1aGd2TgMMda7wCMAvAfhGZnOWlHwH0y/i/HwD/5xU6dYq562bPtl9VxMTE5F/cmdlbAOgLYLdSakfGsf8AGA/gG6XUQAAnATyiSw8dIcIcXM8+y/WXiYmJU9yxxq8H4Mh60Vbb7njAjBk0uLz6qmFdMDHJS+RNK8UPPwD//S+wZo336adNTPIZeU/Yly1j5dZly8xwUhMTD8hbvvHz5jGe+KefgtLDycRET/LGzH75MmOKV69muOMddxjdIxOTPEfgz+w//0zhLlSIOcBMQTcx8YrAndnj4oDJk5mpZPZsJvozMTHxmsCa2RMTgTlz6P7aowfjiW0ZPU1MTHzCuJldhOGJWSu+bNlCQX/jDbq/ml5xJiaa4V9ht+VWt9Vps+VSt1hYny0mBrjlFr92Kc9y/Tq/T9tAuWcPc9QnJTFDa1gY01c3apT5HdepE7QBICau8e8vf8cdwPLlNLaFhfGRD0ILNSMpiVlXp02joNerRyFu1Yo51kuU4HcqwsH07FnGbf/6K/OdnzrFyMChQ/NNWKdJJv4V9tBQIMK4sPc8y4kTTNQQE8M69q+8AnTu7Np7sGZNJnawER8PfPUVc6CHhlLo+/YN6oQNJpkEloHOJDtpaUzIEB3NMN4NG5ikoWtX79yEb76ZgUP79jFb6+rVVO2XLdO+7yYBh7mAC1T27GG5oVKlqIpHRmrXtlJA69Z8rF7NGvStWgEffACULKnddUwCCnNmDzREWAe+dWuq2ytWaCvoOWnThtub4eG0qQRgzXETbTBn9kDCVmro119pYb/tNv9c96abgKlTuTx46CGWkX7gAf9c28RvmMIeKIgAzzxDlX3dOmMqkNx7L9fv99/P0kfdu/u/Dya6YQp7oPD668DmzVxDG1mBpHFjCnzHjpzxA6GKqokmmGv2QGDRImDBAvogBEKpoUaNuJ//2GNMzWwSFJgzu9FcusQCBQsX0uNNK86fz3RFPniQTjZJSdyyCw+np6LFkplvPyTHuN+yJfD00zQS/vij6YATBJjCbjTPPgv07Am0aOFbOyLA2rXAzJkshHjtWqarbNu2QLFiLJiQmkrBP3mSs/eoUcy1brEAffoAjz4KFCnCNl9/nYPBV1/R+cYkT2MKu5EsWkSr+44d3rdx5Qrwv//RhVYpxhi8/TZna3dn4wsX6LAzcybw8stAv36c1WvUYHhxx44cMMy4hTyNuWY3ChHgtdeATz/NnEk9wWrle6tW5Uw+bRodcZ55Bqhe3TO1u2xZoFs3YOlSRh4WKEBNY8AAtt+nD/f+TfI0prAbxdq1FEhvrN3HjnGm/eorzsgLFtAHXot1ddWqDJo5doyD0B13MOBmzhwgIcH39k0MwxR2o5g6lYEongioCGfwJk2A++7jjF6rlj79K1aMmsOcOcA779CoN2eOPtcy8QumsBvB6dP0kvPE6CUCvPQSB4n16/m/P5J7tGkD7N7NQeXll7l7YJIncafWW5hSaotSaqdSaq9S6q2M41WUUpuVUkeUUguUUoX0726QsGAB0265u6cuQoPZxo3Ab7/pN5s7olgxJv4MCQGaNaNBzyTP4c7MngygjYg0ABAFoKNSqhmACQA+EJHqAOIBDNStl8HGli1MHuEOthl9507gl1+McaMFaLTr3JkDTadOwNWrxvTDxGtcCruQ6xlPC2Y8BEAbAN9mHJ8DoJseHQxKYmO5f+0OMTGMfFu2jDOskURHA5Ur06W2b18ORCZ5Bnfrs4cCiANQHcCnAI4CuCwiaRmnnAJwqy49DDauXGFBSndU8T//pNPLmjWMa3dFWhpw4ACDac6epcdcWhpTVRUpwkQVFgtQurR3fbdY6BuwZo3pbJMHcUvYRSQdQJRSqiSARQDcXjQqpQYBGAQAkXrGZecVtm1jiWlXxjUR1rR7/nlufTk6Z9MmYP58YOtWxqXb3GArVaKQh4YyOeWZMyyIuW1bZqLPVq0orO4mrGjUiMuJ0FDT2SYP4pEHnYhcVkqtAdAcQEmlVIGM2b0igL8dvGcGgBkAEB0dbep9p04BVaq4Pm/WLOCff5hvLifXrwNff03L/I0bdH55913mpytRwnm7Vitw5Ah95n/6CXjzTcawDxlCYXaGLaFlfDzPNX3n8xTuWOPLZszoUEqFA2gPYD+ANQAeyjitH4AfdOpjcJGUxD1rZyQksO78F19kT/2cmso978hIruEnTmSQy3/+w1m6RAkopf592CUkBLj9dqBXLw4YBw5w8OnWjV5zrlx3w8L4GQD6zh89Cqxa5eaHNzESd6zxFQCsUUrtArAVwEoRWQJgJIAXlFJHAJQGMEu/bgYRqamuc7cvWMBiGVnr2u3cSWeajRs5Ky9eDHTokDtazVMiIjhYHD/OZUOHDsDo0cztb4+CBTNfK1QIGDGCGoZJwOOONX6XiDQUkfoiUk9E3s44fkxEmohIdRF5WESS9e9uEFC4sGNBsjF1KtVqgIPD228D7dsDw4fTf92dZYCnhIZyObB9O9f/TZrYn+WTkzm723jsMbr+njqlfZ9MNMX0oPM34eFcZzti61bg4kUavxISmBdu/Xoa1gYMcLk2FpF/H15x663AkiU0DHbowMElKzduZBf2YsWA3r2BGTO8u56J3zCF3d/UqAHs3+/49VmzgEGDGHPesSO3yZYuBSpW9F8flWKY608/AU88wcQaAN18CxbM7dgzZAjw+efmvnuAY8az+5s77gAOH6Yw2zPU/fEHt8O6daMhbcYM39fl3tK0KbByJZcQRYrQkm+x5NYu6tTh35Mn6XRjEpCYM7u/CQujQ83OnblfS0zkttiUKXSimT7dOEG3Ub8+t9b69+dMb7HYP89ioeHQJGAxhd0IHAnGzp20ju/YwXDSQClZ3bQpk1fMnw9ERdk/xxT2gMcUdiNo0YL+7jlZuxY4d47766724v1Njx7cX9+0yf7r0dGmsAc4prAbwcMPM8PMn39mPx4Twy2v5s2N6Zcz5s+n485XX3F7Lie1atEWYRKwmMJuBEWLMq9b1u2qlSuZ/vnBB3W5ZFbPOofedY4QYdaaF14A3nsv0wcgK0WK0OZgErCYwm4Utu0qm4PNlClA3bqBWSt9yxZG63XowEHq3Dn6A2SlcGE63JgELKawG0WtWoxmmz0bOHGCan3Nmq6964xg/HgOTiEhNBo+/TRz4WUlJYXusyYBi7nPbiSTJzNEdNcu4PHHWUxRJ1XYa4+6hQsZLDNvXuaxJ56gD8D772fG2ScmZvesMwk4TGE3kvr1mef93Xe57bZ0KaPIAoXz51mxZvHi7IJctizQpQuLU4wYwWNHj/qvxHR+R4TG3dhYfu+JiVxCudCsTDXeaLp0oUfaH38E3l71sGHUOJo1y/3a/fdzq9BGXJxjhxsT3zl5EnjrLZbVLluWv8ns2RyQrVYafV1ob+bMbjS7dlGVHzmStdd27WIqKVdhsHozZQqwdy/w5Zf2X4+OBl58MfN5XJxZz11rrFYmGZ06lTadPn2oCVosjrMD5QxcyoIp7EYTF8eqME8+yQKPZcpwjewoFZU/mDOHW2y//+54HV6lCiPgzp8HypXj5xgzxr/9DGaWLOESqXhxaljz5nH29gFTjTcam/rbvTszz5w/D8yda1x/Zs5kMosVK5yvwZViaqq4OOCvv5iqqkYN//UzWImPZ8Thc88xNiIuDhg40GdBB8yZ3Xj27s2cxfv2pRfauHH0onvgAf/1w5by6ssvgXXrWBzSFXfcwf7bIvWMDtrJ6yxZwm3N7t25nNNAwLNi/jpGIkJVOGtlmNGjgQoVWAfu8cc50nuJ2x5ztpRXW7dybeiOoAN0ALp6lc5B9rzqTNznvfeorn/1Fe0lGgs6YM7sxhEfD2zezP/79weuXeMWSkgIUztfv071uGZNGmgeeshZa96RmkotYsoULiH69/csS2xYGD9D7dp8mHjHG2/QOLthg65JSkxh9yc7dtDz7NdfWS+tQQNaXNu2ZfaXsDDO9hcuUJ1r2pS53R55hK916EBHnKpVfevH6dNcm8+YwfTT27d7d5OlpXFN+eGHvvUnPzN+PPD996zhV7asrpcyhV1vkpI4ak+dSsEdPJhrs5o1OYsXLsy0zjmt3idOMJz00CHmops2jTNw9eo0hD37LHPSOVH3snnNnT9PH/c5czjY9OzJYo3163v/2f74g5pAt27et5GfmT+fA+7vv+su6ACgvHaj9ILo6tUldtw4evqEhzOFUfXqwWvY+eknrmXr1uUa/L77cu+fR0aynFK1atmPp6ZyZrcJtY1du1jo8fff6Y9etSpw553UEkqU4PdqtXJJcO4cZ97YWC4LGjViXHqfPu5XkHXE8eNU3f/zHxaaMPGMM2f4m/38s6bOSNHR0YiNjbW7FvPvzH7xInOip6Rwtjp8GLh8maqkxUKvoPvvz/s+1vHx3DrZsIHbaPfc4/hcm9dcTmEvWJAeUm3bMgecTc2uX5+OFlYrw07feIOCfPw4jX2JiQxWCQvjnn2vXvRhr1pVu6otViu3g0qWpAegiWfYSnAPHuxfr8OsqYf1flgsFsnFhQsiy5eLjB0r0r69SJkyIi+/LHL0aO5z8wLLlonccovIs8+KXL/u+vx33hF55RXHr7/1lkirViJJSfZfP3ZMpHVrkaZNRQ4d8q7PnjJunEh0tEiRIiLJyf65ZjDx5Zcid9yhy3eXIWN25c94Yc/J4cMiL71Eoe/USWT7dh8/vh+ZNUukfHmRNWvcf8+yZSJt2zp+PTVVpEcPkQcf5P/2SE8X+egjXnvbNo+67DEzZ4rcdpvI999T4E084/p1kbJlRWJjdWleE2EHEApgO4AlGc+rANgM4AiABQAKuWrDLWG3kZAgMm0av5jRowN/Bvn0U5HISJEDBzx737lzIiVLOv98SUki994r8tBDzs/79lt+X5s3e9YHd5kxg1rLoUMi778vMniwPtcJZmbOFLn/ft2a10rYXwDwdRZh/wZAz4z/PwMwxFUbHgm7jVOnRDp3FmnQIHBn+S+/FKlYkSq1N7RuLTJ/vvNzEhNFunWj0F+65Pi8H38UiYgQ2bPHu77YIz1dZMwYkcqVqXlZrSJ16oisXq3dNfIDVqtIVJTIzz/rdgmfhR0sybwKQBsASwAoABcBFMh4vTmAFa7a8UrYRfglzZ7NWWvuXO/a0IstW9ivvXu9b2PhQpF77nF9XmqqyPPPc3ZdvNjxeV99RVX7yhXv+2Tj0CGRu+7i46+/eGzdOpFatfi7mLjPxo0iVaty8NQJLYT9WwAWAK0yhL0MgCNZXq8EYI+rdrwWdht79ojceivV+0AgMZEz3Ndf+9ZOSgoF2N3ZeN06kWrVRHr3djzLDxwoMmiQe+3ZjKTvv88l08iRIq++SptJ0aIiI0ZkNzY++qjIxx+717ZJJv37i0ycqOslnAm7O/XZuwA4LyJeZVVQSg1SSsUqpWIvXLjgTROZ1K1LT6Nx45hb3WjefpvOMT17+tZOwYLAU08Bn3zi3vktW9KfvXRp7nW//nrutNSTJnEPd+XK3O+/coVOPg8+yMi26tWBCRPonpucTEeeL74A9uwBOnViYEzZstwX7tWLTkG9e/v2mfMja9f6N7gpJ45GAcmctccBOAXgBICzABIAzIU/1ficHDhAy/OSJdq05w1btoiUKydy9qw27Z07x/a2bvXsffv2iQwfLlKqlEjXrpyhbUa85ctpNLSp8zt30qhWsqTII4+IzJvHNXhaGrWKESPYzv33c12ZVd1MSuJnbtBApEIFkSpVRCZMoFZg4pqLF0WKF9dVhRfxcWYXkVdFpKKIVAbQE8BqEekNYA0AW3RGPwA/aDkIOaVmTSZCfOopOuoYwYsvMngkIsL3tq5do5/5qFGcMU+epJutO9SuDXz0Ed9z332c5UuWBBo3BhYtYkaTJ55gVFuHDnS4mTuXgTU7dtC5o2xZzuDh4XTw+fFHVpDN6tlYuDDznaWm0oFnwQJWo7W57l6/7vv3EMzExdF5zEBvUY/cZZVSrQC8JCJdlFJVAcwHUArckusjIk4Th0dHR0tsbKz3vc3JCy/Q7TBr5lN/sHs3heHECargnnD1Kn3eY2N5A8TFMfCleHEK1Pnz9HSzWqleWyxMAWWxUIDdSVd14wYFeetW+l7v30+hL1o001X5llvYpu1RvrzzNs+do/fekiXsh42LF4GXX6aqP2sW0Lq1Z99HfmHcOH5Xkybpehln7rJub71p8dBMjbdx44ZIjRoi332nbbuuGDJE5L//9ew9O3bQYFayJC3vL75Iw96hQ9lVuzNnqM6vWkXHi+nT+b4GDbi99/bbIqdPu77e/v0izZrR+y46mnvw3pKWRtV+1CjH5yxZQuPp0KEi1655f61gpW9fkS++0P0yecuDzlPWr+ca0l832JUrFNhTp1yfa7WKfPONyJ13UlDfeYfC7Iqff6bA79yZ/fiOHdnX3I7W9+vWcTvw0085kHz9tUibNq6v6+gzDBpEXwBHLrs24uO5QxAdba7lc/Lgg74NuC4A8O9DglbYRWiYmjFDn7ZzMmuWSPfurs+zOQNFRYksWuTY1dURCxZwEMsp8CIily9z6ysign71iYmZry1fTkH/9dfMY0lJPOap0096On38mzQRuXrVvfdYrdy2q13bvYEtv9C5s8hPP+nWvDvCHhyxpUOHMgLMA/uD1/zxB7PBOkKE21ZRUQxR3bKF8d6epoZ+5BHggw8Y8bZhQ/bXSpSgUWzXLuDYMRp+Nm9m2GufPizq0LZt5vmFCwN33+243LI9UlIYWrttG5NPuluDTikWvejZkwbBf/5x/5rBTGgoK/4YiaNRQI+HbjN7erpI9eoiGzbo035WoqJENm2y/1pCAmf9qCiq3FpgU+lfe82xGr1ggUjp0nSAWbHC/jljx9JO4A5xcYzK6t6ddhFvsFp5vZYtuebP7zzyiGuXaA3waestTxASwiQRU6fqe52kJODgQfvZXa5epYXelpetQQNtrtmxIx1o9uyhVd5exZiHHmLttWLF6ERjT8Nxp9pMSgrj4zt2pIX9u+9YitkblOLWJMCtwfxORAQzFRlIcAg7ADz6KLB8ub6q/O7dFKrw8OzHExK4x12rFlMxa13NtHx57pmPGgV07sxly759ma9PnUrh2rWLXlojR+b+HiwWquRWa+72k5LY70aNOLDs3MnU0L4muwgJAWJiqNYfPOhbW3kdW459AwkeYb/lFu5553Qb1ZKjR+nQk5W0NM6sVaowT1xoqD7XVooON7t20U22bVvuaU+ZwvTTMTGszLJiBbBsWeasaqNMGX4/ly5lHjt+nANDZCRTGI8bB/zwA1NZa0W1asB//0vHHqPXrEYSAHX8gkfYldL/C01MzK3WTprEmTEmxmfvKLfyvEdEsJjDyZP0fnvrLWoWw4czH9yaNezL5MmZBjkReriFhnJAGjCAS5HGjTlYbdjAQeL++7VLXZWVYcN47TlztG87r1C7NvD334xLMIjgEXZAf2HPWRZ33z7md4uJ8X8hxkKFaO0HuLx45hkemz2b1v8LF1hVJjycgla2LPP97d3LXH8xMbz5Jk3Sv2xTSAjw2mvUQvyxYxKIFChAO46tVoARXTDsynrQsCGrk+hFoUI0YgGcEQcM4CxbubJ+13TGZ5+xLli1anzcf3/mayJcXkRGUqBDQhjhNnGiMXXU27fnDL95s/0S0PmBRx/lINuhgyGX9+vMHhcX5145Im+5+WZaxfUiPJyqPMDCCMWKMUOoRmTdJnGJbenw9NP2X1eKKvu8eZnaTmKicZl7/bVjEsg8/jiXS2fPGnL54FLjswqjHtx2G410KSmcLT/+WJ81rjssWkTHHWcqeLlyjIJ77z2q8AkJNNQZxYABzKXvQ/26PE3JksDDD+urfTohuIRdRN8QwqgornkXLqTBpW5d/a7linXrsqvtjnj8ce69//IL+6/XboE7lCrFLSgD162GM3QoSzHbloN+JLiEPSlJXzW1SBEWW5g8mT+akdjquruieHG6rn7+uX8LEjgiALagDCUqigPemDF+v7Rfhd1isbi/JvWGCxe4bteTqlXpj961q77XcUZKCncCoqLcO3/IEGD9eu28+nzBYmEsf35m2jQaV7dt8+tlg2tm37bNfQHwFhF6tHmatEJL9uyhE4+7rqx163L3IKfnnxHk95kdoAPYpEm0YfhRnQ8uYXdXtfWFtDR66RmVDgtg5pl69dw/f+lSajy+JvzUgmrV+N3l9zRWffrQ4Pv66367ZPAIuwjVQ72F3ZaSysjstjduuB9yCnC767773J5R3fLk8xalmB4rIUH7tvMSStGOsnix3wKFgkfYjx9n3PYtt+h3jXPneJO+8grXXUb5entiiDx8mMubp54KHPVZ7y3SvEK5ctwpmTTJL/4HwSPsc+e6txXlC9u305LapAm3kX7wX0LdbBQo4P5A89FHQP/+1HiOHQsMIUtLM9bmEUjcdhsjFSdNooVeR3fi4BD2tDTuXeq9HXbxIo1zSrGownPP0VlFI9xWn8PC3BPajRsZk/7yy3T1LVHCrf565MnnDUZ68gUiVatyt+Snn+hKe/KkLpcJDmH/6Sf6p+u9tZRVfW7bFujShfnj/U358qze4ozERFp7p0xhEAzAvrubj14vrl6lBbp4cWP7EWhUqMDow7ZtmaRk+nTNZ/ngEPZPP/WPk4vVmt09duJEYPVqllnyJ7ZEFM5uhjffZBjrQw9lHgsJMT6mfPt29svfUYJ5gQIFmKBk7Vrm4G/VihOZRr9Z3hf2pUu5Fu3RQ/9rhYUxzNXGTTfRovrUU/omzchJRAT32I8ft//68uVMRvHpp9mPJyUZv9fuj+3RvE7dukxs+sQTXMdXqwaMH88CIj6Qt4U9Pp5RZzExtMTrTbFiuZMPtG3LyjTt2/v8Y3i0VnbknLJ+PVNKffddpvrOxqlCFy3qUx99xhR29yhQgOHLmzcD337LXZUaNajiDx4MzJxJ7S4hIVPDc3HfuCXsSqkTSqndSqkdSqnYjGOllFIrlVKHM/7q7Kdqh+efZ6KGVq38c7077mB+tpy88AL9z1u3ZkIIfxAdTQNcVtauZWXWr74C7rwz+2vHjjHqqmRJ//TPHiLMnhMdbVwf8iLR0VTrz5xhpGW9ehzUH3+cKcpCQ2mAdRXk5CjtbI5Z5gSAMjmOTQQwKuP/UQAmuGpH01TSP/7ISqL+LDWUni5SooTjaifjxrFPu3bp35eDB5li2pZeesECkTJlWDbKHvPni3Trpn+/nLFmDevZW63G9iPYSE/nfZCe7jSVtC9Wkq4AWmX8PwfAWgAjfWjPfTZvBgYOpPdRsWJ+uSQAGrgaNqQqeu+9uV8fNYpW1TZtgBEjmMxRL0PU7bdz92H2bM7ocXE0FDqaNQNBfZ46lYZUo3IABCshIW4tY91dswuAX5RScUqpQRnHIkTkTMb/ZwFoULvYDbZtY8TZF1/kVlX9gauorX79KFjr1jEH3N69+vWlUSNWhqlQgVVbnanHRgv76dP0Fuvb17g+5HccTfmSXWW/NeNvOQA7AbQEcDnHOfEO3jsIQCyA2MjISN/Uld9+Y82y77/3rR1f+PlnkYYNXauiVivrz5UpI/L446wio4X6mprKqrVt2rAKTrlyrODijLNnufyIj/f9+t7y5psiTz9t3PXzCZpWcQUwGsBLAA4CqJBxrAKAg67e6/WaPSWFFVDLlBFZudK7NrQiPZ3rckcloHJy8aLIe++JVK0q0qiRyOefi/zzj2fXtFpFjh8XeestlkW++26RefNEkpNFpk1j4UVnhSPHjhUZONCza2rJoUMsT3XkiHF9yCc4E3YlLsz1SqmiAEJE5FrG/ysBvA2gLYBLIjJeKTUKQCkRecVZW9HR0RLraeKC3bvp2122LLcbKlXy7P168N57jCn3JA+61crUUFOnMrd7uXJUqy0WxuCXKME9cKuV3m/nzlH1tj2UoqV96FDuCmRtt107RuK9YufrT09n7PvixVT7/U16OnDPPcy99txz/r9+PiM6OhqxsbF2jSLuWI8iACzK8NcuAOBrEVmulNoK4Bul1EAAJwE8olWHATAD5yef0G1w/Hg6GASKYWfAAKB6dfrKu5vAMSSEAtmxIwXg0KFMQZ44kfHdiYk8LzycWyqNGjF7rMUC3Hqr/c8fEsJtmcaNGQhUu3b215cuZSSgEYIO0F03JIS2BRNjcTTl6/FwqcZbrSLr1ok8+qhIyZIiTz0l8uefPio2OvHkkyIjRhjdi0ymThWJjs5edTUlhUuHr782pk+7dlF9P3zYmOvnQ/TaevOd1FRaq20z3Nq1dLywZeAsUcLQ7jll3Dj6ePfoAdx1l9G9oVfVH39QXV60iE4WEyZw+dOzp//7c/Qo0KkTtbPq1f1/fZNcuFyza0l0kSISe8cdjHpKTGTkVmQkiyWWKMH159WrzMSSnMw96rAwbi01akR1tkED78sIa82iRdxL37EjMPqUmsrAl9BQllvq2JFblf62cxw5QvfhUaM0LaJh4hpna3b/CnudOhIbE8ObMS6OET0bN9Jf22asqlePjjKFC3Ntm5TEIBPb7L9/P32Ee/WiY01W/28jeOwxhpxOnmxsP2wkJ/O7+fVXYOxY/6+Vd+xgCqzRoxkgZOJXfDXQaUehQsBvvzGNbqlSVNdjYhjF5S7JyXRqmTWLQt+lC9tp3twYA96UKZmD1BNP+P/6OSlYkFpG6dIsOlmrFmdZvUlL4y7F5MnccXj4Yf2vaeIZjhbzejwsoaEi/fqJbN6sjTXi0iWRSZNEqlUT6dBB5ORJbdr1lIMHRSpUoP+5kaSniwwbJnLXXTTUrVghEhkpMmiQyJUr+l13zx4aB9u1EzlxQr/rmLhEU6caXx6WevX0+YQpKSJjxtDpZsYMYwItduwQKV9eZNYs/19bhE41/fuLNG+e3VPu8mXuHERGinz8MZ9rxcGD3JEoU0Zk+nQzwCUAcCbs/o1n1yvmvGBBGqTWrKEV/957GQ7oTxo04G7CW28x9NafqZL//JPGuFOn6LiTNYy1RAk6I339NdMeVa5Mo5m9UF13SEujg06HDsDdd9OAun07MGhQ4PhBmNglbyevyEm9ejT4NW3K7TBHmVz0omZNGhHPnqVX3IYN+l5PhIJssTCWftkyx1GALVoA8+fTwFmpEm0dVatybT1hAg16Fy7QJiKS6cl3/DiTJ7z6KgU8IoJr8379OMiMGwdUrKjv5zTRBP9a471xl/WWqVN5I65ZY8w+7/ffA888wz3u116jwUxLDh4Ehg8HLl1imKsnFWKA3F58cXF0Tb5xg7O3CDUxmydfdHTmjkmFCtp+FhPNcGaNDywPOq357DMGrZw65d/r2rhwgQEoJUtmGiZ9WdfaIt7atROJiBAZP572Cq1JTaWxzyTPEbgedHozeDDwzz8MINmwwf8ZTcuUYULK8eMZf9+zJ7cce/TInCWdzfgizCEeFwds2cJCGFWqcKvxwQf1s4GYmV+DkuBV422IcJ+5fXt6uxmJ1QqsWMH1cVwcDVs33+w46m3bNvom2AaG7t0Do+yyScASOE41RqAUHXCioxkVVqeOcX0JCaG/eKdOfG610rV01y7HUW/m+thEI4Jf2AHW0xozhnHxf/wROGpqSAhzyd1+u9E9MckH+PeuT0qixbdQIc5e5cvzf19JSGDO9qQkBoOEhdFltEIFChTAfeC5c4EFC4DevX2/pomJPZKSuASz3YuFC/Ner1DBdapnLduwg3+F/cgRBmnYot4uXWL1C9uatFkzbiE5c86wWmms2rqV697YWOZEL1uWX0hoKPeKbWpxw4ZsOzqayQ6nTjWF3UQbRGhX2bIl8148dIiG2fBwapApKdzOvHaN9hbbvd6iBSu9iGQaYG1boM7aiIrK3kbVqm5311gD3Y0bjJKyfVHr1vFDDh3KQSFr2OilSwya+ewzfgktWmTu/darZ19DuHiRP4at/bVrqQVMnMhr+DBKmuRjrlwBvvySE0daGj0JbRNK/fr2K9Revpz9Xlyzhp6ON24w6rNly0whdtRGfHxmG7b8D/XqAcOGAQ88ABQokIf22dPTRZYtE+nShRlOnn9eZP167lGXKOF7ltaEBJHu3enLXbmyyMSJIomJ3rVlkv84cUJk8GD6TTzyiMjatZ7fi8ePMzCpRAnGMdSvL3LLLSKjR3te8CQpiVmI7rqLiUjffjuAAmE8cao5fJgfQimRnj1Fzp/37ItwxJkzIsWLcxDp2lWkdm3tovBMghOrlQ5aZcqIvP66yOnTnreRns7UYWXKiLzxBu9DG7t2ifTuTQew1au96+POnSKPPZYHhX3/fpFmzURatRJZtIgC+eCDzH+uBfXqicTG8kecP5/eaCNHmrO8SW5OnKDHYuPGInv3etfGsWMirVuLNG0qsm+f4/OWLOEMPXSo12XNAifqzR3mzmUQS9++wKpVLNy4bVtmuSMtgktsFVCVAh59lBFghw8zQ+upU763bxIcLF3KdXi7dtyy9cZH48cfgSZN6FuxYUPu7L9Zue8+7lYlJPBeP3TI+77bw9EooMfD5cz+ySciFSsyGYI9VqygGvTzz54OeNmZMoXrpqxYrSzmULkyixqY5G/mzqXG524xEHt8+SXb2LLF8/fOmsWEKNu3e/S2vKHGT5vGNcuxY84/zYYNLAH166+efAfZ+eMPpli2x4wZIpUq0ZBikj9ZsICCtnu39218/TUNb96q/iIiCxdysHA0+dkh8IX9m2+4Vjl61L1PtHYtBX7rVne/g+xcvEiLqiM++oh11LQyCprkHVauZP28nTu9b2P5crahRenuuXOp7bqZci2whf3PP6maZ1FXwKqx/z7ssmCByO23czvNU27cEAkLc37O88+LPPyw522b5F0uXeJsvGaN921cuMD0ZOvWadYtefddGvjcCDv22UCnlCqplPpWKXVAKbVfKdVcKVVKKbVSKXU44+/NXhgMmG54xAh6BnnCI4/QO+6NNzy+LAoXppedM8aOZYDKwoWet2+SNxk+nJl7WrXyvo1nn2V68ZYtNesWXnmFRrvp031rx9EokPUBYA6AJzP+LwSgJICJAEZlHBsFYIKrdnLN7J9/LmKx5KpACndmdpHMUXTDBpcjXjYSElzP7CIiGzdyzXTunGftm+Q9Fi/m0i1r+SxP+e4777VNV+zbR0czFzYtn9R4ACUAHEeGa22W476VbD59muq7r+uab7/lF+xJxhZXa/asvPwynXpMgpcrV6i+//ab923Ex3s38XjChAnc83eCr2p8FQAXAHyhlNqulPo8o3RzhIjYUrieBau9us+0adzjzlp+2Bt69GD03OLF7r/n8GFmfHGH0aOBlSuBEye86JxJnmDOHPp23H23923Mng20aQPceadm3crFCy9k5g30AneEvQCARgCmiUhDADdAtf1fRP5VvXOhlBqklIpVSsVeuHCBB1NSmBV16FCvOp2LYcMYlOAucXF0lnCHIkWYSdXX9ZJJYCLCe2fYMO/bsFp9b8MdChRgCe9p07x6uzvCfgrAKRHZnPH8W1D4zymlKgBAxt/z9t4sIjNEJFpEosva6rItXsyyRFpljenWDThwANi3z73z4+LoRecuTz/NiDtXRj2TvMfatYx+9GVWX72ak0Lz5pp1yyEDBwLffccIOA9xKewichbAX0qpmhmH2gLYB+BHAP0yjvUD8IPbV506VbtZHWB461NPuT+7eyrsNWpwt8C0zAcfn37Ke9GXAhdatOEu5coBnTtz2eApjhbzkt0YFwUgFsAuAIsB3AygNIBVAA4D+BVAKVftWCwWkX/+ESlWTPsUyIcO0THHFefOidx0E8MDPeHLL0V69PCubyaBSUqKSJEi2ctleUpyMtu4elWzbrnk559FWra0+5LPqaRFZAcAe4vcth6PLtu2cZYsWNDjtzqlenUmAjh3znlV2FmzuEfvaRrmxo2929M3CVz27QMiI7OXy/KUPXto7L3pJs265ZLGjZmZ2GrNTLvmBv6PevNUhXYXpTKj2RyRns5MN94sIWrUYLacS5e876NJYKHFvajX/eyM0qX5OHzYo7cZI+zuWsI9xZWwL1sG3HILUzR7SkgI37dtm/f9MwkstBJ2ve5nZ7i61+3gf2Hfvt07YXMHi8WxMIoAkycDQ4Z4374p7MGFFveinvezM5zd6w7wv7BfukSLoh6UK+d4SyImBrh6lYksfWn/n3+8f79JYPHPP87tO+6g5/3sDGf3ugP8mko6Li4OCQDKli2LG6J9VttmrVvjIwDNlLLtIpC//gJGjeJ+qC+GwfBw4PRpn/tpEiAkJtrP4uppG+HhuQ6rHNtw4sX97rSNsDBe2wP8PrMrOHC10wAr7HygrJF1vrrmhoTQAmoSHIj4vjeuRRve4MW96HdhTwLg41jqkDAAuca6999n/ngtijomJdkdxU3yKGFh/E19bcPDGVYTvLgX/VokQil1AcBJAGUAXPTbhe0TCH0AzH7kxOxHdjztx20iUtbeC34V9n8vqlSsiBiwXxFYfTD7YfbDn/0IvFTSJiYmumAKu4lJPsEoYZ9h0HWzEgh9AMx+5MTsR3Y064cha3YTExP/Y6rxJib5BL8Ku1Kqo1LqoFLqiFJqlOt3aHbdGKXUeaXUnizHfE+F7Xk/Kiml1iil9iml9iqlnjOiL0qpMKXUFqXUzox+vJVxvIpSanPG77NAKWWn6L0u/QnNyG+4xKh+KKVOKKV2K6V2KKViM44ZcY/ok7YdfhR2pVQogE8BdAJQB0AvpZRGealcMhtAxxzHRgFYJSI1wCQc/hh80gC8KCJ1ADQDMCzjO/B3X5IBtBGRBmBiko5KqWYAJgD4QESqA4gHMFDnfth4DsD+LM+N6kdrEYnKstVlxD3yEYDlIlILQAPwe9GmH46yWmj9ANAcwIosz18F8Kofr18ZwJ4szz1Oha1Dn34A0N7IvgAoAmAbgKag80YBe7+XjtevmHEDtwGwBPSoNqIfJwCUyXHMr78LNEzbbu/hTzX+VgB/ZXl+KuOYUfiWCttHlFKVATQEsNmIvmSozjvARKErARwFcFlE0jJO8dfv8yGAV8DQBoDpzozohwD4RSkVp5QalHHM37+LPmnbMzANdHCeClsPlFLFAHwHYISIXDWiLyKSLiJR4MzaBEAtva+ZE6VUFwDnRcS7ROjacpeINAKXmcOUUtnqN/npd/Epbbsr/CnsfwOolOV5xYxjRuFWKmytUUoVBAV9roh8b2RfAEBELgNYA6rLJZVStrBnf/w+LQA8oJQ6AWA+qMp/ZEA/ICJ/Z/w9D2AROAD6+3fxKW27K/wp7FsB1MiwtBYC0BNMR20U3qfC9hLFAOVZAPaLyGSj+qKUKquUKpnxfzhoN9gPCv1D/uqHiLwqIhVFpDJ4P6wWkd7+7odSqqhS6ibb/wA6ANgDP/8uokfa9hwX8NsDQGcAh8D14Wt+vO48AGcApIKj50B4kQpbg37cBapguwDsyHh09ndfANQHsD2jH3sAvJlxvCqALQCOAFgIoLAff6NWAJYY0Y+M6+3MeOy13ZsG3SNR0CBtu72H6UFnYpJPMA10Jib5BFPYTUzyCaawm5jkE0xhNzHJJ5jCbmKSTzCF3cQkn2AKu4lJPsEUdhOTfML/AW4yNc4Hbcy9AAAAAElFTkSuQmCC", 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" ] @@ -4606,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 370, "metadata": {}, "outputs": [ { @@ -4614,8 +479,8 @@ "output_type": "stream", "text": [ "[4032 384 4092 4039 447 493 2204 657 878 2880 1088 4087 2837 3779\n", - " 3093]\n", - "(15, 4096)\n", + " 3093 2395 581 2751 1023 2970]\n", + "(20, 4096)\n", "0.0\n" ] } @@ -4633,16 +498,16 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 371, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2431 319 192 4092 894 4032 3268 2209 969 2963 429 23 3769 1068\n", - " 59]\n", - "(15, 4096)\n", + "[4032 384 4092 4039 493 575 2204 657 878 2880 1088 4087 2837 3779\n", + " 3093 2395 581 2751 2010 3039]\n", + "(20, 4096)\n", "0.0\n" ] } @@ -4672,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 372, "metadata": { "ExecuteTime": { "end_time": "2022-07-11T16:34:57.421237Z", @@ -4686,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 373, "metadata": { "ExecuteTime": { "end_time": "2022-07-11T16:34:58.310764Z", @@ -4697,10 +562,10 @@ { "data": { "text/plain": [ - "1.2498279" + "0.86233765" ] }, - "execution_count": 64, + "execution_count": 373, "metadata": {}, "output_type": "execute_result" } @@ -4711,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 374, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:55:26.961534Z", @@ -4729,7 +594,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 19'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m optimizer_faces \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39moptimizers\u001b[39m.\u001b[39mQR()\n\u001b[1;32m 7\u001b[0m model \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39mSSPOR(basis\u001b[39m=\u001b[39mbasis1,optimizer\u001b[39m=\u001b[39moptimizer_faces, n_sensors\u001b[39m=\u001b[39mn_sensors0)\n\u001b[0;32m----> 8\u001b[0m model\u001b[39m.\u001b[39mfit(XX)\n\u001b[1;32m 10\u001b[0m all_sensors0 \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mget_all_sensors()\n\u001b[1;32m 11\u001b[0m top_sensors0 \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mget_selected_sensors()\n", + "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 18'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m optimizer_faces \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39moptimizers\u001b[39m.\u001b[39mQR()\n\u001b[1;32m 7\u001b[0m model \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39mSSPOR(basis\u001b[39m=\u001b[39mbasis1,optimizer\u001b[39m=\u001b[39moptimizer_faces, n_sensors\u001b[39m=\u001b[39mn_sensors0)\n\u001b[0;32m----> 8\u001b[0m model\u001b[39m.\u001b[39mfit(XX)\n\u001b[1;32m 10\u001b[0m all_sensors0 \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mget_all_sensors()\n\u001b[1;32m 11\u001b[0m top_sensors0 \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mget_selected_sensors()\n", "\u001b[0;31mNameError\u001b[0m: name 'XX' is not defined" ] } From 3484e08411bfa4ce8c2f8c31943c03a2db785f5b Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Thu, 4 Aug 2022 10:53:17 -0600 Subject: [PATCH 21/52] Adding options to call different type of constraints and renaming functions which calculate and update the norm --- pysensors/optimizers/_gqr.py | 204 ++++++++++++++++++----------------- 1 file changed, 106 insertions(+), 98 deletions(-) diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index aa5c700..5fcfb9c 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -27,7 +27,7 @@ class GQR(QR): @ authors: Niharika Karnik (@nkarnik2999), Mohammad Abdo (@Jimmy-INL), and Krithika Manohar (@kmanohar) """ - def __init__(self,idx_constrained,n_sensors,n_const_sensors,all_sensors): + def __init__(self,idx_constrained,n_sensors,n_const_sensors,all_sensors,constraint_option): """ Attributes ---------- @@ -39,6 +39,11 @@ def __init__(self,idx_constrained,n_sensors,n_const_sensors,all_sensors): Total number of sensors n_const_sensors : integer, Total number of sensors required by the user in the constrained region. + all_sensors : np.ndarray, shape [n_features] + Optimall placed list of sensors obtained from QR pivoting algorithm. + constraint_option : string, + max_n_const_sensors : The number of sensors in the constrained region should be less than or equal to n_const_sensors. + exact_n_const_sensors : The number of sensors in the constrained region should be exactly equal to n_const_sensors. """ self.pivots_ = None self.optimality = None @@ -46,6 +51,7 @@ def __init__(self,idx_constrained,n_sensors,n_const_sensors,all_sensors): self.nSensors = n_sensors self.nConstrainedSensors = n_const_sensors self.all_sensorloc = all_sensors + self.constraint_option = constraint_option def fit( self, @@ -85,8 +91,10 @@ def fit( r = R[j:, j:] # Norm of each column dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0)) - dlens_updated = f_region_optimal(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.nSensors) #Handling constrained region sensor placement problem - #dlens_updated = f_region(self.constrainedIndices,dlens,p,j,self.nConstrainedSensors) + if self.constraint_option == "max_n_const_sensors" : + dlens_updated = norm_calc_max_n_const_sensors(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.nSensors) + elif self.constraint_option == "exact_n_const_sensors" : + dlens_updated = norm_calc_exact_n_const_sensors(self.constrainedIndices,dlens,p,j,self.nConstrainedSensors) # Choose pivot i_piv = np.argmax(dlens_updated) @@ -121,7 +129,7 @@ def fit( ## TODO: why not a part of the class? #function for mapping sensor locations with constraints -def f_region(lin_idx, dlens, piv, j, n_const_sensors): ##Will first force sensors into constrained region +def norm_calc_exact_n_const_sensors(lin_idx, dlens, piv, j, n_const_sensors): ##Will first force sensors into constrained region #num_sensors should be fixed for each custom constraint (for now) #num_sensors must be <= size of constraint region """ @@ -155,7 +163,7 @@ def f_region(lin_idx, dlens, piv, j, n_const_sensors): ##Will first force sensor dlens[didx] = 0 return dlens -def f_region_optimal(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors): ##Optimal sensor placement with constraints (will place sensors in the order of QR) +def norm_calc_max_n_const_sensors(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors): ##Optimal sensor placement with constraints (will place sensors in the order of QR) """ Function for mapping constrained sensor locations with the QR procedure (Optimally). @@ -196,96 +204,96 @@ def f_region_optimal(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors -# if __name__ == '__main__': -# faces = datasets.fetch_olivetti_faces(shuffle=True) -# X = faces.data - -# n_samples, n_features = X.shape -# print('Number of samples:', n_samples) -# print('Number of features (sensors):', n_features) - -# # Global centering -# X = X - X.mean(axis=0) - -# # Local centering -# X -= X.mean(axis=1).reshape(n_samples, -1) - -# n_row, n_col = 2, 3 -# n_components = n_row * n_col -# image_shape = (64, 64) -# nx = 64 -# ny = 64 - -# def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): -# '''Function for plotting faces''' -# plt.figure(figsize=(2. * n_col, 2.26 * n_row)) -# plt.suptitle(title, size=16) -# for i, comp in enumerate(images): -# plt.subplot(n_row, n_col, i + 1) -# vmax = max(comp.max(), -comp.min()) -# plt.imshow(comp.reshape(image_shape), cmap=cmap, -# interpolation='nearest', -# vmin=-vmax, vmax=vmax) -# plt.xticks(()) -# plt.yticks(()) -# plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.) - -# # plot_gallery("First few centered faces", X[:n_components]) - -# #Find all sensor locations using built in QR optimizer -# max_const_sensors = 230 -# n_const_sensors = 2 -# n_sensors = 200 -# optimizer = ps.optimizers.QR() -# model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors) -# model.fit(X) - -# all_sensors = model.get_all_sensors() - -# ##Constrained sensor location on the grid: -# xmin = 20 -# xmax = 40 -# ymin = 25 -# ymax = 45 -# sensors_constrained = getConstraindSensorsIndices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices - -# # didx = np.isin(all_sensors,sensors_constrained,invert=False) -# # const_index = np.nonzero(didx) -# # j = - - -# ##Plotting the constrained region -# # ax = plt.subplot() -# # #Plot constrained space -# # img = np.zeros(n_features) -# # img[sensors_constrained] = 1 -# # im = plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) -# # # create an axes on the right side of ax. The width of cax will be 5% -# # # of ax and the padding between cax and ax will be fixed at 0.05 inch. -# # divider = make_axes_locatable(ax) -# # cax = divider.append_axes("right", size="5%", pad=0.05) -# # plt.colorbar(im, cax=cax) -# # plt.title('Constrained region'); - -# ## Fit the dataset with the optimizer GQR -# optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors) -# model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors) -# model1.fit(X) -# all_sensors1 = model1.get_all_sensors() - -# top_sensors = model1.get_selected_sensors() -# print(top_sensors) -# ## TODO: this can be done using ravel and unravel more elegantly -# #yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features)) -# #xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features)) - -# img = np.zeros(n_features) -# img[top_sensors] = 16 -# #plt.plot(xConstrained,yConstrained,'*r') -# plt.plot([xmin,xmin],[ymin,ymax],'r') -# plt.plot([xmin,xmax],[ymax,ymax],'r') -# plt.plot([xmax,xmax],[ymin,ymax],'r') -# plt.plot([xmin,xmax],[ymin,ymin],'r') -# plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) -# plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) -# plt.show() +if __name__ == '__main__': + faces = datasets.fetch_olivetti_faces(shuffle=True) + X = faces.data + + n_samples, n_features = X.shape + print('Number of samples:', n_samples) + print('Number of features (sensors):', n_features) + + # Global centering + X = X - X.mean(axis=0) + + # Local centering + X -= X.mean(axis=1).reshape(n_samples, -1) + + n_row, n_col = 2, 3 + n_components = n_row * n_col + image_shape = (64, 64) + nx = 64 + ny = 64 + + def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): + '''Function for plotting faces''' + plt.figure(figsize=(2. * n_col, 2.26 * n_row)) + plt.suptitle(title, size=16) + for i, comp in enumerate(images): + plt.subplot(n_row, n_col, i + 1) + vmax = max(comp.max(), -comp.min()) + plt.imshow(comp.reshape(image_shape), cmap=cmap, + interpolation='nearest', + vmin=-vmax, vmax=vmax) + plt.xticks(()) + plt.yticks(()) + plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.) + + # plot_gallery("First few centered faces", X[:n_components]) + + #Find all sensor locations using built in QR optimizer + max_const_sensors = 230 + n_const_sensors = 2 + n_sensors = 200 + optimizer = ps.optimizers.QR() + model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors) + model.fit(X) + + all_sensors = model.get_all_sensors() + + ##Constrained sensor location on the grid: + xmin = 20 + xmax = 40 + ymin = 25 + ymax = 45 + sensors_constrained = ps.utils._constraints.get_constraind_sensors_indices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices + + # didx = np.isin(all_sensors,sensors_constrained,invert=False) + # const_index = np.nonzero(didx) + # j = + + + ##Plotting the constrained region + # ax = plt.subplot() + # #Plot constrained space + # img = np.zeros(n_features) + # img[sensors_constrained] = 1 + # im = plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) + # # create an axes on the right side of ax. The width of cax will be 5% + # # of ax and the padding between cax and ax will be fixed at 0.05 inch. + # divider = make_axes_locatable(ax) + # cax = divider.append_axes("right", size="5%", pad=0.05) + # plt.colorbar(im, cax=cax) + # plt.title('Constrained region'); + + ## Fit the dataset with the optimizer GQR + optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors, constraint_option = "exact_n_const_sensors") + model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors) + model1.fit(X) + all_sensors1 = model1.get_all_sensors() + + top_sensors = model1.get_selected_sensors() + print(top_sensors) + ## TODO: this can be done using ravel and unravel more elegantly + #yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features)) + #xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features)) + + img = np.zeros(n_features) + img[top_sensors] = 16 + #plt.plot(xConstrained,yConstrained,'*r') + plt.plot([xmin,xmin],[ymin,ymax],'r') + plt.plot([xmin,xmax],[ymax,ymax],'r') + plt.plot([xmax,xmax],[ymin,ymax],'r') + plt.plot([xmin,xmax],[ymin,ymin],'r') + plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) + plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) + plt.show() From b8b37177a29de63f339c239771dc465593b2cc8a Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Mon, 8 Aug 2022 11:16:05 -0600 Subject: [PATCH 22/52] Few changes to gqr and region_optimal which has the radius constraints --- examples/region_optimal.ipynb | 52 ++++++++++++++++++++++++++--------- pysensors/optimizers/_gqr.py | 21 +++++++------- 2 files changed, 50 insertions(+), 23 deletions(-) diff --git a/examples/region_optimal.ipynb b/examples/region_optimal.ipynb index 64bf64f..54eb348 100644 --- a/examples/region_optimal.ipynb +++ b/examples/region_optimal.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 358, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:04.386599Z", @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 359, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:07.391526Z", @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 360, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:09.785781Z", @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 361, + "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:10.835009Z", @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 362, + "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:11.651751Z", @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 364, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 365, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 366, + "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:22.713344Z", @@ -304,10 +304,14 @@ "\n", " for j in range(k):\n", " r = R[j:, j:]\n", - " # Norm of each column\n", + " # Norm of each column \n", " dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0))\n", - " dlens_updated = f_region_distance_constraints(self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j,self.nSensors) #Handling constrained region sensor placement problem\n", - " #print(dlens_updated)\n", + " if j != 0:\n", + " dlens_old = dlens_updated\n", + " else:\n", + " dlens_old = dlens\n", + " dlens_updated = f_region_distance_constraints(self.radius,self._nx,self._ny,self.all_sensorloc,dlens_old,p,j,self.nSensors) #Handling constrained region sensor placement problem\n", + " print(dlens_updated)\n", " # Choose pivot\n", " i_piv = np.argmax(dlens_updated)\n", " \n", @@ -398,14 +402,36 @@ }, { "cell_type": "code", - "execution_count": 368, + "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:27.911973Z", "start_time": "2022-07-10T04:22:26.180620Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3.6017013 3.6861243 3.727096 ... 3.9694474 3.9748106 3.9125938]\n" + ] + }, + { + "ename": "IndexError", + "evalue": "boolean index did not match indexed array along dimension 0; dimension is 4096 but corresponding boolean dimension is 4095", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 11'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m optimizer1 \u001b[39m=\u001b[39m AQR(n_sensors,all_sensors,r,nx,ny)\n\u001b[1;32m 3\u001b[0m model1 \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39mSSPOR(optimizer \u001b[39m=\u001b[39m optimizer1, n_sensors \u001b[39m=\u001b[39m n_sensors)\n\u001b[0;32m----> 4\u001b[0m model1\u001b[39m.\u001b[39;49mfit(X)\n\u001b[1;32m 5\u001b[0m all_sensors1 \u001b[39m=\u001b[39m model1\u001b[39m.\u001b[39mget_all_sensors()\n\u001b[1;32m 7\u001b[0m top_sensors \u001b[39m=\u001b[39m model1\u001b[39m.\u001b[39mget_selected_sensors()\n", + "File \u001b[0;32m~/projects/pysensors/pysensors/reconstruction/_sspor.py:156\u001b[0m, in \u001b[0;36mSSPOR.fit\u001b[0;34m(self, x, quiet, prefit_basis, seed, **optimizer_kws)\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_n_sensors()\n\u001b[1;32m 155\u001b[0m \u001b[39m# Find sparse sensor locations\u001b[39;00m\n\u001b[0;32m--> 156\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mranked_sensors_ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptimizer\u001b[39m.\u001b[39;49mfit(\n\u001b[1;32m 157\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbasis_matrix_, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49moptimizer_kws\n\u001b[1;32m 158\u001b[0m )\u001b[39m.\u001b[39mget_sensors()\n\u001b[1;32m 160\u001b[0m \u001b[39m# Randomly shuffle sensors after self.basis.n_basis_modes\u001b[39;00m\n\u001b[1;32m 161\u001b[0m rng \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mrandom\u001b[39m.\u001b[39mdefault_rng(seed)\n", + "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 9'\u001b[0m in \u001b[0;36mAQR.fit\u001b[0;34m(self, basis_matrix)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 80\u001b[0m dlens_old \u001b[39m=\u001b[39m dlens\n\u001b[0;32m---> 81\u001b[0m dlens_updated \u001b[39m=\u001b[39m f_region_distance_constraints(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mradius,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_nx,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_ny,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mall_sensorloc,dlens_old,p,j,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnSensors) \u001b[39m#Handling constrained region sensor placement problem\u001b[39;00m\n\u001b[1;32m 82\u001b[0m \u001b[39mprint\u001b[39m(dlens_updated)\n\u001b[1;32m 83\u001b[0m \u001b[39m# Choose pivot\u001b[39;00m\n", + "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 8'\u001b[0m in \u001b[0;36mf_region_distance_constraints\u001b[0;34m(r, nx, ny, all_sensors, dlens, piv, j, n_sensors)\u001b[0m\n\u001b[1;32m 6\u001b[0m idx_constrained \u001b[39m=\u001b[39m distance_constraints_indices(r,nx,ny,piv,n_sensors,j)\n\u001b[1;32m 7\u001b[0m didx \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39misin(piv[j:],idx_constrained,invert\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m----> 8\u001b[0m dlens[didx] \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m 9\u001b[0m \u001b[39mreturn\u001b[39;00m dlens\n", + "\u001b[0;31mIndexError\u001b[0m: boolean index did not match indexed array along dimension 0; dimension is 4096 but corresponding boolean dimension is 4095" + ] + } + ], "source": [ "r = 7\n", "optimizer1 = AQR(n_sensors,all_sensors,r,nx,ny)\n", diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index 5fcfb9c..8afd267 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -241,22 +241,23 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): # plot_gallery("First few centered faces", X[:n_components]) #Find all sensor locations using built in QR optimizer - max_const_sensors = 230 - n_const_sensors = 2 - n_sensors = 200 + #max_const_sensors = 230 + n_const_sensors = 3 + n_sensors = 10 + n_modes = 11 + basis = ps.basis.SVD(n_basis_modes=n_modes) optimizer = ps.optimizers.QR() - model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors) + model = ps.SSPOR(basis = basis, optimizer=optimizer, n_sensors=n_sensors) model.fit(X) all_sensors = model.get_all_sensors() ##Constrained sensor location on the grid: - xmin = 20 - xmax = 40 - ymin = 25 - ymax = 45 + xmin = 0 + xmax = 64 + ymin = 10 + ymax = 30 sensors_constrained = ps.utils._constraints.get_constraind_sensors_indices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices - # didx = np.isin(all_sensors,sensors_constrained,invert=False) # const_index = np.nonzero(didx) # j = @@ -277,7 +278,7 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): ## Fit the dataset with the optimizer GQR optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors, constraint_option = "exact_n_const_sensors") - model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors) + model1 = ps.SSPOR(basis = basis, optimizer = optimizer1, n_sensors = n_sensors) model1.fit(X) all_sensors1 = model1.get_all_sensors() From 8788790ec370fc434b9020a3eb5904992226933e Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Mon, 8 Aug 2022 11:53:17 -0600 Subject: [PATCH 23/52] Fixing dlens issues --- examples/region_optimal.ipynb | 461 +++++++++++++++++++++++++++++++--- 1 file changed, 427 insertions(+), 34 deletions(-) diff --git a/examples/region_optimal.ipynb b/examples/region_optimal.ipynb index 54eb348..1c351f8 100644 --- a/examples/region_optimal.ipynb +++ b/examples/region_optimal.ipynb @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 363, + "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:20.877032Z", @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:22.713344Z", @@ -306,11 +306,11 @@ " r = R[j:, j:]\n", " # Norm of each column \n", " dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0))\n", - " if j != 0:\n", - " dlens_old = dlens_updated\n", - " else:\n", - " dlens_old = dlens\n", - " dlens_updated = f_region_distance_constraints(self.radius,self._nx,self._ny,self.all_sensorloc,dlens_old,p,j,self.nSensors) #Handling constrained region sensor placement problem\n", + " # if j != 0:\n", + " # dlens_old = dlens_updated\n", + " # else:\n", + " # dlens_old = dlens\n", + " dlens_updated = f_region_distance_constraints(self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j,self.nSensors) #Handling constrained region sensor placement problem\n", " print(dlens_updated)\n", " # Choose pivot\n", " i_piv = np.argmax(dlens_updated)\n", @@ -344,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 367, + "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:24.092467Z", @@ -402,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:27.911973Z", @@ -414,21 +414,407 @@ "name": "stdout", "output_type": "stream", "text": [ - "[3.6017013 3.6861243 3.727096 ... 3.9694474 3.9748106 3.9125938]\n" - ] - }, - { - "ename": "IndexError", - "evalue": "boolean index did not match indexed array along dimension 0; dimension is 4096 but corresponding boolean dimension is 4095", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 11'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m optimizer1 \u001b[39m=\u001b[39m AQR(n_sensors,all_sensors,r,nx,ny)\n\u001b[1;32m 3\u001b[0m model1 \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39mSSPOR(optimizer \u001b[39m=\u001b[39m optimizer1, n_sensors \u001b[39m=\u001b[39m n_sensors)\n\u001b[0;32m----> 4\u001b[0m model1\u001b[39m.\u001b[39;49mfit(X)\n\u001b[1;32m 5\u001b[0m all_sensors1 \u001b[39m=\u001b[39m model1\u001b[39m.\u001b[39mget_all_sensors()\n\u001b[1;32m 7\u001b[0m top_sensors \u001b[39m=\u001b[39m model1\u001b[39m.\u001b[39mget_selected_sensors()\n", - "File \u001b[0;32m~/projects/pysensors/pysensors/reconstruction/_sspor.py:156\u001b[0m, in \u001b[0;36mSSPOR.fit\u001b[0;34m(self, x, quiet, prefit_basis, seed, **optimizer_kws)\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_n_sensors()\n\u001b[1;32m 155\u001b[0m \u001b[39m# Find sparse sensor locations\u001b[39;00m\n\u001b[0;32m--> 156\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mranked_sensors_ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptimizer\u001b[39m.\u001b[39;49mfit(\n\u001b[1;32m 157\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbasis_matrix_, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49moptimizer_kws\n\u001b[1;32m 158\u001b[0m )\u001b[39m.\u001b[39mget_sensors()\n\u001b[1;32m 160\u001b[0m \u001b[39m# Randomly shuffle sensors after self.basis.n_basis_modes\u001b[39;00m\n\u001b[1;32m 161\u001b[0m rng \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mrandom\u001b[39m.\u001b[39mdefault_rng(seed)\n", - "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 9'\u001b[0m in \u001b[0;36mAQR.fit\u001b[0;34m(self, basis_matrix)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 80\u001b[0m dlens_old \u001b[39m=\u001b[39m dlens\n\u001b[0;32m---> 81\u001b[0m dlens_updated \u001b[39m=\u001b[39m f_region_distance_constraints(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mradius,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_nx,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_ny,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mall_sensorloc,dlens_old,p,j,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnSensors) \u001b[39m#Handling constrained region sensor placement problem\u001b[39;00m\n\u001b[1;32m 82\u001b[0m \u001b[39mprint\u001b[39m(dlens_updated)\n\u001b[1;32m 83\u001b[0m \u001b[39m# Choose pivot\u001b[39;00m\n", - "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 8'\u001b[0m in \u001b[0;36mf_region_distance_constraints\u001b[0;34m(r, nx, ny, all_sensors, dlens, piv, j, n_sensors)\u001b[0m\n\u001b[1;32m 6\u001b[0m idx_constrained \u001b[39m=\u001b[39m distance_constraints_indices(r,nx,ny,piv,n_sensors,j)\n\u001b[1;32m 7\u001b[0m didx \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39misin(piv[j:],idx_constrained,invert\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m----> 8\u001b[0m dlens[didx] \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m 9\u001b[0m \u001b[39mreturn\u001b[39;00m dlens\n", - "\u001b[0;31mIndexError\u001b[0m: boolean index did not match indexed array along dimension 0; dimension is 4096 but corresponding boolean dimension is 4095" + "[3.6017013 3.6861243 3.727096 ... 3.9694474 3.9748106 3.9125938]\n", + "[3.6794097 3.7269068 3.659267 ... 3.5900235 3.5550506 3.5148866]\n", + "[0. 0. 0. ... 3.4743278 3.474582 3.4667015]\n", + "[2.3921647 2.5788512 2.503426 ... 0. 0. 0. ]\n", + "[2.5788503 2.503206 2.3640764 ... 1.5378283 2.2126265 2.4511552]\n", + "[2.5018702 2.3617132 2.184639 ... 1.5358236 2.2115734 2.4462159]\n", + "[2.3566432 2.176875 2.1142955 ... 1.5334948 2.2102485 2.4421096]\n", + "[2.1710463 2.1073353 2.097805 ... 1.5333759 2.2071729 2.4398267]\n", + "[2.1067212 2.0942876 1.9758159 ... 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0.07139541]\n", + "[0.11451449 0.14769727 0.11642887 ... 0.13121736 0.12778868 0.067109 ]\n", + "[0.14737509 0.113956 0.12288289 ... 0.13100757 0.1126395 0.06261284]\n", + "[0.11374912 0.1216555 0.1406635 ... 0.1304989 0.11002044 0.05684808]\n", + "[0.11957391 0.13970314 0.12264084 ... 0.13049698 0.10970615 0.05512554]\n", + "[0.13605037 0.12209643 0.10962112 ... 0.12182181 0.1017445 0.05476455]\n", + "[0.11113184 0.10287546 0.08311675 ... 0.10001213 0.0989915 0.05465437]\n", + "[0.09725989 0.07466382 0.11779437 ... 0.09465002 0.08514094 0.05395284]\n", + "[0.05755154 0.11122973 0.12960579 ... 0.08432529 0.08489251 0.05360966]\n", + "[0.10401206 0.12465931 0.10700091 ... 0.05413872 0.084133 0.05301797]\n", + "[0.11697015 0.0852209 0.06907541 ... 0.05200624 0.08413146 0.05082748]\n", + "[0. 0. 0. ... 0.04483959 0.06799887 0.05081567]\n", + "[0.06626277 0.05751188 0.04258984 ... 0.03873369 0.06332544 0.03759426]\n", + "[0.05614397 0.038643 0.02621154 ... 0.02552289 0.0528402 0.03668211]\n", + "[0.03481547 0.0256624 0.00327339 ... 0.00450747 0.04175061 0.0208794 ]\n", + "[2.4330802e-07 1.4607911e-05 1.9704923e-05 ... 1.5822996e-05 3.4701079e-06\n", + " 1.2084842e-05]\n" ] } ], @@ -444,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": 369, + "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:27.951889Z", @@ -497,7 +883,7 @@ }, { "cell_type": "code", - "execution_count": 370, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -524,7 +910,14 @@ }, { "cell_type": "code", - "execution_count": 371, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -563,7 +956,7 @@ }, { "cell_type": "code", - "execution_count": 372, + "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2022-07-11T16:34:57.421237Z", @@ -577,7 +970,7 @@ }, { "cell_type": "code", - "execution_count": 373, + "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2022-07-11T16:34:58.310764Z", @@ -591,7 +984,7 @@ "0.86233765" ] }, - "execution_count": 373, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -602,7 +995,7 @@ }, { "cell_type": "code", - "execution_count": 374, + "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:55:26.961534Z", @@ -620,7 +1013,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 18'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m optimizer_faces \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39moptimizers\u001b[39m.\u001b[39mQR()\n\u001b[1;32m 7\u001b[0m model \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39mSSPOR(basis\u001b[39m=\u001b[39mbasis1,optimizer\u001b[39m=\u001b[39moptimizer_faces, n_sensors\u001b[39m=\u001b[39mn_sensors0)\n\u001b[0;32m----> 8\u001b[0m model\u001b[39m.\u001b[39mfit(XX)\n\u001b[1;32m 10\u001b[0m all_sensors0 \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mget_all_sensors()\n\u001b[1;32m 11\u001b[0m top_sensors0 \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mget_selected_sensors()\n", + "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 19'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m optimizer_faces \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39moptimizers\u001b[39m.\u001b[39mQR()\n\u001b[1;32m 7\u001b[0m model \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39mSSPOR(basis\u001b[39m=\u001b[39mbasis1,optimizer\u001b[39m=\u001b[39moptimizer_faces, n_sensors\u001b[39m=\u001b[39mn_sensors0)\n\u001b[0;32m----> 8\u001b[0m model\u001b[39m.\u001b[39mfit(XX)\n\u001b[1;32m 10\u001b[0m all_sensors0 \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mget_all_sensors()\n\u001b[1;32m 11\u001b[0m top_sensors0 \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mget_selected_sensors()\n", "\u001b[0;31mNameError\u001b[0m: name 'XX' is not defined" ] } From 885add494ba3602691979142f5f08c222bb860cf Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Mon, 8 Aug 2022 15:47:56 -0600 Subject: [PATCH 24/52] Adding _validation in utils with determinant and relative reconstruction error --- examples/region_optimal.ipynb | 118 +++++++++++++++++++++++++++++++-- pysensors/utils/__init__.py | 6 +- pysensors/utils/_validation.py | 52 +++++++++++++++ 3 files changed, 168 insertions(+), 8 deletions(-) create mode 100644 pysensors/utils/_validation.py diff --git a/examples/region_optimal.ipynb b/examples/region_optimal.ipynb index 1c351f8..66d2b54 100644 --- a/examples/region_optimal.ipynb +++ b/examples/region_optimal.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 11, + "execution_count": 115, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:04.386599Z", @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 116, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:07.391526Z", @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 117, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:09.785781Z", @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 118, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:10.835009Z", @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 119, "metadata": { "ExecuteTime": { "end_time": "2022-07-10T04:22:11.651751Z", @@ -130,7 +130,7 @@ "output_type": "stream", "text": [ "[4032 384 4092 4039 447 493 2204 657 878 2880 1088 4087 2837 3779\n", - " 3093 2395 581 2751 1023 2970]\n" + " 3093]\n" ] } ], @@ -138,7 +138,7 @@ "#Find all sensor locations using built in QR optimizer\n", "#max_const_sensors = 230\n", "#n_const_sensors = 2\n", - "n_sensors = 20\n", + "n_sensors = 15\n", "optimizer = ps.optimizers.QR()\n", "model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors)\n", "model.fit(X)\n", @@ -150,6 +150,110 @@ "#print('Unconstrained Optimal sensors, n = {}, {}'.format(len(top_sensors0),top_sensors0))" ] }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "img = np.zeros(n_features)\n", + "img[top_sensors0] = 16\n", + "fig,ax = plt.subplots(1)\n", + "ax.set_aspect('equal')\n", + "ax.imshow(img.reshape(image_shape),cmap=plt.cm.binary)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([63, 6, 63, 63, 6, 7, 34, 10, 13, 45, 17, 63, 44, 59, 48]), array([ 0, 0, 60, 7, 63, 45, 28, 17, 46, 0, 0, 55, 21, 3, 21]))\n", + "[3779 4087 3779 878 493 4092 3093 4032 4039 2837]\n" + ] + } + ], + "source": [ + "a = np.unravel_index(top_sensors0, (nx,ny))\n", + "print(a)\n", + "violated_sensorsx =[]\n", + "violated_sensorsy =[]\n", + "for j in range(len(top_sensors0)):\n", + " x_cord = a[0][j]\n", + " y_cord = a[1][j]\n", + " for i in range(len(top_sensors0)):\n", + " if ((a[0][i]-x_cord)**2 + (a[1][i]-y_cord)**2) < r**2 and i!=j: \n", + " violated_sensorsx.append(a[0][i])\n", + " violated_sensorsy.append(a[1][i])\n", + "violated_sensorsx = np.array(violated_sensorsx)\n", + "violated_sensorsy = np.array(violated_sensorsy)\n", + "violated_sensors_array = np.stack((violated_sensorsx, violated_sensorsy), axis=1)\n", + "violated_sensors_tuple = np.transpose(violated_sensors_array)\n", + "idx_violated = np.ravel_multi_index(violated_sensors_tuple, (nx,ny))\n", + "print(idx_violated)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "img = np.zeros(n_features)\n", + "img[top_sensors0] = 16\n", + "fig,ax = plt.subplots(1)\n", + "ax.set_aspect('equal')\n", + "ax.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", + "top_sensors0_grid = np.unravel_index(top_sensors0, (nx,ny))\n", + "plt.scatter(violated_sensorsy, violated_sensorsx)\n", + "# figure, axes = plt.subplots()\n", + "for i in range(len(top_sensors0_grid[0])):\n", + " circ = Circle( (top_sensors0_grid[1][i], top_sensors0_grid[0][i]), r ,color='r',fill = False )\n", + " ax.add_patch(circ)\n", + "#plt.scatter(violated_sensorsy, violated_sensorsx)\n", + "plt.show()" + ] + }, { "cell_type": "code", "execution_count": 16, diff --git a/pysensors/utils/__init__.py b/pysensors/utils/__init__.py index d24d201..6fd2243 100644 --- a/pysensors/utils/__init__.py +++ b/pysensors/utils/__init__.py @@ -5,6 +5,8 @@ from ._constraints import get_constrained_sensors_indices_linear from ._constraints import box_constraints from ._constraints import functional_constraints +from ._validation import determinant +from ._validation import relative_reconstruction_error __all__ = [ "constrained_binary_solve", @@ -13,5 +15,7 @@ "get_constraind_sensors_indices", "get_constrained_sensors_indices_linear", "box_constraints", - "functional_constraints" + "functional_constraints", + "determinant", + "relative_reconstruction_error" ] diff --git a/pysensors/utils/_validation.py b/pysensors/utils/_validation.py new file mode 100644 index 0000000..4a1ee70 --- /dev/null +++ b/pysensors/utils/_validation.py @@ -0,0 +1,52 @@ +""" +Various utility functions for validation and computing reconstruction scores and errors. +""" +import numpy as np + +def determinant(top_sensors, n_features, basis_matrix): + """ + Function for calculating |C.T phi.T C phi|. + + Parameters + ---------- + top_sensors: np.darray, + Column indices of choosen sensor locations + n_features : int, + No. of features of dataset + basis_matrix : np.darray, + The basis matrix calculated by model.basis_matrix_ + + Returns + ------- + optimality : Float, + The dterminant value obtained. + """ + + c = np.zeros([len(top_sensors),n_features]) + print(c.shape) + for i in range(len(top_sensors)): + c[i,top_sensors[i]] = 1 + print(c) + phi = basis_matrix + optimality = np.linalg.det((c@phi).T @ (c@phi)) + print(optimality) + return (c,phi,optimality) + +def relative_reconstruction_error(data, prediction): + """ + Function for calculating relative error between actual data and the reconstruction + + Parameters + ---------- + data: np.darray, + The actual data from the dataset evaluated + prediction : np.darray, + The predicted values from model.predict(X[:,top_sensors]) + Returns + ------- + error_val : Float, + The relative error calculated. + """ + error_val = (np.linalg.norm((data - prediction)/np.linalg.norm(data)))*100 + print(error_val) + return (error_val) \ No newline at end of file From 69a5511f40edf9da8a331cb058762f151abea5a4 Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Wed, 10 Aug 2022 13:10:31 -0600 Subject: [PATCH 25/52] Adding predtermined_norm_calc to gqr --- pysensors/optimizers/_gqr.py | 34 ++++++++++++++++++++++++++++++++++ pysensors/utils/_validation.py | 8 ++------ 2 files changed, 36 insertions(+), 6 deletions(-) diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index 8afd267..3d25af4 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -95,6 +95,8 @@ def fit( dlens_updated = norm_calc_max_n_const_sensors(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.nSensors) elif self.constraint_option == "exact_n_const_sensors" : dlens_updated = norm_calc_exact_n_const_sensors(self.constrainedIndices,dlens,p,j,self.nConstrainedSensors) + elif self.constraint_option == "predetermined_end": + dlens_updated = predetermined_norm_calc(self.constrainedIndices, dlens, p, j, self.nConstrainedSensors, self.nSensors) # Choose pivot i_piv = np.argmax(dlens_updated) @@ -202,6 +204,38 @@ def norm_calc_max_n_const_sensors(lin_idx, dlens, piv, j, const_sensors,all_sens dlens[didx] = 0 return dlens +def predetermined_norm_calc(lin_idx, dlens, piv, j, n_const_sensors, n_sensors): + """ + Function for mapping constrained sensor locations with the QR procedure. + + Parameters + ---------- + lin_idx: np.ndarray, shape [No. of constrained locations] + Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. + dlens: np.ndarray, shape [Variable based on j] + Array which contains the norm of columns of basis matrix. + piv: np.ndarray, shape [n_features] + Ranked list of sensor locations. + n_const_sensors: int, + Number of sensors to be placed in the constrained area. + j: int, + Iterative variable in the QR algorithm. + + Returns + ------- + dlens : np.darray, shape [Variable based on j] with constraints mapped into it. + """ + if (n_sensors - n_const_sensors) <= j <= n_sensors: # force sensors into constraint region + #idx = np.arange(dlens.shape[0]) + #dlens[np.delete(idx, lin_idx)] = 0 + + didx = np.isin(piv[j:],lin_idx,invert=True) + dlens[didx] = 0 + else: + didx = np.isin(piv[j:],lin_idx,invert=False) + dlens[didx] = 0 + return dlens + if __name__ == '__main__': diff --git a/pysensors/utils/_validation.py b/pysensors/utils/_validation.py index 4a1ee70..5940d67 100644 --- a/pysensors/utils/_validation.py +++ b/pysensors/utils/_validation.py @@ -23,14 +23,11 @@ def determinant(top_sensors, n_features, basis_matrix): """ c = np.zeros([len(top_sensors),n_features]) - print(c.shape) for i in range(len(top_sensors)): c[i,top_sensors[i]] = 1 - print(c) phi = basis_matrix optimality = np.linalg.det((c@phi).T @ (c@phi)) - print(optimality) - return (c,phi,optimality) + return optimality def relative_reconstruction_error(data, prediction): """ @@ -47,6 +44,5 @@ def relative_reconstruction_error(data, prediction): error_val : Float, The relative error calculated. """ - error_val = (np.linalg.norm((data - prediction)/np.linalg.norm(data)))*100 - print(error_val) + error_val = (np.linalg.norm((data - prediction)/(data)))*100 return (error_val) \ No newline at end of file From ab4bdbdd8db33f011763fad4dd14e78b482a9e48 Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Mon, 15 Aug 2022 13:10:02 -0600 Subject: [PATCH 26/52] Moving norm_calculation functions to utilities --- pysensors/optimizers/_gqr.py | 178 ++++++++++------------------------ pysensors/utils/__init__.py | 10 +- pysensors/utils/_norm_calc.py | 156 +++++++++++++++++++++++++++++ 3 files changed, 217 insertions(+), 127 deletions(-) create mode 100644 pysensors/utils/_norm_calc.py diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index 3d25af4..6e1c283 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -8,6 +8,7 @@ from mpl_toolkits.axes_grid1 import make_axes_locatable import pysensors as ps +from matplotlib.patches import Circle class GQR(QR): @@ -27,7 +28,7 @@ class GQR(QR): @ authors: Niharika Karnik (@nkarnik2999), Mohammad Abdo (@Jimmy-INL), and Krithika Manohar (@kmanohar) """ - def __init__(self,idx_constrained,n_sensors,n_const_sensors,all_sensors,constraint_option): + def __init__(self,idx_constrained,n_sensors,n_const_sensors,all_sensors,constraint_option,nx,ny,r): """ Attributes ---------- @@ -47,11 +48,15 @@ def __init__(self,idx_constrained,n_sensors,n_const_sensors,all_sensors,constrai """ self.pivots_ = None self.optimality = None + self.constrainedIndices = idx_constrained self.nSensors = n_sensors self.nConstrainedSensors = n_const_sensors self.all_sensorloc = all_sensors self.constraint_option = constraint_option + self._nx = nx + self._ny = ny + self._r = r def fit( self, @@ -92,16 +97,33 @@ def fit( # Norm of each column dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0)) if self.constraint_option == "max_n_const_sensors" : - dlens_updated = norm_calc_max_n_const_sensors(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.nSensors) + dlens_updated = ps.utils._norm_calc.norm_calc_max_n_const_sensors(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.nSensors) + i_piv = np.argmax(dlens_updated) + dlen = dlens_updated[i_piv] elif self.constraint_option == "exact_n_const_sensors" : - dlens_updated = norm_calc_exact_n_const_sensors(self.constrainedIndices,dlens,p,j,self.nConstrainedSensors) + dlens_updated = ps.utils._norm_calc.norm_calc_exact_n_const_sensors(self.constrainedIndices,dlens,p,j,self.nConstrainedSensors) + i_piv = np.argmax(dlens_updated) + dlen = dlens_updated[i_piv] elif self.constraint_option == "predetermined_end": - dlens_updated = predetermined_norm_calc(self.constrainedIndices, dlens, p, j, self.nConstrainedSensors, self.nSensors) + dlens_updated = ps.utils._norm_calc.predetermined_norm_calc(self.constrainedIndices, dlens, p, j, self.nConstrainedSensors, self.nSensors) + i_piv = np.argmax(dlens_updated) + dlen = dlens_updated[i_piv] + elif self.constraint_option == "radii_constraints": + if j == 0: + dlens_old = dlens + i_piv = np.argmax(dlens) + dlen = dlens[i_piv] + else: + + dlens_updated = ps.utils._norm_calc.f_radii_constraint(j,dlens,dlens_old,p,self._nx,self._ny,self._r) #( self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j) + i_piv = np.argmax(dlens_updated) + dlen = dlens_updated[i_piv] + dlens_old = dlens_updated # Choose pivot - i_piv = np.argmax(dlens_updated) + # i_piv = np.argmax(dlens_updated) - dlen = dlens_updated[i_piv] + # dlen = dlens_updated[i_piv] if dlen > 0: u = r[:, i_piv] / dlen @@ -128,116 +150,6 @@ def fit( return self -## TODO: why not a part of the class? - -#function for mapping sensor locations with constraints -def norm_calc_exact_n_const_sensors(lin_idx, dlens, piv, j, n_const_sensors): ##Will first force sensors into constrained region - #num_sensors should be fixed for each custom constraint (for now) - #num_sensors must be <= size of constraint region - """ - Function for mapping constrained sensor locations with the QR procedure. - - Parameters - ---------- - lin_idx: np.ndarray, shape [No. of constrained locations] - Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. - dlens: np.ndarray, shape [Variable based on j] - Array which contains the norm of columns of basis matrix. - piv: np.ndarray, shape [n_features] - Ranked list of sensor locations. - n_const_sensors: int, - Number of sensors to be placed in the constrained area. - j: int, - Iterative variable in the QR algorithm. - - Returns - ------- - dlens : np.darray, shape [Variable based on j] with constraints mapped into it. - """ - if j < n_const_sensors: # force sensors into constraint region - #idx = np.arange(dlens.shape[0]) - #dlens[np.delete(idx, lin_idx)] = 0 - - didx = np.isin(piv[j:],lin_idx,invert=True) - dlens[didx] = 0 - else: - didx = np.isin(piv[j:],lin_idx,invert=False) - dlens[didx] = 0 - return dlens - -def norm_calc_max_n_const_sensors(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors): ##Optimal sensor placement with constraints (will place sensors in the order of QR) - """ - Function for mapping constrained sensor locations with the QR procedure (Optimally). - - Parameters - ---------- - lin_idx: np.ndarray, shape [No. of constrained locations] - Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. - dlens: np.ndarray, shape [Variable based on j] - Array which contains the norm of columns of basis matrix. - piv: np.ndarray, shape [n_features] - Ranked list of sensor locations. - j: int, - Iterative variable in the QR algorithm. - const_sensors: int, - Number of sensors to be placed in the constrained area. - all_sensors: np.ndarray, shape [n_features] - Ranked list of sensor locations. - n_sensors: integer, - Total number of sensors - - Returns - ------- - dlens : np.darray, shape [Variable based on j] with constraints mapped into it. - """ - counter = 0 - mask = np.isin(all_sensors,lin_idx,invert=False) - const_idx = all_sensors[mask] - updated_lin_idx = const_idx[const_sensors:] - for i in range(n_sensors): - if np.isin(all_sensors[i],lin_idx,invert=False): - counter += 1 - if counter < const_sensors: - dlens = dlens - else: - didx = np.isin(piv[j:],updated_lin_idx,invert=False) - dlens[didx] = 0 - return dlens - -def predetermined_norm_calc(lin_idx, dlens, piv, j, n_const_sensors, n_sensors): - """ - Function for mapping constrained sensor locations with the QR procedure. - - Parameters - ---------- - lin_idx: np.ndarray, shape [No. of constrained locations] - Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. - dlens: np.ndarray, shape [Variable based on j] - Array which contains the norm of columns of basis matrix. - piv: np.ndarray, shape [n_features] - Ranked list of sensor locations. - n_const_sensors: int, - Number of sensors to be placed in the constrained area. - j: int, - Iterative variable in the QR algorithm. - - Returns - ------- - dlens : np.darray, shape [Variable based on j] with constraints mapped into it. - """ - if (n_sensors - n_const_sensors) <= j <= n_sensors: # force sensors into constraint region - #idx = np.arange(dlens.shape[0]) - #dlens[np.delete(idx, lin_idx)] = 0 - - didx = np.isin(piv[j:],lin_idx,invert=True) - dlens[didx] = 0 - else: - didx = np.isin(piv[j:],lin_idx,invert=False) - dlens[didx] = 0 - return dlens - - - if __name__ == '__main__': faces = datasets.fetch_olivetti_faces(shuffle=True) X = faces.data @@ -277,8 +189,9 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): #Find all sensor locations using built in QR optimizer #max_const_sensors = 230 n_const_sensors = 3 - n_sensors = 10 - n_modes = 11 + n_sensors = 30 + n_modes = 50 + r = 5 basis = ps.basis.SVD(n_basis_modes=n_modes) optimizer = ps.optimizers.QR() model = ps.SSPOR(basis = basis, optimizer=optimizer, n_sensors=n_sensors) @@ -311,7 +224,7 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): # plt.title('Constrained region'); ## Fit the dataset with the optimizer GQR - optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors, constraint_option = "exact_n_const_sensors") + optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors, constraint_option = "radii_constraints",nx = nx, ny = ny, r = r) model1 = ps.SSPOR(basis = basis, optimizer = optimizer1, n_sensors = n_sensors) model1.fit(X) all_sensors1 = model1.get_all_sensors() @@ -322,13 +235,26 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): #yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features)) #xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features)) + # img = np.zeros(n_features) + # img[top_sensors] = 16 + # #plt.plot(xConstrained,yConstrained,'*r') + # plt.plot([xmin,xmin],[ymin,ymax],'r') + # plt.plot([xmin,xmax],[ymax,ymax],'r') + # plt.plot([xmax,xmax],[ymin,ymax],'r') + # plt.plot([xmin,xmax],[ymin,ymin],'r') + # plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) + # plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) + # plt.show() + img = np.zeros(n_features) img[top_sensors] = 16 - #plt.plot(xConstrained,yConstrained,'*r') - plt.plot([xmin,xmin],[ymin,ymax],'r') - plt.plot([xmin,xmax],[ymax,ymax],'r') - plt.plot([xmax,xmax],[ymin,ymax],'r') - plt.plot([xmin,xmax],[ymin,ymin],'r') - plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) - plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) + fig,ax = plt.subplots(1) + ax.set_aspect('equal') + ax.imshow(img.reshape(image_shape),cmap=plt.cm.binary) + print(top_sensors) + top_sensors_grid = np.unravel_index(top_sensors, (nx,ny)) + # figure, axes = plt.subplots() + for i in range(len(top_sensors_grid[0])): + circ = Circle( (top_sensors_grid[1][i], top_sensors_grid[0][i]), r ,color='r',fill = False ) + ax.add_patch(circ) plt.show() diff --git a/pysensors/utils/__init__.py b/pysensors/utils/__init__.py index 6fd2243..b18f22d 100644 --- a/pysensors/utils/__init__.py +++ b/pysensors/utils/__init__.py @@ -7,6 +7,10 @@ from ._constraints import functional_constraints from ._validation import determinant from ._validation import relative_reconstruction_error +from ._norm_calc import norm_calc_exact_n_const_sensors +from ._norm_calc import norm_calc_max_n_const_sensors +from ._norm_calc import predetermined_norm_calc +from ._norm_calc import f_radii_constraint __all__ = [ "constrained_binary_solve", @@ -17,5 +21,9 @@ "box_constraints", "functional_constraints", "determinant", - "relative_reconstruction_error" + "relative_reconstruction_error", + "norm_calc_exact_n_const_sensors", + "norm_calc_max_n_const_sensors", + "predetermined_norm_calc", + "f_radii_constraint" ] diff --git a/pysensors/utils/_norm_calc.py b/pysensors/utils/_norm_calc.py new file mode 100644 index 0000000..83ab695 --- /dev/null +++ b/pysensors/utils/_norm_calc.py @@ -0,0 +1,156 @@ +""" +Various utility functions for calculating the norm and providing dlens_updated based on the different types of adaptive constraints for _gqr.py in optimizers. +""" + +import numpy as np + +def norm_calc_exact_n_const_sensors(lin_idx, dlens, piv, j, n_const_sensors): ##Will first force sensors into constrained region + #num_sensors should be fixed for each custom constraint (for now) + #num_sensors must be <= size of constraint region + """ + Function for mapping constrained sensor locations with the QR procedure. + + Parameters + ---------- + lin_idx: np.ndarray, shape [No. of constrained locations] + Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. + dlens: np.ndarray, shape [Variable based on j] + Array which contains the norm of columns of basis matrix. + piv: np.ndarray, shape [n_features] + Ranked list of sensor locations. + n_const_sensors: int, + Number of sensors to be placed in the constrained area. + j: int, + Iterative variable in the QR algorithm. + + Returns + ------- + dlens : np.darray, shape [Variable based on j] with constraints mapped into it. + """ + if j < n_const_sensors: # force sensors into constraint region + #idx = np.arange(dlens.shape[0]) + #dlens[np.delete(idx, lin_idx)] = 0 + + didx = np.isin(piv[j:],lin_idx,invert=True) + dlens[didx] = 0 + else: + didx = np.isin(piv[j:],lin_idx,invert=False) + dlens[didx] = 0 + return dlens + +def norm_calc_max_n_const_sensors(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors): ##Optimal sensor placement with constraints (will place sensors in the order of QR) + """ + Function for mapping constrained sensor locations with the QR procedure (Optimally). + + Parameters + ---------- + lin_idx: np.ndarray, shape [No. of constrained locations] + Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. + dlens: np.ndarray, shape [Variable based on j] + Array which contains the norm of columns of basis matrix. + piv: np.ndarray, shape [n_features] + Ranked list of sensor locations. + j: int, + Iterative variable in the QR algorithm. + const_sensors: int, + Number of sensors to be placed in the constrained area. + all_sensors: np.ndarray, shape [n_features] + Ranked list of sensor locations. + n_sensors: integer, + Total number of sensors + + Returns + ------- + dlens : np.darray, shape [Variable based on j] with constraints mapped into it. + """ + counter = 0 + mask = np.isin(all_sensors,lin_idx,invert=False) + const_idx = all_sensors[mask] + updated_lin_idx = const_idx[const_sensors:] + for i in range(n_sensors): + if np.isin(all_sensors[i],lin_idx,invert=False): + counter += 1 + if counter < const_sensors: + dlens = dlens + else: + didx = np.isin(piv[j:],updated_lin_idx,invert=False) + dlens[didx] = 0 + return dlens + +def predetermined_norm_calc(lin_idx, dlens, piv, j, n_const_sensors, n_sensors): + """ + Function for mapping constrained sensor locations with the QR procedure. + + Parameters + ---------- + lin_idx: np.ndarray, shape [No. of constrained locations] + Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. + dlens: np.ndarray, shape [Variable based on j] + Array which contains the norm of columns of basis matrix. + piv: np.ndarray, shape [n_features] + Ranked list of sensor locations. + n_const_sensors: int, + Number of sensors to be placed in the constrained area. + j: int, + Iterative variable in the QR algorithm. + + Returns + ------- + dlens : np.darray, shape [Variable based on j] with constraints mapped into it. + """ + if (n_sensors - n_const_sensors) <= j <= n_sensors: # force sensors into constraint region + #idx = np.arange(dlens.shape[0]) + #dlens[np.delete(idx, lin_idx)] = 0 + + didx = np.isin(piv[j:],lin_idx,invert=True) + dlens[didx] = 0 + else: + didx = np.isin(piv[j:],lin_idx,invert=False) + dlens[didx] = 0 + return dlens + +def f_radii_constraint(j,dlens,dlens_old,piv,nx,ny,r): + a = np.unravel_index(piv, (nx,ny)) + n_features = len(piv) + if j == 1: + x_cord = a[0][j-1] + y_cord = a[1][j-1] + #print(x_cord, y_cord) + constrained_sensorsx = [] + constrained_sensorsy = [] + for i in range(n_features): + if ((a[0][i]-x_cord)**2 + (a[1][i]-y_cord)**2) < r**2: + constrained_sensorsx.append(a[0][i]) + constrained_sensorsy.append(a[1][i]) + constrained_sensorsx = np.array(constrained_sensorsx) + constrained_sensorsy = np.array(constrained_sensorsy) + constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1) + constrained_sensors_tuple = np.transpose(constrained_sensors_array) + idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny)) +# print(idx_constrained) + didx = np.isin(piv[j:],idx_constrained,invert= False) + dlens[didx] = 0 + return dlens + else: + result = np.where(dlens_old == 0)[0] + result_list = result.tolist() + result_list = [x - 1 for x in result_list] + result_array = np.array(result_list) + x_cord = a[0][j-1] + y_cord = a[1][j-1] + #print(x_cord, y_cord) + constrained_sensorsx = [] + constrained_sensorsy = [] + for i in range(n_features): + if ((a[0][i]-x_cord)**2 + (a[1][i]-y_cord)**2) < r**2: + constrained_sensorsx.append(a[0][i]) + constrained_sensorsy.append(a[1][i]) + constrained_sensorsx = np.array(constrained_sensorsx) + constrained_sensorsy = np.array(constrained_sensorsy) + constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1) + constrained_sensors_tuple = np.transpose(constrained_sensors_array) + idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny)) + t = np.concatenate((idx_constrained,result_array), axis = 0) + didx = np.isin(piv[j:],t,invert= False) + dlens[didx] = 0 + return dlens \ No newline at end of file From 67d3bb8b3e6e016a4f901f4d9f7491ff969e10f0 Mon Sep 17 00:00:00 2001 From: niharika2999 Date: Thu, 25 Aug 2022 09:47:15 -0600 Subject: [PATCH 27/52] Adding all updates --- pysensors/optimizers/_gqr.py | 111 +++++++++++++++++++++------------ pysensors/utils/_validation.py | 4 +- 2 files changed, 72 insertions(+), 43 deletions(-) diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index 6e1c283..c802b60 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -9,6 +9,7 @@ import pysensors as ps from matplotlib.patches import Circle +from pydmd import DMD class GQR(QR): @@ -80,11 +81,11 @@ def fit( max_const_sensors = len(self.constrainedIndices) #Maximum number of sensors allowed in the constrained region ## Assertions and checks: - if self.nSensors > n_features - max_const_sensors + self.nConstrainedSensors: - raise IOError ("n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors") - if self.nSensors > n_samples + self.nConstrainedSensors: ## Handling zero constraint? - raise IOError ("Currently n_sensors should be less than min(number of samples, number of modes) + number of constrained sensors,\ - got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}".format(self.nSensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors)) + # if self.nSensors > n_features - max_const_sensors + self.nConstrainedSensors: + # raise IOError ("n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors") + # if self.nSensors > n_samples + self.nConstrainedSensors: ## Handling zero constraint? + # raise IOError ("Currently n_sensors should be less than min(number of samples, number of modes) + number of constrained sensors,\ + # got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}".format(self.nSensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors)) # Initialize helper variables R = basis_matrix.conj().T.copy() @@ -189,22 +190,38 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): #Find all sensor locations using built in QR optimizer #max_const_sensors = 230 n_const_sensors = 3 - n_sensors = 30 - n_modes = 50 + n_sensors = 10 + n_modes = 40 r = 5 - basis = ps.basis.SVD(n_basis_modes=n_modes) - optimizer = ps.optimizers.QR() - model = ps.SSPOR(basis = basis, optimizer=optimizer, n_sensors=n_sensors) - model.fit(X) - - all_sensors = model.get_all_sensors() - + dmd = DMD(svd_rank=0,exact=True,opt=False) + dmd.fit(X.T) + U = dmd.modes.real + np.shape(U) + max_basis_modes = 200 + + model_dmd_unconstrained = ps.SSPOR(n_sensors=n_sensors, basis=ps.basis.Custom(n_basis_modes=n_modes, U=U)) + model_dmd_unconstrained.fit(X) + basis_matrix_dmd = model_dmd_unconstrained.basis_matrix_ + + all_sensors_dmd_unconstrained = model_dmd_unconstrained.get_all_sensors() + top_sensors_dmd_unconstrained = model_dmd_unconstrained.get_selected_sensors() + optimality_dmd = ps.utils._validation.determinant(top_sensors_dmd_unconstrained, n_features, basis_matrix_dmd) + # print(optimality0) + # basis = ps.basis.SVD(n_basis_modes=n_modes) + # optimizer = ps.optimizers.QR() + # model = ps.SSPOR(basis = basis, optimizer=optimizer, n_sensors=n_sensors) + # model.fit(X) + # top_sensors0 = model.get_selected_sensors() + # all_sensors = model.get_all_sensors() + # basis_matrix0 = model.basis_matrix_ + # optimality0 = ps.utils._validation.determinant(top_sensors0, n_features, basis_matrix0) + # print(optimality0) ##Constrained sensor location on the grid: xmin = 0 xmax = 64 ymin = 10 ymax = 30 - sensors_constrained = ps.utils._constraints.get_constraind_sensors_indices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices + sensors_constrained = ps.utils._constraints.get_constraind_sensors_indices(xmin,xmax,ymin,ymax,nx,ny,all_sensors_dmd_unconstrained) #Constrained column indices # didx = np.isin(all_sensors,sensors_constrained,invert=False) # const_index = np.nonzero(didx) # j = @@ -224,37 +241,49 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): # plt.title('Constrained region'); ## Fit the dataset with the optimizer GQR - optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors, constraint_option = "radii_constraints",nx = nx, ny = ny, r = r) - model1 = ps.SSPOR(basis = basis, optimizer = optimizer1, n_sensors = n_sensors) - model1.fit(X) - all_sensors1 = model1.get_all_sensors() + # optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors, constraint_option = "max_n_const_sensors",nx = nx, ny = ny, r = r) + # model1 = ps.SSPOR(basis = basis, optimizer = optimizer1, n_sensors = n_sensors) + # model1.fit(X) + # all_sensors1 = model1.get_all_sensors() + # basis_matrix = model1.basis_matrix_ + # top_sensors = model1.get_selected_sensors() + # print(top_sensors) + # optimality = ps.utils._validation.determinant(top_sensors, n_features, basis_matrix) + # print(optimality) + optimizer_dmd_constrained = ps.optimizers.GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors_dmd_unconstrained,constraint_option = "exact_n_const_sensors",nx = nx, ny = ny, r = r) + model_dmd_constrained = ps.SSPOR(n_sensors=n_sensors, basis=ps.basis.Custom(n_basis_modes=n_modes, U=U), optimizer = optimizer_dmd_constrained) + model_dmd_constrained.fit(X) + all_sensors_dmd_constrained = model_dmd_constrained.get_all_sensors() + + top_sensors_dmd_constrained = model_dmd_constrained.get_selected_sensors() + basis_matrix_dmd_constrained = model_dmd_constrained.basis_matrix_ + optimality = ps.utils._validation.determinant(top_sensors_dmd_constrained, n_features, basis_matrix_dmd_constrained) + # print(optimality) - top_sensors = model1.get_selected_sensors() - print(top_sensors) ## TODO: this can be done using ravel and unravel more elegantly #yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features)) #xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features)) + img = np.zeros(n_features) + img[top_sensors_dmd_constrained] = 16 + #plt.plot(xConstrained,yConstrained,'*r') + plt.plot([xmin,xmin],[ymin,ymax],'r') + plt.plot([xmin,xmax],[ymax,ymax],'r') + plt.plot([xmax,xmax],[ymin,ymax],'r') + plt.plot([xmin,xmax],[ymin,ymin],'r') + plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) + plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) + plt.show() + # img = np.zeros(n_features) # img[top_sensors] = 16 - # #plt.plot(xConstrained,yConstrained,'*r') - # plt.plot([xmin,xmin],[ymin,ymax],'r') - # plt.plot([xmin,xmax],[ymax,ymax],'r') - # plt.plot([xmax,xmax],[ymin,ymax],'r') - # plt.plot([xmin,xmax],[ymin,ymin],'r') - # plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) - # plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) + # fig,ax = plt.subplots(1) + # ax.set_aspect('equal') + # ax.imshow(img.reshape(image_shape),cmap=plt.cm.binary) + # print(top_sensors) + # top_sensors_grid = np.unravel_index(top_sensors, (nx,ny)) + # # figure, axes = plt.subplots() + # for i in range(len(top_sensors_grid[0])): + # circ = Circle( (top_sensors_grid[1][i], top_sensors_grid[0][i]), r ,color='r',fill = False ) + # ax.add_patch(circ) # plt.show() - - img = np.zeros(n_features) - img[top_sensors] = 16 - fig,ax = plt.subplots(1) - ax.set_aspect('equal') - ax.imshow(img.reshape(image_shape),cmap=plt.cm.binary) - print(top_sensors) - top_sensors_grid = np.unravel_index(top_sensors, (nx,ny)) - # figure, axes = plt.subplots() - for i in range(len(top_sensors_grid[0])): - circ = Circle( (top_sensors_grid[1][i], top_sensors_grid[0][i]), r ,color='r',fill = False ) - ax.add_patch(circ) - plt.show() diff --git a/pysensors/utils/_validation.py b/pysensors/utils/_validation.py index 5940d67..3a4ab8c 100644 --- a/pysensors/utils/_validation.py +++ b/pysensors/utils/_validation.py @@ -26,7 +26,7 @@ def determinant(top_sensors, n_features, basis_matrix): for i in range(len(top_sensors)): c[i,top_sensors[i]] = 1 phi = basis_matrix - optimality = np.linalg.det((c@phi).T @ (c@phi)) + optimality = np.linalg.det((c@phi).T @ (c@phi)) #np.log(np.linalg.det(phi.T @ c.T)) np.log(np.linalg.det((c@phi).T @ (c@phi))) return optimality def relative_reconstruction_error(data, prediction): @@ -44,5 +44,5 @@ def relative_reconstruction_error(data, prediction): error_val : Float, The relative error calculated. """ - error_val = (np.linalg.norm((data - prediction)/(data)))*100 + error_val = (np.linalg.norm((data - prediction)/np.linalg.norm(data)))*100 return (error_val) \ No newline at end of file From 5671ebba9f15fc26be002dba8af746ab891247f7 Mon Sep 17 00:00:00 2001 From: Niharika Karnik Date: Thu, 13 Oct 2022 10:16:27 -0700 Subject: [PATCH 28/52] Modifying radii_constraints code and gqr now works as a script for radii constraints --- pysensors/optimizers/_gqr.py | 147 ++++++++++++++++++---------------- pysensors/utils/_norm_calc.py | 85 +++++++++++--------- 2 files changed, 125 insertions(+), 107 deletions(-) diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index c802b60..df2c1b0 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -1,4 +1,5 @@ import numpy as np +import pysensors from pysensors.optimizers._qr import QR @@ -110,17 +111,18 @@ def fit( i_piv = np.argmax(dlens_updated) dlen = dlens_updated[i_piv] elif self.constraint_option == "radii_constraints": - if j == 0: - dlens_old = dlens + + if j == 0: i_piv = np.argmax(dlens) dlen = dlens[i_piv] + dlens_old = dlens else: - dlens_updated = ps.utils._norm_calc.f_radii_constraint(j,dlens,dlens_old,p,self._nx,self._ny,self._r) #( self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j) + dlens_updated = ps.utils._norm_calc.f_radii_constraint(j,dlens,dlens_old,p,self._nx,self._ny,self._r,self.all_sensorloc, self.nSensors) #( self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j) i_piv = np.argmax(dlens_updated) dlen = dlens_updated[i_piv] dlens_old = dlens_updated - + # Choose pivot # i_piv = np.argmax(dlens_updated) @@ -146,30 +148,30 @@ def fit( R[j + 1 :, j] = 0 self.pivots_ = p - self.optimality = np.trace(np.real(R)) - print("The trace(R) = {}".format(self.optimality)) - + return self if __name__ == '__main__': faces = datasets.fetch_olivetti_faces(shuffle=True) X = faces.data - n_samples, n_features = X.shape + # n_samples, n_features = X.shape + X_small = X[:,:256] + n_samples, n_features = X_small.shape print('Number of samples:', n_samples) print('Number of features (sensors):', n_features) # Global centering - X = X - X.mean(axis=0) + X_small = X_small - X_small.mean(axis=0) # Local centering - X -= X.mean(axis=1).reshape(n_samples, -1) + X_small -= X_small.mean(axis=1).reshape(n_samples, -1) n_row, n_col = 2, 3 n_components = n_row * n_col - image_shape = (64, 64) - nx = 64 - ny = 64 + image_shape = (16, 16) + nx = 16 + ny = 16 def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): '''Function for plotting faces''' @@ -190,29 +192,29 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): #Find all sensor locations using built in QR optimizer #max_const_sensors = 230 n_const_sensors = 3 - n_sensors = 10 + n_sensors = 4 n_modes = 40 - r = 5 - dmd = DMD(svd_rank=0,exact=True,opt=False) - dmd.fit(X.T) - U = dmd.modes.real - np.shape(U) - max_basis_modes = 200 - - model_dmd_unconstrained = ps.SSPOR(n_sensors=n_sensors, basis=ps.basis.Custom(n_basis_modes=n_modes, U=U)) - model_dmd_unconstrained.fit(X) - basis_matrix_dmd = model_dmd_unconstrained.basis_matrix_ - - all_sensors_dmd_unconstrained = model_dmd_unconstrained.get_all_sensors() - top_sensors_dmd_unconstrained = model_dmd_unconstrained.get_selected_sensors() - optimality_dmd = ps.utils._validation.determinant(top_sensors_dmd_unconstrained, n_features, basis_matrix_dmd) + r = 2 + # dmd = DMD(svd_rank=0,exact=True,opt=False) + # dmd.fit(X.T) + # U = dmd.modes.real + # np.shape(U) + # max_basis_modes = 200 + + # model_dmd_unconstrained = ps.SSPOR(n_sensors=n_sensors, basis=ps.basis.Custom(n_basis_modes=n_modes, U=U)) + # model_dmd_unconstrained.fit(X) + # basis_matrix_dmd = model_dmd_unconstrained.basis_matrix_ + + # all_sensors_dmd_unconstrained = model_dmd_unconstrained.get_all_sensors() + # top_sensors_dmd_unconstrained = model_dmd_unconstrained.get_selected_sensors() + # optimality_dmd = ps.utils._validation.determinant(top_sensors_dmd_unconstrained, n_features, basis_matrix_dmd) # print(optimality0) - # basis = ps.basis.SVD(n_basis_modes=n_modes) - # optimizer = ps.optimizers.QR() - # model = ps.SSPOR(basis = basis, optimizer=optimizer, n_sensors=n_sensors) - # model.fit(X) - # top_sensors0 = model.get_selected_sensors() - # all_sensors = model.get_all_sensors() + #basis = ps.basis.SVD(n_basis_modes=n_modes) + optimizer = ps.optimizers.QR() + model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors) + model.fit(X_small) + top_sensors0 = model.get_selected_sensors() + all_sensors = model.get_all_sensors() # basis_matrix0 = model.basis_matrix_ # optimality0 = ps.utils._validation.determinant(top_sensors0, n_features, basis_matrix0) # print(optimality0) @@ -221,10 +223,11 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): xmax = 64 ymin = 10 ymax = 30 - sensors_constrained = ps.utils._constraints.get_constraind_sensors_indices(xmin,xmax,ymin,ymax,nx,ny,all_sensors_dmd_unconstrained) #Constrained column indices + sensors_constrained = ps.utils._constraints.get_constraind_sensors_indices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices # didx = np.isin(all_sensors,sensors_constrained,invert=False) # const_index = np.nonzero(didx) # j = + ##Plotting the constrained region @@ -241,49 +244,51 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): # plt.title('Constrained region'); ## Fit the dataset with the optimizer GQR - # optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors, constraint_option = "max_n_const_sensors",nx = nx, ny = ny, r = r) - # model1 = ps.SSPOR(basis = basis, optimizer = optimizer1, n_sensors = n_sensors) - # model1.fit(X) - # all_sensors1 = model1.get_all_sensors() - # basis_matrix = model1.basis_matrix_ - # top_sensors = model1.get_selected_sensors() - # print(top_sensors) + optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors, constraint_option = "radii_constraints",nx = nx, ny = ny, r = r) + model1 = ps.SSPOR( optimizer = optimizer1, n_sensors = n_sensors) + model1.fit(X_small) + all_sensors1 = model1.get_all_sensors() + basis_matrix = model1.basis_matrix_ + top_sensors = model1.get_selected_sensors() + print(top_sensors) # optimality = ps.utils._validation.determinant(top_sensors, n_features, basis_matrix) # print(optimality) - optimizer_dmd_constrained = ps.optimizers.GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors_dmd_unconstrained,constraint_option = "exact_n_const_sensors",nx = nx, ny = ny, r = r) - model_dmd_constrained = ps.SSPOR(n_sensors=n_sensors, basis=ps.basis.Custom(n_basis_modes=n_modes, U=U), optimizer = optimizer_dmd_constrained) - model_dmd_constrained.fit(X) - all_sensors_dmd_constrained = model_dmd_constrained.get_all_sensors() - - top_sensors_dmd_constrained = model_dmd_constrained.get_selected_sensors() - basis_matrix_dmd_constrained = model_dmd_constrained.basis_matrix_ - optimality = ps.utils._validation.determinant(top_sensors_dmd_constrained, n_features, basis_matrix_dmd_constrained) + # optimizer_dmd_constrained = ps.optimizers.GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors_dmd_unconstrained,constraint_option = "exact_n_const_sensors",nx = nx, ny = ny, r = r) + # model_dmd_constrained = ps.SSPOR(n_sensors=n_sensors, basis=ps.basis.Custom(n_basis_modes=n_modes, U=U), optimizer = optimizer_dmd_constrained) + # model_dmd_constrained.fit(X) + # all_sensors_dmd_constrained = model_dmd_constrained.get_all_sensors() + + # top_sensors_dmd_constrained = model_dmd_constrained.get_selected_sensors() + # basis_matrix_dmd_constrained = model_dmd_constrained.basis_matrix_ + # optimality = ps.utils._validation.determinant(top_sensors_dmd_constrained, n_features, basis_matrix_dmd_constrained) # print(optimality) ## TODO: this can be done using ravel and unravel more elegantly #yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features)) #xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features)) - img = np.zeros(n_features) - img[top_sensors_dmd_constrained] = 16 - #plt.plot(xConstrained,yConstrained,'*r') - plt.plot([xmin,xmin],[ymin,ymax],'r') - plt.plot([xmin,xmax],[ymax,ymax],'r') - plt.plot([xmax,xmax],[ymin,ymax],'r') - plt.plot([xmin,xmax],[ymin,ymin],'r') - plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) - plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) - plt.show() - # img = np.zeros(n_features) - # img[top_sensors] = 16 - # fig,ax = plt.subplots(1) - # ax.set_aspect('equal') - # ax.imshow(img.reshape(image_shape),cmap=plt.cm.binary) - # print(top_sensors) - # top_sensors_grid = np.unravel_index(top_sensors, (nx,ny)) - # # figure, axes = plt.subplots() - # for i in range(len(top_sensors_grid[0])): - # circ = Circle( (top_sensors_grid[1][i], top_sensors_grid[0][i]), r ,color='r',fill = False ) - # ax.add_patch(circ) + # img[top_sensors_dmd_constrained] = 16 + # #plt.plot(xConstrained,yConstrained,'*r') + # plt.plot([xmin,xmin],[ymin,ymax],'r') + # plt.plot([xmin,xmax],[ymax,ymax],'r') + # plt.plot([xmax,xmax],[ymin,ymax],'r') + # plt.plot([xmin,xmax],[ymin,ymin],'r') + # plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) + # plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) # plt.show() + + img = np.zeros(n_features) + img[top_sensors] = 16 + fig,ax = plt.subplots(1) + ax.set_aspect('equal') + ax.imshow(img.reshape(image_shape),cmap=plt.cm.binary) + print(top_sensors) + top_sensors_grid = np.unravel_index(top_sensors, (nx,ny)) + # figure, axes = plt.subplots() + for i in range(len(top_sensors_grid[0])): + circ = Circle( (top_sensors_grid[1][i], top_sensors_grid[0][i]), r ,color='r',fill = False ) + ax.add_patch(circ) + plt.show() + + diff --git a/pysensors/utils/_norm_calc.py b/pysensors/utils/_norm_calc.py index 83ab695..f167372 100644 --- a/pysensors/utils/_norm_calc.py +++ b/pysensors/utils/_norm_calc.py @@ -109,48 +109,61 @@ def predetermined_norm_calc(lin_idx, dlens, piv, j, n_const_sensors, n_sensors): dlens[didx] = 0 return dlens -def f_radii_constraint(j,dlens,dlens_old,piv,nx,ny,r): - a = np.unravel_index(piv, (nx,ny)) - n_features = len(piv) +def f_radii_constraint(j,dlens,dlens_old,piv,nx,ny,r, all_sensors, n_sensors): if j == 1: - x_cord = a[0][j-1] - y_cord = a[1][j-1] - #print(x_cord, y_cord) - constrained_sensorsx = [] - constrained_sensorsy = [] - for i in range(n_features): - if ((a[0][i]-x_cord)**2 + (a[1][i]-y_cord)**2) < r**2: - constrained_sensorsx.append(a[0][i]) - constrained_sensorsy.append(a[1][i]) - constrained_sensorsx = np.array(constrained_sensorsx) - constrained_sensorsy = np.array(constrained_sensorsy) - constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1) - constrained_sensors_tuple = np.transpose(constrained_sensors_array) - idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny)) -# print(idx_constrained) + idx_constrained = get_constraind_sensors_indices_radii(j,piv,r, nx,ny, all_sensors) + print(idx_constrained) didx = np.isin(piv[j:],idx_constrained,invert= False) dlens[didx] = 0 return dlens - else: + else: result = np.where(dlens_old == 0)[0] result_list = result.tolist() - result_list = [x - 1 for x in result_list] + result_list = [x + (j-1) for x in result_list] result_array = np.array(result_list) - x_cord = a[0][j-1] - y_cord = a[1][j-1] - #print(x_cord, y_cord) - constrained_sensorsx = [] - constrained_sensorsy = [] - for i in range(n_features): - if ((a[0][i]-x_cord)**2 + (a[1][i]-y_cord)**2) < r**2: - constrained_sensorsx.append(a[0][i]) - constrained_sensorsy.append(a[1][i]) - constrained_sensorsx = np.array(constrained_sensorsx) - constrained_sensorsy = np.array(constrained_sensorsy) - constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1) - constrained_sensors_tuple = np.transpose(constrained_sensors_array) - idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny)) - t = np.concatenate((idx_constrained,result_array), axis = 0) + print(result_array) + + idx_constrained1 = get_constraind_sensors_indices_radii(j,piv,r, nx,ny, all_sensors) + t = np.concatenate((idx_constrained1,result_array), axis = 0) didx = np.isin(piv[j:],t,invert= False) dlens[didx] = 0 - return dlens \ No newline at end of file + return dlens + +def get_constraind_sensors_indices_radii(j,piv,r, nx,ny, all_sensors): + """ + Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix. + + Parameters + ---------- + all_sensors : np.ndarray, shape [n_features] + Ranked list of sensor locations. + + Returns + ------- + idx_constrained : np.darray, shape [No. of constrained locations] + Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. + """ + n_features = len(all_sensors) + image_size = int(np.sqrt(n_features)) + a = np.unravel_index(piv, (nx,ny)) + t = np.unravel_index(all_sensors, (nx,ny)) + x_cord = a[0][j-1] + y_cord = a[1][j-1] + #print(x_cord,y_cord) + constrained_sensorsx = [] + constrained_sensorsy = [] + for i in range(n_features): + if ((t[0][i]-x_cord)**2 + (t[1][i]-y_cord)**2) < r**2: + constrained_sensorsx.append(t[0][i]) + constrained_sensorsy.append(t[1][i]) + + constrained_sensorsx = np.array(constrained_sensorsx) + constrained_sensorsy = np.array(constrained_sensorsy) + constrained_sensors_array = np.stack((constrained_sensorsx, constrained_sensorsy), axis=1) + constrained_sensors_tuple = np.transpose(constrained_sensors_array) + if len(constrained_sensorsx) == 0: ##Check to handle condition when number of sensors in the constrained region = 0 + idx_constrained = [] + else: + idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny)) + return idx_constrained + \ No newline at end of file From 284a752ce5aee15e316beebd637dab85c24555db Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Wed, 26 Oct 2022 19:58:00 -0600 Subject: [PATCH 29/52] stating to clear _gqr --- pysensors/optimizers/_gqr.py | 147 ++++++++++++++++++---------- pysensors/utils/_constraints.py | 41 +++++--- tests/optimizers/test_optimizers.py | 66 +++++++++++++ 3 files changed, 189 insertions(+), 65 deletions(-) diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index df2c1b0..75619d8 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -10,7 +10,6 @@ import pysensors as ps from matplotlib.patches import Circle -from pydmd import DMD class GQR(QR): @@ -30,7 +29,7 @@ class GQR(QR): @ authors: Niharika Karnik (@nkarnik2999), Mohammad Abdo (@Jimmy-INL), and Krithika Manohar (@kmanohar) """ - def __init__(self,idx_constrained,n_sensors,n_const_sensors,all_sensors,constraint_option,nx,ny,r): + def __init__(self):#,idx_constrained,n_sensors,n_const_sensors,all_sensors,constraint_option,nx,ny,r """ Attributes ---------- @@ -49,21 +48,18 @@ def __init__(self,idx_constrained,n_sensors,n_const_sensors,all_sensors,constrai exact_n_const_sensors : The number of sensors in the constrained region should be exactly equal to n_const_sensors. """ self.pivots_ = None - self.optimality = None - - self.constrainedIndices = idx_constrained - self.nSensors = n_sensors - self.nConstrainedSensors = n_const_sensors - self.all_sensorloc = all_sensors - self.constraint_option = constraint_option - self._nx = nx - self._ny = ny - self._r = r - - def fit( - self, - basis_matrix - ): + # self.optimality = None + + # self.constrainedIndices = idx_constrained + # self.n_sensors = n_sensors + # self.nConstrainedSensors = n_const_sensors + # self.all_sensorloc = all_sensors + # self.constraint_option = constraint_option + # self._nx = nx + # self._ny = ny + # self._r = r + + def fit(self,basis_matrix=None,**optimizer_kws): """ Parameters ---------- @@ -77,16 +73,48 @@ def fit( ------- self: a fitted :class:`pysensors.optimizers.QR` instance """ + if 'idx_constrained' in optimizer_kws.keys(): + self.constrainedIndices = optimizer_kws['idx_constrained'] + else: + self.constrainedIndices = [] + if 'n_sensors' in optimizer_kws.keys(): + self.n_sensors = optimizer_kws['n_sensors'] + else: + self.n_sensors = np.shape(basis_matrix)[0] + if 'n_const_sensors' in optimizer_kws.keys(): + self.nConstrainedSensors = optimizer_kws['n_const_sensors'] + else: + self.nConstrainedSensors = None + if 'all_sensors' in optimizer_kws.keys(): + self.all_sensors = optimizer_kws['all_sensors'] + else: + self.all_sensors = None + if 'constraint_option' in optimizer_kws.keys(): + self.constraint_option = optimizer_kws['constraint_option'] + else: + self.constraint_option = None + if 'nx' in optimizer_kws.keys(): + self._nx = optimizer_kws['nx'] + else: + self._nx = None + if 'ny' in optimizer_kws.keys(): + self._ny = optimizer_kws['ny'] + else: + self._ny = None + if 'r' in optimizer_kws.keys(): + self._r = optimizer_kws['r'] + else: + self._r = None n_features, n_samples = basis_matrix.shape # We transpose basis_matrix below - max_const_sensors = len(self.constrainedIndices) #Maximum number of sensors allowed in the constrained region + max_const_sensors = len(self.constrainedIndices) # Maximum number of sensors allowed in the constrained region ## Assertions and checks: - # if self.nSensors > n_features - max_const_sensors + self.nConstrainedSensors: + # if self.n_sensors > n_features - max_const_sensors + self.nConstrainedSensors: # raise IOError ("n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors") - # if self.nSensors > n_samples + self.nConstrainedSensors: ## Handling zero constraint? + # if self.n_sensors > n_samples + self.nConstrainedSensors: ## Handling zero constraint? # raise IOError ("Currently n_sensors should be less than min(number of samples, number of modes) + number of constrained sensors,\ - # got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}".format(self.nSensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors)) + # got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}".format(self.n_sensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors)) # Initialize helper variables R = basis_matrix.conj().T.copy() @@ -96,33 +124,38 @@ def fit( for j in range(k): r = R[j:, j:] + # Norm of each column dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0)) + if self.constraint_option == "max_n_const_sensors" : - dlens_updated = ps.utils._norm_calc.norm_calc_max_n_const_sensors(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.nSensors) + dlens_updated = ps.utils._norm_calc.norm_calc_max_n_const_sensors(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.n_sensors) i_piv = np.argmax(dlens_updated) dlen = dlens_updated[i_piv] - elif self.constraint_option == "exact_n_const_sensors" : + elif self.constraint_option == "exact_n_const_sensors" : dlens_updated = ps.utils._norm_calc.norm_calc_exact_n_const_sensors(self.constrainedIndices,dlens,p,j,self.nConstrainedSensors) i_piv = np.argmax(dlens_updated) dlen = dlens_updated[i_piv] elif self.constraint_option == "predetermined_end": - dlens_updated = ps.utils._norm_calc.predetermined_norm_calc(self.constrainedIndices, dlens, p, j, self.nConstrainedSensors, self.nSensors) + dlens_updated = ps.utils._norm_calc.predetermined_norm_calc(self.constrainedIndices, dlens, p, j, self.nConstrainedSensors, self.n_sensors) i_piv = np.argmax(dlens_updated) dlen = dlens_updated[i_piv] elif self.constraint_option == "radii_constraints": - - if j == 0: + + if j == 0: i_piv = np.argmax(dlens) dlen = dlens[i_piv] dlens_old = dlens else: - dlens_updated = ps.utils._norm_calc.f_radii_constraint(j,dlens,dlens_old,p,self._nx,self._ny,self._r,self.all_sensorloc, self.nSensors) #( self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j) + dlens_updated = ps.utils._norm_calc.f_radii_constraint(j,dlens,dlens_old,p,self._nx,self._ny,self._r,self.all_sensorloc, self.n_sensors) #( self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j) i_piv = np.argmax(dlens_updated) dlen = dlens_updated[i_piv] dlens_old = dlens_updated - + else: + i_piv = np.argmax(dlens) + dlen = dlens[i_piv] + # Choose pivot # i_piv = np.argmax(dlens_updated) @@ -148,7 +181,6 @@ def fit( R[j + 1 :, j] = 0 self.pivots_ = p - return self if __name__ == '__main__': @@ -191,10 +223,10 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): #Find all sensor locations using built in QR optimizer #max_const_sensors = 230 - n_const_sensors = 3 - n_sensors = 4 - n_modes = 40 - r = 2 + n_const_sensors = 0 + n_sensors = 10 + n_modes = 10 + r = 5 # dmd = DMD(svd_rank=0,exact=True,opt=False) # dmd.fit(X.T) # U = dmd.modes.real @@ -209,9 +241,9 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): # top_sensors_dmd_unconstrained = model_dmd_unconstrained.get_selected_sensors() # optimality_dmd = ps.utils._validation.determinant(top_sensors_dmd_unconstrained, n_features, basis_matrix_dmd) # print(optimality0) - #basis = ps.basis.SVD(n_basis_modes=n_modes) + basis = ps.basis.SVD(n_basis_modes=n_modes) optimizer = ps.optimizers.QR() - model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors) + model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors, basis=basis) model.fit(X_small) top_sensors0 = model.get_selected_sensors() all_sensors = model.get_all_sensors() @@ -227,32 +259,32 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): # didx = np.isin(all_sensors,sensors_constrained,invert=False) # const_index = np.nonzero(didx) # j = - - ##Plotting the constrained region - # ax = plt.subplot() - # #Plot constrained space - # img = np.zeros(n_features) - # img[sensors_constrained] = 1 - # im = plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) - # # create an axes on the right side of ax. The width of cax will be 5% - # # of ax and the padding between cax and ax will be fixed at 0.05 inch. - # divider = make_axes_locatable(ax) - # cax = divider.append_axes("right", size="5%", pad=0.05) - # plt.colorbar(im, cax=cax) - # plt.title('Constrained region'); + + #Plotting the constrained region + ax = plt.subplot() + #Plot constrained space + img = np.zeros(n_features) + img[sensors_constrained] = 1 + im = plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) + # create an axes on the right side of ax. The width of cax will be 5% + # of ax and the padding between cax and ax will be fixed at 0.05 inch. + divider = make_axes_locatable(ax) + cax = divider.append_axes("right", size="5%", pad=0.05) + plt.colorbar(im, cax=cax) + plt.title('Constrained region') ## Fit the dataset with the optimizer GQR optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors, constraint_option = "radii_constraints",nx = nx, ny = ny, r = r) - model1 = ps.SSPOR( optimizer = optimizer1, n_sensors = n_sensors) + model1 = ps.SSPOR( optimizer = optimizer1, n_sensors = n_sensors, basis=basis) model1.fit(X_small) all_sensors1 = model1.get_all_sensors() basis_matrix = model1.basis_matrix_ top_sensors = model1.get_selected_sensors() print(top_sensors) - # optimality = ps.utils._validation.determinant(top_sensors, n_features, basis_matrix) - # print(optimality) + optimality = ps.utils._validation.determinant(top_sensors, n_features, basis_matrix) + print(optimality) # optimizer_dmd_constrained = ps.optimizers.GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors_dmd_unconstrained,constraint_option = "exact_n_const_sensors",nx = nx, ny = ny, r = r) # model_dmd_constrained = ps.SSPOR(n_sensors=n_sensors, basis=ps.basis.Custom(n_basis_modes=n_modes, U=U), optimizer = optimizer_dmd_constrained) # model_dmd_constrained.fit(X) @@ -281,7 +313,7 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): img = np.zeros(n_features) img[top_sensors] = 16 fig,ax = plt.subplots(1) - ax.set_aspect('equal') + ax.imshow(img.reshape(image_shape),cmap=plt.cm.binary) print(top_sensors) top_sensors_grid = np.unravel_index(top_sensors, (nx,ny)) @@ -289,6 +321,13 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): for i in range(len(top_sensors_grid[0])): circ = Circle( (top_sensors_grid[1][i], top_sensors_grid[0][i]), r ,color='r',fill = False ) ax.add_patch(circ) + # ax.plot([xmin,xmin],[ymin,ymax],'-r') + # ax.plot([xmax,xmax],[ymin,ymax],'-r') + # ax.plot([xmin,xmax],[ymin,ymin],'-r') + # ax.plot([xmin,xmax],[ymax,ymax],'-r') + ax.set_aspect('equal') + # ax.set_xlim([0,64]) + # ax.set_ylim([0,64]) plt.show() - - + + diff --git a/pysensors/utils/_constraints.py b/pysensors/utils/_constraints.py index 5eb4ae8..b5ce757 100644 --- a/pysensors/utils/_constraints.py +++ b/pysensors/utils/_constraints.py @@ -38,7 +38,7 @@ def get_constraind_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_senso constrained_sensorsx = [] constrained_sensorsy = [] for i in range(n_features): - if (a[0][i] >= x_min and a[0][i] <= x_max) and (a[1][i] >= y_min and a[1][i] <= y_max): + if (a[0][i] >= x_min and a[0][i] <= x_max) and (a[1][i] >= y_min and a[1][i] <= y_max): constrained_sensorsx.append(a[0][i]) constrained_sensorsy.append(a[1][i]) @@ -68,7 +68,7 @@ def get_constrained_sensors_indices_linear(x_min,x_max,y_min,y_max,df): Upper bound for the y-axis constraint df : pandas.DataFrame A dataframe containing the features and samples - + Returns ------- idx_constrained : np.darray, shape [No. of constrained locations] @@ -90,11 +90,11 @@ def box_constraints(position,lower_bound,upper_bound,): Parameters ---------- position: ##TODO: FILL - + lower_bound : ##TODO: FILL - + upper_bound : ##TODO: FILL - + Returns ------- idx_constrained : np.darray, shape [No. of constrained locations] ##TODO: CHECK IF CORRECT @@ -112,17 +112,36 @@ def functional_constraints(position, func_response,func_input, free_term): Parameters ---------- position: ##TODO : FILL - + func_response : ##TODO : FILL - + func_input: ##TODO : FILL - + free_term : ##TODO : FILL - + Returns ------- g : ##TODO : FILL - + """ g = func_response + func_input + free_term - return g \ No newline at end of file + return g + +# __constraintType = {} +# __constraintType['swapMutator'] = swapMutator +# __constraintType['scrambleMutator'] = scrambleMutator +# __constraintType['bitFlipMutator'] = bitFlipMutator +# __constraintType['inversionMutator'] = inversionMutator +# __constraintType['randomMutator'] = randomMutator + + +# def returnInstance(cls, name): +# """ +# Method designed to return class instance: +# @ In, cls, class type +# @ In, name, string, name of class +# @ Out, __crossovers[name], instance of class +# """ +# if name not in __constraintType: +# cls.raiseAnError (IOError, "{} CONSTRAINT NOT IMPLEMENTED!!!!!".format(name)) +# return __constraintType[name] \ No newline at end of file diff --git a/tests/optimizers/test_optimizers.py b/tests/optimizers/test_optimizers.py index bb919ca..d410f00 100644 --- a/tests/optimizers/test_optimizers.py +++ b/tests/optimizers/test_optimizers.py @@ -3,6 +3,7 @@ from pysensors.optimizers import CCQR from pysensors.optimizers import QR +from pysensors.optimizers import GQR def test_num_sensors(data_vandermonde): @@ -49,3 +50,68 @@ def test_ccqr_negative_costs(data_vandermonde): chosen_sensors = set(sensors[: min(x.shape)]) assert all(s in chosen_sensors for s in set(desirable_sensors)) + +def test_gqr_qr_equivalence(data_vandermonde): + x = data_vandermonde + + gqr_sensors = GQR().fit(x.T).get_sensors() + # If no constraints are passed it should converge to the regular QR optimizer + qr_sensors = QR().fit(x.T).get_sensors() + + np.testing.assert_array_equal(gqr_sensors, qr_sensors) + +def test_gqr_ccqr_equivalence(data_random): + x = data_random + + forbidden_sensors = np.arange(0, x.shape[1], 3) + costs = np.zeros(x.shape[1]) + costs[forbidden_sensors] = 100 + # Get ranked sensors from CCQR + sensors = CCQR(sensor_costs=costs).fit(x.T).get_sensors() + + # Forbidden sensors should not be included + chosen_sensors_CCQR = set(sensors[: (x.shape[1] - len(forbidden_sensors))]) + assert chosen_sensors_CCQR.isdisjoint(set(forbidden_sensors)) + + # Get ranked sensors from GQR + sensors_GQR = GQR().fit(x.T, idx_constrained=forbidden_sensors,n_const_sensors=0, constraint_option='exact_n_const_sensors').get_sensors() + + # Forbidden sensors should not be included + chosen_sensors_GQR = set(sensors_GQR[: (x.shape[1] - len(forbidden_sensors))]) + assert chosen_sensors_GQR.isdisjoint(set(forbidden_sensors)) + assert chosen_sensors_CCQR == chosen_sensors_GQR + + +def test_gqr_exact_constrainted_case1(data_random): + ## In this case we want to place a total of 10 sensors + # with a constrained region that is allowed to have exactly 3 sensors + # but 4 of the first 10 are in the constrained region + x = data_random + # unconstrained sensors (optimal) + sensors_QR = QR().fit(x.T).get_sensors() + # exact number of sensors allowed in the constrained region + total_sensors = 10 + exact_n_const_sensors = 3 + forbidden_sensors = [8,5,2,6] + totally_forbidden_sensors = [x for x in forbidden_sensors if x in sensors_QR][:exact_n_const_sensors] + totally_forbidden_sensors = [y for y in forbidden_sensors if y not in totally_forbidden_sensors] + costs = np.zeros(x.shape[1]) + costs[totally_forbidden_sensors] = 100 + # Get ranked sensors + sensors = CCQR(sensor_costs=costs).fit(x.T).get_sensors()[:total_sensors] + + # Forbidden sensors should not be included + chosen_sensors = set(sensors[: (x.shape[1] - len(totally_forbidden_sensors))]) + assert chosen_sensors.isdisjoint(set(totally_forbidden_sensors)) + + + # Get ranked sensors from GQR + sensors_GQR = GQR().fit(x.T, idx_constrained=forbidden_sensors,n_sensors=total_sensors,n_const_sensors=exact_n_const_sensors, constraint_option='exact_n_const_sensors').get_sensors()[:total_sensors] + + # try to compare these using the validation metrics + +def test_gqr_max_constrained(): + pass + +def test_gqr_radii_constrained(): + pass \ No newline at end of file From e011bf83d64cbb11e35e26c8ee4f8938e60a131e Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Wed, 26 Oct 2022 20:43:57 -0600 Subject: [PATCH 30/52] undo old changes to _identity.py --- pysensors/basis/_identity.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/pysensors/basis/_identity.py b/pysensors/basis/_identity.py index 38ef4ac..c31b776 100644 --- a/pysensors/basis/_identity.py +++ b/pysensors/basis/_identity.py @@ -53,8 +53,6 @@ def fit(self, X): ------- self : instance """ - # Store original data - self.original_data = X # Note that we take a transpose here, so columns correspond to examples if self.n_basis_modes is None: self.basis_matrix_ = check_array(X).T.copy() @@ -67,10 +65,10 @@ def fit(self, X): ) ) - self.basis_matrix_ = np.eye(X.shape[1])[:,:self.n_basis_modes] #check_array(X)[: self.n_basis_modes, :].T.copy() + self.basis_matrix_ = check_array(X)[: self.n_basis_modes, :].T.copy() # np.eye(X.shape[1])[:,:self.n_basis_modes] - # if self.n_basis_modes < X.shape[0]: - # warn(f"Only the first {self.n_basis_modes} examples were retained.") + if self.n_basis_modes < X.shape[0]: + warn(f"Only the first {self.n_basis_modes} examples were retained.") return self def matrix_inverse(self, n_basis_modes=None): From 9f1d5e62a671767aba6e2af9d9b714e625a44859 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Mon, 31 Oct 2022 23:15:50 -0600 Subject: [PATCH 31/52] more cleaning, first two constraints fixed --- pysensors/basis/_custom.py | 6 +--- pysensors/optimizers/_gqr.py | 61 ++++++++++++++++--------------- pysensors/utils/_norm_calc.py | 67 +++++++++++++++++++++-------------- 3 files changed, 74 insertions(+), 60 deletions(-) diff --git a/pysensors/basis/_custom.py b/pysensors/basis/_custom.py index b015f98..811e716 100644 --- a/pysensors/basis/_custom.py +++ b/pysensors/basis/_custom.py @@ -6,11 +6,7 @@ class Custom(InvertibleBasis, MatrixMixin): """ -<<<<<<< HEAD - Use a custom transformation to maps input features to -======= - Generate a custom transformation which maps input features to ->>>>>>> 1ccd23fafed0ca39dac6cde204efa228d133df1c + Use a custom transformation to map input features to custom modes. Assumes the data has already been centered (to have mean 0). diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index 75619d8..10eae24 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -10,7 +10,7 @@ import pysensors as ps from matplotlib.patches import Circle - +from pysensors.utils._norm_calc import returnInstance as normCalcReturnInstance class GQR(QR): """ @@ -91,6 +91,7 @@ def fit(self,basis_matrix=None,**optimizer_kws): self.all_sensors = None if 'constraint_option' in optimizer_kws.keys(): self.constraint_option = optimizer_kws['constraint_option'] + self._norm_calc_Instance = normCalcReturnInstance(self,self.constraint_option) else: self.constraint_option = None if 'nx' in optimizer_kws.keys(): @@ -127,34 +128,36 @@ def fit(self,basis_matrix=None,**optimizer_kws): # Norm of each column dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0)) - - if self.constraint_option == "max_n_const_sensors" : - dlens_updated = ps.utils._norm_calc.norm_calc_max_n_const_sensors(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.n_sensors) - i_piv = np.argmax(dlens_updated) - dlen = dlens_updated[i_piv] - elif self.constraint_option == "exact_n_const_sensors" : - dlens_updated = ps.utils._norm_calc.norm_calc_exact_n_const_sensors(self.constrainedIndices,dlens,p,j,self.nConstrainedSensors) - i_piv = np.argmax(dlens_updated) - dlen = dlens_updated[i_piv] - elif self.constraint_option == "predetermined_end": - dlens_updated = ps.utils._norm_calc.predetermined_norm_calc(self.constrainedIndices, dlens, p, j, self.nConstrainedSensors, self.n_sensors) - i_piv = np.argmax(dlens_updated) - dlen = dlens_updated[i_piv] - elif self.constraint_option == "radii_constraints": - - if j == 0: - i_piv = np.argmax(dlens) - dlen = dlens[i_piv] - dlens_old = dlens - else: - - dlens_updated = ps.utils._norm_calc.f_radii_constraint(j,dlens,dlens_old,p,self._nx,self._ny,self._r,self.all_sensorloc, self.n_sensors) #( self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j) - i_piv = np.argmax(dlens_updated) - dlen = dlens_updated[i_piv] - dlens_old = dlens_updated - else: - i_piv = np.argmax(dlens) - dlen = dlens[i_piv] + dlens_updated = self._norm_calc_Instance(self.constrainedIndices, dlens, p, j, self.nConstrainedSensors, all_sensors=self.all_sensors, n_sensors=self.n_sensors, nx=self._nx, ny=self._ny, r=self._r) + i_piv = np.argmax(dlens_updated) + dlen = dlens_updated[i_piv] + # if self.constraint_option == "max_n_const_sensors" : + # dlens_updated = ps.utils._norm_calc.norm_calc_max_n_const_sensors(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.n_sensors) + # i_piv = np.argmax(dlens_updated) + # dlen = dlens_updated[i_piv] + # elif self.constraint_option == "exact_n_const_sensors" : + # dlens_updated = ps.utils._norm_calc.norm_calc_exact_n_const_sensors(self.constrainedIndices,dlens,p,j,self.nConstrainedSensors) + # i_piv = np.argmax(dlens_updated) + # dlen = dlens_updated[i_piv] + # elif self.constraint_option == "predetermined_end": + # dlens_updated = ps.utils._norm_calc.predetermined_norm_calc(self.constrainedIndices, dlens, p, j, self.nConstrainedSensors, self.n_sensors) + # i_piv = np.argmax(dlens_updated) + # dlen = dlens_updated[i_piv] + # elif self.constraint_option == "radii_constraints": + + # if j == 0: + # i_piv = np.argmax(dlens) + # dlen = dlens[i_piv] + # dlens_old = dlens + # else: + + # dlens_updated = ps.utils._norm_calc.f_radii_constraint(j,dlens,dlens_old,p,self._nx,self._ny,self._r,self.all_sensorloc, self.n_sensors) #( self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j) + # i_piv = np.argmax(dlens_updated) + # dlen = dlens_updated[i_piv] + # dlens_old = dlens_updated + # else: + # i_piv = np.argmax(dlens) + # dlen = dlens[i_piv] # Choose pivot # i_piv = np.argmax(dlens_updated) diff --git a/pysensors/utils/_norm_calc.py b/pysensors/utils/_norm_calc.py index f167372..9832bdc 100644 --- a/pysensors/utils/_norm_calc.py +++ b/pysensors/utils/_norm_calc.py @@ -4,9 +4,9 @@ import numpy as np -def norm_calc_exact_n_const_sensors(lin_idx, dlens, piv, j, n_const_sensors): ##Will first force sensors into constrained region - #num_sensors should be fixed for each custom constraint (for now) - #num_sensors must be <= size of constraint region +def norm_calc_exact_n_const_sensors(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): ##Will first force sensors into constrained region + # num_sensors should be fixed for each custom constraint (for now) + # num_sensors must be <= size of constraint region """ Function for mapping constrained sensor locations with the QR procedure. @@ -27,18 +27,11 @@ def norm_calc_exact_n_const_sensors(lin_idx, dlens, piv, j, n_const_sensors): ## ------- dlens : np.darray, shape [Variable based on j] with constraints mapped into it. """ - if j < n_const_sensors: # force sensors into constraint region - #idx = np.arange(dlens.shape[0]) - #dlens[np.delete(idx, lin_idx)] = 0 - - didx = np.isin(piv[j:],lin_idx,invert=True) - dlens[didx] = 0 - else: - didx = np.isin(piv[j:],lin_idx,invert=False) - dlens[didx] = 0 + didx = np.isin(piv[j:],lin_idx,invert=j Date: Fri, 18 Nov 2022 19:08:20 -0700 Subject: [PATCH 32/52] more cleaning --- pysensors/optimizers/_gqr.py | 244 ++------------------------------ pysensors/utils/__init__.py | 16 +-- pysensors/utils/_constraints.py | 127 ++++------------- pysensors/utils/_norm_calc.py | 179 ++++++++--------------- 4 files changed, 109 insertions(+), 457 deletions(-) diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index 10eae24..a0ee978 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -29,7 +29,7 @@ class GQR(QR): @ authors: Niharika Karnik (@nkarnik2999), Mohammad Abdo (@Jimmy-INL), and Krithika Manohar (@kmanohar) """ - def __init__(self):#,idx_constrained,n_sensors,n_const_sensors,all_sensors,constraint_option,nx,ny,r + def __init__(self): """ Attributes ---------- @@ -48,16 +48,14 @@ def __init__(self):#,idx_constrained,n_sensors,n_const_sensors,all_sensors,const exact_n_const_sensors : The number of sensors in the constrained region should be exactly equal to n_const_sensors. """ self.pivots_ = None - # self.optimality = None - - # self.constrainedIndices = idx_constrained - # self.n_sensors = n_sensors - # self.nConstrainedSensors = n_const_sensors - # self.all_sensorloc = all_sensors - # self.constraint_option = constraint_option - # self._nx = nx - # self._ny = ny - # self._r = r + self.idx_constrained = [] + self.n_sensors = 10 + self.n_const_sensors = 0 + self.all_sensors = [] + self.constraint_option = '' + self.nx = 64 + self.ny = 64 + self.r = 1 def fit(self,basis_matrix=None,**optimizer_kws): """ @@ -73,42 +71,10 @@ def fit(self,basis_matrix=None,**optimizer_kws): ------- self: a fitted :class:`pysensors.optimizers.QR` instance """ - if 'idx_constrained' in optimizer_kws.keys(): - self.constrainedIndices = optimizer_kws['idx_constrained'] - else: - self.constrainedIndices = [] - if 'n_sensors' in optimizer_kws.keys(): - self.n_sensors = optimizer_kws['n_sensors'] - else: - self.n_sensors = np.shape(basis_matrix)[0] - if 'n_const_sensors' in optimizer_kws.keys(): - self.nConstrainedSensors = optimizer_kws['n_const_sensors'] - else: - self.nConstrainedSensors = None - if 'all_sensors' in optimizer_kws.keys(): - self.all_sensors = optimizer_kws['all_sensors'] - else: - self.all_sensors = None - if 'constraint_option' in optimizer_kws.keys(): - self.constraint_option = optimizer_kws['constraint_option'] - self._norm_calc_Instance = normCalcReturnInstance(self,self.constraint_option) - else: - self.constraint_option = None - if 'nx' in optimizer_kws.keys(): - self._nx = optimizer_kws['nx'] - else: - self._nx = None - if 'ny' in optimizer_kws.keys(): - self._ny = optimizer_kws['ny'] - else: - self._ny = None - if 'r' in optimizer_kws.keys(): - self._r = optimizer_kws['r'] - else: - self._r = None - + [setattr(self,name,optimizer_kws.get(name,getattr(self,name))) for name in optimizer_kws.keys()] + self._norm_calc_Instance = normCalcReturnInstance(self, self.constraint_option) n_features, n_samples = basis_matrix.shape # We transpose basis_matrix below - max_const_sensors = len(self.constrainedIndices) # Maximum number of sensors allowed in the constrained region + max_const_sensors = len(self.idx_constrained) # Maximum number of sensors allowed in the constrained region ## Assertions and checks: # if self.n_sensors > n_features - max_const_sensors + self.nConstrainedSensors: @@ -128,41 +94,9 @@ def fit(self,basis_matrix=None,**optimizer_kws): # Norm of each column dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0)) - dlens_updated = self._norm_calc_Instance(self.constrainedIndices, dlens, p, j, self.nConstrainedSensors, all_sensors=self.all_sensors, n_sensors=self.n_sensors, nx=self._nx, ny=self._ny, r=self._r) + dlens_updated = self._norm_calc_Instance(self.idx_constrained, dlens, p, j, self.n_const_sensors, dlens_old=dlens, all_sensors=self.all_sensors, n_sensors=self.n_sensors, nx=self.nx, ny=self.ny, r=self.r) i_piv = np.argmax(dlens_updated) dlen = dlens_updated[i_piv] - # if self.constraint_option == "max_n_const_sensors" : - # dlens_updated = ps.utils._norm_calc.norm_calc_max_n_const_sensors(self.constrainedIndices,dlens,p,j, self.nConstrainedSensors,self.all_sensorloc,self.n_sensors) - # i_piv = np.argmax(dlens_updated) - # dlen = dlens_updated[i_piv] - # elif self.constraint_option == "exact_n_const_sensors" : - # dlens_updated = ps.utils._norm_calc.norm_calc_exact_n_const_sensors(self.constrainedIndices,dlens,p,j,self.nConstrainedSensors) - # i_piv = np.argmax(dlens_updated) - # dlen = dlens_updated[i_piv] - # elif self.constraint_option == "predetermined_end": - # dlens_updated = ps.utils._norm_calc.predetermined_norm_calc(self.constrainedIndices, dlens, p, j, self.nConstrainedSensors, self.n_sensors) - # i_piv = np.argmax(dlens_updated) - # dlen = dlens_updated[i_piv] - # elif self.constraint_option == "radii_constraints": - - # if j == 0: - # i_piv = np.argmax(dlens) - # dlen = dlens[i_piv] - # dlens_old = dlens - # else: - - # dlens_updated = ps.utils._norm_calc.f_radii_constraint(j,dlens,dlens_old,p,self._nx,self._ny,self._r,self.all_sensorloc, self.n_sensors) #( self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j) - # i_piv = np.argmax(dlens_updated) - # dlen = dlens_updated[i_piv] - # dlens_old = dlens_updated - # else: - # i_piv = np.argmax(dlens) - # dlen = dlens[i_piv] - - # Choose pivot - # i_piv = np.argmax(dlens_updated) - - # dlen = dlens_updated[i_piv] if dlen > 0: u = r[:, i_piv] / dlen @@ -182,155 +116,5 @@ def fit(self,basis_matrix=None,**optimizer_kws): # Apply reflector R[j:, j:] -= np.outer(u, np.dot(u, R[j:, j:])) R[j + 1 :, j] = 0 - self.pivots_ = p - return self - -if __name__ == '__main__': - faces = datasets.fetch_olivetti_faces(shuffle=True) - X = faces.data - - # n_samples, n_features = X.shape - X_small = X[:,:256] - n_samples, n_features = X_small.shape - print('Number of samples:', n_samples) - print('Number of features (sensors):', n_features) - - # Global centering - X_small = X_small - X_small.mean(axis=0) - - # Local centering - X_small -= X_small.mean(axis=1).reshape(n_samples, -1) - - n_row, n_col = 2, 3 - n_components = n_row * n_col - image_shape = (16, 16) - nx = 16 - ny = 16 - - def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): - '''Function for plotting faces''' - plt.figure(figsize=(2. * n_col, 2.26 * n_row)) - plt.suptitle(title, size=16) - for i, comp in enumerate(images): - plt.subplot(n_row, n_col, i + 1) - vmax = max(comp.max(), -comp.min()) - plt.imshow(comp.reshape(image_shape), cmap=cmap, - interpolation='nearest', - vmin=-vmax, vmax=vmax) - plt.xticks(()) - plt.yticks(()) - plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.) - - # plot_gallery("First few centered faces", X[:n_components]) - - #Find all sensor locations using built in QR optimizer - #max_const_sensors = 230 - n_const_sensors = 0 - n_sensors = 10 - n_modes = 10 - r = 5 - # dmd = DMD(svd_rank=0,exact=True,opt=False) - # dmd.fit(X.T) - # U = dmd.modes.real - # np.shape(U) - # max_basis_modes = 200 - - # model_dmd_unconstrained = ps.SSPOR(n_sensors=n_sensors, basis=ps.basis.Custom(n_basis_modes=n_modes, U=U)) - # model_dmd_unconstrained.fit(X) - # basis_matrix_dmd = model_dmd_unconstrained.basis_matrix_ - - # all_sensors_dmd_unconstrained = model_dmd_unconstrained.get_all_sensors() - # top_sensors_dmd_unconstrained = model_dmd_unconstrained.get_selected_sensors() - # optimality_dmd = ps.utils._validation.determinant(top_sensors_dmd_unconstrained, n_features, basis_matrix_dmd) - # print(optimality0) - basis = ps.basis.SVD(n_basis_modes=n_modes) - optimizer = ps.optimizers.QR() - model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors, basis=basis) - model.fit(X_small) - top_sensors0 = model.get_selected_sensors() - all_sensors = model.get_all_sensors() - # basis_matrix0 = model.basis_matrix_ - # optimality0 = ps.utils._validation.determinant(top_sensors0, n_features, basis_matrix0) - # print(optimality0) - ##Constrained sensor location on the grid: - xmin = 0 - xmax = 64 - ymin = 10 - ymax = 30 - sensors_constrained = ps.utils._constraints.get_constraind_sensors_indices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices - # didx = np.isin(all_sensors,sensors_constrained,invert=False) - # const_index = np.nonzero(didx) - # j = - - - - #Plotting the constrained region - ax = plt.subplot() - #Plot constrained space - img = np.zeros(n_features) - img[sensors_constrained] = 1 - im = plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) - # create an axes on the right side of ax. The width of cax will be 5% - # of ax and the padding between cax and ax will be fixed at 0.05 inch. - divider = make_axes_locatable(ax) - cax = divider.append_axes("right", size="5%", pad=0.05) - plt.colorbar(im, cax=cax) - plt.title('Constrained region') - - ## Fit the dataset with the optimizer GQR - optimizer1 = GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors, constraint_option = "radii_constraints",nx = nx, ny = ny, r = r) - model1 = ps.SSPOR( optimizer = optimizer1, n_sensors = n_sensors, basis=basis) - model1.fit(X_small) - all_sensors1 = model1.get_all_sensors() - basis_matrix = model1.basis_matrix_ - top_sensors = model1.get_selected_sensors() - print(top_sensors) - optimality = ps.utils._validation.determinant(top_sensors, n_features, basis_matrix) - print(optimality) - # optimizer_dmd_constrained = ps.optimizers.GQR(sensors_constrained,n_sensors,n_const_sensors,all_sensors_dmd_unconstrained,constraint_option = "exact_n_const_sensors",nx = nx, ny = ny, r = r) - # model_dmd_constrained = ps.SSPOR(n_sensors=n_sensors, basis=ps.basis.Custom(n_basis_modes=n_modes, U=U), optimizer = optimizer_dmd_constrained) - # model_dmd_constrained.fit(X) - # all_sensors_dmd_constrained = model_dmd_constrained.get_all_sensors() - - # top_sensors_dmd_constrained = model_dmd_constrained.get_selected_sensors() - # basis_matrix_dmd_constrained = model_dmd_constrained.basis_matrix_ - # optimality = ps.utils._validation.determinant(top_sensors_dmd_constrained, n_features, basis_matrix_dmd_constrained) - # print(optimality) - - ## TODO: this can be done using ravel and unravel more elegantly - #yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features)) - #xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features)) - - # img = np.zeros(n_features) - # img[top_sensors_dmd_constrained] = 16 - # #plt.plot(xConstrained,yConstrained,'*r') - # plt.plot([xmin,xmin],[ymin,ymax],'r') - # plt.plot([xmin,xmax],[ymax,ymax],'r') - # plt.plot([xmax,xmax],[ymin,ymax],'r') - # plt.plot([xmin,xmax],[ymin,ymin],'r') - # plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) - # plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) - # plt.show() - - img = np.zeros(n_features) - img[top_sensors] = 16 - fig,ax = plt.subplots(1) - - ax.imshow(img.reshape(image_shape),cmap=plt.cm.binary) - print(top_sensors) - top_sensors_grid = np.unravel_index(top_sensors, (nx,ny)) - # figure, axes = plt.subplots() - for i in range(len(top_sensors_grid[0])): - circ = Circle( (top_sensors_grid[1][i], top_sensors_grid[0][i]), r ,color='r',fill = False ) - ax.add_patch(circ) - # ax.plot([xmin,xmin],[ymin,ymax],'-r') - # ax.plot([xmax,xmax],[ymin,ymax],'-r') - # ax.plot([xmin,xmax],[ymin,ymin],'-r') - # ax.plot([xmin,xmax],[ymax,ymax],'-r') - ax.set_aspect('equal') - # ax.set_xlim([0,64]) - # ax.set_ylim([0,64]) - plt.show() - - + return self \ No newline at end of file diff --git a/pysensors/utils/__init__.py b/pysensors/utils/__init__.py index b18f22d..8d9404b 100644 --- a/pysensors/utils/__init__.py +++ b/pysensors/utils/__init__.py @@ -7,10 +7,9 @@ from ._constraints import functional_constraints from ._validation import determinant from ._validation import relative_reconstruction_error -from ._norm_calc import norm_calc_exact_n_const_sensors -from ._norm_calc import norm_calc_max_n_const_sensors -from ._norm_calc import predetermined_norm_calc -from ._norm_calc import f_radii_constraint +from ._norm_calc import exact_n +from ._norm_calc import max_n +from ._norm_calc import predetermined __all__ = [ "constrained_binary_solve", @@ -22,8 +21,9 @@ "functional_constraints", "determinant", "relative_reconstruction_error", - "norm_calc_exact_n_const_sensors", - "norm_calc_max_n_const_sensors", - "predetermined_norm_calc", - "f_radii_constraint" + # "norm_calc_exact_n_const_sensors", + # "norm_calc_max_n_const_sensors", + # "predetermined_norm_calc", + "exact_n", + "max_n" ] diff --git a/pysensors/utils/_constraints.py b/pysensors/utils/_constraints.py index b5ce757..c392108 100644 --- a/pysensors/utils/_constraints.py +++ b/pysensors/utils/_constraints.py @@ -11,26 +11,19 @@ def get_constraind_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_senso Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix. Parameters - ---------- - x_min: int, - Lower bound for the x-axis constraint - x_max : int, - Upper bound for the x-axis constraint - y_min : int, - Lower bound for the y-axis constraint - y_max : int - Upper bound for the y-axis constraint - nx : int - Image pixel (x dimensions of the grid) - ny : int - Image pixel (y dimensions of the grid) - all_sensors : np.ndarray, shape [n_features] - Ranked list of sensor locations. - - Returns - ------- - idx_constrained : np.darray, shape [No. of constrained locations] - Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. + ---------- + x_min: int, lower bound for the x-axis constraint + x_max : int, upper bound for the x-axis constraint + y_min : int, lower bound for the y-axis constraint + y_max : int, upper bound for the y-axis constraint + nx : int, image pixel (x dimensions of the grid) + ny : int, image pixel (y dimensions of the grid) + all_sensors : np.ndarray, shape [n_features], ranked list of sensor locations. + + Returns + ------- + idx_constrained : np.darray, shape [No. of constrained locations], array which contains the constrained + locations of the grid in terms of column indices of basis_matrix. """ n_features = len(all_sensors) image_size = int(np.sqrt(n_features)) @@ -52,27 +45,22 @@ def get_constraind_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_senso idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny)) return idx_constrained -def get_constrained_sensors_indices_linear(x_min,x_max,y_min,y_max,df): +def get_constrained_sensors_indices_linear(x_min, x_max, y_min, y_max,df): """ Function for obtaining constrained column indices from already existing linear sensor locations on the grid. Parameters - ---------- - x_min: int, - Lower bound for the x-axis constraint - x_max : int, - Upper bound for the x-axis constraint - y_min : int, - Lower bound for the y-axis constraint - y_max : int - Upper bound for the y-axis constraint - df : pandas.DataFrame - A dataframe containing the features and samples - - Returns - ------- - idx_constrained : np.darray, shape [No. of constrained locations] - Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. + ---------- + x_min: int, lower bound for the x-axis constraint + x_max : int, upper bound for the x-axis constraint + y_min : int, lower bound for the y-axis constraint + y_max : int, upper bound for the y-axis constraint + df : pandas.DataFrame, a dataframe containing the features and samples + + Returns + ------- + idx_constrained : np.darray, shape [No. of constrained locations], array which contains the constrained + locations of the grid in terms of column indices of basis_matrix. """ x = df['X (m)'].to_numpy() n_features = x.shape[0] @@ -81,67 +69,4 @@ def get_constrained_sensors_indices_linear(x_min,x_max,y_min,y_max,df): for i in range(n_features): if (x[i] >= x_min and x[i] <= x_max) and (y[i] >= y_min and y[i] <= y_max): idx_constrained.append(i) - return idx_constrained - -def box_constraints(position,lower_bound,upper_bound,): - """ - Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix. ##TODO : BETTER DEFINITION - - Parameters - ---------- - position: ##TODO: FILL - - lower_bound : ##TODO: FILL - - upper_bound : ##TODO: FILL - - Returns - ------- - idx_constrained : np.darray, shape [No. of constrained locations] ##TODO: CHECK IF CORRECT - Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. - """ - for i,xi in enumerate(position): - f1 = position[i] - lower_bound[i] - f2 = upper_bound[i] - position [i] - return +1 if (f1 and f2 > 0) else -1 - -def functional_constraints(position, func_response,func_input, free_term): - """ - Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix. ##TODO: BETTER DEFINITION - - Parameters - ---------- - position: ##TODO : FILL - - func_response : ##TODO : FILL - - func_input: ##TODO : FILL - - free_term : ##TODO : FILL - - Returns - ------- - g : ##TODO : FILL - - """ - g = func_response + func_input + free_term - return g - -# __constraintType = {} -# __constraintType['swapMutator'] = swapMutator -# __constraintType['scrambleMutator'] = scrambleMutator -# __constraintType['bitFlipMutator'] = bitFlipMutator -# __constraintType['inversionMutator'] = inversionMutator -# __constraintType['randomMutator'] = randomMutator - - -# def returnInstance(cls, name): -# """ -# Method designed to return class instance: -# @ In, cls, class type -# @ In, name, string, name of class -# @ Out, __crossovers[name], instance of class -# """ -# if name not in __constraintType: -# cls.raiseAnError (IOError, "{} CONSTRAINT NOT IMPLEMENTED!!!!!".format(name)) -# return __constraintType[name] \ No newline at end of file + return idx_constrained \ No newline at end of file diff --git a/pysensors/utils/_norm_calc.py b/pysensors/utils/_norm_calc.py index 9832bdc..dff6027 100644 --- a/pysensors/utils/_norm_calc.py +++ b/pysensors/utils/_norm_calc.py @@ -3,58 +3,59 @@ """ import numpy as np +from ..utils._constraints import get_constraind_sensors_indices_radii -def norm_calc_exact_n_const_sensors(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): ##Will first force sensors into constrained region +def exact_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): ##Will first force sensors into constrained region # num_sensors should be fixed for each custom constraint (for now) # num_sensors must be <= size of constraint region """ Function for mapping constrained sensor locations with the QR procedure. Parameters - ---------- - lin_idx: np.ndarray, shape [No. of constrained locations] - Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. - dlens: np.ndarray, shape [Variable based on j] - Array which contains the norm of columns of basis matrix. - piv: np.ndarray, shape [n_features] - Ranked list of sensor locations. - n_const_sensors: int, - Number of sensors to be placed in the constrained area. - j: int, - Iterative variable in the QR algorithm. - - Returns - ------- - dlens : np.darray, shape [Variable based on j] with constraints mapped into it. + ---------- + lin_idx: np.ndarray, shape [No. of constrained locations] + Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix. + dlens: np.ndarray, shape [Variable based on j] + Array which contains the norm of columns of basis matrix. + piv: np.ndarray, shape [n_features] + Ranked list of sensor locations. + n_const_sensors: int, + Number of sensors to be placed in the constrained area. + j: int, + Iterative variable in the QR algorithm. + + Returns + ------- + dlens : np.darray, shape [Variable based on j] with constraints mapped into it. """ didx = np.isin(piv[j:],lin_idx,invert=j Date: Fri, 18 Nov 2022 19:45:18 -0700 Subject: [PATCH 33/52] fixing tests --- pysensors/optimizers/_gqr.py | 2 +- pysensors/utils/__init__.py | 8 ++------ pysensors/utils/_norm_calc.py | 7 +++++-- tests/optimizers/test_optimizers.py | 1 + 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index a0ee978..b135283 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -69,7 +69,7 @@ def fit(self,basis_matrix=None,**optimizer_kws): Returns ------- - self: a fitted :class:`pysensors.optimizers.QR` instance + self: a fitted :class:`pysensors.optimizers.GQR` instance """ [setattr(self,name,optimizer_kws.get(name,getattr(self,name))) for name in optimizer_kws.keys()] self._norm_calc_Instance = normCalcReturnInstance(self, self.constraint_option) diff --git a/pysensors/utils/__init__.py b/pysensors/utils/__init__.py index 8d9404b..59b6e8b 100644 --- a/pysensors/utils/__init__.py +++ b/pysensors/utils/__init__.py @@ -3,8 +3,6 @@ from ._optimizers import constrained_multiclass_solve from ._constraints import get_constraind_sensors_indices from ._constraints import get_constrained_sensors_indices_linear -from ._constraints import box_constraints -from ._constraints import functional_constraints from ._validation import determinant from ._validation import relative_reconstruction_error from ._norm_calc import exact_n @@ -21,9 +19,7 @@ "functional_constraints", "determinant", "relative_reconstruction_error", - # "norm_calc_exact_n_const_sensors", - # "norm_calc_max_n_const_sensors", - # "predetermined_norm_calc", "exact_n", - "max_n" + "max_n", + "predetermined" ] diff --git a/pysensors/utils/_norm_calc.py b/pysensors/utils/_norm_calc.py index dff6027..3a5a9aa 100644 --- a/pysensors/utils/_norm_calc.py +++ b/pysensors/utils/_norm_calc.py @@ -3,7 +3,9 @@ """ import numpy as np -from ..utils._constraints import get_constraind_sensors_indices_radii + +def unconstrained(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): + return dlens def exact_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): ##Will first force sensors into constrained region # num_sensors should be fixed for each custom constraint (for now) @@ -106,6 +108,7 @@ def predetermined(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): return dlens __norm_calc_type = {} +__norm_calc_type[''] = unconstrained __norm_calc_type['exact_n_const_sensors'] = exact_n __norm_calc_type['max_n_const_sensors'] = max_n __norm_calc_type['predetermined_norm_calc'] = predetermined @@ -123,5 +126,5 @@ def returnInstance(cls, name): __norm_calc_type[name], instance of class """ if name not in __norm_calc_type: - cls.raiseAnError (IOError, "{} NOT IMPLEMENTED!!!!!".format(name)) + raise NotImplementedError("{} NOT IMPLEMENTED!!!!!".format(name)) return __norm_calc_type[name] \ No newline at end of file diff --git a/tests/optimizers/test_optimizers.py b/tests/optimizers/test_optimizers.py index d410f00..ea9a041 100644 --- a/tests/optimizers/test_optimizers.py +++ b/tests/optimizers/test_optimizers.py @@ -110,6 +110,7 @@ def test_gqr_exact_constrainted_case1(data_random): # try to compare these using the validation metrics +## TODO def test_gqr_max_constrained(): pass From 3408c9b1024effff29ed3d7b7f4d1525facc9323 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Sat, 19 Nov 2022 11:04:29 -0700 Subject: [PATCH 34/52] removing notebooks and unnecessary mods --- pysensors/basis/_base.py | 4 ++-- pysensors/basis/_identity.py | 2 +- pysensors/classification/_sspoc.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pysensors/basis/_base.py b/pysensors/basis/_base.py index f91c131..261cb5b 100644 --- a/pysensors/basis/_base.py +++ b/pysensors/basis/_base.py @@ -43,9 +43,9 @@ def matrix_representation(self, n_basis_modes=None, copy=False): n_basis_modes = self._validate_input(n_basis_modes) if copy: - return self.basis_matrix_[:, :n_basis_modes].copy()#self.original_data @ + return self.basis_matrix_[:, :n_basis_modes].copy() else: - return self.basis_matrix_[:, :n_basis_modes]#self.original_data @ + return self.basis_matrix_[:, :n_basis_modes] def _validate_input(self, n_basis_modes): """ diff --git a/pysensors/basis/_identity.py b/pysensors/basis/_identity.py index c31b776..de90ed2 100644 --- a/pysensors/basis/_identity.py +++ b/pysensors/basis/_identity.py @@ -65,7 +65,7 @@ def fit(self, X): ) ) - self.basis_matrix_ = check_array(X)[: self.n_basis_modes, :].T.copy() # np.eye(X.shape[1])[:,:self.n_basis_modes] + self.basis_matrix_ = check_array(X)[: self.n_basis_modes, :].T.copy() if self.n_basis_modes < X.shape[0]: warn(f"Only the first {self.n_basis_modes} examples were retained.") diff --git a/pysensors/classification/_sspoc.py b/pysensors/classification/_sspoc.py index 531e970..48a795b 100644 --- a/pysensors/classification/_sspoc.py +++ b/pysensors/classification/_sspoc.py @@ -259,7 +259,7 @@ def fit( if self.threshold is None: # Chosen as in Brunton et al. (2016) - threshold = np.sqrt(np.sum(s**2)) / ( + threshold = np.sqrt(np.sum(s ** 2)) / ( 2 * self.basis_matrix_inverse_.shape[0] * n_classes ) else: From 3254b890742753c0d7ec9ad8216f3cbd04be4bb0 Mon Sep 17 00:00:00 2001 From: Jimmy-INL <52417034+Jimmy-INL@users.noreply.github.com> Date: Sat, 19 Nov 2022 11:07:04 -0700 Subject: [PATCH 35/52] Delete basis_comparison-Copy1.ipynb --- examples/basis_comparison-Copy1.ipynb | 962 -------------------------- 1 file changed, 962 deletions(-) delete mode 100644 examples/basis_comparison-Copy1.ipynb diff --git a/examples/basis_comparison-Copy1.ipynb b/examples/basis_comparison-Copy1.ipynb deleted file mode 100644 index 3720615..0000000 --- a/examples/basis_comparison-Copy1.ipynb +++ /dev/null @@ -1,962 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Basis comparison\n", - "Compare reconstruction performance of different choices of basis:\n", - "* Raw input\n", - "* SVD/POD modes\n", - "* Random projections\n", - "\n", - "We'll perform comparisons using Olivetti faces dataset from AT&T." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-11T03:41:30.417811Z", - "start_time": "2022-02-11T03:41:29.407472Z" - } - }, - "outputs": [], - "source": [ - "from time import time\n", - "import warnings\n", - "\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import numpy as np\n", - "from sklearn import datasets\n", - "\n", - "import pysensors as ps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Data consists of 10 pictures of 40 different people, each 64 x 64." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-11T03:41:31.828680Z", - "start_time": "2022-02-11T03:41:31.798246Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "400 4096\n" - ] - } - ], - "source": [ - "faces = datasets.fetch_olivetti_faces(shuffle=True, random_state=99)\n", - "X = faces.data\n", - "\n", - "n_samples, n_features = X.shape\n", - "print(n_samples, n_features)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-11T03:41:32.539676Z", - "start_time": "2022-02-11T03:41:32.532499Z" - } - }, - "outputs": [], - "source": [ - "# Global centering\n", - "X = X - X.mean(axis=0)\n", - "\n", - "# Local centering\n", - "X -= X.mean(axis=1).reshape(n_samples, -1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-11T03:41:34.795977Z", - "start_time": "2022-02-11T03:41:34.789826Z" - } - }, - "outputs": [], - "source": [ - "# From https://scikit-learn.org/stable/auto_examples/decomposition/plot_faces_decomposition.html\n", - "n_row, n_col = 2, 3\n", - "n_components = n_row * n_col\n", - "image_shape = (64, 64)\n", - "\n", - "def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):\n", - " plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n", - " plt.suptitle(title, size=16)\n", - " for i, comp in enumerate(images):\n", - " plt.subplot(n_row, n_col, i + 1)\n", - " vmax = max(comp.max(), -comp.min())\n", - " plt.imshow(comp.reshape(image_shape), cmap=cmap,\n", - " interpolation='nearest',\n", - " vmin=-vmax, vmax=vmax)\n", - " plt.xticks(())\n", - " plt.yticks(())\n", - " plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-11T03:41:35.856425Z", - "start_time": "2022-02-11T03:41:35.669692Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_gallery(\"First few centered faces\", X[:n_components])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll learn the sensors using the first 300 faces and use the rest for testing reconstruction error." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-11T03:41:37.498997Z", - "start_time": "2022-02-11T03:41:37.496833Z" - } - }, - "outputs": [], - "source": [ - "X_train, X_test = X[:300], X[300:]" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-11T16:41:13.678134Z", - "start_time": "2022-02-11T16:41:13.000576Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "first mode is : [ 0.02161312 0.02356213 0.02436712 ... -0.01424331 -0.01122156\n", - " -0.00952435]\n", - "first mode is : [ 0.02641718-0.00283139j 0.03057836-0.0035621j 0.03492383-0.00456587j\n", - " ... -0.02310508+0.00338802j -0.02023996+0.0032135j\n", - " -0.01810705+0.00297554j]\n", - "first mode is : [-0.02504727+0.j -0.02960636+0.j -0.03531519+0.j ... 0.03384322+0.j\n", - " 0.03121531+0.j 0.02839542+0.j]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/abdomg/miniconda3/envs/myenv/lib/python3.9/site-packages/numpy/core/_asarray.py:102: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return array(a, dtype, copy=False, order=order)\n", - "/Users/abdomg/miniconda3/envs/myenv/lib/python3.9/site-packages/numpy/core/_asarray.py:102: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return array(a, dtype, copy=False, order=order)\n", - "/Users/abdomg/miniconda3/envs/myenv/lib/python3.9/site-packages/numpy/core/_asarray.py:102: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return array(a, dtype, copy=False, order=order)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "first mode is : [-0.02291244+8.036276e-04j -0.02671259+7.084303e-04j\n", - " -0.0313051 +7.145689e-05j ... 0.03113792+9.114330e-03j\n", - " 0.02855065+9.065691e-03j 0.02588998+8.364958e-03j]\n", - "first mode is : [ 0.0092756 -0.01193332j 0.01108589-0.01766157j 0.0138324 -0.02483492j\n", - " ... -0.0234978 +0.03297051j -0.02229962+0.03155063j\n", - " -0.02036817+0.02890316j]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/abdomg/miniconda3/envs/myenv/lib/python3.9/site-packages/numpy/core/_asarray.py:102: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return array(a, dtype, copy=False, order=order)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for i in range(5):\n", - " hodmd = HODMD(svd_rank=i+1,d=2, exact=False,opt=False)\n", - " hodmd.fit(X_train.T)\n", - " U = hodmd.modes\n", - " print('first mode is : {}'.format(U[:,0]))\n", - " plt.plot(U[:,0])\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-11T03:44:38.587708Z", - "start_time": "2022-02-11T03:44:38.265806Z" - } - }, - "outputs": [], - "source": [ - "from pydmd import DMD,HODMD\n", - "dmd = DMD(svd_rank=35,exact=False,opt=False)\n", - "dmd.fit(X_train.T)\n", - "U = dmd.modes\n", - "np.shape(U)\n", - "Hodmd = HODMD(svd_rank=35,d=5,exact=False,opt=False)\n", - "Hodmd.fit(X_train.T)\n", - "UHodmd = Hodmd.modes\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-11T03:44:38.901700Z", - "start_time": "2022-02-11T03:44:38.890149Z" - } - }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "object of type 'builtin_function_or_method' has no len()", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/4d/z3xfv_kx21g_zvpk__ybdy5wjqwgnv/T/ipykernel_7875/1947659873.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mwidths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m 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has no len()" - ] - } - ], - "source": [ - "from scipy import signal\n", - "import matplotlib.pyplot as plt\n", - "widths = np.arange(1, np.shape(X_train)[0]*np.shape(X_train)[1])\n", - "cwtmatr = signal.cwt(X_train.flatten, signal.ricker,widths)\n", - "cwtmatr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reconstruction error" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Varying the number of basis modes\n", - "First we'll fix the number of sensors at 100 and see how the number of basis modes used affects the reconstruction error." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-11T03:44:42.079095Z", - "start_time": "2022-02-11T03:44:42.074387Z" - } - }, - "outputs": [], - "source": [ - "max_basis_modes = 200\n", - "n_sensors = 100\n", - "\n", - "models = [\n", - " (\n", - " 'Custom',\n", - " ps.SSPOR(\n", - " n_sensors=n_sensors,\n", - " basis=ps.basis.Custom(n_basis_modes=max_basis_modes, U=U)\n", - " )\n", - " ),\n", - " (\n", - " 'Custom',\n", - " ps.SSPOR(\n", - " n_sensors=n_sensors,\n", - " basis=ps.basis.Custom(n_basis_modes=max_basis_modes, U=UHodmd)\n", - " )\n", - " ),\n", - "\n", - " (\n", - " 'Identity',\n", - " ps.SSPOR(\n", - " n_sensors=n_sensors,\n", - " basis=ps.basis.Identity(n_basis_modes=max_basis_modes)\n", - " )\n", - " ),\n", - " (\n", - " 'SVD',\n", - " ps.SSPOR(\n", - " n_sensors=n_sensors,\n", - " basis=ps.basis.SVD(n_basis_modes=max_basis_modes)\n", - " )\n", - " ),\n", - " (\n", - " 'Random Projection',\n", - " ps.SSPOR(\n", - " n_sensors=n_sensors,\n", - " basis=ps.basis.RandomProjection(n_basis_modes=max_basis_modes)\n", - " )\n", - " ),\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-11T03:44:42.668037Z", - "start_time": "2022-02-11T03:44:42.664558Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "SSPOR(basis=,\n", - " n_sensors=100, optimizer=QR())" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "name,model1 = models[1]\n", - "model1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-11T03:44:44.295653Z", - "start_time": "2022-02-11T03:44:44.291318Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.01081226+0.0041771j , 0.01081226-0.0041771j ,\n", - " 0.01053635+0.01299688j, ..., -0.01331744+0.01571602j,\n", - " -0.01299805+0.j , 0.02183318+0.j ],\n", - " [ 0.01551894+0.00339955j, 0.01551894-0.00339955j,\n", - " 0.00992298+0.01392816j, ..., -0.01557961+0.01544972j,\n", - " -0.01076506+0.j , 0.02271762+0.j ],\n", - " [ 0.01776523+0.00288624j, 0.01776523-0.00288624j,\n", - " 0.01055673+0.0140293j , ..., -0.01877962+0.01605825j,\n", - " -0.00682095+0.j , 0.02672054+0.j ],\n", - " ...,\n", - " [-0.02068003+0.01112751j, -0.02068003-0.01112751j,\n", - " -0.0117961 -0.00428799j, ..., 0.02228742+0.00780084j,\n", - " -0.01379396+0.j , -0.01789464+0.j ],\n", - " [-0.02330952+0.01277846j, -0.02330952-0.01277846j,\n", - " -0.01120195-0.00414563j, ..., 0.02323647+0.00901809j,\n", - " -0.01178332+0.j , -0.01430092+0.j ],\n", - " [-0.02258067+0.01353797j, -0.02258067-0.01353797j,\n", - " -0.01094244-0.00395666j, ..., 0.02169603+0.00869081j,\n", - " -0.01108236+0.j , -0.0114726 +0.j ]],\n", - " dtype=complex64)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model1.basis.fit(X_train)\n", - "model1.basis.basis_matrix_" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-11T03:44:48.136086Z", - "start_time": "2022-02-11T03:44:45.209361Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train time for Custom basis: 4.0531158447265625e-06\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/abdomg/projects/pysensors/pysensors/reconstruction/_sspor.py:478: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " error[k] = score(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train time for Custom basis: 1.9073486328125e-06\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/abdomg/projects/pysensors/pysensors/reconstruction/_sspor.py:478: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " error[k] = score(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train time for Identity basis: 0.0022819042205810547\n", - "Train time for SVD basis: 0.06403183937072754\n", - "Train time for Random Projection basis: 0.008278131484985352\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", - "ax.set(\n", - " xlabel=\"Number of basis modes\",\n", - " ylabel=\"RMSE\",\n", - " title=f\"Reconstruction error ({n_sensors} sensors)\",\n", - ")\n", - "\n", - "n_basis_modes_range = np.arange(1, max_basis_modes, 5)\n", - "\n", - "# Suppress warning arising from selecting fewer basis modes than\n", - "# the number of examples passed to the Identity basis\n", - "# (results in some examples being thrown away)\n", - "with warnings.catch_warnings():\n", - " warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - " for name, model in models:\n", - " t0 = -time()\n", - " model.basis.fit(X_train)\n", - " print(f\"Train time for {name} basis: {time() + t0}\")\n", - "\n", - " errors = np.zeros_like(n_basis_modes_range, dtype=np.float64)\n", - " for k, n in enumerate(n_basis_modes_range):\n", - " model.update_n_basis_modes(n, X_test, quiet=True)\n", - " errors[k] = model.reconstruction_error(X_test, [n_sensors])[0]\n", - "\n", - " ax.plot(n_basis_modes_range, errors, \"-o\", label=name)\n", - "\n", - "ax.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-04T02:50:23.173925Z", - "start_time": "2022-02-04T02:50:22.896146Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.03725206, 0.03725206, -0.04305432, ..., 0.07484102,\n", - " 0.07484102, 0.07520393],\n", - " [ 0.06861386, 0.06861386, -0.075698 , ..., -0.03332578,\n", - " -0.03332578, -0.02807535],\n", - " [-0.0798585 , -0.0798585 , 0.02884919, ..., -0.04854688,\n", - " -0.04854688, -0.05121052],\n", - " ...,\n", - " [-0.00837904, -0.00837904, -0.02156027, ..., -0.07699031,\n", - " -0.07699031, -0.0671259 ],\n", - " [ 0.09092192, 0.09092192, 0.01293033, ..., 0.02918206,\n", - " 0.02918206, 0.04691554],\n", - " [-0.01964494, -0.01964494, 0.04019649, ..., -0.01040314,\n", - " -0.01040314, -0.01088595]], dtype=float32)" - ] - }, - "execution_count": 169, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pydmd import DMD\n", - "dmd = DMD(svd_rank=0)\n", - "dmd.fit(X_train)\n", - "U = dmd.modes.real\n", - "model1.basis.basis_matrix_ = U\n", - "# model.fit(X_train)\n", - "model1.basis.basis_matrix_ " - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-04T02:50:23.226620Z", - "start_time": "2022-02-04T02:50:23.219199Z" - } - }, - "outputs": [], - "source": [ - "max_basis_modes = 200\n", - "n_sensors = 100\n", - "\n", - "models = [\n", - " (\n", - " 'Identity',\n", - " ps.SSPOR(\n", - " n_sensors=n_sensors, \n", - " basis=ps.basis.Custom(n_basis_modes=max_basis_modes)\n", - " )\n", - " ),\n", - " (\n", - " 'SVD',\n", - " ps.SSPOR(\n", - " n_sensors=n_sensors, \n", - " basis=ps.basis.SVD(n_basis_modes=max_basis_modes)\n", - " )\n", - " ),\n", - " (\n", - " 'Random Projection',\n", - " ps.SSPOR(\n", - " n_sensors=n_sensors, \n", - " basis=ps.basis.RandomProjection(n_basis_modes=max_basis_modes)\n", - " )\n", - " ),\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "metadata": { - "ExecuteTime": { - "end_time": "2022-02-04T02:50:58.250623Z", - "start_time": "2022-02-04T02:50:58.124897Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train time for Identity basis: 3.0994415283203125e-06\n" - ] - }, - { - "ename": "TypeError", - "evalue": "'NoneType' object is not subscriptable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - 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\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_n_basis_modes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquiet\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 24\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreconstruction_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mn_sensors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/projects/pysensors/pysensors/reconstruction/_sspor.py\u001b[0m in \u001b[0;36mupdate_n_basis_modes\u001b[0;34m(self, n_basis_modes, x, quiet)\u001b[0m\n\u001b[1;32m 350\u001b[0m ):\n\u001b[1;32m 351\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis_modes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_basis_modes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefit_basis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquiet\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mquiet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/projects/pysensors/pysensors/reconstruction/_sspor.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, quiet, prefit_basis, seed, **optimizer_kws)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0;31m# Get matrix representation of basis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 148\u001b[0;31m self.basis_matrix_ = self.basis.matrix_representation(\n\u001b[0m\u001b[1;32m 149\u001b[0m \u001b[0mn_basis_modes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_basis_modes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m )\n", - "\u001b[0;32m~/projects/pysensors/pysensors/basis/_base.py\u001b[0m in \u001b[0;36mmatrix_representation\u001b[0;34m(self, n_basis_modes, copy)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis_matrix_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mn_basis_modes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasis_matrix_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mn_basis_modes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_validate_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_basis_modes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", - "ax.set(\n", - " xlabel=\"Number of basis modes\",\n", - " ylabel=\"RMSE\",\n", - " title=f\"Reconstruction error ({n_sensors} sensors)\",\n", - ")\n", - "\n", - "n_basis_modes_range = np.arange(1, max_basis_modes, 5)\n", - "\n", - "# Suppress warning arising from selecting fewer basis modes than\n", - "# the number of examples passed to the Identity basis\n", - "# (results in some examples being thrown away)\n", - "with warnings.catch_warnings():\n", - " warnings.filterwarnings(\"ignore\", category=UserWarning)\n", - "\n", - " for name, model in models:\n", - " t0 = -time()\n", - " model.basis.fit(X_train)\n", - " print(f\"Train time for {name} basis: {time() + t0}\")\n", - "\n", - " errors = np.zeros_like(n_basis_modes_range, dtype=np.float64)\n", - " for k, n in enumerate(n_basis_modes_range):\n", - " model.update_n_basis_modes(n, X_test, quiet=True)\n", - " errors[k] = model.reconstruction_error(X_test, [n_sensors])[0]\n", - "\n", - " ax.plot(n_basis_modes_range, errors, \"-o\", label=name)\n", - "\n", - "ax.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Random projection and Identity bases give similar performance, with Identity winning out for larger numbers of modes. The POD basis performs best for smaller numbers of modes, but its accuracy tapers off as the number of basis modes grows large." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Varying the number of sensors\n", - "Next we'll explore the reconstruction error for a fixed number of basis modes (100) as the number of **sensors** is varied." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2020-12-06T21:29:41.188007Z", - "start_time": "2020-12-06T21:29:41.184139Z" - } - }, - "outputs": [], - "source": [ - "n_basis_modes = 100\n", - "\n", - "models = [\n", - " (\n", - " 'Identity',\n", - " ps.SSPOR(basis=ps.basis.Identity(n_basis_modes=n_basis_modes))\n", - " ),\n", - " (\n", - " 'SVD',\n", - " ps.SSPOR(basis=ps.basis.SVD(n_basis_modes=n_basis_modes))\n", - " ),\n", - " (\n", - " 'Random Projection',\n", - " ps.SSPOR(basis=ps.basis.RandomProjection(n_basis_modes=n_basis_modes))\n", - " ),\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2020-12-06T21:29:44.559677Z", - "start_time": "2020-12-06T21:29:41.189444Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train time for Identity basis: 0.015327930450439453\n", - "Train time for SVD basis: 0.037960052490234375\n", - "Train time for Random Projection basis: 0.018201112747192383\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", - "ax.set(\n", - " xlabel=\"Number of sensors\",\n", - " ylabel=\"RMSE\",\n", - " title=f\"Reconstruction error ({n_basis_modes} basis modes)\",\n", - ")\n", - "\n", - "sensor_range = np.arange(1, 200, 5)\n", - "for name, model in models:\n", - " t0 = -time()\n", - " model.fit(X_train, quiet=True)\n", - " print(f\"Train time for {name} basis: {time() + t0}\")\n", - " \n", - " errors = model.reconstruction_error(X_test, sensor_range=sensor_range)\n", - " ax.plot(sensor_range, errors, \"-o\", label=name)\n", - " \n", - "ax.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When the sensor count is small, Identity and Random projection bases produce the best reconstruction error. As the number of sensors grows, the POD basis wins out." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sensor locations\n", - "Let's compare the sensor locations for the three bases." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "ExecuteTime": { - "end_time": "2020-12-06T21:29:44.766935Z", - "start_time": "2020-12-06T21:29:44.561231Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(1, 3, figsize=(15, 4))\n", - "n_sensors = 60\n", - "\n", - "for ax, (name, model) in zip(axs, models):\n", - " img = np.zeros(n_features)\n", - " sensors = model.get_all_sensors()[:n_sensors]\n", - " img[sensors] = 16\n", - " \n", - " ax.imshow(img.reshape(image_shape), cmap=plt.cm.binary)\n", - " ax.set(title=name, xticks=[], yticks=[])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similar sensor locations are chosen for this dataset across all three bases considered." - ] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.5" - }, - "latex_envs": { - "LaTeX_envs_menu_present": true, - "autoclose": false, - "autocomplete": true, - "bibliofile": "biblio.bib", - "cite_by": "apalike", - "current_citInitial": 1, - "eqLabelWithNumbers": true, - "eqNumInitial": 1, - "hotkeys": { - "equation": "Ctrl-E", - "itemize": "Ctrl-I" - }, - "labels_anchors": false, - "latex_user_defs": false, - "report_style_numbering": false, - "user_envs_cfg": false - }, - "nbTranslate": { - "displayLangs": [ - "*" - ], - "hotkey": "alt-t", - "langInMainMenu": true, - "sourceLang": "en", - "targetLang": "fr", - "useGoogleTranslate": true - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "position": { - "height": "306.4px", - "left": "1116px", - "right": "20px", - "top": "120px", - "width": "346.4px" - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From acf2f365beef081eeb0bd362d4cadb628bafdc4c Mon Sep 17 00:00:00 2001 From: Jimmy-INL <52417034+Jimmy-INL@users.noreply.github.com> Date: Sat, 19 Nov 2022 11:07:36 -0700 Subject: [PATCH 36/52] Delete cost_constrained_qr.ipynb --- examples/cost_constrained_qr.ipynb | 428 ----------------------------- 1 file changed, 428 deletions(-) delete mode 100644 examples/cost_constrained_qr.ipynb diff --git a/examples/cost_constrained_qr.ipynb b/examples/cost_constrained_qr.ipynb deleted file mode 100644 index 1695d93..0000000 --- a/examples/cost_constrained_qr.ipynb +++ /dev/null @@ -1,428 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cost-constrained QR (CCQR)\n", - "This notebook explores the `PySensors` cost-constrained QR `CCQR` optimizer for cost-constrained sparse sensor placement (for reconstruction).\n", - "\n", - "Suppose we are interested in reconstructing a field based on a limited set of measurements.\n", - "Examples:\n", - "* Fluid flows (estimating the drag on an airplane wing with pressure sensors on different parts of the wing)\n", - "* Atmospheric dynamics (approximating the concentrations of different molecules based on measurements taken at only a few locations)\n", - "* Sea-surface temperature (predicting the temperature at any point on the ocean based on the temperatures measured at various other points on the ocean)\n", - "\n", - "In other notebooks we have shown how one can use the `SSPOR` class to pick optimal locations in which to place sensors to accomplish this task. But so far we have treated all sensor locations as being equally viable. What happens when some sensor locations are more expensive than others? For example, it might be ten times as costly to place and maintain a buoy measuring the sea-surface temperature in the middle of the Atlantic compared to one close to the coast.\n", - "\n", - "The cost-constrained QR algorithm was devised specifically to solve such problems. The `PySensors` object implementing this method is named `CCQR` and in this notebook we'll demonstrate its use on a toy problem.\n", - "\n", - "See the following reference for more information ([link](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8579238))\n", - "\n", - " Clark, Emily, et al. \"Greedy sensor placement with cost constraints.\" IEEE Sensors Journal 19.7 (2018): 2642-2656." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-07T16:17:17.467243Z", - "start_time": "2020-10-07T16:17:16.153291Z" - } - }, - "outputs": [], - "source": [ - "from time import time\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from sklearn import datasets\n", - "\n", - "import pysensors as ps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "We'll consider the Olivetti faces dataset from AT&T. Our goal will be to reconstruct images of faces from a limited set of measurements.\n", - "\n", - "First we've got to load and preprocess the data." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-07T16:17:17.526638Z", - "start_time": "2020-10-07T16:17:17.469713Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "400 4096\n" - ] - } - ], - "source": [ - "faces = datasets.fetch_olivetti_faces(shuffle=True, random_state=99)\n", - "X = faces.data\n", - "\n", - "n_samples, n_features = X.shape\n", - "print(n_samples, n_features)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-07T16:17:17.543405Z", - "start_time": "2020-10-07T16:17:17.531550Z" - } - }, - "outputs": [], - "source": [ - "# Global centering\n", - "X = X - X.mean(axis=0)\n", - "\n", - "# Local centering\n", - "X -= X.mean(axis=1).reshape(n_samples, -1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-07T16:17:17.552697Z", - "start_time": "2020-10-07T16:17:17.546032Z" - } - }, - "outputs": [], - "source": [ - "# From https://scikit-learn.org/stable/auto_examples/decomposition/plot_faces_decomposition.html\n", - "n_row, n_col = 2, 3\n", - "n_components = n_row * n_col\n", - "image_shape = (64, 64)\n", - "\n", - "def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):\n", - " plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n", - " plt.suptitle(title, size=16)\n", - " for i, comp in enumerate(images):\n", - " plt.subplot(n_row, n_col, i + 1)\n", - " vmax = max(comp.max(), -comp.min())\n", - " plt.imshow(comp.reshape(image_shape), cmap=cmap,\n", - " interpolation='nearest',\n", - " vmin=-vmax, vmax=vmax)\n", - " plt.xticks(())\n", - " plt.yticks(())\n", - " plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-07T16:17:17.805297Z", - "start_time": "2020-10-07T16:17:17.554709Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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w2aylzq3vZa+z91Sd+5GKdk2z4nPc926erI3xHvH61+ta21BZV/e5O/a4Z2T9fNyzsFNbw+5sknjGM/arJfGMqe8YtDSM/fz586v7ZzK5iYmJiYlTixMxuc1mk4sXLx7q6ztjrxkVb1hLDdaVu516riXSjvlwPSTE4yTD7hhLvF37vrYlm06CGzlBrDnnWNpDeuF7xomkw++1DyMJtZtHn+NxvOIVr0iSvOc97zn87t57d+tnXq4zR+fYYBY0kjzr9153swmvT7d3LsfxaATaN8uo8+A+jAzy/r224z7aFlmvMbLT+Hr8rZK17VJmKJ7v2gcAe8ZpCWm93vOcc+211ybZ36Oj+T979myuv/761fUZ3f+dPfU4jO7POsdmOLYtga7PZmPMC32tbVRNR21v5BhUj/e9wHqYFdZ7YqR1sk2we4bwf55F1j50WjzO4Tc+X3/99Un2bbXJUTue+/rwww9PJjcxMTEx8cTEfMlNTExMTJxanIjLnzlzJk9+8pMPqaSpa7Kl7VBkaC7fd7E0IxXg5cSzjVzqraaoKjvTdKuT1mL4OnVkvd5aTMpx8SpVTeHr8dkOO6gGOiP8qE927a2/ef74jLPRy1/+8sNzUH9wLK7jIzz22GM7KsdOvUlIAmuGWziqmG6eMFx7fqySW1NFd84phvfgSDXYOY/YED9Sz9f9SL+tOvM5XZ8d5sIxzCNrUPuB+tvXsZt9nWeug8qJc4mt5Hv2S+0La723t7fqNPakJz3pcB9wbg0/sWqO+WdfdGpkq+T8zBitW8VILd5d7zjVc7cvRqEiVu+5z911mH+74Nc+jp5vfq6vhd2wN/zMZy2qaYX9baczjr3llluSbEMKarv8Pc40NZncxMTExMSpxYmYHAZg3rJIfZ2LNZKG3Vg7V1RLw7RvaaVjaTaAjty0O/df98l969jm5WYEWHM8sGNF5yBipuBgXLtGdyDEg/lccwqhPTuymAG98pWvPDznZ3/2Z5PsB2Mm+26+ayEEly5d2gnA7xg2Qb/M+f3333/YhjHKvmN35S7QegTO6aTlUbKBtUwhZkze76PwgHrOKPyk027A1OwsYGbcOX8hhTOfgOszN3XdPOcwerPPeo4Z+NmzZ1f3cr0XCfyuThZmCcw5bK9zRLGzkkNtzKK7MZt9jwL/6/9HYQ1rmU6OW/duf9gpydoO/tZ5HzFGjjEz7hJYVKaWbPdS91xlr1SWX/v2zGc+M8nWASVJPv7xjx8557777lsNWp9MbmJiYmLi1OJETO7KK6/MjTfeuCNFVOlhxNh46yIZ1OA+pAHe3tZzI41VqQFwHVyRHTxJWx37G4UmjGx1ya5UahsAfa3SkQOgLYl2rIbrIBWhPzcbsLRZr8116CPMDumozgntuE9260dHnmwZF+O69tprV6XxzWYzDKKuIBDUkiifqwTP/21L4hy+J+wFe3Kyy1a8Z9Zsc6MEBV0uw8tpb/T76H5i3F3wOb/5nvPe7ezj9NW5JS2F1zWwDR2maOm6y1nooP0OaAG8NysTpb9cm3Rh3APcP3V/cj88/elPb/tiBlTXlPZGgdzdfTAKZ+mYLvA94GfWmu8Be59zHYa0FmbBfKJFAXx2oonaJ37jnqPPPC+YuyR52tOelmS7V31f8TvPhGQbuuR9PcJkchMTExMTpxYntsk99alPPZQQ+FtTsvBW5bdf//VfT7IN5uPY+jY3C3LmaaSzzpONtzlSCizFAZbPec5zDs+xLcR2m84L0nYNpBX6ahtABeeawZnl1iBnrg3bMHPkr21nyVhvzmfmE1takp1UbaOqEXjDJcmXfdmXJUk++clPJjlZRnSPs47FkiUSIb/XZK1O9MwcMqdenyphOxk1WEvYa3vqyHO1SpfHzYs91eo+YBzsGTO4jgGZxXJ9xslad4H/tuPZ663zUvUarNllgb30jsPe3t7hdT796U8nSX7lV37l8Hc8OekD9xr9hcFX24+TkjNG2AN95DP7MNnOO88x26GYAzRMya5NzvbCLs0WGCWEdzB61ZBxPeZt5AXd2Q2dds/3E89Xnhf1XO9JnhlON9jBmgTWpj6/P/CBDxw5Zy2RQDKZ3MTExMTEKcaJ4+TOnTuX3/iN30iylSIqK+ONf8899yTZlRqQcOrb3HFbSCtIJWtlTZAoHKvjBNEVSGRIcvbMsz2i9htYd2w7QZUsHCfUebUlPbu1zt9lcjrpCanSZWc4F+mr2lWIQ0HKv/XWW4/0jfHXc171qlclSe66664k+/M4sjthj3MquC6RsSVoPKsYT5f2CHbKHJth2WZTr4O0iMQ5Km9S2+Ecp11yLFo9fxSH5barNI4GBGbCMWYStW3WFxaBdoPP9rqtWgDb4OgL1++8o21rwm6D7aRLPeZ+H5fc+9KlS4fPlPe///1Jtl52yW5sHv1k7F15HtvguC9/7dd+Lcku+6+aD/YK1yGROYyjY3gc673DX7Pm2gdgrQx7lDYoV1XHjC2MY1gX28FqH/2ctm2YtliTZLuWPEPsE8D+q4yY9QJoh+yxW5kcz2v25qVLlyaTm5iYmJh4YuLECZofe+yxHQmw2kg+9KEPJdlKB5YezS6S3Qh8zkX3bltWZUJ8Z6Zju0oXZ4NEgdRCHzvJe5Sg1LEm9n6r89QxkaQvb+KYQM6lj5bWq268i39Jdj3MKuvCPkApHf6S4aSzF912221JtlLtPffcs1q+5uLFiy1LBvYYQwK13bFLKGtbJeMx86h7FcbhGCvv77peHh9zPapM3Z0zStD8wAMPJDmajBZNiJkczIE5qrYf+o/XIOtjRt8VzXSmG/aMM1N09mPWhf3sDDVV2+B9fe7cuVUtwGOPPXYk60WSfOVXfuXh/12wlT5hi1vz0PY9a8/BtQKizC1tsIbcj/VZxTG+d13EtoK+8Juvx76wn0SdC+YA5sO6sx/qs4p5Yl3YS6OMVdVbmXO5jjUzXTYde0jiw/GiF73oSD9qwV2uCYtcK/+UTCY3MTExMXGKMV9yExMTExOnFicutrTZbHZUNFDM+h1Bw1BLu+lWdWV1uKjH2GDqYNBkS3ftLr0WeGvVInTaxvDaR9QhDqy0az19rq7K/B9VhtOhdUlvUWXQHn/tcNCpQB1qwXVQk3SJWTHQMzeEBaC2ZD2raoAAzVprbi29zrIsO+tSVbSoROjncc4+9RyrCztHmeSoOsdJZ1Ffj2oi1mOttu6ONUaVoelTt78dKsDasV7Pfe5zjxzn/yfb/TeqBVaPp33vTQcur91fqPlw1CAwuzNRVFXxqM1Lly7lN3/zNw/7wl6sqZ6Ax8a8oZqu96XXjGeJ3ec79b/NIfxFRUxb1cmCOeXeZl4c7lT3UHWUqv0fJQmoamuuzXhQI1vlWcflxBF+1vPXz59kq66kXdTuVlPW+xonFeCQH/pa1cw4oXzwgx887NN0PJmYmJiYeELixExub2/v8M2PYbNKyzA3pHzcwJ3YtSvvMEpGbLf5KnnYOGzJvXOyQPqwQ4jb6q5jWNLmXFhbsnV1dtCsJanOocJMzqVjbBiucOovjuVvx4hvuummJFspkDVGEq6SKecQSvDGN75xpw+1L2fPnt1x165zbBd+5slsqRro7WDisiJOslylZPqCtIzkyTFeU/e3YpSCLBmXUulcxuvxya4zDNI4TK5z6WefVRdrjyPpHULsaOJkvuBykpfXqt+Gk7FfffXVwxCCvb29XLhw4ZAl0be657kW82NHKc5dKy/lzw7A5h6sx9iJoyZLqONLdoOymVP63qWpoj1+8z3hZ1UXssC9wL27pk3henbh7+69OqYKB8I7CLzuVfaGE4PThlODJVvtRX0frCVcmExuYmJiYuLU4kRMblmWXHnllTtldF7wghccHmM3dRe462B9qgN7nbKpe2s73RboypjA9qqUVY/tGKOlertHu62q+yd43oHxZodVSkIKd4ouBxQjuVXpzMHztIFk5QS0tb9IZrYJsiY1bRnzg478y7/8y/O+970vHVxqx+y9fsc8wB4dJlJd7J3mymWgzORq/13Sh8BU+ohUW9N+mdmYVThYt/bJNjfv+67MjW3YDjfpkhX7OycpNpvuih47cYDDOTr7lNmNNTBdIvd67Jo0vizLoY3PAfn12k5KzBi7dWE92BNmnLb3V3d5a31gHLTB3qlj8jMEdumQi6qN4hw0Q0487XHW6zlZfE0ykPTB2RzDuaOSQtYW1N+A18nssGvfTJxxVVsjttiaVH7NFj6Z3MTExMTEqcXnxeSQ7rC7Jdu3NpKGPZc6Dzl7CsEqOinV57rUyKiMzloaMTNTpIkqcdieYv29r1NtcjASl4G3Z2SXeJpz7QHq0u+d3cjjRDpDOu8S85r53nzzzUfar5JbLXiZJLfffnt+9Ed/NGtw0H6F0yxZC8D81D54XznJ85r9qytxlOx6v9b9htRrRsW62GbSXXt0XSflru2PSi45eXk3DkvwaBScVLj20cyI+xk2U21Ptg/Z5mTW2WGNyZ09ezbXXnvtToq2qi3xmjmBhG31tR0nhre2hHNrSjDff9iJvMY1SQNshHF0iTE8Lj+LSJCBnR+tBgyvs5WyVvxlDzOuqhlxX91Ha4nqc5X5cSq4Nbslc+3k4X7e1DkiMJxz7rvvvsnkJiYmJiaemHhcTM6xC/Uta2kMKcnsrLIkMxrbT5Cwnve85yU5Knk4JZeLOjrhbEXHaOrnLm7HsU6WUtdSACEFm312TM72Kex6SGHMDW1WewdSmCUnzoUd1vE5dgeJkfZh7bWIopnWS1/60pYdgEuXLg09Cis8744VqnPL/7kue4TxOAat8ySEkTotlT3a6rHsX/aQ7Q1VGh8VIPUYzAorbPMdJQZPtnvFbMMFMJmbTgvge8NlYepeddyVvRO7+8nljfb29oaxTi7x1ZUXYo8g+XMsGpUuntS2d/YQY+dc9n61XVljxX3je6vGgTkOlz3JXHQekrb5Ork6z0L6WouL2iZGu37e1HE55Rj7ylobgF0x2e4NYmzZh9YOVXbLXLCH7NPRaf7sZXvhwoUZJzcxMTEx8cTEiRM07+3t7ehRq3RkfTkeay6wWiUCpCHKRNgb7c4770yytQ9VGwN9QCdOmQyXXOniliyVm5VVfbMlWCQHe2/RZo2pQfKwpyRS36j0Tu0340IfjeRmxlr7SMwbc0+f8U4im0mylQDpI1IY10Gyqwyca7Mez372s4fxhEYneTnmC+mO/dF5A3IMe+P2228/0objFmv/XvjCFyZJ3vve9ybZLYSL9F/tOY7z9LrbJly/sy3Odt2uWK89E71XmPvK0tjHJNe2tzLSMhJ8tTWRvci2Ld8j9Rx7mnptOwne9rs1m9yZM2dyzTXXHK5pFxvIGG2HpJ9m6XWMZqloMbBdMo8/8zM/c3guyaHx+ESDQJv4KXT73B7azFv1IAS+FzjG/gQdu+UZxL3tNWQf1DnhHmAOnOGEcVFeq2pkiN1knWGOztJSxzkqd2aP3aq94Vj69PDDD0+b3MTExMTEExOPK+OJMxzUtyxSEUzAnoWdlxPSAdIw5yCBIA3dfffdSY7quZFwYC0wHiQ53vpVesDOZJ28paPaVzM520QcN1OL/Fm6dJ7NLiOF7VK2KTkTQB0f0hjjZD4/9rGPHflcz6FPzAWfkewdJ1XH3sUidjhz5kyrYwdIgLY3cm3mr8vxCStDoqYMhzNQVPsa7bDPLMHbblSPQYK3dyv7oNrV7OXqODLQ5V118UqzQe6zGqsKq6UPrLtj3F784hcnyZESNm6f9Ucad7xeHV8X+1b/Vq9Fs4q1vXPmzJk86UlPOlw7e07W/rFvWX9sVjxjqid49QpOtpl77rjjjiTJ2972tiTJL/3SLx0Ze7KdH5439In1eP3rX5/kqD2Z63H/ce/am9Ofk+18OZOUz6n3BhobnhX0hetWWxxwvBrtMa9o1bgP6v3Fdzw/8QAl1yj3ZndfOSOR90dX2JXvrrvuuh0P4iNtD3+ZmJiYmJj4Esd8yU1MTExMnFp8Xo4n0MaqAoK2OxgbaolKqtJt/o8K6CMf+UiSLe19zWtek2TrXFLdqa22sboISl5VDZzvSuR2ia+OJ67UDVCZuKJyVQXeeuutSbaOHqhQmKPO7Z6xo4ZxSbZXBcAAACAASURBVA+of1dqxS7rGIRR+3UV3b1OdvpAhcf16thraqGRKy/Vnf17V2rH6i2nP6r9tlEdRye7zaN2qXsV9TdrhnqH71HVVdUj60o7rJOD0LvURd4jl/O7K0Oz3+0yzr1T+8g5rB33HuN6//vfH6M6h9Trs2edzq6eY5Ut62jX+XrscSpuroXzSbJd2xqmwbwwVtzYmSf2TpdujecPbTgdGb9XE4TLMjGnhBKg4qzVyx1U7kTpa27w7jPmGavuukBrMHLO6MKPbLJxW11YBe1wf7JnOBeVeq3yPSqnZSemqprmHO7tl770pXnPe97Tji2ZTG5iYmJi4hTjxEzu4sWLO0GF1VDOW9uGXo7lc+dGytsalsKb2kmIqzSGRG3J2gVCK/szU0CSsqt9V4jUDihOW0bb1RnHDApWgaHWgfJ1jHbywcCKxIYxuYYs0Ae7KHNdmF1lkIydOUfytSRV+2gp8tFHHz2WyXlcHZt0CRLWkmMx9tc+MP/33ntvku16Mz+Mte47zmVOYUMwR7QD1W2fuWXNzJq7BAJmyb5vHCzdlVyiDzAGxtOxcvrkYp8cwzmwjhpA7ABuruOk3pWpck97/UDnXDJKh9eB5N4u61L74ODr5z//+Um29wUsvQtepvjmu971riTb+5L7hLYqy6W/dnB75StfmWTLVuqzykWTrV3oSju5iKgTqDNewq/q/ek5pY+M24Hy3bjoG+e6mGp9hlgDxrwx54yrJrrmOca4nGC7K8/j8lo333zz4Rp2mExuYmJiYuLU4sQhBMm4yGD9v4OCR6Uakt1ATtt67DJcJR0nTEbScHmZCiRKswDbhOq4rDf32O16XaUol59Hn85npKZqY2CsDuQFSOG2SSa7+mzbi5wKrR7bSVv13I7JXa5N7vz58zusps6TExQ7bZyZSMVa+q5kt9xMHQtjw55jd+3aRxeR9H5wKq3Rd8muRqFLXu2wCo+dtrFF1r4hJePC/YEPfCDJbhKEmkzczIr9B1tjLSojcqHV0T1S7ydrAS4H3lu1PfrFPMHK+R5G55CS2geHwnhN6z3WhTEkuwkf6tw6QbsD7s286zF+HtCnF73oRUl2QxmS7ZqxN5xwvPMfGO1V2vVzrvaVZxT3L39h0d2zmOs4IN+ao/rc4Tfae/rTn96GXYDJ5CYmJiYmTi0+LybX6Uu7BK71WKSKTj/vAGuzB0sgyVZS818Xbe0kxlFBVydhrt/RB7dn6bXOCe27iCl6e3vs1XNgKC7s6GKhta9856S0Tq9TpVDbU5Dc6JtLvNT/V2lrzUMM20o9p86Tg2Od0NgpmpLdskX2QgT83vXPCXKdHLu25bRdZqROKOAx1jZ8vc4mx/8d/GtPvbqHYfkuU4KNyQVEO5ujS6ugfegKbZqJeo47e6VtvTV5t7HZbPLoo4/uaIGqNshJEtjrtG+P2TpGMzfbymxLq+c4HRXX557r5pa/lXXVz9V/YHQPoHVA44JtrjJ9a2esyenYpvvoIsG02RWhdWIMFxJm79TnDseMmJv9JZLtviaZwdOe9rT1ZALDXyYmJiYmJr7EceJSO1Ua4y1cJTczOD7bO6vCEvwo1U+XHsh6bJdNR0LoJHinpbJNoUvQ7CSxlpa6JLuW8j0ex+tV0J5j6Tyu2lcXq/TfLtnvSJodSeVJL7Gt2eSIs6ztVgnNtiJ7WHVljJC2HduERG0bU00p5L3J/DjtWrXv2ebqIplrtsaRjcoMv8LMimOsTbkc2xb2KO+HOp+258EQ6EdnVxkVf7UHcoUZ8XGeuRcuXNiZ+8oIfE1smY6pggHVdqxVcJwf61/3Dv93Ym5sfy4AnOymqLJmZS1e1vGSLhKLV2e1GzqZePf8rL/X9kfFZv387pgjtmyYFhor1qQyWHuC+3nWpYOsCeGT/edn59UMJpObmJiYmDi1OLFN7jgPKdszLLWaMXS/jf5al5zsej3azoAE0Hk7msHZJlL7SHuWZDwHnU3O9gfGYam8ziPX4+/Is2ytGKSlG4+rtun+e03MWLr2ukKxFTW5d6cFgNGasSF5IiXX61gatn3NGUi6TCQeo8vZdPsNWOKkre4c7x1rPdYSGXvP2N5RJV2Py/ZWM5TqZUsfHR/nfV77bi3HqFjqmpfq2t7Z29vLhQsXdmyZdS1HdnvHr1bvSttcR/OzpkEazXWXhNvPARiNx1M9gG2TN6NjvHi7Vq9oPC5H62EtSB2XvZNHz/U6n+wj7lPH1HX2Yxfudam2TuvEb1z7qquuGpZpSiaTm5iYmJg4xZgvuYmJiYmJU4vHpa50Da0ODvpdC/4cpT2yIXjNqG+1qGlvdc8fpWAaqa+Sscpp1I8uoNPu8x5vDfgeBcZD21FbdcZqYNWJVTlVReQgY9N/qzzqmGvfjnOAsGNOpypxG5zTBfTiUGD1HZ/X1Lq+zmivVueY0fq7rXqc91sXClOvW2E1K+oorse4OhWQ75dRhfLaD9cPGzmBdfPAXvUeXXPvruaLtfCTvb294b7g/GTXXd6B3l3V9VHIEn1zmEo91uEunuOqZrNK1upxV2Gv5zhtoVP2scY1RRtjJUSAvo7Sr1XYpOFzee5UVbeTODh131oKN559NtN0JgSO/bIv+7Ik+2kgV5+Dw18mJiYmJia+xPG4gsEtCXYBwpZSu+TKhqUuYIN2V7HbksZasKzd/deqVRuWfi3JWaLsfvM4+EyS1WQ3iauTCTvAskpldoE2M3HC5gqz2y5EwcdWie24ciGWDOta25XeIQ9dOAX/t7PIKJF2hRmuHZJs5K/t2bHAc9w5Ho0SCLjPa8cwN7j2s0+qFsAJAkZORKA6VIzCTtyfztA/Yv+g7q3j5qJib28vDz/88E6IRffcYR78LOmCl/1McgKJUQLt7twR86jnOFia+XLihcr4aAcm54TNZsld2RwzK4cJdWVsnPTCjjagOskQttOx5tpm/d6Jzh1K0DmekNqMBNC33HJLG34DJpObmJiYmDi1eFxMzsyqsz+MUvx08LGWfG3DqjYSvnMaIrugdgUbzVrWbI22p9ml22OpenWnD7NrMudQ4iPZFn0knRP6bl+fc2s/Rrpx2yu71GqWxn29KoHTfmWzo/VeliVXXHHFzvrUEkGWPEe2nC4gHdvA5YYz1HYstY6SLidbe6DDP7wea2EuXcKA7rq1HY+HOUeS7mybtgUfZ4Oux6wlN/D4fI6TKnT3lc9Z06Isy9IWSu7CZpgPp8EC3Vj5zkmwzVq69IWeJ5gI+7v225qJOr6kT0DuObTGguuxL+te5ZnopNQOq6njcojAyNfAdsxk1z480mB1STYczuH7GNt77QPX/vSnP716v08mNzExMTFxavG4mNxasmLr2u056e9re7b72PbH277aHxxg6eBppJhOihwVPu30z51u2MfUz/V72wksKXYFREnPg2cUTMXBzl26KtbF3m2279X57vTlPqa2kezaGK688sphUCZMjjF2wblmuPYC7RjJyI5rm59tJrV922RGpVe6Po6SSdfrrLHbDmv2vFGAf12XUXLykXfi5dwbtnF2bHPExrpzRmymw5kzZ3Lu3LnVFFOcz31gT8XOo9XPMe+7kVYj2WVlbr+bE9vA7flrz1DPQbKbiJ5xkni6zhFaIJ4daEqcmLr20SW9rG3y3urKXoERg+s0cR3Lq99XRo49mjl54IEHVjUBk8lNTExMTJxaPK44uTUJx7FtIym206Fa0jSD421eC0QiFYzSOXUS8MjWZ/1zZUejooV8tv2ojoVj7FWJhNXZXZDukKxILIs05rLxVYKjnZOUAXHpnpH3WyeZVrvnmkRej2V/1Dk2ixh5ha21a7tjV8TW1xvBqcLq/72/12KARh6etkutpYIaJXleS5Pn9kc2xwozEjM5l2tJdu9l29nWPDKrHW9t75w9e3bVG9rPCsePdfZdzmdNPU8jL8FuLD63e/7Z3uTk2yObXf2Ne84Fb3kOVNsV2iDmgITJPDscB9j1wX2x3bXTyJn1+9nJ3+56owTu9ZzbbrvtyHdrBVOTyeQmJiYmJk4xTlxq54orrhgmKa4Yld9Yi0kbJTnmbf6Zz3wmSXLPPfccnoNEgTTkjAMwodqfWiSwnmsvqyrBE5/GuaM4Eh+fbItYwkC53vOe97wj51Rm5fEgteB1SQFMmF1lEJYYbWPEA61KQI5PGSWtraVK7LVVpW2DjBa0Tx+65MBOBmspubOVjjwi7S3WMfrRObbr1v+PbHFrGHmsjUow1WP82Yy7i8s0U72cTCs+xuy6Y79eH++/zpv5OFujcenSpVWmN/JUdaabelwXy5aMtRnuT7LrqegYy3o9ex1y78Kk+Nv5ONAnvJGdRYTrV3uXY+voC4yOtur+c0zgKG7O+z/Z9ZkYrWm3D3z/OP6w0xbSl2uvvXYWTZ2YmJiYeGJivuQmJiYmJk4tHlcIgdG55R6XymhNvWOXcYypd911V5Kj1W9vvfXWJLvGY6s2qvoQlR8qQZKaQvmdBDXZUn/UhiMXaFfuTbbqSvqNio5ku53Ky4l5b7jhhiTJ+9///iTJvffem2Q7v1YdJlvVA9ezi3RH8e2wAVy1u16bc9aSpPocB1UnY/dl0IU5jJIP2KmoU684vMBjR6Xa1cwaVVceBU/X38DlpFCzet8pz9bUiFaZja7XqeWsJhulL6v/H4XkOEC7okvvNoLntAvt4D4fOZN19RA9DqtQ18bs+8QOMGvhAJ2jUe1rsrvuXJfPhAnwuc4Rc4GJ4WMf+9iR6z33uc9NcnR/45xmBz7PQVeR3vvNakWuU8+xOWkULtalnqu1FNf2z2RyExMTExOnFidicpvNpnX9Xyu/cjkG+VEyThw1YHAkMEYCSbbOG2YybrP+jtMGEgEMxKygc+aAodFHzoVJOii9XhtDL8c++9nPPjLODvSRyr/Pec5zkiQf//jHk/TS4EhC9PeVlTF2pG5Lqk6fVY9xQtsR6l4goHNNAhuVwunc9H0Nu453YRouJ4MjABIw7KCyTQfYm+G4TEv9zqzCEr2Pr+Ng7GgUnJi7C7Q+rrRPB7P8EZvpngM+d1TuprZbNS+j/u3t7eWRRx7ZcajpHCYcejNyLqrnAD+r1sonjViwmRfMqB7rBA6jkJmufT5zH8Lkusrg7OOf+7mfS7J9zrhiOFqi2u4oiYKds+o+cLJlJ83utHhOLcfz0yWG6nMHxxnOvffee2dar4mJiYmJJyZOzOQuXry4k46qSpGjgMY1l2FLhwR933333Um2TA4p7YUvfOHhuZ0bbG3LqbqSLZMz2xylv6r/5zenDTJzrCzJBQb5DGsiLKFKlqMwDcpL2M5Xxz+SiG0b6aSfkTTrkIJ6nZpabRSwjRYAyZN2ajHbkdTqhLzd3ulsLsku66vXQ8JEkmUv2Z5S19IBtGYrnU1uVJDWkrvtLfU71gWbMBIvTKGzh66tRW27S3Vm27bnt0vUPSqts5Y+bGQvdH9r8m9rF5LddHse+5rGwD4AtiV1Jb5gYQ7PcXHb7hniZwl/0fjUc8yonMTZBV3R9CT7JWiS5H3ve1+SXUaFra6zd9k2B7h/HKxd++jQL7OzLh2b15/rcE7d33zHs+/tb3/7kWBxYzK5iYmJiYlTi88rQXOXfHmUBNaf63G82ZE0KDlz5513Jtm+1V/5ylcmOVrifVQ2xxJ2leABUpDtbV26G9usrPN3YGkNJB8lU3aC1mo3pC+WfhjXzTffnCT54Ac/mGTrbVnbo/+WRD3u7ljPXyeFWRp/5JFH1r2czpw5ZHKgMi3r7Eef15IPjAKTWYOa9sjz4et0ybYtObusjW1C9dpu196unRagY8vJNiEvGoU6r0i9lpIvx7MZjJjQWqqr49KWdaxmlAzZuHTp0o4dqN7zDoZmjs3su+Bl5pT+j+7tOj7aRzvT2WI9Zn/nIsA8W7p7AqbCs5F9jN2NttDw1O+YG56b9Jm5qsk1sNthM0e74eLEXWpAszz6ZgbZaZBs6+Ncrlvvp7e97W1H+nrnnXe2z3cwmdzExMTExKnFidN6nT17dkfa71KujOLIfFyylaSQQrDBIa2iW37BC16Q5CjDMoOyx5oT9ia79hN/75Ie9TvOqV5TyVaCdymM+huw9AqzqwwVCR2JzXpuvOuYkw996EOH5zKPSPfWn7vAbNcnoytUaZvLxYsXh3YgUsLR784GM/LMHZXAWTvHa4k0WyVCjxlpcJTuqX5nz1zbb7oExo49sx3ZiXsrHE9EW04YXvvk2MYuVvA4jBhcl67KMYlribVtK68p3wzsub6Xu7JPtvm7yGedJ8dFOhmwPXVhWsl2jtnPHIudiPu3rqW9uHmGsD58rqnz0NDgTf3hD384yZbFME6eE5/4xCd2rvea17zmSF/oI2kS6zlcG8YIo8Oj3Zq4+gwxM/beZH7rvWGvTcfL4T/xjne84/AcbIzgzJkzs9TOxMTExMQTEydicnt7e7lw4cKOBFwlKicO9bFdUUnOsdSCjQq2UiPcfa4902znq9czs+JYvu8ynpjJOdMAUhLSXpX6Rol5zSgrO3QpeXsb0Rbsj9iRJPnoRz+aZCuVIQ35ul0iYL6DXVpyXyuwuZag+cyZM7n66qsPpcvqlWU4OesayxyVkTG76Ao2wjhsD1hLQD6yCzoDSe2rY+psK/G467numzNAcE5NOs7/rVUws+uYqu+xkQamjt/7exR/Vr93IvXjim9Wm1wXgwtrMHs0w+u8eWmXNjjGz7dq9+Sewr7lROq/+Iu/uNNHPC65LvcsbJB1qwVCYWzO5ML3aLu6RPHf+Z3feeQ6PE/xWmfcNYMUfYDlcV36xBgcg1n7wDkuR+b7uZ7Dsfxlf7NnK9vkfOyF9VnbYTK5iYmJiYlTixPHyV24cGFHIu1ip4DjyDrvO6QE3tZIX8TDwSrs/VjBb/aucnmL2geOcaFFM7p6LL8h0ViK6OLJbDcaedfVuUOCQcpDgkfKtDcRuvN6HebzJS95SZItU3T5kdonrsucszbVDupxrdmSwLIsueqqq1bXhWtYG7A2T7X92p6LV9JmlZItfcNeOIa1RWJMdqV8lzrpvIc5xmWLRkyy2jnoC9Kxi80y3upVhxTOnmEcZnS2ESW73oKj3Imdh5zj5bx+dU5GdskO+AL4GdJpA0ZaE8fR1fNZf+xanMM9zl/YW7LdE2aO3K/MI/4Fdcz0H40V59jOmux6U5OthL7byxf/hWQbM8e42Gf0nWdGnRvG7tJe7CmyNGGjq88/axVGuUzr9WyDA8QDu7RZcjT7SbK/N9fiLCeTm5iYmJg4tZgvuYmJiYmJU4sTB4OfOXNm1V3a5WqsYupoJUZUHE9QC/DXgbBdGhoAjcfI6kDc2hdTfaPSaju2kBCVz/TNaXeS3XniNwedd6pcV9C2A0rn7o4qATUFKg5UHpcT5DxK5tyl5KnOHiM38DNnzuTJT37yjsG6qji5Bqo5+uRkyxWjtFB2iukcTwDzY3dszkGdVPvA/LPunqc6D04S7ZRmVrVXVTTqSvYGa+v1r3uH73BKsMPGWjJbrre2Tr6eg9hHQeedM47VzB2WZcnVV1+9o9atfRoFovv3qq4m4buTNKDOc8quqiZzKj72DCpinDlq8nWORf3JdZ0wufbR7eNMxj5wesGahAJVqVWbjMNq6/p/nr1cH2cVxkWbN9544865NvfQhhNcJNu9bsc9rk9psfqs8vPzuEQCk8lNTExMTJxanJjJLcsyTKSb7LIiu3Y7wXGyK3FiMHVqqTXHhlFBxS7w24mYHdDZuSi7/3bEMGNdKwdjN9m1BLYucYMkxzkO8K79JvSCkILf8Tt+R5KtpNoVaTW7xABtp5zaB3576KGHVoPBr7rqqp1g6s7xgHaRiu3UsZYeauS+3oUHcG2kZMbq4OAqHTuwnnWxW36X7MCB1XzvUkV1r+K2jvTfOQ3VftRjaR+nAeB74jj3/do3f58cn0LLjlbdMauOAwfhJ97rHfvzfnAoUQ0kZk84HAh2gUah01i5iLLd5nHYqKEdPOcoD+YwkK4QKdfhXDuE8ezi3Br2RFpEJ77n+sxjdXSxo5PLUME6mcc6vptuuinJ1sGNvjjdV1fCjD3IWnBd+lH3B+tUtU9rbG4yuYmJiYmJU4sTM7m9vb1V1mKW5ODRLsEr7IRjkUSdiNU68vqbU3HZRtbZLGxPcd+qpGh2R7ujoOMukbEZsKXyKgk7mNUSryXsaj/kOzNU2wu7FF0OIbDtoQuNqMxrlKDZBXe7tRwlf4VpYWet54zCF8wuugTTtAcLgvGwH7EPVNbOdbDJsodowy7YydYu45RTtg3j0l2ZI7aJUQJyJ32u/UWihsUwj26rS0Tue9zMqCuUbJdxvqf9Lgh4lGC9Ai1AnZekt6+aTcIiSIdFQvNky2hYb9+7DoGoSdC5DumvOIc5NntOtuzOhW/Z59i9qg2YPcFYmVOug9aLPta0dU6EwXXZs5SYqucwF7ZLE6LCPuOcGiIBCDPws7kLF+C+4V70PQcb7fZbtQFOJjcxMTEx8YTE47LJraU9sr1pVLKj87CBYfBWd6oXJNMq0SElmJ1ZD9wlkUbSwGOIdi1h1fY4B4nDDKLTjZuBWL/Osei76/Wcroc+8XtX2sUB6bYfWkKt/TYjRdLqysDQPu199rOfHXrJLcuSZVl22GTHymjXbLmzWZhhmhW7cGO9HmtIX/gM8zKDrNdxwl8nmO0CeknB5kQCzDGfO89MB96bwdZx2VON67FHqh0lOcqwRjY42/Gq9qZLClHRFS729dY8PrmGvVJre4yNvzAS7imYXN07XJNzWDM/uzrPXP4Pk6MvzK2ZT7LLbFwIF3sUtvR6DH20N6X3X103PxtJDkEbPPfqc4DfaA+bmNPKgfqM9PV4vjkQv3uGsFe511wEtT7DRonNR5hMbmJiYmLi1OLz8q5EEqqS7lpsTNKXS+H/vM1r+Y36u2OTkq3U7Vgn9MJIR931br311iRbZoX++2Uve9mR75Ot1MO5tEtf+R4JrkrL9AUQ+4G9hbY/9rGPHR5Dyh8nnuV6SEmdnQo4jY/TetV57BJnJ7ued9WLkz7QxwceeGDI5Eiya4bQpVvjmi6P06UWG3mZmtHZ5liPtQcr4+LcGuvEd/bIZI6d1inZ7iv+OqmvWW1X6mlUtoZx1nMcd8k+YB9iL3L6tNq+7bZmCl3cqYsD+9jOXntcjBPHnD179rD9zo7HHoF5sE6sMXNeE/36HKc9g4F08V2cY3ur2UxXaNV2Ne4B9l0dlzU3XJd72Uy4FgVmHDzHuD62wU7bxrNhlI6R63ali+gr57hYasf4XTCWPrFH7QGbbJ95VVM103pNTExMTDwhceIEzY8++uhOqffKImwj8tu9i3VC4qBdS1K0j9RcGRasCAb12te+Nsk2uwdecBUwJ7IGICUwHjyWKkNFgnF8CpKIPYhqEVPHI8HyyBZgCbWOnXHZ/mUPysqwnBAVBsE4zSDqd7b5IGGt2cOQMu+7776hbcUFd73mHFP7xZi8l6oNYZTM2ZKo7b21fWeCYJ66pL6WYF3WBrZUk/liE3Mx0VEWkbWCsvZ27GxoLuhq9sexeAvWcdob2p6gjqes39nzkrmCodT9djkMDuzt7eX8+fM78bJVW2L2aHubnyX1fPeFe9xZZaovAOewn7jOqFhr/c5sk3uMe7lqG7x/RzF1MLiqdWD96Tf2Q/rsIsH1N5cs89zbc7aDPdK75449ZrkOz+jOe7prZw2TyU1MTExMnFrMl9zExMTExKnFiSuDf+5zn9uhqFWFBYV0iiQoeVcTDKM9Kr9RgG+nCsQ1GOpNcKKDKAl8TJL3vve9SbbGTVSaOK+gIqx0mGMJ2HRKMNQEXfogVCS0h3szbaG+qCEEzJ/rVNEWfXYanDpm1sXG3a6+l1OoWT3hkIb6f1QL73nPe3bq3FXUNe/qrlklO1JBVdWcnR1cGxCsJU52ZXY7bHQphezyjEoS9Usdl6tsOzjayQe6ebIKc5T0ucJqOAeO0yZqrDpmJyT3/qjPAKvSLicpu/t2OXXlvD+qutLp1EbB81V9yP9ZO6u0SeB8zz33JEle9apXHZ5LjUaHR9hRpN4PNjUwB9zb/K21Ia3q5b53Qmjv/zoOJ10e1XBLdh3daINjbc6oZhLm0SFSoAtDcno/9hfPWzuhJbsJK0YJKMBkchMTExMTpxYnYnIXLlzIRz/60XzFV3xFkt64ainRVbg7aRwJGumEY1zSB6miJneGtXAMjig2sr7iFa84PAfJDAYCG4KNVeMtQDqpTi/JbjVrp9BKdp1vkPp+9Vd/Nclu1e9kK5nRPoHEMFLYLxJO53ZOu8yvmXFlNU6m67IsdgpKtpI0c/3ggw8eK1WZJVVJ0MGyTsTqshzJ1tGD9pBEO1ZUr5Fs99HIecUu5cl2ryBRc4yTEHdzC+xAY6eSrqwIsETdpckzG6ddhy7QVr0e94Sdy0ZMsv7mgHyHhHQMHNTk3YaryjvYPdmyINZ0FGRcK2dzL9NPmAEOaYQSve1tb0uSvP3tbz889+u+7uuSJLfddtuR6wP6itYm2e6d45xuOicyxsEccQztd+EgPCPMvq3p6dLtOazGzwPmqrJOhyTw/OEc2ur2jp18uI+7e9ApATebzUzrNTExMTHxxMSJmNyjjz6aT33qU3nRi16UZPs2r/YA3vi8gR0UabtEMnbHNwvkOi984QsPz8WOhUSDJIcU8fznPz/JUQmnnp/sSu6gS5Vl92yHUXj83Rw4ma9L7iRbVgtTefGLX3zkM3DoQr0OY7bdrtPFM1bPNVJfxzLQmxNc+sxnPnNoW0Ead3sVnhf2F7YEbJh1XWyrYF+5TArrVG0W7B2niXJ6t8rKaY95J9zErvWVvbl8SWcTrX1fY3JeW9tIkrEtznbJLkCepMVOcWU7Wz3He8N961K4daEII2w2m1y8eHHHF6DeY+wd2AvaGNaSkCLsRvX/CyCRtgAAIABJREFUPDvQlvB8417D/vbTP/3Th+eyJ1hbtBpcj3OrLwBs0q71TgxdA7qZM9p3cWParMHSAA2O7WzMEc8Bxl37y9rBdh0qQRsdOg1BshuYn+w+460JYz67Qtm1bNcMBp+YmJiYeELiREzu0qVLuf/++w9tMEhHVSJFKnFyYEugVQpzuilL307Ci00w2UoW73znO5NsJWykf46tZdppz4zRCVm7gFd7i1py78rqWFKzHc2lNuoxr3nNa5Js7QMAVmC7R7KdW6ShrtRJ7U/9v/XnSHK25yRb77Na3HRNN06S5m7sya6Nis+sJdfBbpRsWRh7iGPtuejCkXXMSMlOU+e9W8+HxXIurM925GQrjZrB0ZbtrtW+ai822+Rs00jGyXSdfKDzdLa073vSttsKew8yLgfd1/Ord+wo0e7e3l4efPDBnWQG1XuPfWAbKfuC+6MyK8bCWnKfsv72Tvy2b/u2w3O99x2MjXaj7jfaQ5Nk5s7fek84qbZT8nEsfceemGz3hNfSNtTKbnmuucyV55Xj1jy0gfdBFyDPWjB/9K0ryeU9ee7cuWmTm5iYmJh4YuLEab02m81hHBkSQFcGgTcrEhbHdAlebduzjcSeUlWC+4Zv+IYkW0kGRoeNBm+qNSnS0rEZZLKb7siSjZPUVsliVHCVeWROsIckW0aC9xZSvr04O+ZgidA2wa4MvW1xjt2CyVVvMaTL6i14uema7GVZ/+9E3XxmTWt8FP1hL8KoONYJhrviuVwHDYXZYVeeBwn9pS99aZLtGsIKKovCA40+skc+8IEPJEluuummJNs1r+NDkqYNJHiYBPuweuYSK0ofPH8wis4zk7E6FsnzV/eb97y1NZ2t0ees7Z2LFy/mrrvuOpwnzq0p2hg/qcqYL+xR9jCtY4DlOXG179uaqs0e2dzL3MOsU11LFy9FG+OCtHXvuFhq7X+yXVvGW+15tslZo2CW2/2GzZn1dxudZsS2U/stdHGgjJP+W1PRxYGOyp0Zk8lNTExMTJxanIjJnT17Ntdee+1Octpqp0G6MluwvrY7B8kPyQOpHOnBRS6Trffk6173uiPnImEgNXXeR44FGWWgSLZSvzMBMB6Xg6nSiuPgzBwZJ6wt2Ur3TowLRgmb6zmWtmy36JItI8E5Js3ZTZLt+tO3tTL0y7IckeS7ZMuOW3PZHFCZvDM/MJfYIVxyp4sns00YJuSMMcmWDSHVI7nTJ2w+sIJkN2mzPdNgeLB1JPx6bfYvcwODYB9WOw7t4ZGLnYa+s6+5TmUOzJfLEIHu3nAGDyfQ5Z6oa+01XWNyDzzwQN7ylrfkq77qq46Mue5fPzPs3e1YS9qt/aLfsDKXdKl7h2cGxzhbDrZsvq/t2y7NXNLXOrccYybj+8fZompfWAeX3OpiSa3dcmwf5zCPVbPjebQ9zRlQajv0lT3Js551rM83z+OovNfh8au/TkxMTExMfAnjREwOadwZKao3mL0rXaa9K9Rn70ne7rz5bRupb3Xahfl80zd9U5Lkp37qp5IkP//zP58k+eqv/urDcxxrRntIZ4yr6tNt17LU5VIeVbJ26RjGjmTVSfhIpL6OPdDW7IfMn6V/x3bV6zg+znkkK5OjL5y75uXkUjudbZa549rsK9tVuvyDrBVshbG5bE53rmO/2AcUrsXDNdkyNDyLaRfJk3MrOxrZrKqNJ9mNGart2KPM90q1g6ARYJ+zlxwr2N1PtqvYi3KtHBBwvsEuJs77ec2ucv78+Xzwgx889D62HTTZ2qTsKUm7MI4uI48ZLevkPdWxV9pjjtkHsOm6D5xfkn3OMez3aqe2NsN5UF2std7TtmmzN22L7fLemmWaUaF1qefSvjM7uSxP976wFseMvI7La3jNNdfMOLmJiYmJiScm5ktuYmJiYuLU4kTqymSfetrhpKqAUJ9YbYWqzK7dtJmMg1TXyoo4rRUpeWgTN22SMifJ7/ydv/NI33A0WCtBYacXaLQTDTsJau0L6kM72nQGWav1RqomG/e7Y7pq7Elf5sau/U62WtUTdk45LnzgzJkzO2ta+1DT9NRxOOC1gnVBHYXqCTWlE9tWt3P2sdfQleGrs4KDVZ2qySEGya6akj3LMQ4Or2qxqjKvbXn9q2s5fXDYDufaIaBT4Y5UrF4/97f2qVNJAyelXsPZs2dz3XXX5U1velOSbVhQVWHxf9RcdkWnv/U+ceJiPtupx2EJya6zmh0nXDm8HmMHNKeKq3Pi52kXCpP01ddZZ86hLauR67PKx/pZwnMAE0Lddy7L4+cO4+uux/07qiLeBYPX32Yw+MTExMTEExIndjy56qqrDqUkpMwqreCYwBseCcPFNKuh0GVcXM7BaZbqW9uuwUg4OKIgnVcDsN17nZiX61fG6KKMZiIusFklKkvQbgvJpkrUNtaPyqO40GOyW9qHY+gr/ajXGxUgRPri+nUdLXUdK1GdObNjsO/Kbviz2Ut16vH84/6NU4f3Tr0GUn8tVlvHxf6oDNLsjusxT4ynBuVyjNNSAYd61OvVcjJ1HNYs1ONcageYKTsJbh1Hl/Krtlkl65HWwaiMb5REusOZM2dy1VVXHRY7/uVf/uUkRx2CmBeuwT2OMxe/1zJaZtSwE/ffbvP1HAck8z1tV0ct5oz1dSA539e5cWky7l07nHWaETtzsM52JqvrwvnsX+61kUahOsuZubugMf2oTM4JGOzgYtZWr101b6v7Z/jLxMTExMTElzgel02OtyzsqLrk82auKaqS3TI6tcggUhZSsgOgXaKhSivWqyMV8Rn7Ssf+HCgKOtdqS7aWltdsFnbzReqwTasrX2Ip0sUFzQqTrXRpRsrfzv13lIiXY3GJriwKybcGzR5nY1mzxTjd2qiQ65orOhInUrJtc10yYvYX5/j6lSUxVkupdguve5RrYndwQl67SWO3TrZ73v13YeHK/ugL91i3n2tb9ftRaZ/RWtTzu+KoSb/mLhG0t7c3lMaXZcmVV155eP8QFlQLITvwmXVyMd3KIpygHebO3NoGXO9Pa198//Ac4rrJNryBe9mfncIr2Q36dlovl/yqvzMXvm98z3RB9VyXPetCtZ1mxOEmo3CnugZoUTgXDcxoH9Z2HJI1wmRyExMTExOnFo8rGHxUdibZLSY4SvBa7UEOSjQ7sdRX39y2MziY2YVK67EOXnS59q5YpiVoS9JdsKnhkjJd6hoHYzud0siDqY5jVAaoK5dC/53+yoGjVTK17n1NolqW5YhNDqylIxvZI6ut1F6glkCdpLhqEGBu/Ib9xLaRyhydTNx2zo612MbjhLWsP38rk7PHGlK/16vOozUfI/tXVxpltN+8zzpGbG0G8F6t16yakTUmd+7cucO1ZQ2qB6uDoRk7bIn90AUiO30b+8DJiGv/6Qt9sE0MJlefcy4VxTG2d1Vwv9EOmil7tPJ9ta8eVxaM8dR5pI/85TnkvQrqZ+9Fzy/XoQxRst3P3JfeS10SDNqrbH3a5CYmJiYmnpA4sU3uzJkzh5KBS1Lwe7KVQJwoFYmgeipZl0usCXpfx2TU453uxt5HXKdKSy5A6Bg021mS3di5UambTmq1h5CZ25pdxdKxvVTX0izRnj3wOi9V2nVcDnNuT9DROEYS1Wazyd7e3pDV1GvQXyf5tp6+nmP7Kt51xDZhm6t2FRd+ZDxI2B2TY35cCsTsr5sH216tfXBcU7JlINwDbreTsG2T9fVH/artW2MBzMDq+d7P1iBUWEI/zkPu0qVLh/PiJNnJNg6W54CTvbOmXUkqj5U975je6jFrW5jTiaElqF7d9AG/Bcf41ucN8HPTKRWZWyddruPgN1ier1vvafrYJYuun80Kk13fA4+BeEOukezaja0h6+5BngMwQ7NLYzK5iYmJiYlTixMzuQrbBZJtWQ/YmO1dSNRVwkEqQZJF8uDtjTSGF2eXUBbQF3tZ1T4688Aos0bnxWkJ3rp4JydNxt5tZiFdfIyZgWNbGGfHHOkbEpylsKqLt92BuSGDTCfZmVnVjCYdKLpb0RVQ9GfPdZfcG9g7FP0/Unm1ybl9foM9ueRPsitt24bVrcdo77jPa5ldzKhYY8ej1vZpb5SBxH2u35mFeQzdulmr4Lbq3vF3Fy9eHJZM2Ww2efTRR3e8T7FDJVv28JGPfCTJli3AsPDi6/aOy8vAhGmTtjov21HcJMyysj97bdo/ocsg5cw6/mybcMfkiGXjL/c9Gq2upBj97gqd1u+7c72P6Zs9NWs71gqNEtJXVN+QaZObmJiYmHhCYr7kJiYmJiZOLU6srqwVnp2uJdlSUdSV0F6n16qqMqtvXEPJgcldSjAbKK2KqiqAripx/ds5OlgF47p4DtLtAl9p1ynAugBiYCO+XeWZ5xoi4b5alcGa1ArUqIDoi+ePdatqAdapG7NBCIHH2IWDjBL7dqmLbLBGtc14+Iyxv0vQS7uoc/jeSWPrGJ2wwGnWqnOU94j3u9OwdeocJ9V17cNu7ruA3Xpspz4dBXKDtXARh51YxdmZDKqKa81pabPZHO43zBZVdYwTCupp1JQO16ghMN63ri6PowT3RFWP0leeSTiaEB6A41PnqOVK6a5QXtXj7Ek/E30uc1H7eOONN6bCe4XE9F2gtcMLmD/mhrnrQjIYM59ZL6fnS3b3m+/Jbo+iNq6/zQTNExMTExNPSDwuJmdDbXUiueGGG5Js2QNsAUkEiboyD9pBanHpGUv/XRkTuzGbPdn1vrYzkiYqXELHbMzG/Cq1mqGMzqkOB5Zk3K77XOfEacuc8qgz8NvQ7PIjrHE3N8el1eGYvb29Ng0ZGJVYWisRNHIAYr3ZZ07Knew6AuAU5UrhnSOIq9nbcF738CjZ8agETmWbDjNwgDLo1uA4Ruw0c/X/Xh/v3boGXWmqis6Ry4mgO6ekir29vcO1M0Op/bUzFM8f7lvYebLrIOExO81bl4TACQTcj7pODjvgnnWYzjOe8Yydc0irxzl+rrLf67PYacs4hzlg3Wp4lZ9rHOuSUpzTJffmL0nynWKtgv77uWKHl/q8c7jQcQk4JpObmJiYmDi1OBGT22w2eeSRR3aSxVa9M9IOoQSWWji2MivsJ0gFLkjKOUjjVVpDR2xdsqWyzkZGO5YqbS9IdpM4OzWW9fvVVd02Fwe1d+6ytvm5LadW6wqg2kaClEcfq+7fCWfR7dteWUv6uJBqF/Rr2O7U2QNGbLVr3wzD9kfGRds1ENWpuVzc1kHp/n+9ru0EaxI87VZmUD93Qe8+ds2uNuqr0a2B2/P91EnNI3dvvu+SK/heWJZlaFfZbDa5dOnSYWA/Np5uXbiX2ZMufVP74BAoMxszYZ4/ydZe5z0LC4MBdYnhrQ2CoXZFUzmHeXIgN89Z2qJftR32M9e9++67U1Ft89zTTh7vAq98rjZO+sa9wFy4SHQNxXDguJ97XRJp23zXklAkk8lNTExMTJxiPC6b3FoaGqQFp6NBEkVCqG9wPKEALGiUJLa+1R3g7BRdtd/AKWNG+vyuXMpI0hjZMmq7I4+8jrVZggKWfLAjVZucyw8hmSLt2VOqnoO0ZabaeVeaiR5XZqcbT4eR7aor2On5NwOxRys24WTLCLzuowDY+ptT0ZnJdx6Zo/Rqo4TU9dwujVL9vqJLflw/e4665LeG90O1rzgt3SiZeb2Og6k3m80w7RhtwgxgNfUeNwMh+NvJjysjth3LWgDbEqs2p7KfOg6eZS53lOym7/Izsiv9xRrBmBxw7+dd1arBPJkvJzP/1Kc+deTc2kdrfezNyXOianbo9/Of//wkW22Qk1nX+4rnl+fTGpp6j468d0eYTG5iYmJi4tTicTE5/79KD0gCSDB4KHEMb+Qq4SD9OObrOPtAspVwjrNzVOl4VBZnze7gYyxRm2V0bRxXkLL+PmJQsABfv44bewDn2Aup8+bkethHzUz5vUpcZiDH6cY7G0pnDxrFlbG3uoTCoz7BfLuUQkjyzN0o1rKyNsdd2a7VJcz23hx5VXbepF2cZ9fHilEsotmt7Tv13FFhzW7/295lr1X2bPXEww5V52Rk073qqqtyyy235LWvfe2RcVTGw1reeeedSY7aipLdmM5k6xnpe837gfWrzzlrRWjXjKR6O5rhuliv267nOPWbfR265Mgu4Mpnrss90d2DwHZk0KX5w7MeBscaO31Yl5QdRkj/fWzn9zFKA2dMJjcxMTExcWpxYu/KS5cu7XgfdnpnJJpRQtsqrSA98LYmuSlZDBxn0UkeowSiXcLkUXFOl+mobSKFIjEyPks6LqdS+2AWYLZUJRzHpXWecPXcugYf//jHk2zn/mUve9mRcxhnteOhGzdbOu76dRznzp1btctVptcxvlFhzjUPQsY/Klpqabzz8GPfEadpu5Ptb8l2fbuEzLU/3fhsT/HvXaJuZ9pZy+jj75wI2HbKrlyK96bLstR55LsR++f+rbGxzE+1cY72zvXXX5/v+I7vOLShovmp99gv/MIvHLm29wHr1Hnzut+OQetiO50IHrbiPVsZpVkL4JnS7VHuUf66r2aslcnZA9LaAK5T2Sbtcy/YN4C/aHx4biTbslYwOMfwdUmdac/ZWLx36/ui0+itad8mk5uYmJiYOLWYL7mJiYmJiVOLE6krl2XJlVdeuZMuptJr0s/gngrNJBWPEzcnu0G3tIsbMNQYml9TMzno18mVbdyvv7lmFvTaQcLJlj7b4cCONF3qrFHKMeB6enUcDrngGKtLOvWvU1y5H1Xd4/RUnOvg/S6o/nJCB0jr5UDvrjq51RpdNWrgQPRO9VvHV1MmsVetWmcOOjWlVaY2yFslXc+xStVq2M6FfhR8brVVFyxr9T5r6fu3XoM59jF2UqmqL1dFZ5yosnA8qckHnFzhuCS7e3t7O2v47ne/+/B3ng04cTBPOEGgwu/S37GGdhpxfzpHLat87YhS58mOIPTZDk71eYpq1s8kV39n7+BMU8H4mINbbrklyVZN2aUTNJwMgnvnec973uExmHQ8DieuqMHgVqXbocrB/fVYhxmMMJncxMTExMSpxYkdTy5cuNC61ALevBiZkRYcXNhJj2Z0SBiu1EywYbKbMssBtl1gr1PU2IXfxvYK2oW5jRLXrrnLjxhQl3gYqQfDNpKoHUEqc6R9GDBz72DTajRGCjNr4vqd8d1S1/nz548NIXDgeJ1jS8FmOp3LsOd2FDSN5HnzzTcffgcjIEAcKZi17ZiFS4zQN4eOdAHdTn7rYO1u7kbp2xx8XveOU7RxXe+DLvmtU3AxTgcS17nhN+aYvWTJvpvPy00F9+ijjx7uRbRAhAvUa5sV2/miakv4P/Pi/WxtUJ1j5ofnnB1zOk0F4F6mfZdEqloupyBkb8LwcBAx80626wL7smOf04nV812BnDlinqs2zRgl4mAfdGm97CDoMI7K5OygNYPBJyYmJiaesDhxMDhSVdIXzrOem9QyTjtTEzRbWvFbnWO5Lra6ZCtJOUDQpVY6pgPMIDppnGszPgdYd/ZJX8/swklla7As42G+6CPHfOITnzjSRu0rfXGAJePj+6p/d/JY1sL2qeoGXl2Pue5IqoLFjYKbfWwdx+XY/MyG3I8XvvCFSY4yOUuC2JFdAqVL7zYqfdRdf5QCzvaPLj2aNRLsa4+3c+l3WRTWy/aj2leHCNg1uwsOZj8hofN3LeH0SXD27Nk87WlP22H0Vbq3Cz/9hKVzDtqNZGuDs72ZPQ9L4t6v9jyn/nKwucMqku09RrvMEywTBlfX0veftWfc22bvdTw8g2FhZvaEaiXb9bZmB62Hw6sqK7NvAb/BPqs9HIxs2mt+DGb/a+ngksnkJiYmJiZOMZbj9JlHDl6WzyT5+G9ddyZOAV6w2Wye5S/n3pm4DMy9M/F40e6d5IQvuYmJiYmJiS8lTHXlxMTExMSpxXzJTUxMTEycWsyX3MTExMTEqcV8yU1MTExMnFqcKE7u7NmzmyuuuGInmr/G9TjjgI/pzgH+bu3Y0TnHfb/Wrsd1OU45PvZyznk8zj6jufl8cDl5KJ0bscuRWDNn3H///XnkkUd2OvfUpz51Q1HFer2uPc+PP3f9HmHt989nDkdlgbrrHref1vrxhXQMW+ujjxn16XL649i6LvuHY0cvXrw43DvXXnvt5lnPetZO37q94z3kvpxkzR/P/X85azkqB9W1MYqxHGHtfvK8ra3L6LPnuSt349+cwaiDs++M/iZ9LN1DDz2UCxcutJN/opfcFVdckRtvvPEwMNFBf8luEmUn0HXdtdqOkx+PPteEosdVie3qu40qMhPo2FVddsLQ416IXVCm04k5KelawKOv52DJCt88TjTc1V7yC8up1QhUJblssg2iJXD0woUL+df/+l/vtJ3sJ8r9oR/6oZ0g2rqWBNu6Jpjndi29l1No+YbuUqc5dZbntqtl5cQFa/X2PP9OkeX0UV2lbu+RtdRcrrc12qtOfpBs19L3aReobtCOa5/RVu0jvxGQfPfdd+dHfuRH2nZvuOGG/OAP/uDO/VMTSjiInd/qMbUvtQ9+OI/S+9V9N0q2viaMej97fRxYXscDnNjB+67uHVdsd+LzLgWen6c+1jXoanA6v/k+JnkHAft1TIzZz3jmwLXpkm2SBoLqn/a0p+XNb35zRpjqyomJiYmJU4vHldbL0kV9yyK5wJxGUksnEVrqNktZq/7a9bP+redasrZE49Q2yW6pnVGqro4xWLL2OJwaqrZvuK1Okve8nUSVMlIb+Pp1HDXF2Zqa68KFCzvzVlmSU32ZJbn8UO2PWQp71CWJ6t5FajXrM/PoyqXwnSXfbt5GKcycZNkstLuOx2cWWjFiXy6f06VFslYDdOvr9bmcfnRJ0kd7/uzZs7nuuusOGULX7+NSO3UVpq2toE+uCN7d6057Nkq6XTFSF3pP1XR5Th/otG5ODF7390hdOSoT1MH72oyrPiPZi6PkyiTar+Ojv+6LGWqXJq8+Q9ZUoZPJTUxMTEycWpy41M7e3t6q8bGWsvC59W8n6brkxKiNqlcfSVCWbGsfu8S0yVaawMbYSaZmany23rvr90jqs0TfjWd0bscc/NvlOK2MpMzRGOoxtRjnWoLmRx99dIcd1eNHRVJdcqlLgj1ikEiNTvZdz6EPSKWs/5p0CJvABu11X7MbAjP4NTZiO6WZcFf6xu17ftf6aPblRLr1ek7aO7I1dWvEb2tlmpZlyVVXXbVT/qd7Thxnf+w0Hk6qbA1Ft/dtG7scBzQzKtt+Xdy4YmTLtv2wsx97DoDLEdX/+zlqLUdng2Yf+BjmkXuFpNm139ZQ2ObcPRsv14loMrmJiYmJiVOL+ZKbmJiYmDi1OJG6clmWLMtySDGh97VKrNUaI/VXF/cwovFWL1Y6v+YYUX/v6O6oLlHn0u9zbVS16qP20U4rpuhrzjieE89VV3XZKlW3u6YOAZ5rq0XqdTj2wQcfHBqxXU+uxkcBq1PoN1WQqQRd143zUTFadUYtLVQmVV2Jysl9pmK6w2EqqOd36623Jtm6M19OLJCdLuyk0BnZgR0o7IxRz2GsVq35nqzhNdQ2Y71pw44Gda0Y+7OetZ8E3o4ondOHazieP39+6Fi2LEuuuOKKHRVa52RxnIq+c5zyXqR9xo6qm7mpGKknOwe04xzrOhW++2R1sp8ldY6tUnQ4isdQMYqXtWq9U/96Hbl/XG8u2YYkWZXrNa5t2jnluOryk8lNTExMTJxanNjx5NKlSztspYYQjIzcNmBXQ70rCPPbyBGlSp6+zqiybL2evztOEqj9H1V1tlNMlXQttfqY7vojKcXfe7z1N8+J26oMpgt4rX1n/HWtMSDDfM6dOzd0PKJt2nHQbrJlHGZw73//+5NsNQeuSF7b4xhYGEyHPVOrO3MO80KlaK5PFfFnP/vZh+c873nPS7LLEGGSa4kE7MBgabxLLDByEkFiv/fee5Mk99133+FvzA99ZFxef9a2MtVR6ADXZ7z1fuLYV73qVUm2FaDNHDttw+VkF8Lhjb3iwO96/ig4uwtIt/s6/TXD76pym0HZ8ciJLOo51iT5Pu1CVkbZXsx8uvCTzmGvovue/jvpwZoGyRowa6rYF/UZwX0Do/N6Mc4u6Lw6/aw5oUwmNzExMTFxanFiJvfYY48dSjrXXnttkqPMapT7zCmU6jn1//UYS8cORKztWw/Mm5+212xytpVZ/9yN6zhX+wpLOL5ux7QsMTnMAljiqe04gNjj7tLrmIlbUq0SFedgq7j66quPtUmZMVZbktMQve9970uSfPCDHzwyxmobuemmm5JsUwb9xm/8xpGxg84OCcOxncPsqTJHmCDtEdbA9Z2ijnEnx+fw6+xGdsN3OAVs+p577tkZF8zHdo210AXvRZgdfbbts7bHb7fffvuRcdndvX7XMQKDBBTeH10AvFOmrdmnzVYA81bTFda2ku2eoD3ui5G9v/bBqay4p5nTei9bc+RUhGtJL0ZaJ/Y1467jp30/Q3xvdKFgDgJnndxXbLjJbjA797ZtwFWTwf6u+2AGg09MTExMPCHxuJic9b9dElK/8ZFSOknQUgJveiQD3uYO0uyuM0pc2gWDcwxSGBKNJa06RntRjhhPvZ5ti2Zylp5qe8yXPYrWgsHpv1mf2+jseEix9oyjj5X9WWK7995720Dtejy/d0yO/mCL+8hHPnLYbj2n2h2QqFk7mM3IO7SmIeLaToxrVlEZA0yOigrY/pAuaR8tR+1LlyasovMMdHCsA6I7L1WnfmJcJLZlX8A+me9kd2/Yu7Jjt+Dd7373kevccsstR8Zf161LnTcCiQS69a/H1N88Px2rscaINeSzkwJ0CdRHaak6W6P3m7VazEWXTN42PzMs2/vrtW1Pt4anMivbXNnHtnl3zNvsz8H13brZ89PPFFBZdfXmHvXlSL9Wf52YmJiYmPgSxokTNF+6dGkYU5XsvpFdOgFppUuPdlLCAAAgAElEQVTrZS/LkRdilf6dmNR2tU56GCUS9Tmd15H1y6MkuFXysmeXvZ2cELb+v2Nd9XprZUxsX7OdoErRI29Re351aXxgNw888MBqstczZ84M0wUlW8nsrrvuOmwv2UqErAvsqesXfTJ7RiLFhpds2djTn/70JFu7FsymS2xtCd02ODPg2sfLjensvF6xwTmuiLnAc62O5znPec6RMb/gBS84Mhe/8iu/kiR573vfe3gu80cfuJ5ZaN07HAO7wxuWcdc6gka1u68l97548eKOxqdjLb7/R8nQk11PRZd3YU153tTnnD3LOy/A2o/azih+tWN/oxI0XckyXw9Yi2END97RtX2zPsY1stHV76y9s72/ngPr5xj6AqsGdU5od6QRMSaTm5iYmJg4tXhcCZphTWsxUX6bW0Jck4pGrIXCnZ0nzcg219mIbF9w2YrO+9Bsz6zTjKvOTce2unPq9Sz1jTLG2OZU4XF5TmqblvbMuLqYPkt5awmaHcvSlS/55Cc/mWTLpOj3q1/96iTJK1/5yiN/k+RFL3pRkq0H5oc+9KEj/UdCJL7thS984eG5z3/+84+MA2ZDZhXHZSW77Nu2sq4YrOd/VBS4g2O3uP43fuM3Jkle9rKXJUne8Y53HJ7zrne968i4YHusLYyOv694xSsOz3X/KXjJ+Mzaki0ztLftyE5V/894nvKUp6wmZq/xub4Xk3H8qO/xTvoflXiyvav27/rrrz/SLowUltRpkkbezr5u3W/eKzAff9+VkuIYbG51rmvfq/0YmO1Zy1C1acCekqMMNvXcUfYX+tRdx56/Dz300PSunJiYmJh4YmK+5CYmJiYmTi0eV2XwNacHfwelhIJ3Qc2mrE4oCxVFRVIprA3NUHGrHit1HqXvsXqvS1dmFa0pusMC6nfuk1UMVW1C30bGauavU7/YOO26WKjhOpXxKCk2163jYj1wPFlLj7YsS6688srVJLukoWI9cKB47Wtfm2SrmqxOFoyFFFw4W1hV1jkr0H/6ggqKuWBcOH0kW1WcVfbMcZdyyjXn7GjlgO+6pvQRJxz2w1vf+tYkW6eRmh6NOUDl43R5JJfu0np1jjPJdv2Ze8I7ku08ORTHycUxNyRbBwP68IxnPKOtxZhsQ5e8F+ta2kFqZJLonOTsqGXHM+6n6qDhkAQnrWfd6j7w3qi19JLde72OdaQ69fOoqkBRTz/zmc9MslWpWu1bn3N+xtJX9r3HVffqKLWa16TuVeBxcT85zKt+R/+7cJaKyeQmJiYmJk4tTszkkq30YMaQ7KbRMlvqGAESkgO37WqPZAJbq+07dZFTylQjNRKNg2aBXdbruOiTgybNtKpEZSeVUYqzKuE4MNmBpGaOta+unDwqW1Gv53HZCYi5r+tGkDbOCc94xjOGbI7qzsBsKdm6DcOoSIwMI+nCKWx4Zy+ZFXUByVyb5MYOtO7KfNDeKLyhc+3GwQU4rZJTGtW96nHYAcSlhpJdV3SXzeH+sVNRsltKxw5jtFXZn6X7T33qU0faoq81HdvXfu3XHmlvjckxlrWSVCNHoNHzoIJ27dRhJ5YuwJ/+MwewdmsSah/YM/yF4bIPKzPxWjrRg1OP1c+Mh3uCc8zWqrs+e8Tlhly2qXOScViQ56hzVHTIA/1fC8laczLpMJncxMTExMSpxYmZ3N7e3o6uur6hR4HV1iHXYNJRYK3tHWYbtQ+2zVnPXXXjlthHwYprgY5INvTFLut1fGaithdYkqxwcl+n73HAah2f54++WdrtvhulEap9hAkdl1aHY66++uqdYHoYUbKVOJknGJ2ZLjal+hvt0j/+et9V+wPX8R7hc1fqye7lZlr0EXabJC9/+cuTbCV1znnuc5+bZCvJO61UsrVLmrnZTlRTM8GAYRW04bliD1U2bc0AvyH983tdN+x0DtdhXLYFJcmv/uqvJtmGMVxzzTWrWoCzZ8+2CaXBKL3WyHaV7GpwbDuy/bhqPtjzXI/5sTaq0+g4tMMasWoDdhot99HPlDpO9gTX8bOLPtfrOam318RzVn/3M2OUzq4rXOvx2tegfl4LuekwmdzExMTExKnFiZlcta0gIXRFTJ282cyjSgBI3SMdrstLVGnFLA8JxNetul/rndekLzAql1PnpX7flUuxbc4eWlVCcRCkk0bbS7UyLNuhzEjMWJIti8BugqTu5L5d0Dnn1OKixrIsueKKKw7P55zKImxzZQ45hjmoY2Vs2IichNgSd9Xnj2xATklXWZK9HWGV2CcZF8wrSd7+9rcn2QaZE+z+7d/+7UfaYv8961nPOjy32rGSbYqsG2+8McnWc45g9/qdNRLsHbOduu+8n1gTxt0VTXU5FNvhYbB4fSZbpsvaVoZtkKDZTKDaV0cFaL3GlRFwP/KdU3LZm7MLPh8Fknfew6wDexWGu8ZQ2Ru042ck60SbdR55PnMM60KbzH31eh2V2HH6PAe9V9jnYJS0v/YJWGPV2Y2tkZhFUycmJiYmnrB4XN6VLkXRFew0o7EE2Enjlr5t/+BzlTwokmmPG+u5u5gax4K53H2XhmpU5t6ecl2SXb5zolTbEZOthIO0ZYmK37l+TVpsSYfPtg128SX0f2RjqPYir+kVV1yxKlFVey7MsUpo9ii1JI39CTtOsmU9HEOpHRiWGWPdJ+xBJ59lLhlrZQxOUEyaMM4lfgwPw9o3+gqjgeHQVmdztE2WscPoGEO9J4g3ZC4Yn23OzGdlGzAC293N5GvMGKAd1tb7q7OrYA+6/vrrV73murJQVaNj5mQW0RX9PS6dlz3Bax9sg4fte47rc4D5cDyZWVO159smZzbLsfztmJz9FjyGeo49wP1M5DqdndUMHpjZ1WexPd3pC3PVxdSBmg5yMrmJiYmJiSckTszkzpw5cxgvwlu9Sji2L5k1dRIHb3okQKRSsxaXqkl2Y6icjBZJsUoXlu6QJpxYtvPIcmkN29U6z0xnL3EGFMZbPfLoC3OC5M44kNK7hKlOBIutjDZgEthukq1k6NhB21irfQqJjO/WytA/9thj+exnP3u4hrY11fa8LrAG7E7VZgUbomAn82IW2yVDpq+wPOYa6ZkSNV2SWMc40Xc8QqvNlr345V/+5Ul215K+Ib2+6U1vOjyX/jJmmBZ9xxZYJV6Plb5hi+Ev39d9x7nMCWvAfcVa1Ng/+oSXJWvCvUdbdR7ZMzDf5z73ucOCu2fOnDnimdvZgXxPO3tJxzJGJajMkhhfZQu0x/OA9eF71rKeQzujQqFd4mY/A60xOgm6pOh1DMnus9DFbbk+nsF137GvGDv3yMgjubbPb+wz+xzU58W0yU1MTExMTBzgREwODznetjCGziYHnD2gs3MgjSIVI9Fhf+DYl7zkJUmOSh6wEcqk8Mb/mq/5miTbEizV2806dyR2s6Sqv3f+QWwUSDJIuF3pCNu57IH18Y9//Ejfk63khMT04he/+Mg46Ottt92WJHnjG994eC7lVz784Q8f6St5DrlOtW25VIgzq/C3sj/H5qwVMXzwwQfz1re+NbfeemuS7ZpXqdL2H67FHCAJ46WYJL/8y7985DrMk6XmLi8kDIO189ziwYg3ZL22+8oxtFnPgfWQXxNYoobhkaMzSX7sx34sSfKt3/qtR8bjmLvKklhD4uPYi9jq+B7JuyufRPu2DcN2a2wV/WXOYTW0zz1S7dmMHSb3iU98YjUH4bIsh3u0Y9ZmNs7M0bVt1ue8pOyLzkPb2Ypg8MwTz4tqK2U+zLCdX/M4u3YdH+Pqsk85R6/LKDmLTZ0T1pljbNeDiVfNDee45JJzqNZ5ZB9xrIser2VLscf5CJPJTUxMTEycWsyX3MTExMTEqcWJ1JVnzpzJuXPndtyHO/deO4k40XBXvgSgnkINctdddyXZ0n3UifU3p9dCRQJV7oIy6QNqSdRjnSptVB7DCVK7YFmPmfE6zU4FKi6uwzGoQ1A9Mfd1Tm6//fYkW4cTKj+jQvuFX/iFneviCu/1syt2Vfcxt7RTUy8ZDz30UN7xjncczkFXdoO5RO2FCghVxT333JNku+bJdr5R9dEGa0lbqK1rQDJ9sZqStji3JrClfUJXuA7VyhnXm9/85sNzmH+7jo+SbdegevY8fbBjCHurOoKwRvSf+xW1uIOEq+MJ7Tkg36qmes9+4AMfSLJVYXEualGOrWo49jXH3njjja1Kimvef//9h6o5p+Gr3zEvTnbNvNUky5zvJMSj1FbVtZ91YUysoZ0t6jm0hyrYqjin3avt27XfYRrsy/rccWiC03m55FPtA+NwqTQ+YwqpamI7HAH63iW+p7+MGfWukzrUZ7GdVdYc3pLJ5CYmJiYmTjFO7Hhy1VVXHZFOkqMS1agAKVIDb/sqwdvtH8nTkvXdd989vB7AuI804WS4SfKhD33oSF+QbJy6q0qeToU1KoGD1NKVPkH6tzSMxFslOBdJ5De7kCOd00b9v9eJvjLfH/3oR2MgjTMOG6lrH5GKKxMYSVRXX311brvttkMJFLbSSa1Ilg7whmFXxwPWGycR5sshK7T1yU9+8vBc5pL95FALh64k27m1IR6tA2yZtU52y/+Mkpd7DyW7JYmYL+ae9apr7YBkzsVxiz6yFl1aPu4X37/WaNTvWCcYOH85lvut9ok1+Jqv+Zr8xE/8RDqQoJm+dYHCsAQzXJcG6kJgeFaM2KsZUbK9v7kuAfwOwalraSbjEIYu5aEDxq1RcVLvej2zP+aGPnb3NGB/86ywk1KX+J49z9jtfMP1qzbI96sdhjo4TdiFCxcmk5uYmJiYeGLicdnkLCHUN7MDUZ3IlLd6lygVCQp7ExKPJcMqySPRmlEhWSFp1DRiSD2ciwTiQosdY0QKsu0JSQJ2VotK2r6F5FhtWe4jruq0y5xgO0PShskRiJtsGQ+SFGVN1kqf2E7glGddkDvSXk00O7LJPeUpT8lXfdVX5T3veU+SvkwP7bGG2OAsAVZp3Lp8j430WtjOKgMxazWT79JTOa0Xe5f994u/+ItHvk+2TMkJwemL2dJa2iMkata7S5g9SiJOnwnjMDNKtvu3BtrWY5iTKjm7Hdgy69mV5AJrYScVZ86c2Xnu1BAf+jVKd9el5qI9lyJivzlxfLcPRunvuE7to+2DhhNKJLtFUp3ei/3gYsd1zNYUcG69FwBz4ueq9zD7svN1cOiCfQ/WAtkdsuJ+VVQ/jMnkJiYmJiaekDixTa5KCl3pBAcy2nZlW12yKwladw3zwbZRpT9sMfTLb/ROgnOyVnttrSVodjof2226ZMsOPrdkY7tBHas98fAsZE5os7JAWCzSmEuI2BMs2XoYWhdv+86IqSX7HobvfOc7299IB4d3I0HAXckW1gN2yRrCXmpAOvOPJG/bqcsz1f0xssXaM6+zIXgtmT/sk69+9asPz2F9XQLJJVw6CRuvSWsSHBBdvR1HScRdasfJxZPtPupSMNU+131gz0szBidNTna99h588ME2XRfYbDY75WXqurgsE+3bhoRGpPbHyeS5b8ye6rPPnpdmHlyvSwg9Wg9rkuoxfq6aSaLJqPeGkzu7DFnnMT0q7Mz+8h7umLiZmj0/u4TnvgcYj7V8yXYdfO4Ik8lNTExMTJxaPC7vSqef6dKquIihpcfOg4hjkaR4i3NOp3cexc5YaqgSFf938mZLDZ1Nzn+BmU6VrD0OpCUX2KxSkVMh2a7nJMZdIUKugy0TZuTUPHVOzC4tpddxOw7rxhtvbHXnFWbjnccaUjH7in4ipdY4TeadOXRiXBip5y0Ze456LqoUacbAXmVu6XNN4WUptdMU1LFUr0E0FdZyOB6rY5supeJCqKPCovV69qbkevUetM3KzwdsXtUm5/i/hx56aNVWU5PwdsWTWQfHXTnZemURZjqObWMezfSTXWZtNuYUV/Uce23a7t0l9/Y6O8bSJazquJzY2uyszrvPMZO3XbXe7zVRe/3N74cuWf6o7FG3R3kPcM8dt3cmk5uYmJiYOLV4XN6VZjGd9G7dre1SVVrp2F0Hl4Cv7bovliprFD7tcK5LN3S2RnuS2psKqZCxrOnVOcasBum2wizQn81yk61EZd21vZvqOWaxZsZd0likZxJcP+1pTxt6ju3t7eXhhx8+vCYssBYItdfrKNatS0Zs+6PZkqX22o4zXJgtdZKnPRiRKmGONbOGpfzRPvf1k+1a2iPTEnWVuG3zdQkrF4XtCgo7Fs0stMaBcu1qU66AQdY5oS+s7QMPPDC0rWw2m1y6dGnHe6/C94fLZnEPVi2AkxDzF62AtRn1eUBfzBi91l1ZGa+/WUi9L+017jhk2z2rBsnPF9txu+w19M0ZpFy4uoP9LaxFM+vt+mKfh27uWcuqDVizy00mNzExMTFxajFfchMTExMTpxYnrgy+LMuOCmCtnpzpNegCQ32uXWq7IFfTVKuYrEaov9mF2AbgTj1hI7XVBg68THZVmP6MGqeqK1EX2ZXXoRidCsduuF6nbk1GThBet05VjGPLddddN1TFbTabnD9//lDNRTgAKdbqWK1utXG6BqBaBcK8sb9QSXVqV8+pjfqdCsROO7RL6IBT1CW7e9J7iDY6FRdwcL5rq9X19161MwFz5PqGtS9d/b2Kqvoa3duscVVTAswHL3/5y5Psz9Ga8wAqy4p6vNNMVZVf8v+3d269lVxFGy57PJNhuAApMCHkQAgEhYNACBH+LP8GwQ0goSghBJLJOWFIEFEQUSZje39Xj7v201Vte9B3gV3vzbZ3d69ep+5dbx1rZxXWynu9W/+8d7q9YlNIZbZw6Eb1HgVdKjg/y1VyZz8vVj07DCGP0aEkDiyvnLLog/ed+1xVPvcz7gQCVcrD/OxPMPhgMBgMriUeicnZ+aI7L8PnZmnVAcedxMuveXZJ5ZjdVG1kzf2xc4RTj7k/FSq2l6/N6EqqIO3DNrLEi4s6n0imrtxtp5I8HjuYeD4rydTV3p2mqGLRGLg/+eSTVvI/PT2NBw8enEmvTqgcsUj3MEMzHCccjliHkNjt2xWG8z7xPjazcgXn/DcshbRh3B+GVTnqWJJ3GMAW2wRcA+tn7vO4nHqu0yRUzgRebyfvrZy/nBzZyZCZk1wiiXWj/05YkEGCZjuxVeEALqnj5yVX6jbjcGLmiziidZoqtxWxPFtOhweY2/xMdAzRiQQq9um1dBgNn5nJ2bHJDlydI2E+5v/9POX3r9kdc+Jz8z70ntxicRHD5AaDwWBwhXHpYPDM5Kp0PZ1NxgGJFfyrTrtbbqwOWuzsEVXwufXATtlUwVIJcN+ytOL2OAcpExfeKr2S9egASasKqnZIhwNGKybnMAe7VW+Fd3DOv/71r831zXvHRU4jluKbL730UkSs7VDVWO3y7HCQrhBnPtZJ47RV2XGcOJv7wSCqVHfed50tJu8tzydzQALvn/3sZxFRs02HjJgpWoMRsTAiM3fbTqo0eWgmnFILtvbrX//67JoXX3xx79wtJrfb7eL4+HiVhi/DSREYoxMLZy2A17ezYW6lsPJ7zYHe+RnjmeaY7U2VzblL6t1pnSrbrH0PHHaU95/fNw5Y75Jh5HadnN/zWmnInH7R6Qxz8oEqBGNscoPBYDC4lri0TS6jKmMD7H3YJRrN53Teh5bW86+2pSvfr0o55qStnZ57y2u081isdNVO0Ou2zFjyd0jHnbdbZTeq2GTuq71J83deA0thWYLjHKTjxx9/vGV8aAEYD3sH77qIiD/+8Y8RsSQlhu3RZsWibYs4jzXnMduO1tnisqce51DiyIVvXUw1t2NtA9hKGed9TPtI3K+88kpERPzwhz88u8bjsXenGWWGA3m9dysPOc5xaR3w29/+NiL2GQo2WXDnzp1275yenu4xsGrPA+8HB7FXzwmw1qfbU/laPydOv1el6PKzZqaY/Qm8Zzo/gSq5s59l9rFT31WlfXxfP4N+v1ft2fYLqnXuUt15LLm9KvFGhWFyg8FgMLiy+K+YXGUjMcOxNFlJOF15Hnt4VTaMzovTEnC+xnp7S24VuiSnXT8qD1Cn9XIBwuwhaQ8v20/MNvP9zC67pMl5DWgXac/2GyfYzsBr7o033liVHAEHBwfx2GOPrcrcP//882fnvPbaaxER8bvf/S4iIn75y19GxMLsthLl2lbUSYaV7creZm4jzxPxcBR0xUaHRyHjyvfxnHVMir5WiYfd529+85sRsTC5rE3BO5VzXIzWsWRVAWOXZbFtNs8r19iewzmvvvpqRET84Ac/OLvGzPro6KiV4ne7Xex2u82E0sDPe8e0ItYps/yMd/b9/Lfjx6x1yuA575JrV17d9qrs9g5rfJF0gn7GM1xSzMWBPUeVf0T3jqr2jveXvSxdeDWP4zyvSjBMbjAYDAZXFo/E5MxMsiToX2v+t7dRlnTtyWVvNFDpyH0fS+X2QoxY68KrrCi5zQzbKuwp5fiS3I6lTNsyKnsRsB3C3lRZgvNcW/qrvCs7Brdlk7OE+Omnn164XIrnPiLixz/+cURE/OlPf4qIhdnRJ+xeVUYQrwf/205QJQbvvFGRIv/5z3+eHXvvvfciYlkz4uVyktvcj9y+Wbm9Xb238t9OpguDpAgt8XoRES+//HJERDz33HMRsbA8mJ095vI+gI25gCx2HBfPjFjmlHNgtcwN9resbajeAx2TOzw8jJs3b67mLe8DMx6zmMonwHYl73HPT+6/95nfPzCR6nmwLckMu2Io1lSclz0lj8PvMZ+bWed5tn/H3FbaL7+TrQXK70bahd1u2WXdx8qDtcIwucFgMBhcWcyP3GAwGAyuLB5JXbmVssuqOH+/FUpgtSWfDi69SIouU+N8HioZqDZptXxtpQJ0jSkHlFbX2tGE/ru+WK515crMXR2zSr2wZZjPqFRFXfJgO6BU1zz++OPn3tuqjLyWrPNPfvKTiFgCre/duxcREW+++WZE7KsGmX/UQ6g0SSnlVEqVms1qIwe+3r9//+wa2vvOd74TEcte4rMKp7HzA322ag1UTiuugE6fUEHmhNDsZ9S9TnZASjDavnv37tm1qEHtyOAEyLnPTqjNOLmPK5TnMWbnn62EA0dHR224TsTa5NCFGVSqMqslrSLe2tN2TrL6sErRBTqHk0od76QGnfqycnhxaizGyZ7JewfHMZuG/J7zHFXjsZq3CuOhT934KtW0zSSjrhwMBoPBtcWlmNxut9sM/ovonSwqRmXYPRXJb6tyeJd+yJ+V9IDU4vIOVSA5x5B0XQHaEs5WCiCzWs7NLMChF3Yw2QqQ7ioPbyUC7hyF3FaVrirPWxV8X/XFQbMR62BV5hhmgtt+rvLOObBg1vQf//hHRKwrglfhFE5kbRf8zHRwpuhK+YBK2+AK53Y8MYOszqENKpG7zxERv/jFLyJiYcJ2lWfPwn7z/Vzx2sxtyzkGOPjc449Ys74tafzg4CCOjo7O2qVPW+V+uhRqFZOz44n/r5KTgy7hQjWuLomzmVy+jxm12/V7tXKsclgI57AePEMRy76qwsMi1k4ree159rrkztX4ukTXDubP1/j9eXh4uJ2KsT0yGAwGg8H/OB6p1E6V2uesQQXUdkGMVVogB/l1UkqWnqy7vQiTQ/pGSrAdwuEA+d5IP7hJWyquGIrnhvtyjktHRKxddZFiXVIEVMmWzQy6oPSMzi23SszrAPKtUjtgi8kxp2aeSOwuchmx2IO+8Y1v7PWfc1zOJK8pzJk2YEOwwup+/G13eUui+b7eC10JnK11YQ5gkpSMqZL6cg7jwl5IG07NlNfaEnxX2LVKQM65fn7MlHN7+TntpPGDg4M4PDxclZWq0tJ1ZbJ8PP/t+e7SRlVJ3kGXTD4zIT+Ptt85HVrGeUyO/ZbZMvvb4UHcv2JyZve2+W8FYPMutM+BWXulqfA+7gLl8zng+Ph4EjQPBoPB4Hri0kzu9PR0pZ/P0rt/UbtEyVUw+JZ+OaIOEnf7to1VZTLoN5KObXEu25Hbs0cS51YB3cAB6fai2mJUWx5nuc+VLczBoF2gd0al785tVXMPQ9hicQcHB3Hr1q3VeuR+I1Ga5bmMDl57+RoYle0BrJNtpxHL+jvdkZlXHpfLIuHN6eS6sCjGnvvKfW3/3CpfAoP3nmU+0U5ErFkm51a2rNxGHkdXsLby4rPnapfYOM+jmcjNmzfPTetl79dK0wI497znJ59Dv7vndSthu7+vAru70krsJda2SiJtFmgWaq1ExLL3OeY9yj5hD0cstmx7WXpvVja5TkPQaTJyO4bHWfmCVEmiKwyTGwwGg8GVxaWZXKXT3iryeHajjbg1s59OSkICqVImdenErCOPWKQsWFgnaVQxXC5djy2I/5GoKwnOevuLFGO0lMn/lS3G/Qe2LVQFbB2n0hU+zPEqtAtLeuKJJzbtCrkPW0lWzaz53zFx+Zhto2ZN9jSLWLN9zxv7LcflMe94XNIG92d8sKhqHNlukq+ppFb6hrSN9G2Wm/cQNiuOuZipx53h59cerxVD6dJRdamuIupYsC2cnp6ukh/ntey8Kc0uK7ta5TGa+83Y8972/brxVF7PTjzvZybvD8fWdsnXuSYzbOabvQg745lAO5CTgDuJdPeusvYj98Wxil08Ym6/8xa/zLuqwzC5wWAwGFxZXIrJ5QS7/F+dE7HO5rDlIWmvQ/+aW9LO19oz0zaZKt7DUpizL1TsDynILAOpyJ6BlQ7ZdgLaqpLedqXkuxi4rRjCjqlkFmib5kW80wB2gJ///Odlxo+IJdZpq7/nZZiorkUKRaKkL5yDZOr9QJ/yJ/eHwWH7I94sIuKpp57aa5f2HAOXWQt9Y45hmS4qWdmyHBvmNeW+2a7y8ccfR8TCQIDX2DbBiPW6d4nCKzteZWfN96me285OZRweHpbZPAwnLt+Ky7UNyaylOy9/52vMVqp3ZBf76HdZHof3qN+vrGFmgewze36zv+1PkM9xEuQqEbz/t9VqQF4AACAASURBVBbA+2yrWK9jErmW/Z9RlcSaOLnBYDAYXEtc2iZ3dHS0Ykf5F/w8b6atqPdO1+q4jqw7dzE/e95YOo9YpBJ7WSINY9PIJVas0+cavJHsKZltMi5XYnsKbWevQUvF1nd3RQzz2C1RW6+fj5txd15deY1gF8SV3b59ezPW6caNG5uSbhWDlfvmOMaIhcmZlXc6/qp/nEMmFdaLz8xmbAOzjZHjWcJlrziPpllz5f3Id8TFueBmVQDTmgPa87NR9dX26C4/YJ6Tjp17XBm2zeeiqAYaJDO5PE9mhF7/i3ri5XO9R7cKktKXLsYuw3NqLZTfg/l+ztHp5zJrUrwnGRf7j3PzvNvW12nEnJEkt9OVSrOdNGLtc2Cfisrvw++oLc/ciGFyg8FgMLjCmB+5wWAwGFxZXEpdeXR0FF//+tfPynxURmhgutml0MnHrF7r0lBVbsCmyKh1qqBQlw+hXdI6/fnPf46I/RIrGHGdZsv0nbZwVIlYkp6i2oSuUzF5yw3ZxltT/m7u8nddBeR8jdfQ51TOKw5iPS/JbtXHKtTCwb7Mpat9R/Rqoc6hoVKnuwI4qsFKBeR19zicDiliHTpgNaVVwzmwmz59+OGHEbGotK0my7B60KpB74O8Bp3zj9XZlcrJ7fnZrMpPZWeviyRpjljMCRl+v3RlbfL3Vs054UGl1gd2qXdl8sphAnjsjJt9kU0dwI4nTqANqurloAuzqubd6f3s0FOFXXTvDN+veuar9F0ZVR+z2n3UlYPBYDC4lrgUk7tx40Z87WtfO2MkFfiFt6s9sIt/RM9GbKCt0sR07M9SWHaThbG9/vrrEbFIx2+//XZErMuY5PYYO23gTGLjbpY233rrrYiIePfddyNiKXz505/+NCLWSXcjljIvlWNJxFrSrhwPOieSKszBTK1LCFu5Uedk0ucVMLRUVzECj9EJfrekOvdz636cw3qwd5HCK4n6gw8+iIi1owH/02ZOegsTZc+QQMBOM+yZv//972fXwuDeeeedvb46YXiW2vmbcAq7rDOfjLN6nrpkAKAqWeMUYA4KrtJw5ffF1t7JjimVM4fH5gQLTqCdr+n2uN8h+VqnsuuKy1ashblj7GZy+Tkwo/Jz6mem0nJ1qdlAlWzbfe5K4mw58jjMYWt93Sc73uU++p63bt3adHgcJjcYDAaDK4tLB4Pfvn170w28+0XtCkTm6+367mSw/IJX6XUMFxvM5xEqAIN67733ImKxhZCyKUvj9IXCnQQIm3HZtTffB8kdV3Xuh6t6tv3Y7bwLBq5S5XhdrHuvymZ0ZUXMgPK4PMcnJyfbJS8OD9uyP/kelnTdh8quaynRY2feMsPmbxg0jMslSDIrgO3DsGiDax1oGxHx4osvRkTEc889FxHL+sMU6Stt5NAV7vfqq6/uHaN99jJhHBELU2Rveg85wLhKzOB58/+Z/ZkpVKVQ8vH8XVcaJ2O3220er+5hNrll8+nSUPncKpWZbfKsf2U3dNiPtQHsh2zP83vTzKZ7XnO7XYHVCmZUFQPuvjdT9DuqCyjPffR8XmS97ty5M0xuMBgMBtcTlw4GPz4+XiU43kqQabta5dHTSYn+v/Lws8eO9edmQPneTzzxREQsEq+T+eZrkO6xc8DkLB3bEzBiYTwO5EZyg6k++eSTq2tcdqbzyNsq6WHJlL5WNpKuDH3lkQl7YE620uvgHefCnTkNEdJv5YWVv6+CV70HLdGT6qpKZIzkTPsumprvB8u/d+9eROzbzzJyYD9MEXvas88+GxHrkk7MTW6T+8D+Ovt13m+0azuxkw9UHs+2R3V2lSo4F1jSroLF7Vl4XuHLKmC98kbubHOVl7U1Hv50/6sUVm6fz+q94/eZtSNcmz2zbZN3WSN7iOfxVaXQct+2vLm9JzvNWF6zrZRfeQx5TsyIvX7cv1r/nB5xvCsHg8FgcC1xKSb373//O37zm9+cSSJVcdEuLcuWztSS03nsL9+Pv+1J2OnVM5xOjDYqz1Cn3IH9MQcu1lrF5dl7yuXpM6vhXHtg2cuyYzIR67mwF9eWl1qXgLaSTOnjH/7whz0pNOP09DS++OKLs+NOUpzvaYnec1olv7ZkbabgQpH5XK5lPWByVRHV733vexGxeOC6LZBj3WB1r7322t4xGDDzx/fZJoeki+0N9o+nJt/ncilV8c2IZV8z57DDykbT2dUcDxixfj4vEsvpZ3mLyR0cHMTNmzfP1pD5qjwlHbdmTUuG3zcdm6j2ZRe35jFXCZO9LmgS0A5lr14zUJfAcaxfxeR45uibU8RV5bOs0anmPJ+X//ZeMROuUhB2bYDq/Zb389jkBoPBYHAtcSkmd3x8HJ988snZLyheg1txS2CrxApSkO1O1kdXEmclwWRUGRp8rPOqyl6cjrfjvkj9tp3lQpvW9TtRb2UDdExYlzDZdqzcHrAkVc2J4+K6xKk5RvKVV17ZG+v777+/Yjhgt9vF8fHxihlmKZJ5gdF0GRmqRLldIt6tjDCcwxqyn5GkqxgdmPuvfvWrvWs++uijvb7n+7FGzDcSe+dNmr1sKe0D6OszzzwTEQuDy3PE3uniTF3gF0aXx+59Vj0/wHaTzrt3SwtweHi4ac+9efPmas9Xnn2wE5coqlhiF7/V7Z3KFlyxk4iFtWWGzXe20TO32GLzu4Nngu9sN0YbUCXqdsFb5o115/88Ny6tQx+t4aky+3Qx0UaeK+bU67S1d0BObD1MbjAYDAbXEvMjNxgMBoMri0sHg9+8efOM/lau6KbtXZBuhoMHXSPJFLYyYFtFZwNwFfCa1ZHVuVXQOSoA17ayg0NWAdh43FXhzgZpp8yyusWBlZVrNLBzip11ch+trgDch6DkiMWRgmtOTk5W6+82bNjODg6us3cRF2RQJSbI14C8V2kHFR2fqAutUsvgXMI+fE4O6D2vYjJzguopj9t1BFEtWuWV1//pp5/eux/OD67gjfqM0Abfu+qjU5/l7+y4Uanhgc/9/PPP271jdWWVZosxWV3pfVy9f3yOaxPaqSX336YAhxpl1bOd1BzmgBqb1HERy/yjYrST1PPPPx8RSwKA/Eyj4uS7bm6qpBBdij3v3fz8diYVxr0VQO658Pt763fjvFSCw+QGg8FgcGVxaSZ348aNVTmZ7CbbBWM6uLmCDdfAjiFVsk5LD11F44h1FW8bOelrvrarWm5nDlC5GzuZ7lYJii4UozPMZinYYQddsH5V+gT4fjBU0llFrJOonpdg9/j4eJVkNzOgzshs43fF5C9S8Tlif5ysA84BXULjPC67ZWO8R6JFy5HHBWOifYdCcH/mMzse0C57h/9zmIHHRR9wTnBwtiuG52eSoHmz2S1HC7u3O5lDlWTc7ZzneHJ0dHS2Xq7Onu/tMIOqLfehK1djrUyVTJxj1gZU4QIdK2FtYWOkDqz6Rp9ZfzuxZQ2T72OW5FRk+ZhZOH3kk7Hk9GVmiJzr57n6DfB7xikI87isCbtx48YEgw8Gg8HgeuKRbHJ2161ge91WYGjHvrpgwioY2CyQviFN5F/6Kl1TRhWq4O+QOBzYabtSxCJV8h1Sl+ev6s9FGVyeT7OaLuByyxUfNsD32G2yu3kVvL8ljd+4cWMz8epWoGm+pkol5PnwfWwDilgH43d2nNxX5gV3byd3RrKtCtKyF+kLbTGnVQJq5pOgbxd0pc9Z00AYB+PDnpPtQxF1gC999LPnsVSu451mwoka8t9ZA7MVDP7YY4+tGOEWA8nX5j7l8dht3WnvzOSq9GesC+8UPh3GUfXBNjGYN3a2iCWRgIPb2Tuwfp5Pr3HEeu97DfM8Oi0h9rQu6UU1Ptpwyi6HX0WsWTOwhiy/V7xeUzR1MBgMBtcWl2ZyR0dHKwmwktC64pugSrhpDznr1StvNweD28uxSitmm5u9jypPPQeqe+xO8psl+S5hLvetpH9LX7YBdnbLfK1Zrj+3El0D/r9//35E7EtcZslb9tbdbhcnJycXSilmfby9T7Odgnlnbj1Pllor3T5jgvFgV7FkH7GwJCRsPp3AINupbWdA2rZN7q9//WtERLzwwgur+zEO7HpI8sxJTleGvYZj7C+YAvsOFpDXgiBz2KXTo9nzMM8T8DugYkLnaRsyDg8P4ytf+cpq7+cx2yPabMnvhfydmZuPg8ySWF/S+zkFXOWlWNnPM2jz29/+9tl3MEPWymnLsKGyT3J5MFimbYoupprn0XZVkh04hSOfuaQUYO7pM31j/1UeztbAONVi5ZHJsZs3bw6TGwwGg8H1xKWZ3K1bt1aeTBlmEQa/wpUXkCV5e0xaAtkbiLyBaL+ynTkxM3D7VTkRS+yca7tDxQLtZeQ0X1nqM4ODidBWl6g5t9vFjlWSdceAkf7w+KrmJLPXrXX//PPPV7aKKtYpX5P7W9kSXbDTTNEMIe9ZUnFxDRIt9g0YVtYcYBODHSGluuxUngcYAO2b+biga54HroUxIqlj92CcVeJpt8vY3cfszclYYRW2bbN++dmxxqWLla0SKoMvv/yyZXOHh4fx1a9+dcVEMlu253KOv8t9yvdwair6yTNm+zosOmKdosv7zu+JjC4WkTbyuDpvZJ4fEnUTY5fT7tm71V6V7KFsZ6dd9r7LgdmOmeeT+WJuOMb+qlKsWfPFMftNVOWHMmseJjcYDAaDa4lH8q5EukSKzZKnJSlLrdaDR6z1/rYZOVlolo4snXTSf/ZkpI9IFpY0+Mx9Z8yOrbIkUnmAOibI2QO6ucpz0nm7VTa6Ssqq7rMV98N4kPJgPRUjzky1k6hOTk7is88+Oxuz47AqdKzV7WZ0GXZgM5l1wGwcP8S5FNXN9+AYnx1TqLwPq4S4EQtLZk/loqmZPeQ+2hac72dPQ9txvMa5PzzT2GLYX9iJqjg57/mu6Ggl9bMG5zG5O3furLz1so0MRmPG07Hm3AeYkz35+J/75LXgO8fl+RnL+3Frr+drsobFZbg6+y7vsMzKiWl1dibmEXaUtRv2APc4bFfc0j65wGv1bLisGZ8wORdPzd9lT89hcoPBYDC4lngk70ozEewFEWvPsS7rRlW2wpKnbT62D+T7uPwG51YsgPaRZOwZad18xGKL4RwkXevv/RmxSEdI6i5RtOWVCLqcjFuxg75mKw9cVxwVaRAJMUuzXamiCqenp/Gf//xnFZuWvbPon208ZnR5bp3b0XuTtYUdMfcRS45A9oFLkzAnOYcpHphcQ5/s3ZaLpn7rW9/au8aFZekj3meZYbPv2PO+r7Ucud+cw77DboMkX5VC8TNIG/YizM9Vl2fQeUoz6EMuZruVtWa32628bPM+YB95Psy48j1cVsjszywq72/O9V71uyuP3bZQP3OVBsZ96cqQMc6sscLzElhjVvke2LO9KyW0lbHI4+X52fKg9XPLc5QzqgDP02Q8GQwGg8G1xfzIDQaDweDK4lLqyoglPVNEXarj7t27EbEO7ttKnNylBeoqDVdlbFAxod5BhZpdqwGU3kHYDiDNalFUPq78jZrAxuusCrL6k/lCjYWaKqtfuqBzYLVipvUuIdQ5AmT1giuMo97529/+ttdWFXYAttQGu90uHjx4cLYulbMN+8mqWDsKVZWMWWcbzFHRcTz3D3drzuFa1vLevXsREfHss8+eXeMgaeaJ+UOtlJ1HrFKtwkwi6jR5LmvFHLDPK7UY7Vstyty7BEveL1bzAtSvdiaI6Peon+v8LNIHvnvw4MGmujLvrWr/du7yVl/mSt2si00bnpcqCYWdiOwQUo2lS0DuRNHVWuZyVvkah6fkttmLVqm6zfyuYuwurWSHQeaucl7jXDunuO18Lp/MOZ/Mc96PzJMdajoMkxsMBoPBlcWlmVyWqCppHAnNBmqn16qMnVuu9Pl4/lWnHSRNHCWQbCq3bQesOyWPU0RFLFKxXesteVRpj8zuaP+tt96KiIX98ln1oSv0aqksYp32pkt5Vhm4Aezmvffei4i1Q1HV7nnFC4+Ojs6M4eyTvJY4N9ihxQHqVTonwDywPtzPBSMjFscT+sIe+tGPfhQRy5pXTlI4OnTFUvPc0q4dJhxszhznteDaLvzD6Z5yO7SLUwz/0yZzl92zaR/tAs8TjJ4+54QMTplmxwbGm51x+DuH8ZxXcNcMKK8968H8d44ZVeJshyZ1pZ7ymJ24wI5PdoSJWJfYspNH9Szn8ee+2XmIZ4Kwl3xvzu2Kw1aM2I41dqipHN4cknJeAd58jZmcnYyypsr77c6dO9tp4dojg8FgMBj8j+PSTC5iW3JHskQS7JIDZ6ktF7+L6MtmdBJCxGLPgQ2YKVTsz+6qTg2U72/bYleuHYkqS31O5+TAa9hSTq6KRMM8UlCR8SFRO8VN7mNX+qSyG1iP/vrrr0fEwogIts1S7nnBrRmEn1iqrNJRMRakUr6vWIRLBNF/7J4kK67CDxib03nBsC01RyzrYnsXUiUMpQogpx0S58IukPo5XtnmrEHwOZU2BcDUnIi6CgegXfqCdoFr3nzzzb3vq2vYx8xrZbs3O//iiy9amxzJvdmjFUvyethdvtqr9M/aF4eycJ88552tylqpynZpbVYXpJ/7ZCbnRBI8G1nT4wTn9NnpzPL683dlH4xYJ5nP6DRyfg9VWjynOPT7KP/WOJXaVsHdiGFyg8FgMLjCuDSTq4IbqyS7SGpbpVWMLni5S9yc/0bCcDkRpLXsiWOJyaXdCRLNUr8Tvloq8/Eq3Y2ZqO05+RpLVE7F4/RYeU46huXEzJU+Ha+9v/zlL6s5yP3K7VhqrgCTA1ViYcDeYS0dEJqlY+aQ+YDFYFOEPSD9832+j6Vye2LmPjLffNrmUyWRts2F+1K+yMHumTlWiYUj1gUo8/62R5q9oe05VyUucFJx5vzdd9+NiH27kfckY2cvkUAgMzkz8K20XozfTCCzFrNSMyBQJXd3n8zcqtSAnZ3L3s5ZO2M2ZgZZecpWpcIiFubmtcwwY7P2hH24xVC9R7a0QWaz1jJV7yXbQc3+zNAjljllX926dWuY3GAwGAyuJy7N5A4PDzdtZ2ZQLnRZedh0KaUs2VWFPe2dY319VQ7I97E05hLvuV2kCNsUuD/sI8P2PHvIOQ1S7hufsEv+R/qqihZ2yaKr5NiAOaFwJ3ZCezpWCZorhl21f3x8vJJ0c5kPt/vOO+9ExNr+AEOIWNaDfhH7xqcL4eb7mbk/9dRTEbGsIXOb9xCMDUma2DN76OV54lyupX1L6WZRuT3PrW1zeR/QF88tzIdrsFdWkrxTPjn1WW6bc7nWpbicCD23k22p3f7Z7Xbx8OHD1XOZ2Stz6xRjlT3NfTAr41wnMM5t8H5jTPSdOWY9Kt8D2526NIb572pf5bbYy/nZtr+A3weV16jtq7aNuV9539kOSZ+YkyqG2Jo4znH6ujwn3DPvg63UiMPkBoPBYHBl8UgZT+wtk39F7X2DV5YT81a2Pb6zfrbLEJDPsaRhL6TK29FMCnZBn7PEQftOLGwpqUqC7KKIZl+WYnL/HfOGdGSvo+p+HYOrEsEi1b/88st75zo5bV5rJ/Ot4nvAw4cP48MPP1xJ2MRwRSySGZIfbNLlP7JEbQnOUrg1CXkfPPnkk3ufrAvzBuOqCqDC/thLMDoX7Y1YPFO5hjZs96hiIR1zxv1oq4rLo//YLJxomnMZX87OAjNhXSx901ZOys5a+tlnbVy0NWLZvxdJ3guT20oE3jEdvxfyHrUmhz1jLZDt4RHL8wKjZb6YA9Y8lwNyodWuLFQVt+YYWxevrfwjGLvLQzkxfbZ5+5htms4KlPeq18WMuxp3V7DWZa+2ypCdnp5OnNxgMBgMrifmR24wGAwGVxb/VQgBqNR60Fknha0oq51HulACsFXDyO6rUOXK2QK1JNTc5+a2GYfVBgBaXzkPWB1pR4oqjZjbdXovVGuVGtEqhmrsPo+QAYJ9bSyu+sax7Fa8VRn8008/XalX8t7BQcPBzKw3fSSYOmLt5s28uK2qUjSOJt///vcjYm2QRyVYjYn54T4E6ztMI/fJKnur+7fcoJ0YwedWYQcOkfF9K/dzHHasgnS4SA6rcIKHLj1X3od2RDsvxIiA8HxuvuY856oqOTDnsJaoJ+3SX9U5ZE75jnkjxIK9k+sv8jeJ2WkDVaedV/J4+I422Nedq3/+rnOocZKCiOWdiBrWIQOoXJ955pm97yPWz7TNTVVyfu7t95wr3md1pE0PVlEbw+QGg8FgcGXxSGm9QOX0YcmPX22YkFNnRaylOrBVWgM4AbMN0Hb6qGB3aa7NDiI20lbusPl+mflYCu4CHzNsiO1SdPm++dxOsuZ4Ds79/e9/HxFr6XaLbdjhZLfbbbqBn5ycnM0f40LijVi7ZdMufXFS7IhF0kM6fe655yJiMfx7b+VSK+xFp0irKsMDB2c7RKUKfHXaJmsKumrPGU62yyfzma9x2Rc7AHAuUnmulm5nItrKZaci9p1/+NuB8nbwyqiqk285D5yenq6Cm6v3gSuad3s/Yh0wbjd8p7bK/eNaGDxOPOxnnq3srAILIzWf7+fEAhnsefa1yykRVrM1h7TPudaYRKyZHHuS+9J39kx+R9oZj+fK78i8Hxwi4HSJLreVwfp4bxrD5AaDwWBwZfFIweCWzLI0i4TDrytSnoua5l9mlwZxCpkqqTPoJDezpyz1dTpjM6pKcvN9fR9/5vvZBdr3rcqAVP3Px6twgC6NmPXrb7zxxtk1uOv7fm6jCvzPqZQ6u9Lh4WHcvn37bK/Y5hOxZiVeb/ZHdl8HlvKd8JX75TRLXlMkT9uuMpzs2Pvb7voRa/du2zetscjPU2Vzzd9XLtaet648SqWNgImQ/sxpspD08xgsZVsz0mlqcl+2gnl3u12cnp62yQ2q6x8lMby1JeyDau87DAiWxnuPEkXvv//+3jjyOQ4Yt30394l3JAzKyQ1oI+8d9rq1MawhnxW7deqs559/fq9vVSo/ngVYbedrUIWSuLCwExlkduswpPMwTG4wGAwGVxaXZnJZogKVFIaUgrTkNE5ZErL3jZPuOrVR/gXvSlu47H2VbNkeV7ZZVAmMOy8wl+epyknYq22rkCx9dHFOB4FXTM5sCbhsPNJmbu+88ka5jx0z6XBwcLBiJtm2g42AfrqAYpUo17ZC9hf7zbr97O3moGzmxQViq1IkTiTAudhIsseavfNgfUj03M/eb7ldJGunojJryu2axTj4mbXNwcBOW8cxB6xXa+A58fHKGzJ7yG3Zk/I1VcktP0ted3shRqw9pf2Mw5qqfce8eG5pC0ac7UXY6egD+53n8Lvf/W5E7GsBnD6LT3s2oyHL71XsZ54bp77LTJx7M3bb4NirVZJ325q7d2Z+L1m7xDg8nsp2W3lrVhgmNxgMBoMri//Ku7KKJ7OHIpIMEht64OzZ13kdOkmo48vyuUhQ9iysytpYB98lTs0SvGNnumKMVaqzjv1xzlacx3lJqrdKXpjdmtVW69aVsK+kJR/bKnmBXWXLQ87rXpUPiah1+v7f8TcgS9aO2ayKVrof3fjYF/Qte655rT7++OOIiHj66af3vq+eJyTarkhrJS075VMnWTMunsncV2IISV+F1F+VP8rMI2Jtn3Q//Heegw6k9opYlyaKWDM31sHxkxmdl6ZjRF3WKKPzzK7SjFlz4BRarHW2OfueTutlzUKeE/pNX5gTr39eP5gb12AndFJkUBV49XsUVO/BrjwT+6vynDS7vXHjxpTaGQwGg8H1xKWZHBL51vGItZeOS3VkyRRW18Vk2Ya1xXwsGVQxLvxtqb8qAwQ6O2RXZiZLFtafW3KsvIVsP7E0bnaQbUC2XVjKc3/ydx6Pv6/6CL788svNvXF8fLyyxVSJhQEsAjtaJcGzj7o5PY8h5HGY6WyxV//POGx3y38jlSJR03ckd6TmzKwYB1kxuJ9tPpU9t7P90gbzmz1cmVuyynzwwQcRsUj4eF3muekSDdt+XNm4s1bhvLWyRmLLDt15Zud75HnO7dq7tiq9BVhD389xphHLu9AaJGs3cikprucdyZqx/owXpp2LAjuJOHvoo48+ioiIJ554IiL24yS7jETWslV+BDAra+8Yr8uTRSx71BqLLk646st5ttxhcoPBYDC4spgfucFgMBhcWVxKXUnaJqsVMmXtVI2khcHImikragOrxqw2rAzz7oNVMpWKwed0qX+yeqULA7hIqi47ujgAtlJjOll1FyyLCqCi7K54bDVlpVrsVKpVhV6Po0vMy7kPHjxoVcURa5duxmYHlKzqtnqqq7vnxK8R67RTVqXyf3ay4D7uq8Ne8vrbaQRVFmpK+kQfc2jHCy+8EBGLGsoB+LTN85X7YIcQp6tC9ZWfEaurmUerkfI8OomwK047fVYeR3ZK2VJXnp6erlT4eb85JVzneJb3L+fakcFqxKqOnc0grutXJSC32h0VMGpD1qNK0YaqEbWlw4O4Jjs8vfrqq3vHmHMCulGP5/VnHH7v+J1cqWMdekPfHW6Qr+F+/AZ04WOVk1xe4y0zyTC5wWAwGFxZPFKpHUtJW84IliadxDNikfiQUuyej9uyg5kj1pVk3Y+LMCsbpaukzh2Ds3Hfge0RfdC0HR0qg2znym/mltmO18eSaRVW4faqIPOI/Xk2qzgvKPzk5GRV/qNyPGGMSKUO1s/SqhmnDdVIkw6qj1iYFMfYX07YnIG02oXKIJ3neXJ1cvedPsLWskME15BWCekcULYlM7kuXRyhC7Ays8GIRbLuEgLDmLJLPt/ZKcHjq1LCVU5QxsnJSXz22WerhAt5jv0MeY87MXjEMj8uW2WmUGlRXGrLSSiqZ5r+O6Ca9xsOIPka1v/+/fsRsaxhZtK5rfy+taMH4/Q7pQqRsXOM56YKC6FdJyqww2B+Dlxhnfb9u5HZrZnhY489NiEEg8FgMLieuLRN7uHDhyt38vzLbBuYGRB66XyNtuHn9QAAAZlJREFUbUc5UDxi/eueJULriu1evFX6xlKfmVwV6GgJ1BJO5dbaJaF1ctKLhC7YplUlM3YJHEAfmaPKruK+ma1VyJL1Vqmd09PTlcRbFbHkE3bk4OJcngfXaQe+OqDcbs0RC4NBmsSN+e7duxFRryWlfLqiohUDhnm+/fbbe5/WauCun4vCUoTzpZdeioh1AHnlBt4lvXVZliqch7l2IWGYEG7w2U7p9r22lTbF67VVcBd7ru2CGU53Zbbnz3xNV4DW77L8DDgJhcvyVOnW/Ow6nRx9zknEuQ/zxTHacCKL3EfWiP1n+zHfVwn2HdjN/c3gMqN1oVPaddIFnreINTN1omuQx2W73cnJyaYmYJjcYDAYDK4sDs4LpNs7+eDg44h45/+vO4MrgO/sdrtv+svZO4MLYPbO4FFR7p2IS/7IDQaDwWDwv4RRVw4Gg8HgymJ+5AaDwWBwZTE/coPBYDC4spgfucFgMBhcWcyP3GAwGAyuLOZHbjAYDAZXFvMjNxgMBoMri/mRGwwGg8GVxfzIDQaDweDK4v8AT150lEWyrIoAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_gallery(\"First few centered faces\", X[:n_components])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll learn the sensors using the first 300 faces and use the rest for testing reconstruction error." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-07T16:17:17.809087Z", - "start_time": "2020-10-07T16:17:17.806700Z" - } - }, - "outputs": [], - "source": [ - "X_train, X_test = X[:300], X[300:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sensor costs\n", - "In order for `CCQR` to decide which sensors are most important, it needs to know the costs associated with each of them. To this end, one must construct an array specifying these costs. Larger costs/values will make `CCQR` less likely to pick a given sensor, and smaller ones will have the opposite effect.\n", - "\n", - "We'll consider three different sets of sensor costs:\n", - "\n", - "* Zero cost: all sensors have zero cost\n", - "* Center blocked: sensors within a square near the center of the image have a fixed positive cost and others have none\n", - "* Left blocked: sensors on the left side of the image have a fixed positive cost and others have none" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-07T16:17:17.982782Z", - "start_time": "2020-10-07T16:17:17.810593Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cost_names = ('Zero', 'Center blocked', 'Left blocked')\n", - "sensor_costs = {}\n", - "\n", - "# Zero cost\n", - "sensor_costs['Zero'] = np.zeros(image_shape).reshape(-1)\n", - "\n", - "# Center blocked\n", - "costs = np.zeros(image_shape)\n", - "w = 10\n", - "costs[(32 - w):(32 + w), (32 - w):(32 + w)] = 1\n", - "sensor_costs['Center blocked'] = costs.reshape(-1)\n", - "\n", - "# Left blocked\n", - "costs = np.zeros(image_shape)\n", - "costs[:, :20] = 1\n", - "sensor_costs['Left blocked'] = costs.reshape(-1)\n", - "\n", - "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n", - "for name, ax in zip(cost_names, axs):\n", - " ax.imshow(sensor_costs[name].reshape(image_shape), vmin=0, vmax=1, cmap=plt.cm.gray)\n", - " ax.set(title=name, xticks=[], yticks=[]);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we'll apply `CCQR` with each of the above cost functions, plotting learned sensor locations and a few examples of reconstructed faces from the test set.\n", - "\n", - "Note that the mean-square error (MSE) reported in the first row is the MSE across all images in the test set. For the later rows, we give the MSE for the particular image being shown." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2020-10-07T16:17:23.614083Z", - "start_time": "2020-10-07T16:17:17.985060Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n_sensors = 60\n", - "n_faces = 3\n", - "n_rows = n_faces + 1\n", - "fig, axs = plt.subplots(n_rows, 4, figsize=(12, 3 * n_rows))\n", - "\n", - "for k, cost_name in enumerate(cost_names):\n", - " # Define the cost-constrained QR optimizer\n", - " optimizer = ps.optimizers.CCQR(sensor_costs=sensor_costs[cost_name])\n", - " basis = ps.basis.SVD(n_basis_modes=50)\n", - " \n", - " # Initialize and fit the model\n", - " model = ps.SSPOR(n_sensors=n_sensors, optimizer=optimizer)\n", - " model.fit(X_train)\n", - " \n", - " # Get average reconstruction error across test set\n", - " test_error = model.reconstruction_error(X_test, sensor_range=[n_sensors])\n", - " \n", - " # Plot sensor locations\n", - " sensors = model.get_selected_sensors()\n", - " img = np.zeros(n_features)\n", - " img[sensors] = 16\n", - " \n", - " axs[0, k].imshow(img.reshape(image_shape), cmap=plt.cm.binary)\n", - " axs[0, k].set(\n", - " title=f\"{cost_name} (MSE: {test_error[0]:.2f})\"\n", - " )\n", - " \n", - " # Plot reconstructed faces\n", - " for j in range(n_faces):\n", - " idx = 10 * j\n", - " img = model.predict(X_test[idx, sensors])\n", - " vmax = max(img.max(), img.min())\n", - " axs[j + 1, k].imshow(\n", - " img.reshape(image_shape),\n", - " cmap=plt.cm.binary,\n", - " vmin=-vmax,\n", - " vmax=vmax\n", - " )\n", - " error = model.reconstruction_error(X_test[idx], sensor_range=[n_sensors])[0]\n", - " axs[j + 1, k].set(title=f\"MSE: {error:.2f}\")\n", - " \n", - " # Plot target image\n", - " true_img = X_test[idx]\n", - " vmax = max(true_img.max(), true_img.min())\n", - " axs[j + 1, k + 1].imshow(\n", - " true_img.reshape(image_shape),\n", - " cmap=plt.cm.binary,\n", - " vmin=-vmax,\n", - " vmax=vmax\n", - " )\n", - " axs[j + 1, k + 1].set(title=\"Original image\")\n", - " \n", - "\n", - "[ax.set(xticks=[], yticks=[]) for ax in axs.flatten()]\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Observations:\n", - "\n", - "* Using \"center blocked\" costs causes sensors in the center of the image to be avoided\n", - "* Using \"left blocked\" costs causes sensors on the left of the image to be avoided\n", - "* In all cases more sensors are needed for accurate reconstructions\n", - "* With a limited number of sensors, models have a hard time with high-frequency features like the texture of a beard" - ] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.5" - }, - "latex_envs": { - "LaTeX_envs_menu_present": true, - "autoclose": false, - "autocomplete": true, - "bibliofile": "biblio.bib", - "cite_by": "apalike", - "current_citInitial": 1, - "eqLabelWithNumbers": true, - "eqNumInitial": 1, - "hotkeys": { - "equation": "Ctrl-E", - "itemize": "Ctrl-I" - }, - "labels_anchors": false, - "latex_user_defs": false, - "report_style_numbering": false, - "user_envs_cfg": false - }, - "nbTranslate": { - "displayLangs": [ - "*" - ], - "hotkey": "alt-t", - "langInMainMenu": true, - "sourceLang": "en", - "targetLang": "fr", - "useGoogleTranslate": true - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 1f6ec68ef88dc85198d12ef006ccd2c1ea60af01 Mon Sep 17 00:00:00 2001 From: Jimmy-INL <52417034+Jimmy-INL@users.noreply.github.com> Date: Sat, 19 Nov 2022 11:07:49 -0700 Subject: [PATCH 37/52] Delete region_optimal.ipynb --- examples/region_optimal.ipynb | 1363 --------------------------------- 1 file changed, 1363 deletions(-) delete mode 100644 examples/region_optimal.ipynb diff --git a/examples/region_optimal.ipynb b/examples/region_optimal.ipynb deleted file mode 100644 index 66d2b54..0000000 --- a/examples/region_optimal.ipynb +++ /dev/null @@ -1,1363 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 115, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:22:04.386599Z", - "start_time": "2022-07-10T04:22:04.382732Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "from pysensors.optimizers._qr import QR\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from sklearn import datasets\n", - "from sklearn import metrics\n", - "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", - "\n", - "import pysensors as ps\n", - "from matplotlib.patches import Circle\n" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:22:07.391526Z", - "start_time": "2022-07-10T04:22:07.354464Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of samples: 400\n", - "Number of features (sensors): 4096\n" - ] - } - ], - "source": [ - "faces = datasets.fetch_olivetti_faces(shuffle=True)\n", - "X = faces.data\n", - "\n", - "n_samples, n_features = X.shape\n", - "print('Number of samples:', n_samples)\n", - "print('Number of features (sensors):', n_features)\n", - "\n", - "# Global centering\n", - "X = X - X.mean(axis=0)\n", - "\n", - "# Local centering\n", - "X -= X.mean(axis=1).reshape(n_samples, -1)\n", - "\n", - "n_row, n_col = 2, 3\n", - "n_components = n_row * n_col\n", - "image_shape = (64, 64)\n", - "nx = 64\n", - "ny = 64" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:22:09.785781Z", - "start_time": "2022-07-10T04:22:09.779128Z" - } - }, - "outputs": [], - "source": [ - "def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):\n", - " '''Function for plotting faces'''\n", - " plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n", - " plt.suptitle(title, size=16)\n", - " for i, comp in enumerate(images):\n", - " plt.subplot(n_row, n_col, i + 1)\n", - " vmax = max(comp.max(), -comp.min())\n", - " plt.imshow(comp.reshape(image_shape), cmap=cmap,\n", - " interpolation='nearest',\n", - " vmin=-vmax, vmax=vmax)\n", - " plt.xticks(())\n", - " plt.yticks(())\n", - " plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:22:10.835009Z", - "start_time": "2022-07-10T04:22:10.642255Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_gallery(\"First few centered faces\", X[:n_components])" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:22:11.651751Z", - "start_time": "2022-07-10T04:22:11.555299Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[4032 384 4092 4039 447 493 2204 657 878 2880 1088 4087 2837 3779\n", - " 3093]\n" - ] - } - ], - "source": [ - "#Find all sensor locations using built in QR optimizer\n", - "#max_const_sensors = 230\n", - "#n_const_sensors = 2\n", - "n_sensors = 15\n", - "optimizer = ps.optimizers.QR()\n", - "model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors)\n", - "model.fit(X)\n", - "\n", - "all_sensors = model.get_all_sensors()\n", - "top_sensors0 = model.get_selected_sensors()\n", - "print(top_sensors0)\n", - "#print('Unconstrained Optimal sensors, n = {}, {},...'.format(len(all_sensors),all_sensors[:n_sensors+3]))\n", - "#print('Unconstrained Optimal sensors, n = {}, {}'.format(len(top_sensors0),top_sensors0))" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "img = np.zeros(n_features)\n", - "img[top_sensors0] = 16\n", - "fig,ax = plt.subplots(1)\n", - "ax.set_aspect('equal')\n", - "ax.imshow(img.reshape(image_shape),cmap=plt.cm.binary)" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(array([63, 6, 63, 63, 6, 7, 34, 10, 13, 45, 17, 63, 44, 59, 48]), array([ 0, 0, 60, 7, 63, 45, 28, 17, 46, 0, 0, 55, 21, 3, 21]))\n", - "[3779 4087 3779 878 493 4092 3093 4032 4039 2837]\n" - ] - } - ], - "source": [ - "a = np.unravel_index(top_sensors0, (nx,ny))\n", - "print(a)\n", - "violated_sensorsx =[]\n", - "violated_sensorsy =[]\n", - "for j in range(len(top_sensors0)):\n", - " x_cord = a[0][j]\n", - " y_cord = a[1][j]\n", - " for i in range(len(top_sensors0)):\n", - " if ((a[0][i]-x_cord)**2 + (a[1][i]-y_cord)**2) < r**2 and i!=j: \n", - " violated_sensorsx.append(a[0][i])\n", - " violated_sensorsy.append(a[1][i])\n", - "violated_sensorsx = np.array(violated_sensorsx)\n", - "violated_sensorsy = np.array(violated_sensorsy)\n", - "violated_sensors_array = np.stack((violated_sensorsx, violated_sensorsy), axis=1)\n", - "violated_sensors_tuple = np.transpose(violated_sensors_array)\n", - "idx_violated = np.ravel_multi_index(violated_sensors_tuple, (nx,ny))\n", - "print(idx_violated)" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "img = np.zeros(n_features)\n", - "img[top_sensors0] = 16\n", - "fig,ax = plt.subplots(1)\n", - "ax.set_aspect('equal')\n", - "ax.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", - "top_sensors0_grid = np.unravel_index(top_sensors0, (nx,ny))\n", - "plt.scatter(violated_sensorsy, violated_sensorsx)\n", - "# figure, axes = plt.subplots()\n", - "for i in range(len(top_sensors0_grid[0])):\n", - " circ = Circle( (top_sensors0_grid[1][i], top_sensors0_grid[0][i]), r ,color='r',fill = False )\n", - " ax.add_patch(circ)\n", - "#plt.scatter(violated_sensorsy, violated_sensorsx)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:22:20.877032Z", - "start_time": "2022-07-10T04:22:20.866607Z" - } - }, - "outputs": [], - "source": [ - "##Constrained sensor location on the grid: \n", - "# xmin = 20\n", - "# xmax = 40\n", - "# ymin = 25\n", - "# ymax = 45\n", - "# sensors_constrained = ps.optimizers._gqr.getConstraindSensorsIndices(xmin,xmax,ymin,ymax,nx,ny,all_sensors) #Constrained column indices\n", - "# print('The constrained sensors are {}'.format(sensors_constrained))\n", - "# print('The constrained sensors are {}'.format(top_sensors0[np.isin(top_sensors0,sensors_constrained,invert=False)]))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "def distance_constraints_indices(r,nx,ny,piv,n_sensors,j): ##Need to combine all the idx_constrained \n", - " \n", - " n_features = len(all_sensors)\n", - "\n", - " a = np.unravel_index(piv, (nx,ny))\n", - " x_cord = a[0][j-1]\n", - " y_cord = a[1][j-1]\n", - " #print(x_cord, y_cord)\n", - " constrained_sensorsx = []\n", - " constrained_sensorsy = []\n", - " for i in range(n_features):\n", - " if ((a[0][i]-x_cord)**2 + (a[1][i]-y_cord)**2) < r**2: \n", - " #print(a[0][i],a[1][i])\n", - " constrained_sensorsx.append(a[0][i])\n", - " constrained_sensorsy.append(a[1][i])\n", - " #print(constrained_sensorsx, constrained_sensorsy)\n", - " constrained_sensorsx = np.array(constrained_sensorsx)\n", - " constrained_sensorsy = np.array(constrained_sensorsy)\n", - " constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1)\n", - " constrained_sensors_tuple = np.transpose(constrained_sensors_array)\n", - " idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny))\n", - " #print(idx_constrained)\n", - " return idx_constrained" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "def f_region_distance_constraints(r,nx,ny,all_sensors, dlens, piv, j,n_sensors):\n", - " if j == 0:\n", - " return dlens\n", - " else:\n", - " \n", - " idx_constrained = distance_constraints_indices(r,nx,ny,piv,n_sensors,j)\n", - " didx = np.isin(piv[j:],idx_constrained,invert=False)\n", - " dlens[didx] = 0\n", - " return dlens\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:22:22.713344Z", - "start_time": "2022-07-10T04:22:22.699841Z" - } - }, - "outputs": [], - "source": [ - "class AQR(QR):\n", - " \"\"\"\n", - " General QR optimizer for sensor selection.\n", - " Ranks sensors in descending order of \"importance\" based on\n", - " reconstruction performance. This is an extension that requires a more intrusive\n", - " access to the QR optimizer to facilitate a more adaptive optimization. This is a generalized version of cost constraints\n", - " in the sense that users can allow n constrained sensors in the constrained area.\n", - " if n = 0 this converges to the CCQR results.\n", - "\n", - " See the following reference for more information\n", - " Manohar, Krithika, et al.\n", - " \"Data-driven sparse sensor placement for reconstruction:\n", - " Demonstrating the benefits of exploiting known patterns.\"\n", - " IEEE Control Systems Magazine 38.3 (2018): 63-86.\n", - "\n", - " @ authors: Niharika Karnik (@nkarnik2999), Mohammad Abdo (@Jimmy-INL), and Krithika Manohar (@kmanohar)\n", - " \"\"\"\n", - " def __init__(self,n_sensors,all_sensors,r,nx,ny):\n", - " \"\"\"\n", - " Attributes\n", - " ----------\n", - " pivots_ : np.ndarray, shape [n_features]\n", - " Ranked list of sensor locations.\n", - " idx_constrained : np.ndarray, shape [No. of constrained locations]\n", - " Column Indices of the sensors in the constrained locations.\n", - " n_sensors : integer, \n", - " Total number of sensors\n", - " const_sensors : integer,\n", - " Total number of sensors required by the user in the constrained region.\n", - " \"\"\"\n", - " self.pivots_ = None\n", - " self.nSensors = n_sensors\n", - " self.all_sensorloc = all_sensors\n", - " self.radius = r\n", - " self._nx = nx\n", - " self._ny = ny\n", - "\n", - "\n", - " def fit(\n", - " self,\n", - " basis_matrix\n", - " ):\n", - " \"\"\"\n", - " Parameters\n", - " ----------\n", - " basis_matrix: np.ndarray, shape [n_features, n_samples]\n", - " Matrix whose columns are the basis vectors in which to\n", - " represent the measurement data.\n", - " optimizer_kws: dictionary, optional\n", - " Keyword arguments to be passed to the qr method.\n", - "\n", - " Returns\n", - " -------\n", - " self: a fitted :class:`pysensors.optimizers.QR` instance\n", - " \"\"\"\n", - "\n", - " n_features, n_samples = basis_matrix.shape # We transpose basis_matrix below\n", - " #max_const_sensors = len(self.constrainedIndices) #Maximum number of sensors allowed in the constrained region\n", - "\n", - " ## Assertions and checks:\n", - " #if self.nSensors > n_features - max_const_sensors + self.nConstrainedSensors:\n", - " #raise IOError (\"n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors\")\n", - " #if self.nSensors > n_samples + self.nConstrainedSensors: ## Handling zero constraint?\n", - " #raise IOError (\"Currently n_sensors should be less than number of samples + number of constrained sensors,\\\n", - " #got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}\".format(n_sensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors))\n", - "\n", - " # Initialize helper variables\n", - " R = basis_matrix.conj().T.copy()\n", - " p = np.arange(n_features)\n", - " k = min(n_samples, n_features)\n", - "\n", - "\n", - " for j in range(k):\n", - " r = R[j:, j:]\n", - " # Norm of each column \n", - " dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0))\n", - " # if j != 0:\n", - " # dlens_old = dlens_updated\n", - " # else:\n", - " # dlens_old = dlens\n", - " dlens_updated = f_region_distance_constraints(self.radius,self._nx,self._ny,self.all_sensorloc,dlens,p,j,self.nSensors) #Handling constrained region sensor placement problem\n", - " print(dlens_updated)\n", - " # Choose pivot\n", - " i_piv = np.argmax(dlens_updated)\n", - " \n", - " dlen = dlens_updated[i_piv]\n", - "\n", - " if dlen > 0:\n", - " u = r[:, i_piv] / dlen\n", - " u[0] += np.sign(u[0]) + (u[0] == 0)\n", - " u /= np.sqrt(abs(u[0]))\n", - " else:\n", - " u = r[:, i_piv]\n", - " u[0] = np.sqrt(2)\n", - "\n", - " # Track column pivots\n", - " i_piv += j # true permutation index is i_piv shifted by the iteration counter j\n", - " p[[j, i_piv]] = p[[i_piv, j]]\n", - "\n", - " # Switch columns\n", - " R[:, [j, i_piv]] = R[:, [i_piv, j]]\n", - "\n", - " # Apply reflector\n", - " R[j:, j:] -= np.outer(u, np.dot(u, R[j:, j:]))\n", - " R[j + 1 :, j] = 0\n", - "\n", - " self.pivots_ = p\n", - "\n", - "\n", - " return self" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:22:24.092467Z", - "start_time": "2022-07-10T04:22:24.086984Z" - } - }, - "outputs": [], - "source": [ - "\n", - "# def f_region_updated(lin_idx, dlens, piv, j, const_sensors,all_sensors,n_sensors):\n", - "# counter = 0\n", - "# mask = np.isin(all_sensors,lin_idx,invert=False)\n", - "# const_idx = all_sensors[mask]\n", - "# updated_lin_idx = const_idx[const_sensors:]\n", - "# var = np.isin(all_sensors[:n_sensors],lin_idx, invert=False)\n", - "# n = np.count_nonzero(var)\n", - "# print(n)\n", - "\n", - "\n", - "# if any(var) == False: #f_region\n", - "# if j < const_sensors:\n", - "# didx = np.isin(piv[j:],lin_idx,invert=True)\n", - "# dlens[didx] = 0\n", - "# else:\n", - "# didx = np.isin(piv[j:],lin_idx,invert=False)\n", - "# dlens[didx] = 0\n", - "\n", - "# elif n >= const_sensors: #f_region_optimal\n", - "# for i in range(n_sensors):\n", - "# if np.isin(all_sensors[i],lin_idx,invert=False):\n", - "# counter += 1\n", - "# if counter < const_sensors:\n", - "# dlens = dlens\n", - "# else:\n", - "# didx = np.isin(piv[j:],updated_lin_idx,invert=False)\n", - "# dlens[didx] = 0\n", - "\n", - "# elif n < const_sensors:\n", - "# for i in range(n_sensors):\n", - "# if np.isin(all_sensors[i],lin_idx,invert=False):\n", - "# counter += 1\n", - "# if counter <= n:\n", - "# dlens = dlens\n", - "\n", - "# elif n <= counter and counter <= const_sensors:\n", - " \n", - "# didx = np.isin(piv[j:],updated_lin_idx,invert=True)\n", - "# dlens[didx] = 0\n", - "# else:\n", - "# didx = np.isin(piv[j:],updated_lin_idx,invert=False)\n", - "# dlens[didx] = 0\n", - "\n", - "# return dlens" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:22:27.911973Z", - "start_time": "2022-07-10T04:22:26.180620Z" - } 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"[0.13605037 0.12209643 0.10962112 ... 0.12182181 0.1017445 0.05476455]\n", - "[0.11113184 0.10287546 0.08311675 ... 0.10001213 0.0989915 0.05465437]\n", - "[0.09725989 0.07466382 0.11779437 ... 0.09465002 0.08514094 0.05395284]\n", - "[0.05755154 0.11122973 0.12960579 ... 0.08432529 0.08489251 0.05360966]\n", - "[0.10401206 0.12465931 0.10700091 ... 0.05413872 0.084133 0.05301797]\n", - "[0.11697015 0.0852209 0.06907541 ... 0.05200624 0.08413146 0.05082748]\n", - "[0. 0. 0. ... 0.04483959 0.06799887 0.05081567]\n", - "[0.06626277 0.05751188 0.04258984 ... 0.03873369 0.06332544 0.03759426]\n", - "[0.05614397 0.038643 0.02621154 ... 0.02552289 0.0528402 0.03668211]\n", - "[0.03481547 0.0256624 0.00327339 ... 0.00450747 0.04175061 0.0208794 ]\n", - "[2.4330802e-07 1.4607911e-05 1.9704923e-05 ... 1.5822996e-05 3.4701079e-06\n", - " 1.2084842e-05]\n" - ] - } - ], - "source": [ - "r = 7\n", - "optimizer1 = AQR(n_sensors,all_sensors,r,nx,ny)\n", - "model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors)\n", - "model1.fit(X)\n", - "all_sensors1 = model1.get_all_sensors()\n", - "\n", - "top_sensors = model1.get_selected_sensors()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:22:27.951889Z", - "start_time": "2022-07-10T04:22:27.951875Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[4032 384 4092 4039 493 575 2204 657 878 2880 1088 4087 2837 3779\n", - " 3093 2395 581 2751 2010 3039]\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "## TODO: this can be done using ravel and unravel more elegantly\n", - "img = np.zeros(n_features)\n", - "img[top_sensors] = 16\n", - "fig,ax = plt.subplots(1)\n", - "ax.set_aspect('equal')\n", - "ax.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", - "#plt.plot([xmin,xmin],[ymin,ymax],'r')\n", - "#plt.plot([xmin,xmax],[ymax,ymax],'r')\n", - "#plt.plot([xmax,xmax],[ymin,ymax],'r')\n", - "#plt.plot([xmin,xmax],[ymin,ymin],'r')\n", - "#plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", - "# #plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors))\n", - "print(top_sensors)\n", - "top_sensors_grid = np.unravel_index(top_sensors, (nx,ny))\n", - "# figure, axes = plt.subplots()\n", - "for i in range(len(top_sensors_grid[0])):\n", - " circ = Circle( (top_sensors_grid[1][i], top_sensors_grid[0][i]), r ,color='r',fill = False )\n", - " ax.add_patch(circ)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[4032 384 4092 4039 447 493 2204 657 878 2880 1088 4087 2837 3779\n", - " 3093 2395 581 2751 1023 2970]\n", - "(20, 4096)\n", - "0.0\n" - ] - } - ], - "source": [ - "print(top_sensors0)\n", - "c = np.zeros([len(top_sensors0),n_features])\n", - "print(c.shape)\n", - "for i in range(len(top_sensors0)):\n", - " c[i,top_sensors0[i]] = 1\n", - "phi = model.basis_matrix_\n", - "optimality = np.linalg.det((c@phi).T @ (c@phi))\n", - "print(optimality)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[4032 384 4092 4039 493 575 2204 657 878 2880 1088 4087 2837 3779\n", - " 3093 2395 581 2751 2010 3039]\n", - "(20, 4096)\n", - "0.0\n" - ] - } - ], - "source": [ - "print(top_sensors)\n", - "c1 = np.zeros([len(top_sensors),n_features])\n", - "print(c1.shape)\n", - "for i in range(len(top_sensors)):\n", - " c1[i,top_sensors[i]] = 1\n", - "phi1 = model1.basis_matrix_\n", - "optimality1 = np.linalg.det((c1@phi1).T @ (c1@phi1))\n", - "print(optimality1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-11T16:34:56.472709Z", - "start_time": "2022-07-11T16:34:56.456089Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-11T16:34:57.421237Z", - "start_time": "2022-07-11T16:34:57.413767Z" - } - }, - "outputs": [], - "source": [ - "test_sensors = [4032, 384, 4092, 4039, 447, 493, 657, 878, 2880, 1088]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-11T16:34:58.310764Z", - "start_time": "2022-07-11T16:34:58.175996Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.86233765" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.linalg.norm((X - model1.predict(X[:,top_sensors0])))/np.linalg.norm(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:55:26.961534Z", - "start_time": "2022-07-10T04:55:26.877827Z" - }, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'XX' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 19'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m optimizer_faces \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39moptimizers\u001b[39m.\u001b[39mQR()\n\u001b[1;32m 7\u001b[0m model \u001b[39m=\u001b[39m ps\u001b[39m.\u001b[39mSSPOR(basis\u001b[39m=\u001b[39mbasis1,optimizer\u001b[39m=\u001b[39moptimizer_faces, n_sensors\u001b[39m=\u001b[39mn_sensors0)\n\u001b[0;32m----> 8\u001b[0m model\u001b[39m.\u001b[39mfit(XX)\n\u001b[1;32m 10\u001b[0m all_sensors0 \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mget_all_sensors()\n\u001b[1;32m 11\u001b[0m top_sensors0 \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mget_selected_sensors()\n", - "\u001b[0;31mNameError\u001b[0m: name 'XX' is not defined" - ] - } - ], - "source": [ - "max_const_sensors = 1280\n", - "n_const_sensors0 = 3\n", - "n_sensors0 = 7\n", - "n_modes0 = 10\n", - "basis1 = ps.basis.SVD(n_basis_modes=n_modes0)\n", - "optimizer_faces = ps.optimizers.QR()\n", - "model = ps.SSPOR(basis=basis1,optimizer=optimizer_faces, n_sensors=n_sensors0)\n", - "model.fit(XX)\n", - "\n", - "all_sensors0 = model.get_all_sensors()\n", - "top_sensors0 = model.get_selected_sensors()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-11T16:34:59.404387Z", - "start_time": "2022-07-11T16:34:59.378062Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.79669476" - ] - }, - "execution_count": 898, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.linalg.norm((X - model1.predict(X[:,top_sensors])))/np.linalg.norm(X)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-11T16:35:00.194386Z", - "start_time": "2022-07-11T16:35:00.169872Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.88171" - ] - }, - "execution_count": 899, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.linalg.norm((X - model1.predict(X[:,test_sensors])))/np.linalg.norm(X)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:24:41.516465Z", - "start_time": "2022-07-10T04:24:41.495858Z" - } - }, - "outputs": [], - "source": [ - "test_sensors2 = [x for x in all_sensors if x not in sensors_constrained][n_const_sensors:n_sensors]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:24:42.898448Z", - "start_time": "2022-07-10T04:24:42.876231Z" - } - }, - "outputs": [ - { - "ename": "ValueError", - "evalue": "x has the wrong number of features: 8.\n Expected 10", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/karnn/projects/pysensors/examples/region_optimal.ipynb Cell 19'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m np\u001b[39m.\u001b[39mlinalg\u001b[39m.\u001b[39mnorm((X \u001b[39m-\u001b[39m model1\u001b[39m.\u001b[39;49mpredict(X[:,test_sensors2])))\u001b[39m/\u001b[39mnp\u001b[39m.\u001b[39mlinalg\u001b[39m.\u001b[39mnorm(X)\n", - "File \u001b[0;32m~/projects/pysensors/pysensors/reconstruction/_sspor.py:190\u001b[0m, in \u001b[0;36mSSPOR.predict\u001b[0;34m(self, x, **solve_kws)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[39mPredict values at all positions given measurements at sensor locations.\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[39m Predicted values at every location.\u001b[39;00m\n\u001b[1;32m 188\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 189\u001b[0m check_is_fitted(\u001b[39mself\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mranked_sensors_\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 190\u001b[0m x \u001b[39m=\u001b[39m validate_input(x, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mranked_sensors_[: \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mn_sensors])\u001b[39m.\u001b[39mT\n\u001b[1;32m 192\u001b[0m \u001b[39m# For efficiency we may want to factor\u001b[39;00m\n\u001b[1;32m 193\u001b[0m \u001b[39m# self.basis_matrix_[self.ranked_sensors_, :]\u001b[39;00m\n\u001b[1;32m 194\u001b[0m \u001b[39m# in case predict is called multiple times.\u001b[39;00m\n\u001b[1;32m 195\u001b[0m \u001b[39m# Although if the user changes the number of sensors between calls\u001b[39;00m\n\u001b[1;32m 196\u001b[0m \u001b[39m# the factorization will be wasted.\u001b[39;00m\n\u001b[1;32m 198\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_sensors \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbasis_matrix_\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]:\n", - "File \u001b[0;32m~/projects/pysensors/pysensors/utils/_base.py:28\u001b[0m, in \u001b[0;36mvalidate_input\u001b[0;34m(x, sensors)\u001b[0m\n\u001b[1;32m 26\u001b[0m n_features \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(x) \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39mndim(x) \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m \u001b[39melse\u001b[39;00m x\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m]\n\u001b[1;32m 27\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(sensors) \u001b[39m!=\u001b[39m n_features:\n\u001b[0;32m---> 28\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 29\u001b[0m \u001b[39m\"\"\"x has the wrong number of features: {}.\u001b[39;00m\n\u001b[1;32m 30\u001b[0m \u001b[39m Expected {}\"\"\"\u001b[39;00m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m 31\u001b[0m n_features, \u001b[39mlen\u001b[39m(sensors)\n\u001b[1;32m 32\u001b[0m )\n\u001b[1;32m 33\u001b[0m )\n\u001b[1;32m 35\u001b[0m \u001b[39mreturn\u001b[39;00m x\n", - "\u001b[0;31mValueError\u001b[0m: x has the wrong number of features: 8.\n Expected 10" - ] - } - ], - "source": [ - "np.linalg.norm((X - model1.predict(X[:,test_sensors2])))/np.linalg.norm(X)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2022-07-10T04:28:43.703593Z", - "start_time": "2022-07-10T04:28:43.681515Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.0730837" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "XX = np.zeros_like(X)\n", - "XX[:,top_sensors] = X[:,top_sensors]\n", - "model1.score(XX)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - 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"number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - }, - "vscode": { - "interpreter": { - "hash": "3d597f4c481aa0f25dceb95d2a0067e73c0966dcbd003d741d821a7208527ecf" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 9346b796863bb51de1a1c097e096ede2a35432e1 Mon Sep 17 00:00:00 2001 From: Jimmy-INL <52417034+Jimmy-INL@users.noreply.github.com> Date: Sat, 19 Nov 2022 11:08:12 -0700 Subject: [PATCH 38/52] Delete region_qrModified.ipynb --- examples/region_qrModified.ipynb | 1487 ------------------------------ 1 file changed, 1487 deletions(-) delete mode 100644 examples/region_qrModified.ipynb diff --git a/examples/region_qrModified.ipynb b/examples/region_qrModified.ipynb deleted file mode 100644 index 31d4658..0000000 --- a/examples/region_qrModified.ipynb +++ /dev/null @@ -1,1487 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2022-06-24T18:50:42.959640Z", - "start_time": "2022-06-24T18:50:40.955004Z" - } - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from sklearn import datasets\n", - "from sklearn import metrics\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "import pysensors as ps" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2022-06-24T18:50:47.418572Z", - "start_time": "2022-06-24T18:50:47.374771Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of samples: 400\n", - "Number of features (sensors): 4096\n" - ] - } - ], - "source": [ - "faces = datasets.fetch_olivetti_faces(shuffle=True)\n", - "X = faces.data\n", - "\n", - "n_samples, n_features = X.shape\n", - "print('Number of samples:', n_samples)\n", - "print('Number of features (sensors):', n_features)\n", - "\n", - "# Global centering\n", - "X = X - X.mean(axis=0)\n", - "\n", - "# Local centering\n", - "X -= X.mean(axis=1).reshape(n_samples, -1)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2022-06-24T18:50:48.199077Z", - "start_time": "2022-06-24T18:50:48.193238Z" - } - }, - "outputs": [], - "source": [ - "n_row, n_col = 2, 3\n", - "n_components = n_row * n_col\n", - "image_shape = (64, 64)\n", - "\n", - "def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):\n", - " '''Function for plotting faces'''\n", - " plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n", - " plt.suptitle(title, size=16)\n", - " for i, comp in enumerate(images):\n", - " plt.subplot(n_row, n_col, i + 1)\n", - " vmax = max(comp.max(), -comp.min())\n", - " plt.imshow(comp.reshape(image_shape), cmap=cmap,\n", - " interpolation='nearest',\n", - " vmin=-vmax, vmax=vmax)\n", - " plt.xticks(())\n", - " plt.yticks(())\n", - " plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2022-06-24T18:50:49.187561Z", - "start_time": "2022-06-24T18:50:48.993811Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_gallery(\"First few centered faces\", X[:n_components])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2022-06-24T18:50:49.721611Z", - "start_time": "2022-06-24T18:50:49.641048Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[4032 384 4092 ... 1912 3987 2369]\n" - ] - } - ], - "source": [ - "# reduce the X\n", - "imageSize = 64\n", - "image_shape = (imageSize, imageSize)\n", - "\n", - "X = X[:,:imageSize**2]\n", - "n_features = X.shape[1]\n", - "\n", - "#Find all sensor locations using built in QR optimizer\n", - "max_const_sensors = 230\n", - "n_const_sensors = 0\n", - "n_sensors = 399\n", - "optimizer = ps.optimizers.QR()\n", - "model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors)\n", - "model.fit(X)\n", - "\n", - "all_sensors = model.get_all_sensors()\n", - "print(all_sensors)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2022-06-24T18:50:50.340729Z", - "start_time": "2022-06-24T18:50:50.331232Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(array([63, 6, 63, ..., 29, 62, 37]), array([ 0, 0, 60, ..., 56, 19, 1]))\n", - "(4096, 2)\n", - "[2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2688 2689 2690 2691\n", - " 2692 2693 2694 2695 2696 2697 2752 2753 2754 2755 2756 2757 2758 2759\n", - " 2760 2761 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2880 2881\n", - " 2882 2883 2884 2885 2886 2887 2888 2889 2944 2945 2946 2947 2948 2949\n", - " 2950 2951 2952 2953 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017\n", - " 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3136 3137 3138 3139\n", - " 3140 3141 3142 3143 3144 3145 3200 3201 3202 3203 3204 3205 3206 3207\n", - " 3208 3209 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3328 3329\n", - " 3330 3331 3332 3333 3334 3335 3336 3337 3392 3393 3394 3395 3396 3397\n", - " 3398 3399 3400 3401 3456 3457 3458 3459 3460 3461 3462 3463 3464 3465\n", - " 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3584 3585 3586 3587\n", - " 3588 3589 3590 3591 3592 3593 3648 3649 3650 3651 3652 3653 3654 3655\n", - " 3656 3657 3712 3713 3714 3715 3716 3717 3718 3719 3720 3721 3776 3777\n", - " 3778 3779 3780 3781 3782 3783 3784 3785 3840 3841 3842 3843 3844 3845\n", - " 3846 3847 3848 3849 3904 3905 3906 3907 3908 3909 3910 3911 3912 3913\n", - " 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 4032 4033 4034 4035\n", - " 4036 4037 4038 4039 4040 4041]\n" - ] - } - ], - "source": [ - "#Define Constrained indices\n", - "a = np.unravel_index(all_sensors, (imageSize,imageSize))\n", - "print(a)\n", - "a_array = np.transpose(a)\n", - "print(a_array.shape)\n", - "#idx = np.ravel_multi_index(a, (64,64))\n", - "#print(idx)\n", - "xmin = 0\n", - "xmax = 10\n", - "ymin = 40\n", - "ymax = 64\n", - "\n", - "constrained_sensorsx = []\n", - "constrained_sensorsy = []\n", - "for i in range(n_features):\n", - " if a[0][i] < xmax and a[1][i] > ymin: # x<10 and y>40\n", - " constrained_sensorsx.append(a[0][i])\n", - " constrained_sensorsy.append(a[1][i])\n", - "\n", - "constrained_sensorsx = np.array(constrained_sensorsx)\n", - "constrained_sensorsy = np.array(constrained_sensorsy)\n", - "\n", - "constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1)\n", - "constrained_sensors_tuple = np.transpose(constrained_sensors_array)\n", - "\n", - "\n", - "#print(constrained_sensors_tuple)\n", - "#print(len(constrained_sensors_tuple))\n", - "idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (imageSize,imageSize))\n", - "\n", - "#print(len(idx_constrained))\n", - "#print(constrained_sensorsx)\n", - "#print(constrained_sensorsy)\n", - "#print(idx_constrained)\n", - "print(np.sort(idx_constrained[:]))\n", - "all_sorted = np.sort(all_sensors)\n", - "#print(all_sorted)\n", - "idx = np.arange(all_sorted.shape[0])\n", - "#all_sorted[idx_constrained] = 0" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2022-06-24T18:50:51.284415Z", - "start_time": "2022-06-24T18:50:51.107325Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", - "\n", - "ax = plt.subplot()\n", - "#Plot constrained space\n", - "img = np.zeros(n_features)\n", - "img[idx_constrained] = 1\n", - "im = plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", - "\n", - "# create an axes on the right side of ax. The width of cax will be 5%\n", - "# of ax and the padding between cax and ax will be fixed at 0.05 inch.\n", - "divider = make_axes_locatable(ax)\n", - "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", - "\n", - "plt.colorbar(im, cax=cax)\n", - "plt.title('Constrained region');" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2022-06-24T18:50:51.861338Z", - "start_time": "2022-06-24T18:50:51.848985Z" - } - }, - "outputs": [], - "source": [ - "#New class for constrained sensor placement\n", - "from pysensors.optimizers._qr import QR\n", - "class GQR(QR):\n", - " \"\"\"\n", - " General QR optimizer for sensor selection.\n", - " Ranks sensors in descending order of \"importance\" based on\n", - " reconstruction performance. \n", - "\n", - " See the following reference for more information\n", - "\n", - " Manohar, Krithika, et al.\n", - " \"Data-driven sparse sensor placement for reconstruction:\n", - " Demonstrating the benefits of exploiting known patterns.\"\n", - " IEEE Control Systems Magazine 38.3 (2018): 63-86.\n", - " \"\"\"\n", - " def __init__(self):\n", - " \"\"\"\n", - " Attributes\n", - " ----------\n", - " pivots_ : np.ndarray, shape [n_features]\n", - " Ranked list of sensor locations.\n", - " \"\"\"\n", - " self.pivots_ = None\n", - " \n", - " def fit(\n", - " self,\n", - " basis_matrix, idx_constrained, const_sensors\n", - " ):\n", - " \"\"\"\n", - " Parameters\n", - " ----------\n", - " basis_matrix: np.ndarray, shape [n_features, n_samples]\n", - " Matrix whose columns are the basis vectors in which to\n", - " represent the measurement data.\n", - " optimizer_kws: dictionary, optional\n", - " Keyword arguments to be passed to the qr method.\n", - "\n", - " Returns\n", - " -------\n", - " self: a fitted :class:`pysensors.optimizers.CCQR` instance\n", - " \"\"\"\n", - "\n", - " n, m = basis_matrix.shape # We transpose basis_matrix below\n", - "\n", - " ## Assertions and checks:\n", - " if n_sensors > n_features - max_const_sensors + n_const_sensors: ##TODO should be moved to the class\n", - " raise IOError (\"n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors\")\n", - " if n_sensors > n_samples + n_const_sensors:\n", - " raise IOError (\"Currently n_sensors should be less than number of samples + number of constrained sensors,\\\n", - " got: n_sensors = {}, n_samples + n_const_sensors = {} + {} = {}\".format(n_sensors,n_samples,n_const_sensors,n_samples+n_const_sensors)) \n", - " \n", - " # Initialize helper variables\n", - " R = basis_matrix.conj().T.copy()\n", - " #print(R.shape)\n", - " p = np.arange(n)\n", - " #print(p)\n", - " k = min(m, n)\n", - "\n", - " for j in range(k):\n", - " r = R[j:, j:]\n", - " # Norm of each column\n", - " dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0))\n", - " if const_sensors == 0:\n", - " didx = np.isin(p[j:],idx_constrained,invert= False)\n", - " dlens[didx] = 0\n", - " i_piv = np.argmax(dlens)\n", - " dlen = dlens[i_piv]\n", - " else:\n", - " \n", - " dlens_updated = f_region(idx_constrained,dlens,p,j, const_sensors)\n", - " # Choose pivot\n", - " i_piv = np.argmax(dlens_updated)\n", - " #print(i_piv)\n", - " dlen = dlens_updated[i_piv]\n", - " \n", - " if dlen > 0:\n", - " u = r[:, i_piv] / dlen\n", - " u[0] += np.sign(u[0]) + (u[0] == 0)\n", - " u /= np.sqrt(abs(u[0]))\n", - " else:\n", - " u = r[:, i_piv]\n", - " u[0] = np.sqrt(2)\n", - " \n", - " # Track column pivots\n", - " i_piv += j # true permutation index is i_piv shifted by the iteration counter j\n", - " #print(i_piv) # Niharika's debugging line\n", - " p[[j, i_piv]] = p[[i_piv, j]]\n", - " #print(p)\n", - "\n", - "\n", - " # Switch columns\n", - " R[:, [j, i_piv]] = R[:, [i_piv, j]]\n", - " \n", - " # Apply reflector\n", - " R[j:, j:] -= np.outer(u, np.dot(u, R[j:, j:]))\n", - " R[j + 1 :, j] = 0\n", - " \n", - "\n", - " self.pivots_ = p\n", - " \n", - "\n", - " return self\n", - "#function for mapping sensor locations with constraints\n", - "def f_region(lin_idx, dlens, piv, j, const_sensors): \n", - " #num_sensors should be fixed for each custom constraint (for now)\n", - " #num_sensors must be <= size of constraint region\n", - " \"\"\"\n", - " Function for mapping constrained sensor locations with the QR procedure.\n", - " \n", - " Parameters\n", - " ----------\n", - " lin_idx: np.ndarray, shape [No. of constrained locations]\n", - " Array which contains the constrained locations mapped on the grid.\n", - " dlens: np.ndarray, shape [Variable based on j]\n", - " Array which contains the norm of columns of basis matrix.\n", - " num_sensors: int, \n", - " Number of sensors to be placed in the constrained area.\n", - " j: int,\n", - " Iterative variable in the QR algorithm.\n", - "\n", - " Returns\n", - " -------\n", - " dlens : np.darray, shape [Variable based on j] with constraints mapped into it. \n", - " \"\"\"\n", - " \n", - " if j < const_sensors: # force sensors into constraint region\n", - " #idx = np.arange(dlens.shape[0]) \n", - " #dlens[np.delete(idx, lin_idx)] = 0\n", - " \n", - " didx = np.isin(piv[j:],lin_idx,invert=True)\n", - " dlens[didx] = 0\n", - " else: \n", - " didx = np.isin(piv[j:],lin_idx,invert=False)\n", - " dlens[didx] = 0\n", - " return dlens\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2022-06-24T18:50:54.500481Z", - "start_time": "2022-06-24T18:50:52.891827Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4092\n", - "[4092 1 2 ... 4093 4094 4095]\n", - "320\n", - "[4092 320 2 ... 4093 4094 4095]\n", - "447\n", - "[4092 320 447 ... 4093 4094 4095]\n", - "493\n", - "[4092 320 447 ... 4093 4094 4095]\n", - "4042\n", - "[4092 320 447 ... 4093 4094 4095]\n", - "2204\n", - "[4092 320 447 ... 4093 4094 4095]\n", - "657\n", - "[4092 320 447 ... 4093 4094 4095]\n", - 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20]\n", - "3666\n", - "[4092 320 447 ... 182 4094 20]\n", - "2075\n", - "[4092 320 447 ... 182 4094 20]\n", - "452\n", - "[4092 320 447 ... 182 4094 20]\n", - "1039\n", - "[4092 320 447 ... 182 4094 20]\n", - "3483\n", - "[4092 320 447 ... 182 4094 20]\n", - "2114\n", - "[4092 320 447 ... 182 4094 20]\n", - "912\n", - "[4092 320 447 ... 182 4094 20]\n", - "1596\n", - "[4092 320 447 ... 182 4094 20]\n", - "2520\n", - "[4092 320 447 ... 182 4094 20]\n", - "2854\n", - "[4092 320 447 ... 182 4094 20]\n", - "322\n", - "[4092 320 447 ... 182 4094 20]\n", - "833\n", - "[4092 320 447 ... 182 4094 20]\n", - "3133\n", - "[4092 320 447 ... 182 4094 20]\n", - "2833\n", - "[4092 320 447 ... 182 4094 20]\n", - "1228\n", - "[4092 320 447 ... 182 4094 20]\n", - "1574\n", - "[4092 320 447 ... 182 4094 20]\n", - "3051\n", - "[4092 320 447 ... 182 4094 20]\n", - "1373\n", - "[4092 320 447 ... 182 4094 20]\n", - "3450\n", - "[4092 320 447 ... 182 4094 20]\n", - "3963\n", - "[4092 320 447 ... 182 4094 20]\n", - "1136\n", - "[4092 320 447 ... 182 4094 20]\n", - "2087\n", - "[4092 320 447 ... 182 4094 20]\n", - "3932\n", - "[4092 320 447 ... 182 4094 20]\n", - "3967\n", - "[4092 320 447 ... 182 4094 20]\n", - "3037\n", - "[4092 320 447 ... 182 4094 20]\n", - "3607\n", - "[4092 320 447 ... 182 4094 20]\n", - "760\n", - "[4092 320 447 ... 182 4094 20]\n", - "3110\n", - "[4092 320 447 ... 182 4094 20]\n", - "1508\n", - "[4092 320 447 ... 182 4094 20]\n", - "980\n", - "[4092 320 447 ... 182 4094 20]\n", - "1204\n", - "[4092 320 447 ... 182 4094 20]\n", - "1604\n", - "[4092 320 447 ... 182 4094 20]\n", - "1128\n", - "[4092 320 447 ... 182 4094 20]\n", - "1836\n", - "[4092 320 447 ... 182 4094 20]\n", - "866\n", - "[4092 320 447 ... 182 4094 20]\n", - "3791\n", - "[4092 320 447 ... 182 4094 20]\n", - "2545\n", - "[4092 320 447 ... 182 4094 20]\n", - "2089\n", - "[4092 320 447 ... 182 4094 20]\n", - "2533\n", - "[4092 320 447 ... 182 4094 20]\n", - "1432\n", - "[4092 320 447 ... 182 4094 20]\n", - "2198\n", - "[4092 320 447 ... 182 4094 20]\n", - "2405\n", - "[4092 320 447 ... 182 4094 20]\n", - "4085\n", - "[4092 320 447 ... 182 4094 20]\n", - "3879\n", - "[4092 320 447 ... 182 4094 20]\n", - "1121\n", - "[4092 320 447 ... 182 4094 20]\n", - "2920\n", - "[4092 320 447 ... 182 4094 20]\n", - "1885\n", - "[4092 320 447 ... 182 4094 20]\n", - "3695\n", - "[4092 320 447 ... 182 4094 20]\n", - "3296\n", - "[4092 320 447 ... 182 4094 20]\n", - "3451\n", - "[4092 320 447 ... 182 4094 20]\n", - "941\n", - "[4092 320 447 ... 182 4094 20]\n", - "2903\n", - "[4092 320 447 ... 182 4094 20]\n", - "3724\n", - "[4092 320 447 ... 182 4094 20]\n", - "933\n", - "[4092 320 447 ... 182 4094 20]\n", - "3896\n", - "[4092 320 447 ... 182 4094 20]\n", - "3343\n", - "[4092 320 447 ... 182 4094 20]\n", - "1073\n", - "[4092 320 447 ... 182 4094 20]\n", - "754\n", - "[4092 320 447 ... 182 4094 20]\n", - "1110\n", - "[4092 320 447 ... 182 4094 20]\n", - "2910\n", - "[4092 320 447 ... 182 4094 20]\n", - "1166\n", - "[4092 320 447 ... 182 4094 20]\n", - "2622\n", - "[4092 320 447 ... 182 4094 20]\n", - "3095\n", - "[4092 320 447 ... 182 4094 20]\n", - "1470\n", - "[4092 320 447 ... 182 4094 20]\n", - "3374\n", - "[4092 320 447 ... 182 4094 20]\n", - "2722\n", - "[4092 320 447 ... 182 4094 20]\n", - "2138\n", - "[4092 320 447 ... 182 4094 20]\n", - "1956\n", - "[4092 320 447 ... 182 4094 20]\n", - "3637\n", - "[4092 320 447 ... 182 4094 20]\n", - "3057\n", - "[4092 320 447 ... 182 4094 20]\n", - "1226\n", - "[4092 320 447 ... 182 4094 20]\n", - "3303\n", - "[4092 320 447 ... 182 4094 20]\n", - "2506\n", - "[4092 320 447 ... 182 4094 20]\n", - "2207\n", - "[4092 320 447 ... 182 4094 20]\n", - "2156\n", - "[4092 320 447 ... 182 4094 20]\n", - "571\n", - "[4092 320 447 ... 182 4094 20]\n", - "3964\n", - "[4092 320 447 ... 182 4094 20]\n", - "3226\n", - "[4092 320 447 ... 182 4094 20]\n", - "3681\n", - "[4092 320 447 ... 182 4094 20]\n", - "3222\n", - "[4092 320 447 ... 182 4094 20]\n", - "1601\n", - "[4092 320 447 ... 182 4094 20]\n", - "2778\n", - "[4092 320 447 ... 182 4094 20]\n", - "3170\n", - "[4092 320 447 ... 182 4094 20]\n", - "2936\n", - "[4092 320 447 ... 182 4094 20]\n", - "3697\n", - "[4092 320 447 ... 182 4094 20]\n", - "2563\n", - "[4092 320 447 ... 182 4094 20]\n", - "952\n", - "[4092 320 447 ... 182 4094 20]\n", - "2988\n", - "[4092 320 447 ... 182 4094 20]\n", - "2016\n", - "[4092 320 447 ... 182 4094 20]\n", - "1283\n", - "[4092 320 447 ... 182 4094 20]\n", - "1623\n", - "[4092 320 447 ... 182 4094 20]\n", - "2111\n", - "[4092 320 447 ... 182 4094 20]\n", - "881\n", - "[4092 320 447 ... 182 4094 20]\n", - "3377\n", - "[4092 320 447 ... 182 4094 20]\n", - "2594\n", - "[4092 320 447 ... 182 4094 20]\n", - "3858\n", - "[4092 320 447 ... 182 4094 20]\n", - "705\n", - "[4092 320 447 ... 182 4094 20]\n", - "1127\n", - "[4092 320 447 ... 182 4094 20]\n", - "1270\n", - "[4092 320 447 ... 182 4094 20]\n", - "1413\n", - "[4092 320 447 ... 182 4094 20]\n", - "522\n", - "[4092 320 447 ... 182 4094 20]\n", - "2829\n", - "[4092 320 447 ... 182 4094 20]\n", - "624\n", - "[4092 320 447 ... 182 4094 20]\n", - "3413\n", - "[4092 320 447 ... 182 4094 20]\n", - "1326\n", - "[4092 320 447 ... 182 4094 20]\n", - "1800\n", - "[4092 320 447 ... 182 4094 20]\n", - "1174\n", - "[4092 320 447 ... 182 4094 20]\n", - "922\n", - "[4092 320 447 ... 182 4094 20]\n", - "4044\n", - "[4092 320 447 ... 182 4094 20]\n", - "2039\n", - "[4092 320 447 ... 182 4094 20]\n", - "2687\n", - "[4092 320 447 ... 182 4094 20]\n", - "4073\n", - "[4092 320 447 ... 182 4094 20]\n", - "2258\n", - "[4092 320 447 ... 182 4094 20]\n", - "1201\n", - "[4092 320 447 ... 182 4094 20]\n", - "1524\n", - "[4092 320 447 ... 182 4094 20]\n", - "1195\n", - "[4092 320 447 ... 182 4094 20]\n" - ] - }, - { - "data": { - "text/plain": [ - "SSPOR(basis=Identity(n_basis_modes=400), n_sensors=399, optimizer=GQR())" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "optimizer1 = GQR()\n", - "model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors)\n", - "model1.fit(X, quiet=True, prefit_basis=False, seed=None, idx_constrained = idx_constrained, const_sensors = n_const_sensors)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2022-06-24T18:50:54.533405Z", - "start_time": "2022-06-24T18:50:54.528869Z" - }, - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[]\n", - "True\n" - ] - } - ], - "source": [ - "all_sensors1 = model1.get_all_sensors()\n", - "print(all_sensors1[:n_const_sensors])\n", - "\n", - "print(np.array_equal(np.sort(all_sensors),np.sort(all_sensors1)))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "ExecuteTime": { - "end_time": "2022-06-24T18:50:56.112724Z", - "start_time": "2022-06-24T18:50:55.989212Z" - } - }, - "outputs": [ - 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1097 1025 711 1854 1279\n", - " 4093 1600 3774 630 857 2021 1531 3026 2264 2141 57 2970 1090 1081\n", - " 787 2849 2840 2877 3165 976 679 1112 1921 1135 2575 720 3833 1028\n", - " 2238 2393 1282 2336 4053 3103 1006 528 3979 3176 917 3389 927 1879\n", - " 3574 707 779 562 1130 699 1277 9 955 2865 847 2345 1425 1187\n", - " 2600 3245 2211 3029 3236 3577 2978 3786 729 1022 1037 2870 2069 971\n", - " 1003 2009 1223 1189 1050 807 2912 3065 811 3890 2709 2432 3282 3403\n", - " 1052 508 1139 2725 799 2716 901 3497 2214 3706 1263 2859 3032 822\n", - " 1877 1007 4047 3019 3666 2075 452 1039 3483 2114 912 1596 2520 2854\n", - " 322 833 3133 2833 1228 1574 3051 1373 3450 3963 1136 45 3932 3967\n", - " 3037 3607 760 3110 1508 980 1204 1604 1128 1836 866 3791 2545 2089\n", - " 2533 1432 2198 2405 4085 3879 1121 2920 1885 3695 3296 3451 941 2903\n", - " 3724 933 3896 3343 1073 754 1110 2910 1166 2622 3095 1470 3374 2722\n", - " 2138 1956 3637 3057 1226 3303 2506 61 2156 571 3964 3226 3681 3222\n", - " 1601 2778 3170 2936 3697 2563 952 2988 2016 1283 1623 2111 881 3377\n", - " 2594 3858 705 1127 1270 1413 522 2829 624 3413 1326 1800 1174 922\n", - " 4044 2039 2687 4073 2258 1201 1524]\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "top_sensors = model1.get_selected_sensors()\n", - "print(top_sensors)\n", - "## TODO: this can be done using ravel and unravel more elegantly\n", - "yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features))\n", - "xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features))\n", - "\n", - "img = np.zeros(n_features)\n", - "img[top_sensors] = 16\n", - "img[top_sensors[n_const_sensors:]] = 16\n", - "plt.plot(xConstrained,yConstrained,'*r')\n", - "plt.plot([xmin,xmin],[ymin,ymax],'r')\n", - "plt.plot([xmin,xmax],[ymax,ymax],'r')\n", - "plt.plot([xmax,xmax],[ymin,ymax],'r')\n", - "plt.plot([xmin,xmax],[ymin,ymin],'r')\n", - "plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", - "plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors))\n", - "plt.show()\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "ExecuteTime": { - "end_time": "2022-06-24T18:51:00.889636Z", - "start_time": "2022-06-24T18:51:00.885673Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "399\n", - "(230,)\n" - ] - } - ], - "source": [ - "print(n_sensors)\n", - "print(idx_constrained.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "Python 3.9.5 ('myenv')", - "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": "3.9.5" - }, - "latex_envs": { - "LaTeX_envs_menu_present": true, - "autoclose": false, - "autocomplete": true, - "bibliofile": "biblio.bib", - "cite_by": "apalike", - 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"delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - }, - "vscode": { - "interpreter": { - "hash": "be0c9c2c722339dec0978f6a1152ea12dcb8d0ab1123e781ed3c52ac6383bc23" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From b4127083c2aac7f07a8db4ca915b81c7a5084819 Mon Sep 17 00:00:00 2001 From: Jimmy-INL <52417034+Jimmy-INL@users.noreply.github.com> Date: Sat, 19 Nov 2022 11:08:25 -0700 Subject: [PATCH 39/52] Delete region_qrModified.py --- examples/region_qrModified.py | 337 ---------------------------------- 1 file changed, 337 deletions(-) delete mode 100644 examples/region_qrModified.py diff --git a/examples/region_qrModified.py b/examples/region_qrModified.py deleted file mode 100644 index fdadb31..0000000 --- a/examples/region_qrModified.py +++ /dev/null @@ -1,337 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# In[1]: - - -import matplotlib.pyplot as plt -import numpy as np -from sklearn import datasets -from sklearn import metrics -from sklearn.model_selection import train_test_split - -import pysensors as ps -# from pysensors.optimizers._ccqr import CCQR - - -# In[2]: - - -faces = datasets.fetch_olivetti_faces(shuffle=True) -X = faces.data - -n_samples, n_features = X.shape -print('Number of samples:', n_samples) -print('Number of features (sensors):', n_features) - -# Global centering -X = X - X.mean(axis=0) - -# Local centering -X -= X.mean(axis=1).reshape(n_samples, -1) - - -# In[3]: - - -n_row, n_col = 2, 3 -n_components = n_row * n_col -image_shape = (64, 64) - -def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray): - '''Function for plotting faces''' - plt.figure(figsize=(2. * n_col, 2.26 * n_row)) - plt.suptitle(title, size=16) - for i, comp in enumerate(images): - plt.subplot(n_row, n_col, i + 1) - vmax = max(comp.max(), -comp.min()) - plt.imshow(comp.reshape(image_shape), cmap=cmap, - interpolation='nearest', - vmin=-vmax, vmax=vmax) - plt.xticks(()) - plt.yticks(()) - plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.) - plt.show() - - -# In[4]: - - -plot_gallery("First few centered faces", X[:n_components]) - - -# In[5]: -# reduce the X -imageSize = 64 -image_shape = (imageSize, imageSize) - -X = X[:,:imageSize**2] -n_features = X.shape[1] - -#Find all sensor locations using built in QR optimizer -max_const_sensors = 230 -n_const_sensors = 2 -n_sensors = 20 -optimizer = ps.optimizers.QR() -model = ps.SSPOR(optimizer=optimizer, n_sensors=n_sensors) -model.fit(X) - -all_sensors = model.get_all_sensors() -print(all_sensors) - - -# In[6]: - - -#Define Constrained indices -a = np.unravel_index(all_sensors, (imageSize,imageSize)) -print(a) -a_array = np.transpose(a) -print(a_array.shape) -#idx = np.ravel_multi_index(a, (64,64)) -#print(idx) -xmin = 0 -xmax = 10 -ymin = 40 -ymax = 64 - -constrained_sensorsx = [] -constrained_sensorsy = [] -for i in range(n_features): - if a[0][i] < xmax and a[1][i] > ymin: # x<10 and y>40 - constrained_sensorsx.append(a[0][i]) - constrained_sensorsy.append(a[1][i]) - -constrained_sensorsx = np.array(constrained_sensorsx) -constrained_sensorsy = np.array(constrained_sensorsy) - -constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1) -constrained_sensors_tuple = np.transpose(constrained_sensors_array) - - -#print(constrained_sensors_tuple) -#print(len(constrained_sensors_tuple)) -idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (imageSize,imageSize)) - -#print(len(idx_constrained)) -#print(constrained_sensorsx) -#print(constrained_sensorsy) -#print(idx_constrained) -print(np.sort(idx_constrained[:])) -all_sorted = np.sort(all_sensors) -#print(all_sorted) -idx = np.arange(all_sorted.shape[0]) -#all_sorted[idx_constrained] = 0 - - -# In[7]: - - -from mpl_toolkits.axes_grid1 import make_axes_locatable - -ax = plt.subplot() -#Plot constrained space -img = np.zeros(n_features) -img[idx_constrained] = 1 -im = plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) - -# create an axes on the right side of ax. The width of cax will be 5% -# of ax and the padding between cax and ax will be fixed at 0.05 inch. -divider = make_axes_locatable(ax) -cax = divider.append_axes("right", size="5%", pad=0.05) - -plt.colorbar(im, cax=cax) -plt.title('Constrained region') -plt.show() - - -# In[8]: - - -#New class for constrained sensor placement -from pysensors.optimizers._qr import QR -class GQR(QR): - """ - General QR optimizer for sensor selection. - Ranks sensors in descending order of "importance" based on - reconstruction performance. - - See the following reference for more information - - Manohar, Krithika, et al. - "Data-driven sparse sensor placement for reconstruction: - Demonstrating the benefits of exploiting known patterns." - IEEE Control Systems Magazine 38.3 (2018): 63-86. - """ - def __init__(self): - """ - Attributes - ---------- - pivots_ : np.ndarray, shape [n_features] - Ranked list of sensor locations. - """ - self.pivots_ = None - - def fit( - self, - basis_matrix, idx_constrained, const_sensors - ): - """ - Parameters - ---------- - basis_matrix: np.ndarray, shape [n_features, n_samples] - Matrix whose columns are the basis vectors in which to - represent the measurement data. - optimizer_kws: dictionary, optional - Keyword arguments to be passed to the qr method. - - Returns - ------- - self: a fitted :class:`pysensors.optimizers.CCQR` instance - """ - - n, m = basis_matrix.shape # We transpose basis_matrix below - - ## Assertions and checks: - if n_sensors > n_features - max_const_sensors + n_const_sensors: ##TODO should be moved to the class - raise IOError ("n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors") - if n_sensors > n_samples + n_const_sensors: - raise IOError ("Currently n_sensors should be less than number of samples + number of constrained sensors,\ - got: n_sensors = {}, n_samples + n_const_sensors = {} + {} = {}".format(n_sensors,n_samples,n_const_sensors,n_samples+n_const_sensors)) - - # Initialize helper variables - R = basis_matrix.conj().T.copy() - #print(R.shape) - p = np.arange(n) - #print(p) - k = min(m, n) - - - for j in range(k): - r = R[j:, j:] - # Norm of each column - dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0)) - - # if j < n_const_sensors: - dlens_updated = f_region(idx_constrained,dlens,p,j, const_sensors) - # else: - # dlens_updated = dlens - # Choose pivot - i_piv = np.argmax(dlens_updated) - #print(i_piv) - - - dlen = dlens_updated[i_piv] - - if dlen > 0: - u = r[:, i_piv] / dlen - u[0] += np.sign(u[0]) + (u[0] == 0) - u /= np.sqrt(abs(u[0])) - else: - u = r[:, i_piv] - u[0] = np.sqrt(2) - - # Track column pivots - i_piv += j # true permutation index is i_piv shifted by the iteration counter j - print(i_piv) # Niharika's debugging line - p[[j, i_piv]] = p[[i_piv, j]] - print(p) - - - # Switch columns - R[:, [j, i_piv]] = R[:, [i_piv, j]] - - # Apply reflector - R[j:, j:] -= np.outer(u, np.dot(u, R[j:, j:])) - R[j + 1 :, j] = 0 - - - self.pivots_ = p - - - return self -#function for mapping sensor locations with constraints -def f_region(lin_idx, dlens, piv, j, const_sensors): - #num_sensors should be fixed for each custom constraint (for now) - #num_sensors must be <= size of constraint region - """ - Function for mapping constrained sensor locations with the QR procedure. - - Parameters - ---------- - lin_idx: np.ndarray, shape [No. of constrained locations] - Array which contains the constrained locations mapped on the grid. - dlens: np.ndarray, shape [Variable based on j] - Array which contains the norm of columns of basis matrix. - num_sensors: int, - Number of sensors to be placed in the constrained area. - j: int, - Iterative variable in the QR algorithm. - - Returns - ------- - dlens : np.darray, shape [Variable based on j] with constraints mapped into it. - """ - if j < const_sensors: # force sensors into constraint region - #idx = np.arange(dlens.shape[0]) - #dlens[np.delete(idx, lin_idx)] = 0 - - didx = np.isin(piv[j:],lin_idx,invert=True) - dlens[didx] = 0 - else: - didx = np.isin(piv[j:],lin_idx,invert=False) - dlens[didx] = 0 - return dlens - - -# In[9]: - - - -optimizer1 = GQR() -model1 = ps.SSPOR(optimizer = optimizer1, n_sensors = n_sensors) -model1.fit(X, quiet=True, prefit_basis=False, seed=None, idx_constrained = idx_constrained, const_sensors = n_const_sensors) - - -# In[10]: - - -all_sensors1 = model1.get_all_sensors() -print(all_sensors1[:n_const_sensors]) - -print(np.array_equal(np.sort(all_sensors),np.sort(all_sensors1))) - -# In[12]: - - -top_sensors = model1.get_selected_sensors() -print(top_sensors) -## TODO: this can be done using ravel and unravel more elegantly -yConstrained = np.floor(top_sensors[:n_const_sensors]/np.sqrt(n_features)) -xConstrained = np.mod(top_sensors[:n_const_sensors],np.sqrt(n_features)) - -img = np.zeros(n_features) -img[top_sensors[n_const_sensors:]] = 16 -plt.plot(xConstrained,yConstrained,'*r') -plt.plot([xmin,xmin],[ymin,ymax],'r') -plt.plot([xmin,xmax],[ymax,ymax],'r') -plt.plot([xmax,xmax],[ymin,ymax],'r') -plt.plot([xmin,xmax],[ymin,ymin],'r') -plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary) -plt.title('n_sensors = {}, n_constr_sensors = {}'.format(n_sensors,n_const_sensors)) -plt.show() - - - -# In[13]: - - -print(n_sensors) -print(idx_constrained.shape) - - -# In[ ]: - - - - From 616ecf17a43382b2913843aeb922d6a9e7a3eb9d Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Sat, 19 Nov 2022 11:13:50 -0700 Subject: [PATCH 40/52] removing notebooks and unnecessary mods --- pysensors/basis/_identity.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pysensors/basis/_identity.py b/pysensors/basis/_identity.py index de90ed2..c23b919 100644 --- a/pysensors/basis/_identity.py +++ b/pysensors/basis/_identity.py @@ -6,7 +6,6 @@ from warnings import warn from numpy import identity -import numpy as np from sklearn.base import BaseEstimator from sklearn.utils import check_array From 0313052f25c4da120bce4cf98853631eefc5ad7d Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Sat, 19 Nov 2022 11:16:44 -0700 Subject: [PATCH 41/52] removing notebooks and unnecessary mods --- pysensors/basis/_identity.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pysensors/basis/_identity.py b/pysensors/basis/_identity.py index c23b919..1b7b4c0 100644 --- a/pysensors/basis/_identity.py +++ b/pysensors/basis/_identity.py @@ -52,7 +52,8 @@ def fit(self, X): ------- self : instance """ - # Note that we take a transpose here, so columns correspond to examples + + # Note that we take a transpose here, so columns correspond to examples if self.n_basis_modes is None: self.basis_matrix_ = check_array(X).T.copy() self.n_basis_modes = self.basis_matrix_.shape[1] From 023dcb2ebe92ef03fda52d7fac4e6212a9012048 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Sat, 19 Nov 2022 11:18:09 -0700 Subject: [PATCH 42/52] removing notebooks and unnecessary mods --- pysensors/basis/_identity.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pysensors/basis/_identity.py b/pysensors/basis/_identity.py index 1b7b4c0..b4c7827 100644 --- a/pysensors/basis/_identity.py +++ b/pysensors/basis/_identity.py @@ -52,7 +52,7 @@ def fit(self, X): ------- self : instance """ - + # Note that we take a transpose here, so columns correspond to examples if self.n_basis_modes is None: self.basis_matrix_ = check_array(X).T.copy() From c5b1903426af4b8117d8deb0123f654cb5ec15ef Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Sat, 19 Nov 2022 11:22:01 -0700 Subject: [PATCH 43/52] removing notebooks and unnecessary mods --- pysensors/basis/_identity.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pysensors/basis/_identity.py b/pysensors/basis/_identity.py index b4c7827..1965f8c 100644 --- a/pysensors/basis/_identity.py +++ b/pysensors/basis/_identity.py @@ -53,7 +53,7 @@ def fit(self, X): self : instance """ - # Note that we take a transpose here, so columns correspond to examples + # Note that we take a transpose here, so columns correspond to examples if self.n_basis_modes is None: self.basis_matrix_ = check_array(X).T.copy() self.n_basis_modes = self.basis_matrix_.shape[1] From b66409ec074e0caba8ee8a86e55e83d2d8d52125 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Sat, 19 Nov 2022 11:23:41 -0700 Subject: [PATCH 44/52] removing notebooks and unnecessary mods --- pysensors/basis/_identity.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pysensors/basis/_identity.py b/pysensors/basis/_identity.py index 1965f8c..4835088 100644 --- a/pysensors/basis/_identity.py +++ b/pysensors/basis/_identity.py @@ -53,7 +53,7 @@ def fit(self, X): self : instance """ - # Note that we take a transpose here, so columns correspond to examples + # Note that we take a transpose here, so columns correspond to examples if self.n_basis_modes is None: self.basis_matrix_ = check_array(X).T.copy() self.n_basis_modes = self.basis_matrix_.shape[1] From 7fb6978d877a8a60f59b789685736533d7953b2c Mon Sep 17 00:00:00 2001 From: Niharika Karnik Date: Thu, 1 Dec 2022 08:46:04 -0800 Subject: [PATCH 45/52] Revised PR --- pysensors/optimizers/_gqr.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index b135283..ffbbfbe 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -19,7 +19,7 @@ class GQR(QR): reconstruction performance. This is an extension that requires a more intrusive access to the QR optimizer to facilitate a more adaptive optimization. This is a generalized version of cost constraints in the sense that users can allow n constrained sensors in the constrained area. - if n = 0 this converges to the CCQR results. + if n = 0 this converges to the CCQR results. If no constraints it converges to QR results. See the following reference for more information Manohar, Krithika, et al. @@ -49,12 +49,12 @@ def __init__(self): """ self.pivots_ = None self.idx_constrained = [] - self.n_sensors = 10 + self.n_sensors = 0 self.n_const_sensors = 0 self.all_sensors = [] self.constraint_option = '' - self.nx = 64 - self.ny = 64 + self.nx = None + self.ny = None self.r = 1 def fit(self,basis_matrix=None,**optimizer_kws): From 43ee27dc0441bf9eda3a758a3f3f0ba0153947ff Mon Sep 17 00:00:00 2001 From: Niharika Karnik Date: Thu, 1 Dec 2022 11:25:12 -0800 Subject: [PATCH 46/52] Revised PR by Niharika --- pysensors/optimizers/_gqr.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index ffbbfbe..a992c8a 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -42,14 +42,16 @@ def __init__(self): n_const_sensors : integer, Total number of sensors required by the user in the constrained region. all_sensors : np.ndarray, shape [n_features] - Optimall placed list of sensors obtained from QR pivoting algorithm. + Optimally placed list of sensors obtained from QR pivoting algorithm. constraint_option : string, max_n_const_sensors : The number of sensors in the constrained region should be less than or equal to n_const_sensors. exact_n_const_sensors : The number of sensors in the constrained region should be exactly equal to n_const_sensors. + nx, ny : integer, + X, Y dimensions of the grid. """ self.pivots_ = None self.idx_constrained = [] - self.n_sensors = 0 + self.n_sensors = None self.n_const_sensors = 0 self.all_sensors = [] self.constraint_option = '' @@ -76,13 +78,6 @@ def fit(self,basis_matrix=None,**optimizer_kws): n_features, n_samples = basis_matrix.shape # We transpose basis_matrix below max_const_sensors = len(self.idx_constrained) # Maximum number of sensors allowed in the constrained region - ## Assertions and checks: - # if self.n_sensors > n_features - max_const_sensors + self.nConstrainedSensors: - # raise IOError ("n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors") - # if self.n_sensors > n_samples + self.nConstrainedSensors: ## Handling zero constraint? - # raise IOError ("Currently n_sensors should be less than min(number of samples, number of modes) + number of constrained sensors,\ - # got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}".format(self.n_sensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors)) - # Initialize helper variables R = basis_matrix.conj().T.copy() p = np.arange(n_features) From 1c16f677048c3151a50d43dcb9aa837b275a2713 Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Thu, 1 Dec 2022 23:31:24 -0700 Subject: [PATCH 47/52] Removed a validation --- pysensors/utils/_validation.py | 48 ---------------------------------- 1 file changed, 48 deletions(-) delete mode 100644 pysensors/utils/_validation.py diff --git a/pysensors/utils/_validation.py b/pysensors/utils/_validation.py deleted file mode 100644 index 3a4ab8c..0000000 --- a/pysensors/utils/_validation.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -Various utility functions for validation and computing reconstruction scores and errors. -""" -import numpy as np - -def determinant(top_sensors, n_features, basis_matrix): - """ - Function for calculating |C.T phi.T C phi|. - - Parameters - ---------- - top_sensors: np.darray, - Column indices of choosen sensor locations - n_features : int, - No. of features of dataset - basis_matrix : np.darray, - The basis matrix calculated by model.basis_matrix_ - - Returns - ------- - optimality : Float, - The dterminant value obtained. - """ - - c = np.zeros([len(top_sensors),n_features]) - for i in range(len(top_sensors)): - c[i,top_sensors[i]] = 1 - phi = basis_matrix - optimality = np.linalg.det((c@phi).T @ (c@phi)) #np.log(np.linalg.det(phi.T @ c.T)) np.log(np.linalg.det((c@phi).T @ (c@phi))) - return optimality - -def relative_reconstruction_error(data, prediction): - """ - Function for calculating relative error between actual data and the reconstruction - - Parameters - ---------- - data: np.darray, - The actual data from the dataset evaluated - prediction : np.darray, - The predicted values from model.predict(X[:,top_sensors]) - Returns - ------- - error_val : Float, - The relative error calculated. - """ - error_val = (np.linalg.norm((data - prediction)/np.linalg.norm(data)))*100 - return (error_val) \ No newline at end of file From 0a1787f464325de19f23b8dc91d3395ae0f68f9f Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Sat, 3 Dec 2022 22:05:04 -0700 Subject: [PATCH 48/52] removing validation from __init__ --- pysensors/utils/__init__.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/pysensors/utils/__init__.py b/pysensors/utils/__init__.py index 59b6e8b..51f2818 100644 --- a/pysensors/utils/__init__.py +++ b/pysensors/utils/__init__.py @@ -3,8 +3,6 @@ from ._optimizers import constrained_multiclass_solve from ._constraints import get_constraind_sensors_indices from ._constraints import get_constrained_sensors_indices_linear -from ._validation import determinant -from ._validation import relative_reconstruction_error from ._norm_calc import exact_n from ._norm_calc import max_n from ._norm_calc import predetermined @@ -17,8 +15,6 @@ "get_constrained_sensors_indices_linear", "box_constraints", "functional_constraints", - "determinant", - "relative_reconstruction_error", "exact_n", "max_n", "predetermined" From f558ff53b264b5da645ab185c35d4555e202727a Mon Sep 17 00:00:00 2001 From: Jimmy-INL Date: Wed, 14 Dec 2022 14:14:06 -0700 Subject: [PATCH 49/52] fixing and adding validation --- pysensors/optimizers/_gqr.py | 17 +++++++---- pysensors/utils/__init__.py | 6 +++- pysensors/utils/_norm_calc.py | 26 +++++++++++++---- pysensors/utils/_validation.py | 53 ++++++++++++++++++++++++++++++++++ 4 files changed, 89 insertions(+), 13 deletions(-) create mode 100644 pysensors/utils/_validation.py diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index a992c8a..9f6ddfa 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -16,10 +16,10 @@ class GQR(QR): """ General QR optimizer for sensor selection. Ranks sensors in descending order of "importance" based on - reconstruction performance. This is an extension that requires a more intrusive + reconstruction accuracy. This is an extension that requires a more intrusive access to the QR optimizer to facilitate a more adaptive optimization. This is a generalized version of cost constraints - in the sense that users can allow n constrained sensors in the constrained area. - if n = 0 this converges to the CCQR results. If no constraints it converges to QR results. + in the sense that users can allow `n_const_sensors` in the constrained area. + if n = 0 this converges to the CCQR results. and if no constrained region it should converge to the results from QR optimizer. See the following reference for more information Manohar, Krithika, et al. @@ -46,8 +46,6 @@ def __init__(self): constraint_option : string, max_n_const_sensors : The number of sensors in the constrained region should be less than or equal to n_const_sensors. exact_n_const_sensors : The number of sensors in the constrained region should be exactly equal to n_const_sensors. - nx, ny : integer, - X, Y dimensions of the grid. """ self.pivots_ = None self.idx_constrained = [] @@ -59,7 +57,7 @@ def __init__(self): self.ny = None self.r = 1 - def fit(self,basis_matrix=None,**optimizer_kws): + def fit(self,basis_matrix,**optimizer_kws): """ Parameters ---------- @@ -78,6 +76,13 @@ def fit(self,basis_matrix=None,**optimizer_kws): n_features, n_samples = basis_matrix.shape # We transpose basis_matrix below max_const_sensors = len(self.idx_constrained) # Maximum number of sensors allowed in the constrained region + ## Assertions and checks: + # if self.n_sensors > n_features - max_const_sensors + self.nConstrainedSensors: + # raise IOError ("n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors") + # if self.n_sensors > n_samples + self.nConstrainedSensors: ## Handling zero constraint? + # raise IOError ("Currently n_sensors should be less than min(number of samples, number of modes) + number of constrained sensors,\ + # got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}".format(self.n_sensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors)) + # Initialize helper variables R = basis_matrix.conj().T.copy() p = np.arange(n_features) diff --git a/pysensors/utils/__init__.py b/pysensors/utils/__init__.py index 51f2818..94b3713 100644 --- a/pysensors/utils/__init__.py +++ b/pysensors/utils/__init__.py @@ -6,6 +6,8 @@ from ._norm_calc import exact_n from ._norm_calc import max_n from ._norm_calc import predetermined +from ._validation import determinant +from ._validation import relative_reconstruction_error __all__ = [ "constrained_binary_solve", @@ -17,5 +19,7 @@ "functional_constraints", "exact_n", "max_n", - "predetermined" + "predetermined", + "determinant", + "relative_reconstruction_error" ] diff --git a/pysensors/utils/_norm_calc.py b/pysensors/utils/_norm_calc.py index 3a5a9aa..f4c5213 100644 --- a/pysensors/utils/_norm_calc.py +++ b/pysensors/utils/_norm_calc.py @@ -30,9 +30,23 @@ def exact_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): ##Will first for ------- dlens : np.darray, shape [Variable based on j] with constraints mapped into it. """ - didx = np.isin(piv[j:],lin_idx,invert=j= j > (n_sensors - (n_const_sensors-1)): + didx = np.isin(piv[j:],lin_idx,invert=True) + dlens[didx] = 0 + else: + max_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs) + return(dlens) + def max_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): """ @@ -109,9 +123,9 @@ def predetermined(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): __norm_calc_type = {} __norm_calc_type[''] = unconstrained -__norm_calc_type['exact_n_const_sensors'] = exact_n -__norm_calc_type['max_n_const_sensors'] = max_n -__norm_calc_type['predetermined_norm_calc'] = predetermined +__norm_calc_type['exact_n'] = exact_n +__norm_calc_type['max_n'] = max_n +__norm_calc_type['predetermined'] = predetermined def returnInstance(cls, name): """ diff --git a/pysensors/utils/_validation.py b/pysensors/utils/_validation.py new file mode 100644 index 0000000..4e68948 --- /dev/null +++ b/pysensors/utils/_validation.py @@ -0,0 +1,53 @@ +""" +Various utility functions for validation and computing reconstruction scores and errors. +""" +import numpy as np +from scipy.sparse import csr_matrix + +def determinant(top_sensors, n_features, basis_matrix): + """ + Function for calculating |C.T phi.T C phi|. + + Parameters + ---------- + top_sensors: np.darray, + Column indices of choosen sensor locations + n_features : int, + No. of features of dataset + basis_matrix : np.darray, + The basis matrix calculated by model.basis_matrix_ + Returns + ------- + optimality : Float, + The dterminant value obtained. + """ + + p = len(top_sensors) # Number of sensors + n,r = np.shape(basis_matrix) # state dimension X Number of modes + c = csr_matrix((p,n),dtype=np.int8) + + for i in range(p): + c[i,top_sensors[i]] = 1 + phi = basis_matrix + # optimality = np.linalg.det(( c @ phi).T @ (c@phi)) #np.log(np.linalg.det(phi.T @ c.T)) np.log(np.linalg.det((c@phi).T @ (c@phi))) + optimality = abs(np.linalg.det(c @ phi)) if p==r else abs(np.linalg.det(( c @ phi).T @ (c @ phi))) + # optimality = abs(np.linalg.det(c @ phi)) + return optimality + +def relative_reconstruction_error(data, prediction): + """ + Function for calculating relative error between actual data and the reconstruction + + Parameters + ---------- + data: np.darray, + The actual data from the dataset evaluated + prediction : np.darray, + The predicted values from model.predict(X[:,top_sensors]) + Returns + ------- + error_val : Float, + The relative error calculated. + """ + error_val = (np.linalg.norm((data - prediction)/np.linalg.norm(data)))*100 + return (error_val) \ No newline at end of file From 370e9e41347ec8cd90f571ea0f66e0f71b239dc0 Mon Sep 17 00:00:00 2001 From: Niharika Karnik Date: Thu, 6 Jul 2023 14:56:37 -0600 Subject: [PATCH 50/52] Adding example notebook for spatial constraints --- examples/spatially_constrained_qr.ipynb | 1870 +++++++++++++++++++++++ 1 file changed, 1870 insertions(+) create mode 100644 examples/spatially_constrained_qr.ipynb diff --git a/examples/spatially_constrained_qr.ipynb b/examples/spatially_constrained_qr.ipynb new file mode 100644 index 0000000..69eef01 --- /dev/null +++ b/examples/spatially_constrained_qr.ipynb @@ -0,0 +1,1870 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spatial Constraints\n", + "\n", + "This notebook explores the `PySensors` General QR `GQR` optimizer for spatially-constrained sparse sensor placement (for reconstruction).\n", + "\n", + "Suppose we are interested in reconstructing a field based on a limited set of measurements due to constrained locations. Examples:\n", + "\n", + "- Nuclear applications (estimating the temperature at different points inside a nuclear fuel rod where certain areas allow only a limited number of thermocouples)\n", + "- Fluid flows (approximating the temperature distribution of heat diffusion through a plate where no sensors are allowed near the heater )\n", + "- Sea-surface temperature (determining the locations of the rest of the sensors when two locations are predetermined to predict the temperature at any point on the ocean.)\n", + "\n", + "In other notebooks we have shown how one can use the `SSPOR` class to pick optimal locations in which to place sensors to accomplish this task. But so far we have treated all sensor locations as being equally viable (`QR` optimizer). The cost-constrained QR (`CCQR` optimizer) determines the optimal placement of sensors based on the cost of placing a sensor in a certain location. What happens when some sensor locations allow only a limited number of sensors to be placed, predetermined locations are present or restricted areas exist within the physical attribute. \n", + "\n", + "The General QR algorithm was devised specifically to solve such problems. The `PySensors` object implementing this method is named `GQR` and in this notebook we'll demonstrate its use on a toy problem.\n", + "\n", + "See the following reference for more information ([link](https://arxiv.org/abs/2306.13637))\n", + "\n", + "\n", + " Niharika Karnik, Mohammad G. Abdo, Carlos E. Estrada Perez, Jun Soo Yoo, Joshua J. Cogliati, Richard S. Skifton, Pattrick Calderoni, Steven L. Brunton, and Krithika Manohar. Optimal Sensor Placement with Adaptive Constraints for Nuclear Digital Twins. 2023. arXiv: 2306 . 13637 [math.OC]." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/niharikakarnik/projects/pysensors/pysensors/__init__.py:5: UserWarning: Module pysensors was already imported from /Users/niharikakarnik/projects/pysensors/pysensors/__init__.py, but /Users/niharikakarnik/opt/miniconda3/lib/python3.9/site-packages/python_sensors-0.3.5.dev67+g38db2a2-py3.9.egg is being added to sys.path\n", + " __version__ = get_distribution(\"python-sensors\").version\n" + ] + } + ], + "source": [ + "from time import time\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn import datasets\n", + "\n", + "import pandas as pd\n", + "import pysensors as ps\n", + "\n", + "from mpl_toolkits.axes_grid1 import make_axes_locatable" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup \n", + "\n", + "We'll consider the Olivetti faces dataset from AT&T. Our goal will be to reconstruct images of faces from limited number of sensors placed in certain constrained regions and predetermined locations. \n", + "\n", + "Loading and preprocessing the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of samples: 400\n", + "Number of features (sensors): 4096\n" + ] + } + ], + "source": [ + "faces = datasets.fetch_olivetti_faces(shuffle=True, random_state=99)\n", + "X = faces.data\n", + "\n", + "n_samples, n_features = X.shape\n", + "print('Number of samples:', n_samples)\n", + "print('Number of features (sensors):', n_features)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Global centering\n", + "X = X - X.mean(axis=0)\n", + "\n", + "# Local centering\n", + "X -= X.mean(axis=1).reshape(n_samples, -1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# From https://scikit-learn.org/stable/auto_examples/decomposition/plot_faces_decomposition.html\n", + "n_row, n_col = 2, 3\n", + "n_components = n_row * n_col\n", + "image_shape = (64, 64)\n", + "\n", + "# Plot first few centered faces:\n", + "def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):\n", + " '''Function for plotting faces'''\n", + " plt.figure(figsize=(5. * n_col, 5.5 * n_row))#2. * n_col, 2.26 * n_row\n", + " plt.suptitle(title, size=16)\n", + " for i, comp in enumerate(images):\n", + " plt.subplot(n_row, n_col, i + 1)\n", + " vmax = max(comp.max(), -comp.min())\n", + " plt.imshow(comp.reshape(image_shape), cmap=cmap,\n", + " interpolation='nearest',\n", + " vmin=-vmax, vmax=vmax)\n", + " plt.xticks(())\n", + " plt.yticks(())\n", + " plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_gallery(\"First few centered faces\", X[:n_components])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll learn the sensors using the first 300 faces and use the rest for testing reconstruction error." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test = X[:300], X[300:]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Unconstrained optimization of sensor placement:\n", + "\n", + "Consider the case where we treat all sensor locations as being equally viable." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The list of sensors selected is: [2204 4038 3965 320 594 253 878 3618 2331 3999 429 2772 2878 3469\n", + " 1243]\n" + ] + } + ], + "source": [ + "n_sensors = 15\n", + "n_modes = 15\n", + "\n", + "# Define the QR optimizer\n", + "optimizer_unconstrained = ps.optimizers.QR()\n", + "basis_unconstrained = ps.basis.SVD(n_basis_modes=n_sensors)\n", + "\n", + "# Initialize and fit the model\n", + "model_unconstrained = ps.SSPOR(basis=basis_unconstrained,optimizer=optimizer_unconstrained, n_sensors=n_sensors)\n", + "model_unconstrained.fit(X_train)\n", + "\n", + "all_sensors = model_unconstrained.get_all_sensors()\n", + "\n", + "# sensor locations based on columns of the data matrix\n", + "top_sensors = model_unconstrained.get_selected_sensors() \n", + "\n", + "# sensor locations based on pixels of the image\n", + "xTopUnc = np.mod(top_sensors,np.sqrt(n_features)) \n", + "yTopUnc = np.floor(top_sensors/np.sqrt(n_features)) ## change to np.unravel\n", + "xAllUnc = np.mod(all_sensors,np.sqrt(n_features))\n", + "yAllUnc = np.floor(all_sensors/np.sqrt(n_features))\n", + "\n", + "print('The list of sensors selected is: {}'.format(top_sensors))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above cell shows sensor locations in terms of the column numbers chosen as sensors. \n", + "\n", + "These locations can be converted into the x and y position of a pixel on the image grid shown by xTopUnc and yTopUnc. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Sensor IDSensorXsensorY
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" + ], + "text/plain": [ + " Sensor ID SensorX sensorY\n", + "0 2204 28 34\n", + "1 4038 6 63\n", + "2 3965 61 61\n", + "3 320 0 5\n", + "4 594 18 9\n", + "5 253 61 3\n", + "6 878 46 13\n", + "7 3618 34 56\n", + "8 2331 27 36\n", + "9 3999 31 62\n", + "10 429 45 6\n", + "11 2772 20 43\n", + "12 2878 62 44\n", + "13 3469 13 54\n", + "14 1243 27 19" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Sensor ID corresponds to the column number chosen\n", + "columns = ['Sensor ID','SensorX','sensorY'] \n", + "unconstrainedSensors_df = pd.DataFrame(data = np.vstack([top_sensors,xTopUnc,yTopUnc]).T,columns=columns,dtype=int)\n", + "unconstrainedSensors_df.head(n_sensors)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now if we want to place just two sensors in the constrained region defined as $20 \\leq x \\leq 40$ and $10 \\leq y \\leq 40$. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Define our constrained region: \n", + "xmin = 20\n", + "xmax = 40\n", + "ymin = 10\n", + "ymax = 40\n", + "\n", + "# Plot the constrained region and the unconstrained sensors where 1 is the first sensor chosen.\n", + "image = X[4,:].reshape(1,-1)\n", + "\n", + "plot_gallery('unconstrained', image, n_col=1, n_row=1, cmap=plt.cm.gray)\n", + "plt.plot([xmin,xmin],[ymin,ymax],'-.r')\n", + "plt.plot([xmin,xmax],[ymax,ymax],'-.r')\n", + "plt.plot([xmax,xmax],[ymin,ymax],'-.r')\n", + "plt.plot([xmin,xmax],[ymin,ymin],'-.r')\n", + "plt.plot(xTopUnc, yTopUnc,'*r')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.xticks(np.arange(0,64,5),rotation=90)\n", + "plt.yticks(np.arange(0,64,5),rotation=90)\n", + "for ind,i in enumerate(range(len(xTopUnc))):\n", + " plt.annotate(f\"{str(ind)}\",(xTopUnc[i],yTopUnc[i]),xycoords='data',\n", + " xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n", + " arrowprops=dict(arrowstyle=\"->\", color='black'))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Find the constrained sensor indices through the utils function\n", + "sensors_constrained = ps.utils._constraints.get_constraind_sensors_indices(xmin,xmax,ymin,ymax,image_shape[0],image_shape[1],all_sensors) #Constrained column indices " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are three sensors placed by QR which lie within the constrained region, however only (n_const_sensors = 2) are allowed in that region. \n", + "\n", + "Two strategies have been developed to deal with this case: \n", + "\n", + "- exact_n : Number of sensors in the constrained region should be exactly equal to s. \n", + "- max_n : Number of sensors in the constrained region should be less than or equal to s. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The exact_n case: " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the number of constrained sensors allowed (s)\n", + "n_const_sensors = 2\n", + "\n", + "# Define the GQR optimizer for the exact_n sensor placement strategy\n", + "optimizer_exact = ps.optimizers.GQR()\n", + "opt_exact_kws={'idx_constrained':sensors_constrained,\n", + " 'n_sensors':n_sensors,\n", + " 'n_const_sensors':n_const_sensors,\n", + " 'all_sensors':all_sensors,\n", + " 'constraint_option':\"exact_n\"}\n", + "basis_exact = ps.basis.SVD(n_basis_modes=n_sensors)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The list of sensors selected is: [2204 4038 3965 320 253 594 3618 878 2331 3999 429 2772 2878 3469\n", + " 211]\n" + ] + } + ], + "source": [ + "# Initialize and fit the model\n", + "model_exact = ps.SSPOR(basis = basis_exact, optimizer = optimizer_exact, n_sensors = n_sensors)\n", + "model_exact.fit(X_train,**opt_exact_kws)\n", + "\n", + "# sensor locations based on columns of the data matrix\n", + "top_sensors_exact = model_exact.get_selected_sensors()\n", + "\n", + "# sensor locations based on pixels of the image\n", + "xTopConst = np.mod(top_sensors_exact,np.sqrt(n_features))\n", + "yTopConst = np.floor(top_sensors_exact/np.sqrt(n_features))\n", + "\n", + "print('The list of sensors selected is: {}'.format(top_sensors_exact))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display the unconstrained and constrained (GQR exact_n) sensor locations on the grid " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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ynaOmGRlQpYrfMQqWnk7z2Fg4h473qsrgwYMZN24c5cuX9yCcOZtgn5BJVNW8YUd2AYkFvVFEkoFkgMTERBYuXFisDWVmZhZ7nWAK9XxX7dhBmSNHQv6GTjk5OSGdMT4jg3JZWef8u05LS6N79+706dOnZIOdJtT/PYIPGVXVswmoB3yfbz7jtNcPFOVzmjdvrsWVmppa7HWCKdTzaZs2eqBJE79TFCrSv8c9e/ZozZo1ddGiRSWXKYCQ/x7Vm4xAmhZQd4J9tnq3iNQEcB/3BHn7xoSV6tWr8+KLL9KjRw8OHTrkd5xSJdjF8QOgu/u8O/B+kLdvTNj54x//yHXXXcegQYP8jlKqeNmV503g/4CLRGSHiPQCxgMdRGQjcL07b4wpxNNPP83nn3/OvHnzAKet1fYkveXl2ep7CnipvVfbNCZSVapUiddff527776bVq1asWLFCqZOncrc/D0PTImyK2SMCRO///3v6dKlC/fddx81atRg7dq1fkeKaFYcjQkDP//8M++//z6jR49m48aNLF26lC1btpCTk+N3tIhlxdGYMHD8+HHGjx9P69at6devH48++igJCQns2LHD72gRy4qjMWGgevXqfPXVVwwbNoynnnqKhIQEfv75ZzZs2OB3tIhlxdH8epMnQ4sWUL489OgR+D1jxji3APjss6BGiyQiwh133MGaNWvo27cvJ06cYP78+TZwsUesOJpfr1YtGD4cevYM/PqmTfD2286IQOZXi46OZtCgQezbt4+UlBQbuNgjVhzNr9epE9x2G1StGvj1fv1gwgSIjg5qrEhXuUYNosuXdwYrzs11HkVO3tXQ/CpWHI233n7bOdz+wx/8ThJ5bOBiT9ltEox3Dh2CoUNh/ny/k0QmG7jYU7bnaLwzahR07Qr16vmdJHLZwMWesT1H450FC2DHDufuegB798Jdd8GQIc5kfr2iDFxszokVR/PrZWc7U06OM2VlQblyTnE8ceLk+668Ep5+Gm6+2b+sxhSRHVabXy8lxTlDOn48vPGG8zwlxTl7XaPGyalsWUhIgPh4vxMbUyjbczS/3qhRzlSYrVs9DmJMybE9R2OMCcCKozHGBGDF0RhjArDiaIwxAVhxNEGlJ2/La0xIs+Jogur5559n+PDhfscwplBWHE1Q3X777bzwwgs2grUJeVYcTVDVqlWL//3f/2X06NF+RzHmrKw4mqAbMmQI7733HuvXr/c7ijEFsuJogi4hIYEHH3yQESNG+B3FmAJZcTS++Otf/8rixYtZvnw5GRkZdOzY0e9IxpzCiqPxRYUKFRg+fDhDhw7lyJEjLFu2zO9IxpzCiqMJurVr15KSkkK3bt1IT09n2bJlHD9+3O9YxpzCiqMJutq1a5OWlkabNm3o06cPKSkpVhxNyLEhy0zQVapUiXfffZcXX3yRESNGEB0dTVZWlt+xjDmF7TkaX4gIffr04YsvvqB8+fKcOHHCLis0IcWKo/HVJZdcwpo1axgzZgyyaxe0aWM3iTIhwYqj8V1MTIzT5/Hxx2HxYhgzxu9IxlhxNCEgNhZEYOpUyM11HkWc5cb4xLPiKCIXiEiqiKwRkdUiMtBdfp6IzBeRje5jglcZTJjYvBk6d4a4OGc+Lg66dIEtW/zNZUo1L/ccs4HBqnop0AroJyKXAo8AC1S1IbDAnTelWc2aUKmSc0vXmBjnsVIl546FxvjEs+KoqjtV9Rv3+SFgLVAb6AhMd982HbjNqwwmjOzeDX36wJIlzqOdlDE+C0o/RxGpBzQDlgKJqrrTfWkXkBiMDCbEzZ178vmUKf7lMMbleXEUkXjgHeABVT0oIr+8pqoqIgE7t4lIMpAMkJiYyMKFC4u13czMzGKvE0yhnq9pRgY5OTkhnRHseywpof49gg8Z8+7p4cUERAGfAA/mW7YeqOk+rwmsL+xzmjdvrsWVmppa7HWCKdTzaZs2eqBJE79TFMq+x5IR8t+jepMRSNMC6o6XZ6sFeBVYq6pP53vpA6C7+7w78L5XGYwx5lx5eVjdGugKrBKRFe6yocB4YLaI9AK2AXd5mMEYY86JZ8VRVRcDUsDL7b3arjHGlAS7QsYYYwKw4miMMQFYcTTGmACsOBpjTABWHI0xJgArjsYYE4AVR2OMCcCKozHGBGDF0RhjArDiaIwxAVhxNMaYAKw4GmNMAFYcjTEmACuOxhgTgBVHY4wJwIqjMcYEYMXRGGMCsOJojDEBWHE0xpgArDgaY0wAVhyNMSYAK47GGBOAFUdjjAnAiqMxxgRgxdEYYwKw4miMMQFYcTTe2L8fbr8dKlSAunXhn//0O5ExxVLO7wAmQvXrB9HRsHs3rFgBt9wCTZrAZZf5ncyYIrE9R1PyDh+Gd96Bxx+H+Hi45hr4059g5ky/kxlTZFYcTcnbsAHKlYNGjU4ua9IEVq/2L5MxxWTF0ZS8zEyoVOnUZZUrw6FD/uQx5hxYcTQlLz4eDh48ddnBg1Cxoj95jDkHVhxNyWvUCLKzYePGk8u++85OxpiwYsXRlLwKFaBTJxg50jk585//wPvvQ9euficzpsg8K44iEiMiX4vIdyKyWkRGu8vri8hSEUkXkbdEJNqrDCaItm6FP/wBEhKgRg3n0PrwYTj/fLjnHpg61fYcTVjxcs/xGHCdqjYBmgI3iUgrYALwjKomAQeAXh5mMMFy//1OIdy50+nXuHQpXH+9UyD/+1/o3NnvhMYUi2fFUR2Z7myUOylwHTDHXT4duM2rDCaItmyBu+6CmBhnz/Gmm6zrjglrnl4hIyJlgeVAEjAF2ARkqGq2+5YdQO0C1k0GkgESExNZuHBhsbadmZlZ7HWCKdTzNc3IICcnp8gZa958M5WffZYNZcpQ7tAhmsyZw5aePdnn8c8Yad+jX0L9ewQfMqqq5xNQBUgFrgHS8y2/APi+sPWbN2+uxZWamlrsdYIp1PNpmzZ6oEmTor9/zRrVK65QLVtWFVS7d1fNzfUq3S8i7nv0Sch/j+pNRiBNC6g7QTlbraoZbnG8CqgiInl7rHWAH4KRwXgoN9c5jO7UyWlj3LcPDhyAIUP8TmbMOfPybHV1EaniPo8FOgBrcYrkHe7bugPve5XBBMn+/c5Jl/79oXx5qFoV7r0XPvqowFVefPFFtm/fHsSQxhSPl3uONYFUEVkJLAPmq+qHwBDgQRFJB6oCr3qYwQRDtWpQv77TXSc7GzIyYPp0aNy4wFUOHDhA9+7dyc3NDV5OY4rBy7PVK1W1mao2VtXLVXWMu3yzqrZU1SRVvVNVj3mVwQTR3Lnw8cdQvTokJUFUFDzzTIFvf+ihh8jKyuK5554LYkhjis7GczQlo2lTKMaZxHLlyjFjxgxatWrFDTfcwKWXXupZNGPORaF7jiIyQEQSghHGlC5JSUk88cQTdO3alePHj/sdx5hTFOWwOhFYJiKzReQmERGvQ5nSIzk5mRo1apCSkuJ3FGNOUWhxVNXhQEOcEyc9gI0iMlZEfuNxNlMKiAivvPIKL730EkuXLgWgVatWHD161OdkprQr0gkZt7PkLnfKBhKAOSLypIfZTClRs2ZNJk+eTNeuXTl8+DAZGRls3rzZ71imlCtKm+NAEVkOPAn8B/itqvYFmgN/9jifKQW2bNlCp06d+N3vfsfDDz9MUlIS6enpfscypVxR9hzPAzqp6o2q+raqngBQ1VzgVk/TmYjx6KOP8sYbbwR87cEHH+SKK66gU6dOzJs3j6ioKCuOxneFduVR1cfO8trako1jIlXnzp257rrruOaaa6hXr94pr82dO5d33nmHhx9+mMTERObPn0/lypX9CWqMy0YCN0Hx29/+lr/97W/06NHjjKtiRIQ77riD1atX061bN3Jzc1mwYIEzNmSbNrBrl0+pTWlmxdEEzeDBg8nOzi7wqpjo6GgGDBjA1q1bmTp1qnPf68WLYcyYICc1xq6QMUFUtmxZpk+fXuhVMefXrcutWVknF0yd6kwxMWBdfEyQ2J6jCarf/OY3pKSk0K1bN06cOIGqMmfOnFPftHmzc1uFuDhnPi4OunRxRhs3JkisOJqgS05Opnr16owdOxZVpWvXrhw5cuTkG2rWhEqVICvL2VvMynLma9TwL7Qpdaw4mqATEV599VWef/55vvnmG+rVq3dmp+/du6FPH1iyxHm0kzImyKzN0QRdjx496NSpE88++yzdunWjXr16bNq0icsvv/zkm+bOPfl8ypTghzSlnhVHE3SdO3fmwQcfpGrVqtSuXZvdu3dbp28Tcuyw2gTdDTfcwIoVK+jatSurVq1i5cqVfPHFF37HMuYUVhyNL8qVK0fv3r1JT0+nc+fOzt0ordO3CSF2WG18FR8fz4wZM5yZ++8/2en7+ef9DWZKPdtzNP6LjQURp6N3bq7zKOIsN8YnVhyN/6zTtwlBVhwj0eTJ0KKFcw/pHj1OLj9+HO64A+rVc/bMinFDLE9Zp28Tgqw4RqJatWD4cOjZ88zXrrkG3ngj9AqPdfo2IcZOyESiTp2cx7Q02LHj5PLoaHjgAed52bJBj3VW1unbhBjbczTGmACsOBpj/FNQ+/iSJdChA5x3HlSvDnfeSfRPPwU1mhVHY4x/CmofP3AAkpNh61bYtg0qVuTiCROCGs3aHI0x/imoffzmm099X//+VLrmmuDlwvYcI1N2ttMdJifHmbKynGUAx4458+B07cnKAlX/shpTFF98wZHTbszmNSuOkSglxbm6ZPx4p9tObKyzDOCii5z5H36AG290nm/b5m9eY85m5UoYM4ZNffoEdbN2WB2JRo1ypkC2bg1iEGN+pfR05xD773/n5wsuCOqmbc/RGBOatm2D66+HESOga9egb972HI0x/snOdqb87ePlyjlXTF13HfTv71wx5QPPi6OIlAXSgB9U9VYRqQ/MAqoCy4Guqnrc6xzGmBCUkgKjR5+cf+MNeOwx59r/zZtPaSK6NicnqLfmDcZh9UBgbb75CcAzqpoEHAB6BSGDMSYUjRrl9JbIP40a5RRIVcjM/GX68t//Dmo0T4ujiNQBbgFececFuA7Iu1HxdOA2LzOYszt27JjfEYwJSV4fVj8LPAxUdOerAhmq6na6YwdQ2+MMpgCZmZk0atSItWvXUrly5VNfTE8nPiMD2rb1I1qRNc3IgCpV/I5RsBUriI2K8juFOQeeFUcRuRXYo6rLRaTtOayfDCQDJCYmsrCYYw9mZmYWe51gCpV8jRs3ZsCAAfQ87fKt5rGxlMvKIjMjw59gRZSTk0NGCGeMjYoiq1Il/i8EftdnEyr/Hs8m6BlV1ZMJGIezZ7gV2AUcAf4B7APKue+5CviksM9q3ry5Fldqamqx1wmmUMm3ZcsWPe+883T37t1nvBYqGc/GMpaM0poRSNMC6o5nbY6q+qiq1lHVesDdwOeq2gVIBe5w39YdeN+rDKZw9erVo0uXLowdO9bvKMaEFD86gQ8BHhSRdJw2yFd9yGDyGTZsGDNnzmSbXUZoQtjmzZsZOHBg0LYXlOKoqgtV9Vb3+WZVbamqSap6p6ra6VKfJSYm0rdvX0a7/c3+8pe/8M033/icyphT1apVizlz5pCWlhaU7dnlgwaAhx56iA8//JB169axd+9e9uzZ43ckY04RExPDiBEjGDp0aFC2Z8XRcN9997Fp0yYGDx7MiBEjiI6O5vhxu2jJhJ6ePXuyadMmUlNTPd+WFUdDhw4duPnmmzlx4gT/+c9/OHz4MCdOnPA7ljFniI6OZsyYMQwdOjSvV4xnrDga7rjjDpYtW8Ynn3xCpUqVWLFihe05mpB1zz33cPjwYebNmwfAAw884MmVXlYcDQB169YlNTWVu+++mwMHDrB+/Xq/IxkTUJkyZXjiiScYNmwYOTk5vPnmmxw4cKDkt1Pin2jCVrly5Rg1ahQffvgh995yC00HDoRdu/yOZcwvsrKyePXVV7nxxhupWLEis2bNIjo62pNmICuO5gy33HILdadNo/KqVTBmjN9xjPlF2bJleeedd7j22mvp168fI0eOpFy5cp40A1lxNKeKjXXG0ps6FVGFqVOd+dhYv5MZQ1RUFP/617/o0qULDzzwAHFxcRw5csT2HE0QbN4MnTtDXJwzHxcHXbrAli3+5jLGJSL89a9/5bPPPuPIkSPs2bOHgwcPlvh2rDiaU9WsCZUqQVYWOdHRzrD1lSpBjRp+JzPmFE2aNGHVqlXcfvvt1ImKgjZtSrSN3IqjOdPu3dCnD99MmeLcv8NOypgQFRcXx9y5c6n18suweHGJtpHbDbbMmebOBeDwwoXQu7e/WYw5m9hY5+gmz9SpzhQT86vvN2N7jsaY8OVhG7kVR2NM+MrXRk5MTIm2kVtxNMaEN7eNnCVLSrSN3NocjTHhzW0jB2DKlBL7WNtzNMaYAKw4GmNMAFYcjTEmACuOxhgTgBVHY4wJwIqjMcYEYMXRGGMCsOJojDEBWHE0xpgArDgaY0wAVhy9NnkytGgB5ctDjx6/LI7butVZnpDgTNdfD2vW+BbTGHMqK45eq1ULhg+Hnj1PWXy8WjWYMwf274d9++BPf4K77/YppDHmdDbwhNc6dXIe09Jgx45fFmfHx0O9es6MKpQtC+npwc9njAnIiqPfqlSBzEzIzbXboBoTQqw4+i0jAw4fhunToW5dv9MYY1xWHENBhQrOIJ3Vq8PatXD++X4nMqbUsxMyoSI3F44cgR9+8DuJMQYrjt7Lznbua5GT40xZWZCdTUJaGnz7rbPs4EF48EGnS88ll/id2BiDx4fVIrIVOATkANmq2kJEzgPeAuoBW4G7VPWAlzl8lZICo0efnH/jDXjsMeeLv+ce5wx2bCy0bAkff+zcJMgULj4egGtzcpwz/UePwv33w6RJPgczkSIYe47tVLWpqrZw5x8BFqhqQ2CBOx+5Ro1yuurkn0aNYm/btrBunXOmeu9e+Ne/oHFjv9OGj8xMyMzky3//27mhUmws3Hmn36lMBPHjsLojMN19Ph24zYcMJpK8845zEuvaa/1OYiKIqKp3Hy6yBTgAKPCiqr4kIhmqWsV9XYADefOnrZsMJAMkJiY2nzVrVrG2nZmZSbx76BWKQj0fhE/G1iNH8nPjxmzNd3lmKAmX77E0ZmzXrt3yfEe1p1JVzyagtvt4PvAd8Hsg47T3HCjsc5o3b67FlZqaWux1ginU86mGR8av3nxTtUwZ1c2b/Y5SoHD4HktrRiBNC6g7nh5Wq+oP7uMe4F2gJbBbRGoCuI97vMxgIluN+fPhmmugfn2/o5gI41lxFJEKIlIx7zlwA/A98AHQ3X1bd+B9rzKEm6+//pqjR4/6HSOsJH76KXTvXvgbjSkmL7vyJALvOs2KlAP+qaofi8gyYLaI9AK2AXd5mCGsvPzyy9SpU4fHHnvM7yjh4auvKL9vn52lNp7wrDiq6magSYDlPwHtvdpuOHv00Udp2bIl/fr1o1q1an7HCX3Tp7P32mupUbGi30lMBLIrZEJIgwYN+J//+R/GjRvnd5Tw8OKLrBs61O8UJkJZcQwxw4cPZ9q0aezIN/ajMSb4rDiGmJo1a5KcnMwYd2zHzMxMfvzxR59TGVP6WHEMQUOGDOHdd99lw4YNvPfeewwbNszvSMaUOlYcQ4yqkpCQwKBBgxg5ciRlypQhKyvL71jGlDpWHEPM+PHjufnmm7n77rtZtGgRO3bs4MSJE37HCkvZ2dl+RzBhzIpjiHnooYe48sorad26NR07duStt97i+PHjfscKO5mZmdSrV8/aa805s+IYYqKiohgzZgyzZ8/mo48+YvXq1ezZY1dYFld8fDy9e/emd+/eedfwG1MsVhxD1LXXXst3331H06ZN2bVrF+zcCW3aOGMXmiIZNmwYe/fu5aWXXvI7iglDVhxDWEJCAkuWLCE9PR0efxwWL7bbtxZDVFQUM2bMYPjw4c53aEwxWHEMdbGxlIuKgqlTnZtwTZ0KIs7I16ZQl1xyCcOHD6d79+7k5OT4HceEESuOoW7zZujcGeLinPm4OOjSBbZs8TdXGBkwYAAxMTFMnDgRgA0bNvDll1/6nMqEOrtvdairWRMqVXLuWhgT4zxWqgQ1avidLGyUKVOGadOm0aJFC26++WbWrFnDu+++y7V2WwVzFrbnGA5274Y+fWDJEufRTsoU24UXXshTTz1F165dufDCC60N0i/HjkGvXlC3LlSsCE2bwr//7XeqgGzPMRzMnXvy+ZQp/uUIU8uXL2fChAk89thjNGzYkNmzZ5Oeno6q4o43aoIlOxsuuAAWLYILL4SPPoK77oJVq6BePb/TncL2HE3Ea9y4MVdddRXt2rWjQoUKzJo1CxFh7969fkcrfSpUcG5XXK8elCkDt97q3OJi+XK/k53BiqOJeFFRUQwaNIj169eTmJjI0aNHOXToECtXrvQ7mtm9GzZsgMsu8zvJGaw4mtCwdi1cdx1UrgxJSfDuuyW+iYSEBCZOnMh3331H8+bNOXbsmHWu99OJE07Pi+7d4eKL/U5zBiuOxn/Z2dCxo3OItX8/vPQS/OUvzh6FB+rXr8+yZcu45ZZbrHO9X3JzoWtXiI6GyZP9ThOQFUfjv3Xr4McfYdAgKFvW2YNs3RpmzvRum7GxTmd661wffKrOGevdu+GddyAqyu9EAVlxNKFJFb7/3rvPt871/unb12lGmTcvpP8Yla7iOHkytGgB5ctDjx5+pzF5LroIzj8fJk502qE+/dTp6nHkiHfbtM71/ti2DV58EVascL7r+Hhn+sc//E52htLVz7FWLRg+HD75BI4e9TuNyRMVBe+9BwMGwIQJzh+wu+5y/oh5Ka9zfXKy0865c6e32zNO5+8wGUKudBXHTp2cx7Q0sLv7hZbGjZ29xTxXX+2cxfSSda43Z1G6DqtN6Fq50jm0PXIEnnrK2Yuzpg/jIyuOJjTMnOm0A55/PixYAPPne39YbcxZlK7DahO6Jk50JmNChO05GmNCyvjx4/nwww/9jlHKimN2ttOulZPjTFlZzjJjTMho27YtvXv3du6d5KPSVRxTUpxOp+PHwxtvOM9TUvxOZYzJp1WrVvTu3Zvk5GRf7xxZuorjqFFOH6v806hRfqcyxpxm5MiRbN++nddee823DKWrOJqwtXr1at5//32/Y5ggiY6OZubMmTzyyCNs8emSTiuOJiyUL1+eXr16sX79er+jmCC5/PLLGTJkyCl3jly9enXQtu9pcRSRKiIyR0TWichaEblKRM4TkfkistF9TPAyg4kMSUlJjB49mm7dupFtJ9FKjUGDBiEiPPPMMwC0b9+ePXv2BGXbXu85/h34WFUvBpoAa4FHgAWq2hBY4M4bU6i+fftSuXJlxo0b53cUEyRly5bl9ddfZ8KECWzZsoW6desG7eZonhVHEakM/B54FUBVj6tqBtARmO6+bTpwm1cZzsWaNWs4ePCg3zFMAGXKlOG1115j0qRJLA/Be46Ykjdp0iQOHTrE+PHjGTt2LA0aNAj/4gjUB/YC00TkWxF5RUQqAImqmjf8yS4g0cMMxfb222/Tq1cvX7sQmILVqVOHZ599lq5du3LUHVkpNzfXfl8RqmLFinTo0IHFixdTuXJlduzYwaZNm4KybfHqH5WItACWAK1VdamI/B04CAxQ1Sr53ndAVc9odxSRZCAZIDExsfmsWbOKtf3MzEzi4+OLnfv48eMkJyfTpUsXOnToUOz1i+pc8wVTqGZUVcaMGUO1atXo3r07U6ZMoW3btvzud7/zO1pAofo95hfKGTMzM3nzzTd5//33ycrKolmzZkwsoUtN27Vrt1xVWwR8UVU9mYAawNZ889cC/wLWAzXdZTWB9YV9VvPmzbW4UlNTi71Onm+++UarV6+u27dvP+fPKMyvyRcsoZxx3759WqtWLX366ad18ODBOn78eL8jFSiUv8c84ZBx1qxZ2rZtW23QoIHqjz+q/v73qjt3/qrPBNK0gLrj2WG1qu4CtovIRe6i9sAa4AMgb6C+7kDIdV5r1qwZAwcO5N577yU3N9f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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "img = np.zeros(n_features)\n", + "img[top_sensors] = 16\n", + "plt.figure(figsize=(5,5))\n", + "plt.plot(xTopConst,yTopConst,'*r')\n", + "plt.plot([xmin,xmin],[ymin,ymax],'r')\n", + "plt.plot([xmin,xmax],[ymax,ymax],'r')\n", + "plt.plot([xmax,xmax],[ymin,ymax],'r')\n", + "plt.plot([xmin,xmax],[ymin,ymin],'r')\n", + "plt.ylim([64,0])\n", + "plt.title('n_sensors = {}, n_const_sensors = {}'.format(n_sensors,n_const_sensors))\n", + "for ind,i in enumerate(range(len(xTopConst))):\n", + " plt.annotate(f\"{str(ind)}\",(xTopConst[i],yTopConst[i]),xycoords='data',\n", + " xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n", + " arrowprops=dict(arrowstyle=\"->\", color='black'))\n", + "plt.grid()\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(5,5))\n", + "plt.plot(xTopUnc, yTopUnc,'*r')\n", + "plt.plot([xmin,xmin],[ymin,ymax],'r')\n", + "plt.plot([xmin,xmax],[ymax,ymax],'r')\n", + "plt.plot([xmax,xmax],[ymin,ymax],'r')\n", + "plt.plot([xmin,xmax],[ymin,ymin],'r')\n", + "plt.title('Unconstrained')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.ylim([64,0])\n", + "for ind,i in enumerate(range(len(xTopUnc))):\n", + " plt.annotate(f\"{str(ind)}\",(xTopUnc[i],yTopUnc[i]),xycoords='data',\n", + " xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n", + " arrowprops=dict(arrowstyle=\"->\", color='black'))\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare unconstrained vs. constrained (exact_n) sensor indices and pixel coordinates " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Sensor ID UncX UncY Sensor ID XConst YConst\n", + "0 2204 28 34 2204 28 34\n", + "1 4038 6 63 4038 6 63\n", + "2 3965 61 61 3965 61 61\n", + "3 320 0 5 320 0 5\n", + "4 594 18 9 253 18 9\n", + "5 253 61 3 594 61 3\n", + "6 878 46 13 3618 46 13\n", + "7 3618 34 56 878 34 56\n", + "8 2331 27 36 2331 27 36\n", + "9 3999 31 62 3999 31 62\n", + "10 429 45 6 429 45 6\n", + "11 2772 20 43 2772 20 43\n", + "12 2878 62 44 2878 62 44\n", + "13 3469 13 54 3469 13 54\n", + "14 1243 27 19 211 27 19" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yConstrained = np.floor(top_sensors/np.sqrt(n_features))\n", + "xConstrained = np.mod(top_sensors,np.sqrt(n_features))\n", + "\n", + "columns = ['Sensor ID','UncX','UncY','Sensor ID','XConst','YConst']\n", + "ConstrainedSensors_df = pd.DataFrame(data = np.vstack([top_sensors,xTopUnc,yTopUnc,top_sensors_exact,xConstrained,yConstrained]).T,columns=columns,dtype=int)\n", + "ConstrainedSensors_df.head(n_sensors)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot sensor locations (pixels) on the image for unconstrained vs. constrained (exact_n)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_gallery('unconstrained', image, n_col=1, n_row=1, cmap=plt.cm.gray)\n", + "yUnconstrained = np.floor(top_sensors/np.sqrt(n_features))\n", + "xUnconstrained = np.mod(top_sensors,np.sqrt(n_features))\n", + "plt.plot([xmin,xmin],[ymin,ymax],'-.r')\n", + "plt.plot([xmin,xmax],[ymax,ymax],'-.r')\n", + "plt.plot([xmax,xmax],[ymin,ymax],'-.r')\n", + "plt.plot([xmin,xmax],[ymin,ymin],'-.r')\n", + "plt.plot(xTopUnc, yTopUnc,'*r')\n", + "for i in (range(len(xTopUnc))):\n", + " plt.annotate(f\"{str(i)}\",(xTopUnc[i],yTopUnc[i]),xycoords='data',\n", + " xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n", + " arrowprops=dict(arrowstyle=\"->\", color='black'))\n", + "\n", + "\n", + "plot_gallery('Constrained', image, n_col=1, n_row=1, cmap=plt.cm.gray)\n", + "plt.plot([xmin,xmin],[ymin,ymax],'-r')\n", + "plt.plot([xmin,xmax],[ymax,ymax],'-r')\n", + "plt.plot([xmax,xmax],[ymin,ymax],'-r')\n", + "plt.plot([xmin,xmax],[ymin,ymin],'-r')\n", + "plt.plot(xTopConst, yTopConst,'*r')\n", + "for i in (range(len(xTopConst))):\n", + " plt.annotate(f\"{str(i)}\",(xTopConst[i],yTopConst[i]),xycoords='data',\n", + " xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n", + " arrowprops=dict(arrowstyle=\"->\", color='black'))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reconstruct image from test set using sensors placed via constrained (exact_n) optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_faces = 3\n", + "n_rows = n_faces + 1\n", + "n_cases = 1\n", + "fig, axs = plt.subplots(n_rows, 2, figsize=(12, 3 * n_rows))\n", + "\n", + "for k in range(n_cases): \n", + " # Get average reconstruction error across test set\n", + " test_error = model_exact.reconstruction_error(X_test, sensor_range=[n_sensors])\n", + " \n", + " # Plot sensor locations\n", + "\n", + " axs[0, k].plot()\n", + " axs[0, k].set(title=f\"constrained (MSE: {test_error[0]:.2f})\")\n", + " axs[0, k].plot(xTopConst,yTopConst,'*r')\n", + " axs[0, k].plot([xmin,xmin],[ymin,ymax],'r')\n", + " axs[0, k].plot([xmin,xmax],[ymax,ymax],'r')\n", + " axs[0, k].plot([xmax,xmax],[ymin,ymax],'r')\n", + " axs[0, k].plot([xmin,xmax],[ymin,ymin],'r')\n", + "\n", + " # Plot reconstructed faces\n", + " for j in range(n_faces):\n", + " idx = 10 * j\n", + " img = model_exact.predict(X_test[idx, top_sensors_exact])\n", + " vmax = max(img.max(), img.min())\n", + " axs[j + 1, k].imshow(\n", + " img.reshape(image_shape),\n", + " cmap=plt.cm.binary,\n", + " vmin=-vmax,\n", + " vmax=vmax\n", + " )\n", + " yConstrained = np.floor(top_sensors_exact/np.sqrt(n_features))\n", + " xConstrained = np.mod(top_sensors_exact,np.sqrt(n_features))\n", + " axs[j + 1, k].plot([xmin,xmin],[ymin,ymax],'-.m')\n", + " axs[j + 1, k].plot([xmin,xmax],[ymax,ymax],'-.m')\n", + " axs[j + 1, k].plot([xmax,xmax],[ymin,ymax],'-.m')\n", + " axs[j + 1, k].plot([xmin,xmax],[ymin,ymin],'-.m')\n", + " axs[j + 1, k].plot(xConstrained, yConstrained,'*r')\n", + "\n", + " error = model_exact.reconstruction_error(X_test[idx], sensor_range=[n_sensors])[0]\n", + " axs[j + 1, k].set(title=f\"MSE: {error:.2f}\")\n", + " \n", + " # Plot target image\n", + " true_img = X_test[idx]\n", + " vmax = max(true_img.max(), true_img.min())\n", + " axs[j + 1, k + 1].imshow(\n", + " true_img.reshape(image_shape),\n", + " cmap=plt.cm.binary,\n", + " vmin=-vmax,\n", + " vmax=vmax\n", + " )\n", + " axs[j + 1, k + 1].set(title=\"Original image\")\n", + " \n", + "\n", + "[ax.set(xticks=[], yticks=[]) for ax in axs.flatten()]\n", + "fig.tight_layout()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare reconstruction errors on test set for unconstrained vs. constrained exact_n sensor placement:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The reconstruction error for the unconstrained case is [0.12295605]\n", + "The reconstruction error for the constrained exact_n case is [0.11306471]\n" + ] + } + ], + "source": [ + "test_error_unconstrained = model_unconstrained.reconstruction_error(X_test, sensor_range=[n_sensors])\n", + "test_error_exact = model_exact.reconstruction_error(X_test, sensor_range=[n_sensors])\n", + "print(\"The reconstruction error for the unconstrained case is {}\".format(test_error_unconstrained))\n", + "print(\"The reconstruction error for the constrained exact_n case is {}\".format(test_error_exact))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Observations: \n", + "- Since unconstrained optimization places 3 sensors in the constrained region, the constrained optimization with n_const_sensors = 2 removes one sensor (ID 14) from the region and places it outside. \n", + "\n", + "- The drop in reconstruction error between the unconstrained and constrained optimization is $\\mathcal{O} \\approx 10^{-2}$. \n", + "\n", + "However, now if we want to place exactly 4 sensors in the constrained region, constrained exact_n will force the last sensor (ID 13) to be placed in the constrained region as shown below: \n", + "- Note: As the 14th sensor already lies in the constrained region, the optimizer selects the second last sensor to lie in the constrained region. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the number of constrained sensors allowed (s)\n", + "n_const_sensors_exact = 4\n", + "\n", + "# Define the GQR optimizer for the exact_n sensor placement strategy\n", + "optimizer_exact1 = ps.optimizers.GQR()\n", + "opt_exact_kws1={'idx_constrained':sensors_constrained,\n", + " 'n_sensors':n_sensors,\n", + " 'n_const_sensors':n_const_sensors_exact,\n", + " 'all_sensors':all_sensors,\n", + " 'constraint_option':\"exact_n\"}\n", + "basis_exact1 = ps.basis.SVD(n_basis_modes=n_sensors)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The list of sensors selected is: [2204 4038 3965 320 253 594 3618 878 2331 3999 429 2772 2878 1370\n", + " 1315]\n" + ] + } + ], + "source": [ + "# Initialize and fit the model\n", + "model_exact1 = ps.SSPOR(basis = basis_exact1, optimizer = optimizer_exact1, n_sensors = n_sensors)\n", + "model_exact1.fit(X_train,**opt_exact_kws1)\n", + "\n", + "# sensor locations based on columns of the data matrix\n", + "top_sensors_exact1 = model_exact1.get_selected_sensors()\n", + "\n", + "# sensor locations based on pixels of the image\n", + "xTopConst1 = np.mod(top_sensors_exact1,np.sqrt(n_features))\n", + "yTopConst1 = np.floor(top_sensors_exact1/np.sqrt(n_features))\n", + "\n", + "print('The list of sensors selected is: {}'.format(top_sensors_exact1))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the sensor locations (pixels) on image for unconstrained and constrained exact_n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_gallery('unconstrained', image, n_col=1, n_row=1, cmap=plt.cm.gray)\n", + "yUnconstrained = np.floor(top_sensors/np.sqrt(n_features))\n", + "xUnconstrained = np.mod(top_sensors,np.sqrt(n_features))\n", + "plt.plot([xmin,xmin],[ymin,ymax],'-.r')\n", + "plt.plot([xmin,xmax],[ymax,ymax],'-.r')\n", + "plt.plot([xmax,xmax],[ymin,ymax],'-.r')\n", + "plt.plot([xmin,xmax],[ymin,ymin],'-.r')\n", + "plt.plot(xTopUnc, yTopUnc,'*r')\n", + "for i in (range(len(xTopUnc))):\n", + " plt.annotate(f\"{str(i)}\",(xTopUnc[i],yTopUnc[i]),xycoords='data',\n", + " xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n", + " arrowprops=dict(arrowstyle=\"->\", color='black'))\n", + "\n", + "\n", + "plot_gallery('Constrained', image, n_col=1, n_row=1, cmap=plt.cm.gray)\n", + "plt.plot([xmin,xmin],[ymin,ymax],'-r')\n", + "plt.plot([xmin,xmax],[ymax,ymax],'-r')\n", + "plt.plot([xmax,xmax],[ymin,ymax],'-r')\n", + "plt.plot([xmin,xmax],[ymin,ymin],'-r')\n", + "plt.plot(xTopConst1, yTopConst1,'*r')\n", + "for i in (range(len(xTopConst1))):\n", + " plt.annotate(f\"{str(i)}\",(xTopConst1[i],yTopConst1[i]),xycoords='data',\n", + " xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n", + " arrowprops=dict(arrowstyle=\"->\", color='black'))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusions:\n", + "\n", + "- The constrained exact_n case can be used to place exactly (n_const_sensors) in the constrained region. \n", + "- In cases where the QR optimizer by itself has more than (n_const_sensors) in the constrained region, exact_n removes the excess and places them in the constrained region. \n", + "- In cases where there are exactly (n_const_sensors) in the constrained region, exact_n results will match the QR optimizer. \n", + "- When there are fewer than (n_const_sensors) sensors in the constrained region, exact_n will force the deficit to be in the constrained region. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The max_n case: \n", + "\n", + "#### Max_n employs the same strategy as exact_n when: (results are identical)\n", + "- The QR optimizer (unconstrained) places more than (n_const_sensors) in the constrained region, which violates the desired constraint.\n", + "- The constrained max_n optimizer removes excess sensors from the constrained and places them outside.\n", + "- There are exactly (n_const_sensors) sensors in the constrained region (max_n results will match exact_n which match the QR optimizer)\n", + "\n", + "However, there are three sensors located in the constrained region. If we want to place a maximum of 4 sensors in the constrained region max_n will place just 3 as seen below: " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "n_const_sensors_max = 4\n", + "# Define the GQR max_n optimizer\n", + "\n", + "optimizer_max = ps.optimizers.GQR()\n", + "opt_max_kws ={'idx_constrained':sensors_constrained,\n", + " 'n_sensors':n_sensors,\n", + " 'n_const_sensors':n_const_sensors_max,\n", + " 'all_sensors':all_sensors,\n", + " 'constraint_option':\"max_n\"}\n", + "basis_max = ps.basis.SVD(n_basis_modes=n_sensors)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The list of sensors selected is: [2204 4038 3965 320 253 594 3618 878 2331 3999 429 2772 2878 3469\n", + " 1243]\n" + ] + } + ], + "source": [ + "# Initialize and fit the model\n", + "model_max = ps.SSPOR(basis = basis_max, optimizer = optimizer_max, n_sensors = n_sensors)\n", + "model_max.fit(X_train,**opt_max_kws)\n", + "\n", + "# sensor locations based on columns of the data matrix\n", + "top_sensors_max = model_max.get_selected_sensors()\n", + "\n", + "# sensor locations based on pixels of the image\n", + "xTopConstMax = np.mod(top_sensors_max,np.sqrt(n_features))\n", + "yTopConstMax = np.floor(top_sensors_max/np.sqrt(n_features))\n", + "\n", + "print('The list of sensors selected is: {}'.format(top_sensors_max))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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06NFs2bKF+Ph4NtpNjP1l505YssS5NqDxDStyZcSSJUuIi4tj9OjR9OjRg8aNG4fsvpWmmGbPhjZtoFEjr5OYfKzIlRHLli3jqquu4uDBg1SuXJmdO3eWnysolxWzZtlWnA9ZkSsjhg4dysqVK9m4cSPr168nOTmZlJQUr2OZPP/5j3OvjqKOqpqQsyJXhjRu3Jj333+fTz75hEaNGrFmzRo7ydsvZs6Erl2de3YYX7F+cmXQ9ddfz/r169m9e7ed5O0X5fkG6T5nRa6MksqVqWcneRtTJNtdLavsJG9jiqXIIicifxaRfSLyXb5pF4jIQhHZ4j7GBTemOYOd5G1MsRRnS+5d4PT75D0LLFLVJsAid9yEmp3kbUyRimyTU9WvRaThaZPvAdq7z2cCi4GhgQxmiqE4J3m3aMFNaWlw9dWhyVRKzTMyoEYNr2MULjWVljExUIoO2KrKU089xfjx46lUqVIQwplzKe2Bh3hVzbse+R4gvrAXikgikAgQHx/P4sWLS7SizMzMEi8TSn7Pd1NaGhWOHvX9jVNycnJ8nTE2I4OIrKxSf9cpKSn06tWL/v37BzbYafz+ewQPMqpqkQPQEPgu33jGafPTi/M+LVu21JJKTk4u8TKh5Pd82q6dpjdr5nWKIoX757hv3z6tW7euLlmyJHCZzsL3n6MGJyOQooXUndIeXd0rInUB3Md9511tjQljtWvX5o033qB3794cPnzY6zjlSmmL3CdA3kl6vYD5gYljTPj65S9/SceOHRkyZIjXUcqV4nQheR/4X+ByEUkTkb7ABKCziGwBbnXHjTFFePnll/nnP//Jp59+CjhtkbZlF1zFObr6cCGzOgU4izFhr1q1arz77rs89NBD3HjjjaxevZrp06czL/+RchNQdsaDMSF2yy230L17dx599FHq1KnDhg0bvI4U1qzIGRNCP/30E/Pnz2fs2LFs2bKF5cuXs337dnJycryOFrasyBkTQidOnGDChAm0bt2agQMHMmzYMOLi4khLS/M6WtiyImdMCNWuXZv//Oc/jBgxgpdeeom4uDh++uknNm/e7HW0sGVFzpwydSq0agWVKkHv3md/zbhxzp2ovvoqpNHCiYhw//33s379egYMGMDJkydZuHChXQA1SKzImVPq1YORI6FPn7PP37oV/vY35woo5rxFRUUxZMgQDhw4QFJSUsELoJqAsSJnTunaFe69F2rWPPv8gQNh4kSIigpprHBXvU4doipVci56mpvrPIpATIzX0cKCFTlTPH/7m7Mbe+edXicJP3YB1KCyy5+boh0+DMOHw8KFXicJT3YB1KCyLTlTtDFjoEcPaNjQ6yThyy6AGjS2JWeKtmgRpKWduhvY/v3w4IMwdKgzmPNXnAugmlKxImdOyc52hpwcZ8jKgogIp8idPHnqddddBy+/DF26eJfVmGKy3VVzSlKSc0RvwgR47z3neVKSc7S1Tp1TQ8WKEBcHsbFeJzamSLYlZ04ZM8YZirJjR5CDGBM4tiVnjAlrVuSMMWHNipwxJqxZkTPGhDUrcqZU9NTtKI3xNStyplRee+01Ro4c6XUMY4pkRc6Uyn333cfrr79uV7Q1vmdFzpRKvXr1+N3vfsfYsWO9jmLMOVmRM6U2dOhQPv74YzZt2uR1FGMKZUXOlFpcXBxPPvkko0aN8jqKMYWyImfOy+9//3uWLl3KqlWryMjI4J577vE6kjEFWJEz56VKlSqMHDmS4cOHc/ToUVauXOl1JGMKsCJnSm3Dhg0kJSXRs2dPUlNTWblyJSdOnPA6ljEFWJEzpVa/fn1SUlJo164d/fv3JykpyYqc8R271JIptWrVqvHRRx/xxhtvMGrUKKKiosjKyvI6ljEF2JacOS8iQv/+/fn666+pVKkSJ0+etNO9jK9YkTMBceWVV7J+/XrGjRuH7Nljd4I3vmFFzgRMdHS002fO7gRvfMSKnAmcmBjnzu92J3jjI0UWORG5WESSRWS9iKwTkcHu9AtEZKGIbHEf44If1/ia3Qne+FBxtuSygadU9SrgRmCgiFwFPAssUtUmwCJ33JRndid440NFFjlV3a2q37jPDwMbgPrAPcBM92UzgXuDlNGUJXYneOMzJeonJyINgRbAciBeVXe7s/YA8YGNZsokuxO88ZliFzkRiQU+BJ5Q1UMi8vM8VVUROWvnKBFJBBIB4uPjWbx4cYkCZmZmlniZUPJ7vuYZGeTk5Pg6I9jnGCh+/xzBg4x51+o/1wBEAl8AT+abtgmo6z6vC2wq6n1atmypJZWcnFziZULJ7/m0XTtNb9bM6xRFss8xMHz/OWpwMgIpWkjdKc7RVQHeATao6sv5Zn0C9HKf9wLmB6juGmNMwBRnd7U10ANYKyKr3WnDgQnAByLSF9gJPBiUhMYYcx6KLHKquhSQQmZ3CmwcY4wJLDvjwRgT1qzIGWPCmhU5Y0xYsyJnjAlrVuSMMWHNipwxJqxZkTPGhDUrcsaYsGZFzhgT1qzIGWPCmhU5Y0xYsyJnjAlrVuSMMWHNipwxJqxZkTPGhDUrcsaYsGZFzhgT1qzIGWPCmhU5Y0xYsyJnjAlrVuSMMWHNipwxJqxZkTPGhDUrcsaYsGZFzhgT1qzIGWPCmhU5c24HD8J990GVKtCgAfzP/3idyJgSifA6gPG5gQMhKgr27oXVq+Guu6BZM7j6aq+TGVMstiVnCnfkCHz4ITz3HMTGQps28KtfwezZXiczptisyJnCbd4MERFw2WWnpjVrBuvWeZfJmBKyImcKl5kJ1aoVnFa9Ohw+7E0eY0rBipwpXGwsHDpUcNqhQ1C1qjd5jCkFK3KmcJddBtnZsGXLqWnffmsHHUyZYkXOFK5KFejaFUaPdg5C/PvfMH8+9OjhdTJjiq3IIici0SKyQkS+FZF1IjLWnd5IRJaLSKqI/FVEooIf1wTdjh1w550QFwd16ji7rEeOwIUXwsMPw/TptiVnypTibMkdBzqqajOgOXCHiNwITAReUdUEIB3oG7SUJnQee8wpaLt3O/3ili+HW291Ct3//R906+Z1QmNKpMgip45MdzTSHRToCMx1p88E7g1GQBNi27fDgw9CdLSzJXfHHdZlxJRpxTrjQUQqAquABGAasBXIUNVs9yVpQP1Clk0EEgHi4+NZvHhxiQJmZmaWeJlQ8nu+5hkZ5OTkFDtj3S5dqD55MpsrVCDi8GGazZ3L9j59OBDkf2O4fY5e8fvnCB5kVNViD0ANIBloA6Tmm34x8F1Ry7ds2VJLKjk5ucTLhJLf82m7dprerFnxX79+veq116pWrKgKqr16qebmBivdz8Luc/SI7z9HDU5GIEULqTslOrqqqhlukbsJqCEieVuCFwHfB6LoGg/l5jq7p127Om1wBw5AejoMHep1MmNKrThHV2uLSA33eQzQGdiAU+zud1/WC5gfpIwmVA4edA4uPP44VKoENWvCI4/AZ58Vusgbb7zBrl27QhjSmJIpzpZcXSBZRNYAK4GFqroAGAo8KSKpQE3gneDFNCFRqxY0auR0E8nOhowMmDkTmjYtdJH09HR69epFbm5u6HIaUwLFObq6RlVbqGpTVb1GVce507ep6vWqmqCqD6jq8eDHNUE3bx58/jnUrg0JCRAZCa+8UujLn376abKysnj11VdDGNKY4rPryZmCmjeHEhz5ioiIYNasWdx4443cdtttXHXVVUGLZkxp2Gld5rwlJCTw/PPP06NHD06cOOF1HGMKsCJnAiIxMZE6deqQlJTkdRRjCrAiZwJCRHj77bd58803Wb58OQA33ngjx44d8ziZKe+syJmAqVu3LlOnTqVHjx4cOXKEjIwMtm3b5nUsU85ZkTMBs337drp27coNN9zAM888Q0JCAqmpqV7HMuWcFTlTIsOGDeO9994767wnn3ySa6+9lq5du/Lpp58SGRlpRc54zrqQmBLp1q0bHTt2pE2bNjRs2LDAvHnz5vHhhx/yzDPPEB8fz8KFC6levbo3QY1x2ZacKZFf/OIX/OEPf6B3795nnOUgItx///2sW7eOnj17kpuby6JFi5xr07VrB3v2eJTalGdW5EyJPfXUU2RnZxd6lkNUVBSDBg1ix44dTJ8+3blv69KlMG5ciJMaY7urphQqVqzIzJkzizzL4cIGDbg7K+vUhOnTnSE6GqxriQkR25IzpXLppZeSlJREz549OXnyJKrK3LlzC75o2zbncumVKzvjlStD9+7O1YeNCRErcqbUEhMTqV27Ni+88AKqSo8ePTh69OipF9St69ycOivL2XrLynLG69TxLrQpd6zImVITEd555x1ee+01vvnmGxo2bHhm59+9e6F/f1i2zHm0gw8mxKxNzpRa79696dq1K5MnT6Znz540bNiQrVu3cs0115x60bx5p55Pmxb6kKbcsyJnSq1bt248+eST1KxZk/r167N3717r/Gt8x3ZXTanddtttrF69mh49erB27VrWrFnD119/7XUsYwqwImfOS0REBP369SM1NZVu3bo5d2+zzr/GR2x31QREbGwss2bNckYee+xU59/XXvM2mCn3bEvOBE5MDIg4HX5zc51HEWe6MR6xImcCxzr/Gh+yIudnU6dCq1bOPVB79z41/cQJuP9+aNjQ2VIqwY1ngso6/xofsiLnZ/XqwciR0KfPmfPatIH33vNfAbHOv8Zn7MCDn3Xt6jympEBa2qnpUVHwxBPO84oVQx7rnKzzr/EZ25IzxoQ1K3LGmPNXWPvxsmXQuTNccAHUrg0PPEDUjz+GNJoVOWPM+Sus/Tg9HRITYccO2LkTqlbliokTQxrN2uSMMeevsPbjLl0Kvu7xx6nWpk3ocmFbcv6Wne10w8jJcYasLGcawPHjzjg4XUqyskDVu6zGFMfXX3P0tBsgBZsVOT9LSnLOFpgwwekuEhPjTAO4/HJn/Pvv4fbbnec7d3qb15hzWbMGxo1ja//+IV2t7a762ZgxznA2O3aEMIgx5yk11dl1/dOf+Onii0O6atuSM8YE186dcOutMGoU9OgR8tXblpwx5vxlZztD/vbjiAjnDJiOHeHxx50zYDxQ7CInIhWBFOB7Vb1bRBoBc4CawCqgh6qeCE5MY4yvJSXB2LGnxt97D/74R+fc6m3bCjS9tM3JCektKUuyuzoY2JBvfCLwiqomAOlA30AGM8aUIWPGOEf38w9jxjiFThUyM38e/vWPf4Q0WrGKnIhcBNwFvO2OC9ARyLvR5kzg3iDkM8V0/PhxryMY40vF3V2dDDwDVHXHawIZqup22iINqB/YaKa4MjMzueyyy9iwYQPVq1cvODM1ldiMDGjf3otoxdY8IwNq1PA6RuFWryYmMtLrFKYUiixyInI3sE9VV4lI+5KuQEQSgUSA+Ph4Fpfw2meZmZklXiaU/JKvadOmDBo0iD6nnVbTMiaGiKwsMjMyvAlWTDk5OWT4OGNMZCRZ1arxvz74rs/FL7/Hcwl5RlU95wCMx9lS2wHsAY4CfwEOABHua24CvijqvVq2bKkllZycXOJlQskv+bZv364XXHCB7t2794x5fsl4LpYxMMprRiBFC6k7RbbJqeowVb1IVRsCDwH/VNXuQDJwv/uyXsD8ANZeU0INGzake/fuvPDCC15HMcZXzqcz8FDgSRFJxWmjeycwkUxpjRgxgtmzZ7PTTu8yPrZt2zYGDx4csvWVqMip6mJVvdt9vk1Vr1fVBFV9QFXt8J7H4uPjGTBgAGPd/kq//e1v+eabbzxOZUxB9erVY+7cuaSkpIRkfXZaV5h5+umnWbBgARs3bmT//v3s27fP60jGFBAdHc2oUaMYPnx4SNZnRS6MPProo2zdupWnnnqKUaNGERUVxYkTdhKK8Z8+ffqwdetWkpOTg74uK3JhpHPnznTp0oWTJ0/y73//myNHjnDy5EmvYxlzhqioKMaNG8fw4cPzenEEjRW5MHL//fezcuVKvvjiC6pVq8bq1attS8741sMPP8yRI0f49NNPAXjiiSeCcuaOFbkw06BBA5KTk3nooYdIT09n06ZNXkcy5qwqVKjA888/z4gRI8jJyeH9998nPT098OsJ+Dsaz0VERDBmzBgWLFjAI3fdRfPBg+0mz8ZXsrKyeOedd7j99tupWrUqc+bMISoqKijNK1bkwthdd91FgxkzqL52LYwb53UcY35WsWJFPvzwQ9q2bcvAgQMZPXo0ERERQWlesSIXrmJinGt5TZ+OqML06c54TIzXyYwhMjKSv//973Tv3p0nnniCypUrc/ToUduSMyWwbRt06waVKzvjlStD9+6wfbu3uYxxiQi///3v+eqrrzh69Cj79u3j0KFDAV+PFblwVbcuVKsGWVnkREU5l6OuVg3q1PE6mTEFNGvWjLVr13LfffdxUWQktGsX0DZkK3LhbO9e6N+fb6ZNc66vbwcfjE9VrlyZefPmUe+tt2Dp0oC2IduNbMLZvHkAHFm8GPr18zaLMecSE3PqZungtCFPnw7R0ed9PwjbkjPGeC+IbchW5Iwx3svXhkx0dEDbkK3IGWP8wW1DZtmygLYhW5ucMcYf3DZkAKZNC9jb2pacMSasWZEzxoQ1K3LGmLBmRc4YE9asyBljwpoVOWNMWLMiZ4wJa1bkjDFhzYqcMSasWZEzxoQ1K3LFNXUqtGoFlSpB794/T668Y4czPS7OGW69Fdav9yymMaYgK3LFVa8ejBwJffoUmHyiVi2YOxcOHoQDB+BXv4KHHvIopDHmdHaCfnF17eo8pqRAWtrPk7NjY6FhQ2dEFSpWhNTU0OczxpyVFblAqVEDMjMhN9du/2eMj1iRC5SMDDhyBGbOhAYNvE5jjHFZkQukKlWci/3Vrg0bNsCFF3qdyJhyzw48BFpuLhw9Ct9/73USYwxW5IovO9u57nxOjjNkZUF2NnEpKfDf/zrTDh2CJ590upJceaXXiY0xFLPIicgOEVkrIqtFJMWddoGILBSRLe5jXHCjeiwpyblt2oQJ8N57zvOkJCIyM+Hhh6F6dbj0Uti6FT7/3LkZhymeOXO4rlcvZ3f/0kvhX//yOpEJIyXZkuugqs1VtZU7/iywSFWbAIvc8fA1ZozTRST/MGYM+9u3h40bnSOr+/fD3/8OTZt6nbbsWLgQhg5l09ChcPgwfP01NG7sdSoTRs5nd/UeYKb7fCZw73mnMeXPH/8Io0dz6KqroEIFqF/fGYwJEFHVol8ksh1IBxR4Q1XfFJEMVa3hzhcgPW/8tGUTgUSA+Pj4lnPmzClRwMzMTGJjY0u0TCj5PR/4OGNODrfccQc7HnmE+AULiDh5kgNt2rC1f39yK1XyOt0ZfPs55lNeM3bo0GFVvr3MglS1yAGo7z5eCHwL3AJknPaa9KLep2XLllpSycnJJV4mlPyeT9XHGb//3tnxb9lS/z13rur+/ao336w6fLjXyc7Kt59jPuU1I5CihdSdYu2uqur37uM+4CPgemCviNQFcB/3nUchNuVRTIzzOGgQJ2rWhFq1nKPTn33mbS4TVoosciJSRUSq5j0HbgO+Az4Berkv6wXMD1bIsmbFihUcO3bM6xj+FxcHF10EIqem5X9uTAAUZ0suHlgqIt8CK4C/q+rnwASgs4hsAW51xw3w1ltv8eKLL3odo2x45BGYMoXI9HRIT4dXXoG77/Y6lQkjRZ7WparbgGZnmf4j0CkYocq6YcOGcf311zNw4EBq1arldRx/GzUKDhzghh49IDYWHnwQRozwOpUJI3bGQxA0btyY3/zmN4wfP97rKP4XGQmvvcbSBQtgzx549VXrSG0CyopckIwcOZIZM2aQlu/ac8aY0LMiFyR169YlMTGRce615TIzM/nhhx88TmVM+WNFLoiGDh3KRx99xObNm/n4448ZYW1NxoScFbkgUVXi4uIYMmQIo0ePpkKFCmRlZXkdy5hyx4pckEyYMIEuXbrw0EMPsWTJEtLS0jh58qTXscqk7OxsryOYMsyKXJA8/fTTXHfddbRu3Zp77rmHv/71r5w4ccLrWGVOZmYmDRs2tPZMU2pW5IIkMjKScePG8cEHH/DZZ5+xbt069u2zM99KKjY2ln79+tGvX7+8c6SNKRErckHWtm1bvv32W5o3b86ePXtg925o187pE2aKZcSIEezfv58333zT6yimDLIiFwJxcXEsW7aM1NRUeO45WLrUbltYApGRkcyaNYuRI0c6n6ExJWBFLlRiYoiIjITp052b3Uyf7pyMnnclDnNOV155JSNHjqRXr17k5OR4HceUIVbkQmXbNujWDSpXdsYrV4bu3WH7dm9zlSGDBg0iOjqaSZMmAbB582b+ZfeDMEWw+66GSt26UK2ac5ev6GjnsVo1qFPH62RlRoUKFZgxYwatWrWiS5curF+/no8++oi2bdt6Hc34mG3JhdLevc7Np5ctcx7t4EOJXXLJJbz00kv06NGDSy65xNrovHL8OPTtCw0aQNWq0Lw5/OMfXqc6K9uSC6V58049nzbNuxxl1KpVq5g4cSJ//OMfadKkCR988AGpqamoKmIX2wyt7Gy4+GJYsgQuucS5mvODD8LatdCwodfpCrAtOVNmNG3alJtuuokOHTpQpUoV5syZg4iwf/9+r6OVP1WqOLfpbNjQucva3XdDo0awapXXyc5gRc6UGZGRkQwZMoRNmzYRHx/PsWPHOHz4MGvWrPE6mtm7FzZvhquv9jrJGazImcDasAE6doTq1SEhAT76KOCriIuLY9KkSXz77be0bNmS48ePWydrL5086fQU6NULrrjC6zRnsCJnAic7G+65x9l1OXgQ3nwTfvtb5y98EDRq1IiVK1dy1113WSdrr+TmQo8eEBUFU6d6neasrMiZwNm4EX74AYYMgYoVnS261q1h9uzgrTMmxulUbZ2sQ0/VOcK6dy98+KFzKXsfsiJngksVvvsueO9vnay9M2CA0zzx6ae+/qNSNovc1KnQqhVUqgS9e3udxuS5/HK48EKYNMlpp/nyS6eLwdGjwVundbL2xs6d8MYbsHq181nHxjrDX/7idbIzlM1+cvXqwciR8MUXYDdx9o/ISPj4Yxg0CCZOdP4QPfig88comPI6WScmOu2Au3cHd33G6QRcRi59VTaLXNeuzmNKCtjdsPylaVNn6y3PzTc7R92CyTpZm3Mom7urxr/WrHF2GY8ehZdecraqrEnBeMiKnAms2bOddrILL4RFi2DhwuDvrhpzDmVzd9X416RJzmCMT9iWnDEmKCZMmMCCBQu8jlFGi1x2ttPuk5PjDFlZzjRjjG+0b9+efv36Ofc28VDZLHJJSU7nwwkT4L33nOdJSV6nMsbkc+ONN9KvXz8SExM9vdNa2SxyY8Y4fXTyD2PGeJ3KGHOa0aNHs2vXLv785z97lqFsFjlTZq1bt4758+d7HcOESFRUFLNnz+bZZ59lu0en2lmRMyFVqVIl+vbty6ZNm7yOYkLkmmuuYejQoQXutLZu3bqQrb9YRU5EaojIXBHZKCIbROQmEblARBaKyBb3MS7YYU3Zl5CQwNixY+nZsyfZdrCo3BgyZAgiwiuvvAJAp06d2LdvX0jWXdwtuT8Bn6vqFUAzYAPwLLBIVZsAi9xxY4o0YMAAqlevzvjx472OYkKkYsWKvPvuu0ycOJHt27fToEGDkN2EqMgiJyLVgVuAdwBU9YSqZgD3ADPdl80E7g1OxNJZv349hw4d8jqGOYsKFSrw5z//mSlTprDKh/cEMIE3ZcoUDh8+zIQJE3jhhRdo3Lixf4oc0AjYD8wQkf+KyNsiUgWIV9W8yz3sAeKDFbI0/va3v9G3b19PD12bwl100UVMnjyZHj16cMy9kkxubq59X2GqatWqdO7cmaVLl1K9enXS0tLYunVrSNYtRf2oRKQVsAxorarLReRPwCFgkKrWyPe6dFU9o11ORBKBRID4+PiWc+bMKVHAzMxMYmNjS7QMwIkTJ0hMTKR79+507ty5xMsXV2nzhZJfM6oq48aNo1atWvTq1Ytp06bRvn17brjhBq+jnZVfP8f8/JwxMzOT999/n/nz55OVlUWLFi2YFKBTADt06LBKVVuddaaqnnMA6gA78o23Bf4ObALqutPqApuKeq+WLVtqSSUnJ5d4mTzffPON1q5dW3ft2lXq9yjK+eQLFT9nPHDggNarV09ffvllfeqpp3TChAleRyqUnz/HPGUh45w5c7R9+/bauHFj1R9+UL3lFtXdu8/rPYEULaTuFLm7qqp7gF0icrk7qROwHvgEyLtQWC/Ad52fWrRoweDBg3nkkUfIzc31Oo45TWpqKseOHePtt99m4sSJ1K9fP2TtNMY78fHxJCcnO7urIbgBUXGPrg4C/iIia4DmwAvABKCziGwBbnXHfWfo0KEcPnyYaXYxRd9Zvnw5zZo141//+hctWrTg888/tyJXXoTwBkTFKnKqulpVW6lqU1W9V1XTVfVHVe2kqk1U9VZVPRjwdAEQERHBrFmzGDt2LBs3bgSgZ8+ezr06jae6d+/O6tWr+eGHH1i+fDkrVqxg7dq1XscyoRDCGxCVizMeLrvssgIdUJctW+bZKSamoIsvvph3332XF198kYSEBH788Ueyd+2yG0WHuxDegKhcFLkTJ07w2GOPUaNGDV544QUSEhJst8hnEhISWLFiBStWrCBi/Hi7UXR5kHcDomXLnMcg/VErF1cGvv3226lZsyajRo3i17/+NR06dLAi50NSuTLXZWWdmjB9ujNER9td2cJRiG5AVC625D777DNatWrFfffdR4sWLVi0aNHP7XPGR+xG0SYIykWRi4mJ4dlnn2XDhg1cdtllpKen8+WXX3ody5zObhRtgqBcFLk8tWvXZsqUKSxfvpzExETndnnWwO0vIWqnMeVHuWiTO12rVq1o1aoVPPbYqQbu117zOpYBu1G0CbhytSX3sxB2RDTGeKt8Fjlr4Dam3CifRc4auI0pN8pnkQNr4DamnCiXBx4Aa+A2ppwo8qKZAV2ZyH5gZwkXqwUcCEKcQPF7PrCMgWIZAyMYGRuoau2zzQhpkSsNEUnRwq746QN+zweWMVAsY2CEOmP5bZMzxpQLVuSMMWGtLBS5N70OUAS/5wPLGCiWMTBCmtH3bXLGGHM+ysKWnDHGlJpvi5yI3CEim0QkVUSe9ToPgIj8WUT2ich3+aZdICILRWSL+3jGvWdDnPFiEUkWkfUisk5EBvstp4hEi8gKEfnWzTjWnd5IRJa73/lfRSTKq4xunoruDdUX+DGfm2mHiKwVkdUikuJO89N3XUNE5orIRhHZICI3hTqfL4uciFQEpgFdgKuAh0XkKm9TAfAucMdp054FFqlqE2CRO+6lbOApVb0KuBEY6H52fsp5HOioqs1w7v52h4jcCEwEXlHVBCAd6OtdRAAGAxvyjfstX54Oqto8X7cMP33XfwI+V9UrgGY4n2do8xV2Q1YvB+Am4It848OAYV7ncrM0BL7LN17im2yHOO98oLNfcwKVgW+AG3A6iEac7TfgQa6L3P+AHYEFgPgpX76cO4Bap03zxXcNVAe247b9e5XPl1tyQH1gV77xNHeaH8Wr6m73+R4g3ssw+YlIQ6AFsByf5XR3BVcD+4CFwFYgQ1Wz3Zd4/Z1PBp4B8u5KXhN/5cujwJciskpEEt1pfvmuGwH7gRnubv/bIlIl1Pn8WuTKJHX+NPnicLWIxAIfAk+o6qH88/yQU1VzVLU5zhbT9cAVXubJT0TuBvap6iqvsxRDG1W9FqdpZ6CI3JJ/psffdQRwLTBdVVsARzht1zQU+fxa5L4HLs43fpE7zY/2ikhdAPdxn8d5EJFInAL3F1XNuxKB73ICqGoGkIyz+1dDRPIuGuHld94a+JWI7ADm4Oyy/gn/5PuZqn7vPu4DPsL5g+GX7zoNSFPV5e74XJyiF9J8fi1yK4Em7tGsKOAh4BOPMxXmE6CX+7wXThuYZ0REgHeADar6cr5ZvskpIrVFpIb7PAanzXADTrG7332ZZxlVdZiqXqSqDXF+e/9U1e5+yZdHRKqISNW858BtwHf45LtW1T3ALhG53J3UCVhPqPN53XB6jkbLO4HNOG01I7zO42Z6H9gNnMT5K9UXp61mEbAF+Aq4wOOMbXA2/9cAq93hTj/lBJoC/3UzfgeMdqc3BlYAqcDfgEo++M7bAwv8mM/N8607rMv7f+Kz77o5kOJ+1x8DcaHOZ2c8GGPCml93V40xJiCsyBljwpoVOWNMWLMiZ4wJa1bkjDFhzYqcMSasWZEzxoQ1K3LGmLD2/wHJL2KKb4RlaAAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "img = np.zeros(n_features)\n", + "img[top_sensors] = 16\n", + "plt.figure(figsize=(5,5))\n", + "plt.plot(xTopConstMax,yTopConstMax,'*r')\n", + "plt.plot([xmin,xmin],[ymin,ymax],'r')\n", + "plt.plot([xmin,xmax],[ymax,ymax],'r')\n", + "plt.plot([xmax,xmax],[ymin,ymax],'r')\n", + "plt.plot([xmin,xmax],[ymin,ymin],'r')\n", + "plt.ylim([64,0])\n", + "plt.title('n_sensors = {}, n_const_sensors = {}'.format(n_sensors,n_const_sensors_max))\n", + "for ind,i in enumerate(range(len(xTopConstMax))):\n", + " plt.annotate(f\"{str(ind)}\",(xTopConstMax[i],yTopConstMax[i]),xycoords='data',\n", + " xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n", + " arrowprops=dict(arrowstyle=\"->\", color='black'))\n", + "plt.grid()\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(5,5))\n", + "plt.plot(xTopUnc, yTopUnc,'*r')\n", + "plt.plot([xmin,xmin],[ymin,ymax],'r')\n", + "plt.plot([xmin,xmax],[ymax,ymax],'r')\n", + "plt.plot([xmax,xmax],[ymin,ymax],'r')\n", + "plt.plot([xmin,xmax],[ymin,ymin],'r')\n", + "plt.title('Unconstrained')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.ylim([64,0])\n", + "for ind,i in enumerate(range(len(xTopUnc))):\n", + " plt.annotate(f\"{str(ind)}\",(xTopUnc[i],yTopUnc[i]),xycoords='data',\n", + " xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n", + " arrowprops=dict(arrowstyle=\"->\", color='black'))\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusions: \n", + "\n", + "When there are fewer than (n_const_sensors_max) in the constrained region, max_n will only place three in the constrained region even though the user allows a max of 4,5 .. to be placed. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The predetermined case: \n", + "\n", + "- This occurs when (n_pre_sensors) locations are already specified and we want the best locations to place the remaining sensors. \n", + "\n", + "#### Suppose two sensor locations are already specified (sen1 at x = 10 and y =7), (sen2 at x = 25 and y = 30). We optimize placement of the remaining sensors. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The predetermined sensor locations (Column IDs) are: [665 478]\n" + ] + } + ], + "source": [ + "# Convert pixel coordinates into sensor locations (column ID's)\n", + "\n", + "predetermined_sensorsy = np.array([10, 7])\n", + "predetermined_sensorsx = np.array([25, 30])\n", + "predetermined_sensors_array = np.stack((predetermined_sensorsy, predetermined_sensorsx), axis=1)\n", + "predetermined_sensors_tuple = np.transpose(predetermined_sensors_array)\n", + "idx_predetermined = np.ravel_multi_index(predetermined_sensors_tuple, (image_shape[0],image_shape[1]))\n", + "print('The predetermined sensor locations (Column IDs) are: {}'.format(idx_predetermined))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Predetermined sensor locations')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.subplot()\n", + "#Plot predetermined sensor locations \n", + "\n", + "img = np.zeros(n_features)\n", + "img[idx_predetermined] = 1\n", + "im = plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", + "\n", + "divider = make_axes_locatable(ax)\n", + "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", + "plt.colorbar(im, cax=cax)\n", + "plt.title('Predetermined sensor locations')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "665 25.0 10.0\n" + ] + } + ], + "source": [ + "# Print the predetermined sen1 column index and its corrsponding pixel coordinates (x,y)\n", + "\n", + "x0,y0 = np.mod(idx_predetermined[0],np.sqrt(n_features)),np.floor(idx_predetermined[0]/np.sqrt(n_features))\n", + "print(idx_predetermined[0],x0,y0)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the number of predetermined sensors allowed (s)\n", + "\n", + "n_pre_sensors = 2\n", + "# Define the GQR predtermined optimizer\n", + "\n", + "optimizer_pre = ps.optimizers.GQR()\n", + "opt_pre_kws={'idx_constrained':idx_predetermined,\n", + " 'n_sensors':n_sensors,\n", + " 'n_const_sensors':n_pre_sensors,\n", + " 'all_sensors':all_sensors,\n", + " 'constraint_option':\"predetermined\"}\n", + "\n", + "basis_pre = ps.basis.SVD(n_basis_modes=n_sensors)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The list of sensors selected is: [2204 4038 3965 320 253 594 3618 878 2331 3999 429 2772 2878 478\n", + " 665]\n" + ] + } + ], + "source": [ + "# Initialize and fit the model\n", + "\n", + "model_pre = ps.SSPOR(basis = basis_pre, optimizer = optimizer_pre, n_sensors = n_sensors)\n", + "model_pre.fit(X_train,**opt_pre_kws)\n", + "\n", + "# sensor locations based on columns of the data matrix\n", + "top_sensors_pre = model_pre.get_selected_sensors()\n", + "\n", + "# sensor locations based on pixels of the image\n", + "xTopConstPre = np.mod(top_sensors_pre,np.sqrt(n_features))\n", + "yTopConstPre = np.floor(top_sensors_pre/np.sqrt(n_features))\n", + "\n", + "print('The list of sensors selected is: {}'.format(top_sensors_pre))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display the unconstrained and predetermined (purple dots) sensor locations on the grid using GQR predetermined optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yPredetermined = np.floor(top_sensors_pre/np.sqrt(n_features))\n", + "xPredetermined = np.mod(top_sensors_pre,np.sqrt(n_features))\n", + "\n", + "img = np.zeros(n_features)\n", + "img[top_sensors_pre] = 16\n", + "plt.figure(figsize=(5,5))\n", + "plt.plot(xPredetermined,yPredetermined,'*r')\n", + "plt.imshow(img.reshape(image_shape),cmap=plt.cm.binary)\n", + "plt.scatter(predetermined_sensorsx, predetermined_sensorsy, color = 'b')\n", + "plt.xlim([0,64])\n", + "plt.ylim([64,0])\n", + "plt.title('n_sensors = {}, predetermined_sensors = {}'.format(n_sensors,n_pre_sensors))\n", + "for ind,i in enumerate(range(len(xPredetermined))):\n", + " plt.annotate(f\"{str(ind)}\",(xPredetermined[i],yPredetermined[i]),xycoords='data',\n", + " xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n", + " arrowprops=dict(arrowstyle=\"->\", color='black'))\n", + "plt.show()\n", + "\n", + "image = np.zeros(n_features)\n", + "image[top_sensors] = 16\n", + "plt.figure(figsize=(5,5))\n", + "plt.plot(xTopUnc, yTopUnc,'*r')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.xlim([0,64])\n", + "plt.ylim([64,0])\n", + "plt.title('Unconstrained')\n", + "for ind,i in enumerate(range(len(xTopUnc))):\n", + " plt.annotate(f\"{str(ind)}\",(xTopUnc[i],yTopUnc[i]),xycoords='data',\n", + " xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n", + " arrowprops=dict(arrowstyle=\"->\", color='black'))\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare sensor indices and pixel coordinates for unconstrained and predetermined sensor placement" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Sensor ID UncX UncY Sensor ID XConst YConst\n", + "0 2204 28 34 2204 28 34\n", + "1 4038 6 63 4038 6 63\n", + "2 3965 61 61 3965 61 61\n", + "3 320 0 5 320 0 5\n", + "4 594 18 9 253 61 3\n", + "5 253 61 3 594 18 9\n", + "6 878 46 13 3618 34 56\n", + "7 3618 34 56 878 46 13\n", + "8 2331 27 36 2331 27 36\n", + "9 3999 31 62 3999 31 62\n", + "10 429 45 6 429 45 6\n", + "11 2772 20 43 2772 20 43\n", + "12 2878 62 44 2878 62 44\n", + "13 3469 13 54 478 30 7\n", + "14 1243 27 19 665 25 10" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columns = ['Sensor ID','UncX','UncY','Sensor ID','XConst','YConst']\n", + "ConstrainedSensors_df = pd.DataFrame(data = np.vstack([top_sensors,xTopUnc,yTopUnc,top_sensors_pre,xPredetermined,yPredetermined]).T,columns=columns,dtype=int)\n", + "ConstrainedSensors_df.head(n_sensors)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the sensor locations (pixels) on the image for the unconstrained and predetermined case" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "image = X[4,:].reshape(1,-1)\n", + "\n", + "plot_gallery('unconstrained', image, n_col=1, n_row=1, cmap=plt.cm.gray)\n", + "\n", + "plt.plot(xTopUnc, yTopUnc,'*r')\n", + "for ind,i in enumerate(range(len(xTopUnc))):\n", + " plt.annotate(f\"{str(ind)}\",(xTopUnc[i],yTopUnc[i]),xycoords='data',\n", + " xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n", + " arrowprops=dict(arrowstyle=\"->\", color='black'))\n", + "\n", + "\n", + "plot_gallery('Predetermined', image, n_col=1, n_row=1, cmap=plt.cm.gray)\n", + "\n", + "plt.plot(xPredetermined, yPredetermined,'*r')\n", + "plt.scatter(predetermined_sensorsx, predetermined_sensorsy, color = 'b')\n", + "plt.xlim([0,64])\n", + "plt.ylim([64,0])\n", + "for ind,i in enumerate(range(len(xPredetermined))):\n", + " plt.annotate(f\"{str(ind)}\",(xPredetermined[i],yPredetermined[i]),xycoords='data',\n", + " xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n", + " arrowprops=dict(arrowstyle=\"->\", color='black'))\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reconstruct image from test set using sensors placed via predetermined optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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oOIbaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgV6FzsD9xUmk0nccsstccstt8Ta2lrc7373i0suuSRKKRc7a8YYY4wxZgUsqO8idnd3421ve1u86U1viuFwGC9/+cvj8z7v86LX8y0wxhhjjLkn45CPu4i9vb34kz/5k/iVX/mVeP3rXx9//ud/HpPJ5GJnyxhjjDHGrIjdo3cRvV4vHvKQh8STn/zk2NjYiAc84AEO9zDGGGOMuRdgQX0XMRwO4/nPf3489rGPjfX19XjkIx/pcA9jjDHGmHsBVnR3Eb1eLx71qEfFox71qIudFWOMMcYYcwGxoL4TmUwm8ZGPfCQ+9KEPxWQyiSuvvDIe8YhHxPr6+sXOmjHGGGOMuUBYUN+J7O/vx+/+7u/GT/zET8Te3l686lWviq/8yq+Mzc3Ni501Y4wxxhhzgfAqH3ciTdPEjTfeGO973/vi+uuvj4997GNxcHBwsbNljDHGGGMuIPZQ34msra3Fp37qp8ZLX/rSGI/HcfXVV0e/37/Y2TLGGGOMMRcQC+o7kV6vF8961rPicY97XDRNE5dddlkMBoOLnS1jjDHGGHMBsaC+EymlxKWXXhqXXnrpxc6KMcYYY4y5k3AMtTHGGGOMMStgQW2MMcYYY8wKWFAbY4wxxhizAhbUxhhjjDHGrIAFtTHGGGOMMStgQW2MMcYYY8wKWFAbY4wxxhizAhbUxhhjjDHGrIAFtTHGGGOMMStgQW2MMcYYY8wKWFAbY4wxxhizAhbUxhhjjDHGrIAFtTHGGGOMMStgQW2MMcYYY8wK9C52Bu5OHBwcxF/91V/FDTfcEOvr6/HIRz4yHvrQh0Yp5WJnzRhjjDHG3E2xoCZ2dnbirW99a/z8z/98jEajePWrXx0vf/nLo9/vX+ysGWOMMcaYuykO+SD29/fjox/9aFx77bVx3XXXxY033hhN01zsbJk7Ad9XY4wxxlwo7KEm+v1+POEJT4iXv/zlMRwO4zGPeUysrbnPcW/i/Pnz8cd//Mdxww03xCWXXBJPetKT4sEPfvDFzpYxxhhj7sFYUBOj0Sie97znxZOf/ORYW1uL+93vfrG+vn6xs2UuIDfffHP88i//crzhDW+Ixz3ucfHN3/zNFtTGGGOMWQkLamJtbS0uv/zyuPzyyy92VsydxHg8jhtvvDH+8i//Mk6fPh1bW1sXO0vGGGOMuYdjQW3uU5w9ezY+93M/N06fPh0PfehD4+EPf/jFzpIxxhhj7uFYUJv7FJdddlm89KUvjRe84AXR6/Xi7NmzFztLxhhjjLmHY0Ft7lOsr6/HpZdeGpdeeunFzooxxhhj7iV4CQtjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFkBC2pjjDHGGGNWwILaGGOMMcaYFbCgNsYYY4wxZgUsqI0xxhhjjFmB3sXOgFmMW265JT7+8Y/HeDyO+9///vGABzwgej3fRmOMMcaYi4WV2D2IyWQS733ve+NnfuZn4pZbbokXv/jF8cpXvjIuueSSi501Y4wxxpj7LBbU9yCapomPfOQj8fa3vz0+/vGPx5VXXhm7u7sXO1vGGGOMMfdpLKjvQZRS4kEPelA861nPiptuuike/ehHx2AwuNjZMsYYY4y5T2NBfQ9ibW0tnvKUp8QVV1wRe3t78aAHPShOnTp1sbNljDHGGHOfxoL6HsYVV1wRV1xxxcXOhjHGGGOMOcLL5hljjDHGGLMCFtTGGGOMMcasgAW1McYYY4wxK2BBbYwxxhhjzApYUBtjjDHGGLMCFtTGGGOMMcasgAW1McYYY4wxK2BBbYwxxhhjzAr4xS7mXk/TNLG1tRU7OzuxtrYWm5ubMRwOL3a2jDHGGHMvwYLa3Os5d+5c/PZv/3b8/u//flxyySXxBV/wBfHkJz85SikXO2vGGGOMuRdgQW3u9WxtbcU73/nO+PEf//F4yEMeEldeeWV8xmd8hgW1McYYYy4IFtR3I/b29uLWW2+Nra2tGI1Gcdlllzk04QKwtrYWZ8+ejQc+8IFxxRVXxMbGxsXOkjHGGGPuRVhQ3434+Mc/Hr/yK78S119/fTzucY+LL/3SL43HPvaxFztb93hOnz4dL37xi+Mxj3lMbG5uxqd92qfF2prn4xpjjDHmwmBBfTfilltuibe//e3xxje+MZ773OfGZ3/2Z1tQXwA2NjbiaU97Wjz1qU+NUopDPYwxxhhzQblLBfXe3l78zd/8Tdxyyy0xGo3iwQ9+cJw5c+auzMLdmtFoFA9/+MPjiU98YjzykY+Mzc3Ni52lew2llFhfX7/Y2TDGGGPMvZC7VFDffPPN8Yu/+Ivxzne+M6688sp41ateFU972tPuyizcrXnwgx8cX/VVXxUveMEL4vLLL4+HP/zhFztLxhhjjDFmBnepoD5//nxcd9118Wu/9mvx6Z/+6fH85z//rrz83Z6zZ8/G05/+9IudDWOMMcYYswCemWWMMcYYY8wKlKZp5j+4lE9ExF/dedkx5qLxiKZprrjYmbg74efdGGOMmaKqFRYS1MYYY4wxxphpHPJhjDHGGGPMClhQG2OMMcYYswIW1MYYY4wxxqyABbUxxhhjjDErYEFtjDHGGGPMClhQG2OMMcYYswIW1MYYY4wxxqyABbUxxhhjjDErYEFtjDHGGGPMClhQG2OMMcYYswIW1MYYY4wxxqyABbUxxhhjjDErYEFtjDHGGGPMClhQG2OMMcYYswIW1HNQSvlwKWWvlHJ/2X5dKaUppVx59P+nlFJ+uZTyyVLKbaWUPy6lvPpo35VHx56Tvy+bMw/fV0r581LKHaWUD5RS/m7HsZ9bSnl/KeXWUspNpZRfLaU8lPa/spTye6WUrVLKO5aoEmOMuU9SSvn2Usr/uNDHzpFWU0p5TGXfb5RSvvpCXMcYsxy9i52BexB/GRFfERH/NSKilPKkiNiUY34qIt4XEY+IiN2IeFJEPEiOubRpmv0lrn8+Ir44Iv4sIp4WEW8upfxF0zS/lxz7vyLiC5qm+WgpZRgR3x0RPxwRLznaf3NE/EBEPD4inrdEXowx5h7PkcPjH0fEoyPi9oj41Yj4tqZpbq2d0zTNv5k3/UWOXYWmaV50V1zHGFPHHur5+amIYK/wV0fET8oxT4uIn2ia5nzTNPtN01zXNM1vXIiLN03zr5qm+UDTNJOmaf4gIv7/EfGsyrF/0zTNR2nTQUQ8hva/tWma10bER0+cbIwx9wFKKf84Iv59RHxrRFwSEc+MQ2fIW0opg8o5dkIZY1IsqOfn9yPibCnlqlLKekR8eUT8dHLMD5VSvryU8vBFEi+lfGUp5Y/mPHYjDsX7n3Qc8/BSyq0RsR0R/yQi/sMi+THGmHsrpZSzEfFdEfFNTdO8uWmacdM0H46IV0bElRHxd46O+85Syi+VUn66lHJ7RLz6aNtPU1p/t5TyV0fhdf/yKETw+XT+Tx99R9jfV5dSbjgKDfznlM7TSynvPgrV+1gp5Qdrwj4pzztKKV939P3VpZTfLaV8/1FaHyqlPPto+1+XUm7k8JBSyhcehS/efrT/OyXtrvKtlVL+WSnlg0f7X1tKud/CN8SYewEW1IsBL/XnR8T/joj/I/tfEYee438ZEX9ZSrm+lPI0OeaTR0YOf1dFRDRN87NN03zanPn4kTgMLfnN2gFN09zQNM2lEXH/iPgXEfGBOdM2xph7O8+OiFFE/ApvbJrmXES8KQ5tPHhpRPxSRFwaET/Dx5dSro6I/xYRr4qIB8ehp/uh0c1zIuJxEfF5EfEdaAPicCTxW+LQZj/raP83LlaslmdExB9FxOUR8bMR8fNx6IR5TBx2Fn6wlHL66NjzcdiuXRoRXxgR/6CU8rI5y/dNEfGyiPjsiHhIRNwSET+0ZJ6NuUdjQb0YPxURXxkRr46T4R7RNM0tTdP8s6ZpnhARD4yI6yPidaWUQofdv2maS+nvfy+SgVLKf4yIJ0bEK5umaWYd3zTNzRHxmoh4vYcrjTEmIg5F6ycr81k+drQfvLtpmtcdhdtty7FfGhFvaJrmXU3T7EXEd0TELLv8XU3TbDdN8744dIx8ekRE0zTXNk3z+0fhgh+OiB+NQ6G6DH/ZNM3/bJrmICJ+ISIeFhH/umma3aZpfisi9uIoDLBpmnc0TfP+o/L9UUT8HF13Vvm+ISL+edM0H2maZjcivjMivtRtjbkvYkG9AE3T/FUcTk58cYhnIzn2kxHxfXHYa78gQ2CllO+KiBdFxAuaprl9gVN7EfGAiDh7IfJhjDH3cD4ZEfevCL8HH+0Hf92RzkN4f9M0WxFx04xrf5y+b0XE6YiIUsqnllLeWEr5+FF4yb+JaWG/CH9D37eP8qbbcN1nlFLeXkr5RCnltjgUybjurPI9IiJ+FSOucThyexCHDiVj7lNYUC/O34uI5zVNc153lFL+fSnliaWUXinlTET8g4j4i6ZpZhnYmZRSvi0OvePPn5VeKeVLSimPO4pvuyIi/lNEXHfkrY5SynopZRSHQnutlDIqpfRXzaMxxtxDeHccrsT0JbzxKAziRRHx27S5y+P8sYj4FDp/Iw7DLJbhh+MwNO+xTdOcjYhvj4jSfcoF4Wcj4tci4mFN01wShyGFuO6s8v11RLxIRl1HTdNoOKQx93osqBekaZoPNk1zTWX3Zhwuu3RrRHwoDnvvL5Fjbi3T61D/o4iIUsqrSinVSYZx6K14eET8BZ377dh59P9nHf370Ih4c0TcERHvj4hJRLyc0vqqOPRQ/HBEfNbR9x+bUXRjjLlX0DTNbXE4KfG/llJeWErpl8P3Cbw2Ij4Sh+F98/BLEfHFR5P+BnEY8rCsCD4Th0v3nSulPD4OHTJ3BWci4uamaXZKKU+PQ8cNmFW+H4mI7y2lPCIiopRyRSnlpXdRvo25W+E4pzlomubKyvb9IOPSNM03daTx4egwtE3T/EzIhBfZ32mkm6Y5Td//axytl1059ici4ie60jPGmHszTdP8h1LKTXEYmod1qF8XEa86igeeJ40/KaV8UxxO+jsVh+v73xiH3u9F+ScR8d8j4p9GxHVxGPt8V7wn4Bsj4v8ppfxgRPxOHHYqLo2Yq3z/OQ7btd8qpTzkaN8vRMTr74J8G3O3oswxr80YY4wxMzgKGbk1DsM2/vIiZ+eCc28vnzGr4JAPY4wxZklKKV9cStkspZyKQ2/3+yPiwxc3VxeOe3v5jLlQWFAbY4wxy/PSOHzr7Ecj4rER8eXzLGl6D+LeXj5jLggO+TDGGGOMMWYF7KE2xhhjjDFmBRZa5WNzc7O55JJLgr3aTdPEZDKJiIjJZBKTyaTdj09sxzbdn4GXC/In/nj7hWSRNPlYPa/r/67zsnOyYxa5di0trdeutOc5Zp7/Z5Vdfw/Z74N/O9lvSbfrPgW/11tvvTW2trbuijVfjbnHMxgMms3NzYiYts2L2ow7w453caFGZDnfTdMsXI55bP88x3Ie5tmOtLC91l6sUk+z8rtM2su0+7V2oJaHtbW1WFtbi5tvvjnOnz/vtsAszEKC+pJLLolXv/rVMZlMYjweR0TEeDyO3d3d2N/fj52dndje3o6Dg4PY39+Pg4ODmEwmsb29Hdvb2zGZTGJ/fz/29/dbIc6iOyJifX091tYOHee9Xi96vV6UUqLf78dgMGh/9DimlNJ+5/QY3qYGBds4nS5RyfvW1tZifX09bUx4O9JdX18/cfza2lq1MUJdcBpcZt7On5om/nq9XrsP3/VvfX196j4gH7gPnL7mD/nnvKC8nDYfq/cp63gdHBy0/+P7eDyO8Xjc/s/b9/b22m3ozO3t7cV4PJ6qj4ODg/a3+z/+x/84kR9jTM7m5mY897nPbZ9r2AjYaNjuiEM7jmefbSPbgXmEEtv5RdF2RsUWMysvbKtVmGZilG03/uc2jI/P2gO1l5kY7rKd2K7XREcguw9dYjQT/JxXTg9tcpZ/7J8Hbbey/Gr6BwcHbbswmUym2oqDg4OpcvZ6vRiNRjEYDOK//Jf/MleejFGWWocaP1AVyPqj3t/fb8U3vu/t7bXH47iI4wcDP/RSSvqwsQcSxosNCR+XHa9p4Vpra2sxmUyq3gGIx6Zppgy7imNNG9+RVy3vZDJpG5taHjMDP5lMYm1tLQ4ODqaMCxtPzitfn7+jDFzX2qHA/mw7NyAwdIt6bPgcrVvO76xz8T/uJdLB7xSdQBj9zNgbY+aDO8ra2VYxqHa05qBQm6+OBD0u2x9xUrSp4GWbkjlQauVF2pk9ykR1Zg9r7VVmU/n4jMyZVButU4fTPLZPj8nsbXbOrHRmlYvT0XYNaEcl88Zn97bWnkJsG7MsSwtq9TB3PcwQNewxzNKMWHxIp0tscR60h93VA9fjsuvVesa1/Gv9ZPsWFXYQ1VkHQ41JLS9cRm54uH6QJl9rnjIvQtd97DqnVk4tB3e8wDKeLmPMSTJHhIppbM+EjY60wd5o+jU7kaXJHf3sWa+J6lnlW8ZWdcH2tbZ/Xhvb1c7o9lnH6nE1Md3VztTS5vuR3YNaGzPLOabUOkvZbyLTCsYswkKCGg8+C2MVLJmIRggIeoBdRkyFHMPe3Nr5s/LPYjrzArDB53weHBycCLPQ47RHzcNr8xgAFbi1euD/2UPN+VfDw2IZxoSNinqVeBuENHuNuc54P/7XtNQbXms8uhpM3s73olaX+ltlA5p5iYwx86NeaBXFteP13OwYtvPZuZl3MhP1Kqq7Ot+LUDsn80jPcmzoti6ni14rE6bz2LSao6hGzbkE+8vtjNp8piZk+Trz5p3b21ntK0Zz+RrYhjrXttSYRVlYUI/H46l4JBYrLGI4jpr/cGyXd5EfSj1ulkdhljFSryvQ6/HxfG1tCFRU8jW0A1BDwz54e5dg5O81L0cmirGdr8ONlBqqzOPN90WH47he0RHpMn6gFludeUJKOYzFnNUBQ6gRPjV/NqDGLA7sBUI4avM2+K8W6oH0su9d2yK6PZNqu1lUz3LqZPu0DdJ9szy2QG20iuJ5PcZsL7vO68pP1o50ncf7YX/1WuysUceOitZ5ronjss5Kdh6OydosfLLTCfXP4tqYZVhq2bzMs6s/RPVYq/C5K3+08xiUzOOe5XeeMmQCflZeukRhV/oKl6GWX/XOaplr926WRyVLr+s6s5jnmFn3NvtdavmMMauTeY6zY7qeWRU981xzGeZNP7vOKp3wWfVTo2bL7iq6HBe8rauN0WNr+xfJyzzM6sAhzdqiBsbMy1Ix1IDFCXum2ROtcdP6g+XeZMT0JBaeGY7/2Whnw1Rq+NQTvYiQy4y7el34Wl0eGc0jb6vN4p4nz5k3pWZ4URellBOTGZEP3I9sRY7MU8/ftV5wHY2FZE/Doh0MzSu8IRqGo9/PnDsXL/v5n4/X/u2/HbdvbnYOOxpj5qM2elSzhUw2KjhL+PD2WZ7NzEZpmBuYJfyyMtaOU2aFt9XCV/izBue71h5m52eCEujIqJZVPdBq07PrLuuAyq6ffdc8aL70uKwNt5PFrMrKkxIjYir8A6EeWFWBRTWoGTo2vtims8jVSM8Sy/zQqBDk87Bdw0DUiGaNQDbcmcVbqweHjc+sCX/8vau8uo/DQDh0Q40fyqGGmTtC3AAgTdwbLqMaKi5XVyelVkb8cT1xmErWKHEdl1LiOW9/ezz8r/86Pued74zXveAFU8N79kwYsziZ40Cf61roHqeRfUbMFqo1O1Y7Dt8RqqCTsBe5btf/TC2MrZY3zsM8YrhL9KvTgu1oDQ6L65oDBCeZtttsizVvs1ZVmpU3LXNWb6Drd1Vro3gpPWOWYSlB3dWzZyG2yg8zM74Mi272OHRds2t/18Op2/l7bdkmbViyT5yv2xdpSPTYeYYU1asBWHCzh0KNK3spuobs+Ngsr9phWGQ4VH8f2riDb/62b4ve/n77/9OvvTaefu21MV5fj2//lm+xV8KYOwG1a/MIpUyo1gQl2+sL8exeqHTmuU7ESe/qPNeeV3Aum848eaiFINbaJG4futqKmuMoE/LLhs3MWu3Fqz6ZVVlIUJdS2sX6WbywcGLPH/9FnDQigI1j5uGdV1zXPA76sM7qJWuZcayGnGjvHN/5RQYawqKeXU4P12fPArzB8NSrQNW8ZUI+m9xYM2qcNkQuH8teCfTquWx675BH/DZqS2lxXel3zaN+53JzffZ6vfif//JfxnNe//p4zPvfH/3xOPZ6vfiTT/3UeP1nfZY9EsYsSWabM/vDzyPv6/J+1sQVUOG2iFjv6vhn22elV2NWCNoyAr5LaCo1wT5rhSVuSxcRmOqk4WuqqK51nLR9zsqoEzE53VqZ8KmOIv29dgl+Y+ZhKUHdNM3U6hU8bK5D6Jmw7hJKEGcqkmr5wSfEX5cRqPWiI/Jl8/i8TDBq44Fj+M2C2ohkbwnj7xzbzKI8e7EM12mXUEX52NDxuVxnutqIGiDUH+eF6587D7yiC96mli1dxf/rmxq1fvh43cYdEKQxvv/9Y39jI9b392Pc60Xv4CB2h8O4fXPzRD0YY+Yn67yrgOHvKqaz9Lr+z+zQsqJ01nlqG+fNc23fPKK/Kw0VgV3UHFgczpEtP7usHcwEKbczuCaTzYNSZ9K815xHW8xb/24LzCosJKjXJ5P4up/+6fjpL/qi2JHl4C7Gih5qVGthJvP0PGeJdnzPhP682zIhrkIYnQL1cMwyCl1iWuuCOz5cL6UcTyLkZRB5uSnOI+eNOyTr6+tTkxFr9ZPlX9cKrTWa897TUkps3nFH/NGznhXXPOUp8el/8Adx+rbbTtSHMWYxup7lO5NlPIl3ZhsU0V0HXW3LIl72ZfLVtb8mPhepq1ntzCwvd9do8Z15HKOi3phlWUhQnzl3Lq78xCfi+e9+d/zcc57TPjSYdIjJiLwGNb+WPAunyOAft76KPDNUEHi11UTmGc6rCTz+ztdTryk8sKUch3yox7QWxsJe3ux6eg4Py9UMclbm/f39Ex5qNnzIs3rrsZ/LA/HLQhjbNZwFn1lYCsN1pelpHfIyh9l94xCZt37jN8b29nbs7u7Gbz7wgbG1tRWxs9PWkyclGrMas0T1vGKIj+0SOTWHTeZtrU00V8eA5pdHA2v54GP5+vq9tk09ubjOvAI9a+eyeuORxVo+9Hs2ibDLi5yN+ta8x5nnn9sL3sdtOp+HPGgIJOe563eX/W6MWYWFBPWpra1Yi4hnX399PPv662O8vh5f96pXTS2XhxdoQGBDFGfDOvqw8oPMq15AuLHIQloR0YrubIk+fVCyobsuD4J+55hmTlPFXyaoWVAizcxrqwYIx2TnZ0a+tiQcGzeOH9ahOvUssyHjUBY1aloHXD6uI74G55/P43vd6/Wm6pHzlHUq0FBiWBPfcV+0bjwZxZjlUBu5jHdQqYnhjHm9oLiWxhBnorrmsczmomg71uUhnSW250Ftf60NyIR2JoCz9mdW/tTxo9fj+8GiPDuPHTldTiatV5zHo9NdHZisU8DH2KFiLgSLvSnx6HOv14vrH/Wo+KVnPONwexJCoH96HAtmCKiI414qRDSOwznsFcXxnDbSafM8oxc6q9dem3ijPetMiGYGoiaisw5Glj9Nc541ttULMu/wVrZ/49Zb4+WvfW28/iu+IrYvueSEUcV3CGL2LHNMuK78oZ0pzrsuuagivwutc713xpjVWMW7l4nCTKTVrofneRFRvQicty670SWkF2XZdGbVV03Y1vJeE7MqTvXafD+y83gbO8hqbWVtImVtFLqrTHr9rm3GLMpikxIjDid27e/Hdr8ft21sxGRra2qtaYR97O3tTXmr2ZuowppFdMTxj7vX60W/328fUIjp2gOWeStV8OnwHz6zSX3wjLLXtRa2kYk2/GUvSdH1m5E/9uZzPjWMRMujZctCX/i4WkeDX6aTxU//rbe9LT7lwx+OZ73lLfHml7zkRLm0brROuG60/thDzWEe/X6/LTtv1/vO3grOi9YB0tHX4Bpj5qfLo6kjkSxwahPUujytajNYNPG8D71+lu6i4jvzYGbilNG2pIuawMucLrVrdYWtdeWN2+Jse+bF5vTQZmdtgNY/C2YN4cM5GAHVSfiqIzRdzmfNeZL9XlX829liVmEhQX1+czN++Mu/PJ567bVx9vbbpx5GiMEs5IPFND/4/NBprzViOgQB5+EcLN+nQ034n4VhFibChkGFModoqIDOYoHV0HN5slAHzRNQT796d/VcbaSyutTGYJbh1TxBVH/bd3939Gk958/8wz+Mz/zDP4xxrxf/5l/8ixNpcPnxie1ZSAi2cxw33wse1dDzdAiP88J5qOXRGLM4mcDr8gzW9qmDJBO8WbhYJtC5U73oMH5N2GoZ+HsmVuehy+vblbcu2545DrJr8OesOGduc7M81q7Jdjlrz2r2nx0qHNKh+eVrq3ZgIZ51qrJ2GufqCLcxi7CQoL717Nn4+AMfGL/yeZ8XW1tb0RyJ51poB4dh8MOFHy17adlIcq9Xvda1njPSA7O8wSqEdZk79jDzddhTzWnrMZnQ1jjgUkr8yZ98evzvt31a/Ni5b4ivO/2j8fjPuS4e//j3nshrJt5RTvUucKOjQ2/acHF+1HPA3//rN39zfP5v/mY87gMfiMHRes4fuOqqeMsXfEG1YwADCDiPfP8yb7Wmo+donWeGk7dl17A3wpjVUPsxSxh2iepF0mf7praQ9zPziF0Wg7X9NceF5nkVagJ6Vn7mCW3Jwmx4H2BhmrWlnFbWRrGoVVuctWO6TYW+OuKyzo3+jhbptBizKgu/KZEFNHudMSlRV/uA13o8HkfE8cTBiFzcRpyc+YvjszWcaz1c9Ygi3cxjysfgO6ehHpFMdE9Vaq/Xhousr69Hv99Pxf8f//GnxW/8xkvjB8bfHM+Jd8W3nPsv8f958w9EKSWe8IT3tdfitPh6uA8IX8C27J7hmlyvWifwCmTl3RuNYryxET1az3lvNIrdyy6LNYmd11VC0EnKRgeyMA+9Bwj7QQcL2zPjrnH8jN6zLm+aMaZOrZOfdYo5tK/mUdXnOOLkqBbbptpIY9bZVtuL82uCMgujm0egZvVTu2aX0Mv2advB+esqK3+vjWKywOY64Ze44fh+v39iYrqWK/s9HBwcxHg8PrGwQNYmI6/appVSot/vT2kO9bDjWkiP66ero5P9boxZlMUmJYrnmQWUhnTgB980TRoGwrCAwoNdSmmXeYMhzSas8R/HPOM7jss8zploZqGWiWXNN8Qy1w+fq7HXnO+fe91XxSh22/S+MX44vnH/h2PnDcP410/8R21+annXEQGd6ZzVM6epdcDin49H+qfOn4/rn/GMuO5pT4vPeM974swdd7TnQujqCAIPu2XeAw7tYG+Ihnzo0nnc6Ga/0ex//b3YeBqzGpl4Y9FbE268TdERSO1kdw3fq/3l/DC1kBAVl5l94POy1SUWsSmLimm228t4Y2ued8Aefl0RSdsItsnsMMkcI3CoqbNGR205H5njje8/h2dko+PscOHOgTF3Fgt7qCOmRYpOFtBXj2cCO3tQ8KliN/POZqJIjab2qnFMTSxnb+iDQVB0IgYLTxWhXSEIj4wPxffFt8bL4nVxKrbifGzGr8bL45/Ef4yvW/uhap3oWyrxV+t0ZB6frDPBnvXsnv/m1399ez/f+chHRiklRjE9mXIymbTGE/WJPGRL9WlnIxPD7DFiT1fWmPHqIfz747rENedZJcQYM5t5wj0A20wdUeT9a2trMRgMWpvHtgTPLkZD2b5EnAzzUyHOnlUV/dkol4o93q92nsvDNl8Fb1d9rSL8amlr5wOwvWe7rG0f7sdwODyRPo9EDAaD9n4NBoMopcTOzk7s7e2dqC8eGebRa4xuc15ZB6hDhOuZ0+B6Hw6HU+2k1oMxq7J0yAeHd4zH49jd3W1X9uA/DvlgbzX3XiOmhRUEMFZ3wAoX/X4/Io5XfagN8bF3GA81tiF8oJTjuGmgopu3c/lRdvWasKFgzz3OY0opsX3JZtx+29kYxU5sxyhGsRO3x9nYuWQj9a6zh0DvB66pEznYQHLZdeUR9T5kBlO/s6eERyHG43FbP2ysUQfj8bj9PXD9Zp4f9XZjG8rDnRutX/ze1Hj3er0TxtoYsxhZqFaXsGZ7AFuDDjyLMNh7tt2cJg/1sy1Be8PXU9hWat70eza6pc4DDTtgW8VpqKDvEtRdojezlVq3i6TNbRifx84VroPhcBjD4TAijkUvhPTBwUH0er04ffp0DIfDGAwGcfr06ej3+3HzzTfHuXPnYnd3d6oTNBgMWtG9u7sbu7u7U845ztP6+nqcPn16asUn1AW3FerUK6XE5ubmiTrlus3aK2MWZSUPtf6A2QuNh033q+DUuF71IrKHOmJaLOs5+rCwUFRjzWEUDI6roZ4N5DXzltbqDp/Pe95b40Gv+1j8SPMN8d/j6+Pr47/HQ8r/iec9762p4VPPsnq8kbescYAA5WWJuowon6vGm8sOQQ0RzUsbal41/j6bna8eHfVEc0wcG/rM21zzUPPvyxizPF32twaLb9jjwWDQOkqGw2Frh4fD4ZTNxTMNQcajTfPkYZag1jQyDzWuxzaHHQBsc9mbPQ9d+e9yctSOUxuXjQ5nziksWattCQRwRLQdGbb3uJcQ3qdOnYrhcBhbW1tT9Yb7xw4gLLfL+oHL0O/300n2avvxO2HnnUcjzV3BypMSa38RJ+NZ8cChRwtRh4eXvRU8vMTeDP7Ogo0FMsQjezmQNp93ydZWfNWb3hQ/9YVfGHecOhUR0YpuoAaVH1A2QCwAa0ZZPbBXX319/GTzqnj72z8/brvtkvjnl3xvfO7nviWe9MQ/iqbJjauKeP7U66qA5HpgQQ14WDVLS+G1QuEdYkMWMT0bH/t5aTwV2tn1eB/H6vH9Vg8N3wet/9p5xpj5qdkn3afPNrZFxJTAwrMIuwJbzB1rHgXjdojtnF5/1ne2dTV7gO1aDmzLQkjY/mQjlWybtF66hPUsca554JA+dZLwfVCvL6/vzfnfP1o+FZ5mHA8bz4sR7O/vx9raWts+IB20sWjTcb3t7e0TS9exTkAbzm1HVi9w7Ozt7aX3IGtzjFmVpT3UHMqBGbwQUvzgAPWERkQbgrC+vh6bm5uth3Q0GsXa2loMh8MYjUatYcXxLL45TIGHIHkIEeI6Yjpm6wvf9a541Ec/Gi++9tp4wwtf2OYtW+Oay54JRg5x4QeVH2Qd/ptMJvH4x783rrrquikjvr9//DpW5JXTZI8Ce0bUa4/8wRhtbm62dcieaDXimddJRxL4PugwG5cVhosN6s7OTmsUefgv64ypZwrX1waAy66eehbvqBMOH/ILXoxZnJp3OnuW1B4iRCDiUPxsbW2diLHldFnI7e7utvM0er1e64A5e/ZsbG5upuIaf7UlTXEcd7R1u9ph9Uhn5eX/uQzYVhO4WV3qvtqoHJ/Ltn44HE7Za7TVOzs7MR6Ppyb1sz1G294l9rm+9vb22nre2dmJg4OD9p7t7+9PhfKgXT84OIi9vb247bbb2pFrbrt4dHk4HLa/He5koQ3E9729van2BL8Z7ggYcyFZSlBr7zDzWvODrZMA1HBBxPIfP2zsmWYhp95qjpHFnwrqUsqJl5Q849pr4xnXXhvjXi/+/b/6VyfiokEppY0T0/JETK+FyZ7UmheC082GrbrIGoOaINaQl5qg5u8cEsHf+f71+/2pZYyyMrBXmkU1d0q0nrv+19hqNM5cL/yp4B5lDacxZjVmeVZVRMJG7O7uxs7OTtv5VvsHp01EtPN1IiIGg0FsbGy0wprny/C1+FnXuGDNd5fnGt5oju+dx36w04O96jU7XxPqEbPf9KjX5TBJ2Gt2MqjDQv+HqOZ9Wj9oT1jgwlON+mJdkIUeIuRjMpm07TfXMY9sR5wcHeA8YbvWoZYN6XB9GbMsSy2bFzE9MYSNDg/RsZjCfu558uRDiF4VyxDRbCwx4WFtbS1Go1Eb04Vr4DoaQoL9P/pP/2l87q//enzq//pf0R+PY9zvxweuuire+sIXToV8qIc04uTDh7L/yZ98erz/t65uX9DyuM9+b1x99fUn6k/PZYMPakOY7K3R/OEYpKXeCZ7ow+lGHHt3OVaZBaeufILzkR6Ml+ZTPdQQ0RwGsru723pNsF87Ziy+2WBiOJEbahb/bDTZCHNaHP5ijJkP7bTXPMJ6TpYGPJYRx51wdLp3dnZa24EJaxBeEcchYP1+PzY3N6dsQW2eTSYiAW/j8nB4G0ZlIfLH43Eq3jIyZ0rm0ODvLCa7xHTWHkDQIt/4vO2229q63d7ejvF4HP1+vx0d7vf7U+0m0KXosthrtr9Y3WM8HsdgMIjJZBKj0Sg2NzdjfX297RyNx+PY2dmJc+fOxcHBQdu2cHuPbaPRaGoCalf4BjuFtL65Q8f3w5hlWclDrS9p4V4lwHGIkcOwEwsyHgJiUc1e5sFg0IZ/bGxsxGg0asMYRqNRRETr2WBhBUE9FRs2GsX+qVOxvr8f+73e4efmZowvvzwGJB7V886TJPgBvf76q+MNb3jR1Atavvm3fiB6vV5cddV16fASN0BqjFkQszHn8JJanB8btlJKGzfNnvpMeKtHXgU1x5Vzh0gX/2evA3uV4eWYTCZtR2kymbTGFPt5KBLnoSFVrwl+U2hw2RsfcdzYQvBjOBDGGPWD/BtjFifz6Op2BZ132DHYc+5Mnz9/Pvb29lrv9dbW1tTKEqWUVtD2+/04ffr0lOc4mycC2EuM/yOOl0TVc2A70ZGHrYIYjYgTbZ/WgXpXYSPVU8weZO6szBLT7Gjh+lUP7/nz5+MjH/lI3HLLLW2nZTKZtJMI0a6eOXOmHSXmtae5beEVp+A8Q70fHBzEuXPnIuJwAiOcX6dOnYrLLrss1tbW4uabb45PfOITsbu7G+fPn49bb721teWo09OnT7e/j16vF6dOnWpXFMNvCdfn+93v92NjY6O9PjphOJbDQtCJsHPFrMJSgjqie4Yx/6gzj6ZOSFDxyMvisDDm7bykHh5qGI2ISNPDMU3TxKk77og/etaz4o+e+cx40rvfHafoJSVdDYFuL6XEa177ZfEL+oKW8Q/HzuuH8a+vPnxBC3tmZz2wWR60YdBQBz5G63SR0Aa9np5Xiz2s/eEcxM5xxwvhM9yo4HhOt9frtZ0lFv1ZWAhfV70WXb9NY8xiqFjM9mXoqGbE8RrD7BBRQawdbdh7eDS1TcrsbdZuZd5NtRvsHFAnC0Q8O07YSaLhJ1p3s+poFmoTM7vIeecQGxbUEMSwz+yk4Hxre87X4XJz5wjtFdt/npCIfMHrz78nHrnkNj0bFc3aIKStnRKuF4tocyFYSlBn4o490SxuIo4njrBXk4UyC2aN89XVPFQscbwyPJ18bRgzfnAjIn71q76q9UZ87KUvPTy+I64tG4rDw9n1gpavaf5Lmn/1cusnztH60YYmM8xq9NhDwcerB0TvKxsklJVHAeApnyWs+X7pkCw3BOzB4SUXEX+N/zXERe8NH4t8oxOHMkPQO+TDmOXJnhu2oSxoIqbDBHjUj50nCPM7depUXHLJJXFwcBC33XZb3HLLLe2IFryNOB6eS14VSkV+lxOoq1ONdgshC0gLHuqtra2ImH7LIM/34aX/1OGj9cihbNiu4rCrfdI2AemoR35zc7MVsFhZg+0vjt/f3z+x6hX+NNYdkwkj4oQDBO+qQHz09vZ2O2I4GAwOnVynTsXp06fbdhzHbm5uxqlTp2JjYyNKKVNL8mb5Ql1zmwY9wY4bhA4hrxD4xizLSoKaxQ9PRNDeKgtjHjLiPzZA7H1mcR0RJx54iHgNGUAeaudFnHzN6izDWpt0V3tBy/bZ0QlDx2Ee2bXUCCBvtfrT9LQx45h1btiy0I6auITxjIip8IpaGbgsXK8cqw2jx0OpELl8n5qmaV8GoCKZh2l5P3+H94iNLzdmMOrGmMXIOt+KjippJx0iD3ZiMBjEmTNnplaAWltbi5tuuik++tGPxu7ubitiNdwQooudEBHTdjsTo+xQ0HJFRBvHOxwOp+Z27O3txfb2dtx2222tZ/Xg4KANT+z1erGxsdGWRR1N7FzRsA9+YUrmTNKyqH3L7DK3d6dPn47BYBB7e3ttTLKuUqW2U8U5ysvgfHUWjcfj2N7ebtsO2P2Dg4M2bvvUqVNx5syZNqRmf38/+v1+ux3HcT75N8ajFnxt/n3pyAPyhLxbUJtVWDrkg+EfdNaDVnGYeWu1V60e2po3Vq+rk9rQo+Xj+Tykp8M//GByrzYr9+d+7lviQa8/+YKWz/7s3zzRmGj+tZ66vL0absF/PATGDUjW0Kkhmscjr8Ny6rnOYE8BNyRaB9zY8LXR0PDMeM0rd9604dQ/rRf1EhljlkOfb2zjz5rgBmrrsEQahDY63LXY6Myeqa3L8tVVBkbbHfbiclz12tpa+5mVbRaaj8wWZ/uy66iwhg3kuUsQlDiH28Bam8XtpdaPTiDH8WiT4RlmBwqcZhjJxvkI6YQjiTs0WbvVVX9os7J7UftNGLMICwnqzCDwhDNeUxgPA3qjvJyRiuS1teOVOHSmMXsq8J3PxwOKYSWsZcphKBBcWUxdxLGnORNZ6jHgeoDn9qqrrov/efDl8Tu/8wVx++2Xxref/Z549rN/Pa56zHtie/t4zUt+bSvnQxsSHfrMwj7Ucw3Dw5772jW4N1+Dvdm83B0ajZqR1w4RjzDwb0DjvCOmX9rC9y/iWFzrcnsR0y+Y0Y4Qp438oXHWWD1jzPxoZ5XtC49q8aRwHinktkKfXQg+hEvceuut7coU586di/Pnz09NQMRwPcIETp06FaPRaErI4bqARae2R3zM2tpajMfj1jlz/vz5OH/+fGxtbcXW1la73B/bL55Mj2X9NO2a8Mcn6gO2L/NQs/NBHVGaNotf5Afp88oqTdO0Xna0zZnDRutJrwObv76+3q7igb/bb7891tbWYnNzs30t+ObmZtz//vdvBTc6T5ubm+1iBvv7+3Hu3Lm2LcocechL1marUwdtE084tag2y7KSh5rDPPivaZoTQ/3oZfLDnk0y5AeZQ0Wy2GsW1JPJZCoeipdo4wl8CAFhA6rDVhk8s5yHj/D3+Me/Nx7zmPe09XI42WPSei5wTe6BR0wLUDQMmaDW+tSOSq/Xi+Fw2JY9m8THQ3ZZDx/GSYcg9/b22tnQvMg/GywdftVOECaOsoHmPzbInDZ3iDBcqPcLXmz8Hnd3d9u64vxhEiTygPwaYxZHxR/bE17WLCKmlsVjEc7CsZTjtYhhZ9EW3Hbbba2gvuOOO+KOO+5oJzrD/kVEG3KB89mW1Ly+LHR1BFVF9v7+fpw/fz7OnTsX29vbsb29HTs7O206SBs2D+0Z7B+LTh1x01E4PnYejyoL6i4PN2w0JoRn7xJAnaKdYRvNwlxFNZcj4tCJ1O/3Y2trK3Z3d1sxjBj4BzzgAW3c+8bGRlx++eVTjhvuLPV6vamVOvh6WXvGdckTKBmIfqSjziJjFmGlVT5qP+SIk0P62coT6hWYd0iMrw9j3DUExHnSv3mvA0Gm52odsGHCA6pvdUJeWOBqnS1aDp2oqN4MfO8yyuphgCDlpao4Xn54883xvB/7sfitr/3aOH/mTERMxySzAeZRAL0ee9H5PBzDnqeu8BvufPC90uOzEQELa2OWQ22vdmZr9oj/59CuTGRGHIsiFnjadmQeaL52Zv/mbQfY/rFN5DLDFvNKFFx+zUvmbeZrZnnTtgZ1A9iBVAvJ42trSB5sqeZbv2f/Z7aV6wF5QpuoseKw8WjXUTZuL7lzpM4pradaG8fncMihxbRZhaXXocZsa50gwAKPHyj2qqoHAOEKOJ7T4238kMEA4Fq4Ni+9BCOdhY3oA8rCWf/YiMIQqTHk47inixcSlHL4GlY2ttwDV889UI98LQQEPXgV/2wkuO7Ya6MCmjsp8Pju7u6eqJ+n/dqvxYM/+MF48hveEO945SunRgHU4OJcntCoDSIPK8LI8SoA6j3njgp7o3iGP5edGwrUGUJwHPphzPywXdGOfURMiV5dcaMm8jgttW9nz55tbeatt94ap06dmrLbCB84depUu/4wJhFihBD5zq6pNkgFG9a7xotQMFEa18Lo6traWmxsbMQll1wSw+EwRqNRa/9goyOOJ1LzyCrb1ppHugbbfHXcaJ1m4pHP47ZV74PWG9cfh/WUcvjOiY2NjTbsZTQatZMTMakU64yjzFg3WgW4dn60DebtOirJcNsXEe1vk9cUt6g2y7K0oMasZl1cXXutLKxZ+Kmo4jcssdjjB4S9EBx+wb1rCC82BjxkhJg8NlhsdDmGCoIYn7wCBT/A7IlmMd00TWuII2Kq44BhNsBefPzPqIHL/lhU8ncV1BxKwcDY4h6g7FizFOX9B//oH0WP1lz9jHe/Oz7j3e+O/V4vfvg//aeqB5nDNxC/rJ4cFdRspFnsc7wfNwQcJqQNB/8ukCaGh+2hNmYxMlHGNgx2Ut+e1+Wt5naB7drp06fj9OnT0TRNK8747asQcJgfgVADDaHIwvv0muzQgB1BCAKWf4NDqdfrtS8cg5gfjUZx9uzZ1t5zm8jvTIg4ntuhtoxt8bz3AvadwwxZCPP8na50kBY7WDIbreEhOgo9GAzaeUP44/BB5BN5xduQkVe0kYinxjnq2OL65Hxx/fE27oihvRiPx+2LhLo6fMZ0sdSrx/UP6PCLLsFWEy6zvIPaU2fjwZ5IFqM6TKghJmwYINwg9jgmV68NQcc9/Vn1w3kej8cnhHNmQDPvj3p0u4a7eDsP6bH3QYU2e6vVQ89l/Ynv+I54zuteF49+//vbV7f/+ZOeFL/7spdN5Ys7PurNr9VP5lnmetD7rUaVvTAaylH7zTrkw5jVYRvF6DOXhYLg/+w7C22EfnGcNAQWv2F3nuc5a5vUfsH2wYvME7Q5XxCU3N7oXBu9VlZOtoUshme1neyVRj71etq+qHBkm4//ecSY2z++dlcHiee9oJ54gjpGVbUN4G2z7hPvy9oZlEWdYNym8AiBMcuysIdajUrE8UQ07v2rZ5iFEB6a7OGoPTy4Nj+c6gnhuDqe8MivNedeNA/zsUeZBXnEcYgLrziReUC5/ByWAIOE2eCoLw7TQHqZEdWQDzbeKga1zlhgaiiLGiD2zsNzgE4AT6w8d+ZM7I5Gsb6/H+OjV7ePNzZifPnl0VubnvjIDQ8bSw6JQbngAcp+BxxOArGsBh71h1EA9rJkcXtotIwxi8EdURYosDcqzCDw4AGEAObRIqTLz76G/2HyGq7L6+LDLqpQq+UfqPcVecGKFAcHB3H77be3YQr85r61tbUprzi80uyxhb3j13RjlFJHRrW8/MrvmmMJ9YDQCYRRlFJic3OzndjJI3k8twfXw4RQ2EWMdMKDq44pHMdl5HZnZ2ennWC6u7vbvh4eNnp/fz9Go1F7nxFuoQKdf1tIm+8h/864nVO0vPjNQLsg7MOi2izLwh5qFtO6pJmKIDZw2KaosGZq3o6ISK/NRllXmsgm7WWeSTZUbKz4mmyksyFE9cKqgOX8YWlAeFy4Ecjyyy+76apXgDqpNSxcTs63Ds2hnPD+bN5+e7z36U+Pa5785PjM666LS++4Y6qOOT0WvcgLxz+rgUeZuFPE5VVvNpePO3BoaLNrcmdQGytjzGxq9pxH2/j551E62DIdbWNbh/ORLvZDwLKgjpgOb+A8sMOiVg7uDGB08uDg8BXd4/E4zp07F7fffnvbacf1cE2I6MFgMLWiVTZKp52RiOOOA+qJJ2CiPFrfGjO9u7vb5hmCGu0L6hLXYC8754HfNoy603BHlEHDe9jri/Q4hILbLnZisW1GJ4VtO/+vo9l8LRXY2ciAou0Flt01ZhkuyItdamKYjQAbFTYcNWMDdAhHrwFvdUS0vWq+nqbJeWVRpdfj7WxwuSOBNLKhLvWGZtdjr74KRC0ni8ssjIWHybIeeiaaeShPOxGZsI84Nm5v/Nqvje3t7djb24s3f9EXxdmzZ2MknoWsbriTAyOYNRZoKPDbqP0msj8+jsvG90QbMBtRYxZD7SaHh7EN4ucV52XOAh4xVLug9qnmaVU7j2PUtrFt531cniy8g20wO4041EQdI0iP2zDeh8nRnD8W5+pwYucB6hvxx7pAAC83qx2ODMSd872ttdX8G+B2Dq9Oh6DmFZewX9sihu91Ft7I5dN8cDud7UfZ+V5yGArHvBuzKAsL6ky4qPCMiKk1kvEQ8kL+HGPGwooFYsT0pI3d3d2pa6KXi/+5Z89p8OxqwJPuVGRzY4CHk9PlWdkwuuotRn5YTLNh5kmJGqPGDQn+x9rNw+EwNjc3TxhIzjcLRjbCyDMbuloeNU/cgGhHgPOtnaVs+AyTh9bWDmfEwwBn9QbvDw/l8vAlysNly/ZhYiV7fSKi9ch4mM+Y+YEtiDh2FOA5U7HGzxaPOLFjAGsRs3jkdgae7Yg48byzHcc1uN1RjyvbKxyP0SwWU3hpC2wK8joajaZigHlte/VMc7lhd9HOIY+bm5tTbVnEschmWwWydgOhJvBUY71m5JU7Aig/BCvuxf7+fgwGg7ZeeXUUHu3jThCWuUN4B+4T6oxDfDIxr20tys7tCeeb2zXtcLFI13ZEVynB+aifpmni7Nmz7X02ZhlWnpQYcXJpHmzLZnxz6ADHQuPHrz1SHI9Z1SyADw4OWoMG8IDyjGYNF8Anp6cPOZeLDSAeahh0XoA+84irwORj8Zd5SbVHjU6Bvj0SD3+29JIK6pro5j9etYXzpDGGOpSrnRL2HvH/uKd8r7lxAbwiyGQymYpJ1N+iimj1uHMDgXuGxodnkBtj5oftYcSx0GU7EXEy3IyfexZmKh61Y49nG6tD4DtPwGNblNl0zXvmjYRd43kkETHVqYd4hgeYOwqcF6SnHnZsj4ipWHJsw9wfFXdqr9X+wlaiLjnsktswFtM8xwh1hVAXXBPHq+eY00NbuL293d4X/KH9Ug8/22ikyY4q1EntPmUOqOx4FfT4H+3pwcFBjEaj1lllzDIs5aGOOCmi8VpQNWIcisDnYQ1PNnQYJsJDh+vhgcO57BVgMc2GFemxF0TLAKGFa8HAIE32WvBQEtLgcmkZMy8ChBsbksyToeKSGyg26iqWObYddcHeHhaXLO5xXMT0GthcNj5GhT97k9jQ8zHceGXedU5TOxcaYlPrhGieuBHhP70vFtPGLAZGl+B1ReeXvcwgE7c8D0RDJtgucEwv2wG2qRzmgDZHhSinydtU1HH67JWGpxSji9y513YuE39IU2N92SuLusInd/RrbQ3+jziesM1/NXGoYREsMPWatQmR6NioQwL7NM8ou9pcbitRF7iv3C6xBtDfF0+i5PvB9auhRPq7RAepdv+MmcVSkxLxw0ZPWGdZR0wPo6m3FMfykBIeZPZC8HAer9Zx6tSp2NzcnHo4YdjH43G7ODx6n/xabqSPGdwY5uL1L+Hd5h62ejJxTfWUKmzw4fFQY6CeYB4SxUOP+C4skg+DiLR1ljdmV6PR4saJy6MdDu5c8H1nA84CWEcYtHFi44dz+F6ura214h+/La4H/H64s4VhRm0E2WOC66BzhN8CGip4uCymjVmcXq8Xl19+eWxvb8e5c+daBwmea3UwsAdWvYP9fr8NA+NJ5Ht7e7G9vT01Iqid9v39/Xb1JL6eToBjgQV7Ai8ue6I5JhthFxHRxjirMyDieFQUdpA77uxcYW86p8OT0tnTjJfHsKeeYXHY6/Xado9fK85x4JwX9qgjDZ7AyKN4ESfnrWDyI+cP6aOcfE9Qt5x3rgc+Bnab2yKEoqAd1VFe3C+MeqsziJ1QPLIL0EFEOsYsw9Ieah2uZ2GmDx8LF/YQ4uFh0QQjCaEF7wfeOAWDi/8RKwajBkPADw4Mqz5kMNQ84xgPr5ZZPZzai9fhPKAeag17yLys2ZAV1zH+eEiOOxbau+f6Ye80D2XqdZumaY2ydhSyYUu937qf0+bfDJ/HQ8YQwlzf6k3KvNN8LW5w1GuVjQ4YY+YDAgTOCRabEFbZkD23GSzq2E5zqAPsv9pEtRm8DddgYaydehZhHI/NscPs1dSyR0y3ASxWa7ADAG0OBDXbKaTJYWpZWBqHUXDoibZJmaNH7bLaSvaicx2zVz2bcI/tOlqMY7QN5pFWdj5xGF/WIeO/miee70c2kZSvid8iOjbGLMPSq3zw0Ap+zIgtw3f1xEacHLrSsAP2QELUlXL4StIzZ87E+vrhW7M2Nzcj4tC4YY1MPNR4KGAM4I1kTwe8uZw/Fuw8jIR1PWEk4FHQoUaUSYeS2OCjs8AeGnjIUX/4zksxwevAS03x0BUbIfZIREQq4nkYk4dfkW8eXcBoA+ob6SIOPnudPBtAfOKY0WgUp06dOiFweTING3Q17urVx++Ry8UdGOQ/a+Ah3G1EjZkfff5UEGKb7uM/2DUW4TwKmr0GWtsK2GtuU9gmRhyLqYjpZfhYLLOAU9HHZdB0eZIcO4VYeAIdUePtbKciYuqNjDyymp3HsAce/6s3ne+hCnB0RLQdY2eICmBOF202PMo86sj3Dd91DhSuy4sQ7O7utiME2rZxupymXovzze0J3vbM72AwZlmWEtTqXeQfOXuCGf4R8xAYHiD2CkB0whO9vr4el156aVx++eXt8CBCOLa3t9uZ2BwaUcrxBEis7oAhRO71I28YEuKQCuQZYhrXGI1GrbFAGtwQsECFMFVP/fr64QsKsGrH5uZm9Hq92NzcbF8QgJnvKC/Kpg2TCkg2dNyRYUGNfTzBhnvxvA511ulhw61DnDyTXM9ZW1uLM2fOxP3ud78TgvrcuXOxtbU1NVSJvKuHH79DXE+Fu3baUOdcDzzsmnk4jDF11NuavcwE+9lZAjuDVSw4bIy9nuqVzbyTEE9sbyKOw8dgj3nUUcUie4MzJ08W5sG2PBvpYoGqNhjf8Yl1ubn8+/v77auw+XjkSbchbAVeVjiVOK4683KjnUSdcNnZUaYCFmIUopfvIdo/tCEon/4O2CsM2PmDkY+tra10tDTzWquDCFqAR4N5dBKhnzy6YcyyrDQpUb0S7C3Q3j737LVHXPNCIE14a0ej0ZRHFw81Hk414GxIINJ4NQx9gHToUb2xHNvFIRicf/ZeaG8cZeYHn+P8eKKlhnfgmipS+bpZyIXWee3+qaDmBkSH1Fggaxwe3z/1EGjMIHsm4Onm+Gj1fkDEqydMPeO4J+yZ4rdh8XnqqTLGLEYmdCOmBQ97eNlmcGeWR/syW5ZdVzvzEE/Zcdn5QK/N2zQNDRth1DOdiWp8qrjjUVQIfI2d1jyzbYSNY7HKy91lec/qmL38WscsPLO4aG4r1JHEMdFZR4UdNSgX6iAL69EwUqAjwLgPqEuuc07bbYBZlaUENR7WiGh7lyxY+ccaMb1GMhtXnKfgIRsMBlMiWj0ZOBcPFK8NqgaADQkLrVJKu1wOhHtm5CHo19bW2gmR8GhgkgR69zo5h+MAkTY80PBG877MkLGhZ0E/HA7j4OCgFahZb1xjtdlDoK/J1XvChg91jXJheFAFOIeVsPHEdXBPuWHlYVJMeGGPETdU/DvCH0/gRP4Gg8GJlzMgtIQnOFpQG7MYeHYg4NR2sbOC9/GxaDN4JE/nlrAdw4gmT3iDcEKe2HGDOTo4B3kB7HBBXtlxoY4CPo/bNNgjlJmXDoRtA7h+5rlGWwKbNaszwfvgXEJdou1F/dScFBHHL2JhsZ8Jaq4HLjvqGvnl+8SOK+5Q8fwmtC1wxqnTh+PaeR93oLiNyhw6KqD1k3+7xizL0h5qPAhN07ThF3t7e+3Dy95AjlGCYQQaAwYvN2Ytnz59eir0QwU1izn2eLLh5IcdQ0c4r5QSp06ditOnT7cCla8DY4k8wIM6GAymYrwQw43Z7hDdCBNhEdjr9VoRz2Ec6tFmIwEDhSEsFpEHB4drf0Kksrcli0NcWzt+kQKGQ2FQ+VjkOxuyjTie+Q6y+Dzkl0NLNjY2TsSFw6M8Go3i4OAgtra2pl6lq6EzbIx5Mgnqhhs9/o3hHqETxDHzxpj5wLMdMR1Ly/tZULPXlF8IA7vGq0Xw2/WQFntj+Ro6cse2FmlMJpN21SMczx5LdgrwNXm+SibM1L7gGJ5QzeXFNv3k9BAiwaF76mHnPALYSdQ1r6KEdZbVy8vtG5dNr8POEnZqcVhN7T0IHJvMjiB9MQ6uj09u3/Wlbvz7Yhuu3nm9b8gfhzPik8MXa50YY2axVAw1D83oQ81xsRHT64KyGMoMJNJmYcRhEZwmiys+bxbIN/e6a2EVbHhwHPLPgg5rcHOeOX01UtmQF5db/2rDlbwv8yLzcep11saIj8kMCl9Hh+j4PrOhZu+vhrdo+ZqmaT0UOCYrT60O1LPPHnJ85/uU1aExZn54dCjipL1kz2TEyQlpbCt4QjUEJa5Rs1/8XYWvelxhA3TIP2tD+C+D85J9hy1km5iJ4KytYaE/j7BjDzjHdHOZ1XPLx+h5WYwy9ml9qDDXUAp1zmjbl90zvm8cb65tKN9PreMMzaeO3NqhYi4ECwtqFpUR+UPFsVtZTBpvY0opbQgEe1vhad3Z2Tkx/MRpqadCjSKEnXq2kc9SytQ60RgSggcYnlIelsODzp5Orhv21vMEw+Fw2IY+cNhFJvRrgo9j1kejUfuiBZSfPSeZt5pHDLSDwsN5SA9paR1jO8B5akBxLjeaOgkJ57H3C/eCDSA8EZmRV4OpHTf2mNmQGrMcsJkYoYSnGdv12YKt4Xhh9vKyrWeRG9H9Yg72cGbPe63dYdGJVZvYpqlzSNs6/uTrAPXqdo2GZQJaOwu8DfYa7S17m9lrzmXjeUf44+vWbCU7ftS5wR5/5CMbscBiAjp3SJ0yHEKj+5CfWgdFy8D3ByumoPwcWpS1XcYsw0KCujaUwj1qPMwsniKijYFSzwHSxYMzGAxiOBxO9VZhiHd2diIiTng4WVzzPh1eyzyj+MSDhIeuaZp2+G0ymbSCejKZtLOPUSakx6tlsMGD+EZs9XA4jI2Njan4cBgZDl/RGDOuM3Rq8OKBjY2N2NzcbOue44RxH3SVDxaf6gVio6RxherhjoipUYSsw6T3aXd3txXO2Soi6FBhH89+51ECNfDzNHY8AdTeaWMWB88Xj8hhFaWI6TABFnjsTcWLtSKOw/KQtgpqXmJPR/hYyHE4Ids09r5CRHE6vNJE5lHXvKmd0X3spec5QcPhcKptQNlrXvMsLpidG4DT4nrgeTb8GvXz58/H9vb2VBsQcXKCH9LgkUYeOeT8Id/sJEF6PAdJR4K5DOzd19EMHNNls9FGaJuApW9Z8LPjx5gLwcIeag1PYEPAQpUfKIgiGBn+HpEP3eOByIQ6zok4KZJ5IgqY9RDyNZAGP3j8API+Phd50nyz4eQOiTYO2Z/u43LzMfCywLghXW44tL5VaKoAZjE6q/7Yc8FDcJyuGn94g1j0c93pb4JHITTUSMuhXpcMG1FjVoNFX81RgeM0NIG91DhGn+GsnQAqyGrPszoKsjLU0qmlWRthzWyQep65fZwVqpDlXY9VW64dAL4mi1nOJ5dH48Hxndt7/s5tLc7pGs3U34bWnd7rZRwe6mlnka/ltag2F5KFPdQ82SFi+rWtXcMl+oDXBCOMDHrZmGjBw/QqvHVIDJ5NfWlH5mGFYeDJH8gDJlocHByuickTGbAdnmD2wkQcr7E9mUymJjLyChf6x8IUXlSEg+jyeirGMaTGISbasUGecO90WI7h+4kQHm0g2bDqvc8MFAw4DwtzrDR7P/h8bOdJRnzf+drsLcrKp8PFxpjFmUwmcf78+XYyMHfm0U4gbAthcuyQwDm8rJmKnIiTa83DLrAQysQttwmw55nAnSW29XvmoWYxqE4UtGOTyWRqNRINmYQ9ZPvEE/q47ChTrb2tpcleeLQfHGrDZeH7oaMCOvGvVm/axnKHqubgASq+uW3m+uDfg5ab74OGuGS/g5rn3Jh5WUhQY9iKf3j8I+7qubPXUWPhOH0WORxSAUGF49Qbi+34RFwfrs/nsZcAhksnqzXN9BJG/EIYntGsM9IjjoedIOqQRw754PAQbjR4JjQv41dboxrX7ff7bQw1jAcbXW7U+L7V0A7C+vr6lGeYDSJfh+9rzWusE1904iLKw+EkpZQTnQT+ffEMdvZEsPFWr7c2UsaY+YCghqNAny1e6QgvzlCvNOyZhpxxpxrPZ/ZykYj6CFv2bKtgnCdeVtPPbItel/eXUtoXrvCx7LjRUTpu63R0MGs3NZ/qhcV1eFlV7qBwe6XtOV8z4mQsO29XR5nWTeYN7hpp4GvwyinqxUba7BzDNtQD2ng9j7WJ6g9jFmXpkA+NUWLPYGaodOhHHz4+LhvuYWHERkiPQRoQX8iTliG7ngIRygaGvSz8wGbDbSrkat5lHoqr5Ye3147hTkIW5rGIV1Z772qw+Tt7BbT8mZeHfx8wgizy2Rh3/VY4jczzkKVhEW3M6sAG81C6drJrnkV88nNdazO0vchsjMYyZ3mtfXY5FfhYDSGoOY+4jMt6O7ts3axrZmJROyrqUMgELbZrW6YdALXXnMcsLxzqEjHdYZh1L7rqstbO8G+z5uyb9xrGzGIpQc2eYPT++I1GmXHV7/Bi6Exq7gGzAFQDo4aOjQPEdM1roUNJmac1IloPtYZ5YDu82NzDRZ6wjV+Fvrm5GadOnWq9N5hoA6+1Cm4up+adPSFcPojzbOIIz4afFQeoBpUFMxtBDrtAWAj/RjJvMsNDwHoul5m9HjyEizQ4ff5NYBUU/I988pCrLvFkjOmmaZrY2dmJUsrUevRsW2HH+e25/MntRCbqOA1OG0Jen10dVZtFzcOciUm2NxyKUeu0Y5s6oFRYcn2ijHhhDjyqmacY31ncalgM1wnfk/39/fZtwxjN1FWPuC3X+kF+sw6Mtqns2OKJktx+sDOuS/wqeg80Xxpmop0ivVfGrMrCMdQcnhBxLEiyGDmcw8ezQIKAzI6tGShcU68BoYX/EfLA52XGjsuGtPGpwlkX39fjMyOAOMG1tbU4depUK6iHw2EbH42l8zSemtPn/HM5NN6waZqpesXwYdM0Uy9UYIEMMgPF3l+tb6QFoYp8sXHWe6b1HRFTL1bIYrFVUGtnovYd5/J1J5NJ22DN8jQZY3Imk0m7jCnmlsBxoO0Ex0nz0nr6bKpjAuJS7T97xfmtgCyaMpvFYq8WspHFBaud0PPU+aPtmnps2YapXczCOzT8gmGbqKOPSBsOBLQx3OZExNSbfHEPdSnVrK4yZ052X7mTk7Wz2oHpIjs2E9Gcb64L/s5tizEXgqVe7BKRC8nMY4pPNYpqhDJvpIoypbYdDws/3Gwc9NyaN4KHNDXcI8tLlh+eTMgTDzPDovF5uB57ZPWPDUaWB61Hrud5Ygi1XlWcsic6M6qZAcx+D9kQZFafKpJr5c7IRi2QhjFmcWAnuTNce25VwGS2Sr/P+2zXnmHtgGdeYUaPYfuanZO1a+pZZ+8zH5t5edm+sW3sstW1euQ2D8u2YmSYl6fFxHqdz6NzULrKPwtuE/heqDDW77XP2r4sxEPPqd1LY1ZlqVePq+jkhwT/60QCwJPu4KFVr3TmoeYHJWJ6ofeI+oM9K+RAz9WJDDy0iH3ZUFjEsXhmQ8iTCEejUfsK9YjjnjteJsMeDuyDYcMLacbjcezt7bVpwsugPW6khXvAZWHvNsqm91eHyFBPGj/HnmmUmUWuDlHiu04qhWdbJ6yqwObJPRozz/cyM7zqZeH7auNqzGLg+cHEbKxghH1sW/A8c/hHzaOsXm51KNRgG5MJULV1vJ0dLZmzaNb12CuNMD4OZ0TZ+TXk3HaynUP9wPar/arFm+N8dt6Mx+PY2tpqJ60jtPCBD3xgXHbZZXH+/Pn2+hxuWXPkcP4yuuKheTSTO2CzPMS1OGvVIZq3Wp4579z+LOpgMkZZykOtP1rezqKNjZTODGZhzUN7GqMNI8HGB/tm5bGW75qR1Bg/hLFwA1A7Vz3QunLF2tpaG0+NY7gTwsvScdwbjDJeCKCxbSw8kSZvYwHKdYt7VDPSbKzY0GuDk3ldOF8s9FVoI39877M4QIbzz3UIL46eV/NGcPlsSI1Zjslk0tpJiOmImHouI6ZXmoCzgtfL5+dTvb1ZJ1lHvXR7FpvLaStsJ9ThgPPUa842Fm0YPiGos+Mi4kRHXh0S3JaojeL/tR1UO4oOD/ahPcJbEweDQdxyyy2tU0fznLV7KrKzjkdNUEccv22Z67kG2r+aR1tFc60TlAltjffONI0xi7CQoNYHqBZLhu86vJbtr3kksjT4BTEZ7Dntyn/Xg5N5ZrtgQ6hrSetf5gnX7+z1ZYHI3lT2+ugQpdZFVr9d9aQGSutJPTmZIVVhm91f/p7llc9n48tpqkerdn+y32ztWsaY+VHPX+aAYHujHe6I7hAw7sTPa5cz2zbrGc/2a566bFqX/apdW0ddNW2tn1mjsYzabIys7u3tnUgX4R98Xi1kj0cFa4I6qze+r/NSO7arM8V5VweQHqffuXzGLMPCghqT8dhwolfO4o6H7/VhU+GJnrN6dxnurc/yLEKM6oPEYhT/o1wsHnmSCz+Y7F1A/uBd5VeIcxnwHWVgwYn1SdUryz14DI+h3nEOh4TAo5EZYB521OEtbRSy0A81liyo+f6p4UWedOQB5eVQn1rHQ6+biWr+beCeZQ0gT5zl+6leCmPMfMCeIQQNE7cRaoDnil/YFDFtnzQEJCJO2EMexat5mzObwfvgfGB7rvkBLCq7YPsFm8ajrbUOu5ZHxTPbJ25XNd/qpOH00U7Axh0cHMS5c+dib2+vfQ04bPHZs2djMBi0r47nVUFqbS07JzLnWkY2mlmzuzW7rPWE75n953xnx3DYZNMcv8vAmGVZOoZah4JUDMEY4JzMWLKAzDy6EdMzu9lTUfNc8FAfrq15iJgWyzXPddbz5uuyGOXVPFQkZ2EMSJtjsyF0AQ934Q+GGKKajW3mJdAhRJyXiU7NW63zUmuQal4arofsPrPozvKldc73Wq+v57InRes6+70YYxaD52LAVqADq97ViHwictNMD8GrQFbRxmnoKhIq7Hg0TGOo1f5lto3TQXkj8narFrKW2X+UR4Ux1wGHzGQhbZmdBHhbMK6F1Vj29/enHF4Q16WU9hi+LzXhq3HLPFKRdXr4/y4xrXWQ7at5nXn1say+IqZ/D+yow3nGLMvCglqH9CKmV09QIcyGrwZ7uvE/78Nntp1Ro50JMDZiKqTmEZuaHhvSmndCGwj2PuvwVK1+MoGr/6vQVw88ex20TmsGXesuy5fWD5el676pEetqzGYZYM271ht3ALlxqhldY0w3+tzw3A8WLNlLttgDmz3/Kqhr2yKm7b7aVrZlOK8r/pidB1knIOLkC1J0dK1LRGbLdbKN1Dzy+Vkbys4GTgt2jd8uy+fv7u62+UHaWM4V7SDnlfOG62F0lOsa9rUmeGtti6KdCv3L2o5anfP/2fdMoBuzDEuFfLABQEgCHl59UQfIHnr9EWu6EScfNj038y5mBkSNgYpZFXq1OGCkD8MxGAzaz+Fw2A7RcQeDjY2+dhtlqNWJ5gmNFnuCMpHK9Y9tPOzKhpLzo3XCf7jH2fk6OlHLN4ywCuTMIHZ5KVBvWn+4Pl+XPWYR0U7M6fKkGGPqsH1hJwtW/FCnBewt20OM6EUcT9IDeJ7V+wyxzO1DJrR5JErFsLZDmfeYr5mVHddmT2/mnWYhWXshTSao1S7xaK/aXrWdPILLdd80TTv5nZfJgz1EGXAfd3Z2Wo92bWWrefOU1V9X/fLvhEcCEFaTOZU0T+pl17Y3u7aFtVmFhQU1Cyp8ck9SRZAO8WQ//losM4xnjWwfC/fs2pw39UazEWJvNpcP1+UHPYv/5o5F5mmtidZavWd/GsPGZVcPNcqYNUC1BqBWn9k9qHmPuAxcFi0X14nmYR443axx1FAP3Ce8wMAYsxpoH1j06sgWP9uwmZkNYZuWOTe6vrMNxKfu0zzNEnkMt3Ndy3xm+eLVqtTJg2Nq3mk+puat1v9ZVOt3dGp4CVPOCy+1qh51LRdfK2sDModJDa1H9f5riGOmE/R3UxPutv3mQrJSDDWT/djZi6BeQxYza2trbVww9uH4rgeB46X0wVHxy/s4DTYW2MZCG3AZ2ADx26eyoS6+XmY0OQ8wAkhfBTDO5e/aGPF9yDw0egzqiPfxtWG8cU/Zw6PHqmdB88x1zfdZRxP4PO3o6H6+PyiLNmA62sHLdWk6xpjF4JEwOFx4vWXu2PNzruGBsAVq03AuUxNCsBVqp2oOE70+5zcrI76zzcvepcDHc4x5NiqmE+W5rLW2S8uc1QM+u+wb22JOf319PYbD4YlwHb4/fLxeZx6b2iVm0Y7o8drmRMSJ/PP1Ue/4izheSAD7s/o2ZhkW9lBjJmwmRvAgwijgWAztQazqkBQeDEyWwLZaTBqfg+tyXvj4THTj/2wihZYP6WUPOEI9ag9o5rHgdHFt9pLyWq58fmYY2UhrfbC3nMU114t6TLiByRoEFtecP9QFX5frXuuF4ywzUZ0ZOf29qbeew2r0fqLseK0uv1CIjzXGLIbaxPF4HPv7+1N2jGOrYR84/CNi2h5hNZ4M9T5nzp2IXHTWPNUs7FkoZ7YceeUQBH7nAJcnItql6lAHaF80vE7D+JC2ikgdzauJaQ2/0BFXrkN+FTzKgncmNE0Tu7u7bYjceDxuQ+e67s+sTkkNnMerVqn3n+8D6oWvjW38pmP8LrGPvfHscOkaETdmFiu9ejzzDMMg4YeceR8yTy0eaH2NbSacM4/rLPTc2nBbJhgzYRwxvQzdMj1b9tjq96xsmReA4+OysnZ5qLvyrYZ81n6thy6PswpY/h3VvNG1Y2t5wn71eHGjWkvHGDM//MxlYR5sp/S8bBQM+7o8sZm96BrJUg8stqmXPKI+SY7zlY3I6XeuE277tFOAPOqIWmaXOO+1Y7JY9JqNU2cKi1S0w71er3X6ZKO3Wf54hIDrrQvu9OjxtXOzkWxtW9SRk6XnNsBcCJYO+eD/AXq42Y9Te+D80Gi4BBvl7AHo+p55qZFP3cZhKDiHGwN+s5X2jGFwdPgPx6p3Vuuqlk+9Vs0QdW3n0QDMoi+lTPXQ2ehzg5etxakeEqTHf1x+XRpRjZ12YrL60evq95qnGhNo8DvjetZJQzaixqxOZmvhadaONgu4bASL2wD+08nXtXyok6fmwc3y3VWuzLmCUD9ez1gdR/yn53IetTNRy3NXuTJq3nu1h2pX2TGCsmmZdF5KVm/ziOrMuaU2P4PbZnUqQdT3er04ODiIwWDQ7uMRc73+PF50Y2osPSmxJljZULCxwPDL/v5+uz4peyb4x8yvpe3yUHeJ5q6HPNvGAo0NvR6PDoAuk6fH1cIXMqNZ80hnjVFNpHL+WOSijlHv/Epz9XTwOs1Ij6+peeVhOY0fZIOc1T83nJm45nM0JpvzxjHZ+OQhVh7pqNWdhbUxi6P2iZ8rPIewkfpOAvZIq0DjoXoW0yxMa2QCUQWr2svMM66ilcsKAQ3BhteMY8m58Xgc29vbJwRoxHRsuToGeLsKvkyYLiqq2ZbzPWGyOkLZuO4nk8kJ50v2O6i1bbWyZelp/rSd57Y2g+8XlynLY9bWGDMvS3moa+Ioe2CyY3kIBsegp5sZ2ZqgUtRDkfU2VSzieyYes+tw7F8m0LKyLyPaamlnecr+h6HpikXnekJdc95VcGfbcX6W3656ZFhc6zDoPMZNOywaWqT1kp1njFmerJPK4XwM23o+R6k5I+bNB/7PPKBZnrvSqH12vek1m2A4y551xfHOaw+z85RFbSGPGKvgR3qafo2uesjKOG+Zu9pBdpTVHEQW0mZVFhbU/MrrzEAtKqrxnT0WENg8i5cnTETMflEM53XWcbOGfFQw8mS/LCaX88cTQdRoqRFGGXkYUeO02UjofqTPnn0Mb62trbVriGOiTDaqwEOWPDu9y9PDnRQ2YJxvrgOkydfia5dSpoZ4s9CfLCYxM6g62mARbcyFR0fPYGdge9iesx3PRpgykcN2RVHHRZdYy9LVTy1LREy1P2yve71eu9IThwiylx3pcvvCdgtpY3sWRqgjediWvdeARTlvyzo9Wj8s5vl4XgaWX+iSedhrcB1nmkCvycdqm6eiWAUyi3+ko6PdtXbZmGVZOOSDBY/2pnlYJXuoI04O1Ucc//g1vhpDUvwd1+l6AJFX3pY9ONmDxMaNz+XtWVx1LTyFr6NGVL3HswQ10uVz+bXdOkzHdbe+vh57e3sn8h0RU8s+wUBjSK9pmqkZ0tl94zrIhuT0nnCnSe8515l2IroMH+/n3yk3VNn9tzE1ZjVqzyomsvFyqCwUMyGt3lntaM8jgOb15KrtwrbsGF7Ng/OFtwuyoyUi2hA7ddbAHnFoHew48s7X1nKxzdJQDj5HnTDZKG9Wj1z/OEdH/Ph15khHQwW76lo/Zwlbbu943WytF/7OghlpaLn1mHl+M8Z0sVLIRzY0Na9AUcGthqK2DdeoeSp0iDAT2BldPdmI+gRFPq7LkOj1ax0BNYCZIdK0s+E3FbmZRztLj99kyOdyujXDU7tHXUJWRyKQfhb7XKPLGNfuv/5OjDGrU3MkZDZdj6ulxZ9d4ov3z7IX85alq13QP5RFBeY81+nKW01IY58eO2+ate9s+3mbwm14V/vXNaqg3+dpp+dt07vSwPnzdNCMmZeFBXVXT5cXT+fhKPZAo7eP81iw6XATh0sgPXg7uJeaeRdYyHY97PjMhsb4HPVO6Kd6Z9RIZctJodfNQ4mcN96vYSbwauDV55qP7P/RaNSmMxqN2npVTwo+MTMa14O3mof/FC6fLp2o+YqIqSWZ9I2TuBfamdF7w96trtfL6ls+F1lBwBgzDc9vqYkm2BisvMOjfPA2qgOA7RbsDneyce0M9UpmEyA5f13nw/bgk/NeSpl6qddkMomdnZ1omia2t7djZ2ennXyfOUdwLf7M6jfi0G7hle7sKeb8aVk4vEZHQ3FNDuXDdbie+AVYWd2hDdKRSi0fjs/aJh3xzBxi3O5lbax6y9XOc36xPfNMuw0wq7KQoM56hzWjgHABFV4QTjq8xw8E9xzxnZfuYSPLopnJJiDg/4hpw80xaxq6kZW9Js65Y8B5R/q8DiYbuszbgfPUIOpLBSCos+vhOlxPCO/Y2dmJiIi9vb3Y2dlp4x35JQV4OQML6lJKK8AzT4kaXhbUmfcEs81LKe3qLtxYsRHksvE954YGn3oveR97kTjO0RizOJmnj+0lnjF+nnmVDD5ehU4WRsZOmMzecOdbR79q3tbMNqk9VKdGv99vXxK1t7fXvvRke3s7dnd3Y3d3d+olUvoiEr5ulh/YqPF4HHt7e1Nt6mQymbLP2sZxZ0fbDeznNhf3Cddgh4rOVQF8P9Fhyu4J31/Nr3q5tcOAYzQNwHZfyzNPBwrbZ3VujJmHpTzUOku7yyjo/nl+sLPEGQ9HRUw/EGx0s++ZseWevgpqFu61B3JZVEBz/rP9NSGfdRj4Owt0FZvaedC65zTU268e9yxvGXyv1NOg32sNIKej52oeun5zLLSNMauROVB4H543FU9KJtBr+5jaSBa3W7Xr4TNzbKjd0846PKP7+/vVl7MsItq0jOoIyNLJHEvI77ztl9rdWZ2PzKE1y+HUlRf9veg9yPLRBedvHnFtzCosJagj6nFcNZHERkcnL6jnkcUcetq6Ygd7rrOHIzOefDy8DtyDR/nUo6p51deWctlwnk7u47qoGW31mnPMs35mx3fB+e73+zEajdpzBoNBW9fwUGgMIOoCnnFuGCOml0BkOF8qvucR9MhHTcizp1m95pwe3ycO9cCLYCyojbmwZE4Cflb5NdwqztRxMkuka7vBx9UcNLqP00f7wN5l2N7hcNh61hHqsbu7G+fOnYvd3d3WW42J+3wNjFJ2OYBg37Szn7VnmVjW7TUBq50MHolsmubEi3lKKSfab9jv2siu5le/zzM6qCt7cP11OVw4f9pBsEfa3BksFfKRPZxZL5AFDAQXevAa08XpsjhTkVTK9Bv/9Fw+Th8WDZ1gY5kZJn4A+VpYig6NAgwlizrkmQ2i5ocNmQpp9SJnYSmZINdrcpmQ7mQyieFw2H7f3t6OpmnaYUUNWeE0UF/zeC64jCinUqtvneCokxcjpmP+asOSnC8dHmSPkjFmObo8h9k+iLLxeNwewzHKfJ46XfS7dpQzG5R5iWvf+Tx++Qk7NAaDQQwGg2iaJnZ3d2MymcTu7m5sbW214XP8Yhp2DunortaN5nvW/I6szdJ9tTY74uTqFyzmOQyQ5z3BXmroRtYed4ld/sT3rI3k0BLNr5aF6zcT37W613wZswwLe6hB15AQM6tXXTMINcPXdY3sU0Wnbu/KS+ZJUG8K14XWC3cGFu0JZ2XoEv1dYjIzstq5QCdHe/V8DntRuC7UUNUEtd5TrXPtOGT3p5aWosPKWUev63xjzHJkokRH/mrf2fuZeZvnvW5NRNZsiB7DdrA2Igixy2tOd3Umupw+bAMBe4xxTba1XZPZszfX1hwwQOOuZ9leHQXWcJt57WpWD7U6098EX6Pm7dY2apaoNmZZlhLU6oXNBJSKMPQedV3lrh4nrgNhpAZAjYQKM/zBo1xKaSfxYXuX2ObrZL1x9k7zBDee3MHlytLOrqFGT1f5wPHqldF6wbUxzIj0+XW5k8mkDf+Awc686rV8871SD0vE8eRQHWlguOHC+fw7weofWThHNoSH/PPqJewtYg+1V/kwZjl0hYnMZkbUwwG4DeHnex4xXRvt42vPEtSZSORtsDvqgMAERXimd3Z22lU4+MVnSEPbPC0jQug4HxyCyOGDnEfkJesA6GRKbGebqpMksWIHt9lcdva0I5+81jiOz8Iw9V5m7Ysen2kEPidLT7/rcUBDcbrONWZelvZQQ4jgAYqYFtH8gwXak8ZnJoZwDY6f1R430swEGRsPHq7LZm1rJyAT6TCujIZ4sGDjWLOs3EztupnHVs9HR0O9BWxo2ECiHtDJwEx1HLu3t9eml4VC6LBrZtAyj0iXoNbz+L6qoEa4D1MbHVCvEY8a8JCsV/kwZjFqzgEWdnosvkecnGSno1987iKeTr6e5kM9qJkzh9PI2hO2oXjhFZbIq721kG1ZLb+A7ZW+BRerIGX2lYUsH6PXxn4O4wC1UD6kk7XTtdEGrWt877qXWTus+zgPWdtTS5Pvb/Z9VjrGzMNSglonq836Ida8Aiq4VHyx6GVBq8NRWXqZmNZP7t1rj1iNHfe69bXYKqazjkFEnLie1lGXoFcBy3WfeS604cJ3jTNEXUCYc/gH4I5IVs+1jhM3avP8Rmpl52MyT3UXNW8G/xljFiezvdm+Wed27Vf7onaZRVGXVzvzkGfXy2yc2g107LkdQFuj6elydVk+tA3M7BM7lrTtY5HPNr3WrnWFc6Cc/D07husja4Oyes3K3jXS0HXcPOcsgtsBcyFYWFCzqMRDzpMI+dWqfHz2mQnXiOlhL11YXw1G7QUg8MBiEgmGx7Cd13DGcRHTHu8sJo5XhsDrZTHUNx6PT3g9tRes5VZji4kw/Ppx7gjw8fBasNBE54M7JjCKOFYbAV7LGjO7cR+4vpFfbXSy3wfgvC7ioeb0sV3PzzoXNcPIdYQG0at7GLM6/BzqM5zZ+NqnnrOI5zHi5Guza2Te8Ez4ZQ4MhHlgzWmM0tZeHc6OG25b2FZlnnQeRWuaZioshNsO2GVu+9gRpbZey4ZRSs0DO4k4vyzEuf1hwZ9NIsy89F2/g3nsMq6XtQu1EQ8N87BTxVxIlg75iOiezc2w8Kp5ObPhNxXUbDCyYS8W1ixGWZzi3H6/3wpJFeu4jr75keHYW47JRaiBhlp0oR0DFc41jwKuzf/X6rNrKA+NAcfV8b3KPCycfpew1rxl+7LzNO2alyQrY1ZeTaNrVRBjzGos6zXM7PosWJhqm6QCalZe9dpsv9gbCwcFe3A1hCLi5JJvvNwe2hRu21Ss8ltptQy8MorOOeG5KzhfV1LBqKTafezj9k3bGk6bJ7Qjn+wEyfKOe9XVFswbHtK1P6PL6eL2wKzCUh5qFlVsaNbW1k6svYlz1DiqCNZr6HFZPvh8NiZsVBAvjO8Q0YgdZvHNaWaxVex9ZkGmRmeevLOQ57/MM83e6Ux4K5yPWoxYzVOQdX5UUGf3UtPMUI9Jtj9LNwshQWPAvz0Yb2zPPOJ8v/QeGmMuDDUPIfbhU+0UJq5H1O1n7VnV53jWcpgshlkMaghJzV6w2IVg1XzXPNS19ZzVAYG45mw0DfvV6ZJ5wrltw7loa9AOcvq6nGjWnkccO5rYHnM91tp9fW8BO5H4WGWZdidLI/sNuQ0wq7KUhxoPI68nyjHPatjY2DAqzub1YGq8GNLRcI7BYBC9Xi9Go1GbN7zEZDAYtGsxZzHCKBfKtre3N+WJRsgAG1qNqQPcs2ejh5U2kFfkaTgcth50GDsOT8m81QyH4+B4bhRUGKPOEO6hZah58LWcmpdMrM66t11/OgOd0+YhR9QBG04W0hh5qL0Mxhgzm+wZzlZjUnGNZxKT7NAhjjj5mumawyDzPPOLtfQvyztfg+0bbAkLaLyspWmaNtRjfX29fckL7LnaaG5zuDz7+/uxt7c3Vz2zoFaxD3iSYRaqgrwgb8gzyoD7AFu4u7t74p0EvFqU1invj5hu87psOvajDLqaVe2eZfu6QFvAn8ZcaJYW1OwpZO9sJsj4O++rPRx8fNd+FoYaa9zv99tYZAhrXd1CY6jVCLEAxTb2Ruv/OIbLVfMQqwe6y0uteWRPSJY2ewfmCf3Q+5HVPdf3IoJaxaoO89VGMjjvuMdoeJGGdho0ji9rYPV/eyWMWY6sw6p2jz3O2K52i+2o2hA8z5mAZrKOc/acZyIv4ljIq/ec51ywc2V/f7+dk8NOiWzyOwR1Zje7yqVhE+rN1/ZEnUJZeiyoNQwS94s7OFq/mv8sPzUdoCON2nbAhmcj3BcCFdO1ttmYZVkq5EONERvDbIIAT2jjpeRUvGUPHm9j8cR50JU7FvnUoSYuE0QZet/slWYjjXP5ldxaBziOxTF7n+GNRkcAnzgOHQEW1mwEOCZPt2dDlFkDEhEnGr/ab4A/a98zAavpZkaV73eWF/U2ZGll19SJNl1eMGPM/OizpLY8Il/qTCe2a1qZ6NGRKcCT+NRGZ+JOO+fcPkXE1BsPMRGRBTW+R0T75kTkHSI1c45ETMc/M0hX6zazy112NOuYcPvD7ShQ5xi/sIbnBXG9cZ64jrMJotxhqnWeuFzz2OV5hLB2rLI22pgLwdIeao45nvUDLaW0ax3zQ6yiOBPX+D/ieOm6Wk9bQyQgRDEsxy800aE4wN5ODefAK7p5DWPubWsvXw2f5hWhKBsbG+13DvnQfHOngeu9aZpW7OP6GtPNxqQ2rAnP/fr6emtEUSfZyELtPuEcjXPmvGisOdd91mHr+m3ptfV4vhf6EpdsdMIYMx81j7J2VDMPJ+wDT/zORp5wfiYkeRvHJbODgeEOu86bUTHOIX8qqBHyERExHA7j4OAgNjc32zYJ7QxsujpukF/kBfWAl8SoAwTHZZ547OOyZGJUHTooP5eb2xOss60dlMz26wgDf2biWkceNA20R+p4006ZOk74GjpyzHXG+xh7qs0qLC2oaw9T19AQ/68PZU2k6TVnnacGg//PPmvXyh5kNtpZOflPY3hLKXHm3Ln4ste9Ln71y74s1s6ePRHmAa+GGr5sm3ppa/WvDRH/qWDNPNcZs+4VdzDUmPK1OA/ZfeXrZfXdlccs1KQ2emAPtTHLkz2zs2DbBXupoVtsp/TZzUS1OkFm2bCax5udKPAYY1lU3h4R7XedPwMbqPYbjiUWn9wmjcfj1g7qaBrnW+1frR3T/zOnVc2Dq44Zvh9ZSEjW4dG8qJNlVhpZTL4ey3TZ8poAN+ZCsZCghoHQ2dOzYnphkNjA8PEswNQ4q0cB14OXlkMnOEyiS0xnXlAGxpF76RxDp0Ng8/K573pXPPyGG+K573hH/N7f+TttObkDoLFtWSw113Fm/NjAY9hOPRhZI8WGkr3JPCOb64+HIbsMGTcQ/FlrgHV7Zggzw8idHm0EZhl8Y8xy1J4nFU9ZJ5k/1Rap6M2ulYlh3c5p1vIPG8cimj3UPCmQ8w2Rvba21o5g8sji/v7+lO3mciCv6nzp9/snPO2z7FjN4cSinD3/EcchilwPuC7aPW7ntE6zjhQ7d2rOtNpoNnemdBUQ3s/XwmeXY2QeEa0OHmOWYWEPNQ/RReTxXWp41KPLDxuL3ojp9TT1OPwP8RwRbXjE2traVMiHvhyFJ/R1DfOzON3d3Y29vb3Y29tLh+LUE1LzEvzr//gfo0919tT3vCee+p73xH6/Hz//4z8+lV/94xfUaJ1wHiDytSGAYeTwGO7AaN6xr7YGaVaX6j1B3nA/+X+l9rtAXjIPCf7nEBfeznF/ev483itjzHKoyFHxxG1FNsoE26tOl67rZSI68+4CHZnkFX/G43G7ohPHUGdhAgj/wKof/X6/tbf9fn+qDVSHkToGkE94sVEHsGMs6LNwuUxQZ3YS5UeboHUFuFycX70mHGzq7eby8HctP98H5I+PYScOH5OR/Va0w6DbZp1vzCIsPSkR1Ib3FTYs6n3WtGtiWj2jWTgE//E5+r0rnzrUlX3POg44X9P7D9/wDfHit789nvBnfxaD/f0Y9/vxoU//9LievNSL/Gn6+scNADcWXP6uEJ3MK6Tf1TuRieouD3TmoZpFlyjX/GXHzvJ2GWPuXPiZV7uZeatnhWNpnCx/6jW78oQ/Fq88j0TtK18f+xEW0u/3W7tbyvEbajWGGnAsdSaQZ1ETkppftovsec7abQ1tnGXTtUx8vazN4u/q4da8Zm3cItiBYu4qFhbU+uPkB782JJORTThgERwRJ7ypENM8SY9f0AIPNbbrwvUcp8zXYQE6Ho9jd3c3JpNJ7OzsTL1mFoaWvRY6EUZZX1+P20+dit3BIHoHBzHu9WJ9fz/2NjZi59JLA/6amlFU4ctGjlcdgSedPevqoZ5MJlOzy/GZGVz2YOuwoXotdLitZsB4SK/mzZ/XI6XGluMes8Yx63Rw3u2dMGYxOA44YnqiIdsu7MvOmSXUupw0TJfY0rTVXnNIB+w/2zvNZ+Y82t/fj+3t7dbeTiaTdoQRS+tlYYic/1JOvuUQn7BRGsusAhx54VE6eKi5PiDyuYyaRua5Rn7Ufup9Zw+75jNLj387nCdFJ2qibeRRc63XWczTWTBmHpYS1BEnl+HBPjWiOhTE53HYBYc04H+OH2ZjxMvIZSEf2A4RzYI6W66Ih/d2dnZie3s7Dg4OpkI+eKkkHMvLCrGoY5EGEX/6/Pm45qlPjT965jPjKddcE5fcfntExFT9aV3BMGVe44jjlwOwoEYZ0ClAXksp7cx0FtTcIeJGCXmHUWaRygZLO1S1hg3l4SFC9ZDP66XmTg17kjLRrG8YQ50CT0g0ZjlqQkQFmoruRbye8+Sh5r3U67IHnO0Fz4/BdxVt+smiGmmcP38+SimxtbUVW1tbU0vnabuF9yBoPWVtIo6H7dSVijR/7BRiQV1zHGibDnGrIjUb0dT8R8SUXc6umzlO0Gngc7TTwL8bdsJpJ0MdRLXfhbZ3mTfdmEVYapUPhY3VrNAPHN/1P4scjZ1WIa//18I9+AEE/ICyh1P/dFiM/2fDw6EV4LKdnfjqN785fvYlL4n9+98/RqNRvPXlL48zZ87E5tExNQOgwlrF36zjNe4P+9RzwB0iNZizvAt8TJexxnFsvOdtULPOxDzHabz0vB4sY8xyqCjpesbvaiHDz77a/GzOBYvILH8qqnkiHQQ621q245mI5TaUr8eTB9nGs93mNNnBkK1rPYtMOHN+aunN6izx/a6dy6I8a5f4u4pnpbattt3tgFmVhQQ1jAaLNhVM3HuvGVFew5MFcUScEMTsoebJcNz7r31nDzcPZeHhRf71LVgaLsErlHA5OEaZjRi8u6WUeMEf/EE88iMfic9/97vjLS9/+dSQHry9iL3DMCHSjzj21OO14Fw/6qlQWCzraAJ7kTJjgvI2TdN6VSJiysuCsug9zDoq+B/pspe6RhY+pMZV49r1frCHOjP2+sIFY8x8aIcc2yKmBVHXM84icJY9qJ1fs4Haeedj2W5oaFjEtCNHR/HUzmkICUYK1dGgHmcNW2RvNo+oIrQPdhPtE9c5zmmaJnZ2dmJnZ+eEQ0FHE9XzizZFvb58rI4Ech5q9y/zVGeOLf69cN50xAPtCHd4NASFfxvcCeHrcd41L8Ysw1IhHypiIrqFGc5TMaTH8cPGYkdX6uDVKng1j2zJOe3980sEILxqsciInWYhqGXi9UlhlNfW1uJHfvInY0BDZs+87rp45nXXxX6vFz/0fd8XEcfLJbGghxBnb4YaWsTkZWRGMvPec3gKPmvefO4gob75/vDoAZN1uADHCXZ5UNQoavr8O8T/HJozy0ODhszeCWMWg8VLl5CqPVuznAF67LJih+0HO4R05YxMJKrIUy8s7D3bInUiqGe8lDI1x2dzczNGo1Fr5xHegeX34HSB4EVboR0ArCyCsEW261yn3J5wPtE+ZM4yPl9DOgCnhzT5XA3nyOqH613jslUf4FgV1Lxd2wYtizEXkpVCPrIHgUV1zUOt8HFqwLrETtYrnvdByXqvbCjYYM1ryPna3/olXxJffu218dQbbojB/n7s9XrxZ094Qvzuy19+4vrwqkYcr4OqnmWuG/WoZgY7E8oMjAu8GlwGFuNAw2hYnNeukd0b/n2wp4HzpGT3QTtnvE9Fdg3OvzFmcTInSrav67jsnGXEc3aO2ohMPKt90farq41Re6ntSs2JxPUyHo+nvNfcFnD62MehKZwmj8zxyiFZnWT5ymxsrZ5r+2r3m//X0QgW2nrdWqhj9pkdU2PZzpkxXSy9ykfNCOHHj+EpFTXaUwQsoNkr3SV6akar62GpGdjaYv469MXGAPnt9XonjN9tm5ux3etFb38/xuvr0dvfj+1+P27b2Ii18XiqPra3t1vDAePKYS3wPkQcrru9sbExVU9cr+wBqQlR9j6wF5w7MyraUV6etc7hK+qpgkeCPQy1lwSoAc3ur3Z2sj/+rWVeIhbw/KeTVI0xs1EhWTsm6/BGRPW560oL6EicHsPXZNuQeaU1zcxJUOv4c/vEbUOtU4/0IH5Rdwjpw7J7aFNgaznMT8NTUJeoT0yg13xjJJFHZ7VtzWyy2up5nU1doptDSwB7mSeTyYlVqNiZo/nia2ahHTq6ME8+jVmUlQV1xLTnUXud+v+sHjse+uxFJpyHrrx1HcP7WOjhj8MEdC1Ozie+s7HDcTBmZ3d24p1XXx3v+YzPiGf/8R/HZXfc0Xqf+dzd3d3WwPCbtTSejof+sA1wPlkkZiKTRSjqmu8le8K1QUHsH/KQCWn9zg1HVyeIBXwWq599zwS1Dr1mxj8LgzHGzA8L1oh89Qbsx/PP9qLmxeTP7Hq6LxPRbPeyZU61/WKHUOaRVodDdl0+T50BAHaZl7KbTA6XOoX9R6hH00xPdES6PHeGnQScNq/sweF6fM+0/eqqy6xMi7S1XJfIs7atHNaBa9QcNHzv+BPp8PVqowjz5NeYRVjqxS7KMj/CrnPuDHFTMw74rm/EYgMcMT3xhA0LHnB+UDGR5Iee97zo9/sxHA7jFz/nc2JjYyM2jjwR6pGPOBk7jm1sDPA6WzUo2lCod4YNMX9HHmDANHZN70fmxands9qxfB/muWe171mDld1bRUNVjDEXBxXVvH2ec2rCTx0483hVM1vWZd/03JpAZZuaoZ5ZiGEN/cP+LL55HmcS2/iaba3Z8XnEcxd3lq2t/X54f/bZdawxy7J0DLX2lBUYFjZsXfGsESc9otk1Oa2st6miTXu6gL0V6NHv7+/H7u5ubG1tTYls9hRMJpOpITUWxCgfVgZpmmbKW4DX2pZSYjgcxnA4jLW1w/Wza2uVcsgHhsrgWYZYV0ObeWkRzsKiOuJ4SSYV8ahL9X5wHbM3W+sc3/VYvR9ZQ5B1avgz6/Dw7wKjDTx5R8uCiZX2Thtz55N5nVnc6ShfLY2IkwIvE9IsoPXNh1nIhnay1a7NcgiwI4S9ryqmOQ1uU9AOoX2BU4bD69hWabgERhlr3nv+no0ocNnZ0cL3SsvL3zU/WZ3NEuucx8wLrWTn6ShBNjI+73WNWZSVBHXW65vnQesS4V2GSx+smuHQY3ToiLfDyEL48puuOIaaxRuEKYdlsLear8eCGmmgTjjMA+KZt2N5QXxnb/ja2uHLa/i6fF+y0AjuCHD9qOHENXDNmiHkzgSuW+vkZN7sbNhP7xt71Dl/OuSojSofp3ngDoK91MbcOWT2mu0vnn89dtZIVk0Isb3I7B9v17yxfchiuzV0ICsn21F+KZZ6hHEO7D3sG+oDeZhMJm14HRwncAZom8qhIVn51CazyMc1uX3hctWo7dc2fxX72uWB7roX2F8Tynw/V/W+GwMuyItdWEzp9uzYRdKr9bA1Pg7fYRTglVCPA/KowhkiWWOos1dXc3wvb5/lgUe+4H2AgWTvsDYKOA91Ac8rr5+ced9xHvLKceIR02++4k4APtnAYz9fr8uz22Wgag1ZV4dIjaI2mnpvMpGeddQspI1ZnlnPT1ebkAm/zObPm2ZXuFsNnXSdkdmrzNNZ8/ZypyHr9HM+NR20TXgPAXuhVfRqbHGWP803bDp3crLzQVf6nIfMw78s2X2ZJbLnvZc43kLaXCiWEtRqtDgel42I9lLVm1kT3Jk41V4+wh7UoCDubG9vr520p4Yu4vi13XhV99bWVuzv77evjYU4Y++yCkV4jtUbyh5mfoh5OSTM4MZxmIiClTNY5MJbAaELMT4YDGJ3d7dzaE091MgTXioAo833DNfkl+Ag3d3d3TZUZTQatR4UhFBwXlQMa+PD6Wa/A25oeFsW3qJ/3KHCvdKOi3pkjDGLMcubWXu+1PZrRz9LM/M2slhVR0uWzyzPvE+vnx2TbWOPr4pezh/yxfaNnUIqTvlFZRHTL9biEUKdyM/l4TxqmWrhH5xW1h5rp4HvJ4/aqu3WfPFobm1ukdYjPrm+OV/8m9PPzDmT5cuYZVjaQ50Nx0ccx3J19frY+HTt54edf/RslLC8EHtYOfSCqQnq3d3d9s1SCItgDzWumT2ofA0VgGrcYPB1EiAvP6dpoJz8ncuaGUkYJDbaEJkII8F5nB7XE5eV8408cLxg9smowdN7UvNMdXmiu8S0Nk5cJ5lxNsYsziwxPQ/a0WavKdugrE3h5zwTR+oAiTgOR9B5IrXyqThlW6kddg4nYZuTLReaOQRQJn6TMPIKTzULbhbRnF+2c0hXbbK2G+rsytrnWR5w/I88cTlrdIWHdHXEVvFQq2NO68KYZVlaUGdxYQw/SCpi2JBAkGNyHAs9fbhUSK6tHa7bySKODXSXaIeghohGyEe2JJGWQ40WG1M1qBD96rWoic/MKGdGL2OWWOSevxpdbTi4/tgrjjKx0cQ94VfDZ8ZLy5iJ5SycBufUhLX+nri8Ne/GrE6dMaabro7youlkNl+vMU86NbsHdPJh5hCo2V0co44aFe6z8sxp69t3NU9qvyaTyVT4B+xuxPHIaNbO6HctY60+u/Zn3l90IGaRtQ+aNyZz0mQaQZ1LWo7McTNPWY2Zh6UEdWZ0QDbEwzOfWbDymso64xnnZUN3bIz4zYLZa7GRl0ywwbONkA/s7xouRL6yWGLuHOhr07vS0m0Qe+wF5vrlTofWSXY/cBwLXpSBxWttyG13dzfGRy+j2dnZaYf0Njc32/CPjY2NqZVKst8I11MmjjV8hu9TbVlD7Mfr3/n3o9fEfeXOQDZMaozppjayxWRCDufqMeo1nDWCqXnh9kYFE6dVc46omI6IdJ19Fv/ZaFjmCMjqhNsEfWdAzREFG4iXwOA15fv7++33WiegJtJxbK3d6Brl5e/q+NCy6ygBzq3da3Y2aT1kjq55Rhyy8vG9ysprzCKsJKiz3nptyI0fPB3qwsOhRoh739n1sZ/FH3tMOU4LnxCGLO55gmJmQLJhrkwwZuJVy6DHZl4IFcoq3PU4TVuNHKenb59kI6LDmfhkwcrDjxznB4+JGvKsodLGTwV05nme5Z3Wxkjh7fN6kowxdTLht0o6AHYzYnZMq547j3cY8Fte1W7pdz2fQ/fYntS85HptkMU41zyo/Kn1w/ZWHQo1sVkrm16zlveaI6fm3MnOrZW3Cw4L6spPrWOSCWk7VMyFYGlBDUEFwaheXRUw/JDjHBa3+rY+wBP7WIAiDwj5yDwIbPxwfX0D32Qy6RTTAGVUIa3hEzxhpJSSLnGkXoN+v98uh5R5t9VQ4Fy8opbDOPQeZOVCOipa+TocxoLYa06TV1TBqADH/qG+2JizJwb3hfPKITcqjucV2Nw5UKPJ91R/GxbXxixHJrzUUzrPufhfbXcXXYKs5jFHvjKnSBYi0XUNdYCoFxj2v0tsq4c0E3jsHMLx3F7gO9oNTBbndkbbCL1XWg8qWtVRxWJdPdDcXqI8LPazzkPNUYV9OFY919nxtY5W5o1WJ5Ixy7K0oO73++3/vMRabTgO6APFD6iuyoHzFRgQTBjkhwQCFMZAxRl7RiGk4aGGh5UnhGhPl4UhGzTOP1MbzhoOhyfENwwhhPRwODwhQllkD4fDGI1G7bnsFefyqnCFeMU+vAAl8zJERLusIJ9fSmmHGjExFC+owQomnA7qNfNeIE1+Ic4sbzXv4/CdbL/mmxsWNvrGmPlhIaKjUxH5ih3aHmRihp/VmueQBSZfL3uOM4FcE9KZs0TzWctT5mTRMuioKb5nXvpMJMIB1DRN64jhcMeI41CVLudPrW5r4R/IJya049jMScJlY4cYd07UAYd6yUQ8f+Ja2X1kJ1Gt45I5mVgncFmMWZSVQj74e/aAZp7IiGPDx15RGArsBzXvKh5UeEERb62GL/N2Il32utYMfVZe5BFClo0B0qvF8eJ/xLypkdP4bF41hQU/C1R0MHiCi3pNuJy1smdGHcZPRS4ENbZjpCDi0EvPLxrQstd+KyyGeXvW2Gqeu7bhfrMhzrzaxpgLQybM5qVLtK6an3nFNNtjoE4Hzi/SwSeHIqp9xzk6agr4WLWfHLbIb3xlQa0dmayDUBOu6nHmdlRHafU62ubwfVRBzGlrufWceX4LWfpMVzunxxmzLAsLav2Rc0hC9qPOxKwucwfDoh7qmsgDENGaD1wD6fOSd+zF5TTZy8yxdVmsGnsAOH4Y5UV+OJ+6bzQatRP6+DXYLJIxbMdwmAdePc7hIVpvmaeWX/aCY/RNhPyd6xDf2ciy5wKrgei61Fw2baiQL3jK9fejf/wqcpRDt6lYVkENdJTDGDMf+sywjdHQAN5XO0e3Z3YC6c3bCdY0MrGsghffdVk65IvD4RDSwemtr6+3EwRrYSNsl2G/tG3LJkxze6ntha4YwmVDvjJBrR0KlI9tJ7cFXF6uW7a3EN88yqvtUhdZvXcxq7OD7/p74/bObYBZlaUENT+8EFMR+cSJzAvIvd6IaJeVU6PCDyE/8OwRYPFdOxcPNgwdHxMRU+WBUI2IKYHHHQkI2lJKG6JR83BnHY319fXY3NyMjY2NVjhruAgLd+QLdYWXsvDMbu4IqOHS4TXuYKAcOA6TDzmemb/jTZKlHI8OQEzjZTpoaLgDw8Y/i/dGvtTQQqTXxDM+cYwa/2z1GDSKXHYbU2MWQ50N2AZ7x8+yOhWy8IwsPW0rushGvrA9258J6cxZom0Pr0aEP9hjfKJdYHgFKbZNPEeFRa6OOmZlzbzftbJnIR+ZNx2OKra5GI1kG10L+eDVt9CxUmcHUxsh4KUB+RqK6g7WBPjUDoJHKM2dwQUL+dD9ivYUI46NL/dk+Xz2ovJDh6XpNERAh3XUuxox3UvP8nzZzk585S/+Yvz8y14Wt45GU+fxgx4RJwyuPtCcby67TiKBMOa6yYwlG3n2SqtIncfAZMN/fHztHmonB/BQYBa6k9WTouEefE18Z7HM9zjzMtQ8ItnvxBizHBBN/Cyp/ZglWjSmOKK+7r7aHoZHFTkNPr7La822lgWjtk1sU2HT2a7D6aF543BH7lRo/tRxo20ul0P310B6yAvOz0ZItU3I7CSnx+VgZxvnr4aWPfvU42eVFdRGRGr/G7MKCwlqfsi5R8+9VBg0eHoR85WJGzVcHFON9PiB5t47vKPsqWRjxZ8QeOyF1p4wDMnz3/GOuPIjH4nn/97vxa+98IWt55iNmxqjzHBmngX2rG9sbMTGxkasrR2+wjub0Ihj2dDp5JMs5KZWhwDnaJjE/v5+DAaD1isMzwnWPUV9cvw1eyxwDjzUGBFgrwfnW+tFBa56FOD1wDXwyV7pzEDqNvaUqBAwxswHPzPcoeZ9KnxUyOJYFcJ8jVkOGkW94Xy9zGPLXlqe24LXfLPNR7raQWdhyqF4fM3xeNy2hThPbSg7KzRcA2Xj7TXBmf3PwpnP5/A8HXmGLc/W+dfJj3we3g3BI55oh5EHFtyabxzDv43MaZX9PlQ31BwxGfMKdWMyFhbUtQlxEGgqLnXInuEHjB90FugqvnEdFoHYvre31xoBDDexEcAKFJmn9998//dHn0Ijnn7ttfH0a6+Nca8X/+47vmMqLIN7+Tqsh228yD7KwvW4sbERo9GojaceDAbtcZkAZmHK16z17jOPBudlMBic8OCPx+P27ZH8fXd3txXEMJTqGcZ9gDFFnofD4VRHQMtQM24ahgGjjDdaagy1DuPpKIWWH+Vgw22MWQx+ztQpUhMnmV3qEtW169bSZscFH88OGRbUbF8Hg0GMRqOp73ys2iu+Fjs9kAbnYX9/P3Z3d6uOI9gyDtFDvtlzjLZlnhE2Fc6Zx51FLXdG2M7ym4xhO9mhw04aCOm1tbW2vEibO17qDNL7gz/8NrL7zgIbx2rnBN9VTGfOOmNW4YK8KZE9xtjfhfYeeVIfjAp7RCNOvj0rM2o4V72WOrmCe8c45j98wzfEF7797XH1n/1ZDPb3Y9zvxweuuire+sIXtoZDJ4mwh4Prht/YqA8vjtFwDjYYajT4OnxeV6OSNVpsLFHnatQ5jIaNJXsLso6CeqtwPaxNzfeD8zmvoOb7px7proZFG+hTt98eL3/ta+OXX/GK2Dp71kLamBVR0cL2XOkS2l3pL3JOl23M8qGjjhCLHNKnNjpLjwW1huPhPB3lxHb2TKtdVEGN7zXxmHmotc2pMU9dq+Dl9NGuzgpFybzNWb6Rn2XtNOuHrv0W1eZCcMFDPtQzrGRxzxExtRYyT0ZgDwDEN0QaP2yllHY5oYjpCW78wLInlUXvTRsbsdXvR+/gIMa9Xqzv78fucBi7l10WvSNvBTwDGk+tBoEFNU8+4XrEPuRVJ6Uw7IXlumcjM2soUO8Z3w+etIl0cV/Zm4J86mRApMNeJniq+TeSDRXyPeYGhusMx+rkQ762TnjR3w/2Peftb4+H33BDPPcd74g3v+QlJ+raGDMf2lHOOtewzTpymY2azRPyMcuRoN8zx4sew97WwWDQjkbC5qOsLGgzW4v0OWxEO/uZY0LrjQW12nzkE86KrpE4pIk6hGc5OyarNw61yxwX2tmAPd/b22vfXZBNFlevctax6RLZnG8+VvOnOoTraZYjxphlWNhDzb1wiJxsQfls4XYVQhGHP3KsYay9RX4A+AUiENXqqeVrZgI0Yjp+GwIZ5Tl17lz8wWd8Rlz3tKfFZ773vXHJ+fMnDK2Gu2QGgOPpOOYX+/GJ/PKySerxRn2w4EZ9s7HUZYaUWn415IM7LtxZQX4RB8jLzaEjhVAPlGU4HE7dNy4jx+plXmsYYM43ro9r61J+ahjZmH7793xP9Ol397RrromnXXNN7Pd68QP/9t/aO2HMArBHMmJ6BFFHE/mcmlia5TWNqL91UQWaXhM2JruGeqT7/f5UyAeWLmUvLF7KxdvZ6aFhkNzecdk17E1FN8cYZ4IadY00NLab0+6q22w0UPOUpcFtFTuJIKLVAZPlRX8L6vXuyruKa/2sOVV4O9eXhm8asyhLhXxEnPzhZ8Yse1AZ9R7AgPADwBPgWGhzKAGngT/1ULNARzoc9nFwcBD/vxe/OEajUYxGo3jTF37hoYeiUmadDc372dB0eY01z7xt3sYmM0S1us48IbqP0ZjGrByaf+7o6GhE1piqUcsENYt/9T6ox0HTwDV+4Ju+KV7wlrfEVR/4QAz292Ov14sPXHVV/M4Xf3G1bo0xdTLPc9exq5KJ4mW8i9zOaPvBI4oHBwext7c3ZcNZzKrw48nSsIO85BycERHTYpTtcmbjuJ1hW6rLnOI6mZe5Vp96zKw2AaiNZRuNPOlo9TxtHafVdW2ut3mwF9rcFSy9DjX3nNGzw5ASDxFlYorTAggPgKFjAYz9eo6GU9SGd/h/NlboPff7/TbkhGPnar1q9nprLHTE9OobuBZ7elkw4jtPkuMZ3UoWB8eeCxb82hPX9U7Zo6CeXvYs8NAfH8OCmTswaDRKKe3ERg4fYaGu90jvHU9kgbHWoUi+PtcZ5/32U6diZzBoQ3p6BwexOxzG1tmz0Z9TFBhjjlHPbcTJ0cWaE0H/521dIr0mypCfrmupMwV2ZW9vL5rmMAwDoQoq2nBuzUOtYWfazrD9ZM8z57smJLWeNQROOwJch7qCCKeZ1YvOB8JnFhNdc6xweXd2dlphneWFr8ejv1naes1sYnv2++B2mI9Rb342OmzMIiw9KZGH4XU5uojpIajsYVX4R14bMmTRrWsdZ0M4ETE1WZKNHEBaeOj5tdm1mK+aoEY9cFw5jIQKavVK7O/vtzOpGR3aq40MsOeEjRJ7iNWboIKaBTjqQwVz9sdGjNMp5XhNavY46yhE5mXme8W/qWypPG3MAKd5cHAQp86di/c8+clxzVOeEk+77ro4c+5cp1ffGFNHbRDIRA3v69pWm/TcdU6tvcjOZXsCQY3tEccrcZRS2vAyFsMIBUFsNbctvPJU5nTI8sSeb6SnHnB81zKqDeR2ij3wGmLJ12U0JJDblmxEltPM2oGsbdH2WR1DtYmT2jlTr30W2qHX0rrTtGvlM2Zelg75AOpVnuWJrv1Yswc9Czdgz2XEdNy0hhdk1LyhmRjUsAI2uhFRNXz8qZ4D3o7rwLAjBplDHTS/3Ah0eQw4lCUzYpqPbGiOY97VaDGZCObvWWcAZVQPdc3QcVqZoM4acG3Yf+ZLviQiDhuON77oRdHr9WLjRGmMMfOQdexV+IBsu26rxV1nzzVgZ8GstoWvx7aB7Se/MwHeahXUfF2eIM6eWYzS1exT5tTh9LReMqHHbUvm+MExmZjntovriZ0x2UTS2n3UvPC15xG4Gdl9X7Sztcg+O1fMqiy9DjU/dIPBoI03Yw8uGx9e/5KNpH7i+Gw7zufVPPg4GDT12Na8oJl3AWXghf6b5njCHrzy3OtHWrwKCCalIG5Oh/k4zg7eae4kdPWW1Wuh11RPuBpU7d3zJD8Wz6jnvb29NnRDPQ6aHxXTHErDHZXMuOqQYGZQtaHS7wwbeR4pQX11edKMMXXY/vME5szmsGOg5oGuiZkuAcfnRxyPRmbpZ04FHkXLrrG3t3fC0xwR7eR0tdEsKDM7mZWhdgynm7WnIHNyzPKyanpsv7kN4XlA3H6r0M4cHNjOnZZ5HGucpv6m1HFUayNqwj3TFJw22nuLarMsC3uotQfNBhUilD2s7MHWh7gmqjOvBD8k/X7/xCoTOAYhKCrcOR1+WOEhhjHkOPDhcDi1HUal1+tNhTJERLu6BeoDdbC3t9d6ObgucE3+nxfe5/pW+JrYj7dSov4YbVS4IeDOBIY7uS4hYtVL3XWvcE0V4Fzv+hvJvBz6u8jS0zLqfWYPFjo1qHOLaWOWh+16xMkQOX7eATtXVEzP8mB3Pe/Ypp5fFVe6PCmO4Tzjf7zcSu3X7u7uCecFoyODnFf2HOt8lexY/MHhovWr5cVntmKFdoL4eBXKaM/R8eCR0Sz0hOstW3FJHXFZR0nheuffCu/vOjc7lp1pNUeTMcuysIc6i7FikQzPX61HnfUS+QGrPTDs5eDhvZq3gskMmnYKsuNZ4PGkQX5YcW0cx/HBOE9nX+M8HY5rmumX3GiPXL/zECTSyM7hMukx6inhvPA1tVOEvLJ3J+v5q3jW+Gmtbz1P01tkCFHTqp1jUW3Maizi1cvEVG0kbpVns8s+IF0VsjxXRJd74zYKNm3WJD62OWqrs1Ey9ThrXdXqWT3a2XEazpG15Zx/zQM+aza0y0mhITld91a3Z/lb1BmS2X6IavbY2zttVmFhQa3rcoK1tcPZz+i9R0zHVnU9cJwW93DVGGViO4vR5XQ5Hyraa94FCGAsmYQHj0Na1FjyYv54lW0p0+tQa08765js7e21YSZsxNmLoGtOw4OhXhPtMHC5s2Ha2nqhGAqD5x5GiD31fO/Y44381u4Xn8sdmCz8A2lmv6Pst8GeEhyPe8keHAtqYxYDz3pEt5jOPIP4X89TD6ySddxr1+Pjs9E4zRt7V/GHGOrsGiyoddWriONJhhr6AriNHI/HU6OBWXgKt39cD5kI1hANrgceTdY2hJ1gCuepqx3mumRmecO1TNpWzlr9I8sn7+PQxmykHPdw1vscjOliqTcl6jb2Xg4GgxMxwfMYQhyHB1IfSl0SKAsN4DRAJmSRhgpPFZeYXIL97IVGmfEA84tN8CKTUsqUwGTBiPrRTgn2scBVQwixznXKgpoNRuaJwHmaRiamWezj+vgNqHHUmGb1ZnDetFFD/aq413hBHh6dhXqXsmtaTBuzPGxX2S5j3zxeZnVycLoXAqSfCWltb3geSWZr4CTRl5ipk4bbGXaMKNzRyMJEsg6Ctld6XQ3nwKeGXbBTQZ0vai+1/VQbqu1xJnRRV7zqld4jrRfsYy2hjqCsTjm/mcivdSYsps0qLLUONcOeTgg6PLgsnPgHXTOyLKb4HIh1PEwc6pAZK+3hat5VZGoekL6GeXAvWR9qFrrwVvMx3HuHUdbOCZedy981YYWP1WG1Wl3DsGXeCK5XTYPTZ6OM+tcGFfnSdPVPhbhur4V58L3PDD0LcvWKs5faRtSY5cie4+yYiOlnL2Lac6zpdaECWa9Ve57Zdul1MvE4Cz4WbQZPjMwEYy1vGPHDXBwtDzuosutnThS9LovorA3R+qmVOfueHafl12tpB0H3z+qEzMof/u/6nbCjqOv+GDMPC3uoh8PhlBFlIbO/vx/D4TD29/fbcAmdeAHjgOO1V8qeARZEMDizHkImE40s+nltzYjpiWsQ7rrqB8PeDfbc9vv9NjSGJxryKiS9Xq8qlJEOh6mgfKgXNZQseLlDw3WjRozT4etnnQykj1VL0InQmHlOkwUy54s99ToaoR7qpjkeBlXhzMO32e8BowZcftwH/AZtRI1ZDjyrHIaVHcOf7AxhW5t5jxl9RtUWqw3JbFom3LNOvDo/+Hh857YFtpTjcbN1pTObHHG4asgsat5b3q9/Si1shD/Z8ZB5gbvmoHR5jTmfWifqpNEyZ2VX7ZH9XnTEVdPh9pnvmTHLstSyeSqU2IPL4Qm8T0M2YKwyTwGnrx7bzIPK6GQKfph0WKcmILM4LDWu+EQ+19bW2vAQDtVAJ4ONbs2A4DpqCDJjwbF6mSeE66OroeNeOtcJ9mk96rFZnWbX4QZKPTvq1dbGDsewJ4GFd1fcH4fpdHm2jDGL0+WdRvtQs2N6bPadYXukHsxsdEzP7eo41wR9JsQBi0+0aWpXs+tnNjcTmLV8ZnWtYX21dlLbMJRjHruo183a7YwsX5xnfM/yOQ9deZ8l8rNQGDtYzLIstWxe7SFFqAO+848VccUR+UOuMWwspnDdLKxBBSWLbjYeSAcCDGJX4bzp2wf5GO2Vs1HEckulHE7ihNDGsaWUqaX/2JiiY8Lbs/xBXHJesV0bHmzPxHImxtWgZAIa3ml0sPTtkJpnDesAmaDmeuVJQpwf7mzxPed0OW3kMfvt2YAasxxsp1VQ1kKqFhHaeh7T5UjgvHEcc3Zulj6LZLXdag9rdjPzcmf2uPYmwppdqnmoAaeh9jbrOPC9QJk17a5rqrdYOw8spvm7/tU81LW8c95m3VvNN7djuoiCHS5mWZb2UOuEhqZp2lCHtbXD1SpYXENg85rM/KBC/PAn/7gxUSR7iHldUxZuKryQFotqiHkNQYiY7vVjaKhpjl/LzdSGrnq9XgyHwxPH9fv9NOYcq6RwZwRpoh44VlvXCuXGQ8uP89i4cT1q3vXeozxYzYTvCUIoOKRDPfg8lMrDvrxaAOeZ39SIEKJanHgWW42ODcrO5eDrWVAbszh4zjIHCOaURNSFET+3sFuzxKSiQrLWOddzukSTisDMa43PzDOs6XN7g3zyi7iytiCzTfMIRc4n21++P9omal2wqJ4ltvWYWp61jrjN0/dYZOC3oSPjXIZ5OwJw8KHd6vf77e8Yv2VjlmGpSYldPW6OqeWeYJdQ44c6C/XAtSDEOARAQzxqDxGEGH/i+rwShy5Kz54WbkBwfZSpZmjYAHCdaHl0Iicvu6RiHQaawx3YoHBHhMuZeXI1v7P+Vw8NXmLDa1LP8qxw/WsHRsvCjYLWee1aPMzH12EPdVfsnjFmPriTzH/ZalCMduTZk5x19JXavkxUzkorS0MFYlcaXfaO92XCnr3UEHjzhH902XFui3guiuZD7anmcVbHowvVBbwN37VD0nXdWl6y+mFnjZaJr82dF2AxbVZhqXWoWQRhO0QghOD6+vrUxDwW1yyg9btOLmH4WDwULK75odOetwrviONl8Lhnql4W9ZxExNQ1a7Ff2lHI9rOhqXU2dNh0HjEJry7XOV+Ly8TDjagnrUdOe21tbcpDPavDlN0PNfhZOIc20sgvh+lorD53YNjLjU4Ix1+r58aG1JjVmEwm7ZtW+/1+O/k3Ip8QVqNmR+bx0GqYRpdtVe9z0zTt2vqZMM/SUAeDCsMM2CLYHX6zLj7nSTfrpAB2pMyqN3ZAqDNDqQn5bOSwFieuK2rUbG+XTdb7VnNo8XfuvOiytTr6YMwyLCSo19YOX94ymUza0I2IYxHE4QCj0ahdr3Nra6td1QKeVRVtPATF63FiG3t08Z2HitggaG+WxSuLOn4BCcrDIQj8ohYW7DyrPTMkfE0NQeGeecT0K1nVWKlQxTGZkFTDr50C9n7AcEdE20HSGD7uWHBdoqOEZf941RG+X3qe5i3zojO6Fqx2drTxQf55LWuMJKAu+Xengt0Ysxj6fO/v78fOzk4rpDc3N9tOuNrIecSxXmOeTm9mR7uOUxGq183ijznvtbJlopfbC3YCsXOGhWfEcThjFgaiorQ2MtnlhKkJU7Xhs+4Xj0jUxL861ro6LZkzKtueOc662mOEe/C7HNi5YswqLBVDje/qAVDBWIsFY9RDzduzYzVOuCaiZ6E98izkQyekZHnNrjtvY7FIb7ir/jgfnF/2xmr8sdZZlwHj/dohmOUBUQHLsdQR+XJXXZ5j7kxlHhm9V/P8tmxIjVkNPGs6d0IFGZ7xGovYxFpai7QF2mapc2CWOO/ytHaVRe1eVmfYN097crFsmLZltY6TaoGuMs0r5hcps95nvW9uA8yFYGFBPRqN2lel8nZ8wqs7GAxiOBy2PUKd0JCJpkwkg8xTwLG18NxmeeK1oJEOvxFrd3c3dnZ20geYe+362SWMuaesnnmdHFJrbDikAunxWtYYAVCRywaDY7C5XnVYlIcgsV2XnEO9qVDVe8lGlL3C2ZvH2JDpKAXv484B5wPX4fxw3XZ5n7WDZoyZn9rzeXBwEHt7e7GzsxNra2uxubk5tQZ9BgvXzMNZ6xDPEtUqmlh8qthrmqYzpGGWwMvyz+dzftkzjdebs41FXfHopQpR9marDc5C2rTd0W2Zc6Wrjcvuk8JtUlcZuK1Uannj/VzP2W+JJ0JyaGpWZ8Ysy8IhH5ubm7G7uzsV8tEmdjTsgwlqMAwQ1NnDDwPMDzV7M1Xc6ifihfGgcMyeDvOo4djb24uDg4PY2dmJra2tdjJNTYBybB1EqC4Gr71gzid7QDimV4UxzhmPx1MvIInIV8VAndfCSNRgIe9cl+gMaVgE0tOJmdrB4fuIczQtNBxZKEvT5C+IyBrMrGOjnS8W1JkHh39/2W/ZGFNHR4CwDY6K3d3dNtSv3++3a/HrOvI6F6fGvF5Ytme19GaJQ7V1ETFl21iwqdDu8ijXOhNqu2C32Y7rPBcuo7aRKqi1vUV5sv3aSeLrdNEVaqEOH24fOYRRfw9cJv7M9jH6m0L9ZatnsfawoDarsrCHOgvjyHqz/KBkPUb2MoNVfsxd52YPOhsRfqjYY8wPGMe8aciJGjS9Xq0HzfUGgc15UI8sv1QnIn8Ji4ph/a7CHkYW3mQIcPVSaJ61bGrEtJHROuBXljPq/c/outfaSOixXffGGDM/XWIGHWTYGLWVNYE8S7jNQ1camZe8y36z5zpz8ixybQXXUsdNTZhn7W0mdjMxXRPZNQdJF5kXv6YHNI+Zxxv7Z9n1efOn+UKeVbtk7YPbA7MKSy+bh14fDxnBywljur+/H4PBoF2HGudzbxnn6iob2M6iPPNcqkCFWIvIH3y+HjzUmJQ4mUym1k4t5XA4Dh5uvKBFX4Fea1iQBhsreG45b3jYIew5/IJFbtfkHqz72hUfpkY7Iqa83biugnvKZeaOCO5BJrp5VQ7tQOCeog5xL7hOtBHgY2YZP/VgZ7+HzKgaY2aTiUc8sxj5QxswHA5bLyG/ajsTWJmXWW3YvPnR/TXnADtR2G7gepko1Px2hZcpaKtKKVOjuXwttp0150mXF1k9u5mjJRPStTYmC9nQY3RbNlKaea1nCVv9HcwCx+Ga+N2pmNaOhjGrsLCgjjheGD1i+sGEV7ppmvZNgAij4ElkOIdXcmChq/Gz+K4TIiNiStDhe5fIZbE/Ho9bYQ1Bzw+9vhAGRgGeF14iEOkjf11rJkOos+hm46JeCvb2Z8KQDR7KwLBo5W26nZeZ07S5DBDU3PHBfvbkZPeQj9UGiMNZ+P5mHS02hJouG1+tX/WG2JAaszg17yj2wY7v7+/HcDiMwWAQ/X7/ROiCilodWap5EzOHgeYnE2Uaipd5pDNhihHCjMyW1jy+em0+N1sXehbqgeUy1NLp8vbWbL+uNjILbru6BHVXWl0dKz6mJsb5+njpHOo589YbsypLCeqIPPxDHxB+kDimNjN6+uPOPNBM7QHg47visbBf0+FzNC0OhVBDUDOgXcZLy8YdAi67Nho1L80ihi6LB2SvCafJ+ap5FLJ7v0jDwAK5ywOkAjtLJ/uedUZsUI25sKhthxhFZ7lLwF2Ia9acKbPOrQlJ7pRn4r/2OQu2s2zzsvqopadCk5ff60pvFvOeM6vzosfU0q2J5a7rXiibXeuoGbMMCwlqHiLiyXss0Nibi8lsPCkF4RL8B2PAf+zt5dg19WLAG4KJkOypVjGqcCwx9/S1zFz2zEOsZa9dXz23CPMAPFmi5l1ho60TO7JZzNlkx0x0ckOQNR7q5eXVRko5nvSBsnO5ah4a1Ik2vEhTXwePdHmGehccKsNrpKN8k8mkHSWxoDZmObq8jVjx4/z58+0ziOeQw7o4ra6VgPi42v4uUZt5PdVrnXUK+NxsVE5Fde26me2ttVE6IlerA3VucXlUXNccX7V0uD5ntRFZel15yMjqif+v3Ru0d/rb4WvV5vlkZTBmGRb2UOPBgnjCj5DXcY44FjMQWojDra0+AaHEby1EOoDFu650wV4QpIcVMroMUmYIag/1rGE/fLIXFWITcDhHdh0W1Cy4deY58sovt+FVVpAODwXi+nxNFdtZQ6VGlmObWdDjLWNoYGqTf/iesOcK33E9jtHn+6sz1Nn4qzjm+uG3QuKYWUvrGWPmJ7Mf4/E4tre3o9frxcbGRoxGo9ZG6SRofRYzgZoJspqQ7cqXviALsC3hNq5mJ1RA1sRg1g6hzVShzfYwIk4cg+ux6FWbr46QbJuel9UN56vWVnCetD6yfV3bavWnaaKO1NmmS/qqeO5ynlhUm1VYWFBrL497oRr/qg+uPmwq5rJPJdvedawas8wowoPJgj/rzWt5Ge7FZ/syIzOrEdD9POFS0+fjWWjWylzrnWfnZaEo2lFRr0YXfK6KYy6n3rN5fiNsPNUzovF/Wk/GmMXpclpgP6+tzKOcmVhST+g81I5b9HzNj45ioo1YdlSLbRz+VxvH2/CpIW4634bT5PyjDOqA0pGBWrp8LAtubu8ytJ2cJcS1frrQtjYT/k3TnGgrtUNgzIVm6Rhq9aSy+FLPJQQrvMbsScU2nIs02aBEnPQkqmccRkN7pGqQ2PBnDxynzWEtvNoIr6utHmjUjYZc1OLauhoOrodaCEvWKdGRg6xDwOfzveQJnuxhwj1lsauGXsV/zbOA/fjDbHaNX8882V1xmNz44f+1tbUTr5Dn9HmlEmPMfHCHGKOKLNr4GeX15eGp5lWf1Huq6/0DFnSZbanZhcxGcjnYccJtEG/ntoVtIE9w66on1APbNVyLX3jFtihzCLH9qjkxdIIib1Mn19ra8SR7DSHEtXG/eM4POyIyxxOuqXXf1QbOErl8Ts3RxaOZmSNP6xTnz5sHY7pYOuRDf8zZA5KFCvDDiu9sTHRoJvO64ro4BmEGCCthg8RhBNmDhDwMBoMTnhaIZy0LGzE2XJlhYXGtopKvA7JjVFjWjmcjy/WDc7LefFYv7BnBMbyEk9ateqmzhk3vK9/z7K2b6tFiwZsZvazDgs4Od+zUsGKlFxtSYxYDNgCCutYpZeGJNyhi1QWkgedTHTSKCuNa21MTezgGwjgbkdR2Rm0wOzYygVarA9QDf+I7/58Jai0/t6kaN8zl4vOycEtuy0opU/NgIo5HnuGMyNqfrHOTiVmltr3ruOy7XkOdJDVnlabhNsCsylIeajY42T7AgpInzWXxXF3X0qEaPb72wGQezszDywacDWrttd41w8QPJyZf8jGc16wM7M1nA5i9Mavrbx7mPQ51qQI9M/xZA1BLj8+roR2jrmP5PnFj0PU7mze/xpiTwKaOx+MYj8ext7dXfU65s66dZe7UR0yPmmWiaN5ntubA6LJ9tXN4f03MZ8B2ZWFy3DYt4iGd13bX2mkVxeq00jQuJLPa8lXSBV2jjZlzx5gLxcKrfNQeAjUOEJUREcPhMIbDYURE+6KXiJia2FgTyRpCovvxgHCYAk9wjIh2zWfOH5dDX63N18Afi1oOBWGxjPxhZROcl00WVCD+sGY3p11KadPj6/E2HuZTT2ztelnjgftRM6zsqdb1w2sNB6ehnZyuxjciWg+Yek04ffW+AKx7izrFfeAhaPYWGWPmYzKZxM7OTmxtbcWtt94ae3t7J7y8gJ/Tvb291us5GAxOOFxgM3Eep5WNUtW8lzX7pTYP3nHOt3qgs/QZ9WKzHWRHDod2sBe1q2OftYuZA2dRWMBzejwpHG2Bxk5neeS/mtefrz2rvDXPfHY8tyWZp5/zCJaNgTemi6VCPiK6Y6BglDjEo9/vt8aL3xbIBjRD47b02ln+VCzxm/Wy3mvN08BDYSxyNX46i2HTJexwbCbcVTjrIvocNnL2/Pn4u296U/zMS14S20dvH5vHwHI9Zh5b9Rxk9Qojy2ny36x4QqTDDUm2H5818c35VFHNqJjm8i/qVTfGTIMQju3t7djd3W3fRqf2Gt/ZTkRE+3ItpAXYO53ZpFke5lliWr3fmrYKeXzqHI2u9kjnaGSeaZ7En9kg9YbjMxOwi5LVEYRzNmowD4vcF3XMcTm1/mt552M0bFTb+WzUw5gLzdKTEpnsAVdBzZMS8b96gPnV1+qlzIxGZuBwLC9xp6uPIE3Ov07i0HJlYRcoG4tTFtEsrHk7YKPC6an3mz9f8J73xJUf+Ug8//d+L97wohel4pi9Cl0iWu9hzbCzgcs8MRpOk3kG9NzsWL0/tQaJ09bv6iHR2GljzOrAQ72zs9MK6uFw2M5jici9jWyjtUPOtjyzV5nQ1u2169ae/8w21BwTsKc8qspzgDSNLi8s20Uupzo2Znmh5xHTmfjWNpvrYt74Zz2W982TJ5DZdRXc2bGZ8yVz1NTSsSPFXGiWWjav1kOGIGyaZkrIDIfDGI1GEXEY8oFXgPb7/XYYDIIbP/Jsgl0WXsExs03TtN5oxPap6M4EM3uAVZixiM5CMTRWmjsQKrrZm50ZIBXU3On47u/7vuiT4X7W9dfHs66/Psa9Xvy77/iOKaGvL4jRhqXL4KkYZlTgzuudRjr80p5soqGOLPD61JnHAZ/cqUGHLSJiNBpNDStzPdcMtjFmNvv7+3HTTTfFHXfcETfffHPs7u7G2bNnp+w7YBsEW1HKcfjHZDKZWokHdi9iOrwLf7wCBVPzQKrt59AxbFchzx5bnMPnsRe3lDL1UjG+buboQHvEqyVl9lqFdlaWLhum7XNtNFPbwS6xrPXN7easdzRwvubJO5+fjSLoKLSGB2pnJZvsOcsbbswiLL3Kh5L1bPkhxdI7ugQPH4cXg0TkEyfmzRseMvUeqDjnbSqktTwsqPUYfOe4aV1mj8/h62seMgEfEfH//MN/GC/67d+Oq/70T2Owvx97vV584Kqr4rePvNRa9xr3pvetq/efeUYyL4DGCqqgrjVk7JlWj4MKdb6e5lnTz+6bimljzOo0TdN6qHd2dmJ3dzdGo1H7DGed8Yg4YS90dAvOlZp96rJbAKJ1UZHEgjizNWxHWVQjlDHzVNeuw2Wp5bcmZhcV1biGts3Yn010z17skqWdtWerUPvdZGibxA4Y7YBkaVpEmwvNwoKaBWTEdHiB9u4hkHnSCYd/aGhI7QHNHgA1XtnSQRxXBZA+3t7IsEBGHpAn9nxmIQUcp8vl0fNmDTFqzxz/nztzJnaHw+gdHMS414vewUHsDodx/syZqbS0rrqMrgpsNeraOLLhQr3Whs7YOHcZLm6k2POgse98vHZGNLSG75l6qOdttIwxdQ4ODuLcuXOxs7MzMyQr8zaymF5bW2uX3uMXOuFc2CZ+fmvPbC3MQ4XlLNSLqXlBWhDTETHVcc/Or3lD2Ruuy5BmZeFyzCoPi2Ntt5HnzDnE7SCPeOrxtTrtcqwsQpYutxERcaJTxvWtv5ssXQtrc6FYSFBDpERMC154nLlXywITa45OJpMYDocxGAwi4jD8A0M0KqjZMDH8kGRMJpMpT7c+ZGB4NKGPPdUqhtm7jMmC2pHAfnjeWWBHxJSnuUswZ8OVPBxYSonT58/HHz7lKfGHT35yfOZ118XZc+faNNizwkJ4Hi8D30sMx3K9sdjVl6vocCzKDOC5mceLwhwcHMTe3t5U3bAw1nAcHQXA7240Gp2oA/Z+4ziLamPm5+DgIG6++ebY29uLvb296lyHzAbBfqPTXEqJ8Xjc2lGEf+A8DofIgBiFDax5mXGsHq/UJity5x+fbI9wLr6z0wj/8yof2sahDe0Ku+Ntaruz47gt4nOzeUBZCEfNvnI7r3C71VWO2ohDVg5ukyKmJ8TriimaD3UW8e9Rf68W12YVlvJQA32w1eiwQcx6v3qepsuwuKv96FXk6fDiVMHltbcM51vzm/XMef1oLZf27rOOAtebxuHxMb/wZV/WlueNL3zhoaHrqAdOu4Z2YlRMZ/Wq+7JGdBa1cgI2gOwRyoYuszLo7y6L1563jowx00wmk9jd3Z1r7kSGehohrjU+et72gW1XjcxpUxN185ZH7Q2LNg4LQZrckc9GEfmNjBm8Xdvc7NhsNC/Ld+1Tr5Nt1zrjjo0ugajMI6p1v3qg+beUtSnajmVpGnMhWGqVj3keAB6G7/f77bJ5WIea376kxpLFLnsb+GFhw6B54uGg2luTsGQTe1XZO8vlmLfcOEaNkq5o0gWMrR6HbWwU1PvNec7Epl4nM4DZxA3+PxO62SjCLA8ANzhaf3osPtHgameFvSxZR4jrU5eqmiXsjTEnaZrDGOqImHoW1ftXOzfi2N5ASMIGs7jWSd/zdOCz0T6+to6k4fyu2FttozK7yu2eTnzUuoBnXrfPsom1fHR577mcmZDWkWWcl40M8jF6r9kplF2vq+66yAS0To7Xuok4GfaZCWucV7vvxizCwoI6M178g8Z+CMmmaWIwGLQ/bBbUHCbBDxr3avlFIvxAcE+eDS6ENz94GJJUsQWRD4POXgOUJ/OQ1OolE3ooYxa/poYwE7lN00ylx94NNXR8D3Qbf9auk/1pvc/yTuu9qg0Dc13jt1HLJ9LgmHd0vPCJUCSgyxaqweTfbG0SlDEmZzKZxNbWVqyvr8doNDoxesRkYgXPNew7BGYppQ31Amr31EGiHm0VzCy68J3n9LA4y+wm02WzWTyrqER+OMRx1nKuNceOjtgpWh9dnma2f1p2tC2wr13todYfvmfOIS5Prf3IyoPfC/8GZrU9CE/UjgXnRR1AxizLwjHUtd6wPmQq8NhbmHkzQG1YDqiIq+VHvSAcs6aiW4eM5hmGmgVfSw2Z1hmXRSe81ESx1lWXF2MWmQc5O6brPsw6Z1ZcYNf56hnP6raWbtc+Y8xywNnBc0W64nmz87NOt9pkUHuOM1utwr1GJvayNGr7M4+nepi1LcM12YHDael7CjgNFX8qtLMyZ4Kav3P7zOXuCq3Ta+o9YGE9j22eR8RmjpyI2e0KT0rUfNY0hjHLsrSHOgsP4P958ho/YOy15VU/+v1+23vHcfMuQ5SJSg752N/fbw1YREwNLeJ8GJ7BYHDCs8FDjSh7ZlQ0JCV7ULMGQI9VTwlPGkH96J/eH76e1pUyj5eZw0Ky47saIG0YMwOcGcsM7pTh/6xR4N+pim787jJvvDFmPvDM8GRskDkOAE++4xFCHMOjkvBI4rnFdTX0LBNu7CXNJs9lIYT6vetY7QBgG9o4DmvUfMKWa7tRQ/PEiwAoNUHNx2oIh9pIHv3k0Jtah6YmjrNzsv+1Y6Fl1flQ7H3WNHE/9vb22vuBtp3zxNe0w8VcCJaalKgzurM/vFgl4uTwOl7owutS83J6MLZdvVpGV9/QfGBJJhgF5H88HrfGm8UpjC/ymMVgM3wtHMMNRYYKPBbLamA1FhhGZJ5wlEzMZgK2yzOddZ64E8X5yLxKtbJHTBtL3pflhzs+tXXBtZPBeeSQHAhqdLaMMcuxvr5+YmUOpdbRx/PJdnhvb6+1N4PBoBVFeGEMnlucjz8WherE0fhrFrkoA67PabAdYhvFnmi12xDUKnjZ7vC61dq+ZPVX8/5rXjivXHY9h+uG88nebLSXaJuze6dpah70utl3/J8JWxXPLKjxP197bW2tnRuF3xV+O/qbyDozFtZmFZZ6sQt/z/54H5N5VrMHO/O8LgvnQePTILTwgKrRZePL5WHBXLumXpe9BFmvOiPzuixC5gnW/7Nrdolr3ZcZ60W9vVmjVUsr80RrI4p9uo3TWLVujTGHqB1fBG0zWCixgMpCAXHOPPnDZ3Z8zZ50pc+irJauilQVf9k2DUPJbBrnG9tmhYpkNrAW1sFpZza2Vh/ZtmVH/mbpDN6eXR/b2UHXpUe67qMx87KQoMYwCvcY+UcNz2/EyZAQ/HDhzdjf34/BYBDj8TgODg7adarxvZTDtUnRo8/Sioh2wh/nUT2oLKR5QgjSjzg2MPCYw4vAs7FV+KuxYy81hjD5ddsaz8Z5zuLuMuOrQ4+ZUVGDPK+R0LKo8dJjuQ64TngoTtfj5saS06qlr40dhwnpcCqOyybwcH2UUqZGHrLhYGNMN/w81sIq9HiG7SV7qGFz9/f32wnlTdOkrySHcwPnzrJ1WWiHdr71/yxNtrkodxYLzjYf7RtsDrZz+GFX/hZ1AnSNGGShcnw99lBnI5BcTpCJ7i6nU+1T61A90rwNgll/CxER4/G4Xd98NBrF5uZm2w7zOdqW2clilmVhQT0ej0/EL2GfCmp9UyELaghnGEwI6qY5XI4JP2oYG12fNOtJIx/4VEPIDyqMNj+AvV4vxuNxmz82ODCO7DFBY8Ln4zoc8sEvLWARzrF4KtA1xCOLs+PtKqJVbGt9qIHMPD86C73L28t55RAKbkxQfxwfX/NoZfeVQ4P4xUH8W0OeNM6Ry8oNB37HNqLGLAYLaV7JaJaw4mcz4ji0DvNc8DyXchj+gXTxci1OQzvns8TcPHHH6lioAZuu57M95zaC48H1OrxUrDop9JqZzcxse5d3WYWy5olD6jS+Gmllc4k0P/PY1UxM60twcJ+ztyPqi8OQt/F43C7tuLu7G3t7e9Hv99uXuqlmMWZVFhqf4x9g1pPUY7MfKj+Y2jvGg8vf9YHmczMvblrIZAIh5zFbszo7ptZ71nS1frCv5uno8iJ3lY3P1TRq6c1zTI2amJ5V/7V7Na+Izc5fVACfvuOO+Jqf+Ik4fe7c0h4fY8w0i9jhzEPN3zP7WvNcZnlQuuxqV1mybV3tEY7LQkdqf5yexm/rtbL/a22kHpOlkYnprr9562oRuhxetfueHV/LG3fauA3HwgfzeNGNWZSFPdQ7OzsnetL4Qaq4VG9l5iXF0BJ7OnjdaniTOWaZr4lzm6Zpj2N00gVADxZpYfLCeDyOvb29E+dghjBfExMdeVIKzoEHHN5Y/mThXRuW44ZDGxccy71rNZqZcM4M0zwCl40+G6OsYWHvDBs1Xmc2iynURpQ9ObgX8FDxutxIrzZpFHn9nHe9Kx7+138dn/POd8YbXvSi9pxVGwZj7ouoUOsKL+iyMXjO2YZquByOgd3lMArYJrVtbKf4OefVnrg9UTEccXJd6dpoINtDtu+8LSLS9e5rnl4V7mz7ObwO5eP8cnk4v2xXM68zf9fRV4brgs9DnmY5h7g94nxpyIxORORjNQ0daW6a4xChpmlie3s7br/99vYlc6PRaKo+oVUWdTIZwywkqCeTw9fNAu1hq4hTA6QPAxtHiCQW1E3TtEN+vGoICzN+aLKF8jmPmecDxyAfiNvjIcz19fWpa+tqHigL0tdOBq6pDQc+WdydvuOO+Nu/9Evxy694RWxfcskJUV0z5mys5xHTbKCzYTu+jgpn5IlfmpIZIzbYHDOIstaMujZI/NvgdLies0X7m6aJ7/6+74s+deqeds018bRrrolxrxf/6lu/9UQDaIyZj8zBsQhsizhEjsPteP7JeDxu7UG/35+6Lou5TGxxm8E2W8tQKxM7CPC/dv457I2dCpy2ek4jYkrkc91mIX8cioE01PZlI7rqBMucIZpGJug5b9yGaN1qW5V1JLK2CbH0+ibEmqAG3CbzHCicMx6P44477ojBYBCnT58+EXKoI9PGLMNiU7IjDzPIfuCZ8Mt+rPww6hAVC2b9Y2HFsV567a7/+SHFJx4s/uNwj1n1ounNOk/z9Nzf+Z14+A03xHPf8Y6Z59SuNc9floZuy4Yw8X8tdk7LUxPr2Z+SDVlq+rMa8n/39V8f1111VewdxTru9fvxvic9Kb7/m76p2qgYY2YzS0h3tQldx2e2im3zInY1u0bmcMjyq3nPBHfNftWOzcI0svALPU5fhqahH7W05r1Gbfss1A7Pc07WVvJ3vf+1+UNdeWJnGDt9sIQue78XybsxXSwc8sFD9/M+dGoMtReMtanx4OCHj/Oy1TaQBq/2kE2WxHXUk4zvgCfL4dos2HmRe10In9NA3ricsx7aUkp8+/d8T/TphQbsSf133/Ed1XrFH8qow3E1cc/55GNVtKpx1Uk42kChjtgYlnI8c5/ThRcBk0DZQ5EZxazh4PuYeYpu3diIncEgegcHMe71ore/H3vDYWydPRtrk/rSh8aY2eD5rLUFaq8j6qFusBc6qVlf4oTv+B8eyVpsbOZAUDvHXuWsncnSY3vD+/WcWr1kopJhG81tloZ8cJp8nuany6MMslHDrE672v9MH3Q5dvCpkw95xBnHZPeY0+O2eTgctu357u5u3HHHHdHv9+P06dOxubkZvV4vNjY2psJG7aE2q7CUoOZeX1cPFT929vaeuv32eMVP/VT8/MteFudkOJ9X9IBAhmCOiKl4OhZb+r+Kd2zXNytlBgkx2xCpyAcv+6blxLnIIx9XE57KD37Lt8Tz3/zmeNwHPhCD8Tj2+v34wOMfH2994QtPXFMbIf5Ur7uWDWTrlipqPCNOviWMh+O0IcoEPIe3IB9ZRwDncEiQvsAnSy+7L6fOn4/3POUp8b6nPz2ecs01cfrcOYd6GLMiLCYVPPv8PGbPOIANgJhC+uwIwTwXnjui4Q6z7Lv+XztO7UgmDtXG6fF6XlZHs1hWUPMnhzXgf3ZMcHk0rIXT6cpjVl9KNtFUr8WCOmvLtJ3TtFkP9Hq9dkWP7e3t2N7ejv39/djZ2Ynd3d04ODiIjY2NO+X9F+a+ycIvdomYjleKyD2z+uDg75lveUs8/IYb4nPe+c74uc/6rPbhBvhfPQTs+eRt+nCyyEI67A1VDwSjXpRZojQ7NxtG02OyfXecPh27w2H09vdbT+rucBjnTp8+kbcug5Qxj+HOOkec13nJ8qn7WASzUef9LPhrYjnLa5bn17zkJbGxsRGDwSDe/MVffHjMEmUzxuR0iUpQs51ZWvqXrcaENGueXrUHNfGrbQW3KzV7xuJaj6nZ0UXIHFNZm1g7hwW4Cuva9bL0ah0UrZ9aPdTuTeZomvUJ5vkdQS9gRFXDPnZ3d9v/kQc7WcyqLB3yoQIW39kjDA4ODuJr/+E/jB6FNDzzuuvimdddF+P19fjGr/maqSF9nIuXrOzv70e/32/zwJ8wtDoBst/vn+j18rns1cxeDsPf+U9Rwc89f64f9shGnFxneTKZxOa5c3HNU58a1zzlKfGZ731vnD53rjX27GXQtThhCHiiYzZRVIcmswYo6wx0DaWyccti2HlUgetFQ2/4PuroQk1I88RRhIrw7HT+nXIZsnJaWBszP+y4gOd4VqdenQ5Zmvy8aggf9q+tHU9QZJuiNjrrYMMOaKee7Qefy+1Kln8VjZkTpcshkFE7jr3IuG42wVLX5OcVmrJrZUI761DoNfX42l92DId3Arb9+F1xXmp1ovec2wWMfN92221t7PS5c+da7/Xm5mZsbGy0dVtbp9yYeVjYQ80Cjnv+amzYwDZNEz/5nd8Zz/6VX4lHv//90R+PY6/Xi/c/5jHx+s/6rFhfm17yTb0F8CwjVk6X4tPVPdSoIT02/DUvBNLJHip9eNUQcf7ViKpB1kalaZr4hVe+sr3GG1/0osNzKH0enuO8sIclKw/HLGZkngA1tDoqoWSdKl1wP2ts9HpZR6TW+OpvRa9fM8R6T7o8N8aYHH4udZWE7PlrmmbK0cJp6DMI+8arMbAI4zcOqp3RvLE9yOyn2s3MrusxXR2CVQV15piAyMxGdPHJjg621zpSm+Wbr7PIfu1QaL3WHFEsqnW0oXZ/smsqrD/Y2YJQoYjDl7xsbW1F0xy/qA7pWkybVbjLlMTWJZfE3mgU6xTSsDMYxB2nTt1VWTDGGGOWxiEBxpgaZREDUUr5RET81bIXe2zEo8cR409EfOKKiCv6Ef0/j/jgsukZcwF5RNM0V1zsTBhzT2DVtsCYuzFuC8xSLCSojTHGGGOMMdM4eNQYY4wxxpgVsKA2xhhjjDFmBSyojTHGGGOMWQELamOMMcYYY1bAgtoYY4wxxpgVsKA2xhhjjDFmBSyojTHGGGOMWQELamOMMcYYY1bAgtoYY4wxxpgVsKA2xhhjjDFmBSyojTHGGGOMWQELamOMMcYYY1bAgtoYY4wxxpgVsKA2xhhj5qSU8u2llP9xoY+dI62mlPKYyr7fKKV89YW4jjFmOSyo56CU8uFSyl4p5f6y/bojI3fl0f+fUkr55VLKJ0spt5VS/riU8uqjfVceHXtO/r5szjy8spTye6WUrVLKO2Yc+7mllPeXUm4tpdxUSvnVUspD5Zjnl1LeW0o5X0r5SCnllQtUiTHG3OMppbz6yFZulVI+Xkr54fL/svfncfZtZ1kv+s5q1lpVv25nJ4QjSgAFQiAgTRIirfQEgXC4BJAoRPEcBeUqCIogClzAg3JBL3hojh8BQQQuArYktKG7NDvJhkAOEWnTCGl29t6/XzWrqzXvH1XPrO986h2rq59Awng+n/qsVXPNObo5x/s+4xnvGLNpHlh2Tdu2X9W27V9bJ/1Nzr0O2rZ9Ttu23/4/O5+KiooyKqFeH78dEX9R/zRN8+4RcWjnfEdEvDoi3i4inhgRfzkiXmfnPNC27U38fc+a+b8pIv55RPwfa5z7f0fER7Vt+0BEvE1E/PeI+EaU/V0j4rsi4osj4k5E/NmIeOma5aioqKh4s0fTNH83Ir46Ir4gzu3gs+Pcdv9I0zSDwjV7f3AlrKioeHNCJdTr4zsi4tPx/2dExL+xc54ZEd/Wtu1x27bztm0fbtv2h+5H5m3b/mjbtt8bEf9jjXNf17YtzzuLCE4V/sOI+Oa2bX/oopyPtG37m/ejnBUVFRV/1NE0ze2I+LKI+Jy2bV/Ytu2sbdvfiYhPjoi3j4i/dHHelzZN831N03xn0zR3I+IFF8e+E2l9etM0v3sxG/glFzOaH47rv/Piu2YpP6NpmlddzGR+MdJ5VtM0P3cxs/h7TdN8Q4nYJ/V5cdM0f+3i+wuapvnZpmm+7iKt32qa5v0ujr+6aZrXMzykaZq/cDHbevfi9y+1tJfVb6dpmi9smuY3L37/3qZpHtz4hlRUvAWgEur18fMRcbtpmqc1TbMbEZ8aEd+ZnPMvm6b51KZpnrJJ4k3TfFrTNC+/T2WNpmme0jTNYxFxGhGfHxH/FD8/++KcX7kw3N9ZjWBFRcUfI7xfRIwi4vt5sG3bo4j4rxHxETj83Ij4voh4ICL+Lc+/mO37PyPi+RHxJ+Jc6e6F1yX4gIh4akR8WET8o6ZpnnZx/CwiPjcinhQRf+7i98/erFod3jciXh7nM6XfFRHfHeeCzzvG+WDhG5qmuXlx7nGci0UPRMRfiIjPaprmE9as3+dExCdExAfH+WzooxHxL7csc0XFmzUqod4MUqk/IiJ+LSJea78/LyJ+OiK+JCJ+u2maX2qa5pl2zhsvVAP9PS0iom3b72rb9j3uV0Hbtn3VRcjHk+JckX4lfv5TcR6O8v+IiHeKiIOI+Pr7lXdFRUXFH3E8KSLe2LbtPPnt9y5+F36ubdsfbNt20bbtqZ37SRHxn9q2/Zm2bacR8Y8iol2R95e1bXvatu0vR8Qvx3nIXbRt+9K2bX/+YtbwdyLim+OcqG6D327b9lvbtj2LiO+JiLeNiC9v23bStu0PR8Q0LmYt27Z9cdu2v3JRv5dHxL9Dvqvq9zci4ovbtn1N27aTiPjSiPikGhpT8ccR9aHfDN8RET8VEe8QV8M9om3bRyPiCyPiCy8WMH5NRPxg0zR/Cqc9qWDE/6egbds3NU3z7RHxy03T/MmLvE8j4lvbtv31iIimab4qIn70D6pMFRUVFX/IeGNEPKlpmr3EHv+Ji9+FVy9J5234e9u2J03TPLIi79/H95OIuBkR0TTNO0fE10bEM+J8fc5ebL+2hWt3Ti/K5seU7/vG+dqcp0fEICKGEfH/vThvVf3eLiJ+oGmaBY6dRcRbx1XBqaLiLRpVod4Abdv+bpwvTvyYsKnC5Nw3xjmhfpuI+MMOp9iLiCdHxO2L/18efZVhlaJSUVFR8ZaEn4uISUR8Ig9ehEE8JyJ+DIeX2cffi/MZP11/EOdhFtvgG+N8JvGd2ra9HRFfFBHNlmltgu+KiP8YEW/btu2diPgm5Luqfq+OiOe0bfsA/kZt21YyXfHHDpVQb47PjIgPbdv22H9omuarm6Z5etM0e03T3IqIz4qI32jbdpVisRJN0+w2TTOKc3K80zTNqGma/cK5n9g0zVMvFoy8VZyrHg+3bfumi1O+NSL+StM0f7ppmsM4V9X/83XLWFFRUfHmgLZtH4/zRYlf3zTNRzdNs9+cb3/6vRHxmjifjVwH3xcRH3ex6G8Q5yEP25LgWxFxNyKOmqZ5lzj3H38QuBURb2rbdtw0zbMi4tPw26r6fVNEfGXTNG8XEdE0zVs1TfPcP6ByV1T8kUIl1BuibdvfbNv2JYWfDyPiByLisYj4rTifDvt4O+expr8P9edFRDRN8/ymaV6xJOu/HOfTdN8YER948f3/0o8XaX3gxb9/MiJeGBH3IuJXImIREf8r6vCv4zxk5Rci4nfjXKn5f66oekVFRcVbDNq2/adxrgJ/TZwT2V+Ic8X1wy7igddJ4xVxvjDvu+NczT2KiNfHuU3dFJ8f52T2Xpzb9nW3VL0uPjsivrxpmntxHiP9vfphjfr9izhXt3/44vqfj/MFkRUVf+zQtG2d7a+oqKioqLguLkJGHovzsI3f/kMuzn3HW3r9Kiqug6pQV1RUVFRUbImmaT6uaZrDpmluxLna/SsR8Tt/uKW6f3hLr19Fxf1CJdQVFRUVFRXb47lx/sKt/xHn25B+avuWNfX7ll6/ior7ghryUVFRUVFRUVFRUXENVIW6oqKioqKioqKi4hrY6MUuh4eH7Z07dyIiQsp227bd32KxiMVi0fstInrHFotF7zqB35umufK9aZruz8+5X1g3TT8vuy6rw7LvpXxWpb1uup5WqS1Lx5adu26ZNr1n2eyJP3d+np5Dv8afN57btm08/vjjcXJy8gex52tFxZs9bty40T7hCU+IiMt+vrOzEzs7O6mtbpom2raN6XQas9msS6dt2yu2Jev3TdPEzs5O9zv9iPd3puUozciWbMmqa9exadeZBda1m9ZHWNfm/lGYqXZ/oftNyI7r96ZpYm9vL4bDYezu7vY4SImPnJ2ddc8MnyN9Hh8fx2Qyqb6gYmNsRKjv3LkTL3jBC3oEZTqdxmQyibOzszg9PY3j4+PugT07O4u2bWM8Hsd4PI62bbtz27aN2WzWe/gFdZSdnZ3Y29vrOs1gMOh1oog+UWNnE5YZCv1GR5CBndsdReZE3KHs7u5G0zTdp+eX5a06si28LLxud3c3Pc5jSs+/61qWcXd3t/dd94Fl1znMR2XxdmV6Xldd64MtH5C1bds9O2dnZzGdTjujqXNms1nMZrMrA73pdBrz+bz3PMzn8zg5OYnJZBLf+q3fWnxOKioq+njCE54Qf/Nv/s2ejR6NRnHjxo3Y3d2N/f39zl7Lfsxms3j1q18dr3vd63p9nbaOfTzikmTt7+/HaDSKnZ2dmEwmMR6PY7FYxGw2i+l0eqV8sjXKw4kTv8tXuS05OzvrXe+DctrD0qCd+Swj8+7/vLz3g1BnBDUjlLonulZtwvQygcbTzM5THmpbtZ/O1X3T8+O+fD6fx9nZWezv78fBwUHs7u7Gk5/85Pgzf+bPxM2bN2MymXQc5Pj4OI6Ojnp8ZLFYxNHRUZycnMRisYjJZNL5BX2+6EUvWtqmFRUlbP3qcRofkmeBZMaNlR5ckul11IDsdykf2W+ZIctUkMxY8HwZACe2vN6Nla5rmqb7nf/7QMDTZF0yg8T0VQamx+NUdtw58JjKyPRUZ7aRftNxL0tW5hIyI7+pkl0y7l4/d9SqU1aGioqK1XDxgPaAICnz6ylIZLZjmT3gdYTsdXZ+KQ3av9J1yqtkszP/wnqt8msUQNwvLSvXOmkuw7I29zZedX9Y3xKh9rYo2e/S7xx4zWazmM/nMZ/Pr5B5+t2maWI+n/fEHRef6EcrKjbFxoRaREQPrx5kjrJ1ztnZWZydnXUqApXCiLhCwgWlI6UzU5z56Z2TJIrnLkOWF42BjNLZ2Vl3bomc0TCrbE6qXU1fpURk7eDTXxr1Z+XPBh0qV0ZIWR46Exkk1tnPYR7ZsVId3YlkjigrG+um9ueUnp5FKdd0/q7GVFRUrIdMrcwINRVfiickXG5nl4khLmyov7tCzL6u80SgOMB2+8XvmY3LSFfJfpTsupdVPsXh7aL8M/hMn46VhJos3ZIdz8hx6Tv/XzaA8fS8rPRvLK/O0czEZDKJyWQSg8GgG0g1TX9WVOlppkR8ZHd3N50RqajYBlsp1K5uZobMz3M1m+fxuk1G86U0sv9L5E7HqGjQyGQEUOQ4G2W7YusE2su4Tkcu1YmGYh3ympF41oHpZMr2qnL5byVlYhkyp+DIjLcbcLY5nz+WY1keFRUVq1EiYKUBvF+7jKitY/+XIbOJzG9dFZjXul3331eVOztnHYGB+ZfOzb4vE1VK+WX1Wqe8jtJ5q0Qjz8/bWr+TT/gMJGc9XKFXGJIPuDapW0VFho0JNR9cJ4l6yPXJB11/WXiHUDI263ZMloWGqEQGPR+SSSKLTS6FeXjapbJnyrCrsMvUjew7VXSmQ3Wc98kNNvOjcXVyfXZ21lOCabictLvTcmXE1ZQM2TNAQ5ktdFUdOXvi97WqEhUV1wfjYDNSw1C/ZVPqJUJHu5yJHo51BvpUMefz+VIfs0wtz/IrCRDrlNfLkYUY+m/83X1HVr5tsIoE+/+r8iyRZv4tG7Tou56v09PTjigr9no+n8dgMOjdv93d3RgOh3F2dtbNrisuO2K5kFNRsQobEeq2bbtp84xYk0yTSOuBdaOajUaZV8TVGL3SMb8uO15SY7ORKtPy8zQ9x/APnleKZ15W1kxZLinapU7P8on0RuSOit9LITZ0XlTwtZqaxJ0GkHX3abxl6nI2SFnWPn4v+Hs2qPPY8YqKiutBA1v2YQ7Ym6bpwq20CH1VWm4/9SkbRDu3u7t7ZdeQDNlx2RqplbJXWehcJsCsymOZ7Xe/5yGBOmeVulwi3KtIbZbuOmRyXSWXv5dmHTMf6fa/NCPAtjk7O4ujo6OYz+dx8+bNODw8jL29vR4noW/W/Z3NZjEejzsf4jHYFRWbYqvVWMtG5CXF+n/GqPm6yBT2UlmXdbT7WRc3cOumndUjK7P/ni1Q9DZY97dN/y+196rY93VUKs/r8PHH49P/9b+OG/fuFfOtqKi4HtinSJhKsdOOZUSNaWfq7Cqsa1eui8wfroMS4Vz3mLfrOvX5w5yd24QPlMg0STXFOs2a+E5W+s4/zrBUVFwHW+/yUSJwvhiRsdNZp8lG/SRMVCKpuHqcs6ch1WGVIqKyl5SATB0vlVffWe5SWjyn5Fyy+q0Djupp3KWoe9wgF/D5tZnj4nEu+igtcNR5+t+dLttklZH1c/nJ3/37B/zET8RTXvWq+OCf/Mn4T895zpWY6kqsKyrWh4slTupIVhhSkdk7khz6Fe+fnHUrlcl9Ae3Muv3cZ+aYvqfr+fMzuy7Lp1Qmb1cd81C962Kd8MjS78v8ZkRfndY5/K46Zr6YaZXaLuLcr00mk3Ph5PCwR6B9RmOxWMT+/n43IzAcDnuhHxUV18HGhDqLu5Wx45/CPDjVvorccmSpY/zO39cdfWvKkVhmNNwIs94lAs28+L2kFjiZZtqZWqs2z1aC00hliyk9Pe7/mQ1gdJ2Ta6br5XVSW1oUw08P+2FapToyPU6Rsu5ufP/WF3xB7MFQPvMlL4lnvuQlMdvdjS/5/M+/spiloqJiPWRCieyZ9p528UO/O1kUofb0nKTKh5SI9bJ+LF/gg29f+MfvJfKc5Vki7KtIZ6a6M223aZkYUfKH2XVZ2UvlX9cu0l+U1hZxAJbdx+zeryM4LRaL7v0W8/m8R6iVP48NBoMu79Fo1F2j9TbrcIuKigxbzXP4A1cygK5cZ0rysv+XLchYp1zbnLfKINOAZeXLlGcnzZ5WKR9Pd1m51gl/INy4ZffK/0rX+SzEsrSz56J0zrLyr3Igar9v+0f/KF753u8ds/39iIiY7u/HLz/96fHVf+NvpPWrqKjYDpndW/a3LI0My5Tc7PeMLLryfV0sU283wR92yEGprdY9v3TtspnpVSLbJmWnMOL+0PPnjAjDQDSwq4S6YltspFA3zfkbjKj6+ghYanVGoDLllxDpdKK6asonmy5ax7hlnT5TxVU2D0HRcX2yg/J8nsM3DmbknO3K6/VHFZdt6sq+qy+Z0uJTXCT/Irq+cwcXKHLhUfZWR6Wh9spUacHb1wcmLKPqznvj7RURsbe3F9MnPSlmBwexO5/HbG8v9ubzGA8GcffwMFosUKmoqNgcPujX2xFl52gv27a9EtqhNGjfaHdKeclm+2LjLA6ZSjNnuGhL1iGRy3xLidQz3+z8Uh7LzqONU9pe9lVqdDbLvGzAskx51zmZ0i9/wTyyl8CxXPSvjpKKr5e7LBaL7q3KDPtkWemn9/f3u7cuD4fDK/WqqNgEGxNqPXwiY26UXKUUlqnTnoc+S1Na2XcZyazjiyBmZcgUUZ+OcoLn00k6l38aeLCse3t7vVhiknIaXhp+Ekwn0iVC7cgcFFUaTtepTN4GJNQqr9Il0WdbSTUgQV92X1kHJ+jLBlnZwCciumnnG8fH8Svv937xsmc8I979538+bj722H1Xqioq/jjB1TzZh729vc7O6Tf1QxFqt0Xs4wxxIxEmMqV5meLpL+JSnvzMkJ2fEWf/jWXwmcllpJdlom0vkdllZcrqUlqkvsoOigRnZcjqwrBCJ/xcrxPRf6ENyW8W5kgf7OWbzWadz9nf3+/8r9ePz2jTnAuEymP/YhazEuqKbbERod45O4u/+u3fHt/13OfGeO/yUjdobgwygxixOuwju4ZpOgnNjFBG7j29tK4rQixKRN/Jt9LKiGRW/8xQl8JFaCzWMQJ+T9yQynBSlXYinLUB70EWBsP7Xaq/p8V6+f9Zfbwdee2PftZnxXg8jslkEj/y3OfG8fFxLE5Pe21RUVGxGbI+nO2akIkDTnI2gQs3fszLmC1mLOXrpHOZH+M5GZnO0llVr03aguevUqdL5Sl9RsQVH1NKo3Sca1uWcYNsPQzVbBdwVtVH+WjW2OumZ1DPKl/yUsl0xXWwEaG+dXQUT3nDG+LDfvZn43s++IMj4pKEcQGiPrPYaTdMpQeYnU2dqUQ4db4WFeh//palz3TW6Uh0GBw5u3LL+Cz9xuvdwWQj7ox8+jSYT2UtM66uMGSOiMSfBFZKgavFrEumKHvYC9PI2pvTxEyPr5Clmp8tdmV7s65axa16ctbifsTxVVT8cYL6KO2cXqrhCjXtJa/JFotnhMsFAO0g1bZtTKfTbqqfL+hgOWXDmLbboSzmNguL4O/8XrIjqxZRrgKvLw0Altmvkh/M6rMs3nkVqc78kPvrzD9nKjvVa6XhMc5sT5ZNe0vr9xs3bsTZ2VknqPgM8sHBQZydnXXP0bKX/FRUrMJGhPrGyUnsRMT7vuxl8b4ve1nMdnfjs//KX+lINHf4kJFjJ6VxXKVGu7KguN1SZ6WaWjIANEo8Zx1j58TYr3HC6ATQY4SVDstFw5YR6izMhLHOQjY9586JTkrfXVmSceHAhvXQPeFULQcdLLNIscrt7af0/KU5TdP0tjnilHGmGnlb8d4wjjtrl4qKivXBAXDEZWgHp9EjoiMwOoe7eVAoWdYH+bvvIiWipu8R5RBBEjna6Gy2zj9XKbLZMeXtYSAEf2O7ZWo5fcIyMp2RZPq+zEeWbGE2GNH5ywg//Z8GPO773RfSj+g4OYPuW6lttIWe7u1oNOqei/F43D1vItTD4bALFzk5OSnGbldUrIPN3pTYNBFtG9O9vXj5O75j/OD7v//Vc4yscUGC/84YK8bxkpjxDX0k2CR7AvMjvLMKNKSZUsxysIOXQjCYF4+RSHv+Wd7Z/1nYSLaAQ21YUlEyxcfh7aVzd3Z24sbdu/HJ3//98X3Pe14c37p1ZSCQxVPz3lC5Zl4RV9/UyLqzHvruMZR+jtqYAwxvw4qKis1Be8ZZu1J4m1/nyuQ6YPwtF77Ld9D2k/zRJjoppL3Sdf7H47x2Gcl2G5zVg/5nnbp7TLJjmRrt5y1To7PzVy3eLglYEf31TS6o8e2Xusa3Nlx1D71eFJn4XLqqrfsuwcZnVioqNsVmixLb9nynhLOzbqeEs6OjnkFz5YAj07bth2VE9A2bH9vd3Y39/f2uM3CHjNIUnXc2N+y+i0ZG8DJVmZ2STmEVgc4W6JQcTuYMlF62O4grKzTUpelUXxSStRfP54Cmbdv4oJ/8yXjKq14VH/DjPx7/8aM/uldfbz+S4UzB97aiQ+YetvP5vPtfnywr68GyK12qHswrIlfzKyoqVkP2eTQaxe7ubhwcHHTqNBdu++zW3t7elTfbcXo/G/CLREdEb0eH6XQa0+n0CplyuK1knj5zKCh0UdesCr9wZIMGXuc2WHn6Lih+fobSAGAZcfa8S+3nhHoZoSV84JRta8dBgv5XWdjOWtSa+RnWW8+EFiZqZoTf27btnhnxi/39/ZhOp1dEr4qKTbARoT4+PIxvfcEL4j1+4Rfi1sVOCRGXHVEdxmOpI64q1xH9UbzHq0X038K3h0WQDB9wI+JGzzuegwTPiXKJ+GXnKS0n2F4GV25/7dfeO37jp987/tXxZ8Vn3vimeMcPeCje6Z0eStPwMjJ8wYlvhkzVzRyDp3N2dhZf+bVfG/swqs9++OF49sMPx2x3N/7h3/27vevYDiwz2zIbHDFuWupBpuh7GzLvrB4+jef3oqKiYjvs7u7GcDjshXpQeIiIK3YwI7cOt2HcHm8+n8dsNouIS3Ltts3FlVLYQkT5rbUk8YTPdG6CjFz7sVWq/TJC6z62NHOXzeplIZo6Xnr51Sqfk5W7pOzrPgl6lqg4i1/w+XJCLRGP4gt3mtEzJJ8kXzQYDGoMdcW1sBGhvvvAA/H7b/3W8Tsf+ZFxenoacRGv5h2FpFqdkXFu66qCUpT1yXgsN9j6zKYcXVnWMV1DIkfi568716eTRFdMXdXm+TTE/+2/PSN+9Ec/Kf7F/O/EB8TPxOcdf3387R/75xER8dSnvnQt1Vftre86v2SsfdrLFW+GXTCff/ZZnxXP+fEfj3f79V+PwXwe0729eMU7v3P81w/90F7efA78vvF3gaq0/s/um+rI41Shs1kKfvozwftVUVGxGZqmuUKgfaBaItTcVcFjqEtKa0RceRuvCznuf2gPRcycZLvgEdFXSTNV2Ilfhk0Ipn/PxKV10iqp0hmBzdqahNrbPSPgtL2b1NftcKb8iyNkBNrTo7/Jnh/e42Ui0v7+fgwGg+oXKrbGZjHUbT9kgEaL6gE79mKx6FSE7FXkPgVEI+xxTlRpvXPSWLsSnJFRV3wzlZTkWnBSTkJNZZ0hCtzfkqTw3//XvxqjeH6X9mfHN8Znz78xxi8axhe962df3qSLtHyFM42GDJAb4+xFMU4u2Xa+0EgYD4cxOziIvbOzLuxnOhrF6Z070ZhT9PvqxpZpMySGyjWJ9nw+72YlshAgtkk2xelx8hpEVTWiomI77OzsxMHBQW/faYZ6+MwS9/rVy1/G43FE9MMBaD+cIE+n0+5TC8yoVmckMOJqaJjbh0xwEbI1NtlsJvNhuXlORqC9PFS/sxDGEnwQkRFL//TZSrWn/06lvkSc1zm+TACL6C9Wp82nj+W7HPx+a9DkPEN5SaHWtbw3u7u7cXh4eGVBbUXFJtiIUAvZ6Nf/GI/FOGo97N7hM0LtZKo0Eqah4/ZMHhrA7dec3JZIOR1DZhx81wmW3Ym91+0d4rfia+IL4hPiB+NGnMRxHMYPxP8anx//LJ7ffE1avkxhpdqyahS+rGwirCUHcfP4OF7yPu8TD73Xe8UzXvayuHl01FN/2E6comMa2X3jdxp0lo0OigrXMvU7Q3ZN5kgrKirKEEHhNnglUqrz1Xd9tx3aikydphKt2c7ZbHZlfY6TwHVijiOuEmraPdpV/cY4Z58p8/SX2ZaMYPv1mc/hOW6j/drM/ma2OhvUuGK9rMxsew4IvJz0OV6/0n7QmWpeyj+ru/LN8mTbMgyxomIbbEyovWNlI0KfjmPH9FfFRlwdubpiqc6QbcdGIu6ENUvDF/c5wfLt3TIjpj83xNlijpIqsbOzEye3D+Lu3dsxinGcxihGMY67cTtOb4/Sfa6prpJk0rCzTWWcfDQux+YDCyrA2X3/zxdbJLZtGz/+Du8QTdPEjYgr95XbI5EQq32opPiARdfJybo64uc7cfcyU6XQeQzFye5TRUXFarhNoQjB/u62gCEi7IPcuYPHqERrS1aP6SVhW9aXS+JP5kd0vo7TL6lOCkn0HYqWkeiM8GWDCtpDliWr9zp1dv/EMBieo0/OepYGKOsQbX1yMJKBsdLyAW17/hZEPic6l+lS2MmeEdaHPt59UfUDFdfBVoSafzJ00+m0Z/C4F/VsNus24tfxiHzRGFVkn0rkcTeATn5JoIfDYUeiNN2o3wQaZB13tUKfUkeo7HJRYMRVA+mhCE3TxAd90Ivif/kvvxff1P6N+Jb43+N/j2+Jt2leGx/0QS+6MmVKMp29VpX3pxRmwVeuZtOWywi133+Vy9tE99uVHbYBnwd3XiXlivtSMxTI6+r3gAtjGRtfXzNbUbE9ZC+cHMs2q49H9AmMzlksFp0tWyzOd2ZwWybfor2FT09PO6LEQbvstSusDtogDwlgvTKboDL77xQ3Mr+U2bfMH6j8SovnR0RqR+mzspAM2Ur/jfeOeZNIc4CzbN1TRkBZVt/Zw0UzgqTdB2gREYPBIIbDYW8QsFgses9dRHQEXOeqfRTiNxqN4uDgIBaLRYzH497uHstmFCoqVmHrkA8SKz7cvj9oNgJmOhHRGdeIfGeLbErO1VUn07xWBl2EUseeMB7HX3/xi+NbPvRD4+7hYUREL78MPlJ31aI0Us/a8N3e7ZfiX8enxk/91EfF3bsPxBfd/or44A9+Ubzbu/5Srz6uvtNgKS0/xw2rBhMqb7Z9n7YWWqayUDWhMi+jJadD5+OOgIY2S591UjpyNkzfFW3d+9JAxgdffH4qKio2B22FEyD1Qe9vPtsokEzpes50kkxnsc2yQcsIUaYA67inxzqS7LLOnEkrDew9f5ajdG7JNjIft32efykN96GltsmO+/lZ2hkvWEf99fP5HGSDkNJMtwspTCPiUjSjeMPrKyq2xdaEmoQ5C+sodQJOl5H4UjnWStvBYBCj0SiapunInjqDiF8WCkElWgq1fuN1n/CSl8Q7/f7vx3Nf/vL43otXqTP2ulR3KdIR0VNNua+qX++GW9c/7Wkvi3d914fNeJxfUzLuGaFeNrqWA5NS7ws96Ox8lw3my7qovXU/+WbMjCwzdl7X8XnxdmJ+XFjJfcxd+eJ1JWehemkvXA7WKioq1kc2e6g1L1Q1SySoFPNKG8lQj4jLty6y3+v7YrGI/f39KzaayEIaHFn4h4eyZMRYcPupT1/wt4pgrjvgly3Nrmdbu+iktDUTTMGLPlz3wO9TNjsq4Urtz9d5U8zxxf8R0c1quC3WQlQXUjhTyTJQtNHzSFU/4yTkLxUV2+JahNpf4uJ/bmyoGnOEzJcD7O/vx3A47EjxwcFBR4BIikmufWcIGV11bJFrneN7Kn/Qr/5qfNCv/mrMdnfjH/+9v9cLBcnqrnrTGJydnfXCHmhsZMyc1JEoeowYjxHLCHVGKnXu3t5eHBwcxHA47NowM9Z0BrxHTq414NHK6SzGjkaKC4nG43Hs7+/3nhum4Qabhl1tLeNNci0si6nW71Io6JArKirWg2wKCZTIisiq7Bv3/43ox+T6X8RlP53P5zGZTHohduqzjMfOBsPK3wfWysNjcAWml6XdtucvBeFLy3x3DSrxtONN03RhMJm91PWZWl4i7hxY+MBFdZB/ELFlyKMTUv1OO6yX53goCMssULhRqA79nM7x9xCo3bK4bZH5iEiFG/dj9CO83z6ocCFK97OS6optca1FieuOtAkaCd9Ng+EY/sdwDYYmiNRxBKzjulbkUXl9zWd/djznx34s3tX2VP4vH/IhKwmWDLob2le84s/GK3/iz3YvaHn7Z/9cvPM7v+RKu/n/NJjL2myTds0INduVU64lUMHIQmB4TyJyg0Wjx4EHB0F8jtg2MnbZcRpUprFOW7KtPJSooqJiffgMYUTfP2ShCI6SOp2FC7LPkqS6vRb5ZjllJ0iWM+JK8SBTiGW/9N3tHkUI/c/jsmksk7cpP1e1X3at6qdrWeesrLxevs0JvMIyJWpkZaOwI5+bbU2qe8gBh8rJ9Uhso2wmg/l6OfzZqaj4g8DG+1BrhJpNkdC4USHgQ00ySiIsVVpkWcoyXw0qpXk4HHbnj0ajGAwGXRm4SEEqCmOHIyKmT3xiuqfy5AlPSAl1ZtRpjH71V98jXvjCj++/oOUn/3ns7+/Hu7zLy660UURcMdo+bUVj40pHFpNG48/y8VXAfBFDFkOtvJWGk3EvP/cEdYVYaoNPG1JhkqrPBatSKPxaD6WhUiIDykGD569ngVO9fD4qoa6oWB8+U6b+SSKXgfHQtAku0OhPNouhhVSOMxU3C4HIyqOy09a5TcsIrsohBdZJppNyt81O/hh6kpF4r6OvC/E6Kg2vM9Xq0WjUm4nVjB/f9yAfOpvNYjKZ9Ox427a9e6c0ZINFvkWQeT/kO1Qv3V/5fKXtyjbtd7Y2RmXgoIxqOmdlSzMBFRXXwcYKNWOsnFCT/M3n89S4itw6qWPMs4yoPkV6NJV0cHDQhYLcuHEjRqNRRESPeDE+TCo2cev0NF72zGfGy57xjHivhx6KW/fu9XYVIUjeSGxV9u/6gb8Uo5h053cvaHnhML7k6Z/TW5HO65ROZkCpIrsBymYFXI3Wnw9KuIUgz2ddlZ7uD50M66D744pMpnLM5/NuJb/SWywW3XSijHW2X7nINx2tpudEnPWdL9JhfVRfXqfzeW8qKirWA+07B/muvHq/Vd/W7lBuyxguIoLlirdf41P86yq8sn/yP3xbXqZas95nZ2cxHo87YjmZTK6o8rSHHqqn8+QvM4FD+elaJ4lODHV+qewR5356NBr17CSPN00Tw+EwDg8PO5VZBFcCiEL3dP9YDpaV4Xz67i/zUlnPzs663TeYj/6kZPsuYRnkQ8RDZOvlV0uDkoqK62CrGOoSXBWQ8aChcKLoKmw2HeTHOe1HdXfdkWbbtvH9z39+16l+6GM/9vx6lFvnZfA8lr2g5S/H1/auyRTqLE3Wx1WOrCwlhZrt6nmsIpJZe9II0YlmpNw/VTbeMw2Ysik6dyBUwkrlolMt1dHbvBrUiortkPWdkt1kH/aQjux69x+eDs9lqEBmd5aVMStDBrev+q4FzoxjdvEkszkegqHj2Xoa2nMfXCxDybZl5J3KvPsngnbabbb/zrw83CUTdNSmCq2hAJIJXatsdzbgyMSo6gMq7ge2JtR8CDkNk03HcFo9U0ildjL+SnG2PE5Dpo7CV89yNbigTszXjvJ6kUEaOi4iVP1Khr9t2zi9cxh3H89f0KI02W5OKrPO7PHOHDxwKpLGyPPhYEX10CedQzaFyrR4P7k1nl/refPPp+KUv+6v7hG3XdSnFjD6TEEG3zNV5yqUiLMMVaGuqNgOmfK6jJio/3u4h8/eyR67SEIS7n5A/V12hFvrLbPd+n1/f79TMrUrlNtE36FCZE+zdCJ9mUKu71zA2DRNp75PJpPuvOFw2CnHPJeElGGVrhAz35J/UT1VB94fqcNqw4iI4+PjODo66mYZNdPIsAzOtmYiT0R/IajP8rpoxF1aMnLO47y3Lh55G+p5ocrP9DZdE1ZRQWxMqJ2IRVwuIKEho2rJ19MyRIBp6ffBYNBNvylUIyPgTrrUwd2QquPKSDmpjui/TIZkMVsQ5wrIbDaLD/7gF8X/8p+uvqDlAz/wh3ppZE6I+biyTMXA3xiZtSGnYFVWpsmycOEHy5ItyKRT8i2s3Lj5s0JHxDb2GQmVyQ0aHR4JNe8fVWwaS3cybKv9/f3uBQA+CKuoqFgNJ9IZiYy4um0e32Tn0/dOiGgzGALGxdDs+1wER7/kW+RJjKEoI7vgC/fon/i+BLUBRQ/aNhI4J6tKj2qzfN5wOOyVT/mxTBER4/G4Vyf3I2o/V4BpjxXSoTbWdxH9xWIRjz32WLzxjW+M+XzeC3GRrdbgwEUv3xCA7cmyZDPM2Wysysd1MNkzpvqp7dUuOpZdT05RUbEt7kvIB8mLKwJUSrPvEZcjZpIsJ9IkokpXeTuxpiGVoZCK7Woty6jfXVHV+a4+6JynPe3h+NeLT4mf+qmP7l7Q8v7v/1/iaU996RWDyTxJ8FgOlodGylVkDjJIhlVWJ9TZffPP7DuNZ/amS6ZByGFwEJI5XZ8qzRQHpZVNL+rTp09L5/pzmBnvioqK+4ts0O1woSD7naBt4mJ5/vmLYHQdF73peimjtDk6x/2Wl4OCQEYcdZ4+lS9tnIeTlELc2Fa0o7qOa25KWOd+ePkzIYhpOVHVea4aa8Gip+22330ef+fWvPTPJZT2RfdjFRXbYiNC7R1YD7JG+Bp9e5yUVGc3Suyc3Ff64OCgG0GTWHNRATunOhUXuKkcUkKyfJmGjN9kMrnS0V0Z5aI7ddJ3eqeH4s/8mV/otctk0ldNfGW1j8ad5DHcJQv54FaCCo/JprQ45aX/VUYSVd5jnRcRvQUi2oOVRsxH9ty9hVsdaiqVK8hdQXFCr2dJ91PlIHh/NB3p5FrTozs7O9206s7OTpyenlZVoqJiQ8hGuWCQzXBF5DHLvJb2mTt7cOcf2iXtNDGdTjulVoP9xWIRp6en3R7I4/F46UI2hoIpfarF7sP4YirlMZ/P4+TkpCOKtHvahYqkWzZN7SaFV+GOahOfaaRyLTXd7RcXZru9VHoMtXEFWZ/6bTQaxe3btzs/rz2puUOTK/FU/l3A0H1SO9HXcyCgHUeoOCtEbz6fx/HxcU9xLqn6Ozs7vcEOZyx8MLbJIKOiwrFVyIfHRekBVSeL6L/CmyNmf0MSf5dB0c4UPnJ3RVFGTX/Zhvuchsv+ItZbKMj4PRJ6Ogc6CHZwGRWP1+Wn2oQhEiJ9XncSam43yC2HnPQyP52jei2ri6b+xuNx18bcNsk34qczocEcDofdc+F7h2fKD4m62pHqDtG2bXefVUZXNGQwVTa1VVWnKyq2A1VX2rVN4ORFdk2hWAwBc4FDZHk8HvfskYiu/MHp6WnPJjjBE1xRpg+SPY7obxHKuOLj4+PeoH1nZycODg66dChAMU8Rd9ad9aWNZHiL7D4FIaXB3VF8JxUST4az+H1Q/sPhsOffB4NBN2gRWXWhhWVUPUmo5Xe4HS7blyIQ20SYTCa92Wa2C+vgAxFX1skfKpGuuC62CvlwEu1Gzw2gk2t1BCeJTtTZYZiuflNIR0R5RbQbUb6hKSJfuEGVlOUpGXf97iESDD2J6C+Y43QeR8UipYI7qVL9OEVJxYFgHmzXLJ6M1zA2ndOqwze9KZ777/5d/PtP/uQ4vnWrq6MMohtxhuCQiGf15fSfyLKu9bbnPckGPGw3flebyeFVVFSsD9ou/rlN82tK9klw5TqLl6UaWvrLbJA+VUaWVbZQNoziBsuvQbnUcW0fJ0KtxdWyL5qZ9Bk5V2Jl87y+XO8ioYj+xne74n7LHqtNcYUzt5mwIB8lRVrEmcq/4qyzV5P7PeN3Ch2qz87OTudnGHrD+8Pnxv1U6XlzYS4rD8+tvqBiW2z96nGNzLPOLcWQ6qqmvTS1JcjIULmmURNB7co8pwABAABJREFU5IiXi1lcMfdwEE0j8TXkXOwXcXUXCxJgKqTq1K5scFTuRkWb4rPunNpzldYXvdAYZIo9F8v4WwtpQGlQXfEVOGXL6+UwSKjPzs7iA37kR+Jtf/d34/1+9EfjP3/Mx/QW5rhRooogo8XBBWcp/Dq1ixRmn67zP4b7ZAMStp/2Xa1KdUXF+pDd4mwWB7y0o7ThsvW0sRQCIqK3d7B251F/lk2SGj2ZTDpS53seZwIGQwL4SQIvgjsajWI0GnV2Qt8PDg5iOBzGeDyO173udXHv3r2YTCZx9+7dmE6nMRgM4uDgIPb29uIJT3hC7O3tde9KuHHjRo+UsxwsN5VZ1Vdtw4WN8huj0SgODg5694g++ezsLE5OTnqhe/KPHoqosnjoxtnZWTz++ONx9+7dru2n02nXvvJFusfZM8NZDQ1mVF/5Sj0/FNV0PUNEmB6FHtXbxRUdo++hD/SZgYqKTXEtQs0XcXhYAafVGM7AzsbRI0fSPm3Dc0kMadR5PhUAhh1oKowENeJqbBs7tAyZjLOchhtDqiaE2qhpms7Y+vQoVVlXj72dWPaMYEdcVXXo9Kiak2wzfyoh7rQiIv7+l31Z7GPa8tkPPxzPfvjhmO3txVd+8RcXnxlOBXKKjkbNDagr1gzz8Tq0bdvbDorPpc7xxbGaZqxGtKJiMzhB4qwS+142g0T76X1UfZeCgQsd3CVEIQ0kVL6OROAgnIRRogeFCamxTdPEwcFBRzx1/fHxcTz++OPx2GOPxWQy6T5Ho1EcHh52cdfaTWg4HHbhbgzL0AygBgzT6bTnNzkQ4GyhyquBx8HBQW8GkMKO2lttJnVZQpcTYH/Jlj6Pjo7iscce69pd4YyqmxT5VeA9EBRSollDtQHXEvlzx7TEDwj6eV7jfoNtVFGxLbZ6UyLJih5kn8bjaLI0zef/Lwt94Dn6XWk48ZJxV54k1DzuSrkTc+XH0azHYNGBuIHgcf2mkb7+59RWFseVha34QIV/2fUcqGTns0059Sk1w2chvv7v/J348Be+MN7lla+MwXwe0729eMVTnxo/+lEf1TNKVIxVBhn8krJcIrYqaxYWxMEHVQkfVLijKT27FRUVm4Nihvqh+qyLA97f1PddDBFJU5y0244MrkAz32Vl9/SUn0IRJCqcnp5G0zTdzB33Zl5WLuXD9Kmoe92ymTjOTHKLWQk29Ftsr0zwkv/jTALrzV00nFiLmGs2QTPGFMY4m8tjvjDfZ19lu6fTaRcjzrA8n2VUmV1gYnq8J36PMl9fUbENNiLUGj1z8UdEHhenETgX1/HB9VFjpmxE9F9vm3UCfSfJdNKcvXKb00bMe29vryOSdA40eE6aMyhNtpMMMkM0dnZ24tatWz3imxmHbCEi6+sOi/eFqoobVrYtR+1azS3DyhCbo1u3YjIcxt58HrPd3dibz2N+cBCzJz4xBjbNRkfJcqhdtPhIKpScAgcLfl+zQQ4V6uFw2FOqNJBRHfiChTrNV1GxHZzY0mbxOPsYbZ8P6NV/pU7u7+/H4eFhtwvHvXv3urAAxewyDJAqtavjGWgnZZtI6GUHm6bplNPd3d2YTqdxenoa4/E4Hn/88Tg+Pr6yy1UpP6rzOpeE3MMNHVKipdwqZE0hJ2pvtRnLpfqp/dU2UufZfi46SAhRevIPulZKvsqUhWbonmsgIJGLi9j1rCj9wWDQhdq4L1AdCYZ40u/TZ6ie9DNZyEhFxabYWKHm4jQa0kx9dsKXkZZlRIbpO6EmKSf5dRJGlaMUKiGQMKuT7e3tdUTMDYyX0dUBphtxqQSoPowJLqkurig7kfZzfNDBAQlVAv3usw0eN6d7zbjGiIgbx8fx8+/5nvGz7/Zu8f6veEU84fj4Svw329KNNNXlzHE4iWa6qmf2THHwwfu9SqGuqKi4PrIQNNoNFwAi+jNZrqAOh8OOnFElLQkb2ezhMgHEFXPa2WwWjYRYW/P5WqJVJJ5+K7NLrIvbORFTCVbayo+kXDuGRPRji72+sosMj8lmAHQvOVtJv6B8ZOdJYDPhiiq1v39C13IHKbYB/0jAOXjLVGoq1SUfW/JFFRXrYusXu3iHc8Kqh52L3Jz8cOqHRI8ojdidZFKRJKnS74qVdQLt5fIO5eSbRofl47kElRJ/zS7PcWLu9WOsnAYIbF+2QRYn6HHH/r1k2Jm/7tnu7m78+0/7tDg5OYnxeBw/+BEfEbdv346DJESHRlF110IjHxDoe0R0cedSNDhAypStTFlhnWT8qdq4M6moqFgPsmciUK6+OqHmZ0mYUB+UuijlOSJ6yijJIdeNSEXW7yLhilXOBvURV1/y4oo6r9nZ2elIfkR0s4sMCRmNRnHjxo1u733aLrUVBwksl/si/S6Cqe3plIZCIU5OTuLo6Ki7lm8GznynL8in6CDwvlLRHo1GMZvN4t69e12ePHc0GvX23mY+rFtE9MQalY3ljIhuRoDPBd+/QPWZCjxnQrJBHJ87/7+iYhtsRKgzQxTRVx3UMTT1HnG5RY/O1TF+uiGm8eWLY3QNjTGJvAw8w030Ig9Cabphz4y1DKlG6VQkfDFjRPSILsMLIqK3iEMQGfQRNcshwzwcDuPGjRtXCDUXtfg+1PrkHt2lc6hUKH/eXy2q0WJFDljouJQWHaDaQPuuKtyD6rc7WN5LGWvl73XLFCwR5tlsFuPxOPb29rqX9+he8uURFRUVq7FYnO/+w8E+18xEXM4kiZB5H6UYwt03hsNht2OFdpHQ9nQKMVD6Gpy3bdvF23Kg7GIPX8rC/aAJ2V5XuykyRET3ArJbt251+13P5/MYDodx69at2N/fj5s3b3bvVeACeS3ik41z+6r8F4tFt+Dx7OwsTk9P4+joqBeCF3G5H7baVLuJUEigAKbyUFlm3UjWGav95Cc/uWu717/+9fHoo49eGVRp7+3ZbBYnJyedv+TC9mwxOnfgomB1cnISZ2dn3WJP+fLRaNTzH3oGhsNh175S8PmMkdz7AKsq1BXXwbUUaoGki8dcPY6IK0oA48l0nCB5963ySmqwlyObGvIROUfsvNbzc1UjexMjr83UYhk6kTofEfvgRN9pkDlKV/tlI+1MsaVqwTbwc/1eqmxSVnivfFDD+pNg854sFovOCTJsh+3GqTovo/7PlOmSgi1Hqzy5mr2iomJ9UEjIZihJmt2u+4yc0ovo2xgnyG53fFZQtoJl4voZEcNMYWcaKo8vbGY5m6bpdrTg7JcIna8fcsFE6irjiCX+0Ga5PeV2dSqXfBFDFPl75mN4H3kvNPDgC1e0g4jK7rHsvO/aMlBiRURcebEOQ0Pc//i9lCKfPVMUulgH/u5cxO8n865+oOI62JhQc5TLnTMiovs/4rIzuOIpNE3TvX7Uj3HEzAdcSohGn+pkDBNYLBYxGAx6ygC3QlKnkeItI6Q6aeSujiwVVYsVZQQ8Fpd7Huv6iMsprYhLA011mVOKJP+cHpRh1p/v5U2VVmqsDJ1+Z9gDSaXfEw/RoRPxKVtd4+fx92zRH6dC2VYsM5VqvjSA37PBiDt4tjWfVVdtKioq1odsSjZVL2QzdnwngE/Bc1ZJ9kykTC8TkZ2UeiolNaIfIiGy56EWsgkUFERQ27aNw8PDuHnzZs8/OYnnrKQU07Ozs+6NgiTStJlZG7LOghPN3d3duHHjRk8p9p05GE6oPbJFfN0HcxZZ5SVRjYjOb6rtOPCQH75161bXhnq9O21tRMTNmze7+7i/v987JyJ6KrLyd7VY91JCFIUQzTxKxd7f348bN27EwcFBPOlJT+pmczng8w0VfPBSfULFttiYUKtzaq/fs7OzGAwGHZEjoZbR4bR/RD9e2pVpjdo5TaO4NE3337hxI27fvh1N0/Sm7LWNka4XodankzC+LlVGQ/nod5FuGV0abYZIsC6aRnMVXFNfJHo0jmoHrpCWcZLhGQ6HMRqNuus4jar0ufqayobqonvl8eZUalxBIMGl0s6YZhokV4mYBsNWmKYGAiTBVCm4xzTB54lKBB2bnB4HetWAVlRsB/VJEmraCA7OvR9SkaW94WBfNvf4+LgLp5BdUXpSQ2UPVYazs/P9kk9PTyOiv3WoyseZuuPj47h3716cnZ3FW7/1W8eDDz4Yg8GgI3KqA5Vt2X35PtoUhrpR+MhCSHyGjbN3EZdvnr19+3ZEnBPnO3fu9GZKlZ7KoZhjCimEfIzP8HIAoMX8GliozvKl+/v78YQnPCFu3boVs9ksHn300Tg6OrpyjxXaOJlM4vT0tPM5XBOkRZRqb/etDBWRj9PzMZlM4ujoKB555JHOXy8Wi86/vNVbvVVv21z5IV/w6Is3Kyq2wdYhHx7eQHXVCVhJoeZ5AqcR3RgofY+NYkfgFJn/KV9XBrg4Up2daYlwK+ZM9SJoIDnSdhXUF+kpT7ahT5+SmPJ3Ekk30D4F5uqPl5f5KD1Xz5WePwPLVBgnuqojYxn92SCZZ7o+ICqpYj7Vx/Zje3kZKyoq1kfJBnLGKiLfSSPi6htd3V6KvIoksd976ARFAamgbgNYZualmS+Vwxe0R1zdqUS2iG8zVL0ZWsC6Ml8i81PeviTm2haUYDuK/LpvzEIhWUbWlwMe1Yv1l2BFm+42VumofFKZZf+lpot0U7xxv8YwPZ7LAYyEpMlk0ovZZ50zuJ+pqNgWWxFqdRy+ElYdTA+6L05xI6qHl4aBBEt5aM/l27dvd69xvXnzZty4caPLR2qt1AuNeNXZuJ+yvp+enl4hnRxV6/+Dg4PelkQyAkqPyoDKztG/KxCqmxbYSXE+ODjo9tzUvptaoCJlmqucNU1H4unxwTTeMmhOtn16UgbRF21yUSmnBTVbwfudEXEab80w+P7YJycnnaqktqNz5qCAhpvPl8rG+nvsJc9nuE9FRcX6kE2RssjZRZIXbq0m0nN6ehpveMMb4tWvfvUVcvs2b/M23U4Ssr0UPrQ4bWdnp3sNOMUV5cM3KCp/KtNcpC1bOJvN4ujoqGebSK5J7nyA7+RNNsoHCG3b9nadkj8TRBCpsgvMczgcxuHhYW82U4v/qNZqZk422reqI5lXfekn+Dp0+YWIyzdJciASEd1MqAY3TENhGYqz9gWoBwcHcXZ2Fvfu3euuY0y4yLgIeUR0fnpvby9u3boVd+7cicPDw27RqD+Pqje5iA8+Kiq2wVYx1CLPMlB8uLkVEEkYH1wqz/4Ai6iLuCrM44EHHognP/nJ3cbxo9EoFotFt22PyC5XJysPGXwZqSyGmIMETiFGXBoOEbnxeNwZECriDP8Q0ecomQRPRkXOQbF4h4eHvR09uCKcMeFZLDfrRYWCoSgM1xAxZ5q6l1pE4rMIJN58wQzL5CoTr9Oq+AcffLA3MyCH6btv8LnLnitX8z0um4MYbrHE54WDhIqKitVQP4o4349ZtlOiBvulBuY+Tf/7v//78Vu/9VudP+HakcPDw27XjJOTk56YoG3pOFvZtm3v5SiKraWQwJ2QIvpbdNI23Lt3r2cXXTElGaety2bvvL10nVTZ3d3dzuYr75OTk+5FLUdHRz0STfJ7586duH37difEHB4extnZWTzyyCNd+KPagm2sMBLZWo9nJ5GOiJ6v1GvU27bt9uFW2ZS2/lhWhhsqJly+VuEZfAW78uV2ifTvCg3UscPDw1gsFnHr1q144IEHeoSa/oH3R3V1Nb8q1RXbYuNt8yKu7gHtO2hQFXXFojSl5flwWs/jidVJIvr7nfI8peOhEK4qqEwkZz515QsFaYyoFjvx5EjY66rrPO9MNVj256EfpTZ2oktFN6tzlhfPlzpOg+V5ez58Ixbrr9/lwGjkWAeqPUyfbUZyrGeF7e0KfSXTFRXbw22r/0VcfTFXNg3PT9qizG6yv7p9p4jA8AduKeo20MvGATsXaet3Dup5LcuU/e/tQpvFRfOMI+csqCC7STGFYogvWGe5XTHXp98jCl3ePpk4RrtKQp2lQWS+xYURX1TuIgrTcR/tRNpDdyiCVSJdcV1srVD7tJGMlasEEdHbHSMiet+VZq9QF1M6o9Eobt682U0N0ZBQ+dVx30Q/4nIEyuk7draI6BRi5uPQaHp393LXj+l0Gjs7O930mgwfFw5K2Va9RRwHg0G3XydVkJLKISPFjep1rRbWDAaDXvxcFvqhclCRZiycgwaabaf75Asb1eZefikLWkikcBZXlDXzof1L6RR9WlLpSU3Ss8BZEoaISEGhc6GhrqioWA/0AU5cXHghZHNu3LgR7/RO7xQPPPBAt1/xZDKJBx988MrMl/64w8Xx8XFnA/w121KnJ5NJb6cQLhZkPfjpr+r2GbqI/h7KTMvTpe2OiM7+qO00+0p1/OjoKN74xjd2CzHlQ+m/1L4nJyfx6KOPdiq9FtMfHh52/ke7b7gCLRWYwhB9JQcu9B/ark9hkwqdVFvrPrD9lI+ek+l0GsfHx92uH+IHare2PV/seefOnTg7O99/ejqdxs2bN89fIHZwEG3bdqo+31Mh/8uX7/C5k0Ku+qp+9AeZH6yoWAdbL0rkHprc5YPTPiRKTXO5FZ7IZzbFIiMpcss3TpH0cMqNo1URKxoejqhdzRYRV0wet6Rj7C3jvkRsRfgY3qFzRb4Xi/MXIKh8IpEi1NmKd7YFjRpDFUhuZXS0UJMKQbYwQ+EaJKOMKeO5EXGlfX0LQofSoJLArfJk7Lh7iZQIGcm9vb1uVbicgsBBndpTzwcdAQm16k3jqzTUbhUVFeuBhJqq4qpraG/+9J/+0/G2b/u2cXp6Gq997Wvj8ccfjwceeKBnm0ioFco3n89jPB5HxOVAXSElIljcoUnhDwz/oN3ioJoEj0r0qvhaquOso2wklVz5xoODg559n8/nce/eve5lKePxuEc2mdfOzk4XXz0YDOLBBx/sSLRCItVWrlirjSKiRzAVe07hibN/TMMJtQYwItSMVW/btvOf+/v7MZ1O4+TkpBOjJpNJTyCKuNxalQT94OAgbt68GQcHB13YjPyDYvmVl3wMfZjPCLM9dU5VqSuug60XJfr0kYgcyQzP44MsounhIBGXscfsyK58Ck7oqF6WCBKnhVxxVZqqhxvCjHDyz6eavKxqOw9h8RAPncdjWV1pGKjMUqFniAavZflZPv9eOr/Uxgw/oZP1e1maHtYzw2lQPS/e3mx3P+blyO5XJdEVFduj1Af1WxaaQRWU22By32P6FYb5eVr67qQ3I79eVreHHvet4172EjJbVmov93FZ6ARDPGQPCRJWxUoz/GPVzFspjCMLB6EtJehbltnTLHyE4SG+rscHaGojzh67v9UGCT7IWxZmUv1Axf3GRoRaqoS+R/T3v6SKKlAN4MjXDZ/Sk2Lpi9xms1kcHx/3DF+WPsvqxkTXSUXgVCXVc4ELKTQlyZcMyOipXTRiljqhMklNl2PggkRNWTI2nMbCd9AgdnfPdwpp2/MXEnB6j/FhGuz4Qj+1n9RaOgQ30BlpZftlIR9eVrXJ2dn5K3Sl9qtMzI/Tuzs7O72pz4jowj3cCWbOQfde7UJlu4Z6VFRsDirUrvzpN80WMh54PB7H8fFx7Oycvx5bqqpe3y17JmVVL+iSckyb63ZfoQw+IBcRU5iAfqftkprJvZMjLsmc8slsG22t/lR+zc6pPLL5WmAeEd2LSfR6dS6czFR/2Swu8tP1avc7d+50Kv14PL5CimXjXYFmCIjamb6UZFezgvI5sufyNx5GQV/jQg/tMHe2Uj6y91K4FRbigp6Ues4QZKGPKhN3c/H2rajYFBsr1OxQ6iR8cHlcnUsKBEehVH99VErCE3FpkLkIkB2ecAORlZ1xyFQ0OSUYcbk7iAggN5WncXfyy9gtqjI0FIzzYixwpnaXOrhCR9q27bbeYygMF7VwuyOWzUfwrkx4m7ojcqcR0d+yjmlw8KL285AMhogonCXiMsSI6lZG9Fl2V6nZtqUwm4qKivXA9SqZuig7yAWC2jJvb2+v294sIuLg4CDG43Fnz0TSDg8PYzAYdNP6blNcBKA94SCf4Yc+i0fRgjaNyqnPoGWqLdVXkWwKULL/FFFkC0WIqTbrOn7SXsu+7+zs9EIudnfPd1KaTCZx7969norsZfXvVIaXzfBFRM9GK1/5H60v0v1XezZN09uhw32K0paoosGBdvHgriJqW7XrYrGIO3fudGGb/nyQTJPsZ7MZFRXbYGOFOlMFOTovwdVjPezsQExf1+jB56pqfjqxzlRVxkixE1PZ4KpkqgyMy5NB5zZMWRy419tJMskp28MNjOqQqcOZWswQCSfkjLfOHIF/z6b/su+ZYyml7+2t++T3IyPzDBPidYIvHqJS7WVz411RUbEZSnYoI0cR+eCdtpP2kEowCTt/y/o1Zy5J5jPRwMvL3+hrKE4wDIO2z/OgT+OMmA86dI38C2cUWc6s7d0OaxGji1SclWWaXH/EV6yTXFLIYVk8LMeJKV+6poEE75G3hw9+9GyoHlwD5W3l9z8j0fxTGqti4isqtsFGhHpnZ6ebolMH0F6UmeES2JmpFPj0ko7pXC04oAJMAulGlOWUsfDjHlssg8iV0OrEfBEMF7YodMKVaoEdnvGBWiyisBYZFye9qqtG6FKidb6/vjfi3IgdHh72dvmgE+D+nqon7wtBw6PzXWFWG+tcD93R74y11x/bjMaVO57QMeu4HIfypwHNBlwe787ngulXVFRsjmxRom+NKXh8s5RZhSVEXAoBnJGUfRRZ5AtC6AsiorOXbdv2lF7ZPcYqR/RJPkP+GJPtu1WUQHFkZ2enZ7f18hWp7bJxslval1uCDWfjvJwlxfrxxx+P1772tb3QCLaJNg2gP6BSX1KlCYobIsq6jrut6B0R7oMYNsIZAdp8tbUWLnJDgvl8HkdHR91OI65oqz30PKkcqq+EMC1KV7tmg4mKim2wsULNeLCIy5jjLE6JoEFgp/XwCBphElUtOoiIbqrMDarA0A6f5nMyKMPmhFAdU/FaeiGBvrvBILH3zsmpRRkeqseuROu7DCPP5VZzVFe45Z9i6Zin2sOVeH7yO1UgtjXbzlXsbNbAVQfdVzotTVvynvg0LGcaeK/8/vkCGIeraaXzKioqynBRIyNH2ayTD8I10Pd+Lpsg2ynxxdVFkVedw/AGX6wooq38GR5HJZpvlPU1QcqT7aAyU4mVjeaaIA/tE2nmS2hI8ChIqA2VPj/bto3xeByPPfZY7O2dvy3w5s2bsVgsejOX3M7ObS3D7jJQBKOvY4ieyjMYDLp2ZxuzzegjPDxDpFf5TqfTGAwGMZvN4vT0NMbjcecbVTfnDRqYMU2+GZPiUhVVKu4XNo6h5sNHo6BPn8aL6G+mruuc1PA3EkuflnNCV4qBWkYcVVZ3CD71JyXaDbmup7GlgWLbcMDA7XxoXBl/zTjqiMsV3q4Ss5zergyRIFlXPqVBj7dhpla4Q9R5VLNZ/6ztmQfT83KxPtmzxDJmKrvOYf2VhpPpSqorKraD2yT2Se9X7M+ZXaVdiYiePVQf5kLmpml6s5jq17TNTpqpkrOcHKiTmHNW1evIstHOZKIH8xGRpoJbIsyep+CDFvk8zpxmRNYJbjYTSWRKPpX/Zb6pFJ7D8nheKtPOzvl7HHR/+Zr7tr18fTvLJb/MQQnrUPIxLsxUVGyDjQk1SWhEX8nlq12lCLt6G9Hf0SMj1DSi7KRUokmYXaGmMuFpc5cLTnNlpJFTQ1xE59sUqa6ZQdTrxJumiYODg7hx40b3YgOuYta0lhZUrBpoKD+q+FS1pRio3E3TdMoBBz9eZ6ZFuHEi4ZdzpFKVKR0ZaaUTkyF0hVvHmJenIYMY0XcQGkDoehlcTuV6yE5FRcVqyA7QJvkg10mVZtnU37XYkKFqSo8Lh6lEi2Tx3Ijo2U6Gg5Gwe0iH/x7Rt98MQaT9ItlWflJM+R4CzeiS6EtRjogurJB+RIOGLGyO5aNI0LZttwuVK/1+DetOm+n+MBsY0W9oxw5B330rWLblOqSdvlehHrLjCi9V6CR3iqJf44x5Fm7E2W4OmKpaXXEdbBzy4R2CDyiNopMif1AZwkA4+WY6gjqJGxwagNJCEf5f6kTq3BokaDROMuY7ZmQdUUZEAwcZAYZ9cEcTGmdfpOH5ZMqsKzlS8Pld4TJUgXQt1YfMoLrq7GEyJNWeRlbWTL1eplKzbBm5Zxq81ttMbewKVUVFxfooKX38fdk5nH6XTZIIQx8ScUkeRRB9kaDyI4EVQcxiol2xLinpmWKZDSR8lpH/e501mBC5z3yVq7hua/08tYn8ll625oKL2323lyTWOpYNJpa1m+4dQ4Ay+My12of56Jh8veroswYUSDhYYBquUvMeVnW64n5gqxe7+MOXTR/5iNeJMafoaBioLvB85qWRvH4vddhVHWQZWVfn5ciXxtgVWJab9eIe09wuiXkxbZJF7mxBVYdGUvXXtWxPdyxKT0aPRl5puYHMVJkSwVZ6UlfYxvyd5eQAytWlbBDEmQCWLXM4joywZ06hoqJiM5DMLFMhIy63sPNrdJyvxaY9Zdws3+7Kbdl0rZRL5Uv10u2AE2dXc4UsltpFEL7VUXaXbwLU67aVTxZfTPtIZLatNJNYil329J1gZvbaZ18z5drzYFy0wMWJGjgxn9IsgZNlhpRqkKUwF71+3eu5TB1n2bN2r6hYFxsR6mzqh0aKccVSQiMup/ki+p2aL+cg+c5CMaguqCxEyXj7MaqSfj0Nb0R/Zwwf3Xq+CtdQXfmiABn/0WjUhXnQUGjKT0ZGCsdisehCQqgyTyaTHtl2FZckVWEenFpkG7hq48bNFeBM4eBxkvuIfogQ20rtQ2XeVfpMYfbwHh3nM+j3a9n//lxVVFSsDxdXuMCQfZUKNHdV4I4de3t73V7VWpQoIqrFfU3TxHA4jNFo1FvQKBKpMITJZFIssxNo+iflq7rR1rkP0zGVk2Eeo9Eo9vb2un23F4tFb2aS6jh3LCmRVy9zRjwFhdKo3YWM7NIOMm1Pl3bcBS8XXfxzmW3N7DF9gX5TuygEU35yf3+/a+PJZNK9GEhlVTp8hXuJVFcyXXFdbKVQZx3bp4FKI1eBxIkhDpma7XltMzXjhJhpeVyZ0pc6nRFpr5N3YBpaVzGy+D6ROioEyp9hGowz9IWVWdyah39QJXCludTe/lvJWOo8xja6gXWSzHvPgUDmKHS+g0Q+u+/8zL7Xqb6KiutB/Uj9kP00U0ndX9DuKxyCg3Sf0RLZ4gA/GzQTSqvU391mZdfyPJJThnvwe0T0iD9DF5YtRvT81KZe9owAyi+44MJzaf9p30tikbc///d25wxqdj9Kfpz+l23sz4rH5KuNRbb9njGtZep05gMrKjbBxoTaH2p1dP7pGBdHUK2OuDol46EJOkegEkHDkk0TlUhUaUReMgBCaTTO8ktF4dSeDG7WmTU9KVWaHZptSOOrc+lsGF/GduDCFhp/7nTBmMWSI1mXbLJ+Pu3L+yvnQwWfW22tOzVHkIhnhFtl4mxKVq6Kior1QDvqdjzruxnR4XGR0Gw9jvqrZtuoUCs92UtfkF4aZGf9nXaBZY2Ino1gHTm7xncKMD5ci9ilsjO2OLP5667tyNqYNl3p6S2CmXjAY+5DWFf+nq3BYVt7WE3Jh5QGLtkxlsMXr5OPcJbcBxP01UqXPr0k4lRUrIuNQz6kmvIBdqVZqir3iPTO4wTLVV0HjSUJYJa2ypWNjJeFBOgzMwDsvD6Q0MJDTfPRsLJDcwqLLyihYaVRbZqmW7WtRZBO0rnXtk8X8n5ERC8Mx+PQsnq7oV3mnDLl2Ym8h8FkhHqZQ3bVxhUjP+7Pg6aBmZYvGqqoqFgPHMxzqn7V1Ln6G4mZwuIkGEwmk+5FWiSGzO/s7Cz29/djPB53JIv7F7sSWlI3aX9lk3Sc5RVoM7gIUWQ5IjqyPx6P4+TkJM7OzuLw8DBGo1Fvz2blx/hggf6qJBSwXKwv49D1Yheuv8mIss8YZPdwWXicD2JcAc/KmpFYlku2nbs1cbGnzuMARrt+sC3VDu7zfGeZ6gcqroONFerSyDMjNQylKCEb9WfXLhvFb1L27P/M+Hr5sv9Jbj2kw4maKzJyKHQsJNI6xqk7b/fSoMAJbqYWsw7uOLJ2Kqn+DJkpXcO0fbpwWTlLyIzvquuyQUEl0hUV18M6fcp/d3Dwn6mhJGW0sRHRkVnZLdlGzvqVZt9YnmU2RaB/y/yVysY1Kfyu8i4LW1M+q2YGMzvNdvP28jy8vvRHWd2Z17KyZT7J73/p2LK6sjzyi1l+WZ5Kg7OyfL6cx1RUbIuNFepMAaCR0+pqjvayaSwnmJ5mxNUFFV4WhxsJqso6xk7JjpypvNx5g0ZQRl7qql4Trpe2LCOHrGPTnMdB82UrJYJKBYNKPR2I18PbRipxqb3pHEjwPaTE287LzPvLfFwhWteIriLKXhd/3tRmvJ9ShTYZkFVUVFzCB8gUF5zY+XU6P+JyNksq9O3bt7sQuqOjo+7FHvzTm3M97lbIdhJRWaiYZgRTKBFf1plvDqZCfXx83KnEN2/ejIjo3kGQvVqc9j2zS6tIbMTlIEKEk/dEAwv6P2+HZW1Cf535TQpf2e/8bRnob/lc+azB3t5e9wpyvopcabiv4SCIz4vf4+oPKq6DjQm1EzdOoVAtKI3uSy/RIClz9XtZeSJylYGkih1NxoXl8/zoCDwemHXRDhx67be+c9SbkXG2m8pZGmW7U+C0XbaHKeuv/Ek0Ne3Fsni7te3lGyjdALkT8Pvsu3B4PTKDmilYmbFbdl1mPOmguAOAVtqvKldFRUUZrs6SBEUsV6adUCvcbzgcxp07d+Lw8DCm02ncvXu3C5e4fft252NEqHW9/AdDQrI/F0ncppdQUslVdvkAhVeMx+MYj8cxGo3i1q1bvZd57ezs9EIn+WISEmr5q00UYUHtIZ8UET0lX75D5ee9zO6z58f76yTblW6hFJbnYg2fI4pWHu4xnU678CC1VXb/XWRim9Ffrcs7KipK2Crkg5+EE08SlWxaJ+uYm4wQlxnuzDhwVO7qtccRU8lg3Qh3JPrLOmWJSPqf18fPX/fakuKyzGD471kaq1QdP6dUj3VJ7DrnlJ5F5uW/rTOtWlFRsRpuDzJb7+fyf1dCFWMs8utvUfRrSY4ZNqf/afOl4nre6yinWT0Y+qHzaHNECjWI93xKvm8bpZR5llRa7vQUcVXgWdUO69jjzO9y8OLtxTLzWifgFO5Yt0wYKpU984/LnteKik2wMaF2RZSjaCex6ricduIK3CyUgNNTEXnssTqWG1iBhtRBA+ZqgMrs8cul0fv+/n6nSjCmKyuPkHV+7t3KRYbLDLWOaacQNwruZJieFBap6dw4n46CKq4r3YK+My9ug8eyutqu++wDHFcvOLgpOR0q63rO1D5qc99ZhDsCVINaUbEZ5vN5R35JmCKurqnJQPtGwnzz5s2un56cnHSLzY6Pj3t23cmRbChnp2Q3shdnZQMBwokxbYRC9aiKqnzT6bSzNYeHh3F4eNiFhDBt9z+cgaRNZl29nJlgQH/Kt/oeHBzEaDSK2WwWd+/ejclkktraLJ/S/cvy9UGCC22eRnZP1cZOwtWO0+k0xuNx710RngdnDzzkL2vDbKa4omITbPViFyfBAskUN+ynkYu43KVDZJDHOQ1Y6ojLSPc6naFp+lv5ZcbXFUyqC+zcItSueHt++s1VUZFAkrtsxMzOzrbXLiAikmwzGSsPc9FxNzQsq0+PUUHIHFJGqDPDxdARtTEXXvJcdyK6PlOXOThT2fXqeOXphFoohSFVVFTk0GA1s9cRufLsA2wdj7i0b4PBIG7evBnD4TDato1HHnkkmqbpCHVE9LYlZfoUNTJ12klsBp3rIYAsK8kfzx+Pxz1boxe8aGcPD4dZFZayar1JpoYzbfnY6XQau7u7cXh4GMPhMCaTSffyHKblA4tMqOHvGXwQQL+qa73M7ms8PZ3HtUqLxSKm02mcnp52ZfVBHQm1RJaSXyKprqjYFluHfGRYNhLl9ezwNCyMJ+Y0kY+clRe/u1FZVkZ9+vRgVpds2om/lerMMmXqquD1LdVxGUok3okuf/O6L1ORSm27rC08z1K5N1EDSmXMiHhmIL2cFRUV18Oy/ruN0kdSKJKswa8LIG5j10nXCXPpOoWHsC7ZbBntN8/hbk+exrrtoL/SNZvaT56vRZ8RVwUF+lsXzko+KiPIVJh5rr6vq4i7j/KBhF/vIp8P2krPTFWnK66LrUI+/MH3BXje6TQ6XCwWMZvNrrzIhC904S4hQqZ6+Kfy2YRYcxTPkAGhVAau7NYiiawt3AC4EXfFl79lZeBo39UI1p1hGHyN+u7ubldXbqVEQq/pQW+rTLEmOeX37K2X2TPB8vN+lPKOiJ4i5gskpZpp/1r+7vuCV0WiouL6UB/ibOMqxTEjLSQ+mspvmiae+MQnxgMPPNBN8S8Wi+5V054OF2xHXNpsXx+jMjphXlVP7uHMsBJ/Jfn+/n63o0fTXM7WcnCgtJxQcuaQijt3dHJQnBJ821btw6+F2Q888EA88YlPjOl0GsfHxzGbzXq2cxmRZ3mX+VnNvGbnlMh0RtC1x7d8tcJY1NZZeTTzORgMur3KJ5NJT0RiHi7EVFRsg41DPjxWmg+fjIGOO2mSUXDDJGIZ0Y+RZrrsfBnRdaKp30qdg+cx5ILqRZbnzs751k4ylr6rhZfXibSPjpdN67lKsa4apPvTtm1XTpXLY429jCVCXWo33jfV298qxrpk5WY5vB5MxwcSbD8Sar1J0kNZsjdwZvlWVFSsBx8Qe19ap1+5EKO42J2dnbhz5040TROnp6dx7969rn9PJpMrb8xjGJ+rmrSjWUjHKiHGB9/6rpfJaOcRkebDw8PY39+P+Xze7UJRsu/67kJFadCf2e0sPNH9psoxGo3iwQcfjJs3b3YhE6enp9E0TY9QZzHwIqpefpbNfYQLSZnPdrGDtpu7umg3FBeG2KZqN/pq1l8iGMtDYamiYlts9epxHw0TNAz8zo7m4R4yxvrUOepoPt2UTUll2PS3jMz7b4zVyhbf8dxSPk4a/VrmUTLwPqLP0qUCzm2eSmEQUlt8FO8OkvfUDSjLsUy9yJwuBxcsQ/YMrIovXKedqiJRUbE9Surzuv0pu96PM/RP9nY0GsWNGzeu2CltP6frnMj5lq+l8lDZlsJKwk+iSWFFRE0kkosMVSbay4xM0r7xnBKysDpPM6unyGlExGAw6OrATQN4L3kflokk7hco7mQobbWrmWz9RuGE9zZDViaWLYuPr6i4H9hKoXaDx46XLYCLOH+YOf2Tjfa1clrp0ADJoLmiTSwjl368RMaykBWNhGVQuMAkix32NmPbkRi6EVU+NMzZq3x5HY2RHAw39dfnzs75nqTKR6/hVn30nbtkUA3Q98xpOunlMbY/jbPK4QtFZrNZb7Cl81jv7L75Pc5mPTLyX1FRsT2oovJzFZHyATXDDJQG7cPOzk7cvn07IiLu3LnT2Qa+bno6nXaL7e7du9eFhTBcIlN0WR7azYjobKNsN9XTnZ2dTommQt225y9Ay2bK6DcY6qj9q+UndTwjph4jrHJTEPLFojpH9lwvntnb24sHH3ww9vf3u0V+2kf79PS0W9Ttfl/pZfeToTROllUP+VDFcsvvajBycnIS4/G4S0NtyV2bNKDJfEU2q632YxrZZgMVFdtia4XawxjY8SOudmR+ukIYcbkdnxtjXSMlwElbCa6k6pO/exlYbiewipcuKcgk18zD/3dC7fnKqFBR9hAXtjHTYfwiVRYZV8XmyXnpPE7hcbs5tk8Wb8i281mDknLNY3yW9Ml7nKk02acba7ZV9qIZfq8KRUXF9UBS7THUm6QREd06G0E2dzgcxmg06siniK52rBAJOz097YihykSVeFWZXKSQH+Kf4o0Hg0EcHBx0L2sRoSYxVZ0iLsMf6CtcrMn8acnPuS9g+UmoWQa1gUI7bty4ETdv3owbN27EbDbr4tePj4+jaS5DQPQ2QrZh1pZ8FiQOqZz6LhKtF6MdHBz0ys0tCSno6NlQGzKc0e+h+wwX9hSDrWesouJ+YOMnKXuAfWqIo3gnWp5Glp6PwHd2drpFKj7aZhpZXvw9y8sHACUjxsWH2ajWydw6aqiTcD/fy+LtlpH7LA3Vj4s+fR9mfbJN+dbHUhgM4Qq0jJfXgdOHgisgbONS7DPz9LyXlU/fafwrKirWRzZLpeMk1fw9s820N9kAXQN7EVUnP3zpi1Tj3d3dGI1GvTU7HiLmtkf+an9/vyec6DzO0Kk8nEmUXaPCLpQIngij72Kic+TzVKds1wzaSCfpfi7rLYK6t7fXxU9zdoELAeUvMjuZEXb5DAoaHipJMsx3P+h+Uhn3MBR/rlgOtjfLo+MlkcsHXhUV22AjQk3SWAploOLsC9z0G6fRnAQztotTXpraUYdk/JzDFziUSKngioSfQxWBabjB0rVUQ7JRsqdR+qNCzZcI0IjK6DE/r7d+Hw6HXT20qb9UCTeWUoIiolPnpVTIuWT3lsdlpJbdHxpXvlaW33kNlRz9lk3H6hrVnccZYiQDXpXqiorNQDKsPimbLwUys38czDItH+TSr3BgT3HDQypkJx988MG4c+dO6p9ciGF4W0bQpIzOZrMeYRZxV7jGdDrt7SGt8nronb7LvnL7upOTk27Bpc/UcbcSDh5UXu3NrfQzcq/ySUGX8jwcDmM4HMbh4WGnHJMs+2wh8+GzQFu8atCl7wo/5D3XzANnGRyZX2eoZET0Zlo5s54NAPjegoqKbbCVQu2GkJ02Uzt1ngyWK7n+3UfqJKJZzJh3Kg99KCm3OodkmZ3Rz83IOMvFaTcaIA+RcZKY1cEJfolwq+w+TejtJ1Iuws+XumgQ43XwHVvcybmilClMmVLPtnGFytUttp2rXkyPhtPviw80vOzVgFZUbAe3OdnAepXtXDZzRKKrF5TIfklVzWY0+VZCJ3hOqLUAbhnJz44xTEGkT+X12T6fiaQ4ok+PESYB11ay3q6MPWZ4Be0et+iT4qwt8qTKK5RiNBp1AgrtP22wyqSBjfsDKvXZm5U5q+BrdXT+dDrtBmfMv/QcZcILy5Tdh4joDYD8JWcVFZtiq+AhJ9WOEtEiKXPDWzLKJGa6ngaGHcQNZQafgtIxdsSMuLGTZmXld9bRR+x+DdPPCHNGqr2OmdpdSp/GV0ZEagYNF1/nSoPs6gdJc0aeSyTYy06USLqTbR+8ufNjWkyP5/t0cEVFxWbIlEjvTyVRwm0iB+20nRrw017JNrl6WSJNGanW75x184G28teCaangJK7MO+LSz/jaG9lPHacownLs7u5231V+rn9R/bnT1DLxhWWUHdW1Ci2ZTCZxcnJyZWa5NDigEJXZVhJqV4Rpd7NPlTGzzZnY5Ao6ny9dr3ul2QEXiTgLUVGxDTYm1HrIM6OZ/e8dS6NQgiEXPrpnR1THV6dYLBZXFia40snffLEfDV22ECWrk0bmOodGQIaII10aSa93iTi78uC7fXhMnuqWjcq9nXX/NKXXtm03taYpQLav32fWndOSjO/LBh4l+D3KyDTDe1gmnw72Z4dpeliIyixHWY1oRcVmUL9Vn+SxiL7A4YQvG+jqu+wnY2n1XfbXt8dzokzBgyEDJNdO4FhmDrgJvU788PAw2vZygSIH+wy/8O9qg+Fw2IXrMaROYXyySSLWSpflpp9g+IfILuHx1wyhlBqsPxdBSjN48gVsV1erM9vqBFtldsHIxRMeYxiND2h4nu4tBx/aJlD+Ts/HZDJJwx8rKtbFfVnempERH5G6IhjR31fa05NxcQPH30UOXaVeRuLcAGWEmmk5sea1y0IfXGHJQj4iVu9aUVKtSyp06X7wd5afbUCj6MbNy+B19jANXsf7WipT6f5lqhGVi2XKk+eZOfHMuFdUVCxH1reX9fXS7Jkrpz74dbLr9sIJddaXadcyIp7NmDJv2j3ZH8VNMyzB7auHYpBQ+9oREWoppxHRvRgmoh8/LfvrPiyz1/yfKi7PdxFi2T0iGG7oITNs75LQ5mVlvLnAkD8vH+tA/+P+2/PiLLQGdy5+VVRsg60JtRPFiFjaEfQbR84ZOcwIEGPmPD3vHEK26plKLztvNl3GMni+pRXKVEB5nCpHiQiXyHM2jadyefuXjIpPh0kVadvzFeYy4m3bXtlNhdcpDw4uSHT9vnqZ3GlldWd8HgcvWehINtji8dK0Iu+ThyBVVFSsD1ebBZ8Jok30/u+Cg45TVfR0M0JN8s20eJzEj4vHM/tBAit/IXsp1Zm7X+j/3d3dODg46M4fjUa9NCKit5sIBQ2WfzAYdPlzjYjqS3vJUJTRaBQHBwdX0j4+Po7xeNwNAqjiss0prmT3lWAbusBE+6r0s3RIann/+ay4/ScBZ97iHXreOGtMn+mil5e1omIbbLUPdWkqJ6KvOHJELyPIFdpESV30sIMS+Yy4+rIU/a8pHhlDKgRO6nVtqT7KJyJ6oRxcaMEFHzTOno+Ol+qlvEgqaZQYSpOloXP0G8M1NN24WCy69mma5goJXdYOpcEHf1eZ11GCS/ePSkjJafJ/d5j+O41oFoJUUVGxGhlBYl8XCVQ/U2iEEycSHbc53JvYB8JOkHxnDO/XPgDP7BHtCZXtnZ3zhY6DwSBGo1GMRqOezScGg0G3W8bBwUHcvHmzU7W5E4faj22jeu/s7MR0Ou0+tWYoq1dEdGnv7u7Ggw8+GE94whN6AtJ8Po9HHnkkjo6OusWd4/G4135OarkgNAsn9FloHxQtFv09xTORxducx9yvEXo2JAZxnQ9D+RTSwWfPiTS36quLEiuug60U6oz8lpSKrCOUrlv1IJPULSPTNADZlkUee1ZKk2X1LaCytnCjVCKQWRtkeWequV9Lg08D5Odk+bEN+H9EHjfn04pO8F01cUPMMpbaxZVqlnEVIc8Ua1df2Gb8rKiouL9gH3TiRri9WnXc+7f/iWBl9jAL7yiVOytPNuB3G81Fh/v7+912pXwhjQsqsp0cQChWnMdcKBGogCvOm6LRbDaL4XDYrZdhmAkHQSTrmW9xG+tiiftD1k2fSrOEkh/L7oWXLRuglUJ6skFE9QUV18HWixIjLhc6uHGIuEp+CSeHJYIqVZWKsr4vI4Qk0NyrWSqDpscYVkD1mfXxzk1jzaklX0DDTuxt4fHabqS5cNLr520l5cLvBfcHVTmyKTgpJzrXQ3KUVuY8SkR/mYK9zFj6AIZl1Oe6pLqUtgw6dwfInHxFRcVqZH08Iq4ofbJrPFfXyu7of5I8peELEfmiFVdYV9kIkjuVObuOMdQMHzg9Pe1skcgp7dRgMOhCOqRqy99oNpDtpbZx2zwej7vjUra97oLe2qgQE+0DzvYfDodx48aNLjZb7SBl1v80C+22nu247Hng/fXzM99Ios37Q79HDqA25jPggwPdMx9s+QuBVAZ/RisqNsHWCjWNTdbZIi7JtwxGRP+tRRkxU/o6J4tzLsU/8zi3JJJxUScUiSTRduIaEZ1B8WnAbJooe8MTjYIbBA+L8NhuhcbwGMuotvTpTw4O2J6uKChfqSga6U+n0yv3MiP16xDpTBXm7w53tp6Wnjc9V+4QswEd7wGnjrn3bEVFxfXgyqn6a8TVmSuB4WAMTRCJ9AE4bRRtLAlxaZCc2StXM3m+iJeECsbiHh0dddv3iTxHREd6SaIHg0EMh8MeuSbpi4gr7wRQ2RgeKZVbAgrL3TRNjEajuHnzZuzt7cXBwUGMRqPuHqhuItxqP7WzbCFf164dP+jj2W4lgSW7rz5QcX/EtFeFeMoXyW8pzly2nKGB3MHJfWZGqMlTKiq2wdb7UAvrTt8JJHslQu3puUpZInT630lyiTRTec1eN6vRu3fwbJqodKxEErP6L1OBvd4lrFJbs99khErtmpXFr/c6+fes3o5VhDq7136d/l+HKFdluqLi/mDZlHnmIzKFMuJy0LvMzvlvWZ6ZLcnKUErLybWr1VKJuQ5H6WYksVROv8Z9XSZKZQMAihnKs2maKwKPz04yf6rD+nTiy8GA+4GSfc628PNzSrY8a6tl4Tb03bxvPpjLdiVZJ/+KimXY6k2J7HxcfOJk1EfbTmDcCDCPiP7+1EQpzEOLMjS95ausqcYqto1hFuyUVHvbtu1G8R7mkakjKqNPK7L9pJYztk4j7uFw2Dumc3QdBwcMjRB4T9RGboRZVxrtEmH2gYinUSLLmUqUITOM2R9ValfdqYRIYWIZdH52/yoqKjaDdljIFp6VCBr7Ls+VTZHy68SI/Tmiv0OFypGRRPbtLFzCbYPHe4vca2GgyjmdTmNvby9ms1nPTsuWakEh7T0Vato6LqRUPXS+Yqn1e7Y3t6D2eeyxx+Lo6Cgiotsnm+RS7aB0pFhPp9Puz19047OLtMUR0ZVRxzlrrPPle6kcu3+gXfeZDa6HyvyMz2hIaW/btrerCp8nKfGlmPuKik2wtULNEA6OAp2M6XzGv656cLPQAR33UTw7GUMlGMemqS4u0iCh9h00fFTOjqm6ZluusUPLMKiNMiWmRKg1XajyZ4SagwlvSxkHDWRouElI2a4MoSExdRLt8dS8T7yGJNcHFtkgye+3KxD6PQv18KlS/Sbjrf/pLOuWeRUV24O2kbtB0L5xsBvRD/dzQs3+Tp8S0e+76te8xtOP6L90xGNnV5FpbvfH9UJ8W+NkMond3fOXrjDMYn9/v/MXylthGrLvbsfVBjpf9lZpeXlUh9ls1qu/RALt5CGSrLLQtioMUsRaO1PpfA5eeC90//SpNLnLhsI8WEfFeDfNuWo+mUx6oRp8rnhP/JmjHy/xh8xv079RVNFOIC7IVFRsg40IdYlEcXS4bPrNY1+FVVNHq+AEc9M/KhvK3xXqiP4LalTvUrnpTDIC6WXIFiJ6GRlT7cqtI5u2K5Xd1XPWOYM7gdInv3seq9L27zxGJ+LnuxNnHbPjq8pTUVFxFZmiy8H9toNUH1Rn/iIbwPun2+5ldpvpOLnzvCmqiBzqk68np18REed3z9fzoDrra5Uyv+IDC5Jv33eaMwAqd7ZHuJePgyC2o7ebn+fHSwMotgvDfvx8L1cm1vi5mYLtos8yDlNRsQ62UqipjPrOFozz6jLZ2+td6zFNmRIpUPHIiDNHq5x60wiciq8vStT3rBw0nBrRTiaT4gsKdnd3YzgcXolV43lcaMitjQ4PD2M0GnXhKQpX0WIW7lRSGp37fqgyDDLwVHKpMNCYSAHQcVff3ViV7huneleReX+u3OCWkBlR1l1tr/95H11tWZVXRUXFVXCBrwQIKoHZLGZGbFyt5t7C0+m0O64/7uwgeD9meJ4vHOeOSG67ZJ8jrg4aSHJFBE9PT2M8HneKq2YVZcMHg0G30JIL57LZXC+/yqNraM98JtTrQpuvNmQ+CkmRXxOxVlq6j1S/S2KH23UfBPEey5/dunUrmqbphVtoEaSLHxSbNBPNTQY85Igz0WxPbRnYtm1vZyuPM6+o2BbXjqGOuDRepZEqFyXwf1cjeE6WbxbqwF09GPIhw+Z/ItzcNo9lprKhTqvOpg5f2hZPAwcuZGSbUGnW9J+MLw0wQ0AYFrKzcxkHrnRZBk4zsk6MFeb5dBS6f9qb1F/Bmik32ZSl30t3AszP7zevXZfgZgoNy+vk2u+bP58VFRXrQYSNYooTO7dT2cDayRfjcfnCE9ll2UFeSx+wWCxiMpl0OxaR9HNALWKeKZpZPf0tsrKTFFtkzw8PD7vdOxaLRVden3n0+pPI0tcxX7fj3tYk1CKM2QBE7UtSy8ENxbFMCOM9VHs4L2B6XMipN0n6biJ8IZqLPMtmovn8cFcsloW2nzvJsH2qQl1xHWz96nGhNMVC6GH1B9/Pd4Uzy4ffPVzCt5Yj6Sv9r/TY8Un6GGvrKssmHbBpmrh9fBx/8T/8h/i+5z0vmtu3i/VYFgbiZc/UX1fIdZxY1sZ+X2jcvL4ZofY0/XymlV3H58BVdy9XBi9j5sSr4ayo2B7L+o/bANnRLF625AMyu0ASSTLKP7eHDJfgtZ73KsIoIhjRXyxPsUTn0VdIaXYhJgtlUdoiewzb4J/7HRL87F5IJKE9LYlWbpd1PFs7o/rSXi/jAowF5+JQpu82331k5re8PpmPLME5RUXFdbDVi12yEaRPmXDqhp2ZI0d2RBqpbAqehJIb5HPhntRdKRg67n8lUh3RN4gaPXP1M1UON96r8CE/8zPxlFe/Ov78T/1U/Ozzn98L4VA9XJ1mfWR4MudBI8W4OSkoPtJXm1L9ocrE1eQcEPE++M4oq4y0/pRXyZk6XN32AY0Pcji168p0aeq5oqJiM2SzPezD6nuTySROT087m+o2o2Tzs0F2xOWbCCOiR8JI3GezWTfFLz/BgXxWF6Xn+bVt29vOjzObHkIi+6NdQSKitwMIyS9VYe0gonPpZ6iAUyxh+2Wzjbu7u50SLHsuv6DzOEhgeVhWlceRCSv+ne3LV3vL14xGo26xIsFZb/nFrM48j/5U/lLt4m2vsivMcrFYdP69EuuKbbF1yAfBKXOuDqaxKJEwpstzPC92JMYikzgzjCOLuc4MeKamMPZORojqgOpWUgocX/V1Xxf7aJdnPPRQPOOhh2K+vx/f9a/+VToA4DGPTfTRNw2pnBa3RNJCGL7ghvGNgk9DZko91Q7W2eueqRzEpkYrU+KdUKs+WYjLMjJdSXVFxeagqBKRK4Nt23bkliJLRD9m2JXHkk0VOfJZvLZtuzyo6iqNzK7RZjCE0EMUVQeVl6SXJJFkUfZXYR9N0/S2uJNYQ7vUNE0X+hdxuT5JNp2+NeJyTY7axdeHqKwacJyennbnyieQjPrgSGnT5yyb7cv8Am0rybwGELu7u3H79u3eoMgHDGrvDE7q+UxERMc9VA+/Tn6Wz01Fxba4LyEf+iwplMuu8Wv9mHcGdhp1+kyB1vfSGwe9vK700ij7QkSSsizWmEZyZ2cn/sn/9r/Fx/7kT8bT//t/j8F8HrP9/fjN93iPePj5z0+VHU+Ljisj0FRmOBhgHXy6jHs0Oyn19vewC1d26RSXTSnq+DKlaB2iXVJLvA40ztm9Yz0rKio2g9suV6d9H3g/z1Vp2Q+3e8xPn7rW+72IoeKjCdlrfec1LIP7BSeXmg11MipFNBM92B4Rlwqs2+iIy9hunpPtk0wC621IO6zt93S+CD6VdaWtcJNsNlJ5sm2y797m/l3l56JWtaHypM9Tm7Mt3WbzHigPF6Ay/5k9ZxUV22JjQk2VIVMX1CEzwuoG2MM8BFcgqDJHRG/XDoZ5aKcMTp0pPMR3+SBIRrliezwed/tycrN7GjpuUJ8Zvd3d3Xj88DAmg0HsnZ3FbG8vdufzmB0cxOQJT4g9dPrMmNN4KG0ZZk0ttm3bhaScnZ115aZaLUNKVcNH/e5kuJqe6gsdpd9LKh2etoeXOKleh9y6MaTDoQLtA4vsWuXJWZOKior14GSSNsxthtt3XZ+Rcamxbk9dVFE+VKK5GF39PFOOI3ISSD8j+xoRvRnEg4ODjlDL5oqsk3AKVLB5TPbq5OQkxuNxp1Cr7NyVqRRe5yF8ymcymXQ+8eDgINq27XYc4c4eJJV8vbp8pdqaIRdCFvJDm8x7TfFHZYiIGI/HcXR01OWnsjJNhT7SdjvhV34U0LKZaZWFQhlnNCsqroONCXWm7qnTcES56uHMRpARVxc2kHQrLopvPPTvvttHFkrBmDB1RC4AkZEkkXa1gAbCFWK2yZ2Tk3jBC18Y08EgHnrv945f+XN/Lt77JS+JO/fuXXESTupKCrV+8zJRlRbRJqHmyJ9TW7x/vD+MG3SF3ENFVpHp7P4zTa+/D3pkNPn8LQu/YRu5au2qRCXTFRWbgwTP+7BsjPdJX0PjYkGmInp+FB68HzN0Q9cydCKiH5boIHFdLPpvYZRN1A5NTE92shRSxuNut2azWYzH405oYAiCyqBtU1V/fWZ2jHZP14qEyqa7yKDvqq+HHmYzu5yd5e4kTF/nczDDwYd8FX12RHSqOmeWWWYPT2SbkD84mdZ95V8l0xX3C1sT6hKJcbjiys5bOt8VWxkyHvcYumynj0zxpbEhuWS4hH/nQCGiP+3lnVNGQMT6o37xF+MdXvva+MX3eq940cd9XIxGo/iJt3/7ODw8jANTWjOCTAPF+nsbuuFwBcN3QKH67WqxD5Q4fcpFoH5PqFJk99iVad4bJ7VUtbJnjHn4d94/f0bdAdFhVlRUrA8O9CPK6yREynidfy/ZZj8/y4OzZK6EZwJEVg+SYw9dy/zPsvIs83NKNxNiRGR99o0KLPMWIVWZsoX9mXjCfFgmvRtBCjUFLN4/1Uuk1/MjOBuq85W3bDzbn88SSXP2e3YvMn+2LijgVVRsg40INVWHZdsgNc1lfBavJZFzlaJEorm7hX6jKi21QN95PCORMi6ckpQavVhc7l+qY77amIaO6ej8iHOj/E3/5t/EAErIsx9+OJ798MMx39uLb/zar+3FumnhoxRlpcGRONuBL19hG9J4yVBxZ49sEYurDnSCTEtTfmxXkWveN39eqNwoXZ8CLhk+V1FKqg9Jsyv1DMnx/HwGpKKiYj3IlrrNFlwd9EV8OkdpMSzDSe8qwYZ+ISPuVKqpNuvPt6OTzVCaTXMZduAkTmVQ/eQrmG+p7eRfFK5HP8dyUF2mIkyfo3DHiP6ModJX2rJ3qguhOgo6l7tmcLGnzwzItqteTJ/vVDg5OYmdnZ0r702gP2PICQUY8gVyAtpxpeeDgOw+0DdUX1BxXWy1KNEfRCJTH3kdr8/gBDv7vuzPO513EKrrJNQevuEKpzptNlBgfUQWv/B5z4tPfuiheO/f/d0YzOcx3d+P//70p8cvfNIn9UbBrvjToXiohto2C63wkXs20idZ9vIyDcHVHhlXLgjNpt14r/k8UOnPpnKXoaT6+DFvx1WzKFRBKioq1oer0yW11mfUOLgmAfJFjP5d/0vlZD4kWbyOZaRAQXvldkvlIMGSkKH8PG/au5I983p5DK/axv0JBQmmLR/FNnVBhMIJyXpm99QmTl45UMl8v/uYzLbz7ZF6cyTrUCqLt1/m5+j/vYwlLpKlW8l0xXWxdcjHshXcGWnSNBKneYisgyx7yL3TLCNNSj/Lk/XxKTg3bm5II/r7N7M97t28GeP9/dibz2O2txd783lMBoM4unkzmgsDojakgiBlwsM0pBAMBoMuX5Jt1k3nu1HJQlaymQL+7/eWb2rk612z+5TF6el/3o+SksNr/a9Empf98d65Ia6GtKJiM7iqSGLidtNFgYj+TkO0AVQWGQJREnAyQcAH8lQhOTvI/HVONsB2++l2xe2f21i/lmlmIhDtXNM0XVvJj1LR13GudxF8pjMTQFwZzurOgQXrRsKq4xRa6H84s3njxo3eOiWlkQ2q2LZUpOkfs9h8tq3fY9YpE7cqKrbBxiEfrhS6chpxqXq6kqtdJpaRHHa8bCEEO4WXK/teMrIEiTTDBFyhVpnYubkYgyEh8/k8bo/H8bPv/u7x0vd5n3j2y18eD9y926nO7MTj8bg7PplMevVXeyiURQtMpJhoio6GQuTb29nVb6+DG3cZV7bXaDTqCLUbMYfHy7mjULk5KHBjTfKs7z6D4DMLWfw0FyJxIOD3tKKiYj1k6yFcWInoD/ClYGaEVOfS7rvIIbhN1rWEiDOJ6XA4jIhz+zcej9N1FhmhVj5U0zMhw0M+ZI8llng+suVUw0ku+WKus7OzbjGkBBkSbYXg8L4orIIL9Omr1fY6Jt+h9FROzlC6LyZZleijtuB+3KrnwcFBF099cnISx8fHXZuIXGcCHT9ZH/1PkSQiemEtXJOkdF1MybhFRcUmuC8hH3xI1TFJYFydXPXQunFepwyez6bIiNs6I1YaQRLXpmnimz7iI2I0GsVoNIr/8JEfeb4Q0VRbOQwaQhlMfkZcGnXGIGZkkIpQqQ6ZA8kGOUJGQH3aMLtfJMzLFCQ61FLbL/vfP0vXsJyZql5RUbE+sv4W0d+JQ7Yii40mAXUV0sWUDJnS6L8TmQBQsg9ZPh6+wvNL5VxWB7dFXi4OQpap9cv8FFVsL7crtlTI/Xd9Zvbc6+ThNKUBFsNQVA9vG28fJ8M+o53lSbHG25n1rIS64jrY+sUuTjodJJl8YEtYZhSzvEtplM7PiBfVTCkBfKtXpl7onOwtXFme8/k8Tk9PO4eieDHtja0pMH1XfBmVGhlS1U8jfb5Ol2XNBgZSCzzmjupOZjxL9XN1qERsnSh7GjzflWuvzzoDHqlHVLYIThFWQl1RsR0ym0rQdnAmqxTS4Aqtz5bJPkZc7hjBfFgm/3+Zv1DeOj+zd8qfM3Z8+YrvBMVt3lROkjmfvdXbFP1aLSjUYj62m9JiiIYUaqrLvD/yR9kAwIms0qbfY/n1nQMo+ijGoev+K2+m4XHdGWFm2xPuI0mwWT/3K+5rWM+KiutgK0LtBIegMXK1cRV5obHJSByxisj7eV5OEmatXNaemJPJpBcC4uloGksjbZZVZaJRHI/HERG9eLHRaBSDwSCapum9ElzfRbRlLBRXrfbUC2s4vefldEKqzfwJDw9RG2ZtmTm9bCqzpN7IcJbKnN0fOa11CDavo6OTseafbzFVUVGxOdQ/HR7fmoWVUWDg+ZyJc1sjgsVFchlZYvky/0M7RsLPsvI6CSAR0SPJsqm0T0o/K5MTT9WHYgnDXWSvWSbWwQl1xKXow/R0n+RHaPezNqESz3KQ3PrMpofD0Icp77ZteztFaXCkNuE90TkMmeHghTteqT4uzGXPAjkJ1fJlg6+KinVwrVePlxYn3E/4A05DlxG57Dg7K9PN4m8Zr8c3X2VkLquvH3N1W7HS3MGDJJe7iLB9ZWD39vZ6RpavrGU7sNz6YzweZxConmigkBFkN0bZ/dkEbtB90EOU7nHp3HXUs6pOV1T8z4MTWH73vped6wQpI3xcxLZMZHGUxAMKOrLxHlLA8ntoC49HXNpf5se05Dcz1bgE2eiSDSuR5BLcDvN4VqZSWmwzD6/wAUZWPlfuXa2m/1n2bKzThhwslepfUbEprhXyoU895B4LlY3YI/pbtQlULVxRLnUUxvByFM7QCf3mZZAavVicv2L89PS0C88Yj8exWFzup6kys176no2GM2LYtm1vyov/D4fD3uvRpW7PZrNuJK72HY/H0bZtR6alOJTuEQcMeuujK+s6RkNNVZz3YDQaxXQ6jZ2dna7camMfsLiTc8fjxxkWkqkJrkzTWenPd2hh2ow351RzRUXFZpCyytkjkjypyLLL2uc/4jLMjPsmM+yNpNa3rlPeAlXLUjmVBt8toHLQRziZ42whVWySRe6sUZqxVdm4aJB+K1NJVX+1I1Vd+Q6q6UqLddH1mgml+s16coDCcrON/dXjfk1m6/m7nom2ba8o8ppp9ZkClVV1J8fgG5D5zGSgb9D/DvfhFRXb4FpvSpQR8emliKsrZjPldlkeSoPXO6l25YAklYTRr4k4D7+QcSWhPjk5idPT014oiMqUKQM0HKuU08ViEdPpNCKiFyvN+g6Hw17bRvS3PqI6rjq4ksDykWCqvjRWcnacbm2apqd8c2pWYSuaktVx7k/NspRINe9rNrvhz9iyPxJvvvUyI9T+jFVUVGwOElwSTO5MxJeB+EtKqGKKVPmLOJiehyn47GJmQyIu+73KenJy0tlevqiFZRLxGgwGMRqNer7H81ZIg/tFhsG4TZQtdlLNWcSI6EijCLXah35R9XLfq/9VB2/XzFfQZrtYRcGKyj3jzzm44Uwo09B3ll9rgbLdTkioIy59IUNC3Ce7D3ZxxttCbb/Md1dUrINrhXw4GAZRmr4vTfHzYeYInudkKihJHRdOZNM4UhQiovd2LC5KdMWTZVKn9IV4LD+PZeTb24KKfpZGpvpyWo3hLO5UPM3MWOgaOkWm46ovByyM8XYDTedTqmvWhtlnCVnbrYNMMa+oqLgeMpJGlGakXCWlSqvzaft95qqkOPI6fjJN/intUr7L7EQmFGTfs/K6PSzN7Ho7Zv7H81wlZK1jM92mq43UPu7z/FhWJvp3ne+KO9PTOZl4xDKW6pO1jRPtSqgrrouNCbWPHpdNs5Ck6ZMEWQRNIQ46hzHFpdW9yptxyf4Wv8yYkBhrMcl4PO72JOXrxtWBMwLodZc6qvKx3G5gMmNL4s5wBBof1kF1ZxlpUDw9KkPunLhjia/EVjtpFmIymXTtPJlMYjgcxs7OTvfq24xgO7JQjYjotTvP4QJDTjFnKpXParDdNJgqhchUVFSsB9pS2hUnp96XPbwhIrqwBC1O4yvCda7Si+iH60XkZJYhEhr4R0QX/rC/vx+j0eiK6KEZOKWT+biMiAkkfj7glzJNFdxD2rSDFGclXTlnXiKkvk83RR9fiK4yejhfVk8fmNAfcYGih2pSUFFefDb0O2cepMKz7eQHFfKhtnAfw3ZyH+2iEWcEptNp5/O1GUFJDKyoWIWtFGo3moIbFyeRJDUR/VenMgSBndJVbKbH85cRaoZOyFizs08mky5uOluxnpHCTI1h2TWdmI3iea6HIbA+rIfX18mi/8/03MA4QWcMItUjlXsymcRkMulUBJHm+XzeTdXO5/POcdGBMe4xaycfYHkIR7ZjBwm0k+vMwXmedBSl+1tRUbEcmV3hYN1n0jJCTcK0v79/ZavLLJxuPp93W5tmW6qJnDF2WGXizkqj0ahnnziwp62jqOI2lQKIhx/qd6ZLQi17pNnStm1jOp32xAH6D28X2j/CY6V94MG4bxdYeG9Zj3UINdVm1l35M6aedVP6vGdsNxfH3IexDu5nM7+oZ0JlFKHmfaio2AZbh3y4Ss2HcBkJjrjs0Bxd7+3tpSPDUoeIuByhS+XOlAqVJyNeDGNYRqbduHjnZocuLRTJOqkrKG60/Jj/ueKcGcXs/0zxzgY/+nMnp3ZT/HbbXi404WArqy/z9LKVVJ9SnXie14NGfVk5luVVUVFRxqp+kw3mS7azZL8zpVe/8RwvT8lmur3MyuM+h75ChN79SWlQLr/CbVjlc6RE87gU6kxUcCIpUp7FEFOIcXi76lwSb/3vbbZMMPMQHs8vg98PV7B5nGlnfo/1cpEpoj9TXHo2l5W1omIVtlaoueqXo+0sZtan4dlZtEgu4mp4R4mYu3KgkTJfRarQBSqwLBuNoRb3SUFhB+WnYrV1DvOVEVs16pcB5PQWVV++8EWrzL3tueBEI3ouwnQy7CSTKq+mupaN7Nk+un5n53wnFcVSz2azruzD4bD32lfd82yVPtvGlehsQODqtJ6TUiiI15nPnu5pNaAVFZsjsyv69Fkvt2Mkfdme1T5Ypm2IuFRHfTEelU8O8iMuF8DJXrp995kr7YwUET2iy9C0kqJJVVpk+eTkJO7du9eFGsjucj0P97Vm2EdJpdVvCpuRX1AoHvfszu4V66/zXaihX+VvuoZlod0ttSvVdt5D3ksn0/T3vL98ZshFyCtYNz1Xam/3ndnArqJiXWxFqPUQRlySzIicTPu0FDuMSBqnBomMBHsHovEjgfKRvBsSN4qKEfY3YrHOzFOdnOfzZQP6I7HmyHk4HMZwOLwy4pZR4bZAbE85BZFyOgifftP5fl9cpffFmF53GnzeL80sSKnW/4wjFFiObNCSKeF8Dpxk+/OVkemsznQeGfGuqKhYD6W+KFtOwuKzU07KGGKmY1lom/sR2UwfgOs4zxWJ17FMSeUnCfN0Oo3pdNqzJRH9kI7Mjsi3nJ2ddVuyKmRFYSu0r9ptirY5E0y8DsPhMA4ODmJ3dzcODg6631meVWXNCDUJu3wgQ3RYFr7xUfePIlPpufHZWN0D942umNNfZv7enwmVhTHq9Ots04qKbbA1oS49+BlIlP27wjW4b7TghDriUlFkJ6Py6HGyysPhHY6GNgtNIElkJ2Zn9ikzwdOjQfIROEfdmdGnWsCBBcud3R/WOasPy+fElaTTt00S9AbIiOgGSlneuiec2vN8SveJ5WOZsnJn19KZKO8sz4qKiuXwvsbjgmxy1vd4jtuAUn8WaHPpezzcgPZG/7uy6nXJBtgiYa5Q89qszCLEUqJJorX4kQRPeWcCh29jynaQDRcpp09QehHREysy8YS2mSo+FWfWdTab9fwV06N/oC/IfJOTaT4LylPnZ/fdn6fd3f5bNj1Nf6acuFdCXbEtNiLUPjJUZ5Nh4MMf0e+0rjzKePholteWlEOSUp3ro3caNX9VuBt6HY+I3p6XJFs0ApymUlhGZvwzw6L8bty4EQcHB53SnO2MwcV9OsapPd+037fzk/F3p8Y3QOo8GSHOGlDBZ+yf7uVsNuvylePQwiINFmjcszhrtZETaleuXX3mFLBvgejk39Pm/dIioUqqKyo2g0ie+qrPENLuuTAScXX7NPVj7v9POyBQjCDxdHHEbVzTNJ29dPIusszwNtoTHWc5CV2rcug6LqCcTCZxenra2VntVCLbpXx8R6NlkP1USMru7m6cnp52RFezmGoPzig7iaSNpnDDsBF/+ZjC/hhSQXHH28i/cyGiPy8ixVS5+YpxD/dRnQaDQcpDslfEi3twoFVRsS22Vqg9zspHdSVi7cdJqrNr+PAzD5+iUUdWOupg3GNaRkEdlCN9ddQHTk/jM37oh+I7P+7j4jFsiE8j5LFbyxRqtg8JtQzJzs75GwcZLuJtzD+GfGRqtbcfFRqWkdtRUQlwguu7bKg91bZsAzkFLTD1KTmlX1LTfQDljo/Ems+H/2WqUUbWVYeKiortQEXTRQW3exF9u0QSo/6tP9pmv46EmrbJQ808T4YrZLsGMW+KBxKNRJj1xlnaEZFhT28+n3dhHiTXDCHJxAGW27/7zlgUKXzWlL6Fs4v0LySpDCFkG2vwRJtPUc3JvyvItMnKm+Q78/8kugLV7Oy50/PBNHiPSgMUKvpVoa7YFhsTaid5OsZRpCvG7GycPiKy0bgToWyajorA7u5uFwvt6oNP82dTRU3TxEf+wi/E27/mNfGRP//z8f0f/uGd8WJsGUliFufMTl1SqPmqXaoINDpOpPW5KiyE9VG7KW2VS8q62k0OQQZUbakYaY7y/b5FRE/pFaH2sgo+Pcj77SApdtXIHc8qNcfTZPnXvbaioqJvp/S/I7NN/j/FCPVzkigXGDzdzM6RoBO0HbJ3PtvlO3HI9vH9BL5eR+WkzyHBlzLKdSiZr1PZ/Y2R9IFU+ll/loukU6Sd9Xd7TELuIZWcNfX6cveR8Xh8Rfnmd/p9kmfOdrvIlS3E9Hvubc9ng7adabMMnn5VqCuug41DPrgiVyNZdgiSJYYMZPFbjO1iZ+B5GREudXi9EtsNJw2Nv2ZbaXzdN31T7KOMz3744Xj2ww/HbHc3vvwf/IPerhzM3xeLkPRmHV9lPTw87PZBHY1GMRgMem1GkJhKRVA6bmD8/2zqbX9/P4bDYU+JkdM4PT3tvmtKcjwe95QKX4Wu43oeRMj18gSpGapDZixVdm8rfteqe95XOqqSQs00+NzRmVdCXVGxGTJhQv3IF45nqp/sGYmq7Kf8CH2Dhx0oP/Vx2UWVTes6nHQ2TdN7OQzthXbfkK3xfaN1jpNnloWkjDbTdzHSNbQ9FEpkL2n3qJp73abT6RWfmBF0+m0fpChdlVn+RsczMWmxWMS9e/e6UJPDw8MujMNJqu6ljumFPnoOSLR9RxYSf1e/5QO4UF/Pj+6fBBjtRiU/x7ZX+1RUbIOt35TI71Sn+TBKASVcWRY4NZOp0ErbyRLTIXkiyVKnkoFX+Ie+7+/vx1f81b8aH//TPx3v/hu/EYP5PKZ7e/FrT3ta/PBHfERHDp1Qs7P3GhWxzV531Y+xaJpecwOta3wBY0ZG2W4ZyWZ6HhrD0bqrIVKcPaZRaWXKieqktuA91H1QWmwTv8cOJ9Il8uxgWjfu3o1P+O7vju//lE+J0zt36hRfRcWWcIVPx4SMTJNc8bj6NgmT+4RMAfXfSaS444SXl0o0y50p1E6eaYOykLGMVGczbRlIWCnMqDya/cvaVm3I47TdWVv6fWC6ItTuc+Q/GZqj44vFIobDYc+Gl4QTJ8iuIDOcyH0bofZ0FZwE3f1JyeZXX1BxHWxNqD38gXGzy6bgSdjUEZQGFQr9LoPFPKWA6hr9HtF/2Yt3KBFtqq1K802HhzHe34+9+Txme3uxd3YWk8EgTm7fjt0Lg0JSS0Pj00QiyMzXHY3agsqJzhdcVXUC6soGCWn2mZFslqFt2xgMBlfuD4ms4gfpVFwJptrgrxkmafc6uOpAtUIG3uMc6Ty9rZ3wLxaLeP8f//F421e9Kj7wJ34ifvgTPqEa0IqKLaH+WxJDSMiopPpaD12X/ekcEmPaW/ket/O0DywXZ0WdUKvMvgjabQx9C0lcVvbd3fN3C5CERvTfjpjNkEmhlSjF17JnIXNUpkl06U+9nf2elRYSqv5Z+2d+nvadgwLu2e02moRa7UnhxfNzX7hO6GP2jLD87mMrKjbFVrt8cAGcOowIlqaA3EjQ+HLEnBGnjBBRRZWRobHT+fpkmAnT1TUMQZAKe3h0FP+/93iPeOn7vE+87y//ctw+Pu7qys3yOZ3JkbsMDRVaGm4n3gy14EDFofooDzow1ZmhNyWj4OUlmVebKH0u3pEz4wp8kWsqNZze3N293ApRdeeAye+1ykOiT8WKu5A4ofZnSqBT/Ptf9mWxj3Oe8dBD8YyHHor53l78f776q69cW1FRsRpuo0mcNZ3O8Dq+8IniiquVXJciQk2Cqb4v20AbJHugEAid5yETKh/z8d08nEQrtIKhAvRLrIPIvsLrOCjIXnNN9Vs+xENftJhRtlTgrONkMuntvFUSDUhm5a9Kyrfq7q8Ppy+iTZdYxUX0yrMU28z0PHSlRKoF8hEhG5hlMww+01kJdcW22HpRIr/79AwXHTiyESrJLjsRH/CSesnv7BiMM/NpKVd5ZQC++SM/MkajUYxGo/iPH/VR57FW0Tf2TqizN245YcxUApbd65NNafF7Vvdl7Z2pGB4v5jMPrkjIsKsNXNmhgdL910CLBjMz7q5S6VyqPyXVyO87BxQ01t/wuZ8bH/7CF8ZTX/nKGMxmMd3fj//2tKfFTz/3uWm7VVRUrIeSDeIsVEmJJZaJCiSs2QL2dVRI+oWMRPn5buNILEshG7Rl+uRAw+OZWX4SUQ0gOCCgr1wWCiFhI/MlWVm9/bPzVvlz1jnz20yb4soyH5apzSW4D16GzOeu83xWVKzC1tvmiVCSZPJ131RVI652IoLkiGnzOPPmSmSN5jliVt5te7lVkkhgaZTKEIUs/EJ5u4HM/tQeqrfHq7kBl/IqRWNZrKCO85P5uPHy8Ips1M5Fhv7yguxvmROKiK4ufAMlB12+I4zDDbKuk2rkZWHIDNuFDvHujRsxHgwuQ3rm85gMh3F861bsrWGEKyoqLsFBtJPIiMs4YpI/2bWMjNIeyb5TpInovwGPg3RdJ3VZ4MIz2orMZjlpJPnd2dnp9nmm39BvLliU7FpE9HwiVWEOFJi2h1nIDnobUqF2wkhfSqWcfkBEngIR/QUFJdZR92pvby9Go1HPh8k/+3sPstlrfvpzsUxlZ5qE+zC1ke494+S5C4vHhVdUbIJrxVDzL6JvXGi4eF2Wns7X/67I6jMbcTdN0yOBpekkKhTMh9ODWsCRKZ+sg/44qPBPhn9QaRV8OtHbj+mxHUoKc3YfvM48n+2eqTYZoS4truE98ilATT2SRLtB1fdsgQ/r5mXgM8Y25r3lOTeOjuKh937veNkznhHPeNnL4tbR0VrGuqKiog/aLSdu+l4KB1iHUJNUMu1slkp2gXs4axH52dlZnJycdGF1FGtWKaOuiOuNhxQpdD7JZsSlP3Miqf/5QhPlxbp4iAyJr9JiWzIMkzZQdVOoitpU+1Nrb+z5fN7bcYNlF2H2esp3qpx85bmX1+GEWulxEJWp27rW/898NUOAVAcKWCTUHk5YUbENrvXq8YxMEt5pSiN4Vwd0vncQqh7ZiJQGy0keVWIfwTtpd6NeUjE8nVWgKktD5+UuGXoe46jbFYWsPrzWFWw6RiepmZKTlS0jyExL94/GM1Ny/L7S0DLN0l+Wv/Dvnve87jn8oY/92NjZ2YlRlAd7FRUVy5H1e33ftk+VxBP+5rY6Ip+N87KVbHXJli+zDfyN/lDIfNQyn6hj2W8USxj+kQkqnl7b9kP13H9n9cp8RjYD6m2RkWAXg9geDp/N9fr49SV/xz/eB5bJ7/myUJ6KinWw1aJEYmdnp9sZgvt+sgNzKso7eskg63+ew7ART0OKiRsAXZfl552+bc+nByeTSTd65wplhrRkG+PzDVJSQvgqW+alUbEIIxd7UA3IDFdm2Lg3q4fMuMFh2+gcGRNX+3VfNXr3XVX8XvDeOVn3e+aGMhtYsJ5UqJcZTabnzpbPQvaMVVRUrIcSUVF/5QJoErmMYDEcxAfxQtZnlY9sMq9ROrLN7r98MSMFBR2XPZSqS3uTkWkpo64gsw4qs2+56jOcTFflV76ZH1aIi7ZgdYFkOBz2dqqSv9FLvly9p63XW3l9Qb7vcqVzPVSH95j3UD5C5zJt93PLngnVST5cafO+sozZYMb9ZUXFpthqUaLHjWlLH+6a4SNnj91y8pUpCOzU+l/kVmAH0Hd1yMXicu/pVWqJOt1icfkGLU3LybDyTYKqk67VTiAybDKWekGKE0qGligOXISYMeJOXAkaBeWp48tG7nR2NDrcd5WOTfGDbmy8Xek8OUBh2zIERs8HDa8r0aw/Cb8/K9n91H3ze1yKNayoqFgfJeUx4nIhs353ZZTXuU/xQbbOKQ2CGavN3UE43Z/tAkG/kYW9Mb62aZoYjUZX7AZ9DuvGt8uy3BzUy16ToJOkUnFWuf1dCAIFFW3RR0LN+rJMTdP03rHAdqfv1ctQSHR9kKI05Ys8f7ftKiuflyy0hOXyOHPWQ9xA/rYUmshnhulTAKqo2AZbh3zo0/8i8j2ReV2WXjbyZMchEVpmXEXaVI6s3KX/lZ6HljB+l6NZH/lmxpXqB+tKY0/DpdF65niy8jvBXVYvEmof9bvhychmdr+XDVSYpu6FL0xdNrByw5+pPplTXydtL2dFRcX28D7E2SZXAWWD9Emy7Wm5r1kGnpul4fHY2bWlennabjOd/HE2VddIbCEJzXypq9N+Dv1f1j5+Du+DIFvqL1/LfEhJLXcf4PZ6FXgP6Lf9ej4vvC6z2yUCXfIJFRX3ExuHfGghBUmrOpVeNc2FgaX4YKUncJRLFTKbqpF6rN9Li9kyIy1VgFNXVANYbi5UiLhcYKFROuu3v78fs9msS1flk7qbEVQZCpFoGbfsZSgMX/EN+F0VX0XG6cQiLlUCb2sPQSGZ5at7ue80DZrfG94/DiZYDo/hJkpTeB5HrnOpIHnMue9zW1FRsRkolvjx+Xwe4/E4IqL3OuiMGDvRY3gWFVG+WlwKsM94ZkRM9tHhRFckWNdqEZvqIGGFSraLJfr0GUwvGxf/yWeozgy1YPllj7mvs8D/Xfmm7VMaVOTZNrKHfDmZQkWkIHP3E9lShQYq3IQDCyrT+nThxAcGmWhSggtq2sHD/X+mbnMXklKoUUXFutiYULPz+SIHvXLbY3J9SihTB0iqOMJ0UqV8GC5Bg+Urnf1alcVjwXRMIKFWOv7mL5ZT4RIyOgwP4RuxRCCz+Ggn1Kobp79k6Fxl8ek4jx/3P88/U6X9fA0cZPh1ryOit10V7x0HRwz/cXWCba66+0DJB1s+4KB6pGfDd32RQ3KDXlFRsT5cmXXM5/M4PT2NiIjRaNRTNz1emoIGbQfP9zA6lSHi0k6J2PG3iL5Yk/kexh/LhnCnjYjLXUNkg7gzUzbjleXptpnl8/rT/lP04UvDvA2YLuOJ2T4SEhTap2t5P7xdh8Nhj0z7Gh+1GdcYkVDLD7p/yQQNEmCfLc1UcW9j+RA+PzzufqekaFdUbIONCXWmjkZEzzDSoETkyvSyh5cPuSucVIZ1blbOUvm9o5MwMn/VSQMGxtK5ouKfKr/UhpLq6oY16+Csq6u7KiuJJMNV/Hdvl5Jz8d/caHMgxfj4ZSgZUVcLsnN8UMVzsv/9/vk5pYFdRUXFdlgmXmS/OWnL7I2fn9mQbGCcEVyS2lI+mQ3ljKYrxm6fMptMosZ1Mk74qVYzDa2vkd3VH2cxfVDCMnEmmYMZ2lu2M31FppCXBJoSSveI+WWhOG67M9+1yp/xWPa8cLaBfxUV22JjQq1poGxUuVgsevt9cpEI1UKO9D0tnceN19u27Y2uOT1UMkxM10f4+p8rnn1qqGmaGI/HPUXZHQQNpkbwVFN80YaHv/gCGY6ofWTuzoKGjHV2Z5VB5XaD6EZXcAVZ93F/f79rn9ls1s0ccCCk9pSDoqPJ2kR/HIgoz+w+CT67EBG9F8uofjSkWftVVFSsB/WzTLGUohuR76+v4277XZWMuLRX8he+eJokLBMk5E80g+qklnkx7cFg0O3dTLLJGUSuhSmppm6zOUBwG8zfXTXlubSN/F3t4Da1aZrOJzl5ZDvxfPmvwWDQLbh3Up0d83tWmm3Ws6MZDJaBNjrzSXz+HGxDT48+THxFYZ2np6ddGGNFxTbYeFEiR8YigepEXGlMJTOiv2CP5CqiHAPFnSVofLjYQ8gMUolQk+xnhoBly4gnjZyMhYi0DBd3/BgMBt2566SXxZgRvJ6DDLaL2r2kEjFfppXtikInNZlMeu1IBYdTbT4QcEfJ5yIL7fCwEbWL2pjpqe4O3+YvK8sqhaWioiKH21f2I9oGkjcnlUpnmb3Tca5lIZH1l524nWGYmELmmB/LrfUgi8Wit1bGyTHTVrl89jarhxPOiKuL/uh7SHbZfmpTF5kirtpUqszcZYnCA9uY4o6EpywGPvOf2T3Lwil0jfyjRBnt0OF8IeMKWfsybfoRhqFyXZPItIg1d+SqqNgUW+1D7eopFQffYYOGYlmYhh93wxgRvbi2EjHVuW7gGZaga7NRsxMtxr2VBgBu2GgMaLxdTXESyIUnarvsHjDcguov03ajwvvk6fpgpNSuMrTKj7MGjHVUPX3AwHZ3B5qdz99UT+4S4tf5c0RVgs69oqLielhGfCNWT9HzPBIft/mCk0tXhpUGzxM02HelnN9ZLooCPrj3snPbt1I8edZuJVurNDK7RtCel373/ykssW6ZLxTZpdBCuDLt+1jr/sgvUBjx2GuVYZltpk1f9vsy8LnggK6i4n5hq32oS8cGg0G3YEOj2ohLVYAhISJnIkgMFeD+nzSi7KhUg7MFIBF9NUAd1wkoCXBEWU0h2aXi4XnRaHEE7udqpbaHoWi/Txo0XieFQd+53yfDGFgmGj1+97KyLJkxl1qhxYhUZhRawRXWfv+4mJSDJC7wEVQfTcHOZrNe+Xh/soGYrtWUbdu2PXXb71lFRcX1oH7u9oN2l4Nt/qmvZjHOOzs73a4hZ2dnMR6PYzKZ9PyF8lHfpn3jonOlzxA9J98SCRSO0LZtTKfTK7sYucosm+72SfnLlrnv8Nhm+iYn8ryWO25kAg2vcV/JPas91IZl13UKy2Abc/9q7QKiNE9PT2Nvby8ODg5ib2+vu2ez2aw7V/WT78jKoOOqi/KnL3AFmmAaOof183OXDVIqKlbh2osSdTzicvu2iOhNLzH+OFOXXV3wFcEkX7pGachQCxoVs2w0gspfZD6iP9VV6nRUoTnNJ5TUCW2n50oEFROSa/6mQUdJ2WG+VKtdjWcZM+WDZXOH4KCD4rZSJMsi1Swvv3u7Z2V0RUq7xdAZZ/uW8lpN5el3f1ZKdayoqFgNJ4Y+s+S/OWHibCOJnfsL2fDpdNr1adlrihucGaRoQHLtRNFnxVhe31qTf656M0TCQzgoUrjwQXXX7SF9pZdRaSsUg2EbWdiI6utCytnZWRfGl6Fpmp7I4+tWKP5wbZT8pQQlvoHX28MXo2fPGQcw+lQ65A5ZeImQiWVs1+oLKq6LrbbN81G0HkTGJlM95bRPhox0OZnmpwyI8lo1dZONPHkNY7VdeaVBp8pdGkULVBwcDL1guUpEl7+5suOQ0fDQCBpDOrR109U1NKI+7bfKIFF1UVloADMizWeAzsUdLuufGWgOpjyfVc9PRUVFHyJOEgTcTmmGSN8z+5CRIB/UM/zC827btpsF1XUkoyXxp6RO6jraei8by0DCTDKt3913+cyr0iTRc4GB18t2Mv0SAWWbZDOUTugZTsl8IvpKuKfPNCSwsD4+gCIJZv39fqyjFJdUdZZR4HNFcY0DmYqK62JjQj0ajTpj6YT67OysU1uHw2G3wOPk5KQ3HcSH3o2OE52sYyrkg6NUVxNUXjcqNNQkn3xdKVVXDzPwcmVtpPNkLDID5KEeTgwzgpspydkom/GFbiS9bm17uTWUq77e9jxXzoFTcN7mvNdKx5Xr0j7VvDdsQ77QwRUhv5b7xfq916cUk4qKivXRtm2Mx+No27ZbVOa+gPv2LyObPlXP86l4CrPZrMtbhD6iT5q4+DAjdVlMsJNq+gSl79dQ9aVwJIJGUUDlZhtQodb5DKngTCVD6USAuYjShQX54eFw2Ls/bHP6IYXzcScQDVr4ojIXNXTtwcHBlV2e+J0DL9llHlsmLjmcBHN2Nxto7O7uds8DB3Luj6q4UnEdbKVQM16JRpRGUB3Vp3iWqZ+l405iOapdpyNkebNjcdW4TxuKoLoyv6zMXkcec6PhZDkrOz/9u//PdvK8fCqR53PQsGw2gYMXV6jXQTYgyRRqEuPsd4dvB6VzZXS5b6wP2JZNE1ZUVFyFCA0HvE7oSAzdRunTbQEH5DrHFdOI6PkgXcfzKTj4jKKrn7Rf8lmr7LFAH5eJHD7Ad1U+azPOprkIk9kq/k8Rp20vF46zjiTzTFshJLKlXFgowp/ZSyrUvIfMS+3MtJUexacSqS4p8fJDzNdFNdaRolw2w1r9QMV1sNWrx6kY8KHPRrsRlwsgSoQ2I6qrHmx1dJFdj43ysIpsSyNunSPFw8koO6zK61v6sH38u4+kabj5FsmI6J3HqU7PR3HBbvyzPy7I0ScVWh1zw5c5K9XHia3fS12j8rG9HbzPHCSVBi0+sHIFm/dn2TSek/eKior1QRIWkS8Id2JJm6z+yb5LG6drZDNIit3GSCH3MmRhZfQFFBW4wM3z4GyYD8JVBn5yj2wNOmT/aCdl75xok+y5T2Gbq5wlVVZ1zAgwxS+RWV+0zh27eC7TUJvN5/NuoSjvJZV6D4Ok6EGFn+UU6JN4b6i485768YiroosLMBpQrCsOVVQ4NiLUu7u7cePGjZhMJilZ0bTKYrGI4XDYGSpNi7FTywBwisuVC8GNJVULpeExtTSEzJ/GTItcxuNxHB0ddXnJMGq3En33xYWuOmejYq+nOi5jnGlAI/qjZ9VNZXJyzrrqesU400jJ6MqI81N10P6crlC7QuH3UZ90MkxD+bj6z7aKyPekZj2ZD/NW/bwcmnlYpjx4XHlFRcV6yAi1ky795n02Ino2lTYuE1tKM0naScIFDO0M5OcqFERhfhJkaLOcgLdt24VN0K64gEAVlIsnJdwwbQ4QdD3PIbkkueZCyIi+/fKy6HcuzI44t+fD4bAToriIkGF0DGHhfeBr2gXtgqJ8pFZPp9OeL9K1anv6UF+zxOdMaWSijM9KCi6qqF6MZWfoIhX2ioptsNWLXRQbRyMW0Z9OU+d0BZUoEehNyY2f73m5WqprRPJoIEW81bkVo6ZO7iPpTOF0ZUH1o8KhaUISbF2rNnPynIVisL11HsvMcohE80/1J2HPnF9mZPz+lYw68/fBg87Nrvd6Zgo+66WylBY8Lit7RUXFZiip0xm8b9MOZmosyWXE1fUcSidbDBgRnUqs81xZpcCR2YaSoiu7k4WF+FobV6hLfo7HqNjqOG1dFrrhaXg9fKaRKrSLKjxH62wodGS+VWWkf6RNF1nPeIAPtpxXuL8qhSNm94y/ZddmPmeT8MWKCsfGhFogQWWHpSKqEbDislxZ1ghRaZRGmkrbpwx5rgyDTy06dI7UEe5zrLT4Su2Iq7tiiHDT+Ljy4vl7WZqm6fZIpvrAdiP59OkwT5eqgccLZ86IJFSGhgoyoQUp3o6ZKs30ZATdsLNN+T+dSXatyLI7WbbDMnLv7cdnp5Lqior1sQ75cHLppIdE2sMH3A61bduR5La9XBztIXOyIXrrndRY7Zm8WCy6dEoET2DICWc4m6a/EJLlYzl0/Xg87uWXtRNtN7fzY1hIFlft5WWZVBbaZxJoneuLKvXdfY4TeL5zgNdnizN1vudPsq71WU6odZ9LYo0TZQ6umB5nabOBVGkWpKJiXWz1pkR1Nu2xTAVWhk47foh86jtJEmPHOAXjcVJCRiidQGVxVjxXRo4vCtCG8yLTzItlprFhZ3dFl7FnNMKM34u4JN50KjIqCsGgAsOpL78vWSgIFRWSR57n5XPlRQaQaoHSze4Vp24zqByZoZZaHtHfhYTkXUZ6HcPnjkP3jnmuo65VVFRchRNSH8QKHKxng1wqwJwx5EI3hmi0bduF4rE/6zrZ9PF4HIPBIG7cuNGFhYhQu0iicgqyRyTJfHOgK8Z6MY3EGdmo6XQaJycnMZ/Pr+wI4nHbIup8WRd3SiFhpm/Jyp7dDyfJJKkUbnSubDDFDeatttS1/pIXrmWhj+dsLHdi4m4iFMVKPkjf+Y4C2nJxD+XphJrPMdu7+oOKbbGVQu1TMG4UIvokkdvDZchIsY9OMwJFw8HPbEqodI0r414GEtLMUHkHzn5nXgxT0HeOol2BYDhGNjXnbeiOrBTj6MoG8/Tvap/MkLuDLA1kMjWABp3tT6Kf3fNloRylPDMlKiP3FRUV64FEehkyu5qpjevYDLeBXoYsH/qrkt3O7E5G0Lz+XoaSP8nq5PVeppiX2ivzgVm7+H1weN78n8eXiTMcGPm1y9rU28d9nZfX7TX5gfs2IiPT/nsl0xXXwUaEWg9kRPQWckRcJbEa5Wpkrx0/NOoVkSGR5L7G3G5PaWuVrnc8EU8pACpr1okzg8r4uGWkKzOo+sxIs+rgbeht6Uoxz5Nq43GGOlfHVHdOubmyzXR9QUiJDJeUJJadioaH7JTaTG3F+8OXHtAQ85nwlfKOzFmrTfh8sPzZ64ArKiqWg33UY2ydSKm/ScnlNbou+yRJYhgc1VOGHso20O7Jt3isNUPH9N135ZBCTGU3I8uz2ayb6dSfrh0MBldsK9VY+hiuPZJCvrNzuZ2dykLVVtdPJpOIiBiNRl37sBycOczC63xRovuoiMuXnUl9p4/xrQPZ3lSHOWPBZ4Y+hgOTLJxD7cbnzkMXeX+5ODR7pmq4R8X9wMYKtR5GdVg3MprqYifb39+P4XAYEf3FczSaIkqatuPCR3Y2bTfnxNBHziLyJLoZmfb0XfllvUmWM1WFJFLlWCz6r0IXOc7IO4ktSXA2veexhyKMNI50BGxrJ536zoUjmbpD48nfeZ85hce6qR5OtH0qTu2iZ0nHdd6y6eOI/otifIqVTtif1WpIKyrWB/uc+mxJ1aTd0vZqmZ3NbKrAUC8JNAy3iOjHLtP2KV+3byRrtAkMY/TZO5WF9oNhJgz9kA87ODjorlN+9IlKxwULpcXXejtR5UBAhFovc9FxCjtef/nyzEbq3rmdpV9z0cLvP7cbdAKrY07K+ayw3VVnhXHQp7Nc2cJ6Hnfxj3X03VcqKjbB1osSOSJkJy2dWzK2bnj5wK/7YGdKtP8uZOEgpfK4YfeyO0gY7wdWTUGV2jMLYShNh7nanyn57lw4+mdZWKasrKX6LStTqSyCz3Tw/FL5PK9qRCsqNkfWl1bZX/bhZaFWJTuSDaIzoSSiP/Omfk4ylpErX5+Rze65uEFS7buHuD32mT2vV2bLXCXO/NCqtvffGCJBZO2apZmR7OxatX/2rLioVELJd61zHbnEsvrws6JiW2ytUGvU6aNldn6FaHD0yb0oNbVFddmNlfJkiILSp6qg85WerqPh9KkgKqslRdZDWzwmPItnZhpU8r38/unhGquMDVUA3h+qrqyTq+v6nqnVvA9UCjykhfecsWw0TkxXISxUeUhoaTwZPuKqhuAzC1wwqnbUPeTqeT4vlVBXVGwP2usSSYuInvroRIs2KiNnbitWCRsMkWB4AsWD6XTaC7WIiE5Bj4i4ceNGHB4eRkR/P2dhsVh0SvR0Oo179+71Xnom25O9tlt1ln3TjiRciMn6MGyNC7aZlurgiyl5DgcZUu1Zf/rVLCSG/rZt2yv+zUks77/KrDYjN6A/cJSErGzGkz7CZz8zcBGsv+K+omJTbK1Qk0QvG516HK+Mhgi1drXgdSRNmVKptAV2HsZZ8zcSTP2mcsiAsXOKgHHHDtaDRNs7Oqe+GIpRgo/wSXy9nu50fB9Wnsu21LlO5EsqkRs3El0NiFzNWWcA4MqOnJLKQqPM7zSUdFjefu6kPa6c5ZAj4561FRUVmyNTAPW/dq4oKcqZ/cvSKsW5Zn6BfkbkkYIA3+y3s7PTrfGRPRAZvnHjRkREHB0ddURb9lbEVfHTJycn3bXKn/afto62nWROb32kXXaxhVvTCu4DFLaSiUhqM25XlwkgTkxVVsYte+hnRJ+A03+yXh6K4vfc7Tg/+dxwNxH+0beUCLWOc3eYKq5UXAdbE+oM3indSDqhK408s/T4/7rTRN5BPfaW5SjFBvvvJKYsOwkqr12lNvM4jXCW/6rvy9rS8/Rye9sta1cnt64c+8DF081mI0p5uZFcVid9cjah1DbVaFZUXA/L7LHbg3XTElydXpWXn+u2P1POmV4WfiDCqO8uMGRlouJN2++D/uzP83bBJhOcRKzll/wc2WeGujhZ1/kUx7L2dHJdejcCbbXSWxa37LPcq/gAZ2CX+busbb1uGSeoqNgW94VQO+nRwyljxEUSg8EgBoNBt9hCyoHSiegbKIZ6LCOB2cIHhSpIgeQxXwSSGQZftSyiS3Xa91zWcZ6ThY1kKqvO9ZcWcPW1D1AYzqDRvlQD1VPp8y8bFOgeZOq17g0NEN8wuc4In1OI65Buncv9qdUe/N3ronKrTThNmuVfjWlFxWZo27bbJ98VVe/Dy/oXbb9sh2ydXrolu0375+ElPriXLZJN9dms3d3zF75QpYzo28vhcBg3b96Mtm3j8ccfj/F4HG3b9kIuaLcZmjEcDrt6yJ5LvVY5tduJ7KjItvJXKKLq7XHfrtTSbzBcTm0o/7C/vx+j0ah7nTrTUvl5Pe+DL7bUfuBqO4bRReSvRue5Cs9hG5WeEX5yYFAanPB50H3Ss6HZCdZN560jSlVUZNg6hrr0m/487ktbB9HIOLHL0nPiyTIsU0a0y4fixDIS5QQzIl+ISMVB5DVTibtGvYibU7pKuzSiVz7c2s0JMHfwoPpNh+bhKbxXruByEFEitaW2pdOiES3FOTPN0uAr4uo+3fr07Y6yclKhoUrEaVJNbWZKT0VFxfpQ/y+pvplKnAknTogjome7OEhnWABJE6/nVD+3G80ItfJmWADLvL+/H4eHh109RYBJTmVXKAxRGKEtZp4i0szX20zrezLF2AcUypPt44skuXNWRPTsIe8JCbzSUEiKQlw04OBgir43I9I+eJKopR1bVEbVx5+tTGnmIITPgYtr/vZJ1UmE2tOvqNgG90Wh5sNdInAMZxDBpXFjXBrhSqTn6XCjozSdTPuI1FXmiH7IgC9EpArq9SfRZQiHiHGp/dhOLBPJ/O3j4/jU//Af4ns/8RPj5PbtdErR60T1we+Pf19lVNxQUcmgelK6T5njIugg6WRL5Ndj83xw5NsGlupUUVGxGdjns/7FPhmx/gA2U2D9L1O/aXtkO9zO8XpPJ1OcPT3l4+nItnMxpF/HNmGeUmkpvtDXeN0pnmRxyCWCTH+U3RtvR/cLbB8KZtn9pd8uCSHZwMrbzQWr7H4ROr90bfb8Zc9BRcU22OrFLt5JM6Kpjnt2dhbD4TBGo1FEnId/aNS9v7/fGQQtTmzbtnsleEZ2I/IFdeoU3JNUI2mVmZ3GDW2mCtPo7O/vXzEiGXmOiF7YBhVnGkhHKT22c0TEh//oj8bbv+Y18WE/+7Pxwo//+FSh9gU5JceUITP+rnq4Qq12L5FqwRXtzHCpvL4XbFYHGn5/HiPOnzWpH1lozjKSXVFRUQb7KHfWoW1m/LGTNJEen7HTjBSJOtNSPlQyMygd5ue2lHVRfoPBIIbDYW+WMeJy4SDP9ZlChkDI3zh5VBiHQiciosvPiXy2mH1vby9Go1Hs7u52u5Swnai++gymwjxKO4U4dD13eeK18uUR0c0IlPwC2587a2hAIZGNz5bqwJAQimM6n0o+fYG/aIzKtXOWVbuBVFSsg61CPvy7k1B2oIjLaTGNwhkbpoefU2dZPj6izgyprvG9QKfTaUrKqRZ7CIfKTYPu8YJUkxnPzJAW/+4jccLJukh8RMQXfcVXxD7iiJ/5kpfEM1/ykpjv7cX/+yu+oqc68Hs24l5HrV3mdPxFLBlJ5vStb2voBtHz4NShrvPZBA6K+MnBkSv+rH8l1BUV20P9k/27JEj4dauIi8e+Km2GdLmdWZWP2wid4+olCZwECqXjL6xy0F6TSLMMqoOIpo6NRqNeGek/mR7DSTggcf8rKH2uJcle3pLBfVZEXPF3vhDSwy3oi5UO/QXLTtuu9qb/dIGFoYAeEkkOQmGJ96KUb0XFttiIUGekkA8xR+wZ8RTB8fNKC+Qi8tg7GfMSKdXvNIgOjYjd6JfImcdC+wg3qyOPK81lYSvLnNE3fO7nxoe/8IXx1Fe+MgazWUz39+PX3/Vd48Uf+7Gpg/A0M2TTfqucFc8pxf6pnpxl2AQ0xiyHz0i4A+Knqylsd5a55PgrKiqWgyQlmyHK4H004ur2lxkpjbga0iF1XOf77BrtNm2yILJHYicCJ9En21bTSTntE4mZE3HZIZbbCasTQrdRVKFdcVWZPU+WjyQzs+8i+Vq4x3ZmGqqnSK1vpycfybAbny2VDyYx5sDG4575LCgNF9FctOHz4vdP19et8iruFzZWqH3xHjsGjYNGsFoQsL+/302pac9Pvapa5NdHjIJ3DBobBzvYdDrtvrtRHgwGcXBw0DPqJGI0xjSyLBtVaZE3kuqIfsgH26+kGrtCLONyfPt2TA8OYm8+j9neXuzN5zEZDuPk9u2lDqwUYsJ89cfpw4wky/hwRTzjCn30T2fg9zQjsiTQXCzC8vlz4io0/6j8lJ4pN9wVFRWrQeKmmUWGxTmZop11cusDYtpL2RQuHMzIG6FjDN3gIvGmaWIymcR4PO6UYpLSw8PDzqcdHR3FbDbrfAnrIOKpeioPLuLjLhLc7YqgykufQxs1GAy6esrWsuzauUP3hjtbsY2p9rINdZ3I7WQyidPT0zg7O+vSpogUcf4SnNPT0x6hVjuIyKs9lLby4SBoPB73yD7rrPJx8EAoH+0Rns0M8H+2qc+wVnW64rrYSqGO6BM/Vxj8ODuLhzXQAPsfOxCJdMRV9ZTKgKYh1WlkJHid7/jAdLxsMkRU3FUvEm8ZEhpukW5vH29DodSpd3Z24ubRUbzsWc+Kl73P+8R7PfRQ3D46WkkEs3tW+t3T8vOzKb3sPBLgZYqVDyIITvmRIDN9zhQ4aeZ9ydqcbV/JdEXF5mBfolLq/S0bSNPG8reIvtKbpVFSV12NpRKcDey1y4OnwfhiKdT+xkFXXV1I8dA1knb6J6XHtssGIRJmqD4zD50TEVfEBW93KtT8nce0BkmCGAdBul7tp+soqtFHRvS3z2MbKh3nBe63lpFppZ/5q+wY25btWFFxXdy3F7tknVedSgoBOydj1BxcoMARtowTO0M2Xe9KBl8pqk+FJCgNqt404Bnh8t+ztnCnkamkpXb0//X3g5/+6V3dfuRP/snzdtzJQ3BUr6yt3EB522QGzKfklp2r4/w9a5+sDF5O3e9MafewGnfcmdP2+pTSrqioKKMkhvD3jBg6aHed2Lh6qHTY16nkusro/Zq+hL4hU8ulGvuLTJQu7RfVZV88Rz9IEk612GfYvPyMgdZiPZapafrvIeBghOq20i75Op0vMs1t90i0uYUeiSyFFF3nzwLbmM+NfudWhyXRjPn54nuGfKjcIu0qk75rwMDBROavKirWxbUJdUakNWJXJ+cIfzAYXNndg8SaI1WOxiPyrXioglLZ0LV6xSxXKkdEt0o6M2rKK1M1lrUBy6HrNJhQ3VgGwollZjjUtrpeYSaZI3M115UIKgZ0RE6c/RiNFB1giVRnhspVEo9h9HRo2DULoGeH32Xwee8yR6U0eU8qKio2g888lgi1z2C6jc2Ilz65G4TsGgfJ3Kc6C3FgXjpf6SqUQ2EhnI3c3d2NyWQS0+m08x1UgGWnaEf4inUdz/aj1vl6zTjjwNl+3ClqMBh0hPr09DSm02kMh8M4PDzs0uJMqOwtw10YhpGpsmy/6XTa7SLCUA0RetltqsvyOQz/0J8GETruseQsswg/FXUXYPgM0HeKKDPkRvdJaWsvbS8n72dFxTa4r68eFzKl1A0vO5KTHXU2N7765F9J5SaRY5yb0qFRyAjkOmpyVm/WX99p2N3pMD+CBNfbqeSgmJ+Xx/P2dtJ3tgHPWUeJdpBIZyr1OuD1Pj2cKR2aefA2L7XBpve4oqJiOUgat7mO/bU0GGfon9unzC4yfV2XKZJ+bbY2pATZH/maTMFn29CGkYxm6bItRWS1JSh37XA/WRIVsvBJtgPDVPhJkspwD+VRas+sPhTDsmu9TKU29zbijKsLQJy5pu9jm1VUbIuNCbU6ghsxJ07ZQyuDQ4Wa8cciudrmzo2Qo0Ra2Um4kpvnabStz8zw0NBwypHGyNVbjsLVVowty8izG0Od7wbHBxeryCHT429Ze2Zkmp8cnJQcmGOVQ11VptI1JWfFAQfbiu2vZ5D72lYjWlFxPWShH7STEVd376FYULKpBO2n2w4XTlx1pgJJe06fpPMUCuCxzxRvdD1nzOi7Dg4OunaZTqe9Qb6TaNUhoh/OwnqOx+OIiG7xnVTrw8PDbvE/VVddT7+m+jNvXSdSzEX0ejX7zs5Ol6/UXdWNa4Wy0B0+E2xLrWGiqs9QFrflbJPMXitvtQtJta7RPeKMJ8W7LMykomITbEyoSXJLsbcRly9WcdKlTquOxN0x9HIXxkVl+SsvIYuB5dSe/mQA9TtjqyMup/J8NXXTNL2O7jt1cOTLY1TQM1LNjuyhDrw2m071+0G4I1N6pUFANv3Hc3yLJld26BjXVRSYBw0t29XLo7bgFCoHSRmhzqaAlYZUlmpEKyo2h6utGamO6AscUjipSNLmug10IYGElOcxBEEizXA47KnZEZchJL6VHXfR4DmyTbQZJGN6uQmV2+FwGLdu3erI5unpaSfa0A9qxw8SSb5pUX+z2SxOTk668qh+BwcHcfPmzdjf34/j4+O4d+/eld2RNFiI6L98hWruZDKJ2WzWhaBExJXynZycdPZSgtFoNLoSMklV2BeN617xnjAkknHXWQiMPv358ueBzxzDhUTquRjV08uEoYqKdbFVyEemZmbxtD6VRGPIB1lGzVXiZQ/3Oh3AR8s6po4lI8ZYqlJcsBN8DyGh6kFV1PMsKdXL1ObrdnIn8NumsQ7xXEaqr1OPTIn241k+GVFf9WxVVFSsj2X9z5EJJf7/pnampGb7QHuZ8k1S5nHGHJTr0wfqEdEbnDOuWGFoy2w87VRm6yjYUDhg/LUr6pmd8zagqMJ2Z/v5W3B9h5KSPaW9dfj17qOF7FkotRvTY4ipz/RmA79KqCvuBzZ+9TgXKmSk2uOX+DtHzIoB0wKCwWDQW3gmcIrO1e6I/lsBmZeT6Yz4copQRpIrqr1zihBrlE2FWco6lWyN6Nm5aQCoyjjx985OQ8j2dGMfcXX7KNZf94btVVKEPfYsO9/bSAoO1SvdR5aF7V4CHYfujZwIF/uwTm4Y+WyqHaU2uROpqKi4HtwO8VPfS6Fa9B209bKrVGgVsiV/pH7NHS3Y5/mZzfhRNZWCzrBF2jxdz3VAKreT6v39/e6V5FTGI6In6NC3ZOJNyVdwhlHXc4E938fAduCiw4jokX9/dwJn8vzecFcTKeNsZxJnXScOoPQZi+3n0scpbf6p3dwPKB3NKnCgoedE92NdsaiiYhW2JtQZWWX8lkimoM7jL3nRamXFPbXt+aby7GweD6X09enEzAk4OwuJnb+SXKRY5aPxp5FSOfgbFWiuHJ7NZt13Lh7ReT4dlhnujHCrPah8uMKhc5i2D0h4D70N5UxIkJ1M65ODDv6utubAR+3k5/p13LpK6TNMKHtJQdZu2WBI+bjqU1FRsR1Kqh9/Z3hHpjSSvNH+ktxGXO64wT7MeFySU8L9BQmlrvF9nj3ETfbDX+HtSm/TnIcY3rhxI87OzmI8Hvd8p2xWJj7R7pIku5gl+y+75gMOhXRwACCSrWsZcsk29IECBwNqY8Zvi7gzHl35imyrnlpQKWLvfpqii8J4qGS37eUaKPpi/q4Bje6p2lNts7Oz092TTFSqqNgUGy3D9ukhH7GWHko/7uqEKxauOmfTOJnCUaykKRJeNjea3HuTRk3/u4HN1ON1j3lay8q4ST5Z+5euKf3u94yf+r6Oypw51mX/Z22R3W9Xm5Y9f7eOjuKvfvu3x63j46V1qqioWB+lvlMKCfBBv/uBiOWhGULJ1vk5/F3faTeytR8lRTRDJlS4PVo10OAxV1O9bTJb6epqZhczW08imdny7LqI/iyDrl3H/1+XtPo9Kt3rrE7uM/y9BZVQV9wPbKxQj8fj3sPrhpDnUjXMHliNcDUC1YhSI1wSeN8oXiD55iJIgeWLyNUAnbdYLLp9Salkc89s5e1l10iab2BUR2bZlRfPURoOxvJlo2hdr/K4kWMd3YA6ljkrth/V99JCENVvlUKtujNfdxBSeSKit6Je6bn6nDmFpmniQ37mZ+Ipr351/Pmf+qn4T895zpXYuoqKis2waiDtpIWiiJM+KZZSOn0xmabpdYyKp9ujkjpN7OzsdAsKFVLStm33ngTZbb7ynHVRHnqTYtOc74oxGo268A795osb27bt2UOpvBGXoRNadKi0JpPJFeKsBYhcA0T/I+XYVWz33x7mIZ8jv0VFXO0j/xfRF9o4OJE/jbj0zQLroplIlYflpw91+GLGLGyDz4ZvdsDdZph/RcW22IhQa/qIUCf2UX42cvbraGDVqRUKoq18+DYjdWwaap+qy0bdHgIQ0d/vUx1WaUyn0ytKOOO6Vb+IS/X77OysI9h0Jjs7O90xkl8ZLbUfofqobFTKSx2+9CIXljdTKXSup52dE3HVgclIZaSf95ehKQz38LRLWy0y3IP3VL+XDOJXfd3XxT7u9bNe+tJ41ktfGrO9vfjHX/AFvbauqKjYDiUhI1MHaaO8bysdiRO0N7TBfGFHSfn18hDaxYL+QDac4QXKxxVa/a4QyMFgEAcHBzEYDHrpMdRA17JOHAgsFoveS1v29vbi4OCgt1Wdt7d2D6GoQJvMkEnaVbevWfiKbx8YcU7+R6NRL3SHBNjjwP3FKl4OlcHVbn/pios3LLNe3uaDHt53louhmix/RcV1sfFr4rIpoOs8kE6s3dCSmPL/zGCXpsWycxweukIjwVi6Upvwk8eza5YpxTfv3Yvnf8u3xI1790pNdiWPVX+lui27nnCCXiLt2TV+Xen3LD3/XZ/ulLP0Vbev/MzPjJc97Wkx1SKmvb345ac/Pb72b/2t9JqKiorNsI7tZ39lv12lDJb6O8mR/kr58fxSubM6+Kzfqnp5PWj/nZDzHPo7ltdjspmOt1+pjuuUO2snEWkJT1xDRGT5rWOj6ZeWteE69fDyuN8rze5m6azyaxUVy7BxyAfVVnWcbDrGr3PVUcosF0O4WqBruHuGru8qcDFKdfLrI+aIy1GqRvSZMdI0GpVbN3S+aEPHOa3H/Bi64ftSOz7gJ34i3vZVr4r3/7Efix/+hE/ozqWxpIrANmVds88sDdWR90XnZ4ZQizy83Xgep9ZUd07zqux6BjKjxw33OcXo08X8TiifuzduxOn+fuydncVsby/2zs5iOhrF6Z07sQuHUVFRsRl8pi3iavyujnFPfYJ20Ps094d2uy7s7u7GaDTq2X+lpYXuXIAn4krl1VVQ+qeDg4NOOWZ4GkPOdO6yGPBMKZW6vL+/3+06oQWEUr8jLt+ZoOvof514Z342I4gKa6HSS784m81iMpl0qv1oNOrZdl9HpTaQveae3Sozy8aZXJ9ddX+wjOCWBCS2G++XnjmF4vg9WTarXlGxChvvQ02CpP+XKcM6J1t8p3QYj8wpN3XmnZ2dLgyDqkRE9KbnGIJQmgqkYfMYb5VR1zKeTcbCwzN0LcmjGwXVmSNy/S9j8ve/7MtiHwb7GQ89FM946KGY7e3FV//jf1xUHnw6Tw7OVWh+qv46RhLN42qvbPS+ygBl5JzH6ESzeGqlQQWKsxXMQ8eyZ7Ft27h1fBy/+F7vFb/0rGfF+7z0pXHj3r0iEa+oqFiNTFDIbAb/1zGSMl2b2WqRTRFqCR20b7LNbdt2Ozaob8unUAiifeN2db6GhSRZRNd9Fwk6Qw8j+j4vazMKRqon6y9Sq3qW1hDpXFezKZQwf7Wrl9sHA4oBl4+SqKG6yd66yiz/zTA8HwgoDZXF7bD8UMmHe7r8Tr/Ll9woL+XtO5LxXlRUbIuNFWpXI1zpJKF0IrlYLOLgscfiud/6rfHvn/e8OAJhYnpUJ0l8ZFhdMeYn4Yqtb65f+s4RNeuwDkqDCpXHCaXwLz/v8+LDXvjCeJdf+7XYn81itr8fr3za0+JHP/qjr6jFWZlKBqHUPk6kVVY3fFmddGzVdGbWZpmSrBkIqiS6d64eZWllChPxbR//8TEajWI4HMYPfezHnp9j6VRiXVGxGdhnSB5dOCGyAa+fT58gf8PdlZZN4Wd5i/jJtvB82g7mqXPWsQ0lRTqbvS3Z8WXtRaFmWRoaPCwrL32pvmcClN8PKvssc7ZdKW06fUpmw1Vub0cXnlyt5uCBe2R7+/ggR8+Pb8NYUXE/sLFCrReVsJNnf4y30gM8n8/jWS96Ufyp3/md+MCf+In4wY/8yO4B50iZYRFa1KHFHtmIlKuQPdwi4nIB4u7ubrfnJTsslU8aCRpW/3O4mloyICqbDwru3rgRk8EgdufzmO3txe58HuPBIO7duBFN9B2Qv+yGdVS9/EUAbBvWKSvXqnMzg857TsPMMpbaQ8oGp2Fd1c5ULj0rUp94H1i+bJBDQ19RUbE5aN/V1/liK6q+bj+oJoowy/4q1KNpmt5rwLVIjnnTbjM9F1JkI3wWS7aHCqxmQ7mbUMlOrBJsnNzxGvlSX7yo37XjlSvB9K1q78lk0u3ApZ04/F6xPtpVRUo4lXLabd0PLURU+ExE9NRf2mOFsLBOTdN0u7QwhIXp0fdLpVb5GCKi+6H21kYC4hHco5wvtVE9+VIbF31qyEfFdbCVQq0H10kOyYyrw3/5r//12EMH7EIadnfj733O5/TS8tAO5cu4LIGdggQpU1i1PR0VCyFTdn1kS3KagW2hTu/l4HFXiA+PjuIl7/M+8dB7vVc88+GH4+bRUY+w7uzsXBmNkyzTAPJ+sa1chfHyZ3VxhYAOK3NQfk6Wh+fFmQdO17oDIWjEaZSd8JdUCN6rioqKzeAqM+NpfVCbXRdxdWvQiMstSWnrtZ1eRiozkYV5OtGmiurfSSYzW10C7eGy810l91hkt/eyg5y544vE9CdSyfbL7hfV5v39/Y5kyjcydIVCiEI5NPCh+EEhjN+zNvf7xXLrntNP6hlQuI+uU/0jLmOl2faqA4UalZffq0JdcT+xsUK9Lb73n/yTeOb3fE+8/S/9UhfS8H8/9anxXz7kQ/6gihARf7R3dfjeT/mUzpD9l4/5mHPj8IddqIqKioqKioqKiqVoNhmhNU3zhoj43W0ze4eIpzwY8VZtRNtENG+KeMNvR7xq2/QqKu4j3q5t27f6wy5ERcWbA67rCyoq/gij+oKKrbARoa6oqKioqKioqKio6KMGj1ZUVFRUVFRUVFRcA5VQV1RUVFRUVFRUVFwDlVBXVFRUVFRUVFRUXAOVUFdUVFRUVFRUVFRcA5VQV1RUVFRUVFRUVFwDlVBXVFRUVFRUVFRUXAOVUFdUVFRUVFRUVFRcA5VQV1RUVFRUVFRUVFwDlVBXVFRUVFRUVFRUXAOVUFdUVFRUVFRUVFRcA5VQV1RUVFRUVFRUVFwDlVBXVFRUVFRUVFRUXAOVUFdUVFRUVFRUVFRcA5VQV1RUVFRUrImmab6oaZp/db/PXSOttmmadyz89kNN03zG/cinoqJiO1RCvQaapvmdpmmmTdM8yY4/fGHk3v7i/z/VNM2/b5rmjU3TPN40za82TfOCi9/e/uLcI/v7lDXL8DVN0/z3pmnuNU3zyqZpPn3F+Z/WNM3vNk1z3DTNDzZN86D9/qlN0/zaxe+/2TTNB27SJhUVFRVv7mia5gVN0/xK0zQnTdP8ftM039g0zQPLrmnb9qvatv1r66S/ybnXQdu2z2nb9tv/Z+dTUVFRRiXU6+O3I+Iv6p+mad49Ig7tnO+IiFdHxNtFxBMj4i9HxOvsnAfatr2Jv+9ZM//jiPi4iLgTEZ8REf+iaZr3y05smubdIuKbL/J/64g4iYj/E79/RER8dUT8lYi4FREfFBG/tWY5KioqKt7s0TTN341zO/gFcW5Xnx3ntvtHmqYZFK7Z+4MrYUVFxZsTKqFeH98REVSFPyMi/o2d88yI+La2bY/btp23bftw27Y/dD8yb9v2H7dt+8q2bRdt2/5CRPx0RPy5wunPj4j/1LbtT7VtexQRXxIRn9g0za2L378sIr68bdufv0jvtW3bvvZ+lLOioqLijzqaprkd53bwc9q2fWHbtrO2bX8nIj45It4+Iv7SxXlf2jTN9zVN851N09yNiBdcHPtOpPXpF7OBjzRN8yUXM5ofjuu/8+K7Zik/o2maV13MZH4x0nlW0zQ/1zTNY03T/F7TNN9QIvZJfV7cNM1fu/j+gqZpfrZpmq+7SOu3mqZ5v4vjr26a5vUMD2ma5i9czLbevfj9Sy3tZfXbaZrmCy9mOR9pmuZ7fTa0ouKPCyqhXh8/HxG3m6Z5WtM0uxHxqRHxnck5//IinOIpmyR+EaLx8jXPPYhz8v6KwinvFhG/rH/atv3NiJhGxDtflP0ZEfFWTdP8RtM0r7kw3AeblLeioqLizRjvFxGjiPh+HrwQIP5rRHwEDj83Ir4vIh6IiH/L85umedc4n/17fkT8iThXuv/kirw/ICKeGhEfFhH/qGmap10cP4uIz42IJ8W5WPJhEfHZm1Wrw/tGxMvjfKb0uyLiu+PcZ7xjnA8WvqFpmpsX5x7HuVj0QET8hYj4rKZpPmHN+n1ORHxCRHxwRLxNRDwaEf9yyzJXVLxZoxLqzSCV+iMi4tciwlXd58W5cvwlEfHbTdP8UtM0z7Rz3nihGujvaRERbdt+V9u277FmOb4pzgnziwq/34yIx+3Y43Ee3vHWEbEfEZ8UER8YEe8ZEe8VEf9wzbwrKioq3tzxpIh4Y9u28+S337v4Xfi5tm1/8GI279TO/aQ4nw38mbZtpxHxjyKiXZH3l7Vte9q27S/HuR3/sxERbdu+9GLWcH6hln9znBPVbfDbbdt+a9u2ZxHxPRHxtnE+Kzlp2/aH41xgeceLfF/ctu2vXNTv5RHx75Dvqvr9jYj44rZtX9O27SQivjQiPqmGxlT8cUQl1JvhOyLi0yLiBXE13CPatn20bdsvbNv23eKcuP5SRPxg0zQNTntS27YP4O/XNilA0zT/LCKeHhGf3LZtyXAfRcRtO3Y7Iu5FhBzC17dt+3tt274xIr42Ij5mk3JUVFRUvBnjjRHxpALx+xMXvwuvXpLO2/D3tm1PIuKRFXn/Pr6fxLkAEk3TvHPTNP/5YnHk3Yj4qugT+03AtTunF2XzY8r3fZum+Ymmad7QNM3jcU6Sle+q+r1dRPyABKI4F5rO4tz/VVT8sUIl1BugbdvfjfPFiR8TNlWYnPvGiPiaODdI9yWmrGmaL4uI50TER7Zte3fJqa+IC9Xj4ro/HRHDiPj1tm0fjYjXRF9lWKWoVFRUVLwl4eciYhIRn8iDF2EQz4mIH8PhZfbx9yLiT+H6gzgPs9gG3xgRr4yId2rb9nZEfFFENMsvuS/4roj4jxHxtm3b3onzGVDlu6p+r46I55hINKprcir+OKIS6s3xmRHxoW3bHvsPTdN8ddM0T2+aZu9iAeBnRcRvtG27SrFYiaZp/kGcq+MfvkZ6/zYiPq5pmg9smuZGRHx5RHx/27b3Ln7/1oj4nKZpntw0zRPiPG7vP1+3jBUVFRVvDmjb9vE4X5T49U3TfHTTNPvN+fan3xvngsN3rJnU98W5rX2/iwWEXxrbk+BbEXE3Io6apnmXOPcffxC4FRFvatt23DTNs+Lczwir6vdNEfGVTdO8XURE0zRv1TTNc/+Ayl1R8UcKlVBviLZtf7Nt25cUfj6MiB+IiMfifBu6t4uIj7dzHmv6+1B/XkRE0zTPb5qmtMgw4nz67ykR8Ru49ov048X/H3hRxlfE+bTdv42I18e5weTilv9XRDwUEb8e51N0D0fEV66ufUVFRcVbBtq2/adxrgJ/TZwT2V+Ic8X1wy7igddJ4xVxvjDvu+NczT2Kc5u71vWGz49zMnsvIv6vOI99/oPAZ0fElzdNcy/OY6S/Vz+sUb9/Eefq9g9fXP/zcb4gsqLijx2achhuRUVFRUVFxbq4CBl5LM7DNn77D7k49x1v6fWrqLgOqkJdUVFRUVGxJZqm+bimaQ4vwuu+JiJ+JSJ+5w+3VPcPb+n1q6i4X6iEuqKioqKiYns8NyL+x8XfO0XEpy7ZgenNEW/p9auouC+oIR8VFRUVFRUVFRUV10BVqCsqKioqKioqKiqugY3eZnR4eNjeuXMnIiKkbLdtG4vFItq2jbZt4+zsrPd7RPR+53FXx9u2Db4DRd+bpun+/Pj9xLrpZWVclkapTuuW47r/Z3mvKtO656zKt3R+dm42W5I9L/pcLBbpbzzO35lW0zS9Z/Kxxx6L4+PjP4g9Xysq3uzxxCc+sX3KU54Si8Uizs7OYrFYxMnJSdy9ezfm83nMZrOYzWadTc9sz87OTs+2Z+esY5scJbu06rpSfsvOXaccPEc2yK9bZftKx0t21K8ttfOyGWqWdZntzvJap0zL8syuXcYheDxrY3/W9DuP7+/vx87OTrz+9a+Pxx9/vPqCio2xEaG+c+dOfOZnfmbPiE6n0xiPxzGfz+P09DROTk7i7Owszs7OOnIzHo9jOp125y8Wiy4N7xxN08Tu7m5EROzu7nZ/e3t7MRgMug6wt7d3pYOwU2VGrASl6d+ZTsngezkyZ6HvOzs7abmzPNkO/p1/no+nxzx2d3djZ2enKwfbUsdZVuW5t7fXK7fy0b3xNtHvKrenl9VX905kmMZTz4sGbIvFIubz+ZVnicfdAM/n826wp/KcnZ3FeDyO2WwW3/zN37z0GamoqLjEU57ylHjxi18cJycn8eijj8Z4PI6Xvexl8SM/8iPxhje8IV7/+tfHa1/72pjP5z37MRgMOuJycHDQfR8MBp0dGgwGna3a39/v2bmIc3uU2ZKMKOl82aL9/f3Y3d3tCUGyexHR5es2mqCdVZkj+kTO7ShtnK7VdbJhTEO2zEUmlpv5kExqQMM2kf+UD1W5lfdisbiSj9KUnXc7zrLqfLWzt8nZ2VlXH5Yrs/n8vlgsYjabdZxiOp126cjmK23+37Ztzz+NRqMYjUY94ry3txcHBwcxGAxiOBzGk5/85Lh582b87b/9twtPfUXFcmxEqAV1Wic7PnpUJ+ADz45VGkkvFouuQ0ZcVSJLI2tXxbPj7MgEjVt2nOVhWmdnZz3jKQOtOrix05+Tyk3hKu3u7m5nFL39dEzGhWVR+ZSOGzr+rjr6ceXPtDetm9Jj+qvOX5UWjbKeu4jo7pMP6CoqKtaDbMF8Po/HH388jo+P401velM8+uijmu3p7H0mBmQihgsEzEvn+f8udDAfPyZkwgttVkmg0XHZV58NK5FE/ZbNyJbOz5RXt2sR0YkEvGY+n/cGC03TxHw+j/39/S4f2vAszww6l3lmbUq4oLNMYeb5/O73tfQ/0+Q98t/9+cvuQ0XFNtiKUIugnJ2dxWw26zowjYJG2BphSk2cz+cdsXGQDIqoEuwcJSLH726kltWnpHQ4Gc+I3NnZ2ZXjIob6rnKoXiS0PM8Nf2ZgMlXfiT1JqTsIJ5ssI+uttL09sqnaZQaxhMzQse3WuT5z0u789NxNJpOeEs+BQkVFxWbY3d2NyWQSr3vd6+LRRx+N17zmNfHqV786Hn300ZhMJl3IB9VUnynzY67uuj0p9fWMXGVpZMTPbZ0Ubfm5LB1dJz9IZdbV74ykSfHNyqTzpbjqHNZL57nfpe/l+bu7uzGbzZa2CYml23wKOCUSm5XRyS/bJvNvXi4eV9n39vZSVV3XKH2KQER2r3QfdW1FxTbYilBHXCVn7CT6XZ8+Lc+pd0EPt+dRylfXl85hx8g6uh/PiCUhhZdGwA0CFd9SPTh6ztIopZvB6+Wjcv/063yAwrL7vSypFzS0HNwsK/cq+KBgG8LrzopG9rrlq6iouJyhOz09jePj4zg+Po7T09M4PT2N2WyWqrfZINiP+zXZd08vO8dFAmEde6r/aVMzWy1fxjAI2hknffpO5X6Z4rtMNXa/SwXZfWTmq5flU/JtXpZlApD/Th+fDTIyW196brLnxUGf5O2XDSgqma64LjYm1Jo65+jYO7VGyAzx0Ig5i6NapkRnv5c60rLOkJHd7HwSax8h0/A5Ic6IstJjp85UCZJahp5kjiIzeEzHnVhp0KC8SobNy8VjJLqZkkFn6QMrEv914G1EeFv7ddmzyrJUVFRsh7ZtYzqdxsnJSTz22GPxpje9Ke7duxeTySSm02lERM8OlMIz+J3+YFUcc3Ydj3t4iGzOfD7vxV9n9ZLivCw8wsmXfiP5FjiDyTouG9yrHFlIhyvUXibaWqqxSiNTikskmcdKqnNEfz3NOvda5aUIw/tGX7K/v9+l27ZtxyV0/u7u7pXNEPRb6RnhsVWcoKJiXWxEqPUwu9pMBZr/S6XwRYo0UK5qKh8ns46MhEfkI18/x/PJFI9MNWBMNMkhrydR1fFSvmwHXq+8OGXlxJRpMY1lxnCZYfNyZost6WQ8ZMLJPNOg8/D/XUXK2iurly/2Ydv5wI6fbD86+4qKivXRtm1MJpO4d+9evP71r4/f//3fj0ceeaRbgK5FbwzlUH9dRm5IyrLQBH7quoysZtcx3K5E1j1cgmkwFMPLIuLn6WW+jETX68NyOOnNCHjm7+hjaSd5fjZIyNpO5V52nNf5uZkfZ/0p1ng5OTugMFDdOy5C56JJKf+6RvmxfjzmMxCVUFdcBxsziSyUojRaz6aa/qjAO8+qjrRuPbbpkMuU8lJ6LD/L5GX067MpQjfcTKd0D7PfSvXKBhRez1L9hdJgwJ1i6Tm8ee9e/JVv+7a4eXR0pczViFZUbAYpqFqboJ2e6AucyJSUZkcWO70u1gnzWNfWE8vsXmZD+b/H52bH/U95Ki2RSZYtI91Z2UuDh3XbZNksgX5nPHwmxJT+RPi5i0hGvplHlk6pnquenZIwV1GxDe5rDHW2o4cbk8xQ+dQMR71ZB9J5hEbb3kE2Vaw9TX7PRuDrduws3VJdWB7Va1k8WOk6V571v9R3D+XQ/eH3zEHN5/P0eOZIeT/ZNvyfU6Kr1GJX/3lPIvJ4xIiID3zxi+Mpr351/Pmf+qn4jx/90VdmEqohrahYH4vFIu7duxd3796Nxx57LB599NE4uhisqj9Kpea2nJkNJ7FypZOqNpXNTH0sQfbT15dw15+MhHH2iqR2WZu4LeEAgyEcrtZ7/lJffdBPn+v+htfrd20xyHZjW2Y2W3bf1ecs3t3vNctSIveuunPGkOfpXJVf1+3u7va2FCTH2Nvb6/wJy+LPFZE9mxUV22DrbfO8Q+iPU+xa5c1O4+lERNcRaDB9tMqHnlNDTCvrDJoqctJXurZEqpcRZ+bl52ZpZIQ8K09mTL0dXWUgGdYxJ9SldsoIZqY4eTk8Txosv97r5kavFFfI8+nk5Chp/InP++Ivjj1M4T7rpS+NZ730pTHb3Y0v+fzPT/OqqKhYjrOzs7h37148/vjj8cgjj8QjjzwSJycnHeHRnyuXq0i1h3r4tTzG65eBSi7XqNBe0f4J9EmlWThCpFvfPX8vUyYIuI11gYB5kCB7WzrZzQi1zi0p1WwX+uSMaO/v78fe3l5ah6ydXKkvta2INJ+dLKySSj8HTmon97sc+JUGNhUVm2LjkI/S1F1pgcN1iYqTU/8tOy87Xrpm3TKsyrtU3pLh32Q07E4nS2eZUcicgR+TMfKB0rJpzVL4SDYtmZH8ZWlnA4KsXZYp2k3TxLd84RfGK97zPWN2sbBlurcXD7/bu8X/8df/elreioqK1Wjb80WJ0+m0eyui7960KWEp2ciMKK5Ki3BCntmhiKthdv65TplXoWSHlwlOy+ADFZUp+8t+Yx02uc6V7mzg5MqvH6NKX5qJZh09nazeLGupbo5NfHFFxTJspFA3TdNNqfj0GQlRRsYilu/Y4PlkRDT7npFHHs+MUvabTzs5KS6N0jkKZieXKqDr9alRPA2Bv0mLqqvKwX0zWX6V26dBdQ5Vmew+sA2a5jL8gmoDz/EXyOh7to+s1CrOYJQGPSz3qnPUfplqze87OzsxefDBmB8cxO58HrO9vdg7O4vJYBB3Dw8j/gjF9FdUvDlhNpvFG97whnjTm94Ujz/+eNy9ezcmk0nP/umNdKW3qWb93QlXiYBlPsLtBW2q7IVsBlVRD32L6CvBJXLJvAS3SfSV/N3PlS1XnVkmryMXbmohpAsTajOFfGQhNcyXZfa215uKnfgKJaLsIonAuvH5YJx4KVST1+n5ki/yjQ/8WSCyNUb06xUV22BjQq3Oxekx78wlZVNp8JMEsTQK5vlZGiSPTgAj4kpZHd7hmQbzWDUqpkFhWEpGuvW/ykejuMzRuLqSOShHFj+o46z7Oo6DsdwkyXJaEdF7MYLS9t1cMsOVqRlejlVEnNft7e1F27Zx4+goful93zde8t7vHe/5i78YN+/e7U2lZm1WUVFRxnw+j0ceeaSLnT45OYnpdNqROJIxJ8ben7Pf/Br265J4EHF1Vwn6GZFkX8ynbdeygbyHVyhtQWWjLXVfsCw8TmVzn5OVJeJqGIYIdeZ3dS9kj/madBJq2m8SZ5Ly/397/x5k65fe9WHP3t27961v55zfdcZYVjmBmfEgRzMjdHEEEhKOLAYJSZEL2cakSthQ6A8IFpUQcCUgQhmMEyLiIKCISbBLVVYRG4w0AgYkywghzUgjxC0TSkFG1kia3+2c7t733b3zx5nv6s/77efd3Xufn+wY1rdq1769l7XW+77P813f51lr6TyZrWSZdAzNtKE8dS9X2zXLZljJ/BHPExGFXCt91K8H923jCW1tXlHxUOxEqLvX1/Gtf/7Px3/60Y/GFDdd2+jmLJzOB/i+G5cPRNazd7WW+3DbbWRayAib/9e2rRPBzGFwO//c9hBn/7e12UMNQRY1oMEnWfZtVBeqLlSrVY5Mie50Og2ntU2ByJwIy57VIzuGXh/7d/6dmM/nsVgs4vt/w2+I2WwWsVjcaZOKioqHYbPZlDmnfXaPbXAbmQkTfOd+et9GOH17fs9UYn1nJG8XQkXbnq2Ym53ffVZW7m3twEipTzXnRDnrlHhHQ+9t23lbuxjiQpOLRpmgRrjows7BtvsqI8Vtbdh2v1S7X/FuYidCfXJ1FZ/3xhvx1X/rb8V/9q/8Kw1FWsuQa+5pLebihDYzLBmcMInEMRzmPWyOitYxMmRqgP8vOBlmr52gcWOIjK821ZW/0fA4+c5WImwjpg4Pg2WE2js6bGMSapWHDoThN0//8Pbh7yqDd0jcuLuzYxqJRxfobLStUpXW63Xj+laDWlGxO1arVVlyXCsjcpluRunaCI7PPpGlJpDocvtsoFlGvlzg8bQC+gHOYdxG/NqEjzahyG2d0EYUVT/aLOLw8DCOjo7uKNRcsEbodrsxGAyi3+8Xn5WlfOjlBJ1EN7PdvJ4alMjrp3rqWJnIlg2y32w2JTf/+vo65vN5w39l14919jZ3P8LBnn6MKq5UvAh2ItTj6TS6EfGlP/mT8aU/+ZOxPDiIb/2Wbyk3qG56TZtHwu3qQBuZ9hvayXNbuIYGk7/5udoM4kP+pyNwI5mRQG4f0b6ClPZhaoafO1N12tqizVi7wqt35gp6qgrV4Tb1wjsdzOHjNjKIJMgsZ5uacl/uH+vF9mHuphNz7lcNaEXFbri+vo533nknLi4uYrVaFZsfkUfUSMI8baHNrmRCB22CE2qmXgiZ6NCWkugigZddx8uQkbhtn0laVR5vhywCe3BwEEdHR4UgcxVBRQpoQ4+Ojsr22WqGPDbPz5xkF0icUCv9wmcTEUnXObIoMj+z3jqPhDqWNfNjbW2tMqt8fg/wulY/UPGi2G2lxM+9Lw8P4yc///PjP/9Vvyo1TNt6kHwQdMOz96netpNYV6b1H4/bRtxdNXCinRnviEjzydq2z3rtNPhtjsKJuI+U57F8f7ZJW73b2p7vhNfLtxk9exZf/93fHf/Vv/VvxfT09M65neyTbOt/qk7bIMPKMtHottXB69N2fbM2qqiouB9SESmkROR2i9/93YmoH4O2kfNaO8lThJId7m3HdPHFy3yf/faBlq64ttVpW7naysLtXGRwEpp1WDyHmp/ddlMUUft4x8fLlRHqrF5OYPWeEeqIKPNNK/J8fX0di8WiVUhpu4YPuQ5ZGSsqdsVugxIjns+UsF7HrNeLd/r9uJnNGgu53DcPtSvIJNX+oPZ6vQYJY9qHp1xkBlLHypY3zwxkZkwzRcR76e4Y3LDRwLghzoi+5+JxYI/vo3blAEF9z4xHFm5j2dxJcbtutxtf+vGPx3t/5mfii//qX42/9ht/Y9mXnSCWs61NMsXd66h2oyOg0c1Itav7WUdH1+QhufUVFRV3cX19HRcXFzGdThszZtAGZGSUz7RevmAVt9X2/X4/xuNxIYieUndzcxOLxaJBvnRMDTrk7BNKTSSZi4iGipvNXCTf40Tt5uYmlstl8Vey4duijbKxnmbiUTt+Vnsp3aPX6zX8w2azaWwzHA5Ligi3HQwGxYe2RS59MCXh1/Xo6Kikn9A/Zqo9bX4bKdbv6/W63GeLxSLefvvtRtoOlyPnDFRsY96PulasH31mNiiyouKh2IlQT0aj+NP/5r8ZX/iJT8Tp06d3FGmpFdlSq3w4GV6LuEt8+LC5QiljTHhvt42oysB5r99n5cimx8sId0aKSSS9R++Gxo0Uy7+NsLsD0mc34I6MTKss3vaO/80f+APRg7H58I/+aHz4R380VoeH8R/+gT/Q2DYrO9uCKTNsV80gQmfjHZFt7damuLc5A32uqKjYDTc3NzGbzWKxWDQIdduz70SbYkOm5joh6vV60e/3G0TchQVPL+QgaD8HyVNG9LLOABVYDrbLxAu3xfzMmZLcdxCZXWZHRSRZtlI2XURbKR/9fr9sq7YbjUZxdHR0h0w72dTv2xRldXg8ohtx12/6frTdrL+20TVSR+ry8rKxr7d5dj3bfAbbXf7noYNrKyoy7ESon52dxS+8+mr8F1/91XFxcRHxOQW6LdVD796jpOLJm9kfCKVAKLVBRsyRPUiuiGYkmuf3eTYjIp1mSL+3GWonkFSoMwfz9//+vxz/8G98QfyZq98ev/X4T8X7vuJT8f73f6pRVrVF1mb63Xv6ajN9945GVq9tg0/+5Ld/e3zVxz4Wv/wf/IPorVax6vXi0x/4QPyNr/3ae40VSTwdD8tFRZyG1rfPrjeNspPrTAFh/VimioqKh4O5v05eMnKklw9W1DaMPsr2Kv93OBzG8fFxY0o+Qb7GRRxNm6l3kkQpqlKufaAfyxSRk235s06ncyfy2kbKnGhvg/uzrC3Zpqqf7DjVfKrVh4eHcXx8HP1+/46/ztIm+dkHcqpMIut+D9Cfsi3db3ob67Ouaa/Xi6Ojo7Iap6LfGrio8vPa+PlZjqzD4PdIRcWu2C2H+nMGhHlz/mLKB8Nvq9UqIu7ONhERdx5CHxjH30m8fF+qHU56uU1GeknAGfLTObKwlKdzsOfOEeky0PpPx/l7f+8L4mMf+/r446vfFf/z+Jvxv776zvid3//Ho9PpxAc/+FO3F8kGe/AYNHRsG3VAfMCh4MRZ73Jg7vBWw2GsRqM4WK9jfXj4fKGU4TAWjx7FYTRHc+sau9H0kK7K0aZc63c6Ub9WPHabsuIdDe8sVFRU7AaRGaV6eQqG2xfZRI8Gyk7o936/H8PhsMxmMRwO4+DgIM7Pz+PVV1+NXq/XmKaP/kgE0m2Bk1CdV6Ss1+vFYDAoZF42kHXVeZSGoHMqjzyi2cHX/04iSfi2ISPMEZHaQtrPiKZCPRwOYzAYRK/Xi7OzsxiPx3F0dBRnZ2cxGAxKfVRmfXb7rzJzsKLq2ul0SjoJ28HbPOsIZP6d5725uYlHjx7Fer2OyWQSR0dH8fTp05hMJvHZz342ptNp4RZqc7fzzh3cB5C76J6oqNgHOxHqiLs91WzEtL9oAH1qu1IQ9G5106snqfes5++9YSef/nBl6jKNf1uudNZb53YcHJg5EzdOnU4nvvu//M0xiNv5kH9H/Mn4Hes/GfP/qh9/8IO/u9TPDadebGOq966meHqH15Ntwwn86SA2m00cX13FT33Zl8VPfcmXxBf8yI/E8eVlSmoFV6Sz3j/bS9/12fMe26ITbYq0XzdXKioqKvYDI49OjCLupjK4YEEbFnErQpDUKl1BKQrHx8eFUCsHWgplplCrbJkvEIHabDZxdHQUg8GgiB8i1LQpmsZNudo8Bkmcp55sy0NugxPQNgLNtqXtd4VadRoOh4VQn5ycNHKoVWbVkW3FMnEGL0YRPZor+G9tZLoNUtYjnncULi8vS1Tg6dOnJW/dy63Idtt96ed0LlNRsQ/2ItROoqVGU52mUfF9mIrghoFqh6uRGUHmux/LlRESairRTq6daKveXn8S006nORBF/zMUxTbsdrvx+fH/jT8Wvyd+Y/yXMY5pTGIU/0V8Q3x7/IfxW7v/caPdWTfmULNN2wxHlhP+kBWxaHQ2m0389W/7tnK+H/kX/8WIiBh3bkOf3mly8u8DKCPuTt/EtmtrU9XLQ5Oe4sJr5fdFNlC1oqLi4XA7vq3Dmm0jmyOCp7xepSNoIOLh4WGcn5/H6elp9Hq9WC6XMZ/Pi60RmdJgOxIiF1lkC+mfRNxJviOioWpqEZubm5uYz+dlzYVutxuLxSK63W5jQJtsIG0W7aK3UyYC+Lu3J7enD/SBi2rL0WgU4/G4pGfwWCTgmUItHB0dlXrStm7LhX+3MBgM4vT0NCKe+6/JZBK9Xi/m83lERFGXORNIlkqakekswllRsSt2TvkgSWGYROE/Kge6wX3GD5GkTDnggBOGrnw0c9vCKfwsA9Hv9xsGRtuJPBI6dkRu8BT64yIGEfkE9aqrlkLV7zru7GwUF89OYxDzmMUgBjGPiziN+dmgYdBYL9WJJNUjAlT2VQ+2IdWgrO29g5LdBzquKzWu2nAbtcFqtSrKAtvZDZrKz4Ep+t1Vf4HnZNoRj6fr0el0YrFYVEWiomJHUBWMyKeJ4yvLmxaZHo1G8eqrr8bjx4+j1+vFyclJIbknJyfR6/Xi+Pg4zs/P4/DwMObzecxms5JKyNk1BKbcSeWWbXebTVVaviriOYETQZ9OpzGfz+P6+jomk0ksFouYz+fx1ltvxWQyKWRbdmnxuZVYvUPPSKZD5eW4oW1tyjanP5PflLJ/cnIS/X6/dEq2YZvd53n+h8Krr74aZ2dnZXDi1dVVXF5eRkSUTtZsNmt0mDqd57OQZCCnYcpLRcU+2Dvlg2SOOdUZyWtbnUrICDHzhUmQ3Zi4UfFjUaFmz/3g4CDOZ7P4zd/7vfHnP/rRuByPnzcIpnHK6k4i6wps1k6+H9XUr/zKvxav/cWfj+/a/Pb40/Hvxr8bfzre0/m5+Mqv/Pi9ZJFpJvqP7ySf+k7CnBFqDc5xw91WL9Zd5VFHo41QU7FvSwEhwVZbSa32FJC2dmqLKOh/b7+KiordoedqmxKZqde0zb1er5GOoPfBYFDSPEajUcmnps9hObLxGCLULszQFjNverlcFjI8HA5jNBoV+8YxKTr+5eVlUUS1sqH8lrb1HOvMh3h7ZmNQ2tqfbeypIbL56jT804DBYBDX19cxHo+LeMcBphF3xbBtJNkFqYqKfbEzoY5o5lxlAzWoRjoBY9iN5FZT++jhFwFUbpuUZvWQpSKTAJMoHh0d3VGoqVB0u934mh/6ofj8z3wmvubHfiz+0td8zZ3jefkjbgfRUCGPiEZqQtZWTqgPDg7i/e//VPy5m2+JH/zBfzUuLs7j9539ofiKr/hr8YH3fypubvKphdiW/t7mtKjUSMVoG5Gd5Zi3qRZcHGCFGV980IraTfeH9vN8Q27P81EF4wAkLz/bJiszQ9TsaGULPFRUVLRDNv8hih470VILj46O4qWXXorT09M4OTmJ119/PZ48eVJsvt5FqAeDQQyHw4YIcHNzE/1+v6ykJ2VW54yIlFDLZjFyJX8im6BUENkRlUmkbTAYxHQ6LfNwHxwcFGLN6foYqaW9kt2jz4popih4ipzKTYGAwgDJZDbTxz9N4oHSP+RLFLXYbDYlgsBoA+E+k+1N31VRsSv2UqgVGtGAEIX+9NlVad6gJEIidxowISItcq0R3zSWMn4ydiLXVLCleogca0oknfd/+wf/YGNO5S/51KfiSz71qVgdHsYf+4N/cOtAOA91Cuv1uqHSkiTyPxLHTqcTv+JX/Hi8//2fKsby+X+3xJ3n1f7M7fO5vD2fjh2JwWDQ6Lhkuef6zOhARq7Z+WEHiyBZlvPValdyTkwH4mIMntfGd9ZT7UFnxfZl+zElhg703czzq6j4ZwGbzaakOUQ007aybfVskxy/973vjddffz3G43F83ud9Xjx69KihrjKHmmkZ8guytSKwmtFCNlpEl4IK0/xITPUbCTAxGo2KDR+NRrFcLksaSETEbDaL5XJ5R11WzjVTBFUHCQOqH1Vtj+7RFrK9I5oDzfWfjql2Y6rLPw04ODiIJ0+exMnJSSMtp9PplHQQruQZ0bw3affZzlVcqXgR7KVQR9w/9y/hpI2pGz44TiSYE/gzdOUkWvOJRtw+JFSodSwSxO/6Pb8nfu33fV9jTuX/9/vfH3/ja7+2EHOvB+uWpYT8/b//L8ff+2v/UvyZq98e3zr+rviffvkn41f8ih8v/3uqC43utra7D66KZ71v1Z3tk01R5MdlWg3JtV66Dm3GXvnU/E1Og23MtuEqkW3OmUrCPmG6rBPxT5N6U1HxS422TnQbRB5ly0WKx+NxjMfjGA6HZTYH2R3mAssvuM2kLev3+4VcdbvdImTQXxAZwWQ6iOpHAYG2x8tG+8p2IunX8WQ3NcYm86VevizqRxXb1Wt//dMGiSKKeNzc3BQ+cHR0dGecU+YnCfdFFRW7YmdC7Q8ue3QyFOv1+s5oYd3c7I3TSOpB4MDBbLSyDKc+S3UlRPZkbEWu9d+q32+fUxlE0wkbjRVJ9U/+5AfiL//lry3zSf/uyZ+I3/nxPx7d7kG8730/3vqAZsow//OUBm0jA80y8Xgky1InmENOgqztHdxG14Dn8FBlVna/TzR4iGHP5XIZy+UyIm6nZFIdqW5nyozmwKVj4bXzRYQi7q70xfu0oqLiYZBCzehkxN1Ze9j5Pjo6isePH8d73vOeGI/H8frrr8crr7xSFGsKLTrGcrmM9Xpd1GeKJIT8gqDp9Zzg7goXB/QuWyU7SAFItlL5vdPptKjXKqtIuQi8yk6bz8HnUscZaaVfpV/q9/ulbIoCdrvdEhl8t2ydbLM+q25+bX4pIe6g1KD5fB4vv/xyzOfzuLi4KG0UEQ2uwvuUuezbRKaKivuw9ywfWb4R0y64PeeEHAwGjXQNbc/8aBJrpm0odDcYDGI8HsfBwUGMRqMYDAYR0SSanCpJKR/E8WQSf/fLviz+7pd+aXzwR34kTi4u7qzCRULNujoB/n9+z2/K55P+vn787z/4O9M8riydQsfW/zSeesmIZSouj6d2lSPzDgyNR5Yzzlx37ecdAG6TlZvHW6/XJT2EuctHR0eNuUQ5WwhzB0mw9ZIx1/3F8+t3tg0dHTt4bPeKior7sdlsGnmqbQN8RfREeF955ZX4/M///BiNRvHe9743Xn755Tg8PCwpfxHNDrk621SDH4p9iB3rwOgf7RGniJUN4eA/Rt8Wi0WZBUR2iyTv4OB2hUMvh2YncULtKrpEBR1vOByWKKAIdafTifl83lDb3XYTTNfzCKrOlU02oIgAOxnvJlhndiCOj49jMBiU6zOfz4u4t1qtyqwsEdG4RqoH0z8qKvbFu3K336fAymgw/cIJm7ZzVTYjntyOBIpq5H0pFN/3rd9ajMAPfNM33Rvm2fb/tvmkf0v88bK/G+v7Ht6HpCbw/yzP2ckzoWuyq0LLtqWTYEgz255qC/OgeX8wXMvoBh1Rm+Nmmfw3tQGV7qxDU1FRcT9caNAzxsFzgkQNRRmV3qHBglk+KyNcOt8vNTglKoUjJ5NSzSUAeGc/G7DvedlMJyFJZLpHNhjRI39M+WBaiRNiLxM7C2xv356fdR7fhvVhuiUnFXi3bCzLz2PKd2h8lVR/ddK2DU7Uta2oeFHsRaid3MlocBlY5oCpJ6mHTeox86c9zcOnuOOxZMg7nU6ZXD8iioFTmSKiMTKboGEgaeeDRQND48X/b25uWueTnp0OGkZL71kah5DlLXt+Xpai4eqOK9Uku65ssFOS5ZtRnefyrm2GktcqywNkXjXVHKovMooi2nRybvxo2KV0U02KiPQ8nU6nhEOrMlFR8XBIfXWVUtGkiNso3PHxcbz66qtxfHwcr732Wrz88sslbU/HIiGT6MI5pjkQ3FNMMnuoVDIn6LRl9BsaOMi5iKVER9zavZub5yslavDi5eVlzOfzWCwW8ezZs1gsFnFxcRGTySRms1lMp9OYzWZlKr6IKGoxc7tlfziL0WbzPE/75uamMWiRQgMXk4mIMk+3CKTaQdfLxRO2ieyl6q79GIVg54ApegIJdbfbLak8ikJIUebvGihKMSRLTVHaoA/cZKRgNBrF+fl5qb/q/c4779yZISqiOb1sFnWvqNgFeyvUJEvMdfWeoEJXPg8oCaar1Z7y4akkfj714KUc0EhoIIlUcCfMEXfTG9p67K4wRDxPZWibT/pX/+q/UurIXnWmGLepyyTRTqidPDt59UhA1ilwFVvHJahWyJFw36xNeX6W25URzlgio+btfHBw0LiuXgeGYOkQ1cHivaZ3dbQUFqyoqHg4SND0TOo51nOm5/7k5CRee+21GI/H8corr5Tp8ZjzTEItUi3CxudbpE6pIC7sFJHjc7Nu6HedI5vmTykBtB+e2iHfos+aRWQ+n5fyKLVjMpkUIq13lvfg4PkUe8p1lo0mYZW/1D6cg18iANtBODw8bB2XojrymnGWrvV6XbbRfNwisepwKHWC9lXXmuKYyLLGOg2Hw7JIz3A4LFPecbIBTkIwHA7vrImgnHS2pc6tYzD9U/8rNenq6urObCv8rHapqNgX71rKh5Mqv1nZE8yIIMkVX21KLsHQFBUFleM+wsTj890JtU/Qr/N+8IM/Ff+Pzb8RP/ADvy4uLs7jf3f6h+LLv/xj8YH3/0RKAAmS4IzU+sPP8nJ75kZnqnhbG7b1xnlNqebLkej3bcehg/VBjdnntrIwLcTDlfrf2y2LNDD3TvtVZbqiYnfQNgoixBG3xFHT5I1Gozg+Pm6olx4ZVOdXpEeElnmwJFX0Mzq/XiKxFENIllXGiCjE1FMlOM82x3dwMCbzyLU8uV7ch3aLijBTO7w9HW7zs/+z66R3poQwoizVV7nHqqNItD7f3DyfAlCfXVxR+RhtVh64ttMCLCTQFNu4krHqpO8qI6MT2sY5hwQaCmpZJMMjGPddg4qKbdg75SPiNlTHfDIZEBpODSjUQ+QKqwxvp9MpK2TpYdDvTHtg+J4GSwYt4rZnLqWDqQ9tpD4iJ1meL0biqnO/730/Eb/8l3+yGK7n6kUzbKh8MsHLoXrqnYMItR8Hk2gbGTGp8FlZ28pNctqW1iISTWfi5+Hx2EHicrhSK1huRh40AEfnjYiGc5URd4PnSjkNrn6nitbv9+Po6ChVeCoqKh4GEsOIZiqGFt7QfNPve9/7YjQaxZMnT8rgckUupXyuVqvGrEzz+Tyurq6Kf5nNZkXd1SIeEc18bj3n8/m8qMqz2ayQP5FHRsyoXHNxGIKkVzaQaS+LxSKePn0aq9UqJpNJvPXWW6WcTuJJDnV+/u/CEqOM7Iio7hHNQZucbpD2T212fX1dZh5Zr9cxnU6LLVR5SajVWdhsNg1CvVgsSiqL22T5ei2IMxwO49GjR41VMdnh0mQD2v709DSOj48L0RYxVnv7ZAOqJyOcWrp+uVzGeDyO0WhU7gP5MPKRqlBXvCh2ItSZykmySfJIcsVJ+T2v18mjtpVR8XmqnfDyfDIQHFzCvCme21XNtnQHVxUiblcI1Hlp0F0J0cMrA6OOhSulmUqv3rqnZeizBmCo0yJHwIE1fi1kwFlud0wy+Lq2rg4xROgDeHTN1Naqg3Ki6TD9fuA95QqO6kWlhdeOjoPKOB2POlgql9q+KhIVFbtBYkbbbwcHB0WVfvnll+OX/bJfVggTU/o0y4/I3MHBQUnjWywWhezN5/OYTCbR6TzPP55Op3eUXSrCyiNeLpfx7NmzkpoxnU5jtVo1UgtpK0ejUSF7FDJUP6a9yeeIUL/zzjulzBcXF0W99ihlt3s7bR5TWVz4cH/lg/ZlT9XePu6IKXYs93q9LuXjLCTL5bJ0YEioqeAr5UNRAKbKUNDYbDaFSB8eHsZ4PI6nT5+W3PnRaHQnn1rzkR8dHRXiLp8p/6F2pwoun+a2nOfXIFj6NvIPii7VH1TsixdeelyGYluaAZVgpia0hd9d9WBIRkRuW4hfJIyGKSPz2cCHtlQBpgywF0sjwu1o6EkIOahPZfX0CpZpW3u62q424TGy+nhPvu2c2pahTw7O6b/9dvwv/pP/JP7yb/7NMTk5udNmDE2ybehgPEysF3+XwdP2Dg/z+fX3evFcNMoVFRUPh55JPufMUdVsHiLRnNFD9pzpEtPpNCJuZ3HSaoSKMIo8ilBTxNCxZFs4cHAymRRyKGItm+eiAnOKRXxZVwoJtGUsh7a/L32A/7kNV1sxMptFMwV1ENhRYMRSZFTqunKkSbIVicyIJX0Nx5+o3kzJU928M8Ic9cVicaf8Ov56vS5CkTo8LtxIJKN/pdpPPsKoqDpB7jsrKt4N7EWomcfmOWLsMTNcRcWZBDfiVqHVvlJ09cDwQdF+NNw06E6+aGAUVnLVl+SXD68baz24GnlNg8OwIT/LWcgQcQAKH3QaTamsNLJZp4D7qZ5OoH0Uvsqa5XZ7GJQhTh+AdHNzE1/8vd8b7/nH/zi+6GMfi49/4zfeUfy908QZNZbL5R0lmeE372iozWi46eyY9yj1gnO/6t7xe/HdntapouKfBehZc7sluzwajeK1116Ls7OzskQ0I48RUcL30+k0fvEXfzGePXtWlGqRu2wQ4Xw+j9lstjXlhAMLpcBqm5ubmzJ3sU/ZKlvNeupd52kb2Chbxv1IuOnzSLhp15m6oTUb5CPotzxiqTz1w8PDODs7i9PT0/Kf/PSzZ89K215dXd3pZFCJ5nghplz0er1S19Fo1PBzrrp7OZWPLULtUVutKXFwcBDPnj0rHbGzs7MYj8elPBHPV0dcLBaNaIeOR4FEfEDL2CuioPrpfnRRqaJiH+ytUKsn771ZJ8lZKgNveFeunSjRCGl7Eq22RUdUBhojLVHKHOSIZooEDaf+I9Hmg0pkRFyGiYaGxJyT9gvsedNgZikf/E5HxVQJV5ud8POakuBmuena/3f8e/9eHCLc++Ef+7H48I/9WKwPD+P/8h/8B3euA+um84hcU03WfnxXp4j3FjtXdEpSRdgp4PUkeXd1uhrTioqHQ/aMzxJtd6/Xi/Pz8zg/P4/xeFzIEcm3iNVsNotnz57FO++8E/P5PN5+++2yCAeJqT4zj5dRUtptdrBFvvmcbza3U3PSZ0ndFgGkDeE7BaQsbcztLevCbSKa6jR9lM/TTVuvc6tNlZes9IbBYFDsuHy10mZEqNWhoTJNf5VFj5W/HBFluW9dS06551FcKc0Rt2maEc99oNRqiU/dbjdms1ljCXGKbJ3O87FWus84O8jR0VEMh8PGNZVP4D4U/rRdRcWLYueVErPwVkSee0z1wo/DfagC8D07v5+Tvzn5jrgl3OzhOzHN6khC6waRjsPr5N9pWGkY9J93JvxY3lFwIk04GWWbeGhM9WL7sV4i3d4piIj4v//+3x9f/hf/YvxP/t7fi95qFcvDw/j/fPCD8UO/4TfcIcasv19Hdzic4s6vrxNvldHbipEOlsXPyeO23W8VFRX3w5/viNupSPUsikQxWnV5eRlXV1cxnU7j2bNnJddZ+b1um/RSvm/2/Lu9FNny8nlerfajQKP93IZlA9dIGrOIYrYPt/FoIyOXbR1+btvv98uAPk65J1WYg/GU0pL5TdratjLrmjJ/Waswym/6wE7PWacvov/jNLgqq8g2x9/Ij2pdC/2u8tNPiOyzg0TfoDJ5RLWiYlfsTKg50EJGhuF4zrAgY8ZRtCJt7G3qPxIfHY/n1n7Mj+WLc1zrfz5sMlIM/bvhpGra6XRK753Txd1HxDLCqLbTcZfLZSP9RXWlquzkWdsxv8wVbO1HY80cbs8Dp5LL8nNqKg427Xa7MT07i+VgEAfrdawOD+Pw+jrWw2Fcv/xy9D7Xnjo/Oyc8PtM2+PI5rlmfiNsQHY2yyq57RqrGzc1NI9TreeC6jtWIVlTsDirEst0Rt7ZfhDUiYjqdNnJu5/N5/OzP/my8+eabMZvN4jOf+UxZGOXp06dFoY64K1boWY5oTheqgdmyATofU8X0fTAYxCuvvBLj8bjxu9scKp2qI9Vq+QVF8XQuqcfazkUN+UUp0irvaDRqkERG0NxGaXvNivHo0aOyn9ISpUYrl/zq6qpRbrWhyCjLTPWf6rLaQpGH6+vrePr0acxms0b5qKZ3u93SeZFvcfst8s82Pjg4iDfeeKMQ8rOzs5IKc3x8XFRsDSQdDAZF5WaUkykuajveP6pb9QMVL4KdUz6yhy172DtGrLg/ja8fm0bH/+P+2kYkXoSUPX4ZxPtmy8jqpnOwnkL2W8TdKfcyYs0VnJTO4AMV2YbZKyPR2j67HjKW3oauvruanoXwdO1Gl5fxE1/0RfGJL/zC+KJPfSrOr65SNcUVJo9KZCFbr6Mrzt6hyvbR9iLf7JCxvSuZrqjYDyQ+Dtl/KZWMdt3cPJ8h4uLiIt56662YzWbxzjvvFGVa08+1HZtwFZd+gIIJbbr20ywkGpDnvkfpBBQoWG8psyKnFDhc9fQUPCqzFJ98UKGIn7cD/RyXdKffo8/h3NiMMkc0I3nZtaV4prZUesV4PC6pOyKkFD+4aIvynCVyMDWSijSjooQ6Zxq0uNlsypS88vOqD+8DdcB4TOcBLvpUVOyDnRVqPgAMGbnR8R6+QmHssXN+aO2z7XwkdSRLepcBU8+W27lh5PF4Pk9noWGmgu4vEjNXMUhSM+KctSdB4ys1gwq1/neSSiPMzoFe2Qh1llV1cTJ/cHAQ3/ut3xrT6TQWi0V87KMfjdPT0+hbCgrLoPPIuHKOVIdHH+g0Sap5f7hxptOjEq57kdMt8r2iomI30AaSJOr502C3TqdTlFyleVxeXsZsNourq6syeFCzT0RsX6wk4pZQO3k7OjqK0WgU19fX0ev1SpTt6uqq2FERag2ElEJKdVyEVrbHSSjzcZXDHBExm80a/oD+Se1EAs1oLqeE0/EEj75yP0YFmaOu9qT/5PaZb6PQIgU+IhptrbxtLvpycHDw3Bd8bq5xjxoLjBxSSc5sOSPil5eXZSXJm5ubsp7A4eFhEYBcGVedGAXudu/mTHsZKyp2xV4Ktd5JAKkCRERj1aOIaBAaPcAKL0k1ZO+eAwYjbvOgtD1DYTIMDLkxtYOT9at8Mp4Zqc7yy6gkqzeuh70tBcWJHNMfVHcZBg83usLMgSqj0ehOO2VkmY6HhJ5TJGUk1MloRDSMuBZj0HlVZpbFVW4q0VzkZTAYpNMgMiWDHQi1g9qVTts7RPxPA16YMsM2ySIOFRUV28FwvXd+fTBcRMSzZ8/i6uoqZrNZ/MIv/EL84i/+Yszn83jjjTfKHMiadYJpeQTt5WAwKIPQSFBPTk7i9PS0lHGz2ZSp9qbTaYxGo3j06FE8efKkzG8tov/06dPYbDbx6NGjcmwnZ7JN8isikMvlMnq9XjkW/ZiIYafTKTnPsoVaUVADCmX3fPA7/YvEFU5JeHBwUDotSvO4vLxsiAiM4OqYalfaa9WTUxqORqMyV/Tp6Wk8fvw4JpNJ/OzP/mxcXFzEcDiM1157LR4/ftxYtIW+UjnQ6jQ5RM5VFnWyNF2irvt8Po/BYFBmAOn3+2UWEdWRPkmDKD0SQR9XleqKF8HehNpJp6ceuBrsSiCNk8BZNLLziSwdHBwUg8tyUP1mWTwfNyIavV4/X1sIU6qLK6NME4lo5mV52dhxYC6wq9PeDlSkZThlNLw+fHm9MvWWKRfZQBRPJVG5qcCwE8H6kuB7mJFOkCkYbCuGbVVG3lM8R1v0gMfTfUaynd0HFRUVDwOfHVdK9eyJHGlWj+l0GrPZLGazWZm1Q0qnp5h5KgIjaH5O2ScRVUIpdpyWTiTMB8bRXokEqjyCRy9FhOfzeSG5jMyKWDLSx5fb9mxQfxYBJuFXG0mAkpjgogF9FEEbzjbnVHkqGxfmEfFVJ0Hvw+GwRAjUrkxLibjlAhKtBEY5dD0YWVQk4fDwsEzD2u12G1OykiCzzei7vF0rKvbFXoRaxoGDL3TDc3SvyE82byd7/B5qyR7qiNspbjQogaGdiFsV++bmpgxYoAF1eO4bjbGOp3pxMnjVzVM1+E4lnE6FpNpzm1V3hk0ZHpQBYxiNSrivzqjjqUycm9WdVkQzB1wdloyc0zB7qotvnyk7Cskyf61NNaDKrftOZWBait9bNJ7uuFQ/r0tFRcXDkQkpHDcju6SUipub54PDLi4uYjqdxnQ6LUSaHVsq3bTb6sSLLB8cHMTx8XH5TKJ8fn4eL730Utnv5ub5HMqz2axMraaVEyeTSTx9+jQWi0W8/fbb8c477xQSLft7fHxcCLOPz5HiqjTG9XpdButRhSVZ1pR48lGeAtIWWeVnjwBrpg11VEhgZXclOHl6iK4Vl1JXGszFxUU8ffo0IqIo01L6NRix0+kU9fr4+DhOPrfQl9RmpqhEREkXlCIfcSsa6bpoEL/aiBFIdVxUH62cqXuEKZAuwOgeo5/2/ysq9sHOOdQ+MlgTvTOPWTekSAt7luXEn3uwNFDBFU591n5atlbG7fj4uJxDRFEDL0ajUTG8IqCag5JkT7ll2kZhNj3gWkGKKzsxr8sVUCeFbDM6l4jmPN2uHDAnTi+F9xQqZNpDxO1iOzoHOwA6BxdLUN3oIOggqci7uiEHpXp4/h3vkyxPmg5RZWd7s1PR6XQaipHuM+2XdUj0YuoP7wHej5VQV1TsB0Yh+czp2VXnfTabFTInwjqfz+PZs2clb1qD+/Ss6rjyD1JZI57nKp+fnzdUZpLU0WgUr776arznPe8pfuH6+jqurq7i+vr5QjJa5ENk+rOf/WxJPfnFX/zFQirn83n0+/1CsEXuZL80VR3J6M3NTbz11ltF2WXbaOYOEUnPoZaN17EzQi0cHR2VY69Wq5KWIfVffkdtSEGBPlrbqm2YKrJareKdd96JN998Mw4ODsqCPeoc6bpFRJycnMTZ2Vmcn5/H48ePG8uxDwaDODk5KedXp0oq9uHhYRGClstlvP322zGZTIq/Hw6HpY3lQ9UZ22w2RSkfDAZ31G6PkjOaTLRFaCsqHoq9FWo3olStM1LppIoE1JVf3vgZ2ZTh0bEZBsoIFkkfy5SRYPWSWRb1iD385sjIGevCtst6wZ5aQeWYaitJctb7pgrOFIrsve282mZbXllbW7QpxkyJ0Yvtz+tHIs6ysExtSgLPp22zEKO2rYpERcXuyNLSvGMe0cwf9oFynnKV2bqIKIRcn33OZXagSUy1j1ZQ7ff7ZRCbFGQSOc5xrd+0P9Vc938qF9PYGPVUJJRqNFM/svQNEmBvU7UVFVYKN1xAzNuH/oL7q35cNZHtonqQjFPUYTRYBJmRWfp5tb0UZbWb7LFPKav/mNbIiCzLTSHP/cU2v6F2qKjYF3utlKiHXVPXKDRGssjBFK4+66FXeEfHJNGhMqte6qNHj4oCMRqNysOi0JXCSlIs9NBxOVV9Zg+eBkz7dzqdUj8Zfp1D81nqYY5opnZ4KooIXcTtCGPlmWmQ4Wg0Kr11DVCRUqFBKxx8onOQVDM3mN9VBjeANGQ06qoPrykVZ4Ycda2ZsuMdGKk72m40GpWlcQWFBrWgg8qcheRcRaBTa9ueaUeuwmfOqqKiYju8Yy6VmCldEVGUT4XypSySUMvOaX5hkWE9t7KTh4eHcXx8HOfn59HtduPi4iIuLi6KvSaZVRn1/He73Tg+Pi52/eLiorxrMJtsNclfRDQGGWqeY86LzKgafYlstsqnMmZ53zyf2pO23m2UvjM6u9lsChEWtC/bhNFMrZ6o9JfVahWz2ayx5gSV8Kurq8baDvIBGqyo9JPpdBqXl5clHWM0GpXzS+1XVKDX68Xjx4/j5ZdfLmLQ8fFxo6y69hr8eXV1FcvlsixDzqgrUz68/i4m0X/W8TQVL4K9CHVEc5UkznSRkbmIuwMMut1uWf1Ix9F2Ec9v9MFgUIzo+fl5vPLKKyV0TzIfEaVXKyWCJFCGQUbRV03SedVJUK6cXjJWeliVu8U6cpAblzdX2QQRPYX95ChIrpXSwZk9MkJNok7y6HXzHj2NCcOYVAKkzLhxobMR4ZVhzQy+OxoZSoVs2X4K8VF1YLupXlSHXP1WPUioVWeVWb8xB9KdWUVFxXZQ8ZNNEKkSsVZ+sWxw2/R4ijgOBoN4+eWXYzgcFnt9c3MTT548iddff734hPF4HNfX1/HTP/3T8fM///PR6XTi5OSkkFiWT2pop9MpM388e/Ysfv7nf74QR84qQdsq3yQCF3GbZjgYDGK5XBaSrvNTKacwwnbxtRFoQ2mf/XiqV0Q0CCCFI0Z2mYdOAi5VfrFYlGXINX2hUnB0zZSGJ78wmUxisVgUvyE7Kr+sjtPV1VU8ffo0JpNJbDabOD09bQhuBwcHMZ/PyzLjjx8/jtdeey263W6cnZ3Fe97znpjP5/GZz3wm3n777ej1evHkyZMYjUYxnU7jjTfeKMLYbDYr1zoiyuxZEVHKx3x83rP6rnaphLpiX+xNqNmzphpNeC+Q+2bbZecQ2fG5Ohn+Vz6xyJiHwkiiqOLqHB5qe8hLpE1hMKZnUPnM0kCyUB970a5asJPh6RnbUjK8/h7yys6z7Vwsu4itD6D0egqsL68j02s444en++gYqpOTab9+EdE4PtUU7t9W9oqKit3QprYySsROP+2+7B/Hish+Xl9fx3A4jJOTk5JXTAWSnXLCo1S0bSTaPhZD9k2f6d9EXjkOReNVJLgwLSFL4XCbQ5uf2TTaWdaLYDqK6pP52Sy1Q9fDhRiB5VXnwsutdo1opvjwevMcLJP7T+WPR9wuwCPSLYVf4hLHZknQEqlW/Tz6qm31XT60EumKF8VeOdQiRTQUenCkOHDeaP0uBVk9Xh1P0MPJ0drj8bgxiE3GkCF8hsVIuDMySYKlcw6Hw6KE6zw0XhHRGDxxfX3dGIypeUdlMDjPspQPnUtl1EhzX17W24LtpHp4J0MhtszRuMPQ9dOAFqreVEdUBl7viNs8QbUJc+O8E8NUDCnhDAtztLy211yh8/m8kcvo+X66T7S/34+e4qL92aGiQl8JdUXFbmAnWc+uBo+LWCpnWuTTQ/NM6ev1ejEej+Oll14qA8t17CdPnsR73vOe6PV68eabb5b5q7VcuSKeIrpUxTVjBPN2ZZdJXn1GKPkf+SJFRSlIaBBft9uNd955Jw4ODuLq6qqUa71el/QVXxqd6TEkivzMvGwfUBhxm9utmVRkr30cENNCOPiQgxIjouELJHJQyY2IuLy8jMPD58udy/+IFOuaaJYRRWNvbm7i8vKyKOER0RDARI6VTnJ8fByvvPJKXF9fl7mulW4yn8/j6OgoHj16FCcnJzGZTEr9dB7ZeqaXqJNG/6Q2ZhSzomJf7EWoGYrabDaNXGMORODcoyRrWeidx9ZN7oSavWTmBlOR9F5zRFO5cCWThJppFnzwIqLkhunhE4lW/SJuV39S2eVUNF8m1WguFUtC7aRadVObivxRUVZOuMpII5rNRqJ66hqK6Op/tSvLwM4Op0b0tBK2NxUZnUfqA6eGkgPTf2pnOUGOJKcaQpWb5Fp1oULCaIIcic6p9q+oqNgNVFD1/GrAoOyP8nM1S4N8hIhiv9+PR48exXg8bqR89Pv9QrQfPXoUr776ahwcHMR0Oo133nknLi8v49mzZ2XAHAfTcaltpRUsFotCzGQfPDInmxRxS6glAnB6WNk6TVHH2aZ0TpJUtZFyy+XTVH/ZY5FuCg4ezaPCHnHrG5SKKF8ZcTsWRmRV12E6nRb/RWW73+83Ukqk8DLdRIRaKYralsdiNFBjbZQ/L0LNyKyI+dXVVZlJ5D3veU90Op1473vfG+v1Oi4uLuLTn/50EY/Oz8/LvloUSJ02F3U4QFV17XafLxCT5VRXVOyDnQk1e/QcELctjK6Hy/NfeTx9Z494GzHmebelPDiohrqyGtHMC6Sy62FBbw/W21Ng/OH2V9YmWdu0XQe1sac3qL4PaRtPB3Glmu9tv9Eo6dz+csVdqTrcXgaZ9cmIfhYebUPbPVeNZ0XF/vBnMKL5bEfEnUgR4bnGWvlQ08plEcqIaJBaCi06l3K0fRYRwe266sJoHNPwnHj6MRhJ07tsW2ZraA/5uc2uZWKL22DacLfF+j+imRrBOvixGe0TKWaKBf2a+83MPygikKVbtvlh3SMcS6R0EG3PDgjrxMHoyuXn/clUUbZPRcW+2JlQZ7lIbiT5nUSURNwfeoYNOXBDkMrhxsUHFlJBd7Kk31kWHUtl5qAEqg4MG0r54CAWqts8N41Xp9NprCAlJYdqcZa+4gP+2PmQoqD5ORVKVZ2oNtDJ0fBpwA6vCZ0f25wOwp1CWyeH10tGeT6fF6XG759O53b+WbWbrhunaGJ78fieS+idA+/MVFRU7Ac9m3oGOXPE06dPo9vtlkFum82mhOUV2Yx4vpz148eP4/z8PE5OTuK9731vmelDA/0imrnYeu61kIj+V1qBZn5g7rJmVtKUbkoVoB9SWoA+67iKQsq+UB1WdFYKsWw+28jJo+y7Uu48YselxGnfaEtJeN0ncnyRp22oDoqo6rh8l19ihyHi1ifqeFplksehqER/opQUTVvIDot8mXy/Bh1qGfnRaBTHx8fx2muvxWAwKKkqKs/p6Wn0er0yqFKRiouLi+JnyCdUx4xEV2JdsS92ItQynvoc0ZwMnZ+lKsrIyjjQgPnADxFqEVMaAIWntH1GlvWu/TIFkmEzbkejoTLqwZQxoALhM2booWU+b0Q0lBkRZ4UPucKXD1ZhbnCW1yViyNHxSpcQOBBIxFntyW18Gjq2QdZWbEuWk2E+qgQEjTnTTFQf1V/19vtJ9WlzNh4KdWVb14okoKKiYn/Q5olszefzksuqtA/ZTj2LTBPhoiCvvvpqnJ6eRr/fLwr1ZDIp6R0SMUT6NIUqhY+Dg4NCrM/PzwuRlu2lfdLxlMon4sW1DpR7TGh/pra4Gk6y6z7KB9qTTHsaoO9LccNFLp2X7USBQ6RWgykzFd330/GVSiPQh+ud+9IWKw2GdZDvJ+ntdruxWCzi6dOn5R7QtXj8+HEMBoPGojGr1aqk5kgcUlmVbso2VTuyQ9PWjhUVu2BnQk1CKngo7L5jCB7qdwKsB58hG6rQOp4+c5U9GgkaNT3gfHCUSyfj7yEjkU45Aw9hZtPUZaoriRyNIRUMr1eWNuGkm4ZT7SpyS8Wb+9+HtvSP7HNbmggdVcTtyGo5P15HLxPVcf7v714WKvBZuXjMioqKFwPtVEQz7UDRMnWCPVrJ8Q/MKdaxRMA1jmI2mxWRQ/trEB/JLtVPHYepAAIFHqYACFl6B5FFvnwblsOJJElslv7BbdznbQOjc2xz9yFZPTKbzDKwviLB/J8EXOdUOY6OjsrkBJ4WqZx7Tv2nSPDFxUWZ0EAdM48+Kr+dgwzdn7tPVtm2+e2Kil2wM6FmSCwiymwMWX6ZwJu2jTTyIdZx1IOOuB19HHGrjPrxaIg2m01j3kmdz1MW9NBxxLeOx6VkfdYJkWoOcFNdOXBls7nNP9MoeKoQciiZUiE1RduzF8+BeLouo9GoMTiEDsJTVNqMB6MHJKdqP4FEWU7Toxe8LiTVOjZVaXUIPIde117l1bG4DZWmzJnw+rvjqqio2B1S/TjYWJE+pT/QXnj0T7ZMCuTZ2Vk8evSoLJKiKNazZ8/i5uamzGms+aA1S1G3ezuwTHZXqre20aBmpiJIoWSZBKV3eM427YrgdnjbWA8ONGRngjbQU/3YUaG9lQ3Xfy4a0b6q46DB8lRnaTtZ50w8I9mOiHLdNptNGeTIulI4ur5+PvWhyqFrw1k/FotFvPnmm2V7+c633367LFuuAZdawVFrWZydncV4PC73yuHhYYlmZGmpugezCG1N+ajYF3sp1HzoRNLaBu1l6qA/dP4/0wCUByeCrIeNYS8qGhF5nndEc5oefZdR9nzhzaa5QqJylDeb5/nK7EWrrHqxB83zyaByqjwaUg+DuXH1F+spAq6ZQNRjV89e7cJORna99DuNkKsT/F/1ZYoPOyVtijYjBbqOVK09x47OKjOOTtjbOnd+D25rh4qKihwecZM9Y7oclesMnDJuOByW9A4dZz6fl1k5JpNJvPPOO2UVP9kGiQZUs0XWRQg5H7EIGcm1q+oic1RSXWBwJdftSSZW8Lyesub/Zcd0QsvfdU1YVgkdTGuhX1Db0d/6GBW/5qyzVi1WSiTH3TBvXf5bOdmcwk557Eqbuby8LPfPcrmMw8PDmM/nMR6Po9/vx+PHj2M8HjdErU6nU8j6ZDIp44r0n9qE74yeelSi+oKKfbHXoMSIuGM8PAzjD3mmSru6rP89z7mNnPk5ZHBUJldYN5tNg/ixTlm9fOJ71tNVaMLDilx6lqpzRpa3TaHH9stUA29bGQ2FIhUKYxl5juyase14vakUU1HwfTNiS+egd05JmHWy+PIOlNfB7w22gd971YBWVOyH7LmTQrwtzJ7to3mjOe3afD4vg8ym02lcXFyU3F+JGlRYmdpB+0xCKf9Cu84OfmZ/VFfaHdkM9zHaVnacvztJphJMf0Bl18uQ7c80Dk9j4UwXVLHlbyJuUyWJrNPgggnbmT6R51SbKeKg9hDR9txzlVvXWL5SApdm+BAB9wHo7Oix0yXo3mQHwvlARcW+eOEcat3oSvuQouv7+MOsHi7nt/RzeJ6ZtqOh9hHG+k3KtsPztrVfZlBk6COioVYrjy8iGr1bHzUsEq05qUejUZnvejQaFbVE4UmmezDUp2Nl6Qpsa4YMOUBSZeQgm21TSfFasYdP45qp1CSvbQbaO0VOqvWdBt/z/nQclUP7eaqH369SZBi+bWuLioqK7cjsQ6fTXDEwsyN8biVaKI2Dz+N8Po+Li4uiSl9eXjbUzIjm2gO0CyR8st2K1skOMCWAxJgiDutFtdqPT/us2UQ4kDqimZIoAi21loMmlZbR1tb6zoijcsnVnlxMzK/DwcFB8UtMXYxo+rP78r+vr68bawVoJpfZbNYQMQ4ODkqqzvX1dZycnJQorX7nypM6ru6jy8vLMuh+tVqV5eP9Osnme9oQlWpdY/otRVPkMysq9sVe81DznaotVWp/+El2SbxopCLuDlDMeo5OIr2HSbXBy5ERVO91RzQVan2mA3AS5sfmORTe0rRAMmjModbDTIXiIe3vZaBSQedAsp3lAmbHaiPFJM8KLWofT51h27SpwbpfsrQhvw/kjLPyCyxDZiAzQ1xRUbEbaDP5TJIwu3CRPWtUqCXOaGrNp0+fls9alITguAtBhJkCiFRQCjycqUnHzQSciGiQdNk9lV32kEKIbC1FAh7fI7I+w0fE3dQ27q/PnkKi82hfEmsOIKQv8A6JSLCfl+0ge02lX52V5XJZOkc6v67vcrksi6yo3UWepUpvNptyL8i/6/vx8XGpq0Qo+mxdB6Uhan/afBJs3qfVD1S8KPZaKdHf+RDTMGVpHFQbuQ2VRP7uhEgPe6autj0kbeTTCbAfj4Sa4cE2EuYEnUoE86E1LRPDThpcQWIpY7DZNPPH6VTcwPr5mUZC58Z2bcuV9jbz77zuMloZyfb293LSyLOMDtWBKrqT/AxtRF7/tRHzioqK7XAy3SYECBlhZb60VrrTbyLXXIWXzyufbRJDqZsUatw+yN7QR/h27Hjr2J7eqO3ct9GPZefIBB3Wi/tloo+3p0fyBM+P1nemmtDH8bq4v1Vk2cUKikROzv0aMaoYcTvoXESZHTEeS8vIswwEIxQu2DlvEXRvtc1JXVGxC/Yi1DSgNCwaGBIRDdWADzkVWA0cYZqAGxYaXj6Y25ROfSa55z6uoLedxxUMGplMjc4GDVJ9VtiKxHOz2ZQ2U2//4OCgOA8NpNEDr3QW5kNnnRAZLq5wxdkyRNzdUbAtPXUn4i75Zv15rEyJcbVc9wNz3fi5zQn6tYyI1mvtdXEjz45SRUXF7uDzRHvAd4G2MeJ2arxnz54VIq2B3sqdVuieg8H1PIsMRTQXe3J75CljsjFtnXCVzXN7I+JOhJLiSduAQ5WB9s+FDxJIwsUQ1SHiVhjhIEKPRPrgPLULB4AeHh7eWVnSc6tJUvVZbcLl1UmKeb2obHNmMC3HznPyGuse2Ww28fTp05hOpzEajVLRhvXn+CWp6c5XJJipM+Azg1VU7IKd7h4+6N6T5f9uYP2hzAxL1qvnvlS/7ytj1nMVvKw6j6utqqMTat/X66WXT49EkkhSqPaTAWK7cg5uN1D8ToPbpvxSQVY96WDalGgqwVlb+3cq1Cxf1knKohV8d7gan91f28ro9fROWkVFxe7wZ4jPtyN7XpUuIMKs9Ayp0z44nOdjSoP7C9qezEZ4jrBDQoeOx7pmpDcjy9n/bf9ts2OuXrPcPHYmgtCWM7rqPmvb2BdvMy8P86V93+zeoJ/z6LTzAJZdnS35xOwaeycli2gLrt5XVLwIdu6OicyRaIqw+Q3t+XMReY60P9C+j3/eN+81I1TZNnzYtxEuPsQ+VzQJtRsrkmjvlev/g4ODQqiZi8fl0Jkflhn4iOZUTX49MkOXtVdGnKn8+P6sC9NNeH2zDsd9hNrLmqlPbft5xEHbtqWXVFRUtOMhzw478Vknn79TKPD0OhcMnOD5IHeprx4lzYQbJ9TZOA7tz+gc20D1kU3bZr9orymoZFHDNgJOwYezT7Gs8smZv9Pv2oepL1Ro3R8zL1l19jZwFZvnpc3X77pu3W63LPyiGZ+ya8DjKi+b7cPVjXVOEWaVNSPPuvfkcysq9sHOCrVICUk1CRNX69OsEgINqk9or4eKDxxBRZUGRIbBjfu2WSz0OXuwuI0bJKqi+s7UDqVmuHGlMddxVb6st8+HXgM9RLplRHheV5HptGhoFQakw8qUDRp6D3cSXJFKL5ZB707q2T68B9oUdoLf1Z6u1mfkXnnqdFg6xn3qUEVFxf3wZ4og+fJnPuJWseTsGz5WJBsoR/FCogb9iyu5PBYHrRGugme2lcKAzscFbjJkJJriSkRzPApzgNWuLsSQUOt4Khf9NM/PzktElEVWIqIsyNPWYeLvTpo1kwqvA/2zBirKZ0k4EpmXb3OBiPcVr5lmGPFrJ46i+0Nqtsiy+1u9NFtLJdQV+2LvHGpX+vS+zahmD2pGvHgcVxMz0uTq5EMeiG0pDv6/h9H4H8kyjWBbTjjJr6sV+p1tLFJMI0g1Y5uKnpHTjHDy/SFQWbOOTNa2Or63VabCPFQxzrbbtq93EFiuioqKXzq4f3A7JBu4zZ6xw05yzciX5y23Rc/cN2WCArfP7Fmb/aINaxN02gidCxeZj2vbp6297iP3XgelR/C3THjifi6q+LlduPHroTZua8c28cOVef7m9dsmzLjQVlGxL/ZWqP2m5VQ1fDh2RVvqwrZjPZQgtj3IEflgO9XRlRGGk6S2asUvH0znJDFzFlQl2sixG1CVjwM3PD/O88XlfGg8vHOQpZLwN5aHbeZ1pDFlub0D0QY3rjqP/uM183ZRWfm7FAodR9eUCk5FRcXDwA6/q836v43suW9oswt8ljO1mQKGfBAV44yUCf6dJJizXrhNdqXay+Bt5MSS6ncmwvj5MvGJ7S9/zFTArLxs17a0FkWVFWVme/tqu7KhVIydxPLa6jhcuMz9hI59fX1d5tT2crDzxLbQi9FXT4fh/bStg1JRsS/2ItR6mD3lg4StzZBFtK9GJcPZBj4Qbf/rfNlABJ6X/7G82QPo9ZAx0AwcSlmRMeKD6aSQJFCpHZky4+VzlToi7qRusIOQGYxsMGTmIJxA06l5Ko0rKm2GiuHN+4yWqz7e/o5MdVaZOe2h0mYUJm5TgyoqKu4H7QNJcmbfPS/4IeSbqrO28/OR0DuhzkSdts68kzZtk83uxI69RyO9rE6oKUSwI5LVMyubH4cLszAd0+vsx6XAIRweHpYlvHXNXHDyiADT/ry8gs7j94mXSddQKY5+D7F9dRwn0rT5bddN19d9fSXVFS+KdyXlg+SrjZhkqqLe2Vt9iHpJcN/7yJeT8Yeo2G7gfVv21Hn+tv28V+5GchvcIHK/jFRuq1fmaPz/trZ4CMl9CB5CrO87T3YM3kfaZlv9KpmuqHgx7BuRFO4juXz3CKC/+Dv3c/u1DSTI21Iedq1XRpaz8rfV4b7yZz5iFzDNg7nM3q4ueGTHcV/f5gez/TLhLIta6lhZXbOOhMohQast1aeiYh/sPctHRD6ljk8vpxuXhJqKIdVhrjTludL+UG5TsoVtD0ibasoH1RcoEfRZy8b6al1tJI//8Z2raVGpcILuyjcHqLA9ZPDY0fCXlAYeI1OrI5qLGjixdidHtaWNtOuczKnL2tlJsM7vCgrvId5rHLTT6XQa89UqtKiBihUVFbshI7aCEy++RzTTPPRMZ0Qns/Nuc9qIt2/rdozl4HbZlLCsk5NWzXRF1Zf2XqkLjCbuooay/XQ+2VmmtlDsok2n3c0iAR4lZbtvNpvG5AK+NPp9QgzLRCWb77Td2VSGrmJnhJ6CnqLEvhCQ+zNtz/uhTp9X8SLYOeWDITD9FtG80RmG0Q3uv3e73QahpjHV8SLiDlH03/i7k8IM3DYznJp7lIaA9dMxVC/lTdMA8Tx88NsUU06g7wYqS41pux5OoBlmo9HodJozgDAvXmD4kSTWVYuMUPPaZdeG6geJvefFZYoGy+NREv1Ow8lpkBgO5vRQD0lDqaiouIUTIbc7bgsyNVN2i35D22Ype54mwuOyDE62OMOI2zmvk97bbDW3i7gr/Ph2tNHbSHTWMfC6Z/V0Qs3UQPovkWz6PI8ae1ky33N0dBT9fj8iohBWt8GsC+vsufIRd+cCZ/qMCyf+f5tgo3vH/Rp9FcGOVkXFi+CFlgVy4+AP0n370Rj5Te0EiuB2JKuZMuHg720KrG/rBpr5e3x/aFu5wuH/t5X3oQ985tzcAPE3bpsZ1+wabDtPRNxpk/vaR/Xbdq5se777cbJjPqQcFRUV25GJKkTbc0ZydB8yO6/ztT3PTtxe5Hl/qL3dRridUPr4Fx84R8LZdv777LmD18mPmXUustxlt/PazscbteWBsz3uQ1v9eJ5t+/L9oefaZZ+KijbslfLR9rCrx0x1mmqiDIpCYL1e704KidJG/MH1m/6+FAH/z0HCzt+olEg9zerIEc4ZaWX52oi0VNltigXb0NM4eCwPi8nY9Xq9iIgyCpyqtdQNGvvMwLphUxm2KVOucmRlZjvouNk1bMvPdGel+06LA2TLA9/XdhUVFfdDCqCiS+v1ujEndEROdvm8+zZZNLAtrYRk2e2Dn4cR0La8XO3H/b3s/l12md89Ukk7tFqtYrlcRsTtQmDX19cxmUxiMBg0ZtngPNL0EbSrHg3U3M/yr6yH+15vd9bPUx07nU7xI4x4sg37/X7jmnAhlazdsnvEIxoCr7+nVvJ42zp4bb97qqLasKJiH+yc8tEWLvOcNz7Y+p+98k7n+SIlIqbMXRKxU1hexLPNgAq7PAhO4Gh8feQvobw1LUTDaYDcOKjNMkKdDc7UOd1oer2caGekP6vLYrEon2kUeV2Z8uLn8zJyLthtIdc25SRrn7aIRBtIqnVvaWEIOgrPTW+7LhUVFQ+DL6IR0Zy5oo0At0XoIu4SqTZyTj+T2QeRQuZOOzLbet94iqzsbeVnJ3+z2cRyuYz5fB4RzxdQmc/njd/pS1gWt4sUnUioOd0d/ZrbR+9kMIdaYCoJc5szf8ffeTz/nRwh85nyKW0ClPytfBfT+TJS3XatHKqfhLKKin2x1ywfEbmyQPAGz3qpgh5wV7RpCPm/9n/RXmSbMpn1qgkq0zQKLI+nO2x7mDOlxo+b9dZZ1m2dC+YotzkYGk4u+ZoRXTdS95HdtrbM1CBedxL+zCGSSD+kfSsqKt49eIf1Ic9ZG1HKtvmlwLZje5lYv8zWtHUASB49lzmrKwWPzN57OQT3re5HaKu3Ke5tqi7bwHPXs+soMuw5y6yPq95ZfbO6cJ+sHN6WWX3b7k8XfbaJPxUV92FnQi0luW1AA0Neesh4o0o9dJKW9WKvr68bS3jzHG2GPFNB2shtZhgY0spIsvfYfcUunY/tQ2PqYc1skImWS21LK8nSNtQRUZnYRvou9bnTea5Wi/CqrDoeowmbzebO4JNslLQ+83p6O/I681jsTCkthWQ8I+WZk3LDz+tTUVHxSwPad44v4fPIznJENAZFkxxSUNF+TrCyHF++++8Rzdkb3La7muvLgLs95LHkB2SvuMy13mVrufT1er0uCvVmsyl239NmMkFK6Ha7JW2SfpfTuCrVjzZaNt3hRNRV6G632xC2mA+v8pP801fR52cRDJ4jE2K840DV3Tsv3vnRcZW2kpFttVlN+ah4EeyV8iGjx98j7j6ANIAkXhkJ9xWkZBioVFKt1veHEGevw33b8GHWOX1FMNaPZNt7uTyHD8hxJySDTNKepX1kxDprCxLciNucdVd8WX46jdVqVa6JDLAT6zbD7/X036lC857S9XUH23YNicyhqmPxS6l8VVT8s4iHPofa1m1H1tl128IOenaeNjLdZovayiwb48SMCjN9FG2oiyoi3yKzItc893q9juVyWfZVugFtOs+T+TkKDm3TpnqdKGhQyODv2o9pJVSrXQmWz5YYs1qt7vgn1oU+7yHXsy3im10n7/D4cTOyrTZkR6aiYh/srFBnIRHenFnYxY2CD8QjmVJPnkbKySRJUltv0h+YXQxz9qDrgfMyuXK9zUiIsPJ3J+Be5vu2y0h+dizmiWmgC1UNV3vZzlQACK+vk2q/7lQ2ZJiVK0+Dp3q1nUfwzor/xnZk/fiehWIrKiruR2ZvtsHJDDvSWVRQ+7jtprKtcjhoi9pEFNoh2gSSNE5L6ukbHmV0Au7noz3yufrbOv2sM+2Y22Laa5LYtmtAYaUtiidy2u12G4MZWSY/XhvJ9fp723jbR9xNEfFr4y8/Z1s6S9bG9PEPvZ8rKhw7EWo9fK5Os0frPXruK6MoYuqkmLN9cOEPkkEpmG2DASPaJ/xv633SqDiZa9vGe9pumNyIe119X6+Lq9SZCq5R4W15dN4Wg8Gg9MLn83l0Os8Hhi4Wi0YoT+dTiIyjzrXE7TalhwaUx/X2VB044EXlZaoPw8fZtaHj85k9eG5fkIjh3EqqKyoeDj2799lWB5VOpg9KRKEd5QwX9B8kdNtSukiY20iSEzKmdlBdXq/XjYjdZvM8gjoYDMrMHKpbtvR1RDQUaNo6+ruMgKss2QJcanf6AqnfrKOOo++KQLJT4D5L+y0Wi9LuNzc3ZaEXV9zV1kxt0THafHVGwIWMUHN5dbaJzpldS7Yl60dBSu13dHRUCXXF3thLoWbYR+DDGnFLrrUPCWWWHydwNamI2wdVx+GDTVLL8mV5Z34e7seQWZsayt+3EWDB2yeDH8OVZT+f19PJJtuNzkf/Kz/s+vq6TNmk6+SjxqnIt6kCRKYC8Zo7YXVlRtvQSdOZ0on6tW1TSNQ+fg7eq1ldKioqHoZtRLpNcfYXFWpH9p/buW3n3qZ+c9tMieY0cyTaLDM7Fm5LsjJn4oi3TVa+TKDxNA8q1B4FdpvMsmbn5W+0nySh3n7ZKslsqzbfSptNdTrzizqXfBbfKeSRoPP8WX15XWoOdcWLYC9Cfd///nDywW1TN7cZXn0X6WOv0wcobjOcEXFHiaZS2taL1vauTLPcrLf3uP3lxNnf/TwsZ5tRaiPmvg3VfpFqGhsaWpJYgSSe9czqxH0eSlrbDDs7UH4sJ9Ntx83uqfv2q6io2I5tdqfNHrNTy9SybMC5p2/Q1pIwu+CQbZOVi+KPK9T6Twq115nnUgSPKSkSJRRRdT8jgWO5XMbNzU0ZkE5hg3Y/U3Ip6uj4qoNsPaOLmb1zsUH+1q8Xc8Rpjw8PDxvqMdszU5oFRrKzNBb69LYOh18PJ/Osk9CWnllzqCteBDunfPDhbSNJrh5475EGJzumtpcByh5snqet15up1Awrev6zQHLp4HyVTtK8Z8ye+305ddmL5LUtT5rOxxcVyOouxzIYDEo9NTcqw5oMl/EYdBKqL8l1Vi+1m+CGjb/rPzfYdJh+nzDM6CE+J8+8XgznVkJdUbEbnFSS/BF8tnwMiQQS7esD5WiLSar5nTMDuX0kaXbIJnvkTTZwtVoVgrxcLmO5XDbSNViPiNS2i7IAAG7hSURBVGikWdDeyl7qXJ1OM71ttVrFZDIpaxt4W0g5JZllOovahPuzg3J0dBTX19exWCzKolcumvBaueLsBFx1oC+dz+fR6/VSMsvOgN6Zfkc/S7vNsqmu3inK6pHNxsL5t9nGTKnUtaoKdcW+2Euh3oV8+MMlUp0ZPT5MruhSoXSDyweV7w5XeqkWULluqyv3o6rCh5dGWnXZFlrTcdke3iZtr+wY3u5efxk4dgwyhdqdG4+h47d1qjJno32ydiUyFckVBr8/SJS3wfdnfSsqKvZHZm/9+dU2bWlbvn12DO3vxxF51D46X5sv4HPvnXMnlSSxUn2zepNAkmxqm/V6ndp4CQLycz6eJSIa6wOwrDxOJk6oDBQqMnWa++ndUzi0Pzs6IvmMHFMwY+eH46K8Lm3XSeV7SBSR/IGEXf+xLNm5fKBoRcWu2JlQCxmJcWOREeYs5JWpGpkC68dsI5neE9WL8zr7lHQeLhRc9aBC4Xl3/M5ecqZO89hthsIVadaRZDIrt18ntpnqIFWDyoLK60oBj81cPZbF24ov3ivZPeNtTOPGz/d1Grw87PD473xVVFQ8HLSx2whIm217yD6+TZb6wc8axOj/61i0l55DLDvBeZt9jAnJGFcApM1X+oPOTUKt40c0U+d0zogoy5OLjHKApt6loDux9Sig2pE+Tukf3n6ZP8muV5vf8Uiy2phtz2vbhm3/ux9jxJVCUBva/uNxa8pHxYtgL0LtxCtTTZkGwIeEaiiNcUaOsvwn3vSZUafx0GeFwjhTSLfbbQxAcGLnn5nXRiPJEBqJtY8I9+NlbUM4wcwIpZQN7wywPaiacNnWo6OjMmOL0j/klHw0OcmzE+ttHR9eq/vqy2unz7yWHhFoA7fxTl9Gpn0wTUVFxf2gXd1GfLVtRHNgnn53EtMmEmRkyFVaqqQESbYPLIxozv7DtDelfJAw0x7JJil1jH6ANpPn1/Hkf3SOxWIRNzc3MZvNyj79fr+cWyqtFopRG6v9Zc/dv4h4ipyLUJNUu13VvlT/qSiL/Mt/eMqkix76jdfffRm3za6zQ+2na6rjtuWIc79MMNN/Pud1RcUu2FuhbgufuOLKm5WgoXSlVp/9IfXzONoUXX+IXQHNyuTEWvv4uV0JZWfDnUP2MDu8Xhk5zc69Tb3JiD3TP/zlBtY7SjwGz+nlZbuzLR7SiciOsStYjzaHfB/Jr6ioyCGydh/aFOd90KagkkA6mGrgdtqPm42DyQiabBPtB0k3pwTMiKvvx8491WiSWpFq1kNl8XbJyutENusIuZ/IPmcpHZkdbdu/7Tpt+y9TuF2g4rYP4SB+7IzkV1Tsgr1zqL2Xr/+yG7LtIc6IEh8GT9VgXhrTAUjuXNXUgA59lyqtWS5Ytra0AieGTOfgSwNYON1SWxv68fnydBTWz4lyplAz5Og5anQkOq7SPxgyJYH3lAuPMPg1dAVKuC+1ou0eua8jQgfpzsqNZVWmKypeHN1uN0ajUcMGO3HJnlM9gw8JrVOdzQSFiGZaQVvIXwT35uamzNSR2Qj9xm0oPsiPKE1OiqhsD5Vq2WYp3p1OpzE/M0Wd1WoV0+m0kP7lchmHh4cxHA4bc1xrJg35mTYBSp8ptigVpd/vN9Jb2NZqz4yMZoSWxJp5320qbyYOqZx+Tfw+8PLx+NngSX/XwNA2f1+JdMW7gb0VaqZ86KZtUyvcGG5TjyNuHwISairEJHgkXCTcIpWHh4dlwnuSa/6ekVfWkWWW4eQMEfpMQq2H3B/Ytgc3U4rbXrwGOiZVajoBNzKuqnBBgJubm1gsFneupXdUaAy5jdfRr6mrSLx32rBNheBnH4Tin3ld5Uwqoa6o2A8i1P6MedqBR6UyVbONYHt0T+fgOWW39Hmbuil7va1jz7QNnY/2VD7k6OiobKdjauo7EUyWWeVzf9bpdBp506vVKnq9XhweHsZqtSqLjUREmeZUKR/sIOh/kmvZOW2jQegqr/yGR1SzyLOTXm9nDaqkKOTIhC+/R7JrQ99Bv6o21X6eesl7S23i5XMuUol1xYvgXZ2H+ubmdqS1K9fsTfP3bb1EGp1sP37ng5IpvT79HAcoOmFsq6MbmOxBz1RSGg43NE6273u1wcvjxNKJdFaGtvNsK0t2/fxatZ3vobivM3Jfu/j/WRtVVFQ8HBIrRGQcGZnOtvHv99mFNgUzIs/ddvLeRtpIzDOFVp+zSCHJHGe6INH3/fWdYkhEM81Dq0dKtJGg46+IKHNM63htNtbrkJHmNtx3HbP3XyqCymgHz5ld2/sio9UXVLxb2EuhlsLrJJLhG6Ktx9pGpreFA317zudJktzv94tSrc8K1+mdynVmYDTgUD19pgvIyLE33DYgwpV8GTUpBko/0UthPv6verbNTkIDTwenOtGxuEJ8H6H2KICnfPA8LIvQ5qBYhrZj+G+uHFFp9w4Fja3/nuUiVlRUPAxSqDWgjmF3kVM+q3on2SLh5NLjbSljbZ1jHjvLJXbhw/eNuDt3sYsrsr+ywTyefMF8Pm+QYPqkTqcTw+Ewjo+P4/DwMAaDQfT7/YiIomzzvBFR5o0+ODiIxWJRZtJgaonSNubzeVG5PcdZ6RicP5s+Viq2fuNgS79W3i5sL6HT6ZT6c1tPU/GyuJ/cZpfdnjPamPkyHpfH1n7qHFZUvAj2ItRUgjVKOeLuKFm+a0SwH6dNDdXxMpLHB5VkTwRUaQwiosPhsBgTvfd6vej3+w0jSaiDwDSObPCIz1NKA0NV2DsVbYRa5Wb59b+Xtc1gMKTHmT2YppMp8fo9u05Ze99HqN0Q67dtqnXbtZc6Q5LMYzL0p3vNjbMb4GyAUEVFxf0QoRaRE/R8ekeVBE7PLtO09N2FjftUVEbfMruSRQ2zY/DFcyt1UGkYJIIRt2kkq9Uq5vN5Iab9fr+xn2z98fFx4zcdQ3nWtKnz+byUZzKZlDZi3rbafjqdxmKxKNdlOBw2Ogksi6B2k7rNVMm2iKfsPxVhEWiVVcfyQfw6jgtmTna1rYsnDtpwduh4zuze4XZqT80YUlHxItibUGeGri0XjiTJ893aVM1dy+CEb9vLUz3awl86niub20KHGUnNsK08Wfn8dV+bZUSyzZl4m7LO28hv9j37j+dpO152/Lb6tW1/n6Lh9a2oqNgfIoltqvE2+PPo6qF3wLdFudrslG+zTYHMjp8JCRm2/SchRQIKybWUYAof/N2Jn8rptjx75/YuLmR19w7FQwaQexnuuw4eMfSUGC9Tdj7+vy2V56F8ovqFincTOy897ot6RNw+wArdEVKDRbY9nEcSx9+zzwwJ8l2DMdQD18ARfdccnVKuta1U36w8/sBeX1/HfD5vDELxnvvR0VFjP7VNZjSlkB8cHMRwOIx+v19+Ozo6Kmq1lpxVHWSYnah6+gLVal+Axjs4JLs6B4+XOTS2f3bNqBplDiAzYDwelZqHKMjcj6FKtQ3vU1fO6tyjFRW74eDgIMbjcUREXF1d3XnmnDC5LdDzppmFOJhPhI5RJ8KJW3Zcbsv0EydlXlbaEfkIt0f0GYPBICKep20wbUXphP1+P0ajURweHsbJyUmcnJwUlZi+Q3ZZPotK/3q9jtlsVtIoZP91vogofkNtpsjqfD6P6XRa/CBtKqOtVJj5f2b3NeuI9hEf4EBHijLuk6TGq11ZVyfJzhG4PX2z5vfmbCO8F5iq6NjWWauo2AV7K9S8OZnq4EZts9k0buaMeEXcP4VN9mCRpOqzCCjDalQFOMsHianA1ZeYxsFZPLIZIthZ8FHGVLv1YlpHv9+PwWBQfiOhVjllLJlPTeNDg5j12t1IcX+OnFbnx0dTZ9ehrSOUKehtZXLHmN0HbWSaRpygEaex9v/8ulVUVDwM3W43hsNhrFarMp2b256MvPr3Tuc21YP2i6JGm4KZ2absPNk4CS+bvnM8DW2t2zJty0HwyolmpFGEutfrxXA4LOSaqyNy+lb6Sp1nPp/HZDKJ9XrdULeZRilBRjZR43+Uh81OBdvFBzdybFRGamWjaV/Z7i6auOAjkPiyvtm1or/y60B/nOVRtwl12X3g/1VU7IoXnuXDb9yI3Hjq5s9m7cjyZR2uSvvL56PmZ5YzU1MdTv6ocmTqc1sbeQ/8dDKJb/lLfyn+wjd/c3ROT+8YKa9flpLi5d/mQNoUAhpEfffc76w96DC9vpmxysqobTOHuA3ZeTPV6b686Ieo3RUVFfcjWxyLoN3JSEyb7fV9svQDEnDfd9uxtxF7kkcKH96x17ZSsaU4a1o7Em0JJkdHRzEYDIrgk52zjVBLUNlsnq9wq7E/EkN0bpWVY31EONWOmX/guTio1P/TZ4/O+vXy9uZ5ee2yY7dds8ymZ76zbZD5Q3xNJdMVL4q9Uz64EpQPZIhokjA94Bm55Xf9v41063ycrUMqAlMk1JPX9sxN9lk9sl6vevnL5bLxyubszNqJRkMG9Nf+8A/H5/3sz8ZX/NAPxX/zLd/SMNpU1TnjB+vj5NqJPw2pBoqq905jrfpS4VCZmfIR0TTCPEbbtczagmoTf2ObbztG1qHx68C2YGTB70dX5Su5rqjYHZ1Oc6BeRqY8R1bPndtH39dJnXfW5VOEjNS7AOCRKtaDZaS/6Pf7MRwOIyIaKq5wdHQUp6enMRwOy5Lh8/m8HLfb7cb5+Xm8/PLLMRgM4vj4OE5OTqLb7TaWNaevlK1XHSJuc9Wvr69LWqAvIS6/sdls4vLyMpbLZaxWq5jNZjGbzaLX65W0lGyWKrWNBlV6O2WCGRVj2l4OvlfkgvtxBhBeg4zg6zx+HdVWah+mDHnaaZvY47+RnFdU7IO9FGo+XPzuoSI3jG4gI5qGLKIZRtPx9T+JNxdx0YtpHK7utr27MyBx0+htvbsBzNSULDT1h/6j/yh6MMYf+cQn4iOf+ESse73483/qTzVIdVYf1iULPzpZVLhPBlIGTOQ8mwVDZffQq44rR3h9fV1y6HR9MoLMa8ft/PM2pVqOc5ua4oTaOxaZwpF1gioqKh4OEuosxSuiXXGMuA35u43QNhRgMiXa/YMrrNy27dl3Uu9+QyJHp9NpLKCibY+OjmI8Hsd4PI6Dg4OYz+d3Zosaj8fx6NGjGAwGMRgMYjgcNoQL2lf6AUJ+YLPZlNQREWzWR/ay0+k0VmlcLpex2dyu3JitlaC247v7xewz4USbs7bwf+8QZVPqEm1pJSTVag+dz31Qxie87bzOFRW7Yu+VEgUSKt6UbkzdKDqp1W9+bCfSJNHs0ctIuQrd9hvLzAeVq2nJAPkgRFdJSaCzcNgf+W2/LX79D/xA/Ev/6B/F0Xodq14vfvoLviB+/Fu+5U77eRu6YeBvWTpKFu7T8uR0NCozFQoPp2l7N6DMf2u7J9p+pxpN4p6hzSFnaTdZdIRlzxyIyrRNHa+oqLgLEUqfsSJ7zqjAat8M3MZJtf9PuH3y7263fbu2NDuf+s3V7MPDwxiNRnF6ehq9Xq+sbKg5qVV+Tn3qU7XSflMscgFK3110cb9wc3PTGNjoUU2/hl5/bzdtp3KoDNlxXIghwee2fo9k4o6TcBdOHCTXIsttaagu1Ph5Kir2xV6EWg8L1V797sYz+50PsiuuPB6nZep0mnOCSjngYAx+ZqoEB/Tps84VcTuocrPZxHw+LwsV6LN6+Rr4p3cRVnck3jaXx8ex6Pfj8Po6VoeHcbBex2o4jMWjR9E7uLtaY9Z23v4i9BpwojLe3DxfPnyxWDTUahoZtWM29zavAdM/OFOI2kCjxnUs7wQQHh7UedqUhOzeUZ3phPw6UH3h6HUej8Y0a4eKiortODg4iLOzs1gsFnF0dFQieNtWTaQYEtF8/tvsRnY8+ohMlGmzGToPy6VjMH2APkQDDbPzD4fDeP311+O1116L+Xwejx49ivl8Hs+ePYvPfOYzMZ/P4+joqMwLPR6PS8oH6+zpJGovphjKzvd6vTKAnQSd7S/7f3h4GNPpNGazWSOSQLDu7Ii0dY441V+br+JqkeQC9PVe34zc61pmPpbtxHJ52b1sun76zdMDK6GueBG86wr1Q0MnJJHang8cH6xMYaaCkKnRVE6y7/7gMM1jsVgUI6aUCY4i5gNIlVoPZKaCH0+n8ckPfzj+7pd+aXzhJz4RZxcXadttU0szNYKKtKvSMkYMham9mWeWOSS1u9ToTClx1UmvTCHK7hfveLUpJA43rq5AuTP1falUVWW6omJ3SMCgiCF7QaIT0exMR9ydZcGJ9UOQpSXwmLKJ+v6Q43N8CMfhqL46H6fGOzk5ifPz81gsFtHpdIqQ8dnPfrbYW83YpNmcKJ6QUNM2qeycoUPHkgLd7/djPB5Ht9uN5XJZVmrUWKLNZtNIgczaSv/pOjjR9Ta77/rof6Z70i/Q97qfyJRv35bX0lM2Mp/C8nLcV3bPVTJd8aLYiVC3kRaHh8a0rz8M3NdVY1c+PR+axs/JdPaiGu0GVqTTiSkHvkXc9oK9J++5vKzTydVV/Jbv//74nm/6plg9eRL9fj9+4PM+L0ajUQziLjkm+VXONp2HRwT4e0bKvcOShcEyZZlGmPVi3qQP7pSxdAVG/2U5a24AWTbvQPi91kaqPe3Fj0W05X9WVFS0Q5FCtwEP2c/tl7CNXPP4TpLui+xtI0rab9sYlox46ZyKeiq3+ebmJg4PD+ONN96IiCipIPP5PAaDQfFpUphVBxFqqdKsR7fbLfseHx+XQY4aNNntdmM2m5Wp9RilvLq6KlP1KYd7uVy22kORWrVr23VwYYT/0cd7R8rTOzi7lOw3Uw23qdJtRN99TXb93a/qt6pSV7wIdlaoSVTa5n30B0n7qQfsxE5wRdqJM8k1lxBnegeXGPdZLUSe9VkPM/OkF4tFSZ2QOk11gKSX5FvKsOohQ/yv/uiPxr/w3/138VU//MPxA9/8zcUwinySxEfcGjI5F426VooLB8qQKLJN9VkKBRVnpma0TYJPMi2DLuPPdtViMyTrNEZU7gneI27AMpLf1pEjceZvnC9cnZI2dSabZ7aiomI7lPKgxamkTke0E1jaoIgmmdJ+beTHSTOVTwoe+t/Lsk34OTg4KPWgP9F0dyJenC1JtkPzTI/H43j11VdjNBrFpz/96Xj77bcL0day4MPhsOw/HA7j5OSk1EV2bDKZxHw+b/iIXq8X4/E4er1evPbaa/G+972v9bosFosYDAax2WxiNpuVNBCl6IzH45jNZvH06dM79pmdHdloV4Nl+9l+bYKI2oy+wX2P78uoKAdeZtfTxRJB52uLUrgyzvsvSzGqqHgodibUvKEzVdORhWH8WNqujdzxfxo0kkmSOs/DytQNPmwkxdmLKqs+36eG/Ik/+2fjKJvZ4/Aw/sx3fucdJUZGROeMiDKY0MOO2Swo3o5sG5bZnVB2Hdra3Um5Rw28nXVuHZ/KwX33A41v5hQz5YK/8/pldWQHkO1ZUVHxMDAqeN/zQ3vSlnKXjenw6Jw/5xRmmIqmbfjeVi7asiyN0Ek895OQMxgMYjQaRUTE48ePYzwel1UURWrZgVAaCG2+0g1JWrW9UkaOj4+3tnO/34+Tk5NSlsFgUIQPLRomv+L2lDac6RmbzSYdnOntyGP57y60UKxxW+/73Gf/3Sdn/IHg+fz3qk5XvAj2HpTovbyIu8qmP2Sacs0HqPkD0EZ07jOQ+4ZsVDZP+fD0EJWPcMKqz7/vW74l/pd/+2/HF/7Mz8TReh3LXi/+0Qc/GH/7m76poUJrYCFVWxk8Dh6UKn10dFTOS0XByyRlmteKITwntm6Q3HBqX849S3KdGVqeV+2pNvL0j4cYzUyhdnU6u25+P3hdszasqKhohyJUXM2VRK3Tuc3HddKd+Y7NZtOYs9j/y+yCjkXb2zYlnB/PU9UoQCgnPCLuDPpm1HSxWMQ777wTo9Eo5vN5jMfjOD09jclkUojtbDaLy8vLuLm5iclkEtPpNG5ubhoLtAgaUDidTssYGPrTzeZ5Ssg2aO5ppTH2+/04PT0taSMuiGQCUUQ7KW0j1L6dvrelA2WCCdM9ROq1bZZDnV1fF3WyaHm2D31UJdUV+2LvHGp9F9oUUW0n4yASlPUQBX8IPZfLw/962B5y7KxOMsSaFUJpAp5D7b16kUo/383NTVyOxzHv9eJwvY7V4WEcrtcx7/XicjyOroWiNAiy230+WptTAsp4cyYTKTHKw2M7KYePzovtxPqyM0OVRsfKlCcNQlL5nIxye6ocPvDGiTWNK8vq0YK2jo/P/uHkWm3Na7aLwlZRUXGLTqcTw+GwzK+sNAPN10zb4hFFhtUzkp11cGVLSJy5rSueJF5tzzbTCXUspXHoN874NJvNGlHQyWRSZvM4Pz+PiIizs7N4+vRpjEaj6HQ68Qu/8Avx9OnTWCwWMRqN4vz8vEHMmbu9Wq1iOp3GxcVFsWVe58lkEm+++WYMh8OSv93pdGI6ncbl5WWsVqt49uxZmXta+dOsL31MW4TRBaJd7w0eo01wyTpFJMCZ79gmbun68/5x1dvP7ap21tmrqHgoXmiWj203uHKMtZ1v7+Ea7t/2AG4797tRj4zI3ad2bFNnT2ez+OFf+SvjJz7ykfjiv/N34vxzSoX+Zx611B0ZFf5Ho0eF39uT5FT708Hw3Gx7ns/rJnhajRPwbW2j/Z08Uxlouy6s2zb1mttvO1ZWvkqmKyp2AzvUWZqdthG2TU3pz+9DSRxtmhPphxDqjOxRteZMTi4IqBM/n89jMpnE4eFhXF1dxcHBQSwWi0aqHhdZ4doGTAFx8ihkuePz+Tw2m02JCkREWRGR0+fJrmvZcq/jQ+DXcpuN9WNm90Qb6KMyn+I+jMd3UIB7SKfACXhFxb7Ym1D7Dd5GWHz7+7BNncjO78d86HYkzlKmlX4hxVi/8zg0jDqfn1PG7M/8a/9aUR/+21/za54rOVdXxcgxjUPOSQNIRJ51fBoFGWsZXw9XZaq0wpYeEpXRdwfk5xPocFh3J7vcnvvx87b7wVXpNvXZ0ztcxWYOPMvNwYiVUFdU7IZOp1MiZMzNpT1pS/nIBBZ9d5KkbbrdbsMW8/+Iu4MQGYHzaKnKn3UGhOvr61gul7FYLMpCLUrNk8Lc7Xbj8vIyNpvbKKOWIdfy35eXlxERRSWWT7m6uirRxOPj4xiNRiUlUqkZmm+agyY3m028+eabEdEc9K31Em5ubuLq6iomk0nc3Nwu8hIRZQYRTa/nvsKJp0cvdU5ve24rPDSNzn2OyuQ52xReOAOJH0f3XSY4ZWVmznwm3FRU7IKdCLX33LepAW7E2FtsO3YWlqGK6uTPH5LMeOqcnjNMwiWSrBw2rXIlA+WKtWb+8LlKWX5uqzlKRdY5glyEWsdZr9eNGUxkuH0giUKEmtfU24zGiWoK55/WcSLuLju+LTeaRk77t+UttxFqbycvu+dHO0n2PHfWn4qSO3WGWukoKioqHg7ZLS0uMhgM4vr6uizyIhIcEXfItD/jEe1zEPP5zzq+ToIFEnG+6z+Vy5dOp6+YTqcxmUxKKsZyuSx2V+V9+vRpmZXj53/+50tKxXw+LwRZbXRwcFD8ymq1isvLyzJzlCKPmtHj6Ogozs/Py8wgqufTp0/jF37hF2I6ncZ8Po+rq6s7ueeymwcHB3FychJHR0elg0AlO4vAOuF0H8DrRBFDYFtmaLO3mfAl0YjCjcQszrSS+Q+Vy4UYHl++QLO6qO0qKvbFCynUPlAgI17vRm/PyXhG5ttenpPFY2ak0x8+qpy+jxuPNgWWTkPHOjw8bEy/p2PxMw0Ve+c0mJy60Oum/VztFbaFSdl2JNBt2z4EuyrB2bkesu19KsO20HRFRcXD4AofO6yuItK+CW7ftoX63Ze0pSBkxL0NJPptvoHpE+wEKJIoMYZYr9dl6tVO5/lqvlz1V+3gxE/lJ9HTIHTWVcefz+cxnU7vTEHngoVenm6SjTHZ1r6Zys+Ok2/3bsCjB22+p+2dyH5jpKKi4kXxQoMS/TcSSR9E4GG87GHm78zBjsgHOzJc5/M4M/yY5XIpvePm5qb02q+vr8tSrRykGHFXgY2IO6ox1W9+J0FerVaNMpCkcrL/zeZ21HuWf6g6ahtXknkd1KZaPl3HlMFXPRRWU3t6h0HhTk7DpLSVLKza5tT8GmYE2O8Hd2ZtqR9MD2EnT04qS1GpqKjYDVKoOZ+yonCcBz7r5LpNEKlxhTgjTm1Emv8R9xFqT61TlHKz2cR8Pm9EK+VT6BeOjo5SGyZCfHx8HI8fP46jo6N4/PhxnJ2dRbf7fIYQDXKkYKFB3yLTWvFQbSnbrVQOpSqw3XS86+vruLy8LAMrr66uYj6fl3MvFos7beR++r6owH0dG/6nSLPanmXNrhtzzFVPjzC7j/FIhPsFdQB0XvlctmdFxb544RxqPexC1tOPyFNAuB1Bw6D/2LunQswBcgqd6UHx3i1D/FIWbm5uysASkWs3oqwryaNU5rZ6uLGQk4m4zaXzeU77/f4dZVtGQYZYBLjX6zXCfQyxyrDQ8GhfjvD2gTFZPiE7OcvlsoQvh8NhId4eOs0UhW3KRRaBYCcmy6emcuQDiLyTRwVN5/KIQkVFxcOgcSDD4TBOT08LWdLCWLJv6si32fvsuSN5bMuVpbqt8riN8Wec+9Pe0eZtNpuiLmu1QU//kyiiurEjcHPzfEo85T8fHx/HkydPYjAYxPn5eZnC7uLiouRkUyGVIk1CrbZU2TVNIVdyZOSS4shyuSxpIUoT0WelIrbZ/TYxJPuebevXmhHP7BjuA3htScb1H8Up8gQei+XiPcPoihPq6gsq9sXehPo+ErLtAcuMa6Z6k6AprEUVNSLuGBKGv1zVIBnm9Hgy/K5+ukKSqe1ZHbxt3NhnZDNTaLPzUIlQOXU+dkSYIqJt2+AGx8vPNmHuGpWdiOb0WG1qU3a9t5Upa6PsHmoz/tvuwfvKUFFR0Q6ORxAB1Pc2W+n7u116iI3YZsva4OVx4Yfn1ssjYdw+S6Oj+qpxMSRqnAPapxKUvadt97EgTBOhnWU7ensrEqt3CklOdumnHiqCuDjB9nwIMnKdRTr9uCyn4yG/se08bamiYl/sRah5I7pxI4HKCBq358hs/k+SyBucxpAKq0i0BhdQoc6Iqiu3mmOUqkobOXPFkwZNx5Ziq/18xDnbhHl8bDdX3zPVuE3pcQPE6+DKvToUfp1YruVyWdQADao8PDyMxWJRnKhUa90X2wyxOymS/yyVoy3Ng06G//t1o4NwlaKiomJ3dLvdMv/0eDyOiCgDn5fLZUwmk6L0koBG3J2Ok5E7IlMuXeigveFxSVq93Dymysb5s1VWKdT8TYMOZTeVend4eBjD4TAODg7i9PQ0XnnlldI+SgtRVFGEu9/vl/Lp/MpvPjw8LOl1VMi1aEtElLQ7T2Wj/Vee9Xq9jslkUtI/lP5HP8ZrxEiei0m8LkxnpA+/r0OVpWG6DyM/4H10n4DVRqhVN3VydM2Oj49LNEF+rKJiH+xNqEn02pQGz5fi71KJ9SAwfYKkLlMSeH72pDVQhITfSSVzqfSbcuWy2UuyXrAbA6rl+l/lYJ50ZlwyFZqEmm3s7dB2DO3vxFXlIFmW4XIFne2r6aM2m00jdWK1WhUDJOPP9I82gk5DyRfTOJw4c5onT//IIgtsF6rl7nQrKip2R6fzfLDdcDiM0WhUSPF0Oi1kMCJSIpQ9d3w+MxFFtlTbupiQEWq+u6DBc5DMS1BRuoRPL6cyKlongjoYDOL4+Li8v/zyyzEcDsu5PKrHzzyvpr9Tep9P56Z0GtWHM1TQNqqNFotFSfmYTCYxn8/vtKH7WR1rG7HUdfJxNln0kNeI7ZFdN581iqBCT78hf3sfWBalegwGgxgOh9Hr9cpCRVWlrtgXe6d83DeoIAPJInNhSYYJkSTuH9HMpxaxklKgMvk+DOM5ofZwmuAGOjMCzFuW8fOBJq7MsO1kXH1JWBqstl68b5uV19s/C89lZJOdFU4/pzZXe9NRLJfLksOu81A5yYyuE2pXoByumGT14G/+rnIwxFlRUbE7REyUMzybzRp2xyNGmeIse9k2Xdk2O7Dtfy9n23eKMfrelmaR+QfaeanOyn2WupxFbXksKcabzaYM6pRt4qBDqeK0YxSPmD/sM3/onN4R2QYKSllKyEPav+0c2zpBbWXJjrtN4fZt2vyj/GxN+ah4UeytUGv0cRaa0Wc9BDSWuomlaopIy7A+BL5krB52qdwko27AqFjLWHLwoRNYKgROdEUktY1m6CCRzOrU6dzOPe05XJyTmuoD1QQRby2oQIPAtidJVZ1dbZExz/IS1QZK+XBVQ6FOzc2qUJqmieIx2Dlw0Fn4LB08pyvXrmzze2bwWUeVX/dLJdYVFQ+HOtLD4TAeP34c4/E41ut1vPHGG+UZ5EJSXP5aNkB2lyl+To6oHrd1yDPcl3aW2QnZIJFbDeKmsukCitI6jo+P46WXXorj4+M4PT2N09PTGAwGZe5n5TBrzmoOTp9MJjGdTosqrvS6TDlmO0RE9Pv9iLgdqOikPFNuScTvI7UuPHEOf/nEbeXLfmvzBSS06hTQh3ExNR4zU8n1Xf9pNqqIaPhMDu5Uh6iS6op9sReh5kPKlIas9y1kaiHTEnRc72VmucKu6EbcXW6UOWBZuNEfVKrb/kDJ6PDY3qvNQmYqF/fX8fr9fsmhI1FnHrJPi6TjceEXknJ2IkiinXwyJ0/133bt2OFguNEN7Hq9LoYrU+V90KIrHVSEnEw7qd6mcGfhQrY9y9a2fUVFRTuoTiv39OnTp3dSzaiUMppF25NF//zZ9GiXR9S8bJkdd8h+cVYgLt0twceJI8vDhW1OTk7i9PS0rHwohZrn0yqLTCWhfVWaierhdVKbu4iiz2oX2cQ2ESNTY92XCrSPnmbpIoXbfcI7Iy5kqE2pLqu9XfDRveQk2lV4gek1HkGWD9Mg0vuU+4qKNuy1UqKHS/RfG0h6uB2V67YcqOxBYtqIjLO25QPPY2RGmSTL56zW7yy3DBTJK42Dl5NtxrbgQ6xzs8fs52BdfD+miUgFcrBjoWunATWsuxPYNmWYzkzbKWdSahTbNjOUfr1JiL0cGcmmE/R9/bh8V33YJpk6X1FR0Q49gxG3zzLFANpGkUbauIi7ETOJM9q/zW+4qprZmkx19X1oR/jOgekkiE7ANG3gaDSK4XBY0j1knzMf4NFBFwKy6Fp2LJYjornUuiKl3e7twFHle8tOt7WJk2SHb08+sK8dpfiU2evsOnAwJbfPfIAT6SzC7NenomIf7KxQO4lcrVZxeHjYUDAjmkQmIzskxlR/Hd7jpXLhISKel/vK2MgAOemW0hARjcndmcPtBpUpGm09/bYevwzdcDiMbrebGmGSa/7uA1vUo2YOXRsZZXvIcYj8SqlRagcNvE8xKFWJ94I6N5o3lVPqkfBzPzfOJMm8hlS7OABR7ypTRr7Z2XLIIbSpGhUVFTk2m02Zx3gwGMRmsymDu5Q/PBwOY7lclsWy9OwfHR0VW8K1AKQYyy5y0HFEMxfW5xsWvKPOfVh2RiVpNzQA2zva8jUayMbBh2dnZzEajeL8/DxGo1EZqC0VVTYxIgqhZbqLVHHv4NM/uR2V71FbaWD9ZrOJwWBQRBN1UtS+2WwqbJe2gZ8UsTT3tk8DKHJPn87rwHc/r87hv/EzCbzuI4+O85oS9LE6Rr/fj9FoFKPRqBxH16GiYh/sTKhp0GQw2sJKJDVOtCNul+JWCMyJJx/wiLs9TBFBfVd4keeW8cyUDi+LGwaCaR2e+9wWUmN70cDIMCtHWqtjsVw0ojQCGYl2Qs36Uzlgfb0N2amheqN3faYj8naXgxDBZseL15qhSrVRmzNxFcvJcqZsZ4Rax+S5dHzmSVZUVNwP2QVGzLgwhogfU8zcFtE3qDNP4kVb5M8nbS5TMjLi5sfS+QUSTRFqwn2d0jz6/X6Mx+OSL61ZInyRK0ZFmTtNYcCFALZRpk6zHEq1EIEWeby+vo7RaBTL5bL4GS4Ek11Tt638j5FJKeEUxRj5434PxbY0HlfrPYKr88rPeLupA0Jfys6f0DY4tqLiIdgr5YO5sB7aE3izZw9nxO3Nyzw27cv/BRE3PjAkYSTUrsx63qyrwfp+OpnEb/nYx+I/+7qvi6eDQdmHHQeqrzSebCcn1ISMIQcfOqGmIWYYlITfVW03IqwbjbTClWp3XTtOWcT92IbuyLQfFwvgcX0+cKblOHi9ub07Hp8iz8m0H8PB+3SbEa+oqMhxc3MTk8mkkEiuNsfB1ALtsgZB8zn29AfPXaYNo83zZzdTRfl7W6ebtkPnUj2khqp+Uqj1Gg6Hd9TS7DxqN52DqXGZr9R+WXTPhRz6Eb3W63Uh1N1uN8bjcZn+NCPxIuUUuTJyz2vJ+6FtZpHsOukcLni48MZ7g9ee/IPHIGH2c/q+Hi2tokrFi2JnQp3l+IoMcpAHVc+2gWZ6EDx/OVOZ+RDQGLgCTCVVx5CR4qwOVHep+n7NJz4R/8LP/Vz8uh/5kfhLX/M15Rzq9UdEw7g5qVV9tg1u6HQ6xRArxKkwaPZQc4BiZkx1TNWXxoJgu/vAIKlIXEhAoU92cLJ21e9qV6WSaMYP5qt527fVmY5F9wkHDDnRdqfooGNgR4DRi4qKiodhvV7Hm2++GY8fP44nT57EcDgs5Hq5XBafIEIo+yRCt9lsyowWJNGdTqfYHdoK9wWcnpPPtqej6Xf3D24zSOgjbm30eDyOTqdT1N3Dw8M4PT2N4XAYJycn8eTJk3jy5EkjCkdxieegTZO6HPF8pg6Ww1V8wvN+ZetF9iOizP6kYyl/+vr6+XzZ19fX5bsijvIDTA3R+RmVFCRMydcLIuIeLdX2vJZM5fHfsg6Jq9MZqfb7gjyB9wT9t9qN17+iYh/snUOtG5okj8TYe6DeE47IV03U/tyH6qn2ywi6juEKJh9SJ5oyzn/0T/yJ6KEMX/KpT8WXfOpTsTo8jO/4vb+3qLoqX6bWC1IISHK9DaV+6CW1gs5BEEFn2wuuyLKDwbb088vo8Dq1OR2l4+j4PJ9fSx5P141laCP5vB6ZUsPz+fX1+8CPmX1Xx6wtZaeioqIdNzfP855lGzj9WDbAnCRHeaoutHDGKHXKI279CYlVZgNJnNt+d/LsaWbaR7ZdkTyq75peTS/lkGe+jmV0wu6iwkN8pvtfgRFRTydUTvVoNCq5291ut0x1KnKt4+vcrkBn11R2VP4rS/vgtWgD24Ft59ed27MNeI94XjX38XdGkxnhrqjYB3sRat6EPrLbe+cCiVJmwNiTpGHh/0xJEKniw8EwFXv6Ao0Wc/+63W78H/7tfzu+4W/9rfiCn/7pOFqvY3l4GP/wfe+Lv/Lrft0dhTXrUKgMEdFQvFlftiEN1kMHi7D8rs6oXCTV2pdl02caQJaJirQ7HeU5StHIBjzqmvhAUJ7b7wGWQfA0HbaTr16WKdR+rwnHl5fxTd/zPfEX/vV/Paanp61tXlFR0Y7r6+t45513YjQaFSKidIjlchnj8bgM9prNZo1nk9GxiCgET6IBbUnbIEOO3xC2ReRkDziDh89xrTJotbyzs7M4OzsrNkydhZOTkxgMBmVqPJ9pSsfudDoNe+n2VefVcRUZzHyB6uLqrHcyZCtln7WPooXHx8els6Jon/KtNROL7KxypJVTLlvrqXmy6RwH5QRY14fE3X2UjqHfnCeo7Uh83f+z3mwTpRox2styk5hXUl2xL/ZO+WAulwwhRwC39Ug9J8pJoQwGDVBEMy2E4UEdw99JuKnq8ngMD70xGsX04CAO1+tYfe593u/H7OwsDj9XRynUVIupUKs8JJFOlllmhTuZNuLH0jGonNB40EllZSEy8i8jpZ49Q3M0Xuqs6CUD7AMXOSBR7zJa6mSw3VgHlYtk2I2in5OqddbGJNSbzSa+/Ad/MH7ZP/kn8at/8Afj+7/u61rbqqKioh3L5TJ+7ud+LobDYRlMqDmpIyLm83lcXV1Fv9+PyWRS7DzFl4imEMCZhATZWhI0CRayC5l9zUQM+ScStM1m0zie1OfDw8N48uRJPH78uEQRVXZNkzccDmM8Hke/3y9pFDouVz7UZy+bjjcYDKLf78fNzU3MZrPiQz0Fsk35dd8Q0RSo5F/Ozs5iMBjEer2O2WxWbKkWsFmv1zEcDoudlXKtGVpErj0tg0SYHSVPT2T5sigGUzl9QR1XlSm26F6Sj8r+U9mZE697Qr97ZLmiYlfsPcsHiR1TBrznmCELf1GJzggRFVSRKeVjteWraRtXPfxB1YN8PJ3GD//KXxmf/NCH4kt+6qfi9OqqYQwimoq8k3Vtx98YbmSbsFeekUeWkaE0kd1MkW1razdGvD7+G8kr66y25vXmtSTJVTmpROk3P7/fB0IWgfDry04XHWimVP3+P/yHowfH+5FPfCI+8olPxPrwML7zj/yRtO0qKipykPwJiuQxPYKqJaODtGn0KR69yuBkk/aVtkmgfaIPcTJKJVcpfprDmQLKYDAoecpZyh+P7cp05hfVbrLtOl4mkDCyyXOpXeg7GK0UsVY5OFOTbD39i44jcq2pcdvamNeL9j5LzWm7tvSFPA6vL8/hbUh/zAGSvB6ZQq5yui+vqNgVOyvU/rB77rRuZic6mRKYKdHK7XL1WWEqkT0+8MyBcuVbD6IPbFGvWgPT1ut1/F+/8ivLyO3PfNVXPR84khDWjIxG5IMrmb7iYS4+5HQ8TuJ5Dv5HwukOhoZE24kQE1QDqBDQuJG8Oon3V0Q0ls71eUGpSOl3Gkcn6rquLGumUPM+o0HW5//Tt31bfM3HPx7v//Snn6f09Hrx6fe/P/6br//6qkhUVOyI9Xodn/3sZ+OVV14pudSdTieGw2FsNpsyFzPtklRAqaA+KwSJtxMg2gnZF9pb7ZeRaUEii2ytfJjmlO71enF8fBwnJyfR6/Xi5OQkxuNxSQXhtHn6TrtJsukkd5vQJOVcKR+M7Ek8cfvr55UfI6mk7WeHoNvtljnDmfLBNI/1el0GMKqu6/U6JpNJXF1d3bHnTvrld306XLW50ku8HXwclI6n+vC8rG+mhHsnR3XVYFPt4xGO6g8q9sVeCjVvYI6SJaGOaOZx+QNHeJiorSfP1f10TP1OVZplZSqEGySdUwsM0LhE3Ob6sWfMY7szYC/ZOxn+kLvxyFQX1pHtoe04EIUzZ3jvm9eA5df/2aIonpdMtUXX0NvRybGm09N5MhUpO76r7uycsFyuUPP4POdms4nL4+OYHR3F4fV1rA4P43C9jkW/H9PT0+i13G8VFRU5lPLx6quvNtIURqNRIakkcHouRWZE4KRwS/DwKJVP3RZxa+87nebsS/Q9EXenzWPKGFMVB4NBnJ6eRr/fj8ePH8dLL71UFGpuo1k5dFyVWal7+o+CgX7LOg4qF9MlOGiQY4Y4uxHhUUT6PJ5fKrjOyc6AyLyuh+yszqdZqFQu97O8NvQdagePNip66dFoTr3qdaUv0P2nmWT0Tp+oOuo/zSijWbVOT0+LT2GUJUtFqah4KHYm1BF3FVoncW3kZBtp8XBR9n9buI5qtYeJtA8fNL37NhG3KreTu7Yy0wl4SMsNeqZOe5jLj+ntozbyTkebcuwkWKBx8/SJNpK7S5qJXzMZL14bbeOEPLtG2j8j0X4/+PlVz+PJJH7sC78wfuIjH4mP/MRPxOnVVcuVraiouA8cT8EOrQsY7ORm4ziEzJ75Mxxxm/63zT63HYv/M0VFM3Yoh5qzMGk7Dq6marotRaUtmqntZcc40Nsjb/zs5/EIsOyk+zuWxwUiTjDA9BaRZynUnU6nodSzTbOOjPtyv5aZUJVdU/erKuu2e4CqfESzE+OdiyyyUVGxD/bOoWZvW3NecrAew3x6wKnEthEzf0AygyDV07eRQWJvXWXkw0uD4+dVj5U5dd5j32w2d/bXtqq/Bjcsl8vGABEq1DQqTF3Rw57l59FI8D8flEhj72oC24QGnIqQDLzUEaoHPIfKQrVB7SlHoTZ3YkynyzbR9gQ7PL4NnRrVIYaLIyL+02/4huIUPvbRjz7Ph4zciFdUVLTj4OAgHj16FOPxOG5ung/4kgq4Wq1iPp+XJce73W5RBLU0NwcgyvZk4H96RhXql/2iHXS7TMIaEWWefS0VfnR0FOfn5/Hyyy+XQZUnJyd3bCmnx5Od1PmzMrtvo73UfrPZrLG92lGpiOPxuJSX9lNw38DOSyZYuILr71LiWUe1ndJ0uNIj56qWXVYdXGTytLy265zNcEJfQMGFg995H1C55hoZ7ERp3QdXwmuksuJFsPcsH7o5GTrzaeWY50uFwkcJCyRcbYNLdMOLVLcps1lOsY7tSgX34wIlGnnNvDsZKxoTEWwNUlEnQuRfzoOqMgk1OwJUZLN0GX0X1K6c1o/bZwoPIwpMo+AoeH3fbDalU5ARat4HMlqe88xy6h7IIhEk95nqzHbXbw7eI5k67+pWJdMVFbtDs2Bo2rzFYlFI9XK5jNlsFldXV4VQn52dNQi1njuG27eplfwsG6MxGhRvMiWYKRkSK8bjcbz88ssl/P/qq6+WQYjD4bAhwHQ6naJge5mzNIysDBG3Ax+1H/OmpfQvl8syXuj6+jr6/f4dH+bpgx4NoN2joJJFkdm+8uWdTqeR/qGXp0KqrPQf9BkirCTd8oO8xplYQv7A7eRLI6J0PLwOug9IqJWzTUIt8l99QMW7hb1zqPWZ32UwSB7b8qF5vMyQOvEVydb/WQ/7IQ8FH1I3Lg4SLg8LUQF2wqr/qcbS4GlbbU+Cydk12sKFbkh5nmx7r0tGuLeRTHaMdE3ZUWpL02k7d9u18vbOOkr3pbBkx+O2WSeuoqJiN3S7z5eyHgwGEXF3sS12tAUngY7s//tIdnYMf+eYE0XylBOt6e+U5kHBiOKMk1EXYtrq42UXKGRoaj2S1M1mU3J+JWTx3Pf5ruycLKfbTifsjM5SrNFARk4AcHBwkM720pZK0WaXvYw61n3pGO63vNNxn43PBJiKin2ws0LtPWYOnNCk+D5IMJus3Y+rdxIn7cPttE02j7EbmoyI+pyiNCC+vcJd6nGLOFJJ1vk5JRGnGOJCJKyP11mTzmtgpLZ1A+EjmsuFNIU6SwthO5DcMk+P/3nemedJ+iI67mSoSnCQprcBFQedIxsUudk0FWq/f/xzZhyz/MJqRCsqdsNoNIoPfehDcXZ2VlL+mFPd7/fjpZdeKqkfk8kkIu5GqqhWRzRXXI1ozqfs07npeFIqfVYR+grN3nR+fh79fj/Ozs7i9ddfL8ulazYPHoOpaS4EyM5wAJ4TcZZfxFyfpeKv1+uYTqcxnU6LYrparaLf78d6vY7RaFRmH9HcydkqvCLd8j9MxfPIJO3yZrO5s2qviLMGYUq1v7l5vjqm5qLmZ0UnFGnVQH/Ndy14ekUmbujdfYXqzjKrHeQ72kQo8goOhpWvVURgNpvVfOqKvbEzoVYIJftPo6JFDDl9mpNkPjTaP+L2weADz/+dHDrJ43b87sqseuAyRCTXOi5VBBlYkluSUYazuIgJOxSuSus4nP6H5FwvkmiuwkjCzVlDaNyzMJ/O4wSY15LGjj141VeqhNI8uMqW2lvGi7OR+DEFdsBEnEmqtY3fF6wvy897g79n+fuVUFdU7IbhcBgf+MAHyjMl+yN71+v14tGjR4W4KLROGyCClOXX6tnnjB6050wv86ijtvXUiOPj43j99dfj+Pg4Tk9P4/XXXy/zTFOgIanncV24cZuVKddtxyOhvri4iKurq0JMRahlV7WQjNqMKyvKt0hQYadAYBqFyiA/q2vgfpgdFJFjEX6R6Ol0WqZM1LYSLNg58Cit22CCCjX9FcUs1ddTKEXmPaLAYzO1kUo8fWj1BxX7YmdCzbQL/02Ezwkit9d2QttDlRE9/u8KdkbUMuXaiaaXnyCh9VHMVGy9LWhcuQ0Nqrcj60XFWOVwou8KhSvM/N3rlbW7k+1sWzoqqjLbUk20v5cp+4/15nv2UlvovPfdJ95eFRUV+0MCSkQ+ToNpFq4s0y76oD6qy7QnmW1psxW061RupbSKoCovuu082efMNroymrUVP7OMbTazDe5HOYCdUda2TkFb+dReTN+keMa6UyyRH1gul0XlFQdwcu9ii87jbez+mXV24SurD6MK94lGGaepPqJiX+xMqDU6NntpudJut1uW88wUXD5gunn1sPFBZQ+cBoMDUZw0Z0aDxkLHYM/U0yh0TI44puKhY7JeDJlp0IP3lGl82/KOM/KpOtOIuEGhEeHvbANBRtBVe4YSvWNCss/BKdpHoTgdn/vSiTLkSFVA++lcTBuSWp05VDpRbxNX7XXd2VHK2qqiomI7lEPN2SAiojEjkGwgp1lbr9dlJov5fF7SAegXKMzIrvD5pg2SgkqVPCIag8S1aMv5+Xm8973vjfPz8xgOh2WWjywiSjVTZYpo+ikfRMhl0umXGJXU/M+ank/l1DzP2cA9gb5SbcQl2EWueQ2UipEJSWo/+TaleWTbamyUUnh0fZnSobpqkOXBwUEsFouIiMY9QkIdEWnUu20ubU7hp/uL0PkVuVA91R6e5qmIOverqNgXe6V8UEUVNptNY1Qtp5DT/x56aiNIMlaef03C7PtnKgDTQfQQ6WEkoaaxZlkYbszKy7QMzz/TA6020TFIqDNjQCPOskTEHXXHnYzatk2x9XNxe1eTsmvjoTLVM6KpHGTXScoFib+OyWvsirUTap8/lO1AI632krFnXmGmZlVUVDwcUnw560JEk+z6QLqI27mrRUT17LNDrudU5FNo6+QzPYT2S8uIHx8fx2AwiEePHsVLL70Ujx49in6/H+PxuBB3paTw+E60vZ76X6mNskNOqFVuqrXMhSZJFPH0tA1XT0W6Rcp5bqZoiFzL97Et3fZz2XW2da/XK9EIrpqo/TSzC/OYRcDlIxjpVRnVJmwj2nNdf73UZkdHRw2RjFxEdfY28fRBCmyqD9u3omIfvNAsHyQwvMFJXthLZDqBK72ZMsCHRA+jzsEeaEb+/DsN8LY8Wu7rKq7X2wmgcri4v+rCB9/rzPbzeVQdrDfrwU4E23lbONLJL40st8+MkM7DWV1oaN356Z3lVPmy65+p4pnT0jF5fIEG1NubqIS6omI3MDpHUqaBbLThUhsZ+aPNELxD7N/9+eVz67bMI4ZatEWDE5mG4oS5Tajh9202g0o77S/tpYimlNZ+v9+IdmqA/2AwaMw+wplKJNY4edQAd7UDBzOq48K0PR6bS6rr2lJZ54B+rkTodVCHhttqNc22a+oEP7sOnpZC8s7tXJRyddp9FK9NRcW+2Fmh5gwJ3qPXNESr1aqEsbREqEJrJJ9UKiNu0z0Y+tKDwQnlb25uSu+eD7urvq6OU91lnfibTwfkBpq9XW8bGlsOIlSeHqFwpAyEHmiFD92o6J2k3PMTSYa5D7d34uxpMJyNg1DbyihTEe52u0XB4O8ZOXZCTZXKz6cohUKI7qAyZ0cCLeWJihednb8qKioehs1mU8L5sj2aRWO1WsXV51YhXS6XJXd5sVjEdDotoXUpjTc3z2eP8DmpM0U6IlKSynemLwwGg7JYy/n5eTx58iSePHlSVFxFyHy2DiGzNVk6Bu2qiKnqRnInX6Uyyg8q8qsyHR4eljm7e71eDIfD4kelrA+Hwzg+Pm6IMPSvm82mpGh2Op1CzmUb3S/If6ssKrfIcsRt1K/b7ZZtFaXo9Xpl1hJFMjV7SqfTKYMZXaghAff0D7f38ve8Jow8kz8woqrOlBR4DrrUMbSMfXZ9Kyoegp0VapJUQr1XDpLw3m9E00DxRfKV5bRFRGNxFT1oVJFVjqwX7CoEH2wP64nskfQrXWGz2TTCnG1ETL9rSiknbjc3N41ZRvSu81L59fPwOK6au/qtMqvdsvYnuc4Uan1WuVROhuU0IEXn0kwv3q7eDtyG94LfDyLU2s4dn64zrzdTk5jPn91/FRUVDwfFDj1DIn7KB57NZoVoyX5G3D7zIjVaWdHtNJVEj2oJbrcogPiy4poebzwelzxuRsAy2+D+ZJu94H7yez5exTv0JLdMXzk4OChtSeVYpJr1YmqMxCam0EQ0CWNENK6HyiQRjKl52tb9un6XrddCLuoQqF4i2ZyooC1yyI6ICDF9EcUnb3f5ULbhcrmM+Xxe7k0n0n5dmQZaUbEP9lopkcZNIJESwVJv876RuVk4PvtOQ5utkpTlJet82UPiA0w8jEmi2ZZKkRH4bXXLVNW2NmnbJ9uO6izVBRkPlZMknZ0jJ9gefWBZGMbzwR8OlitrE7+XMjLNbVkvHZ+O3ff1yARJtneyKioqHg7aDUG2Q/bBo1LZM8dnU7aF0UCCtoL2q62jLKX35OQkRqNRI8Ka2YLsGPpN7yR+Iq6cxo2D3WQf3U7LDsvfkNxS5eagw8xuZf5FUVFGCiV+kFDrfDpHv9+P09PTODw8LIvN6Fp6+qPqKSgCrSir6ilBiXnm3q5tIhv9Lgk+7z+KRTo325P7tN179FGVTFe8CHYi1ArrKUzmRIQD8BQ+Ug6XQkNcAMXTLyKaaQL6rneSQoWl2Gv13NqIZiiODwvPoZQCEuqI25HG3FfbZLNOCK7Atqnp3vtWudwA87htYDtxEB9VYTc+NP46vuorZyOQyGrRAbVbpjx7e+gYNF5qSy5PTtVG7Zw5PeamZ1EKHcNniWHHifWspLqiYjdQwGBHO+I5wZL913PHZ9nTwvSsahCh0gU1VzMJpdtzRgBF5CXoDAaDePLkSbzyyitxfHzcSL+T7eGCUp7m5xE7bSfSyc6DSLWUegkOTvI2m02Zw1/tKBuq4yilQwvfeHupLbmvyqoccfeltP1Stnu9XozH4zupFkotYeodzyPQV8kv9Hq9WC6X0e/3Yz6fl46D1GoKcyw3j+0+0P0LO0SqB68Jo6XyKT4wUdB1cM5RUbErdk75kMGkcpCRNa7aRPKWEa8slEd4b1yqJAk0DQ5Jl8rnD6MrmTR6GUGk83BFISuv9tXxHpKXxfK1/ZcRVt+e5VcZmdvMNvGBGNn1aVOoPdS6DewMqYyuTrOjxagBCTXDxWpf1VHn8evrv2e/VVRUPBzbVN2I2yniKBBse/74u0QY2fj7yiG74mWQ/1Ee9WAwSO1EVhft7z5L+c0ccMmFTbifr2joBJjnykiviDXFDbeH9IkCx9VwvA9tvtI7JHa1wX2Hi0A6v1RuqvIi15zFRPupE+Qdsuwcbb7F0xlZb68zr7nfP/xc/UHFi2Bnhbrf75c8KSfVvKm5dKlPjRNxl9TSyNJ46NhuOPSfDBzLQYOYKdQyjFxWXDl8OjbPQ4XXiVrWCaATcNIoA0BnQYfg22lfNxasQ9a7b1NZmEusbZj3pv1doWZd2t65j8rD6+udHjdmEdGIFPg9QIhwk6Q7YafzajOSJO4VFRW7gbaVCjVn2WjroJNc6Xe3FZ62wPPyeE6IOQNGxK0aPZ/P4+bm+fzMbls7nU4Mh8MYj8dFxZWiLX/HVW8jbufi9xkk+C7i6YJMNu+x0jWkcmvmDIpI29ohIkr0cLO5jfRFNGfFiIiS1iH12Ik1VWkOsifJV7ur88LBnVrxcbVaFcLt6ZNOhnmttY2PQfKURLWDc4NOp9OYYo/+ynmC+6+Kin2wM6EejUZlQIegG1jG4Pr6uoR/OKAiomkASGYy9SJDpkAyn9rDQzIGzJ2LeG4sZFQ1WT0fZG1DJVS/cyaMth40SbiMsau62k7l5kBEEV/17KnE8BwsB406jSeNE6MFqiMNoAZ+ZqFCXTNPk9C1oBriOXdyINtAh8VrxevBDgUNJztVuj+YjpLdTzTWFRUVDwdJI8m07Nd6vS4zTPgUaPQXJNRMLdBn7+g7XKxQ2USIlRoou3JxcdEYByTbITupmUB6vV6cnJw0zqWFy1Q+DsbzqJ0TXyeJmovbOwxKleH82bKbSpnMrgVtsVJlZHOVv8xyaWVDEWG1AxdL0TVTJyMiiqKtNpC/EGFWu2he79FoFDc3NzGbzRoLqfG+YZ64wAVXlJajsnKdB3YWdAz5J81O4r6LdaNyztTPiop9sFfKBx8IEj32BDloLZv+zZEZxofgIWEaltX35dRs2lYPGVVkDnxjj/kh8B42SSl77Bzgp3ZTZ4HE0FVbz2Xk/96B8c4GOzWdzu2KjFSJnaz657Zr4J2GrOz8nCnFWQfCf1fd/LNHNbaVuxrRiord4c8xbQ5JnG/L/7PoX2Zn2p7/7BkmudU2butYBpFLTa+maUAJDkDM/BlTOVgXry+38/qoLFT4PYXP6+z2TERXopEPUCfx7HQ6ZbpCzRWtzoH8NtvCFWISY073KhFNr7Z0T5J8/53rGvD8bb6XbU+RygUinsPrU/1AxYtgZ0IdcasAuDLZFhrixP5ugDyX1qfLo+FyI0YjzLBN9rDpeFS1ZWy4yhNHP8vQU82lEpEZep5L+7uxYPnpVNRGPqpaeWltkPHRZ7ZXG6Fm+fSZ15FGyZVytYWr0jq2yst68npl14bOgMeiY2oLy6mM/M57ytvDr1cN81VU7A6PFmZpXxFR8phl22iLI24FByqQnDWiLfVM5yCpY+dcqQ/z+Twmk0lRPZkaFxElvUJEUP5BZd9sNnF1dRWTySQWi0V89rOfjXfeeSdWq1VcXFzEYrFonH8wGMR0Oi02WfXl7CXz+Txms1nxP2o3pvFpyjetRCileT6fR0TEdDqNi4uLxmwbm80mptNpWR6cc/hzJhJ2CtQOuk5SjaVWT6fTmM1m0e12y1Lm9GvyHbTvOsZ4PI5Op1MUak6zyjbRtVeKhjiDtmEUQb4xW6+h7V7sdrslYqHcdvfJVKsrKvbBztPmyXDIMLhx0wOjB1KfOZNERDTIWDb4LCN/HvJzgy61Ocux8nPK0KoXT6PGhUuksitXW+TNlRdXR1hX5j/TmfjADE63RAPoOWb+wKus+sxjunLrZN7LwcUVdDyF+Oi8pOr7yGmS6kxFauvw0Jl4m/n+2TKxrm44eN9q30y1qKiouB9OpGUT3B7IfgwGg/I9ommLnXAOBoMYjUax2WxiMpmkHWCqrj4zh2yPyOpkMinH1eImKkun0ynLkJPI39zcxOXlZUlvvLi4iMlkEsvlMt544424uLiI1WoVl5eXheAKKj9THFRWEUmlMWw2m0YutVI01PmQvZvP5w3VOetkqG1ms1mZA9zTJTxlhD6Ji8Yo9aTb7cbx8XEcHx/fSdXQQikRt+k6vO5HR0dxenoag8EgZrNZ9Pv9mM1mjevJ2VE0V7iTWs0OQlKtfb2j4FxAvk31UcoK6xJxm3df56GueBHspVBH3B96IbHzfLIMWRjej/3QG/0hJClTV7alAvjnLDfXVXCV2Y9BRZUPtNI89F0dAyezDpF9L3NbfbNyZwq66kKnyRCqt4uO29YubdfBy5iVydvV68kysHOh39runbb2qqio2B98pkSqV6vVHTXVI0nbImxCZqva8qvZUaeK6uIECZk67IvFIiaTSaxWq5hOp4VQz+fzWCwWRZTxFR4ZWdR7RJNQc9o7RgY5WN7Jt14cRO7pbVK/NcieU6hybYhMqKCApPIqH5qLuGRpFG67BarL7OzQV2zjCbxOmaqcXXf337wenrLKY7UJVhUVD8VOhDozFlSceQPz4dM0PRFxJ0zD/bkErIf+eZPz4WVqgRsoEU0aHc/RczLlijvzmn1GDSeovp/qKUXb2ycbvc0pmPg7STVDm1RefaS5f3YS7+3ruWwZ0Wb+HUksB366Eu5to238vuEx6WAYxeDsMmx/no+gSkT1X/t5qLKiouJ+uC2IuNuplbp7cHBQlsjmSoqMDEbcDlKMuB2z4pHNiCipINkzyzExGsS3XC4LKVwulw1bpajcdDqNg4ODst/19XVcXFzEs2fPYr1ex2Qyiel0GtfX1zGZTIpiPJvNYrVaNUQZ+TuKIyq/R8iIg4ODuLq6KgPpuDgMU+u2kVjZSM6WxDE8mVBBPy3FmNd1Op3GYrHYqiJnqXhqD/kHTdMXEWXQoq47r4mQ+Q1GJ9UmnHVE154dEF1n5cZLWa/2v+Ldxs4KtR4QKaL8nSF7kjyNto1ojqplbrBymDnRvo4TcVdllJHgg6z99aDwQcuIm6urrAtJsM4lIkwy74NeHFQoqDa7wk3iLoeh/dkJ0XF4bO/hk0R7Lz5Lc6Cx9/SUzPhmHQimUzDfO2tjv2Yky94p4Tb631M+6Nx5vRglcaPN8meDISsqKraD42iyZ1zE5vDwMI6Pj8uqhfIFl5eXMZ1Oy35UPp1ke3400zpEaGVr5IcWi0WZiUMzdIhQs6O9XC6Lrby8vIxnz57FarWKt956K956662iRCvVguRN4282m00hibTLbaA/Y0ofB/D52Btt2+/37/gCP7b8FGetkP/ScXQNWQamYKieSo2MiDg+Pi7+iQSY14zk3Ql1toIjlWN2qNwfRNzyBPpvXhOJcoqGKF+anTkSex1T5ankuuJF8EIpHxFx56EnSSSpcwNCxVV46I3s27kC/JD93AHcZ/weAtZ9G5yoZuXKjsP9qMw+tHxtx3Y12T97nqQ6Qn68XcrCc2wzZP77LgYvc0b+WzWeFRW/tCBZzvyBtuFv3lmPaKq7PvA5G1+j47SRL51DOcadTicWi0VJ45jNZkWJFilnB8Ijanp/qA30ennZMx/LiOt9xyXaIo1ZZJL10WcKVSLXJLxOqFUPtj87DrzW2XVv8wWMTnqU0+vrx6eQxzbivrv6sIoKYi+FOuJ2nmFPSWCvVP9xMAFn/+BvTkb9AXEi5Q+/VmXyh5hqAvPWdAw95BqMQkWcBsCdQUQ0VAjvMLD+bQMK7yNzakfWRd9dgXb1V//RwG8ztNqWHRNX4GWM1L5tKoI7GwcdEsOTvHdUFirY247JunnbU+XKBq+0RRcqKirakaXP8VkSUdS8wdfX12VgYLf7fPYIzVyhAWFUVmV/FG3MomObze0qfbS7DirXSulgNFXHffbsWbz11luxXq/j2bNn8ezZs4a98u35nTbEI31taTFsJ5bV29lXGlQdM7ul/zeb5kqOR0dHpR2pcnMfRocFpo1o5hGPHND2c7C6ogeaJUQLvWglTB9oSFDp3hZZld9XR0vqs9JTpEgzn5sRdr+OFRX7Ym9C7WSOxEv/M5TjU9/ogdQMEn58fndjlfVoI+KOekpjRuKkzyp3tihIW/0ykuxlUt15jKwdfX++syzMRWszKjymE0TW1c/j5aYSLSdEki6jRULtKRP3kV9dNxLqrH2YquPOm3BDmKW80CHr+Dp3ncy/omJ3eGSJz2xEU4GVkKIc1ogoBE/TtVEQ4TPJdADZVimVFEBcBc8iciLUbvNkx54+fVrSPCaTSUwmk2LzPK2Oyit9lPtDiUdtYz/4PeugcHAjy6FzO2j7WHYRy8PDwxiPx2UWLvpG5h2rHEzhox9keoiu8fX1dVH4b25uytS08/n8Thl4zbJOCdNO6C/ki7SPzy4if05CzSkT1Y4uUGVKfUXFLnihlI9tCqS2acvt1Y3NsF0btpGdrBxOpp1YZ8fIyK0PnKSh9DCSk2kS8bbjO/jAu+KSteG2XLtt18e3c7SF2yKiYYRImtnmmSPzY23bxjsGrvLcVxca/SzEeF9dKyoq7oc/w5nYEnF30JuigZp1IiMyPJYf021tRLNTTbvhKR9uoyJuI5wklMzV1blYTqZecMo79xkqm2waFyrxDoerz+xQUJCSf8gEBhJvfVe0j9fB24qvbYqw/lNdOMjTxZpsf/ou76hwe/7Wlo+e2XURau3n81ezDhSqKomueDewN6GOyPON2FPUd6oS/X4/jo6OyoNIAqlj+v6ZcfVy8PzsbXc6zyfGZ96X3r0Hz2M4WeYIdM4WkaV9UIGXCq86ZQ8uiXM2KIUGl+fk7zRqJN1OQtm+fv22kVYZZc4dK6dDh5SNmr7P0WWgmuMquHcW3BEJ7oS40AJDsPep6RUVFU2QmOrZ5DNI4hxxS5DOzs5iOp0WFdMJq09tR4LKFWx9ULy2pT3SzBRKO1BEygmrtl2v12UBFynZJKMi//JhFGmUnqLjk7xR6WU0TvXS8QRX+KXg0461EcyIaPgztUe3223MwU31mGswsJOg1YN7vV5jVgwN4ry+vi6zkrzyyitlrurFYtG45iqTBDTNS84UIBF0RQ/6/X70+/1GB+fg4KAMDJXazZS+TqdTFpO5vr6O0WhU5gNX3Q8ODsqsLboHsk5QRcU+eOFBiYIrwuyFcsJ85jCxt92m4HoISOdqU31pvJQ/JsOQqRPaNwudZYo6Vc+2njmXW2Xv2EdGqx6CjLarqjw3y+pLurqiTaWX5WtTte+Dp5GQ6N5HqrUP8695vXgtqHjRYbHsToI9zYOdIF6HiOZqnJVMV1TsB4/60SbLxnuq3ng8jrOzs+j3+3F1dVXIHMdiRNzmDdPWkZy5PfBySUxhGomnmOm517R619fXZVCip4FR7CABpjqt80ZEmV2CdpDpFCy7pnHL7JHs2Gg0Ksd8iDBDQi0/0O/3yzVRfbliMO2uft9sNjEajRr11XYizsPhMF566aXSydH5RXx5b6gdxQeUjkHxR9dtMBiUsVHy5R6d8JUPuSqnyLryquUHtFCOq/kVFS+KvafNEzJ11kmwh5uyULwbxm1ksK036SE9lUODI7SNfnODSbSRMxosGi5PxaBC7Sqyg+fJiDOJ++lkEr/pL/7F+M+/8RtjdnbWULG9zGq/jGzqvGyDNgW4rY2pNvugm/v2bds2U7G9fNvaz5V9D/dp+4qKincf3mnnbxG3yrWUSn2mKk17pv3dbkms0Wdu5+lxtFfyA8JmczuIjoOsSRxdZWe0i6kXIvpMM2izi7RLIn4ispw3muQyI9SZTXRBhYPLWe8sHYYKNb/zGjJtUkRYC96o7FxhWOUQ4eaUtlxinG2terRdd6ULUYDybZxzsN1E9tu4REXFPth5YRf2OiNue9AaeOEDQ66vr8vggIgooRwRTS6Lqgd8Pp+XPK1dbnaqJZyL8j7QSLlDyNIudEwSXZJupXmwR+5kmed2Y0AF3Mn1V3/84/F5P/uz8Wv/5t+Mv/L1X5/m3lGNlWHV8bNzOmndRqZdlaaCzLw0b1tdE8/V47b8jUqSzyTiHTd3pGoTzXuaRRKoclVUVOwOfxadxEbcPrv6bzgcFoV6MpkUkqtlviOag6jdvukZFmlqE2c4CE3nYGoKbaDU2JubmzLfNBVwikAit/JnKq+UdtkapYfQHlJ4kMrd7XZjNBrFcDiMm5vns2Esl8tSd/mS4+PjhhKr87Kds8HhVMV1XTK7TTLvSj7TXrTwTcRthPTm5qYsgHNw8Hy+59FoVI6n+qoTxXmu1amS36fP1rEFtYX2OT4+Lm0oP6cIQkQU5V8K9dHRUeEk4h1OyNVOFRX7YK+UjyxMT+NKgxgRpTepB069V5I+/dbW884U1qxcegD1wLjyIdA4u4LrSjQNdMTtYEUqFiTRnhuu/ahyex10PDoHGeVOpxO/9zu+I3oIF37RJz8ZX/TJT8b68DD+z3/4D99Ry6kOZQTZDQhVY2+vLAWDhpkvT+XIjr0tJUQvDgqiI6Ia5eHErEPCNm9DTfmoqNgfLki4giwCRrW10+nEcDiM4XDYUKRpezN7rN8kuNC+chvaAdoc5VVH3EYlOQhRJJNzU5PEiyyLtNGOKb2Egy1dAVabMB1Bub4kriR7R0dHMR6PyzmpstJeKu3EFWeP/LrtdrubTSXKjpJsvNpdHQGJZ8PhsLGQmzoaapOI246W0iPpI3wWLtp55YBLLFGqDP2NogdMvaQS7ulFmS+pqNgHOxFqGix9583IB9d7jbqp9dIUdyTSDyXNetj8dxlZ/55tv21wh6dxMHTkhp2qNENLVFYyFdlVfn85CfyPf/fvjq/6/u+P9/3Dfxi91SpWvV58+gMfiB/6uq/bagDa/ssIvZPcjPDSCGchQ7ahK+PbypidV4abeYA8Ht+zThEJdVukgtekoqJiN9BWu+3msygbSEVS+bMiXBQ4JLjQdnjkKxNJ3A5ERJmNwkUSndPTPWjfMlKs82ftsM3esl2U+sKcbI1D0TYi1GozDqoWnPTSLrNN9Jn7s06ubntdstQV7itVX/nZ8ut+H+ha+1oU4gkUQOjr3X5n95nK5Ck6fl/wN/ERF48qKvbBzgq1HurMmMkIkkSrpy2D0e/3Sy9TN7OrASTjhB4u9pId2oeDPzI1VOEfhZVUL0+zkOqczexBQk1Vwsk3CXUbgc8MND9Pz85iNRjEwXodq8PDOFivYzkYxPT09M61oGPid28D5pFFRCNCkKVuMBSYKSGeb+f14u90BA4qLt4W+u7Omu2u+0dKEjs7PiiS16eiouLhIGnTO59LKrcRt/bm6Ogo5vN5PHv2LObzeRwcHMTFxUVJw2oTV0iyaVuo7GZ5xkoliGjOhKF0ARJMLZdOX6Zze0oby9amArvgpNdoNIrxeFzmhJZC3ev1Cvmnb1EONSOEWZpHRNyZw1twe6z6cLYmbZf5BheztO9msykKtZaWJyFm3rPaQsoyfTDTQcUnRLq1PVOInFTznlTdlWLi6xDofpHSrfpk4ltFxUOxs0KdkTc9gG3qBMkOVWkSUz+P3rMe4zaiHXGbT8Yeu/ewqTxk9XPiLMOmfbmN14+KaDYNnsONcVvbjyeT+NSv+lXxqS/6ovif/diPxcnV1VZVhHViu6rebpSc6G5TqHkc386VkG1la9tG1zBzrNqX5DnLo3wIWb6v/SoqKrbDbX7mH6hERkRjhgelApJ8toFqNu0HUwS8HMzl1bY+lalsH186vqdHsM5t9W4j3WoHCTBSqkWW6UezbZmOQXut7WXX+bt3eGifXa3Orqv/n5FyTTvY6Txfvn25XDbOyfNyvBR/FwHWOeinVLesXXkMRqnZhiqL3nVcHlv7V1Tsi50VajckfoPTeOjB0UACje6VKk0CymNKqXCi7EaEcEPM3mzWi+egC/bIM5JNIryNfG1LO3Cj4m1KA+JtrTb5S7/ltxRj+vFv+IbnbZkMqND2/J7BCfG2FA5P9+D2+iwwb6/NULsy5PX1MmZgxCBLx3E1q83htXV0Kioq2uHPq/sAtyVSKzudTlEkNR9xW5QuGzDG42fn5H8OqrsRcUcAcZJH28NxIr66q5TarFPvYzpEHEWSmQYT0ZxNhEoqfaXssXws1WL5V+aEexswCqy6SxV2Ui7CrOvm5dR5tLbEZDIp/n84HBYhir6YPMEVfN5T+l0qdnZvqZ1VfsFTMJniw/91vuoHKl4UL0So9V0PJg2JjIYMpx4G5ot5bnLE7cAPLotKRdQHtLFn6ikhetcIbJWXZWSYKyNlbtCz9nADSmVUCgQNCPcjnGCqbJyGSYZUv8tYEZla6+dhfrJ+47FdvcjIdqb8q240vBlxdoOddSSy8/rATzqoNmeWqVZeloqKioeDz40rsPqNBFTPukjkwcFBHB8fx3w+LyQwI8vyB4TsgrbnvpnYomNq5qfr6+viX5QapnmWKazQbnKgoqaI4/8k1Gwf2SaRaaUFalCiSKdmDZFiz3qpjCKMjAjrGmhgpdpG/m+5XJbBilS33S7qdw4G1/aefsd257zWmrf6rbfeiqurqxiPx/HKK6+UwZo+44iOwZQQ92e6BzS9Ijsyur/YPiTFjIj7fNVZ1Dgj8xUVu+CFVkp08OF0UsPeNnPJXE10suXfI+6SvewBcNKnB5Ejk/WQa5TxNoX4ofXP2iNTSTPF1HvfbjippLNt2s7ZVj5XfL3urkxnL7Vhdry2fb0sVES2wcm+4B0H76y0vXTMtnaqqKi4H3x23LZF3LUj9AciT5xGLcM2ksNOsf7fFrKnYrtarRokmupn2/l4DD+P+ysXbjwt0F8inayPp4jIt5IMul8hYXYxIqKZh04iSYGq0+ncGU+TzbyRiTASsCKiCGr6j4oyz+Xfed3Zpn6NCG3jnZrMBysCXhXpincbOxNq3dgkXxlp4oCJNmOqF3vyIpLbbnQZFn3OCCvz3jjIRIaCy5lymVmqHDRIqpP3gl2xZfk8JWLbTBPax19Zx4Pn4XsbqNy3/e/Xz+vlM25kqvMucAdwH6mOuJuP5y9Xh1gPlo9KWkVFxf5wwsI0K0YA9XxLbex0OnF8fFyEjuPj45hMJkVBjogGQXLbpHPrO5/ztk68FE6JEprvWb/LL2RCBesV0RRkPO9ZvqzbfT4AczAYlM9K3dDc071er0ybt9lsypLsTPNwMqh2ZXR3tVqVpdw5Y4lAuy2CznQRtRUVYvpvbePiltpDnROVU353Op029uc1ky9mp4mTHrR1MNSp0H4+fkm/c9rZzEe4uOIcoqJiV+w1bV5E82FrezEERYPjhFpTAqlHqweHS406kedN70olH2AZS07kHvHcwMhw+XH0cNIoaRCljIAbB6Y/yDBzIIXv422qbTPF1o2BrkGmTvjxM6KaKQvbSLUv5sK5prNzZufyOmfbZMqWg3mINJba1x0Q7z9uzxHzdSBKRcX+yEiLnlPm9oowKdXh7Oys+ICzs7OYTqexWCxKigJVUMJtjs99r9+czPt3ije+aErmXyT+kEhrMB7rJUJ9eHgY/X4/RqNRIciae3o8HpdZPk5OTmI8Ht+JRmpmD6+7fJt8ZqfTKUumq73ZcaDvFBmV36WY5Eq4CDrtpOcd8zun9ZN/1fLy7GTQn+gYameJauwYUX3mDGEUUzigUeDiPplK7ap21pGqqNgFe6d8ZAQ6+9/JiqeAkGi39SAdJKd8tZUxS03gf04SI+4OuPNwEo/Dh58pGSTZvr3Xh3X2TsMuPeY2grvt923Xj84n245gvb1u/J6da1sZ/ViuRGe/C1lk4L7wYUVFxcPQZoO3/U4CpBkuRETbxjwIbQKBf886/gSV2W63G6vVqkHO7ju+hAadx9PwqJYyl3db2gf9IH/3c1M8cBGIIkLE3bx2f2+7Zll7Zm3v/pvpIep46HhUv9t8SVsnKjsXf3eir2Nl9XqIj6mo2Ac7Lz3OsJKTLPWKta0TK4bDNOOHBhvoXYMYNRenHmQnqjK6nn/nJD/LdaOCrLLpXOpNa6T0arUqD5kTa67IpP0VjtN5GE70wZesT0akaRCc3Go/bsc28uvGd+7bRpDZ2chy8dhuqk9Glkl6aaz8ONtUeaoUvIfoRHhMVxq87FQ2tnUQKioq2uEpHk6iGUnzsHxExHA4LPby/Pw8ptNpY/lp2gz5HH2m/XK7cnNz00iBkGIrRddtNG2DbL8rmlRGlYPNctC/+YuqtZbCzhRWnUd+RJFbEkGWle0jdZn10rF5DNlykvV+v1/GEXlqiPsalVHl43oOiuDqGsrPiitofmqWgzNtdTqdMliUUx2yc+SdCL1rsgMiSwPRPUIhTL/xOlRU7IOdCLUIZ6b6+sPY1vvVgyjiLBIuQr3ZbMpDqmViSWQV6hKoUHp5MpWVgxGkSnDOTE89kWGSERJx1m/dbrcQZqqezNXWeXgMDvrI2tMJJTsGal+Sdl4j7ufHdtLeBl5Pz5t20DHQ+NEp0eCynkSmZKnNdN18OdlM0cgcLHP/mPLh+egVFRX3g2STz7+Taiea3H48HsdwOIyIiPPz85jNZoVU+3NN5VMzTmRkWs+7ZuIQwVsul9Hv9+P4+PgO8eKy3Uyj4FRrskFKRRQ55HLa3nFgeqP8ngg1Z6Ug8eP82CTUtOmqK9uEKTb0LVx5UL7CU+DUvqvVqrSZthHpZg40589mTvjR0VGsVqu4uroqx9EKih7hJfGnT+X0gRR0aP9diVa6jWZK4T3Ke9DvE/raiO2rJ1dUPAQ7JwxRsWxTLree0EJjftNv+6799E7C5sSayH6nAss6yJg4oeRKWVm924g8jXy2T0b8eTwvc6ZS++9t4bm2sr2ISpupz/7/Q+Aqs//Xdi+4cXXoeOOLi/hf/bk/F8dXV2V7P15FRcVuuO/5oYLq27GDLPWWea9tJN3HSbiaLbjtpf2m3ZR910vqqN6Zl6zffOVEt1uZraKv8jJS6Mjq7e2RpcVkfpLnabt2PB7r1HY92zpOTHXx9B2vq/tA3hOezpEJc95R85ShrLOSdb6y+lVU7IudUz4Wi0X5HHF3tCy3lfrn+cgCb3rNv6levB4qqdnMxdJ5I27naJZy4Uole+4Ee9x8QNfrdRn9TWeQDQ6JaC5xrvOoPKqfBkR6e22DFFWpEhysyF4+j0lj6yuD+TXh7/5Z3/UiceUI/Mw5ECwTVz1kHTIj5/eWzsGl3RmWVQjXHQzr/ZV/82/GP/9P/kn8mv/6v47v++hHG8evRrSiYndknWXaTNlBz+9V5FEK7HA4jEePHsVm8zwt4PLyspDWxWJRnm0py0oX1Hmkfq5WqzvzJbvYwgGStE0cdC1f49soFUVTw3Hhkk6nUyKdGcFVWaloK71wsVg07JmWRHdFX8eUjaSK779rX6W+cPYUlq3T6ZR0j+VyWbYV3BZ7tCEjyVLhb25uil8/PDwsZSAfYP2YbsHvVPQ5zaIi2ScnJzEajcoiQSp/lmOue4A+nnWrCnXFi2DnlA+tmBRxl8i1EepMCdV+nLGBITIdT6qAjEPE3aW/mTvmvWsvH8P8DPnQKLM8+p/H03kjouRaMzXFl4eVEXGSLEPoxFhtrd8y1ZnHiIjivLS/yrdNJXeCTmRt6CRb7eTHJtyxqXPAtuC9k90nNIjMPeT5dQyvy3f8sT8WPTiSL/rkJ+OLPvnJWB0exv/x9/2+xv1UUVHxMLhS6f+xo0uFkGRUivRgMIjz8/PyXL/xxhtl+WotFuJpY04eXUBw25ARatlIn83J/ZiOIzuvz55+oLxhjs0hSKhXq1VjphDVTVPpufLr7ctr4HaPZVD6C3OZWX+3tUr7ENFmG6gOvJ70Lez4KKWOgpgWWGPdsk6PfhMZFuEXsRZHUGfm+Pi4pPLoGtzc3DTSd+hneC1JtOnzKyr2wc4pH07IMiLURtD8f3+o+CAxnCeCzd99PxrNTHl0pUBl8rLRQJAYZ3Xe1i7CtrQYnksYPXsWv+m7vivGl5fpse+7Dn7cbLtdjkW0KS+O+9R4Hkfbb9vOQ7xt1zk73x/5bb8tPvX+98fyc85h1evFT33BF8R3/q7flTqrioqKh+G+Tqg/s06+tI1Ik1az0+wfnv9LUsfzZyqq+xQOYvbImkCyxfQOpoRIEPBUEP/M8rCsPv1oljrihHOb4MH/22x7W6qLi15t40n4XfXP0l3Y/rqOFKc8FcPTWLJ6+LF5TTnItI2kO1nP2pDHv8+3VVRsw84pHwzdu8HIblB/oPWfE2b1ZNV7J+mVyiG1gsbVR2RTrY647XXq2Dqmevb6TiOnUJxCeP7AZtP/kIDT4PPc23LTdKwv++t/Pf65n/mZ+JK/+lfj49/4jQ1H5B0SXQ+2OdtMn/lb2/kzsu/XUm3haR6+r4xY5jh1nfS7Umk2m01jiXjvYKnd24wwnS0N6LPRKOb9fhxeX8fq8DAO1utY9vsxOzuL7ubu4NmKioqHIVNOZQsYkdJA9s1mU5TZ6+vrGI1GEfHcpozH42IrHz16FBERk8nkjg3msSOiobzSNmuOZamZVCQjohDgiKYNl0LLOpKIS/mUCtrtPk/pU2qKzv/o0aNG9JXnVCqLor1SXTebTQyHw4aNy8A5plUf2U4f68OBhlL99bu21/E4OJ/n14BKdTYuLy/LtYq4teGqr+bjXq1WZbEe+XCH6t7pdBodDJWL15URyn6/H+PxuMzuQZVZc1lTodZ1ZhoqOQCvdUXFvth5HmpNN8eHXWGcDPrd86YiotF71c2/2WxKWEyEUQ8WDaeHhxQS0v/e21dY0FUNQQ8bw3okhpouycN5TlizersK4CkkERHf/u//+3GIfOwP/eiPxod+9EdjfXgYf+w7viMlrgxVMoWCJNvr5+3H8qs829QKv+5tZJ/7ZYoNOzfqBGXRB/1GMu3/M43Gz3NzcxPHk0l84kMfir/zxV8cH/rxH4/jzzkE3g8VFRUPB0WNzJbKpuuzfs/G03S73bLgyXK5LCsobjabIrTwPLT5EmBI5EWA5U+Yg6v9ScR1bBFG5frqOCKb8i9ctEv2VnXUyocRt/5N9khEttO5XYil2+2WlAzZsG0paCTN7gcz9Vn14bsr7JqJQ2VzscI7ISqrVGiKGUrFGAwGZXYVlYfiicCUC3YEVEYdl6q2OkmDwaBMvUgwHZPpo7z3Mn/KuldU7IO9FnbJpkVzQqXPHr4ZPXsWX/dn/2z8hW/+5rhM1EYqmE6G287jD4er566ochs/Fo9HAuz18LDarnBl9P/27d8ev/ZjH4tf8Q/+QfRWq1j1evHpD3wgfuDX//pUmXYizHJuI8xZeb2eLON9yIxSVj9va90bdMxt6Tzb0jL8fnFnEBHx53/jb4zBYBBHR0fxV7/u66rBrKh4F+BRSf4uAuQCB8kgCY62VbqHXv1+v+zHCKD7CEL/Sy2m0OG2k7N5kGjyOFTbGY2MiIZ4QTKoAZJUzUVepWozhcTTMuT3HCL02obqrtdDectSz1k/DhD0elOs8HZl2/C6iGxnZeGEAg+1vZl/zyIULFtWRvmDTGTSebJZQCoq9sHOKR9UcJ2wZr1aGq31eh0f/tjH4p/7mZ+JL/+BH4j/11d/dWMie++Fcs5oqQ3saZJMOlFmb9hTTlQmL7fKzrmS3YhmnQRXdN3Is6xOGnW8i/E45kdHcbBel9SE+dFRPBsOo7u5TSPx3O4sOsDwaNYTZx21jY7t5NZ79m31alOfCTeCco4eqqQy0dZmUsA4w4e2pXHMOkDajvdwRUXF7vBOMyNKIsNUdjWThBbvEtnq9/sxGAxivV7H6elpQ31eLBYxm81iMpkUGyWSqnQGFwK4bDdtJ1MetMw5c595LA0S5ABHkWKdR36EaQez2SwuLi5ivV7HcDgs9oqpjLJ3SmVUuoUIsFIXaMsjnpPy2WwW6/W6pFZI5WZ6hXzuxcVFPH36tGFjdQxuy04Ho8YeaWQniqkuk8kkIqKkWOg/LSeva+aRR8KjqLLtKqfUf7WL9uF1Z3qm6qCy+/XVfepKe0XFvtiZUOuGJxl1suUq4fX1dfwbv/W3xiGm4/nIJz4RH/nEJ2J1cBDf/m3fVn7XgyBDLMLVlv9GAsYykFBR5Sb51X58CLXPfS+2CZEpJlRksm11zPHVVXzywx+On/jIR+JDn/xknFxdNdRjHYOjtdl7Z8qHwq2sLx0Ly67t2NZtnQy/H/zat0UGuI3XXy911rLtHK5gZ8Se7c/PzLu/TwGvqKjYDn/2ZW8lglDd5eqzJJUKz4tYL5fLuL6+jvF4XGyZcpuZJqDPtMPX17cr75JkyW4qZYNKLdMKqf72+/2IaEbxqMaqfrTNzMNmSkrmOyQcUc1lWqXbKHUKVqtVSZWT7eT0eCrHbDaL2WzWsIHqTKhjkIlBJMD0ixTU9D3idgrAm5ubRvqOXu5jM3Elu6/UxvQNjBpk23MSA6ZouhCncnABnyqwVLwI9kr52Ad/4Y/+0fjwd393fN6nPlVSGv7+L//l8Ze/4ivetXM4Yf0fG77nN/2m8rB/7KMfrUSvoqLinzlUUrMfMlJaUVHx3x86uzyEnU7njYj4b/c92edH/POPI17eRGw6EZ23I974xxH/ZN/jVVS8i/i8zWbz8v/Qhaio+B8DXtQXVFT8/zGqL6jYCzsR6oqKioqKioqKioqKJmpOQUVFRUVFRUVFRcULoBLqioqKioqKioqKihdAJdQVFRUVFRUVFRUVL4BKqCsqKioqKioqKipeAJVQV1RUVFRUVFRUVLwAKqGuqKioqKioqKioeAFUQl1RUVFRUVFRUVHxAqiEuqKioqKioqKiouIFUAl1RUVFRUVFRUVFxQvg/wepxDA8+b2UZAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_faces = 3\n", + "n_rows = n_faces + 1\n", + "n_cases = 1\n", + "fig, axs = plt.subplots(n_rows, 2, figsize=(12, 3 * n_rows))\n", + "\n", + "for k in range(n_cases): \n", + " # Get average reconstruction error across test set\n", + " test_error = model_pre.reconstruction_error(X_test, sensor_range=[n_sensors])\n", + " \n", + " # Plot sensor locations\n", + "\n", + " axs[0, k].plot()\n", + " axs[0, k].imshow(img.reshape(image_shape), cmap=plt.cm.binary)\n", + " axs[0, k].set(title=f\"Predtermined (MSE: {test_error[0]:.2f})\")\n", + " \n", + "\n", + " # Plot reconstructed faces\n", + " for j in range(n_faces):\n", + " idx = 10 * j\n", + " img = model_pre.predict(X_test[idx, top_sensors_pre])\n", + " vmax = max(img.max(), img.min())\n", + " axs[j + 1, k].imshow(\n", + " img.reshape(image_shape),\n", + " cmap=plt.cm.binary,\n", + " vmin=-vmax,\n", + " vmax=vmax\n", + " )\n", + " yPredetermined = np.floor(top_sensors_pre/np.sqrt(n_features))\n", + " xPredetermined = np.mod(top_sensors_pre,np.sqrt(n_features))\n", + " axs[j + 1, k].plot(xPredetermined, yPredetermined,'*r')\n", + " axs[j + 1,k].scatter(predetermined_sensorsx, predetermined_sensorsy, color = 'b')\n", + "\n", + " error = model_pre.reconstruction_error(X_test[idx], sensor_range=[n_sensors])[0]\n", + " axs[j + 1, k].set(title=f\"MSE: {error:.2f}\")\n", + " \n", + " # Plot target image\n", + " true_img = X_test[idx]\n", + " vmax = max(true_img.max(), true_img.min())\n", + " axs[j + 1, k + 1].imshow(\n", + " true_img.reshape(image_shape),\n", + " cmap=plt.cm.binary,\n", + " vmin=-vmax,\n", + " vmax=vmax\n", + " )\n", + " axs[j + 1, k + 1].set(title=\"Original image\")\n", + " \n", + "\n", + "[ax.set(xticks=[], yticks=[]) for ax in axs.flatten()]\n", + "fig.tight_layout()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare the reconstruction errors for unconstrained and predetermined sensor placement on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The reconstruction error for the unconstrained case is [0.12295605]\n", + "The reconstruction error for the predetermined case is [1.34766841]\n" + ] + } + ], + "source": [ + "test_error_unconstrained = model_unconstrained.reconstruction_error(X_test, sensor_range=[n_sensors])\n", + "test_error_predetermined = model_pre.reconstruction_error(X_test, sensor_range=[n_sensors])\n", + "print(\"The reconstruction error for the unconstrained case is {}\".format(test_error_unconstrained))\n", + "print(\"The reconstruction error for the predetermined case is {}\".format(test_error_predetermined))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 11d5bc703fe953857b09e3b070e220a416294016 Mon Sep 17 00:00:00 2001 From: Niharika Karnik Date: Fri, 7 Jul 2023 15:18:37 -0600 Subject: [PATCH 51/52] Adding max_n and predetermined tests to test_optimizers.py, deleting comments from _gqr.py,spatially_constrained_qr.ipynb and _norm_calc-py,adding kwargs documentation to _custom.py to address Josh comments --- examples/spatially_constrained_qr.ipynb | 196 +++++++++++------------- pysensors/basis/_custom.py | 3 + pysensors/optimizers/_gqr.py | 11 +- pysensors/utils/_norm_calc.py | 4 +- tests/optimizers/test_optimizers.py | 50 +++++- 5 files changed, 143 insertions(+), 121 deletions(-) diff --git a/examples/spatially_constrained_qr.ipynb b/examples/spatially_constrained_qr.ipynb index 69eef01..33ce0b8 100644 --- a/examples/spatially_constrained_qr.ipynb +++ b/examples/spatially_constrained_qr.ipynb @@ -27,18 +27,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/niharikakarnik/projects/pysensors/pysensors/__init__.py:5: UserWarning: Module pysensors was already imported from /Users/niharikakarnik/projects/pysensors/pysensors/__init__.py, but /Users/niharikakarnik/opt/miniconda3/lib/python3.9/site-packages/python_sensors-0.3.5.dev67+g38db2a2-py3.9.egg is being added to sys.path\n", - " __version__ = get_distribution(\"python-sensors\").version\n" - ] - } - ], + "outputs": [], "source": [ "from time import time\n", "\n", @@ -64,6 +55,16 @@ "Loading and preprocessing the data:" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import ssl\n", + "ssl._create_default_https_context = ssl._create_unverified_context" + ] + }, { "cell_type": "code", "execution_count": 3, @@ -73,6 +74,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "downloading Olivetti faces from https://ndownloader.figshare.com/files/5976027 to /Users/karnn/scikit_learn_data\n", "Number of samples: 400\n", "Number of features (sensors): 4096\n" ] @@ -134,9 +136,9 @@ "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -183,7 +185,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The list of sensors selected is: [2204 4038 3965 320 594 253 878 3618 2331 3999 429 2772 2878 3469\n", + "The list of sensors selected is: [2204 4038 3965 320 253 594 878 3618 2331 3999 429 2772 2878 3469\n", " 1243]\n" ] } @@ -207,7 +209,7 @@ "\n", "# sensor locations based on pixels of the image\n", "xTopUnc = np.mod(top_sensors,np.sqrt(n_features)) \n", - "yTopUnc = np.floor(top_sensors/np.sqrt(n_features)) ## change to np.unravel\n", + "yTopUnc = np.floor(top_sensors/np.sqrt(n_features)) \n", "xAllUnc = np.mod(all_sensors,np.sqrt(n_features))\n", "yAllUnc = np.floor(all_sensors/np.sqrt(n_features))\n", "\n", @@ -282,17 +284,17 @@ " \n", " \n", " 4\n", - " 594\n", - " 18\n", - " 9\n", - " \n", - " \n", - " 5\n", " 253\n", " 61\n", " 3\n", " \n", " \n", + " 5\n", + " 594\n", + " 18\n", + " 9\n", + " \n", + " \n", " 6\n", " 878\n", " 46\n", @@ -356,8 +358,8 @@ "1 4038 6 63\n", "2 3965 61 61\n", "3 320 0 5\n", - "4 594 18 9\n", - "5 253 61 3\n", + "4 253 61 3\n", + "5 594 18 9\n", "6 878 46 13\n", "7 3618 34 56\n", "8 2331 27 36\n", @@ -396,14 +398,12 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", 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", 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" + "
" ] }, "metadata": {}, @@ -877,9 +873,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, @@ -961,8 +957,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The reconstruction error for the unconstrained case is [0.12295605]\n", - "The reconstruction error for the constrained exact_n case is [0.11306471]\n" + "The reconstruction error for the unconstrained case is [0.12293136]\n", + "The reconstruction error for the constrained exact_n case is [0.11314685]\n" ] } ], @@ -1050,9 +1046,9 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -1151,7 +1147,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The list of sensors selected is: [2204 4038 3965 320 253 594 3618 878 2331 3999 429 2772 2878 3469\n", + "The list of sensors selected is: [2204 4038 3965 320 594 253 3618 878 2331 3999 429 2772 2878 3469\n", " 1243]\n" ] } @@ -1178,26 +1174,22 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1550,18 +1536,18 @@ " \n", " \n", " 4\n", - " 594\n", - " 18\n", - " 9\n", " 253\n", " 61\n", " 3\n", - " \n", - " \n", - " 5\n", " 253\n", " 61\n", " 3\n", + " \n", + " \n", + " 5\n", + " 594\n", + " 18\n", + " 9\n", " 594\n", " 18\n", " 9\n", @@ -1657,8 +1643,8 @@ "1 4038 6 63 4038 6 63\n", "2 3965 61 61 3965 61 61\n", "3 320 0 5 320 0 5\n", - "4 594 18 9 253 61 3\n", - "5 253 61 3 594 18 9\n", + "4 253 61 3 253 61 3\n", + "5 594 18 9 594 18 9\n", "6 878 46 13 3618 34 56\n", "7 3618 34 56 878 46 13\n", "8 2331 27 36 2331 27 36\n", @@ -1696,9 +1682,9 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", + "image/png": 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", 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" + "
" ] }, "metadata": {}, @@ -1755,9 +1741,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -1833,8 +1819,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The reconstruction error for the unconstrained case is [0.12295605]\n", - "The reconstruction error for the predetermined case is [1.34766841]\n" + "The reconstruction error for the unconstrained case is [0.12293136]\n", + "The reconstruction error for the predetermined case is [1.30984724]\n" ] } ], @@ -1862,7 +1848,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/pysensors/basis/_custom.py b/pysensors/basis/_custom.py index 811e716..e54bf79 100644 --- a/pysensors/basis/_custom.py +++ b/pysensors/basis/_custom.py @@ -27,6 +27,9 @@ class Custom(InvertibleBasis, MatrixMixin): """ def __init__(self, U, n_basis_modes=10, **kwargs): + ''' + kwargs : Not defined but added to remain consistent with prior basis functions. + ''' if isinstance(n_basis_modes, int) and n_basis_modes > 0: super(Custom, self).__init__()#n_components=n_basis_modes, **kwargs self._n_basis_modes = n_basis_modes diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py index 9f6ddfa..97e0607 100644 --- a/pysensors/optimizers/_gqr.py +++ b/pysensors/optimizers/_gqr.py @@ -26,6 +26,10 @@ class GQR(QR): "Data-driven sparse sensor placement for reconstruction: Demonstrating the benefits of exploiting known patterns." IEEE Control Systems Magazine 38.3 (2018): 63-86. + + Niharika Karnik, Mohammad G. Abdo, Carlos E. Estrada Perez, Jun Soo Yoo, Joshua J. Cogliati, Richard S. Skifton, + Pattrick Calderoni, Steven L. Brunton, and Krithika Manohar. + Optimal Sensor Placement with Adaptive Constraints for Nuclear Digital Twins. 2023. arXiv: 2306 . 13637 [math.OC]. @ authors: Niharika Karnik (@nkarnik2999), Mohammad Abdo (@Jimmy-INL), and Krithika Manohar (@kmanohar) """ @@ -76,13 +80,6 @@ def fit(self,basis_matrix,**optimizer_kws): n_features, n_samples = basis_matrix.shape # We transpose basis_matrix below max_const_sensors = len(self.idx_constrained) # Maximum number of sensors allowed in the constrained region - ## Assertions and checks: - # if self.n_sensors > n_features - max_const_sensors + self.nConstrainedSensors: - # raise IOError ("n_sensors cannot be larger than n_features - all possible locations in the constrained area + allowed constrained sensors") - # if self.n_sensors > n_samples + self.nConstrainedSensors: ## Handling zero constraint? - # raise IOError ("Currently n_sensors should be less than min(number of samples, number of modes) + number of constrained sensors,\ - # got: n_sensors = {}, n_samples + const_sensors = {} + {} = {}".format(self.n_sensors,n_samples,self.nConstrainedSensors,n_samples+self.nConstrainedSensors)) - # Initialize helper variables R = basis_matrix.conj().T.copy() p = np.arange(n_features) diff --git a/pysensors/utils/_norm_calc.py b/pysensors/utils/_norm_calc.py index f4c5213..e991a9c 100644 --- a/pysensors/utils/_norm_calc.py +++ b/pysensors/utils/_norm_calc.py @@ -7,9 +7,7 @@ def unconstrained(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): return dlens -def exact_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): ##Will first force sensors into constrained region - # num_sensors should be fixed for each custom constraint (for now) - # num_sensors must be <= size of constraint region +def exact_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): """ Function for mapping constrained sensor locations with the QR procedure. diff --git a/tests/optimizers/test_optimizers.py b/tests/optimizers/test_optimizers.py index ea9a041..1866c7d 100644 --- a/tests/optimizers/test_optimizers.py +++ b/tests/optimizers/test_optimizers.py @@ -107,12 +107,50 @@ def test_gqr_exact_constrainted_case1(data_random): # Get ranked sensors from GQR sensors_GQR = GQR().fit(x.T, idx_constrained=forbidden_sensors,n_sensors=total_sensors,n_const_sensors=exact_n_const_sensors, constraint_option='exact_n_const_sensors').get_sensors()[:total_sensors] + assert sensors_GQR.intersection(forbidden_sensors) == 3 + - # try to compare these using the validation metrics +def test_gqr_max_constrained_case1(data_random): + ## In this case we want to place a total of 10 sensors + # with a constrained region that is allowed to have a maximum of 3 sensors + # but 4 of the first 10 are in the constrained region + x = data_random + # unconstrained sensors (optimal) + sensors_QR = QR().fit(x.T).get_sensors() + # exact number of sensors allowed in the constrained region + total_sensors = 10 + max_n_const_sensors = 3 + forbidden_sensors = [8,5,2,6] + totally_forbidden_sensors = [x for x in forbidden_sensors if x in sensors_QR][:max_n_const_sensors] + totally_forbidden_sensors = [y for y in forbidden_sensors if y not in totally_forbidden_sensors] + costs = np.zeros(x.shape[1]) + costs[totally_forbidden_sensors] = 100 + # Get ranked sensors + sensors = CCQR(sensor_costs=costs).fit(x.T).get_sensors()[:total_sensors] -## TODO -def test_gqr_max_constrained(): - pass + # Forbidden sensors should not be included + chosen_sensors = set(sensors[: (x.shape[1] - len(totally_forbidden_sensors))]) + assert chosen_sensors.isdisjoint(set(totally_forbidden_sensors)) + + + # Get ranked sensors from GQR + sensors_GQR = GQR().fit(x.T, idx_constrained=forbidden_sensors,n_sensors=total_sensors,n_const_sensors=max_n_const_sensors, constraint_option='max_n_const_sensors').get_sensors()[:total_sensors] + assert sensors_GQR.intersection(forbidden_sensors) == 3 + +def test_gqr_predetermined_case1(data_random): + ## In this case we want to place a total of 10 sensors + # 2 of the sensors are predetermined by the user + x = data_random + # unconstrained sensors (optimal) + sensors_QR = QR().fit(x.T).get_sensors() + # Predtermined sensors + total_sensors = 10 + n_sensors_pre = 2 + predetermined_sensors = [8,5] + + # Predetermined sensors shopuld be included + # Get ranked sensors from GQR + sensors_GQR = GQR().fit(x.T, idx_constrained=predetermined_sensors,n_sensors=total_sensors,n_const_sensors=n_sensors_pre, constraint_option='predetermined').get_sensors()[:total_sensors] + assert sensors_GQR.intersection(predetermined_sensors) == 2 + -def test_gqr_radii_constrained(): - pass \ No newline at end of file From 082a963ca73be79ed704905ff42cfbb4a89db494 Mon Sep 17 00:00:00 2001 From: Niharika Karnik Date: Fri, 7 Jul 2023 15:40:48 -0600 Subject: [PATCH 52/52] Adding the cost_constrained_qr.ipynb --- examples/cost_constrained_qr.ipynb | 370 ++++++++++++++++++++++++ examples/spatially_constrained_qr.ipynb | 10 - 2 files changed, 370 insertions(+), 10 deletions(-) create mode 100644 examples/cost_constrained_qr.ipynb diff --git a/examples/cost_constrained_qr.ipynb b/examples/cost_constrained_qr.ipynb new file mode 100644 index 0000000..e1156d1 --- /dev/null +++ b/examples/cost_constrained_qr.ipynb @@ -0,0 +1,370 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cost-constrained QR (CCQR)\n", + "This notebook explores the `PySensors` cost-constrained QR `CCQR` optimizer for cost-constrained sparse sensor placement (for reconstruction).\n", + "\n", + "Suppose we are interested in reconstructing a field based on a limited set of measurements.\n", + "Examples:\n", + "* Fluid flows (estimating the drag on an airplane wing with pressure sensors on different parts of the wing)\n", + "* Atmospheric dynamics (approximating the concentrations of different molecules based on measurements taken at only a few locations)\n", + "* Sea-surface temperature (predicting the temperature at any point on the ocean based on the temperatures measured at various other points on the ocean)\n", + "\n", + "In other notebooks we have shown how one can use the `SSPOR` class to pick optimal locations in which to place sensors to accomplish this task. But so far we have treated all sensor locations as being equally viable. What happens when some sensor locations are more expensive than others? For example, it might be ten times as costly to place and maintain a buoy measuring the sea-surface temperature in the middle of the Atlantic compared to one close to the coast.\n", + "\n", + "The cost-constrained QR algorithm was devised specifically to solve such problems. The `PySensors` object implementing this method is named `CCQR` and in this notebook we'll demonstrate its use on a toy problem.\n", + "\n", + "See the following reference for more information ([link](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8579238))\n", + "\n", + " Clark, Emily, et al. \"Greedy sensor placement with cost constraints.\" IEEE Sensors Journal 19.7 (2018): 2642-2656." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2020-10-07T16:17:17.467243Z", + "start_time": "2020-10-07T16:17:16.153291Z" + } + }, + "outputs": [], + "source": [ + "from time import time\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn import datasets\n", + "\n", + "import pysensors as ps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "We'll consider the Olivetti faces dataset from AT&T. Our goal will be to reconstruct images of faces from a limited set of measurements.\n", + "\n", + "First we've got to load and preprocess the data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2020-10-07T16:17:17.526638Z", + "start_time": "2020-10-07T16:17:17.469713Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "400 4096\n" + ] + } + ], + "source": [ + "faces = datasets.fetch_olivetti_faces(shuffle=True, random_state=99)\n", + "X = faces.data\n", + "\n", + "n_samples, n_features = X.shape\n", + "print(n_samples, n_features)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2020-10-07T16:17:17.543405Z", + "start_time": "2020-10-07T16:17:17.531550Z" + } + }, + "outputs": [], + "source": [ + "# Global centering\n", + "X = X - X.mean(axis=0)\n", + "\n", + "# Local centering\n", + "X -= X.mean(axis=1).reshape(n_samples, -1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2020-10-07T16:17:17.552697Z", + "start_time": "2020-10-07T16:17:17.546032Z" + } + }, + "outputs": [], + "source": [ + "# From https://scikit-learn.org/stable/auto_examples/decomposition/plot_faces_decomposition.html\n", + "n_row, n_col = 2, 3\n", + "n_components = n_row * n_col\n", + "image_shape = (64, 64)\n", + "\n", + "def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):\n", + " plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n", + " plt.suptitle(title, size=16)\n", + " for i, comp in enumerate(images):\n", + " plt.subplot(n_row, n_col, i + 1)\n", + " vmax = max(comp.max(), -comp.min())\n", + " plt.imshow(comp.reshape(image_shape), cmap=cmap,\n", + " interpolation='nearest',\n", + " vmin=-vmax, vmax=vmax)\n", + " plt.xticks(())\n", + " plt.yticks(())\n", + " plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2020-10-07T16:17:17.805297Z", + "start_time": "2020-10-07T16:17:17.554709Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_gallery(\"First few centered faces\", X[:n_components])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll learn the sensors using the first 300 faces and use the rest for testing reconstruction error." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2020-10-07T16:17:17.809087Z", + "start_time": "2020-10-07T16:17:17.806700Z" + } + }, + "outputs": [], + "source": [ + "X_train, X_test = X[:300], X[300:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sensor costs\n", + "In order for `CCQR` to decide which sensors are most important, it needs to know the costs associated with each of them. To this end, one must construct an array specifying these costs. Larger costs/values will make `CCQR` less likely to pick a given sensor, and smaller ones will have the opposite effect.\n", + "\n", + "We'll consider three different sets of sensor costs:\n", + "\n", + "* Zero cost: all sensors have zero cost\n", + "* Center blocked: sensors within a square near the center of the image have a fixed positive cost and others have none\n", + "* Left blocked: sensors on the left side of the image have a fixed positive cost and others have none" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2020-10-07T16:17:17.982782Z", + "start_time": "2020-10-07T16:17:17.810593Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cost_names = ('Zero', 'Center blocked', 'Left blocked')\n", + "sensor_costs = {}\n", + "\n", + "# Zero cost\n", + "sensor_costs['Zero'] = np.zeros(image_shape).reshape(-1)\n", + "\n", + "# Center blocked\n", + "costs = np.zeros(image_shape)\n", + "w = 10\n", + "costs[(32 - w):(32 + w), (32 - w):(32 + w)] = 1\n", + "sensor_costs['Center blocked'] = costs.reshape(-1)\n", + "\n", + "# Left blocked\n", + "costs = np.zeros(image_shape)\n", + "costs[:, :20] = 1\n", + "sensor_costs['Left blocked'] = costs.reshape(-1)\n", + "\n", + "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n", + "for name, ax in zip(cost_names, axs):\n", + " ax.imshow(sensor_costs[name].reshape(image_shape), vmin=0, vmax=1, cmap=plt.cm.gray)\n", + " ax.set(title=name, xticks=[], yticks=[]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we'll apply `CCQR` with each of the above cost functions, plotting learned sensor locations and a few examples of reconstructed faces from the test set.\n", + "\n", + "Note that the mean-square error (MSE) reported in the first row is the MSE across all images in the test set. For the later rows, we give the MSE for the particular image being shown." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2020-10-07T16:17:23.614083Z", + "start_time": "2020-10-07T16:17:17.985060Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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ZgbZdQ0NDaG1tRV9f38jnp6TtYh4RXaRNk1JO+Gfu3LlyInp7e+XDDz8sExMTZWZmpvz9738vbTbbhD5LdKmGh4fl7373O5mRkSGnTZsmH3nkEdnf3z+hz3q/2wHlQzB/JppbXV1d8qc//ak0Go2yoKBAPvnkk9LpdE7os0SXwuFwyD/+8Y8yNzdXxsfHy3/4h3+Q586dm/DnAeyVCuaWDCC/YsX7778vFyxYIAHIdevWyUOHDikdUkwLtO06ePCgXLNmjYTnAdJSMo+IpsyF2rSArlRNlFarxezZs7F69WoYDAYUFRVN6GoB0WSoVCoUFRXhqquugt1ux6xZs6DRhOQrrhidTofy8nKsXbsWiYmJKCgo4BVgCikhBAoLC7FixQoMDg6irKwMOp1O6bBoEtLS0nD55ZcjPT0dDQ0NSExMVDqkmBZo2zVt2jTMmzcPGo0GO3bsmLpAieiCQnLGaTAYsGzZMlRWVkKlUiEjI2PCQ0WILpVGo0FjYyNmzpwJKSXS0tKg1+uVDiuo4uLisHLlStTX10OtViMjI4MdFhRSarUadXV1yM/Ph8vlQmpqKgwGg9Jh0SQUFhbirrvuwtDQEBITE2E2m5UOKaYF2nZlZmbitttuw7XXXosbb7xxCiMlogsJSVE1UkhlZGSEYvVE4xJCIDU1FampqUqHEjJqtRqZmZnIzMxUOhSKIcnJyUhOTlY6DAqSuLg4zJgxQ+kwyCvQtstgMKCgoAAAeC8cURhhFzcREREREdEksKgiIiIiIiKaBBZVREREREREk8CiioiIiIiIaBJYVBEREREREU0CiyoiIiIiIqJJYFFFREREREQ0CSyqiIiIiIiIJiGgosput+PUqVM4e/YsHA5HqGIiijnMLSIiIqLIFVBR1draikceeQTbtm1DR0dHqGIiijm+uXX27FmlwyEiIiKiAARUVHV3d2Pz5s3Yvn07ent7QxUTUczp7u7G008/zdwiIiIiikCaQBY2GAwoLi5Gbm4u9Hp9qGKiCGC329HZ2Yne3l4YDAaYzWYkJCQoHVbEGsmtvLw85hYRERFRhAmoqMrJycFvfvMbZGZmwmw2hyomigDnzp3DX/7yF3zwwQcoLCzELbfcgrq6OqXDili+uZWRkaF0OEREREQUgICKqqSkJKxduzZUsVAEGRwcxP79+/HSSy+hqqoKy5cvVzqkiMbcIiIiIopcARVVRCOMRiNKS0uxaNEiFBUVISUlRemQiCKSlBLt7e04c+YMXC4XsrOzkZubC7VarXRoRFHB4XDgzJkzaG1thcFgQEFBAdLT05UOi4iiDIsquiQpKSm4/vrrsXjxYsTHx6OwsFDpkIgiksvlwu7du7FlyxYMDQ3h+uuvx0033YT4+HilQyOKCgMDA3jllVfw8ssvIyMjA3feeSeuuOIKCCGUDo2IogiLKrokBoMBpaWlKC0tVToUoogmpURbWxt27doFi8WChoYGOJ1OpcMiihp2ux1NTU3YuXMncnJysHr1akgpWVQRUVDFVFHV0dGBY8eOYWBgALm5uSgpKUFcXJzSYZFC+vv7cezYMXR0dKCvr0/pcChGqVQqFBYWYsWKFbBarSgpKYFWq1U6rEvmm1dE4cBgMKCiogJXX3010tPTkZubG9EFFdsuovAUU0XV559/jo0bN+Krr77C6tWr8bd/+7csqmJYW1sbnn76abz//vtoaWlROhyKUWq1GvPmzcP06dPhdDqRnp4Og8GgdFiXrL29Hc888wx27NihdChEAACTyYQ1a9agsbERWq0WZrM5oosqtl1E4Smmiqr+/n589dVXOHr0KGpqauBwOJQOiRQ0PDyM06dP48iRI3C73UqHQzEsJSUlaiZ78c0ronCg0WiQnZ2N7OxspUMJCrZdROEppoqqvLw8XHPNNaipqcGCBQtgMpmUDokUlJKSgmXLliE1NRUvv/yy0uEQRYXk5GQsW7YMKSkpkFJi06ZNSodEFFXYdhGFJyGlnPDCdXV1cu/evSEMJ7SsVit6e3vhcDgQHx+PadOmQaOJqbqSfDgcDvT29mJoaAjXXHMNDh06pNh4kEjPLaIRvnkFAIWFhfuklIo+GZz5RdFEqbaLeUQECCH8tmlRX1G43W44HA643W6o1WpkZmZG9FhqCh6tVjv6rBKdTqdwNJHN6XSOzlin0WjYWRHDfPOKpo5vDmq1Wj7nLATC5TjHtosoPEX9mU9rayv++te/oqWlBcXFxVi4cCHS0tKUDosoarhcLhw8eBC7du2ClBINDQ2ora2N6BnsiCKJ3W7HJ598gv3790Or1WL+/PmorKxkYRVELpcLn376KXbv3g2n04n6+nrMnTuXRQ0RjYr6ourMmTN4+umnsWfPHqxatQolJSUsqoiCyOl0Ys+ePdi4cSOcTid++MMfYs6cOSyqiKaIzWbDzp078cQTT8BoNEKr1aK8vJxFVRA5nU7s3bsXGzduxPDwMO6//37MmTOHRRURjZrSosrtdsNqtcJms0Gj0Ywe/KeCy+VCIPePEUUqp9MJq9UKh8MBnU4Ho9E4JSdXUsrRHyKaOiN553a7mYMh5LtvuY+JaKwpLap6e3vxzjvv4ODBg8jMzMSKFSswa9askG4zNzcXN998MxYvXozi4mKO9aeo19zcjLfeegsnT55EWVkZrrzySmRmZoZse2q1GnV1dXjggQdGh//xKhXR1NHr9Vi4cCH0ej20Wi1qa2t5lSrINBoN6urqcP/9948O/+NVKiLyNaVFVV9fH95880385S9/QUVFBWbOnBnyoionJwfXX389XC4X1Go1D4IU9VpbW/H8889j586do48QCGVRpdFoUFNTg4qKCgCem6g5UQXR1NHr9Zg3bx7mzp0LgBNVhIJarUZ1dTXKy8sBeI577DwiIl8BnfnY7XacOXMGRqMR06ZNC/iAolKpkJCQgNTUVEybNm1KChyVSgW9Xh/y7RBNht1uR3Nz82huTaYo0Wq1SEhIQFpaGkwm05QUOJzxj0hZWq2WJ/khxuMcEV1IQEeH1tZW/PKXv0RVVRWuu+465OTkBLSxlJQUrF+/HpWVlUhJSUFJSUlAnyeKVq2trXjkkUdQVVWF9evXIzs7+5LXlZ+fjzvvvBOrV69Gfn4+zGZzECMlIiIiorECKqq6u7uxadMmXHPNNViyZEnARVVCQgIWLVqEBQsWQAgBlUoV0OeJotVIbq1duxbLli2bVFFlNptx1VVXQUoJlUrFPCMiIiIKsYCKKoPBgKKiIuTk5FzykDqe5EUHh8OBzs5O9PX1Qa/Xw2w2w2QyKR1WxBrJrdzc3KAMV+X9FJHJ7Xaju7sb3d3dEEIgPT0dycnJfGA5hczAwAA6Ozths9kwbdo0ZGRkTGgYYaS2AZEaNxGFv4CKqpycHPzqV79CVlYWhxTFuO7ubjz//PP44IMPUFBQgFtuuWX0JmkKnG9uZWRkKB0OKcRut+P999/HSy+9BK1Wi/Xr12PVqlW8j4NC5tixY9i6dSuam5tx+eWX48Ybb5zQMShS24BIjZuIwl9ALXVSUhKuu+66UMVCEWRwcBB79+7Fiy++iMrKSixbtkzpkCIac4sAzzPGjh07hldeeQUGgwHV1dVwu91Kh0VRrK2tDdu3b8fhw4cRHx+PNWvWTOhzkdoGRGrcRBT+2P1Jl8RgMGDmzJlobGxEUVERUlJSlA6JKOKpVCrk5uaioaEBer0eWVlZHPpHIZWcnIyqqiokJiaiuLgYBoNhQp+L1DYgUuMmovDHooouSWpqKm644QYsWbIE8fHxmD59utIhEUU8vV6PpUuXori4GGq1Gvn5+Rz6RyFVVlaGBx98EENDQzCbzUhOTp7Q5yK1DYjUuIko/LG1pktiMBgwa9askD+8mSiWqNVqFBQUoKCgQOlQKEakpqYiNTU14M9FahsQqXETUfi7pKKqo6MDx48fR39/P/Ly8lBSUgKj0RiUgEK5biJf/f39OH78ODo6OtDX16d0OAA83/9jx45hcHAQubm5KCkpmfBwnAuRUqKlpQUnTpyAzWZDYWEhiouL+bBQCrqBgQEcO3YMHR0dSodCFNFsNhu++OILnD59GkajESUlJcjJyQnLtouILrGo+vzzz/HYY4/hyy+/xJo1a3DvvfcG/MwqJdZN5KutrQ1btmzBBx98gJaWFqXDAQAcPXoUGzduxNdff41rr70W99xzDzIzMye9Xikl9u3bhyeeeALnzp3DTTfdhDvuuANJSUlBiJroG21tbXjmmWewY8cOpUMhimgWiwWvvfYann/+eZjNZtx7773IysoKy7aLiC6xqOrv78cXX3yBo0ePoqamBg6HI2gBhXLdRL6Gh4dx+vRpHD58OGxmWOvv78eXX36J48ePo76+Pmjffyklent7cezYMZw9exbt7e1wuVxBWXckk1JCSgkAfH5ekAwPD+PUqVM4fPjw6L6lyOabJ0IITp6CqdknTqcTra2tOHz4MPr6+tDb2wsgPNsuIrrEoiovLw9r1qxBbW0tGhsbg/rgvFCum8hXcnIyli5diuTkZLz66qtKhwPgm+9/XV0dLrvsMsTHxwdlvUIIFBUV4brrroPFYkFtbW1QHjIcyaxWKw4dOoQTJ04gISEB1dXVKCwsVDqsiDeSVyNXQTdv3qxwRDQZDocDR44cwdGjR6HRaFBZWYmSkpKY7oSYqn1iMBhQX18Pi8WC1NRUFBUVQQgRlm0XEQEikJ7Euro6uXfvXgwNDaGnpwcOhwMmkwlJSUlBm6EqlOsm8mW329HT04OhoSGsXbsWn332mWLdr/5yKzk5GWq1OijbsFgs6OnpgdvtRkJCApKSkmL6xOjs2bPYuHEjtm3bhpycHDz00ENYvXo1e+EnyTevAGDGjBn7pJR1SsY0kl8UuIGBATz55JN46qmnYDQacd999+G73/0udDqd0qEpZqr2icvlQm9vLwYGBqDRaJCcnIz4+HjF2i7mEREghPDbpgVUrUgpMTw8DK1Wi+zs7JCcfMTFxSEuLu6CyzidTjidTgghoNFognbSGQoulwtOpxNSSmg0GhaIYyj5t9TpdDCbzQCg+FWbqcgtk8l00Su/DocDLpcLQghotdqwLbqklHA6nXC5XFCpVNBoNAHH6nK50N/fj/b2duj1egwPD4co2tBxu91wOp1wu91Qq9XQaDQQQih63PHNK1JGMI+rUkoMDAygvb0d8fHxo8VyrBgvl6Zqn6jV6nFnZwyntouIvhFQS9vZ2Yk//OEPKCoqQmNjI9LS0kIVl192ux379u3Dvn37oNVqMW/ePMyZMycsCyuXy4VDhw7hk08+gcPhQF1dHWpra2O6h89XJP0tQ803txYsWHBJUxxP1sDAAHbt2oUjR44gJSUFCxYsQFFR0ZTHMRG9vb3YuXMnvvjiC2RmZmLBggXIz88PaB3x8fFYtGgRdDodUlJSUFxcHHFXqVpaWvDhhx+ivb199IGmSUlJOHr0KHbt2gWbzYbq6mrU19fz5CtGDA0NYffu3fjss89gMplw2WWXYdasWZf83dbpdGhoaMBdd90FnU4XU8doKSWOHz+Ojz/+GIODg6iqqkJDQ0NM7xMi8i+goqq9vR2//e1vsWrVKsycOVORospms+GDDz7AE088AaPRiIceeghlZWVheUBzOp345JNP8Oijj8Jms+G+++5DRUUFiyqvSPpbhtpIbl111VUoLS1VpKjq7+/H66+/jueeew7FxcVIT08P26Kqu7sbL7zwAl599VXU1tYiJyfnkoqqlStXYvHixVCr1Re9Qh6OTp06hc2bN2P//v1Ys2YNSktLkZiYiAMHDmDjxo3o7+/HPffcg8rKShZVMcJiseDtt9/Gli1bkJ2djcTERJSWll5yUaXX67Fo0SLU1dVBCIG4uLiYOUZLKfHZZ5/h8ccfR2dnJ26//XaUl5cjNTU1ZvcJEfkX8PA/h8MxeilcCVJKuN1uuFwuuN3usJ/5ZiTWkXhDZXh4GDabDYDn5tZIOIEK9d8ykvbJSG65XC7Fc2tk6FA4zw44Eutk9plKpbrokEin0wmr1QqXywWdTgeDwRBWQyKllHC5XHA4HHC73aP7wXffBPvvaLPZRodKhntexaqRIWsj34uJstvtGB4ehtvthu5p7QIAACAASURBVF6vh8FgGC0aQtXpEO7H6ZE2amSYrZQy5vcJEY0voKIqMzMT999/P2bOnIn09PRQxXRBer0eCxcuhE6ng1arxdy5c8O2h0ij0aCurg73338/XC7X6LCBYLPZbNi5cyc+/PBD6HQ6LFmyBA0NDWG7X0aE8m8ZaftkJLdKSkoUuQIMAImJiVi5ciWys7ORkpKCkpISReKYiNTUVKxbtw6lpaXIzs5GQUFBSLZz8uRJvPnmm2htbUVVVRWuuOIKRa4i+pOfn49bb70Vy5Ytw6xZs5CamgqVSoXq6mrcf//9sNlsqKurC9pJmcPhwK5du/DBBx9ACIElS5Zg/vz5vFc0jMTHx49+TxMTE1FWVjahq1RSShw+fBjbt29Hf38/GhoasHTp0qDNQDqecD9OCyFQUVGBe+65BxaLBTU1NSHdH0D47xMi8i+gljAjIwMPPvgg1Gq1YkPY9Ho95s+fj7lz5wLwjPcO14ONWq1GdXU1ysvLAQBarRZarTbo27HZbPj4449Hh9ElJyeHdbE5IpR/y0jbJyO5pdFoFMuthIQELF++HIsXL4ZKpQrrYarJycm4+uqrsWLFipDGeurUKfz5z3/GwYMH8Z3vfAd1dXVhVVTl5ubihhtugNvtHv3ujJwIlpSUQEoJrVYbtP3jcDiwZ88e/PGPf4RarUZiYiLq6upYVIWRuLg4LF68GI2NjRBCjH4nLkZKiaNHj2LTpk1oa2uDzWbDvHnzQl5UhfNxWgiB2bNno6ioaDSXQtGG+wr3fUJE/gXUEqpUqpAcYO12O/r6+mCz2WA0GjFt2rQLNtJTcWALlqmIVQgBo9GIpKSkiBsqEKr9E2n7JFS5NTw8jL6+PjidTsTFxSExMdFv4yyEgF6vD/t9BXw71pHphYeHh2EwGDBt2rSgfae0Wu3oox3i4+PDaugf4Om4MRqN570eqhn/hBCj+1itVo8OD6PwMVJIXUohbTAYkJCQAKvViri4uJB/3yPhOD3Vs2dGwj4hovGFRffiqVOn8OKLL6KpqQlVVVVYu3YtcnJylA4rYuj1eixevBgpKSnQaDSoqamJ+Z5j7hOP48eP4+WXX0ZbWxvmzZuHa665BikpKUqHFVQtLS146aWXcOzYMZSVleHaa68NeNIKf2bMmIG77roLXV1dKCoqirp9FyitVouFCxfCZDKNDjOMxbyKRkIIVFZW4sEHH8TQ0BDKyspCPnkLj9Pn4z4hilxhkant7e147bXX8NFHH2HNmjVYtGgRi6oA6HQ61NbWorq6GoCn9zrWe4+5TzxOnz6NF154AceOHYPNZsPSpUujrjA4e/Ys3nzzTbz77rtYuXIlFi1aFLSiKicnB+vWrRu9OT3Wh+BoNBpUVlaioqICgOcKa7hdvaNLI4TAzJkzMWPGDABT87flcfp83CdEkSssiiqj0Yjs7GwUFRUhOzs7rO/lCFc8uTkf94nnpvWCggI4HA6Yzeao7PE0GAzIzs5GcXExsrOzgzpchoXU+ZhX0WvkYcFTid+n83GfEEWmsDjDKiwsxA9+8AN0d3cjOzsbmZmZSodEFBVmz56NBx54AP39/SgoKEBSUpLSIQVdbm4u7rjjDqxevRpmsxnZ2dlKh0REREQxJiyKqrS0NCxZskTpMIiiTlZWFrKyspQOI6RSUlKwaNEipcMgIiKiGMbry0RERERERJPAooqIiIiIiGgSWFQRERERERFNQljcU0VERETKamlpwYkTJ2Cz2VBQUICioiLOxktENEEsqoiIiGKc2+3G/v378cc//hFnz57Fhg0bcOedd0bdc+2IiEKFw/8opkkp4Xa7lQ6DKKqM5BVzK3JIKdHT04OjR4/i0KFDaG1thdPpVDos8oNtF1H44ZUqilk9PT349NNP0dzcjHPnzikdDlFU6O3txcGDB9Hc3Kx0KBQAlUqFGTNmYN26dejt7UVtbS0MBoPSYdE42HYRhScWVRSz2tvb8eyzz+Ldd99FS0uL0uEQRYX29nZs3boV77zzjtKhUACEEKiurkZBQQGcTicSExNhMpmUDovGwbaLKDxNuqhyu91wOp2QUkKtVkOjYZ1GkcHhcKC7uxutra1wOBxKh3Me5hZFIofDga6uLrS2tiodCgXIZDKxkIoA4d52EcWqSZ2lSSlx/Phx7Nq1C4ODg5gzZw4aGhpgNBqDFR9RyKSlpeHqq69GQUEBtm7dqnQ43yKlxNGjR7F7925YrVZUV1ejvr4eer1e6dCILig1NXU0rwDg0UcfVTgiougSzm0XUSybdFF16NAhPP744+jo6MDtt9+O8vJyFlUUETIyMnD99ddj7dq1eO+995QO51vcbjcOHjyIf/u3f0NPTw++//3vo6KigkUVhT3fvAJYVBEFWzi3XUSxLCjD/+x2O+x2O1wuF6SUwYiLKOQ0Gg2mTZs2+v/hRkoJp9MJm83G3KKI4ZtXRBR84d52EcWqSWWjEALl5eW4++67YbFYUFtbi/j4+GDFRhSzVCoV5syZg7vvvhtWqxV1dXW8AkxEREQUpiZdVJWVlaG4uBhSSmi1Wmi12mDFRhSzhBCoqKhASUnJaG7pdDqlwyIiIiKicUz6urFGo+HlZ6IQYG4RERERRQaV0gEQERERERFFMhZVREREREREk8CiioiIiIiIaBJYVBEREREREU0CiyoiIiIiIqJJCKioGhwcxO7du3HixAlYrdZQxUQUc5hbRERERJEroKKqpaUF//iP/4hnnnkG7e3toYqJKOa0tLTgZz/7GZ599ll0dnYqHQ4RERERBSCgh+AMDAxg+/btSEhIgMViCVVMRDFnYGAA77zzDkwmEwYGBpQOh4iIiIgCEFBRlZiYiMbGRtTU1CAhISFUMRHFHOYWERERUeQKqKjKycnBb37zGyQlJSEzMzNUMRHFHN/cMpvNSodDRERERAEIqKiKi4tDbW1tqGKhEJNSjv6/EELBSGgs5hYR0fjYdhFRJAioqKLI1dPTg0OHDqGlpQUZGRmoqqpCenq60mERERH5xbaLiCIFi6oY0d7ejmeffRbvvfce6uvr8ZOf/IQNExERhTW2XUQUKfjw3xBzuVyw2+2w2+1wuVyKxeFwONDV1YUzZ86go6MDdrtdsViIiCi8se0iIgoMr1SFkMvlwuHDh7F37164XC7U1NSguroaWq12ymNJTU3FypUrkZubi6KiIk6GQERE42LbRUQUOBZVIeR0OrF79278/ve/h91ux7333ovZs2cr0jCZzWbceOONsNls0Ol0MJlMUx4DERGFP7ZdRESBY1EVYk6nE1arFXa7HQ6HQ7E4NBoNkpKSFNs+ERFFDrZdRESBYVEVQhqNBnPnzsV9990Hl8uF+fPnQ6fTKR0WERGRX2y7iIgCx6IqhNRqNWpra1FRUQEpJXQ6HRsmIiIKa2y7iIgCx6IqxLRaLbRaLaSUGBwcRE9PD4QQMJlMiIuLUzo8IiKi80RS22W1WmGxWOByuRAXFweTyQSVipMbE9HUYlE1RWw2G3bs2IHt27dDr9dj1apVWLBgATQa/gmIiCg8hXvbJaXEoUOH8Nprr6Gvrw+LFi3CypUrkZCQoHRoRBRjwuOoGAPsdjv279+PzZs3Iz4+Hnl5ebjsssuUDouIiMivcG+73G43Tpw4gW3btqGtrQ0ajQaLFy9mUUVEU45F1RRRqVRISkpCQUEB4uLikJiYCCHEhD47ODiIrq4u2Gw2JCYmIjU1VZGpbaONzWZDV1cXBgcHMTw8rGgsw8PDOHHiBEwmE9LS0nj/AkUsm82G7u5uWCwWpUOhCbhY+zJVbZfL5UJ3dzf6+vqg0WiQlpY2ocJICIGEhATk5ORAr9cjNTU16of+hVPbRUTfYFE1RfR6PZYtW4acnBxoNBqUlZVBrVZP6LPHjh3Dtm3b0NzcjEWLFmHDhg1IT08PccTRr729Hdu2bcP+/fvR2tqqaCytra34+c9/jrq6Otx4443Izc1VNB6iS9XR0YFt27Zh3759SodCE3Cx9mWq2i6LxYLXX38d7777LlJTU7FhwwYsWLDgogWcEALV1dV46KGHYLVaUVxcHPXPsgqntouIvsGiaopotVpUVFSgvLwcACbc0wcALS0teOutt3D06FHo9XpcddVVLKqCoLe3Fx9++CFeeeUVpUNBT08PnnvuOdhsNlx11VVKh0N0yUby6uWXX1Y6FJqAi7UvU9V2Wa1WHDhwAM8//zxyc3NRV1eHxsbGCRVVhYWFKCgoCDi+SBVObRcRfYNFVYCsViuam5vR19cHk8mE3NzcgHrFLuWAn5SUhPLychiNRhQWFnJoWJAYjUYUFxejvr4eR44cUTSWuLg4lJeXo6ioCAaDQdFYiCbDaDSiqKgI9fX1AIA9e/YoHBEB/tuuibYvoW67tFot8vLyUFNTA7PZjNTU1IC2GQvF1IhwaruI6BtCSjnhhevq6uTevXtDGE74a2pqwqZNm7B7925UVFTgzjvvREVFRUi3efbsWTQ1NcFisSAzMxPFxcU88Q4Ci8WCL7/8El1dXbjvvvtw4sQJxVrlkpIS+fjjjyM9PR3FxcWIj49XKhSiSfHNKyklVqxYsU9KWadkTGy7/LddoWxfAlm33W7H119/jebmZuj1ehQXFyMzMzMocUQbpdou5hERIITw26bxSlWABgYGcPDgQbz99tuw2WxYv359yLeZnp7O4X4hYDKZUF1dDQBITExUNJbExERcccUVisZAFAy+eUXhw1/bFcr2JZB163Q6lJaWorS0NCSxRJNwaruI6BssqgKUmJiImpoa2O12VFRUICkpye+ybrcbzc3N+Oqrr+B2u0fHfU/0Jt+JcjqdOHXqFE6dOgW1Wo3p06cjLy8vpoZDEBGRf+HYdhERRRMWVQHKysrCbbfdhrVr146OS/fH6XRi586d2LRpE+x2O2655RbcfPPNQX8avc1mw7vvvoutW7fCaDTijjvuQFZWFqddJyIiAOHZdhERRRMWVQEyGo2YOXPmhJaVUqKzsxOffvopbDYbli1bBpfLFfSYnE4nWltbcfDgQRiNRnR2diKQe+WIiCi6hUPbNdIucRQFEUUjFlUhpFKpUFJSgnXr1sHhcKCsrCwkV490Oh3Ky8tx3XXXQa/XY+bMmVH/8EMiIgqNYLddUkqcOnUKhw8fHn2WVFlZGfR6fRCjJiJSFouqENJoNLjssstQWloKKSWSk5NDMh26Xq/H8uXLUVtbC5VKhZSUFI59JyKiSxLstktKiQMHDmDjxo04e/Ysvvvd7yI/P59FFRFFFRZVISSEQFJS0gVvCA6GkUIqJSUlpNshIqLoF+y2S0oJi8WClpYWtLW1obu7G263OyjrJiIKFyyqiIiIKGRGhhPedNNN6O/vR2NjI5+1SERRh0UVERERhYwQApWVlZgxYwbcbjfi4uL4gHMiijosqoiIiGKU0+mEzWaD2+2GVquFXq8Pyex8RqMRRqMx6OslIgoXLKqIiIhiVFNTE7Zv346zZ8+isrISS5YsCfl9wERE0YhFFRERUYxqamrCli1bcOzYMdx0002oqalhUUVEdAlYVIWA1WqFxWL51thxPjeKiIjCjVqthtFoRHx8PHQ63YSH/rlcLlgsFlitVmi1WphMJk6RTkQxjUVVkEkp8dlnn+GNN95AX18fFi5ciBUrVsBkMikdGhER0beUlJTg7rvvRnd3N2bNmjXhq1Q9PT144403sHfvXmRnZ2P16tWoqKgIcbREROGLRVWQud1unDhxAlu3bkV7ezuEEFi0aBGLKiIiCjuFhYXIzc2FlBIqlQoazcROC/r7+7F9+3Y899xzmDNnDubMmcOiiohiWkBFlc1mw5dffon4+HikpqZO6gnr0UoIgfj4eGRnZ0Or1SI5OZlD/+iiRnLLZDIhJSWFuUVEU0IIAa1WG/DnNBoNUlJSkJ+fj8zMTM7sR0QxL6CiqrW1FQ8//DDmzp2LG264AXl5eaGKK2IJIVBdXY2HHnoIVqsVxcXFvEpFFzWSW3V1dbjhhhuQm5urdEhERH6lpKRgw4YNqK+vR1JSEkpLS5UOiYhIUQEVVefOncNzzz0Hq9WKFStWsKgahxAC06dPR2Fh4bdeI7qQkdwaHh7GqlWrlA6HiOiCTCYT5s+fj/nz5wNgO0dEFFBRFRcXh7KyMkyfPp2X+i+CDQwFwje3DAaD0uHQFJJSoqurC21tbXC5XDCbzTCbzVCr1UqHRjGgr68PLS0tsFqtSE1NRXZ29oSHH7OdU4bVakVLSwv6+vowNDSkdDhE5BVQUZWTk4Nf//rXyMjIQGZmZqhiIoo5vrmVkZGhdDg0hdxuN3bv3o2tW7dieHgY1157LTZs2IC4uDilQ6MY8Pnnn2Pz5s04deoUli9fjltvvRVms1npsOgC2tra8PTTT+OTTz5BS0uL0uEQkVdARVViYiJWrlwZqliIYhZzK3a53W6cPn0aO3bswMDAAMrLy+FwOJQOi2JER0cHdu7ciSNHjiAjI4NXPiJAf38/9u/fjzfffFPpUIjIB6dUDyMWiwVNTU3o6upCUlISiouLMW3aNKXDIopoUko0Nzfj5MmTcLvdyM/PR35+ftgMrxNCIDc3F4sWLYLVakVRUdGEp7Ummqz09HTMnz8fmZmZKCsru6Sh/Wy7plZCQgKqqqowNDSEPXv2KB0OEXmx5Q4jbW1t2Lx5M3bu3Imamhrcd999qKysVDosoojmdDrx0UcfYdOmTbDZbLjlllvw3e9+N2yG16nVasyfPx/5+flwuVzIzMzkfXU0ZcrKyvDggw+O3lOVnJwc8DrYdk2trKwsfO9738PatWtx++23Kx0OEXmxqLoIKSWAqbkhd2hoCF988QU++eQTGI1G9Pf3h3ybREqRUk5JXkkp0dHRgYMHD2J4eBhLly6F2+0O+XYnSgjBe+ko6CbadiUlJSEpKWlS22LbNbXi4uJQUlICAIiPj1c4GiIawaLKj+HhYXz++edoampCXFwcysvLUVBQENJtJiUlYeHChYiLi8Ps2bORnp4e0u0RKaG7uxufffYZOjs7kZWVhTlz5kz6pO5CVCoVZs6ciWuuuQYOhwNlZWUcXkdRi20XEZEyeGbhh8ViwWuvvYZt27YhMzMTP/zhD5GXlweVShWybWZmZuLWW2/FddddB6PRyIaJotKZM2fw1FNPYffu3ViyZAl+/OMfh7SoUqvVuOyyy1BSUgIpJZKTk6HX60O2PSIlse0iIlIGiyo/XC4Xuru7cfLkSTidTlgslqCv3+12QwgBtVoNIQT0ej1ycnKCuh3yT0oJt9sNt9s9OlRGyVgcDgdUKlXYTKAQKjabDe3t7fj6668xa9Ys2Gy2oK1bSgmXywUp5ei+FEIEZYgTUSRg2zW+8eImIgomFlV+GI1GNDY2wu12j85mFKyD8NDQEPbv348jR44gPj4eDQ0NmDlzJg/yU+zcuXPYvXs3Tp48ia6uLkVj6erqwp/+9CdMnz4d8+bNu6SbxSNFRkYGVq9ejeLiYlRUVCAtLS1o625ubsauXbvQ1dWFmTNnoqGhAYmJiUFbP1G4Y9t1vkiNm4giC4sqP0wmE1auXImFCxdCrVYjISEhaAfgwcFBvPXWW3jmmWeQmZmJuLi4oDZ8NDFtbW3Ytm0b3n77bcWLqra2NvzqV7/CqlWrMGPGjKguqnJzc3HrrbfCZrPBYDAgISEhaOtuamrCk08+iSNHjmDdunWYOXMmiyqKKWy7zhepcRNRZAmoqHK73bBYLNBoNNDpdCEdo60Uu90Ou90OIQSMRmNITsjcbjeGh4fR39+PxMREPuhTIVJKWK1WWCwWuFwuRWNxuVywWCwYHh5WPJZQcLvdsNvtcDqdUKlUSEpKCskwR6fTicHBQQwMDMBqtYbVLH+xwu12w2azReX3OBKMDMdTqVQQQkCr1QZt3Uq0Xb7fJ7VaDZ1OF/Cxg20uEU2FgIqqjo4OPProoygpKcGSJUuibgpgm82GXbt2Yffu3dBqtVi4cCFqa2uDfvIXHx+PxYsXw2QyITExEbNmzWKPmQLS09Oxdu1alJaW4sknn1Q0FrPZjLvuuguzZ89GamqqorGEwrlz57Bjxw4cO3YMOTk5WLZsWUhmJCssLMTNN9+MtrY2VFZW8gGkCujq6sJ7772HL774QulQYtLQ0BA+/PBD7N+/HwkJCVi8eDEqKiqC0sYo0XadPXsWO3bswBdffIH8/HwsXboUeXl5Aa2DbS4RTYWAi6p/+Zd/wdVXX42KioqoLKo++ugjPPbYY4iLi0N8fDyqqqpCUlRdccUVuPzyyyGEgMFg4AFeARkZGVi/fj0cDgdeffVVRWMxm834yU9+Aq1WG5UPfu3u7sbLL7+Ml19+GfX19SguLg5ZUXXbbbfB7XZDq9Vylj8FdHZ24sUXX8Qbb7yhdCgxaXBwEO+++y42bdqEnJwcZGRkoLy8PGhF1VS3XZ2dnXjhhRfw1ltvobGxEaWlpZdUVLHNJaJQC6ioGhlWoNVqo/KANDIrkE6ng16vD9ksbCMH9bEnzyNDl2w2G7RaLUwmU1CHbtC3qVQqxMXFAYDiM+6p1eqovqoihIBGo4HBYAjp8UOj0cBkMp33utVqxeDgIKSUiIuLQ1xcXFQew8KBSqViQaswjUYDvV4f9GH6SrRdI0MYR36fS8lbf3FHKt/97XQ6lQ6HiLwCKqqysrLwd3/3d5gxY0bUXaUCAL1ej8svvxyJiYnQaDSoq6ub0pPtjo4OvPbaazhy5AiKi4uxZs0aTJ8+fcq2TxQqaWlp2LBhA6qrq5GZmYnCwsIp27bL5cKePXvw9ttvw+FwYOnSpViyZAlP+kMkIyMD119/Perq6gAAP/rRjxSOKLaMTFSRnZ2NhIQEzJkzJ+QdCKFsuzIzM3HjjTeioaEBOTk5AV+lika++7utrU3pcIjIK6CiKj09Hffccw9UKhU0muibOFCn06G+vh41NTWjV62msqjq7u7Gm2++iddffx0LFixAXV0diyqKCsnJybjyyiuxfPnyKT9+uFwuHDp0CFu2bMHw8DASExOxYMECFlUhkpqailWrVo1OEsKiamrFxcVhwYIFmD9//ugV4lAXVaFsu3y/T9F67hEo3/1tt9uVDoeIvC5p+F80GymkXC4Xenp60N/fD41Gg5SUlHGHFQWTVqtFamrq6Dh4nU4X0u0RTRXfWchsNhva2tpgtVoRFxeH1NTUkH/XExISkJWVBZvNhsTERA79C6FgzzhHgXG5XOjt7Y2atovfp/P57u/m5malwyEiL3b5+GGxWPDGG29g+/btSE5Oxvr167FgwYKQnoxlZmbilltuweLFi2E2m5Gfnx+ybREp5cyZM/jzn/+Mzz//HNXV1diwYUNIJq0YodFoMH/+fJhMJrhcLsyePTvqO4codrHtin6++/vhhx9WOhwi8mJR5YfVasXevXvx3HPPIS8vD7W1tWhsbAxpw5ScnIzFixdDSgkhBHvTKSp1dXXhnXfewY4dO9Db24vly5eHtKhSqVQoLS1FSUkJc4uiHtuu6Oe7vx999FGlwyEiLxZVfmi1WuTk5KCyshJmsxkpKSlBayicTifa29vR3d0NrVaLrKwsJCcnAwAbJIp68fHxKC4uRk9PD6ZPnw6j0Ri0dQ8MDKC1tRVWqxXJycnIysoaHYrE3KJYwLYrNnB/E4UfFlV+JCQkYM2aNaiqqoLBYMDMmTODdgAbGBjAq6++ijfeeAOpqam47bbbsGTJEh4gKSbk5eXhrrvuwnXXXYeMjAxkZWUFbd0nTpzA5s2b8eWXX2Lp0qW49dZbg7p+onDHtouISBksqvzQ6XQoKytDWVlZ0Ndts9lw9OhRvPnmm8jNzcWyZctGh00QRbukpCQ0NDSEZN1dXV348MMPcfDgQSQlJWH9+vUh2Q5RuGLbRUSkDBZVCtDr9SgpKcHSpUuRnp6OzMxMNkoUM/r7+9HU1ISenh6kpaVhxowZQZudLCUlBQ0NDUhOTkZFRUVQhxYSRQK73Y6TJ0+itbUVer0eM2bMgNlsDsq6w7Xtam9vx9dffw2bzYacnBwUFhZyxkAimnIsqhRgMplwzTXXoKGhAXq9Hjk5OWHRMBFNhdOnT+M//uM/sH//fjQ2NuLee+9FSUlJUNZdUlKCBx54AIODg0hLS0NqampQ1ksUKSwWC1599VW8+OKLSE9Px913342VK1cGpY0Jx7bL7XZj//79+NOf/oSuri6sX78et99+++i9XkREU4VFlQK0Wi0KCgpCOuMZUbiyWCw4duwYdu/ejbS0NAwODgZt3dOmTcO0adOCtj6iSDNypeqTTz5BXl4eurq6gjZELxzbLiklzp49iwMHDqC9vR1z586Fw+FQOiwiikEsqqaIw+HAF198gRMnTkCj0WDWrFmYMWMGVCqV0qERTamUlBQsXrwYqampqK2tnXSP8rlz53D48GF0dnbCbDZjzpw5SEpKClK0RJHFYDCguroa69evR2pqKgoKCkJyNWlwcBBHjhzB6dOnkZSUhPLyckUmhRFCoLCwEKtXr8a5c+dQVVXF59ARkSJYVE0Rm82G7du3Y/PmzTAajfjBD36AgoICFlUUc/Ly8nDHHXdgaGgIJpMJ6enpk1pfc3MzNm3ahI8//hiLFi3Cj3/8YxZVFLMSEhKwevVqLFiwABqNBunp6SEpqs6dO4f//M//xCuvvIJZs2bhRz/6kSJFlUqlQnV1NXJzc+F0OpGcnBy0ezSJiALBomqKuN1udHd3o6mpCfHx8ejt7YWUUumwKMxIKeFyuQB4emDVarXCEQWf0WhEfn5+0NY3PDyM1tZWNDU1oaioCDabLWjrJoo0arUaZrM5aJNT+ONwONDR0YGvvvoK8fHxQR3GGygO+yWicMCiaopotVrU1tbie9/7HnQ6HcrLy3mVis5z6tQp7NmzB729vSgtLUVtbS17XS8iPT0dq1atQmFhIcrKgfNP/gAAIABJREFUyjg5BdEUSEhIwOLFi2E0GpGTk4O8vDylQyIiUhSLqiliMBiwZMkS1NXVQQiBhIQEaDTc/fRtx48fxx/+8Ac0NTXhhhtuQHFxMYuqi8jNzcVtt92G4eFhGAwGJCYmKh0SUdRLSUnBtddei5UrV0Kj0TDviCjmBfWs3uFwjM66o9Vq+ZwIHyOFVEJCAqSUcDgcGBwchBACOp2OBRYB8OTQwMAA+vr6YLVa4Xa7R19nbo1Pq9WOXp1yuVyw2+2w2+3QaDTQ6XSKT/lMFI3UajWSk5M5dTkRkVfQzuRdLhcOHDiAjz76CE6nE/Pnz0dDQwN0Ol2wNhE1hoaGsHPnThw4cAAJCQm4/PLLUV5ezpM/QlFREW677TZ0dXWhtrYWCQkJcDqd2Lt3Lz7++GMAQGNjI+rq6liIj+PUqVPYsWMH2tvbUV5ejssvv5wnfURERBRyQTsrczqd2LdvHx5//HHYbDa43W5UV1ezqBrH4OAg3n33XWzevBlZWVlIS0tDWVkZiypCcXExcnNz4XK5oNPpYDQaMTw8jD179mDjxo0QQkCr1aKqqopF1Ti+/vprbNmyBYcOHcKGDRtQWVnJooqIiIhCLqCzMqfTibNnz0Kv1yM+Pv68mcnUajU0Gg1cLldUzloWTCP7SqvVcsIKGuVvaJ9KpRrtoBBCsAD3Q6VSQa1WQ6fTQa1Wcz8RUcRyu90YGhrC8PAwVCoV4uPj+QwuojAWUFHV1taG3/72t6ioqMBVV131rWdSaDQa1NfX40c/+hEcDgfq6up4lcoPk8mEK6+8EpmZmTCZTBz6Rxek1Woxf/780Y6K+vp6XqXyY8aMGbjjjjvQ1dWF0tJSXqUioog1ODiId955Bx9//DGSkpKwcuVK1NbW8nyBKEwFdGZ29uxZ/OEPf8CaNWvQ0NDwraJKrVajsrISZWVlnhVrNLxa5UdcXBwWLVqExsZGCCGg0Wh4kCS/NBoNampqMGfOnNF/M7fGl5eXhxtuuAFSSqhUKhafRBSxhoaG8Ne//hVPPvkkcnJyMH36dNTU1PB8gShMBXTGodVqkZmZieTk5HGHKKnVap7sTZBGo+EJH00Yc2tifIdJUmi53W709vaiv79/dCY4Tv8fm+x2O3p6ejA09H/Ye/NoW9O6PPB599lnuOfcoSaq7i2oAdCCKqoogkVBAREbbJpBKESrlTDokoim7aQ7ulaWnZhETXfW6nR6dceQNt1xpVutQBsVXFCitCAisXBALVEBU0oxCHVrulPdc8+4z9d/nPPs++zn/N5v2Gfadet91jpr7/MN7/d7f+9vft/v3RcwNzeHK664oixT2wX0ej0cO3YMJ06cwNVXX41Dhw4dNEkFBQU16BTVX3vttfjJn/xJXHfddXv+a+0FBQUFBZOLxcVFfPSjH8XHPvYxHDt2DG9961vxile8olTRn4Y4efIkfvmXfxkPPPAAnve85+Gee+7BN37jNx40WU95HDlyBG94wxvw3Oc+F4cPH8btt99e3sEuKJhgdEqqrrzySrz97W8vL8oXFBQUPM2xsrKCP/qjP8L73/9+HD9+HC960Ytw1113lRnVpyFOnz6NT3ziE7jvvvvwqle9Ct/yLd9SkqpdwNzcHO644w580zd9EwCUhKqgYMLRef1ZUer9weLiIh5++GEsLi7i6NGjOHHiBObm5g6arIlGVVV4/PHH8cgjj6CqKlx99dV4xjOeUWS2YIiNjQ08+uijeOyxx5BSwokTJ3DFFVeUIlEDnnjiCTzyyCMYDAa46qqrcPXVV6Pf7+PEiRO47bbbcOWVV+Lyyy8vfHyaYm5uDjfeeCNuv/12PPvZz8b8/PxBk/SUQp3vKkXsgoKnDspLPROKhx56CD/7sz+Lz3/+83jpS1+Kd77znbjxxhsPmqyJxvr6Ou6//378wi/8AtbX1/Ht3/7teMtb3lLWoRcMsby8jI997GP44Ac/iPn5eXzXd30XXve615X3G2swGAzwmc98Bu973/vw5JNP4u6778Z3fMd3YH5+Hq9//etxyy23YHZ2FjfddFMpYDxNceLECbzrXe/C6173OlxxxRW44YYbDpqkpxSK7yoouDRQIokJxalTp/DpT38av/M7v4N+v4+3vOUtB03SxGMwGOChhx7Cxz/+caysrODWW2/F+vr6QZNVMEFYW1vDgw8+iN/4jd/AwsIC7rrrLlRVddBkTTSqqsJXvvIV/OZv/iZOnz6N5z//+VhbW8Phw4dx88034+abbz5oEgsOGEePHsUdd9xx0GQ8ZVF8V0HBpYF9TaqWl5fx0EMP4ZFHHsHhw4fx7Gc/G1deeeV+kvCUwWWXXYYXv/jFmJmZwa233oqFhYWDJmniMTU1heuuuw6veMUrsLa2hhtvvPFp837H4uIiHnroITz++OM4duwYnvOc5+DYsWMHTdbEod/v49nPfjZe+cpX4tChQ3jmM59ZltY0gMsk77rrLpw/fx7Pec5zwt1fCwoKxsPT2XcVFFxK2Nek6oknnsAv/uIv4jd+4zfw3Oc+F+95z3vw8pe/fD9JeMrgxhtvxHve8x6cO3cOV1xxBY4fP37QJE08+v0+Xv7yl+P6669HVVW49tprnzbb+p48eRL33nsvfvu3fxsvetGL8AM/8AO4/fbbD5qsicPc3Bxe85rX4Oabb8bU1BSe9axnleClAb1eDy95yUtw4sQJrK+v48SJE2VZUkHBLuLp7LsKCi4l7PtM1YMPPoj7778f58+fx6lTp/bz8U8pHD16FLfeeutBk/GUAivq+qPUTxdcuHABX/jCF/DpT38a/X4fTz755EGTNJFgRfi66647aFKeMkgp4Zprrik/o1FQsEd4OvuugoJLCfuaVC0sLOCOO+7AhQsXcMMNNxQDUlCwSzh69Che9rKXodfr4ZZbbinLagsKCg4cDz/8MD73uc/hySefxHXXXYebb7657AxYUFBwyWJfk6orr7wS99xzD1772tdidnYWV1999X4+vqDgksXx48fx9re/HXfffTcOHTpUZhUKCgoOHJ///Ofx3ve+F3/5l3+JN73pTfihH/qhklQVFBRcstjVpKqqKmxsbADYnM727XWnp6dx7bXX4tprr93NxxY8hbGxsYGNjY2hvJRNA2KobkV8mp2dLUvaCoagvFRVhV6vV7Y6LzgQLC4u4ktf+hIefPBBnDx5EmtrawdN0q6h+K6CggLHriVVg8EAn//85/HAAw9gMBjgtttuw2233VZ2iSrIYm1tDZ/97Gfxp3/6p+j3+3jRi16EW265pQSAhvX1dfz5n/85/uRP/gQpJdx+++14wQteUDZYKAixsbGB//yf/zP+6I/+CKurq7j11lvxwhe+EDMzMwdNWsHTDNdffz3e8pa34KUvfSle9rKX4fDhwwdN0q6g+K6CgoIIu5ZUra+v4/d+7/fw3ve+FysrK3jPe96Dm266qSRVBVksLy/jk5/8JP7dv/t3OHToEP7e3/t7uOmmm0rwZ1hbW8P999+Pn/7pnwYA/NAP/RBuuummklQVhNjY2MBnPvMZ/NRP/RTOnTuHd7/73UWvCg4Ez3ve8/CDP/iDWFtbw/z8/CXzMw/FdxUUFETY1eV/S0tLOHPmDJaXl7G0tFR+VLOgFlVV4cKFCzhz5gxWVlawvLx80CRNLJaXl3H69GmklIpuFTRidXUVp0+fxrlz53DhwoUiLwUHgrm5OczNzR00GbuO4rsKCgoi7FpSNTU1hRe96EV497vfjcFggDvvvLNUbQpqMTMzg5e97GVYX1/HzMwMbr/99jL7EqDf7+Obvumb8P3f//1IKeHFL34x+v193WOm4CmEXq+HW2+9Fd/3fd+H5eVl3HXXXeU3bwoKdhHFdxUUFETYtcis3+/jJS95CW677TZUVYW5ubniyAtqMTs7i1e+8pW44447kFLC3NxccUwBpqenceedd+KFL3whAODQoUNlWW1BFr1eD7fffjue97znoaoqzM7OFltcULCLKL6roKAgwq6Wu4vzLugCOqNLcXnIbqPwqaALii0uKNg7FN9VUFAQoWxVU1BQUFBQUFBQUFBQsAOUpKqgoKCgoKCgoKCgoGAHKG+7FxQ8TXD+/HmcOXMGGxsbOHLkCI4dO1Z+V6WgoKDgKYbV1VWcOXMGFy5cwOrq6kGTU1BQsIWSVBUUPA2wsbGBBx54AB/4wAdw/vx5vOY1r8Eb3/jGS+bHOAsKCgqeLjh58iQ+8IEP4IEHHsDXv/71gyanoKBgCyWpKih4GqCqKvzVX/0VPvjBD+Lxxx/H5Zdfjm/91m8tSVVBQUHBUwynT5/GJz7xCdx3333Y2Ng4aHIKCgq20CmpWlpawmc/+1kcPXoUx48fx9zcHE6fPo1HHnkEa2truPLKK3H11Vej1+vhsccew2OPPYZer4drrrkGV1xxBVJKw7aWl5dx8uRJnDt3DgsLCzhx4gTm5+c7Eb++vo5HH30UTzzxBKanp3H8+HFcdtllndoomGy0kZPdkMGlpaX97toIVLdOnDiB2dlZnDp1Co888ggGg8GwXwDw6KOP4vHHH0e/38fx48dx+eWXj7S1uLiIkydPYnFxcairMzMzuOyyy/D85z8fp06dwvHjx2u3AF5ZWcHDDz885Pvx48exsLCwpzwgqqrCE088gUcffRQbGxu4+uqrcdVVV5WliruI1dVVPPLIIzh9+jTm5uZw4sQJHDlyBIuLi3j44Ydx4cIFHDt2DMePHx/ZRXBjYwOPP/44Hn30UQDANddcg6uuuiqrV5OCyHcV7B52w59fathL3zU3N4frr78et956Kx588MH97lpBQUEGnZKqr33ta/ixH/sx3HnnnXjnO9+J6667Dn/8x3+M97///Th16hRe//rX45577sH09DQ+/vGP40Mf+hBmZ2dxzz334HWve93ID5Y+/PDDuPfee/EHf/AHeMELXoB3vetduPnmmzsRf/78edx333349V//dTzjGc/A2972NrzqVa8aMT4FT200ycnGxsauyODXvva1g+jeENStl73sZXjHO96BZz3rWfjDP/xDvP/978eTTz6Jb/u2b8N3fMd3oKoqfPSjH8Wv/uqv4ujRo3jb296GV7/61SMJx5e//GX83M/9HD73uc8NdfX666/Hi1/8YvzIj/wIVldXceONN9YGPY888gje97734Xd/93dxyy234B3veAduvfXW/WAF1tfXcf/99+MXf/EXsb6+jre+9a1405veVALhXcTp06fxS7/0S/jkJz+JG2+8Ee94xztwxx134Itf/CLuvfdefOELXxjK4nXXXTe8b21tDb/927+ND3zgA+j1evjO7/xOvPGNbxz53bSTJ0/i3nvvxe///u8fRNdCuO+64YYbDpqkSwq74c8vNeyl7zpx4gTe+c534rWvfS1++Id/+CC6V1BQEKBTUnX27Fl8+MMfRq/Xw913342qqvDVr34VH/vYx3Dy5Elcf/31ePOb3wwA+Iu/+At85CMfwcLCAu64445tU9Tnzp3D7//+7+O+++7D2bNn8cY3vrEz8cvLy/izP/szfOQjH8Ezn/lM/M2/+TdRVVVJqi4hNMnJbsngQYO6NTU1hTe/+c2oqgpf+cpX8PGPfxyPPfYYnvOc5+BNb3oTqqrC5z73Odx333245ppr8IpXvAJVVY20derUKdx///341Kc+NdTVlBKuu+66kQC5DufOncMf/MEf4MMf/vDQ4e8XBoMBvvjFL+KjH/0o1tbW8IIXvGBfn/90wOLiIh544AF86EMfwu23347Xvva1AIAnnngCn/rUp/DpT38aU1NTuPvuu0fuGwwGePDBB/Frv/Zr6Pf7+Bt/429gMBiMJFXUqw9/+MP72qc6uO8q2F3shj+/1LCXvuvo0aO48847AQA/8RM/sT8dKigoaMSBrafxQHBS2iqYLOzl2F7KcnOp9O1S6cckovC2YLdQZGk7Ck8KCp5+SF0UP6X0GIAv7x05BQUHhhuqqnrGQT286FbBJYwD1S2g6FfBJY1906+iRwUFAGp0rlNSVVBQUFBQUFBQUFBQUDCKsp1WQUFBQUFBQUFBQUHBDlCSqoKCgoKCgoKCgoKCgh2gJFUFBQUFBQUFBQUFBQU7QEmqCgoKCgoKCgoKCgoKdoCSVBUUFBQUFBQUFBQUFOwAJakqKCgoKCgoKCgoKCjYAUpSVVBQUFBQUFBQUFBQsAOUpKqgoKCgoKCgoKCgoGAHKElVQUFBQUFBQUFBQUHBDlCSqoKCgoKCgoKCgoKCgh2gJFUFBQUFBQUFBQUFBQU7QEmqCgoKCgoKCgoKCgoKdoCSVBUUFBQUFBQUFBQUFOwAJakqKCgoKCgoKCgoKCjYAUpS1QIppS+llFZTSlfZ8T9OKVUppRu3/n9WSumXU0qPp5TOppT+LKX0vVvnbty69rz9fVdLGmZTSv8+pXQupXQypfTDDdf//a3rzm3dN2vn/7uU0kMppcWU0udTSjd1YElBwa6h6FdBwd6h6FdBwe4jpfQPU0o/s9vXtmirSil9Q+bcr6WUvmc3nlMwHvoHTcBTCA8BeBuAfw0AKaXbAMzbNT8P4E8A3ABgBcBtAI7bNZdVVbU+xvN/HMA3brV9HMAnUkqfq6rq1/3ClNJ/BeBHAbwawNcBfBDAT2wdQ0rpbwN4N4A3Avg8gOcAOD0GTQUFu4WiXwUFe4eiXwUFGWwVD34EwHMBnMOmzP0PVVWdyd1TVdU/b9t+l2t3gqqqXr8fzynII1VVddA0TDxSSl8C8DMA7q6q6iVbx/4lNg35/wjg2VVVfSmldB7AK6uqeiBo40ZsOrbpcZxSSunrAL63qqr/b+v/fwbgG6uq+u7g2vcB+FJVVf9w6//XAPgPVVUdTyn1AHx5q62Pd6WjoGC3UfSroGDvUPSroCCPlNKPAPgHAL4HwMcBPBPA/wHgGQBeUVXVanBPf8ziwo6RUqqwqTt/eRDPL6hHWf7XHr8L4GhK6eaU0hSA7wZwb3DNv0kpfXdK6foujaeU/lZK6bOZc5cDOIHNKiLxJwBekGnuBcG116SUrgTwrK2/W1NKX91aQvETW86qoOCgUPSroGDvUPSroMCQUjqKzVnQv1tV1a9XVbVWVdWXAPzXAG4E8I6t6348pfRLKaV7U0rnAHzv1rF7pa13pZS+nFJ6IqX0j9Pmsttvlfvv3frOpbTfk1L6StpcbvuPpJ07U0qfTimdSSk9nFJ6b0pppmV/fmtrJhcppe9NKf1OSul/22rriymll28d/2pK6VFdKphSemPaXBJ8buv8j1vbdf3rpZR+NKX0V1vn/2NK6YrOA3IJoBiibvh5AO8C8F9ic9nB1+z8PQA+BeAfA3gopfRASuklds3jWwLOv5sBoKqq91VV9cLMcw9vfZ6VY2cBHKm53q/F1vXP2vr+Wmwu7/gvsLks5N2ZtgoK9gtFvwoK9g5FvwoKRvFyAHMAPqAHq6o6D+Aj2NQV4m4AvwTgMgD/Qa9PKd2Czdmtt2OzgHAMmzNedXglgOcBeA2Af0JdAjAA8PcBXAXgrq3z/03HfhEvBfBZAFcCeB+A/xfASwB8AzYTxvemlKifi9i0D5dhc2nt30kpvaVl//4ugLcAeBWAa7E5C/5vxqT5KY2SVHXDzwP4WwC+F8DP+cmqqk5XVfWjVVW9AMA1AB4A8CsppSSXXVVV1WXy9/kWzz2/9XlUjh0F8GTN9X4ttq5f2vr+L6qqOrNVlfk/AbyhBR0FBXuJol8FBXuHol8FBaO4CsDjmaV8D2+dJz5dVdWvVFW1UVXVkl37nQA+XFXVf9paLvhPADS9W/MTVVUtVVX1J9icjb0dAKqq+sOqqn63qqp1ke9Xde8aAOChqqr+76qqBgB+AcB1AH6yqqqVraW4q9hMsFBV1W9VVfWnW/37LID3y3Ob+veDAP5RVVV/XVXVCjbfofzOlNLTbt+GklR1QFVVX8bmuvI3wCobwbWPA/iX2MzadzQNWlXVaWwq+O1y+HYAf5655c+Dax+pquoJAH+BTUVShSgv1hUcOIp+FRTsHYp+FRRsw+MArsoE/ye2zhNfrWnnWj1fVdUFAE80PPukfL+ArRndlNJNKaX70tbulwD+OUaTuy54RL4vbdHmx/jcl6aUPpFSeiyldBabiRKf29S/GwB8kDPY2JwJH2CzOPO0QkmquuPdAF5dVdWin0gp/c8ppVtTSv2U0hEAfwfAX245g53i5wD8WErp8pTS8wF8P4D/p+bad6eUbkkpXQbgx3jtljL8AoB/kFI6klJ6FoD3ALhvF2gsKNgpin4VFOwdin4VFFzEp7G50+Vb9eDWkrjXY3PjCqIueX8YF5emIqV0CJtL7sbBTwP4AjY3ozgK4B8CSPW37AreB+BDAK6rquoYgH8rz23q31cBvN5mseeqqvIlxpc8SlLVEVVV/VVVVZ/JnJ7H5lacZwB8EZvZ+5vtmjNp9Hc+fhgAUkpvTynlKncA8E8B/BU2dz76JID/pdrajjaldP1WW9dv0fjrAP4FgE8A+MrWPf9U2vpvsbnE4uvYNCrvA/DvWzGgoGAPUfSroGDvUPSroOAiqqo6i82NKv51Sul1KaXptLnT5X8E8NfYXDLbBr8E4E1bG0HMYHP527iJ0BFsbut+fqsA8XfGbGec556qqmo5pXQnNpcKE039+7cA/qeU0g0AkFJ6Rkrp7n2ie6JQtlQvKCgoKCgoKCh4WiKl9G5sbg7B36n6FQA/urV0FVs74X1DVVXvkHtGjqXN37r6SQALAP53bC6f++6qqj6l16bg5wlSSr8F4N6qqn4mpfTNAP4vbM4M/TE2iwuvrqrqlVvXZrdUt3a+F8Dflvu+AcCDVVUluf6vt2j8Tyml7wTwv2Jzue8nAXwJm79L16Z/PQD/PYAfwOZSwUcB/EK19bMITyeUpKqgoKCgoKCgoKBgF7C1fPAMNpOfhw6ant3Gpd6/naAs/ysoKCgoKCgoKCgYEymlN6WU5lNKC9jc5OVPsTnbc0ngUu/fbqEkVQUFBQUFBQUFBQXj425svuf3dQDfiM2lcZfSUrBLvX+7grL8r6CgoKCgoKCgoKCgYAcoM1UFBQUFBQUFBQUFBQU7QKdfO56bm6sWFhZGjqWURv7q4Nflrs/NnkXXN820tZ2JG3fGrk2fm57X1Ma4iPrkx3by7HHv3atnNvW3qqptcsrvp0+fxuLi4n78FkSI2dnZRt2q63ukV+PoS1fsVnu7KZdt0KX93bIx49Cx13xoC6WjqqqwzzlaH3744cerqnrGnhHXAvPz89XRo0cBXByvlBJ6ve11Re2f9lOvdX5E36Nru2Kn+tVETxvaDsLO78UKmr3Upa70Rv6/61jw+6lTpw7Ud83MzFTz8/NDmqI4LydzXWVxN7FbMua2oGs/mvx622uVhjbH2Zbaw+j5e+nfxmm7i77oc3J2OqKh1+uh1+uNpVudkqqFhQW84Q1vGCFiZmYGs7OzmJqaGv6R0I2NjSGBKSVMTU1henoa09PTQ4dGR+WddKcWKV/k+PRYdJ40KaLr6mhyYxENMvusyNGdE+auSae3q30l/XX8zNGSg/YxaiMn/LmApA0f+v3+tutyydT6+jrW1taG/d7Y2ECv10O/38fU1NRQJlNKeO9739u633uBhYUFvOY1rxnh2fT0NGZmZrbpjfOMfzMzM+j1ekM9dNmsMyx1PG3Si9x1RKRzek/URiT/4zrdXq83QgPlti7pVF1VW+Z0RwlGk2Ot0xX/7nak6XrvR+7/iFaXEdpoflKm1tbWhnqlfJmamhrac+Xvj//4j385y4x9wtGjR/Gud71rhGbqVK/XG+n3+vo61tfXsbGxMTxOfaR/Iy+qqsJgMACAke/A6FhpQhYlckSdzY7QNM5sL0okI9ni/zqOOdQFy5EcNPUh+ox8cy5Yj9rs4pfGgfpa53XuuZGcaHzk7bkc8Xp+/1f/6l/tuB87wfz8PL75m795xPf0+/2hP+r3++j3N0NN+l4AI/43F0vkQPmq8y05eHxUp2NNtKgMRvGht+u6zxg4ioN1rFUvo/jJj0V9GwwGIzG5PpM2LhqHLnGD06rt0Z7m4r+2Y8k2+/1+ll5vfzAYYDAYDHnD7zyu/ez3+5ibm8PMzAx+6qd+qhVNik5JFYkDMFQMfncGaicJNzptnAbRNvuOoHRESV+kBBx8Hstl7DnDXmcgtM2mPtQJigtnXTJV95y65K4OTFK8bXcCUaAaPT8HH4voORHtg8Fg2zMjZ6U0PhXh8uk6pedyctxGBqNnRfrubanOAaPyGY1rW3mkvamjXcc71yd/Lr/XBWxdksHc89romyeDngTl6I+OtU2scuebAmXa1N0IVvcC7rvcZ6n+aELleuX2W9GUAOegtkjtk+t13fOi81NTU41BvtPXJSGq60vb+3O2q8nfRgEd76uLQ5r8uWInwbs/z59TFwOo7EX2F7iY2E8CaB80SfLilf/PuNETB17rPI9mivW6NjPJap+i2K5Obry/bLtOjiLfq+1GibjGOy6ndXKosporUKgPbuvvctd4f5p8at2zmvRLeRiNtyerkc1ra5vJGy18dEHnpEpRZ+D8uhzqKgRtgoAu9/l1kcGMBD+XWLU1aE2Orwu6KMJ+JAqRMqiA52hsq2wexNQZqDpeaJXEHfdOHOZBo46/dc5ip7JRN6ZN412nd3XtK9yB7ldS3KYgkAuUomu8nbqAfVwam2jJIZc0uIPaKb37AQ9Qu9DbVIxSdPE/ueMeUEYy0SYhaPP8cfjRpf29QlO80aZQl7svartrctVGJ9oEsU5rU3FsEhAlkNHqgFz8qDOpwGjxVq9tSowjfYmSNW+ry7jt9jgwHsnNZnextXXFCT/edK1fl0uocrTlkk9g+ySAt5OLeaOiQ10fcrFiJBOehHZBp6Qqcky5SkPUCQ+2o/a70JJDG0eh9+usm85u6HWuyG2EL1K4SJCb2smfCtopAAAgAElEQVTdp+3nkoM2RqdJoXYjwMslpm3o1fvV4Gg/IgOtVeherzd25WG/0SS/7gCiqk9UucnpZ5MctnUikTzVGTwPVLrIWW6WtA1yNLVJ6qJkom2VL9dund1satOfX2dX2wbiuQQ3F2z6zMokJ1g5e+yFljb31qGNPETjzuNNutE20fJ79yIQzwVWO01cVabbtBX5l6gA0AVRVTwHf3akl7mEwP1XZKO8H7nlhgcB97meGOWu93ujazQ+i+5t6+s8sWpji9ugyU/ngnmnOTrWNk5OKYXJSZcYk9+bYrScnPtKCvfR0cxjlMzoc9rSXhcPeV80FvT4kDz3PKALOs9UkVHqQKP3h5RgXqMMrhu4NglLU5bchiG5SrkKQF07TQFEXULVBE9G9F5/V61tW4q2yj2OYFFGora6KLvTldLmGmyVQR53eayqaihzTe/0TAI0YczJVSTfUdDQNLZ1DidKeroG/VEFKWf09Tm+NKMN2E6bZRF192s7bQIoRZTk1tm2rnaqrp26xDUXhBHuBL1PkRw22cOuvNtP5IoNPFf3jkVd0upoGt+oMOTXN+mBF5jYblvdZNt+vdPVlNB4MhDR3OR/mngd6Vdd/+pQN45t+to00xEFnLnnupzkdC5K/HN9OAiQL/4aSN1fl2J89LwIdUUFT3A1saoreNX5zNwzI53Iwe1R7tWNHDRRVJraFFhybUVt1PEGyC8zpo3y91OBeAKjTVJV5/v8Po8P/Rr11+ojNcHqis4esM1DIsdax7wu7TcZlTqj68zN/Z/rw7hrzccdHN6rqMvsm9CV7q7t17URjdtuOoXIsXcN0J9qGLePXWUm+r5TRAFFm5fjFdSFnS7fbGOL2laqDxq5YLErj3IOKHes69gdBKIiVxvb3CYQ6WrLfDzqAsxcwO3+qE5GfflUU/IQfR8H4/K8jqaudI3j98bBbjyjS8IwLi/3A03yyGvayGHb8RuX/13lY7f0Y9wCVDTu+ykD+uycXOpntJeCX5s734WWNmhK4tmmv3PWBWO/U5VzApFzzVXfvXrg5yKoQLlw7zQh07ZIt0//100d516UjCoIbYIcr0I6cjNCEQ1tjrc934S69cD66d/bgOPOMVDeqhHm/3XjpcHmQTsmrd7lkKu67ARtjCLpq+NRXZWrzfXA+LNNTbORdS88Kx2apOmSE/bdZ9SaqpwRz+rGLzemkU2NbGlbG0rkZryjtnm922s9p7booPUpB/cdXXfbI1xG9H4/lit81Pm/JuTGK/IZ7rsi5GQgZzcj1NmSJqjNVp1yGzQOv9pc21ScyhUzXWbq5F9tRk6PFNSnnN/cSeC3F8gVX/wv8nNRwbptIh3JSY425b0vXSbaxCk5/Wsaj7rlkOpjtL1INyIo3Tnfk5PLqB1gu133vvpMVCTj/tw6G9sUG+Z4HdkJ513OnkQ+WWfWuqJzUpUTXlcEnQ50JfJlCznD4p0iUzQYjoKftvRHyCl7U/ueOKoQdnEEUZC3U3RNosYN2Nsss/Lv/L+tE88pNz8jvrv85RzVJKMpcc6tR8+NZZPOtZGBOkPZNjAlmpKmpmQzoieXvKj9yemY80B1OOeMnR4/r2PSxlYB9du/N41RW9muW3YZBY91+qvHJk23muS1i33m+OV2CWx6fkqpke9tEhqXDZVTT/qA/K5qUTuR76oL7toei/yNB0D6rK6oKxA4bVFAVQcf5yiZahNjuBxpG8BFe6jxjvNHt4Y+aHjC5HzlOb02F3dEOtGWp01+z3nPODUqGLV9bt3/iiY/5rQpDW0SorrEz/1hGz+vRUZ/dcev81cX9BWMiDZNwiJ0jUEivhF1chXJYlWN/kxGV+x4AbwrTu5cDm2CW78m90LeXhqXyDjkruH33UpW2tLW5lzTmHSlsQvP68aoafzHTZZzs4dd2t4v5Hjvs2lNjqMtxuVpE8/GGePd3IGxDd25rdb3Cp5Q6bG29zYdqzuu77SOi3HHfVKRSwJycuHHo3ew2vIgCsZz5+ro7fJMoml5fpvApA57VbDaLf+523TtZn+fajrUBppMtE1g28ZObRPoLtirOC16zrgx4m7JyU5sSt3GaLnCQ1SAyhXs6pLE3PPboimG30l80HmmKqpAEF2rqnWVLa3QRIgSrC4Vp7o2o/89c6570Xg/DaPTVCcIXqWI2mhTWcxdW7fMqu45kcHN0ceKUm5Kmvf6udwL6jupSOwFIhmO9CraEanJ8eSqrESdPrb9v+21XZe/Ru3spfNr6/jdLuTkOtdmXRLk59sEvkpPxH9dQZDbOISyFI1X7rjb60kqVihII/kQOffoHv9OPkdBAhDPwrrfdP/ms6e+kUiONqVDUScjUbGpTj5dzsdBF53K3at9beuX6q4Zp191AWPb59fpaKSL0SzDJM1SuY+K4kQtSOu5ulmQJvlT+dfrm+jlvVF7bfxg1F4ObWx513Hskky6PKmNqksu1B51STBct/SZri+5+FnHNRfb+OYc2m6uT/yMYsgo/hxHv8ZKqpSI6FjEhCgArzOKdDh1ndOskmjzcmT0PKetrr3cYLvhzz2vib62hj63mUZbBcglLnXX5c6P08+6Y1Gg6sFNLjjyRHtqagqDwaB2569JQFtDqQavLkHOIQog6xLU6N42z/D7u/A5WirR1sA18c6f05W2qD13jJEdjPTV33uJnEzTsVyQ4DoUXRct8YjgCZOfS+nicjbXwUmEB2O5oDiXUGlf9d6IR7p9L2cMI1n2e9u8Y1j3jlTOLrSVpyZ7nAt2HE1t566P5NnHI0eXH2tCW39dl1C5XtXpUy4Oye0s6H3aSdC3F8jFg9G5KKGK2qv7X8fLdbkLzdpWDq6PbWnOnesaK/l9XfQvkhPaaiB+f9iv3wmNOjY5u6a+L0q6mvqoz9Tn5GLMtvwflwc7eqcKaFeNjdBlKYoLRiRcRNNsSR2j6jaDqENu8HYqmHuJXKWA53LoQlv0MnfbNnWM6xLNJsVrGodJcUqKrrrUNlDJBVmuR00OIOJpG2PcJeGP2tyJTml7pGUntOXaz9EXzdpWVfOMQdtj0f912Cu5n0R9cuT8CdCcXEfJaV1CFT23ia42aKIzem+5jbzkrqkr3LTVy65+Lddu07Gu/mucomAdfcD4O7t5G23kcRJ0bjds0jgYJ7nca9tXx4M2ccpu86yN3dmNGDZHN2W0i23dz+sUntiNg84//jsYDMJAOTK6vK7rj67mqoN+rC5QJLS60CZoitqrq1A0BfS5Y9HOgkpz10ROeZHra84ZdRWkrsFbk/OuMzq5BLptQMAZD68QazuT4JSqqsL6+vpQZ7rIlCN6WZg8aKrIe1UxR2vuuCfA/gz2z6u6UT9zs3FtoW3oc5p0q02FOadjLmNtl0TrONVdE31vizZVwNwzIrnJyckkzlJ5FZO/kcLxinZSq5uB2InNyOmdoq6Q1NQW7/cVF9pek7y1lTVtJ+JJXTu+m2ZUqPH76/xU07HdCPjdb/DT6cr1xfnrwbSPeWRnJsVn5dDE57YBsV7bZtxdVyMf5Drh9rwuucjtGhtdq8/377ljLkt8Thcb7d9zvl59Y5v2o40l6vyI88d309PrI7+svlPPaeHKd6+lzXPdbIpnnN5x7IJirJmqNgpdt9NJm13icptRAPEshv6v1xC+1j8njBHaOJU2cHpzQt00uxM9r011zo2P05NrS//P0dylytLFmTe167vOOC2qWDtVlr1Gk26pgXH51cQpFzj5Zy4IGjdgj967yX1X5H44MEdHGx5F/zfJbpd3uNQpEb4j4NTUVMj/pvbq+N+0yUAOalfrAtU6/rVJlnLvaU0CIrsXzS7l7J/ysIu9q6MnQhRc566PgjBge4CihTq1FXqN0+CfUZ+bdsfNJVJ1z6xDne2qCyqbnt0mgeV9SoMnVjkb57Y4as9picYg184kwO1KVxqb4po2Mx1truGzvHgdJVZuH5WWaImtylGdTOUSoy5w3czJXpsiTk7mmujzWMufp+OhiVl0H1c2uYwzpsn5KX0NhP9HNEd9ixLxnfqtsX+nKoc2jqDpXlYF6rYtrhv06Pq69uqcQZvBaTIG/pxI+MYJIOsw7rsidQlVLkivo6HumjYOpskojbvMYjcrE3uFnRhbHaNotqau/abgI0LdtqtNwU6kEzmn3CWh6nq+7X05J5arKnsb0THXlS7JU9NxpVW3ENYgwvneJVHLYVL1SlFn73JFjkh2m/xV9IJ+7lq/pq6KzuubihLR7LU/v8n2Rjoa0dIGUf+6oMtzIlvfxofvFg28NqdXbivbXNNkJw8KO6ElsqlRrJR7nuth3XPGoVVpa5oY2K0xGbedJn7lkpsc7bmEJvLzOZ/YxQd6QsW/3CquXFGsrk/+/LpjXTD2O1UppZGlSpFyR4aey7Acykwywq9t8+Kut6e01ClcXYAZGWD+5RKHSMDYXnQsojcXbHalv06onca6Khvba3pRs00i1JRANY1n7lxOBnX532AwGFmSOglOiYEu+cKKjcONosphv98f8pa62ev1wrbaBlY5WvWT3/14Tge0opS7LhcsDAaDbRvY5JypylnOyXRxgM6zSD+ZtLhtaPqcmpoKZ6jbyHaXIEw/tbLbNhDhtZ5wsK2o75OCaGlIhFwyFV2nfdXxczvZZM/qxpC0tq1Ce3tqJ/zP6WuT3ESBT5t762Zi62KC3AxBVGXm9VH/fUxydOSe6+f1Gp+hjYorbQqA0fJv3qs6O25isFdoslOqczo+uXfg62Sd46hyzDa5IZWPR84XdU3AIrsRJSiKnC+LkLMDTf5Dr42WMufa17ZVvqLjGnNE/WJM5X5PY17nkeqkjinjGdpVtsGx1T9v12MjbTPig9KisfK4BfuxZqqaFDpSjGggoqRLFc4FOXrHInet0+uMyy3dUfpzwhwdd2Pc9v2tyMGx3ZzxjPheFxzmnJ7zqi6hanp+dI0Hvq5odbRGPPa264yY90eNrxuOcZVntxE5DUUT3/U+/YySqsgxOR11dEZy4wG7t5VLwprWSWtbWrGPihPaXk639N7IXvB4dKyNc6xzXD5eKoM6TlFg6PRG9tOf3zQ2dFbabtcdyPQYg5ougcRBwPnh9iEn4/p/3RhpUFCnXzm7GtnzNsleJBMuc0S06xd50OS/Ir/XJrnK6VXT/3X2PqcHdbQq3OdEflGvUR6qzVbfkkuinUdNyyG9z1FBYxKQsz05G5A757zLvbfqvlv5o8/gsa78aiOnLiNt/HWEnB1poi0XbzltPJ+zVfxseu/J/UXUj+iZHncBF+U+ikNSuphI8bu/IuDxcUSzFtKjxM754vd23QuCGPt3qhR1WbFml7n7gbiSp9fmArI6QeG13kaucl/X1+i7Bq/eRt2SDQpEbkmI0poL+rrA2+gaHOb6q8j1Japw1FUMI37naFIe5e6r6zfpmpQX63P8rQty9Z5c1cd1ya/l9W2QS2qi4J2fuYA0aiOSOU8CtA0PbquqGpktyumWXt/W+ZGuNm3keA/EgWhOJ+rkus4GRnR5shDNYEf38No6h6rtt931c7/hMt4meSBcHp3nPs6R/un13nZ0PELkC+qCKdKi7df5Hb8ngi8hdnnxmYK6WagIdefUr3qRJWqjzuZF1+Z0THUtSqr8+rpkAMi/N53re86nTRI88WyS57rEqkv7mqjqsaYNxprg/ic632SDd6NgG9mHnMxHcWNdX72g5ucITU5UhiPfEcUmmti4P/ciXHTM/V1UzHA6ckvxm3iyU3RKqlK6mEECscPVa/WTA6D3R+0rcrM/bQIKRRTY+YDlAle/VqHZdCQAHsTwHJB/2TkS5BxddcY1orfJ0GibdUFgFBREyubjVNeGX5PrUxtDF1VMVcm1n12D6r2COv0mPvA6zm7we7/fH/I+l2j5DFYdPY4oYIgSJv0e/biyf9claC4vOtY0wvzRy9wyNO1DpFs+9k166HbAZbsuGdJlmG4ncgGf2zRPlN2uRDYwd568U/7V8UeXdOhzffmst1f3DtBBwHWf9LIf7td0bHXcfPdKHx/VL85URfBiXM6PuV9x5Pyi973JZuf8LD9zgWJEX06vcnYt11/to9sAl9Xcc9TO5XjpgaA+05+dsyn6vy5R5p/z2+/xPuhz9T1InufSp9yY7yeiIDqyc+yD2jEi4oEe13Y5c+G+PIoxIlrq5MuPR6so6uxaFE80PTOn+7lzekztkS/bq7MrdfGn+zcA6Pf729qcnp4emY33BCcnD4PBAGtrayOrGoB4KSBp9TgipYTp6ekROx7pg/qvtquTIrnpgs4zVV6liYxPThDaEKqOSZ/nL1TXvT+hoJHz456wuZJ7P+oCHj+nvIimnvV5ddm08jPnLOuSLv+u/fDkKqInEvDoU42dfrpTycmAn/f+O/05AxU5PeWDJpWRUz5IKN8cUXCiyRMNa5RUKT/ojPy+6Bn+3YMafs85Q73WeQ7EG2botTld1u8ppVC32yDn/HKos1/RMU+QdWzrrndo8MBrNIGpGy8PNng9AzJ+13t1nP27O23nQXTPJKGOxkiOfLx0jKJADrhon1TPvM1IDnL+Jcdv7YMGE03vz+qxXFKlz1AZUxny422Ro8X77OeVlihhz/GVNtHh9i7y4U1FXG1H/3f5ivyfXu/89HbrYqZJ0rFI5lRfcv3WYw7nn+tXjk/KUw/WHbnlgZ5g1Okh2+niUxx190a6pv5lnFmZJtnRGNF1iGMwPT09/E7esnAHjE48cMzW1taG7Sp9ucKivjvlOq722/ul+qf+VxPEvcBYy/9yiZUaocjItRFwDwj1uDIx91J3ZPQio6U01b2nURcI5RyTtuWKT/gaVU8W9XnuwKL2IwPjPNDjUXVdj9c5dw8GVKmUJ+5UtO2I37njUcLUJlBoCtL9OQeNOucZXaPffStSnznkpwZ7HiBG7StyAbfeG9EbBSesvnriVecMvMjASlcU2Pj33MxJpFt1MhHxJqc/URDVZoy1nVz1LvfptJK/3sder4f19fUw6I+CV++D8j23XGeSdItQu+TV2i6Jdp0vqLsnsp36PfKZdfKi/YjeMXBaoyDfr/Giidve6Blt0NSXnL7UFQ+ittXX+AYw0b25Qp3zM3rfJ9cfHZNoBsGfNU6/J7FwAeRXokSI9CFnr2ZmZoYBviZVtD/r6+tYX18f+gYiKmgp3zQGc11pSqz9vI+Jx0O8totPaMvL3L1t9IXw2J7HPN7meMzOzmZ1h9dwvGZmZpBSwvLyMlZXV7fxS2eUOfMEYGRjMZWVupViSgfbUL7Pzs7Wxh47Qeflfz4t1+v1tk3N6Zp6DeBSStnALvcMfVabl2ebjGPktHzZSmSofGbLnxk5RF6rz+Zxna703ehyVYeck/WktoknkUKpcPH/CF595//cec55lVt/n+OP94m84jHnidOtFZO1tbWR4D1KBsYNEPYCUVDmBjiqhJP3uiwiSnJ5rfIOyC8FyDnHKJiOdJbXRMv7IsOnS1oiuJznZoH1Oi1eeIBTV2yoo4PoEhD7MT7fE9XIXrrjiJxz3TipU9RZDfLE+er9quMr2/RnqlOcFCjPPbHSftYFOVFCou2rrkX6w3PA9iousH2GMwoa2A4w6kdyuqZQHdC2cj5FZSiy2frMpmKE9j13vV4T6Y/f43S7zYvai/6cJvrlKFD265w/HJONjQ2sr6/X8icXH9Txx+3rJKCuuBrB9YBLyTwQ50wjg3JvU+V/bW0Na2trqKoK6+vrjTv85nx/pGfROKmMREvQ1J5rGy7HdfYmsu/RrGqOt13a1rhC7+v3+8OxUR7Mzs5idnYWwMXER3OCfr+Pw4cPY3Z2FjMzMzh8+DCmp6dx6tQpnD9/HisrKyOJ8MzMzDDxWllZwcrKylCPuJKANE1NTeHw4cMjq6PIC7XzrsMpJczPz2/jqfKW9jEXAzdh136nqs7RKzNU4aL3qyLjq+fc6UWBdo6+KNAhTTrVGQUJdHZRJULp1kHn/dq2K2wU0OjARjzQykeumhzxJOp39Bm15XxIKY1s4a3fnT5tM+e8/ZnuzKhUynt9nvMnqkLkHFLkNA8CXRU5Crp0bH08tKDB+4lIRusMMvmrYxcZcg/0XLe0HRrjKNHw50btaV88UFZDmatM+ZIStzE5IxzxP/oeyWAOem0uyPRrFWrvtHilNscTW5c9HnNHxfaaArpJ0CmF29voe2Qvx3lO7rj/RUmYH6sLkLSoqclupBvRGOdk2/1LLsEmTVGwGPGjzp5ENkzvycUE/hnxLNIlTWSVR8D25URRYuS+KLKH5J/7wgg5+xE9j/9Piu8C4mJSE7zgx+RpenoaU1NTmJ2dHfqt2dnZkRiMPGVQrnapDQ1NSZW3EemKFusIHXOV3TbFh4iONudybTqtimhJXVTE6/f7w2V+2i6TIADDZDaliyscOJZMvhYWFjA7O4sLFy6M8I3jpzv8ra+vD2ezdNKBfZieng6LgK5jlBMWN0jfXqJTUlWnxJFjcqfu3yOhdQNO5BKNOqEhXW7s9JPtajVBna8/p42iRs/gJ4/VrQGNlC8KopR/+k5Azom5k9K26r4roq0u3ZEpLxWRgfLjTpfKBCsWvDeaWXMH6gE4nzVpCRWhs7xAPFaRPukx3bxCQQOlwRE/laeRHGmizOu0Db3WtwaP7skdV1rrnJ4f03H2NtwJe5tKa9dggPe3CVajAFATFdcl18eoT/68nG32cc0F0xH/FKTPZ1acr114uV+I+pNzsDna2/qBuvaicdMAQYOTqampbUGifvfKeISoSKjf9d6oX3X2URO5nGxqOyqHde1G/j+6z38bx/VakzUPuPU3jVQXm3Qgp3+RbfEijuup05uLYdrq+kEh58P9XI6fwEVZWl9fH7Ez/OQqC/KafwzoVb6Ul7l4KGeb6+y6Ho98EG15LhZWOfGCc44vdbamSQacBvZHV6y4PGlSxOOqK0r/+vo6gItLL3k9YzX+8Xyv1xvOIrIdvuPEpJrPW1pa2ratOX0ki/j9fn+kkBTxhTHP6upqOAZqv3YDnWeqPLFR4VFjo8mRBgwelNdVjv1YpCQKNawqkLmX2fVdk8jZKvNzyZsHf65EqixRkN+ESCk84OI5CnVUPffrI0fqbfsMEDC6GUJuTas+IzeToH1zh6H9VN56FTFndEgLaY2m5LXNSVlC4WOtqEukNInyF0c9oVBj4kFVTlbYnut4NL4uo94/DyJzgXsuOFN5c73i8WgWItd35bcGV3VjUzeDEAVbkR56kUj1ygOCyPk5791Z6PXajr5jwv/pAJVXkW7xu26MAmwvELUJBg4CbtOjpAIYtd38PzruMxw6TrmATaFBYI5v0fsgKh9RsB3xPUo2VO+VDm1b7+O4s7JMnfIdJSNa3Bbl6I38pvNReZez65F+u11iv0lfk1zwWLQsMOoD2/XxdZ8TBb3q/3QpmycUuxUI7hR1vsPhPl1nWweDAS5cuLDtnRttV4P5lZWV4cYH/X5/uETw6NGjmJ+fz9pT9WlRDJOLtfR6H3/Xe++v/q994LHIzjuPc21GMYzrPJOQXq+H2dlZ9Pv9oTxTj5eXl7G2tjay8khlbm1tbaj/TpM+i8dXV1eHfF5eXsZgMBiO2fr6+siyTs6IDQYDrK6u4uzZs8PCEmmhn+Tn7Ozstpl66oXGfqurqyP6TZnRZHC3sGvL/yKoQEeBBRFN20Vt1f2vzyM0QK+rSEbKMDU1Ff74lypXU/AV3eMVjpwC1SHn8NVwRLNjOWcVta33abtaMVKH7xWd3BR8lEjV0aHjU8dvGueIN1ESqvdHznMSEAVjkdPK6RnPR0GCOnbtu8uLPyOXVLlzieiNAqoc7+uCr1zAFY1v1G7UZk7GnI5It5ueGyWG+sl2cuMX0eM2xP+A/A6pUfU112elz2mNHGtb/d5v5PQ8R2eTf/GZkahy3YYehRfwUkrbZmJ4PBfAdUHTODfdy8+o8u42ICpY+DVdacj1IUpwXS79eJRQ1T0vlyACsQ2N9MJp0Pt1hU1kQyfVZynqxlGTJf2fQffy8vIwofR+cktuYDOpWl1dBQDMzMzg0KFDw+SKwbryERiNByI7Fsmi22SdldL3fdrILsfct8qvk7lcu10KwqSbhVhuSU75z/l113HyXmVQ+86ETJMczliRX1oQ8HgypTRc/rexsTF8p0t5TP3QFRMcD6eJx3O+VHkYTSR0ReekKjIeOlg6C0VGaNbrDlhf9FMoA3PBjsONj1+Xa0MDDaXDq/n+jBw9bvAixdR7osq6ftc2/Dc3eFyDZN+pRY13nVF2nueqO7nZrIhnkfPxgD7n2KPx4jO9UkdF07bd+Kl8TqJDchlTQ6OGhbrF6o6OCdvwyia/R8GQI5ol0XfacsGT3q+yqDLqMu90KW2RbKghzQUoKmM+U+XypwGY0h3xhjx2J6hOL6oea5We10SJjQYakY1x2+C0Ot+iYEHpy81I+DN5rycV6qx89n6nAf9eI+KN9sXpd5tUF8yoP4zGMtKvqI2IFj8eJehKn/uRXP95zMfYn6vXuExHtkXlRnfw8jiC8Hcq9bjrmMYPHmBFM0FaePMX351v+nztJ9/NcF2LbLbz2zc1UD2JbJAG3Z4oRjw6KHjckIsd/J6oDc5cABd5xlmd5eXl4cwCNzFg8A1clJ3p6WnMz8+PjE0UN3lM5Dqtx7ywTPvH31tiore2thbajwhR3JOLVz3OaZqIiHyA7pbLJZNra2s4e/bskLdLS0tYW1vD9PQ05ubmhqtfmKAqfJtyLwrq5mUbGxvDXf/W1tYwMzODjY0NzM3NYX5+HlNTU8MEeW1tDcvLyzh//jwGg8HIBlzc9ILH5ubmRmZyc3GDjqGOpet40+YmbbDjmaoo0GaHp6enh8Yu2n1KAz+dUucx3uuGvy4IiOAO36FLbzzgj5K9aDt39o3XUICjmSKlhfyIhCJ6f4hTtm6Mo36pE/RKXC740zbV0KsT9+k+9QwAACAASURBVPcpfCyjWZAoIPQA0I2dGkJtK3KuUQCgxo9Twe40J8EpOXyqmzzn/9PT00Pd4gu9wChf1IFr8N12RthlNXLobYJCJoOu4z72HiT4McoH29LnaJKjskU+aN9Jh89CeyCpfHIbpEGZy0/EY7V/3r4mNtoP57cGGNHx3HfvA1/wVR2JEDll5RH1yRMHn22ZJLjcAaOONrJxURv+vx7TcY1mHrUNPRbZ41yArkkb7YHb7KjPPF83Nh7cu9xG90YJnCc1npQ52L7voKbJBJ8R6bQWcckb75NujOWBr/upXMyh9OnOfjreGkwq9BlaudfzpF/tuhcQo+LQpKDJf+TkRxOxubm5oZ1nHxcXF7G6ujqcxbpw4cLIjnMppWFSMz09jcOHD48kzjoL4nA5I599G2+CY0MZYGLFhAQYfWcp4oHHUeq/3C+qj+O9TQmV6yTvYxu03YuLi/jrv/5rnD59epi4bmxsDDeW6Pf7mJ+fx5EjR4bL8/QVA/aTm4soDfrO/WAwwPnz5wFsbmoxNzeHmZkZLCws4PLLL0ev18OpU6fw2GOPYWVlBYuLizhz5szw/Try9PDhw0P56Pf7WFhYwOrq6nAJn/Zbx3t6ehqHDh0aPp+JOK+tqotLBFX/xvFju5JU5YLvyKF4R3JBVtQ+ETkgtum0+Xe9RgU2qtrWPcfb83a7wINH54tfW/esKNDLBaxR2xF0HCOeRv2Jnhklq04HeeuGru5ZTruOp9/fFFRMCjwZ9yDNzxMeULtuRUGDtqF8jAIDfYbS4zqr4+hBCbD9BfumpEr74pWmSBddRyJb431SZ6VBaCRj/L+NLHlfcvd5sN3GRvo5D7jYL/Kdy5o9QW1y0hFyduGpDLdvub432XvlZy6A9+ujAl4kKywU6YxGnV12W5q7zqH60HSt6xyP6XddmhNd559utwiVcS0cRPZKx9DtUl0CqjLteqVbpqud9fva8Coq8ul4Ke2ehHtbBwn3VdG5CDoOGkcyadEERHng48A4jjMbzpOoKJCTxTo51jFx+WSSl1IaeU9H9Z860DbhdB41IfIN0TktMDJR1aSqqi4WKvr9/jC58U20dHx0BYnGBzymxU4tQPCP40a6OPun8qQzxaRHiyUqI+6fOHa66YbyhnzZqS/rnFS5gEfZtzKThOuLiFo5zwU6uYCRn02OjLS6MYqcoRoxpUkr2mrcog0a2hg2ttmkuF3ggRiwfStk52VdoKCOWh2QO7fIIfHZUYAQBXzAqDy4MviUPaEGSulje5GhZp/IGw0uJ6Xa1+ttX86XW0ah/XX+6Ti5bnnS4N+dHg/w9fpoJlnPpzQ6o6OzZzSOGxsX12jnnk2o49REk3xg/9xO+F/UZ9dh/a789SDHaVTZ1mfpuzHqJNQWNhWitE/6v+qcbiKglVaXHfLSq6W83gN3vU+fHwVAkwrSF+mGVsYje17XN0+g+Kk6Suj46RirHntbroMazHiQEumi2kQtzmgw4kGF06J90GA2VxzJIfLx2gdFzofoch/2Td+54LWRD4h8Sx0i/6cBqdodn3HwdvgZ8c2vpe0kqGNa/Z8Uv0VEvIySWvZLl4ypvfKNl3q9HhYWFnDs2DEMBgOcPXsWp0+fHi4V46wDr+cMBrdjZxuKnJw22bCU0nA5nNoRzlRduHABwOiMta4q0W3h9ZoovqmqauQH2qMEIZcY6vXeL7VzADA/Pz+UZe64pzNpqnfcnIjH+efvvqlv4Tn+ra2tDZdv9vt9LC0tDWOzmZkZVFWFhYUFHD58GGtra0N94azZwsICDh06NNQRXdHldJHXXpDWXQNJI2e8aE9yqxWaMNaP/+ofjwMXg0J1ygBGtrxUBkcBEO9V40Tn0cZgq0DxXm3Pr1Un5UnV6urqtp1+mF3rDnguvDk6PYmMAtY2YxDR6ooZKawbdn+mJ02REup0u8pALqlSx+lLO9xJ5ZI/5a8uh+M1nvwqb9VA6pIZyumkOCYNclRmeS4KjNVBaTtNCYQe03Fw6PLJKFH2wETlT/WK0OruysrKcNy405Pqvo6x88G/qy2JbEpTEBPxKGcbctVw1XuvXk9NTQ1toO845Y4oF/Tlxkn1ajAYDO2rLk1yGiM7pkuWtL85G+XtANtnTNratP2Ay2LEV+1v7toIaqc0yHYbVYfIHmuCpkEnn8UZSLXH3i/2jYESx5l/OR8Q2RsvhLFfkWxEfHP/rH3XT+2j38/qtSZ22i6TDw3+1Edpv1zelV9ug7W/asc4I6Fyw4DNeREVASPbRP1Xf8D2NdjzMTpIuL5ENLn9Vb6r/tGOzMzM4MiRIyOvkfR6PTzxxBP4+te/jpWVlWEio/EGk6pDhw5ti1W9SBT1I7cKBNiUJf7uko7d6uoqlpaWcPbs2REZnZmZGS5XO3To0LAvOtb6HI111ce7TEa6EfXLi3iExoeHDx/GzMwMVldXh+8osXChNKi/8tiQ/VXo+47ax7W1NSwtLQ1nv7jUbjAYDN/jWlhYwJEjR4bLK9fX1zE9PT08zuuUTpUxLw4pnylfyqeU0pAm0r4vSZXChU2JVqHRQMf/9wCI0GPKmJwS+H2OnNH3a/RaN/Qa+Kvh1WO5IMn7mAvucgFfHaI2lGfuOOsMsNKlfcw9T+mN+qZ8iWjt2rcc7XXt5545Kc7IkTPmjlyg4rJVx3N17Dle54J5b8NlX+/156gT1T+VtcjosY02NsZpaOJFrl+uz0389Ocp7yI6PRiJ7EhXOfX+uw3KfVfU6UfOTk2iPtUhxwv+7/3RpMvbIaLCQi6Q87ZUl9o8U/2o2w0PylTnmuylyqYGT9FfdC7Hmzp48FNHV6RH0ayW2gwgfrc6GucIkU56McWfr5/+PbpGfa6OrfvgSfVdRMRT72sbPdLkl9tnM9nq9/vDwDfiR84X6LmIrro+KFzPOVZMSJhksPDhvq3tGDodOd8W9cGfk5sJZgI/MzOzrUgQFVhyY+tFPD0WFWjIr9XV1eF4kk7u4OjFCiZgLFx4UaUt/9TX+liMEy8odn1Lda1m6W8NANsdgQYpOvA5ox8JoR6ryyxzykVaVTD4xxcfeW1VXdxFTv/39jRr953ZeI3+ABoz8Qie5asQ5Jyv8lSnj31WyfmhL3aqA1bnykyf7bUxZjweVVh0BsSdec5YRnITyQaV1vmlMzCT5qA8qIn6H20zy3NAvMSPyDmznAFRnkXt54I+3utLdTgmOlPFKf5erzc0pPzfEwLqiy718CBLddgr6VHw1hQA+KdXvhW0GVHwTLnTxFBn5qLlQ66zbvzpgPhd7/cKHttT56rFIdV3fZ7brqoanXmpq6BOClRvfJYFwMj7EI7c7GR0zGc3ooBYv9OWeuAVPdvHhf1Sn6sVeU9APOGiLDpdkazovbpBQO53XnJFGLalsuJLmN3XKc1uq0ifLpt2v6S6H62+cF2NlmNRx1hR53ffOEf55rZc6Ynsg44Vg00+W/0VZSZ6d+igoH11O8L+ABju3AZgZCaXuqnBrgf9XDp35syZ4Y5158+fx+Li4kjsor9PyVmrubm5Ed/A5xIaf/pSMb2m1+thbW0NKW3OSi8uLmJxcREXLlzAhQsXhlvBq20hPbrlu7ddF58qP9R/+DWut+pXvG3VT121o7PfHBPOttE/67LhqGChOsXzTJK4amN5eXn4d+7cOfR6PczPz2N+fh7A5pLEq666aqgHTKDn5+eHv7G1vr6O8+fPD2MCjwOUlsjGuI7Tl+omJOPo19hJlQukGwb9kbCcYdS13uyUJi6EClLUyboKt9ObE15VNk2q/FeiaQi404kHG/zUdcPROlRdC6qOKUpUVAhyiUzUNw/WvArmfFLjx6DMExx3xF0TkijIoBB7suQBJa9RGnitfmp/2AdfdjWJSyiA0apXtASMxlyrOuq8csEH28n11R1/xFPXrabACRjdit2TKi5RY/GFVai6ZJ1910RAA0s/FgV8dQmVQ3UlkkcPlrjGX/mka8v1XT5dhtpm6ZzaSH12tBunJlVaIPHd3CKZyS1/UTmLlqJ5QjYpAR8wauN9Sbee1+uB0Zl+d9bOoygYz81uarCsz1H990TV7R5p8vedgHhZmdsKlQe277ZF/0iHvuegP6oZ6VWTHaLP8eAsStJ9jDwQ1lkLDfo0CNXn6LEokeNz/VpNhJ3H2oaOTc4m83oPQlnM1eBYfbrTddBwm6j91MIagJEt03VcNXlIKY0kllNTU8PA/uzZs8Ok6sknn8STTz45tK0MjAEMl99pjKHLZZ12YDTuiN6d1/Pr6+tYXFzE+fPnsbS0hKWlJSwvLw/bYdssADI50dgR2L67I8/pMb02klHvh8pNrq/kt+7Qy9+u0niYPE0pjeyK5/rocbj2AwBmZ2cxPT2NCxcuYGVlZZgQ8Z24q6++evge3KFDh3DllVcO43D6TP3xX93BL8oxFMpL3VRDwcSP7fi7Wm0x1kYVRFPy4hUyHvfBUEQCrMF8nRFRw6gGLrrGjRjhAh71Maooev/8/1yyR5q8IsjvrshRpVkdZVs0GWMPOHbLeEfVW9LTNtj14KeOv9E4TlISpVCHnUMkX3rc/3LyX/e9LVRmVafdRqiR9iDEAxGX93HoUztAuxHpDGn1tnV3IAaaCg16/XgTva5Xer0nvUqj0+335+C81P9Vb5yuKBD2Yy5fHgRPGuoCW553W+qzG1GfHZEMR8FHZIf4fJcFQqv7bVFnA7Q/UVLJ47lALqKlyddFcF+jtjAaM/eXHi9EY+TxhCdw3pe6sfUxchpVd70gnNN/j1ncdvr5SfRhER9yhdjIngAX/WDk63z2SoN8H/9oPPTZkTy34Snp0HdZfSZEi7f6flxk3z1+ieQ9F4N5vE3eEGq/6mJutqPJO9uN6Pbv0f/uz1n48CKFr2ThtVziR3vAvqlt9YKI9sn5VKd/vMeLW+Ng7JmqqDNaGdZlMN5ZTwxSuli1091aNEN2pvisEhD/WKF/j9rQWSNNBlVRmB1H1btIgSPHqdVoTk9X1eiSAtLmAum/wZFSGs5wkVZf6uRGRgUyUlo13OSrV2p09yEdx8hAkS5tu875aCVV5czbrFMKhwcMKiuRAz5o6FiS38DoUkZFLhAiIuMGjP6Irt4fOXo/7tuq6nNcv1yfVB5Ix+zsLICLlSLV/Qg+c0qaIjuhCVEkL7qUgdAXjfXF1Uhe3OmnlIbVLr0umhml3aNtiX5vSJ+nwZVuLZvb/Ef7FY0Z+dPrXVzSEjlC55knHaSBz9ypU9pLRDObPK6zmtGMhgdvXg322ZK6Aon6ENIRJSqR31L6Ip/B+3zsPKhxW8LgN6Jbr+cYu03Va/VTbU7kz6N+eHt6rdoF3WiF+hPRymvZdy5Fcj64zeKfzziRLteryE+llGpnrFQGFOSrrpYhf+oKu/sN76vLvyY+vhNf02qHKCA/evToMGY6c+YMFhYWtq0o4C5x/H0ibiyhO83m/KUmRKr//GRsyx/L5aoLPotL/Xq9Hg4dOoRjx45hdnYWc3NzQxmhzQcw9DOctdGdcZsKGhGYqDAZ8YRWeZqLydTX5MbB+ab80yWeKSXMzs7i0KFDwyWQc3Nzww0ruNEIf4eMfebvSnkSFtlG1T09rjYqKo5qjE/Z5C6Ivoy/LTonVe6QfWkbCY+W/1FQNVjnQOkOL/rdjTAZ5QmAfnoioALJtpzxukOhJlt6nwpKm51BIienDkaNpC6B4/N1aZD+ojV5zndLtP9UCH2+B2i5JEJ3DyNdLsR0YB6Qarse4EVjRuSSNE3qvB0PMNomRpGBiWYqDhLso44jMEp7G2gC70mIBvNurPU5UQIbyZOe13aAUYfhMsE2NDDSwkW0/FH7o9soR+OoNiPn+KOtUweDwfB3O6Iqa9Rn/q/6oI6L5/V+2jfavCgoZFs6NmqXtGLq8qH8U73K2U1CbbMmehH4fHdokwi3RdE5YPQdH02egIu2VxMofZfH/Vo0nhoYtwmWIjlTffKZ1wgeKAKjY6V+qa6irddQLqOdDb1/HmypjdA+qu/hfZ6AeJIGYCRWyMmgjhPfy3D+U5a1oFJXEFRaPPCL9MxjjLrCrN7rPOjiC/YDEW/Uv3BMdcmrym0k4ypD6hMOHz6Mw4cPo6qqYYDOAjPbnJ2dHW5owWVnHjs0ya0XHjlGLLhxa3Bu7d7v9zE3N4epqalhQjc3N4ejR4+GRTMuA+Qx0qPvfjIG6hLcq3xx0wxg9MevWchsaodtqc5rsqljCYz+LIzycmZmZrhrIv9WV1eHy4dJJ2mdmZkZFic5flV1cUdu3qOvFyhviUh/9JjqJOOPtbW14Y9Nj6NnY22p7oZIM0UPriJEFT7/yxnHKHByUAjaoi5h0+SCtEZ9diehgVhkLPXepkBEeaH9ipKlXHt6X+4a7YsnSCqA+kK3VlLcQOr0uCdEem00XsprNSwe5Pv4e7/a8HZS4BXLKCnoSq87fk+CI375OGpw0CUJ9QDWg5PIEHr7OTnVT6e7jh6/PupPlDg2te9y3ZSAOTw4zjkID6b1z/sXJTpqJzw4U1vHayP91vabEq5JRDQWbls0SCCiYl2UQBFd5NF9Zx1cp3y8tE2VcaU1gspFdM4TpDZ99PuA0aSM/7veaB+031ERr21yqvZHZ/1VR9VXRd8VnlxGOqt+LLIhUTLibWifxpWZ/UbOZjrNXrDS+6PvKtMbGxsju8Tp+6rcxMgT5zp6I9oj2eBskm42pnQxqdBlf9FKCu+391PjrlwBW+EFaQDbNpJR+6/PclutfoX/cxZHfbfbyZy/U53j6giODycM+F6XJ3O5+KfON2sfPfb0ZF51s9frjcwUjoPOM1W6202dwVHh0Y7rzIQKnO4qotPyOUNF6Iu1ej4aANLF82Q0s2RPHrRSwC0f/fdcooQyMnRewQa2V7C0Eq2GmPdp1SSnnBR0FZSmdfgqbH4vgJEd9MjryFm7cLOaoHJCXmgCETlVNUI6i8d2dSmZjq/Lj/ezjQM7KChPaMRptCO9A+Ig3gsUUWXJK/BsVx2WB+yuz/p8d5SkkbrlCZX+rzPAOn5Kl6+79v6qnEeOUGcfvJLmL+eqc+T1LiNepGDb0fp+HdM6+rgET3npbZAHukxEX/R3O0D6vZCl41RV1bBCGI2T223+HwV1dLyTDJUnQqvBKmMqa5FO6coKYHvRLHo2EO/OFyXBhI6d2j5/l8Ntn95HWadceyIePdv9NnnFAFZfcvfgUp/vyVxOf/xc7ryPA69zH+x+g/QpHzTQdB+jdsH7pnY1mjWnb9NPjVfcz0czntovp28ngd9uwvusfFJfAowus+VMAMdD+cp2PdbjuHLs+SOwjOMYq+hukBqs5+gn3FeSFu5UNxgMcO7cueGSNV2F0ev1RmbHODul/oYywFkq9mV6enpog/WVDu0vZ7oi/+jxLJfRcUldSgnz8/PDzT5UplUm+TxuEsKEjrvvcSZHZZV20vuoMdny8vJw05GVlRVMT0/j8OHDQ/u5vr6Oubm54Tj7q0Oqzx7H6BjmYmqH95cywzifSwDH0a/OM1XT09Pb3o3QQDsKsvScGyUVfB8o3sf2co7KnYM6SG2D36MM1gNBPa8BE4BtGXWUVDk9hC/LUqet90ZGIMdbntNAJxf4tkms9H5VEj/PPrgzVwFX56/88GWjGhTzGk8+uSQjcoLsp4+1thHxNUq+DgKk1YMsJvXaT09Qeb8vVdCASB2f/+k9zh83UnxmFHx5lVz1yeVabQb7SMOm+ut65UUHpckNreo073d+0+Z4UKvBkBczoqqe6kTEX+8H+618jYLCqODApIoBtTogtS+eXGmfNdAjT92Zqy1xWYvkh+cmHVExJpLdqqq26ZTqklajyVOFj6UfUz/qS+X1mcDoMmkGHewLdSmiVf+n3WURwcfZ4cFLVBjkcd7vS+Y9qVJ5VjnMvT8ZwduMAi+lR4NHLunlZyTDHqRqHKBBN+VD6VGb6zbBt/QHtm9g4vJFut3mKE05n76fiPTeZUMLFaSfrzBo4cftl8cBKodMYjSpAkaXurk/ycWR/kyOAe3jysoK1tbWcP78eZw7d27EPmgsy0RqZmZmZEdb9lmfr35ZZVblhG0zEYv8r79DxZ8sWVlZGSZVXGJOXqrt98IObYz7J93JUfvgSz09BqXP0kKf7qMQxQa0DV6McT+vY6e8UJmJ4j4Hk0baB9/Fty06J1W5ILTJOOv9QPyeB9tp+sxVkbyaxGBNM2i2UedQdGbE+6tGVvum54BRA6BrQKPt1XU9sDpaNR6a7BGqDBpo58bBnbsbFzfcPs7qnHheA1yvXruy8rhWolxxcoGFBpmkpSkhUoOscOM9CU4JaJ4560KnO6QcvE2tgOr40Zj6b1q4TnkbuWTWE0W2oU5HdUnb0GvUmbCvSocmCl7l5bVaRfagJ6KPbUQV9SYdc2jSrDQR/lIwadBfktdKKfvjtjYKWAi36210SpNO7tKkn5M4W9XkWN228BqtXOtnzoZEwT2hTt71hLKs7Udt+Sfb9aKUntMEO6eTzpOouOm0R/dHxZs2aGvfVA9dN7wf6lfUFmj/KM/Om5w/pY7pszRm8CCQ/FediRIFj5NUBtoEhQeJSD48RuM5/fS+eoLqBUFt3+M+n3HxIpD6AR1n5Xsk1+r/vOisxQ7ddEhnLiO/oDKs56ampoabavA6TdBydoezSVVVDd9H8uIRkz0tJgL59zD5HpqOrfLcfZsmNewbixdaCFT6c5ssuSxp4dVlzeXPbbf3IbJxHEtdktj0PnEOnZf/eUVOmeyDE1W2CBVIP+eOh9/5x6UvqkjMzvXXmXW61KvRmqT4NKsqYERX9KNg7vQ0QIqW3+n7RppUKS8pDFqhIO/YRlNyqePlVQeHOnhdGqH38qVMfaZOd7M64gmYjrvOSmo1R4NsV1bSwcCN/XdESqLGkn8ppXC51UGBfFGnoAbZDU5dsqRVNg863DEpOL6UTV1ixsoXx1qX8HrwpwmZVrY8QIuW7kRVWZVt1RO1K5rQu5N1J8Y2qWM+C+sBr7fLZzudbEN53OQwWKmNdvIEMLJMyXf88+CYVVL2TYNGdfKq0xwPrcqqDYqCHq+a8j72XWe9JgUuW5Gz9EBcZ3b4XX+vxXXL7buOve/gprpI/eLsU6RfGsh7ohXJoULlRH/MXhH5av/O+yirKoO8xlcseCDkcYLKG/+P6Ir6o3ZAEf3+l9ozAMPfuKHOMPBT3xUF3coTVv71Wepz1c6RLvXd7p81caDv0nGgfpGHOmt2kKDvAkZjpJWVlWxsx/t0GS2/q/xHicnGxsbITK0WpKPN0TSm8xjI/UhKaWQ1DMEf9qUNIa1zc3Mj7wRR9vR1FtdR6vPq6urQTpPG+fn5bTE2Ey1dMkjwOrU3tFkaEwMY0qqxt/oyjRvW19cxMzMz5KvumujvI6qP2djYGC714zhpPKEFBmB78ceLJZpUV1U1Qjf7HCXdaht17NmmF0V0LKuqwtGjR7cVqtpirI0qcgGdOxY3Qi6oXpVgG4QGPB4sUVA0yFhdXcXy8vLIryJzMJyxeq8LvCoyn6300SB60hfxgbRNTU0Nd4gBMBJE6Y8Mu1Cp8Ppa2iiR0j+ly8/VJVXaZ68KKO81MSS9/LVsfc+DoIAyEXODEgWdkfKqEahLEiMnTwfgCjcJ8GVm/r2pr5Fu5RxuJAMcX07767bi3DZW3zNidUxl1ZMZX57kiRWw/QXbyBF7ckPnp2ugKRf6oqkWTDT4A7avq87ph9sk5XGOt/x0unWM2Vfy2PmusqnHo+CA0B/BrAtulX630+SvXu/fdcadQYLuZkXZmCToCoC64N0TUg02/L0XItIn9U0+S8LnaIC1srIy8u6S+846m6Df3Wbzk8/yPrt/drlxqI65v4/oju5XqIw3Xav/u0yr387JOvvvq0Hon7Vgq/z0RNP7SjvkATGvU3nR57qP0354Eq3JxUEnUg76YuUvg2/qDhAn88pTLWQoNIhW+6fvw/umDCoHHrxHtEc8pU/hznQemHNWg4WmaGdk7bfGjV6QADCSpGuCzWc4TzyB0D4zziIv/RUbTfj4PxNEjdW04KPXq7/WxEd91dLS0kgRhv5J+cS4QAuZGud5YTY3TpFuRtd7Usf/WSQZDAaYm5vD/Px8Ntepw9gzVbpxQ5QcaQe0I7nzUeCigudLkXi9dpqDpQLPduqYQ2Ggsctlp7lBckPh5zVY0QqLV77ZB32WwpcdeLCZS2o9QYwU0Gn1WUSlTzdNiBybVtHUaOifVrT4P6srfI7eHzlwdTqRPDiUTqV7HMXZbTBQ1QRTk0IPknOJYFMiFQUuKhOaEPH5VXVx7bhW4whPcNkfbQMYTar4XDXwTnsuYHOZVdojnXDeKG3KE7/PEyLvVw652RAP1KNzUcLi7akDy/3RGdFZqKPyWT2HJxORjYjsuM50RSsQDhpR0B8lyX6d96OOd2qvPDgHRmcrNJjQoCFapuN+Se0Y29dgJdcXhc7QakLsgR5pjHSK5/15HizX6UuOl2yHNBKeWPnzdEc40sqkn++T8ZzGDMoTf06O5tXV1ZFlQ7xPi6Oqw8ozT5qUf35tZP8i2TwI9Hqbv8fE4oDGUT72UYLjP6OhM8IeO2k8SFAm1Xfxniiei2RKx0nHj+3r7BTlhrqqfk19YZ2fIL1+HzAaA/JTZ9lVB10f+d3f+9dEPho/FtJ0jDQeYxuRD9fk1leaaLyuNEcTE0q/FmK1aMpjUeGPtPMeXsfx8NjW7ZPnEvuy/C+lNGKsKGjqKLyDJNCDc//UwfP3H7wioQLL5WOssNN4ci98r/wSKvDKTDWwvV5vxNl5v9S55hyHDpa/5Bc5p1wwopV4QivxUZDjNGiVW3/TQceJfZ+ZmRn5sToNMtkHXZ6gQYAv5/PgWg0gqxm8n5UafnLso+qSK32Or84Xn22pS0T2C73e5g8WalXbxyaiM2e8PZjJBUTkr88sqSzqsFojwQAAIABJREFU8i4GLTTE7sCA0aqRHtMZZDX0+mPfvFeTIzeeUULFpRRuvPmphlSPq8F3x8VPX5JJGtQ5K58JL4JwXFh19LHg9T6jpWPsL/TnNgGhPWS/+ZsfulGBBsouNzqO7nBygZ0GR+P+xsdeQ+2Q+hjtY6R3OtZeQFDe64oDn03k+HEHLp3t7ff7WFpaGo4VfarvNgtgm8zyOP2eXuv9ATCi61VVjWwB7UGIBv5R0UKTb18V4HQoz3mOcsg2Vf5yhTXKtxd0ySutMrNtXwrsOsXAtdfrbZtV5L0qLwCwsrIywl+1mZ4wqZ7RBrju1SUfbEN9aFSIOgj0+31ceeWVWFpawvnz54c/hMtZD+1npGf0Afzjb0xpLLG6uoqlpaWR5X46joxplpeXtz3PN0VQ+aa8s6igM1K+yoK6xbhS2yMob5Rt94GkS2fVtB3yTWWKs0XRroCEJgj9fn/4w7m6O6e+F6a0uNwyWVSe1MVaXDar9LF9jWdUjzUp1jhcbTMTI4/JuSyxqqrwR+s5XtRnjwdoK9jvKOkm//Zl+Z+uZ/RMz6/V75HiqzAqw8kw/c7lfQoacX5SgMio3HKqKEFSwXen69d64KbCGT2D/2u/2gQpkePWdl043fnymAeSylsdQw1gdabKg1udqfLgX/mnAq3GVGlWh+UVCT5beeHt8RleuaqD3jsp1T4PoFxem+iM9C1KwFymyffonT41cv1+f7jcTmnRpD5X9dcKkQanHpR5gKAyr07K++Dy7PcrTzwZZB+UF962fnrfgO2bbugztWKt/NDkkuc1oXJ7qH1QTE1NbXtfx/uvAQifX6cjkZOK7JnTozMvk6JXjpzt90RSv7tMuf3WYF95rUuRNPHUwhH1Sl/i1mVEWrDU51FWPKCPggC17yoTugRO5SOSee2n65VWfSMfpLzT+zzBiQLFyIb5EljXIZ2pYj/JS48JlH6Oj55TPXR/xECeOqh2RHXWkdPBJr+l97bxCfsFBqFVVWF5eXkk4SAPo+Vb2gcN7BlIawEKwHAWLNJJPsNtIZ8RLUH3uI3jqLGI/nCuz9Kw78DopIDKTQ4qI7T3WmBUv8m+abLnPlAnN9R+eLwXLcmO7J3SwPMaK/B/jQO86MDjvsEar3E74+8cavHH41Wn12MEh46HjqXPUmtBmTakK8baUh3AyNaV/vBcAKxwo6gD4jMqfB6Pa9U+l7xxML3KFF2rxzRQd8MXKZRunBDBK41uXJQ3Wm3LOZqIXt6bEzblufdfr3Fl9nuInPLw+ijAdWWl81peXh5WG5aXl4eGRe91RxY5fVWMyEhEY63XHDSoW5xdYJVGExJ1+PxUPqsBjJJ+1S09z4DOk/YoKFejrt+bKqYc+yghjxIqGjanyeVVj7k+aT99Rj1KzNQherLgfanrZ87xRrroQazKtDuxqD3KO/tNmdEqK9/V8Yqp9ldtsDontbM5m8VrcgnppMD5TxkDtm/Vm3PYhFZ7PSn34oU68ZmZGczOzo4UEqJqt26i5LRrEUplVRMELxxEPln1S5P/qL/qj2i/PalTHY6CTe+HXqtLhD3JVZ+iNCnvNBCPkmS1i+5bdfx8FsRp1Xde6LfYxtLS0nBmxcdDn63fXb7UJvoyMN5X59cOAs6rKI5w3fJiGmdnNRGrqourVLhKSeFjzJkt9y8qy1p4UJnXcfIZWbbl/fN2vcDIJNyLMHx2FDdqEYA2YG1tbbjhhMdAfp+Cz49iRO2/8kp9Af2IyqTyXfVM/Y3qPAscqls6fjqGuoRWaeI73cCmL+NMoc66KbxNf5bbB9LMvQ12uslSp6Sq1+sNly5wwLlMzBMBHfjIIbAjLsDsnHaUysXjZFbETM2cGSz6j48Co8s61CFQ6dSQa//VkbAd7VskgOpg9Ly/1KjGgc46CriiTNz740JDaDvuDN2Qq0KqEXFeVtXFXaUiuti27jyja365q8758+dx4cKFYfWW07e6nFB/kycKgkgPj3ui4UHIpDgm6pYGeVqw0LGJ1ldHxl8dE+/T30rhOHJ5LIARx+SBIz/12W6A1GF6/3TpmQfufr3qlVcBo/FSGdalo+44SWOu2s3z/HRdVlli29EyP4XrY6SD3ib/101sIpr0OJeo9Hq9oRNm//juh9ogfTZlw2lV+6D2SQMTX70wCfoUQR0oZU/tZjTL47PH0Wyo+jDqg7ZNuZ+amsL8/DyOHDkykvj4eGrSpAWqKNnRsdLlf9HvyXiw6foQzTBE/icKQHP+hvyOxoJ/ymOvHEfJqxeayF9dMsbzWmRTffeZefYvWpak9M7Ozg6XxS8tLWFpaWmop9ztjjEHbZEXwzzA0/Psr24wQB1TOqOi8kHCZ108QfKZIvaRtoS727Ff7mu8MK7yo+0Bo8u0gYu7pm5sbAyDfML1j89lnOFF2qgIonbbbbge81gk0k/KjfZ/fX0di4uLw5Vakf7rMS5hZPzAGEJjrtxEg8awvmoIQBh3My5j4qNjSN1eXV0d7hytNkZjedJL6PJbzoAyPnRdjQqsXmhhsd6LxPzkDzx7nNgVY21UoQqtQZEHC+o0gNi4u2GLEi5PcKLgyhM4d3YePPF+Gixvry54i6p6DEicNhUwpc2TGXUUnrx50BfRqMY5R3uujaiPOb7yWDSGGlRFNKhRomIoTzSZ9p1i9Nk+O+N99nHUzyY+HiQYBAAXg+RoaY1CZxei4MWDIq+q64yTBlp6vwf6RG5qP3rH0mUiKlAo/Lj2M0LOHhHKvygw1naidn0McvruiOSNx9vKXK66p310GtX+MqiYmpoaJmlenc0VwaLgJeIP6YlkcFLA8dL/c/oV2RK3e2xPfUw046u6rC/h+7uTOhZ8hiZHmgRFVWPtg49Tnd1jv3iNBpiRnHoQ6nz1a5ug7VEuPaCNAtPcrIe2F9GhY+aFIe+z08/gDBj11RsbG8Ofc2EBmH3RmIZteLXc7XfUB71Wv0+C7yJy9sKLzDynRQ2VX46Hxm25OEjb9XggShxyfCX82XrM24hsJuEzVFFixc8oFtbVVrrza45mlS/fSI5LG6N+Kj3OY7dPPkY87zNltAk6HtpPLR5rXOhyonG/7kYcPZ/tKTSh0iKFF1V5jG3vRKc6JVU0QPobFW4ItDN6nzKUQY1WOVWQVIC08zq42p5WtHhON7XwhI206rsLelwDPg3EfHrVlSZSeHWQ6nBzDkvpVd5FPOZ5HtMgSR1dlNj68zQp9kDbHSgdhSesTq8qiDoXVvZ5HZeSaFt8MZXJl75sqRVV0qtbsEZj6gGRLoOrM9b7BfZZ9UqTby1mcFwix8NzlF9d/sqAOloCqA5Q21WwXd1a1vuggWbOwapj1ecpdPbHHWv0THXQ/izvT1MA6DKsuzISDJ78em8nal8TRA/kmVhHSY4+x4tEPK/3apKu57mphy8VpNP1JWR1gYPbYad9kqA64oGz9tvhttQ/o8Idl+/qOSZSumxMecvxit4nIA28z3cR9CBP5cRXhxD+3gVB+6o+oE3BxvsDjP4GpPJaoUVND9o8EYn6xnbVN2gxU32tryzx1wN0NszlQfVXZUbPMfZgG+SzXsO2eSyyw/qnvPdi8yQgpTQSxCv/XW71nF5L397r9YZ2SPvpMSQTYd0EQRNefTZw8fdCeQ9pIdx/AqMTCJ4I630eC2mfdVt5LzppIUHlhzqT89cR/11/9fcPVaa54YTqjtLEMcjJZMQH7Tt5TXp1nDSmUf3RghP1gita1J7QV/lv7bmuuH7pNTpmmrTqp8cQXdE5qeIUHo1RtLZamQCMTr/6YPqfvrSrCuUBiE4NqlAx2OFvJWkA54GZV9RVYKiAKhxqwPWTvPH2NWChsii9kaJo4JcL1vSZw4HcWi7gRlodE4/5/3yuBuO6FIgBGvvEMec48X5/ni8Tiaac2R6NCK9ZXl4etsENElRmdEcff08nF+CqAeT3SXFM7L/rFnDRCev/XijQAMz5pQZDp7e1cqa6y4RG5ZSyvLFx8YeAdSbNgypg+3IJ12FerwY50i0fMz1OfmhQ7A7VA8ec7kX6nNLFF3/1HnWeXlxqqjC7HSEYhEe2T3nrxR2dmdIgOLJ7/B05TUJVl/m/3qs8iWyQLleLdnuaJJBGjh+wfVaYyCVQHnRoQKaBg8qIBoNcJqYBpdqxpaWlof1TeNJB3dKkSpMn9am6CoD2nH3RPrJd8ipKKpwn2j/VOT3PY1Ggor5L21ceRjqlBVjlh/NLA3pdIqbv6/g7bLxWbVjUJw/eWLTQBIH36qf+4LCOg9PuvPQkedzAbzdBGwmMvluj530MyAf1UxwT3UVON3BhW+4HeFz9DJ+lxUXK0+rq6jb7pzGajhXb1sJabjw8yQeQ9eE85p/ansfaXuTW56gcDAaDoY3hckedsYlme9gX75s/R22N+giN8b04rGOrekOe+I8n8/n81CKYvl/l8qW65bN0Pm6kj8sS+Z30qm3uis7L/7wiF1WwIkRBF/9Xg6wCqgOuiqK0OB0uXJ5Vd2WSBhYaxPmz2LYbSBUKD1By33O8akOrCg5pj5xZFLDlaPRx8PaaxpbXakCi1+rad6VHE2dH28QoCgSVtklClDh40sMx9aoXEQWCXvX2xCRa/hq1pYGDJs56fcTrXDCl96m8Rs9vGq8oOfR2cv/X0cS2KbdR4OiIgqSoT/zMBa56zG1QdG+uXe1vlMC6rVXnmoOPjbcxaboFxIUvgnIT+ReHJp1Ru24vnbd1MqTXt/EvUR8jevWZtAWeHPr9zqecD/Hxz9EU3a98d/13ffaAmkGy9pHHVIbZz5xtiHyJt0E752MTtan+XmMFpaGtnjxVfBdtpPMpF4dE48ox0CKNJ805GdHvnvxERRC35y6/7nNz9jDyV/qd+qqxT5QIRfZEk702460xgRZbtc9eBNBr/D6NuzymbbIFylefrdYCKz+jMdNxIy0eH2k8pJ9t4g1N+qIi5rjolFRtbGwMfyGZWR2nfckcfycjF/x6VVV36tNdQpT5WjnydghmvLocTJVRKwhekVCagNFtHSPBI9gHnbXRKpQ7URegtgGJK7gGSOSfGyp+9/HQygd5pNdrxVWXSaqjIK9VaXiePIgqqRoYcFx9ja3v1NY2yNP+aoDoY6EGOLfsZz+xsbH5EibHhp9a7Y7GFsA2GVXD49Pb2n//nRdg+yyLygSr29zNR6vtqhNa9fcKryfI2r7LfeRoedyNrxpmXuP3eULCY55kuMFVPYmCTtdfjkmUCKtMqwPwIDByDmr7okRMj+kMotpkrZSymu5OxQtR/hzaa+DiS9Ee7LqdPmhUVYWVlRVsbFzc7EZnH1UfeMyXT0ZtRr6BY897OXPMaij5Q7vn1VHdwUyh48yZHbftOuOS810afKjd9ufwej/mz/OCqNOl/PRNMNQ2uxxHQW0UBKv+eHt8ptoenz3XYqkHoG5b/n/y/m03jiTZtoadVFGq7gVsYF997/+E+2Z1l0iVxP+iMFMjB6d5RFLqUjZ+A4gMxsEP5naYZu7hwbbnmbXWFeYwzqEvJP7gdfaXM4WRH39j6N4o7fr06dN6ff0+49TkIfc32SEIp66udT1bwnchyTfOdBiA53zqb7b88fGvDbWckG/vGtv+O3FB8uxOZlTaWDYM6ICR5yKT375dv05jXMq+xYfHfvN1mbTBvGPgTL0IceaPOmcbmg11+I4pA6yMDxMbvpb2GBeZj8QMGR9u4sTVUfa37v9Zujmo+te//nVxDmuty1SbDWAboNZJv4hHA0Jme7vFlMGpQe8i5OiajOILwwR0DBAMNlyWgZ4B1VrXW8G3iJwAjnWu1bMwa11vH27lMSBqQJwGjfeRl5w65/K6nKMBa7NPnO7ebX4QEJFjGoCQAyHyzrLGPrMeggzyty37+RWUoIoU3crxWm8zow4AGEzEuWUJBZewclcqjpF1NETDFoNMI0+HwrXyHsfWBxKBUv7ncoU8v9Z1QMXNT6hLfketGU46bTooBmWTc06ZDITZTtqvgKuUZdnbBVUtuGqyz+u2ly7HMpP22XmTN6kzSzmaQ2Yf7oW+ffu+pNhJCwO03L/Wd/mjzNqvsCz+MnuaADY7nSYwcmCXcaP8h1h/2sMghUGVbULKoiw32Uk9BDCWQYLJ+O4Ggiyf1H8HHlx21fpKIljn+ze5PzYvviV6wHHMsiMnXWgLnHSaeJIxazyn76d+MQHrYC/8ybNT4oK/v5LSrvDh8fHx8k70Wm+/T8i+5zeva6x1jcEoP+krt1+nvXIAbZ/BMjnzQd2Pf+Nz+bVOToGVrxGnpM35tAJ1N31viYPw0W1qSTZjHn7+6NOnT1c6u9Za//rXvy67WNI+NPtNn+VkuN8TpF1keU9PT5cdw71kk32gv5xw7M7HxB7aXr+8vKzn5+eroM969SN08/I/gxZH/KSjBlrwJgN/RGGunTqFP4PRgALv4QBSqKc++nyry6CtBQf5bRkUOqipbCo3hWjHv9Yn8jLAoIFK0s7At3MtCMr/u7az/2epGcAzbfy7ieBtrbfAw7LL821sIsfut+XHRq3JFongoslyzjujxvLclh1P0pdGky7bgVI3Wh+pM+ZX49/Uzul/1tnq2vWL/bGD531NPs7oPtvghM/Urma3b9HJX0VnfA2d+CT/P1L3Wt91c5oBtSy3djaAZbltyRHSDpTsdNP61frIa03WqE8O/HZ+pAEq6zf9VXxYa5/tF2cmqQtndD7lTTOAbPeORw5GiQlc5z34rVDa5vdaPQPx+nq9dJ0YkoGOZabZwNCUSDad8TdTOVOZE/5t/qPJH4PtSfantk9+wn1pdTKgYTudEGh8yB8TKElO2U8aK+Rckw3zzmP9HttrfBQ5c+C61n5W/ha6eaOKpuDsvDOoZNzr6+ubjS1M07T7xFCCFS538CwMf3N8xmC28qZ2HAG4ZMceHr7PvLEuO/NJiQwI11qX7Bx5RwViJmfiifsZhafzzzOWAb9b43aS2thM/+fcBOBZJmWEgeHr69uNRsybX02vr6+XXcG8RIb3rNV3flvr7fak7GPKDe/5cngyjDuAFGKZ+Z+/a7397IKB3yQXrGO6nnppJ/i9E7bb9XsZQsraLVlOpi+2zMEi22H9md5V84zdTge99Db8c4BF2gHUCViSJ7QVDrTCM876pjzPoBzZzL+T3NYG+ngcW0ybbMCYcpmlpXwSINs3PTx835nT7ZzAY+hIv1JOfpt/9mwox426xOfaCgnzjHVP48B+hlexfcEIrNd9bYEV/X/INrQFNik/Sy7Tb/OW4NPvd+x0inayzYhOmIQy49msNjvxK+nbt79WMPE7lGt9t38fP368rLTIpikcrzzDLa8NdNfqGxLxHsoGifxm0Dv510btPuM9+x5jQb7awpUnfg/NuvXw8P3zC3nGNnuytVOZnI2LPbFNb/ri2cH2Gk3jG+9h0iNlNxsWsp1pGMTy4H5zHLy6osnBNIN2ht61pfoEtthpCjo774a24CyBV3Pq7dn80kkdAba27CbkAd8pW+pyIDX1izuxBHj4fjsngz5S+stpTAq7HVLLJpinXhbT+Mf2rfV2uV7ONT7QMR0JrYPqBkLtZGmkuNTHW0ZbZn8lJagyHxsIiaw5S+b+5DjbqJKcuCCYsKNg/Q8PD1fLyhrfJkByhigjk975fS2uzeZ7QjbUdnqRl1ZnnP7j4+PF5hk4k8eR9Q8fvn8PxHJrotH3efIj9bbxJjU+02E1HtgG7N5XaEtEHaiQR/eQrCB5GV8DuMwcE4ikv7bFHHvazJZ99dIagyEDiqmd5G2zXRwfl9XaZVvIDHaejY6ZWj9SrvvA8wwmmbTIvc1ON75SDrmMp/HCbcg5LsFsz9uOEGN4KRvLbTrUdM46GfKS0rW+L61mu341Jaj69u3bJbCiXXx6elr//Oc/18PDw2X3Wc9OBbM4scwkMHHUDsA3UN50hLb+TAJoCs6bL069vP7w8HD5KK+xE5PA1kXKmPVy0n/20UmE19fXCw9ps/I/d2+13aIO2lfwvGXevGmzQu6Lj1MHXztpGJYJMfIufMgrRn6OGOdH/NfNy/9IDTTQAFpwDCAaIAw1g9rqPwp4Gk2OpdFOUVvwdBRwOtv9o21vbc1xG4MjnvJ59p3vCNBYtP7ayba2kN7LCwcBdFINSE5jcy9EhzK1y+cNNJoeeqx3Tr4BOzr8M868yVYz/Gf0t4EN/m+ZTsZtKmuqd9cOOhdnsmmEdzPwjbjsw+d3QSXbZWdiHZ36eovcN73K/7zHsnZPumVqzrz5LspZA17sq7Op1s1G1q1baZcYOCND7If5wP4xeGq6eOSneV/KcxJy5x+sYyaDZIJE96UFT+RBymh1kHZAvNnh0Bmwdqbse6HgAy6roq0g5mkzbbbfu7FpskZ5Z6AztXX6PeuLjG0mHMQ+vhfzsd9Te6Y6bavZRye/JpyQX9on+hUGgc3fNP9Nf0r/x6DxaCyOsKuPGVRNY90w/nvoXUGVDQQzxsxm5t7cw5kZGtSd4BwZurPgk+0N0eA24BGh4bmmyOxrM6Dp29PT02Xa1TxgPTa6jTeph9lDZlbZtvCc2WbuVDQFHunv169f38zs2XmTmD08ygA529B4mD5Y4T0jxnOmXcB5T07KPLBjyj0hZm5cDq9PM1U7EES9o9EkTcZvd++ZgIqymzbZ+HOGykuWKM92em5HA1vWI2dQKbNZuvL6+nrZwMf3TAE9x2WyBztyud4oowFKP9ccqf+3vWp2zzN2Z7K/fyfRF621ruSGL77z/lDsZwNjmSE1uHDSIuQZGdfV2py6mo2bAOIR4OPs29PT09Wv28/l6gRT7M+0ouIMsPTqDD+fpfOpx/3jUjJu2NSWvKc+881gmcdHiZqUz/Fyn6exbgFCIwcfxF6/kl5fXy/fvvv06dOVX6bOBQM5+KG8TsDefU3Z3IDKm4ydtaG5v4HqFlBwzLlyq+FAjjn5wbI8A5Vy4ru52cRuBZg3YjEvwxOOyZ9//rk+fvy4Pn78eFkWxw3c1vq+bHEKOugbSLZ/fL+Rm2dw1r35mKMAizyz3jYsalxquftRfbopqMpgOPtDJed0HwWAGd487zW1rb5mbHbO+pYpu120aodk48n7mS30gHCKNDsRPTx8//p7ExoblQnouL9T4MCgilOnAdtc79940rLppiaIDciaKEs0BO0dLQObyOIus79TtmbE74moW24n+7NbAkCDlTJZ9g7ws/wpQ3wrHdVF8lIp3k/H4Pd/DLiagaZ+NFBFfrfgne1+fLz+Uv0k6+28nUjOTUkWO/iJl3SePD+1yWCwlTkBC2eho6f3qFe0Lwym/K4A5WOtt3aE9iO2lM+c4ef0/452AcAkezzXbAiDJvMhNjnvYH779u0qcRC/bh01sLbdMZBsy+hoo5gEzIdNrd+eKaZ9YJlnQBr5s7vfvJ0CqJ0u2tY0eTBQN2j/lfTt21+79z0+Pl62quaW/wxw+d4Ut103xrAORcfMV86O8b1pAmcGbHmWcjnZ7RawGhf6OfY5ZbTdPPk//ZD9Ossl3mEZvGeyPSk7H6NO8iR6nXF5eXm5lJExTMLIftUy0NrqcXXSjzwhHjujo+3eFkix3eQFj+m/fpTetVFFaAL0fmateQkJBWGiHZN9vr2r04KhtH8nHGb+rn2t/JybgLHLnZyf6YgXE/ghteCYz7jc6d4j2gVWt4IJy0qWY/ke1zEFT2ed670Sg8wzoNl0hv9HgGK6Tr4TYLX6z+h/G1cmcaZ226CeIffLs1S+3kAj2+Dj3bkfoWbfeP4WQDmVOV2/B3B3lo6Wu+S4BdDTva2cMz6o2fzJp91CZ0FJfg0Gfb6Bl0kPYpemem4lti2BUuMn2xO70wKd3Ov2NbJP3ckB/9/JFfm8C47Ic99HTHEv9Pr6elnVMgH/nDOIPdIvPnemHdP58HSaHSL5Hs+mTnjGY8TgLM+4381H5XnaIc7mNNrJY/Tj69ev6+np6ZIMzPbuj4+P6/n5+TKGDIYZoEx26qwsMtjiWEz+suHaM/e05X7NhrW+/Ai9a0v1td7u9tYazxkSL7V7ff2+E+AUyKz1VqGmwIHndu2+JSDwc60tOceMxOvr62V68/Hx8bI5RZSLwsS/qc9Tm0xNOFOO+cqp5Wkr07SJ07YTD89mxCf++RrLZJtsZCh3OwDkZ5ht+RnZif802RjQwLXlLf7e1y4AmurzcgqPF8flKChKGR5fy4IdUu7zDknpd5yDnUYyoS2z13jJ/1lOnosOxOG4f24Dn59s1QS8Gjg0z/2Mz03LjnagnufOjCf728q+J8AXitx4ZcXR/WtdzwjRztIuccaKst0AwA4cTSCe16b25ncadwLah4fvy9K57KcBQm6c4E0ULAds/xn7auDJdtC3Pjw8XLLt/K6RdaQtpW3t2AU1Z3jczk8yxeu0feSzfd8Z2bwHPQuf803E33///c0rBt6unksBp5kl8ohBxpE/o9w1PWN7fJ6y22z3UX2U12xO5tlSfj9trXW1WohyEP7w1Zn0yb6L5ROXxt59+fJl/fvf/15PT0/rH//4x+UDvP/f//f/rf/7f//v+te//nXlWyNbfJ9+wvmNjN9IwQLsG5+ZiIFuC5Dsd6fZKc5mhRhI3pKENb0rqLJRaQI3CaAHh2BkretsgIVrao+X6ZkmBrV7JwM1GS8rfoAXgWJ27JkC0TadOhn7CJX7ZCDkWRz3mQYnO8xMQcdab6dtDQ6tFGcM/S6gcp/9zNkAgf006KXxvAfHNFEba7fdQDf3mXcOQF0uy6ccsKyWveP5Sd+8KxCXzZqmAMvBsPXSbbVtIn9a/2mY85u27IIbBvvMpOf5xkcS+eB6zso8+WT++b7JTk7Ak7QLCO+dwqMsdaPdJlm/7PcMEHM+z042xUBgAnxHz03+gomInS0mUOW7ie47QYYBcIKZCdixXTvayWl8EwNbLi1z4qPpf6jZml0gdGSXpud5bFuRccn5AAAgAElEQVTN52K/plUs7HPDA/fmt759+3bZLj0B1Vrf5ZyyyQREgmRiw5B9QLOFkz3LeeKlaTxIxjbpW8MLHJfIa+xLfhNUtfvWul7CyHaFH0wC2WZMfpk8Y3CUpbvBpWnft29/7dz4//7f/3vzaRf6P+NC46mzsUDazNeCeL4Rd8FtM1sOnFpbzDPHIW7HWZxJevfuf80BT50wWWgM4s8IO+tjvbcEUFNbj4KnBvK8kUOExev1QwRZuz6TYhyOomgbBRsAZ8kMClmXeW7Q7mtu2xmQdpbOLBXw/e5Pc4hn5fY/TROwm/4P7UAJfy1rDbBPdXBcp+BiZ4jOAKsJNHiWlDLbAMzOMU8B5URNfndl8yV62jbWOS3h2AV7P4uarBgMTvLk+3N8Vk7vidoYRjbOyC/HkLZyCiZ2wML3nAmqGk2Am8cMJAP82oxxIwZUt47xxNeWWLH+MgETX0r/yvKnxMBEU9Ki8eLIz5jf0/O8338uk+XdOxGsUk68dCy8Cb+nvppf1M+zMwlN7s74ouncZD+Nq6Zx3enWLlnSMNyZvqzVEzn5rqnLzVJAPkdsSH5yBcgZ38t6Uu5Zmu49steMMY5kwbx872zVu5f/rbWulgelwXxRcDK8MZLMxKSsFjg48OIfp/6ngW2BCwXD2V0qOXc29JKRydgxS5idVSx45tFOkVMmeeexIBnM8cOuaTOdmR2bx26aQj0yVpOTvtVJeHwIqKfsDO9Za13NHqbd/NjdPZCde9rGNc1rzXJjZ24daH1tOnWkT6QjAOjzHr+1+viutd7sasj7kmH7+PHjlR2wc2JGy7NGTQ75XAO9zLBNs2wZL34zyx8bNDhoH4FNX3d8PatHk/Mgr6k7bTlWmwWzPZsC7nugJs/sl+Uj1yewTBnZzeh6FcD023RwAhO02Wxfm31r4xr9yQvrbbcw/p8dwPg7ASiS22d5aePj2e/X179mpzJjlXsCCqNr0bHMotmWNV2izzMxi97AtHeja76Iv7lOfjc/xuCRfElZLUH4qynj9PLysj58+LC+fPlyWTaWmY/InZfCpU9eDrjW2w+oZ2z5LaWUwzLXmu0lkyCWC+sA/cGOiBEz85vEBf1aA+7sT9OXtDmvyzgBnvY1eYjvIX79+vXr+t///d/18vKynp6e1u+//36Ryf/zf/7P+vjx43p5eVl//PHH1YZHE4YwNj+TdHFQ2HCi+9ZwJPmUY054sHwGgb4n/Y8MZmnxe+iHgioDtyhDiwhDNO4ObNzpFljluDkzO6WzTp1Oj+1qU7Z0UGtdf4yvZSs5U8XtP5tzP6IdX00B41QIGoYsq6CyGix4inWttyCgXSMg8PTurX1IOeRB+yM14G6lIkC6R/Dn36N2NqdCQORzO31iO3ZB0zT+PJ7kugEVj2fbATJjyl3/4pgasG1gqMnMe6jJH98TJQ/SxhyTGh/PBKt0uo3aNf/fsqtHTsyzg23G5t50isS2Hc10WjZpE+2/wov2zt1k92wzX1+v3/WYNqmIHE2zJEws8RyXJ+U9KgJ7yqbBJRMXt47zzu47wUowRN7Fh/Jj5ryfAVauPTw8XNm/1jcSx32nW1PAtMuosz8E4U2vCPx+lr36OyhywaD269evV4GV/flab9+vbYlr+yvraOSDZVvmqBfWLfO4BVwsJ/1da10FLfRRO3zCetIfB0e2y6m7BdUuk/9//fr1klxPQiK6wqXQCbAeHh4u9xgrNVn0e0xHCWD+v9Mz86Bda35xrTXaFJIT8M2+30o3B1XNATmAaY1pxmZiMpWEg0pDR+UzSMnz0wwEnX/uzwAwEKHRo+ClDDot18/r5Nd7HFLoCFSbxx4fG2pmPNhnO5a13s708NjEJRlNGZrBbMbrTHaoHTee7DJ79wIA7UA4djZore/WEQYiNo47kJP639P+POs2pR2RjQbmWYbBLg0lHVautxkqgzMnbqbz1JXm2FuQ2xxhHLeDe5bj9t/K60nuW5tMbQysrztn9vDwfYc1BldnkgC/gjiGXGVxdH/7neSn2Szrrm1R/FiOd0DBRNmij4pPsy8i+ONx2pUyyau11lVAdZQwcbt34I88CDHJOunrWt9nfHJMX0Zekp8OYlt7w4PJj/nd0FDbMMjtSNnWPcuN233v5D4ziZT+0R6udR3AJtDnOFnvmr1tQVMoZTj5QL5Oemub1gLBtd5+RJd/O9l34Dn5BbaRz092xrLOgCK8Dz9Cz8/PV8mcx8e/Zg2zkQUnA2jjySfOIKfvsWuTLWv+ZooTLEe7IGiqr9mt5ut+VOduDqrSCX9wzdOpFA4bOlIY1RTGjicDx91ISG3ATSybACzKYcfEXVyceaAxpMN5fHy8LPnjOxYRzvaCrx0bz7U+NjK/Ume+M5B+pn47eQIEzl4lm9GyEEdkx5R+NR5aeZpBsiJOYDDXmK3geEdOaTB+JVEPaARpCNd661gmXji4mLJbZ4DlLgibDBWBRMDb9GHRtNN9oM1IH/JdjZQTeaRukcJTBkqNDy0503hMHWDAxH4057bW2x2bUgbLnNrAMi3r1qsjp2X76vFoY0BbRHvJ/q11vUPcGfvwd1JkJssypzY2R5vjCXwZhK11LdP0mzlvP2Y5af7LfoJ+iUEG7/emHFzqMgHc9Jsyy+V1bo/5NvGTdTWKbkaGMuvEZVRcPpdZrvis8JjfYORSsfw5edj4bF/E/33fBKKbvTYodBuafk60u/Z3kXFQZCQ7AQYT0e45+KAstt0cCfpTJ+3/Wj0ZkPqIcUKUAfoI4yjW2fqeuq2Lk5027iD+aEGV5ZQbylgfGyZ2Qic2h3aQM9jRtejVy8vL+vz582Vmq30ImPUftanxb8dfygkTQW2VGPnH+omLLEsNax61e0fvmqliw6aOnC0rv425zlbwmIyiYDflacEdr9Eo0jhm0OywGtiw8hls5R5vs8wy2IcWUJ0hgzkalLbengYv58jHlJXAjH2ZgCj70gDapDxtfHb3HDkfGvsGNtmXeyDKfRtD64N/aThyroEz3+Nzu/bdeo9BBDN7IWd4myzYedlItiz6ZA9IzKq5D60dBr7N9rHeps9NR9m/tuTriPdTQMXr5rHvuwWgTc7Q/Lgn3aLN8/vA0zOTvk2y1ewNxzdJLdrPSU+ntrCeNvNkctKQ9zJRaV9I+d7NUv2o/ISazSMIDA/opwxeJ9kLiEzdE7/ZlzYj1XxKuyd1Um9tA/hcs9H/zRSZenh4uBrHNutCH+GZScpfyppsTzs271vg5TZNZTai3W0Jw2lcJ/zcfIOfdSKm6fwUWKS/Pn54eLgkK9f6noAi/ptm1twv1tXsv8+dsXvsuxMe5p8xqeVm0vufpXM3BVXsRIQoTPa2kLmfv6Ezzmx3P4XKQY2zD36+ASS/OMpIncFWE15n7eiwWvbmTBC6y/4dBTIkBr98zvxIXzl+U0aWmdamXH5mAm0tQN05FjuhKTNohbMDJP/vBfBNRN0y+D4yAAQOOW4ywHvPUgMlHkca6h3oM6CYZIAg0OB42qSitTt10nlPAPGIJ814N57QXu3ak/uc1W3PsJ5mL3ZOoo2TQcfuWd5P22b79t+iX9nSebJFPN8AwRGw4+wRx3PawIKyN40ny/YSJN+3W/JnX0A+0K9nbD2rb35RFp1MoM41MNj8/lHwa5DcwCn1y7PFk4zS9zdQR56xHW2MbCv9LpXpyMaz7femY7bNa11vOMS2O5m81lub6CArz5ImPhmn5l77rxbM0b5Nfcxx7OHDw8MVdrStTHnhTUtSMKF2Bk9PWLmdO5IXY4yU/+HDh/Xp06c3SzfbygpirF2bTDs807C3bTXHa+IJ8QJls9mPozbt6OaZqhhwDnimA72koDWKBq0BjqkjzQlYCOjMaVQ9PdwGpO3GQyfVllbQ6Eeh1lqXD7s9PDxcLT9wtB9qM1ONDw1w7jISa32fSue0NPlJ3vEF37W+B6hewph7qFRt9zI7mZbhYxvsoHyvx8flkC+5J0tCmmG7N/BnPry+vq4vX76stdZldysacVLrRwNnzo63+lu7pqy69cjjlSURBHUu+whEcFlFjumY2o5ku76Rf7ZBtCm7Mlg/jT6DPpdtkG2diw7R6FtOdwCz6YP1fAKCbewaSLQtDN8zDtPy5l9NtM/krTc1WOvtOzPkgZNoDIZTjskgjP6oLZOZZirbOz7sV9Ol2EDqD/uU+u3/Hh4eLkt9Eni2Hf9YF99zZL8beKVNbjJNfWB7LLPpe84bQOYD92t9l1XzeOoT2z0lHXxfG7c2JlMQ1nxg41HabOD9K8n9//Lly/rzzz+vvlkVO0HcRZ++1rqy7/nuVSMGzfm/3dtAtoPxtM0YhXLlwCxtJdawnuWetdZlG3P6rLQ/NoSYMvelbMsSce6EY8yT9N0rX9LW6DsxcXYJfX19Xc/Pz5eltV++fLngk934HAWmE9k25bnmn7zaxTEF/VLkMtc4KzfZ5lvo5pmq5mxpKDmd2Dp4S115djJia10vl7GB4j05R2UyELJT4V/LLNGA8x4bTBrAM0ZwAkzsz5TB8f9WPC9ZIfDLbBXLbQbFU9JWHo7ZLotuZ5O+tb6zvCk49r2TvJI390zkKzOs3qThiNz3HzEYR/V43J2waEChtdG6bFCT56fdNHdtbM7RgIYvWTdqDty6wn5YF1IG3wNhRjVE2zbNWvH/ye5N9x7pSasrjojy2cbg3vQr4+NsevrNrOVu3Bsvd8HuWm9XV3B5KMu1HW6JqAbq2Jbp/ikhMwGVta5tTxtPy8WR73L7Gj9DTaZi/1IGA9spuLVeTj6Rz9mPH+nKlEhufupI3yYZ3PHqXsiy0xIHUxI548cAI9caL1wn+TPxyucpg20MJzzCdjXs6GPypOkU72FwtZtRtf60e8z7nY+kPqx1HajwvcW8CtJ21Wzts1/b+Rm2ZUqcH9nYCWOw721H5PbMe+imoIqNSrQa5r6+vl5mZPjiGxvNzEoDYDRwdlI2oKzbRAVt53PsZRM5dp1sp5XdLwlP4OnsQJ1xTGknnQH7YEEk3/O/HXOeO4rW+XI177dDcx1TH1sm3b9n/8KDts6+GbN7C6xoYNN+ZladScvvxPdmkGh0aJQ5Hgy8yZ/U7ZnflNWSFM5e21FO4IJOiS/P0lbQCXhMz2Qs23X2uc1w78DTrj7Lc/pAIJGx9lLAo36Zb62u1t4jW0OZ4Fh6PK1f95Q9J1k31lpXy/8a4Eq/Db5NkyOf7sm7Ve0e66TbbH/VgJ+TGi2xQdDn7+mcsSOub+d32Q/TBLjO6uvj4+PVGLJc6s/j4+Mls75LAjQ5mfqwS1zweiuDPssJ2YYh/Htv/mutDlAz47RLNnvjGwfS/Du7c2fzdSbjzDP9anrx4cOHy0wVV2xNmMPPso3GmFObd/1qNPlE23b7QdqL9M19Sj93fvFMYMU6W1A9jZMnTmwHMj5fv35dHz9+vFzLOFEmWc576Oblf2Ecp8wSuWbtZQTLjWpRtwcxBrIZs3S+Gb8WhJH4vI1Xcxi+h5kwChADMwPIlOWgZuJrawvb7//Z7zaDMxliOwU6YI+P/2egxT41JWOZre0+nvppANHGksEtAXgDfDHMbvOvJmZcI1dpJ7/Nstb1EjLLmOWS5buvzfk4sDI1A5y6Uve0KYrr4liSuGyQu/2x7dxBiefJJ8+eNmogqoFDyl+TGRpm9s32qs00TruUUZe4pG5Xv/vitvGcM6S+n313YGtdCniizt0Tkf8Zx3ys9MOHD5cPSa/1VjZtU9k/XksdEy+bDPu8fZiBu229+5hnqI8MmhwkByilLC8NSnmcVW32krIxXTdAm0Ck7cvkE8kbv49C3gW4cxfbyT8e+a7WH16zDO2wSPhAjOF63M705570y/LI9n/79tcOcpEh7l7HBFID6V5O7JUJk01PG6y7ljePlWXReu++xnZEtj5+/HiFg798+bL++OOPijX4rlnDazlv0N/w862BFX0Ux4TUeJS+kfdJTJn3/G398vEOh7VxbnhiZ3/WWlfjxT61NjZccobe9fFfK4/B9TTIRwoQatly/+YZtmPHyNxDo0VwY0A0tW8y+i0Ls+vr0XWW0drI/1t7WA/5thsXBo6tntfX1ysn5SwgDaLfnXFd5uPRfQY37vPEg+Yw7ymQakRlbk6H1EBVc+Jn5Y38njLpPLbs+LyNvpMHra35nV72dV+mvt0yvpFh2pnW9zP2xn3h8aSj6WMLqqb2TsDAx27TERhp7W9tZztae+6ZDPrOOPRb+mX5sI4x+GlLuUOeWcp91r30g+U032Td47lp9sx9OHN9px+uvwHF1s9dndSfta43B3E9GQMneRtIu6W/re9TGebBUV8n3bpHXWv2gYkhUsOLrU+WkTP93vnBST9d9s4/+dfBsf14C4Z2RNtgOmvD23OmZtPPtMv2g+W5/ImOZP6Wsnb3sa2vr691o7TduN9CNy//85IzLkn69u3ben5+vgJAyU5kTSaXWby+fl/THiEk6GInKVweCBvMnOO9VoIp02cj7az8WuvqxeAGEHezNmdAzdQHt5HlUKHdFs6AtIySy9mBo4yXs4zO/L3X2Lc++5235ojaeHKJKP9eXl7Wy8vL1djcI7FfX79+Xc/Pz1fZZcooZ0/Zr8an5pj5bNOjZnB2Y5Hra73d8cnj5Rnhh4eHy/eo6KxiL5ylZRlcHsx2HwVetjdsv2WRtiv3Hcln+DDpLttD/vMZ6h3738ZrR5MzbNeaXnEcMsOWbDRfXG7Zz19F9DU+v9a62IXIWfr+7du3q80QmESy7QifjhIYKT/HTSdZno85k51rOR/552xxmzn2zKNnqkL05eybM9PkZbu/tdU+eZJd2wbal+bL3Jac9wvp1LGzduJsUNNsB6+5D628lmDJX3uP/F6I8h1+Z5MRY0YmtjMO/g0Zm006kzbscNZUrn/dl7X6e8LRp6yq4EotzralXMox7Td1u/lL8on9iC0OUb4889pkbsIE5lH6E77QHx3pEKnZNdbtOnkvcU971cOYxatuyPv8z5jkR+ldM1WsmBHft2/f1ufPn6+Ywem2DIJ34GmguSlFO59617r+yFuurbU34Cwz/3vgGghswk6homFg+wOArfwWSAsd+UDHT2XMB4d5P9/FaQEixzXt8zgbALSdkwx0LZy7jEtzvGnL5FAn4Oe+tR3inp+f18vLS23Pr6IpIcBgIUHVhw8frpwUA68QAb/f9bPjppNzOwgkc64ZJ/OectSojR+XIT09PV3tGhXn4C2eUxaXSx7t7EUe7+TPcujxoH1xYMg+OlHkdk+gMGNDO8lr/lurL31sfWP7DDCcfLFO0d5mh7ivX7+uz58/X96d+Pjx45XjugdiQGRQ4e/wRW+4ecVa38eoAZi13gbBKZ/Uzk9y6F+Pj59py6Dpf3mOy2qtrwSTTGrQrzFxMS2zpX+efJqDRJeZeyyP1lHrjon8agB+R1Nfm982hrCO0W5PyV22jUvVaf/uMSHoICQ85ysibZajBVMtaRF7yrLPtOeILI851+7hLn9s19PT0/r06dMbu56de+1XI/McywRnaTvrdr+a3Tc2TpmWyVaeqemffS+X06Yc9qfx3j6Iv0fBDfWG39UyX3ic8jy+7LfvOSMzE/2UVCIFnIPs7HfIWb6dYpjBdoa+pxkonzti3AQmLJTNudlRkvhci6iPaBK8JqCpo4HeM4LT+u1j39/AB/v+XicwjcNRfxpA5drstGvq099JbIOVm+dbAOsymmHJcXsmv5bJI/nYgSSSM2V+xgCEfyY7k6kvt9KZvk92od3D/lL2J4cxjcMt/WmgfALqTjTt2jTVlT9mYXe7Kt0LOXPJ8wwe1jrOeJumGayJdsCJ53Y+rPmBSb/a72683B/ec2TTd+CI9zSQR1vmPvme1jbzddIpgvRWTspyW8zzqZ/TOO1sSLPZ1Lcj0Hov1MYj7XeyzvdNZTU7uQugj3z7Wf5ZT458Wfqy89dH7Wltm4KpXPO9Z8ucjoMhjxIW9nGTLTvyM5MONzKv30P2+e8th3TzluqJwDkDkhkSUjKXET4OfjJfWULADPRaHRy0QMyD14z8lJ1Pua0eAjxm8lIWv0FAYhTfXqCn0nF2IP+3+6yoVOjUlTqyM5+zIOHVDqg2aoYsvJj61p53HZOTJrV2mg87YxGe5u/l5WU9Pz9fXqYnr5yN/lXk2ZY2k/Pw8Nf3Wj5//nw1xiE6q2bMG4jL7LEdlsepBXsTOHWdlHmCvGTUMw78WHBkIBs4xE54KQVpAkt0CkdghvrIwIi/1EMvr8p9U+AyLd1rvJ70yiArRNtAYOpMXbMrbrf1l0myjEWW0jIb+/r6etn44Z4o/YlNZMaZchJ586zjZPdNu3t24GoKjnjO97p/tNHxWdw9M32wjjnpRP2KLrF9BFoN2Oza7n7QLlBnWGcLvFiXg5AjcDaB+rN0BNR3/T8C4Wtd73iX7wEFV7APR235u8iz8SbiniwX5mZKnDWd7BST0Ucz8mutNzbPwdxRQsP+zstp2ca0P7tif/78eb2+vq4//vjjMoNP+W72lb+Nv2t9X6bcPh5s/oXopzxDmjqNV70hCGerG+9iZ6aE1YSD+T/7w2OOBXGs/VvuYxzBbxK6vTnvcd6Nwxl618d/aYA/ffpUDV6MAZ18ArCUwSAoy5lIR4apnecygEZTcMXyGJgQePs4fMjgcVkMA54JiLk/zTFQkMhb953CFOeZeyM0nJLOM+zDxBOSQTvb3+6ng0wfd9nPBtxbmTa8jQj+vnz5cgmqAgAD4O/h3Y+Hh78+sudd4CyHa/3VL767GJ7FsHMmLiCR9eSXfOPyHwNy69pURtMtAzEep2+xCQyq/DyXcdqZuO6JWmBlg+w+t2coe/m1k2Uf84xBE39NbEdLDLE8Owa2rdXRAJ2v2RY7iIucPj8/r+fn5yuwFMDE5Zu/mmL/1rreca+BuYDYNpZtA57dTCwp4KQBh538Nt/K89bHlqxIUMUlMw6o6MfoN1p7GlglhXdMbDRb0dpunVnr+l3GKaHSQHJLWPB4d522Z1fGWtd+dtIvHzfeUjYyFvloLK9znCf/9yuINjTEvqZPTFInSPFHrG1ziT04vgbIISeunXCaguoWmJPXTMZEXrNc/bfffru8W5qd/2Ijc522aPKfbk/k/suXL5ckFs/H1hoPRQeZtPD790yiUOZTR7BJeNf4xvGMH2hjwvF1e4kL0y6PBcez2UXGJuzPmSA658/Y5B296+O/NMKs2NEvjy3ILIMddNZ1B5rOnjvTJ/7RmbYXfCeDmPob+LGRTdkGRZMxyv/hS+qyU2rAzzMEfpbtbHVNy1nOOIn2DA3hWvvsbaunUVMe8tY0ZXV+FXH8pnY3XbDsTP3l/Uc8PKJdGS6H8u8MeQw8s+otU01DedS/W+iIT25/M7ZnZL4d08Gv1YOgnb66HjvnI8B4KzW9au24R6AXIrA7uod9zSzuLct4prJ3YzPx1OQZUANP+jHq1OQD7M/P6Fhr13SOfWvtbs80ebcPOfPcWVux68/kJ1u/2rNHumB7nl8GkDnX2nSv1DAGrxEXemaCNGGTdo3kmTxjmp385XeHDVOWE56xGXnXtK3AugW4u4/G0K2cyV8c4VeXcWQTKP9N/ya92gVH7Vli6JZIv4WPO3n6GXRzit7AL44mHWV2zIAhy7C4Q1TKbODdTqwNMgd75yxbFtHOZ611lU34/fffL0sbGYnnuUyRpp1cmtQUk/Wkv+6f25h7W2YjsxK834GfHfDEx/CbdbLtJiuPp4vdJ2aNfI9nSRoYmNrDzDjlkiAoPGKAzL7u5ObvJL9szRknglWOJ5cA5mXgBmBCU/DZ5HCagufxkUHk2HGJxO+//375n7PdeS4ZOdqMFlSxv83w004dASCX5+ej85ZFP9+AJO9rG06QOFPX7GJ7lllc/7EMzyiYBxzfta51g0tQ8j8D42RLPSN0DxTfxGxm+PPnn3+ujx8/XjZJaUAhKylaMNQC4tx75LCtTzy3A0X0IznHDZ/iu+KLuXQptpGzUtEvf/utyWfqp61tfm4HsuzfJr40PZvGYeLdBGBtz3yNv7bN/rUutn6G7Aet37T9kVOP91rHnwK4J2rjkj5Gv9a6xh0G0s1+e5z9N9m1yVfwN8/Fdnjjl8fHx/Xp06erVUGfP39ez8/P63//938vG2LxdQPWkWSN+8V2x3af8WEtYPL5yWcbE0XOwifP3McXEn82GZ2ojeGZhJV3/Dvy/+YD75v0+Ufp5qDKg0zQ+vj4/f2qCbRzKRLXqO8MXM41sGfDZAaF8QTWOc/BiUON8qQv3M2lAZfUxa2FWTcDIgZm3EreoM38tkJQmCaHl9841Aai85s2cGnLrh47Q4NQjkUDc5Pwe0bNTrqVE+IOaZElA2IaRNa5y5L9XZS2cdked7Lzu0YGczF8a+3fi8mvnfkRUOC1Rk0+KP+Pj9/XzT89Pa1//OMfF10L+KNNiaPNbo3pG/t0BFitq1Ng0RyU9SUylbYRwOaeXbvs0FLHtEyCdbu97g/Lyrkc0/YdUbO/PG5Jr9jOtd7ugvgjszr/CWKwRx464AilH7HVLy8vV9uQt/EMTSsuWDafcwAyAZ9csz1Lm2In6LviA1gugXuWlx3NBk8BUdrdkl9HOmodOupvs1sE3+Yd5XCqZ9LXyLffOZ1sJf1M41Or7yioYlvtzwPW74nMy+YXeC3AnPbdPtrjSt/HY69sMAZ0e46O+Rw/kBvbn6Xr+Wh4lkE/Pz+vf//73+vz589Xs1VcmUX7POFdt3v66Db51Pw9r+100niCWD9JJS7TTJvyLGW96cAu4OFvjpueEkNPWDDXyF/aj9w/8d7tupXe/TKJDd1a1waOjWods2M6Yqjr3jGRdedaznn2w39UmDbLQ4VowJNtNJ+aM2K7pva3Mibne3R+187J2bS2WCjZ/mksj8b1TDt313byFIVM/dMM4QgfVLwAACAASURBVK+myfjZkfv+td6CkN2shAGg65qMb+Orz1Nv+G5Hjtv7R2xLki8ta84+nqUW9Ld3Y3x/q8MBu/XgyB7k2HbAZRz1Y0dnA+HWNpcz2fK0nQmPncO+J7J+ecwM2po+Wfbf02/b/JTF38bb6FLaFFn2JhQ7W0Lg7kBqB36anNLut/p2/Xefm7xNbTkLiJodtP4e1UHgbv/HXz7rMZzaMLWdvwyKz/jRe6HG2ykZ5WMnjY5kYqr3jC87knnPwlC/knhJkOH3flub2//2O5QbzhylTsqeN0pKObQV+d962maWmOBu9sR8sx2zbp+V18aHiWeWiV0C0OX792fSzUGVp/vSKC6BC/jxIJpa0GJDY8Uyw1kuQbMNkgXMx/njTBUDq9znpX5eN8s+p010emljpoOpfI+Pj1cvB7a2Hjkv96sJOp0iZ6x8r59nnXzWih4y0KQz3wU1dq4t02titpmZlNT39PR0aa/XOd+bg+JOXZzB/PLly1U/13oLAKdza80zVc7wNZ0MWR5C1BVmzxlUcWltNqegMefORsn2UddCE0hhP530oMyFny7L/Ww2xrOwDw/XLwIz8KPOUkcyK95kmm31ManN1lufOb6tHvfNYLk5Xt9vALGr815omsXljqANUNgftGUok5NufKYeT7I3taP5Bto+zmxzlUVWU2STEe8q13Sd7UybPNtncLabgUqZbbZhsl22bZbViX+cfbQee1bX5RFzUJ7bDEOzF5MdbXa2PXOEd9yuX0ltaXqIY9oA/Fpvv9V0S0DV8IPrbs9NvqzZxviwhhfzGsbz8/P6/PnzxYf5+4K5t+Gq9DHffWI7mITk0mu2MW2hvc55b7CR82lHm8nmUmny0MEdbT+/RUZbZd7v9MJ8z//sJ6nZq4ZBW/2hZk+mZ8/Qu2eqKPhpwBRc2BmFdoAvxwlCjjJ6Jjo779yXtsUBWcCoQDuDHMXxtKz77rW4HiwKapYa2LFTWXjc3rWaeN2E2YLM/xsP+L/Hm0a+jRnv47gfKVPaMjmQBjhYNschY8eA9l6CqgBybtqQ8/n7+vXr1XtU1q1mfJrxoqNKWQSHOcfnQwQ9NMKc5WVg6OV/NLZ0MFkGkuAqyykcuKcPO9DGfhowZfwb+dqk/6Q4QutKxirjml/KNIOxUJZIt3FyX20vHVhN/Zvspony5CDi27dvF3vZgOc9EuWHxwZ1zXdRJwlgqDe0eUcO3zbb560n1C8nBOm73Afaz9iQ7E6WZVfsO8myyvbzmbS5zai0vttm2b81Hu3OTeW3QM/JCr4vx7+U6UDrqB0cv6bfbK/tNvlofXZ77sV/uQ88P8lTjtfqSaFp3M/21ZjF7XDg3pJrbRwpU0xafPny5fKpEy/LdEDZ7O/kZxhYUZb+/PPP6vvzRz/bEjOpK7bEOyA3nEpeOEFgX8XjKUmyG8sJc/Ja08szZXJ82/FROUf0rqCqGcBJWdZ6OzWYe5gN8jVTnMJuoPy8o2U7mvaXdtthOJKdBK6VN7XN4LVlBawkfH7K+rh9DpQMoOlQ3M6pbxyDpkDNMLqOqc7GAzt1O9AWWOwczj2CvuZgrWPm79RHG4tm0PjbZLUtkbOOt4yYDbnHhL+UxQAdzng3gNPsSgMd5NkUSPE8M9fsewOSdn6TUTef7Xw45nm26SPrtkMz2D2yrQYH7E+zO2nT2WDsHnVrrTkz3eR/svMTX3fP7Oqa7Oxab99RO1rG47rZVgbaTX4m4GO5oA460dXKoRwbxBz54R2xXW0spuQI605/uKmWfbH1lZsLECe4zQbSO19nm5r6G9kGnuXXf5pukfHds7vrzc75twHtaWyO6ms22nKdWV8m1mk3WQ6TpfTlrrv5i+b3mn9loNfsBu+jD5h0r9mTNl4OjBvfG46Z6mrPT/edeeYW+tEkxbs3qoiAT07g9fX77iatDA9iMzIe0LZUws+yDAvNdMw66XRcTmaR/OJh7m2OzhkOX0u/mEWsA4UP1PJ5f49qUkBnV8w7AzbynUpghSAgbYFnA8RZNun6rPD58zIxti38I/hjeVT01r5JwX8FpR2RB8qoM1Uc37Xeyj51084o97H/Ted8/+7PS/7aTBRtAtsXGeWHZPPn5MYUwJAf07KF3GMH6Bl3O+lWD/u+C4ZSNpeksr72QdnYmtw7BVOW7cz0+bk2juQpjyeblWddP8l9uxe9WuuvthM855xByVrXctr8FHnmcw3sHtkY10EQNIEjt9tynPZZZvjxc97flumQmM1OW9xfJ75YbwsGmk9poM7X8+dlwR7HJEfaOOa8fWazrdYzL31s1OTF/Zr6az/la7Yx90Tsj5MwzZZMv37mDJBudjdlTER77/Iod+5Lxj5LaKNTsTNuT3SZK6WYIKUPcf+jt5E/LhGkLYpOEi9x508Hfe7b4+P3jW5IxrppL23Q6+vrZfkfg762sURL0O3k4IysE0v4fBtjltvw6o/Qu7ZUP/Nr4Gpjt1Y35HYUk/GZ2tScjsHVEWDgxyGbw21Aqxl+l7Fzzg38t6WUfJ7BRJxMfpvAtPawnnZ/aysd0xE1gW3bi7LsBkgmZYtyO0tKh+exulenxL4fvUOXPtCouYyQAwWfy/nJ+Lj+BsYdVPmetd7KGdseuU0wRScSYxmZMdjJMc+ZH75+y65ZTZbc/xb4TGOQ8qYMIe+dlv7wHRgnZBhU7eyn38tzBtUJGNfNcz6+p4AqlL4ZFDTyDGCz+Qz4d3zmGNuu+jrHgn8GKJYZttvXOQvTloxZp9lH92EKfthu1jnxbLJtrKMBJLY14K1hjIn/zf9OtsP2KjqWOt2fqe527OA+59KXnX9qMnTP9N42tiT1ERFLmE9TsDq19cj3ZvwfHh6u3oV/eHh4s5xurbfbgTMhTkxsHU/AElzX5INYkP6X70sRM/D+tCFJeuM6rhxhAq9hMX9yhHaW+kriBI3HgOUfjdkZHTxb7pEO7ujmoCpM4gyIO9UMrs81Iz4ZoB2dUYJdXwx+KOCtvCnr5jZRqP1+CY2y62QWjPxsQZWzHc1J0zFTqUMNOB0Z9gYY+WtiuyI/XLPbgio+2861wMhO2ADiFuP8qyhjRgM96UmTi7VmZ8Tj6LDLbWM46ZUD2Ym3LZgiIOV9Xqbk5XlNNlLuDti6HW4Py+S1FsS9vr5e6ZIzby04bWPhdrve3bHLXut6qdF0TzvncaP+0xHaXtFOnq3zV1HGyasseL3ZNF5v5fla42kb9+n+I4Bw5OjTL68SyVi2ZUqW38aXDx/+2j56rdl25M/yQ1vf6mhJmrXevp/leujjGg9bQoDtvUXHWjtcx1ma/CXtyxHGeC/g+xXU7CGv5deBOJdhT75lh1F47SiJZn9rm2eZNg5kwBN9cbunmarpe0/2J9GjBHCkXLc/ajNiTW+CTfPeM8tPQNd4zv4xeUMb63r9LCcx1uqvtuz8F49v9TmT7/oR/Xr3O1WMxBujeM4zKr53rbdLKc4q0OS4OCCsuwUs/PWyGSpYm/6fKMt6Pn36dPm4KYEYlZVZAC6N8zImK9uUXaQhzo5rmdr1uyLuWwOQBr4hgl0+S965zTnXXoScxr1RM8buvzcRsYzco2OiQV6rz/TmvvAyumgeT0DN4DL15LcBoKZbNt5up51pdKsZ4rXWZekfN4AxeE+9dCIpp8lxc45t6RrLb/wwKI0MU57ZL5bvGQfzpBGBxQ4QeGyzNMT2ie3ieYMJ3mN+TDMeHP97pthf2rjdjKHlzkD+6J3DI0c/3eMAJzym7rTx8XhknPltSMp+lkPGhrT+Ra6d8Z5WN7SAYDezbL6y38zaNzvFzYfCN49BSxiaJnvQKLzaleX6Jl3y8w2ss942+3cv1GScCbFQ48Va67LxAvGGMWELYCeZ4qx9k0m33XKf4+gcxyUf9H19/f6tsA8fPlw+BPz4+Hj59qJnmvMdV/Yn34k7QwyqHPCFvFS3+WQmU7jb9adPn66Wnq+11vPz89U3WINZbWdYB5M6TIZa1yeM4qTlbszatR3Rfpp3P4N++DtVa3XHzPt8P8+3e24B10dObFquMAmkQYyB0E5BHQBFULNOdQqqorwEs1GeEIGz+8wyzMfUy1+2l0aM/WnLSEw0mpPBItkpu1xn+90W0m48W2BovuzK+tVkvdrdNxkmO6QQg6A2Bq0dt+g1r5GsN5TdXOcMSNMzG1ca9QlIsY+TjPJ568fUFut6s4eNnxNvGrUAyOXx94wuHgHbdm2tt4DP77LcI9AjUVaavTvzPI/P+KhJRyZ/ufORBAGmFtRMbbc9bMuMubLCqyzWug5CaJuZAKENtlz52bYKI23jLniTPZ9s3U7X2K4d2PaxbY2fIyD1dT7bdJv8OxrHe6M2xvblnHnK+SabEz44slEsh+VNNrzhzrXWG/tg3/Tnn3+uL1++rG/fvl0Sgb/99tvlUxkJVIy7GFS1WZhdv6IjDPKmZ7wiqckNMWrammDMyfIWGHtcGu9TBgMv8zi8sK61xO/Ul/dQ5KjhzR/1Ze/6TtU05R3ha4EMAXwbBCqLO8z7Wp00jgwyKHwNkLkctjPHfE/JL+dyCSTLjHAmoMoMETMIzvRytop8Nj93TsT8iDP08o6JXH8TZtc5Lany9fTRPD6iZjQmkNuuUYkJZtyXe3NYj4+Pb2Yopj62cTKYsS5Ns3vWIwODo/IbWGCdDTSknCkp4DIZGLZlpNH/aRaI97L+2Avqih0Cz7ONO13kDHezjbvnd86z9Zt6O43bZOsmmzi1hTS1/56o6UK7PvmeqX+t70e+q8lfiICyHTe9zHX6ptC0CqQtF/LsVIAigyq+70j9dLvMH/PVwMr8a36Os8Xu/5G8t3unMhp/d34nNOlrs91uX7Pn7E/j387G/SpqOGWttwmrFnQRJ7isyfY1maeMEmvxWbaL+IDYK8T3fLM5BYOqHK+11sePH6+wRjAYg5fUudb1Si4SN2oiPxyY8pf9Iv9cBoOqhg+ZRIvOpf/UPyc1PSbE4OF1ym82zn1IOVPMQTrjbxp2+tk6dFNQxQZkECzc/vgvz3sAGpDL/5NRm9pFsgHKwO4ANGeL/HXstd5+/M3OKeU+PPy15C7TqP/4xz/W//zP/1wAYIIqvniYqe/UzyAr5U4gjorp5Tl2kvl/EiQul5uMuzch4PhNDuRovDgGlo1W3uR0ck8zRP6zgb8HythzfNtSJfbRY82yTDZwdEaN3zxuutP037rG8WLigC/2trqiLzTua603oI9LRuwIJtBB/jWnw/7xHsrN9IFw8yS8+PLly9XsNe9r4+f2HukYHcSRfkyAcBrjtHFahmg7OOnorySORXPQ9FHmVQMsBg6+3sqfrrdx8AvflNMpgGXSIrbcPiIJvsfHeYlSrj89PV1l1p+entbr6+vlmzysk/w1Ebw2XrE/5JNXVkSPpqBqwibU9cZ7lzX5rCMg1vzekV+0LedHyY2BJtm6F2rA+Iy/ZSDDj9vSlzAImWwYz/E9JeoFKeXRJqcuB2TZ4a8FVUy4f/r0aX39+nX985//vARS+dD9b7/9dnkVxP3huIcP+ZAwMSl1yfLUZn5Zj/tOf5T+s985zswcdZ7+3vbItsA43Py333QZ8e/G3Y4Zmi571s33Gl+Q3uvD3rVRxURTxEew1aYSPSA747fW3rjcwghH5qwnDOdLdBbqta63v+U1T/tGwfzOQwSGfbeQ+rj95VnyaArAdjyjctpwGTA2QPye/wncGlhobSO5Xa19vNdKfO/Oaq232by038Zip0uNJxPAPquXrZ2ToWvGbDceky7QWTDI9GzkGVs1BTLWZ7Y11w34zIepTjpuO/KJp/7foIx8baDF47zLzrVZ7ckWTLbqvQ7p7yLPBL+nvdaJs35p0ivrcfOXJI+r/ZaB2lpvdxfjbFV+/bF6Ai8DkTN2oQVPTbfZJ183mDR4OpqVtv82v3f2r+lbq4P3Nn80nWs2+Nu379vB/zeRx+sMeXySUKCNpKx5DIwR1rreTKeNr9vcxonP51336FbeqeI78DmeEuKcoUqSIvWEX9S3L1++XHSAdp08Nu6beN/0rOnlzle3mb+GKyeZdlvos498HO83TeO7s53u48+kdy3/o5B7oMP0JqgN6HgwI0CmJjRNgc8IlNvCvnC2JtH5Wuvy1fW11uUdqSgGs9fkTRTw5eXljfBQ+XbRciNnMBsPrBRrzdsw01j4JUjyvikm7zlyNj53hqbnp3GeDE/ryxnw/XfSEUCh3JIvdEJNH/1s/m+GpenSrk0NGDWDbOPM73o0eWYyIsbUWTXWdUSpJ3zwO4oM1NgmOg7W5ayZbZ750wKwSUatV9aBSaYNCEKs+2ipAwOqI1tqfXMff/ayih8lOmW2zecpJ5Fb6lbu83LwybeZqOdNZo7sQO6hjtl35dMEqSOg7tu3b5fkXtpPWab/DniMvoRnfGk9fsPH3v6ZejDJF21E5Mf3xJ8SyNrGMYh0fc3mT/av6VUbh3bvdL6NH+8L0f60hIvt5r3QxCcD6GZH+Mv+eswmntn+OVFO2ulmcBKxEWeqiJHY7gRaj4+P648//livr6+XzSt+++23C4a0rzZGY1+fnp7ezLg1v23/4b88T/7QZvPbV25TcLBXMTWf1TBhk1G2qREDau8OyOusK78t0ON9R5iBcvgeunn5H7Nf7foE/DyI7Vn++phk8HUG9LlNHBCuv+VHR6NILodLKF5fX68clIUl62wfHh7Wp0+fLtvS0gHSKdrAGxzTmNJ5TFPcNCzsJwOsGAkGVa0MA8umsLtg5sgx7Zytj5thdr0TOG1yeA+OaXK8a3WDmOP82hHvDBeNZhtTA5GQ9Y5Zb7c7skWgFeD1/Px8FSwFuPllX5b38PCwPn78eLXzaJMrnmtZtPYNkdRHPf7w4cPVsmWWyxnqNm4Gky3RxP9bQGxqQKSBNs80e/ybHk5ObxdguS20ifcI9jgGk31qfoIyzzGa7FKum8zLCRQ34DnVy+Vw8V0BeNzoKPUF5GVJUsaNG0IQ2MVPZBfZtb77S86INV0nwKG/an2zvUh7fU9sB9tgnnPragbME1YgPydc05a+NlBLf+7y2+/kl5rvt53ZZeLvgWzrbIe4SsByvdZ3TNN8zFRf82lNRkKWCSfVX15eLoEUZc5j/O3bt8uugF+/fl1PT08XHcyyWdpF1tvGN/ckqEoQ4cS3g8/8+i/3cmfp9J8TBi25z36xva4z/pL9mmS79Z/jkPbxnoZ5JzrSc+vimefP0k9d/jfdc/T/rfeF3tvxnSG0w7Bic1BjKOiELOxRgiw3YRbEWQf3uQGwM/yf+sj6d9mPSVhbxjZlT2DxzJhacRqAONtvPzddvyfAZ5qmuUk2xJSPBsaOxmyiM3yanINlalpGEDIgdxDh80fyOrVzF7QQmMWInyn/rMFmm1u28ojf5jXLXOt4Jqq152foAoOV/H9vOkZ5PHK6P3JtAnDNNr7X3nn8rV9clhTKTG+O+d4VZ4fYFv7PJI0TgS2Dntk8t7UlBDyb7T7SnzYfTZDGLDdtSptdZPkt4JvG4sy5nU0465+Okhn/zTT5LZ6nTB75RPqTnd/b8Y3yywAmsuZ7aG+dwP7y5ct6enq60rM///zzTXDANvqdurXeJjp3NNk1t5f6x2R+8x/EqmzvER9bG3a6tcMuO2xxC73nmVvpXTNVDw8PV9OFZsQuyzllHnZgO8cekDPGL3XSSXhAogBrrauZKgYhXEqRIOnDhw+XTSaSBcx5vuAbgPbp06f1+++/X/HFwtICHAK7ZBsMLplhaRtO0AGSVwZkvMd8drbJmcQ2Ls2xTPc4GJqOeW8LNncG9fX17Q6Ubs+voIyP29LGaRdcNIc86UXTzxa8+BlnmSaZYobv9fX1olcEQ6wrTmzXbvKpjbNlyU4kMpP17LzfCZP8NSOe/tiWUWf5fOMlgyiOn8fEY+P+NPK1Nl5nqAFc1sHyCRL4/71QZCf2dAIZ7Bf9VUt2tLE9C4Ka3bJd8yoBtmutdXmJPPLIdz2oh+l39Ovjx4/r27dvlxmrbKyU9vv348ePV9/YaXLIOhnQuD/mC8ErZ6ommfPyw/DDbXp4uP4IawLJiSbw3rL37rv/p5/c+TGfsy22TNo+30vywuMarBAZaLbNzzQ86TrO0M42umzKT3452/v8/HyFj9zOlnT7888/1x9//HGZqfr27dtlFUZWYvD9qvapm2DstsQ4voVY0X4/x5xha4kWBnrso8vY6QCf4XL9XCOGbfjE5VF22CaTN++IvlDHJ4w60ZEMnqGbZ6oCJAge3KjJSIRovHaNnwTF9zRnOIG+KdigIkUQbLypXBnMBFK//fbben5+vigMFSV8+sc//rF+//33q6VGu/4fCRX7kKWK6Y+XXPH+CUQzS5j7CTapJGfG7z3UAPUuKGhj38ri+LV1/ffgmJKw4E6Q7B+zryHL9mS0JmBvx9z0bReAun3pB9tkwMSMuuuPrrQAx/W4jJxj25qDbUFhymh1up+ppwVMrGsCP7zu8hqP25js5N5tPrpv97wBRSuL/eNmBu+p8z9FkcVpC37+tmfXupY3godQs6suw3XsgArly5ni/PGbOdQ1L2PyjNLHjx8vmfQEVS1zHvr9998v9yQQ49Jdtos8YJ0N0NJ3pc1cTUF+T/WY5wRya10HpOxjk+PUaz4cBVXWlTbeR6C0yVKzvbmPS5B/NU1g1CD9TND4I22YbH7TzcgkMSH1iUmKCS84sEoZ//rXv9bDw8P697//vf79739fbaueZX15Hz+7bJpPxIdcORGdpY67n/lNYOig6mgpLHWOs3R8dqeD1CXL+M5Opg3kaVtSTrnJOSbLaDebbJhfvNb8+i30ro//2oDsdtBqz9oZNQa5nMbQ3TNnjGBj+PRsiIAwgs3B4MC6jVxb/u3bt0u2vAkWlYMC5XYz88+MxBRUNZ6ybPbFvLGwTbOOuzFgn9zXo+eOqAHcVt7ksH4l7WTwDD92sjvp15njiZpxc5vdnyOwQUPYjDZldK05gzwRHUbrY0sQtf/dLwKdMzaERB2y3v0smvTirPzvAipSc5j3SLRlocmPNbkm8bkzjtj1TjK+s2MTQKBfa7JL38X3hZ2YcLum5AATIBM5OcTzucbVId7AifrVlp6zjW67AdaRj29yQb62+ycbsTv+GXSPPsx0i935GWD21rbxOLJCDMWAgIHE5DuIs7jqJEEaZ2sY+JMvXuFgm8oVYtR3+hCWmb5Ez87O9oUmPxyayjsKmCdd47MNZ1qHWXbDuayvndvZgffSu75TxdmKKaBqz5IacGqDNwVTu7J9nsJHRXHGOxSh/O233+ouRg5a8sznz5/fZNlT18PD9fersmnFw8P3j8O5v+3jjDSmdBrenYYZywYmGeDtMvceD1ObLr6FJofcwMKufIPcBkRI5vE9OCfKJZfO8FrjVwPjrd8O0M/2O4ba5/JrIEc9o3OiTmTGoAG11m5mzTiD2/qXvzYrbXCV5+jo3E++GNyWFjCoYpn+blizY3QGEzXbcCudcTTkL8ey3d/krPHhnqjZ/dAuICQgyr2Z9Yo/mDK/O+Cw+3/iP/9y3TaYS94c+HAm6F//+tdl+RG/nWbdfnj4vnQ9Ky4+ffp08WneRdD9Yf/J+9iGl5eXqw0B/CI+M/zNh1k3OCacvd/5tF2QssMXHIcG9s4EXW0JmXnmcc9z96Jv4W2bbTtKvobIy/fYugkks2z7iNRHf+UdkJucMehx2/mb71jtsF1mqjyDxW8hZmVT9CMJ/egNeZ5nXl9f1+fPn9fnz5/fJDfYHuO/XPdS6fQ399q/sg3T+LUZqzbBcrRKhWPAPvgeB2O0K6yPbXdbbqV3balOw+xBaQ20IeGgkKHMCLiMHTXAbTCaX2bBpsy5BY7AiuXTAazVBSGOwgqTpYBxWF5OEcXgsZcDUki4hIJLAZnt44dKGfxx167Gg/zfjJaF2v2fqBncaWypBLupZz7jZwkQ2N5Jdn8FGbzd4pjtyOOYTDZEjRh85JmQDSCdCPnNJTzN6DKocnnuN51awOxa6yrAmfox8S78cX9cd8tami8uJ3o1ASXL7wRmUxbHq40ZHcyOF2wzz7msoyBvAt73TrSX9F/2NwbYud+BFm1JA44TT454Rd6yDdbvtIMUeaF+8d0/+r58WNTgwzq91lofP35cv//++/rw4cP6n//5n8vxx48f37wnwrbQj9D/Erw+Pz9fwGG2S+dYcclTAjny3H6XG0c1P+bfdn0ak91YTfa63euAfvJDU/kNEP9KogztwPSt/G3PNLt+lmwDMhYO5JvPspzR5lJWnbxvY5j/I88Jmv75z39evSLC5bkJsiLfwXpub/Tl9fWvoCrbuzc5Z/DGdtKHOXnJ59lPkvGkeUk8N/GHfGeyj9jGuJNtadiV9s99+Vn0ruV/oW/f+gfqmlM4UxafpzPZGZszAP4WIhgymPQ7K2kHnZANC98hCT08PFxlIRgsRZgbAKNg+M/L/7J2Nu1rwerO0E1jOh0btB2NPe/fzcCEL7cSFei/jcy3lpyY6ChIPUs2wkf3tl8SjSCN4gTydyBlksWpXbvrO+BqGdrJk+V5N2bWDdbV2mEbkHpu6edU93Tu/59p8l/N7vK++EOXM9FR0HVmTGzTGznAN8hwX9oKjnyMNIFPvltlgMlAqgWeXpaeP353qm1EQ1213tLmNVtxFoAzEGtludxbzp+hJm/t2AHhvdDOjpzBdNMz7+HpZHfzS9s+2f215iA85GPjtp2euYzX17+S40ymc7dolp9r3kU65FdB8kzjSWuXy9vJ+3RtGm/rLnnKYMv1NmxzBnvc4h9/Bv1QUEUyA9v0GgWZhpdkAzkN2tmMx06BmlGKY2BZ6dPj41+7J/F7Hc5iM5jhPWt9j6ozjZsZqt9///2SfeNSDDqonUKs9f0Dba+vr5fvCqTd+fMGGmt9X77Zptw9Dj42z3fZs/YMDRUzETuDaEjv4AAAIABJREFUe0vQ4EB9ase9EPlhXjgzZKNMoGEeTQ7iyBg2/WDZLcCgrkSXLDs5d8bxpZ4pmcF7WHcDpi144vLYlnWz3Wg7Dk0zFHm2zQKzr7yX7WBbbQeanpwBgRNIsa1rZe9sQIi8+29KaOwSg7keW89EonWFqy+m5MhOv2ivfZ5ySvvAb0z5ufiUlMHvN8VHrPV91peZae7El00tAuYC/toy9tSbOps/Z4DF2SnuxEsb5pnCHdl2TEt2Ux4DYfuu5jcm/WlJypYQOcI8rWz+su334r+O7Evuse1g0L4rd3d+4sXkS4jVjA1dJvWx2XzPOLaEgldxuA7uhPv6+ldg9fj4eNG5vIbCbzjG1nipYngZfvJD2fZV2RDLO/E2/vEcz7NvZ/yPiTaz+Y7wjziVvG4YlXW2OCT93Onaj9DNQVWLPqfBaP83weW1M0DBGYEJpOS3MXzKNkztz+Dn189GGda6Br7cQTD32jFFYT58+DCuUW/tZPu4wxMVqQlf+NeWavic+df4aSMyAT8rKdvAAPtoSYNBxZEiGDDeM9l472TT/SewmnRpJ0Op07ptOgpWd6CiyUCr3/VM0/wpM47By2btEF02QZeNcHO2bdkYHSKXp7KMo+U9nBVwnRkPJxOOlj+yHzu7y/uO5GMHLsm/tmTkV1PTp7NLYGmruJyO5WaM2lIY3us2tTbu7JRXUxz5YdafhGD8WMasgc34qPDi5eXlYnfjC7ND2lrr8l4I67TcNnnnMvr2Mn3Gwdn6MyDOQJZ2JjrpFSb2KZOtcn0su7U//W60k5lW9r35MfdvkmHbytzbsALPT5hyN1ZNBjhD2mZ52G76j1DkxomT5oN5jb4gFJzHbc6/ffu2np+fLwn8LPuL/LtOJgXpJ1g2sWD4zw0vjN2mPrVz9pFndJK8TJvtL7zaw6tbiAF3uuEg1rGDfeOuvWfpp81UhRhVHjlpXp8M1ZHxoNIZOLTnpyBlF7lS4GIMPIs0AVweG5xxapeBVMpidm5y/jQWdJLOzu2E54hHZw24DWHOmR9+xvVO07xWFs8AkFqGYtfue6IWBJCODFcDVmfqPFPemXKoM0fGlvfT6Lcggm3K7/Tn+3bP59gZxem5Xd8bwH0PAGptt1PePdvsz1Hbd+1wm95rr++JprbGhx0BuQnwna2r2VbL/k5vJh/XnmO5nBXY2ffms9b6nohIcjBBJpcuHYFr6lj7dEnjCxN/5JGTJ2d9lmeIzvBzJ/tTGe/xL2nbf9Ns74/SWVmenpls/5QoO4OJGl48ki3KoIMUvh/UyDM0CYiYyGEyYPJTR343fDCGanjMfTsTQO3oFkx5Cx35nyNc0O59D90UVD08PFxNLU5Ox98Dafe04wid7z8SZmeiGIjwf7bJjusITDFDQeDHD6dlWZ8NKoXfgJMZhcfHx6uZqiOgY0PPII2RfeO3lZ6BYo7bUgnzmPWzbB9PRoz3tsyVDUQzRkeOp4FMzordCzWgEPL3KFr7zxiNSaZaEMdymg5O4862UxanmRU+1zKAroe6yqwjkxM0/q3Nzs4H3NkO0EGyHdQJLtnlcQOCE+ibgui0I8+zbLbJfbXNae1uOk0dm5zzzlEfgc9fSe475c3EjLqfj93nMkAn2lp9PudyPcahpvf0JZzd4XjnOetUfA31Jv7DPo5lhl5eXi7lRm/c7sl3sE8p020n8fs+eZnfPtL+a7Kj5JnHOWR5oD6ljJaw4/lpa/7JT016YvvE/kY273FG+Ihoa0IE+Jzt35Wx1n4lhGfNaeM5UxWabHWTiV3QHF1g8JN+eVY05OW30emXl5cLrszyP+6EmXZ7BqzxuB3vZryJw8iDNpPM45YMMM+aTZja2/ybqT3HOq23tutH9d5C75qpmgylG5XjCdxPHeFs1xGx/Al8N6NJI8yZnQb+DNC4/WzWpRLseFBoHL3ZRRwSla4FVZPhmBSFgNBl5f/256VTvHfit+u2U2ZbLaRNVgw8J3lyptF9nnjlMlzHr6JpTNZaF9Bj8jQ4gbb5NwG7nZNgmfxt9xpg5voEzt0m6z31x/VRn8IbAkODOztw6uT0l74efaSb9zjAas75yH6SCAgaaKR9oe05CqraWOa6dWans3nG1+5Bn0jWqba0hzQ51IxDk7XU4+SQ2zC1J895N1b7tWbLms2LbWBfOeZZCrjWulrl0HhCWYjPyl+esc8ImUfsk4n3cUfc6BX/XIf1bTe2vNaWVbX/yQPbJZbbbOJEE+ht9Z4p716pjb8xBQF+e27SRx/TJhp/tWTaDhs1HGqc1/pJ/JlNXWIvmv0MZopdoV4FY+bdxXzOIFutWw4pzxP+tj9Me6nLfPZI7qbrDrB+RH5bX3ht16YdbuZ4/gz/dXNQ5aV9Daiy4dPAHlFTOl9fa16212gCrTsl4bP5pcDtlgJyxsfAMEqdrEJbG3sEVug839Nn9qf1xcZtGkcKZMvCNJqA2c7JcIzOOul2zUHHfwNNumU60y8blMkxuf6m8x4P1n12qdqZsdyBCuqTA/o242CHe2Y5yERHutX0aFeO7/NvA9rkg3ni410/Go93wUW71/JxT9R4fATaGjlwiQ3nMfVlt9yHbVnr7ftXtwIJksuinWfyjECqAaz0waA192fmy36r2XSev6Wt7c/PNx1k2dOsVNO1M/6JZJloK27yu7N3Z4On99iq/ySdae8RhtvZklvKbPb8aEZvSsKTfN7tYz+aD4gese/0QWyny4meZRdOf+eR7XPipbXP7Y7MUs53ersrn22IDv8MuhXv7K7tcMSP0ruW/5nxHiiC7Px/pmwDn7Wup9In4M16eI8N7BQ0ZODNVAsZz8eB0qFmZyXzh7MJnObNFrX5vy3xMT8aeHXfSXFKdE4+TrvpbF1X/qZxtZLunFAT3h049zjxPI3QVI+vtZm8X01Nbqb2sV9e/pcxIoDYAcfmzFp5HP/WDstsW44U53G2XTy2/LO8LGHisiYDxgZcWuby7Mxl+srESXSXM1W3LP/zjGPO83+2nf1yAGW+NxvZ2uFZFj9LHjbZvKeZX5OXhHEMDbidEHCGmbpB/jAQmRJMzf/Y7k92Pm1rWf20y2Md+Xx9fV1PT0+XNnKnwvimx8fvWzXnfAAd6/cKi8mvuo1eGuvrnJnKR1GzQ26W/+Vc2tfKaHXTVrZ7qKdtvHzM+ulTmERlQGdd5XgdBQ8hyte96dhOZteabYb74d2SW5m2d2u9XaKZ/9vKo6nNOzk6wiYhr6BpMsPVUbSzXJLoAIVLYddaV9899Ux3w5Bewuc+TUsBvQprWtrX5JLfwHIg6XbRBnuiotkKltdWpNh3+rf5zNauW+nmmarW+FADQs1ITcTOuuNmEOs5usdG08yiQFAhGuhwOS0Q8vIjGlkbgLXWZQdAZ9otQFZQOya3ewci/MfzE/8aP5uQN/IYTffugivXMdW9a1MDRmfl855o56B2/SEY3N3j+/lMc2hn6uXvTp/bcSuHRpBONHRmCbGDqqneRna6TZ/scHY2hWVNfNjxx31ox2534wdtb1u64TH3+da2e6AdiPZqApNnH2iP+cv3ko7a0Y6bfDRbOOmG7SDl0rrHwCTjzp3CAlZssykXBFpTv+iHQn4nqrU3QVTep+JnR7h7mflhX9ioAbX2XPO55rl57xm+lvhyGT7f7LTP3ZOOTfy5haxPxI/GhO77lMRj2fxd63ty/AjvtWtOxLB9uUbZiWxlRZLbyUCQPo3vCaetmbFi0OUlsQ2LOtAn79g/YnwGLB4rj4HHibiY/ZzINniyJ2zDdC3Xd+PJMtb6jgV+FBP+0O5/Fo5dQ9wJO5AGEHZGZnIqE1jzfbecZ/tb9oLvVYX47ZCc57IFDxxns1q9fIbb4jZH3IKp5kBc/o7aWEwgoIHvIyA/0RH435Vrh9ScsAOGX0XUo7XeLlVp9x9dm/RhF5BNZAcy1XtkxHaAkn1nmTZ8DQC3AOLIxrh8Z/JybuoTHR6dorOUkwNq5PN0nrsg0305Al7tfAua3I8zcvOrdeln05mM5ZHPOUPm9+76JFOeyWeQQh8TIJby6CuyRXre12jg1HW0thrUcSXElFlPwOQEhftiu+3j9ms+GUy7LwT3rQzXy2f8fOON+bmTISd8pvp/FbENPzOwmuo4U87Oxq3Vl4zuEmJ5JuU0P3TW/rI9Kdvv0rtNls2sdvLy47XW1fueDbs2HHuEN87ge8p1gsgjarrgtpFsj1v84T5ZJ3NuF0e8l25e/pcPkTm7O9E0WzX9P4GoM3VNwV1jrNvYIuQ2KGutN04qZfLF2pxLGU9PT1f1NQDUwKDb8ttvv11eWDyzVMEfjJscCHloPky8pAFqGQlPcbe6zgjvlGEhD21wfF+byeBz9+KY2jIRj5Xljs/HmKUsZvpyz+SsprE44xjOjEXTJ8uiZ548dg8PD6NMtcCUTnpnPyjDE39cdpyGZ6e4/a2N+BSk5VpzDiFnMVvwafC16/c0ZjsQ3QL0xp970CdTa/sUpKf9Xu5OnWpyEhA92ao2xq7T43Kkn7nm3W1DXIJjnaDcR6aSJEyZWe6X5eqUqdZP29TwK7NNOeYqD+pPruXDp8zE59hJQvrUlJPrHivWmTFu/WCSxLY4vLOuhI87oJj2eqx5P20GdZ3XU8+v1rXwarJb/N/2r/Gn4b+Jmt42OxjyrCHrjdwYK621qszZt7TVSPYZjSdOxqcMYgHzLBg8HwqObv35559XesY+G7NNAUuza82PNHzq/tNfme+sb7KbbHfjA8eg3XsGXxI3nPGbR/Tuj/+m4rXeTh+yoU25moEinXEiZ+5vDmkH5HfGyWAwFCWIYAdQMehKIJrMX4DXUX9YNxWETmgy3lQeK9LPolbPzxBK0gQ02j1ny+Lxz+THz6JJRo8caHuuLaE44uME+Nr1W516c7y3jEGry+PK7NhRlt3/O9M/2SYeE3z5N/ftgg2CM9tR65X7xDJvDWpYzvSZjLPUHNN/K01yMvXrjC3Z+Z2z93tsj/wEj22b1+pgjgFDKM/m70z2ubWLvouzU0xEZsnf5MP8xzZ6Z912bwPLBMzGNC3gTjmNbGPPPMPnfP+k240Hv5J+lj9tMj0F8EfPHtkhtpU4zkGIj/08A2+P19SGxieWvwuYrcfmD3XGkwVTwDH1zXVObbeO+d6dXFCWb/Udu0kb1j0Fpy2Y+lEZfldQRQPWru+UvV3bAb219mtBSWTuBOrPCIsNsI+Z3XO9yRYwM5h1tHEYcUzJcicbl3ocldPBPD09rU+fPl05mSm6t0PK9z12DmtXN3n28PDwxik6G9oyeS3rcaRwdCJnqBm2pjxHdf/ddCR/k1OZMuNtzPg76cdR4OL27siBxREQZEByxsA+PDxcsnOs8/X19Qq4TQ6O9iq6yPOW2eYknP1sSwAn+aUzb87RzzVnwfadAbzNbrpPpl2gsdOve6IjUMHA9kfb33jReMRjyv0ZYJ4+cGbJSQHbb8/+5n3e9J9+ncFGEobxXV7qOi1Py3K++J+PHz9e+Qv+2ZfEX3IFyBRc2Z/YN1tv2/brxgKtbI6vx6XpD/kxAbYJ0FGvWxL2ZydJfwbt8NR7giLy5ChJu9PbhjlDTaZ43vfskiscb8tekuq7gMv2oMkMdS738ztuOQ5O9com6pL5MeFdy1r0ouFW9ittiF1Jf2hvWwDZdJzXcm/Dq75/CrbNa5bzI/b/5uV/txj9dm0yPtNzrGOXoc0g7zKurmOKvv2CuxUkL8ymvjiafLDt69ev68uXL1cO6PX1r+970DFx6ZAV0PXHCcQx5RyFvQkE2+6Pxq213tTfnFAUg3zzvWkLZy2pPGwTjYqBTqNJYeywPLb8y/jwmsHpryTy2bxvjnaSffImYzGBtaM+T/rh9u3K8vi2uq17lKOcowFO/xmArbUuL68zqG/BptuXNnIXzCZn/FAq2009jKyl/uYQQnZWLtfjbgCc+wNwuWTSz7cyGx8ajybesW7yfVfPr6LYqBxz3DNudPS2D2u9BeimHSh0wNYABIOjSU8M3PLctClLs8MZq+gLfVcAvOUp5yaQ7/EP8R2p33777ZIQpK2jnpLnTIrwPSv2ycvOTfaR5AnLcUKQdmuywRPIPwvsd2TfxSXPaR/7/quJtiO6xPMTTml+oGGFnd4xyGB9Oww4BefTtSajU5Cce/ic63C7Q02H2rPUw9fX10vCPHqW5KBXNZ1pF9vn8/Q59MvEiPZLISZsUmfk2/W2+ie/2MaRtnIKXsNDJ2So2+/RrR/aqOI/RU3p0lkbkeZkeM3l7eqclKQpne/NdWfv6MwizAReeXbawILnHh+v15nTqTRBcpvb2vGmbORZnnU2noBk9zeNSwQ3QelZh8N++nwDIi0IMY/uicwzjov7dQtNs1lHz4SmwPeIhxyv9KGNHe9lfWkH3xdxsBiASAOZ+gy67BDtXDyTa5magMBOvn2veTOd3xHtDcFgy26ekXHff0a+qEcE7LfU+3dT43V+HVj9CNG+TCBxcvi7Mlt7j4g+gkvkLLvWH8580o95g5Z2P+vO+x0fP3682gKa+tcAnN9RPvIxjRdT2a0MXuMY3eKbzo7ne3TM8tJs+j3Sj8yo/Sd89K0BlbHRWvNGUhkfBgbTTCsTktE74sK1+mwz/WA2leGMMIMqB7Ms7yh4ccKUOsHZJ7ePsk3sMvlKlu1++5lbfdkuoDqyv+9NVvy0oOrImd7ClF25Bsi+x881gcr/DhR2bW7gkMSsHmdFCDaY6Wb2qX27o/V9re/TvAGALQPkjDEBI4Ft+sLva/lbW1bSidj2afaCY+dMQNpiw8HympI1+aCcHAG/BnzugVrgR2CUe/LrPnA5kI0My5jq5m/Ae4jjbHC01vziN8eBbeE9DJ54ns+0ADkZu+hW5KkFgyxvcqyc9eMzqac5IfOYAJKzCZwRs761MaCusu3mrUHxDnA2B9aC3sleG0zv7rlHmvTrqM1Nl47qmermTGv4GN1M2db9td5+b8ltmwKItXomnOAsgVGzmQR+zX43e5slSdEBf1enBTbpu5MdtjW+h31PGd5quo2Lxz/Ptj6yr9P48vyR/kz3WKc5bpSNNp6/gprNzq+XiU28251vNizlnU2CuAwHGWu93TGZ8td8ApMNSeyxPCYVWJ59tTEi63HynM95pohtTR9ZTv6nj+Z562T6x5UiTOCxv8aueSYBGGfhGxacqPF9R0cBb44tb9ax99LNQVUDpWGgs7ymnQNiZphMtiFp56d2umyDjCPnxOPmsKZAxSAwxExellOwrJSXOujcUtaUWWfb6PRsJKIkXn6YsgjyUt7Hjx8r+GvO1WPI4DH3mSekCfw1RW/jxbbYMe12IrwXMtCLgXh4eLgs12mBCZ0XAfhab2dRbURsqM0f63QLSAiUbNRtSG1UTXZ0NOqTDeAfAx8HgGu91T3zP7y2XHEp7/RsS5bEsXr3MgZVbB/fWSGw9VKFBrzsaBvgJJ8JdggQOA4TEEp/2/KJe9Uxtz86wb42m8ZnJj92ZIfWun4/mM7fQICAxD7naPaGNM1kuL/t+znuA3l2Rg8cHE3yTrL8sc8NIMbOeAkkwSN33nUf8n/+nLig/pEPLGtHrS+sz2PG6/Rb08ZW9zJTRbnmOfsM3sNnzMdWHn3dETVdyPl2vQVTDOS5gQptIn0CZYhLXvmx6hD9hHFhjhnopH6ed1/bLNjU95bUsG/KnycD/G5l2tiW/yWgis8l/mx+wm0izp1wNcl6mj43+5U2Ntz+I/RTZqp2juY/Uf7Oadtp2mBZiKZgife382utKyfhdrXBoTPKi78hKm8TPJbHl9ubohEYWDANpCZgzHaln3ymOd7Gsx010DLRmXKbwW3Apl2/R2LbJ2Ds67tza/WtfK0zt/LJbZuA3gRUWA51awqqbCjZbgeObfYshr7JSp5vs21xMEczMwRLzq5Pf9RLOmdm98jjVi95nGPaOJZhmZrky2CwBU236PE9Upx/o12fdnq5K9PXpyTDNBY5NoCy7d61nbJJnTkDLqwD5kN8l7P29COUe/rMBrBpE/i/5daBF8Ew22p9udVfnTnf7O9Z2+3Aa/Kzuzb9KrIsrdV146gMH9M2knbY08lXy0+OrTMNIzas5d/0OQF+Vvhkcxa3jQG8k6BuD9+Hb/baQdWRTHNztCnpsVsK73FgeexHeObk1UQ7vN2eew/ePPv/e+mmoKop+plGHXXazKYyTAFOqzPRc6u7GS4KTXtpdhICgh5vDpDfNpPC88xIEABGEbOphbPVbAuzc3Qok9OLwhCwkfh9AypTc0xr/aUoCQxtWEw72SFYO0OTcrl9rs/OKn3gM7+SIr80jFx2OYEJ9j3GK+Ud9Ysy5TFqgbydjpeF2jg7OGmgnG2hLhzNVPnZ3JtMGu/nEtZ8c4fPUccYVJm3zGw1/nknwvDOLxLb7pg3bebA9bb/2acA3KZbzS62YIvnJ543ebsHfSI137DWdZLI4N3X87/LvQWYk/iBTrYtZXG5KNtCueHqAvsuAznKOPu5k7Nm1+1fYnMYpHE2mHylT20+pf22gIn8CK9a9p2+i/33rG8bP+v7ZBvT56ntZ2Rjkqtmb3jPe+TuP0Fsl7e2n3SnJQAmbJJrRxjQ5Fkx1ueAJH+001mu+vT09GYZX8o11qKNb7s1r7XWly9fLpuZ5bnYKCc+XScx3zQxYL76f2/KlGtckcSAKDNFv/3226Xd7Ls3xOBz2Q2bm7cRN2a8d5McCTp3icYmH81HGlO/N26Z6F3L/wxE3bCzjfGMSY7NvDNl2rlZodk2OiYrDKd5W7aaA0JltGK1zDrbk6CKhvrx8XF9+vTpkkl/fn6+mnq1w+M6X4NQg7P0LcLZlu4xqGJmho7JH36OcrTlUm2M8rsD1hw/HtMIZNZgV5dBhMdnp1B/N7Et4SkzWRw7ZrkaOUvedHPnqLksIdcsz5Q3jhmBXnSL2ekjHvDF+BZUHZEBXYi7Zr68vKyXl5c3ACn1xCk6M8l7qZPUcSdLMl5ZAuKgijxkOxgYsiy2mX8GYdQfLychEYBO9/Bet8W2756SFCa3KUHNWtfJiIxNjvk79Ss8PwK61kXzzn6QG/lYvwLa2m6w9A3U4cin/ZJnkUMGpbnekpep/9u3b+vl5eWN7WUfHFQ1HlGWW1BF8rsr5AM3sTHPTeHXpGPGP+Qv2+4x4x9tr8eB17lbLct32fdCtAvZGe6onc3u7AKrqd6p7Fyj76GOEk8E8ySQ+vjx4/r999+vjnmv8QPriu799ttvlzLYhj///HM9Pz9f8cg8bK9ORKbTjsj8DlM1Xjf/Zj2j7gf35XurxH/pL3Hzw8PDxb7mA8Xpb8qmHfLSSo8Pdce4w/znvQ5QczwlK47s9xn6j+3+956G2UDtymr3tmfJvCPAQAWzk6LC/Pnnn1drufNLZ0BBbUbTiuR+UpCOAgg+43J83QrkLOsEsuzoHLC0af+c5/PvBVs/Kugcfzv7ewquSJTbs3pxpsxbrk31OoPL9voZG8icY7/iRAxmee8Zsvw2AHKmPNff+sJAk9k2B6J2DE337UwpowZ6DrBIPneLjNA2HcnJPerLe2jnF3a843MZl1v10ePLcgMezgAl+iz+73cccr91bOLBLpBpMmL9soyz35S1VjZ/WabLac/6Xic8rTsG9Dtd+5ly7/43Xkw2+Uf94X+KDFzbxkOhW+w6y3+PTTu6pwUWDLT8UeoJ2KcMBlVOfuc5482cd+BsWW0rrKYAwrxq9mKiM7yefBv9oe9p/HJ7W7vP+KYd2S9P139Uv27+TpWzX2lEA6qTkZgMyRRcTMbbz7XI88OHD+vl5eVq6RuBEQOo19fXq4iZGcHQlO0iDxLRO3iikeY3q7jsKJkAzlRNmTIqq3foa9n3aYkJz3NqNTxk1pzt+fz58/ry5cuVkrI/vLdl/diWSdloxI6ylRmHtsMi+5x+NJD7q8gGd63rnbgI1lt7m054HNt9ExlEUJ7Xun5XieDNma6U4UQFN3hhcJIlAqybM1UONkK8zjJCfMeDSy8oj+xjMnDOMDbeOMBiBpA848vIdoSc+Xp+fl7Pz89v6vQ37yIfzUakXQ1wT0C80QQ424yiZ9d+tU6RYiPZ9rWuwR/vdYDQgLiPd8HDbtWF20SARR2Z7ED++NFPk4OFnFvr7XuW9G1sP/WkbZyQ2VjOiLF89s164lUrbEuuTcuD0nb6X8qk+5D27761RV9lP0bQ6zazn+2vtZ18pn4RH3h8DO5/NWXcpxmAHHOsQ9NYmhp2nAB2w5huS7ufsy4fP368bNKVzwGkrwxqmlymfC4hbH7DPtN8a7rg2ehgu6bj5p/9W7un8c0+0n1xwBmM/PLyclluH5vQ5KTpiAO/HeYx1m4YYtKl1p8foZuDqqenp8v7CAQLu+VIvJcDS2fWACOnCM+QwRd/bQRZFwMOCjyXFLCO/FLImtOxYhAkf/ny5Qr8R7m+fft2CapeXl6uQJMHn4A2Sk/hdLus/FRq84uBo7eIz9/z8/NFFiYF5bRxfs3nSTlaQHUEAptjDKWfaRczPfdA3ECB8mLQt9Y+q9mcVM43ZzeV2YyOy/vy5UsNdKzbkVVn8Gyo+XxLJHDZ6mT0ub16iLLgHYy8pDWz0cxUTvKdNvidM9+TX+66yV2RKK9enkieE3S1ZEV42d51Y/umgGoH+NhOOqu21PRewF4oMtf8Qn7tmJtD93gS5DYgw7J31O6J3uR3reud0jhWDKDdTsqWbbD1yzYz91FnpveBHcwYuPh+zwhQLtf6noSgrMZfNf55OWfKsB2J/21BVWjSq7bbJcm6ZB1rq06aHeM71W4jZfUe9CztoAxxHFobd4Hmzh8f2ZeGI1lu+Nvq8MzU09PT1fK/LLOlDf306VPFXfTbScw7GcG+T5vWMKnSVlLRN9GmTXq8s0PWVT+3ww4M8MMHJlf454Cq8YPlkK8T2ef6t9khnydB9bqJAAAgAElEQVS/dpsMHdHNy//YYXfSWRx3oJEF3P/vmDlds3C1NkXoYuRiNCOoOW6CYiOb8gh0uIUk6+P1FlRFiaKADEaacBOsGSg1ABTyzNJab2cjaCDsVCbnSmNmYLxTSo7p0fERGShM97yn7P8U0VA7cF/reqx3dGQ039vXNn6c/YmMMhFiHUqgElm3UWuBQvjge8gz6pATGgTK+aPuMSj0Lx1F+jlRAweNZwRQyeClTs5kuQ+kFkztbGxzUv6/AZCpv9Yr2+HGi3ukoyU8JN5DX9LGe/JnO/1r4xe5sD2PvlHvIk8OUta6lpe2+ZEBxnuDqgBS+4kzQVWSLfSZ5InfC5zwBfmV55yoSh/cLo5N8107f3Ir7XDNzoa3cu5Bz24J8H5Ge5su3cK3UOTN42ubnCQ39arhw4yrdYVBVfwOE9K2RQ27pc+cCUrdX79+fbMRhG1PC5jYX9/DZ48wG/lHu5M27ewO27BLUkx1k29n6D1ycgu9K6gi+F/r+4BnCjjnaCibIyaAbEu7JqDSnJQNHweMWWcGIVGYL1++rOfn5/X4+HbTCu9iRgBno88lOS3L5GDMxv7x8fv3DegAm4EnYH14eLiaqWq8TN/8QmB45V/2LbNq6WcDuB5XK5qNVlMWtsvT3NMSJsuGAbnJihy+3ItjIsUYZeaEL4Dmuu8POUN4ZLBcVmuL5SJGk8FQgpa09eXl5Wo5ReTbDinlceY3dVFX2s5+1MnoTfpLB0j+EWRyFjblUva4JW4LSDhjvAtEbKPiqGkz6MypO1PZbVxM7A+XUrUZ4FxrPN6BUFOzQfdA6X98lZNiDkjWOsdj+79ml3b8cPn0XbT/GcMsu45+xV95aRLLsly5fbSffGat6+CkzdY8PDxc/MvOZ7WgKtn8lNWWYrpvDqqm4CI6nmNjluYPTPTlR3rG35TnttHHOUh0Yil1trq4sdSvJvvutd7Olk72y/8fjYfvNTBPe3Z1sU3Ri+jT6+vrxedGz/h8np1mqqYkuPFfxthyMLW/8TnPNv9hHjaf4rrIF/Ke7Wo+Y7KX7O/nz5+vMLLbwvqYaGllu07P9Lkd7of12TaRge6t9K6NKprDSEMZJFGYGvg+EqCUkzJ8Lw0riU6CTCQwSmT/8PDXEibuXMLdyiJEcRgp3wP7559/VoCU+8mHCFlbl85A1LNC7B95mDZ/+vTpStnIv8wg2DGR2tKztDVZlUkxaWiaMvh+jiF5bYfDDCjvT78b2DMwcP0cv3t0TGt9b2PATJYGNgNPagaHzo117a43akFBdIfAMRnzh4eH9fz8fLUToJfJUV5sEDlz42WkLIMghMkIB+O0JWkvlwE5wRMZy46cuZbfBgrbWKYP1P3X17+ylbEZsR/mN+WYS1UN3Ngvk/W9rX+fgirrzE6v3Hfz5F6ozYTQRpNXrf9MzjUbmHvoc2gfG9mPhjJO9GceK4Mslmmb4kx0+tCCKv8ycRHdpK3n+8cGlZafthTYgI3Hvse8m4gz0eQx9aclUwySzbdGPt/a2vCRx8f+jL9u673omHkWasCW13bnWnKjEc87mbh7lokO+tbwODv0BSP6o7ePj49X71pR15kEpN7sgmXa55TnmbAcN7myvvLenJ9wpGlKZHvTjYbxWjKFyU7yovWf7Z5w5HRv058WNJl3Lnvq3xn6oeV/psbIDGgDdGZOq+sI6J0hBzV2mAmywkiCwvx9+fLlYsAsjAGSAUgWoJCNu6Pl9LcFVf5b67vScTZjcgyhaetK1mkH2AyDiYBgqtvUrllZpnsmgzwZKpd5pn2/kqwj7ttZJ9XKnHT3PRQHYvBmoMCgnm0IMLBO5jjyF3DErKJBocFfwFvqoYwSHHrmL+2P40pA1GQn5RoIp07OOnvJMN+b4DuWKaPZCc84pT9HYxSaHBVlYwqaGiDlrIHBvu+9N5ra1nSN5xxYkRLMu7yzPm+iyMJu3KyDvIfy5ODMAVMLgiyHPGadBHC+j7LfyqNOrXUtqwTLt4Adrwgx39KWnGugivxoAM/Ht/oWA0C2lW0Kf23/7kHHmm2cZH3io+1Kq2OyTWu9xSCNbONz7PEN5rMPigxTHp2Moz4xIW39crtTBj+p4vFtM+kpg37Msph7WkDXbHV8p3UyNI0v/SrroX8+CnIatXF3e888d/baj+jVu2eqqDhknDNUFHA6XD4/lZ1nqQhnKQJtwUyZnCniILeBilA5IOG5ZDUoVHye5bBsK7QdYNrse8nHtb4vcWRfmzJagQhqPRNAPkzZlVCUiGCD/CE1Y0EwkmcnAJh7eL4tm2jPceYrf2e+sfV3kfUq8pqPyk6g6qjM0JTltWxxDB005TqTFJzxaaB0aufDw8PVC8AtM5eNG9ZaV0GQnYvtkcszxVm29wIpe6nbdTFga+PC5VD8bg8BJoOpNnPN9xyZPczsXwMzjWxf2r12lM5+uhzalDhh8u+9yyf+00TwwyV7BK+2uWtdg58WWHFcyUPWy3P2gQTO/D/3MyDxxkG5Tv2d6kkdkamWvGN9k/5S7lOW++32e6Zqret3U1LWzpex/skm0s9OyQAnfNkH94c+18sqfWzZ97inrvhc+vldBp8+O+24Bx1reMgYcK3vcuQgJNQCAdIOxPP5ta4/rO3yG3/pZ1sd2TSMfm+tdVkaTplN/XnWMzStX7uAg+Uax9kmGZ8dJSAmXBgfY79M+ed592VKqtB/kP87ol1uwS/Lb22ZyuQvy0i/3osLb979b2JIU6Jcp6AmcztlIvw3GS4/Z7KDc2Y32eEEEswmOLo2yOf0Z7bLzHtZVjr2iwPFNlMZeK0NvO+NgHsNrcuY+MQtP+n0XMZUjoU940sA2BRv5wzOgESXR0UmeWeytI1ycQ+Oaa23WWcDcL6sfcS/ds78pJPbOZxpDKZZI2aHW6IldQdcRf6sY+ljgiouAbQTa0vjzsgQg3Hq1WRQG8jzWETG8n5mgionLGKDaDctv7QlXAKVeskz/9p+2Mac1a3msFgXZycaIL43sn0x8GAikHbVNPmx5vx9f7PP7byDJPqrHO+WGrncEIE5y3AfUgaDoF0AZPti2Q7AtH/w8/w1L1JWtrp2opJ+1n4rf072xSfFtmRJ8y5D37BJ4/OOaOcZVLW6GcgnqXIvvivtIti3vTfPmEg9sk3WpSNcaBwaXEKiP+U91rnX1+9Lta3bWd4+tduJ6oaj6CPbygnzJWNv/rYEDoMgEm0ffW3DbNQX+l/Ww/6Sb7Yra71dWuixbUS+N12bYoNca/fSXnh8jjDWjt69IDeV2ulE8emY2Fje5wCmAZafRQYsmalK1q85I7bPWRgK2sPD9cvvEbwQhXrKhJwRLLepOY20dRdUNcPvwMRGfTc2DrIm4LArYzej0OoxWZ5S5tReA9J7oGYYpuw4s30h85tO6yxRd+1sLG9s96RbzsTa+DtBQB1jwiCZ+RxTXqJ/pgaYyae1rl9CJ/EFZfOH9aZNTacJ9uwsW7DSypjazTGejP8OqJ6lqa1tJmEHsO+FLA9TG88A5rXe7jbXnvf9AexTmX7WAU58VvwMdY16spOttmmLdZNtoX7ZVzux4j6w/X6nI0CtjUkDbC47dsPkRC7vT1umGaScM445A/wa2Y76mbP2wO+u3qN+rXW7fdnZZ57bAecjmmYs0oa11ptghrrRZnMzBkwmuQ+0iZPfjq+zDBizmlcTnz2z1e7z0j7bbbffbaDuNn42eQ41fZ/GtvlAt2+qZ6Jm5zjZ86O48Ic2qnClybwmmqbg8f40mGtT8z8dzlrdwUzG221xGcwIvLy8XO1GkiU+U4aFQsjMejY5SBl0bgFWyWS0gIp1UmBcv/nnPpp293hGgGCdAVUUih+UZAafSk/H6ixE2m6FoOA2QGbAxvHkH40el0ul7XmGWdcYMgL5eyPKYcBT+tVmKGkAafwmAGdjRr7xnMtmEJFlFnQ6/M7SLgu+1rqSL+pKZnrWWpelhbEvsTFZNngr2cmx33Z+uzLIRx7nj/YsM1LmdSsvfNsFKgaG7ltzmGxr0y3aHNbhmTzOmFmfbcfuibyBzrSMi2Bh57BjJ5uuhWfmAe8/IoK36P/r618fh/7jjz8u57yVcp51QMC/zFAFTEyBFWUnqzlMkUNmvCdglWN+3Nqzr0e8SZn5nqP9YtqRNrWdOan7blvK4bvV3qTKY2jMELIe2C9n3Bgo2w67HiZ470HP0oe19gGV+WNfRfJMjOkI//ma5SttnmwefRf1rNXBoKqt0InscMzZX2IULgfPbLT73ey5cWKbqbKt5uZs9mG7WdBpBty2r+lY6uZY2Je6T8YwR7sCtnbyGpM77Gfqyhi+Fxf+0EyVj2lw8g5IlMPX0zkqTwN5uW9SMD/D51wWwXc2lqDCNKfShJCMzzV+W4bH3EnQZMfdFCXEpRfNAEyDP/Gszbo58EhddFLhg9vO4IyKwnMGe5PDtxOagJ9BHwMMA2Uv93N25t7IbSLQznXLy1rXOpX/z8xWNQeU42bESeF/nM7Ly8ub9wtZh0EbZSQOirta/f/Ye/dY3drurGvMtfdae7/7e/sdoEBAoI2lyEmJfwCKJEaRCBIEFZWD0iatwYL8ASppOB+UhENiRBCMgkgrAmIhYoSihoOYIsEAxQYhQls+pLS0pV/f993vOk//WOt69u+51jXuOZ+197vX+r5vjmTledac97wP4x6Ha4z7nvdDQCmnM02vjm5X3Unv2UeRv/uh9rlliA6X2cXkRJLsErhTTkmUQdYtMJdsAttOoFm0Zt5G5PrlNirpTqfDj4G6OfLVgy4I8O9elp8OBNiH+5Dq4jY/6QG3JXlQldqmT1Y/mQSsygc3dGPVn4MQL+Pjp0w5sPEgycnxgPuXq6ur3bZbr7OzEdfX13vbuNxe+Fy4fqb5opyRBwmXJCyQxq/nXyeT/lERx6hEG+8lrOaUbOjSM4eQ6u9kmfaOq8DcacC65IMcZ7AtyrPLBonynLYMJh/t2NvbdZyqT9+CRxl3/XPdd312eU47MkjyW0w6Jj/ifNE9txVdO6m/xMw+Xtb/1laqkuB5x8WwZHhprGi8WD5lX5f65OQTwrpT5lXPyMEmENodMcn7ao/3lg5C6CbZnbSDGZVLhsqFJBGzFOwfAynd158HJO6IE19Gc5mMS6ojAUq1mSgZNfIqXX8MRFlwmfCVCwKSJYe1dH9kxETSjQRE9Yz/sW21o08HRPqe+j1NN0HW9fX1bvVKcusnclblbUIJhOk7jS5X2DtnNpIXOi79+fYQ8sFfBlbbfLnZ+51Wrkgjpz5yXj7WNfW6Y32MekVymXAagR0vT3+YfEYKJngv2b7Otrld6ICEr4TwWspOS56kV2zHD0JwvWZ5ArhUzikF6lV15/2UZJeoD/TLIl8t8O/eL9lR/k9f4dQFSG7nSCP/73M50p+uT4+BOI7ON7tOeMKqs9kddRhIz3e8cl+U7MKa9lNfhWHTQWnsc9c3Jfi0muzj4c6h1D7lPCUs9Ld00BL50405fU/lRniOvOh0Z0kXl/qn/0dyIj6klbBD6OCVKt/e5kZeAnV8fLxnnAi6OTAukbJ8Us4lw0TA53XM87xbndJ3ZtEZTDiR4XRMDljkmNimwJ/qIQhROc8G+7h9eTQFKgn4qayAIucnjYdlCFifPXu222qVAGnn4DlnnSFxRXel78AbDZgHIMmJutI+tlWqeX61DYQvrXJc/p6PGz797wc5uMFPc8I93SIGTwIqfJ+JvNfqLLf7uUxSdvQcVz8dQOmP86iX0+VcHIRR9ygXzAYnPaS8zfO8p8ssc4jM+KE0sgeUz/SSszJ45JOTOyKXb7cBDqL5XX1LvGd7nMeRI9b3Q9/nexuUAF9KEojcH6V39xiYEzS67ogoh4nSqiaP3+fvtiU9oR9z2eKpkUkmHJyNZN19levv0hg9o63+8SAo3xVBkg1w4pZu2gvv32h7UKfnfC4lN0b8kgz4+9tum/TZ+XkPvh4DqV/pp2RYhp9MoHO3gpdL805yPnnCP/n8zt5Rp+jf6Dc5PtkE4gzufpAMq/0U7HBMJycnQz77+Ee+YeSvPDnPujg/up98QrKZrKOTA/bTeZJ8GvuYxs45Tm1qTlx3WI9sBbf+3ZcODqoSaPFJcKGm8hCcjJQvCUJShlSG9+kIXVnSci4nlhPgAD8pNjN1qlsTpTIETmrDT0sbrdbR2Kaxqh3dPzk5ueOYHFj5fGgsvlpFB6xMZjfn3ueR8e+AoDurzhC6g0zztNT+Y3BObtBFzk/qFGV0BGTc8CSD5eXTfZ8XB5w+L3yecsZVmRRQMQNe9WrfOYM6/pgwAyLqlvjILXXdihj1l/fSoTMekCwFPjxNTHX4aX4OhN2xp/qTHid9SnbLv6c2uqTWSGdGzvwxUNra5YFVWvFJ89yBhqWgQmXSHDho02faaeF9GoERrp6yLH0Xx0yb341zJJ8kr4MBIIMKD4g82eNj9RUm1k2d5pgJkpfmb3Rd80dMs9bXLYHOhC+8z49Nv5Lv4j32fSQvSxivap+PnIOqV9vnOlqao6R7eq7rGwMQJvZ5LbXvmLMLMrp+Jl67T0iJM7WdxjHSCx8v+8Lvo3nzfrHP+p76uYaWdHrUN9rB1/Fh9wqqEnBycjDrW2C6ulV2dJ/fO8FS2x0Qr6rd8qqMr55JGQQHPy4YzDpTQOis/EVeD6R4bUTdRDODxj45+FsTaBDwpeVm7wvbSPPhvPL21zgiv+ZO2lfhHKiz7GN1TJ7BFa35vsTDkb6yDPsyChYIRKVDytr5gTNVFYOW5FT0SWDE4EnPMajiuyLdyq//qKL4xyxpCriY6Oh0ZwQoWEb6pLa4ik2ecOWeQDDNhTthXut0JzmMVDat4qTnErB8TLqV5KKzaQT8HbncelsqQxl08JcC1g7QMdGgPtLeeT2uU5qP9PtmzNAmwMd6nRgQrQHAHIue4/sq1GkdCKV2PLhiP9k2x5K2ByYddnldK8MeGHdtdPaiA7pOLitL8/IQ5IE/9SAdFqJn1gZb/hxpNJfsm/R6yW8mH8WAg/auat9fjdqnLrie0de5fenmufPPouTr/FnXKbcxXveoTY7LeUTeuK/yv6SPadw+T2vm1vvNFUU/2GytPJIOfqeKStOd8+8K71nnNUuPbHNNf0Z1dJT60Tkj//Os8pMnT3a/maFtSR75Hh3lvevcypG2fnT8oMBwPpITZZv67jyjIPNdK4I/BX4cu5P/Hg/rcMFPIKwzJCzn/XflXOMQ15Z7G+SgjwZY993o0UmMQJ7Lia45z90g8tMNoiitOHE1piMmKTq7oP99WV4nbjJpwBVhOg+untEOcZzJQfM+T5RM5Hol/XBgoTGST12mXf1i/9Mx2cnGuB5088h+OaWkQwpCksMkOHhMlBJXXOEUUcb9KHHd9333zmeW532WoR2reuU/XZ48mKC+z/O822rfJfNo5xSoSK80lnQCp4PGJCve3yQ3HDd3Nug7D59h+0yUdD4y+UPfZaG+M5AhSCYRu6Q/zoVjnY5nCRAm0OYgkn6blNp6aNKY+FoIVx+FexIvdJ1JYQ861pDbqrRalWz9CC+63+vknP6K5b1+JR2pm8Rax8fHe3rZBWpLPEl+3HfC0B6qjPPCE6ejgMvLdH12HnE12fU2kWRj5M/XBoNub2RLedjcoXTv0/8Y+Sdyhh7Sua5suu6C0GVVWcYNmTvHLuObHBYNqISCARbBDd9TooBrjzyvd2NPAUnVK2UVJeFkmxQyOpeU4SMA7ubGHYArvfN5rTykehL479pZW+9jIDd8VRmMjYJu0VqH686rA0VuvBIY6eyBj8F1zPvcJTsYPDGgOjo62vsRULVDAN0BmM4A8747eV3nfQ9Wl5wvg6Cqu7/dwWeYLHGg7n9rEgXdvc5Rkq9d3QnoPVYdk0ys8V0dOGM5l7s1esT/U7mqu1uq3S5QJ533TGzQb3UAhknAtOOikysm0Fx2UlDgwE5j0PbcFBARRPmOlxS4eqa/8yFrKM2Lz9l9gFf3zGPUmbXkQa/+/L23JZ3wZOEooZXq8+spIbGWUpAwqqO73uk9n3O8xYR4l9TQvY6oH0xMeD94EFbq4yE64+S+j9f0fYQJ1gbCiT9uJzq8mFa07zveex1UIQHx/c40rlKukQP2TGoykMlwdcKdVl864eX9lNFj2yw7TdNuu4I7VGa/PKvMLKSPg0GLl0lAJwm7+pIMPh2ZZy6cugifL86nwGmUOfTx8p6DAa8jBYGudE4OMjp5SG0/JKWVDeqWAmfy5Pr6eg88OV9Zh9OId2mrROKf5FPv16Xgi/3wIK7jg2f5pE/pxXQ9w/bcPvh2RF3noRAdj9L4CQxFnb0h8bl0b9QPD254nTaM1ztQ6NcPDZzckTvwSGUempIN5G81pXlOtphBMWXbv6eAiPPj880EgJ/65WXdt+izs2nJz2qOqAPsF20L6/CkR+pfIvoi9yOsm/55tIOjA7e0jX599K4Nx5h4mOaAc+1zkvrWrbLxOW+fweSof4+Jrq+vd79Fdnx8XFdXV3Gr830CpjWBEX0TfUFn13wVSr6Gvy3K+6kOl+MlnFL1yv9IzvVd8ru23pGsJj3riLaKtmIkq07dCnDScV/x7+zIyL6MfLLIecYAlluiWd999eveK1WMALtl1g5gk7ncstP9Dg/r5L1pmu6s8NAQ8xk+5yDUs3pJiTge9kt8YDa5qnYn7qk8gy31gYpMR5J4lsbjzp8KQGeUAqkE/nzOGKzxOefDEgCruqs8rmDdfNOxLBkpgQDOq4MJ8vsxOaXk8N24+SeBK3mYDn5wR+RBh4hOnfo4Ckj1zMnJya5ven+JfUk/Apz4ULUfWHHM2kbCsg4U/f3INE6NPzkGB968RjvT/XikqDu5jD/irL50/KjaT9Z4AMiVB18Bo46toQ6I63uXNXWQzlXEx0IOzDV/BDRuo52oYwQGbCO1yzpTNtR1Iv3Oi69gV+3riPrnffJx6BmCTb0LqbpT1pb1Uc7WBFMeHFS9eo9KOsjt5QnUeWJwBL4cWHW+a2T7/H4Knjj+ZGO7vum7PtU/8pV80xiIGx5bUOXzcXl5Waenp7tg6sWLF7u5HtmZRMk+rpW7NTbIg6qqfbxBn+TtOxZMY0vzmeyN2wnXZz/wzOvusE4Kqkb4uguo1gYbTHwmzDZN++8Vp4Cmw7ne31G5NTZMW/5oo0b2ZQ3d66CK0XWfWBrfJYMjorF6HcORlJB9oXPphC2Nl0JK5aMDoKF0YfXvpKXAoXNkHvw4H7rrrNP7x3F2/fb+dH328ofMKx3am3Qmydg9FuKcdCuw3v+lcazlO/WCfWEf2E9/hvpF/ffg3GUn9a8DMQqg3XF4ENX1l99Tme7aCOSyz87LUZk1fUjgwOfIx9c9dwgl/SZYGa1QP0a9qroL3HWNn/5d5L6C8kdeJxlM8uLtrtlt4X3rgNVo/P7p+sO/UZ1rAbHqVj3sq+8c6cbZ2aDuvvsatyVdf5d88CjzP+rb2kCAdd+H5w9NGqtvvU4YcTSW+2CEVMdagMxAxX1g1bItHenfaCxplTz5lzX++5Dxvmla8kXUd/J6TVywpLeHjNnn2eftdfl3cFDlHVLGyYMTAR7PpjkzUxDGtg4lMl8OgplHAjFmeTtnmeruts0RVFa9WolTJJxOJVK9a0DJyCF7FpB9dUVNhkL99qPgCSaTknTAeDSGLmBbo2TJ4ekZblFMspXaeUgj5JQMh29/maZX20+r7p6kmLLMlNe0ncf/p/Fz5zJa2WECIQE9B+OuL16nxujZOxG3LaWVOnfkh4AgX9Fgn/U9ndKYyAGqr3iof4l37Av7Ps+vfqCb9akNfSr4HG0NI3/Sdw8WaKs72/UYiXZAsndxcRFXd7TSmgJK8lxllgAX54XXOp9C+XX7VbXukB232/LTnujUaoK+qwzfb/I/v85+L/VHfeHv8XFevKz7t6W6vX++nZBj7+pyfUwBnfeRdVI2Rn2uupvEWkuOlx6SXE/E66urqzo/P6/T09M6OjqqFy9e7OxRN58dBkw8XRN0+HxzrtzmehIwkWOJEaX7nliU3ske8TluT/ZdCI7jHE/67ha/pu/pFGqXa/ap4/FozOoj+9qNwf0eqesb75PPSZZk22jj9Gza4XUo3Wv7nzPj4uLijkHh8YR0wCIHEwQrFOzkjLwv3XW1SQVeA9rVt7S9x42+/vjr2ZeXlzvlODk52fvxQdXtp5ukYGSNkaWyEAgk4y+lkTBRmdNzVFJ+qn/8ngKvDsA6mPBn0rhdvrqAwOv3edK40pLzQ5MnIKpeASGeQPf06dOd0vOkLg+oJPcq66s4VXeDDQb9lGnJSXJczj8lW9iGl1EbI2Pu9dPRSM/SUb3pRDef867vDmbned7plconQ079dX3iH50fHSRtoPrLPjmxzZRt0yfnlXV1OsS58zI+9ymbutZuPQTRVrvz7WwQE1EOClRWdau8yug+wbwD9LTS531Ov6fI95nTs65LnHfXJ25TPTp69QPf2h6T/BJ5QblfmnvySbaN3zs+8B51hmNVGdoEjo3+j+Rz6fzTs7KnyXctzcWaa6IOtCbbNQo03xalsUtuLy8v6+zsrF6+fLnbavXs2bPd3NDecixLGG2Nz6acj3Di6BoBt+rj4SyUJQ+2kq9k2aX+SL4YBJBvbD/pn+sF/1IS3u8nvLdGx0fb7tx+0H8Rl3aynfCB3yO5TDE2IV/FhwcLqqryqo2EaDTwQ+r/KJyzC4cLjhtsL+sZlgQEZVAEUOikOK7EOxc23vcxeHspgODzHriqLvbpsQQYI+qcF+8ncJwyF49tvD4XDrz4d4jzXUsduPRsqgO1pT5IntOqi4P51Hc6NNaV2kmkFZsRb9Y4pERrgS3JV+/XzJHrqIPdtc8vAYmuza6Mg3S//hjIHaVkhzLkepfodezHm+bHITZ7VIZBtPOJiRURE6TSK11fS/6D9+lYZ9fHtKJnvoMAACAASURBVLKdAiuuxq/FESPZdn/twHktjXBRtwNgqb4lX/i2aARo53nevUdKe7pk/94E/hvVkVbLRkkkx4Bd8LCmbSfHLLRNSYY7X+DlUkDVBVqON1h/R0lu2Y/UP7e3CXMs4bu1/fN+qc+eDOow833oXu9UKcr1aI7/a1tFynYd0lYCPWnCPMNKheGhEJ6FHRk6D2y8nAdBNL5UBiphVd66obJJIDlmb0vlOvCXAkBfNSQfXPg7oJQMjges3XPexyXeJ56480+ZfRnzeZ7r4uJiL+vMOro+vE0iTzyDpGtaMdEKDbOwKkOedcG//9/xmiuVvM4Xazsd8qBW9z0Y8nl1gJecihw0x9jNP23AksHugE2yc2nL2AjAuW64/na05PjT/4k6/V4zZpePqn0Z5SoMeb900trbJgF57ayY51crvVWZj1yVEnVB6ZINSc57tA1K/NSqsdr1kyzTSovLl+uo5lR90lyxbrdH3bg7gDICg1x9Z9bcbQjlyfuVrnX9Svxxf+zU+TqS78Ihf1N9vv1K7Xuf3HZ63zVnr5NRf5PU6Y7m+fT0tC4vL+vk5KSePXu2Wy04OTm5U0eyjR0GWLK3o/46v/1ZzhHteydTrOeQFUTJwzRNu5/XcZ3ibglPyHE8/t371elOF0x1+pswrJfxa1yRSivcaS7S/wkfj4gYcZqmndwle5V2XxxK9/rxXzkn/5FXZpz4mxjpyOKR0NHYMBBIRnaNI/N3bWjwRwJFh8cJX/rx26q6AyjUj5StZ3840QwCEhjzpWdSF7QkvqU6qQBJQUfArgOM6bmk3D7exBMX/pRBFoCq2gd/lMfH4pSqcuCjzxRUsYw/09XJDFsC2qQucGDdCfRM0xRXTvX8aOulg3lvl7pDPnRZbG75STrsWylG4IrfXZY9AHWecSyut76tyJ93++n2ynk0ogQm2R4TZuyLBxf+npmDWP/dvIcmzTmDKp5A1VFnA3lf9XftdtszRd0qre45cK96tS1pFLgmfZDseBJFMiob48+O+q/7HqR1gU7V3XcSkzx3NqkLsCizHejrwNxobCNfOk37Pz+R2lQZ7hRxvXEep6S1yypt7ENSN1e6p0Dh8vKynj17VicnJ3V8fHzH9vpYU2DJ6yxbddcHdThE933ek+1OeGX0Mxzu66ryqYGp33w2/W7UEiVbPZob708XvPh3f495iTSmpaBqVFea1yWs48+pzePj411QxcTE2phiie61/W/UqBuOzrAnRViipQl0B+FZwDQhLO/XqnImcZRddPKAwbdQ8dOfIyBMAnhIILWGCPyWeJ2M0H3pUOPhPPW6+Jme86zoY3BMTqmv3LLEwFL3HWyMnEpyIPzU97TNzoOJtA1vzfgS+KBujYBqVT6QhGUYPHnyR2UT+E+OdNSPlHwZPetblLw8gRr7k4KutEKXxpDovk57ZLdIh2Rr3wZ5Zj+Bnqq7J0g6HeK/1gAFlev+T6CDfsv7NKLR1h4GBfRXIgZEfk3XGVSNaGm7OinpYxprApKOCQ6182v5+rp+0Puqtv0++5WC0cdEqb/+jnBX/k20eeh8dz5Q5Prh7XSfS+SYcA2GSX1nHeknHO7D37XPLAWwXqartwuYRu2+KfzWBeuH0r1P/0ukk17YMS3z0njKEGhrluoV0dmlJfwRExlwSKgIoFi/2uiMtpdXuZTZHG210rNp25DueabLt0esEchRv8jHzlnz07MJibxvIwdPIMk6U8DIPvi4KEMpo87nvDzlShnZx7L9T0T9YJ+46qZV4Hmed4eipNUMN1CaK2XaElDz78mZu+PXNkSu4HpfSKNkALPdXfDL8Yzmze+lzLj4MTKgS8aVJwhx9WzUJ47FM85cvepWURjMpoyh5m2UmXdHvJSs0HOca7bl43osOlX1Cmh4kiKtFOqedlmQt0waVuWtd6IE7qtyIJ/qcL/g9pZ+agT0SEwAuB76mLS6wL44eVbceevEd2u6wKuTWz032rLZ8Vyf3W/9uE3ryG0k6/HV8A4gsp1UH3ko+5rkq+PxQ5LznSR8+MEHH+wOrTg+Pt7xLSXB0283pja7+64Xqa6ER3Tf604JGdbj+pr4kORsZHvVju/OSTxIMpnGlnRkqR7y03FkF4y4/nV9SNTpYxcz6Hq3is+2XM7eZIB/74MqklGT0tDZy3gyy67rCqqm6e5JMDImqU2nNKEp2zhNN0dWUmlckbsJ9DHT+PnkOyBUP3wLjery05cEAJjNceDiJN4mPjk/OwBQtf/uyVqh79rs7vHaSLk74OeBVQIOPhcOWv3I5McEAKvuGlcGVU+fPt2drMljibt5ok5QD71MZxx5LzkClqXsrx1fmnfaCbblMjCSuzSGqn2w2G398WRE6qdTupZAN+Ww67vby9RGCo5Yrguc07yq/VFAlE5VdN64bj4WmudX71e6bVZfJRM8tVbgj/WQlKCgndGz3bHj7EMXqIi4+qP5dl1PRwIncoDkfNBzaWun16NPJhQoA+kk2sQD6jD75pn2bquRy7P4ka4TEHZjGvkBX5VPAZWPsbNP3WeyFwruk4ylFZ/HQsnmXVxc1IcfflhPnz6td955p54/f37nB8ypl6TEzzSXXTAz6lenq9QVzn1n25I99L7pM93z+SQv+BMjyc9RdpiQdj/RXfPnEm/YL45xrW6me6Nra/C4rvsijPjp9XcYJvX/PnTvoKrq1V5wKjsBrGcBE4BVmSXDkADNoc/ICXQA1CdJ4+O1pfY7ZVmaPDqTqv0jZ6vuHiOb6uiCkyVykJWE+760FnyupRHo6+alG9OorsdA1CM6GdcxD5QIHpbqT4ZtJGN6xp9dMqAjHnf3klMdBXbpf++jDG23Qpn+T07WgVpnx3id9ay1e04JoKax+vUlnXb7nGRjtNLe6dZjoiQ36qfb+Q4QJxA8WlFM5HJAGtVDMD8KyF1PUz0jH5VkLCUDO5minrocEJSyPbcprlejct5X9qMLnrpnDiX3mQ40R4DTP0fANIH1x6pjo35J1yTDkmPtZhK5HPDaEo3s4CHPe38YVKtfvsp9CDE41v/08+QlMYAnrBj4dUEfgyc/E6B7ncXrZVn6riW8QbvF/5cwyhqesu20s0r8W7OT503RQUEVGcvJOzs7q+vr6zo/P98FWTpGfJ5vTl7j7+x4nfwRy6rs/Kqy8CQB13USncLR0dFe/2jABU71qVUkVzA+Q3KjQmfdZcXZFwLmxDPW7TzwHwOtqju8ZcbLjXXKtnlfSS7A4knqnztlKpj/JT6R325U/Jm0zdP7oH5eXl7e2yi+aSL/1DfpDrfKXl9f7162v7i4qPPz892zMh48cTNlUPVJ/rpx7JycPp3nBKPJUCc97UCX65Anbbx/PqYOdHb3R+NN+sTr4rsHUNwul1a2fbukr0gtBTbJkazJVibiimeaE3fuut4BPq20PybyMTiAcb9SdXdV2+WGv8fIduhHaIO7XRHJRlbt+zzZbvaNoNN1N63yJttOu8o+peSjy6h+JN554/qW7GsCQ7IP5Ad3vnBsbte4Ypba64DXUj99/NQ12gLvH5/xxDLlwo+A53MaF+eePpB+4aGIeOXy8nJ3MEXV3blQf+d53q1YSX8cg0zTtNsuqvsi340i6oCyY6VURvzVH/WH13mIBPVy6TRGzj9xB+WFdpM4h8/Sz6oc5ZBEuaTcuuzqU/zmNZ4IqvkaJTuST3C9Y/87bDIix51pfrkwkXZ1OE/1/No+dPRa71RJqLQ1SQpVdSM0T58+3QmPnDUnVMFE1d1jYru2q/LL8Z0hVd16TkRHwnHRkbA9X0ZMfeKxl1V3fxyTTjA5L3duHTh0UFNVO0HXNe47J+Dr6uqAlzvizlklJ9WBOAeBFPZum4fqTM5f9XBMzidvX2V9m+VjIPafp2wmw3txcbHbl065lW51pwSyHT3DTxpKEg23A50u49XpadKFro90NORPWnFyvdM1f3ZkPF0muTVF1/mugztdBjQuf57Q4PWqugOe/T7bG/Wb/4+CKtqnEQCm/XL9TeUfU1Dl864/t9lV+yfMcdtNx5sOFDtA9kBhRA783OdRPx3Ap2dcdryfo5P4ur6pHj2b3pEe6RlliP5K99zvUE498+zvHXV2K600OyXd834lvUrtOzh3/iSwmfjjAJbY6zH4LsmNMGAnQww+zs/P6/T0dHcam+rgT+B0PqXqro6M5iABfpURL30u3dbRX+mPtmLJp7BN8YGfjkfcv3X6Q9l2m5uSD5TbFGCpjDCl+qD6pWuOKd2udBgvUXd9VC599zYcO438n8/zfeneR6rzkyDXf+fCn+EyLydmBPrdkWjyEzg5xGmpHrZDQe4msKvLy4wMgsbjn74Sk8qQfykDviQUSfCdPAB1cuPD68kpefmOt8z8dGNwI5qWlfW/G72OD4+BOPcuCzTgVa8CcTrWFDynMS8BCdc3pxEgcUO/9PMDdF7OixF1Mt45uBRUeVtJr1hG/KZcsV1+eiaP5RIwTvqyRK5LDtBGTmxtvaN5S2N6nXY/atL8E4gmQCZ50XxzTr18Ajpr7EoCQEs8c5/nwCbpTArGUp8deIo6HROlbZOpfAcIU1lf8e3KuQ6PEnJr2h6VT4B7pB/e9xEgTrK0Zh6WgOrbJCbXtXuiW7WhPvlWQLertMUJl6314c4jB9KHPMP7XUCXaOSHiJXXBGZdH0dtp7IJL2hcHzVeSr7wTdUrGuEI17k3Nb6Dgypt8dO2JCmQgiYuwStbfn5+vve7VWKgMuxpadzbTQanC8IoGLouItDhARFsW47Cg6J0GiAzKl6frnvgKWeosnzG3/egAfKxTNOrbDmz4/M87y3jupCNTidLYMn5R2Oetqx0YKPLiDhI99VCGlXyhCsIKbjk0nValdT41jrjj5Lm+dUPFCvTd35+vuecuOqkMto6ofE/ffr0zqqVqAP6JD6b9M4NIT9131d1xH9updV1yS2dyqh/boRdR12HdE18HTl5tx3SL31P23Epn91qhPrI+UvAYSkY8hVCfTLbmPQq2ck0lxx/0nPqlQcgDsi1+vBYyPXL7eqTJ0/q+Ph4z/4zGGHZqlfba/k7aSI9Q5DomXC3rTzwoQtOVM7tMeeNux54Pc0z+8oTep1vDjwcDDIJJH1O9phEmWXd5DX7neZCfjT5jeSXNN5kG1M/U+Djuua89U/ON+dD8+gyQR9FHpLHaV4fkq6vr+v09LRevnxZ3/M937Pbjp6CRcrd+fn5LrlwcnKy89f6o365vrh9V52iUeDV4UzZaPa7S8p2fPe59iQpV6d48IQHW13d/j//Rv0aUZJXYkrqGf1Aasv71K3+se01vn7EB16njqQVP/ZR1AX/96WDvZ4EI/1N07RzpBpU1avTtuZ53oG+tDXBAcuIGMwkgU9Kp3v6ZJCXJtqVy8fldUqANIkURh2QkQyMB1XpnSDvG3ng9z0zydWfzvB05ALpgdHIeDlfOkVLfXHH3Y1fnwSybJflxddRUPlQNM+vTifTJ7f+Ve2PR8vwFxcXO2fE7bSdkUgrwiR35h0gSnOj55OzZ/8oU56xT5kl9Ulz6w6WPGTddG7ckjLSqwRCSQ56yKOUaVUdS07eg5wl3Uz6tGalyh2oj59tUxa8bb5H0IGakZy9baI++eFKVfuJIMpZ0inZdQ9oEiWHTfvEBF6qowNyTOD5vQ5EjHQ2+UF/p4rP+3Z4XSOAHO3UGMlG8pF+n+DV+1F193h4ltEndXwE7EZ/9PnOo9G4vA4fVwL8qa+PIaiqql0S8MMPP6yzs7N6+vTpLlCqujunCiK4dVSn2zre0LVkk0fj73wEn/MEWJKnNL9pBbjrD0G+B0705/x/NBaX4SRLh1DikSeDDg06DpmXzj+OfH1Xtmr/sDe3hSo7Wt1/Xbr39j9/18MH1D3PvyWAV9Vn+6r6zAT/Hzk81uOgwifSjQOzs10f/DkFVrqeAJsvxfJe6jczOp6lVrCheykQSsArjcEzrWyDQJOgNAVQnfInY5YcVaqrI5c3v+7tPjQR+NEIczWnKp/+Rx0h/wj69NltP1iipFNJvzg3Lrcq7+PRPT862AE+HZmDU7XpL87zGp9L/Gf/Ulaafanaf5cjJQu8/nQ96Ysoge0UPKV+doAirVh6felT5TmnXAFW2RFIfUiiv3K/dXR0tBdArfFLhzh78S0ldJYASPdddacttrTLvmrMhGSnnx3AX/IVLvvehiflqsZHoK/xTx2lBOBa/Uxlku9JYJB2JJGPld9T8OT1Pjb90krV6enpLqh69uxZHR8f37G5KbiSjPjfSA5SsOXXu3a7eUm2NtlA1Ud/JB+T8O/ITnBsxDdqI30f9X+JEgZL8pwSdUvy7H5p1Fd/XuR8SjYolU06MkpoeT1vUp8ODqrOzs72tvzpFDLvGEEV7wv8COh7JOyBhCsc63Yh1L3OIZAIstyx+DYhKhAni1tJknIkMJ/Ar/c1BRyJP75Mri0pBITcVsJVwnSKWQf41BbnzR2WG0KCE/ZJ37ltJgWqNLicV30/Onr1cjOVKPHN36Hosn4PSfP8amvt2dnZrt88LYllq/ZP3JMsil8MMtw4V+2DYQ/M3AkksOPBkYO4dIQptxhpriUXrhMau9qjfKaVO/aV5Qg4KUvOS9ZDOdd31w9PVIx+A8754LykU3b97MpSx13vZXepW647BAPep+7QIMods4HcckZ9fCy6VXXTd9/+l/o4TdOdrX2cU/ogvg/isuXO3f1cAjeJksySPLgj/3ndfax0zBMVLhvpgCbqPfmXkm/zvP/7iBwz5TYBuBSAOS+WwDLnkb6QK0y0EarTAWbqi/fD/9g254YyljCI15NWN7qVuIegy8vL+q7v+q5677336ru/+7vr7OysPv7xj9fJyUmdnJxEPlXt2w5tBby+vt69FkJMU7Vv7/XX2apuJcJtqvOR804Z9iQgn3PfKT+cVov5P3WNiUAmvNxe6Hoay8j3uOym3+Vy3VuLEcUH6nm3G6gLcpb6zudd5z2+0K4UfRJX+ry7Xjn2uQ/da/uflGGU8U5Rv573wGQ0CHcEIyO69H8HvsRICTgVkuDRxyFjm1atVC+VxFcR3JFVZUDjAZjIAxUHeQSEvO/j78iNgD6TYyKlrGD6S3PjAu6rd8np0jE6JdCcwNRDA0DJH7PpPHa1M6rJ8S7pFCnpMOvm/6Qkj7pO0OUgTZSCv/R/ao91puDOgaSuk5LTZVnXG/WByQKvc8lBpJV517Gkn53TS/93IHytw3Cb6Lzh/xoT7yWw/Rhonvd//Nf15OjoaO/EzLV65HbLAUDVvs/jXKndjpZAjPfDASSTFfrObDoBY1qx6vrHwJI8SvKXgKQDvORTRrJ+CJHPHhyl8er/rv9JJ5Pv6uQm+S4PuFkHbXpV//tDD0nzPO9Wqk5PT+vs7KyeP39+B+ewfNW+XhBXEiD7qZCso7tOIj47hKgTnY9zf6TPbsWqaydhwRR8pPEmeeyIbSQsRrzIv5RUTHWvsWmHUCc3iVxXui3e6bmlug+lg1equCVJv0fAjLAHVTKWfmSrA0JmLdKk8CAGP5TByQWATO0Y2wFEGjQGPB5MEdjL2bCcP6978zzv3oupunvCFMv5eLlSpd8LEShgds4BVxf0JD6Sb90zDhTS/RGlwIBGy8fuzpGZo6RcPk/s030M7kdFrlcEgmnlQsT+exCfAJOeIU/otN0heFDH9ul4XCc9+OM18Z3vhrBv7CP7wcDHAYbK+G+xEbh4/9NppdKn5FRom0Z6NHJCCTB2K2OdszoEcJJvPhduG0cA0e2188zB6WPRK5E7XI5N73N0vkCUdM8P1unaZh+SXLJcoi5gln/gEcgcnx+NzHlyUJfelUrk/o0+r5NHXnOdSr7JExde55Iv0vekv5TXJTld0q30v/sw1TPij/suT5SN2n1Iurq6qvfff79OT0/vJFlEST8S/lNy4/Ly8s4pwPT3yZc7sQ8uN1V94j/Vk3xawnnEXe5r+bzbYrYlnRy9r0oivhuNh77F8Zr63CXpucLl36lXqQ9pHtbwPfXf69Wf75xw++52t6v3TejVwUGVXijUViW+YMhBEORL0fRyPQ+wmKZpByJp6DjxZFwCUiTPGpDWGDP2V8ruys1Jm6b90wLVTwLMJEAO/MiTNfMgHgv88Uf0jo+P72T+NH59LgVWVBQqIhXJeUtw1Sl+4n3KHHuf2RcfF+thXZeXl3V+fr7nlDpQ9NA0z/Put97Oz8/vbFHiao90i0bbkxKUK843ExNMeHD+CLj4WbUvFx6s+XtLHvykoJlEoydDLv1LMqL5drminpKP1NNETIroJDg6In0fUQJ1qYzq8kRIp5tVd7ds0OF1zsxBWafvuufzlJIVklPNkzted2iPgWgTmMXUnGtr6tHRUR0fH1fV3fdoq3Lgz1MhU5Arv9nJgso48CJ1c0w9kk7TFjJoow+lj6TdlD/2cbKvXI0cjSvZcMqt/BWBmraok/8i+tIEJNN2Jd8i7/7Q/U033s7G8H/On//xeeqsbK9n1rn9OcnDY9Kvq6ur+u7v/u7dabXpQIaqjL/opyR3FxcXOz/H32CkPe3GLtn0lb2EwTQHKWhl/7w9x6n6pP3Ws74QwP8dB7Md+j226+SYriPiN3/W8Z2X5dj8NRPOafKN4q1jiFQuXU/jEM8dBzCJ7slyYijHmckG6//70L23/3EAuk4DkjrMjncAiYGUytPZ06AvTUJnKJeeo6KxrPrlB05U7SuGTjpjH1KfNAbyzhVpqZ/uLOREupWNkaPzcixLBRs5uwQolsgdEK+JKBtLzzvg6XjngPGhiUDP9WPpOX5PoJa6w7mhgWJg5k6D1OmPy5aDMHdgXo8MOYO6BGTS6liSSbbpDtD77fc9U+efJOprSmIkPrn+cQyuewlAUs+WbJrbSwfvaQWDz/G7/phFJY/Ts4+Fku8hD5hQ8+cSENdzqR6VJTEBsMYPkTq7Sl2VX3KgQ52j7LIOD1hGYJTXFBj49QT2ksy6rLt+pb50icLR9+TnUvmO72vJdaajZNe8jpEOPSbdur6+rrOzs71gymmNfZJdUYCVdvYknJLaSn6L5HV1vkH9WzMeypdsoq4z2a86xSsvq89uK3zqA8fT9bdLjnq/Rz7P9ahboXLfwlW8EZ+XdM3vU9d8hb7bycLPVOfr0kFB1fX19S6LrgMqmLX0iVHnpSzTNO2tatEJuBGZpv59JJIb2LSViM+5ILJufVc/nj59GoMj7wuzTnRePoEudAzc1E5aremIWQPPevM3YrweVxYCSM7h6AVf1eNz1zl/N2J+7GVyLA5COvDn2YkEnNhnBqFS9jetWIcSdSsd+6yMXXputPKrTLLrU9U+j2nA15yC5gCaRGPruuWOReXV/y5g9+QNv1NG3MgS8C85R8qKb3MQLzzp4+OnjCe5Yh3UWWYxR9n3pH9pjpI95XXyiLykzDHAJ88pn06HJL3eJslXeTaTSZe0+uuJhmRLPWCRTVmjI1ytWHL0KSDwMeq6rzZpXvj7hdJ71Uc/vcZ3qT3qM/WR4xTR7/hWKeoE23T5pi/hNl22yVUw11/yMvmdju/u00bPOah0P5bAIBPWvnrIvqTvD0nzfPNOVdVd/zriGa9z1wF3NDHA8gN5kl1P9lb1p7b1rMvcCIf5HHR2QTLu9sF5IZ1M2Nb7PZKFTqZZXzo0hvYg7cpw35TK+FyrH11CZcS7ESW9YTDfJYNo613/nL9r8XdHB2//Oz093QVS3P6nIImnvahzGjSFilvXJDxc7dE1FyrPdvvyMEnXuD1DdRzioFiGxo/Cw60XXZTs9eg5Pku++TP+v28ZomKkH95Mjo51uLAzYBtlG6v2BTUBT58f8inxigGV1+nyRaDkgDsB76Ojo917Bh04fNs0z3N9+OGHdXV1tduySDkiACcfudrG5Xdua1Kw4svz1E/KMue76u57FwmUV91dgeEnDRUDJZ8XzaP+JyBUPQnwuWxpXEmvOrlkfd0KEuVwCYAlm+QrYLKXzodko7Tt09tjn7okBXnjuuXP0VnJtqutqtq98+BBA+2K8/mhiUGVxsetgJxTJsc8oPJ58dVu/xFdybnrjOZTZRjcjPxRBxpZjvrFNlWeGXL1NwEUPat+ichDAr8EbhPPkozrmtueVJ+DPU80pISg98N9VLJtbPsQueb8+yq26mPCh7yXPPJ7B0YfC11fX9fLly/ryZMn9fz582i/REmexAMllhVkTNPNqYAk9wnED4456De4SuJ6wd09/g72CCN6e5x3+j3HTuqPDqKS3yN/xNfEN8oi9bpLrpEfnrhzfeiSFNIhx+1pjp1/9A1eluMZ+S0fj+TF8fiovPTJg0v2JeGcQ+le71RJ6d0Jc1ITyNHACJ50fzQAFzROuoMttpsife+b/69nKGzpeZWj4qZtEF077K+AbAKp7JcLAMFZclKpfQe+LuxU+hRIsYxfGxmizriOKAFp1ufyk8Bzkgs3JlxNeShy40C9EiW+E6Txea6m8ntqt+qVXjED2JVN190grdEBzgP/5NySbi3V7UYxZQfd+bkR7XTCwdgoqKq6u1LlzsX34Hs7fi3p9X3kNunH6I/6of/94CH21/XtsRD77Drm85mCINr8qruBhoMdBsApg7oGRDglvjIbT9vOcTDh54nJBCI4/hGg7Mq5j9E13/Hg5Ua+y8sxa84yo3r9z8efcMtIlkc2kd8dq3g5xzhJNhyoPhYSLmQgm3DT6Hn6oSXd7HxMktU0n4kS4E91dPfTPDr+dJlTm/R1rIv2g7JIGUg+g7QUVPE79YrjHulQSuokn9fhQvLG+9uR2x3KjtdHIg7q7NWb0K17n/7H7Io6o0yqr3xwFUo/CKcMGeumoU/ZL/ZBzsEzVSRfwnXjmSZZhl+Gwu+5shPE0pF1y5C872MmCOSz7mhStoGKwK1iXVCYXk5kmynb5/3yOpcUR/V2S9pprsmv1F7VY/Os+AAAIABJREFU3eVcZiVSP/Rr75Tlx0DpBV/Oh17i5nxz6x/14NmzZ1VVe3o6TXl1SOQAmkkDGvzkAJ2Sc2MWn8bL5Vx6QN3yFacOfI0Aspdjm06dHkpXuvrJ0w5Y8KXftFUmAUw6Ye+nj6EDjD5+zp8f3qDvXJHi6o0TbUkCGI+FpO9Jz7gVU2W5lda3avo7AqpXPJdOpoyz6k9BP2lJtxwo6Zn08jv1ynWbPPGti94XzwwnefB3cJOfSn/ua3zM1EHXCX76tnjxp/P5Ps41IK8DZiTKg/qsFWBfGfEdF9ylwLFrS9lj8l3iEw8oE3W2qWr/QAbxirKr1St9F8995YmymeZYz1FvSWlF0b+Pyro91TX5be2aoG+lvfc5H5H3STzs/CGfSwkh387nekKMyG2Ynd50upOeSf87PvCxklf6444jn/d5nne7f8SDk5OTvT6xzQ67rqWDgyruS3fwos46+NAWQWb6nj17tidAVKolB0MgQCeXouQRdUEAmevRLwNJVyT+cVmWQsagMYHS9FxyPuxT1d13L3yJlvxwRUkgjtfStjPymJlc5yXJDe1ICbuAak02wmXTx6VtqjyB53UV6XVJuuXj5Zz6fKnsxcVFPX36dPe+ohyTghjpCd+vInD0LFkCSS7bNGq6n2SH5KDP5Ua2omtP1zVG2hgvr3Gl7Wvep7Vzz4RR0lmVSbrqskiA1Om287JzinQMvNYFxHqWdoyyR/3hPnRPeHkf5XA9CHsMRLlIB8E4YFDfCeSYNJRTpv2r2t9JcXR09xCHlHwbZfV9zkTdik8CaiL3VQQiyacl/qkelaH+imhjkn/hWFKWfGnrOgFkBxI9cTEi1u0YhPqbsMnItrgP4nW15SCRspn6+eTJkzo+Pt7Z/a7thyL1r/MBVTmI1acSGSp3fn6+48XJyckOGAtndj7DcY/bUJalbmoMap91JHyiOqkbtB+SZw96JJfzvH/wGe1sx7/OXnhf2Ff3H3wmYQy2Qzzpr5a4X+76nQKs9F3/J5uX7Lfrj2M9+jXhH8kRx5107r56dfDpfylwSAY9MUTC3hmNQ/vhhsrvp+tL5A4qTb4ElwLJSWXfXDFpmF0xpWCc8OSMEp8drHlGkOQAfU0AN1KUpMhvktyYOR+9b5RR/Z9AKB3+Q9PaPnt/HSClAINOZE0/CMB1zfvI+wkYLjnTDmSlbbCUL8ojeeGAUf1h1r3r09r5J4D0sbguq99exutICY2lPiY+duPryOe06u6PcXo5B9ujOT6Er2+LuoCBRFvNZ6gTlDUHXQkMHNrHQ+TRP5Md7+w07YKApG+7pX6RByrDsuxXt5qUMuopgdeN1+tMgMzLLa02+Jx/FER+uhyxjNtWXU/z+hjJeX8Iuc+hPaJd6hIJa+aPz6TyaSWV/evq9MRk1y6TmV5/Cgq8/c7Wu455nzt/y/532K9Lioz4ka7dV79GOpL8WIdTtVqYfIDby9fp771Wqvg+lIMI/gI2jYcERntvtUrgkacMf3qRzPuizySgXsbBg2/dIFE4aRjonHxsKp+CueR4uRrgfXDlScuxzhsGR76NJYEwz4C7wvi9pOA+ziXqAq+RYrgCpWVeBoleVtk8//0TfefL4g9J8zzfeSmXfSZAoBz7S/V6AZb6pAyNtiFIRqpyptf7pbqZEHHZ9wx8Mlr8TNveaMxkBzx7SCJA6/q5RD5+r891jFm6LrPlz7C8jzttvWX7HYAYjYcBUQJp7I/+eOokZYplvS46ILcRhwKqj5rm+e77wCKXQcoOt/9V3fCBh9y4PabeJd+S+JJ8gtthlksAJwFZ6iOf73zQPN89aMOf1fi5Bd8DcNmpDqh1K1vSB+m6rwBrnKOEIetQWfr6Nf7Ky6wBWcneOY/TWPi8b8HlYUOeBGU9j4HId/J8VJ5EXnGlSq9g6PcbxSPiTOoacenSXCddcV+UfJMT7SFllzojXWFQJTtLnMl5H/VvrT/g80lf/J4nBHUtHU6RbIMoBV5rsLx/Og99ZYrXeDAVZaHq5sfd9ftnz58/rxcvXuzsdFrI8Pk/hA4OqrQ3kQp/cnKyY7wEh0LlQdg8v1rylNJwuZSOSZPIyfH94ikbn4A6nRK/kzpQ0NUtEJd4la750i4dEZ9LTlPlPAPO69N095Sw5IjTc6Sl1a5pmqLD6HgwUqgO+OkZGhqdmEO+pPESLKqcj4fG4jEEVTz1iIrt7944z5hFZjbGV4fppPQDpxy365TPS2qXetuNi/V3sqSy1H+fM3+Gcqt2mLDxIDS16f93cuDyIiPOcXVtOI89i+/l3Tmlla/UFseZ5s774kGVdIs65uVd5vgjyeqrO9jHQgJk/m5GAre6pq3r1L/ux+olF0nmloJMzhXfj3Rg55l69xFJx5L+VlUbbLAf3j+3MQIkbIOg2gNt11mXHW6LoswmH+Xj1HcPqogN1gZWaX68Ta+LOsjvaTuWvwOr6wSK8l0J0HZJ04ciBlN893cJXDvu0bZ1vo8oXTs/P9/Ve3x8fCcxmFb+Upvsc7qennP76iQc4s8TbzJhQBvk7XALdWenVHYJe3a4z8t0iXXxyV894H3apUTJ93SUbBXxH9855JZJnpqZdOri4mJ37P/Z2Vmdn5/X8fFxPXv2bGdvUjLxPnSvgypGAHj0LB2Qrq01dG6Iu6DFQcTrUmrXjechRAPQGRd3NryfnCaFvctIJIVY20bnpEXJaXj7ad4TOV9ZlqAuPeNlaGQ7me0c9kMQA4m1wHQ0Pi/X8WlkrB0kkrejdtbqX5KtZNhTH9P/elbBFYFv93zXhlMChQKRaQypzg4Qe9/ehP3q5sL1gwG4P6P+uV4tbSvtHP5DEcfb9TkBMpbnjotDdDPJWipL37IEUjq9Hcmy95vJFgdxAoHqSwKXDtBSEOT+hYkh3kvv97ovJD86O8Gx8brrFFfYOh6znTU0kh8nzUWyJ0x2kA/kr4//MdCSXfOypBEudL/v91K9I4xwyFhS3ZSdLthXmQ5HdbJLv8V7lH+NxRPsLh/sn2M8twNp7hJWHPHqvj5rhOW7eV87v4lvDNQ9EH8TvvfgoOrs7GyvI9M07f02ATtPoaewc+mTDl33+adoNL2kz4nmNiS2l5RX5RxULzkkfdLxpElwJ5OuUxmZmXPe6pPZal8W1n3/zjaTQut/boHrDOMImPF/8sjbd0qATkTwoi2nkgfx3U8AUnmtpmrJ13np20oeQ1A1zzerwOS95nHUP5YXf7itlvcdFHIlK22DpK50fWZ5Am1fkR2RG2X1KbXBa50DYB0jB7yk9/xf5dJLzOlZL+PbKUbEMY/ARyLqowdC7JNkhb836Ac48DmuzPGeVrg4f7Spj4Xku2hDlO1W32VrSGmLSVXteCbdSfziYQmiTt5ow3yly4Ei5yhto/W63V84UGEfUjv6rutapSJ/+Kcx+zi1kuE+c82uiLQq7WOk3XSed3aEY0t2YkkXO79Y9UpGJFPsYzoQQNvV9Yy2K3E8HHu30vK2KeGPzu6PbKZ4TR8lHya/pjLcFUXZIZai/0q4j7uMEtZK9lt+xXGm7nnAQ/lgX7l9kc+nZMpI/l2ffOeI+xvWIV6m1Sd+Hx32kpIT9DGjYIdjcV10H+SHU7Cs18H+yDaL3/N883ug3/u931vHx8d1fHxcz58/3+MnfcR96F5BlTouhibg5wz1yWRQ5QNIgPz6+jpu1XJQ545d39MkU7AcpDk58NO1ZFS7T/JA+/KrXm3DYLDp7abgif3tnGviE3nj9SWDQiFO5A6Vn8kB+jOj+gnc+Iz66S8eyvjqGQUqGqfL2mPJps/zvBcATtN059jPRLxH/mgbhVNyWDSohwCI1CcGVqN+sw43+nSsLueeNffvHViknfF607iTjWFZyiDbc6CnawlEJUei8smOsa3RXHHMXjcBnPREOubg0oME2ib1Qadocl+62rmvU/ooSPrFZAJ1TOM7OjraO8bZg1Tdc+dOHZSMcguqgqyqrGfuJ+jzqvb56eBrJL8uG+6jR9/ZhoMX/6va3y54n6Aq+VfRaDVY90dJRdXnwCvhgeTTHXw7r1I9VbULkugXOz+toEqfZ2dnNU03W91kq13H1tjYt0HJJh9CLtvSH/oo2Z+jo6PdadI6bdB1YAS4iXXSilPyCS57bvsdA3nyX8+w7qRb6XUSr4+40d8l9/f1XG/cR7JuJ9aRgjr2jf7Keeu6OsIW5KNsLbf8jYIqEbflK6hiUuLi4qLee++9Ojk5qXfffffOO2y+0HMoHRxUedSvzq95MdE7udTxBNJVV3IaFGrVnwRGZahQawAj2+/63d1L15ODSyAqKXin+Etgy9t2pVrzrPfZr6WxOvBYWzeVSdeTIjHIT8f+M4DvViwemhLvunntZMkNVDef8zzvgWEPYliOn6m/Tq5XIzDkRH1Pffexd3Ll9oLXRzrUle++d/Mw0k+Wc733hMqIXF7cMYlYpwcJvkugayclv1Sf7zo4xIa8LaLvcj+2Fgi6LaFdqsq/kcM2u0THfcaS5o+649RllFWff1/SU7ZHQEegpjIO8Kg7Sz6Lyb/Ud/6/pGMdJfCdyqzhiwNs1kmflvCSdIm7DYRTPFvf8eGhaEmHloB1Kq9PD1jIAwf0h5A/l/QgUefPkv/08p0f1/ekp643rju++uxtJf+WgqDR82t4e18MmXSvm3/njT+X+qQ/BlZMvPP9vaUD8tbQwUEVX6bXYJQxcEOhyeJqgm8XELBLbTlgUESpzITartoHC50C81q3ZMksl1NnFJJSr5lov6aJTUpF50JB4YqNl/E+qV96jsKWlD0JuVMHtv2eKw//T3zjdj9uoXB+0QheXV3V+fl5vXz5cvddK1Ukba04Orr5zYLHcIqS+s85rbpZvvZMXFXtzTtXE+h0mFTwOach0VxIztKRrMmYJSOtZ9QH31boRpr6yjJJB9QPlzl3TN4/jc0dSXIYSW86x+SZLa/LD6Tw1QZ9ui1NW0DUjvPDgyfOJ+97Zr5bAeaYCe7Ozs7q9PR0Z6+5tU19oV7x5MrHQsx4iriFaCljS9ssu6RMqMuz6hYf9X83t4cAER8DZdFXfdW3kUy5rLtM+rj0v39SlxMvu5UV3/7HvqwFc95m8ikp2KGtGvnskY1xfnH7tZ/il+yx6r+4uKgPP/xwt8NCuqRtqsfHx3V+fr7TPdb3GChhCZLb+aq646NYVjwU0Tdx3NRd6WPSEdWb+sRyHkCnfnt9lHveXyPPXWBBIuZ23O0yyXrcx3m/KYujZHPiX+fTU7+Tz/c/1u8HUvjW86psB1kfV8CfPXu2s9lnZ2f13nvv1fHxcb377rv14sWLevr0ab3zzjs7nXJ7dwgdnDbjqTUU5LRH1MkFIzGV5ICBTsGBsLZtJOF040XgqX4RFCWwz2d1LwnY0vjTOB2I8Wh2joFjoTKojhRUcdk4OT0apDRep1FwtbY8ee+AhPfplDqj4e1r24QCMQVVPGZeYJI/aPfQjknGw8GR5qg7/SfNmScwfJ5F/DHgqtrLNLtTSfNL2VJ/vF2CNn++M/D8nhwax5rAk9+jLnTteJ/SfZbrQHiXAGFQ5ZnmBNiW5LHL2HU2lY6IusWA3Hmo69qO9OGHH9bV1VUdHx/vTtyULlGvHmNQJf3Sd5Hbv05WdU3zx+2CVfuBl4jXKMuHrlY5HylDsg3U85Ffch/nftT9Iq/z06/rO/V1yV5RT9wHrQFzXne3K8U/U1CV9KarK43L6/TttbSNPlY+c3Z2tvdelQChtqlKx/zku4cm4g8nyoUogXiReESdlS/Rs7IztGG+ypDsKvvk/3flRnLMsXUYqPM1o/4k8jbWBlX89EUI6h6DCZ/PtX6p03unLvnH7wyquu26HIvXzXMYnj59ujvp78MPP9wlL05PT+vs7Kyurq7qnXfe2UsKjOZrRPc6/a+q7jiGkVCs7Rid1poBLTlAv9d9T+U7ZfLPVM7vdYo2AozpOVfOJeDXgcVOYNJY1ii70xLPOweta75N5z7tS1a1sso92NwW2BnChyCNt1PsBAzS8+RdB3pES9u/Rnzqrr1JWgJmr1PXIfc7HdVnCticlmxkd39JBxJI9PtL89j1z3cWVL3as66ttsfHxzug052q9BhoCZgf8vwSz3nPAx0C69QnBzBMKLpdlJ1Lx5CPaOSXEuB1n5fqST4tJR1SnSNflfq4dO0QW9bJ6SGYJunYaHW/a09tKqiSXmmnRtoW+NhoFFiI1va746sfsa06O1vmMtXpLAO6lDRMY6TeepklXLaG/Fn68U7PUl892T5qb5Q4cBvg/On40M1NwjVLn6I1cjRN0+6o/6raC9i1cqX/O5t9CN3rd6pOTk52J2eIWYx8ORiROkmg6IrCT77km+ryLUpkujv0bg8ynZaflOYB05qAyoVrJEi6n8Cz88bH7plVOcKUceX3tJzbGTs3OhzLCDAl/rM+Hw95rS1J8zzvnUbGlSoPCEbB5/n5eb3//vtVdbM16fnz53VxcVGf+MQn9k4VehPvObwuUbc8e8Qy+uzAAfno2265AupAreMl63QD5rxLzoK67DI52rriY0/XXe+TA0zb5EbgOumN6193Td9HDjTNnfO9y+Im0Oz6kcbIMl3QnRya5txXjR3YXV1d1bNnz+rdd9+td955ZydrjyWLXvVKv/yHVDvwy+ecR+KBEjS+i0J2RfVyCyx10uVGbbh8i8/MMqtPfoBB8icizYv60gFM+teR/zqURr50zSoy60n9pk50cs2EgCcMkk3l2Gm3ErijrnBbLZ/t7J1jofPz8/rggw927V1fX+9WrE5OTh5VYMU+agVpJC/EAJ0PcLwhvrrNPDp6dWhF5xtUXp/EfaqDiXwdkOArX1zB7TCV+6OEdVjnEo3khT7UMbie4/ZJ2R+OxdtKNjthL2/Tyy/plcu795X6KbliXzqe+Jxzi6x+/+wzn/nMDl++//77u1WsFy9e1DvvvLPj7X13MB2MJimIyfiKMX6kKb97IJKYrbaSYLKeBARSf1PQp3oIKul4Upvpk+2nMRPwJeCXnAr75PWOHJAb784AeB/VT+fnCKw5EF9yTFzKT/1wx7QEeEakLLp+8E1805Ylbt1J/X0IooNMR70vybqXS0vsLocEJ2nOKIvePsG/yzUBDkHhWqdyKKBiv1h21NZozruAKl0fgdnOtt2HOv3k3JDv/lwCkakNT6w4AOWpmvwRTr0HUrUvy4+FZIPS+7+JF97/EVhItpr10MYxgdfpR1eP95d2bGnV2SnJqgMrt+167j40AphL3/m8g9hurK4vnZ9aoxP6dADO+ui/1up6smECkaenpzVN027bUtX+ygxPqXxo4jyRB7qXxtnhrLTdlfiBfodJHv+Np9Q39x8+7y7jHV5kmc5PSZ+87e6ZRCmJJxnhogPL0o447uIqXJLPEaZdut9hwfQ/iT7HVx27+UltOjHQ1nduG626+SHgly9f1jy/Oh1W9d7Xzj2eVOJGG2200UYbbfRZS/cFIhtt9NlGjyERu9Hjo+kQwZim6R9U1bd+dN3ZaKMHoy+a5/n7PVTjm25t9DlMD6pbVZt+bfQ5TZvv2mijj4YO1q2DgqqNNtpoo4022mijjTbaaKON9mnb/rfRRhtttNFGG2200UYbbfQatAVVG2200UYbbbTRRhtttNFGr0FbULXRRhtttNFGG2200UYbbfQatAVVG2200UYbbbTRRhtttNFGr0FbULXRRhtttNFGG2200UYbbfQatAVVG2200UYbbbTRRhtttNFGr0FbULXRRhtttNFGG2200UYbbfQatAVVG2200UYbbbTRRhtttNFGr0FbULXRRhtttNFGG2200UYbbfQatAVVG2200UYbbbTRRhtttNFGr0FbULXRRhtttNFGG2200UYbbfQatAVVG2200UYbbbTRRhtttNFGr0FbULXRRhtttNFGG2200UYbbfQatAVVG2200UYbbbTRRhtttJKmafoV0zT9V2+67Iq65mmaflhz709M0/Rlb6Kdje5HW1C1gqZp+pZpms6nafpCu/6XbwX8i2///8HTNP0P0zR95zRNn5mm6f+epunLb+998W3Z9+3v31zZh2fTNP3eaZq+d5qmvz9N0y8blP0x0zR9/W0/5nD/+0zT9EenafpgmqZvnabp5x3Ajo02eqP0Oahf/940TX9pmqazaZp+33pObLTRm6fPNv26Lf9Lb8t97+1zz3DvJ07T9BenaXpvmqZvnKbpJx3Iko022qNpmr58mqa/Nk3Ty1u5+13TNH1y9Mw8z79pnuevXFP/IWVfh+Z5/mnzPP83H3U7G/W0BVXr6Zur6ufqn2ma/vGqemFlvqaqPl1VX1RV37eq/u2q+nYr88l5nt/F3x9a2f6vq6ovva37n6uqXz5N009tyl5U1R+uqq9o7v/Oqjqvqh9QVT+/qn7XNE0/emU/Ntroo6DPJf36e1X1H1XV713Z9kYbfdT0WaNf0zT9i1X11VX1k2/L/6NV9etv732fqvrjVfVbq+qTVfVbquqPT9P0qZX92GijPZqm6d+vqt9cVf9hVX2iqv6pupG7/2WappPmmadvr4cbfTbRFlStp6+pql+A/7+sqn6/lflxVfX75nn+YJ7ny3me//I8z3/iDbX/ZVX1G+d5/ofzPP/1qvovq+rLU8F5nv/GPM+/p6q+ye9N0/SxqvrXqupXz/P8/jzPf76q/se6caAbbfRQ9DmhX7f3v26e5z9WVd/1hvq20UavS581+nVb9vfM8/xN8zz/w6r6jSj7E6vq78/z/N/P83w1z/PXVtU/qKp/9Q31c6PPI5qm6eN1E7D/knme/+Q8zxfzPH9LVf0bVfXFVfVv3Zb7ddM0/ZFpmr52mqbvraovv732tajrF9zu/PmuaZp+9e0K8b+A57/29rtWfb9smqa/c7sy/CtRz4+fpukbpmn6nmmavm2apt/RBXdhPH9mmqavvP3+5dM0/R/TNP0nt3X97dtV3i+fpunT0zR9x4StgtM0/fTb1evvvb3/66zu0fiOpmn66mma/tbt/T98mwD5vKMtqFpPf6GqPj5N04+cpulJVf2cqvraUOZ3TtP0c6Zp+qGHVD5N08+bpukbm3ufqqofWFV/FZf/alXdZ3Xph1fV5TzPf/MN1LXRRm+KPlf0a6ONHiN9NunXjw5lf8A0Td9XVXoTVfVjDunvRhvd0k+squdV9XW8OM/z+1X1P1fVT8Hln1lVf6RuVkj/W5afpulHVdV/Xjc7f35g3ax4/SMLbf+kqvrH6mZF9tdM0/Qjb69fVdUvraovrKp/+vb+LzpwXKKfUFXfWDcrz3+gqv5g3SRPfljdBIy/Y5qmd2/LflA3iZdPVtVPr6qvmqbpZ60c3y+pqp9VVf9sVf2gqvqHdbMj6vOOtqDqMFK276dU1V+vqv/P7v/rVfW/V9Wvrqpvnqbpr0zT9OOszHfeZg309yOrquZ5/gPzPP8TTbsS+s/g2meq6gvuMYZ3q+p77dp969poozdJnwv6tdFGj5U+W/Tr3VC2bst/Q1X9oGmafu40Tce3mfYvqbtbGTfaaA19YVV95zzPl+Het93eF33DPM9/bJ7n63meP7SyP7uq/vg8z39+nufzqvo1VXXnfVujXz/P84fzPP/Vukkc/Niqqnme/695nv/C7Wrxt1TVf1E3wcp96Jvnef6v53m+qqo/VFU/pKp+wzzPZ/M8/6m6eQ3kh922+2fmef5rt+P7xqr679Du0vj+3ar6lfM8/915ns/qZrvvz/583Ca5BVWH0ddU1c+rm60IvnWibrc2fPU8zz+6bt5X+itV9cemaWJm7Qvnef4k/v76inbfv/38OK59vKreu8cY3rd6XqeujTZ6k/S5oF8bbfRY6bNFv9xH6ft78zx/V92sGPyyunnf66dW1f9aVX93RT822sjpO6vqCxvw/wNv74s+PajnB/H+PM8va3n799/H95d1m3yYpumHT9P0P023B7VU1W+q/eDuEOI7kR/e9s2vqd2fME3Tn56m6R9M0/SZugmU1O7S+L6oqv6oki11k7S5qhs78nlFW1B1AM3z/K1188Lvv1S2XBzKfmdV/ba6EcbX2lt6u6/82+o2k3FLP7aadzoW6G9W1dNpmr70DdS10UZvjD5H9GujjR4lfRbp1zeFst9+G1DVPM9/dp7nHzfP8/epm3eBf0RV/cXX6eNGn7f0DVV1VvZO3u2WuJ9WVf8bLo9Wnr6tqn4wnn+nbrbc3Yd+V1X9P1X1pfM8f7yqfkXd3fL6UdAfqJv363/IPM+fqKrfjXaXxvfpqvpplnB5Ps+zr4Z/ztMWVB1OX1FV//w8zx/4jWmafvN0c9zy02mavqCqvqqq/l85g9ek319Vv2qapk9N0/QjqurfqarflwpON/S8qk5u/38+3R5Je9vvr6uq3zBN08emafpn6ibz9zVvoI8bbfS69FmtX7f/P729/6Sqntze/7zbBrHRo6RHr1+3Zb9imqYfNd0ca/2rWHaapn/yduvfx+sm8Pv0PM9f/wb6uNHnGc3z/Jm6OajiP5um6afeytUX183prn+31uOiP1JVP2O6OQjipG62v903EPqCunlF4/1bXfmqe9Zzn3a/e57n02mafnzdrGqLlsb3u6vqP56m6YuqqqZp+n7TNP3Mt9TvR0VbUHUgzfP8t+Z5/kvN7RdV9Uer6nuq6m/XzZLov2xlvmfa/52PX1ZVNU3Tz5+maZQZ/7VV9beq6lur6s9W1W+d5/lP3j77Q2/r0svFX1Q3y7qq78Oq+huo6xdV1TtV9R11s2/2q+Z53rLyGz04fY7o16+6vfbVdfMy8Ie31zba6EHps0G/bq//lqr601X1d26f+bWo65fXzbasT9fNFq1/Zd3oN9roLs3z/FvqZjXot9VNMPN/1o1s/eTb94PW1PFNdXNYwx+sm1Wd9+sGX6163ug/qJuA5r26OSVz7c8WvC79orowwnQIAAAgAElEQVRJtr9XN+9M/WHdWDG+/7RuVrn+1O3zf6FuDsn4vKNpnpfepdtoo4022mijjTbaaKONluh2++D31M0Wvm9+6P68afpcH9/r0LZStdFGG2200UYbbbTRRvekaZp+xjRNL6ab3wL9bVX116rqWx62V2+OPtfH96ZoC6o22mijjTbaaKONNtro/vQzq+rv3f59aVX9nPlzayvY5/r43ght2/822mijjTbaaKONNtpoo41eg7aVqo022mijjTbaaKONNtpoo9egg475ffHixfzJT36ypmna+zs6Otp96rv+r6rSatg8z3V9fV3X19e77/M87/5ELM9rvL60wnafFbjU7iH1Lo2hK7OG9n9/8f5l7tPem6hXdXR1+X1+pr7wOr87T3Vdssbv/Hvvvffq9PT0bfwWRKQXL17Mn/jEJ/b0ieOibj158mSnW1W1N65Ot1x/+OnXXnf1Oj2/Rh/W1OP33+RK+5JsLpV7nXYO1belMqM+j9oa6ZV47c8kvbq+vt7d/47v+I7vnOf5+y0O6iOk5Ls6/1KV+Zt0Sde9XHquuz+iNbLwJnWgswmdfb0PHaI/o7L3vXdoH0blOz1L/m7k40Z1u1337y9fvqzz8/MH810f+9jH5k996lN7fXYs6DZlnuc6Pz+vi4uLXT2Us5G8ObaUrXG7w/KJOlke+cfRsx+1rnb2d23da2X+Mexec70h3hHJrhIzPX36tJ49e1ZPnjxp9UbPVlVdXV3tZIZypM8PPvigzs7ODtKtg4KqT37yk/WVX/mVdXx8vOv4s2fP6mMf+1g9ffq03nnnnXrx4kU9efKknj9/Xs+fP9/r+OXlZb3//vv14Ycf1uXlpYxBXV1d1eXl5W7QV1dXu09+lwJeXl7W5eVl28/r6+vds2K2kysjr7HuBEj5P5WYE3h1dbXXFy+vSV5LS6Dr6Oionjx5sjeeVIbjUl3JuJNvT548WQ3KyB8Pvp8+fXon4KYRZrBwcnKy68Px8fHOSKuOJ0+e7Mo/ffq0jo+Pa5qm3byr30+ePKl5nuvs7KzOzs7q+vq6Tk9Pd9/Pzs7q6uqqvu7rhr+F+ZHTJz7xifqKr/iKOj4+rufPn9eTJ0/q+Pi4Tk5O6smTJ3u69QVf8AX17rvvVtUrmZM+nZ2d1cXFRX3wwQd1cXFRl5eXu7FKb6hTVbXHsyXd6hyX7qmO5ITYTkqoeN0dONWfbEZXpqNOzjsAJNlKz6uONW3pf+qTgxDvU2e/ElDp+ig9od6wbuqk9Emf0sPz8/M6Pz+veZ7r6dOnuzokV9fX1/Xhhx/u7Pnp6elurn/7b//t39oy6C3RJz/5yfqFv/AX7nzW0dHRnm2+uLjY+RfZk6p9OZU+XV9f18XFxZ0Ehsu0ZFP+TTpAme3sp+6NZMuf6eQ+yUVXVvygH7u+vt71g2NVfUl/Rv3wMbmMJ9/R+Tz2oUvESVadD6OAx0l1ePvqg3Rafkx+igln6aG+Sx9lX4QJqKfzPNfLly/r9PS0Li8v67333quXL1/usNTZ2Vn9uT/352Kf3xZ96lOfql/8i3/xnn9+/vx5fexjH9vzY+TNxcVFffrTn65v//Zv38NhwglHR0d7WLDq1dzIRx4dHdXZ2Vmdnp7udPL8/PxO/2hvE+7jd8o8gbhsmeu5iPZzpItdcpH3aR9kp/jMmwiqkl1JeJi6qDLUfX12vsdtXbJ34i31iX5S8uM2Qljm+Pi43nnnnXry5El9/+///etLvuRL6t13362zs7P64IMP6urqqj744IN6//33d3Orz/fff79evny5w4Ky0/r8+q8//OfvDv5BSjFGSi8HpO9yuA7EXQDdmTODkSZLbc7zvFPOBKiqaifYI0PJ/riQEDDoehdUeUaFyukCqb7JcPpKXte/kWPRfQpkAqMjwR/V7cq35jnyvhuTyqSyVCyNi38dCJaMsB468uPj47q6utpzWDRaD0kcq3RJDkl913c5HdHV1VUE2A6aySMlHMSDqtrTrap9g5iCcF7Xvara1d89S3lMQbgcy5IMdXxM1IG8JMPJ+XQyOmoz3R/pWqLr6+thssTtD/8ftTOywam/kgvVS5l5+vRpXV1d7T6rbgKTpbl6m7R2nP5dlOR8zbwn2U+UANNI5t6EzUr29+joaGdPyDPeZ5B1SFt6nv+7HHb+n8+merv/O//l872m76P5G42pkzkvmxIr0zTtcJVstwJ+37Hw0ORjlA13nnUJZedVsmFrbJq3R/+4VBfnoptrl4cu2HHbzGeWZKmq9mTBMeqoX2vqHNGI587jpflxf5/sqvMi2cDRfQbfSnx5wpVzqn5cXl7uxSqOFzsMskQHB1VyrsfHx7tM5snJyW7ZTRkEB34ufHTUvEbG03EnhZGTT204JQVV4OPAWu1QsVKGg+2lIEERL7MX7Dfr836zji5bnYKPpKxLGY7OuKQMn/7vnJccLgUyKY4Hll6/B1Ke5Ut9HRliBSRa8ZShVcbiPsrzpslXB7Taq+/KxkjfqmpnPBLPKBO8n2RNvPMsmcu/64XXw+8ydnRClH2XC7cRTFCwn95vPZvGs2T8kyx7PbRHa4FYqsfb7Sj12XlBB5H0fsRXyoPq4moW+8jVqwSE6JDk2Lga+pjI9cJtdprnqj6gWgLYrDcF4ZzTBCRG9XbJoLVy2cmhdFR9SiBoTSJqBLx0zVeauv5UHbZi2/UnYQzvSxrXKJGXrtHu+veubfeD0sd5nndZ+svLy91q6tHRUZ2entbFxcWj8F1pDlJQxZUfbsdawn/89PYcB6WVJA/WOS9cse7kSeW6MSZeOHX64H1Nq6J8bk1yJclzp0Nq2+tN8t0FSCP7NfJ5nU4mG+x4gWW0QqkdSScnJ3vJIV/oUTJQq5tKXqaV0UPp4KBKjVEwZQD80xUqAZ/0PZWp2gcSS6DEHZfXz3qSU6MSiRIgdL7wuyuyU4qGu+BqlKVzoRw5lhE5r9P9jvfsZ5cZcqIxkVA7+PC/zjB4H0jcUiFQwPofC3GMSkpIn/jnPFiSw07PKH/kXReU02FRvj3p4XLkesQ+JD1y/aWDcJvS8TH1P7W/htY4iaUgU/+/jrz5VmMHHR0/U78432ucJPXH62JwRoD4mHSL1I310P4uySLvJx2sWh+kOLAfJQN9XIcEt12fXca6Pqc+jdpKZfz/+wRUXcDW+WHqw5pVwJEPTH1cwwfvM69L99wPjPzh26bOLiZ/0iX2Ot183QRNAsqOK9asBvHZlEg/pN+pTNf2kq572fSdvF3iZ7dS09mYJboPJiWmEDmvdV+rn9ze58EXfRXHyR0Y7NN99eqgoEqOlX/a/setf0nRU0ClQTLjKrDgq0BVtfeujBiZhCs5Ll3XJ/fL+jsOKcvBvrBND7C6wNEzzarTx9o5qsRTdyLJqXR1ev2pzrXgNI1vJJBJScQDn/+luR312R3l9fX1TkafPn1al5eXUdEegjrd0jtV0jHKguuHzz8zpC5vbJdb9Q4xvHQoySB5hpL94JY21yEaSsqGB+ycO5eVzjE7wBLfu/F1Trgbpz+fvqsvpLT3XH/cz88yvg0rBbkqRx50trgDNPruvNY1leNqVrey9VCkeXYHSxvhvEx1yJY8efJkZz864KHrzIB2dvlQG+S2bQnsLCUB0zi1RVjPO088yPbxJv+0RtY8KB+BnGQL/Hr6PqrX5yLNcdXhwWrqC2XR+yD+sh1tA6+qnY94TEQ9SsBWfpg7LLp60n3iDOpAR6M5cjtJnU6UfK6ud+0lvRnRSIdHyapO9hOWfV0aYbvOFi7VlzAh/5bsF33dhx9+uNMfrvKenJzszZ/er9U7r3znvOr+W6wPDqoUPOnv5OSknj9/vlN27f0lcZm3al9hOqNCx81Pf8dAUSn7yO9dIODLxLxOgM/v3jfW12UINTb21RXTwaf3WQKSxuh9Tw5L7zekfifeOCVD4c+syU4m40Pw7ODPQTV5MQq63MAQzOjFRr4cOTISb4umadrTIeqWttZqC6O/r0jiaoF4QSCYjGwKVNLqkvjIxMAS0OE1tcO5HyUpOPcpWPdttKP2XYZ0X7xcklmuxui6O68RLcmY5oFjS8+6rqcVK5WjDAggu32h/R2BE+oJea5Ahe9JHB3dvED+GJIVJF/ppW2mjUiJB32O5t9XcnWPusfvVctJqBHI8n4kXUjUBTOqI423s++HBHK+ipn8vgcda2XI9TgBM7aRVvtHOtyNuXvG2/d2Hfu4zB0dHe38Nss9e/asqqpOT0/r2bNnwyDgbZPzVbogGyUfoENhRrJKP+QYiz5L/JFu+2mCidJ1yRl9pOym92uUwOva6Hwl6xQxAen6MBpXF3StlW3vwxIlPzsqxz52PtvxLMnjAT6r/6+urur999+vy8vLevfdd+vFixc7PfJtoYwdLi4u6vT0dOd30yFYa+neB1W4QSCQGxmgpWX5JYDj2Wq/nxi+NA720QGmqFsedmGgEeRzS46XdaS+cvwddfxcokOM8sg43Ie6+g7N8CRKPEiyO5K5t0lJp3ybxyF99bG6gxC5bNOhLdXfJRO8HNtJesrn/L4HXuyn6l3acpocjc/9SPf4/T7yMpLz1ObSKgZ5xcCKwU5K2qS+HCJP/r3zA48toKrKfuYQ2+oySJ0aAZfkR5IMJf3pdKWrf4mW5p3jWDO+kS/s2ulAk99fMy5PoC1hgI/CznvgcAh531IwSJBNv0Ab9hip8w3+LhU/SaNxuR4esnWvaj9wSfSmeEoccwh28l0Ia/mT+Jn8Z6I1ZT4qcn8/oiXd5mni7pvcJyoY5+snr8uD1z6oQscdKrvu2wW6jB+vjYBcAmgOqFKE7QabQpqEJ50G50eh+9iYdaRB58lqqf9pxUDkL3irT37wB/vh9bMcv9OYLRmTkWCtzQA5cEigPhkOjXmapt1BHxwD51zP+MrnyFgfHx/v6tER0A9NVH4pOleuuvepqvLKaQIyPh+eLU99GgVWh4JIEp0gbQVlQIESdTI5UMoAyXmVgrI1oCQdptPRmix2V977JkqrBL5l2bcIUh/8tEs973z1PnR9ckCjurlL4eTk5NFt//MgOq0G0Lbz2SQ77rdcHpNOag6YDWe9pA6MdTLYrVr6KmIiT6qkPnWZ8K4+DxBYpwfeqaxk1ynxlX6v41UKwFQf51198f6xbOKPnueq8BLfUxJW49YzOl2ZR/NfX1/X8+fPh1vo3hb5KoDPC30XV9aSbND/kef+2gS3pnZ9SskJfl9jnzkv3Ym2qZ5RQov1ejsjbJXsy5rk5yHkO7JSP7r73Rhc/4hDHZO6HR75Ia+/6gZjnp2d1TzP9eLFi73EtOqVTF5fX+9hQa38Lv2szBIdvP1PndSWpJOTk73jnpOh6uryekVUIH66YtBJqVw6sU7XXShpsDnJ3BrGAIv9o0C7YVMwoHo5md3yp4gBFw2y/1aO6k5KkDLwIwDdKXoqM7qf6kl1JodPo8WsnwyoKP32WJIT/2N/tCf96Ohot8/2MWTWqVvSKyYs+DtfI/K5Xzuvo6DBgbTLXQJfVbWXYOCcMjDmKpPqVcIigT3KM8FpCgxoJ3wsDl78vgO9Nbxnn5ac0sgZJnl0IDiaJwf8CbSSv3RiqV7VNU3723SZ6RP4Ozo6Wtze8xDEFd+qfdBOvtLe+MqSy68fM0+b3NlB8bp7l8+fSUFVSgakExdd1xK5TGqcvgKaghPvq/hDUJLe++mCFl7rjgzvgqqk691W6RRUOcCj/00gkPzwsr6lfckW6FkGVfJTKsv3Pabp5regHovvkh93f0Kf5jaaOuM6KZuS7LZjmVHA2tE0TXuBmfsfXtP3bh5Tm539Xwo8kh6wbrfPbu+936mN1L5fS/1fE4RW7SdUumQdg/A0j2nu1/jg6+tXvz2qhDztCGVS+E9tP3/+fPeMkhdr/L3TvU//4x5mgvjRxHVCtnay3tRzS9fdWDMwSmXpeD0A7ITADbHKJ6Umn5MhHjnxVEcHdtYqkQubK2BShnSdffUVKAJT3UsB5MhhOu/JA89gPBaiPtHJJjlyo3QojQznSLe6bJbmOs1XIgJUX7k6hFy+XOa9TAJjPoa1RlzPJGc6SmTw2TSe7hkGNskeuFNm1txBvxzXKED2funT+ZPA0lLQ8LaJc+nOPq00jerRp9v7Q/qydH/Jxvk9BtBr2vFyI/1xfV4KVDr/l9pIMufBb0fJJpKUnOmoA5fs18jeqY0kT3z3ey25zfFgQz5Bq6yPzX+J3AeP/pbqGM1Rus7PVN6TCB7Y3Zc6fHMoPXSQfOg4RhiP+p70hM8s6eravmthJAV2CT8rgCcuZIxzKN3rnSpFePrjGfDqBDOgGiSzGR5hJ4Ps1IEX/Z8+RVz9UBvd+NL/3QpTckYMkpKQOfCh8WBwxWxommDV5VmdNM5RZmIEpEfA0FcmRV0GqQOQ+uNzFxcXO8BNHjMDKp4wuKCj0XUHf1xVff78+R3FeyhSX9OJmvpUOQLieX7143cuY107Xs6DWOpqlzHk9hbX37RVYing8D51WWHOKzO3iZxnDkLdsbsedkGZy7vzb3Q9rco7eZDiAFgOwOc5jU/Oyg+/IE99xSYFmtI3X8nxLGBV7TKAj4XIM676p/nlnKxZTXTZpA3vyAOONL+ud2y3C6q6IMN3f/A7dcP709nRJbCh1alkWx3skE/UyaSfS3YtUbeVh2OljqZgMLVDXnBnisYl38w2CCzFW/89HX0mnXz27NmON2dnZzuePQZyWdTvLXKHhcZGXDOybV3yiG3RB6YVED7jWEv2McnCSNYOwVV8hj7Fy3dtjMpRj2h7kp53dY18cHqu6wfLpATM0dHRHZySXndhvyQviboATT8ALDyoGCVhACYqhLmqancgzFsLqrQ0rRPJCPgIABJQ46SIyV4/PzshHwG1quwMu0h5FGBR+WQI/MQdOkGWVZt0sqmvSXkIHpeOqVcfE8BlGy74iYfpJMVOwZyf7rhl5JjldIfqRkYAcJqmuri42P1Am4hb91hHN1aNjQ6aR/8vgfK3TeofAysPqrivPv2NjF4KakZ66jLrQcNSsC1ycJfIdX00Dg9+Rg7HHaf31eWI+ut6zn7qu5JG/L/q1TxV1V7mbA1IHLWbgKqPWd9pr7m9lkkayRf55eMk4HNeeXAxTdOjC6qq9t/XIHAXOegdySrBoN53qboLLJ28zrSiy5XqJf/octLJk2wH7XCy9ak+6rvzLfHI9Swl3xzYEBOkuenqFrkNSDLs9dB2EOStJY5LP9HheuWYQLZoFGgysGBfhQl077Fs/0u2gX4s2Rrimi6oInZwXCVyn+S+zCmtHibZd+pka+leShikOhOG0WeHcVl21Kc0lsQb52P3bNKRblx8lcb9Ne1K1d3XcTTvkgHeTwkXPacfxL6+vt69muS2zGV0mqbd9vWqVzjzrQVVFHz978Z4bWcSU7q/pDidAnUC6yBeRCfhgJN1pK0AIwGmMR2N268n57NmtUz/JyVzB+PAkP+zzs5hVeXDJ5wfXoaKqbqoONqypO/6zRuVkbIxi0Ued/Lnc+Fy/NBEp0rg5zrmQehI/kTdvOvTgyiXl3Tdgyr1qzuJb+REdM0N7ei5Jb1bmtND59zbTnwkT8hPX3VbCgbT987xuq0g/yVP0qmrq6udXtFx8f0CBpns45Isuv49NNgjUbf8u+6n/idbWzWWheQrWJa65Cu6mgsH/MnmpjlIfiP1obs+CtI6v7m0mjeyx6P+jKizYV5/Zy+SDjKhNOKf7nHlmXrEYCrZ2BTw8Z7jKp8TJgceyxbb1E+uAni5pGNLfE/kNtaveR/TARddux2m7Oy23+v0Yq2/PoQXLleHtjX6rLqbYOnq6K4zkeA2UNd0Pa1SylYySdr5l4RrpTM+NuoTyxwSwzjd66AKnvqnrVQ0BGQAGcbOeoeV7eUJNzwa0ZVHk6tn9N0nN7XNexpXuj5SHt3n1oLOAUkwuATplDIzvMeTtag8FJrUNsfATDX5RiCbxuwGKgV63iYdDuvRNTeukhdmGTRuKhb5pCVaBlqsn3NC3skRHR0d7ebvoYOqadpfofJT/5jhSye9dcGV5sJ1SyfcUM+qXq0u+nZdtun61rXb6ZV/X9K9pbKJ1gLH7jkPKPy+r0yl1UK3XSnYHI2BAItlu8CGgQy/P3ly8/stkneeqCkQyO+yVZJJ8o332Rf/Lll9TCR90g9CVt21T9P06sRRX2WnHVJ5fWqOpVMd6Jde8bnkE9Rux0P1vzvMwQFuCpY8UZKeZV/IMyb4RqttHlyPfKrLOdtMQMmTrIlffs39AcnrGCUMeJ2gTLzgbgjqkyfNZHt9tU5+IPFIPH/+/Pmu/EOS7AR31EjPfKVKvKqquGNE5HLJ/zn3+t2reZ7r/Px8Z3N5qAf7Kf6xbrezLq9VeYsc7/N7h/ESJjqEHD+l+6MER+d/03iW8MSo/k5vWB93cfhziY981cPfeUoYs+rVb0/p/sc+9rG6urqq09PT3e8oHh292g7+zjvv1NXV1U6O7vsbcK91pDp/7NczUv6/C7FTAicCggQlVftgzsuk7EIylOyPZ1NGgsmxdQCwC3BEvgRNnqVMCvnLdihQDti6/hEkk4fuZJ1/aXuLAyu1yee7Y0jdiMnBODBjO6pvnufdUZi6Twc8UjS1pzZ5pPJDE3WLWydSxk9Eue/kkY5oCfx335PcpGAuZR27FVYfO6krO9pC0WWvRsCxs0VdQMXxiRdM5siZy8mnlapUn/OhA3G85tcJanhfuiFAIT75ffWHfWDwqGe4ZceDONXTyetDEYFfesH/+vr6DvB1sOc2zn2J65XqpS4ygeFA0U8j6+SG5O/bpoCI8pCyxfo/jZM6LFn1+5pvgr4UVI3GIWKdfMZ5KN76ezQJHHY+KFHCCv7JpJx0RL6Lvl27LBgIuR4Rswgwin/+HrXz+dmzZ4/Gd/nJtNIzbqmqenWSscrwfXDaoZGs8L70qUvIVy2v/DtA53x0n13/ukCEbdPmj/we+ZZwLeVyFFAlW5Rslz+TxpkCUpUfBX20q0w+dQGY2mJdbns1bx1vdLy65vb58+c7uTg9Pd3Jm4KqZ8+e7ZL6L1++vPcq8L1WqvSdRjMpdjL0DvIcnNFQSlHcWXFiaVSZBVRbVTnqphB4ts+DCh8LDX8y5MnQ+f+pjftSUiY3AIl/DF59TlivK51nFHQtZVdHRqxqf6+tiOBMYEekQEt9pwN2cidIZXSlfGjHJP6lVTwPWkdgNcl5OsQiBWKdfPh3D8j1vNdFIO/9cxkhYEvjcEPrpOd5f8kxJ50cOU7Xh6Q3brNS4JXaE6WtXQkop8wq32GkHKWjg7UFUEef650QjZOrVinZ0ukLfcJjCqqq9nXMHaYHAV2QUnU3AUh54A4A14equpOooEwlcOMAz4l1eDKPc69xCPyyrQTyaKulKyngv4/vSkFTIveRLotuf9wWkc9eppNND6JTslg8db/h9pnvX3fv3aUxk/dev/Sb25Ueg+8Sr9ivlODx8fDeIbJE2fWkutvbJMspMGDQq+f8j9f5bLLlHOdIf9X2KOnozxA3pbo7fNnh4K6ODk8s9c+JtsN1WDzktmc9475raQ59XEwi+ooy+6t5V7LeV1gPoYNXqjyTziVcDsiNMA0dswsXFxe7JTePInUvARgqlZjSbb1IRlX/i5l0NskZcizdlgvySGXdoKSomn1M1LWVxuP36cS1TC7e8rv6QIFPmcxOQb2P4msXVDFb53yq2j+V5eLiYred4Pr6evdJJXHjJ8Xg9iUqq9rSyTCPwTFxuwQPp6C8dWCbeuHZO17j9gjJgG8LZCDgCQvPDKfAwoEeVzbYV9GS8+XYfWVxFGD582zfdV3PObh1R8pxJ55xW6VOIUrOubNRTsnWKLuWViFSUENbrEyc9Eq2XHqlrJ1O7kq2UP1i/9UOwdVakPA2SHZbv6/op47JXiiYFE9oYynf9F9nZ2c7O6og1W2pB6wuYw5s9D3Zd7e7frqaxivybVn0T6R5frW1JgHeDsAkH+n9SMA5+RYPuJIe0gbJnqUEBm0Rn/N+JD5If/incS3xW9tHXcfmed47jUzPUVeXgjrOgba/P4bkhXDh8+fP68mTJ/XOO+/sVqlowxSIaszSOc2jZJT22W2P5rqq9k56Oz8/r/Pz8zuA2slXo9im81vkskOZX4MdkvzzuSSXTGw5jWxr8jHJxrC8t93xr8OcSYdILsPSUfomT7BTZjwxxHKcN45bMqFddVoh5fd5nncyI596fHxc5+fne307hO61UkVAzD8HKQ6mNFD/Y4ZPSiJFI/jntokE/mlsKQxkMo2syF8sTcrMiZfT5XXxR+34kreuJ+UaKchasD9SFge7BMfdFksPXJMD1F8KikR8F4wKSMWhUui++iGjIkXSqWLTNO0BFe8XwZ2XcQVdaxg/auL7UwwqaehpTEQd8E/zrj8Pnrrvet710GWrk6MUsLpRdYDhRpU8oJ6uNXiuw4l35J9n0lgPtyCn99O05Y9JIY6XPGP7yamKf95v2VXfW0774tvC3MaIh5eXl7v3YyVjHjyrXwREXbLEA6vHQuwT30+surv1LwWkrksEgkxi6NNlhc93K70jn8VrlEuBWfU1BTO0JQKxHKsnOBywHMrn0TUGTaQO2OkafVHSQembrntCiAGY8zD5LoEryo14qdWnzm9IT3Tv6uqqTk5Odu81cv69fdq3lCAhnzSXj4WePHlSz54929v2R9tddddGpADHyeXFg2rNO5NYXcJC9s35L/JEFecl8VqA/z6UAiy/lnQl1ZF8rGPYpGMsy3uOk3k97broxrDUb/exrMuTeJIV8Vy6TfnyoEp4RThPdcn+S4ak28KrJycnb++dKgfD+s4BdxPjjoWf/E4HRfDnRpJOjsBOzE3ClAATs3cknyCCfZHvZ5ciMOtCvnn9CUzRuCQF0DPd1itec96Ix2nVSuVS4Mn2WcxymTwAACAASURBVL87Bj5zdHS0E0yvy8EfASHv05lWVZ2fn+/qPj4+rnmedytXbiz1PdUro94ZpbdNdDTJ8VT12xFErg+cf9cxJilcB3k96WsChQ7EOW8jR+l6SDCRZMaN6Khen3cGV7rveuf2YQTyXLfEA+mT7Bfr13WXu865dKDJgVvKsCaQrbakm2rz/Px8J2d85yHxw3nrQMDl9jERAyvRPL86hpfXEihZo4NVd1f9KTPdPT3rdj3pigfJVa98kQNCtqFDERykixjE0Xd1viCNn+W9/12CLo3J+eMrhm7LZJeYzOB4lnybxlpVe0nThA00RpXj2Jl84JY//jQIV65cn5wHqX/uLx6Spmm6E0SlnSjsN4NV51unY/xOPOP+y59lEOBJEl2jPU2JPcqRjz1dJ3Wy1pVJPmdtPSqX7ArvdeVH9qvq7uFU5CHnbu143WfRRzMpoWc6/+8y4nKke5zjTu8kzzzQ6BC694//csVGUaBns2XcUmZB4EPZBX2en5/X2dnZ7trZ2dkdASGw8+1tBHPu7PUsmV91dwtFImbNmeljNssFgYbF+8G+qD/+qf6oPSlw53R1jQohnlxd3by0p/+5TJ5WIQi6GeWTRgoqXgnA+HhF/htnThLwo6OjnZBrm8E0vXr5XFsheOoPHZ4HU8xEj5za2yKOhXqlsXNOHZxV7c87ExJMTEinNP+np6dVdXcLrSc8uG2Q2Xg3tt5HjUufrgsce/3/1J1ZbyNJkq2NlFKilsysrp4FGGBeBpj//4/mZdDALN3orspUaklJvA+JL/jFoXlQUvWUeB0QSAUjfDG35Zi5uUft0maq9lP+zCPcZ8e7M8SdMed+2rACHgVl+N8G2umV6CzTmPE42oX80t+RvukMvmmVRqFLtetSJBgHBTqi+75//z69XBTZv7i4mHTQkvH1vDqyf0zFp//lCroBb+pQX/PqZAIt2ynuTYcsgxYG/NwzShmlHn9aFrzqbnC43c73+WCbMo3NQYuq3SoX3ztwaxolDzp7BT4evYg3aZi6xXrJtLQj5S0EBN9cOqeWsWXJrQ1eDeZad8JlAnhkg3F/+PBhemmv9Rr3J8D0XPhvuz2elar1el0XFxd7hyt1+1fspAJc1+v1ZIusY435/IfO5ZPtIta9nSNQtR9cyvnLzBAX2zR+GwUyMqBhfZ9Bik6uaM9O3RK/upiG2Ydsj8/UU5ZV/574c9T+oetJp86ZZewEgjKV1PKT8w1mSv+Dtiy7OTcnJyd1eXm5d8jKS8ub31OVEQcrcE9Mgq3O6HjwCQgzlaIDfrn6YmEcCYZBvgk8AjpWqjC7UwIoNiQo4K4sMXhH886IpbB4jDkPBscG2dDBtDRooJ3uZWhpUHMMqUBScBPojZwq34uxXK12OeW5kjmiS6f8vFr13qWL8jmaNwJaVfsrkwkefC2je34uQV/KFsGQpHUawjT2KDIiglnsKDAeBxL8u5V8jt9yXVWt/KVzRUlaZOmMu2kGnToZyr6nIs82GEOupkIHR8CzDpyiUf2WQ+SL1KTVajXlk3c8kvIy0lvHtkpl2jl9m4KsjZzH1Kd5jZJAJZ2ukZx0spt1dO08Pz/P9gMT5LPeNFhxqot/y9Xwke0ajTX1NH3rZDb1vevsHFnLWNLHMuiAEUFDj63LcHE/uu/b7fzF8c68SPp0DrYDRaQGE4CmTy7dXOTvVf3+tfcq6HY7oSPHhPvhwcxSSZ5J+Usb5kBhBiPsCKSud3E72XfzqO2QgxDYZwe6Op2+NFedXOXzvp71JJ7q9FZegzauI+VqhCWW+mza2ynMflr35Pgya4WSuGOp/W7stNu1adr6VPPXlt+0UmXl3TF95pmj+AxCDPSJNnn1yscSZ/0orXTOqnpBSiKbOfBu7SE7quLrjk50kcEOxIwUZWeM/FsnnDmWLN3ydtabbfMMzGZm8nizHfqQbXb974rp0z3bKQ87Ueax1Wo12yz+knflJOh479I5fvmbSwfGHK1ltZeoHhE+ZIs6LF98N6j2faOSddBn/27eM907Z5frnaFOZdfJ9UimXH/nWI2K5aRzZNIB7vrp5zr59mf3XGeQcmx5f7Yzio57zp09sF6vJ955fn6eVk9NM+jilY1j2lNVtf86EPPLEthZ0mmHdFzqWk5Z7ICNN6Wj4xJkd/V3K1V8Z0Ul5yLteLciMhpL0stjfEuBNl0dOLrJy/AbBX3/9PQ0m9uUt0N0Tdu/RFcfAGKb6cBrJ4foSGMjxmnHcUmGq8bbFt6jdH01eE1QnYFDY6208b6WuslZE+5L1eGAaYcHjdVG+hndZocA3rLeOyQXI9DfOZappzpbkb8ttZl2zvgt7XiHqzs7+RJni0/oNeLflCH6wqFlri95y/qh4xGPxzLke35LsP3VB1X4XR9OCzPxMyXm/v5+dqIfYA9D/fj448Q/XtZ1e3s7S/9LD7lzqiyICewSKObk55I030kxwzCl09StLIwcIXv69Ml07b67dFGwQwCycxasyFww+OwtyPdAdQLrsWbknOe4tqQQmBePI9OHzF+ksLB6giPlMSMspBd082LleAwljVECAhfzVa7wkhJxf39fDw8PdXNzsyeTdqpcp5WmVy69mjlyrjoDAZ1ToXZL6yO5Mnjx9ar9ledOiXfAxG0tReCqdnycK2bwIUAZ3qQ+ZCidUhviJdplMT3SsHdy7u9L4MtgcLvdHbZxenpat7e3s4g+qRFeuU+De2xyVbVLIz4/P6/NZjPtwyQgl0HCUXSWuvxZtb+nzYa9Cx5AJ0e6q2riD7/ou1vNcGH+vFLgPQGjNJnsk2Ut2/G9BngJ0NLZHs1Fdy2dIPOdaWh5dOAMWnYOU9bt3+xo+XlsPnaE34lidycfj1b48l6yAZyyRv9Y0e+cXvebAMF7y5n5xg6St4UwRoNY7mEe0ZfoHxdvG7m/v58woh1RaFe1vyqZxRgx08M8ro62zrjw714xNjbunOJ0WrJtHDTzr1d/LHu2JTjnPOOxWFeng2j+skNr+z/SRx5TFvc1AxojrFc1XxRJXFT149Tm8/PzvQUX811VTU4Y9zp4sVr9eHfVxcVFPT8/193d3ezUv9H8Hyqvdqr8Z8Ik0Dco61L1bLgcselAYjoUCfy63E8znB0Zb872BDI+n+xjRZzjHdFiNAkj7/2l5dDzFiwb9EMlI+82bhY0xmfg6rnP+/27BZ3raey6cYzo0PFaOgJV/ZJzlhEofe8yAh6dvPn/pEOmSTiNLxVyBiSWjNJvLaZ5rrZ4Tl4TSXfp5rxrr3uuq4M+ZNS861fKlP/v+kNbXcAlS+qdER2WnPJD/bA8pT5eAilJsyU6v0dJwDdKkaSkbLmkHejKyC4kL7m+DDQtOTij9rw6xfes3987+zaqv6MPxQCK/19TDslrJ2teHWBOc7+E+5t9tmOVwSzzSzoO9CdpllF008ZjSlCdfzk3I3odk91KmTCtHFDq6Jt0NCbj+Q4vGh+aHilfXemwBNezPo/RDo/HDGhPJ+kQJuwCHd19ec3t0BdjHre/hBk7HdjRJa/n/V3dHT55ySpQ3j/CKJTE+twPv3SLElW78xTSPv8WuXp1+l9uRuyUftXOgbFzlECPFSsfXNExpB2EBFodYZkIihkhU8KsLPnO/2dnZ7NoX7ex1++LcT67V2mSIarmm5TtlVNMV/c3IxgeR6ewE0B30VfTire0Z92OZmfpGLeLqjsy5LZHqSlJ61TQ/rNx9WZyb+ROnoCOb3Fy/97FRiZXZeDzVG6jIIQdKFaCR6u41OXCCkw6Belk8yzGkjnO/SoU5IfIb8571s/vXiU2oKF/VvIe41LU3fzcGULX6Xby984BdTRwxFudsWaevXKR/A8tvPqQoLNqFwU2H43aTBBuMIQed0aA5cpAyQ7XMQI+O1TwqsdgOnk+u9WqrNdGnI3VI9DhFb1coUeu+U7b7ltGlil2oHjnWNVOfpI/E7x7PL6W122DEvh0AK3DBtnv7nfb985GQBc/x2oHr94wDeHbrIP7sPOWtcQEnge/B5FimiRNTdeUMetwaMw8OfKeAdBjCQj6IAF4HznwWEZAOPVoOjgODEKj9Xo9y3jwKhi8gD7tVleMiUarL+nIc83y1DlHFD/jz1wIOIRBEr+MirFyPt/xpsdTtVtRtS43dmAOcp6yTehOfcYniQu7LDDwS/I2fgMOZPLPCPfTvnGo60j6vWRORuXVK1V+kZYVUDIWTpKdJ64Z+HEiHeCPyINBd05cOi2j5dsUTF9PJrVgwmQ4TEw8RsoMkE6X6zA9MoKQS6tdikeCRtOgm5tkQCsMA3DqwRjSbxsB08R1pwKysKUBTPDqE7Y8l35BL8XKLN8lhXCYJigp8yOCyXwgyAbgXWTnvUpG7jIyWjVPHzLos9Ehze/u7m4voOFoqEuCK4NOO1R2dtL4jXgTeluR2hh2oMCBDstWd68Nb9U8BaeTizSQXRQrnarUcb7XspyKvjPWnXHMCF5nnLvTITsQW1UzfWWAbzlaAooGRhgrInroaj+X8vSSaOTvWbBdjM/pdQ4KmIeQF+ut5BfrL+jAc9DLfGDnieup6zv5PD398dJi26Ocd+tMn3DouvOF3+ZbeDADF9ZLpgG8nrK2BAB9f8oXdE8adYE3079q/90z1uv0MwF5ypttvp2qbjzwk+28nbeUS6cSjgIXHnc6gB4/NBoB6N+7WDdZd5j37SAmnhn9Ve304OPj4+z0YsaeqeGdfaD9lBcHtWkr5anTm5TtdjvpQi8ipKOeARFohl7p5J3nbZfNH519MIZN55UxwOfOyILX0ykxj1Gf92Vbf7rPFNLu0KtsPaBu7sngH3RLrIouNk+Yx7L/ftY6wUEt99u2jPl8iw37TS//9URnB9OD7CK6OeCcoJFiNoPTh/Su3aeOMMngrsfjc7TK1zsA7HzsVLZpKFIpdn31GEeTa7rnfQkQu+dG88hY0ii7WFFB/3zGc2aB8fWlcVpZds+bh9jfknxnR+qYS4J9X+uUquVo9N1RphEfjK51sm1e8LOdQqPYuMAruR/IdSWQSbmjWNYtb/7MOTdds29LJfl5VKznXlM6mrqfqXdGcpbPWY5zHA405FykvrIhslzhPIx485hKp/O6P5eleUw58dwQzOE3R0c7Z4LnRnybGSIZaHId2C07e+v1egZSUnZNl679Tgfk7/lsYoJOzpfa9X12QFyw4eyn4jmDdutA9zcdFTsFduTcftLb7R+ybx0taZtnU493/V6q671K6o+quS5+SfAyx5uOl3+3LvQ8pfPtgCD3dHI7kgHPe9oI8AbfE6ynTul0OfeP7LA/D9Gve5bx8azH3PXVzzsInU4aqbYuiT2ZG4IPDhy4b+l00s/MmHFfRngmr3W+x/91efNBFR5oB362211kiI3z2+12ijawSoX3moqvUzz+fZReRz9GKRLU0fWZetNhwpPvHCminx1zVNVsbPb4O/BiJWJhTobolDd1dMaPOSJy4L50ioi63F9KRmL8echIIiRdNDSVVio2O7XQwi9ZfXh4qA8fPkxLwycnJ3V/fz97w3vSB0csnfn3KPTZqSb+nkrNq1KsSCFPyJgjLdTviJSjTV7po6Tygi9HPOOxdIDKugNj1Bm8NNB5elkCLFZPbCwtS/lcygifdt4pOQedweQegF0a/6peblwSOLkP8IJXn3IjdNbDJ32ynunktAsIcT9/gPKMPHfz3RnQ9y45Npc03I6CJoirqhmosIyNoqEGliMgk7ahA9S2O6lj7SSzEgx/wr8Gf36Hod9b6DaXAiWj/71S7DosHzl220vqs6OR0fesuwPeHu92O3+vk+Un5b2zXaNx+no6BPCDU9JyPpkrryZU7Q5m8BiSPl4Bfc9i3Um/rMNGtpX+O3PJc5jzCU/znO0R/Ug85cA1pesPfTeGs90Z8YUzgHi/akcb9898lw6A57Nz5HKMiSlzjNSRY+baer2uzWYzS2NF3tAxXk3yu2PNo7l1x04VuhIbmTbZNoT5PTn5sUWAunOFiyyAzFJbwtS2wbnK3D3/1vKm9L9kZKcF0KlMR2LJkBPJOPGP0/2s7KmXzw4guXSAx+mJFDNhOlOeLMaTezn4328PPz8/n+2lMnNUzXNnbVhp0xNr5jATdc/ZWHJ/5xzY4Fp5Zx8wAPzug0NGBtSAI/vmdulfCk3V/vKvBcJz2UWgEGScRdOZa5wkxO85Dhvj9y4G8OlUGaCRRouCw6ni9Brvo0pjDSjHuR2lHrhAJzaBWweMHGHPF/JmmewUV6Zy5CoKdVsnYIhQvu5HOlgunWPVRaatJzDiCRrcVwOd1D3dvoKuWIasgzKAk+mTqUPoZ6a+GNRa7jIoZGBvo2QnvNtn6jk5lpL6Mh0W7mF+0aceR+rKdHweHx9nJz4aYDEvo+ivi/vo9h1syZN3DQIzQuxgyii98e7urh3zoQBAOhLuv79b90OHlGnutw1M2tN37rVspsNRVXtAHb3nAE8GHczzXmHJcSZe6cAc16xj3VfAo3VKAtbcHuC+HYvtShxjTNbxi3UK9gzH3sVjB2R7fjs5giaJJbl/VKA/Jwb7BE3bto6/n56ephOsn5+fJ1zrNs1rdkLdt9Vq/I5B2rMOSllNvkusnk7ayclJbTabCRfk9dXqx/tALy8va71ez05h9OtrwB4ZXHBffdAR3y2H7uvT09N0Kp/b4Y8gSxcMzmKbZYcZ3ThyTN9aXn1QhQHEqBOpFHMvRyqEVEwYtlzFSA+3i0T4mWQmwAUlf09gYSbs0uEy8tm1y6eZzOO1U0IfXzIHbi/nIa9lmy6dQ7SkeDrD0gHrriS9Gbvp7RQOhGcULc3+dAbRiqcTnCVw83uWpEP3R8kxdvulcrzmP8uN+Z17R+0u9d3FctEB0qzX/Gw5TNCfbXag2PL0GkXZ3ZNOi7938zHipUOGnHsOyTD/v1Tuu7ElTTO4dKiP+dkFXBL4HEt5CT+PAErHtx2dR/LT6eyuDto2fyc4O9R32uuAleXe8o9eSOffDkw31y8pS3Ty/53sdmVk/zq6Zr87O8796TiZ7ksg3iV5xn1N3nKblhkHu5aCXscmYyNe7kpnp0f6hLqh2ch+8+m0sfxt1JeuvqWSQTe+sxrqldXOtub1DJxy3Xgo64C3X6ofRrqv020OepuHsyT/Jv3ymvvOeNMeuYCJWZnyQRfZj5fo9pyTDkO8BC8slTen/6XzkR13BMJLvD7xD0BoQnoVw8zpCJqdOgNmUp4SqLifKYQdw9vQjPZU2ftFEeYpS/QvI1oZxU9jmAwICE7G6cAw11Es0NQrO9zj0oE6loRTudM/xu458ApLAl/66NQV3jfQgZAUOAtol4KafMjqzenpad3f308RKJ/+ldGe9yrQ5fT0dDp10isTCXScWst3y0inoFxS8Sf4sIHyCyqhq2WiK52z603i8Aj/QwPPoY2MjYsNsuXHkWx4pFuZOBQEYGzQpwvaeHPy9+/fp/c55Wqz5415ceoB7VvGkh7Wt6PgD/JhmuaqoMeVdXSGEx1gnU87zA19zn5kdP29y4hmLslbXnHkWfQGRp7CvCErppNXipKffNpbAmTo+Pz8I70VWbQzlLaDtpOXqNs86tQlO3B5yuN2u50djJL0gkbW3Ul3+pTOU/Kb5yBXzaCl0xm7KPNIR+R1H0DiOVgC1mlrOnvlqL9tPXWnTDo7Brqzevjhw4cZT6V+PIaVKvRKYq0ROPX8OvXP75viPgfoqDN1pe9HZgHtxmhLjhu/Q2/0J4fDpA7lE7ngWWSUfvKMaVW1ywRivKzC3d/fT/eRBVU1f5eanRJj5VwpSqyYfeG6x+D5QUacmXBzc1Nfv36dsAeZQE7Ry8w12k2cT1sOXiamo760o77f1xNvug9Jw07HuL63ytabnap0rCidwPgEMiYjlbkdJUruUUhQ72u56sXvnqBMv8nJSdDhNL8RgPz+/fseUDcdLMwJXhC8jEZ0EzyaDz/r6GMKFnPC3hPAuo1o7pdz/jvt5VGgPq3RCgIDmqmRGSE/Pz+fnCqP2ektnk/nUmeUyKCPvrJnD+fD6YbUmXnQ71GYE5wpn1S2Xq8nAGC6O1Dh1ark6w4E+Ejs1WrV5kSvVrvUMf9OOz62eVQsk4DO5DMbTorlOBWdAxYJKrv7+d4p41GhT6lzTDPq9J5RP+cgRuqY7sQ45sT07+bxJQ6CHSHT0vot05vT8K3X65lj73bsOKR+gj+PyamqqkWapb42wEtHtmrfXnm/zsnJyexkL/YUpBNj59uAkMK9rjcjyKa9I/RV+xFoj8/9yBQi2uQU0aqadBJj7WzTiBcd1U8HnOey0D+AIn1ERzJWUuRsC7s5dV+78Xve3UfT1rI7ao/rKVdZN/TwH31D3zJ29Ejy4DEFLlKuOkeias4vtmMG7gnE+T9TrNG3Dv5mijLPes6TZmAAO7ToPs8X/WF+80RaB17Agl0gPh2WDBJQFwFn94/2Unbu7u5mY3I9pl/aWWNe7LP5lX6Tzvi3v/2t/vznP08Ba6flGf9ZBmi72zrUBWByjB3utjx185njs5zTH+idzzPmDHK9tLwp/a8DL6mwu+hQMoU91IyC+T4IkpFo+uPvafyz713xJHReft6bQM7jWkovyLHxvB3HBEFL/YU23RgonpPOKc3+JDPnXJoGL/Hm3VcbmlQ4bnMEtKkv6Zv1jISw48NjKqMV4JEsdTKVEaw0TC4JMhzdG9GW50Z1Zt18zza41kWc6dOIpzu+TTnkPjsBfqaLmmX/ze/dKoLH4SgYbaT8pwFzX33/CKgmvZf42GNMw5XzOirZ9qgfHd06Z+vYSuqzUeTf+ojvI350fZ2+dDsAP1/LfgEEvMdmtdo/zrtqnoqdTjL8aWeXtu1UEfDMMRg0+RP68Nnp5RHdR3PCX7dtoGp+qAV0TAfH9OvmwUGi3OuB/sv57my5naauD2mTOpn0fa4/eWZkD/9/LC/RD0v2nN9dLL+jLScAcT9vYG28ieOO7I9wXhbq64Lp2S6flis72onFRm2mbl7KWOjKa/R1h9VyMQMauiTuq9q9fyxT7VPHdrbUvyPDqUtHJRdYql6eZn2ovMqpWq/X06Y2mMrLq7e3t3V7ezt7/1RGwPHAt9vt9ElUAEIasDjyYMPg+yxAHeAyoZLBYWRvYmPCHKH1ZHEQgAuRvPX6x+EVFxcXQ6Xs4n5l5D0ZlWte4va9CZIdbXRUyP3BQFfND40wrTjVJo1RphOmorFgsWKVbVtx+h0hBp7QcbRU69VTIiIGHtTjl9YlXd+7rNfrury8nOSLMUA/ZClT/ap2ETzGywZbFJgNv/mYOWUVz6AiFQw8Ypl09Na82jlPVTXjW4wWz/Oc06H47tWVzWZTm82mquYRc7dv/uMzDaefQ/75H/600aMeg7LRu/VoIzeg02efYpVOcQJBr+pRGE+CtdQXnn/rNuslr3zY8bNsQn8fKONiejMGnIBjKavV/vvuiBbT1+49OLZ1zAW8byBugI5sdvLllBnLmt+hRDEYZPWLftze3u45LZ4z3lPl+X96eqpv375N/fVYmdsPHz60mQP0J2UrbQXfuxTTEU84Um97f3d3N0XgoZVPErPtNy0SP0BXpzOyus/9PGOaUKcPLEAOkB3b/C4Kj+w8PT3V+fn5nl5NG5hptol9bBetw9+z2K4nHuhK2l/Tv2qui6wrHRDwXGPXyRioqplevr29nTJWONTJdbjkKcHgU1aN+O73k6IraOPx8XGSM/MLfFQ1d7xs56kf3mI1zFlD5g/m3wfkuPjEWOM1xoZcZt2mj/lzs9nUp0+fZofQsWJsfOUVOduDtDHME3SyrbEz6IwnaEFK9ePjY93c3MzsvGmTY7DDaxyVDvlrHM0ZzV9zM8ray3gYiqen3cl+3QlkBgkGBPzZqeoiwDZSLJuaAExslwLlYscNxspIgRWaGbBql7uaDMqR3jyPUzXytq1M7HDmNZfO4Uyjyn0dXWBgmJFnUNJOS+rG7T/PTwfcKDYi+VvygE+Wy/qsRBLsOvrvlDmDcdPrvY1QV1BYHAHvVAfzf6b5JWDvVkA6p9RzCa0dCUtF4/d/cT2BOsUBjoweAgyq9lfmnBpsAOS5xejwjCNyHY9VzZ177rW8Gyx6lS75JGU2nVzuSdo74ED/qd+0MQgh9aKLWmY/s48db3XOk+s136RDZVkagSXrTPTCMaTVuiQdoTvz6ACh6eN0r0zVTqNsuXJwy6eC3d3d7fE4feicZ3jq/v5+6gNp3P6jGIwy7qofoOrXX3+djkTm88OHD7XZbOrk5KSur68nO2AnubOZVfvvQTOfZ/8SPGdJu0/6tvW+dYLtnOfVQVewiR1XsEqCa8+TeR762K6AWeyUOhXa8uN9sR6j9VUnk9Da+svPHItTVTVffbEOfE3JceC0nJzs9hImrrMtwwk3RkTmwKO3t7et7GTgyvbMoN7OdFXt6Wyw783NTT08PMwc7ouLi6keeMA60s6bx+7xGtPYlsOPzuiiDp+amCcs2vnIYLbHT/vn5+cTvb9//z4FDgnygI+9+us+Mk47VehbnE7GwnjN8/l6laqq+/v7WaDBdPEY0hnNQKSDWL9Fpl6d/kfJCEN6dgY9MKHvMSMbMOVEuL00OL53RARPaK508JwNgB0VPGgzlZ2TNDSMxQogDU5Ovr8vjc+ODPVb+aKMedafFDMWz9O+AXwqdCtvO8oAYOYNZh8ds2wHtgMD5huPx46R++ff4SEbsREgNYh0O8dYusgmY63aRaKIZndL7iPgDS0N0G1kLMvUbWfEfNIpopFcZkQoo1bwG1FtHA7zH7RIh47rtE+f7Th2fcwxdLxqgJMKOGXX/VgqbtdRvfv7+6nNPCwnHVGvFKZD53kaOU7dM10d3YqZ59jz8RZQ9XuUJbuVMoZOS11SNX69x6hN80vyTtok+pByCm/we4LwEd/xTL7eJFelsdPeD0Y7IoiYdwAAIABJREFU3l+cetsybnuQcmGnJm0X/HXIEbdM24Zazrkv59mrA4BCl862wgP8NtpPZn1B6WQvdZCDFcZBpnPy5zGW7Kfnf9T3l+gK6FpVrX1LbLQkZzlffNqmud/gRTsyBvSA/PV6Pa2ScbQ4ThV7LZlTnHPrc+pK5y0xcNU8mNYtWpiXqubvY/KKMHShbWeOdHgIvvX+bbKgcGjJiPKKVTfvOQ/872CUcbTTMD0/KWtdnS6pMxMv+/sIn76kvNqpsnLfbncRARjboO/i4mJask+vsWo+4SNPkwn69u3b3ipJB4bSseEaz9rhoU82FgaPjiTQv6enHykU9MsC7/SYy8vL2m63s2VjPHHohlBQT373gR5O2aBtK9/z8/NpadljZU4ctWQOiYwyNisA08jRVVJkECRv6kTBMF6vGnluHNWz8XAEI+lJn7xiwicpYaTN8X6FzuGkPqI6XvV475JOCt/5hD7n5+d1dXU1KUJv5u6cBq6ZjwzGidpU7SJD8I1BEvxB9Jj6HZ2nGJgkcK/arcRU1QRu/L4cBy6oB77hk9ORiPBZ6dIv66lunpEpGwlHx8zD3Itx8YlVNkTWO2mwkh6WM/rx9evXurm5qaqaIo3IuMebKRPwdYJtO0QOWqTe60AutHYKVNKXk+nQQYCM1zgev1cx+IWXM3WFsVTt0roNgtDJXj2wYU5jbL4xEDJ/2rFKHkUOU47hTwM910EqFDz28PBQv/zyyxTNpy9Ot8FxYNX84uKiTk5Opv+hG3REDuzwZaCK+5FvgFJmFLj/Kauuz3aTP9pMbNC1//T0VDc3N9MhHN0KiAMV7pNTI81L2ablzraO9uABZydwaFPaRMZgx9m88d6FcRqPQRNjr6qdHUPOjA0NnBm3t2Ugi3Z+rb958b2DwJY52kGGvcrnzwwyrFa7tHNkge8XFxd1fn5ed3d39d///d/15cuXur+/r19//bUeHh7q7OysLi4u6vT0tP7whz9MslVVdXV1NXPM3A/323LOeKGND7tAh282m7q4uJjNkZ0uY1g7SvQts5XsmLrNX375pX799deJ9g8PDxN9ndWQ9cEztoHwNuMlHRv+8SIAz9v2uT4HPS273Wok9dMPSq4Qvqa8aaXKDom9y+wU0S0rKBt3fstIFwJjgG8i04d0rCBCRksoAKd0xpgwK+WMSDoy3BkSOwEsfzrlBkONsTbIZJnYk2uw6jQI57H6hC8rb9PDbTEPViCmkftgwwl4pB8orm/fvk1L6V62BYBbWNbr9XT6XqYiUUaAz6uYBnR8B3jaMGWELNuxE/HWiMT/VbFi9ZgdzWIZvmp+kk0aCRsPgBh18buV+mh1w2DdoMAy1O0J8dw5SttFHO28O8WXvpyenk4g2P3zqYnum4MDGeVMGciopgMspo/7hkyYTil3S5E/Aw2M2/Pzj1SKL1++TEEZHCmCAF4RrtpPFbSTZD3j/mQQI/+sn/1nI8d4vUoAL3ZpJO9dEjh5DtMAQ1vva7BzjW6zPaKkLnF7DgyajgmWu75bHvLT94za5uh/ZAwQZB17enpaHz9+rKqaeA8geXl5WVW7vRnUaV6AfvBKBiJoGztB9D51i+WbMRl4jRz2zv7zx1xjw29vb/cwAjbceoC+ORMi26FkvyyTrtt7YXBo0evGITm32d6xlATJjKGqDxKmnc/7/D/6hHvtICHDDvBYnxtHJd0SP8IjONvMS1VNAbfV6sfWDpwPnr+5ualffvml/va3v9X9/f30idywDwsMRACKNjLYjk4iYAUPmXe4F5p5X+TFxcV0f/KNHfXHx8dplYmgfDpBDnBad339+rX+9re/zTAxwT/wIA7kUkkMT98csHfQEL5JvnNdXQDCgZoMevlZ0+gt5dVOVRLAYITOGuTjGHnZMo171Q5kJVAzeBtF4jsniZIGzsAu6yGC5ms2Ul7tyEnr6vQ4E6xmv9x3fzLm/J7FoNaKZqSAzUDuu4Go74EuGMOnp6fabDazKJJXoawI3F7OY0YYDBysbBO00M+ksY1e8mVnaI+lWPk7Kuk+OsKXDrvlMY3Zob+kg3l79NkBx/yevD4C2ubDjGq6dCAfXgOc5HyP5B1apj6Cd/xpPuwCSDgxSZdO2afMIU9VO/7fbDYTEHVgyocPdA5TFo+to+fo3oyU077Teu1AW1917R5LsSNAqpkdJDtSjmpTUp4c9HLmQTpQnW1MmlmGKclLI9vh6xnxBigiD4A329sE/rkvFdnqwJ1p5fm3s85qA/KFM+EMDsuBgZGDmdmGxzECzVW7DAzv2WXPs0Eq/c4AU8pcykbqNObWtjQDLNbltl2554Z2O51+zCXHCs8nsO30hIM03If+W6/X00prh1OypN1yu0t9z/o8HzheBMBWq9WU9ueDaA5hjM4+wI85tgy4Q0vbBmfdEOzOuo2PPQ/Yr5Rl9yn7Y8yC4wV9qubphtYJvmYHzvJuncRiCLrG7zlN7Eeffd1/ObcdtuR6BqheWl7lVBkUo6jtLaMkVqvV5KGi1Dul58gGn06JYFXG0a4EgZ3j4cm3cGK4rKQ74XLU2V6vUwOt7M2AHZD0apIZyv3vTndjzKzSdQ4s9IZe3O90kxQqz6UjGQiElR33+zQ5DCMpjvSFP9KEOt7ZbnebJeET2iFyasfWq1lWhjZkTgtjuR1hTH7IMR+Lc7Xdbqd0HcaSoAr+OTs7q81mM5MpoloAni6azHfo4usJFKwwR84X/R5dHwGQTl6qdifdPT8/z/YUcQ9AjD/SHEjDyJPBqmqmNxiDHSWMAakP8JuBlIGqDzWwMXIxKPL82HlKR4U557fNZjMznLSV+xUdQbfRshGxYzmKwCWQNJAhhRlZzbTlBLzU09HmvQryAU8DztCrAG0Ca+n4ICdVuygqEdqvX7/OVi/tWBmMVe2iwJYP25gONKee95+dFwdbDL4cEMNms2pl2UeOSElzWvXFxcW0UkX9djytL3AODLBoh/H7hFwi24An77vixDynfTtqnuDOAK5qlzGD3FDf1dXVNAavfqOjzNcJCLmHzIgEdAZjzKUBsOfEqxVOt4SHaJvvudp5LIGLtKXWQ77u4BM0zJU/6nOA9sOHD3V5eTk531++fJmd2AndCeBbjpCllzg48Ci6l2vofuSH+WTF8+7urn755Ze6ubmZ2YiR85sOPPfaKbO+6epBLglMoJ9JP4Te0Mz9YnzQH9qwSmf6JZ42xuDP77TDdvuAF9snaAxWdCp7lxJM/eAe20N4zEEZigNE3p5iGWWcxvSHMM+h8uqVKjrgTzOujUceKTwCsWlMbMhgrnSaLCgvIUBGmdJp6ErHSNTldKI0itDBoGYpzSyZ1+PtjHo3LtOmS2npxua23X7nuFhBLtENYYJeGCb4gD+MrBWGDSaCjpFO5839y1UqA2LG2tHUCvy9C/PmgEXVPDe/W6li7FX7G3k9bkfP83vS1iC8i7iN+Lgbk+c8Uz1cH8V9db59OgsoUitOO6Lwjeu23gCoUJf5zEo/n8/N/e5bV+Bz60fLWQJh+N48bvql7GXQwX3y/Qb23b3ZlvVWpj2ZHp3+9fPHVFJ/et+FwZRlxgejeLwZobVTNXI2zcO0b93mFWqKdRi8kxHnqp0Ta9BKf623fbqqdQ1zbx3KClLqVeplpSr3L9vxsGNgnsiVqpRd+mue7JxPaGNHI6PjjoZzD84VQDyBGgAaubFcQG87U2kbM3DnefKnecLOKLSzLngp1jmWYl1tOiUNO52CLFTtdJlPxjXeSVmg+JrxzqjkHFuPGbvyZ3vGse0+bbLDul179GuJb8wL6QCCtQlm2zHzHsDMsuiwsOUoHSrmDNuZ2CIDKMiOcVzaMMZQVTPcZryJbmUeOxxv3UfddrpGK1Y5P8b6S7yyVN708l8TLDsDQZyCRcQqlYInrQO6nROGsvFyshU4z/p9AxAbA8IEd8qbsXSfBkNMuFMLUOBJFxs8aITRwtA5CoJBdgTH3xkPbdo5GYEcFytqz5mNh4/NN/2d0mlhpF2Pk2hGB8pdNwLjAxOYIxvmESjs+KVTCL43HY1jMVIGZN3qg3m+aveKAo9lRBtKzn2CBUpGvb1Ky1zbkU+HCT1ArnYH5H0/bQK0koc5LCEdKQNAeDMVOnJpWaXfVvSWB3jYh3sYqNLXpLdL/gZNvLKaho2ofYIng1oHH/wG+65P1oGp63K+OiDb8Y7/T4OVxv8YinUCfGyHwveh09NpyayEDgAx5i4IQtveh+coewfcUp9V7QIPVfODTBK8cq9BBn0nKyDBqw9KYD6RFwN+bI4BFKtMPEMx+PKqGfX4IAGfyEe7DgDZacmAA7SyXfSeOGMECjyAjqLOs7Oz2QFAlgGuMSbzfBYftGD8kPq2m3PmJG205fcY7Bb6yHNih6Pjyxy3aeLxO2UTnOAVkgx0V/3gc1aTHERDt3b4wEFEB/JSb/qZ9Xo9OXpVVR8/fpz0BCvgm82mrq6upiBCp6dtj7rgNv3idx+s5ANrwJPfvn2rr1+/Ts96r30XEEw7bxmjZNCF+SFV/cuXL1Obvnez2czezeV2PLaq3bYVBxzcz6qaVgbNF94PajzJtQy6juxfyl6HHV9SXv2eKisIiAOzzSqWE5AvP3QuewJcr5ykc2CB9MTiOfPdnquZhvPsMWQudrwyOp6Cl4qNCHM6VnYwDf6qdo4NUc6qXaoSCgQB5X/vY2BcKInHxx8vnMtxGQR3yho6Mo8Ipk8tzPr8LLTP93tAZ0dwnWrjejOVwdFxC3kCxnQIOuepExTzGpGlYzBOVfODEKyEoSt0QJEDqPI0SY+5c7LNn1yr2t+jBaCxUXOdgDPmmrqoD8Bvhe5+VdWM/3zqWm5ydYoeYJL0At4B4rE/PDxMEelv375NzgptmtaWO/MVaQ0+IAZwR19TR8GvGHYX0lNZAXAkEXoABBkDfbbzx4Z/GxXTPb8n2HGbzLn5zLrP8pmyZmDA852MH0PxKq31DbKUTiTzjNPg33LVFz2dIIzCc84gILJt++Ln0Ou5+py2N/Vg1f5rKOAtTgVz2pv7Rxot/ODUNAAkB1gQoCCNkBPFqna22tFrF4Nu0jCRGegAaDJuYN4YD7bc9RFgYW7Nz6YVjiGn2CavGLNwqAV8wdi+f/8+BGzMH3NwcXEx9X8UlEydaGfKuMYBgvcuz8/PdXd3N/GLsYF1kG1XHnxgnOLgBSmRVTXZGo4uJ92M+r03D/6kDWyYaYnOos2uODhBSRxVVVPa5sePH+vx8cf7sB4fH+v8/Lw+fvxYHz58qOvr69nhL9CI9Ffk2Y49ugne4BCMp6enur29ra9fv+4dlkRaMjTllEGvpFt/05/MqrB+NC5k79Y//dM/TbT7n//5n/rrX/+651hj675//z4dbIbuMcZIB84HMlnncgo4B4Ag55vNZravCx5Ab3klzzxm251O9u+2UtV5eP6ti9TRwVQmrqOLGnDdk2xjTzsUf2dS3KZBSNdvBJDvVgj24ikJQJwP6rrTEeDTINnfGV8qAddrGptOBj+eH/c7+9E5KU73MThwf3w4AEJpYObVlKqdAfQ8uu88b37IiFKOacRHNladw+T6j8EwUbLPaVS5ZoBsPkz5TLq4QCM7ExSD4hEvObXT9DRwR2E7gOJ2MzJn/dHxpWmRvJZKmO8YEw4osBM9Wtnx/xjldL4tp6mjmJ801u6vHRGPGQNQ9UN2vNfQushRSvNKlpGzlbTqnLBDMpQgNes6pmJecCDM/bferepX7yhdxNUR+dSbdtxt1xxwcFAKWXHgi7/UWW7LskG/uuAY9sZj8QqS7Q/2jRUrAhQELZCvBJvZNteTv5gTj4s2nRnh8aVt9XOdbrS95Jr3bzEXVbsVLNuldHjye/bFc5y80slM1pfzZaxx6P7fuzgAYR1tnDOaL/OM+aZqfshFOkmJx5I2xnHmYb77NRy50uY66E8GN9zP1Wp3joDPBADUe6V3JFvb7XaWZssrNIyhoTNt+yhz+oWOw3lNWie/mGadHSOQggPGnnX6nnvbPO8EaQiKVNXey5ehe+rElBfbvI6n4JXOX0gdbnn2fLrtt8rVq5wqRyQckeN7RqIZeBoYGNQrGV7BygMazPQYAq/4VP1gZDYMr1ar6ShKIgF2VrwpNZ0dfrezk4QeOZOeLNrgmGgmG6HwqlxOvPPL6XdugOa3zhnzeBxRtfdt5UHf7BSmguuM8siAGISn41g1P4ghgQfFaROMwwA4QQ6GL4/e7+bHPPhbBejvWdIpNjjI3w3omT/zMPNO9CYjg77mZ81TpOD5fUwGE1W79ENHmzLYwHhytZV6EngYeFXNAUXVjqetH3De0iGAjzjOFj6y8TFt6JP5B0cMR4j7DI7zgBf67UhjN9+mG8856EPUsapm0TuDRAxeR9eX8nXKCDS0rmalPx1g86Od52Mq7pOzGPgt+5z61ME2B2OSxgZeSU87SlU1A0zfvn2bHc5CvYAt+MApuO5rtt05Lf7NdHD/CQQA2Ii4EwnOOTcWQBe4f8h9pkn6E7tk58TzNrK9xh7uV86r5850gb+5t6sPmjiFOZ0br1S6fdtR6OoVCuuz7fbHyuW3b98m8IqN9Dvilsb7XgUae86zpH2p2h0kYFpQX8oY/E/Gi50qVlGcCWQ9BOC3PTX/Wo69enx5eVnX19e1Wu1esp62wZkLrJwQbMBmOG2W8XU09Jgp6WycnJzU1dXVbMXIdqtq59StVqvpHVpgowzo0CfsWjorVTu85mAPNIRHP378ONHw7u5utn0Be3Z9fT3NI3sxzQ9eTUqbQn99qIx5g6DO3d3dtJr14cOHurq6qouLi/qHf/iHurq6muli1wGPpgP7Flz4aqfq27dvk6Pi6BUE4M9MhGFw/jjCgYBgMDDevic9UhMfR4A0HV6aSV141h8+fJjSFHIyraBIB4CZOhpAbEc+UHjUyVhPT0/r5uamTk5+5PnmHgoLS1XNItSAWujBmGwgoC2K13PgZ0x/C0PVfEOpX3TZFSsUG1QDdgu6DT604tQ2BDT76+VZ5gTwaAWVKUcdbTwe86RXH45hpQrgw5g6RZZOMZ+dU2Uj5BP/oKtTBa3EoHHVbq79pnj3wful0iExn1OQ56p5elJHf7+PwnxGPavVj2NsAXLn5+eTXPNsVU2RdIyVjUqXwpUrdpZrAg+msYNA7rtlCH60QWbcnk8DPu4h9SgdNkc209kyrV/rVHVywu/WE+nAOuKbAPpYCmDAdLY+t173aVKAKpxb2zCvXqZzZfqY36mT/QZEbh2JNwBHnwJWvJ8gZcwOBO1YZqGDwaj76SADexfOzs7q8vJyz/ZV7XTA4+PjbF+SV4cYS6YfjYCkedn32AHid4Nyy1vn3FuGUq/aqYKXqc9OuGmNXmTOvKJM3QBV7D91ZFo088b9V1dXM0xweXk5m9djKg5eVe07tp4b62cfNOQAKvbfNgtsR2odfEF9rIr4NS/Q8+vXr3V7e1tV8wO36B/z//T046XQX758qaenp/rnf/7n+vnnn+vs7GyWKmsesQ5g9da6Hfz1/Dx/XU+ukpjP+Q7duBfe+PTpU1X90PGfP3+eBSWpj344dbXDOsbQLrYBBBWsH56enias8OHDh/rDH/5QHz9+rO/fv9df//rX+vr1694cky5PsAZc5+AB6fDQ27Ybp5dx4jPYl/j69Wv95S9/mW05wpn6x3/8x9kWHS9wmFe9CPGW8ur0v4zYMQFdxCQjc3Q8QU6upligUolX7RjBSpV2UlicGsP1QyXrYBw5hqUIkY0FzoxBiYXbyh4D52hArkhBJ9N9KWKVBglDm88nUPI8unTtmK4Gj/70+BiTAZgFHj4z03cKIRW4BTCVvMd1jKCv60+O7yV1JBAZKW1HaOxgo/ytVFOGDWySX0b8aGPRyZXHyPx19fOM9UQ3nzYM6ShY9tLhyTpyXABFtzl6nue6+jxe9EnqMH5z0Cpp3NHJ8rOkp1yXn0+ZSrnKuu2keJ6PTcaq5octUSwjnQ5OveFxZul4IecBw+5DJrIO95fSRU+7+TUvpez694zad0GuJRlzPXZY/enod8cTaZu6MaVDZdoAWGnPdsLjTVkd6Syue0UlnVgA9cgWup+WHfNY9surPdbNdlKPNWCR/RnpgE5vVM1PIU6bRX0ECC0DyXu5hw6HvtNZrtt/BvN2pm1LbC+NNROL2sZ5rG7XZYTnfJ+dM4KGLqajM5y6oCxtdgsMDpwk73v8q9Vq9o4sB+Y8BmdtOFsMLE+qJOO1TTS97Bv4XuNtFlnu7+9ne/g85q6k7X1LefV7qvDWUVyds5HfGejj4+O0PId36TP5HTEmCmwFh2LzIQpWpBmB5NObUB2F70D2arVL2ekiVCNg04GL1epHGqJzW1MB0CaGx+DP7wDLZxgLfSUaYFrCgLSdSiSVBgcBoMRhdkfyrOCp3wqFwu8dWOEQAgvf8/OPVdDb29uJbxC8VACOlvtAFPgRw3NxcTGtiOVqKjTxvor3LPTJkW/zFrQ2zVOJEDnlf3iHulxWq9UkC+bzPOEL3oR+9DWDCvDOdrvdW81BCeb7YEZK033s6FS10yms4riujLJ1cgoN4Y0E0Hb+Dsm5n/MenVydgDbWU+jRvI4RIALL3OQKcgdiMVA+QbGjq1cB/en9qJ4Xrz4TOTcIQm8nfx1DYW7S2BsgWFYYfx6045d85vvKoFHKjFfMrf+J9D4/P0+63JHgqprtZWAlKNPK4HWnjPGuqe12O9MFrHZV7Y50tx5OJ9JHi8PvyB7FwVP+jAMcqLEd4Lnc45xOURcY6lagiHK7f+Z96wXLcaYc0X9WerER6Dj2zlxfX0/2KgPFyUvwHnbS0XJjKejInPuPuQQXcM972y767r01xjbunw8uAfje3t7W//7v/9Z//ud/7mGGf/mXf5lOmEPuyHqpqunAgvX6x3vm4AHPB88lJk2MhBPCnH3//r2+fv0629NoPjHAN59W7e9VtW7nd3Q8enK9Xtf19XVdXV1Nz8LLXm2juM3z8/O6vLyceIuANRg730mJDrHdScfTNjADmavV/JA15tLOaFXNeNV6B7qRScZKlg8lubi4qKenp/ry5cv0nNOYkSHjt9PT06m+jx8/1ufPn+vy8nI6SCT5kXHbjqUD+tryJqcKxcXA/HsqPRiV9AlOLGHCMx0gc8cNVjAwgGUbxowa0Q9P/PPz896pIiYkTIQCs6NgYehWeaywuQ9hJWUSeqEMR9EoJhlBR9E64lK1cwiqdgY1Iz1e8k36UNwnnCqP3Y6Zr1sJEWHgfjs3aQAxTgAFaAFfYfxxSE0rO1NOoeiA3+np6ZRGwTwZ3K/XuyNRM1L8exdkK1dA0qnKiB7ygnG6ubmZDEnnoFEybQa+8vzmNc97KmJ4x+09Pz/PDCHfCYJQJ8DAy/CUkWMFyKUvGZBIpypl2cCnC2ikkcRIpRPIb470ZcTOxSDWBtmyb6ffTpX7N4o48j9Bi6rdvqzUzQn6oVHqSOjlyCUnSjkwBng/OTmZ5PtYCjxqGtmZMpDPwBE2hD0vDghSnEEALznNya/PyBRfnjd4c6oY93ansdoO2TG6vLyc5JH3RfoUS4/fQSqcnAw+WZfYHtB3xmyaJt9U1cTr8J0BWALqXN3hHsul59Z7TBL0Vu0cVMaADvIpa/Qfh4pnCVJ8/Pix/vCHP9TJycl0Ah1pY5zIeXNzM+2PY14t9waDiU/sVGEb2e4AGL27u5t0EQ7hexbT6fb2duJXzzW8QdZOpmz913/9V/3Hf/zHNCbT6/Lyctrb9+3bt9nKIUeWM49sgXCwnr02OH7GpBl0JL2WMX358mWyUQ6YedzUk3abkrY3nwM7EaxiP9aXL1+mvZa//vprff36dQ+r8vf58+f69OnTdCLu5eVlPT091V/+8pcJS0EL09h22EF2ip2pqprJEwc/bbfb6T1d9I26+XNffVLxycnJdOQ8c7ler6d73K6P0semc7KgsQqpyh8/fqyffvpp5lRl0Mhz5QUJ6/LXllen/6WSO3RvPpcrW1lPpyA8IfnXPZ/XDPB9j6Np2Zck/Ki/XV/zmaV2KDhSaTSzT10EJJ0Wxjzqn8fWOYadR9+NL8fivljxJN0NODEwGMJchj40huyPx5XOoYv7ZDDwnuUQb3UAw0q2+52S9Mqo9IjfXbp7DIq4J6M9I8Pj/ucc5Rg62uR4zced0vT9GUxZot2ofYodTPfjEE+N2qNf3dykDhj1CxnDufUqbfZ5pFtdv8fiVS2D/+z3ewcqupK2I53VkRPvT+tzP2N9vCRXS7zR8ap/S32f+rdr10GHl8i4x9CVtOOHfh/xaAZActxpO+lf9pfPkbxkG1lG82HHjmInCceB/WTouVz5Gzm+lsGUG2TMgRl41kHSY5KxJXuUGGBko/LTdEo+6gLZ7oPtD46UbVF3WmX2DVobm5gnHZDpsN3o/6SLecGZHjgl3uvoABA63gEI6sAGdG3Db52dzTkaYfa0o16l9YogTlVXh8sInyAH2+3uwDNnHaSjxHdjzA7TWh8SjFnCGy8tr3aq3OnsZAIae3r8hift9BYzGCDAkbCqmt3n32EyvGY2rFft3glCBJX7XEcyzgjgZQ5n/t7Rhz6z/GlhyPsSNHWKyZEX2uxW0sy0MCB1V80Z1sunju65mNmgM9c9t7RvnsiTaOCBVCJEnaw8+N2C5HQICxvAkRUsln8/ffo0GS3mnJQNxn8M0b6qfYBQtQ8yOseAZ4jW5YELVtzMsdvznOf37Xa+OZT5oA4bLPrkgzG8kmFl67Emr1Mvq6DoiRFtqubpwZ1j52il0yF8AinpXQY3nbFwm17pSR7KqJ8NlvWJjb/nJvWpAUSuilh38pzTSjxnHb85wOGVN69OcYoqEUXPgQ9AgRe9ev3exfbF7z8yzc3DTg9iLrySf39/v2eot9td6mvyhNMi+UNu8v1u/p1sDr9PzXJUNT/pj09nLlhxAAAgAElEQVRWJmnb4Mb6Lh0t883I2XDE29FfxmPeNHj1ChcrqdRveaiay4RtZIKeTjbM57bvnmvb8k5Xev+O5wh9AU4gRQm6ZVoeKxy8r4h35pAh4XGdn59PdLm6uqrLy8uZPTPWgceOISCIHe0CsqnPXNCdV1dX9e///u/1008/Te8zur+/r59//nnCDF7dc9YSq4TQGkfXqxqsKLJibyzYBaT5tG2wI+KSepyS9ZrPq+aZD2TuMDbm+evXr/XnP/95wiyZ0mp5+PbtW/31r3+dVutIUWXFer1eT6m/HoOxNKtVqRvtvNrmsuL9/Pxct7e3E65yBlp34qCdnIeHh7q5uZlkx+92ZHxnZ2f1+fPnenr68X6qh4eHur6+rk+fPk3vfmN1jzpo1+/XS75jpcyBKs9z2u+Xlt/0nio7T5m+wnUzAIDaS6wJkKygO+cqAQkgHGZhWdNKPV+waOXkya6av/vJ/TLI85/H0DktNgydk2mFxPU0CjYw7rdpmIDb4/FxsL4O43TKOZWFIzamC/V2DiP3PT4+ztJXUIgZjUMwvBnVq035Z2FnPBgmjtP89OlTrVa7JWfSz+BZG8BjKMlL5ttcXaGkA71arWZyY6fKDrHr81wn8PepRygig2nqM3+SooKCc7+tuBlrGmHq9PVO0XE/Bikjt9Yj8ICBkU9IZE8aeiQBtuXQ/U4A3elIy4ujeNZHGB/kyZE4t2cZsWG2U+V0DuhD/01vvmd6UjphThG8uLiY7akyrxGpB/gcU7FTBX2guVPGqvZfD4KegH6AANsCiunJfOW7WbiOrDJXGRWGH91X05znzSupQ71Hi767WKd30X9Hgu1A285VzR2XdKQshwAl6FS1c2jpo2UCGwAQso2k7VEKYRes8Zh5xvqV/tPfzqkCyLFvjaCO0+TtwBOIII2QAIWBMnKF7WKPDPzqIDH8eAy2yxjGqwuHnnEg7N/+7d/qX//1X+v29rb+9Kc/1S+//FI//fTTRKd0qhj34+PjdFoz183z2Hzv6SfIl4deVO1s6Hq9noF86/0MamSxrfAYjSXRL9hT9n5X7TDTly9fphfqsle6s2/r9Xrab3V2dlY///zz5EixBxBa5cqVAx12MtBZaXcTJyI7dqq6l3nT1na7nZ3I/fDwML1GgG0M8La3tiBnOEsXFxd1fX1dFxcX9fz8PGFH6ORXreBUGZ97PBms5B7zxWvKm1eqcsktlVwKOvca2Dki6oiUJ96D82dGeQ0W3D8v66VjdWiMbi/7kN/9XDf+pEMa40OKsYv0uE9dnaPnuvnLOnP8/j3/H/FA116Xs+s58fx5Dru5dUEwbICt6J3Xm2M61rLEc50DDW1QtFbSWW+uWHX35MoM9+Y92Z+syzzuSOYSz49kziAqeb4LhnR/S/LvIIXb7uS26/9LZHpEz9G9zOdSPaNncxzwx0ju3WbOWwYzXkKLYyrJK9Zd2dcETdZJ/vPzuarY0Rhw4b50qbEd6F8q5m+cMK7bQXQbL9GFnlv4EIeQa5ar/J/2uder4C7W0W47xzgqtv9Jy7STHgvX85nsg8eHEzvSj9igJbnxp4N8PoI6n3kJHd6rdPY5ZYNi/gCbMebn592hLbk66ZX0rIvv6fh0DlCnB/xbR3fzwSF5XLKF2Qev9Np5cyDPAevOFljOvJcaO29Hcam/fF+SH8uQC/QyhutKBo1My8wO6Jx0aISzDS0dDCT4lY7+CK8fsmOvLa9yqlar+aZ1A2Qmm0hBAmUGne+1yfQ1vHGvDDmauN3uNjlSD39VuwgRXq4jZRY62t5udymIjJH+WJEmc1thUtLYui4r26Rfd7/7Q/9gWkf5fZ+XNL2qkPW6HzlmR0C7VSnmmnt4NmlDccTOK1TQ1qmMTgn1ZnmecdqOBd1K4+LiYtr4mKcBEfWk3/TBUdJjKOkAOMoFDzsCZN4CXDBW19eN1QGJbNfpUFU7oJepgvCNHWTz7YivDzkflDSCeQCAnWj325uRbahMZytvj9Fjo17GnnJIW6vVai/VgPE6wm7g7D4krU2XNOLuQ47F/U+nF7qZtvTP6TDoa6LkHz58mKUjZTqm9SF9y9Xr9yzr9W7jvwFGAqQEE6vVatovgy5Zr39snnfdVct7c5h7Dmvh/qqaVh+8YuXV4M4BydMEXVfVXEa9IZ9ieRqBLcuE+SNTZgBzHDxg2bH99Xen6WMfMsPE88TzS85vHviQ88n/jCmj0knvTIvfbrfTinauYNnBIsXPoNkHHVhOzDNXV1f18ePHaaWK1Qb+HG33PL53AXt0KwD8hs6BB1g1urm5qfV6PRvvx48f6/HxcTpohRWW6+vrKdOJFQvaTwcAO4D9RCcDxpknBwqYG2NIpzAn7uocqy6IT//hc/qTh3VV1XQ6NlkTlt1u9Q/58MEPPA/dP3/+PK3W8V61dNKRLesSpwNCZ9sYOzxkIWEHvaqMHeJe8JhPd7Zza32EvWHeWOnFJrGaRmYJ/Xx6eppW7LxSmAEmxkmfU1+MdONSefVKVRd1sXfq5XznMNM5lA2Dy0iBBw/xV6t5OpPrzWiEHTgTKBl9yWnIum0kunvcz05Zus2MXKUhzr54PBk9dj/MsM53N00ZS7ckzVg8l12qhEv23UrO/IISyTRDA98ci4+Ppv1ulcsGB6G7uLiYBBEnPnN1XUfO7TGUdHDSEcr0pKrdKp2Vt+fXTpJBdsd/5vsEH3ZI3Rc7VlbAWX/XXtK/Aws2ep5zeMV8bkcqI38d3Swv0Jg+8DuAsmrHi24T/vJG45zHTm6XxtuVpG/3nJ0at9HpvXSiPT7+ABiZ6mgdZv3nMR5DQSYcvTQdq/oIswGjnURO1Ms2XFcGPZIX7TAQYbYdsUx1bVTN58lybT7PQIptCu13c2V+waGgXgN8HC7vVbTd9cEA0JB0LM+N6WOdbyBu4G5gm/ow5Ytx5thyJcN4xTiH5wGmnNznFFCe8ysJeN6rLL7m+h0Q5MXQ1p8pazm+9yyMpXOqGHvVfA8uJ9aenp5OR19X/aDD3d3dBJSh8eXlZZ2dnU0pXimnll2n1xkb0VfziWXBK2QOfFuW0zFPm0a9tgtPT7tj4NEpfnkugTv2lPnEvpGtsMxaPpx+d3JyUh8/fqz7+/v68uXLTO9lX/O7bWK2nXyIDaa/Jyf76arMv/GyT+4zXuhkCgeR0/182qD3sqKbP3/+PJPFTr8bV/Pd5S3y9aY9VSOw5O8doOgAfAem/JdecSpNgzrXOTLonTLqmHT03fcbpHeOZv6WbXbGvOtvAkCEOX9LuuU9I0HKkveb0Tzmrt4RvTJ6SN/SyXI7CSBeGjUwHZb4wIJ2LMWAiL8ukuqIboKpl4wpDbNBBtdeWlInEDFD2VGnnYp8nrH7WgISyxLX7Hx3tHMwpRu7wXI6KebvdBi84XeJ/30PMun5TMCUz2V9/G96pyGyHup0jkEi19OodnzkZ61/KQkSj0muXDr71PU1aWK6LNXtZ1xyPiwXPNMBf+tySgaI3G7aQdfrIIBXkpP/3E7agYyYJxBLBzSv074Pohk9l6XTIR3N/Zk0cpuj50xb9KJp58OnTL/Un5bRrvi35I/sc1eOQcYSsy0B5KpeNmzHUs+YdzPzppNH68Cqmjl0I106wrRe8XWQwnxqXZ5tmA8cXE49zTMEEKjHYx3ZzsSqHGzhAIX/rG/g2e32h3PJqlrqorSL9CVTNNM5YREFubVj2dGD51NPMg7jnqRVzr/xXfeXvsPfq7w6/c/5rrkaVLW/ytOBnMwXTU/VIMmercEIxPWZ9ywL81xG2ekXY3GfbTiS6AmuPPlEkxx5cDuOrOdmdr7bo3ef8fwZN+PwhumM9jkyAFN5lY82R8arm0vPj+chDUUHjl2XXyTJ/ZlCmUo5aZnAIhWNlVUqRZ5xlND3vHehL6SUrNfraS692du8bf7391EEtmo/Ag1PpbOWh12kAYPvaYu5YBmd51InZJ86I+QoISca2bEhHY131lXtot/IETRBNyTw89xbhnOFIPkuUx47A+Dn+OuAufVTOkkjHmEMGayBLumIds5h1fxkTvclwYNBKPJL+rX7T4QdfXcMgI9i2jut3CfjdQ6WbZcPReIZz+9SQMP6mDm2rDqFvWr/UBhH1ynJIwamHpP76mCW59QHQaB/SE2Cj32yXepmrwigU8zjTt0z8GMe4GWvpneOyHa7naLw1uMuhzIPLI95/8hBoM+OrNu56k7NtIxmYMzzZyyQoM/z1DlYf08w+FtKd1CFUwI9Rx0u9Epn1S5A45VOaIzD0Mkh9AeLbbfb2YoPNi1lLu1UVc1WVo0FD9HcTjLyAlbk8BFW3ayjn56epvd25WFkXT/NP/xWVfXLL7/Un/70p1manGliu1i1swPM1Wh1ysVYHf3Bcz6FcbPZzPRnZj+5DtssBzA4zMKHnT0+PtbXr19nqdS2g9ADfqIfjDdP/4WunUP52vJqp8pCksaYgfA5cqwM3Hg+T+fguomfigdGN4FsSBxByLpdHCHJ+g0a6I+ZwADG+zp87KmP/0562tCPoi58pkNnentsMPiSswCNOgDXAWi3P1L2fn7UHv10ioeNF8X08hz4nu65vM9Rmfw95/s9S/K0QRe0y+CEozUGfd38ZTHt0qlNxzoNfQfSk56plJbq6fqUgQucKniGVE/vX8h0P++pyv6kwaBu0nqq5uml1hvIlvvjko6Wx5Y0TgOc90G7LKkXrd8MbgwKoaeBehpraGWjZv1oMMlY6auDTCOn8D0L88LcOTgx0mHWzwYjCcSTr3NOzH+e1wRqtkOp40fyvCSv1imu08/ZlqVjaLD0+Lh/aq/vT35LHrM9IvhBP3zfEqCxs5999j1p0xmn+2b75QAKJdPEGINXqQGjboPPlMVubB1wzblLPdCN4z2LbUA6VaaDaZljtP1yfXZcnXptG5iBHe4xRjFvwRt+IXruCYbPnPLqgDcl554+e0UGnYsdS0xop8qv/jDIX+IFf263P04M/dvf/lanpz9eK3N9fV3Pz8+zNExjZcuC90J1ts1tZ2YCc+/FA7ZfjDCu2/G8e8w+IfPh4aHOzs7q+/fvdXt7W3d3d5PjyNjcb7CUx+q+ePtJhyXfUt60p8oRVSa6U+YUM+pIEaQCzJKA3teXiGGQRjtusytWsq43lYTzpB2ZqZqfsmXhGbVnxnaU0kYDOsP4Hc1yvI4QptMyAnBc75wyGxeDwyX6Q4/OcHT353xaOS7RLxWeFW4apASj713o04hPRs7IWw2rDVxHaxS6DVa2afolrbNfGI3OsfA1O2rQgoh5gjWUqSOaaSjcVytvaGngZEceQ97pDTtgCZZztSmjflXzQwRcDKYTDGb7+ZxBh/VT0rnTnd0cuHS8lf1J/j0WuVoqBnQGaAateb8/+e7rnQOfspB1d/w6skFd+w5Odrow+dCg13bLB91YZjKokHyEPHqF2jbLwGYkF26LMhqDxzHCBbnKnsUg1eN1sM8lbZftZccDBvEJhBMLuW7T3/3LOTlWGct+JZ3yXq/6WQYtS6kXrWvQ+9R3d3e3tzpmZygdJwcq3U/LnvHH0oqx+2YdzN+hhQGv5IycpmyTko4rttaHenQ2LPEE7Y9wtefRNEvHJJ/tsEuOL+XOY1mv19N7JNfr9XRsOqtMPnyJfhH8yoy6kR+SsrYU3Fkqr3Kq1uv1bLMyitIM2y2RpvFydDAVVBYzKkrb0Xwv4XZLd4CyTvG5TSL8HTET5HnJk3SXzWYznUzCcjP3eCO9labpA00Yp4XXKYAJYq28LfhOp7BSYjyHosnU1RnwDmgnvUw32jbDjpRsB7ApBpvZ9zRGfGbKIf3GQR4pj9+7rNfr6YCNTJdgLN2qi+dkycFKBZd87hPrABcoyjROVt4U86LrznTW/D1BgvdO8Z0IX9XuABwcLdIB/HLUTmkSxUx+fnh4qJOTk5lB+/79+94Jfna8ttvtdHCDgQ/t5Iox4+nkJUF0gqwREHdbDuo4DWfJwUng0wED5MaAzgCI/nHdL1/OVfljLIABeJ2otFdnPC9eBbXtsv1zKsnIKJvevrdzinxfgm8+bXN96A9/li94kBeEIlt5epeBq/Uu9dlhJziB3kiA7Og392dgtlvtdkmbxt4PaGR9bjq4L54zBwwtuwmal2wDPFG1e8dP1S4gY9mwfBmzpKwhb3kIBtc81mNyrjzWXCnn9wTWtsNPT7t3DDkt1tjPPAOIBmj73qqa2VGeSznL9L78varHMmljjbtoj5UTUqIdALQ93W6303u2WL213OI4wk+JH5N36Q8v4M0Vv3zGYzfezCBMziP3oYfAWRS+e96SliPslVidFD1kHz/g/v6+np6eJj2GngHLpy7y/OYffbX8v1WufvPpfznJHbCzMC0Bv64k8PVAvUzpiIbBu7/Tl6raMyCH+mDjkitVuc/MDsvIMJk2HcjM9pn8XM2wANpQkMZk5et+uZ1u/M5T9nxZ4bi9NEQ5d918j3gg+5UGJH8f8VVGoNy/pMcxFIPw5INO4fv6kkx1ID2/d7KTqTsJ7FycVmbQPSqdgrXs+AhigJ/7i6HF+XlJmwmYGKMdJqfGdY5qAs4O2CSwyMipdZblytFE64MlXZl0c5SU9jJC6rrT2Pm66ZQlr6VcHpNcuXT6wcG+nGvu65wf12cdnSmVh4B58ljnjHV8kEA1+5U8m6A3j5j3Si1lib/pE7xctUvNIgAETbFPvATZdecqWI6R9pHNUZ8SCFbV5OTilHjOOke1kwe30YHLDvtkkM+BKDtPpoXvzeCl8cwxOVOUzr6mDlm6B55x4NBBe/MIvMp85sERtGcnBt6wE5UpYSlD7qt5PNtJucoMJv+fY16tdi9zz7Ri06nrR/K//8exIF3Ojjw0NGbOOaJfuTrcYfsluhmzdq8EcH/ytwwecw1ZYYxp1x1k6mx454t4Dm0j31Le9J4qR0SrdpGZ7GwXTfMAEjx2k9KBwJx8AJYjimzIo/1UgETHuIYxSKanvwZ5Bn6sTrEaZeGyEvQYRow5MsBWCpmi4TEnY2NoOgW+tOpTtTOO9A9aWqgToGTbVv5pkCwcaeQ8N8kPLgkafN0AhzoZh43psRgnQI5Xga2Ik5ZV+0Z8VDrnaXRPKnCiP45Mj1aq3IZltwPo9Nu87eCDTwXCabA8WWegYB2ttAHo+mG6O1pOFMzXyemmj6mALeN2puzseGw5h1W71WiPyd+T3p0T5fsN3AzOkk8yQJH1ed5cd7f/xXQ4Bply8RzYiaeYZl1EOqPX8G0HFglG2H64+Jl0tH0P190/t5PPjXRs2lp0DU6VAWfKUPY5+XEEqjr7jnxlhDjrTACMPuCTfXspU7m6bGC+3W5nGCHlqaMlpXP+cg46GqWDzb22SyNswPOW3wTTXZ+OoaT+6Wys57rTWVzHccrAGys/tpdp73kWG0a7iZdSHjs5Szp3etSLDc4csIzd39/Xer17x6JXj0Yy1DkcI1uWNLZtXJLPtJfJW6ZDh7s6meJ3YweK8aQXBBJ7+9pqtdpzmKxjcbTJOthsNi0dl1bJ3Pe3Bt3fnP5HusDDw8PeAK0wYW5vSnV9+U4XE4FipZ3120FggxzM2nmqFlyDok4x8j915ka48/Pzur6+noQmU5eenp5m16BHMnA6nDnpTm/00iaf1JdLx1XVRgcwqiMj4WLao7TsoFA6xzWBn9voHB/GYfq8lKntyDrqRWTU4Mi8g4J7bxC4Xv94AaLnhhS3qhquqnYBi6oejHVAIq+5DqesOnqa+xU6mee791nQrzRiudLCZ77gdLvdzhxO6iICTroDzzkFibZTrzhQ4vuIcPsQnExvY/XM9dqAZpDFfx1gdTpFx68uHVBOHWa5wnC53RFwzBU4GzrLK9c6vf3e8pRltVrNTmqFPzyOTBvhuv+nLjteXo0hGOXUE+7j2ewXvJ7ptZYb80PVfJ/pyBl3/cynT6LlZapOmWEc9MPtZ4BqyZlKfsSGbjabenr6ccqZT2VzHbazHqMBa+otB2fctu2VVzTSXnXzYz3YpdzZVrse62T6AM0ZA/R2MJZ++c+2a+RgHIus2dZU7WxWroAyNs8lNPO2icfHxymNzbbaBxedn5/XZrOZpUsy16SkcVJpV9KJMialXcZmOvsgCq479dopf2wR4b1cz8/Ps0B84ivLUKdbs8+d80EhrRK6UzqHx/zrurPetGv+vdNZnZx1Je8zj1jfQhef9A2tofH9/f308mjbtZOTk713lnby81vk6tXpf6ncDHiydIDLE2AgnWUE8Lt77WlX7XJCISbCZmeqE6SOqSiZ04vgexN9TlS3yuBxjIyghYprfLeDYlBgA5c0tmLIOexo6Tnw/Lk/tJMCaKdoiSlz3Pl8lg6M+Lv/Tyegazcdh/cugI6Uq0PCX7UPCjwHvoffR3UkCHI+d6YuWFbSQbBOsDyZzr6WeddWgAns83/aNID1ylYny6a5jVc6Pek4ZF1p6AwgUu47x7Gjf+qG0bz7ukFZzk3KPs+6dA5U167rzFWG0TPHVMzHOcdV+3sLUkfbhpm/LHMpI3yOHHto6T6NUq99v9vrwAvF8oTNApw6ANC9cmDJBo94K8eYMpI2pHPOEoAngKOObrUg+04wweAKIHYI7C3pnO4+04j+WVb8mbzV0XjEh+7PMcpbykU655TkcfO1MQ6BLetr80QGrGmnw1fZ9sg+mFe638wb1v3IWKb9Wc68LQMHcBR86fQyNO3sURawcOqM1EFpc7q54t6kv/9PuucWkZGuyrHY5prGySvWG/CMX62UczbS/UnHQ/h1qbw6/Q8G6UBEpsZ0gGuk+Jcm0N8T3JjQJpijFr6n20yf11PZrVarWZSJ8bNix/uhPLnn5+e13e4i4R246lbS0oh3hrWj06jv/J5093zZCHXz4Ho9Vvc96cfzHrfBn9MZc0UiHYtUBLkqSltevfA90LoDN0sA4vcsBjzd2G080gns5sk81Blly2cCZM9pRzMrT/NgB7Qzkk2xXHeRL/OL63O6x3a7bQ+oSBAzMqzJJznGURR6NBbzdsq6ecyrx64/9QFjGBnPlH//3unj7G/SIj87Z5o+pV7v2jmmYv3glZAEqMyDVzQ63ZDOAd/5zfO5NH+Wp7RXVXPQ4VSpETjpxp02GqeK667DDk2+5sS6iT55fK4HPvY+kZFj+fz8PEWPnYJrHdPxeh6uY7oyXq/8GVzm/sUl8IytTx73AVSJibKvtGMe436neXMPq4ed3HUy+Z7FutG6bgReO7Dr69AD3vLBFfADGRxeqaI+dFemKHdzPALPyXfUXTUPYnWBMztT2EVnzjw9PU2rbV4FNYZlDJ1N7UpHY3jMK+m8h7GzZYmJO73W2cNOR47wx8iJXcK3ec39wGk0Bk7d0zmU1MGBJ9b/bvstMvbmgyrMMKQupMJ1p0aA3wPxNSuzjPBQ3yGA0wH+Lt0wJ73rj8dMGsnZ2VldXFxMqX739/f1/Pw85X2nwUaAHIVZcqpsYDtgTJ2ML1/mafrnmA0cGY/BRAKknEfmI9MpOmM4AlwwdTpVVfOVwa4kWHEahQ9XyJSVjDYf00rV+fn5jE6OcNmZ8B4fF/Nv8n2nLDqQZlnr0p5GUUf/nzLayan7k4AlgaCj6PmCbWTOwM17PFNndGA1AyK0bSBLG3mMumnDPPmIagM/0522rMw7R8oy1vGp59N12EHNtvLelAv+t87rAlpun+/HIEtdQdck6OsCN9vt/gvTc2yuo2q+R8B62xvEO9r5r2oe4OP/Ts46nh7VCy/SPwM/n8xXVdNJtgbv3J+nlzGmTvenPBrkGXwbODNHpNUmvyV4dR9Tx9k5NO2579ApjZ3tHtnz7JfpnfYLnGQnJE8HrtqtaLgOgDH67xDQ/j2LnVe+vyQ9EdoZnPtkaY7OZpuJnQO3xz76u7u7icZ+v1Hat8R6iRvpV85jOlrmC2NjHKaqXdrm3d1dffv2rZ6enury8rI2m81s6wHtdcEY64WRjk2sx3idNgpWt17qnCXTJ8dMyUM1XLpAS+LC7GvnyLhf1mXwgQMS3GcnluCEaQkdqBN5XNpT+pryaqcqG0owlEaiKwlsujY6UN4Z8M4JsIC7DTNmx0Rumzq4llFnK0IrQ0pOtPuY1zs62DCPvHuPI5/1b13/ko7OWYVhU3izHp61Ea2aH/DROWamq/mmAzujseV3t9U5p6lEzU9vFZ6/d0k+T34fyQF8UjUOXnRtdd9TkaeTzv3wiO9Nmcrnkneq+tWCER3Mq3bYuvHm9UPfO3ol2M1xjkoCWj+f4+5WPJKmBohd26Nr2Vb3TNdm1pFjsE7Kuo9FlrryUvmq6tOFuu/8j260U2W6pS7MoKNLl67jsuRUpaxU7R9rnA530iBXOLs6TaORnrUMLjnjrqNqPyWyG1eOp3O+/JwduNVqt2cn9WfWkc6b+2jaJD7IPjPGkax2wNaH15hGh+p7r9Lx4tI8L9FilCVg25JB0cxugCe87xz57MpIL3RjGWEY8ycBb8+naTQK6KbuXsJ/3D/CkB29so1OD3r1Kcfutpb6NsJh2e/u2tJY3R+Cf117XZvUkfraeML3vaW82qlyZ+mAUwrOz8+njePcxyC22z5/1MUK1eCJ+xEMg/gUPn/3NUeeO6Cd91lYvBKy2Wxm4I7x4P1aKdBGpyz4vTMMVfOXhHYbek2z0RxZON030yhT87r6XQ80soKyMDDmkXHx5yia5bEuKbklIEQEa7VazejH+I9llYqSY8pxe7N099yIr32fv1uhdEp81FZXb9feSNG7L51C971ptBzRdbQtaZZK020vAT/3rwOT5m36ZRpaHzgwkLKdfXP/XqvYPX+j+ztd6TZSVikJHjsd5GdN/2MCfFX7AYiq+f5SVm08h36O+1I3wZOd82A9WLXjHxwnt8PvI/Bn/uj4puuv+101j6zzTqDtdjutDuXqC/WRrpTALMeVcuUUnDy1mbwAACAASURBVG4+Ur+Ytk4/zMN4eBaHsQNUbsft2bFaeib/EjzbbuZ9maKX89HplE6G4R/qos9eyT6GYj6zTOTcpE4wLavm8nh2dlafPn2aVi+/fv06vfzVf2xJMO+5HWM+32f8lXOVZeT8eMw+RMkrVTc3N9Nq0fX1dVVVXV1dTSvF1jfuY2ZRUA45MlU7RxJdk3rK+jkDOB0G6TCYaei2jR+63/3bUrH9N1/l6uHp6enkb/CZ9iptu213BnCSlq8tb3KqYAJHwlDMOE1e/kcZoFydWkHJPNVsz0SysCY4rOrPt+e6jWZn3LoIBMu5LDVaYAzcM72E3z2uTEtIZe2+miFGhumlhTnwGB1t7kBitwpoxySFxGUJ0LsPdmzeAsKWQCRjWK12J8HZGL9EsH/vMuKHUfqN77NsHVqV6wx+AowRbUZ1HEoHOJRO4LptiD1fBlFV83fVJaChXYyLeZu+OLUwP7u5GBkG85tB59I+mDT+I9rZMI3KSN4yWNOBmpFTZsCTEb1DjtWxBiyqeofSaZu8bLJqvgKRcgLdvB8mgT+6knmwDbIsmzfS0aL4Hv+lXXCkvLvOGEiBMwj0eEwnH2Xul2Sapl2qo21XApcEbhTuJTV8u/3xglSnwzE+70fx8wns+A7dkw9c3J90pDyG5A33q0vxtUza3r405cgA8/n5eTrt9xiKx5/jq1peoYLnquYHF52fn9fnz5/r8vKyHh4e6tdff51S5z59+jTRGaeK5x306pyUlMOk/SG9ZRlnvh2Y8V57Tt+7u7urzWZTHz9+rNPT07q6upq9cNs4z/9bp6QuH/Wrc8KgJxjQMo7cJi4aOZLZnuc3Ha1RsKALxGWdGbzM1zk59e/h4WFKFU0foXNQE8+ip92Pt+LtN69U0fAInLksAV9+76IYWccIzNmAGfAnaEnFlsKRitjgwJOb4/QkpgLtlHGnaEb0SSFZema9Xu8ZZCuLTpmNrmWE9VA5BN7y3rzH3x3FH/FGR8+udEomjeSxlM7wjED836ONrs7/a3q4/RF/ZZ/SQLuuQ8a6a3skVx2vdP2x3qJYXtLQcG/ntHVGKJ8dfX/JXKVh6Pq+pI+S7mk8R+XYZKtqfw6SFqnT89klG+T6re8zqJbgKHkgbZFlZGTkDSTd3278tpWu0wFE61X3Ke1bB946kGU72/XJfct7naY3etZ9/a2lA34vvX/0e4dBluzPIdvZYaxjKSOZWLrX/yet/HqM7XY7O7RiRFcH2LjPmI3v3kvYyfOoLAWwXE/alNVqd2J0lxnU6aauvZeUtDHgYV/3fvyque15if5+iX5PGfLKK3Wk/aHPfjadMPpvrG9H6CV9H9nC3ypbr3aqOkbh056uN7m6w47cevUFYtszN5Egcl63gklQD4E6sO/JcaTIk070ztGTBBk2lN0+KkcMnRKQjJvGs6Ozo50WwIx0JX26PnEogjfeZ39SOdC3t3rwnZEagRgrQ/fNhr+LhrkeNrp6PDxPxKaLur9XYd4ywsxvFEcrbTByVaRT0qPPJWCd132v5TdXV6tqbyXIgCnnrQMcHhv3csQudaQx8/dUuh19PMa8F3rnSWNLhd/Nf2mkaMf6bWRIl+RtCfh1v8FfOQ8d0DOYdj9GvNWN85iKdWP2O1d2Or3kqHjnKCw5ZPniWYM624Ptdrvn2FBnZ//gobSzKTPuI896U/35+fme/rB+zT3Endxh001bxtil8tvWuK+8b84vJ6beDpSO7D+fCZQMwEa8Ci7oHBj6brq/RCdwr1c00lFNeho8wkfMwejAovcoj4+PbaZO1csCoB63nabr6+tp3N++fZsOILi5uWmDIJ73XCGGnl6h6p7r+plYLHGG52W73U79e3h4mDDx5eVlXV5eTqu+rruznz5QImXHdOv66GtVuxVjeObi4qI2m019//69fv3117q/v9+rK+dwqXT0ys+08V0do8BW5wBDx4eHh2kl26vZidN9MI95oKNhZyNeWn7Tnip/piK3F2kmNPjLdKFcpusMttvq7umYv1uByjEYhHFvnkzn592Gx85vTp1LRzOZtQPDHfg7JOhe7sxVNeiLsfTLjJ2O0vXRpxS+1qFaWo2gdMYt07qyf2nYO7qgSDy3zEceDX8MpaNDB9Cq9ldvzIN2tpKPRmPNenxfp1wAFVm3vyd9c/k95dB1m3etMzK9OGXaKTkGTku0SF5M3eYTQ5MuaWBd3/Pz/KhoGwzo1wVJDpWUTV+zLksdxXffw29d5DRp26162WhnO8dWPI4MpqRT1QEYaGT+y/pHdgueHKXDZB+XSoL9JR3B73mvg1Loww8fPkyvKOAep93CwwarKTtdmnonXyPcYNt5cnIy7dEmkJKvVHC9XXDwEEhO8Jj9zuBttmsdYJ3Y1WVZA+DlHusOdGKvPQ+82PYtoO/vXdDLXcpjVb8CZVyQ+AYanp2d1fX19fR6mr/85S+1Wq0mp6qqZhjN9ZufjI0SJy0FVUe20H1NDMn9d3d3k3zAs5vNZjrxL+2fbYH/7FAt2YgRVjQe5Ej3k5OTury8rPPz87q/v59esOy6Um5sU5NGo2J76TGkLUpd4PFkfdyHQ3V+fl7Pzz9SYW9vb6e+dosfuYetk7WXYKVD5c17qrrSKYXOmHtyukkZOUpmqu6a+zGakOxvx0BJ6K5/I6WddXYK5u9RRuNxH7K9pT6gAJbm5beW0VznfC0ZudHcdPO9BG5SkN67dPxd9TrgNaJPV++o/c5x8HfPVecwZR3dHLy2JN929b203pHC9PWRgcr7sy+jtshX79pMY/kaXhzxsA3p6P7XtGGe6hyyqn4f7P8vJemSgM98gA5xVkXeR1kyytSTTvFScT8O0bebj2zDe6gcrFo6/OVQP7ugQ/bjkM3ufreDZ/A6qm+pD0vltboq52RJX3eOxkva8Jj/L+zy36sc0oWvLalzMsOEz8Quh9ryHFgvj56zTHCfbSGfDiL6Hgcjso6X0iFxc5bX2g7fz0EgVfOX9XJf2l0HGrKfvs9OkoPbvpfvKTsvxTgdzvPz1keWn0O647X0dHm1UzWKkvGZ6XBc829VO28zPeQRsMmorj34FIp8zvW7pNKmX14FcT0pFGlk+T83quYqV/Yxy0ixH1IapEmMlLVPj+t+zxMVHTWwYe3AYVdSSEYgsItsdpuAk9Y5/9SLkvWcojgyikEU9BhAYI4nV6IOGVTuB8RbhpAZr/Bku1Xj9850fU1ZHTlYlhX+nLbUgVh4oDMojkibRrnBPvXBSGd1vztKCG3TUXREvzscg6h/Go3OOTT9XsKLSw4NdeRBM6Z96oDRKml3GEDKYr60umr5HSbvVXIVhRWA5+fde5JS93vuGKPffZK2qDul1XJSNQcRmer7Et3a6T3PST6b9oT7/PJRj3vpkIUcT8pM8gj1QP+MvCducF2Pj491f39f6/V6ivJ3BZ1n2rvepIv7NKJx14Y/ecarashDp/MsJ2SJ2MZRZ6dzLYMjvX0MxfOGvjy08tDR3jQhrWu1WtUf//jH+umnn6Z0r+fnH+8pvL293avHh6ZU7eYt01DpYzpNh8aZJ7wijz6ZDtzBSX/mUzuIXWDFPOPDj2h7NO+Jzxmzeefp6cc7VVnt/Omnn+qPf/xjPTw81M3NzXQqKO8GW3Lm3N8lfGJs2tGzc6g6J420YFbewG/QuusP2J104ru7u+n9lokt+L6EfQ6VVzlVBqwmRKdArGDMeFZw3ZJ/B9aqambE/Hsu3yZQynqyZAqQX86b48s2OofKNEiDlIxzaNJM5+xDjtsOnftHSdDjcfj/THfo+v8axyoj5h3vdP3N9pKv3N+c/3Qqqnb7IfhuBeN9P+9Z0jnpHIrRM1XzdB/Lm0GYXxBtpeO2R6tMXT+XAiBZcu64NwF+ArPuN37nM9PXRkoxFab1BcZqJFtZj2WlA0eptEcgteuv2+8ig5bPkUPE7wY4GJjOoIwATjdvnUy6vhEPvFdxnyxfXq2xzHiOPGant+NEVc336WSQp+PFka7LNjtH27xnXdrxoe+3XcsUNM8nKTJLvOpPt2f6dWMdyR/8bH1iR+ny8nJygB8eHiYd3712BB233e6CAg7gWB5yLEnnznbnWADLnU03HiDYCs3THrtOCvPiFGTKIWzzexfPf9e3l/TTOqVq9xLk9Xpdnz9/rtVqVbe3t/Xly5d6fHycTnzD8eC5lPPEMdbJnbxy39I4KXwnLROswVzDuwQJujnL/ll3m69GuMu/+Z7cLkGhH5vNpn7++ee6vr6e0udub29rtVrNnKrExLTj7TKdnvA1dKb73fkQaUscoPO+VuQc/sgAlelGEAk5Yvw+ydTj6Gj90vKmlSqDdwtSKuIOkNuIdwZ/ZFxSAXb1vaVYCEcKLvtCu53xODT+LCnodkbdj2w/6/Rz2W4a2iWwnsacutNwjvqR1w85l/n/S+mW9/rPbaYy6cqxGKV0ggyYU+hHAIrCPaOxHRrzyCnxb6MVrZfU7X6m8uX6S/oyGs9L5tx1LilQ0zINMHPUrRa5XyMDn/1ZKl3Aw59dvymdUez6+RL93ZVOpxyLXFG6AEwC2ATeozEY+C1FYLvvS3qX7yNnKksnNx2457d0Il+jH7sASOoj/z6SCT+7xL+HxpABEACY+2w555muD/7f4/I4lmyTDyroVsc6nT2iyUg/LNHlvUvHz6/p50ge0omEfvDAZrOpq6urvSARwVKeS3lKPhn1h3nwSotl37zhvfN2nGk/XzmQOLbDLtzre0alk7essxsnDkpV1dnZ2TQG76VPW5k83tVvPeQxjWjuNGTXR8aH5Z5+HwrejXCFcZZp8fcor3Kqnp+f6/b2dvYSQEf585QaNhA6ncDpR8l09rRHBs1gc/TbS4ljgo8OaPD7uBhDN/mOPFnoU7EkQHbbVbt3ftlAdIzjVUDXmWk3VgB2iBlHGnC3k7n11NeBtaQn//v+JUCW9R1yxmzAfCJU9x6DjAi6ziVe+z3L8/OPVAYfjGLnyvPIOBOwW/l5zBmQOOQsdX++x+2Z3zoFlXNo3n5+fp69d6oDZzaSHdhM0MR4l4yP57xT0Ka5ZYxn06msqikFwXR330anEnHNn8wxdaRxdZTdujDBrvnFEcUsyTekhZkXu7l0XZ6/YyzI10j21+v1tIGcaPNqtUvXSaCDbiEFZUlO8tOy4Hs7ByrBjH+r2teT8IVPwrIe2Gw2k/1mvG4n9ULa23SsbAetk8wbHSjM/qZzBU04rYy5YpP9yclJPTw8zE6ESxDIc9TBYQq2z6ZP2mkDx7RjXEOunELpdD3XYbBtmhtDdKcMewzWBcfmXKVetOPw0gCfbZ7rADtS16dPn6qq6vPnzxN/mQ8eHh6mAxi+fPkypQjmidP+zP4kn5DpYr3hQ75YkfJK1Xa7nd57h2NF/ckbVbuVGMbq9NLOOcngN/1OXOr2uAc9wcuJT09P6+eff64PHz5MBz/wnq3b29spW6zDS2kLKE6rTMzMONBDuUUD3+Dbt291d3c31QEt6Q/t54uxE0uaTtaTXmlM/fSW8ur0v8yXhVHoiFOsIEzXORsoC0wCtVRkMFoaA+7pBLczYr6e37nHStpgKYUt+3cozc59TfDjCc/xdcXgyp48v5mhYLgE27mUPWovhbQzknn/ktOVEZSsY+S8+Vr3Z8NpY9eVHP97loxm2cDk6VtO4eJe80o6BKn0Um663zqglZGrdOhGSt4FPWKnIHmgc8SyPx2QTf4fzXsaRfqeugc+Queg85ImHi+fmZpE6fqUewhHpRtjlpQpO+SjwrjhrTyhKudl1Efro2Mr2C4HogxsHWlGxwHSuJ50R8bgi5E8ZoGGHS9330d6yno/HZludfTDhw/T6zSSDl3bFNMpxwAtSMtZ0jW29baBpkXS0EButVrNXmJK6hf3J40S2FM63dABL9MpZcnOk52oDAolwE080s1l4ofOuT0mh4qSc9Y5rC+po2ruTFbt9oSfn5/XZrOZ9BSYk5PsAOK3t7eTc2D9fmiVimIsYUfGf+w/Ojs7q4uLi+mFvjhVdk4YU1XN7Hc6VyP9tKR3/ZvtUDpV7gM0IM3v6uqqrq+v6+rqqr5//z7tZ7u5uanVapcO+PDwsDdXHS3NC5YVywuO1MnJj5M+Ly4uZv32cfXgBhwhnCvX183hCGPiSxAQhcd+a3l1DT47HyH3pj0vuSbgdxS4K4eMdk7caCIPlUMGPw1LNyGd05cCUrVPB7dho8I90Mm/V9UM/HVRHxgOo8Wb6EeGA2anPq9i2aH0cwk26VfVfhQkx2maUTLq2c17GjeUjp2Mro40atmXYyvMX9VOsJOeVrSmB3M22hvWAeLOKeB7rsZkPw85NwngbSQNDLKPvr87Lcj81/E/vMxzKPOkQ7Y3+j+NEPV7TAk8rQ9dr+W6myM/c8gBMh8buIzu6UrKB1G+LkpuGR/1p/t+TKXjZ++nTODulap0lqrGq+apm90mejZLp/c6fk958LPdXJv3DNayXzmeTl93suNr3i/kYp7vdLIDLwkiUw85oGtHuKqmSHvVDqOY7mlvk0+Ttzudmf1P5ylXBHxP1k+Qxn3x2FPGE4BTjkHeUqclj41oyz2uh987DIZ9xFlJXsvVSuzjZrOZrvt3101xYI0DRZLu3rNHf5wVY95N53cE8uHRPN2Qe3yQjlMRO5pa1u0Y+l6PGyfl9PR02k+VupAVJJycTo91Tpt51/LhBQg7RN7XzHx6haxbjfa43A/T2/3heofJ3U6ehPjS8qb0PybUZ75X7ZZfuZfJPDs7m7zKBGSUjsny8Aobgg7QjQBLtsH3BEYJGP1nQO9n3G+/d4LJdMTQdOQzAb+dMG+8NZObvsyBT1Jcr9ezJVJHBiletmZspoOZmYIzkwq0i8zZYIzAbOekOcpipcCnTxrzaUp5HwKbkemuHINhIrrmnGbTnrRG7iU6x3tcbDSqao93ec4KPBW/Hbs0CJ3xGzleVfvRZtrPOpJ/XPfDw8OMn7KOTrkamFihJtChLYM098t8WLUDQb7H8gEdiOI5WklfCToR3cxggOU0DWQW0y/p30Xh8zfLh+91+myOMaO2Ocf5d2yFcVivEn2t2tH05OSkLi4u9vgV+crxO8CD7iQCmm3nCgTP2OF6CWju5jdlHjk4OzubpdWkzHfAizZSTtO5YNyM2Sk4doZMe+q17TKNPD70mVekWA1Yr3+kXBl0f//+vW5vb6cViy7oVrUDxoyhc/Y6kDoCrwbUplGuZBm4wTv0MfGR6UffuZ86Ot37HiVt+Xa7nfiflYjEeDw30uGmgcfK90yntENUtaPtzz//XJ8/f96jvds1v5k3quYyywoJJwYzZzhvpO5xmIrbNI8k72DPfbT5t2/fJr732Kv2X2qPDNNfUrepv3Pw6B+YHtt1fn5e5+fnU5ot8mbaex7djnkhA7SJG7nX83F/fz979vn5eVqBtA7O0mFM6O2sA9qxvu2cwIeHhz38+9Ly5vS/XHI3KDFQqpqfmuSBLxmMdAJGDpV/74Cf66RvKUhZZwJT7vWYcry+xyAw7+ean3NfOsZEeaQz5nuszGCMnCfTNE9U6RReJ4TJuFZ8jspZ+JNOpmknaElHO1I4UFxPBTX6dFnik/cqli2vOHV99fjtbI0MVTq7qZTgeQOhqnHevv9/idLpjJj7m0Y220kHM/vSjdWla8fXsz3zjSPK7MlwMMTPGQSZV/P+LkhindDpvyU65Xi66HDWkTxhebGeM2hYKsfuUFXtG3sHjqw/kS2it9yT91IMWqzvHVG2/eF3X7PNqDpsH91OOouuE9uR6ZymQ1WfaZA6aMQ78IaPNKYv3kPtoE5mZFg3ZWAHOeQTXQV4rKrpZC/6misStJ92vsMlnWxkgCbnYRRY7LIpLCfwTqdjPZbsT9ZzDCUxRmfDlgI+eW86VnZ2eIktjjzzm0Hg9Xo9OSpV/es+qnbzz6EIHZ5cusZcw9e5Ct7ZFv+feCWDEcY42KSkK3+Zamfb4q06lreHh4fabrfTC8Cfn59rs9nsYQzrG/cb5zZtvTFF7vcH8zgg7BP9uP/h4WFy0N3+iI8sp6krR/NQtdNZ9CX7+9LyKqcK4o3yF5N5YPoOLIycigQrCeyWDI2foz/8vwRSR4YplUTXpldHEpjYSGc/XHKi6TuGw8aa7ylQGAnGQH57xxjJUI7Co+R5HvobiCdIdB8AItDNoDTnvaNrFw31HGRUwbRIcDKat1R0x1CsmBz1Zrwomaqdgu1ASYLhqtqbg865sMLOVUt/5jU70JTk13yONrnm6LV/4zuBBdezBCbcv+57938+b94a6amO1gaCpGz47/T0dFpdTJpk3/Nat5rgfmVfsx7LRfKA9R06I9+50j2XdBnR+73LarWabTTfbndRUeYs+Tidk4xy+1nX0QUjkmbd/x3/j/gh7ZXHxf+WeZ5LUNLt5ev4pEuF4nk7EAlYbf+4x/sT3Y+UV9svvgN6CUDZ1pyfn0/1j/bOOCCZMpMgC93TzVXa+XSwuGansJNPz18CVq4bc6Uz/dYUpf+r0vH9IfzhZ/1pebJcObAP30DjpMcIOOeff++CmpZ7VkQd8LXz4rardoHlnEf43tkBXvmlHycnuwPhHKxIXrYsdvbeGIyCneNZbNb9/X19+/ZtttrO/Z1jkllcid1Tll1fBnQ7nWq8k7yUOsn6rbNxzA20ZauM+cyrka8tr3Kq1ut1XV1dzTxiM7GBn5WsI1QdoLbS93cTvP4fdW+73EaSbNk6SImkqrvPOTPz/o84ZmPW1RIplYj7Q7bBlYs7EiCru4jrZjSAicz48PCP7R6emdOffkTjmP/bgq5AIplp59ocTsab+fAFitzK33tZH531zLYULxlSgmlnxwmAGpi5ubmpT5thlsJ8ZubPTjJboemXL2wOsfSBIJBGz7zzddwep6KzXQOHlpVhG14zGl9mnj+abm5uNpkhOuLwnDXl4WsACktBaXxjZCIX4QvnTb0kuDHQtIOh3PD4zPYhDc0Q8pNjbY6B53os/D9z5DgNrFa2wcY+PGTfTpo4MUEeGLQF8GWtcg3LSJjAyHhZJkhe04k7+Eu/DTCugKD5ZJBNvfIa2C61TO9H083NS6kY1yg3sf/48eNU/sKSkaxdZClzZRvxUfF/vC+A/RtUU1YNRLmGllVm7ZuMp23LaMZNX9NACbPP8Qd5IABlgmOlz4xtaQEMAyr6gNid2L/j8TiPj4+veDvz8o4ZrtHnz5/nH//4xzw/P5/Kl37+/LkpHTLo5rhoQ/1EUssRdSk6wTK0FZCnn+e5DLwCaplcSwlWxtyA6kcT9aLZbANxJ1p5HnWCAXL8NfnE2x6c0Gj2ifpCO9XwnzGlA7bPnz/Pw8PDya6krJt22piK38OD+/v7U3lu5C+JxNw6E3nnLR4cN3WK8hVZItGOzbw8cfpwOJx2hfJHDBUeN2LJuIOn8K8FKfa7GTP9j7Ec44mZFwzJZHB4xPOytsQZud0i5Y+Rj6enp9MLpt9K79qp8iQ52TCUZV8WbpIBns9xdLr63shOydcaHFJIOecVLww26JS44BQ2giNnNgx0qBAUjJWw5Xc6tryRO2tCIWmGkA6B9fHMEpnHEUpnR7gDdi5w4XV28jbOdHb839e3PnjNexTmP0ne7XSJLZ2HwZh5lmtsnBpPvTZ0dG6L57OdPce4p/9ci8y5ycylwOE9YN7jZd/NFqxs1cz2YRyxh9ThOECebyeX47nGmTeuyQr8tTl5Hu6Lum6ww2sJlE3XAPBWFKfrdeBaef2jV7QxMz0pZ1DH89JW0zWfFz022PP5K7DHNtp42bfPo4wlyAjYa/cecUz0Z/ZXtsucE4PYXO85xS4QZNOXHg7bnUgGt60Mi/rFvpiY2uPbnk1t/irrZMDdggjv2jUfmDlcg7618e3Z7iZDPs9BTgt4zG/rX/PvBPwtGFsl4GkXI8dMRCdB5rEFHzJoJ0akngWvJajKDsrMnF4ePPO63Di2jAnJJp/8n/aE55/Dvyt5Y3WNk2rkdwuqLOfhiZN49IUeH+fgZDr7cV/E6rG5mcPqwV/n6F0v/6UhdlkOhTrMZX0kJ00m2IhaeFtQxO92MFYUX2Plc7Bgp0KBjYPhDcr+npv7ct7My4M8qAQJgpzNSlCScVMAqNDMSBgMpwww8012xzxkYMJSLB6PwkThyeuc0xSVYNxrz985vvRP52OjYFCUawl+nBGjjDqjfA3OyeCacuf7DPz+FmfGZ7bA0OvYnFrIuuWME/WGiYlGqz4IsNh+25Vdtd1sANeen/7N11EmVv14ByJ6SBuYdpgsoi47053v1DuXqLDt8K4FN96ZSJuUA9/nY2BH3rDkzfrhpAX5aNm4FjL/YqezfrmvoMkB7WprtwHH9mmyDNMmkp/etbbONzmg/QjxOLPLOefTp0/1EcfWG46ZPtRBhpNjPMZ7seKnsvOQNYgekcexfdz5cXIzD4khHkjbx+PxVEZIPkcPDdj5fiuvb3Y3OfcGUqkT2a2grGXMDGJdEpa5B/D9GeD3nyDiDxKxAoPiBuAbXiPWNH7x7vHK/jAQzXHq1KqywoFB1iP6wx1c3vee/29vfz30Juc/PDxs2piZTWlt5IcVWjO/7hlM/+1hUvZ5kZ2Hh4f58uXLq7b/9a9/zePjY33/GXme81frSnISmPy072w4lDLgAIiyYjvEIIx9x3ZE3mw3mr/K+X/Gf735QRUEPC2w4sAyCd78F2FLe7nmLcCPDLfR8iJy3CQyj9dyKzEOJv/HeEeRUo+ZBc05cUY5L1u7T09Ppy3GT58+nZw4y+uYXaOSBuBQcVrmI+fyqX95Ek1ehtecdYjP7A/F4Lfg1bsnqyDYmUgqRgOyNKD8zPxdaplAI+u1CqrSH51SK2f8qylgyQA4jt5PG6Jj4PeQddQBBbNc0bXmUBg0NT1rCQv2T4DCvrI2mXd0j9dm/pdkz/jXQKUDX9l9WQAAIABJREFUDF7Hm2OZbTSQdgYuOk9wNjObxEmI7RLIehfLgM/GnQ7DII7j5drEllCmCPxImc/t7e2pnGVmTjpmW0Nbbcd0DXoVyvrGXsSm/fbbbyen+/Xr15mZkz2e2SZ0uHaUZf9vcLkKqDw+tpPvDAqi6ylVyTn0EZT93KCftSEYjB/jfA6Hwzw8PMzf//73ub29nb/97W/zj3/8YwNOMhYHeFnv+I/2F300CEy53sw2aIid5xMzc04CnZwXX5s2M/eUUFGvAipjU+0HjWk4Zq5P81szL6VQWRvvvnO96H9iAwnWCZCjW5n/n7nv499NDSTTDmWO9LnGCeSv7R6BM3EbvxvX0HetkiT2Y+2cjJuBVZ6o+fDwMA8PD5sHLpDu7u5OT9H78uXL/P3vf99gSOrHzGx4k3nf3NzM9+/fT59MNjQZJD793//7f8//+l//a4NP//jjj/m///f/zu+//3564Mfj4+Mr38614UNCbAPD45CTPTnG+7My35aca+tAGWn4NFicNjN+P7oS7E3Zaz6Ln+/RrTfvVBl4kclt0hba9ntjkoVtNZbWhwXCgt7AIdtqQeMKpNpp5DPZh7xsMePIH++d4q5VxsLdq9aXx2KgwwCLx3Md+Zfve+C1rZH5yXYM2HMsfFgpDzOGHi8/rfgN1K/kaG/9P4oa/2fmlfzNbI3WyrCeo0v00OPz3yqgegu5vVVbdrz53s4hOZhctWtH3gJUy4r1cG9uM9vkkYMa60ucpseYa6hjGUOucVKKAcXMS+DUeEgg9BbdsC2+Bp0yeTyxu/nOdTag4+clbZssfy2LvvdJsOBER6M932lfRbkiqL+7uztVXHBXhDsCTTb3+ieY8q6Zr2XJ4QpIOmBL2/4enxvi/Sn0O7YH1AfrO3dJqLvp1zpEHnvdqPfm0cp3vdf2fwTRBtq+kPZkdtUu2/Zf9Kb5DOrPOb/TxmPs1fAG79MLFkwAkN1Z47HIAoPIJBecyMs8SNwJy31fTKb8+PFj7u/vT/casuSQ8k7ZMq7zeDPmhsPb3Bou3OP/nu3lOrR+qC9tzVdY8r3+681BlY2lBShGkI7AzsoGmIwjc7gTYQPoSHPvO/+8RbnHwHYNDS8pgVQyEn/7299OipOgygoRoZqZzduhY7C9u9P4w5KHUPpNe3EgyXaswDvXy+vT+MYyGStdvjO7zfHQidBx0EHa0TDAYOaFGVjvGjqrYpm4lp2qmdnw1aCn6VYD7S1D9JadHmduqAM8x9fZQbbMH/tjgE2bQNvQDCr7sO7aydHIck527DlO4NhKwFjiu+IjncReoJWMWStFcPa1OazMiTYkMmIA6OMt0UAQyj/ys+nVzGwAL3cfr0WvSNSFw+FwKqvhzeKhpl+m/OZAttnPPX217uYcl/1RdqwfzKYbeJFoS2lDb25+PYjqf/7nf+bz58/z97///bRTlRvYj8eXHZ+sd3aLaKebTSF/WAUS3mc96G/yiGfyjDvLBGeZr0vuwxfuJliP2CeDN/LJvstBkMl+jffNRNbiq/noageZ5OG/K5v+n6Am9zPzaozELb42vo74iPI0Mxs8SHlo/n3PD6Vf+5x2XfgfbJZdl2/fvm0wlZ/ud3d3d1rf7G5lnSPz5BeTABnD4XCYx8fH0/HscHnuobu7u/ny5cup3DDlvOT//f39CaPy1gmW3vIvOtjsW8a4Jw9cX5/vYw62uD7UKVZfhMeUAQeIvmUiOk+7wLFYRi+lNwdVjZgBNfA7HLb3g3gb0YzO8Znto76TLZvZPhCDQrXaFj4HDukMOJ+cQ6eV/inIWYD7+/v59OnT/Pbbb/Nf//Vfm/K/mZdgJ/PKnBh0kA90snSSBNrObMVos1wwWYnHx8fN1nIzOC1DvwpW+UQmXp9jNI5c1xhFOyyOO8DA4K4FUMkC0THxmpnt/WycC7eFP5oMuDK/mZfHsWbHgeM1SG9t2vD6dwfKTYfcHv93aVwznuzTQdVexqoFWg440pcDAgcxDjIJUFJWEwNsMMAXIjYwRZti58OAi3rNEqQcb6Uxq0/ymNlFjrE5rejezOt37HgXwckLBlXevWCA6sfxXgsx0ROQfH9/Pw8PD5vytdiH9qADympss/3IzGVJwJnXj3rOtZZfj2Nmq2MZw6p8Op/cIQr4ur29nf/+7/+e//N//s/c3d1tgqpv376dnob19PR00g/KTmTDwRTLojKGz58/n+7XCtikP0hS8fHxcVO2HjseOeeOax6RH19A/tOemX/087Qn4SHvl6L/pn2mPnEtqIe+zzpBVYB2ALjtC8fO5E9s1rUEVSHao/xPm9eCUdoqlqm5giftRc7iu1swZZ/l8Tmosy8M0S8eDofNvTm///77SVazfjNzCnwYSGXnlwEWgf/MnB4sZp3h0yX56gDr2+HwUsL76dOn+fLlyzw8PJzWIHNL0BX+hc/R6zwVNcec9Cff2i6dMWnW1TjLfpxtUyfTl/FkNl2iR4wNmOCnrthftqAqevoeendQ1SJWO+1VVEvgaJDhPla0cj5Uij0ASIpQmchkLnBTOs6bgKQFPXt/5u/e1uiKeG1AwwoENorgrZw7QUFri0Eo+Wpe2snvAQpm/82v9p3/Wxb2xv7RtNIBrmm+N6Dt6/fmuzq3/fn31l8LAtpc2vnnqOmbHe3eGGwTWtuxDXSiJDsE6nVLRrx1Hu03X2snnP7ZDmX+XL+ej+2N5a7Nb4/f105cQ9rsvd1Ig+9cv5eYoe1rdmhP53hNA5ik2MpL15t+gkkqJq0Y7GSu58BUm5P7bDui7MdBfNOrlS1KPwxczbvWFte2nRefRj7T9rSxNZ5ljOZBw1N7dvka9WzPBzQZtCwzqF3ttvL6vfZ9Tmur6fNqHsRAh8PLPTvH43GTzE27LVBYjdPXUF4iI5E7VjFZBmy7MlYnFRm0GV/Rp+ST/oUYpCVEzUuuKan5zZWfabyy7hj7cexM3rLttySEL6F3BVWeDI/z6XAta0uD3zIMjByZWWrnrowMGdOIWfL8z8VgsJcbgkM/f/48ZQqiSDTeGUMytYycv3//vrlZjnPM+B1c7IH/9EUHwvmFn42H58oGPIZzzp/jyyeNAI+zdIRKluA2chQFJk9spDzWNo+Mn5lezmsPDP2VtAoUZ+aUkTFQmHkNvGa2xiq/8SbYdrOmwTqp6ZiTDu3aBnTcHjOKnlOua2OJHNkJODg4Hl/eQ5Tv1E2X04RPfIRtHAj5yh118qIFeDx/xesVkAu1LB/1nscucXYBy0x+rADyHhFgUqcclH400X60QDT6ZdsbO8Ydkmb/mk+0fDegeM5+XTKvPYq8BQTmf+7ysyyJfvjp6WkOh8Om5C+/hSd8KFLmlzZ4jf0sAyZmtAl+GNi1ebeA8/b29rSbYD6HHy3Ach8tkZB5cawND2XMBKttnZscOUijD2eJJ3czPpqCh9rDCFYgnb6d58YWxS7t+YyZrV31bQNp07aVeMC4xfJEzHQ4vDz4K+P8/v37fPr0aX78+LF5SAQxJGU/u1p8qBaxZz4zj5z/8+fP0w6s9TDfQ+HP//t//29+//33mZlXu+7ERWkn+DRYlQ9RC28aro8ezMxpjDnu3fPD4XB6CAt3kKwDxH1cU7bFNkPWqYwj/fBpi5Sn4AJi4/cEVu96UMVMdyCcNB16iyQbmMunn4TXAiqPaaUce9SARK4j8J/Zlv6lVCLHozxcID7tJm1GgLLAVAiCUhuBldOlUTfv82kF8h/Xjp8OpFpQ1XjcgAaDVIOW/OV4tvzTFrf/Mz47YI+hKQLLkRwgtDY+ijg/B1UxICzRm1nv1PqTBozgygBjT94MEv3HediBNqIs8PqV4wwR5Ma4xkgfj9ut/5lt+SeDKj5tzPrJ8uOARY7Ju7Arnvn/jClreM5wN2DY/idYZykWHTTbdECa46ugqgWrTY9i0661/I/2I38pxXLSL+e3JEYL+vd2iVZ200CFtvYcNflpY00JW2SYT5ZjUEWZTlA181LWnbXN9xZUBdhSFqI31CfuRBkX7OGM8Il9UHe9+8X2rRNtDWmD23oy2RL+E+CxHe7uvRWfhBhIOYgk4P0oIrDm7R20RTnWdIsybP9nrGJ/5R1Atz+zfTEtAyQHC/lkHywzpd1OGf7Pn7+eqnx7++u+dZbc5b523ssTnxKdo+9qGCx+P215PPRpnH90Lk/4S6DEZEP4lnuumExMQBU/GDJubBifJbnBb5xj7vk6HA4nG8OEAeWKa2KZ4/2HKx9KOeJLlKnbLFdP4vXPJCv+1D1VbSI2KhSUS6K+5oQaoLdyrQzLijnNSLex0MC7bzoZKoxrndO27+tZBZUrB9zmH7Jx4vhbEMV2V3xoBqr17esvNfINIDIY3FvX1tbe+SsevqWPv5JoUHisgd6mWwYClqdzutRk0u2yLf7GcdnBrvSujZ/ybHmzTBNkcSwtsbCnX/5s7Tnrmu9Nv9jGSv/25K/JwXuIvLSdCDlw9Thntu8pXJ1rmbkWsi6t7B/l1eDcu+bn7E2+X6Jvvm7mfLlQo9U6pp29JJvBJoFS83dNpy4ZK/Ul7ZP3Ldm1Z+c4/hzn+hBENR92LumzN49czzXMTsbKtriNt/otJ8I+kpq/8A7de4jywKRsO4f/+5N6y/Hm+6rvS2SPSeEkLpigc4CfYIzf3a/7iCyTB/xrPpZ6wQDM76ViIoAJkpX9XvmQpgftPB9fBdHkC5OFPt/j8vhsz8lT27g9u/wWevN7qrIFmQh6ZZyYncjNdy1Amdk6jvaXc/LHLTpmwfaYYSbmkwub8xxEcEES8WdeMy8PXjgef20tPj09zdPT04lHLjU5Ho+brViWGrEs0Fn0HHcZGynZx2TI0o8zWs25tNJMf287JORl2iGfm6HlJ8cUo0Enuzona0OHSqW0czbAXvHmI4lGY2a7TpTV8JKP5l8lADz3fGYtWQroJ9Lljw+babJDWeQOsHepWylA1tK7ko3YJ7NoaTtZPQKome37P5jhs26Zj3SamZvHSP2xUY4ceweW82nUkiQMHBufaMf4nXwzX5ouNgcUPvhmYwPJrEsejHMt9Pz86wELLrtpyS37g1x/OLw8qMCVBuZTK80Jj9IXbamDE9tbnx+y/7Ke5RgrKwKsPn36NE9PT/Pw8DDPz78eOf7t27dTuRCfykd/T+CVbDb9Mm23ARDtyePj46uxEgDTD2a8x+PxtENgXWNJYoiPZadfZPb9XEBlvNDOyXg4j+Px5T4bytaefnFHL+cR49Be0VZ/NGX+SSazsiLfZ+aVvtkW0eZF3yILuRWDfoNPfAt5d9Nlq/x0MJ/2Z6ZiN46bsvv8/Hx6mEt2XlJmlttF7u7uTn4g68ZkDcnjz3hyjTFzriFOyFyoT76d5ebm5R1Y2XUjJqKvpqzv+Qt+OhDmGkcf//GPf8zhcNjg+tiUnJv2WDLM3e74fZef8qEw5GceJx/MnjG3h1m9ld68U8WSFZd0OWqmQrF0ySA77YZWYJDXeseH1+V7U9wQHZFBrMEp50MjzzKA8CGOKo/AzDE7DxpKzsvlgQaDLP2yYwlP+Uh1GzsS+ec2HEhxfczr8DOfKyeV68xztxkFd4bJssH/o+gtg+d5MnPjzOxHkgOQmW2w0pIAdFg0zjR8LRAykCQgaZmqpkt2ZqGsIefkuRkIsh+204y015HEcgOOnwbbASWdrOWHehAnz/9p8M1r99d4ZjCcYy2p4IDIOkleWrcYUBlQ+lwHFdY1yyZtf+aawOpa6Hg8nsAD52A/ETIYNNinfrT7E2lXWLlAWbDdtR9r53N8IZedGaitZIaPaZ7Z3gsxM5sX1+fJZRw3/RR1Ke2tHjSQc3O/FvlOIMuxZy2azNOGUeeyNn50da7hXFke5nPM7xDtjNtjEMExEuSSj07IWF8pC/67Bso4s/6+14W20vYk33NeKNgp36NnMy+2MElkXkvAnmRKEuHEQpRV3sO9h2FyXXalaAsjf09PT6fNhzwxlhsLGS/1tPlCBjORm1WSwvP3dTmvJeTTZuwUEzmW5/A88tkCqvDD+J7t8eEeX758Od0HyacMphSPY0mA1CoIHJwmqLJNsf1mopT8sU+4lN68U0Xjv3deo5YNeAvZALu/SxjgNs6Ng0aX2ZcQjbcf7xmFj5DwyUa5bgVK8+mAYAWQOR4HoFYgKttqDORVA4/8/5I13Vubdr0zN2+hJg8OBFb8+UhaKfIlvGvH99bVa2r+rAKFS8jruQqobLBJBAsEHyvAmX7aWNs1bzGc5BOTSM7C5VyOYw/0NEdqHp4b1zmyk8nnnty0c8wjgsV80qa1gPcjiaDfQMRJtRVZri3PbDPHIiMrG/lWvSIwMQC0jvG4AUf4MTOnewksqwSw8V9747qEqEcBpgRNAeQ8n7LEuTCZSX/mQM4y+hYMwDVtwP8cXaJz7q8lhZrPJy74SNqTYc/ZiWGe12xp+0yfOebElAM4227rhfs+FzQkGJjZvjYmvzOZwXVicoTAvvmRtB0ZZyLYyfYWWDXbm6CEQXxbI/KS9ibHfR8k+bPy+SZuKPCBIWyfY+SYVrt7PM/XvNW+vyc+Cb05qPr+/fvmXQ3+vSkYHYyZZYOYYGRmNuc2Z8E+PQ737//ZZyMzODfzZdEjcNlO/f79+6mcgdHx3d3dPDw8nL7z5sS2cORhMr5UKmdEXf8+85KVd+Yzu1jMhNvA0YFa4Q24G2Br81nxlv2Z33ScbVdr1RfBXdqiIX9+fj490SaB8DW86yMgh7uSmUN+p+Fp/NozYgZ/zPa0zHn7a21Sr3OMmSHeJOzdNa8x552sFflCgOUx0aas7AUzlC27TuBIPkVXcq3XgskSvh+nrUcDAXuUNaJcUy5M7Jc2hs6OvGcfK1mio145qfDyeDyeyp+vrfwvZW2//fbbae7mxUwvjW7AInNPFjrfneTKZ0tWOOFlgNXADT9dRsuSq8wpJVTUzdjB7N59/fp1I2eHw6/33eR9Nr/99tvJ5/M9UOQbd2Ayv4zTCbz20CCuB+fIJOXt7e2pnIr3sKSN2E/ekN8SHOahQaj/945lwxRcL+pR0yfbAcpIKLsnGXvwQHxWbNc1EGW72b3M6+npab59+3Zaoz2+kawToZR3zWzxIn1+ds2jA9SH5teYTHB/x+P2CZ4s73Q5YfAZExbtPWRM9KWdlLPFzrCEMTthDetQX6jLt7e3px2h+D/qIHfQWJ4YvctYHeyTL+fWivz1rvLt7e3J3vga+uc8WKfNmefFVuSJprYj5D3teEqhs0t2Ce40vTmocoQcauDPE/f/q4CpOfSmZOeCKZ5nxWjMasEYFygvdCPQaUENz/ny5ctJSaysfGu6gQoBHBWKfGegYEHx/BisrvperVWcKTM+5mXL9jZHZUO1WmfuCjYjzb59zFlNAr/wkeWV1xBUzWwfnduMvvVtxcsVWfecCUofNMjknftyIO5xOKPE8xhQJWHBAJ/AiHq2AhKRlVXmjnOiQbdOka/km9fIPCWYDW/iYPeAmO2Sx7Cyf+HnzPoBGE23GEScs81tfCuHSlARnXL9/kfS8XjcPM0qNpl8PpdECJE/4SUBCPuMTFI+8xttFXXE65ljtHe8b5EyyMRfSxpSZpwgmNneM5tAKqCE9yew5Mr2P/Nr2fIcY8Iwx61LK5uS8cQ3pb/c+G980vwRdewcHmA/tHvN3jbdOYdj+Gl5yDEmUX1f1TX4LiaZZvoOwfF4PAU4lmfKT0vWp00fp4+JzCexY56ljZXOz2wxCpNlPD8PoDgcDpvAx36F/iZBS3wC7/tjAjFjPhwOp+TBzEuJcfyifSDvF6Rf49iTUH9+/nX/V86N3jAgsYwzqZBj5Bs/Mw/7MMopfXts8e3t7fzXf/3XJjB20Bh+N2r4iHaQG0G20znGe9S5Rm+hdz/9bxXAtPNWwUozmM0Qr2gFtjm+S8fJ42a8gT37pEN1hvJwOGzqRPMug1V21I6ASkbA7YDWc7bhXxHn2UAz26LgeY577dMZXUqXnru39jSSBkveubpm4vy4rjN9R2YFkt3WTC+PZF/u+9Lx7unXKnub33kzbgxcnNhK3twuySC2yYVlpP02szXKK34xqKLxd/bdvNqzVyte5vzwZs+JNYC91/ZKpkJtvLRPKxv1UdTsKXnS5rnyHQQaDJwDEps9XbUTeX+rHHC929havwymCVwIAunHmHn3PZcs32FA7fX3vT/cqWr3tnCM3GnljgD1jOX0vN+xZeu9ttxV2NMdriVtENvmteY514bntcQF+yQf2/droj1fE32b6cm5mdf3WDOR0BIdvJ42l3Yn8pIdP1KCiXxv+KYlChxgJCnogCSy4jUm/ghRbxIAMvFOHWEyhHxo9oA6nnOjIzmfFUsZZ9rORgADV+scedO+m+f+nvFnfvH1DJgZsIfn5KX1h2uQPhrmWPn8P+u33hxUOcNFg2Tn0DLABAC8jn/eXfA5aW/mdcRJh9CAj9u0Is9s62QtSM0ZNqdi8MZt09XNrQYlzvr6XhA7tzYXr136ZJkKnU6L5CmEq3KDFfAisGQWhrxrAGQvEGgyxTE6u8EgKtvx2UK/pvI/jt3k9TEPaIRmtpnsmRcDSDmkg3ZWa6bLkMdow+X1yv98So+DIJ5D2QhotV6w7cw5WTi255IKBkcsEzIQIxCkvhFEE/RxHHyBKtfBoC3H7FTYH53yypbReXudEji47zYG77xwvVaylH4y1gBlvuPkmojlQJSbFoxQ1hrvKcd07lw/rluzc+GtfaX9YQtQGRSEuIvDdWO/LMcN/fjxYx4fHzfl0Wzv58+fc3d3d3ofD20Q9ZIPWcpTxPLdvjE2OOcz+ZY53d/fnzLw9/f3p7IflnsFNJOXeQlrgLN3D5r/94MVzOv8URcM8qlrLZjiucm4r/olyOPOlF9Q+pZkyX+SHFDQphAz2lbtBZ0zs9lZts/j9/TDHamUqQbrUAZy7krHM7b0E9wwM6c2U1KXoIoYjW2TKIs8Ft34+vXr6TaSyH/4F965rJv+2onylAve3NycyuuOx+PpSYT53fdnxXazFDG8ZvldyFiftpAJk5zLIDK8eHx8nN9///3UX8bKNvMQNtpFB33pL2vk323nmSiyjXgPvfvlv/m+Ar/53YaHn2TyKhO9F0EalMXJnGMI+3BgyPa8ECFG0BljPr0DkpKJOI9s1wegZTwOMOywDPJyjvmd9ijIzahTAG0MQ75hmOAh7ezx2nw+Rysw73G1IMzjzF/m2IJR37/00dR4ZONJ+eA5dGQxNFwvrnEDevm/BbZtTG8xOtatPfBBec08CARbQJDrfJ+i15xzdmbeuteCnMPhJaPnshAmXwLu8qqFmdflWv6efhvfvUZeB65Hs59OkKy+ew18zGOyDDDTGv26JqJNXZVxtmsamafUu/y+AtT0e2yvZebP2SbaOfZrH0U/F1ASok1339Eb3tfI1wNQT1imnvVneTVlIvfcBcA5AM/YGXykT5Z5ff78ef74449ToOWMO+fFoI58S8beSRnyOPaCvjXfCdAutZ8Geqt15To4ufRngN+/m4wJKPuRGdsMl9/yfLbVdMHynuta4MVbL+zzV0niXMe1ZSI9tj5PyGR7kcGGXe1viOWCER8fH0/2wK9Myc6Y/Sd3dMgfym6ujZ1i0tL8y/n5zFoxUOGasQyVyVG2n/MZ0EY2cuz79+8nzBx9z+4ak30csxOHzQb7L9R06s/q1ZuDqpWhMK0EitSCpHxSaVzP3EDiHtA2WembcjsA5LUMRJpj5sKu2iLgpULaQTVHsALfDTzRwZCXvtZ1qq0m32t5CfgzCF6Rg6AVMSPs9ltblj2DUirkR9OlunXu2hZkUd7aXwxsDCfpEnDnfn1fBHnMcdDJcFeG6xRjOjMnR8b+aPQtt2nHSRvKBvkXuWGWn224n6YD5HXbqWry1tbe4PucwV/Jj/WWursC/pfIYgMOXrdrIoM22/KZ9X1qe0TZarxr4DnyvrLvLpfyd/pH/oUcKLUx83t2uHI+wZSzvWyzJf4YVLk8cE+GPdcGRht4dLDlRGGOsV0GJTmfuwErvfDcY0dW1Sf8voctLBv8brBHm+pE0kdRsyskyiv9S5Nv6sAehtyzW/YDTjCsSv/p6+IPHRTQ9q8SVvl/D/8yGe+EfGSKiT3jRet7HtxCucj/GQsTXeEJg5R8fv78eR4eHk47VbEP2aUzzxL4uD+SfXiCxMzNmJqyRNvTfm9rEV61sZwjYpi30p++p6oJjA3XpYZ05nXWrwFAgyY7IwcQrX8yrQFrG9a0wYX1jlETKJc7sT2W7jGYOh6PpyfUtbJCOhjfMOzvjSjAFHASM4N2Zuy/AT5nYAmgve50YiQb2AYMTRwb+eAdB66V72n4KGpy2BzwypE4gOL/My+ZNu+Okg/Mllse+PS91fjzt9p2n3kdjLNNl4qSL2mTOsjxJ3tHHrK0wDbE1zsYyO4Y5Ttzy2dKDu1k6PQIJjLHjJE6bZnNmGjDXNK8AmWmtH84HE5O0jzxWlImmn5Sn5utv6ZkRchyxx2A2MKZdTC4Akjkj4FFfm9P1uK7bgyeGvhnf/xzCWPIump9zLl3d3ensee+3+fn57m7u9vIOMEk/W0yzSznS/mnH0xgG21bQ1klP1L6ljXii4BpI2xfUmaY42mT99OwjJ5ZbgaatE3mK8+x3bT+G+jynIwnnxwrg4DoJl/X8pFEWW4BqW1v7Ljtc9piRY75uQeO2b/Bv7EJX27LPz+ILbustP0pQTMWzRhY0ku7sMe7lDmmdDrtxA9kHJF5304S/ZiZeXh42JTIZpxpP20Hm2YupMwxlHP5ND0+AMS4Kv4t82L7afv29na+fv06Nzc3p7E3f8jyw+jwCr8Q19Ou2ke3daB93ysHPkfvDqoykBUYbgO18Wy0Bx7z2RTWWejVmNwmhcDAf0XnFJvtrBaGxrPdv9EcrB1S44v7Opd5PQesOF47sZYFMAhwxSvBAAAgAElEQVR1m40Hb6W2O9jATuurBdcf7ZT+XbSnI+ey6XT+Wefw6q1ltTRsq6xvyLKz2sWJ4+D9SgQzBEHnbIJlJsf8P4FQ+qGza3NbyRf1huDdc2n8bPp17lqSgdcqMPOYz7XL8dnWXzKuv5oIcGhLvc7tupYQcrv53vSPIChtZBwr/9B46n7tw3j93tpy3JTJ6FeSMKtAbKaXzrAE0EkDB4p7PG2/r4La5nO5c5RxuSLFa2E+hQcmJxPa7l34yr72fGzzjfb5zT5d007Vyv/kWPxKzp95nYCiHPE8f8//XiMDbV7HMWYs3nGyDjHhl7YT0KY/930O+7V5+Z6e8Mb4OTxy39E58pT6zX4YhJAPpPDEAQyD1ZXNJ18bVs1LkW9vbzelxJnDaizmX8O+Tqxaj87piuf7HnrXPVUOBAhi9owfr+f31Z8pAkuj2QRs77q2EJcQz6dy8feZeWUUbDQzZt8w6XI/Zi/aTZoOXsgzK1t+Y10zjTUV6Ny86ayYpaBxtEG0oDYBXx1rtMqcex1aUJjrcwN2U+KPoPCQWVQab+tMy2DPrIHu3vnehXRfq4DM+nhpINWI68b5coz5/ZzR5Zwpj6Scz11ayq0zZsniM3BzfzPbDKiz0Q3Q8tNPqToet/deki8r4LbSmRVAtW5y7dqf+6Lcsg+CmmuilZzMvIBxZoKdCGxz9q6Ld1mbXeb/5P3M9r6g5rtYjsP33jS7mPMzN7bH9WLAkTkmo+y2ubuXHann55cb9fm6itVOW3TzeDy+snFciza35l/dPgHczC+9vL+/39iFvIvGmXSvtQH6OczBMTX9t/3w+ufYOX+4Wu+PIPOFMmtsQrCe47a9Ie4wsBxuz5flO483nMTqDOswscJePw3zen05HwJ+9tNsr3eFWW0QnnHnm36sJUJZxcVxeD451uy3saDto/14syu0s58/f56//e1vJ7uRnbSMN9e5//Cy2Yl2Ow956zVuerkXh5yjN7+nKtv92a4jyGg7K2YK22J0bfDHLN5qq9wLuKLmvFagrxnXXONF5EIEdBEYBxRRqJpiMKjip99yT+NAnhjUWKHJW/N7ZehXNbkz23sIeINiU7A4MCqpDeMqwOJ3AzavaVs784CKnvPD72twTtStyNPMnPjsrLDXkGQA6GNpN0Z25vW9OyvwYH5nbfnksZVza9TAEs91Zs1zoZzSdtju8PwGQltQxbp6lsS6VJG8yP8GvM3uGFimDb9/xoEhdS58b/bWwWhswArshNe+Z8w8Zv9+qEf4c67k4q+mZmMMePIZwBAbYXDDkrEWVFGv7MTN95mtTeKuVgNdq3scVvpFf+X55zrqLn17gqp8zszGX3379u1VUJXvrTQo/yex5d/oi1b6k/WwTjVbk7nFF+fBFuHhz5+vX6Zq+c65SdAwmbwKbBo4tq9a/U7QuGe7CYY/mqIr9OXNR3PdYyvb3HIu7RD1rmENBr/mS3hJDHZ/fz8zv+SZT7/kWFZBOwO8dq8gMRL5wzJ29xMZNbac2eLE6EAekEG55Ln0RcfjyxP9Is8soQ0RE8QmpL2MM+vB8ljjzKxJyvKDieOvMs8vX76cEptfv36df/3rXyeeJMBa4XfbQ9ow2oeZ2ZQ4Zj7N93k+76F3PVLdxscOoP1GWoGdvWtmesBzKVFRGwi95Pr25wxaMyj8zJgN4KLQvHel3ajp+TQgxe9ciwYa97JdKzDtLAC/X7Imdu4kB1gmGoD3OBSD8PfIwn+KaJTocPz7it4zD8uyA4Rz/awM3jnyOhvkt7Y9Xhpvt02nuwI9+XTmlN9nXt8n0+bodWq7aSTvoK34/1bw1AKqS6j12cDqXr8rG3VN1EDq3tyzlk7cWcbcZugSHrZ1bv7DmdhL9e2cbvK3ALvMuyUfHFj66X4roMn+2txWY232aI/H5ndsBYNR3s/T7oVa8cdjvpT3K10+Z2vbfH3ttVDz5TOvk6B7+mR+WM4v1aM9mx9qib9LbJjXceWz9sa4+s3z4LgYiO7t2u3hBO5medwtYM9x/55P+s7VnJisMg8YZLfEqXlj/tAu+nuzK2mXCWO33fp/C717pyqR8Mz2EaergXigactObub1TsbMvjCamlE+Z5h9vc/hIu2VOnkOq8VkuV+yeskm8EbmBrzMh7RJXrK/BiQI4DnnJrxcC94c24SvGRqPneUWzelQOVqWv2UonVlv/KARuCSI/ysp631zc7O5OTaZqL0xrozAnoOgczP4pnMz7zKmGHbrRa7ZM1rpo/3e5mOA1hxudJJ6x4x22jNPmM1K2zxndR9XruU4OP6sp+caJ8K26Cg5FrZxOBw2O1Lh8TmwkfPNK+sVj3Nd9kDCSu72gM1HEuUpc29BlWWi7di2TOZK3wwici7t0cy8yn6vxmU7mRvK7VvTH69dleMQQHFM8VPkBx+i9PT0dNrp8WPXm/9mKWL6v7u72/g561XGbXm0/8113FEmP9Mmy6B4nElNrqn9mx+SwXGxfNQlSda79meyXXEli8f7EUQerYK/ZmMocwyqvFNjPvMVG9l9ZD8ck//fw3zpO+dTpngeH/ed/7M23sWkX8o47Q+4xtk99bVZ7+zeGpdx7DNzwufcZeL6REfP4WLyhTaB4893BtHEEExeZP35kB6uZWzPCu+R9yTKlpNOnB+xScPBvvf1PfTmoCrPkk898uFw2AiLgSwn0oDsquRoBcp5To77HFO7mZP13mQ4jZsBZRYrC2tnScGiUrP9CBhrzp+enk78dClgc3oEVy494TqQ715Hzt3znHl5g30UxOekLyqQswzun0pCBaTBjAI38NecE5WuBZxcRwcR5Nc1OKZsz/PxpARM5u05ZxbiepG4Fk6K+PysTc7NMetBruV9YWnbDqsFdOx/VfriMbGcwHqxFxQYsLE0g3OJHrI8hGNtDiljyJpmzrQdvF9qlYyYmVdlVDNbx8a1aWQg6qSQgSCdkm3qpXJ3rUFVZJOyT/uQT9rbnEvgT/lk+6Hmr2jbZuaUREmfrB5IUMVy06wZ+2aZpe2twQ/XmOfSnmZu4UvG8f3799PxgPkfP36cXlZK35XrMpfMge+cIll/Vr7f8uggk/2k3If6y5K/6CL5HH11WVfG5X74nf6QerT6awGVAzTKFPWOAWxwwzUQ8Q2pBZde06xT8/uxkw7OsoaW/3y3LPGYcaOxR8btAOZw+FXSlj6JffMS3YZ72pgcfGQ++d+lj/SptvctqJqZ004y28s65SXoLh83T+ibOA4GOE7UWIdubl5KiLm7zRLfBMjhif0VsTfbzjnBrH5gFflvWaCtY8Kk2aFL6d0v/22D8jn+/meJwrfnzMOggDaCbQq0DcAKBBiUNIGjcrC/BurCN5b95Rh3sLxLtSLzexXAXkI2OOyfv4WvBNnk+QqUW9CbI+Fx87LxtX1vvOF1/L5X+vFXEnWKgO/c+l2yvgRq5wKNvWP83gLZvbFQflZytrrOc2ljtBw0HW/E81tmkmM4Z3fauOMUqRe0UzOvd7YDJAgqGsBo817J/Aqc+rivewtdyvOPINvEpl/k6VtklHSJ/ZyZV/LAdTLwXPkx2uNWPtX8VTvXdtbyTn/EQKX5LgckbLvtOPFzlbTYu8Yyy/lEx/IZ3jLYM3hN35nXymbyeIBe83Mrf3fOB66IcnstvuscrezKyvf7+x5fY8d8v3iz1Y3H1iXLkBPHLRE8s77Xmza+Jahtb/b8s+lctcLKtq2o+Q/zph03kWe2cbRhe4HcSq+ZrDqnc5fwkAHzav5voT/19L840Nz42cokeM0lg7Wz53ELKY0Kr+O1NKJt94eRrheB/fFJSL45OAvBTGNzTATKdELctcpx3pAZ3jqz2r5zfWa2tbOcz8oAeM2otJxL251itsm7gHRcBJPmd7INq6xejmUN2pZv5kiHk3MoM1yzjyY7St6Y2kBy+LgHDL3DQEDOjEwz9DPbRIEfOpDzuPvEkiWPhePw95aE2AtQDLZaIESjfm59CbaY7aOsUpZX5YjtM9+Zyc7/dtye46dPn067Ge07y9jYZnM0nqP1KzrV9G5lFzMf7rxxTVvw+1EUObeNtf5YtvaoyTrXke3xf8uO+0+JdbNrM9uHpGTdrGPxG5EXPnCCdpDnZSx8MAd381mizvfqODvvRCvljqVbBpDH47G++ybfDV7pEyir3A2mLwovoieZw8ycHmCR3StiBcoy7Y2PZz34Hh6Ojb7N/ow+zrJDuaLNZrnlR1J4zkCbdiO+OnPIbuDMiyzzvUqfP3/eVMrQ16RN7haGHMy3caaN7Ja4NC1jsL27u7t7hTVmtqV81hsHvLQNfJCE5cm7JZl/+MjdnegMd9XSFueS6+/u7jaYeBW4eNzkMcst8+lzrP/G5pmjd+by8A3vGGastF+Zv7Gg50UyPt4LIN+rV+8Kqhr4d5RN8s6JF7IBQn43WJl5EVAf8zVUAio9z7NAciwzW0NJBWu8mdm+12cV+DCoIg8ZTGWLlEaK/7etbq6N+6ch8BYpx+/58NoQAXro589fj/jkvWBcJyq6wQvXYeUsHWxFkdrLZk25loFCC0g+mhqQcda6XUMe0+D4HAMqZpN4XfjY9JX6GJ1o5bQtYGjglWR70ABvzmuA37J1jgzOVu+6yrzyGado2VkFEtFt6m2Ag/UvbXhN/Z3AImNtZSfUizjXNm/+0ZE1u+62Z7b16HuO7aOIoNkgtdmBPefaZLrJuq91wJG1ZNKMa8OnVjU9YABB30ZbG/9F38XgJ2OnvNt/0Z/wMeoE9vZB9FWUM+st+2sJL/K1BVBsP3rRSuRj/9MmExUB2Lk/jEkt8pP64KRD+l4FVfZfzc/RRxvHZK3o468lqIotYpDR1iNlo5GV8I9rxPI698EAwQGMk+Ym8n9m5uvXryf/ypf5ckzBMXd3d/Pw8LBZa/edxIVtSqs4ymdwngOr6FaupW1O0El+h3f5pBzl/8zBfLUecnzEAhxf5kV7wwQrbYYrZNwex//58+eTnjOIzl/0auYlubmXBLQNpo1q9j68/zO48M1BVXOwq/NIHqSBGhVhBSBbv2TE6vc9Jq/aDdloNiB3bkx0FBzDasH5naDOjmsFONv/jbeeRwOJbK+tR1s7H19dx3F5jHvXORuxB/5W/X60IzKZbzNbebkUoNoI8foV3/m/x7B33h5wdubK35su+Nw2tz1eEECtrreMOAhaOeOWdWzlVn+GVm14vAbOPO8Su8Q2LS9NflaysXf9tdM5Oduzhe18fz8HVnj+nl/0OefGTfmwrWzBTOt3dcy+h+V+zadd4mffIise71sDd/LHAQ0Bacg6v9KNhg1Wc+U5HP+lesM1uGY6N6+W+LHc5rw9bGgs1PTD/OYn23Rgu9fvOTu7+p/f23itPy1J1vi4wpG8runz3v+NjFOILZzIZQDW2sknA0AmT7zRwfXx2vhzz3433jRc/ZcEVYfDoW7LktGsLeYkWlaMUb0z5DOvt9eZAUubHAuNzSpiZRsGGJznzGy2Wbkl7TIhzjPXp23uPB2Px1NG3yV+jO55Iyqjcc6PQr0KtDgXrh350gyUv8/02l2udzIiGWvGt7q/YpX5o3zlvGQwnE3PMW9lrwwe+eQsy0fT4XDYZMtCXO9Ge7sTM9ugJfxl2c/hcNisEQ3WnuM2oFlld1qiIN/ZVnYP3B7ndElAZYOYNpm94/iZ4U6/BrGrWv3mGEi2gek33zln2lLqHnd/WLqU9eH47Ng4Tmb9QtajvTIKA0rOJ+OiLcuuwDWRdWJmyzODBNswrnMLtHmNZTZ9MPts+Wm6znb4Ymn7NY49OwMzs7GdBo32IZ4Hx01fxXfp5WEJpKbnK0BoMEiehicuu2q6lmujF64WMVHveStD/s8OFnci2prRjth38RzvBgdbXPLyZpJ3Y1qFzV9NtL3khwOUhr9Y6jYzpxK1PLCAMs+nsOYz8rfyk/GrxHEZU0rhPn/+PA8PD6/wTV5Gm3ZsPz0vyxp9qX1gdqi4G2Zsm5doswyV8k1isiB6GuIOEp8EyDE2POZ50ibwe9aNu5UNq3Ldmx2KPGRN/ACNyDtjDOKYnEM+OeYwtsk4np+fTzvw3LF+jw97V1DlwIoMbpGpGczzaQg5aWcT6MT5IjIa/ggVf+MYKLAZS5tjro/Ry2Lz5WIrw55r+Z2KwG1q/k8+RZFc8/v8/LwJjBwENcCaObHswL8xUxByFj7K6lpmKjP56+Ca42PZYGShbeG2sfOPxtIKz7E1g0a+X0NQNfPyIu2QAVOjplsEtPydRt5BTNYka8nPXBvi2rRxrYCS5TPHCYD2AEJzmCEbPzs3XkPZbGCm6dXKcc68fms8+zKwoz5k3LQpXgM6ONrXHz9+bPhPoO51sQNkW9Qh36PoQKo529ZmHGRLPH0ktfHvHWu2y8kekteUgY5tD/tqcm+blMAmsuGSP7Y3M5u1bC/RzRplXL4XpfmF+C0m/OijPN6VzSKvfK6vY8lX8w1uh+NsxPXLZ86nX80T07Je+d9t5RrjooyPusey9axJSwrmWo+Za0W7cQ1k8Oq1Is5bBVX09Z8/fz5hpJD5kCDh6enphI0avvn8+fPmXqKMKcdSFkdsQ7t6PB43a2S/TLljota2mFgkukNcODObctq8TDs6TqzbEqG2BTPzSraMfe17W2DvwPCSoIqYkHNP/7zHzsnt2KysD/nmJIL9GufQcDDnlGMcd+zZn7lf8V3lfxnUnuFcGYhz7TLYooEiyCfYo/Ozg7IwcHHyO4nXGqTzeCPzYhXgGKRR2Nr/zKLNvDyGtzn85iCtTAw892h1DgOm1dy5PqTGQxo683n1fwvoV/K2Wgee24DVNVAz3HuGgte0c1byHQdHEN+AuPvbMzhtp8XUDLipXXupLl4C6qJrtDMtKLSOtv48Ro/TwGMle+ccwszLPY357dzOkNfdx5qdOzdOz3Wlu9dCtrmXOkyvX46t1s2JJmdM99poyYuZddIg/VjHG0Cyn7Afsv95D6Bw+xkjd1ga7/eON8xBX89+aNMIgMNPrv2erWTwGf1qPLdPyp+D2NWfdW+V4DSfOMZr0LNzsuJ1aD4782+7b/ndMpzfeI7Hs7Jl5n0bj/XHNoQPaWFQ1YjJ9AD3JNGTpODx7FRRH3OOfTMTLuZDq1JY8TXnGl8TG7SgxfxuSUGvURtL0wXqemt7z3/RXpAY8K1k870+7M1BVTrmYGycmnNt4MqZCl5rkMOHOYQC8DOW1aefhGcHZQYyc+ubuq1kDFYyVjtD704lS5Fta5YEZgvyjz/+mMfHx1OpxY8fPzY38tnAcE0I/Ch0vEGbQtsCopVQxZCckwO2E6Dh7GC+U0a4G9ie8JdtYZb+UV7oDNvYPFc+xfGjKWMjWOa7bJiFo0xGt6xHzGDlWPpxhtAvAuT3Ff+oRyz9op62TGMzfo0X/tszeHR41HOPm+M7Hl8y0s7et/kwc8oxMNPobGnTA9q45hQaICBRX2IDnQls4CvfvSvMY6xl93UZo3cB7NziyK+JYre4M3EucKAdyvWrndsAD2eCZ14/gdV6yd9WwQbPyzpnfJQR7pw4u56xWC9SQsUHJ1FWqauRFZ7DseaTSUCWcH3//r3apYzRSYuck7VjINISHDPbbLz9fbNn/J22NL6oPdiEspHMesq3WW7JhymlrC1ZeO7KUIcyJmbKbc9mZoNPPprO4S+uddvhjRzTLrHUy+2xTT61lLiRQTZx3Mxs+E4e0kdS9oPNZmYT7PjWjmZPqHuxIV+/fp1//vOf8/z8q+zs6enp1AbfX8p+om+r3Zr8Flm7ufn1cIr7+/sNnl2tGeef8x34GxOTp/yf+ml8Sr7SVnANXf1B/0KbxvWlzNAvs8KKc6P9i6zw6aTvLa19c1DFiZ5zSLyGjsfG1M6EipTaWvZpJaNx5GLNbJ3IaqeqgW0KKQWI17JfKpiFhucwwPL9VQmg8pkAKwIQ3nvr1Pyms+G4I7znslsGv6tzm8HPdZYN89CO2grTnkCWsfnRz62PFvCFNz7fTxr6SOKaJhDO2hOchwjS2g4lQYT5GD1jVj1/DkxZi019ZFKhARXKEHeELFerwCqfqwCBIDnEe6BWuxLkpR10soQG3+SDZY+OI2Pj+rRdwL0g0eu7GjuTJTxunmXMtG0Ohtpx/s7vDC4pWw6uroWyznx1hfkU4ppFT6x75oltDh1/44Ovab7QviPHOKdVmWZLGNFHst2UUDHZEGLS6hJ55bgZHMzMKTHIJEDGl36cOM0x8qWtBduLfPK42zSGaH4h40o7GQt54wSs/RP1qj1hk4nE9H08viR7VniHPHsP8Pt3k+WT42SAw6QT5dE2nfaDto39OcHhBGMox3ludCfHVv6IusOyvLx0mViLMtgSNsF1P3/+nG/fvp2S5k9PT6cSRgZV37592+xURaYJ/MmjzOH+/n6+fPkyt7e38+XLl9PvHM+5sbagikFb5I+JAY6FT1nN+vmWkCY39kNZA9s275wxEKO/XO2OZyy8Z436Tp6+ld6MJrk7FGYY5GWAGZR3SHieF3vlaMxM/96MJsdMBfBCEuwZKLYbXgkqmRVfBVW5hjfCZfcpBpSBFm9QDMCj8MQwWRnSJ42Yx5P/ueM187pkjyDboM6U83yfDsdkQE+D2ACeDW0DMWy/UTOSXJMGVD6SWtbV+kXlj2x6ngQbDTDmk8DRhirnUJ9XwITyn+PWUxtTjqcBNLZto9iAoe9XpG7GESYb6Fp9zi9tRicJCr1GnhPll/cNrsjy3oB54xt3/RlQtTXkNXZCq6BgBZ5XQJq8uxZdapTHJ5Nnq6DKfJp5fc8Y9YzXhZpt5u/N5/A6yrd9F9vyOFqbJv4W3aD+zry8Ayvn+DHqTAimzfCTj81mcEN+Oghqc8gxvlqi2UTbB/pt+us2/9U6Ua9yHstsW+Bk3do7Rh+XPq1fXONr1a3V2BoubDiK53C+Toa3PihPtFcNh+V4/je+8FyazgWIe6eK17YxRybjgxhIJYFOkE9f5rZYvdJw1O3t7Uk/uSlAH23/12xZZP9weNnNy1iav+RuEdujnOfalW9xQEVZMLZp6255WuEkrlGTQfrlt9K7UvRWDgt8GJsXeVE5uBheGAsvJ5/vucbAnAwx2PKLdX3dzAtQozGOoM9stwI5H24xsu0Qx/Tt27f5+vXr/Pz5cx4fHzfZiVz/+Pi4KQ/MeFKeFcfm4GVldOwkoiD39/ebLXeO1c6IWfkVsKbyUznMB393tq9l/rwl7E/P3cpnIkhpivUR5EBk5nUygFk9Zt8yf4J57kLZEPF/Aw3qo/WDY2Vw0hxAKLuszeDRCbAP6pbnwO/USWacXE4bkBfdydjbOMITlpgwk0Vb4/U7HA7z8PDwKqCkrJoHq7IF8qElNag/fGgFx2UgfzgcNi8xJyC8JODKsayT5Yey2nj0kfT8/DyPj48z8+sG9ZnZyK6DHVZIEEykrZnXttD6swLo0YmUpRAA2h+2MlSvBf1iiL6Ids7HZ34FTL///vsGfEQfElQ9PT1tytUDKp+enk6JQtopgrqUIsXvNEAVGed7aLgesW0uC2qgiC+X5XnGLKTYz1b+Y/7mM2V8kSnvVEWOWMqeEsCU/5EPWdMEbwaBbb7vBX7/bvK93tQXB9JNfygDlDG+DJqBT4hlfNQ/+oi0Gcp65kEI1Lf0TZ9hrEdfwvGxfSYVch0fqvH09DTfvn1b3gqSfuyD9iiylKT87e3tfPv27VWJavjBJLhtfOQ4PsrfZ7Zllxk7dSA61UrBG9bnwyksL8Y1DPRy3IFm1th9BSMYWziJcm4zYUXvevnvKvjJRAzeGC0SzMSZ0AGZmS16bc5qLxI18AuxHI6Blhcy7eU3tseMNh8tS4ATQXh8fJzHx8eTUjmo8i4VlYP8bQCN69Ki9swt/HbGlWM1mDq35gRTDtLaZ8jKS2DJwMmOnsrfDC15YLBh2WjXfhSFz5wTx2gnlXWk/DZQ3QJ96pmz8ZQPgx8DFBr9OBcHbfyfY/Afx2pwmU/WPwdkxYk5uIvzo241YGKezPR7xFZ2KP0SPIWX4cEqMHKGkWvCT8oFbWX6zTgd9FGWfL/Cag38f1szHgtZr66JIiOfPn062VSCsJmegIouZH3yP4PHUONh+73ZrWavaVcZGNn551y2ST1i4o+PdA89PT2dSpE495TwzMw8Pj7W4MkVF+kv+sidwTzRjXI/0wMD8jF9ck3If849/zd/ZWBq35Dx8nfiGNtY3wPSvlPf+D22ogUWq/nsyco1ENfIa9CwBO0aZSEy5ES1g+noZIIqJsLMR/fJ0jVWNERPnDDkDlJ2bY/HX4l33o8cPjgYi+5FzxhgsZyQc2DAtsICxLDE3v6bmbm/vz/5JldVUedzjPeckccJoON/iduYDPXaG9dw7RmAUedyfksu0nc2ubNPpb3e81OOK95KbwqqsuhhbgaXgXhyHCAZlQkyWGigeGZbT0vw6HHxOoPBEB0T+4gCECRFQHyDLIEdFYYKQaLz5o6UwWKo7XjlPBsOzj39k+cNEIeixM5Kt7IlGi7yjesTXjgrYefg49wFo3KuMuieG9e79cO5k+cEzJdkgf4KcqbYTtygjPyYeV03HVrxhsas6YuduQ2THZFlmXaCBpXz4/rRIHPniWuX9Yqxp3NhaYODvjgxywqJBj2ZLIJSyqf5lPFl18gyOzOnTJ6dPnVgZvsumjgxy3v0rulUvnusOWY94rWWqRYA8HwGJgS5Lej4SCJICU/5AIWWTQ/4yjrwfg2CgRUAyHHuGlvmQ7bV+bTdSn+UU9sDtjEzG3/F6gfKZfSH684MPCs+2u40g7bV+ocftt/hUfq0LNrOO6DKZ/OFJPK/gTyW9jUiqIsutd2p1V+rvmjyQ3xCGWjB5DUkMagT+d/UfIz/D3+dQGA/uc4Bude0BWkkyy5vwbDtDQ5k8oCJCq9B01n7lbTr9ec8IzMed9bfSRodEP0AACAASURBVAX6VSYiwiNWXuWcyCLXJNfQTti2eL70y4+Pj9V/cHy2oZkvd4cZTDdf5jU3741r8zvb5hjcfkuEXkJvDqoSXc9snz6TiTPSJRDK9SugRGNJh5YJ8rx8emHJkHzacFKYeT7HHWbyRj22k61aOzob+rSd9lpdOs8n+JvZ3utFIcgTlMgrzolZaAojhTfZRGbP2AaVnaUNBGVZfypx+idlrWgUMw6WhOR/KvbKMdGJzbyuEbYsEKh7+/4agqoYaxrBVW0y14vldfwk+GpyySQBwYYzPk5EeMzM6sXphKjTNIA22FwjZ+pynGOhHQiPqNd2eM/Pv0q//vnPf87Pnz9PJTgz24xd+Go7xWygd2EzvvD4589fdfPMSM9sXyQeXUppgkEZ14uZUDsd6xWdQBIhJAZV/OQTyph5tMxYfjhnJyxagukj6fn5143fM78yts/Pz/Pbb7+dfm8Z75nXpXsEabb7Oeb18acTV7e3tzWYcIDC5F/O5Q7xKpHG+z8oT5GllPClHwJS35zP9aVN/fnzV+m6XwQ88yI3nz59mu/fv2/kkOOmzNEW2S/7SaVZX9oL+kuek7a5U0QAmvUwqDKmOBxe3m8UX+gqCtqAlP7F9rSHTZnsu/hHP30NyYvYUBLxiHfGTeEZbXfWyTaZwUZ78MXMbGxfkgkt8IieEdPlj/caErvR3jUsyLFQhpjMoZ9rmCs8sc/IWIhd7F8zXwdCLUij7Fof027GfDgcTmXR1GH6r+fn5/nnP/95Kjv87bffTiV91qmsZY5FNyIH9Gt+UiOxkDFEsACfyGlcEFsRbBv8Rd6HP2+lNwdVNKgtODHznp+3j/zNMX5nnWT+GgBsE1wBvjhCX0OQyEXjU72cAcjccoy1rlYIj8XOYMU7j281T46vKab7zpwc9OU6OhcHupx/2st6EYD67xIiX2MU2yM87aSaIqWdPTkwMCc4uAanZOfY5kP9aLrGzCdBBa93e2y3nbMy+DNbXWrBl9tnAByHw3m0LCHHwaAr8yXvrFv8YzaShtzJAOsBgzsCA87TDjpzNkjN9+jQzEuwFV2jDLSA146aY6ATpZ6Qmv40eXLQaMp5tD97AOEaKLKT6gPLa5u3M6n83yDK1xPkEDRSfhhwNR10ooDXNXlrftJgIgDJgXnGmT4YFDN5Ql1vu1aeO3d9zZOZ1zvEAX7eVWUSxb7dgX3GzqQi9ZEBDXWMumb/z/ETTBJgrvwVk2H8fi5BnP9ty97qb//TZKyQY6EWULWgdeb1Otku0pc0eeca0GfQ7nO83JHiuNtOlQMo+p3V7qgDK6/hCn9YVviwiOiJAyrzkMdT+tx41daB7bbEXuaTMmHOI//f399v5HQPg7T1tt9rfotkLGI/xfVtc77k+Dl68z1VEU5mv1YKTkausjJ2MDy+N1kaNzsat0XFMlhbUdo18Msxz9EKuQKX3GHg2EJ5uAeJbbG+1UaCjpHzsGPgeDlGAlY6JgIRvgOBzsp9GeSRDw6YZmYTqHl9V0Awn3ZKJBu/tm1/Lc6Jih/DshqbdctkY9WC+BUYdjsGfR4z/3JNA+UNCFm3KH+r8bQ5R7cIFCmLuQn2+fl5s1NF2WG2joaZtqDxmuNtGa7ImncIMp5k4lI2YXvJnY3YD/J4lWiw7cr1K72y3rGflml2AEUbdG07VQHstGUsi5vp/sP+pX0ymdF+T/8Mzsgv89V65gBmZnsDPpMMTe/Tf86JnaW85+El9ilMrhGoZp0jjwSunAOTptmhse+izJNoDyN/2c1n8JNzec8k7QZ3l8kjyidl2vJgnnL3IONypp/nr3wYZYOflBfrk/2Wr/soilw4+Jl50T3yP/6NVS3Gcf7LOZadUNbEdskJP69/C6poLxh4E0vyHAZKq7Hf3v56aAsDkZntu9yan45/SBCe3aJ2v33O5xwpzy71szxyrKbwhXpuLOyxM7hjYOjd1nwyuZFjHHezqfThljHSnu+yTrVkx6X0rnuqMsAYMmaQmqMl4GkR9N53GlAyh4vqqJbBBZWCN66zDxvgXEehI6iIYTVvbAhntsEL76egAeCuEe+7aMCSddk2sASkdDpUtrQZw8BzotzOOrJ2/OHh4aRU5HPacNa9ZZScyXNdumWAJUlWGitZjpFnztL65X3X4JRioJmVYSbKDteG0iAqvzG4IG+sq82Y8xrr1sxLWUV0i2NqSQOCMmeS2pp5Lvnz+3doDygPGV94mzEmMTGzdTp8X0zG4nIb89A6mj5yjkt8Z14cVkrOYk9SJsGyas6PMmKnZr1h5ryV3xAIEmRbxzJ29mnbSdCRv0uTV38VhXcpY5t5eYln9GzmdULAAJnHbfNjw2lX6aTTDn0hz+X54bltl3dUCPD45Dp+Wl6SFPNLZx2opBwm/p27P5THnJNAtdmWXEe5DlHeCBLt09LG8fhSKh1dSlkU9exwOGzKwKgTPi/ksiPL/uoJtS3hwtI/6hr9ZeZp3OQksP+ukbjWDp74kvX88T5tJzcapsifgwwmv1gqyGQWXzodvWLQ1N4LtrpNoD20gjywbc18cqsDMdPMax9DXkb3IkPELLFlxLPk8fF4PD21k9hob+3sX30NsaFLWOmvqWfxWfR36TP63XBzS9Y3zEeMM7P1ryHPhf1xp9GJ9v94UOXB8P+9ATSg699WZGfm31b/NwZ6vPzdjsVznnn9ZLw2HvdFJ8G2KVgzs1EeGg+ekzFw+5XAhjzm/GJErLS8NuNjxsTlVzFiDt7Sh7MWHA/H29Yo/xu4OeuwJyurtWjBwyqI+CjyWPd2qUgrvqZN8pHAjjw51zavsU6ZpzM9y5U5URb35tfk2GNajZVGODJrJ01ZJY+aznFu7t8yz08mFRhEUudzjOCPOpMxcQ4cy55N9byaY2q2zICv6az50/6/Jv2aee1IGQTOrGWLwU5LXLTvlx47ZyOtX5QDgseZl3uN8pt1Nm3zz8kby03OcXa6lawykWKAnb7pu8yTBphaEMldulzre2PTVnYFGu9bYBWeGnOYZw6O9uzSpf6r6Yvx1TXqFWnlV4hPLvG/5HX7jclvt8O1zCf7zB9tQOMxjxvv2obs8YJrT3nx/U0c//G4TaAlGAkupH43jJTvCcptB1ZjNf/beed8NudsvN1sDHeFVvKzh/VNb8GKDSP+WXz4p99TxUxaSgEaiDdDDR5zDoFDA9zcSmTET8G20LtEKWP12GZ6aZozDgZlK/60+TXDyDnk/VGrhU1QlX7ySQXnfVfMGHh3KuQMEXdK0kaITtwvJfZa8RjHTaNog9BAQMsE2thZWTNfP5zCJRSXBi//aVqBiqwXx+ugOsR7Fy1fBE02IAYiNv50XtQp1ps7wLYhDa0Aaa5jENIyXytiKQ+TBLw+WULKYgBUZNKZ8vxm4N120kx27iHKeBynr6EOsvTJO0ntscy2Zz7e1njl8Mg/24zII21tjvGBO9dAASmxBzc3N6fdy9vb25NtYADVghG3SVsaeZnZPoFy5vU7UAw22CaDPQJA2jz6Jut7y/J6zZlh5jyiB2nHZVtp02Woh8P2vT/kT4g7VbTd5mUDh5mjgbQTfNHvtk6XgLb0SYxAvvgJfh6z24zeNMzQ7ELWIz6Lu1Oct234R5Llc+Z1xQADAupMC0jsw2N/yWfrA9s6Hl92mUJ8GIF9v32mecoA6Obm5rQu3OXIbw1HrtaHSWvuDlHG2TZ1Pp8tqGPSpfl4+vGGwXirh3GZ8RjnmLX69OnTPDw8nNrIeS6zzJq1YGylT80Om8wP4+PwiL4qn3w6I6tY3kJvLv+jkZ95qWWemVPGqy1kPrkI/KOzodEnkxjUhDGrTIEDKpZPZPytTpNjdCDA4zaQq4yVFZ/K4m1tGhArbD4ZjJA3XAc6ZI6DvCN44yeDsObsaUBvb2/n+/fvr5xMxkNDGvngGhrYmai8XAtnL3OuKUY0vMlWeXtS3DWQeZB15NOCXEPNv5TEGLBRn/acSdokYGLCgkCGpRUMsjwuZtX25sp1zv8sG2RwYPJ8QtS7lF4YCDM5w/IpzpWlTw00p6wh8mS9tcM2wOM1AXQpjeGTN8MTOzXeH7YCpnaILajy+jQwzXnQxjqrm/fwXQvRUQY0/PjxY56enk48JJAm/wgGwxNmfxmIsBzOTtz8y+8OHOy7KCergIVtsC0CJ8q8gxonvNKfA+t8xh5nnAzyZ14/aY9+06CRfDI+CDlgYmDm9SFvVkGH18ZrTn6z/IqlufRJjcKP6DD1jIFxiLxgmTqDKuOrjw6oMm6XOTOYiK8OEWNcElQxsGDbTj444Zcx5V6+nz9/ztevX0/J4Mil7XMbi3fG8noO67Bt7ExP8lH++NLb9MW5pD2W68682Br6v1xv3Mo5Pj+/vFM1ycY8xOLp6Wn++OOPzZP4OPbItOcZmc04v3z5sllrjtdk/EEMvEr88doVTqX8EbNkDuGvn8idY39JUGVqgk3QwPP22riU9qJYHr+0zRXQ21vElpU6B05acJlx2jHacFjZaQzcFgFiaGUsOPY4JY+Hzpdjs0Fn1oO0MvwEJ5zDinjuW8ggpTmka3JOIQPvS4y+v5tWgWsowZOdg/v7T/CNa9t0g2Nvc3AQ0cbkAI/zNWDmPAlG3T/LFnJNKHK3mhPPCzHzn+90hHRwOecS/q9s2Uom2vnnHJfnEv5cExGk0W/xb8WnfHq9IyM+N7/nk2D/kjW7VLdWa0hfPLOVH8oUz9lLcNku+H/af7bP+5gCZkLUnxWPQy1Zy3XL+D3XjCf6wgw126ZtsH2zjTAPQkxAXbJG5BX/v+SP514LrWxBs3tvaXN1feOLdy4tXxzbinfncEsj20pjlrZ74mDC+td0ju0yUcPrnIS0b8zvtncNYzX/1vSh8aL5EWNh8sPU7OqevWu22X9cB45pZVveQ++6p4qRd7J9qV1mBsqLYUFyhM3JeLeFoMhCkd9DDTQxm9ycZ4QkUX4y1+1dL+zLmTp++rqMw2As5EymBdHr4PZd0+7z285X1nBmTmUwFMDwuzloPs1sZYgS0PB+tPCBn81R0Sn7XrIVPziOyGYyMH6/Q3hyLeDPDiQZ8XymVIk7OqSshXWQDqdlO7kuTbdWlP74f+SQWTWPdQXY7USyppQPBzmU0+gUd5Vcztf4m2PegSYYs+N5ft4+wTNj8A3NtJXe8TMF3NGmZKfscDhsspbN0XEO1q3mjKhXq4wgQW6TIz8hK78ly35tFLtwPP7KNn/9+vXE42Rs2wMN6I8M5r3GLQCwA2/nmCjz8V/ceUp7DXDQd2bN2jjT3rnKAeqRdy/zybnHN1j2uKOxmrv11YEwd4+pU5wD7Rp9DvsnPmBQ6PnnHO5WESus+GQ8Yd5aBlhFkcoK2pNmzy9NrPynaQVWaZeMH6hf5DdtbEvepO180q6H/1yHtBUsQzkJ5XzuDrPvrE3a4KPCjUOpRy6L9hwyZj/AxT7Pcky/ZKwZWT0ejyfMy/k8P/+6zcRPkT4ej3N3d7fBWpx/5sf3hHIXut2rzPK/mdn4ZvIh44rdyJrQpzV/1GSCNiC2PnbD6+D1ouwSL7+F3n1PVYzT9+/f5/Hx8bTN/ePHjw3Y4KcNABea/9uIZvJtNyW/GVzwOwXRIIljmdnWfeflYY0y7ihhzrcxNxH4sl+PN8pLp0E+OeBqpYz83/ykgqU/Bnss52Kf/J33buXaNl+uj4Op5mxIBrxUAJ5DHkWGGFTx5ZW8B4ilgB9Jzeje3Nxs7vmIUfdjY1fBSANvBiK+zkFLfieRXwQiWRc+stzglOfyWvJh5rU80cAy203jF93m3OMsPYfww8Eby/gouwSfbe3ymXWiftApOVlknnp3ixlX2gOvtfnqv2aTWuDVMvLkJ21DbJ/LAKN7vKfhWiiA9efPXy+r/fr166nc8suXL/Pz589Xdr8FALSlCXZbQGW/xATOXmCRa6gnDcQ3HWHbsXEpecp4w4foRsqP+HJP2we/kyl8YMlujq1AZAPDK6Ju81646BX//DAK+ojYBSdC7KvzO48xKGAZoH1S2uT8aa+ohzzXYJdJCZarGxjTln+078o8ZtaVBMQnLamdT/OJ7UWe7OMoR/RjTPay9CtjoS7R77TKFt5vczgc5uHh4ZWe0w9xbu1R//QpCT543IEKfWXGbVkN8YXI2exoWJv8S9t87QH5TruVclbKvwPVtJkAzP0bh2aslJdWZshxMdnAPnNecJ/1xjrjJEzG9d6k4Lt2qkjOzvEcOyG30wStXUtaAW+fb2DQhJLgMeOjw2hZ8RCz+RQqAxODnnYsCxpjQOORsTVDv5pv4xMFrglh+uMY2Ya/rwyfqY27XdvG29o1aPD8eJyZTWe8bNyugZzxao6UYI7UZOItzpb9+VobHBqhfKeDMlBP+6RVCStlz5ljyw7Hzbaa3Fr32B6dG6+Po7Rhb7zL+TzvEntoJ9Hm0Gil73s8J60CqUupBQ8+di16ZaJ9YGCYtXaWu9nDtNP82DlqvGt+YmabsfZT+jiXXEdQkzFThhvZHr9Xtlobq3N5jfU34z8nk3tjWM3T5/L81e+rv0vn0tpe2VnrjxNgpmsIqEwrjDiz5TllNJ/n5NttrMh+1MfpWxp23JtX89FcM9vTm5vtK3riJxg0tPnZNjc9ZYC50lOe0/xO7BGT/W4vtNo1a77T49oj+0ZXwPC8c76V57YgaqV7/y5684MqcjM8AX8yKo+Pj6ddK2bVVs6V0WBzMgbKzeCssmJ0LnwcJd/WvionYyYg3xton/kVtRMEWuEZ5Tsz7J0gfrfw+KbZOEsKLp9QY+E271j+FmOW7HqOt0wODQW3dpvDzjkZM9/Jw8B2z4CaaLhoqMxv71Qxc84Mc3tHxEfQ8/PzPD4+bjKbXKdkLvO7wb4NUci6RMC/+uOOQwN76efz58+bna/8np0qByLM4Ob8Nlaem/OY0WvAceblQTnJTnmnyDs7aTt6k/KvnO829gBVKDI/M6cXPc7Mq5JTr8vz8/Ord+o0kMzfk1V0Jjxr1HZ2V467JZAa0I1c8IZeP6wkspqboa+NAty+f/9+unH97u5uvnz5clr/8IY2NX/NWceGWmbpq47H1+Wh3DV3Mi1jZVl9yDrFMZEC4OiDMq74rlbyTjmnTZ3ZJh7dZ47z5aak/G7fZR9A/9B2zji3HCNIzScz6i5PCnEXJfxJ5c3t7e0mM0/ccC6oW9mJ1Y4mg3s+rZYYKfNg+e1H+66Mrc03cv74+Dgzc9oFXtlS+/L8GTvRVmYnyMmsBsZpn90vgx3qTHBj5Dy+zRjUMp1PJsrb2PhAiMgXMU7O5/ijw3zvU4j/eweMNiZtcGfOVSCZe/idssHgEGPOjP379++byq2cwx2qfBpb20fRvpyTdftUvtuPdqPtcrH81kn4t9Cbg6o8IYkTDtjjfSus1zTZWJMZNNKrjJ4z9y6lo2FdCV5ASciOrDlRj8+g0Uo88/Iy0JzPkjnO2xSQnP5yHZ2EHR3LM8hjC8jz80sJTNbQY8tLMQ3+uKZ0UjaOdE4MFFqNOakZnUbkoctRXEJB8MI+okiR2Y+kBE4BOQ5+A1ajWy1IIA/SpmV25fgoGzS+DiiYzYo80mEdDoe5v7+f+/v7mdm+KNS7bJb7pusZM/snf3Iey/bIu9gABpIGZTG6cRbhJYNv89i8pFO2TXPwzpJTG3bPdwXc4ui8s+5giW14vD6HgYD53+bDIMqg0OXD10BZo6x3wB4Tgk78xHZRxpnII4hjPw1MEXzRTvG3XM97GpxpzTkz88pGGIRxXXgdba9t8gqIZQz0iznuYIZ6v7LpGVNLlLAv6inJvphlXb7vyUHySq/o25i0IH9awqMFByv9zVpZ7ykT1LHmf7MuLXD9qynr0Pz5zC8s8e3bt5mZeXh4eCUjMy9+xwl7ts3AP+vLl/bObBN/ltuZeSVnJAYt9Bnxtbk2cpF1jJ1rSbvIrvtcJaPb/OnXGPi3UnjOg20GXzQ/SozacCP5mlf+ZA2cyAjPcm8UdSPntKSi/+dY7OuJR6xjxjKxtbQPLWHsmKJh8kvp3S//pdDxOEF+Bstr3YYNSzu3XdOUwkQBtGHfyzgxc0VBMSizMDGQWwHGfKfQtfPpRNg+xx1h8Vg5ZvNjBVRd+pLa+ubwVyCOxzxufnIdvN7+zYCuOTDz7xJZWcndRxIDGsuGQdwl8t/IBqgZJDvxlfMx4CRwIQhxdno1/lXARdlaBWmUUeo1b5hngGhikE/ndTxuSxv3eLqSbfLL9mwv00xw7T/rlAOoPVrZDJL1iWNe2W86sfx/rUSdYtAbp2+5anOh/WW7q+8NHDQ94HqsqikIfiKnPI+yTEBhW50+Vg9fcCLEbTOr7nE3UNt0hDaCvDJ5HEw8xu4QGNP/E4i6f5L1jXah+btmT1rbDbQZADJQeq99vwZa2b/Vb8YkKx3i+eew5B6uzDHLXDuP+sUqJ8oH73F2cBL5MI6inrFvJzvYRqqHLNNMfjsw5ZiYMPRus4NzroX1oVVGrPzIiveWb7af/60bPNft7vkvH1v5rvb3HnpX+R+BRhbkeDyeyinC9Nz8SmFiZs5gnRO24WFWzzdvNsZRqFKGQyGgI3FAxHE05czYnLW6u7vbAL6ZOe0IeQ4uR2tzZqacfdIZMkuyKv+iovParE3+5zrwO0FGoxZEhe/hCbPqdsA0PGzTik8e8XqOjRm+1b1UzlSsMmx/JUV/olv5DF9Sdnt7++spZdwtssFZgXCCQAMeGhLyy4YyRpXZH8rl4fArm8Ub31c7b60P24OZbRlu+nh+fn614zMzp92HzMXvJKMB9jEGbHvv1KD98vitg+QN5zCzvSk65zY9MyB2ptA3DTcd4/rRqdoBt7nObLPodjw59vj4eLJrLeD4SGpO/+fPn/P09HQq/0vZKuWB4MI7TKsg3f6N/orfnUQIz7hzc3d3tznHto/HmTXPnHnOKjDPrgxlZ+blIVTPzy/vHTPgTZVK4zfPMxHIOXA0ULY/bMCMss/EY0t67lGu+/z58zw8PGx2rOjvPRfOmf2tsAB9O0v5WH7ktm0j93T3r6aMt+1chH8zr5OuTNBSTuy77b8j56zgIJ5cBbHRP5Z3pt180pek7bu7u1M5M20Cy8Uz5pV+sh/qofXacsQ5hHgu7Qx/Dx8oS+knPsMBBPnE8yP3xHT2M+2Y12wvSfXp06fTTibHwETzqoR3hU9tv9mebTlLbr99+/buCqZ3lf85wk/HeXT1z58/T4+c9DkteODk3R8Vg8EJ2whgtJHNp7cpZ7aZAt4bxDGzbCJj5PgpRJ8/f577+/sNaD0ej6dSLRuD79+/v3oqTAOKz8/bmnoaUtdaN4dB3rcsXHhE5ckxgr+9+7/Cm1B47dK/pnQOpkmeE4GHwQUzyw4OWoZzpdgfRQmcDoeXJ4TFYOYzBp7GuyUmbJjzGT3xOQaBbtOBNoOPlv3iywxzPwL7SGlmdke9zp4Ha7q53gQpmQdfvprMXvps+hGeGAgxA8/AnjvMGYeBAh21nSwdFF82SNlvARvbSKDKv5VutfXbA5qeD+WTAZUBb/jNx0BfOz0/b0vXHx8fT08C9H00M1ugl/mahw3I8Rj110COvsvglOe0dZ2ZzRNCQ83W5zNymMCBgcnMzNPT06liIcDY9jV9cB7UgRwnmR975X9uw+CSY+B8WhVKS9K1ccfm0H9Rx9q4Gr+JF2Zm87joEH2+X0/gtk1NBj6KuA62HyxTW9m2FtQ0Ocjx2BfyLDY1/TT9o37Er7A/jju3ujw/P298ngMktu1KLfOHZP870x8g5Pm4xDc85a5tqGHY2JfMzTJKHgdb0re3e+KY1FvhUNs9jik6F9yQWzc4jpWtXfGXbdMmU8doM4MnElyl/7fSnyr/y0RpIP3HRZ95fc9GJt6+t773jE0DEDOvn1bWnExzfqtxeiE9XwNSjiMA0/crMVhI21Ye8qHxxkRFPwegm3C2jADPpXHM+Svntqdw5K+NQiOe43k7C9GAi3mw4t9HkMfJ45GRllnKd35a/kk0Nua3dchybCc4s70vwzeHx8EwMGets4OSNk7/luva3P2oWd5bRT60gIj9+re2HpmXAyme5zVJ29zl83Uek23Xim8rwG1aOb49G0fdWdnK6F2uuTayHY2djTOdmU0pIOdjv9fs8h4Q5KfH1HzFCkS0NT8et+/mIe9XJaQr3eYYnJzk+ueTuuUEno973ud2uXzMgYqTOjOzSaK6DSY9Q9RNBlV7vuuSXS+235Ir+c16tQq4m85dA52TefPJPofn2b60th1geIfI/p3X51YH287mA4zLvEYee+yEbfM5vq10kYH5Ody0t2O58qn0oZTR5otW5cGcL/Ef9T/rE0zARAJ1i2PYk+1m49rve0S5sG//s/TmnSpm2pzlzXPhkwFM5t0C3oS9ZXYy4VC7rgkIj2fc6YPGlr8TEPCG8uzMcfFdFkIikNwT9kTlEbp8Hg6H044EhacZoZXxSVszc9o5bEDL68GxMvOQTCbXn9QAXxSR2V46aI/HxqNtIfPT19HIJuNAWeVnMhFZ43+nQr2XmvP0mlIm+f4qr2vkhckEO2ueY+DeghWu3wrws+SPWV6WC7Js1wkOjscZzZDHZ/1iBo67QN+/f3/FB15rx2J5pKHP2PjekraTa2CbdshnZ3Bpi3h+2qA+UkfNn1USg47bDo2yQhkJYIhdYnktn6z5/fv3k73x7ts1kG0L+Zyn1v748eO0U8VgnHzL/wliDLJWYDK/r/xY7OTq6Zlux32wosGybMDGa1cyEjlLO9ktpoz6qZYGneQJE1328aux7JF9cv6YTSePyfMGyKw7fGgJA8xVIGjdjoxFjgy6Z2ajK9QxZtNdIkg9vLaHwYSIZ4zhEtQwKOdfcKTt88ycfAgfMPP09LTBbunHAT4Db/ozVtM4AIuOpzQtvqS9Oyz9ZUenBW5MMAaDkNpuqn04fwvxsRY/AgAAIABJREFUSXwNo/GaYDPL8+qWCa4NS/TIY77fig98Sindp0+fTk9XzZrF1vo2h8yh2VLOP/1z1512yDrONlh2v/KTe4HqHr15p+rnz58nkORIm4vJ8ggvMgWxTSjnrIIAkwW3fZ/ZljykPY+HRotbpwQ0dqiZb87jJ8fiMfFek+fnl63IVQRO8MPxcw4z290a8n7FZ65NMwQUrhZQkS8z27pqOyQC6Oboz/1mIGJe2An5t8y3GY9rIRtrg17KZTMeNkQGyivQZ8BlsM5ALOc4gI5DyXcGVRyDgww7nQb+fH5bNzqk29vbTXkIdYuJChpjJ4rMnwYQV0Q+p22vC50Pd6xs8MlrAjfqq4MpzsF609Y451mnPGfbF+qV7wm5NvJ8o0d5/QLtcGSIySUn7mZeAwAH45eOizaTO71eJ9p6/haAYRla7cbu2QHrpH1e/rjeDLBawo82OeNqfp5kneF3ji2lXLE/SfA4kKL+N9Do9eYx85I8XJGD7vSdtqk7TafSBn259fAa/JfHYNvh36wzsV3238YOWdO8xocl4EyQsmTMa2lbGvlptjL/N9nPn3e/jHus28TL9kMM5Kgv9EseY9pOImF1WwjxhHdib25+PbX26elpGUwcDi/l/PFpoQSmxAK0D/n97u5uc3+p+eGqgCZnDGLzSX3y741WicuVTXwLvTmoYscUWg4qE2rZ2xYQmJjFWP15PBlLM8JsN+esFJ3jJnhN+wbqXgwqgedBA8HjzvA522hyMMGxG5BznishdTs09pw7+1+R7+9omdYV4LsEiHg+dJzN+ezJDef90dSAsHlogDKzfYgI+engI58EXW3uDlhyTcaTT44zzsBGlfro9hn8cMwrAETDmzms5k5g18AKZYPzbWVe/L/dU8TfOb+3rn0c8yUAdw/YWSfSRktmkMKv2CzKCZ2VnZb5GboWsEdqwSTLx2ZekjJ8THyqFSiDrV1Ts61Nttj/ygawH+qkwRXbyBpyHSnvHAN/z3Hu6Dd5b36Hc7etaskfy6n5SX/q3yjXBLIuP7LPdjutDQLwtr70kU2uzFf2z/Obv3dguuLNCh/81XQ8vtxryncuEcxzB6nxtAFhBj85x9gtfR+Px819t7FjM+uHeK1sIXWvyTnPyRgYNDmR3PSdr9hgm/R7TkLw+oYB9/Adgyf7EfvxBIjEHSHuiLl9tuH7L80HBpj04dSX0CU7RrZBDV+GKFf0ewxm/wy9O6jKYByRhyk/fvyYx8fHVwvnSe6B6xyzQ7dzihCw3pPMCtHYch7OZjMbRIMXZ5aXhHKsNzc3p63LlPVlqzrjdcYrY2jZ9L2gphGzxOZxjAODXIIIA6DD4bABE3TiURqD7hxP+QWPkU/Pzy83Jq/AHz8NwJuT5fqkdMAlSAY2LQD9aHIwykAlRKCX+dr508iTDKgaGM55aSc6lMzvyhHxqUApX5rZ7mxyPWl0vasY+XNZ0cz2vVe8Mbg5ALadl9Far+m0bVMIcqxXrfyDa8j1Ik/ZD3lCEMzgirzm+/Y87szX8zgej5us5Oo+kQacCRpZ2k1bE14aAFwL4GtEfhNsRJaenp7m27dvG18Su29gQBDgYCD88w5eCybov7hWtJXNVxKE83f6St4jtpJ3+rc85Sy7d9YTUrOjDrTbnH1tqM015/J3A1g+KIo7VfYDsRGs3qCvYjlSPr0jbT7PvC6Pph4ZQ3B+lAk+uIYJHvbZsNFH+67j8TiPj49zPB5f+YmZF2xCXVsFHAThM1ufGB7x9+DM8NWB1eHwsotpGTRmZJ8OrPLQAifxeI3tbOwDE6DpP+MmD4ihcn76i+2PLsbuEP/ywRrWndiv+/v7zfqQ5wx0okNpL3KW0trwnnwKXz5//jxfvnzZ4OiMI98ZfAeX8hh96DnsS33mteQ3ybezWO4oj++hdz2owh2ulJ3ApQGNPYqTMyh2/3QurIuNkHBcBi8kAzwHcAFRUR6DHipcBDHtsk6U0bnL/7y7sgK8dqAEgo1s5NO/DRv5Rr6yz7THtsgHGs4Gvr0W7MPHvXZe/yYb5uPq3FBTuo8iymsD23bUM9sdHQcW5r1lvwGdvbGtwB1BDgPCUJPLpof+blCa+ea61RMEKVcOzBgU5BwnYqiTAaXNXpB/LO9r8+Gxdpxjb4CbesXz94j62dbONnv1fc/58Bj7W83/I6n5AiYkOBc+sjlgJeUqzSaTGi9Xukb9aZljB8CrwMZjsTzFxq4CqugHfRd3qlbZ/j07vOfHeD5/51o03lJfZ7avCPBOVfqgfVzpHtshkHcQ1sa0d5y8oY60dVgF3a1924mPpPDHFQAh2gwC51ybT67VTK9qoGyEaNtzHc+n/jjAts2mH2H1QFtbH6ONaEkr+xQnHRrP4u+MkXKs2ROOj/JEHbH8ue0kJ2IPaB+iW5RX9plkENeQfYXPbDvtNXxqXu/ZEvrH5ks5R2Jc63yTs0vpzUGVHZCdUo7n3qSWDWgBCZUrREHcC7C8u8E+GlkISDamPD8KQ3J21u0QwLE/ZtxZw59dlhiqvYW1ULcxs98oYsbHseYcvo/DYM5GjkrQMqoEdB7LOVDINpy9aoaEWRE7dZ6X8XOn5BooxsgZTys4HdjM6xIBrjGNWf6PzF3ilB0sre53suMyGHGigiClgS7akONx+/6dOIf8vsrozsymDd4zQ0MeR2t+OVvcgPI5OV7p1aqsyHrCMe0BuUvAt88jUOGnj9H2tYRTA8TXStEx7kywTIxyx5KifP/06dMrW84dLvLLtsj8Irj2ujQ/t+It/YTXy8cyFupmjiV4Yj/cqaLdN8BxkO0Awb57ZjagkuQdNfucFhhxzJlb2jGI5svKwwvv3vIzfa4CQ/Kh/U5+k0+0UUzcusS4gdY8UIX8/EhqNsVjdnDhXT0nQp2EZ6KjyVPWsd1Ll2tm5lUfDBDSHm2C+6A/tJxTlvLJd2hx17S9OudwOLwKtgj47XfbZsVqdyZzbPJEuxO9aHYyPOO5bIO6F1/LtSRmaf4nPKKd5jhD0cdVMib9Egfx+MzLxsfKnyWofI8/e1NQFYXmIGmIs6gRphxPOZwBAkvx7HBmtmDZBptlPwb/diZ2AjlmQxxmEjyytGIVQNp58Klgdl4Zd7aKj8eXQIoZAY7Va5BxG/jR+DeQ5AyF14Dg2S8UZTBiQ8S2zVe2zzmQ722uzBzw93Z+QAGBtuWG5PW9hsAquuXyAfKARinv1+E6EYC5bX63ITN/qAv549OCTM7w2sC1B7/YcVhX+R43viibT/ej3lDnMgaX0xCIOCOaeYRsc3isXWfbFh6EL1ynVtJpx+nfaScddDcwuAq2WnBsHSEQzN/T09PyfTp0/kw6XQPgCx0Oh01ZMu3czc3NKbE181JSxHK8lEKnLQOrmW22nTbIpX8EG2mTuj/TSzlnXmwXZbI93GilX346Wq7PS34zv/CBL/xtvrMlpqhHLr8L+SmguY7BTLOF6TPyaZvTxkT/TVuadiwT1qNmHw3cCYz3zjUvmXT1U2kbQM4YW8D6kdSCqob5rC+hJOG5DsaZDXOR8oQ59hW7H9vPc1MWGH/68+fPzb3A7if6nhK61TrR1nIMWesVbgxx3gxwyOdgF/qMltwxXgwfiPlyOwttCRO81AsHgkxShPJ0xPSTXas8gTe6mGvDe/pT6qHlP220J182O0tZYDs3NzebBFq+Z17k01voXfdUZZANsHICWfSmAC2gIVmR9gwHAUQDJW+hLDiNpCNdts0FzCJnG9iKydr28InHLzGSdDqrgCS0Cspo6M27Vv4w8/qldGwrv5s3q/FfSi0YXs2TAeleYBlqGc+PJoOKUHPUNEKUVRrTyB/b2eNJjjnAcXC3F0y3sbZkyUqvPT47tszT2b8GkuJEwq8WKJnH1s1VQNWuNVlnWuLBzrSt2zly0LwaF/u1DeF3A0UDmtX6tfW/FloFmUyUMcPp+wCY2Wz2L7THO/5OarbTutLmsyebbX14Pq+jHnEMBKMroNYSIiyZcqKQYzHICW9ty9z+Su6aTplnlPvm22gHVjaNfdCXeqz8rcmB/1a+q83D4/hockC1R5YlymDblSFvZl4nHNKOg+/8lqRezuMa39zc7CZgm6ylj8hrKxFkgsQ6tqebPJZrrOtOQnice37dGJa7UbSBxn5JPjEx6jnbJrBd9k+b29Y5363T1smVn2xrxt/atV4T+om30rvK/zgQD4iCn+yes9htIs4urbLiTTgsnDbCvn41LzvLw+ElyqaghJhpYAapGdL88R4QR/6ND40fq7IkztECQ35YYZgRzPdkcSzIeVx267Pxfc8pNUdt0ENek2IADofDKcuVTBCDK4InGttryfCRaDjoHHwODSyTFz7fzsc8ZpBEJ9GCusgJdcCyQQPo0qcm2wY5nl9ruxnW9GeDTZ614CyUNpo987lNN5tjIc+sY+HxyojnPNoMrytfcOzfvOPl8TED2Jw7bRFLkbm7Qf2ivFwCqj6KmHUNX7jGWVs+oOfHjx+njCpBEdcnvLJuzLwG2TxmyvVsh+0H1PD4ChCu7Ct1nWT9tT44eKLeWT7p68/N1eefC2bcBsE4wW2zGVn79EkAueKDecTdKALc/O82aN9sf8K/lilnYN/GtcIJH0GXAFCP3fJKGaDssAIhFL1MO8EjxkjhYd6hl12ZYJjn55cqihXIDzlpTp/Kh2NwfBxHrs+O8Eq+jT1Z1UC8w+QDr+N4OaaMhUFF5Jl9208RF67sfNap4S3ucNF2ZXOB/dOW5J175Cl1ynaNctTiCGJv8mIVTBsvvIXefU9VqDmWDDqTYqmby8HswOmkLXR7BjqfKzBxrp0sFIWzgSXOk2UXyWa27IFBXQObPEYw3PjQ5rMHjELc2qbAJ3iiYePL3CjMflw2jd+5YI/j2wOnNnBW/MPhsHGg3L5nSRjPv719eZElS0+uEQC6BCXUQH4CSurYTJdXgwp+Z/bJIIMGsgUCGbPXi1lqyzbXur0ANyVXlMuMlccukbWVjeI4zNd2rF2fMVHvnaSwvpGXaZ/lX3aE0TGDLCdHOEcCSfPbjmbFhwCS6AzXkkmJ/z8EVZEzA/iZ1zvdBBx5CWlKg5g8C89bsiC0cv78zY7d4zKosg9i9ruBKa+5Az0GSdRTB1WRFZYgugTddmbm9etFvC5p89JgKuO2/Fq/yHf6gYzJvGzU5LkFkRxL0zkDOK5fdCzg3/7f8uq1uQby2jV5ntnea2jdsG1nkjTlYjOz8fPH4/GEXyiXLPt+enqax8fHubu7m7/97W+nEsHw3HzOOENZWwZKwUAuzUvQk/L1JHtjS75+/Tp//PHHq1J/38dFDBZ5oT+kntsPeuxtPRwoMVBx8oll0lmrZjfyDIXgxMwhlVvhY9bHOsN7p/mUQepT1i39U24YcDZMHJ/L5FjDA+T3XxZUmTiBFbhnwNOud6ZvRW+Z5Lm22rkWbB5n/3EeEXAC2ta2geo5umSePqcpUxtLFMWBFBWpgY92fvhF5bpk3G8JGPPJNbDxdpDqzOrMdZb8mRxY+jcbyWY0Te03y/Vev+28vXNn+uOSG7ENZ6D4u/9fzcff/ww1WV7NqY236YoDHTsM8iJjeOt8Gg/3zrXNM5heJXuaDl+zfhlgh7zO9lsrXbvE3rmv9/i3vf59zmoMOWev/z3d37NF7J+B3yXB0cz6Meo8Z3Ut+07/9LUcv3Ww2dN8v5TcV65P227TwZXH3gJ08/LaAqqZ1/hoRZ5vC67YHq8hkU8tScLr2A/lbA+zcv1W+ub5ewxOUOz565Uf3lt386vJWuOL18G08rnNR1Hnff1qB3qPp+bP4bAtSfR4rX9M3jScwL7emky5lN71SPXmkHKcGSpmPBORunyMmc9kJNyXDasXohni5shWIIO7GKtSAAoEs0yJsI/Hlyi8KZf54x071rQ7E9bGTKUNcUvY2ZP07ZvlnZHwlq8VKcfZ9vG4vdnYT3BqmQMbxHxP1ul4PG5uLg3/2CYdeM5pxjptk5d/Rmn+U9QMs2uqV3LgDG3LiprX2WJvOyAeT9adfOMO0somGHiunAePHY/HzY2sHlM+XfaxKgOhgW07f+3/lcFt44/9sN4xu+esoHnWkhnkZc7PWrMsxHOyjFDHZ14eVmBaZcRXTpDrFXvYMtHXQPZNIWZ8uUbtyZNcC2YyVw89aDJrGeFat3PPgcCM2zaN7XLtqA88L7/zHirqfHYN0mfsb3YU6I+4K8s50RbQ5ri0yPMiX30OqQEp+jFXAMQv0T+1ZBzna5mxH2vgOedRTjzu8Di7VpSf9NkSHNdClFHfc2MwnbFnR4fX5Lr2yfWlrNHO8l5I+r38Hr0lfzkm8pY7GrwfnvLaAqY84CU7VXkIzuHw8tA2zou7MvTnkd2MIztB7V1q9vdPT08zM/Pw8PDqYTy8LnNw6ayrLMgjYsHoRXb0iUOcxAjPbC9Y9WB9T/tN7u33XG3Dtrm+LLltMuWk/FvpXUHVCozQcbFcIIMMw/LiLT4JhQJsoGWnsqJ2Xb7z00SnROPNNgx0MmYGVRbsnJv/A75aUBiAGydFvu59XxlXO/D0HeEOAG3b11RonkNlYx9xwMwS2Jhx7C3gYb/kG6+LcqfPBv6oFJSdvXFcA/hrwYwNWQMVdjB5mpEfzfr/sfeuMbZt2X3XmLVrP+px7jn30X3d3XG3QTHgPMgXkkAUCUGIiImCAwpgEuhuKUHCkSI7BiwrTycRiIQIhEhIIl4hfpAYm0QgESsWogkgG4k42MYKH3Dsdrux771973nWu2ovPlT9V/32v8Zce++qc05tt+eQtvbea801H2OOx3+MOddaEYvbSchryjgTCzSG7oRYj4j8zAJbnzcacAKUruv6Pe8qz5VgtjMUXPvvDIx4FtOv4Xhrhl8vO+Yx6gr/+1yyLm639cBQY9S3B7k1wM25zsbHueT2awfCnnH1837v1SbolJMDdz8nmYu4dtLanqVjPKex8x2GrsOUB/KaZVy3a34sm7dsvmpjdRniMR2njksPIxafUicSHwg8CQgzf8tAhgEJfTCDCudVxqOaLXe9cj/m/dNcZkkgjtll2/vlIFHXMWDy8UqHVIYve9X1Lwv0vWziXLjfdfmmLdajtzM5GcJsDFC3t7f7l86Xcv1EOL+vmvhE751zP0tfK9nW9kCV8X5pHngPl7YcchuggpmdnZ3+OrWnF1ez37Td6vPp6Wl/+4VsMHWEwaCCKr3wV8e5cOHjFw4Vvxisau7cB8pueALEE/KcgwyrOWZ0LMiETsS1feM2eu8b5cRlj/JB4hipx+vQre+pckVxw8LvDGBxAAR/Do5W6Y//J/D2MpmR9DFlhpyZKP+tiXUgTOdB8Mex6ToGYfqu8ZfXerbN+0gHQoF1Z5yBAM/qOU+cr+R3NnerzmcNKGbykxld79tQnzaJHExH3ATJlO9sjPxehY8OjnnM5aTGQwbTtXay/9n4OU7Vq2ydt+8G27Od5BMzb1k/M7Dq9iMDddQPtx2ZLmXj9LLOa/LCbW+Nh1k/s7lxWynKQKrTba7ZZCKfnTJdqn1WsTU+57fpa6bHJAdbLm8MoihbWX2Uba+Pdbk/JdFer+PbPSFW06PsWtepdXSn1s8sYJKdqpX1FY2a73L5oh2r9WcTKJvrZRiAvBhKeNTmzEE0j3nbxGwKggjIM4DtCaIsecFxMjjWxxNRHGctYZq1p+PuozOZXsZ7P8fkJSnja1anz0PN57gs+7hWsYOr2tfsOuLyofHw+zZ0q/dUMRJltpRC45kgj/J1DSN9CqHK1IyRv7HZHYIDEp3LBJnKlGXwddyNqI5JmfjkHo5L19DoEjiRF5PJ5MaYdT0FIyIWHsXuyq3fvg1D/OD86FvZCfG3djOmO1cfE+vMnEoNkLJOzxg6WOUY/ak2XOVgkErlXcXJvk6ibkXEDXnPltN1nP89aCAPlHkrJd9iGrH4Di/W7Zm9iMUMM1crvG3XX107RJrHrusWXvzLxIvLhMpTLmSfuMWCfcr6zH6rvIh2xbN66rfLFLdRuGPleDNQ5nPKNnRe3y4HGQjQPPEaOjrOr9tk9cMfWJHZPfZtE4i+KguadD7i5s4KkcZLHZ3Pr292p+7Q3vCbdtRX/TNyAJDNN+UpCzSYHdY5+lh/sA8DS+pGjW+UUddJEn171mf6LNWbPXAik3/ys2Yrazqm67MdIuq3z6OOs1/+wA63J3wHEPENAT11nbLGfnB18C7g71WQ+/sMqEfEgq5lNojz5gCdeJF4IqOtrcXtcvP59VY1XatjwnTqm1bSIiL29vZid3c3Ihbf9ySSHdCK0vPnzxcwjPyTfJD8MPkiedCTCulXOB71URjN/QP1xh+wwTIMNLV65/iX/dI4aRso8/4gIA9kOP/qs3gm3hPb1W6DyeY7s1nu530roBMfjJK9+29VWjuoIuMzpouhMgg00j75HKwG4gGRBx6uRNlvv56TzO8M3NTuJVJ96jdXpyKun9JFw6dr1O9a5jozwhkg9dWsWlBFPrnT5XHyimOnAfA6vK6ao60BVc6Lf1wJuP/XnQ7rp/JJNre2thbAuIMKOv9NoWyLFnnmWzYzHrpx4YdbeHze2E7tHgSR5tcNqC+XE7BxvlgPvyPqN35Th3w+vV8EZwRD0+l0QT/JF2/HAwTvj3SEvHFeUd6YmMi2HpHXLquZzcrskgduHlhFLN7f6raBPFBZgj7W44CQ83xbZ/Sqif7H++hAPXPcHDO3xbmsSAfJD308cZXJyhDR/3GeM/nI/AvboJ+oPbrafZH30e1PVoZ8EWUBQ8RiQJXV7/6TuscXbGfJBfm3GtDjscyf1uyO+u+2ifaPOqI2CSBddpwv+u2rH5tIWd/0n08V5nG3d5znrK6afc78i2+V5uO8hRNOTk76/5PJJCKuX0Av2drb24uIiBcvXvTBluRLwYvupzo8POyvZbCvsSsI45gjYgHQTyaT1PcS4+jbMRCv4bsaHauLZ7peiUm25/KssvxNe5YlAjQPapfj8m2JWZzgWMHlR/pNv+6+XHzI/JOO86mRr3X7X8TNdzToGDvMsrV7IjiozOG50/G6/X9Wjgas5rSyIK3WnoPAbPXDj7PumtN2AXZeZUEQAy2/xgMijj8DtO6ElvUzc6R+TQamhyjLCLpc+HE3ArU5HrpuE4j6IsOb/eb8Og05fta9DCR4n7LjLttZP/Tbrxtqi/LkBnVVO+BtMZNeW/miwc0cOsszCFL9y/ilcsuAc8aPVa/Jxs5xZWN2vnpQnvVJ5HzbZNI42dd1bICuzcCz88ETYt4P/535w2W6N3TcbfM6NOQjRA7+a+WWteF16hwDJ+/LUEIpO17zzVk/+H+VYErlIha37mfXu0x4nW5LhtpZxXa/TqLNzs6t43Nr+uJjrrXlZTOcOYTxMpyooEG/GcTU+sSVrxquqtkRb9v1wccZsXhfbIa3mdihLcv47Ng16yODKiYNfT48me52w8eQ4dWsXvJ4mY8c0jeO52Xo1NpBlQaQBVDqUJYl1Tnd+Esjw0xeRCzclKbr+cz4zKlQELI++yoAKVOgDJwtA46+iiNe+aqWFKT2UAZd71F3Nl7fslITHP1WPzJAnGVRaw7LDZWPWcrDh5VwPlgvx3ByctLLiQyErqHREH84D6pPDwNxh+bZ401ySuo7l+tpkMQPZdL4tCCW5ZNteD5icQ64tStLhPA355/1Ua98VVD89RtvXfaGVl5c551c3/w4M8EkT0Zk2wwy3aPM0b5lCY1sTNn4WbYGpjJHmq0cyJm646ZMcL65tYQOj++cc35nQTl1itnQ2hbT+yACDe9zxM1tm7RPKscnXPH9K3zxZQao+dAY92HUKQKFzD77dVmw4NvbHMSwLs9cqw5mkN1niFfiF200x8PxksfMRntgw3FE3Ny6znlUOfkr2UT1TWV8SxLroy5z/BHX7/9zOan5DK5CidzvUPc0L9Tjra2tmE6nNwDrkH5l9u11U9ddbuPivPBc9p0R7ZzGyV0nfGce9VU88KCAPki+k6vDun40GvV8V/0Ri7s3ptNp7O/vR9d18fTp0zg+Po6u6xa231EmKYfT6XRhB1BE9KtY6qeegkjcQ1shTEOfQ3liWd+OTvkRD6WH4/E4ZrNZTKfTG35G/ef1/l41PoBDO5vEO8koV7ZUv+MSBUd8OrhvsaSM8JvBoeM+2lTKieZMD/XwsbnNW4fWDqpE2UqCBikh8AgyMwwOeskMDmgoEMoAPvuSOYoaUVmzutg2nZ8bE3dU6lsNRFEwaPBdONh3Zh24tasWhGV95odbH8kz/qZAOkAgDx281OaLfNCL8gjGCeQkOzRGmVwwmPXtkbVrbqM8L5vUh2yLB3XCwRP1p3ZfAMupjSyo8r5kcuJGl+Uj4oaOax6HAgcRs1RZf7KyKk/+aLzunHQd5SHbQ+68UXkvK6rx3cFodi375o6NZT2oYhKJ357QksNw2+rgnWVlJ31lzeeHc+2O8r7BnhMTA64vvL8p4uYcEVR4QK16yNPModfqVv3u92r+h/+zYId1qqyT+91M33lt1jb/6ztbwaoFkkM6PWQv5Ks8eOIx38LMFQP1qVY3sUKm15lvZSLC7YjKkNf0Y7SnlJ3s2hqYvk9SX7P5dF1Q+WV2joG4ynNLlo6zDvKEfBKuYLKfbTLh7gl/0Xg8jt3d3X6cCoLoPxTMKNBQ/3nvE7EJx5s9QZrtj0ajhaAlwzTeJvlDf6/2yHPej+wYldiLSSRtdyyl9EGZY13XIbdJ9LWllP5JjuqjxpPZS8oYZY1lshiD2081Lt1K4096vQ3dKqiqgVMHgRHLtzpkzoGM8GNsyx3BUH+zvjllwH9dsJ1NMA0Ay9AY0JCIssDVecJrHYBnPPA61D/2ueaEfVzrUNafjN9uLDROKqyPOQMBpAyUOj82xTllmVF9a6w1B+8ON2IxyGQ9/tvry/iaAa1MpxwgrqJDPrbMuWThunSlAAAgAElEQVTOplaXvmtBvQdLpGVzwP56fQRqmSPIAHOt/041fi+rh3xzeXHdp/Nx0Jnxo9bWplKNF6sSA5Ds+qx+XuNUC2ZYn9s/77snL4dI9QytIqoftBtsvwZwBL68716OY8hsRLZqyHMMknxrFftfCxCd3Ba47anJSaYH0pusHu8DE6+sk/xn3/y+xQwz3BfR9wwFyxkGXFavyut6/9TmQUQdEmUBfuZrfXWL9akdr0dBPh+Q4deRJ2yTq+BM4FHG+WFiM7svyWWPdVB/fG6cjy6j5I+vDrMM6xtKsPv8en9oA2t4zuWOOpRdm8lfJgfr0tpBlRSbWx70UcTnwsAyupaD0ATLcPDJG5kgukPJHJLK0MAxCGEd7CeVr5b1qzmwDHRyb6sHhnJszAxTQbMtBT4X2aqfCyrbd55RGei0ObcO0p0nyxyQA1FljTgv/puZFfGDDlSZEmUe+NJg9lVbdNS+r9BsCgDsuutsZ+3JMzK6Losany+Xu7H0B1W47Og49UH85juUSO7k9ZugzY2t65eOuRz4apfKsh3yxuVPtkZ886Dc62Efs+1/WdDK9lVGY6s5AV8Z4LnMybjDYb9rYJJzkN10y3K8nhlIbgFmvxzo8el/m0zkm2/xqe1Q4LUcozLUBFLSYZaPqAck5Hs2jxGxsNXJ54K+0+v2+p38WOYP9TsDQq7rbMttufPPcYDfn8ix0VZorAKdfEcR/Riz+hlf3Ua43+m6rl/d8FXYzAbSV+taygl5SpzhvCavOB5l0cVr1b8p+qYxy0dTJyKuV04i4sbYdb6WxKFtEa9Ul9rhikZGqofteeDOsai9yWQS0+l0AV9EXPtRlqWOjkajhe1wtNlqIyL6LX3aRhcRfXsezHF1RbS9vR2z2SxGo1H/9ELySfIiG0Kcpy1/tScIOul63prDa6fT6cKTDX3HkWyjeCV+qX9cgfSHyYg/Cjj5kCD34dR96r8/EZoYgLqphJPji3VoraDKDakiYzdWDGiygMeJg/JgisYsIn9Ahv5nkT//e3ZH5/1pXB4E+Lma482O+/U+UeSlxkCFGApSsi1/DgKz62jUyPts/Nn46Dg9sKoR++YZV6/DHTADTjrlDOQ66KCx8CAqA+L3SRwPxxxxM7lAyoIZD5xVzvWUH7aVGRsHhrXAlH112XLyNglGXE+zQIjn/T91yIFUrS72P5NND+5q49Hvmu1zoMpjy+TRx1Ljr8u92zWCGR2jPWK2k2PO7OomJSiWkc//qg50aP49KeCOPiLfxkKgV7P1bt9UXv9Vf9anVWWK13gbEYsrMPqv9vze15pv4Ie+zWXYeUQQxhUqBVbkhdss1lEjym/tt/7X8I7zw7cNqt++gsCAMrs/ze3/OvL6ukhjFiCNuLmilPF/lbFwDsgbPmyhhndq7WTJi8z/EcRTx5gQqJFuf9E1vNbHwAWK0WgUs9lsoY81fKbASwFDpgduO6g3DIo49ow8QRERC0kM8aimE/IrPpfEO+w7dUz8ZnKX9YqHOsYEMH2c657j4Kzd29DaK1VkgpQpuzmWxpYGkYbfBUcRKo03jW8GTtz4UNl03h1hBsJ8orNMhgQ/Exyd98nOeOfOxo1wzTnX5iIzTqs4UQdH7oTJF3fMQw7T+Zxd7xmVWp0e0NLZSQ6Zqa31oUZDwf7rJB9X1m83DL6PnfrGefNV2CyBUAN7Tn7tMhmtBRU+loibgZQbQPKIfWf7XjZbjcv0lZSVr7VNnhBY+jG1NxQALZPb7BxBmTtGv9F9SBckTwyqak9sdf5sig4NkcanrOiyIJAykgVDWVn+zgDU0PyrXK3+zO8sq0sfrhpnQZPb2VoQ5vJV0xGXNa/Lk10MVLIASvzTtyd6asnbzKdnPsTxTLZDpOaj3F6xTpbPEijZnGS8y/Suxuv7IvZp3WR6JpMejGW229vmVjae5yqnbxkV0faxb1ql6rquf8S6Byoe+LJN90dcVZN9zbawsg+e+OIiggcrKqM+e5vsH31w5r+FF/QwB/KZdWicCmyIZ6m7qo9zxjLqq8s8gynOresQ/Z7rdc0eeSBVW8hYh9ZeqeJNXXS8mkiutOgmNh3jk0QI4EtZfGeHb6FwYSIzMgPsiuegrcY0N8KMvikoXmfE4hYbLtezPIWXfVF5joeCQAGjEvt7qkQ06lRUz0iwHXfa/Kai+5PT+DsDXJ7tFGW8pDKwbvKAxo9P1RLvatu2MsORAd/7IpcbT0hQR2TgZahkdGhsZVRdt8RX8q6WsSHRCXGe/QZY573q1XVenzsFD54cqPCG8AxQ0DDTqPrY6PB8/EOAifWJmO1zQOF8zJy95lF9cIfDa6lXBAsOJrz/Tlm9kit9PLPnc0EHlIGjWqB9H9R1XZycnMTp6Wn/tCfqP7Ob5F1tFWgZ2K3xRyCBbblMqv6IuCEXXsYTmrSFstUZCCQwirj5oBXyRn3OgAn54tn7LDjLALc/zS97ch+PC2cw0+59dn6Ql7Qjwinquz8wwMfggWSGMWTHnVfsGwEqbYdveXZ58s8m6Jj6eH5+3j+ogVjQATVthAc4NUwXcT1/fJhEBuBJOsZtfNoeprpPTk7i+Pj4Bp7QwymkRy9evIizs7M4PT3t62cfebuC2iBO4dPlptNpTKfTG7xkIosBIO3IZDLpx5n5cz3RT3Pj20WpawwmODfCI7KbR0dHcXFx0ddNXB9xqTdHR0cLWJm6ygBNdasd+q7j4+OFgI9jVv9qyQS1o3eIZXrK/+Sp48676NXaK1Vkmkf1NGA+EDJD57OoPFshGrrGgUwWeWfBiY+JBtTbYBlnttfNSamB+wyc0Dg7CM2Ab0R+r4SO8zcD0oyvGWUO1NvPAimOzcGDA3v9JlinjPAceextOzCpjYO0CYGUk8uOjtG4RFzzic5KjiwD0tStGjgi1XiTgaQsE1Sri9dzrlxuavJER0p+Ubb9nrnMONJR+WPbKaMOnIfGmtmlmq5Rf1y+yQ+SAz3aO0+WkDe1fnr9tWC2BqQ9IM9oaCXldZPmj6CZ8x1xM6jO5N35sSrg9rl2HfMgJus/22EGO7uOfjG7ny5i8V4WTwQM+QaSj1l8G7IzbN/7Sr2hfPsKA+Xe5SzzAbKV6rN/aFs4r1ld/Li9dZ9PfdSY6Z/VRqaXbgdrPN8Eysaf2cAM89GW8VzEYjIiq6Nmg3xVputuBrDsnwfCus4TmbpfiP2sBcoMfOhDaMclD67HmS6wT1zZ8TZUJmJxFdj1yvGy2yAGQrx/3fVQfrR2n9kqCRy16fd/uWy4fmXznWHimk8lb8nHu9DaK1Wnp6c3VmIyo+bC4AzMlMsNcpbBUHvuBJaB5GXM8nMUrMyAulGjMGUG2wEizzGoqq3seFDFLP+QANWATeb8Vb/zwXnCPjlPWN98Pl/IHGUgM3MQ+maWikCUhocy4v0gEM6C4U1xSBHXmSEGylmWj8Yl4pofWYYuo8xQsb4MpHNes3mu1Z/1ITvm+rRMTzNwJKID4Li8DnempZSFx9DW9D7TFYKIWl95DcvWArAaD2ogmMGAf9hX/816ycshMOkB1Tog/L6o664fPqA595u/yavaapL7o5p8Rdy8T2poXmr+R+dYzs85IHKbKfvgOsbxcJcHEzbuR6gXpVw+Atm32JJ/PD6ZTGIymSzIquomyNO8MJDiY9JrQZOPm3xVGfpcriDIlxLX+NxkdpD6nQE+YhZeW5srzqknh7L6l+Ge10H0wzV8lwUHTrJtzied40f1ECNQ39wv0KfpvPojn8vXJXBc2W4l3wLnc6P7jCJiwZ/7Q2V03GXeecgARXVrhZV9kh/zWyJUh1a5iKfYf29T/tSTleIbH6/ut+pI34hva9g/ww0KNhkw8Tq3o5on6ma28stVav0WZshw/7q0VlA1n8/j8PBwoRN8HxWFvUYZAOTEUqgkyM54B34ZKOFvz7y68LDvrnjZtVmA5kbaDbVfKyH0DKbzxUEe+cOnppA4xhq4dUcsofVAxh0WHUy2/YDjpJEjQFH7ETffGUTH7E8lFJ9F3Gef9SG7d4Ln+P++qeu6hZfQaamdvKfcOx85Z1n2neckR+7wI+IGz8UnZnAdVDtA5Jy47GUBG3lQA500drz/wQOe+Xy+sL0rCxzZJ+mqZ/4zufE6alvD2J8MNNUAB+vyJ6NlbbgzIm/4ccfquqzrZKu8nzX98a2Vzp9NAHyiruv6LSzMNpN3DFj5YmSRyhLsRyxue6MNIzjzQEUJJ4KboTnib/GfWW73iw426U/dZki+5GsjFt9jF3G9Fanrun77k+wI+0S7Tp2NuLx5fzqd9rrGstkYsu1/7Lf7Gl99YBnOoUAgX2IqHdBvZuLd97st9LbIX9pT2XbKks+3+MK+ZFvaM1t2n0R+eQDjY+UxziVxSBaIEe9k+IA2yRNEmU7JBmhb3unpab9F0OVOW4c1f1wJov5ojHrAhYIPXePvq1J5JRtoV8g/tacnXiqoOjo6itPT05hOp7G7u9vXxactSm649VHySJtEIv+0HZzyKwzrOJcJEM4J51u2Ucf93jL22XXbg1jOL+eWOkRcrnlS3bID3k/O57r0Uh5U4cHUEJivGYEM+DGIYp1D9dcAcgZ6MsfldTqQ5P8sMPMAITOKFMKhvvg13k83tN6XGhjLxuZGhwFV7To6d/UpU1CS8ywbM8tl5dnfZXKQ8c7LbAJRZjzgy3iRXb+sjKimQzV+Okj3j1OWWdLxWt+H5HSovM8vwSDl0o2wiI66BnK9bdaZ6W1W3tt1WmVuxdOMt15+XQDmANGBkde3TM83jdwucwwMOiNyEEiiX8rmYogvNXvtur5sLLK1vgKV2UX2MxubAxYmFnzMasOTkASIKu+8EOBie95nD46GVj/ICx8b23b/kvllZtQZ7HniaMj+ZfNeCx6G5MvlNBvHLxVyuVj3OvLK+UF58fPUp5pPE2W8Ztse8Hq/MlKwwOSeyzJ5Q3lnQJLV60nm8/Pz/tUCtddgSM+yVTBPJjp/mJzmNwMVbv3jokjNrvp4aE+ya71PNZ47j+gz3fYzmej+bx084rT29j8HfdmA9O2GkgOlYPD3fD5f2Mfq78NiX1Se2QMGZFwOlBNaBZDom6tAy8DkULkaAOS1bjhdgB1YaTzZW6cpSASbzI6wHrXB895HOoUhI8+MAZ0ihdaNna80ZZT1m3JFRZbM6KZ0Kr3PHeX5vonGi3LrmRqR61jETXDMsrWMcM2AZPNFqskIAVNmvDPHR9nL2nBHSpugrJc74prOkkfkOe0abQXlM9Nzjj0Dojyv8ZI3mT3wedYxX+FQm3SiNVvJfvg4WcbnTnVzy8Tp6ekN5+Rj2DQQmCWm+Js+it/ZCgiv4WoNn3SVARTxsZRyY8VM7dF3ud1d5jvcTtJ/uHxwjHrAgLbAep91HYGg9yPTYdblTzljXdk2cbcnWQY/a8uDGPJC//n+I+4m8bp9pcrLEGOIOHdum7O+q5wy58qw64EMmW3L6twUygJhx3yaB9pcXceyNbmnDXWbRltVSrmx+kR7pn4Qi6isdJQBt9qkvOp6XadgSg+T2tnZ6flyenq6sLrsNkRjiLi5a0i/j4+PIyL6BzJo9Wp3d7d/vDpXX3S94yT1g/d+abzaYqjy0+m0nyO1KxnV2PwJnY5dKRPkpWwOV/e4rdFto/ulTP66ruv5QjysazRHTMJSb2sJs1XoVi//dWbVVpII4HxZ01dHMoUTGOa2BAdM6pPOa9kzA5k+CTUQ56Bb3zTKWTmCDI+M/TpOmDtAHcu2GXBbiteTKSYNRo0PBLWsj5lE1rfMkBP8aY6p4GpTCuUgkeeHjKrfMKkxaqlaT/Vx4KHrycP7dk6SdZcTDx4duNCYqZ4aLwWeNDd8kqPzh/0QXz0Yywym+qTrXec96FC9us6zRU4EFB5MuPyqjJ9jRtr1wBMZbp+8fx4QZtd5wONg2e2ZyvF6/s5AhPMry+CqXrZNG5PxmGWZrJCOZf2ozcV9k/NB365j7sOy7K7rFO2cgvzMrvgWu4hrHeZ9GtwS4wDU5582nj7Rgwn2m3PM99yof1kiU/VrzD7fbNP1POJ6C6HbJ27ty3TSg6qaLLs98LFzVZpb9PnktEx32S+XIdo6jZm643NFcrwk3To/P4+Tk5M+IehzoAA4q/O+yLFgFlhF3LxPXPeziveepGD9Pt8uxzrOQFkyp22nLCt58Mec8+l6LKO5p/wTkOsFuBpX13UxnU7jwYMHvdwfHR3F2dnZAn90LduLiP4l1+Tj2dlZHB4e9v3R+HZ2dmJ/fz/G43EcHBzE8+fP+22NEdd4KXtBL/3EyclJnJ2d9dsRI+JG/w4PD29g89lsduO+MGJgbtXkHHJOtD0xYtF+1ZIpmXy5PFDmfLu65sm35LL+29CtXv6bkXem9lnlWmbQnEleBw2hG0UHa66U+q71q2aca3QXA8f6aeB9VbAGorL63ADJcWZjzsZX4423z3pFVJ7s2mUykjlMpyHhp3GlIvkYNyGgEtUCSFHNqWSGROVrdfBD/an1hcFPRH07xyplSHfJCNXmn+Ni/yPihl7pGMvX5qHWT9dN8nQZrSp7rHMdsMZrs7Zr52rOSWPMEkur2KX7pmWy5rqRgcMh2zUajRYCp4ycTwIXLqsOFlalzI57PTXfyHEzuNM1DPR8DDX/sQwEUdY8qZfhBsqs+7mMF95HT3byk+luzR5kAGyd/mbEtjYtKbEKZfNbo5rMkFbhgfM3myeXpWVz6ok7BtUcl86xnYi48SA3BRo1++DyVrM16hNxDdtQ+76ylsml84C64UkR8Y8rvOqHJxGW4T4nvz7zKRq3U41vrI8JM7d1Nfte6+sqtPZKlZMcSfbxATmI43FG5AI8ntmiQmTRJyeHT3byLGJmUGvgQuRKmwFQn8TMQLNPmaBxJdCX/XVNlrH0PjJrl/GNc+GA2efIwSnrcSDA+dZ/7yvnu/bAhGx8Wdbes5f81jYKkQyaxsDtTJsABF22lBFTJtffJk7d8xURGlnP8lJGvX3uMeYWCtbDFS5ey28R51C6l+lHBnyyc8zcq34ad8+gs5/Zqgz7mAEZ6ojXy1VjX5Xz42rXM3WZo6DOsM3MeXE8bkc9g8s+ZbxXG67ftEXM2roNyHRrk0g8kb6Tj77dRXNE3cqyrfI1mtuISz7oPV/UHxFXczgHkt/RaNRn8LmVRkSb7XNcAyTkgRNthfvoDOTQL3h/mM3P/LK+nfcZOOIqYTaeoeDDwbNkWHLJTD3tnM//MoDu9rjWvtdDe+zywP6SX45Zuq7bGN+Vkdt+fuu381LXZDzR/NAGSW8jol89l/zxSXeZzXNdVp/pG6WH9ItuL6mn7L8HVuPxOHZ2dmI8Hi+skEUsPhDKfYbbbq7k0S8xWaDriaFll3St+OA7D4jhfSeRZM4TE7KDGoNwV4Y3KMtaPVP93K3jZTWPItcFYgvKkOqRnabdlZxwC/Bd/datgioXKkbimgROphsiXsslQ52TYPneUxEnVOVoeMRU3wrIvvuE1Mbnx/0afmucEnxfHXMny8llXR70kX8S6ohr4Sdf9K2l74ibQZuuVVl3ADrPVYRa30RsQ3uLa/yTnHh2ZVVQkMlU5nCoKDREfKTmJoI/kQyB9oRLVyg3DLZIro8EOc4/EQEGgQcBDgMjryPTJY1DVLtO5RzQU5/VLmWGj0OnU2DbNKTuiNyu8LoMGOk6Pl2J15PPXq/mribvDih8ztwxcd7oQLN5zUAK+cM6CYTdzhKI+mpfZpc3lchjjdeDKuc5gyoRbZC/M4Zbb1SOPJJ90nH5DOmz61xEnnhRG2qboNHJwatIyQq1qd+ZD2AZHZfP0ZwT3HlAyXZq/WE7HtxRL50vPO/88i1/fiuDeO33lbEODz5dnshj2hvyT781784z6pfPA31WLUm0CeTz6vNL+fbzBPyaLwZg5E3Eoi3WcfpLTwyLfB7pP3SN67TbT2JND6qYeCrl8p6rvb29uLi4iOPj4xu2gNeJD/rmXHN7HYNq+WrJjAed8/nl9j4GgQq0dC0TuOShB4v+lFjpjXCDgjfen6Z2ZRc1Tj1kQ8GdBzb6Lxsrm0D+6P5Dx8o6r6CW+K/ruoUYQXNyV326VVDlypIZGTofP0+gwPoyA0ujnkWgYngW2btjqSm4lx0ql5FPQq0eBlMvmzw4W7XvLM8g0Hnometl5EZSx2pzn4E8v551L5sjd7xuWB1gb5JjWqYXHpzzfE0WRRmI5zn/MAig0XaAsM7YModLuRhyyJntodzWeFEDbsv6ehtadp3r6jrXrnOe9tJ1WmWdRzXdG5rvTGY2SZ9EtZWj2j2d/F3zTzxfy7qzPfJYAMr7RRCxqm1aZvOXnc/8JANO9VfHCXx1jL7Ndbd2zvXVkxLLfFoW+DgIpFx6hn2IHzVb6fM7pDOrzJ/zctk8uh/bFKr1O0sE8X+mXwL8NR5mGGXIljpv+d8TfawnS4LVeJ7JYNbeMvzi2MXL0V54+47NaFMy/MPrOE9u57Lr1BcGk7RZy/T1LuRzlPk3/s54Lt74zpvXGlSVUvpoTzeUzWazmE6nfYTLCWKUS0HJBDoTslo2yx0Os18sozpEFPbsZkiV91WWrstv6KYQU/iZ7fKyBPgeYKlttcM6MqeXOR79J1DwDL4rS6a4zPYPKav3yxWMx6m0DkB0nH1mdsh5RNmIWHz8qNfPVUFlh3nj6SatVDETqvdNMDOjuZ1MJgvlRW5YmPny7Jn0lUZQ/NBNrnr3hOSVmTtv18GLSOPxrVQEkOxvZitUj77VX+oQb7LX9QwEJQ+6mVhlMoeSZbsyZ6Nr1C/XCZI7MX6zXs5p5vSd3KFl+ldz1Ow7r/EHFmRPIKMNVr2+fXlTSPrE8U2n0z5LyjHS53AlmLJB2c/sGG05H8Tgdodbz9QHz+TK6ddAPgMy2m2erwGFbIXFZYFzrH5n58Qv+ksnrvpl8uwrQTU/zrY1DvfB4jcfTpHdo+xzS37I7tH2eLKYdob8Jh/ok12GOA7aNp9jjsFXa+6bajZP5zxxQR64bZNOasXD74ve2rrcsqVj1Befx9oqFWlra6t/yIS2F3bd5cuqtTVNeunyzzZOT0/j7OwsSrl8Wt5sNuu3+umcP/BC9oYrXvRnZ2dn/YMoVJfew0iSv5ZclFIWVly1guSrWY7Hs23QtFNcGRN/hHcjFnEu/ed8Pu9Xr8RHEceS3cNJnmsunPwBFxmmo2wQ++g69T/D3OvQ2kGVjLaYPpvN+r2r7Kgm1TN02X1CdOyedfagyh07DRojTk4G21cbNGg0glR2B15uzGuA3B0AnxRDoKKyNMgcK4VKdbnTywAoy5CfOsdvlfFj/O+Pt1TdfDltBiz8252RB2AZ+PMggYpBJ+6KyXZ8jjd1+5/GJUMlI+/b9xRUuZx4FtZlVEZRxKBGRF7p0dkM6tz5q9+sx52Y5FvbCRgU++pXBp74XwER286COI2XesZ7XzwQU1361lww6GEZjl//abNUt8htTjYfrCfLGjoQE2W64zrpY45YTLJk9sb/ez8jrvfAy75xe+CmAL6I6IEYwYvk0bfYUQYJGIb4TL/i4E73DUTcfPoZt9eqXU8OZAktkfQu8wk6z2+/PuNTllSkflNn2T/qC20Lj6l9BoDue90HeH85Jg9QfQwC5TqflaFOO+mcb4lUeeIK2kHfWsgkoAcT7rcJtD3hxHFskn6R3GZwXh0PONaTvjEg0KP+qVfSp4hrG5TZcvaJ/SGNRpdPt6N8X1xc3Eho0taJOO+6L2kymcTOzk6f+FR9DPB1reNd+i6+2Hd7ezt2dnYWHmPu/NZTBZm4Jz7iPV6OC8QX3pohPikgc7y0vb0ds9ks1WX2gfPovsHxh8sI/YrziPOnPuvJmZndcN9NvfX+34Vutf3PjYIbwppS1YIQUu16MjsDFl6OdXhWnWVVHx0DQfmy/mZl5CBohDkuFxp+OwDycrUxDNHFxUWaAXDH6FlODyKzvvjc1KhWfui62rl12nLwu+lUc0C14HIZ7wk6akEP2+Y1tfMqk4Hu2nU+pmU8iMizti57LMNraXxd72pjcgev37UVl5rRpu5nQH2IDxmAzmyal1k2Xw4EauMZmkuXucyOkTZR7zS3ul+XviuivoXH7Zd+Z+QgeWi+/RyBhH47QMj6wv+UFfenQ7rg/WKd7IO347Ltq0ZZ3zO+s3zWb9eBbHxul2j7mEGvjTc7RvDrZTPfOFTnKlTTL9GmJAEzWkXna3aEfKxd54Ew5zlLCLA972PNPmb64Yn2ZePS/yx4qNliHwPHSPmtjcPHsKr9rWEz6g+DqWzHk7fPut1He7tqo+bHV6Wh/mRYttbvmn1dlW4dVCnLxxeeRSw+LEG/tUzITAtJzGSWnWBE5/k7C9qYVaRj8j2T/eBxA5/q07giFh+LqXaydv2mwZpyKdrnNRyjKyAFuWass6w8yVfh+ASUGqke3jzJFywz6OSHY86cEDN8LO9OS2XIb2YqSC5LlAPJp26O5Ivq3OluCgCkbmWZMg80JN8ZCJGs6Ulk3MJAGcicleoQUf+ypXJRtupJGfFtpSQCKj7pUFsnVJfa1VZk6sn5+fnCahYzxQ5SPfDh1iM6O27D9THXgJaOuQPKwISvJjvPyTNf2cjALhM57JPkxpMnGehwhy2d6rrLbTGyZZJL1kX7tUnEscsucGufAi2C762trT7jTMfvSUX9p9/jSy0lq9vb2/0WIV4nfXc/GLH4MCjNh6/G+/iYFecTxTx5qGPMHqsd8Yh1sE81cKJ558qB+ud2X+PN9IgreaqfOu32jv2SvdAWMo3B589/qy6f4wyIE+i6ztN3ifeuZ7S5rpta3RROcFtd84f3RbW59OCZY/QAiMlvnx/tzuBWWMcxo9EoZrPZQhCiuvQSWD6UweWfdphzI/3c2dnpV5Bo81QPddkTepnPoC/TKtN4PO6fRqeHSsgHRsTCw7ccBxVNbXcAACAASURBVHjw5T6+Ji/accQVH/FFfTs5OelX72az2QLuyJIWsmXCMq5/7BufdOq7JxyfDck7ee3yIb5xviRz2pbpc3Lb5MWdgyp/5CKDKnVODlkDcAfuBsiZSEFnNOtGXcrnE5gBGTopKqA/LYVUAz3sfwZGuEonYWVGgteqLt+WxvpqACwDMQI9DKaGBNMdC5WG41RZ39LAdlWGToo8GeoHhb6mUJQFGkH9lnxSoVTGQe0mkPqufvtT/TzApiFyYE7DIBmnQVaZTLcIvlwPIxb3H2djIHngTRmp8d2DCRldL5M5b9cr9lmyyD3dlAW/l8blw7ctuC4M9dHHJqo5XC9PPXSHP9Qe2yBwywInyg7b0TldJxupFXCu+lA2Ni2oioje0Wc2K+Lm000ph7KxWTJJlPGZYMGDIP2noyeQYD3eZ/qCDDQRWNEvq22XLSYf3NdyfKzHeatvAj99PGnhspsFaUwi8FgGerjtVud1r4nPqeu/6yR5ndk5B17kFfkneXH+ZHrqvsvvQ8p8d62u10neB7ctmY3SMd++73wUCXPK1wlHUl9ll7qu65/kRp8qmZQ/cZliwtvHp2BDwU5m+yXf3K4fcRPTOl8orxonx0+f5dgw008GOCrjMuw4mN8eEOqesPl83gf7xGdMRBFT8Eng7jcz/FWzp0wi+Jgzf+xBleac2ybZf98WnPm/dehOT/9bpjCZ4aJRFWXOXYJQU043at52JkjLxpS187IoE4hVrvGJzfgecS18dDQuaNm+1Bog0H+RA7nMKWbjzYwk+095qK1y0DhmClXjqRs7tsmA476dEmlofinTbpxc9lXG6/CPymeGqFaO/fHM5Ko6Ryfqq0rkQ0R9u1qNb5mtIF8IepyHTBJoPPx2ftZ+D8mUn6/NuddbGzcB8Kp2pqavJDlR9oU6I4cpwOJjuK1TepW0jNe1eZEM+D2+LOe/9Z/1e90iJgRdXjM9jlh8hD/lWef44a4D1e06x/rdd9Sodm7IN+g6HvckjgNRAs+sz6WUhYDeV/tUxv1IDWyuqj9ZAorEgNjnnPOX8cbBbS3JsQnk48rArpNjEJc7lSEA1jxnPkM01DYTU7XEobepMqtghWwMqtOTTJltyMZDXjhmqdXBXVM18oSObIIHMD4ffg+q+szxEdf5vdMZrmW/nY8cR5ZkZwDp71B0G+t8ZlD1snRp7QdV+PuFHHBQILnMOpvNeqYRiDFKZTu16JcTn2394vKwiEYpE0YHW1lk7XzQd2a0h0A+eUMlych5wjac3AlS0CnwDor0rT5l9bMsb+yugXnOk9oUX8ljGrEaqKbiDgHmLJjUDajOG2Wa1I/sPU+vm6hbni0SMZDhg0O0CsxscwaM/OEclAltj+ANtTRQlF0aJFEW8Nb0IQN8NYeqb8oIecbz/oAIlqdh5xOWaF+YgdN4WBeBqMqovx5UcB4zx846fSzkYW3MvJ4vIa6tYrlzYl1Z8OBtkA9bW1u9Xsk28OlQQxn++yS3e6QhAE675XLNz5DvyAIUXadVaT3RjFuRNP8+l/7QIwXA0gF/5wvBA2/6963DLm+iLNFS45mDnsxeq86IWAA1GrsHg1xd1neW2Va9HlRxjlw3qHseNGdgmu0zo00dc312OXEe6HqCfm1jZh1MYKj++/ZdEYsAN/NRtW3f4jHlXNhIPlwPZaJf0vZjtk0dZX3uA/luOJ8f3tISEf3WeenWEL+9HZcx/nf/LrvpD7TQeW0hdkxMWyZ+n5ycxPHxcXTd9RMMfa44Hj3Ap+sWb1HRij51SVh+a2ur30oZEQurQFz50nZGjqmUsmDrFACyPvpzYhpt1dMY6KclI1xV40OG+OJjjZMvPna7l9nBVWjtlSp3JKIMRDGYkSH3YMwFn22oDoIYB0NuOFU3HVFmjGvjykCJX585ZNZTK5s5GV9dckNeC6yy/rM/LhAMRCmwvnKYASGerzmyDKBFLGYW2FYGmMkLnWemzyk77qBchkjy56t1zuP7pJrT93Ey0InIHbeu07c+fp8cgZUHO6zD+8S+iKh7NPpZ+Syo4ljZngOcGvil/LA8x8F+elDjekH+kxRE+Pw4yGQfPVBx3nqbztusHxxPFsTUwGzNJg3JnPrDADRLgrDeTQB7TpSXoaywg+CaXfdEXSa7omx+mNjy13DoGoI/1uO8J+AiWKGvFPCIyB82k/nUrE9efplPGuIDbYA/cl718/5m8t7vX1F9WVDlQVPW3yFdynixTMdW9d/Z9dxGlq0G0+7fNznmy5JxOp9dF7EYkBEH8J6ciMX75KhDrJ9y7LaXcu9y4TLCRKXrdo0oh0Pl6a8y/+uyKxlgMO+3sMivK4DQddl8cXFkPB73gYZesEv5I4ZTYp0LA9w+yXq50OE89/livzXntL+SAd5CpDJM0HDhRtdLh5hc8t81X7AubQaabNSoUaNGjRptBL0sgNGoUaNGv5yorGM8SykfRMQXX113GjW6N/pM13Ufu6/Gm241+iqme9WtiKZfjb6qqfmuRo1eDa2tW2sFVY0aNWrUqFGjRo0aNWrUaJHa9r9GjRo1atSoUaNGjRo1ugO1oKpRo0aNGjVq1KhRo0aN7kAtqGrUqFGjRo0aNWrUqFGjO1ALqho1atSoUaNGjRo1atToDtSCqkaNGjVq1KhRo0aNGjW6A7WgqlGjRo0aNWrUqFGjRo3uQC2oatSoUaNGjRo1atSoUaM7UAuqGjVq1KhRo0aNGjVq1OgO1IKqRo0aNWrUqFGjRo0aNboDtaCqUaNGjRo1atSoUaNGje5ALahq1KhRo0aNGjVq1KhRoztQC6oaNWrUqFGjRo0aNWrU6A7UgqpGjRo1atSoUaNGjRo1ugO1oKpRo0aNGjVq1KhRoxWplPKHSin/+csuu0JdXSnlV1bO/c1SyudeRjuNbkctqFqBSik/W0o5LaW8Y8f/7pWAf93V/19RSvnBUspXSilPSyn/dynl81fnvu6q7Av7/Csr9mFaSvkvSynPSim/WEr59iXl/+BVuWdX102vjn866UNXSvm3bsGaRo3uTE2/GjV6ddT0q1GjYSqlfL6U8pOllMMrufsLpZRHQ9d0XffvdV33+1apf52yd6Gu676x67r/+lW306hOLahanX4mIv5V/Sml/NqI2LUy3x0RX4qIz0TE2xHxr0fEe1bmUdd1+/j8tRXb/66I+Pqruv+piPiOUspvywqWUv7ZiPjOiPgtV+X/wYj4ExERXdf9HNuPiF8bEfOI+MEV+9Go0augpl+NGr06avrVqFFCVwH5n46IfyciHkbEPx6XcvfDpZRJ5Zrt19fDRr+UqAVVq9N3R8Rn8f9zEfFXrMyvj4i/3HXdQdd1513X/d2u6/7mS2r/cxHxp7que9x13d+LiP8sIj4/UPa/6Lrup7quexwRf2qg7Gcj4m93XfezL6mfjRrdhpp+NWr06qjpV6NGRqWUN+IyYP8DXdf9UNd1Z1ey9C9HxNdFxL92Ve67Sik/UEr5nlLKs4j4/NWx70Fdny2lfLGU8mEp5Y9erRD/M7j+e65+a9X3c6WUn7taGf7DqOc3lFJ+pJTypJTyC6WUP1cL7pLxfKGU8vuufn++lPK/l1L+o6u6/n4p5TddHf9SKeX9gq2CpZTffrV6/ezq/HdZ3UPj2yqlfGcp5aevzn9/KeWttSfkq4BaULU6/WhEvFFK+YZSyigivjkivicp8+dLKd9cSvn0OpWXUn53KeUnKufejIhPRMSP4/CPR8SvrlT3q5Oy75ZS3rZ6S1w6pbZc3Oi+qelXo0avjpp+NWp0k35TRMwi4r/jwa7rXkTE/xgRvxWHvykifiAiHkXE97J8KeVXRcR/GhG/Jy5l/WFEfGpJ2785Iv7huFyR/WOllG+4On4REX8wIt6JiH/i6vzvX3Ncot8YET8RlyvP3xcRfzUukye/Mi4Dxj9XStm/KnsQl/r0KCJ+e0R8Synld644vj8QEb8zIv7JiPhkRDyOiD9/yz7/kqYWVK1Hyvb91oj4exHxZTv/L0XE/xoRfzQifqaU8n+VUn69lfnKVdZAn2+IiOi67vu6rvtHK+1K6J/i2NOIeDBQ3stGUv43R8S7cWkoGjW6b2r61ajRq6OmX40aLdI7EfGVruvOk3O/cHVe9CNd1/2NruvmXdcdWdnfFRH/Q9d1/1vXdacR8cciolvS9p/ouu6o67ofj8vEwa+LiOi67u90XfejV6vFPxsRfykug5Xb0M90XfdfdV13ERF/LSK+NiL+ZNd1J13X/a2IOI3LACu6rvtC13U/eTW+n4iI/wbtLhvfvxkRf7jrup/vuu4kLrf7/q5fjtskf9kN+I703RHxtyPiH4ibWyfiaqvCd0bEd5bLm4L/bET8jVLKr0CxdyoKPEQvrr7fiIhj/H4+UP4N/NdvL/+5iPjBq6xMo0b3TU2/GjV6ddT0q1GjRfpKRLxTStlO5PoTV+dFXxqo55M833XdYSnlwyVt/yJ+H8ZV8qGU8g9FxH8YEf9YXN73uB0Rf2dJXTXiPZFHV33zY2r3N0bEvx8RvyYiJhExjYj/9qrcsvF9JiL+eilljmMXcZn08OTNVzW1lao1qOu6L8blDb//XNhycVL2K3HplD4ZEXfaW3rl7H4hrjIZV/TrIuKnKpf8VFL2va7reiUopezEZWaybZ1otBHU9KtRo1dHTb8aNbpBPxIRJxHxL/Lg1Za4b4yI/wmHh1aefiEi+uTDlXy+XS8+SH8hIv6fiPj6ruveiIg/FBHllnWtQ98XEf99RHxt13UPI+Ivot1l4/tSRHxj13WP8Jl1XffLKqCKaEHVbej3RsQ/3XXdgZ8opfzpUsqvKaVsl1IeRMS3RMT/S2dwB/orEfFHSilvllL+kYj4NyLiLw+U/b2llF9VLh8L+keSsv9CXO57/Z9fQt8aNXpZ1PSrUaNXR02/GjW6oq7rnsblgyr+k1LKbyuljMvlKwa+PyJ+Pi5Xd1ehH4iI33H1IIhJXG5/u20g9CAinkXEiytd+ZZb1nObdj/quu64lPIbIuJ349yy8f3FiPh3SymfiYgopXyslPJNr6nfG0UtqFqTuq776a7r/s/K6d2I+OsR8SQi/n5cLon+81bmSVl8x8a3R0SUUn5PKaWWuYuI+OMR8dMR8cWI+F8i4j/ouu6Hrq7Vuzs+fdXHH4qIPxOXDufnrq7541bf5yLiu7uuW7bvt1Gj10ZNvxo1enXU9KtRo0Xquu7PxOVq0J+Ny2Dm/4jLlZffcnV/0Cp1/FRcPqzhr8blqs6LiHg/LlfB1qV/Oy4Dmudx+ZTMVV9bcFf6/RHxJ0spz+Pynqnv14kVxvcfx+Uq19+6uv5H4/IhGb/sqDSb1KhRo0aNGjVq1KjR3elq++CTuNzC9zP33Z+XTV/t47sLtZWqRo0aNWrUqFGjRo1uSaWU31FK2S2l7MXlqtdPRsTP3m+vXh59tY/vZVELqho1atSoUaNGjRo1uj19U0T8f1efr4+Ib/4q25761T6+l0Jt+1+jRo0aNWrUqFGjRo0a3YHaSlWjRo0aNWrUqFGjRo0a3YHWevnv7u5u9/Dhw+i6LrTCdXFxERcXF9F1XVxcXMT5+Xl0XRfz+TyGVsFKKSt9WL5Wj/9f1u4yWqXdVfpxW7rLtRl1Xbe0f+v+z3h811VPXl/7zf/rtOdz6uN59uxZHB0dvY53QaS0s7PTPXz4MCKuxzWfz1Pdov5F5PJKHdra2lo45r9r9LJ1ZVUdvms7d6Ws/mWyts7YeGxZvbXzNZ1Y9dgqOuQyltmEra2tVL5IX/7yl7/Sdd3Hqg29Btrb2+veeuutBd90cXERZ2dn0XVdnJ+f97oWsWgz9b21tbXwW+dq+rfKt1839PsutEwOh9pctw938QPr2oJlx1+FL639puys8t/rYJ/5GY1GMRqNUn2LiHj//ffj2bNn9+a73n777e7Tn/5076/m83kcHh7Gs2fP4vz8PM7Ozno9q/kg6ZZjP//Nbz+fUU2W1/F/q8rkKv3IbP9dsFUNB/C8X1vj8yq+YBkuz9papU9DbWbXZjhoSL/YD5c1nefx8XgcW1tb8f7778fTp0/X0q21gqqHDx/G5z//+bi4uIjT09OYz+fx4sWLePr0aZyfn8fjx4/jgw8+iPPz8zg+Po6Tk5Mbg1LHR6NRjMfj2N7eju3t7ZjNZgu/t7a2Ynt7O0ajUcqImtJtbW31TjNj6Pb29o1jJLUj40VHShpyuA5kRcsUgGNcdu2QwmfGvGaQOUaCcBeyIeURQGEwwHNuTLPrvb8COX5eRlu/va4aL7a3t/vx8Lfk63u/93urdbwOevjwYXz2s5/tkxTz+TyOjo7i+fPncXZ2Fo8fP46vfOUrcXZ2Fufn53F+fvny962trd7h0glPJpMFfRqNRrG9vd0bC14XkYMqyQrlxkll6fhr5RyUZs6y5vTYDvuSOYOag6uNIeury2pNtlXe54D1ZODB6yAxITWfz9M2Nf81J0MbSP0RuHFdYt1qj9fKXrud2Nvbi52dndja2orJZBLj8fjGeL7jO77ji0uZ/orprbfeim/7tm+Ls7OzODo6irOzs3jy5Em89957cXp6Go8fP44PP/yw5+t8Pu91RN8a39bWVu+v5IDFD9kT6Rt9nY5TVjL/xnooL5n/4DGXb9cTfbudl030tl1+h3TbZTCTIb+mpvdZWY7FfRh9m/cz83nOtwwr1MCh9FFj1G8GDrLR8/m892MK4BV48LfqoE+aTqcxHo9jPB7Hw4cPY39/P7a3t2N/fz9ms9kC8Pv2b//2dF5eF33605+OL3zhC3F4eBiPHz+O4+Pj+LEf+7H44R/+4fjggw/i/fffjy9/+ctxfn7e+6RSyoI+7ezs9L/lu/RbOkg903xkGDHipmywPHVyNBotJFooL2p3SP7pI9XniEXfQDwl3afMsU3ae9pjJVRdhtVvtkN7rqCWPNne3o7JZBKTyWSh32p7Pp9X/Z8H+OJ95nfEZ+cJE8TsV81u0KacnZ31+nR6etrXIz1S3fzfdV3f74iI2Wx2Q4e2t7djZ2cnJpNJTKfT+PjHPx77+/vxrd/6rRWpr9NaQZUYQCYw4ydjom+B3oibYKJWN79J8/m8V4AhYpsiTt7FxUVsbW3dEFCWLaX0glULzrzfcsLs79A1tXNDzsvP1wKdocyE/tdWL5xWySj4tVQ0AsKsvM+DeK4+6rcbKXeKbohqY+dxGs9lsvmqieOSDEuvzs/P4/T0NE5PTxecMYE6nXKtfucDZTaTceoCz7Gsl/E6RC5vLD+0kqYPDfKQPmbzvIyGyjhI5PFl7Q7ZsYxquuL1aq5po2rj8GNbW1t94EBeUw/p4D2h4Y6STth9w6aRO2YFWEoAKlHI8tQPjlU6OBqNej0imGBZzhV5RX+WJal4nZdxkm/jnMqOeJChY1myS+PR8aFVSJcfjiUL1nlN5n+HbDXPu+3w+lmH65TbN33TzzgwHeqP1+VUCyp5XjZN8tZ1XR98KIEmnRWukmxsAklOzs/P4+nTp3FwcBAfffRRPH78OJ48eRIHBwc9EM4C9izZxuOOD1TO/2dYpnZMVAuovayXo3xpzjIfmGETyhf7USvvcso6eIzYV8cU3BMfnJ+fx3g87tvJsG4NSzrfani7lkiJuE4EezlvM5v3DB9k/1kn56iGA93GDmHXZbRWUFXKZWTnkfDJyUmcnJzE8fFxHB0dLUSKus4Hlhk7ZwqNHQfM3xljSdnkDjkbMVNOKQP97pAyoWZ5z6CsAvSyerLfHOcqQRWzOn6Ox1VPZgC8HxpjZig867FsXMyYcFxUejp9P0+5Y7aYffOM8qorGK+aqB/SEQVTh4eHcXBw0DvXiGt9FJjSOB101MBRZhRZluWXGRgZyZp8E5R79o8y4k4y6xPHy37qOjeg3p8soBuioa3Mt9FvymvG12UBG9ut6dfW1tYNO+dz7/Vl88fMo9piplJldI1nfjeF1CcmAA8ODrTtNw4PD+Pk5KQHfhqj/JZsklYjlN0VwBURENKPOF/pb7L5yeZvmR3OKFvVyWy++nh+fp4CWx/fkLzXMt41faTvqq02cHWPZQgMCdg57pqO0H9oHujH+F2ziTVg6LwYAqcCvlz1kAyORqM4PT2N4+PjGI/H/QoW/fkm6NloNIqTk5N477334vHjx/HzP//z8aUvfSkeP34cJycnCyvk4jNXgvnhCpF/iyiH7k9cRlXe68jAP/tYyvUKLhOZWfJS/s0TT7SFtUCYSV3vk8oT12RJCuoc69EqF8uPRqM4Ozsb5EmWVKC+qM1M/30MmT7T/znOZxnWyePq+/b2dmprdA1XlR1HkB+cK+4Wuo1u3Xqlih1WJ/zDwS0DGqo7Azl+fTZhfq4G7Gr1ZCtLQ86M54bGxkg5q28VvmTtLqMhPmQOdqiejMdD9XofMiIg47EMpPp4MjBZ65MHid7fVXjwuohGl5kg30ri4HjIqatepxq/KK9ep/SelPVjSD68/lpZ8cFX0rz/GaCjjeK3j2sVqvH2rjKTzdNtQHONyIN1ronIeZ4lbbz+GmjeJOKWKwZYp6enC8DF7ZODiQyQZW1xBYv1+Cqjr2DWeFwLhmvkK9qqg76Wvo9zV9MV3zZcAzPOy4zID7frrDtbVXc+MMDN7IWXZ/tuu3iMNuS2VJMXtu+roFyR8k+W6b9PEv+Pjo7i4OAgDg4O4ujoqN9qm63iuP+tJXp5TfY7k5csoMquHwp2/X+2Ik0S5uVOHV0XETcwi35zBW9o5cftb9Y/lhV+cDl2mXPb5O1kuHkZls3447aFts8psys1uVkFw9G+1mwBeXCXpODaQZUalnJru5+2JmU0NFgBaGaieDxj7DpBQc0p1QIAN6o+4Q42lymr6qLieLsaQy3yZ31Z32vj1veyD8e1ipDWDFet3FAdGf+6ruvvfYu4ueVLfD8/P1+op+u6BR6786eiM9DdBBDoAEJZcW6rzcrWDJkcib6VARySr6E5URl+k9xp1vSWjsR1ne2prMrV9NbroFOgLIgPrs+1VasagH5ZIMbtklM2XpbXWEaj0QIv2W+Oh9nSLKFRsy9c9WMmPdMdB6KboFck2XPtruCWWj5wycdGn0Dgm/kKB/UMFpYF9LwmA4o1MJX5soz3vvLFlR33PVw5cPL+ub0gmKMOLxu3l6vNA8dAchvPctmxDDwO+dkaUKQP9/YYKNZ8csYL/ifectC3CUFV13X9boonT57ERx99FM+fP+91LOJ6zJntGJovL78OJtF/X62VLp6fn9/YuePjkkwObZVzAK5znlCJWJRxlxFen/Uj297nmCHzW5Q/v8eJds2DqoyfOlbz1xHX911pbMvmWv3N/DX5WUrp700Vb7mVXRjH77UnFhoak/P+tQZVHkwdHx/H8fFx9X6HbBBUFt4MnClQtr1maDVjiLLo2yeXgupAk9kKKheNa60fHlipnNfnTmvdyXXeZwrF72UG38GSlxNloDUzck6ZEfFrXNmZrcuyvuqPG1TVTwNVu8fldRPnX/ql+z38Phj/LaJM0YlRt2rXEFB6/cuMDRMQLmOqqyZnDvZ8zji3NafKftMY085k4MdlKhtXBl5WtXOrHBsK2Lx+jYEgnfzRPPgN0cxsawu3khI1cMZr3B6rfrdntW0Wm0BKVGirunTr+Ph44aZnbflxGaGcUi7II9puX03wrHBGPhc1PtaC15oMZXY4s7NsU/Ockcp4osZ5w3prPqXWD98W5XpcG+NQn7O+ZDzJzqsfmS3J/CaxSsaHDOt4gMeAituSIm4mTu6Luq6Lk5OTeP78ebz//vvxi7/4i/Hhhx/2eqWtsvRBQwk+n0facLbJb13nvqt2nd9bn/XFt86xDm7L876Ucv3wGr/O5YvYzMfDfnjgkwVhmf/i1jjabZbPAsWMd+r30HFe52Uz3eL43Xayn8TdW1tbN5I2mitu3Rbe1jVqj+PjMV+JfG1BlSs9l6SzpUtRTflrAs3rXFF8UlR3rX0HHazLj9FB6pwb8qHtSxn44nkPyrIytWPLyB3lMqPl/zNhr7XjRGNJJecy77L6vO8MmnyMy2RmWTsOFtfl9askGQwZCj4pJ+Im+BmiVcZXA1j6eEDl+uJEgCka6qvPvwdnmVyzvK5h4oO6zFXJIf2vHb+tcX1ZlI1Xx7O58G1UvNbt5lCG1MvXzq1y/r6JOs97fj0wql3LOobaWKUfNcp2RbifyOpxu7Yuef3cYlzbmsMtg54UXKUvQ0EVy6iNVcr7dU60A6tcX/PJd7ULNV3Jxlizr5tim9QHbVNXkoIPSIi4CWZXtQ1edh2bkq2gqr9D2MvH5u369stsLjzgpW6pbx4kDBFtVVaW54fwaA0PuG0ZwmrLsIQnC5bNWYZn3K5438U/rsJl+LXm8zNyjPJagyo9jUwrVfqIAbzxy4mDVlTJrLoeLsBjGcP8KTIiGfshAOLCR+bLYfiEsp4sA18bZ/Z/KNio7a3Povyh9ry/4hkFckgYI/Kn+GXZPv535aTzzYxjzZGUUhaCCy/H/ebZCpnzWrIl+WU2fxOyfRHXuqXHhZ6envZbKLS1dpVMX0Qs6BF1KWIYAHM10+c1cyKcx0xGasmHrAwNacTi1qnM8LqOZ/ePqD7KoQO+ZY61FpwsM+a18bq98WByWT94HdvxcTG45La1zFllgN0DroxHW1uLjxImcN2UFWCREn9ardLHn/pHoszLJi1L2gxR5teGqAaqa6uqEfUVjCEgldWlMdfshv6LJ66bWSCfyWqNDy7LKlvzy8v4SX+RjcP54e14ucw3ZX3Sb4Fn/ZYP0s32/loS34ERET320usxuE3+vmg+n8fz58/j2bNn8eTJk3j8+HG8ePFiQX5kI9wnkT/69lUtHnP7mvmrIdKceSKAryjJ5pzyLfwxpD/Z3DHI5Ha+bLWX7Su5muGkGpbl9Tqvx8+Tb+RlhkcV9PkqFHnOD+d4n6Fz9gAAIABJREFUyDdxbDonmc94oLJ8gFDXXe7A4OPmuZq7vb29kCDiXLK/pEw216W1NJID502+fC/DkKN2wWFQxQnXoHhM17hT8rYIIlbN8nh9UhqCvMwQr2LIa47Ms/FZmex6V7yhKDwLnjKlYnkfl89jJpy1/nMe2BcvmxkJtUUjVAuYHfBnvJWM+f0nmWLdB0nm5vN5H0wp63dycrKgW3w3B6+X0aH+ZE4sc0KrzKnzd9l5tue6zzkWKKMDIEh3yjJKDgJ9a0NEHnSpjMun2wYe49jIO7cj5GetPMeQtcPzHEsNkLqT5fjUxpDNcNs4dM4BkNri8U0igRPqFV//UeMHt/3Roa9KrlvLEiLLMqbywZrPWuaa9VG3WGfmp31+fdXT+8bExZDeZmNTO94v/y2+1+qr+SteH3H9yHheV5tL17EMBPL+jRrfPKPOMgwWCCqp79Q7BVVd18VsNkv7/brp4uIinj9/Hk+fPo0PP/wwPvzwwzg8POxBL3GeJ/mG7Kjb5CxBSMC/CgjmVjrKqnjMdilv3BJcC6JJtBu1wIrHKGM+Hpe1rA3Hc/xmvXxP15Bf9n6TL56c13kdY7DvY8j4RJw4FKxKRyg7nngVnvAtg9T/DO8PyeO6tHaag0KpiWbUrQG6Aa910AH8kMLxXBbkuAOobV1Qn7wPWbllx4botpNS6xPP+ViHnJXO1R5LSyWu/fYxZYI5NI4McOq6zEi5ouh6LzvEp4yWOfX7pszAEEBF1GUzAxhONcPLelg24uZTi+hw+JvOv2agMxAj4+grtENAzvu6KshVvausCmX95O9l/M6SFF5PrX8+pmXyyf7qd01va/WtAhRWsYtDQdt9EZ03X8jqN4DXriVPh2RnSPeGrolYbTsv+8S+rVK+Nue1+fPftet9vpfp1hBlejdEtW1WNT3y8a7Kv2VlhpIRtyEH/jxOn7AJ1HXdwkNf9OCXiBwvrGIHMzyWXbsuAJa/8gSWf9OnUSaHeL6u7GayqLaXJVYyyoIx9z/OQ37czmXlsuv84/g84x/7KN/vr4bh9Ryjbz2uLbT4tbXxkF4GFrz19j/d6KsbfHnjmL9MlUuXXClRFl1vttaH2XW+k4HOxwGN+saPG3cGgSzvgUTm3LKAYFUHmoEtHidA85U5r4OZglpZCqRvr9RvvUm61g/+zsai7+y+J84BM+T+pJ1SygKg4bwxW+Rzqr75iogHaDU5UD+4krMJJN06Ojpa2J6kBwpk2Tldx/n1jFTtQTA1OY9YnGclUDgXDIA8q6cyNX0ZGv+6gDwDSNSVVZ2492/Ivuh8Nh+Up4yXWT/cbumYn/N+Zn0VUXeYjNDcZXrH/05DfXQeDcnXfVHXdf12db2XSvd+6N1MtI9DsrJsXA4w3PY6fzwjzz6T1wR6tK++Q6TWH/5fxqvst9eZ0bIVqprNcZC7Sj9r9dFPqB7yz/XG2/P5WYVfXbeYBPPkM7dwZcka6k/E9c4MYSud09bwiLsFry+Tzs7O4oMPPoiPPvoonj59Gs+ePYuTk5Oeh9qqSAxQs9MZOJcf8/+ZLtWwE/GBJ2d926XXIb6zLpcVtiWifdzaun6nXRagsSwDDddvHyMxlbCy4yXxTDtcMiyQ6UjGe+Fznne/k80TEwEcty8M8NHyNfzmfNDY1Sa30nL1N5sv9cHnoxYHrEK3WqnSErT2o+tJZdnT5UT+6EMqHCeMQQOFIFM41qe+ZYJKhTk/P+/3MMvI1RS0ZuBrQK1mfJetjrhxGTLmFNSISI0UVwqdf/rvQVXmaLLtO+yz6nWFcQCg8lxCZ/8dOPhSrkBP5hTd4FCJ+LRFB+wKqGgk7psUVJ2cnPTv+BDo41NtaHx0HY97YOWGMWLxsaekzOi4Q6hl9il3DKyyzNsyPmSUgaDavGX71DM7UQNSmc2hnHuyQmV8P3lNh1Wf864GpNUn1wHXWwIBd7CaO99GmAVuGXFL5TLwOzQ390EKqviSet6vOJlMFgDBqpSBLH0PBVS0fyzLegnUfSyUDSaunHhsSCZFtJ+q12VV/6XbQ/VxXMv04TaUXUf9cZCb6VbWx6H/WXsEcrKPOuZBVk13vG/ZllS+v2qZvr4uOj8/jw8//LC/l+rw8DBOT08X7KF8kwdHmY32c36N+7EMx0Tk2zd1nLbQ7Xp2H07E4nuoMoymvtGWU7e19ZTls4QW9S2zGSLaq9Fo1AdVngBVncI8nBe2wbaJH8h7Bcg1nWWfVAfvZSf/mYDL5ix78mJmS9hORPTYdj6f91jf29O1vsWW7dzWLq19TxUFkitUtYHr2wGI77H1gIqTmQUbjNDpoGiYGEzRwGaRf9bXoXEtM7Qid5Y1HvnxjMgDrztzBN5X30/LgEzEY7VgsNa+Z0c4Bz63NHD6phEicV5ptFQ222vM3xmP3eDdN2W65QY3kzk3FP5hMOZ6WJvfbF7ZT086+HnezO+JlpohrjnYdWnV61yOeazGS5UVD2o2KpPzZX2QM3Tgp28PAj2rltWlY8x0b29v9zLD97z5PNcSVPy/DNRtgl6RfNWAOrZsLLcBsJk8Z4BwSGZrPMz0VOTyvEpgE7GYnGFii3XdZk6XtT3kA7NxDdWZ9fc2fa/pv/hDP+fHdY68dMpspx8nONaxLMi9b+q6biHBvurWRMrwkKzV5KM2R1k7/n+I/z6vqxL93NC72dh+Zs+930N88ESrylB/qde1RA/7kPm3IX/oPHCcobK+qynjj/ogHDqUkKjxdBkPa/LysnRq7aBKq1LK+El4sgwEM+EciFYHxuNx/9F/RdweffOhFfzt7aifBNrMsMqR8jeJIDADFZ7lVvmhya7xskZDYNdvEswAla9kqYzzNVOWZQDY++nALWL4DdmuyOSHP0pcWblSSr9PO8t4uOJw2xNXzJiZFG/G43FMJpONAICuW1mmJqJ+X5T0QvqkueaKr24g9WDLt5RSFrJVSP5W39VfZrvdAGdOhLzXcWaelunWkMHM5INy4StanvQhZSvaKqtzsmFDQJHkfOWxjPe1jFqtDQdnvOeBbejpkkOr/dJx6iVXltmf7GEq90ka+8nJSRweHsbh4WH/oArfIqLyy2zCKvZxa2urtzH6LZ+WZbw5x75NPQOA7lezgJ5bcHSd97/2AJ8skGE9zoMMZGW7KWp8dD3MPrVVgmV6UGuTY3G99mPU0+3t7R5DbG1t9bIk38Vtpd5WtpWTH9pSX13mXG6C3zo7O4v33nsvHj9+HEdHR719IWbJdg7o2/2PJ9nFewY7LJ+t9mYA3H2XbzEjT/mOo5o81WSZga/LaIbtasGCxqcVF08eb29v93aFWJkvNRZtbW3FbDaL6XTa22fXMcqfB2kMdjKMwPl0/EEcoboyn5bZ4K7rFl7Qfnx8vGCvs/njmJ3njp2lX9k8rJJsy+jW2//8hkTPwompXdctGFW/j4pAMAOAnByBXz5dJNvGRCY7qGZQRRAvYqBCprIc7x+rAUVSBhpr5WuKmgHgLKgST7JMOu9dkzI6oOSxjA/qd3Y+U47MSfK4O3bx1h2Uyp6dnfUyxmV6KQjBX6a8GQjJXtT3uomGXk8ky4J+UQbaOdfSJf/t91q5I5POqU6fX3dE3PJCYO4GUu34mF03uA2D86rzTsyqsy3KWi2w8rIu+5kxzoAW+Tv0mOMMyA0FVZnjcTA1pE8iAjONQVu4tVqlDHNGWR/cxmb83LSgSs6Z9wPLxozH44io2+pV+EyiDHGLsQdVnjiorVZ6m/KtPObbq3wVxevWPHELjOtKbZxDoDLjwxC/XM8yfaTeZkmLWhuZXg1R5q/oT1WPymgeasBQgRWBNvtW+0guVQd3f7AP9+23Ii798OPHj+PZs2cLT9OMyINdl0vqieMan3+R21+XM8q7yLFeRJ60kj4w0eB9V30ZZUB+6DflynfhEGO5/BETa1FCfaTMqK7JZNKXp+3JZJvt089n2NL5w22GXHRQ2cyX+G+OW+0IY7CvNVuZ8Vp9JsahDHBebxNMiW4VVOnDbOcQeHag4oPjvS0OBrWiwpsdBVwyRSKTsg+XFbmnlkQw4ICu67oFsOvAswZMRJlxWKZ0bmAyoSaRz3SyDKoo+G7k2A7Hzt/O+6EAhuUzkuJqP7MUSdvIGLjL0XCvOuvWeGurZW4UN8EpiTK5zQBO1mfKhAdJ/M/VXhkWyQVlxOeWq73sJ+cjW7IXUV7daKkdZo04V5xPT4AQMPKY183fmbHN9Mrl1e2XfnOfOZ0bqQb4PEDRcT/P4+w3f9cAuDtF3f/KuWOwnNk3D1zZZwcjNSd339R11/dRcLwRNwNxHdN1tTIkyg0Tggqk3KHzvgbqO8GcMtUE5QTX7Kfa0XXeX5cz543kY2h7Tu26jFc1Hg2R6z1/u31jOxmQrQUrzhcHhMQnGbj1vnJ+aIe4es/y2QMtyDefH8qtJwA3Qce67jJhQZ8cUV9J5Dm3YS4fvMaxo74d6MuGZXYxq1P8daKcODbix1diPcCujWmoX7W+sFyG8Wgj3JcxucOVKi6AqF8cL/2yB78ZxvOgqub7/HctqIqI3l/JN11cXPSvmvFra/7d+1Gbh6yP69BaQdV8Pl+4uVcZCU2IJk0kA00DKIZPJpPY2dmJnZ2dGI1GMZvN+uun02kfWfO3AJ9+ZyCJAGsoGvZlPxqCLBvB67RCRwfFVbCa83Iw6UCQSpopLwNNv059dyVjIDWbzWIymcT29nbMZrMbQVVWX9Zv8mmZwrgB0HkHx+KHzkmBLi4u+i0Fp6enMRqNFjLOvC9Ec0pFJhjM5nVTSI6AW0ciro0YdShzTkw2TCaTmM1mMZvNYjwex87OzsJ56ZlWK6VPW1tbMZ1Obzy4wwE09YtbwrhSmG3RdOfiQa0yUa6/nsjxlVACTToX8scDRRFlnjdVZ0/xo0Ohg5pOpzd4ybb9N22Kr6Rnc8zrnJfsfw0ccP5ot1+8eNGv2IxGo347twAS+cv2NSelXK9wUZ8IHDeF5Lv0fqraC7WZeBDVzmfgSnIwm81id3e3t9f0b5RF2inuhKAtzBJEGVCjHmdJAU8kMlHBHQKUoQx4MEhhEObJDZZ1G5zZF/o4/+ZvbrElH7OVUfKPcprZUdkAB/jkJa8hhiDgOzk56W3gZDLpHzcuIEgfp4RGFoyofj0UTOMbj8dp4uq+6OLiIp49exaHh4fVRHsWkHiCL9vyzbIqP51OY29v70ZyWB/pOgG46lTiVnVJ5slLJmjVHw/cGEC4rZ7P5/27xEq5TpIM4SJi6CxJwX7pt/glnSDWU30ss7Ozs7ANmbhQuN2xsr6z3Uf8zw/xuSdGvA7qYS0w0vHz8/Nezk5OTuKjjz5aSI75E8hpU2r67LaQule7/WIZrX1PFZ2yG1OCFhcWGkxNtJYjuSKl/ec6P51OF34L+E0mkxv9q01QZnhoEDmWZYBGCsuMpwyc+MAAy+ugcDLDwbZp1F0wM6fu/WZQxXvVGFTt7u4ubKF0R+L9zijLGIq33qcagMjmjIGrgvazs7PY3t7uHydbSulBkepkdopAs6aotdWN+6LaCrA7JFGma0xayLjRgDJJQQAonZtOpzdWWjyo4icDf35/lWe+eD3HpC1pzgcGVRmpzkwnasGoByXM5hGUujF2ICBezmazG0HVsqTDUFBVA58aZ/YkTToWXsu58+054/G4f3Q/9c4DXq+beiWQIie2SYBPJKcssOtAym0uj0XcfMpqFrgT/I3H4z5BoaCKQIjXUMZrc+d9yuy0/CJtgerQNx8uRd3lvAl4sh2fT/WJQTb7yWSqytPPcOwedNF3SR/5WwGq66gHwxqbbwcX+fy53tf8V6ZXDI50r5USDiqv+2KyoDnDChyD8AST1j6e+6L5fN4/TZM2wm2v85T89uShKAPF1C0GVeyP89VvFfD+8+nU7LP66f33bfScS9pj1eW6w7YdB/rvjJfkn8YkDE1fwlVyYmglWIUFJ5PJDczsAYeOZz6APGJi1rFrJgOO1dz3qIzmSDHE8+fPF651nmfzmflktwl39WFrb//LnvhHA5ct9zG7q4lV8KQJYPCk1SuBFTopCkRGzJbxWEQdVFOAyHROFO8d0VKkQAiNp2cD2R77UJusmtEf+miMFBwGsBnQVnDFOas5EeeFztecpJPX6YEoeaI5oQMqpfRblpRVZzZQdbJcZtBdBmqA4T5IsuUZExoCZmdliHmOc8uP61CWpPAVYfbLg6ZlQUEWELlBIyDR9Zo7OTkFzb49rQZAanzjceqJ/3cnlR3LVqp8RT27CZh9YL8zx+X9ypy0J0Joe1xX2Q6TFRHXTlfvlRHPHSho7LKR7pwyR1sLgu+LuKrmMkQ+Enh5dtXnUkETd19odXh/f78/L/mgbSIPCfw9gSHKMq0kzxCzbs4/kxZZcjGr23nlsumUAVHyzY876KX/ks9XckiYIdNHJ7dRNfCl37XtSm5LPKlEn6agVFhJAeHJyUlfj+4NUbAfsWgTM/+bfTbBd0UsBtVulzKArI9jCJVhYM3kVSllQbdcFxhI07Zub2/3QanbW+mNsJ0//IF9isgDLsmAsAj7U5ufGl6qldW32w//cPWYq+tcDdSq1fb2duzv78d0Or3hjyhbGZbNkkDE55lNZTkfj4+3hhuFcQ4PD3ubRt+m/nNuhvBAFjRmPnlVWnv7HzMSEbFwU3vN0erYeDyO/f39PjJ+9OhRnyXXyslkMokHDx70KyvcNpE5DE5IBkykmEMgh4LP8xQwPjzg+Pi4/88XSGrJWcql6wX8BS4FTMgjRucUAgd5/mEWQAbKl3blkB48eNAv9e7s7CzsS3YDyPrcwdN4ZoZGv2ugQA5nKNBkEKsb6o+Pj2M6nfZP8BqPx/3LPHUNs3pUrgwILgMPr5O6ruvHKZLMyzEzgKJcyFju7e3Fzs5OzGazeOONN3qAJwfEbRPT6XRhdUWOJHNUzlP1N5tznSPYkK2gPDGbyIeSaMVEW148ccEtGbWlf2YuaSc8OPJ+e3JI5T0rKcchoLy/vx/7+/u9QyYPM73VMQaL4pv66v3mWFgHgWUm+5o/tcNdBtpWe3JyErPZLA4ODuL4+DgeP37c2zVtCXQH5UBIekZQc9vtE6+C5vN5v9WxlvjxVRGRxuzyIf3c39+P8Xgcu7u78ejRo96fPXjwoC/DbXkZyMj8JolyQNvM/lOuKQO838VX67TlVj5dIIP2V+3Tv5I/9AM1YK06Mt/lPBZvudInW8at69zhwlX4zBaRJ+6X6Ic4Dg8UOF7pEjGC6tXx8/Pz2N3d7W3Z9vZ2HB8fx8HBQW/v+bQ89rEWUImoY/cdVAnQCuf4KrongDRfLsdM3mg+hVOYbH/06FG8++67MR6PF+aAq4CSIbeFGQ6Uj+m67saqsj8ZmO24PfVt0yzPZIVju2XzlwVNEXHDz7BOlaHtETYYj8fx8OHD2Nvbi8lkEg8fPozZbNaPR33W75r8Z0k84k/yIcPeQx/6X9F8Po8333wzzs/P4+DgICaTSTx58iQODg7i/fffj8PDw4VErCdzHT8Jh1PvhUlkH2+jW2sHVdmeawoqAQ/LUFH44X0fAip7e3v9b90P4su8GYBn5EwG1vZgu1Gv1Tefz/vH79Jw+MuDpThaTRHPlOGtZcd84lwBMxDj5zxrR+fER9fLUQn8iT+ZUCtLkwVV5LF+uzzUHNMqzsCzTHpMqFYzIiKOj497cMqtjNpzTqCd8dD5eZ/EwCDi5jYfzi8BFudQKybaBiQHwUB6b2+vByweVEmuHcTLyHhW1OWP806AwWV7GTPNv7b8SSaUZSNAZz8018yWsUxmvL2/HjTWEhYs68EWs36+Tdkfme2GXb+zYNX7M9Q3ndecUa+YaeRv3sextbXVP/nu9PS0H9/R0VHPUzqW2g6AjLiyuQnkYCgj53l2LvN3squz2awHKru7u30yg+CMspmtqOs32424+fChWoafNlbzPR6Pq4/75kq/+kQfvgz01Wxo1rfa+cyP6ZuvW5HP4tYvbVvmvdi0m9Qtz7y73rEvXO3I5saBJwPQ7e3t/vpSrkHm0dFRr6eTyWTh3h4R++WJLdlElt0EHfOk1jIbnCWEKGduZ+WfNO/SLQVVkm3e6+4rVZ4YYT9oh31nh/SWcqP7MYULWQeBPBOHPlerYg/XkVoQRd4SQ2RYUAGqbJWSPxqfJzvdJuqbu9aYDMgwhOY1G9MQxiVphS3iMlh8/vx5r2NPnjzpE2bebyYFM7n0Nj1Jsi6tvf2PmWcHCXQSCiTEZAEQKcRkMok33ngjdnd3YzqdxhtvvNErDletBPzcWKp9KqSU28F+9k4mZ6bXG7G4zCkFlxPSI+XluKREUjCd80yJG1Afh5NnrdQfL19TPlcoOiyec/5oztQHd0ZODjTEB80N5059y4JJ8smVtOu6/j0L8/m8NwoR0d/Iq5UtOSXKgmckyYdVjdyrJN7rQgflWT3fFiNd2d3djZ2dndjd3e1XqmazWTx8+LDP8Ga6xVVggiqRDJXPfQ0gshy3cFJXCfK1GqKsuQJp6ZS2zWRZMeleJmeZzGW/OZaa/ui3Ejvc9sWtSUz++Jy5fshWuX6Rr277vN8MqpidpTNnMCEdkX4okBU4GI0ut9hOJpM+uFLgS8fJseiYJ442QadEkuHsfsWIxSQcZUA2TLwVwNvf3+8ftPT222/3uqWVKgVYtLGsN6J+fyx1KQOgmnOCPY2ReidwqeST/BhXpFSHgzTaTAYM3o766ZiA78rxtryOZUFbBg718XuxM5zgQZXbMZcH9TMLwOnLmczzRBLtnvp4cnIS4/E4uq6L4+PjXm9l33Qt65d+eR84x7cBfi+b6LdrGEuUlZFNFcjXbiZtTVOyfXt7Ox49ehRvvPFGnww6Pj7ueSf/MRqN+tUXEfWGukdbKQya4VnNkR50o9VvYUHOJVfpGXgzIOH/DBfy95Cu+LXUYeFuJn+EsbV4wW2z5I18Sc2WTyaTG1tXySvv41ASbl3SbpyIyyTGwcFBjMfjOD4+jojo8QqfEOjJV36LMsy+Lq39oAouccoI89n3DlxKKf1EjsfjePPNN2Nvby9ms1m88847vXN65513FpZ6pQAScLXvRNCcGRg6yhrJYam8163JkbHUqpXAh5bxCT71QAVuPVCdPpYsINK1DroiYuGJd74NwwGhFIn32HD1jtuYOGcMqtjfLAPONsnLiFgAcO7o3HhoJdAdrjJ/DJrU//Pz8z7YJc/l1PgUPSq9O+n7BoAcPx2UgkY6BD7lS3ujp9NpvPnmm/322o9//OO94Xz77bcXsn3SWZ/3iPzhI3JYbtg9EGHQtCwIJwA5OjpaWLmaz+dxeHgYBwcHcX5+HoeHh30ZzZm2Wwh0ZCsjngmlzhDwZYaWARW3GOk3n+6mYFZzpHniCjkTF/qmLtGIO2hlHW6nqFMeDIsUrDKYnc/nMZvNen1SJv3o6Ki/x0o3AgsAag+721Sfaw9uN4EEZPlULvE0YnGbjIOviEs+y2/NZrP42Mc+Fg8ePIi9vb345Cc/GXt7ezGdTtPVKU/yuY11+5nJDRMprMNXIbl9Rx9t62Q2X9tkZB9PTk76/wTyqiuTVQ/CCBIVPNQA9BC5PdF8aKVK336PqJJEXI1w2XR/rPb0zWDVAZZ4ke2yoC5oGze3hCnpp622pZQ4OTmJJ0+e9NuehTPcnmXBoMpswhZb+S5usSP5fGarrEwMvvvuu/HWW2/1tyzIVz148KDfbvvo0aN+O+XR0VEv09RvERNbvB+PKyrqO1eneF+v9L7rujg8POwTgQcHB/1TRT/88MM4ODjoAy7JoHSL+FS664kR8kx4R0mJIZ6S5/I9+lbgpC3J0+m0D0yHaJmuqv77onfffTcePnzY+6kXL17E8+fPIyL6QFuJQa5WZg+5i1i0EXfZWrt2UFXbAiRDRuAgoWVQxS1KyvRp9Wp3d7d3KhGLAl6LHOmwsuyXO7UsC1BbrdCYNGZl+Vxh5NCkBG40lkW8Q4LrBiLLWLjj8mPZ3Pi3l3GARkBKp+OBnI+FWUqvz49xRcMzO6pHzrrrugWHqsBLwLPGMwfQWZ/vgwhePGiJuLn9x7f9MQulrUjSL2X7xCsFVczs1uZRxl+BDMn7l/Gcv7NjXJnSN+//UOB8enoapZQ+ABAfqPMe0PlYPPvvtkH9yvjhHwbkvppFvSJI9yw6bYtvc/Ssn8ZbC5yGiHNHgCbgEHH55DiVUbb17Oysv6dPdk3f6sdQBnWTKAu8ORdDoI/+TGBe+iXfpd0XWgl20MY2NJ+yW26jBYJYBwN6yjVthrZwMqhS0kn6xaBCcuHBkfoasZgUpAzp222n/KTKs41lMlKzw5kuur4xSeaJPfZf85EFVZ74IzFw1fUZv6hbXHGSv4q4DFyVLBbIz3AD282SU7Xj90WrJFMynhGD6FYQ7ULR92w2W7h3UfdX0W+yH243Vb9wKJMX5CGTIboXLiJ6XZd8yxZGXNvo58+f9ysjmlPJlMr6PVfEtj73lKNVElSOG9xn0X7VAotfaqTE4N7eXp804kNHIm6u5A3piydTbkO32v5H5ckAhSbQb5CbTqfx8OHDfmvSO++8E2+88Ua/RUkZHAePQ8bWASDPRSwCKw8KPfCKyN9lUcp1RlPROUF+RPR712V4eY8Pt9x4/6j03u8MiPnY3ak4UPDxZ0JDR5AZdifP7oi8XgFjyktmVEWeCc2yheKLeC/HJCOrlQ/x2x0/ecbM732T5MYfJS+eEWjxHh7plnTowYMHsb+/H++8807s7e312SkFUKrPZSsD7gxwHYxG1J+IxDKe/ZFTIVijPqk/Mo7qKx/YQYeldmvbYDIbkX28vNuzocCK4+Eqvuu76zLPa/yqg/wWX7g9lNctW4kn8KZuMbMs2xURfWAwn8/7bRWllIWnl9Vsbo2vm0B+c3lE/cmwIgE9Zc31MJi33nqr376+u7vb37fo9XlSjICH5Pb98PqEAAAgAElEQVTRg2r6Wcqz5sntOQNgrl7RNg/NE2WFK1y6loFSLYnFVT6NMdt9QR558JMBY/JC5AHjENVs1ZAf0DkBc/aVdXDsBNBd1/X36kREv4Xs4uKif/iXtpNpxwW3cCrhSJ5sin5Jp1bJ7HN+lViYTCY9Fnzw4EF84hOfiLfffrvfsq5vBVVKaNCezufzmE6n/VMVpbNqM+J67hhUuSwKF6i8gi7eE6k+Sb9ms1kcHh727+nSiqTmTtfxlhHqO/WJcu2JNn1Th7gCQx9KH+67cjZhZ87LJG0FlP3V6mXXdf1KIlcdSRkuiFh8uNO6dOuVKk2qg1RNmLacjUaj3hlNp9N455134s0334zd3d342q/92nj06FGffeB2Mw3UjWvttwcEQxldB+s0iGR+FnRo3MpYKTt4cXHRZ58uLi56ICKlUhkP2JjtdkNP4ffAICIWAlcGoBnw0zV+H4c75HUy4bVsoMbJe2QosBlw93nhfSDsI/kkkH12dhb7+/v9tovDw8MFIOF99hWGZaD0dVDXXW8bUYaZT5LjfTs7Ozt9hlP7z5WkePToUTx48CA+9alP9cGUtqlxnj2oFw+47dPB81151HXdjXsNJYsEDgqYNUY5IyUuVI4gmW04KCZYon7wP3niiaIsccF7FNkX9UH2MHPW3hddo2/WR+DG++1YljbQEyhe3rcVqR/cUhUR/b1qesl2KaV/IqBTLcDahGSFiMBPsuRJKSfxRfr25ptvxqNHj2J3dze+5mu+pr9/6uHDhwugK2ubPMoCUwbHrptctcrqp4902+73z3EbW9Zfyojst+wRH4KiOtVPr0Pf6pN8QLbaXWufYyK+0Ie6owQeecD6hwL+29q1Gs7Y3d3t+ca5jbhOvkbEwisktF3p8PAwRqNRv9U24vrBTm6f7ttnieS7agGml5UdY4D0qU99Kj7xiU/E3t5efOYzn4k333xzYe55TxV3MCmh6sGnko3yOQp2lIyUTyWIlp7qGIMgknZUzefz/umO2hIYEXF0dNTvrGCCQPNMudAYtKpLTMegXB9PjpHfEYtJCJ3jfb/EFF8tNBqN4u233+6fbi09KqX0WwP5BNSIm4kzTw5Lnm5Da69UedaaxECBQJ/Cwns79KGhVBusL+LmzeV+nEHVMhpyfr5VwftSM840AEN9cGOf9WcVoz/Un2x8/O9juy0RHPM/M2x8Io4HTzQs3i/ug61tOWWmlLLmKwq+fD7Ep/sk52fE4lg5Ns8+UbeoX1rhkvPOdJe8cN3S+ZdBnLNsGxaPsR9DgZ3L3pB98nLLxpXpkINiyi7l3BM3KlPrf/bbAWamawxe3P64fmf1sV+6ntuv9SRNbj3zen2eltnA+yQGDMx014g65n5LusZ73UTr2Hcvv4ps1sgBFgObbF5qwJd23JNc1LFshdhXozSeWltZn4Z8U82X1QI2BiO1el4m0c65baUsRVzf4tB1XW+rfXdF5gPZ1n3rmtuVZaQA4v/n7k2WG0mSrGsFwAjOMVYOlSW96F2//9v0olclUtXVOURwJoMk8C9CjuP4pTo4ZFYGvt9EKAABuLmZmg5XBzPHUcYxOjw8HDLBnPKGbHpvkO0894fP+ZwSeAcU0G2dU9HpAAcimR/ryDohEzk222rTKYOHOD18x/v8y/ElhstgmXnmMWz4/4eG74BOXi6Xo+d22pGt6st/3TbJ3KNjec6PWXi/etFgtsVivXF0Z2dnEBbSdJT6ESkgipipy6lJW+lYWT/HGHkOZjSfIOOoAMJAKQT7PPxHZIONi2ycJIpjw+R7QgvPFWGz4bfTyu+6ssKMhHZg3bTLPgC4HbDj/XK5HD1XyUDNQk1000Y9M2yst8fZPaQyeYA5kOlkTaD9bPbwhDXo5+zgNigZ6OljYS3UNsqOOHFggv8ohXS/Uw6SHYCqcXlvypnH8ZLGoQiWdz5DriibuLq6GsopKI8hC8nnrLMj6tYFZP2mMlHwgstmrQcw/vCv9ROyzfc0jC19cf8pg5mGEN7vnjfVgckOtCWA71r27fWHRmwO39vbG/iIP/aSmnc8jk33/hbNToKjkNaBdorREexJxHa9f/9++Ozg4OBB9HfKwaX5N9bvjIE2m81GJ5llJsu0pj/rSc/VjwPBRiFrrCVZSL/3yWrIn0FcztlOtTNNli3bE9t56xyu78pWkfXO5pHJ65zYqvH+xO4P0N3J0XOb+SwfR7FYLIZTj5k79IX+tlnojzzCelvaajU+ZtyltcYg0BmZ+fDhw3DIy1//+tf6/vvvh8xV8npVDXiDLJQDHm44VTSOXk8n57mNcYBreLWdcYmdgy3Yt8vLyyGLxVhZU2wJY7fc+AAVB4r9GxwH45zd3d2RDYIOPDftj9LR2FveM7dcm39nm8/nw4mRnAT43Xff1fX1dZ2eng40qho/8sl8ahvw0uDgi49UN+hzhKBzqtjXgWGi5M9OFYxmgFM1vck9Dbk/fwohDPBcO4sxsRNUNd4zgQLEIPGKsSIdbMcLocoSHgtdGgI7Iy47snGwA4OgQVfTZcrrZs70xfh8kk6WfqSxZvxp6OyM2ej55LmsfaYfGxCXUtDskLGhFaXB+gAEbZxtgLcpDY5hxXBwRKt53UaELBQn/LFhHjmDX6AB16ciheYGZlaK6XSZF59TKlpVrSzYQHGKJo4UMsSpdDhVFxcXw28w5KyzeRBaepwGzBm5RK6sW8jwOStqPeFoprNwGD5ohX6zQ2l+d8DFZSfubwowdk6Vwb0dYr6zLHcAE4fdxhqdeX5+Pow5M9FZbrxNDUDOH5+l/rR+wIHa39+v9+/f14cPH2pvb28oaUcWLZuZAZuyVwZ5XVAKx8q0zUaAKvU9+tPBL9snHKabm5tB71DqSfmS9/34GGmDuCydYUwOgpkW5hXb1tQ56IXEGy4pT1qlk9bRymuTMuLyMK+lA39PaQ7EQncHIFyVw338eBYfC+0SZ9vUp2aF/oxmp9Dl/t3voP3u7m59//339Z//+Z91cHBQf/vb3+q7774bZM5BZmPEqhplhZ7aXgLuOzxWNQ4sg42cdfKBEA4Y39zcDKcDGqs6kNWt62y2PtAknarMpsEj9Le/vz/IqctIKek2Zk/bQssEgzG5cbHHwTjzUSN/ZPOcLb8EwVgfTq+FPwlcVNVojZjH7w0Kvqj8LyNIVogMxgprk5LKSCkTS+cI5jFze0wwkyNOXfOeFTtVLDiGJ50qK0rXmOefaYOwmSk758bgBvoZzGZUz/P2nNN4P7aOKYwWEowqjOkjmdOpSuPm8dupsmOUYM9jMu1zHTLyxXu+89obYKbD90dFaP7IZjomjyRvGBjYIciIMP26n5x7AmpnRsyHCV7SSeuCG4zToC6dIB+nbvn0fg54oPtzRNitM1LpVHhuaSzMq56XdWAX2DANUzfhUJrPrVdS3rx2ncxY12bWrXPE4JWq8VPnucb9ee6sNboIGjorsY0yRcv1pTmgkHrEdiujzw5sZbMT3t07g13+je2GZW0qyu49hpbXrrLC+8nsXOaf5c0Z5Tz5rmr8HLp0tjYF8vw+nYS0ZUmfzgFlLPlbj9drZtmwUwV/UxEAUOagn6c4V8YBdggzsMdcwTZkcJbL5ehkSOie9i7n+a2adaJ1f8pBVQ1BUPbMUupHkDCd9A5L/BnztX0xj6aNcMAi8Z+vs63LTKvtRuIx61jTM39v+nC/dMKnxmRM3dn8xK++T/7G8yFzhA4l2PtHBdw8fveJPCG3q9Vq4K+q2nhgBWv7e9qznCqAtKNFeM/2jp2yBOQiSD5xJUE5Cq1LS6ZCy4U3SJkCH0TpbGyqaqiR5zc+5CBBmQEhUVsifzxXh+eCdJmq7M/RZSv4qunFTaMCrRPkbFpHl+7RlyNNRDJ532WtiE5xPffv6G+nmnpnFOz+/v5o/qY990yDabqZ/2az9ek8GWmF3qbttzZItOVy/SBBl9k4co5ht/HJGnNH9Wzs6MvBDGTFn6djxPWOmprWvi6NYdU6EmSew3GwcrczsVwuh+wUGSm/Z00Bgawv2T0bY0en7TSi9G0suoANCtqZYq7N8j/0RzYDDvSGQWvKXjqIUw61acxaeL7+PPnFawxv0Lflit9RcQDoY72cwYCvvK7b1My7tIzSQjee/3Z4eDh60C97qey8wAdVfdDCtEhQBv8ZxPl5dSmnVeO1ctZ/am0tX5eXl0Ok9tOnT3V9fV3X19d1fn4+RHXPz88HW28bAG+n7uXABew2YyA63MmXX+l7NpuN6GvQZszBGPyZK09soxyQc/Ma2UYZBPqZjtgryq0zOGx96WAQ+sr23/rWTsje3l69e/duBNTRbTznCrrYWfvWDSyQ2QrWqGrNs0dHR/XDDz/U0dFR/fjjj/Xdd98N+Iu+rJ/RZ66cMR9YtqacXvBLF3iw/quqIVvb6Wd0smnvCqWzs7Mh+3tyclI3Nzd1enpaFxcXQ+UFpey02exr1sgVEw5YgIlxupfL9f5WB1O6oOLOzs6AKUwH1svBEWhvG++5W7agvx1EYzSanar5fD6UdZKNJLPkz129xtp0/oDtKHR08BJ79e7du9HBczc3N/Xp06cRbS3/jNtO53Pbs/dUUWMNkTm5JLNUENIKKiMR9IHCccQrWxd5ZRwuc0gnjXFX1WjPhpUZzyBwRKBqfCIMc7NCw6lCqChR8h4Q37NT8DZGKJHOMOc6JPhizDhXjNeN/xk7fcE8V1dXdX5+Pihy19kzB5cmdSVXNjZeSwsMAGU+nz94cKqNjZ0qg19HJwwamaOPG8dA2bA/5rR+i2bD5IhYOk4+cc5A2XLF2njeyRMAEu6RMofSNDjg/04h26A5a8bGUTvKGfmyIuc3Nzc3w4P7DO4wWvAigAWDkbyHo2Wn0yn+BAA2uMwFehE1NjDKKHICQejIegBcoSXjR864F/16baz7zAM+RMIRdSJzfiYZ77Nl5N7ONvehlMSAwwYwI/Tb6FS5WYf4PVFVAj7sB2YvBzrajkJGqDPYZz3Nn3WpI92Xl5fD/9gR+MtBFnQdQSkHBzs7ihxjq3799ddBnrBjyJFtgHV91fqYecbEPg1KlEwXAioef0a9uY/trIMVdo64dj6fD/S5v/+6v5KyYAcyjQmmeNK6EdvhyPru7u7oQd9HR0eDA4YDmP0xN2QdHYDusQOK/LI9wo717e1tnZ+fj/jTmcBtcqosBwRHk2+Pj4/rxx9/rMPDw/r++++Ho9O9B8pOFbSyLXLAAbpW9VtBCNAZ73APBy9pidccBHSZut+DXR1Ip8zv4uJicKZ49XgXi6/Hr7P3CftlpwX+4Bp+hz1CFuiXtrOzM8hC1TqDWlUjHGydbYeJ31C1hGzhdFo/wK+sNfzq8j+CP/v7+8ODnPf394fj0PlDLnAyCXBlYBR9BV24N33s7e1V1dp2M++7u7sB507ZAGOA57YXHalu45EAyR6mJ0sziDLIyigU96N14MJRLhbcCtRKmc9gDvfrPv17j8H3tAefBsLGNPvqwK1p6zHZwJhJc6yms6/pnLJuLAgQBgDwjCOFskDADPy9R8xj7w7YsLI1YDMA78aXgKVzdLv5+h78flsdKlqCsU2t4wk7ofzGgI57VI2Be0cXr6+dKkeqfK2DHl5XgEzqh4yIcc90pDMqm3/WQ/QLTfL7LlgzFcTp1iPl2s4YcuCx2gHhcA1o6ayEwQFzSANl/sboQHPmbF7f2dkZ0cJ90YfXLwMV1sUOQOV6m886um1TSwCaTqr5l0CFS9arxgGtzrZlUK6qHsiA9SfrP+VU+Qhm0x6nxk4G98n1cSCFyDoZRu+dcplgZ8/sLC2X6wh62kjrXr+nLRaLkd1IPnH02DRz0GM2mw3OIbYLWvkglcQEtiPuv+orHxOY4XCDxWIxyjBxPc5Wgj3TG0DqIAR9smY05CzlGTo78NrZ9m/ZOnm3vnXwh0wfD8vOsnVjLHADzoPtTwbWrJ+4P384Mmkn4HPGWFWDHk4b4+ynM8k+oCMD6T7Yx9cw1rSDDuo/pj+nAif+vlsnXqErtgcehV/RPczRsuUAJ++9T9VriUOIU8X+wapx6bL3oaHfeN9hYcboLCW/MS9Yrgj4OHjf0ZQ+XmrDnl3+d3FxMXIOXr16NYAEIqEZaeBayg9QHDCho98suu8J0TK6vFp9za5kiYK/dzMTI+iz2fpZJCncNBuKBP5pHCwUTk3mfAw83QxKnZ0ggjbl/OWRo5m9qKpR+hohcYbt4uKiPn/+PIpYoiwctciItNcsMyeOoDMHyv+IQpD+5QHRZurkBz63UBjkGABwr8ViMRha8wHruQ0NOqcgW3F67n7uhB9GiCx4fWwk4H8bCTcDwFSwNiQ0ZzJYc5ckuZaZZsevMyLpKBlwmi/dB79JpcpnfrgtdK0aZ9imFKnHY5r5HhhtB3l86hqG3dkC6M2rjbaDQszJY4fe5nFHA7tyW9bGhs5ljc5wIbfpGACsoRmG0cEZ031bmgFulkmadjs7X0+rPT4+HjIUlD2uVqtBh7DROWWtq5RIuTMIc4SfP59qiY20/XPmkewUQMSRXmdsbXMBfZ8+fRrA38XFxcCTab+qxjLL+tKgIWMhowNN9/f3B0BjBx2edgCAufl5adAbHeAgKofZ3NzcjAIXmQFM25zbAKCxMQEHkZC1omzJpUuWN/4YfwaSOt1hh8k8wBjIQnBdBiE7nPMtmp2DqjE2IgPH86j+67/+qw4ODurjx48jTIAugUddjukSVTALWR4e9Fr1cO874D91r7eCeN2t432IllvyEbxGwPnm5mbAURcXF/Xrr78O40xHzg4C9/f3iaOyEsNOh7GgnRSXFkIbaEYQB/r4gDXGa6cK3bFarUZOFbrM46ChjyxD79+/HzJVh4eHbZktv3/z5k0dHR2NdBz3RJ8jK1XVygYn2X758mU42Mv7SKtq5Gyl/ntOe7ZTxUO1mICV1uvXr0fAxqAeIbu5uRmUMilSK0oDG//ZqFj5nJ+fj5wqxpIESQPvDcc4LfaYuYbrMgXLazL1pigD3xvQpVPlskmDI5RPgk+DHKdaHXnlt9AEY0T9MKUfZ2dn9dtvv42UDszrvVaZDXQE2FFUGwwLCcJEZJB68cPDwxH/uM9cPwPnbgzdWEw/K69taQbStJw78zZvoDANwgAhWSPuzAiyYn5CDh15c4bXTpf517zn8k47Vc6CwIs2UFXVyoadKoPSXEuAEdczVkeYO9nNzFY26yEbPPaBmGdd+uv3gFaMVe69Mh2sU5O3mYOdIECrgy92cimDTaeK9wA4l9FyLffLdYKmvobxsb5TUdRv0UxX6wNHNNGzlPrhVPkRBd5DAM850OSSaNszgzD0qvfb+BrvUyOYZeBn8MF7A66qGh3zzloaiLDfA91OsLRq+sAR1h7Q7wAluhZ5hycdTMsACmthfnZQkHvyO5fDeR8zQM+g1Y6u7RQ0BmgbxDN3ZOP4+HhU5gf4p9wSm2ZZwo7hbKFjOrtdNd7Tx/1Zb+TUlQG0BNLfstnh7j5z2eR3331X//Ef/zHQDpoRIGRdcZoo38ZpxrZdXFzUbPZ1P9Ll5eUD3nJmyI+3OTk5GWwjGAjdSQYVmnKyrrGY58d9cjuEAxaXl5eDnDkYTcNOWackRrS9794bb0Nv41vruhz33d3dMD6fTsjWlnSqnMlzWT62zrqsao1pwAUErT5//jwEJSit9f4qnlf2+vXrwXlDPxPggu4OElp+LBu+P7o9dRh2AR3x0oDFs0//Y8HT2OYkMhWL0sKbJdoAI9oYwXRWRNkfiwfBzdQdyHZqzwTswLqZmnmaUQzc3LqsQi5KKtcO6GVUIjNoBogWpPx9OlVmFqdPAdh8578u6pDODIw4RWvfn3691yUdMNMjwW/eu1sD1p7+vZ45nm0wSlXjlLx5slsHZxn4jaPkjsi5Hho54zddEMMRPpcnOdvliDVgKPnA87J+6Pg/P+tok/1tWjvznrMRyIYjdxmdsjFj3Lkm6eQyDsuVgV3nvKd8ZJ/WgaZdNybrQ987dQdzcglSOsdV41PUGNtT6L5p3bahdfYps1V2sDogUvWwrAv5MuBIh8p6j98A7NNpd0DQIMoOcB5akzyaAQDG7fGYb9PumUa8T4ebKDjyBEgiKu1sejpVHqPtcXfojnW69ZPfp7OfczAN7Ghi23Pem8o+fWgE9EiZQ64yuGk+RA8b1OXaPSZDUw7wn90SG1WtnW8CO+xJhC9ynV06d3l5WVXrSghnJR08wKnKIIbxjA+T4BAxsKOz62mLHITE+fFc0aXmx5R9fv+Y3pzSTVXrgKVtWIdlaTiJdhYJUjhwR4AcjGBHi+Bq51xYb5Jl5hXnLuV8KvBGljnHT/93d3ejfcIZJOEPe2ad1wVsHYzGZ0hs+XvbizJVLp9wGtUldPaUiZb7wXZkRlyraYJ1DJhKB6IYTHaAgLH6QY1mOHv9XJtjMOBNp8VOFn11zIhRNcjzGH2gh6N9fJ6Cbe/cpRepsPg9ZVtEgohAELEx+E4Qb4DGE6oNAOzIZHTFxsuGqarq6upqmNvFxcUoY+j7wU+OetKfBcJz9r4Iyk4TwGwL8CPa47WGX1ar1XC4wGw2G/GG15WSJCJyKMksjzUPmRZ8xnvo430W/p77O0CRQYGM1rrfDtR1QD9BVPInRjHvDS3hG2+URVfBWza+0DmDL2nUTKcsNUmDa7Du4IFPO4L+Nzc3A20TgBoAmkZe3xy/QYzf8wBSoofQAiOYWQ7Tm/t3rXMOtqFBT9PP2V6cFXSwgQj0RR7IlmC7WO88FMHAGXmmfIbfV41PzbXzsr+/X1U1ZNBcjtY5UzQ7wuhIxmhZdjAF22Tn0uVP3Ido8WKxqOPj4zo6Oqrd3d368OHDwEd8ZhuVdjsf2QGvpkPrwETuV7HNzyCpnRvWn1fP7eDg4IEuIqLta9ERADGyWB73zs5OXVxcDP+zZsZMlutOP0Kf1IusQTqK37rBy2nr0VUHBwf1448/1tu3b+vjx491fHw82k9VVQP2uLy8rH/96191cnIyyuoDwNNesw88g+nWP84qk4nhN8vlcni2EfIOH1FV5Xnyyn26MaEDjL2qxnZsCm9mEMXy7pMpbeuMt+bz+bBvbWdnp96+fVtv3rwZvkNmTk5OBtpS7WVHMwNEVTXi8+Xy6ymEzPXg4KAtX4f2OU70Jk4Vegf8d3BwMOi4k5OTwRl/+/ZtHR4eDuOp+pqVv7m5Gdk4Y2oHP1arr4frHR4ejnwOvkdP/h7ZevZBFQZleI4oRDOFBR+DVFUjRUoq1tG+LjJPSyb1otmQuUHIDnT5wV8GCB3Y9Jy6NDC/QWF3ypDxsnB2CBIIOtpgMFc1Ps3EDiNp0KnoGPT1BkpvpHSZgWnstWQMOzs7o3KrjGRADxs6SoJc+oTwIvAIleeOALvUzRFOr5+Vme9tw5Vrsy2OFbyLsYdfMFJV64gx/FG1VuzIAqU9KEjXQ+N40Z7iXCKvUxkX1sOKMx0q95X353N4lDGl0eF3m8YLz/nPoHmxWIzKdnzkO8bEuiJ5Px131s0ZoqlMleUcns6yYvOk5whtUvcwjqSfG7yCoUGWGKuND3Q3AOS1W8Mp4zMVGNum5nV12VnnPAN0cQAAJThK2B6vfVfWY6fKAMv3Bkw4wry3tzcAP5f8mb9y7Q3UMpqe+s8BKvS0A392rnCqXr16New5A5jiVL19+3ZwONIxwWkBnLkUOWXMtrhq/RxJ74lKQGqetVNFg152SllnxmGZ8ecJwrBd/B77Zr3io9kJWkAPB0Y8T+MavoNnt63ZTnkNbM/evXtX7969G07S9CEEzlhcXV3VycnJcNz/b7/9NtgsOye8974ey0IGgRgjDpixw2q1GkrKHCDHliIT6eQm7zlLmfRJPNn9pmqsk7Ls1MFy41juDU3Zp0RggIe4e08iJZQ4VeBv6zLrsMRRrAP6gbMUWEtjEmPy1Wo1+A1V6z2TVV/tFFkr9OR8Pq+rq6shIWJ/g7kTcE6M6LMSGDs6zdc4iMXvfk970SOODTaydYY0IwiOquIN22v0QnqCGYWqWpe9LZfL4dXNQMYAy6DLv+UvgV1GZ20AFovFKALosjoLH/PKjNzUn41upyzM4OlMGYR6DdIB9WcY9wR5RKFyDZgz90mnugPV9GFBs9KjIbAIoI0c43GpiCMSNpKpdAA2nt82gD8bJvMdzZkgGwMDJdd0W74cUe+co46nvH44sAYTvGa03xngTjk565EOyyaHizlaYVu5+/+M5Jpe3Xr7d1bASYcEetYTnbPnIIwNtmvA4duMsmffq9Vq5Gx387Cs+/uUrdVqNZTf4twBKtLI5Bp2spJBjqT/NjTr3wSqvBqo8N5rwL4DwBlgrnOo+T9lNB1S7mNAAIAyX+Tm86kslfVqfsfcmDMO2/39/QAy3K8zVgZ9zuY5qzcVfLA+tr2yDfeYsx/Gi8x1MkXLQKab7219Yfrg9LAm1nsef+e8st4EsxwUtH3qWmeLvL5eg22wV1Ot00uJtVw+zv9nZ2d1fn5el5eXdXJyMgTc2e+Tcsuf9yhO0c78bKeWlvtsuM4ZU1cOeCxdNt6OQ2Zo7FC4+TeJa4x1pvSqf+ustoN2YAAf0OD9ydAqMa6zVXlP1tRBOR6PgIzlYR/WYehF7m3HFBvFew4vyUNMcLJIRvB5Bi3gNVeQWIdwfzvdL7FhL3KqEmi4JRBIQ++0vR+SyyZTbzB0Kg+hTLBOWtT39zjt1HBSmlPzGe3znDJalMCJqBSRDG+M9GZjRzrtJMGoXOuxELkhmtctsI2Py8as+J0V4n4pVDhYLjchIuB5sw4oGAMFn6wFU3s96I+5GbD49ByaHUTWg4cjeoOjFaWBEIoQOpHyxXGhhMP88S3bcvm1/K/qayQ4jdN8Pn/w4FGME+tHacPNzc2otNbPmKKEqWoMOniPYkIZcooVf46OuT3lFEcDhMzeGHR1gbWtJjYAACAASURBVAWUIc/74NruHlNO/RRogWdSx1SNn8mTEfekP+to/WCjA339bD9nlzE+lElXrQ/poA/G6mAGVQDoT2dP0AGMyZnKi4uLms+/lovc3t4OG+xXq9UI2Hc63uDWQa2k/bY09IsDC3Z+qtYbvG30caZub2/r8+fPQ3T37OxsiO6aV01r2z7ukbzK/Sh3cSkmv6mqISvk8XGPLDeqqpEu9B9yDf8A/s0njC8znIzHhwwcHx/X4eHhg0NSEgTafiFr3LcDur4mnQkHT60jmO+XL19GIJjfOmBApQU0ZEyULLoRxHPANO9reYOvXr16VdfX14PepuzNQUB+n0ElyxgYILHVNjXLk9eMueK0VFVdXl4OmXKc0L///e/1yy+/1NXVVf3jH/8YHp77+fPnkc7qAkk+EAP944NDsGngFdaS//f29ur777+vw8PD0ecG29ZnDq55XeABYz/kATtKv+ZLZMXlx+gEOwoOXKT95fc7O19Py3v//v1wnYPJHEp2cXExPOQ7q89wSDxmY2RnmaAFGcj7+/v6/PnzgGVoxg1gmdlsNkqseH2NVSyfP//886CX3r59O+D/o6OjIcjD4SJ7e3tDtgt6LZfLUbkjtDP/MLeXBgWf7VR1SjJvbiVoIQMAGNC7rhLghLFySvb+/n7kYFWtDQef5zirxqVL3lTrzeoIZXq2aZSc5eEe9uZdT59pVIAQ9LACmspaMXZ+04FZmgGco1qdMc9SkA4wZ6TWBhmDZVCCYXEdeUaUXLaYWTz6YbyOctLgCa6xoTdITyNcNQbHOFTJr9+6ObrdOVUWetYDWlBGyZ4eTjxyqYw3oHr+XnPW1BmobhzpzGQW2FHwDjD5vZ2tLoOZY3eks+urcxZpnWPla21IGZNlq1sry1DOdao/G1x0GEDasmfH0LLnTDP6AXpb3zLnBHxVNQSE7u7uhgDTarUaHoRuRzdpmPQ2gO5k91s3ZMS6IZ2NjCozfmjt/accQ5zrbx1mXpjiuVx/9CfOLWvljBDyBSjJ6GrVZqfKThO8aCeN31rfWEcwPsbsPa9ee9tR5pvj69bpseZAiNeJtaJyxM36zJ/ZmXSW0OsKXdKWds6zQWoGSpGxDBZ2FTaml3WG6bQtdqtqcxk5jqHL1Y0/rq6u6vT0tH799de6urqqT58+DRkqjiZ/iiOZjryra5xxMgbjOk4n9L5I09vZ2AzUsb5+FIKz3SmfrGWnB3BCM7jD/9wz6ct1BDfYc504ztUr0NU2Ou1Vrq3xCbScz+eDM2MsnzwO/RysISCYttRZNOtUGg46B1msVqvRo1zgNdtu1pzASGLNDMq/tD3LqcKwZwTYhtrMnIaGCfnkkfwfD9X9OarD5A1IaFbUjJM9JVnza6FIZsnvLJT873pS/zb78iJ6z4azNdCHLANzcYQsBc9jwYgk0LYjBy2ravRZ0o255IZqxkCU0nM24yM8MLodNpSJhcfpe2dRbKjSOfIzEVAigFTu7TVztAh+coaxA/7fsk1Fo/yZeQ1+8h45eM0bTnlvOaIRnTKfdSAZOsJz9JfOnseeUaip+Vq50WeCeoMbrq0aRw/9P3znudqQeNwdCE7edfbHWQnfu2vQwPrImTGOaYYnHbAgWMFemqq1EaI01lHXqvVjE1ar1ZD5yjnC+7PZbPgt+0Gsq7JEEQNu4OHy0tSx29IccTWv26albfAjPy4vL4djhvNZY+msGTB1QQm+49Wg39Fixs06AKycacqDUarWIIZ7pk6x7Vkul4ND7wgxvGGwlzJlm5SyBuDivpZp2wDTwc00zbVzS32TVRZcj96y/jNYxJZgT1ya5MyFx5a21Xqiavx8LfaEAPhc6tTJCp+RxXCA2Jhom1piJutO26DZbDZkdCj5Ozs7q6urqzo/Px9VXbhcv7sfzbJjAP/69es6ODgYaI9uOz8/HxwCnCoOx0AOnCVzEJd1q1o7Is6Esqep6uthXNYp1rvQyU6Ug5NZKu5mPWtHzPYbXsTZgZ5ODKS9h6+M7zx2sJdpzT4uPxh4sVjUmzdvHhygltjDB4ekT5HOljPFZ2dng71Ch5F59x5N39MBI+7TBXdyjM9pz3aqSNsZjCAsjm6xMP6OUiQWlvcIFk4WmSq8ToTU5QLezGtADRMeHBwM6XeYP50SAwIf0mBlxjxdMwog57dmPitxHMZ8TpAFjEgj95vN1s+/IdKAMragWcEsFovhBCOuR/nirJq5DNoMltIYnJ6eDmNmnG/fvh3uCw1QQF++fBnSrpQRGkwmo69Wq6GcCyXnLBrK1OWgFxcXw3yvr68HhfjmzZsh0ggfOl2Oo4VSB/wBnLbFOG1yqAzCk98wBshRPrjPUaqUU5drGjRZXqAf9F4sFgMQd7YkQZsVI+uIkegitgZk8LNT8QZ9pg3jM/CjvzQaGAaP0cEQ7u8xAQwMsm1cHEXktzhInvvV1VV9/vx52ODOuN69ezfQpZPfqq8llu/evRvuw5jRLzYcV1dXg9G5uLgYaGd9jFOIkcU4LpfLoZwTI+UoICUX0Js+Deq3zaGCVi79g2+cbXG00vrpy5cv9euvv9Zvv/02yJKz7TTzWZbrzGazUUbSwSr0OrR2Rj4DRi7zZHO/g3ZVNcpq+WAA63oH7NDvLl3i3naWDP4MAP1nuTJwzPVgDnY00xm3DrEusU33vZA9HwbCXF1BUTUOTHBIEg9+trMKhiBoaD1hfke3GDgDyvf29mq5XA77XbBX1ulu/jxLvi3r5r1v3UwT+N+YwwckVFWdnJzU+fl5XV1d1f/+7//Wv/71r7q+vq6ff/55CF50ASg31he9xMEE5tPj4+N68+bNMMbVajXYrsvLyzo4OKj379/Xx48fh+dfgUk/f/5cq9Wq3r9/P/SdAB2dj7OME/Hly5d69erV0Bc8jqxCJ3jCTj262M9BSxmyXvE+R5wXyzN24OzsbFTtYVtMn9A1q4kI0FF+zzP8Xr9+XW/evKkPHz7UxcVF/f3vf6/T09Pa39+vH3/8sT58+DAEBJFXYzrwZdeQP8aCo03VAOt+fX1de3t7w8mAu7u7A85jjnakUr8mD28KdjzWnu1UdRFIKzkDQkeaUDJZxpNZHP484S46lc4bhECYMV6Oltng5fidbYOYeQ2M5yhF0qCLBsMQvNpQ+Dd+b3AKOPPck8Yeu718R5wB06ZnGn36wqhgcLxJ8P7+fqTQvUZWMGZYR0BYHzufBr8ZJXG5AA7ibDYbDBUKjPm5BMp8i0AncNomh2rT//7MsgX9nfl1dtigxGUx2VfyQsqAnXpH0aboZ35KB8sGwkGOLjuVICv/HH3OsWySd8bWlWslXcjMGdy5DLG7b96P63B0rZeQNcZkmSLCSsTaY7OTYHBaVYPMEuywkbBThrEkmMNG49lsNgRHMEyeH5/ZAXWGZduaZR1adE4331etg0EYc5fSZjlhBiTS8eeVexkcZwCgA1Bp8wzObFt9TdrGLqiIw2ZHm78M3nVZKf918paYIYNCjIXPec2/dCA22XH/OdjT6Tc7ZeAGZ1dsl2h2No0XUuc5I47tIpKeY0qauV87g9sWsHAzjTJjYnuOziFocXV1NRz+4hP9MlDT2YLERonZcFbclsvlKBBASSu6EvrbljrAwXhoyCf3xRm6vr4eHB1wU9os5N1//ix1lO/Ja8qgkx7G1QQyLEsOZrp1ds06gjJxsAAPb8b5wVHkdX9/f8gUOpDHuKvWfE/1Bc3yaH3HmAhe7OzsjAKWvE99lrrQvOvfvKS9aE+V38O0RK34HJA3m60zJy4RcRrPzOW09v7+fh0dHQ1RJiKlPpBhKjrCYuPxs/AuAXDE1kKfxHV2gM87AGwBd7QEJYpzhEGFOe/u7tqSG88ha2vNCDAk907hsXLOteO3vFIP+/nz5/r06dMItLFZmrmYIYmqsCHVYAslwm99kMH+/n69fft2iOoAAlFIKDEbVmjG8fzwBPyGw2aFPAVwtqXNZrNRlAqFmsfxZraKObp01konnVoyec48uGQS2bJSrhqf9GeDReACQ4NC9XvG4UxVVbURMgNO+uicX/NzAlOP2781QHFZBaWq0M33RZelYYUmdtJp7gvZolzs4uKiPn36NGzWtlHnYJoEx9Z9lgNo6sw+ug69d3d3N6wtThZzIsBi+gN2oNXl5eUoOMX4bJT5LbxZ1e+l+5aNdTcPENk176ATr66uarVaDSV/dqoMDKvGWc0O1C0WiwHUdYcluLSTVzZ9AybQC5aPlA0DDYNw5p/Ay78zKOGe6cjYHqGLOtuEnUVnwbsZlKOZlgkMrRc8F2gALxK9vr+/H7If8HjqLOjFfaE71S0uz6O5ygRex9bRlwN/Bq7OOjN+QLb3pTDPBLOmgwHzNrUOX5hf4U8qKpbLrwcGnJ6e1uXlZV1eXg7OlLGhMZ2DOuAoH5DCs9HMixxu9Ze//GW4DizDMzLRk5eXl3VxcVGfP3+um5ub+u233+rTp08DH7HuR0dHg9NkpwA58F7Vu7u7QZat060r2MeXeyYtW+bLzrHKU/4IjOGs2onBaXFgwHaOtcKuOBt+enpanz9/rqoaMlToSQ6omM1mQxbr6Oiojo+Pq2qddWJNWc/Ly8u6ubkZMGTVWtewLmC93FeM/QUrzufzury8HAVTHOBywMbYuAs4dsmcp7Tf5VSxmJw64hNKEKCqGm3g88PFHAWwUQYEHB0d1du3bwfhgOC0JAxMjtdMmRxKz8bTp47YGXLkOoEbfaWS4zoDPxtOlw4ybke9LUyOfhv0IXjpeJoZEFoyS1PRPY8dRmfPwNnZWd3d3dWnT5/q559/HkoeoOvh4eGgNKADPEAKmvIg09QG/eDgYDihhb5vb2+H9LCfWO85AHagG+lfK1h4kug+hjCjrtvW5vP1BnB4xil9K14bVeTMJ5t5kycRWNMHZ/bw8HAkMzQ76o7iJijDGTHvo2Ap8bCidsqfsbtcz/fl+5Qv9IT52s6m58BcDcyYrwM0OOF5IiTX26myc8VYfW9+D/Dm4BBOsTo/P69//vOfdXl5OdpQTJkKwQGCK/CEj4kFoGGoKZ9lXn5IbFUN5ZoEK6CnwQu6eLlcDkaYMezu7g56gADQ3t7eSBcix+xHyQjyt24GedDFz1syAOSUrOVyWaenp3VxcVE3Nzd1cXExOFvpUFXVSD4M5nmGUzrF8D70RIdyCuNyuRyy8Q585B6qDDQ5oGew7rFVrfeI4FjQNzLjDHcGCdFFfpaOnSnT1uVQjMX6ndLxqnE2DDzB+nXZVnQehx3c398P+ofALHqO+1o+sUeLxWIAgj5QgXsjDxmAslNqbOMgEjxCNhgd6IOdMmiVmUrGkcGbbWl2gjPIgpPOOgHocVqur6/r5ORk2EcFHkE2jDNYC2h6eHhY7969G2Wb7KgcHBzUDz/8UD/99NNIb7LV4PLycngQLA7V//3f/w1liP/6178Gx4KTHFk/Bw0I/JNh8XNXf/3110HHmjbwD84EcmX77747p4oGT0IfSvTIAqLnoaFtFnxkfAVtXDZ4e3tbnz59ql9++WWQlx9//HFwkFm3qqrj4+N6+/ZtvXv3rj58+FA3Nzf16dOnurm5qb29vTo+Ph7uj2NNNgs8CEb/7bffhm0fR0dHtb+/P9DYPgZlumTMsFHOehk3V433cbnZD3hue9GR6jRHfxwFpGUUrcsGVa2VLBkNFEs6SClUGbWt6tO/BjyZ7vNY3F9Gi7q5Z/N8M5rgv7xf9pF0yVfm00WZPa/sb2q8WX5JxgrvH8PmCGb26+hKF0kzKOe3KJY89apqvSfAUTyPgfkzV5dSOsKbUdHHaPItW/JtGqaMMmdEpVsX9131MLvMe4Nt+nB5kvmXZgNhAOYokrOlDqCkLngKn/p3nk/++fMp2mZ2AgcB3vL7HIPn392LZuDrQ0R8II0dTNMQOkFfO5dZAuHoOOPBWAOYAc2ADWfU7fhyDzuQriyw09jNveOTbWkZtXRm0+P1ujlIwdpkoCrvYV6rehhYQ6flGjvqnZmZBNNes+TNLpCW71M2oMcm4J6Blcwqdfo9/5C1tIcdTa2LunF3POq/HL9b6hLji/zeejZp4aoI28l0AL0u1oOpZxzM6lqu5bbYspR380aumWXLhyc4yOM+rKuralQFA25w9YHlyc6Jg7M4+6wbOtRg3odo8RnXW/7SkWRcGdxgvR2o5tVYtZOtDNx19DbvOuPkyoKkT2Jermd+Ln82XZiHHTJXejgAg5PkQIP1jCtt0p/AKcpxz2bjbD395tYib6tB73Y6s2svla1nOVUMPpmdzIRTr1UPszepEEidewJEAObz+aj8zwqZqJSV7u7ubr1//34Ygw2DSxoM8Hw9StCERPAMbKAB11NCAFDKGmCncL2QMAlKAceRKOXu7m69efNmoGsekYmBdjQ/72ugyDUIDMyHUvMGwDzOEgHiXjB1Gk7Wh02ZMDcREg7TIKKwWCyGjNVsNhsiTkQZPn/+PNAYuu7t7Y2AJLxDLTTzIQtiRYUgbgJF29AYL4dCZKay6uEz06rGTg5GxMrL4I6Mhktj7u7uhtIZ90tm0ZnfBBmsh2XLLR0oOzg0OxUuYSUKZaXNfOA96x5HAJ3hQw6ZO8obXrVhysw18+GkITvyBjfwPBFX/3EwDzIBTWyQHJlHnjAePHssjYfLnsiI+Zk4x8fHNZ9/rS+Hl3zi6nK5fhZTNsa6WCzq+vp6ZChns9lAUzsbdqC3qcF3dh4yUFFVw4b61Wo1ZKccQa8aZ1tMC59w5c/Zm0bAynYo9RnZogREzIGMJzT2PqgMSMJPdggTlKEvM7PSOUvum4AY2fXMTkEP6xnG7FJngg7pxKRjwzwMfMmqOrvKK78n4zq1p6SqBhn49OnT6JCE+/v7UflUygi0hB4+fINrTEvstsuisdM4aOgl60vbeQc3t6VZv1etn6uIPjaAJrPukj87Vehonj+ELUMX+RmVR0dHwyE/p6endXp6WrPZOovoIK/lbT6f19HR0VBtcXp6OryyNsZptlM+eILM8KtX6+cmoWMyyGDH/bFHEWQgOAPr6VQ56IbjAy7KskPPZ7VaVymQrSPTxXvbLQeCyOiTqXZihAMs7u/Xj6I4OzsbSvMODg6G+4P5yQ6+evWqPnz4UN99991gl4+OjkZjZe3JgFOi/fr165GT7gBHN/8M3GDLXZHy3PZsp+rLly8PymcALzgHVh72kA1U7HFCVF4BSJSSAXrMML/++utAtNlsNpz6cXBwMCKOHQxHHAHqLCpEdokSxjAjLe7DDy0mtew528F0Q3Bc2sgDAvn/7du3D0ooWHzGYqWN8WDO3LczIM5IITRsFE3AZWVeVSNnxcAWZe9T4RgfD7eczWaDAwcNOFXn48eP9f3339fNzc1QMoNx894UBJWUdFUN+0Bev349gD/G64hRAv5tMkxV62g6hgUnm7IU+B3QkBkDX19Vg4JDnqA1/E0f8DXlToCM5XI51KRzXZddMjDKiJUVuJ0qZwnQFwb5KHI7yimfrKujlcyRkirrldlsXe9tgL1YLAb5MZBDVhirI9PWMY6Y+bQll455AzZ0N/izo+oMFr9hPfLPe1JY59lsVm/evKnF4uuJnR8/fqy7u7tBTzM+xmP6ulzEm34z6mqwwNzTWdyWBl9mBNnOD3oO/QSgxqki+ERQwgEMgJX3JPI9fDibzQZ+qFqv7WKxGHQukVnGy5pbvrwvhbl5/bHLLpfPzI3nm3aRZnDIa+dQ4UABpNAz6VRVrXnezg809VrBQ54j82Adrq6uBuBnHWGnKt97v2dmpXyEtrO/jNUOjkEvZUnsN8ZmOoBimq5W632L6Er0FTR3ENM63mXapsu3bp1uhx+8T9j7tqeOTucwnr29vfruu+9qf39/cGSWy2V9/Pix/vrXvw5ydXh4WPf39/U///M/9c9//rNms1kdHx+Pytq9dpThcSLgyclJ/fOf/xxwkE+bI7CVDvH5+XlVrQ+y2dvbqy9fvgyOGvd3xsylgqYLdt1OmIP4VetAuftjXlVrLGgbZBtRNT6l1k4Y8n9zc1Pn5+cDFsRRub6+Hj1HFt2BE3ZzczMqobZ+wHkm4EdZ7ps3b4b5MV8CjzhVP/74Y83n83r79m399NNPdX19Xf/4xz/qt99+q1evXtXHjx/r4OCgLi8v6+effx7sFkFhMAiB0Kp1Vho6QMN0NKHLv92p8iLS7FlnBCybPXFeYSgUL+AHw8B7FBtep6M8BjWpwJ7bpq6hXwNKl8k4om1BSBol0M9MT4JFv8/oRTdnM0iXLbCxYqwpfDQUSvZBJM5rbgOVoA+6cV1mDEnBY4RXq3XJEsoNY+O0NMYn1wdjZEVj4J7Rz21qdjSsYDv56sZvnsuIGADIAKlq/fRwBz2gk4MPVeN9TknHLsLMWDbROq9ztspZIfOnHUjTyXNLWtowZWTQPNLpgPw+5SvnYL5POnVyY7r7fr6OdbDe8T2gGU43NHOpp7N09IeMOAuTgH2TXNkYJS22qT1G/06ve67WfVXjQEaW7vkzl/f4Pr4v93D2yTJgO5MZJZrH40DYlFw6e2CezeucyTP/246njcs/BzDyOstT8nw6VWlbGPcmfnMf2Z8/z3XA0UwHz/06G8s8HGjoAHDelz5Y3yk71enRbZMxWvIJzfbbfO15wCd23KHJ/f197e/vD8FnVwHRv19pKduWYa938pJ1IXbGfU4FCAjEWXeY722rLF+pmxIz2+Z5Xm7M0XydNi3p4Wog65rs2+PFwcxxQ9eqcblnp1fThmZGlqBU1fohzThe4BrsmwNC6Elki/mZ5v6tcfnvtV0vcqocOem+61JrfOboJoKxu7tbx8fHQ2rUr4ABNstZ8dIfQnhzc1NnZ2dDJICIXyrSKdDkPhEMP/zNHqxLb/IhuS6pcm2oo952lngmDHQ4ODgY3vvBaek8JvObIRFEp4LNYC7xo3+YrqqGLElGLolO7OzsDKV7rHUnhI6IeGMhgn52djYYn7dv3w58Qukj5QKO/lMTzYEZzKWqhiNMbcgSZCf47cb9ZzcrTn9mJ4DxGnhYYTqKReaGZxtBEz8YGWPE6Uv0kfdfrVbD8zucCaoaBxlSodqA+Pc4x5RsMicbW5dJkankPs7kEYzJUxPz3o4SugzDhsAZPpyONGysUQIwj9vRcfqkzIT+CRC5lIN5Mg/WyVnJdDJtQJHrqhqeh0UZMfM9OjoadBubi8lOea8XOg0dQ6kLgISxOaLutd82pyoBBn/WywAiH53ukjLrCWdsjo6OHmTs+Y61rxqXhhpMw+MnJyfD/gOivAafWXa+XC4H4JkgzHrZDnBVjeaNM+3sJPtabS9dou5gn+UlKxcs97QugAGtGAt0oZrBYMxleeiNDDw5Mu1yTNYTujBHl7pnhN+AGztlfUz20wdpkbVi/Jz+lqWctNwykNlBxsE8bdu2Rc48XhwiaIJzQZYRmcoyLeTl6Oho2Arxl7/8Zcjy0vfHjx/rp59+qlevXtUvv/wyPN/q5ORkoLnl1tkxTpKz04wOtJ53dqmqhmcgebuGaT+bzYaDHebzeX369KkWi0Wdn58P46JSALmFN521chA0HQcHaZA7rz38RRYX7GNdgNyDC8FXWQlUNZad5XJ9sI5l+uzsrHZ2dobnrtkesCaUzyI/y+Wyzs7OhoxYVQ04AN2JfUK/fv/993V/fz88C4vSQ55V+v79+zo+Ph6qbJDjs7OzQfaRIVetGLt4Hz+0e0l7dvlfRvtom4ypFwLiUYOKY/Xhw4fhtI5hcErfzmazodTOxLAw39zc1Onpab169WoAD3aqWOx0quwIMreq9Wl6OX9Hdinp4XuaDZ0BMQbWe9D29/dHJz/hVFH65WinjaSdKhwnGCcVWUY4HfUGLCLgt7e3o6fKu6Th7u6uLi4uBoFnX1wXMXSUB6fKfML729vboT8AOw/ss1PFXivADyfMrVarURkTysD3SaCKQ9FFyr5VSyFmrAQWHLn0eDP6RfkBjvl333038JEBMaAYmUw5sIOFsrJjkuvOWHIOXVTIgKVzdjGEGD/LonkVo824MiLuP/jCpV8es50q3y+NbdVYl6SMOYtkgA0fZpbLThXzoozMDosdKe6TQRY7VRguAMpi8fXEJsqU9vf3B0fOZR/IF4aW+3m/SDpVNvbbBPbcnG0zn0I39JM3qDNv60F4yPtdMdL8ob/N2zjd9M1vAXbIIk5V6mkHFixvWXIIn9EM2u1geG3RnzhQ2exQ5X6VjMD7tctyWSfARzgeODaMm/XxHiTv3bTe29lZH1GPfDmYZvmwU+3sIOvghhywDujj+/uv5ZnYbYKSlID5NDIDe2MSdFsGddEDtvkEhmjbJl926FlTgnuWpcvLy4F21inM7/3793V4eDgq/2M7wKtXr+r9+/f1ww8/1GLx9QH0nz59qrOzszo5ORnKlQlI+MAJ1pcyP8C5eclZDOxc1dqpwtZw2I+xA6XCDoYkT/n0PeQWkO99ichZVio5aAHNHTBPecbuV61tFg4L6wC+MrblWuaGvkinHqeKfW781n3ZJoAb2E+HU4Uco7vu77/uEeaEwZ9++qlms1n97W9/q7u7uzo9Pa3//u//HoLo7969G65lTyR22Y4TAVgHVeA/Tlq1bk0885T2u07/czPg29QyEuD6SwwJ/TiK62ZvlkV2RKuqhu/cnxfagNCANeeUxM9rvWCPLYB/Y0cmwX5GPdM56zICU7T3ZwmA7fz4fo7ScG8i6FXro7DpD0G1s5vZQcCgx8J1zNEpWkemGBOgI8fqyCHjAzgYRGX0ISOS29A6p6RqLPxJWysBR4zJyCBbVdPlBVxv2gIgu0iX17MLonAvXk37dKzTWezWgt9ZFvyX9/R1GUU1Dfy7KRnO8W0KILk/GyDkxYGeDCDlPTt6TMm+x22gmE5eNz50bf4h287O5do7yMNamE+2qW3i+wQmVWP9aFlA/k22VQAAIABJREFUNyboMa34zgG1qrHjjp5jHNC74w+PwzLDOnbOyyY6pAxYRhlX6pV0ijY5TH7t1mDTe2if/Ja6utMFyBH6zoDU9J7CFf5uky7K9zkuxuKAivnLmS6usT31vZ2RThpsi92q6p1m5mRc1pV+VY1PjKMKgSoenBrvL4f2dmxYQwee8hCxpNlUcND8ZEySzkf2gTPlV7BLx38ZmJiy0fl50p33Hk8GYDznqoeP1ki97XvjHHkPbdLQvGt+zvuDF7lXynGnC+ARHCN4xH1bH3tO2EJsHcEVxugAs+nzkvYspwoC2/CmEUhCeoB2lIg8vH37dihRwlMkeuA6T6fOd3d36927dyNDh9I6Ozsb0v9EBRi7Fwcvtmp98h99WFCsCHlPP0QcTRsrb6JZvq+VP0oXLx9GyYfBQXOuTxqnMHjM6VT5d/Tr0kLmb8DlDYpEnYjAMCbGTL9JP9OFcc1m670fHJLA7/xchvl8Phgm0uuHh4dDieBisRiN0Zm1VPIG2Mmb29IsOzaodlyrxmNeLtdH3+/t7dWbN2/q4OBgePieM4opW3Y+Dw8P6+PHjw8MPiCFbCBjMR0BfQQ9uM7OeBqhBC7JM/Ai/NI5/nYk3c9stk7puxzLWUu3DgxidNwncu35pIwyZqLn5kuXknG9yzssP6y/aQ0PZPDBB48Qrby7uxuiiXYImIufuQPtOVnLZc3WO5QuMkZ/zzpPlTp9q4bu8mEvfA6fETW1HnTgpqqGAAXRdKoiXMrj6DM6C3njQKUMMDkT48xT7iexczybzYbM4xQvm08sPwb2LlGHRxLk+wAqR9Yd1MqgoPnZIDUdHAfF7JhnuWM6INgJ3psHec0N9MiIwTW0M45wKaAj7XZgM8DnrDL3e/PmzXAwzPn5+Qhko7PhN+7toEjVOpuFTDpwtG12y2sODXn+03w+Hw4+MH5arVaDfTk4OKgPHz7Uu3fv6vj4uP72t78NJwBy+EPVeG8W9Oe0aL4nU8+JcOZRDlCh3JZKDPOEs89kCLF/VfVgnbgebITuzi0U6UBkENSHtvF5BtndV9XDY/xtv5B1y7bttrPD9OtXAki2R1U1wlysLXqS67kv96r6KnuUJ5JZti3DqUbHcBAFW4bANT/++OOw794ZQsrdOWiDjCX40nqAhhxme4l8vejhv8kYGUky8MvBQeRXr14NJ7dAJJ5sTa2nTx2xc4JDBgPRnMZn3w3KL42OSwHcj0Gdx96Vibn+u6pGCsXMZUZ0VsoRz3SoMGIZzTTt0zDbqbLz4pZAx4bOY+VzlEJuYid9z5ygkedvmsErjNXGDweYB7wRZcj9MlXrCCSOKBH1q6urkTGeiuzYiDMf8+e3bMmjVeNIn9fIjc/hFaJ6R0dHg3y5vCYdNfMwD+bz+pj/MSoYUNPS687n8NEUH3a8YHrAW8zNezlwkgx8MzKXBitBsmneZbAyIuesnaPGBjoGd9AjnaqpqCCvBr3WnYwPHkeXpjNmUMgDNx2ssB6GrswHucIhMMBmTgQ30NUOVkxFor9lY46We+sG1mXqOkdK9/b26ujoaDhdkb0W3m+Ibq+qoawS/kRP2XnDHmXQiX4MxGnz+fqB4anHrPs913SAzLs4EC47434O7rkEMCPsKY8d8KsaZ+nyj2ZH0kFN2/TsOzMCBpLWVR1/QnOAMeuRmMf9OvOYoHyxWD9QGNCa+69xLkzvKadquVwOpy8zZ6/5NjTW3jqKk3r5zFkc60n0kx8c+8MPP9SbN2+GclpwAqV+8C88eXBwMNCR/VOLxWJwrgjeA9xdxm6sAiYBfIM7WK8pjGi8lHjRDk8GdZxhSYfKFUPdtZYH4yzf13RKvOv9pF02La+jfz+0u2pt061jfa2dT0oiPQfrD9vrm5ubYX/w4eHhsBZsGfKDhW9vb4cyTeSXsYJbTFPoaIwwRcentt/98F+3KXBk0NUN2AAD44LQ4WnCpAYxAA8zSDeuXOxu/Ga0BK/pyBjoGPSZOabo1SnpDvwnTacMT9cyouDrGK+dX4NyDIQjQlNjcT8oE/rIMkv+uvk5Ao7C8jjcf4IFAwSPJdfYyi2zLI/R889q5q1U9ClTyeudjHUApZMtR4QtW/SXQCGBWfJkOrDmF8ZihyGjy5ZXX1e15tGMgndtk7y4z+57z+Ex0JIGyOOfkqdc3+yvm1vex8GKTj94zYkiMh8MCVler3+OAdnqxsEcDAozC7eNzSXMU+tAg9boIgAcDoijyeZ106WqHshRVQ0BJAAmfXr/n9c3+XaTrfH4zZPWFWmzDc5yzB2Pmf/SwUr9yncGZ12/HU5wtjZ1XdV4o7v7sHNnR3I2mz0AxvTDnAlEkVFivVwSZj1LcMFBSOs4AKbtj/tIfZ26wnbAcrwtzetWNR432Wzv6zYmsF53JtTr74Ml2PuZwSUOt6JlVQI60Xanas0/lgP3Y32W1/KZ6WA+8j1SFjo5SDnK33jdn4oF/Wq70WFxv5/CF50e8uFPXlvbKsbBPkMHOL1mBBDgFfbEnZ6eDjIElklMzn43Hzwxm82G8r/ONmUA8/fYrxc5Van8DOJdogRTYoDM/C4tcykZZ+VzItHZ2dmQ3uM5BRgrGx0IYcDQCbeZsNvH0AEXzxsP2PfhXi6b8AJZOFGkZKe858Xes53MNCQelxefz6Epa8C1SRPGxnVd+UPVOs3rdWae3sSbUcqqsWPqNeDPzhIbJ+fz+XDC4M7OzvDME/eHoVutVqOHI5v/rMSy9AzFCU0eA87/7rZarSOWzNUnGBncOJvoQzlQaq45Z41Wq9UQMb+/vx8e7kcqnlIxeNWRWCtkxmfjiSxaORvsWKl6/To5Yf0cGTTPuYTLGZqUA+5t45QAOseNbNrodMazA5qeu4EboNnr1jnKfm9lnqDLWT94wHJt/WHZJGrncih0jgGLdRtZYmcMcC7MX47OWqad4duGBo2cOZwKWlQ9rPPnj1IkIuc+BZW+fMhH8hyZINtBTotzdBp+tM3y2lfVSD753jxpMGMgn46xZZzmstd0AKAF9ssZZGy8y+9Nf+aOnsvyUfqnX/jcfwkqoZv74HPma7uUAIpr+HN2yC3l1LLtklcqJ9DP2DVnS7FhfGbZg4egj7NW5o0pp/rPbKwn6w690SvX19cPHEZe4S0cqcPDw3r79m29f/9+eG4XWOTk5KSWy+XwzCOeF4U8zefrwwbQV2S/+A0H8zjgBW94TDRX4ySWSJ2RQZAumGI97nW0vXWw2DJl7JZBEvOydYBL+x18JuNum2o95TlzLXPgPl5T1m21Wg0HX3iu1jv391+PxWccrI1PA7y5ualffvll+P1q9XUv12+//VbHx8eDbUJucLoWi6/PZTw8PBx4ZWdnZ8hqOlhBgwezvNpr95z24kxVCrONK0rECiejRVYsgClSdBzxCPBj/xOG37XLBqGdE+Kx2ePv5kDLiABEpy9efW8r/E2lBTZITvc6S+Qxs8gZnciIAJ/7urzGc891ZLxEcO2cWHANFPh/Pp8PZQkGuQkGUrnQ6I/T3hAwaMJpWAkgARQul0w6+/7QxODW/XzLBi0NkpAL5gxvGwg6OuS9AxlBrarhqNW7u7vhwbQYLEeFqtaGJo2NgwcZgOhKB3KOnYNl3kiHMQEpEUx0ideZ66rGZbYp555jfmcHhjHCz56Hm+drueS3fGeAzXcJulPxm552UHHWMLzZTEd4y/rXGRHziGnNGkAD+jXA8NjhV3h3GzNV1lk2ogZJGcCC7n7eGyebsTeD6KiPorc+c6NP+iCqur+/X1X1QH5p5knzV0Zq+W06HlUPS9TzHnaezTddAME2L8Fg8mXqImeNUubyvXkp7VjqH2jCd86mG/CbVqnz4I3cm1u1fhxJVQ1AjubN99Di4OBg0GMeN2M1nTLgglPL/OnbMtrp2T+7sSYZXIYvAdibHEDANScAE7Cgn+vr6+G0vouLi/r06dMQeLeuh1ZgxQw8+jAsn4ibzjb0z0BSVb+/3nzodemCNfzOstIFLPJz95n4yp+zJh7rfD4fzQe6gPegnbEyc+yySWn72c5DkNYy60AL9og9Wt57yr42/AgetwM+2dnZqevr6+FU7A8fPtTh4eEQwELX4rBdXFwMe8mthzN4aTyS2ck/xanyQnYCnQvP++4aRzcBexxRzhGklINlVLEDOVMEmFLWGd3tjFD2aecsf9sZQtPCiscGzdE3O0oGWZ3zxPv8Y5zcO41IrkU6ehiFdAK9bvRtA5L0yPt0NLXCMeDpTnZ0xG/TKUIJPLwGpuk2GCSa17nLZtjpyJaAx8aXSOFisRhkiyAFx4DnkcF2JDwGxpn37sYxJUvuzw6kwUhVjYycnRvzGmNBEVqeOn43v6bhYN6drvIcPNZNjlcanXT8Eqy53MSyNiXfBsWdgWW+yIejlQ6SGBwwPhvXHEcHxH2/zjHcttYFBKe+q3pYUtvpWnSRQV2X6bB9JLJqAAdPTsmZ5TLH38ndJt3PnKZkBXpkpijvk3bPASHTysGs5K1OJvxdBm2yOZiXc0y76vklrfJ9F0xljlPBS2RssVgMOtb36eTKc+z0Sa6d1+klwO+Pbt2YCPqkDaFl0JNr/DiN1Wo12C4OHri8vKzT09NRID6DU14bBwrsVLBWxhLWibZHOVfr87RDyVuuYPLcO34D8zhr1enczh6Yj8xLpodL4HxPO50dXkvH0bLK3Kz/rA+5JzQjkAs9cLZyLxrjZo2d2b+9vR1O/sMJ89ozXjCtHW9a4g8HL35Pe3H5n1tnAAyQO2ZbrVZDyd9i8fV5AyghIhI+4x9D5cXDINjzTOE12E/wmZvVppgp50cmx1GwXMwEpSgH18p3/3PfTEVm5NrvUxnjiD4GgKFPlhR1GQRozZj5Hb+dikKlYUrjllEfg1Xosr+/P5SQ5nMXrCinwEGCPYTcY9qGZjBqZ8cOr0FZGnIrD0dTkTOifUSTLi4uHtzTBsJ0Yp1xug1wEnAZZPl9J6udjKGAu5KMLMPINYdHeZ/rboNA38yTKCbfu4yFcZsmCQpYC5oj8dzLDk7O2ZHQDoxbl2V2NenA+iXdrQN9AEy3ZskTuVk5AyLQktdtK/+zbjYPVT3UU15PR42Jppv/Meg+cRHwl2DcWR3ABd/buKfT6z4YV1WNbFBGtV1i7+u41qXP1uP5e/p3yVKCN/O4s3QGhd4z63lxD/RKZ7Ohea5n51BOrbvnmeubjhu0ZQ2QA+sIwL9BJNfRF1lM23ruz2/YB7TJ2U/ZTL30rZvpb8BtDJiyh9MCjaEtJX227dfX13V6ejraFuKsRtW4VBre8P84bFR/uKrK5WHGI9aLnpd1dfZvG0gmG/5mrLaVyBVZGx+kQYneFK353/aSyh3oyT3RLV4H+I9T9JhD1XjbiuXC8+PVMk/wdrVaDWWwyNJisRjKNu/v7+v4+HioAOBznCvGw2F18/m8zs7Ohgz/7e1tvXnzZkRP1gSZzxLSrByw3sWBY92mMO1j7Q9/TlUaJjs6fIcg+dQRFtIPh/ODEtN7rXroRHWgmvulU2Vj5N+4H/frzaU2JLx2Hr77Y5HT8CWDJk2teDoDkv3YiOc1fvUYuc7A2BH1NAT0mw9I3dSStjQbLKIUCWQQCkBCtxduau2TD7222+JM0RifI3xTjkMaDfMm64cyBbih7FB8fjBe10yjBIe05OWqXulaBjoeReFaIVrhubm/BO4JnpJmU9Hw5BHTlN+YV3O+3SvvM+I5NaekaTr+Hk+3NrlOplEXUTeINe/4EQu53ptkhnluCnJsc+voaXp1ZTpV43l7b5oPAKE5gJXGPe9H36Zl8p6dXK6z3PHZY3Kb65Xyk3xi+iRPWC6xHdgSy1Das8xEeS4OXpoWlseutC91wRRPd3bU+iKxjKPjy+XDx0zMZusMhYNQpj/3dpDQh6d02TzzxpRO/hYt+SixC06j59CN25kqH6Z0fX1dnz9/Ht6DEd1wTkxnnCYcYUo2nbmHX53t8pyS3tbPTho405HbFLy3Nvk+g2V58l/V5goV3hszOejmgCC/9bYBxsd65Tp2pX+mg/GGs1M4UDjI3N9VaOzZhe7IEdmp1Wo18AI6hP+Pjo5GwS7PGZ3gIDDXWzfayTKf/h6ZepFT5Zt3oI9mhdQpN5cI8Fvvm3IqkWtsRMxINBbZteOpwGwg04hNAdh83wkbzYvi8Znhu02JCSQtrFni5Pnakc2x2ajbaFkJui+Y0ACftcyWwpZ0MzDY5PWbNzwe8xjrRqZgao6mSSp4nED/1pH4b93MT+kA8j2KIZ1FaNA5YwmuDbKnxmC+8toZWBqIT73vQFc33/yMzz3fpIHHSOPeGVFP5wYeMt85M9M5J/7f4zavp47wmE0LR5lznbPv7vup39AMXnNOtASMSa+kq+e4yWn1/bepGeRVPeS7zgnpAmDuD5lzli7/+G3VurwmdRn0M29XPTzkyOuSDpS/S9trnZlOF7RIXvF1rKezMnxusJR0pj8Cj1zrk7symJEynbS3PuA+UyCd37gvzxca51hZT3Spo/cec+qmjp7Qivc5Hv7HoTLfeL7Jn5uw17doaW834YOq3mlZLtf7pyhT5zOfBG3+yftXjR0fVyj4OzdsmuncyVvVQ73ZBQlSlyZOy3t0gX/Py9dtwm++VwZAPHbjOjtW8KAxp/ncjst8vj7xLzGCD15LfZKya8eG/YfgDDvj7uvu7q4uLy9HY3CzPrWzmfR0g7cc+HpJe5FTZUcH4uNR23NFEb169aq+fPkybIyDOK45hyh4qelYJShwyQPvTXyXQlk4HDnLhcq5TRmaKbBopWcmoe3u7o4OXMjaWQst/VjRc29nalIR8N4RGj+DwHTrxu9NtSgil6ikIaEZmPkzWkaVaDbUuc6O/iYtLMguc/I62NnITcVWSNtkmNJRtAwksLXRtgFDttL4OvObYM/3Nj+iGB2MyFOLHJHz9TmvlCHGNtXg7S5oAj8kcE8DkdHLKaDfGUjzR/K8752Az5/zl6W9rJsfgOmx8JoOTMerHR18H+u0pKP1WdW4ZNFG2cEhO0zJSxjszvH6lg1D7IhxHqRg/e3PXJXAH6C7au0o+cQ/5DYBCvSFTl1ArAOjLr1LeezsHPN14zc+CMOBUQdoEty7P/TIavV1E3lG613KxwEEfAdIBsCkjrMNMs86Yp+6hPF1Dk63rp6j+bTTLXaqvJbJ3wbtXl9XsDDfLNc0iMuxpWPGOFmPbQtepI6vmq6SSfsBZjw5ORn4BP1IOS06k+/NowDiqvXhCi7hhm7Wa8ZLOT431iB5JgOUyFQeQGZ5SwzjYDf/G5u6Geea5lXr4IG3kjgLlQ4MtEPmoNPOzs6D58NmJVbaf2S+an0KcTpGXi/Lmber+MAxJ1xsY5DFz58/1+XlZR0cHIwSJx6nExieYx645iAKDmF3ANRT2rOvmvI6zVRWejayySCdMsgMVXddeusdM3YGqLsm59bN1/NOJ7Br9A0QTkduajwJ9M1Idlo8zk7wPD4rZIxfOiVJU0cZGXcKFILh8XQRoMdat77+LsG/y5h47Zw8mgUHI905J9vQPKYuEuqWRjeVrPkoT5HqghQeg/mgkxnzsB2q/G2O13OkGZDlOGyYk0ZThtrjTlnrWtIx6TLl1Ph3j/G86ZROZcr9ptaBlSke8bg7Bwdw3/XZZUcScCYtUp62Sa5onb1y64B49xvonnzb8dAU+Kf/jvcBXW52Vqb0dtdPNstE/rYbd35vJ93ZJ89ltRpHtLHLDpROZXzSdibfpv3LuabO6Jyq7pr8fTpVBoTP5W3W2zazm1Pqqims5fFv477FpGfV5gNg3HBEfYgSjpT3CNm55X4ub3PfCeQ7WemqltwckLX+24RP02Hqvp/67jF8SUvd7b67gGnqIWM3/3VOZs61m5tlPwO9U7yReiPnkjLD2HG4cdy6NU7fwHopm4OCv6f9rkwVA3CmoZuYDZCjdpnd8nvvpUkjzu+c8UlmcASkY6h0YCyYBuFOk3ZM2kXKPb6qdbbK42QzoZ0/2hR42iTEjl7Y0cgIs7+venjyoJk3U+I2qqZPZ9AeU6b5/2q1zq645RqmsuR1E8g1T/EbA5ttaLPZOivEOpjG0D3pkbLYGWbfw5E7ap3dHPUmom2lhMx1yvcxY8B4rNCngIodgW4+dkhy/NSjp1I3HR5rHg80z0jllD7owJrH22X1nal1SdUmvk5nht/wPp/b04HHTr6Shx77Lfcx4H4qnf/sZvBuXdDxcYJbtw4gTDlqm+jQ7SPqnJ4sF/f3znh1cp+BD1+XwHRKJm170sFAl5pnGSc6yw5YBgHcD+OdomcnC3bGnrIm2UcHahmXQRlOrfmbOXblRc5YZGQ/g1KmWVV/Cpv73oQH/uyWuKFr5oMEzA6Gow+hc2YGjXVyfU1f9iPNZrPR+6q+5M4ylbo86WuHpZNbV3NsCjQ7SJ6yZTrkGN2fg83GCR4rfJv2yHPkGmM+Z2qMC7jO65bZ8o7nfV9Xu/C5MQcPB6YKrlsD98s+LdMHR9z+BjiJsXYOFLznQ6me057lVEF8Az6iCY5EMSmn7DlnnkhDMqaZj+u8sF202AJjBVY1fiiaFdFjkR0DnKlUv4GpMyepnKtqdIqIgahPhek8eiscf+6xpOF0eaWdKkpeiP6kELjEywrHJ4O5JIO+zbBToLVzOv09fAAwyNLLTtl19JgCOfRvp8rldAam37J1xsXlIz7yPAHJcjl++GqC4Kox8DAY4DuPg+9JxXf7qBIUZD9T4Ic5ZGlaF/1LZch80wi4fxwqn9DmfrnGgYYpwJxOVTqCHvMUADe/ZySQUzTtSCFXecS9adl9nnzsdUGuUo95M7V/n/14nbv/oQ0lI/TRlbF9q5ZzpzlQYaDmsdumwVMGeQnuzDdTv6EvP3Otkyma9XTn8E1FV/lt7jFEZ9ghM53Mf9DJOtp6KasH0Ku2Ibb9jNf6wPvLfFhDgr90iHOeponnk3Tr6Jy2yUE4g0I+M5BF//o3CbDBArnWzAf62u4lX3V2bxtbOjBuxh5pV6rGz8yjzNT83emWDgtS7uWtIPzW723fUkdk0MFrkrYW+XRQbwpbdI6UbWPV+FmDiRONm4zFMuDBuKbsbAbCeBBvVQ0PbZ7SSf48HSdKrb0O9h8I6FrW7dDhJHWBEubvNePkwVw7n+brB3Fb33Rr4u0Tz22/+/S/BEp8VvUQXNtZ4TMrQlrnnHiC6YlPRUDyus7wZcso3aZUYI69E6CMMHQAawqYcn0aCxuWTsA3jdUGxnM2AMhxZJSB9wloDVDcb0efvNZ7OaZ4IdsUUOoa8zMvbKNBgn7dmvO/W84jaZ30seHo1jUNRedMdfw81TrF1c0rx9HJQSf/+RvmlXLV9Tk13se+78be0aFbK8Zn+iVNUkazb/fbyTPXEKHMtUIup66bmpN5Mx25DGA9hTf+7Da1HtkeM6aPzauzQf68+00GKnIMU+vleVh/51jz2nRYuEcnkx2Pdn++v50xXju5yT5TJmznNumEx+Qv9VBHo6QXLW1a2kgHmT0fALbXLmWoG8cmGcz5baMN29Q6/k85sMM4te7WPw6kZ6USNE+7yvt0+tKxSh3f0TvXN21o9sP/m0B9YpspfuyumaLXFC+l7DEH7//ks6lSfX6T9ibv7f5SX3ldp+hoPkmblrggsfwUHab04ktl63c7VURpqmp4knEXIafxwEMzm4XALf93ZNZRnYzk5rX2zjum8/9ZztZFs/huCkBMMXAyiT3tFOQuMuD5pKGzculKH6vWGUDP2VHGFIquzthKyJsTkx58ZnpnZrJrWfZEpMW1tqYnkQfPy/e0UrVi2DaDhAF2ZHO1Wh9bnxneBNb8hr5c+pWGwdmFqdbVNrsZbM1mD/e1paLifqyPlV2XpUp5SFDn917jLK9JA51yk/fdZJCSN71Hb4q+6XjQMjviDAg8PzWupKFlmOYMhEuJ3Bf0yvl2wIQ+vfb+Mw8a3Gxbs26zE2H68rnBhIFZ6n3mend390DuqBLItUfe8yCYHKudlc6BzlLLzhZ53M5Y5j6Erpzbc3XEv1tby25muDq57wCXS+5NA77L33vu3Vp3LWXcdsm2tbvefA3oBPP49L50qvg+5T2D0aYdut522TTalkwV4+6yTnw/hb2S7x2I9RoYn3RZJ2dzUrYsY1zjlv9bXnwaXgYdUg/kGJJGHa8b5/j61E8eV44Z2uYzAi1rHq/pmr+hLRbrE/x8P9tBeBp+Np5NGffa0o8z7x6TbeD9/f3wzK0ch+2UaWF9AY2zNNL8tMlJfUl7llOVypTF5DQOp9oSBKBAOOmDlKDL4zpDxn0NiDpCuESjqh4A7QRXU20KHHrxO+BHSw+ceRtE5ck0jLcDojnWBIcWbN+jcwa55/39+khb34eSJINIrttk/KFbNtPcBtV8RJ/JL/RpJdOtB7QzbUjtptDTZ/LntrRuz4RPQeLZD5YB8xprytp3p9eYbp2STkcl14nf+NXXdfxa9TBSbb7w9V3EaVM/rHNXw43spTHJz3IOpod5B9pPjdPgm35wMuif6wzsWQuXOmek3+/dj8Gxm8Gsx+T55ueWmYwUpr4zkJjP1w+MtdF66elJ/+6W+1+8pl7rdMCqHmb5TAN+699gm7I+38Cv6mGGOpvpn7r+/v5+pB/TqfJnDgx0kfBOZ3Mf7MAUaEy9BL06p6q7b9X4ZEmPnX4MpjoaZb/53nKf75Pf81ro0K1V7hezo2U843sYbHp8GazBNkNX8NbvAX5/dDOf2G4kVuOzlB1a2gdayqEdDcueHVg7VR1u8bq7pe7nN+bjvDYdqs5eplNlGbQz2s2zG1v244f3+hTSnHP222HqnZ2dIUnCmtkGe87GiMYjHieN+ySf5JiMk21D3W8GJtOZgiYd/ue+xlPWO79Hvv4Qq7cJDPk3VlZWPlPOUtc6wfD7TUwzRaTHCDdl6Px9pyA3CWtnngAUAAAgAElEQVTXnjKX7vtuLGlc+a5TIlbiedhBOj0Ar2z+TRrKfE2nakoIE9Rvamkgp4ziNjcrCrd0IlLRJRDqHO7H7vuUz7pghmV6il836YWntk2y4DGnkk6ZesqYOnA2xa8JJqfuZ7nzXAzk3X8GlEz7jr5576pxxsXjT77YBMI72mz6/v+V1hnzpHn326m+Ul/auZgKRE0B9G4MjHOqnxxLjr1zJqrGACl/1/XfteTtzo5NgZTOken01mP28KktbeBL+8pxW9bs5Bo82q5mewp9kkbb4lBlm+L3p7bHHB2/Jkbo8E++5x7+f1OzXG8qf3vuvB5b604v55g33XfKFj21QVtn6XxP02UTDRxkmZLtKb2WQZoc22M235/7lWb5zeDGS9uznSo8UhogzsojwTiRldlsNpyEx2IZAFoxGeCnwpkyNl1UN38/xcj+37/t/ty3D9WYOrTCdGKznJm0S4ubPh731FxMb1K3ZDZyA63HaLp6nfyHx+9opYXJY8nrrPz8/yYD2Qlt9p1r4shMRlJMZzuQv9ew/pHNstHxGfzFb2nORmRmwPLTKWT3vUkGqsbHAtMMtp4SVHHfvocdQuaYh0lk6V3ychdFTyfT43bmwfSeiuwxZpfpZXYmI13cy/qg43tAmPvO+1vpp1x0Zbj+/ZTR6yKF+btONyFXjJ1Tmjbx0TY08zLOrJ1V0zR1pL83jdKhQd5sF6aCEdlX0o77diWa9Odsl58/5TXL8s/sB/1e9fBEwM4pcD9+tXP/GOj3dbPZ+qHzmXVNIIvc5dhMP997ir5d1neKX6fAbX7myDmfO2tu+fYY02aZLv6Mtbq5uRnsQSef36J1zg0twbdfq8ZBIeOG1GPdwTemUbfWnT4yFjGdMwBi3kt7nLqB36DLLQ/mS8sZ936KvPi+lnFsfOKytGl5D9vctEWZgHBWn2fOVX197ure3t6kbkk+8Jgsb341r3fbT9KOd3o0qyXwQTLzzm99D/PDpvMUNrVnl/+ZqJuImMzEXipAPxNyau/+/n500lfeo2M6M8tUJCGvN+E6IJHCwu+6k+lQmn6YoQGhGcjgz/fowOhsNn44ZJcSTvp7Pt2TrlOIUvhms3W9LH0yNwyYH2bs08M6oOHxQhcrp00O1dT/8FWmo11P3Dl4pnN+/lSF9u9s8AZ85rVCMbqW2UoBuelkBce8i4ibTp3TY4VjHk568r7L0nY8msbPipZmJ5HvOiDnvs2PHnPOt3NCcgwdbzAmaN6NPWWuA2pemykdMJUNTr1hMGLZSkOV/NEZvRyrQW6CpnSqtgXcPda83in3HSjLdTDdkoZT71M+U043AUD/zt91e0dsWywztl1dH9Y5PqEvy5E7kFTVHw3tuW7Sr9hRP5S5kwl/Bk/6ZLGplrrP68q4EiAnPvB65Bp1eMffe9/Ipn1UubfZfWf5qU/H24aWYNi8YuDbrQPNGCSzIg6M0hLXuF+PoaMln28KBKZ94j45Ds/DNrLrz3K2SS46PZRz7+aZThXzM45gnN67iAzyXTeWzh69fv26dnd3q6oe2IBOZ6SttQxWPXxWmEspPXbkxN9P6Ut4B3yY9jHn1QVhn9ue7VR1xH1OY0ENsDcxEO0x5+o5Le+36b6P3TMFxOnKdIK66x7rs2O+qWsfAzadIHdGsGpNk3SGbIi6vvjc3z91vaxMaZsUTEd7X0efmxTnlFB+i2bls0nhdzTt+KMz8pv+z9bJescf+X0Cj26sz2mb+MhKvBtH53Bsol9mdzrZmBof77t1S1DMawI40zV5t5MP9+P/DRw6/n6M7zuZyus6+c/fbVPrMnNV9cDQbmpT+rNr0Ihg32Pgf9MY/LmzH1NOz3PnYsD32DXd65Sdss14TA90PNPZNAOsDJS5r67vKcCbjtWmcU318dhnplXS5rFmvbAtzhQtdWu2KV40QH6seW0SqG/CBxl4eml7qs3qeNrfWa/aOfY+INugxzBUx9ebeH2TDezs1qagUmb1MrC4Cf9u+vwp8/N9Nl3r16fe6znXdO3ZTtXe3l7d3d0NTzNORu88U3uV9/f3dX19/eDZDfymOz3lMVDD/TcZeFoXRc7odmcUYPpkGAsEET6X0XGtmY5+KcugTzP6bDY+bayqHmR6/J3nmE8c99yyT4/fysc14S6Z8ykszqT5/j69LufcrQ/XTwnIlLEl+nB7eztESsxr0LzjG/PcY9HOP6PNZl+zi1mqmc3RpaoaRUJZG2dKqzbXH3Nvj4PXqTWxXKes+9rsswNYGBKXNsI7fqZNdxgDc+vu082Ve1ru/L15xeNhrhn5SyNZVUMpnOlCRrs7iCRBremXvJtOV9LcrymXeb9sTwHCBvOetyOfZGV4Dt+2tPl8PipVcVbkMUBsPY7OwW5lSW4nD9lfjmvqsymghK5AVvL5VcnLU86u186R5qnfGwSmHJiW0IqAHM+xTH2R1zP3qUyy/7csVq3LIDOK382jal3S5KChaZU0nAJcUzil689OFfTuMojQwacdog/AXlOnzH2Lhq5m3TgFMytZ8hpo3NE1g1v5Pnmp+013H+uvrjqg08upM7v/WR//77K5qnFp7e3tbX358qWqatCX9/f3dXFxMTy/lNP3XB1hXrWNNiY1pukeIk9feaIe33l+ifHQPdArA33z+bx2d3dHa5LVMkm3jkcys0nz+ruqzXoksXbXNmFCdNBLceGznKr5fP3QLoPaKSDmheQzlxdg5GDAqZThpjb121TW/Jbm1KFbKnMrb9evZqmWS7bMzLR0cOyodCdRZR/+TdaYe24dCEVBO+Wbv89o7Wq1asflNH+nEK3cNmVa3DqFOGXY3Qe8ROnlarV6cLJixz8pmNviVGGUppzPBOCM2Uq3uy4BGd9PyUZnuLJPfzZFZ5qd2w7sZUqfZoerc447x4P+EgSlw5SnpSVI6+iSQDA/Nz3cvIcSGeqCDl7TKf03Fb3sgImDJI7ue9z53jrPn7M2aRxT31jfb9Ppf7PZrHZ3dwd+6sCSf1vVH/fMQy3hodls9kCndo5SAiz/vrt35xAwnjyK/SnVI/BB53hZf+ZY0gnive1jB0CTlxmnS+DSaUmHKNcpeZT70KCDH8HhuXsdOqyS9IKnUw9O2SiabX3HZ3bOrfu6tfdYoQnXGjt86+ZxWQ4yk1o1Xo8pHVT1UAamHLSpky89Nsbh8bmlrFc9fsBSN/ap8Ts4v1qt6suXL3V9fV1VXx+ye319PfrcgRKPJXHtYrEYzY/PfBR62lnrbMubed7zSV3j0uPEFf48ZcifM5+pEj7sTfIE77EvGYDl2nSsptYqG/NLHfKc9iKr54VgkJ3ysHB1ys8L6993BJ4Cx7mgm4BGgqypKHyWF3SLkkbGr7zvHAE7Ot4onUo4HbopI5ZjMl3z+m6seb+k71QjwpdrZt7oMmv+7VMAucfR0TPn0F37GO88BZT8Gc2ANB2kTQocoAcved03Zf82fZ6G8CnjnmrJv1wzJVtWyDn/KSDTyYp/z/3c96a19324blOWahMNHMzIyHg3l8fonrKTYCVp5N9kmYYNUQcsU8agg7MJbjaW2wL4qh5mwg2uHuNfWq53F/2mb7cum+r3T3GuDGbSAZkao+eWPGVwZV7O/rtrHqOXeSZBVNKiA9LdPZM2XNsBxpTH7O+xNUhwlt9PjX9KhvJ9p6tSH24KSE5ld75VS5o/x2ZsArtPlcuXtE19T2G91Psd79LMo7nenb3wmhubJF92NhOdPlWdkHw6Reep7I5pkAG1KbucAeKcT2a/uvl2c/E13TiSllP6o2sdln2JfD3LqbISJms1n89HJUsoIoxqRmPsjFF24GudsfJkp4Rg6kz/TYrJc8nmMZEFYQzdtRYUvkuGZz53d3d1c3MzMgj0y0MjkxFdmgcY65jZTpSzgflckhy3jarP9O82QHt+XudctywRSqVj+ljBeL3MM27uY6oszAJrhWP6Va3T5K9fv/7mxsnzsTDzP2ubEVjKSPKAF6KGGWVy3x19O2X32LjzfUYDzV/O6KYhYb403jMX98XYvBGVe+eam36mtXm70x3dZ85MP7bp2DxsGWZsZEkzQ59rROvAoFtmsfjLrDFr3EVtoVXOyXN3EKjLUlWtHx65LW02+1q2Yj6gea42+rkW5rHr6+tRhNhZvAQCrLcdFn/P/box25Gyrs17wZPOgNiR8Zpb97OufMa6dY6Vn5PHPT2H1N9+ZdyeK/dw0GE+nw/rlA8w9XogRy77xm5jS/mddWauhf/SrmZp02M6IoOxyVsGpy5thq9st1P2jAVsI7oMy7dspqeDqxnQsE5ygMa6G13U6asE576/X/PzqofPME1bxb0S5/Fn3e++XB2DTmB9eeXkRtYZXEimCuz16tWrByWUm4KklhtnxV21Y/6CJmCIbGnPrAcst9ZxzIfxp+y6QsO2qQvg8Ht+k3P1mtJXl0joMCi08nr7vtDsTyn/q1orDiLjfGbwz6Q3MQDXUW9thZxCaafKypXfTjlV3ecWgs7psoFBGKpqxJweS7dwnSPB55ykx74OwJUVrkEfY+xKu3wfA1b2GDHH7Jv++cwAF6FjTRHQrn7btH/KA0O7Znqm85wM37WpNejukzxj5/9bGyaviQEIMtU57emokLLOzMhTgHkX5Zr6bfa3ifb+zkrPJTId7TvFSH9dxMzGIrPfdqqszP1dzitBkGUiZSl/l32473wYtR+2bYC0KXsKuEhA0NE+QQafWT92Mjql07j3JsfEcrUtDWPfGd2p32eDz6pqFGzDecyABO+nwMmm9U7A4cCWP09+6+Se6535TZDIZ9771/HOY6WTvncCGdsE86Z5x7Lh79zQh/Ch7aWDvHbAcnxd1o/vuvt2NnfqO//GtLBTnvY3g6HmE8teOszf2m7RnitHGUztdIW/N/90fabMuaWtMF27ls7xJjtgnVhVo8CBbbG3KuRWEO+Tq1qXryLnOBfW+0lfY27T0s5Jzsm22456Z9ON7W2Du+ABmBZ8b7yVc5kKDEyt59R5C5tw7lS/OUfTMk8Sfk57dqbKC5LlX1Vr4ptYi8VitDncCwpBDOhN/E6A/N2UUunAQPe536P4qC/PulPeu16V18ecBwsjc7aitMKFBo4eWnDdZ4I6K+du3TaNo2s4VvmsExxD1oMo5mNCkkrBY+iUXYJS3qdxt4CipFN5W/GZT7elJSiveljils2ObdV4M/lsto5uGkR3ysqRQprp1hmgTn7yuzRE3XM6ppwq87QBIddnpN70yzFtAklpGBJ4bwJD/nyKPjluz51Ie2Y7vNZeE38/pQ8Yk+87pXeT3nZ8kzY2pJ3hTZuwTc1zYZyeU0fLKdvD9/ymo5H1oKPvU/Kdxp7run1sHqOBQzozXu8pZ8H3BkxkZsHjnAqceZ6JE9JOmZboJfp7ShDOQDF1edoQ29YM0iY4nVrzx5qdO+7JPKwvOkcK4Mk65ljdzL/+24b23PEkP9hJnsJ+5qd0grvsL81BuE4n+zdTNqRbP/N5yry/S13scaKL0pZ19tZzNs+lzFi/GUtPrQHz4bquwZ/GfV7vxJdTDmlisSkHN/kjA1Yd7s375v27+3Q0ti58iXw926m6ubkZ0mc4GGYkE9wpQK7viHx///VEwNvb29rZ2Rk9aLbbhGgG9HOTcoHo31HsNDpV602Wq9X4VBYWCKa099pFFl2mRLNx6bzk2Ww2Gl9GSbiGSEYaIPr/8uXLANBubm6GOSAouc8qwRPNAoixdn+cSENEkPIlP9S4K2mayvRl9DRpaqBn+rg0ZDZbR4vt3KdAsGakdhnnNjR4L0GWZatzRAxic9+OeRd5yggR9+LVBsvgpVMuHVizATOfUVab+6RSkedckKsMEliZO3jD/XyQjK/jGmiQ40Embm5uRrLCd5ZV+krAnjTymOkb4wSfmh/JSiRI6TIhfJ6Gxhl3803KuwMxCfLcMnPe0bcDCNvS0JGUNcNz8E+eJGU+9lzMdxlxNi8DZqrWG8cNEBIYZ9bKepOWZWxVYyfdGeq0d7a3qWvdL/TJ6gnPxZkyeKdztCzjjNWy6mx8V61huroflyBiixyhN4D+8uXLcK1L2n1trq3vma0DoElr29qUK68br8ibaWbHNu00tNsWWYMW5t+nNPOWyz8doDWPsO7mB8vjpsx4BoSmfmNaW7acZXLg3eu1t7c34KO0F8nXlqPE0MhJygA0c7bLTopllK05ZME8R/rhf3jQjqHvbX3B9hVkH9zl34PfPX/bjikHvHPCaJ1ThWy5xNJ4v1tL09LzY97okdevX794W8iznSo2xduAWokxEQu9mdUGPvtNR8KguWqs/M2Em1p6rb6nf+M9SF4UK7gUSCu0dAiqHtZ8Jgj1d2kMrTi6U3U6pvER41++fBkZmi6S0BngzjFNwAhITwfAn5tH0rmiTxtgz9lrlPM0vTqhy7V3S5CU0c1v2ZhTAikaNKh6OO5UyFXjclE/eNsgLuXQAQOD9E1j7kCaedxync6R79mtgfvpgJavcWaOaz3HqXlYrqwr7BDm0e6WUV/X0WcTvVKuPF47i/5N0sky5v5zfh0NcjxdoMVzTGc05XAq4LQtLW1T1bhcpzPyKTfmu9RdprGvc/+5Dp0cTzlb3X3hz6oaAa20r7x2Nojx2vGwjcnApv9cIu75JTjy/8knzHMT4E3HiuundCHzc7VJ0jNB6VPalCwlfa1D+LNTxfW3t7ejbQbdeNJZ9jy2SdY2jSXXjs/yz3o7W/ddh1mm7p02Yeq3XUYqqyyy/M/BAu5lm9iNOTPRKbObeCz/z8CkM1UOQngsnS3q7uvPrF/siCT9bBe69Z0KBqS+qHp4lH7ey7bLwUT7G/4sfZbUx9DsT9lTZUcKAjBpR65QYo7AOMJpBpwyZkQvrMCtcBwZSUVkcOlo0ZQQ8xvXv9JPGkkLBL9JAcoxYyzsyCRI6WhqunROiJklM1FJgwSmXR+d4evWxLT2MZl5xKWZN401zQYjs1YJcpMPrSQ7Bcb9pxRUrvE2NCusDFp0jnvK4JRydgbZoMYGxtmIDrx3Tm/yM593fGnwQAOUpZOUYN+RzC7DRkvHsHNKErT6nsl3BkVTgDTBmXVD/tZjhD7oSsbkYJLXtFtbr5fv5+x7GhqvTTpRppH/79YoWwf+t61ZTqoe6rFs1pcA/03gseNJ92NesX1LULRJHqb+7+7HfWh5ffIxgQ+yvskXrnoAtHUla6Zbyk7qkRxfHrZhoMt9uIfp3TmlCQiRFd8zs61uU3rQNNskV53sJf0N0Bkrz3ryfQHPgL5tyFJlm+L/qt6x4vOqcRDD792f5Y/+UkbhS4+p+003LlfFZKbKdsyHO6SuZPx2oslIVdVkwBm+5Bmwrv6hD/N5YjqPxQ4bcyBjztiMi3MNU266ig/rAtsuqs3M/9CzyzjREh/nmPh9ji9bJ59JK1/byVFig+e0ZztVpMTM7JxSUlUjYmZ63KlOrnUzkNkUVTBDEoXncxM8gR3CkUDDzGXFZ0cwyx4A9faQTRPoYaby+Bh3B1TSsfEY83e83t7eDifL+EGOGTHwK3P07zGQWa7HmFzqgfGtqlHJH6ln+oFeqUQYy9S4+MzZzdxgzG87RzjvY2BuJd0plm/R7GA4FY3MQPs0qLz6t1a83g/H7zFa/J9rmuCIMaXzar7pSh2clk+QRkuHz46F+6gaB3AMkLM0NsFqOlWpMDsnir980Dlr1c3FMpMgKsdhoImhRncgT8zNfWc/nbNlnZfAzmAuQZ51V+dc+TWb6e25bUszT1hOqsZHwFufwHfoe763g5Uy0jlFDqARcKqqB8Enj5OWoN3gYwpYmf6sczobWVLLtVQ5uF/LJeWr9LMp4GG+Sh1mp4HfUq6EXbIMopsAm9wzaZb0dIPeSSPo09motCO0LmBk2bNj6KCt6eLgh/WFo+TWAdb1myL+f3ZLPTGVSeucaetkgDfOgNfC9j6D2ilP8Fyuo+/rZj7wH4kBDv9aLpf15cuXQUYSz3IPl9zZZvvZffCcy/9ub2/r4uJipP9NC/O/+cy2ebVaja63k/r69eu6v7+vm5ubQb6MT3Ot0pakrkm5WSwWdX19PZzAmXjAspf6I22GZc5jS92U1/q3lkl/lvjBa+S1eolsPfv0v05Z2ZB0DJu/nap99QJAOAjP9/RhkG0B6vqy0uqOMjVz+j5+7+/SOPmv6iGAc1+pELr7mYkM/nNM2W++ct0mcGNlbhpAZ4+pG5/fZ101RjjB+nMMgQW7i+bSDHryNymU3T22sVnwaclvOa8OYPOKfPo3nTyn/HU8SJ8JHPhdF9B4jBe9vjlPxtLRaKpNGVXTNHnbUWPP0UDYdEpZtXwnrTAquSb+Po0HfO2+u/kkHVK/WR/kunU0c/8doMy5+fru821pBmiO5j6mk54TeOnAiR2wtANem7SNHYD0WOwk53W+BrvsR2Pw2jkKCaA8NvoDBKfTYXpadqd0lu2G+TJtk+fQ2VpeH3OITDP67njcfeV9TOcOPPp9OlKmeerGqXXxeMy32+BQZevGZBDt33T60nyS2Cg/y0AH7zOgvwkHWA8m7xl/+I/vEpO4T/S9HQ5+0wU6uY7vkjeZn/k/8V5n101PZ/+6wOAUduiC83ZOqsYHgGXGyc6dnecpXTbF19DlMV1sebb88Z3H0t3r92SCn+VUzefz2t3drdVqnfFxNN0RQE/CisTRMTOZm5nEn9EsQBnt8G8MiPL9lOPhBUsl5lSsFbIjdQmU0lGhcZ0jnnnPLirPb0wTM0cKUwK/pOUmunvzopudFUeGbDwZS0Z6ubaLAqSSSdCYc/RYu2iH++hKWUzXbTBOzuZVrTOe5j8+nzI2CcI6xc138Gd+brDkazuD5khQRvc7UNStTTbuY9kmqu2onp/B5eiSZafLXkEfR/dMbzsxRPM2BRj8PnVcAqxch5Tp2Wz2/1H3tsuNJMfZdgLgF0gOZ3ZnV7LCJ+DwT5//iTwHYFuWZGu1MyTBT7w/Jq7m1TezAHIkLflmBANgo7u6Kis/7szKrp5db73vNq/IgMB20YCtS3btc0zpRLvjnb4ZRLwXWiy+raCPfA7n8JnndDaiky23YTlzNtRbo7udEVB2oJHA258JfKzjthG2FQn8851t5o3HAaC0zckNiiD64URNbtpgf4LssjrAShUbWQGaM7jo7t0lmbATfo4s9Rv9z7nMuTD/umcvMzjMoMrzZxnM5DPneIXKK3ZvSS/1oyN785JrOp3o/Bzfs1IqbeYI/3nOXH1h/+XVIfsh20POMc7JChCXzTKvfu7Yz8Y7gENX0RHjLe7TJSi5F+eiS8m/tOuj+erOw46lv7EujfxK0q7fE6PDP+vaLrka/eZ2u+ToS+jVQdXp6em0fIjQ+I/BdVF+B5RzGb9qPAHJyBFYsSNBeDP7YOfXAcWqeUkbztD3IEhjArP0JZXRWULK/0YZUx+zA7CxRXiWy+XUXmbdEvRwHLIxSCdggc1t5D1PuUxPpsWA0EFpOgILcAY7eU9fm3K2a7w4PY+J9rwk/1ZEwsJ8ZN4pr6WfyJ2v9fHks/lo/nTGLZ0VupJyD0+7rJ7JjosSngwE+OwSKSn/ziA52ABg2A5lyall0fYGvhwdHU1jOTo6mkqR0FcHke5zOmiPiWNZAmkblgbeAWoGgwa1zK15aRlxtjBtZ0cJPAw2kgw8DMjpJ7sZvhdaLpd1cnLSJi74HAHaDJi6JE8H5rOU17Ls89L2cTyBuIFKF1iRBPDxqrmfoD/IB/7Lu3R5PGmDAX7spPjw8FDHx8czvbRsVs2TLJ0P8nXsNPv4+K3MEJxxc3Mzlciar8fHxxOvkTf32/w1j21TrIvdn9twMJXlf7mLn8F156eSDykTuTKYiSPs3i7w+FvQYrGYYSWT/QnnVs1XNDmeQLYD7P40pd93QtdkjGaMaCzD755TAnzPq/EYCQYnBTzv9j20T3vYS+6BzF9fX0/XsJjhhN/9/f10rm1MJo/sx+knvtJ97DC1dYD55DfjEJLmnb55jjjW2Zqcz45G+kK5oedl9MyYr8v+pQ/4Ht16NZokk2VjZGEfKVbnBKAuwOk+u6CqqwlNwzYKqjpg11E3VlMHWDpnmApsHmSgZyOT989+pMMc9W/0P22Yd1Vz8DAKREdj4Jq8jt8ctHFPg3nTSzIG6azcTgf26c+uef2tKfvRAbeO4N3IeNBWXmPK4IlzuqAqV0DS+WW7fHLtaCyZ8HDfu8Ciar5aYF6l3mQ7Ha/NBwMal5Z2df4jsJsG244rKY979Tv5nzwzyOyyr/7eZb/9f94n59WJjI7ekz4lvaZkyo6/k59d143kLeV+BD6Sdtm1/BzZ5VGboz/6lMACHXbAji3P++SxzuZw3EFkF7A4Mcu8AAq5l+1g53cSp3TBrPk3Wt21r7RP92fOReefkqy/7m/ylHl5yerQb0W2mbvIsvX30i6f0/mRqqfNEnYlLGzrc04722ccY5lGXrtKppGspPx7VcqBDfLucdCXkZ65v+lXRpg9A6H8bqyYY8p7dteP5mnXb6MYovOR2RfjhtE9s73X0qtXqk5OTqaVERunBCEeAIHYcrmc9vHPjCHt7wrAOmFJQJfC6WPcA0qHV/X0DiNnWxm39633xhEd8HEWP52sM+ppFDsgaMr+IsxsdZ/Ozhnr5Bvnmc8jHmMgstY0++cI3+PwdZ3Adk5s5FRyDH5IeGRoccJ8eiXjPZRQIGPO/uyaly6D7A0OuprgBPUvAWDpBBNAdPrate0yUPqafHd/LUMHBwez95B1KwXMo1fNLWcd+OiMNnxldXCxeHoe0M+MmSe0ZZ50POR7B9acOXc/XmvcMzOcQMC1/LsAntuDcNjppFgdy5WZ96BXkPXfBFjZ5yOq5pULtt0pB3xm4shVBAY/fHcpNdezgkRfE6w7sdFtZlQ1Lyvmc7vdTg/Te8WK/jDeHBfync/rGahyXlIHOhX/yr4AACAASURBVH2cPtEXHqb3io/bdgn9arWa/LOrSrzCn8Ft54/I5CPDI4DW6c7I90Dc03aYY0560BfLj6sP/KjF9z5M/4+k5fJbBZPlK+1Y51ccCO+j1KfumtSnEX5Alryq2/nE7XY7O8fz12HEfF7fZaCU9YGbc2WY+b27u6urq6tJJlgRXq/Xs3dgscOeNxljfMgv/XbAw9/BwUEdHx9P13ab1nS2PnmUvCfJmH68m4cRls05STnI/rn9DIS7/nYbMe3Cpq+lVwdV6/W6FovFtANK7jbmcxFsjPNy+W25HuPnaxiwjbMdTwp+N/GjjHECw/zuQMDBjgPAw8PDOjo6mpQxy4AsHM6yWcCtPDb8mV2Dn6OlUY6z9G+lTR6mcPmPvuYxKDM2uSSaZTRWki4TkoEAfR+B8k64u3m18cp2fO+sw2cO3ksJxfHx8Wz3zKr5NrOW43RaHovHaJ74/5SNTBCY0njuCvY43gUO9NO6le37PAdKLu1JmcuSP853/3xNd0/LrwNtdOnw8LA2m82sfDH5MLJVSaMMpUFh9ilpV7CY2cJdOpbf0w5YLvLe1iv/77/3Qg7+ctwdsOsCd0pMuzkZBdj8xp+fE6Rtg5AMxDyXXbLFz/J0NhxdSxtn+0rb1lmP2UEh4JEkg4Osh4eHafUI/+dxOgC0zWZ7Z/eDceUzLTl/9JXyKQf0i8ViNnbLZT475jmir04edasWL9Gfrn2fi21PGbIs2J/Sd2zce/BdBFXJzyxByyR3t7oxCrLsZ4wjfE8Hp51d9L0zkdCRS/i4n21/4kL0gGCIsXiVyXa7wyR+juru7m5K7t3d3U0vpK36lvDwYzgOEvndARa6xTn4Sz++Yr21T+uSs3x2OM2PWdifmTqcmzKyKzGDTfD9LU+eO4+Hc4xPO3/79wRXry7/8/MIXW06A2eQkAVyX8c5b5fAp+HaBRqsfNnfnFw/Y8N3DFlmgLO/OdbO8Y548NpJ7MYxOsd9HoGqURt5/agvu/rRtTMCfCiJwUXXh5HzGvUx+/aPzEz8o2gkC6PAyMfcRrY54lNnuNKpjVYu87vvvWu10OPMLKbnH2OXhteUK6K7dKnj3a7++Vg6wLQred2ugMrjTCfSBbjdGNx2gvBRH14TVHXzl6BmZEdfYwd+a9qlC925fPqvCy6hURC5C4An6OyAdwJPtzMK5nNeuudY08aMwH36LScJfY8cWzdej6crI+6eR9k3Z1xHsOffuooY+LsLHH6P/O7yj2knbed8v5G/24Ub3poWi8UsmE7qAqrunPz/e/CI7WLqaurRyNYZe3b+je/plxLQZ9UQbef1/A/ucTKb353IIGhxiaCTDiQpduGobgwd5hjRvnnsPv9ZsmrM6Hvu82OjtvaNfR+9eqWKCPno6Gi2M0kuFWbnEQ6ELUsEO4PmVZSc9AQPeTy/Zz8ya0WW2GU//s55zlRX9YGlHYz7T9aMFb/j4+NnfXfUjqI4a56O20bC/WRpF3DqLIr7RbuLxWIqIcylcxtwO38b0DQWafTt7FJwR4bNc5W8NTkrmkvhBuidM3oPTgnqwJuB02jjARtexlrVl5DZoY8MzK5Aqvvfx7kvbYz0g00DfDzfR+Vs88nJybP7WT68Ym6e0SeXD3WOkjYSfFqH0KvMgjEHybdOttKhp93s5NNgNnXCepg20vIyWjk0L7neG7d0wZ5lzOcZyHRA9S1psVhMc8cD4ua7d9HqfFPydpRQ7DLSVX2gjE20v7Pc4wOQkVyp6VYkubdXbqkO8Tnb7dOLZLOMjO/Hx8dTub/HxCrzwcFBnZ6eTufg27bb+cvG/WiAV6Sc1WdDGIPD5L15OeIN4DPLzRkX/fMD/q4ayfI7Ph0A5v1Th3MuEngnucoir+F79ot7vgf9YqWKTRbMK+yibQefXSJnuXxaCfWcd6A/x26edwGTba9lxtdWPX+3Uc6hfY2rSLy6SlUDgRCyxbjW63Wdn5/XwcFBnZycTFiQFS7ft6qmUtjValU3Nze1Wq2elRmic5vNZvauOfhKH7EPmRypenpfovGt9dl8Tb6YXxC4Ms8dYRjreMrIiEbJGtrufGriGc+7n9X8Hvqul//6JXzQw8PD9H+CFxsUC6ANuZlpQe2AedamO6Doln8757hcLqdA6vj4eNpFyM82dFuWLhZP28FmlG+QgxCbBzg36r9xdKNlzgwqcYQ+pwO4BIkOftJZpSHzsRQ6K4mXUEdg0HNqgGDej4TWY/XScyqq+02/4LuBeQICt/HeV6uqnuTA40qDlMv/8DafK+pAYEedwenAm++Vwa5LLBOkIv+pP36BtVeJj4+Pp7Ljrm/cJ4Mqy3gGBxn0uR95Lr9brwDnOCiDSM9lx9suUB7NgYGe5Xn0jCLH7FhSXrJ/yJATTeZDAhQ7QfrqufH9djnD35Kw9y41S/vMfFY935DJlHPh49zLx1J3nLQy8LQuG8zYZ6b8d4C+6unZYBIB9jXIHc/gom8G8OgdfhH5R97xk6enpxMgtP44IWg/5KAKP3pzc1ObzWY2J9wn/R1jMBDi/3yxsP2NbU+uBKDT2I6UbSdQclVtxH/LnW1NF3x1JYD+tE66T++FCKoA85CDUo+7sx1OGBhDpf/KwMpkOzSyvfm4Q9dGyiH3HiXbIeT77u6uNpvNFJywq6938V2tVnV+fv5sZ1/7Fc85+rFYLOry8vKZPj08PEy8v7q6ml7QfXp6Wuv1eiaz7gsE37CPTjQkT+CZ/S3zTBBFX53k4D7mc2dDuzlxINfNbSZY8hp/WnZ8Hvzk0YHvpe8q/0MonJncRzasBtqeECiB3D4F8DVVzzeOSAPFOBKMOcPlY3lPt2NQM8remndu3/3NMXT9zox1BjUeG+MzaHOwmiCJfnQBk88xD5K6QCyPJajoqBNo33N0/wzWunYSQOfY3oqQnxGQyznJ79atqnq24pjGqkt8jO6xK5gajWXkDDt9GQW+Tp647Y4nTgYYbHY2Io+loR+NeTSWru+7yH2z4Tco8Ny5DIRzc3WKv5T/znbu0qPXOhPzKbONf49j+meQQYop++lVAOahk7uRHcrssOckAWVVzfTWv6UMd7rZ2QoHQfZlOUeWG/+eQbxXs6qeNnFxErK7f/ZvpC85LmfT83zrSIKyTn8JgvK5VO7DOQ5yIR/rsEhnT15KHn8XCOT/IzD5Xig32oFewpddNrfz+50t687fdb9d/qtr37ihS6hAu34z3vSGSuA1+o5u+XiCf/q5y8fx6fO7pHmOPe3bvgA+scMu2whlsnOUnPVY8n7+PXW00+N9NPL5r6VXBVWr1aouLi5quVzWZrOZBR4vZWAayS5a5lyvsJhRzjSnw7LgJ7hzsMFDnpRHUMLg6+1IbMQ7sEc7OBgbda8ccP98+B7qJpYM+XK5nB6MJIDww3reGYbvrCw+Pj5OGfWHh4dp/nz/XCVEGbPMr3OSu4A35/FbBoTJA37vQI37bB6bfylDBg4YNuZiBLR+a1ouv5XEsQmKSyCSx5Yp/w+/4JF3uUnKVZJdQcS+/7sglT7kai9yajCW8s+qG/N3eHg46Sdt03/PL3OLHI8ojX72f7t9WiFInliWrMtkybPsKZ2Ed5OyI+j40N3X48yxVz0li0bAoZN365p1M/tFn7xTVPJkBK7empbL+SZLHUCBrBuUUduuL5fzEiWT/+/atm54Jd56yy6AL1nxQxYsF2THqYpwVjpfOJr6Yp9nO0npussJOZ4bgDw+Pk7vfLMc44O226fyOwKe1GnLIWNIfvMyYK8QOFDFV2eyzXOA7Ps8yzNteZe1LKvdFWB5dbEDv+5TgssOmHb45q1ptVrV2dlZVVV9/fp15+61HlfaYa9sGmfZLqVN64LPEajn+kwojvpqOfCmILahxiOUqLODpcvX0cnT09M6ODioDx8+1IcPH57pjmUQvTXWvb+/r+vr68nf5A61VTXpJzxDdjebTV1dXc0ed/GYXUaYwWyuNsIb9Nkr6h6D566Tf1bl7NcZawZK9nVg9QzOXN6dFW2W11Hgm2P8Hnr1M1VnZ2f18PAwvYysA9jZyQQOMH4f8EGIqp52eKt6vpxsUGAH061KOcNGEHR0dDSVOXjiDAgtMAAjG1RqY3NZ2P02f3InqQQu5qn7Sn8N4Lbb7XQcYbaz9A4vCJ0Vh/46Y5rK1QGpHFM6D4Q+HQS/5fVQguJUxjRoHeBAfjrlMaB4D06p6qm0tmr/+3TSyJnfyMKo1GQU/I6yUbn6YNnuAGUXwOZ4vGK7a2zMn/tnubB9gHBSzrK7fzn21D/zqXtWKR0A4+gCxM7GpdPwPHZZxCwP9L1zbjo9yz4lJfgwqOxASQZWmb19T4kKE6DGjrYD/Rz3Dlbp67IKYOT3OrJu2A66TI7zOpuXADIDWpfM5i5xljfrp/2mddMrUoeHh7Ver2u1Wk3PAzuI7kCT9ajLJLtcz3qaZe/YRvwe46XciZeg5nOTjNe7AtrXmYcObtNuZZmV2/d4OhkY/WYZ6Xw/cmrg6Pl+LwFV1VPCgiCXefV8dwFM/g+vM/Dic+TPuwAq7SjnJXbM/rivtmXWrQ7zpJ+jJNZ2kaAKXSLA8s7NLlu1XnCfzWZTl5eX0/Pv3NdlrcfHx9Pzjcj/w8PD7BUFmYh2UIWMuzy4C2yQz5H/6mwB5/n+tskebzdX1omcB2yKF2K4JnHLLvzqe30PvTqowrjtAm5p+DvjwecIEFc9X3YfGadkmu/l31MY7FgMwvKc7Bfg10Ln2lhfh7CnYI7uY354xcF9s3HpwJ63k+Z+OBzmo3tmwMYLog07/bznCEDQfre0m8A2wWCCfvPE7e66f3cft+N5eGsyeLNjqnrunK1P6RAcPKcDGjk3G7nUm1FQnEYwnbz/z2SFHU1nOLO/aSTpcwfiDHZH/cp7wDO/7wznY7uS2bPuHp3BTrvE+D2nL5VB+DE6vwNbnq90RgY/2Y+RDnbn5tjeI+XqdI41wQDfM9uKbmVVwkuoay+ByAgQ2k76z4F9lrOnr9tun5JdBFoJZE35f5fM6cZv35DAmPH7np08dzajqmaA0ZtjeEvpxBrcF/22DOQqVvoJJ0DMgwR+mTXvbEUnC6NzOt/U4Zj34L/2JVNSvvN4d113TYcBO9kd+X3/PgquUs+QPZej5rlOQlQ9bXnuYMvPN56cnMyS9p1ud0EVq0zb7Xa2GIBd4t701XLvJKQTl6nHlmfkf5dt7HBF54N8fq5cdm2P5qyzO6kP2W/TS/Tl79GpV5f/ff78uZbLZf36669VVc8e/HWGhY65XhQagQMLh43gLgYlsEvQYCfj0gbXt7JcaiXIDDRCenJyMvUTIIaiOGOYwcrNzU3d3NzM+k+gmqtWyUM7Re6NMlfNyzYODr69LO7xcf4CxZubm7q/v6/b29taLBbPdsEy37w7093d3bNNEHIO3Ld0Ltl2jt9zj5wY/HXgzjzq+jVyRmRuKSmhnPKtHdPBwUF9/vy5vn79Om3YUPWUxUn9SeBu0JKBBdQZzpwzy3sXxJsykEhZxdGQffMOWwY1LtvhOuv8zc1Nff36dWqvK7G9vb2dnJg30mGF18Ayr+U+3h0TJ0Q7BsFcZ7m07bATzRUsgGDKbpfAMYjzPQ0E0wl79TVXezmn08ku0BhlZa2nVU9JH3TLwct7oeXyW3ktVRZVNW2KYuBRNQeILufcbp9WgaueeJoJLijButuxXic/3ecu4EigZ/5jy0a7/3H9ZrOZNqugxKh7MbTvT/DlbZ39O+OgL5k4wF/Ca1d1jOy1bQiytV6v6+DgoG5vb6fVx6urqymw8lwmIPTqE3roUi3vVNatVNkGeNWKcyDG7V0lmdNMWnbJGttQ2x6Dd6/evSUtFvPNGzpMl4nVxIojnJDAvvN7mXTt7t/hkxFmoI+WYzZLqppXH0FHR0d1cXFR6/W6rq+vq+rbqhLtLpfL+vTpU/388891cnJS5+fn9eHDh1oulxO+SltgvOqgitJv/Bv6C4+ww9vttr58+VK3t7d1d3dX19fXdX19XYeHh1OJInLrd87BG1Z4k09dcjzxGvNrH9TZl9xNO9vwudwn5xFewR9kJnXS7Xc608UN36Nbrw6qPnz4UDc3N3V8fNyWwWF4U7myDKaLivnf0WzWJJsS1PlYnmdQlSDLYMjPBPicjsEPDw9TfX6W6BlYAGQuLy+fZQYMRiwcCQLtmLqH1DGyVTVTstVqNS3/4jQQODs9zkW42b4zlaULbg3mR/OLctLfLjtKO/Qvj9OO+TMK8jpZsaPOpfO3dkyr1bfdgDCW+dJLG51dpXPwM42Jf88VL5Pv1Tk6OyhoBIgMTjKT5/aq5gGDA8Sqp7prZ6d9Dwf+6Bp6RFavqt+VjeDJ5RHuDyu6GRxlwsD2wn+2e+ipeeCANR2O72M9hdI2dUA4562br6ongGjblzqXffO4PNfvsQQQ4EdQ7kqL9DO2rwZ+DoRszzjXn12wlHPUAYcEi6OAw+V66INBdiYS7V+YT0Ai8u0+Z8IAXama77SaMsl1XXDPmN0/jzFtmm2Hx3FycjJ7PQlzx3PCfv4pgyr6bZ1Dx81/+wVn+k325Rl0pc5ap9wfg2Hzj/F3Qa7xReKqtyAHVaMEQ2c3rG9pszpMwP+dP8prqp6/q6qbGygDu7Tn1rNcEUX3zs7O6uzsrFarVW02m2dzc3Z2Vj/88EOdnJzUyclJrdfrmYzbFhmjmPy8vEtyUz6RW7AAzxzy6gKOZXkflIGneZ5JgY4y2HJc4N8zKN4nz7TX2U2uhR+dX+w+E0fu8pMvoe/aUt3vqehq1AHodPglxHk5yfsCKmgE8hzgjcBf1143dtrCePhhWAydgzRnEqu+ZUZ57snv5MlxerLTMSU/7VxzVY3jdnQOnjID1o3ZxoW+daA8g6b8PcG4lT6BdRdwZ/A9mq8O7HocCTjeS1C1WHx70HWz2UwykysnaQRyznJJvQt+nWHrHJ3b6ebRctDJLNeN+N6R5wubMhobTodz3S596BIQ9D11nvF0ztcrGCP7goPqwBFtcM9sw3pl8jwnb5Mnvj5BY/Ylg6XO/rlNH0vH5DEkP/9ep/TPIttkni2FDLI63iVvLYdVz3nZBVVQx5eRwx/xMH1aJkJ8nsGhH2Ano15VUzbbdgPwl8HySI+dpMxnhque3iFl/4GOkvhzBUZnl3PMR0dHU6WIs/reaKYDfh5nJnQAgT63803WcctPBnQdhumOdzpGe/A8Sz3fi++C/05WdGA9dWKXHx8FVvm7qQt8s13PTZ5nH5FBVeIrzq/6Fuycnp7WxcVFHR4e1t3d3bRJ2Gazmfrv5xz9ugIneByE+7j9LjiukwOP0ZtdcK8RFs7xJ984zzgwgyD3wXbQeM/npowkJsw2+Uzdyj4YL3ey43NHejrCR/voVUHVwcFBffr0qW5ubur09HSWIaITMKXLwO7qqM/BYY3OzwnPpdiuhM/CC9P57ACVx8O1KMTFxcVUdsBOLDaqDrDISFQ9Zd4oV4Bvzk4YJGaWjiwc2QaPx8Gbgfhy+VSuYUPEuS6vNF87sEnbBq1WBmfirNi75j7b8GcXlPm6UQAHdYYxH97OF1y+FVH+t91u669//Wttt0/lognQLLMG7PCA1chcrXJQNQoSbFT93h4bqSztycAAvpvPyCj97jLczqhTzmdAu91uJ+Bn2edeHr/7slqtplVcO0g7Tz9c7UCKFbB0og4eSJKkvNOGwZqDLJeR+ZOsY/I3+ZU6xrh8j1F23GPJwLcLCHJ+E3jkaj/z/l4I+eM5hKr5TowG4thGxucs8Hb7tGrppNou8F/1VAI/yuR3QC0DNX93Vtb2wCs/tnWA3vPz8zo+Pp5WdW5ubur6+rqOjo6mjR+Y9/V6XWdnZzuTNOixA6mjo6M6PT2d7T64WCymDSUeH592IiMgYnWevlbV7NntBLsc94YAVI5cXV3NEiL5jGVVzfxh96D8Syh1MJOktgvGHCbbuNSrvFfVt+dl2OCAFbu39l2r1ao+fvw4JY2xmaMSdOxTBioZsCZ17ZlXXdIsg7tR5RPfka2qmnRnuVxOWGF0//V6XX/4wx/qX/7lX2qz2dQPP/xQm82m/va3v9V//dd/1WazmfQCvaL8z2NOeYVf+MPHx8dpJZZV2wzSzH8wxMHBQV1dXdX19fXMD5s89pGvMc+waZngyfHQnv2J/WeOtwvwmMtuRdh8cr+y79k35o9jrloZyeBL6NUrVbz9GSM9Uv7uWp/TZRm6iDQHn9TdzwrSGSlP2ksNEm2w5MoL1XAWZNtyJQhhB6TyDFo+W2UA5XvSFsazy1RwXgZV6ZwT+GSGzryzIFvZUnlcKgNPR5nBEWUQ5THuos448n+Cvgyw7JizJO0tCMPMA6xku6rmmZnM4iTt0h2ARvLVBs7Xe579DIUD8F1y2/E8AzD30wmQqpqSBxg5JwkMKpFReINx5FyvbOV4LSedY+9kMIMQJyYsyxnAmHfoSWZ1O550oD0DH4NDj8lOrZs7t5sBOt932erkY+rdeyIH4OZprjSM5sKyYDBk+actl5GP+tHJoP/fNYaq5y+u5v7dXBDc8GzIcvlt9zzaYOc2EzuUIUd+yL3TC+4B2CPIOjk5qdVqNd0Ducd3UpqErhocdbxIf7ZYLKbnqxiLweGIl+iG+W65T1/IfEM+JytsvHJt+enwUup7R/7dzy4SsL4lLZdPL4omCCF5nDa0W62A0r+9FNRmZYVpFPDuaz8T8S7Fs7wgf0dHR/Xhw4dp0WGxWEwJ0T/96U8z/EhiJ1/j46DKPp++e+c+2vILvkmA3N7e1mazqfv7+ylhgU1Ke2HiN+ahq2rpAtERpdzbNmUyNu1JtwKW545ss/1Yyhdk3e9k7nsDqqrvCKoSmI8MHx02JdCwwCRI6yJJt9Mp5ujTIMFtZRY4GcnYDOgIjNjdpStXzD8HHICozC7yuwWv42MHVC1EOV/Je5yWgYSzI9kn35PvzlDAv9F7CkZCOhJ2H8s+GEDuCi46cOeg0LzLTUXeihaLp2c+XJ+estxdlwZnFNjauZh8zEAwM0VJu/ifssn5CTqcHeJa66MNZ97TARf9TQdOaS5ZYztg8y1r0junluPxKoYdYW43PgJr5lfH3xyLbVUXZKVOdDrYAbuUH9/Lv3ncHl83lvcYVEEG5dgG+6QMkvhuYGa/l3xHZnb5RT+r1Z3X6VYmCfO7gSB2DdtmW+9VBAdc6VNPT0/r7OxsAljeZILVcDAAwM6g0YDPMmH5pS0CK/rUjRvZZOXYc8P4vfkQyZd8bxLnd9QF1KP5SR/VzV1nv3PlOHFCRymztq9vTfiuXFF8yXVOTph2BVijINfVPh326e6T/Vks5pu8GOu6GsKf3JOVcBLvj4/fnrX/85//XFU1JUo3m02dnJxM80lwzBjwJaxOeRzL5XK69vz8fNr4wsmS6+vradv1x8fHabXq69evs1XO9Xo9bVrW8SZX5Efz0NnBDpekfUn/1GGATFblXGZCPr8nJtiHNTus8lp6dVDlpUbKCNIpjQxQ1Twr2D07YcdlpcP4ZpBio9oFGp4g2nEWhYAAQ83vfE9gtN0+1aInmPLYkgc4IDazuL6+nvpBOxlIdRNK5u8lc5VgCB6SFfQf9++yPsw73wmqcM4OpHK51zIwWhlLoMv1HTmDYfkw3+yAciUvDaW3JX1LWiyeVoFPTk6mXRqtWz43MzlV86Cp26iCc2jDx2zYDNYMPF/CI59v/kMOPHAgzlBb/3MFoRur+dPdh+yeeZcrSm4PvaeP2Aey67kCB69zYxGTga/7Z3k3nzjf4Nw2lBU42kmA5nHRXmcL7ShtV7MdywPXdeDF5xkovBdiLIBv75LlpBBzn0mNTHoxz07Y8GmeYue5xhlj+7lcCfP/1nfrY+7+x3e/rJf7LhZP2XPLKCVlDvSWy+WUeV+tVnVzczNtyrTZbCYwhk09PDyss7OzqYrl/Px8tktdJi83m820AyFBkMvi0V2vUjtZwTMrtuu5ExrzmLri8Tsxk4HSviBsF+iCj/tWmTv7lnqVZbV+fumt/VbVU5WFN+rqysxN9s9VzyteRrx1EGOskCDe9jT70rVrO8w4vLENcm0cyRjQA95DdXZ2Vr///e/r9PS0/t//+3/1f//3f5M+Xl1d1c3NzfQi8tXq23vfPnz4MI0FX3R5eVmbzWZmK9Czw8PD+pd/+Zf6t3/7t+G83NzcTDtVX19fT/pEuebZ2VldX1/XL7/8MsOv8MP23r95Jdn42585Z/AM2+KgeZSAst1MrJzzaWxpciKnW63sdI9zugT0S+jVdU8GWwYZFmh3LqPVDgQk+B4pYipFruoYvHSrZLu+d4GQS4sM4hzgddHzrqg5Az9nu3etmiX/9/GKc01WnDTqI15me36exgEUxzITOVr5MlkZEnjmMRTzJVkEB1cGlv6zYX9LcuBncJR6lHOUZCOVgXnyrDMg1iGDse4+IzLo6661DNqJuF9pK0bkc0bgBZkxsHX2u7uP++fVsOS/QXjVfMUr+9UBoEwKVc1r1a1LXRDjvtKX1LmU7y4IpZ3MSFru6O+ubLQDq/cA+Dpy37tgl0+vVHbBFf/nSm8GqE4eek7tN1P+Mrtq3c++8Ictc4DlfiDLzoYzFicLaMM7lGU79NcrVV5pB/wDTkcrEg8PD9MmGU6aIsfeIt26tt0+7Vho3tMnKknM4/Sr+bv5bp1KOR8B/8RAu6jDDZ7TpPRZr1kR+i3IsrevTym/neynzXLwmXMGH8032kj+7sNL1iljXP7PQM7XoQcnJyd1enpaVVU//vhjnZ2dTYlwb8jCtX6ti5MBNzc3zwLnxWIxJYLPz8938vn4+Lg+fPgw9YVn8Far1bThHMmjxAmWL+M79M7j7vjotvK4j3m1KoOttKPdJ99TXzuMNMIxnd6+BHuM6LseJsFoOxuG89gFPjLi7YzKLoc/Ak953mhSu/+76ZDnvQAAIABJREFUftihEUhZsDebzbNx5iqMx29H5vIaZ0kRLgeqdoS8M8BtGxB2gmRQ4Ht7BcCg1oYjhdFBIL/zcHFmNBhzzm1nQO1EuiBuNO+Wl+TLSNk57yWlq781uV/olkGJ+ez+MibLQAdgEiSPKHlpHo1Wgu2EdoFuz2W3nG+ZGWWL4VGunvHJiqrBpceBjNK+daGTTa59fHycZYc7eUt+2yZ0sp33MdDnnvS7s5t+zs2UK1G7dCLJfOlWajqAmbLx3gAflDY6wUGnOw7Aq554en9/P1u95y+zrgkKuuOLxfx9WG6T75Z7bITtxWLxVEWSQVCuCGc5rH2BA7FMdrAKBADEB2CjWK2gkoW+pL5st0/PnFXVBBKtq+ajX91SVbOMu5/zshzCK9vHBF5++bFXxJyg6IIqKOfQ8+bg0Hqb5feWDeQr7Ylls7Opb03L5fLZrtAG6+hF1fOXBHdVCLZt3W8doKYt8y5XIDvKACJ13QkBAnw2C7MPurm5qb/+9a91enpam82mzs7O6uLioi4vL6fg5vr6ur58+VKPj491eXk5bajSVcs8PHzbZOLq6mpKIqQss7HMiHg3FXp/fHxcFxcXU8IkKy8SR0Lpq/x9n274/124ILGek4PoEedm4oPvXR+so93CSXeNMcL36Nd3BVVEui5Xogwwnz8YddyD7QKqPOYM8EuyIbnyso/sRJO8MoPhR6m8WQdOwuACA1H19KJkwBK7ybj8ziAWg88nE+6SDia+A3suf8gXvDng8vbumSGyo6J/XsHju0sqacfCD3Do5iP5l/OQwNrK4YAtAYvv5RIKZ1XfCwAEFKFX2+12qr1G1ii56f5c5giNDEIH8Dvw7AydV878aXl1UJV/VXPAnmCGvnqba/cNvfPLZR0Y2zZYPhPwM06/j6pL5CSIZmwuZYLHNsQJSAHf1OMneLfjyHlxdtD6n6UYBg62v+Zf2lzb2BxzBxK7dtxP24b3sPKblCDZ9tLZ6AzmHbgyh2nXqp6XM7n8Nqs5zH/Pu20nx7FR2AbkHhCWyZd8Gex2u31WRpeJBCcfsOX4F29NzvgtywbS7Ph3cPD0gl6TbYGDPVc8UPrM81Bcc319PSvLZYzHx8fPkhcES+hgbuDkseOfOZfdCUc6kkHALhCafjF531EC57xPzu17CKoWi8W0yRJ/yF3VXE8ykZkVClX9hjAmJ3xGNg+5zqBq5Octa7RlPav6Vk738PBQm82mrq+vZ37l8vJy2uXv06dPVVX18ePH+uWXX+r09LQWi0X98Y9/rF9++WXaPfvTp0+z4MyPJ9zd3dXV1VX9+uuvk7zkmC8vL+svf/nLpGvo/tXVVX358qXu7u7qb3/727ThE89Tebxs6pJ+PTGB7dVrZcNtjHxIFxjbnxqDJ2bo5pX5t/zY9nb3Ttw40tF99OqgqnOg/N+VCnkyOgOVgCuvS/peI7JLGHZFu55sAyWDtaq5QvvTBjRXlsj8eeJfYjgTuPlYJ0Rdlrv738KVALNTCIM85MIrKgk6DFqgXRn8bh5Gvyffd/2eAPu9kFdXDPp2BX5p8PaN6TW/d3IwuiaD2pfoW3fc+mHn6HtkOeeor7mS1RnTfcaZa6ueZ1n30T6Hvqud/I0kRRcs8f8/EmSNMn+jvnZ24b1R2jf3k+9pG+2HOrvJtQY+HINYVUGWHcQ7K2owkT5hlKywLfN3y54TafmqAgdR7ot5keAGwlaxWuXSZSffzKeUXwClqzMMwDMQ87x4XOZVJ3++JufMiZmU405+Xkoje5OB+0ts8nvUJ8i21jI44uWupEtisZcCefM1g6mXBFUjfAPvHRgb2BsXbjabury8rIODg/r69WutVt+eRUTGq56ev83n2rP83veCEgc8Pj5OVVOsDlZVXV9fT89QkVBBp4+Ojp7hspf6sw7b7ju36/u++1lPOrlPPXL7SYlF98lT4ufvoe/a/c+Zfr5jkOiIB5lGMWvMczBV85rVLpJMsGkDXVXPlJw/v3cgSyXsjJxR4rizzovFYpZFNNhzJtuZPpTJ7dIvJtsg0MaA872DmYMqnCOCyHcyewSDVmL3DZ4525r8TuCWc4eDytUS99EyYrnyfeiL52GfAr/EMXl8dv4vNSr/TEKeXOvMZjDmbxrEqvmzBMmnfQEDx1OXMnBxYOFEir8bIPF9VyDTyYH11yu/ee9dRjuBX2fMM1mS/YCfJsuMS2r9EL3va34gc7vmAd3NRJMdDOPnwX50y9n+7h4jUNPJU+d0R87Gdnc05++FzJv0D5YtV0VAtmOALNrqVheSR77W82ued8Bvl+21/eV3ywPAz++4ub29fSar9lu07XLzLDP07qR+mD+rJbKfo3HhQ+1zR8FjtuMqDs+V77EPmOEPE8ya59aJXB1OGgW26W89llHSx/b4verWYvFUemr/hZ2xXGZiqktS8H/yhHOWy/lGPf696vnGFOhB+oEM9jMghHju7+bmZnqZL/rBStNyuawvX77UdrudVjvX6/VU8nd7e1tfvnypqvk7Qu/v7+vr16/1+Pit9Pb8/HzaNIbns1ar1fQ+KiqcwEd/+ctfqmr+vPzNzc1Uqvj169e6vLysx8enFwFX1WQT2Ho9sVYGH9bh9E85D51vfgllkspJD45zf1ewdQkUPlOP83f32b7a/HgtvXqlKoMq/hika8O7gOklAJj7ZPYhM2Z2aFaIbhXNip8ZtlRMj8Wle6vVanqOzEBitP//drud6m+ragaCMoAyjYwOn2msq2pSGsbJuS6ZoC/5XhD6w/hSaUaANANIzx2/YwRH9c0dQOMaFDtX2kaA0crWtZ0Blevu35oAytRXPzw8TKUUBMjectgBlTOt5lMmOji/ag7YE1ClPKdeGYg6oeBnPlyL7vuNxu72q2oG4ixv3WqR7+N+uP0ROahyOZR1k+/uE7sz5sp1rkLQJzu0kS4zhw5QAI22Uy5V845o+8oVzD/u6WdGOwCaQUPy0mUru4DJeyDzzwkgJ9wyOPV1qTM5Lz7X3w3mMgjgeIIDX2NfkPKViTwAkkvNsfnYkCxxd0mQdYD2lsvltGWz7Sc8w2dkwqP7zO+2Hc767wqqTMnLnIMMxrqEhTcP4P5ZFp+rS3x3gJrJHAPdXFXLPuV8d2NIu7Yv2fhbEb6LZ+lOTk7q4eFhehEw8lT1HNsYx+x6BMHndhVRnAfl4wOWaWMWPpO/3JP5urq6qsvLy6ksj515/U60X375Zdqt77//+79ruVxOW6gTJMEj+/S7u7v68uXLhKPRBXb6Ozo6qk+fPk07BjLOX375pf74xz9OO2Z+/fr12SMW4K7ValUfPnyYknG3t7ezFa0uUZD4Ke1RJueq+tLNkR8Y4a4OJ5LItD5j44zvO99Kv1IPU8fw04yrexToJfTqlaqM6tMgjJx7gqMuY5NOu5vULuOQfUsByPMzAOr64wng96zn7JxmClAGPr7G10L7AIn7RZs+7rF0QmQjNup/BrBdH0aBTc5HzpHb4NgoK+LzRvRS4NaN8T0BP4OkXN1wiUrHjxEAS11M+c9M+Wi+kl+djo36UdW/rC8p5yPBUKf3HViyTfI5Hs+oD13w6fEaODqz3cm+nbWvT716CSjydSNe7LO/SSNZ4rfuM/vbzfv/H6hLyDk42QfYTLtkKQF01fMXwHZBWAcquHaxWLTzm9d6ZcSbI+QqnH0bbTsLTFDVVaOk/HV9sryOgs+X0Ei2vXrfgcGunarn7+Ax8LaujfrPfcwLz/eoL8kTrt2nO4lXXnrdb0WLxTyZ7cRbJgz4btDqeU0dgZx86uxQhyl93S65GPkMX+tSOgeCJFf8rjWI5BsyxYYsXnX0s+iJywz2j46OZv1y+5vNpq6uriY97vTSWDCrljLQ2MffPMacdEm5fxQlRhgFgaNP0whH/SOS7K8OqliZIpvOg6kOQJwNGgkqv4+cdH4mwHMW2Aqc5UgJEvnfCuF+ddkTG0GX0WEw+J5Z4JETyTHQ3+QD/acf2+3TQ7V2loyHLIWzHWRCsvQA54HDJJvBGGjTfPExA5LOYHVzZifVZf7cfraTIIjvzuonrz2PbqsrA3xr54TBJSP8+Pg4Zf2QO79zKYMgy5DH70y6ddNO3U4vwWXutEdGL3XMTsJZx3T8nW7QR2SRe3Tzks7P8kE/cpXMPMk+efMWdCf1xPaF1WD0b7vdTg80G4xVPa0A2DZZX1LWX5KssN7yW+5iZlBnnjM3GWhap7okWcdH28TUp872vjUZkDFONoWhn35X4cixduPqgHXyPMFAfk+Zhsj00z7t5Tsbs33sPud2JY2ci67e39/Pdvbj91zNyxUc2rBt2Rfc39zczN5HBfD0syYGm9gE9MbynbjDupnznvPoFYrlcjkBZJdr2rc6AO1kwP3p/BDXviSpZ1CdOoVtzpc2vwUtFt9Wqvy+pcVi8UwGzYfkjdtinIkTEiSPgin/ZtoXVHWywHvdeK+aZdT4r6qmFduUT/zG+fl5/fjjj3V0dFQ//vhjffz4sZbLbzsHsvGF7S47aBJQ5Tv17O+wuyPM8/DwUF++fJk22/j69WttNpvp3jc3N894lH6742ni647P2Q7HulXb0bxlRZX7Y3xsOcnkhm0V55nn+VhTrpi+lF5d/ufa2YeHhwkIMtkoQzKWAbrEwIYHhnEuBDMMGlP4DAQNgLpMtoG9a28zaGA8OZmO8FEqHAr32Wc0ErCMjGIH/hxY5TMVXs0AKFILbKeaQJRj3gLXvGAcJsDZKAgy3zkXp+hA1ePv+GSHOJrjXcC9M9gOphyIvyVhRL170mazqePj48lBUatuJ8W1Xs3yQ7T8XvUceHVBfwZUDloNnD0f/qQtlwGnnFvHnBn27mUOqhIwmWe06+Avd0DjntyXccAzv9Igt7bOIBIQYxCJTcxAyX1Ht5LvGSR3ttNzbJ3ieAIu67//Tz3JeeuSFdZD2mEcHejrAOB7IeQN+aDUtuqpfNoJMjtdrh+BC343oPb59gdOFiC3uTU4lEkQfKj137rLObz7ySAs5Yr/7UcM1uiPAyrO75IQ6TtdHZH3vLm5mZ49wUdxHX63C6qcRPSnV5a9Iuc5yWcabV+c8HACK30svEjf2AHJDAT8f64oJH8SJ6Sf2pV4+q0JnVqv13VxcTHNAS+MJvBgrjt5qOpxkAOIUYInMYjtk881LvH1iStSjxgL8upyORIxjC3xF4kbnpf6/PlznZyc1KdPn6btzX/99dfpGS3jL1amHFTBS/ruRxmMr9Ady+vt7e1UIkjJIN9vbm5mfBhhKlPO1whbdHPd+SO3kTbTc+uAjN+YBx+zTUj9Sz+NfmVQ9T269V0bVSQoTZDegYOOydm2P7vffc7ou+/XOcBkrJVgVz9HmcsE8CNAv8sZj6gDhiNQllE5YxoFdBn4+PcRdfMJ+Ej+jfiYoG7Xud353fW72u5ko5PX90DOllm/MnGwKwPMOaN64G6su3jj30fHkzowyfFujpwsyeDZwCLbzmAgg4IusDSPMM4jWcj7ZwCSoIfERiYv8jvjyDn4XhvhxIXvtW+uTTmeXedle3nde9GnpASsrsXfZRNSjkft5rmjQCafxcoALNscAfPuHrb9GViPQOUI2HZAxG2jP12A4IAog4uqp9eIOADK7/zt8inZx+7TPLGsZgBrHnSykNd02KLj7Qj87woo9vk9y/B7ICcr/eJnP+eyyy50fq2b16RdvnBE2Z9M4vve/KVc+nwfz2fwCDjhixO5rqryHCNHThwQKPlvFIDb/5hIFvBJuWK38uvAdISlTdy/C2ZfSp0OjGxVhyv2BXKjY+nHs2LrtfSqoGq5XNbZ2Vk9Pj7W1dVVHRwc1NXV1ZRZz7czuxSlA3k5WR1TcdRpgNKBJ/jyahhAZ7F4eleSHcMuR0KbfjDXDPfyr1epUlDNQwsgbfC9Gxf98ucugt9uc7vdzjLtXdnAiA82FuYLmUvzexRUem68/efI6SRYzPnwZ2bwPG7P3XsuU1qtVnVxcVHb7baur6/r+Pi4bm9vp1VgDPJi8fSC0aoa6laCAY51v+fxLPVj9ScDHeucAxiv9jjIgLbb58vunItcug+j1QLLgVdZaS+z2ZY/Z/VSfkyWH77TJqv1Kb+77Fi2nckQX9MdS4BXVbP+s2VuVxJBP2wj3a6BWmZNzU+30yUAPM/vhfBPBjd3d3d1cnIylZ9dX1/Pxp6+qQN51kWD7pFeerMSn+/SHd8Dnj88PMzsLZQBDte7fM7yNypr4Z4Ge/6zTJlXZPGXy+X0AL51pqt22G63zzL/XoHnOysdWQ7IOOmL/ZnllXO9CggfsrrE/jhtS85VZss72fD9Uxe5xvdK+2CQ574YlHfXvQUtl8upwuLs7KyqvsnrZrOp29vbury8nFZ8zJOqua2Ex7k7pc/zPGWS2cnHzqd19tdt0je/X4u+Iq8+tlwuZ6vBy+VysjFs6nJxcVG/+93vJv7Yj+KzWDGnf9wfmT84OKjb29s6OjqarZTxYt+qp3fH2ganTPLc1f39fV1eXk4VTOiYZc1zZP1IGU45Zl44bhw8ktPEFHmuban1v0u4dHg+yXYVX8CcnZ+fT6uK2LPX0ncHVWdnZ7VcLmu9Xs+20IS5jtj3ZXd8vBP8DqB3wZQdgHeScQDk+2L8LBApOChA1q5aaCzYzgymU7YR9H38XBb3pI1OmPeRHbt55PIHl+Cl8vi4AzQbnhxLGsyuv3ZQjDWfWctAt8ug20imscxrvdLjueTvvdByuazT09O6v7+fFPvr16/TszrelcY1/+mgOoeS/yf5mEv7HKg4WOnmh77QHk6GOarqAwT0ysEuY0xwRxsZIOCYbH+Qccu2gypALMEV98gAMIMq6yjH08l7rLTt0szku3W1c0BdsEUfDBSxu161sl6OHFfapgxmrZ/mpfUoM6+vsVe/FdEf185TYmOZ6JIQvn40tgyiunMMzAHbXlVFBjNRkSuv3X2cMScQSVtpPfHcjmTMekd//cJQ+m99Qg4738Wnn6ki8+5+c5/cmc++nfOZL0pwPRbzzPaJfts3eMwOqPx/lkVad1M26F/6UPPV9ioTOiPf1SWb3pIWi8X0LPDp6ek0bhLv7H7cgeGu/9aFnBc+u0c3+G3k8yzL6cMgB3R+3pbAI7EO+rparaYg5eTkpM7Pz6fPn3/+udbr9XQvsJh3y/XOg04qUBZ4c3MzswHb7dOrEhiPMYJtATy6ubmZyv8uLy9rs9k846H9JMT4ds1/JuC6uekCnwyoPG8eS9rSbiU8seUucl/AICcnJ7Ver+vw8HB6mXXy4iX06meq6Iyz/R1D8hoHCB3oG4G9DEzyM8FHMj8n1atnNozdNe5bt6rhNgxuO2Dm/qWTcTYyje1oxasbW/7WGa7koWnX/Hls9MmKlMYyx588SUD5kn7lfL/EqXgOEyh2juytKAFUt/w8ckDWLY6NAPSu8SZfRnzr+pD/d/PZ6X8CBY6loc37JhjpgpCRPL4URHaUY+v+EgAnX7vVjZf0u+uvgzrzzg/EZ3lWB0J8fd5jF6V87JKTt6YMsN337HeCZbdhnqdM77LJBmAjO7nP/nVZXe6VcuNzOjDe2T/baScTGCdBkJMm6BMZdo51CVH3NYEfv2UAZmDY8bcrI+z8bAfq8l67dHCXTr6GRvbD1PkpJ6nei9+C6BfPEF1fX8/GZXzE+Xya99irjvbhoJfMT+dH3A62k/9HJXedbnuuWH3iWShWmWzr0+5XPb0TC30iqNpunzad4s/PSvreThjbF+zCyvvstTGGbWDO32t43/HuJf3peO928vqRz877V80Tht+rY68Kqmw4eX8OW0RmBxKIu9P7AM2IoRmB2jA6e2DySpmFgr5wfScYNmR+9w4GvKqmpdhRlpPzcyJTwZh4n5sBmx1Yjtf87xwsY3EwlNnQjvjdm2PgULvNL9y/jlKoF4vFZBw6gMnn6Nm9lJU0Mhh7v5iwe/HzW9N2u53eZ+EH6Uf9M39GGXKvzOwzaHxP552lJkmdY7AO0I7J8sfccLwDEf4zMHKiw7/b2Tir3dkA7mVdGzkJrksDbd6w0mbemCd8cr8sa6Bdb/rjucr2upVKn5t65X7tkv3Urw4cYxfzmQGD//dCj4+P08YvgD/vXJtlVtjzzIhzLAMc/94l1dI+d05+FPQg67m5Tudzc2Ml7IjL17OUPec5H3LnNwBeVU3y6bJP3t3jhGvng6q+vXyUkjD3f5//yGAsN/hIn+Ygy/7K405edsnMbpXFNAJr6f+NJ5Azz5X7AB/znaAu4TTQfytiPOv1un788cc6Ozur+/v7+vOf/zzxjBXH7XY7q0awbHnDoM6WeRWps0sjPnSlf6acVwctBDi8+Nf+xzZ9tVpNJX7n5+f1008/1fn5eV1cXNTFxUWdnJxMZbOswvJOK8b18PBQl5eXdXV1Na2OsQHDyP96XGy64/J2B2bdCk7K5a7AxnrFSq9L7ff1rzuWNgayTi4W8823rPcd3vb/3Xz50ReXY7qUnaD4ewKrV69UYbRdD9pNiIXUQMQDHv3f/fYSBz1y/J3SUavqgKgziK4RT8Dkyc4smLN4XfbXOwv6N/rBNV1Wk//z3vl79qkDq1X9SlK2h0PCOOb18OklRo5x2RF2QM8BUv7mcfnc/L0DJXZK7wX4YQBxOjiqbsyjzG5HuzIuOYejoKoD2B11AdboXmnMcpxdUMVx2nPfOBeD22XiRjxzciWp06001Haudl6Wa/qayRS3a0DcBWQeC23y6Wtpz8Gi79MBF1Nnz7O/5rvLVwxg3otuVT3pF5UKDgZS9u3kLTe0A+XcAsQ66mxj8nMEbCxXCTbcLwcklknPj0tt/QwUY6D8zokpxpplehlUuRSwS1q5vNe7E/q8l/gPAysn5G5ubmYl7slrgzH7sM6OpOz4r6MuCBj9nquG9p0+P0trUwa6hNpbEH09OjqankX55ZdfJvvDn3mO7IDBLFPdn4Nj46NMdHV98/kjQnZdZuryU0rtOntK+3758YcPH+ri4qLOz8/r9PR0Wqny/W5vb2u5XM7KCv36G0oOGUeOCZ53fsf4CrkdJbp2VcXkcWMP27TOZ+1K3mVAmnYWniYu7xIuiZX2YSMvAmUCyzjxe19X8KqgiujZjLUhyknz4Oz0k4E+/j3UKc1r2koj/pKgAIH1p5/jGvWBNnO79jTKu1befNyBa+f8d42hm4cRH2zUHFCm4PvcUbupgFU1OeV0hu7rSxxXR13A0AH2t6THx28PnjprZmAzkqXOqZhHuwIiG87umpFe+1z0ziVt7t/onl0/TS+dF9sTy15neOmLnbNl0ud2x3fpUmakfX7HvxGv+N7pSh7fp9vc13qa9+r0ycAh++mV8ZSvrkT6vVGC407u8jzLVrbzmrG67dF9Ojl5iU5WzV+tkG1koiRXvQA22Mi0KawicB/zwatCyZsuSB3NA8DPCcXHx8dZGWLnmzLYcx/zWawMQpx4cH9Nnc/tdK/TJbcFaHdANUpg2Lb6XPN9l/7/lmR850SPA0YDfW8f7o1/bJvhUyZrRzrivvj4Lt/la7gnOM5VOTn/HQhnS/nT09Nar9dT6Z+fQ+Za5tHt5kqM8VY3LrflflQ9JdsXi8WsCoSVNJ7/4lm3EU86nGbK8y3Lu67bRbYxnb/q5iH9zggT0y6f5l+32dJoVXofvSqoenh4qL/+9a9ThMzAyEzRIRufjGyraraLUQpGl33pAISFMwGBmb0LlGZglO2OqIvWAS98ZhYU/jkLwvmjlRMrEhkJlo7dj7zGoLID0+ahDY/5nOUTZBXJpHCNlS+NX44n70u/3A6/d6A4+7vPsSCnCR7ey/s9THd3d/U///M/0wOTlstdIDyfz7FOdJlQdGsEyjAwfFqHmCPzznOSWbWUw1zFMMijfz7f85y8MNBgXHx2q6nul/mXWTrrQF6bwKrjQTp/z4fnCTth55l6k3NvfQegjOSfc7gf826HS7u7ViOtnzmXueU/2b33StYZBwnpKzjXAK/q+UosQGJfcJX+xEAqM+55vu0WPtYbyIzuiywxN8wPYI9yJYNC5MQP6HsVAR+QGxdZ9lMfcytpSpqdVLT/yV0D3Seu8+5s6BC7AGYA4t0EvbJlfmcpoANM5j03Bkps43mz3UK+HCja5o18fwa+zKPn1zvoviVtt9vpPUfsBM0D/5Tdr9frur29raurq+l5q+VyOe1W6u29KYNfrVa1Xq8nGbAu2l6Zf+ZFlrt1vpR7m5fci/enZZKO+cBXe0OKjx8/1unpaX369KlOT0+n1WBkm3dNVdUU1Lj0kdWxxHHGmw4IrFfwihcVb7fbCUugb/yOTu2a09FmIGA9VttSbxzgpS4wD/7M+3KPPObvDuKQo8QQI/1wsEsbx8fHdXp6Wqenp1M7fvH6a+jVK1WbzWZWlw11GZfOKXWldlVPguoly6oxKN8VhGR/RpNop2ChdaY2l3u5zgEZbSdQQyls4J1Rc416F1TYMTmDmAAsP+0EOsdrJ/iSwMJOxE7FPPbuhQZuCVhHNFKwDvh3QdmoTYOK/J/r9/Xtt6DHx8f68uXLtOMMO1m5b9lHGx4D+rzGwUfV8xLTNHg2jin7DiDyPunckON98546Oupfl1ixHORDxQmMrB/mU2dLsh/W747Ho++j4CudhvWaY+iYz8s5hLJN989z3yWQ3K+RXqVjNXjlOuvZvoTHb03mm5NbKcsZwCZ4TqfP7/sScdl2BuC22SYnRgAr5rnbHc2z5wlgR3AGcMfuOKjiu9vkz/7Fc40cUtFCkM13+p0Bifva6QjPiORqEf3BB1kOq55ebJzj8XW2Ffbt6XN935Rvy1EmHGkX7OMAyXNpWXIyLL+P/P5bETqFfBJA2Oe6zM8vzOV66wCJsbSbHd/5HbIudn4z2+L+kIMNgiqTAyT89PHxcR0fH9fZ2dn0/BS7xxknp09zKaplNJMW5lHn190P5BWAoivBAAAgAElEQVSsSQDx8PBQp6en03OP7IVgfJ5z2tk/fks/cnh4OPHefr8LiF5Cu3CD/a9xijEt+pYLD5xPcof+5m6wzMv30Ks3qmCvfgwzgZKNYYJpM8KTlUFY/m9KADECmm6nc5bdvbIP7nMHeCzQ3sYScj1mB7hsbGkjHSXG5/HxcZaxgt9VT8ETRpfsgB2hHYPbR5ms3N0cVdWzgIT+G3R3dbGpsHYwyeeOcn7yGsbezXUH+tyG5e+tnVLVt7m8urqqqpreTdVlcjNT5YTFKFDOcXZ6x6fnutObBAQj3enmN58ZAgh1GUU76HzAPB2b5dGgyf3wJgoGhTgdwE6CJsbLdQBOA6HufAM389zz4BU6rvEYc+46nqbs7tMlt5nf3UbyL68Z6dVIBt6abGctb8hCF0R1tt9kGwulnu0i67gTkOhGyoDbtDxxLAMc2wSu8QqIgyrbcuSY7/ic9PMes2UiM+AjGTMAsj02KOJ6b9rDO32wDfmsTvLVq7Tp82jfwRP/Ewikjo8wQTcnlPx1+pf+KfFFp2eZHHoP9Pj4WJeXl1Mg4Q01OnzEWFjpsA46mez/M1jqfFknY3lPH3/pvBrvLpfLabMrkhH+4xVDDtC7+8A37oEPqqpn17qvyKVlwxUgVTUlTPx3f38/BVXL5bdXI4Hlu0AufZ0xZYffjfOYs9T7kV3kHplo9EqZ2x3pXLaxC8d0+trp4vfQq8v//va3v00vNiMCdgbMu45AXVaoar4824GOqn6L31FQlcBmdC2f3e/ZZ4/dgBPl4oVuFkgi3gwsXX5gA26nBlC8v7+fXu6G8mZG3WAzeWegCWBz5E5EbmNmoWVperVa1d3d3ZSJsKCmwQR4ug/Zv30rF27LQHIE/DJYtDPKjSmy5C8Nw1vS/f19/e///u/0PorDw8O6u7t7tvtT1fMlda+aYlhyLgyEUkb8Zz4ZgDqoyewbx9P4cQ/PuQ0yCQPKSW3UMntOe94wxokdnPPj4+P0bi/321koAzFslsmOBX5X1azs1oGPS3jcRgfW7RTsEOE3etr1ye2lA+qos3vJ75x/t536QV+RRQe4toujgO8tablcTs87ZMDgvyx983xVPS8xyt8yYON42h37ROYcHnY7W2HPUj85XlWz9wHl80P0jQfpeRdLtolsW2c9jtSN5DH94sF+3z/55f+tEwme0dX7+/spuOKFzdiHzKbTv3zXUL7Ater5TmP0Y7T7KjzwbnUduB35Luub/ZPtEnPiLLz50YH0t6L7+/v6y1/+Uj/++GN9/vy51uv1FGDd3t5OfgtsBj+YC/AGSWSDcsrvEsvYjyC75gXzYz3hePIx7bSDuqpv88WLjReLxbTKc3BwUBcXF7Ver+vDhw/1+fPn+vz589RX61LVePWbVaaqp4Sq+0W/c54zIYJM2aexyyltrVbz92k9PDxM/2eprMsELfNd0oQ5s/3DlmXZqu2Mx0pbeawLSo1XEv/Bs5SLrKDq7Ct8G9m4l9B3bakO0Dbw7Zy0rwPwOTgxvRRod+13bficLqv2EjCdRsuTyXgBGBYC77bktrp2MQoYZS+beze4fL5lsVg8c5y03a1s+H4mG3MrcGbLGYtXyiyYu8aY4L3rR85f9383N905XZbPDu49BFFJj4/fNqo4PDyc6pRxJg4WcyxVc0Pl3x1QmTqAk3qRq1BpgDJhkEGTKYGTs/LYBLdhI5f38PNwXr3FEaS8O6jqngVzex5vlyV0ttQgarvdTjYg58QG3bKfuml+2lG4D05IvRZMpa3eZQdHupX9tj3q7vWeCLDh4KVq/pxrgp4ua23KMebvu5J2KVMG+eiF79vZAe7B9y4Ych+sV16xyoCANp0cyaRJ6r+v61ZmuvGPqBuP72mQ7t3+umDDc2k/x/HOFj4+Pk5Jra40eGTfEm/YHqfN9rnoe9pvzstrLCfvgR4fvz0Wgv3NZGaS7eLoOT3rAInlqvl297ZnUGK19D2dXbaMdEEMc8SziF6FY+tt/nimLNvpsJGDtiybztWblGf6ZYwDWWadAKN/i8WiTk9PZ8/HkzgnwKJ97p0rUd2cooMu1e18wT7fYD6YdznvPt88sIwgQ9nnTh+Zh6p+462X0qufqaJEiQyCX/iXnc6l72QSAdauCaCtzrh0ZAbn5whQJBDNIAAH5G3kUS6Wey14/JZtE7Vn+zg2KxeTi7Dn1pnwykqHUeD+VfP3U+X5zAH98iqayz1OT0+nVUk7Wguy26dvXjKGOrBmQ9fNg6lz6BkcZFDFWLLEpft8K3p4eKgvX75UVdXZ2dmUWbWzgXBczuiZzLtdujUC1R3QSGCX4NRt4ARz/rJssEt2ME9+lxgyagBCQsPg0wCL/ucK22KxmK1kOfnRJXu8WuFVOwd9jKUDdJltM2FL8tyUU+skOs5mHJ5zOxPz18dyzhwk7gqGOtuZeub+vjbo+y3Isp4rG/zOJ/rlDS269mir8yNQAig+u5IZZCvns9PFLrDhdwdPowAnA4s83gWV9KtrD0LXttunsqnuWbBcCYas0xlsAG4t2zyD41Wp1FlAOuSEpMtw0x9kiXkHztKG5jmJQzpepdzg851g4fcsGX1rPXt4+LaB2enp6eSPqDS4vb2ts7OzaQMAdri1zXYwg533O0Gr5slcyHrg5FxVvzkM7SDf3tmvKwdlI4rlclkfP36sjx8/zpIRq9WqPnz4UCcnJ9O26YzH92LFzDLqwMn3daKQ85MYS67SZKBp+2RZZ8Oa8/PzyVfjT8B5PO+P76VSiWfMkMP0Qeg18+jjJs9Xyjd99mMpGVx6kaYLkJAdJ2yd9OHZMttI7ung7HsCq+8q/4PJR0dHE5PTsGDUnYXLDI6Drm6njlHgQ/u0lWTmAtAcrPj3BIlWcGi1Wk1lfnxn/BcXF9M7CDIgcyahqmY7F9mYOqiCHh8fp+VZg0k7KDvWfL8BpSAdXxAeFJ7SKTswE2VTnJ8ljP58fHycFJMAMmtjk0YBFL91QMCUINGllJmVzSXht3ZIEC9LdO0zLwq0bAEUzONOBzon4uOjgCj/5x4OZFxfnuUpjMXy1+lqtwrqbODx8fFs5yeDUq6hDIPMMo4X0GVd3hdAWl9tJyiPIMillMKBFDXr6EcC0i77WTV/9jLtHue7HT8bYnLQbOCRzmZXkOxzO+DoBIVlIDPSCfjeC3lOdyVSsMfIU1XNdoGy/viT3zow1wVFVU/BAwktfttutzNw1c2bV1fdN9p2AiNXFPk04MBuZ0DV+bWUHcbhEiN0kNImJ2E6uXSAlSCR8cAX/KJLm7CX3lUQvchAzn7MfOAa7oPvNchPWelsaPKLcTmB4+CJtg2gDehHG3x0ZY9vQbe3t/Wf//mftV6vpwom3llVVbXZbOrr1691fHxcl5eXE3/sP6rmySl0LnnC7/DVyW7jSNqr6oNcZMY2mnl38hwf8/nz5/rxxx9nydnVajWVFK/X6zo7O6vj4+PJZ9Au96Fk1f233IMtSfpdX1/PdrVMHc55t75Yz1z2ht34+PFjnZycTI+ZEFTxkuP7+/tar9dTgMUKFjs3EmBl0tDBkP2v7VD2r1vNJGBDBmx3E7/bxztJmDu8GofwmAXzjEz48Yvf5D1VCIaXDfdl8RLIZDYoncKova7djjI4yeAmg6qq+UsJrdgWAF9vgG4HnBlhg/wEWC5H6oytQZKVykrvgK0DYm7P52QmxQDSgke/7Tj5znhpA6BJOwb7HZDpgJ7/7xzFKPBKSuXKAHTffd6Ctttvm8DYqLk0IfuZ8z7iJ8dG43yp0ej0OYMqjCPOKdt3BrrT49RVy3fXD8+tnYaznBmw77Ib1jXG5Z3QvDLY2TT/dQFcjmM0xqq5s2Uc5l+C07SlydfkgedwF1/cRmZGO1A5CiDfA3nco4AqbcW+a6B9tsl+LTO4tqEOTruAyn3K+yXgsH6M5HHfmNy25Trta67WdQmXzl911I3LY7IPx3c6+Ug/3S/ri32xQVked199fBffk3/Z9+7abp4yaZXXdcH6W5EDAIgg0qVyXr0giZuVSpYfg/Jd/it5l/NvQs8ymMqAxCs6BwcH0/Pz6e9ItpMM3OUbcoWqkxn4Bv6yPKQeM5a8F3xJ3OXruYeDeesReuFEFAEWz+WOeOz5so51+Gs0t8aXbqfzdV3QbL2yz/Z8dCtl9HPkl19C37Wl+sHBwbRC5W1Mc9JZieI7x7sluy5ahXICYFSSmZk7tPjhXjOXezvzl85rufyWFQes5Q43vm861xRsC30aehOCn2CtMwKM3Stm5peNzGIxf58WAv/w8DA9F4LAee6YH2f4eOM32QmAAG1yD/+NFMHjHsnASwARBtAZCC/xGjgnT9+S0K3NZjNlS7yi6zkwuHemj9/SkDi4hUZAgDasC+noKXPwNq7mKeUEu4AmvGd+lsvl7H02tOVERSZkcACWP9p0IoPxWj8zMWDD7wSCeUG/7CAMDgwA0ikkSMJhdXYnVwoYl50AdtcBH+d7jvP7yCZ7XuiD58LP3/g5nATN2Kz38ryHKX0UMkdiiTEZyFqWRrvapQ6mfbbdq5pvnuLz3W4+hN7515y73BIYGQbwAQy7pBznmzrQaz76WgdZyCUAzPweyZzHlDJsHjthk4ldfAxtuM8eT87Dayj5b0CfAJPzrCP5nKh9k4E5QL27R2fz3pLu7+/rT3/6U/3ud7+bnq1aLBa1Xq9ru91OK5UeB6sBrIZkssr2NUGw7RwrY16Z5bouoIKwmy4rXywW0zunDg8P6/z8vD58+FCHh4f14cOHOjs7m/kpvnuDNo/FCbGq58mQzjeygob+2MfhYxID5n1Z+ba8ZLLA9pt3irn8zyV/9/f30+owY72/v6/Ly8v6+vVri2mN1dBvB9VVT3bErxYyH1yZYd9q7MZ9Pd5uRcy8hl/EBmdnZ9M1udI5wi+76LueqVouv71AjAlOp8vEwhTXmWaAYSCRwuZz/L07l+8IDEHV0dFRnZ2dTQ4sgQvgAePvZT87HgMKjJ9rMjvDlmNN54QgeWz5GwCKnXFSoCD6Y0HLtm2Q+fSLUg0ObbjsrM03slMYAr9gjk9nOAjE8zmhDvx1NMpuoGg4LQdVfvmv5xOZwmi8tWOCn+v1ujabzUy+LWtVT8/sIbsG8VnnTGAL7cq+GMQbNLkP8BSARq282/ALDEeGySUJOER01o6ANumH9cgACv3INtO4cozxUZKB7GBcXdLm/hiU+X/64V0Isz/oHEDe5X8GfC6xzedEcK7WM8aOY0m+p0OxLNhOJDlQsl5l8Ou/blX+PRBz4OdXGMtisZheiusdyBLAMAdJ6EbaNfu8vA697ObAfsWllgla0m5SrmT5tiwDIgDr2U76Ic8nclX1tOsmYzRYY1zoRsqhy13NK69yd6sW3giGP54FdrlfBlXwmE0n0BmoC2jdv5EcpS1xX9PeGkMwTj4N0N0e8mi+ey79gti3Jsr/fv/7389K1k5PT6dAxSAePgFoAfHecTgTcpyTPoFjyL83tEg/CBmTsDrshPnFxUUdHx/Xjz/+WD/99NNk+3wOu/XRLn3GfvCb/RjHOpxHv1w654ow6xv97lZo6Evnx9wn+IQfgif4Lz8WAqZ4fHyc9hBwQiNtWyY+jVudeGc8BHK2Q/bnOdbEjd5h0jg/K2f4jZ0mV6tvL5e+uLiY+W9o10LPLnpVUFU134I7A6NdkZ2Ng6/JiNugoKMU0LwHnzZiFrI8t1N2r2pkFjZBRPYp/8fwpUB1RrsDQw5wOic/Ak/md8cjB22p+FaAqnk2zWOxMjhjZgNgZ4sDzHlLedhFo8DKffccZpYv+TBynm9BHRiv6g3x6Lek1C2vsuzqRxoU+Ge9sk5098sx+Bzuk2PyWDLT3I0FB2DjbqeWuoRedEDMfekyzpaVXaDL97bTdVKDYxmYdGNN/vn8BMH75HkkJ7vIc5/zNJK/77nPP5s6PUpdGo3RbYzm3b6rO2efnUk7lTYs9TaDDAN+j8N+0HKWct1lwLv7dXzofHan8+ZnynbXXgdIXZLkZAfANue0am5futWy7HPnV7vPkezvmuuUr+5/r/Dl+D0Gr4S8JblkPZNJxjsp3ymL0MjuZ8LCNm/Eh1Fb/h2eU31BUEuFkvWHJJPbpB+5eND1o5MZYzz7Mz8CkFgw75N+D/lwcJH9SVl10J++iYSgFyDw/+ZpF8yOfLl/6+zNPmxNX3fJgO1h1TyQzQCzW+F8Lb16pYoozzt8pXE32EaxusCAgVTNd+5y9tcTn4pgQ54ZIZc6nJ6ePtuNzyDMDshZARu7qqfd+8iOdNkCxpTBz3b7lMFfLBazTSu4l8dm8koVD0JmFtCrL2R1quYrAt17pRIYW9BdJuRVEpyYgelisZitpjmTnmB3ZPxee9y8snNyIO0srRXLBswPor8HYv7ot4Nn5K9qXp6TsmjHhrxyvg2tAY1lCXKQSkaPbDcrVW5ru93OZNvGLjdxMCFvlAJ45cVlHwZi3gjHuxdRPplApVt52mw2U2bV7/ew84cvZPK6gMcBDnaRNt1X24NM0HA/b9qDDmE/cHSWWewtfDSo7GgUVHs8GTRbDgzSXabmEq33pE9V85I67CDysljMs9xOGDFmjvlVIun3DF7s33YB3wTSPMfB9seAO7f98PDtgWrGZBlzaWZmq1lpptTHD3L7HVf2b5Yr7NDJycmsL2nXXbmQPtTPOzqLbeKYgVjqMXLGtQa6ln/3cbX6tgHO3d3ds82lPB9VT9UAlnmOdzalCwrch7QZ9lX5CAF2Izex4biD5dG7tH5LWq1W9cMPP0y71t7c3Ew48e7urjabTV1dXdX19XUtl8tpZeD09HSaD/wD89WRf4Mn+Aj8hPmeiUH7/KqaZOD09LQ+ffpUR0dH9enTp/r555+njTY+fPjwbM69dTp6ZD+VfU6sa5DPddfX17Pz4SM44OzsbOpvJl1pM4MW+yWOOfDKYMufy+Xy2RjhHSWbxph+l5UXXrBTbt+BSye7iWFzDv3dNsd4E354BYvvrsRBhzoM9b169eqNKshI+NkaGz4ARWaGYIJBiYUwgaIHhmFxhjdBoO+F8WSpku0uDZQS3DjYsvA70nafHEgkWcisBBgZn2OnnMR1zgB5pz7GYYX3dQayy+VytoRt50spmdtwlsRz6DHCU641EPGndyXk2k62/JlOyXPGnNtgcA1jY1y5WpllJllW8JaUQbFl2uM1aHCgYL4id3m8uzb70B3LTB5vj2e7fc+XZcdZN9fb28BaVn0/y0sG5sgu8uegqtttzOfbpjioMrDmXg6sEkBa7l1WRqmsbaF3rrJe2Yb5XtzPZWUGmtZZg094baD6GkdhW54BtW1Gp1eWrVHg9lbkoJm/+/v7CdQZOBvs20lvt/NSmarn2xozfpfxjMiZU9tidhNzxtz2PsEGvzsQ8Gqyd+t0W4C2zIKn7tqPWZ8Yp8+BNy4rh/+LxWIqT0wf6XlCps0n67MTFH6flLPsnOvxAYhJTGWw63tap83LDKKd5XbARFvct0vEpF5xL8uqZcrXcC7H35IODr7tjseW6my4xC7E19fX9fXr1ymo+vjx4yyogi8uvUp/nMGJ+Yi9S5/Z4SqX58G/s7Oz+vnnn6dSsN///vdT0nC9Xj8LbNHL7HOXZB9hO+Ncl92xYOHEBLjKpb3GfCkjTvhYxnMhwdeY59vtdrIli8ViVgrIn6uYwKXGqcYAYH7+93f7CdsC8xL7mL7XiUWCzxwDcuCgisStgyoCwH9EQvBVQdVIeUfZnhH53NH311Iau8yuZiYoI/2c3G4smXHqFL+qZg+QejncK1U8xMu9k2cWPGcPV6tV+2BfCtSor15qzf67Dwmu8rgzLZyTjiUNwN9DI9nIPo+CgmzHIODvXe79R5AdgSnBd/6eY+5km3Z8fRrhbDvPS8Bs0J2GHTnI4L+TCRtL99WBgg005GwXusU5Trq4r9040UkHNeaXs16WlZF+dfal05GuLzmnBFQOpjPp0Ml82gbzeCQX/i3b7u7x0nPeCwFC81mwqufbjjuBkTKaepNj55MVYf/eJYwSBFqn8m8fP/fx332wjjqQ8gqS/U4maQBVXYkS11l3AL8GXJnAND/Mo1y5Sh3kOuuJ54/fXSbkckHrbupxfnZz91Lq5CRtoT/z97wu7e5b0XK5rLOzs2kF04ky61CXLOd7Uvf7vkCrayM/4ZkTQ6wIO5mRm5Bxr9RF67bHO+qL+w5Z53jG14EK/q0LpvfxcXRP9zN9QMqkK2Zy/4H0zavVqt0F0v46+5Rz6E+PqcMKo/ZS9tIGj8g253vjkVcHVVmKkEa5y57k5NtwGTx1NFKanHSycESk/PnB+jRAnXE0cLJxsFB0y5KjSDyVhKz4/f193dzcPDOMvk9XT3t0dFSbzWbmhKvmmTUDhlwJcN9wUH7Y05k3B2rOcNAu5+eKH1lg5iYdnfk8ymjvUwKUFeX2c19dwMg9DcC9rP29CvSPouVyWR8+fJge7qW/NiJsS5tjNJgwEK96bmg7gDYCCS4BY5XKm7R4NRAyyEkQ6fdHAc5sO6qer6pst/P3rtlgevUKB8TKtFdVRk6Htv1ulXRYVfVMrzw/ORfMA7zu7FvazyQDQNo3CO2SRP7LQJxrPD/wrbO9dt62KwliEyzRT//2XogSJa/msNp+e3v7rJQKnnn1BVtJe1XPwS28I4vK6mkXSKNT6JjL/gB33cYSkG1f6jSytVgsZnrCuNg4yFnx9OXohnUOufRGKrmyBZ/Qc+QVWc9ghv4mL12W3iUVWRGDz5x7dHRU6/W6Hh+fXmLPOfQX/jsQtK8FSNKP7n1yXh3LQMKrep2MeKWXecvgHr7Y7vjzvaxUnZ6e1n/8x3/Ux48fp/eG+Rmr4+Pj+umnn6YywMvLy6qaJ3+oLrJcGIdVPd/cyLaR9lix6FYWkZ+Tk5M6OTmpT58+1fHxcX38+LH+8Ic/1Hq9njbPyGQ8cpFBVNW8PJx5z2DM/WdO+e53rF1dXdXV1dW0cnJ3d1fHx8d1f38/PcZyfn4+2zQocbV1xzrk1R9kKHGrnx/zKg8bc2CbHh8fp52KqULhO6uUVFrwOADvw4I6Xw8l7s+EImN3n+EDtmeEa6yj3iAFfWJl8Pr6+rsS7q8OqjAGkA2oje5LS4te4nh3AV5n8gikKJlIMOg+2Om7D86CZRkTvyOcHk/WlcKPm5ub2mw2k7Cxsxt8s6G087Zzs2IeHh7W7e3tLINQNd85LYWM8Tj48XFn+LMOnr4mf8xD991OoFvJcPDIZ8pAAocOfGJY829XUMX9bLRzBeStaLVaTdk+O2obQQO5qnkmJ4Gsg1woA4MMNrItn59ZfoODBI204WAe4wwYclDPWO0gIYJgL80jDwTHGEZAMhm1lIluRcw7LPmZQwdSDtBcPuUxulTBvOx4bR2wMzGwhByEerUgExaj4Mz9dP8csJkM7Jys4Jp0ygkgGBNy8l5otVpNz3LAF6oFcMwGztgGEkQEJy77S50zEK96SkSlnnKukxZOABJc8eet1yEHZgmeLJPWMcYG4EX27+/vp918HRjd3t5OegcZoMFDZ9nzWV4HsdYR6wW63wXpzI+DNIIq+zTaRW5PTk5mumJ/7r4xTvt198W2L+1I2mQnFWwH0vfZd49WKE0ZlDGe9AdvRev1uv793/996pv5zNbcP/zwwwRekam0S37fZ9XzYMXJX+MbeE5g7ettizwH5+fn9Yc//KHOz8/r4uKi/vCHP0y+l7bdl+xXJpdsM5zUSp/Vteeg6tdff62vX79OeJGgarH4lqjhZcPwDBlywiefrezwrf2JdQtdNf9tq/x8P2WeDw8PdXV1NZX3O4lC3xiLExgZXCZ1dtOJSvtqYx1kzxUJqSOer8TTtvXfgw1fvfufgZUHaxopeXYwnX4HfEaUxsoOiu8dU/NzV/ue8Fy6ZjzZXhrdbqx2fvmZQpdBVbaPQUmFdWTv+5rXL+HBLl7ZcPj/XX+QwaJ5l+177D533/xmewYZDqp2BWC/JS0WTyslmZXM8/w957jjddXLyifzeAecR44/75Fy63YNnBiDwVUG7v6zUxj13fPqgGTUx7yG+6Rj3Ed23BkodUFnUmcvfH3yIuU+kxe7+t/prcdgINLRS4Dce9AraLFYPAtOkEOc/4h/uSrF9xFZVziX+4zukaDaG4QQPKfsW5dSFtIOWM6r5rv4ph3sbI/lOb9nYJNZYrdhG5z2K3XG9+j8oOfWn16B8ridMDROcIKTVeuq5+9xG/m67EM3D/Amg6jX4hK3O0qm/NaE76p6blOr5jYlV5hs9zOhNsIPI/9m/2FbbnlGr/xMsJ9fHN2n+572zfLNeR2v/L2z6y+l1AFvpuKkzygwHPXP8ln1FPBmvz1mB2as/DsplQGeE06dT+/8XPI8q7W68diP2xeOcNMIm7+GXhVUkU2nBMgZE0+EH+xFWZwB6laxMgvQAQz/b4cCGEVR+O7tMPeBsI4sWF49snA6UOE8HyMLmUa+uyYNQhK/U19LZsbjGoFdlypwzJ8Jqgx8/d2OeGQMOLfrH78niHHmIK8x3zCKZCP5boW1s0d5yaAkvZfyv9VqVZ8+fZo5Xs8ZcoShgj/uu1cuMKZpiEaOit8M7LwSxQrw6AW9ltt8jtDOMgGnSzApiU1Q5/6n46RNxm05NfCwMR3ZgQ70ma/mnwkZzH7bmWWfrG/mv7+7D9yXzL3LmpygsCNMfej6T59yY4MuwzeSnRHYf0+0Wq3qw4cPk/18fHysk5OTaXVls9lMZXaUlVXNNxuhdDsBVjdPGexwnsGOV3+92x/lSaxc5TsWLUOsbBGI5eYKVXPAaf/lMmhkJCtR8vlg61H6glzp6wIfrxJ1Pt7HMlNsPpqHVXN5dxkZ80u23GVJ2NPNZjPJROo6MmC9sl3wXCAntsGcgw0FrzBnLwGSzCF9se19LRD/Z9ByuZy9bwp7wPOqj3Ezc/oAACAASURBVI+P0wq/NxnB3qN/6JznEt6x4sWYjcuy/A8M6lVlMCsv9v306VP967/+a3369KnW6/W0+59XmzJwoD3LsvvgjSW8MZHlCd7gW7fb7SQP9JP3QLnyJ4k2wdq+p/XCc4D8Z9LWvsg+ABnNc9ERyjmZX5f3MVb0b7VaTe8ptYw4qKqqWfAGpU2iP97MxRUkkG2c9cy6bluNfvq676FXl/8BrDoHmoCpqs9wdUESRttGLSkjUiYYZ+L3Cvj5qgQxNvyQgUECEzsJDAH378bMbwbG7iu/ZwC2C9ingtuRZ5bTY8hr81j22d9zTG7HGckOZNqxdM4jnYLnwwKffUeZ/HzPyAnZ4dvIdue8NS2Xyzo9PZ3+T+eB4ifPquYJicyIGwRZ/5JfkB27V3yz/C/L6vLTZUAuiUl5sM6nY+W4ywsduNk4Y1wNJB1Epb53QCQdKdQFp8kzg2W3nXNkGzYKPhKwZzLKc+BgykEbAKaqZnXsI311yUjnoDL4S12zPfOx90Lol3dfBTg4Mednc8xHgzso5zlXopDb7IfBeiYu8GH2X9y/A90uW3QwnFlhJ+T8PFQGR35+iORd1fw54vQjCfg7XXUgBh8yGE3y+aPgwfx2YoP2bSeur69nz5GZN8gvNinvYUoQmj7Mc2RddTVNVzadwVXy2HbZPvKt9Wy5XE4JirSbfn6taj42Sk8JRpAxgifa5vouSK96nkTF/2QC4/j4uM7Pz+vk5KR++OGH+umnn+qHH36o4+PjOjs7m/Q7S17ddvpSxsnvrHQa43QJLeunk3LWd+Q2S/jSR4G1ctdbdJwgwpsy+TnoLmiCX07gMjesStJXVnlXq9X0/LOfa8ImkMTATuajNSnPxkCJAeCZd/XGB9rneoMP24RMyhrn0J9deHwXfdczVQZFNo5Zdsc1KYhc43ahTmg7o9NF0A4oOsOE4RyNzW3QT3+nH6w82agyoS4V4b7ODqYDsqM0AKyaZ7i51vd0vxEIBDBXZey8zTfzOcczWtlKB5n886fHked2c2pQ012TJX8GtAlYTXZ4ed+3dkr0A2di4NGtFqaMVc11xDrpYGff/fkc8agD06OMU6cPozmz88E5pQ1hLJmVrHrKGHINQUHaKWcJzSMcofXdwNfgtyu16oAvbRNQms95fSeraQPz2s7GdfYx+Zft5Vz7uqRODkagsOv/e6SUkwQRu3jctdVRZ5tSNu03/d28zgQLetSVlBnMZHJh5F/53s1bp/95vJNJB3TJs+7aHEP+4d9SD9DP7GOuXGADvOkPgC2flXZAl3O+S/9GK9ueQ1PnW0e6uq+ttyAHzpY9NjdwQOLAu+ppA5IE+okZ8v+OP1DKDzriYIEt03mOKnUjbVp+9/+78IP13rKFDjvYWCyeVp7zmUT66o1p7JNIrmQAcXt7O7MV3uACvIFvtG7RpoMZ6xl9cz9YZcoxoGM+l1X/0Zx2WCH5nvjcNs/nZZIkV6lSjz0330OvDqooufIyLkwG/LOLiTs6ArX+3edVfTNOjoIBaJkxz2yfA5aqef2mx+IJcabbBjsBLMLH0ijZQXY3sRPZbrfTw9AskZIpzewD/XD/+CPD4MjdfGNeMBAuGeGc1Wo1KWUGZr5/B9i9moMCelUinb4DzpQfr7aYDyiuDWsatzQk+WflNVjPLJLLOVKh3oqWy2+ZdDsgy+JqtZrkwBmiqueZzKp5CaYf8PWcea5teM0j8zqDdXQdmXDAYwPMPVKvUo5vb29nD5hzL3YWMvBhpcFZOK4blV7w55JcstecA/Edh+aVhAR+bMebcmQQtwvEun+MsVsRgWfOXmZf0uFV1TPHaEq98tx15yXoJ9OYYDfH+Z4oARfA5uTkZCqPsY1L4F/1/BUcyVsH8ZkV5jyvWHSbU2TwYNvmshdna2nfu3L6hfO2Lx1Zjx1UOAlhmSMJxl+WwZL5z2SZz3FSoivXT7DnHYiTPz6GTWEjCpdWcR79YgUPu0A5F3zqkkNdcrP7H71MfJEA3nOTQXe2B686nf6tabvdTjaUvrO73t3dXX39+rWqarLv6/W6bm5u6urqaiqz8jsveXeg208saZ5loJIJCr9bkRf6fvr0qT5//lyfP3+eVnOwu15NSZn1J/dMsr0gOGFsBvguLXZghQz63Yu804v32OGLWGFbr9d1fn7+LBHugJeX3IIXkZ9uAxB8mvWh6ilgqqoJUy2Xy+lccCpJi6urq8mmsqviYrGYbEL6CgdhWQqY8UPiC+bDK33MpzekIKBmJc6ryLTh59tfS68OqhCQDny7FCFBu0F5F1BliQBC2EWeaXDS0acxcpDDn0F11bw+2gAz78MEWWEd8HnXm8xAWUl9zb4Ind3PErQyLvqRWQiTg60EtTbUafAZk1c7UIYEoeafeZzncJ7nMoMC8wWAYr6MMrnp3Cx/zNuujP1bkY0p/E8HzHkJOrqVAtO+2uAE5rmCmkAKst7malh3XQcQzXsDNebYQI45tH4xnyQu3HYGerSPHiSoyf5X1VRW7KSNbQ4Awttxm+/dPbJEwfzyfHX96eanm3vb6My4Z5spBylbqV8pB6OyyvcaVEEeDw46H56vep4Q8LVVL9Mv2vGxzPRmRr1L8Pn8qvmrNLhH2uGUg65ao1tx7uxkysfI/9q/OCHoPibvATPeJtr2Al/rMif77+SDbajnkx3VDKKtL5YBEgY5fs9n2rBMSGQQ1vkabJeT0y6nHd0r5eotCD9AX/Bl6/V6CmKvr68nsI2trpo/Y0ficLPZzOwmdp62LaNpe9KXwXfkx68tODs7q7Ozs2cvwXbSwe1kcLcLN/g6+yNjncRfDnBcyrharSZeegXJj7kwLpdJPj4+zlakIAcNVTWbD/pEAoLf7A+5LhN3yD7JCL87koQFgY59euJk2sYO+BGCnOcOY2IHzMPb29tp520vvCSWoo20J6+hVwdV3bIZAuE6YRt1GyaYN2o/2/bxquc7wCW4eKkj70CCx5J98mqIs3GOZlMRbCww0C6/66LgDoT6ATqTM6QAOwdYPi8ddY7ZDg7y/Toj44Coy7rleDrHk/PsoCczo4yN6xNkdLJjPiVYzWDkrSkdh8FT1RPgyWxcyn4GtMjOaHUj/6r6ch8nJuzwDcLS4ed8WEY87qqa1WdbttL4WX4w+t59aF9QhaPhd/jd9c3P7jmoGsmaqbNfmeBJvrmvuwBuZ/vgYwdgkYHOtrnEcZcM5D1T9z3m9xZQme8GrVDaeK5J29HZbGSx41+e4/ulDmfAwnl28lyTFQedfcUHIQ+A2i4IRF8Yt+/ruba9NQDCNvt82vV3Jyy9Gs54uoSgdTp5xf/IcPoPA2WvBhBUrVarZ89X8f9oc6ORHHT4wXPtObIvGun4vjZy3t+SuqSo7bfBedpAj9s6mnLclXH5XvSjwyrIyOnp6fQ+SCe1vTK9qw2O8WnwT/CSK/7oRSZ17XeWy6dnjBI70YZ9T/a14yVjPj4+nskY+uSgivtxj+Pj47q4uKiDg4PpXatOUni+GSfEhh+r1VOl2Xa7nd5h5ufOOnnu+O6g1EGe5Y+xwTMHqJ43n5+yZ/39Xr36ro0qquqZoXTHyCZlpGlAxsBoN1eHzBw7IL53Bpk2M/hIUOOJqqqZY+vGzCoPhgEFcX8JmOywCSj9YHBmBGyc07HyhyDa4JiXi8VTLS6KlCuFdnrJF86Flygtc0bJYoJBlHGXU3AwRz/Me4/DJW2ZwcewkJlxqadlw9dYQVx+klno9wAA4b/JWdvr6+vpPJf2IHd+bi/5wLleVfVKqTNOlj/roucp/7pAOOfbZAfpgKlqXmoKAbIyEFmv11Of/GBsZqoMBg3ottvt7AFrP8Due5MZNxD0vCUQ92+c68SIwZtfrprlVlAmN6wPTjzkdfQpwYOfJUmn7evSFtku51wy7pSV90LYMW9cYDsA+IAv5pUdsOfZK6oZlPG7j5scCLjkrfMHzA1y6KoEUxdkVM2TKllWxX3wL1XzMhuXDHEuW1AnOaixrwBMOfnlHfDIsi+X8wy6gZIzy4zd3+EPoIv7e6WKdphTypWWy+VUCWLwmxsvwGMnBH3cPPJ19GOXnTPA5hpXZ3BOAs0uuHsLylU184mXMcMDP9+WeBCdu7+/nzaW4AXdvMvJQYVtX9W8igZ5Q8dOTk7q8+fP9bvf/a7Oz88nfec67mvZcVs5x5xH4OEAEl1gxQ4fnUB/u91OO1LCR/sx/tC5jl/2/VxLX3kkxDpZNQ/YWOE6PDyss7OzZ0kNygy5l589tPwZB/DC4sPDb+9VPT4+rs1mMwWP3TON7rfbzkA0gzHjEuNCvxgcO2LbnElG5IA2/fkaenVQldkSOwwMHwqWTnnU2Yx6dxmSXQbmNUxw/xOEJzByIJfK7DEm8LQxRfmyhC/HZEPJ/xh7K1ECF5f2GUAaILkvOT7OwelZuBD+7G/21XOcStCNNxUd45VZhAwEEuB6DkbUBfMpC29JGYhUPW2jznePswO8rgXPtmnXzsFyx2fO1yhznwDAoHOks8n3TkYyGeCAKK83+MXuuM/ojcduB2m+G3Sb0kl2JQHJb/PBbdBHznHmrQvGMkAe6ZTnM+1qzuvIeZkn2Y9Oh32PBN2+93tIWEC2yx3PXQZj+d6XhOlAl89N/vNbp8f8xmcmGJElbL3BebaZfgQflM9ijhIflkHLmgO2zmZkVpx+OHB0kJH65WSd9T/tn32AA00ncWnv8fFxVl696/ty+bQ198h2dT7DAZOv63Sny7bvSkKk/xzp41tQ+oO0+16pctJl5EPS9xOU7wsg4XXqXNUTtuG5KvYGGN3bY+H6xIo872RZ88tvfZ1LWo0R08bzO9dazp20dzvZb8tDJnm6ZDWlfvksfhL9yZUf2xeSFfSbeSPAyk2d0Fe+u73uHiNZt63wXBn3JRYZ4ZGUidfSdz9TZUMJcEGgEDav7DjaNBOsABZyrsvjvmcKuQU1HxJPBbaD9bkeq4XFD+ST0WbyMoL3/XOjgM55dQDJfBqRlZJ+peB7njpAxPhoAwH3kq83yuhqfs3LzvnQJ2cJ+a3L2Pk6wE4GUf5/H2hJZ+SHFhPEvyUh0+iYAYbfTeaVP1YrkL8uIz5yRp2RYp4SZOfqFPdKR+lzuT/zkkY/nQcPDKdx83EbSVOnX+5/Zy8yGLCNSnth/U/gTIYsr3N/kHuvOue4zL9sK8nyzTykfc3MJHPllekuA5uAJO/X6Q18YFX7ex3SP5PsPwyAOWYZ4beRffLYumCqAwYpo8l3qEvKOWuNnMJv23qv6ib5viY/I+xrLT/IeI4tg277HidXPQf2iylv6EjyDbtmv2MQ5fu7OoRVgAyM8UXgDK/mYkvhJfdMPnZzXjVfGfbvjL0D/F0SovNdnNP14a0pcQx86PAa5DkBYHM87X0G676v20sb/P+1d2a7kSPJtjWGMlNjdVYB5/br/f/Puq+nUDidg4aQUor7kFiMxS3z0FBVLfUBDRAixCCd7uY2bDM3Or0zXtV+Verm5qYeHn6+v8l2gD6enp7W+fn5vJrDypaf63W/vOrcYTrk0/23HmdCj9VzVrsoWaUPqVsdX+1jXIlBn+AJeA+5z+DKq1P2Hw704DsYwklenmNkoy3bCvMkfYuPo5vWh4wr4EPa6WmaFhv6OCBNXezs/kvoVeV/MC0NrEvgyBxZCG2cpml6FGzYmTF5FkgDBC/720kibAi9M9hW0HwY0WURHOPTk2pHhmAxRo67rMfKQlYgjaMdgwPQnPgEfAkw02AnCErnn0afz2ma6ubmpq6vrxfgz9egpAm23WfGXLXcQt5BLf02LzxeDJrLXroSGPPBctIZcsaz2ex3rXlrsvFnRx/3ebvdznpHqYtLFZDBDEhSFjhup2dCpyyTzJeTJtwvwbxBE//zSf9o0wGBgyrLsYP6TJZ0fU6e2sHB0zTCNu4eA9+7d/SYr34QuANOLu3gQWw75s4pjMj3N+DOOYBfBrYZFBqkJxjqnJLbSYdeVXOyAp6+J/K8ODHIcfRqt9s9CpC7B9dHjnwUuIxsUmeTvdLiTDJ20PYTMFRV83O02Meuf4Ad2wPLiIMa22h0J4FP+mj0uGq5CYHHh3xiC9BrwBf3ctDvYNLl6PDUCUYnBA32DGbRG/hmOWCueLDdNqezMRngdJ/pqx2U+tzUt1yhMw/fC1mO3W/4/ePHj5nHXSVC1b4Sw8+3GTNaJ6p6+9JhAGSU4AQ/VlX19evXRbVDyhw7BH78+LF++eWXxb3YPMwJRmQu561LLFjfeFdXBo2UTfr9Wsg+etLNhX0RZZPYinx9yGbz86XmWYnx4cOHBb5izgg0q/bVUU6S2BfDF3buZXfj6+vrhT2x3Pi5Mcil8yRMjIWM+ZEN2sAuuNwYWXHiFxmkL36f30vpVeV/Xrr34NO4ZqSeRibbNqVx6fqRYJnr7HQcoGQ2aJTxZiwoMudmpt7CkL/5oUNv4dpN0mjiuox1BlseSzcXXIOAJQ/MG86p2m+X6QxHAreuH/nJfGHc3FeDWO7P+b42g8I03sxFAp/8nvPUyeFbkYG/n5mqWtbkGxD5L/Wx43/ej0+fa501dfPN/925lt2qpf7YAFrm0B0HgwkyrZPuv/nlPnUy9RLKsXRBVVfWlyDbY3TtfreK3c2XeZn3d4Bw6HrzIcHaqL28Z9674xVz8lqe/11En7oxp8/yNcmD5wTB5s8oAMt7ZV/ddtow+xfApwPsQ8A7729+pI1Pv9K1476hD/aP5pv9Q9oy60HyJ5MWtGl7wfmjFe+cRyd00h+5OgKQ+5zM9SE9fspPjvxgp28pH++B0m8nLxxcdP7ISbwOW1T14+546GO2dZyTvsZ9IMBg621v+gU5AdP5GOuNx5Lj7fTLGNTPEnqBoJOrDicS7Nzf3y92kE77T7skaD58+LCo5iEYNi9GfsfBGQker7RnxQg0ssG0x8q27z/ydeY9c8yYcy64R47ntdjwVeV/LlHixkSLXqkiMmRVgM4zyARlzsJZQAzansNAR81dhmkUZPm7I1qcBIpkIMRE8z4OgBJZC96TUbV8vxb99mRP07TIyLmvXgVyoGe+k520svI7fCDTkfyDh4DT7XY7L4/n6gBz55US93MUaKXx6ebZWWKO2ah0m1QwrkPgJRXK936Os/y7CYPlrVHNe/OB8WMEXWaRK4YJGFybnquimTBJsJUJBfrdGbcMRCwv9NdBMYFWrhhX/ZRFdMsZJPfb5R2doU1jDlm2nb1y2x24yzE7eOzA0c3NTR0dHc1lJ/nAMvzItuFR5zg516C0A9NZ3mT+00bOuxMQXWDlPme/zYf3RAYkBnCe96qfY6LUJ22DfQyyDP+qlvzJFRrsu/XVNs88Mx9tB3LF9vb2dn5HEPfx2CxL9oWp35Zx+uESJ9rOUi63Y5+bfiH5bP0yLzI5xPm3t7eLcivrvfVkmpYrVV0yzokNZ7ftr2wLDXwt4x6759U0CnoySLSNTv3358jGvjV18zxaDTg5OZnnxnazqhaBNn7Ou8l1+uh7dPKMHFMlcHl5+ajCiU9K7cAa+C36vtvt6vv373V5eVnb7bb++7//u/7nf/6n7u7u6uvXr7Xdbhf3Pzk5qaurq0e4Btna7XZzVRD+zzoPvmTVFDzJitPNzU1VVV1dXdXXr18f2Zyrq6u6urpa+E7jTL5bpj1P6BirVldXV3V9fV2bzc/NXbw6B+8JVo3hqMCZpmleqYKf8MY+iJWyzmYaR6O3DuIymE1ZxL5TGZR6ZZ6M9PcQvSqo8oOyzhw7s8PLw46OjmaBsLO2Q8P4uz1+s+DTBwdzJisYIJs2U9gQMv7PAIDreJbl9vZ2UWOdZLDk5VyCEwM0jyMdU+cE3K8EYpzDtpluj3FwjoPbpwIJOybua8ey2+1LGw0qcMYJ8Oz4snYYckmGj3m7Xb8Yk+M25u6js04osVfgHKS+tWPabDbzUj96435mQIXB8RbJ8M5ZqW4Fy+f7uOXPf7TJpwOCTuYcuGUywM4xSw6cMLD88ILe+/v7+cWQjMO15ak3nlMDwbQpnJdyibyOSgvtRFyS5HY6kEztuvU79Z3v+WqEDHAYTyZc7NidBEvnwrl2VPBp9HJp/zlgcz+xg++FkJeU69R/eA6gQNbM9wwaOrtlkOxj5ldWfCRAdP8AhQTm2F6CKnjuJFoCKMgBkfUB24yeuuzbCbwuYEug5gCms7WeB3YoM2DiHinXtiMJnpkj99svR8Zv4Lu8Gy86xH1dDurEb/oX+mrgN0pEJNjju21vtu+2DfCMnd6aMpjCJmZ5OD785ORk/r/q8XNyVXvdODk5qbOzs9rtdnV5efkI5Fo2bOOta+j+7e1tXV5ezu3yAlz6Ao5iBzyCuYeHh/r27dv8PquvX7/W5eVl3d7e1u+//15fv36tu7u7+vbt2xzkQPTf5W70FZvhZwD9uIifZcdnGmfi57tAE95cX1/P7wjL0rm0WbZzfrEwZYibzaYuLi7q4uLiUUKGl+lW7Us3Pe+fPn2qf/zjH3VyclLX19d1fHxc19fXi/l0OTrvEsvAhl0Dc4HCZYtpT10+in3nvV8uN7TueUONl9KLgqqq5bIhgNYZQAMsGx2ClOfSKDt6aJBdpqSqDyBQ4tGqymazWWSz0kEm+VyvVFkx3Pd0PjbSzk4d4g3toSTdezXs/MwL89dj7xxTEk7bgsrxri1TOkAfd3Dme5lfHYDuxtvdl/51jvityU4ggR+/Z7Bjx5t/XfuHxjqSN7dn/XJyJAMBB0bWL68KZckm7XUZbhvFBH1pe3Ic3M/ApAsEPD4DQgOqji/J724MnM+47dy6BEoHSl9j3Lv2RjbFoO6QHHXX2YnBg5H+vzfq+ulAI/nV2U2u8bFDeph+Mm1z179OJ7pAxc89ulymk/en+JL/W3e71QHk1X7/0HhGlMEDvKEP6U+xHVAG9OY11MlozlmnC52t6c7pxtLx4ikdS721jfpP0K+qpSwRWJEg8DmWMc6tel6Zl793Ni5Xf5z4zXmzPUOOttttXV5e1t3dXV1dXc1B1c3NTW2323kVLF9ZkAFzrrhULbGoEwZOYGYAxp9fsJuB+m73cxWMpD7jJvjITW0SP5OEpr9HRz+fjyJwMV5xQNzhaY/Zq92W5S5+6Oa6k4eRb+tsZ2Ktkd6nfD2XXrVSZaNpkOqypOPj4zo7O5v3qPeqgjOlfni8y0A5aIPIDGfE7bIAlCEnjHb8UHxmlyGyypvNpm5ubhZLgmlUKf+jby4F5B7dhg+Mh2yEwV/eL4XGQZizRCks6XTMK5P/77LePiezKll+le12istcecUiS7T8u2tzO2dmOU3K9uz8DWzeiqZpmt9nYYPiOUTOyeKwgopeuZzCc5EAiPHCA4M65DRlz79bV7zsbnKpDnONUSfr5ky5dd202+0W5X8up7UzcdbZ/c1VdO6XD97nXKQTzESHv1unM6vekUs6zf9uzjOAdjB7KLng+XAGN3nE73mPQ+Axx5b2acTX90AZkDgYQE79vhZKXSy/Bh4JzsyDpOSrVxAzEZk2iWAC/4LvctIOPaiqGeSlP7DOYWu8Q54zzE4+OPtM0pB+MfaTk5OFbYbfz5kT5gU7YDyw2+3myg/G3wV1plwxARN0QBJ+cI3n0mNJvTTe8Wqgq3dyjIcCceavK3XimBNJ6ZPfitKnuE8cY5Xn6OioLi4u6ujoaF4tADt5s4bkZ9X+fYWpF/Z7JuYBHeE5IQIDSudt429vb+vq6mquFEHnvn79Wl++fKkfP37U5eVlXV1d1f39fV1eXs4rR9fX1wtMiU9mcy36Tv9d3ZO+4ujoqL5//z4/QgO+clLRfO4CGXTIO8+6QqIL6h3wgLU8r1dXV/OmWaPVJC9o+Jh1jRXkqppX2pn3UfVD+i5XUsATsJ1thYNQ5pln5Vhhw1b8FcnAVwdVqdRE0/x+cnIyCy1BFW3AFD98ZpCfzjudNku5nng7Mww/k2hBou+Ut3nFJQUBRbFQO6C0Y3LJHw7Nzvfh4aGurq7m55QYby5N+j4OYDuQa35nUOUVQn7PJfkO9HXOMJ0r8+JVPD6zXGqk7HzfbDaPyjMSzHmes57W/DiU+YMv9/f3i4ePszTmrQidybnrABjGDBnxnEOZBcT5OIPM8QyspmlaJCU410aa65hH63e3OmUw690XGTvkhAvHCaQMIulTVc2OLldWXeqZQZVLSruSU2fj4J2DLfMYnvD9UPmfj9mW+PdMiPC/t4RFln2d58r8c5mEzzsUvOUcuF+Wh2zHvBkFlW9F+IZ0npZ9HDNBFc8pODnmpFA64A7gVz0OmJEx9CZXCT1n3I/nKJwQ5PkKklzcG/+YthT/gl8GXHb+z9l0svEkReAloMUJH/f9OTJgPnqFyS9rvr6+XpTUp+zb/nnctjUEVQ5cnXClv9aJDHDTJzmoAtzbt6aMPJXINHCkTfwevxkTjfzdv5sYe+K4qn1CADm5uLioDx8+1NnZ2Vwy9u3bt7q6upqvy2Rx1eN3NVbVLI/oNXqR/mq73c5JSJLexpDM6+3t7czXb9++1ZcvX+ru7q7++OOP+uOPP2ZsR5LPAJ7n/kgEOog4NE+eR/sEJ5m7xC+BjYOzrm0nhYzB0v86uExszTjZubaq6uLiYoEroJRjbEUGVTyy4v54BSmTFF1Q5blOvA0+ZVXUWImA3sEdbdKf1wZYr375bzoJf09DzkBswBI4MJBsq6NuwAZ5+XsatW4CnKGjH45sEQzuYYOcTrbrg/tphc8MgttPIJRgLttyzTvXuG9+RifH6/vknCcgNLCwofc9R20eAiGd3NjIZKa3I7eV1IHZNNJvRXYkz+lLyoOfpWBMLls9NMdJnSFJncm+WBbzewJRA/PuMyl1azSObMcBHfpsZ805XXCe/e9sG84lA8fMIiZ1gUnHF+uvA9iurTzW6VWeP7LbXbujFZicj85HvBcyr+3ku7HmS1iBEAAAIABJREFUH3LzVAn7cwKq5GUnW13f6b/9VgLZbryeD2Sqqh4lNFN/R/f3dd44xtUofOJb/dmBI9rkzytzBEZ+v1u2kUCesaLf6KZXNZ6yg928pHxnAsK24ikskPPUnfOUvv6nUfq5bix5zHOdAcBut3uUYEq7Y70Z4T/uQVn2NE3z8/BsS86KlBMZnQ5m8PscynFl31Ouq2oRaD/Vril5PUriQb4H+odupQ/knE4/zf9MKHa+6pAfRZ/dNtc8F2NaFt1ud91L6FUrVY8aUTlVGpZpmuaH9TKyHK2UeKKr9lFsToCNNkYMg8ynHWLVfqL8oGq3BSvnene/zCAYxLr/kPuN04FXBmYGeSMDbOVKwdvtdgtj0AlqAj63282HHUY+hGsAmWCe89hAIfvKtbkCgGxlWdZm83izCa8smFepKOaH5cRtdO9FeAuapv2mBCNg63PhEUvY5r9Lgg6RgXy2z+/+NAji/s5ap2FDLjOr3znRvH+2bfDSOQDKkdLBjYx4As387mtdluznVKB0KB2fPf50oB2wQKfMa/6YN8CE58i/J+DjXp2jzrFnvxIMpZNMHro64b2QE0n0nf5mVQC6yKqwAX7ycARIRkGwKx68esqxDACcJKiqRVVEArxDAJSVFew52XVndd229ejm5qaurq4Wc/3wsC/rYgXC5UoduM3+TdM0t22+Wu/INh8qz5mmaV6pc7bbPsoJCvppfU09tY9ysJiVKr6nx+Ux2MemjJgnuRKevsk2o9PZt6K08YkpEhfc39/Pm0VsNj93lWP+vLMvPsQVMbmZibFTvmOxCyqYFz+T1CXQv3z5Un/88Uf9+PGjvnz5Ul++fFkEUXm+/7dcua/Z36plif4oqDKfXc1j2975HvsMP3f/6dOnmY9e7fI1Tl5AXgCgvDhXEC3b8JckycPDw1z9xcuAP336tJjXUSCYfid/r9pvdQ/GTDvOyhS6y2dinT+DCV8cVNEBgxUoI08mnW0qKbmzAU9FdDvd/ZNwCDmpfm7B1zkjgdDki77sxDLjZYeR5QZdKQ/HMc4EVc7MZfa/A58JhByo0D+er8mx+n87xQ4cOeixgfBDlQZyduZWXkqxbNhyCRdHBN9sEH3/fOYqwUbKiAGoeQqvbLTtgN+S6Jd1K8F2jhdZ5E3rDvCdxUqw4HZ8L/rh37ogHAdkBw/4qXocVLmU8ClD5f67XxkgGNRULY2pja71LAOptA1dlo4xdP02X62f6IHPMbizI057mbbDK9iZmewMf+dsfZ4BgM/v7j9qM8GvzzOQfyqo/3eSZdlZbdtw+uyd33jFwWazfzWIM9CdvzOA595Q2ifbu0z+ZXKA/71qgz91YJT67CQTO17hk9JGElRlO36JqP/I5LP5gAELY+38uXnG6wZynmxH3J/OXieA93bMHge+Lf1I6qt1LgOZbs7sr6xvzIvJyUTsAljKc5JtdUHXewmqOj4a19gukNQkIVhVM8j/8OHDvCsfbRibZcLUiSXzLVfDOntFUGX+4sfu7+/rX//611zyd3l5WZeXlwtslIku5tBBkm0i48lkmP2A/+/k0ljT/UgsDiXW4xqCC0qdeTkzsgjfXQ5Xtdwh1/7FpYLM8f39frdeEjAcyz6knTIPuUfaHic7jdMdGOVz6LZ/1mHase6/NrB68e5/I6drgc4/Ok72zULgwKIzEEzwc0CvhdEgfwQO7bj8l9dyPp/dnyfEPLGg4KzhQ2YWqx5nFe30+cxMBcczy4EgHuJXp7j5PZ17XmsHkvNlI2ZAknxKB5xOygKeQYHpEDi08RoB67ekUfCUvyeIs2ygVza2vt7zhxx290l963hk3bGsJRiyjGfQkPdIIGr5ct8MeHa73ZyRctBn42zZckbMQb2fr3OQ9FJ6STCR4zvUXgZvI9m3nHM8zzF1Y+4CL/cBcND1y9e9Jxr5rhyrv2OLkCHzqpvn0T347Pxk1eONUMznzt/YdxlgdKAn7aWDS//u39IWdTbBACTHY99xyHenrLr/3dh9XcfvkZ8ySM3VhkNzab/c6epTczvSDfcx75d2Ps/N+7wH6vhe9bjk1QEtgRVJ21xlyraTL13AWfX42U/P9wi/QQQTDipcbcG93E8nu5BfnuPJOcInT9Oy6iGDTt/DPDDvHFh1Mmzszf/2hfZ95pX/zLdsm98Yizf+SN9zSNbRrQwSzTOOHVpw6XhNUsU8y+Sgx/pX6NSrtlRPIXSW3ICHF4aRVdts9rvy5cqQAwwbv3zWKJ2QrzdATOeRht0bRKA4FiAmm4fbIBvKdHL8vtls5hfcGfSSFQD4GXimY99sliUU/J5G55AA0Hb3HEBn7NOQOQDy+0q6ANV89SYhnZDbyBpM8DI2A+Y0YO675cUGrDvfhsG89X3ekgzeqh6XvGZ21BstVC2N+TQty0q9zauzeg5izLN05jmPnm9n730P5ITsOWOEulVt/3F96p7fLeESEUogrU/OcqZjt/zl97RHo4RMPiyfjsP2KsGnx/SULrs/6YjT7nROIefW97I9s63lvrb3o+fPLLMd4HkPNE3TwpYjY7aDXRKCd5p4QweyrWmPfF3aV/hh3bXtMcipWr6fintYBngQ3pu4OIFhf+f58oqPN4uBnPnl/6qaNxawjlGyQzv50LflIcFV+hdf0yVWGPuh4GZ0ny6Q8qY2XSBju+Bd0DrQz/xRxsRmB9zfZfD8dSATmXDWn3OxoU42vpeVKuMgr7qZfwRPVXt79Pnz53nzLr8TE1643DPHy3wgg6l/TlqjL8ai6FbiK8798ePH/JJfbwyDD0aHj4+P51Uer3KBdRPA25Z6BZNx0R6U+sxKnoOrUZBRtfR18AN86veusork0l/jZ/SFyhh4j6zf39/PuxX+85//nN9lxYvJLQ9eZOG9ZS4HnaZpsYpItYCD3KOjo3mzEGwP/IDf7OJ6f39fZ2dn8/vCGPvR0dG8yRUy8Ffo1IvL/9J40RkY599xELywC0MOIxBeJs6Gv2r5TJONqYH4ISBCv7rVGiuAyyZy+XCUacosh/+30ruv7HTDGB3sdAEObXTBnq/JDAxtpCOBh4dWr7I/CaqdDU0QjHL59wx26EeukGCoAD4oiXk4Gi+EwcuAimvsFDn3vQRVVY9Lx8zfzNbAPwwwAJvzrVfMWSYaME6ei0N6lcGTHUSCiExacD33cJsZPOKU01EafFbVwoBaDjODnzKc/UhA5WAsV7M9bn43MM2MZva/O5bn+re8lwOfvJf1zXPtxI6dm8eLXc7+OJnVjTftia9LwP6WhD1NmTJ4SDtVtQeEfLddNy9yHiCy8PYD6b+cLHF7nm9n0NEpjvFMlcthOtmmPyP7n/bG8udEoZMmBnY8m+EggHZtO9Jnpn8ZJTFoN7EA16W++B7+nj4sEzyQky3O9negK1cYONfJobQTnR2yf/IceF67ao63po6v9AvepJ04Pz+vz58/1/HxcX3//r1NDlQtdahqud26S+mSx/QLDOmSwi5pUVXzluv39/fzRhVeAKjaJ4ePjo4WQZATm9y3qmZMY1l0aZ37zhbflnmfQ3LDr2IY+WrLE3xEb9j50wEVAaUxMOMgiXN2drYYL+cRPJ2entZ//dd/LXZYTltpG+OAm4QpesO5BF7s3smzYdabxI/wCptFwEbyA3v748ePxasnusqdl9JfUv7n3zB4Ls0xY9OJUHtpZ9KBt3R+o8F3WSwbpTzWGV/uMQI9CXTTGWdUnsCPc9JQu2/8bqOajoRzq5ZBmvnUBSBkgTwe9yOzaL5POsJOHuhvji+diA2CeTRycu6HKdvu5id54GXg9+KYpmlaBD8dYK7qyz9SPh2E8dmV1hggVdUjHci2+T/noQP/OY4M7kfAz3OY8+h5yxLR1JXUm06eunvjALjWq8oZMHW62MlSJ/9JCTCzX2mj3G6X9c/+jfS166vv2fEsA4AEHe8xqHKSbMTHBKteGfbK6EvHlvKecmd7mdc5c2zQmQGIx5AAnft43J0OW0674Kbq8bMrHDMoMZ+cHLOdsS1OP5D9tM2wPGaQP5Jv2wbLQSfbxiiWgbQjz9Fnf+/sg/uepUldW11i472SZbzjFYCaFQu+J8bIcrLUH89Nhzk6HEAiybTb7TdW4M++rOrxKz/AsByjfYK93NSM+5g874B//E6uchJgdEFVh42MA8AXXeCZOJFjVcv3QeYcJo58eHiYX4pM38H4TnASdMEjAmfbQNsf27Wcd0pHWX3qznElQOoY+vRc//gUvTioGjnSqpojdyJJygKIBGEmD/464rUAO4IHLLAUOE3TvESY1AlVLp9CZAo6xTF1joqJ6SbKipbG2UDDhsPC5D5wnbPl9CnH7R3f7IgNJhirFSTH2gmWlS5LTDwePr1TYu6qBXjIQJNz82WYOV7LnfmRmXhnQar2uz0ahNOn0RL6v5vog406BLBznxkPY6fkweUJXhmepmkRIPDuDgNI7+TpPnV9TYDKd2e2LGvcx89WZgCReuS5qqrhg61PlUGkDJm6/y3vfvcJn7xMMo9nwqJqDJgP9clymgFjJm+Y9wSXPs/3TzBp25b8GiUrXEqHvmef/oqs319F0zTNZTrWAa+4nJ6ezr4A34W94/00+LWbm5sn75eO27+l86967FPpK3306pSz+pABpfUkbXoHnExd39IHGgghe15BSEBt+aUP5geUiU4os+emBHuZbPH9XZHiseX4aM82AP7ar+bY7KsNVM13fH624c0a3J9cScXOdMHXW5F5hzxkgJyPiZyens4rVZeXl7Oeffv2bV75cKA/KsF08j4TsuAKBzbeSTLBNL7r4eFhfh9VrmpyHwIcNtugv+imEzLc28EZ88pq12azqbOzs9kO8W4269bHjx/r4uJisSLDfc3nxLPYEm8aZZk1BndAl5ic+Ts62u8yXLXfIfrh4WF+STI2lZ2/4SMbazlR9fDwMAfWu92ubm5uFjpH2xC84JqLi4uZh+Bd4pGqmlcAWaliIxuXgWZQBp9eSq96T1VniBl41os7q4dQMpEsx/p3Z1yZdP4nyh4ZZMhge+Q8MvuXbabi+tMGJEG8FS6zJAk0GDvGNJ0q1zk7mcEhY0HZDRocLLptZ+LdF/M9+elrfa7nI51gBpP5f4I5H38K1Ccl0ExgZyDVgff3RJ1MovTJ1+RhOiBnZi07lgkySdzblIAnqQusMnhHhxlHRwneUiY8V4wt66cTZIzmNu1BOh/GylgA1wZK0zTNDi+z74zR/3e863hh25WA2P3O3w8FbSNQ3QVW/p7nJF+xycxv2oX3pFfIS67weD4/fvw4Bwpkn7Hl/m4wduh+yXPu+dSKhHWJawxyuleAWA74ZAzZPtStGo3sOf3mHK/ecP9RsozfO1uRlKtvnOfr7dNou+N99gE57Vb58jqDM7/qI4Pj7GeX8Xf/PW7bamMm7p18MT4a8e+tKX0RcgEPvCswQck0TXV6elqnp6cLGU5fMDrmINeJhcRdzAHyw3NWVXtb7I0pXL6ewZcDJoC7ZRI74Q04RnLh0jSe/XHwYsD/6dOnOj8/n+9p2aZN2wf/JX70cWMOt5OrVPDKvIQf9OP6+rru7+/r+Pi4Tk9P5746qe79ArBr2N3EEonZLD9VNb9ImLJJ6xc2kLaxi/Q3S00Te/7tQZUpA4A0SGnUDOwsLB4c0aWzSDYgTKAfBnSg5eugDpyn8etWgUZgwsrbAXiMY9bRp6OzE7MTT6Hw9Z0RtbMxQMAo0K/MlsOXUYBs6oIs9zVlwWQnAH/M3wTNBqR8JvjrvnO+DajlyyAm+/waxfk7KMt6EkAj56lPHmOnd4cATtUeyACS0UUcA9ckdU7CRtrf6V9HCWQ8d9YbjGRmLK2PJsuC++y+d+OBJ/TZgVHajRHIybZHgVXe/7myaEBsh30I9GEXEtx19iavT5BkXRoFqO+NLP+pC9YTBwy2n58+fZqz2OYHvscBie9j/gJWckXYoCd1CXqKt5b3Th8Yp8/3veljAi/O7ZJVbjeDqgQnI3CXY+yCL+tdJ3c5zpyDqsf2p7NdHcFPvkMJ7J3tHyU/0m7ZzqWP6nTVYwbvvBedS73q/nfCzysTPE8DTvGcoVudHe/uxXWd72OVN/ntJFHO40g3R76x688hPlFRw8oKzxh5VYmgCp4ljqx6XC6XMmR81eEs/nKVK8di3+P54FpW9x4efj6v5aSt5YC5zgom7G5u1oLuZ7KukzP6ZD/uv04Xs2rptXr1oqAqjVsKFkxhaS8dU9VyGY/o3Dt0wYAclO9tpTAjk6FVj99qzXHXzlqAcwnQINaBoP+IfFl+9M6H7pc3RbCgeweZzlmMjL+dlY0OGQZH4YwR/tsR5MOandNyO3l9J9AOdOEhpXi+zsrj1c109CkbqehWOOaJZV6AjOuJff8E3m9Bu91uLlHs3jfjPtr4kNkxL3mIMwNVO2H/5Y6BVTW3k6VAo0DKQQblMlVLeTHQ43yDNScjnOHz8a6sieMGifApd+/k91FAlbplnfHKL21yDCeRQNuJjgR1nV3qAGXXT+s2PPQupgYKmazAwXkc6WTSUUGbzb7swm2YH3n9e6EO4HIc3THA22w2s05W1fysw83NTX3//r222+2sN1XLZyo6526bic22n0o5yQCi873mMaAsdW3Ehy57z+/c37rUBWwJDker6NleHu9WzRLU8kk5bscTyH4/V+yYhxxnp2uMndUq/CW4xslJMIX7l3qYPsdYwb7Ldjl57aDNoPktyXiGT88/v3vHOez2zc1NffnyZX5f2devXxfJ9U5nHWgZ+3iFp3vuiLKyqmW5KHNgu4vuZ8IkAy7aSoxCO9Z/84e/s7OzOj8/rw8ffu4OzUrVx48fZ6xrv8gzVbYlXclf1XKX7UyqZ6AERsxEqG0640/7w7W73W5eqTo7O5vtEeeD71wqzAoTuvHw8LB4PyAxBXiB81291fkr88I6nJuTIS+seDGetK/Ppb9spYrBpJPuDCwCChPtTDwIBx+eRAtTGpX87n4kAOwClXQs3fg6AJJZj66ePZehq5YrVfSryxS6dj6DDa5D0cwDZ8LyePIr59f38vGckxG/7CwNnlFcrkunw30zIO5kK3/L4BdZS8XzuN8D+MugpONtjsUG2rKeOpbHDaohO+kMvnzucwGQj7Fi2sla2ojMKCXY8GpvzrkD8qpaXOMxHMpwJziwvYEv6JoTCjbAtjG08RwDbRB9CCjZSXeAw9le60w6+A4QJBCGF50ceQ7+E8n2074ImbGuIXcZOFQtdTeDKvQtAQzXQZ6LDMIM6PK6HMsokIZsM/ysR/oHrhsFVdY97g1QysCc750/gB/+jXvbDtrfZ/Kio7R9nV6NgqmOt35+sgsoU+fTv3SYgf/TX0GjlQfbmfcSVEFP+ej0y1U/x0kiw4m8UVIASjtnH5W4k3742R7OJdCzD3LQbv3IYN5jHo17FHi5sgnA7xU7y3l3rnWCPnMf2y8fz6C3S3iMsL3H042dPrOr9zRNtd1u51e6dIkW65WPEwRxD+5p2zzyX7YrPm79SuxkrPEUTniKXhxUpQOhs/xm5hB1ejXCRtpZXupAGUyCSxss7/9PKQVtJmi2cU2lSEPNvbtM4iFecF6Cog4wZhlBGpk0Dh5DCn5nwHyv3W43Cy0O2qDRc5XGGl54rJ6HzB7YuGUZCH1M4Gw+mDqQSJ99Xd4DGegcO0qaq1J5/VuSs02Z2cpjGFUMT1UtjBCBK+DJGa3NZvPomQyX1pKZ50FR8/E5QMTneLXGO32mjNPvDuDn7yk7BnOmTG5kH/N/24GqZZYu+5vJCs7x+GwX8r5dEObv1lnbjEPO2jalG2Oel3aqA6kuh+iANPJ1aGXkvZJtlefKdppyG/wMwC9XTKse89vzloky5hh56fTcyY/dbveoosDU+buctxyvgyo/y9DZQ+uSK0/ctp9r7ACUx+Ix20b4HFdb+LOqFiuyhwKa/H9kC0a6Yj2terybmm1dF+T4XPvIzs6l/nZ9zuvfi//qZK2zNf6DH6xM8L4iZCfbP+S7O7nvbK3JPrGqT8KlD4aMIb2xWtVSNhObOTFBgoaVf+wL1TVVy41/+KQqI22GnxmlD1ScODGYPLBMM3bwXQZmBE0dnuS8+/v7+UXAl5eXM17hfX+0k1UftgkjjOi4opMt40uPNStimIece+7XyeBz6cXlfy6XY9IANVmSQtSHU2LS/BZtP1TnHc0QVhjv71U1L4taSC0UoygTQWBplzGlYmREnGDSDLeCwSfOsSJlzWZOcoIvGwULte+b/TVfLTTOVuTDed3KRWdAONdywFyxVFvVvz9oJKgJGNMo+14O3rjWSgQffIzj3hnGy+C+11vSw8PDvGsQRtBlSC7tcmkc/Wd+Pnz4MJehQOgP/MQg+aXAPodgjR1ysrYZeThkeJx9RqfRcxxJB8LTiaRMWJ88x24vkxgJ8kxdAOjjbsNOwA6M89OxpQ514K8DyK4/NyhLB98BQcpmcmw+x/PYgRH0wyU0CRThiUs3Uu/fG3nszK0BTB7HL2B7/PD3ycnJ/MJS+6WO1+afdfX+/n6R9OK8DBjgpWWItpL8u+0DsuMXrOcD9D7X1ycQ8cYNkM9x354jC53/fXh4mHcX47t3P0THmJPUr6zssL1IMGZ75nH7eh9jjA4Gs61uDtI3pV1Cf9Ouduelf3tLsl2y7FgWjJGQf+To6OioLi4u6ubmZvZvXcDkYB6yP8MG8b3DgLRJmeb9/f1sA9AJP6Zg+wshr5vNZt4+3L/n4yz2Rd7BF/1jowoCD3YT9Ptd/Rwx12XZOXNgrODg5fb2dvFOO37vEqfWM5/vxQ/LI9iWoIvdf//444/6/v17nZ+f1z//+c/5Oe3ciZA2XB7Yle0SP6TNsA57kzj7NPPduyd2lVJdQPdcevVKVTplM8ffLVCADgCKo1afk5mtbsXEgJ5Jd0TNuWZMggwDqXSsHdjpgo406Nl+8qILCMwzyM7fv1nwc7zZRtXjzFrO0VPUjce/ZWkL7aZQdjzo+pwAJOd7BFo655UA4bllBW9Fdsh2yp1eMQY/B4VBgleMmfK7BA4c4342qNmXkU4kdYkBBy2MZ5T0SEp5GcmT53kEWLog0PZlFLDkfXyPkc3z/8/hW9Xj98ulnCbQeE57h2Q97cto3Am887fs23ulrn8em3lhnUJXXIZq3RrN8YgnGVj7E0qdfakejvywd5jLzD/86ICxgyqSPXmdS3Pdj/TnSX6RJ2SMgZ/PlWE/2wKmoJ2RLh2S0QTPI/J857wckoORfcjzsp/d9SNf+lb01DjSLxgXAqC9xXZHh+awSwoc8jFeubm7u1sEUl4FGd3PbeR9LHujoNn2JP9ygyjzCh1GBjPBmD6CoCl9edXyuTTrfGKP1CsnF40v066xYlVVc0kjvxlz+F75v+fdPM05MtlnjfAC7WPDO2zwWnpxUOUAJIWF31MQ/KyPMxZmIg+IwUAy9w58vC22gynKlLIEJRXabeTKkleCnFlncpzp55hX5eg7v7P8mdkAJi+DslTiNNr+nv01j+h/rijx3edldq8LmvhzVqCbf8bmUqz83ePL52K6ORk5LOSNrI/7YnImj2u4t7Ne3dj/3QSPbWzMV2ejcEAOBvgd/vOej6qaM0tcm2W1LguwwUT+kV/I+p5jgNIBWgcBOl5659zcvhadojzJJTR2TvQpA/AESikjln3rhjN5uaKdWbwuAE1+dPfszkmAnk7ANqYbm4Fn2hj4nzbUcmbH3YFu5jbnyIChC8LemhJ4cIxP8xQyj/LdUAQWHz58mMthDDw6gJNkAGd9Yk78HJ8p+8j5tJd9SB1xZtqfVct3PGXJTG4iY7LMdLw/1PdOZpywRS8ZIzbJCRrrsR/ON6/ThqWNRQ4yyZL+y7o78qGWpVxJsJ/sVqa4Z85Zztfo2reiEU+dwKvaywCrDtM01cXFxYwxLi4u6vLyclFxYXuXMsS9+d86MQp2bcvBawTqYD1v+GRKnbHcGf86SNpsfq5+n5yczN8p4+PdVB8/fpy3VN/tfm6IA47k3LS38NUycnd3t8DOGfhZZq3b9mNVyw1ejBk4Bz00X6pqDlCtnz9+/Kirq6vF9Z4z7Jx1zhgxMScyBSaw/crkV1U9Kk1OP+C+mMev0a1X7/5nRjIAB0rOcnlnFTppoEcAU7U3QExa1f5hNsgA0KUq3NNMsZF1MNGBDP7nkzZtkBmjgRvZlgTx8IRnWswrO4cERumIE4Tx3c/g+LuXf12LbkNi8GiFgaxg3ljCBsuCa6fqbJNBMNfn/CTvfR/30Xw6Otq/aDoBZAJFf7fM2Si+Ne12y10t03E+POxLy3igFRlywEEA4hfnUZ7QAZ9OD5hv3oqeBt3AvZuzBNu+F/PkDL8drueFtrzDoXXPBtTnW7/c1yxZok8p5w8P+x3GDKxJ4DhDmQmKLsBKEJBADLKeZPCYgQxtJd8tLy799csbU7cSvLm8y4DCji113oDKwcV7oZSjDKAsq04E8luW1FD+Bz/9Mmjf0zY+AR/z4qRCVZ9Q6vSJcx1wdPpsYNetVBmE+Fy+T9O0KBscrUiNSnoT0ObY/OJ66xFz0YHCqn0JV+odvPC9EpilX8jAzm1YPka6bN7DU9ssAzhAcuo1babtN7+tl7aV74U64Er/naBCHyl7+/z58wzuP3/+XFdXV7Xdbuf5zaAbMqas6h9lyLLuqsebxXAP5DjnDHJilj7hN9igweOyPT0+Pq6zs7N5/tn9+vz8fN7975dffqnz8/NFIL7ZbOYd/3LsyB+Jjmn6uTnEzc3NzG8Hj9gj2wbwBKV9xhXIPEGafZ+DGfueqn1AxjW3t7f1/fv3OUh0CaOTe8bzrNjZdtp2w4/cIIdrTX7uN/GJsU3il9fQq3f/q3pcYgCT/Hsqv42HjTqTiJPB2fM9jZMNnwMTK5mNYIKZBOJQtuUgyt8NHDqHhqA4mrcBzsATebt0AAAYV0lEQVQsg6s03tlXA6NDn4e+p4E6RHnflAELq8FJJ5g+z+2lI+vu7+vTEcLj7n6HgqqX8OHvpHTC3V/VUu8cGOK4ukwNRDsOZFLu+HMJUgKd1K2n+O/xORCxPqUOOfGQ39OZur3U3y6Ad1DY6VImHnyP5Id5luN/jmwl6Ms5STs7uibP7frRAXbzJAGReeV7+K/T/fdI2b/83tkydAlbnkkkVlZHq3p5j+famk6fDCzyvOeMu7Mj3RwmcAR4+f9ckcrjnW8dBSS244yZ9vy/MYRBYfLI4DH1/hBvsp8jHTSl3ue1XfudnnUgLo87YPHne6FOxp46bhDs1d9cUe1sSzenXdBlG97Jgm36ZrOvULIMHmofP8N9LKcOjq0jHmP3Z1ycyUPf21irW9m0bHo8HZYYzVnHz473th9O7MEjl/k6KTPSz1Eg3d3LxzPYo61uXIf057W69aKgapr2UTydfA5wttOxQWAP+9vb28XGE1V7piOkzry7HMCBDu918LJyGssERiMlqarFag/3ILL2Q7424M4ETNO0eCDPSucH8dKwGih3fcsVqRyTH+q1st/e3rYrWCNedPdOcGWHSzagM4Z5XcqMx9XNl69FPo6Pj+dsRvKE6/k0WHCG4r04pmma5h3Fuqyy+ZBG23LDmNEtB1tVfb03wDCDBWRmmqZ5JdnvKDJ5DlJuExRU1Syf3uSGefJ4sTkAK8ZAv6tqXoEYZXPtaJzkoN+pV4wbfvi49Y3jfpdRBlhdRjT5lsfsgL25TWa0s03rG3PqfiTAQPeTRwCbTt/d77zne9GlEQHAsxzdv3ssGfzbfjjIqFquDKUNM8jHT2WyrQMqGVg5YZmgoaOnAuHuL1eysCOUKxFsORPNGB2ApnxatxlLVkiknCKrXjFze4eSHb6+878ZrHT+ymWEo7lNGgVPPh8eot/eDMZ8cr+wAd4Mxn1+a3Jfk5fJawcJHD89PZ3189dff62rq6v5nVJdtUv36EaXcEDfWGkBHxivWMc9x8xP2jevkORqadUeF3V/Xr06Pj6ej3m+Ga9fhuzdRrmH+2r+oMcel/Wb89AXB2yu/rFNy6QH94SHtg9eDGEO8UnoPHbF/UBP4XO+Y5Rz/diAA0k+ee+raZRots5Z1w8lyp6iFwVVm81mXra2IRhlZzOgqtov1cF0hIyaVhhrkMx1TDxLlNyj6ifTDCQ6w58GN4Em5NIkP+MBAGH3Ee7Fb/TDQZ/HhjGAfxlR29iOghwLe5ZN+LgdOI7FOx7aUR/KPHQAkXFmQOWXGKbTt9B2wW4GVenM3CfuywviDHar6tGuMKl46ZjeA03TNAeJ7KRoPpr/5t0ooWF9IqNuwsBmINrJUVUtHp61QUydsq5hsJNoO5MRCTqsYwaw+TymyyLtODzHyIz7nH0yKENvnEgx0HJQZaOfADHv4znqgBq/pX553jIo5NMybuCRuzgZnHOtna7LS7Ev1mPPPX3qEkKdTXkrgjcOqjyHBoUElU46VS03F4JHyKYTV8g05ITHdrttA7r8P32r9asLjjw/pi745X+DuAzi8XMkeAAqmRB0n2gnAZltAfYgkxDmAXpv35SlhdZFl+Kmr8+24asz9hlUMS7sXxcYuR+d/nbf6Q/8Q4aw995Zzf3pVjoMoN86qEpb2wWpHovHwLnn5+d1enpaVVW//vprXV9fz4EVMmXb6oRWVR9QMefs0AfIv729rePj47q4uHgEvnM3anCuy5zBeGBO/GHuQNsFVLTDvBNg5fyCEWnLQRW87fQG2bZNSuzNXBinWo6QfXhmLMvv8Nvv1/IzYp8+faq7u7v6/v373M52u32Em2mPeyTO7vypFyYSZ1J6yQ6KllHLYMqJfVnVMqB+Kb24/C+VxoYqieNpZGAqTs5Gww4oI3cARQLJNN4dIB+BmxFhrOm7l3UNSlAugxUENpUcgMvYkm9MegYW2a/MOndOrAseOx5l2y+hNJg+lt8PtZ396cZvxU8DzblkQzpgkSDEMvTWTok+dNmq7rwRwZ8sG+iu89yYH66Xrho/lG05G43nEKFfDqwMYNPIW6ZTvgyqON9jc/vYrE7mEsAm8DZw60Ac7bjN0bGn+JSJhxEPu+u6czrdTz46sCKxM7Lt2c/s73vQqaQM/Lpg0byoWupUZ+t8TtqZvHdnD/05KlWFkLuu/57b0fUdP9zflIO0SfkHGRAlb7KfHS8MmvGNbtNz5/a6v24u3Z+nZLrTlz9D3Xwmz0eBiPvdjek9BFSmp2zWoX7b9pBY9HsSU765HgxpTGCfBWWgndjIvsZVE6nLBL8O6NOPdTa+k8/Uo84+d3KRMmXb081F9umQ33D79nGj+RwFz5msgXeHsKjHw/kdPu7+5x5eDfRYU9/SPo3G9xp6cfkfUbmjvcwUVS23sOY3Z/gQQAINFMjvyuHarMvc7fYZCjP24WH/QLYz736/SOfw8kHGdHgGnCgVYwGAklnPWlkbTANGgjGIvjn6Njk4HAVVLlPy9wSHHB857jRA8DmdbWZfOuBh2emUnH7leZ4v+Eg2BKPLCwMdrJpHyE/VfmMLAyTu58+3InTL5X9Vy2SAjY2X9+GTt6S9u7ubN0ghA3hzc1NVeznOgNQOwsedHaKUwvKTcoR8ZMaeNjvnge4wPq88GWztdsvVL8tl52DMM6/iWFY6IJUrUV6xyvr8zjEcAmVODqVdwgY4kWRyGUSCNI/doCH748yjS5D4Y8XUJdippx4L5+VKxUsTWX8nbTabOTOMDXfpTgcIzKNcfU9bZ5kzuEKOyTpTCgMhBzzAbTBCvzmvmwey3ugQ/tP2Nlcnnbjpymf8Hiuv/rq8x7zIZE4CsRyvgZBl2HaI9qxDln2uz6QR9qED48yH+Um7aW8tGx6vyW0kQE7e2H5R1eEMf17PPeFDPt+WfH1r6viTttdzA0+22+3s2z98+Pmept9++612u58lYt++fZvll1XeaZrmFab0G/CYKh76kfilavk+KY4bO4EnM5hBNkim397ezn1ndYOtxDOQMr/s59H/7Xa7KDl09U8GYvZl6Ut8nGvx3fkeS9sLcBSP5MDDqser3rnq2Pk9bO7Dw8P83i3wflZJeXy5GvuUfcI2/fLLL3V2dja/SJr+p63zijlVPebDv22lyp2HMuubYJjJ5HfKtnAmGDQAn3fXoj07E5jgjATkoMdlhO5jCjeClzsEWjgQYjbRoO9c46DKgUYqsh2PgyoD45EBp6/ZP/NoFEBlkGQHXbU0zgkYzWMHiGnoc5xuNz9HINQGwyVerkNO8AcA7HZuo3367ucB3Zf3QA6K4OsoU2SwnUEVRpjdyG5vbx+9yJREgJ+NQp79fGPV4+cXaLvTPfrm/qWu2Vl5PFni5myhz/MqGoSR5lo+s4+5+ufkgvtnenjYv5TZOtZRBhPZlv/PwCp1y/bTQDX/EkAnn1N+0j4D7FzGi17xDKZ50QXPo7KU9wT6ACjwFJmxjUsbXVWP7FvOU9VSB6uWcsVxB56QdQvZTttvme3kz6Xo6DjfHeS5bQeOabud6fXzVS6FSnlzm+abbUKnY+5P59ccVBks0k4GIpxH31NX8vlj39t+kXNtg/L+3M+JUvsV2nZwx/Xw0mXzyVMH7ZZBZ+HTr74VWfc7uYAP2G4HJwQk+PGTk5P69ddfZ37+/vvvtd1u6/b2dn6hrGXXesZxBwwOihP/OaiCp9Yv+wbaoh0/akKgYHn28/0cMzmo8jPw2+12HhvbrFvnusDdc9D5TNspbHraJyc4PAZ2DnbpfGcbHVQ5qU0S/OHhYV5M8espPLbEyx6X7TZzarxEQHtxcTGXdTIHTmz5XrYLlimP7TX0qt3/cuJMyaQErxYMnLGZA8OqlplP13Gn0xsBigwssp+QjXkGdPyeQSBt+K3QXQadNrk2FdX3txNMA9Vl2LqgKr/7HLeRv/m4z0+A5M80pocCqvyeDm9EVrQENM5MJWDo2nG/39oRJXWO6RAoHx2nHQKm0eYhPt9ym5mwpFyBGPGx62vKHefln1cZDbhGwDMNuWXXDjez9QZT9M/ke2eZ7Wi83XieopHO2B7wf9deJzMdD6zLGUB0f4f0Ou+dMvXe9KtqmTnn07LU2YjRHzbIYDmDaQcVvocTgl2wYLAzIttsr+B49cVBSSe3nU/s5vu589n5hkOrlSnPI14/xYNDbY/aSH4nBvB4ujF2x9MuWZewW3xmUOvxu9+H/Op70rGn+pL+LQE45wCcnTz160MsW1X74L3DmeiaZdHBjH1ikgF3EvhqmpbVHSTdj472z/fn2MyrrhrLWPOQHGRfcw46/+P2u8UD26SRDuQ9snIi+wJ/bQe6lW7O7TDQyH9xrpM+WUGQ+PE5eOW59mdEL35PlTPW2RkGx6Q44OC8zWazeDhwmqa6vr6ey5XIkvv9HTgIshBetUph4DpeyGhy9JnCRqYgXzhHH1EgHJWzInzn4fujo6P5QTn6kMu3dsq5UuW+JkgcrV64ztebNpgMHnNnI45n5p5PC/tTQCwzF8n7LEPs+mqF8EO9p6enc9mfH6RGcTHE5lc+oGnePwfA/DvIwUQHjJ2ldhBZtdyMA6fxj3/8Y27ny5cvVVVzRq1q+SC0eeZd76xbWS4AD7slcl/rrJhlznJC3zje8cOOJuWFOaafBqq5gsbY3VY6Bp9LNhW9c3azG3eWEptnkJ3IiEa/dfPud35kqWJm5qv2Tq1b9aUE1eUSI2DH9wzA3jPZ/tPfnK+u/DJtB0CvalkyZrvWbbIE3y2jlhPasv+zvCCzfm9MynuudLr87/j4eOFHsAldYqWb/5zjBPnmEaAyE4m2cf6efHZAwvxk5clTiQvass1MO+JNM+xbOT/5QJ88dvxT3jvB3273s6yNVQivVFmGvDLlyhf/WUffmtJeJeZj7MjtbrffNfX+/r7Ozs6q6ievz8/P58TDb7/9VlVVl5eXc0ldloZx71xdxCfwDib46JWJquVjI/ZdrNR4jA6E8TvYEAIryhS5/2+//bZY9fU9KWsEr3rn1dPT04UedOR3UDEeV1bY/nvzCVb/OG5MjX0xf60L3pXv27dviySRd0YEB1MVwAud8VtJfj+i/ZexPvPqSqnj4+M6Pz+fN9PpbK1Xqphn22/kM+f6NfSqoKqLiOkInet2HWNwWTZGYIXhyYCKewIceLkZSpl1n96Ny87B4DPHYCCCQNnZWEBp11kKlwV2pR6OplNQnWW3M7fyjoIqjFc+PJnbU2bwaUeXx5MMLjxnmVlIh+i+pkOy3Pizy3Tw592S/E4LxpeOhmPZxwTV74XMBwOuzC5VLbOaDgwZKzq23W7n585ubm4WiQDaQM7v7+/nZXMHG1XLZyFIfFBq2IHpDMxsJK2THq9LPh3oWhZ8LxtBat8zM5llhNatquXWzpYF5D0DFduzNLoOft1vf7cDzuOja9wn63KCq5SXLqhyQoTrcFCUAWbS5FDA1OkXoPAQ2H0Lov+AFgdVnv8sF+rshwMrB2P8jzzCC9+b86rGGyghHy6LNX8zGeUEBcklbID1zgkK96ELbEaBVJ7XXQNZt1K2betTP+mjwU4mmzxvKWsJkLh/JiXoI9fwlwnCjgcZ4Hiucl4Yx263f6YKAIgPc0kZ/s56mvx7L0GV+drNA2PkO8c9b8ZVvBT39va2Li4uZlzjREYm5+CZHzUx7wDXTkJxvYMx2kaHSbLTDgEH9g2sah/GGD9+/LgIFi1LBDPTtH9Z72azmcvz0A/rdZJtfOpQJh4I4oyZGTdtEBDe39/PfUufm4EoffXLweE9ZXknJyfzrovWgZQXl9/Zb9mn2nc56XBycjJvy2/ifycmUva6xIzH/lL6y17+60+DGKjrtAWe6JcI30aV62mD84+Ojurm5mZoWPPeGUhlf+w08zwmdbRcX1UL5e6UnwcFLRhu27xzWx5fl/mrqkdBlbPvBt/co1vWziDIY0xKfo0AZHcPfhvNV4JoO5gMSDtHZ5lx/xx8+B7vDfxBydPOuXeBtp2J6/ZHZQ8JFr3pR6cTeV/PxShYzmv9acDT6aWTDpkcsDMzEE5d8XybtwnMsm+54pP3TOoAavKAMVU9fs7LPE8agdiOx+bPIYfhwMoyMboWHjrgynvm2N8LpUzbPjhASXubSaxp2tf2+9yUh86GWp+w71XjdxJ29jvJwVFmXTnmDaC8yQ3nuH3uaf1J8NbNrXXP/LYsdWNI3ep4n8ezvZyHLujymKuWK1bmWfq1xDX8Zh9ufUg/0+GELjHZ2eTUb7fT2eW3opE/t1+xfKRMWcc418EnyVTjG/THQXPKpROyVf3LtPlO0oz2nbhI7GGZdMkt5ICATTOwG1U1BzCbzWYR1GUijHF1qybgOM7xKk+Og+eYWEXz+LrXhxinGW+Zr+aN5yXtjfvi3Zmf6x8S76Q/7/rW9RH96vw29zlUEvpcenFQZRBFZ535YyCOSjsiI+EH2Xho0TskMRmZGZymnxmGq6urur6+noXDq1aj/luxbfycxff5fDrgYymR4MTvFLAy2FhYqKj/rNoLSmalcqWravkeBRuIDKqsFGmY81rIDshG3g9hdhnuQ85gJJzpULKvueuUV6jIqHcOZbPZPMpopSE0PznnPQHAzjmkk3dQQIaT81h2//jxY2232/r8+XN9/Pix7u7u6uTk5BHvWZ14eHiYSw5sbJEpO8Np2j+Myz39MOsooIIsgxi5blcryx3zlDqJnnhHJsbgFVuvYmVSwU7B/c7yP/TDc5F6hY30iqltjQOqDEK6ADTBnP/P+46AbLaBPfWOmn6uNcdrQOD6dQMay+CfdUx/ByErLqXqgunMkGamFN7hC1ze6nex0J55YYDp+9rZWwbxMelL3X/zn+oQ9+vm5mb2D4zdwcBut1uAPa9Udysk9l2d7abvKUcATPs6xuvdermOPnOdQaBBMN85bpBqGXZpP7zLBJ31OpMMkEE/c0TpLOPxvGbb3tHNdtP6l+ViOfec75Kmt6aUE/sXAiKv8LApkjfFgZdUV1DGvtn8XIXabrd1fX1dl5eX8/zBP0rbklf4wqrlbpouf9tut/PqTepP1c85ZyUEnUHfuQ967xK06+vr+vr1a/348aNOT09n3eNRkarla0woy6uqOQjabPbvNTWPf/z4UdfX1/Xjx4+5zG6z+bna5VI79OTr16/1r3/9az6GjvH4jY9DTrT63sbR9IWA7fLysqpqgZG3221dXV3N4/HmRqPknH0P+IR+sgqYONrz7qo065ntkfXUz/L9mWTFq1eqDO6cdRsxqbuWWv5pmurs7Kw+fPi5g5630bQTgNGnp6ezQKTze4pgYjpTwEGXiahalgkB3NKRdJkq+mcjs9ls5rHRJ863kHR11H47uAUPp8LYHFSN+JDZNINQC6UdaDqJLlPg4NrnO3jpgi4DD/9lEHcom8D5XZ+czep+fy+UgRPHLKMJqFOWyfJRv7/b7Wbnklk9BwCsGNuZZGDFfXEkGVxAqUOMozunavkcCeTMegZDflUCDnJU/mcZxla5P05GpF51TjaTS5kBzey3HYTn2ON9KrhPGcjA1YmElAm3b73y81MOAg1GMxFhncy5TPvwXogxubTRtjznkvMtE1X7caFHDuA7OU9wnjbUc+jMKrJn+zoKquxrnMyjLT9j60DEcoBMMSb4YL0DCHYJBfu97CvH8VEkJ6FMZmQwZhCUxzxPmW23HvjT85PjzPnJQNG2mSob/LZX+FMWON+6Y/4DPhOHGFNke/bR74WSd+YTMsccI1P4ESc9CMROTk7mV3icn5/PPOZZJ9shvptX9/f38w6Z5pcTIQbsGaijT/SnamnfwUz2K8Z9fi6LwDATKrQJ/vWqDv3LYL9qHxiy0y99JWBlPPTj+vp6XoCACCgJDq0fvp+xKP11uSr/V+23h394eFiUcvLnYAe+mBJHuD++71MYlHlj3o0BOv/IOLN/L6X34/VWWmmllVZaaaWVVlqp3ley8z+JusBkpX8PTS9h/jRNv1fV//v7urPSSm9G/3e32/2ft7r5qlsr/S+mN9WtqlW/VvpfTavvWmmlv4derFsvCqpWWmmllVZaaaWVVlpppZVWWtJa/rfSSiuttNJKK6200korrfQnaA2qVlpppZVWWmmllVZaaaWV/gStQdVKK6200korrbTSSiuttNKfoDWoWmmllVZaaaWVVlpppZVW+hO0BlUrrbTSSiuttNJKK6200kp/gtagaqWVVlpppZVWWmmllVZa6U/QGlSttNJKK6200korrbTSSiv9CVqDqpVWWmmllVZaaaWVVlpppT9Ba1C10korrbTSSiuttNJKK630J+j/A2uvrVDVYLFWAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_sensors = 60\n", + "n_faces = 3\n", + "n_rows = n_faces + 1\n", + "fig, axs = plt.subplots(n_rows, 4, figsize=(12, 3 * n_rows))\n", + "\n", + "for k, cost_name in enumerate(cost_names):\n", + " # Define the cost-constrained QR optimizer\n", + " optimizer = ps.optimizers.CCQR(sensor_costs=sensor_costs[cost_name])\n", + " basis = ps.basis.SVD(n_basis_modes=50)\n", + " \n", + " # Initialize and fit the model\n", + " model = ps.SSPOR(n_sensors=n_sensors, optimizer=optimizer)\n", + " model.fit(X_train)\n", + " \n", + " # Get average reconstruction error across test set\n", + " test_error = model.reconstruction_error(X_test, sensor_range=[n_sensors])\n", + " \n", + " # Plot sensor locations\n", + " sensors = model.get_selected_sensors()\n", + " img = np.zeros(n_features)\n", + " img[sensors] = 16\n", + " \n", + " axs[0, k].imshow(img.reshape(image_shape), cmap=plt.cm.binary)\n", + " axs[0, k].set(\n", + " title=f\"{cost_name} (MSE: {test_error[0]:.2f})\"\n", + " )\n", + " \n", + " # Plot reconstructed faces\n", + " for j in range(n_faces):\n", + " idx = 10 * j\n", + " img = model.predict(X_test[idx, sensors])\n", + " vmax = max(img.max(), img.min())\n", + " axs[j + 1, k].imshow(\n", + " img.reshape(image_shape),\n", + " cmap=plt.cm.binary,\n", + " vmin=-vmax,\n", + " vmax=vmax\n", + " )\n", + " error = model.reconstruction_error(X_test[idx], sensor_range=[n_sensors])[0]\n", + " axs[j + 1, k].set(title=f\"MSE: {error:.2f}\")\n", + " \n", + " # Plot target image\n", + " true_img = X_test[idx]\n", + " vmax = max(true_img.max(), true_img.min())\n", + " axs[j + 1, k + 1].imshow(\n", + " true_img.reshape(image_shape),\n", + " cmap=plt.cm.binary,\n", + " vmin=-vmax,\n", + " vmax=vmax\n", + " )\n", + " axs[j + 1, k + 1].set(title=\"Original image\")\n", + " \n", + "\n", + "[ax.set(xticks=[], yticks=[]) for ax in axs.flatten()]\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observations:\n", + "\n", + "* Using \"center blocked\" costs causes sensors in the center of the image to be avoided\n", + "* Using \"left blocked\" costs causes sensors on the left of the image to be avoided\n", + "* In all cases more sensors are needed for accurate reconstructions\n", + "* With a limited number of sensors, models have a hard time with high-frequency features like the texture of a beard" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.9" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/spatially_constrained_qr.ipynb b/examples/spatially_constrained_qr.ipynb index 33ce0b8..33600bb 100644 --- a/examples/spatially_constrained_qr.ipynb +++ b/examples/spatially_constrained_qr.ipynb @@ -55,16 +55,6 @@ "Loading and preprocessing the data:" ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import ssl\n", - "ssl._create_default_https_context = ssl._create_unverified_context" - ] - }, { "cell_type": "code", "execution_count": 3,