{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Convolutional Dictionary Learning\n", "=================================\n", "\n", "This example demonstrates the use of [dictlrn.cbpdndl.ConvBPDNDictLearn](http://sporco.rtfd.org/en/latest/modules/sporco.dictlrn.cbpdndl.html#sporco.dictlrn.cbpdndl.ConvBPDNDictLearn) for learning a convolutional dictionary from a set of training images. The dictionary learning algorithm is based on the ADMM consensus dictionary update [[1]](http://sporco.rtfd.org/en/latest/zreferences.html#id44) [[26]](http://sporco.rtfd.org/en/latest/zreferences.html#id25)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import print_function\n", "from builtins import input\n", "\n", "import pyfftw # See https://github.com/pyFFTW/pyFFTW/issues/40\n", "import numpy as np\n", "\n", "from sporco.dictlrn import cbpdndl\n", "from sporco import util\n", "from sporco import signal\n", "from sporco import plot\n", "plot.config_notebook_plotting()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load training images." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "exim = util.ExampleImages(scaled=True, zoom=0.25, gray=True)\n", "S1 = exim.image('barbara.png', idxexp=np.s_[10:522, 100:612])\n", "S2 = exim.image('kodim23.png', idxexp=np.s_[:, 60:572])\n", "S3 = exim.image('monarch.png', idxexp=np.s_[:, 160:672])\n", "S4 = exim.image('sail.png', idxexp=np.s_[:, 210:722])\n", "S5 = exim.image('tulips.png', idxexp=np.s_[:, 30:542])\n", "S = np.dstack((S1, S2, S3, S4, S5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Highpass filter training images." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "npd = 16\n", "fltlmbd = 5\n", "sl, sh = signal.tikhonov_filter(S, fltlmbd, npd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct initial dictionary." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.random.seed(12345)\n", "D0 = np.random.randn(8, 8, 64)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set regularization parameter and options for dictionary learning solver." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lmbda = 0.2\n", "opt = cbpdndl.ConvBPDNDictLearn.Options({'Verbose': True, 'MaxMainIter': 200,\n", " 'CBPDN': {'rho': 50.0*lmbda + 0.5},\n", " 'CCMOD': {'rho': 10.0, 'ZeroMean': True}},\n", " dmethod='cns')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create solver object and solve." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Itn Fnc DFid ℓ1 Cnstr r_X s_X ρ_X r_D s_D ρ_D \n", "--------------------------------------------------------------------------------------------------------\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 0 1.00e+03 1.14e+01 4.96e+03 6.62e-07 1.00e+00 3.92e-03 1.05e+01 1.00e-01 2.77e-03 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 1 7.70e+02 6.67e+01 3.52e+03 6.96e-07 9.75e-01 5.43e-02 1.05e+01 9.11e-02 9.63e-02 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 2 6.20e+02 1.24e+02 2.48e+03 7.46e-07 8.00e-01 1.17e-01 1.05e+01 8.26e-02 2.24e-01 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 3 5.50e+02 1.55e+02 1.98e+03 8.52e-07 3.59e-01 1.36e-01 1.05e+01 8.03e-02 2.05e-01 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 4 5.39e+02 1.58e+02 1.91e+03 7.64e-07 2.39e-01 1.28e-01 1.05e+01 6.38e-02 1.83e-01 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 5 5.57e+02 1.47e+02 2.05e+03 6.95e-07 1.92e-01 1.17e-01 1.05e+01 5.53e-02 1.25e-01 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 6 5.33e+02 1.36e+02 1.99e+03 7.25e-07 1.53e-01 9.49e-02 1.05e+01 4.75e-02 1.03e-01 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 7 4.94e+02 1.27e+02 1.83e+03 7.72e-07 1.28e-01 8.04e-02 1.05e+01 4.11e-02 8.85e-02 1.00e+01\n" ] }, { "name": "stdout", 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1.19e+02 1.12e+03 8.61e-07 4.67e-02 3.76e-02 1.05e+01 1.59e-02 6.01e-02 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 15 3.40e+02 1.18e+02 1.11e+03 7.34e-07 4.37e-02 3.56e-02 1.05e+01 1.49e-02 5.68e-02 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 16 3.33e+02 1.17e+02 1.08e+03 7.91e-07 3.94e-02 3.40e-02 1.05e+01 1.37e-02 5.48e-02 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 17 3.31e+02 1.16e+02 1.08e+03 6.94e-07 3.72e-02 3.23e-02 1.05e+01 1.29e-02 5.27e-02 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 18 3.25e+02 1.15e+02 1.05e+03 6.96e-07 3.40e-02 3.08e-02 1.05e+01 1.21e-02 5.10e-02 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 19 3.22e+02 1.14e+02 1.04e+03 7.10e-07 3.23e-02 2.94e-02 1.05e+01 1.15e-02 4.91e-02 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 20 3.17e+02 1.13e+02 1.02e+03 7.22e-07 2.99e-02 2.83e-02 1.05e+01 1.09e-02 4.74e-02 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 21 3.14e+02 1.12e+02 1.01e+03 8.45e-07 2.86e-02 2.71e-02 1.05e+01 1.05e-02 4.57e-02 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 22 3.10e+02 1.12e+02 9.89e+02 7.15e-07 2.67e-02 2.62e-02 1.05e+01 1.00e-02 4.42e-02 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 23 3.07e+02 1.11e+02 9.80e+02 7.58e-07 2.56e-02 2.53e-02 1.05e+01 9.65e-03 4.28e-02 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 24 3.04e+02 1.11e+02 9.65e+02 7.46e-07 2.42e-02 2.44e-02 1.05e+01 9.26e-03 4.15e-02 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 25 3.02e+02 1.11e+02 9.59e+02 7.36e-07 2.33e-02 2.37e-02 1.05e+01 8.97e-03 4.03e-02 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 26 3.00e+02 1.10e+02 9.47e+02 7.56e-07 2.22e-02 2.31e-02 1.05e+01 8.68e-03 3.93e-02 1.00e+01\n" ] }, { 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1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 197 2.44e+02 9.71e+01 7.35e+02 8.32e-07 3.12e-03 5.08e-03 1.05e+01 1.77e-03 6.82e-03 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 198 2.44e+02 9.71e+01 7.34e+02 8.71e-07 3.10e-03 5.07e-03 1.05e+01 1.76e-03 6.78e-03 1.00e+01\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 199 2.44e+02 9.71e+01 7.34e+02 7.28e-07 3.08e-03 5.03e-03 1.05e+01 1.75e-03 6.75e-03 1.00e+01\n", "--------------------------------------------------------------------------------------------------------\n", "ConvBPDNDictLearn solve time: 86.53s\n" ] } ], "source": [ "d = cbpdndl.ConvBPDNDictLearn(D0, sh, lmbda, opt, dmethod='cns')\n", "D1 = d.solve()\n", "print(\"ConvBPDNDictLearn solve time: %.2fs\" % d.timer.elapsed('solve'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Display initial and final dictionaries." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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PcdSFkbj00kvFUfCOOuBSaI9ChdTZkjq4UjdZgsL01CnynHPOEUdhJuqeSYEuCslTKLtevXriRo8eLY5CxtR5k4JuFDz++OOPxVFHbAr2EhTUCiGElStXiqNjTZ2AKciXGsIKgYsZUNiNoGNIwdFy5cqJo1D2559/Lo4KRHTr1k0cnROaE6jAAQUuKexG12HhwoXFUfidQnHUrTUqdB1SJ+rPPvtMHI1LOn6vvPKKuNSO3dR52BzkuOOOk47wFStWlNc999xz4mheLFasmDian2geowA2QeFcCmvSdUTBb1oHqDjM/fffLy7q/Eldu+k4U1EFmusoWE1h/6hFbgh6v6jXNF1z9H5U3IDC9FGLpVBH7BB4vqMxQ8HljRs3iqOO2lRUJWrnbSqKQZ3VKQxOxSBofymoHXVdjkrBggXFUXibxiUVBaD1rFWrVuKiFn6gY0pz1r59+8RRQQcaG2eccYY4uk9KvedIN3ZD8F8+jDHGGGOMMTHhhw9jjDHGGGNMLPjhwxhjjDHGGBMLfvgwxhhjjDHGxEKOAue7du0KY8aMyeYoKLdlyxZxv/zyi7iBAweKo3AUhTcvvPDCw27rIShcSkHXq666Shx1Qm/WrJk46nZLn0shwFq1aonr3LmzOOqYPHjw4EjbRyFFCgWvXbtWHAWXCAqNUQBr/Pjx4ui4UMDp/PPPF0cBUerUTqxZswY9hbCoCAAFsz744ANxHTp0EEfBahoLBAVCKXBJQbQqVaqIu+iii8TRuaPiA1E7nE+aNEkcHQMalxQIpTFDAVj63U6dOqXdzl/7DAqY3nbbbeLo+NFcSZ9BxzQ1cE77ZQ7yww8/hAkTJmRzFN6kNYk6+RYoUEAcdbqndY+KfRC01qSG5tO9H13n1Fmd5jVaGxYuXCiOwvS9evUSR0VVaF2hMDIdPwrskqNzRNAxoGIdFIin65LOG90P0FpDoXaCzm8IfO4ICqHTGjJ9+nRx1Jmd5m2CCtjQ+l+pUiVxTZo0EUfrKHXyXrZsmbio44PWECpMQ4FzugeidY/WBroeoq6tQ4YMEUfFDPLkySOO5hgavzQGKXCeej9G+3oI/+XDGGOMMcYYEwt++DDGGGOMMcbEgh8+jDHGGGOMMbHghw9jjDHGGGNMLOQocH7MMcdIaIXCTF27dhV35plniqPALnUHpeDS2LFjxVHHdArZUHh46tSp4qiTdP/+/cU9+OCD4ijg1KBBA3EEbUtq0D8EDqZTJ89//OMf4nr27CmOQpRRA5PU2TYjI0McdbGlsDR1Bb/66qvFvfrqq+J69OiRdjuzcv3116N/+eWXxVFAjzqfUgiTgmPbtm0TFzWASMeGrqXKlSuLo1AnhdOo4+rq1avF0ZghateuLY4C9vR+FAymQC0F5Wh/o0JFIyiwT/MJdbGlUCEFW9u1ayduzpw52X6mY2IOkkwm5VhPnDhRXkdhYVovqAgChZ6XL18ujoLGdG3R+aTrcvLkyeJoraGu53QNUhGEqlWriiNojFMBEBr3tG/0Opp3qYM1nQ+CupRTSJ6ufdo3KhhB4fzZs2eLK1y4cNrtzAqFgkPgNYSOId1T0TGkLuwUok4XgE+Fgt90/KljOh1XCi9TB20a5zRHE9QxnT6Xrmtak6iAEo0tckWKFEm7nVmhcRR1bqMxTcc0df0JgYP4qYVRDtel3X/5MMYYY4wxxsSCHz6MMcYYY4wxseCHD2OMMcYYY0ws+OHDGGOMMcYYEwsJCtikIyMjI5mZmflf3BxjjDG/RkZGRsjMzNSEo/E6ZYwx/wMcbp3yXz6MMcYYY4wxseCHD2OMMcYYY0ws+OHDGGOMMcYYEwt++DDGGGOMMcbEQo46nO/YsSMMGzYsm6PAOnVcpM6bw4cPF1eiRAlx+/btE0ddsakzNXW2pa7HCxcuFEfbTB26qYtj06ZNxVFHbOp6Tt3bCQpVUlf27777TlzevHnFUXfpr7/+WtxHH30k7pprrhFXsmRJcZUqVRK3ePFicdRJeuTIkeIaNWokjjqrPv/88+L69esnLoQQVq1aJY46gVJXUhq/GzZsEEfdZKkj/EUXXSTujTfeEEcdYS+++GJxn3zyiTjqNH7OOeeI++abb8S98sor4t577z1xU6dOFTd06FBx1KX88ccfF/eHP/xBHB0DmouKFSsmrnv37uJovNG5pM7v1HWaoGuzRo0a4lI79G7evDnS+x+NbN26Nbz++uvZHHX3pQ7CtJ59/vnn4qgz+KZNmyK97qmnnhL32WefiaM5gjqhU/dm6spM69kJJ5wg7scffxR31VVXiaN1irqFDxo0SBx1UaYu5bTN1HWbunjTvPHss8+Ko7mduoefdNJJ4mhN/+mnnyJ9Bu0bjY3nnntOXAgH78dSoWO4e/ducTS2du7cKe64444TR/djtN1PP/20OJoXyT300EPievfuLY6gsU9j+qabbhJHY5XmaLrno/NO95r79+8XR+eDuPfee8XRfS91W6dtps/98ssvxdH1ldrNPAS9JzpcZ3n/5cMYY4wxxhgTC374MMYYY4wxxsSCHz6MMcYYY4wxseCHD2OMMcYYY0ws5ChwvnPnzvDpp59mc+XLl5fXzZ07VxyFWuvWrSvu559/FkcBYPpc4vLLLxd35ZVXiqOAM4UPq1atKo6C2hQ0HDNmjDgKnFPwa/ny5eLomFL4dePGjeIuvPBCcVQAoGHDhuIICkvv2bNHHIV4a9euLY4Cib169RJHY2P79u3pNjMbZ5xxBvrZs2eLoyB/ly5dIn12hQoVxFFYc+XKlbg9qdA46tatmzgKiVFQmYoAUICQzhN9LkGBSwrTDxw4UFz16tXFLViwQByFZ1u2bCmOzi9B540CdHQMqCAGzROlSpUSR6HdWbNmZfuZgsfmID///LPMgxQGLVeunDgqRpA7d25xW7duFUfXG4U8iY8//lhc1JAszWMUfqf5mI4LBWIJ2l+6zuvXry+OioxQMJrW0ajzGpEvXz5xdFwo1E7XXIsWLSJtHxUJiVqUggojhMDBb5oXaZ/pvFO4nPYl6txD5zNqgJ2OK631xYsXj7R9dD4JWkMKFSokjoqv0DGl+0W6R6DPpQIHBO0bXYenn366OBqDFJKn36VrM7WIAgXfD+G/fBhjjDHGGGNiwQ8fxhhjjDHGmFjww4cxxhhjjDEmFn714SORSFyXSCQyE4lEJn2PzBhjjDmSZF2nKCthjDHmf4dfDZwnk8m+IYS+IYRQsGDBZGoYmgKYbdu2FZcamAyBO/lS92zqIkrdFYnrrrtOHHUBnTJlijgKflLQnR7KvvrqK3HUcZqggBOF+CkQv379enEUaqfzUa1aNXF0jojWrVuLGzFihLiyZcuKo1AbhewoGEjdTKM+JA8YMAA9FR+gcBV18m7evLk4CspRQJ9CrES7du3E0TVC20dFCuh1NGbouFJX8QsuuEAcdVanc0cd4qkLa/v27cUNGTJE3LRp08RFDfYPGzZMHIUFqQNuo0aNxH3xxRfiKCxM3ZXbtGmT7efUwh9HO1nXqSJFiiR37dqV7d9TO++GwIUHaO2iogVUnIOCroULF06/0VmgoDttM3VgpjF5/fXXi6NQK60hFBQmKHRL8zuFWimcS6+j7aNu9aeddlra7cwK7RvdD1DhGwoAU5dyKjJAa/rhwrhZWbJkCXq6B6L5k7aR9o/m2aj7R9D9Ex1rKnBw7rnniqMO53/84x8jvR+NN4Lun6jgBBUFoiIyNHfQdUj3SnT9d+/eXRyF+GlOiBq6p2IpVPSFjmnUzwjBX7syxhhjjDHGxIQfPowxxhhjjDGx4IcPY4wxxhhjTCz44cMYY4wxxhgTCznqcJ43b17pPEkBnXnz5omjjpUUEqNuy9TFkrowEhTAKl26tLhJkyaJo+ASha0oJN+5c2dxS5cuFUch78zMTHHU8ZvCqhR6mjNnjjjqvJk3b15xFPImqLNqzZo1xX344YfiKDBFXbypW31qsDSE6J1BKeQZAofEKOQ4Y8YMcXS8KMhH20jBNoK6jVJonzoLUyfgDh06iKMxSOcpKj179hRHxSqWL18ujuYT2o86deqIGzx4sDgaW88995w4On4TJ04UR9WVKHhLY4iua3KpndB/y7n4vXPsscdKwJeCrjt27Ij0fhS6bdq0qbhTTz1VHI1xggLAdE0TVJyDOmqXLFlSHAXso1YLo+uS5lRaV9auXSuO1vmiRYuKozWYilcQFNKmcC69Hx0XCg/TWKO5ndYuol69euhpfETtxn3mmWeKo22kwjRRrxsKnNO5o7FP1xyNBboPjFoYgKDPoN+le1cqIkNji4oeRL2fJSjkTXNC1OISVPiBimnQcUkd++5wbowxxhhjjDni+OHDGGOMMcYYEwt++DDGGGOMMcbEgh8+jDHGGGOMMbGQo8D5gQMHpKshhV+pMyMF76ZPny6OuoAPHTpUXNSwFnVl3rRpkzgKolFop1+/fuKom/H3338vjrpkE3SsKOxGISXqSE5Bo6+//lpc48aNxVEn3yZNmoibOXOmOApvUcCeAmxUoGDFihXi9u3bJ+7ss88WR1DAMQQO2S1evFhcw4YNI33O5MmTxVHQk8KCBAW9+vTpI466lFNAkqAO2ldddZU4GgvEfffdJ+7Pf/6zOAplU2CVjimF+yhESZ9LjB8/Xhx1kqdr88033xRHQUgK/NExeOWVV7L9TF3VzUGOPfZYCX/TPJEvXz5xq1evFkdFUGgcfPXVV+KidjincUoFTwiaN6J2ZaaO6eedd16kzyVWrVolrnz58uKaN28ujuZ3chSIpZA3QXMEzYl0DdK1OmrUKHFUjIDWJCpeQVBxjXTbQ93QqTjFokWLxEXtJh+1oAuFjaMWiKHCLY8//ri4u+66S9y1114rLmqHc7rXbNOmjTjat7p164qjc0TXCBUVoKIABI1pCvvTsZ87d644Wm8p1E6fkXovR2Pq3++Z9l+MMcYYY4wx5j+IHz6MMcYYY4wxseCHD2OMMcYYY0ws+OHDGGOMMcYYEws5CpyfcMIJoXLlytncl19+Ka+rUqWKOAqIXn/99eJGjx4tjrorUhdggsKg1EU5db9C4G7h1LWbwowUXKJgYKdOncRRl1jqZk5h+qjBKgojU0ieAuwEhQpr1aoljgJ61IGVwpvUlZ26o15xxRXpNjMb8+fPR9+1a1dxW7duFUddcKkrNgUQp06dKo6KNxADBw4Ud+6554orV66cuPXr14ujcF9qR+0Q+Lqh43LPPfeIo+7j9LkDBgwQ9/DDD4ujoBwVUaBAPI1zgjoI0/VPBTZobNH5KF26tLgKFSqISx0b1KXdHIQ6nFNoN3/+/OLonFPYkopS0BpH8zZB8xiFNaMG0ynoTuFX+txp06aJa9eunTgKatNcTvP2WWedJY4KqFColeYcCskSFM6tWLGiOCowQudjw4YN4gYPHiyOitdEnYfSFS2g+wm636HAOd3vUIEYYuHChZFeR9tC921UcIa2mQoF0fvROKfrhkgtVBECh8tp7NO1TvcxNJ/QGFy2bFm6zcwG3fPRvQR1l6drne7l6HfJpYba3eHcGGOMMcYYc8Txw4cxxhhjjDEmFvzwYYwxxhhjjIkFP3wYY4wxxhhjYiFHgfM9e/aEGTNmZHPUsXrt2rXiKLBGwVnqqEmdWaN2aqau4qeddpq4bdu2iaPQLYWyKHhMHU0pCEVQCHDcuHHiLrvsMnEUrIoaUqQwOHUBps+l93vxxRfFUfCYOo23b99eHHUGpVAbdeemIDgVSwghhNq1a4ujjsYrV64UN3LkSHzPVKij6bp168RRt2E6J3SNUBfsCRMmiKPCAHT8qYs6dT0n6LqhbrzUNf6jjz4SR0FPur6oGMRbb70lrkuXLuJomynoTvMJhWfpus7MzBRHodhUR2F9c5BkMinHmsKgFOSluZK6QVNRAAq1U5iWoGA1jRcKxFMQmsZ91OAshX0JCvtSF2VyFLqltZqKzdB1TkUfokLnl96Pjh/dr1AQl67XqCHozz//HD0Vv6A5i8YgdbYnaG6LCu0zdaKnwDSNcwqcX3nlleKomEvUbuEZGRni6DqkogdUkGXp0qXi6Hqle83f0kmePoOuayq6QXPR3r17xdE9X+r70Xk8hHGRj5cAACAASURBVP/yYYwxxhhjjIkFP3wYY4wxxhhjYsEPH8YYY4wxxphY8MOHMcYYY4wxJhZyFDjfv3+/BFspKBM18FuiRAlxZcqUEUfdQSkkRtDrqAP7t99+K46CUNSJ8uWXXxbXrFkzcZUqVUq7nVm56KKLxNExfeSRR8R16NBBHJ0jgjqDz5s3L9LvUndpCiRRIG7BggXiKLBLAT0KfkYdG9dffz166g46ffp0cY0bN470OdQxlM5J1C69FDinIP+cOXPEUQiQOu3SZ1x++eXiKNRO0L5RwJSoXr26uE8++UQcBeref/99cRQSJagL8Icffiju/PPPF0eBeApMdu7cWRyFAP/5z39m+/ndd9+V15iD/PLLL2H9+vXZHF1vY8eOFUfjlMLRFEJt0KCBuKFDhx52Ww9BQVwKPdPYpTAoFUGhECrtb9TwNl2/tC1U2KNjx47iSpYsKW7jxo3iKIhL+0scruNyVqjACAW3oxaboMIoUbeZipOEwOsUzeV0T0WFgmh80BoeNQhNr6MwM91n0fGitYu2mY5/1AIdVKghtchSCCG0atVKHBXEof2l8546v6dzBI1BOqY0fosVKyaOCklQcQ46R6nXF52fQ/gvH8YYY4wxxphY8MOHMcYYY4wxJhb88GGMMcYYY4yJBT98GGOMMcYYY2IhcbhASCoZGRlJ6shrjDEmPjIyMkJmZma09OxRhtcpY4w58hxunfJfPowxxhhjjDGx4IcPY4wxxhhjTCz44cMYY4wxxhgTC374MMYYY4wxxsRCjjqcL1++PLRr1y6bo+6+1Gl4xYoV4kaNGiXuoYceEkddIidMmCDuiSeeiOSo+3Dx4sXFRe3QSd29qcP59u3bxd14442R3i+1Y28IISxcuFBcixYtxM2aNUscdY2nLqDU+f0Pf/iDuMcee0xcuXLlxFEHVuqyuXLlSnEZGRniqJs5dae97LLLxFGH+BBCaNq0qbhdu3aJmz9/vrh169aJS71mQgihRIkS4tauXSuuS5cu4qhrd40aNcQtXbpUHJ33IUOGiKOuznT8ia5du4qjsXX//feLO+GEE8RRB1fqEkvdkGmbqdPrTTfdJO62224Td95554n75ZdfxNGY/vzzz8XR2KDrf9u2bdl+pm7p5iDr1q2Tc5cvXz55Hc3vqcc5hBAKFSokrmrVquLo+qU5/+GHHxb3wAMPiKMOwnT9kqN1isYpdbCmLs/XXHONuP79+4sjaC6hTuPHHHOMOLoWaC6mrszPP/+8uHfeeUccrf00DmjOoTFE20dd46kL9d133y2OxkYIITRu3FgczW1RO2XT+MidO7c4mttoHnv99dcjvR+NBVpbqXN5/vz5xZUuXVrcscfqrS7N748++mik3925c6c4GgvkaC2k/aDzEfXei+a7qHPC8uXLxVGHc7quy5Qpk+1nmv/+/ftp/8UYY4wxxhhj/oP44cMYY4wxxhgTC374MMYYY4wxxsSCHz6MMcYYY4wxsZCjwHnevHkl0EwhTwplUuCcwl8///yzuMWLF4ubNGnS4Tb132zdulUchdOWLVsmjsLvL7zwgrjq1auLo2AVhcaJb775RhyFlCjM9NFHH4lLDQGFwIGpw4WDfg06b5s3bxbXsGFDcatXrxZHwe08efKIoyBUVChsGQKH7CnIR6EuCkxTiO21114TRwFEgoJedD3QMaTrtXXr1uJGjBgh7pRTThH32WefiaPA+eDBg8VR6L5Pnz7iaExPmTJFXPv27cVRgQO6/gm6rqlzNgUw6dhTuI+udZonevToke3n9957T15jDpIrV66QN2/ebI5CrRRMpWNfqVIlcXQuKaS8atWqw27rISjknUwmxdF4ocA5XasUhKY58LfMQ+RoP+h1NHdSWJrciSeemHY7s0LHgNYuCqHT79J+UMGdAgUKRPoMggLAIYRQq1YtcTTvlCxZUtzcuXPF0XEtW7asOCoMQNA9EP0uFZeha4nOE30Grct79+5Nu52/9jo673S90hiksU/QvEMhdIIKstC5pPsxeh3dV9avX18c3Selfsbhjrv/8mGMMcYYY4yJBT98GGOMMcYYY2LBDx/GGGOMMcaYWPDDhzHGGGOMMSYWchQ437t3r3QHb9Wqlbxu/Pjx4igYRCFZCqgsWLBAHIU8CeqKSV2PaVs+/PBDcdQVc9iwYeIqVqwojoJQxJdffimOgl8U2KWA05VXXinuqaeeEkdhcOpCT1Agu0mTJuJGjx4tjsKRHTt2FDd8+HBxFCSl4Hy3bt3EnXHGGeJCCKFu3briaCzQOaEQK70fdXBNtz2pUKiQQmd0LVGImvbthhtuEEeB/w4dOqTdzqzQmG7WrJk4CrbReb/66qvFvfnmm+KoqyuFten9Jk+eLI6OQWq4OQQOmJYvX14chelpfkoN2FPHZPP/k9qNmroUU7CaCpRQuJR+lwoZ0NiICoVzaR2lOZquX9o+6qJO1wxB8wuFxunY07ZQwJYKRtAxpe7jBBW5oXWPPoPWVjofdD9AgeLKlSuL69SpkzgKtYfAa1/U8XbqqaeKoyI0NBaiBqHpuqHzTtccBaFpHFExHSrAQPeBBK3Ba9asEUeFHyhwTuFtGqt0rCh0T9DryNG20DbTvfW4cePEURGk1PNG5/EQ/suHMcYYY4wxJhb88GGMMcYYY4yJBT98GGOMMcYYY2LhVx8+EonEdYlEIjORSGRGbT5kjDHGxEXWdYpyDMYYY/53+NXAeTKZ7BtC6BtCCGXKlEnWrFkz278PHDhQfue+++4T98ADD4hr166duOnTp4tr0KCBuLVr16bf6CxQEJcCZlHDhxMmTBBHXbbnzZsn7vzzz0+7nVmhoDaFsmmRrVKlirjOnTuLa9u2rTja33r16qXdzqxQUOv9998XV6NGDXHUqZW6utJxbtq0qTgaQwR15w4hhEKFComj4GOFChXELVy4UByF9qg7KB0bgvaZAmHUOZ4CYL169RJHQT4Kpjdv3pw3MoW7775bXGrxihBCOPvss8VRUQsqEEH7RuOSrhHiggsuEEfdbulc0lh99913xVFAl4J8qZ1jqdvv0UzWdapo0aLJ1M7dNDaoczkVN6Bz+d1330V6HRXiIChMS4Fuel3UkDd1M4/qCApC0+/StpCjYiQUqqYgPr0fQXMnfQZ1g6Z5nNZMKjZBa0XU7tfp9o3mHdoXmmep+MWqVavEUVCbwtEEbcsvv/wijuYyeh0F9Ck4T78bdZupYEzVqlXFRS1wQIUG6N6Q5g665yDoGBx33HHiaFxSd3kqDrN06VJxtLY2atQo288jRoyQ1xzCX7syxhhjjDHGxIIfPowxxhhjjDGx4IcPY4wxxhhjTCz44cMYY4wxxhgTCznucJ4acqpdu7a8joIyFPKmANeKFSvEUdi6Z8+eh93WQ1BHZ+ouvWTJEnFTp04VR+G+m2++WdzMmTPFUWfqM888UxwFninwQyE2Cti3adNGHIWyJk6cKI7OUffu3cVVq1ZNHHWm37Rpk7jFixeLo+AiBb+mTZsmLmo3UwrEhcCdXikUT13sv/jiC3E0fuvUqSOOwllUbIG66lKnUgpWU6EG6oZKXXrpM0aOHCmOOpfPnTtXHJ0n6tBLwflFixaJu+iii8TRtUTjiKDgN4U8aQxu3LhRHM1FXbp0EZeZmSkuNaQYNax6NHLgwIGwe/fubO7cc8+V11GXeJoT6Hojtm/fLq5s2bKRfpegYDVtH4VLaf2htYY+I/XYpYM+l9YVmsup0zXNTbT+UNg3aki+ZcuW4saPHy+OChQULFhQHBUeoDU4T5484qJew/S7IfCaS0F5Ok809ukYUhic5jaC1gsqokChfTo25cqVE0djge4/aVsIWs/o+FEhHhofJUqUEHf66adH2j46RwQFyen9hg4dGukzqMALFYyhIk2p95CHuy79lw9jjDHGGGNMLPjhwxhjjDHGGBMLfvgwxhhjjDHGxIIfPowxxhhjjDGxkKPA+QknnCBdYSkkmy9fPnHUGZi6QVNYi7o/UjdO6oBZrFgxcRSUoZASdTim0PhXX30ljkJsjRs3Fke899574igYSIFdCjNRB0zaPgrs9u/fP+12ZoW63VIgnj5j0KBB4ihYSeOFwsMU8M4JFNaiLrG0z1SAoXjx4uJef/11cXSeCBqrNBaomy91y6XrddSoUeIoZEdBXoKKPFAYPH/+/JG2hQKJVDSCzhEVPahZs2ak7StSpIg4uubod6mYBoV7KdCZGjql/TIHyZUrVzjppJOyucmTJ8vrKKxK6wVd+8uWLRNHYVp6P4JCrbR9FOimQPzYsWPFUUD5t0DXIO0HrV0U1KZwOa0DNPajhrcp3EwBW1pD5syZI47OOXV+piB41K7sdK+T7vcpnE6vo8I5NPfSeaICMQTd81HBDgr3U9GSqNcIFTPYsWNH2u3MCgWk6dxRMSLaFhofdI9A6wVtMxVuonWFzhut3zRWaY2j+4t+/fqJS72Hof0/hFcwY4wxxhhjTCz44cMYY4wxxhgTC374MMYYY4wxxsSCHz6MMcYYY4wxsZCjwHnu3LklEF66dGl5HYWKKCRGHYQplL1y5UpxY8aMEde2bVtxFMaj7s0dOnQQRx2Jn3/+eXEUcLr44ovFvfbaa+KuuOIKcVdddZU46nCeGv4PgcNgAwYMEEdheurOfcstt4gjBg8eLI66+z722GPiqKAABQMbNWokjrqRU4CNSNfhnILL1B2YCgNQF1bqmkpBVNo/gsJpJUuWFEeFEKhjOgUSb731VnEUsqNiAWeffbY4uq6/+eYbcTRPtGvXThwVjZg+fXqkz6CQHUHHoG/fvuKooMBHH30kjgK/FBakrripY5XmHHOQY489VuYUCqtSR22C1qkyZcqIo8A0FTcgaNzT+1GxCQp0582bVxyNGQrTppsXU6GALc0l+/btE0dzCUH7Rp8btQs9zRFFixYVR/MGHT+6VqkwCs33Ubuy0/oYAh9DGqurV68WR/tCBU9+/PFHcdRRm6BzQus1jVVaR2ldp/FBRRkoYB8VGh90jdD5TC18EQKfj3nz5omLOqbp3rBOnTriqDgP3etQMSK656B7otTnA+qqfgj/5cMYY4wxxhgTC374MMYYY4wxxsSCHz6MMcYYY4wxseCHD2OMMcYYY0ws5ChwftJJJ4W6detmcxRceumll8TVqFFDHAXqZs2aJY7CpVEDl/fff7+43r17i8vMzBRH3cwpTEsBMwplXXrppWm3Mys//PCDOOp2SV1sKTR2+eWXi9u2bZs4CmrRZ1B4uFu3buLo+FHIk8Kg1157rbiJEyeKW7BggTgKoRHt27dHTx2mKWBG43f9+vXiSpUqJW7cuHHi6Nw1b95cHIX26Bqh8Cd1V03XQTfK71LHeoL2jc4njcHRo0eLa9GihbhatWqJo6AcnSPiySefFEdji44zbQsFb6mwAn3GmjVrsv1M59scJE+ePNLNt2DBgvK6/v37i6PQKM1PFOyl+YDmWYIC57Qt9Bn0u9SlmOZjGrtRC3bQGKTto2NFAWC69umaobU/aodzGgcUqqZzTo7WZfoMKjxAHcAJKnwTAnePppAyBabpWNO8SGPw559/xu1JheYxOtZULGX37t3iop4TGjO0v0TU4jc0Bqm7PEH3dxS6jzqmaY075ZRTxFHhJireQHMHnXP63NSCGHQuDuG/fBhjjDHGGGNiwQ8fxhhjjDHGmFjww4cxxhhjjDEmFvzwYYwxxhhjjImFRNRQSwghZGRkJCmYbYwxJj4yMjJCZmZmtFbURxlep4wx5shzuHXKf/kwxhhjjDHGxIIfPowxxhhjjDGx4IcPY4wxxhhjTCz44cMYY4wxxhgTCznqcL5x48bw8MMPZ3P58+eX11E3SeoSWaJECXHU+XTDhg3iqHPiX/7yF3F9+vQRV6ZMGXFvvfWWuC5duohbvny5OOr4/cUXX4ij/b3uuuvEvf322+Kom+lJJ50krnTp0uLmzp0rjrqetmnTRtyzzz4r7tFHHxU3cOBAcaldhkMI4aGHHhJXs2ZNcZs3bxZH3XMbNWok7rvvvhPXo0cPcS+//LK4EHhsUWdW6g6akZEhjsbMN998I446FT///PPipkyZIi5qJ286rpMmTRJHHd2JihUrirvjjjvE9e7dWxxdDzVq1BD38ccfi6Prq1evXuKoe3tqt/AQQhg+fLi4e++9V1yzZs3ELV68WNz06dPF3XbbbeIGDRokjubASy65JNvP1DXaHGTz5s1y3dDxok7eNO5p7qU1jrpL0xxBc+rTTz8tjjpE0/ZRp/Ht27dH2hbqykzH5ZlnnhF3++23iyPOOOMMcdTlmbaZjmnDhg3Fffrpp+JeeeUVcffcc484KrpDxzlql+yo70ddrZ944glxNDZC4PWfxgLdoxUrVkwcdWGn9YLWx0ceeUTc66+/Lu60004TR8eVPpfuY2jfaK2mY/3kk0+Ki7rNJ554orjRo0eLo2uJxjS5Y4/V23MaH7S2FihQQByNjZ07d4qjfaM5kDrTp97zHW6d8l8+jDHGGGOMMbHghw9jjDHGGGNMLPjhwxhjjDHGGBMLfvgwxhhjjDHGxEKOAud58uQJDRo0yOYofFSwYEFxFDDbuHGjOArTFi9eXByFcQgKM02bNk0cheIWLVokjkLZS5cuFUdBo+rVq6fdzl97PwokValSRdyQIUPEFS5cWNz48ePFUUhpx44dabczK99++6042o/zzjtPHAWXPvnkE3EUSKSwdK1atdJuZ1YoKBxCCMcdd5w4KipAwXYKeVNwlAoNpAvApzJmzBhxF198sTgqAlCqVClx27ZtE9epUydxdJ7mzJmTdjuzQsFWOs5UCIGuTZpjdu3aJY4KAFDhAoJCijQn0LV+yimniKNAJ4UZixQpIi71WEUtCHA0kkwmw88//5zN0fGiNYTOEY1JCmCSozmVoHNOcy8VpaDrt2PHjuKWLVsmjsZu6rFLBx1TKuZCRSn69+8vjopNrF27VhzNsd9//326zcwGbTMFxOm+JmrYf//+/eLy5csnjo4VkS60u2XLFnFUxKN27driKGhcoUIFcTRXZmZm4vaksnv3bnF0jl944QVxtIbTeaJjTesFvY6gdYoKNZCj6/X4448XR9cw3d9F3WY6zvR+dC9M9ya0JtG+0Xo7f/78bD/T9XEI/+XDGGOMMcYYEwt++DDGGGOMMcbEgh8+jDHGGGOMMbHghw9jjDHGGGNMLOQocL5z584wYsSIbK5p06byulmzZomjwDkF/jp06CCOgnxjx4497LYegrqAUndP6rJNXY+p6/kFF1wgrmXLluKoczFBHSaps/K4cePEUffXkSNHiqPuzR999JG4qCF5CgBTF28Kpk+cOFEcBeIo7E/HtFy5cmm3MyvpAn/UJZ6CxhQSbd++vTgqcDBv3jxxZcuWxe1JhQJrkydPFnfmmWeKq1SpUqTPWL16tbglS5aIo4AjQV3F6fhR4JcChDTHnH322eLoOFPQnWjevLm4zz//XNy5554rjoJ8VKCAXKtWrcQtXLgw28+HC/IZXVsokEzBagoaU+C3bt264qizMgVYiQ0bNoijIGlqB+EQQihUqJC41HU6BJ5To3YfJ1KLz4QQwqmnnirugw8+EFetWjVxVACE1lvaD1rnCQrx0jmnID6FjAkKbtNcTAVUCApah8BrLo1VCqZTAJ7GGwWNoxb7oUIINM5pPqaCHXTuKJhO4yPdMYzyGRQaj/q7v+Vzo/7u7bffLm7YsGHiaG2lOZAKOtAxoGIEqWONrq1D+C8fxhhjjDHGmFjww4cxxhhjjDEmFvzwYYwxxhhjjIkFP3wYY4wxxhhjYiFHgfPcuXNLt1cKM1MI+IorrhBHnWOnT58ujrqcNmnS5LDbegjq1kiBurvvvltct27dxHXv3l3c1VdfLY66dtL+EvXr1xe3efNmcRTu+9vf/ibuoYceEkfdKSkk+/XXX6fdzqy8+eab4igIRUFh6sZ5yy23RHrd0KFDxVFYkLq8zpgxQ1wIIcycOVMcFUKg80lBL3o/6vTaunVr3J5UKARI+1e8eHFxdH1RKIw60dMYnDp1atrtzErbtm3FURicCljQtdSzZ09xTzzxhLgbbrhBHBVCIBYsWCCucOHCkVzUECtd67R9H374Ybafo4ZVj0YOHDgg4W8KodL5uO6668RRp3sKb1IBhaih0SpVqohbsWKFOLreqCDL1q1bxZ111lniKERNcxNBv7ty5UpxdD9A1xatt/QZdExpf6MStVs9XdMXXXSROArnUoGIqAVoqJBBCBwMpnsCKkZC3a4pfEyfXblyZdyeVGi9oHuvG2+8Udxnn30mjoo30P5ScRO6Xgk673Ss8ufPL47OO92z0OsSiYS4qMH+Tz75RBwVPKJ9o8IUNFYpXE7XZmphDwfOjTHGGGOMMUccP3wYY4wxxhhjYsEPH8YYY4wxxphY+NWHj0QicV0ikchMJBKZ1MDGGGOMOZJkXafSfUfeGGPM/wa/mmhJJpN9Qwh9QwihePHiydTwDXX3psAPdXCkDt0UvKGAU4ECBdJvdBYGDBgg7q9//as4Ci5Rl00K48ydOzfS68aMGSOOArYU/KIu4NT9lbqKUriPOqFTIOnWW28VR9B+UFidQtD0UEudsylYRR3FKURF0LaEEEKxYsXE0XGlQggUfKxatWqkz3777bfFvfjii+IaNWokjjrH0/GiG7MePXqIoyA/jcuSJUuKI+bMmSPunHPOEbds2TJx1IWZuvH+/e9/Fzdo0CBxS5cuTbudWaFA8qpVq8TRtd6xY0dxb7zxhjia26jwQ2pH7dmzZ8trjmayrlNFixZNpo5z6iT/l7/8RRwF0zdu3CiOujJToQC6ZqiDPc0bjRs3FkchVBp/S5YsibR9FMRduHChuAsvvFAchVAnTZokjtYpKlTzxRdfiGvevLk4Ws969+4tjqBCK7ly6f+DpXktIyNDHIVuaR2lkDzdExHpiktQAJtC7DSnUuiZCg1QwR6aAwm6J6B5u2/fvuIorN6iRQtxo0ePFkeFH2gNISggTuOD7lNpLNDrKIRN8wmNI4LW9HfeeUccFZGhtf+5554TR9c6zW0nnnhitp/p2P3739L+izHGGGOMMcb8B/HDhzHGGGOMMSYW/PBhjDHGGGOMiQU/fBhjjDHGGGNiIUcdzg8cOCAhTAqOUTiNQlMUxKXuzRTQofA2hdN69eoljgI1pUqVEkeB4nbt2omj8NY999wj7vXXXxdHUFCYgqnU4ZSCrhSyW716tbg6deqIGz58uDjqBk+B5zvvvFMchd/o/FL38UqVKomjsH/nzp3FETVr1kRPYXAal6nhqhC4azcVZaDjT4EwgsJ4mZmZ4q6++mpxH3zwgbgtW7aIoxArBQgp/EkUKlQo0usoeEsBbLo2p02bJq569eriogY9aU6gsUpjf8qUKeIo3EfhzVGjRolLDYMeLsh3tJNIJOT4UFiYAs6nn366OCo8QGFLCohG7URPYXCa30866SRxNG/Xrl1bXLNmzcTR2vDWW2+l3c6sUBGJJk2aiJs+fbo4mstpTNP1QfMuHSuCfpfmpqgBZTrnNL9Q8JjCzQQdqxB4LqJu3G3bthVHBXZojaPxVr58edyeVGhNooIODRo0EEdj+r333hNHxWFo35YvX552O3+NqOfpt3Qup7EfdY6Pem9CBYAWL14s7pZbbhFHwf5169aJSz0fDpwbY4wxxhhjjjh++DDGGGOMMcbEgh8+jDHGGGOMMbHghw9jjDHGGGNMLOQocH7SSSdJEIhCbJMnTxb34YcfiqMO4hTopvejjovEs88+K446RJOjLqf33nuvuEsuuUQchdioQydBXUVLly4tjsK51PmYgtUU6Kb9pa6dBIWtKOBEgTh6XdGiRcVRd1Q6BtS9nUgXlqbPocA/hbAoZE+dyylESGF8CqFTQJwCtXTdnHXWWeJq1aoljgLxRM+ePSO9rkCBAuKidomlwDmF7vfs2SOuTJky4sqVK5duM7NBIWAaBytWrBB38cUXi6Ntpv2l85EafqfrzRwkV65cErhM7RAfAs+VFKKmUDaFWimYTp2LCZoX6XcpzEzXORVBoKBr4cKFxVGhCoK25fvvvxdHayEdK+rUvHLlSnFVqlQRR6FbIl++fOKoKzgdFxoHdB3SttAcRkULiHRhetpu2sZt27aJu+KKK8TR8aeA+MaNG3F7UqFrhMLq9DoKQnfr1k0cFfag7atXr17a7cwKXXN07uh6peID9H5RO6FHDZznz58/0utobFSoUEEcFRlq2bKlOLqHTB0vh7t/9F8+jDHGGGOMMbHghw9jjDHGGGNMLPjhwxhjjDHGGBMLfvgwxhhjjDHGxEKOAud79uyRbqXjx4+X11GX2BIlSoijEMuiRYvEUVDm7LPPPuy2HoK6qBObNm2KtC29e/cWRx0wFy5cKC5qMKh169bipk6dKo5CVNRh+6OPPhJHndCHDRsmjsJqBHXy3bBhgzgK9lOojRyFo+iYRg15UpAs3e9TOJ2CgJ9++qm4NWvWiKMQ5jnnnIPbkwp1tiXo+qKupDS22rRpI46uhwEDBoijayRPnjziKLA6f/58cXSsihQpIo7C5dSpnTpbU0ddmncohE6FM6jIABV0qFixojjqrpwaYo1aCOJo5Pjjj5eCDlGD5BQCprFL8xPNJxQKJgoWLCiOQtnJZFIcdaGmgiy0TtH1MWLECHG01uTNm1ccFZagAhm0Nnz33Xfi2rdvL27u3LnionaSp/WCQui0LRQup+NH4WEaV1QAgKACNCFEL3BAr/vkk0/EUUEMKpJTv3593J5UaLzRmkn3i7TPtAbTOkBQURCCrmEKftP1T/tLY4G6j9N1HfU+hu4HaFtoP6jIAAX2qZAEFblJXado7vz39qT9F2OMMcYYY4z5D+KHD2OMMcYYY0ws+OHDGGOMMcYYEwt++DDGuGjpnAAAIABJREFUGGOMMcbEQoKCLunIyMhIZmZm/hc3xxhjzK+RkZERMjMzNc1ovE4ZY8z/AIdbp/yXD2OMMcYYY0ws+OHDGGOMMcYYEwt++DDGGGOMMcbEgh8+jDHGGGOMMbGQow7nS5YsCQ0aNMjmevbsKa+jrtiVK1cWN23aNHHnn3++uP79+4u78cYbxdWtW1dcp06dxFEH4aJFi4qrUaOGOOrMSl0cqaMpdZi89NJLxd1zzz3iqKvo+++/L+62224TN2nSJHHUubRPnz7iatasKe6+++4TN2jQIHHUBZS6bo8ZM0YcHdPixYuLo27VzZo1E9euXTtx1157rbgQuOsndWGlbrmrVq0SR52tqbs3dWu96aabxL366qviNm3aJI72mTqIU4dfujapkzK935AhQ8Tdeuut4qi7Nx0X6opbrly5SK8rVqyYOOpS/uKLL4p75513xH399dfiOnfuLO7ll18WR12i6bpetmyZuF9++SXbz9Q12hxk3bp14a677srmqHMxjfuqVauKo/EStZs5fUb37t3F3XHHHeIKFy4sbu/evZE+g7ojU7dl6oT+8ccfixs6dKi4F154QdzatWvFRe3kTXN+1O7NNG/86U9/EnfVVVeJo3sOmu9nz54trkqVKuIGDx4sjub2QoUKibv55pvFXX/99eJC4K7Yu3btwtemQnPvnj17xNF2lypVShyN6UcffVQcra10X0Tnk6DX0fvR2H/mmWfE0ZimDvF0b0Ofcfzxx4s7cOCAODqXNMfQMX3yySfFEVE7ptM5ot+l6zC1wzl1Sz+E//JhjDHGGGOMiQU/fBhjjDHGGGNiwQ8fxhhjjDHGmFjww4cxxhhjjDEmFvzwYYwxxhhjjImFHFW7KlSoUOjVq1c2N3bsWHldw4YNxVHFH6pURNUaGjduLO6DDz4QR9WuqOJP2bJlxVFFLapK8NNPP0V6PzouVH2DqF27tjiqPnTOOeeImzlzprjmzZuLGzhwoLgyZcqI++GHH9JtZja2b98ubvHixeJOPPFEcXPmzBFXvnx5cVQxjapJrFmzJu12ZiW1ctshvv32W3Fdu3YVR8e6devW4qhCElWnoqoaBFVDoqppNAbpPFFVN6rcQWOBKpARHTt2FDd8+HBxdFyoAtGUKVPEVahQQdzy5cvF0fVAUOUyOi6LFi0Sd9FFF4mbMWOGOKr0RhVCrrnmmmw/U+Uxc5ADBw5I5Z5TTjlFXpdaQSyEEEqXLi2OrnOan6jCYdRrmqq37d69WxxVf6KKVQQdA5oPqIIlkTdvXnFUMYmq4lBlK4Kq7FBlIFqrCZpLaB6ialD0GePGjRNH9yELFy4UF3VtpYpaIXCFTqrkSXMWMXr0aHF0v7N169ZI70f7F/U80Tin36WxReODKmARVLGO9oPGJW0f7Qc5OkdRj1XUanJ0XOh+jF5H91lEaoW+w22b//JhjDHGGGOMiQU/fBhjjDHGGGNiwQ8fxhhjjDHGmFjww4cxxhhjjDEmFnIUON+7d2/YsGFDNpeRkSGvGzVqlLimTZuKo8DURx99JI6CwXXq1Dnsth5ixYoV4igs1KRJE3H58uUTV7BgQXFFihQRt3//fnEUYKUgPgX57rzzTnF33323uC5duogbOXKkOAo4pp7bEEI49dRTxRH0uxQ2IteiRQtxZ555prj33nsv0uvSBfRSOeaYY9CfddZZ4v785z+LowB8ZmamuFq1aomjUPEdd9yB25MKFRqYN2+eODp31apVEzd//nxxFDCjYOvs2bPTbmdWhg0bJo6O8+TJkyO9H13/VJSBAqZLly6N9BkUOqXztnPnTnHVq1cXR+F3Cuiefvrp4lKPS9Sw6tFIrly5JEhJ4XIK01IQ+rvvvhP3448/iqPrI2rQde/eveJobaAAO4Vkv/nmG3EUaqW1MGqgmI5V9+7dxb355pviKNRK8zEdP1pD0s3lqdBxofWW1nkqKEBjaMmSJeKokEHUa5jmlxB4LqIAPJ1P2mc6hsWKFRO3bds23J5UqMDBli1bxFFoPOr5pOsh6jVH0PmkezkaCzSm6XW0b1ED3QS9H31uahg8BJ47aFvoHNF8R8V60uG/fBhjjDHGGGNiwQ8fxhhjjDHGmFjww4cxxhhjjDEmFvzwYYwxxhhjjImFHAXOc+fOLQEkCvxSsI06qVKnYQouU+iJukYTFN6k4Cd1Qh86dKi4qAHs1atXi6PgYrNmzcRRCJCOC4X9KfBDrm3btuLofNx///3ievfuLY6CvRTinzBhgjgK3U6cOFEcdY0+7bTTxFH36/r164tbsGCBuBA4YEafc8EFF4j78MMPxdG5o1Bc1I6m1PmYri8q6PDuu++Kq1mzpjg6nxQwbdmyZdrtzAoFLleuXCnu5ptvFjdixAhxFKzs3LmzOBpvUTtCUxfr9evXi6MO8RTobN++vbgvvvhCHJ2PXbt2Zfv5twQUf+/kypVLgpQU6KbuvhTUpDk/akGGdGHhVKhjPf0uXYO01lAXZQoeUxA/KrTOUxCfrulnn31WHM2JhQoVEkfB3qhdvGkeosIhc+fOFUfHvnHjxuJobqpdu3akzyDSdYmm9YLWKRq/NLbo2ND5pJAyQdcDrfW0dtF1SAF2GjMUjqY1naDPpd+lY0/nieYdCslT0J3mBILuIalYEn0GXUu0v/Q6Ghup4+9w++C/fBhjjDHGGGNiwQ8fxhhjjDHGmFjww4cxxhhjjDEmFvzwYYwxxhhjjImFHAXO9+3bF77//vtsjjqIN2rUSBx1LqbgLAW/KTRK4W2CwpsUAKbwa6VKlcRRSPmyyy4TN2fOHHH16tVLu51ZefHFF8VR127qdE2hMdpm6o5K7uKLL067nVmhABt1dKdunNS5vESJEuKoOzftW9SQ5xlnnIGeOmBT2JDGL4U6qbMoffbAgQPFPfLII+KoizqFzug4PProo+JefvllcTTeZs6cKY4KTjRo0EAcFVGoUKGCuK+++kocXet0bY4aNUocXXObN28WR9CcQL9LwdE+ffqIo3A+hSNvuOEGcZ9++mm2n6OGPo9GkskkBq5TSQ3xh8DzE7myZctG2hYKZRIUYKfQKI3JUqVKiRs/frw4KiJB12BUaP6jYCqN1dtvv10cXTMUKKbjErUjNkGh4OLFi4sbO3asuPnz54u79NJL/58/l0g3hmhdoeNF44POCRXxoSA/nWOiXLly4mgM0rbQvEiFZFLvR0PgYgtRoUA87S+tZzt27BBH546KPFA4nwrGECNHjhS3adMmcTQ2ohYUoHFA4zf1M+j8/Pu1af/FGGOMMcYYY/6D+OHDGGOMMcYYEwu/+vCRSCSuSyQSmYlEIpP+JGWMMcYcSbKuU1G/6mSMMebI8KsPH8lksm8ymcxIJpMZ9L13Y4wx5kiSdZ2K2pzLGGPMkSFHgfNffvklrFu3LpujcAqFrSkQ+/HHH4ujUMw999wj7rXXXjvsth6CAmEUpqUQ26BBg8RdeOGF4mg/qBsnBWJr1aol7vzzzxeXetxD4IA4FQCgrqepAdYQOATdqlUrcQQFISmQRNt8yy23iKPAHwXOqHsrhZEJCoeHwGOQAmEUHKMO2Jdccom4AQMGiKOu8wQF6iig17BhQ3HUwfWhhx4SN3z4cHFdunQRt3bt2rTbmRUqDHDdddeJe+ONN8QtWbJEXPfu3cXRNUzHlILGBHWErVixorhvv/1WHF2HVESB5kUqAJAaxKewrzlIIpGQ64H+GlKgQAFx1H2Yri2C5nyax4hvvvkm0uvatGkj7s477xTXu3dvcWvWrBE3e/Zscfnz54+0LVE7JpMrWLCguJtuukncY489Jq5+/friaM4mKChM9wgU0m7durU42g+6/6ExFHVs0PodQgiTJk0Slzdv3kjvScVISpYsKY7WSHpdVH744QdxdJ9A54kKzlChhm3btomLGkKPWsyAjh91OL/jjjsivY7WZbq/IKhDfNTQOB0/gq4vOm+pn0Gf+e/3jPTJxhhjjDHGGPMb8cOHMcYYY4wxJhb88GGMMcYYY4yJBT98GGOMMcYYY2IhR4HzQoUKSTdv6npMYcjp06eLowARBeAoxNupU6fDbush6tatK27evHniKPQ8ZswYcRQUpgA7dcClDqIEhbIbN24sjgKsFGyL2kn1s88+E3fgwIFIv0uhOAoz0Ti44oorxFGHXgpC0vij8DsF5ylcGgIHH2kMUqdxKhZA1Xc6duwojgKcBHW2py6nVDCBQuMUWKMxTSHAqIFzCspSCJ2C5BR2oxArhcEpZPv555+Lo67sH3zwgTgKKdM8RkFZOs7Dhg0TR2P1xBNPzPZz1IDt0UiuXLnkeFHglOZFKkZA1y9dq/R+Ua9pKpJBHZOpQAEFj//+97+La9mypTiat2fMmJF2O7NCIVlaf2iepX2j4/fII4+Io+uSgt8EdVymEDTNp1T4ZuLEieJozYx6T0TzH4WHQwhyLxZCCFu2bBFHAWcab/Q51JF81apVuD2pUCib1hCCxhbN+fv37/9//gwidd4IgcdW+fLlxdHaQPMJjXPa33T3J6nQ/tKxovsx+gw6phQcp3GVWvCAXnMIr2DGGGOMMcaYWPDDhzHGGGOMMSYW/PBhjDHGGGOMiQU/fBhjjDHGGGNiIUeB8x07dkjn42LFisnrqPs4BbMogE1dYgsVKiSOQjEEhecoPPzSSy+JozBzo0aNxFF4bty4ceKoeyaRJ08ecXPnzhU3depUcdTxu1u3buIoeEfHmbo3U1f2ChUqiKNA8TXXXCOOOmx37txZHIWbKQwWtftzzZo10ffp00dcjRo1xFFRgdRO1CFwAJ460VJxBILGdOHChcVRh/Mvv/xSHAVRaXxQp9fDdTDNCnVwrly5sjg6xxTao8+lYPDq1avF0TgnmjRpIi7qMS1Xrpy4fv36ibv11lvFTZkyRdyePXuy/Ry1++3RyP79+2XM0JxAoUwKYFKhCwr8U/AzaviVQryp5zwELlDw7rvviqOCEVR8gYqW0BpCRA0AU+iUxi+F1SkMTuto1KIqFOylYgR07f/pT38Sd9ddd4lbtmyZuIULF4qj/SXShempkzeNX7qnWrp0qTjquB61KANB55jGB70uasA56n1g1PFBRC3iQ93gCRqDUcclQeseHT+as2hsRC2mQdd/6rFyh3NjjDHGGGPMEccPH8YYY4wxxphY8MOHMcYYY4wxJhb88GGMMcYYY4yJhQSFRtKRkZGRpK7Oxhhj4iMjIyNkZmZGS/sfZXidMsaYI8/h1in/5cMYY4wxxhgTC374MMYYY4wxxsSCHz6MMcYYY4wxseCHD2OMMcYYY0ws5KjD+ZIlS6TD73nnnSev69q1q7hXXnlFHHXepo7E1LWTumLecMMN4p599llx1HW2dOnS4ooWLSruwIED4qhTe7Vq1cRRJ+RHHnlE3OWXXy7u6quvFlemTBlxb731lrjjjz9eXP369cWdcMIJ4j7//HNxjz/+uLg77rhD3Pnnny+OOoCPHTtWHB2XESNGRPoM6sbZsWNHcdRtPQTu9FqnTh1xtN3XXnutODqG8+bNE0cd0x977DFx1CmburW///774qjz8aRJk8TRfrz66qvievToIa5NmzbiqAszdT3/9ttvxdG1RF12qYtwq1atxFGHcxof//d//yeOjvOKFSvEUcf5nTt3iqP9pbGW+rvfffedvMYcZN26deG2227L5mgOpM7A1DG5Xr164qj7MHUpp/e77777xPXt2zfS71L3YZq3qTM4dRumtZXe79577xV35ZVXiqtatao4Wkepezt1kqZrlY4LzSW0zXRN0/5G6d4cAne1pvuLvHnziqNj8NRTT4l7+umnxYXAndQzMjLEnXjiiZE+m+YU2mdap7p37y7ujTfeiLQtmzZtirQtVETi1ltvFUfHJU+ePOJuvvlmcS+88IK4Y445Rhyd4wIFCohbtGiROLo2S5QoIY7uNe+55x5xffr0EUfbTGP6xx9/FEddzzds2CCOurJH+b1D+C8fxhhjjDHGmFjww4cxxhhjjDEmFvzwYYwxxhhjjIkFP3wYY4wxxhhjYiFHgfOTTz45NGrUKJurXbu2vO7TTz8Vd9VVV4mjkNhpp50mbvjw4TnZzGy0bdtW3ODBg8VR6IxC7SeffLK45cuXi/v+++/FNWjQIO12ZqVIkSLixo8fL27y5MnilixZIo4CwBSUowBmlFBRCCG0aNFCHAXsKfh1ySWXiKPAVOXKlcVRyJOC4BQozpcvn7gQQihfvrw4CmXTmJkyZYq4NWvWiOvUqZO4tWvX4vakcs4554ijMD5dS3QMKRC/fv16cRUrVhRHYUFi/vz54ii0e9lll4mbMGGCuDPOOEPc9u3bxW3ZskUchR4JmtsojEdB3mOP1amVxhvNHbly6f8TSi2iQOFBc5BcuXLhXJYKhS3LlSsnLn/+/JF+lwqAUGEJggoU0BwdNRxNawhtc9T5naDraNasWeIooEzXPhVBoeuIigfQsSfouNC27NixQxwdZ1qnCDqXdJ0TFFAOgdeQt99+Wxwdf5rbaP8o9Jxue1IpWLCgOFqvaU6lQgi0fbROUcEZ2g+CAvY0PqjwA20z3UtQgSLaNxrnBG0z7S9tH90j0DGlsRqlKAOtjf9+z7T/YowxxhhjjDH/QfzwYYwxxhhjjIkFP3wYY4wxxhhjYsEPH8YYY4wxxphYyFHgfM+ePWH27NnZHHWJpnApdXrs37+/OAqcUjdJ6hZMrFy5UhyF0Cl4R2HauXPniuvVq5e4P/7xj+IobE1hcAreUZj2gw8+EEfd0SmkSB1EKahFwV6CzkfUED8F4ihATcG0s846S1z16tXTbmdWaFyFwB2rL7zwQnHUaZc6etKxoWArHS/ib3/7mzgKXNI4oo7aTZo0Ebd582Zx1PmdrmuCwod169YVR+OSxkepUqXEUfhz6tSp4qgDbrdu3cQ1btxYHO0vHRf6jObNm4ujwPnhusIewoHz9CQSCQk+Umic2Lhxozhaz2rVqiWO5pOo1zSFRikcTZ2padxTuJzCn/S7UdfWZs2aiRsyZIg4KvpAxRfo2qLzduqpp4qLGoKmz6VCH1GD+HT8aG6ibabjQqQ7H8OGDRP34IMPihs1apS4cePGiaNjTcVN6F6EoM+lubJmzZriqOM3dYmnwj70GYcLPmdl9erV4qpVqyaOQuj0GVRIguYJuk+lsDpBRRl2794dydE2UzCdHBH1OIfgv3wYY4wxxhhjYsIPH8YYY4wxxphY8MOHMcYYY4wxJhb88GGMMcYYY4yJhRwFzk8//fTw9NNPZ3PU+Zk6JFLIhjo4UuisatWq4qJ2KR45cqS49u3bi1u4cKE4ChRTKItC1Lfddps46vxOUJhp165d4qirOB1n6qhJATjqChy1cywF0+l333vvPXEUPG7durU4ClbR/laoUCHdZmYjXQdRCiX269dPHAXCKOxWqFAhcc8884y4GjVqiLvgggvEUdCTAmt0jr/66itxxYoVE0cBTiqYQGFBgsKzFLamDuy0b9T1nIKBVKQgalg7tbhGCBx6pA7xNH4HDx4sjsbgzJkzxXXv3v1Xf88cJHfu3KFkyZLZHBUAoLDwySefLI7GC81FRL169SK9jtYpmkvo+qD1guYcCqFTp2YKYBMUpqfiK++++644OvYUHi5atKg4Ct3THEbQHEHrBRUeoP2lQDEVzaAO0VGLEaQbQ9Qt/K677hJHBXaowAYV2KF7BxpbBAXvKbhMhW6mTJkijrpxE3S9Rg3J09pPY5WOAd27Ll26VBztBxUVoOuVoGuYihlQsSSa76g7etQCFi1btsz28+HmSf/lwxhjjDHGGBMLfvgwxhhjjDHGxIIfPowxxhhjjDGx4IcPY4wxxhhjTCzkKHC+f/9+CcZQ2LpRo0biqGNox44dxX3yySfiKFhNwdmuXbuKO/fcc8UNGDBAHIVzqcsmhd8XL14sjjqDUjCQoABWgwYNxH355ZfiKFREneRfe+01cRQApoB4q1atxFEomDqFU/CYuopmZGSII26++WZxDz/8cKTfffbZZ9FTB3HqAr5gwQJx1J2awttU9GDNmjW4PalQsI0KMFCIlYKPFOCk0Blt3+mnn552O7NCAU4KBtLYomDl9OnTxVFoj4pflC5dOu12ZmXixIniKHTfpk0bcRS0q1KlijiaY2guSg2nUkdsc5ACBQqECy+8MJuja52CpBQWpnNJRQG6dOkS6XcJKoxA1wwFgFNDniGEUL16dXEULi1Xrpy4qOFcmjeoEALN5bNmzRJHYV9aCym0THMEMWLECHG0H1Tog45L1M7qr7/+ujgqHkDQXBcCF2q56aabxNGY6du3r7hatWqJK1u2rDha9wg6n3Q90P0TXQ9UNILmYwpCRw33U0ERuo+he9J27dqJe+ihh8TRPSR9Bo1Lgo4fzWNUUOjMM88UR/MiFWWgY596/R+uMJT/8mGMMcYYY4yJBT98GGOMMcYYY2LBDx/GGGOMMcaYWPDDhzHGGGOMMSYWchQ437dvX9i8eXM2R4FzCkzOmzdPXNQA65AhQ8Tdfffdh93WQ+zevVtcjx49xE2dOlUchYCoayd1uxw6dKg4Cr8TFLq/5pprxFHolEKFN954ozgKH1FIKbVTcDpom2lbqCPstm3bxFEwkEJo1FE3avjwxRdfRE/BQgou075ccskl4l5++WVxS5YsEUedSonUazAEPjb58+cXR52AqbNt1DDk1q1bI72O9pfmDrq+6LjUr19fHHVDptdR91yCwofUnZYCtVQQg8L+FFilrrh16tTJ9nPU4hVHI1u3bpWAL4U3CxYsKI4CsRRwpnNJ1+XhApdZoZAnhbdp/FFhhMmTJ4u74447xNG+0TVIUIEHKuZQsWJFcRSCpi7KFFqm4jW01hAUdKd5g+4H6N4kanduCvbTOSfSFZeggiJ07ihUnFqQIYQQJkyYII7m7ajd5KloCUHrKN230bx47bXXiqPu3lTchKBrjq4Rur+j/aVjRYWCqHDOwIED025nVipVqiSuYcOG4mhs0NpP+0v3FzQHpl6bhzvu/suHMcYYY4wxJhb88GGMMcYYY4yJhV99+EgkEtclEonMRCKRuWvXrji2yRhjjIlM1nUqan18Y4wxR4ZfffhIJpN9k8lkRjKZzKDvfRljjDFHkqzrFDW/MsYY879DjgLnO3bsCMOHD8/mnnnmGXkdBZe+//57cdTxt3bt2uLGjRsnjgJr1IWxQIEC4qgjJIVnKHgzevRocddff724+fPni/vHP/4hjrpiUhiMAmsUssvMzBRH+/bZZ5+Jq1mzprio3W6pW/2YMWPEUSizQ4cO4ijIR8FjGhtRg9sUmAyBu8lTKHHGjBniUq+PEEJo1qyZOAqEUvCOaN26tTjqMB+1W/Mbb7whbtCgQeKo027U8GHXrl3FUaiQxvTy5cvF1atXT9xjjz0mjrr20jVH0Dig/6u+evVqcXQDTIUVKPRM13BqgDpq0P9oZO/evRI6vfzyy+V1NMajdO0NgQP/FPw+++yzD7uth6AwM4U1aaxRcJm277XXXhNHcwTNdRTspe2jIhL0umrVqomjQiH79+8XRwVt0s3lqdAcS4UlaG6iIDMVtGjatKm4qOsZUb58efR0T0BzDK2R48ePF0dFbWidogI7BAWw6X6C5lQqrEBjn8L4xx13nLioneh37NghjoLaa9euFUf3mtTN/OOPPxbXp08fcbTOE7QGFy9eXBwdPxqXtG90f0Ed51MLRNDxPIQzH8YYY4wxxphY8MOHMcYYY4wxJhb88GGMMcYYY4yJBT98GGOMMcYYY2IhR4HzPHnySAjzpZdektelduMNIYTZs2eLa9u2rTgK8VKXWOreTFCYjMKvFLpt0aKFOOquSttMwUUKyhHUdfLbb78V9/+1d6ZBVlXnGl6HhEFBEVEQm2ZoRkEEm26GQFBAEkZFAkYUNKIIMRoFhyQgQpmkLI2BlAKmTFkxIQQESTAEVCalGWQSBBqZWkCaJsgUR1I24rk/KO7tPu9z2t3JvZsu7/v846nTfdbee+219rZ9v4+6sHbp0kUchYWys7PFUSnlqJ1BqbNtixYtxFGId+7cuZHG0rhxY3EUKO7atWvacZaEQvchhLBs2TJxFMIcOHCgODoWCvxTQD9q523qTk/XqWfPnuKoY+1tt90m7vXXXxfXr18/cXPmzBE3ZMgQcRTgpDAaBQ0pdEpFI+bPny/uiSeeEDdjxgxxVORh6tSp4qg4AgXiCQqIUvCTim6khigp/G/OUKVKlZCZmVnKFRQUyOcoSEohYFrzCQq/Rl0/o3ZWb9mypTjaG6jAC42PvjdqZ2q6LylwToHijIwMcbRXUyCevpd+lhgwYECk8VFo+f333xf3wgsviKN1l65l1DHv3bsXPc1LChpTuJ+CxlQshdYiCiQT1apVE0fzl64x3ZtUVODkyZORxhL1PqRnTTpXVLigsLBQXPfu3cVNmjRJHBVuoqJKQ4cOFVdUVCQuPz9fHF0POjY6V1QoiJ7BUyviUjGMs/gvH8YYY4wxxphY8MuHMcYYY4wxJhb88mGMMcYYY4yJBb98GGOMMcYYY2IhETWIE0IIOTk5Seq+a4wxJj5ycnLCxo0bE+d6HBUR71PGGHPuKWuf8l8+jDHGGGOMMbHglw9jjDHGGGNMLPjlwxhjjDHGGBMLfvkwxhhjjDHGxEK5OpwfP348/OEPfyjlqCPx4sWLxVE3Y+ogTl2sqQvw8OHDxVGXbeoaPW3aNHH16tUTR102qQMmdeOmjtjUTZa6rU+fPl0cdabu2LGjOOq8SZ1U77//fnGDBw8WRx06O3ToII46vVLnXeqoSR1r8/LyxFE38y1btoj75S9/KY66nj/77LPiQuBrTN1Lu3XrJm7hwoXihg0bJm7t2rXiqIPuk08+KW7p0qXiqBsqdcCla3LppZeKa9asmbhEQnNj+/dJ/ENtAAAeDklEQVTvF/ezn/1MHN2Hhw8fFrdq1Spx7du3F0f313vvvSeOrvvEiRPFUWf6sWPHijt06JC4cePGiaMO8W3atBH31ltviaP5kjrP//nPf8pnzBmOHj0annvuuVLu+PHj8jnqZk5rUfXq1cXl5uaKoz2OugD37t1bHO1xqccQQgidOnUSV7t2bXG01+zZs0ccHVv9+vXF0T5F9zlBXbdp/jZp0kQc7SutWrUS169fP3F9+vQRN2PGDHGffPKJuO3bt4ujfZ66RtP+QZ2es7KyxN13333iHnroIXEhhFCjRg1xtDfTvnLs2DFxdCw0bvrcPffcI+6pp54Sd/HFF4vbtWuXuH379omjZzR6Jvjyyy/FURf7Bx54QNyUKVPE0b5H54W62NNYUruAh8B7NT0rPfzww+Lo3NP9T+tEdna2OFrHyDVq1EjcgQMHSv2butefxX/5MMYYY4wxxsSCXz6MMcYYY4wxseCXD2OMMcYYY0ws+OXDGGOMMcYYEwvlCpxXqlRJAmoFBQXyueuuu04cBYgo7LZ582ZxnTt3FkdhHIKCN7feeqs4Ci63aNFC3I4dO8RVqqTvcBQ+olAR0aNHD3HXXHONuNWrV4ujEFpxcbG43/3ud+IoFExBTYK+l8KWNF9q1aol7pZbbhG3fv16cRSEpLAvBY8pnB9CCEuWLBE3cOBAcRkZGeJq1qwp7pVXXhGXTCbFUaCOoHG3bt1a3NatW8XRNaGCBHXq1BFXpUoVcTT3CQp60pivuuoqcRRMpyAfrTvPPPOMOAqnEhS6pyIUqUU4QuBg5aeffiquefPm4ijclxqId+A8PadPn5YQMV0P2hto3T569Ki4ZcuWibvjjjvEffDBB2WO9SxFRUXievXqJe7VV18VR+sG7V0U/KYQNQWAiZ49e4qj4zj//PPFUcGIvXv3iqOwNYXzN23aJI4C57Ru0BpGIXQKGdPeSmRmZoqjuUZUrlwZ/dtvvy1uwIAB4r744gtxtA9QCJ3WrKjHTOOm8DsVS6HCDzSnaZ+itTHqPkXHSy5qOJ/2Cxoz7XFUEIOge+mjjz4SR/sKPSPQmCkkT/dI6tpR1jOv//JhjDHGGGOMiQW/fBhjjDHGGGNiwS8fxhhjjDHGmFjwy4cxxhhjjDEmFsoVOP/oo4/CokWLSjkKjVJAjwLdJ06cEEfBMeok/dprr4m79957xVHYl4JBV1xxhTjqPkyhJwqxUTj6xhtvFEdQJ1oK4lMIqGnTpuKoYzqFabt06SKOutVTkI/GnNrtMgQOaVM4igLZFOjcvXu3OAp0ERSODIELIVAYnzpqt23bNtJ3U8fVqAUJTp48KY7GR6FCCpMtWLBA3LvvviuOgm3U/ZWKANB1p/vw4MGD4qibNHULf+SRR8RRN3MKOBLUqZiuEQXxqZP8lVdeKY7CoFRIIvUc0PU2Zzh16pScf1oXKThLocz8/HxxFPKmADF1yu7evbs42i9ovlD3cQr2UgEQCuLScdBYCNrTqXgIFQAZPny4uKhzmgrQPP744+LGjx8vjtZdWk9pT6eQMUF7ABUJobA/cemll6Jv2LChuKefflrcoEGDxFHBHlrLadxR96moRXdoX6FCHPT7Vq5cKY72f7rGBB0vnRcKg1OonfYp+lnqPp6Xl5d2nCWhe47mOa13VBCD5hvdDxTETx0L7fFn8V8+jDHGGGOMMbHglw9jjDHGGGNMLPjlwxhjjDHGGBMLfvkwxhhjjDHGxEK5AueXXHJJGDFiRClHHSs3bNggjkJxrVq1EkedqKnzadTAed++fcVR4JRCO1lZWeIopEyfa9y4sTgK01977bXiKOxLwR0KR1JInoLuS5cuFUeBKQoVERR+p+Addc+l76C5QQF2CmDOmzcv7ThLki6ElpOTI66wsFBcu3btxFFocv/+/eIo6End0YnZs2eLu/3228W9+OKL4ijw98Mf/lAcnRsKR0edH3Q/HD9+PNL4Ro0aJW706NHiqDv6mDFjIo1vyJAhkT5H9z8VoaD1hAoUUOdtug937NhR6t9Ru9/+f+S8886TsDaFVel60DpLhTgo9Ez3Oa1FtE9RcQgqvkDfS/sFdaumtZLCtBSSJWifpw7b9L20hlEHdjoHdevWFUfrAUHHS2sOBY+pCAoF+2kOUQA4aodz2udDCOGb39THN1qPmzRpIm7nzp3iqlatKo6KwUQN3tO5ppA8Fb+hvYEC09QJna5T1CI0VCiEOnnTd+zbt08cFZz4T/ZMgs4z7T90/ug8UyEEetY5cuSIuNRnNAqqn8V/+TDGGGOMMcbEgl8+jDHGGGOMMbHglw9jjDHGGGNMLPjlwxhjjDHGGBML5Qqcf/bZZxIyo6Bxdna2OArtUBiFQtkUaqfurwQFM6lzMQWwqZP3ihUrxFGn19Rgfggc8iIKCgrEUQf2nj17iqNustRxnkKFY8eOFXf99denHWdJKIRGATYKBVM30yVLlojr1auXuGnTpom766670o6zJOlCaG+++aa4u+++W9zMmTMj/c5HH31U3J/+9Cdx1CmbiFqkgObvrFmzxK1du1bcddddJ27hwoXi6JoQFI4k6HspjEv3dbdu3cRRN2kK4hMUBh86dKi4zZs3i6Pw4aFDh8TR3KeAaGqBDepQbM5QrVo1KQJA93Tr1q3F0fWgQgsvvfSSOAp+0rpNUFdh6kh+4sQJcZ06dRJHhUeoSAvdMxRWJ2jMVJCBzgGF/SlYTfcC7Q3XXHNN2nGWhIK4tE/RswndvxdccEGk76UQLxWqIdIVIqE1lebqk08+Ke6GG24QR+eBil9E7XBO465evbo4CnmnFtgIgZ81W7ZsKY7mDAX+CQqSEzSP+vfvL46KCpx//vniqMBL1IIE9ExFzwNUNIeOl55TqWgErU9Rz18I/suHMcYYY4wxJib88mGMMcYYY4yJBb98GGOMMcYYY2LBLx/GGGOMMcaYWChX4Py8886T8Njvf/97+Rx1KafuihTypsAfud27d5c51rNQaIe6l7Zv317cb3/7W3E/+tGPxFGQ74033hDXoUOHtOMsSW5urjgKOFF4i84VBcQosEtBrcGDB6cdZ0kowE6BuPHjx4uj4gHU7favf/2ruOHDh4ujrqdERkYGegp00zWmeUSB5OnTp4ujjqHUTZagACcFzPLy8sTRPD98+HCksdB1og6pBIXYKLBG90jHjh3FURfhqKFiCgYTdC/Nnz8/0viWL18ujrpT03mhLtG33XbbV/6cOcPnn38uRTuoaAkVeKCCHVG7KFMhEwqSErQWUTdo2s/mzp0rjgo3bNu2TdzixYvFpc61dFAxBwoK0/mjMDLdl23bthVHYfWoaz6F5OlaUuie9kwqAEDBbdrPoq5D9PtC4KITFJSn76Eu8R988IE4CnlTqJ2g5w46r1QQhzrHUwEQOo7KlStHGh9B85L2W3oeo+sRtQs9nauoawcVBUgtuBECn3taT+geoTXmscceE5daWKGsQjP+y4cxxhhjjDEmFvzyYYwxxhhjjIkFv3wYY4wxxhhjYsEvH8YYY4wxxphYKFfg/IsvvpCQaIMGDeRzFOBq3LixOArUNGvWTNyaNWvEUXiToGAqdR+mzqzUvZSCN9QlloJo1AmZumIeOXJEXKtWrcStW7dO3MGDB8U1atRIHAUwKVhFnbgpNE5hX+oqeuedd4rbsGGDOArZUQi6adOm4p555hlxvXv3FkdB6xA4cE7haOoiPHv2bHEUJj1w4IC4/Px8caNHjxY3YcIEcTSPKLBKQVQKjVO3YSoqQF3PCeqQSt9LwXm67jS3KFhJ9wN1mCXoe+mcrl+/Xly1atXE0TynNYbmUI8ePUr9m8LS5gynTp2SECvNPwqIUqiVguQ0d6lTc7169coc61lortE+SkU3KGRMwXnqAk6BU9oLiU8++UQcBZTpXqXCEtu3bxc3b948cdnZ2eJoTyconEvnoHbt2uIoaE0/W6dOnUi/j/YUIl1olwrO0PdQwHnq1KniqHgL3SMUvCc2bdokjtYtukdoftDcovHRM1/UbuH0OepsT4UVCNpraB7R3kXXjYh6z2VmZoqjfZ4KANBz/syZM8WlPr87cG6MMcYYY4w55/jlwxhjjDHGGBMLX/nykUgk7k4kEhsTicRG+jOrMcYYcy4puU/R/3JgjDGm4vCVLx/JZPL5ZDKZk0wmc+j/TzbGGGPOJSX3Kfp/4Y0xxlQcyhU4P3XqlITRKExLXSIpBEQdovfs2SOOgun0s8S4cePEjR07Vhx1FV6xYoW4rKwscfQXIeoWGrXz5qBBg8RRcIe6e7788sviUrtOhhA9dEvha6Jv377iKDS+cuVKcdTNdNeuXeKoAzt1uv/e976XdpwlSXcOqJv5X/7yF3F0/um/uubk5IijudWnTx8cTyoXXXSROAq7UgEB6mgctUPqxo0bxaXrvpsKhRTp/FNncOomS+OjogxPPfWUOOo2TDzxxBPiaO2gNaGwsFAcnfuhQ4eKe+6558SldnWmjrjmDJUqVZKQOIVGqYM9rdFUHILuc5p/UbsUU9iaQp60n+Xl5Ymjjt/FxcXi6B6kNXrkyJHiaB0iR8VNKNhL52/v3r2RfpauJUEhb3o2oetLzzUUHqbnAQqhR+3KTgUF0v08fZbWaDqHFN4moj7HUNENWstpn6IiD9Shm46XHBWIIGidoOOg8VHRCAp00/MOXQ/al4kpU6aIo8IyNFepm3nUc0outWBMWcUJnPkwxhhjjDHGxIJfPowxxhhjjDGx4JcPY4wxxhhjTCz45cMYY4wxxhgTCwnqopqOnJycJIVOjTHGxEdOTk7YuHGjtsU13qeMMaYCUNY+5b98GGOMMcYYY2LBLx/GGGOMMcaYWPDLhzHGGGOMMSYW/PJhjDHGGGOMiYVydTjft29fGDZsWClH3V9/8YtfiDt69Kg4+lnqDkodJqnD+YgRI8RNnTpVXGoXxhBCuOWWW8RRp9f69euL+/TTT8VR5/dXXnlF3AsvvCDub3/7mzjqzLp582ZxN998szg63jVr1oijTtzUqX3ixInixo8fLy43N1ccBUGpMyh1hL311lvFbdq0SRx1Qh09erS4RYsWiQshhG3btomj7q80L6ljKH1u1apV4u68805x7dq1E/fss89GGh+5I0eOiLv44ovFUVfcLVu2iKtdu7a43/zmN+KmTZsmjjq4UjfkNm3aiOvYsaO46dOnR/pcfn6+uJ///OfiqKs93XN0nul+veOOO8TNnj1bXPv27cWldsAuKiqSz5gzFBYWhjFjxpRy1Mmb1nLqdl23bl1xVapUEffxxx+Lozk+ZMgQcZMnTxZ34sQJcRdeeKG4RELznDQW6j5Oc5fG/Mgjj4ij+7xly5bi9uzZI47WF+oGTZ2kqVs1fe4nP/mJuFmzZomjTs30bELu8ssvF9e2bVtxdN3oHqY9btKkSeJC4L2G5i91z6bzT3P6m9/UR0RyNO4JEyaIo47XND6C7sPPP/9cHO1n1A3+4YcfFkfPMdTtns4f3V89evQQt2vXLnH03Evn5cEHHxRHz7gFBQXi6Bk89Xk+hDPP+anQcyDdN6ld1Mvap/yXD2OMMcYYY0ws+OXDGGOMMcYYEwt++TDGGGOMMcbEgl8+jDHGGGOMMbFQrsB51apVQ5MmTUq5Dh06yOdmzJghrlGjRuIoHL1ixQpxFD6mkDdBAWcK9u7YsUPce++9Jy47O1schd0ovDV27Ni04ywJhXsOHz4srk+fPuL2798vjgLUFLY8efKkOAoVERTAuuyyy8QVFxeLy8rKEpdMJsU9//zz4ijUTgF2Yv369egpeE/ntWbNmuIoJEYBxAULFoijAgcUOE8NdaVzdG++88474qpXry6Owq50jevVqyeOoO8gKMxI4e2XX35ZHIUe6TxHHTPN/SuvvFIchfgfeOABcXT/9+/fX9zq1avFffvb3y717zlz5shnzBmSyaQEqaMWX2jevLk4urco2EuFJSgMTtBe06JFi0g/u3XrVnF9+/YVR/OK1mgK8RJvv/22OAqX0/4dtXDLwYMHxdHeSvsyQfsKhfOpKEXlypXF0b5MQXAK3tJaR9SoUQM9FQag4DKdf1rLKZRN4WgKfhNUOIOg80DHQY7um507d0b6XoLuB5qDNGfoGZfmBwXT//GPf4iLunbQ+aM5c+jQIXH07EX3cOPGjcXR/Z/6/ENr51n8lw9jjDHGGGNMLPjlwxhjjDHGGBMLfvkwxhhjjDHGxIJfPowxxhhjjDGxUK7AeTKZlDAadSWloBGFmSnUlRqsDIE7flPHUKJTp07idu/eLY7Cbg899JA46oA5aNAgcRQWopBs69atxVGIioJf1ImSQooUxqMgFIXzqWs0cdNNN4mjzpsUzqegJgWeGzZsKG7+/PniKFRN3W7pe0PgcDnNN+p8unTpUnEUQKSiB2vXrsXxpEJFGSgI/eabb4qjTs9UBKBZs2biunTpIu6NN95IN8xSFBYWiqPCCjfccIO45cuXi6NzT4F96sAeNehJIUAKJFLXXgpvUoEImld0/48cObLUv9PNXXMmiJu6ztD1oHlPYUtaPyl8TCF0mqdEx44dxVFIft26deJGjRol7le/+pW4gQMHiqNCKxSmJWrVqiWOQtB0n9M9Q+FXuhfoWlJXa4KuOa2TGRkZ4iisTuFhKpBB55mOI/U+T/f7QuBzTfOICs4sWrRIXJs2bcRdddVV4qhwAUF7MxVkoVAyFX2hvStqAaCoRWjoc3Qf0p5Oawc9i3Tt2lUcrRPpCg2kQvPoggsuEEdFBqjrOUHXiK7l1VdfXerfixcvTvs7/ZcPY4wxxhhjTCz45cMYY4wxxhgTC375MMYYY4wxxsSCXz6MMcYYY4wxsVCuwPnp06ely+Qll1win6NuiBQCoiDV+++/L45CVHv37i1zrGehQDd1tk7t3B5CCBs2bBA3ePBgcRTGobBq7969046zJBROo1A7BY8pHEXdZCnsT524J0+eLO7xxx8XN2nSJHEUfu/Ro0ek8VFYjYLgNIeo0zWRrus2hfGp6y+FrceNGyeOgvx5eXniunfvjuNJ5f777xf36KOPiqNzTcE26hh8/PjxSGOh7rlE586dxVFIlO5rClZSN1kKxFPRg6ZNm6YbZikGDBggjgowUKD2z3/+szgK2VIA9te//rW41157rdS/qduvOUMymZTzSuFNuh5UKOTYsWPi6tWrJ47mKYVBCVq3qYPwgw8+KO6nP/2puHvuuUfckiVLxNH+ffnll6cdZ0noXqWgNp0DKphAaw6FoCkUTPcgQeeUjpcKglDAlp4RKHhMzwhldX8uCRXhCYGL5NB4KFC/YMECcbSH0PnKzMzE8aRCzztUlIGgIPSBAwfEbdmyRRwVTKCu7MOHDxdH+xkVBaHjoEImX375pbgGDRqIW7hwoTi6lwgqfhF1LZo9e7a4b33rW5G+o1u3buJoTU2H//JhjDHGGGOMiQW/fBhjjDHGGGNiwS8fxhhjjDHGmFjwy4cxxhhjjDEmFsoVOK9UqZJ0maSwFoVOqKNmajfEEELYuXOnuO9+97vlGWYpXnzxRXFDhw4VR51jKUB05MiRSL+vqKhIHIV7KEw/YcIEcTNnzhRH3ZspjEchNLpG7dq1E0fHQVDAiTqSU1CWOr9fe+214k6ePCmOOqFSMJ3YunUregqTUqdXCtTT76TQGc1pCqYTNKdvvvlmca1atRJHQbm33npLHB3v3//+d3FRuyFTYQAKiVJHY+reTuOj4+jZs6c4Ct4SFGakIgUU2qW5QR2Sd+3aJY7CoKtWrSr1bwfOyyY1OE4FI+i+/PDDD8XRfUkFQCg0Tp2Gic2bN4sbPXq0uMcee0zcvffeK27p0qXiKHRLUNEX4qabbhI3b948cXR/RO0aTfst/T4KxBJU4IHupcLCQnE0N6iQzhVXXCGO9sLc3Ny04yzJiRMn0FPBmWHDhomjoPz48ePFUREAclSQhaCCLHSNqeAMBb9pTaVnNDpe2lcIOjZ6zqK1gwotUbEAOqejRo0SN23atLTjLAnNafqOWrVqiaNCSzRXaS8kUost0LU4i//yYYwxxhhjjIkFv3wYY4wxxhhjYsEvH8YYY4wxxphY8MuHMcYYY4wxJhbKFTivWbNm6N+/fylH3TMpBERBV+qyTcFlCqFTEI0YOXKkOAoQUhCK6NSpk7i5c+f+2z9LUECcQmw1a9YURwUAKNBNgT8Kz9G1JGgs1KmdwowU1JozZ4446hpPgc5+/fqlHWdJqENnCNyBlMZDYWYKdVHQMzVAHAIHq4kxY8aIo0ID69evF7dv3z5xdP5feuklcXQc1OWYoCAffW/z5s3FUTEICsCtWbNG3H333Scu6pymbvDUlZ3WNhoz3V8UhL7sssvEDR48uNS/t2/fLp8x/0NquJ+6SdNeE7VLMQWrBwwYIC5qEJqCwk8//bS422+/XRztZxQ4TS0WE0IIBw8eFEdznKB5T53BKbB76NAhcVRQhKA1n7p9ExSwpXNA57RGjRriaI2g80z3fkFBgbjOnTuLS/esQ8VqVq9eLe7YsWPiLrroInFUTIPWd9rjiBtvvFEcdS6nzva0d9HPVqlSRRwVJIg6p6tWrSqOAv/NmjUTR4ULJk+eLI4K4kyZMkVc69at0w2zFFTMha5RahGOEPj60vmj9ZPuh8zMzFL/putzFv/lwxhjjDHGGBMLfvkwxhhjjDHGxIJfPowxxhhjjDGx4JcPY4wxxhhjTCyUK3D+jW98I1x44YWlHIWrqBPyj3/8Y3HUAZMCKqldE0MIoVevXmWO9SybNm0SR6FCCqtSp2vq2Pjuu++Ko47T1MX2+9//vjgK8lA47frrrxf3zjvviGvatKm406dPi6Nuy1EDxT/4wQ/EURCKOoBTN9m+ffuKo6AWBZmjduilDtshcCdVCui9/vrr4ii0F7XTOIUwienTp4uj+4ECiRTypnuOOrNu27ZNHBU4ICggTgFCKlxA5y8jI0McBVtpfPT7CCpCQYUa6L6mbuYUJKeiBXS/zpo16yvHYc6QSCQkOFpWp93Un43iKKxORQAomEpQgYfUIgMh8H72r3/9S1ybNm0i/SwFcel4CQrnUjifguQUxKffR9DaGfV+oPBwo0aNxNHaRCH+rKwscUVFReLomYP2RyLd5+i6f/zxx+LofFHIO+p4qEgGQc9FVCyA9iQKW9O8pLlF1+7AgQNpx1kSKlxAc5WeMdq1ayeOCgVdffXVkcYS9TzTGkPPVPTMRyF5WifoeYzWjgULFnzld57Ff/kwxhhjjDHGxIJfPowxxhhjjDGx4JcPY4wxxhhjTCz45cMYY4wxxhgTC+UKnBcXF0cK81LwhoJeFCCmAGZubq44CuhRB3EKjVI3aOqETkGtP/7xj+KoKzOFxul4CQrOUuh+5cqV4qZOnSrurrvuEkdhawqItW3bNu04SzJx4kRxXbt2FUeBbOrqumzZMnHf+c53xNWvX19c1A7W6cL0FF6kOUPBYOpGS2OkOUNdRAnqhE4Bs+XLl4vLzs4WR53jKThKIXTqREs0bNhQHAXdyVE3+BEjRoij8CcFjelzBBWh+PDDD8VR+LhBgwbi6NhoDpJLvebUMdn8D6nh1KhB8qjhcgpHRw10ErTOUpERKvBC69C6devEUQGVunXrRvpegvaL4uJicRToplAr7Q0UKKaCEVG7btP5o3WXwuVR77k6deqIo3kQNdif7nMUSKZ1m6Au1hQGp/NPhTMIum9of8zPzxdH9xJdOxozfY6e5Qh6bqMQOhU8efXVV8VRAZXULuAh8LMhFY0gqFAQ7TVRCyvQnvnZZ5+Jo+IGqfOqrGcx/+XDGGOMMcYYEwt++TDGGGOMMcbEwle+fCQSibsTicTGRCKxkf7MYowxxpxLSu5T1P/AGGNMxeErXz6SyeTzyWQyJ5lM5qQ2GDTGGGPONSX3KedhjDGmYpOgTrxpP5xIHA0hvB9CuCSEcOz/alAx4uOoWPg4KhY+jopFyeNomEwmtZqG8T5VcfFxVCx8HBWLr+NxpN2nyvXy8d8/lEhsTCaTOf/B4CoEPo6KhY+jYuHjqFh8XY4jLr4u58vHUbHwcVQsfBwVi6jH4cC5McYYY4wxJhb88mGMMcYYY4yJhX/35eP5/9VRnDt8HBULH0fFwsdRsfi6HEdcfF3Ol4+jYuHjqFj4OCoWkY7j38p8GGOMMcYYY0x58f92ZYwxxhhjjIkFv3wYY4wxxhhjYsEvH8YYY4wxxphY8MuHMcYYY4wxJhb88mGMMcYYY4yJhf8CFusmylh1bNcAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "D1 = D1.squeeze()\n", "fig = plot.figure(figsize=(14, 7))\n", "plot.subplot(1, 2, 1)\n", "plot.imview(util.tiledict(D0), title='D0', fig=fig)\n", "plot.subplot(1, 2, 2)\n", "plot.imview(util.tiledict(D1), title='D1', fig=fig)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get iterations statistics from solver object and plot functional value, ADMM primary and dual residuals, and automatically adjusted ADMM penalty parameter against the iteration number." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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sv127dvHtb3+7UWJ78MEHmTZtWqOcS0REpDpKMFXh95oW+RYRacGcc/OdcxOSk5MbfK6EmFCCqVgJJpHWLj4+nqysLNauXcvChQt56623mDp1ati+vXv3Jisri9WrV7Nu3TreeOONY14vLS2lS5cuzJs3rzlCFxERaRRKMFXh83g0RU5ERCKSGBucIqed5ESkspSUFGbNmsUTTzxR4yYAPp+PM844g02bNjF79myuuuoqxo4dy4UXXsi2bdsYMmQIALNnz+aKK65g7NixpKen88QTTzB9+nSGDx/O6NGj2b9/PwBPP/00I0aMYNiwYVx55ZUUFBQ0y/sVERGBKCWYzOxOM1tjZmvN7K5QW3szW2hmG0P37Sr1v8/MNpnZp2b2zaaMze81SspUwSQiIrUrnyJ3WFPkRKSKXr16EQgEyMnJqbZPQUEBixYtYujQoQAsXbqU5557jvfee++4vmvWrOHPf/4zH330Effffz8JCQmsXLmSr371qzz//PMAjBs3jo8//phVq1YxcOBAnnnmmaZ5cyIiImH4mntAMxsC3AyMBIqBt83s76G2Rc6535jZZGAy8DMzGwRcDQwGugD/MLN+zrkm+bnY5zVKA6pgEhGR2iWGpsgVaJFvkRPGbz/6LRv2b2jUcw5oP4CfjfxZnY+rrnpp8+bNZGRkYGZcfvnlXHTRRcyePZsLLriA9u3bhz3m61//OklJSSQlJZGcnMzYsWMBGDp0KKtXrwaCSagHHniAgwcPcvjwYb75zSb9XVZEROQYzZ5gAgYCy5xzBQBm9j7wLeBy4NxQn+eAxcDPQu1znXNHga1mtolgcmppUwTn83goCzicc5hZUwwhIiItRPkUOa3BJCJVbdmyBa/XS0pKynGvla/BVFViYmK154uNja147PF4Kp57PB5KS4OfQTfccANvvPEGw4YNY/bs2SxevLiB70JERCRy0UgwrQEeMrMOQCFwMbAc6Oyc2w3gnNttZuVX467AskrHZ4famkSMLzhrsKTMEeNTgklERKpXXsGkXeREThz1qTRqbLm5udxyyy1MmjSpWX+wzM/PJzU1lZKSEubMmUPXrk32lVlEROQ4zZ5gcs6tN7PfAguBw8AqoKZv5uGuymHrjc1sAjABoEePHvWKz+cJDlcaCBCjNdBFRKQGCVrkW0RCCgsLycjIoKSkBJ/Px3XXXcc999zTrDH84he/YNSoUfTs2ZOhQ4eSn5/frOOLiEjrZjXtbNEsAZj9imBV0p3AuaHqpVRgsXOuv5ndB+Cc+3Wo/zvAg865GqfIZWZmuuXLl9c5nmf+vZVf/G0dq6ZcSHK8v87Hi4ic6MxshXMuM9pxRFt9rxNV9b3/LW4+qxc/HTOgEaISkfpYv349AwcOjHYYJ6VwfztdJxrvGiEi0hJVd52I1i5yKaH7HsA44CXgTeD6UJfrgb+GHr8JXG1msWaWDvQFPmqq2PzeUAWTdpITEWmRzGysmc3Ky8trlPMlxPhUwSQiIiIirV605oC9ambrgPnAbc65A8BvgAvMbCNwQeg5zrm1wMvAOuDtUP8m+ybv8wT/JNpJTkSkZXLOzXfOTUhOTm6U8yXGeDmsNZhEREREpJWLxiLfOOfOCtO2Dzi/mv4PAQ81dVwAvlAFU4kqmEREJAKJsT4KtIuciIiIiLRyWsW6Cn9FgkkVTCIiUruEWB9HjmqKnIiIiIi0bkowVVExRU4VTCIiEoG2cT4OFpZEOwwRERERkahSgqkKVTCJiEhddGoTy978o9EOQ0REREQkqpRgquLLRb5VwSQiIrXrlBRL7uGjOKcfJkRaM6/XS0ZGBoMHD2bYsGFMnz6dQJjvk9u2bSM+Pp7hw4czcOBARo4cyXPPPdegsdPS0ti7d2+DziEiItJQUVnk+0TmUwWTiIjUQaekWIpLAxwqKiU53h/tcEQkSuLj48nKygIgJyeH733ve+Tl5TF16tTj+vbu3ZuVK1cCsGXLFsaNG0cgEODGG29s1phFREQakyqYqvB7tQaTiIhErlNSLAC5miYnIiEpKSnMmjWLJ554otbqxl69ejF9+nRmzJgBwIMPPsi0adMqXh8yZAjbtm0D4IorruD0009n8ODBzJo1q8niFxERqQ8lmKrweYIVTKUBVTCJiEjtlGASkXB69epFIBAgJyen1r6nnXYaGzZsqLXfs88+y4oVK1i+fDkzZsxg3759jRFqq2JmvczsGTObF+1YRERaGk2Rq8IXqmAqUQWTiIhEIKU8wXRYCSaRE8EXv/oVR9fXnqypi9iBAzj1f/6nzsdFujZbpP1mzJjB66+/DsCOHTvYuHEjHTp0qHNcjcHMngUuBXKcc0Oq6TMGeAzwAn90zv2m0mteYDmw0zl3aWPHUd3YzrktwHglmEREGp8qmKoo30WuVGswiYi0SGY21sxm5eXlNcr5OrWJA1TBJCLH2rJlC16vl5SUlFr7rly5koEDBwLg8/mOWRy8qKgIgMWLF/OPf/yDpUuXsmrVKoYPH17xWpTMBsZU92IogfQkcBEwCLjGzAZV6nInsL6G41PMLKlKW59I4ohgbBERaQKqYKpCu8iJiLRszrn5wPzMzMybG+N8beN9xHg95ORH9R89EQmpT6VRY8vNzeWWW25h0qRJmFmNfbdt28ZPfvITbr/9diC4I9zf/vY3AP773/+ydetWAPLy8mjXrh0JCQls2LCBZcuWNe2bqIVz7gMzS6uhy0hgU6hiCDObC1wOrDOzbsAlwEPAPdUcfw4w0cwuds4VmdnNwLeAiyOIo9qxI36DIiJSZ0owVRHj0y5yIiISOTOjU1KsKphEWrnCwkIyMjIoKSnB5/Nx3XXXcc894XMnmzdvrqhASkpK4vbbb6/YQe7KK6/k+eefJyMjgxEjRtCvXz8AxowZw1NPPcVXvvIV+vfvz+jRo5vtvdVTV2BHpefZwKjQ40eBnwJJVQ8q55x7xczSgblm9grwQ+CCho5tZh0IJraGm9l9zrlfVz7QzMYCY/v0CVcsJSIiNVGCqQpVMImISF0pwSQiZWVlEfVLS0ujsLCw2tfj4+N59913w762YMGCsO3lu8ydYMKVbjkzK18vaYWZnVvTCZxzD4eqj/4A9HbOHW7I2KFz7gNuqWHMRq1yFRFpTbQGUxU+ryqYRESkbpRgEhE5TjbQvdLzbsAu4EzgMjPbBswFzjOzF8OdwMzOAoYArwNTGmFsERFpQkowVeEP7SKnRb5FRCRSnZJi2atd5EREKvsY6Gtm6WYWA1wNvOmcu8851805lxZqe8859/2qB5vZcOBpgmsn3Qi0N7NfNmTshr8lERGpiRJMVfg8oV3kNEVOREQi1KlNLPuOFFNapmuHiLQOZvYSsBTob2bZZjY+1P6WmXVxzpUCk4B3CO4W97Jzbm0dhkgArnLObXbOBYDrge2RxNEIY4uISD1oDaYqfKEKJk2RExGRSHVKisU52H+kmJS2cdEOR6RVcs7VumObHMu5+n/fdc5dU037xZUevwW8VcM5FgOLq3ntP1WelxCsaIo0jhrHFhGRxqcKpir8oTWY9Cu0iIhEqlNSLAA5WodJJCri4uLYt29fgxImrY1zjn379hEXp6S4iIg0DlUwVfHlLnL6giIiIpHpeko8AJ/vL2BI1+QoRyPS+nTr1o3s7Gxyc3OjHcpJJS4ujm7dukU7DBERaSGUYKrCX7GLnCqYRESkZq60FPP56JPSBo/Bp1/kc/HQ1GiHJdLq+P1+0tPTox2GiIhIq6YpclWYGV6PaRc5ERGp0dEtW/j09EwK/vtf4vxe0jok8tme/GiHJSIiIiISFUowheHzmCqYRESkRsWff447epR9s4JrzvbrnMSnXyjBJCIiIiKtkxJMYfi9Hu0iJyIiNXIFBQAcXryYo1u20P/UJLbtO0JRSVmUIxMRERERaX5KMIXh8xqlAVUwiYhI9QKFhRWP9z/3PP1PTSLgYFPO4ShGJSIiIiISHUowheHzqIJJRERqFjgSrGBKPOdsDr31Fv06BLf61jQ5EREREWmNopJgMrO7zWytma0xs5fMLM7M2pvZQjPbGLpvV6n/fWa2ycw+NbNvNnV8fq9RqjWYRERaJDMba2az8vLyGnSeQGiK3ClXXkkgP59OG7KI8Xm00LeIiIiItErNnmAys67AHUCmc24I4AWuBiYDi5xzfYFFoeeY2aDQ64OBMcBMM/M2ZYzBKXKqYBIRaYmcc/OdcxOSk5MbdJ5AYSF4vSSdey6etm058s479OnUhg2qYBIRERGRVihaU+R8QLyZ+YAEYBdwOfBc6PXngCtCjy8H5jrnjjrntgKbgJFNGVxwkW9VMImISPUCBQV4EhKwmBiSvvEN8hctYlDHOFUwiYiIiEir1OwJJufcTmAa8DmwG8hzzr0LdHbO7Q712Q2khA7pCuyodIrsUNtxzGyCmS03s+W5ubn1jtHv8VCqNZhERKQGgYIjeBISAEj6xjcIHD7M6QU72Z1XRF5BSZSjExERERFpXtGYIteOYFVSOtAFSDSz79d0SJi2sNkf59ws51ymcy6zU6dO9Y5Ru8iJiEhtyiuYAGLS0gBICxwB4LMcVTGJiIiISOsSjSly3wC2OudynXMlwGvAGcAeM0sFCN3nhPpnA90rHd+N4JS6JuPzahc5ERGpmSsoxBMfD4AvJVh0e2pJMLGkdZhEREREpLWJRoLpc2C0mSWYmQHnA+uBN4HrQ32uB/4aevwmcLWZxZpZOtAX+KgpA/R7VMEkIiI1q1zB5G2TiCUkEH/oAEmxPj5TgklEREREWhlfcw/onPvQzOYB/wVKgZXALKAN8LKZjSeYhLoq1H+tmb0MrAv1v805V9aUMfq8pgomERGpUaCgAG/HDhXP/Z06UbY3l359k/hUC32LiIiISCvT7AkmAOfcFGBKleajBKuZwvV/CHioqeMq5/d6OHK0tLmGExGRk1CgsBB/fELFc19KCiU5OfQ/K4m/r96Nc45goa6IiIiISMsXjSlyJzyfxygNqIJJRESqV3mKHICvUydKc3Lp3zmJvMIScvKPRjE6EREREZHmpQRTGFrkW0REanNcgiklhdLcXAZ0bgPAul2HohWaiIiIiEizU4IpDL/XKC3TIt8iIlK9cAkmV1jIoGQvHoOVOw5GMToRERERkealBFMYPo9HU+RERKRarrgYSkvxJMRXtPk6dQIgJm8//TonkaUEk4iIiIi0IkowheH3eiguVQWTiIiEFygoADiuggmgNDeXjO6nsGrHQZzTjxUiIiIi0joowRRGrN/DUSWYRESkGsckmNa+DqtfxpcSrGAqzckho/sp5BWWsHXvkWiGKSIiIiLSbJRgCiPW56G4tCzaYYiIyAkqUFgIgCc+Ad79X3j9FnzFO4Bggml4j3YArPxc0+REREREpHVQgimMWJ9XFUwiIlKt8gomCxyCvB3gAnjfvh1PQgKlubn0SWlDYoxX6zCJyAmrrKyM73//+9EOQ0REWhAlmMKI9QWnyGntDBERCSdwJDRF7uBnwYZLp8OBbfjaxlKSk4PXYwztlszqbCWYROTE5PV6yc3Npbi4ONqhiIhIC+GLdgAnolh/MO9WXBYg1ueNcjQiInKiqViDaf8aaHMqnH4jrJyDz7eHkp07ARjcJZkXl22ntCyAz6vfc0TkxJOWlsaZZ57JZZddRmJiYrTDERGRk5y+8YZRnlTSNDkREQknUBhKMOWshPSzwQxG3ERcYh5HN2zAlZQwKLUtR0sDbNunhb5F5MTUpUsXLr30UgKBAPn5+RU3ERGR+lAFUxgxvmDe7WhJAOKiHIyIiJxwKiqYivdC+lnBxsHfIu7UB3CflnB082YGpnYBYO2uQ/RJSYpWqCIi1ZoyZQoAR44cOaaC6cEHH4xSRCIicjJTBVMYseUJJu0kJyIiYbjyBJPfQacBwUZ/HPGnjQagaM0a+qS0we811u9WNYCInJiWLl3KoEGDGDhwIACrVq3i1ltvjXJUIiJyslKCKYwvE0yaIiciIscLFBYC4PE6iGlT0e4fmInHH6AwawUxPg99U5JYt/tQtMIUEanRXXfdxTvvvEOHDh0AGDZsGB988EGUoxIRkZOVEkxhVKzBVKIEk4jIycDMepnZM2Y2rznGC5m9DV4AACAASURBVBwpAK8H8wKxXyaYrMtQ4tqVULRqJQADU9uybpcSTCJy4urevfsxz71ebXAjIiL1owRTGJV3kRMRkaZlZs+aWY6ZranSPsbMPjWzTWY2uaZzOOe2OOfGN22kXwoUFOCJ8wefVKpgovNQ4tqXcHTrDlxxMYO6tGXv4aPk5Bc1V2giIhHr3r07S5YswcwoLi5m2rRpFdPlRERE6koJpjAqpsiVaA0mEZFmMBsYU7nBzLzAk8BFwCDgGjMbZGZDzexvVW4pzR1woLAQT0xon4zKCaakzsSnxuFKAxR9tpEhXdoCkPX5weYOUUSkVk899RRPPvkkO3fupFu3bmRlZTFz5sxohyUiIicpJZjCqJgipzWYRESanHPuA2B/leaRwKZQZVIxMBe43Dn3iXPu0iq3nOaKdVXuKr71129x5NBePDEe8MaAL+aYPvGD+wFQsPxjMnqcQkKMl39t3NtcIYqIROzTTz9lzpw57Nmzh5ycHF588UXWr18f7bBEROQkpQRTGFrkW0Qk6roCOyo9zw61hWVmHczsKWC4md1XQ78JZrbczJbn5ubWOajOCZ3ZkreF3blbgwmmytVLIf5+w/EnlVGwdCmxPi9n9O7A4s9ycM7VeTwRkaZ0++23R9QmIiISCV+0AzgRfZlg0hQ5EZEosTBt1WZonHP7gFtqO6lzbhYwCyAzM7POGZ9TE0/l7K5ncyDvfbp7449Z4LtC56EkpjzPoeXLcaWlnNM/hX+sz2Hr3iP06hSmv4hIM1u6dClLliwhNzeX6dOnV7QfOnSIsjJ9/xURkfpRBVMY2kVORCTqsoHKWxt1A3ZFKZZjXNX/KgKlJRykGGKSju/QZTgJKUcJHCmgaP16zunbCYD3P6t7xZSISFMoLi7m8OHDlJaWkp+fX3Fr27Yt8+Y1y2acIiLSAqmCKYzyXeQ0RU5EJGo+BvqaWTqwE7ga+F50Qwo6s8uZvGMxfEEJxHY6vkPHviT2SIClUPDhh/S4aSi9OibywWe53HhmevMHLCJSxTnnnMM555zDDTfcQM+ePTly5AiJiYnRDktERE5yqmAKQ1PkRESaj5m9BCwF+ptZtpmNd86VApOAd4D1wMvOubWNNN5YM5uVl5dXr+O9Hi8dYttxwBPgc3/M8R3M8A0YTUw7Y9+zfyL38Sc4KzWW5dsPEAhoHSYROXHs2rWLQYMGMXDgQABWrVrFrbfeGuWompaZ9TKzZ8xMpVoiIo2s2RNMZtbfzLIq3Q6Z2V1m1t7MFprZxtB9u0rH3Gdmm8zsUzP7ZlPHqF3kRESaj3PuGudcqnPO75zr5px7JtT+lnOun3Out3PuoUYcb75zbkJycnK9z9HOfwoYzPMWhu/QfRRdMnOI69+HvTNn8u1H7uKMDf9hY05+vccUEWlsd911F++88w4dOnQAYNiwYXzwwQcRHWtmz5pZjpmtqaHPmND3901mNjnUFmdmH5nZKjNba2ZTG/Ieqosj3NgAod1JxzdkTBERCa/ZE0zOuU+dcxnOuQzgdKAAeB2YDCxyzvUFFoWeY2aDCE6NGAyMAWaambcpY4wJVTAVK8EkIiJh+PDQzjneKNvP0bKjx3foMZr4DiX0+OlVpL/2Kv4+fbgr6xUO3HUHgcJqklIiIlHQvXv3Y557vRF/zZ5N8Lt5WKHv608CFwGDgGtC3+uPAuc554YBGcAYMxsd5vgUM0uq0tYnkjhqGFtERJpQtKfInQ9sds5tBy4Hngu1PwdcEXp8OTDXOXfUObcV2ASMbMqgvB7D7zVNkRMRkfDKyuhaVsYBV8oP3/khOw/vPPb1LsPBGws7lhE3cCD95jzPnNOuIDnrQw6+9lp0YhYRqaJ79+4sWbIEM6O4uJhp06ZVTJerjXPuA2B/DV1GAptCFUPFwFzgchd0ONTHH7qFmz98DvBXM4sDMLObgRkRxhF27IjemIiI1Fu0E0xXAy+FHnd2zu0GCN2nhNq7AjsqHZMdamtSsT6vdpETEZGwXFkZ7QMl/K79aLYc3MKUJVOO7eCLha6nw+Z/AuDxetkz5kq2d+zBwb+8jHNai0lEou+pp57iySefZOfOnXTr1o2srCyefPLJxjp9td/hzcxrZllADrDQOfdh1YOdc68AbwNzzexa4IfAdxph7A5m9hQw3Mzuq3pgQ9fpExFpzaKWYDKzGOAy4JXauoZpC/vN3MwmmNlyM1uem9uw7aBjfB6twSQi0gI1yj8PgTLAMeaUgVyUfhHr960/Pmk0+FuwZw188QkAp/dsx1+7jeToZ59RmJVV/7FFRBpJx44dmTNnDnv27CEnJ4cXX3yxYj2mRlDtd3jnXFlouYxuwEgzGxLuBM65h4Ei4A/AZZUqnxoy9j7n3C2h9f1+HWbMBq/TJyLSWkWzguki4L/OuT2h53vMLBUgdJ8Tas8GKk8O7wbsCndC59ws51ymcy6zU6cwW0fXQazPoylyIiItUKP881BWipmD2CR6n9KbQ8WH2Fu499g+Q78NHj9kBQt1R6a3Z3HXDMri4jk49y8NeAciIo1j69at3HPPPYwbN47LLrus4tZIav0O75w7CCymmrWczOwsYAjB9VqnhOtT37FFRKTx+Wp60czmU021EIBzriFXoGv4cnocwJvA9cBvQvd/rdT+ZzObDnQB+gIfNWDciMSqgklERKrhSkuDq4bEtKH3KekAbDq4iU4JlX7cSGgP/cfAJy/DBVPJ6HYK7Tu1I2vQmZz+1lt0uutO/Kmp0XkDIiLAFVdcwfjx4xk7diwez5e/O8+fP78xTv8x0NfM0oGdBJfG+J6ZdQJKnHMHzSwe+Abw26oHm9lw4GngEmAr8KKZ/dI590B9x26MNyUiItWrMcEETGuKQc0sAbgA+FGl5t8AL5vZeOBz4CoA59xaM3sZWAeUArc555q8tEhrMImISLXKyrAYILYNfU4Jbmq0+eBmvtrlq8f2G3YNrJ8PWz/A0+d8rhjehcd3j2R2YDH7nv0Tp97/P80fu4hISFxcHHfccUe9jjWzl4BzgY5mlg1Mcc49Y2ZvATc553aZ2STgHcALPBv6Xv8V4LnQTm8e4GXn3N/CDJEAXOWc2xwa73rghjrEcdzY9XqjIiISsRoTTM6595tiUOdcAdChSts+grvKhev/EPBQU8RSnVi/psiJiEh4LlAWXOEjpg0d4jqQHJvMpoObju/Y6+vB3eQ2LYI+5/Ot4V158p+byRl1LvbKK3S85Uf4Gm+9ExGROrnzzjuZOnUqF154IbGxsXU61jl3TTXtF1d6/BbwVpXXVwPDIzj/f6o8LyFY0RRpHMeNLSIiTau2CiYAzKwv8GtgEBBX3u6c69VEcUVdrM9DcZkqmEREWhozGwuM7dOnT/1PUlYGoTWYzIzeyb3Zkrfl+H4xCZB2Jmz6B/Ar+qQkMaRrW15KOpc7l/yDA3Pn0um22+ofh4hIA3zyySe88MILvPfeexVT5MzCrY8tIiJSu4gSTMCfCC6s9wjwdeBGwu/O0GLE+rwUlaiCSUSkNmZ2T02vO+emN1cskXDOzQfmZ2Zm3lzvcwTKMANiEgHoc0ofFmxbgHPu+H/O+nwD3vkfOLgDTunOmX068uy/85l85tc4MHcuHW++GYuJacA7EhGpn9dff50tW7YQU+UzSEkmEZGGKykpITs7m6KiomiHUm9xcXF069YNv98fUf9IE0zxzrlFZmbOue3Ag2b2L+q2m8NJJdbnIa+wJNphiIicDJKiHUCzK/tyihxA71N6k1+cT25hLikJKcf27R2a/b15EZx+AyN6tuf/3t/CvovGkfCfezj09tskN96uTSIiERs2bBgHDx4kJSWl9s4iIlIn2dnZJCUlkZaWdlIm7p1z7Nu3j+zsbNLT0yM6JtIEU5GZeYCNoQXzdgIt+kqkNZhERCLjnJsa7RiamwsEMHMQG0ww9W3XF4AN+zccn2Dq1B/adgtOkzv9Bk7v2Q6AZe37MKZ3b/b+4SmSLrwQT1wcIiLNac+ePQwYMIARI0bUeQ0mERGpWVFR0UmbXIJgNWuHDh3Izc2N+JhIE0x3EdzJ4Q7gF8B5wPV1jvAkEuP1cLRUazCJiETKzOKA8cBgjl2v74dRC6qpBAKhCqZg8dbQjkOJ8cTw4e4PObvb2cf2NYP0s2Hju+Ac7RJj6JvShuXbD3D9/9zHjvE3kfvYDDr/7KfN/z5EpFWbOjX87wPz589v5khERFqmkzW5VK6u8UeUYHLOfRx6eJjg+kstXqzPy9ESJZhEROrgBWAD8E3g58C1wPqoRtRUAg7z+MAbvIzG+eLISMngw90fhu/fYxSs+jPs2wwd+5CZ1p6/rd5FwvUXcsrV32X/7NkknX8eCZmZzfgmRKS1O+ecc6IdgoiItCCeSDqZWT8ze9rM3jWz98pvTR1cNGmKnIhInfVxzv0vcMQ59xxwCTA0yjEdx8zGmtmsvLy8ep/DBQLgO3axw1Gpo/j0wKfsL9p//AHdRwfvdywDYERaO/KLSlm3+xCd770Xf9eu7Pqf+wkUFNQ7JhGRulq2bBkjRoygTZs2xMTE4PV6adu2bbTDEhGRk1RECSbgFeC/wAPAvZVuLVasT1PkRETqqHxnhINmNgRIBtKiF054zrn5zrkJycnJ9T9JwGFVdtMYnRpMIn20+6Pj+3fsB/Ht4POlAJzdrxN+r/Hqf7PxJCaS+quHKPn8c774+S+CySsRkWYwadIkXnrpJfr27UthYSF//OMfmTRpUrTDEhGRk1SkCaZS59wfnHMfOedWlN+aNLIoi/V5lWASEambWWbWDvhf4E1gHfBwdENqGi7gwHfsgriDOgyijb8Ny3YvO/4Ajwe6j4LPg1PoOraJ5aIhqcxbkU1BcSmJI0fS8bbbyHvjDXb99Ge4Eu1iKiLNo0+fPpSVleH1ernxxhtZvHhxtEMSEZFGNnPmTIYMGULPnj15/PHHm2ycSBf5nm9mtwKvA0fLG51zYeYBtAyxPg9lAUdpWQCfN9I8nIhI6+Wc+2Po4ftAr2jG0uQcx02R83l8ZJ6aWf06TN1HwWdvw5G9kNiR74/uyZurdjF/1S6+O6IHnW6fhMXGkjt9OoH8fLo+9qh2lhORJpWQkEBxcTEZGRn89Kc/JTU1lSNHjkQ7LBGRFmfq/LWs23WoUc85qEtbpowdXGu/V199lYULF7Jy5Ur27t3L0KFDmThxIj5fpOmgyEWaObme4JS4JcCK0G15o0dzAon1B/80xWWqYhIRiYSZ/b9wt2jH1diccwCY13vca6NTR5N9OJudh3cef2DPM4L325cAwXWY+nVuw8vLsyu6dJxwM6c++CCHP/iAz2+6ibL8/MZ/AyIiIS+88AKBQIAnnniCxMREduzYwauvvhrtsEREpBHNmDGD3/72t/j9flJTU/H7/QQCAb7zne9QWlrKvHnz+PWvf90oY0W6i1x6o4x2Eon1Bf9xOFoSICEmysGIiJwcKv/sHQdcSkvcRa4stAGE5/gE06hTRwHw4e4PGdd33LEvdjkN/Amw7V8w6DLMjIuGpDLjvY3sP1JM+8Tgxabd1d/F2zaJnT+bzPbrr6fH00/j69ChSd+SiLQ+ZWVl3H///bz44ovExcUxZcqUaIckItJiRVJp1BRKSkpYvXo1/fr1A2D37t107NiRmJgYrrvuOu688068Xi8zZsxolPEi3UXOb2Z3mNm80G2SmflrP/LkFeML/mm0DpOISGScc7+vdHsIOBfoGuWwGl35ItwWZvp071N60zG+I8t2hVmHyRcDPb4KWz+oaDp/YArOweJPc47p2vbii+k+80mKt2xl+7Xfp2TXrsZ9EyLS6nm9XnJzcykuLo52KCIi0kTWrVtHXl4eW7ZsIRAIcN9993H77bcD0K9fP1566SUefrjxlkyNdNLdHwA/MDP0/LpQ202NFskJJrYiwVQW5UhERE5aCZyAazGZ2VhgbJ8+fep3gvJd3sJUMJkZo1JHsXTXUpxzmNmxHdLPhn9Mgfw9kNSZIV2S6ZQUy6INOYw7rdsxXducdRY9nn2GHT+6hW3fu5Yezz5DbK8T7s8pIiextLQ0zjzzTC677DISExOjHY6IiDSylStXcu2113LNNddw5MgRxo0bx4QJE9izZw8PPvgg06ZN45VXXuG6665rlPEiXYNphHPueufce6HbjcCIRongBFUxRU4VTCIiETGzT8xsdei2FvgUeCzacVXlnJvvnJuQnJxcvxOEpsiZJ/wldNSpo9hftJ+NBzce/2L62cH7bf8CwOMxzuufwgef5VISZs2/hNNOo+cLz+NKS9l+7fcpXLO2fjGLiITRpUsXLr30UgKBAPn5+RU3ERFpGbKysrj00kv58MMPWbNmDT//+c85fPgwd9xxBzNmzOCGG27gtddeIxBonLxHpBVMZWbW2zm3GcDMegEturSnooKpRAkmEZEIXVrpcSmwxzlXGq1gmoqrqGAKn2AanToaCK7D1K9dv2NfTB0GscmwZTEM/TYA5w1M4S/Ld7Bw3R4uHpp63PniBgwgbc6LfP7D8Xx+/fV0mzmTxFEjG+39iEjrVd26Sw8++GDzBiIiIk0iKyuLiRMnHtPWpk0b/vKXv1Q8f/311xttvEgTTPcC/zSzLYABPYEbGy2KE1D5LnKaIiciUjMzax96WPVn77ZmhnNuf3PH1KTKK5jC7CIHkNomlR5JPfhw94dcN6hKubHHC33Ogw1/h0t+D75Yzh+QQr/Obfj1gvWcNyCFOP/x543p2ZOef57D5+PHs+Pmm+n66CMknXdeo781EWldcnNzefjhh1m7di1FRUXRDkdERBrZ4sWLm3W8iKbIOecWAX2BO0K3/s65fzZlYNGmKXIiIhFbASwP3ecCnwEbQ49XRDGuJlFRwVRNggmCVUwff/ExJYGS418c/n0o3B9MMgE+r4cpYwezY38hf/zXlmrP6e/cmZ4vvEDsgAFk334H++fMwTnXoPciIq3btddey4ABA9i6dStTpkwhLS2NESNa9CoYIiLShGpMMJnZeaH7ccAlQB+gN3BJqK3FKp8iV6wEk4hIjZxz6c65XsA7wFjnXEfnXAeCU+Zei250TaCGRb7LjUodRUFpAWv3hlkzqdfXIbk7/Pf5iqYz+3TkvAEpzF6yLexaTOV87drR49lnaXPWWez5xS/54v9NwWkHKBGpp3379jF+/Hj8fj/nnHMOzz77LMuWhdkFU0REJAK1VTCdE7ofG+Z2aXUHtQSaIiciUmcjnHNvlT9xzi3gy+tIi+FCCSDzVn8JHXnqSAxj2e4w/6h5vMEqpi3/hNzPKpq/O6I7ew8X86+NuTWO722TSLeZT9LhRz/i4CuvsP2GGynZs6d+b0ZEWjW/3w9Aamoqf//731m5ciXZ2dlRjkpERE5WNSaYnHPlK//93Dl3Y+Ub8IumDy96YkL/OBRpkW8RkUjtNbMHzCzNzHqa2f3AvmgH1egCoR8eapgid0rcKQxoP4Alu5aE73D6DRB3Crx2M5QGK5C+3j+Fdgl+Xl2xs9YQzOMh5e676Dr99xStX8/Wy68g/7336vpORKSVe+CBB8jLy+P3v/8906ZN46abbuKRRx6JdlgiInKSimgNJuDVMG3zGjOQE01ibHD988NHW9wGSCIiTeUaoBPwOvAGkBJqa1EqKphqmCIHcF6P81iZs5Ivjnxx/ItJp8LlT8LuLFg0FYAYn4fLM7qycN0eDhZENu2t7cUXk/7qq/i6pJJ962188fOfEygsrNsbEpFWp6ioiEcffZS3336buXPnMmDAAP75z3+yYsUKLrvssmiHJyIiJ6na1mAaYGZXAslmNq7S7QYgrlkijJLk+GDJ8KGiMAu0iojIcZxz+51zdzrnhodud56IO8iZ2Vgzm5WXl1e/E7jaF/kGGJM2BoB3t70bvsPAS2HEzbD0Cfgs2Ofqkd0pDQT47dsbIg4ntlc6aXPn0v6GGzjw55fYesW3KFjR4tZWF5FGdP3117N8+XKGDh3KggUL+PGPfxztkEREpAWorYKpP8G1lk7h2PWXTgNubtrQoivO7yXG5yGvUAkmEZGamNmjofv5ZvZm1Vu046vKOTffOTchOTm5ficoC06Rq62CKS05jYHtB/L2trer73ThL6HzEHjjFji0mwGntuXms3vx0kc7WPxpTsQheWJi6Dz5Z/SYPRtXVsb271/HF7/6FYEjRyI+h4i0HuvWrePFF1/kRz/6EfPmzeNf//pXtEMSEZEWoLY1mP4aWm/p0iprMN3hnKtmYYnamdkpZjbPzDaY2Xoz+6qZtTezhWa2MXTfrlL/+8xsk5l9ambfrO+4dZUc7+eQEkwiIrV5IXQ/Dfh9mFuL4sp3kfP6au37zbRv8sneT8jOr2bRXH8cfPtPUFIYTDIFAtz9jX70SWnDL/62DudcnWJLHD2KXn99g3bf+x4Hnn+BzZeOJX/RojqdQ0RavvLFvQF8vto/y0RERCIR6RpMt5jZKeVPzKydmT3bgHEfA952zg0AhgHrgcnAIudcX2BR6DlmNgi4GhgMjAFmmlnNPxs3kuR4vyqYRERq4ZxbEbp/v/wGrAYOhB63LOUVTLVMkYPgOkxA9Yt9A3TqB2N+DVsWw9LHifN7ufXc3mzOPcJ/NtV9jXRPYiKn/u8D9Pzzn/EmJZF92yR23DaJkl276nwuEWmZVq1aRdu2bWnbti1JSUmsXr264nHbtm2jHZ6IiDSymTNnMmTIEHr27Mnjjz/eZONE+pPFV5xzB8ufOOcOmNnw+gxoZm2Bs4EbQucqBorN7HLg3FC354DFwM+Ay4G5zrmjwFYz2wSMBJbWZ/y6UIJJRCRyZrYYuIzgtSULyDWz951z90Q1sEbmymrfRa5cWts0UhJS+HD3h3yn/3eq73ja9bBpEfxjKqRmcMlXvsZDf1/P7CXb+FrfjvWKM+G04aS/Oo/9zz9P7hNPsvmSS+k0aRLtf3AdVql6QURan7Lyz7FqmFkzRSIi0kosmAxffNK45zx1KFz0m1q7vfrqqyxcuJCVK1eyd+9ehg4dysSJE5ukgjXSCiZPlSlr7Yk8OVVVLyAX+JOZrTSzP5pZItDZObcbIHSfEurfFdhR6fjsUFuTU4JJRKROkp1zh4BxwJ+cc6cD34hyTI2vIsFU+2XQzBidOpqPv/iYQPni4OE7whUzoWNfeOUGYvN3cM3IHizasIdNOYfrHar5/XQYP57ef5tP4ujR5Pzud2y98tsUfPxxvc8pIiIiIiePGTNm8Nvf/ha/309qaip+v59AIMCIESOYNGkSP/jBD/jTn/7UKGNFmiT6PbDEzOaFnl8FPNSAMU8DbnfOfWhmjxGaDleNcD+hhF2UwswmABMAevToUc/wvpQc72djTn6DzyMi0kr4zCwV+A5wf7SDaSqutBiIbIocwMhTR/Lm5jfZeGAj/dv3r75jbBJc/Wd4+usw9/v84Lt/5cUPt3Pn3JW8dusZxPrqPzvc37Ur3f8wk/xFi/jioYfYft0PaHvxRaTcey/+1NR6n1dEREREIhBBpVFTKCkpYfXq1fTr1w+A3bt307FjR/bs2cOoUaN44oknADjvvPP4wQ9+gDfC77fViaiCyTn3PPBtYA+QA4xzzr1Q81HVygaynXMfhp7PI5hw2hP6x4TQfU6l/t0rHd8NCLuQhHNulnMu0zmX2alTp3qG96W2cT7yClTBJCISoZ8D7wCbnXMfm1kvYGOUY2p8oQRTJFPkAEaljgLgw90f1tIT6NAbrnwWctaSsugn/O7Kr7B21yGmvfNpfaM9RtL559P773+n4223kb/oPTZfdDG5M2cSKCpqlPPL/2fvvsOjKrMHjn/fmcwkM+m9FxKSQGgBQpeOyIqAFbsoNlxlXRvqrtt+q+Kuu2sXu9gBFRAUBMWCCigkBJAOAUICoSYkkJA27++PO4SAlPRJOZ/nuc9M7tx73zOAzuTc855XCCGEEKL52LBhA0eOHCErKwuHw8Gjjz7K5MmTSU9Pp2fPnlXH2e12TKaaTnA7u9pcYRMwG/gMOKqUqlOJkNY6D9itlDpxG3c4sAGYB0xw7pvgHAfn/muUUu5KqXZAIvBLXcauLV+bhaLSChyO2q3iI4QQbZHW+mOtdVet9V3On7O01le4Oq6GpisqAFA1mCIHEOYZRqxPLL/k1fCjK3EEDP8brJ/NhfkfcXVaNNOX7WRPQUldQz6FyWYjePI9JCz4Aq8hQzj4/Atkjb6EwkWLa71qnRCiZXvxxRfJz893dRhCCCEayerVq7n++uu59tpr6dq1KzExMdxxxx2nJJjWrFlDTExMg/Tfq9G3Y6XUZOBvGBVMlRjT1jTQtY7jTgY+UEpZgSzgFoxk1yyl1K1ANsY0PLTW65VSszCSUBXA3Vrrc3cmbCA+NgtaQ1FpBb42aYgqhBDnopRKAqZh9NTrrJTqCozVWj/u4tAaVqWzsrWGCSaAnqE9WZK9BK11zT68B9xrNIL8+h88NC6R2atNvPzdNh6/tEsdg/4tS2QkUc8+w7Gfr2XfE0+Qe++92Hv3JvTRR/Do2LHBxhFCNF95eXn06tWLHj16MHHiRC666CJp8C2EEK1IZmYml1xyCVdfffUp+9PT0zl06BDu7u6YzWb+9a9/Nch4Nf12fC+QrLWu/XrJZ6C1zgTSzvDS8LMc/wR17/lUZyeSSoUl5ZJgEkKI83sdeAh4FUBrvVYp9SHQuhJMFUaCqaYVTACpwanM3jqbnYU7aefb7vwnKAVjX4CDmwn68vfc1WUa01buZuKAdsQHe9U18jPy7NObdrM/peDjjznw/AvsuPwKfK+4nJB778WtAaabCyGar8cff5x//vOfLF68mLfffpt77rmH8ePHA7i7OjYhhBD1l5mZyV133fWb/QsWLGiU8Wo6RW43cKRRImjGTiSVZCU5IYSoEbvW+vR5YBUuieQclFJjlFKvHTlSt481XXGigqnmTRC7BXcDIHN/Zs0H159gVgAAIABJREFUstqNpt9mN/6w91ES3Au59Z1V5B8rq024NaLc3PC/9loSFn1JwM03c+SzeWy/aBQHX3lV+jMJ0coppQgLCyMsLAw3N7cTU+YSlFL/dnVsQggh6ue7774jOfkci8w0sJommLKA75RSjyql7j+xNWZgzYEkmIQQolYOKqUScK70qZS6Etjr2pB+S2s9X2t9h6+vb90uUOnsweRW88rWON84fKw+rDmwpnZj+cXAdR/jdjyfuZ5TqSjI5d6ZmY3WK8ns40Pow1NI+Hw+9v79OPDss2RdPJrCBQukP5MQrdDzzz9Pz549mTJlCgMGDGDdunVMmzYNjNYUra6HnhBCiMZV0wRTNvAVYAW8q22tmq9dEkxCCFELd2NMj+uglMoF/ghMcm1IDe9kBVPNp8iZlImuwV1rn2ACiOoJN8zGo/QQn3tPZfOWzXyzaf/5z6sHa2ws0S++SMz06Zh8fMi9/wF2XXc9JWvXNuq4QoimdfDgQWbPns2iRYu46qqrsFhOSZxf4qq4hBBCtEw1SjBprf9xpq2xg3M1Hw9JMAkhRE05V40bAQQDHYAhwAUuDaoxVNZuFbkTUoNT2VawjcKywtqPGd0LbpyNT2UBn9ie5JX5P1JW4aj9dWrJs28f2n36CeGP/5Oy3bvZOf5qcqdMoTwvr9HHFkI0vh07dhAbG3vKvhtvvBEArfVGV8QkhBCtSUuvAK9t/DVKMCmlvlVKfXP6VqcIWxCZIieEEOenlPJxTqF+USl1IVAMTAC2AeNdG13Dq0sPJoDUkFQA1h6oYxVQdG/UDZ8Sbj7CU0f/zNSZXzfJlxZlNuN35ZUkfPklgXfcQdGXi9g+8iL2/evfVMjy5kK0aOvXrz/l58rKStLT010UjRBCtC4eHh4cOnSoxSaZtNYcOnQIDw+PGp9T09uvD1Z77oExJ7vZNW5taHarGTeTkgSTEEKc23tAPrAcuB2YgjGl+lLnqqGti7OCiVr0YALoEtQFkzKx5sAaLoisY2FXTB/cbppN9DuXc+uWu3h73jQmjhtZt2vVktnLk5D778Nv/HgOvvgih995h4JZswiYeAsBE27G7OXZJHEIIepv6tSpPPnkk5SUlODj4wMYv0hYrVbuuOMONm6U4iUhhKivqKgocnJyOHDggKtDqTMPDw+ioqJqfHyNEkxa69NvZfyklPq+NoG1REopfG0WSTAJIcS5xWutuwAopd4ADgIxWusi14bVOE5UMClz7RJMdoudJP+k2q0kdyYxfbHc+gV+b17KuIyJbIx6n449h9TvmrVgjYok4qmpBN46kf3PPcfBF14k/4MPCZp0J37XXIPJam2yWIQQdfPoo49WbVOnTv3N60899ZQLomoaSql44M+Ar9b6SlfHI4RovSwWC+3atXN1GE2qplPkAqptQUqpi4CwRo6tWfC1WSh0JpgKisvYfbjYxREJIUSzU5WF11pXAjtaa3IJqFbBVLseTADdgrux7uA6Kh2V9QpBRXRH3baYMuVB7PyrKVk7p17Xqwv3xESiX3yRuJkzcE9MZN+TU9k+8iIOv/c+juPHmzweIUTNZWRkkJGRwVVXXVX1vPpWE0qpt5RS+5VSv57jmFFKqc1KqW1KqUec+6Kd7Tc2KqXWK6Xurc97OVscZxobqvoF3lqfMYUQQpxZTb8dV69gqgB2AG3if8w+1SqY/vrZejJ3F7B0ylAXRyWEEM1KN6XUic7VCrA5f1aA1lr7uC60RlDHJt9gJJhmbp7JtoJtJAck1ysMz/BkNl4xlwMf30jX2TdTtvNHrKP+D6xNO1XN1q0bMdPfpnj5cg689DL7nniCg6++SuAtN+N/zTWYPGXqnBDNzQMPPHDW15RSNb3MdOBF4N2zXMcMvARcCOQAK5VS8zCmVD+gtc5QSnkD6Uqpr7TWG047PwQoqX7DQinVXmu97XxxnG3s08cQQgjRsM757VgpFaO1ztZat626rmp8bRYKisvQWvPjtoMcPlbG4WNlBHjKFAAhhADQWteu23ULp+vYgwlONvpec2BNvRNMAGldOrOoci4Zn07h5ow30Flfoca+APGD633t2lBK4dm/P579+1O8ciUHp01j/9P/4dBrrxNw8wT8r78es0/ryjMK0ZJ9++2353y9JkkmrfVSpVTcOQ7pDWzTWmc5rzkDGKe1ngrsdV6jSCm1EYgETk/+DAbuUkpdrLU+rpS6HbgMuLgGcZxx7DOMIYQQogGdb4rc3BNPlFKfNnIszZKf3cL+olK27j/K4WNlAGzcW4clpoUQQrQOFc4KpjokmKK8ogjwCKh/H6ZqLkqNJeiqZxhf+hcOFVfCu2Nh/h/huGs+q+y9ehHz1lvEzfgIW/fuHHjuebYNGcq+qVMpy8l1SUxCiLP79ddfmTVrFu+++27V1kAigd3Vfs5x7qviTAx1B34+/WSt9cfAl8AMpdT1wERqvjLpWcdWSgUqpV4BuiulHj39RKXUGKXUa0eOHKnhUEIIIU44X4Kp+u2L+MYMpLka0D6IvUeO88I3J6txN+yRBJMQQrRVVRVMdZgip5SiR0gPVu5b2aBL1l7SNYJeQy7hgsJ/siz0OnTGO/ByX9j6VYONUVu21FSiX5lGuzmz8Ro+nMMffMj2kSPJue8+StaudVlcQoiT/vGPfzB58mQmT57Mt99+y5QpU5g3b15DXf5MZVBV/+NTSnkBnwJ/1Fqf8cu11vrfwHFgGjBWa320vmNrrQ9prSdprROc1VSnjzlfa32Hr69vDYcSQghxwvkSTPosz9uMS7qG4+3uxvw1e4j0sxHi7S4VTEII0Zad6MHkVrep0n3D+5J3LI/souyGjIoHLkzm+gs6cN2uS/hP1Atod2/44EqYcxeU5DfoWLXh0bEjkU//m/Zff0XALTdz7Mef2Dn+anZefwOFixajy2WlViFc5ZNPPmHJkiWEhYXx9ttvs2bNGkpLSxvq8jlAdLWfo4A9AEopC0Zy6QOt9eyzXUApNRDoDMwB/tYQYwshhGg850swdVNKFSqlioCuzueFSqmiag1dWzW71Y1LuxvVvH3aBdApwocNkmASQog2S1c6V4CrQwUTQN+IvgCs2LOioUICwGRS/OWSFP50cQde2urPX8NeRg98ENbOhJf6wKYvGnS82rKEhRH60EO0//ZbQh99hIq8PHLvvZdtw4Zz4PkXKN+716XxCdEW2Ww2TCYTbm5uFBYWEhISQlZWVkNdfiWQqJRqp5SyAtcA85TR4OlNYKPW+n9nO1kp1R14HaN30i1AgFLq8fqMXY/3IoQQogbOmWDSWpu11j5aa2+ttZvz+Ymf20y3zuv6xGBSMCgpmI7hPmzbf5TSivotMS2EEKKFqkeTb4AY7xjCPcNZsbdhE0wn3DEogd8PSeC9lXn88cAllE5cAp4hMOM6mHkDHNreKOPWlNnLk4AJE0hYvIiol1/GPaUjB6dNY9vwEez+/d0c/eEHtMPh0hiFaCvS0tIoKCjg9ttvp2fPnvTo0YPevXvX6Fyl1EfAciBZKZWjlLrVuX+BUipCa10B3AMsAjYCs7TW64EBwI3AMKVUpnO7+AxD2IGrtNbbtdYOYAKwqyZxnGNsIYQQjahut1/bmI7hPnz/0FAi/Wws+HUvFQ7N1n1H6Rwpc7OFEKKt0ZV1b/INRh+mvuF9+Tr7ayodlZhNDb8I30MXJePp7sZ/Fm8m+7Af029ajG/Gy/DD/2DzQkibCIOmgFdwg49dU8psxnvYULyHDaUsJ4eCWR9T8OmnHP3mGyxRUfiNH4/vpeOwhIS4LEYhWruXX34ZgEmTJjFq1CgKCwvp2rUr06dPP++5Wutrz7L/4mrPFwALTnv9R87cI+n06/x02s/lGBVNNY3jN2MLIYRoXOebIiecogPsmEyKDmFG4dbmvCIXRySEEKIu6r1CUMWJCqa69WAC6BfRj6KyItYdXFfna5yLUoq7h7Zn2vU9+DX3CNe/nc6B7pPh3kzoMQFWvgnPd4elT0PZsUaJoTasUVGE3H8fid9+Q8R//4MlPJwD//sf24YOY/ekuyhcvBhdVubqMIVolXJzc1m2bBnZ2dkUFBSwdOlSV4ckhBCihZIKplqK8rcBkFtQ4uJIhBBC1IXWej4wPy0t7fa6XcCYvqXMdatgAhgYORCbm43Ptn9Gakhqna9zPqM6h/PajWbu+iCdcS/+yGs3pdH5kv9Bn0mw5B/wzePwyxsw9E+Qen2d+0o1FGW14jt6NL6jR1OalcWROXM4Mvczjn73HWZ/f3zHjsH38svxSE52aZxCtBYPP/wwM2fOJCUlBbPZqKY0WiQJIYQQtScVTLXkYTET5OVObr4kmIQQoi3SJyqYLHVPMHlZvRgZO5IFWQsoLi9uoMjObGiHED6Z1B+Aq19dzrLtByE4Ca75AG75EvyiYf4f4JULILtx+kLVhXt8PCEPPED7b78h+tVXsPfqxeEPP2LHuEvZccWVHP7gAyoLClwdphAt2ty5c9m8eTMLFixg/vz5zJ8/n3nzpBe2EEKIupEEUx1E+tvYc0QSTEII0SY5V5GrTwUTwBVJV1BcUcyinYsaIqpz6hzpy5y7BxDhZ+Pmt1cya9Vu44XYfnDrVzD+XWOq3FujYMFDcOxgo8dUU8rNDa/Bg4l6/jkSl35P6J/+hHY42PfPx9k6cBA5991H0XffyRQ6IeogPj6e8vJyV4chhBCilZApcnUQ6efBJunBJIQQbZKurH8PJoDU4FTa+bZj9tbZXJZ4WQNEdm6hPh7MurMf93yUwZRP1vLLjsP8c1xnbFYzpIyDhGHw9d9h5RuQ+REMuBf6/R6sno0eW025+fsTcNONBNx0I8c3bKBg9hwK58+naOGXmHx88B42DJ/fjcKzXz+UtX5/P0K0BXa7ndTUVIYPH467u7urwxFCCNHCSYKpDiJ8bXyzaT9aa5mnLoQQbY3D2YPJUr8EhlKKy9tfzn/T/0tWQRbxfvENEd05+XtaeXdiH55fspXnv9nKupwjvHxDDxKCvcDdG0b/F3rfAUv+D759HFa+DoOnQPeb6p1Qa2geKSmEpaQQMuUhji1bRtGXiyhasoQjc+cayabhw/G+cASefftisttdHa4QzdLYsWMZO3asq8MQQgjRSkiCqQ4i/W0cL3dw+FgZgV5yt0cIIdoS7ZwiRz2nyAGMSRjDcxnPMXvrbB7s9WC9r1cTZpPivguT6Bnrzx9nZjL2hR+ZekVXxnaLMA4ITjb6M+3+Bb76K3zxAPz4LAy8H1JvaHaJJpPViveQIXgPGYKjrMxINi38kqKvv+bInDkoiwV77954DR6M1+BBWGNjXR2yEM3GhAkTKCkpITs7m+RqzfNvvvlm1wUlhBCixXJJDyal1E6l1DqlVKZSapVzX4BS6iul1Fbno3+14x9VSm1TSm1WSl3kipiri/AzVpLbU3DcxZEIIYRoclVT5Op/jybQFsjQmKHM2z6P8sqm7YMyKCmYBX8YSMdwH/7w0Woem7uO4+WVJw+I7g23LITrPwXvMPj8Pni+uzGFrqK0SWOtqRPJpoh/PUXSTz8S8/Zb+F9/PeV79rDvySfZftEotl80in1Tp3L0p59wSN8m0cbNnz+f1NRURo0aBUBmZqZUNAkhhKgzVzb5Hqq1TtVapzl/fgRYorVOBJY4f0YplQJcA3QCRgEvK6XMrgj4hEhngim3QBp9CyFEW6MrK0Hpejf5PuGKxCvIL81n3vamX7kpzNeDj+7oy52D43l/RTZXTFvGzoPHTh6gFCSOMBqB3zAbfCONiqbnUuHn16C8+d5oUVYrnv36EfrIwyQsXEDC4kWEPvYYlpgY8j+awe5bb2NL337s/v3d5M+YSdnu3a4OWYgm9/e//51ffvkFPz8/AFJTU9mxY4eLoxJCCNFSNadV5MYB7zifvwNcWm3/DK11qdZ6B7AN6O2C+KpIgkkIIdowhwMUYGqYWeb9I/rTLbgbL2e+THF5cYNcszYsZhOP/q4jb9yURk5+CZe88COfr91z6kFKQfvhMHER3DgX/GNh4UPwfCqseAXKm//noTUmhoAbrifm9ddI+nkFUdNexnfcWI5v2kje3//O9gtHsm34CPb8+c8cmf85FQcOuDpkIRqdm5sbvr6+p+yT/qJCCCHqylUJJg0sVkqlK6XucO4L1VrvBXA+hjj3RwLVbyvmOPe5jJ/dgs1iZo8kmIQQou1xVKKgwRJMSikeSHuA/SX7eX/j+w1yzboYkRLKgnsHkhjqxT0frmbKJ2s4Wlpx6kFKQcJQY+rcTfMgIB6+fNioaFr+EpQ1fYKsLkw2G95DhxL+t7/RfskS4r/4nNDHHsO9YweKvvqaPQ89xNaBg8gaM4a8J56k6JtvqCyS1WNF69O5c2c+/PBDKisr2bp1K5MnT6Z///6uDksIIUQL5aoE0wCtdQ/gd8DdSqlB5zj2TLdR9BkPVOoOpdQqpdSqA41451EpRaS/TRJMQgjRBp2YIkcDztbuHtKdIdFDmL5+OkfLjjbYdWsr0s/GrDv7cffQBD5Jz+F3zy1l5c7Dvz1QKYgfDLcsgAmfQ1AiLPoT/K8DLHwY9m9q+uDrSCmFe0ICATdcT/SLL5K0fBlxH88i+P77cQsOpmDWLHJ+fzdb+vZjx9VXs/+ZZzm2YgWO0ubZh0qI2njhhRdYv3497u7uXHfddfj6+vLss8+6OiwhhBAtlEtWkdNa73E+7ldKzcGY8rZPKRWutd6rlAoH9jsPzwGiq50eBZxWu1913deA1wDS0tLOmIRqKBF+NpkiJ4QQbZHDgVKAqWHv0UzqNolrPr+GGZtncFuX2xr02rVhMZt46KIODOsQwn0z1zD+1eVMGpzAfSOSsLqd4T23G2hs2Svgl9dh1Vvw8ysQ0w963gwp48Bia/L3UVfKbMbWpQu2Ll3gjttxlJZSsjqTYyuWU7x8BYfeeINDr76KcnfH1qM7nn374dmvLx6dOqHMLm0RKUSNHT9+nFdeeYVt27bRpUsXli9fjlsDLFwghBCibWvyCiallKdSyvvEc2Ak8CswD5jgPGwC8Jnz+TzgGqWUu1KqHZAI/NK0Uf9WmI87+wqbb3NTIYQQjcOoYGr463YK7MQFkRfw7vp3XdKL6XQ9YwNYeO9ArukVzbTvtjP2xR/J3F1w9hNi+sKVb8L9G+HCf8LR/TDnTvhvB/jyUTiwpemCb0Amd3c8+/Yh5I9/JG7mDKN/08sv43f1eCoPHebAM8+wc/zVRsPwu+/h8HvvU7ptG1o36n0uIeplwoQJrFq1ii5durBw4UIefPBBV4ckhBCiFXDFrYpQYI6zgaAb8KHW+kul1EpgllLqViAbuApAa71eKTUL2ABUAHdrrSvPfOmm42+3kl9cjtZamiEKIURb4nCgGun2zKRuk7hhwQ28lPkSD/V6qHEGqQVPdzemXt6VER1DeWzur1z28k/c3D+OB0cm4+l+lq8QnkEw4A/Q7x7Y+QOkv21UNq14GWIHQM9bIGUsuLk37ZtpIGYvL7yHDcV72FAAKg4e5NjPP1O8YgXHlq/g6JIlxnHBQdjT0rD36Ik9rSfuSUlS4SSajQ0bNrBu3ToAbr31Vnr3dun6OUIIIVqJJk8waa2zgG5n2H8IGH6Wc54Anmjk0GrFz26lrMJBSXkldquUFAshRFuhKx2NUsEE0C24G+OTxvP+xvcZGTeSbsG/+bh0ieEdQ+ndLoCnF21m+rKdLF6/j8cv7czQDiFnP8lkMvo0xQ82qpkyP4D06TD7NlgYAKnXGVtIitHTqYVyCwrCd/RofEePBqAsJ4djy5dTvOJnitPTKVr4JQAmLy9s3btj79kDe8+eeHTpgsnDw5WhizbMYrFUPZepcUIIIRqKfKLUkb/d+GDOLy6XBJMQQrQljsaZInfCfT3vY2nuUv6x/B/MumQWbg20Wl19eXtY+L9xnRmXGsHDn67jlukrGdstgr+OSSHI6zzVSF4hcMF90P9e2PEdrHrb6NO0/EXwj4PuN0Dq9eAT0RRvpVFZo6KwXnUV/lddBUB5bi7FGRkUr0qnJCOdA88+B4CyWPDo3Bl7zx7YevbE3r07Zj8/V4Yu2pA1a9bg4+MDgNaakpISfHx8pDJfCCFEvTSPb60tkJ/dCkD+sTIi/VpO81IhhBD1ox2ORv0FzMvqxZReU7j/u/v5dMunXN3h6kYbqy56xgbwxR8uYNp323np220s3XqAx0ancEWPyPP/uZhMkDDM2Ir2weYFsH42fPO4sYV2gU6XQpcrjcRTK2CJjMQ3MhLfMWMAqMjPp2T1aorT0ylZlc6hd96FN94EwD0xEVvPHth7pmHv2QNLRMtPuInmqbLy3N0mJMkkhBCiLiTBVEcnKpgKistdHIkQQogmVelo9CUyRsSMoFdYL17IfIGRcSPx9/Bv3AFryd3NzB9HJDG6SziPzF7Hgx+vYe7qXB6/tDNxQZ41u4h3KKTdYmwHt8Gmz42E0zf/NLaoXtDlKuh0mVEB1Uq4+fvjPWwY3sOGAeAoKaFk7TpKMtIpXpVO4bz5FMyYaRwbEV7Vw8nWowfu7dujGnj1QiGEEEKIhiIJpjry93RWMBWXuTgSIYQQTUo3bgUTGNUDj/R+hGs/v5aHlj7EKyNeaTZT5apLDPXm4zv78cEv2fxr4SYufOZ7buwbx+Rh7as+J2skqD1c8EdjK8iGXz+FdZ/Awinw5SPQbjB0vRo6jgF3r8Z7Qy5gstnw7NMbzz5Gk2VdUcHxzZspSU+nOD2DYytWUPj558ax3t7YunXD1qM79u7d8ejSFbNXDRN6QgghhBCNrPl9W20h/E5UMJVIBZMQQrQljdnku7ok/yQe6/sYf132V57LeI4H0h5o/EHrwGRS3Ng3lpEpofxv8RamL9vBJ+m7uWdYe27qF4eHpZYrp/nFGP2aLrgP9m80Ek3rZsHcSfDF/ZB8MSSNgoShxop1rYxyc8PWqRO2Tp0IuOkmtNaU79pF8epMSlavpmT1ag6+8CJoDSYT7snJ2Lt3x+bcLJERMr1JCCGEEC4hCaY68rMZd2YLjkkFkxBCtCmN3IOpussSL2P9ofVMXz+d3mG9GRg1sEnGrYtQHw/+dWVXbrkgjqkLNvHkgk28s2wXU0YlM6ZrBCZTHf7MQjrC8L/AsMdg98+wdiasnwu/fmK8Ht4NEoYbyaao3mBpfauyKaWwxsVhjYvD77JLAagsLKRkzVpKVmdQvHo1BXPnkv/hhwC4hYQ4k02pRpVTx44oay2qyYQQQggh6kgSTHVkdTPh5e5GvvRgEkKINkU7Gr8HU3UPpj1I+r50HvvpMWZeMpMwz7CmG7wOOoT58M7E3vy49SBPLtjIvTMyefPHHfzp4o70jQ+s20WVgpi+xnbxf2BvJmz/BrZ9A8uehx//B2Z3iOkDsQOM/k1RaeDh27Bvrpkw+/jgNfACvAZeABjT6kq3bqV49WpKMowqp6JFiwBQ7u54dOqErUsXbN264tG1K5bIGjRkF0IIIYSoJUkw1YOf3UKB9GASQgiXU0pdCowGQoCXtNaLG20whwNVl2qcOvJw8+A/g//D9QuuZ9JXk5g+ajp+Hs1/OfsLEoP4fPIFzFmdy38Wb+aa11YwKCmYBy5Molt0PeI3mSGyp7ENegiOF0L2ctixFLK+h++eAjSgILgDRPcyqpuie0NgorGSXSuj3Nzw6NgRj44d4brrACjft5+SzExKMjIoWbuW/BkzOPzOOwCYAwKwdemCR9cu2Lp2xdalC2a/5v9vSgghhBDNmySY6sHfbpUm30IIUU9KqbeAS4D9WuvO1faPAp4DzMAbWuunznYNrfVcYK5Syh/4D9BoCSbt0EZFTRNK8EvghWEvMOmrSUz+ZjJvXvQmVnPzn/ZkMimu6BnF6K7hvLNsJ698v51xL/3EhSmh3H9hEh3Dfeo/iIcPJF1kbADHj0BuBuSshN2/wIZ5kPGu81g/iLvAaBoePxiCkpr877KpWEJDsFw0Ep+LRgKgy8s5vmULx9etM1atW7uGo0uXGr2cAEtsDLYuXbF17YpH5064JyVLA3EhhBBC1IokmOrBz26RKXJCCFF/04EXgXdP7FBKmYGXgAuBHGClUmoeRrJp6mnnT9Ra73c+f8x5XuNxOKAJK5hO6BXWiycHPsmD3z/IP5b/g8cHPN5ipjl5WMzcOTiB6/rE8PZPO3l9aRa/2/ADozqFccfgeHrE+DfgYL5GT6aEocbPDgcc2gY5v5ysdNpkrMqGd7gx7S6iB0T2MHo6uXs3XCzNiLJYqpqH+19zDQCVR49y/Nf1lKxdy/F1ayn+5ZeqFesALNHReHRIxj25g/HYoaM0ERdCCCHEWUmCqR787VZ2Hy52dRhCCNGiaa2XKqXiTtvdG9imtc4CUErNAMZpradiVDudQhm/8T4FLNRaZzRqvA7tsl+wL4q7iKyCLF5e8zI+Vh+m9JrSon7Z9/aw8IfhiUzoF8frP2Tx7vKdfLk+j56x/tw+MJ4LU0IxN3TyzmSC4CRj636Dse/wDtjxvTGlLmcVrJ/jPFhBcDJEdD+ZdArt3CqbhwOYvbzw7NsHz759qvaV79vH8Q0bKN28meMbN1G6aRNFXy+pqnQyeXvjnpyER3IH3Dsk49GhA+6JiZg8WuefkRBCCCFqThJM9eAvFUxCCNFYIoHd1X7OAfqc5ViAycAIwFcp1V5r/cqZDlJK3QHcARATE1O3yFxUwXTCpG6TKCwr5P2N71NSUcJf+v4Fs8nssnjqwtdu4cGLkrlrSAKzVu3mzR93MOn9dOIC7dzcP44r06Lxcm/ErygB7Yyt583Gz0cPwJ7VsCfDmF637WtY85HxmsnNSDjFD4H4oUYDcbfmPz2xriyhoVhCQ/EeOrRqn6O4mNItWzi+aTPHN2+idNNmjsyZg6PYeZPNZMIaF2ckmzp0qKp6cgsJblEJUCGEEELUjySY6sHXbqXweDmVDt3wd1yFEKI7MziqAAAgAElEQVRtO9P/VPXZDtZaPw88f76Laq1fA14DSEtLO+v1znORJm3yfTqlFFN6TcHT4smra1+luKKYJy54AovJ4rKY6srT3Y1bBrTjxr6xLFq/j9d/yOLv8zfw38VbuLpXNBP6xxEdYG/8QLyCIWmksYFRrVOYaySbctNh10/ww39h6dPGanURqUaiKawrBCZAQDzYAxo/Thcx2e3YUlOxpaZW7dMOB+U5OUaV0+ZNHN+0mZLMTAoXLKg6xuzvb1Q5JXfAPbE91pgYLDExuAUHo1phs3UhhBCirZMEUz342y1oDUdKygnwbL13M4UQwgVygOhqP0cBe1wUyylc0eT7dEop7ul+D3aLnWfSn+F4xXGeHvw07mZ3l8ZVV25mE6O7hjO6azirs/N5+6edTF+2k7d+2sHIlDBuGRBH73YBTVcNoxT4RhlbylhjX0kB7PwBslcYDcR/eR0qS0+eYwswkk2B7SE81WgiHtgezC0v8VcTymTCGhODNSYGnI3EASoLC43pddWqnfI/+ghdevLPSnl4YI2OxhIbgzUm1rhOrHEtt7AwlLllVeQJIYQQwiAJpnrwtxtJpfziMkkwCSFEw1oJJCql2gG5wDXAda4NycnhQDWTKWkTO0/E7mbniZ+f4O6v7+Z/Q/+Hj7UBVmZzoe4x/nSP8efRizvw3vJdfPhLNl+uz6NzpA8TB7Tjkq4RWN1cUP1i84OOY4wNoKIM8nfAoe1weLvxeGib0dep+vQ6/3ZGoikwwZhqlzCsVVc7mX18sPfqhb1Xr6p9uqKC8rw8ynbtojw7m7Jd2ZRlZ1O2cyfHlv6ALju5Iq+yWLBER2OJjsIaGYklMgpLVBSWqEisUVGYfX1d8baEEEIIUQOSYKoHP7txV7KguOw8RwohhDgbpdRHwBAgSCmVA/xNa/2mUuoeYBHGynFvaa3XN9B4Y4Ax7du3r9P52qFRbs1nWvQ1Ha7B0+LJX3/6KxMWTuCZIc8Q5xvn6rDqLdzXxpRRHZg8LJE5q3N566cd3D9rDVMXbuLyHpFc2SOKxFAXrvjmZjUaggcn//a1/J2waxkc3GoknQ5th6xvoeK48bp3BAS1h6AkCO4AIR0hJKXVJp6UmxvWqCisUVEwYMApr2mHg4r9+51JJ2cCaucuynJzKFmdiaOw8JTjTd7eWKKisMbGYo2LxRoXh3tcHNa4OMx+fk35toQQQghxGkkw1UNVBdMxafQthBB1pbW+9iz7FwALzvRaPcebD8xPS0u7vY4XaHb9Y8YkjCHEHsL9393P+M/H8+c+f2ZswthW0WDZZjVzXZ8Yru0dzdKtB3lv+U7e+GEHr36fRZdIXy7vEcnYbhEEejWj6YH+ccZWnaPS6Om08wc4uMVIPq39GEqPnDzGK9SZcEqBEOdjcAfwaNlVaeeiTCYsYWFYwsLw7NP7N69XFhZSnpNDWU4O5Tm5zue7Ob5xA0VffQWVlVXHmv38sDqTTcYWizU2Fkt0DGYvz6Z8W0IIIUSbJAmmegjyNr7M7i8qPc+RQgghWgujB1PzSjAB9Anvw6djP+XRHx7lsZ8eY9meZfyl71/wsnq5OrQGoZRicFIwg5OCOXi0lHmZe5i9Ood/zN/AE19sZEhyCFf0iGRYxxDc3ZrHFMZTmMwQ3cvYTtAaCvfAgY2wfyPs3wT7N0DGO1BefPK4gASI7AERPYzHkJRWnXSqzuzjgzklBY+UlN+8psvKKMvJpWznTmPbtcuYdrd8OUfmzj31OgEBzibj0VijjZ5PluhorDExmAOasL+XEEII0YpJgqkewn08sFnMbN1f5OpQhBBCNBWtoZmuHBrmGcYbI9/gjXVvMG3NNNYcWMNTA58iNST1/Ce3IEFe7ky8oB0TL2jH5rwiZmfkMGd1Ll9v3IevzcIlXcP5Xedw+sQHYDE3v2RgFaXAN9LY2o84ud/hgCPZRtJp36+wJxN2/gTrPj55jD3Q6O8U0hEiexpbSAqY285XO2W14h7fDvf4dr95zXHsmNHnaVc2Zbuzjal32bspXrWKwvmfG/8dO5k8PY2+T5GRWMLDsUREGFuk8SgJKCGEEKJm2s63kEZgMikSQ73Yuu+oq0MRQghRQw3Sg6mZTZGrzmwyc2e3O+kT3odHfniECV9OYELKBH6f+ns83DxcHV6DSw7z5tGLOzJlVAd+2naQTzNy+DQjhw9+zsbHw42hHUIYmRLG4ORgvNxbyNcek+nkNLvk353cX5RnTLM7uMVoMH44CzZ9AavfM153s0FEKoR1gdDOENbZSDpZbK54Fy5l8vTEo2NHPDp2/M1rjtJSynNzKcs+mXgq372b8uxsileswHHs2CnHKw+Paomnagko5+YWGopyayH/toQQQohGJJ+G9ZQY4s2P2w64OgwhhBA1VO8eTA6abQVTdakhqXwy5hP+m/5f3l7/Nt/u/pZ/Dvhnq6tmOsFsUgxKCmZQUjAlZZX8uO0gi9fnsWTTfj7L3IPVbGJA+0BGdgpjRMdQgr2bUc+mmvIOgw4XAxef3Ke1kWzKSYdc57b6Ayh3JkmUCQITjWRTaGcj+RTWxbhWG2Vyd8c9Ph73+PjfvKa1xlFYSPmePcaWu4fyvXurfj6+aROVhw6depLZjFtoCJbwk0kna0wM7u0TsCYkYPZqHdNUhRBCiPORBFM9JYZ68WlGDkdKyvG1WVwdjhBCiMamNao5T7uqxsvqxd/6/Y2RsSP5+7K/c9PCm7gh5QbuSb0Hu8Xu6vAajc1q5sKUUC5MCaXSoUnflc/i9Xks2pDHt7PX8Se1jp4x/lzUKYyRnUKJDWzBDaCVgoB4Y+t6lbHP4TCSTvt+hbxfIW8d7P4Ffv305HleoRDRHcK6GpVSftHgGw0+kcYKeW2UUgqzry9mX98zVj8BOI4fp3yPM+m010g8VTiTUSXp6RQuWHBK83G30FDcExKwtk/APT6hKvHk5u/fVG9LCCGEaBKSYKqnpFDjrtTWfUWkxbXO5YWFEEKcpDXNssn3ufSL6MfscbN5Jv0Z3tvwHot2LOK+tPsY3W50q+8tYzYpercLoHe7AP48uiOb8opYvH4fi9bn8cSCjTyxYCPJod4MSTaqn9Li/Jtnk/DaMJkgMMHYUsad3F+SD/vWw961sDfT6O20ZRGgq52sjOomvxgISjJ6PAV3MB69w42EVhtn8vA4a+8nAF1RQXlODqVZWZRu207Z9m2Ubs+i4JNP0cUnm7ebfX2xxMScsfm4W3Bwq/9vUwghROujtNbnP6oFSktL06tWrWr0cXYfLmbgv7/lycu6cF2fmEYfTwgh6kspla61TnN1HK5W18+J7b074BEfTuSMbxshqsaXuT+Tqb9MZcOhDaQGp/JIn0foFNjJ1WG5xO7DxXy1YR9fbdjHql2HKa/U2Cxm+iUEMigxiEFJwbQL8mzdv+hXlMKRHDiy23gscD7m74QDm6D44MljrV5GY3H/WAho53weZzz3jQazVHKfi3Y4qMjLo3T7diPxtGsn5dm7jV5Qe/YYlWdOymYzmoxHRmKNjDQakJ/YoqIw+/k16r9L+Zxout8lhBCiJTrb54TLKpiUUmZgFZCrtb5EKRUAzATigJ3AeK11vvPYR4FbgUrgD1rrRS4J+gwi/WzYrWa27JOV5IQQoiWod5NvjVEh0kKlhqTy0eiP+GzbZzyb8SzXfn4tYxPGclfqXUR6Rbo6vCYVHWCvWo3uWGkFK7IO8f2WAyzdcoBvNu13HmNjUKJR3dQ/IRBvj1aWRHFzP1ntdCbHDhqr2R3YBIe2G1PvDm6FrV9BZenJ45QZfKOMZJNfLHgGgS0AbP5gDzCe2wON6ij3ttmTSJlMVT2avAYOPOU1XVZG+Z49lO12Jpyyd1O+J5ey3FxKMtfgOHLk1GvZ7VgjI7BEnJZ8io7CGh2N2cenKd9ai6KUigf+DPhqra90dTxCCNGauHKK3L3ARuDEJ+AjwBKt9VNKqUecPz+slEoBrgE6ARHA10qpJK115Zku2tRMJkViiBdb90uCSQghWoL6N/lu3qvI1YRJmbgs8TJGxI7g1TWv8tGmj/hixxfclHITd3a9s1X3ZzobT3c3hncMZXjHUAB2HTrG0i0H+H7LQeauzuWDn7NxMyl6xPozOCmYQYnBdIrwwdQCGr7Xi2cQtBtobNU5HHA0Dw7vcK5ot8OoesrfAZsXQPFhONtXNe8IiOxhJLX8252shvKNAlMLn55YR8pqxRoXhzUu7oyvVxYVUZ6be8pWlptLee4eijMycBSd+j3U5OOD77hxhP35T40Xs1JvAZcA+7XWnc9yzCjgOcAMvKG1fqqm59Y3jrONrbXOAm5VSn1Sn3GFEEL8lksSTEqpKGA08ARwv3P3OGCI8/k7wHfAw879M7TWpcAOpdQ2oDewvAlDPqfEUG+WbpGV5IQQoi3QGjC3jl+Cva3ePNjrQW5IuYEXVr/AW7++xRdZX/BQr4cYGTuydU8NO4/YQE9u7OfJjf3iKKtwkJGdX1Xd9PSizTy9aDOBnlYGOqfS9U8IIszXw9VhNx2TCXwijC1uwG9f1xpKC41EU8lhKM43ptsV7oH9G2DvGti6GCrLTp7j5gFBiUbPJ98oZ9WTs/rJM9hISNnbZr9Ls7c35g4d8OjQ4YyvVxYWUp6TYySddudQnrMb94TfrpLXwKYDLwLvnulF52yFl4ALgRxgpVJqntZ6w/nOdZ4fApRorYuq7Wuvtd52vjjOM7YQQohG4qoKpmeBKYB3tX2hWuu9AFrrvc4PFYBIYEW143Kc+5qNxBAvPknP4UhxOb72VlY6L4QQ4lSaVtfoOMwzjCcueIKrkq7iiZ+f4MHvHyQ1OJX70+6ne0h3V4fnclY3E33jA+kbH8jDozpwoKiUH7YayaYfth5kbuYeACJ8Pege60+PGH/SYv3pHOmLubVXOJ2NUuDha2ycuRk2jkoj4XQ46+TUuwObIftnKJoDjorfnmMPMpqPByU6H5MgOMnoAdVGq58AzD4+mFNS8EhJabIxtdZLlVJx5zikN7DNWTGEUmoGxo3jDTU4F2AwcJdS6mKt9XGl1O3AZcDFNYjjrGOf/50JIYSoqyZPMCmlTpSwpiulhtTklDPsO2NncqXUHcAdADExTddwOynUyJNt2V9EL1lJTgghWjWtQbWSCqbTpYakMmP0DOZsm8O0zGnctPAmhkQP4d7u99Lev249q1qjYG93Lu8RxeU9onA4NOv3FLJy52HSs/NZvSufL9buBSDIy8rAxGB6xPjRI9af5FBv3Mwte3plgzKZwS/a2Bh86mtaQ2mRs/rpMBzdD4e2wcEtxrbpcyg+dPJ4Nw8IbG8kngISnNeNMfpB+UYZvaZEU4sEdlf7OQfoU9OTtdYfK6XaATOUUh8DEzEqkuo1tlIqEGMWRXel1KNa66nVT6xvnz4hhGjLXFHBNAAYq5S6GPAAfJRS7wP7lFLhzuqlcGC/8/gcILra+VHAnjNdWGv9GvAaGCs/NNYbOF1iqNGscss+STAJIUSrp3WrrpQwm8xcmXQlo+NH88HGD3hz3ZtcPu9yxiSM4fepv29zjcDPx2RSdInypUuULxOdlTp5R47z845DfL1xPz9uO8ic1bkA2K1mUqP96BHjT89Yf7rH+OFnt7oy/OZLKfDwMTb/uDMfc+wQHNpqJJwObDYqoPZkwoZ5v+3/5BVmJJx8o042IPcMMvb5RBhT8OyBshJew6rxTeKz0Vr/21l9NA1I0Fofre/YWutDwKRzjFm/Pn1CCNGGNXmCSWv9KPAogLOC6UGt9Q1KqaeBCcBTzsfPnKfMAz5USv0Po8l3IvBLU8d9LpF+NjytZrbuq+lnnhBCCFep191phwO0QrWBKhSbm43butzGlYlX8tavb/Hhpg9ZuGMhVydfzW1dbiPQFujqEJutMF8PxqVGMi41Eq01OfklZGTnk7Ern/TsfKZ9v51Kh/F7dkKwJz2d0+p6xvqTEOzV+huHNxTPQGOL6Xvq/soKKNoLBdnGdmQ3FOwynu/NNCqijhec+Zq2AGOlO+8wIxnlH1dta2esitfKpsg2ohrfJD4bpdRAoDMwB/gbcE9TjS2EEKL2XLmK3OmeAmYppW4FsoGrALTW65VSszDmTFcAdzeXFeROUErRPtSbLftkJTkhhGju6nV32lFhNPlu4avI1Yafhx/3p93PdR2v45U1r/DRpo+YvXU2N3W6iQkpE/Cyts0l52tKKUV0gJ3oADvjUo3qr+KyCtbsPlKVdPpqwz5mrcoBwMfDje4xJxNO3aJ98faQqppaMbtVm3p3hgbkYCShig8aSaeiPDi2H44eMB6L9hkJqrx1cOy0RVzcfSAkBaLSjKSTX6wxjm80uMt/C6dZCSQ6p7nlYqwKfV1NT1ZKdQdex1gYaAfwvlLqca31Y409thBCiLpxaYJJa/0dxmpxJ8pVh5/luCcw5ko3W4khXnwvK8kJIUTrpiuhFfdgOpcwzzD+3v/vTOg0gRdXv8gra15hxqYZTOw8kauSrpJEUy3YrW70SwikX4JRBaa1ZsfBY6Tvyicju4CMXfk8u2SLMRtTGb0eq1c5xQba2/QKfw3C7HayUulcSo8a1U/5O43t8A5jBbxfXofK0lOPtQWAf6yxCl5MX0i+2Jh610r/rpRSH2GsAB2klMoB/qa1flMptQC4TWu9Ryl1D7AIMANvaa3Xn+vc04awA1dprbc7z5kA3FyLOM44thBCiMajtG6yVkVNKi0tTa9atarJxntt6XaeXLCJzL9eKP0UhBDNmlIqXWud5uo4XK1OnxOlRWzqkYb/hT0IffajxgmshVh/cD3PZTzH8r3L8bZ4Mz55PDek3ECQLcjVobUKhcfLycwuICM7n/Rd+WRmF1BUaqyqFuhpNaqcYv3oGeNP1yg/bNa2l/R0KYcDju5zTr/LPrnl74T9G4zXACye4BMO3uFGryd7kDHNLqAdBMQbm83PpW/lTORzoul/lxBCiJbkbJ8TzWmKXIuWeGIluX1H6d1OGn0LIUSr5KgwejC1oSlyZ9MpqBOvjXyNXw/+ytu/vs3b69/m3Q3vMjZhLDd3upk43zhXh9ii+XhYGJQUzKCkYAAqHZpt+486q5yMqXVfbzSSGG4mRUqEDz1i/OkRa1Q5Rfh6SJVTYzKZjMSRTzhE9z71Na1h36+Q9R0cyYWiPVC4F3YtM/o/lR879XirN/hGgk+k8zHKuK67j1EB5RNhbLISnhBCiGZOEkwNpHOELxaz4oVvtvL2zb1kGWIhhGiNHJVGDyazfHye0DmoM/8d8l+yC7N5Z/07zN02l9lbZzM8Zji3dL6FrsFdXR1iq2A2KZLDvEkO8+a6PjEAHD5Wxurs/Koqp5krdzN92U4AQn3cq6bV9Yj1p1OED+5uUuXUJJSCsC7GdiblJUal06FtcDjLSEIV5sKRHMhb+9u+TyecWAnvN1us0ZDc4tFob0kIIYSoCfmG3ECCvd15/NLOPPzpOp5evJlHf9fR1SEJIYRoaA6jB1NbavJdUzE+Mfyl31+4K/UuPtz4ITM2z+Dr7K/pEtSFq5Ov5qK4i/Bwk1+AG1KAp5XhHUMZ3jEUgIpKB5vyiqqqnNJ35bNgXR4AVjcTXSJ96RHjV5V4CvGRvw+XsNggpKOxnUlFqTHF7nih0XS8cI+RhDrinIaXuwo2zDUqKqs7PQEV3QeSRzX++xFCCCGcJMHUgK7uFcPPWYd5Z9lO7huRhIdF7hQKIURroivLAdUmm3zXVJAtiD/0+AO3drmVudvmMnPzTB776TGeXvU0l7W/jPFJ44n2iT7/hUStuZlNdI70pXOkLxP6xwGwv/B4VbIpI7uAd5bt4vUfdgAQ5W87pXl4hzBvqcBuDtzcjQTRuTgqjZXuqvd/Kth1agKqy3hJMAkhhGhSkmBqYJd2j2T26lyWbz/E0A4hrg5HCCHEaZRSY4Ax7du3r/3J5WXGoySYzsvT4sn1Ha/nug7XsTJvJTM2z+C9De8xff10BkQO4JrkaxgYORCzSf4sG1OIjwejOoczqnM4AKUVlazfU0iGs8ppRdYhPsvcA4DNYqZbtC9do/xICvWmQ5g37UO85IZZc2QyG9PifKMgtv9vX3dUQnlx08clhBCiTZMEUwPrEx+A3WpmyaZ9kmASQohmSGs9H5iflpZ2e63PrTASTEp6MNWYUore4b3pHd6b/cX7+XTLp3yy5RMmfzOZEFsIF8dfzOj40ST7J0tT6ibg7mY2+jLF+AOgtSa3oISM7IKqpNP0n3ZSVukAwKQgLsiTDmHeVUmn5DAfYgLsmE3y99Vsmczg7u3qKIQQQrQx8g25gbm7mRnQPohvNx1Aay1floUQojU50fNEqm7qJMQewl2pd3Fb19v4fvf3fLb9M97f8D7T10+nvV97RseP5uJ2FxPhFeHqUNsMpRRR/nai/O2M7Wb8uVdUOth56Bib846yOa+QTXlFrN9TyMJf84wm94CHxURSaPWkk7EFe7nLdx8hhBCijZIEUyMY3iGErzbsY/2eQjpH+ro6HCGEEA2l/EQFkySY6sNisjAidgQjYkdQcLyAxbsW83nW5zyX8RzPZTxHz9CejI4fzcjYkfi6y+doU3Mzm2gf4k37EG9Gdw2v2l9cVsHWfUfZnFfEprwiNu8r5LvNB/gkPafqmABPK0mhXnQI86lKOiWFeuPlLl85hRBCiNZOPu0bwfCOoXi5b+Smt37hhWu7M6B9kKtDEkII0QB0RbnxRBJMDcbPw4/xyeMZnzyenKIcFuxYwOdZn/N/y/+PqT9PZVDUIEbHj2ZQ1CDcze6uDrdNs1vd6BbtR7dov1P2HzpaWpV02rLPeJy1ajfFZZVVx0T62WgX5El8sCfxQZ60C/YiPsiTSD8bJplqJ4QQQrQKkmBqBMHe7sy9ewB3vZ/OpPfTWfbIMLw9LK4OSwghRH1JD6ZGFeUdxR1d7+D2Lrez8fBGPs/6nIU7FrIkewneFm8ujLuQS+IvoWdoT0xKVjtrLgK93Onf3p3+1W6oORyanPwSNu8rYnNeIdv2H2XHwWPMycilqLSi6jirm4l2gUbiKTHUm5RwH9oFeRITYMdmlUSuEEII0ZLIN+RG0j7Ei/9c1Y1xL/3EjF92c/ugeFeHJIQQop50pbMiQxJMjUopRUpgCimBKTzQ8wF+zvuZL7K+4MsdXzJ762xC7aGMjBvJoKhB9AzpicUsN3GaG5NJERNoJybQzoUpoVX7tdYcOFrKjgPHyDp4jKwDR8k6cIxNeUUsWp+HQ5+8RrC3OzEBdmIDjOuc6PkUF2jHzSwJRiGEEKK5kW/IjahbtB992gXw1k87uHlAHBb5MiSEEC2bs4JJpsg1HbPJTP+I/vSP6M9jfR/ju93f8UXWF8zcNJP3NryH3c1Ov4h+DIoaxMDIgQTbg10dsjgHpRQh3h6EeHvQJz7wlNdO9HjadbiY3YeLyT5UTPbhYn7ecZg5mblVDcatZhMJIV4kh3qRGOpNXKBR8RQTaMfXJslGIYQQwlUkwdTI7hwcz8Tpq7jgX99wda8Y7r8wydUhCSFEm6aUGgOMad++fa3PPdGDSabIuYbNzfb/7d15dFzned/x7zMLZsFg3zcCIMFdlkgJpBInlqVadiVV8i6vbdITx6rbpInTkxPLdZul5/jUStrETpzEsRM3dmpZUpQollLJdiJLouxIpsRFEsV9FQEQAAlwwb7MvP1jLoYDYuECcuYS+H3OuefOvPe9M8+8GM4DPnjvO9zdejd3t97N8MQwW7u3sqVjC1s6tvDsW88CsLZ8Lbc13satdbdyU9VNFAQL8hy1XKq51ngCGJ1IcrB3kP09A+zrHmBfzwBbj/TzDzu7pvUrjYdpq0qwtq6YdfXphcbbqhMUa6kCERGRa06/IV9jd6yu5o8+ehNP7Ojij589wNsaSqZNFRcRkdxyzj0FPNXe3v7pyz454BUrCuJXNyi5bPFwnNubbuf2pttxzrH/9H5e7HyRLR1b+MYb3+AvXv8LIsEIG6o3cGvtrWyq3cT6yvWEAyo0XI+i4SA3NJTM+HbewbHJzEynt/qHONY3zIGeQZ7Y0cnfvHws06+6KMLyqkJaKwtp9mY8NZXFaSqPURILY6aFxkVERBZKBaZrzMz4wMZG7r2xnvv+5Mf893/YxY2NJdQUR/MdmoiIXCZXtQ4Aq2jNcySSzcxYXb6a1eWr+eW3/TLnxs+xrXsbW7u3srV7K3+844+B9Ayom6tvZlPtJjbXbmZtxVpCAf0qdD1LREKsq0/PVsqWSjmOnx5mf88gB3vT29G+IX74Zg99Q+PT+hZFQjSWx2kqi9GUta8vjVFTHKUsrgKUiIjIpdBvVTkSDgb40odu5P6v/Qs//9CP+IWfbeG//Zu1+oVFROR64lLpfUBr6vlZcUExdyy7gzuW3QFA/2g/r3a/yivdr/BK9yt8efuXASgMF2YKThurN7KuYp0uqVskAgGjuSI9W+nCmePnRic43j/M8f4ROk6n13s6fnqEI6eGePHAKUYmktP6FwQDNJbFWF6VYEVVISuqEqyoTtBWndCaTyIiIllUYMqhDU2l/PA33slXf3SQv/rxEdbUFnF/e1O+wxIRkUvlfYuc6Usbrivl0XLe0/Ie3tPyHgBOjZzKFJy2dm/lxc4XASgIFHBD5Q1sqN7AxuqNbKjaQGl05npAcn0rjoZZX1/C+vqSGcecc/QNjXO8f5jus6N0n0tvb/UNc/jkEFv2n2Q8mcr0ryqKsKKqkLbqBG1VCdqqi1hRXUhtcVR/RBQRkSVHBaYca60s5Pc/fCPHTw/ze0/tprWykPaW8nyHJSIil8ClpmYw6VvkrmeVsUruar2Lu1rvAtIFp529O9nRu4OdvTv59u5v881d3wSgpbiFjdUb0wWn6g20FLeocLCImRmViQiVicisx5Mpx/H+YQ6dPH/p3cGTg3xvZxcDo5OZfolIKD3bqTrByuoiVoMrs90AAB0KSURBVNcmWFVTRENpTO8fERFZtFRgyoNgwPjDj9zEh//8JT78tZe4/5ZGHvrQjQQC+oVDRMTXNINpUaqMVXJn853c2XwnAKOTo+w6tYudJ9NFp2ffepYnDj4BpGdD3VJzS2ZbWbqSoAqOS0YwYLRUFtJSWci71p6/9M45x8nBMQ72DnLIKzwdOjnETw6e4u+3d2b6RcMBWioKaa6I01qZyMx+WqFvuhMRkUVABaY8aSyL86PffCdfefYAf/HCYaqKIvzWXWvyHZaIiMzDpVz6hgoKi1o0FKW9tp322nYAUi7F0bNH2dG7g+2929nWs41/OvZPACTCCdZVrGNdxTrWlq9lXcU6lhUvI2AqQi4lZkZ1UZTqoihvX1E57djZkQkO9Aywt3uAI6eGONY3xMHeQX60t5eJpMv0qy6KsKIqQVN5jKqiCNVFUepKojSVx2ksi1GkApSIiPicCkx5FC8I8eBdazg3MsmfPX+I08PjfPbOVfqGORERv0p5i/9qxumSErAAy0uXs7x0OR9a9SEATgyeYFvvNnb07GB3324e3vMw46n0t5MlwgnWlK9JF50q0kWnluIWFZ2WqJJYmPaW8hlLIkwmU7zVP8yhk0PTLrl7Yf9JTg2Ok0y5af1L42Eay2I0lcUzRaemsvS+sSxOrECFbxERyS8VmPLMzPi9964nGg7wf18+xg/e7OE7v3wra+uKL36yiIjklPMW97Wg/iO31NUl6rg3cS/3Lr8XgInUBIfOHGJP3x7e7HuTPX17eHTfo4wlxwCIh+KZotPU1lLcosvrlrBQMMDyqgTLqxK8m+nfdJdKOfqHx+k6M3L+2+5Op7/5bn/PAD/a28vYZGraOfUlUe/xClleWchNTaVsXFaWy5ckIiJLXM4LTGYWBbYAEe/5H3fO/Y6ZlQOPAi3AUeAjzrnT3jmfBz4FJIFfc879INdxX0sFoQC/c996PnlrM//ur37KJ77xMr/3vhu454ZaQlrnQ0TEPzIzmPTZLNOFA2HWlK9hTfkaPrDyAwBMpiY5fPYwu/t2Z7bH9z/OaHIUgFgoxsqylawpW8Pq8tWsKV/DyrKVxEKxfL4U8YFA4Pxi4zc2zvwmw1TKcWpwjOOn08WnY33DHDk1xOGTgzyxvZOBsUnev6FeBSYREcmpfMxgGgP+lXNu0MzCwI/N7Bngg8CzzrkvmdmDwIPA58xsHfAxYD1QD/yzma1yziXzEPs11Vad4JEHfoZPf/tVfu27O/jTmiL+5BMbWVVTlO/QREQWDTO7D7ivra3tss91mUW+NetELi4UCLGqbBWrylbx/rb3A+mi09GzR9ndv5s9fXvY27+XZ448w2P7HwPSl+O1FLdkCk5TxaeKWEU+X4r4TCBgVBdHqS6Ockvz9CLS1ILj2es7iYiI5ELOC0zOOQcMenfD3uaA9wG3e+3fAp4HPue1P+KcGwOOmNlBYDPwUu6izp3mikK+/+u38f03u/nt7+3ivV/9MR9pb+Ljm5fpsjkRkavAOfcU8FR7e/unr+Dk9F4zmOQKhQIh2sraaCtr470r3gukCwJdQ13s7d/Lvv597O3fy87enTxz5JnMeVWxqkzRaXX5ataUrdFi4jKrqQXHRUREci0vazCZWRDYBrQBf+qc+6mZ1TjnTgA4506YWbXXvQF4Oev0Dq9t0QoEjHveVkd7Sxlf/H97eOSV4/zNy8f4wMYG/u3PNLOhsZSAFpgVEcm9qRlMKjDJVWRmNCQaaEg08K5l78q0nx07myk47Tud3r/c9TKTbhJIX2K3qmxV5tK8NeVraCttIxpScUFERERyLy8FJu/ytg1mVgo8YWY3zNN9tkrKrHN+zewB4AGAZcuWLTjOfKsuivKVj23kfwxP8GcvHOT//OQof7+9k4bSGP/+7S3cd1M9tSX6JVJEJFemFvlGCzNLDpRESthct5nNdZszbePJcQ6dOZQpOu3p28M/Hv5HHt33KJC+xK61uHX6bKfyNZRHy+d6GhEREZGrIq/fIuecO2NmzwN3AT1mVufNXqoDer1uHUBT1mmNQNccj/d14OsA7e3ti+bC85J4mM/fvZb/9M42nt/fy8M/fYsvPr2HLz69h/X1xdx/SyN3v62OmmIVm0RErqXo+nU0f/dhIlewfpPI1VAQLGBtxVrWVqzNtKVcis7BzvOznfr3sb13O08feTrTpzpWfX5dJ29rLGrUJXYiIiJy1Zhzua3DmFkVMOEVl2LAD4GHgHcCfVmLfJc7537LzNYDD5Ned6keeBZYebFFvtvb292rr756TV9LPu3vGeD5fb08+VoXuzrPAXBjYwnvWlPDu9ZWs76+GDNdRiciM5nZNudce77jyLfFnidEzoyeyVxaN7UdOXuEpPcrVDwUZ3X5alaXnS88tZW1EQlG8hy55JvyhHKEiMh85soT+ZjBVAd8y1uHKQA85pz7RzN7CXjMzD4FvAXcD+Cce9PMHgN2A5PAryzGb5C7XKtqilhVU8QDt61gX/cA/7ynh2f39PDlZ/fzR/+8n9riKO9cVcUda6r4ubZKiqLhfIcsIiIiOVQaLeXWulu5te7WTNtYcoyDZw5Om+301OGneGTfIwAELUhrSWtmIfGVZStZUbqCmniN/nAlIiIi88r5DKZcWap/dTg1OMZze3t5bl8vLx44xcDoJKGAcUtzGbcur6C9uYyNy0pVcBJZwvSX6bSlmidELpRyKToHOtl7ei97+vZkZj31Dvdm+sRDcZaXLGd56fL0vmQ5K0pX0JBoIKg1yRYd5QnlCBGR+fhpBpNcQ5WJCPe3N3F/exMTyRQ73jrDc/t62bL/JF/90QFSDgIGa2qLaW8p45bmMja1lFNfGst36CIiIpIHAQvQVNxEU3ET725+d6a9f7SfQ2cOcfjMYQ6fPcyhs4d4uetlnjz0ZKZPQaCAlpKWGcWnhkQD8XA8Hy9HRERE8kQFpkUsHAywubWcza3lfO6uNQyMTrDz+BleOXqabcf6eXxbB99+6RgANcURbqgvYX1DCTfUF7O+oYT6kqimw4uIiCxR5dFyymvL2VS7aVr7ufFzHDl7JFN4Onz2MG+ceoMfHP0BLuuLfssiZdQn6qlP1NOYaMzcbkg0UFdYpwKUiIjIIqMC0xJSFA3zjpVVvGNlFQCTyRR7uwd49Wg/r3ecZVfXWZ7b10vK+92wLB5mbV0xq2qKWFmTYGV1EatqEpTGC/L4KkRERCSfiguKuanqJm6qumla+8jkCEfPHuXouaN0DnbSNdhF12AXB04f4IXjLzCeGp/WvzxaTn3h+aKTClAiIiLXNxWYlrBQMMANDSXc0FCSaRsZT7Kn+xxvdp5lV+c59naf47FXjzM8fn5d9cpEhFU1CVZWJ2ipLKS5Is6y8kKaymNEQlqHQUREZCmKhWKsrVjL2oq1M46lXIr+0X46BzvpHOika6grU4Taf3o/zx9/fkYBqqigiLrCOmoLa6mN16b33taQaKAmXqP1n0RERHxEBSaZJlYQ5OZlZdy8rCzTlko5us6OcKB3kAM9A+zvGeRA7yCPb+tgKKvwZAZ1xVGWVcRp9gpONcVR6kpi1JZEqC2JkYjoLSciIrLUBCxAZaySyljljJlPkC5A9Y30pQtQg510D3VzYugEPUM9dA938/rJ1zkzdmbaOSELpYtNRQ00JGZulbFKXeovIiKSQ/rfvlxUIGA0lsVpLItzx+rqTLtzjlOD47zVP8SxvmGO9Q3zVv8wx/qGeHZvD6cGx2c8ViISoqY4Ql1Juvg0VXiqLY5SUxyhMhGhIlGgmVAiIiJLSMACVMWrqIpXsaF6w6x9RiZHMoWnrsGuTDGqc7CTF46/QN9o37T+kWCE2sJaauI1VMerqYnXUFOYvl0br6WmsIbyaDkBC+TiJYqIiCx6KjDJFTMzqooiVBVFuKW5fMbx0YkkPedG6T47Svcs+385dIregTGSKTfj3KJoiCqv2FSZiGS2qful8XB6ixVQEgsTDQf0V0oREZFFLBaK0VrSSmtJ66zHRyZHODF4go7BjkwBqnuom57hHrb3bKd3pJfJ1OS0c0IWoipedb4IVViTLkRlFaOqY9WEg+FcvEQREZHrmgpMcs1Ew0GaKwppriics08y5egbHOPE2VF6B8Y4NThG3+AYpwbHOTk4xqmBMQ70DvLS4T7ODE/M+TgFoQClsTAlsXThqcQrPJXEwiSiIYqjIRKREIloiKJomETEa/Pux8NBAgEVqERERK5XsVCM5aXLWV66fNbjU+tA9Qz30DvUS89wT/r2cC89Qz3sP72fFztfZGRyZMa55dFyauI1NBY1prdEet+UaKI2UUs4oAKUiIiICkySV8GAUV0cpbo4etG+45Mp+ofGOTU4xtmRCc4MT6T3I+Ocnbo9nL7fcXqY3V3ptux1ouZiBomCEEXR6UWo7OJUUTRMvCBIvCBEYSRILBykMBIiVhCksCDkHUu3RUKaUSXiV2Z2H3BfW1tbvkMRkRzKXgdqfcX6Wfs45xiYGKBnKF186hnyClDD6bWgDpw+wPPHn2cidf6PXkELUltYmyk8NRU1ZQpRzUXNJAoSuXqJIiIieaUCk1w3CkIBakui1JZcvBiVLZlyDI1PMjA6yeDoJAOjEwyMTd2eZHBsgoHM7fTxwbFJTg+Pc7x/mAGvbXQidcnPGTCIF0wVn4LECkIUFgSJR0LEw0Gi4QCxgiCRUJDo1P3w+dvRcPpYrCBINBTw2oNenwARb18QVCFL5HI5554Cnmpvb/90vmMREX8xM4oLiikuKGZl2cpZ+6Rcit7hXo4PHKdjoIOOwY7M/rnjz9E/2j+tf2WskubiZlqKWzJFqPpEPQ2JBsqj5crjIiKyaKjAJIteMGAUR8MURxc2fX0imWJ4PMnIeJKh8cn0fmyS4Ykkw2NJhscnGR5PetvktP3QWJKRiUnOjUzQfXaE0YkUoxNJb0sxnrz04lW2gJEpPmUXoqaKVOlCVYCCULoYVeDdjoSC3v58e6bftLbg9GNz9NUvxyIislQELEBtYS21hbVsqt004/jQxBAdAx0cHzjOsXPHMttsxadYKEZ9YT0NRQ3UF9ZTl6ijNp5+7Kp4FVWxKqKhy/vDmszOzJYDXwBKnHMfznc8IiKLkQpMIpcoHAxQEgtQErv66ywkU46xyXSxaSRTeErOKESNTiQzx8cmU4yMe8eyzh3L6nt2ZILxyXQBa3wyvY15+ystas2mIJguOoWCRigYIBzw9kEjHPTaA+n7oUCAcGiqz4X9s/oELXNeQShAKOsxQ4H0Y2YeP9P/Is891Z45P/24wYCpSCYiIldFYbiQ1eWrWV2+esaxoYmhGd+A1znQSddQFzt6djAwMTDjnKKCIqpiVZmCU1W8irrCOhoSDTQWNdKQaCASjOTipfmOmX0TuBfodc7dkNV+F/AVIAj8pXPuS865w8CnzOzx/EQrIrL4qcAk4gPBgBEvCBEvyN1zOucyhadM0SmrGDU2mWJsMjlre3bbWKZwlWQy6ZhMpZhIOiaT6f1EMsVkytt7x4dHkkx69ydSXnsyxbh3fDLrvNm+ZfBamCo0ZfbBwPT7mb3XHpy9PTijvxEMeIWs4BztU/eDc7Rnni9A0NL3KxIFbGqZ+e2NIiLiX4XhQlaWrZzz8ruhiSG6h7rpHurm5MhJTo2cone4N7Pf0buD3uHeaWtAAVTHqzMLj0/tV5WtmrXItcj8NfBV4NtTDWYWBP4UeDfQAbxiZk8653bnJKJnHoTuN3LyVCIiC1L7Nrj7S1f1IVVgElmizIxIKL3WU1G+g5lHKpVdhDp/O7twlV28mpjqN9V+QXFr3CtmXVjcSjqXLmglXaawld6npt9Pzt4+MpE8355Mtyeddzw5z+NdYRFtU0sZf/uZt1+DERcRkXwpDBeyonQFK0pXzNnHOUffaN/09Z8GOugc7GRr91aeGnoKh+Oe1nt46LaHchh97jnntphZywXNm4GD3owlzOwR4H3ARQtMZvYA8ADAsmXLrmqsIiJLgQpMIuJrgYARCQSJLOJPK+emF5zO71OzFKjS7dFwMN9hi4hIHphZ5tvwNlRvmHF8PDlO12AXAQvkITpfaACOZ93vAG41swrgi8BGM/u8c+5/Xniic+7rwNcB2tvbr2wK9VWeDSAicj1ZxP9lExG5PphNrUeV70hEROR6VxAsoKWkJd9h5NNsiyo651wf8JlcByMispQs2T9tiIiIiIjIotMBNGXdbwS68hSLiMiSogKTiIiIiIgsFq8AK82s1cwKgI8BT+Y5JhGRJUEFJhERERERue6Y2XeBl4DVZtZhZp9yzk0Cvwr8ANgDPOacezOfcYqILBVag0lERERERK47zrmPz9H+NPB0jsMREVnyNINJREREREREREQWRAUmERERERERERFZEBWYRERERERERERkQVRgEhERERERERGRBVGBSUREREREREREFkQFJhERERERERERWRBzzuU7hmvCzE4Cx67w9Erg1FUM52rwY0zgz7j8GBP4My4/xgT+jMuPMcGVxdXsnKu6FsFcT5QncsKPMYE/4/JjTODPuPwYE/gzriuNacnniUWYI8CfcfkxJvBnXH6MCfwZlx9jAn/GdVXzxKItMC2Emb3qnGvPdxzZ/BgT+DMuP8YE/ozLjzGBP+PyY0zg37gWOz+Ou2K6dH6My48xgT/j8mNM4M+4/BjTUuDXcfdjXH6MCfwZlx9jAn/G5ceYwJ9xXe2YdImciIiIiIiIiIgsiApMIiIiIiIiIiKyICowze7r+Q5gFn6MCfwZlx9jAn/G5ceYwJ9x+TEm8G9ci50fx10xXTo/xuXHmMCfcfkxJvBnXH6MaSnw67j7MS4/xgT+jMuPMYE/4/JjTODPuK5qTFqDSUREREREREREFkQzmEREREREREREZEFUYMpiZneZ2T4zO2hmD+YphiYze87M9pjZm2b2617775pZp5nt9LZ78hDbUTN7w3v+V722cjP7JzM74O3LchzT6qwx2Wlm58zss7keLzP7ppn1mtmurLY5x8bMPu+9z/aZ2b/OcVx/YGZ7zex1M3vCzEq99hYzG8kas6/lMKY5f155HqtHs2I6amY7vfZcjdVcnwd5f28tVcoTF43NV3nCLznCi8V3ecKPOWKeuPKaJ/yYI7znUp7wGeWJi8amPDF3LMoTC4tLeWJmTLnPEc45benLBIPAIWA5UAC8BqzLQxx1wM3e7SJgP7AO+F3gN/M8RkeBygvafh940Lv9IPBQnn+G3UBzrscLuA24Gdh1sbHxfp6vARGg1XvfBXMY13uAkHf7oay4WrL75XisZv155XusLjj+v4HfzvFYzfV5kPf31lLclCcuKTbf5ol85gjv+X2XJ/yYI+aJK695wo85wnsu5QkfbcoTlxSb8sTcz688sbC4lCdmPmfOc4RmMJ23GTjonDvsnBsHHgHel+sgnHMnnHPbvdsDwB6gIddxXIb3Ad/ybn8LeH8eY3kXcMg5dyzXT+yc2wL0X9A819i8D3jEOTfmnDsCHCT9/stJXM65HzrnJr27LwON1+K5LyemeeR1rKaYmQEfAb57LZ57npjm+jzI+3triVKeuDJ+yRN5yxHgzzzhxxwxV1zzyNtYTclXjgDlCR9SnrgyyhMoTyw0rnks2TyRjxyhAtN5DcDxrPsd5PmD2MxagI3AT72mX/WmIn4zl1NHszjgh2a2zcwe8NpqnHMnIP0GBqrzENeUjzH9H22+x2uusfHTe+2XgGey7rea2Q4ze8HM3pHjWGb7efllrN4B9DjnDmS15XSsLvg8uB7eW4uR78ZXeeKy+C1HgP//LfspR4B/80TecwQoT/iE78ZXeeKyKE9cPuWJS5P3PJGrHKEC03k2S1vevmLPzBLA3wGfdc6dA/4cWAFsAE6QnmKXaz/nnLsZuBv4FTO7LQ8xzMrMCoD3An/rNflhvObii/eamX0BmAS+4zWdAJY55zYC/wV42MyKcxTOXD8vX4wV8HGm/8KR07Ga5fNgzq6ztOmrQq8eX42v8sSlu85yBPjgveazHAH+zhN5zRGgPOEjvhpf5YlLpzxxBQEoT1yOJfN/CRWYzusAmrLuNwJd+QjEzMKk3wDfcc79PYBzrsc5l3TOpYBvkIfpzM65Lm/fCzzhxdBjZnVe3HVAb67j8twNbHfO9Xgx5n28mHts8v5eM7NfBO4FPum8C269qZB93u1tpK+5XZWLeOb5eflhrELAB4FHp9pyOVazfR7g4/fWIueb8VWeuGx+zBHg03/LfssR3nP6Mk/kO0d4MShP+Idvxld54rIpT1wG5YlLl+88kescoQLTea8AK82s1atgfwx4MtdBmJkBfwXscc79YVZ7XVa3DwC7Ljz3GsdVaGZFU7dJL+62i/QY/aLX7ReB7+UyrizTqsL5Hi/PXGPzJPAxM4uYWSuwEtiaq6DM7C7gc8B7nXPDWe1VZhb0bi/34jqco5jm+nnldaw8dwJ7nXMdUw25Gqu5Pg/w6XtrCVCemD8uP+cJP+YI8OG/ZT/mCO85/Zon8pYjvMdXnvAX5Yn541KeuHy++7esPHHZltb/JVwOVnm/XjbgHtIrqx8CvpCnGH6e9DS014Gd3nYP8DfAG177k0BdjuNaTnpF+deAN6fGB6gAngUOePvyPIxZHOgDSrLacjpepBPSCWCCdOX3U/ONDfAF7322D7g7x3EdJH1t7dT762te3w95P9vXgO3AfTmMac6fVz7Hymv/a+AzF/TN1VjN9XmQ9/fWUt2UJ+aNy5d5wg85wntO3+UJP+aIeeLKa57wY47wnkt5wmeb8sS8cSlPzB+H8sTC4lKemBlTznOEeQ8iIiIiIiIiIiJyRXSJnIiIiIiIiIiILIgKTCIiIiIiIiIisiAqMImIiIiIiIiIyIKowCQiIiIiIiIiIguiApOIiIiIiIiIiCyICkwiHjMb9PYtZvaJq/zY//WC+/9yNR9fRESuPeUJERGZj/KELHUqMInM1AJcVkIws+BFukxLCM65t19mTCIi4h8tKE+IiMjcWlCekCVIBSaRmb4EvMPMdprZb5hZ0Mz+wMxeMbPXzew/AJjZ7Wb2nJk9DLzhtf2DmW0zszfN7AGv7UtAzHu873htU3/dMO+xd5nZG2b20azHft7MHjezvWb2HTOzqcczs91eLP8r56MjIiLKEyIiMh/lCVmSQvkOQMSHHgR+0zl3L4D3wX7WObfJzCLAT8zsh17fzcANzrkj3v1fcs71m1kMeMXM/s4596CZ/apzbsMsz/VBYANwE1DpnbPFO7YRWA90AT8Bfs7MdgMfANY455yZlV71Vy8iIhejPCEiIvNRnpAlSTOYRC7uPcAvmNlO4KdABbDSO7Y1KxkA/JqZvQa8DDRl9ZvLzwPfdc4lnXM9wAvApqzH7nDOpYCdpKfangNGgb80sw8Cwwt+dSIislDKEyIiMh/lCVkSVGASuTgD/rNzboO3tTrnpv7iMJTpZHY7cCfws865m4AdQPQSHnsuY1m3k0DIOTdJ+q8cfwe8H/j+Zb0SERG5FpQnRERkPsoTsiSowCQy0wBQlHX/B8B/NLMwgJmtMrPCWc4rAU4754bNbA3wM1nHJqbOv8AW4KPeddlVwG3A1rkCM7MEUOKcexr4LOnpsCIiklvKEyIiMh/lCVmStAaTyEyvA5Pe1NS/Br5Cejrpdm9hvJOkq/0X+j7wGTN7HdhHelrrlK8Dr5vZdufcJ7PanwB+FngNcMBvOee6vYQymyLge2YWJf3Xit+4spcoIiILoDwhIiLzUZ6QJcmcc/mOQURERERERERErmO6RE5ERERERERERBZEBSYREREREREREVkQFZhERERERERERGRBVGASEREREREREZEFUYFJREREREREREQWRAUmERERERERERFZEBWYRERERERERERkQVRgEhERERERERGRBfn/TPYF+CNvvIYAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "its = d.getitstat()\n", "fig = plot.figure(figsize=(20, 5))\n", "plot.subplot(1, 3, 1)\n", "plot.plot(its.ObjFun, xlbl='Iterations', ylbl='Functional', fig=fig)\n", "plot.subplot(1, 3, 2)\n", "plot.plot(np.vstack((its.XPrRsdl, its.XDlRsdl, its.DPrRsdl,\n", " its.DDlRsdl)).T, ptyp='semilogy', xlbl='Iterations',\n", " ylbl='Residual', lgnd=['X Primal', 'X Dual', 'D Primal', 'D Dual'],\n", " fig=fig)\n", "plot.subplot(1, 3, 3)\n", "plot.plot(np.vstack((its.XRho, its.DRho)).T, xlbl='Iterations',\n", " ylbl='Penalty Parameter', ptyp='semilogy',\n", " lgnd=['$\\\\rho_X$', '$\\\\rho_D$'], fig=fig)\n", "fig.show()" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }