diff --git a/docs/conf.py b/docs/conf.py index 501beeac2..40f9f67bf 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -24,7 +24,7 @@ author = 'Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer' # The short X.Y version -version = '0.4.4' +version = '0.4.5' # The full version, including alpha/beta/rc tags release = version diff --git a/examples/tutorial_particle_physics/4a_limits.ipynb b/examples/tutorial_particle_physics/4a_limits.ipynb index 3312095c2..d4cbfad4b 100644 --- a/examples/tutorial_particle_physics/4a_limits.ipynb +++ b/examples/tutorial_particle_physics/4a_limits.ipynb @@ -130,19 +130,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "15:16 madminer.analysis INFO Loading data from data/lhe_data_shuffled.h5\n", - "15:16 madminer.analysis INFO Found 2 parameters\n", - "15:16 madminer.analysis INFO Did not find nuisance parameters\n", - "15:16 madminer.analysis INFO Found 6 benchmarks, of which 6 physical\n", - "15:16 madminer.analysis INFO Found 3 observables\n", - "15:16 madminer.analysis INFO Found 15117 events\n", - "15:16 madminer.analysis INFO 10004 generated from sm\n", - "15:16 madminer.analysis INFO 1080 generated from w\n", - "15:16 madminer.analysis INFO 1207 generated from neg_w\n", - "15:16 madminer.analysis INFO 1399 generated from ww\n", - "15:16 madminer.analysis INFO 1427 generated from neg_ww\n", - "15:16 madminer.analysis INFO Found morphing setup with 6 components\n", - "15:16 madminer.analysis INFO Did not find nuisance morphing setup\n" + "07:33 madminer.analysis INFO Loading data from data/lhe_data_shuffled.h5\n", + "07:33 madminer.analysis INFO Found 2 parameters\n", + "07:33 madminer.analysis INFO Did not find nuisance parameters\n", + "07:33 madminer.analysis INFO Found 6 benchmarks, of which 6 physical\n", + "07:33 madminer.analysis INFO Found 3 observables\n", + "07:33 madminer.analysis INFO Found 15117 events\n", + "07:33 madminer.analysis INFO 10004 generated from sm\n", + "07:33 madminer.analysis INFO 1080 generated from w\n", + "07:33 madminer.analysis INFO 1207 generated from neg_w\n", + "07:33 madminer.analysis INFO 1399 generated from ww\n", + "07:33 madminer.analysis INFO 1427 generated from neg_ww\n", + "07:33 madminer.analysis INFO Found morphing setup with 6 components\n", + "07:33 madminer.analysis INFO Did not find nuisance morphing setup\n" ] } ], @@ -223,13 +223,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "15:16 madminer.limits INFO Generating Asimov data\n", - "15:16 madminer.limits INFO Expected events: 2.107031712222278\n", - "15:16 madminer.limits INFO Setting up standard summary statistics\n", - "15:16 madminer.limits INFO Creating histogram with 20 bins for the summary statistics\n", - "15:16 madminer.limits INFO Building histogram with [25, 25] bins per parameter and 20 bins per observable\n", - "15:16 madminer.limits INFO Calculating kinematic log likelihood with histograms\n", - "15:16 madminer.limits INFO Calculating p-values\n" + "07:33 madminer.limits INFO Generating Asimov data\n", + "07:33 madminer.limits INFO Expected events: 2.107031712222278\n", + "07:33 madminer.limits INFO Setting up standard summary statistics\n", + "07:33 madminer.limits INFO Creating histogram with 20 bins for the summary statistics\n", + "07:33 madminer.limits INFO Building histogram with [25, 25] bins per parameter and 20 bins per observable\n", + "07:34 madminer.limits INFO Calculating kinematic log likelihood with histograms\n", + "07:34 madminer.limits INFO Calculating p-values\n" ] } ], @@ -271,9 +271,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "07:34 madminer.limits INFO Generating Asimov data\n", + "07:34 madminer.limits INFO Expected events: 2.107031712222278\n", + "07:34 madminer.limits INFO Loading kinematic likelihood ratio estimator\n", + "07:34 madminer.limits INFO Calculating kinematic log likelihood ratio with estimator\n", + "07:34 madminer.limits INFO Calculating p-values\n" + ] + } + ], "source": [ "theta_grid, p_values_expected_alices, best_fit_expected_alices = limits.expected_limits(\n", " mode=\"ml\",\n", @@ -305,9 +317,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "07:34 madminer.limits INFO Generating Asimov data\n", + "07:34 madminer.limits INFO Expected events: 2.107031712222278\n", + "07:34 madminer.limits INFO Loading score estimator and setting it up as summary statistics\n", + "07:34 madminer.limits INFO Creating histogram with 20 bins for the summary statistics\n", + "07:34 madminer.limits INFO Building histogram with [25, 25] bins per parameter and 20 bins per observable\n", + "07:34 madminer.limits INFO Calculating kinematic log likelihood with histograms\n", + "07:34 madminer.limits INFO Calculating p-values\n" + ] + } + ], "source": [ "theta_grid, p_values_expected_sally, best_fit_expected_sally = limits.expected_limits(\n", " mode=\"histo\",\n", @@ -332,9 +358,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "07:34 madminer.limits INFO Generating Asimov data\n", + "07:34 madminer.limits INFO Expected events: 2.107031712222278\n", + "07:34 madminer.limits INFO Loading kinematic likelihood ratio estimator\n", + "07:34 madminer.limits INFO Calculating kinematic log likelihood ratio with estimator\n", + "07:35 madminer.limits INFO Calculating p-values\n" + ] + } + ], "source": [ "theta_grid, p_values_expected_scandal, best_fit_expected_scandal = limits.expected_limits(\n", " mode=\"ml\",\n", @@ -366,9 +404,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "07:35 madminer.analysis INFO Loading data from data/lhe_data_shuffled.h5\n", + "07:35 madminer.analysis INFO Found 2 parameters\n", + "07:35 madminer.analysis INFO Did not find nuisance parameters\n", + "07:35 madminer.analysis INFO Found 6 benchmarks, of which 6 physical\n", + "07:35 madminer.analysis INFO Found 3 observables\n", + "07:35 madminer.analysis INFO Found 15117 events\n", + "07:35 madminer.analysis INFO 10004 generated from sm\n", + "07:35 madminer.analysis INFO 1080 generated from w\n", + "07:35 madminer.analysis INFO 1207 generated from neg_w\n", + "07:35 madminer.analysis INFO 1399 generated from ww\n", + "07:35 madminer.analysis INFO 1427 generated from neg_ww\n", + "07:35 madminer.analysis INFO Found morphing setup with 6 components\n", + "07:35 madminer.analysis INFO Did not find nuisance morphing setup\n", + "07:35 madminer.sampling INFO Extracting evaluation sample. Sampling according to [10. 10.]\n", + "07:35 madminer.sampling INFO Starting sampling serially\n", + "07:35 madminer.sampling INFO Sampling from parameter point 1 / 1\n", + "07:35 madminer.sampling WARNING Large statistical uncertainty on the total cross section when sampling from theta = [10. 10.]: (0.004146 +/- 0.000431) pb (10.386887565947704 %). Skipping these warnings in the future...\n", + "07:35 madminer.sampling INFO Effective number of samples: mean 38.713146554037266, with individual thetas ranging from 38.71314655403725 to 38.71314655403725\n" + ] + } + ], "source": [ "sampler = SampleAugmenter('data/lhe_data_shuffled.h5')\n", "x_observed, _, _ = sampler.sample_test(\n", @@ -381,9 +444,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "07:35 madminer.limits INFO Loading kinematic likelihood ratio estimator\n", + "07:35 madminer.limits INFO Calculating kinematic log likelihood ratio with estimator\n", + "07:35 madminer.limits INFO Calculating p-values\n" + ] + } + ], "source": [ "_, p_values_observed, best_fit_observed = limits.observed_limits(\n", " x_observed=x_observed,\n", @@ -415,12 +488,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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UKT7//HM2b95csWEyJiaGSZMm1dovOzub1mVioXq9nq5du3ptp0KhaDrqucak0sUDFeE8RcimV9EVZyANFkTZ2oyuNJ/Q/z2LcJbiDo6lMHEh4TgId+dSoIvC2z+jb7/9lgsuuIBOnToRERHBrl27iIiIqLXP4cOHSUhIOKO0EMC1115bMR8+efJkpk2bxtSpUxkxYgRDhw5l9OjRXHPNNZp39ysUimZDQKWLqzWmSkhLJDnXf4utwzhAINwe0VbPs8CWNI7sKd9RGtSaIp0ViywlxF3g9fiLFy+uKEVxxRVXsHjx4gaxe8GCBRWlNKZN86jdP/TQQ3z77beMGjWKxYsXc+ONNzbItRQKhW9p6etLoCKmakhzGAUXv0v03BHgLKl03Er+xe9UqEOfEiF/ppFLI6UiqNZxc3Jy+Pnnn0lOTkYIgdvtRgjBrbfeWmu/pKQkUlNTKSoqIrRSGW5vSEpKIikpiSlTpnDOOeeQm5tLZKTaKKxQKPwbFTHVgK7gGLpTWUi9BakzIvUWdKcy0Rceq9KuSGfFjokwdx4Gahcc/eabb7j22mvZvn0727Zt45dffqFdu3akp6fX2i84OJgpU6bwxBNPYLd7IriMjAwWLlxYa79Vq1ZVfJs6dOgQOp2O8HC1QVih8HdUVp6KmGrEcuA7hHRxqscUis97mJCNrxK853PMB77nVP+pFe0kkK+PqijNnquPwUXN+yoWL17M9OnTqxybMGECb775JgcOHGDAgAEVx5955pkq7R599FFefvllRo0ahdlsJjg4uEriROU1pp49e/LWW2/x1Vdf8fTTT2OxWDAYDLz99tua96ooFApFU6AcUw04WvUkb8K/sSeOAKBo1NPYO4ylpi8mbnTkVUojz9XH4K4hU6+m9aQ777yTO++8E6ieiTNx4sSKn00mE08++SRPPvlkxbHy9uUl1U/nvffe+9NGH26wVSgUioZGOaYacCSeX+2YPXHEGUNmJwbyddFEuLOwurLJ07fyOlNPoVAoylGSRB7UGlMDYRcmCnSRmLAT7s5tanMUCkXLopsQYo4QYmLdTf2fgIyYhBDVdLGEED7VUwOwiWCK8KiRu6XBp2rkWr4l+YuOXF1tT9dJ01KVVut71KKD5i9aeVp15LS011JlFrTpzmkZ25fvUevYTaWVV88IKKD2MQWkY2pKTunC0EsXwe5CXOgo0WlL8VYoFIqWjnJMPqBIH4HO5SLUnY8LPXZd7XucFAqFQvEnfrPGJIT4txDipBBid6VjTwshUoUQO8selzamTVJKNp3cpH0xEijQR+HESLg7F4O0+8ZAhUIRcKh9TH7kmIBPgItrOP66lLJf2WNFYxp0sPAgM7fN5GDhQc19JYI8fTRudFhd2eikZ73k9ddfZ+TIkYwePZoxY8awfft2ALKysmjbti1z586tMs6gQYPIzs6ucmz+/PnMnDmzyrG77rqLzz//vOL11q1bGT9+vKZ1GoVCofAH/MYxSSnXATlNbUdlVqetBmBN+pp69ZdCT74+GgFEuLLZtnULK1euZOXKlaxZs4YFCxaQkJAAwLJlyxg4cGC99fOeeeYZ3n77bXJycnC5XDz++OO8+OKLmhd8FQpF06IipuaxxnSfEOJmYBvwsJSyxlxsIcQ0YBpQrTaRt9hddtacWIPL7QLgu9TvPM/Hv6NNcBuklBh0BkbFjcKk9y7TyCWM5OujiHBlUXjiAFFRURXlKyrbuWTJEmbNmsVf/vIX0tPTiY+P12R7XFwcd955Jy+88ALdu3enX79+DBo0SNMYCoVC4Q/4u2P6F/AcnmWb54DXgNtraiilnAPMAUhKSqrXV4gSVwkfJH9Ati0bs86MW3oUEwrsBby15y1sbhvR5miGxAzx2jEBOISZQl0El40cxPP/eI+hQ4cycuRILr/8coYNG0ZqaionT55kwIABTJo0iaVLl3L33Xdrtv+2225jwoQJrF+/nm+//VZzf4VC0fQEYgSkFb+ZyqsJKWWGlNIlpXQDHwCDfXk9q8nKxyM/Znjr4QghcEiPMKtDOhBCMDx2OB+P+Jhwk/b9SaW6EHShMez4bh5v/v15oqOjmTZtGvPnz2fJkiUVhf/OphyGXq/nhhtuYNy4cUpFXKFoWagNto2FECJeSlkuv30lsLu29g1BqDGUZwc8y3Wrr6PUVVpxPMwYxjMDnjmrzapFOisGvYPLzuvO8OHD6dGjB19++SUnTpwgMzOzQjE8IyODgwcP0rFjR83XEEI02cZAhUJxdpyFJJHaYOsLhBD/AUYDrYQQx4FZwGghRD88U3mHgbsaw5b0knRybDmYdWZc0oVe6Mm2ZZNekk6b4Db1HjflwAH0QjKwfThWVw6/796Fy+Xi1KlT7Ny5s6LdK6+8wpIlS3jooYca4u0oFApFs8JvHJOUckoNhz9qdEOAtelrcUs3F7e7mKndp/JB8gcsPbqUdSfWcX3H6+s9bnFxMTNnzqQgPx+zAToltSOpfSI9e/as0m7ChAncddddFY5pzJgxFVHQpEmT6NGjB//973/5/vvvK/osW7aMNm3q7zQVCoV/oERc/cgxNSRCiBrTpL2dhusS3oWXB7/M4BjPktaDfR5kWNwwhNQ2TXb6H0y/fv1YscKzFcsg7VidmbgxkGeoWiqjV69erF+/HqBin9PpXH99zQ7ypptuqnbMl2UvGkorryZ9Qy17sHz5HrX8zrXWvPKlnl159qc3aNG+02qLlveo1Q4tY/tSx1DRsASkYzpbzo09t9qxcifVUDiFiQJ9NFZXFuGubPL00VBDHSeFQtGyCMQISCtqlbwJcegsFOojMUkb4a5cUFWcFIoWj9pgqxxTk2PThVCkC8ciSwhxFTS1OQqFQtHkqKk8P6BEF4ZOugiRRbjdelUqQ6FowajkB+WY/AMhKNJbK0pluNFjU6UyFIoWR6BOzWlFTeXVgpQS28aNjfSHIspKZZgIc+eiL1OdUCgUipaGcky14Eo5QMEjM3AdONBgY77++uuMGDGCUaNGccEFF7B9+3YcDgfPPfcc5503lL5jrmbIZTeyceVSRJlW32+//Ubr1q1ZvXp1lbHi4uKYNWtWxet3332Xv//97wD8/e9/p1+/fowdO5bhw4dzxx13sG/fvir9s7OzSUxMZN68eVWODxkyhJwcvxJ6VygUtRNQkkTKMdVC6Y8/ep5/+qlBxtu6dSs//PADq1atYu3atXz11VckJCTw0ksvcfLkSdauXcvadev4bN5ciosKCXd7MvUWL17MkCFDqmnomc1mVqxYUa1eUznTpk3jxx9/5Oeff2bSpElce+21VdouW7aMAQMGsGTJkgZ5fwqF4uypZ1beXinlNCnlsqa2vyFQjqkS0maj9LvvKPlmOSXfLKf0W89mWNvyFX8e++47pM1Wr/EzMjKIjo6uUvYiPDyczz77jBdeeKHieGRsWy694lrMspRgVwHLli3jrbfeYs2aNZSW/qnfp9fruemmm5gzZ06d17788ssZNWpUFedWXmojPT2d9PT0WnorFIrGQqWLK8dUBVlaSvF771P00ksUvf46sqAQAHdBAUWvv07RSy9R/N779XZMo0ePJjU1lfPOO48ZM2awYcMGDh06REJCAmFhYVXaluhCKRHB7Nz8P5IS25KUlMSwYcP4sSyKK+e2225j4cKFFBTUnWrep08fUlJSAEhNTSUzM5P+/fszceJEvv7663q9J4VC0bAox6QcUxV0ViuRn83DNGIECAGOsgQEhwOEwDRiBJGff4YuXHvZC4DQ0FBWrVrFa6+9RnR0NFOnTmXDhg1nbF+oj+CzJSu5YdIYjNJWY0mMsLAwrr32Wj788MM6r1/5D3jp0qVMnOiZjr788svVdJ5CofAbAjJdXAhRTefqTDptpx/Xh4VhffEFsq+8CnelaTNduOf42ZS9AI+21/nnn8/5559Pz549mTt3LqmpqRQXFxMaWnX/ksvlZtGKH1n+w2peeOsjXOjIycmluLiYkJCQCvvvuusuxo8fz/XXX48QotqjXCNs9+7d9OvXD51Ox9KlS8nMzGTRokWAZ5rx8OHDFaU2Grt8hhACi8Vy2vt3ed1fq1aelt+jL/XsTn/PDdVWqy2+HFuL/p1WfTot7ZuLVl4gRkBaURFTDbjT0nBnZ4PZDAYDmM24s7Jxp6Wd1bgpKSkcqJTht3v3bjp16sQNN9zAzJkzsdvtgMdJLFiwgLVr19KrVy927tzJgc3fcXjzciZOuKxCCLacyMhIJk2axBdffHHGa3/zzTesXbuWK664gpSUlIpSG9u2bWPbtm1Mnz5dRU0KRRNTn2m8QHRkyjHVQOnq1eB2Y7nsMlqtWIHlskvB7aZ09ZqzGre4uJjp06czfPhwRo0axb59+5gxYwaPPfYY0dHRnH/++YwcOZJbbrmF6OhoFi9ezKWXXopb6Mk3tEIA111yPosWLaw29t13310txfv9999nzJgxDB06lIULF/LVV1/RqlUrFi9ezCWXXFKl7YQJE6pME44dO5YBAwYwYMCAKinpCoVC4WtEIHrbjh07ytmzZ1c51rVrVzp06OBVf9uWLSAl5iFD/jy2eTMIgXmw9yrjWj5bb9oapQ2rMwunMJKnb4X0Uo3clxInWqbQamt79OhRli5dWuVYcXGx12NXzlb0Bi3ThFqm8rROiZVPyXpDcHCwprH9ZZqwpUzlTZkyZbuUcpCmTqfRt29fWbnOmjd8++233HHHHU+pCrYBTk3Op7KTaiocwkyBPopwVzbhrhzy9VGoUhkKRWARiMGCVtRUXjPDrguiSB+BWZYS5spranMUCkUDo9aYVMTULCnVhXrUyN2FON0mSnTeTwcpFAqFv6McUzOlWBeOQdoJdefhEEacQluaskKh8E9U2Qs1ldesKdBH4UaP1ZWDDm37eBQKhf9xFtN4SsS1pSClJG1vgd9+I5HoyNdHo8NNuCsHVZpdoWj+KBFX5ZhqJe9EKevmHiI/Q1sqcm0sX76cmJgY9u/fX3Hs6NGjjBw5slrb6dOns2yZ5++svDTGkCFDGDlyJBdddBE//vgjTmEkacgEBo65nHFjRjNmzBhmzpwJwLZt27j44osZO3YsI0aMqCiJ4Q3lckUNycaNG7n11lsbfFyFQhFYqDWmWji6K6/iOSKuYSrKLlq0qKKExYwZM7zu99JLL5GRkcG6deswm81kZGSwceNGAKTQsXzBPBKjzOTroiqq395///188MEH9OzZE5fLVSHg6g3lDlGhUCgam4B0TEKIGjf11aWP5nK4Obo7D7fLMyV2aIdHSeHgLzmERntKUuj0gsTeEeiN2oPNoqIitmzZwpIlS7jxxht59NFHq9h1+gbAcq270tJSPvvsM3755ReCgjxOJy4ujiuvvLKibYkxAodwEe7OpUBnwKEzk5WVRVxcHDqdDp1OR48eParZlJyczAMPPIDdbsftdvPxxx+TlJREx44dOXjwIG63m8cee4yNGzeSmJiI2+1mypQpTJw4kUGDBjF58mR++OEHnE4nc+bMoUuXLuzYsYOnnnqK0tJSLBYLr7/+Op07dz7j+yx/r1o2m54tTqfT67ZaNthq3QSrpX35794X7bVq/GnZNKvl8/PlJlitY5+tLmZ9UckPAeqYakIAZlcxdn3wGRUTnA43v36fTkmBA71RIN2eX7j9lJPty47jckiCwo206RZeL8e0YsUKxo4dS+fOnYmMjOTXX3/lnHPOqbPfoUOHaNu2bbXSGJW58sqr0Ot06KSTW66dwO33PsTdd9/N0KFDGTZsGGPGjOG6666rtsP/008/ZerUqVxzzTXY7fZqigjLly/n2LFjrFmzhqysLEaMGMGUKVMqzkdFRbFy5Uo++eQT3nvvPV577TU6d+7M4sWLMRgMrFu3jhdffJGPPvpI46elULRMAtHRaKXFOCaddBDiyCLIocNmCMOmD8ctqjoXc7CBSx/szqavjnJif2FF5OR2gV4nSOgZztBrEjFa6qc6vGjRIu6++24ArrzyShYtWuSVY/KGxYsXEx0djU46sTpOgiOTGQ8/yNVXX83q1atZtGgRixcvribUOmjQIN544w3S0tKYMGECHTt2rCIdtGXLFiZOnIhOpyM2Npbhw4dX6X/ppZcC0Ld2rk/WAAAgAElEQVRv3wpx2YKCAu6//34OHTqEEAJHefkQhUKh8AK/Sn4QQvxbCHFSCLG70rEoIcRKIcT+sufI+oztEkYKTa1x6swEOfOx2o4T7MhGL6veNE0WPSNuSMIUVNX5mII8x+vrlHJycli/fj0PPvgg/fv355///CdLlizx6ttRhw4dOH78OIWFhXW2dQsDBcYYBGB1ZNIpKbGimOCePXuqCb1effXVzJ07l6CgICZPnsz//ve/Kufrsq98Ckin01VMj73yyisMHz6cNWvWMHfuXGz1LKyoULRElPKDnzkm4BPg4tOO/Q34UUrZBfix7HW9cOgsFJliyTe3wa4PwewqxmpLI9R+EoP7z8y74lw7pYWe6TydXqA3CkoLHRTn2ut7ab7++msmT57Mzp072bFjB7t27SIxMZFNmzbV2Tc4OLhaaYwTJ06wYMGCGtu7hJECQwwrVq0hzHESId0cPHgQvV6P1Wqt0vbw4cMkJSUxdepULr74Yn7//fcq5wcPHszy5ctxu91kZmbWWtiwnIKCAuLi4gD48ssv62yvUCg8qLIXHvxqKk9KuU4IkXTa4cuB0WU/fwqsAR49m+u4hJFiYzQlhgjMrkLMzkLC7Rk4hYkSQwRHfytASug4MJpzLopn53dppGzO5ujuPHqObF2vay5atIgHHnigyrGJEyeycOFC7r//flJSUujTp0/FudPV0WfOnMkLL7zA8OHDMZvNBAcHVyRPgGdqsHxxt1evXrzzzjt8smglDz/9KkFBQQijhXfffbfaYvHSpUv56quvMBgMxMbG8vDDD1c5P2HCBNavX8+oUaPo1KkT/fv3J7yOCr733nsvDzzwAHPmzKk29adQKGonUByNEGIQMAJoA5QAu4FVUsqcWjvih2UvyhzTN1LK3mWv86SUEZXO50opq03nCSGmAdMAWrVqNfDdd9+tcr5Dhw4V1Vmr9UVichVjceajl04OHZDYDKHEdmtV0SZ9XwEIiO/ifVl1X3623o5tdhUT5sqhRBdCkd67WdDTy1OUV8zNycnhkksuYdmyZcTGxmqyo6ZxK3PkyBFWrlxZ7breoqUt+C4rT2tmoZb2KiuvOv6UlTd58uSzLnvRu3dveXr5l7pYuXIl99xzj9+UvRBC3ArcDxwCtgMnAQvQFRiOx0E9KaU8eqYx/CpiOhuklHOAOQCdOnXS5BEkAps+FFvZ9F5i5zz0shinzU6pwYpdH0x8V+8dkj9h04egl3aC3UU4hYnSegi+3njjjRQUFGC32/nrX/9a4ZQUCkXDEwDp4iHAcCllSU0nhRD9gC5As3ZMGUKIeClluhAiHo/39RFVHZTZWUCoIwuXQ4/NEI5NH4oU/rYsVzfFeisG6SDUlYtTGHAKs6b+lSvbKhQK39LcHZOU8p06zu+sa4zm4Ji+Bm4BXip71hbnVkJK6WV4Xu6gQjG6S7A4Cwh25mJx5nmOG8Jxi+bw0ZUjKNBHE+nMINyZQ54hFreoX3ahL/C3fyyFQnH2CCE6ANOBJCr5GinlpLr6+tXdVQjxHzyJDq2EEMeBWXgc0n+FEHfgCf2urc/YNpuNwsJCwsLCNM0dO3RBOExB6N12LM4CLK5CLK5C7LpgSg3huHTaoo+mQgod+YZWRDhPEu7KJs8Qgz9Uv5VSUlxcTEFBQVObolD4BQH0RW0J8BGwDLSVP/ArxySlnHKGU2PPduwTJ04AYDafnSMR0o3RXYrBnYlA4hJGHLogXDrvF4NrorESJQzSQZC7CIfIpFRXsxyOFlsaom1BQQE7duzwehyFIlCpT/q3HzuyUinlW/Xp6FeOqaEQQtSYCXTyZPXlqfpmlRlcp+iQ+z+6ZK0k2JlLrqU9u1tfRUZoLyiLyHx1gz/dlro4PQPtnOxl9M39js0x17HfWl3VXEvGmpbNs6WlZ1ZpDwoKqpZFpiWbS0t2FlCxH8wbtGSs+VNWnha7tWTZge8y7bRmwmlpr7TyGp03hRCzgB+AihuFlPKXujoGpGNqDJz6YPa3uoiU6LEk5m2m58mvGXHkdTKDu7E77iqygzs3tYlnZFfUZUTajnNu5gJK9FaOhzaMLJJCoTh76umYugkh5gDL/KgmUx/gJmAMf07lybLXtaIc01kihYEjkcM5Zh1Mh9x19Mj8hgsOvkha2Dnsjr2CfEu7pjaxGlLo+DnuVsak/pMRJz5iXfxUUkP61N1RoVD4K3v9ZR9TJa4EOkopNUvmNL/cZz/FrTNyIHos33Z9id9aX0Wr4v2MP/Asg49/QIjdhxnu9cShC+KnNveSZ05gZPqHxBfvaWqTFAoF2mWJ/JhfgYg6W9WAckwNjEtnZm/MZXzb7SWSW11MQsEOLt7/JAPS5mFx5Da1eVVw6IP5sc195JvjGX1iDnGn/mhqkxSKFk8AaeW1BpKFEN8LIb4uf3jTUU3l+QiHPoTdra8mJWosPbKW0zF3HUl5G9gXPZ4/Wl2KS2+pe5BGwK4PYVWb+xif+haj09/npzb3kmbs0NRmKRSK5s+s+nZUjsnHlBoj2BF/A3ujL6T3ySX0yFpBUt7PJLe6hIORo3CfZZp5Q2DXh7IqYToXHn+d0env8W3c/eSaEpraLIWixRFI6eJSyrX17aum8hqJU6YYtrSdyo8dHqPQFEf/E/O5ZP9MOuWsRudu+kJ6Nn0YP7a5D4ewMD7jPUId2U1tkkLRIgmQaTyEEOcJIbYKIYqEEHYhhEsI4dVOeuWYGpmc4E6s7fAIa5L+j1OmaAakf87FKY/TMXcdQnq/f8gXnDJG8VObe9FLB+Mz/oXZVdSk9igULZEAWmP6JzAF2A8EAXeWHasT5ZiaiMyQ7qxOepS17f9KqSGCQenzuCTlSZLyfkZIV5PZlW9uw4+x0whx5TE+4z0MblV9VqFoTALIMSGlTAH0UkqXlPJj/qytVyvKMTUlQnAytBc/dXiMde3ux64PYXDaJ1x84CkS8zYhpCZ5qQbjpKUja2JuJcqeygUn/42uiSM5haIlEShOCTglhDABO4UQrwgh/oqnJEadKMfkDwjBibA+rOrwOOvb3YtTmDgv7SMuOjCLdvlboAkc1PHg3myIvo6E0mSGZ33RJDYoFC2N+kRLfuycbsLjY+4DioF2wNXedAzIrLwzaeXVhBbNOa16bFoo/+PKMg9mbdQg4vN/ofuJxQxN/YAueWvZ2e42iixtzmpsre1Phl/IbqOD3ie+wh0UzW/x11foAJajRVfP4dCW5BEaGup1W60adVo0/rRozgUH1yyMeyZ8WWVWizac1r9tLTpyvtScayo9O18SCFl5Qgg98LyU8kagFHhGS/+AdEzNHqEjPWIQ6dYBJOasp3fafMbsfYJ9sRPY13oCbp22G9TZsC/mUizOfLpk/YBbGNkTdxU0w2KJCkVzwR8djVaklC4hRIwQwlQfSSLlmPwZoeNo9EhOhPejT9oXdM9YSkLeZn5teytZYT0ayQbBrvjr0bvtdMtcTqg9g23t7mw2dagUCkWTcRj4uUztobj8oJTyH3V1VF99mwF2Yzjb29/Nho7/h066OP/AS/Q/+iFGZyOlcwsdOxJuYVf89bTJ387IAy8SZM9pnGsrFC2MAFpjSgO+weNnwio96kRFTM2Ik+F9+Kn783Q7sZTOJ78lrmAnv7WZwvHIYdXWfhocIUiJuYgicxznHn2P0SnPsSlpOpmmRN9eV6FoYQTCGhOAlFLTulJlVMTUzHDpzPzeZjJruj1DsSmWQUfnMPTga1gaKYI5EX4Oazs/jlsYGHngJRIKtjfKdRWKlkAARUsIIZZVFm8te8wTQjwghKhVLFQ5pmZKQVAi67o8wa8JNxJdvJcxe5+gTd7Wxrm2pS2ruzxJXlAiw46/T6ec1Y1yXYWiJRBAzukgUAR8UPYoADKArmWvz4iaymvOCB2HYsZzMqwPg46+x+DD/+Ro5HB2tb0Rp15b2rJW7IZw1nd8hEGH/8XAE18Q5Mxjd8wVvp9SVCgUzYX+UsqRlV4vE0Ksk1KOFELUWgBORUwBQLEljnVdniC59eW0y93AmOQniS5K9vl1XTozG9rdw4GIEfTMWsGg9E+bVE5JofAlUoKj2PdfvAIoYooRQlQsQpf93KrsZa0p5CpiChCkMJAcfxUZ4X0ZeGQO56e8RErsJfwRdxUu4btfsxR6tsffRKnBSq+sb7A4C9jY9i6VTq4IGFylguLjRoqPG5FOiL+gGOG7vfYBk/wAPAysF0IcAATQAfiLECIE+LS2jsoxBRi5IZ1Z3e1ZeqfNp8vJFcQW7GJb4l0UBLXz3UWFYE/s5ZQarPQ/8QWjjvyD9e3uw27wKjNUofA7pITSLD3Fx4yUnjSAFJijnYS0c3husT69tt86Gk1IKVcIIboA3fF8aslSytKy02/U1lc5pgDEpbfwa7tbORHej/7H/s3o/c/wR9xV7I+52KeqDQeiRlNqCOe81A8Yd+gFdsRdT3poX7XupGg2VERHx4y4SnXoTG7COtgJaevAEBIYDqOxEEIYgbuA8nWmNUKI96WUdWqTBaxj8lYnTIueWHPT/MqLHcLPkT3offhDeqf/lzZFv/Fb53sptcQ2mC2nf34lUeP4JaotPfe/y4hj/yQ74hz2dbiV4pBETZ+1VrTq8JWWltbdqAxvdRcBjEZtFYm1fNa+nOLRohlZH1uaetzarwklJ3UUHjFQkqEDBJZWLqJ62QiOc5d9l/Ph3F0VWwKngi3wL8AIvFv2+qayY3fW1TFgHZPCg90Yzs6uD9Mmcy09Dn/M8F2P8EeH20lrNdJnkUx+eHc29f8HbdO/o+PR/zJkx8Okxl/EoaQpOI1qek/hP9hyBSe3m3CV6NCZJNbOTkITXRibMDqqp2PqJoSYAyyTUi7zhV314Fwp5TmVXv8khPjVm47KMbUEhCAtdjS54T3pnfIOfVLeISZnG793nIrDGO6TS0qdgWMJEzgRO5KOR+bTNv174jL/x8GkKaTFX4T05eqxQuEFtjzBiU1m9CZJzMDK0VHNRBTto3XeDva2vc6ndtUzAtorpXyuoW05S1xCiE5SygMAQoiOgFdpu83GMQkhDgOFeN6YU0o5qGktan6UWGLZ2msWSWnL6HJsPhG/7uOX7o9SGNrRZ9d0GMPZ23kax+Mvotuhj+mW8gEJad+xv9Pt5Eb289l1FYrasOULMjaZ0RslcUPtGILP7AyCbJl0Oz6fhJyNlBojONT6Euw++kIHAZWV9wiwWghxEE/yQ3vgdm86NhvHVMYFUsqspjaiWSN0HE64nOyIc+if/AqD9zzNjm7/R05EX59etjikPTv7PEOr7C10Ofgx/X97hszoc0npeBslQfE+vbZCURl7gSBjoxlhkLQedmanpHeV0Dn9azqcWAHA/jZXciBuIi59rWo6Z00AOab1QBegG2VZed52VBtsWyiFIUls7jObEkssA5NfJC5rg+8vKgRZrYawadDbpHS4ici83xiy7X7aH13oWYFWKHyMywEnNpoReogbasd4BqdkduQx+reH6Zy+lBOR57Kmzz/Yl3BtozilANlcC7BRSmmTUu6SUv4qpbQBG73p2JwckwR+EEJsF0JMO/2kEGKaEGKbEGJbfn5+E5jX/LCZotjS6xnyQrvSd/+btM1Y1SjXlTojR9tdxaZB75DZagidDn9G/11PEVSS3ijXV7RchACdUeJ2grsW7QGXMGE3eKbrLPYcTM7Gu6c0d+ckhIgTQgwEgoQQ/YUQA8oeowGvtNKak2MaLqUcAFwC3CuEqKzBhJRyjpRykJRykNVqbRoLmyFOQwjbe8wkK6IfvQ7OISl1aaNd226OYk/3h0nucg9hRQcZvP1B2h3/GpSskcJH6AwQN9SG3iQ5scmMLa/mzFSnIZj/9XqBXe3vILQ0jfN/f5K+h97DbM9tZIubJRcBrwJtgdcqPR4CZnozQLNxTFLKtLLnk8BiYHDTWhQ4uPVmdnR7hPToYXQ7+jmdj3zeeFNrQpAWfyGbB71JbkRfuhz8mIE7ZxJcfKxxrq9ocRiCPNN4eqMkY5MZW/4Ztk0IHcdix7Kmzz84GDeBNtkbGP3bw5gcBT61r7lHTFLKT6WUFwC3SinHSCkvKHtMklIu8maMZuGYhBAhQoiw8p+BC4HdTWtVYCF1BnZ1uZ9jrcfRMXUJPQ5+AFLbpsuzwWZuxa5eM9nT/a8ElaQz+JeHSDqyAOF2NpoNipaDIdiT+CAMkoyNZnL3Gs4o0Oo0BJPcbgrrev+d5LbX+zQjDzybnbU8/M0xVaKtECJcePhQCPGLEOJCbzo2l6y81sDisl3yBuALKeV3TWtSACJ0/N5hKk5DCB1Sl2JwlbC7871IXSP9mQhBRuxIciL60vXAR3Q88gUxWRv4o+t9FIV1ahwbFC0GY7AnVTx7l5H8fQby9xkxR7oIbesiuI0Lvalq+1OW1hyxeHVfrTcBpvxwu5TyTSHERUAscBvwMfBDXR2bhWOSUh4EzqmzoeLsEYL97W/EoQ+h69EvMLhK+LXrX3HrG08t3GGKYE+Ph8mIOZ9uKe8zaMcMjra7gsPtr8OtM9U9gELhJcYQj3NylkBxqoGiY3qyfzORvUcS3NpNaFsnQbG1b7xVnJHyEPRS4GMp5a/CSw2uZuGY6oMvtPJ8ZQNo18rT671XTtA6tsFg4GTXG9AFR9Ip+V3O3fsyv/d7CpehekKNFh05s1mbczO1uZIj3ccSu+Mtkg4tIj5vGycGP05JTPWNuVrH1oIWHTmbzaZp7JKSEq/b2u21lrCphtPp/TSolrag7Vu6y+V9MktT6QGaQsDU1U1EFzf2fEHhMR1Fx/WcSjejM0lCE9yEtnNhjpA+1yT24whIK9uFED/gKXfxWNlyjFf/TOp7gOKMnGh7KXt7/x9h+b/Te/tjGOyNn4bvNoVzYsgTHB39FsLtIPHHu4nb/Bzm3H2Nbosi8BECzBGSVn1ctL/ITtwQB0ExbgqP6EhbZ+L4T0Z8vezZ3JMfKnEH8Dc8mnmnABOe6bw6CdiISdEwZMWNxmUIpvuuF+n9y+PsHvhikwixnoobwqGLvyBm9xwiUhYRcegbTkX3Ia/zVRQmjgWqR0xSSjKLPXcRg05g1AsMuvKHb9XiFc0foYPgODfBcW7cDihK02EvEPh6yVWr0rvW9r5GCNFdSpkMlE9tdNQ8a9PgVikCjtxWg/njnCfp+euz9PrlCXYPeB6XMbTR7ZDGYE72f5CsXrdjPbSciJRFtNn8DM4db1DU9UoKu15Dvjme7ceL2XysiC1Hi8g6deavtwZddYcVbNQRZtETbtYTai57NukIM+sJM3uew816wsx6IoP0BJuUGG1LQGeE8Pa+dwB+HgF5y8PAVDx7l05HAmPqGkA5JoVX5EUP4I++j9Pj19n02jmLPf2fq3HNqTFwm8LJ7TaF3K7XE5SxFfPvC4jdPZfw3Z9ywN2Xtc7xbDUMYEDbcPq1CcaoFzhdEodb4nRJnG7Pw1H2s8ON57zLzSmHm0Kbi5NFTg5k2yiwuShx1HxD0gnoERvEuW1DGNwuhC6tfCtXo2gZNPesPCnl1LLnC+o7hnJMCq/JbXUuyX3+RvffXqTnzqfZ3X82aEh+aCiklOzNsrH2UBHrj7Qms/huWjOZv4Su40pWMVr3Ko6QeIpiLqcocSLOsIQq/V3Shc1lo8RVQqmrlFJXqedndylhhjASghMINfwZEdqdLgptlR8e53U8387WY8V8sj2LT7ZnEWHRMyghiGGJwfSPD8JsUEu4Cu00d8ckhLiqtvPebLJVjkmhiZzYoezr9X902/0KXfe8xoH+j/u0XHtlpJR8nVzAoj15ZBQ5MepgYEIw/++cSIZ36EJM6Plkux+h5Nhawvcu4Jf9c/ksYxF5pmCKjRZO6XSUuO3YaxNJK8NqtJIQlEDb4La0sbQhITiBhKAEultbo69US+q2QTHkljjZdryYLceK2Xi0iFUHijAbBAPbBDEsMYRzE4IIM6spP4V3+JujqQcTT/u5cuFCCSjHpGh4suJGYrZl0WH/Rzj2fsTR7lN9fs1Sp5s3fs5kzaEi+rS2cGO/SIYlhhBStsZjNpeVNNcZOdL6HN4v2sQWSzrtdKF0spUQWpBJsNuN2RyFIaIH+siemIJbY9FbsOgtBOmDMOvN5DvyOX7qOKmnUkktSWVT9iYKKknQGISBOEscCUEJdAvvxqQ2k4gMMjC+i5XxXawUnSrlt4wSNh49xcZjp9hw9BR6AX3iLNzcL5LuMWq6T3FmpJTNPvlBSlmReSeE2FH5tbcox6SoF6mJV2IuyaDN4a+wBcWS0f5yn10ro8jBsz+d4GCOnVsHRHFdn4gaM+pc0sXXqV/zn8P/QSK5rcNtTEyYiEFnwFB4nJAjKwk5vBLz798D32OL7klx+/EUJ43HGda2Ypwh0UOqjJtvyye1JPXPR5nT2pyzmV15u5jRfQbBZettRr1gQJtgBrQJ5p4hkn1ZNjYePcVPh4qY8X06953Xigs7q/LyijMTABFTZer1ZpRjUtQPITjYbRoWexZJf7yH3RJDbuthDX6ZX9NLeGHNCZxueGZcHIPbhtTYbl/BPt7Z/w6Hig9xbtS53NX5LmItsRXnnWFtye99G/m9b8NQmErwkVWEHFlJ1C9vEvXLm9iiulOcNJ6ijpfhComrMnaYMYzuxu50D+9e5fiqE6v414F/8diux3iy15O0Mreqcl4nBN1jLHSPsXBNbysvrjvJGxuyOJRr586BUeh1Kl1dUZ3mvsbUECjHpKg/Qk/KOY/Rc8sMOv/6Er8PfoXiiO519/MCKSVf/5HPe1uySAg3MmtMHG2t1eWIip3F/OfYf/jh5A9EmaL4W8+/MTR6aK17lJxhCRT0voWC3rdgKErzOKnDK4n65W0id7xLcdKF5Pe6GXt0j1ptHBc3jlbmVryc/DIzfp3BEz2fIMGYUGPbMLOe58bG8dH2HJb8UcDhXDuPjYzFqO1jUbQAmrtjEkIs489IqaMQ4uvK56WUk+oaQzkmxVnh1ltIHvAsvTc9QLdfZrHnvDewBZ9dqXS7SzJnez4/HjrFkHbBzBjRmhBT1QQLKSUbcjbwyZFPyHfkM6HNBG5IuqFiSs1bnKFtKOh1MwW9bsZQmEp48nzC9i0k9NC3lMQNJr/XzRTHD+VMOjT9IvvxYt8XeW7Pc8z8bSZ/7fRX+lmrSyYB6HWCaedG0yHSxNubsnhwRRqPDrOSaFXuSeGhPmtM/uaY8NRiKqemvUx1EpCOSQjhtZaclh3JWncv+4tWnhY9O63tzWYzBAVxePirdF77F3r88hQpI9/BZa5erDEkpOZpuMpkFTt5+qdUkjNLufO8Ntw5tA260z6btFNpvLXnLbZmbaVLeBdePPdFBrUb5LXNcKbPry30GIKr9GnkjrlYts4h6Mf7cEd3xTn4blw9rwZDdYWJNrRhbtu5/PV/f+Xl/S9zf8/7mdBuwhmvfX10NL0SW/Ho1weYuTqHR0fGMzyp7g3LxcXFWt6iJt0+rZqRWrT1tPxta9HVA203Za3/Y011w2/uEZOUcu3ZjqE2WigaBFtYIofPewHTqRMkbX4c4dImZgrwe0YJf1lymMO5NmaNbcO0YQnVnNLSI0u54393sDt3N3/p8RfeGfoOXa1dG+pteLBYcQ+djvPe7TgnvQt6I6ZvH8Ly3rkYNrwBJTnVusQExfD+Be8zKHoQb+x5gw/3foi7lnpWfeJD+eSGHiRaTTz9YxrzdmTj9rMbjKJpaO46eUKIZUKIiUKIalMBQoiOQohnhRC31zaGckyKBqO4VV+ODpxJaPYuErc9jxa1yxXJeTy0/CgWo463J7VnRIfqmWtfHPiCt35/i37R/fh4xMdcnXQ1ep0P9wfpTcg+k7HdugrbdQtwx/bC+L+XsPxrIMaVMxF5R6o0DzGG8NyA55jQbgLzD83nxV9fxO46856p2FAT/7isHeM6hzH3l2xm/5R+RpUJRcshAERcpwIjgGQhxFYhxAohxE9CiIPA+8B2KeW/axsgIKfyFE1HftsxpJZmkfDbP9FtKuHI4Gdw17Lu43JL3tl4kq//yGNgQjBPjGlT42bUufvn8mnKp4yNH8ujfR/1rUM6HSFwJ43AnjQCkfkHhi3vod85D/3OeThH/g3nuXdDmT16nZ4Hej5AfFA8H+z7gExbJs/0fwarqfrUJoDJoGPGyDg6RVn4YGsmqd8c5elxCcSHqXUnRfNESnkCmAHMEEIkAfFACbCvTGW8TlTEpGhwsjpP5lj/GYRlbKXT+gfR2/JqbFfqdDNrZSpf/5HH5D6RvHBR22pOSUrJx/s+5tOUTxmfMJ5Hz2lkp3QaMqYHjsvepPTurbg7X4hxzXOY5l+DKDhe0UYIwXUdr+OJc55gb/5e7t90P2mn0s44phCCa/pEMvvCBE4WOblv6RF2pnn1/6sIQAIgYqpASnlYSrlRSrnTW6cEyjEpfERO0gQOnzcbS/5BOq+7D+F2VDkvpeTJH1LZfKyYB4a3ZtqQ2Br39Sw6sojPDnzGJW0vYUafGVXkgJqUsDjsV3yI/dI30GXswvzRBegOrq7SZHT8aF459xUKHAVM3zid7NLsWoc8t20Ib09KJCLIwN++O86WY9qSHRTNn/KsPC0Pf3ZM9UU5JoXPKIgbTqm1IzpXKfI0Pb1f00vYkXaKv5wXy8QeEWccY2vmVpJCk3io90Po/K2+tRC4+lyP7bafkBHtMS28GfOB76s06RPZh2f6P0O+I5/f836vc8i2VhNvTWxHm3AT72/JxOUOvJuOonYCKWKqL372n64IJMIyNhOcm0xGt5vhtEjn6z9yCTPruLR7zWsv5eTac2kd1Nr/nFIlZER7bFMW4o7vj3XlA1iSq2pUtg9tD0BGSYZX44WY9NwyIJqjeXbWHSpscHsV/k0gOSYhRJAQopvWfv77365o3khJ3B//xhYcT077S6ucyip2sP5wERd3tdZZGiLXlkukOWsg7AIAACAASURBVNKXljYMFiv2yf/BnjAU6+q/EfTbvIpTVpOVGEsMe/P3ej3ciA6hJEWY+GxnjoqaWhgB5JQmAjuB78pe9ztdBeJMKMek8Anh6esJzksmo/stnF6LenlyPlJS6xQegFu6ybPnEWlqBo4JwBRC3qXvU9phHOHrnyN4+7+g7MbRM6Ine/L2eD2UTghuLIua1qqoqcVQnzUmf1MXr8TTwGAgD0BKuRNI8qajckyKhke6iUv+GFtIW3LbXVjllMMlWZ6cx+B2IbQJr659V5lCRyEu6WoeEVM5ehP5F75FSddJhG15ndBNr4KU9IroxcnSk2SVZnk91Iiksqhph4qaWhKBEjEBTillfn06KsekaHCsaesIyk/hRI9bq0VL6w8XklPi4vKetUdL4JnGA5pPxFSOzkDBmFc41ev/EbLzA8LWPU1Pq0fc1psEiIphyqKmY/kqampJBNAa024hxP8D9EKILkKIt4EN3nQM2A223upiadHP0qon5suxtbTXOvZZ6fBJSev9X2ALS6Qo6SIMlZIeDAYDX/+RT5twE+clWavJDZ1OocNzM24V3AqjsfYNp1psBuocrzJafo8m059RoG3s84ggK8Hb/sVAZzEmnYnkgmTGJY7z2o4xXSL5fGcOn+/MYehl8ZpKZWjRndM6HaT18/YVWrX1mgPNXSuvEtOBxwEb8B/ge+A5bzqqiEnRoARn7SQoN5nsrtdXy8Q7kF3KbydOcXmvyDqdEvwZMUWZo3xi6+m4XS4+Xf487oa62Yn/3955x0dVpf///cxMeiGVBELoAaUpioAiFgTBBoIFbOjadd217a7r+v3t6rpFdy3Y1lV37RV0lSKKgCigglQFKyVAQgupkzr1/P6YCcYQyMwkM3fKeb9e88rMnTnnPnNz537uc85znkdoGnMXjSf9ltQf5zFIxfFN1Wa/ujCJcOXxueyqtrNip17XFO1EkbeEUqpBKXWPUuoEpdQI7/MmX9pGrcekMYbsH9/EGd+F6l5nHfLee5sriTcLZw1sfxgPWgzlhWiO6eO1/+Oh8jcpXDeQcSMv7LR+m074JQDDv3uOV1UDNpeNBPOhWcoPxyl90+mTlcAbX1cztleKLjAY5UR6afVmRGQZbVSwVUqNa69thz0mEbmro334sI9JIvKDiGwVkd8He3+awIivLSFtz0oq+01Dtbrw1tldLN5SzfiiLqQn+nY/VGWrwixm0uPTg2HuIXy0+UVQig83v9jpfTeNuJnB3cbgQLFj9UPtN2hBs9dUanWyfIf2mqKdcPaYvNnB/ysib/vw8d8Av/U+/h+e0PG1vuzHb49JRGa3fAkcCzzobz9+7M8MPAVMAEqBNSIyTynl+yyyJiRkb3kLZbJQ2f+CQ95buq2eJqfi/MG+D8tV2irJiM8I2uLamrpKXvzgPlzedEmr2QFi4ktVzCNv3QyA2RTHFRPuIT21g8OJIhSN/TN8NJXvf3iToZlHYT/60ON0OE7pm07vjDje2FTNKb211xTNBGuOSUSeB84FypRSQ1psnwQ8BpiB/yilHjjCvrYD1/giTEqpda02fSYiPtVqCmQoz6qUurb5hYg8HUAf/jAS2Oo9IIjIm8AUQAtTGGFyNpCxYyE1PSfiSjz0Iv7BlloG5SUxIDfJ5z6rbFVB9ZaqreW8a11ChVlIUAq392JvNQmvN3yKTYRsl2Jy7U0dFyYgMzGbbkn5bMxM4srFv8OVczTEt12KvTUmES4dlsHflh/g810NjO3dftFFTeQRiBfkx+dfBJ4EXm7ecLgbfzwi9fdW7a9WSpX5ujMRafmjMQHHA/m+tA3kVvSvrV7fE0Af/lAAlLR4Xerd9jNE5HoRWSsia2tqAgqd13QEpRC3A2cbogRQ3ehiQE6iX10O6DKA4tpiNlZs7AwLD6FX9wG8Pnkhx9mSEcDhFSaHSRDgOFsyb075kMJu/Ttlf/sb9nOgqZys3GGIciH1vqUoaub4Ao+o761ztPNJTSQTYBLXEc3XP+/j+tb9KqWWA62rXB688VdK2YE3gSlKqU1KqXNbPXwWJS/r8AzdrQO+AO4ErvGlYbvCJCK9ReSfIvI/EfkPcI6I9Gp+Xyl1aDnPzqWtMYu2JtSe9UZ+jOjS5cj51zSdjzsuhYbsoaTuW93m+5lJZiob/Yt2u7T/peQn5/PI14/gcAfnYtw9txcvXPsFqa3mj9Pc8MK1X5CfU9hp+3pt62sAzHTEo8zxOAtG+dXe7vKc9olmHUyrOYS1zdc/7+NZH9v5dOPfjIhki8i/geEicveROlZK9VFK9fX+LVJKnamUWumLUb6c4XOB7/nJ3TsGWC4iT4mI76FFgVMKtLw69AAOX9xGYxh1+aNIqv4Bc9Oh9yqZSWYqG3yvaAuQaEnktiG3saN2B7O3zW6/QYCs/34FlWYhwe3GohQJbjcVZmH99ys6bR/lTeUs3LWQSYWTKCxdg7P7CRB3+AKKbdHk9AhTgkXPL0UzIQ4Z9+nGv4VtFUqpG5VS/ZRSrYf6PB2KTDvSwxejfBEms1Lqv0qppUClUuo6oB+wA/BVlTvCGqBIRPqISDwwA/ApEaAmtNTljwYgdf+hXlNGov/CBHBS/kmMzR/Liz++yN6GvR22sS3mr30atwgnOvP48LxFnOjIwy3C/LX/7rR9vLn1TVzKxRXdz8Rc8SOOXqf43UeT0+PWaWGKbkIsTMG48T/vCI9zfenAl+CHJSJyi1LqSbxKqpRyAv8UkR8DsdoflFJOEbkFz6phM/C8Usr3bJiakNGUUYQzIYPUfaupabWOyeMxNQbU76+H/pqZH89k1qZZPDDyAb8yMfhCUe7x3CXHcflZnpUPT1z/Ma9+8CCoztlPg7OBuTvnMr5gPL0OeH4yzp7+C5PN6zEltpORXRO5GBACfvDGH9iN58b/0o50qJT6RUeN8kWY7gDuFpG1QHfvpFoDcCJw5JKcnYRSaiGwMBT70nQAMVGXN8ozz6Tc0CLMOzPJTJPTTYPDRXKcf+ls8pLyuPqoq3nqm6dYsW8Fp3Tz/6J+JC4/+3eHbjur85bnrSlbg81l45xe5xC3+jncKV1x5Rzldz8/CZP2mKKZIIaLvwGcBuSISCnwJ6XUf4N54y8i5wCDgYORT0qpP7fXrl1hUkq5gb+KyKPAeDzrljKBzQQ/Ii+s8PdO3Z/PB7PvUFKXP4qMXYtIrP6RpsyfLr6ZiR4xqqx3ktTFtzt+h+OngIcpPabwwa4PeGzTYxybcSxJlp+HndtsNr/s9CfHmj+fdToPHa78bN9npFpSOTplAJZdn2HvMw6nt09/LkLBHMqL1PPPnzyQ4ZohoTUBCtNAEXkWmK+Umn+Yz11ymO1BufH3BkkkA6cD/wEuBL70pa3P/1Vv3qN5Sqk/K6VuV0o9rZSqDshiTdRSlzcSgNR9q362PTPJK0wBzDMBmE1mbht8GweaDvDy1pfbbxAmuJWb1WWrGZk7koSK7zDZqrEXjg2oLz2UFxsEOL/0g1Lq+sOJkkGcpJSaCVQppe7DM8rmU5irPsM1nYorMYvGjIGHhI1nNHtMjYEJE8DgzMGM7z6e+bvm0+gMbL4q1PxQ8wNV9ipO7Hoi8btWoBDshWMC6svmDRdPMIeHt6IJDiEOfggmzT/SBhHpDjiAPr401MKk6XTq8keRXLEZk6Pu4LaOekzNnFN4Do2uRlbs77xQ7mDyRdkXmDBxQu4JxJeswNl1CCopsCwSzUN5eo4puokiYVogIhnAP4H1eCK53/CloRYmTadTlz8KUS5Syn5KlZWeYMIkUNUBjwlgaOZQCpIL+LD0w46aGRJWla1icOZguijBsm9jwMN48NM6Jj2UF71EkSihlLpfKVWtlHoH6AUcpZT6oy9t9Rmu6XQas4fgsiST8/2rpO79DNxOzCYhI8nC/g6m0xERJvaYyFeVX7G9dnsnWRwcSupK2Grd6hnG27kMUS4cPU8OuL96u17HFAv4m5IoXIM6ROQrEfmDiPRTStmUH2XWtTBpOh1liqNs6E3E15XQa+VvGTh/Mt3WP8TFOTtZtq2GPVZ7h/o/q8dZZMZn8qf1f6LGHp55EZtcTdy/8X5SLamM7zKY1JV/w5nZH0fe8MD6c7hZsq2OAdnxOrN4lBMtHhMwGXACs0VkjYj8RkR6+tJQC5MmKFT2v4Afz5vPrjEPUt/1eDKKF/D7st+y1HIb+xc9RFxNccB9ZyVkcd9x93Gg8QD3bbgPp7tjw4OdjVKKRzc/yjbrNu4ZfDtFi+8CVxPWiY+D2feS7i15e1MF5Q0urj4+NNV8NcYR4HDeQBF5VkTOM9r+ZpRSO5VS/1BKHY9n0e4wwKcfvq5gqwkayhRHbfex1HYfi8lRT86BL3B9PZcp1jmY35uNLeto6vqeTX2fibiSu/rV9+DMwdw59E4e+PoBnvj2Cf5wwh/CZm3N3J1zWbx7MVf2u4Iz176IuXo7Nef+F1f2gID6q2xw8vrGCkb3SGJonn8Z2jWRRSBekPfzPyil7g+KUR1ARHoDFwPTARdw6Gr2NtDCpAkJ7rgU6vpPxtnnPC58ex2n2FdyI1+SvfZhstY9SkPPcVQPvRp79iCf+5xQMIGddTt5Y/sbDMgawEX9LwriN/CNzZWbeeq7pxidO5qbSr4nvvQzrOMexBFgiDjAi2sPYHe5tbcUI4T58JzPiMhqIA6YDVykvDX1fEELkyakWMzCzLFHc/v8ZKqOupTrT2kgdetc0n+YQ8rOJTR2G0X10Ktpyh/pU39XD7ianXU7mfXVLHqm9WRUnn+lJDqT8sZy7ttwH3lJedyvckn54SnqR9yCzY9Kta0prmzi/e+rmDI4i4L0wIYBNZFFsFISGcCVSqnvA2mo55g0IefY7imM65/OG19VsIPuVB1/K7su/IDK428lrnob3T66ge7vX0bCtg/BfeR0QCYx8Ydj/kDfLn35v9X/xw7rjtB8iVY43U7uWXUP9c56Hsg6jfw1T9E08HwaRt7aoX6fWVVGUpyJK4/L6SRLNeFOtISMtxQlEVngT1vtMQWRYJ4w/vQdzDswf/LItcx9d92IbL7YUcvjK/dw/4TuiCRSPvByKvpfRHrx+2R++ypdFv0KR3ovrEN/QV2/c8Ecf9i+7z3mXm5ZdQt3fnYnT4x6ot2S7Gaz74lkffmOT333FF9VfMUful/AsZ89RFO3kZSN/iM0NR2xXVu59ZpZt7ue1SV1XD8yh5Q4aPSz0GK4nCMaDUcoPtgW2mPSGEJ2soUrjsvmy9IGVpXUH9yuzAnU9J/GjnPncOC0h3Bbksj+7F4K3j6btM0vIY76NvvLS8rj3mPv5UDjAe7/6v6QRuot2bOE93a9x4V5ZzB9zX9wpvfkwLhHAo7AA3C5Fc+sPkB+WhxTBmV0orWacCdaPKZWbPDnw1qYNIZx/qAMemXE869VB7A5Wy0SNJlp6HMm+ya/xf4zn8HRpTdZax6mYPaZdNn4b8R5qCcyOHMwtw++nY2VG3nq+6dC8oPdZt3GrG9mMSz9KH73zUcoczxlE/6FSujSoX4/2mKluMrOtSNyiNel1GOGaBMlEYkXkWHAo95Crz4RM2e82dnIgOIXibfrhOjhgsUk/OrEXPbXOXllw6Hl2AEQoangRMom/Ye9576GLf8EMjb8i27vTiVp1zJo9cOcUDCBi3tfzIKSBczdNTeo9lvtVu7deC+plhQe3ltKQmMlByY8hSvNr1GLQ2h0uHlxXTmDuiZySp/UTrJWEylEyzomby2mbcDjwJPAVhE568itPMSMMGVav6Nw7weMWf8repW+h7g7lhpH0zkM65bMpAHpvL25ih/LjzwfY88dyoEzZrFv0n9QlkS6Lr2Vrot/icW662efu3rA1YzOHc3TPzzNuvJ1h+mtY7iVmwc2PUB5Uzn/tCWTd+B7asY/gj1ncIf7fuvrSiobXdwwMjds1mZpQoe/6YharGMKt7IXDwOnK6VOU0qdiqcu06O+NIwZYSrPOo4vhj9KVfrRDNj5KiduuBOzs8FoszTA9SfkkJlk5h/L9/uUfdzWbSR7p8ymcuRvSSjbQPd3p5Ky7umDEXxmMXP3sLvpldKLP2/8MwtKFnTqnNMPNT/wuzW/Y035Gu6I78cJOz6ndsw92HqP63Df3+5vZPamKk7rm8agvKT2G2iijigazitTSm1t8Xo7UOZLw5gRJoAEWwUJ9ioAHJYUlPhX4lsTHFITzPzulHz21zm4ZX4JW9rxnAAwxVE7+Ar2TJtHQ89xpK2ZRdb8qzDV7QMg2ZLM/cfdT++03jz27WNcs/IaluxZgkv5F9nWktL6Uv7y1V+4ZdUt7KjbwW0547j8+8XUD7mChiGXB9xvM0u3WvnNB6XkJlu4cVRuh/vTRB6BiFIYC9M3IrJQRK4SkSuB+cAaEZkmItOO1DBmhCm34ktGfHMfcc5aNg24lTXD/orbnGC0WRovw7snM+ucQgS44/1SlhfX+tTOlZxL+Wn/oPr0B7Ac2EzOnMkkFC8GPJF6s0bO4i/H/YVESyIPbnqQGz+/kZX7V/r1Y660VfL4t49z7WfXsvrAai7vdzmvDbqbX2ycjSNvOLUn+pRl5bC4leKFteU88Ok+js5N5InJPclO1is5YpUoESWARGA/cCpwGnAAyALOA849UsOYOfvLM4fzXd9r2JN3Bm6Tz8EhmhDSLzuBJycXct/Svfxl2T5217qZOSIXU3vzLCI0DZyKI284GUvvJHPRLTQMmo71xLuRuCRG5Y7ihJwTWLF/BS9tfYn7Nt7HwOKBXD3gakbkjDjsPE69o57ZxbOZUzwHh9vB2T3O5oqe0+i56TWSlz6IOymL6gmPHnF9VXs0Otz8c/k+VuyoY9KAdH59Uh5xukJtTBMta8qUUr8ItG3MCJMyxVHazaeAEI2BZCZZ+MdZBTz2WRkvrC2juKqJu0/vQWJc+869K6M3Fee/Qeqax0jd+B/i9q6jZvzDOLOPwiQmTs0/lZO7nszivYt5ddur3LXmLoZlDeOaAdcwNGvowX7sLjsLShbwytZXqLHXcFr+aVzV/0r6ln1H+ruXYa7fR8PR06kddQcqMfA1RgfqHPz+/RK2Vti4cVQu0wZn6GAHTdQIU0eIGWHSRA7xZhO/GZtH/9wU/v3FPnbXbOdvZ/Wka6oPnok5nrrRv8VecBJdlt1F9jsX0tT3TBqPvhh791GYTWYmFUxiQsEEFpYs5NVtr3LrqlsZmTuSXxT9gpL6El748QX2Nu5lePZwrh94PQNVAmmf3k/irk9wZA+kesIsHPmB1VVq5vuyBu5euJMGh4v7J3RnVE8dFq7xEI1C4y9amDRhiYgw49gcemUm8OfFJdzw9nb+Mqkng/OTfWpvLxxD+UXzSF33L5J+nEvS1vdxpvei8egLaRw4DXNaHuf3Pp9JhZN4b8d7vLn9TW76/CYA+qf358EhDzIicxjJXz1PytqnUGLCeuLdNAy9HEwd+9ks21rD3z4uITPJwmMTe9InS891ajx0oOxFVKGFyQ/8PQH8GZbxtzyyP337a/eR8rd1xA5/cTqdDMsx8chZ3bj34/3cOreYX5+YzRn90g75rN3eVlXcOKzH3IoMvoHUko9J3/oeaasfJvXLWdQXnoq1aBr2bqM5O/dsTs08la11W7G77RyfeTzJ+9eTsehc4muKqS0cR/mIO3Em50F94xFtPlJePaUUr39dxSsbqhjUNZE/np5HstlNY+OR+2ymZa5BX/Anj6G/54g/52swL5zBtMOoC36Adg4UkWeB+UavZRKRO470vlLqkfb60MKkCXt6ZsQz6+zu/O3TMh7+rJyv9zVx1oA0BuYk+CSMypJIbZ+zqe1zNnE1O+iy7T3Sty8gddfHOFK6Udv/fMz9p3BMxjGYGyvJ/vxPpG1/H0dqAXvHPU5dt5M6/B1sTjePfnaAT4rrOKNfKree1JV4sxxGUDWxTICCGE6FApvvHAcCJwDzvK/PA5b70oEWJk1EkJ5o5i8T8nl+XSULf6xl8bY6CtLjGNc3hXF9U+mR6VvCVEeX3pQfdxuVw28hpeQT0re8S9ZXT5P59TM0dhtFQvlmTM5GqoZcQ9XQq1GWJPDTm22JtcnFZ7vqWfB9Ddsq7fziuCwuHqqDHDSHJ1I8u8OhlLoPQEQ+Ao5TStV6X98LzPGlj7AWJu8XuQ5P/DvAH5RSC42zSGMkFpNw/QnZXHZMJit31vPx9jpe2VjNKxurGZqXxPj+aZzSJ43UBB8WTpvjqe99JvW9z8RSW0L61vdILf4AW9Ygykf+DkeXPgHbaW1y8fmuepbvqGPj3kbcCrqlWfjj6fmc1Csl4H410Y9Syu9hfX8/H0J6Ai2HBOxAb18ahrUweXlUKfWQ0UZowoeUeBMTi9KYWJTG/joHnxTX8/H2eh79rIwnVx3gxJ4pjO+fzgk9UrCY2vdMnGmFVA7/FZXDfxWwTVabi8931vHJNisb9zbi8orRhUMyOKV3Kv2y4rWXpIk1XgG+FJF3AQVMBV72pWEkCJNGc1jyUuOYPjSDy4bn8GO5jSVbrSzbXsvy4jq6JJoZWZjCiIJkemUkUJgR16klJGxON5/vrGPxVivrdzccFKMLBmcwtk8q/bUYaQIg3IbmAkUp9VcR+QAY6930C6WUT3WZIkGYbhGRmcBa4E6lVFVbHxKR64HrAbp27RpC8zThgIgwMDeRgbmJ3DAql7Wl9Xy0xcrqXXUs3mIFwCRQkB5P78x4+mQl0DvT8yjoEt+mZ+VWiqpGF2W1dsrqHZTXOzngfZTXO9leaaPB4SY3xcIFQzI5tW8afTMsWow0HSLS55hakQxYlVIviEiuiPRRShW318hwYRKRJUB+G2/dAzwN3I/HDbwfTxr1q9vqRyn1LPAswIABA8L6P6UJLhaTMLpnKqN7puJwKXZb7eyosrOjysaOKjvbK218trMOt2r+PBRmJNA707OA90CdkwNeIXK1OpPizEJusoXcVAun9U3jtL5pHNMt6WDaJH9CtDWatogWYRKRPwEj8ETnvQDEAa8CY9pra7gwKaXG+/I5EXkOWBBkczRRRpxZDnpGP0WxgsMNu6qbxcpGcaWN7/Y3YTJBbkocQ/OTyU2xkJsaR3aSma4pFnJSLHRJNGuPSBNUwlVoAmAqMBxYD6CU2iMihy5CbAPDhelIiEg3pdRe78upwGYj7dFEDwkWE0U5iRTlJLb72TCOetJEGVGW+cGulFIiogBExOeQ1LAWJuAfInIsnqG8HcANxpqj0Wg0wSWKhGm2iDwDZIjIdXimYf7jS8OwFial1BVG26DRaDShJEChCZuURM0opR4SkQmAFc880x+VUot9aRvWwtQRgnEX4e+QjskUvDqMwcyD5o/d/hwTfwMDzGbfKww3NflQ9bYFwZon8vcc8eeY+Hv8gvm/CVaOOn/t8KfvSPFEArQznFISASAiDyql7gIWt7HtiMRMBVuNRqOJBKKotPqENrb5VBQvaj0mjUajiTTCXGh8QkRuAm4G+onI1y3eSgM+96UPLUwajUYTRkTKkOMReB34APg78PsW22uVUpW+dKCFSaPRaMKISBcmpVQNUCMijwGVLbKLp4nIKKXU6vb60MKk0YQTShG/YAGm7dtxjh6Nc/hwiPehpLwmagg3oekATwPHtXhd38a2NtHCpNGEC0qR8PobxC9ahBIhbt16VEoKjtGjcYw9GVfPnqCzTkQ9ke4xtUBUC+OUUm4R8UlztDBpNOGA203Cyy8T//Ey7GdOwHbJJZi/+464FSuJW76c+KVLcXXvjv3kMdhPPBGVkWG0xZogEGWZH7aLyK/xeEngCYjY7ktDLUwajdG4XCT+93niVq7Edu452C+6CERwDRmCa8gQaGggbvWXWJYvJ2n2HBLnvI1z6FDsJ4/BceyxEOdb9V6NJsTcCDwO/B+e7D1L8VaAaA8tTBqNkTidJP77GeK+/BLbtGnYp0w+dLguORnH6afRdMpYTHv3Ev/Z58R//jkp/3oad0oKjlEjsU2ciFuXe4kKosVjUkqVATMCaauFSaMxCrudxCeeJG7DBpoumYHjrPbXHrq7daPpwgtomjYVy7ffEr/yM+KXryD+0+XYx46l6bxzUVlZITBeEyzCVWj8RUQG4BnGy1NKDRGRYcBkpdRf2murhUmjMQKbjYRHZ2HetImmmTNxjD/Dv/YmE84hQ3AOGYJUVZG4YAHxny4nfuVKbONOx3bOOZCaGhzbNUElWjwm4Dngt8AzAEqpr0XkdSB2hcnXf5Y//1R/86sFs29/8tn5e+L6k6/MHzucTqdfdvjTd7jUSPLpWDc1kf74E5i2bKH2qiuxjRkDNlu7zQ6bny4lBdv06ZjOOIOUBe+TuHgJ8Z8up+H006gfPwGVkuznt2ifYOVqDKeLslHlTgI8BmGXxBVIVkp92eq36dNFIGqFSaMJR6ShgbRZj2HZsYO6a6/BNnJkp/Xtzsmh9qoraZg0kZT5C0j9cBHJny6nfsJ4Gk4/HZXYfu0pjbF0ICVR2CVxBcpFpB+ewAdE5EJg75GbeNDCpNGECKmtJf3RWZh376b2phtxDB8OQbjjd+XnY73uWuomnknqvPmkzZtPysfLqJs4kYZTxuoFu2FOOHmNHeSXwLPAUSKyGygGLvOloRYmjSYESHU16Y88ivnAAWp/dQuOIUOCvk9njx5U33wTccXFpM6bT/o775CydCl155xD40knQhDLsmgCJ1qESSm1HRjvrVxrak5N5Av6zNRogo3TSfpDD2OuqMB6669DIkotcfTpQ9Wtv6by9ttwZWXS5bXXyP77A8Rt2RJSOzS+ESUlLxCRbBF5HFgBfCIij4lIti9ttTBpNEHGsnUbln37qJt5Bc6jjjLMDvuAAVT+5jdUX3M1pvp6sh95lC7/fR6T1ecbWU0IiKJ6TG8CB4ALgAu9z9/ypaEeytNogox57x4AHAMHGmwJIELTiBE0DRtG6kcfouMUJAAAIABJREFUkbLoIxK+/x7rxRfTNOJ4nYvPYKIsJVFWq4CMv4jI+b401B6TRhNkTNXVKJMJlZ5utCk/ER9P3bnnUnH373Hm5JDx/PNkPPMsppoaoy2Ledxut1+PMBamZSIyQ0RM3sfFwPu+NNTCpNEEGVN1De4u6WEZbODs3p3K39yJdepUEr75hpw/30/iqlVBiRbU+EYUDeXdgKdooM37eBO4Q0RqRcR6pIZ6KE+jCTKm6ipUlzDOBm4203DmBGzDhtHl1VfJeOllmtatw3rppbgzM422LuYIY6HxC6VUWqBtw+8WTqOJMkzVNbgzw1iYvLjy86i843asF11Iwg8/kvPn+0la+RkYlAEhFgnEWwpXIRORa1q9NovIn3xpq4VJowkypupq3JFSP8lkomHcOMr/3//h6FlIl9deI+vhR7CUlBhtWcwQLcIEnCEiC0Wkm4gMBVYBPnlRUTmUp5TyK5eXrwQzH5s/eeHAv1xlwbQ7UvsOGXY7pvp6bCmpNDU1dairUEZrOTIyaLr5ZpLXrCFj7jyy//4AdSefTM05Z6OSk/3qO5g558L4ohzzKKUuFZHpwCagAbhEKfWZL22jUpg0mnDBbPXM8bozuhhsSQCYTDSMGkXj0KF0WfgBqStWkLxhAzXnnUfdyBPCMpgjGoiWcHERKQJuBd4BjgauEJENSqmG9trqM0ujCSJmb/i1q0sECpMXlZxM9YUXsP93v8XZtStZb7xB3qzHiNPDe0EhSobxAOYD/08pdQNwKrAFWONLw7AQJhG5SES+ERG3iIxo9d7dIrJVRH4QkYlG2ajRBIK5uhqIbGFqxlFQQNmtv6bi8suxVFaS//AjZM6ejam+3mjToooommMaqZRaCqA8PAxE1ALbzcA0YHnLjSIyCE9p3sHAJOBfImIOvXkaf5CmJuJKS5GGdj32qKd5waorUoIf2kOEhpEnsOeeP1B7yimkfrGKbn/9Gymff6Gj9zqBaIjKE5HfASilrCJyUau3f+FLH2Exx6SU+g7anOyeAryplLIBxSKyFRgJfBFaCzW+IE1NpKxcSdqSpZi9d9GulBScXXOx9+2LragIW9++qKQkgy0NHeYaKyouLuq+s0pKonraVOpHjyLz7XfIfustUr/4gorLLsWZn2+0eRGNv8EiKvwKBc4A/uF9fjcwp8V7k4A/tNdBWAjTESjAE2LYTKl32yGIyPXA9QC5ubnBt0xzEGloIPXTT0n75FNMDQ00HXUU1SNPwFxjxVJeTtzevaR+8ilpSz9GmUw4CguxFfWnqagIe9++UV3ATux23AkJUZuDztG9O2W/uoXkdevJfPdd8h96mOrJk6kbe3LUfudgE2DwQzgVCpTDPG/rdZuETJhEZAnQ1q3UPUqpuYdr1sa2Nv9rSqln8RSloqioKLx82yjFVFtH2iefkLpiOaYmG41Dh2A9cyKO3r0O+azY7cQX7yBhyxYStmwhddknpC1ZijKZsPfqRdWM6Ti7dzfgWwQZESTah7hEaBhxPE0DBpD9xhtkvfMOSd9+S8Wll+AOp/yAEYBSKlCPKZxQh3ne1us2CZkwKaXGB9CsFChs8boHsKdzLNIEiqmmhvSPPybls88Rh4PGY4+h9syJOHq06cwCoOLjsQ0cgG3gAADEZvMI1dYtpHz+BTnPPcf+3/4WlZwcqq8RGkwSM3nn3OlpHLj+OlJXriRj7jy6PfgPKi+ZQf2gQUabFlFEQbj4Md5ceAIktciLJ4BPwyPhPpQ3D3hdRB4BugNFwJfGmhS7mCsrSVuylNRVq8DtpuH446k9c0JAcwoqIQHbUQOxHTWQpsGDyX3scbJeeYWK666LrvUxYooZYQJAhLqxY2kqKiLn5VfIfe4/JJ54IpVTJqMSEoy2LiKIdI9JKdXhALWwECYRmQo8AeQC74vIRqXURKXUNyIyG/gWcAK/VEp1fkoHzRERm42M9+aS8sUXIEL9qJFYzxiPKzenU7Iz2Pv0oXrqVDLffpu0xYupnRg9qwKUSExGqznz89l3x+1kvP8+acs+IXHbNg5cfhn2wsL2G8c4UeAxdZiwECal1LvAu4d576/AX0NrkaYZy/795Pz3eSz791M3Zgy148/AlZXV6fupP2UsCcXFpL+/EHuvXtgMrPTaqZhMSBReOHzCYqF6yhQajjqKnNdep9usx6g+6yxqxp0eXV5xJxKO4d9GEBbCFAx8dYf9ueMP5gkTzHxi/tL8PVM2fkXXOXNQFgt7r7+OxqIizwfs9qDsd9+0afQoLSXrxZcouf22qFj7k+x2gdvd4Tx5kYyjb18a77yD3LffIfP990n87jvKLpmBU5fUaBN/rwXhdO3oLPRti+ZQnE6y584l/9VXsefnU3L7bT+JUhBRCfHsu+pKxOkk/6WXwekM+j6DjsRO8MORcCcns/+KyymbPp2E3bvp8fAjpK5fr49NG0Ty4trOQguT5meYq6spePrfZKxYSfXYk9l9040h9VwcubmUzZhOYkkJOXPnhWy/wULFWvDDkRCh9oQRlNxxO468PPJef4Nuzz1H3IEDRlsWNjSHi/v7iDa0MGkOkvjjFno8+ijx+/ax7/LLqZgyBSyhH+2tHzqUqtNOpcsXX5C6bl3I99+piHjmmLQ4HcSZnc3uX97MgfPPJ2FXCYUPPUzmh4sQh8No08KCSE9J1BlE7RyTxg/cbjI+/pjMDxfh6JrLvpkzceTlGWpS5VlnkVhSSu7b72Dv1g17pC6+NXnnMJXSmRBaYjJhPXkM9cOGkr1gAVlLlpC2fj3lU8+n4eijjbZOYzDaY4pxTI2N5L3wAlkffEj9scey+9ZbDRclAMxm9l9+Ge6kJPJefgWCUPgxFCjx/sSicLilM3Clp1N26aXsvvEGlMVCt/8+T97Lr2BqbDTaNMOIdW8JtDDFNHH79lEw6zGSv/+B8vPPp+yyS8NqEaQrLY2qceOILy/HXFdntDkBobxDoRINgRxBpKl/f0ruuJ2Ks84iZfNmesyaRfzu3UabZQh6jkkLU8yS9P33FDz2OGKzseemm7CGadLNZqGM1Au7SogHwGSzGWxJBGCxUH3GOHbffBPidFHwxJOkrV4dU/Nzen7JgxamGCT5m2/If/4FHLm57L79Nmx9+xht0mGJdI/D7RVWU5DWfkUjtt69Kbn9Npr69qHrnLfJfestJIaOn/aYtDDFHMmbNpH30svYundj7403hH1lVXdcdAiTaI/JL9ypqey99loqJ0wgbd16Cp54gvg9sZG/WXtMWphiipSvviLv5VewFRSw74YbcEdCJm9zZAuTivd6TFqY/Mdkomrimey99hos1lp6zHqM7HnzkSjPoqGH83S4eMyQsmEjXV9/HVvPnuy97tqIKc4XNUN5WpgCpnHgQHbd9TuyFi6ky4oVpG7cSPmUydQPGxaW86IdIUrqMXWYqBUmX/9ZrkgNQ/bjZEzfsIGus+fQ2Ls3pVddiTKb4QiLGf3pO9j5A0V5fqSOhsaIzDfn9tZFc9bVtWm/KYjJTDsj87sRfbdJfDz7zj+f6uOOI++9ueS/8ir1RUWUnT8FR05OaG0JMjq7uB7Ki3rS160jf/YcGvr0ofQXV4VVOLgvKLOntIvJGZlZAZo9JrMtdibvg0lTz57svOWX7J98Hom7dtHrkUfJ/uijqMoa4W/gQzQKU9R6TBrosmYNef97l4b+/dh9xRWo+HijTfKbg0N5EerZur3HXEfldSImE9UnnUTt0KF0XbiQ7KUfk7ZhIwemTKY+wsulBDJnFI3CpD2mKKXLqtXkv/M/6ouK2D1zZkSKEoCK8OCHg8Kk55g6HVdaGvtmzKDk+utQZjMFL7xIt5dfwVJVbbRpHUKHi2thikoyPv+c/Pfeo+6ogeyZeQUqLs5okwLGfTD4ITI9Jsxm3BaL9piCSGO/fuy87VYOTJpEyo8/0vvhh8lctixih/diPSIPtDBFHZkrV5I3bz61gwax+/LLDw6FRSrK0jzHFJkeE3jmmUx27TEFFYuFqtNPY8edd9BQVETuh4vo/dDDpH79dcRljggwXHygiDwrIucZbX9nENlXLc3PyFyxgq7vL6R2yBD2XDIDvIEDkUyzp9G80DYScSUlYa5vMNqMmMCZmcmeK2eStG0bufMX0P211z3Re1Mm48jNNdq8dulAuPgPSqn7g2KUAWiPKUpI++prjygNjR5RAoiz1gLgSE832JLAcaSnE2e1Gm1GTNHYrx+7fnULZc3Re4/OInvRoohIbaQX2GqPKSpI3LmT/DlzaOjdm73Tp0eNKAFYaj0XdGcEC5MzPY3k4h1GmxF7mM1UjxlD7bBh5C5cSPbHy0jfsIGy886jftCgsF2cqxfYao8p4omrqKTg5VdwdklnzxWRP6fUmmZPI+I9ptpaXZPJIFxpaeybPp2SG67HHZ9Awcuv0P3FF4mrqDDaNM1h0MIUwZgaGil48UXE7ab0qqtwpaQYbVKnY7FaccfF4Y6QFEpt4UxPR9xuzA16nslIGvv2Zeetv6bsnHNI3l5Mr0ceJWvxkrCK3gtkGC8aPabour2OJZxOur/2GvGVlZRcc3VETOwGQpzViiMtLWyHXXyh2duLs1pxpaYabE2MYzZTfcpYao89htwF75OzZAnpGzaw7+KLaerdy2jrAJ2SCKJUmJRSPufAC+Y/NWh9K0X+/94lZds2Si+YRm3PnnCEcOpgnujBzKvndrsxV9dgT03FHgGT1oejMSnJ86SiEnurvG7+5MrzNz+dP58Pl7x6ocod6E5NZc+M6VSfMIL8d/5H4TPPcGDCBCpPGQtBtMEX9ByTHsqLSLKXLydz/XoOnHYqNcOHG21OUImrrcWRnma0GR3CkeaxP33btohbUxPtNPTrx45f/4rawYPpumgRPV58CXNtraE26aE8LUwRR9qmTXRd9BE1Q4dSdsYZRpsTXJTyCFNa5AY+ADhSU3HFxZG7di2JZWVGm6NphTsxkT2XzGDv1PNJLi6mzxNPkrxtmyG2aFHyEBbCJCIXicg3IuIWkREttvcWkUYR2eh9/NtIO40mcdcuus95m4aePdk9bWpEz7v4grmxEZPLddDjiFhMJlxJSSgg85tvjLZG0xYi1Iwcyc5f3owrMZHC/z5PzuLFYEDyYC1O4TPHtBmYBjzTxnvblFLHhtiesCOuqorCV17FmZ5O6eWXR3T+O1+Jq21eXBt5wiROJxnffou4PPMF5sZGBMjasBFbZhYAymzCOngIKoKzWkQbtvx8dtzyS/LmzSfn42Ukby9mz4zpOLt0CZkNOvghTIRJKfUdGFB8LEIwNTRQ+NLLiMtFycyZuFJTYmJNTHxz1ocI9JhMdjvdl35MXG0tbosF8f6/LI2N9PjgA0xOJ460NOoGDMSlhSmsUPHx7LvwAhr69SX/vbn0fvwJ9l14AXVHHx2S/evghzAZymuHPiKyQUQ+FZGxh/uQiFwvImtFZK01itK/mGw2er74EnEVFZRedhn2rtEZFt4Wcd6sD/YIFCZXcjLf3XQjNQMHgggm78XG5HZ7ho0GDuS7m2/ClZxksKWaw2EdPpziW27BmZFBj5dfIW/evKCvedLrmDyETJhEZImIbG7jMeUIzfYCPZVSw4E7gNdFpM2ZcKXUs0qpEUqpEekRnCWgJeJ00uOVV0ncs4fdl8ygoV9fo00KKXE1VhSR6TGBZ1K9+OKLcLVaHOxKSqL44osOVrfVhC+O3Bx23nQjlWPGkPnFKno99S8sNTVB3acWphAO5SmlxgfQxgbYvM/Xicg2YACwtpPNC0vy5s0nZft2dl90IXWDBhltTsiJb15cG8G5/+Krq4mrq8NlsSAuF6IUcVYr8dXV2DMzjTZP4wPKYqHs3HOoL+pPxuovcQZ5kXQ0Co2/hPVQnojkiojZ+7wvUARsN9aq0JDx5Zdkrl1L+WmnYo3ytUqHI95qjegceYAnAEIpKo49lq1XzkQAATK+/c5o0zR+Uj9wILtnXhH0GyXtMYVJ8IOITAWeAHKB90Vko1JqInAK8GcRcQIu4EalVKWBpoaExF27yJ+/gLqiIg6M99vRjBrirDU0ds0z2owO0Zifz9ZLL6W2fz9QCnt6Ovb0dBrzI/t7aYKHjsoLE2FSSr0LvNvG9neAd0JvkXGYa+vo8fobONLT2T39YsPToxiGUsRZa7H2LzLakg5R26/fTy9EqCkqIuvrr6nrFR552TThhVIBFwqMKsJCmIKBr/9cf06CoOfVc7kofP11zI2NbLvuWuzx8YfNgRfMHHW+5hkMph3mxkbMDgeNqSk4wij7c0ep7NuX3HXrSNq6jZp+fSM2V16w7Pb3ohwuOf46E+0xhfkcU6zRbdFHpO7cye7Jk2nKzzfaHEOJ94b82yN8jqk11t69cMXFkbF1q9GmaDRhixamMCHjq6/JWbWK8tGjqT5mmNHmGM5BYYrQUPHDoSwWrL17e4QpCu90NR0n1gMfQAtTWJC4dy8F8+ZR16sXeyeeabQ5YUG0ekwAVUX9SbBaSdIJXTVtoKPytDAZjrmhgZ5vvIkrMZGS6RdH9JqdziTeWovbZMIRhVV5a7wBEZl6OE/TCp35wYMWJiNxuegxZw4Wq5WdM6YHfeFeJBFvteJITY3KqERHaip13bqRsUULk+ZQYl2UQAuTcShF9/nzSdu6jb3nnktjYaHRFoUVCVVV2DJCl9E51NT07UvKvn2YbDajTdGEGW632+9HtKGFySByl31C1rr1lJ16KlUjjjfanPBCKRIrKmjMzmn/sxFKbWEhohSppbuNNkUTZuihPC1MhpCxbj15y5ZRNfxYys4YZ7Q5YYelvh5LUxNN2dlGmxI06gq6o0RILSkx2hRNGKFFyUPULrANV1K3bKFg3jxq+/dj95QpUV+FNhASKyoAaIxiYXLHx1Ofl0eaFiZNK/QCW+0xhZT4igoKZ8+hqWtXSmbM0BF4hyGp3CNMTdlZBlsSXOoKe5CyZw9ymOwemthEe01amEKG2O30fONNlAi7Lr1E1+I5AokVFbji4qJyDVNLagsLMTmdJO/bZ7QpmjAi1kUJonQoTynlc763YOacO/h5pej53lwS9u+n+PLLaEpPh1b2BTNxY6TlyksoL6cxKwtXmOQx9Ad/8qvVdOsGQPLOndT4kIIqmLny/Ml9B/6dr5GYsy+Qz3cGgSRx1VF5moDI+fJLMjdtYt/pp1Pbv7/R5oQ9SRUVUT+MB+BISaExK5O00lKjTdGEEdpj0sIUdFJ27qL7oo+oGTCAsrFjjTYn7DHZ7SRYrVEd+NCS2oIepJfu1nnzNAfRc0xamIKKpbaWXnPmYM/IYNfUqWDSEXjtkVhVBUBjVowIU2EPLE1NBwM+NBotTFqYgofLRa85b2Oy2dgx/WLcSYlGWxQRJB0MFY/+oTwAa48eAKSV6rBxjYdYFyXQwhQ0ui/6iNRduyidfB5NebqMtq8klVegRGjKzDTalJBgy8jAnpJCup5n0mgOEpVReUaTtWYtOatXc2D0aKqHDjXanIgibfduGnJzURZLbMy7iNDQtSuJFZVGW6IJAwLxgqLRa9IeUyeTsn07BQsXYi3qz54zJxhtTkRhsttJKy2lpndvo00JKbb0dBK89ac0Gn8TuIZSmETkfBF5TkTmikjQisdpYepE4svL6fXWbGw52ey68MKoLNkQTNJLSjC53VT36W20KSHFlp5OXGMjJofDaFM0YUCwgh9E5HkRKRORza22TxKRH0Rkq4j8vh3b3lNKXQdcBUwP9Du2hx7K6yTMDQ30ef11MJkovvRS3ImJsTEU1Yl0Kd6B22Kh1hsQECs0Z7iIr62lKSs2gj40hyeIQ3kvAk8CLzdvEBEz8BQwASgF1ojIPMAM/L1V+6uVUs1ll//P2y4oaGHqDFwues6eQ1x1DduvnIkjRibuO5suO3ZgLSz0zC/FEPb0NMBTHFELkybAobmjRGRti9fPKqWebdXvchHp3ardSGCrUmo7gIi8CUxRSv0dOLf1TsSTDuMB4AOl1PpADPWF2LoCBAOlKFi4kLTiYnZNPZ+GXr2MtigiibdaSa6ooGzYMKNNCTk2r8ek55k0HeAGpVRdAO0KgJZrFUqBUUf4/K+A8UAXEemvlPp3APtsl6gVJl/zR/lzd9JWn9lr15G9dh37TzqJiqFDf5YDr6N9d9bn/b0DC1bfR+o3e9t2ACp7FuL0ZtsOZj7AYBFIfjVnQgIKsFRX4zjCPJPZz2z0/tji7/kXrPx3/n5Hf/7vwczD14kkAw3+NgpQlADaOiiHPahKqceBxwPcl8/o2fkOkLKrhB4ffkhN//7sGXe60eZENBk7d2JLSaEhRlIRtURZLDhSkkmw1hptisZglFKNATRL68AuS4HCFq97AHs60F+noIUpQCy1tfR5+23sGRnsnDZVR+B1BLebjJ07qe7dK2YLJzalpZNQq4VJA3i8Jp/pgLcEsAYoEpE+IhIPzADmdaC/TiEsrqYi8k8R+V5EvhaRd0Uko8V7d3vDGH8QkYlG2tmMOJ30nT0Hk81G8cUX4UrU6YY6QmpZGXFNTVTH8PycLS1NC5MG8Ntr8tlbEpE3gC+AgSJSKiLXKKWcwC3AIuA7YLZS6hu/DA4C4TLHtBi4WynlFJEHgbuBu0RkEB4FHwx0B5aIyACllO8Fg4JAjw8XkbJ7N9svupCmrl2NNCUqyNi5EyDmhSmruNizxCBGvUbNz/Bprskfb0kpdclhti8EFvpuWvAJC49JKfWRV7kBVuEZ5wSYAryplLIppYqBrXjCGw0jcf9+stevZ9+YMdQcfbSRpkQNqfvLqOvaFUeyXyMYUYUtPQ1xu7E02Yw2RRMG+Og1dWRuKawJF4+pJVcDb3mfF+ARqmZKvdsOQUSuB64HyMnJCZpxTXl5/Hj11TR07xa0fcQa3593LpamJqPNMJS9w4axZ/hw7S1pWnJEr6mDc0thTciESUSWAG3Vj75HKTXX+5l7ACfwWnOzNj7fZiijdzHZswD9+vULauxwQ482tVETKCI4k5KMtsJQYm1RsaZ9lFKNRwhxj1pvCUIoTEqp8Ud6X0SuxLPS+Az10+KEsAxl1Gg0mhDRptcUzd4ShMkck4hMAu4CJiulWv4T5gEzRCRBRPoARcCXRtio0Wg0oeYwc01R7S1B+MwxPQkkAIu9rusqpdSNSqlvRGQ28C2eIb5fGh2Rp9FoNCHmZ15TtHtLECbCpJTqf4T3/gr8NYTmaDQaTdjQaq4p6r0lAAmXHGOdiYgcAHa28VYOUB5ic9pC23Eo4WKLtuPnhIsdED62HM6OXkqp3GDsUEQSgHylVFvXtagjKoXpcIjIWqXUCG1HeNkB4WOLtiM87YDwsSVc7IhmwiL4QaPRaDSaZrQwaTQajSasiDVherb9j4QEbcehhIst2o6fEy52QPjYEi52RC0xNcek0Wg0mvAn1jwmjUaj0YQ5Wpg0Go1GE1ZEvTCFUxFCEblIRL4REbeIjGixvbeINIrIRu/j30bY4X3PkMKMInKviOxucQzODtW+vfuf5P3OW0Xk96Hcdxu27BCRTd7jsDaE+31eRMpEZHOLbVkislhEtnj/ZhpkR8jPDxEpFJFlIvKd9/dyq3d7yI9JrBH1woSnCOEQpdQw4Ec8RQhpVYRwEvAvETEH2ZbNwDRgeRvvbVNKHet93GiEHQYdk5Y82uIYhKxwmfc7PgWcBQwCLvEeCyM53XscQrle5kU8//eW/B5YqpQqApZ6XxthB4T+/HACdyqljgZGA7/0nhdGHJOYIuqFKZyKECqlvlNK/RDMfXTQjrArzBgiRgJblVLblVJ24E08xyKmUEotBypbbZ4CvOR9/hJwvkF2hByl1F6l1Hrv81o8pccLMOCYxBpRL0ytuBr4wPu8AChp8d5hixCGiD4iskFEPhWRsQbZYPQxucU75Pp8iIdHjP7erVHARyKyzlsA00jylFJ7wXOhBroaaItR5wci0hsYDqwmvI5JVBIWSVw7SrCLEHa2LW2wF+iplKoQkeOB90RksFLKGmI7gnJMfLEJeBq437u/+4GH8dxIhIKgfu8AGKOU2iMiXfFk3P/e60XEMoadHyKSCrwD3KaUsh6heJ+mk4gKYQqnIoTt2XKYNjbA5n2+TkS2AQOAgCe+A7GDIBdm9NUmEXkOWNBZ+/WBsCpIqZTa4/1bJiLv4hlqNEqY9otIN6XUXhHpBpQZYYRSan/z81CeHyISh0eUXlNK/c+7OSyOSTQT9UN5kVCEUERym4MMRKSv15btBphi2DHx/sCbmYonQCNUrAGKRKSPiMTjCQCZF8L9H0REUkQkrfk5cCahPRatmQdc6X1+JXA4bzuoGHF+iMc1+i/wnVLqkRZvhcUxiWqUUlH9wDOBXwJs9D7+3eK9e4BtwA/AWSGwZSqeu3MbsB9Y5N1+AfAN8BWwHjjPCDuMOCYt9vsKsAn4Gs8Pv1uIz5Oz8URtbsMz3GnU+drXex585T0nQmYL8AaeYWWH9/y4BsjGE3m2xfs3yyA7Qn5+ACfjGTr8usX142wjjkmsPXRKIo1Go9GEFVE/lKfRaDSayEILk0aj0WjCCi1MGo1GowkrtDBpNBqNJqzQwqTRaDSasEILk0aj0WjCCi1MGo1GowkrtDBpYhoRMYvIY956O5u8mTc0Go2BaGHSxDp3A9uVUoOBx4GbDbZHo4l5oiKJq0YTCN5cdFOVUsd7NxUD5xhokkajQQuTJrYZDxSKyEbv6yxgiYH2aDQa9FCeJrY5Fvij8pbrBj4CNnozfL8kIs+JyGUG26jRxBxamDSxTCbQACAiFjwlJuYD04C3lVLXAZONM0+jiU20MGlimR+B0d7ntwPvK6WK8RQKbC617jLCMI0mltHCpIll3gCOE5GtwDDgDu/2UjziBPo3otGEHF2PSaNphTda70mgCViplHrNYJM0mphCC5NGo9Fowgo9TKHRaDSasEILk0aj0WjCCi1MGo0TUt22AAAAM0lEQVRGowkrtDBpNBqNJqzQwqTRaDSasEILk0aj0WjCCi1MGo1GowkrtDBpNBqNJqz4/+YyucPavONFAAAAAElFTkSuQmCC\n", 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" 16.66666667, 16.66666667, 16.66666667, 16.66666667,\n", - " 16.66666667, 16.66666667, 16.66666667, 16.66666667,\n", - " 16.66666667, 16.66666667, 16.66666667, 16.66666667,\n", - " 16.66666667, 16.66666667, 16.66666667, 18.33333333,\n", - " 18.33333333, 18.33333333, 18.33333333, 18.33333333,\n", - " 18.33333333, 18.33333333, 18.33333333, 18.33333333,\n", - " 18.33333333, 18.33333333, 18.33333333, 18.33333333,\n", - " 18.33333333, 18.33333333, 18.33333333, 18.33333333,\n", - " 18.33333333, 18.33333333, 18.33333333, 18.33333333,\n", - " 18.33333333, 18.33333333, 18.33333333, 18.33333333,\n", - " 20. , 20. , 20. , 20. ,\n", - " 20. , 20. , 20. , 20. ,\n", - " 20. , 20. , 20. , 20. ,\n", - " 20. , 20. , 20. , 20. ,\n", - " 20. , 20. , 20. , 20. ,\n", - " 20. , 20. , 20. , 20. ,\n", - " 20. ])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "theta_grid[:,0]" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/madminer/__version__.py b/madminer/__version__.py index cd1ee63b7..98a433b31 100644 --- a/madminer/__version__.py +++ b/madminer/__version__.py @@ -1 +1 @@ -__version__ = "0.4.4" +__version__ = "0.4.5" diff --git a/madminer/limits.py b/madminer/limits.py index 33154841e..9823d3aed 100644 --- a/madminer/limits.py +++ b/madminer/limits.py @@ -43,7 +43,7 @@ def observed_limits( include_xsec=True, resolutions=25, luminosity=300000.0, - n_histo_toys=10000, + n_histo_toys=None, returns="pval", dof=None, n_observed=None, @@ -83,7 +83,7 @@ def expected_limits( include_xsec=True, resolutions=25, luminosity=300000.0, - n_histo_toys=10000, + n_histo_toys=None, returns="pval", dof=None, histo_theta_batchsize=100, @@ -363,6 +363,9 @@ def _make_sampled_histo_data( sampler = SampleAugmenter(self.madminer_filename, include_nuisance_parameters=self.include_nuisance_parameters) all_summary_stats, all_theta = None, None + if n_toys_per_theta is None: + n_toys_per_theta = 10000 + n_thetas = len(thetas) n_batches = (n_thetas - 1) // histo_theta_batchsize + 1 for i_batch in range(n_batches): diff --git a/setup.py b/setup.py index 6be047d43..0e5b73c2e 100644 --- a/setup.py +++ b/setup.py @@ -20,7 +20,7 @@ EMAIL = 'johann.brehmer@nyu.edu' AUTHOR = 'Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer' REQUIRES_PYTHON = '>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4' -VERSION = '0.4.4' +VERSION = '0.4.5' # What packages are required for this module to be executed? REQUIRED = [