From 983470ed4ef099d63807ee9a7b5fa3336a7315b5 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 19 Apr 2021 09:34:17 -0500 Subject: [PATCH 1/5] Adding StatePoint.close(). Updating some examples. --- examples/jupyter/chain_simple.xml | 1 - examples/jupyter/mdgxs-part-i.ipynb | 223 +++++++++++++++------------ examples/jupyter/mdgxs-part-ii.ipynb | 81 +++++----- openmc/statepoint.py | 12 +- 4 files changed, 175 insertions(+), 142 deletions(-) delete mode 120000 examples/jupyter/chain_simple.xml diff --git a/examples/jupyter/chain_simple.xml b/examples/jupyter/chain_simple.xml deleted file mode 120000 index 96f6fa8818e..00000000000 --- a/examples/jupyter/chain_simple.xml +++ /dev/null @@ -1 +0,0 @@ -../../tests/chain_simple.xml \ No newline at end of file diff --git a/examples/jupyter/mdgxs-part-i.ipynb b/examples/jupyter/mdgxs-part-i.ipynb index ea1dd7bcdfa..ae31859a4f5 100644 --- a/examples/jupyter/mdgxs-part-i.ipynb +++ b/examples/jupyter/mdgxs-part-i.ipynb @@ -363,17 +363,20 @@ { "data": { "text/plain": [ - "OrderedDict([('delayed-nu-fission', Tally\n", + "OrderedDict([('delayed-nu-fission',\n", + " Tally\n", " \tID =\t1\n", " \tName =\t\n", - " \tFilters =\tCellFilter, DelayedGroupFilter, EnergyFilter\n", - " \tNuclides =\tU235 Pu239 \n", + " \tFilters =\tCellFilter, DelayedGroupFilter\n", + " \tNuclides =\tU235 Pu239\n", " \tScores =\t['delayed-nu-fission']\n", - " \tEstimator =\ttracklength), ('decay-rate', Tally\n", + " \tEstimator =\ttracklength),\n", + " ('decay-rate',\n", + " Tally\n", " \tID =\t2\n", " \tName =\t\n", - " \tFilters =\tCellFilter, DelayedGroupFilter, EnergyFilter\n", - " \tNuclides =\tU235 Pu239 \n", + " \tFilters =\tCellFilter, DelayedGroupFilter\n", + " \tNuclides =\tU235 Pu239\n", " \tScores =\t['decay-rate']\n", " \tEstimator =\ttracklength)])" ] @@ -403,15 +406,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=4.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=6.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=5.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=8.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=7.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=14.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=13.\n", " warn(msg, IDWarning)\n" ] } @@ -483,26 +486,29 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", - " Date/Time | 2019-07-19 06:56:34\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | d4df243d920d12c3d268a4c20941950d2299d678\n", + " Date/Time | 2021-04-18 13:10:08\n", + " MPI Processes | 1\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", - " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", - " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", - " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", - " Reading Pu239 from /opt/data/hdf5/nndc_hdf5_v15/Pu239.h5\n", - " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for H1\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading Pu239 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Pu239.h5\n", + " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for H1\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -563,20 +569,21 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.7388e-01 seconds\n", - " Reading cross sections = 4.4709e-01 seconds\n", - " Total time in simulation = 3.9290e+01 seconds\n", - " Time in transport only = 3.9005e+01 seconds\n", - " Time in inactive batches = 1.4079e+00 seconds\n", - " Time in active batches = 3.7882e+01 seconds\n", - " Time synchronizing fission bank = 1.8814e-02 seconds\n", - " Sampling source sites = 1.6376e-02 seconds\n", - " SEND/RECV source sites = 2.3626e-03 seconds\n", - " Time accumulating tallies = 8.3299e-04 seconds\n", - " Total time for finalization = 1.1533e-02 seconds\n", - " Total time elapsed = 3.9783e+01 seconds\n", - " Calculation Rate (inactive) = 35514.2 particles/second\n", - " Calculation Rate (active) = 5279.54 particles/second\n", + " Total time for initialization = 1.1312e+00 seconds\n", + " Reading cross sections = 1.1046e+00 seconds\n", + " Total time in simulation = 2.7801e+02 seconds\n", + " Time in transport only = 2.7794e+02 seconds\n", + " Time in inactive batches = 1.0359e+01 seconds\n", + " Time in active batches = 2.6765e+02 seconds\n", + " Time synchronizing fission bank = 2.5796e-02 seconds\n", + " Sampling source sites = 2.3811e-02 seconds\n", + " SEND/RECV source sites = 1.7176e-03 seconds\n", + " Time accumulating tallies = 2.5310e-02 seconds\n", + " Time writing statepoints = 6.3601e-03 seconds\n", + " Total time for finalization = 2.2876e-02 seconds\n", + " Total time elapsed = 2.7919e+02 seconds\n", + " Calculation Rate (inactive) = 4826.80 particles/second\n", + " Calculation Rate (active) = 747.234 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -644,7 +651,9 @@ "chi_delayed.load_from_statepoint(sp)\n", "delayed_nu_fission.load_from_statepoint(sp)\n", "beta.load_from_statepoint(sp)\n", - "decay_rate.load_from_statepoint(sp)" + "decay_rate.load_from_statepoint(sp)\n", + "# Close statepoint file now that we have the info we need\n", + "sp.close()" ] }, { @@ -888,7 +897,6 @@ " \n", " cell\n", " delayedgroup\n", - " group in\n", " nuclide\n", " mean\n", " std. dev.\n", @@ -899,7 +907,6 @@ " 0\n", " 1\n", " 1\n", - " 1\n", " U235\n", " 0.013336\n", " 0.000061\n", @@ -908,7 +915,6 @@ " 1\n", " 1\n", " 1\n", - " 1\n", " Pu239\n", " 0.013271\n", " 0.000053\n", @@ -917,7 +923,6 @@ " 2\n", " 1\n", " 2\n", - " 1\n", " U235\n", " 0.032739\n", " 0.000149\n", @@ -926,7 +931,6 @@ " 3\n", " 1\n", " 2\n", - " 1\n", " Pu239\n", " 0.030881\n", " 0.000123\n", @@ -935,25 +939,22 @@ " 4\n", " 1\n", " 3\n", - " 1\n", " U235\n", " 0.120780\n", - " 0.000549\n", + " 0.000550\n", " \n", " \n", " 5\n", " 1\n", " 3\n", - " 1\n", " Pu239\n", " 0.113370\n", - " 0.000451\n", + " 0.000452\n", " \n", " \n", " 6\n", " 1\n", " 4\n", - " 1\n", " U235\n", " 0.302780\n", " 0.001378\n", @@ -962,65 +963,60 @@ " 7\n", " 1\n", " 4\n", - " 1\n", " Pu239\n", " 0.292500\n", - " 0.001163\n", + " 0.001167\n", " \n", " \n", " 8\n", " 1\n", " 5\n", - " 1\n", " U235\n", " 0.849490\n", - " 0.003865\n", + " 0.003866\n", " \n", " \n", " 9\n", " 1\n", " 5\n", - " 1\n", " Pu239\n", " 0.857490\n", - " 0.003411\n", + " 0.003420\n", " \n", " \n", " 10\n", " 1\n", " 6\n", - " 1\n", " U235\n", " 2.853000\n", - " 0.012980\n", + " 0.012985\n", " \n", " \n", " 11\n", " 1\n", " 6\n", - " 1\n", " Pu239\n", " 2.729700\n", - " 0.010858\n", + " 0.010887\n", " \n", " \n", "\n", "" ], "text/plain": [ - " cell delayedgroup group in nuclide mean std. dev.\n", - "0 1 1 1 U235 0.013336 0.000061\n", - "1 1 1 1 Pu239 0.013271 0.000053\n", - "2 1 2 1 U235 0.032739 0.000149\n", - "3 1 2 1 Pu239 0.030881 0.000123\n", - "4 1 3 1 U235 0.120780 0.000549\n", - "5 1 3 1 Pu239 0.113370 0.000451\n", - "6 1 4 1 U235 0.302780 0.001378\n", - "7 1 4 1 Pu239 0.292500 0.001163\n", - "8 1 5 1 U235 0.849490 0.003865\n", - "9 1 5 1 Pu239 0.857490 0.003411\n", - "10 1 6 1 U235 2.853000 0.012980\n", - "11 1 6 1 Pu239 2.729700 0.010858" + " cell delayedgroup nuclide mean std. dev.\n", + "0 1 1 U235 0.013336 0.000061\n", + "1 1 1 Pu239 0.013271 0.000053\n", + "2 1 2 U235 0.032739 0.000149\n", + "3 1 2 Pu239 0.030881 0.000123\n", + "4 1 3 U235 0.120780 0.000550\n", + "5 1 3 Pu239 0.113370 0.000452\n", + "6 1 4 U235 0.302780 0.001378\n", + "7 1 4 Pu239 0.292500 0.001167\n", + "8 1 5 U235 0.849490 0.003866\n", + "9 1 5 Pu239 0.857490 0.003420\n", + "10 1 6 U235 2.853000 0.012985\n", + "11 1 6 Pu239 2.729700 0.010887" ] }, "execution_count": 18, @@ -1094,7 +1090,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -1103,7 +1099,7 @@ }, { "data": { - "image/png": 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IS0UFXoWF/OmXX3AvKcC9tBT30lI6VFRg0unwLcjHsSoHPrE/ds+NH1L9eRJSQKkRDnntoMwIZUYodQCjCaJyocIIR911gH0gfnTfhzxwahW5HeCwJ/zaCcqNQM3l7Fy3bm65N9qzX53Fh2ZzYth/AfgVWAPaRTc5gckZHKuhdxFIPetTr+ZhttptP/PzZznh8j+o6gi7YuBkrPZ1lQtUeIBjEQgzFPszKDSIXdvc7LaPiYHERNDpoHNn7ePkBM7O2ic7WwvmnZ3hgQfgoYfsr9/HH8OZM+DrC717Q9eu4O6uPTgoiqIoinL5uyL+ZAsh7kdLX6FHjx4tum+TrK79usT6rynZlZkN5q2t/pJiihpdX48eAAsWDBgYffImHmeG3Tqfmj9CYqYb3RjAALyqvXCobmRngKE2YK8TG18Xdw7Z2eShA5D5pKPddEl1Fe/PgFIXKHITbB/mxBlvJ/I7acF7YRew6K3rAnoH+9vltFHH97GxtdMfjBvXZNl6k4kqKdGJunM47dqBa15/HZ/CQoJPnGDizz/TNT8f/5w8nKuq7LbvedbSYJ837k/knt3286p1kO+sfSTQrUgL7ON8i+Af9ut2LcqnWzEcd4fTHaHaABiqtI/d99SE2flUg/ILvDeB515tIuCnJs8dILlwMGD/DUrvOwU56hfMFR6cPDqak8evhkJvKPWBUl+QAqo7gMmZnTsbBuJz52qBeH0dOmgBeXk5SKlN//3vcO+99utt2gReXhAVpa2jKIqiKErbas9A/ARgW0XczTqvASnlO8A7AAMGDJAteRDVDtVQcX7rdvL3aLi9rhoaxogAmDHXrUc1nr6uDdZZafkUE6ba6Y50xMv6zxtvjnEMRxzpQhduDbnv/A60CVGjQuymO+o86HmsLpKrH8ibBSCgyqjVrvv94m633C8sGJ0JLOdxF0kh7IJwgOrIMLbHxNRO/2vq1NqvPYqL8Swu5rSnJ26lpYQfy+D7evuMdnFpUI7RAr6l2qeGWxUMPN3wtnkxLoU/JGpf5zvBr35w0gWOu8FRTzCYwbUK9naBLG99w7I6lp37xK2cdQ3vnUqfneBqfbjz29twI4sOdBYw6zlY/jDwit3i0k67QOcCBb20gN2qrEz71CgshJONvGe6+WaosN77Xl4QEKDVqtd8srMhKAiuugquvlpLwVEURVEUpeW0ZyC+FnhICLEKGAQUtnV+OEBuySnKysooKCjAZDJhMpmoqKigvLyciooKkpKSOHnyJIWFhdx6660Nth86agjp6emUlZXh6+tLRUUFhYWFFBUVUVlZabfuDX8Z2WB7szBrVbdWpdZ/mdjXvieSyIyQGXbzLBYLM11n4qH3oJexFzOvnUkncyfMRWbMxWaqz1ZTllKmPShI6BPby277gHI38pu5NnqpbedcqX06dfe0W97XEsm316ZjNkCpu+D4CAeyAwUZAZDew8KhTiYKpPaU4tBIvoR57A2Qk9No2QWurhS4ag8upc7OlLq7N1hnzZ33MPRv8/EoKaH/4cNM//Zbgo8dIzIjA5cK+6crX7/ABtv/wS2Qmme/ThUw5mjT16I0qqrBvOWdh5B21IFkPwOmgB44OnekpKqEkqoSCisKSTuTRmFlISaLieuG+jTY3tW7hILKBrPr6KxPeHozgcENX5NU3TYWHAoAMGaPQHdiCFUng5H5QZAfDPoKKPGD6o50b5gVhe3teeaM9klIaPxQPvkE7rjDft5TT2lB+rXXqhp1RVEURbkYrRaICyFWAtcA3kKI48AzgBFASvk2sBEYB6QDZcA9rXUszdHr9bi6uuLq2rC2GmDEiBHNbv/tt982uayyspL8/HyysrLIyMhg1KhRDdbp1asXBQUFlJSUYLFYqK5uIi8FGDxqsN30mTNnSC3W8sZ/4RdWfraSgIAAoqOj6TOiD7179yY1NZVrrrmG6N7RGDzsv90O/g743OZD9ZlqZLXEsYcjVTlVtR/TmbqaemEU6BztO9npkmLmKGAwgfsZiftXlUTZLBdOAmHQYfRzxHFAR6j3HDLEy4vh5eWcNZnQA046HSerqsiuqsIk7WuwPRqp/U6IjaUqP5/Tnp5sGjCATQMG1C4L0unwqq6muKqK3mfPMlUIbq6/gwtIpu7YrWeDebe+vQ1SUrSJPuUwcaKW59EnCiIitEi3QwcQAikb1sgvHPUse3P2klOSg6ezJ2XVZeSW5ZJbmktOSQ6FlYW1617bL7TB9sKxuPYhrrrrj9D1R/vl6JBYcJDuiJBV2HZiVF2t5aNXVoKliTc6tgYOtJ8uKIAXX6yb7tMH+vaF6Oi6j68v6Bu+SFAURVEUxao1e02Zeo7lEniwtcq/HDg6OuLn54efnx9XXXVVo+ukp6fXfi2lJD8/n+zsbLKzszl27Bjff/89WVlZ5ObmNsiP37mzYVL4sWPHOHbsGOvXr6+d99xzz6HT6Thx4gRdunSpne8c6EzUf6Ia7KOGqcREyd4SylLKkFUNA8nytPKmTx6QFRKJpDKtHNPphjXKY446MjixCz63+mBwr7sVLVKSV11NalkZe0tKSCsvJ8jZucH2GRVN5xQdtlg4rNeDszPJzs4c6tixQSCeumED7jodXYqLtdyNnBzt/8xMOHoUfv4Zjh/X8jwiIxsWkp1d9/X+/dqnRocOWivL8nKIjES8/DJcc43d5v836P+aPH6AkqoSsgqzSM5LZkj3IXbLqsxVCCHs3qbUJ605U1WikK6+RrtlRiOEvBpDVOcoxva8iSGdbibvlAPZ2dppHT4M//sf5OVp6Ss96z2H/Pyz/XT906/h6ak1Sl23Dhp5llIURVGU3zXRWE3d5WzAgAEyLi6uvQ/jspCYmMiCBQs4cuQIeXl55ObmNlmjbjAYqKqyBm9WW7ZsYfbs2YwYMYIpU6Y0WmPfHCkllScqKfqliOq8amSVpCypjNLkUsqSyqjOrTuWDr07cFWi/cPI/nH7yf9aS44xdjHiPcEb9xHudLq+Ew4+DucsP6O8nB8LCkgoKaHMYiGvupqUsjIOlZXZZOdr7vL15aOICLt5Qbt2caSiAle9nj/5+TGza1dCnJ3trhGgVR+bzVoVsq2uXRtPvm7M88/D00/bz/vhBwgLu+h+DaWU5JXlkZqXytGCo6Tnp5N+Np30/HSSc5MpriquXbf4iWJcHOoi4b05e+m3rF/t9A3BNzAqcBQjAkYQ6xeLQWfAIi3odY1Xaa9frzX+zM/XLk1zdDqoqrKvHT9+XPsMGgT1L7eiKPaEEPFSygHnXlNRlCuNCsR/Q6qqqjh06BD79+8nMTGRbdu2sW/fPsrKyggLCyOlJo3C6s477+TTTz8FwGg0MmHCBCZOnMj48ePx9va+5OMpOVBC/sZ8in4pwmOkB91md7Nb/rP/z1RlN6wpR4DrAFdc+rrgea0nPpN9EPrzj9YqzGaSy8rYcOYMPxUWklxWxstBQdzWubPdesZt2xqkwHQ2Ghnl4cFN3t50dXSkv4sLrs2lsJSVQXIyJCXBwYPaZ+9eLcq0deCAlrZSo7paqzE3m7VWkj/8oHVG3kKklGQVZbH7xG4STyXy3Kjn7JY/9d1TvLjjxUa37WDsQHTnaPaf2s+owFHc0+8ebo1s2D6ixunT2iVITKz7JCTU5aD36AHHjtlv8+CDsHSplh00eLDWq8vQoarrRUVpzO81EI+Pj+9sMBjeBXrTugMQKkprsQAHTCbTfbGxsacbW0EF4r8DZ8+epbi4uEFqS3BwMIcPH26wvk6nY9iwYXTv3h1HR0fuvPNOhg0bhtFobLDupYjrH0dpYinSdI57UAcBTwfQ87mGedoX62RlJf47dzaX2QFoXUOGdejA/6KiCO3Y8fwLOHFCy9/4+mvYtw/27NGqhmt8/jlMmVI3bTTCyJEwbpz2CQmBU6dabRSgbce2Mf/7+SScTKC0urTZdd0d3Tn7+NmGbwqaYbHArl2wciUEB8Mc++736d1be2ax5ekJ48drqfbXXgseDTuaUZTfpd9rIL5v3761Xbp0ifDx8SnS6XRXVrCiKIDFYhG5ubnuOTk5STExMRMbW0cF4r9jixcv5rPPPiMxMZGysua74rvvvvtYvnx5qxxHaXIplccrKdpVRP7mfIp2FjXoEjLi8wh8b/e1myctEqG7+LyGSouFnYWFrD1zhkNlZfxUVESBydTouqlXXUVoS3YNMmcOLF7c9PIePSArS6smfvBB+6C9BUkpSc9P58djP/Jj5o9sy9jGsUL76usBfgPYc/8eu3kf/PoBqWdSmd5nOlGdm25n0JSoKO0lQlN0OnBz09Lq//53+5cJivJ78zsOxI9ER0efVUG4ciWzWCwiMTHRMyYmpldjy1UgriClJCUlhbVr17J27Vp27tzZoJePf//738yaNctuXnFxcZO9zVyK6rPVnF51msyXMqk8XgkCRpSPQOdQV6NcnV/Nz34/0zG6Iz3+1gOfW3wuqMa2MRYp2V9SwrozZ1iRk0O6tTGop8FA/rBhduv+WlzMHcnJPNa9O1M6d8bpQrsHMZlg505Ys0arNU9ObnrdXr201pNt5FjBMX7I+IFPEz/lh4wfWHzDYh4Y+IDdOv6L/Mku1hqrLrxmIfNHzr/gckpKtPSUI0e0xpy2bV9t3XmnNoqoovxe/Y4D8YyYmJi89j4ORblU+/bt846JiQlsbJkKxJUGTp06xZo1a3j//feJj4/HYrFQUFCAm1vdEO1lZWV4e3sTExPD3LlzueWWW9C3Ql915gozxbuL8Rhhn6eQNieNE4vrxn9yCnbCb4YfvtN9cerhVH83FyWzooKPTp7EWa/nr/XSeq7du5dvC7Q+vEOdnVkfHU3IpdSYHz+uBeQbN8KWLVBqky4yeTJ8+aX9+t98A/7+rV5VXF5djk7ocDTUjeaTX5aP18teduvdGHojDw58kOuCrkMnLjyVU0otr3ztWi1rJzW1bllWFnSzb15AerqW8qIovwcqEFeUK5sKxJWLVlZWxt69exkyxL77vCeffJKXXnoJ0LppXLJkCVOnTqXjheRRX4KdgTupPNbIaDgCfG7xIeDpAFxiWqe/PLPFgtP27Q0aeo7x8GCmvz8Tvbww6i6hXVF5OXz1lVZd/NNP8OuvWifdNaQEHx9tBJ4JE+D117Va8zay/9R+Rnwwwq6f8xrBnYL5S+xf2JS+iTui7+D2qNvp6HDh98TevfDqq9qooGvX2i9buxZuukl7Dpk9Gx59VPW8ovy2qUBcUa5sKhBXWlx4eDipttWWgLu7O/PmzWPu3LmtkrJiy1xuJvPlTHKW51BdUI2lpOGoNE69nAheHIz3+EvvAcZWpcXCs0eP8vbJk43mlPs5OBDs5MQtPj7M9PfH4VKC8hMntIjT1rffaq0ZQYtAFy+GBx5o0y5HzBYzm9I38ebuN9l8eHOT63V3686ROUcw6Fru2Pr10wL1GgMGwAsvaJdEBeTKb5EKxNtPamqqw4033hiSlpZW27x83rx5XV1cXMwLFy48VTMvPT3deMcdd/TMy8szCiG4++67c+fPn38aoKysTAwaNCi8qqpKmM1mMWHChLOvvfZaNoC/v390x44dzTqdDoPBIA8cONBonuLhw4eN33//vcuf//zns619zraaOr7bbrst8LvvvnP38vIy2V6b+p5//vnOK1as8JFSctddd+UuWLDgNMBzzz3X+eOPP/YRQhAeHl72+eefZ3To0OGyC0gb+15fjOYCcdUdkHJR3n33XW666Sa7dJTCwkKeeeYZgoKCGDduHLNmzSK7qcTfS6R31tNzQU+uzrqaoaeGEvFpBJ7XetqtU3GkggM3HuDowmbGrr8IjjodLwUFcWboUDZGRzPRy8vuB+lkVRXbi4p4+PBh/HfuJK+qkS4az1f9IBy01JUaUsL//Z824NDHH2u551VV2v+tSK/TMz50PJvu3MShhw4xd/Bc3B3dG6xXVFnU4mXX77o9Lg6uvx5GjdJeICiKorQ1o9HIq6++evzw4cMH9+zZk/zee+91jo+PdwJwcnKSO3bsSE1NTU06ePBg0nfffef23Xff1b4q3LZt26GUlJSkpoJwgI0bN7olJCS0YI8B56+x47v33nvz1q5dm9bcdnv27HFasWKFT0JCQnJycvJPHVUfAAAgAElEQVTBTZs2eRw4cMDx6NGjxnfeecd37969SWlpaQfNZrN49913O7X+mVyeVCCuXJRhw4axZs0acnJyeOWVVwi2SdjNzc3l66+/5q233iIwMJA9e/Y0s6dLp++gx3eaLzHfxBD7aywu/WxSUnTgP6uRYLYF6IRgrJcX/4uO5ujgwcwPCKCLg/1ARAXV1ehaupr2b3+DZ5+1H6oyLQ3uugsiIuC++7SBglas0AL1VhbiFcKi6xdxYt4Jlt24jAjvuoGTXhrzUoPa8NS8VMyWc4wC1IycHC1zZ+BAcKxLXWfbNhg2DIKCYNmyi969oijKBQsICKgeNmxYGYCnp6clKCioPDMz0wG0LoHd3d0tAFVVVcJkMokL6Vxg8+bNLvPnz+++fv16z/Dw8MikpKRzj3jXysaOHVvi4+PTbI1PYmKic79+/UpcXV0tRqORoUOHFq9atcoDwGw2i9LSUl11dTXl5eW6bt26NRiNsKioSHfNNdcEh4WFRYaEhEQtX77cE2Dp0qWdoqOjI8LDwyOnTZsWYLJWPC1ZssQrNDQ0MiwsLHLSpEm1/R0/++yzviEhIVEhISFRCxcu7Azam45evXpFTZkyJSA4ODhq6NChISUlJQLg8ccf7xIYGNg7NjY2LC0tzbG5Y2kJKhBXLom3tzePPPIIKSkpfPDBBw36Kq+ursbBoe1+Z7j2dSU2PpaQpSEYvAx4TfDCwdu+/MqcSop+bdma2h5OTizs2ZNjgwfzWlAQLtZ0lFldu9KpXv/rZ6qrG/RKc0E8PeGZZ7RWjM89Z9/hdnq6VjN+5AjcfbeWaN1GOjp05P7Y+zk46yA/zviRmbEzuT/2frt1SqpKGPTuIGLejmF18uqLvg6TJsHu3VpnMvWzco4c0eZ16QJ5KrtUUZQ2lpqa6pCUlNRh5MiRJTXzTCYT4eHhkb6+vjEjR44sGj16dG2L/DFjxoRERUVFvPLKK43mUV5//fUl0dHRpatXr05PSUlJioyMvOjXrLGxsWHh4eGR9T9r1qxpMp/0XMfXlL59+5bv3r3bNScnR19cXKzbsmWLe1ZWlkPPnj2rH3zwwZyePXv26dy5c4yrq6t58uTJDf4or1692q1Lly7VqampSWlpaQcnT55clJCQ4PTFF190iouLS0lJSUnS6XTy7bff9oqLi3N65ZVX/LZt23YoNTU1admyZZkA27dv7/DZZ595xcfHJ8fFxSWvWLHC56effnIGyMzMdJo9e/bp9PT0g+7u7uYVK1Z4bt++vcNXX33VKTExMWnLli1p+/bt69jUsVzYlW+aCsSVFqHX65kxYwaHDh3i9ddfr+1hZejQocTExNitazKZLi0QPQchBP4z/RmaO5SoLxv2KpI4NpGE/gkk3ZWEpbphbvmlcNDpeLh7d04OGcKLPXvyUlCQ3XIpJQPj4+kXF0fqOfpuPycPD1iwADIyYOHCxkfAsenppq0IIRgeMJylNy5Fr7PvSWfOpjkUVhZyMPcgf1n/FypNjTS4vQD+/vDWW5CSonVzaKuyEjr9bl92Kspvz7x5dBWC2PP5TJ1KQP3tp04lwHadefPoeq4ym6q5bmp+YWGhbvLkyUH/+Mc/sjp16lT7B8ZgMJCSkpKUmZm5PyEhoeOePXucAHbs2JGSlJSU/M0336QtX76889dff91oLwNHjhxx6tu3bwVAUlKSw+233x5www031LbSX7JkidfTTz/tO2XKlIDx48f3Wr16daO//OPj41NTUlKS6n8mTZpU3Nj653t8jenfv3/FnDlzcsaMGRM6atSokKioqDK9Xk9ubq5+w4YNHunp6Yk5OTn7y8rKdEuXLm3w27p///7l27dvd5s5c6b/pk2bXLy8vMybNm1yPXDgQIeYmJiI8PDwyB07drgdOXLEcfPmzW4TJkw46+fnZwLw9fU1A/zwww8u48aNK3Bzc7O4u7tbxo8ff3br1q2uAP7+/pVDhgwpB+jXr19ZRkaG49atW13GjRtX4OrqaunUqZPluuuuK2jqWM73OpyLCsSVFuXo6MicOXM4ceIEzz33HB988EGDde6//34mTZpETk5Oqx6LEAKd3v4Wz34vm5K9WiXF6Y9PsztsN0W7Wz6P2cVg4ImAADrU69Lx7exsjlZUsK+0lOg9ezhWXn7phbm7w/z5WkD+/PN1AXm3blqtuC2LpU3SVRpjkRY2p9c17Mwry+P6T68nPT/9kvcdFKS9CPjmGwiw/vl9+WX7wUwVRVEulK+vr6mwsNDuF3l+fr7e29vb9NJLL/nU1ChnZGQYKysrxfjx44Nuu+22/Lvvvrugsf15e3ubhw8fXrxu3Tp3gJ49e1YD+Pv7m8aPH1+wc+fOBt1MnTx50uDq6mquGd06MjKy6j//+Y/dyGtxcXEdFi5ceGrVqlXHPvzww2OrVq1qNHXiQmvEz+f4mjN37ty8gwcPJsfFxaV6enqaQ0NDK9atW+fWo0ePyq5du5ocHR3lpEmTCn7++ecGAX6fPn0qExISkqKjo8vnz5/v/9e//tVPSiluu+22MzUPEBkZGQcWLVp0UY3RHBwcav8Y6vV6aTKZmswXauxYLqbMxqg/U0qrcHFxYcGCBYSEhNjN37JlCx988AFr164lLCyM9PRLD8IuhKySCEPdz1rF0QoSBieQ9nAapuLWbeAI8JZN49VqKfljUhJpl1ozXsPdHZ5+WgvIZ86EL76wT6IGeOUVrcvDoy3bgPV8mC1mHrrqIVwc6n7f/njsR/ot68dHez9CSkmFqeKSyrj2Wu30t22DP/3JfpmUcPvt7XLqiqJcodzd3S2dO3euXrt2rSvAqVOn9D/88IP76NGjS5544oncmoCwR48e1VOmTAkIDQ2tePbZZ+162MjOzjbk5eXpAUpKSsTWrVvdIiIiKoqKinRnz57VgZaDvHXrVrc+ffo0qJ1JS0tz8PX1bTIdpbKyUhgMBqmz1jw8+eSTfrNnz85tbN0LqRE/3+NrzokTJww157BhwwaP++67Lz8wMLAqISHBpbi4WGexWPj+++9dIyIiGvzyz8jIMLq6ulpmzZqVP2/evJy9e/d2uOGGG4rWr1/vWbPfU6dO6Q8dOuRw/fXXF61bt84zJydHXzMfYNSoUSUbN270KC4u1hUVFek2btzoOWrUqEZr/wFGjx5dsnHjRo+SkhJx9uxZ3ZYtWzyaOpYLuQ7NUYG40qYef/zx2q+Li4tZtWoVplbu4cOW/0x/Bp8YjOsgV4SjNSCXcOKNE+yJ2sOZDWdatfyVkZH0telr/ZfiYvrGxbEsO7vl0nXc3bU+yAcNsp9/7Bg8+SRs2KANBPTLLy1T3nky6o38bdjfODL7CLOvml3biLOkqoQZ/5vBpFWTCHojiFd/fvWSGnMCjBjRsCvDhx6C//4XQkLgxRcvafeKorSDRYvIlpL48/msXMmx+tuvXMkx23UWLeK8alI/+uijoy+88IJfeHh45MiRI8Mef/zx7KioKLu8ui1btrisWbPGa8eOHa41tcyff/65O0BWVpZx+PDhYaGhoZH9+vWLHDVqVNHUqVMLjx8/bhg8eHB4WFhYZP/+/SOuu+66gltvvbXBK9qYmJiK/Px8Y0hISNSWLVsa1Ehv3rzZZfjw4SUWi4WZM2f6jx8/vrCm4eilaO74JkyY0HPYsGHhR48edfT19e3z2muv1eaPjxw5MjgjI8MIMHHixKCgoKCoG2+8Mfj111/P9Pb2No8ePbp0woQJZ/v06RMRFhYWZbFYxLx58xo8OMTHxzv37ds3Ijw8PPKFF17oumDBgpOxsbEVTz/99IkxY8aEhoaGRo4ePTo0KyvLOGDAgIpHHnnk5PDhw8PDwsIiZ82a1R1g2LBhZdOmTTvTv3//iNjY2Ijp06fnDh06tMmHiWHDhpXdfPPN+b179476wx/+ENKnT5/Spo7lUq9vDdWPuNJmpJS89dZbzJ07lyqbLv0GDRrEihUrCA0NbdPjKT9azqEHDnH2G/tuWV36uhCzNQajh7GJLS+NWUpezsxkQUYG1TY/fzd4euLv6MhTAQH0dHZu+YLvuQc+/FD72tkZduyA/v1bvpzzFJ8dz7TV0zh05lCDZdOip/Hp5E9brKyjR7X0Fdtfd489prV1dWqZgVgVpdWofsQVWzk5Ofp58+b5b9++3e3OO+/MKyoq0r/44osn33zzTe+VK1d6xcTElPbt27f8sccea7RWXGl7akAf5bKSlpbG9OnT+cWmRtbZ2ZmXX36Z8vJy7rvvPjwaa3jYCqSUnP7sNOkPp1OdV9d7kt5NT2xcLB1CWq/b1l+Li7kzOZmkeqkpTjodrwcHc7+fX5MNgi6YlDBmDGzdWjfPwQHee69hK8c2VFpVytzNc1mesNxu/tzBc1l0/aIWK0dKbQTORYvsg/GoKK2Xx3Z8HlGUc1KBuNKcu+66q8eKFSsy2/s4lKapAX2Uy0pISAg7duzgxRdfpKbxSXl5OQ899BCPPvoogwcP5mgbJfIKIfC9w5eByQNx6GbTzaEAB7/W7Xaxn6sr8bGxzO3WzW5+hcXCspYeCEkI2LwZ/v53qOlOsaoKpk/X8sotFi11pY11dOjIOxPe4cvbv8TDUXv40gs9f+7/5xYtRwgtPX7/fhg8uG7+wYNaBs8777RocYqiKG1GBeFXNhWIK+3CYDDwxBNPsHv3bnr37m23LDU1lQcffLBNj8fB24HBRwbjeb0nwijov6s/BpfWHzLeSa9nUXAw38bE0Nmmv/E3Q0Jarja8htEITz0F+/ZpI3HWqBkfPiwM/vrXVh+VszGTIyaTOCuRUYGjWDJuCRE+EXbLz5af5UzZpefv9+4NP/8M//43dLC+7DCZ4C9/gZgYrctDRVEURWkrKhBX2lXfvn2Ji4vj0UcftZsfHR3d5seiM+qI2RTD1dlX0zHcvj2MlBKLqWX7HLc1xtOT1KuuYoqPD3/r3p2h7g2Hi28xERFaNHrDDXXzvv9ei0JffRXmzGm9spvRza0b3971LX+J/Yvd/EpTJRNWTmDI+0M4cvbIJZcjBMyapT2P2Kak7N8PPXtCrsqqVBRFUdqICsSVdufo6Mi//vUvVqxYgV6vZ+zYsbz00kvtdjz1R+IESJmRwi9Bv1CVe9EDmp2Th9HIyqgoXujVq8Gyt0+cYF9JSSNbXSR3d1i3DmbPtp8vBIwb13LlXCCd0DV4E3Dbf2/jp6yfOHTmEEPeG0JBRaPd816w4GCtm8PAwLp5p061S4aOoiiK8julAnHlsjF9+nS2b9/O6tWr0dUbiSUhIcGup5W2dPzN45xacYrKzEp2Be6iaE/LDwBkS1cvEH0nO5uZaWkMSUjgm/z8livIYIA33tDyNGqut6cn9O3bcmVcoqzCLH7K+ql2Otw7HHfHlntb4OIChw5BbKw2/cknMOB31yROURRFaS8qEFcuK1dffTVO9fqTS0hIYNiwYVx33XXkt2Qgep5OfVI3NoOlzMLe0Xs5+/3ZZrZoOdkVFTyYlgZAmcXCvPT0lutvvMasWbBpE/j5aUNT+vvbL7e0XkrOubg5utG7c10bgm3HtnH/uvsvuZ9xW0Yj7Nmj9eY4dWqL7VZRFEVRzkkF4spl7cyZM4wcOZLy8nK2bdvGlClT2vwY+v/cH6+JXrXTlhILiRMTKdxV2OplF5rNeOjrRlc+WFbG0pbuUQW0xppHjtRVDdc4fVrrZuSbb1q+zPPg7uTOpjs2MTFsYu28d399lz9+8UcqTZWYLWYs8tIfFISAoUMbzv/kE619q6IoiqK0BhWIK5e19evXU2KTG20wGNo8RUXoBdH/iybmuxgc/LX8cUuphcSxiZTsa8G87UZEdOzInthYgmzeEjyUlsZTR460fM14/ZFtTp6Eq6/Wqotvuklr0NkOnI3OfHn7l9wVc1ftvC+Tv2T8Z+N5YP0DzFgzo0WC8fq2b4e779ZG4bz77hbfvaIoiqKoQFy5vN1+++1MmjSpdvrrr7/m3nvvbfkg9Dx4jvYk5tsYjD5aN4OmAhN7x+yleF9xq5Yb6OzMrv79GeTqWjvvxcxMHk5Pp7C6upktL9GOHdqQlKD1qJKT03plnYNBZ+CDmz7g4UEP18777uh3vPvru3y8/2Me2/JYi5d56611WTkrVsDnn7d4EYqiKMrvnArElcuas7MzX3zxBbNmzaqd9+mnnzJ//vx2OZ6O4R3p800f9O5auojpjIlfB/9KWXrZOba8NN4ODnzXty/jOnWqnbf4xAl67NrF+ydPtk6hH35oPwylo2PrlHOedELHousX8fdRf2+wbPeJ3VSaWrYT8G+/BWfnuun774fExBYtQlEURfmdU4G4ctnT6/UsWbKE+++/v3beCy+8wBtvvME//vEPLG3cmNC1ryuRKyPB2rmJpcJCfGw8lsrWPY6Oej3/692b23x8aucVmc38OTWVjWcufbCbBlaurOtoW0qYNk2LTtuREIKnRjzF0nFL7eYP6T4ER0PLPihER8Pu3VBzuYuKtJ4djx9v0WIURbnM6fX62PDw8MiQkJCosWPH9iouLj6v2Ck9Pd04aNCg0KCgoKjg4OCo559/vnPNsrKyMhEdHR0RFhYWGRwcHDV37tyuNcuef/75ziEhIVHBwcFRCxcu7Nz43uHw4cPG5cuXe17a2V04f3//6NDQ0Mjw8PDI3r17R0Dz52qrqfWaux6Xm3nz5nVdsGCBb0vtTwXiyhVBCMG///1vxtn0cf3www/zxBNPMG/evDZPVfEa60XnqXW/Z7rc2wWdY+v/OBl0OlaEh3OVTZqKBTjeGkNCurnB119DaKg2XVUFkyZp0anJBIcPt3yZ52nmwJm8P/F9AGbEzGDhqIWtUk7v3rBlC9Rc7uPHtWC8sPXb6SqKcplwdHS0pKSkJKWlpR00Go3y1Vdf9Tn3VmA0Gnn11VePHz58+OCePXuS33vvvc7x8fFOAE5OTnLHjh2pqampSQcPHkz67rvv3L777ruOe/bscVqxYoVPQkJCcnJy8sFNmzZ5HDhwoNFaho0bN7olJCR0aMlzPV/btm07lJKSknTgwIFkaP5cbTW1XlPXo+3PrO2pQFy5YhgMBj7//HP62w6HCLzxxhv88ssvbX48EZ9E4DXJi8DnAgl5LaTNynXS69kQHU2gtXHlQFdXbvc5r78LF65zZy0S7dZNmy4thbFjYeJEGDRIG46yndzT7x62zdjG+ze9j4PefhAmk8XUYuXExMDq1Vq366Clp0RFtWvKvKIo7WTYsGEl6enpjqmpqQ4hISFRNfMXLFjgO2/ePLta3ICAgOphw4aVAXh6elqCgoLKMzMzHQB0Oh3u7u4WgKqqKmEymYQQgsTEROd+/fqVuLq6WoxGI0OHDi1etWqVR/3j2Lx5s8v8+fO7r1+/3jM8PDwyKSmp4Uh0bai5cz2f9Zq6HvUVFRXprrnmmuCwsLDIkJCQqJo3AkuXLu0UHR0dER4eHjlt2rQAk0n7G7BkyRKv0NDQyLCwsMhJkyb1rNnPs88+6xsSEhIVEhJS+9YhNTXVoVevXlFTpkwJCA4Ojho6dGhISUmJAHj88ce7BAYG9o6NjQ1LS0tzbO5YLpQKxJUriouLCxs2bKBHjx6183x8fIit3+1eGxBCEP1VNIELAtu8bG8HB77p04cHu3ZlR79+eBiNrVdYjx5a94Ve1i4c8/O1mvIzZ2D8eMjLa72yz2FEwIgGI3GuP7Se6LeiOVF0osXK+cMf4P3366ZPnIA+faC8vMWKUBTlMlddXc3mzZvdoqOjL/gnPzU11SEpKanDyJEja7vaMplMhIeHR/r6+saMHDmyaPTo0aV9+/Yt3717t2tOTo6+uLhYt2XLFvesrKwGAe31119fEh0dXbp69er0lJSUpMjIyIvuTiw2NjYsPDw8sv5nzZo1rk1tM2bMmJCoqKiIV155xft8zrUx9ddr7HrU32b16tVuXbp0qU5NTU1KS0s7OHny5KKEhASnL774olNcXFxKSkpKkk6nk2+//bZXXFyc0yuvvOK3bdu2Q6mpqUnLli3LBNi+fXuHzz77zCs+Pj45Li4uecWKFT4//fSTM0BmZqbT7NmzT6enpx90d3c3r1ixwnP79u0dvvrqq06JiYlJW7ZsSdu3b1/Hpo7lwq68RgXiyhWnS5cufP3117i7u+Ph4cGGDRswtmYgeoEqT1WSfHdyq6fLhHTowJLQUBzqjUJaYjJRYmq5GmEAIiK04LtjvTeFgwfbt2hsZ+8lvMfElRNJyUth3GfjKKpsuVFQp0/X0uRr5ObCnDkttntFUc5h3jy6CkGsEMTOm0eDHOI//5luNcufeYYGObxTpxJQs/yVV2gQQDalsrJSFx4eHhkdHR3ZrVu3qjlz5lxQ7UNhYaFu8uTJQf/4xz+yOnXqVNuYyGAwkJKSkpSZmbk/ISGh4549e5z69+9fMWfOnJwxY8aEjho1KiQqKqpMbzOWhK0jR4449e3btwIgKSnJ4fbbbw+44YYbetUsX7JkidfTTz/tO2XKlIDx48f3Wr16tVtj+4mPj09NSUlJqv+ZNGlSo12C7dixIyUpKSn5m2++SVu+fHnnr7/+2uVc53o+16Sx61F/u/79+5dv377dbebMmf6bNm1y8fLyMm/atMn1wIEDHWJiYiLCw8Mjd+zY4XbkyBHHzZs3u02YMOGsn5+fCcDX19cM8MMPP7iMGzeuwM3NzeLu7m4ZP3782a1bt7oC+Pv7Vw4ZMqQcoF+/fmUZGRmOW7dudRk3blyBq6urpVOnTpbrrruuoKljaep8m6MCceWKFBkZycaNG4mPj2fgwIHtfTi1yo+WsztkN6dWnOLwY22fQ326qoqB8fHcdvAg1S3diHXgQFizBhxsKme6dGkYnLeTSlMlnx34DIn2AFRYUUheWcvW1n/yifbsAdrlePPNFt29oiiXoZoc8ZSUlKSPPvooy8nJSRoMBmnbUUBFRYUO4KWXXvKpqVHOyMgwVlZWivHjxwfddttt+XfffXdBY/v39vY2Dx8+vHjdunXuAHPnzs07ePBgclxcXKqnp6c5NDS0ov42J0+eNLi6upprKqEiIyOr/vOf/xyzXScuLq7DwoULT61aterYhx9+eGzVqlWNpk5caI14z549qwH8/f1N48ePL9i5c2dHgPM51/NZr/71sNWnT5/KhISEpOjo6PL58+f7//Wvf/WTUorbbrvtTM33KCMj48CiRYsuauQ7BweH2ho0vV4vTSZTw/yYZo7lYspUgbhyxRoyZAi9evWymyelZPny5eS1U7rEwVsPYi7WHoqPv3Kcgh+b/F3U4opNJiJ27yalvJxNZ8/ywKFDLV/IH/4An32mjQv/zDPw2mstX8ZFSstPY9fxXbXTfq5+dHPr1qJlCAE//ggLF8KuXe3eo6OiKO2kW7dupvz8fENOTo6+vLxcbN682R3giSeeyK0JCHv06FE9ZcqUgNDQ0Ipnn332lO322dnZhry8PD1ASUmJ2Lp1q1tEREQFwIkTJwwAaWlpDhs2bPC477778uuXn5aW5uDr69tkOkplZaUwGAxSZ31j+uSTT/rNnj07t7F1L6RGvKioSHf27Fldzddbt25169OnT7nFYqGpc7XV1HrNXQ9bGRkZRldXV8usWbPy582bl7N3794ON9xwQ9H69es9a67bqVOn9IcOHXK4/vrri9atW+eZk5Ojr5kPMGrUqJKNGzd6FBcX64qKinQbN270HDVqVJMDgowePbpk48aNHiUlJeLs2bO6LVu2eDR1LE3tozmGi9lIUS5Hp0+f5o9//CM//PAD69evZ82aNQ3yh1tbxMcR7Ineo3VlAqTcm8KAXwdgcG39H7Wv8vLIt0lJOVVVhUVKdC19DW65BTIyoGsjvUtZLKBrn+f73p1788nNnzD5P5MB2HV8Fw+sf4D3Jr7XoveB0Qjt1I29ovyuLVpE9qJFNFnTuXw5x5cvp8kORleu5NjKlRxravmFcHR0lI888sjJgQMHRvj6+lYHBwc3CBq3bNnismbNGq+QkJDy8PDwSIDnnnvuxB//+MfCrKws44wZM3qazWaklOKmm27Knzp1aiHAxIkTgwoKCgwGg0G+/vrrmd7e3g1SHmJiYiry8/ONISEhUUuXLs249tpr7fKpN2/e7DJ8+PASi8XCgw8+6D9+/PjCmkaSl+L48eOGm2++ORjAbDaLW2655cytt95atHnz5ibPFWDkyJHBH3300bHU1FTHxtYLDAysaup62IqPj3d+4oknuul0OgwGg1y6dOmx2NjYiqeffvrEmDFjQi0WC0ajUS5evDhzzJgxpY888sjJ4cOHh+t0Otm7d++yL7/8MmPYsGFl06ZNO9O/f/8IgOnTp+cOHTq0PDU1tdEGr8OGDSu7+eab83v37h3l5eVV3adPn9KmjuVirqlojxEKL8WAAQNkXFxcex+Gchl6+eWXeeyxuhEWP/30U6bZJvW2keNLjnPk0SNYKrRovPO0zkR8EtHqDwXVFgs37t/PNwVaLbyjEMQPGEBUW6SOSKnlaXz1ldawsx1z9v+545/87bu/1U3/4Z88NrTlR960JSU8+SQ8+ijYjLmkKC1CCBEvpRzQ3sfR1vbt25cRExPTfq3BryA5OTn6efPm+W/fvt3tzjvvzCsqKtK/+OKLJ998803vlStXesXExJT27du3/LHHHmu0VlxpXfv27fOOiYkJbGyZCsSV34yxY8eyadMmANzd3Tl8+DBeNT19tLFTn54i+c7k2umw98Lwu/ei0scuSJHJxLBffyWxVKsc6e/iwq7+/TG2Zi21lHDHHdoAQADz5sGrr7Zeeec8HMm9a+/lw70fAiAQfHzzx/x47EfeHPdmg64OL9XhwzB6NGRmQt++8OuvLbp7RVGBuHLB7rrrrh4rVqzIbO/jUDTNBeIqR1z5zVi2bBmu1pFXCgsLmd+O+QO+d/jS5d4utdNpD6VRcrDZnpxahJvBwMrISByttSQiHcoAACAASURBVO8JJSW8mNnKv4tPntQG+amxfTtUNHhL22aEECy7cRkjAkYAIJFM/2o67yS8w/Pbnm/x8l54QQvCAfbuhUWLWrwIRVGUC6KC8CuHCsSV34wePXqwZMmS2um33nqLzZs3t9vxhCwOoUOE1nbDUm5h78i9mMsvqnejCxLVsSN/71k7bgHPZ2RwT0oKvxY32Rbl0rz5Zt0omzodvPUWODXodapNOegdWHnLSjyctHEwanpSefnnl8kpadmReJYvB9vxlN5+G8ouORNTURRF+T1QgbjymzJ9+nRuvvnm2ul77rmH/Px8SksbjAvQ6vQd9YS9HwbW1HDTGRMHJh1ok7Lndu/OMHet5ycz8GFODnckJ1NuboUHgWee0YafBK2x5l13XRaRaFfXriy+YbHdvMVjF9PFpUsTW1wcvV5Li6959khLg8cfb9EiFEVRlN8oFYgrvylCCJYtW0bnzp0BOHnyJCNGjCA8PJzc3LZvo+I2yA2XmNqxDiiOL26TWnG9EHwYHo6TTQPR5LIyPmiNcdmdnLT88JqBfZKS4JFHWr6ci3Bnnzu5MfRGALq4dCHIM6hVyunbF2xexrBkiRacK4qiKEpzVCCu/Ob4+PiwfPny2umDBw9y/Phx7rvvvlYf7bI+IQR9vu2DrqMO95HuXH38avTOjY+S1tKCnJ1ZFBxcO20Qgptaq/FqRAS88Ubd9Ntvw+rVkJgIP//cOmWeh5p88VkDZpH8YDJjeo1ptbLuvRcmTqybvucebfRNRVEURWmKCsSV36SJEydy77332s3Lzc2lpKT1G0zW5+DlwJBTQ+j3Qz/0Tm0ThNd4oGtXrvXwINzZmYTYWPxbM3f7vvvg1lvrpqdNg379tFSVdmy82dW1K/8e/+/afPEaZdVl5Jc3GCfjoglhny+enQ2RkVDV5JAbiqIoyu+dCsSV36zXXnuNwMDA2ulbb721tleVtmbo2D5jZwkhWBkVxb6BA4l2cTn3BpdWGLzzDvTooU1XVoLZrDXk/Oc/W7fsCxSXHUf/Zf2ZsWZGi74l6dxZuwQ18vJg6tQW272iKIryG6MCceU3y83NjQ8//JDOnTvz5ZdfMm/evPY+pFpSSjJfzqTyVGWrl+VlNOLQSD/iWa1RS+3pCZ9+aj+6Zng43H57y5d1kY4VHOPq964m9Uwq6w6t492Ed1t0/5MmwcCBddMbN2rPJIqiKIpSnwrEld+0kSNHcvToUSZPnvz/7N15XFTl/gfwzzMLoOyLLIKAwMAwLIOiWQIZ8HODzCUxNE3r5i21a4mmWWoupf28al41fnW9lVCJlaKJkUhe9IJpsRRXGRhZRBaBRJZh2Ic5vz+GYVFWmzND9bxfr3l5zpkz5/uc6d7hmWe+z/fRdVO61F6qxQ92P6BoYxFyI3MHf4GG1SsUWHDjBkTp6ShhozMeGAi80b2yJTgcVWd8BGhVtGLn5Z1QKBUAgFG8UTDgaT5dR11FxdUVyM0F9PU1HoKiKIr6A6AdceoPb/To0Q8cS09PR13nUvDaduf/7qC9qh0AUHepDrWptVqLzTAMHsvKwunqasg7OrC5qIidQG++CTg7A6++CqSlqdJWRgA9rh4q5BVd+9aG1nha9LTG45iZATdvqh49sqMoiqIoqhfaEaf+VMrKyrB06VI88sgjeOedd3TSBo+jHiD63R3TwtcKwSi1U80ltb4euT1qfN9sbka7Uqn5QIaGqjKGBw+q0lVGCEII/jnnnzDRNwEA3K6/jS3/3sJKrHHjemfoUBT1+yOVSvUEAoFXz2NRUVFjt23bZtPzWEFBAX/KlCnurq6uXm5ubl67du2yVj/X1NREfHx8PD08PERubm5e69atG6t+zt7e3sfd3V0kFApF3t7env21o7CwkH/06FGtfpgOdE8RERHOFhYW4vvfm/v1dV52dra+UCgUqR9GRkYTdu7caT3QdXSlr//Wmkb/TFB/GgzD4P3338cXX3wBADh06BDy8/O13g6eCQ9u77sBnfM35VlyVH1epZXYj5uZYalN92dKm1IJDluj1eq64j0lJvYuc6gDDiYOeH/m+137B68dxJWSK1qJXVyslTAURWkZn8/H/v37ywoLC3PS09NzP/74Y+vMzEwDADAwMGDS0tKkUqlUkpOTI7l48aLJxYsXDdWvvXz58s28vDzJjRs3+s1VTExMNMnKynrw510WDXRPL7zwQvXZs2cH/QPa13lisbg1Ly9P0nnPEgMDA2VkZKRufqIeAWhHnPrTkMvliImJ6dp3d3eHnp6eTtpiv8oejhscu/aLNhVBIVNoJfbfXVxg2DlU+9/GRnxRpYUvATIZMH8+EB4OvP46kJfHfswBPO/3PGa6zgQAMGCwLmkdGlob8HPFz6zEKywEJk0Cxo8HkpJYCUFRlA45OTm1BwYGNgGAubm50tXVtbmkpEQPADgcDkxNTZUA0NbWRhQKBSHDGABJSkoy2rp167hz586ZC4VCkUQi0cofroHuafbs2fIxY8YM+kdrsPPOnj1r4ujo2Oru7v5AoVeZTMZ54okn3Dw8PEQCgcBL/YtAdHS0hY+Pj6dQKBQtWbLESaFQXf7IkSOW7u7uIg8PD9G8efPGq6+zfft2G4FA4CUQCLzUI+9SqVTPxcXFKzIy0snNzc0rICBAIJfLCQBs2rTJ1tnZ2dvf398jPz9ff6C2aAKrHXFCyCxCiJQQUkAIeaOP5x0JISmEkJ8JIf8lhISx2R7qz83Y2Bhvv/121355ebnOyhkCgONbjtAbq/o8batsw+13bmslrq2+PtaPG9e1v+XWLbR0sLzap0wGXOkcdW5vB956i914g1Av9KPPVc2iTL+TDseDjngy7kk0tTcN8urhCwgAMjNV288/D2h5XSmKorRIKpXqSSSS0dOmTetauEKhUEAoFIpsbGzE06ZNk4WEhDSqnwsNDRV4eXl57tu3z6qv682cOVPu4+PTGB8fX5CXlycRiUQPvTqBv7+/R8+0EPXjzJkzA/4x7OueNCEuLs5i4cKF9/p6Lj4+3sTW1rZdKpVK8vPzcxYsWCDLysoyOHnypEVGRkZeXl6ehMPhMB9++KFlRkaGwb59++wuX758UyqVSj766KMSAEhNTR19/Phxy8zMzNyMjIzc2NjYMVeuXBkFACUlJQZr1679taCgIMfU1LQjNjbWPDU1dfTp06ctrl+/LklOTs7Pzs427K8tmnoPWOuIE0K4AD4AMBuACMBiQojovtO2APiKYZgJACIBRLPVHooCgJdeeglunatN1tXV4d1339VZW3hGPLj+vXvJ9dIDpZDf0M6CQxvGjYM1n6+K29qKQ2Vl7FRQAVS1+yZP7l5m8tFHVStv6piTmRNeeeSVrv26ljrcabiDA1cPaDxWz+kIFRVAerrGQ1DUH15UUtRYsoP4D+Wx+ORip/tfv/jkYqee50QlRY3tK05P/Y1c93e8vr6es2DBAtf33nuv1MLComsCDo/HQ15enqSkpOS/WVlZhunp6QYAkJaWlieRSHIvXLiQf/ToUevvvvuuzwUfioqKDPz8/FoAQCKR6C1atMhp1qxZLurnjxw5YrllyxabyMhIp/DwcJf4+HiTvq6TmZkpVaeF9HzMmzevob/3oL97+q1aWlrI999/b7ps2bI+KxZMnDixOTU11WTVqlX258+fN7K0tOw4f/688Y0bN0aLxWJPoVAoSktLMykqKtJPSkoymTNnTq2dnZ0CAGxsbDoA4NKlS0ZhYWF1JiYmSlNTU2V4eHhtSkqKMQDY29u3Tp06tRkAJkyY0FRcXKyfkpJiFBYWVmdsbKy0sLBQzpgxo66/tmjqfWBzRPwRAAUMwxQxDNMG4ASAufedwwBQ/4/FFMAdFttDUdDT08N7773XtX/kyBHcunVLZ+2xXmwNQ7/OVMEOICciR6MLzPTHmMfD2z3KebxVXAxxRgZq2ts1H0xfH1i7tnv/5k2Aq90VRvuzOXBz18RNADAzMIOLucsAr3g4L74ICATd+zt2aDwERVEssLGxUdTX1/f6wKqpqeFaWVkp9uzZM0Y9olxcXMxvbW0l4eHhrhERETXLly/vM+fZysqqIygoqCEhIcEUAMaPH98OAPb29orw8PC6q1evGt7/moqKCp6xsXEHv3PwRCQStX311Ve9fkLNyMgYvXPnzqoTJ07cPnbs2O0TJ070mTox3BHxodzTwzp58qSpSCRqGjduXJ+pK76+vq1ZWVkSHx+f5q1bt9pv2LDBjmEYEhERcU/9BaK4uPjGgQMHHqrvqKen1/XHlsvlMgqFot98ob7a8jAx+8JmR9weQGmP/bLOYz1tB7CUEFIGIBHA31hsD0UBABYsWIDHHnsMANDW1obNmzfj1KlTkMk09kvTkBFCYPZ499LrzXnNqPpCOxM3V9rZwa1zyXsFw6BOocA7t1lKj1m3TpUgDQA1NSOmJ2o52hKvT329a99E3wTPeD3DSqyTJ7urOCYmAt9/z0oYiqI0yNTUVGltbd1+9uxZYwCoqqriXrp0yTQkJES+efPmu+oOoaOjY3tkZKSTu7t7y/bt23t9iN+5c4dXXV3NBQC5XE5SUlJMPD09W2QyGae2tpYDqHKQU1JSTHx9fZvvb0N+fr6ejY1Nv+kora2thMfjMZzOuT9vvvmm3dq1a+/2de5wRsSVSiX6uydNOHHihMWiRYtq+nu+uLiYb2xsrFy9enVNVFRU5S+//DJ61qxZsnPnzpmXl5fzANV/j5s3b+rNnDlTlpCQYF5ZWclVHweA4OBgeWJiollDQwNHJpNxEhMTzYODg/sd/Q8JCZEnJiaayeVyUltby0lOTjbrry2aeh90s+52t8UAjjEMs58Q8hiAzwgh3gzD9PrpgxDyVwB/BQBHR8c+LkNRQ0cIwb59+xAQEAAA+PLLL/Hll19i06ZNvUbLtcV1rysqj1WiQ9YBQx9DmE411UpcPoeDPS4uiJBIuo7VtreDYZh+f3Z9aAYGwL59wNOdNbs/+AB46SVAdH+2mva99uhr+CD9Azzu9Dh2Be8Cl8POaL2vL7BiBfDpp6r9119X5Y3TEocUNTQHZh64c2Dmw41+AkDcwrjbcQvjhj3aEBMTc2v16tWOGzduHAcAmzZtuuPl5dVrvdzk5GSjM2fOWAoEgmahUCgCgB07dpQ/88wz9aWlpfwVK1aM7+joAMMwZO7cuTWLFy+ul0gkevPnz3cDgI6ODvL000/fW7hw4QMjQmKxuKWmpoYvEAi8oqOji6dPn97Y8/mkpCSjoKAguVKpxJo1a+zDw8Pr1ZMsf4uB7mnOnDnjr127ZlxbW8uzsbHxfeONN+6sW7euGgCmTZvmFhMTc9vZ2bm9v/NkMhknLS3NJCYmpt//HpmZmaM2b97swOFwwOPxmOjo6Nv+/v4tW7ZsKQ8NDXVXKpXg8/nMoUOHSkJDQxvXr19fERQUJORwOIy3t3fTqVOnigMDA5uWLFlyb+LEiZ4AsGzZsrsBAQHNUqm0zwmvgYGBTfPnz6/x9vb2srS0bPf19W3sry2/9f1VI2z9DN7Zsd7OMMzMzv3NAMAwzJ4e5+QAmMUwTGnnfhGARxmG+bW/606aNInJyMhgpc3Un8vTTz+N+Pj4rn09PT3k5ubCxUXzqQmDqf62GspmJawXareUKsMweDQzEz/J5bDh8/GVlxceNzMb/IUPFwwICQEuXVLt/8//AEuWADduAPv3sxNziOpa6mBmwNJ991BerkpRae4c8/r734ENG1gPS/3OEUIyGYaZpOt2aFt2dnaxWCyu1nU7RprKykpuVFSUfWpqqsnSpUurZTIZd/fu3RWHDx+2iouLsxSLxY1+fn7NGzdu7HNUnNK+7OxsK7FY7NzXc2x2xHkAbgIIBVAOIB3AEoZhcnqc8x2ALxmGOUYI8QRwEYA9M0CjaEec0pT8/Hx4enqio7NiyNy5c3Hs2DGYsdURHaGu1Ncjrb4ea+3tMYrt3O3sbGDiROD+RYSuXwe8vdmNPUxlsjLca7oHsa1Yo9fdtg3Ytat7/+bN3vnjFHU/2hGnBvLcc885xsbGlui6HVT/BuqIs/ajKMMwCgCvAEgCkAtVdZQcQshOQshTnaetB7CSEJINIA7AioE64RSlSQKBAKtWrQIAGBgYYPbs2X+6TjgABJiaYpOjI/udcAAQi4GVKx88fkDzlUoeVkNrA7b8ewsEhwV47sxz6FBqtrTjhg0Ar0dS4DPspKRTFPUnQTvhv2+sZicyDJPIMIw7wzCuDMO823lsG8MwZzu3JQzDBDAMI2YYxo9hmAtstoei7rdt2zasWbMGRUVFeOmll3TdnC6tla3IicxByx2WSgoOol2pxM0mzdfTBqAaDjbtkQf/5JPA++/3f76WVTRUYO+VvWhRtOC/Vf9FbHasRq9vYgL8rce09LIyQKGdtZwoiqKoEYZOE6L+1MaMGYMjR47Azk5jlYh+M8kyCa6OvYq7X95FwasFWo3NMAwSqqvh9uOPmJ6djWY2FvoZM0aVn9GTqXYmqA6mpL4EAZ8GoF2pKuPoPcYbAkvN543s3Qs4OwNbtqjyxnm6njZPURRF6QTtiFPUfRQKBc6ePauVet59xq9VqCrsA6iOr0Z7LQu1vfuRLpNhYU4OSlpbUdLaiiPl5ewEevllYM4c4NtvgbNn2YnxEMaZjIPQSti172frh0DHQI3H4fGAoiLVjwOdpYEpiqKoPyHaEaeoHv75z3/C2dkZc+fOxcWLF3XSBsGRHiOwSqDsUJnWYp+9dw9tnV9ACIBGNkbEAWD0aFUHPCysu7j2CEAIwZ7QrsJO+OL6F7hedZ2lWKxclqIoivodoR1xiup09+5dHDlyBOWdo8B79+7VSTtGOY+Cw3qHrv07R+6go4mlDvF9Njo6wrIzT4IBYKbNnIn6euDdd4ES3c47CnQMxJPuTwIAGDB4699vaSUuwwB1Gl23jqIoihrpaEecojpduXIF16+rRj8JIXjkkUegvL/Mnpa47HGBvpM+AKC9uh0V/6rQSlwTHg9vOzt37b9fVgaFNt6DmBjAyUmVNK2jL0A9vRvyLghUQ9YJNxNwpeQKbtXegkKp+VmVbW3Am28CZmbAhAkavzxFURQ1gtGOOEV1mjNnDtzc3ACoJi3a2tqCo6NlDzl8Dhxf715FtvTvpVC2aedLwUo7O4zpTFwuaW1FfDXLZXwVCiA3VzUiDgD/+hdw56EXz9MIXxtfPOv7bNf+/C/nw+2wG05JTmk81tdfA3v2ADIZUFwMXLum8RAURVHUCEU74hTVicvl4rXXXuvaP3jwYNdiP7pg+4It+NaqDnFrWSvufKSdzqkBl4vVY8d27R8oLWV3VHzjRuB//1e1bWgIfPSRqrKKju18Yid4HFVqzt2mu1AySuy7uk/jk3iXLAGMjbv3//EPjV6eoiiKGsFoR5yielixYgXMzc0BAIWFhUhISNBZW7ijuLB7qbus4q1tt8B0aKeSyyp7e+h1zib8saEBY69eRVZDAzvBXnyxe7uxEXjkkRFRSmS8+XhEiCJ6HTM3MEdDm2bfB0J61xX/z39U6SoURVHUHx/tiFNUD4aGhnj55Ze79vfv34+GhgY0sNUJHYTxhO6h0o66DlTGVGolro2eHp61senav9vejv2lpewEE4lUpQzV/v53duI8hFenvNq1zePw8Nn8z2Cib6LxONu2Aba2qu07d4Avv9R4CIqiHhKXy/UXCoUigUDgNXv2bJeGhoYh9Z0KCgr4U6ZMcXd1dfVyc3Pz2rVrl7X6uaamJuLj4+Pp4eEhcnNz81q3bl3Xz5C7du2yFggEXm5ubl47d+607vvqQGFhIf/o0aPmv+3uhqe/exroXvuiUCjg6ekpCg4OdlMfs7e393F3dxcJhUKRt7e3J9v38rCioqLGbtu2zWbwM4eGdsQp6j6vvPIK+J0jsmlpabC3t8fBgwd10hareVbg26raQngEbdXaGypd5+DQaz+lro6dBX4A4I03urc//1y13OQIMMVhCqbYT4GjqSN2h+zGKP4oVuLo6wOvvNK9v3+/qooKRVG6p6+vr8zLy5Pk5+fn8Pl8Zv/+/UPKnePz+di/f39ZYWFhTnp6eu7HH39snZmZaQAABgYGTFpamlQqlUpycnIkFy9eNLl48aJhenq6QWxs7JisrKzc3NzcnPPnz5vduHFDv6/rJyYmmmRlZY3W5L0+7D0NdK99eeedd2zc3Nya7z9++fLlm3l5eZIbN27ksnsnIwftiFPUfcaOHYvIyMiu/YaGBhw6dAjNzQ98ZrCOEALBIQHGrh6LAFkAnDY6aS22j5ER/se8e7Bl/bhxGMXlshNs6lQgsHPhnPZ21ZL3t28D586xE28YTi46icK1hXg94HVWRsPVXn4ZGNXZz8/OBv79b9ZCURT1kAIDA+UFBQX6UqlUTyAQeKmPb9u2zSYqKmpsz3OdnJzaAwMDmwDA3Nxc6erq2lxSUqIHABwOB6ampkoAaGtrIwqFghBCcP369VETJkyQGxsbK/l8PgICAhpOnDhhdn87kpKSjLZu3Tru3Llz5kKhUCSRSPTYvfOB72mge71fYWEhPykpyXTlypXDrgQgk8k4TzzxhJuHh4dIIBB4qX8RiI6OtvDx8fEUCoWiJUuWOCkUqgpXR44csXR3dxd5eHiI5s2bN159ne3bt9sIBAIvgUDQ9auDVCrVc3Fx8YqMjHRyc3PzCggIEMjlcgIAmzZtsnV2dvb29/f3yM/P1x+oLcNFF1amqD5ERUXhs88+69o3MjJCUVERvLy8BngVO6wjrGEdMeCvfKzZMG4cxvD5WOfggMkm7HVCAQCbNgFpaartQ4dUD0NDVYfc1JTd2ANwMHHo83iHsgNcjua+mFhaAs8/D0RHq/YjI4GqKkBHhXsoirpPe3s7kpKSTGbMmCEb7mulUqmeRCIZPW3aNLn6mEKhgLe3t6ikpER/+fLlv4aEhDRmZWV17Ny5076yspJraGjIJCcnm4rF4sb7rzdz5ky5j49P44EDB0onT57c8lvuy9/f36OxsfGBD7P33nuvdN68ef3mZfZ1TwMdV1uzZs24vXv3ltXX1z8QMzQ0VEAIwfPPP393w4YND3TU4+PjTWxtbdsvXbpUAAD37t3jZmVlGZw8edIiIyMjT19fn1m6dKnjhx9+aPnoo4827tu3z+7q1at5dnZ2iqqqKi4ApKamjj5+/LhlZmZmLsMw8Pf39wwNDW2wsrLqKCkpMfj888+Lpk6dejssLMwlNjbW3MfHp+X06dMW169fl7S3t8PPz080YcKEpr7aMth73Rf6EU9RffDz80NISEjX/vLly3XSCde1mRYWOC4Ssd8JB1SrbKrfY4VC9aivB44cYT/2ECkZJc7dPIcnjj2BXf/ZpfHrr13bvV1d3d0ppygKiEqKGkt2EH+yg/hHJfUefQaAlQkrHdTPv53y9gM5vItPLnZSP7/vh31WQ43b2trKEQqFIh8fH5GDg0Pbq6++OqyR3Pr6es6CBQtc33vvvVILC4uuElQ8Hg95eXmSkpKS/2ZlZRmmp6cbTJw4seXVV1+tDA0NdQ8ODhZ4eXk1cfv5JbKoqMjAz8+vBQAkEoneokWLnGbNmuWifv7IkSOWW7ZssYmMjHQKDw93iY+P7/ODPDMzU5qXlye5/zFQJ7y/e+rvuFpcXJyplZWVIigoqOn+59LS0vIkEknuhQsX8o8ePWr93XffGd1/zsSJE5tTU1NNVq1aZX/+/HkjS0vLjvPnzxvfuHFjtFgs9hQKhaK0tDSToqIi/aSkJJM5c+bU2tnZKQDAxsamAwAuXbpkFBYWVmdiYqI0NTVVhoeH16akpBgDgL29fevUqVObAWDChAlNxcXF+ikpKUZhYWF1xsbGSgsLC+WMGTPq+mtLf+/XQGhHnKL6ERUVhalTp+LUqVPYunWrrpvTizy7z4GG3zcORzUq3lNAAODtrZv29OGs9CzmxM3B5duX8UH6B2hu12y6kocHIBB074+geasU9aelzhHPy8uTxMTElBoYGDA8Ho/pueBbS0sLBwD27NkzRigUioRCoai4uJjf2tpKwsPDXSMiImqWL1/e59q5VlZWHUFBQQ0JCQmmALBu3brqnJyc3IyMDKm5uXmHu7v7AyPeFRUVPGNj4w71fCaRSNT21Vdf3e55TkZGxuidO3dWnThx4vaxY8dunzhxos/UCX9/fw91m3s+zpw5Y9zX+f3d01DuNS0tzSg5OdnM3t7eZ8WKFS7Xrl0znjt37ngAGD9+fDsA2NvbK8LDw+uuXr1qeP/rfX19W7OysiQ+Pj7NW7dutd+wYYMdwzAkIiLinvq/UXFx8Y0DBw48VL1fPT29rtk5XC6XUSgUpL9z+2rLw8SkHXGK6kdYWBiuXLmCBQsWoL8RCW2r+KQCP9j9gAy/DDT8rP1KLmUtLXi9sBBf//orOwEiIwHH7oWMsGQJMHcuO7GGKfNOJt6/+n7Xfm1zLa6UXtF4nPfeU/3r7w/0yI6iKGoEcXBwUNTU1PAqKyu5zc3NJCkpyRQANm/efFfdIXR0dGyPjIx0cnd3b9m+fXtVz9ffuXOHV11dzQUAuVxOUlJSTDw9PVsAoLy8nAcA+fn5et9++63Ziy++WHN//Pz8fD0bG5t+Z++3trYSHo/HqBele/PNN+3Wrl17t69zhzMirlQq0dc99Xf8fh988EF5VVXVf8vLy68fO3as6NFHH2345ptvbslkMk5tbS0HUOVep6SkmPj6+j4w0lFcXMw3NjZWrl69uiYqKqryl19+GT1r1izZuXPnzNXvW1VVFffmzZt6M2fOlCUkJJhXVlZy1ccBIDg4WJ6YmGjW0NDAkclknMTERPPg4OB+/6CGhITIExMTzeRyOamtreUkJyeb9deW/q4xEJojTlH9IKTvL8IMw/T7HJuUbUrcXH0TTKvqC3vB2gJMSNXemuhfVlVhSW4ulAB8DA2xcMwYE2lVsQAAIABJREFUzb8PfD4QFQVs3gysXAmEh2v2+r8Bn8vHf0r+AwAgILjywhVMcZii8Tjz5wMlJcC4cRq/NEX9rh2YeeDOgZn9j3QenXO07Oico/2WXIpbGHc7bmHc7f6eHw59fX1m/fr1FZMnT/a0sbFpd3Nze2DUOjk52ejMmTOWAoGgWSgUigBgx44d5c8880x9aWkpf8WKFeM7OjrAMAyZO3duzeLFi+sB4KmnnnKtq6vj8Xg85uDBgyVWVlYPpDyIxeKWmpoavkAg8IqOji6ePn16rzzypKQko6CgILlSqcSaNWvsw8PD69WTKX+L/u7JzMyso797BYBp06a5xcTE3HZ2dm7v67plZWW8+fPnuwFAR0cHefrpp+8tXLjwgVz8zMzMUZs3b3bgcDjg8XhMdHT0bX9//5YtW7aUh4aGuiuVSvD5fObQoUMloaGhjevXr68ICgoScjgcxtvbu+nUqVPFgYGBTUuWLLk3ceJETwBYtmzZ3YCAgGapVNrn5NLAwMCm+fPn13h7e3tZWlq2+/r6NvbXlod5T4mmV4lj26RJk5iMjAxdN4P6E8rLy8O+fftw7949nD59WjdtWJmHyn911hLnAAF3A8C3YH/xm+aODjhcvYqazpnoAPBvsRjB5iyUsG1qUj2shpzCqTXBMcG4VHwJALA5cDN2h+7WbYOoPwVCSCbDMJN03Q5ty87OLhaLxcOurPFnVFlZyY2KirJPTU01Wbp0abVMJuPu3r274vDhw1ZxcXGWYrG40c/Pr3njxo19jopT7MrOzrYSi8XOfT1HO+IUNQTl5eVwdHSEOicwOzsbvr6+Wm9HW3UbfvL4CYoaVYfY7ZAbHP7Wd1UPTXsuNxefVal+cXzc1BQpfn7gaPOXgcZGVRUVHTqdexoLvloAALAcZYnSdaWs1RanKDXaEaeG67nnnnOMjY0t0XU7KJWBOuI0R5yiBqFQKDBz5kz0nJgTGxurk7boWenBZXfXpHhUHK2Atr5M91zg5weZDOWtrewH7egAzpwBZs9WzWRs7/NXTa15yuMpOJs5AwDuNd/DF9e/YD1mQQHw1FNAzQNZohRFUX2jnfDfD9oRp6hB8Hg8TJnSnQv8+OOPY+/evTprj/Via3BGq/6v23i9EQ3p2pm0OcHYGNM663krGAZHysvZD6pUAi+9BJw/D5SXAwkJ7MccAJfDxSuTu5fAfP/a+/jk50/w1sW3WIk3fbqqikpCgip1nqIoivpjoR1xihqCdevWdW2npaWhuLhYZ23hmfBgvah7gZ+Kf1VoLfa6HjMI/1lRAXmPnHGNq6kBnn1WVVAbAAgBfv6ZvXhD9JeJf4EhX5UiI7krwV/O/gX/e+V/USbrd47YQ+P1mE4fF6cqrU5RFEX9cdCOOEUNgbe3N2bMmAFAVabpiI4XmbFb2V2utDKmEgq5dnpoT1pawq1zHfY6hQJ7S0pwrb6enWCmpsC1a6pRcUC1sM8uzS+iM1xmBmZYLl7e61gH04HPsjVfa7Dn/8za2gCJROMhKIqiKB2iHXGKGqKeo+KfffYZ2tr6LeHKOiN/IxB91URJpo1B+QdaSBMBwCUEr9rbd+3vKinB3woKWArGBVas6N4/d46dOA/hb1P+1mt//WPrsUy8TONxXF2BJ5/s3o+L03gIiqIoSodoR5yihmj69Omw7+yEVldX4+DBgzorY8jV52LU+O5qHeWHtdMRB4AVtrYw4nR/dGQ0NOCGnKWVPp9/vns7KQko03z6x8MQWgkx03Vm176jqSMcTNipXrNyZff2Z5+p5q9SFEVRfwy0I05RQ8TlcrFsWfeo56ZNm7BmzRoodJS4O+51Vb72KPdRGP/OeK3FNeLx8Ix1d466s4EBlAOc/5uMHw+EhKi2lUogJoatSMO2/rH1WOG3Aj+/9DPWTlnLWpzZs4ExY1Tb5eXAxYushaIoiqK0jHbEKWoYli/vnRtcUVGBCxcu6KQtNstsMPXuVEyRToHdCrvBX6BBz9naAgAseTw8Z2MDXyMj9oL95S/d2x9/DHz+ObBpE3vxhmi663R8OvdT+Nn6sRqHz1fNWVX78ENWw1EURVFaRDviFDUMQqGwVynDxx57DE5OTjppC4fPgZ5Vnyvysi7Q1BTf+figYupU7BjP8mj8/PmAmZlq+9YtYNkyYO9eoLCQ3bgPobGtEbdqb2n8uj1T5U+fBn76SeMhKIqiKB2gHXGKGib1qDiPx8MTTzwBLy8vHbdI+ziEYJalJfgcLXyEjBrVe0hY7ehR9mMP0a3aW3jhmxdgu98WL3/7ssavLxarisiovf22xkNQFEVROkA74hQ1TJGRkTh8+DAqKyuxe/duXTenS1tNG+78645O28DaKp8vvNB7/623gLXs5WUPV11LHY79cgzyNjmSC5NZqSkeEdG9PQLKqVPUn4JUKtUTCAS9RluioqLGbtu2zabnsYKCAv6UKVPcXV1dvdzc3Lx27drVNZGmqamJ+Pj4eHp4eIjc3Ny81q1bN1b9nL29vY+7u7tIKBSKvL29PftrR2FhIf/o0aPmmry3oRisfdnZ2fpCoVCkfhgZGU3YuXOndc9zFAoFPD09RcHBwW7aa/nw9PXfVFt4g59CUVRP5ubmeOWVVwY/UUva69uRHZIN+c9ygAHMgs0w2nW01uLXtbfjq7t38WllJZZYW+NvDixUD5k4EfDzA375RbXv5ASMHTvwa7TkQuEFhB8PBwPVlxAPKw+U1pdqvIrK9u3Av/8NvPyy6kFR1MjB5/Oxf//+ssDAwKba2lrOhAkTRGFhYTJ/f/8WAwMDJi0tTWpqaqpsbW0lkydP9rh48WJ9aGhoIwBcvnz5pp2d3YCz/hMTE00kEokBgFqt3FAPA7VPLBa35uXlSQBVh9vW1lYcGRlZ1/Ocd955x8bNza1ZLpdztdHe3xs6Ik5RGsAwDBobG3USm3AJ5NmqTjgA3N55W2ux25RKbCwqwks3b+KaTIZPKyvZC/bSS8BTTwFnzvROmtaxRx0eBY/TPabx5dNf4rFxj2k8jr29Ki3+9dcBY2ONX56iqN/AycmpPTAwsAkAzM3Nla6urs0lJSV6AMDhcGBqaqoEgLa2NqJQKAghZMjXTkpKMtq6deu4c+fOmQuFQpFEItHN5KBBnD171sTR0bHV3d29a5GNwsJCflJSkunKlSur+3qNTCbjPPHEE24eHh4igUDg1XPUPzo62sLHx8dTKBSKlixZ4qSuUHbkyBFLd3d3kYeHh2jevHnjAWD79u02AoHASyAQeKlH5KVSqZ6Li4tXZGSkk5ubm1dAQIBALpd3vfGbNm2ydXZ29vb39/fIz8/XH6w9bKEdcYr6DaqqqrBnzx54eHhg48aNOmkDz4gH85Duz4rqs9VQKlgrKNhLU0cHYioquvavNzbidksLO8Fefhn45htg7lxVKZERwkTfBHM95nbtf3H9Cx22hqIoXZNKpXoSiWT0tGnTuhZYUCgUEAqFIhsbG/G0adNkISEhXSM3oaGhAi8vL899+/ZZ9XW9mTNnyn18fBrj4+ML8vLyJCKR6KFXk/P39/fomUqifpw5c6bfr/eDtU8tLi7OYuHChfd6HluzZs24vXv3lnH6mU8UHx9vYmtr2y6VSiX5+fk5CxYskAFAVlaWwcmTJy0yMjLy8vLyJBwOh/nwww8tMzIyDPbt22d3+fLlm1KpVPLRRx+VpKamjj5+/LhlZmZmbkZGRm5sbOyYK1eujAKAkpISg7Vr1/5aUFCQY2pq2hEbG2sOAKmpqaNPnz5tcf36dUlycnJ+dna24UDtYRPtiFPUQ2IYBqdPn8abb76J/Px8HD9+HC1sdUIH4fKeC7jGql/9Ouo6UHO+Ritxzfh8zFMXuQbwkp0dnAwMtBIbACCXA+np2ovXj2W+3fXlv7j+BTqUdNUditK4qKixIMR/SI/Fix8sZ7V4sVOvc6KiBs1v62/kur/j9fX1nAULFri+9957pRYWFl0jIjweD3l5eZKSkpL/ZmVlGaanpxsAQFpaWp5EIsm9cOFC/tGjR62/++67PmvBFhUVGfj5+bUAgEQi0Vu0aJHTrFmzXNTPHzlyxHLLli02kZGRTuHh4S7x8fEmfV0nMzNTmpeXJ7n/MW/evIa+zh9q+1paWsj3339vumzZsq7Umbi4OFMrKytFUFBQU59vFoCJEyc2p6ammqxatcr+/PnzRpaWlh0AcP78eeMbN26MFovFnkKhUJSWlmZSVFSkn5SUZDJnzpxadaqMjY1Nx6VLl4zCwsLqTExMlKampsrw8PDalJQUYwCwt7dvnTp1ajMATJgwoam4uFgfAFJSUozCwsLqjI2NlRYWFsoZM2bUDdQeNtGOOEU9pNzcXKxataprX6lU4vr16zppi/FEY4xd1f03peJfFQOcrVnLO2uKA0B8dTUUSi2MxstkwOrVqjzx8HCgvZ39mAOY4ToDY0arvpCUN5Tj8u3LrMZrbAS2bQOmT2c1DEX96dnY2Cjq6+t75TbX1NRwraysFHv27BmjHlEuLi7mt7a2kvDwcNeIiIia5cuX1/V1PSsrq46goKCGhIQEUwAYP358OwDY29srwsPD665evWp4/2sqKip4xsbGHfzOXwJFIlHbV1991SsHMSMjY/TOnTurTpw4cfvYsWO3T5w40WdKxXBHxIfSPgA4efKkqUgkaho3blxXLnlaWppRcnKymb29vc+KFStcrl27Zjx37txe9W59fX1bs7KyJD4+Ps1bt26137Bhgx0AMAxDIiIi7qm/KBQXF984cODAsKsR6OnpdVUQ4HK5jEKhGDAnqL/2sIl2xCnqIYlEIvj5dS/m8u6772Ly5Mk6a4/dX7o/L+6du4fWilatxJ1hbg6bzj8QFW1tuFjX598fzeLzgS+/BBoagLt3db7cJJ/LR6R3ZNf+3it7seLMCsTnxms8VmkpYGIC7NoFfP89kJGh8RAURXUyNTVVWltbt589e9YYAKqqqriXLl0yDQkJkW/evPmuuqPo6OjYHhkZ6eTu7t6yffv2qp7XuHPnDq+6upoLAHK5nKSkpJh4enq2yGQyTm1tLQdQ5SanpKSY+Pr6Nt/fhvz8fD0bG5t+01FaW1sJj8dj1Okfb775pt3atWvv9nXucEbEh9o+ADhx4oTFokWLev0U+8EHH5RXVVX9t7y8/PqxY8eKHn300YZvvvmm10ILxcXFfGNjY+Xq1atroqKiKn/55ZfRADBr1izZuXPnzMvLy3mA6n2/efOm3syZM2UJCQnmlZWVXPXx4OBgeWJiollDQwNHJpNxEhMTzYODg/sc4VcLCQmRJyYmmsnlclJbW8tJTk42G6g9bKJVUyjqN1i+fDl+6azkceLECZ1WUxntPhqm00xRf7ke6AAqYyrh9Ab7iw3xOBw8a2ODA2Wqkn2xlZWYaWHBXsALF4ClS4Gazs98FxfVELGOLfNdhsM/HQYAJBUmAQAq5ZVY4LlAo3EcHAAjI9WPAoCqpvi332o0BEWNTAcO3MFDjIp2iYu7jbi4Yc9mj4mJubV69WrHjRs3jgOATZs23fHy8uo10pGcnGx05swZS4FA0CwUCkUAsGPHjvJnnnmmvrS0lL9ixYrxHR0dYBiGzJ07t2bx4sX1EolEb/78+W4A0NHRQZ5++ul7CxcufCAnWSwWt9TU1PAFAoFXdHR08fTp03t94CUlJRkFBQXJlUol1qxZYx8eHl6vnjj6W5SVlfH6a9+0adPcYmJibjs7O7fLZDJOWlqaSUxMzLDf28zMzFGbN2924HA44PF4THR09G0A8Pf3b9myZUt5aGiou1KpBJ/PZw4dOlQSGhrauH79+oqgoCAhh8NhvL29m06dOlW8ZMmSexMnTvQEgGXLlt0NCAholkql/U5qDQwMbJo/f36Nt7e3l6WlZbuvr2/jQO1hE2Gt7i9LJk2axGTQISBqhPj1119hb28P9Wzu/Px8uLnprlRq5eeVyFuWBwDgWfAQUB3Qby6jJmXL5fDr/P+lASF418UFS21sYK3HwuT+khLA2RlQf3bduqXa1zGGYSD8QIib9252HSMgKI8qh52xZn/dfOEF4NNPVduPPAL8+KNGL0+NMISQTIZhJum6HdqWnZ1dLBaL+6y28WdWWVnJjYqKsk9NTTVZunRptUwm4+7evbvi8OHDVnFxcZZisbjRz8+veePGjX2OilPal52dbSUWi537em7Q1BRCSFQfj78QQvwGey1F/dFZW1tj9uzZXfuxsbE6bA0wynVU17aiRoH6H+q1EldsZASxoSp1sIVhsL6wEMerqgZ51UNydOydHK3j91yNENJr0qaTqRN+WvkTbI1sB3jVw3nnHUBdhOCnn1RlDSmK+nOwtbXtOH78eElpaemNPXv2VDY0NHBNTU2VW7Zs+TUnJyf3+PHjJbQT/vsxlBzxSQBeBmDf+XgJwCwARwkhuqnXRlEjiHrJewD45JNPsGPHDly7dk0nbTGZYgKOYff/rcve1/wKj/15zrZ3h/PTykrtrLT5ySeANiaIDsGzPs92bbd2tMJrjBcrv0iMHQv0+P43Ur6LUBSlA7GxsSW6bgP18IbSEXcAMJFhmPUMw6wH4A/AGsDjAFaw2DaK+l148sknYW6umqBeXl6O7du3Izo6WidtIRwCq6esoGerh7GrxkLwD4HWYi+xtu71gbLAygqsdY/nzQM633Pcvq0aFh4BxpuPx9bHt+Js5FmUvFaCUfxRg7/oIfVc0yg2dsR8F6EoiqKGYSgdcWsAPScltAOwYRim+b7jFPWnpK+vj8WLF/c6durUKTQ1/ea5Mg9FdFyEqRVT4R7tDn17fa3FtdXXxywLC+gBiBgzBpE2NuCylZ+urw/Mn9+9f+AAsGoVcP48O/GGYWfwTszxmAM+l91Fh+bM6f4uUlwMxMWxGo6iKIpiwVA64l8A+JEQ8jYh5G0AVwAcJ4QYApCw2jqK+p3omZ7C5XKxf/9+rUySHGkOCQSoDAjAV15e8BjNctWnhQu7t7/+Gvjww+4ZjCOIklHiYtFFNLf3WfXroenrqzrjauvWafTyFEVRlBYM2hFnGGYXVHnhdZ2PlxmG2ckwTCPDMM8O/GqK+nOYPHkyhEIhAGDSpEkICQnBqFHspSWMVK6jRsFcW8vPh4YCZma9j507NyJKGap98NMHGP+P8fifz/4H30i/0fj1n+3xCXz3LpCVpfEQFEVRFIuGuqBPFoCvAZwG8CshxJG9JlHU7w8hBB9++CFyc3Nx7do1uLu767pJAABluxK/fvUr5BK57trA1oRNPT1g7tzu/QkTVKkpI+QLUKuiFaklqSipV82jOpV7SuMxpk9X1RRXO3dO4yEoiqIoFg26oA8h5G8A3gZQBaADAAHAAPBlt2kU9fsybdo0XTehF+lLUlR+UglGwWDMwjHw+tpLa7EVSiXiq6vx9d27SJfJUDBlCngcFhbyXbgQiIlRbcvlQFCQ5mM8BIZh4BXthcJaVV1BE30T2BpqvowhIcAbbwDV1cCaNYAOS9hTFEVRD2EoK2u+CsCDYZh7bDeGov5ompqaMJrtXOl+tFW1gVGoRqPvnb8HhmG0lrf+i1yOF/Ly0NhZyuM/9fUIUc8s1KTp04EnnwTCw3tP3tQxQgimu0xHYaaqIx4hisDhsMOsxHrrLVYuS1EURWnBUIaoSgFoZ1UQivoDaG9vxwcffICQkBA4OTmhvb1dJ+1wWOfQta2UK9GQ0aC12Cfv3u3qhAPApbo6dgLp6wMJCcDLLwM2NuzEeEjLxN2L+5yUnESLokWHraEoiqJGoqF0xIsAXCKEbO65uibbDaOo36vCwkK89dZbSElJQXV1NVJSUnTSDrMgMxg/Zty1f/dr7S20tmDMmK5tUy4XWx21OK2EYYCbNwc/j2WPOTwGF3MXAEB9az2+vfmtjltEURRFjTRD6YiXAEgGoAfAuMeDoqg+PPvss6ivV/2IRAjBzz//rJN2EA6B81vOXft3v77L3kqX95lkbAx7PT0AQH1HB9JkMvaDdnQAmzYBLi6AtzdQW8t+zAEQQnqttBmfF89qPIUC+OwzIDAQKKHr7FGUxnC5XH+hUCgSCARes2fPdmloaBjShJeCggL+lClT3F1dXb3c3Ny8du3aZa1+rqmpifj4+Hh6eHiI3NzcvNatWzdW/dyuXbusBQKBl5ubm9fOnTut+746UFhYyD969CgLOX8Dq66u5s6aNctl/PjxXi4uLl7ff/+9YV/nRUREOFtYWIgFAoHXUI6PNFFRUWO3bdvG+k+tQylfuKOvB9sNo6jfq/k9cpXDwsKwadMmnbXFfLo5uKZcAEBLcQsaMrWTnsIhBPOsrLr246urtRCUo0pTKS4G2tuBM2fYjzmIpz2f7to+m3cWW/69BbtTd7MSy94eeO454MoVYP9+VkJQ1J+Svr6+Mi8vT5Kfn5/D5/OZ/fv3jxn8VQCfz8f+/fvLCgsLc9LT03M//vhj68zMTAMAMDAwYNLS0qRSqVSSk5MjuXjxosnFixcN09PTDWJjY8dkZWXl5ubm5pw/f97sxo0bfa7MlpiYaJKVlaX1SUh//etfx82YMUN269atHIlEIvHz8+sz7+6FF16oPnv2bP5Qj/9Z9dsRJ4Qc7Pw3gRBy9v6H9ppIUb8vCxYs6NpOSUlBc7NmF3IZDo4eB1ZzuzvEd7/SXnrK/B7pKWeqq9kdjb9xQzUSnpur2jczA+p1P7XF18YXzmbOAAB5uxzvpr6Lf/z4D3QoOzQeSyTq3j6l+UqJFEUBCAwMlBcUFOhLpVK9niO627Zts4mKihrb81wnJ6f2wMDAJgAwNzdXurq6NpeUlOgBAIfDgampqRIA2traiEKhIIQQXL9+fdSECRPkxsbGSj6fj4CAgIYTJ07ct2ACkJSUZLR169Zx586dMxcKhSKJRKLH7p2r3Lt3j/vjjz8av/baa9WA6guFlZVVnx9os2fPlo8ZM0Yx1ONqMpmM88QTT7h5eHiIBAKBV89R/+joaAsfHx9PoVAoWrJkiZNCobrMkSNHLN3d3UUeHh6iefPmjQeA7du32wgEAi+BQND1y4JUKtVzcXHxioyMdHJzc/MKCAgQyOXyrioGmzZtsnV2dvb29/f3yM/P1x+sPZow0Ij4Z53/7gOwv4/HoAghswghUkJIASHkjX7OWUQIkRBCcgghx4fRdooakTw9PeHh4QFAVTXlwoULOm3PmIjuDnHFxxVaS0953NQUFjxVYaay1lYcKS9HEVtfSlxcgF9/7d5PSQFee42dWMNACME8j3m9jv3a+CuulF7ReKxXXunebmpSZepQFKU57e3tSEpKMvHx8Rn2B5lUKtWTSCSjp02b1rWog0KhgFAoFNnY2IinTZsmCwkJafTz82v+6aefjCsrK7kNDQ2c5ORk09LS0gc62TNnzpT7+Pg0xsfHF+Tl5UlEIlHbw96Xv7+/h1AoFN3/OHPmzANpyFKpVM/CwkIRERHh7OnpKXrmmWecZDKZRmvTxsfHm9ja2rZLpVJJfn5+zoIFC2QAkJWVZXDy5EmLjIyMvLy8PAmHw2E+/PBDy4yMDIN9+/bZXb58+aZUKpV89NFHJampqaOPHz9umZmZmZuRkZEbGxs75sqVK6MAoKSkxGDt2rW/FhQU5JiamnbExsaaA0Bqauro06dPW1y/fl2SnJycn52dbThQezSl3zePYZjMzn8v9/UY7MKEEC6ADwDMBiACsJgQIrrvHAGAzQACGIbxAqD7v5wU9RsRQnqlp5w+fVqHrQH0x3b/qqmoUUD2kxbytQHwORzMsbTs2l9bUIBPKirYCTZ6NBAW1r1/duT8aDdP2N0RH8UbhS8Xfgl/O3/Nx5kHqH+EqK0Frl3TeAiK0q2oqLEgxB+E+OO+0WcAwMqVDl3Pv/32g7m9ixc7dT2/b5/VA8/3o7W1lSMUCkU+Pj4iBweHtldffXVYuXb19fWcBQsWuL733nulFhYWXeWkeDwe8vLyJCUlJf/NysoyTE9PN5g4cWLLq6++WhkaGuoeHBws8PLyauJyuX1et6ioyECdFiKRSPQWLVrkNGvWLBf180eOHLHcsmWLTWRkpFN4eLhLfHy8SV/XyczMlObl5Unuf8ybN++BXEaFQkFyc3NHr1mz5m5ubq5k9OjRyq1bt2p0kYSJEyc2p6ammqxatcr+/PnzRpaWlh0AcP78eeMbN26MFovFnkKhUJSWlmZSVFSkn5SUZDJnzpxaOzs7BQDY2Nh0XLp0ySgsLKzOxMREaWpqqgwPD69NSUkxBgB7e/vWqVOnNgPAhAkTmoqLi/UBICUlxSgsLKzO2NhYaWFhoZwxY0bdQO3RlEG/xRBCAgghyYSQm4SQIkLILUJI0RCu/QiAAoZhihiGaQNwAsDc+85ZCeADhmFqAYBhmF9BUX8APdNTvvnmGxw7dgw5OTk6aYuR2AicUd3/Vy97v0xrsXumpwDAKTZzxSMiurdPnmQvzjAFOAbAcpTqC0lrRyu8rb1hqNfn3KbfhMvtXUo9nt25oRT1p6HOEc/Ly5PExMSUGhgYMDwej1H2KNHa0tLCAYA9e/aMUY8oFxcX81tbW0l4eLhrREREzfLly/us42plZdURFBTUkJCQYAoA69atq87JycnNyMiQmpubd7i7uz+Qg11RUcEzNjbu4PP5AACRSNT21Vdf3e55TkZGxuidO3dWnThx4vaxY8dunzhxos+UiuGMiDs7O7fZ2Ni0hYSENALAM888U5udna3RPHVfX9/WrKwsiY+PT/PWrVvtN2zYYAcADMOQiIiIe+r/FsXFxTcOHDhwZ7jX19PT6/pZmMvlMgqFYsAFNvprj6YM5eeEjwEcABAIYDKASZ3/DsYeqhrkamWdx3pyB+BOCLlCCLlGCJnV14UIIX8lhGQQQjLu3tVejitFPaxJkybBwUFVx7uurg7PP/88Pv74Y520hXAJzEPNwTXiwvJJSzi+rr1SgjPMzTFc5b9TAAAgAElEQVSqxyJCjxgbo63HHy+NCgsDDAxU29evA1IpO3GGicfh4e1pb+PTuZ+iakMVRGNEg7/oIfX4/of4eFUlR4qiNM/BwUFRU1PDq6ys5DY3N5OkpCRTANi8efNddUfR0dGxPTIy0snd3b1l+/btVT1ff+fOHV51dTUXAORyOUlJSTHx9PRsAYDy8nIeAOTn5+t9++23Zi+++GLN/fHz8/P1bGxs+k1HaW1tJTwej+F0rmj85ptv2q1du7bPDtRwRsQdHR0Vtra2bdnZ2foAcOHCBRMPDw+NLpJQXFzMNzY2Vq5evbomKiqq8pdffhkNALNmzZKdO3fOXP3+VFVVcW/evKk3c+ZMWUJCgnllZSVXfTw4OFiemJho1tDQwJHJZJzExETz4ODgAasVhISEyBMTE83kcjmpra3lJCcnmw3UHk0Zysqa9QzDfKfJoPfFFwB4AoADgP8QQnwYhun1rZFhmH8C+CcATJo0if5poUY8dXrK4cPdqymeOnUK+/fv19rqlj15nfYCh8fCEvODGMXlIszSEqeqq/GIkRFeHjsWemwsdQ8ARkbA7NmAOhXo//5PNWlTJAIWLWIn5hD9bcrftBInOBgwNVXNUy0uBn74AQgI0EpoimLfgQN3MNAI6NGjZTh6tP+f/OLibiMu7na/zw+Dvr4+s379+orJkyd72tjYtLu5uT3QGU1OTjY6c+aMpUAgaBYKhSIA2LFjR/kzzzxTX1payl+xYsX4jo4OMAxD5s6dW7N48eJ6AHjqqadc6+rqeDwejzl48GBJX5MhxWJxS01NDV8gEHhFR0cXT58+vbHn80lJSUZBQUFypVKJNWvW2IeHh9erJ47+VocPHy559tlnXdra2oijo2NrXFxcMQBMmzbNLSYm5razs3M7AMyZM2f8tWvXjGtra3k2Nja+b7zxxp1169ZV93dcff3MzMxRmzdvduBwOODxeEx0dPRtAPD392/ZsmVLeWhoqLtSqQSfz2cOHTpUEhoa2rh+/fqKoKAgIYfDYby9vZtOnTpVvGTJknsTJ070BIBly5bdDQgIaJZKpf1Oag0MDGyaP39+jbe3t5elpWW7r69v40Dt0RQy2MQtQsh7ALgA4gG0qo8zDJM1yOseA7CdYZiZnfubO1+3p8c5HwL4kWGYTzv3LwJ4g2GY9P6uO2nSJCYjI2OQ26Io3bt06RKCg4MBqMpYbd++HevXr4e+fp+VqP6wbjY1wZDLhb027vv4ceDZZ3sfe/xx4PKg01q0rrS+FA4mDhr/YjZ/fnflRpEI0FFGFKVBhJBMhmEm6bod2padnV0sFou1UPv096+yspIbFRVln5qaarJ06dJqmUzG3b17d8Xhw4et4uLiLMVicaOfn1/zxo0baVqBDmRnZ1uJxWLnvp4bSke8r2UBGYZhQgZ5HQ/ATQChAMoBpANYwjBMTo9zZgFYzDDMckKIFYCfAfgxDHOvv+vSjjj1e6FQKGBnZ4fq6mrY2dnhhx9+gLOzs66b9ccmkwHW1kBra/cxQoCyMmDsg3O7dOEf1/6Bz/77GTIrMnF91XV4W3tr9PoHDwLr1qm2ORygsbE7Y4f6faIdcWq4nnvuOcfY2Fi6tNcIMVBHfCgL+gT38RiwE975OgWAVwAkAcgF8BXDMDmEkJ2EkKc6T0sCcI8QIgGQAuD1gTrhFPV7wuPxEB0djR9++AFlZWUjphPOMAxkP8nQXKy7+uasMTEBZs7s3vf1BWJjVcdHACWjRMLNBGRWZAIA4nM1P6Ny5UrVdw8AUCqBxESNh6AoaoSjnfDfj35zxAkhSxmG+ZwQEtXX8wzDHBjs4gzDJAJIvO/Yth7bDICozgdF/eFE9KzkMQIUbSlC+aFydDR0wPpZa4g+Z2/i4P06GAZX6utx6u5d3Glrw9deLK1uvHBhd/lCPT1g6VJ24gxTq6IVbofdUCZTpbDyOXzca9L8uIOhoSpVXl8fWLtWlZlDURRFjUwDTdZU19d6oHwNRVEPj2EYnUzYBICGzAZ0NKjm/dxLuKfVtqTW1SEkOxvqZLjy1lZ28sbnzAH8/IC5c1Wd8hFCn6cPgYWgqyP+9+l/x6uPvspKrG+/ZeWyFEVRlIb12xFnGOajzn93aK85FPXH1NbWhoSEBJw6dQqFhYX48ccfddIOh7UOqD1fCwDokHVA/rMcxhO18137rVu30HNGyjfV1Vhtf39FUw0wMwN+/lnz19WAecJ5SClWTbtJKkxirSNOURRF/T4MZUEfA0LIGkJINCHkE/VDG42jqD+KyspKLFmyBHFxcfjpp58gkUh00g7z6eYw9O1eTKb6rPbmQS3osbjPFGNjrLTT6JoIg2vWfU78XI/uNc0u3roIWat2VjmlKIqiRqahFPT9DIAtgJkALkNV73vAougURXVrbW2FSCRCW1v32gtJSUk6aQuHx4HTW05d+zXfPrBOBGvmW3WvKP2LXI4Wthb26ampCdi9G3jsMUAgUM1e1CEnMydMsJ0AAGjraMN3+Wwt0dDt2jXgL3/R+a1TFEVRfRhKR9yNYZitABoZhokBEA5gCrvNoqg/Dn19fYSEdBcaWrduHV577TWdtcd8hrlqZQAADRkNaK1sHfgFGuIyahR8DVWj8a0Mg/M1WvgSoK8P7Nun6o2Wl4+IlJV5wnld23E34vB/6f+HBGmCxuMoFICFheo7yCef0OopFEVRI9FQOuLtnf/WEUK8AZgCsGavSRT1x7Ogx9rj165d09lkTQDgm/FhFmTWtV/znW5GxeOrWU6L+eknYMoUoFaVEw8OZ8R1xL+RfoPViatx8MeDGo/D46m+h6gdOqTxEBRFUdRvNJSO+D8JIeYAtgI4C0ACYC+rraKoP5g5c+aAy1UNQ1+9ehV37vS/SrM2WIRbdG1XxlRqLe78Hnni56qrkSGToUGhYCfYmDFApqpeN/h84PZt4MUX2Yk1DD7WPhhvNr7XscvFl1HdpPkvJnO7U9Jxj67QQFEUNeIMZUGffzEMU8swzGWGYVwYhrFmGOZDbTSOov4oLC0tMW3atK79b775RoetAbhG3K7t+rR6KNu0k0Dsa2gIl85lHuVKJSZnZeEsWz3E8eMBT0/Vdns7kJ3NTpxhIoT0GhUfazwWB2cdhB5XT+OxNm7s3r5+Hair03gIiqIo6jcYStUUM0LIWkLIAULIIfVDG42jqD+SnukpJ0+eRFpaGhobG3XSFvPp5t07HUDt97VaiUsI6ZWeAgDfsjlUGx7evT2CkqR7dsQZhsHqyathoq/51T9dXIBJnQujt7cD585pPARF/T979x7X5Hn3D/xz5UREIJzkLAdJICQcBHS2AkVBAWFWi7Wlrp32Wbv9qnt0YqfTqfOwVftMXdd2PK22W2V71HVoXVUKUgsO7UEOlSKRACKCB1DkEMI5yf37IwSCctQcUK/365VX7yR38r3uSOGb6/7e1/exJpfLeSKRaFD3sdTUVLetW7c66z9WVVXFnTVrlp+vr69UKBRKd+7c2V/C29HRQYKCggL8/f0lQqFQunbtWjfdc+7u7kF+fn4SsVgsCQwMDBhuHFeuXOEeOHDAbrjnjWGkYxrrPjt37nQSiURSoVAo3bFjx4Qtax7q39RUxlKakgnAG0ApgCK9G0VR47B48UDy9dVXXyEqKgpnzpwxy1gsfS3BseOAcAmsQq3AcRipt5dh6ZenEAAWrLH8GnpAiYkD26dOAQwz/L4mFDE1AvG+8dgVuwtfLf8KLGK8z0Dv+x9OGP6aUIqiAHC5XOzdu/f6lStXygoKCi5//PHHTkVFRXwA4PP5zLlz5+RyuVxWVlYmO3PmjM2ZM2f615E9e/ZsRXl5uezSpUuXh3v/zMxMm+LiYktTHIvOSMc0ln0KCgr46enpU4qLiy9fvny5LCsry/bSpUtG6OL2aBvLb38+wzCpDMP8jWGYg7qb0UdGUY8Zd3d3zJo1eMGhTDPO0s6UzcQz3c9gRvEMCGYJTBb3aRsbOHG0if9UHg+/8/Ia5RUPITISsOmbab52DcjPBz75xOxrirNZbGS9nIXfRP4GYkexUWMtXDiwffq0djUViqIMy8vLqzcyMrIDAOzs7DS+vr6dtbW1PABgsVgQCAQaAOjp6SEqlYqM54L97Oxsqy1btkw9efKknVgslshkMsPXsQ1hpGMayz6lpaWTQkNDldbW1houl4uIiIi2I0eO2Oq/XqFQsObMmSP09/eXiEQiqf6sf1pamn1QUFCAWCyWLFu2zEvV98vr/fffd/Dz85P4+/tLFi9e7AMA27ZtcxaJRFKRSNQ/8y6Xy3nTpk2TpqSkeAmFQmlERIRIqVT2f/AbNmxw8fb2DgwPD/evrKy0GG08xjKWabC/E0JeB3ASQP86ZwzDmG6pBYp6TCQlJfV31bS0tMQUvdlhU7NwMc/EBIsQHBCLMdXCAtOtrIy7ggyXC8TFARkZ2vtz5mhnxZ2dgQULjBf3ATEMY/DPQyoFXFyA+nptjfiOHdobRVHGIZfLeTKZzDI6Olqpe0ylUiEwMFBSW1trsXz58tsxMTH9dYmxsbEiQgheffXVO2+++eZ9V23Hx8crg4KC2vft21c3c+bMrocZW3h4uH97ezv73sd3795dt3jx4mF7xAx1TKPtM3369M4dO3a419fXsydPnszk5OQIQkJCBtVjHjt2zMbFxaU3Ly+vCgDu3r3LBoDi4mJ+RkaGfWFhYbmFhQXz8ssve37wwQcOTz31VPuePXtcv/nmm3JXV1dVQ0MDOz8/3/LQoUMORUVFlxmGQXh4eEBsbGybo6Ojura2lv+Pf/yjevbs2dcSExOnpaen261cubIpPz/f8rPPPrMvLS2V9fb2Yvr06ZLQ0NCO4cZjTGNJxHsA/BHAb4H+DtUMgGnGGhRFPa4WLFiArVu3AgBsbW2x4wnNiJ69p07cqBITBxJxXWnKiRMTJhHv6O3AwYsHkVmViRuKGyj+RbFB358QwNNTm4gD2o/iCf2xo6hxG+6L8XCPt7a2spKTk313795dZ29v338VPIfDQXl5uayxsZGdlJTkW1BQwJ85c2bXuXPnyn18fHpv3LjBiYmJ8ZNKpV0LFiy4L9mtrq7mT58+vQsAZDIZb9u2ba4KhYKdlZVVDWhnievr6zlVVVX8trY29s9+9rPG5OTk+1r3FhUVycf7GQx3TKPtExYW1rVmzZr62NhYv0mTJmmkUmmHbvUwnbCwsM7f/va3U9944w33RYsWtSYkJCgBICsry/rSpUuWISEhAQDQ1dXFcnJyUrW2trIXLlzY7OrqqgIAZ2dn9f79+60SExNbbGxsNACQlJTUnJuba7106dIWd3f37tmzZ3cCQGhoaEdNTY0FAOTm5lolJia2WFtbawAgLi6uZaTxGNNYEvF10Db1MV0vbIp6TIWFheFXv/oVoqOjERsba9b1xO+lbleDPdnoX/5N796E++mngbAw84xlCCzCwrrT69Cp0pbLXGm6Al97X4PGWLZMu6w6AFRUAGo1wH4M/6mpx1tqVZXbn65fdx3LvilOTo2HJZJr+o+9JJN5Hbl9u38WYK2Hx619QuGIa8k6OzurWltbB/3f0tTUxPbx8enetWvXlIMHD04BgKysrEpXV1dVUlKS79KlS5uWL18+5BpFjo6O6qioqLYTJ04IZs6c2eXj49MLAO7u7qqkpKSWb775ZvK9ifitW7c41tbWai6XCwCQSCQ9n3766bWEhIT+CdHCwkLLv/71r3UsFgt37txhr1q1ymOoRHy8M+Ld3d1ktGMaaZ+1a9c2rl27thEAfvnLX7p7eHj06D8fHBzcXVxcLDt69Khgy5Yt7l9++aViz549txiGIUuXLr37l7/85Yb+/n/4wx/GdcEnj8frvzCIzWYznZ2dI5ZkDzee8cQcr7HUiFcB6DDmICjqScFisfCnP/0JixcvhrW1tbmHA3WPGvLX5fja/WvkC/KhajNtATHDMChrb8ee2lpkG6vTposLEB4+cP9Xv5oQ64kDwA8NP2DWR7P6k3AA+M+1/xg8zquvApaWwFNPAe+9p+1tRFHU6AQCgcbJyan3888/twaAhoYGdl5eniAmJka5cePGO+Xl5bLy8nKZp6dnb0pKipefn1/Xtm3bGvTf4+bNm5zGxkY2ACiVSpKbm2sTEBDQpVAoWM3NzSxAW5ucm5trExwcfN8FLJWVlTxnZ+eeex/X6e7uJhwOh2H1/Y+9adMm19WrV98Zat+ioiK5bsz6t6GScI1Gg+GOaaz73Lhxg6M7hlOnTtm+9tprg37R19TUcK2trTUrV65sSk1Nrb948aIlACQkJChOnjxpp3t9Q0MDu6KighcfH684ceKEXX19PVv3+Ny5c5WZmZm2bW1tLIVCwcrMzLSbO3fusGU2ABATE6PMzMy0VSqVpLm5mZWTk2M70niMaSwz4u0ALhJCcjG4Rny10UZFUZRJqJvVuPXRwJf9xs8a4fJTF5PF33ntGn5XUwMAWDplCuLt7Ud+wYNKStLWZiQmatf0myDcrN1Q2lAKACAguPD6Bcxwm2HwODY2gFKpLVOhKGp8Dh48eHXlypWe69evnwoAGzZsuCmVSrv198nJybE6fvy4g0gk6hSLxRIA2L59+40XX3yxta6ujrtixQoftVoNhmHIokWLml566aVWmUzGe+6554QAoFaryZIlS+4+//zz981ih4SEdDU1NXFFIpE0LS2tZv78+YPqrLOzs62ioqKUGo0Gq1atck9KSmrVXUD5MEY6pujoaOHBgwevyeVyi+H2AYBnn33Wt6WlhcPhcJh33nmn1tHRUa0fo6ioaNLGjRs9WCwWOBwOk5aWdg0AwsPDuzZv3nwjNjbWT6PRgMvlMu+++25tbGxs+7p1625FRUWJWSwWExgY2HH06NGaZcuW3Q0LCwsAgFdeeeVOREREp1wuH/ai1sjIyI7nnnuuKTAwUOrg4NAbHBzcPtJ4jIkwoyznRQhZPtTj5lo5ZcaMGUxhYaE5QlOUQfX29uLrr79GZmYmYmNjERcXZ5ZxfO32NXpuaSdbBHMECM0NNUncK52dEPZduAoAAjYbjRER4BhjurarS9vvfQJmorM+moULN7R1I/98/p94QfqCmUdETTSEkCKGYQz/DW2CKykpqQkJCaFlsfeor69np6amuufn59u8/PLLjQqFgv3WW2/deu+99xwPHz7sEBIS0j59+vTO9evXDzkrTpleSUmJY0hIiPdQz406I84wzEFCyCQAngzDjLvIn6Koof3+97/vv1izoaHBbIm400+ccH3PdQBAx+UOMBoGhGX8hHUanw8vCwtc69ZOLP3c1RVqjO003bjx+aPvYyYJvgn9iXhWVRZNxCmKGpGLi4v60KFDtbr7P/3pTz0FAoFm8+bNtzdv3nzbnGOjxm8snTUXArgIIKvv/nRCyOfGHhhFPc6OHDmCt99+u//+F198AY3GNG3m7+W72xdcR+1FQL0NvWgrHrG0zmAIIUh0cBh036jNfXRu3wb+9jdgyRLgyBHjxxvFAtHAxaRZVVkY7SylIdy5o215T1HUoy89Pb129L2oiWosf/W2AfgRgBYAYBjmIujShRT1UEQiEbr7ZoL5fD4OHDhgkgRsKIRNYJ8wUJvddMp0LQIS9GrCvzDWxZr3OngQ+K//Ao4dA/71L9PEHMFMt5mwn6T9HG4pb+HCjQs4U33GKD8PH30ETJkCODkBzz9v8LenKIqixmksiXgvwzCt9zxmnqk7inpMhIaGwslJuwpTV1cXXF1dce/6qqbk8OOBmem7p+6aLG6MrS14fXXbpe3tuN71UL0qRvfxx4B+N9PTp4Hu7uH3NwE2i40434GypMi/RWLe3+ehsqnS4LE6O4HGvorbqiqgt9fgISiKoqhxGEsiXkYIWQaATQgREULeA/C1kcdFUY81FouFhISE/vtffPGFGUcD2MXbAX3fA9oK2tDTMOxKWQZlxeEgSiDov5/V1ASNMc8MpKcDeXna7RdeAAoKAJ5JukWPKMF34GdBpdEuIflFpeF/JpbrXXqv0QDff2/wEBRFUdQ4jCUR/28AUmiXLjwEQAHgV8YcFEU9CRboNZoxdyLOEXDAcx1ISBsODblkrFEs0KsT/011NVJkMuMFS0oa2NZoALF4QqykEi+MH3Tf38Efk3mTDR7HxgaYobf2xg8/GDwERVEUNQ6jJuIMw3QwDPNbhmFm9t1+C2BcnY0oirpfXFwcdA0YvvvuOzQ2NvbXjZsaIQQsi4FfBw3/MGEirlcnflelwhdNTegx1oWriYkD26dPT5jaDBcrF4S6DCwbuXvebrwWZpymQykpA9tm/v5HURT1xBsxESeEPE0IeZ4Q4tR3P5gQcgjAeZOMjqIeY/b29njqqacAaDtMhoSEYPnyIZftN4kpyVP6twnPdLPEAZaWmKpXHtKuVuOiUjnCKx6CVAp4emq3FQrg/MT5VZYgTIDQXohfzvwlfGx9jBZH/7tITg7QY5oqJIqiKGoIwybihJA/AvgrgCUAThFCfg/gNIDvAIhMMzyKerzpl6fcvHkT2dnZUKlM22Zex321O4R/FiKqPQrh34SP/gIDuXcZw1+6u+NHNjbGCja4POXUKe3FmteM3jxtVNvnbEflf1fivcT3EOISYrQ4YjHg5aXdbmsDvqZX/FAURZnNSDPiSQBCGYZ5CUActHXhTzEM82eGYYy8tAFFPRn0E3FAu4KKXG6evll8Dz48VnuAbWn61VtecHLCL93dcSooCLuN3YJePxH/8EPA0RFYscK4MceAy+aaJA4hg2fFd+40SViKoihqCCMl4l26hJthmGYAlQzD1JhkVBT1hNBfxhAATp8+DalUasYRmUeMnR3eE4mQ6OAAS2Mv4zh37kCnzbY2QKnUlqgYqxzmAbT3tOOE/ATeOPkGKu5WGPz9w8IGtvPyaHkKRVGUuYyUiE8jhHyuuwHwuec+RVEPicViYcmSJUhMTMR7770Hf39/cw/p8WdpqU3G9bm7A1evmmc8Q1jx7xV49siz+KDoA5ysOGnw99e/YFOjAT77zOAhKOqxwWazw8VisUQkEkkXLFgwra2tbUwtgKuqqrizZs3y8/X1lQqFQunOnTv7Z106OjpIUFBQgL+/v0QoFErXrl3rpntu586dTiKRSCoUCqU7duwYdnGMK1eucA8cOGD3cEc3PiMdk77hjq+kpMRCLBZLdDcrK6vQkY7RnFJTU922bt3qbOw4nBGeW3TP/b3GHAhFPanS0tLMPYRBehp7cOvjW7h9+DacXnSC10Yvk4/hbm8vLrW3I9rW1jgBkpIGlgyJjgZycyfEMoYaRoN3v3sXsjsDSzh+UfUFUp9ONWgcKytg6lSgrg7w8dEua0hR1NAsLCw05eXlMgB49tlnffbu3Ttl27Ztoy4txeVysXfv3uuRkZEdzc3NrNDQUEliYqIiPDy8i8/nM+fOnZMLBAJNd3c3mTlzpv+ZM2dabWxs1Onp6VOKi4sv8/l8TXR0tF9ycnJrYGDgfUtqZWZm2shkMj6AZiMc9riPSX+/4Y4vNja2XfdZqlQquLi4hKSkpLSYavwT0bDf6hiGOTvSzZSDpCjKdMqeK8PV31xFe0k76g/WmzR2m0qFp4uL4XT+PBJ/+AFdarVxAumKpPl8bc/3CYJFWPio+KP+RHxJwBJsfWarUWJ99RXQ2gpUVwP3XKpAUdQwIiMjlVVVVRZyuZwnEon66wi3bt3qnJqa6qa/r5eXV29kZGQHANjZ2Wl8fX07a2treYD2bKhAINAAQE9PD1GpVIQQgtLS0kmhoaFKa2trDZfLRURERNuRI0fum5HIzs622rJly9STJ0/aicViiUwmM0lnspGOSd9wx6fv888/t/H09Oz28/MbVBynUChYc+bMEfr7+0tEIpFUf9Y/LS3NPigoKEAsFkuWLVvmpVvc4P3333fw8/OT+Pv7SxYvXuwDANu2bXMWiURSkUjUf2ZBLpfzpk2bJk1JSfESCoXSiIgIkVKp7B/Yhg0bXLy9vQPDw8P9KysrLUYbjyGM6fQKRVGmU1lZiRMnTpgtvn3iwLrenZWdUHcZKRkewuqqKnynUEADoEOjQX5rq3EC+fgAZ84ATU3Av/41IWbDdRKEA1023azdEOUVZZQ4QiGdCaeo8ejt7UV2drZNUFBQ53hfK5fLeTKZzDI6Orr/YhSVSgWxWCxxdnYOiY6OVsTExLRPnz6988KFC9b19fXstrY2Vk5OjqCuru6+RDc+Pl4ZFBTUfuzYsary8nKZRCJ54Cs9wsPD/fXLRXS348ePW4/3mPQNdXz6zx8+fNj++eefv3vv644dO2bj4uLSK5fLZZWVlWXJyckKACguLuZnZGTYFxYWlpeXl8tYLBbzwQcfOBQWFvL37Nnjevbs2Qq5XC778MMPa/Pz8y0PHTrkUFRUdLmwsPByenr6lPPnz08CgNraWv7q1atvV1VVlQkEAnV6erodAOTn51t+9tln9qWlpbKcnJzKkpKSySONx1BGKk2hKMqEmpqa8NRTT6GyshJWVlZobGyEhYWFycfh/BNnXN18FdAA0ACt51phP89+1NcZAo8Q6Brcu/N4MGKzeyAmxpjv/sAShAnY+422EvCLKtpxh6LMrbu7myUWiyUAMGvWrLY1a9Y0Xrt2bczLHLW2trKSk5N9d+/eXWdvb9/frYzD4aC8vFzW2NjITkpK8i0oKODPnDmza82aNfWxsbF+kyZN0kil0g72MBewV1dX86dPn94FADKZjLdt2zZXhULBzsrKqga0s8T19fWcqqoqfltbG/tnP/tZ41BJZFFR0biX6hrumPQNd3wA0NXVRb788kvBvn37rt/7urCwsM7f/va3U9944w33RYsWtSYkJCgBICsry/rSpUuWISEhAX3vwXJyclK1trayFy5c2Ozq6qoCAGdnZ/X+/futEhMTW2xsbDQAkJSU1Jybm2u9dOnSFnd39+7Zs/9JZ/0AACAASURBVGd3AkBoaGhHTU2NBQDk5uZaJSYmtlhbW2sAIC4urmWk8RgKnRGnqAmgra0NGzduxNW+CwaVSiXOnTtnlrHwPflwe2PgDGvzaZOVHw7qsjmFx0OcvWm+AKCzU9tp85NPTBNvBFGeUbDkWgIAqpqqUNVUZZK4jFG/9VDUw0utqnIjeXnhJC8vPLWqyu3e51+Xyz10z//u6tX7LrJ7SSbz0j2/p7bWcaxxdTXi5eXlsoMHD9bx+XyGw+EwGr0OwF1dXSwA2LVr1xTdjHJNTQ23u7ubJCUl+S5durRp+fLlQ9ZCOzo6qqOiotpOnDghAIC1a9c2lpWVXS4sLJTb2dmp/fz87lsy+tatWxxra2s1l6v9PiCRSHo+/fTTQQ0RCgsLLXfs2NFw5MiRa5988sm1I0eODFlSMd4Z8bEc00jHBwAZGRkCiUTSMXXq1PsaZwQHB3cXFxfLgoKCOrds2eL+5ptvugIAwzBk6dKld3X/FjU1NZf27dt3c7T49+LxeP2/7dhsNqNSqUY8JTrceAxl1EScEOJHCDlACDlNCPlKdzPkICjqSTd58mT8+9//7m/mExkZCR7PJCV/Q7JPGEiAm043mSxujJ0dOH1lIheVStzqvu/6JMOrrQXs7YH4eOBXvwLM1FBJx4JjgRifgdn67KpsMAyDHrXh1xjs6QHWr9c2+Zk8WdvbiKKo0Xl4eKiampo49fX17M7OTpKdnS0AgI0bN97RJYqenp69KSkpXn5+fl33Xtx58+ZNTmNjIxsAlEolyc3NtQkICOgCgBs3bnAAoLKyknfq1Cnb11577b5fwpWVlTxnZ+dhfyl0d3cTDofDsFjaNG/Tpk2uq1evvjPUvkVFRXLdmPVvixcvbrt3X41Gg+GOaazHBwBHjhyxf+GFF4b841JTU8O1trbWrFy5sik1NbX+4sWLlgCQkJCgOHnypJ3u82loaGBXVFTw4uPjFSdOnLCrr69n6x6fO3euMjMz07atrY2lUChYmZmZdnPnzr3vePTFxMQoMzMzbZVKJWlubmbl5OTYjjQeQxlLacq/AHwA4AAA0xWLUtQThMViISEhAQcPHgQAzJs3D1FRxqkNHgvbObYgXAKml0F7STt6GnrAczb+FwMbDgeRAgHyWrSTLFlNTXjV1aCTD4MpFMD33wO6U7+trcCFC8Ds2caLOQYJvgn9yxa+ff5t7Dq3C2tmrcGvI35t0DgqFfDHPw7cP3oUWLbMoCEo6rFkYWHBrFu37tbMmTMDnJ2de4VC4X2z1jk5OVbHjx93EIlEnbrSlu3bt9948cUXW+vq6rgrVqzwUavVYBiGLFq0qOmll15qBYBnn33Wt6WlhcPhcJh33nmn1tHR8b7cKyQkpKupqYkrEomkaWlpNfPnzx9Uf52dnW0VFRWl1Gg0WLVqlXtSUlKr7iLLhzHSMUVHRwsPHjx4zdvbu3ek41MoFKxz587ZHDx4cMiWxkVFRZM2btzowWKxwOFwmLS0tGsAEB4e3rV58+YbsbGxfhqNBlwul3n33XdrY2Nj29etW3crKipKzGKxmMDAwI6jR4/WLFu27G5YWFgAALzyyit3IiIiOuVy+bB/yCIjIzuee+65psDAQKmDg0NvcHBw+0jjMRTCjHI+khBSxDCM6fpdj2LGjBlMYWGhuYdBUQb3z3/+Eyl9CzzPmjUL3377rVnH8/2c79F6VnuxZMA/AuD8E6MvpwoA+J/aWmyorgYALJ0yBZ8as8HRn/4EpPYtDWhtDbz6KvDGG9opYjOqbq6G77u+gx6b6z0XXy03/MlILy/tSQFAu5jMqVMGD0E9pL6/wzPMPQ5TKykpqQkJCWk09zgeBfX19ezU1FT3/Px8m5dffrlRoVCw33rrrVvvvfee4+HDhx1CQkLap0+f3rl+/fohZ8Up4yopKXEMCQnxHuq5sdSInyCErCSEuBJC7HU3ww6Roqi4uDjoTiNeuHABjY3m/ftjFWrVv33zw3GX4T0w/TrxU3fvYvnly+jWDHkt0MOLixvYZrOBffvMnoQDwDS7aRDZiwY99kPDD0YpT0lOHtg2dlNTiqKMw8XFRX3o0KHaurq6S7t27apva2tjCwQCzebNm2+XlZVdPnToUC1NwiemsSTiywH8GsDXAIr6bnRKmqIMzM7ODk8//TQAgGEYZGdnm3U8TM/A2bK2gjaMdvbMUAInT4Z7X318h0aD9IYGfG2sZQwlEsCt75qvlhZgAp1t01/GMCUwBfVv1oPHNnx50BtvDGzn5gK9vQYPQVGUiaWnp9eaewzU2IyaiDMM4zPEbZopBkdRT5oFep1V3nnnHaSkpJgsAb6X8ysDpSiaLg06qh66vHBMCCFY4OAw6LHTzUZauYUQYP58vUCnjRPnAegScSueFVytXMFhGWe1WZFIW54CAEolYOaKKIqiqCfKWFZN4RJCVhNCMvpuvySEjHn9TIqixi5Or1SisLAQ//znP1FaWmqWsdjMtIFNpA3c/9sdM8pmYLJossli65enuPF4+ImTk/GC6Zen5OQYL844zfWei7zlebi7/i72xe8zWhxCJuxHQFEU9dgbS2nK/wIIB5DWdwvve4yiKAMLCwuDnd3gpV5Pm2mWlrAJwvLDIHpXBCuJ1egvMKBYOzs4cDh4ztERW729IZ1sxC8B8+YNbJ8/D/z859pmP2ZeWHsSdxKivaONUo5yL/2TAkePAsYqyacoiqIGG8u5zpkMw4To3f+KEFJirAFR1JOMzWYjNjYWGRkZAIClS5f2r6TyJBFwOLgdEQGWKVrPOzkBoaHaZQw1GuDAAe3jFRWAv7/x44+RhtHgh4YfwCIsBDsHG/S99ZuMymRAfj4QHW3QEBRFUdQQxjIjriaE9K+jRQiZBrqeOEUZja48JTg4GAkJCfDw8DDziMzDJEm4jn5tho6ZL5bVd7LiJFz2uCD0w1C8lf+Wwd/fwUHb00hH912EoiiKMq6xzIj/GkAuIaQaAAHgBeBVo46Kop5gL7zwAhYuXAgXFxdzD6WfpluDptNNULep4bzMNOuJ30vNMGBBezGnwcXFAW+/rd22swP+9S8gIsLwcR7A3Y67kN2R4U6HduWxL6u/hIbRgEXGMo8ydrNnAye1PYRw65ZB35qiKIoaxqiJOMMwZwghIgC6c7RyhmFoI2SKMhKBQACBQGDuYfSr+3MdrqReATQA15Fr8kT8/xoacPzOHZxpacE3YWHwtzRod2GtiAhg0iSgsxNobgb8/AA+3/BxHsCGLzfg4+8/BgBYci0x33c+WrtaYTfJbpRXjjPOBiA8XNtZUyQafX+Koijq4Q2biBNCYhiG+YoQknzPU0JCCBiGOWbksVEU1aezsxOTJk0yS2wLdwug7+K93sZedN/qhoWrhUli/6elBZuqq1Hbrf3uf7qpyTiJuIUFsHWrtl58/nxg6lTDx3hAcb5x/Yl4sFMwDi85bJQ4kZHaG0VRFGU6I82IRwP4CsDCIZ5jANBEnKKM7I9//CNOnTqFb775BrW1tXB2Nn1ZiH2CvbYorW8Rkbun7sLtNTeTxC5tb+9PwgHgQlub8YL95jfGe++HEOsTCwICBgwKbhagtasVAv7EOWNCURRFPbhhiwwZhvld3+YOhmFe1b8B2Gma4VHUk6ugoABpaWk4e/Ysenp68OWXX5plHBwrDlxeHahXb8lrMVnsOL2lHC1ZLHzs52ey2GAY4OpV08UbhoOlA8JcwwAAakaN3JpcM4+Iop4ccrmcJxKJpPqPpaamum3dunXQrEhVVRV31qxZfr6+vlKhUCjduXNnf/ODjo4OEhQUFODv7y8RCoXStWvX9s9kuLu7B/n5+UnEYrEkMDAwYLhxXLlyhXvgwAHD1qONQUZGho23t3egp6dn4KZNm4a8cGn79u1OQqFQKhKJpAsXLvTp6OggwMifyUQz1L+pqYzlap+jQzyWYeiBUBQ12Jo1a1BTU9N/v6yszGxjcV/p3r/dnNMMRmOaNbaFkybBy0JbBtOh0Rh3RlyHYbR93729gWnTgNu3jR9zFPOnDSz0nXPFuB13OjqAjz/WVuhcv27UUBT12OByudi7d+/1K1eulBUUFFz++OOPnYqKivgAwOfzmXPnzsnlcrmsrKxMdubMGZszZ870N0c4e/ZsRXl5uezSpUuXh3v/zMxMm+LiYiPU5Q1PpVJh7dq1npmZmRUVFRVlR48etdcdk87Vq1e5+/fvd7548aKssrKyTK1Wk48++sgeGPkzoQYMm4gTQsSEkCUABISQZL3bCgBj+iAJIQmEEDkhpIoQMux5X0LIEkIIQwiZMe4joKjHlH6XzZSUFLz1luGXrRsrq1ArcBy0lWy9t3uh/EFpkriEEMzXW1cvx1it7vUxDPDdd0BtbV9Q87eanO87kIj/W/5vbM/bjrSCNKPEcnMDXnsN+PJLuowhRY2Vl5dXb2RkZAcA2NnZaXx9fTtra2t5AMBisSAQCDQA0NPTQ1QqFRnP6k/Z2dlWW7ZsmXry5Ek7sVgskclkxu/yBSAvL2+yl5dXt0Qi6eHz+UxycnJTRkaG7b37qdVq0t7ezurt7UVnZyfLw8OjFxj5M9FRKBSsOXPmCP39/SUikUiqP+uflpZmHxQUFCAWiyXLli3zUqlUAID333/fwc/PT+Lv7y9ZvHixDwBs27bNWSQSSUUikXTHjh1OgPZsxrRp06QpKSleQqFQGhERIVIqlf0f/IYNG1y8vb0Dw8PD/SsrKy1GG4+xjDQj7g/gxwBsoa0T193CALw+2hsTQtgA/gJgAQAJgJcIIZIh9rMGsAbAd+MdPEU9zubrtTs8f/48GDN2eiQsAvv5Awlx82kTJMR95uuVpxg9Ef/6a8DVVdvcBwBsbIDGRuPGHIOIqRGYxNFerHuj7Qa2nd2G/y00ToNjX9+B7X//2yghKOqxJpfLeTKZzDI6Orp/xkKlUkEsFkucnZ1DoqOjFTExMe2652JjY0VSqTRgz549jkO9X3x8vDIoKKj92LFjVeXl5TKJRNLzoGMLDw/3F4vFkntvx48ft75337q6Op67u3t/LA8Pj54bN24MSqR9fHx6V61aVe/j4xPs5OQUYm1trU5OTlaM5TMBgGPHjtm4uLj0yuVyWWVlZZnutcXFxfyMjAz7wsLC8vLychmLxWI++OADh8LCQv6ePXtcz549WyGXy2UffvhhbX5+vuWhQ4ccioqKLhcWFl5OT0+fcv78+UkAUFtby1+9evXtqqqqMoFAoE5PT7cDgPz8fMvPPvvMvrS0VJaTk1NZUlIyeaTxGNNINeL/7qsH//E9NeKrGYb5egzv/SMAVQzDVDMM0wPgCIBFQ+y3E8DbALoe5AAo6nH1ox/9CDY2NgCAuro6VFRUmHU8dnEDCXHDoQaTxY21s4NuCuM7hQKnGhuh6JsZMTihcKAUhc0GqquBNWuME2scLDgWeMbrmUGPXbp9CTfbbho8VrLeOllq2rqNesINN3M93OOtra2s5ORk3927d9fZ29trdI9zOByUl5fLamtrfyguLp5cUFDAB4Bz586Vy2Syy6dPn648cOCA0xdffGE11PtWV1fzp0+f3gUAMpmM98ILL3glJCRM0z3//vvvO2zevNk5JSXFKykpadqxY8dshnqfoqIieXl5ueze2+LFix+o7u/OnTvsU6dO2VZVVZXW19f/0NHRwUpLS7PX32e4zwQAwsLCOvPz823eeOMN96ysLCsHBwc1AGRlZVlfunTJMiQkJEAsFkvOnTtnU11dbZGdnW2zcOHCZldXVxUAODs7q/Py8qwSExNbbGxsNAKBQJOUlNScm5trDQDu7u7ds2fP7gSA0NDQjpqaGgsAyM3NtUpMTGyxtrbW2Nvba+Li4lpGGo8xjaVG/P8RQvpPRRBC7Aghfx3D69wB1Ondv973WD9CSBiAqQzDnBrLYCnqScLlcjF37tz++6dPnzbjaABGNTAj3/5DO9QdpsnSHLhchFtrJ2s0AH586ZLxZsZ17e4BbRZ6/rxx4jyAON+BUiVvW28cXnIYNhZD/q19KD//+cD25cuAwujzQRQ1NlWpVW55JC98LDfZSzKve18ve0nmpb9PVWrVqMs/OTs7q1pbW9n6jzU1NbEdHR1Vu3btmqKbUa6pqeF2d3eTpKQk36VLlzYtX758yKvaHR0d1VFRUW0nTpwQANoZZQBwd3dXJSUltXzzzTeT733NrVu3ONbW1moulwsAkEgkPZ9++uk1/X0KCwstd+zY0XDkyJFrn3zyybUjR44MWVIxnhnxqVOnDpoBv379+qAZcgA4ceKEjaenZ7ebm5vKwsKCWbx4ccvXX3/d/2VitM8kODi4u7i4WBYUFNS5ZcsW9zfffNMVABiGIUuXLr2r+6JQU1Nzad++feOeeeDxeP1/uNhsNqNSqUasCRpuPMY0lkQ8mGGY/g+PYZhmAKEPG5gQwgKwD8C6Mez7c0JIISGk8M6dOw8bmqIeGfp14qdOncLRo0fR1WWek0f2C/QmORig6XSTyWLrl6cA2vXEjRdsoCQIZv7yo0//gk1FtwIvSF+AFW/IybOHMmXK4O8ieXkGD0FRjwyBQKBxcnLq/fzzz60BoKGhgZ2XlyeIiYlRbty48Y4uUfT09OxNSUnx8vPz69q2bdugU4Y3b97kNDY2sgFAqVSS3Nxcm4CAgC6FQsFqbm5mAdra5NzcXJvg4ODOe8dQWVnJc3Z2HrYcpbu7m3A4HIbF0qZ0mzZtcl29evWQydJ4ZsSjo6Pba2pq+OXl5byuri5y7Ngx+yVLlgxKpr29vXuKi4ut2traWBqNBl999ZV1QEBAFwBoNBoM95no1NTUcK2trTUrV65sSk1Nrb948aIlACQkJChOnjxpd+PGDY7uc6+oqODFx8crTpw4YVdfX8/WPT537lxlZmambVtbG0uhULAyMzPt5s6dO+IMf0xMjDIzM9NWqVSS5uZmVk5Oju1I4zGmsbS4ZxFC7PoScBBC7Mf4uhsA9LtiePQ9pmMNIBBAXt8pHhcAnxNCnmUYplD/jRiG2Q9gPwDMmDHDfIWyFGVi+nXi2dnZyM7OxpkzZxATE2PysfA9+OA6ccH0MrB5ygaWfqa7gH++nR129V08ySEErjwjXqsUFwf8z/9otyfAhZo6gU6BWDVzFZ72eBrzps0zeIt7ffPnD5TJnz4NPPus0UJR1IR38ODBqytXrvRcv379VADYsGHDTalUOqjDeE5OjtXx48cdRCJRp1gslgDA9u3bb7z44outdXV13BUrVvio1WowDEMWLVrU9NJLL7XKZDLec889JwS0FzwuWbLk7vPPP3/fOaiQkJCupqYmrkgkkqalpdXMnz+/Xf/57Oxsq6ioKKVGo8GqVavck5KSWnUXST6MvlVPahMSEvzUajWWLVvWOGPGjC4AiI6OFh48ePBaTExM+8KFC5uDg4MDOBwOpFJpR2pq6p3RPhNdjKKiokkbN270YLFY4HA4TFpa2jUACA8P79q8efON2NhYP41GAy6Xy7z77ru1sbGx7evWrbsVFRUlZrFYTGBgYMfRo0drli1bdjcsLCwAAF555ZU7ERERnXK5fNg/FJGRkR3PPfdcU2BgoNTBwaE3ODi4faTxGBMZ7QIwQshPAWwC8C9o23o8D+APDMP8fZTXcQBUAIiFNgEvALCMYZgh12AjhOQBePPeJPxeM2bMYAoLR9yFoh4bDMNg2rRpg5Yx3LBhA3bv3m2W8WhUGrA4xksAh9Ot0cAuPx+dDIMwKyvkTZ8Oa85Y5gMeQFcXYG+vbXcPAKdOAWVlwKpVgDG6ek5AZ84A8+Zpt/38gPJyYByLPFAGRggpYhjmiVtVrKSkpCYkJMT8V0tPMPX19ezU1FT3/Px8m5dffrlRoVCw33rrrVvvvfee4+HDhx1CQkLap0+f3rl+/XpaQjBBlJSUOIaEhHgP9dyof8kYhkknhBQB0BWrJjMMIxvD61SEkF8CyAbABvBXhmHKCCE7ABQyDPP5mI+Aop5QhBDMnz8fB/rWkXNzc4OPj4/ZxmOOJBwALFgsfBESAqmlJRyNORsOAHw+EB0NZGVp7yclaf8bEqKdLZ9gVBoVOCzDfimJiAB4PKCnB6ioAA4fBpYtM2gIiqIekIuLi/rQoUO1uvs//elPPQUCgWbz5s23N2/ebP7GB9S4jOmvat8s9qcAPgegJIR4jvF1mQzD+DEM48swzB/6Hts6VBLOMMyc0WbDKepJpF+eIpFI8Itf/MKMozGfaFtb4yfhOvp14joTqEzlhuIGNn65EeH7w7Hw8EKDvz+fDzjr9Zj7+4jnPymKMqf09PTa0feiJqpRp1EIIc8C2AvADcBtAF4ALgOQjvQ6iqIMIzY2Fn/4wx8wf/58hIWFmXs4g/Q09oDnaKLk2JT0Z74JAZYuBSIjzTeee/Soe7D7vLY8ic/ho0vVBT7HsA3r5s0D/vY37faFCwZ9a4qiKKrPWGbEdwJ4CkAFwzA+0NZ8f2vUUVEU1c/e3h6bNm3CzJkzwWazR3+BkXVe68TFuRfxH6v/4Nup35qs3b1Oh1qN7KYmrKuqwkVjtbyXSrWNfQBtp81164BFQ7VBML1PLn6CsP0DX8h61b0oqS8xeJzXX9eu5vjCC8Cnnxr87SmKoiiMbfWTXoZh7hJCWIQQFsMwuYSQd4w+MoqiJqTeO71oyRtYwart+zbYhBt+PeshY2s0eLGsDCf7li+04XAw3fq+5W8fHiHAggXahbTnzx9cp2FmzpOd0dKl/fyn2kxF6RulEPAFBo/z9NNAg+n6NlEURT2RxjIj3kIIsQLwHwD/Rwj5M4D2UV5DUZQR1NfX4x//+Ad++tOfQi6Xm2UM1mHWIBYDS2g0pJsuW/v0zp3+JBwAcoy5nvhHH2lb3m/fDnjd1xvEbJ7xegY8trYcqE5Rh/Ze+uuYoijqUTWWRHwRgA4AawFkAbgCwPBXB1EUNaqf/exneOWVV/D3v/8dWbpVPUyMsAgcFjr031eWKk0WO9a2v8kvWADenDp1+J0f1gRdr28ybzIipkb03/+y+kszjoaiKIp6GCMm4oQQNoCTDMNoGIZRMQxzkGGYdxmGuWui8VEU1WfVqlXIzMzsv5+dnW22sYjeEfVvK84rTNbu3sXCAsGTtR2gNQC4LBMtp1hUBOzYAURFadcUNzP9Lps51cZfzYVhtB/BjRuj70tRFEWN3Yh/xRiGUQPQEEIMX4BIUdS4+Pn59W9LJBL8/ve/N9tYLNwtYCnVNrdhehi0/KdllFcYjn67e6OWpujbvRv43e+Ac+cmRNv7+b56ifiVHFxvvY5zteeMEuvll7XLGc6Yof0uQlEURRnOWKaTlABKCSEfE0Le1d2MPTCKogbTX0/85s2bCA4ONuNoAPs4+/7t5tPNJosbZz8Q93SzkeO2tACbNg1ev28CrCce6hIKh0na8qCG9gZMfWcqXsx4EaN1Sn4Q9fXaxj7AhDh0iqKox8pYEvFjALZAe7Fmkd6NoigTCggIgJubGwCgpaUFhYXm7X9lN39gZrrxpOm6UEcJBLDoq9++3NGB611d0BghAQWgnQp+5x2gtq9fxp//DHzyiXFijQObxUbstNhBj91suwnZnVGbHo+bfkdNU52AoCiKelIMm4jrumf21YXfdzPdECmKAgba3evkmHl6ctK0SUDf9YxdlV3ovtltmrhsNiIFA9Vy4UVFOHDrlnGC6drd9wefpF1cewLQrxMnIJg3bR46VZ0Gj5OSAnD6FrptbaV14hRFUYY00oz4cd0GIeSoCcZCUdQo4vQ6Pp4+fRpqtRo9uroBE7PwsBh0//ant00We75eecrt3l6cNuZUrX6XzQlQH66jn4jz2DyceOkEZrjNMHgcS0vgmWcG7k+gj4CiTILNZoeLxWKJSCSSLliwYFpbW9uYrhKvqqrizpo1y8/X11cqFAqlO3fu7P8W39HRQYKCggL8/f0lQqFQunbtWjfdczt37nQSiURSoVAo3bFjx7Df/K9cucI9cOCA3XDPG0tGRoaNt7d3oKenZ+CmTZtchttv+/btTkKhUCoSiaQLFy706ejoIACwdOlSb3t7+xCRSDShO7Snpqa6bd261ehNJEb6YdJfu2uasQdCUdTo5s2b1799/vx5ODo6IiMjwyxjYU9mg++jbatOuASqZpXJYsfZDf7b861CYZT6aG0wvUT8yy8BtWlWiBmNl60XAp0CMcd7DrZGb0WP2nhfyOLjB7ZpIk49aSwsLDTl5eWyysrKMi6Xy+zdu3fKWF7H5XKxd+/e61euXCkrKCi4/PHHHzsVFRXxAYDP5zPnzp2Ty+VyWVlZmezMmTM2Z86cmVxQUMBPT0+fUlxcfPny5ctlWVlZtpcuXbIY6v0zMzNtiouLLQ15rKNRqVRYu3atZ2ZmZkVFRUXZ0aNH7XXHpO/q1avc/fv3O1+8eFFWWVlZplaryUcffWQPAP/1X//V+Pnnn1eactwT2UiJODPMNkVRZuLk5ITQ0FAAAMMwaGlpwWkzZkZ++/0w/ex0PNP9DHy2+5gsboiVFRw5A42Bj0mlIMZa91siAfpq89HSAhQWAo2NQEeHceKNw8VfXETu8lxsitoEGwvjdTfVT8RzcibMdxGKMrnIyEhlVVWVhVwu5+nP6G7dutU5NTXVTX9fLy+v3sjIyA4AsLOz0/j6+nbW1tbyAIDFYkEgEGgAoKenh6hUKkIIQWlp6aTQ0FCltbW1hsvlIiIiou3IkSO2uEd2drbVli1bpp48edJOLBZLZDIZz7hHrpWXlzfZy8urWyKR9PD5fCY5ObkpIyPjvvEBgFqtJu3t7aze3l50dnayPDw8egFgwYIFyilTpgw7c6NQKFhz5swR+vv7S0QikVR/1j8tLc0+KCgoQCwWS5YtW+alUmnf5v3333fw8/OT+Pv7SxYvXuwDANu2bXMWiURSkUjUf2ZBLpfzpk2bJk1JSfESCoXSiIgIP4WtcQAAIABJREFUkVKp7P/jsWHDBhdvb+/A8PBw/8rKSovRxmMIIyXiIYQQBSGkDUBw37aCENJGCFEYchAURY1dvH5WBKC0tNRMIwHsY+1h+4yt8ZLgYbAIwbqpU/H2tGm4OGMGfmRjvCQUhAyeFU9O1taJnzhhvJhjxGaxTRInKGigNP7uXeDAAZOEpagJpbe3F9nZ2TZBQUHjvhhDLpfzZDKZZXR0dH8HNJVKBbFYLHF2dg6Jjo5WxMTEtE+fPr3zwoUL1vX19ey2tjZWTk6OoK6u7r4kOz4+XhkUFNR+7NixqvLycplEInngU2Lh4eH+YrFYcu/t+PHj1vfuW1dXx3N3d++P5eHh0XPjxo37xufj49O7atWqeh8fn2AnJ6cQa2trdXJy8phyx2PHjtm4uLj0yuVyWWVlZZnudcXFxfyMjAz7wsLC8vLychmLxWI++OADh8LCQv6ePXtcz549WyGXy2UffvhhbX5+vuWhQ4ccioqKLhcWFl5OT0+fcv78+UkAUFtby1+9evXtqqqqMoFAoE5PT7cDgPz8fMvPPvvMvrS0VJaTk1NZUlIyeaTxGMqwiTjDMGyGYWwYhrFmGIbTt627b8S/ehRFjUQ/EXd3d0dBQYEZR2M+v/HywnpPT4RYWRn/i4DeRbK4eVPb4WaCreVX3VyN/y34X/wq61cGf28WC/DwGLi/f7/BQ1DUqKpSq9zySF54HskLr0qtcrv3efnrcg/d81d/d/W+2l7ZSzIv3fO1e2odxxq3u7ubJRaLJUFBQRIPD4+eNWvWjGuZqNbWVlZycrLv7t276+zt7TW6xzkcDsrLy2W1tbU/FBcXTy4oKOCHhYV1rVmzpj42NtZv7ty5IqlU2sFmD/2Fu7q6mj99+vQuAJDJZLwXXnjBKyEhob+U+P3333fYvHmzc0pKildSUtK0Y8eODZm7FRUVycvLy2X33hYvXtw2nuPUd+fOHfapU6dsq6qqSuvr63/o6OhgpaWl2Y/+SiAsLKwzPz/f5o033nDPysqycnBwUANAVlaW9aVLlyxDQkICxGKx5Ny5czbV1dUW2dnZNgsXLmx2dXVVAYCzs7M6Ly/PKjExscXGxkYjEAg0SUlJzbm5udYA4O7u3j179uxOAAgNDe2oqamxAIDc3FyrxMTEFmtra429vb0mLi6uZaTxGIqJ2tJRFGUos2fPRmBgIH7+85/j3Xfpkv4moVebDwBgs7VLiEwQ7T3tEL8vxsrMlfjzd3/GrTbDryKzcOHAdlmZ9rsIRT0JdDXi5eXlsoMHD9bx+XyGw+EwGk1/To2uri4WAOzatWuKbka5pqaG293dTZKSknyXLl3atHz58iE7nzk6OqqjoqLaTpw4IQCAtWvXNpaVlV0uLCyU29nZqf38/Lrufc2tW7c41tbWai6XCwCQSCQ9n3766TX9fQoLCy137NjRcOTIkWuffPLJtSNHjgxZUjGeGfGpU6cOmgG/fv36oBlynRMnTth4enp2u7m5qSwsLJjFixe3fP3111ZDf8KDBQcHdxcXF8uCgoI6t2zZ4v7mm2+6AgDDMGTp0qV3df8WNTU1l/bt23dzLO+pj8fj9f/2YrPZjEqlGnEmZ7jxGApNxCnqEcPj8VBaWooPP/wQycnJYJmqzfsIWr9uRfnPynEh4ILJ2t3rYxgGsvZ2VBqrbtvJCeirzQcA/P3vwL/+ZZxY41TVVIXf5f0OXDa3/7HTVwx/3cAvfqH9L4+n7bLZ22vwEBT1yPDw8FA1NTVx6uvr2Z2dnSQ7O1sAABs3bryjSxQ9PT17U1JSvPz8/Lq2bdvWoP/6mzdvchobG9kAoFQqSW5urk1AQEAXANy4cYMDAJWVlbxTp07Zvvbaa/ctC1VZWclzdnYethylu7ubcDgcRvf3YdOmTa6rV6++M9S+45kRj46Obq+pqeGXl5fzurq6yLFjx+yXLFly3xcMb2/vnuLiYqu2tjaWRqPBV199Za07vtHU1NRwra2tNStXrmxKTU2tv3jxoiUAJCQkKE6ePGmn+3waGhrYFRUVvPj4eMWJEyfs6uvr2brH586dq8zMzLRta2tjKRQKVmZmpt3cuXNHnOGPiYlRZmZm2iqVStLc3MzKycmxHWk8hsIZfReKoqjh9Tb14vvI7/sv6W7+shmOz475rO9DO37nDlZWVuJWTw9ed3XFfn9/4wSKiwO+/x5wd59QVyve7biLvd/sBQBYsC3w9ry3MddnrsHjuLoCpaXaa1cnwHc/6gkk3Ce8KdwnHHYG1P+A/3X/A/7Xh3teclhyTXJYcm2458fDwsKCWbdu3a2ZM2cGODs79wqFwvuSzJycHKvjx487iESiTrFYLAGA7du333jxxRdb6+rquCtWrPBRq9VgGIYsWrSo6aWXXmoFgGeffda3paWFw+FwmHfeeafW0dHxvl84ISEhXU1NTVyRSCRNS0urmT9/frv+89nZ2VZRUVFKjUaDVatWuSclJbXqLhx9GH0rwdQmJCT4qdVqLFu2rHHGjBldABAdHS08ePDgNW9v796YmJj2hQsXNgcHBwdwOBxIpdKO1NTUOwCwcOFCn2+//da6ubmZ4+zsHPyb3/zm5tq1a/vLfYqKiiZt3LjRg8VigcPhMGlpadcAIDw8vGvz5s03YmNj/TQaDbhcLvPuu+/WxsbGtq9bt+5WVFSUmMViMYGBgR1Hjx6tWbZs2d2wsLAAAHjllVfuREREdMrl8mEvao2MjOx47rnnmgIDA6UODg69wcHB7SONx1CI0Zb8MpIZM2Yw5u4oSFETSU9PD7799lu4urpCJBKZZQznHM5B1aS9et0x2RGBRwNNEreqowOxJSWo7dY2E/KysMDVp54yTs34tWvalVLEYu0FnBOEWqOG0x4nNHVqJ81K/l8Jgp2DzTwqypAIIUUMwxh+kfgJrqSkpCYkJMR0bXsfYfX19ezU1FT3/Px8m5dffrlRoVCw33rrrVvvvfee4+HDhx1CQkLap0+f3rl+/fohZ8Up4yopKXEMCQnxHuo5Oq9BUY+wAwcOwMHBAdHR0ThgxqUsBFEDnS7bS9tH2NOwpvL5uKPX0EhsaYl2Y81We3kBAQETKgkHtCun6Df3yarKMuNoKIoyBxcXF/WhQ4dq6+rqLu3atau+ra2NLRAINJs3b75dVlZ2+dChQ7U0CZ+YaCJOUY+o3Nxc7N+/H0qldjWs7Oxss41F/IkYhKtNUDsrO9F9yzTt7i1YLMzVa+6zyNERVhwTVdzduAF88gkgk5km3ggShAn929lXTPdz8IidUKWoJ0Z6enqtucdAjQ1NxCnqEdXS0gJdmRaPx8PTTz8NtZlql7m2XAgiBmbFm3OaTRY7Xq/dfbYxW93r27JFu57fq68Chw+bJuYI4nwH1jnPv5YPZY8SKo1xOp1euKBdSn3KlMErqVAURVHjRxNxinpExcTEQLe+bE9PD3bu3Inh1ps1Bbu4gZlpcyXiX7W0oFdvSTGjqK4G6usH7k+A9cTdrN3668J7Nb2I/iQajv/jCGWPcpRXjt/f/gZ89pm2ueh//mPwt6coinqi0EScoh5RAoEATz/9dP/9HDMnhPZxAwlxU04TGI1p6hb8Jk2Cl4UFAKBNrcY3CiM2/mUY4JlngI8+0t4PDweWLJkQNRrxvgONnopvFaO1uxV5NXkGj6NbxhAA2tq0FToURVHUg6GJOEU9wvS7bJqzRhwAJgdPBsta+yult6EXyhLDz8YOhRAyaFb8d1ev4uNbhm9o0xdscLv7Z58Ffv3rCXEBp36duE7hTcOvMBUSAtjaDtyvrjZ4CIqiqCcGTcQp6hEWp5cUnj59GuZcjpSwCZiegfj16fUj7G1Y+ol4Xmsr0ow5TaufiJ82fOOcBxUxNQKW3IE+E7nLc7FtzjaDxyEEWLp04L6Zv/9RFEU90mgiTlGPsPDwcNj3JaH19fVYsWIF0tPTzTIWwiKwChnoYNx82nR14rF2doN+mRUrlbjdM2zTuYcMFjuw/e23E6bVvQXHAnO9tY18ptlNA4HxZun1TsRMpO8iFEVRjxyaiFPUI4zNZmP+/IE1pNPT082WiAOA2xtusImwgfDPQoR9F2ayuAIOB0/b2PTf/4WrKyYZq/3jlClAWN+xqdVAbq5x4jyAt2LfQuV/V+LK6iuI9o42WpzY2IHumoWF2gs3KYqiqPGjiThFPeL068QBID8/Hx0dD93J+IG4rnBF2LkweKz2AMfKROt594m3t4ffpEn4pbs7fuHmBmtjrieuX57ypz8BixYNXMBpRsHOwRDaC40ex9YWmDVLu80wwMGDRg9JURT1WKKJOEU94vRnxFksFg4fPgyOqZraTCAbPT0hnzUL74lECLW2Nm4w/UT8P/8BPv8cyMgwbswHoOxR4lTFKaOsKR4SMrD9xz8a/O0piqKeCDQRp6hHnIeHB6RSKSZNmoT4+HiEh4eDx+OZe1gmxzFWKcpQZs8GLC0HP5aXB5jpTMRQfnLsJ7B/2x4/PvxjXLhxweDv/+MfD2w3NEyYUnmKMii5XM4TiURS/cdSU1Pdtm7d6qz/WFVVFXfWrFl+vr6+UqFQKN25c6eT7rmOjg4SFBQU4O/vLxEKhdK1a9e66Z5zd3cP8vPzk4jFYklgYGDAcOO4cuUK98CBA3bDPW8sGRkZNt7e3oGenp6BmzZtcrn3+ZGOW0elUiEgIEAyd+5c45+ue0BD/ZuaCk3EKeoxcPz4cTQ3NyMzMxNeXl7mHg4AoOtWF2r/VIueO0a6aHIM1MZaRcbCApgzZ+D+0qVAaen9ybkZMAyD8sZyXGm6gl5NLwDg9BXDX1EZHw/o948qKDB4CIp6ZHC5XOzdu/f6lStXygoKCi5//PHHTkVFRXwA4PP5zLlz5+RyuVxWVlYmO3PmjM2ZM2cm61579uzZivLyctmlS5cuD/f+mZmZNsXFxSb9BaNSqbB27VrPzMzMioqKirKjR4/a645JZ6Tj1vn973/vLBQKO0059kcJTcQp6jEgFAph0dfUxtwYhsEF6QV86/YtqlOr0fD3BpPGr+/uxrarV/F0cTF+XFpqvED65SkMA4hExos1Du297Qj+32B8d+M7AECAYwCcJt83SfXQOBxg82bgH/8AWlqAefMMHoKiHhleXl69kZGRHQBgZ2en8fX17aytreUB2pJBgUCgAYCenh6iUqkIGUfvgezsbKstW7ZMPXnypJ1YLJbIZDKTnPLMy8ub7OXl1S2RSHr4fD6TnJzclJGRYau/z0jHDWhn8rOzswWvv/76kJd0KxQK1pw5c4T+/v4SkUgk1Z/1T0tLsw8KCgoQi8WSZcuWealU2hK7999/38HPz0/i7+8vWbx4sQ8AbNu2zVkkEklFIpF0x44dToD2bMa0adOkKSkpXkKhUBoRESFSKpX9H/yGDRtcvL29A8PDw/0rKystRhuPsTx5haQU9YTo7u42S3JOCIGme6DNfMP/NWBq6lSTxGYYBn+5eRO/v3YNAGBBCDrVakzSn7o1lKQk4No1bUL+zDOGf/8HZMWzQqRnJHJrtKu5rI9YjxXTVxgl1rZtRnlbinqkyeVynkwms4yOju7vaqZSqRAYGCipra21WL58+e2YmJh23XOxsbEiQgheffXVO2+++eZ9CWt8fLwyKCjo/7d353FRVf0fwD9nWGVfRRgFF0B2EDDNJVMrSTJcstQ0KzVTKx+xR3PJ1HqyfpWVmU9qi1qPmimaC4pWmGsmkKYgiyKyibLvA8zM+f0xMAtboHNnEL7v12te3XPn3vs9cxqH75w595zK9evXZw0cOFByP3ULCQnpX1lZ2eQD8YMPPsgaP358ufq+rKwsY7FYrPxJs2fPnrUXLlywaHxug+Ze94IFC3r93//9X3ZpaWmzH8JRUVFWPXr0qDt58uR1ACgsLDQAgISEBNO9e/faxcXFJZuYmPDp06e7fvXVV/aDBw+u/Pjjj53Pnz+f7OzsLL1z547B6dOnzXbu3GkfHx9/jXOOkJAQ79GjR5c7ODjIMjMzTX/44Yf0IUOG3Bo7dmzfHTt22M6fP7/o9OnTZvv377e7cuVKUl1dHYKCgnwGDBhQ1VJ9hEQ94oR0ImlpaXj99dfh6emJF198UW/1cJzkqNyuvFqpkZgLiTGG/fn5ynIt50ioEGiFT3d3YP16ICysQwxJUae+3H3MDVpxh3QOkZGRLoyxkLY8pk6d2mSM3tSpU93Uj4mMjHRpLo66lnquW9pfWloqmjhxYr8PPvggy87OTvnBZ2hoiOTk5KTMzMy/ExISzC9evGgKAGfOnElOSkq6dvz48bStW7d2P3r0aLOJbnp6umlQUJAEAJKSkoyfffZZt7CwsL4Nz2/cuNF+5cqVTlOmTHELDw/vGxUVZdXcdeLj41OSk5OTGj8aJ+Ht1dzr3rVrl7WDg4N0+PDhLd48ExwcXH369GmrefPmiY8dO2Zhb28vA4Bjx45ZXr161SwwMNDby8vL58yZM1bp6ekmMTExVuPGjSt2dnaWAoCTk5Ps5MmTFmPHji2xsrKSW1tby8PDw4tjY2MtAUAsFtcMGTKkGgAGDBhQlZGRYQIAsbGxFmPHji2xtLSU29nZyZ944omS1uojJErECekkKisrcfjwYWzcuBFpaWk4ceIE5HLdJMCNid8Qw8jFCADAazlKTpXoLLb6KpuzevTAUGtrncVGdTWQkaG7eC1QX+7++I3jkMkF/1tCSKfk5OQkbdybW1RUZODg4CBdt26do5eXl4+Xl5dPRkaGUU1NDQsPD+83efLkopkzZzb7oefg4CAbPnx4+aFDh6wBoE+fPnUAIBaLpeHh4SXnz583b3zO7du3DS0tLWVGRorPVB8fn9o9e/bcUj8mLi7ObO3atXd27959a9u2bbd2797d7JCKkJCQ/g11Vn8cOHCgyVRTvXr1qs3JyVEOM8nOztboIW/Q0us+c+aMxYkTJ2zEYrH/iy++2PePP/6wjIiI6KN+bkBAQE1CQkKSv79/9dtvvy1+8803nQGAc84mT55c2PBFISMj4+r69etzm3tNrTE2NlbeKGRgYMClUmmrY4Jaqo+QKBEnpJO4efMmIiMjleXa2lrcuHFDL3UxFZui+2TVuOTCw4U6i62eiJ/S1VQeqamKaUTs7YEXXtBNzFYEOAWgh4VigoOi6iLE344XNF5GBrB0KTBnjqBhCNE5a2treffu3esOHjxoCQB37twxOHnypPWoUaMqli1blt+QKLq6utZNmTLFzdPTU7J69WqNG2Nyc3MNCwoKDACgoqKCxcbGWnl7e0vKyspExcXFIkAxNjk2NtYqICCgyU2NaWlpxk5OTi3e9V5TU8MMDQ25qH7mqOXLlzu/8cYb+c0d254e8REjRlRmZGSYJicnG0skEhYVFWU3adIkjS8YcrkcLb3uL7/8MufOnTt/5+TkXNm2bVv64MGDy3/++eeb6sdkZGQYWVpayufPn18UGRmZd+nSJTMACAsLKzt8+LBtTk6OYUO7p6amGo8ZM6bs0KFDtnl5eQYN+0eOHFkRHR1tU15eLiorKxNFR0fbjhw5stUe/lGjRlVER0fbVFRUsOLiYtGJEydsWquPkGiMOCGdhK+vL8RiMXJycgAAR44cgYcebyC0f8oeOZ8r6lJ4qBDun7m3+HOuNg23toapSASJXI7U6mpkVFejd7duwgY1MgKOHFFsnzsHFBcDtjqfaUyJMYYx/cZg+2XFSjvrz6+HlYkVlgxdovUFfw4cACZMUGyLRMCmTYrmIETb1q9fn3svvaINdu3adWvXrl23/vlITdu3b785f/581yVLlvQCgKVLl+b6+vrWqB9z4sQJiwMHDth7eHhUe3l5+QDAmjVrcp577rnSrKwsoxdffLGPTCYD55xFREQUTZ06tTQpKcl4woQJ7gAgk8nYpEmTCp955pmyxvEDAwMlRUVFRh4eHr6bNm3KePzxxyvVn4+JibEYPnx4hVwux4IFC8Th4eGlDTdQ3o/6GVEyw8LCPGUyGaZNm1YQGhoqAYARI0a4b9++/VZKSopJS6+7LTHi4+O7LVu2rKdIJIKhoSHftGnTLQAICQmRrFy5Mmf06NGecrkcRkZGfMOGDZmjR4+uXLx48e3hw4d7iUQi7ufnV7Vv376MadOmFQYHB3sDwIwZM/KHDh1anZKS0uJNrcOGDauaMGFCkZ+fn6+9vX1dQEBAZWv1ERLjQk3vJZDQ0FAeFxen72oQ0iG9/PLL+O677wAAq1evxjvvvKO3ushr5ThrfxayCsWwiIFJA2Hu3eRXV0GEXb6MmOJiAMBXnp6Y6/KPQ0Hv3Zo1wLp1QE3932UvL2DXLiAoSLiYbbDryi5Mi5qmse/zsM/xxqA3tBqnogJQXz8pKkqVmBPtYIzFc85D9V0PXbt8+XJGYGBgs7NtdGV5eXkGkZGR4tOnT1tNnz69oKyszOD999+//cUXXzjs2rXLPjAwsDIoKKh6yZIlzfaKE927fPmyQ2BgYO/mnqOhKYR0Ik+oTakXE6Pfm/RExiLYPKGa6er2tts6i60+POXnggLEFBUJF8zZWZWEP/wwcO2a3pNwAHi83+Ng0PwF4nDqYa3HsbAAxGJV+ebNlo8lhNy/Hj16yHbu3JmZlZV1dd26dXnl5eUG1tbW8pUrV95NTEy8tnPnzkxKwh8clIgT0ok8/vjjyuEfFy5cQJGQCWgbSItUS6vn/6S7vwvqifjRoiKMv3oVlTKBblgMD1dtX7zYYZaYdDBzwEDxQGV5ovdErBu9TpBYr7+u2j51SpAQhJAW7NixI1PfdSD3jhJxQjoRe3t7DBo0CIDiJprNmzdj586dequP+jSGNTdrUFdcp5O43mZm6Kk2h7pELscv9UNVtE4sBoKDFdtSKaDnXyLUqU9j6GjmiBCXEEHiqA9FOX4cqLrv0amEENI1UCJOSCcTERGh3F6+fDlmzpyJUj310naf0h3MiMG0jynEi8QQddPNRw5jDE+q9Yr3MjFBN5GAsceNU20fOiRcnHaa6D0RS4cuRezMWGx4coNgcTw9FUPjAcUMjr/8IlgoQgjpVCgRJ6STGT9+vEZZKpXqbby4sYMxHql+BIPTB8NjvQcMTAVfpEwpwsEBAGApEmFq9+54Qi0x1zr1RPzIEeDnn4H33xcuXhsF9QjCB499gEd7PwpjA2FXxVb7/of9+wUNRQghnQYl4oR0Ml5eXvD09FSWhw4dCmdnwdckaBEzEH7KwuaMtrFBTEAACoYNw4f9+gkbLDgYaJiZpbgYGD8eWLECuHOn9fP0QCaXoaymyQxp9039+9/27UBentZDEEJIp0OJOCGdkHqv+JNPPonhw4frsTb6YWpggCfs7GAs5JCUBowpFvRp7OhR4WO30d93/sbLP78M50+cseLXFVq//kMPqeYP5xzYINxIGEII6TQoESekE5oxYwY2b96M3NxcrFih/aTrXskkMlSmVf7zgQ8i9eEpxsbAv/8NhAhzc+S9uFl8E99d+g75Vfk4mHoQ2l5DQiRS3bMKdKjvIIQQ0mHRypqEdEJ+fn7w8/PTdzWUSs+XImVWCqpSqmBoa4hhBcN0Gv9ubS0OFxbiYEEB/q9fP3iaCbBq8ahRgKkpIJEAtbXAK68A7tpdxfJeLT2xFJ+c/0RZrpXVIrc8F2IrcStntd+8eUBRETBliuLlE0IIaR0l4oQQwdXdrUPVNcWcdtJCKarSq2DWV4BkuBkVUinGXbmCP8vLAQBDra3xb1dX7QcyM1PM41dToxim4uj4z+foiLOlM2RcMY/64J6DcfblsxAx7f8gOnOm4kEIIaRtaGgKIZ1ccnIyPvzwQwwdOhTJycl6qYNdmB2gNmHKnR90dxPj93fuKJNwADhYWChcsJ07gX37gJdeAqythYvTThO8VBN9x+fGC3KzJiGEkPYTNBFnjIUxxlIYY9cZY28183wkYyyJMfY3Y+xXxpibkPUhpKvhnGP+/Pl46623cO7cORzS0xzXIhMRHCIclOWyc7pLBJ92UMUVAfhU6BlUGtPyWOx74WbjhhBnxXj1OnkdjqQe0XONCHlwGBgYhHh5efl4eHj4Pvnkk33Ly8vblDtdv37daNCgQZ79+vXzdXd393333Xe7NzxXVVXF/P39vfv37+/j7u7uu2jRIpeG5959993uHh4evu7u7r5r167t3vzVgRs3bhht3brV9v5eXfvt3bvXqnfv3n6urq5+y5cv79HcMa29drFY7O/p6enj5eXl4+fn5627mrdPZGSky6pVq5yEjiNYIs4YMwDwJYAnAfgAmMoY82l02F8AQjnnAQD2Avg/oepDSFc0d+5cxMbGKsv6SsQBwH29arx0SWwJpBVSncQVm5jgIUtLAIAcQLKuln08ehSYOxdwdQVu3dJNzFZM9J6o3I5KjhI8XnU1sHEjcPGi4KEIEZSJiYk8OTk5KS0tLdHIyIh/8sknbRp3ZmRkhE8++ST7xo0biRcvXrz2zTffdI+PjzcFAFNTU37mzJmUlJSUpMTExKRff/3V6tdffzW/ePGi6Y4dOxwTEhKuXbt2LfHYsWM2V69eNWnu+tHR0VYJCQm6GeNXTyqVYtGiRa7R0dGpqampifv27bNreE3qWnvtAPD777+nJicnJ129evWaLuvfEQnZI/4QgOuc83TOeS2A3QAi1A/gnMdyzhv+Kv4BoKeA9SGky3nkkUeU23379sW+ffv0VhdTN1OY+5sDAHgtR/EvAi0534zxar3iBwoKdBP088+BLVuA7GwgOlo3MVuhnohHp0Yj9mYsvk74WpBYU6cC5ubA668Db78tSAhC9GLYsGEV169fN0lJSTH28PDwbdi/atUqp8jISBf1Y93c3OqGDRtWBQC2trbyfv36VWdmZhoDgEgkgrW1tRwAamtrmVQqZYwxXLlypduAAQMqLC0t5UZGRhg6dGj57t27bRrXIyYmxuLtt9/udfjwYVsvLy+fpKQkYVfsqne1bc7TAAAgAElEQVTy5ElzNze3Gh8fn1pTU1M+ceLEor179zapX2uv/Z+UlZWJHn30Uff+/fv7eHh4+Kr3+m/atMnO39/f28vLy2fatGluUqmiQ2fjxo32np6ePv379/cZP358HwBYvXq1k4eHh6+Hh4fyl4WUlBTjvn37+k6ZMsXN3d3dd+jQoR4VFRXKxS6WLl3ao3fv3n4hISH909LSTP6pPtogZCIuBpClVs6u39eSWQBowitCtGjs2LEwMFAMzk5PT0ddXZ1e62P/lL1yu/CwgGO1G1FPxI8VFaFaJhMuWHk5MGsWcO6cat9vvwkXr428HLzg5aBYh14ik2DUjlGYf2Q+SiWlWo9lbq4akXPmjNYvT4he1NXVISYmxsrf37+6veempKQYJyUlmY0YMaKiYZ9UKoWXl5ePk5NT4IgRI8pGjRpVGRQUVP3nn39a5uXlGZSXl4tOnDhhnZWV1SSBHTNmTIW/v39lVFTU9eTk5CQfH5/ae31dISEh/b28vHwaPw4cOGDZ+NisrCxjsVisjNWzZ8/anJycVhPs5l776NGjPXx9fb0//vhjh8bHR0VFWfXo0aMuJSUlKS0tLXHixIllAJCQkGC6d+9eu7i4uOTk5OQkkUjEv/rqK/u4uDjTjz/+2Pn3339PTUlJSdq8eXPm6dOnzXbu3GkfHx9/LS4u7tqOHTscz5492w0AMjMzTd944427169fT7S2tpbt2LHDFgBOnz5ttn//frsrV64knThxIu3y5cvmrdVHWzrEzZqMsekAQgF81MLzrzDG4hhjcfn5+bqtHCEPMDs7O4wYMUJZ1ufQFEAzES/YXwAu1834aS8zM3h26wYAqJTLcbSwEPm19/x3q3UWFsCxY4qEHAC++ALYtUuYWO000WuiRrlOXoej17Xf/xEZqdqWSBRTGhJyvyIjI10YYyGMsZDGvc8AMGfOnJ4Nz7/zzjtNxvZOnTrVreH55hLAltTU1Ii8vLx8/P39fXr27Fm7cOHCdv2sVlpaKpo4cWK/Dz74IMvOzk7esN/Q0BDJyclJmZmZfyckJJhfvHjRNDg4WLJw4cK80aNHe44cOdLD19e3qqEzpbH09HTToKAgCQAkJSUZP/vss25hYWF9G57fuHGj/cqVK52mTJniFh4e3jcqKsqquevEx8enJCcnJzV+jB8/vry54+/3tZ85cyY5KSnp2vHjx9O2bt3a/ejRoxbq5wQHB1efPn3aat68eeJjx45Z2NvbywDg2LFjllevXjULDAz09vLy8jlz5oxVenq6SUxMjNW4ceOKnZ2dpQDg5OQkO3nypMXYsWNLrKys5NbW1vLw8PDi2NhYSwAQi8U1Q4YMqQaAAQMGVGVkZJgAQGxsrMXYsWNLLC0t5XZ2dvInnniipLX6aIuQiXgOgF5q5Z71+zQwxh4DsALA05zzmuYuxDnfwjkP5ZyHOnagKcEIeRBERKhGhB04cECPNQG69eumnDRVWiRF2Z+6uWmTMabRK/7ctWtYcfOmUME0V9nMzAQMO8ZMserDUxgYXgp6Ce522p/r3McH6N9fsS2TASdPaj0EITrTMEY8OTk5afv27Vmmpqbc0NCQy+XKnBoSiUQEAOvWrXNs6FHOyMgwqqmpYeHh4f0mT55cNHPmzJLmru/g4CAbPnx4+aFDh6wBYNGiRQWJiYnX4uLiUmxtbWWenp6Sxufcvn3b0NLSUmZUv5ytj49P7Z49ezRuRomLizNbu3btnd27d9/atm3brd27dzc7pKI9PeK9evXS6AHPzs7W6CFX19Jr79OnTx0AiMViaXh4eMn58+fN1c8LCAioSUhISPL3969+++23xW+++aYzAHDO2eTJkwsb/l9kZGRcXb9+fW5zsVtjbGys7AEyMDDgUqmUtXZ8S/XRFiET8YsAPBhjfRhjxgCmADiofgBjbACAzVAk4XcFrAshXZZ6Iv7bb7/h5s2byMlp8p1YJ4y6G0FkrPrYyd3c7s/QezZBLRGXco5DhYWQCzWjifoqm4cPCxPjHgQ7B8PVWjGHOgfHFL8pCHUJFSTWM8+otn/+WZAQhOhNz549pUVFRYZ5eXkG1dXVLCYmxhoAli1blt+QKLq6utZNmTLFzdPTU7J69WqNOVtzc3MNCwoKDACgoqKCxcbGWnl7e0sAICcnxxAA0tLSjI8cOWIze/bsJr8ppaWlGTs5ObX4s15NTQ0zNDTkIpHi83b58uXOb7zxRrNDCtrTIz5ixIjKjIwM0+TkZGOJRMKioqLsJk2a1OQLhlwuR3OvvaysTFRcXCxq2I6NjbUKCAjQGOqTkZFhZGlpKZ8/f35RZGRk3qVLl8wAICwsrOzw4cO2De1z584dg9TUVOMxY8aUHTp0yDYvL8+gYf/IkSMroqOjbcrLy0VlZWWi6Oho25EjR7bawz9q1KiK6Ohom4qKClZcXCw6ceKETWv10RbBumk451LG2GsAYqCYQfhbznkiY2wtgDjO+UEohqJYAPiJMQYAmZzzp4WqEyFdkZubG4KCgnDp0iXU1taiX79+mD9/PjZu3KjzujDGYD3EGsW/FMPQ1hDdenfTWeyHrKzgZGSEO/Xj5I0Yw+3aWohNmp2Q4P6or7J57Rpw4wag62kTm8EYwwsBLyCtKA0TvSdiSK8hgsWKiAD+8x/F9uHDgFTaYX4YIA+o9evX57bWA7p169bsrVu3Zrf0/K5du27t2rVLK1MYmZiY8MWLF98eOHCgt5OTU527u3uTXusTJ05YHDhwwN7Dw6Pay8vLBwDWrFmT89xzz5VmZWUZvfjii31kMhk45ywiIqJo6tSppQDw9NNP9yspKTE0NDTkn332WaaDg0OToRCBgYGSoqIiIw8PD99NmzZlPP7445Xqz8fExFgMHz68Qi6XY8GCBeLw8PDShpsn70f9bCiZYWFhnjKZDNOmTSsIDQ2VAMCIESPct2/ffqt37951Lb12f3//6gkTJrgDgEwmY5MmTSp85plnNH4ajY+P77Zs2bKeIpEIhoaGfNOmTbcAICQkRLJy5cqc0aNHe8rlchgZGfENGzZkjh49unLx4sW3hw8f7iUSibifn1/Vvn37MqZNm1YYHBzsDQAzZszIHzp0aHVKSkqL49mHDRtWNWHChCI/Pz9fe3v7uoCAgMrW6qMtjHeAOW7bIzQ0lMfFxem7GoQ8UNasWYPVq1cry66ursjIyED9F2CdqrpeBZGxCKauTWa8EtyrKSnYfPs2RAA2eHhggVi7S7xrGDdO1Rv+ySfAoEGK1TcHDBAuZgcilwO9egG59WlTbCzw6KN6rdIDizEWzzkX5qeLDuzy5csZgYGBOprm6MGWl5dnEBkZKT59+rTV9OnTC8rKygzef//921988YXDrl277AMDAyuDgoKqlyxZQjfa6cHly5cdAgMDezf3HCXihHQBly9fRlBQkLI8ZMgQHDp0CHZ2dnqsle7Fl5fjamUlnrK3h3392ErBbNmimEccUPWOT54M7NkjbNwOZO5cRTMAgL8/8Pff+q3Pg4oScdJeL7zwguuOHTsy9V0PotBaIt4hZk0hhAgrICAAbm6qhWvfe++9LpeEA0CIpSVm9ughfBIOaN6wKan/1frQIaCs4ywvH58bj+W/LoffJj/8dfsvrV9/0CDV9tWrQGVly8cSQrSHkvAHByXihHQBjDEsXLgQa9aswaVLl/BoBxojIK+TQ1Yp4Lze+uLiAoSEqMrm5sCMGR0qG/343MdYd2YdEvMT8XOK9u+ofP55oP5eMXAOfPON1kMQQsgDjRJxQrqIRYsWYdWqVQgMDNTL2PDGyuLKcOmJSzhtfhrpy9N1Hl8ik+FoYSE+ycr654Pv1dNq956PGKEYp+Gs1Zmv7tnCowtxKFU1r/yx68e0HsPEBAgIAOztgenTNSeTIYQQIuCsKYQQ0prrC6+j7JximMadH+7A/TN3nX1BKKmrQ8/z51Epl8MAwItCDVd5/nngnXeAwYOBCRO0f/37UCQpQmWdond+svdkfDf+O0Hi/PGHIiEnhBDSFPWIE9IFlZeX45tvvsHs2bP1Vodekar1vqRFUpTH3fcibm32amoqKusX45ABiC4sFCZQv35ARgZw/jygx7Zujvoqm4kFiTA3Nm/l6HtHSTghhLSMEnFCupiamhq4ublh9uzZ+Oabb3D58mW91MP+aXsYOal6oe/8704rR2vXSFvVAnNuJiZ4xMZGuGBqN8kqdYDZqsa4j0E3Q8U87kn5SUgpSNFzjQhpQi6Xy/U/jo6Q+1D/Hpa39Dwl4oR0IZxzPP744yguLlbu27Ztm17qIjISwfsHb2X57q67kEtb/KzSqqft7ZXb2TU1sDAw0ElcJCcDq1Yp1n+/pdU1IdrNzMgMYe5hyvL+5P2Cx5TLge+/VyzuQ0gbXM3Pz7emZJw8qORyOcvPz7cGcLWlY2iMOCFdCGMM1tbWyvKYMWOwbNkyvdXHdqQtjF2MUZtbi7q7dSg+Xgz7sfb/fOJ9cjYxwWArK/xRVgYZgMOFhZjZo4fgcbFoEXCs/qbI3buBpUuFj9mKid4TlQl41LUojPMch+yybIxxH6P1WNOnAz/9BNTWAlVVqinWCWmJVCqdnZeX93VeXp4fqOOQPJjkAK5KpdIWxybSgj6EdDE//vgjpkyZAkCxwmZ6ejoMdNUj3IwbS24g6yPFzCWOzznCd7evTuJ+mJmJt9IVs7U8ZmuLE4GBwgWrrgY+/RT44gsgL0+xb8gQ4OxZ4WK2QYmkBI4fOUIqV3VRu1q7Iv2NdBiItPue8PAArl9XbLu7A2lpWr18p9ZVF/QhpCugb5iEdDHjx4+Hg4MDACAzMxPHjx/Xa32cZjgptwuiCiAt1c24handuys/AH8pLkZiRQXu1tYKE8zYGNi0SZWEv/UW8MsvwsRqBxtTG4zuM1pjX2ZpJn5J137d3nxTtZ2bq+gZJ4SQro4ScUK6GBMTE8ycOVNZ3tKwBrmemPuaw9BWMUqO13Hkbs3VSVxXU1M8qba66MCEBCxq6LLVNgMDxdiMBqmpQLduwsRqp4neqtlTGBgm+0yGo7mj1uPMmgU0NHdVFXDkiNZDEELIA4cScUK6oDlz5ii3Dx06hJSUFFy5ckUvdWEiBiNH1ewpuZt0k4gDwFwXF+V2tVyOffn5KK6rEyaY2pcfHDoECDVlYjtF9I+AAVMMQ+HgeGfEOwh2DtZ6HENDYN48VXnrVq2HIISQBw4l4oR0Qf3798cjjzwCAJDJZPDz88PUqVOhr3tGxK+JAQAiUxGshlrprB5P2tmhp9pE15aGhrheXS1MMG9v4KGHFNt1dYqbNTsAJwsnRHhFYKDLQGyL2Ia+tn0FizVrlmr72DEgM1OwUIQQ8kCgRJyQLuqVV15RbkulUiQmJiI+Pl4vdXF+2Rn+0f4YXjUcPt/76GyFTUORCLPVlpyf1aMHBlpZCRdQvVd82zbgxg0gJka4eG30/YTv8eecPzEzaCa6GQk3ZKZPH+CxxxTbnAPffitYKEIIeSDQrCmEdFESiQQuLi7KOcX79u2Lr7/+GiNHjtRzzXQrWyLBptxczHF2Rh+hx20XFQHOzpp3Kjo5AdnZirEbHYxEKoGpoalWr7lnD/Dcc4ptAwPFjZvdu2s1RKdDs6YQ0nlRjzghXZSpqSleeOEFZTk8PLzLJeEA0NPUFO/37St8Eg4o7lZ8+mnNfXfuACdPCh+7jUolpdh0cROCNwdj3pF5/3xCO0VEqL5zyGSK9Y0IIaSrokSckC7slVdewfTp03Hq1Cl8/vnn+q6OEuccNbdr9F0NYagPTwGAp54CLCz0U5dmJBckY0H0AvyV9xf2JO5BqaRUq9c3MQEefVRVjorS6uUJIeSBQok4IV2Yj48Pvv/+ewwfPlxn47JbI62QInlWMs7YnsEfff/Q2ZL3DTjniC8vx/zUVETl5wsTZMwYzbEYCxYAgwcLE6udTmacxAv7Vb+SyLkccbnaHwr4/vuAkREwdiwQHa31yxNCyAOj4w1KJIR0WTW3apD3bZ6ynL8vH07PObVyhnaty8zEips3AQDJVVWY6Kj9+bRhZAQ8/zywbx/wwguAj4/2Y9wjRzNHpBalAlDMKX5xzkX4dffTepyBA4HyckXvOCGEdGXUI04IAaCYxjAmJgZTpkzBJ598opc6mPuaw7iHsbKc81mOzmKX1NVhTX0SDgCxJSVIF2oqw3ffBW7eVPzX1VWYGPfAt7svRriNAKCYU3xP4h7BYlESTgghlIgTQur98MMPCAsLw48//oivvvpKb3OKu7ymWmSn/K9ySMt0s+S9jZERnnJwUJYnOTigj6l2ZwxRMjcHRI0+fmUyoH4GG31aMHCBcntL/BbUymgtekIIEQol4oQQ3L59GwsWqBKw69evIyEhQS916b2iN8wDzAEAvIYjf59AY7Wbob7S5q8lJZDIdTBGXSoFvv8e8PMDXn1V+Hj/YLzXeLhYKtrhTuUdRF0T/m7Kc+cAtWntCSGky6BEnBACZ2dn+Pr6KsuLFy9GSEiI3urjNEM1Ljx3U67Oeucfs7VF3/pe8BKpFD8JdcOmuqtXFWPFk5OBn34Crl0TPmYrjAyMMDdkrrL82R+f4dPzn+Jo2lGtx6qsVEyjPnSoYsn7c+e0HoIQQjo0SsQJIQA0V9o8fPiw3oamAIDTdCcwE8UsLuVx5Sg8WKiTuCLGMEdtpc3NubnCBvztNyAyUlW2tAQSE4WN2QZzgufAUKS4l/9CzgVEHo/ER+c+0nocc3Ogrk5VXrFC6yEIIaRDo0ScEAIAeO6552BpaQkASElJwenTp/VWF5MeJhDPFyvLqfNSwWW6+WLwkrMzDOuncjxXVoYf8vJQUCvQOOmSEiA2VrFtYgJcugQ884wwsdrB2dIZk7wnaeyLzYjF9aLrWo81Y4ZqOyMDeMAWeyaEkPtCiTghBABgYWGBadOmKctbtmzRY20ApxecgPqpzWtv1yJ3i8C90w1xjY0xQe2mzRnJydiWl9fKGfdh3DjA01OxXVMDfPmlMHHugfpNmyImwudhn6OHRQ+tx1mzBmhY1DQjA/jzT62HIISQDosScUKIkvrwlJ9++gkff/wxtm/frpe6WPhbwMjRSFnOWJuhs9jqN20CwJe5uagR4sZNIyPF6jYNvvgCuHVL+3HuwTDXYfDv7q8s97HpAwtj7a8AamMDTJmiKm/dqvUQhBDSYVEiTghRCg4OVt6kWVtbi3//+9948803UVqq3WXO24IZMLh/5g4wwHq4NUL+1N3NoyNtbJQ3bQJAkLm5cGPmJ04EBg1SbNfWAqtWKbalupm2sSWMMbw17C2sGL4CGQszMK7/OMFizZmj2v7f/xQ944QQ0hVQIk4I0fBKo3nkCgoK8MUXX+ilLt2ndMfDuQ9jwKkBMO0l0JzezRAxhlfre8X7mJpiipMTTA0MhAnGGPDhh6ryjh3AwoVA795AVpYwMdtomv80vDfqPfSy7iVonMGDgdBQxbZEAkyerHkTJyGEdFZMnzMj3IvQ0FAeFxen72oQ0mlJJBL4+voiPT0dABAaGopTp06hW8NA3i6isK4OVyoqMMLGBqz+5k1BPfUUcOSI5r7XXlMMV+lA/rr9F06kn8CSoUu0et2zZ4Fhw1TlefOATZu0GuKBxRiL55yH6rsehBDtox5xQogGU1NTbNiwAQDg6OiIf/3rXx0uCddFB4K9kREetbXVTRIOAOvWKXrH1R0/3mG6hqVyKV49/CpCtoRg6S9LcT7rvFavP3QoEBysKm/eDGRnazUEIYR0OJSIE0KaCA8Px9atW5GWlobnn39e39VRKr9cjouBF5H2Wppe4pdJpdiYnS3MFwF/f2DmTFW5d2/g778VN3R2ACWSEhxJOwIOxWtffHyx1mPs36/6LuLq2mG+gxBCiGAoESeENGv27NmwtrbWdzWUMj/ORHxQPCr/rsTtLbdRV6zbLG17Xh56nT+P169fx667d4UJsmaNYj5xQDFouqZGmDjtVCurReiWUGSXKbqo3e3c8W3Et1qP4+oKrFwJbNwIpKcDffpoPQQhhHQolIgTQtqkpKQES5YsweHDh/US336svXKbSznSXtddr3haVRWW3biBMpkMAPDvGzdQWb+tVa6uiiz0jz8Uy91bWWk/xj0wNjDGG4PeUJZvldyCnAswnSOAtWuBBQuajtIhhJDOiBJxQsg/+v333+Hm5oaPPvoIixcvRp0exgyY+5jDZqSNspy/Px+1dwVa8bIRK0NDjcR7kKUlzEQCfXzOnq2azrADWThoIQaJFfWqk9fh5Z9fhkwuwJcRQgjpQigRJ4S0qra2Fhs2bEBZWRkAIDU1FXv37tVLXXz3+sLM1wwAwKs4Mtdl6iSuk7ExPujXT1k+UlSE69XVOomNrCzF7CnqC//ogYHIAN88/Q2MRIox6xdyLuDzC58LHrewEIiIAEpKBA9FCCE6R4k4IaRVxsbGkKutKunm5obJkyfrpS5Gdkbo+5++ynLOf3MgyZLoJPYrLi4YaGkJAKjlHPPT0oSfveXiRaBfP+DLL4H//Af46y9h4/0D3+6+ePuRt5XlFb+uwFdxX2HOwTmCtMWaNYCTE3DwIPDss1q/PCGE6B0l4oSQf/Tpp5/CtH6lyVu3bmHz5s16q4v90/awfEiREPMajltrdbMkvAFj+MrTU/mh+UtxMX4U6qZNQLGy5h9/AA1fgiQS4NIl4eK10VvD3kKAUwAAQCKTYN6Refj6r6/xc8rPWo915QrQMCLoxAkgPl7rIQghRK8oESeE/KPevXtjxYoVyvKKFStwV8gktBWMMfT5j2o6jdtf30bmR7oZohJsaYnXxGJl+Y3r1/GvtDTIhOgZZ0wxmXZDJjp2LPDSS9qP005GBkb49ulvYcA0Vxp979R7Wu8V37FDNYkMAGzdqtXLE0KI3lEiTghpkzfffBPu7u4AgNLSUrz11lvIycmBVCrVeV1sR9uiW3/VIkPpS9Jxe9ttncR+t08fOBsbAwDy6+rweU4Ovr0tQGwDA8UiPw0OH+4wS02GuITgzSFvKstGIkVyru3Fj8zMNBcW3bIFSEjQaghCCNErSsQJIW1iamqKL9Syou+++w7BwcGYPn26zpNxxhj6fdQPUMv7au/obgaVz+q/kDT4MidHmGBPPQWEhanKCxYA32p//u578c6Id+Bp7wkAGOM+Bo7mjoLEmT1b1QScK+5b1cN3P0IIEQQl4oSQNgsLC8OECROU5bt37+LHH3/ErFmzdF4Xh3EO8PrOC8yIwWOLB9yWuuks9mRHRzxmo5pK0VGo1S8ZA378UXM6w9mzge++A158EThwQJi4bdDNqBu+i/gOOyfuxMEpB+Fs6SxIHMaAzz9XLTB6/jzwxBPAjRuChCOEEJ1igt/1r2WhoaE8Li5O39UgpMu6desWvL29Ua02fd/GjRuxYMECvdRHWiGFoYWhzuPeqK6G359/YqqTEz53d4eloYB1KCkBRo9uOi7D3Bw4cwYIChIu9j1Yd3odBooH4rG+j2ntmqtXK2ZRaWBiAvz+e4eccl3rGGPxnPNQfdeDEKJ91CNOCGkXNzc37N+/H2Zmivm8e/XqhalTp+qtPs0l4dU3qyHJFnZaw37duiF7yBB86+XVJAnX+qqbNjbA8eOAv7/m/spKxQqcHcj7p9/H8t+WY+z/xmJP4h6tXfedd4AlS1Tlmhpg+HCgtFRrIQghROcoESeEtNuYMWNw6tQpBAUF4dSpU7Czs9N3lZTKEsrwp/efiAuIQ12xsCuA2jczJOVKeTnczp/Hjrw8LQezV8zh5+Wl2mdgAOhhWFBLSiWlWPv7WgCK1Te3XdqmtZlUGAM+/BCYO1e1b+pUwNpaK5cnhBC9oEScEHJPQkJCkJCQgN69e2vsr6qqwqpVq1BTU6PzOtXk1uCvQX+B13BIi6W46H8RMonulmHPkkgw+K+/UCiVYmZyMv6r7Zs4nZyAX39VLPIDAJ98AvTt2/o5OrTp4ibUyFT/3ytrK5Ffla/VGF99BaxdCzz5JLBtm1YvTQghOkeJOCHknjWerq6qqgoPPfQQ3n33XUREREAi0c2qlw2MnY1h9bCVslybU4vESYmQZOqmHt/n5aFKbRXSAwUFqFEra4WLC/Dbb4pJthcu1HwuMVEx2baephWZGzoXYz3GKsunMk8heHMwzmed12qct98GoqMVveTq9PDdjxBC7gsl4oQQrVm9ejUSExMBADExMfjf//6n0/iMMQTFBsEiyEK5ryi6CBf6XUDyS8moTK4UNP58sRiPWKm+CBwvLsZjly+joFbLUyu6ugIzZjTdP20a8MorQEAAcO6cdmO2gV03OxyaeghrHl0DVj+3ZE55DkZsG4Ev//wSZzPPolQizKDu0lKgRw/Fukfabm5CCBEKJeKEEK0xUV8GEcCSJUswd+5cnDx5EnK5XOsrLzaHGTCE/hWKXkt6KfdxKUfetjxc9L6IC14XUBRTJEhsGyMj/BIUhNk9eij3nSktRXB8PFbfvIn5KSmIKSwUZiXOgweBv/9WbF+7pkjE9dAzLmIirBqxCtHPR8PW1BaAYrz4a0dfwyPbHkGPT3rgrV/e0mrM2lrA11cxuczRo4Cjo2L4Ck1xSAjp6Gj6QkKIVn322WdYtGhRk/1isRjOzs6ws7PD8uXL8cgjj2h9JcbGio4X4db7t1D6e9Ne2F5Le6HfB/0Eics5x/rsbPz7xg009wlrY2iIjEGDYK3N+cfXrlVMLaLO1RV4/XXF3ONmZkD9iqC6klGSgUl7JiHhtua0iw+5PIQLcy5oLU5uLuDtDZSVNX3u4YeB6dOBiAhALNZaSJ2i6QsJ6bwE7RFnjIUxxlIYY9cZY026QBhjJoyxH+ufv8AY6y1kfQghwvvXv/6FAwcOwM1Nc4GdnJwcxMXF4fjx43j00Ufx3//+V+N5uVwOuZbHU9s9YYcBJwdgwNkBGsNVAEC8QDMrKzlbgpurb6Iwpt83vQYAAA1ISURBVBDSivvrSWaMYXGvXojy9YVNM/OL18jlsGq0P6myEqlVVai+16kPn35aMVxF/bqZmcC//w307KnoMra0BAIDgaioe4vRTr1teuPsy2fxctDLGvsn+0xucuzcQ3Ox9ve1OH3rNOS8fe8DFxfg5k0gNBQQNfqrdv68YkHSnj0VPeWvv65YoZMQQjoCwXrEGWMGAFIBPA4gG8BFAFM550lqx8wHEMA5f5UxNgXABM75c61dl3rECXkwyOVynDt3Drt27cJPP/2E/HzN2TOys7MhVuui3LZtG1566SWYmppCLBZj/PjxcHBwgJGREQwNDZGfn4/U1FQYGxvD398fS5cu1bje0aNHcfz4cQCAi4sL+vTpA0B1Q2lmZiaSDyaj7I8yBLgFYFnyMo3z1w9ZjwvnFb203dEdzg7OMHY2hrGTMUQmItzMvIn8knwwEcPo8NF44csXNM7/eMbHuHb1GhhjcPd0R3eX7gAD6rgc6RIJ4uKvorCkGADQe/ITOPCe5rSDgx99A9V5dwAA3fr1ga2DLcxFIpgZGMBMJMKNhKuolVRDBGDm0ll4ccYojfMnDluAitIyQCZDfzsrmN3OAWprweo/46/UAtL67ZXh/hi+bb3G+WN7jYNMKgUYEODZF4YmxoqsViSCHAyXrqYoj/0m5nP09OqtLOel52LmqHkAAJFIhKBAH41rV1fX4EpSMqTyOnDG8f3FLXDroTr/959/xYr5HyjONxDBTGwIQ5HqIZcABdnFYIzBxMQYx28c0rj+f9/bhD2bDwEMMDE2gZ2zM8rLgKpqAHIA8grUVBUAAIyNLfHrrT0aN3rOn7QGV87+oTjf1Ax2Tk7K5xkDKopLUFaq+H9n4+CAg39/rxF/1pOLkXZZ8afN3NwCy79ahuGjg6Et1CNOSCfGORfkAeBhADFq5WUAljU6JgbAw/XbhgAKUP/loKVHSEgIJ4Q8WOrq6vixY8f4U089xY2MjHj//v2bHLN48WIOoE0PJyenJueHh4e3+Xw/P78m53t182rz+YNsBzU538nAqc3nh7uPb3K+GczbfP7EYQuanG8Awzaf/9aEZU3Ob+u5AHj01kMa557bf6pd5+ekZWqc/3L4rDafy8Ca1D3C99k2n28K0ybnD+n+WJvPt2V2Tc73swjROGb1gk+bHHM/AMRxgf5W04Me9NDvQ8h1ocUAstTK2QAaL0asPIZzLmWMlQKwhyIhV2KMvQLgFQBwdXUVqr6EEIEYGhpizJgxGDNmDKqrq3H37t0mx2RmZrb5egYGBtqsHgBAZCoCqtt2LBPd39h2E3Z/owIbZiS5Z7a2muWsrOaPayt+f7+sWpla3l/8Zkfi6+psQgi5d0Im4lrDOd8CYAugGJqi5+oQQu5Dt27dmowfB4Cvv/4akZGRuHLlCkpLSyGTyVBUVASpVAqpVIrMzEykpaVBKpUiOLjpz/6BgYG4fPkyAKBHjx5wc3MDV0sQs7KykFO/wE5z5z888WGUHC0BALj2ckV3q+6QVcggr5YDMuBm9k0UVhYCHAgIDGhyvk93H8juKMZ3u1q6ws6ifrXR+ircKLiBcmm54lhvnybne1p6ILsyGwDgZtUX3UwtIOccMnDIOZBddB01MsU3BQ9v9ybnuxj1QoVUcbei2MYDxoaaM9hkFiZDxhVj3139PDRPNjCAo8gJ0vrn3aw8IGoYbM0Vv5xmlKUqD3cQO2qcbmVvA1tmD0Axa4qblWb9pLI6ZFXcBAAYMkOYdDPVeD7Axx+2UYrzjZgxxJa91Z7lqJFKcLtK8WWhm4FZk9feo5cDbJMU53cTmaG7hYvG89W1VbgryQUA2Bk7NjnfsbsjbPMV55sbWsK+m5PG85U15SisVQwbcjFr+t51sO4B20rF+dZGtnDp7dzkGEIIaY6QY8QfBrCacz6mvrwMADjn69SOiak/5jxjzBBAHgBH3kqlaIw4IYSQroTGiBPSeQk5a8pFAB6MsT6MMWMAUwAcbHTMQQAz67efAfBba0k4IYQQQgghnYVgQ1Pqx3y/BsUNmQYAvuWcJzLG1kJx48lBAN8A+J4xdh1AERTJOiGEEEIIIZ2eoGPEOefRAKIb7Vulti0B0HRCWUIIIYQQQjo5WuKeEEIIIYQQPaBEnBBCCCGEED2gRJwQQgghhBA9oEScEEIIIYQQPaBEnBBCCCGEED2gRJwQQgghhBA9oEScEEIIIYQQPaBEnBBCCCGEED2gRJwQQgghhBA9YJxzfdehXRhjpQDSGu22BlD6D/vUy+rbDgAKtFzN5upzP8e39nxbXnvjfa21jbbbQ9tt0doxbd3fnnJHbw9tvzcal7vav5XO9N5o7Rht/Ftp/FxHb48H+d+KG4CXOeeHtHhNQkhHwDl/oB4AttzLPvVyo+04XdTxfo5v7fl7aY9/aButtoe226K1Y9q6vz3ljt4e2n5vtPZe6Qr/VjrTe6O1Y7Txb6WZ5zp0e3S1fyv0oAc9HozHgzg0pbkegbbsO9TKc9rW3uv/0/GtPX8v7dFa22ibttuitWPaur+9ZW3q6O+NxuWu9m+lM703WjtGG/9Wutp7o7l9Hbk9CCEPgAduaIq2McbiOOeh+q5HR0HtoYnaQ4XaQhO1hyZqDxVqC0JIWz2IPeLatkXfFehgqD00UXuoUFtoovbQRO2hQm1BCGmTLt8jTgghhBBCiD5QjzghhBBCCCF6QIk4IYQQQgghekCJOCGEEEIIIXpAiXgrGGPejLGvGGN7GWPz9F0ffWOMjWeMbWWM/cgYe0Lf9dEnxlhfxtg3jLG9+q6LvjDGzBlj2+vfE8/ruz76Ru8JFfqs0ER/SwghLem0iThj7FvG2F3G2NVG+8MYYymMseuMsbdauwbn/Brn/FUAzwIYKmR9haal9jjAOZ8D4FUAzwlZXyFpqS3SOeezhK2p7rWzbSYC2Fv/nnha55XVgfa0R2d9TzRoZ1t0is+K1rSzPTrN3xJCiHZ12kQcwDYAYeo7GGMGAL4E8CQAHwBTGWM+jDF/xtjhRo/u9ec8DeAIgGjdVl/rtkEL7VFvZf15D6pt0F5bdDbb0Ma2AdATQFb9YTId1lGXtqHt7dHZbUP72+JB/6xozTa0oz060d8SQogWGeq7AkLhnJ9ijPVutPshANc55+kAwBjbDSCCc74OwFMtXOcggIOMsSMAdgpXY2Fpoz0YYwzABwCOcs4ThK2xcLT13uiM2tM2ALKhSMYvoZN+qW9neyTptna61Z62YIxdQyf4rGhNe98bneVvCSFEuzrlH89WiKHqwQMUiYS4pYMZY48yxjYwxjajc/ZitKs9ALwO4DEAzzDGXhWyYnrQ3veGPWPsKwADGGPLhK6cnrXUNlEAJjHG/ouutfx2s+3Rxd4TDVp6b3Tmz4rWtPTe6Ox/Swgh96jT9ohrA+f8JICTeq5Gh8E53wBgg77r0RFwzguhGP/aZXHOKwG8pO96dBT0nlChzwpN9LeEENKSrtYjngOgl1q5Z/2+roraQ4XaomXUNpqoPVSoLTRRexBC2qWrJeIXAXgwxvowxowBTAFwUM910idqDxVqi5ZR22ii9lChttBE7UEIaZdOm4gzxnYBOA+gP2MsmzE2i3MuBfAagBgA1wDs4Zwn6rOeukLtoUJt0TJqG03UHirUFpqoPQgh2sA45/quAyGEEEIIIV1Op+0RJ4QQQgghpCOjRJwQQgghhBA9oEScEEIIIYQQPaBEnBBCCCGEED2gRJwQQgghhBA9oEScEEIIIYQQPaBEnBBCCCGEED2gRJyQTooxZs8Yu1T/yGOM5aiVzwkUcwBj7JtWnndkjB0TIjYhhBDyoDHUdwUIIcLgnBcCCAIAxthqABWc848FDrscwHut1CmfMXabMTaUc35W4LoQQgghHRr1iBPSBTHGKur/+yhj7HfG2M+MsXTG2AeMsecZY38yxq4wxvrVH+fIGNvHGLtY/xjazDUtAQRwzi/Xl0eo9cD/Vf88ABwA8LyOXiohhBDSYVEiTggJBPAqAG8AMwB4cs4fAvA1gNfrj/kcwKec84EAJtU/11gogKtq5TcBLOCcBwEYDqC6fn9cfZkQQgjp0mhoCiHkIuf8NgAwxm4AOF6//wqAkfXbjwHwYYw1nGPFGLPgnFeoXccZQL5a+SyA9Yyx/wGI4pxn1++/C8BF+y+DEEIIebBQIk4IqVHblquV5VB9RogADOacS1q5TjUA04YC5/wDxtgRAGMBnGWMjeGcJ9cfU93CNQghhJAug4amEELa4jhUw1TAGAtq5phrANzVjunHOb/COf8QwEUAXvVPeUJzCAshhBDSJVEiTghpizcAhDLG/maMJUExplxDfW+3tdpNmf9ijF1ljP0NoA7A0fr9IwEc0UWlCSGEkI6Mcc71XQdCSCfBGFsEoJxz3tzNnA3HnAIQwTkv1l3NCCGEkI6HesQJIdr0X2iOOdfAGHMEsJ6ScEIIIYR6xAkhhBBCCNEL6hEnhBBCCCFEDygRJ4QQQgghRA8oESeEEEIIIUQPKBEnhBBCCCFEDygRJ4QQQgghRA/+H2OmgFbMn4ZAAAAAAElFTkSuQmCC\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1141,6 +1137,31 @@ "plt.legend(legend, loc=1, bbox_to_anchor=(1.55, 0.95))" ] }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[CellFilter\n", + " \tBins =\t[1]\n", + " \tID =\t3,\n", + " DelayedGroupFilter\n", + " \tBins =\t[1 2 3 4 5 6]\n", + " \tID =\t13]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_tally.filters" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1150,7 +1171,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1190,7 +1211,7 @@ " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 8.779139e-08\n", - " 4.658590e-10\n", + " 4.659945e-10\n", " \n", " \n", " 1\n", @@ -1199,7 +1220,7 @@ " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 7.149814e-09\n", - " 3.559010e-11\n", + " 3.565137e-11\n", " \n", " \n", " 2\n", @@ -1208,7 +1229,7 @@ " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 9.527880e-07\n", - " 5.055905e-09\n", + " 5.057375e-09\n", " \n", " \n", " 3\n", @@ -1217,7 +1238,7 @@ " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 1.303159e-07\n", - " 6.486820e-10\n", + " 6.497988e-10\n", " \n", " \n", " 4\n", @@ -1226,7 +1247,7 @@ " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 2.353903e-07\n", - " 1.249083e-09\n", + " 1.249446e-09\n", " \n", " \n", " 5\n", @@ -1235,7 +1256,7 @@ " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 2.032895e-08\n", - " 1.011928e-10\n", + " 1.013670e-10\n", " \n", " \n", " 6\n", @@ -1244,7 +1265,7 @@ " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 4.720191e-07\n", - " 2.504737e-09\n", + " 2.505466e-09\n", " \n", " \n", " 7\n", @@ -1253,7 +1274,7 @@ " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 2.626309e-08\n", - " 1.307315e-10\n", + " 1.309566e-10\n", " \n", " \n", " 8\n", @@ -1262,7 +1283,7 @@ " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 2.827915e-08\n", - " 1.500614e-10\n", + " 1.501050e-10\n", " \n", " \n", " 9\n", @@ -1271,7 +1292,7 @@ " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 2.430587e-09\n", - " 1.209889e-11\n", + " 1.211972e-11\n", " \n", " \n", " 10\n", @@ -1280,7 +1301,7 @@ " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 1.477530e-09\n", - " 7.840413e-12\n", + " 7.842692e-12\n", " \n", " \n", " 11\n", @@ -1289,7 +1310,7 @@ " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 6.994312e-11\n", - " 3.481605e-13\n", + " 3.487599e-13\n", " \n", " \n", "\n", @@ -1312,20 +1333,20 @@ "\n", " score mean std. dev. \n", "0 (((delayed-nu-fission / nu-fission) * (delayed... 8.78e-08 4.66e-10 \n", - "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09 3.56e-11 \n", + "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09 3.57e-11 \n", "2 (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07 5.06e-09 \n", - "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07 6.49e-10 \n", + "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07 6.50e-10 \n", "4 (((delayed-nu-fission / nu-fission) * (delayed... 2.35e-07 1.25e-09 \n", "5 (((delayed-nu-fission / nu-fission) * (delayed... 2.03e-08 1.01e-10 \n", - "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07 2.50e-09 \n", + "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07 2.51e-09 \n", "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08 1.31e-10 \n", "8 (((delayed-nu-fission / nu-fission) * (delayed... 2.83e-08 1.50e-10 \n", "9 (((delayed-nu-fission / nu-fission) * (delayed... 2.43e-09 1.21e-11 \n", "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09 7.84e-12 \n", - "11 (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11 3.48e-13 " + "11 (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11 3.49e-13 " ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1334,7 +1355,7 @@ "# Use tally arithmetic to compute the precursor concentrations\n", "precursor_conc = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) * \\\n", " delayed_nu_fission.get_condensed_xs(one_group).xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) / \\\n", - " decay_rate.xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True)\n", + " decay_rate.xs_tally.summation()\n", "\n", "# Get the Pandas DataFrames for inspection\n", "precursor_conc.get_pandas_dataframe()" @@ -1349,7 +1370,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1363,16 +1384,16 @@ { "data": { "text/plain": [ - "(0, 7)" + "(0.0, 7.0)" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1420,7 +1441,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1429,13 +1450,13 @@ "(1000.0, 20000000.0)" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1487,7 +1508,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/mdgxs-part-ii.ipynb b/examples/jupyter/mdgxs-part-ii.ipynb index 1c996c82a21..a7740fc2675 100644 --- a/examples/jupyter/mdgxs-part-ii.ipynb +++ b/examples/jupyter/mdgxs-part-ii.ipynb @@ -319,7 +319,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -388,17 +388,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=1.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=1.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=5.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=6.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=6.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=17.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=17.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=23.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=23.\n", " warn(msg, IDWarning)\n" ] } @@ -486,26 +486,29 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", - " Date/Time | 2019-07-18 22:07:58\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | d4df243d920d12c3d268a4c20941950d2299d678\n", + " Date/Time | 2021-04-18 12:29:49\n", + " MPI Processes | 1\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", - " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", - " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", - " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", - " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", - " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -566,20 +569,21 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.2397e-01 seconds\n", - " Reading cross sections = 4.0321e-01 seconds\n", - " Total time in simulation = 2.0407e+01 seconds\n", - " Time in transport only = 2.0154e+01 seconds\n", - " Time in inactive batches = 1.0937e+00 seconds\n", - " Time in active batches = 1.9314e+01 seconds\n", - " Time synchronizing fission bank = 7.8056e-03 seconds\n", - " Sampling source sites = 6.7223e-03 seconds\n", - " SEND/RECV source sites = 9.5783e-04 seconds\n", - " Time accumulating tallies = 9.2006e-02 seconds\n", - " Total time for finalization = 1.0890e-02 seconds\n", - " Total time elapsed = 2.0869e+01 seconds\n", - " Calculation Rate (inactive) = 22858.4 particles/second\n", - " Calculation Rate (active) = 5177.70 particles/second\n", + " Total time for initialization = 9.3825e-01 seconds\n", + " Reading cross sections = 9.2114e-01 seconds\n", + " Total time in simulation = 1.5872e+02 seconds\n", + " Time in transport only = 1.5607e+02 seconds\n", + " Time in inactive batches = 8.3963e+00 seconds\n", + " Time in active batches = 1.5032e+02 seconds\n", + " Time synchronizing fission bank = 1.2433e-02 seconds\n", + " Sampling source sites = 1.1456e-02 seconds\n", + " SEND/RECV source sites = 7.1083e-04 seconds\n", + " Time accumulating tallies = 2.5981e+00 seconds\n", + " Time writing statepoints = 2.4262e-02 seconds\n", + " Total time for finalization = 1.7827e-05 seconds\n", + " Total time elapsed = 1.5971e+02 seconds\n", + " Calculation Rate (inactive) = 2977.49 particles/second\n", + " Calculation Rate (active) = 665.252 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -638,7 +642,10 @@ "mgxs_lib.load_from_statepoint(sp)\n", "\n", "# Extrack the current tally separately\n", - "current_tally = sp.get_tally(name='current tally')" + "current_tally = sp.get_tally(name='current tally')\n", + "\n", + "# Close statepoint file now that we have the info we need\n", + "sp.close()" ] }, { @@ -1104,7 +1111,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1171,7 +1178,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/openmc/statepoint.py b/openmc/statepoint.py index 6e90bcaf7c6..0ce32065738 100644 --- a/openmc/statepoint.py +++ b/openmc/statepoint.py @@ -153,9 +153,7 @@ def __enter__(self): return self def __exit__(self, *exc): - self._f.close() - if self._summary is not None: - self._summary._f.close() + self.close() @property def cmfd_on(self): @@ -490,6 +488,14 @@ def sparse(self, sparse): for tally_id in self.tallies: self.tallies[tally_id].sparse = self.sparse + def close(self): + """Close the statepoint HDF5 file and the corresponding + summary HDF5 file if present. + """ + self._f.close() + if self._summary is not None: + self._summary._f.close() + def add_volume_information(self, volume_calc): """Add volume information to the geometry within the file From d3314107844fcdfaa4ccf4a0e04ab951d53b8928 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sat, 26 Jun 2021 08:48:03 -0500 Subject: [PATCH 2/5] Updating jupyter notebooks to either use context managers or close SP files. --- examples/jupyter/cad-based-geometry.ipynb | 152 +- examples/jupyter/candu.ipynb | 701 +-------- examples/jupyter/mdgxs-part-i.ipynb | 352 ++--- examples/jupyter/mdgxs-part-ii.ipynb | 244 ++-- examples/jupyter/mg-mode-part-i.ipynb | 181 +-- examples/jupyter/mg-mode-part-ii.ipynb | 370 ++--- examples/jupyter/mg-mode-part-iii.ipynb | 115 +- examples/jupyter/mgxs-part-iii.ipynb | 764 +++++----- examples/jupyter/pandas-dataframes.ipynb | 1625 +++++++++++---------- examples/jupyter/post-processing.ipynb | 356 ++--- examples/jupyter/tally-arithmetic.ipynb | 336 +++-- 11 files changed, 2318 insertions(+), 2878 deletions(-) diff --git a/examples/jupyter/cad-based-geometry.ipynb b/examples/jupyter/cad-based-geometry.ipynb index 9fa56f6fbdf..3699360a63b 100644 --- a/examples/jupyter/cad-based-geometry.ipynb +++ b/examples/jupyter/cad-based-geometry.ipynb @@ -174,7 +174,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -280,70 +280,76 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 2c0b16e73d5d81a2f849f4e2bfae5eb5319f2417\n", - " Date/Time | 2019-06-18 08:54:17\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 11:58:36\n", " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading DAGMC geometry...\n", + "Using the DOUBLE-DOWN interface to Embree.\n", "Loading file dagmc.h5m\n", "Initializing the GeomQueryTool...\n", "Using faceting tolerance: 0.0001\n", - "Building OBB Tree...\n", + "Building acceleration data structures...\n", + "Implicit Complement assumed to be Vacuum\n", " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.15789\n", - " 2/1 1.05169\n", - " 3/1 1.00736\n", - " 4/1 0.97863 0.99300 +/- 0.01436\n", - " 5/1 0.95316 0.97972 +/- 0.01566\n", - " 6/1 0.95079 0.97248 +/- 0.01322\n", - " 7/1 0.96879 0.97175 +/- 0.01027\n", - " 8/1 0.94253 0.96688 +/- 0.00970\n", - " 9/1 0.97406 0.96790 +/- 0.00826\n", - " 10/1 0.97362 0.96862 +/- 0.00719\n", + " 1/1 1.16613\n", + " 2/1 1.05158\n", + " 3/1 0.99824\n", + " 4/1 0.98119 0.98971 +/- 0.00853\n", + " 5/1 0.99111 0.99018 +/- 0.00494\n", + " 6/1 0.98946 0.99000 +/- 0.00350\n", + " 7/1 0.96858 0.98571 +/- 0.00507\n", + " 8/1 0.96709 0.98261 +/- 0.00518\n", + " 9/1 0.97415 0.98140 +/- 0.00454\n", + " 10/1 0.93691 0.97584 +/- 0.00681\n", " Creating state point statepoint.10.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.4575e-01 seconds\n", - " Reading cross sections = 1.3129e-01 seconds\n", - " Total time in simulation = 1.6897e+00 seconds\n", - " Time in transport only = 1.6829e+00 seconds\n", - " Time in inactive batches = 3.1605e-01 seconds\n", - " Time in active batches = 1.3737e+00 seconds\n", - " Time synchronizing fission bank = 2.8739e-03 seconds\n", - " Sampling source sites = 2.5550e-03 seconds\n", - " SEND/RECV source sites = 2.8512e-04 seconds\n", - " Time accumulating tallies = 7.8450e-06 seconds\n", - " Total time for finalization = 1.7404e-04 seconds\n", - " Total time elapsed = 1.9588e+00 seconds\n", - " Calculation Rate (inactive) = 31640.8 particles/second\n", - " Calculation Rate (active) = 29119.1 particles/second\n", + " Total time for initialization = 1.4751e-01 seconds\n", + " Reading cross sections = 1.0076e-01 seconds\n", + " Total time in simulation = 3.0461e-01 seconds\n", + " Time in transport only = 2.9733e-01 seconds\n", + " Time in inactive batches = 5.7728e-02 seconds\n", + " Time in active batches = 2.4689e-01 seconds\n", + " Time synchronizing fission bank = 2.9442e-03 seconds\n", + " Sampling source sites = 2.6400e-03 seconds\n", + " SEND/RECV source sites = 3.0032e-04 seconds\n", + " Time accumulating tallies = 4.2046e-05 seconds\n", + " Time writing statepoints = 3.0596e-03 seconds\n", + " Total time for finalization = 8.3508e-05 seconds\n", + " Total time elapsed = 4.5681e-01 seconds\n", + " Calculation Rate (inactive) = 173226.0 particles/second\n", + " Calculation Rate (active) = 162018.0 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.97020 +/- 0.00750\n", - " k-effective (Track-length) = 0.96862 +/- 0.00719\n", - " k-effective (Absorption) = 0.95742 +/- 0.00791\n", - " Combined k-effective = 0.96203 +/- 0.00898\n", - " Leakage Fraction = 0.57677 +/- 0.00317\n", + " k-effective (Collision) = 0.97672 +/- 0.00532\n", + " k-effective (Track-length) = 0.97584 +/- 0.00681\n", + " k-effective (Absorption) = 0.96793 +/- 0.00613\n", + " Combined k-effective = 0.97097 +/- 0.00582\n", + " Leakage Fraction = 0.57298 +/- 0.00284\n", "\n" ] } @@ -437,7 +443,7 @@ "outputs": [ { "data": { - 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"text/plain": [ "" ] @@ -602,21 +608,23 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 2c0b16e73d5d81a2f849f4e2bfae5eb5319f2417\n", - " Date/Time | 2019-06-18 08:54:35\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 11:58:41\n", " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading DAGMC geometry...\n", + "Using the DOUBLE-DOWN interface to Embree.\n", "Loading file dagmc.h5m\n", "Initializing the GeomQueryTool...\n", "Using faceting tolerance: 0.001\n", - "Building OBB Tree...\n", + "Building acceleration data structures...\n", + "Implicit Complement assumed to be Vacuum\n", " Reading Fe54 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", " Reading Fe56 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", " Reading Fe57 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", @@ -624,10 +632,12 @@ " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for Fe58\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", - " Initializing source particles...\n", + " Maximum neutron transport energy: 20000000.0 eV for Fe58\n", "\n", " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", "\n", @@ -645,19 +655,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.9659e+00 seconds\n", - " Reading cross sections = 6.3783e-01 seconds\n", - " Total time in simulation = 1.5112e+01 seconds\n", - " Time in transport only = 1.4102e+01 seconds\n", - " Time in active batches = 1.5112e+01 seconds\n", - " Time accumulating tallies = 2.8790e-04 seconds\n", - " Total time for finalization = 3.2375e-02 seconds\n", - " Total time elapsed = 2.0224e+01 seconds\n", - " Calculation Rate (active) = 3308.59 particles/second\n", + " Total time for initialization = 9.0900e-01 seconds\n", + " Reading cross sections = 4.7368e-01 seconds\n", + " Total time in simulation = 4.5284e+00 seconds\n", + " Time in transport only = 4.5248e+00 seconds\n", + " Time in active batches = 4.5284e+00 seconds\n", + " Time accumulating tallies = 2.4557e-04 seconds\n", + " Time writing statepoints = 3.1711e-03 seconds\n", + " Total time for finalization = 1.4047e-02 seconds\n", + " Total time elapsed = 5.4540e+00 seconds\n", + " Calculation Rate (active) = 11041.4 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " Leakage Fraction = 0.62594 +/- 0.00117\n", + " Leakage Fraction = 0.63090 +/- 0.00246\n", "\n" ] } @@ -679,19 +690,16 @@ "metadata": {}, "outputs": [], "source": [ - "sp = openmc.StatePoint(\"statepoint.10.h5\")\n", - "\n", - "water_tally = sp.get_tally(scores=['flux'], id=water_tally.id)\n", - "water_flux = water_tally.mean\n", - "water_flux.shape = (40, 120)\n", - "water_flux = water_flux[::-1, :]\n", - "\n", - "pot_tally = sp.get_tally(scores=['flux'], id=pot_tally.id)\n", - "pot_flux = pot_tally.mean\n", - "pot_flux.shape = (40, 120)\n", - "pot_flux = pot_flux[::-1, :]\n", + "with openmc.StatePoint(\"statepoint.10.h5\") as sp:\n", + " water_tally = sp.get_tally(scores=['flux'], id=water_tally.id)\n", + " water_flux = water_tally.mean\n", + " water_flux.shape = (40, 120)\n", + " water_flux = water_flux[::-1, :]\n", "\n", - "del sp" + " pot_tally = sp.get_tally(scores=['flux'], id=pot_tally.id)\n", + " pot_flux = pot_tally.mean\n", + " pot_flux.shape = (40, 120)\n", + " pot_flux = pot_flux[::-1, :]" ] }, { @@ -702,7 +710,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 20, @@ -711,7 +719,7 @@ }, { "data": { - "image/png": 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\n", 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" ] @@ -750,7 +758,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/candu.ipynb b/examples/jupyter/candu.ipynb index eaf1e36d217..30cdd78bc62 100644 --- a/examples/jupyter/candu.ipynb +++ b/examples/jupyter/candu.ipynb @@ -121,7 +121,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -130,7 +130,7 @@ }, { "data": { - "image/png": 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ksYj4dNudRsQZ25+T9IikGUn3RcSzbfe7xJWSPiHpGdtPjZ+7MyIezjyOEuyWtG8cyMck3ZKiUa4YBCpX+nIAQMsIAaByhABQOUIAqBwhAFSOEAAqRwgAlSMEgMr9P9Ske2C/wpRUAAAAAElFTkSuQmCC\n", + "image/png": 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" ] @@ -176,7 +176,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -185,7 +185,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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" ] @@ -242,7 +242,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -251,7 +251,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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3434414152534totalflux0.1143940.004919
3534414162535totalflux0.1143520.005322
3634414172536totalflux0.1108900.005051
\n", - "
" - ], - "text/plain": [ - " level 1 level 2 level 3 distribcell nuclide score mean \\\n", - " univ cell univ cell univ cell \n", - " id id id id id id \n", - "0 3 44 1 100 2 5 0 total flux 2.08e-01 \n", - "1 3 44 1 200 2 5 1 total flux 1.97e-01 \n", - "2 3 44 1 201 2 5 2 total flux 1.90e-01 \n", - "3 3 44 1 202 2 5 3 total flux 1.95e-01 \n", - "4 3 44 1 203 2 5 4 total flux 1.91e-01 \n", - "5 3 44 1 204 2 5 5 total flux 1.90e-01 \n", - "6 3 44 1 205 2 5 6 total flux 1.82e-01 \n", - "7 3 44 1 300 2 5 7 total flux 1.66e-01 \n", - "8 3 44 1 301 2 5 8 total flux 1.69e-01 \n", - "9 3 44 1 302 2 5 9 total flux 1.60e-01 \n", - "10 3 44 1 303 2 5 10 total flux 1.59e-01 \n", - "11 3 44 1 304 2 5 11 total flux 1.49e-01 \n", - "12 3 44 1 305 2 5 12 total flux 1.51e-01 \n", - "13 3 44 1 306 2 5 13 total flux 1.54e-01 \n", - "14 3 44 1 307 2 5 14 total flux 1.66e-01 \n", - "15 3 44 1 308 2 5 15 total flux 1.57e-01 \n", - "16 3 44 1 309 2 5 16 total flux 1.65e-01 \n", - "17 3 44 1 310 2 5 17 total flux 1.57e-01 \n", - "18 3 44 1 311 2 5 18 total flux 1.60e-01 \n", - "19 3 44 1 400 2 5 19 total flux 9.66e-02 \n", - "20 3 44 1 401 2 5 20 total flux 1.18e-01 \n", - "21 3 44 1 402 2 5 21 total flux 1.06e-01 \n", - "22 3 44 1 403 2 5 22 total flux 1.11e-01 \n", - "23 3 44 1 404 2 5 23 total flux 1.12e-01 \n", - "24 3 44 1 405 2 5 24 total flux 1.10e-01 \n", - "25 3 44 1 406 2 5 25 total flux 1.00e-01 \n", - "26 3 44 1 407 2 5 26 total flux 9.54e-02 \n", - "27 3 44 1 408 2 5 27 total flux 9.26e-02 \n", - "28 3 44 1 409 2 5 28 total flux 9.55e-02 \n", - "29 3 44 1 410 2 5 29 total flux 1.14e-01 \n", - "30 3 44 1 411 2 5 30 total flux 1.08e-01 \n", - "31 3 44 1 412 2 5 31 total flux 1.07e-01 \n", - "32 3 44 1 413 2 5 32 total flux 1.12e-01 \n", - "33 3 44 1 414 2 5 33 total flux 1.15e-01 \n", - "34 3 44 1 415 2 5 34 total flux 1.14e-01 \n", - "35 3 44 1 416 2 5 35 total flux 1.14e-01 \n", - "36 3 44 1 417 2 5 36 total flux 1.11e-01 \n", - "\n", - " std. dev. \n", - " \n", - " \n", - "0 7.04e-03 \n", - "1 5.27e-03 \n", - "2 7.82e-03 \n", - "3 6.47e-03 \n", - "4 6.43e-03 \n", - "5 4.89e-03 \n", - "6 3.85e-03 \n", - "7 5.82e-03 \n", - "8 8.30e-03 \n", - "9 3.09e-03 \n", - "10 5.91e-03 \n", - "11 5.31e-03 \n", - "12 6.65e-03 \n", - "13 3.67e-03 \n", - "14 4.73e-03 \n", - "15 6.54e-03 \n", - "16 5.94e-03 \n", - "17 5.73e-03 \n", - "18 4.58e-03 \n", - "19 4.47e-03 \n", - "20 5.45e-03 \n", - "21 4.72e-03 \n", - "22 4.21e-03 \n", - "23 5.08e-03 \n", - "24 4.15e-03 \n", - "25 5.08e-03 \n", - "26 3.62e-03 \n", - "27 4.00e-03 \n", - "28 4.02e-03 \n", - "29 9.53e-03 \n", - "30 7.24e-03 \n", - "31 5.72e-03 \n", - "32 5.00e-03 \n", - "33 6.24e-03 \n", - "34 4.92e-03 \n", - "35 5.32e-03 \n", - "36 5.05e-03 " - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "output_tally = sp.get_tally()\n", - "output_tally.get_pandas_dataframe()" + "with openmc.StatePoint('statepoint.{}.h5'.format(settings.batches)) as sp:\n", + " output_tally = sp.get_tally()\n", + " output_tally.get_pandas_dataframe()" ] }, { @@ -1108,7 +429,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/mdgxs-part-i.ipynb b/examples/jupyter/mdgxs-part-i.ipynb index ae31859a4f5..972dee54abb 100644 --- a/examples/jupyter/mdgxs-part-i.ipynb +++ b/examples/jupyter/mdgxs-part-i.ipynb @@ -406,15 +406,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=7.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=7.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=13.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=13.\n", " warn(msg, IDWarning)\n" ] } @@ -489,9 +489,9 @@ " Copyright | 2011-2021 MIT and OpenMC contributors\n", " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.13.0-dev\n", - " Git SHA1 | d4df243d920d12c3d268a4c20941950d2299d678\n", - " Date/Time | 2021-04-18 13:10:08\n", - " MPI Processes | 1\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 12:02:59\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", @@ -515,82 +515,82 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.21670\n", - " 2/1 1.24155\n", - " 3/1 1.21924\n", - " 4/1 1.22486\n", - " 5/1 1.21719\n", - " 6/1 1.24330\n", - " 7/1 1.22322\n", - " 8/1 1.24133\n", - " 9/1 1.21840\n", - " 10/1 1.25141\n", - " 11/1 1.21217\n", - " 12/1 1.25625 1.23421 +/- 0.02204\n", - " 13/1 1.22056 1.22966 +/- 0.01351\n", - " 14/1 1.21757 1.22664 +/- 0.01002\n", - " 15/1 1.24571 1.23045 +/- 0.00865\n", - " 16/1 1.26489 1.23619 +/- 0.00910\n", - " 17/1 1.22323 1.23434 +/- 0.00791\n", - " 18/1 1.26108 1.23768 +/- 0.00762\n", - " 19/1 1.23145 1.23699 +/- 0.00676\n", - " 20/1 1.23548 1.23684 +/- 0.00605\n", - " 21/1 1.20446 1.23390 +/- 0.00621\n", - " 22/1 1.20533 1.23152 +/- 0.00615\n", - " 23/1 1.22520 1.23103 +/- 0.00568\n", - " 24/1 1.18367 1.22765 +/- 0.00625\n", - " 25/1 1.23614 1.22821 +/- 0.00585\n", - " 26/1 1.23746 1.22879 +/- 0.00550\n", - " 27/1 1.23626 1.22923 +/- 0.00518\n", - " 28/1 1.21334 1.22835 +/- 0.00497\n", - " 29/1 1.25169 1.22958 +/- 0.00486\n", - " 30/1 1.25579 1.23089 +/- 0.00479\n", - " 31/1 1.23828 1.23124 +/- 0.00457\n", - " 32/1 1.26911 1.23296 +/- 0.00468\n", - " 33/1 1.20090 1.23157 +/- 0.00469\n", - " 34/1 1.28606 1.23384 +/- 0.00503\n", - " 35/1 1.23129 1.23374 +/- 0.00483\n", - " 36/1 1.22535 1.23341 +/- 0.00465\n", - " 37/1 1.20367 1.23231 +/- 0.00461\n", - " 38/1 1.22886 1.23219 +/- 0.00444\n", - " 39/1 1.24056 1.23248 +/- 0.00429\n", - " 40/1 1.25038 1.23307 +/- 0.00419\n", - " 41/1 1.21504 1.23249 +/- 0.00410\n", - " 42/1 1.20762 1.23171 +/- 0.00404\n", - " 43/1 1.20597 1.23093 +/- 0.00399\n", - " 44/1 1.24424 1.23133 +/- 0.00389\n", - " 45/1 1.24767 1.23179 +/- 0.00381\n", - " 46/1 1.22998 1.23174 +/- 0.00370\n", - " 47/1 1.26195 1.23256 +/- 0.00369\n", - " 48/1 1.23146 1.23253 +/- 0.00359\n", - " 49/1 1.22059 1.23222 +/- 0.00351\n", - " 50/1 1.24724 1.23260 +/- 0.00345\n", + " 1/1 1.26527\n", + " 2/1 1.23398\n", + " 3/1 1.25383\n", + " 4/1 1.24224\n", + " 5/1 1.22254\n", + " 6/1 1.20745\n", + " 7/1 1.22674\n", + " 8/1 1.22724\n", + " 9/1 1.23185\n", + " 10/1 1.22019\n", + " 11/1 1.24342\n", + " 12/1 1.23258 1.23800 +/- 0.00542\n", + " 13/1 1.20626 1.22742 +/- 0.01103\n", + " 14/1 1.19410 1.21909 +/- 0.01141\n", + " 15/1 1.24556 1.22439 +/- 0.01030\n", + " 16/1 1.27632 1.23304 +/- 0.01207\n", + " 17/1 1.25083 1.23558 +/- 0.01051\n", + " 18/1 1.23155 1.23508 +/- 0.00912\n", + " 19/1 1.27501 1.23952 +/- 0.00918\n", + " 20/1 1.24863 1.24043 +/- 0.00827\n", + " 21/1 1.18837 1.23569 +/- 0.00885\n", + " 22/1 1.21978 1.23437 +/- 0.00819\n", + " 23/1 1.22815 1.23389 +/- 0.00754\n", + " 24/1 1.24244 1.23450 +/- 0.00701\n", + " 25/1 1.21128 1.23295 +/- 0.00671\n", + " 26/1 1.22836 1.23267 +/- 0.00628\n", + " 27/1 1.21573 1.23167 +/- 0.00598\n", + " 28/1 1.19115 1.22942 +/- 0.00607\n", + " 29/1 1.24854 1.23042 +/- 0.00583\n", + " 30/1 1.24486 1.23115 +/- 0.00558\n", + " 31/1 1.23967 1.23155 +/- 0.00532\n", + " 32/1 1.24406 1.23212 +/- 0.00511\n", + " 33/1 1.24808 1.23282 +/- 0.00493\n", + " 34/1 1.23056 1.23272 +/- 0.00472\n", + " 35/1 1.24209 1.23310 +/- 0.00454\n", + " 36/1 1.23203 1.23305 +/- 0.00437\n", + " 37/1 1.21629 1.23243 +/- 0.00425\n", + " 38/1 1.22928 1.23232 +/- 0.00409\n", + " 39/1 1.23665 1.23247 +/- 0.00395\n", + " 40/1 1.24100 1.23276 +/- 0.00383\n", + " 41/1 1.26373 1.23375 +/- 0.00384\n", + " 42/1 1.25002 1.23426 +/- 0.00375\n", + " 43/1 1.24100 1.23447 +/- 0.00364\n", + " 44/1 1.25701 1.23513 +/- 0.00359\n", + " 45/1 1.23027 1.23499 +/- 0.00349\n", + " 46/1 1.25747 1.23562 +/- 0.00345\n", + " 47/1 1.24960 1.23599 +/- 0.00338\n", + " 48/1 1.23535 1.23598 +/- 0.00329\n", + " 49/1 1.21318 1.23539 +/- 0.00325\n", + " 50/1 1.27184 1.23630 +/- 0.00330\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 1.1312e+00 seconds\n", - " Reading cross sections = 1.1046e+00 seconds\n", - " Total time in simulation = 2.7801e+02 seconds\n", - " Time in transport only = 2.7794e+02 seconds\n", - " Time in inactive batches = 1.0359e+01 seconds\n", - " Time in active batches = 2.6765e+02 seconds\n", - " Time synchronizing fission bank = 2.5796e-02 seconds\n", - " Sampling source sites = 2.3811e-02 seconds\n", - " SEND/RECV source sites = 1.7176e-03 seconds\n", - " Time accumulating tallies = 2.5310e-02 seconds\n", - " Time writing statepoints = 6.3601e-03 seconds\n", - " Total time for finalization = 2.2876e-02 seconds\n", - " Total time elapsed = 2.7919e+02 seconds\n", - " Calculation Rate (inactive) = 4826.80 particles/second\n", - " Calculation Rate (active) = 747.234 particles/second\n", + " Total time for initialization = 2.7487e-01 seconds\n", + " Reading cross sections = 2.6530e-01 seconds\n", + " Total time in simulation = 2.1335e+01 seconds\n", + " Time in transport only = 2.1311e+01 seconds\n", + " Time in inactive batches = 9.3940e-01 seconds\n", + " Time in active batches = 2.0396e+01 seconds\n", + " Time synchronizing fission bank = 9.9228e-03 seconds\n", + " Sampling source sites = 8.2374e-03 seconds\n", + " SEND/RECV source sites = 1.6638e-03 seconds\n", + " Time accumulating tallies = 8.4502e-04 seconds\n", + " Time writing statepoints = 7.4764e-03 seconds\n", + " Total time for finalization = 6.2220e-03 seconds\n", + " Total time elapsed = 2.1623e+01 seconds\n", + " Calculation Rate (inactive) = 53225.7 particles/second\n", + " Calculation Rate (active) = 9805.91 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.23256 +/- 0.00308\n", - " k-effective (Track-length) = 1.23260 +/- 0.00345\n", - " k-effective (Absorption) = 1.23111 +/- 0.00186\n", - " Combined k-effective = 1.23135 +/- 0.00184\n", + " k-effective (Collision) = 1.23688 +/- 0.00324\n", + " k-effective (Track-length) = 1.23630 +/- 0.00330\n", + " k-effective (Absorption) = 1.23291 +/- 0.00216\n", + " Combined k-effective = 1.23399 +/- 0.00202\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -685,17 +685,17 @@ { "data": { "text/plain": [ - "array([[[5.14223507e-06, 1.16426087e-06]],\n", + "array([[[5.15913423e-06, 1.16842422e-06]],\n", "\n", - " [[2.65426350e-05, 7.58220468e-06]],\n", + " [[2.66298632e-05, 7.60931827e-06]],\n", "\n", - " [[2.53399053e-05, 5.73796202e-06]],\n", + " [[2.54231809e-05, 5.75848069e-06]],\n", "\n", - " [[5.68141581e-05, 1.04757933e-05]],\n", + " [[5.70008690e-05, 1.05132542e-05]],\n", "\n", - " [[2.32930026e-05, 5.45658817e-06]],\n", + " [[2.33695515e-05, 5.47610066e-06]],\n", "\n", - " [[9.75735783e-06, 1.65150949e-06]]])" + " [[9.78942387e-06, 1.65741521e-06]]])" ] }, "execution_count": 16, @@ -757,8 +757,8 @@ " 1\n", " 1\n", " U235\n", - " 9.345817e-08\n", - " 6.616216e-08\n", + " 9.493755e-08\n", + " 9.395351e-08\n", " \n", " \n", " 199\n", @@ -766,8 +766,8 @@ " 1\n", " 1\n", " Pu239\n", - " 1.574816e-08\n", - " 1.114955e-08\n", + " 1.602797e-08\n", + " 1.586127e-08\n", " \n", " \n", " 398\n", @@ -775,8 +775,8 @@ " 2\n", " 1\n", " U235\n", - " 4.824023e-07\n", - " 3.415087e-07\n", + " 4.900384e-07\n", + " 4.849591e-07\n", " \n", " \n", " 399\n", @@ -784,8 +784,8 @@ " 2\n", " 1\n", " Pu239\n", - " 1.025593e-07\n", - " 7.261101e-08\n", + " 1.043816e-07\n", + " 1.032959e-07\n", " \n", " \n", " 598\n", @@ -793,8 +793,8 @@ " 3\n", " 1\n", " U235\n", - " 4.605432e-07\n", - " 3.260339e-07\n", + " 4.678332e-07\n", + " 4.629841e-07\n", " \n", " \n", " 599\n", @@ -802,8 +802,8 @@ " 3\n", " 1\n", " Pu239\n", - " 7.761348e-08\n", - " 5.494962e-08\n", + " 7.899251e-08\n", + " 7.817094e-08\n", " \n", " \n", " 798\n", @@ -811,8 +811,8 @@ " 4\n", " 1\n", " U235\n", - " 1.032576e-06\n", - " 7.309948e-07\n", + " 1.048921e-06\n", + " 1.038048e-06\n", " \n", " \n", " 799\n", @@ -820,8 +820,8 @@ " 4\n", " 1\n", " Pu239\n", - " 1.416989e-07\n", - " 1.003215e-07\n", + " 1.442166e-07\n", + " 1.427166e-07\n", " \n", " \n", " 998\n", @@ -829,8 +829,8 @@ " 5\n", " 1\n", " U235\n", - " 4.233415e-07\n", - " 2.996976e-07\n", + " 4.300427e-07\n", + " 4.255852e-07\n", " \n", " \n", " 999\n", @@ -838,8 +838,8 @@ " 5\n", " 1\n", " Pu239\n", - " 7.380753e-08\n", - " 5.225504e-08\n", + " 7.511894e-08\n", + " 7.433765e-08\n", " \n", " \n", "\n", @@ -847,16 +847,16 @@ ], "text/plain": [ " cell delayedgroup group in nuclide mean std. dev.\n", - "198 1 1 1 U235 9.345817e-08 6.616216e-08\n", - "199 1 1 1 Pu239 1.574816e-08 1.114955e-08\n", - "398 1 2 1 U235 4.824023e-07 3.415087e-07\n", - "399 1 2 1 Pu239 1.025593e-07 7.261101e-08\n", - "598 1 3 1 U235 4.605432e-07 3.260339e-07\n", - "599 1 3 1 Pu239 7.761348e-08 5.494962e-08\n", - "798 1 4 1 U235 1.032576e-06 7.309948e-07\n", - "799 1 4 1 Pu239 1.416989e-07 1.003215e-07\n", - "998 1 5 1 U235 4.233415e-07 2.996976e-07\n", - "999 1 5 1 Pu239 7.380753e-08 5.225504e-08" + "198 1 1 1 U235 9.493755e-08 9.395351e-08\n", + "199 1 1 1 Pu239 1.602797e-08 1.586127e-08\n", + "398 1 2 1 U235 4.900384e-07 4.849591e-07\n", + "399 1 2 1 Pu239 1.043816e-07 1.032959e-07\n", + "598 1 3 1 U235 4.678332e-07 4.629841e-07\n", + "599 1 3 1 Pu239 7.899251e-08 7.817094e-08\n", + "798 1 4 1 U235 1.048921e-06 1.038048e-06\n", + "799 1 4 1 Pu239 1.442166e-07 1.427166e-07\n", + "998 1 5 1 U235 4.300427e-07 4.255852e-07\n", + "999 1 5 1 Pu239 7.511894e-08 7.433765e-08" ] }, "execution_count": 17, @@ -909,7 +909,7 @@ " 1\n", " U235\n", " 0.013336\n", - " 0.000061\n", + " 0.000056\n", " \n", " \n", " 1\n", @@ -917,7 +917,7 @@ " 1\n", " Pu239\n", " 0.013271\n", - " 0.000053\n", + " 0.000050\n", " \n", " \n", " 2\n", @@ -925,7 +925,7 @@ " 2\n", " U235\n", " 0.032739\n", - " 0.000149\n", + " 0.000137\n", " \n", " \n", " 3\n", @@ -933,7 +933,7 @@ " 2\n", " Pu239\n", " 0.030881\n", - " 0.000123\n", + " 0.000116\n", " \n", " \n", " 4\n", @@ -941,7 +941,7 @@ " 3\n", " U235\n", " 0.120780\n", - " 0.000550\n", + " 0.000504\n", " \n", " \n", " 5\n", @@ -949,7 +949,7 @@ " 3\n", " Pu239\n", " 0.113370\n", - " 0.000452\n", + " 0.000427\n", " \n", " \n", " 6\n", @@ -957,7 +957,7 @@ " 4\n", " U235\n", " 0.302780\n", - " 0.001378\n", + " 0.001264\n", " \n", " \n", " 7\n", @@ -965,7 +965,7 @@ " 4\n", " Pu239\n", " 0.292500\n", - " 0.001167\n", + " 0.001101\n", " \n", " \n", " 8\n", @@ -973,7 +973,7 @@ " 5\n", " U235\n", " 0.849490\n", - " 0.003866\n", + " 0.003545\n", " \n", " \n", " 9\n", @@ -981,7 +981,7 @@ " 5\n", " Pu239\n", " 0.857490\n", - " 0.003420\n", + " 0.003228\n", " \n", " \n", " 10\n", @@ -989,7 +989,7 @@ " 6\n", " U235\n", " 2.853000\n", - " 0.012985\n", + " 0.011907\n", " \n", " \n", " 11\n", @@ -997,7 +997,7 @@ " 6\n", " Pu239\n", " 2.729700\n", - " 0.010887\n", + " 0.010276\n", " \n", " \n", "\n", @@ -1005,18 +1005,18 @@ ], "text/plain": [ " cell delayedgroup nuclide mean std. dev.\n", - "0 1 1 U235 0.013336 0.000061\n", - "1 1 1 Pu239 0.013271 0.000053\n", - "2 1 2 U235 0.032739 0.000149\n", - "3 1 2 Pu239 0.030881 0.000123\n", - "4 1 3 U235 0.120780 0.000550\n", - "5 1 3 Pu239 0.113370 0.000452\n", - "6 1 4 U235 0.302780 0.001378\n", - "7 1 4 Pu239 0.292500 0.001167\n", - "8 1 5 U235 0.849490 0.003866\n", - "9 1 5 Pu239 0.857490 0.003420\n", - "10 1 6 U235 2.853000 0.012985\n", - "11 1 6 Pu239 2.729700 0.010887" + "0 1 1 U235 0.013336 0.000056\n", + "1 1 1 Pu239 0.013271 0.000050\n", + "2 1 2 U235 0.032739 0.000137\n", + "3 1 2 Pu239 0.030881 0.000116\n", + "4 1 3 U235 0.120780 0.000504\n", + "5 1 3 Pu239 0.113370 0.000427\n", + "6 1 4 U235 0.302780 0.001264\n", + "7 1 4 Pu239 0.292500 0.001101\n", + "8 1 5 U235 0.849490 0.003545\n", + "9 1 5 Pu239 0.857490 0.003228\n", + "10 1 6 U235 2.853000 0.011907\n", + "11 1 6 Pu239 2.729700 0.010276" ] }, "execution_count": 18, @@ -1090,7 +1090,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -1210,8 +1210,8 @@ " 1\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 8.779139e-08\n", - " 4.659945e-10\n", + " 8.808003e-08\n", + " 4.352878e-10\n", " \n", " \n", " 1\n", @@ -1219,8 +1219,8 @@ " 1\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 7.149814e-09\n", - " 3.565137e-11\n", + " 7.175417e-09\n", + " 3.446159e-11\n", " \n", " \n", " 2\n", @@ -1228,8 +1228,8 @@ " 2\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 9.527880e-07\n", - " 5.057375e-09\n", + " 9.559207e-07\n", + " 4.724119e-09\n", " \n", " \n", " 3\n", @@ -1237,8 +1237,8 @@ " 2\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 1.303159e-07\n", - " 6.497988e-10\n", + " 1.307826e-07\n", + " 6.281133e-10\n", " \n", " \n", " 4\n", @@ -1246,8 +1246,8 @@ " 3\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.353903e-07\n", - " 1.249446e-09\n", + " 2.361643e-07\n", + " 1.167114e-09\n", " \n", " \n", " 5\n", @@ -1255,8 +1255,8 @@ " 3\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.032895e-08\n", - " 1.013670e-10\n", + " 2.040175e-08\n", + " 9.798408e-11\n", " \n", " \n", " 6\n", @@ -1264,8 +1264,8 @@ " 4\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 4.720191e-07\n", - " 2.505466e-09\n", + " 4.735711e-07\n", + " 2.340368e-09\n", " \n", " \n", " 7\n", @@ -1273,8 +1273,8 @@ " 4\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.626309e-08\n", - " 1.309566e-10\n", + " 2.635713e-08\n", + " 1.265862e-10\n", " \n", " \n", " 8\n", @@ -1282,8 +1282,8 @@ " 5\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.827915e-08\n", - " 1.501050e-10\n", + " 2.837213e-08\n", + " 1.402138e-10\n", " \n", " \n", " 9\n", @@ -1291,8 +1291,8 @@ " 5\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.430587e-09\n", - " 1.211972e-11\n", + " 2.439290e-09\n", + " 1.171525e-11\n", " \n", " \n", " 10\n", @@ -1300,8 +1300,8 @@ " 6\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 1.477530e-09\n", - " 7.842692e-12\n", + " 1.482388e-09\n", + " 7.325898e-12\n", " \n", " \n", " 11\n", @@ -1309,8 +1309,8 @@ " 6\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 6.994312e-11\n", - " 3.487599e-13\n", + " 7.019358e-11\n", + " 3.371208e-13\n", " \n", " \n", "\n", @@ -1332,18 +1332,18 @@ "11 1 6 Pu239 \n", "\n", " score mean std. dev. \n", - "0 (((delayed-nu-fission / nu-fission) * (delayed... 8.78e-08 4.66e-10 \n", - "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09 3.57e-11 \n", - "2 (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07 5.06e-09 \n", - "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07 6.50e-10 \n", - "4 (((delayed-nu-fission / nu-fission) * (delayed... 2.35e-07 1.25e-09 \n", - "5 (((delayed-nu-fission / nu-fission) * (delayed... 2.03e-08 1.01e-10 \n", - "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07 2.51e-09 \n", - "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08 1.31e-10 \n", - "8 (((delayed-nu-fission / nu-fission) * (delayed... 2.83e-08 1.50e-10 \n", - "9 (((delayed-nu-fission / nu-fission) * (delayed... 2.43e-09 1.21e-11 \n", - "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09 7.84e-12 \n", - "11 (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11 3.49e-13 " + "0 (((delayed-nu-fission / nu-fission) * (delayed... 8.81e-08 4.35e-10 \n", + "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.18e-09 3.45e-11 \n", + "2 (((delayed-nu-fission / nu-fission) * (delayed... 9.56e-07 4.72e-09 \n", + "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.31e-07 6.28e-10 \n", + "4 (((delayed-nu-fission / nu-fission) * (delayed... 2.36e-07 1.17e-09 \n", + "5 (((delayed-nu-fission / nu-fission) * (delayed... 2.04e-08 9.80e-11 \n", + "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.74e-07 2.34e-09 \n", + "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.64e-08 1.27e-10 \n", + "8 (((delayed-nu-fission / nu-fission) * (delayed... 2.84e-08 1.40e-10 \n", + "9 (((delayed-nu-fission / nu-fission) * (delayed... 2.44e-09 1.17e-11 \n", + "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09 7.33e-12 \n", + "11 (((delayed-nu-fission / nu-fission) * (delayed... 7.02e-11 3.37e-13 " ] }, "execution_count": 23, @@ -1377,7 +1377,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Beta (U-235) : 0.006504 +/- 0.000007\n", + "Beta (U-235) : 0.006504 +/- 0.000006\n", "Beta (Pu-239): 0.002245 +/- 0.000002\n" ] }, @@ -1456,7 +1456,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] diff --git a/examples/jupyter/mdgxs-part-ii.ipynb b/examples/jupyter/mdgxs-part-ii.ipynb index a7740fc2675..a9102dfb8fb 100644 --- a/examples/jupyter/mdgxs-part-ii.ipynb +++ b/examples/jupyter/mdgxs-part-ii.ipynb @@ -319,7 +319,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -388,17 +388,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=1.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=1.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=6.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=6.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=17.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=17.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=23.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=23.\n", " warn(msg, IDWarning)\n" ] } @@ -489,9 +489,9 @@ " Copyright | 2011-2021 MIT and OpenMC contributors\n", " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.13.0-dev\n", - " Git SHA1 | d4df243d920d12c3d268a4c20941950d2299d678\n", - " Date/Time | 2021-04-18 12:29:49\n", - " MPI Processes | 1\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 13:44:53\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", @@ -515,82 +515,82 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.03852\n", - " 2/1 0.99743\n", - " 3/1 1.02987\n", - " 4/1 1.04397\n", - " 5/1 1.06262\n", - " 6/1 1.06657\n", - " 7/1 0.98574\n", - " 8/1 1.04364\n", - " 9/1 1.01253\n", - " 10/1 1.02094\n", - " 11/1 0.99586\n", - " 12/1 1.00508 1.00047 +/- 0.00461\n", - " 13/1 1.05292 1.01795 +/- 0.01769\n", - " 14/1 1.04732 1.02530 +/- 0.01450\n", - " 15/1 1.04886 1.03001 +/- 0.01218\n", - " 16/1 1.00948 1.02659 +/- 0.01052\n", - " 17/1 1.02644 1.02657 +/- 0.00889\n", - " 18/1 1.03080 1.02710 +/- 0.00772\n", - " 19/1 1.00018 1.02411 +/- 0.00743\n", - " 20/1 1.05668 1.02736 +/- 0.00740\n", - " 21/1 1.01160 1.02593 +/- 0.00685\n", - " 22/1 1.04334 1.02738 +/- 0.00642\n", - " 23/1 1.03105 1.02766 +/- 0.00591\n", - " 24/1 1.01174 1.02653 +/- 0.00559\n", - " 25/1 0.99844 1.02465 +/- 0.00553\n", - " 26/1 1.02241 1.02451 +/- 0.00517\n", - " 27/1 1.02904 1.02478 +/- 0.00487\n", - " 28/1 1.02132 1.02459 +/- 0.00459\n", - " 29/1 1.01384 1.02402 +/- 0.00438\n", - " 30/1 1.03891 1.02477 +/- 0.00422\n", - " 31/1 1.04092 1.02553 +/- 0.00409\n", - " 32/1 1.00058 1.02440 +/- 0.00406\n", - " 33/1 0.99940 1.02331 +/- 0.00403\n", - " 34/1 0.98362 1.02166 +/- 0.00420\n", - " 35/1 1.05358 1.02294 +/- 0.00422\n", - " 36/1 0.99923 1.02202 +/- 0.00416\n", - " 37/1 1.08491 1.02435 +/- 0.00463\n", - " 38/1 1.01838 1.02414 +/- 0.00447\n", - " 39/1 0.98567 1.02281 +/- 0.00451\n", - " 40/1 1.05047 1.02374 +/- 0.00445\n", - " 41/1 1.01993 1.02361 +/- 0.00431\n", - " 42/1 1.01223 1.02326 +/- 0.00419\n", - " 43/1 1.06259 1.02445 +/- 0.00423\n", - " 44/1 1.01993 1.02432 +/- 0.00411\n", - " 45/1 0.99233 1.02340 +/- 0.00409\n", - " 46/1 0.98532 1.02234 +/- 0.00411\n", - " 47/1 1.02513 1.02242 +/- 0.00400\n", - " 48/1 1.01637 1.02226 +/- 0.00390\n", - " 49/1 1.03215 1.02251 +/- 0.00381\n", - " 50/1 1.01826 1.02241 +/- 0.00371\n", + " 1/1 1.03409\n", + " 2/1 1.02768\n", + " 3/1 1.01526\n", + " 4/1 1.03910\n", + " 5/1 1.00144\n", + " 6/1 1.00485\n", + " 7/1 1.03532\n", + " 8/1 1.01159\n", + " 9/1 0.97707\n", + " 10/1 1.06530\n", + " 11/1 1.08615\n", + " 12/1 1.00438 1.04527 +/- 0.04089\n", + " 13/1 1.03145 1.04066 +/- 0.02405\n", + " 14/1 1.04785 1.04246 +/- 0.01710\n", + " 15/1 1.04634 1.04323 +/- 0.01327\n", + " 16/1 1.02264 1.03980 +/- 0.01137\n", + " 17/1 1.00157 1.03434 +/- 0.01105\n", + " 18/1 1.09443 1.04185 +/- 0.01216\n", + " 19/1 1.02810 1.04032 +/- 0.01084\n", + " 20/1 1.01800 1.03809 +/- 0.00995\n", + " 21/1 1.02815 1.03719 +/- 0.00904\n", + " 22/1 1.00064 1.03414 +/- 0.00880\n", + " 23/1 0.99013 1.03076 +/- 0.00877\n", + " 24/1 1.01182 1.02940 +/- 0.00823\n", + " 25/1 1.07537 1.03247 +/- 0.00826\n", + " 26/1 1.02924 1.03227 +/- 0.00772\n", + " 27/1 1.01499 1.03125 +/- 0.00733\n", + " 28/1 1.01749 1.03049 +/- 0.00695\n", + " 29/1 1.04072 1.03102 +/- 0.00660\n", + " 30/1 0.99990 1.02947 +/- 0.00645\n", + " 31/1 1.03239 1.02961 +/- 0.00613\n", + " 32/1 1.02613 1.02945 +/- 0.00585\n", + " 33/1 1.04340 1.03006 +/- 0.00562\n", + " 34/1 1.05081 1.03092 +/- 0.00545\n", + " 35/1 1.02511 1.03069 +/- 0.00524\n", + " 36/1 0.99923 1.02948 +/- 0.00517\n", + " 37/1 0.97758 1.02756 +/- 0.00534\n", + " 38/1 0.99628 1.02644 +/- 0.00526\n", + " 39/1 1.06004 1.02760 +/- 0.00521\n", + " 40/1 1.08287 1.02944 +/- 0.00536\n", + " 41/1 1.02731 1.02937 +/- 0.00518\n", + " 42/1 1.02634 1.02928 +/- 0.00502\n", + " 43/1 1.04269 1.02968 +/- 0.00488\n", + " 44/1 1.05044 1.03029 +/- 0.00478\n", + " 45/1 1.05183 1.03091 +/- 0.00468\n", + " 46/1 1.02770 1.03082 +/- 0.00455\n", + " 47/1 1.07099 1.03191 +/- 0.00455\n", + " 48/1 1.04467 1.03224 +/- 0.00444\n", + " 49/1 1.02996 1.03218 +/- 0.00433\n", + " 50/1 1.03418 1.03223 +/- 0.00422\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 9.3825e-01 seconds\n", - " Reading cross sections = 9.2114e-01 seconds\n", - " Total time in simulation = 1.5872e+02 seconds\n", - " Time in transport only = 1.5607e+02 seconds\n", - " Time in inactive batches = 8.3963e+00 seconds\n", - " Time in active batches = 1.5032e+02 seconds\n", - " Time synchronizing fission bank = 1.2433e-02 seconds\n", - " Sampling source sites = 1.1456e-02 seconds\n", - " SEND/RECV source sites = 7.1083e-04 seconds\n", - " Time accumulating tallies = 2.5981e+00 seconds\n", - " Time writing statepoints = 2.4262e-02 seconds\n", - " Total time for finalization = 1.7827e-05 seconds\n", - " Total time elapsed = 1.5971e+02 seconds\n", - " Calculation Rate (inactive) = 2977.49 particles/second\n", - " Calculation Rate (active) = 665.252 particles/second\n", + " Total time for initialization = 2.5756e-01 seconds\n", + " Reading cross sections = 2.4233e-01 seconds\n", + " Total time in simulation = 1.2099e+01 seconds\n", + " Time in transport only = 1.2046e+01 seconds\n", + " Time in inactive batches = 8.7582e-01 seconds\n", + " Time in active batches = 1.1223e+01 seconds\n", + " Time synchronizing fission bank = 4.8051e-03 seconds\n", + " Sampling source sites = 4.1028e-03 seconds\n", + " SEND/RECV source sites = 6.7903e-04 seconds\n", + " Time accumulating tallies = 3.0247e-02 seconds\n", + " Time writing statepoints = 1.4253e-02 seconds\n", + " Total time for finalization = 5.3700e-07 seconds\n", + " Total time elapsed = 1.2369e+01 seconds\n", + " Calculation Rate (inactive) = 28544.8 particles/second\n", + " Calculation Rate (active) = 8910.20 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.02207 +/- 0.00343\n", - " k-effective (Track-length) = 1.02241 +/- 0.00371\n", - " k-effective (Absorption) = 1.02408 +/- 0.00356\n", - " Combined k-effective = 1.02306 +/- 0.00307\n", + " k-effective (Collision) = 1.02658 +/- 0.00374\n", + " k-effective (Track-length) = 1.03223 +/- 0.00422\n", + " k-effective (Absorption) = 1.02640 +/- 0.00361\n", + " Combined k-effective = 1.02849 +/- 0.00321\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -720,8 +720,8 @@ " 1\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000099\n", - " 2.275247e-05\n", + " 0.000079\n", + " 1.583613e-05\n", " \n", " \n", " 1\n", @@ -731,8 +731,8 @@ " 2\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.001260\n", - " 2.852271e-04\n", + " 0.001001\n", + " 1.983614e-04\n", " \n", " \n", " 2\n", @@ -742,8 +742,8 @@ " 3\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000800\n", - " 1.795615e-04\n", + " 0.000636\n", + " 1.248704e-04\n", " \n", " \n", " 3\n", @@ -753,8 +753,8 @@ " 4\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000630\n", - " 1.397151e-04\n", + " 0.000503\n", + " 9.721681e-05\n", " \n", " \n", " 4\n", @@ -764,8 +764,8 @@ " 5\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000023\n", - " 4.861639e-06\n", + " 0.000018\n", + " 3.397815e-06\n", " \n", " \n", " 5\n", @@ -775,8 +775,8 @@ " 6\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000002\n", - " 3.879558e-07\n", + " 0.000001\n", + " 2.710107e-07\n", " \n", " \n", " 6\n", @@ -786,8 +786,8 @@ " 1\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000091\n", - " 2.062544e-05\n", + " 0.000092\n", + " 2.407283e-05\n", " \n", " \n", " 7\n", @@ -797,8 +797,8 @@ " 2\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.001162\n", - " 2.584797e-04\n", + " 0.001165\n", + " 3.017676e-04\n", " \n", " \n", " 8\n", @@ -808,8 +808,8 @@ " 3\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000737\n", - " 1.626991e-04\n", + " 0.000739\n", + " 1.899840e-04\n", " \n", " \n", " 9\n", @@ -819,8 +819,8 @@ " 4\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000581\n", - " 1.265708e-04\n", + " 0.000582\n", + " 1.478539e-04\n", " \n", " \n", "\n", @@ -842,16 +842,16 @@ "\n", " score mean std. dev. \n", " \n", - "0 (((delayed-nu-fission / nu-fission) * (delayed... 0.000099 2.275247e-05 \n", - "1 (((delayed-nu-fission / nu-fission) * (delayed... 0.001260 2.852271e-04 \n", - "2 (((delayed-nu-fission / nu-fission) * (delayed... 0.000800 1.795615e-04 \n", - "3 (((delayed-nu-fission / nu-fission) * (delayed... 0.000630 1.397151e-04 \n", - "4 (((delayed-nu-fission / nu-fission) * (delayed... 0.000023 4.861639e-06 \n", - "5 (((delayed-nu-fission / nu-fission) * (delayed... 0.000002 3.879558e-07 \n", - "6 (((delayed-nu-fission / nu-fission) * (delayed... 0.000091 2.062544e-05 \n", - "7 (((delayed-nu-fission / nu-fission) * (delayed... 0.001162 2.584797e-04 \n", - "8 (((delayed-nu-fission / nu-fission) * (delayed... 0.000737 1.626991e-04 \n", - "9 (((delayed-nu-fission / nu-fission) * (delayed... 0.000581 1.265708e-04 " + "0 (((delayed-nu-fission / nu-fission) * (delayed... 0.000079 1.583613e-05 \n", + "1 (((delayed-nu-fission / nu-fission) * (delayed... 0.001001 1.983614e-04 \n", + "2 (((delayed-nu-fission / nu-fission) * (delayed... 0.000636 1.248704e-04 \n", + "3 (((delayed-nu-fission / nu-fission) * (delayed... 0.000503 9.721681e-05 \n", + "4 (((delayed-nu-fission / nu-fission) * (delayed... 0.000018 3.397815e-06 \n", + "5 (((delayed-nu-fission / nu-fission) * (delayed... 0.000001 2.710107e-07 \n", + "6 (((delayed-nu-fission / nu-fission) * (delayed... 0.000092 2.407283e-05 \n", + "7 (((delayed-nu-fission / nu-fission) * (delayed... 0.001165 3.017676e-04 \n", + "8 (((delayed-nu-fission / nu-fission) * (delayed... 0.000739 1.899840e-04 \n", + "9 (((delayed-nu-fission / nu-fission) * (delayed... 0.000582 1.478539e-04 " ] }, "execution_count": 18, @@ -972,8 +972,8 @@ " x-max out\n", " total\n", " current\n", - " 0.03245\n", - " 0.000677\n", + " 0.03154\n", + " 0.000643\n", " \n", " \n", " 3\n", @@ -983,8 +983,8 @@ " x-max in\n", " total\n", " current\n", - " 0.03180\n", - " 0.000659\n", + " 0.03030\n", + " 0.000662\n", " \n", " \n", " 4\n", @@ -1016,8 +1016,8 @@ " y-max out\n", " total\n", " current\n", - " 0.03072\n", - " 0.000677\n", + " 0.02981\n", + " 0.000595\n", " \n", " \n", " 7\n", @@ -1027,8 +1027,8 @@ " y-max in\n", " total\n", " current\n", - " 0.03104\n", - " 0.000652\n", + " 0.03083\n", + " 0.000732\n", " \n", " \n", " 8\n", @@ -1061,12 +1061,12 @@ " x y z surf \n", "0 1 1 1 x-min out total current 0.00000 0.000000\n", "1 1 1 1 x-min in total current 0.00000 0.000000\n", - "2 1 1 1 x-max out total current 0.03245 0.000677\n", - "3 1 1 1 x-max in total current 0.03180 0.000659\n", + "2 1 1 1 x-max out total current 0.03154 0.000643\n", + "3 1 1 1 x-max in total current 0.03030 0.000662\n", "4 1 1 1 y-min out total current 0.00000 0.000000\n", "5 1 1 1 y-min in total current 0.00000 0.000000\n", - "6 1 1 1 y-max out total current 0.03072 0.000677\n", - "7 1 1 1 y-max in total current 0.03104 0.000652\n", + "6 1 1 1 y-max out total current 0.02981 0.000595\n", + "7 1 1 1 y-max in total current 0.03083 0.000732\n", "8 1 1 1 z-min out total current 0.00000 0.000000\n", "9 1 1 1 z-min in total current 0.00000 0.000000" ] @@ -1111,7 +1111,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/examples/jupyter/mg-mode-part-i.ipynb b/examples/jupyter/mg-mode-part-i.ipynb index c393ecc417c..dfcb88aeca6 100644 --- a/examples/jupyter/mg-mode-part-i.ipynb +++ b/examples/jupyter/mg-mode-part-i.ipynb @@ -18,9 +18,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -48,9 +46,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Create a 7-group structure with arbitrary boundaries (the specific boundaries are unimportant)\n", @@ -119,9 +115,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Initialize the library\n", @@ -178,9 +172,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a Materials collection, register all Materials, and export to XML\n", @@ -205,7 +197,16 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/shriwise/opt/openmc/openmc/openmc/surface.py:1511: FutureWarning: \"ZCylinder(...) accepts an argument named 'r', not 'R'. Future versions of OpenMC will not accept the capitalized version.\n", + " FutureWarning)\n" + ] + } + ], "source": [ "# Create the surface used for each pin\n", "pin_surf = openmc.ZCylinder(x0=0, y0=0, R=0.54, name='pin_surf')\n", @@ -391,12 +392,24 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, + "execution_count": 10, "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -435,9 +448,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "tallies_file = openmc.Tallies()\n", @@ -523,44 +534,42 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2017 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | 966169de084fcfda3a5aaca3edc0065c8caf6bbc\n", - " Date/Time | 2017-03-09 08:18:02\n", - " OpenMP Threads | 8\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 14:36:11\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", " Reading cross sections HDF5 file...\n", - " Reading tallies XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", " Loading cross section data...\n", " Loading uo2 data...\n", " Loading mox43 data...\n", @@ -569,7 +578,9 @@ " Loading fiss_chamber data...\n", " Loading guide_tube data...\n", " Loading water data...\n", - " Building neighboring cells lists for each surface...\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -578,40 +589,31 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 1.3630E-01 seconds\n", - " Reading cross sections = 3.0827E-02 seconds\n", - " Total time in simulation = 8.9648E+00 seconds\n", - " Time in transport only = 8.2752E+00 seconds\n", - " Time in inactive batches = 2.4798E+00 seconds\n", - " Time in active batches = 6.4849E+00 seconds\n", - " Time synchronizing fission bank = 1.4553E-02 seconds\n", - " Sampling source sites = 1.0318E-02 seconds\n", - " SEND/RECV source sites = 4.0840E-03 seconds\n", - " Time accumulating tallies = 4.2427E-04 seconds\n", - " Total time for finalization = 1.7081E-02 seconds\n", - " Total time elapsed = 9.1340E+00 seconds\n", - " Calculation Rate (inactive) = 1.00813E+05 neutrons/second\n", - " Calculation Rate (active) = 77101.8 neutrons/second\n", + " Total time for initialization = 2.1712e-02 seconds\n", + " Reading cross sections = 9.5683e-03 seconds\n", + " Total time in simulation = 1.6152e+01 seconds\n", + " Time in transport only = 1.6108e+01 seconds\n", + " Time in inactive batches = 3.8534e+00 seconds\n", + " Time in active batches = 1.2299e+01 seconds\n", + " Time synchronizing fission bank = 2.8962e-02 seconds\n", + " Sampling source sites = 2.3951e-02 seconds\n", + " SEND/RECV source sites = 4.9397e-03 seconds\n", + " Time accumulating tallies = 4.9472e-04 seconds\n", + " Time writing statepoints = 2.4687e-03 seconds\n", + " Total time for finalization = 1.4768e-03 seconds\n", + " Total time elapsed = 1.6182e+01 seconds\n", + " Calculation Rate (inactive) = 64877.2 particles/second\n", + " Calculation Rate (active) = 40654.8 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.18880 +/- 0.00179\n", - " k-effective (Track-length) = 1.18853 +/- 0.00244\n", - " k-effective (Absorption) = 1.18601 +/- 0.00111\n", - " Combined k-effective = 1.18628 +/- 0.00111\n", - " Leakage Fraction = 0.00175 +/- 0.00006\n", + " k-effective (Collision) = 1.18598 +/- 0.00158\n", + " k-effective (Track-length) = 1.18629 +/- 0.00194\n", + " k-effective (Absorption) = 1.18569 +/- 0.00111\n", + " Combined k-effective = 1.18574 +/- 0.00111\n", + " Leakage Fraction = 0.00184 +/- 0.00006\n", "\n" ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -635,22 +637,23 @@ "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "# Load the last statepoint file and keff value\n", - "sp = openmc.StatePoint('statepoint.' + str(batches) + '.h5')\n", - "\n", - "# Get the OpenMC pin power tally data\n", - "mesh_tally = sp.get_tally(name='mesh tally')\n", - "fission_rates = mesh_tally.get_values(scores=['fission'])\n", + "with openmc.StatePoint('statepoint.' + str(batches) + '.h5') as sp:\n", + " # Get the OpenMC pin power tally data\n", + " mesh_tally = sp.get_tally(name='mesh tally')\n", + " fission_rates = mesh_tally.get_values(scores=['fission'])\n", "\n", "# Reshape array to 2D for plotting\n", "fission_rates.shape = mesh.dimension\n", @@ -694,7 +697,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/mg-mode-part-ii.ipynb b/examples/jupyter/mg-mode-part-ii.ipynb index ef9e00017be..01ae6bf370f 100644 --- a/examples/jupyter/mg-mode-part-ii.ipynb +++ b/examples/jupyter/mg-mode-part-ii.ipynb @@ -102,7 +102,16 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/shriwise/opt/openmc/openmc/openmc/surface.py:1511: FutureWarning: \"ZCylinder(...) accepts an argument named 'r', not 'R'. Future versions of OpenMC will not accept the capitalized version.\n", + " FutureWarning)\n" + ] + } + ], "source": [ "# Create cylinders for the fuel and clad\n", "# The x0 and y0 parameters (0. and 0.) are the default values for an\n", @@ -278,7 +287,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -287,12 +296,14 @@ }, { "data": { - "image/png": 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\n", 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"text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -472,7 +483,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/mgxs/library.py:412: RuntimeWarning: The P0 correction will be ignored since the scattering order 0 is greater than zero\n", + "/home/shriwise/opt/openmc/openmc/openmc/mgxs/library.py:418: RuntimeWarning: The P0 correction will be ignored since the scattering order 3 is greater than zero\n", " warn(msg, RuntimeWarning)\n" ] } @@ -554,11 +565,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=66.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=66.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=11.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=11.\n", " warn(msg, IDWarning)\n" ] } @@ -603,50 +614,51 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 6c2d82a4d7dfe10312329d5969568fc03a698416\n", - " Date/Time | 2018-04-22 15:02:43\n", - " OpenMP Threads | 8\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 14:38:16\n", + " OpenMP Threads | 2\n", "\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.16513 +/- 0.00090\n", - " k-effective (Track-length) = 1.16337 +/- 0.00104\n", - " k-effective (Absorption) = 1.16479 +/- 0.00080\n", - " Combined k-effective = 1.16460 +/- 0.00068\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.16510 +/- 0.00094\n", + " k-effective (Track-length) = 1.16422 +/- 0.00106\n", + " k-effective (Absorption) = 1.16519 +/- 0.00081\n", + " Combined k-effective = 1.16502 +/- 0.00071\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] } @@ -671,7 +683,7 @@ "source": [ "# Move the statepoint File\n", "ce_spfile = './statepoint_ce.h5'\n", - "os.rename('statepoint.' + str(batches) + '.h5', ce_spfile)\n", + "os.rename('statepoint.{}.h5'.format(batches), ce_spfile)\n", "# Move the Summary file\n", "ce_sumfile = './summary_ce.h5'\n", "os.rename('summary.h5', ce_sumfile)" @@ -772,11 +784,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Material instance already exists with id=1.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Material instance already exists with id=1.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Material instance already exists with id=2.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Material instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Material instance already exists with id=3.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Material instance already exists with id=3.\n", " warn(msg, IDWarning)\n" ] } @@ -876,32 +888,38 @@ "outputs": [ { "data": { - "image/png": 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\n", 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EJNVQTa3GCcwnkXzb/2xVbGqUrRaqZQlbslDVuwBEpI3zfrfXQTWVAPeceRB9O7bm3neWsqm4jCcuLqBjm+xEh2ZSXDwfoNFe64InPueFK49gzIBOYY+tqq7hX7O+49Ij+5KTmQ7YjLgtVdiShYgcJCLzgUXAIhGZJyJDvQ+taUSECUfvz+M/OYxFG3dx1mNzWLk16fOcaeaiKS2EXjrVm/SzZVe5q+Nem7+B+6ct5aEPV3gSh2k+3FRDTQJuUtU+qtoH+CXwhLdhxc4pB3fjpQmj2FNRxTmPz+Hz1c2mU5dphiJ7uIfoOuvi+3s8vuH7q3BL9+7rYbjC+dLVWGIsLqvk/mlLrYttinGTLHJV9RP/G1X9FMj1LCIPHNq7Pa9fO4bObbO56MkveO3r9YkOyaSoRHUnjbRB3Ks4t+wq57i/fso/pq9i6oKN3lzEJISbZLFaRH4nIn2d1+34ekg1K706tOa/1xxJQZ8O3PTyAh78YHmL6hNv4iOSEdxuDo1F19lof88nf7mO37+5yLmWu3NcMOlzdpRWAFBVY/+/UombZHE50Bl4zXl1drY1O/mtM3n28pGcO6InD3+0gl++vIC9VdZTysROVG0WQT7kLym4nu4jwkqpX76ygH63vR3ymAfeXxZy/11TF3HftCV1tq0vLNv3xnJFSnHTG6oQuDEOscRFVkYafzl3GH06tOaBD5azoaiMf140gnatsxIdmmlhmvosbWqbRVML1nNW7WDOqh2s2LKbpy49nMrqGqpqrJ0iVYVag/sh58+pIjKl/ituEXpARLjh+IE8fP5w5q8r4uzH57B2R2miwzIpINY1m6ESQly76ToX21ayt8G+j5duBWDI79/Fap5SV6iSxfPOn3+NRyCNEZH+wG+BfFU9N5bnHj+8B93yWzHh+bmc9dgcnri4gBF92sfyEqaFiaQ3lKs2iyDbgiUQb0Z8NzzpjtKGycKvsrputKu27aaiqoasDFsJIRWEWlZ1nvPjcFWdHvgChjfloiLylIhsFZGF9bafLCLLRGSliNzqxLFaVa9oyvVCGdmvA6/935Hk5WRwwROf8/Y3m7y6lGkBovlmHelH3CaQ2uMT9G3/nzNWc/sb3/LIRytYtc3GODV3blL+JUG2XdrE6z4DnBy4wVkzYyJwCjAEuEBEhjTxOq7079yG164dw7Ae+Vz34tc8/ukq6yllohLRdB8u0kSoJODVOAtV5V8zV7N9975SRLT/G16eu54HPljO8Q9Mj01wJmFCtVlcICJTgX712is+AXY25aKqOiPIOUYCK52SRAXwEjC+KdeJRIfcLP595RGcfkh3/vTuUn7z+rc2qMhELNZfMUKP7vbGqm27+ePbS4Lui7TXlUkdodos5gCbgE7AAwHbS4BvPIilB77p0P3WA0eISEfgHuBQEblNVe8L9mERmQBMAOjdu3dUAeRkpvPwj4fTu0MrJn6yivWFZUz8yWHk5WRGdT7T8kTXdbbp141lm0U04yP+/fna2AVgklKjyUJV1wJrReQnwEZVLQcQkVZAT2BNPAJ0Fl26xsVxk/BNTUJBQUHU//3S0oSbTzqQPh1y+c3r3/Kjxz/jqcsOp0e7VtGe0rQgsZ51NuRqeq6v1HT+ML74Lvh0Obe/sTDodpM63LRZvMy+5VQBqoFXPIhlA9Ar4H1PZ5tr/sWPioubvlbTeYf34pnLRrKxqIwzJ87m2/XNYv0nk2DRLWfR+Ke8GpTnt3TzLtfnmre2sHZEdzTs/1Dz5iZZZDhtCAA4P3sxgu0rYKCI9BORLOB8IKLxHKo6VVUn5OfnxySgsQM78d9rjyQrPY3z/vkZHyzeEpPzmtQVSZWSq2NjMd1HiH0nPzTT1TlEgo+xiMTpj85q0udNYrlJFttE5Az/GxEZD2xvykVFZDLwGTBIRNaLyBWqWgVcD7wHLAFeVtWIvsbEsmThd0DXtrx+3ZEc0LUNE56fy9Ozv4vZuU3qiaY3VKSlAv/R8eyxZ50DjZtlVa8BXhCRifi+pKwHLm7KRVX1gka2vwO804TzTgWmFhQUXBXtOYLp0jaHlyaM5mcvzeeuqYtZu2MPvzttCOm2XKupJ6IJyl0NynN3xlg2cDd2rlhcY1d5JXsra+jc1hYia27czA21ChjVnFbK80KrrHQe/+kI7ntnCf+a9R3rC/fw0PmH0ibbTb41LUUks866Eex0/iVU4/1t/+rn54U/KIyj/vQJxWWVrLn/1BhEZOLJzUp5XUXkSeAVVd0tIkNExLMR1U3hRTVUoPQ04fbThnD3+KF8smwb5z4+h/WFezy5lmmm4vwAX7GlJE4zJ8fmxorLKmNyHhN/btosnsHXjtDdeb8c+LlH8TRJrBu4G3PR6L48fenhbCgqY/yjs5m7pkljFE0KiXk1VIhjdpRW8IMHZ/Cb10J3W420bcMqV00wbpJFJ1Wt7T7rNES3+EUgjj6gM69fO4a2ORlc+MQXvDrPVt8zEfaGcpFaQh1RUu77lv7Vmp21VVNNccPk+Tz26cqg++auKWzy+U3z5iZZlDqjqBVAREYBSdlh2utqqPoGdGnDG9eNoaBve371ygLum7aEapujuUWL9ayzwdQujBTw+ViUBqYu2Mif3w2+4JF/3W3TcrlJFjfhG++wv4jMBp4DbvA0qijFqxoqULvWWTx7+Uh+Oqo3/5y+mqufn8vugMXtTcsS6+8KoVbRqz2mGS5J98SMZrcyc4sXNlmo6tfAMcCRwNXAUFX1Ym6oZiszPY0/nnkwf3Aavs95bA7f77SG75YosnEWTTum+aWIfe55ZwkLNyRlBYVpRKhZZw8Xkf2gtp1iBL4J/R4QkQ5xiq9ZuXh0X5657HA2FfumCPnKGr5bnMgauF20WQTrOutUOvm76QoScgxEpEnFm4WUGtpZWhH+IJM0QpUs/glUAIjI0cD9+KqginEm7Es28W6zCOaogZ1547ox5LXK5MInPueVud+H/5BJHRE1cEcnFaqhwJfsyitbfF+ZZiNUskhXVf9X4x8Dk1T1v6r6O2CA96FFLhFtFsH079yGN64dwxH9OnLzq99w7zvW8N1SRNPAHfqbvLvJoZpjd9fbXvuWA3/3bu37lVtLbNxSEguZLETEPzz5eODjgH02bDmM/NaZPH3Z4Vw8ug+TZqxmwnNza7s6mtRVE8V6WaFqo0Lui3JuqfDik3o2FZfXeX/C32Yw9k+fxOXaJnKhksVkYLqIvAmUATMBRGQASdp1Ntlkpqfxh/EHcff4oXy6fBvnPv6ZNXynuMjKj9GVNus/ykOVZnbvrWJzvYeyMdFoNFmo6j3AL/GN4B6r+1rj0kjSrrPJ0GYRzEWj+/LsZSPZVFzG+Imz+fI7a/hOVTFf/CjItmBzQzU2KO+MR2Zx1J/t27ppupBdZ1X1c1V9XVVLA7Ytd7rTJp1kabMIZuzATrxx3RjatcrkJ//6nJet4TslRdQbyvkzZE8ml1VUjZ1j9fbS4DtCiFdvqMaU2jilpORmUJ6Jkf6d2/C60/B9y6vfcM/bi63hO8XEYw3u2vUsIr9UszD0jvcSHYIJwpJFnOW3zuSZyw7nktF9eGLmd1z13Fx2WcN3yoh9NVSqpoS6rC0v+bmZojxXRNKcnw8QkTNEJNP70FJXRnoad40/iD+eeRAzlm/jjEdmBV0L2TQ/8RiUF+zzzbHrbCBrV0l+bkoWM4AcEekBvA9chK/R2zTRT0f1YfKEUZRWVHPWxDm8MX9DokMyTRTZrLM+EY++lhD7YuDTZds8OrNpztwkC1HVPcDZwGOq+iNgqLdhRSdZe0OFcnjfDrx9w1gO7pHPz//zP+54cyEVVVF01jdJIS6zzgb7fAxbpd/6ZmPMztUUCzcU8836okSHYRyukoWIjAZ+ArztbEv3LqToJXNvqFC65OXwwlVHcOXYfjz72VrOn/SZ9Y1vpmLdwB181llvm7jjvVzrykamPz/tkVmc8ehsALbuKqfGOoMklJtk8XPgNuB1VV0kIv0Bq2CMscz0NG4/bQiPXngoSzeXcNojM/ls1Y5Eh2UiFMka3LFqvI73wz3Wzn5sdoNt7y7cXPvzpuIyRt77EQ99uByABd8XcePk+ZY84szNFOXTVfUMVf2T09C9XVVvjENsLdJpw7oz5fox5LfK5KdPfsGkGasiXhbTNBMh5oZKk7ozywYKVg3VnBu4y4JMJnjNv+fV/uwvZU9f7mtLueq5uUxZsJFtu/fGJ0ADuOsN9aKI5IlILrAQWCwiN3sfWss1oEtb3rx+LCcN7cq97yzl2he+tnmlmoloGriDSU/zPf6rqhs/KrCBPJYD6eL91STc3Fa18fhHrjtvNxSVWS/COHJTDTVEVXcBZwLTgH74ekQZD7XJzmDihYdx+6mDeX/xFsZPnM2KLSWJDsuEEckgy1CJxZ8s3Jwv1gXPeJdSKqpDd+go2uNb96J+iersx+Zw8kMzPYzMBHKTLDKdcRVnAlNUtZLUHTyaVESEK4/qzwtXHsGusirGT5ydND1VTHCxGpGf7nyLrgpyvmBrcKeyy5+ZC8C2kr1s3WUdPxLFTbL4J7AGyAVmiEgfwMp+cTSqf0fevnEsg7vlcf2L87lzyiL2VtmiMcmoMoI5ykM1cKc5/zODJZ8GK+WJF9OUJ58NRb6G7u3WVpEQbhq4/66qPVT1h+qzFhgXh9hMgK55OUy+ahSXj+nHM3PWcNbEOazaFrzLoUmcUG0M9YWuhvL913RbDZXoyf9M6nPTwJ0vIn8TkbnO6wF8pYyk0xwH5UUiKyON358+hCcvKWBTcRmnPzKLV+Z+b72lkkhlmPr3QCEbuJ2Hf9CShccjuO33yQTjphrqKaAEOM957QKe9jKoaDXXQXmROn5wV6b97GiG9czn5le/4Wcv/c96SyWJYG0M0UhLa7zNws+rh/qC9c3ry9ZLX65LdAgtgptksb+q3qGqq53XXUB/rwMzoe2Xn8MLV47iVycewNvfbuKHf5/J/HWFiQ6rxYusN5Q6fzbcl1HbG8pdSSVYLdQLX6x1HUtzdutr3yY6hBbBTbIoE5Gx/jciMgbfMqsmwdLThOuPG8jLV4+ipgbO/cdn/O39ZTa3VJwFfsOPphqqOki2SHdRsghl5dbd/Pb1hVF91phg3CSLa4CJIrJGRNYAjwJXexqViciIPh2Y9vOjOHN4D/7+8UrOemw2y21MRtwEliYiaeD2Z4tQpZHgbRbBllWte4x9YTCxFjJZiEg6cJGqHgIMA4ap6qGq+k1cojOu5eVk8sB5h/CPn45gc3E5pz0yi0kzVtlKfHEQ+O0/mq6zESeLep83Jh7CrcFdDYx1ft7ljOQ2Sezkg/bjvV8czTEHdObed5ZywaTPWbfDViHzUmDVU3UkJYsQ1EWpwzotmXhyUw01X0SmiMhFInK2/+V5ZCZqndpkM+miEfz1R4ewZNMuTnxoOpNmrKIqgvp0416daqgYTffh3+d2BHf9QXktbdxFcZn1BvSam2SRA+wAjgNOd16neRmUaToR4dwRPXnvF0czdkAn7n1nKWc+NpuFG5pXt8jmoLI6ygZuF3nF9dxQLSw51Hfq322OKK9lhDtAVS+LRyDGG93bteKJiwuYtnAzd0xZxPiJs7libD9+ccIBtMpKyjWsmp2qgHaKiEZwh9ynDc4dSgvPFawvtA6aXmu0ZCEifxGRBr2eRORqEbnf27BMLIkIPzy4Gx/+4hjOK+jJpBmrOfGh6cxYbmstx0JggoikgTvUQkm11VBBko+/iilwbqiM9JZdDWW8F6oa6jhgUpDtTxDHaigRyRWRZ0XkCRH5Sbyum4ryW2dy39nD+M+EUWSmp3HxU19y9fNz+X6nNYA3RWC7wt4IuqyGGoHt3xOsC2z99glVyEhzU6NsTPRC/YZla5DfZlWtoYmlXhF5SkS2isjCettPFpFlIrJSRG51Np8NvKqqVwFnNOW6xueI/h2Z9rOjuPmkQcxYvp3j/zadv72/jD0VVYkOrVkK7DhQVuF+NuBQzRv+UkOwVeSCzQ1lJQnjtVDJokxEBtbf6GxragXhM8DJ9c6bDkwETgGGABeIyBCgJ/C9c5jNyx0j2RnpXDduAB//6hhOOWg//v7xSo5/YDpTFmy0ieQiFFiyiCThuqmGCpYs9h2zrxqq/qlawpTlJr5CJYvfA9NE5FIROdh5XQa87eyLmqrOAHbW2zwSWOnMP1UBvASMB9bjSxgh4xWRCf6Zcbdts7p4t7rlt+Lh8w/llWtG0751FjdOns+ZE2czZ+X2RIfWbAS2K0RSsgiVLGpLFkHOVz8NWG5PLi99uY6CP35ATYoNiG304auq0/CtjjcOX0ngGeBY4BxVfceDWHqwrwQBviTRA3gNOEdEHgemhoh3kqoWqGpB586dPQgvtR3etwNTbxjLX84dxraSvVz4ry+4+KkvrautC/5G7ZzMNPbELFn4/vziu/rfqfZN92HDZpLT799cxPbdFWxOsVX9QnadVdWFwCVxiqWxGEoBV913ReR04PQBAwZ4G1SKSk8TflTQi9MP6c7zn61l4qcrOe2RWZxxSHd+fsJA+nduk+gQk5J/LEReTmZEycJNm0UwWem+73iRjOkw8ZOWBlRD0Z5KurdrlehwYiaZulBsAHoFvO/pbHOtpaxn4bWczHSuOro/028ex3Xj9uf9xZs54W/TuXHyfJugMAj/QzuvVWbINob6akLMVhvYblS/DSkrw/ffNtRkgdbgnTj+Lw+7UmyNmWRKFl8BA0Wkn4hkAecDUxIcU4uW3yqTm086kJm3HMdVR/fnwyVbOPHBGVzz/Dyrngrgb7Nom5PBnooqivZUuFonvaZOw3h1vX37fq6ol0gynTEVgdtTq3a8eatNFik2BUlCkoWITAY+AwaJyHoRuUJVq4DrgfeAJcDLqroowvOm9LKqidK5bTa3nTKY2b8+jhuPG8DsVds57ZFZXPTkF3yydGvKNeRFyl+a6JibTXllDQ9+sJxn5qzhta99BeOyimpu/e83FO+p+/D4zev7Fu0p3Vu3F1VgqaN+I3dmet2Sha83VMv+N0gm/v8Ou8pTqyu6mzW4nxWRdgHv24vIU025qKpeoKrdVDVTVXuq6pPO9ndU9QBV3V9V74nivFYN5aH2uVncdOIgZv36OG45eRArtuzmsme+4oQHp/P852tb7DgN/8O8U5ssAHbv9b0vd5LIf75ax0tffc9DHy2v87nAHFv/7y5wX/2qrUx/NVRgyaJerrDckXgtsWQxTFWL/G9UtRA41LOImsBKFvGR3yqTa48dwMxfj+Ph84fTJjuD372xkNH3fcw9by9m5dbdiQ4xrvxVSB1yfcnCX/3kf+D7/wx8gNcvCfgTTLD9JfW+odY2cDsli/WFZSzaWPd33tYxSYzygMTeEtss0kSkvf+NiHTAxQSEiWAli/jKTE9j/PAevHndGF69ZjRjBnTk6dlrOOFv0znvH5/x33nrIxp30FzVVkO1yQb2TfkRqnqu/jf/knoPlsBqqBMfnFFnX7A2iydmflfnmPIw7SXGG0UBVY27ylKrpO3mof8A8JmIvIJvPNC5QMRVRHG1fQU8fWqio2gxBChwXhX9a9i+ey9bt+6l/M1qFk4VOuVm06ltFm2yM+I7svjgc6HA+0mTy5wqpM5ts533vge1f23t/31fBNQtLdRfd/uiJ79kzf37fmcV6JibxY7SigbXq99mEUzRnoafM94rDPh7T7U1NtxMUf6ciMzFN7EgwNmqutjbsExzlZWeRvf8VnTLz6GkvIqtJXvZurucLSXlZGek0TE3m45tsmidle5t4tjsNB7HIVnsqagmI03o1d7Xp36LMxjLXxU0ZcFGoG47RKhxFACfLmt8FgJ/19k3/td4z/L73lkaPnATc4WlgckiuoTtP0d7p1ozWTSaLEQkT1V3OdVOm4EXA/Z1UNWGQ0sTrM6gvMveTnQ4LZoAec6rS3kl7y/awpQFG5m9cjvV25QBXdpw8tD9GHdgF4b3akd6WowTRxxLlqV7q2idlU4PZwDW5mJfsqhfDRW4ZnYEM5kD8MXqHbz45Tre/N9GjjuwCwDllY2fZEULazdKFoVONVTnttls3x1dsjj07g8A6pQ0k0GoksWL+KYin4evVBz4v1mB/h7GFRVVnQpMLSgouCrRsZh98nIyOXdET84d0ZMdu/cybeFmpi7YyGOfruTRT1bSITeLYw7ozLGDOlPQtwPd83Nqp7RoDnbuqaRDbhad2mSTlZFGidMN9psNxdw5ZV/v73AlC1Vt9L5/POnz2p8/WbY1RpGbWNta4vuicOB+bVmzozTksX95bynrC8t4+Pyk7C/UQKPJQlVPc/7sF79wTKrr2Cabn47qw09H9aFoTwUzVmznk6Vb+XTZVl6f76tW2S8vhxF92nPgfm3p3bE1HXOzSU8TCvdUsLGojJLyKrrm5XDyQfvV9kBKpMLSCjrkZpGWJgzulscCp43ig8Vb6hwXqs0CfNVV44f3qLPt6csO57Knv6p3nhgFnmKemLGaHu1b0aNdK3q0b0XH3Ky4f+nYsmsvmenCwC5tmbe2MOSxEz9ZBcCD5w0nLdYlaw+46tUkImcDY/GVKGaq6hteBhUtmxuqeWnXOoszDunOGYd0p7pGWbJpF/PWFta+3v52U8jP3/3WYi4e3YcJR/ev7YkUT3NWbefrtYVs372Xnk57xWG929Umi/rqdJ0NqEHqnp/DxuJyVm3zfRMN7Bk1blCXmMedqu55Z0md9zmZaXRv50sePZ0k0qtDa/p2zKVvp1zyW2XGPIZNxWV0zcuhc9ts9lRUs6eiitZZoR+zO0orajtHJLOwyUJEHgMGAJOdTdeIyA9U9TpPI4uCVUM1X+lpwkE98jmoRz6XHNkX8PUq+r5wD0V7KqmqriG/dSY92rUiLyeT5VtL+Of01TwxczXPf76WC0f25ozh3Tmoe37Yb2k7du9lzY5SRvTpEFGM/vET2RnpLNtcwoVPfAH4HkpH7t8JgGMO6MzTs9cE/XxNIyWLiT85jLMem8N/563nxuMGcPCd79f53N3jh/K7NyOazKBFWnDHiWwoLGNDURnrC/fU/ryhqIzFG3c16FnWvnUmfTrm0rdja/p2ymVQ17YM7pZH7w6to/6mv3pbKf065dLRGaC5vaSC3h0bPmYDS5mbisv479fruX/aUpb/8RTX1/pw8RaG9cynS15OVLFGyk3J4jhgsH/VPBF5FrDfXOO5VlnpHNC1bdB9B+6Xx4M/Hs514/bnoQ9X8MycNfxr1nfkt8pkeK92/KGwlJyMdJYu30Z+q0zycjKoUVi0sZi731rM9t0VPPTj4Zx5aN1qn7KKaorKKuiW33C20FMemklmehrv/eJonpq1b1xDeWUNA7v6ZuQ95oDOXD6mH0/N/q7B52vU153yuL9+yh/PPKh2+yE92wGwoaiMAb+d1uBzF43uy/x1Rbw2f1/vp3atM+v06Te+waL5rTIZ0j0v6H7/l48120tZs6OUNTv2sHZHKV+tKeTNBRtrS365Wekc2C2PId3yOKRXOw7t3Y7+nXLDVmlVVdewattuzivoRe8OrQH4bkcpvTu2bnBs4BiMjUXlTJqxGoCdQbpK17e3qpphd77P3qoa+nfK5eNfHRv2M7Eg4eaUEZG3gOtUda3zvg/wqKqeHof4olJQUKBz585NdBgmjor2VPDhkq3MW7uT+euKuKvwFgazlsXap8GxOZnptUuh9u2YS252BhlpQnqasGrbbnaUVlDQp33tutalFVVsLi5n2+69ABzWuz0L1hfVGSU9vFc7cjLSa98Xl1WyZPOuOtfNzUonNzuDrSV7aZWZXjuYb1S/jizaWFzbMB5oVL+OtT9//t2O2p8z09Jq19EI5uAe+XzbwiZ7DPy7ilSNam21UWlFNXv2VrGnorq2BNg6K52OuVm0zcmkTXYGaUESR8neShZt3MWALm3Iz8lk3rpC+nRoHfSLR1llFQvW+/59+nRszcaiciqrazikZzsWrC8KeT+BvwdNve9Acvk781S1oLH9bkoWbYElIvKl8/5wYK6ITAFQVVsX2yRcu9ZZtT2uAGq+uprKBf9haGUNVTVKVY0iQHZGGm1yMiirqGbJ5hJWbgvexfR/3xfRKjOdNBGK642u/npd3YZLgTqJAiCvVQYdcrPqfFMsraim1BmwV3++p4Fd2zY4b30j+3bgyzW+HutVNTUU9GnP3EYaUXOzMjioez4LN7ashBGtNBHaZGfQJnvfI1FRyiqr2VVWxbaScr4vLAPKECA3O4O2ORm1ySM9TdhYVIYItGuVSUZaGhlpUvvvXV9lwOqKFVU1tV1Nk3maFjcli2NC7VfV6TGNqAkCGrivWrFiRaLDMUmusrqGZZtLWL6lhJLyKnbvrSLbGfD29bpCduyuoLSiioI+HWiTncF320v5aOmWOuMbLj2yL789dXDtqOr6Zq3Yzp/fW8o36xt/aPv70//gb9MbjI+o39f+zImza0eEr7n/VFSVfrc1XLjS/7m+t4Yeb/TmdWMYP3F2nW1/PncYt7z6TcjPJSOvxyUUllbw1ZqdzF1byNw1O/l2Q3Gdhz7Ab384mKuO9o0quO6Fr/lyzU6+uO34Bm0gb32zketfnA/AacO68fXaQjYWl/OfCaNqu0lnpaex/J6GbRiB/6YZacLKe38Yk/sTkaaVLFR1uoh0xVeiAPhSVZOyo7c1cJtIZKan1Taqu1VcVsnWXeV0yM2iXeussIMJxw7sxNiBY6msrmHpphJOf3RWnf0nDN7X2+nlq0fXDshqzD1nHcSpf993jnD16D8/YSAPfdjwi9Oo/h04cL88hvWse+8j+rSnYxJ0R05G7XOzOHHofpw4dD/AN2ngtxuKWbShmNKKag7t3a62owPACUO68Pa3m/h89Q6OHNCpzrl2OAP2Dujahk3F5bX/jnsCSpwV1TUhx97Em5spys8DvgR+BJwHfCEi53odmDHJKL9VJgO7tqVjm+yIRp1npqdxcM98vvzN8XW2X3nUvrGt7XOzGNGnff2P1tG/U8OlbZ++7PDan8cM6MgNx+3rOv7zEw4Iep57zzqYO88YWudBtOQPJ/PShFHNos9/MsjJTOfwvh24dEw/rhs3oE6iADjloG50bpvNn95b1mAlxA1FZWRlpDGkWx6bi8txmscaTLzpdkyNqtYOCPSKm1lnfwscrqqXqOrFwEjgd55GZUyK6pKXw4p7TmF4r3a0zclo0HPn8Z8cVvtz17yGfe9bZaU32Da6/74GzkcuOIxfnjgoZAyf/urYoOupt8pKJzM9jfQg32TzcpJyoumklpOZzh2nD2HB90X8+tVv6iSM1dtK6dcxlx7tW7F5V3ltUqi/YmL9wZur6rWxVdUoyzaX8O/P1zLyno9YWq9TRSy5+Q1Iq1fttIPkWo7VmGYlMz2NN64bE3RfYJ/568a5G1yak7kvgQQrExzSM7+2501Wehp9O+WGPF+wEtPVx+zPX95b5ioes89pw7rz3bZSHvhgOWt2lPKnc4bRr1MuX68r5OiBneiW34rqGq2dfLKs3iJY//u+iMP7+sYDrS/cw/EPNGwiPumhfVPYf7etlAP3C951uKncJIt3ReQ99g3K+zHQsDO4MSYmendozbqde+okgUAP/OiQ2m689QWr3vZPlX3C4C7cecbQsNcP1i3Uy6Vzjx3UOeQsu83dDccPpHfH1twxZREnPTSDgV3asrO0gpMP6lY7eM/fUF5/EazLn/6Kb+86CYB1O/bU2delbTZbS+r+HnjZvBG2hKCqNwP/BIY5r0mqeot3IUXPVsozqeDdnx/FbaccyNn1Bgz6nTOiJ9ccs3/QfcEaQ085uBsAh/ZuT8/2DQeI1edfHjaQlz06jx7YucnnCOwokIzGD+/Bhzcdw7XHDqB1djrXjdufk4Z25bDe7etUN24oqpsQKqqDj6U59eBuHN6v4QwEM1ds58lZDQeExoKbBu5+wDuqepOq3oSvpNHXk2iayFbKM6mgdVYGVx+zPxmNdMcNxd/1N5C/W2/9RtbGDOzallevGV1nW40qh/T05v9VuI4C/TuHrjYD+MdPR8QqHM90apPNr04axOvXjuHmkw5ExDcQ9E/nDGNU/w7k5WQ06GLd2L/Z8F7t+MHgrg22v/DFOu5+y5vlhtz8Nr4CBEZc7WwzxiSZYMkiy78Ma4iV9eor6NuhTumlRpX7zxnW9ACD+PHhvULu//iXx7LIqYoJ5ryCnlEl1mRx7KAuvDRhNL88cVCDZBFYogss3PVs34r9g3RS8JKbv+EMVa0dhur8bB2xjUkiZx/mq7IKVg3lX1mvsWQxtHselzqTNwa69ZQDuf3UwYCvEX1wtzxevnp0g+OaKidz38JRfjNvGVfnfW52482rfz73kJjHlAgXjOzNyHpVS3k5GbWTDgZ2jOrdsTVt49xDzU2y2CYitVN6iMh4YLt3IRljIvXnc4axsJFv3/6G8r2NJIu3bzyq0Ybvi0f35eaTBnHlUb5lbbxqQJ1963F13vfq0Jqh3fMY2GXft+e//ugQDuqRl7LdeLMy0nj+ipGMGbCvK/Su8ir++v4yamqU2av2PXYHdW1LlyBdq/3++t4ybpg8v3am5Fhw87d+DfCCiDyKr2fe98DFMYvAGNNkGelptGmkKibL2R7NgyMrI61OF97B3Rp2yww1R1Vjrjlmfy45sg9LN5U02OdPem/feFSd7f65v/79+Vpuf2NhRNdrLrIz0nnhylGsL9xDcVklz8xew8RPVjFvbSGfr963knVGehoZ6Wk8dWkBHy7ZyotfrKtznkc/WQnASUO7ctqw7kGvtbGojOKySjLShA+WbAl6TCA3032sAkaJSBvnvS3ua0wzMtCZ5v2w3qFHh7sRONHeIxccSrf8HAqccQAXPfkFM1cEr3QY1LUtd54xlO7tcigpr6qdYiXYjKxtQlQ5AbUrLW7fvZfyyth9c04mPdu3pmd7uPnkQbwybz2fr97J2Yf2YGS/Dpw6rFvtcccd2JUj9+/Et+uLg84yfP2L81GFE4d2ZW9VDcOctVKG9cwPOV9ZMG56Q/1MRPKAUuAhEflaRE6M6CrGmIQZ0ac9M28ZF7Yh2a3TD/F9Uz3uwC61iQLg+SuOaFBFdFAPX0nkgpG9GL1/R/p0zI1oLq5QOrXJdtUVuDnr0nbfIM2jD+jM+SN70zan7gp/OZnpTL1hLP++4oig57hh8nwG3f5ubaIA6iSKNtkZ3H/2wWFjcVMNdbmqPiwiJwEdgYuA54H3Q38s/mxZVWOC69Uhdg/Vv5w7jJt+cEDQRuepN4zl//79NYs37eLDm47hs9U7WLhhYdAFgOp79vKRtPNgqdNU0atDw1JYoLEDO/HUpQVc/kzotXzGDerMAV3b8vMTDqgzfcwFYa7vJln4m7R+CDynqoskWaZBrMdmnTXGezmZ6fRrZMqQPh1zefvGsWwt2UvXvBz275zLQd3zONRFFdgxBzR9cF4q6+WiFHXcgV25YGRvJn+5jnvPOpjfvP5tg2OevmxkVNd3kyzmicj7QD/gNhFpS91xF8YYU0tE6OrMcSUirhKFadxZh/bg9fkb6Ny28d5Pgf4wfii3nnIg+U4p7YTBXchvnUl5ZQ27yqJfitfN4kdpwHBgtaoWiUhHoIeqJu3qKLasqjEmVZRXVrOrvLJO+4UXYrH4UY2I9AQudGqfpjvVPcYYYzyWk5ne6KSS8eSmN9T9wM+Axc7rRhG51+vAjDHGJA83bRY/BIarag2AiDwLzAd+42Vgxhhjkofb2bfaBfxsU7oaY0wL46ZkcS8wX0Q+wdeN9mjgVk+jMsYYk1RCJgunJ1QNMArwrwr/a1Xd7HVgxhhjkkfIZOH0hLpFVV8GpsQpJmOMMUnGTZvFhyLyKxHpJSId/C/PI3OISH8ReVJEXo3XNY0xxtTlJln8GLgOmAHMc16uRryJyFMislVEFtbbfrKILBORlSISsv1DVVer6hVurmeMMcYbbgbl9WvC+Z8BHgWe828QkXRgIvADYD3wlYhMAdKB++p9/nJV3dqE6xtjjIkBN4PyrhORdgHv24vItW5OrqozgJ31No8EVjolhgrgJWC8qn6rqqfVe7lOFCIyQUTmisjcbdu2uf2YMcYYF9xUQ12lqkX+N6paCDRlVtce+Fbb81vvbAtKRDqKyD+AQ0XktsaOU9VJqlqgqgWdO9vslcYYE0tuxlmki4ioM+OgU42U5W1Y+6jqDnxLuxpjjEkQNyWLd4H/iMjxInI8MNnZFq0NQOCSXT2dbU0mIqeLyKTi4siWCzTGGBOam2Txa+AT4P+c10fALU245lfAQBHpJyJZwPnEaAyHqk5V1Qn5+TYjiTHGxJKrKcqBx51XRERkMnAs0ElE1gN3qOqTInI98B6+HlBPqeqiSM/dyPVsWVVjjPGAm8WPBuLr0joEqF19Q1X7exta9GzxI2OMiUy4xY/cVEM9ja9UUQWMwzdm4t+xCc8YY0xz4CZZtFLVj/CVQtaq6p3Aqd6GFR1r4DbGGG+4SRZ7ndlnV4jI9SJyFtDG47iiYg3cxhjjDTfJ4mdAa+BGYARwEXCJl0EZY4xJLm56Q33l/LgbuMzbcJrGekMZY4w3Gk0WzuR+jVLVM2IfTtOo6lRgakFBQVOmIzHGGFNPqJLFaHxzOE0GvsC3pKoxxpgWKFSy2A/fNOIXABcCbwOTYzWAzhhjTPPRaAO3qlar6ruqegm+NbhXAp86o6+TknWdNcYYb4TsDSUi2SJyNr5BeNcBfwdej0dg0bCus8YY441QDdzPAQcB7wB3qerCxo41xhiT2kK1WfwUKMU3zuJGkdr2bQFUVfM8js0YY0ySaDRZqKqbAXtJxcZZGGOMN5pdQgjF2iyMMcYbKZUsjDHGeMOShTHGmLAsWRhjjAnLkoUxxpiwUipZ2AhuY4zxRkolC+sNZYwx3kipZGGMMcYbliyMMcaEZcnCGGNMWJYsjDHGhGXJwhhjTFiWLIwxxoSVUsnCxlkYY4w3UipZ2DgLY4zxRkolC2OMMd6wZGGMMSYsSxbGGGPCsmRhjDEmLEsWxhhjwrJkYYwxJixLFsYYY8KyZGGMMSYsUdVExxBzIlICLEt0HB7oBGxPdBAeSdV7s/tqflL13sLdVx9V7dzYzozYx5MUlqlqQaKDiDURmZuK9wWpe292X81Pqt5bU+/LqqGMMcaEZcnCGGNMWKmaLCYlOgCPpOp9Qerem91X85Oq99ak+0rJBm5jjDGxlaolC2OMMTFkycIYY0xYliyMMcaE1aKShYgcKyIzReQfInJsouOJJREZ7NzXqyLyf4mOJ1ZEpL+IPCkiryY6llhItfvxS9XfP0jd54aIHOXc079EZE6445tNshCRp0Rkq4gsrLf9ZBFZJiIrReTWMKdRYDeQA6z3KtZIxeLeVHWJql4DnAeM8TJet2J0X6tV9QpvI22aSO6zOdyPX4T3lXS/f6FE+LuZlM+NYCL8N5vp/Ju9BTwb9uSq2ixewNHAYcDCgG3pwCqgP5AFLACGAAc7fwGBry5AmvO5rsALib6nWN6b85kzgGnAhYm+p1jel/O5VxN9P7G4z+ZwP9HeV7L9/sXq3pL1uRGLfzNn/8tA23DnbjbTfajqDBHpW2/zSGClqq4GEJGXgPGqeh9wWojTFQLZngQahVjdm6pOAaaIyNvAix6G7EqM/82SViT3CSyOc3hRi/S+ku33L5QIfzf9/2ZJ9dwIJtJ/MxHpDRSrakm4czebZNGIHsD3Ae/XA0c0drCInA2cBLQDHvU0sqaL9N6OBc7G98v8jpeBNVGk99URuAc4VERuc5JKcxD0Ppvx/fg1dl/H0jx+/0Jp7N6a03MjmFD/564AnnZzkuaeLCKiqq8BryU6Di+o6qfApwkOI+ZUdQdwTaLjiJVUux+/VP39g5R/btzh9thm08DdiA1Ar4D3PZ1tqSBV7y1V76u+VL3PVL0vSN17i8l9Nfdk8RUwUET6iUgWcD4wJcExxUqq3luq3ld9qXqfqXpfkLr3Fpv7SnTrfQSt/JOBTUAlvjq3K5ztPwSW42vt/22i47R7S/37ain3mar3lcr35uV92USCxhhjwmru1VDGGGPiwJKFMcaYsCxZGGOMCcuShTHGmLAsWRhjjAnLkoUxxpiwLFmYFk1EqkXkfwGvcNPcx0VAXN1DHHOHiNxXb9twEVni/PyJiOwWkQKv4zWpz8ZZmBZNRHarapsYnzNDVauaeI6wcYnIAcC7qto/YNv9wB5V/YPz/lPgV6o6tynxGGMlC2OCEJE1InKXiHwtIt+KyIHO9lxngZkvRWS+iIx3tl8qIlNE5GPgIxFpLSIvi8hiEXldRL4QkQIRuVxEHgq4zlUi8qCLeE4Ukc+ceF4RkTaquhwoFJHAWXvPwzeK15iYsmRhWrpW9aqhfhywb7uqHgY8DvzK2fZb4GNVHQmMA/4iIrnOvsOAc1X1GOBaoFBVhwC/A0Y4x7wMnC4imc77y4CnQgUoIp2A24ETnHjmAjc5uyfjm+sHERkF7FTVFZH/NRgTWouaotyYIMpUdXgj+/zTUs/Dt1YDwInAGSLiTx45QG/n5w9Udafz81jgYQBVXSgi3zg/73ZKH6c5bQuZqvptmBhH4VuxbbaIgG+1s8+cff8B5ojIL/ElDStVGE9YsjCmcXudP6vZ939FgHNUdVnggU5VUKnL8/4L+A2wFHcLzwi+RHRB/R2q+r2IfAccA5wDjHYZgzERsWooYyLzHnCDOF/xReTQRo6bja/9ABHxrzEOgKp+gW99gQtxVxL4HBgjIgOc8+U6jdt+k4EHgdWquj6y2zHGHUsWpqWr32Zxf5jj7wYygW9EZJHzPpjHgM4ishj4I7AIKA7Y/zIwW1ULwwWoqtuAS4HJTnXWZ8CBAYe8AgzFqqCMh6zrrDEeEJF0fO0R5SKyP/AhMEhVK5z9bwEPqupHjXw+Jl16reusiRUrWRjjjdbALBFZALwOXKuqFSLSTkSW42tYD5ooHLvCDcoLR0Q+AfrjWwjHmCaxkoUxxpiwrGRhjDEmLEsWxhhjwrJkYYwxJixLFsYYY8KyZGGMMSYsSxbGGGPC+n8q+Wz1Gp3AuQAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -949,38 +967,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 6c2d82a4d7dfe10312329d5969568fc03a698416\n", - " Date/Time | 2018-04-22 15:04:03\n", - " OpenMP Threads | 8\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 14:41:06\n", + " OpenMP Threads | 2\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -988,11 +1005,11 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.16541 +/- 0.00086\n", - " k-effective (Track-length) = 1.16590 +/- 0.00096\n", - " k-effective (Absorption) = 1.16469 +/- 0.00046\n", - " Combined k-effective = 1.16480 +/- 0.00045\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.16517 +/- 0.00094\n", + " k-effective (Track-length) = 1.16588 +/- 0.00106\n", + " k-effective (Absorption) = 1.16485 +/- 0.00050\n", + " Combined k-effective = 1.16499 +/- 0.00048\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] } @@ -1068,9 +1085,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Continuous-Energy keff = 1.164600+/-0.000677\n", - "Multi-Group keff = 1.164805+/-0.000448\n", - "bias [pcm]: -20.4\n" + "Continuous-Energy keff = 1.165017+/-0.000706\n", + "Multi-Group keff = 1.164985+/-0.000485\n", + "bias [pcm]: 3.2\n" ] } ], @@ -1161,7 +1178,7 @@ { "data": { "text/plain": [ - "" + "Text(0.5, 1.0, 'Multi-Group Fission Rates')" ] }, "execution_count": 38, @@ -1170,12 +1187,14 @@ }, { "data": { - "image/png": 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\n", 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W7ttO0ainj35X4DkLbx5cTkgbLk/PP4SQ1QchU3IfSWv9ltlx1hD3badQ1BPoh9DyTXRzWP0tdh9o4vtLFhNeHLYako5XmI5wOhl92Y7ThjTMt1v69YI2MtdxatNhRt2Y2RVm1mRmTWt5VjXHaTNa+vXA9jbHWUepJ9DPpeUrRzdn9dd7fqBReEd8X9ZsJh7HaQ/ct51CUU+gfxDYJr5itjthbMqkMs0kwrRyECbL/ZN5Kq7T8XHfdgpFXa9AkDSGMNl3F8K0fD+RdD4w3cwmxfcwXw98nDBDzxGJ1/nGcne01dtVORW7+lsyOj3pT887FiY1/9L31qRm/Px/TWq+t8kPk5qRPJnUvEt6jPQzGbMx/pnRSc1Dsz+V1Nw+9MCk5ovPTE5qNt026RrM+1nGa9cTIxi5twlbOL3mg9O28G31bTL+KWHcH69MGA+MSw/B7Tt2XlJzX49PJzWDMgZ4v0J63PrO859Kap7eZHhSc13F+WtaMpSXk5qvvVM+m+fq7LXBlKTmgWm1ZqMM9N4h/WxmyaYZ3XpLXkwIDsZsZuPH0VuY43Vy2bKzSz4vAw6rpw7HaQ/ct50i0WEexjqO4zhtgwd6x3GcguOB3nEcp+B4oHccxyk4Hugdx3EKjgd6x3GcguOB3nEcp+B0yIlHGNoDvr1Fbc2hK5LF7Dw8/d72hyalE4JIv5Yazkgnnv10fMbLDa/KqGtqRpLb5Iy6pmXUdV66ri9k1LV4/+5JzXGkk4UmbJ2RMHVWWtIudCE5nw6zxiaL2WH4g0nNzL/umrbn7rQk5/xvemaGr81OS7a7aVZS89PdM+p6Ly3hkfR+TZuYUdfQtEQ57XVEWsIBiZh4ffVJUPyK3nEcp+B4oHccxyk4Hugdx3EKjgd6x3GcguOB3nEcp+DUMzn4UEl/lvSkpCcknVZBM1rSYkkz4t/ZlcpynI6E+7ZTNOoZXrkS+LaZPSypD/CQpHvMrPyF6n8xs4PqqMdx1jbu206haPUVvZm9amYPx89vA0+x+gTKjtPpcN92ikZDEqYkjSDMtFMpBWcPSY8CrwCnm9kTVco4HjgegA2GJWcJ6ntienrOh/6UkQx1cEYyQ0ai05VKa46bkK5qwdTeSc3AgzMSOU5JS/hchua3GXVlJN5sOGZ5UnMBmyU1E5q+nK5s826117+WLqKZen27hV8PGpY8L6cMvzhp06U3nJnUcFSGXx+ecW5fyNCcmJbwbIbm6Iy6vpFRTnpSLNgyo65hGeVMSR9nm5Su64SZv0xqfrfqSzXXL/5z9STSuh/GSuoN3AZ808zeKlv9MDDczHYCLgXuqFaOmV1hZk1m1kSPjGm1HKeNaYRvt/Drfu7XTvtQV6CX1I3QEG40s9vL15vZW2a2JH6eDHSTNKCeOh1nbeC+7RSJekbdCLgaeMrM/rOKZtOoQ9Kusb50n4vjtCPu207RqKeP/pPA0cBMSTPisu8Re7bMbBzwJeAkSSuBd4EjzCyj89Bx2hX3badQtDrQm9lUoOZTBjO7DListXU4Tnvgvu0UDc+MdRzHKTge6B3HcQqOB3rHcZyC0zFnmFoOzKotOb3Hz9Pl/CItuWGfdDLDFzZIlzM3LUkmgQH0229JWpQz49XMDM2RaYntn9bctzCteeyq9HE+9fp0OV886tak5raxR9UWXJ2upy3o1XsJ232y9qxn/XgzWc5DR6fr2uXbGQlBY9KS34xPa07qm9ZkzWZVfYKkD9k+Q/NOhmaTtOSh+9KamRmJkmPvzDEnncW3amWX2oIaQwH8it5xHKfgeKB3HMcpOB7oHcdxCo4HesdxnILjgd5xHKfgeKB3HMcpOB7oHcdxCo4HesdxnILTMROmNiE5E89o/pwu5870ywQ/lpHw8HZGAsa5GS8unJJR1+jB6br4R8ZLEnNm0Hk1LdEb6bqGZ+zX8HRVWbMi/W5+uq6RPxpZc/2Lf1iaY027cP7CC5Ia5bwk85sZ5z9j1qeTMup6MuP8jzwsXRcTMvZrx4z9WpxR18vpunbJmIHrvt9m1DUmXddZ7yWSoYCZPXasuX6K3q66rhEzTM2SNFPSDEmr5X4q8CtJz0l6TNLO9dbpOG2N+7VTJBp1Rf8ZM3u9yroDgW3i327Ab+J/x+nouF87hWBt9NEfAlxngb8D/SSlZ4F2nI6N+7XTaWhEoDfgbkkPxRnvyxkCzC75Picuc5yOjPu1Uxga0XWzp5nNlbQJcI+kp83s/jUtJDam0KAGDGuAWY5TFw336+7DBjXaRsfJou4rejObG//PByYCu5ZJ5gJDS75vToW3+prZFWbWZGZNbDiwXrMcpy7awq+7DuzXRtY6Tm3qCvSSNpDUp/kzsB/weJlsEvDVOEphd2CxmWUM7HOc9sH92ika9XbdDAImKoyj7QrcZGZ/lHQigJmNAyYTpjh4DlgKfK3OOh2nrXG/dgpFXYHezF4AdqqwfFzJZwNOXqOCuwMjakuO5JZkMbNvTic8fOypDHteTkuezUmGypjRJ+uM7J+ua+4LGyc1Qy5JTw31WMZ+fez2pASuytDcna5rzH63JTWvrardF76CbjXXt5VfL3u/J/94Z9uamt9tfFCynMMyZuvKsuyctGRSxvk/+BsZdX0+LbH+6bpef6N3UvM8WyU1uw/JOIZ7pyXfuiuteSjjGO6SEYfmb1fbr1fW8Gt/BYLjOE7B8UDvOI5TcDzQO47jFBwP9I7jOAXHA73jOE7B8UDvOI5TcDzQO47jFBwP9I7jOAWnQ84w1XeDRXxqt9pTt3yeiemCnk5LbP+0Rhkz1gzqkdbws7Rk3g59k5pNn00bNGRiOhmKTdKSj52W1pA2GZoyNBmza23Ca0nNmC6Ta66fnDUFUeN5/82uLLmj9nucPvKVZ9IF3ZdRWc45OSMtOTgjWfCFKzdNarY8bl5SMz/DZQeNX5LUDLz90XRB+6Ul/CRDkzFL1y4ZyWsLtksngp3Af9Vcfy4Lqq7zK3rHcZyC44HecRyn4HigdxzHKTge6B3HcQqOB3rHcZyC0+pAL2lbSTNK/t6S9M0yzWhJi0s0Z9dtseO0Me7bTtFo9fBKM3sGGAUgqQthGrVKYx7/Ymbpl2w7TgfBfdspGo3qutkHeN7MXmpQeY7TUXDfdjo9jUqYOgK4ucq6PSQ9CrwCnG5mT1QSSToeOB6AzYdy1xu1M5m+0T9jyqLzLCnRoIyZZjISizZclq7rnoyZZvadmpHM88t0XYzO2K+cGa8y6sqahSojGSrnfH0nY/agT6+6v+b6xVyWYcwH1OXbpX694bANOfUr36tZ2U4vZmTgXJ8+Tkt7p89Jr5xz8o90XVuek+Fru6Ulg67M8Ovd03WNn5YuZqw1xq/fTVfFbhl1DcyYNWzGN0bVXL+Uv1ZdV/cVvaTuwMFApVTWh4HhZrYTcClwR7VyzOwKM2sysyb171+vWY5TN43w7VK/3mBgrzaz1XFq0YiumwOBh81stdx0M3vLzJbEz5OBbpIGNKBOx1kbuG87haARgf5IqtzaStpUCvc/knaN9b3RgDodZ23gvu0Ugrr66CVtAOwLnFCy7EQAMxsHfAk4SdJKQnfWEWYZHVaO0864bztFoq5Ab2bvAP3Llo0r+XwZrNmTL8fpCLhvO0XCM2Mdx3EKjgd6x3GcguOB3nEcp+B0yBmmzNZjxbLuNTXTMjIwDrk7I5FjeoZBf8/Q7JmRDDUlo5yc2YO+lq7LMmxWRm4O389IhpqbLua+wbsmNXvNT9d18iZ/SGpGdHmx5vqneS9ZRlvQi6V8nBk1NS9skTFbU0ZyTa+jMgx6Jy2x/um6lDFLG2PTkqyEwm9nVLV9WsP2GX6dMbvaDb/8YlKz2yXpuq497fCkZiiza67vzvKq6/yK3nEcp+B4oHccxyk4Hugdx3EKjgd6x3GcguOB3nEcp+B4oHccxyk4Hugdx3EKjgd6x3GcgtMhE6ZYKXizZ03JXUP2Sxbz02U/Std1V4Y9GTPWvFZ9cpcP6NuU1vS8KK1hg7REMzLKyZnOOmPCKyanJSMeqZ3ElF3O2HQ51953Um3B273TFbUBi+nLXdTOLlpKenKSLSfdltQs/VPanl6fTGtWrkprZt6UzlDa+ftPJTX73p6uix4Zmoyo9la1OcNK2DBjVqyjMs4Fu6clq+iS1DzCx2uuX1qjAWVd0Uu6RtJ8SY+XLNtY0j2Sno3/N6qy7TFR86ykY3Lqc5y1gfu1s66Q23UzHjigbNlZwL1mtg1wb/zeAkkbA+cQZozcFTinWsNxnHZgPO7XzjpAVqA3s/uBhWWLDwGujZ+vBQ6tsOn+wD1mttDMFgH3sHrDcpx2wf3aWVeo52HsIDN7NX6eBwyqoBkCLd7EMycuc5yOivu1UzgaMuomTqFW1zRqko6XNF3SdBYtaIRZjlMXjfbrZQuWNMgyx1kz6gn0r0naDCD+n19BMxcYWvJ987hsNczsCjNrMrMmNhpYh1mOUxdt5tc9B7bPaB/HqSfQTwKaRxscA/y+guYuYD9JG8WHVfuRN6DRcdoL92uncOQOr7wZ+BuwraQ5ko4FLgT2lfQs8Nn4HUlNkq4CMLOFwI+AB+Pf+XGZ47Q77tfOuoJCN2THolvTjjZgeqULqQ+5kfQUOnvzf0nNpIxZbQ7eKymBKenjuLR3xsxA6TwwuD1d14sZ+/W/GVUdl+EfUzPq2vPkjMouy/DF2em6uveqneW1cp+9eH/GIxnTjzWWnZq62uTpfWtqhkzP+L1oSh+nnNma3k3XxMEZ539KRl2jc/z6rozzf1GDTtsZ6bre6pmua8OMpDPuzdivF9N1nb5F7QTQG5p+w7zpcysW5K9AcBzHKTge6B3HcQqOB3rHcZyC44HecRyn4HigdxzHKTge6B3HcQqOB3rHcZyC44HecRyn4HTIGaZWvt6DeVdvWVNz07FfTpaz9zczkqGGJiVwWFryZEbSyMjXMuqamKE5PV3XFhPSxRx3ZlqTlQyTcwx/m6HZPV3X9476YVKzYm732gJrn+ubBQzgKmrPUTK6aUqynL2OblAyVEYS04p+Gec/5+UP6QmmYP+MZKh9MsrZOy15o2u6ro1r57YBMD5jJq93c5IybeOk5hc//EFtwSt3VF3lV/SO4zgFxwO94zhOwfFA7ziOU3A80DuO4xQcD/SO4zgFJxnoJV0jab6kx0uW/YekpyU9JmmipH5Vtp0laaakGZKmN9Bux6kb921nXSHnin48q89wfw+wg5l9DPgH8N0a23/GzEaZWVPrTHScNmM87tvOOkAy0JvZ/cDCsmV3m9nK+PXvhDkzHadT4b7trCs0ImHq68CtVdYZcLckA/7LzK6oVoik44HjAdh4WLLSaeyWtmxGWsLLjZmtZ9+cmWZyGJOhmZShyTizVnsiJiAv8eY3s9OakzJmKnqJTZKaC847P13ZvMT6V7NnKarbt1v49UbDOPfyn9Ws8Ecnn540aq9VDyQ1OTNDMTp9HH6S4SPn5iRD7Z6heTFDk5G8yLNpSf+cxKsZacnYnON8Yvo4n8mpSc1BP6qddfiXPyyquq6uQC/p+8BK4MYqkj3NbK6kTYB7JD0dr6JWIzaUKwA0ImOuNMdpQxrl2y38epj7tdM+tHrUjaSxwEHAV6zKxLNmNjf+n09I7t+1tfU5ztrCfdspGq0K9JIOAM4ADjazpVU0G0jq0/wZ2A94vJLWcToK7ttOEckZXnkz8DdgW0lzJB0LXAb0IdyyzpA0LmoHS5ocNx0ETJX0KPAAcKeZ/bFN9sJxWoH7trOukOyjN7MjKyy+uor2FeLjRDN7AdipLuscpw1x33bWFTwz1nEcp+B4oHccxyk4Hugdx3EKToecYYqXgG/Uljze+xPJYsZO+XVSM35iRjLU2UkJnNeYxAk+nlHXyRl1ZcxClcOBGQkhz2YklHF4WnPGhPHpcnqnJYx7MiFYllFIG9AXOKD28fzhSxcki5l5045Jza0zMs5JxkxM507J8LWPZ9T117SECRl1bZ+ua8Wr6WK6vZlR13Hpun7L55Ka2eNOSmouuvqctD0rE+sXVE/G8yt6x3GcguOB3nEcp+B4oHccxyk4Hugdx3EKjgd6x3GcguOB3nEcp+B4oHccxyk4Hugdx3EKjqq8brtdkZoMEvMtH5FR0KFpSc8DFiY1W/d9Pqk5muuSmllskdSsoktS8+9cnNQ8wqikZiSpxCLYaXZ6up7/GZrOvPk1Jyc1f/juF5IaLnwtraFPYv2emD3cmIyyNUA7NBm3Jfz6jnQ5O585Nan5fEZBQ0lPDXbMfRPSBm2Zljw8dPukZucb0lNV3XPUnklNH95Oanb/66NJzYOf3CGpOYXLkpoHpu2V1CSToQBSp/3SJmzO9Ip+nfOa4mskzZf0eMmycyXNja9xnSGp4gR4kg6Q9Iyk5ySdlarLcdYm7tvOukJO18144IAKyy82s1Hxb3L5SkldgMuBA4GRwJGSRtZjrOM0mPG4bzvrAMlAH+fBTPdvrM6uwHNm9oKZLQduAQ5pRTmO0ya4bzvrCvU8jD1F0mPx9nejCuuHQItOwDlxWUUkHS9puqTpsKAOsxynbhrm2y38epH7tdM+tDbQ/wbYChgFvAr8ol5DzOwKM2sysyYYWG9xjtNaGurbLfx6I/drp31oVaA3s9fMbJWZvQ9cSbiVLWcuMLTk++ZxmeN0WNy3nSLSqkAvabOSr58HHq8gexDYRtIWkroTBkROak19jrO2cN92ikhy4hFJNwOjgQGS5gDnAKMljQIMmAWcELWDgavMbIyZrZR0CnAX0AW4xsyeaIudcJzW4L7trCt00ISpwQbHJ1Q7pwv6wcFpzYwMgzJmNdr+5oeTmlfeG5zUrN9jaVIziPlpgzLowXtJzfq8m9Tc90ylEYpl/D3DoEQuEQCXrcgQdUusb8KscmJJW6JuTcaAxE5OTbfHnbaaltSM4pGk5lQuTWr+wqeSmtkterEq869cntRM4TNJzXNsndS8Sb+kZhDpxLuJfD6pefS83ZMaNk9LshKmvplY/14T9n4rE6Ycx3Gczo0HesdxnILjgd5xHKfgeKB3HMcpOB7oHcdxCo4HesdxnILjgd5xHKfgeKB3HMcpOB00YUoLgJdKFg0AXm8nc1qL29z2tNbe4Wa21t8wVsGvYd055u3JumJzVb/ukIG+HEnTw1stOw9uc9vT2eytRGfbh85mL7jN4F03juM4hccDveM4TsHpLIH+ivY2oBW4zW1PZ7O3Ep1tHzqbveA2d44+esdxHKf1dJYresdxHKeVdPhAL+kASc9Iek7SWe1tTwpJsyTNlDQjTHTe8YiTXs+X9HjJso0l3SPp2fi/0qTY7UYVm8+VNDce6xmSxrSnjWtCZ/NrcN9uK9aGb3foQC+pC3A5cCAwEjhS0sj2tSqLz5jZqA48pGs8UD5byFnAvWa2DXBv/N6RGM/qNgNcHI/1KDObvJZtahWd2K/BfbstGE8b+3aHDvSEiZmfM7MXzGw5cAtwSDvb1Okxs/uBhWWLDwGujZ+vBQ5dmzalqGJzZ8X9uo1w365MRw/0Q4DZJd/nxGUdGQPulvSQpNR8iB2JQWb2avw8DxjUnsasAadIeize/naoW/IadEa/BvfttU3DfLujB/rOyJ5mtjPhtvxkSZ9ub4PWFAtDsTrDcKzfAFsBo4BXgV+0qzXFx3177dFQ3+7ogX4utJh5ePO4rMNiZnPj//nARMJtemfgNUmbAcT/jZmBvA0xs9fMbJWZvQ9cSec51p3Or8F9e23SaN/u6IH+QWAbSVtI6g4cAUxqZ5uqImkDSX2aPwP7AY/X3qrDMAk4Jn4+Bvh9O9qSRXPjjXyeznOsO5Vfg/v22qbRvt21PnPaFjNbKekU4C6gC3CNmT3RzmbVYhAwURKEY3uTmf2xfU1aHUk3A6OBAZLmAOcAFwITJB1LeMPi4e1n4epUsXm0pFGEW/FZwAntZd+a0An9Gty324y14dueGes4jlNwOnrXjeM4jlMnHugdx3EKjgd6x3GcguOB3nEcp+B4oHccxyk4Hugdx3EKjgd6x3GcguOB3nEcp+D8P5G8dStRKxN0AAAAAElFTkSuQmCC\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1258,38 +1277,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 6c2d82a4d7dfe10312329d5969568fc03a698416\n", - " Date/Time | 2018-04-22 15:04:39\n", - " OpenMP Threads | 8\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 14:42:24\n", + " OpenMP Threads | 2\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -1297,11 +1315,11 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.16379 +/- 0.00090\n", - " k-effective (Track-length) = 1.16469 +/- 0.00101\n", - " k-effective (Absorption) = 1.16315 +/- 0.00052\n", - " Combined k-effective = 1.16335 +/- 0.00050\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.16293 +/- 0.00093\n", + " k-effective (Track-length) = 1.16281 +/- 0.00107\n", + " k-effective (Absorption) = 1.16309 +/- 0.00049\n", + " Combined k-effective = 1.16306 +/- 0.00048\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] } @@ -1327,8 +1345,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "P3 bias [pcm]: -20.4\n", - "P0 bias [pcm]: 125.1\n" + "P3 bias [pcm]: 3.2\n", + "P0 bias [pcm]: 195.7\n" ] } ], @@ -1415,38 +1433,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 6c2d82a4d7dfe10312329d5969568fc03a698416\n", - " Date/Time | 2018-04-22 15:05:16\n", - " OpenMP Threads | 8\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 14:43:41\n", + " OpenMP Threads | 2\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -1454,11 +1471,11 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.16471 +/- 0.00093\n", - " k-effective (Track-length) = 1.16412 +/- 0.00106\n", - " k-effective (Absorption) = 1.16449 +/- 0.00050\n", - " Combined k-effective = 1.16441 +/- 0.00049\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.16377 +/- 0.00093\n", + " k-effective (Track-length) = 1.16368 +/- 0.00111\n", + " k-effective (Absorption) = 1.16357 +/- 0.00047\n", + " Combined k-effective = 1.16357 +/- 0.00047\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] } @@ -1489,8 +1506,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "P3 bias [pcm]: -20.4\n", - "Mixed Scattering bias [pcm]: 19.5\n" + "P3 bias [pcm]: 3.2\n", + "Mixed Scattering bias [pcm]: 144.4\n" ] } ], @@ -1514,6 +1531,19 @@ "\n", "**NOTE**: The biases obtained above with P3, P0, and mixed representations do not necessarily reflect the inherent accuracies of the options. These cases were *not* run with a sufficient number of histories to truly differentiate methods improvement from statistical noise." ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# Close all StatePoint files as a matter of best practice\n", + "sp.close()\n", + "mgsp_p0.close()\n", + "mgsp.close()\n", + "mgsp_mixed.close()" + ] } ], "metadata": { @@ -1532,7 +1562,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/mg-mode-part-iii.ipynb b/examples/jupyter/mg-mode-part-iii.ipynb index ea1e773f8df..52a6ff23912 100644 --- a/examples/jupyter/mg-mode-part-iii.ipynb +++ b/examples/jupyter/mg-mode-part-iii.ipynb @@ -362,7 +362,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -371,7 +371,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -642,7 +642,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mgxs/mgxs.py:4144: UserWarning: The legendre order will be ignored since the scatter format is set to histogram\n", + "/home/shriwise/opt/openmc/openmc/openmc/mgxs/mgxs.py:4872: UserWarning: The legendre order will be ignored since the scatter format is set to histogram\n", " warnings.warn(msg)\n" ] } @@ -688,19 +688,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=1.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=1.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=11.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=11.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=21.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=21.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=22.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=22.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=12.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=12.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=18.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=18.\n", " warn(msg, IDWarning)\n" ] } @@ -764,25 +764,25 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | ed7123f4e7ce7b097c4b2bfb0ef5283eaa8afaea\n", - " Date/Time | 2019-10-04 10:54:35\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 15:15:04\n", + " OpenMP Threads | 2\n", "\n", - " Minimum neutron data temperature: 294.000000 K\n", - " Maximum neutron data temperature: 294.000000 K\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.83866 +/- 0.00102\n", - " k-effective (Track-length) = 0.83799 +/- 0.00118\n", - " k-effective (Absorption) = 0.83968 +/- 0.00103\n", - " Combined k-effective = 0.83900 +/- 0.00085\n", + " k-effective (Collision) = 0.83621 +/- 0.00102\n", + " k-effective (Track-length) = 0.83692 +/- 0.00114\n", + " k-effective (Absorption) = 0.83714 +/- 0.00103\n", + " Combined k-effective = 0.83686 +/- 0.00084\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -903,7 +903,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] } @@ -980,7 +980,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 31, @@ -989,7 +989,7 @@ }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1049,12 +1049,12 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | ed7123f4e7ce7b097c4b2bfb0ef5283eaa8afaea\n", - " Date/Time | 2019-10-04 10:56:58\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 15:16:33\n", + " OpenMP Threads | 2\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -1062,10 +1062,10 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.82471 +/- 0.00104\n", - " k-effective (Track-length) = 0.82466 +/- 0.00103\n", - " k-effective (Absorption) = 0.82482 +/- 0.00075\n", - " Combined k-effective = 0.82477 +/- 0.00068\n", + " k-effective (Collision) = 0.82516 +/- 0.00104\n", + " k-effective (Track-length) = 0.82465 +/- 0.00106\n", + " k-effective (Absorption) = 0.82332 +/- 0.00074\n", + " Combined k-effective = 0.82369 +/- 0.00066\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -1117,7 +1117,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] } @@ -1170,12 +1170,12 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | ed7123f4e7ce7b097c4b2bfb0ef5283eaa8afaea\n", - " Date/Time | 2019-10-04 10:57:24\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 15:16:54\n", + " OpenMP Threads | 2\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -1183,10 +1183,10 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.83564 +/- 0.00103\n", - " k-effective (Track-length) = 0.83572 +/- 0.00104\n", - " k-effective (Absorption) = 0.83478 +/- 0.00073\n", - " Combined k-effective = 0.83504 +/- 0.00066\n", + " k-effective (Collision) = 0.83683 +/- 0.00105\n", + " k-effective (Track-length) = 0.83685 +/- 0.00108\n", + " k-effective (Absorption) = 0.83600 +/- 0.00077\n", + " Combined k-effective = 0.83624 +/- 0.00070\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -1261,8 +1261,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Isotropic to CE Bias [pcm]: 1423.5\n", - "Angle to CE Bias [pcm]: 396.6\n" + "Isotropic to CE Bias [pcm]: 1317.7\n", + "Angle to CE Bias [pcm]: 62.5\n" ] } ], @@ -1296,7 +1296,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -1400,6 +1400,17 @@ "source": [ "With this ratio its clear that the errors are significantly worse in the isotropic case. These errors are conveniently located right where the most anisotropy is espected: by the control blades and by the Gd-bearing pins!" ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Close all StatePoint files as a matter of best practice\n", + "for sp_file in sp_files:\n", + " sp_file.close()" + ] } ], "metadata": { @@ -1418,7 +1429,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/mgxs-part-iii.ipynb b/examples/jupyter/mgxs-part-iii.ipynb index d75255d73b7..60f44ac619c 100644 --- a/examples/jupyter/mgxs-part-iii.ipynb +++ b/examples/jupyter/mgxs-part-iii.ipynb @@ -343,7 +343,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -564,21 +564,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=126.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=126.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=21.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=21.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=3.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=4.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=96.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=96.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=15.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=15.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=114.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=114.\n", " warn(msg, IDWarning)\n" ] } @@ -622,107 +622,111 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", - " Date/Time | 2019-07-19 07:12:55\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 16:01:58\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", - " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", - " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", - " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", - " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", - " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n", + " Reading H1 from /opt/data/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /opt/data/xs/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.03784\n", - " 2/1 1.02297\n", - " 3/1 1.02244\n", - " 4/1 1.02344\n", - " 5/1 1.02057\n", - " 6/1 1.04077\n", - " 7/1 1.00775\n", - " 8/1 1.03892\n", - " 9/1 1.01606\n", - " 10/1 1.02209\n", - " 11/1 1.03259\n", - " 12/1 1.03331 1.03295 +/- 0.00036\n", - " 13/1 1.02027 1.02872 +/- 0.00423\n", - " 14/1 1.03901 1.03130 +/- 0.00395\n", - " 15/1 1.02000 1.02904 +/- 0.00380\n", - " 16/1 1.04469 1.03164 +/- 0.00405\n", - " 17/1 1.01862 1.02978 +/- 0.00390\n", - " 18/1 1.03265 1.03014 +/- 0.00340\n", - " 19/1 1.00489 1.02734 +/- 0.00410\n", - " 20/1 1.04533 1.02914 +/- 0.00409\n", - " 21/1 1.01534 1.02788 +/- 0.00390\n", - " 22/1 1.02204 1.02739 +/- 0.00360\n", - " 23/1 1.02181 1.02696 +/- 0.00334\n", - " 24/1 0.99207 1.02447 +/- 0.00397\n", - " 25/1 1.03041 1.02487 +/- 0.00372\n", - " 26/1 1.03652 1.02560 +/- 0.00355\n", - " 27/1 1.03793 1.02632 +/- 0.00341\n", - " 28/1 1.02099 1.02603 +/- 0.00323\n", - " 29/1 1.01953 1.02568 +/- 0.00308\n", - " 30/1 1.01690 1.02525 +/- 0.00295\n", - " 31/1 1.01938 1.02497 +/- 0.00282\n", - " 32/1 1.01800 1.02465 +/- 0.00271\n", - " 33/1 1.01598 1.02427 +/- 0.00262\n", - " 34/1 1.01735 1.02398 +/- 0.00252\n", - " 35/1 1.01080 1.02346 +/- 0.00247\n", - " 36/1 1.01267 1.02304 +/- 0.00241\n", - " 37/1 1.01907 1.02289 +/- 0.00233\n", - " 38/1 1.02333 1.02291 +/- 0.00224\n", - " 39/1 1.01516 1.02264 +/- 0.00218\n", - " 40/1 1.02797 1.02282 +/- 0.00211\n", - " 41/1 1.03949 1.02336 +/- 0.00211\n", - " 42/1 1.01456 1.02308 +/- 0.00207\n", - " 43/1 1.02376 1.02310 +/- 0.00200\n", - " 44/1 1.01917 1.02299 +/- 0.00195\n", - " 45/1 1.01631 1.02280 +/- 0.00190\n", - " 46/1 1.02381 1.02282 +/- 0.00185\n", - " 47/1 1.04002 1.02329 +/- 0.00185\n", - " 48/1 1.01059 1.02296 +/- 0.00184\n", - " 49/1 1.02647 1.02305 +/- 0.00179\n", - " 50/1 1.02451 1.02308 +/- 0.00175\n", + " 1/1 1.04638\n", + " 2/1 1.00498\n", + " 3/1 1.00535\n", + " 4/1 1.02695\n", + " 5/1 1.00781\n", + " 6/1 1.02035\n", + " 7/1 1.03808\n", + " 8/1 1.02532\n", + " 9/1 1.02783\n", + " 10/1 1.01371\n", + " 11/1 1.03205\n", + " 12/1 1.01947 1.02576 +/- 0.00629\n", + " 13/1 1.01843 1.02332 +/- 0.00438\n", + " 14/1 1.03269 1.02566 +/- 0.00388\n", + " 15/1 1.03879 1.02829 +/- 0.00399\n", + " 16/1 1.02251 1.02732 +/- 0.00340\n", + " 17/1 1.01274 1.02524 +/- 0.00355\n", + " 18/1 1.02454 1.02515 +/- 0.00307\n", + " 19/1 1.01993 1.02457 +/- 0.00277\n", + " 20/1 1.00665 1.02278 +/- 0.00306\n", + " 21/1 1.02200 1.02271 +/- 0.00277\n", + " 22/1 1.03748 1.02394 +/- 0.00281\n", + " 23/1 1.02803 1.02425 +/- 0.00260\n", + " 24/1 1.03052 1.02470 +/- 0.00245\n", + " 25/1 1.02027 1.02441 +/- 0.00230\n", + " 26/1 1.02597 1.02450 +/- 0.00216\n", + " 27/1 1.01776 1.02411 +/- 0.00206\n", + " 28/1 1.02652 1.02424 +/- 0.00195\n", + " 29/1 1.01063 1.02353 +/- 0.00198\n", + " 30/1 1.03300 1.02400 +/- 0.00194\n", + " 31/1 1.02020 1.02382 +/- 0.00185\n", + " 32/1 1.03720 1.02443 +/- 0.00187\n", + " 33/1 1.02797 1.02458 +/- 0.00179\n", + " 34/1 1.01994 1.02439 +/- 0.00172\n", + " 35/1 1.02626 1.02446 +/- 0.00166\n", + " 36/1 1.03183 1.02475 +/- 0.00162\n", + " 37/1 1.03410 1.02509 +/- 0.00159\n", + " 38/1 1.00919 1.02452 +/- 0.00164\n", + " 39/1 1.04388 1.02519 +/- 0.00171\n", + " 40/1 1.01254 1.02477 +/- 0.00171\n", + " 41/1 1.01584 1.02448 +/- 0.00168\n", + " 42/1 1.01002 1.02403 +/- 0.00169\n", + " 43/1 1.00277 1.02339 +/- 0.00176\n", + " 44/1 1.00672 1.02289 +/- 0.00177\n", + " 45/1 1.03974 1.02338 +/- 0.00179\n", + " 46/1 1.00460 1.02285 +/- 0.00181\n", + " 47/1 1.03954 1.02331 +/- 0.00182\n", + " 48/1 1.01414 1.02306 +/- 0.00179\n", + " 49/1 1.02016 1.02299 +/- 0.00174\n", + " 50/1 0.99791 1.02236 +/- 0.00181\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 5.7635e-01 seconds\n", - " Reading cross sections = 5.4002e-01 seconds\n", - " Total time in simulation = 7.0174e+01 seconds\n", - " Time in transport only = 6.9687e+01 seconds\n", - " Time in inactive batches = 7.1832e+00 seconds\n", - " Time in active batches = 6.2991e+01 seconds\n", - " Time synchronizing fission bank = 3.9991e-02 seconds\n", - " Sampling source sites = 3.4633e-02 seconds\n", - " SEND/RECV source sites = 5.2616e-03 seconds\n", - " Time accumulating tallies = 4.9801e-04 seconds\n", - " Total time for finalization = 1.3501e-05 seconds\n", - " Total time elapsed = 7.0791e+01 seconds\n", - " Calculation Rate (inactive) = 13921.3 particles/second\n", - " Calculation Rate (active) = 6350.11 particles/second\n", + " Total time for initialization = 2.4372e-01 seconds\n", + " Reading cross sections = 2.2745e-01 seconds\n", + " Total time in simulation = 4.7933e+01 seconds\n", + " Time in transport only = 4.7887e+01 seconds\n", + " Time in inactive batches = 3.4521e+00 seconds\n", + " Time in active batches = 4.4481e+01 seconds\n", + " Time synchronizing fission bank = 2.0174e-02 seconds\n", + " Sampling source sites = 1.7952e-02 seconds\n", + " SEND/RECV source sites = 2.1985e-03 seconds\n", + " Time accumulating tallies = 1.1888e-03 seconds\n", + " Time writing statepoints = 1.2929e-02 seconds\n", + " Total time for finalization = 3.9800e-07 seconds\n", + " Total time elapsed = 4.8193e+01 seconds\n", + " Calculation Rate (inactive) = 28967.6 particles/second\n", + " Calculation Rate (active) = 8992.65 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.02434 +/- 0.00173\n", - " k-effective (Track-length) = 1.02308 +/- 0.00175\n", - " k-effective (Absorption) = 1.02494 +/- 0.00175\n", - " Combined k-effective = 1.02408 +/- 0.00144\n", + " k-effective (Collision) = 1.02393 +/- 0.00174\n", + " k-effective (Track-length) = 1.02236 +/- 0.00181\n", + " k-effective (Absorption) = 1.02412 +/- 0.00167\n", + " Combined k-effective = 1.02362 +/- 0.00128\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -771,7 +775,9 @@ "outputs": [], "source": [ "# Initialize MGXS Library with OpenMC statepoint data\n", - "mgxs_lib.load_from_statepoint(sp)" + "mgxs_lib.load_from_statepoint(sp)\n", + "# Retrieve OpenMC's k-effective value\n", + "openmc_keff = sp.k_combined.nominal_value" ] }, { @@ -854,16 +860,16 @@ " 1\n", " 1\n", " U235\n", - " 8.089079e-03\n", - " 1.461462e-05\n", + " 8.099261e-03\n", + " 1.626934e-05\n", " \n", " \n", " 4\n", " 1\n", " 1\n", " U238\n", - " 7.358661e-03\n", - " 2.302063e-05\n", + " 7.326723e-03\n", + " 2.168273e-05\n", " \n", " \n", " 5\n", @@ -878,16 +884,16 @@ " 1\n", " 2\n", " U235\n", - " 3.617174e-01\n", - " 9.467633e-04\n", + " 3.613773e-01\n", + " 1.025247e-03\n", " \n", " \n", " 1\n", " 1\n", " 2\n", " U238\n", - " 6.744743e-07\n", - " 1.750450e-09\n", + " 6.739270e-07\n", + " 1.911057e-09\n", " \n", " \n", " 2\n", @@ -903,11 +909,11 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "3 1 1 U235 8.089079e-03 1.461462e-05\n", - "4 1 1 U238 7.358661e-03 2.302063e-05\n", + "3 1 1 U235 8.099261e-03 1.626934e-05\n", + "4 1 1 U238 7.326723e-03 2.168273e-05\n", "5 1 1 O16 0.000000e+00 0.000000e+00\n", - "0 1 2 U235 3.617174e-01 9.467633e-04\n", - "1 1 2 U238 6.744743e-07 1.750450e-09\n", + "0 1 2 U235 3.613773e-01 1.025247e-03\n", + "1 1 2 U238 6.739270e-07 1.911057e-09\n", "2 1 2 O16 0.000000e+00 0.000000e+00" ] }, @@ -943,13 +949,13 @@ "\tDomain ID =\t1\n", "\tNuclide =\tU235\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t8.09e-03 +/- 1.81e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t3.62e-01 +/- 2.62e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t8.10e-03 +/- 2.01e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t3.61e-01 +/- 2.84e-01%\n", "\n", "\tNuclide =\tU238\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t7.36e-03 +/- 3.13e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t6.74e-07 +/- 2.60e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t7.33e-03 +/- 2.96e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t6.74e-07 +/- 2.84e-01%\n", "\n", "\tNuclide =\tO16\n", "\tCross Sections [cm^-1]:\n", @@ -964,7 +970,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1269: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/shriwise/opt/openmc/openmc/openmc/tallies.py:1216: RuntimeWarning: invalid value encountered in true_divide\n", " data = self.std_dev[indices] / self.mean[indices]\n" ] } @@ -1076,16 +1082,16 @@ " 1\n", " 1\n", " U235\n", - " 0.074556\n", - " 0.000144\n", + " 0.074479\n", + " 0.000151\n", " \n", " \n", " 1\n", " 1\n", " 1\n", " U238\n", - " 0.005976\n", - " 0.000019\n", + " 0.005950\n", + " 0.000017\n", " \n", " \n", " 2\n", @@ -1101,8 +1107,8 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "0 1 1 U235 0.074556 0.000144\n", - "1 1 1 U238 0.005976 0.000019\n", + "0 1 1 U235 0.074479 0.000151\n", + "1 1 1 U238 0.005950 0.000017\n", "2 1 1 O16 0.000000 0.000000" ] }, @@ -1154,7 +1160,16 @@ "cell_type": "code", "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ WARNING ] Group cross sections by nuclides are not currently supported.\n", + "[ WARNING ] ... Contributions from all nuclides will be summed.\n" + ] + } + ], "source": [ "# Load the library into the OpenMOC geometry\n", "materials = load_openmc_mgxs_lib(mgxs_lib, openmoc_geometry)" @@ -1200,7 +1215,6 @@ "[ NORMAL ] Progress Segmenting 2D tracks: 100.00 %\n", "[ NORMAL ] Initializing FSR lookup vectors\n", "[ NORMAL ] Total number of FSRs 867\n", - "[ RESULT ] Total Track Generation & Segmentation Time...........4.2139E-01 sec\n", "[ NORMAL ] Initializing MOC eigenvalue solver...\n", "[ NORMAL ] Initializing solver arrays...\n", "[ NORMAL ] Centering segments around FSR centroid...\n", @@ -1215,264 +1229,264 @@ "[ NORMAL ] CMFD acceleration: OFF\n", "[ NORMAL ] Using 1 threads\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0: k_eff = 0.823216 res = 9.828E-02 delta-k (pcm) =\n", - "[ NORMAL ] ... -17678 D.R. = 0.10\n", - "[ NORMAL ] Iteration 1: k_eff = 0.779788 res = 4.642E-02 delta-k (pcm) =\n", - "[ NORMAL ] ... -4342 D.R. = 0.47\n", - "[ NORMAL ] Iteration 2: k_eff = 0.738779 res = 9.633E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -4100 D.R. = 0.21\n", - "[ NORMAL ] Iteration 3: k_eff = 0.710046 res = 8.556E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -2873 D.R. = 0.89\n", - "[ NORMAL ] Iteration 4: k_eff = 0.688781 res = 5.190E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -2126 D.R. = 0.61\n", - "[ NORMAL ] Iteration 5: k_eff = 0.674128 res = 3.585E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -1465 D.R. = 0.69\n", - "[ NORMAL ] Iteration 6: k_eff = 0.664928 res = 2.516E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -919 D.R. = 0.70\n", - "[ NORMAL ] Iteration 7: k_eff = 0.660304 res = 1.866E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -462 D.R. = 0.74\n", - "[ NORMAL ] Iteration 8: k_eff = 0.659481 res = 1.471E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -82 D.R. = 0.79\n", - "[ NORMAL ] Iteration 9: k_eff = 0.661799 res = 1.248E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... 231 D.R. = 0.85\n", - "[ NORMAL ] Iteration 10: k_eff = 0.666690 res = 1.123E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... 489 D.R. = 0.90\n", - "[ NORMAL ] Iteration 11: k_eff = 0.673664 res = 1.049E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... 697 D.R. = 0.93\n", - "[ NORMAL ] Iteration 12: k_eff = 0.682301 res = 1.001E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... 863 D.R. = 0.95\n", - "[ NORMAL ] Iteration 13: k_eff = 0.692239 res = 9.638E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 993 D.R. = 0.96\n", - "[ NORMAL ] Iteration 14: k_eff = 0.703171 res = 9.329E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1093 D.R. = 0.97\n", - "[ NORMAL ] Iteration 15: k_eff = 0.714835 res = 9.055E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1166 D.R. = 0.97\n", - "[ NORMAL ] Iteration 16: k_eff = 0.727008 res = 8.803E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1217 D.R. = 0.97\n", - "[ NORMAL ] Iteration 17: k_eff = 0.739503 res = 8.566E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1249 D.R. = 0.97\n", - "[ NORMAL ] Iteration 18: k_eff = 0.752162 res = 8.335E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1265 D.R. = 0.97\n", - "[ NORMAL ] Iteration 19: k_eff = 0.764855 res = 8.108E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1269 D.R. = 0.97\n", - "[ NORMAL ] Iteration 20: k_eff = 0.777472 res = 7.879E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1261 D.R. = 0.97\n", - "[ NORMAL ] Iteration 21: k_eff = 0.789924 res = 7.647E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1245 D.R. = 0.97\n", - "[ NORMAL ] Iteration 22: k_eff = 0.802140 res = 7.410E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1221 D.R. = 0.97\n", - "[ NORMAL ] Iteration 23: k_eff = 0.814061 res = 7.168E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1192 D.R. = 0.97\n", - "[ NORMAL ] Iteration 24: k_eff = 0.825643 res = 6.922E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1158 D.R. = 0.97\n", - "[ NORMAL ] Iteration 25: k_eff = 0.836850 res = 6.672E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1120 D.R. = 0.96\n", - "[ NORMAL ] Iteration 26: k_eff = 0.847658 res = 6.419E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1080 D.R. = 0.96\n", - "[ NORMAL ] Iteration 27: k_eff = 0.858047 res = 6.165E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1038 D.R. = 0.96\n", - "[ NORMAL ] Iteration 28: k_eff = 0.868008 res = 5.911E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 996 D.R. = 0.96\n", - "[ NORMAL ] Iteration 29: k_eff = 0.877535 res = 5.658E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 952 D.R. = 0.96\n", - "[ NORMAL ] Iteration 30: k_eff = 0.886625 res = 5.409E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 909 D.R. = 0.96\n", - "[ NORMAL ] Iteration 31: k_eff = 0.895281 res = 5.163E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 865 D.R. = 0.95\n", - "[ NORMAL ] Iteration 32: k_eff = 0.903509 res = 4.921E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 822 D.R. = 0.95\n", - "[ NORMAL ] Iteration 33: k_eff = 0.911317 res = 4.685E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 780 D.R. = 0.95\n", - "[ NORMAL ] Iteration 34: k_eff = 0.918715 res = 4.456E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 739 D.R. = 0.95\n", - "[ NORMAL ] Iteration 35: k_eff = 0.925715 res = 4.232E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 699 D.R. = 0.95\n", - "[ NORMAL ] Iteration 36: k_eff = 0.932329 res = 4.016E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 661 D.R. = 0.95\n", - "[ NORMAL ] Iteration 37: k_eff = 0.938571 res = 3.807E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 624 D.R. = 0.95\n", - "[ NORMAL ] Iteration 38: k_eff = 0.944455 res = 3.606E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 588 D.R. = 0.95\n", - "[ NORMAL ] Iteration 39: k_eff = 0.949996 res = 3.413E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 554 D.R. = 0.95\n", - "[ NORMAL ] Iteration 40: k_eff = 0.955210 res = 3.227E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 521 D.R. = 0.95\n", - "[ NORMAL ] Iteration 41: k_eff = 0.960110 res = 3.049E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 490 D.R. = 0.94\n", - "[ NORMAL ] Iteration 42: k_eff = 0.964713 res = 2.880E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 460 D.R. = 0.94\n", - "[ NORMAL ] Iteration 43: k_eff = 0.969032 res = 2.717E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 431 D.R. = 0.94\n", - "[ NORMAL ] Iteration 44: k_eff = 0.973082 res = 2.562E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 405 D.R. = 0.94\n", - "[ NORMAL ] Iteration 45: k_eff = 0.976877 res = 2.414E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 379 D.R. = 0.94\n", - "[ NORMAL ] Iteration 46: k_eff = 0.980431 res = 2.274E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 355 D.R. = 0.94\n", - "[ NORMAL ] Iteration 47: k_eff = 0.983757 res = 2.140E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 332 D.R. = 0.94\n", - "[ NORMAL ] Iteration 48: k_eff = 0.986869 res = 2.014E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 311 D.R. = 0.94\n", - "[ NORMAL ] Iteration 49: k_eff = 0.989777 res = 1.894E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 290 D.R. = 0.94\n", - "[ NORMAL ] Iteration 50: k_eff = 0.992495 res = 1.780E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 271 D.R. = 0.94\n", - "[ NORMAL ] Iteration 51: k_eff = 0.995033 res = 1.672E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 253 D.R. = 0.94\n", - "[ NORMAL ] Iteration 52: k_eff = 0.997402 res = 1.569E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 236 D.R. = 0.94\n", - "[ NORMAL ] Iteration 53: k_eff = 0.999613 res = 1.473E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 221 D.R. = 0.94\n", - "[ NORMAL ] Iteration 54: k_eff = 1.001675 res = 1.382E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 206 D.R. = 0.94\n", - "[ NORMAL ] Iteration 55: k_eff = 1.003597 res = 1.296E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 192 D.R. = 0.94\n", - "[ NORMAL ] Iteration 56: k_eff = 1.005388 res = 1.215E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 179 D.R. = 0.94\n", - "[ NORMAL ] Iteration 57: k_eff = 1.007057 res = 1.138E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 166 D.R. = 0.94\n", - "[ NORMAL ] Iteration 58: k_eff = 1.008612 res = 1.066E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 155 D.R. = 0.94\n", - "[ NORMAL ] Iteration 59: k_eff = 1.010059 res = 9.980E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 144 D.R. = 0.94\n" + "[ NORMAL ] Iteration 0: k_eff = 0.823342 res = 9.831E-02 delta-k (pcm) =\n", + "[ NORMAL ] ... -17665 D.R. = 0.0983\n", + "[ NORMAL ] Iteration 1: k_eff = 0.780160 res = 4.646E-02 delta-k (pcm) =\n", + "[ NORMAL ] ... -4318 D.R. = 0.4725\n", + "[ NORMAL ] Iteration 2: k_eff = 0.739336 res = 9.631E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -4082 D.R. = 0.2073\n", + "[ NORMAL ] Iteration 3: k_eff = 0.710750 res = 8.566E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -2858 D.R. = 0.8894\n", + "[ NORMAL ] Iteration 4: k_eff = 0.689598 res = 5.205E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -2115 D.R. = 0.6076\n", + "[ NORMAL ] Iteration 5: k_eff = 0.675031 res = 3.605E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -1456 D.R. = 0.6926\n", + "[ NORMAL ] Iteration 6: k_eff = 0.665895 res = 2.538E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -913 D.R. = 0.7040\n", + "[ NORMAL ] Iteration 7: k_eff = 0.661318 res = 1.889E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -457 D.R. = 0.7443\n", + "[ NORMAL ] Iteration 8: k_eff = 0.660529 res = 1.493E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -78 D.R. = 0.7904\n", + "[ NORMAL ] Iteration 9: k_eff = 0.662872 res = 1.268E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 234 D.R. = 0.8490\n", + "[ NORMAL ] Iteration 10: k_eff = 0.667780 res = 1.140E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 490 D.R. = 0.8995\n", + "[ NORMAL ] Iteration 11: k_eff = 0.674766 res = 1.065E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 698 D.R. = 0.9337\n", + "[ NORMAL ] Iteration 12: k_eff = 0.683410 res = 1.014E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 864 D.R. = 0.9527\n", + "[ NORMAL ] Iteration 13: k_eff = 0.693354 res = 9.759E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 994 D.R. = 0.9623\n", + "[ NORMAL ] Iteration 14: k_eff = 0.704290 res = 9.438E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1093 D.R. = 0.9671\n", + "[ NORMAL ] Iteration 15: k_eff = 0.715957 res = 9.154E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1166 D.R. = 0.9698\n", + "[ NORMAL ] Iteration 16: k_eff = 0.728134 res = 8.892E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1217 D.R. = 0.9714\n", + "[ NORMAL ] Iteration 17: k_eff = 0.740633 res = 8.644E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1249 D.R. = 0.9721\n", + "[ NORMAL ] Iteration 18: k_eff = 0.753297 res = 8.404E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1266 D.R. = 0.9722\n", + "[ NORMAL ] Iteration 19: k_eff = 0.765995 res = 8.167E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1269 D.R. = 0.9719\n", + "[ NORMAL ] Iteration 20: k_eff = 0.778619 res = 7.930E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1262 D.R. = 0.9710\n", + "[ NORMAL ] Iteration 21: k_eff = 0.791079 res = 7.691E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1246 D.R. = 0.9698\n", + "[ NORMAL ] Iteration 22: k_eff = 0.803304 res = 7.447E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1222 D.R. = 0.9683\n", + "[ NORMAL ] Iteration 23: k_eff = 0.815235 res = 7.199E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1193 D.R. = 0.9667\n", + "[ NORMAL ] Iteration 24: k_eff = 0.826828 res = 6.947E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1159 D.R. = 0.9650\n", + "[ NORMAL ] Iteration 25: k_eff = 0.838047 res = 6.693E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1121 D.R. = 0.9634\n", + "[ NORMAL ] Iteration 26: k_eff = 0.848868 res = 6.436E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1082 D.R. = 0.9617\n", + "[ NORMAL ] Iteration 27: k_eff = 0.859271 res = 6.179E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1040 D.R. = 0.9600\n", + "[ NORMAL ] Iteration 28: k_eff = 0.869247 res = 5.922E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 997 D.R. = 0.9585\n", + "[ NORMAL ] Iteration 29: k_eff = 0.878788 res = 5.667E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 954 D.R. = 0.9570\n", + "[ NORMAL ] Iteration 30: k_eff = 0.887894 res = 5.415E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 910 D.R. = 0.9556\n", + "[ NORMAL ] Iteration 31: k_eff = 0.896566 res = 5.168E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 867 D.R. = 0.9543\n", + "[ NORMAL ] Iteration 32: k_eff = 0.904810 res = 4.925E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 824 D.R. = 0.9530\n", + "[ NORMAL ] Iteration 33: k_eff = 0.912635 res = 4.688E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 782 D.R. = 0.9519\n", + "[ NORMAL ] Iteration 34: k_eff = 0.920049 res = 4.457E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 741 D.R. = 0.9508\n", + "[ NORMAL ] Iteration 35: k_eff = 0.927066 res = 4.233E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 701 D.R. = 0.9498\n", + "[ NORMAL ] Iteration 36: k_eff = 0.933696 res = 4.016E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 663 D.R. = 0.9488\n", + "[ NORMAL ] Iteration 37: k_eff = 0.939954 res = 3.807E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 625 D.R. = 0.9479\n", + "[ NORMAL ] Iteration 38: k_eff = 0.945855 res = 3.606E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 590 D.R. = 0.9471\n", + "[ NORMAL ] Iteration 39: k_eff = 0.951412 res = 3.413E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 0.9464\n", + "[ NORMAL ] Iteration 40: k_eff = 0.956641 res = 3.227E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 522 D.R. = 0.9456\n", + "[ NORMAL ] Iteration 41: k_eff = 0.961556 res = 3.049E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 491 D.R. = 0.9448\n", + "[ NORMAL ] Iteration 42: k_eff = 0.966174 res = 2.879E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 461 D.R. = 0.9443\n", + "[ NORMAL ] Iteration 43: k_eff = 0.970507 res = 2.716E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 433 D.R. = 0.9435\n", + "[ NORMAL ] Iteration 44: k_eff = 0.974571 res = 2.561E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 406 D.R. = 0.9430\n", + "[ NORMAL ] Iteration 45: k_eff = 0.978380 res = 2.414E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 380 D.R. = 0.9423\n", + "[ NORMAL ] Iteration 46: k_eff = 0.981948 res = 2.273E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 356 D.R. = 0.9417\n", + "[ NORMAL ] Iteration 47: k_eff = 0.985287 res = 2.140E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 333 D.R. = 0.9413\n", + "[ NORMAL ] Iteration 48: k_eff = 0.988410 res = 2.013E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 312 D.R. = 0.9409\n", + "[ NORMAL ] Iteration 49: k_eff = 0.991331 res = 1.893E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 292 D.R. = 0.9402\n", + "[ NORMAL ] Iteration 50: k_eff = 0.994060 res = 1.779E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 272 D.R. = 0.9399\n", + "[ NORMAL ] Iteration 51: k_eff = 0.996609 res = 1.671E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 254 D.R. = 0.9393\n", + "[ NORMAL ] Iteration 52: k_eff = 0.998988 res = 1.569E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 237 D.R. = 0.9390\n", + "[ NORMAL ] Iteration 53: k_eff = 1.001209 res = 1.473E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 222 D.R. = 0.9386\n", + "[ NORMAL ] Iteration 54: k_eff = 1.003280 res = 1.382E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 207 D.R. = 0.9380\n", + "[ NORMAL ] Iteration 55: k_eff = 1.005211 res = 1.296E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 193 D.R. = 0.9378\n", + "[ NORMAL ] Iteration 56: k_eff = 1.007012 res = 1.215E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 180 D.R. = 0.9374\n", + "[ NORMAL ] Iteration 57: k_eff = 1.008689 res = 1.138E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 167 D.R. = 0.9369\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[ NORMAL ] Iteration 60: k_eff = 1.011406 res = 9.342E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 134 D.R. = 0.94\n", - "[ NORMAL ] Iteration 61: k_eff = 1.012659 res = 8.740E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 125 D.R. = 0.94\n", - "[ NORMAL ] Iteration 62: k_eff = 1.013825 res = 8.175E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 116 D.R. = 0.94\n", - "[ NORMAL ] Iteration 63: k_eff = 1.014909 res = 7.642E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 108 D.R. = 0.93\n", - "[ NORMAL ] Iteration 64: k_eff = 1.015917 res = 7.142E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 100 D.R. = 0.93\n", - "[ NORMAL ] Iteration 65: k_eff = 1.016853 res = 6.675E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 93 D.R. = 0.93\n", - "[ NORMAL ] Iteration 66: k_eff = 1.017724 res = 6.235E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 87 D.R. = 0.93\n", - "[ NORMAL ] Iteration 67: k_eff = 1.018533 res = 5.822E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 80 D.R. = 0.93\n", - "[ NORMAL ] Iteration 68: k_eff = 1.019284 res = 5.436E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 75 D.R. = 0.93\n", - "[ NORMAL ] Iteration 69: k_eff = 1.019982 res = 5.074E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 69 D.R. = 0.93\n", - "[ NORMAL ] Iteration 70: k_eff = 1.020630 res = 4.734E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 64 D.R. = 0.93\n", - "[ NORMAL ] Iteration 71: k_eff = 1.021232 res = 4.417E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 60 D.R. = 0.93\n", - "[ NORMAL ] Iteration 72: k_eff = 1.021790 res = 4.119E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 55 D.R. = 0.93\n", - "[ NORMAL ] Iteration 73: k_eff = 1.022308 res = 3.841E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 51 D.R. = 0.93\n", - "[ NORMAL ] Iteration 74: k_eff = 1.022789 res = 3.579E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 48 D.R. = 0.93\n", - "[ NORMAL ] Iteration 75: k_eff = 1.023235 res = 3.336E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 44 D.R. = 0.93\n", - "[ NORMAL ] Iteration 76: k_eff = 1.023648 res = 3.107E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 41 D.R. = 0.93\n", - "[ NORMAL ] Iteration 77: k_eff = 1.024032 res = 2.897E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 38 D.R. = 0.93\n", - "[ NORMAL ] Iteration 78: k_eff = 1.024388 res = 2.696E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 35 D.R. = 0.93\n", - "[ NORMAL ] Iteration 79: k_eff = 1.024718 res = 2.510E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 32 D.R. = 0.93\n", - "[ NORMAL ] Iteration 80: k_eff = 1.025024 res = 2.338E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 30 D.R. = 0.93\n", - "[ NORMAL ] Iteration 81: k_eff = 1.025307 res = 2.175E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 28 D.R. = 0.93\n", - "[ NORMAL ] Iteration 82: k_eff = 1.025570 res = 2.025E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 26 D.R. = 0.93\n", - "[ NORMAL ] Iteration 83: k_eff = 1.025814 res = 1.886E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 24 D.R. = 0.93\n", - "[ NORMAL ] Iteration 84: k_eff = 1.026039 res = 1.752E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 22 D.R. = 0.93\n", - "[ NORMAL ] Iteration 85: k_eff = 1.026249 res = 1.628E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 20 D.R. = 0.93\n", - "[ NORMAL ] Iteration 86: k_eff = 1.026442 res = 1.517E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 19 D.R. = 0.93\n", - "[ NORMAL ] Iteration 87: k_eff = 1.026622 res = 1.408E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 17 D.R. = 0.93\n", - "[ NORMAL ] Iteration 88: k_eff = 1.026788 res = 1.308E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 16 D.R. = 0.93\n", - "[ NORMAL ] Iteration 89: k_eff = 1.026942 res = 1.218E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 15 D.R. = 0.93\n", - "[ NORMAL ] Iteration 90: k_eff = 1.027085 res = 1.132E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 14 D.R. = 0.93\n", - "[ NORMAL ] Iteration 91: k_eff = 1.027217 res = 1.049E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 13 D.R. = 0.93\n", - "[ NORMAL ] Iteration 92: k_eff = 1.027339 res = 9.760E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 12 D.R. = 0.93\n", - "[ NORMAL ] Iteration 93: k_eff = 1.027453 res = 9.076E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 11 D.R. = 0.93\n", - "[ NORMAL ] Iteration 94: k_eff = 1.027557 res = 8.434E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 10 D.R. = 0.93\n", - "[ NORMAL ] Iteration 95: k_eff = 1.027655 res = 7.827E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 9 D.R. = 0.93\n", - "[ NORMAL ] Iteration 96: k_eff = 1.027744 res = 7.266E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 8 D.R. = 0.93\n", - "[ NORMAL ] Iteration 97: k_eff = 1.027828 res = 6.737E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 8 D.R. = 0.93\n", - "[ NORMAL ] Iteration 98: k_eff = 1.027905 res = 6.255E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 7 D.R. = 0.93\n", - "[ NORMAL ] Iteration 99: k_eff = 1.027976 res = 5.803E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 7 D.R. = 0.93\n", - "[ NORMAL ] Iteration 100: k_eff = 1.028042 res = 5.383E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 0.93\n", - "[ NORMAL ] Iteration 101: k_eff = 1.028103 res = 5.017E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 0.93\n", - "[ NORMAL ] Iteration 102: k_eff = 1.028160 res = 4.618E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 0.92\n", - "[ NORMAL ] Iteration 103: k_eff = 1.028212 res = 4.306E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 0.93\n", - "[ NORMAL ] Iteration 104: k_eff = 1.028260 res = 3.999E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 4 D.R. = 0.93\n", - "[ NORMAL ] Iteration 105: k_eff = 1.028305 res = 3.706E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 4 D.R. = 0.93\n", - "[ NORMAL ] Iteration 106: k_eff = 1.028347 res = 3.429E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 4 D.R. = 0.93\n", - "[ NORMAL ] Iteration 107: k_eff = 1.028385 res = 3.213E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.94\n", - "[ NORMAL ] Iteration 108: k_eff = 1.028420 res = 2.943E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.92\n", - "[ NORMAL ] Iteration 109: k_eff = 1.028453 res = 2.740E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.93\n", - "[ NORMAL ] Iteration 110: k_eff = 1.028484 res = 2.531E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.92\n", - "[ NORMAL ] Iteration 111: k_eff = 1.028512 res = 2.369E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.94\n", - "[ NORMAL ] Iteration 112: k_eff = 1.028538 res = 2.186E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.92\n", - "[ NORMAL ] Iteration 113: k_eff = 1.028562 res = 2.026E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.93\n", - "[ NORMAL ] Iteration 114: k_eff = 1.028584 res = 1.858E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.92\n", - "[ NORMAL ] Iteration 115: k_eff = 1.028604 res = 1.760E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.95\n", - "[ NORMAL ] Iteration 116: k_eff = 1.028623 res = 1.612E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.92\n", - "[ NORMAL ] Iteration 117: k_eff = 1.028641 res = 1.496E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.93\n", - "[ NORMAL ] Iteration 118: k_eff = 1.028657 res = 1.382E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.92\n", - "[ NORMAL ] Iteration 119: k_eff = 1.028672 res = 1.293E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.94\n", - "[ NORMAL ] Iteration 120: k_eff = 1.028686 res = 1.191E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.92\n", - "[ NORMAL ] Iteration 121: k_eff = 1.028699 res = 1.112E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.93\n", - "[ NORMAL ] Iteration 122: k_eff = 1.028711 res = 1.005E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.90\n", - "[ NORMAL ] Iteration 123: k_eff = 1.028722 res = 9.443E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.94\n", - "[ NORMAL ] Iteration 124: k_eff = 1.028732 res = 8.583E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.91\n", - "[ NORMAL ] Iteration 125: k_eff = 1.028742 res = 8.028E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 0 D.R. = 0.94\n" + "[ NORMAL ] Iteration 58: k_eff = 1.010251 res = 1.066E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 156 D.R. = 0.9369\n", + "[ NORMAL ] Iteration 59: k_eff = 1.011706 res = 9.981E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 145 D.R. = 0.9362\n", + "[ NORMAL ] Iteration 60: k_eff = 1.013060 res = 9.344E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 135 D.R. = 0.9361\n", + "[ NORMAL ] Iteration 61: k_eff = 1.014320 res = 8.743E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 125 D.R. = 0.9357\n", + "[ NORMAL ] Iteration 62: k_eff = 1.015492 res = 8.177E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 117 D.R. = 0.9353\n", + "[ NORMAL ] Iteration 63: k_eff = 1.016582 res = 7.648E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 109 D.R. = 0.9353\n", + "[ NORMAL ] Iteration 64: k_eff = 1.017596 res = 7.147E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 101 D.R. = 0.9345\n", + "[ NORMAL ] Iteration 65: k_eff = 1.018538 res = 6.680E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 94 D.R. = 0.9346\n", + "[ NORMAL ] Iteration 66: k_eff = 1.019414 res = 6.237E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 87 D.R. = 0.9338\n", + "[ NORMAL ] Iteration 67: k_eff = 1.020227 res = 5.828E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 81 D.R. = 0.9343\n", + "[ NORMAL ] Iteration 68: k_eff = 1.020983 res = 5.442E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 75 D.R. = 0.9338\n", + "[ NORMAL ] Iteration 69: k_eff = 1.021685 res = 5.080E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 70 D.R. = 0.9336\n", + "[ NORMAL ] Iteration 70: k_eff = 1.022337 res = 4.739E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 65 D.R. = 0.9328\n", + "[ NORMAL ] Iteration 71: k_eff = 1.022942 res = 4.422E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 60 D.R. = 0.9331\n", + "[ NORMAL ] Iteration 72: k_eff = 1.023504 res = 4.124E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 56 D.R. = 0.9327\n", + "[ NORMAL ] Iteration 73: k_eff = 1.024026 res = 3.843E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 52 D.R. = 0.9317\n", + "[ NORMAL ] Iteration 74: k_eff = 1.024510 res = 3.586E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 48 D.R. = 0.9331\n", + "[ NORMAL ] Iteration 75: k_eff = 1.024959 res = 3.341E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 44 D.R. = 0.9317\n", + "[ NORMAL ] Iteration 76: k_eff = 1.025375 res = 3.115E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 41 D.R. = 0.9325\n", + "[ NORMAL ] Iteration 77: k_eff = 1.025762 res = 2.898E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 38 D.R. = 0.9303\n", + "[ NORMAL ] Iteration 78: k_eff = 1.026120 res = 2.703E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 35 D.R. = 0.9327\n", + "[ NORMAL ] Iteration 79: k_eff = 1.026452 res = 2.519E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 33 D.R. = 0.9318\n", + "[ NORMAL ] Iteration 80: k_eff = 1.026760 res = 2.341E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 30 D.R. = 0.9295\n", + "[ NORMAL ] Iteration 81: k_eff = 1.027046 res = 2.180E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 28 D.R. = 0.9312\n", + "[ NORMAL ] Iteration 82: k_eff = 1.027311 res = 2.028E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 26 D.R. = 0.9301\n", + "[ NORMAL ] Iteration 83: k_eff = 1.027556 res = 1.889E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 24 D.R. = 0.9315\n", + "[ NORMAL ] Iteration 84: k_eff = 1.027783 res = 1.757E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 22 D.R. = 0.9305\n", + "[ NORMAL ] Iteration 85: k_eff = 1.027994 res = 1.632E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 21 D.R. = 0.9284\n", + "[ NORMAL ] Iteration 86: k_eff = 1.028189 res = 1.521E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 19 D.R. = 0.9322\n", + "[ NORMAL ] Iteration 87: k_eff = 1.028370 res = 1.412E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 18 D.R. = 0.9285\n", + "[ NORMAL ] Iteration 88: k_eff = 1.028538 res = 1.311E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 16 D.R. = 0.9287\n", + "[ NORMAL ] Iteration 89: k_eff = 1.028693 res = 1.222E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 15 D.R. = 0.9316\n", + "[ NORMAL ] Iteration 90: k_eff = 1.028837 res = 1.134E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 14 D.R. = 0.9279\n", + "[ NORMAL ] Iteration 91: k_eff = 1.028970 res = 1.056E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 13 D.R. = 0.9314\n", + "[ NORMAL ] Iteration 92: k_eff = 1.029093 res = 9.786E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 12 D.R. = 0.9268\n", + "[ NORMAL ] Iteration 93: k_eff = 1.029207 res = 9.093E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 11 D.R. = 0.9292\n", + "[ NORMAL ] Iteration 94: k_eff = 1.029313 res = 8.445E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 10 D.R. = 0.9287\n", + "[ NORMAL ] Iteration 95: k_eff = 1.029411 res = 7.848E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 9 D.R. = 0.9294\n", + "[ NORMAL ] Iteration 96: k_eff = 1.029502 res = 7.272E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 9 D.R. = 0.9265\n", + "[ NORMAL ] Iteration 97: k_eff = 1.029586 res = 6.769E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 8 D.R. = 0.9309\n", + "[ NORMAL ] Iteration 98: k_eff = 1.029663 res = 6.294E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 7 D.R. = 0.9298\n", + "[ NORMAL ] Iteration 99: k_eff = 1.029735 res = 5.827E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 7 D.R. = 0.9259\n", + "[ NORMAL ] Iteration 100: k_eff = 1.029802 res = 5.413E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.9290\n", + "[ NORMAL ] Iteration 101: k_eff = 1.029863 res = 5.032E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.9295\n", + "[ NORMAL ] Iteration 102: k_eff = 1.029920 res = 4.648E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.9237\n", + "[ NORMAL ] Iteration 103: k_eff = 1.029973 res = 4.328E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.9313\n", + "[ NORMAL ] Iteration 104: k_eff = 1.030022 res = 4.004E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.9249\n", + "[ NORMAL ] Iteration 105: k_eff = 1.030067 res = 3.734E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.9326\n", + "[ NORMAL ] Iteration 106: k_eff = 1.030109 res = 3.452E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.9246\n", + "[ NORMAL ] Iteration 107: k_eff = 1.030148 res = 3.195E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9253\n", + "[ NORMAL ] Iteration 108: k_eff = 1.030184 res = 2.979E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9325\n", + "[ NORMAL ] Iteration 109: k_eff = 1.030217 res = 2.750E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9230\n", + "[ NORMAL ] Iteration 110: k_eff = 1.030247 res = 2.541E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9240\n", + "[ NORMAL ] Iteration 111: k_eff = 1.030276 res = 2.364E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9302\n", + "[ NORMAL ] Iteration 112: k_eff = 1.030302 res = 2.178E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9215\n", + "[ NORMAL ] Iteration 113: k_eff = 1.030326 res = 2.022E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9285\n", + "[ NORMAL ] Iteration 114: k_eff = 1.030349 res = 1.896E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9374\n", + "[ NORMAL ] Iteration 115: k_eff = 1.030369 res = 1.753E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9246\n", + "[ NORMAL ] Iteration 116: k_eff = 1.030389 res = 1.619E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9236\n", + "[ NORMAL ] Iteration 117: k_eff = 1.030406 res = 1.500E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9266\n", + "[ NORMAL ] Iteration 118: k_eff = 1.030423 res = 1.406E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9375\n", + "[ NORMAL ] Iteration 119: k_eff = 1.030438 res = 1.287E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9154\n", + "[ NORMAL ] Iteration 120: k_eff = 1.030452 res = 1.193E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9269\n", + "[ NORMAL ] Iteration 121: k_eff = 1.030465 res = 1.095E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9174\n", + "[ NORMAL ] Iteration 122: k_eff = 1.030477 res = 1.033E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9437\n", + "[ NORMAL ] Iteration 123: k_eff = 1.030488 res = 9.328E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9029\n", + "[ NORMAL ] Iteration 124: k_eff = 1.030498 res = 8.716E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9344\n", + "[ NORMAL ] Iteration 125: k_eff = 1.030508 res = 8.527E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 0 D.R. = 0.9783\n" ] } ], @@ -1502,16 +1516,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "openmc keff = 1.024078\n", - "openmoc keff = 1.028742\n", - "bias [pcm]: 466.4\n" + "openmc keff = 1.023625\n", + "openmoc keff = 1.030508\n", + "bias [pcm]: 688.3\n" ] } ], "source": [ "# Print report of keff and bias with OpenMC\n", "openmoc_keff = solver.getKeff()\n", - "openmc_keff = sp.k_combined.nominal_value\n", "bias = (openmoc_keff - openmc_keff) * 1e5\n", "\n", "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", @@ -1554,6 +1567,9 @@ "mesh_tally = sp.get_tally(name='mesh tally')\n", "openmc_fission_rates = mesh_tally.get_values(scores=['nu-fission'])\n", "\n", + "# Close the statepoint file now that we're done getting information from it\n", + "sp.close()\n", + "\n", "# Reshape array to 2D for plotting\n", "openmc_fission_rates.shape = (17,17)\n", "\n", @@ -1615,7 +1631,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1660,7 +1676,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/pandas-dataframes.ipynb b/examples/jupyter/pandas-dataframes.ipynb index f5e5142b51c..a8b042e48fc 100644 --- a/examples/jupyter/pandas-dataframes.ipynb +++ b/examples/jupyter/pandas-dataframes.ipynb @@ -255,7 +255,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -410,6 +410,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "zsh:1: no matches found: statepoint.*\n", " %%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", @@ -435,829 +436,836 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.12.0\n", - " Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n", - " Date/Time | 2020-08-15 07:10:20\n", - " OpenMP Threads | 4\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 15:27:04\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /home/master/data/nuclear/endfb71_hdf5/U235.h5\n", - " Reading U238 from /home/master/data/nuclear/endfb71_hdf5/U238.h5\n", - " Reading O16 from /home/master/data/nuclear/endfb71_hdf5/O16.h5\n", - " Reading H1 from /home/master/data/nuclear/endfb71_hdf5/H1.h5\n", - " Reading B10 from /home/master/data/nuclear/endfb71_hdf5/B10.h5\n", - " Reading Zr90 from /home/master/data/nuclear/endfb71_hdf5/Zr90.h5\n", - " Minimum neutron data temperature: 294.000000 K\n", - " Maximum neutron data temperature: 294.000000 K\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 0.53544\n", - " 2/1 0.62631\n", - " 3/1 0.63917\n", - " 4/1 0.67203\n", - " 5/1 0.69300\n", - " 6/1 0.64862\n", - " 7/1 0.63937 0.64399 +/- 0.00463\n", - " 8/1 0.67696 0.65498 +/- 0.01131\n", - " 9/1 0.63216 0.64928 +/- 0.00982\n", - " 10/1 0.70996 0.66141 +/- 0.01433\n", - " 11/1 0.69761 0.66745 +/- 0.01316\n", - " 12/1 0.68662 0.67019 +/- 0.01146\n", - " 13/1 0.64374 0.66688 +/- 0.01046\n", - " 14/1 0.69121 0.66958 +/- 0.00961\n", - " 15/1 0.72125 0.67475 +/- 0.01003\n", - " 16/1 0.72706 0.67950 +/- 0.01024\n", - " 17/1 0.69623 0.68090 +/- 0.00945\n", - " 18/1 0.70953 0.68310 +/- 0.00897\n", - " 19/1 0.69026 0.68361 +/- 0.00832\n", - " 20/1 0.68633 0.68379 +/- 0.00775\n", - " Triggers unsatisfied, max unc./thresh. is 75.24758750489383 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 84938 --- greater than max batches\n", + " 1/1 0.56788\n", + " 2/1 0.66344\n", + " 3/1 0.67801\n", + " 4/1 0.64662\n", + " 5/1 0.69912\n", + " 6/1 0.72720\n", + " 7/1 0.71337 0.72029 +/- 0.00691\n", + " 8/1 0.70564 0.71540 +/- 0.00630\n", + " 9/1 0.69707 0.71082 +/- 0.00639\n", + " 10/1 0.69745 0.70815 +/- 0.00563\n", + " 11/1 0.68982 0.70509 +/- 0.00552\n", + " 12/1 0.69541 0.70371 +/- 0.00486\n", + " 13/1 0.69724 0.70290 +/- 0.00429\n", + " 14/1 0.71009 0.70370 +/- 0.00387\n", + " 15/1 0.69614 0.70294 +/- 0.00354\n", + " 16/1 0.68883 0.70166 +/- 0.00345\n", + " 17/1 0.68635 0.70038 +/- 0.00340\n", + " 18/1 0.70665 0.70087 +/- 0.00316\n", + " 19/1 0.65747 0.69777 +/- 0.00426\n", + " 20/1 0.69228 0.69740 +/- 0.00399\n", + " Triggers unsatisfied, max unc./thresh. is 93.89673349356296 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 132254 --- greater than max batches\n", " Creating state point statepoint.020.h5...\n", - " 21/1 0.68310 0.68375 +/- 0.00725\n", - " Triggers unsatisfied, max unc./thresh. is 71.20148627325992 for absorption in\n", + " 21/1 0.70753 0.69803 +/- 0.00378\n", + " Triggers unsatisfied, max unc./thresh. is 88.01427279112123 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 81120 --- greater than max batches\n", - " 22/1 0.68679 0.68393 +/- 0.00681\n", - " Triggers unsatisfied, max unc./thresh. is 66.94650483064697 for absorption in\n", + " WARNING: The estimated number of batches is 123950 --- greater than max batches\n", + " 22/1 0.71245 0.69888 +/- 0.00365\n", + " Triggers unsatisfied, max unc./thresh. is 84.70931054978398 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 76197 --- greater than max batches\n", - " 23/1 0.67440 0.68340 +/- 0.00644\n", - " Triggers unsatisfied, max unc./thresh. is 63.553590826021285 for absorption in\n", + " WARNING: The estimated number of batches is 121992 --- greater than max batches\n", + " 23/1 0.65293 0.69633 +/- 0.00429\n", + " Triggers unsatisfied, max unc./thresh. is 86.59494174559383 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 72709 --- greater than max batches\n", - " 24/1 0.67483 0.68295 +/- 0.00611\n", - " Triggers unsatisfied, max unc./thresh. is 60.37873858685279 for absorption in\n", + " WARNING: The estimated number of batches is 134982 --- greater than max batches\n", + " 24/1 0.66415 0.69464 +/- 0.00439\n", + " Triggers unsatisfied, max unc./thresh. is 83.04784122808066 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69272 --- greater than max batches\n", - " 25/1 0.71558 0.68458 +/- 0.00602\n", - " Triggers unsatisfied, max unc./thresh. is 60.34535216026281 for absorption in\n", + " WARNING: The estimated number of batches is 131047 --- greater than max batches\n", + " 25/1 0.71052 0.69543 +/- 0.00424\n", + " Triggers unsatisfied, max unc./thresh. is 81.86075666631334 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 72837 --- greater than max batches\n", - " 26/1 0.71853 0.68620 +/- 0.00595\n", - " Triggers unsatisfied, max unc./thresh. is 59.60875760463032 for absorption in\n", + " WARNING: The estimated number of batches is 134029 --- greater than max batches\n", + " 26/1 0.64576 0.69307 +/- 0.00468\n", + " Triggers unsatisfied, max unc./thresh. is 77.87299751571092 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 74623 --- greater than max batches\n", - " 27/1 0.67455 0.68567 +/- 0.00570\n", - " Triggers unsatisfied, max unc./thresh. is 57.228951643423976 for absorption in\n", + " WARNING: The estimated number of batches is 127354 --- greater than max batches\n", + " 27/1 0.66691 0.69188 +/- 0.00462\n", + " Triggers unsatisfied, max unc./thresh. is 74.42216649772053 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 72059 --- greater than max batches\n", - " 28/1 0.69435 0.68605 +/- 0.00546\n", - " Triggers unsatisfied, max unc./thresh. is 56.194065573871285 for absorption in\n", + " WARNING: The estimated number of batches is 121856 --- greater than max batches\n", + " 28/1 0.65694 0.69036 +/- 0.00467\n", + " Triggers unsatisfied, max unc./thresh. is 71.15138613523133 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 72634 --- greater than max batches\n", - " 29/1 0.67706 0.68567 +/- 0.00524\n", - " Triggers unsatisfied, max unc./thresh. is 53.86022066923874 for absorption in\n", + " WARNING: The estimated number of batches is 116443 --- greater than max batches\n", + " 29/1 0.69674 0.69062 +/- 0.00447\n", + " Triggers unsatisfied, max unc./thresh. is 69.56665853404124 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69628 --- greater than max batches\n", - " 30/1 0.69294 0.68596 +/- 0.00504\n", - " Triggers unsatisfied, max unc./thresh. is 51.73413206858763 for absorption in\n", + " WARNING: The estimated number of batches is 116154 --- greater than max batches\n", + " 30/1 0.63556 0.68842 +/- 0.00482\n", + " Triggers unsatisfied, max unc./thresh. is 66.8810661090428 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66916 --- greater than max batches\n", - " 31/1 0.69108 0.68616 +/- 0.00484\n", - " Triggers unsatisfied, max unc./thresh. is 49.71174462484801 for absorption in\n", + " WARNING: The estimated number of batches is 111832 --- greater than max batches\n", + " 31/1 0.69809 0.68879 +/- 0.00465\n", + " Triggers unsatisfied, max unc./thresh. is 64.26124103679346 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64258 --- greater than max batches\n", - " 32/1 0.68089 0.68596 +/- 0.00466\n", - " Triggers unsatisfied, max unc./thresh. is 50.80993117627794 for absorption in\n", + " WARNING: The estimated number of batches is 107373 --- greater than max batches\n", + " 32/1 0.64298 0.68710 +/- 0.00479\n", + " Triggers unsatisfied, max unc./thresh. is 62.020922539187175 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69710 --- greater than max batches\n", - " 33/1 0.67698 0.68564 +/- 0.00450\n", - " Triggers unsatisfied, max unc./thresh. is 50.46659333785448 for absorption in\n", + " WARNING: The estimated number of batches is 103864 --- greater than max batches\n", + " 33/1 0.68559 0.68704 +/- 0.00461\n", + " Triggers unsatisfied, max unc./thresh. is 60.42853939278924 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 71318 --- greater than max batches\n", - " 34/1 0.68167 0.68551 +/- 0.00435\n", - " Triggers unsatisfied, max unc./thresh. is 48.852656603250665 for absorption in\n", + " WARNING: The estimated number of batches is 102251 --- greater than max batches\n", + " 34/1 0.65886 0.68607 +/- 0.00455\n", + " Triggers unsatisfied, max unc./thresh. is 58.33270366370514 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69216 --- greater than max batches\n", - " 35/1 0.67760 0.68524 +/- 0.00421\n", - " Triggers unsatisfied, max unc./thresh. is 48.5685583427197 for absorption in\n", + " WARNING: The estimated number of batches is 98684 --- greater than max batches\n", + " 35/1 0.68743 0.68611 +/- 0.00440\n", + " Triggers unsatisfied, max unc./thresh. is 61.22701811574928 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 70773 --- greater than max batches\n", - " 36/1 0.67628 0.68495 +/- 0.00408\n", - " Triggers unsatisfied, max unc./thresh. is 47.77661998216646 for absorption in\n", + " WARNING: The estimated number of batches is 112468 --- greater than max batches\n", + " 36/1 0.69354 0.68635 +/- 0.00426\n", + " Triggers unsatisfied, max unc./thresh. is 59.98985062145117 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 70766 --- greater than max batches\n", - " 37/1 0.66736 0.68440 +/- 0.00399\n", - " Triggers unsatisfied, max unc./thresh. is 46.57810773879176 for absorption in\n", + " WARNING: The estimated number of batches is 111568 --- greater than max batches\n", + " 37/1 0.65095 0.68525 +/- 0.00427\n", + " Triggers unsatisfied, max unc./thresh. is 58.08611480075532 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69430 --- greater than max batches\n", - " 38/1 0.71026 0.68519 +/- 0.00395\n", - " Triggers unsatisfied, max unc./thresh. is 45.876107560616674 for absorption in\n", + " WARNING: The estimated number of batches is 107973 --- greater than max batches\n", + " 38/1 0.74212 0.68697 +/- 0.00449\n", + " Triggers unsatisfied, max unc./thresh. is 56.42863990001401 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69458 --- greater than max batches\n", - " 39/1 0.67674 0.68494 +/- 0.00384\n", - " Triggers unsatisfied, max unc./thresh. is 45.30341918376721 for absorption in\n", + " WARNING: The estimated number of batches is 105084 --- greater than max batches\n", + " 39/1 0.70258 0.68743 +/- 0.00438\n", + " Triggers unsatisfied, max unc./thresh. is 56.00920365845771 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69787 --- greater than max batches\n", - " 40/1 0.69360 0.68519 +/- 0.00373\n", - " Triggers unsatisfied, max unc./thresh. is 44.018056562863386 for absorption in\n", + " WARNING: The estimated number of batches is 106665 --- greater than max batches\n", + " 40/1 0.73458 0.68878 +/- 0.00446\n", + " Triggers unsatisfied, max unc./thresh. is 54.62032757641821 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 67821 --- greater than max batches\n", - " 41/1 0.70987 0.68587 +/- 0.00369\n", - " Triggers unsatisfied, max unc./thresh. is 42.781078099052785 for absorption in\n", + " WARNING: The estimated number of batches is 104424 --- greater than max batches\n", + " 41/1 0.66256 0.68805 +/- 0.00439\n", + " Triggers unsatisfied, max unc./thresh. is 53.09826256808107 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65893 --- greater than max batches\n", - " 42/1 0.68780 0.68592 +/- 0.00359\n", - " Triggers unsatisfied, max unc./thresh. is 41.60877069209228 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 64063 --- greater than max batches\n", - " 43/1 0.69223 0.68609 +/- 0.00350\n", - " Triggers unsatisfied, max unc./thresh. is 42.44932759168626 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 68479 --- greater than max batches\n" + " WARNING: The estimated number of batches is 101505 --- greater than max batches\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 44/1 0.69561 0.68633 +/- 0.00342\n", - " Triggers unsatisfied, max unc./thresh. is 41.34743776753899 for absorption in\n", + " 42/1 0.68262 0.68790 +/- 0.00427\n", + " Triggers unsatisfied, max unc./thresh. is 51.64336419771116 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66680 --- greater than max batches\n", - " 45/1 0.67503 0.68605 +/- 0.00334\n", - " Triggers unsatisfied, max unc./thresh. is 40.97332186124358 for absorption in\n", + " WARNING: The estimated number of batches is 98686 --- greater than max batches\n", + " 43/1 0.70218 0.68828 +/- 0.00418\n", + " Triggers unsatisfied, max unc./thresh. is 50.670570340468814 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 67158 --- greater than max batches\n", - " 46/1 0.67290 0.68573 +/- 0.00328\n", - " Triggers unsatisfied, max unc./thresh. is 40.24678448931756 for absorption in\n", + " WARNING: The estimated number of batches is 97571 --- greater than max batches\n", + " 44/1 0.67643 0.68797 +/- 0.00408\n", + " Triggers unsatisfied, max unc./thresh. is 49.69199356343392 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66417 --- greater than max batches\n", - " 47/1 0.67355 0.68544 +/- 0.00321\n", - " Triggers unsatisfied, max unc./thresh. is 40.42620640829592 for absorption in\n", + " WARNING: The estimated number of batches is 96308 --- greater than max batches\n", + " 45/1 0.67103 0.68755 +/- 0.00400\n", + " Triggers unsatisfied, max unc./thresh. is 48.44632012698348 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 68645 --- greater than max batches\n", - " 48/1 0.71383 0.68610 +/- 0.00320\n", - " Triggers unsatisfied, max unc./thresh. is 39.90662320308606 for absorption in\n", + " WARNING: The estimated number of batches is 93887 --- greater than max batches\n", + " 46/1 0.65599 0.68678 +/- 0.00398\n", + " Triggers unsatisfied, max unc./thresh. is 47.34469866924748 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 68485 --- greater than max batches\n", - " 49/1 0.68389 0.68605 +/- 0.00313\n", - " Triggers unsatisfied, max unc./thresh. is 39.075369568753906 for absorption in\n", + " WARNING: The estimated number of batches is 91908 --- greater than max batches\n", + " 47/1 0.64796 0.68586 +/- 0.00399\n", + " Triggers unsatisfied, max unc./thresh. is 46.49104551849076 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 67188 --- greater than max batches\n", - " 50/1 0.73148 0.68706 +/- 0.00322\n", - " Triggers unsatisfied, max unc./thresh. is 38.57054567218653 for absorption in\n", + " WARNING: The estimated number of batches is 90785 --- greater than max batches\n", + " 48/1 0.69638 0.68610 +/- 0.00390\n", + " Triggers unsatisfied, max unc./thresh. is 45.709058495601944 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66951 --- greater than max batches\n", - " 51/1 0.69796 0.68730 +/- 0.00316\n", - " Triggers unsatisfied, max unc./thresh. is 38.21572703507316 for absorption in\n", + " WARNING: The estimated number of batches is 89846 --- greater than max batches\n", + " 49/1 0.69484 0.68630 +/- 0.00382\n", + " Triggers unsatisfied, max unc./thresh. is 44.85300785365741 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 67186 --- greater than max batches\n", - " 52/1 0.70691 0.68771 +/- 0.00312\n", - " Triggers unsatisfied, max unc./thresh. is 37.50971908717773 for absorption in\n", + " WARNING: The estimated number of batches is 88524 --- greater than max batches\n", + " 50/1 0.69678 0.68653 +/- 0.00374\n", + " Triggers unsatisfied, max unc./thresh. is 43.84946296160474 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66134 --- greater than max batches\n", - " 53/1 0.69104 0.68778 +/- 0.00306\n", - " Triggers unsatisfied, max unc./thresh. is 36.824732312223716 for absorption in\n", + " WARNING: The estimated number of batches is 86530 --- greater than max batches\n", + " 51/1 0.72852 0.68745 +/- 0.00377\n", + " Triggers unsatisfied, max unc./thresh. is 43.787094662843266 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65096 --- greater than max batches\n", - " 54/1 0.74368 0.68892 +/- 0.00320\n", - " Triggers unsatisfied, max unc./thresh. is 36.20814737643575 for absorption in\n", + " WARNING: The estimated number of batches is 88202 --- greater than max batches\n", + " 52/1 0.69916 0.68769 +/- 0.00370\n", + " Triggers unsatisfied, max unc./thresh. is 43.14074745408116 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64246 --- greater than max batches\n", - " 55/1 0.67371 0.68862 +/- 0.00315\n", - " Triggers unsatisfied, max unc./thresh. is 35.48607231512293 for absorption in\n", + " WARNING: The estimated number of batches is 87478 --- greater than max batches\n", + " 53/1 0.70809 0.68812 +/- 0.00364\n", + " Triggers unsatisfied, max unc./thresh. is 42.78319111442477 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62969 --- greater than max batches\n", - " 56/1 0.67846 0.68842 +/- 0.00310\n", - " Triggers unsatisfied, max unc./thresh. is 35.34421893287461 for absorption in\n", + " WARNING: The estimated number of batches is 87865 --- greater than max batches\n", + " 54/1 0.67049 0.68776 +/- 0.00359\n", + " Triggers unsatisfied, max unc./thresh. is 41.90152321392576 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63715 --- greater than max batches\n", - " 57/1 0.66351 0.68794 +/- 0.00307\n", - " Triggers unsatisfied, max unc./thresh. is 34.67062652878957 for absorption in\n", + " WARNING: The estimated number of batches is 86037 --- greater than max batches\n", + " 55/1 0.67612 0.68753 +/- 0.00352\n", + " Triggers unsatisfied, max unc./thresh. is 41.247941809938425 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62512 --- greater than max batches\n", - " 58/1 0.67049 0.68761 +/- 0.00303\n", - " Triggers unsatisfied, max unc./thresh. is 34.22135922543247 for absorption in\n", + " WARNING: The estimated number of batches is 85075 --- greater than max batches\n", + " 56/1 0.68035 0.68739 +/- 0.00346\n", + " Triggers unsatisfied, max unc./thresh. is 40.46515891712033 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62074 --- greater than max batches\n", - " 59/1 0.66967 0.68728 +/- 0.00299\n", - " Triggers unsatisfied, max unc./thresh. is 33.66881484408945 for absorption in\n", + " WARNING: The estimated number of batches is 83514 --- greater than max batches\n", + " 57/1 0.70520 0.68773 +/- 0.00341\n", + " Triggers unsatisfied, max unc./thresh. is 40.05474461357334 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61219 --- greater than max batches\n", - " 60/1 0.70271 0.68756 +/- 0.00295\n", - " Triggers unsatisfied, max unc./thresh. is 33.19914799505717 for absorption in\n", + " WARNING: The estimated number of batches is 83433 --- greater than max batches\n", + " 58/1 0.70938 0.68814 +/- 0.00337\n", + " Triggers unsatisfied, max unc./thresh. is 39.36707588204034 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60626 --- greater than max batches\n", - " 61/1 0.70035 0.68779 +/- 0.00291\n", - " Triggers unsatisfied, max unc./thresh. is 32.65594936729897 for absorption in\n", + " WARNING: The estimated number of batches is 82143 --- greater than max batches\n", + " 59/1 0.71202 0.68858 +/- 0.00333\n", + " Triggers unsatisfied, max unc./thresh. is 38.66137944699882 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59725 --- greater than max batches\n", - " 62/1 0.66274 0.68735 +/- 0.00289\n", - " Triggers unsatisfied, max unc./thresh. is 32.15622046485561 for absorption in\n", + " WARNING: The estimated number of batches is 80719 --- greater than max batches\n", + " 60/1 0.66559 0.68816 +/- 0.00330\n", + " Triggers unsatisfied, max unc./thresh. is 37.98682899231639 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58945 --- greater than max batches\n", - " 63/1 0.68607 0.68733 +/- 0.00284\n", - " Triggers unsatisfied, max unc./thresh. is 31.601225649282494 for absorption in\n", + " WARNING: The estimated number of batches is 79370 --- greater than max batches\n", + " 61/1 0.65921 0.68765 +/- 0.00328\n", + " Triggers unsatisfied, max unc./thresh. is 38.029460967594694 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57926 --- greater than max batches\n", - " 64/1 0.66518 0.68695 +/- 0.00282\n", - " Triggers unsatisfied, max unc./thresh. is 31.12129365572805 for absorption in\n", + " WARNING: The estimated number of batches is 80995 --- greater than max batches\n", + " 62/1 0.66584 0.68726 +/- 0.00324\n", + " Triggers unsatisfied, max unc./thresh. is 37.381474566315354 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57149 --- greater than max batches\n", - " 65/1 0.65999 0.68650 +/- 0.00281\n", - " Triggers unsatisfied, max unc./thresh. is 30.641988019531464 for absorption in\n", + " WARNING: The estimated number of batches is 79656 --- greater than max batches\n", + " 63/1 0.69512 0.68740 +/- 0.00319\n", + " Triggers unsatisfied, max unc./thresh. is 36.76408883031811 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56341 --- greater than max batches\n", - " 66/1 0.67843 0.68637 +/- 0.00276\n", - " Triggers unsatisfied, max unc./thresh. is 30.320463443580458 for absorption in\n", + " WARNING: The estimated number of batches is 78398 --- greater than max batches\n", + " 64/1 0.72240 0.68799 +/- 0.00319\n", + " Triggers unsatisfied, max unc./thresh. is 36.18011705137993 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56085 --- greater than max batches\n", - " 67/1 0.69295 0.68648 +/- 0.00272\n", - " Triggers unsatisfied, max unc./thresh. is 30.06051080038397 for absorption in\n", + " WARNING: The estimated number of batches is 77237 --- greater than max batches\n", + " 65/1 0.68739 0.68798 +/- 0.00314\n", + " Triggers unsatisfied, max unc./thresh. is 35.60153732818344 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56031 --- greater than max batches\n", - " 68/1 0.69158 0.68656 +/- 0.00268\n", - " Triggers unsatisfied, max unc./thresh. is 29.7400907913873 for absorption in\n", + " WARNING: The estimated number of batches is 76054 --- greater than max batches\n", + " 66/1 0.63734 0.68715 +/- 0.00320\n", + " Triggers unsatisfied, max unc./thresh. is 35.03171980405627 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 55727 --- greater than max batches\n", - " 69/1 0.69825 0.68674 +/- 0.00264\n", - " Triggers unsatisfied, max unc./thresh. is 29.278619659445805 for absorption in\n", + " WARNING: The estimated number of batches is 74866 --- greater than max batches\n", + " 67/1 0.67068 0.68689 +/- 0.00315\n", + " Triggers unsatisfied, max unc./thresh. is 34.51557177789461 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 54869 --- greater than max batches\n", - " 70/1 0.73637 0.68750 +/- 0.00271\n", - " Triggers unsatisfied, max unc./thresh. is 28.945044018568716 for absorption in\n", + " WARNING: The estimated number of batches is 73868 --- greater than max batches\n", + " 68/1 0.67713 0.68673 +/- 0.00311\n", + " Triggers unsatisfied, max unc./thresh. is 33.96995049011678 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 54464 --- greater than max batches\n", - " 71/1 0.64301 0.68683 +/- 0.00275\n", - " Triggers unsatisfied, max unc./thresh. is 28.73677928804667 for absorption in\n", + " WARNING: The estimated number of batches is 72705 --- greater than max batches\n", + " 69/1 0.71729 0.68721 +/- 0.00310\n", + " Triggers unsatisfied, max unc./thresh. is 33.74180433617202 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 54508 --- greater than max batches\n", - " 72/1 0.71506 0.68725 +/- 0.00274\n", - " Triggers unsatisfied, max unc./thresh. is 29.20796537704291 for absorption in\n", + " WARNING: The estimated number of batches is 72870 --- greater than max batches\n", + " 70/1 0.69537 0.68733 +/- 0.00305\n", + " Triggers unsatisfied, max unc./thresh. is 33.2256923807649 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57164 --- greater than max batches\n", - " 73/1 0.69203 0.68732 +/- 0.00270\n", - " Triggers unsatisfied, max unc./thresh. is 29.56297016014581 for absorption in\n", + " WARNING: The estimated number of batches is 71762 --- greater than max batches\n", + " 71/1 0.67200 0.68710 +/- 0.00301\n", + " Triggers unsatisfied, max unc./thresh. is 33.22256499834962 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59435 --- greater than max batches\n", - " 74/1 0.69208 0.68739 +/- 0.00267\n", - " Triggers unsatisfied, max unc./thresh. is 29.545241442413783 for absorption in\n", + " WARNING: The estimated number of batches is 72852 --- greater than max batches\n", + " 72/1 0.71865 0.68757 +/- 0.00300\n", + " Triggers unsatisfied, max unc./thresh. is 32.735500285071055 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60237 --- greater than max batches\n", - " 75/1 0.65717 0.68696 +/- 0.00266\n", - " Triggers unsatisfied, max unc./thresh. is 29.284013224166248 for absorption in\n", + " WARNING: The estimated number of batches is 71804 --- greater than max batches\n", + " 73/1 0.69590 0.68769 +/- 0.00296\n", + " Triggers unsatisfied, max unc./thresh. is 32.26730455835508 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60034 --- greater than max batches\n", - " 76/1 0.70992 0.68728 +/- 0.00265\n", - " Triggers unsatisfied, max unc./thresh. is 29.00034584995327 for absorption in\n", + " WARNING: The estimated number of batches is 70806 --- greater than max batches\n", + " 74/1 0.68222 0.68762 +/- 0.00292\n", + " Triggers unsatisfied, max unc./thresh. is 32.10780833972545 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59718 --- greater than max batches\n", - " 77/1 0.65590 0.68685 +/- 0.00264\n", - " Triggers unsatisfied, max unc./thresh. is 28.867967845905174 for absorption in\n", + " WARNING: The estimated number of batches is 71138 --- greater than max batches\n", + " 75/1 0.70643 0.68788 +/- 0.00289\n", + " Triggers unsatisfied, max unc./thresh. is 31.818936053589276 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60007 --- greater than max batches\n", - " 78/1 0.64439 0.68626 +/- 0.00267\n", - " Triggers unsatisfied, max unc./thresh. is 29.016012595317935 for absorption in\n", + " WARNING: The estimated number of batches is 70877 --- greater than max batches\n", + " 76/1 0.67658 0.68772 +/- 0.00285\n", + " Triggers unsatisfied, max unc./thresh. is 31.799932820980022 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61466 --- greater than max batches\n", - " 79/1 0.66295 0.68595 +/- 0.00265\n", - " Triggers unsatisfied, max unc./thresh. is 28.626245464278142 for absorption in\n", + " WARNING: The estimated number of batches is 71803 --- greater than max batches\n", + " 77/1 0.67857 0.68760 +/- 0.00282\n", + " Triggers unsatisfied, max unc./thresh. is 32.26711337228593 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60646 --- greater than max batches\n", - " 80/1 0.66672 0.68569 +/- 0.00263\n", - " Triggers unsatisfied, max unc./thresh. is 28.24218910063624 for absorption in\n", + " WARNING: The estimated number of batches is 74969 --- greater than max batches\n", + " 78/1 0.69108 0.68765 +/- 0.00278\n", + " Triggers unsatisfied, max unc./thresh. is 31.82734318749477 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59827 --- greater than max batches\n", - " 81/1 0.69110 0.68576 +/- 0.00260\n", - " Triggers unsatisfied, max unc./thresh. is 27.917349908027763 for absorption in\n", + " WARNING: The estimated number of batches is 73953 --- greater than max batches\n", + " 79/1 0.68226 0.68757 +/- 0.00274\n", + " Triggers unsatisfied, max unc./thresh. is 31.40561277542678 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59238 --- greater than max batches\n", - " 82/1 0.67481 0.68562 +/- 0.00257\n", - " Triggers unsatisfied, max unc./thresh. is 28.01946018168837 for absorption in\n", + " WARNING: The estimated number of batches is 72993 --- greater than max batches\n", + " 80/1 0.67648 0.68742 +/- 0.00271\n", + " Triggers unsatisfied, max unc./thresh. is 31.182065681219466 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60457 --- greater than max batches\n", - " 83/1 0.72216 0.68609 +/- 0.00258\n", - " Triggers unsatisfied, max unc./thresh. is 27.931394620754766 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 60858 --- greater than max batches\n" + " WARNING: The estimated number of batches is 72930 --- greater than max batches\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 84/1 0.68429 0.68607 +/- 0.00254\n", - " Triggers unsatisfied, max unc./thresh. is 27.713137470531738 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 60679 --- greater than max batches\n", - " 85/1 0.65458 0.68567 +/- 0.00254\n", - " Triggers unsatisfied, max unc./thresh. is 27.364539968246927 for absorption in\n", + " 81/1 0.65907 0.68705 +/- 0.00270\n", + " Triggers unsatisfied, max unc./thresh. is 30.770354748329673 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59911 --- greater than max batches\n", - " 86/1 0.69966 0.68585 +/- 0.00252\n", - " Triggers unsatisfied, max unc./thresh. is 27.178113974435043 for absorption in\n", + " WARNING: The estimated number of batches is 71963 --- greater than max batches\n", + " 82/1 0.63285 0.68635 +/- 0.00276\n", + " Triggers unsatisfied, max unc./thresh. is 30.494055493974837 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59836 --- greater than max batches\n", - " 87/1 0.64776 0.68538 +/- 0.00253\n", - " Triggers unsatisfied, max unc./thresh. is 26.941566345072534 for absorption in\n", + " WARNING: The estimated number of batches is 71607 --- greater than max batches\n", + " 83/1 0.65128 0.68590 +/- 0.00276\n", + " Triggers unsatisfied, max unc./thresh. is 30.31368198832468 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59525 --- greater than max batches\n", - " 88/1 0.62737 0.68468 +/- 0.00259\n", - " Triggers unsatisfied, max unc./thresh. is 26.73959660667411 for absorption in\n", + " WARNING: The estimated number of batches is 71681 --- greater than max batches\n", + " 84/1 0.65957 0.68556 +/- 0.00274\n", + " Triggers unsatisfied, max unc./thresh. is 29.927509740512043 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59351 --- greater than max batches\n", - " 89/1 0.69779 0.68484 +/- 0.00257\n", - " Triggers unsatisfied, max unc./thresh. is 26.490234865810894 for absorption in\n", + " WARNING: The estimated number of batches is 70762 --- greater than max batches\n", + " 85/1 0.69936 0.68574 +/- 0.00271\n", + " Triggers unsatisfied, max unc./thresh. is 29.684480624889716 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58951 --- greater than max batches\n", - " 90/1 0.67312 0.68470 +/- 0.00254\n", - " Triggers unsatisfied, max unc./thresh. is 26.24036001465229 for absorption in\n", + " WARNING: The estimated number of batches is 70499 --- greater than max batches\n", + " 86/1 0.71538 0.68610 +/- 0.00270\n", + " Triggers unsatisfied, max unc./thresh. is 30.42821373418053 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58533 --- greater than max batches\n", - " 91/1 0.69289 0.68480 +/- 0.00251\n", - " Triggers unsatisfied, max unc./thresh. is 25.936795778335345 for absorption in\n", + " WARNING: The estimated number of batches is 75001 --- greater than max batches\n", + " 87/1 0.67737 0.68600 +/- 0.00267\n", + " Triggers unsatisfied, max unc./thresh. is 30.08396896848712 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57859 --- greater than max batches\n", - " 92/1 0.69884 0.68496 +/- 0.00249\n", - " Triggers unsatisfied, max unc./thresh. is 25.695465963215582 for absorption in\n", + " WARNING: The estimated number of batches is 74219 --- greater than max batches\n", + " 88/1 0.67516 0.68587 +/- 0.00264\n", + " Triggers unsatisfied, max unc./thresh. is 29.820034394835567 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57448 --- greater than max batches\n", - " 93/1 0.71351 0.68528 +/- 0.00248\n", - " Triggers unsatisfied, max unc./thresh. is 25.49001212499821 for absorption in\n", + " WARNING: The estimated number of batches is 73812 --- greater than max batches\n", + " 89/1 0.71831 0.68625 +/- 0.00264\n", + " Triggers unsatisfied, max unc./thresh. is 29.52222916170289 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57183 --- greater than max batches\n", - " 94/1 0.65602 0.68495 +/- 0.00248\n", - " Triggers unsatisfied, max unc./thresh. is 25.350859463183905 for absorption in\n", + " WARNING: The estimated number of batches is 73217 --- greater than max batches\n", + " 90/1 0.69057 0.68630 +/- 0.00261\n", + " Triggers unsatisfied, max unc./thresh. is 29.192694352726093 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57203 --- greater than max batches\n", - " 95/1 0.72223 0.68537 +/- 0.00248\n", - " Triggers unsatisfied, max unc./thresh. is 25.157803279393637 for absorption in\n", + " WARNING: The estimated number of batches is 72444 --- greater than max batches\n", + " 91/1 0.72527 0.68676 +/- 0.00262\n", + " Triggers unsatisfied, max unc./thresh. is 28.882854654006614 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56968 --- greater than max batches\n", - " 96/1 0.67930 0.68530 +/- 0.00246\n", - " Triggers unsatisfied, max unc./thresh. is 24.92205849077747 for absorption in\n", + " WARNING: The estimated number of batches is 71748 --- greater than max batches\n", + " 92/1 0.68240 0.68671 +/- 0.00259\n", + " Triggers unsatisfied, max unc./thresh. is 28.746406095976795 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56526 --- greater than max batches\n", - " 97/1 0.66201 0.68505 +/- 0.00244\n", - " Triggers unsatisfied, max unc./thresh. is 24.653967027285237 for absorption in\n", + " WARNING: The estimated number of batches is 71898 --- greater than max batches\n", + " 93/1 0.68284 0.68666 +/- 0.00256\n", + " Triggers unsatisfied, max unc./thresh. is 28.55969476551871 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 55925 --- greater than max batches\n", - " 98/1 0.71110 0.68533 +/- 0.00243\n", - " Triggers unsatisfied, max unc./thresh. is 24.566957281211884 for absorption in\n", + " WARNING: The estimated number of batches is 71783 --- greater than max batches\n", + " 94/1 0.67323 0.68651 +/- 0.00254\n", + " Triggers unsatisfied, max unc./thresh. is 28.562644465757202 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56134 --- greater than max batches\n", - " 99/1 0.69409 0.68542 +/- 0.00241\n", - " Triggers unsatisfied, max unc./thresh. is 24.581943149247135 for absorption in\n", + " WARNING: The estimated number of batches is 72614 --- greater than max batches\n", + " 95/1 0.67226 0.68635 +/- 0.00251\n", + " Triggers unsatisfied, max unc./thresh. is 28.266619522119196 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56807 --- greater than max batches\n", - " 100/1 0.71197 0.68570 +/- 0.00240\n", - " Triggers unsatisfied, max unc./thresh. is 24.444112571967217 for absorption in\n", + " WARNING: The estimated number of batches is 71916 --- greater than max batches\n", + " 96/1 0.69225 0.68642 +/- 0.00249\n", + " Triggers unsatisfied, max unc./thresh. is 28.090316080385687 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56769 --- greater than max batches\n", - " 101/1 0.71713 0.68603 +/- 0.00240\n", - " Triggers unsatisfied, max unc./thresh. is 24.18957776896016 for absorption in\n", + " WARNING: The estimated number of batches is 71810 --- greater than max batches\n", + " 97/1 0.67708 0.68632 +/- 0.00246\n", + " Triggers unsatisfied, max unc./thresh. is 27.821561823906112 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56179 --- greater than max batches\n", - " 102/1 0.68143 0.68598 +/- 0.00237\n", - " Triggers unsatisfied, max unc./thresh. is 23.97797098066553 for absorption in\n", + " WARNING: The estimated number of batches is 71217 --- greater than max batches\n", + " 98/1 0.68583 0.68631 +/- 0.00243\n", + " Triggers unsatisfied, max unc./thresh. is 27.550146728325792 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 55775 --- greater than max batches\n", - " 103/1 0.69936 0.68612 +/- 0.00235\n", - " Triggers unsatisfied, max unc./thresh. is 24.253000406602812 for absorption in\n", + " WARNING: The estimated number of batches is 70593 --- greater than max batches\n", + " 99/1 0.64118 0.68583 +/- 0.00246\n", + " Triggers unsatisfied, max unc./thresh. is 27.31334482933601 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57650 --- greater than max batches\n", - " 104/1 0.65247 0.68578 +/- 0.00235\n", - " Triggers unsatisfied, max unc./thresh. is 24.593482483379837 for absorption in\n", + " WARNING: The estimated number of batches is 70131 --- greater than max batches\n", + " 100/1 0.67711 0.68574 +/- 0.00243\n", + " Triggers unsatisfied, max unc./thresh. is 27.045284650239612 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59885 --- greater than max batches\n", - " 105/1 0.66517 0.68557 +/- 0.00234\n", - " Triggers unsatisfied, max unc./thresh. is 24.37904760701804 for absorption in\n", + " WARNING: The estimated number of batches is 69493 --- greater than max batches\n", + " 101/1 0.68084 0.68569 +/- 0.00241\n", + " Triggers unsatisfied, max unc./thresh. is 26.783343195485262 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59439 --- greater than max batches\n", - " 106/1 0.67814 0.68550 +/- 0.00232\n", - " Triggers unsatisfied, max unc./thresh. is 24.142311084988883 for absorption in\n", + " WARNING: The estimated number of batches is 68871 --- greater than max batches\n", + " 102/1 0.69024 0.68573 +/- 0.00238\n", + " Triggers unsatisfied, max unc./thresh. is 26.507109203671433 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58873 --- greater than max batches\n", - " 107/1 0.67788 0.68542 +/- 0.00229\n", - " Triggers unsatisfied, max unc./thresh. is 23.935477435724106 for absorption in\n", + " WARNING: The estimated number of batches is 68160 --- greater than max batches\n", + " 103/1 0.66758 0.68555 +/- 0.00236\n", + " Triggers unsatisfied, max unc./thresh. is 26.75706619598529 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58442 --- greater than max batches\n", - " 108/1 0.68016 0.68537 +/- 0.00227\n", - " Triggers unsatisfied, max unc./thresh. is 24.532504688648594 for absorption in\n", + " WARNING: The estimated number of batches is 70168 --- greater than max batches\n", + " 104/1 0.70685 0.68576 +/- 0.00235\n", + " Triggers unsatisfied, max unc./thresh. is 26.50422283567677 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61995 --- greater than max batches\n", - " 109/1 0.66963 0.68522 +/- 0.00226\n", - " Triggers unsatisfied, max unc./thresh. is 24.354532539671386 for absorption in\n", + " WARNING: The estimated number of batches is 69550 --- greater than max batches\n", + " 105/1 0.65411 0.68545 +/- 0.00235\n", + " Triggers unsatisfied, max unc./thresh. is 26.700673042186363 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61692 --- greater than max batches\n", - " 110/1 0.67556 0.68513 +/- 0.00224\n", - " Triggers unsatisfied, max unc./thresh. is 24.16165322902175 for absorption in\n", + " WARNING: The estimated number of batches is 71298 --- greater than max batches\n", + " 106/1 0.66632 0.68526 +/- 0.00233\n", + " Triggers unsatisfied, max unc./thresh. is 26.660322235538125 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61303 --- greater than max batches\n", - " 111/1 0.68273 0.68511 +/- 0.00222\n", - " Triggers unsatisfied, max unc./thresh. is 24.00069508298176 for absorption in\n", + " WARNING: The estimated number of batches is 71794 --- greater than max batches\n", + " 107/1 0.72095 0.68561 +/- 0.00234\n", + " Triggers unsatisfied, max unc./thresh. is 26.493921637614655 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61065 --- greater than max batches\n", - " 112/1 0.69505 0.68520 +/- 0.00220\n", - " Triggers unsatisfied, max unc./thresh. is 23.791656279909404 for absorption in\n", + " WARNING: The estimated number of batches is 71602 --- greater than max batches\n", + " 108/1 0.68486 0.68560 +/- 0.00231\n", + " Triggers unsatisfied, max unc./thresh. is 26.261958180540656 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60572 --- greater than max batches\n", - " 113/1 0.69385 0.68528 +/- 0.00218\n", - " Triggers unsatisfied, max unc./thresh. is 23.667941020219764 for absorption in\n", + " WARNING: The estimated number of batches is 71044 --- greater than max batches\n", + " 109/1 0.70300 0.68577 +/- 0.00230\n", + " Triggers unsatisfied, max unc./thresh. is 26.026316770700436 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60504 --- greater than max batches\n", - " 114/1 0.65352 0.68499 +/- 0.00218\n", - " Triggers unsatisfied, max unc./thresh. is 23.469658485546123 for absorption in\n", + " WARNING: The estimated number of batches is 70452 --- greater than max batches\n", + " 110/1 0.63861 0.68532 +/- 0.00232\n", + " Triggers unsatisfied, max unc./thresh. is 25.926028971288112 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60045 --- greater than max batches\n", - " 115/1 0.68339 0.68497 +/- 0.00216\n", - " Triggers unsatisfied, max unc./thresh. is 23.259123161328624 for absorption in\n", + " WARNING: The estimated number of batches is 70582 --- greater than max batches\n", + " 111/1 0.68795 0.68534 +/- 0.00230\n", + " Triggers unsatisfied, max unc./thresh. is 25.717124436169865 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59514 --- greater than max batches\n", - " 116/1 0.65854 0.68474 +/- 0.00215\n", - " Triggers unsatisfied, max unc./thresh. is 23.06250977653337 for absorption in\n", + " WARNING: The estimated number of batches is 70111 --- greater than max batches\n", + " 112/1 0.64471 0.68496 +/- 0.00231\n", + " Triggers unsatisfied, max unc./thresh. is 25.505784119998573 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59044 --- greater than max batches\n", - " 117/1 0.66907 0.68460 +/- 0.00214\n", - " Triggers unsatisfied, max unc./thresh. is 22.874382198219536 for absorption in\n", + " WARNING: The estimated number of batches is 69614 --- greater than max batches\n", + " 113/1 0.68637 0.68498 +/- 0.00229\n", + " Triggers unsatisfied, max unc./thresh. is 25.844123747859776 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58608 --- greater than max batches\n", - " 118/1 0.68165 0.68457 +/- 0.00212\n", - " Triggers unsatisfied, max unc./thresh. is 22.709602691165983 for absorption in\n", + " WARNING: The estimated number of batches is 72141 --- greater than max batches\n", + " 114/1 0.70597 0.68517 +/- 0.00227\n", + " Triggers unsatisfied, max unc./thresh. is 26.41373710688748 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58283 --- greater than max batches\n", - " 119/1 0.70967 0.68479 +/- 0.00211\n", - " Triggers unsatisfied, max unc./thresh. is 22.509869996658225 for absorption in\n", + " WARNING: The estimated number of batches is 76053 --- greater than max batches\n", + " 115/1 0.67258 0.68506 +/- 0.00225\n", + " Triggers unsatisfied, max unc./thresh. is 26.35672818667791 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57769 --- greater than max batches\n", - " 120/1 0.65543 0.68453 +/- 0.00211\n", - " Triggers unsatisfied, max unc./thresh. is 22.422104322874098 for absorption in\n", + " WARNING: The estimated number of batches is 76420 --- greater than max batches\n", + " 116/1 0.68671 0.68507 +/- 0.00223\n", + " Triggers unsatisfied, max unc./thresh. is 26.123859680914105 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57822 --- greater than max batches\n", - " 121/1 0.67305 0.68444 +/- 0.00209\n", - " Triggers unsatisfied, max unc./thresh. is 22.32834321567902 for absorption in\n", + " WARNING: The estimated number of batches is 75758 --- greater than max batches\n", + " 117/1 0.65467 0.68480 +/- 0.00223\n", + " Triggers unsatisfied, max unc./thresh. is 25.891227809553317 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57838 --- greater than max batches\n", - " 122/1 0.68206 0.68441 +/- 0.00207\n", - " Triggers unsatisfied, max unc./thresh. is 22.155196965032374 for absorption in\n", + " WARNING: The estimated number of batches is 75085 --- greater than max batches\n", + " 118/1 0.68299 0.68478 +/- 0.00221\n", + " Triggers unsatisfied, max unc./thresh. is 25.663579354019788 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57435 --- greater than max batches\n", - " 123/1 0.71125 0.68464 +/- 0.00207\n", - " Triggers unsatisfied, max unc./thresh. is 21.96683678913398 for absorption in\n", + " WARNING: The estimated number of batches is 74429 --- greater than max batches\n", + " 119/1 0.71640 0.68506 +/- 0.00221\n", + " Triggers unsatisfied, max unc./thresh. is 25.798931576936234 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56945 --- greater than max batches\n" + " WARNING: The estimated number of batches is 75882 --- greater than max batches\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 124/1 0.65918 0.68443 +/- 0.00206\n", - " Triggers unsatisfied, max unc./thresh. is 21.78216358933223 for absorption in\n", + " 120/1 0.67682 0.68499 +/- 0.00219\n", + " Triggers unsatisfied, max unc./thresh. is 25.600502408827925 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56467 --- greater than max batches\n", - " 125/1 0.68122 0.68440 +/- 0.00205\n", - " Triggers unsatisfied, max unc./thresh. is 21.681928420505304 for absorption in\n", + " WARNING: The estimated number of batches is 75375 --- greater than max batches\n", + " 121/1 0.64612 0.68465 +/- 0.00220\n", + " Triggers unsatisfied, max unc./thresh. is 25.442400998883397 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56418 --- greater than max batches\n", - " 126/1 0.66900 0.68427 +/- 0.00203\n", - " Triggers unsatisfied, max unc./thresh. is 21.60631058168512 for absorption in\n", + " WARNING: The estimated number of batches is 75094 --- greater than max batches\n", + " 122/1 0.65743 0.68442 +/- 0.00219\n", + " Triggers unsatisfied, max unc./thresh. is 25.314147051671682 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56492 --- greater than max batches\n", - " 127/1 0.66742 0.68414 +/- 0.00202\n", - " Triggers unsatisfied, max unc./thresh. is 21.468291123480988 for absorption in\n", + " WARNING: The estimated number of batches is 74980 --- greater than max batches\n", + " 123/1 0.67460 0.68434 +/- 0.00217\n", + " Triggers unsatisfied, max unc./thresh. is 25.120655024301723 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56234 --- greater than max batches\n", - " 128/1 0.66971 0.68402 +/- 0.00201\n", - " Triggers unsatisfied, max unc./thresh. is 21.313238544974386 for absorption in\n", + " WARNING: The estimated number of batches is 74469 --- greater than max batches\n", + " 124/1 0.63622 0.68393 +/- 0.00219\n", + " Triggers unsatisfied, max unc./thresh. is 25.173748425745888 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 55879 --- greater than max batches\n", - " 129/1 0.68183 0.68400 +/- 0.00199\n", - " Triggers unsatisfied, max unc./thresh. is 21.314008888585132 for absorption in\n", + " WARNING: The estimated number of batches is 75418 --- greater than max batches\n", + " 125/1 0.74014 0.68440 +/- 0.00222\n", + " Triggers unsatisfied, max unc./thresh. is 24.969908358640748 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56337 --- greater than max batches\n", - " 130/1 0.68403 0.68400 +/- 0.00197\n", - " Triggers unsatisfied, max unc./thresh. is 21.159444482258046 for absorption in\n", + " WARNING: The estimated number of batches is 74825 --- greater than max batches\n", + " 126/1 0.66532 0.68424 +/- 0.00221\n", + " Triggers unsatisfied, max unc./thresh. is 24.763458789173267 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 55971 --- greater than max batches\n", - " 131/1 0.69137 0.68406 +/- 0.00196\n", - " Triggers unsatisfied, max unc./thresh. is 21.24931160989673 for absorption in\n", + " WARNING: The estimated number of batches is 74206 --- greater than max batches\n", + " 127/1 0.66151 0.68406 +/- 0.00220\n", + " Triggers unsatisfied, max unc./thresh. is 24.715390259532615 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56899 --- greater than max batches\n", - " 132/1 0.67481 0.68399 +/- 0.00195\n", - " Triggers unsatisfied, max unc./thresh. is 21.164512281281944 for absorption in\n", + " WARNING: The estimated number of batches is 74529 --- greater than max batches\n", + " 128/1 0.66861 0.68393 +/- 0.00219\n", + " Triggers unsatisfied, max unc./thresh. is 24.532144885232228 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56893 --- greater than max batches\n", - " 133/1 0.70390 0.68414 +/- 0.00194\n", - " Triggers unsatisfied, max unc./thresh. is 21.084176856795946 for absorption in\n", + " WARNING: The estimated number of batches is 74030 --- greater than max batches\n", + " 129/1 0.69363 0.68401 +/- 0.00217\n", + " Triggers unsatisfied, max unc./thresh. is 24.350853769848076 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56907 --- greater than max batches\n", - " 134/1 0.67961 0.68411 +/- 0.00192\n", - " Triggers unsatisfied, max unc./thresh. is 21.48684646255342 for absorption in\n", + " WARNING: The estimated number of batches is 73533 --- greater than max batches\n", + " 130/1 0.70857 0.68421 +/- 0.00216\n", + " Triggers unsatisfied, max unc./thresh. is 24.17444245442967 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59563 --- greater than max batches\n", - " 135/1 0.65362 0.68387 +/- 0.00192\n", - " Triggers unsatisfied, max unc./thresh. is 21.41235779526348 for absorption in\n", + " WARNING: The estimated number of batches is 73056 --- greater than max batches\n", + " 131/1 0.67714 0.68415 +/- 0.00215\n", + " Triggers unsatisfied, max unc./thresh. is 23.98323256055421 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59609 --- greater than max batches\n", - " 136/1 0.63946 0.68353 +/- 0.00194\n", - " Triggers unsatisfied, max unc./thresh. is 21.30299014546295 for absorption in\n", + " WARNING: The estimated number of batches is 72480 --- greater than max batches\n", + " 132/1 0.67410 0.68407 +/- 0.00213\n", + " Triggers unsatisfied, max unc./thresh. is 23.867449107003456 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59456 --- greater than max batches\n", - " 137/1 0.64818 0.68327 +/- 0.00194\n", - " Triggers unsatisfied, max unc./thresh. is 21.159761745415484 for absorption in\n", + " WARNING: The estimated number of batches is 72352 --- greater than max batches\n", + " 133/1 0.69079 0.68412 +/- 0.00211\n", + " Triggers unsatisfied, max unc./thresh. is 23.685770754896556 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59107 --- greater than max batches\n", - " 138/1 0.68975 0.68331 +/- 0.00193\n", - " Triggers unsatisfied, max unc./thresh. is 21.000094566393475 for absorption in\n", + " WARNING: The estimated number of batches is 71816 --- greater than max batches\n", + " 134/1 0.67606 0.68406 +/- 0.00210\n", + " Triggers unsatisfied, max unc./thresh. is 23.73618218941846 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58659 --- greater than max batches\n", - " 139/1 0.67280 0.68324 +/- 0.00191\n", - " Triggers unsatisfied, max unc./thresh. is 20.853656171297644 for absorption in\n", + " WARNING: The estimated number of batches is 72685 --- greater than max batches\n", + " 135/1 0.69637 0.68416 +/- 0.00208\n", + " Triggers unsatisfied, max unc./thresh. is 23.69643071032316 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58279 --- greater than max batches\n", - " 140/1 0.66857 0.68313 +/- 0.00190\n", - " Triggers unsatisfied, max unc./thresh. is 20.709591767033707 for absorption in\n", + " WARNING: The estimated number of batches is 73003 --- greater than max batches\n", + " 136/1 0.67044 0.68405 +/- 0.00207\n", + " Triggers unsatisfied, max unc./thresh. is 23.57110141899803 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57905 --- greater than max batches\n", - " 141/1 0.68175 0.68312 +/- 0.00189\n", - " Triggers unsatisfied, max unc./thresh. is 20.560813068689416 for absorption in\n", + " WARNING: The estimated number of batches is 72789 --- greater than max batches\n", + " 137/1 0.69621 0.68414 +/- 0.00206\n", + " Triggers unsatisfied, max unc./thresh. is 23.48120874987551 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57499 --- greater than max batches\n", - " 142/1 0.72210 0.68340 +/- 0.00190\n", - " Triggers unsatisfied, max unc./thresh. is 20.54814917791921 for absorption in\n", + " WARNING: The estimated number of batches is 72786 --- greater than max batches\n", + " 138/1 0.71568 0.68438 +/- 0.00206\n", + " Triggers unsatisfied, max unc./thresh. is 23.304158910242545 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57851 --- greater than max batches\n", - " 143/1 0.67361 0.68333 +/- 0.00188\n", - " Triggers unsatisfied, max unc./thresh. is 20.4177880049802 for absorption in\n", + " WARNING: The estimated number of batches is 72236 --- greater than max batches\n", + " 139/1 0.69041 0.68443 +/- 0.00204\n", + " Triggers unsatisfied, max unc./thresh. is 23.13692986052565 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57536 --- greater than max batches\n", - " 144/1 0.65862 0.68315 +/- 0.00188\n", - " Triggers unsatisfied, max unc./thresh. is 21.229890183572195 for absorption in\n", + " WARNING: The estimated number of batches is 71738 --- greater than max batches\n", + " 140/1 0.68872 0.68446 +/- 0.00203\n", + " Triggers unsatisfied, max unc./thresh. is 22.97436361147689 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62654 --- greater than max batches\n", - " 145/1 0.69713 0.68325 +/- 0.00187\n", - " Triggers unsatisfied, max unc./thresh. is 21.34513800240435 for absorption in\n", + " WARNING: The estimated number of batches is 71261 --- greater than max batches\n", + " 141/1 0.68717 0.68448 +/- 0.00201\n", + " Triggers unsatisfied, max unc./thresh. is 22.918186999320493 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63792 --- greater than max batches\n", - " 146/1 0.72980 0.68358 +/- 0.00188\n", - " Triggers unsatisfied, max unc./thresh. is 21.60165210412777 for absorption in\n", + " WARNING: The estimated number of batches is 71439 --- greater than max batches\n", + " 142/1 0.70148 0.68460 +/- 0.00200\n", + " Triggers unsatisfied, max unc./thresh. is 22.90664792171847 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65801 --- greater than max batches\n", - " 147/1 0.70004 0.68370 +/- 0.00187\n", - " Triggers unsatisfied, max unc./thresh. is 21.596734424310384 for absorption in\n", + " WARNING: The estimated number of batches is 71891 --- greater than max batches\n", + " 143/1 0.70002 0.68471 +/- 0.00199\n", + " Triggers unsatisfied, max unc./thresh. is 22.800726503137792 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66237 --- greater than max batches\n", - " 148/1 0.68882 0.68374 +/- 0.00186\n", - " Triggers unsatisfied, max unc./thresh. is 21.447240534346236 for absorption in\n", + " WARNING: The estimated number of batches is 71748 --- greater than max batches\n", + " 144/1 0.68586 0.68472 +/- 0.00197\n", + " Triggers unsatisfied, max unc./thresh. is 22.666610393936825 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65783 --- greater than max batches\n", - " 149/1 0.70401 0.68388 +/- 0.00185\n", - " Triggers unsatisfied, max unc./thresh. is 21.424993974056104 for absorption in\n", + " WARNING: The estimated number of batches is 71420 --- greater than max batches\n", + " 145/1 0.64403 0.68443 +/- 0.00198\n", + " Triggers unsatisfied, max unc./thresh. is 22.850786290626733 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66106 --- greater than max batches\n", - " 150/1 0.72110 0.68413 +/- 0.00186\n", - " Triggers unsatisfied, max unc./thresh. is 21.27792348945665 for absorption in\n", + " WARNING: The estimated number of batches is 73108 --- greater than max batches\n", + " 146/1 0.64825 0.68417 +/- 0.00198\n", + " Triggers unsatisfied, max unc./thresh. is 22.780126501480474 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65654 --- greater than max batches\n", - " 151/1 0.65918 0.68396 +/- 0.00185\n", - " Triggers unsatisfied, max unc./thresh. is 21.378637401006184 for absorption in\n", + " WARNING: The estimated number of batches is 73175 --- greater than max batches\n", + " 147/1 0.66962 0.68407 +/- 0.00197\n", + " Triggers unsatisfied, max unc./thresh. is 22.713317279463887 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66734 --- greater than max batches\n", - " 152/1 0.67751 0.68392 +/- 0.00184\n", - " Triggers unsatisfied, max unc./thresh. is 21.25974745003047 for absorption in\n", + " WARNING: The estimated number of batches is 73263 --- greater than max batches\n", + " 148/1 0.67953 0.68404 +/- 0.00196\n", + " Triggers unsatisfied, max unc./thresh. is 22.555984073152782 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66446 --- greater than max batches\n", - " 153/1 0.69302 0.68398 +/- 0.00183\n", - " Triggers unsatisfied, max unc./thresh. is 21.16055371271148 for absorption in\n", + " WARNING: The estimated number of batches is 72760 --- greater than max batches\n", + " 149/1 0.70028 0.68415 +/- 0.00195\n", + " Triggers unsatisfied, max unc./thresh. is 22.467338789571908 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66275 --- greater than max batches\n", - " 154/1 0.67102 0.68389 +/- 0.00182\n", - " Triggers unsatisfied, max unc./thresh. is 21.0227808264386 for absorption in\n", + " WARNING: The estimated number of batches is 72694 --- greater than max batches\n", + " 150/1 0.66858 0.68405 +/- 0.00194\n", + " Triggers unsatisfied, max unc./thresh. is 22.317894720265652 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65857 --- greater than max batches\n", - " 155/1 0.64427 0.68363 +/- 0.00183\n", - " Triggers unsatisfied, max unc./thresh. is 20.882547322553506 for absorption in\n", + " WARNING: The estimated number of batches is 72228 --- greater than max batches\n", + " 151/1 0.64729 0.68379 +/- 0.00194\n", + " Triggers unsatisfied, max unc./thresh. is 22.256511579898202 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65418 --- greater than max batches\n", - " 156/1 0.68488 0.68364 +/- 0.00181\n", - " Triggers unsatisfied, max unc./thresh. is 20.797424126850476 for absorption in\n", + " WARNING: The estimated number of batches is 72327 --- greater than max batches\n", + " 152/1 0.68669 0.68381 +/- 0.00193\n", + " Triggers unsatisfied, max unc./thresh. is 22.10461716023024 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65318 --- greater than max batches\n", - " 157/1 0.67337 0.68357 +/- 0.00180\n", - " Triggers unsatisfied, max unc./thresh. is 20.67015745584828 for absorption in\n", + " WARNING: The estimated number of batches is 71832 --- greater than max batches\n", + " 153/1 0.65299 0.68361 +/- 0.00193\n", + " Triggers unsatisfied, max unc./thresh. is 21.973197597061482 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64948 --- greater than max batches\n", - " 158/1 0.66662 0.68346 +/- 0.00180\n", - " Triggers unsatisfied, max unc./thresh. is 20.568519956722266 for absorption in\n", + " WARNING: The estimated number of batches is 71463 --- greater than max batches\n", + " 154/1 0.68069 0.68359 +/- 0.00191\n", + " Triggers unsatisfied, max unc./thresh. is 21.8366209055588 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64734 --- greater than max batches\n", - " 159/1 0.62697 0.68309 +/- 0.00182\n", - " Triggers unsatisfied, max unc./thresh. is 20.47085213159483 for absorption in\n", + " WARNING: The estimated number of batches is 71054 --- greater than max batches\n", + " 155/1 0.69270 0.68365 +/- 0.00190\n", + " Triggers unsatisfied, max unc./thresh. is 21.693707386898733 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64540 --- greater than max batches\n", - " 160/1 0.68300 0.68309 +/- 0.00181\n", - " Triggers unsatisfied, max unc./thresh. is 20.34586209866351 for absorption in\n", + " WARNING: The estimated number of batches is 70598 --- greater than max batches\n", + " 156/1 0.67507 0.68359 +/- 0.00189\n", + " Triggers unsatisfied, max unc./thresh. is 21.593867638588357 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64168 --- greater than max batches\n", - " 161/1 0.68918 0.68313 +/- 0.00180\n", - " Triggers unsatisfied, max unc./thresh. is 20.23505377614212 for absorption in\n", + " WARNING: The estimated number of batches is 70416 --- greater than max batches\n", + " 157/1 0.68589 0.68360 +/- 0.00188\n", + " Triggers unsatisfied, max unc./thresh. is 21.661355782353464 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63881 --- greater than max batches\n", - " 162/1 0.70939 0.68330 +/- 0.00179\n", - " Triggers unsatisfied, max unc./thresh. is 20.21114215977674 for absorption in\n", + " WARNING: The estimated number of batches is 71326 --- greater than max batches\n", + " 158/1 0.69068 0.68365 +/- 0.00187\n", + " Triggers unsatisfied, max unc./thresh. is 21.5257902006611 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64138 --- greater than max batches\n", - " 163/1 0.69681 0.68338 +/- 0.00179\n", - " Triggers unsatisfied, max unc./thresh. is 20.163170350438893 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 64241 --- greater than max batches\n", - " 164/1 0.66454 0.68326 +/- 0.00178\n", - " Triggers unsatisfied, max unc./thresh. is 20.109882525638955 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 64306 --- greater than max batches\n" + " WARNING: The estimated number of batches is 70900 --- greater than max batches\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 165/1 0.68804 0.68329 +/- 0.00177\n", - " Triggers unsatisfied, max unc./thresh. is 20.033653897136055 for absorption in\n", + " 159/1 0.67616 0.68360 +/- 0.00185\n", + " Triggers unsatisfied, max unc./thresh. is 21.48299566167353 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 71079 --- greater than max batches\n", + " 160/1 0.67492 0.68355 +/- 0.00184\n", + " Triggers unsatisfied, max unc./thresh. is 21.352926864543797 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64221 --- greater than max batches\n", - " 166/1 0.66078 0.68315 +/- 0.00176\n", - " Triggers unsatisfied, max unc./thresh. is 19.916131324861123 for absorption in\n", + " WARNING: The estimated number of batches is 70677 --- greater than max batches\n", + " 161/1 0.69218 0.68360 +/- 0.00183\n", + " Triggers unsatisfied, max unc./thresh. is 21.3145021538723 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63867 --- greater than max batches\n", - " 167/1 0.65762 0.68300 +/- 0.00176\n", - " Triggers unsatisfied, max unc./thresh. is 19.85097950868946 for absorption in\n", + " WARNING: The estimated number of batches is 70878 --- greater than max batches\n", + " 162/1 0.66037 0.68345 +/- 0.00183\n", + " Triggers unsatisfied, max unc./thresh. is 21.1820405677372 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63843 --- greater than max batches\n", - " 168/1 0.69267 0.68306 +/- 0.00175\n", - " Triggers unsatisfied, max unc./thresh. is 19.729436003984436 for absorption in\n", + " WARNING: The estimated number of batches is 70448 --- greater than max batches\n", + " 163/1 0.65640 0.68328 +/- 0.00182\n", + " Triggers unsatisfied, max unc./thresh. is 21.086966040066464 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63453 --- greater than max batches\n", - " 169/1 0.67859 0.68303 +/- 0.00174\n", - " Triggers unsatisfied, max unc./thresh. is 19.61178427698242 for absorption in\n", + " WARNING: The estimated number of batches is 70262 --- greater than max batches\n", + " 164/1 0.65113 0.68308 +/- 0.00182\n", + " Triggers unsatisfied, max unc./thresh. is 20.95392716842 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63084 --- greater than max batches\n", - " 170/1 0.66545 0.68292 +/- 0.00173\n", - " Triggers unsatisfied, max unc./thresh. is 19.495197332905757 for absorption in\n", + " WARNING: The estimated number of batches is 69817 --- greater than max batches\n", + " 165/1 0.70691 0.68323 +/- 0.00182\n", + " Triggers unsatisfied, max unc./thresh. is 20.837836560668773 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62716 --- greater than max batches\n", - " 171/1 0.66716 0.68283 +/- 0.00172\n", - " Triggers unsatisfied, max unc./thresh. is 19.47044861415963 for absorption in\n", + " WARNING: The estimated number of batches is 69480 --- greater than max batches\n", + " 166/1 0.67295 0.68317 +/- 0.00181\n", + " Triggers unsatisfied, max unc./thresh. is 20.769653148158348 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62936 --- greater than max batches\n", - " 172/1 0.70008 0.68293 +/- 0.00172\n", - " Triggers unsatisfied, max unc./thresh. is 19.382801191970024 for absorption in\n", + " WARNING: The estimated number of batches is 69457 --- greater than max batches\n", + " 167/1 0.66620 0.68306 +/- 0.00180\n", + " Triggers unsatisfied, max unc./thresh. is 20.676559522637568 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62746 --- greater than max batches\n", - " 173/1 0.69417 0.68300 +/- 0.00171\n", - " Triggers unsatisfied, max unc./thresh. is 19.270038663528165 for absorption in\n", + " WARNING: The estimated number of batches is 69264 --- greater than max batches\n", + " 168/1 0.66855 0.68297 +/- 0.00179\n", + " Triggers unsatisfied, max unc./thresh. is 20.72956424681471 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62390 --- greater than max batches\n", - " 174/1 0.66458 0.68289 +/- 0.00170\n", - " Triggers unsatisfied, max unc./thresh. is 19.281533726364312 for absorption in\n", + " WARNING: The estimated number of batches is 70049 --- greater than max batches\n", + " 169/1 0.69800 0.68306 +/- 0.00178\n", + " Triggers unsatisfied, max unc./thresh. is 20.880997251947857 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62836 --- greater than max batches\n", - " 175/1 0.65867 0.68275 +/- 0.00170\n", - " Triggers unsatisfied, max unc./thresh. is 19.234847030404104 for absorption in\n", + " WARNING: The estimated number of batches is 71512 --- greater than max batches\n", + " 170/1 0.69435 0.68313 +/- 0.00177\n", + " Triggers unsatisfied, max unc./thresh. is 20.817259102451693 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62902 --- greater than max batches\n", - " 176/1 0.69631 0.68283 +/- 0.00169\n", - " Triggers unsatisfied, max unc./thresh. is 19.13381099709457 for absorption in\n", + " WARNING: The estimated number of batches is 71510 --- greater than max batches\n", + " 171/1 0.65628 0.68297 +/- 0.00177\n", + " Triggers unsatisfied, max unc./thresh. is 20.7516484421445 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62609 --- greater than max batches\n", - " 177/1 0.71142 0.68299 +/- 0.00169\n", - " Triggers unsatisfied, max unc./thresh. is 19.022563643493143 for absorption in\n", + " WARNING: The estimated number of batches is 71490 --- greater than max batches\n", + " 172/1 0.68216 0.68297 +/- 0.00176\n", + " Triggers unsatisfied, max unc./thresh. is 20.633920344795456 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62245 --- greater than max batches\n", - " 178/1 0.68640 0.68301 +/- 0.00168\n", - " Triggers unsatisfied, max unc./thresh. is 19.03176453708651 for absorption in\n", + " WARNING: The estimated number of batches is 71107 --- greater than max batches\n", + " 173/1 0.67774 0.68293 +/- 0.00175\n", + " Triggers unsatisfied, max unc./thresh. is 20.517288947571853 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62667 --- greater than max batches\n", - " 179/1 0.70448 0.68313 +/- 0.00167\n", - " Triggers unsatisfied, max unc./thresh. is 19.088395456136563 for absorption in\n", + " WARNING: The estimated number of batches is 70727 --- greater than max batches\n", + " 174/1 0.71232 0.68311 +/- 0.00175\n", + " Triggers unsatisfied, max unc./thresh. is 20.39737483131609 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63405 --- greater than max batches\n", - " 180/1 0.70538 0.68326 +/- 0.00167\n", - " Triggers unsatisfied, max unc./thresh. is 18.98864751831452 for absorption in\n", + " WARNING: The estimated number of batches is 70318 --- greater than max batches\n", + " 175/1 0.65712 0.68295 +/- 0.00174\n", + " Triggers unsatisfied, max unc./thresh. is 20.340808883881543 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63105 --- greater than max batches\n", - " 181/1 0.65591 0.68311 +/- 0.00166\n", - " Triggers unsatisfied, max unc./thresh. is 18.911017891051518 for absorption in\n", + " WARNING: The estimated number of batches is 70343 --- greater than max batches\n", + " 176/1 0.67973 0.68294 +/- 0.00173\n", + " Triggers unsatisfied, max unc./thresh. is 20.225760579774068 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62948 --- greater than max batches\n", - " 182/1 0.72818 0.68336 +/- 0.00167\n", - " Triggers unsatisfied, max unc./thresh. is 18.808510226366458 for absorption in\n", + " WARNING: The estimated number of batches is 69958 --- greater than max batches\n", + " 177/1 0.70262 0.68305 +/- 0.00173\n", + " Triggers unsatisfied, max unc./thresh. is 20.188734303225726 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62621 --- greater than max batches\n", - " 183/1 0.67896 0.68334 +/- 0.00167\n", - " Triggers unsatisfied, max unc./thresh. is 18.825142861337717 for absorption in\n", + " WARNING: The estimated number of batches is 70110 --- greater than max batches\n", + " 178/1 0.66234 0.68293 +/- 0.00172\n", + " Triggers unsatisfied, max unc./thresh. is 20.36584641647614 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63086 --- greater than max batches\n", - " 184/1 0.65442 0.68317 +/- 0.00166\n", - " Triggers unsatisfied, max unc./thresh. is 18.79514029258707 for absorption in\n", + " WARNING: The estimated number of batches is 71760 --- greater than max batches\n", + " 179/1 0.67778 0.68290 +/- 0.00171\n", + " Triggers unsatisfied, max unc./thresh. is 20.266332203155603 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63239 --- greater than max batches\n", - " 185/1 0.68885 0.68321 +/- 0.00165\n", - " Triggers unsatisfied, max unc./thresh. is 18.76276176864163 for absorption in\n", + " WARNING: The estimated number of batches is 71472 --- greater than max batches\n", + " 180/1 0.63949 0.68265 +/- 0.00172\n", + " Triggers unsatisfied, max unc./thresh. is 20.153693315273593 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63373 --- greater than max batches\n", - " 186/1 0.68893 0.68324 +/- 0.00165\n", - " Triggers unsatisfied, max unc./thresh. is 18.690155368597363 for absorption in\n", + " WARNING: The estimated number of batches is 71085 --- greater than max batches\n", + " 181/1 0.66641 0.68256 +/- 0.00171\n", + " Triggers unsatisfied, max unc./thresh. is 20.06530998234231 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63233 --- greater than max batches\n", - " 187/1 0.68918 0.68327 +/- 0.00164\n", - " Triggers unsatisfied, max unc./thresh. is 18.590144288270153 for absorption in\n", + " WARNING: The estimated number of batches is 70866 --- greater than max batches\n", + " 182/1 0.70248 0.68267 +/- 0.00171\n", + " Triggers unsatisfied, max unc./thresh. is 19.994849139740012 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62904 --- greater than max batches\n", - " 188/1 0.69854 0.68335 +/- 0.00163\n", - " Triggers unsatisfied, max unc./thresh. is 18.61460656150607 for absorption in\n", + " WARNING: The estimated number of batches is 70769 --- greater than max batches\n", + " 183/1 0.68906 0.68271 +/- 0.00170\n", + " Triggers unsatisfied, max unc./thresh. is 20.092026018527292 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63416 --- greater than max batches\n", - " 189/1 0.66324 0.68324 +/- 0.00162\n", - " Triggers unsatisfied, max unc./thresh. is 18.518608099237504 for absorption in\n", + " WARNING: The estimated number of batches is 71862 --- greater than max batches\n", + " 184/1 0.64815 0.68252 +/- 0.00170\n", + " Triggers unsatisfied, max unc./thresh. is 20.010212645636404 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63106 --- greater than max batches\n", - " 190/1 0.69450 0.68331 +/- 0.00162\n", - " Triggers unsatisfied, max unc./thresh. is 18.425351661292233 for absorption in\n", + " WARNING: The estimated number of batches is 71679 --- greater than max batches\n", + " 185/1 0.66689 0.68243 +/- 0.00169\n", + " Triggers unsatisfied, max unc./thresh. is 19.898829466229213 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62812 --- greater than max batches\n", - " 191/1 0.68953 0.68334 +/- 0.00161\n", - " Triggers unsatisfied, max unc./thresh. is 18.328779429843646 for absorption in\n", + " WARNING: The estimated number of batches is 71279 --- greater than max batches\n", + " 186/1 0.68573 0.68245 +/- 0.00168\n", + " Triggers unsatisfied, max unc./thresh. is 19.796150792368255 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62491 --- greater than max batches\n", - " 192/1 0.66621 0.68325 +/- 0.00160\n", - " Triggers unsatisfied, max unc./thresh. is 18.28094973389564 for absorption in\n", + " WARNING: The estimated number of batches is 70937 --- greater than max batches\n", + " 187/1 0.66673 0.68236 +/- 0.00167\n", + " Triggers unsatisfied, max unc./thresh. is 19.701525271465893 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62500 --- greater than max batches\n", - " 193/1 0.71102 0.68339 +/- 0.00160\n", - " Triggers unsatisfied, max unc./thresh. is 18.19949730061142 for absorption in\n", + " WARNING: The estimated number of batches is 70649 --- greater than max batches\n", + " 188/1 0.65564 0.68222 +/- 0.00167\n", + " Triggers unsatisfied, max unc./thresh. is 19.864636825432147 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62275 --- greater than max batches\n", - " 194/1 0.65341 0.68324 +/- 0.00160\n", - " Triggers unsatisfied, max unc./thresh. is 18.159054737369345 for absorption in\n", + " WARNING: The estimated number of batches is 72218 --- greater than max batches\n", + " 189/1 0.69960 0.68231 +/- 0.00167\n", + " Triggers unsatisfied, max unc./thresh. is 19.855911612557982 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62328 --- greater than max batches\n", - " 195/1 0.70061 0.68333 +/- 0.00159\n", - " Triggers unsatisfied, max unc./thresh. is 18.082465954324594 for absorption in\n", + " WARNING: The estimated number of batches is 72549 --- greater than max batches\n", + " 190/1 0.68178 0.68231 +/- 0.00166\n", + " Triggers unsatisfied, max unc./thresh. is 19.76297244962571 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62131 --- greater than max batches\n", - " 196/1 0.69339 0.68338 +/- 0.00159\n", - " Triggers unsatisfied, max unc./thresh. is 18.043133483791827 for absorption in\n", + " WARNING: The estimated number of batches is 72262 --- greater than max batches\n", + " 191/1 0.69760 0.68239 +/- 0.00165\n", + " Triggers unsatisfied, max unc./thresh. is 19.760934073441348 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62186 --- greater than max batches\n", - " 197/1 0.64411 0.68318 +/- 0.00159\n", - " Triggers unsatisfied, max unc./thresh. is 18.019303623546417 for absorption in\n", + " WARNING: The estimated number of batches is 72637 --- greater than max batches\n", + " 192/1 0.66122 0.68228 +/- 0.00164\n", + " Triggers unsatisfied, max unc./thresh. is 19.804180280770698 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62347 --- greater than max batches\n", - " 198/1 0.66626 0.68309 +/- 0.00159\n", - " Triggers unsatisfied, max unc./thresh. is 17.968092739058083 for absorption in\n", + " WARNING: The estimated number of batches is 73348 --- greater than max batches\n", + " 193/1 0.69346 0.68234 +/- 0.00164\n", + " Triggers unsatisfied, max unc./thresh. is 19.801359777905503 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62316 --- greater than max batches\n", - " 199/1 0.67839 0.68306 +/- 0.00158\n", - " Triggers unsatisfied, max unc./thresh. is 17.91968515142146 for absorption in\n", + " WARNING: The estimated number of batches is 73719 --- greater than max batches\n", + " 194/1 0.67831 0.68231 +/- 0.00163\n", + " Triggers unsatisfied, max unc./thresh. is 19.738915362738556 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62302 --- greater than max batches\n", - " 200/1 0.66459 0.68297 +/- 0.00157\n", - " Triggers unsatisfied, max unc./thresh. is 17.82970764669685 for absorption in\n", + " WARNING: The estimated number of batches is 73645 --- greater than max batches\n", + " 195/1 0.65285 0.68216 +/- 0.00163\n", + " Triggers unsatisfied, max unc./thresh. is 19.653034572834297 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61996 --- greater than max batches\n", + " WARNING: The estimated number of batches is 73391 --- greater than max batches\n", + " 196/1 0.66672 0.68208 +/- 0.00162\n", + " Triggers unsatisfied, max unc./thresh. is 19.625879744409815 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 73574 --- greater than max batches\n", + " 197/1 0.67536 0.68204 +/- 0.00161\n", + " Triggers unsatisfied, max unc./thresh. is 19.52848856663241 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 73227 --- greater than max batches\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 198/1 0.66675 0.68196 +/- 0.00161\n", + " Triggers unsatisfied, max unc./thresh. is 19.51191007087699 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 73483 --- greater than max batches\n", + " 199/1 0.64492 0.68177 +/- 0.00161\n", + " Triggers unsatisfied, max unc./thresh. is 19.511121460032857 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 73858 --- greater than max batches\n", + " 200/1 0.69441 0.68184 +/- 0.00160\n", + " Triggers unsatisfied, max unc./thresh. is 19.41826259948151 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 73534 --- greater than max batches\n", " Creating state point statepoint.200.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.9309e-01 seconds\n", - " Reading cross sections = 2.8108e-01 seconds\n", - " Total time in simulation = 1.1321e+01 seconds\n", - " Time in transport only = 1.1242e+01 seconds\n", - " Time in inactive batches = 1.6721e-01 seconds\n", - " Time in active batches = 1.1153e+01 seconds\n", - " Time synchronizing fission bank = 2.2958e-02 seconds\n", - " Sampling source sites = 1.8701e-02 seconds\n", - " SEND/RECV source sites = 3.9403e-03 seconds\n", - " Time accumulating tallies = 9.9349e-04 seconds\n", - " Total time for finalization = 5.2200e-07 seconds\n", - " Total time elapsed = 1.1620e+01 seconds\n", - " Calculation Rate (inactive) = 74758.2 particles/second\n", - " Calculation Rate (active) = 43708.5 particles/second\n", + " Total time for initialization = 3.2763e-01 seconds\n", + " Reading cross sections = 3.1447e-01 seconds\n", + " Total time in simulation = 2.3755e+01 seconds\n", + " Time in transport only = 2.3701e+01 seconds\n", + " Time in inactive batches = 4.2962e-01 seconds\n", + " Time in active batches = 2.3325e+01 seconds\n", + " Time synchronizing fission bank = 2.0652e-02 seconds\n", + " Sampling source sites = 1.7317e-02 seconds\n", + " SEND/RECV source sites = 3.2308e-03 seconds\n", + " Time accumulating tallies = 1.7916e-03 seconds\n", + " Time writing statepoints = 8.5764e-03 seconds\n", + " Total time for finalization = 7.5200e-07 seconds\n", + " Total time elapsed = 2.4088e+01 seconds\n", + " Calculation Rate (inactive) = 29095.3 particles/second\n", + " Calculation Rate (active) = 20900.3 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.68198 +/- 0.00141\n", - " k-effective (Track-length) = 0.68297 +/- 0.00157\n", - " k-effective (Absorption) = 0.68161 +/- 0.00145\n", - " Combined k-effective = 0.68209 +/- 0.00118\n", - " Leakage Fraction = 0.34033 +/- 0.00074\n", + " k-effective (Collision) = 0.68290 +/- 0.00158\n", + " k-effective (Track-length) = 0.68184 +/- 0.00160\n", + " k-effective (Absorption) = 0.68167 +/- 0.00151\n", + " Combined k-effective = 0.68200 +/- 0.00134\n", + " Leakage Fraction = 0.33996 +/- 0.00079\n", "\n" ] } @@ -1341,13 +1349,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[0.04508259]]\n", + "[[[0.04969994]]\n", "\n", - " [[0.0221707 ]]\n", + " [[0.02200773]]\n", "\n", - " [[0.10763375]]\n", + " [[0.12289506]]\n", "\n", - " [[0.05107401]]]\n" + " [[0.05303359]]]\n" ] } ], @@ -1415,8 +1423,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.27e-04\n", - " 1.02e-05\n", + " 2.10e-04\n", + " 1.04e-05\n", " \n", " \n", " 1\n", @@ -1426,8 +1434,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.54e-04\n", - " 2.50e-05\n", + " 5.11e-04\n", + " 2.54e-05\n", " \n", " \n", " 2\n", @@ -1438,7 +1446,7 @@ " 2.00e+07\n", " fission\n", " 7.19e-05\n", - " 1.82e-06\n", + " 1.83e-06\n", " \n", " \n", " 3\n", @@ -1449,7 +1457,7 @@ " 2.00e+07\n", " nu-fission\n", " 1.89e-04\n", - " 4.69e-06\n", + " 4.75e-06\n", " \n", " \n", " 4\n", @@ -1459,8 +1467,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.35e-04\n", - " 9.82e-06\n", + " 2.19e-04\n", + " 9.36e-06\n", " \n", " \n", " 5\n", @@ -1470,8 +1478,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.71e-04\n", - " 2.39e-05\n", + " 5.33e-04\n", + " 2.28e-05\n", " \n", " \n", " 6\n", @@ -1481,8 +1489,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 6.88e-05\n", - " 1.61e-06\n", + " 6.98e-05\n", + " 1.63e-06\n", " \n", " \n", " 7\n", @@ -1492,8 +1500,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.81e-04\n", - " 4.15e-06\n", + " 1.85e-04\n", + " 4.24e-06\n", " \n", " \n", " 8\n", @@ -1503,8 +1511,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.31e-04\n", - " 1.13e-05\n", + " 2.23e-04\n", + " 1.02e-05\n", " \n", " \n", " 9\n", @@ -1514,8 +1522,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.63e-04\n", - " 2.76e-05\n", + " 5.44e-04\n", + " 2.48e-05\n", " \n", " \n", " 10\n", @@ -1525,8 +1533,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 6.95e-05\n", - " 1.76e-06\n", + " 6.69e-05\n", + " 1.65e-06\n", " \n", " \n", " 11\n", @@ -1536,8 +1544,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.83e-04\n", - " 4.53e-06\n", + " 1.76e-04\n", + " 4.27e-06\n", " \n", " \n", " 12\n", @@ -1547,8 +1555,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.07e-04\n", - " 9.85e-06\n", + " 2.09e-04\n", + " 9.24e-06\n", " \n", " \n", " 13\n", @@ -1558,8 +1566,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.04e-04\n", - " 2.40e-05\n", + " 5.09e-04\n", + " 2.25e-05\n", " \n", " \n", " 14\n", @@ -1569,8 +1577,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 6.48e-05\n", - " 1.45e-06\n", + " 6.72e-05\n", + " 1.65e-06\n", " \n", " \n", " 15\n", @@ -1580,8 +1588,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.71e-04\n", - " 3.81e-06\n", + " 1.77e-04\n", + " 4.32e-06\n", " \n", " \n", " 16\n", @@ -1591,8 +1599,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.20e-04\n", - " 1.07e-05\n", + " 2.11e-04\n", + " 8.34e-06\n", " \n", " \n", " 17\n", @@ -1602,8 +1610,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.37e-04\n", - " 2.60e-05\n", + " 5.15e-04\n", + " 2.03e-05\n", " \n", " \n", " 18\n", @@ -1613,8 +1621,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 6.76e-05\n", - " 1.78e-06\n", + " 6.47e-05\n", + " 1.40e-06\n", " \n", " \n", " 19\n", @@ -1624,8 +1632,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.78e-04\n", - " 4.63e-06\n", + " 1.70e-04\n", + " 3.66e-06\n", " \n", " \n", "\n", @@ -1634,49 +1642,49 @@ "text/plain": [ " mesh 1 energy low [eV] energy high [eV] score mean \\\n", " x y z \n", - "0 1 1 1 0.00e+00 6.25e-01 fission 2.27e-04 \n", - "1 1 1 1 0.00e+00 6.25e-01 nu-fission 5.54e-04 \n", + "0 1 1 1 0.00e+00 6.25e-01 fission 2.10e-04 \n", + "1 1 1 1 0.00e+00 6.25e-01 nu-fission 5.11e-04 \n", "2 1 1 1 6.25e-01 2.00e+07 fission 7.19e-05 \n", "3 1 1 1 6.25e-01 2.00e+07 nu-fission 1.89e-04 \n", - "4 2 1 1 0.00e+00 6.25e-01 fission 2.35e-04 \n", - "5 2 1 1 0.00e+00 6.25e-01 nu-fission 5.71e-04 \n", - "6 2 1 1 6.25e-01 2.00e+07 fission 6.88e-05 \n", - "7 2 1 1 6.25e-01 2.00e+07 nu-fission 1.81e-04 \n", - "8 3 1 1 0.00e+00 6.25e-01 fission 2.31e-04 \n", - "9 3 1 1 0.00e+00 6.25e-01 nu-fission 5.63e-04 \n", - "10 3 1 1 6.25e-01 2.00e+07 fission 6.95e-05 \n", - "11 3 1 1 6.25e-01 2.00e+07 nu-fission 1.83e-04 \n", - "12 4 1 1 0.00e+00 6.25e-01 fission 2.07e-04 \n", - "13 4 1 1 0.00e+00 6.25e-01 nu-fission 5.04e-04 \n", - "14 4 1 1 6.25e-01 2.00e+07 fission 6.48e-05 \n", - "15 4 1 1 6.25e-01 2.00e+07 nu-fission 1.71e-04 \n", - "16 5 1 1 0.00e+00 6.25e-01 fission 2.20e-04 \n", - "17 5 1 1 0.00e+00 6.25e-01 nu-fission 5.37e-04 \n", - "18 5 1 1 6.25e-01 2.00e+07 fission 6.76e-05 \n", - "19 5 1 1 6.25e-01 2.00e+07 nu-fission 1.78e-04 \n", + "4 2 1 1 0.00e+00 6.25e-01 fission 2.19e-04 \n", + "5 2 1 1 0.00e+00 6.25e-01 nu-fission 5.33e-04 \n", + "6 2 1 1 6.25e-01 2.00e+07 fission 6.98e-05 \n", + "7 2 1 1 6.25e-01 2.00e+07 nu-fission 1.85e-04 \n", + "8 3 1 1 0.00e+00 6.25e-01 fission 2.23e-04 \n", + "9 3 1 1 0.00e+00 6.25e-01 nu-fission 5.44e-04 \n", + "10 3 1 1 6.25e-01 2.00e+07 fission 6.69e-05 \n", + "11 3 1 1 6.25e-01 2.00e+07 nu-fission 1.76e-04 \n", + "12 4 1 1 0.00e+00 6.25e-01 fission 2.09e-04 \n", + "13 4 1 1 0.00e+00 6.25e-01 nu-fission 5.09e-04 \n", + "14 4 1 1 6.25e-01 2.00e+07 fission 6.72e-05 \n", + "15 4 1 1 6.25e-01 2.00e+07 nu-fission 1.77e-04 \n", + "16 5 1 1 0.00e+00 6.25e-01 fission 2.11e-04 \n", + "17 5 1 1 0.00e+00 6.25e-01 nu-fission 5.15e-04 \n", + "18 5 1 1 6.25e-01 2.00e+07 fission 6.47e-05 \n", + "19 5 1 1 6.25e-01 2.00e+07 nu-fission 1.70e-04 \n", "\n", " std. dev. \n", " \n", - "0 1.02e-05 \n", - "1 2.50e-05 \n", - "2 1.82e-06 \n", - "3 4.69e-06 \n", - "4 9.82e-06 \n", - "5 2.39e-05 \n", - "6 1.61e-06 \n", - "7 4.15e-06 \n", - "8 1.13e-05 \n", - "9 2.76e-05 \n", - "10 1.76e-06 \n", - "11 4.53e-06 \n", - "12 9.85e-06 \n", - "13 2.40e-05 \n", - "14 1.45e-06 \n", - "15 3.81e-06 \n", - "16 1.07e-05 \n", - "17 2.60e-05 \n", - "18 1.78e-06 \n", - "19 4.63e-06 " + "0 1.04e-05 \n", + "1 2.54e-05 \n", + "2 1.83e-06 \n", + "3 4.75e-06 \n", + "4 9.36e-06 \n", + "5 2.28e-05 \n", + "6 1.63e-06 \n", + "7 4.24e-06 \n", + "8 1.02e-05 \n", + "9 2.48e-05 \n", + "10 1.65e-06 \n", + "11 4.27e-06 \n", + "12 9.24e-06 \n", + "13 2.25e-05 \n", + "14 1.65e-06 \n", + "15 4.32e-06 \n", + "16 8.34e-06 \n", + "17 2.03e-05 \n", + "18 1.40e-06 \n", + "19 3.66e-06 " ] }, "execution_count": 20, @@ -1702,7 +1710,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -1835,8 +1843,8 @@ " 1\n", " U235\n", " scatter\n", - " 3.81e-02\n", - " 4.13e-05\n", + " 3.82e-02\n", + " 4.48e-05\n", " \n", " \n", " 1\n", @@ -1844,7 +1852,7 @@ " U238\n", " scatter\n", " 2.34e+00\n", - " 2.41e-03\n", + " 2.52e-03\n", " \n", " \n", "\n", @@ -1852,8 +1860,8 @@ ], "text/plain": [ " cell nuclide score mean std. dev.\n", - "0 1 U235 scatter 3.81e-02 4.13e-05\n", - "1 1 U238 scatter 2.34e+00 2.41e-03" + "0 1 U235 scatter 3.82e-02 4.48e-05\n", + "1 1 U238 scatter 2.34e+00 2.52e-03" ] }, "execution_count": 24, @@ -1885,8 +1893,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[2.41367509e-03]\n", - " [4.12533801e-05]]]\n" + "[[[2.52057084e-03]\n", + " [4.48210552e-05]]]\n" ] } ], @@ -1947,25 +1955,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[0.0131914 ]]\n", + "[[[0.01301475]]\n", "\n", - " [[0.01252949]]\n", + " [[0.0136507 ]]\n", "\n", - " [[0.01241481]]\n", + " [[0.01260538]]\n", "\n", - " [[0.01194961]]\n", + " [[0.01293374]]\n", "\n", - " [[0.01186091]]\n", + " [[0.01219012]]\n", "\n", - " [[0.0127257 ]]\n", + " [[0.01191931]]\n", "\n", - " [[0.01358576]]\n", + " [[0.0127884 ]]\n", "\n", - " [[0.0130368 ]]\n", + " [[0.01339325]]\n", "\n", - " [[0.014031 ]]\n", + " [[0.01309524]]\n", "\n", - " [[0.0141883 ]]]\n" + " [[0.0133674 ]]]\n" ] } ], @@ -1981,13 +1989,30 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Print the distribcell tally dataframe" + "Now that we're done getting data from the statepoint file, we'll close it to free the file handle for subsequent OpenMC runs." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, + "outputs": [], + "source": [ + "# Close the statepoint file now that we're done gathering info from it\n", + "sp.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print the distribcell tally dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, "outputs": [ { "data": { @@ -2057,8 +2082,8 @@ " 3\n", " 279\n", " absorption\n", - " 7.11e-04\n", - " 1.10e-05\n", + " 7.23e-04\n", + " 1.05e-05\n", " \n", " \n", " 559\n", @@ -2071,8 +2096,8 @@ " 3\n", " 279\n", " scatter\n", - " 8.91e-02\n", - " 6.60e-04\n", + " 9.08e-02\n", + " 6.39e-04\n", " \n", " \n", " 560\n", @@ -2086,7 +2111,7 @@ " 280\n", " absorption\n", " 6.75e-04\n", - " 1.02e-05\n", + " 1.09e-05\n", " \n", " \n", " 561\n", @@ -2099,8 +2124,8 @@ " 3\n", " 280\n", " scatter\n", - " 8.35e-02\n", - " 6.11e-04\n", + " 8.49e-02\n", + " 6.75e-04\n", " \n", " \n", " 562\n", @@ -2113,8 +2138,8 @@ " 3\n", " 281\n", " absorption\n", - " 6.10e-04\n", - " 1.02e-05\n", + " 6.22e-04\n", + " 1.06e-05\n", " \n", " \n", " 563\n", @@ -2127,8 +2152,8 @@ " 3\n", " 281\n", " scatter\n", - " 7.75e-02\n", - " 6.10e-04\n", + " 7.85e-02\n", + " 6.36e-04\n", " \n", " \n", " 564\n", @@ -2141,8 +2166,8 @@ " 3\n", " 282\n", " absorption\n", - " 5.67e-04\n", - " 9.88e-06\n", + " 5.66e-04\n", + " 1.01e-05\n", " \n", " \n", " 565\n", @@ -2155,8 +2180,8 @@ " 3\n", " 282\n", " scatter\n", - " 7.11e-02\n", - " 5.99e-04\n", + " 7.17e-02\n", + " 5.80e-04\n", " \n", " \n", " 566\n", @@ -2169,8 +2194,8 @@ " 3\n", " 283\n", " absorption\n", - " 5.06e-04\n", - " 9.35e-06\n", + " 4.94e-04\n", + " 9.18e-06\n", " \n", " \n", " 567\n", @@ -2183,8 +2208,8 @@ " 3\n", " 283\n", " scatter\n", - " 6.39e-02\n", - " 5.53e-04\n", + " 6.35e-02\n", + " 5.45e-04\n", " \n", " \n", " 568\n", @@ -2197,8 +2222,8 @@ " 3\n", " 284\n", " absorption\n", - " 4.35e-04\n", - " 8.22e-06\n", + " 4.34e-04\n", + " 8.41e-06\n", " \n", " \n", " 569\n", @@ -2211,8 +2236,8 @@ " 3\n", " 284\n", " scatter\n", - " 5.62e-02\n", - " 5.18e-04\n", + " 5.54e-02\n", + " 5.15e-04\n", " \n", " \n", " 570\n", @@ -2225,8 +2250,8 @@ " 3\n", " 285\n", " absorption\n", - " 3.73e-04\n", - " 7.90e-06\n", + " 3.58e-04\n", + " 7.60e-06\n", " \n", " \n", " 571\n", @@ -2239,8 +2264,8 @@ " 3\n", " 285\n", " scatter\n", - " 4.76e-02\n", - " 4.92e-04\n", + " 4.70e-02\n", + " 4.81e-04\n", " \n", " \n", " 572\n", @@ -2253,8 +2278,8 @@ " 3\n", " 286\n", " absorption\n", - " 2.98e-04\n", - " 7.30e-06\n", + " 2.83e-04\n", + " 7.08e-06\n", " \n", " \n", " 573\n", @@ -2267,8 +2292,8 @@ " 3\n", " 286\n", " scatter\n", - " 3.82e-02\n", - " 4.17e-04\n", + " 3.84e-02\n", + " 4.47e-04\n", " \n", " \n", " 574\n", @@ -2281,8 +2306,8 @@ " 3\n", " 287\n", " absorption\n", - " 2.05e-04\n", - " 5.96e-06\n", + " 2.19e-04\n", + " 6.36e-06\n", " \n", " \n", " 575\n", @@ -2295,8 +2320,8 @@ " 3\n", " 287\n", " scatter\n", - " 2.86e-02\n", - " 3.72e-04\n", + " 2.99e-02\n", + " 3.89e-04\n", " \n", " \n", " 576\n", @@ -2309,8 +2334,8 @@ " 3\n", " 288\n", " absorption\n", - " 1.22e-04\n", - " 4.12e-06\n", + " 1.18e-04\n", + " 4.29e-06\n", " \n", " \n", " 577\n", @@ -2323,8 +2348,8 @@ " 3\n", " 288\n", " scatter\n", - " 1.82e-02\n", - " 2.59e-04\n", + " 1.85e-02\n", + " 2.75e-04\n", " \n", " \n", "\n", @@ -2358,29 +2383,29 @@ " mean std. dev. \n", " \n", " \n", - "558 7.11e-04 1.10e-05 \n", - "559 8.91e-02 6.60e-04 \n", - "560 6.75e-04 1.02e-05 \n", - "561 8.35e-02 6.11e-04 \n", - "562 6.10e-04 1.02e-05 \n", - "563 7.75e-02 6.10e-04 \n", - "564 5.67e-04 9.88e-06 \n", - "565 7.11e-02 5.99e-04 \n", - "566 5.06e-04 9.35e-06 \n", - "567 6.39e-02 5.53e-04 \n", - "568 4.35e-04 8.22e-06 \n", - "569 5.62e-02 5.18e-04 \n", - "570 3.73e-04 7.90e-06 \n", - "571 4.76e-02 4.92e-04 \n", - "572 2.98e-04 7.30e-06 \n", - "573 3.82e-02 4.17e-04 \n", - "574 2.05e-04 5.96e-06 \n", - "575 2.86e-02 3.72e-04 \n", - "576 1.22e-04 4.12e-06 \n", - "577 1.82e-02 2.59e-04 " + "558 7.23e-04 1.05e-05 \n", + "559 9.08e-02 6.39e-04 \n", + "560 6.75e-04 1.09e-05 \n", + "561 8.49e-02 6.75e-04 \n", + "562 6.22e-04 1.06e-05 \n", + "563 7.85e-02 6.36e-04 \n", + "564 5.66e-04 1.01e-05 \n", + "565 7.17e-02 5.80e-04 \n", + "566 4.94e-04 9.18e-06 \n", + "567 6.35e-02 5.45e-04 \n", + "568 4.34e-04 8.41e-06 \n", + "569 5.54e-02 5.15e-04 \n", + "570 3.58e-04 7.60e-06 \n", + "571 4.70e-02 4.81e-04 \n", + "572 2.83e-04 7.08e-06 \n", + "573 3.84e-02 4.47e-04 \n", + "574 2.19e-04 6.36e-06 \n", + "575 2.99e-02 3.89e-04 \n", + "576 1.18e-04 4.29e-06 \n", + "577 1.85e-02 2.75e-04 " ] }, - "execution_count": 28, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2395,7 +2420,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2442,37 +2467,37 @@ " \n", " mean\n", " 4.19e-04\n", - " 6.86e-06\n", + " 6.85e-06\n", " \n", " \n", " std\n", - " 2.41e-04\n", - " 2.51e-06\n", + " 2.43e-04\n", + " 2.55e-06\n", " \n", " \n", " min\n", - " 1.68e-05\n", - " 1.07e-06\n", + " 1.65e-05\n", + " 1.25e-06\n", " \n", " \n", " 25%\n", - " 2.06e-04\n", - " 5.09e-06\n", + " 2.08e-04\n", + " 4.81e-06\n", " \n", " \n", " 50%\n", - " 3.98e-04\n", - " 6.90e-06\n", + " 3.95e-04\n", + " 6.87e-06\n", " \n", " \n", " 75%\n", - " 6.17e-04\n", - " 8.44e-06\n", + " 6.19e-04\n", + " 8.77e-06\n", " \n", " \n", " max\n", - " 8.70e-04\n", - " 1.52e-05\n", + " 9.04e-04\n", + " 1.51e-05\n", " \n", " \n", "\n", @@ -2483,16 +2508,16 @@ " \n", " \n", "count 2.89e+02 2.89e+02\n", - "mean 4.19e-04 6.86e-06\n", - "std 2.41e-04 2.51e-06\n", - "min 1.68e-05 1.07e-06\n", - "25% 2.06e-04 5.09e-06\n", - "50% 3.98e-04 6.90e-06\n", - "75% 6.17e-04 8.44e-06\n", - "max 8.70e-04 1.52e-05" + "mean 4.19e-04 6.85e-06\n", + "std 2.43e-04 2.55e-06\n", + "min 1.65e-05 1.25e-06\n", + "25% 2.08e-04 4.81e-06\n", + "50% 3.95e-04 6.87e-06\n", + "75% 6.19e-04 8.77e-06\n", + "max 9.04e-04 1.51e-05" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2515,14 +2540,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 0.47449458604689265\n" + "Mann-Whitney Test p-value: 0.4886239022425303\n" ] } ], @@ -2551,14 +2576,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 2.499381683224802e-42\n" + "Mann-Whitney Test p-value: 3.2855436374070945e-42\n" ] } ], @@ -2585,19 +2610,19 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - ":4: SettingWithCopyWarning: \n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " scatter['rel. err.'] = scatter['std. dev.'] / scatter['mean']\n" + " after removing the cwd from sys.path.\n" ] }, { @@ -2606,13 +2631,13 @@ "" ] }, - "execution_count": 32, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2635,22 +2660,22 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 33, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -2688,7 +2713,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/post-processing.ipynb b/examples/jupyter/post-processing.ipynb index 8d9e1096620..8684ccaae86 100644 --- a/examples/jupyter/post-processing.ipynb +++ b/examples/jupyter/post-processing.ipynb @@ -236,7 +236,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -344,157 +344,161 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", - " Date/Time | 2019-07-19 06:22:24\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 15:28:06\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", - " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", - " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", - " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", - " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", - " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.04359\n", - " 2/1 1.04323\n", - " 3/1 1.04711\n", - " 4/1 1.03892\n", - " 5/1 1.02459\n", - " 6/1 1.03936\n", - " 7/1 1.03529\n", - " 8/1 1.01590\n", - " 9/1 1.03060\n", - " 10/1 1.02892\n", - " 11/1 1.03987\n", - " 12/1 1.04395 1.04191 +/- 0.00204\n", - " 13/1 1.04971 1.04451 +/- 0.00285\n", - " 14/1 1.03880 1.04308 +/- 0.00247\n", - " 15/1 1.03091 1.04065 +/- 0.00310\n", - " 16/1 1.03618 1.03990 +/- 0.00264\n", - " 17/1 1.04109 1.04007 +/- 0.00223\n", - " 18/1 1.02978 1.03879 +/- 0.00232\n", - " 19/1 1.06363 1.04155 +/- 0.00344\n", - " 20/1 1.06549 1.04394 +/- 0.00390\n", - " 21/1 1.03469 1.04310 +/- 0.00362\n", - " 22/1 1.01925 1.04111 +/- 0.00386\n", - " 23/1 1.03268 1.04046 +/- 0.00361\n", - " 24/1 1.03906 1.04036 +/- 0.00334\n", - " 25/1 1.02632 1.03943 +/- 0.00325\n", - " 26/1 1.03906 1.03940 +/- 0.00304\n", - " 27/1 1.05058 1.04006 +/- 0.00293\n", - " 28/1 1.03248 1.03964 +/- 0.00279\n", - " 29/1 1.04076 1.03970 +/- 0.00264\n", - " 30/1 1.00994 1.03821 +/- 0.00292\n", - " 31/1 1.04785 1.03867 +/- 0.00281\n", - " 32/1 1.03080 1.03831 +/- 0.00270\n", - " 33/1 1.01862 1.03746 +/- 0.00272\n", - " 34/1 1.05370 1.03813 +/- 0.00269\n", - " 35/1 1.02226 1.03750 +/- 0.00266\n", - " 36/1 1.02862 1.03716 +/- 0.00258\n", - " 37/1 1.04790 1.03755 +/- 0.00251\n", - " 38/1 1.03762 1.03756 +/- 0.00242\n", - " 39/1 1.02255 1.03704 +/- 0.00239\n", - " 40/1 1.06094 1.03784 +/- 0.00245\n", - " 41/1 1.03842 1.03786 +/- 0.00237\n", - " 42/1 1.00628 1.03687 +/- 0.00249\n", - " 43/1 1.04916 1.03724 +/- 0.00245\n", - " 44/1 1.06237 1.03798 +/- 0.00248\n", - " 45/1 1.08153 1.03922 +/- 0.00271\n", - " 46/1 1.05649 1.03970 +/- 0.00268\n", - " 47/1 1.06265 1.04032 +/- 0.00268\n", - " 48/1 1.05728 1.04077 +/- 0.00265\n", - " 49/1 1.07343 1.04161 +/- 0.00271\n", - " 50/1 1.04640 1.04173 +/- 0.00265\n", - " 51/1 1.05143 1.04196 +/- 0.00259\n", - " 52/1 1.03639 1.04183 +/- 0.00253\n", - " 53/1 1.04846 1.04199 +/- 0.00248\n", - " 54/1 1.02435 1.04158 +/- 0.00245\n", - " 55/1 1.04806 1.04173 +/- 0.00240\n", - " 56/1 1.04798 1.04186 +/- 0.00235\n", - " 57/1 1.06621 1.04238 +/- 0.00236\n", - " 58/1 1.05734 1.04269 +/- 0.00233\n", - " 59/1 1.04581 1.04276 +/- 0.00228\n", - " 60/1 1.02682 1.04244 +/- 0.00226\n", - " 61/1 1.05971 1.04278 +/- 0.00224\n", - " 62/1 1.02357 1.04241 +/- 0.00223\n", - " 63/1 1.02645 1.04211 +/- 0.00221\n", - " 64/1 1.00711 1.04146 +/- 0.00226\n", - " 65/1 1.06171 1.04183 +/- 0.00225\n", - " 66/1 1.03444 1.04170 +/- 0.00221\n", - " 67/1 1.05875 1.04199 +/- 0.00219\n", - " 68/1 1.04640 1.04207 +/- 0.00216\n", - " 69/1 1.04376 1.04210 +/- 0.00212\n", - " 70/1 1.07078 1.04258 +/- 0.00214\n", - " 71/1 1.03916 1.04252 +/- 0.00210\n", - " 72/1 1.01843 1.04213 +/- 0.00211\n", - " 73/1 1.03666 1.04205 +/- 0.00207\n", - " 74/1 1.04625 1.04211 +/- 0.00204\n", - " 75/1 1.05277 1.04228 +/- 0.00202\n", - " 76/1 1.04944 1.04238 +/- 0.00199\n", - " 77/1 1.01898 1.04203 +/- 0.00199\n", - " 78/1 1.03283 1.04190 +/- 0.00197\n", - " 79/1 1.02304 1.04163 +/- 0.00196\n", - " 80/1 1.01539 1.04125 +/- 0.00196\n", - " 81/1 1.03988 1.04123 +/- 0.00194\n", - " 82/1 1.02138 1.04096 +/- 0.00193\n", - " 83/1 1.02473 1.04073 +/- 0.00192\n", - " 84/1 1.03810 1.04070 +/- 0.00189\n", - " 85/1 1.07438 1.04115 +/- 0.00192\n", - " 86/1 1.03048 1.04101 +/- 0.00190\n", - " 87/1 1.06778 1.04135 +/- 0.00191\n", - " 88/1 1.07341 1.04177 +/- 0.00192\n", - " 89/1 1.06729 1.04209 +/- 0.00193\n", - " 90/1 1.05069 1.04220 +/- 0.00191\n", - " 91/1 1.07675 1.04262 +/- 0.00193\n", - " 92/1 1.06470 1.04289 +/- 0.00193\n", - " 93/1 1.02609 1.04269 +/- 0.00191\n", - " 94/1 1.04761 1.04275 +/- 0.00189\n", - " 95/1 1.08802 1.04328 +/- 0.00194\n", - " 96/1 1.04162 1.04326 +/- 0.00192\n", - " 97/1 1.04573 1.04329 +/- 0.00190\n", - " 98/1 1.03232 1.04317 +/- 0.00188\n", - " 99/1 1.03473 1.04307 +/- 0.00186\n", - " 100/1 1.04505 1.04309 +/- 0.00184\n", + " 1/1 1.05252\n", + " 2/1 1.03787\n", + " 3/1 1.01943\n", + " 4/1 1.03989\n", + " 5/1 1.06679\n", + " 6/1 1.03713\n", + " 7/1 1.02400\n", + " 8/1 1.04289\n", + " 9/1 1.05130\n", + " 10/1 1.00878\n", + " 11/1 1.06773\n", + " 12/1 1.03922 1.05347 +/- 0.01426\n", + " 13/1 1.05156 1.05283 +/- 0.00826\n", + " 14/1 1.06049 1.05475 +/- 0.00614\n", + " 15/1 1.01018 1.04583 +/- 0.01010\n", + " 16/1 1.04020 1.04490 +/- 0.00830\n", + " 17/1 1.05579 1.04645 +/- 0.00719\n", + " 18/1 1.01592 1.04264 +/- 0.00730\n", + " 19/1 1.06881 1.04554 +/- 0.00707\n", + " 20/1 1.02985 1.04397 +/- 0.00651\n", + " 21/1 1.01496 1.04134 +/- 0.00645\n", + " 22/1 1.05330 1.04233 +/- 0.00598\n", + " 23/1 1.05170 1.04305 +/- 0.00554\n", + " 24/1 1.02888 1.04204 +/- 0.00523\n", + " 25/1 1.04083 1.04196 +/- 0.00487\n", + " 26/1 1.01235 1.04011 +/- 0.00492\n", + " 27/1 1.02785 1.03939 +/- 0.00468\n", + " 28/1 1.04556 1.03973 +/- 0.00442\n", + " 29/1 1.05400 1.04048 +/- 0.00425\n", + " 30/1 1.06213 1.04157 +/- 0.00417\n", + " 31/1 0.99934 1.03955 +/- 0.00445\n", + " 32/1 1.04433 1.03977 +/- 0.00425\n", + " 33/1 1.05184 1.04030 +/- 0.00409\n", + " 34/1 1.03971 1.04027 +/- 0.00392\n", + " 35/1 1.05272 1.04077 +/- 0.00379\n", + " 36/1 1.06881 1.04185 +/- 0.00380\n", + " 37/1 1.03344 1.04154 +/- 0.00367\n", + " 38/1 1.04726 1.04174 +/- 0.00354\n", + " 39/1 1.01440 1.04080 +/- 0.00354\n", + " 40/1 1.03534 1.04062 +/- 0.00343\n", + " 41/1 1.04429 1.04073 +/- 0.00332\n", + " 42/1 1.02142 1.04013 +/- 0.00327\n", + " 43/1 1.03895 1.04010 +/- 0.00317\n", + " 44/1 1.05985 1.04068 +/- 0.00313\n", + " 45/1 1.04737 1.04087 +/- 0.00304\n", + " 46/1 1.04796 1.04106 +/- 0.00297\n", + " 47/1 1.06708 1.04177 +/- 0.00297\n", + " 48/1 1.06523 1.04238 +/- 0.00295\n", + " 49/1 0.99626 1.04120 +/- 0.00311\n", + " 50/1 1.04077 1.04119 +/- 0.00303\n", + " 51/1 1.06327 1.04173 +/- 0.00301\n", + " 52/1 1.06508 1.04229 +/- 0.00299\n", + " 53/1 1.03689 1.04216 +/- 0.00292\n", + " 54/1 1.02899 1.04186 +/- 0.00287\n", + " 55/1 1.03267 1.04166 +/- 0.00281\n", + " 56/1 1.05790 1.04201 +/- 0.00277\n", + " 57/1 1.04353 1.04204 +/- 0.00271\n", + " 58/1 1.04657 1.04214 +/- 0.00266\n", + " 59/1 1.02914 1.04187 +/- 0.00261\n", + " 60/1 1.04882 1.04201 +/- 0.00257\n", + " 61/1 1.01905 1.04156 +/- 0.00255\n", + " 62/1 1.03995 1.04153 +/- 0.00251\n", + " 63/1 1.05377 1.04176 +/- 0.00247\n", + " 64/1 1.02909 1.04153 +/- 0.00243\n", + " 65/1 1.06892 1.04202 +/- 0.00244\n", + " 66/1 1.04216 1.04203 +/- 0.00240\n", + " 67/1 1.03473 1.04190 +/- 0.00236\n", + " 68/1 1.04114 1.04188 +/- 0.00232\n", + " 69/1 1.04955 1.04201 +/- 0.00228\n", + " 70/1 1.05464 1.04222 +/- 0.00225\n", + " 71/1 1.02859 1.04200 +/- 0.00223\n", + " 72/1 1.05387 1.04219 +/- 0.00220\n", + " 73/1 1.05039 1.04232 +/- 0.00217\n", + " 74/1 1.04338 1.04234 +/- 0.00213\n", + " 75/1 1.05838 1.04259 +/- 0.00211\n", + " 76/1 1.03831 1.04252 +/- 0.00208\n", + " 77/1 1.03555 1.04242 +/- 0.00205\n", + " 78/1 1.05684 1.04263 +/- 0.00204\n", + " 79/1 1.04267 1.04263 +/- 0.00201\n", + " 80/1 1.05813 1.04285 +/- 0.00199\n", + " 81/1 1.03512 1.04274 +/- 0.00196\n", + " 82/1 1.07081 1.04313 +/- 0.00198\n", + " 83/1 1.04476 1.04315 +/- 0.00195\n", + " 84/1 1.05153 1.04327 +/- 0.00192\n", + " 85/1 1.03939 1.04322 +/- 0.00190\n", + " 86/1 1.04218 1.04320 +/- 0.00187\n", + " 87/1 1.03688 1.04312 +/- 0.00185\n", + " 88/1 1.03480 1.04301 +/- 0.00183\n", + " 89/1 1.05089 1.04311 +/- 0.00181\n", + " 90/1 1.06251 1.04336 +/- 0.00180\n", + " 91/1 1.04054 1.04332 +/- 0.00178\n", + " 92/1 1.05340 1.04344 +/- 0.00176\n", + " 93/1 1.05938 1.04364 +/- 0.00175\n", + " 94/1 1.02741 1.04344 +/- 0.00174\n", + " 95/1 1.08249 1.04390 +/- 0.00178\n", + " 96/1 1.02858 1.04372 +/- 0.00177\n", + " 97/1 1.03983 1.04368 +/- 0.00175\n", + " 98/1 1.04715 1.04372 +/- 0.00173\n", + " 99/1 1.07443 1.04406 +/- 0.00175\n", + " 100/1 1.04461 1.04407 +/- 0.00173\n", " Creating state point statepoint.100.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 6.4445e-01 seconds\n", - " Reading cross sections = 6.1129e-01 seconds\n", - " Total time in simulation = 2.0000e+02 seconds\n", - " Time in transport only = 1.9970e+02 seconds\n", - " Time in inactive batches = 2.9966e+00 seconds\n", - " Time in active batches = 1.9701e+02 seconds\n", - " Time synchronizing fission bank = 4.0040e-02 seconds\n", - " Sampling source sites = 3.1522e-02 seconds\n", - " SEND/RECV source sites = 8.3459e-03 seconds\n", - " Time accumulating tallies = 9.3582e-03 seconds\n", - " Total time for finalization = 4.6582e-02 seconds\n", - " Total time elapsed = 2.0072e+02 seconds\n", - " Calculation Rate (inactive) = 16685.4 particles/second\n", - " Calculation Rate (active) = 2284.19 particles/second\n", + " Total time for initialization = 2.4568e-01 seconds\n", + " Reading cross sections = 2.3233e-01 seconds\n", + " Total time in simulation = 1.1761e+02 seconds\n", + " Time in transport only = 1.1757e+02 seconds\n", + " Time in inactive batches = 2.0641e+00 seconds\n", + " Time in active batches = 1.1554e+02 seconds\n", + " Time synchronizing fission bank = 2.1808e-02 seconds\n", + " Sampling source sites = 1.8421e-02 seconds\n", + " SEND/RECV source sites = 3.3183e-03 seconds\n", + " Time accumulating tallies = 2.6283e-03 seconds\n", + " Time writing statepoints = 4.2804e-03 seconds\n", + " Total time for finalization = 2.2731e-02 seconds\n", + " Total time elapsed = 1.1789e+02 seconds\n", + " Calculation Rate (inactive) = 24223.6 particles/second\n", + " Calculation Rate (active) = 3894.66 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.04342 +/- 0.00159\n", - " k-effective (Track-length) = 1.04309 +/- 0.00184\n", - " k-effective (Absorption) = 1.04107 +/- 0.00140\n", - " Combined k-effective = 1.04195 +/- 0.00117\n", + " k-effective (Collision) = 1.04491 +/- 0.00149\n", + " k-effective (Track-length) = 1.04407 +/- 0.00173\n", + " k-effective (Absorption) = 1.04203 +/- 0.00169\n", + " Combined k-effective = 1.04355 +/- 0.00131\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -551,10 +555,9 @@ "\tID =\t1\n", "\tName =\tflux\n", "\tFilters =\tMeshFilter\n", - "\tNuclides =\ttotal \n", + "\tNuclides =\ttotal\n", "\tScores =\t['flux', 'fission']\n", - "\tEstimator =\ttracklength\n", - "\n" + "\tEstimator =\ttracklength\n" ] } ], @@ -578,19 +581,19 @@ { "data": { "text/plain": [ - "array([[[0.40767451, 0. ]],\n", + "array([[[0.41279586, 0. ]],\n", "\n", - " [[0.40933814, 0. ]],\n", + " [[0.41176924, 0. ]],\n", "\n", - " [[0.4119165 , 0. ]],\n", + " [[0.41096843, 0. ]],\n", "\n", " ...,\n", "\n", - " [[0.40854327, 0. ]],\n", + " [[0.4095409 , 0. ]],\n", "\n", - " [[0.40970805, 0. ]],\n", + " [[0.40836217, 0. ]],\n", "\n", - " [[0.40948065, 0. ]]])" + " [[0.40852022, 0. ]]])" ] }, "execution_count": 17, @@ -624,32 +627,32 @@ { "data": { "text/plain": [ - "(array([[[0.00452972, 0. ]],\n", + "(array([[[0.00458662, 0. ]],\n", " \n", - " [[0.0045482 , 0. ]],\n", + " [[0.00457521, 0. ]],\n", " \n", - " [[0.00457685, 0. ]],\n", + " [[0.00456632, 0. ]],\n", " \n", " ...,\n", " \n", - " [[0.00453937, 0. ]],\n", + " [[0.00455045, 0. ]],\n", " \n", - " [[0.00455231, 0. ]],\n", + " [[0.00453736, 0. ]],\n", " \n", - " [[0.00454978, 0. ]]]),\n", - " array([[[2.03553236e-05, 0.00000000e+00]],\n", + " [[0.00453911, 0. ]]]),\n", + " array([[[1.74741992e-05, 0.00000000e+00]],\n", " \n", - " [[1.83847389e-05, 0.00000000e+00]],\n", + " [[1.68457472e-05, 0.00000000e+00]],\n", " \n", - " [[1.68647098e-05, 0.00000000e+00]],\n", + " [[1.75888801e-05, 0.00000000e+00]],\n", " \n", " ...,\n", " \n", - " [[1.71606078e-05, 0.00000000e+00]],\n", + " [[1.79971274e-05, 0.00000000e+00]],\n", " \n", - " [[1.87645811e-05, 0.00000000e+00]],\n", + " [[1.89308740e-05, 0.00000000e+00]],\n", " \n", - " [[1.94447454e-05, 0.00000000e+00]]]))" + " [[1.75231302e-05, 0.00000000e+00]]]))" ] }, "execution_count": 18, @@ -682,10 +685,9 @@ "\tID =\t2\n", "\tName =\tflux\n", "\tFilters =\tMeshFilter\n", - "\tNuclides =\ttotal \n", + "\tNuclides =\ttotal\n", "\tScores =\t['flux']\n", - "\tEstimator =\ttracklength\n", - "\n" + "\tEstimator =\ttracklength\n" ] } ], @@ -722,7 +724,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -731,7 +733,7 @@ }, { "data": { - "image/png": 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4JpZwdr/PmHEaqvSFWTLOOqGXEnSfF6v713/gj/jRv/s/pS/nIjQMjNF0tkpN+fhmU7G8a4jISsX026URk/HO7lT4JsCZno3jJpsBt3TALS/tqTLwRSm/+OYGIwPCSc8OCH/c66vhHBMzv/SrY3AbQT+asW78mM8DDx+Qe/eh3ivj6azbWDVpc/7IMF2XTHkbVm6O0Dje2RTtyOtbrqtCt2oSbKn3rXNMV+QD73teBd6EqrtO+u9FMdYyFg2jjMsDIdxkk86oAhZJYqFBcdTEb7kUoZaRIiEDUdh1KGwVbZic9XGTTa9NiOZEoZWHI+S2ynWNuSTdZ+XaRgwKtxhsIO+w5S/kOLvUjX6T8XKdFZYJiUbgnVnr0dSTotz8ZCTA2Z2CDpSxk1dBNMELVwdIZkR5Tj+8gXd96AQAP7/vId7h/gQAlQVpx8laAWvHyUP1nUJtcU6mWd4jk0x+m0di2lABD6/h3yWwzE+f/BBjX5H2Z+71SF1tRT8EWLzPxQrLcxi+P0J5UOKqFEcVI4+VzZjEsesGTz+Sx8pJMK0L//sIdcN+2fBpm6nvNx5cvmLpRaEfBi9aw2J1r7S9+a+rxAxuvnbIJfWSTARWxSK6YiC2QR9lAp+NPVhneZ+ZOB0lXqRAZUDJpNdZb3bkdS6dT6AjHelIR24Qua4WulPwGH0wR/EtFtFlmUvWt2syJ42zymSUitkYK49oNh4QV/TCgQg1w+de3+TQdVmgjuJYKHC+8UPQSJgY68bhp7yxgbMmt3jlAz6p02KOxpZ0EI43NeuRviD9a8RtXEFi6D3lghJII7+twepNsqEYXdfET8r5ypC0Nzi6RuGCWKKJGUjOiFV8+b1p3H7pa/Jxxdyd0hd3yCU+J7CDKoWwX5ClhY7Bn1wWDOIPvTuY6BEO+dVnJahMbFkHzJ+lg5rYk3I+FCYI9evkrCCmSfQzGRZMnBpqFubWiKzYwSZmM/5NOBcJmC9uzMcysFV5ssGVrmZMHRh43tSnNYtvkU3m5FQrfs7MPWAlZbnil0JEl028m+1yXeKCQ2lSVk0Lt6cYfEaoROFCotWnJzSzb5Eykd4K6qRxwor4wf034ioIwRCf09huW+z3jnTkdSrXF0MPWVSH4lQ+m8bd2KIB1jMGQ094FCYNDtpdp1CT5XUqUqN2okmXg9xmUYZOUQeMksw5n6XbTdjcPsNamYpQ7xMYITodpjghx7WMRdcFKVtLWdhGETSAxLxhnIQU5WEpE+6pUh4S2MOutRIuRI3H4/jBdY5MyoRT7Q1RGWzSGjURo7j3fPw5Vq6Kt4y7HsM2Sjc8UKZcNJBKTrGyLMprdHiNK6tSpzb32H22QnaX/JO+QKB0lQ+Z8wIJLdwWI79dlKF93CbeJXBO918kyU3IhJa82qIZNpNZ2FWNY7D67hPrLN4puL6qWkFiED+iqGXkOTTi6QB/9yKtJB1WQ9F02Eyfcaj2mLDCWbkuMa9xCnLsRWHpoNxveVgz8njDPB+b6IJh31QSsEXuYS4WDVgu0RUoTJj2Y5rhJxvBeHSkI69X6UAuHelIRzpyg8j1jeXia5xiA6seChyFqn06iAnSc8wKls2l0Sj1rPwwtQF6TRREtMXIAwLFeN0p6v1isS7cFiYiBBGqxqKsjdVJNzfa7s7ineoO+tKMSOg74BsHnS1/WaLWK23Wk3aQVUidTVDbIhawv7tB3fCso4YDf+LB7Wy8UxLaTJ0eomJixkRmHNghsVce/ouD1AaNBdnvkt0jx/EjKeppE1WyS6PyYuouhLv4ib2PAvBfpt4qdb8jFsBJmQs+xWEzH1tQGpF+lya8IAuRthSRB2Vzc+kgJKfMCialAo59M41bpccKVh7VkVQwTvE5m8idEunyHeOn+dzem+WHJzKMflWcBi58KE101SSyyCu6H5VxWbkZotmmc5RcZtd8Bk/IZu7FH0zjxQ3ks2azeEDuvbajglqMBH3wS3Le9iGca0FsjuzJMvJ4jcjlZaxKB3PpyOtbrqtCb0Qtsjuj+CHF4DPyNa7tjFMVZ0JWb/VQUVE0o58Lsb5FFJPlQm6TfMhDT9dYuke8deIrHvkNxms0SwCRjE+KZle/18/MvaIwDvYtcawuEEY942P1GwW9HGVku8Tqnj88RHpK2s9PWkFat/SUR/WKieG9LUKXicMSeHtGFVcXpG5nsMLAX8oks3gQ3Kwc1yc9hrdKv7oiVc6+IBG+lC9x4kFC7Da9RiMTVf7g6JukA2bCCU8UqV0ReGbxNtj8acGfp9+WYnm/XBcfLTC6SxTtxefHgwxQmTOKwkZT3Yo8C4DougnkFYLQBxalvmwaz/Qj2lshOy0Y/2eeeQPVQSk/+XyNWp/B1tdbTJjieAsWKY95AVzTDD9c2GBR7ZZJJr6giKwZuuMIuHvknYi8lAhgsPiCJrtb3oP0ZaibuWbsC8uUtnSbcWtQ2j2EbzIodaQjr1fpQC4d6UhHOnKDyHXeFIVatyK+oFk8JJyLoacKZG8SqzO6YuM7JpLifgkTAOCmfUJFw5YYDbO6z1i0R6wgtkcjpnEzcsH0FTH5rXstUpdkzjqxuIP6sHFQyVnokliXyoa1R4UTX97aoLDdcMXTVbzFmOmXFfDGe0/4LL9drHtt8mT+9Ju/yBcWbgJg6TMbmL3HdByfeL9YndGwy9ys6exoFjUkdThnYsTnjPW6zQVb2t/fv8jz1TG5T5OAw5pOs+PtlwE4fWyC5X8rO6uRL0L/MZPIY62LKymxgFWYYPUxG+2l94g87uxej/KwgS6EvEPqvGI4KpuPKxcH8YwzjzuVILloePUhiM/K84meusLcD2wGZIWxYnjwfkhR6TGOTUVNxEAxIeOYlbnoMv0WsymaamCb7EbxOYU2YX/tGgEMt3S7R/dxk1O1jcu/dLiP1IyY8VbFZW1bksaRjmNRR17fct2Dc9l1iC83qPaZMLHJMHnRC2TOgpsw0MG8DhyFnLxFRBh8RPIevceMAhxXjH9ZMOrL700Q7hUlaZ02YWWH3SAGjB8BbbwjrRWb2JwohuiapjRo4IXFELUeUSqNpVgQQwVFEHo2t9VC503C45iU/fz8HqoNE1hqfx18k7j6+RD5mijXmtKExwWW8LUidEEUYGEjuEOG8lK3UEapPf/odnr3iTJebEIbQ2VOnjbJ6KM+a4tS99C7FmlmXystZLAM9PCv7vsCv/XgO5q3EHh2Dj6lWLzDhPg9JspybV+D1Ypo9/qIS/q43GOtV+OZSMaVyTqhrNR96cOb6T8uAHxuu8IPGXisoWnEpZ3EtApohokFUb7ZneEgZG7XUYt5M/nFF8D5c5nkc5PQ95JJqbenytpBEyjsTDhI9O0mFYUx43E6lMEp6mAPoCMdeb1KB3LpSEc60pEbRK6rhe6HobjBxw85AbNj6dYI8Tn5vTAOMePW34i1YnuE8wR889m7VcA3DhUVFz4oVmX6oiI3bHJwGpfw6JwDxmqzapLMGGQjsiQ+MfQdrxLOyfnp+2xGtsnG5dojQ1QMRBMuQD1lkk3XwCmYcLtvkjgls0+OBha8rSC6WVgc67uSwZRplyzqa1JoybVJrplwwDfVweQ3jU2HUCZtj3VwncVloYb0nDAbh9kUEcO8aWyuBlb+QiiDFZIbHRleY2FdQuZ+4tl7wURyTA4WKRoGT3k2GkBR6atiOa/tU4T+q0BV6j0uyXcsSDuPDhG9Q1guuhohdE6sZTepWd1lXh/bo3xYoKXQqQRhGRZCVR2suJrxd7rPeRRHTGLvQYsuE7K3NAwpE+3RasDSLVJ3X1eRg1uuAvDwsUNBRMbB52qsbzYrJQ09p8qEKh0TvSOvb+lY6B3pSEc6coPIt7TQlVLjwJ8Agwgh7ZNa699RSvUAnwY2AleAD2qt175ZXbF4jd23XOFMdZKI4SdHsppqv9k4q0K1x+Cvc5quy2IhF0fsINXcQpdFdMVskO5o8cwjOZ/0cyatmok17oc0hf2CLidORUhdNREGE4qYMPSYekcsyGXpJxrYvy9WqncQIlljldddahmDEXstT1H3MRMNMQGNjFi6H7vzS/zRJXHfLyvQJnt993MWy28wFqil2f3+0wA8OzVBd1qw9bVE0zkf4kqzY1ys5HPbheJojZbp6RJL2PUsfvGH/gaAn3/2B4LrFk8OYI/IDuSGvnWumJAEh0cv8+Wn9krdc4q62Z8omxjym/66zvoWGUu1GmYtISsfb2+RD01KbPL/fOyNgTdp34s+2Z3G8/SiTcE2ae88GHpa6JTL+5PBpnXEWO3rm+wgkFl+l0vqrKyOvBh0nRa6ZfpiCN8RW2N5eYjPb5IVR4q2pCI3RWiYDd3krMYuu0Gc/Jcrr+a73ZF/WKxoFGtQoo7qeDQ4r8qGOry4jF+tflf6dqPJy4FcGsDPaq2fV0qlgKNKqS8DPwY8pLX+VaXULwC/APybb1qRb7FaiRPZkaNWNc4iTyVIzMqHmNuigngssRVFdEWUcXy2weX3CAF54KiPtg1DwwkHDivhnCJzQZRqbdW4jacU60lpZ/xzS0y9TxQDt+bo/pRsnPaeUMRW5DptqyDbTs8pn9Wbmu0oXAMRRVcV5T1GYQ7K7t78k6MoVxTQJ555K3ZEJqLElI0+LJu2y3fEg2iG9vk4L0YE83nX9pd4MSu8+mo8RHFJlPq+TbM8ecnsFo/Iy37P5nNcKcgkkq3E+WpuJwBbR5awTL/9EcXVrPCzp+Z7wUA0jvIDBk2tB2L7pO+5kJRNTUPJJONIX7KIPSvj0/ORKaImfm/oSpTiJrmHwg6fxCUZ50Zc4CIQn4HsLrm2MqjY8IAo6exNsoFr16D3RRkTp5QMfBDiC5r1XfIw13ZYgebufcmnuZCsdYnzlTwrTdhw2HObFUt3JKn+v9/2gvNVe7dft2LZNO7eB8D84Qhqvzzv92w6wfd1HQNgf7iBi2GYYWMrE7JBtybgl1w59+nsbTxwaTcA6sUUw0/J7B/66rEgoXlH/mH5ll+A1npea/28OS4Ap4FR4D3AH5tifwy89x+rkx3pyD+GdN7tjtxoorR++ctUpdRG4FHgJuCq1jpjzitgrfn/PySRyTE9/PGfQmuIGs/LgecbFEYNt3lN4/9z2YBbPzIQxLvuukCQX7OdmlbbXcGaliVc4qqiPGKsN7Pu8MaqRE/JxqHlCgUPwBut0v/FiGnTI3FK6IEz7x6lsNXEZu+qM9wv1kblM4NUBox36IF1SlcNz9sMnR/1g83XoclVFqYFZ0ifcihMtiJAemNiaQ/355idEkvbTjZwzkkfvZ1F3LKBkGYcbr1XYJmMIyuCr17dgn9crNi9bzvDxTUxb3OFGG7OBATzFMmLMp61Xo23UdpUM1F+8L7HAbj/c2+gbuiZKiOrIL9h0fWC1OFFobhFVi2/cfen+bd//iNSJqSpD4m1PvYFK9jcLG7QZKSr1DOKwmbjTbpjHv2rstTOT8h9haqaRlTGcvX2BpjN3MSZCN6tAtVEHk2RuSTtr29ycI13aGJWY7smfEFSsb7L5FSdtyjtqLHwS79P7fLMKyKjfyfvdlr16NvUW15Js68pUY48w/UP3kLuvQL9/Y8Df8RN4VbIBUfZwXFVmyBxtB6Jq30cJXakZ1yKfa2xjNUeV+G2sh41U8dZN8KPH/0xALr+NkHmr54HQLt1Xg/yoL7/qNb61m9V7mWzXJRSSeBvgH+ltc4r1XpIWmutlPqGM4NS6sPAhwHsvi6ssIf2VRDlrzRgk1iSj3p9s015Vb5ea2uZxNMCkq7eXmfgUYFO8pOKoaflIS7pWMCKyU9qUjdLUsn6o6LoSrEIyelmREJNaczc9HSUnqNm4tjTS/Fek2s0r0mfbeLmUWYPyXG8TzFwVF7anrct84JJOJEcFgWkgPinRdHmB6MkjTNRI5YJMvk04hrflfoWs2nwzAQ1HWXzPeIsdPLyCLdtlzQ8z8fGefK57QBsu0li1/zc7i/zwKDEUjmxMMzmPrnfQjmCnZYx8YoOha3NZCCQfFEmi3s+eIS/eOKwPK8hlwkTHqEZskCVQlRMrBm9uQR5Ue4/94UfhgETm2agTPx5eT6rOwl46KURO9gHacRh+BFpvv7QMGWTdar7nCyd8xMRfPPNhrIhNv+HDXn+AAAgAElEQVS1jOHSrWFyswI3+YMaaEXUbGLujThEFqSPhQ0WyoR17HnzPPtSa3wxagp+m/JK3u329zpK/BW1+1qQ0Lh8NGc+OsZ/fPf/AOCu6KPB71Xt4WoDoaBxtXzLERUiqkS9lP1rY+w0yzTFo5UrtoZ7Tbmm8t8ZrvP0HZ+UH+6Axz8uUOFPf/7H2PEJ+T4a0zPf2c3eAPKyQEellIO88H+utf6MOb2olBo2vw8DS9/oWq31J7XWt2qtb7VTiW9UpCMd+a7JK323299rh8jX/tyRjnxX5OWwXBTw34HTWutPtP30P4F/Dvyq+fu5b9law8JfiRBdsqmMiXVX71YMPyY/d59vAGJRhsot7jlAfqNYAW5Ss7JHTDzfAWMcECop1q7KrD1k2Cy+09pca8QUg88YL8MEZA8I5LF8ALpPSR0rb3BJnpK6nRKkTsqxd2cO/03CROmPFkkOyaZe46i050U0hfvEQt6cyXNlUere/faLnHpiEwDJGUVju9RhKc34iNBseiIlnp2eAGDLhiWePSr58LomcqwnxEpdLZswCaEcF9ek7nS8Ssk1uVVtH8d4mL5v9xH+5Ik75YbCfgCdHM+OQkLGPBxzWSubwTUc/PhoEW9Q5vfqepTojIl82OcRmzErleeSrBySOjIvhVjeJ69P6oqmPCzV+SHN6s1Sz/CTDQa/IrqwtFOgl/6HrqJrMlZdBzaSvUks/khOk74g19lVTbggzzC+WGf+sGFGaIiYaIulTS6DY0I8sS2flWoC1//2NkVf1Xf7BhJ7UMgDp//dRr74fb8FwJjdCnzmoqm1WdlNkMUHfGNqW3gYdAwXjWNgl/bj1vXXQjIBFAN42mymKiuw7G0Ud0Xl2Z/4wO8y8z55x+/7wkfZ9UtTADQWFl/x/b+W5Vti6Eqpu4DHgBMESDG/CDwD/BWwAZhCqF3Zb1ZXbHhcT/74x/Ai4G4TXNjPhgmZeB7DT3hYdWmiMOYE0QFDFcXgc6IEpn+sgWVLma4vJFg1YWiT0xauSfHZTOhc3FlnfExgibnjQ4w+Ytgn57NkDwoss7JPQguA5NdshhhwipqyCQlQ21HBrxvaYsRj+6i8LBcWREnFYnXyq6J0Y13X0q9qFfkQ9k7McHpRYsaEQh7hkCjG/kQpKHv24gibJqXuhVyKt28UYPozL9wCgKraQbiBD97yHOuuKOVn5yfIXxaIN7M5y9qUTDRWVfHOu4Vy+JWr2+hJyIQSd+rMfEkmkfIu6W9XVznoh1Ka9atSn7NmYRnHp3q3T3ymmetTY43JNcrSNFGJ+kKcyLCp62SK8S8ammWXTD7xY1fJH94IQLXLopFoTtRQ2tDMhq3Z8PkWw2h5v7SpGoo+k+h7fasdhCQA+P4feJI//eGHWDiZfdkY+qv1bt8QGLpSzH/0DgB+/6f+EwB3RDxco1BdPHyjK1xaOsNBBQq4XdoVsKs17fwU++tKEzBfHFSApwOUDbPFVtc+1oiBYnytiaiWXfpsTSb/f/kHHwFg5DefEs+z17i8ahi61vpx4B/6SF7jb3FHXs/Sebc7cqPJt8Vy+U4lPjCut33go7hJRWXQJDZYt1q5JC8RWMXVEY/ovMzliVlN7j5j6VUcbOOsM9ibo/pZcZxZu8knPGggDcO9Dj2VbjkBJSFlEjxU+lubslYdkibtnFP0Ahd/L6wojpncpFvrkqwCiQLoDcpqwV4QE7Ex4BK7bI53F9FTYq3bk0V8E6irUXUIRWVp6OYj9D8hc+m7f/arfHZqDwC3Dk4HY/Xg2R2oBbE2/AHZ7NNVm/iU9MM6uM77Nr0IwJ88cSfJy1JfZcjHLjetW+jbK5CHY/nMvygrhIE9i+QM5KKPyGauHybYnF2rxlg6LuOqR6vEjsWCe0/OyBiu7mkFw7LqCrtuntuGOqEV6WPvcU1uswx0zymzOpqtYh87L/Xt38b5f2pgo4pFyARDq/V5RBeN6/9xLwgB0YiqoM2VPSpI9hHav05xJcHCv/9daldeGcvlO5HXsoVub5kEIPzfy3x6y98BLX54TTcCa9lGUTXWelVrwm1c8qb1HVaKurk2qlSwoVn2vWu451+bhiSqVNCmo1QAwVS1H5RtB9NsuKY+p62P7f0F+OD598H/YdhbFy5/2+PzvSKvOsvlVRElzjuNBKS2iutgKlqjLyaY9IvRzQw9bRRG2GbgBRNnZJtD7GnBU1SvDp5uPl7DNc4wdtnCeVbw2LTB0BcP6iAjj9vXwHfkdjPnfBYPGyW+btNzRgp5UYtwwSzptziB7Ra7GA6YFt6AJnlCXhDboCsF28GR8C34Z5M0klL3jsElqp60OfXIBPVuUXTJOSvAnF1t43qisbbHF/FMo1+q7SY2KQyQdFwaWivEObT3DABn1gb4q3MCxfS+YAeMj+RMa7+h2u+z/owo5pvvPct0t4GZcknqhuboGIep3/rA/+CnHvxnAFgViy0HZHJpaItLm4bMIGsacbmf6LKFm5JrR26dJ1+V+mpriSCpRiOqGHlMBs7yZExKo1FSrgm7G7UZ+7L0tTBm0XtSyi7dEiFcaO53KOImUqM7HCa3Rcqndq2yvi4TZzUbZ+PGJbIR87A78rKk9P7b+Piv/TcAbouUKPvmm1At9dnEyr9WoTbFo4VVFXxNyjLGRNu1dpuiB4Kcs4a9eo1SlslCzrcrfh8CTN5RUDVewQlL4TbrVhD5GpX2uW2f56kvSY9/8ed/gsT9z/zDA3IDSCeWS0c60pGO3CByXSGXdHJU37bnI8zenQwYLPH9q7iPCnOjEYfqsGFirNj4hg3mh1oMiEYCysMmsuBjmqVbzZL+ZIvLurbDnDvts/AGY7mO5gl/Xjb61nZrkleNc0OkFescBY1oazx84wh02+QVyg3jGGN5rFbFMuw3K4sjJzeR6DcbhEpTNDx11VDER6WMZfmkDU96rRRjIC3nV4oJ9DPSr9hdK6wsidNS30CeyYxs6IYMzpByqnz5jLj72wsRxh4SG6Y86BD7kXkAphe7CV2VGxp6yqOelvtce2+JTFJ2i2tuCN/Qgwoz0p4OabZvk5x7c/k07974EgB/c34froGQvESL2RDK2cEKJnWFYAMbBd6AQFL9D4VZFTQpSEUXWbbwzBgPPdPqX3S1QW5SVjDrh2sk09LXwmJSomYCmfM+tbRhuYxDeq+MT+mZPuoZn9lP/Da16ekO5PItZOYXxR/hmZ/8ROAIVNUNnK/ZrmyHXGraD6xsV4tlLNdpmvwXF4Jjj5ZF7SNWNYgF2XyLmtZkVLWsbA+otakkJ7DiW+fa64srFdQXV3awQdvOnIkow9jSLof+88cAGP/lJ7/x4HyPyvcm5KJBeZrMeY+5N8sp71QPIUNPH3vjNBcuyPLe31whdE4cNsLrKoj5oW3oPW48DXdZAewRWWuQ3SkPzjcepvNv8eh91jg3LHQzeE6URD0TC7wP+443WNsuZYrb6thxmVD81QgDfy8zylN3bcXJSEOZVIVSVZR7xTXxaIo29azxHvUhVlVBX0uOwdNTLuWSyTxk+WxMCWni6mIP/naD59QdYmlpZz0f502bngLgoZUdABx5fB9pAxHnD1aZeYuBfjYWSVryWut8mOQeUXQLuhc3LecnuvOBE5Hv2qR7ZE8iZbxKy4Oas2cMfmXBp67cacbeYuQOmSzmTgzi98kkMvz3sL7F5HPN+VQLJktRBQomxO3ybT6Jq1J/02lJ+ZARCB3laVb3SNn0BYe1W4xnoa2pH5dJbuKZBoUxE2r5kIRBlrHVFMoGNqrAvrddYC3xyhyLXk9y8Tdv5/SHfg+AstZ4xhPT1xpfNVkp8tdDUzUwjMu1y/lSWyC0Kk3cWgeK1qeluD1UwDRxFNdMANLetSyYZh2uvlaRmxBO2G19cZQVMGEKuvF1cWKiysJqq/25j/w2AHu6f5rNP/v0Nx6k17B0IJeOdKQjHblB5Ppa6BZ40RCrN9l0nZRTxY06WIJffn4Mx3CbY88mgpyihUMVks8JRuOmIC++OkRXwHmbuPDPxXvxTDKHoV3C7Kh/epDEolgg5UGHpVuljnqXpjYm9sH0iIWVEst9/H4H56fEcp4NdbF0m6wQEgMlSotiaWc9m5CJIRsyVvG+Qxc48ag4BCkPBt8o0MWVSwP0PC9DnN9k45uoifduO8vjs8Iu6O0ukoqIZXlr71Uemd8CwJoX54vLEnVuQ1zI8ac3Fek9IPFlko0Qy9Oy4bmxL0vWpI/TIZ/8KQNhDbXYN4uLo/gb3OA5FHIyFmNvF+tbf2mYooG7IpfaQpx6MH1R+PYkfZw5qa+e0tQOCWykjySxTUgN9/YCrErddk+NYkIs9NgFsaYtF9YlogGVfiewuFcP19m0QZ5bSPmcNxEoa2mbwc9cACCzZwOXPig2SKS7yp0bhLXwTGiCo+c3Uq61EdM7co1c+K3bATj1wd+jaFzxXXTA5/bQWE2noCbnW7c45xYCjTR/qH0DpNYCqm0wixWc14F1bcHXsVzaN1bbpa4tbOPf4LRx3S1aG7M17Qcbqq7WASwUDjZZfSSopoQjaFrwZz/0n9hm/SQAWz5641jq15nlotAhhWpAYZOBCEKwYbcolVS4RqEuH/70WAyn0LbbflgYH9bxFL7pdXlY07gkTjSxvEIZx6L5C6KAoiOKcNF4Pw55VIzCj3RVST0tmItdh8Jh0Ubv+5Uv8YfnxbmiL11idl0UW0+izOg2UaS25ZMJywQwVzKUP62CIFipZIV3Dgv+/GelQ3iRbnOfPuODopi3xJYIj8tL9qHuZ/hXZ34QgL966hAHbpZYLtkX+7nkiGIejMq9v3vLCY5lJbZGybcC6mXY9og75jNxNL03S5yW2gMDAfvFakDXZRO//F/MkqvKvc2vyj1seudVzp2TmDbJaU3FxGap7y9izZlYJaoVkGzxLh/LOFt5+yt4FTnOfDXF9h+8CMDZhzbj9ZrgZAfl3gvLScIL8gCdgiZk/KrGHvYpDUv71R6LsJlDwOfKh2WyHHqmFuD2E71rPHpJJr9wxOX7bj7B/bEKHfl6mf63hzn3wd8HoKgbAc7soFrOQlrj8w0YLYFyv1aDe20YdbQt1E20DfKotl3ShE6qbdfFmyGfgarZ04kq3caCafNGVa062icES+uALQPtirx5j02lDh4uUTMVuHic+qBAT7cs/Qxj/+G1han/Q9KBXDrSkY505AaR62qh19OK6XsjuEmfSNYkLej3uHJRoIPYTIhG0oRE3Z4j9KBsjNUzEWph2Urpv2OZ5QWxKpM9ZULPS5lQVdzEQULfAoTPxQIeevcJi9wWw5TJJajtkjKqbJN8TizQLwzeROWM1FexgLRgPrGQy+a0QDun1wfZlhZoIO+KlfvCyUkwzk5d/VX+64m7AHALYXreKtepU71Mn5QN35GJR/gnqeMA/KfVN5AvSz0ff/NnWGnI5urpbYOUcnL+Qkp2hNfL46yvyjJk+8Z5bn/bEQA+f/pm/KpYHuEFh9UVMW8jCajtFatVWT69JtvRxmSWB6/KRmv/gBDoL8z1s3OHRKs7n59Aj8l1FqDNJvNbDx7nS89LtMdwd5Wk4cfHnAYLJyX+h1PSHD8mcFLMhcwZZZ6nYCvDmwqcd+R5b33zZVb+740AVHsdKr3yfPJbPeyKcff35dkCTN8bpntIILGLi31ETshzS85ovrTzFvJ5E+axIwAUPyCZs577yG+T9+VDsNpYIY5S13DFm9ZdkyFS0n4Abbi0Nhpd3YJA2i1nR7U2S20FLk13fn0NpOIZa7zWBuc066tqFVj8bsvlBE9/PTsG5Fz7RmgTcmmWcYEuy1jl2g+co5r/Azz+k7/Buy58FIDkX7+2eerXVaGHytD/gs/irRZVg2GrmgUmw09lBJxu+fBLcykidwvUEDqbpDEh5cO2h7Msyr263EXILLfye2pEk6KkrUui9EIlTa3LxB4ZUEzsF4U1v56mYuAUMm4Qbjb2x+NMnhcMf+lggvB9AhP0Rkus1ARDn3t6BOsOKd8McKXiDeKnpb6V8yP4/fKiHLztPOdXRbk2uhtBMKlH89v5zLI4BQ1Eitw0JJDTLz3zbn7ptv8JQDTskh4STVb8C4EiijtAGerg+4Zf4G8XJFOMXg/T+6KZrGKKwkFRxo1KjGhMxiTqNFg6LX15sKsbZTfxUvl7cNMUZ1dEKeuQxg61XvyxLTKBPb80HpzzPYs9/dLv09nBIHtQcUyeM0h2IcuECa48KZNZ9uYCIeNhe3RpOwNmrFZvtojPm/C9UZ/kOQPnRKCwWZRRbDbE2qxM5k53lUbCUN2iEFtSqK8FZ1/HYm+Z5D/82n8BwMdvYeFt0ImvW4GyStoP4IqmogsrFUAX0KITeqhAKUfbVHVNt0ExWmPTjP2igmMHHcBmVeMCLBh7k5Wir4FfSibgmo8KMP6o8gMmDLQmERd1zUQj7bWk3WHKRgX3aSnFr//aHwDwK8fej3f+0tcP6GtEOpBLRzrSkY7cIHJdLfRGDJb3WoQqCs8sqXXM5+6d5wB45NxWYs+KJVzc4FOfkWPd5fP2HRJ58AvP30x63nCxdzQY+6rMsleGbaoYlkPGJF4Yd6j1GkvTInCmqdcclCPXHd5yiWP/cxcAyvMpjZvwvRXN8rRsaK6d7MM3jjH+qMvK38nGZPFWsYSd6Qilja1IgeElGdbnjm7lnttkg9QfVOxKzgHwpxcOsW9wNhiXI+cEoghFG5ypiDX+k1se5ZcfeTcAEePKH85BzZgev/HZ9zBxm6w4rJ4a+Tcby+R0InAsYmeBkmGzjE/Mc+ebxPJ4Yn5T4LR0NS/3+MLMGPXVaPMWaMwLnLFh9zy39EgYgPPFAbIVw2WvRnm4InSVSKqGY1guQ89UCF+STdnTvzyAY2Cj6riYz1EgVDAbV9uqLB2UTXDf0XgGhrNiDdb3GWtsJkzc5CutjHikzxrW0J4wvVekzdKooueUh92x0AOJ/mGRvWbz3tUtNku5LS+noxTrfsvCLjehE/O/Tctadto45p4msKir2go2L702S9xts+KhVV42QMUyb36PEeUFZUta4dO00D3Cpu7mNc02/baVQXNV4GmFa2xUp/m70tfcc3vcmfYwvttNVrDQfyvj3f2NN4JfC3JdFbpVh9QUJBYa+CZgw8JtIR577CYAhm9exJoy2cEtO/AmdZOah/9WIIqeZU3OQCR2V53Vm0TxxKeheTuxRfm9PNRynCls9ZhZFuVlX4kSW5P2j8bH6ZqWh790X530c6KAek/WUCY+68qtHvEZqTt9JERk3dC+ks2gVZrYrAkmdaJB1SQrK45bPPyk3Jsf83l2YAMAh0av8uSUKPGuZAUrJ9du3jjP3SmZuP5s+Q7GTVahuYLAFbElRXncMHUmS9Qacl1/d4GlFZOEuW3N5V1OMrxXwvGeOTXOuYxg1yN96xw5JdzP23YLI6UvXuJkWSaT2HAR61G5icJLIzwUFwrhxh+4GIQaro3WUSWTJHotgTLPaubuGN47BJrRfj3ITtQ/IrF7lpfSsEm0/2B/ntxlg73nFSGjUfySQ3zKOC2t6YBZ40VtknMmvPItPtl9xmnq8x65SSdgP72eZf5j4gV6ZPPvUPabStcPcPF2B57S12QOakrZKE8Hv6WUVcvzs11spQOsXNoysfOVFyhaHxUc2+hA2fqq9XtTHOUHHt8lv0VbtNCBcq9rK6ivqu0WnNN2rd1Gc2xi7PWvUdBN9ksUK/BU/dPNn+UNH/tZAIZ/87XHfOlALh3pSEc6coPIdbVplC9QxsqeENFVE5FvoE7mqCy7l/Qgk4uy9Jl9cwRlEiuEs1bLceWd63gLJu/oUpSeM2ItZ3c4VA+Ko0vVwAX0V7FeMmFiYx6+CUd74O6zvPCwwAX+xSRZ8d9h5HMOecn7QKXfwTEW48DTNok52axd2RPBM2vP6lbZtBz9nBMkul7aHyJmkqVYdfDjbUvaZYGQHl7ciUoKRJPoqfO/3fd5AI4WNvKpFXEAefzsVvZMCqSyNCH3lRtw2LtRoJo/mPwbPlOUuC6/e+KeYKOp1usRXTJhhw+usqtbOrM2GKdSMOOcSxIxXPCdty0A8OX5HUyMCgyTDNc4ucPEY/AU3Sekvvn/tglLFhmkXwpT6zFhVgc8urYL+yT8qZ7AnX/070JkBc3C/6wwdeztkGiGL7g8gDbr+8iaptprWEpRj8PvFc+zRx/cE1jesSUo9xuormGh0vLsl/dHKY97eP+L17XYgwN88qeEW+22sTnaGSzXsk24xrpuSlQ1w+TawUZkO3ziYrUgja8R6xrL2Bzr1vUuVlDGNY4UjvLFug762GqrCctY6Gtgl2+QU+Maa91rY9i0c9LbVyjNYxcdWPG+1vzuT/5nAH79z+7FW/yGmTW/Z6VjoXekIx3pyA0i19VC90NQ6bNoxDS1jOEnX4hQEyYakVVFYYNY0aGiCkICOEUo7Da88dk0fRNC/8uXouQmhaJY3OjhnBerMmyocow1qGWMK/C8E8zqLzy8ncTNYlGuzXZhmxR4s/f5KMN5zlzUxK8Kb3t9V4qrP25wv0aV0NNi6fZ9Vf5Wu6B4u6wsosdjWO8Q7vlovMzMutxceTkRuFnGpx0qo9LH2XAXv/mCbH76w1XuM5u/f/TG/84npu8DoGY2FicmlrmjRzY2/2P2Dh5dFE9J7SusGSnjDdewTA7QtZUUX8ltA+DA5FVeuCTHjZUw2sRB//MH3iTj6kFjohqU/bHbn5B+PH0n+//5CQCe/7M9QYq+4oQmuir30EhYhP5KvForvYpG3AvGM2H2MOLLcq40FiK/SyzrzIsODbMQ0BaMPCo01Xk/xUN52XuI1BRuxthSSzaRnDyH2OUwnom7nrng0YjZqNd5OPTTv7SR7Y6sJKtaB7iw3cY9d/W1Xp5NsdHUdTMMgLFulR9Y0R4qsMpTqhFY9uW2jQtLtaiK7fZ7VPnX/N9sp0mZbbfOq9puYext2LuFvqbfXtvGadNybz/2gr9+gL1bimsSc6QMP72m/RY9U8H+iHz3pz++kW0feW1Z6NcXctFg1zVdFyC+LB91PWVTmDAhVJc1blIGPD6nSE/JhzzzVh2wUhI9ZcbTolVOHd1KbqeUGdy0wvrTsulX75Ky3X+fDJgP2d3Q6GoyXjT1FYFtQjmbkImOGN+SC+KzLPxQBDcr2iaUqWJdFejGT/j4hvy+st+8KH01fDOJ7Pr+s8GLeuTSBFtGZGPzYs3BX5UJoLGviHLlZdoxvMSJrOAY20eXGInI5uH52hD7M8IuOZuRjcNcJcrnZ8WxZyBeYKUg/Wu4NlHDHFF+JOCBZ54LBwrzWGiMhmH/OKshDt0piTKeeVxgm213XOH0C4I3+RsVrvkgVMPiK6cFnrInNJh4NLFjsSDph11R9Lwoz+Tcz8UgK/ep6ha7vv+sjMV22QROn4CqCeqY2+XhGP768ONlGinZQXVT4OTk/NhXyky/VSC0ws46GCaTXYWEIQrlN9h0n/WZvTad6+tGQuPCunrwnZ+guehud7hp3wyMKAJF77RtdBZ0KIBa2jcUm2ECLDQJq03x+i2lH2x+ahUo55JuhWi2lA7qBgIF25ws2icFR/kB5OJqi4hqB0kI2mn1r41Zoy0yxpOweV/tG6i0wS9RpSgY9ktYKVJtMW2a8uA7PsFPj0lYjsZMi5X2vSwdyKUjHelIR24Qub6QiwOlEUjMwfS9YgGmrlh8/w89DsD9X7gzoKglr2oaMZlvRh7WzN8llpl/OsqJboEx4nvXGDaZ7HOVKNVxgWWaOS2zb6yhSyYQVN7GSoi5nk5XqL4gfOrqeB17VsqXClEiMSkTi7okRqW+0unuYDlvly3CebOUNZb9gY1TvLQoOeXClse5NaFefnjf45woiDlqD/ucWRE63/6xGc5nZZPwUPcVxg6KVf4rQ1/hXSd/RO7fqXNuSuiKuze1Ek9sSIklfOSJHUS2iNu+rth4Ji9rI+lTHjVeoyM1EikxW/XZLiwDYb3/7U9wKi/9baTESjl5YZTYilx3anGI4zPGjE65pE2yiXLEo1E0OU0bkpAEoBHXnP3Xxg3/WJTyLVLeDnkBz735YN0kxC9LHeEcFDbK+ezueBCmwa5BdEXG9uo7YgwekR8K+4o0zslzi2Q1/U/I6mfm+wZY2atoPMzrUs58TCz0DaEYRV+WTQ4qoOXBtVBDM2p8e8REu23Tsd2attsCbxX8pgu9RTywhK1rLPH2+tqvvWZD82ukHapp9xr1UNTMdRHlBWVs1eprTdvXwC/tljkIbFMz/bYtl0jgpaqvsWbdYPO31ZdBO8TZj8o3u/lnXxsW+svOWKSUsoHngFmt9buUUpPAXwK9wFHgR7XW9W9WR2RiXA//ws/Qc8yiYpIgZC76zL/LuKefj1IxDig7fztHcWsmuHbujQZ3c1Xg5HPLbeeDbD7bk4v80VMSQ2Vik+BeS/kke4bFmWe6kGFu3vDQV8IB/lse9dAJeYFTPSXqL0oZf3uRVEKUYXY2Q/yqSZQx1gjWNX1joojH02vMFKSvy/Nd3LFTuN1XC928dVigjdPFIY5cMpCGa+PEW0P1nm2CUecbUc7lBF6ZX0sTi8hY/Ltdkrz3vYkin8wJV7zmO/zHk28M6qgvikLNnLJYOyjXjY5kSUfkHg72TLFQE67680vj1L4iE0qTqRJfUFTfJBj24fHLfPWs4O1qNUzPNtlvuHvkPCfWpf2zl4ZJn5BJtjTuB5EfsVoTnZvx6HvGfEx1aac4ZhFZM6ETbvLpfUEuLGxUwWRe6/XITMjYNh7ppbihBW31ZITxszyboec5eSZrN/v0PWdx+vO/RWnl28tY9Gq819/NjEXKCfORU6cAuCu62IrTwrVOQ+2JJ9qTRlT1y1+ku20qsIlzl3UogFnctroc5V/DhPHbeONNiRsrqabtAFppx81dbV0D/wSYO/41TJjmBFTX1jUTQHcJB48AACAASURBVFOaEE9ctSfVIFDuYdUG4WhNVDXZNxZPVgWa/cTO/Wj3m74G/6jycjMWfTuQy88Ap9v+/zXgt7TWW4A14F98e13sSEe+J6TzXnfkhpGXBbkopcaA7wN+BfiYUkoBbwZ+2BT5Y+CXgD/4pvU0wFmzqPQrKsMygw4/7aKL0g21LwcmkcTpf52m7zGZbetdCmtEdp4Hugv0xeT4yPmNjI+I9ThV6CYzLBDE1fOyOXrLnosBtLG7b4GFZYFqElvX2X1Y+NfPXN7IWL9YgwtrKXTMLOvOJqnUhUET3lPEn5PjyHKIxJyUWfHFKl+JpejuFcvxXxx6nJcKYsX2xMr0mIDf1YbDv7zlqwD8zhNvxc0KK2Xb9jnuPyITb2gthDUp5Tf0rTG9KvX/r+xeAE5VVvj0JfGYLV7qwqqbjdCJMsNbBX6Y1/3gyvm58/3Mm+PTqdHgOUS6qkG0yfCiwB+Rdy6xs0t46I9c2sItm64CcNTdSP6YMFges/zWhpRWFLbIM8xMrDOQlPu/+OwG3B6xvJRrURoz1pbZnE1OQ/aAyRubqRH6oNxv5s/7SU0LGLC6K0rOk5VS97omZmLhv3PyFLcnZfXzb3LvI/V+uefci8Ms3+7R+Mq356r9ar3X301Z/+At3BOTKJPybJqu96044S7Xenm2p4Zr977029gtIBzuJpvF0+oaaKW9bNMSt5WmpA3ECRR0e2gskXaLu/l7VHmBZd3evkubWz8KWzcZNK3NVw91jdXfDvs0+9Sso6xb4QustjGx23jotlJBgg9X+7wtLjb9//OhA2T+9Kmvu5/vNXm5GPpvAz8PmEyc9ALrWusmUWwGGP1GF17TWFkzeMRj5eYQ0UV5CNkdFskrJpxmLk3Y1Nhz2qJolEF8XlNYFgU4m4tQGZVu26sO064o7NRAkeK6wA5On2C4R09sYmhSlFTDt/FLJgfoE1Ge2i8KOj7lML2h9eIlDC5dWo7jpEXpufkIapNBHi2NVZe+HNwjyuVw90V2RCTy4FOlLbyzV0Ljfil7E385fQCAueleViqi1RJ9ZSolgSvOnRshZML9sh7CeV76dXmPzRs2S6aeh54WZsuvvuNTfHy3OCH9f9G3UXpAMPbiBsUHxp8H4HeuvpVQVsbHH60SmpK+6uF6gGPX5xP0nJKxzZo8nuvFGP/XjocAWK2+mzPLMinSsFDbRFkvXu0JqJ/Dj1jkNpkIi6Ue1pMGHov7xKZkPJWG8XunAFj6a2Hy2DVN6qzZs9jfIP+YtBPqA8sVdkxsxSch8y2z92j0ijzXL9o7uD8r4xmZcXB7zB7CcBXVsF7JFv+r8l5/NyX/T4pBrBJbqWsYLe24eVORrftWi63SVtZuw7FboWxbA+qjrlGW7RTCdigm3AazNKEQXytKRnn7aBxzvsmksmm1HVEe5eakoPy2uC9WULOtGtfAO+3hAaw2nL3VjyYLpgU3tWdUah832/RR7ktT9AWyLP2TPJk/5XtevuUnoJR6F7CktT76ShpQSn1YKfWcUuq5Rq30SqroSEdedXk132uXTnLqjnxvyMux0O8E3q2UeicSLC8N/A6QUUqFjDUzBnzDbWCt9SeBTwKkMmPacjWJWc3KHWIEVUYVqQvG4q4oaoMyeyen6/CjYoHlnhwkOSUzdWncZzglS/Bsb4rQkli64WczREbEatA3icU7NLnKD26Q7/XvF3ezfZt0ce2JDVhlqe/n/tn9fGr2EACD8TzPTsvGZXg5hFeQMukZC+42KdRyMaqmj6eWhoL7vL8oUMjSC4P87gf+EICZYmtTd+j/Z++94+S4rjvf763qHGd6cgAwGOTMnEWJpCiLkmk5SnqOki3bclxb613Z3rfvo30bvPv2WV57g3cd5JwkWcGmZIkSRVIMIEgiEyASB4PJOXUOVXf/OLeqa0DIorQ0BIF9Ph9+2Oiprrp1u/rcc3/nd35ncIn5FyQa3frfRzj/SyKO9aMPPclfvCyQS+cRTcWQQojXeOppo0nQKdu+D3/5PfQPS9HS3PEeGvvlPrf3LPDonNTYR9sq6HmJ8mMn4qi7zbjnUuT6pI1eqV+TL8oxw1tEGuDiZCcf+JOfB6TpSKJfovJYR5lq2bBS5ptsmsU9ymelRJcU9awp4OqqUK8YlUxLM/l5mc+oSYoWBxTZCxJrlbtjhOUy5IdcCkPyetMjDabvlmg9VNRQMm0El9tIGlG1tgsOU5tkfqMjMdywRtW+oXzo6/ZcZ1Tu6svymaKYP775j8ha8hso6boflUeV5fOsbSAf5G6b0a6P5gKl9erVhT1ynlezVoLFPHVt+9E3Giom0r68kMlnq6w7nxxbo5kILbnNnXNYOX5EX9EhEpb8JtzLJAmudO4lJ2zO4a6L4L3XNa39Rh62Uuta9Hn2sRv/mH9tiSwH7qu58deKfV2HrrX+NeDXAJRSbwF+RWv9Q0qpTwDfjzACfgz47Nc7lxtSlLpDLNzRAFOIkxgL+XosaxtCVAZMYc8dcUpn5Ufdf8phZdhsvZIOF+YEZlGWxjJoxeo2aGTEw9zcJ/DHai3O8bzQjqbWMuQXTSXpTnBNf9F/8/R3sWmj6Ua02OtXZaoNVWxTzFTb5EBdpkpXbDAVils75HMnv7SDG98mebW2O8v8zrgwHpZLcWJ/K05nbYui1ivje/nXh/xen4dXNvJ/7ZJF509X7yI+IQ+f83KG+G5xwN5WtxIPk6/InOgNZW4cFF9zcmKATFrom9Fn0n6/1lpvHaYETdize5zxFRlLZSEOPTLnQynJQYxPD1A33aIS/QXKl+Rz4bzCMk48uqxw98giW62EqdfFJewZHvNpjs5EAt1nio9OxTG/O4qmMraR0qzskM8NPFn3NWicWNi//uxtUZ+CGpkO4xg14NR4U5FRaej6ssxFfhMk5pV/rddir+dz/a2wxn3S3GRH+GmqfqGQ5Xcgql6mpOg7OLW+k5BnV9qqF3XIh1CCcIsUDTUrSF1fk8VZp6oYMyyWig41Kz4DhUieec7cG1OwcjSIofvHBBaRtKqvY9D44w047iCDJhLA3j1VyQSO/xyitY+hO2gSSo45EGnOeeixb2pTd1Xs/6Sw6MNIIukCgj3+4eszpJa17Ftqree6Zd+29g0VFmmtnwCeMK9HgNu+oc8rcCLQ8UKI2IqslMpxWNsow4jkNUOfMpnscIOuo7KyTt8dxTZl3eGFEGpcoseYK0UtAE5Mo5ISERy9JFF55FycV7aa9vGuorNHostGp0VtwcASmaqvkx4/GscaNNFBrIG6JOX+9U1lkgmDk+YqVBbl/fE1Yc1YNTg5ZwqLQg2W5oTvvWFwkYnbXP/66XNyn8kHZ+lLyliOH97CybgkDKNzNpUus51rq9MoSgTa2ymResOxCNvy92SiSsVsJZXlsnpWCm52f+8IZ56VMvuGbRPukgTx6Zc2snOPSAlUayGqpgXfommtV++sQ8MUYxzOorfLhNd1xOeHx5Y0Nw6KlkzDtXlyZKv/emuvME4uRdopmaYaxa01cs/LGGtZUx4e1sTmjY7PeB5luNIT9+e44XZJAp94ehuDnzMMp5TsvkD0Zow0DV1/sUa9Xa6TO1Fm9s4mvPWN2v/pc/2tsCkDScVUiApekY97RVXFINwStGBrOOBVKogR3Ctyv5OqQcWAFJdDMk2d8pAPv9SwSaomh9uDTry/W5eNw7PLz93Uo7H96H/VjfrnidBUbfSSojbaH6tNs5jIQvvQUrB1HbCOy+8E9HC8Od/42KuGes3YVZfPDRc15W6LNdOwObqgKBkn2v+kxmoYLHa1wuIecTbhglSZAtQ7GljT8o/Br5SYvU0YEIWMy9t3SYHFFw4Jza+edbFCcu53bD9F1WhGPP7YDV5PZ5xkjbdvl899bvWA3x0nsrnM5rtFvvbSajtrebmO1sqX9V0ek4Wgc0pTf0wcytL+Om09gvHf1DnO+CWBh6yCTeVWSQrXD/YQvUcewnBeYS0YrDGhifYJdOKeSVEzzr2cMU7xfIZynyws0ZEY4zfIOLb2LPDBm58A4GhpiJfSJg+wZBG9IAtX26Jm6HaBVy7MdrJvhzj3oZSwgCrDIc6eE9ikvKEBpsK2besyhZI8yAsdYabLsojF7Do/uU8EvF5Y2cSpQ8PmHly/YXb70RAFGQqNuBF2GrF82GTqwRwdJ+V+YgsWx58Vz50ZhdXNgZzJMwZ/77HJijQM1Z4k4/d7bJoomVe4oqTq9WpqvwQEToCpUtF6XZGM97qkm5h3UjV8p1YJ6JzYASpiUJzLc5BBaqCN9p2ypVwsj/oY2PCLmJcZB0382/uMnLPpfjxmiaVcbLMABRkzFR3yPxe0GjZhHP+YqKr6Y7zcwspdR8MMWiyAVdiBhaSkBcdLqwjsy7/qnNeatbRcWtaylrXsOrGr21M0rZm918EqSmd5ALdXEdsgK9/M7RlCJhrsOWzhmMqIxIyLZZCI4uZmV/nlnXEyYybSLVg8PidMk6hZphpxTfS4RNaP1PfRYYp/6u0OoTWJGDoeSfD5O0yyI1clY3jrlVqYuZJAO+lojbVTUlwTqsFO05vzlSV5b2VnloFbJEG5erqPfFHgjGemh8l0yzULiRjupEAEtgWTCxLRu5srYFQYda6GMn1UrTCEVmSMIdN2rtFVR3nRyypEwrLtvLjQwUfyDwPwE1uf5Yb9Mr6FcoqZJYF/4tkiIbM17W3PM52X908dHpLr1RXxNQOLHMhTXTLt9VwLZ8aItuRqDKckEfz4+DZeNFIGrIWhVyKjnq5VFo+LfMHaVu3vZuiSv9eW4sQX5Ltf64CxhySOS78CHS+Z5FVYUc0YjvtmzdIOmYdIHta2yOmKgxHCJmCyHKh/5zL6K9cu++D1tncOSwOQOg4JZVgulK94rCQom3MT8wPQpqxtTVvNZGSg8YQXybpa+RF1MCoPmqstH/5w0f7xYeX4iVPvOBmHPL8OTXVPi/WRfrO9nOPfQxCKSdKEcsLK8XcZnpZLnabWjINqQjFKr2uw4Vldayo+UwafQeRozTvMnL/0qju/duyqOvRQQdH9TIi1IYX3fLkRqIwZKdsGVDYbcaEnXdyQ/NhTkzVqbabDzu+sMfWAca4VzfwNBu9bUb6cqyeepd++jPV5gUVu33GOkbx8rm3bNOPPiKDR7L0OqfNynR0HJvyxjix3+PjhzHN9uGnzYBUsTj8r8IJXEUmuwWrZUDEsjbMiD4HKFuFJub7Vp3F6jFZ1IkTqmDjJyKqm1GMYILUojQ7Z4qmVEFZV3l88a+63ofjAQ18G4PeWHyDxVYFznDaNvV8WoqRV5dhxGd+23ZP+w1yoRDm5LBWs0VAD2xSXLObkepGzUaqdJq8xkqJtl9Adi+UIfltJpfnsC7Jo3rnvPAcXxLuGO8vU12RRKv9DDxHj/0t9mt03jwJwbkZyGY2EptQj31ltoEpoVuZKh2DyLd53aeHtxsMrFt1vFj2esTM9JI3We3hNs3yDPERdz9lkfzPGpdlviLb4bW3vbT8EQBibVVP8ElbKl8YNWlitZ7R4mHpMaVy/0KZZCRrUZrG+Bo4VLArynHvCqvvQjKVcf2FYR2fkynDIld6L4PoFSTHV8M8ddMByfRMcXqEpddVV61guQVqjR0+MrYObmpa2Qv6SV9EOD7cdA+Al9l9xTq4Fa0EuLWtZy1p2ndjVhVySmrk7HZSryB2TtWTt/hKRMwIzuGGNvSgr8uhPV4hKBT1T98RIjctqWr4950f3+Y0WjaSsoF1HXRpmL1ltM4UoR3Mg5BNykSIjSKR7caYT2yxl0ZkQRZOUXa4mGLkkcIGq2Oy+UTJwiXtqaLP6XzgxiG0i5+6nZfrm31plY1b0YJZTKTDi/4vLKdydJqnSUyBuOOlzb67T/4TgBaGFPNWNwlAZfzCKKgfU6GZMUksCa7QFnx6XhO+OG8Y42yWFSuFIg8UlSX7+1pkH/GX6wolB3nr3cQBeXu71YZ7GUgxlNF5CPUYad8jyx60SDYovy87CGaiQmDaRViHmz/2N2TEaO808O2HOIPNWvKPOjn4pVjr18gYuLsq9ffjAowB8vOsWlIme1s73k5wyW+R717DOy05N25CcNFGXDRNm3N1bF1mbk+uUt2lUVa5fbVeET46gyt86NbyraVYsxt5Is6w/CH7YgXL/iqdVopqRWylYiKNZx/rwSvi9ZKZAIeu7GPnXCSYxaUbI9cuSp97/r1Twc6XOScFj1jevsAOc9Cb8EmywYV0BRokqx4/WXa18CCkozRvTrg+zWDSTyXXt+lF8QoV5U0x25P8pFsOtXJvdVK4uy6WuiE/JJReNQFPiRLI5mLLCLRr4YYNL1zFxhqubQz7LpdSnfAzdiYJr2BOVnE2p13xZRvdbW4KXA3z6K7fTeVQ+l8pZOAIRU78lz20Dgn8fnxrgfbc8C8Cff+HNHJ4U+mM8WqP+tCwGem+F4UFxWOf6xaF25Ap+d6HakM3LLwucE5oOY5dNHuCrWeZuk3Ht2TrJhe8cAqDzRIJyh5EGrioiS/I6ewGSM6btniOwRLkbVl4Qh7aQ0KS2ySJSGM36VZuD/3qekR8TZ9xIab50WipIY6kqvUb7ZGoqTnRRrlNWMhE333SBoy+alnZJqGdk3gY+G6Fq4KZqO4RMNeZnJg74+jFztQynJ6VqNn4kwXRaHPPA5gW/A9TzeaFSRkMNzj8ukJCddrGNKHf9QtrvHBVdwpdXVi70fEbgrLmb4yhTfJR+xSK6Zpgz42Xq+4bRR2Sernezerp8dovHwvDMe7+OOHLPPCEqRzdx5KAgl4UmGcC0wWixmHOU3BBhU7lVcsMBpkqYmJL3RajL02+58uY/SGcMFiEFKYn2ZePwXgcXA6+K1FLuuuKnoPRu89imNowHIbkEoRh86Cms8LsaRS2LMB4W38xVWD1duJfGr3h/32prQS4ta1nLWnad2FWN0HUIqh0uyaFV1KSwLPqeq5DfIAm1WkoRX5LVNu+kGH23RKgbP+1QaZftUXxONyGXvTXsJVmp1+4vkcsKz3tuTrjSumyDZbZvaYe5O2X9atu4SCYq554+1supsESX27rn2RWXaP3Gu89xc1YkZP9heg/22yRhmo2UuScnKoujCwInrObjfOVfSXON5Q/kUQa6qG+s0vC2e3cVYFEi1+VKnIbZIWhLETMNH5ZudEhcMhIDSlPok3sr9ZqIYUURLpjzhRRrvRK5utk6yvDGR39oA7U2mcPQQImGkemNPZdmcqfAMm57g1LaRGZxiYYOn9kMbSbacxQ9Q8JZn3xbO6nzpmnAsjQEASjXwvzhubvkdSnK9+yShNGnZm/nRzdLafTu2CQ//9QPyT2X5Ia35Bb8BiXJCYvV7TLWzqP4NQgzd2sfkiv1K2bulGElphXheyT5a13IUTP9Z5e3xYjkNe7JN0Z8ohMxH1qJYVPSXoFOM6kXbGIBgWhdNyGNmKWbkftlMrQgEa0X3UYDDJKEVfcTkG4gjRjG9Yt4vHPKedbz0D32i8dykWbUr4ZqAL/4hwCDRvjk2j9X3VwzSf1VTJgw7mUMn2YxkQe/WDTlENKquctxtfZ3KDYKx8gp6ESMa9WuemGRXVKUijHivULnG3sww4bHxLnO3RhlYaOh5a1A36PmYVoogxann5goMPGgEWVK1qiVTFFOMUxvv+DS2Q2Cb52f7CY8Jp+zdhSIPC0ONfpsO+P3GkZH3KUzJQvB6ec386+0QAPpnUu8eFwgCLtgcf994rAePbKP997/PACuY7ZjxTCT95kqy7U4oYJ5P2Hx5t2Cw4/mc9hdUvE5NdZBwohMVbOw9CbD7Ik06N4ix1ya7iB1TB4cT8DKiYDzNiMZeySL9tg0mToP3CpkqmCmf6LYxsRxqUItbtD88H1PAfCnz9wNtjy08VPiaCtdLpFNgqdXJ1KspeXabz1wmue7jfSt5RL/tCxibbtLjEwJy6b/0xE++Q6RtVUhzafGhQaaHSqTaRd8rGToqCfPbMXIY9B5okqnyZNYVYdyr6FvxlzWtsgc2hX8nEWpz8WdlkAgk1K4BmEpd7u0vaxwv3aXs+vOLDxsu+G/VwmITMVUszuPo6GimwVEtSAtMVBo42uM61e/J9dqwhlNzfRma7jqZcVDNcNQsQPMm7BycPR6t1MPOGsL5dMWY8rxi4ycgLyvq9fDL97CEIRc/HMHBLusgL57OFDV6tJcRCqBvEJFu75AV1iFWTNsIh+fuQbtjRHStKxlLWvZG8CuboQec4juWiVkuawuJ817mpEfMCt7pEbfF2VICzc0d1uFjXHyG2XtKfZm6TwpscdSLU31gET3b9t7iv0pgUWOrEnBy7lGL5Zhc1QW48QfMNv1SB3bFNxETyZY2miI00ozeIMoNY5OdLJpmyQ/1z7dxwsfk6iT2+p8+Ks/IOfJy1gT8xbl7RJld3euMW8ZrZkLcZ7PSnRbWothm5J4O9lgs4Fw5j82hDUvoeb+28Z4d88LALzQMcyjmZ1yna8axsndq2zKCT98zMn628G29iJfPrkLgHceOMnhBUnKDqRW/TlEw58eMfKfcQcqMuflHjkgNmdRSso8dG1bZH5KdkFfdbbQmJT33bhL6EGJ4hfGuomlzM7q5hgeDvbeew7y2RFpyPEbT7+TaFaiGvu8nKORcnGMWuX0XVE2fkF2VWvDSZYNayYxYlPPyDHZEZeCaXqttCKy4iVLta+uWNpXpVCK+RH79W6q1GRYuIFmx0nVjM8q2l3XzMFr8uBoCHvqnboJswRlcIMWbMzsepG2csmbyQ4HmjdDs1y/hkWEJvwRbGrhRdRB+YBm9B1sqvFqZUa5ZrPxRdqqNeGfwI4iuNsIdmXyS/5V8HzNpHFdN/uvXj4bUWU6q5WuTYYLtCL0lrWsZS27buzqVoraLt3pAtVGiOKUqaDcWqC3TaK0lUf6WRVGG3YFygLX4sSalaXVnKKWFWyu/6t51nYIBvzCzEYenZTIMNkjmHhv3zIzDTnJWw+c5ssvSRSrOwuEw445n6Y0Z/qFlhTjx4T0bYU1M2Pyun5XGWtKMOXkuQiRuyTS32hogw3dLC9+eaQf2yQaEzcv+PdejURw8jLurg3LnHlhSO7t3jpeKeax8UHOL0pFZTpWpWgkBLThyb9z0zm/2rOyv0T2GYl6nfMdhG4XrPpLj95ExOioH1vaQNebZZexs32OJ8+J+JVuWAH9Z/lfI9VsELGwkMZOSvibSVZYyEo01t6V5+0bRPf9r0/eQm3S1A90OESMYNpfPXcH77vraQD+8pE3o83cevTRSG+J+JNGa72oOffDco7uFyTpClC6q4B2JdZYCMeIyy0QWdVUDJ0xnActpyZ6Ni73c+1Cm6+rubPz1E0iNGGFWXJkd+gqaWwBXiQux1cvmxevtVzacn2uekXb67Dmy60eFPIK8MojgShakpsml6StpjyAtvxoPKYa/mufHhlk0ivHT5pWdGidPEBz/PY6PD0arEL1281Z/r0EBciCSpK2ryQJMauZIC0FFBZ9OiUOMROhu/PN3/W1ZlfVodcqYS6e7SPZnye2W5xhNOTQGZesX7GgWdsaUFjrkYntecaisMEkShY00VU55vwPJsmcMw/QmQ5ipttPUYmTsHtdbCOp+5Xz29mzRRgsZ17c5EMxqSlFzagZuhFoO2P4vX0W5Q3i1KzFKFGTxHRtWFkxcJF5IJYW0ngidyrsEjlt4IVigprpVhm7YZXhQYFzTp7eSMzw7cNDVeKGcRMNN1g8JjzzQlwTKpiim14Zx6MjO3ENg8ZditIwFP5GHBprXoLUxT0nLJ+tt44RMXK7B8eGaGuThW51pJ3YnOGhG6ioVrdQpqFHONbwYZaBgWnyhyX5WW0L8dVZw1UvhtCeZKXGn59aDj4zakqjtxb984S6ZJtam0vQuNsoStZsMA3C525VxE0f0fDxFKVtMiepWYuNfyasorEf3ULFyBPYFYu8KdrKdBconW3ja1CfrztzKxVeMs/v3rAmHFBYdL9G6b8/NQrSymuCgd/guY61zjF75jnAIBxTCsAmwDrnGmS2+GMK/NsJdEHyPle7DCgInjuoyOh40riBcv/LzblMviAc4LgH9WqCvHY7OH8ECqu0Jqa8Yiubo6aQ7VotKoIW5NKylrWsZdeNXdUIHaXRUYfiWIae5+Stmfscal+SCLCwz/WXmOiC7feezG+0fOre2lZR4wPInVQs7zba26eVr86Y7JZIdG0uhZ0y4lPnEpyqit636qwTHjXty7a4ZM4bfvordapZ0x8xrygbah+dVWolgT/iu1com1Z2DUeOzXXmWbkg0E52yzL1ihy7trNOzEARN/ROslaX9xPdRSKnJIru61zk5Qsyrl1bJ5lNe1xXB8cxfN0xib4bO5uRSyhvUeo3vR8DPPT2lyz2vE9U4Z5+fjfRfpmLvX3THD43JNffkMddkut7EbJVU8SHJHJuNCwG9gjOcWduhOI9cv18Ncr0adlB2D1VXEObDC9ZVDpkru48cJ6a4Q8OJlYY2ivb009N3AjA+HKU7BMCk63s1mgzxzrhUJViXJLjFt2Py7yFKg6LDwiVNDavqRvueX57w4eN1qbTpBaaPU7fCPa3K7cCsK3zWV+QK6yUz88GAtWXkFDN9+qB4NYNRMvN5hSm3WMgyRhTjp8ItZS7jkt+pb6jwUSpfdkxHn56pbJ+O9B4IqjeuC6iRr9KSEyuo1+lc17Rli9pkLxsHE16YpOPn1BNLr8NFA33PGOF+NuVm81frl1s7+o6dFdh5UPEZy1WTBea1AXLL/+2qgpD7aaWdSl3muKS7VVSL4sDVg1ITRrGywA4WfkVL+21ic8ZvnJBjrVXQ1hp2bpXept9TLv7VphflkVkeN8kF9JSwp8dUUzfZ5TjxizChsVRX4phD4tjzE9k/AYO9YY8+GsLSYb2CpwyfryP275HcObnzg+jd0t+4NxyF+moaebwPyS8mQAAIABJREFUxQylB+X9XZkZ3nn3SQA++oV30rFdCnocV7HsCBPHSZky/Nwak9OCK9mbyty9WWRyxwvt1MziMpVs56mXpa3PB+9/jD85ezsAZ+Z7UKYx9vffcoxP2wKLRL8i51vdVycalsWvuJDFzcj9jldyjEzLXOnlCJaZw2isxptvk/v88pdu5M0PCKH82PwA79ggC8pMNcPvPPEdMt684ZVvLFPNGdZMzCFzxuClnRYhA0MVN7iUjYxD7pRieYeBxybw+4v2Pmmhf0QWi4VTXRT3VHHj1+4P7fW2z4+KpMOvdj5LwvIcsKaivQbPTYclvPMm08P2GS/KL7oJQipBZxlscOFBIUGIJKxcasa5X66Y6H22qENfV8tlHUQT6FHqWXDBSaiGz3mva8sfT4eqXrH034OVgteLKe2zgIILXEVD2vLOoUl7c4vmC5ckB9fPaa5Va0EuLWtZy1p2ndhVjdCtuvCdE9OapX2yLHYdd1kdlmE02hrETQu4cBF/C509GvWTi5E9q+STErl2HlHUDETSc0hTNtv+zOdla7jy/Xkapk9meMUmuiQrb+GVbkKG5zy+2EZXr6nOfLjd303V9zeZFh1Hbbp/UKLB+VSK+hclYl29SVb+XVumSIUl+r4U0Rw8aVqp9eZxDkoEvNibpLhRovIb3/cyZ5cEulipJ/iDibsBuPOOM5yaFxmCtbU4VtGU3BshraloO0nD666/nKEwKDuRZLhGPCT3NrBtlJe+sAOAfxjcw9s2nwGk2UYxKuP9s6fu8UXAatskCopOhyiNy32prRXe3i9RyGfH9vOWrecB+MqJXTgGIlEKHn3ScPOj2teaH0iv8vFzopleq4TQMbO7MJz+SyPdlHbLPcRGYuTOyK5g8i0hn1fu5Bo4JgFVGAzR86LhLSct4rNmd9avKLwi18ydgaV4yFeLfCOYc0TqBKxbFXXdhB08pcBaQIUxbbl+FBpTri8DEEztCaTiCV412S5e6X+wCjOmHEqBZGmzCUWT5RJVzjqo4/IWdvLZV8M2QaEuB+Xz2h2au4k6zeRmXTcrQWuBqDzI1PF46BbrmT/BaNZ7bSt8CMtWal07usbRb75v7dWyq+rQ3RBUO10q3ZAaM7S0/RYh2d3T/5hC2zL5pW6L2KK8Xtlu0X1EftTzVhtxo5Ka3wTtsrun3KHID8nr5azBvUIO6og4//SYS0P8H4v31KBqnCVQOGiogrcsUzltZAUupggZ3L7aBmePSoFQdusyqzfIALJH5YQzHWkarmDSbUMrFE4Lnl5ZaCMz5ykVKqoVWWieO7gTuyQPyqE9Xk4d1uoxSuYYt2FJF2ygEW8+VMVF0xjDheOXpIDo1uFLPJgTB/z06jZ/IZx+oY/oHfKPhbkMkawsOl3ZAvMvCszU9bznOJul9PdtP8fHjopOSzJTYU9KGkwcGtlPuVfGNNyxyCWjpFgqRZlekXm+VAvR1S4L1/5NUzx6RKikU4eNjnFPHcssCpUNNZaWTX4g7bD5VmEhTXx5I6UNZnuf1Vg1OT6/yyKyIq+Hf+g8x56ThbPUp4Ry+eomOtet9T1r4LsPhnADiouuD200nbsVeN8JOLKYamLH4uyaThLAtrTvGMXRrqcEwmWwCU1nHZQBqGA3nXGA/pi25HdUcsPrWTNXoCoGF4uYctZDKoHG0JfL/do0pQ4iyr3sfjFjbb6OqGZj6GigUKvgVul/usq1bi3IpWUta1nLrhN7TRG6UqoN+ANgLwJK/DhwFvgbYAgYBd6ttV7+x85j1SE+bRFd1bSfkxLyWibM9F2mBLhuU+42gvIzUDOKgJX+OuPDssKnToRIzDZDsbVNZk1S4JpkZc8z8p622ogvSvRS7ghR6TLLcMPymyOEww6uyb3s757muCcGdKTNj9DdO1YJnZYIdGW0jd5tprDANJ6Ye7mLtm2SzIyHG0T2zwOwtJqkZhgx0WW46c3Cp35qbSebPyPjeqUtRXJQItoz091s6pIp3Jhc5okLEoE6FRnrpoEFRi9IZK1t/F6k830pfuPo22UOx+J0mGKipRe7uTAtuw9laSIRiXzu7bnAJy05z9x9Mo5dm6d4eVSi6NV6jPCEnLuQCzE7JPde3NQgfUEembPFzQzdIZrQ8fYl5styn1OjnczWZbdyIVzHMgVKLJtS7Zci5HfIe3bBJr9dJl9HXC6ckB2HldWETc/X9N5FLrXLrqntBP53ePjUMKTls7X+Onopsq6c+7Xa6/VsX20LPymNSw5Vw+wOS6QbTISGleUzXoLcaks1C42igfkqBZgmXqGOo1VAVVFdVkDUbEEXTKwGC4iCUbIf9aObEXWgX6iX2FzHjgkydgKCXJdLFFiB970EaPAaNb/IqKk66ejmDoZ1XHVNTHlSAk21xfONMKEnjnGt22uFXH4b+ILW+vuVUhEgAfw68JjW+j8qpX4V+FXgw//YSZQr1YH6uxYZPyJ4bWpM026SxmvD+CyKUp8mtEuUBd+2YYQjc9JsohxqSle2v7TG4j4DdZyFsGFSzN5vMJmKhfeNhNtL1MvyWKuyTccWccC1L3VS2CfHHzy0k/RFOcdtP3iCJ86LQ01YmlqfEfFP1pmZEQcTvyBOzx2usTRp8DWlUXEj8zkepThgVB0biqeOizbL9u1TjL5d7kdHGzSOm05CXQ6XzL29Mt6NHTErjWn8sPS5ARJvErzfupilZv48MtpNZEbuLVxQ7GyfA+DUTRaFsoyxr22Nubyp2tQ2G24TeGPmMXGio5kc8bTJA6zm2HKXjKTcCPO5i3vk3Ms21TaDV3Y2KNQFLjl3qZe37hHGy/xymropclr43CD7vlsWsdNhWUCqbpINQ7Iglj7ZS/T7ZKwzZ7qJrJgfdRWq+4VCWX6+E2WKiRoJqJncx427L3L+Efl+qjmb0HDBL4z6Bu11ebavtumGLM4fPPLDvHjHxwBpduHNQEW7vhZJpekXpb9oAGrwnF1COb4SYZC26JmldLMIKVgFirBYgHUytVdq9OyZ59x9NchAk2gC2jAVHVrXSCOpaq+6jhPohRrsThSU/fXHESiwspQ4b/jaSJ2DJmpc5I8ffR8D7qmvceS1Y18XclFKZYF7gT8E0FrXtNYrwLuAPzGH/Qnw3f9Ug2xZy/4prPVst+x6s9cSoW8G5oE/UkodAA4D/wzo0VpPm2NmgJ6vdyJtS3Iw/pkO2iqyLs7e4xKdMyyXRDNar2UVVkhW1y8d2k9sxvBOcxpMsqKWzpILLJprJtL2CnHQUBmUOCAer+FMS0FLaniVlTXDhR5yyfXITmB1JUfVyAc8fmgv3QZa+RdbH+VXnno3AMlElb5egVRS2ySiXa3G6YhJZne6lKHSkPvJDFR55YhE4o22ZvOK6nCIhuFMt/eusZqQsei1MM6MvI4NFijPy2srapQHH5rBNonIsX0R3KKJypN19Ba5z8a5JItVgT8W5jN0dsm95atRX6pgX2KcBzOin/4vbv9+mctj7dRTJhqKaRYdSeyqXNWHdtycQ/aUYT00wizOyleuOlweOyvMmgd3vsx8RXYCx4tbOHlEioIyr8h3lt+kmZiVSY71KuoVOXfmvMXKAbmHxGiYyElz7w4+P1250P+0PBPH4sPYOZPoS7vYoyl07RtOCb1uz/a3yjKfSVG/o9lKzZsB0fgWS1rNRF9FN2EH6Z/ZbPjgKTJ6Ccqi22xRFzRLaVzDALMDETA0k5ERXJ8tE9RPD7JSPJZLWDlETBs7h/XyAV7kHgnorltof1xhXNxA6b53Dx4zIKi2WL9sp9JMDiu/7ZxNM2IPo/wWf6lPpV81D9eivRaHHgJuAn5Ba31IKfXbyBbUN621VurK4gpKqZ8CfgoglG3HiWmWbnBJvyJfVLi9hGu0Udy4S6VT3g8+J+kRm+4XZAteHIxR7jA4d0RR6jXH3LxA6JTQ2DzH5KRcotNyi2s6hdUtDviO/lGOL0h15pLlsmw6Cd1x51kOjQwBkDwZZ9aIw/zK9Lt5aL84wJ/qfJKPmyq9gwvirObzKc6Py28+NBUhvU/Eu5bdOAkj4AWQ2CgPx6VLXahOWXw6kiW25mThuLjSQcoUH12a6vA/55riqfHRTtr7xEHfvHmM8bxANQOpVfrjAsVMDLT5Al+RS1HmGwJJfe+NR4jbcv3fPPMgm9sFcmpPSC5jrCMDWfn79sFZEiEZ37mFbsLPyUKYfdcMl7TQLQm7tHVJkkFXIty+cRSAG1Nj7OsUbP1flr6f6VkZY6lf7sGdSRCJmR9vNEp1VMbXmdf0fFW++7nbHUIGPms71xTtSk43mLjfUEmHlsibphnqQor47hWs6Hrn8hrsm362g891jMQ3et3Xzdr+5ggHPyJzfGdsxddykQYOxgINo+so/5iatkiaAMHSep3crmdXqgIN4/oMlYq2SRtnHGS2OCgf244px4dl1p3nMujEs0gABw9SIq0Agyao5bJee2b9NVDrKYnBJd/7lAUkAotCOMBuOVQRH9D2N0eu4frQpr2WkGYCmNBaHzL//iTyI5hVSvUBmP/PXenDWuvf01rforW+xU4mr3RIy1r2rbJv+tkOPtdholdtwC1r2T9mXzdC11rPKKXGlVI7tNZngQeA0+a/HwP+o/n/Z1/LBZWr0FGXNaPBYo8ncYy0atuJEKU+WQf7nmswvlEiD9WvCRUMZ7U74fPT5+7QWKY9WTxcZzFp+lNulujzjp5RDi8I5KGAzrjAIpcKOQbTEjkPZZe4OSsJwP/55QeJm+Kf2L1FGiahWKuG6IlIZPzro9/DbMEkF025fXEmyZ0HpPhm+q+2MJqTcQ9tmWVxWY7NGaVDAHslhBs3PPxssllavxLh/rvFt4yOdbFjhyQuR+YkWlcKlk0LtsNrCaJnJHJeindzfo/cz6b2ZUpFA5F0N/ij+yRh9v9ceJcf1RQKMU6MD8k1Y031wsEvyP1M7N1EZb9E7kzFqGw2UMxqmnC7lKMkn06hHNN4Y6vmXFoi96de2sHWYZFNnJ5pb/Z0NTIJ2aEV+JJpY/euScZOCbMme76EXZZIb2V7ls7bhKkz2ddO6pTcTz0VwjWsGaW0rzx505vPcn6pk6+xSfya9no/298K0/UaP//Z9wNw+D2/hWUizbQK+QVHFe1iZI4uK71fP1/Bgp5/zOxAO7iYctYV9ATP7zNblONH8SUd8qEWK6AT4yU2LaWbLJbLvk43wLKxfAZPszeoHfhswkT/Na38RGnFbe5IoMn8CUJVdmAHA/Bzfydzu7X+3D86J9eKvVaWyy8Af2FYACPA+5E5+LhS6ieAS8C7v95JlCM61uGzYV9Y6pa7znJ8SuCPcm+KTZ8XaKWwMU7Xi+ZzrmbhJtma17IQzpus9rxF9g4JniZm29FGKrdhBGEeOb2P6IiwYir9dWZXBBZpdNUYGhSYY6mYYLkiW+Z9N15kMi/XWc3HST4jO4r6Js0fl6Wa0040cA0T54Ed0i/06aMHeOEpYbBEblKoqtzbxHw7G3tkcbk01YGuGXpmX8XXY19dThKaFsy/68ZZbk5eBOATxdu5MGMoh2PiuNN7Flkx5xjuW2AiKguHPp+i+IqM++J0OyHDRGlsqvCB534MgBs2jlNqyHW29c9R6ZbHeeyswcEHyqxtlOv0PVtmWsnr4pa6rwETCTcom3OEipplkRMhVLBYOCVj7TmuGcuIox/sW/LxctcUEK0uRugxjcAnF7PokIx14UACxxRQ1dodlp4SLE1tq2IKGLEcfLrp0ulOkgbOev78ZhJnoujCN1Un97o8299K2/FfpPvVxPfB5lAQ9pBnLKYsX+MlDJQCsESwmbTHfsE4tKAgVpAFE7QghdAKiGNdrrcSDQhyeTK4HrRiX4aJBzXNvSCkou2AJK57RWy/rq1ANakR6gs4cAu32VuVJiXR0dqnMMriJ69nHYudvyVz++2i+/aafgFa62PALVf40wOv73Ba1rKra61nu2XXk13d0v8IFDY5pC7Z2GVZBZ+/MIRt+MMd5zTjb5OoODmpqWWaq7CX99jwb59l6cfvBKD9rMuKSdLFgPa7Zau/NycEhUen9qH2ClTSFm5g9clJliba2J6VyH481M5AQhKKZ1e6+f09fwbADx/9cX/LF1lRRLeb3pcjbWBK1+/OCszy4k0b2NwuidCTEwP8meEF/8hXf5KJo1J9dMfdZzh4XHjTbb1FaoYJU5+P45oem4trSf7Vp38QAN1RZ7BTItBLhtftfqGTrnfKuLORMhs3SLZwtivNxqS8PrvazcWzAmMkU1WqFQlvD58cxsrItjd2Ku7PqzaNJNyZGEWhpLN8q0334zKm8t4GysAca9NplGGSdPzIGI1PbgKgkYL0zXL/iS9lqD8m3+HsxiRxU0vgXJSdUnQJil4i+ytJKibBXWuT3RuAqivKm00tQc3CQ1LqSbA7JWls2S6lM0bPJKQp7ynjxt9Atf8Ba4xLFPnw536Jl971XwHpx5lQ8t2XdN1v4lALwAkWwoABKLrNaN2DHyJq/XwmzBex6to+5JFQDvlAwjOYoPSiaxvtR+th5fq/K4+rXnFD60r/g+fypQcCcIqt9DrIJeJz1ZvFUc3eodqHVsKq2QAkodY3tvBYQG1WiIrpBvXwI7/EtvFDfDuZ0lfocPJPZbH+DXrTT30IHdLUNwsWa03HfE1st6PuY67JkzGSU2ZrVnQpdhu8ekD5P3DlQGOPKefUCm2+xNC5hP/3arucI7qxQDkvWGyqrew7unoxDLWmgJfXSWl/9zSvrDaZJjPjhsYXc2h/Rs7zpp+Shs6fP7eH2AvixL7jhw9SdOTvS7UEL17a6I/PMddRtibzgmkvZ8Gm7xUZ3OHUAks1GftTx3eSG5CxrKwaFlAhzG8/8OcA/N+n3kVhQvD05ECewrwcYxVtLNNKrtHe4EdvfxaAP3/8TSjT5SbzCqzcJY5RG00bK94gZGAgziWp9coPzE408CY8dCFORobKwq0uKmMWg3yYISO+NTaTI3RJ7q3rmEvd5ApWRNEXe1ee+JfS/r070WYhWed+WaxqDZvyIckrVHoc4lMyxmqni5Mx+ZajYepy+1TbNY2uOjP/5r9SvThx1RW6Miqnb1ff+oDe7unmI899DoADESi5shDX0b7DCsIvQU5QJEDd8xxgRbMOZvHglyAUE1aQd5vQSpB9EtRvuZJ574eV62PewYpVYF3RUFDsyzMngKHLfayXyo0pHcDHA9cO3KetFGE8DF/xYlXyXr951wM4s1fkelx1+7L+5GGt9ZV2kuuspeXSspa1rGXXiV3ljkWgQ5regw3yExLFLd3SQBmtkvB4hNSEOdTVJGcMRHBulsr9plTe1jhmae08ppkZMkVENcvXGUmPyyo9f5Mis0Wi3PKxHFbGcG7bSzAqkXB61wr/bo+QGD70/HsoG27zcwd34rSZKHUlRNhEvfUezS0/IZoOY0WT8NMKda9AHp88fpPfX/SP7/1D5rskjPy1T/4QbDKCpUpTult2FvXVKCdPSxRf3BZhZESSlNFc2e+CdOBmKZ9/aaqPv5y7A4D3Dh/h95fvkc+NZcidkosu310lckbmtpFV/OkzkswlWycyJXMVfdccjEgE3HHMyJ1+36Lff3TSThKZkbms9UDbcZnwcjcUJH9Noq9AaVIimYFt8ySNnkj0TJxaTua5+xdHOP7iFnnfyPXqlzI0jHpDcaNLeqvMm7OSYFubFGwdfGoP7eMm0tO2n7yy6orwpIzFiUkhGkBiWlEiDPU3jnzulcyZneMD/+2fAfDsL3+UqGlq7Oh6Uyo3ILVr04zSLZrRnVeSb6tAVB6I1p0Ac6ToNmPCtKd/jHDcKwG9F3+MqHW6Lp7VAwwWAoqJHoTjotY1qqhfQSo3otxXJXYt1kfmfv/Vy6Anb67CyuaX/8dPA9A3+yzfbtaK0FvWspa17Dqxq4qhJzs26L1v/yWiqw6lLq/1mKL7sMQPczfFiC0Zyl1MaGoA4YKm3GVW7Qh+xBZd0r76XmbUZW3IRIHm77ElTcMQcOsZ/I7xqUsWHtvKuX2NSkkiV10K8RN3fRWA1Uacp2eHAZg91+Xj0kqDHhSOtqf7nQjXeVuPiFP9ryNvYvOAUCInnx0geaMkC/OnOmiYfqHxCZu6EZlqJF3aTxls8HtnGB83nPOSjWqXqNfDpGudDoSM2FfV5oFbpHr1y0f3gKmSDM1G/ByDG8Kn9lWqYWpFQzmMNXCWZSfSfsLQAG9q+A0iejYt0Z+SRPHRs0MkO4RKWj2fIbJq6F3dLpFl+Wz6tnnmpyRBGV4MUTc4N7b2v4zIgkx4ra9O+pSMI7aosd8rGOXy8z1sumcMgFdmO1ETkrjtOKFZuNGcrqKILMv5sqMOi3tMoi0PjSSM/v5HqUyNv2Ex9KDFn+zhb7Y+AjQTfgB1nAA/Xa8T8PIbX2iPy96sNnVpYuXB5hF2ADPPu2EfN7cCiVArwFu/XCkRhOIYPI8TUHgMVof6tMXLkrVeub+jXy20FVXrI/SY8oTBtK937mrtR+jvG32I1XsWXzXGb7W9Vgz9qkIuThRWhy3aLkDHUdlqF7ZmqeZkG13p1lRukwKczs80y6lXhy1KGwX+SJ8LYXKOVNuVv+1e3KeImBJxU+HO0i3NhF5sPIJOyMNWzSnazskxayczfp1fZUON7rCwMh6Z2MvqIWHQdF3UlM3CUenShM7K2DL3iuOuuTbjFYFHvnPPSR55Sfp1knOpjQksk5lSdL8gC9f5H4v6Cc9IyGEWgT8an++DXXKfOqzZOSiJxjNrAjfFOso0RgTmaHTXeGZcpAc6B1dwjDPWL3awcpMsBL39y5Sq4jzrs3EiawZe2VukYDo5WQ8L9JNp2D5rZO58J3nTDcmKNdjZZcZxKMve75IOSA3X4uyCYRiFGqTPyHcYW9S4hgu9tN/lhhski3r0tIxVlW0qneZHenuRmkk2bzja4GJE7tNNumy6UZpqLMwNgGu21CvKh9MmH3IIz3vSyYq2sy72td9/4KpZ7ScSHH1U5mdXpEbMOKyS6/odeYJ6L7bCZ7n4zZsvO6fnSIP6KEUd8hOXCdVoNp1W65tjeJ+10Ot45jIOta6IKBbQl3Gu0N8URNrAu4d13HJjQU0bzyJK+cVUYRRhc/d15fBiTX4nhfdngWvPob9Wa0EuLWtZy1p2ndjVTYpa0EhrErM1nJQpq09Z5I5IaG3VMyysSgQaqjRY22hgmS4XqyRrT7WjWe7ffbTuwytrm0JeLoXCkInK2ypUp000PaJJTsn5tILYskTrK9ts9FaBFGIhly8vSmfvXblZjqxIBDp3l8P2bRIxnp/opm6iW08PHOChdmk4cLoywK/e/g8A/Oe/e5cfNZZ6NRd+xgiPuQ61pyQqX9pSZ3i38OYvZrqgIsd0blhh8rNDACTMFkJPplEGqul6IsL8mwyctJgmvGagDQs6n5ZoeWFXN5EtsuPQKYcDN10A4EP9j/KBYz8qx4xJVH7zvhGOhaTatPdZmLlHvge7bHG4LNG13e1y6Jx5HXW4aaOIcL1wbjNd90tCcyUfp75qRLMqNi9/xWjK7xMIp7Qcp/NpGetMqrlrWNmi/ETVlk/UuPhBidyzaxrlRWkFTXxe4sbBz4WYFY00suddlncpnCdomTHnwkX++a/+HACf/+hv+bBLWFl+4rDkNomLMaWIWgaO0F4iUvjpIBF82ousA63rYsoJUARdSgFIJRloJRcUywrSFcFUjwa02JsiYM1oft05aPLnHa39Rh5hWAcR+fdloJWS6/jSCFaAg17XLr/2YUmEps5/e/HOL7er6tDDeU3/kw1mbo+x4XNSEu9EEkzfJz/etW0OKZFVYWFviMp2gSg6cgXcvxNsObaimXlY3p8hTkh8MZYDazsMKyUvD1XyCyk6TZn50k7l49apMZi9RW5daY0zYXjrVVjOyesXTm6BAwJd5J4Lcy4k1TCJ9jKJqDiVueNGSqCtwelBoX88ubCdqmMy5gVFY7dASI35GLpkZHXPhIia3phOOMxIpMtcJ0L+Pjm+/HQnxe1yP7FZs7BtqtFxUJx1fkihiobFEHdxTK1QpQ+ic3L/Wmn29cpisdiW9NUZ//XF76a4IPfZtUkW04c6X+LUJilIys+m2fgFo73xs0usnJDFJzmuyPfJT+Z9e5/jxJrcczRV5b5+00h6cjsLBqvf/Hd1VofNojcpbB/dq1g0DcLj0xalTaZ3aI8me1LubeaOOL0dUiS21B2n0i3X7HlOMfkWOV/ulPbhtlBVk5hWWJdjBG9wS31c9Efu2vLPOfHz/03e1FJ0BOv513W03xC5FMDcg80wvNxVXV+mwug5+gDmHUav47B7UIDDeugGmueF9RxzN3B9S2sf42924V1/bjvQDzQW6KfavJegzG4Iy/z13t/9JTZ84tuP0XIla0EuLWtZy1p2ndjVLf0PKaptNk4Ulm6UZKFyoSIBIJkLNvlbhUESjjbo/ZRUP+YHO4m6noiTYvgP5PiLD7tUBkyv0VfChFa9Nlbyv+6Di1QGTTmhsokuyh9Wbq/6NNTEuSi1nJwjOlxgynSvj3eVcIzIV6k3QnRSIsNyMUQpatgy2wVGyMYr9IcN370RZq3SrAJVpvJ1+94Jzp2W2vrCRpdyr6lqLYAykbu2oLEgoXZ9qEH6vLyf3ypRS65rjeV7ZU5y7UUaRv991x2jnDkoUEi9vUEtZyCpHfNsiEsEfujcZt6ySzLBx+f6sY2Q2fyswCz/izfReEVgFhWD5W0mDqqHfG16u6oJm4Twp756H2tbzXZ8Q4m//9u75P77G+zbI2yVU+/vIxoTJpCnhFgbydAmeVVKfdD1nElMfc8yK3ulgjR1Iczis7IjcvYW2N4tSarRlY3kTmt/rnpNUFXqlD61l/VaaJmxwd94lu3dPwvAuXf/D8ra1AwEdL+jNBOGCcur/HQDioTNqL2oQz4n/HJ5AO+3V9HWOnVEz2yHCEZFAAAgAElEQVT0q5guEdx1SUwvKo8pRd5t8sk9C1Z51mgmdsOoZs1C4Hzuuh2HYctpze6PCyS19d9fH9E5XGWH3si6zH9nleTzcVKT8lCNvy1Cw2hwJCcVA93iGKOhBkUtzis+r+k4LI5Jue2MvEsw2g1fdqgn5Qta3i7QB8BbDojHeHFuH8VNxuFfBPvNAvO8uWeSyX8uBS8XH9YkJkwBxIUsa3sNVXApTNhQ9Ko7y9y9VYp7wsrl8bNSx76vW+CMQyND/Nu1d8g91kM4a/K4dY5pSvvl+ucnu7ntRoElnn9xO3ZJzt3/TIWL3yWLxfIBh/Q5+UrsGmQvCoZQMI2wl5dSJE/KYmHPRGncI+e+MN9JvUuOzZyM+LmEuY4MjxT2yj8qNic+Jq+Xb3KazZvNb235ZCe2kU+JrMAv/dwn5dyVHv722JsAWLrJIWyoimtb8AuvQmeTlIcMs2ZgmVPjAt24pRC1aRmv52x1RFN7WBbC6kgWx9BKa5MZOjfKd2893cGStDGFsSRnV8w5sprlneYHGdN+JyO7IpDb16gwbxmw9ZcFftmuf5bT7xG9F0trwsr0D8X14Yo6zQ5ItUCBjoebh3F9FUNHNx1wXQcojQqfuWJfBr/EfGnb5hcWbDYR85UPA2X7inUl/JWvQbcOlvCDOHNP6kAgJrnffZ/4RbZ+6NtDEvcbsdZPoGUta1nLrhO7qoVF0eEB3f/vfo6Nfx6ikvMYH7Dw3ZLZzGVKDGdle33w5S3YqxKt5k4oqm0GoqhoPxFaHFDE55pb8PyQvB9ZaSZbvN1dcVOD3GCT+z1zSeCKaK5MfVJ2Ajqs+dhDvw/AT/3NT+OaUv29g1NMFQSKuav3In//8j45z3mBR6rdDvFJE+mEIHKT7Cbya3Ew0XpyzKbUK5GCDmli/ZL8rF1Mo/vkOt0da8wvyXUGu5Yp/5lEulZD7jH6/hkuXTAt4KKuXwiU612lUpPraK1wzqT9eajcJJPVKISx17w5VzRyEqFHM0LDcR2LulF1JKTpH5DdTK0RIn9YMLEgz9uNQOQGuc/Mn2ZwQzKWqbc1wLCAQqs2jazRwU6bfqHJip9Ec7+S85O57i1rVMcE8rHqyte8r/Q5aHP8Q7ee4ItP3SDn2bxG6aLMVXTJwg3D2P/8KJXJVmHR17OJXxd47IWf/S/+ey6uD7m46wqRvIIgvS4Z6eWfw0DeYyGh/UYadZq89nDgG5Ho2jvG469rSboCaUutU4T03g8qQ16pjRwIhOQG2DyelYx6oqM1b/3dfwkIDPXtZNdkYVF42aL/byNM3G/Re1C+ioX9NqknxaHO7Y6zUBPnEaorel6UY0qdCk+hc3mvS9RUHW74UhFtvmQ3alPpECgmtihfana0ytIOeW/jd0xw4ZDIvT784CGeMQ/h8uEukqb6sHhTmV/+zQ8CYHdDJC4wwk8OPMnvTtzn38dAlywMi+bvqhyhnJZzpI7F/D6d+dU4iXEZayMGiWkDF0ShURKnu+XWcUZelIKaBUujjMZNtLfB5ENyHqduHs6pDiLLxik3QiSmzRb57TZlo6uicjWilSaQ2FgRJ52+EKKwySwoYcev4GyMJc35FLZZ/MJFxVJG3q9VQtimSbXVUNR2yQJhj8TRT0kepNDbhIewNZbpB2pXIWmaShcH5eTlDRp31sBGvdpffKOPZ6jslh+e6i9RXpJj4tMhnyn0xWdu8DHStj9L4xr4pZ7WpEdpsVxeow3+B3FmD57/BX7r/xP2y23RGAVXAgsPL3fWqTSud7SO75SbBT12wHFbrFdzDDreJlvFqwJtOunKZdfw3g8uCiWtyRqcv6rddQ4+ZZmCOPOgVHWDkbosRb/yL3+WwU9+eznyb9RakEvLWtayll0ndnVL/2NSyOOGXJZ2ygrbfsal7aRs7zOXsiztMmp6EZgyhTMeOwWAdIMNfy17/6l7kww8IaXryzubnPT2M/Ji8i1JalmzZVzMYRqVc2JlgIUViWid/johT8tlKULfD4wCcP75TcRDkvT7DxfeQXtMouV9iQnCXRJ7fHpBtv8P7zrJ3x28GYDIqubSuOwyVMWmnjaaLQkNpnCjsqGGMu3SRhdyRLdJ8U9xMUF8i7BCfm7jV/iN2kMALJwQmEXb2teU0XvylF2J8tPhBkWj+9L+eIyiBPzU4prwisxzpUv70gdYGmU06J2E2S63V3FMe7uqght6hAf+0tNbfW0YqwbxY8JyqXRo8nvle8iciLI6LN9bx7OaxLxcZ+pum9U9JnFqxtGeLdLZJ/ots3+9icrbTIJUKwb+0uzUbkqSGzP3kIH6Jbmmm3WwTYHZ0g6bkGnTGllRIgfxxhZb/IYt9YlDfOTYewBIfGyNvxr+IgAO8iyFsQibL7/kOn6y0qUZxScDkXvasv1iJZdmJB7s2RkJcMWDSo/BQiE/sldNNksw2s9adhNauexLrxt4xWte8b7Rh0w5PyS/zYuGXotdVQw90bNBb3vPh1jZ3SA+aYpsRl2/G5HSUEuaLzauiC/IH6bf4pI7avpa5rXPZljbbBFbMF9sAUqGCuidr9ytyQg5hZXdmv6vyrFzNwa64GyuoGZlmxZdtCgbGmTmrO1L3Pbn1sjFxHscvzSIZZgbrsG+la1xDP6cuhCiNGAq7RIOiVF5JK06NG4XZ31j/wQvmh6km28dpyPWbCB9ZlGc9/JMBky/zVS7LFAf3PEUv/unDwOw9aFXOH7eeG4LOrtlUVgYb+PAbqnOOjPbTW3OaOJosHLyQ3WqNvGM2V4bHN6yNNUJg2FXlY99RxZsYmZBXdtVJ7wk39vAEw3KnfKdpCZrrGyVOVzdgs+WcSOw4RZpdD3+ghQh1dsd7KLR8Iho2jYJfFWqRGiMi0PvOKaYv8MwEwoWTkpe545aTVZMGqzb5LM828aGj53h4MqnWK3PtzD0b8aUYvpD0gnsd3/Og2G07yCruuEzR+ra9SEUR2u/YMdG+YyShGVTDUj1BgXCPGcbdMbBhtXuFT4TVmodLu6dO6Fsv0DIxeW40WT5mf/+8wD0ffTgOqncb1drNbhoWcta1rI3mF3lwiKJmmNzIZyYrJrzN0PmgpFhnWqgDSwRqmhS4xJFDn/cpthvtFwyzRZ0WkE9ZaLHYU3ayAas3CxQQCJTIe8IE8KNuawMy+22n3UJmVT78p4a9rRQLRoxuGG/qAOeLG5lICdRb74a4aG+UwDMltJsGJYLHZuUqFOfT2HovBSGG/QPiwrjwos9fsSvGgrbsD8Ont5KzPDQz13oY99O0UQ5/fxmdt46CkDsL9pZ2Wruea9k+z43u4+iKZVfqiR8hkq9GmLlJWHtWIMVZooCxVSX4qiUHB8diWIbSVrrzmW/aKqal91GKlfCNQVTdtkme9oUNd1aZs2oYaqY4yspztzW5Dw44SjOdwrjJflYjlK/2VJHNCPjImuQNJz+cD5ERKaV1Z0O2bh8xytjbWjDa1/eHSbUIRBX5lgSuypjXf3OApYp1KqNpgidMJK9wMqD23G+GKNl36RpTd9vSsLw3//52wF4+SNDfOUdHwWgPxQNHNwIsEkC5f1K+cnSYHQOTdnaYCQefB0OSNl6hUKWwpcjcALSBBaWD/nUtcuSK8/NWz/3IXb/v/Lb7Ju5vpOfX8tek0NXSv0y8AGkVOAk8H6gD/hroAM4DPyI1qYE7WuYjmiqg3VQmv4+cQBTYx3okHyZ8wdCVI1uR3TWphEXB1TLKHoOyw98YU/ch1wia9D2inyZq5UQhUFD70uaXpeuRcSIVrkRy6fIzb6lwdAn5HUiWmdhm9GAKVicf0TEpHSPy+SCOAz7fIJD2SEAyrUwNcdoexs4o/2SFN0AJEdDTCnB0GM7CoSME49EG9imGCN6NEk9ZaryRsO8vCxVnk6bw9iKXLN4B+iInDN+QqCIzqGL2Fm5t6mTPey/TfCkbKTCE7UdckOFMKWUcbZRh9RJ+SHm99SwDA1UX8oSNbTJSErOVxrNYJkGzNkNKyymhMGSTFXRL8nElXY3CBfkfhKz2m/wrFyoPSd6PE6n9gtKElMWVbMFji7Lm2ubwTbiapmzNmMNoWaGKgpt8PF6dx1rRua23KnofEm+n8qzadb2yOIWbijabxNZ38JjPczco6k/wzdsr9ezfT2Z10dz+8/M8fMb3gvAmV8e5PfeJZTeO6Nl7ADm7VlB14kYBx+kEEKARhhw9JYPvdjr9GWaEreWX/hkYfnMlQYOzxq56n/2mfex47elzdn28ed5davpN5Z9XchFKTUA/CJwi9Z6L0IlfS/wn4Df0lpvBZaBn/inHGjLWvZ6W+vZbtn1Zq8VcgkBcaVUHUgA08D9wA+av/8J8BHgd/+xk9gFRe65MEu3NKj/lSgV5mKwfLfpeLgSpv9xeZmYLDJ7u+FIuzB7i4FF4tD7nESStbYQriGorm11SY/I+tR5izAnLh3rp2ogj6G/d6ilZbVfdiIsHJDrHMjNsSkrLJsjr2yisclE98UIasZwwsuwMy3R4EtTfbxUkqgyc9ZIBtQ0ysAC1Zz2NWVq6RDaayRxLknuDpEKWHbxeeBWA78LkJNSqCckMtabXbKDch8F07v06Rd2kRiURG0pFuXYRdNn1bGIml6b1S6HUtFAD3WLgpE+iKaqREynoMKBCpxKm2ua5NKMhbMkc1w8UEc1TDR0vM2fw+hIjLrpy1orWKQvyevVbRbZC6aP682QmDRsnk7Ne97+NAB/PyqyA85khnyPYb7MRfxo3olqYqZhRXwm4jcxCRehETdzWIPotOl1mnNZe0qeofJGkSRQ31x49ro829erNcYl+t36oQn+84eFybX83pspfa88m394w59wW9TsCN2mkqP809QvKOUnLsMK/3XzuGZRk30Za8U79kTN4ceOvh+A1KfStP3NEQC21J97w0flQfu6Dl1rPamU+v+BMaAMPIpsQ1e01t5cTgADX+9cyoVwUdP7uE1VkAXym/ArHu2Sxext8n5kOUml24hgjVr0HBKmR609wth3mIpGpYnPmYcj5JDfKo6n/KIMxck1sFOyRS/2xKV93f9u70xjI7uu/P67b6m9WGSxuTd771ar1S2ptVgt2ZJlWZYcTWLPIJolHiT+4GAwQIBMFmAyziAf8iXBAIGT+RDMYJBBPniM2LDiGcsaZWzLtixra6lbUqtbvbObZJNNNnfWvr26+XBOPUrIBFYmIimR7w8QLD6+enepW/ee+7/n/A8SeFQeljJff+0wib3ifZJ9J07xuM4wZQ+/2NENgW+/KwfM/+HEX/KHb/wqAKv3yMISuxkjMSITLe90EZf1gb4fWGZPyMxUHmkTVzfI0oEWptnx5nE4eL/wfjEn4B8/8RoAv//cV0J50SCtVE1/BecVccFib0BCqSXe7qI6ujas40qRVAfbHDom/Pz0ao5qv7ZtOUbylrweOCl9trpfoi0Bkj/MwoB6k+QsmXFZoMqjbXrOy/X0TIvSsLpEDrRw6jKUcpcsVdHVotUV8PKcaOZUNPl2csolpgue07LhZB0kCT+fyqFW6NXUyBlmJbARv2BJi9MMjZyhoXLIsWWXRm8QBp99WHyUY3s7wDZlvHV/8zW6vynX/p1zguZj4r4781Ac926Z6J/cfYHfzIub4F0xiBs1OGwT7wNJ4cSD5pIGhX17+QGev34EgPaZHMOvyHfM//kZRlrvrdVlHdq3FfBhKJce4MvAXmAYSANf/LAFGGN+xxhzyhhzqlUv//I3RIiwQfj/GdvvH9dNotx3ET4e+DA2zePAdWvtPIAx5nvAp4FuY4ynlsxOYPpve7O19s+APwNIDI/a1QMO+fMBJhBLb8cZyy216EbumWFyVg47bn9wkjOvyQFleVeblaJYnf3PX2O0IVTDzc/4Yfh7tc+BETk4bbpi6jlll9QlsQxaScvynXrwUnZodYkB5lQdatdV+yQNuTc15HyhTUODgryqZcXI9X+7+htYX6UFLnhatsWeE2+a/OU2flE9W5oB9R65t+uaw/SS1PvA525w9ao02jYMN/9yDwC1zxR5Li5ckPUtNc3xaTXpsu+3cB9R838iR70mbWuPtvC7hbay4+nQDz8+XObSNaGHaDj4u2SXY2aSNHLS/zeeEGup6yphEFZlfxOjPvA7+gp86lGJ8nnh+iHMWfFVXzzq4eumpPusR2ZWD4UnSlz7h9IXXsnl5ukh7U8pr3RnDfmUwJ+Ms++70p5Lv5Njxyn1cKp6VFVSuTYYhFIP9f01/IJ8DtndKxQWhZJrdaQR/t/ttr/z2H7/uO4y+e1rMLYD/BdOA7DrhbXL54BzSEopJ5HAGRBvJ5ta80QyFY2FuDVPu6a0K5adrFnihFcjfBh8mAl9EjhhjEkh29LPA6eAnwFPI94AXwW+/0uflGwT3F6iPpUOPSQK+yCWkq3cxOQO/DmZpE4Hu0Hd2AZecmnKd5eFL+wLpXezk7AsGePoGgM7sRZEA9BKQXxJJ/x+syagFYPEoibNHWoTn+9MJKCJzZl9qsGBP5VJ6vqXUiQPi1dOcKmbxKTUvRP4FCQcqgPy+uYXWyTHZEFpZT1ymoy6tAvsEaF2lipJ7jqsGe6f20/XhO7ubZYX71ZvlUSb7B4V+SrJYvbkros8+9cnpO3HVvCelwm/OmCoVzXrkmdDoSz3VJaDL8skfvU348TOpMM2d/jmrjFpe2K5Tf6C7HsnfsWn3Suv5yd7+MWP5cuYLVhKo2ueLZ3PpO1D3xmxUiuj6VAQLXtN+hQIKTbHswSr8hk3d9Up3CYUUnzBpe/nMm9OfXkEV7/f9T5DkNSoxLOJMGipUo3hLmli6v0Fyoupv0uk6Ec3tiP8X9Gu1WhP3NjsamwL/FLKxVp7EngGeAtx63IQy+TfAP/KGHMVce/683WsZ4QIHzmisR1hq2FDQ/8T+0fszv/4uxgD2RfEvFt8qBHKrZq6Q+KWWtHHitQ1N6WpeGSvyPXKkCUzsaYmqO6o9D40y+JrQmPURsS6zJ31w4M+pwXZKbG4b93v4KoiYeb+BWovyv6+mV2rq1+C2CMSILRaTOFdFSvZrRp6L3Qsavm1cNSjrYqEg6+1KA+K9b/0+RrtklQgecOj0aMWfaYNTkf935KYiIXtqfdKHXfddouEJ+0YO7VL+sS3tLNSdrK7RtzXJLwv5an1qe/uqiF/UZ6RWGiweFS2uKsP1eh/XhNzdxmKe7X4yprUsC8bCCpDlvw5eb16ABrqlULbcPzIdQDOvXoAowdZjZ0NUpfl2e2YBG4BzN9tiKuaYid2oHZ7lfhl6ct9n7/OlV/skWcMtojNSr8l5w1dk1JmecClJM0niIGzUwV7rqdwVdcmtiLPH/uLb1CdjeRzI2w9RKH/ESJEiLDNsLF66G7AcL5A8Zkh6j3KxRY90qNiGpZmMmupyi5l6JeMbdR6DeUR9VOtyyElwMphSM3KcxZfHyR/QcW89slDajt8rKoK9ly0lPtl/UpPGwoPCLm7dDkPw6oTHregiRh6f+Azs1v8v0d+AkuH1wy/0tAH3a56LgdUBuTaxJcNuHrgOheHnLxOLFnqQkUzuneeqVvqb17xSMhGgEY3pCfkObcGs9Rvyi7GGVFC+VacRE646upikqbWtf8LszTeEJ/s2CoUR+UZ88cTOPU1C7zxFTmAzP5JjiVd65tSDTJj/lru0Jq4agI0htZExpP5Kst14eo/9cgFLi2JkNjCfJbKLm3nLY+mKjg6e8sEFzqCX/KM2FgyPEzt8mv03DsPwOrJftI3NcGCa8M+Xr4rCF0Y3Rosj+i4cQjPAfpPVVg+nIxyikbY9tjQCb3R8JiY6CO+E1opmUSTsy6VtnhF7PtBk4mnZNLtPWNCv+Tuq63Qd7ntQ0OzF5nAUrpXJmZnOkG9S68vyhvru+rEpuR1edhh8LWqvo5TmVRvlluG1SMyM/hLLo7m5nSbHruf04Vjv09FNVTuPzrG269ITtFOcoZmxqWtPRlbcMOgGKcJRp2j3ToM/UKeN1sbJH9V7qkMGPyy+lOXoLRThflnUtguqUtQFNomc9Oh3Cuv3ZKLNyOvi8UU9cNyb320TdcZqUC9L8DRgKfR/mUmxlTJ8UkHN60eBstyb++5JvVu6ePivQ2amseTtiHVq1mPWi5Tbw8DMH8gAyflQHP0Uoupz0k5XdcsDf0cGsUYvmr2NA5IeYnzSbomZOZ95/nbQ6oqe8PSUiVFt2FJLsn4qE15NDTPd9/1BqtjQtf4JcPAKVkl6r0xWikT7TcjbHtEX4EIESJE2CLYUAvdNA3xGY/MJDSzspbkrrUoqmhUedAP/dOFZhErzWm5JBfFqpt+uomZUxPYQua0CnjlxE0RCPNRJveXqY3LvYEPrZRYoDNfbOIuqsvboqVxQ7vBgPe6nIwu3SY0CUD1s0V8jWZ989x+um6u0RggOuFGfaH9VYd2Z/cxXKQyIeal0/xgVvqCBFDSzLXwalK+V7ahEmH3VUMrIbuL+QfkeeXRNv6UtidGeChbGbQ4Rd3BpIW6AYgtuvRcUFXLuWH6p+T10hGw06pUlpRnNzMulT6p4NAPfObvUVfBHRXiP5Q2VHcYGhqRGkxnyCqNsnCHx64fqWhWqcXV35b27HzepSoikLTHxeLv+9NXCR69B4DMlIOv9FngE+5U5o8bMj/X6NglN4wALez2Q5mE1JylsFulDHYa/FKophAhwrbFxk7obfDKBq/WDhMVTD/dJHVWVQBdh+xhSRJdbuTJXZF75h9pgmqLxGIBvobqV8eztJQZqA206LmgHHkn2Gimh2C3ep+8HjD+a/r/Sx49lzRxbNzQ965MUsURl5T6lmNh/m4NS2+5JM7IauE7UHlAKIieH8mkmJzyiWmuhcKhgPi8TK52yNBW+dr5Ey6ZMbmeviGaNCAqkPVulQJNm1ArpTJgCPYJTZE+IzeXDjbxb8pCNHK6zuIdMrm7dUMQU7pizsHXRaH6aImVQBao5BwURzsStpJYBKCwR+q09OtFgqvCd2durtFJ9s0cS/dqgudxn73PSL/NPBQPqaUd77VYOCb1ii955M6qwqUbsPigvLf7LaXBnrqfRpeUuXI7dFbF2IoJ5Qhiq4apx7zwc1s8Jvev9gd0XZbry7dD39uqrrliiK1anEjUI8I2R0S5RIgQIcIWwYZa6NYRWqTS71DRw8y+5+MsHVMLNRfgXRTHcseubcFNyeWrn/0FAN96/rOUezRvYGDC7Xhidi1cvHOIlpm2xJfFAly408MkNJpxxFDtV03uaw4zj4tp1/32WnfMH3do5KWcgecT1FUTq56HYElM0/x74q7RfcmhuEesaPP4KqWKuI4kfpoDzalp4wFFzWqfuRgL/cb7TsOSRrs6TULFwVbakn1NdcgH1VNnoMByIBWZIx5qjKfmLCsH1Ze/DYWDYn171uCpg8zAq6tc/ppY4DYZ0HNF2to5lAyuZ0Kf8ZnPgt8nbQtupMifUuGta3US4+Ipkzg8FEbVLhzzaKU7uVPBqo9923fxVTxt5U6x1FcPOaQntY3JdhgPEFuxrIrSA82eFmiyjfJALPSQ8QsuhcPqTTPjESvIaxNYCnvWDqYjRNiu2PgJPd3GtB26XhauZO6BNtnrGn4+54UTtDVQU9dGp2757thxqXDJkLopN5k2uLXORGIoKb3SmVC8qoOroedeGfx7hSopNDOYpioF9kLXOaEDKoOW4t4OEWtJaKCLdSyFA51sPobUDXnvjceFzog/uEjxvFAy9ko3QV555jk/fEYj1ya9TxMi9/hkx6WUaq9D73vKFy83Q1ooM+ZRfFAqH1TkGcGlPL0qJVDYb2lp6L1z3ZBSumL5aDt0VbTX02uh8rvTpCc77n8uSyJoR9eYTuhxqIoTDG7JwZmTyd94luIe7cNajMVjos2Su9ZiZX+H+4eaSh/Elhy8ckepkdCVMHNZKJnsjTYFDWqyqYDYLVXh6wGjQW5u2cFV/ZbV29phhqpdd8xQ/M5w+OxOTtOecwW8WpqJWqT4EWF7I6JcIkSIEGGLYEMtdLcOXWOG5GKbpcNqiY67+AWxrBbvD0hNSJWcptAbAEE2oLwi9EP6fSH8U0/Y8ACyfajEbYMSpHLhnd0AlHa1cQaFczDG0pwVi9pbcem/WxJWLL8yGB6mNYaamLKU7xcNux8VnfJLF0dwc0IZJN5K0ntOzN56t9IWkz3Yp5TbuJnAVZ95OQSWy7EVh0JOTGrPh+W7Ne1dwQvzqK7ui+EWVZ1xog1G2lzbobuQfJPSqKpHDtfp/2EsLGfxYMc334TJKXrOQ+9pOWReOdoTHoTWegzHHhdT/8KzIgZW3hWElv3ASZg9YcN62wOys6nNpcId0fIhL6R8EsvttdR0822WDkv7m9l2SCGVjjT0Woyc7gpM4IdtG3izHQYT5a6vaZtX+lzKGky09NcjtHVMlPa18Cq6U3qym67xNtaJ3FwibG9s6ITutEShcPbThp73lGf1CPVW/BWX6qBMOtlxh67rnWzQHhl1uUvPNlm4U10OZ+DYE5cAmZjGjdAEqU6Oil0Bjx2Q/7/wyl24rU5+UcvCKYmsdMxaYgV/wSd5S+/xYeFbIiLSnTB0uqo8YkMvjVhBFpbl23yCkvz/0YfPUW7JA6dLOSrPir5M8MQyibeEW3eakJyVNux4t87sA8LJ5661QaRSqPUYEvPSkJqeDdA2oWte16kEtx7VIKh0k+6fyeS/dLclf9qEfTv/QP7/+ByWj1gufF8m8k6SiPwZh/z5qna3wUj38GufO82LfyQZJqq9hEFDbY9QVbE86BLThag07NLUZ+Yum7W6mzU6rNPf9bwlOSfPW9nvhloyNx92yagkS/WRIq0bshBmrxsyM9LmyrGA1UOaWDjdIjXrfcAtNEKE7YjoKxAhQoQIWwQbaqG3ErB0xKUda9HMSNGFQy3ic3roF2sz8Lrcu3JQvFQAdpxrhx4VNx/xyEyon/mA4e1JSRqR+ZSJ+ZoAAA5GSURBVPQSq5PiAeJWZZ2yiYCfvijpsfwaxJfE6iser5Mak0PZlXsb2CnV5+5rktPDu/KQobRLKYhTa+nWvLIJfbeDhFIrLdj/bbHWX50/yv2PXZC6ju8gk9G2v9VDOyb17r5iKeyVOs7dGw8P/brGyiwdlTe0UobCPWKNJielTkHcpdktO5jcmINbULqp4ob+3vnXfYq7lXK5FHDzMaU0Xnao5XX9zjUwE7IrGHxDE1NcXaZ9VSim6pN3Y4xQJN+9cA/x3xDTubyQwlGawzQNu36k+U1HkgSa23XHjyco3Sc7m5mHXEygjWuvKWQW9snL5JyheLdQVcMDK1S+L9uC/tOWhTvlnmAyzYHvCG81d1+G5YNi3mdOEybyKO6B1YOEfvERImxXbDjlkpq19L5nqGs2oP7XXEwg38zlw4bCHvniD77RZGWfTGSVYRtSIc2uFtU+qXZtuElMtT1KsSQ2p8FC7U7mYRMKONX7A+rqxRGfiIceIm6iRV1Fu/L9BZaOCkXRNWbITsmbC7s9VlTvJTHrhRNHrV/K88oOc8c1EvLtgPdmxIXk6a+9xvdqDwDQTgb0v6xJNQYcGkeFl879LEl1h7Rt/B9kwujT3nNtCrd9MCL10LEbXLkpjZh/yOP44XHpt3qKyXND4b3VEXWVdD0GX1L3vyEnXDicRT8U35p9QCboQXow+yTENIgbzLi6YQYQaAhu1wpkbkqbC3scWmnNmOQa6jnpw7Hf3Udcg6x2nGmH7pS+erNYZ42qqeUt7ox05lwsS3BIkwqfM2GEa9e1KkFCPYW6DNkb0p5qr0Npz5qoWs9bcLOT9CZChG2KiHKJECFChC2CDbXQ27HO9tiEeit9Z1rUcmKy5c/bMICnOOqRntOM9IfBreo9Z1zqKvkau+WRndCHGygqFdIYEPphdHSR+AGxVqdfHKWeF4uuPtgircEylVsJjC5rxfJavsPSqCWId3zIIaV6L4lFy9I9Uq/Bl+SNzSQsPigURfEIGPXD/sFzJ9BscGTOezgtlSF4tcQs4nGTnWzg64HqwnFDMChRNM1rcfxlaU//IzcBuDw5iDsrlIPXhDOpnQA4jg39vVNzbfyX1SqurOnJ1ntt6MWy/ztlCvtli1LrlXsrfS5uvXNwuaZcuOPtdkgxxYoWvyJ9OPRyjYlfEcu97UGsoDEDTSiq5QxOqDfTNSa//apdo0p2OaSn5Y+VeprmoB54Dvkk5+SeufvSNJW2yl8McOtyf2nYIdAUhV1nYyzfbgl+SoQI2xobOqF7ZcvgyYCFox6uRv8tH/Coa47dwTfaGHVVmPl8wPCP5HX/SYPbkMlp/h6HkZ/LF//GF1xWD6iGSd2QFE9E4kvqZRLvJnVWqIP6YDvM9uOUvJB+yEw4FA/Is5uFOG4nH+lAg/w94vJ3Y66H7E+VUnlplpWHhZqY/YzQCLFll84bR//KZfGI1Hv3s4tMPy7qVK0k5K41wr4YOCm8cGl3kopSPm4VzLRmFfrSCukXZOWaGFBXEbuWNjM9DcEx4RhGu1eYy2ny5tU+Rn8s3Ha9N076PemU9FSOxWNyT70vQXlYyuwksc6/Z6lr4ujkYpvB1zRhdJ9LrNDRorcs3iFtNi2f/re031KG5IJq1tzlY9Paz80Yvrpthh4xQ07oqth/OggFwfwiNHVhrx+u0jiiLpHvpqgOSzlLuCQWVV8nAd6CunCmYNf/qnJrtbOQRIiwPRFRLhEiRIiwRbChFnozbZg54WKsxbqa0/OGpZnVpA5dLnOfEyvWWfGpDKjmR2pNnTAxL1YgQDsesFMP/ebu8ULLr3OA6swkwsz0bsMQ19ydQcyGXhmFO5qYmgbFjPvhzqEyFLBUkUIzbybJnxerd+5zg7RrmhBDvUz8ogFNpLGy35B9WPiC6Up/uBNodMHSEbG+Cwc8Rl7U6xlDfFVuuu3XL3NmegSA8pVugmOqcnhFpQkONhi+ZwaAKXeIxOtiwV9N9oQ0xtDJBtd+VRo98osW84/K86yB3rPShunPZmmoZZ6a7ljlTXpOiU7L4oMDLB6V6/1vt2ikpX+yU3Uw0obiLifsw56zq1z5J8qV0WZ0VHY2c9NDYSxBx3+9tDuQHQ2Qnqww8xn9gHbUaDfkujMXJzmjZd5o00rK9dSsDS19E4BXlWf2nm+xcGeK1sXIPomwvbGxckYGcCDw17iDRs6EKclWbgNnWSfrRJuGaqY7ATQPSdBLbdUnM67RpHXD1BPyXuu08Ffk295JV7fn3iluviBujY3dDewtmRhjK4bcdc0GlPTDQJdWylI5INfjV5M48+rdMdli9oTQFZVhS+yWcus71Mul5NGtGisLx9s0TkmuuUzZSiYdRGyrNqT67t0Nqr1C4Sw9XMdqdGrrLw6RUFnhet5iPZXh1UnMm/cp9+ui1NWiuUv59oUk6WFxLRzvzZKckWfcutcPF5TkLcvY00pGW0tOMyZVNS1eedDHrcqkHCu1iS9pXw641PLahlQifO00oDwon098JY2ryZt37VimA2MJOfTVO6RfTbyN0Wdf/a00Nq801FKM3NXOWUqd60/r5N7waOwRamlpn6XnJem30ReKtDIyVmp5X0XBiBBhWyP6CkSIECHCFsHGarnUIHdJQstLYjhTHmmHllVyT5HWZDa8v5N42Mk08a8K/TH0ThDqjKT2r2Jf1WTLJ1bJvCLvXTmkMrA/GsVRKsJZ9NlxRjVjjsHcvWLd+UUJ/wcwbbMWom4IvStqPS5NrZbTJAxX71jQ1Z0tjGq3do0Zyjs1M9DxNl6v7CzcixmSaoHWexJhxiJb8eg7KdfjhTare+V1a38NuxzTcjr1g8VxaW9mpEitqluLTJPmu3JQGzM2TPDRyBqGXhKL+epXuklPSb27rzZpxzr5UOXhqbkWtz6lB8jdluGXxXIu7PbJX9RDycOuaMwgXiadcP/lQzH6v6P5SP9pheWi7Gya+YD0lHy43rLuQroD6rqzoauJpxmYjCU8fF24K46vOjH5iw0qmrEpdjYVSiMX9qVDudzEckB61uCs5bOOEGFbYmPlc11odhmS822824UiyCUazM/IVj/7TJbhcZkAr38phbNXXCSadQ97WHiZlWImDDrxT3Uxd5/qhjzfFboFdlwinSYhh+6VDQ2dlHvftfScFp579e6+MAp19uE26UsywVgD8RV5XnnI0EqqJvlFcFrq/tit3Pu0R3G/XMvcMKzskIUoNufRt1/qbc4kWTyqE/pQC9NQumLOpdrXSavWDqNT79x5k5m/kZBKtyHPnj9uSE0pFZJJYjVq0604NPdJv2XfSIaufcEOw+RTsgB0X7L0npU+XzyWCReU7DVp18RTDjt/IvWu5l0W7pTFYvTZOa5+VXiZ+MpadObwN06y+LVPSd9WLV5ZMxktZ8n9XGiR4j5Yuku12ft0YbuRJqWUUHXACaN3vRp0XxHaZtkkwwxMxV0+3sVYeE9HG75r3IZ6+at7fdyGjSiXCNse0VcgQoQIEbYIjLUblxTAGDMPlIGFDSt0DTu2WbmbWfZmlbvbWtu30YVu8riG7fc5b2bZH+uxvaETOoAx5pS19r4NLXQblruZZW9mmzcL0ee8Pcr+uI/tiHKJECFChC2CaEKPECFChC2CzZjQ/2wTytyO5W5m2ZvZ5s1C9Dlvj7I/1mN7wzn0CBEiRIiwPogolwgRIkTYItiwCd0Y80VjzCVjzFVjzB+sYzmjxpifGWPOG2PeM8b8nl7PG2N+bIy5or971rEOrjHmbWPMc/r3XmPMSW37d4wxsXUos9sY84wx5qIx5oIx5sGNarMx5l9qX58zxvwPY0xiI9r8ccF2GdubMa61nE0Z25/Ecb0hE7oxxgX+K/D3gCPAPzLGHFmn4lrAv7bWHgFOAP9My/oD4CfW2oPAT/Tv9cLvARfe9/cfAf/ZWnsAWAa+tg5l/jHwN9baw8BdWv66t9kYMwL8c+A+a+1RwAV+i41p86Zjm43tzRjXsAlj+xM7rq216/4DPAj88H1/fx34+gaV/X3gC8AlYEivDQGX1qm8ncgAewx4DtGVXAC8v60vPqIyc8B19EzkfdfXvc3ACHADyCNSEs8BT653mz8uP9tlbG/GuNbnbsrY/qSO642iXDqd08GUXltXGGP2AMeBk8CAtXZG/zULDKxTsf8F+H2gkz6nF1ix1mq66nVp+15gHvjvuiX+b8aYNBvQZmvtNPCfgElgBlgFTrP+bf64YLuM7c0Y17BJY/uTOq637KGoMSYD/E/gX1hrC+//n5Xl9SN37zHG/H1gzlp7+qN+9i+BB9wD/Im19jgShv6BLeg6trkH+DLyxRsG0sAXP+pyIqxho8f2Jo5r2KSx/Ukd1xs1oU8Do+/7e6deWxcYY3xkwH/LWvs9vXzLGDOk/x8C5tah6E8DXzLGjAPfRranfwx0G2M6ypbr0fYpYMpae1L/fgb5EmxEmx8Hrltr5621TeB7SD+sd5s/LtgOY3uzxjVs3tj+RI7rjZrQ3wQO6glxDDlceHY9CjLGGODPgQvW2m+871/PAl/V119F+MePFNbar1trd1pr9yBt/Km19reBnwFPr1fZ1tpZ4IYx5ja99HngPBvQZmRLesIYk9K+75S9rm3+GGHLj+3NGtda9maN7U/muN4osh54CrgMjAF/uI7lfAbZfr0LvKM/TyGc30+AK8ALQH6d2/so8Jy+3ge8AVwFvgvE16G8u4FT2u6/Ano2qs3AvwcuAueAbwLxjWjzx+VnO43tjR7XWs6mjO1P4riOIkUjRIgQYYtgyx6KRogQIcJ2QzShR4gQIcIWQTShR4gQIcIWQTShR4gQIcIWQTShR4gQIcIWQTShR4gQIcIWQTShR4gQIcIWQTShR4gQIcIWwf8G6pQtqHouA6wAAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -763,7 +765,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -954,6 +956,16 @@ "plt.xlim((-0.5,0.5))\n", "plt.ylim((-0.5,0.5))" ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Close the statepoint file as a matter of best practice\n", + "sp.close()" + ] } ], "metadata": { @@ -972,7 +984,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/tally-arithmetic.ipynb b/examples/jupyter/tally-arithmetic.ipynb index 0e8b53ac71c..5f04c688466 100644 --- a/examples/jupyter/tally-arithmetic.ipynb +++ b/examples/jupyter/tally-arithmetic.ipynb @@ -243,7 +243,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -421,11 +421,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=6.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=6.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=3.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n" ] } @@ -478,78 +478,82 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", - " Date/Time | 2019-07-18 22:51:02\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 15:46:22\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", - " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", - " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", - " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", - " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", - " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 0.96168\n", - " 2/1 0.96651\n", - " 3/1 1.00678\n", - " 4/1 0.98773\n", - " 5/1 1.01883\n", - " 6/1 1.02959\n", - " 7/1 0.99859 1.01409 +/- 0.01550\n", - " 8/1 1.03441 1.02086 +/- 0.01123\n", - " 9/1 1.06097 1.03089 +/- 0.01279\n", - " 10/1 1.06132 1.03698 +/- 0.01163\n", - " 11/1 1.04687 1.03863 +/- 0.00964\n", - " 12/1 1.02982 1.03737 +/- 0.00824\n", - " 13/1 1.03520 1.03710 +/- 0.00714\n", - " 14/1 0.99508 1.03243 +/- 0.00784\n", - " 15/1 1.03987 1.03317 +/- 0.00705\n", - " 16/1 1.02743 1.03265 +/- 0.00640\n", - " 17/1 1.02975 1.03241 +/- 0.00585\n", - " 18/1 0.99671 1.02966 +/- 0.00604\n", - " 19/1 1.02040 1.02900 +/- 0.00563\n", - " 20/1 1.02024 1.02842 +/- 0.00527\n", + " 1/1 0.99327\n", + " 2/1 1.05535\n", + " 3/1 1.02165\n", + " 4/1 1.02898\n", + " 5/1 1.02521\n", + " 6/1 1.04782\n", + " 7/1 1.07014 1.05898 +/- 0.01116\n", + " 8/1 1.07724 1.06507 +/- 0.00886\n", + " 9/1 1.06268 1.06447 +/- 0.00630\n", + " 10/1 0.99628 1.05083 +/- 0.01448\n", + " 11/1 1.07257 1.05446 +/- 0.01237\n", + " 12/1 1.01603 1.04897 +/- 0.01181\n", + " 13/1 1.01047 1.04415 +/- 0.01130\n", + " 14/1 1.03639 1.04329 +/- 0.01000\n", + " 15/1 1.00699 1.03966 +/- 0.00966\n", + " 16/1 0.99098 1.03524 +/- 0.00979\n", + " 17/1 1.03990 1.03562 +/- 0.00895\n", + " 18/1 1.02076 1.03448 +/- 0.00831\n", + " 19/1 0.99685 1.03179 +/- 0.00815\n", + " 20/1 1.04833 1.03290 +/- 0.00767\n", " Creating state point statepoint.20.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.4427e-01 seconds\n", - " Reading cross sections = 3.1628e-01 seconds\n", - " Total time in simulation = 3.7319e+00 seconds\n", - " Time in transport only = 3.6302e+00 seconds\n", - " Time in inactive batches = 4.9601e-01 seconds\n", - " Time in active batches = 3.2359e+00 seconds\n", - " Time synchronizing fission bank = 2.8100e-03 seconds\n", - " Sampling source sites = 2.4682e-03 seconds\n", - " SEND/RECV source sites = 3.2484e-04 seconds\n", - " Time accumulating tallies = 4.4538e-05 seconds\n", - " Total time for finalization = 9.3656e-04 seconds\n", - " Total time elapsed = 4.0859e+00 seconds\n", - " Calculation Rate (inactive) = 25201.2 particles/second\n", - " Calculation Rate (active) = 11588.7 particles/second\n", + " Total time for initialization = 2.5195e-01 seconds\n", + " Reading cross sections = 2.3830e-01 seconds\n", + " Total time in simulation = 3.6172e+00 seconds\n", + " Time in transport only = 3.6041e+00 seconds\n", + " Time in inactive batches = 4.5282e-01 seconds\n", + " Time in active batches = 3.1644e+00 seconds\n", + " Time synchronizing fission bank = 2.4821e-03 seconds\n", + " Sampling source sites = 2.0826e-03 seconds\n", + " SEND/RECV source sites = 3.8781e-04 seconds\n", + " Time accumulating tallies = 2.8497e-04 seconds\n", + " Time writing statepoints = 8.5742e-03 seconds\n", + " Total time for finalization = 3.1545e-04 seconds\n", + " Total time elapsed = 3.8763e+00 seconds\n", + " Calculation Rate (inactive) = 27604.9 particles/second\n", + " Calculation Rate (active) = 11850.8 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.02889 +/- 0.00492\n", - " k-effective (Track-length) = 1.02842 +/- 0.00527\n", - " k-effective (Absorption) = 1.02637 +/- 0.00349\n", - " Combined k-effective = 1.02700 +/- 0.00291\n", - " Leakage Fraction = 0.01717 +/- 0.00107\n", + " k-effective (Collision) = 1.03497 +/- 0.00681\n", + " k-effective (Track-length) = 1.03290 +/- 0.00767\n", + " k-effective (Absorption) = 1.02663 +/- 0.00590\n", + " Combined k-effective = 1.03085 +/- 0.00535\n", + " Leakage Fraction = 0.01728 +/- 0.00077\n", "\n" ] } @@ -631,8 +635,8 @@ " 0\n", " total\n", " (nu-fission / (absorption + current))\n", - " 1.023002\n", - " 0.006647\n", + " 1.025847\n", + " 0.009998\n", " \n", " \n", "\n", @@ -640,7 +644,7 @@ ], "text/plain": [ " nuclide score mean std. dev.\n", - "0 total (nu-fission / (absorption + current)) 1.02e+00 6.65e-03" + "0 total (nu-fission / (absorption + current)) 1.03e+00 1.00e-02" ] }, "execution_count": 21, @@ -712,8 +716,8 @@ " 0.625\n", " total\n", " ((absorption + current) / (absorption + current))\n", - " 0.694368\n", - " 0.004606\n", + " 0.695924\n", + " 0.007275\n", " \n", " \n", "\n", @@ -724,7 +728,7 @@ "0 0.00e+00 6.25e-01 total \n", "\n", " score mean std. dev. \n", - "0 ((absorption + current) / (absorption + current)) 6.94e-01 4.61e-03 " + "0 ((absorption + current) / (absorption + current)) 6.96e-01 7.27e-03 " ] }, "execution_count": 22, @@ -790,8 +794,8 @@ " 0.625\n", " total\n", " (nu-fission / nu-fission)\n", - " 1.203099\n", - " 0.009615\n", + " 1.203449\n", + " 0.01381\n", " \n", " \n", "\n", @@ -802,7 +806,7 @@ "0 0.00e+00 6.25e-01 total (nu-fission / nu-fission) \n", "\n", " mean std. dev. \n", - "0 1.20e+00 9.61e-03 " + "0 1.20e+00 1.38e-02 " ] }, "execution_count": 23, @@ -869,8 +873,8 @@ " 1\n", " total\n", " (absorption / absorption)\n", - " 0.749423\n", - " 0.006089\n", + " 0.74975\n", + " 0.009032\n", " \n", " \n", "\n", @@ -881,7 +885,7 @@ "0 0.00e+00 6.25e-01 1 total (absorption / absorption) \n", "\n", " mean std. dev. \n", - "0 7.49e-01 6.09e-03 " + "0 7.50e-01 9.03e-03 " ] }, "execution_count": 24, @@ -946,8 +950,8 @@ " 1\n", " total\n", " (nu-fission / absorption)\n", - " 1.663727\n", - " 0.014403\n", + " 1.663575\n", + " 0.020557\n", " \n", " \n", "\n", @@ -958,7 +962,7 @@ "0 0.00e+00 6.25e-01 1 total (nu-fission / absorption) \n", "\n", " mean std. dev. \n", - "0 1.66e+00 1.44e-02 " + "0 1.66e+00 2.06e-02 " ] }, "execution_count": 25, @@ -1020,8 +1024,8 @@ " 0.625\n", " total\n", " ((absorption + current) / (absorption + current))\n", - " 0.984668\n", - " 0.005509\n", + " 0.984639\n", + " 0.00883\n", " \n", " \n", "\n", @@ -1032,7 +1036,7 @@ "0 0.00e+00 6.25e-01 total \n", "\n", " score mean std. dev. \n", - "0 ((absorption + current) / (absorption + current)) 9.85e-01 5.51e-03 " + "0 ((absorption + current) / (absorption + current)) 9.85e-01 8.83e-03 " ] }, "execution_count": 26, @@ -1093,8 +1097,8 @@ " 0.625\n", " total\n", " (absorption / (absorption + current))\n", - " 0.997439\n", - " 0.007548\n", + " 0.997372\n", + " 0.011707\n", " \n", " \n", "\n", @@ -1105,7 +1109,7 @@ "0 0.00e+00 6.25e-01 total \n", "\n", " score mean std. dev. \n", - "0 (absorption / (absorption + current)) 9.97e-01 7.55e-03 " + "0 (absorption / (absorption + current)) 9.97e-01 1.17e-02 " ] }, "execution_count": 27, @@ -1168,8 +1172,8 @@ " 1\n", " total\n", " (((((((absorption + current) / (absorption + c...\n", - " 1.023002\n", - " 0.018791\n", + " 1.025847\n", + " 0.028224\n", " \n", " \n", "\n", @@ -1180,7 +1184,7 @@ "0 0.00e+00 6.25e-01 1 total \n", "\n", " score mean std. dev. \n", - "0 (((((((absorption + current) / (absorption + c... 1.02e+00 1.88e-02 " + "0 (((((((absorption + current) / (absorption + c... 1.03e+00 2.82e-02 " ] }, "execution_count": 28, @@ -1260,8 +1264,8 @@ " 6.250000e-01\n", " (U238 / total)\n", " (nu-fission / flux)\n", - " 6.659486e-07\n", - " 5.627975e-09\n", + " 6.670761e-07\n", + " 7.788171e-09\n", " \n", " \n", " 1\n", @@ -1270,8 +1274,8 @@ " 6.250000e-01\n", " (U238 / total)\n", " (scatter / flux)\n", - " 2.099901e-01\n", - " 1.748379e-03\n", + " 2.099931e-01\n", + " 2.336727e-03\n", " \n", " \n", " 2\n", @@ -1280,8 +1284,8 @@ " 6.250000e-01\n", " (U235 / total)\n", " (nu-fission / flux)\n", - " 3.566329e-01\n", - " 3.030782e-03\n", + " 3.571972e-01\n", + " 4.203420e-03\n", " \n", " \n", " 3\n", @@ -1290,8 +1294,8 @@ " 6.250000e-01\n", " (U235 / total)\n", " (scatter / flux)\n", - " 5.555466e-03\n", - " 4.635318e-05\n", + " 5.555637e-03\n", + " 6.200519e-05\n", " \n", " \n", " 4\n", @@ -1300,8 +1304,8 @@ " 2.000000e+07\n", " (U238 / total)\n", " (nu-fission / flux)\n", - " 7.251304e-03\n", - " 5.161998e-05\n", + " 7.269233e-03\n", + " 6.814579e-05\n", " \n", " \n", " 5\n", @@ -1310,8 +1314,8 @@ " 2.000000e+07\n", " (U238 / total)\n", " (scatter / flux)\n", - " 2.272661e-01\n", - " 9.576939e-04\n", + " 2.276519e-01\n", + " 7.674381e-04\n", " \n", " \n", " 6\n", @@ -1320,8 +1324,8 @@ " 2.000000e+07\n", " (U235 / total)\n", " (nu-fission / flux)\n", - " 7.920169e-03\n", - " 5.751231e-05\n", + " 8.069479e-03\n", + " 5.324215e-05\n", " \n", " \n", " 7\n", @@ -1330,8 +1334,8 @@ " 2.000000e+07\n", " (U235 / total)\n", " (scatter / flux)\n", - " 3.358280e-03\n", - " 1.341281e-05\n", + " 3.363165e-03\n", + " 1.147434e-05\n", " \n", " \n", "\n", @@ -1349,14 +1353,14 @@ "7 1 6.25e-01 2.00e+07 (U235 / total) \n", "\n", " score mean std. dev. \n", - "0 (nu-fission / flux) 6.66e-07 5.63e-09 \n", - "1 (scatter / flux) 2.10e-01 1.75e-03 \n", - "2 (nu-fission / flux) 3.57e-01 3.03e-03 \n", - "3 (scatter / flux) 5.56e-03 4.64e-05 \n", - "4 (nu-fission / flux) 7.25e-03 5.16e-05 \n", - "5 (scatter / flux) 2.27e-01 9.58e-04 \n", - "6 (nu-fission / flux) 7.92e-03 5.75e-05 \n", - "7 (scatter / flux) 3.36e-03 1.34e-05 " + "0 (nu-fission / flux) 6.67e-07 7.79e-09 \n", + "1 (scatter / flux) 2.10e-01 2.34e-03 \n", + "2 (nu-fission / flux) 3.57e-01 4.20e-03 \n", + "3 (scatter / flux) 5.56e-03 6.20e-05 \n", + "4 (nu-fission / flux) 7.27e-03 6.81e-05 \n", + "5 (scatter / flux) 2.28e-01 7.67e-04 \n", + "6 (nu-fission / flux) 8.07e-03 5.32e-05 \n", + "7 (scatter / flux) 3.36e-03 1.15e-05 " ] }, "execution_count": 30, @@ -1385,11 +1389,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[6.65948580e-07]\n", - " [3.56632881e-01]]\n", + "[[[6.67076123e-07]\n", + " [3.57197248e-01]]\n", "\n", - " [[7.25130446e-03]\n", - " [7.92016892e-03]]]\n" + " [[7.26923324e-03]\n", + " [8.06947893e-03]]]\n" ] } ], @@ -1415,9 +1419,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[0.00555547]]\n", + "[[[0.00555564]]\n", "\n", - " [[0.00335828]]]\n" + " [[0.00336316]]]\n" ] } ], @@ -1437,8 +1441,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[0.22726611]\n", - " [0.00335828]]]\n" + "[[[0.22765194]\n", + " [0.00336316]]]\n" ] } ], @@ -1501,7 +1505,7 @@ " U238\n", " nu-fission\n", " 0.000002\n", - " 9.679304e-09\n", + " 1.382881e-08\n", " \n", " \n", " 1\n", @@ -1510,8 +1514,8 @@ " 6.250000e-01\n", " U235\n", " nu-fission\n", - " 0.854805\n", - " 5.239673e-03\n", + " 0.858278\n", + " 7.512185e-03\n", " \n", " \n", " 2\n", @@ -1520,8 +1524,8 @@ " 2.000000e+07\n", " U238\n", " nu-fission\n", - " 0.082978\n", - " 5.346135e-04\n", + " 0.082753\n", + " 7.469156e-04\n", " \n", " \n", " 3\n", @@ -1530,8 +1534,8 @@ " 2.000000e+07\n", " U235\n", " nu-fission\n", - " 0.090632\n", - " 5.981942e-04\n", + " 0.091863\n", + " 5.596607e-04\n", " \n", " \n", "\n", @@ -1540,15 +1544,15 @@ "text/plain": [ " cell energy low [eV] energy high [eV] nuclide score mean \\\n", "0 1 0.00e+00 6.25e-01 U238 nu-fission 1.60e-06 \n", - "1 1 0.00e+00 6.25e-01 U235 nu-fission 8.55e-01 \n", - "2 1 6.25e-01 2.00e+07 U238 nu-fission 8.30e-02 \n", - "3 1 6.25e-01 2.00e+07 U235 nu-fission 9.06e-02 \n", + "1 1 0.00e+00 6.25e-01 U235 nu-fission 8.58e-01 \n", + "2 1 6.25e-01 2.00e+07 U238 nu-fission 8.28e-02 \n", + "3 1 6.25e-01 2.00e+07 U235 nu-fission 9.19e-02 \n", "\n", " std. dev. \n", - "0 9.68e-09 \n", - "1 5.24e-03 \n", - "2 5.35e-04 \n", - "3 5.98e-04 " + "0 1.38e-08 \n", + "1 7.51e-03 \n", + "2 7.47e-04 \n", + "3 5.60e-04 " ] }, "execution_count": 34, @@ -1605,8 +1609,8 @@ " 1.080060e-01\n", " H1\n", " scatter\n", - " 4.541188\n", - " 0.025230\n", + " 4.542677\n", + " 0.037555\n", " \n", " \n", " 1\n", @@ -1615,8 +1619,8 @@ " 1.166529e+00\n", " H1\n", " scatter\n", - " 2.001332\n", - " 0.006754\n", + " 2.019002\n", + " 0.011992\n", " \n", " \n", " 2\n", @@ -1625,8 +1629,8 @@ " 1.259921e+01\n", " H1\n", " scatter\n", - " 1.639292\n", - " 0.011374\n", + " 1.622688\n", + " 0.011889\n", " \n", " \n", " 3\n", @@ -1635,8 +1639,8 @@ " 1.360790e+02\n", " H1\n", " scatter\n", - " 1.821633\n", - " 0.009590\n", + " 1.834317\n", + " 0.012322\n", " \n", " \n", " 4\n", @@ -1645,8 +1649,8 @@ " 1.469734e+03\n", " H1\n", " scatter\n", - " 2.032395\n", - " 0.009953\n", + " 2.040064\n", + " 0.012967\n", " \n", " \n", " 5\n", @@ -1655,8 +1659,8 @@ " 1.587401e+04\n", " H1\n", " scatter\n", - " 2.120745\n", - " 0.011090\n", + " 2.107758\n", + " 0.012781\n", " \n", " \n", " 6\n", @@ -1665,8 +1669,8 @@ " 1.714488e+05\n", " H1\n", " scatter\n", - " 2.181709\n", - " 0.013602\n", + " 2.175480\n", + " 0.014165\n", " \n", " \n", " 7\n", @@ -1675,8 +1679,8 @@ " 1.851749e+06\n", " H1\n", " scatter\n", - " 2.013644\n", - " 0.009219\n", + " 1.983382\n", + " 0.012931\n", " \n", " \n", " 8\n", @@ -1685,8 +1689,8 @@ " 2.000000e+07\n", " H1\n", " scatter\n", - " 0.372640\n", - " 0.002903\n", + " 0.372359\n", + " 0.003689\n", " \n", " \n", "\n", @@ -1695,25 +1699,25 @@ "text/plain": [ " cell energy low [eV] energy high [eV] nuclide score mean \\\n", "0 3 1.00e-02 1.08e-01 H1 scatter 4.54e+00 \n", - "1 3 1.08e-01 1.17e+00 H1 scatter 2.00e+00 \n", - "2 3 1.17e+00 1.26e+01 H1 scatter 1.64e+00 \n", - "3 3 1.26e+01 1.36e+02 H1 scatter 1.82e+00 \n", - "4 3 1.36e+02 1.47e+03 H1 scatter 2.03e+00 \n", - "5 3 1.47e+03 1.59e+04 H1 scatter 2.12e+00 \n", + "1 3 1.08e-01 1.17e+00 H1 scatter 2.02e+00 \n", + "2 3 1.17e+00 1.26e+01 H1 scatter 1.62e+00 \n", + "3 3 1.26e+01 1.36e+02 H1 scatter 1.83e+00 \n", + "4 3 1.36e+02 1.47e+03 H1 scatter 2.04e+00 \n", + "5 3 1.47e+03 1.59e+04 H1 scatter 2.11e+00 \n", "6 3 1.59e+04 1.71e+05 H1 scatter 2.18e+00 \n", - "7 3 1.71e+05 1.85e+06 H1 scatter 2.01e+00 \n", - "8 3 1.85e+06 2.00e+07 H1 scatter 3.73e-01 \n", + "7 3 1.71e+05 1.85e+06 H1 scatter 1.98e+00 \n", + "8 3 1.85e+06 2.00e+07 H1 scatter 3.72e-01 \n", "\n", " std. dev. \n", - "0 2.52e-02 \n", - "1 6.75e-03 \n", - "2 1.14e-02 \n", - "3 9.59e-03 \n", - "4 9.95e-03 \n", - "5 1.11e-02 \n", - "6 1.36e-02 \n", - "7 9.22e-03 \n", - "8 2.90e-03 " + "0 3.76e-02 \n", + "1 1.20e-02 \n", + "2 1.19e-02 \n", + "3 1.23e-02 \n", + "4 1.30e-02 \n", + "5 1.28e-02 \n", + "6 1.42e-02 \n", + "7 1.29e-02 \n", + "8 3.69e-03 " ] }, "execution_count": 35, @@ -1728,6 +1732,16 @@ " filters=[openmc.CellFilter], filter_bins=[(moderator_cell.id,)])\n", "slice_test.get_pandas_dataframe()" ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Close the statepoint file as a matter of best practice\n", + "sp.close()" + ] } ], "metadata": { @@ -1746,7 +1760,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, From 8d36b9b81336c45b2bd9d9de84278b71f5f13bd1 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sat, 26 Jun 2021 18:46:14 -0500 Subject: [PATCH 3/5] Add a note about unable to write errors for summary/statepoint files on OpenMC re-runs from Python API. --- docs/source/usersguide/troubleshoot.rst | 17 +++++++++++++++++ examples/jupyter/chain_simple.xml | 1 + 2 files changed, 18 insertions(+) create mode 120000 examples/jupyter/chain_simple.xml diff --git a/docs/source/usersguide/troubleshoot.rst b/docs/source/usersguide/troubleshoot.rst index 7fb5723d932..259b21aa45e 100644 --- a/docs/source/usersguide/troubleshoot.rst +++ b/docs/source/usersguide/troubleshoot.rst @@ -45,6 +45,23 @@ with the :envvar:`OPENMC_CROSS_SECTIONS` environment variable. It is recommended to add a line in your ``.profile`` or ``.bash_profile`` setting the :envvar:`OPENMC_CROSS_SECTIONS` environment variable. +RuntimeError: Failed to open HDF5 file with mode 'w': summary.h5 +**************************************************************** + +This often occurs when working with the Python API and executing multiple OpenMC +runs in a script. If an :class:`openmc.StatePoint` is open in the Python interpreter, +the file handle of the statpoint file as well as the linked `summary.h5` file will +be unavailable for writing, causing this error to appear. To avoid the situation, +it is recommended that data be extracted from statepoint files in a context manager: + +.. code-block:: python + + with openmc.StatePoint('statepoint.10.h5') as sp: + k_eff = sp.k_combined + +or that the :meth:`StatePoint.close` method is called before executing a subsequent +OpenMC run. + Geometry Debugging ****************** diff --git a/examples/jupyter/chain_simple.xml b/examples/jupyter/chain_simple.xml new file mode 120000 index 00000000000..96f6fa8818e --- /dev/null +++ b/examples/jupyter/chain_simple.xml @@ -0,0 +1 @@ +../../tests/chain_simple.xml \ No newline at end of file From ae7ff201606ab7243061cc0ba605c4baa19d6493 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 28 Jun 2021 08:21:51 -0500 Subject: [PATCH 4/5] Apply suggestions from code review Co-authored-by: Paul Romano --- docs/source/usersguide/troubleshoot.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/usersguide/troubleshoot.rst b/docs/source/usersguide/troubleshoot.rst index 259b21aa45e..e5a68d68123 100644 --- a/docs/source/usersguide/troubleshoot.rst +++ b/docs/source/usersguide/troubleshoot.rst @@ -50,8 +50,8 @@ RuntimeError: Failed to open HDF5 file with mode 'w': summary.h5 This often occurs when working with the Python API and executing multiple OpenMC runs in a script. If an :class:`openmc.StatePoint` is open in the Python interpreter, -the file handle of the statpoint file as well as the linked `summary.h5` file will -be unavailable for writing, causing this error to appear. To avoid the situation, +the file handle of the statepoint file as well as the linked `summary.h5` file will +be unavailable for writing, causing this error to appear. To avoid this situation, it is recommended that data be extracted from statepoint files in a context manager: .. code-block:: python From ff5b73d4d90c39aed69d19278bbef985b330cb0d Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 28 Jun 2021 08:57:14 -0500 Subject: [PATCH 5/5] Addressing suggestions from @paulromano. --- examples/jupyter/candu.ipynb | 691 +++++++++++++++++++++++++- examples/jupyter/mdgxs-part-i.ipynb | 83 ++-- examples/jupyter/mg-mode-part-i.ipynb | 49 +- examples/jupyter/mgxs-part-i.ipynb | 392 ++++++++------- 4 files changed, 953 insertions(+), 262 deletions(-) diff --git a/examples/jupyter/candu.ipynb b/examples/jupyter/candu.ipynb index 30cdd78bc62..3e5bc72834b 100644 --- a/examples/jupyter/candu.ipynb +++ b/examples/jupyter/candu.ipynb @@ -121,7 +121,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -130,7 +130,7 @@ }, { "data": { - "image/png": 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nIh7NXEcJbpV0bHwAflHSgUka4WO4QGVKn94DSIzQA5Uh9EBlCD1QGUIPVIbQA5Uh9EBl/h8FjJsanv3x1AAAAABJRU5ErkJggg==\n", + "image/png": 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" ] @@ -176,7 +176,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -185,7 +185,7 @@ }, { "data": { - "image/png": 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" ] @@ -242,7 +242,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -251,7 +251,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -319,7 +319,7 @@ "outputs": [ { "data": { - "image/png": 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level 1level 2level 3distribcellnuclidescoremeanstd. dev.
univcellunivcellunivcell
idididididid
03441100250totalflux0.2113850.010406
13441200251totalflux0.1820150.006170
23441201252totalflux0.1849110.005997
33441202253totalflux0.1931770.009182
43441203254totalflux0.1934220.007610
53441204255totalflux0.1888080.005277
63441205256totalflux0.1914240.003950
73441300257totalflux0.1549320.007590
83441301258totalflux0.1536880.006581
93441302259totalflux0.1614710.004471
1034413032510totalflux0.1512020.008047
1134413042511totalflux0.1539050.005394
1234413052512totalflux0.1532890.010077
1334413062513totalflux0.1715590.007727
1434413072514totalflux0.1638020.006032
1534413082515totalflux0.1537710.005325
1634413092516totalflux0.1590700.005367
1734413102517totalflux0.1500780.005973
1834413112518totalflux0.1500650.007176
1934414002519totalflux0.1054390.004338
2034414012520totalflux0.1085020.004957
2134414022521totalflux0.1030880.007733
2234414032522totalflux0.1022370.003835
2334414042523totalflux0.1071330.006022
2434414052524totalflux0.1041770.004377
2534414062525totalflux0.1102810.005753
2634414072526totalflux0.1031710.003678
2734414082527totalflux0.1035340.005595
2834414092528totalflux0.1072400.003574
2934414102529totalflux0.1081620.006636
3034414112530totalflux0.1185500.005535
3134414122531totalflux0.1144350.006232
3234414132532totalflux0.1092270.003769
3334414142533totalflux0.1131480.006165
3434414152534totalflux0.1120060.005454
3534414162535totalflux0.0999730.004764
3634414172536totalflux0.1049520.005613
\n", + "
" + ], + "text/plain": [ + " level 1 level 2 level 3 distribcell nuclide score mean \\\n", + " univ cell univ cell univ cell \n", + " id id id id id id \n", + "0 3 44 1 100 2 5 0 total flux 2.11e-01 \n", + "1 3 44 1 200 2 5 1 total flux 1.82e-01 \n", + "2 3 44 1 201 2 5 2 total flux 1.85e-01 \n", + "3 3 44 1 202 2 5 3 total flux 1.93e-01 \n", + "4 3 44 1 203 2 5 4 total flux 1.93e-01 \n", + "5 3 44 1 204 2 5 5 total flux 1.89e-01 \n", + "6 3 44 1 205 2 5 6 total flux 1.91e-01 \n", + "7 3 44 1 300 2 5 7 total flux 1.55e-01 \n", + "8 3 44 1 301 2 5 8 total flux 1.54e-01 \n", + "9 3 44 1 302 2 5 9 total flux 1.61e-01 \n", + "10 3 44 1 303 2 5 10 total flux 1.51e-01 \n", + "11 3 44 1 304 2 5 11 total flux 1.54e-01 \n", + "12 3 44 1 305 2 5 12 total flux 1.53e-01 \n", + "13 3 44 1 306 2 5 13 total flux 1.72e-01 \n", + "14 3 44 1 307 2 5 14 total flux 1.64e-01 \n", + "15 3 44 1 308 2 5 15 total flux 1.54e-01 \n", + "16 3 44 1 309 2 5 16 total flux 1.59e-01 \n", + "17 3 44 1 310 2 5 17 total flux 1.50e-01 \n", + "18 3 44 1 311 2 5 18 total flux 1.50e-01 \n", + "19 3 44 1 400 2 5 19 total flux 1.05e-01 \n", + "20 3 44 1 401 2 5 20 total flux 1.09e-01 \n", + "21 3 44 1 402 2 5 21 total flux 1.03e-01 \n", + "22 3 44 1 403 2 5 22 total flux 1.02e-01 \n", + "23 3 44 1 404 2 5 23 total flux 1.07e-01 \n", + "24 3 44 1 405 2 5 24 total flux 1.04e-01 \n", + "25 3 44 1 406 2 5 25 total flux 1.10e-01 \n", + "26 3 44 1 407 2 5 26 total flux 1.03e-01 \n", + "27 3 44 1 408 2 5 27 total flux 1.04e-01 \n", + "28 3 44 1 409 2 5 28 total flux 1.07e-01 \n", + "29 3 44 1 410 2 5 29 total flux 1.08e-01 \n", + "30 3 44 1 411 2 5 30 total flux 1.19e-01 \n", + "31 3 44 1 412 2 5 31 total flux 1.14e-01 \n", + "32 3 44 1 413 2 5 32 total flux 1.09e-01 \n", + "33 3 44 1 414 2 5 33 total flux 1.13e-01 \n", + "34 3 44 1 415 2 5 34 total flux 1.12e-01 \n", + "35 3 44 1 416 2 5 35 total flux 1.00e-01 \n", + "36 3 44 1 417 2 5 36 total flux 1.05e-01 \n", + "\n", + " std. dev. \n", + " \n", + " \n", + "0 1.04e-02 \n", + "1 6.17e-03 \n", + "2 6.00e-03 \n", + "3 9.18e-03 \n", + "4 7.61e-03 \n", + "5 5.28e-03 \n", + "6 3.95e-03 \n", + "7 7.59e-03 \n", + "8 6.58e-03 \n", + "9 4.47e-03 \n", + "10 8.05e-03 \n", + "11 5.39e-03 \n", + "12 1.01e-02 \n", + "13 7.73e-03 \n", + "14 6.03e-03 \n", + "15 5.33e-03 \n", + "16 5.37e-03 \n", + "17 5.97e-03 \n", + "18 7.18e-03 \n", + "19 4.34e-03 \n", + "20 4.96e-03 \n", + "21 7.73e-03 \n", + "22 3.84e-03 \n", + "23 6.02e-03 \n", + "24 4.38e-03 \n", + "25 5.75e-03 \n", + "26 3.68e-03 \n", + "27 5.59e-03 \n", + "28 3.57e-03 \n", + "29 6.64e-03 \n", + "30 5.53e-03 \n", + "31 6.23e-03 \n", + "32 3.77e-03 \n", + "33 6.16e-03 \n", + "34 5.45e-03 \n", + "35 4.76e-03 \n", + "36 5.61e-03 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "with openmc.StatePoint('statepoint.{}.h5'.format(settings.batches)) as sp:\n", " output_tally = sp.get_tally()\n", - " output_tally.get_pandas_dataframe()" + " df = output_tally.get_pandas_dataframe()\n", + " \n", + "df" ] }, { diff --git a/examples/jupyter/mdgxs-part-i.ipynb b/examples/jupyter/mdgxs-part-i.ipynb index 972dee54abb..c37547a337f 100644 --- a/examples/jupyter/mdgxs-part-i.ipynb +++ b/examples/jupyter/mdgxs-part-i.ipynb @@ -490,19 +490,19 @@ " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.13.0-dev\n", " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", - " Date/Time | 2021-06-26 12:02:59\n", + " Date/Time | 2021-06-28 08:47:21\n", " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", - " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", - " Reading Pu239 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Pu239.h5\n", - " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Reading H1 from /opt/data/xs/nndc_hdf5/H1.h5\n", + " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n", + " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n", + " Reading Pu239 from /opt/data/xs/nndc_hdf5/Pu239.h5\n", + " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n", " Minimum neutron data temperature: 294.0 K\n", " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", @@ -569,21 +569,21 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.7487e-01 seconds\n", - " Reading cross sections = 2.6530e-01 seconds\n", - " Total time in simulation = 2.1335e+01 seconds\n", - " Time in transport only = 2.1311e+01 seconds\n", - " Time in inactive batches = 9.3940e-01 seconds\n", - " Time in active batches = 2.0396e+01 seconds\n", - " Time synchronizing fission bank = 9.9228e-03 seconds\n", - " Sampling source sites = 8.2374e-03 seconds\n", - " SEND/RECV source sites = 1.6638e-03 seconds\n", - " Time accumulating tallies = 8.4502e-04 seconds\n", - " Time writing statepoints = 7.4764e-03 seconds\n", - " Total time for finalization = 6.2220e-03 seconds\n", - " Total time elapsed = 2.1623e+01 seconds\n", - " Calculation Rate (inactive) = 53225.7 particles/second\n", - " Calculation Rate (active) = 9805.91 particles/second\n", + " Total time for initialization = 2.6337e-01 seconds\n", + " Reading cross sections = 2.5312e-01 seconds\n", + " Total time in simulation = 2.6057e+01 seconds\n", + " Time in transport only = 2.6028e+01 seconds\n", + " Time in inactive batches = 9.8414e-01 seconds\n", + " Time in active batches = 2.5072e+01 seconds\n", + " Time synchronizing fission bank = 1.1907e-02 seconds\n", + " Sampling source sites = 9.8847e-03 seconds\n", + " SEND/RECV source sites = 1.9926e-03 seconds\n", + " Time accumulating tallies = 1.1168e-03 seconds\n", + " Time writing statepoints = 7.9423e-03 seconds\n", + " Total time for finalization = 6.1998e-03 seconds\n", + " Total time elapsed = 2.6333e+01 seconds\n", + " Calculation Rate (inactive) = 50805.8 particles/second\n", + " Calculation Rate (active) = 7976.89 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1090,7 +1090,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -1137,31 +1137,6 @@ "plt.legend(legend, loc=1, bbox_to_anchor=(1.55, 0.95))" ] }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[CellFilter\n", - " \tBins =\t[1]\n", - " \tID =\t3,\n", - " DelayedGroupFilter\n", - " \tBins =\t[1 2 3 4 5 6]\n", - " \tID =\t13]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dr_tally.filters" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1171,7 +1146,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1346,7 +1321,7 @@ "11 (((delayed-nu-fission / nu-fission) * (delayed... 7.02e-11 3.37e-13 " ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1370,7 +1345,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1387,7 +1362,7 @@ "(0.0, 7.0)" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, @@ -1441,7 +1416,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1450,7 +1425,7 @@ "(1000.0, 20000000.0)" ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, diff --git a/examples/jupyter/mg-mode-part-i.ipynb b/examples/jupyter/mg-mode-part-i.ipynb index dfcb88aeca6..97a73d1e0dd 100644 --- a/examples/jupyter/mg-mode-part-i.ipynb +++ b/examples/jupyter/mg-mode-part-i.ipynb @@ -197,19 +197,10 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/shriwise/opt/openmc/openmc/openmc/surface.py:1511: FutureWarning: \"ZCylinder(...) accepts an argument named 'r', not 'R'. Future versions of OpenMC will not accept the capitalized version.\n", - " FutureWarning)\n" - ] - } - ], + "outputs": [], "source": [ "# Create the surface used for each pin\n", - "pin_surf = openmc.ZCylinder(x0=0, y0=0, R=0.54, name='pin_surf')\n", + "pin_surf = openmc.ZCylinder(x0=0, y0=0, r=0.54, name='pin_surf')\n", "\n", "# Create the cells which will be used to represent each pin type.\n", "cells = {}\n", @@ -393,7 +384,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -402,7 +393,7 @@ }, { "data": { - "image/png": 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\n", 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" ] @@ -563,7 +554,7 @@ " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.13.0-dev\n", " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", - " Date/Time | 2021-06-26 14:36:11\n", + " Date/Time | 2021-06-28 08:49:29\n", " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", @@ -589,21 +580,21 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.1712e-02 seconds\n", - " Reading cross sections = 9.5683e-03 seconds\n", - " Total time in simulation = 1.6152e+01 seconds\n", - " Time in transport only = 1.6108e+01 seconds\n", - " Time in inactive batches = 3.8534e+00 seconds\n", - " Time in active batches = 1.2299e+01 seconds\n", - " Time synchronizing fission bank = 2.8962e-02 seconds\n", - " Sampling source sites = 2.3951e-02 seconds\n", - " SEND/RECV source sites = 4.9397e-03 seconds\n", - " Time accumulating tallies = 4.9472e-04 seconds\n", - " Time writing statepoints = 2.4687e-03 seconds\n", - " Total time for finalization = 1.4768e-03 seconds\n", - " Total time elapsed = 1.6182e+01 seconds\n", - " Calculation Rate (inactive) = 64877.2 particles/second\n", - " Calculation Rate (active) = 40654.8 particles/second\n", + " Total time for initialization = 2.1582e-02 seconds\n", + " Reading cross sections = 1.0391e-02 seconds\n", + " Total time in simulation = 1.7437e+01 seconds\n", + " Time in transport only = 1.7387e+01 seconds\n", + " Time in inactive batches = 4.2441e+00 seconds\n", + " Time in active batches = 1.3193e+01 seconds\n", + " Time synchronizing fission bank = 3.2421e-02 seconds\n", + " Sampling source sites = 2.6798e-02 seconds\n", + " SEND/RECV source sites = 5.5395e-03 seconds\n", + " Time accumulating tallies = 5.3440e-04 seconds\n", + " Time writing statepoints = 2.9755e-03 seconds\n", + " Total time for finalization = 1.7629e-03 seconds\n", + " Total time elapsed = 1.7465e+01 seconds\n", + " Calculation Rate (inactive) = 58905.6 particles/second\n", + " Calculation Rate (active) = 37899.4 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", diff --git a/examples/jupyter/mgxs-part-i.ipynb b/examples/jupyter/mgxs-part-i.ipynb index 3d5711adc16..10e02e96cc4 100644 --- a/examples/jupyter/mgxs-part-i.ipynb +++ b/examples/jupyter/mgxs-part-i.ipynb @@ -33,7 +33,7 @@ "outputs": [ { "data": { - "image/png": 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Widi+P/wQePBAuhyEEOJoVEwTQogTkeJhQUv6jI0Fzp2zfwZCCJELFdOEEEII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id0CkRvp07At9/657mkrd9A4GCaiIjIQ9262W/du7ueQ1q3/v2dfx49OrCv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\nCvr164fFixcbWOistvUb7MLDw13+Ef/nP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+ "image/png": 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\n", "text/plain": [ "" ] @@ -154,9 +154,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a Material and register the Nuclides\n", @@ -179,9 +177,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a Materials collection and export to XML\n", @@ -199,9 +195,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate boundary Planes\n", @@ -244,9 +238,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Create root universe\n", @@ -283,9 +275,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# OpenMC simulation parameters\n", @@ -384,17 +374,20 @@ { "data": { "text/plain": [ - "OrderedDict([('flux', Tally\n", + "OrderedDict([('flux',\n", + " Tally\n", " \tID =\t1\n", " \tName =\t\n", " \tFilters =\tCellFilter, EnergyFilter\n", - " \tNuclides =\ttotal \n", + " \tNuclides =\ttotal\n", " \tScores =\t['flux']\n", - " \tEstimator =\ttracklength), ('absorption', Tally\n", + " \tEstimator =\ttracklength),\n", + " ('absorption',\n", + " Tally\n", " \tID =\t2\n", " \tName =\t\n", " \tFilters =\tCellFilter, EnergyFilter\n", - " \tNuclides =\ttotal \n", + " \tNuclides =\ttotal\n", " \tScores =\t['absorption']\n", " \tEstimator =\ttracklength)])" ] @@ -424,9 +417,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another CellFilter instance already exists with id=3.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyFilter instance already exists with id=4.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n", " warn(msg, IDWarning)\n" ] } @@ -464,146 +457,138 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2017 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.9.0\n", - " Git SHA1 | 9b7cebf7bc34d60e0f1750c3d6cb103df11e8dc4\n", - " Date/Time | 2017-12-04 20:56:46\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-28 08:44:52\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Building neighboring cells lists for each surface...\n", - " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n", - " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n", - " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n", - " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", - " Maximum neutron transport energy: 2.00000E+07 eV for H1\n", + " Reading H1 from /opt/data/xs/nndc_hdf5/H1.h5\n", + " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n", + " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n", + " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for H1\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.11184 \n", - " 2/1 1.15820 \n", - " 3/1 1.18468 \n", - " 4/1 1.17492 \n", - " 5/1 1.19645 \n", - " 6/1 1.18436 \n", - " 7/1 1.14070 \n", - " 8/1 1.15150 \n", - " 9/1 1.19202 \n", - " 10/1 1.17677 \n", - " 11/1 1.20272 \n", - " 12/1 1.21366 1.20819 +/- 0.00547\n", - " 13/1 1.15906 1.19181 +/- 0.01668\n", - " 14/1 1.14687 1.18058 +/- 0.01629\n", - " 15/1 1.14570 1.17360 +/- 0.01442\n", - " 16/1 1.13480 1.16713 +/- 0.01343\n", - " 17/1 1.17680 1.16852 +/- 0.01144\n", - " 18/1 1.16866 1.16853 +/- 0.00990\n", - " 19/1 1.19253 1.17120 +/- 0.00913\n", - " 20/1 1.18124 1.17220 +/- 0.00823\n", - " 21/1 1.19206 1.17401 +/- 0.00766\n", - " 22/1 1.17681 1.17424 +/- 0.00700\n", - " 23/1 1.17634 1.17440 +/- 0.00644\n", - " 24/1 1.13659 1.17170 +/- 0.00654\n", - " 25/1 1.17144 1.17169 +/- 0.00609\n", - " 26/1 1.20649 1.17386 +/- 0.00610\n", - " 27/1 1.11238 1.17024 +/- 0.00678\n", - " 28/1 1.18911 1.17129 +/- 0.00647\n", - " 29/1 1.14681 1.17000 +/- 0.00626\n", - " 30/1 1.12152 1.16758 +/- 0.00641\n", - " 31/1 1.12729 1.16566 +/- 0.00639\n", - " 32/1 1.15399 1.16513 +/- 0.00612\n", - " 33/1 1.13547 1.16384 +/- 0.00599\n", - " 34/1 1.17723 1.16440 +/- 0.00576\n", - " 35/1 1.09296 1.16154 +/- 0.00622\n", - " 36/1 1.19621 1.16287 +/- 0.00612\n", - " 37/1 1.12560 1.16149 +/- 0.00605\n", - " 38/1 1.17872 1.16211 +/- 0.00586\n", - " 39/1 1.17721 1.16263 +/- 0.00568\n", - " 40/1 1.13724 1.16178 +/- 0.00555\n", - " 41/1 1.18526 1.16254 +/- 0.00542\n", - " 42/1 1.13779 1.16177 +/- 0.00531\n", - " 43/1 1.15066 1.16143 +/- 0.00516\n", - " 44/1 1.12174 1.16026 +/- 0.00514\n", - " 45/1 1.17478 1.16068 +/- 0.00501\n", - " 46/1 1.14146 1.16014 +/- 0.00489\n", - " 47/1 1.20464 1.16135 +/- 0.00491\n", - " 48/1 1.15119 1.16108 +/- 0.00479\n", - " 49/1 1.17938 1.16155 +/- 0.00468\n", - " 50/1 1.15798 1.16146 +/- 0.00457\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.18505\n", + " 2/1 1.17297\n", + " 3/1 1.16184\n", + " 4/1 1.14929\n", + " 5/1 1.09928\n", + " 6/1 1.18675\n", + " 7/1 1.19772\n", + " 8/1 1.17470\n", + " 9/1 1.17208\n", + " 10/1 1.09993\n", + " 11/1 1.14342\n", + " 12/1 1.10127 1.12234 +/- 0.02107\n", + " 13/1 1.19914 1.14794 +/- 0.02834\n", + " 14/1 1.18411 1.15698 +/- 0.02199\n", + " 15/1 1.14556 1.15470 +/- 0.01718\n", + " 16/1 1.20337 1.16281 +/- 0.01621\n", + " 17/1 1.13853 1.15934 +/- 0.01413\n", + " 18/1 1.18208 1.16218 +/- 0.01256\n", + " 19/1 1.11842 1.15732 +/- 0.01210\n", + " 20/1 1.15248 1.15684 +/- 0.01083\n", + " 21/1 1.14903 1.15613 +/- 0.00982\n", + " 22/1 1.23456 1.16266 +/- 0.01110\n", + " 23/1 1.18876 1.16467 +/- 0.01040\n", + " 24/1 1.13591 1.16262 +/- 0.00985\n", + " 25/1 1.19559 1.16481 +/- 0.00943\n", + " 26/1 1.16947 1.16511 +/- 0.00882\n", + " 27/1 1.13198 1.16316 +/- 0.00851\n", + " 28/1 1.15329 1.16261 +/- 0.00805\n", + " 29/1 1.16538 1.16275 +/- 0.00761\n", + " 30/1 1.18229 1.16373 +/- 0.00729\n", + " 31/1 1.15060 1.16311 +/- 0.00696\n", + " 32/1 1.15460 1.16272 +/- 0.00665\n", + " 33/1 1.13875 1.16168 +/- 0.00644\n", + " 34/1 1.13479 1.16056 +/- 0.00626\n", + " 35/1 1.21125 1.16258 +/- 0.00634\n", + " 36/1 1.15914 1.16245 +/- 0.00609\n", + " 37/1 1.10457 1.16031 +/- 0.00624\n", + " 38/1 1.17215 1.16073 +/- 0.00603\n", + " 39/1 1.18462 1.16155 +/- 0.00588\n", + " 40/1 1.15361 1.16129 +/- 0.00568\n", + " 41/1 1.14983 1.16092 +/- 0.00551\n", + " 42/1 1.14087 1.16029 +/- 0.00537\n", + " 43/1 1.18725 1.16111 +/- 0.00527\n", + " 44/1 1.19094 1.16199 +/- 0.00519\n", + " 45/1 1.17371 1.16232 +/- 0.00505\n", + " 46/1 1.18552 1.16297 +/- 0.00495\n", + " 47/1 1.14194 1.16240 +/- 0.00485\n", + " 48/1 1.12045 1.16130 +/- 0.00484\n", + " 49/1 1.18476 1.16190 +/- 0.00476\n", + " 50/1 1.17063 1.16212 +/- 0.00464\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.0504E-01 seconds\n", - " Reading cross sections = 3.6457E-01 seconds\n", - " Total time in simulation = 6.3478E+00 seconds\n", - " Time in transport only = 6.0079E+00 seconds\n", - " Time in inactive batches = 8.1713E-01 seconds\n", - " Time in active batches = 5.5307E+00 seconds\n", - " Time synchronizing fission bank = 5.4640E-03 seconds\n", - " Sampling source sites = 4.0981E-03 seconds\n", - " SEND/RECV source sites = 1.2606E-03 seconds\n", - " Time accumulating tallies = 1.2030E-04 seconds\n", - " Total time for finalization = 9.6554E-04 seconds\n", - " Total time elapsed = 6.7713E+00 seconds\n", - " Calculation Rate (inactive) = 30594.8 neutrons/second\n", - " Calculation Rate (active) = 18080.8 neutrons/second\n", + " Total time for initialization = 2.9038e-01 seconds\n", + " Reading cross sections = 2.7861e-01 seconds\n", + " Total time in simulation = 3.9987e+00 seconds\n", + " Time in transport only = 3.9829e+00 seconds\n", + " Time in inactive batches = 4.9461e-01 seconds\n", + " Time in active batches = 3.5041e+00 seconds\n", + " Time synchronizing fission bank = 5.2042e-03 seconds\n", + " Sampling source sites = 4.4431e-03 seconds\n", + " SEND/RECV source sites = 7.3804e-04 seconds\n", + " Time accumulating tallies = 3.6977e-04 seconds\n", + " Time writing statepoints = 6.6301e-03 seconds\n", + " Total time for finalization = 1.9945e-04 seconds\n", + " Total time elapsed = 4.2948e+00 seconds\n", + " Calculation Rate (inactive) = 50544.8 particles/second\n", + " Calculation Rate (active) = 28537.8 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.15984 +/- 0.00411\n", - " k-effective (Track-length) = 1.16146 +/- 0.00457\n", - " k-effective (Absorption) = 1.16177 +/- 0.00380\n", - " Combined k-effective = 1.16105 +/- 0.00364\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.16239 +/- 0.00461\n", + " k-effective (Track-length) = 1.16212 +/- 0.00464\n", + " k-effective (Absorption) = 1.15435 +/- 0.00325\n", + " Combined k-effective = 1.15666 +/- 0.00304\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -658,7 +643,9 @@ "# Load the tallies from the statepoint into each MGXS object\n", "total.load_from_statepoint(sp)\n", "absorption.load_from_statepoint(sp)\n", - "scattering.load_from_statepoint(sp)" + "scattering.load_from_statepoint(sp)\n", + "# Close the statepoint file now that we're done getting info\n", + "sp.close()" ] }, { @@ -697,7 +684,7 @@ "\tDomain ID =\t1\n", "\tCross Sections [cm^-1]:\n", " Group 1 [0.625 - 20000000.0eV]:\t6.81e-01 +/- 2.69e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t1.40e+00 +/- 5.93e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t1.40e+00 +/- 6.00e-01%\n", "\n", "\n", "\n" @@ -724,6 +711,19 @@ "data": { "text/html": [ "
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