From 83f19e8cf1ade0a85f980f3b86c70c3053f9d019 Mon Sep 17 00:00:00 2001 From: Will Boyd Date: Wed, 23 Mar 2016 13:50:27 -0400 Subject: [PATCH] Fixed bug for Python 2 reading of distribcell paths in Summary API --- .../pythonapi/examples/mgxs-part-i.ipynb | 309 ++-- .../pythonapi/examples/mgxs-part-ii.ipynb | 1082 +---------- .../pythonapi/examples/mgxs-part-iii.ipynb | 487 +++-- .../examples/pandas-dataframes.ipynb | 1594 ++++++----------- .../pythonapi/examples/post-processing.ipynb | 328 ++-- .../pythonapi/examples/tally-arithmetic.ipynb | 374 ++-- openmc/summary.py | 2 +- openmc/trigger.py | 7 +- 8 files changed, 1350 insertions(+), 2833 deletions(-) diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb index 6b78e9d53..01cd7cd7f 100644 --- a/docs/source/pythonapi/examples/mgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb @@ -518,9 +518,10 @@ " Copyright: 2011-2015 Massachusetts Institute of Technology\n", " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.1\n", - " Git SHA1: 34381b40a9445a727e360873aaa6ef892af1cb6a\n", - " Date/Time: 2016-02-07 15:58:16\n", + " Git SHA1: 5f252e2df51930b9175fd41bafa8db01f3eaeb92\n", + " Date/Time: 2016-03-23 11:41:09\n", " MPI Processes: 1\n", + " OpenMP Threads: 16\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -546,56 +547,56 @@ "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", - " 1/1 1.19804 \n", - " 2/1 1.12945 \n", - " 3/1 1.15573 \n", - " 4/1 1.13929 \n", - " 5/1 1.16300 \n", - " 6/1 1.22117 \n", - " 7/1 1.19012 \n", - " 8/1 1.11299 \n", - " 9/1 1.16066 \n", - " 10/1 1.12566 \n", - " 11/1 1.20854 \n", - " 12/1 1.14691 1.17773 +/- 0.03082\n", - " 13/1 1.17204 1.17583 +/- 0.01789\n", - " 14/1 1.14148 1.16724 +/- 0.01529\n", - " 15/1 1.17272 1.16834 +/- 0.01189\n", - " 16/1 1.18575 1.17124 +/- 0.01014\n", - " 17/1 1.20498 1.17606 +/- 0.00983\n", - " 18/1 1.14754 1.17249 +/- 0.00923\n", - " 19/1 1.18141 1.17348 +/- 0.00820\n", - " 20/1 1.15074 1.17121 +/- 0.00768\n", - " 21/1 1.15914 1.17011 +/- 0.00703\n", - " 22/1 1.14586 1.16809 +/- 0.00673\n", - " 23/1 1.18999 1.16978 +/- 0.00642\n", - " 24/1 1.15101 1.16844 +/- 0.00609\n", - " 25/1 1.13791 1.16640 +/- 0.00602\n", - " 26/1 1.19791 1.16837 +/- 0.00597\n", - " 27/1 1.19818 1.17012 +/- 0.00587\n", - " 28/1 1.14160 1.16854 +/- 0.00576\n", - " 29/1 1.11487 1.16571 +/- 0.00614\n", - " 30/1 1.17538 1.16620 +/- 0.00584\n", - " 31/1 1.20210 1.16791 +/- 0.00581\n", - " 32/1 1.20078 1.16940 +/- 0.00574\n", - " 33/1 1.14624 1.16839 +/- 0.00558\n", - " 34/1 1.14618 1.16747 +/- 0.00542\n", - " 35/1 1.16866 1.16752 +/- 0.00520\n", - " 36/1 1.18565 1.16821 +/- 0.00504\n", - " 37/1 1.16824 1.16821 +/- 0.00485\n", - " 38/1 1.18299 1.16874 +/- 0.00471\n", - " 39/1 1.21418 1.17031 +/- 0.00480\n", - " 40/1 1.11167 1.16835 +/- 0.00504\n", - " 41/1 1.11545 1.16665 +/- 0.00516\n", - " 42/1 1.11114 1.16491 +/- 0.00529\n", - " 43/1 1.14227 1.16423 +/- 0.00517\n", - " 44/1 1.14104 1.16355 +/- 0.00506\n", - " 45/1 1.16756 1.16366 +/- 0.00492\n", - " 46/1 1.13065 1.16274 +/- 0.00487\n", - " 47/1 1.11251 1.16139 +/- 0.00492\n", - " 48/1 1.14731 1.16101 +/- 0.00481\n", - " 49/1 1.16691 1.16117 +/- 0.00469\n", - " 50/1 1.19679 1.16206 +/- 0.00465\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 1.17479 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", " Creating state point statepoint.50.h5...\n", "\n", " ===========================================================================\n", @@ -605,27 +606,27 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.2100E-01 seconds\n", - " Reading cross sections = 7.4000E-02 seconds\n", - " Total time in simulation = 8.3830E+00 seconds\n", - " Time in transport only = 8.3670E+00 seconds\n", - " Time in inactive batches = 1.0330E+00 seconds\n", - " Time in active batches = 7.3500E+00 seconds\n", - " Time synchronizing fission bank = 4.0000E-03 seconds\n", - " Sampling source sites = 1.0000E-03 seconds\n", - " SEND/RECV source sites = 3.0000E-03 seconds\n", + " Total time for initialization = 5.7200E-01 seconds\n", + " Reading cross sections = 1.3300E-01 seconds\n", + " Total time in simulation = 2.7830E+00 seconds\n", + " Time in transport only = 2.1610E+00 seconds\n", + " Time in inactive batches = 4.1200E-01 seconds\n", + " Time in active batches = 2.3710E+00 seconds\n", + " Time synchronizing fission bank = 8.0000E-03 seconds\n", + " Sampling source sites = 5.0000E-03 seconds\n", + " SEND/RECV source sites = 2.0000E-03 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", - " Total time for finalization = 1.0000E-03 seconds\n", - " Total time elapsed = 8.7140E+00 seconds\n", - " Calculation Rate (inactive) = 24201.4 neutrons/second\n", - " Calculation Rate (active) = 13605.4 neutrons/second\n", + " Total time for finalization = 0.0000E+00 seconds\n", + " Total time elapsed = 3.3710E+00 seconds\n", + " Calculation Rate (inactive) = 60679.6 neutrons/second\n", + " Calculation Rate (active) = 42176.3 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.16131 +/- 0.00453\n", - " k-effective (Track-length) = 1.16206 +/- 0.00465\n", - " k-effective (Absorption) = 1.16096 +/- 0.00364\n", - " Combined k-effective = 1.16120 +/- 0.00325\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", "\n" ] @@ -751,8 +752,8 @@ "\tDomain Type =\tcell\n", "\tDomain ID =\t1\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t6.81e-01 +/- 1.88e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t1.40e+00 +/- 5.91e-01%\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t6.81e-01 +/- 2.69e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t1.40e+00 +/- 5.93e-01%\n", "\n", "\n", "\n" @@ -780,7 +781,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -795,19 +796,19 @@ " \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", "
1 1 1 total 0.668323 0.00126411total0.6677870.001802
0 1 2 total 1.293258 0.00762412total1.2920130.007642
\n", @@ -815,8 +816,8 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "1 1 1 total 0.668323 0.001264\n", - "0 1 2 total 1.293258 0.007624" + "1 1 1 total 0.667787 0.001802\n", + "0 1 2 total 1.292013 0.007642" ] }, "execution_count": 20, @@ -891,7 +892,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -908,23 +909,23 @@ " \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", "
0 1 0.000000 0.000001 total (((total / flux) - (absorption / flux)) - (sca... 4.884981e-15 0.01127410.000000e+006.250000e-07total(((total / flux) - (absorption / flux)) - (sca...0.000000e+000.011292
1 1 0.000001 20.000000 total (((total / flux) - (absorption / flux)) - (sca... 1.221245e-15 0.00180216.250000e-072.000000e+01total(((total / flux) - (absorption / flux)) - (sca...-3.330669e-160.002570
\n", @@ -935,9 +936,9 @@ "0 1 0.00e+00 6.25e-07 total \n", "1 1 6.25e-07 2.00e+01 total \n", "\n", - " score mean std. dev. \n", - "0 (((total / flux) - (absorption / flux)) - (sca... 4.88e-15 1.13e-02 \n", - "1 (((total / flux) - (absorption / flux)) - (sca... 1.22e-15 1.80e-03 " + " score mean std. dev. \n", + "0 (((total / flux) - (absorption / flux)) - (sca... 0.00e+00 1.13e-02 \n", + "1 (((total / flux) - (absorption / flux)) - (sca... -3.33e-16 2.57e-03 " ] }, "execution_count": 23, @@ -970,7 +971,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -987,23 +988,23 @@ " \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", "
0 1 0.000000 0.000001 total ((absorption / flux) / (total / flux)) 0.076219 0.00065110.000000e+006.250000e-07total((absorption / flux) / (total / flux))0.0761150.000649
1 1 0.000001 20.000000 total ((absorption / flux) / (total / flux)) 0.019319 0.00008616.250000e-072.000000e+01total((absorption / flux) / (total / flux))0.0192630.000095
\n", @@ -1015,8 +1016,8 @@ "1 1 6.25e-07 2.00e+01 total \n", "\n", " score mean std. dev. \n", - "0 ((absorption / flux) / (total / flux)) 7.62e-02 6.51e-04 \n", - "1 ((absorption / flux) / (total / flux)) 1.93e-02 8.65e-05 " + "0 ((absorption / flux) / (total / flux)) 7.61e-02 6.49e-04 \n", + "1 ((absorption / flux) / (total / flux)) 1.93e-02 9.46e-05 " ] }, "execution_count": 24, @@ -1042,7 +1043,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -1059,23 +1060,23 @@ " \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", "
0 1 0.000000 0.000001 total ((scatter / flux) / (total / flux)) 0.923781 0.00771410.000000e+006.250000e-07total((scatter / flux) / (total / flux))0.9238850.007736
1 1 0.000001 20.000000 total ((scatter / flux) / (total / flux)) 0.980681 0.00261716.250000e-072.000000e+01total((scatter / flux) / (total / flux))0.9807370.003737
\n", @@ -1087,8 +1088,8 @@ "1 1 6.25e-07 2.00e+01 total \n", "\n", " score mean std. dev. \n", - "0 ((scatter / flux) / (total / flux)) 9.24e-01 7.71e-03 \n", - "1 ((scatter / flux) / (total / flux)) 9.81e-01 2.62e-03 " + "0 ((scatter / flux) / (total / flux)) 9.24e-01 7.74e-03 \n", + "1 ((scatter / flux) / (total / flux)) 9.81e-01 3.74e-03 " ] }, "execution_count": 25, @@ -1121,7 +1122,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -1138,23 +1139,23 @@ " \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", "
0 1 0.000000 0.000001 total (((absorption / flux) / (total / flux)) + ((sc... 1 0.00774110.000000e+006.250000e-07total(((absorption / flux) / (total / flux)) + ((sc...10.007763
1 1 0.000001 20.000000 total (((absorption / flux) / (total / flux)) + ((sc... 1 0.00261916.250000e-072.000000e+01total(((absorption / flux) / (total / flux)) + ((sc...10.003739
\n", @@ -1166,8 +1167,8 @@ "1 1 6.25e-07 2.00e+01 total \n", "\n", " score mean std. dev. \n", - "0 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 7.74e-03 \n", - "1 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 2.62e-03 " + "0 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 7.76e-03 \n", + "1 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 3.74e-03 " ] }, "execution_count": 26, @@ -1200,7 +1201,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.11" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb index 0a8d7230e..5d8da5da1 100644 --- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb @@ -34,14 +34,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/lib/pymodules/python2.7/matplotlib/__init__.py:1173: UserWarning: This call to matplotlib.use() has no effect\n", + "/home/wboyd/anaconda2/lib/python2.7/site-packages/matplotlib/__init__.py:1350: UserWarning: This call to matplotlib.use() has no effect\n", "because the backend has already been chosen;\n", "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", "\n", - " warnings.warn(_use_error_msg)\n", - "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:11: QAWarning: pyne.rxname is not yet QA compliant.\n", - "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:11: QAWarning: pyne.ace is not yet QA compliant.\n" + " warnings.warn(_use_error_msg)\n" + ] + }, + { + "ename": "ImportError", + "evalue": "No module named ace", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mopenmoc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mopenmoc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompatible\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mget_openmoc_geometry\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpyne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mace\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mImportError\u001b[0m: No module named ace" ] } ], @@ -70,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": true }, @@ -93,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "collapsed": false }, @@ -127,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": true }, @@ -153,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": true }, @@ -181,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": false }, @@ -218,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false }, @@ -243,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": true }, @@ -270,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "collapsed": true }, @@ -308,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "collapsed": true }, @@ -333,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "collapsed": false }, @@ -364,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": false }, @@ -388,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": false }, @@ -425,188 +434,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " .d88888b. 888b d888 .d8888b.\n", - " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", - " 888 888 88888b.d88888 888 888\n", - " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", - " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", - " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", - " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", - " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", - "__________________888______________________________________________________\n", - " 888\n", - " 888\n", - "\n", - " Copyright: 2011-2015 Massachusetts Institute of Technology\n", - " License: http://mit-crpg.github.io/openmc/license.html\n", - " Version: 0.7.1\n", - " Git SHA1: 263266f4f8807fd38c6ac282fae259ae73fa1eee\n", - " Date/Time: 2016-01-20 18:12:40\n", - " MPI Processes: 1\n", - "\n", - " ===========================================================================\n", - " ========================> INITIALIZATION <=========================\n", - " ===========================================================================\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", - " Reading tallies XML file...\n", - " Building neighboring cells lists for each surface...\n", - " Loading ACE cross section table: 92235.71c\n", - " Loading ACE cross section table: 92238.71c\n", - " Loading ACE cross section table: 8016.71c\n", - " Loading ACE cross section table: 1001.71c\n", - " Loading ACE cross section table: 40090.71c\n", - " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", - " Initializing source particles...\n", - "\n", - " ===========================================================================\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - " ===========================================================================\n", - "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.22593 \n", - " 2/1 1.24245 \n", - " 3/1 1.24545 \n", - " 4/1 1.21868 \n", - " 5/1 1.22429 \n", - " 6/1 1.22607 \n", - " 7/1 1.21456 \n", - " 8/1 1.23816 \n", - " 9/1 1.25060 \n", - " 10/1 1.22806 \n", - " 11/1 1.19821 \n", - " 12/1 1.19897 1.19859 +/- 0.00038\n", - " 13/1 1.22119 1.20612 +/- 0.00754\n", - " 14/1 1.20701 1.20634 +/- 0.00533\n", - " 15/1 1.24784 1.21464 +/- 0.00927\n", - " 16/1 1.22413 1.21622 +/- 0.00773\n", - " 17/1 1.25050 1.22112 +/- 0.00817\n", - " 18/1 1.22006 1.22099 +/- 0.00707\n", - " 19/1 1.22813 1.22178 +/- 0.00629\n", - " 20/1 1.22791 1.22239 +/- 0.00566\n", - " 21/1 1.22729 1.22284 +/- 0.00514\n", - " 22/1 1.19867 1.22083 +/- 0.00510\n", - " 23/1 1.23796 1.22214 +/- 0.00488\n", - " 24/1 1.22412 1.22228 +/- 0.00452\n", - " 25/1 1.22638 1.22256 +/- 0.00421\n", - " 26/1 1.22181 1.22251 +/- 0.00394\n", - " 27/1 1.19055 1.22063 +/- 0.00415\n", - " 28/1 1.20683 1.21986 +/- 0.00399\n", - " 29/1 1.21689 1.21971 +/- 0.00378\n", - " 30/1 1.23670 1.22056 +/- 0.00368\n", - " 31/1 1.21396 1.22024 +/- 0.00352\n", - " 32/1 1.21389 1.21995 +/- 0.00337\n", - " 33/1 1.24649 1.22111 +/- 0.00342\n", - " 34/1 1.23204 1.22156 +/- 0.00330\n", - " 35/1 1.20768 1.22101 +/- 0.00322\n", - " 36/1 1.22271 1.22107 +/- 0.00309\n", - " 37/1 1.21796 1.22096 +/- 0.00298\n", - " 38/1 1.23842 1.22158 +/- 0.00293\n", - " 39/1 1.23080 1.22190 +/- 0.00285\n", - " 40/1 1.23572 1.22236 +/- 0.00279\n", - " 41/1 1.21691 1.22218 +/- 0.00271\n", - " 42/1 1.24616 1.22293 +/- 0.00272\n", - " 43/1 1.21903 1.22282 +/- 0.00264\n", - " 44/1 1.22967 1.22302 +/- 0.00257\n", - " 45/1 1.22053 1.22295 +/- 0.00250\n", - " 46/1 1.24087 1.22344 +/- 0.00248\n", - " 47/1 1.20251 1.22288 +/- 0.00248\n", - " 48/1 1.20331 1.22236 +/- 0.00246\n", - " 49/1 1.22724 1.22249 +/- 0.00240\n", - " 50/1 1.24798 1.22313 +/- 0.00243\n", - " Triggers unsatisfied, max unc./thresh. is 1.32110 for scatter-p1 in tally 10054\n", - " The estimated number of batches is 80\n", - " Creating state point statepoint.050.h5...\n", - " 51/1 1.22253 1.22311 +/- 0.00237\n", - " 52/1 1.24330 1.22359 +/- 0.00236\n", - " 53/1 1.23251 1.22380 +/- 0.00231\n", - " 54/1 1.21133 1.22352 +/- 0.00228\n", - " 55/1 1.24503 1.22399 +/- 0.00228\n", - " 56/1 1.22013 1.22391 +/- 0.00223\n", - " 57/1 1.23877 1.22423 +/- 0.00220\n", - " 58/1 1.23793 1.22451 +/- 0.00218\n", - " 59/1 1.21018 1.22422 +/- 0.00215\n", - " 60/1 1.22417 1.22422 +/- 0.00211\n", - " 61/1 1.23094 1.22435 +/- 0.00207\n", - " 62/1 1.23310 1.22452 +/- 0.00204\n", - " 63/1 1.22488 1.22453 +/- 0.00200\n", - " 64/1 1.22702 1.22457 +/- 0.00196\n", - " 65/1 1.18834 1.22391 +/- 0.00204\n", - " 66/1 1.23112 1.22404 +/- 0.00200\n", - " 67/1 1.21611 1.22390 +/- 0.00197\n", - " 68/1 1.22513 1.22392 +/- 0.00194\n", - " 69/1 1.21741 1.22381 +/- 0.00191\n", - " 70/1 1.22484 1.22383 +/- 0.00188\n", - " 71/1 1.19662 1.22338 +/- 0.00190\n", - " 72/1 1.23315 1.22354 +/- 0.00187\n", - " 73/1 1.22796 1.22361 +/- 0.00185\n", - " 74/1 1.21417 1.22346 +/- 0.00182\n", - " 75/1 1.21020 1.22326 +/- 0.00181\n", - " 76/1 1.23413 1.22343 +/- 0.00179\n", - " 77/1 1.22184 1.22340 +/- 0.00176\n", - " 78/1 1.20309 1.22310 +/- 0.00176\n", - " 79/1 1.23458 1.22327 +/- 0.00174\n", - " 80/1 1.20724 1.22304 +/- 0.00173\n", - " Triggers satisfied for batch 80\n", - " Creating state point statepoint.080.h5...\n", - "\n", - " ===========================================================================\n", - " ======================> SIMULATION FINISHED <======================\n", - " ===========================================================================\n", - "\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 4.3200E-01 seconds\n", - " Reading cross sections = 9.1000E-02 seconds\n", - " Total time in simulation = 2.2239E+02 seconds\n", - " Time in transport only = 2.2234E+02 seconds\n", - " Time in inactive batches = 1.3715E+01 seconds\n", - " Time in active batches = 2.0867E+02 seconds\n", - " Time synchronizing fission bank = 2.3000E-02 seconds\n", - " Sampling source sites = 1.7000E-02 seconds\n", - " SEND/RECV source sites = 6.0000E-03 seconds\n", - " Time accumulating tallies = 2.0000E-03 seconds\n", - " Total time for finalization = 9.0000E-03 seconds\n", - " Total time elapsed = 2.2288E+02 seconds\n", - " Calculation Rate (inactive) = 7291.29 neutrons/second\n", - " Calculation Rate (active) = 1916.88 neutrons/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 1.22327 +/- 0.00148\n", - " k-effective (Track-length) = 1.22304 +/- 0.00173\n", - " k-effective (Absorption) = 1.22407 +/- 0.00129\n", - " Combined k-effective = 1.22373 +/- 0.00113\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Run OpenMC\n", "executor = openmc.Executor()\n", @@ -629,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "collapsed": false }, @@ -648,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "collapsed": true }, @@ -668,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "collapsed": false }, @@ -703,46 +535,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multi-Group XS\n", - "\tReaction Type =\tnu-fission\n", - "\tDomain Type =\tcell\n", - "\tDomain ID =\t10000\n", - "\tNuclide =\tU-235\n", - "\tCross Sections [barns]:\n", - " Group 1 [0.821 - 20.0 MeV]:\t3.31e+00 +/- 1.88e-01%\n", - " Group 2 [0.00553 - 0.821 MeV]:\t3.97e+00 +/- 1.24e-01%\n", - " Group 3 [4e-06 - 0.00553 MeV]:\t5.50e+01 +/- 2.02e-01%\n", - " Group 4 [6.25e-07 - 4e-06 MeV]:\t8.83e+01 +/- 3.56e-01%\n", - " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t2.90e+02 +/- 4.54e-01%\n", - " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t4.49e+02 +/- 4.10e-01%\n", - " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t6.87e+02 +/- 2.56e-01%\n", - " Group 8 [0.0 - 5.8e-08 MeV]:\t1.44e+03 +/- 2.82e-01%\n", - "\n", - "\tNuclide =\tU-238\n", - "\tCross Sections [barns]:\n", - " Group 1 [0.821 - 20.0 MeV]:\t1.06e+00 +/- 2.30e-01%\n", - " Group 2 [0.00553 - 0.821 MeV]:\t1.21e-03 +/- 2.25e-01%\n", - " Group 3 [4e-06 - 0.00553 MeV]:\t5.82e-04 +/- 3.09e+00%\n", - " Group 4 [6.25e-07 - 4e-06 MeV]:\t6.54e-06 +/- 3.27e-01%\n", - " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t1.07e-05 +/- 4.39e-01%\n", - " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t1.55e-05 +/- 4.12e-01%\n", - " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t2.30e-05 +/- 2.57e-01%\n", - " Group 8 [0.0 - 5.8e-08 MeV]:\t4.24e-05 +/- 2.81e-01%\n", - "\n", - "\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "nufission = xs_library[fuel_cell.id]['nu-fission']\n", "nufission.print_xs(xs_type='micro', nuclides=['U-235', 'U-238'])" @@ -757,34 +554,11 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multi-Group XS\n", - "\tReaction Type =\tnu-fission\n", - "\tDomain Type =\tcell\n", - "\tDomain ID =\t10000\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [0.821 - 20.0 MeV]:\t2.52e-02 +/- 2.19e-01%\n", - " Group 2 [0.00553 - 0.821 MeV]:\t1.51e-03 +/- 1.22e-01%\n", - " Group 3 [4e-06 - 0.00553 MeV]:\t2.06e-02 +/- 2.02e-01%\n", - " Group 4 [6.25e-07 - 4e-06 MeV]:\t3.31e-02 +/- 3.56e-01%\n", - " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t1.09e-01 +/- 4.54e-01%\n", - " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t1.69e-01 +/- 4.10e-01%\n", - " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t2.58e-01 +/- 2.56e-01%\n", - " Group 8 [0.0 - 5.8e-08 MeV]:\t5.40e-01 +/- 2.82e-01%\n", - "\n", - "\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "nufission = xs_library[fuel_cell.id]['nu-fission']\n", "nufission.print_xs(xs_type='macro', nuclides='sum')" @@ -799,152 +573,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py:872: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", - " return c.reshape(shape_out)\n", - "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py:872: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", - " return c.reshape(shape_out)\n", - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.1-py2.7.egg/openmc/mgxs/mgxs.py:1303: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" - ] - }, - { - "data": { - "text/html": [ - "
\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", - " \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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
cellgroup ingroup outnuclidemeanstd. dev.
1261000211H-10.2340220.003645
1271000211O-161.5603050.006280
1241000212H-11.5880250.002815
1251000212O-160.2851470.001392
1221000213H-10.0107760.000186
1231000213O-160.0000000.000000
1201000214H-10.0000230.000010
1211000214O-160.0000000.000000
1181000215H-10.0000000.000000
1191000215O-160.0000000.000000
\n", - "
" - ], - "text/plain": [ - " cell group in group out nuclide mean std. dev.\n", - "126 10002 1 1 H-1 0.234022 0.003645\n", - "127 10002 1 1 O-16 1.560305 0.006280\n", - "124 10002 1 2 H-1 1.588025 0.002815\n", - "125 10002 1 2 O-16 0.285147 0.001392\n", - "122 10002 1 3 H-1 0.010776 0.000186\n", - "123 10002 1 3 O-16 0.000000 0.000000\n", - "120 10002 1 4 H-1 0.000023 0.000010\n", - "121 10002 1 4 O-16 0.000000 0.000000\n", - "118 10002 1 5 H-1 0.000000 0.000000\n", - "119 10002 1 5 O-16 0.000000 0.000000" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n", "df = nuscatter.get_pandas_dataframe(xs_type='micro')\n", @@ -960,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "collapsed": true }, @@ -982,143 +615,22 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multi-Group XS\n", - "\tReaction Type =\ttransport\n", - "\tDomain Type =\tcell\n", - "\tDomain ID =\t10000\n", - "\tNuclide =\tU-235\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t7.81e-03 +/- 4.75e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t1.82e-01 +/- 1.89e-01%\n", - "\n", - "\tNuclide =\tU-238\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t2.17e-01 +/- 1.31e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t2.53e-01 +/- 2.08e-01%\n", - "\n", - "\tNuclide =\tO-16\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t1.45e-01 +/- 1.50e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t1.74e-01 +/- 2.66e-01%\n", - "\n", - "\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "condensed_xs.print_xs()" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py:872: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", - " return c.reshape(shape_out)\n", - "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py:872: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", - " return c.reshape(shape_out)\n" - ] - }, - { - "data": { - "text/html": [ - "
\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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
cellgroup innuclidemeanstd. dev.
3100001U-23520.8281270.098842
4100001U-2389.5822950.012550
5100001O-163.1573580.004725
0100002U-235485.2176490.916465
1100002U-23811.1760810.023196
2100002O-163.7881670.010090
\n", - "
" - ], - "text/plain": [ - " cell group in nuclide mean std. dev.\n", - "3 10000 1 U-235 20.828127 0.098842\n", - "4 10000 1 U-238 9.582295 0.012550\n", - "5 10000 1 O-16 3.157358 0.004725\n", - "0 10000 2 U-235 485.217649 0.916465\n", - "1 10000 2 U-238 11.176081 0.023196\n", - "2 10000 2 O-16 3.788167 0.010090" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = condensed_xs.get_pandas_dataframe(xs_type='micro')\n", "df" @@ -1140,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "collapsed": false }, @@ -1159,7 +671,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "collapsed": false }, @@ -1204,182 +716,11 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ NORMAL ] Importing ray tracing data from file...\n", - "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.574633\tres = 5.948E-317\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.679931\tres = 4.254E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.660910\tres = 1.832E-01\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.658975\tres = 2.797E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.642976\tres = 2.928E-03\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.625710\tres = 2.428E-02\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.606520\tres = 2.685E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.587277\tres = 3.067E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.568777\tres = 3.173E-02\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.551415\tres = 3.150E-02\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.535708\tres = 3.052E-02\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.521916\tres = 2.849E-02\n", - "[ NORMAL ] Iteration 12:\tk_eff = 0.510221\tres = 2.575E-02\n", - "[ NORMAL ] Iteration 13:\tk_eff = 0.500691\tres = 2.241E-02\n", - "[ NORMAL ] Iteration 14:\tk_eff = 0.493392\tres = 1.868E-02\n", - "[ NORMAL ] Iteration 15:\tk_eff = 0.488317\tres = 1.458E-02\n", - "[ NORMAL ] Iteration 16:\tk_eff = 0.485438\tres = 1.028E-02\n", - "[ NORMAL ] Iteration 17:\tk_eff = 0.484705\tres = 5.896E-03\n", - "[ NORMAL ] Iteration 18:\tk_eff = 0.486045\tres = 1.510E-03\n", - "[ NORMAL ] Iteration 19:\tk_eff = 0.489362\tres = 2.766E-03\n", - "[ NORMAL ] Iteration 20:\tk_eff = 0.494546\tres = 6.824E-03\n", - "[ NORMAL ] Iteration 21:\tk_eff = 0.501481\tres = 1.059E-02\n", - "[ NORMAL ] Iteration 22:\tk_eff = 0.510041\tres = 1.402E-02\n", - "[ NORMAL ] Iteration 23:\tk_eff = 0.520094\tres = 1.707E-02\n", - "[ NORMAL ] Iteration 24:\tk_eff = 0.531507\tres = 1.971E-02\n", - "[ NORMAL ] Iteration 25:\tk_eff = 0.544144\tres = 2.194E-02\n", - "[ NORMAL ] Iteration 26:\tk_eff = 0.557872\tres = 2.378E-02\n", - "[ NORMAL ] Iteration 27:\tk_eff = 0.572557\tres = 2.523E-02\n", - "[ NORMAL ] Iteration 28:\tk_eff = 0.588072\tres = 2.632E-02\n", - "[ NORMAL ] Iteration 29:\tk_eff = 0.604293\tres = 2.710E-02\n", - "[ NORMAL ] Iteration 30:\tk_eff = 0.621101\tres = 2.758E-02\n", - "[ NORMAL ] Iteration 31:\tk_eff = 0.638382\tres = 2.781E-02\n", - "[ NORMAL ] Iteration 32:\tk_eff = 0.656032\tres = 2.782E-02\n", - "[ NORMAL ] Iteration 33:\tk_eff = 0.673950\tres = 2.765E-02\n", - "[ NORMAL ] Iteration 34:\tk_eff = 0.692043\tres = 2.731E-02\n", - "[ NORMAL ] Iteration 35:\tk_eff = 0.710227\tres = 2.685E-02\n", - "[ NORMAL ] Iteration 36:\tk_eff = 0.728423\tres = 2.628E-02\n", - "[ NORMAL ] Iteration 37:\tk_eff = 0.746558\tres = 2.562E-02\n", - "[ NORMAL ] Iteration 38:\tk_eff = 0.764569\tres = 2.490E-02\n", - "[ NORMAL ] Iteration 39:\tk_eff = 0.782396\tres = 2.412E-02\n", - "[ NORMAL ] Iteration 40:\tk_eff = 0.799989\tres = 2.332E-02\n", - "[ NORMAL ] Iteration 41:\tk_eff = 0.817301\tres = 2.249E-02\n", - "[ NORMAL ] Iteration 42:\tk_eff = 0.834292\tres = 2.164E-02\n", - "[ NORMAL ] Iteration 43:\tk_eff = 0.850927\tres = 2.079E-02\n", - "[ NORMAL ] Iteration 44:\tk_eff = 0.867177\tres = 1.994E-02\n", - "[ NORMAL ] Iteration 45:\tk_eff = 0.883017\tres = 1.910E-02\n", - "[ NORMAL ] Iteration 46:\tk_eff = 0.898427\tres = 1.827E-02\n", - "[ NORMAL ] Iteration 47:\tk_eff = 0.913389\tres = 1.745E-02\n", - "[ NORMAL ] Iteration 48:\tk_eff = 0.927891\tres = 1.665E-02\n", - "[ NORMAL ] Iteration 49:\tk_eff = 0.941925\tres = 1.588E-02\n", - "[ NORMAL ] Iteration 50:\tk_eff = 0.955483\tres = 1.512E-02\n", - "[ NORMAL ] Iteration 51:\tk_eff = 0.968562\tres = 1.439E-02\n", - "[ NORMAL ] Iteration 52:\tk_eff = 0.981161\tres = 1.369E-02\n", - "[ NORMAL ] Iteration 53:\tk_eff = 0.993282\tres = 1.301E-02\n", - "[ NORMAL ] Iteration 54:\tk_eff = 1.004928\tres = 1.235E-02\n", - "[ NORMAL ] Iteration 55:\tk_eff = 1.016104\tres = 1.172E-02\n", - "[ NORMAL ] Iteration 56:\tk_eff = 1.026816\tres = 1.112E-02\n", - "[ NORMAL ] Iteration 57:\tk_eff = 1.037073\tres = 1.054E-02\n", - "[ NORMAL ] Iteration 58:\tk_eff = 1.046883\tres = 9.989E-03\n", - "[ NORMAL ] Iteration 59:\tk_eff = 1.056257\tres = 9.460E-03\n", - "[ NORMAL ] Iteration 60:\tk_eff = 1.065205\tres = 8.954E-03\n", - "[ NORMAL ] Iteration 61:\tk_eff = 1.073739\tres = 8.472E-03\n", - "[ NORMAL ] Iteration 62:\tk_eff = 1.081871\tres = 8.012E-03\n", - "[ NORMAL ] Iteration 63:\tk_eff = 1.089613\tres = 7.573E-03\n", - "[ NORMAL ] Iteration 64:\tk_eff = 1.096979\tres = 7.156E-03\n", - "[ NORMAL ] Iteration 65:\tk_eff = 1.103980\tres = 6.760E-03\n", - "[ NORMAL ] Iteration 66:\tk_eff = 1.110631\tres = 6.382E-03\n", - "[ NORMAL ] Iteration 67:\tk_eff = 1.116943\tres = 6.024E-03\n", - "[ NORMAL ] Iteration 68:\tk_eff = 1.122931\tres = 5.684E-03\n", - "[ NORMAL ] Iteration 69:\tk_eff = 1.128607\tres = 5.361E-03\n", - "[ NORMAL ] Iteration 70:\tk_eff = 1.133984\tres = 5.055E-03\n", - "[ NORMAL ] Iteration 71:\tk_eff = 1.139075\tres = 4.764E-03\n", - "[ NORMAL ] Iteration 72:\tk_eff = 1.143892\tres = 4.489E-03\n", - "[ NORMAL ] Iteration 73:\tk_eff = 1.148447\tres = 4.229E-03\n", - "[ NORMAL ] Iteration 74:\tk_eff = 1.152752\tres = 3.982E-03\n", - "[ NORMAL ] Iteration 75:\tk_eff = 1.156819\tres = 3.749E-03\n", - "[ NORMAL ] Iteration 76:\tk_eff = 1.160659\tres = 3.528E-03\n", - "[ NORMAL ] Iteration 77:\tk_eff = 1.164282\tres = 3.319E-03\n", - "[ NORMAL ] Iteration 78:\tk_eff = 1.167701\tres = 3.122E-03\n", - "[ NORMAL ] Iteration 79:\tk_eff = 1.170923\tres = 2.936E-03\n", - "[ NORMAL ] Iteration 80:\tk_eff = 1.173961\tres = 2.760E-03\n", - "[ NORMAL ] Iteration 81:\tk_eff = 1.176822\tres = 2.594E-03\n", - "[ NORMAL ] Iteration 82:\tk_eff = 1.179516\tres = 2.437E-03\n", - "[ NORMAL ] Iteration 83:\tk_eff = 1.182052\tres = 2.289E-03\n", - "[ NORMAL ] Iteration 84:\tk_eff = 1.184438\tres = 2.150E-03\n", - "[ NORMAL ] Iteration 85:\tk_eff = 1.186682\tres = 2.019E-03\n", - "[ NORMAL ] Iteration 86:\tk_eff = 1.188792\tres = 1.895E-03\n", - "[ NORMAL ] Iteration 87:\tk_eff = 1.190775\tres = 1.778E-03\n", - "[ NORMAL ] Iteration 88:\tk_eff = 1.192639\tres = 1.668E-03\n", - "[ NORMAL ] Iteration 89:\tk_eff = 1.194389\tres = 1.565E-03\n", - "[ NORMAL ] Iteration 90:\tk_eff = 1.196032\tres = 1.468E-03\n", - "[ NORMAL ] Iteration 91:\tk_eff = 1.197575\tres = 1.376E-03\n", - "[ NORMAL ] Iteration 92:\tk_eff = 1.199023\tres = 1.290E-03\n", - "[ NORMAL ] Iteration 93:\tk_eff = 1.200381\tres = 1.209E-03\n", - "[ NORMAL ] Iteration 94:\tk_eff = 1.201654\tres = 1.133E-03\n", - "[ NORMAL ] Iteration 95:\tk_eff = 1.202849\tres = 1.061E-03\n", - "[ NORMAL ] Iteration 96:\tk_eff = 1.203968\tres = 9.939E-04\n", - "[ NORMAL ] Iteration 97:\tk_eff = 1.205017\tres = 9.307E-04\n", - "[ NORMAL ] Iteration 98:\tk_eff = 1.206000\tres = 8.714E-04\n", - "[ NORMAL ] Iteration 99:\tk_eff = 1.206921\tres = 8.157E-04\n", - "[ NORMAL ] Iteration 100:\tk_eff = 1.207783\tres = 7.634E-04\n", - "[ NORMAL ] Iteration 101:\tk_eff = 1.208590\tres = 7.144E-04\n", - "[ NORMAL ] Iteration 102:\tk_eff = 1.209346\tres = 6.684E-04\n", - "[ NORMAL ] Iteration 103:\tk_eff = 1.210053\tres = 6.252E-04\n", - "[ NORMAL ] Iteration 104:\tk_eff = 1.210715\tres = 5.848E-04\n", - "[ NORMAL ] Iteration 105:\tk_eff = 1.211334\tres = 5.468E-04\n", - "[ NORMAL ] Iteration 106:\tk_eff = 1.211913\tres = 5.113E-04\n", - "[ NORMAL ] Iteration 107:\tk_eff = 1.212454\tres = 4.779E-04\n", - "[ NORMAL ] Iteration 108:\tk_eff = 1.212960\tres = 4.467E-04\n", - "[ NORMAL ] Iteration 109:\tk_eff = 1.213434\tres = 4.175E-04\n", - "[ NORMAL ] Iteration 110:\tk_eff = 1.213876\tres = 3.901E-04\n", - "[ NORMAL ] Iteration 111:\tk_eff = 1.214289\tres = 3.644E-04\n", - "[ NORMAL ] Iteration 112:\tk_eff = 1.214675\tres = 3.404E-04\n", - "[ NORMAL ] Iteration 113:\tk_eff = 1.215036\tres = 3.180E-04\n", - "[ NORMAL ] Iteration 114:\tk_eff = 1.215373\tres = 2.969E-04\n", - "[ NORMAL ] Iteration 115:\tk_eff = 1.215687\tres = 2.773E-04\n", - "[ NORMAL ] Iteration 116:\tk_eff = 1.215981\tres = 2.589E-04\n", - "[ NORMAL ] Iteration 117:\tk_eff = 1.216255\tres = 2.416E-04\n", - "[ NORMAL ] Iteration 118:\tk_eff = 1.216511\tres = 2.256E-04\n", - "[ NORMAL ] Iteration 119:\tk_eff = 1.216750\tres = 2.105E-04\n", - "[ NORMAL ] Iteration 120:\tk_eff = 1.216973\tres = 1.964E-04\n", - "[ NORMAL ] Iteration 121:\tk_eff = 1.217181\tres = 1.833E-04\n", - "[ NORMAL ] Iteration 122:\tk_eff = 1.217376\tres = 1.710E-04\n", - "[ NORMAL ] Iteration 123:\tk_eff = 1.217557\tres = 1.595E-04\n", - "[ NORMAL ] Iteration 124:\tk_eff = 1.217726\tres = 1.488E-04\n", - "[ NORMAL ] Iteration 125:\tk_eff = 1.217883\tres = 1.388E-04\n", - "[ NORMAL ] Iteration 126:\tk_eff = 1.218030\tres = 1.294E-04\n", - "[ NORMAL ] Iteration 127:\tk_eff = 1.218167\tres = 1.207E-04\n", - "[ NORMAL ] Iteration 128:\tk_eff = 1.218295\tres = 1.125E-04\n", - "[ NORMAL ] Iteration 129:\tk_eff = 1.218414\tres = 1.049E-04\n", - "[ NORMAL ] Iteration 130:\tk_eff = 1.218525\tres = 9.777E-05\n", - "[ NORMAL ] Iteration 131:\tk_eff = 1.218629\tres = 9.113E-05\n", - "[ NORMAL ] Iteration 132:\tk_eff = 1.218725\tres = 8.494E-05\n", - "[ NORMAL ] Iteration 133:\tk_eff = 1.218815\tres = 7.916E-05\n", - "[ NORMAL ] Iteration 134:\tk_eff = 1.218899\tres = 7.376E-05\n", - "[ NORMAL ] Iteration 135:\tk_eff = 1.218977\tres = 6.873E-05\n", - "[ NORMAL ] Iteration 136:\tk_eff = 1.219050\tres = 6.404E-05\n", - "[ NORMAL ] Iteration 137:\tk_eff = 1.219117\tres = 5.966E-05\n", - "[ NORMAL ] Iteration 138:\tk_eff = 1.219180\tres = 5.557E-05\n", - "[ NORMAL ] Iteration 139:\tk_eff = 1.219239\tres = 5.177E-05\n", - "[ NORMAL ] Iteration 140:\tk_eff = 1.219294\tres = 4.822E-05\n", - "[ NORMAL ] Iteration 141:\tk_eff = 1.219345\tres = 4.491E-05\n", - "[ NORMAL ] Iteration 142:\tk_eff = 1.219392\tres = 4.182E-05\n", - "[ NORMAL ] Iteration 143:\tk_eff = 1.219437\tres = 3.894E-05\n", - "[ NORMAL ] Iteration 144:\tk_eff = 1.219478\tres = 3.626E-05\n", - "[ NORMAL ] Iteration 145:\tk_eff = 1.219516\tres = 3.376E-05\n", - "[ NORMAL ] Iteration 146:\tk_eff = 1.219552\tres = 3.144E-05\n", - "[ NORMAL ] Iteration 147:\tk_eff = 1.219585\tres = 2.927E-05\n", - "[ NORMAL ] Iteration 148:\tk_eff = 1.219616\tres = 2.724E-05\n", - "[ NORMAL ] Iteration 149:\tk_eff = 1.219645\tres = 2.536E-05\n", - "[ NORMAL ] Iteration 150:\tk_eff = 1.219672\tres = 2.361E-05\n", - "[ NORMAL ] Iteration 151:\tk_eff = 1.219696\tres = 2.197E-05\n", - "[ NORMAL ] Iteration 152:\tk_eff = 1.219720\tres = 2.045E-05\n", - "[ NORMAL ] Iteration 153:\tk_eff = 1.219741\tres = 1.903E-05\n", - "[ NORMAL ] Iteration 154:\tk_eff = 1.219761\tres = 1.771E-05\n", - "[ NORMAL ] Iteration 155:\tk_eff = 1.219780\tres = 1.648E-05\n", - "[ NORMAL ] Iteration 156:\tk_eff = 1.219797\tres = 1.534E-05\n", - "[ NORMAL ] Iteration 157:\tk_eff = 1.219814\tres = 1.427E-05\n", - "[ NORMAL ] Iteration 158:\tk_eff = 1.219829\tres = 1.328E-05\n", - "[ NORMAL ] Iteration 159:\tk_eff = 1.219843\tres = 1.235E-05\n", - "[ NORMAL ] Iteration 160:\tk_eff = 1.219856\tres = 1.149E-05\n", - "[ NORMAL ] Iteration 161:\tk_eff = 1.219868\tres = 1.069E-05\n" - ] - } - ], + "outputs": [], "source": [ "# Generate tracks for OpenMOC\n", "track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=128, spacing=0.1)\n", @@ -1399,21 +740,11 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "openmc keff = 1.223729\n", - "openmoc keff = 1.219868\n", - "bias [pcm]: -386.1\n" - ] - } - ], + "outputs": [], "source": [ "# Print report of keff and bias with OpenMC\n", "openmoc_keff = solver.getKeff()\n", @@ -1434,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "collapsed": false }, @@ -1474,251 +805,11 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ NORMAL ] Importing ray tracing data from file...\n", - "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.495594\tres = 5.948E-317\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.557312\tres = 5.044E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.518115\tres = 1.245E-01\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.509016\tres = 7.033E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.496279\tres = 1.756E-02\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.488357\tres = 2.502E-02\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.482659\tres = 1.596E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.479523\tres = 1.167E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.478568\tres = 6.497E-03\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.479590\tres = 1.991E-03\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.482388\tres = 2.136E-03\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.486774\tres = 5.834E-03\n", - "[ NORMAL ] Iteration 12:\tk_eff = 0.492575\tres = 9.091E-03\n", - "[ NORMAL ] Iteration 13:\tk_eff = 0.499632\tres = 1.192E-02\n", - "[ NORMAL ] Iteration 14:\tk_eff = 0.507799\tres = 1.433E-02\n", - "[ NORMAL ] Iteration 15:\tk_eff = 0.516943\tres = 1.635E-02\n", - "[ NORMAL ] Iteration 16:\tk_eff = 0.526942\tres = 1.801E-02\n", - "[ NORMAL ] Iteration 17:\tk_eff = 0.537681\tres = 1.934E-02\n", - "[ NORMAL ] Iteration 18:\tk_eff = 0.549060\tres = 2.038E-02\n", - "[ NORMAL ] Iteration 19:\tk_eff = 0.560984\tres = 2.116E-02\n", - "[ NORMAL ] Iteration 20:\tk_eff = 0.573368\tres = 2.172E-02\n", - "[ NORMAL ] Iteration 21:\tk_eff = 0.586133\tres = 2.207E-02\n", - "[ NORMAL ] Iteration 22:\tk_eff = 0.599207\tres = 2.226E-02\n", - "[ NORMAL ] Iteration 23:\tk_eff = 0.612528\tres = 2.231E-02\n", - "[ NORMAL ] Iteration 24:\tk_eff = 0.626035\tres = 2.223E-02\n", - "[ NORMAL ] Iteration 25:\tk_eff = 0.639676\tres = 2.205E-02\n", - "[ NORMAL ] Iteration 26:\tk_eff = 0.653402\tres = 2.179E-02\n", - "[ NORMAL ] Iteration 27:\tk_eff = 0.667170\tres = 2.146E-02\n", - "[ NORMAL ] Iteration 28:\tk_eff = 0.680942\tres = 2.107E-02\n", - "[ NORMAL ] Iteration 29:\tk_eff = 0.694681\tres = 2.064E-02\n", - "[ NORMAL ] Iteration 30:\tk_eff = 0.708356\tres = 2.018E-02\n", - "[ NORMAL ] Iteration 31:\tk_eff = 0.721940\tres = 1.969E-02\n", - "[ NORMAL ] Iteration 32:\tk_eff = 0.735406\tres = 1.918E-02\n", - "[ NORMAL ] Iteration 33:\tk_eff = 0.748734\tres = 1.865E-02\n", - "[ NORMAL ] Iteration 34:\tk_eff = 0.761904\tres = 1.812E-02\n", - "[ NORMAL ] Iteration 35:\tk_eff = 0.774897\tres = 1.759E-02\n", - "[ NORMAL ] Iteration 36:\tk_eff = 0.787700\tres = 1.705E-02\n", - "[ NORMAL ] Iteration 37:\tk_eff = 0.800299\tres = 1.652E-02\n", - "[ NORMAL ] Iteration 38:\tk_eff = 0.812684\tres = 1.600E-02\n", - "[ NORMAL ] Iteration 39:\tk_eff = 0.824844\tres = 1.547E-02\n", - "[ NORMAL ] Iteration 40:\tk_eff = 0.836772\tres = 1.496E-02\n", - "[ NORMAL ] Iteration 41:\tk_eff = 0.848462\tres = 1.446E-02\n", - "[ NORMAL ] Iteration 42:\tk_eff = 0.859908\tres = 1.397E-02\n", - "[ NORMAL ] Iteration 43:\tk_eff = 0.871105\tres = 1.349E-02\n", - "[ NORMAL ] Iteration 44:\tk_eff = 0.882052\tres = 1.302E-02\n", - "[ NORMAL ] Iteration 45:\tk_eff = 0.892745\tres = 1.257E-02\n", - "[ NORMAL ] Iteration 46:\tk_eff = 0.903184\tres = 1.212E-02\n", - "[ NORMAL ] Iteration 47:\tk_eff = 0.913367\tres = 1.169E-02\n", - "[ NORMAL ] Iteration 48:\tk_eff = 0.923297\tres = 1.128E-02\n", - "[ NORMAL ] Iteration 49:\tk_eff = 0.932972\tres = 1.087E-02\n", - "[ NORMAL ] Iteration 50:\tk_eff = 0.942394\tres = 1.048E-02\n", - "[ NORMAL ] Iteration 51:\tk_eff = 0.951566\tres = 1.010E-02\n", - "[ NORMAL ] Iteration 52:\tk_eff = 0.960490\tres = 9.733E-03\n", - "[ NORMAL ] Iteration 53:\tk_eff = 0.969168\tres = 9.378E-03\n", - "[ NORMAL ] Iteration 54:\tk_eff = 0.977604\tres = 9.035E-03\n", - "[ NORMAL ] Iteration 55:\tk_eff = 0.985800\tres = 8.704E-03\n", - "[ NORMAL ] Iteration 56:\tk_eff = 0.993761\tres = 8.384E-03\n", - "[ NORMAL ] Iteration 57:\tk_eff = 1.001491\tres = 8.076E-03\n", - "[ NORMAL ] Iteration 58:\tk_eff = 1.008992\tres = 7.778E-03\n", - "[ NORMAL ] Iteration 59:\tk_eff = 1.016271\tres = 7.490E-03\n", - "[ NORMAL ] Iteration 60:\tk_eff = 1.023330\tres = 7.213E-03\n", - "[ NORMAL ] Iteration 61:\tk_eff = 1.030174\tres = 6.946E-03\n", - "[ NORMAL ] Iteration 62:\tk_eff = 1.036809\tres = 6.688E-03\n", - "[ NORMAL ] Iteration 63:\tk_eff = 1.043238\tres = 6.440E-03\n", - "[ NORMAL ] Iteration 64:\tk_eff = 1.049466\tres = 6.201E-03\n", - "[ NORMAL ] Iteration 65:\tk_eff = 1.055498\tres = 5.970E-03\n", - "[ NORMAL ] Iteration 66:\tk_eff = 1.061339\tres = 5.748E-03\n", - "[ NORMAL ] Iteration 67:\tk_eff = 1.066993\tres = 5.534E-03\n", - "[ NORMAL ] Iteration 68:\tk_eff = 1.072465\tres = 5.327E-03\n", - "[ NORMAL ] Iteration 69:\tk_eff = 1.077760\tres = 5.129E-03\n", - "[ NORMAL ] Iteration 70:\tk_eff = 1.082882\tres = 4.937E-03\n", - "[ NORMAL ] Iteration 71:\tk_eff = 1.087837\tres = 4.753E-03\n", - "[ NORMAL ] Iteration 72:\tk_eff = 1.092628\tres = 4.575E-03\n", - "[ NORMAL ] Iteration 73:\tk_eff = 1.097260\tres = 4.404E-03\n", - "[ NORMAL ] Iteration 74:\tk_eff = 1.101737\tres = 4.239E-03\n", - "[ NORMAL ] Iteration 75:\tk_eff = 1.106065\tres = 4.081E-03\n", - "[ NORMAL ] Iteration 76:\tk_eff = 1.110247\tres = 3.928E-03\n", - "[ NORMAL ] Iteration 77:\tk_eff = 1.114288\tres = 3.781E-03\n", - "[ NORMAL ] Iteration 78:\tk_eff = 1.118191\tres = 3.639E-03\n", - "[ NORMAL ] Iteration 79:\tk_eff = 1.121961\tres = 3.503E-03\n", - "[ NORMAL ] Iteration 80:\tk_eff = 1.125603\tres = 3.372E-03\n", - "[ NORMAL ] Iteration 81:\tk_eff = 1.129119\tres = 3.245E-03\n", - "[ NORMAL ] Iteration 82:\tk_eff = 1.132513\tres = 3.124E-03\n", - "[ NORMAL ] Iteration 83:\tk_eff = 1.135790\tres = 3.007E-03\n", - "[ NORMAL ] Iteration 84:\tk_eff = 1.138954\tres = 2.894E-03\n", - "[ NORMAL ] Iteration 85:\tk_eff = 1.142007\tres = 2.785E-03\n", - "[ NORMAL ] Iteration 86:\tk_eff = 1.144953\tres = 2.681E-03\n", - "[ NORMAL ] Iteration 87:\tk_eff = 1.147796\tres = 2.580E-03\n", - "[ NORMAL ] Iteration 88:\tk_eff = 1.150539\tres = 2.483E-03\n", - "[ NORMAL ] Iteration 89:\tk_eff = 1.153185\tres = 2.390E-03\n", - "[ NORMAL ] Iteration 90:\tk_eff = 1.155738\tres = 2.300E-03\n", - "[ NORMAL ] Iteration 91:\tk_eff = 1.158200\tres = 2.214E-03\n", - "[ NORMAL ] Iteration 92:\tk_eff = 1.160575\tres = 2.130E-03\n", - "[ NORMAL ] Iteration 93:\tk_eff = 1.162865\tres = 2.050E-03\n", - "[ NORMAL ] Iteration 94:\tk_eff = 1.165073\tres = 1.973E-03\n", - "[ NORMAL ] Iteration 95:\tk_eff = 1.167202\tres = 1.899E-03\n", - "[ NORMAL ] Iteration 96:\tk_eff = 1.169255\tres = 1.828E-03\n", - "[ NORMAL ] Iteration 97:\tk_eff = 1.171234\tres = 1.759E-03\n", - "[ NORMAL ] Iteration 98:\tk_eff = 1.173142\tres = 1.693E-03\n", - "[ NORMAL ] Iteration 99:\tk_eff = 1.174980\tres = 1.629E-03\n", - "[ NORMAL ] Iteration 100:\tk_eff = 1.176753\tres = 1.567E-03\n", - "[ NORMAL ] Iteration 101:\tk_eff = 1.178461\tres = 1.508E-03\n", - "[ NORMAL ] Iteration 102:\tk_eff = 1.180107\tres = 1.452E-03\n", - "[ NORMAL ] Iteration 103:\tk_eff = 1.181694\tres = 1.397E-03\n", - "[ NORMAL ] Iteration 104:\tk_eff = 1.183222\tres = 1.344E-03\n", - "[ NORMAL ] Iteration 105:\tk_eff = 1.184695\tres = 1.294E-03\n", - "[ NORMAL ] Iteration 106:\tk_eff = 1.186115\tres = 1.245E-03\n", - "[ NORMAL ] Iteration 107:\tk_eff = 1.187482\tres = 1.198E-03\n", - "[ NORMAL ] Iteration 108:\tk_eff = 1.188799\tres = 1.153E-03\n", - "[ NORMAL ] Iteration 109:\tk_eff = 1.190068\tres = 1.109E-03\n", - "[ NORMAL ] Iteration 110:\tk_eff = 1.191290\tres = 1.067E-03\n", - "[ NORMAL ] Iteration 111:\tk_eff = 1.192468\tres = 1.027E-03\n", - "[ NORMAL ] Iteration 112:\tk_eff = 1.193602\tres = 9.883E-04\n", - "[ NORMAL ] Iteration 113:\tk_eff = 1.194694\tres = 9.510E-04\n", - "[ NORMAL ] Iteration 114:\tk_eff = 1.195746\tres = 9.151E-04\n", - "[ NORMAL ] Iteration 115:\tk_eff = 1.196759\tres = 8.805E-04\n", - "[ NORMAL ] Iteration 116:\tk_eff = 1.197735\tres = 8.473E-04\n", - "[ NORMAL ] Iteration 117:\tk_eff = 1.198674\tres = 8.152E-04\n", - "[ NORMAL ] Iteration 118:\tk_eff = 1.199579\tres = 7.844E-04\n", - "[ NORMAL ] Iteration 119:\tk_eff = 1.200450\tres = 7.548E-04\n", - "[ NORMAL ] Iteration 120:\tk_eff = 1.201289\tres = 7.262E-04\n", - "[ NORMAL ] Iteration 121:\tk_eff = 1.202097\tres = 6.988E-04\n", - "[ NORMAL ] Iteration 122:\tk_eff = 1.202874\tres = 6.723E-04\n", - "[ NORMAL ] Iteration 123:\tk_eff = 1.203623\tres = 6.469E-04\n", - "[ NORMAL ] Iteration 124:\tk_eff = 1.204344\tres = 6.224E-04\n", - "[ NORMAL ] Iteration 125:\tk_eff = 1.205038\tres = 5.989E-04\n", - "[ NORMAL ] Iteration 126:\tk_eff = 1.205706\tres = 5.762E-04\n", - "[ NORMAL ] Iteration 127:\tk_eff = 1.206349\tres = 5.544E-04\n", - "[ NORMAL ] Iteration 128:\tk_eff = 1.206968\tres = 5.334E-04\n", - "[ NORMAL ] Iteration 129:\tk_eff = 1.207564\tres = 5.132E-04\n", - "[ NORMAL ] Iteration 130:\tk_eff = 1.208138\tres = 4.938E-04\n", - "[ NORMAL ] Iteration 131:\tk_eff = 1.208690\tres = 4.751E-04\n", - "[ NORMAL ] Iteration 132:\tk_eff = 1.209221\tres = 4.570E-04\n", - "[ NORMAL ] Iteration 133:\tk_eff = 1.209733\tres = 4.397E-04\n", - "[ NORMAL ] Iteration 134:\tk_eff = 1.210225\tres = 4.231E-04\n", - "[ NORMAL ] Iteration 135:\tk_eff = 1.210699\tres = 4.070E-04\n", - "[ NORMAL ] Iteration 136:\tk_eff = 1.211155\tres = 3.916E-04\n", - "[ NORMAL ] Iteration 137:\tk_eff = 1.211594\tres = 3.767E-04\n", - "[ NORMAL ] Iteration 138:\tk_eff = 1.212017\tres = 3.624E-04\n", - "[ NORMAL ] Iteration 139:\tk_eff = 1.212423\tres = 3.487E-04\n", - "[ NORMAL ] Iteration 140:\tk_eff = 1.212815\tres = 3.355E-04\n", - "[ NORMAL ] Iteration 141:\tk_eff = 1.213191\tres = 3.227E-04\n", - "[ NORMAL ] Iteration 142:\tk_eff = 1.213554\tres = 3.105E-04\n", - "[ NORMAL ] Iteration 143:\tk_eff = 1.213902\tres = 2.987E-04\n", - "[ NORMAL ] Iteration 144:\tk_eff = 1.214238\tres = 2.874E-04\n", - "[ NORMAL ] Iteration 145:\tk_eff = 1.214561\tres = 2.764E-04\n", - "[ NORMAL ] Iteration 146:\tk_eff = 1.214872\tres = 2.659E-04\n", - "[ NORMAL ] Iteration 147:\tk_eff = 1.215171\tres = 2.558E-04\n", - "[ NORMAL ] Iteration 148:\tk_eff = 1.215458\tres = 2.461E-04\n", - "[ NORMAL ] Iteration 149:\tk_eff = 1.215735\tres = 2.368E-04\n", - "[ NORMAL ] Iteration 150:\tk_eff = 1.216002\tres = 2.278E-04\n", - "[ NORMAL ] Iteration 151:\tk_eff = 1.216258\tres = 2.191E-04\n", - "[ NORMAL ] Iteration 152:\tk_eff = 1.216504\tres = 2.108E-04\n", - "[ NORMAL ] Iteration 153:\tk_eff = 1.216742\tres = 2.028E-04\n", - "[ NORMAL ] Iteration 154:\tk_eff = 1.216970\tres = 1.951E-04\n", - "[ NORMAL ] Iteration 155:\tk_eff = 1.217190\tres = 1.876E-04\n", - "[ NORMAL ] Iteration 156:\tk_eff = 1.217401\tres = 1.805E-04\n", - "[ NORMAL ] Iteration 157:\tk_eff = 1.217604\tres = 1.736E-04\n", - "[ NORMAL ] Iteration 158:\tk_eff = 1.217800\tres = 1.670E-04\n", - "[ NORMAL ] Iteration 159:\tk_eff = 1.217988\tres = 1.607E-04\n", - "[ NORMAL ] Iteration 160:\tk_eff = 1.218169\tres = 1.546E-04\n", - "[ NORMAL ] Iteration 161:\tk_eff = 1.218344\tres = 1.487E-04\n", - "[ NORMAL ] Iteration 162:\tk_eff = 1.218511\tres = 1.430E-04\n", - "[ NORMAL ] Iteration 163:\tk_eff = 1.218673\tres = 1.376E-04\n", - "[ NORMAL ] Iteration 164:\tk_eff = 1.218828\tres = 1.324E-04\n", - "[ NORMAL ] Iteration 165:\tk_eff = 1.218977\tres = 1.273E-04\n", - "[ NORMAL ] Iteration 166:\tk_eff = 1.219121\tres = 1.225E-04\n", - "[ NORMAL ] Iteration 167:\tk_eff = 1.219259\tres = 1.178E-04\n", - "[ NORMAL ] Iteration 168:\tk_eff = 1.219392\tres = 1.133E-04\n", - "[ NORMAL ] Iteration 169:\tk_eff = 1.219520\tres = 1.090E-04\n", - "[ NORMAL ] Iteration 170:\tk_eff = 1.219643\tres = 1.049E-04\n", - "[ NORMAL ] Iteration 171:\tk_eff = 1.219761\tres = 1.009E-04\n", - "[ NORMAL ] Iteration 172:\tk_eff = 1.219875\tres = 9.702E-05\n", - "[ NORMAL ] Iteration 173:\tk_eff = 1.219984\tres = 9.332E-05\n", - "[ NORMAL ] Iteration 174:\tk_eff = 1.220090\tres = 8.976E-05\n", - "[ NORMAL ] Iteration 175:\tk_eff = 1.220191\tres = 8.634E-05\n", - "[ NORMAL ] Iteration 176:\tk_eff = 1.220288\tres = 8.305E-05\n", - "[ NORMAL ] Iteration 177:\tk_eff = 1.220382\tres = 7.989E-05\n", - "[ NORMAL ] Iteration 178:\tk_eff = 1.220472\tres = 7.684E-05\n", - "[ NORMAL ] Iteration 179:\tk_eff = 1.220559\tres = 7.392E-05\n", - "[ NORMAL ] Iteration 180:\tk_eff = 1.220643\tres = 7.110E-05\n", - "[ NORMAL ] Iteration 181:\tk_eff = 1.220723\tres = 6.839E-05\n", - "[ NORMAL ] Iteration 182:\tk_eff = 1.220800\tres = 6.578E-05\n", - "[ NORMAL ] Iteration 183:\tk_eff = 1.220874\tres = 6.327E-05\n", - "[ NORMAL ] Iteration 184:\tk_eff = 1.220946\tres = 6.086E-05\n", - "[ NORMAL ] Iteration 185:\tk_eff = 1.221015\tres = 5.854E-05\n", - "[ NORMAL ] Iteration 186:\tk_eff = 1.221081\tres = 5.631E-05\n", - "[ NORMAL ] Iteration 187:\tk_eff = 1.221144\tres = 5.416E-05\n", - "[ NORMAL ] Iteration 188:\tk_eff = 1.221206\tres = 5.209E-05\n", - "[ NORMAL ] Iteration 189:\tk_eff = 1.221264\tres = 5.011E-05\n", - "[ NORMAL ] Iteration 190:\tk_eff = 1.221321\tres = 4.820E-05\n", - "[ NORMAL ] Iteration 191:\tk_eff = 1.221375\tres = 4.636E-05\n", - "[ NORMAL ] Iteration 192:\tk_eff = 1.221428\tres = 4.459E-05\n", - "[ NORMAL ] Iteration 193:\tk_eff = 1.221478\tres = 4.289E-05\n", - "[ NORMAL ] Iteration 194:\tk_eff = 1.221527\tres = 4.125E-05\n", - "[ NORMAL ] Iteration 195:\tk_eff = 1.221573\tres = 3.968E-05\n", - "[ NORMAL ] Iteration 196:\tk_eff = 1.221618\tres = 3.816E-05\n", - "[ NORMAL ] Iteration 197:\tk_eff = 1.221661\tres = 3.671E-05\n", - "[ NORMAL ] Iteration 198:\tk_eff = 1.221703\tres = 3.531E-05\n", - "[ NORMAL ] Iteration 199:\tk_eff = 1.221743\tres = 3.396E-05\n", - "[ NORMAL ] Iteration 200:\tk_eff = 1.221781\tres = 3.266E-05\n", - "[ NORMAL ] Iteration 201:\tk_eff = 1.221818\tres = 3.142E-05\n", - "[ NORMAL ] Iteration 202:\tk_eff = 1.221853\tres = 3.022E-05\n", - "[ NORMAL ] Iteration 203:\tk_eff = 1.221888\tres = 2.906E-05\n", - "[ NORMAL ] Iteration 204:\tk_eff = 1.221920\tres = 2.795E-05\n", - "[ NORMAL ] Iteration 205:\tk_eff = 1.221952\tres = 2.689E-05\n", - "[ NORMAL ] Iteration 206:\tk_eff = 1.221982\tres = 2.586E-05\n", - "[ NORMAL ] Iteration 207:\tk_eff = 1.222012\tres = 2.487E-05\n", - "[ NORMAL ] Iteration 208:\tk_eff = 1.222040\tres = 2.392E-05\n", - "[ NORMAL ] Iteration 209:\tk_eff = 1.222067\tres = 2.301E-05\n", - "[ NORMAL ] Iteration 210:\tk_eff = 1.222093\tres = 2.213E-05\n", - "[ NORMAL ] Iteration 211:\tk_eff = 1.222118\tres = 2.129E-05\n", - "[ NORMAL ] Iteration 212:\tk_eff = 1.222142\tres = 2.047E-05\n", - "[ NORMAL ] Iteration 213:\tk_eff = 1.222165\tres = 1.969E-05\n", - "[ NORMAL ] Iteration 214:\tk_eff = 1.222187\tres = 1.894E-05\n", - "[ NORMAL ] Iteration 215:\tk_eff = 1.222209\tres = 1.822E-05\n", - "[ NORMAL ] Iteration 216:\tk_eff = 1.222229\tres = 1.752E-05\n", - "[ NORMAL ] Iteration 217:\tk_eff = 1.222249\tres = 1.685E-05\n", - "[ NORMAL ] Iteration 218:\tk_eff = 1.222268\tres = 1.621E-05\n", - "[ NORMAL ] Iteration 219:\tk_eff = 1.222287\tres = 1.559E-05\n", - "[ NORMAL ] Iteration 220:\tk_eff = 1.222304\tres = 1.499E-05\n", - "[ NORMAL ] Iteration 221:\tk_eff = 1.222321\tres = 1.442E-05\n", - "[ NORMAL ] Iteration 222:\tk_eff = 1.222337\tres = 1.387E-05\n", - "[ NORMAL ] Iteration 223:\tk_eff = 1.222353\tres = 1.334E-05\n", - "[ NORMAL ] Iteration 224:\tk_eff = 1.222368\tres = 1.283E-05\n", - "[ NORMAL ] Iteration 225:\tk_eff = 1.222383\tres = 1.234E-05\n", - "[ NORMAL ] Iteration 226:\tk_eff = 1.222397\tres = 1.187E-05\n", - "[ NORMAL ] Iteration 227:\tk_eff = 1.222410\tres = 1.142E-05\n", - "[ NORMAL ] Iteration 228:\tk_eff = 1.222423\tres = 1.098E-05\n", - "[ NORMAL ] Iteration 229:\tk_eff = 1.222435\tres = 1.056E-05\n", - "[ NORMAL ] Iteration 230:\tk_eff = 1.222447\tres = 1.016E-05\n" - ] - } - ], + "outputs": [], "source": [ "# Generate tracks for OpenMOC\n", "track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=128, spacing=0.1)\n", @@ -1731,21 +822,11 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "openmc keff = 1.223729\n", - "openmoc keff = 1.222447\n", - "bias [pcm]: -128.2\n" - ] - } - ], + "outputs": [], "source": [ "# Print report of keff and bias with OpenMC\n", "openmoc_keff = solver.getKeff()\n", @@ -1788,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": { "collapsed": false }, @@ -1814,32 +895,11 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "(9.9999999999999994e-12, 20.0)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEhCAYAAAB7mQezAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRETE6eQvWE4Yr8iVHuYAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a loglog plot of the U-235 continuous-energy fission cross section \n", "plt.loglog(u235.energy, fission.sigma, color='b', linewidth=1)\n", @@ -1875,22 +935,11 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py:872: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", - " return c.reshape(shape_out)\n", - "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py:872: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", - " return c.reshape(shape_out)\n" - ] - } - ], + "outputs": [], "source": [ "# Construct a Pandas DataFrame for the microscopic nu-scattering matrix\n", "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n", @@ -1918,22 +967,11 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAADUCAYAAACWNDiHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create plot of the H-1 scattering matrix\n", "fig = plt.subplot(121)\n", @@ -1981,7 +1019,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.6" + "version": "2.7.11" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb index c3f19aa22..83d99976a 100644 --- a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb @@ -32,7 +32,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.py:1318: UserWarning: This call to matplotlib.use() has no effect\n", + "/home/wboyd/anaconda2/lib/python2.7/site-packages/matplotlib/__init__.py:1350: UserWarning: This call to matplotlib.use() has no effect\n", "because the backend has already been chosen;\n", "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", @@ -420,7 +420,6 @@ "plot = openmc.Plot(plot_id=1)\n", "plot.filename = 'materials-xy'\n", "plot.origin = [0, 0, 0]\n", - "plot.width = [21.5, 21.5]\n", "plot.pixels = [250, 250]\n", "plot.color = 'mat'\n", "\n", @@ -470,7 +469,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN\nAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAADFBMVEX///9yEhLpgJFNv8Tq\nQYT7AAAAAWJLR0QAiAUdSAAAAAd0SU1FB+ABDg4MCdjYNQMAAASWSURBVGje7Zs7buMwEEBzieRc\naYaB48KVisSFj7Cn4BFU2I37LVan8BFc5ABb2ICtpSSaHP5EUqOAzsIOAjwEGjjiZ/hEDZ+eiJ9n\noHxe6fHvW4BPDmwHEMAaYBdAEb+5Amu/YNlyQLgP4xGhiG9avmwvsBF/t/FkY2vj69NLD1f41Z6Y\niw3Gvy728ceVuhLhwY8bA0fij8EgO/6wjH2pF/lKxvf3tNG3Z+BRt4oHh/Znt5bu+iQd+/Z/Xp8B\nmiO8X0X/n7KQNbWIZ1wMJjEUPwBuuI1hfcMZxv9Pj19/AexrYH84KASFV41nhe8Ku/4f+nSpu3eN\nsdadjpBLFPF6pIE76Hx4QeiMfy/yVQi/cf6mxx900jk4ScfGlc4/q9v8c9sPxhpN4wn3n+qepeqe\nAK5x/3WZfieGx+8h6Uv8DCNHeAfjv3Q8q0VjwJCesrFbP2X+7NZPidAjE7hAyGTSFOvnLX8erfw9\nYCV+BL4p7DL1gH3SNvK3Z/0Qn3HE64dn/eLifx1Fd/62eP4NVyLsJx1Cce2bf/7mfL+Kt6UB+ivt\nm+YasT88u6Yi2z+M+lrpT432J4F9pw+mZOH+rP3pLP2pEzFhaiCdzESGcOvBO5g/peMt6d2lYo39\nd0ivNUvwXyE6KhVb/ssh7r8LMRAs/1XrD0DcfxfiP8DrD54/AFV0/av6eP/6acQH/NcTr/KH6JCY\nCnezMOi/8v5H/be7f9N/tdNyluC/sv3V+rnWTvuxUNj/tbax81+u0fDfSuttOt7B/Ckd3zVvb7ra\nfzFq6XWxifqv0f8x/2XZ+PBfw39tFb5YyPTz//z+u9P+a+KnTvoO3sH4Lx3fiyzXTutgbxrgx8F/\nbdNNR+2/Uq/YuH9dLRXW60cVk14DK2P/aJkinQ7yDfZfR3pH/Feg47/532/6r196/cgVDu3/liK9\nDgLyX2260U5vMfr9dxvBh/+i+CzptVHE73V69WOj/ddBT/53toKdTV8j/5vrT9b+7/eun9P2f6P+\nm7T/G/GPkP/m481/6xHpHcNu/PJhKFbi18SFi2DhHcyf0vHYf09Sb4ON/iXR9d/J/U8Zf5dZxj91\n/s3ovzzqv3b+IfvvSNL1o5V/belNzP8P/5XxqdLhxdn9N6ZiQf+d6n8z+OeP919K+5P7nzr+Ss+f\n0vHU/EfNv8T8T11/frr/Uv1jFv+l+Ffp8V88ng9YwTT/pz5/EPuf+vz1H/pv1vM39fmfvP9A3f8o\nPn8Kx1P336j7f8T9x//Bf4n7z6T9b+r+O9l/qe8fSs+f0vHU91/U92+z+m/++8d7eX869f0v9f0z\n+f039f176fFfOp5xWv0Htf4E9fSU+hfsv1Pqb/D4h2n1P9T6I2r9E6n+ilr/Ra4/o9a/lZ4/peOp\n9ZcbYv0nsf70pXUe2rLqX19acv0ttf7XfmjOrT+2kxbE/Dd4fmZC/TW5/ptaf156/pSOp55/mNF/\nWx8y238vD//1+++k80fk80/U81elx3/peMZp5/+o5w8b2vlH7/7viP8mnJ/JPf9Zev/X9oes87fY\nf21MOf9LPn9MPf9cdv78A0xugrwgDfcHAAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDE2LTAxLTE0VDA4\nOjEyOjA5LTA2OjAwdS8nUQAAACV0RVh0ZGF0ZTptb2RpZnkAMjAxNi0wMS0xNFQwODoxMjowOS0w\nNjowMARyn+0AAAAASUVORK5CYII=\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nrkJggg==\n", "text/plain": [ "" ] @@ -560,7 +559,7 @@ "* `NuScatterMatrixXS` (`\"nu-scatter matrix\"`)\n", "* `Chi` (`\"chi\"`)\n", "\n", - "In this case, let's create the multi-group cross sections needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we will define `\"transport\"`, `\"nu-fission\"`, `\"nu-scatter matrix\"` and `\"chi\"` cross sections for our `Library`.\n", + "In this case, let's create the multi-group cross sections needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we will define `\"transport\"`, `\"nu-fission\"`, `'\"fission\"`, `\"nu-scatter matrix\"` and `\"chi\"` cross sections for our `Library`.\n", "\n", "**Note**: A variety of different approximate transport-corrected total multi-group cross sections (and corresponding scattering matrices) can be found in the literature. At the present time, the `openmc.mgxs` module only supports the `\"P0\"` transport correction. This correction can be turned on and off through the boolean `Library.correction` property which may take values of `\"P0\"` (default) or `None`." ] @@ -569,12 +568,12 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ "# Specify multi-group cross section types to compute\n", - "mgxs_lib.mgxs_types = ['transport', 'nu-fission', 'nu-scatter matrix', 'chi']" + "mgxs_lib.mgxs_types = ['transport', 'nu-fission', 'fission', 'nu-scatter matrix', 'chi']" ] }, { @@ -681,7 +680,7 @@ "mesh.type = 'regular'\n", "mesh.dimension = [17, 17]\n", "mesh.lower_left = [-10.71, -10.71]\n", - "mesh.width = [1.26, 1.26]\n", + "mesh.upper_right = [+10.71, +10.71]\n", "\n", "# Instantiate tally Filter\n", "mesh_filter = openmc.Filter()\n", @@ -736,8 +735,10 @@ " Copyright: 2011-2015 Massachusetts Institute of Technology\n", " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.1\n", - " Git SHA1: ea9fb637f63f9374c7436456141afa850b84acf9\n", - " Date/Time: 2016-01-14 08:12:09\n", + " Git SHA1: 5f252e2df51930b9175fd41bafa8db01f3eaeb92\n", + " Date/Time: 2016-03-23 12:10:16\n", + " MPI Processes: 1\n", + " OpenMP Threads: 16\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -764,56 +765,56 @@ "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", - " 1/1 1.02650 \n", - " 2/1 1.01386 \n", - " 3/1 1.01045 \n", - " 4/1 1.05511 \n", - " 5/1 1.04873 \n", - " 6/1 1.04558 \n", - " 7/1 1.03840 \n", - " 8/1 1.02086 \n", - " 9/1 1.08845 \n", - " 10/1 1.03932 \n", - " 11/1 1.01271 \n", - " 12/1 1.03448 1.02360 +/- 0.01088\n", - " 13/1 1.04395 1.03038 +/- 0.00925\n", - " 14/1 1.05477 1.03648 +/- 0.00894\n", - " 15/1 1.00485 1.03015 +/- 0.00938\n", - " 16/1 1.04523 1.03267 +/- 0.00806\n", - " 17/1 1.01328 1.02990 +/- 0.00735\n", - " 18/1 1.01476 1.02800 +/- 0.00664\n", - " 19/1 1.01490 1.02655 +/- 0.00604\n", - " 20/1 1.00926 1.02482 +/- 0.00567\n", - " 21/1 0.98504 1.02120 +/- 0.00627\n", - " 22/1 1.00397 1.01977 +/- 0.00591\n", - " 23/1 1.02556 1.02021 +/- 0.00545\n", - " 24/1 0.99808 1.01863 +/- 0.00529\n", - " 25/1 0.99638 1.01715 +/- 0.00514\n", - " 26/1 0.99615 1.01584 +/- 0.00499\n", - " 27/1 1.01843 1.01599 +/- 0.00469\n", - " 28/1 1.00315 1.01528 +/- 0.00447\n", - " 29/1 1.00633 1.01480 +/- 0.00426\n", - " 30/1 1.02159 1.01514 +/- 0.00405\n", - " 31/1 1.03395 1.01604 +/- 0.00396\n", - " 32/1 1.02672 1.01652 +/- 0.00381\n", - " 33/1 1.03778 1.01745 +/- 0.00375\n", - " 34/1 1.03807 1.01831 +/- 0.00369\n", - " 35/1 1.07854 1.02072 +/- 0.00428\n", - " 36/1 1.03524 1.02128 +/- 0.00415\n", - " 37/1 1.03100 1.02164 +/- 0.00401\n", - " 38/1 1.03853 1.02224 +/- 0.00391\n", - " 39/1 1.04089 1.02288 +/- 0.00383\n", - " 40/1 1.02150 1.02284 +/- 0.00370\n", - " 41/1 0.98470 1.02161 +/- 0.00379\n", - " 42/1 1.00658 1.02114 +/- 0.00370\n", - " 43/1 0.98652 1.02009 +/- 0.00373\n", - " 44/1 1.02787 1.02032 +/- 0.00363\n", - " 45/1 0.98800 1.01939 +/- 0.00364\n", - " 46/1 1.00286 1.01893 +/- 0.00357\n", - " 47/1 1.02559 1.01911 +/- 0.00348\n", - " 48/1 1.03729 1.01959 +/- 0.00342\n", - " 49/1 1.02538 1.01974 +/- 0.00333\n", - " 50/1 1.01478 1.01962 +/- 0.00325\n", + " 1/1 1.03852 \n", + " 2/1 0.99743 \n", + " 3/1 1.02987 \n", + " 4/1 1.04472 \n", + " 5/1 1.02183 \n", + " 6/1 1.05263 \n", + " 7/1 0.99048 \n", + " 8/1 1.02753 \n", + " 9/1 1.03159 \n", + " 10/1 1.04005 \n", + " 11/1 1.05278 \n", + " 12/1 1.02555 1.03917 +/- 0.01362\n", + " 13/1 0.99400 1.02411 +/- 0.01699\n", + " 14/1 1.03508 1.02685 +/- 0.01232\n", + " 15/1 1.00055 1.02159 +/- 0.01090\n", + " 16/1 1.01334 1.02022 +/- 0.00900\n", + " 17/1 0.99822 1.01707 +/- 0.00823\n", + " 18/1 1.01767 1.01715 +/- 0.00713\n", + " 19/1 1.05052 1.02086 +/- 0.00730\n", + " 20/1 1.03133 1.02190 +/- 0.00661\n", + " 21/1 1.04112 1.02365 +/- 0.00623\n", + " 22/1 1.04175 1.02516 +/- 0.00588\n", + " 23/1 1.01909 1.02469 +/- 0.00543\n", + " 24/1 1.07119 1.02801 +/- 0.00603\n", + " 25/1 0.97445 1.02444 +/- 0.00665\n", + " 26/1 1.04737 1.02588 +/- 0.00638\n", + " 27/1 1.04656 1.02709 +/- 0.00612\n", + " 28/1 1.03464 1.02751 +/- 0.00578\n", + " 29/1 1.02528 1.02739 +/- 0.00547\n", + " 30/1 1.02799 1.02742 +/- 0.00519\n", + " 31/1 1.05846 1.02890 +/- 0.00516\n", + " 32/1 1.03811 1.02932 +/- 0.00493\n", + " 33/1 1.00894 1.02843 +/- 0.00480\n", + " 34/1 1.02049 1.02810 +/- 0.00460\n", + " 35/1 1.00690 1.02726 +/- 0.00450\n", + " 36/1 1.03129 1.02741 +/- 0.00432\n", + " 37/1 0.98864 1.02597 +/- 0.00440\n", + " 38/1 1.00017 1.02505 +/- 0.00434\n", + " 39/1 1.03635 1.02544 +/- 0.00421\n", + " 40/1 1.07090 1.02696 +/- 0.00434\n", + " 41/1 1.03141 1.02710 +/- 0.00420\n", + " 42/1 1.02624 1.02707 +/- 0.00406\n", + " 43/1 1.02668 1.02706 +/- 0.00394\n", + " 44/1 1.05940 1.02801 +/- 0.00394\n", + " 45/1 1.01149 1.02754 +/- 0.00385\n", + " 46/1 1.06958 1.02871 +/- 0.00392\n", + " 47/1 1.02674 1.02866 +/- 0.00381\n", + " 48/1 1.02542 1.02857 +/- 0.00371\n", + " 49/1 1.03516 1.02874 +/- 0.00362\n", + " 50/1 1.06818 1.02973 +/- 0.00366\n", " Creating state point statepoint.50.h5...\n", "\n", " ===========================================================================\n", @@ -823,27 +824,27 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.1800E-01 seconds\n", - " Reading cross sections = 1.4300E-01 seconds\n", - " Total time in simulation = 4.1206E+01 seconds\n", - " Time in transport only = 4.1193E+01 seconds\n", - " Time in inactive batches = 4.1760E+00 seconds\n", - " Time in active batches = 3.7030E+01 seconds\n", - " Time synchronizing fission bank = 3.0000E-03 seconds\n", - " Sampling source sites = 2.0000E-03 seconds\n", + " Total time for initialization = 5.2200E-01 seconds\n", + " Reading cross sections = 1.6000E-01 seconds\n", + " Total time in simulation = 6.0800E+00 seconds\n", + " Time in transport only = 5.6140E+00 seconds\n", + " Time in inactive batches = 6.1300E-01 seconds\n", + " Time in active batches = 5.4670E+00 seconds\n", + " Time synchronizing fission bank = 2.0000E-03 seconds\n", + " Sampling source sites = 1.0000E-03 seconds\n", " SEND/RECV source sites = 1.0000E-03 seconds\n", - " Time accumulating tallies = 0.0000E+00 seconds\n", + " Time accumulating tallies = 5.0000E-03 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 4.1648E+01 seconds\n", - " Calculation Rate (inactive) = 5986.59 neutrons/second\n", - " Calculation Rate (active) = 2700.51 neutrons/second\n", + " Total time elapsed = 6.6230E+00 seconds\n", + " Calculation Rate (inactive) = 40783.0 neutrons/second\n", + " Calculation Rate (active) = 18291.6 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.01805 +/- 0.00261\n", - " k-effective (Track-length) = 1.01962 +/- 0.00325\n", - " k-effective (Absorption) = 1.01554 +/- 0.00339\n", - " Combined k-effective = 1.01711 +/- 0.00235\n", + " k-effective (Collision) = 1.02763 +/- 0.00343\n", + " k-effective (Track-length) = 1.02973 +/- 0.00366\n", + " k-effective (Absorption) = 1.02732 +/- 0.00319\n", + " Combined k-effective = 1.02826 +/- 0.00259\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -981,14 +982,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1642: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/wboyd/Documents/NSE-CRPG-Codes/openmc/openmc/tallies.py:1996: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" ] }, { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -1006,23 +1007,23 @@ " \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", @@ -1030,23 +1031,23 @@ " \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", @@ -1054,13 +1055,13 @@ "" ], "text/plain": [ - " cell group in nuclide mean std. dev.\n", - "3 10000 1 U-235 0.008064 4.062984e-05\n", - "4 10000 1 U-238 0.007336 4.459335e-05\n", - "5 10000 1 O-16 0.000000 0.000000e+00\n", - "0 10000 2 U-235 0.361327 1.902492e-03\n", - "1 10000 2 U-238 0.000001 3.536787e-09\n", - "2 10000 2 O-16 0.000000 0.000000e+00" + " cell group in nuclide mean std. dev.\n", + "3 10000 1 U-235 8.055246e-03 2.857567e-05\n", + "4 10000 1 U-238 7.339215e-03 4.349466e-05\n", + "5 10000 1 O-16 0.000000e+00 0.000000e+00\n", + "0 10000 2 U-235 3.615565e-01 2.050486e-03\n", + "1 10000 2 U-238 6.742638e-07 3.795256e-09\n", + "2 10000 2 O-16 0.000000e+00 0.000000e+00" ] }, "execution_count": 31, @@ -1097,13 +1098,13 @@ "\tDomain ID =\t10000\n", "\tNuclide =\tU-235\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t8.06e-03 +/- 5.04e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t3.61e-01 +/- 5.27e-01%\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t8.06e-03 +/- 3.55e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t3.62e-01 +/- 5.67e-01%\n", "\n", "\tNuclide =\tU-238\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t7.34e-03 +/- 6.08e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t6.74e-07 +/- 5.25e-01%\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t7.34e-03 +/- 5.93e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t6.74e-07 +/- 5.63e-01%\n", "\n", "\tNuclide =\tO-16\n", "\tCross Sections [cm^-1]:\n", @@ -1201,7 +1202,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "
100001U-2350.0080644.062984e-058.055246e-032.857567e-05
4100001U-2380.0073364.459335e-057.339215e-034.349466e-05
5100001O-160.0000000.000000e+000.000000e+00
100002U-2350.3613271.902492e-033.615565e-012.050486e-03
1100002U-2380.0000013.536787e-096.742638e-073.795256e-09
2100002O-160.0000000.000000e+000.000000e+00
\n", " \n", " \n", @@ -1219,16 +1220,16 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1244,8 +1245,8 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "0 10000 1 U-235 0.074383 0.000280\n", - "1 10000 1 U-238 0.005959 0.000036\n", + "0 10000 1 U-235 0.074860 0.000303\n", + "1 10000 1 U-238 0.005952 0.000035\n", "2 10000 1 O-16 0.000000 0.000000" ] }, @@ -1326,126 +1327,126 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ NORMAL ] Ray tracing for track segmentation...\n", - "[ NORMAL ] Dumping tracks to file...\n", + "[ NORMAL ] Importing ray tracing data from file...\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.854316\tres = 0.000E+00\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.801593\tres = 1.522E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.761131\tres = 6.380E-02\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.731467\tres = 5.066E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.709897\tres = 3.910E-02\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.695111\tres = 2.954E-02\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.685967\tres = 2.085E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.681511\tres = 1.317E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.680926\tres = 6.520E-03\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.683509\tres = 1.046E-03\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.688659\tres = 3.848E-03\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.695861\tres = 7.565E-03\n", - "[ NORMAL ] Iteration 12:\tk_eff = 0.704674\tres = 1.048E-02\n", - "[ NORMAL ] Iteration 13:\tk_eff = 0.714726\tres = 1.269E-02\n", - "[ NORMAL ] Iteration 14:\tk_eff = 0.725701\tres = 1.428E-02\n", - "[ NORMAL ] Iteration 15:\tk_eff = 0.737329\tres = 1.537E-02\n", - "[ NORMAL ] Iteration 16:\tk_eff = 0.749388\tres = 1.604E-02\n", - "[ NORMAL ] Iteration 17:\tk_eff = 0.761691\tres = 1.637E-02\n", - "[ NORMAL ] Iteration 18:\tk_eff = 0.774081\tres = 1.643E-02\n", - "[ NORMAL ] Iteration 19:\tk_eff = 0.786431\tres = 1.628E-02\n", - "[ NORMAL ] Iteration 20:\tk_eff = 0.798638\tres = 1.597E-02\n", - "[ NORMAL ] Iteration 21:\tk_eff = 0.810618\tres = 1.553E-02\n", - "[ NORMAL ] Iteration 22:\tk_eff = 0.822303\tres = 1.501E-02\n", - "[ NORMAL ] Iteration 23:\tk_eff = 0.833643\tres = 1.443E-02\n", - "[ NORMAL ] Iteration 24:\tk_eff = 0.844598\tres = 1.380E-02\n", - "[ NORMAL ] Iteration 25:\tk_eff = 0.855140\tres = 1.315E-02\n", - "[ NORMAL ] Iteration 26:\tk_eff = 0.865249\tres = 1.249E-02\n", - "[ NORMAL ] Iteration 27:\tk_eff = 0.874914\tres = 1.183E-02\n", - "[ NORMAL ] Iteration 28:\tk_eff = 0.884128\tres = 1.118E-02\n", - "[ NORMAL ] Iteration 29:\tk_eff = 0.892891\tres = 1.054E-02\n", - "[ NORMAL ] Iteration 30:\tk_eff = 0.901206\tres = 9.920E-03\n", - "[ NORMAL ] Iteration 31:\tk_eff = 0.909080\tres = 9.320E-03\n", - "[ NORMAL ] Iteration 32:\tk_eff = 0.916522\tres = 8.745E-03\n", - "[ NORMAL ] Iteration 33:\tk_eff = 0.923545\tres = 8.194E-03\n", - "[ NORMAL ] Iteration 34:\tk_eff = 0.930162\tres = 7.669E-03\n", - "[ NORMAL ] Iteration 35:\tk_eff = 0.936387\tres = 7.171E-03\n", - "[ NORMAL ] Iteration 36:\tk_eff = 0.942236\tres = 6.698E-03\n", - "[ NORMAL ] Iteration 37:\tk_eff = 0.947725\tres = 6.252E-03\n", - "[ NORMAL ] Iteration 38:\tk_eff = 0.952869\tres = 5.830E-03\n", - "[ NORMAL ] Iteration 39:\tk_eff = 0.957686\tres = 5.433E-03\n", - "[ NORMAL ] Iteration 40:\tk_eff = 0.962192\tres = 5.060E-03\n", - "[ NORMAL ] Iteration 41:\tk_eff = 0.966404\tres = 4.710E-03\n", - "[ NORMAL ] Iteration 42:\tk_eff = 0.970336\tres = 4.381E-03\n", - "[ NORMAL ] Iteration 43:\tk_eff = 0.974005\tres = 4.073E-03\n", - "[ NORMAL ] Iteration 44:\tk_eff = 0.977426\tres = 3.785E-03\n", - "[ NORMAL ] Iteration 45:\tk_eff = 0.980613\tres = 3.515E-03\n", - "[ NORMAL ] Iteration 46:\tk_eff = 0.983580\tres = 3.264E-03\n", - "[ NORMAL ] Iteration 47:\tk_eff = 0.986340\tres = 3.029E-03\n", - "[ NORMAL ] Iteration 48:\tk_eff = 0.988907\tres = 2.809E-03\n", - "[ NORMAL ] Iteration 49:\tk_eff = 0.991293\tres = 2.605E-03\n", - "[ NORMAL ] Iteration 50:\tk_eff = 0.993509\tres = 2.415E-03\n", - "[ NORMAL ] Iteration 51:\tk_eff = 0.995566\tres = 2.238E-03\n", - "[ NORMAL ] Iteration 52:\tk_eff = 0.997475\tres = 2.073E-03\n", - "[ NORMAL ] Iteration 53:\tk_eff = 0.999246\tres = 1.920E-03\n", - "[ NORMAL ] Iteration 54:\tk_eff = 1.000887\tres = 1.777E-03\n", - "[ NORMAL ] Iteration 55:\tk_eff = 1.002408\tres = 1.645E-03\n", - "[ NORMAL ] Iteration 56:\tk_eff = 1.003818\tres = 1.522E-03\n", - "[ NORMAL ] Iteration 57:\tk_eff = 1.005123\tres = 1.408E-03\n", - "[ NORMAL ] Iteration 58:\tk_eff = 1.006331\tres = 1.302E-03\n", - "[ NORMAL ] Iteration 59:\tk_eff = 1.007449\tres = 1.203E-03\n", - "[ NORMAL ] Iteration 60:\tk_eff = 1.008483\tres = 1.112E-03\n", - "[ NORMAL ] Iteration 61:\tk_eff = 1.009439\tres = 1.028E-03\n", - "[ NORMAL ] Iteration 62:\tk_eff = 1.010324\tres = 9.496E-04\n", - "[ NORMAL ] Iteration 63:\tk_eff = 1.011141\tres = 8.771E-04\n", - "[ NORMAL ] Iteration 64:\tk_eff = 1.011896\tres = 8.100E-04\n", - "[ NORMAL ] Iteration 65:\tk_eff = 1.012594\tres = 7.479E-04\n", - "[ NORMAL ] Iteration 66:\tk_eff = 1.013238\tres = 6.903E-04\n", - "[ NORMAL ] Iteration 67:\tk_eff = 1.013833\tres = 6.372E-04\n", - "[ NORMAL ] Iteration 68:\tk_eff = 1.014382\tres = 5.880E-04\n", - "[ NORMAL ] Iteration 69:\tk_eff = 1.014889\tres = 5.425E-04\n", - "[ NORMAL ] Iteration 70:\tk_eff = 1.015357\tres = 5.004E-04\n", - "[ NORMAL ] Iteration 71:\tk_eff = 1.015788\tres = 4.615E-04\n", - "[ NORMAL ] Iteration 72:\tk_eff = 1.016186\tres = 4.256E-04\n", - "[ NORMAL ] Iteration 73:\tk_eff = 1.016553\tres = 3.924E-04\n", - "[ NORMAL ] Iteration 74:\tk_eff = 1.016892\tres = 3.617E-04\n", - "[ NORMAL ] Iteration 75:\tk_eff = 1.017204\tres = 3.334E-04\n", - "[ NORMAL ] Iteration 76:\tk_eff = 1.017491\tres = 3.073E-04\n", - "[ NORMAL ] Iteration 77:\tk_eff = 1.017756\tres = 2.831E-04\n", - "[ NORMAL ] Iteration 78:\tk_eff = 1.018000\tres = 2.608E-04\n", - "[ NORMAL ] Iteration 79:\tk_eff = 1.018225\tres = 2.403E-04\n", - "[ NORMAL ] Iteration 80:\tk_eff = 1.018432\tres = 2.213E-04\n", - "[ NORMAL ] Iteration 81:\tk_eff = 1.018623\tres = 2.038E-04\n", - "[ NORMAL ] Iteration 82:\tk_eff = 1.018799\tres = 1.877E-04\n", - "[ NORMAL ] Iteration 83:\tk_eff = 1.018961\tres = 1.729E-04\n", - "[ NORMAL ] Iteration 84:\tk_eff = 1.019110\tres = 1.591E-04\n", - "[ NORMAL ] Iteration 85:\tk_eff = 1.019247\tres = 1.465E-04\n", - "[ NORMAL ] Iteration 86:\tk_eff = 1.019373\tres = 1.349E-04\n", - "[ NORMAL ] Iteration 87:\tk_eff = 1.019489\tres = 1.241E-04\n", - "[ NORMAL ] Iteration 88:\tk_eff = 1.019596\tres = 1.142E-04\n", - "[ NORMAL ] Iteration 89:\tk_eff = 1.019695\tres = 1.051E-04\n", - "[ NORMAL ] Iteration 90:\tk_eff = 1.019785\tres = 9.673E-05\n", - "[ NORMAL ] Iteration 91:\tk_eff = 1.019869\tres = 8.899E-05\n", - "[ NORMAL ] Iteration 92:\tk_eff = 1.019945\tres = 8.187E-05\n", - "[ NORMAL ] Iteration 93:\tk_eff = 1.020016\tres = 7.532E-05\n", - "[ NORMAL ] Iteration 94:\tk_eff = 1.020081\tres = 6.925E-05\n", - "[ NORMAL ] Iteration 95:\tk_eff = 1.020141\tres = 6.372E-05\n", - "[ NORMAL ] Iteration 96:\tk_eff = 1.020195\tres = 5.861E-05\n", - "[ NORMAL ] Iteration 97:\tk_eff = 1.020246\tres = 5.388E-05\n", - "[ NORMAL ] Iteration 98:\tk_eff = 1.020292\tres = 4.956E-05\n", - "[ NORMAL ] Iteration 99:\tk_eff = 1.020335\tres = 4.555E-05\n", - "[ NORMAL ] Iteration 100:\tk_eff = 1.020374\tres = 4.187E-05\n", - "[ NORMAL ] Iteration 101:\tk_eff = 1.020410\tres = 3.850E-05\n", - "[ NORMAL ] Iteration 102:\tk_eff = 1.020443\tres = 3.539E-05\n", - "[ NORMAL ] Iteration 103:\tk_eff = 1.020473\tres = 3.254E-05\n", - "[ NORMAL ] Iteration 104:\tk_eff = 1.020501\tres = 2.991E-05\n", - "[ NORMAL ] Iteration 105:\tk_eff = 1.020527\tres = 2.747E-05\n", - "[ NORMAL ] Iteration 106:\tk_eff = 1.020551\tres = 2.526E-05\n", - "[ NORMAL ] Iteration 107:\tk_eff = 1.020573\tres = 2.321E-05\n", - "[ NORMAL ] Iteration 108:\tk_eff = 1.020592\tres = 2.134E-05\n", - "[ NORMAL ] Iteration 109:\tk_eff = 1.020611\tres = 1.961E-05\n", - "[ NORMAL ] Iteration 110:\tk_eff = 1.020628\tres = 1.802E-05\n", - "[ NORMAL ] Iteration 111:\tk_eff = 1.020643\tres = 1.653E-05\n", - "[ NORMAL ] Iteration 112:\tk_eff = 1.020657\tres = 1.519E-05\n", - "[ NORMAL ] Iteration 113:\tk_eff = 1.020671\tres = 1.398E-05\n", - "[ NORMAL ] Iteration 114:\tk_eff = 1.020683\tres = 1.283E-05\n", - "[ NORMAL ] Iteration 115:\tk_eff = 1.020694\tres = 1.178E-05\n", - "[ NORMAL ] Iteration 116:\tk_eff = 1.020704\tres = 1.082E-05\n" + "[ NORMAL ] Iteration 0:\tk_eff = 0.854370\tres = 0.000E+00\n", + "[ NORMAL ] Iteration 1:\tk_eff = 0.801922\tres = 1.521E-01\n", + "[ NORMAL ] Iteration 2:\tk_eff = 0.761746\tres = 6.349E-02\n", + "[ NORMAL ] Iteration 3:\tk_eff = 0.732367\tres = 5.029E-02\n", + "[ NORMAL ] Iteration 4:\tk_eff = 0.711075\tres = 3.869E-02\n", + "[ NORMAL ] Iteration 5:\tk_eff = 0.696557\tres = 2.912E-02\n", + "[ NORMAL ] Iteration 6:\tk_eff = 0.687673\tres = 2.044E-02\n", + "[ NORMAL ] Iteration 7:\tk_eff = 0.683470\tres = 1.277E-02\n", + "[ NORMAL ] Iteration 8:\tk_eff = 0.683129\tres = 6.141E-03\n", + "[ NORMAL ] Iteration 9:\tk_eff = 0.685949\tres = 7.889E-04\n", + "[ NORMAL ] Iteration 10:\tk_eff = 0.691329\tres = 4.181E-03\n", + "[ NORMAL ] Iteration 11:\tk_eff = 0.698755\tres = 7.875E-03\n", + "[ NORMAL ] Iteration 12:\tk_eff = 0.707786\tres = 1.077E-02\n", + "[ NORMAL ] Iteration 13:\tk_eff = 0.718050\tres = 1.295E-02\n", + "[ NORMAL ] Iteration 14:\tk_eff = 0.729230\tres = 1.452E-02\n", + "[ NORMAL ] Iteration 15:\tk_eff = 0.741058\tres = 1.559E-02\n", + "[ NORMAL ] Iteration 16:\tk_eff = 0.753310\tres = 1.624E-02\n", + "[ NORMAL ] Iteration 17:\tk_eff = 0.765800\tres = 1.655E-02\n", + "[ NORMAL ] Iteration 18:\tk_eff = 0.778371\tres = 1.660E-02\n", + "[ NORMAL ] Iteration 19:\tk_eff = 0.790897\tres = 1.643E-02\n", + "[ NORMAL ] Iteration 20:\tk_eff = 0.803273\tres = 1.611E-02\n", + "[ NORMAL ] Iteration 21:\tk_eff = 0.815415\tres = 1.566E-02\n", + "[ NORMAL ] Iteration 22:\tk_eff = 0.827256\tres = 1.513E-02\n", + "[ NORMAL ] Iteration 23:\tk_eff = 0.838747\tres = 1.453E-02\n", + "[ NORMAL ] Iteration 24:\tk_eff = 0.849847\tres = 1.390E-02\n", + "[ NORMAL ] Iteration 25:\tk_eff = 0.860527\tres = 1.324E-02\n", + "[ NORMAL ] Iteration 26:\tk_eff = 0.870770\tres = 1.258E-02\n", + "[ NORMAL ] Iteration 27:\tk_eff = 0.880562\tres = 1.191E-02\n", + "[ NORMAL ] Iteration 28:\tk_eff = 0.889897\tres = 1.125E-02\n", + "[ NORMAL ] Iteration 29:\tk_eff = 0.898776\tres = 1.061E-02\n", + "[ NORMAL ] Iteration 30:\tk_eff = 0.907202\tres = 9.986E-03\n", + "[ NORMAL ] Iteration 31:\tk_eff = 0.915181\tres = 9.382E-03\n", + "[ NORMAL ] Iteration 32:\tk_eff = 0.922724\tres = 8.803E-03\n", + "[ NORMAL ] Iteration 33:\tk_eff = 0.929843\tres = 8.249E-03\n", + "[ NORMAL ] Iteration 34:\tk_eff = 0.936550\tres = 7.721E-03\n", + "[ NORMAL ] Iteration 35:\tk_eff = 0.942861\tres = 7.220E-03\n", + "[ NORMAL ] Iteration 36:\tk_eff = 0.948791\tres = 6.744E-03\n", + "[ NORMAL ] Iteration 37:\tk_eff = 0.954357\tres = 6.295E-03\n", + "[ NORMAL ] Iteration 38:\tk_eff = 0.959575\tres = 5.871E-03\n", + "[ NORMAL ] Iteration 39:\tk_eff = 0.964461\tres = 5.472E-03\n", + "[ NORMAL ] Iteration 40:\tk_eff = 0.969033\tres = 5.097E-03\n", + "[ NORMAL ] Iteration 41:\tk_eff = 0.973306\tres = 4.744E-03\n", + "[ NORMAL ] Iteration 42:\tk_eff = 0.977297\tres = 4.414E-03\n", + "[ NORMAL ] Iteration 43:\tk_eff = 0.981021\tres = 4.104E-03\n", + "[ NORMAL ] Iteration 44:\tk_eff = 0.984493\tres = 3.814E-03\n", + "[ NORMAL ] Iteration 45:\tk_eff = 0.987729\tres = 3.543E-03\n", + "[ NORMAL ] Iteration 46:\tk_eff = 0.990742\tres = 3.290E-03\n", + "[ NORMAL ] Iteration 47:\tk_eff = 0.993546\tres = 3.053E-03\n", + "[ NORMAL ] Iteration 48:\tk_eff = 0.996153\tres = 2.833E-03\n", + "[ NORMAL ] Iteration 49:\tk_eff = 0.998577\tres = 2.627E-03\n", + "[ NORMAL ] Iteration 50:\tk_eff = 1.000829\tres = 2.436E-03\n", + "[ NORMAL ] Iteration 51:\tk_eff = 1.002920\tres = 2.257E-03\n", + "[ NORMAL ] Iteration 52:\tk_eff = 1.004860\tres = 2.091E-03\n", + "[ NORMAL ] Iteration 53:\tk_eff = 1.006661\tres = 1.937E-03\n", + "[ NORMAL ] Iteration 54:\tk_eff = 1.008330\tres = 1.793E-03\n", + "[ NORMAL ] Iteration 55:\tk_eff = 1.009877\tres = 1.660E-03\n", + "[ NORMAL ] Iteration 56:\tk_eff = 1.011311\tres = 1.536E-03\n", + "[ NORMAL ] Iteration 57:\tk_eff = 1.012639\tres = 1.421E-03\n", + "[ NORMAL ] Iteration 58:\tk_eff = 1.013868\tres = 1.314E-03\n", + "[ NORMAL ] Iteration 59:\tk_eff = 1.015006\tres = 1.215E-03\n", + "[ NORMAL ] Iteration 60:\tk_eff = 1.016059\tres = 1.124E-03\n", + "[ NORMAL ] Iteration 61:\tk_eff = 1.017033\tres = 1.039E-03\n", + "[ NORMAL ] Iteration 62:\tk_eff = 1.017933\tres = 9.596E-04\n", + "[ NORMAL ] Iteration 63:\tk_eff = 1.018766\tres = 8.865E-04\n", + "[ NORMAL ] Iteration 64:\tk_eff = 1.019535\tres = 8.188E-04\n", + "[ NORMAL ] Iteration 65:\tk_eff = 1.020246\tres = 7.562E-04\n", + "[ NORMAL ] Iteration 66:\tk_eff = 1.020903\tres = 6.981E-04\n", + "[ NORMAL ] Iteration 67:\tk_eff = 1.021509\tres = 6.445E-04\n", + "[ NORMAL ] Iteration 68:\tk_eff = 1.022069\tres = 5.948E-04\n", + "[ NORMAL ] Iteration 69:\tk_eff = 1.022586\tres = 5.489E-04\n", + "[ NORMAL ] Iteration 70:\tk_eff = 1.023063\tres = 5.064E-04\n", + "[ NORMAL ] Iteration 71:\tk_eff = 1.023503\tres = 4.671E-04\n", + "[ NORMAL ] Iteration 72:\tk_eff = 1.023909\tres = 4.308E-04\n", + "[ NORMAL ] Iteration 73:\tk_eff = 1.024284\tres = 3.973E-04\n", + "[ NORMAL ] Iteration 74:\tk_eff = 1.024629\tres = 3.663E-04\n", + "[ NORMAL ] Iteration 75:\tk_eff = 1.024948\tres = 3.377E-04\n", + "[ NORMAL ] Iteration 76:\tk_eff = 1.025241\tres = 3.113E-04\n", + "[ NORMAL ] Iteration 77:\tk_eff = 1.025512\tres = 2.869E-04\n", + "[ NORMAL ] Iteration 78:\tk_eff = 1.025761\tres = 2.644E-04\n", + "[ NORMAL ] Iteration 79:\tk_eff = 1.025991\tres = 2.436E-04\n", + "[ NORMAL ] Iteration 80:\tk_eff = 1.026203\tres = 2.244E-04\n", + "[ NORMAL ] Iteration 81:\tk_eff = 1.026398\tres = 2.067E-04\n", + "[ NORMAL ] Iteration 82:\tk_eff = 1.026578\tres = 1.904E-04\n", + "[ NORMAL ] Iteration 83:\tk_eff = 1.026743\tres = 1.754E-04\n", + "[ NORMAL ] Iteration 84:\tk_eff = 1.026895\tres = 1.615E-04\n", + "[ NORMAL ] Iteration 85:\tk_eff = 1.027036\tres = 1.487E-04\n", + "[ NORMAL ] Iteration 86:\tk_eff = 1.027165\tres = 1.369E-04\n", + "[ NORMAL ] Iteration 87:\tk_eff = 1.027284\tres = 1.260E-04\n", + "[ NORMAL ] Iteration 88:\tk_eff = 1.027393\tres = 1.160E-04\n", + "[ NORMAL ] Iteration 89:\tk_eff = 1.027494\tres = 1.068E-04\n", + "[ NORMAL ] Iteration 90:\tk_eff = 1.027587\tres = 9.825E-05\n", + "[ NORMAL ] Iteration 91:\tk_eff = 1.027672\tres = 9.041E-05\n", + "[ NORMAL ] Iteration 92:\tk_eff = 1.027751\tres = 8.319E-05\n", + "[ NORMAL ] Iteration 93:\tk_eff = 1.027823\tres = 7.654E-05\n", + "[ NORMAL ] Iteration 94:\tk_eff = 1.027889\tres = 7.042E-05\n", + "[ NORMAL ] Iteration 95:\tk_eff = 1.027950\tres = 6.478E-05\n", + "[ NORMAL ] Iteration 96:\tk_eff = 1.028007\tres = 5.959E-05\n", + "[ NORMAL ] Iteration 97:\tk_eff = 1.028058\tres = 5.481E-05\n", + "[ NORMAL ] Iteration 98:\tk_eff = 1.028106\tres = 5.041E-05\n", + "[ NORMAL ] Iteration 99:\tk_eff = 1.028150\tres = 4.636E-05\n", + "[ NORMAL ] Iteration 100:\tk_eff = 1.028190\tres = 4.263E-05\n", + "[ NORMAL ] Iteration 101:\tk_eff = 1.028227\tres = 3.920E-05\n", + "[ NORMAL ] Iteration 102:\tk_eff = 1.028261\tres = 3.604E-05\n", + "[ NORMAL ] Iteration 103:\tk_eff = 1.028292\tres = 3.314E-05\n", + "[ NORMAL ] Iteration 104:\tk_eff = 1.028321\tres = 3.047E-05\n", + "[ NORMAL ] Iteration 105:\tk_eff = 1.028347\tres = 2.801E-05\n", + "[ NORMAL ] Iteration 106:\tk_eff = 1.028371\tres = 2.575E-05\n", + "[ NORMAL ] Iteration 107:\tk_eff = 1.028394\tres = 2.367E-05\n", + "[ NORMAL ] Iteration 108:\tk_eff = 1.028414\tres = 2.175E-05\n", + "[ NORMAL ] Iteration 109:\tk_eff = 1.028433\tres = 1.999E-05\n", + "[ NORMAL ] Iteration 110:\tk_eff = 1.028450\tres = 1.838E-05\n", + "[ NORMAL ] Iteration 111:\tk_eff = 1.028466\tres = 1.689E-05\n", + "[ NORMAL ] Iteration 112:\tk_eff = 1.028481\tres = 1.552E-05\n", + "[ NORMAL ] Iteration 113:\tk_eff = 1.028494\tres = 1.426E-05\n", + "[ NORMAL ] Iteration 114:\tk_eff = 1.028507\tres = 1.310E-05\n", + "[ NORMAL ] Iteration 115:\tk_eff = 1.028518\tres = 1.204E-05\n", + "[ NORMAL ] Iteration 116:\tk_eff = 1.028528\tres = 1.106E-05\n", + "[ NORMAL ] Iteration 117:\tk_eff = 1.028538\tres = 1.017E-05\n" ] } ], @@ -1477,9 +1478,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "openmc keff = 1.017105\n", - "openmoc keff = 1.020704\n", - "bias [pcm]: 359.8\n" + "openmc keff = 1.028263\n", + "openmoc keff = 1.028538\n", + "bias [pcm]: 27.5\n" ] } ], @@ -1553,20 +1554,18 @@ }, "outputs": [], "source": [ - "# Export OpenMOC's fission rates for each pin cell instance in the fuel assembly\n", - "openmoc.process.compute_fission_rates(solver)\n", + "# Create OpenMOC Mesh on which to tally fission rates\n", + "openmoc_mesh = openmoc.process.Mesh()\n", + "openmoc_mesh.dimension = np.array(mesh.dimension)\n", + "openmoc_mesh.lower_left = np.array(mesh.lower_left)\n", + "openmoc_mesh.upper_right = np.array(mesh.upper_right)\n", + "openmoc_mesh.width = openmoc_mesh.upper_right - openmoc_mesh.lower_left\n", + "openmoc_mesh.width /= openmoc_mesh.dimension\n", "\n", - "# Open the pickle file with the fission rates\n", - "fission_rates = pickle.load(open('fission-rates/fission-rates.pkl', 'rb' ))\n", - "\n", - "# Allocate array for fission rates in each fuel pin\n", - "openmoc_fission_rates = np.zeros((17, 17))\n", - "\n", - "# Extract fission rates for each fuel pin\n", - "for key, value in fission_rates.items():\n", - " lat_x = int(key.split(':')[1].split()[3][1:-1])\n", - " lat_y = int(key.split(':')[1].split()[4][:-1]) \n", - " openmoc_fission_rates[lat_x, lat_y] = value\n", + "# Tally OpenMOC fission rates on the Mesh\n", + "openmoc_fission_rates = openmoc_mesh.tally_fission_rates(solver)\n", + "openmoc_fission_rates = np.squeeze(openmoc_fission_rates)\n", + "openmoc_fission_rates = np.fliplr(openmoc_fission_rates)\n", "\n", "# Normalize to the average pin fission rate\n", "openmoc_fission_rates /= np.mean(openmoc_fission_rates)" @@ -1589,7 +1588,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 44, @@ -1598,9 +1597,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAADDCAYAAACmois2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAADDCAYAAABJYEAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1636,7 +1635,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.6" + "version": "2.7.11" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index 85f64ff6f..1ccff330d 100644 --- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb +++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb @@ -382,7 +382,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nYII=\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nYII=\n", "text/plain": [ "" ] @@ -500,7 +500,7 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -567,9 +567,10 @@ " Copyright: 2011-2015 Massachusetts Institute of Technology\n", " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.1\n", - " Git SHA1: 34381b40a9445a727e360873aaa6ef892af1cb6a\n", - " Date/Time: 2016-02-07 16:01:57\n", + " Git SHA1: 5f252e2df51930b9175fd41bafa8db01f3eaeb92\n", + " Date/Time: 2016-03-23 12:21:14\n", " MPI Processes: 1\n", + " OpenMP Threads: 16\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -596,35 +597,46 @@ "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", - " 1/1 0.54958 \n", - " 2/1 0.67628 \n", - " 3/1 0.70618 \n", - " 4/1 0.66601 \n", - " 5/1 0.70876 \n", - " 6/1 0.69708 \n", - " 7/1 0.68623 0.69166 +/- 0.00543\n", - " 8/1 0.69159 0.69163 +/- 0.00313\n", - " 9/1 0.69908 0.69349 +/- 0.00289\n", - " 10/1 0.63865 0.68253 +/- 0.01120\n", - " 11/1 0.65439 0.67784 +/- 0.01027\n", - " 12/1 0.68518 0.67889 +/- 0.00875\n", - " 13/1 0.69507 0.68091 +/- 0.00784\n", - " 14/1 0.70129 0.68317 +/- 0.00728\n", - " 15/1 0.71336 0.68619 +/- 0.00717\n", - " 16/1 0.68725 0.68629 +/- 0.00649\n", - " 17/1 0.72579 0.68958 +/- 0.00678\n", - " 18/1 0.67149 0.68819 +/- 0.00639\n", - " 19/1 0.67771 0.68744 +/- 0.00596\n", - " 20/1 0.68035 0.68697 +/- 0.00557\n", - " Triggers unsatisfied, max unc./thresh. is 1.09851 for absorption in tally 10002\n", - " The estimated number of batches is 24\n", + " 1/1 0.51036 \n", + " 2/1 0.64436 \n", + " 3/1 0.64874 \n", + " 4/1 0.65998 \n", + " 5/1 0.68369 \n", + " 6/1 0.69058 \n", + " 7/1 0.68288 0.68673 +/- 0.00385\n", + " 8/1 0.69483 0.68943 +/- 0.00350\n", + " 9/1 0.70348 0.69294 +/- 0.00430\n", + " 10/1 0.69969 0.69429 +/- 0.00359\n", + " 11/1 0.67170 0.69052 +/- 0.00477\n", + " 12/1 0.67661 0.68854 +/- 0.00450\n", + " 13/1 0.69571 0.68943 +/- 0.00400\n", + " 14/1 0.67433 0.68776 +/- 0.00390\n", + " 15/1 0.67744 0.68672 +/- 0.00364\n", + " 16/1 0.65256 0.68362 +/- 0.00453\n", + " 17/1 0.66657 0.68220 +/- 0.00437\n", + " 18/1 0.66887 0.68117 +/- 0.00415\n", + " 19/1 0.68238 0.68126 +/- 0.00384\n", + " 20/1 0.64423 0.67879 +/- 0.00435\n", + " Triggers unsatisfied, max unc./thresh. is 1.40549 for absorption in tally 10002\n", + " The estimated number of batches is 35\n", " Creating state point statepoint.020.h5...\n", - " 21/1 0.68105 0.68660 +/- 0.00522\n", - " 22/1 0.67168 0.68572 +/- 0.00498\n", - " 23/1 0.67520 0.68514 +/- 0.00473\n", - " 24/1 0.67940 0.68483 +/- 0.00449\n", - " Triggers satisfied for batch 24\n", - " Creating state point statepoint.024.h5...\n", + " 21/1 0.66266 0.67778 +/- 0.00419\n", + " 22/1 0.67656 0.67771 +/- 0.00393\n", + " 23/1 0.67643 0.67764 +/- 0.00371\n", + " 24/1 0.66192 0.67681 +/- 0.00361\n", + " 25/1 0.69848 0.67789 +/- 0.00359\n", + " 26/1 0.66274 0.67717 +/- 0.00349\n", + " 27/1 0.69746 0.67810 +/- 0.00345\n", + " 28/1 0.67485 0.67795 +/- 0.00330\n", + " 29/1 0.67427 0.67780 +/- 0.00316\n", + " 30/1 0.66531 0.67730 +/- 0.00308\n", + " 31/1 0.68457 0.67758 +/- 0.00297\n", + " 32/1 0.66592 0.67715 +/- 0.00289\n", + " 33/1 0.65929 0.67651 +/- 0.00286\n", + " 34/1 0.67252 0.67637 +/- 0.00276\n", + " 35/1 0.71827 0.67777 +/- 0.00301\n", + " Triggers satisfied for batch 35\n", + " Creating state point statepoint.035.h5...\n", "\n", " ===========================================================================\n", " ======================> SIMULATION FINISHED <======================\n", @@ -633,28 +645,28 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.0900E-01 seconds\n", - " Reading cross sections = 7.8000E-02 seconds\n", - " Total time in simulation = 4.9560E+00 seconds\n", - " Time in transport only = 4.9400E+00 seconds\n", - " Time in inactive batches = 7.3100E-01 seconds\n", - " Time in active batches = 4.2250E+00 seconds\n", - " Time synchronizing fission bank = 4.0000E-03 seconds\n", - " Sampling source sites = 3.0000E-03 seconds\n", - " SEND/RECV source sites = 1.0000E-03 seconds\n", - " Time accumulating tallies = 1.0000E-03 seconds\n", + " Total time for initialization = 4.7000E-01 seconds\n", + " Reading cross sections = 1.3500E-01 seconds\n", + " Total time in simulation = 2.1470E+00 seconds\n", + " Time in transport only = 1.8480E+00 seconds\n", + " Time in inactive batches = 2.1900E-01 seconds\n", + " Time in active batches = 1.9280E+00 seconds\n", + " Time synchronizing fission bank = 7.0000E-03 seconds\n", + " Sampling source sites = 4.0000E-03 seconds\n", + " SEND/RECV source sites = 3.0000E-03 seconds\n", + " Time accumulating tallies = 2.0000E-03 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 5.2780E+00 seconds\n", - " Calculation Rate (inactive) = 17099.9 neutrons/second\n", - " Calculation Rate (active) = 8875.74 neutrons/second\n", + " Total time elapsed = 2.6360E+00 seconds\n", + " Calculation Rate (inactive) = 57077.6 neutrons/second\n", + " Calculation Rate (active) = 19450.2 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.68264 +/- 0.00405\n", - " k-effective (Track-length) = 0.68483 +/- 0.00449\n", - " k-effective (Absorption) = 0.68225 +/- 0.00336\n", - " Combined k-effective = 0.68275 +/- 0.00346\n", - " Leakage Fraction = 0.34345 +/- 0.00167\n", + " k-effective (Collision) = 0.67866 +/- 0.00337\n", + " k-effective (Track-length) = 0.67777 +/- 0.00301\n", + " k-effective (Absorption) = 0.68234 +/- 0.00332\n", + " Combined k-effective = 0.67987 +/- 0.00255\n", + " Leakage Fraction = 0.34141 +/- 0.00198\n", "\n" ] }, @@ -771,13 +783,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.18257268]]\n", + "[[[ 0.12916959]]\n", "\n", - " [[ 0.07111957]]\n", + " [[ 0.06336943]]\n", "\n", - " [[ 0.40880276]]\n", + " [[ 0.33288738]]\n", "\n", - " [[ 0.16407535]]]\n" + " [[ 0.14666158]]]\n" ] } ], @@ -800,290 +812,301 @@ { "data": { "text/html": [ - "
\n", + "
\n", "
100001U-2350.0743830.0002800.0748600.000303
1100001U-2380.0059590.0000360.0059520.000035
2
\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", - " \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", - " \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", + " \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", + " \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", - " \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", + " \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", - " \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", + " \n", " \n", " \n", "
(mesh 1, x)(mesh 1, y)(mesh 1, z)mesh 1energy low [MeV]energy high [MeV]scoremeanstd. dev.
xyz
0 1 1 101110.00e+006.25e-07 fission2.02e-043.69e-05fission2.37e-043.06e-05
1 1 1 111110.00e+006.25e-07 nu-fission4.92e-048.98e-05nu-fission5.78e-047.46e-05
2 1 1 121116.25e-072.00e+01 fission7.62e-053.74e-06fission7.00e-055.15e-06
3 1 1 131116.25e-072.00e+01 nu-fission2.04e-049.88e-06nu-fission1.85e-041.28e-05
4 1 2 141210.00e+006.25e-07 fission3.75e-043.86e-05fission4.04e-043.09e-05
5 1 2 151210.00e+006.25e-07 nu-fission9.14e-049.41e-05nu-fission9.85e-047.54e-05
6 1 2 161216.25e-072.00e+01 fission1.07e-041.26e-05fission1.00e-045.08e-06
7 1 2 171216.25e-072.00e+01 nu-fission2.78e-043.16e-05nu-fission2.63e-041.34e-05
8 1 3 181310.00e+006.25e-07 fission5.64e-045.60e-05fission5.82e-045.00e-05
9 1 3 191310.00e+006.25e-07 nu-fission1.37e-031.37e-04nu-fission1.42e-031.22e-04
10 1 3 11316.25e-072.00e+01 fission1.49e-047.25e-06fission1.38e-041.03e-05
11 1 3 11316.25e-072.00e+01 nu-fission3.88e-041.78e-05nu-fission3.59e-042.54e-05
12 1 4 11410.00e+006.25e-07 fission6.69e-044.44e-05fission6.88e-044.25e-05
13 1 4 11410.00e+006.25e-07 nu-fission1.63e-031.08e-04nu-fission1.68e-031.04e-04
14 1 4 11416.25e-072.00e+01 fission1.65e-041.09e-05fission1.62e-047.43e-06
15 1 4 11416.25e-072.00e+01 nu-fission4.33e-042.89e-05nu-fission4.22e-041.93e-05
16 1 5 11510.00e+006.25e-07 fission9.32e-046.90e-05fission7.62e-045.69e-05
17 1 5 11510.00e+006.25e-07 nu-fission2.27e-031.68e-04nu-fission1.86e-031.39e-04
18 1 5 11516.25e-072.00e+01 fission1.83e-041.10e-05fission1.80e-048.16e-06
19 1 5 11516.25e-072.00e+01 nu-fission4.77e-042.77e-05nu-fission4.71e-042.08e-05
\n", "
" ], "text/plain": [ - " (mesh 1, x) (mesh 1, y) (mesh 1, z) energy low [MeV] \\\n", - "0 1 1 1 0.00e+00 \n", - "1 1 1 1 0.00e+00 \n", - "2 1 1 1 6.25e-07 \n", - "3 1 1 1 6.25e-07 \n", - "4 1 2 1 0.00e+00 \n", - "5 1 2 1 0.00e+00 \n", - "6 1 2 1 6.25e-07 \n", - "7 1 2 1 6.25e-07 \n", - "8 1 3 1 0.00e+00 \n", - "9 1 3 1 0.00e+00 \n", - "10 1 3 1 6.25e-07 \n", - "11 1 3 1 6.25e-07 \n", - "12 1 4 1 0.00e+00 \n", - "13 1 4 1 0.00e+00 \n", - "14 1 4 1 6.25e-07 \n", - "15 1 4 1 6.25e-07 \n", - "16 1 5 1 0.00e+00 \n", - "17 1 5 1 0.00e+00 \n", - "18 1 5 1 6.25e-07 \n", - "19 1 5 1 6.25e-07 \n", + " mesh 1 energy low [MeV] energy high [MeV] score mean \\\n", + " x y z \n", + "0 1 1 1 0.00e+00 6.25e-07 fission 2.37e-04 \n", + "1 1 1 1 0.00e+00 6.25e-07 nu-fission 5.78e-04 \n", + "2 1 1 1 6.25e-07 2.00e+01 fission 7.00e-05 \n", + "3 1 1 1 6.25e-07 2.00e+01 nu-fission 1.85e-04 \n", + "4 1 2 1 0.00e+00 6.25e-07 fission 4.04e-04 \n", + "5 1 2 1 0.00e+00 6.25e-07 nu-fission 9.85e-04 \n", + "6 1 2 1 6.25e-07 2.00e+01 fission 1.00e-04 \n", + "7 1 2 1 6.25e-07 2.00e+01 nu-fission 2.63e-04 \n", + "8 1 3 1 0.00e+00 6.25e-07 fission 5.82e-04 \n", + "9 1 3 1 0.00e+00 6.25e-07 nu-fission 1.42e-03 \n", + "10 1 3 1 6.25e-07 2.00e+01 fission 1.38e-04 \n", + "11 1 3 1 6.25e-07 2.00e+01 nu-fission 3.59e-04 \n", + "12 1 4 1 0.00e+00 6.25e-07 fission 6.88e-04 \n", + "13 1 4 1 0.00e+00 6.25e-07 nu-fission 1.68e-03 \n", + "14 1 4 1 6.25e-07 2.00e+01 fission 1.62e-04 \n", + "15 1 4 1 6.25e-07 2.00e+01 nu-fission 4.22e-04 \n", + "16 1 5 1 0.00e+00 6.25e-07 fission 7.62e-04 \n", + "17 1 5 1 0.00e+00 6.25e-07 nu-fission 1.86e-03 \n", + "18 1 5 1 6.25e-07 2.00e+01 fission 1.80e-04 \n", + "19 1 5 1 6.25e-07 2.00e+01 nu-fission 4.71e-04 \n", "\n", - " energy high [MeV] score mean std. dev. \n", - "0 6.25e-07 fission 2.02e-04 3.69e-05 \n", - "1 6.25e-07 nu-fission 4.92e-04 8.98e-05 \n", - "2 2.00e+01 fission 7.62e-05 3.74e-06 \n", - "3 2.00e+01 nu-fission 2.04e-04 9.88e-06 \n", - "4 6.25e-07 fission 3.75e-04 3.86e-05 \n", - "5 6.25e-07 nu-fission 9.14e-04 9.41e-05 \n", - "6 2.00e+01 fission 1.07e-04 1.26e-05 \n", - "7 2.00e+01 nu-fission 2.78e-04 3.16e-05 \n", - "8 6.25e-07 fission 5.64e-04 5.60e-05 \n", - "9 6.25e-07 nu-fission 1.37e-03 1.37e-04 \n", - "10 2.00e+01 fission 1.49e-04 7.25e-06 \n", - "11 2.00e+01 nu-fission 3.88e-04 1.78e-05 \n", - "12 6.25e-07 fission 6.69e-04 4.44e-05 \n", - "13 6.25e-07 nu-fission 1.63e-03 1.08e-04 \n", - "14 2.00e+01 fission 1.65e-04 1.09e-05 \n", - "15 2.00e+01 nu-fission 4.33e-04 2.89e-05 \n", - "16 6.25e-07 fission 9.32e-04 6.90e-05 \n", - "17 6.25e-07 nu-fission 2.27e-03 1.68e-04 \n", - "18 2.00e+01 fission 1.83e-04 1.10e-05 \n", - "19 2.00e+01 nu-fission 4.77e-04 2.77e-05 " + " std. dev. \n", + " \n", + "0 3.06e-05 \n", + "1 7.46e-05 \n", + "2 5.15e-06 \n", + "3 1.28e-05 \n", + "4 3.09e-05 \n", + "5 7.54e-05 \n", + "6 5.08e-06 \n", + "7 1.34e-05 \n", + "8 5.00e-05 \n", + "9 1.22e-04 \n", + "10 1.03e-05 \n", + "11 2.54e-05 \n", + "12 4.25e-05 \n", + "13 1.04e-04 \n", + "14 7.43e-06 \n", + "15 1.93e-05 \n", + "16 5.69e-05 \n", + "17 1.39e-04 \n", + "18 8.16e-06 \n", + "19 2.08e-05 " ] }, "execution_count": 25, @@ -1112,9 +1135,9 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEaCAYAAADtxAsqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEaCAYAAADpMdsXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhnwAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1137,7 +1160,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 27, @@ -1146,9 +1169,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU0AAAEZCAYAAAAT73clAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVAAAAEZCAYAAADBv319AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqJlZov8PnPpCLiDrzj8AAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1218,7 +1241,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -1232,148 +1255,148 @@ " \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", - " \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", + " \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", + " \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", + " \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", - " \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", "
0 10000 U-235 scatter-Y0,0 3.71e-021.15e-03010000U-235scatter-Y0,03.77e-026.49e-04
1 10000 U-235 scatter-Y1,-1 2.66e-043.23e-04
2 10000 U-235 scatter-Y1,0-4.17e-042.74e-04
3 10000 U-235 scatter-Y1,1-2.28e-042.37e-04
4 10000 U-235 scatter-Y2,-2 2.57e-051.99e-04
5 10000 U-235 scatter-Y2,-1-1.15e-041.85e-04
6 10000 U-235 scatter-Y2,0 1.51e-041.59e-04
7 10000 U-235 scatter-Y2,1-1.22e-042.80e-04
8 10000 U-235 scatter-Y2,2 7.65e-06110000U-235scatter-Y1,-12.54e-041.81e-04
9 10000 U-238 scatter-Y0,0 2.33e+001.31e-02210000U-235scatter-Y1,03.65e-052.70e-04
310000U-235scatter-Y1,1-1.70e-042.19e-04
410000U-235scatter-Y2,-27.47e-051.54e-04
510000U-235scatter-Y2,-1-2.35e-041.34e-04
610000U-235scatter-Y2,0-5.51e-051.79e-04
710000U-235scatter-Y2,1-1.27e-041.54e-04
810000U-235scatter-Y2,21.72e-041.40e-04
910000U-238scatter-Y0,02.34e+007.62e-03
10 10000 U-238 scatter-Y1,-1 2.45e-022.27e-0310000U-238scatter-Y1,-12.46e-021.71e-03
11 10000 U-238 scatter-Y1,0-5.87e-052.80e-0310000U-238scatter-Y1,01.15e-032.17e-03
12 10000 U-238 scatter-Y1,1-2.80e-022.54e-0310000U-238scatter-Y1,1-2.39e-022.15e-03
13 10000 U-238 scatter-Y2,-2-4.86e-031.58e-0310000U-238scatter-Y2,-2-3.92e-031.38e-03
14 10000 U-238 scatter-Y2,-1 5.57e-042.02e-0310000U-238scatter-Y2,-1-1.19e-031.58e-03
15 10000 U-238 scatter-Y2,0 6.24e-031.63e-0310000U-238scatter-Y2,03.22e-031.45e-03
16 10000 U-238 scatter-Y2,1-6.48e-041.55e-0310000U-238scatter-Y2,11.27e-049.70e-04
17 10000 U-238 scatter-Y2,2-1.03e-031.31e-0310000U-238scatter-Y2,2-2.70e-031.21e-03
\n", @@ -1381,24 +1404,24 @@ ], "text/plain": [ " cell nuclide score mean std. dev.\n", - "0 10000 U-235 scatter-Y0,0 3.71e-02 1.15e-03\n", - "1 10000 U-235 scatter-Y1,-1 2.66e-04 3.23e-04\n", - "2 10000 U-235 scatter-Y1,0 -4.17e-04 2.74e-04\n", - "3 10000 U-235 scatter-Y1,1 -2.28e-04 2.37e-04\n", - "4 10000 U-235 scatter-Y2,-2 2.57e-05 1.99e-04\n", - "5 10000 U-235 scatter-Y2,-1 -1.15e-04 1.85e-04\n", - "6 10000 U-235 scatter-Y2,0 1.51e-04 1.59e-04\n", - "7 10000 U-235 scatter-Y2,1 -1.22e-04 2.80e-04\n", - "8 10000 U-235 scatter-Y2,2 7.65e-06 1.81e-04\n", - "9 10000 U-238 scatter-Y0,0 2.33e+00 1.31e-02\n", - "10 10000 U-238 scatter-Y1,-1 2.45e-02 2.27e-03\n", - "11 10000 U-238 scatter-Y1,0 -5.87e-05 2.80e-03\n", - "12 10000 U-238 scatter-Y1,1 -2.80e-02 2.54e-03\n", - "13 10000 U-238 scatter-Y2,-2 -4.86e-03 1.58e-03\n", - "14 10000 U-238 scatter-Y2,-1 5.57e-04 2.02e-03\n", - "15 10000 U-238 scatter-Y2,0 6.24e-03 1.63e-03\n", - "16 10000 U-238 scatter-Y2,1 -6.48e-04 1.55e-03\n", - "17 10000 U-238 scatter-Y2,2 -1.03e-03 1.31e-03" + "0 10000 U-235 scatter-Y0,0 3.77e-02 6.49e-04\n", + "1 10000 U-235 scatter-Y1,-1 2.54e-04 1.81e-04\n", + "2 10000 U-235 scatter-Y1,0 3.65e-05 2.70e-04\n", + "3 10000 U-235 scatter-Y1,1 -1.70e-04 2.19e-04\n", + "4 10000 U-235 scatter-Y2,-2 7.47e-05 1.54e-04\n", + "5 10000 U-235 scatter-Y2,-1 -2.35e-04 1.34e-04\n", + "6 10000 U-235 scatter-Y2,0 -5.51e-05 1.79e-04\n", + "7 10000 U-235 scatter-Y2,1 -1.27e-04 1.54e-04\n", + "8 10000 U-235 scatter-Y2,2 1.72e-04 1.40e-04\n", + "9 10000 U-238 scatter-Y0,0 2.34e+00 7.62e-03\n", + "10 10000 U-238 scatter-Y1,-1 2.46e-02 1.71e-03\n", + "11 10000 U-238 scatter-Y1,0 1.15e-03 2.17e-03\n", + "12 10000 U-238 scatter-Y1,1 -2.39e-02 2.15e-03\n", + "13 10000 U-238 scatter-Y2,-2 -3.92e-03 1.38e-03\n", + "14 10000 U-238 scatter-Y2,-1 -1.19e-03 1.58e-03\n", + "15 10000 U-238 scatter-Y2,0 3.22e-03 1.45e-03\n", + "16 10000 U-238 scatter-Y2,1 1.27e-04 9.70e-04\n", + "17 10000 U-238 scatter-Y2,2 -2.70e-03 1.21e-03" ] }, "execution_count": 29, @@ -1432,8 +1455,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00131009 0.01310707]\n", - " [ 0.00018089 0.00114976]]]\n" + "[[[ 0.00121338 0.00761835]\n", + " [ 0.00013952 0.00064888]]]\n" ] } ], @@ -1501,7 +1524,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.04537029]]]\n" + "[[[ 0.03284934]]]\n" ] } ], @@ -1530,7 +1553,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -1544,143 +1567,143 @@ " \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", + " \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", - " \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", + " \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", " \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", "
558 279 absorption9.27e-051.33e-05
559 279 scatter1.35e-028.05e-04
560 280 absorption8.41e-059.92e-06
561 280 scatter1.42e-026.12e-04
562 281 absorption9.12e-05279absorption7.14e-058.26e-06
559279scatter1.25e-025.75e-04
560280absorption8.50e-056.16e-06
561280scatter1.38e-024.58e-04
562281absorption1.04e-047.54e-06
563 281 scatter1.45e-025.90e-04281scatter1.55e-024.15e-04
564 282 absorption1.12e-041.24e-05282absorption1.22e-049.98e-06
565 282 scatter1.63e-027.29e-04282scatter1.68e-025.73e-04
566 283 absorption9.18e-057.12e-06283absorption1.14e-048.02e-06
567 283 scatter1.62e-026.61e-04283scatter1.66e-025.44e-04
568 284 absorption1.04e-041.10e-05284absorption1.06e-048.37e-06
569 284 scatter1.74e-025.99e-04284scatter1.64e-025.14e-04
570 285 absorption1.11e-041.14e-05285absorption1.23e-049.19e-06
571 285 scatter1.80e-027.74e-04285scatter1.70e-025.34e-04
572 286 absorption1.25e-041.20e-05286absorption1.14e-046.70e-06
573 286 scatter1.83e-028.28e-04286scatter1.75e-025.68e-04
574 287 absorption1.19e-041.30e-05287absorption1.14e-048.10e-06
575 287 scatter1.75e-027.57e-04287scatter1.72e-024.93e-04
576 288 absorption1.13e-041.40e-05288absorption1.06e-041.07e-05
577 288 scatter1.82e-027.82e-04288scatter1.72e-027.73e-04
\n", @@ -1688,26 +1711,26 @@ ], "text/plain": [ " distribcell score mean std. dev.\n", - "558 279 absorption 9.27e-05 1.33e-05\n", - "559 279 scatter 1.35e-02 8.05e-04\n", - "560 280 absorption 8.41e-05 9.92e-06\n", - "561 280 scatter 1.42e-02 6.12e-04\n", - "562 281 absorption 9.12e-05 8.26e-06\n", - "563 281 scatter 1.45e-02 5.90e-04\n", - "564 282 absorption 1.12e-04 1.24e-05\n", - "565 282 scatter 1.63e-02 7.29e-04\n", - "566 283 absorption 9.18e-05 7.12e-06\n", - "567 283 scatter 1.62e-02 6.61e-04\n", - "568 284 absorption 1.04e-04 1.10e-05\n", - "569 284 scatter 1.74e-02 5.99e-04\n", - "570 285 absorption 1.11e-04 1.14e-05\n", - "571 285 scatter 1.80e-02 7.74e-04\n", - "572 286 absorption 1.25e-04 1.20e-05\n", - "573 286 scatter 1.83e-02 8.28e-04\n", - "574 287 absorption 1.19e-04 1.30e-05\n", - "575 287 scatter 1.75e-02 7.57e-04\n", - "576 288 absorption 1.13e-04 1.40e-05\n", - "577 288 scatter 1.82e-02 7.82e-04" + "558 279 absorption 7.14e-05 8.26e-06\n", + "559 279 scatter 1.25e-02 5.75e-04\n", + "560 280 absorption 8.50e-05 6.16e-06\n", + "561 280 scatter 1.38e-02 4.58e-04\n", + "562 281 absorption 1.04e-04 7.54e-06\n", + "563 281 scatter 1.55e-02 4.15e-04\n", + "564 282 absorption 1.22e-04 9.98e-06\n", + "565 282 scatter 1.68e-02 5.73e-04\n", + "566 283 absorption 1.14e-04 8.02e-06\n", + "567 283 scatter 1.66e-02 5.44e-04\n", + "568 284 absorption 1.06e-04 8.37e-06\n", + "569 284 scatter 1.64e-02 5.14e-04\n", + "570 285 absorption 1.23e-04 9.19e-06\n", + "571 285 scatter 1.70e-02 5.34e-04\n", + "572 286 absorption 1.14e-04 6.70e-06\n", + "573 286 scatter 1.75e-02 5.68e-04\n", + "574 287 absorption 1.14e-04 8.10e-06\n", + "575 287 scatter 1.72e-02 4.93e-04\n", + "576 288 absorption 1.06e-04 1.07e-05\n", + "577 288 scatter 1.72e-02 7.73e-04" ] }, "execution_count": 33, @@ -1738,425 +1761,27 @@ }, "outputs": [ { - "data": { - "text/html": [ - "
\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", - " \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", - " \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", - " \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", - " \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", - " \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", - " \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", - "
(level 1, cell, id)(level 1, univ, id)(level 2, lat, id)(level 2, lat, x)(level 2, lat, y)(level 2, lat, z)(level 3, cell, id)(level 3, univ, id)distribcellscoremeanstd. dev.
0 10003 0 10001 0 0 0 10002 10000 0 absorption1.23e-041.19e-05
1 10003 0 10001 0 0 0 10002 10000 0 scatter1.78e-028.08e-04
2 10003 0 10001 0 1 0 10002 10000 1 absorption2.17e-041.96e-05
3 10003 0 10001 0 1 0 10002 10000 1 scatter2.89e-021.26e-03
4 10003 0 10001 0 2 0 10002 10000 2 absorption3.18e-042.03e-05
5 10003 0 10001 0 2 0 10002 10000 2 scatter4.05e-021.27e-03
6 10003 0 10001 0 3 0 10002 10000 3 absorption3.86e-041.80e-05
7 10003 0 10001 0 3 0 10002 10000 3 scatter4.86e-021.34e-03
8 10003 0 10001 0 4 0 10002 10000 4 absorption5.01e-042.60e-05
9 10003 0 10001 0 4 0 10002 10000 4 scatter5.71e-021.72e-03
10 10003 0 10001 0 5 0 10002 10000 5 absorption4.84e-042.58e-05
11 10003 0 10001 0 5 0 10002 10000 5 scatter6.08e-021.58e-03
12 10003 0 10001 0 6 0 10002 10000 6 absorption5.32e-043.90e-05
13 10003 0 10001 0 6 0 10002 10000 6 scatter6.91e-022.25e-03
14 10003 0 10001 0 7 0 10002 10000 7 absorption5.77e-043.92e-05
15 10003 0 10001 0 7 0 10002 10000 7 scatter7.67e-022.34e-03
16 10003 0 10001 0 8 0 10002 10000 8 absorption6.49e-043.90e-05
17 10003 0 10001 0 8 0 10002 10000 8 scatter8.16e-021.61e-03
18 10003 0 10001 0 9 0 10002 10000 9 absorption6.80e-043.17e-05
19 10003 0 10001 0 9 0 10002 10000 9 scatter8.77e-021.96e-03
\n", - "
" - ], - "text/plain": [ - " (level 1, cell, id) (level 1, univ, id) (level 2, lat, id) \\\n", - "0 10003 0 10001 \n", - "1 10003 0 10001 \n", - "2 10003 0 10001 \n", - "3 10003 0 10001 \n", - "4 10003 0 10001 \n", - "5 10003 0 10001 \n", - "6 10003 0 10001 \n", - "7 10003 0 10001 \n", - "8 10003 0 10001 \n", - "9 10003 0 10001 \n", - "10 10003 0 10001 \n", - "11 10003 0 10001 \n", - "12 10003 0 10001 \n", - "13 10003 0 10001 \n", - "14 10003 0 10001 \n", - "15 10003 0 10001 \n", - "16 10003 0 10001 \n", - "17 10003 0 10001 \n", - "18 10003 0 10001 \n", - "19 10003 0 10001 \n", - "\n", - " (level 2, lat, x) (level 2, lat, y) (level 2, lat, z) \\\n", - "0 0 0 0 \n", - "1 0 0 0 \n", - "2 0 1 0 \n", - "3 0 1 0 \n", - "4 0 2 0 \n", - "5 0 2 0 \n", - "6 0 3 0 \n", - "7 0 3 0 \n", - "8 0 4 0 \n", - "9 0 4 0 \n", - "10 0 5 0 \n", - "11 0 5 0 \n", - "12 0 6 0 \n", - "13 0 6 0 \n", - "14 0 7 0 \n", - "15 0 7 0 \n", - "16 0 8 0 \n", - "17 0 8 0 \n", - "18 0 9 0 \n", - "19 0 9 0 \n", - "\n", - " (level 3, cell, id) (level 3, univ, id) distribcell score \\\n", - "0 10002 10000 0 absorption \n", - "1 10002 10000 0 scatter \n", - "2 10002 10000 1 absorption \n", - "3 10002 10000 1 scatter \n", - "4 10002 10000 2 absorption \n", - "5 10002 10000 2 scatter \n", - "6 10002 10000 3 absorption \n", - "7 10002 10000 3 scatter \n", - "8 10002 10000 4 absorption \n", - "9 10002 10000 4 scatter \n", - "10 10002 10000 5 absorption \n", - "11 10002 10000 5 scatter \n", - "12 10002 10000 6 absorption \n", - "13 10002 10000 6 scatter \n", - "14 10002 10000 7 absorption \n", - "15 10002 10000 7 scatter \n", - "16 10002 10000 8 absorption \n", - "17 10002 10000 8 scatter \n", - "18 10002 10000 9 absorption \n", - "19 10002 10000 9 scatter \n", - "\n", - " mean std. dev. \n", - "0 1.23e-04 1.19e-05 \n", - "1 1.78e-02 8.08e-04 \n", - "2 2.17e-04 1.96e-05 \n", - "3 2.89e-02 1.26e-03 \n", - "4 3.18e-04 2.03e-05 \n", - "5 4.05e-02 1.27e-03 \n", - "6 3.86e-04 1.80e-05 \n", - "7 4.86e-02 1.34e-03 \n", - "8 5.01e-04 2.60e-05 \n", - "9 5.71e-02 1.72e-03 \n", - "10 4.84e-04 2.58e-05 \n", - "11 6.08e-02 1.58e-03 \n", - "12 5.32e-04 3.90e-05 \n", - "13 6.91e-02 2.25e-03 \n", - "14 5.77e-04 3.92e-05 \n", - "15 7.67e-02 2.34e-03 \n", - "16 6.49e-04 3.90e-05 \n", - "17 8.16e-02 1.61e-03 \n", - "18 6.80e-04 3.17e-05 \n", - "19 8.77e-02 1.96e-03 " - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "(('level 1', 'lat', 'x'), array([], dtype=float64), Filter\n", + "\tType =\tdistribcell\n", + "\tBins =\t[10002]\n", + ")\n" + ] + }, + { + "ename": "ZeroDivisionError", + "evalue": "integer division or modulo by zero", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Get a pandas dataframe for the distribcell tally data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtally\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_pandas_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnuclides\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# Print the last twenty rows in the dataframe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/home/wboyd/Documents/NSE-CRPG-Codes/openmc/openmc/tallies.pyc\u001b[0m in \u001b[0;36mget_pandas_dataframe\u001b[1;34m(self, filters, nuclides, scores, summary, float_format)\u001b[0m\n\u001b[0;32m 1609\u001b[0m \u001b[1;31m# Append each Filter's DataFrame to the overall DataFrame\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1610\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mself_filter\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilters\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1611\u001b[1;33m \u001b[0mfilter_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself_filter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_pandas_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msummary\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1612\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilter_df\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1613\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/home/wboyd/Documents/NSE-CRPG-Codes/openmc/openmc/filter.py\u001b[0m in \u001b[0;36mget_pandas_dataframe\u001b[1;34m(self, data_size, summary)\u001b[0m\n\u001b[0;32m 739\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevel_key\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel_bins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 740\u001b[0m \u001b[0mlevel_bins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevel_bins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 741\u001b[1;33m \u001b[0mtile_factor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata_size\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevel_bins\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 742\u001b[0m \u001b[0mlevel_bins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevel_bins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtile_factor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 743\u001b[0m \u001b[0mlevel_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlevel_key\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlevel_bins\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero" + ] } ], "source": [ @@ -2169,85 +1794,11 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/html": [ - "
\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", - " \n", - " \n", - " \n", - " \n", - "
meanstd. dev.
count2.89e+022.89e+02
mean4.18e-042.17e-05
std2.39e-048.82e-06
min1.81e-053.82e-06
25%2.02e-041.49e-05
50%4.02e-042.11e-05
75%6.15e-042.67e-05
max8.92e-044.43e-05
\n", - "
" - ], - "text/plain": [ - " mean std. dev.\n", - "count 2.89e+02 2.89e+02\n", - "mean 4.18e-04 2.17e-05\n", - "std 2.39e-04 8.82e-06\n", - "min 1.81e-05 3.82e-06\n", - "25% 2.02e-04 1.49e-05\n", - "50% 4.02e-04 2.11e-05\n", - "75% 6.15e-04 2.67e-05\n", - "max 8.92e-04 4.43e-05" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Show summary statistics for absorption distribcell tally data\n", "absorption = df[df['score'] == 'absorption']\n", @@ -2266,19 +1817,11 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mann-Whitney Test p-value: 0.414863173548\n" - ] - } - ], + "outputs": [], "source": [ "# Extract tally data from pins in the pins divided along y=x diagonal \n", "multi_index = ('level 2', 'lat',)\n", @@ -2304,19 +1847,11 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mann-Whitney Test p-value: 3.28554363741e-42\n" - ] - } - ], + "outputs": [], "source": [ "# Extract tally data from pins in the pins divided along y=-x diagonal\n", "multi_index = ('level 2', 'lat',)\n", @@ -2340,43 +1875,11 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/smharper/.local/lib/python2.7/site-packages/ipykernel/__main__.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 the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEZCAYAAABb3GilAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Extract the scatter tally data from pandas\n", "scatter = df[df['score'] == 'scatter']\n", @@ -2389,32 +1892,11 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEZCAYAAAB4hzlwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot a histogram and kernel density estimate for the scattering rates\n", "scatter['mean'].plot(kind='hist', bins=25)\n", @@ -2441,7 +1923,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.11" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/post-processing.ipynb b/docs/source/pythonapi/examples/post-processing.ipynb index 6526f0307..7daf1d1cf 100644 --- a/docs/source/pythonapi/examples/post-processing.ipynb +++ b/docs/source/pythonapi/examples/post-processing.ipynb @@ -350,7 +350,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nFMTl8QAAAABJRU5ErkJggg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\ngSrIowAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] @@ -460,8 +460,10 @@ " Copyright: 2011-2015 Massachusetts Institute of Technology\n", " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.1\n", - " Git SHA1: ea9fb637f63f9374c7436456141afa850b84acf9\n", - " Date/Time: 2016-01-14 07:02:06\n", + " Git SHA1: 5f252e2df51930b9175fd41bafa8db01f3eaeb92\n", + " Date/Time: 2016-03-23 13:12:56\n", + " MPI Processes: 1\n", + " OpenMP Threads: 16\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -488,106 +490,106 @@ "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", - " 1/1 1.04894 \n", - " 2/1 1.01711 \n", - " 3/1 1.05357 \n", - " 4/1 1.03052 \n", - " 5/1 1.06523 \n", - " 6/1 1.06806 \n", - " 7/1 1.05161 \n", - " 8/1 1.04199 \n", - " 9/1 1.05010 \n", - " 10/1 1.04617 \n", - " 11/1 1.04894 \n", - " 12/1 1.06806 1.05850 +/- 0.00956\n", - " 13/1 1.05002 1.05567 +/- 0.00620\n", - " 14/1 1.03471 1.05043 +/- 0.00683\n", - " 15/1 1.01803 1.04395 +/- 0.00837\n", - " 16/1 1.05588 1.04594 +/- 0.00712\n", - " 17/1 1.07503 1.05010 +/- 0.00731\n", - " 18/1 1.02786 1.04732 +/- 0.00691\n", - " 19/1 1.00071 1.04214 +/- 0.00800\n", - " 20/1 1.05587 1.04351 +/- 0.00729\n", - " 21/1 1.03886 1.04309 +/- 0.00660\n", - " 22/1 1.04335 1.04311 +/- 0.00603\n", - " 23/1 1.04057 1.04292 +/- 0.00555\n", - " 24/1 1.01976 1.04126 +/- 0.00540\n", - " 25/1 1.05811 1.04238 +/- 0.00515\n", - " 26/1 1.02351 1.04120 +/- 0.00496\n", - " 27/1 1.05261 1.04188 +/- 0.00471\n", - " 28/1 1.03355 1.04141 +/- 0.00446\n", - " 29/1 1.02797 1.04071 +/- 0.00428\n", - " 30/1 1.03758 1.04055 +/- 0.00406\n", - " 31/1 1.04883 1.04094 +/- 0.00388\n", - " 32/1 1.03557 1.04070 +/- 0.00371\n", - " 33/1 1.02947 1.04021 +/- 0.00358\n", - " 34/1 1.03651 1.04006 +/- 0.00343\n", - " 35/1 1.03331 1.03979 +/- 0.00330\n", - " 36/1 1.05947 1.04054 +/- 0.00326\n", - " 37/1 1.05093 1.04093 +/- 0.00316\n", - " 38/1 1.06787 1.04189 +/- 0.00319\n", - " 39/1 1.01451 1.04095 +/- 0.00322\n", - " 40/1 1.02351 1.04037 +/- 0.00317\n", - " 41/1 1.04826 1.04062 +/- 0.00307\n", - " 42/1 1.04228 1.04067 +/- 0.00298\n", - " 43/1 1.03214 1.04041 +/- 0.00290\n", - " 44/1 1.04950 1.04068 +/- 0.00282\n", - " 45/1 1.06616 1.04141 +/- 0.00284\n", - " 46/1 1.07039 1.04221 +/- 0.00287\n", - " 47/1 1.00292 1.04115 +/- 0.00299\n", - " 48/1 1.04477 1.04125 +/- 0.00291\n", - " 49/1 1.03360 1.04105 +/- 0.00284\n", - " 50/1 1.04783 1.04122 +/- 0.00277\n", - " 51/1 1.03985 1.04119 +/- 0.00271\n", - " 52/1 1.02507 1.04080 +/- 0.00267\n", - " 53/1 1.03477 1.04066 +/- 0.00261\n", - " 54/1 1.00412 1.03983 +/- 0.00268\n", - " 55/1 1.02239 1.03945 +/- 0.00265\n", - " 56/1 1.04308 1.03952 +/- 0.00259\n", - " 57/1 1.05534 1.03986 +/- 0.00256\n", - " 58/1 1.06667 1.04042 +/- 0.00257\n", - " 59/1 1.06458 1.04091 +/- 0.00256\n", - " 60/1 1.00304 1.04015 +/- 0.00262\n", - " 61/1 1.05038 1.04036 +/- 0.00258\n", - " 62/1 1.02904 1.04014 +/- 0.00254\n", - " 63/1 1.00249 1.03943 +/- 0.00259\n", - " 64/1 1.01779 1.03903 +/- 0.00257\n", - " 65/1 1.05335 1.03929 +/- 0.00254\n", - " 66/1 1.06231 1.03970 +/- 0.00253\n", - " 67/1 1.02382 1.03942 +/- 0.00250\n", - " 68/1 1.03796 1.03939 +/- 0.00245\n", - " 69/1 1.03672 1.03935 +/- 0.00241\n", - " 70/1 1.02926 1.03918 +/- 0.00238\n", - " 71/1 1.05834 1.03950 +/- 0.00236\n", - " 72/1 1.04332 1.03956 +/- 0.00232\n", - " 73/1 1.05613 1.03982 +/- 0.00230\n", - " 74/1 1.01963 1.03950 +/- 0.00228\n", - " 75/1 1.02228 1.03924 +/- 0.00226\n", - " 76/1 1.04842 1.03938 +/- 0.00223\n", - " 77/1 1.02157 1.03911 +/- 0.00222\n", - " 78/1 1.02810 1.03895 +/- 0.00219\n", - " 79/1 1.05030 1.03912 +/- 0.00216\n", - " 80/1 1.02391 1.03890 +/- 0.00214\n", - " 81/1 1.02488 1.03870 +/- 0.00212\n", - " 82/1 1.04957 1.03885 +/- 0.00210\n", - " 83/1 1.03499 1.03880 +/- 0.00207\n", - " 84/1 1.05922 1.03907 +/- 0.00206\n", - " 85/1 1.05898 1.03934 +/- 0.00205\n", - " 86/1 1.02242 1.03912 +/- 0.00204\n", - " 87/1 1.03278 1.03904 +/- 0.00201\n", - " 88/1 1.06134 1.03932 +/- 0.00201\n", - " 89/1 1.04521 1.03940 +/- 0.00198\n", - " 90/1 1.04277 1.03944 +/- 0.00196\n", - " 91/1 1.04214 1.03947 +/- 0.00193\n", - " 92/1 1.05610 1.03967 +/- 0.00192\n", - " 93/1 1.04531 1.03974 +/- 0.00190\n", - " 94/1 1.01534 1.03945 +/- 0.00190\n", - " 95/1 1.03971 1.03945 +/- 0.00187\n", - " 96/1 1.07183 1.03983 +/- 0.00189\n", - " 97/1 1.07214 1.04020 +/- 0.00191\n", - " 98/1 1.03710 1.04017 +/- 0.00188\n", - " 99/1 1.02532 1.04000 +/- 0.00187\n", - " 100/1 1.03965 1.04000 +/- 0.00185\n", + " 1/1 1.03019 \n", + " 2/1 1.06141 \n", + " 3/1 1.03988 \n", + " 4/1 1.02696 \n", + " 5/1 1.06159 \n", + " 6/1 1.03855 \n", + " 7/1 1.03452 \n", + " 8/1 1.04526 \n", + " 9/1 1.02137 \n", + " 10/1 1.02129 \n", + " 11/1 1.04810 \n", + " 12/1 1.00454 1.02632 +/- 0.02178\n", + " 13/1 1.06176 1.03813 +/- 0.01725\n", + " 14/1 1.02927 1.03592 +/- 0.01240\n", + " 15/1 1.06158 1.04105 +/- 0.01089\n", + " 16/1 1.02692 1.03870 +/- 0.00920\n", + " 17/1 1.06703 1.04274 +/- 0.00876\n", + " 18/1 1.02341 1.04033 +/- 0.00797\n", + " 19/1 1.06256 1.04280 +/- 0.00745\n", + " 20/1 1.04829 1.04335 +/- 0.00668\n", + " 21/1 1.01742 1.04099 +/- 0.00649\n", + " 22/1 1.01629 1.03893 +/- 0.00627\n", + " 23/1 1.01145 1.03682 +/- 0.00614\n", + " 24/1 1.05042 1.03779 +/- 0.00577\n", + " 25/1 1.02543 1.03696 +/- 0.00543\n", + " 26/1 1.04643 1.03756 +/- 0.00512\n", + " 27/1 1.03020 1.03712 +/- 0.00483\n", + " 28/1 1.04088 1.03733 +/- 0.00456\n", + " 29/1 1.03885 1.03741 +/- 0.00431\n", + " 30/1 1.05497 1.03829 +/- 0.00418\n", + " 31/1 1.01946 1.03739 +/- 0.00408\n", + " 32/1 1.07049 1.03890 +/- 0.00417\n", + " 33/1 1.05920 1.03978 +/- 0.00408\n", + " 34/1 1.04910 1.04017 +/- 0.00393\n", + " 35/1 1.03827 1.04009 +/- 0.00377\n", + " 36/1 1.08004 1.04163 +/- 0.00393\n", + " 37/1 1.05729 1.04221 +/- 0.00383\n", + " 38/1 1.00328 1.04082 +/- 0.00394\n", + " 39/1 1.04603 1.04100 +/- 0.00381\n", + " 40/1 1.03193 1.04070 +/- 0.00369\n", + " 41/1 1.05548 1.04117 +/- 0.00360\n", + " 42/1 1.03566 1.04100 +/- 0.00349\n", + " 43/1 1.02848 1.04062 +/- 0.00340\n", + " 44/1 1.01806 1.03996 +/- 0.00337\n", + " 45/1 1.05404 1.04036 +/- 0.00330\n", + " 46/1 1.06319 1.04099 +/- 0.00327\n", + " 47/1 1.03238 1.04076 +/- 0.00318\n", + " 48/1 1.07148 1.04157 +/- 0.00320\n", + " 49/1 1.06016 1.04205 +/- 0.00316\n", + " 50/1 1.02051 1.04151 +/- 0.00312\n", + " 51/1 1.04903 1.04169 +/- 0.00305\n", + " 52/1 1.06004 1.04213 +/- 0.00301\n", + " 53/1 1.04790 1.04226 +/- 0.00294\n", + " 54/1 1.03742 1.04215 +/- 0.00288\n", + " 55/1 1.05670 1.04248 +/- 0.00283\n", + " 56/1 1.02739 1.04215 +/- 0.00279\n", + " 57/1 1.03133 1.04192 +/- 0.00274\n", + " 58/1 1.00078 1.04106 +/- 0.00281\n", + " 59/1 1.06328 1.04151 +/- 0.00279\n", + " 60/1 1.02275 1.04114 +/- 0.00276\n", + " 61/1 1.04295 1.04117 +/- 0.00271\n", + " 62/1 1.06079 1.04155 +/- 0.00268\n", + " 63/1 1.02148 1.04117 +/- 0.00266\n", + " 64/1 1.04801 1.04130 +/- 0.00261\n", + " 65/1 1.03501 1.04119 +/- 0.00257\n", + " 66/1 1.07021 1.04170 +/- 0.00257\n", + " 67/1 1.01764 1.04128 +/- 0.00256\n", + " 68/1 1.02806 1.04105 +/- 0.00253\n", + " 69/1 1.01645 1.04064 +/- 0.00252\n", + " 70/1 1.03971 1.04062 +/- 0.00248\n", + " 71/1 1.06581 1.04103 +/- 0.00247\n", + " 72/1 1.03359 1.04091 +/- 0.00243\n", + " 73/1 1.02155 1.04061 +/- 0.00241\n", + " 74/1 1.06730 1.04102 +/- 0.00241\n", + " 75/1 1.03557 1.04094 +/- 0.00238\n", + " 76/1 1.03795 1.04089 +/- 0.00234\n", + " 77/1 1.02976 1.04073 +/- 0.00231\n", + " 78/1 1.02257 1.04046 +/- 0.00229\n", + " 79/1 1.05500 1.04067 +/- 0.00227\n", + " 80/1 1.03306 1.04056 +/- 0.00224\n", + " 81/1 1.04693 1.04065 +/- 0.00221\n", + " 82/1 1.02975 1.04050 +/- 0.00218\n", + " 83/1 1.07900 1.04103 +/- 0.00222\n", + " 84/1 1.02915 1.04087 +/- 0.00219\n", + " 85/1 1.03153 1.04074 +/- 0.00217\n", + " 86/1 1.05792 1.04097 +/- 0.00215\n", + " 87/1 1.06045 1.04122 +/- 0.00214\n", + " 88/1 1.08821 1.04182 +/- 0.00219\n", + " 89/1 1.08077 1.04232 +/- 0.00222\n", + " 90/1 1.06569 1.04261 +/- 0.00221\n", + " 91/1 1.04921 1.04269 +/- 0.00219\n", + " 92/1 1.04849 1.04276 +/- 0.00216\n", + " 93/1 1.06074 1.04298 +/- 0.00215\n", + " 94/1 1.04030 1.04295 +/- 0.00212\n", + " 95/1 1.03190 1.04282 +/- 0.00210\n", + " 96/1 1.04525 1.04285 +/- 0.00207\n", + " 97/1 1.08086 1.04328 +/- 0.00210\n", + " 98/1 1.04070 1.04325 +/- 0.00207\n", + " 99/1 1.05730 1.04341 +/- 0.00206\n", + " 100/1 1.05036 1.04349 +/- 0.00203\n", " Creating state point statepoint.100.h5...\n", "\n", " ===========================================================================\n", @@ -597,27 +599,27 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.6000E-01 seconds\n", - " Reading cross sections = 1.0600E-01 seconds\n", - " Total time in simulation = 2.5756E+02 seconds\n", - " Time in transport only = 2.5751E+02 seconds\n", - " Time in inactive batches = 9.7270E+00 seconds\n", - " Time in active batches = 2.4783E+02 seconds\n", - " Time synchronizing fission bank = 2.1000E-02 seconds\n", - " Sampling source sites = 1.3000E-02 seconds\n", - " SEND/RECV source sites = 8.0000E-03 seconds\n", - " Time accumulating tallies = 1.3000E-02 seconds\n", - " Total time for finalization = 1.4600E-01 seconds\n", - " Total time elapsed = 2.5809E+02 seconds\n", - " Calculation Rate (inactive) = 5140.33 neutrons/second\n", - " Calculation Rate (active) = 1815.75 neutrons/second\n", + " Total time for initialization = 4.4800E-01 seconds\n", + " Reading cross sections = 1.3300E-01 seconds\n", + " Total time in simulation = 4.6592E+01 seconds\n", + " Time in transport only = 4.4918E+01 seconds\n", + " Time in inactive batches = 1.1940E+00 seconds\n", + " Time in active batches = 4.5398E+01 seconds\n", + " Time synchronizing fission bank = 2.2000E-02 seconds\n", + " Sampling source sites = 1.5000E-02 seconds\n", + " SEND/RECV source sites = 4.0000E-03 seconds\n", + " Time accumulating tallies = 3.1000E-02 seconds\n", + " Total time for finalization = 2.7300E-01 seconds\n", + " Total time elapsed = 4.7345E+01 seconds\n", + " Calculation Rate (inactive) = 41876.0 neutrons/second\n", + " Calculation Rate (active) = 9912.33 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.03912 +/- 0.00160\n", - " k-effective (Track-length) = 1.04000 +/- 0.00185\n", - " k-effective (Absorption) = 1.04240 +/- 0.00156\n", - " Combined k-effective = 1.04078 +/- 0.00127\n", + " k-effective (Collision) = 1.04225 +/- 0.00171\n", + " k-effective (Track-length) = 1.04349 +/- 0.00203\n", + " k-effective (Absorption) = 1.04192 +/- 0.00172\n", + " Combined k-effective = 1.04213 +/- 0.00141\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -717,18 +719,18 @@ { "data": { "text/plain": [ - "array([[[ 0.4107676 , 0. ]],\n", + "array([[[ 0.41161103, 0. ]],\n", "\n", - " [[ 0.40849402, 0. ]],\n", + " [[ 0.41135796, 0. ]],\n", "\n", - " [[ 0.41014343, 0. ]],\n", + " [[ 0.41058715, 0. ]],\n", "\n", " ..., \n", - " [[ 0.41049467, 0. ]],\n", + " [[ 0.40919256, 0. ]],\n", "\n", - " [[ 0.40982242, 0. ]],\n", + " [[ 0.41057119, 0. ]],\n", "\n", - " [[ 0.40996987, 0. ]]])" + " [[ 0.41225079, 0. ]]])" ] }, "execution_count": 20, @@ -764,30 +766,30 @@ { "data": { "text/plain": [ - "(array([[[ 0.00456408, 0. ]],\n", + "(array([[[ 0.00457346, 0. ]],\n", " \n", - " [[ 0.00453882, 0. ]],\n", + " [[ 0.00457064, 0. ]],\n", " \n", - " [[ 0.00455715, 0. ]],\n", + " [[ 0.00456208, 0. ]],\n", " \n", " ..., \n", - " [[ 0.00456105, 0. ]],\n", + " [[ 0.00454658, 0. ]],\n", " \n", - " [[ 0.00455358, 0. ]],\n", + " [[ 0.0045619 , 0. ]],\n", " \n", - " [[ 0.00455522, 0. ]]]),\n", - " array([[[ 1.95085625e-05, 0.00000000e+00]],\n", + " [[ 0.00458056, 0. ]]]),\n", + " array([[[ 1.92422804e-05, 0.00000000e+00]],\n", " \n", - " [[ 1.78129859e-05, 0.00000000e+00]],\n", + " [[ 1.58028832e-05, 0.00000000e+00]],\n", " \n", - " [[ 1.89709648e-05, 0.00000000e+00]],\n", + " [[ 1.56204065e-05, 0.00000000e+00]],\n", " \n", " ..., \n", - " [[ 1.56286612e-05, 0.00000000e+00]],\n", + " [[ 1.98926652e-05, 0.00000000e+00]],\n", " \n", - " [[ 1.65813279e-05, 0.00000000e+00]],\n", + " [[ 1.70440988e-05, 0.00000000e+00]],\n", " \n", - " [[ 1.67530331e-05, 0.00000000e+00]]]))" + " [[ 2.05592499e-05, 0.00000000e+00]]]))" ] }, "execution_count": 21, @@ -819,7 +821,7 @@ "output_type": "stream", "text": [ "Tally\n", - "\tID =\t10000\n", + "\tID =\t10001\n", "\tName =\t\n", "\tFilters =\t\n", " \t\tmesh\t[10000]\n", @@ -867,7 +869,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 24, @@ -876,9 +878,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAC4CAYAAADHR9Y0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAC4CAYAAAAohb0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n87qEMGb9LAAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -908,9 +910,9 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEACAYAAACznAEdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEACAYAAACgS0HpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAEmBJREFUeJzt3X+sZGddx/H3p6wtPy0rpne0Ld0qP9walTSxkKDxRk1p\na7Jt1DSgUbBGTVA0Sgy7atyNMSklMajRmiiKi2ltFozQKLa1KVdiIlQopciuZRW6tKs7+JOIJriV\nr3/M2e5wd2b3uTN35s7c+34lk848c86Z73329H7uc55z5qSqkCTpQi7a6gIkScvBwJAkNTEwJElN\nDAxJUhMDQ5LUxMCQJDW5YGAk+f0k/SSPDbXtTvJAkseT3J/k0qH3DiQ5nuRYkuuH2q9N8liSTyX5\n9c3/USRJs9Qywngn8Jp1bfuBB6vq5cBDwAGAJNcAtwJ7gRuBO5OkW+d3gB+tqpcBL0uyfpuSpAV2\nwcCoqr8G/mNd883A4e75YeCW7vk+4J6qerqqngCOA9cl6QEvqKq/7ZZ719A6kqQlMOkcxmVV1Qeo\nqlPAZV375cCTQ8ud7NouB54aan+qa5MkLYnNmvT2+0UkaZvbNeF6/SQrVdXvDjd9rms/CVw5tNwV\nXdu49pGSGECSNIGqyoWXmkzrCCPd44x7gTd0z18PvG+o/bVJLk5yNfAS4OHusNXnk1zXTYL/8NA6\nI1XV0j4OHjy45TXMo/aVlatG/tutrFy1FPUv4sP6rX+ax6xdcISR5G5gFXhRks8CB4G3Au9Ochtw\ngsGZUVTV0SRHgKPAaeCNdfan+EngD4FnA++vqvs290fRrPR6e+j3T4x599ydtN+f2R84krbQBQOj\nqn5gzFvfPWb524HbR7R/FPimDVWnhTAIi1F/vRgM0k7ild4zsLq6utUlTGyZawfr32rWv71lHse9\nNipJLWJdO9Vg2mncCGN0u/9+0vwloRZg0luStMMZGJKkJgaGJKmJgSFJamJgSJKaGBiSpCYGhiSp\niYEhSWpiYEiSmhgYkqQmBoYkqYmBIUlqYmBIkpoYGJKkJgaGJKmJgSFJamJgSJKaGBj6Mr3eHpJ8\n2UOSwMDQOv3+CQa3XR1+bNQl54ROr7dnM8uUtAW8p7e+zOj7d2/8nt6jtuG/qTRb3tNbkrQQDAxJ\nUhMDQ5LUxMCQJDUxMCRJTQwMSVITA0OS1MTAkCQ1MTAkSU0MDElSEwNDktTEwJAkNTEwJElNDAxJ\nUpOpAiPJzyb5uySPJbkrycVJdid5IMnjSe5PcunQ8geSHE9yLMn105cvSZqXiQMjydcCbwKurapv\nBnYBrwP2Aw9W1cuBh4AD3fLXALcCe4EbgTvj7dwkaWlMe0jqWcDzkuwCngOcBG4GDnfvHwZu6Z7v\nA+6pqqer6gngOHDdlJ8vSZqTiQOjqv4J+DXgswyC4vNV9SCwUlX9bplTwGXdKpcDTw5t4mTXJkla\nArsmXTHJCxmMJq4CPg+8O8kPcu69OSe6L+ehQ4eeeb66usrq6upEdWpRXMKoI5ArK1dx6tQT8y9H\n2gbW1tZYW1ub2+dNfE/vJN8PvKaqfqx7/UPAq4DvBFarqp+kB3ygqvYm2Q9UVd3RLX8fcLCqPjxi\n297Te4vM8p7e45b131raHIt8T+/PAq9K8uxu8vq7gKPAvcAbumVeD7yve34v8NruTKqrgZcAD0/x\n+ZKkOZr4kFRVPZzkPcDHgNPdf38XeAFwJMltwAkGZ0ZRVUeTHGEQKqeBNzqMkKTlMfEhqVnykNTs\n9Xp76PdPjHnXQ1LSMpr1ISkDY4caPVcBG/1lb2BIi2OR5zAkSTuIgSFJamJgSJKaGBiSpCYGhiSp\niYEhSWpiYEiSmhgYkqQmBoYkqYmBIUlqYmBIkpoYGJKkJgaGJKmJgSFJamJgSJKaGBiSpCYGhiSp\niYGhLXYJSc559Hp7trowSet4i9YdapFu0eqtW6XN4S1aJUkLwcCQJDUxMCRJTQwMSVITA0OS1MTA\nkCQ1MTAkSU0MDElSEwNDktTEwJAkNTEwJElNDAxJUhMDQ5LUxMCQJDUxMCRJTaYKjCSXJnl3kmNJ\nPpnklUl2J3kgyeNJ7k9y6dDyB5Ic75a/fvryJUnzMu0I4zeA91fVXuBbgL8H9gMPVtXLgYeAAwBJ\nrgFuBfYCNwJ3ZnAXH0nSEpg4MJJ8JfDtVfVOgKp6uqo+D9wMHO4WOwzc0j3fB9zTLfcEcBy4btLP\nV5teb8/IW6BK0kZNM8K4GvjXJO9M8kiS303yXGClqvoAVXUKuKxb/nLgyaH1T3ZtmqF+/wSDW6Cu\nf0jSxkwTGLuAa4Hfrqprgf9mcDhq/W8jfztJ0jawa4p1nwKerKqPdK//hEFg9JOsVFU/SQ/4XPf+\nSeDKofWv6NpGOnTo0DPPV1dXWV1dnaJUSdp+1tbWWFtbm9vnpWryAUCSvwJ+rKo+leQg8NzurX+v\nqjuSvAXYXVX7u0nvu4BXMjgU9ZfAS2tEAUlGNWsCg/mKUX25kfat2Yb7gLQxSaiqmU1STjPCAPhp\n4K4kXwF8GvgR4FnAkSS3AScYnBlFVR1NcgQ4CpwG3mgqSNLymGqEMSuOMDaPIwxp55j1CMMrvSVJ\nTQwMSVITA0OS1MTAkCQ1MTAkSU0MDElSEwNDktTEwJAkNTEwJElNDAwtqEtG3sej19uz1YVJO5Zf\nDbLNLfNXg/iVIdLG+NUgkqSFYGBIkpoYGNuE9+6WNGvOYWwTmzNXMa59UbYxaHffkEZzDkOStBAM\nDElSEwNDktTEwJAkNTEwJElNDAxJUhMDQ5LUxMCQJDUxMCRJTQwMSVITA0OS1MTAkCQ1MTAkSU0M\nDElSEwNDktTEwJAkNTEwJElNDAxJUhMDQ5LUxMCQJDUxMCRJTaYOjCQXJXkkyb3d691JHkjyeJL7\nk1w6tOyBJMeTHEty/bSfLUman80YYfwMcHTo9X7gwap6OfAQcAAgyTXArcBe4EbgziTZhM+XJM3B\nVIGR5ArgJuAdQ803A4e754eBW7rn+4B7qurpqnoCOA5cN83naye6hCRf9uj19mx1UdKOMO0I4+3A\nzwM11LZSVX2AqjoFXNa1Xw48ObTcya5N2oAvMtjdzj76/RNbW5K0Q0wcGEm+B+hX1aPA+Q4t1Xne\nkyQtiV1TrPtqYF+Sm4DnAC9I8kfAqSQrVdVP0gM+1y1/ErhyaP0ruraRDh069Mzz1dVVVldXpyhV\nkraftbU11tbW5vZ5qZp+AJDkO4A3V9W+JG8D/q2q7kjyFmB3Ve3vJr3vAl7J4FDUXwIvrREFJBnV\nrPMYnD8wqs82o31RtjF+2+4v0uD3QFXN7GSiaUYY47wVOJLkNuAEgzOjqKqjSY4wOKPqNPBGU0GS\nlsemjDA2myOMjXOE4f4izXqE4ZXekqQmBoYkqYmBIUlqYmBIkpoYGJKkJgaGJKmJgSFJamJgSJKa\nGBiSpCYGhiSpiYEhSWpiYCyhXm/POXedk6RZMzCW0OAOc7XusZOde9tWb90qbT6/rXYJjf5m2p39\nbbXjlnU/0k7it9VKkhaCgSFJamJgSJKaGBiSpCYGhiSpiYEhSWpiYEiSmhgYkqQmBoYkqYmBIUlq\nYmBIkpoYGJKkJgaGJKmJgSFJamJgSJKaGBiSpCYGhiSpiYEhSWpiYEiSmhgYkqQmBsYC6/X2kOSc\nhyRthVTVVtdwjiS1iHXN2yAcRvXDqPaNLLvR9kXZxsa37X6knSQJVTWzvyonHmEkuSLJQ0k+meQT\nSX66a9+d5IEkjye5P8mlQ+scSHI8ybEk12/GDyBJmo+JRxhJekCvqh5N8nzgo8DNwI8A/1ZVb0vy\nFmB3Ve1Pcg1wF/CtwBXAg8BLRw0lHGEMOMJwhCFtxMKOMKrqVFU92j3/AnCMQRDcDBzuFjsM3NI9\n3wfcU1VPV9UTwHHgukk/X7qwS0bOAfV6e7a6MGkp7dqMjSTZA7wC+BCwUlV9GIRKksu6xS4H/mZo\ntZNdmzQjX2TUyKPf98QBaRJTB0Z3OOo9wM9U1ReSrP8/dKJjAocOHXrm+erqKqurq5OWKEnb0tra\nGmtra3P7vKnOkkqyC/gz4C+q6je6tmPAalX1u3mOD1TV3iT7gaqqO7rl7gMOVtWHR2zXOQycw5jl\ntt2/tB0t7BxG5w+Ao2fConMv8Ibu+euB9w21vzbJxUmuBl4CPDzl50uS5mSas6ReDXwQ+ASDP+MK\n+AUGIXAEuBI4AdxaVf/ZrXMA+FHgNINDWA+M2bYjDBxhOMKQNmbWIwwv3FtgBoaBIW3Eoh+SkiTt\nEAaGJKmJgSFJamJgSJKaGBiSpCYGhiSpiYGhHcgvJZQm4XUYC8zrMOa/bfc7LTOvw5AkLQQDQ5LU\nxMCQJDUxMCRJTQwMSVITA0OS1MTAWAC93p6R1wVI0iLxOowFsLHrLca1L/p1Dote36B9J+132n68\nDkOStBAMDOkZ535liF8XIp21a6sLkBbHF1l/qKrfdy5JOsMRhiSpiYEhSWpiYMzZqFNoJWkZeFrt\nnI0+hXaxTi3dmfWN3/Z23Re1/XharSRpIRgY0nl5dz7pDE+rlc7r3FNtwdNttTM5wpAkNTEwJElN\nDAxpIs5taOdxDkOaiHMb2nkcYUiSmhgYM+JNkSRtN17pPSMbuynS4l/tvBjbWI5tL/u+q+Xlld7S\nUvGeGtq+DAxpU52ZDD/76PdPeUaVtoW5B0aSG5L8fZJPJXnLvD9fmr9zQ8Qg0TKaa2AkuQj4LeA1\nwDcCr0vyDfOsYbM5ub1o1ra6gA0YFSQfWOogWVtb2+oSprLs9c/avEcY1wHHq+pEVZ0G7gFunnMN\nm6rfP8G5/9Mf3NKadra1rS5gSmuMH5Gc2MK62iz7L9xlr3/W5n3h3uXAk0Ovn2IQIgvjHe84zAc/\n+DfntL/3vX/Kf/3X57agIumMS84Zva6sXMWpU09sTTnacbzSe53bb387n/70x8e8O+70Smkezr26\nvN9/9shDoBdd9Fy+9KX/aWofFzq93p6RoxpDauea63UYSV4FHKqqG7rX+4GqqjvWLeeJ7JI0gVle\nhzHvwHgW8DjwXcA/Aw8Dr6uqY3MrQpI0kbkekqqq/0vyU8ADDCbcf9+wkKTlsJBfDSJJWjwzOa22\n5eK8JL+Z5HiSR5O84kLrJnlbkmPd8n+S5CuH3jvQbetYkuuXqf4kVyX5nySPdI87F7D2X0ny8SQf\nS3Jfkt7Qe8vQ9yPr3+y+n1X9Q++/OcmXknzVUNvC9/+4+pel/5McTPLUUJ03DL238P0/rv6J+r+q\nNvXBIIT+AbgK+ArgUeAb1i1zI/Dn3fNXAh+60LrAdwMXdc/fCtzePb8G+BiDw2t7uvWzRPVfBTy2\n4H3//KH13wT8zpL1/bj6N63vZ1l/9/4VwH3AZ4Cv6tr2LkP/n6f+peh/BhdW/dyIz1uK/j9P/Rvu\n/1mMMFouzrsZeBdAVX0YuDTJyvnWraoHq+pL3fofYrADAuwD7qmqp6vqCeA4013bMe/6YfPOzZ1V\n7V8YWv95wJmfY1n6flz9sLnnRc+k/s7bgZ8fsa2F7//z1A/L0/+j6lym/h/Xzxvq/1kExqiL8y5v\nXKZlXYDbgPeP2dbJMeu0mlf9fzH0ek83JPxAkm+btPDGz5+o9iS/muSzwA8AvzxmWwvb92Pqh83r\n+5nVn2Qf8GRVfeIC21rI/j9P/bAE/d/5qe4Q0DuSXDpmWwvZ/53h+l841L6h/l+Ub6ttTrkkvwic\nrqo/nmE9GzVJ/Xd3Tf8EvLiqrgXeDNyd5PkzqHFsSS0LVdUvVdWLgbsYHNZZFNPU/89sbd/DBepP\n8hzgF1jc75uZpP4z62z1vj9cy/ncCXxdVb0COAX82mxL2pBp6t/w/j+LwDgJvHjo9RVd2/plrhyx\nzHnXTfIG4CYGfyVeaFuTmmv9VXW6qv6je/4I8I/Ayxat9iF3A997gW1Nal71fx9AVf3vJvb9rOr/\negbHxz+e5DNd+yNJLmv8vEWs/6NJLtvkfX9W9VNV/1LdQX/g9zh72Gkp9v8R9X9r177x/X/SCZrz\nTNw8i7OTLxczmHzZu26Zmzg7cfMqzk7cjF0XuAH4JPCidds6M/F6MXA10088zbv+r+bsZPjXMRhW\nvnDBan/J0PpvAo4sWd+Pq3/T+n6W9a9b/zPA7mXq//PUvxT9D/SG1v9Z4O5l6v/z1L/h/p/oB2v4\nwW9gcEX3cWB/1/YTwI8PLfNb3Q/4ceDa863btR8HTgCPdI87h9470G3rGHD9MtXP4K/1v+vaPgLc\ntIC1vwd4rNsJ3wd8zZL1/cj6N7vvZ1X/uu1/mu4so2Xp/3H1L0v/M5hkPrP/vBdYWab+H1f/JP3v\nhXuSpCaLMuktSVpwBoYkqYmBIUlqYmBIkpoYGJKkJgaGJKmJgSFJamJgSJKa/D/gtsoePYnNigAA\nAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -951,13 +953,13 @@ { "data": { "text/plain": [ - "array([ (1.0, [0.2712169917165897, -0.04844236597355761, -0.1887902218343974], [0.3889598463000694, 0.8470657529949065, 0.36220139158953857], 2.2746035619924734, 0),\n", - " (1.0, [0.080729018085932, 0.19838688738571317, -0.38053428394017363], [-0.6604834049157511, -0.6893239101986768, 0.2976478097673534], 0.7833467555325838, 0),\n", - " (1.0, [0.019430574216787868, 0.06594180627832635, 0.23329810254580194], [-0.7472138923667574, 0.13227244377548197, -0.651287493870243], 1.1632342240714935, 0),\n", + "array([ (1.0, [0.09184833943086697, -0.23957301713645024, 0.35275256701073315], [0.6943502674814661, -0.18996972225593808, 0.694110373553384], 1.8499326750790277, 0),\n", + " (1.0, [0.09184833943086697, -0.23957301713645024, 0.35275256701073315], [0.5159084442036803, 0.8277435289738373, -0.22063346854291235], 0.08218797495117988, 0),\n", + " (1.0, [0.20806026831237243, 0.2806741437409227, 0.5336799247482618], [0.1279254678841002, 0.5275600667322506, -0.8398306083110433], 3.0474103747828187, 0),\n", " ...,\n", - " (1.0, [0.18544614514351207, -0.0113070561851496, 0.5468392238881264], [-0.8006491411918817, 0.43855795172388223, -0.4082007786475368], 1.4358240241589555, 0),\n", - " (1.0, [0.18544614514351207, -0.0113070561851496, 0.5468392238881264], [-0.5150076397044656, -0.34922134026850293, 0.7828228321575105], 1.5771133724329802, 0),\n", - " (1.0, [-0.2722999793764598, 0.22680062445008103, 0.2987060438567475], [0.9207818175032396, -0.2884020326181676, 0.26265017063984586], 2.932342523379745, 0)], \n", + " (1.0, [-0.19031412582761326, 0.09385643210497185, -0.22790301416803052], [0.5932941753311154, -0.7955471372215329, 0.12290961710458291], 1.9935677118802835, 0),\n", + " (1.0, [-0.19031412582761326, 0.09385643210497185, -0.22790301416803052], [-0.8965339181960187, -0.33727511250097575, 0.2871801386091118], 9.772336043367204, 0),\n", + " (1.0, [-0.19031412582761326, 0.09385643210497185, -0.22790301416803052], [0.5932941753311154, -0.7955471372215329, 0.12290961710458291], 1.9935677118802835, 0)], \n", " dtype=[('wgt', '" + "" ] }, "execution_count": 28, @@ -1033,9 +1035,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEUCAYAAAA8+dFZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nE4ckqRUThySpFROHJKkVE4ckqZX/D2CKDT5bRBuyAAAAAElFTkSuQmCC\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAETCAYAAAAYm1C6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEDMzy8QJxMzMMnECMTOzTJxAzMwsk/8Pmq0TNsrj52MAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1082,19 +1084,11 @@ "metadata": {}, "output_type": "execute_result" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python2.7/dist-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " if self._edgecolors == str('face'):\n" - ] - }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAD7CAYAAAC7dSVGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDQ2NFuT/AMH9R/ORrjlAAAAAAElFTkSuQmCC\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWUAAAD7CAYAAACynoU8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGt0ITZQ1NDQ0uhH/DznvrI6ebgS5AAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1127,7 +1121,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.11" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/tally-arithmetic.ipynb b/docs/source/pythonapi/examples/tally-arithmetic.ipynb index bbadd1ac4..da9cb2dd1 100644 --- a/docs/source/pythonapi/examples/tally-arithmetic.ipynb +++ b/docs/source/pythonapi/examples/tally-arithmetic.ipynb @@ -366,7 +366,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nVE6KjgAAAABJRU5ErkJggg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nSvCBBQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] @@ -570,8 +570,10 @@ " Copyright: 2011-2015 Massachusetts Institute of Technology\n", " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.1\n", - " Git SHA1: b9efc990c7eb58f4a41524d59ae73396c9929436\n", - " Date/Time: 2016-02-23 10:52:44\n", + " Git SHA1: 5f252e2df51930b9175fd41bafa8db01f3eaeb92\n", + " Date/Time: 2016-03-23 13:22:51\n", + " MPI Processes: 1\n", + " OpenMP Threads: 16\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -598,26 +600,26 @@ "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", - " 1/1 1.05992 \n", - " 2/1 1.05251 \n", - " 3/1 1.05204 \n", - " 4/1 1.02100 \n", - " 5/1 1.07784 \n", - " 6/1 1.04814 \n", - " 7/1 1.02335 1.03574 +/- 0.01239\n", - " 8/1 1.02415 1.03188 +/- 0.00813\n", - " 9/1 1.10331 1.04974 +/- 0.01876\n", - " 10/1 1.05452 1.05069 +/- 0.01456\n", - " 11/1 1.07867 1.05536 +/- 0.01277\n", - " 12/1 1.04203 1.05345 +/- 0.01096\n", - " 13/1 1.04482 1.05237 +/- 0.00955\n", - " 14/1 1.04117 1.05113 +/- 0.00852\n", - " 15/1 1.07581 1.05360 +/- 0.00801\n", - " 16/1 1.04235 1.05257 +/- 0.00731\n", - " 17/1 1.02710 1.05045 +/- 0.00701\n", - " 18/1 1.01970 1.04809 +/- 0.00687\n", - " 19/1 1.01022 1.04538 +/- 0.00691\n", - " 20/1 1.01449 1.04332 +/- 0.00675\n", + " 1/1 1.03167 \n", + " 2/1 1.03535 \n", + " 3/1 1.02709 \n", + " 4/1 1.00637 \n", + " 5/1 0.99250 \n", + " 6/1 1.06116 \n", + " 7/1 1.04289 1.05202 +/- 0.00913\n", + " 8/1 1.04779 1.05061 +/- 0.00546\n", + " 9/1 1.04695 1.04969 +/- 0.00397\n", + " 10/1 0.98778 1.03731 +/- 0.01276\n", + " 11/1 1.05810 1.04078 +/- 0.01098\n", + " 12/1 1.01539 1.03715 +/- 0.00996\n", + " 13/1 1.08644 1.04331 +/- 0.01060\n", + " 14/1 1.06425 1.04564 +/- 0.00963\n", + " 15/1 1.01768 1.04284 +/- 0.00906\n", + " 16/1 1.05877 1.04429 +/- 0.00832\n", + " 17/1 1.02195 1.04243 +/- 0.00782\n", + " 18/1 1.02488 1.04108 +/- 0.00732\n", + " 19/1 1.06285 1.04263 +/- 0.00695\n", + " 20/1 0.98751 1.03896 +/- 0.00744\n", " Creating state point statepoint.20.h5...\n", "\n", " ===========================================================================\n", @@ -627,27 +629,27 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 8.4700E-01 seconds\n", - " Reading cross sections = 5.8300E-01 seconds\n", - " Total time in simulation = 1.6037E+01 seconds\n", - " Time in transport only = 1.6026E+01 seconds\n", - " Time in inactive batches = 2.3070E+00 seconds\n", - " Time in active batches = 1.3730E+01 seconds\n", - " Time synchronizing fission bank = 5.0000E-03 seconds\n", - " Sampling source sites = 4.0000E-03 seconds\n", - " SEND/RECV source sites = 1.0000E-03 seconds\n", + " Total time for initialization = 4.5200E-01 seconds\n", + " Reading cross sections = 1.2900E-01 seconds\n", + " Total time in simulation = 2.0330E+00 seconds\n", + " Time in transport only = 1.9420E+00 seconds\n", + " Time in inactive batches = 3.1000E-01 seconds\n", + " Time in active batches = 1.7230E+00 seconds\n", + " Time synchronizing fission bank = 2.0000E-03 seconds\n", + " Sampling source sites = 0.0000E+00 seconds\n", + " SEND/RECV source sites = 2.0000E-03 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", - " Total time for finalization = 3.0000E-03 seconds\n", - " Total time elapsed = 1.6899E+01 seconds\n", - " Calculation Rate (inactive) = 5418.29 neutrons/second\n", - " Calculation Rate (active) = 2731.25 neutrons/second\n", + " Total time for finalization = 2.0000E-03 seconds\n", + " Total time elapsed = 2.5040E+00 seconds\n", + " Calculation Rate (inactive) = 40322.6 neutrons/second\n", + " Calculation Rate (active) = 21764.4 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.03935 +/- 0.00682\n", - " k-effective (Track-length) = 1.04332 +/- 0.00675\n", - " k-effective (Absorption) = 1.03845 +/- 0.00598\n", - " Combined k-effective = 1.04024 +/- 0.00523\n", + " k-effective (Collision) = 1.03965 +/- 0.00597\n", + " k-effective (Track-length) = 1.03896 +/- 0.00744\n", + " k-effective (Absorption) = 1.03976 +/- 0.00606\n", + " Combined k-effective = 1.03991 +/- 0.00536\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -738,7 +740,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -754,8 +756,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
0total(nu-fission / absorption)1.0401660.0090691.0368470.009685
\n", @@ -763,7 +765,7 @@ ], "text/plain": [ " nuclide score mean std. dev.\n", - "0 total (nu-fission / absorption) 1.04e+00 9.07e-03" + "0 total (nu-fission / absorption) 1.04e+00 9.69e-03" ] }, "execution_count": 26, @@ -798,7 +800,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -815,11 +817,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
000.0000016.250000e-07totalabsorption0.6947070.0066990.6920340.007217
\n", @@ -827,7 +829,7 @@ ], "text/plain": [ " energy low [MeV] energy high [MeV] nuclide score mean std. dev.\n", - "0 0.00e+00 6.25e-07 total absorption 6.95e-01 6.70e-03" + "0 0.00e+00 6.25e-07 total absorption 6.92e-01 7.22e-03" ] }, "execution_count": 27, @@ -860,7 +862,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -877,11 +879,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
000.0000016.250000e-07totalnu-fission1.2012160.0122881.2022980.013385
\n", @@ -889,7 +891,7 @@ ], "text/plain": [ " energy low [MeV] energy high [MeV] nuclide score mean std. dev.\n", - "0 0.00e+00 6.25e-07 total nu-fission 1.20e+00 1.23e-02" + "0 0.00e+00 6.25e-07 total nu-fission 1.20e+00 1.34e-02" ] }, "execution_count": 28, @@ -923,7 +925,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -941,12 +943,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
000.0000016.250000e-0710000totalabsorption0.749250.0082570.7491510.009003
\n", @@ -957,7 +959,7 @@ "0 0.00e+00 6.25e-07 10000 total absorption 7.49e-01 \n", "\n", " std. dev. \n", - "0 8.26e-03 " + "0 9.00e-03 " ] }, "execution_count": 29, @@ -989,7 +991,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -1007,12 +1009,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
000.0000016.250000e-0710000total(nu-fission / absorption)1.6636160.0186241.6634350.019976
\n", @@ -1023,7 +1025,7 @@ "0 0.00e+00 6.25e-07 10000 total \n", "\n", " score mean std. dev. \n", - "0 (nu-fission / absorption) 1.66e+00 1.86e-02 " + "0 (nu-fission / absorption) 1.66e+00 2.00e-02 " ] }, "execution_count": 30, @@ -1054,7 +1056,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -1072,12 +1074,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
000.0000016.250000e-0710000total(((absorption * nu-fission) * absorption) * (n...1.0401660.0219281.0368470.023674
\n", @@ -1088,7 +1090,7 @@ "0 0.00e+00 6.25e-07 10000 total \n", "\n", " score mean std. dev. \n", - "0 (((absorption * nu-fission) * absorption) * (n... 1.04e+00 2.19e-02 " + "0 (((absorption * nu-fission) * absorption) * (n... 1.04e+00 2.37e-02 " ] }, "execution_count": 31, @@ -1136,7 +1138,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -1154,82 +1156,82 @@ " \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", + " \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", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
0100000.0000000.0000010.000000e+006.250000e-07(U-238 / total)(nu-fission / flux)0.0000017.377419e-096.627781e-077.082494e-09
1100000.0000000.0000010.000000e+006.250000e-07(U-238 / total)(scatter / flux)0.2099892.303838e-032.099843e-012.003686e-03
2100000.0000000.0000010.000000e+006.250000e-07(U-235 / total)(nu-fission / flux)0.3564203.951669e-033.547246e-013.854562e-03
3100000.0000000.0000010.000000e+006.250000e-07(U-235 / total)(scatter / flux)0.0055556.101004e-055.554185e-035.316706e-05
4100000.00000120.0000006.250000e-072.000000e+01(U-238 / total)(nu-fission / flux)0.0071558.053460e-057.151165e-035.480545e-05
5100000.00000120.0000006.250000e-072.000000e+01(U-238 / total)(scatter / flux)0.2277701.079289e-032.278981e-016.424480e-04
6100000.00000120.0000006.250000e-072.000000e+01(U-235 / total)(nu-fission / flux)0.0080675.254797e-058.073636e-034.374754e-05
7100000.00000120.0000006.250000e-072.000000e+01(U-235 / total)(scatter / flux)0.0033671.647058e-053.369592e-038.971220e-06
\n", @@ -1247,14 +1249,14 @@ "7 10000 6.25e-07 2.00e+01 (U-235 / total) \n", "\n", " score mean std. dev. \n", - "0 (nu-fission / flux) 6.66e-07 7.38e-09 \n", - "1 (scatter / flux) 2.10e-01 2.30e-03 \n", - "2 (nu-fission / flux) 3.56e-01 3.95e-03 \n", - "3 (scatter / flux) 5.56e-03 6.10e-05 \n", - "4 (nu-fission / flux) 7.15e-03 8.05e-05 \n", - "5 (scatter / flux) 2.28e-01 1.08e-03 \n", - "6 (nu-fission / flux) 8.07e-03 5.25e-05 \n", - "7 (scatter / flux) 3.37e-03 1.65e-05 " + "0 (nu-fission / flux) 6.63e-07 7.08e-09 \n", + "1 (scatter / flux) 2.10e-01 2.00e-03 \n", + "2 (nu-fission / flux) 3.55e-01 3.85e-03 \n", + "3 (scatter / flux) 5.55e-03 5.32e-05 \n", + "4 (nu-fission / flux) 7.15e-03 5.48e-05 \n", + "5 (scatter / flux) 2.28e-01 6.42e-04 \n", + "6 (nu-fission / flux) 8.07e-03 4.37e-05 \n", + "7 (scatter / flux) 3.37e-03 8.97e-06 " ] }, "execution_count": 33, @@ -1285,11 +1287,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 6.65702880e-07]\n", - " [ 3.56420449e-01]]\n", + "[[[ 6.62778145e-07]\n", + " [ 3.54724568e-01]]\n", "\n", - " [[ 7.15488656e-03]\n", - " [ 8.06673774e-03]]]\n" + " [[ 7.15116511e-03]\n", + " [ 8.07363630e-03]]]\n" ] } ], @@ -1317,9 +1319,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00555533]]\n", + "[[[ 0.00555418]]\n", "\n", - " [[ 0.0033668 ]]]\n" + " [[ 0.00336959]]]\n" ] } ], @@ -1341,8 +1343,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.22777006]\n", - " [ 0.0033668 ]]]\n" + "[[[ 0.22789806]\n", + " [ 0.00336959]]]\n" ] } ], @@ -1371,7 +1373,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -1389,42 +1391,42 @@ " \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", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
0100000.0000000.0000010.000000e+006.250000e-07U-238nu-fission0.0000021.283958e-081.338459e-08
1100000.0000000.0000010.000000e+006.250000e-07U-235nu-fission0.8685536.880390e-030.8641417.363278e-03
2100000.00000120.0000006.250000e-072.000000e+01U-238nu-fission0.0821498.837250e-040.0821116.090952e-04
3100000.00000120.0000006.250000e-072.000000e+01U-235nu-fission0.0926185.195308e-040.0927034.695215e-04
\n", @@ -1432,16 +1434,16 @@ ], "text/plain": [ " cell energy low [MeV] energy high [MeV] nuclide score mean \\\n", - "0 10000 0.00e+00 6.25e-07 U-238 nu-fission 1.62e-06 \n", - "1 10000 0.00e+00 6.25e-07 U-235 nu-fission 8.69e-01 \n", + "0 10000 0.00e+00 6.25e-07 U-238 nu-fission 1.61e-06 \n", + "1 10000 0.00e+00 6.25e-07 U-235 nu-fission 8.64e-01 \n", "2 10000 6.25e-07 2.00e+01 U-238 nu-fission 8.21e-02 \n", - "3 10000 6.25e-07 2.00e+01 U-235 nu-fission 9.26e-02 \n", + "3 10000 6.25e-07 2.00e+01 U-235 nu-fission 9.27e-02 \n", "\n", " std. dev. \n", - "0 1.28e-08 \n", - "1 6.88e-03 \n", - "2 8.84e-04 \n", - "3 5.20e-04 " + "0 1.34e-08 \n", + "1 7.36e-03 \n", + "2 6.09e-04 \n", + "3 4.70e-04 " ] }, "execution_count": 37, @@ -1465,7 +1467,7 @@ { "data": { "text/html": [ - "
\n", + "
\n", "\n", " \n", " \n", @@ -1484,91 +1486,91 @@ " \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", - " \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", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
0100021.000000e-080.0000001.080060e-07H-1scatter4.6193980.0401244.5910220.043961
1100021.080060e-070.0000011.166529e-06H-1scatter2.0307570.0112392.0324810.010876
2100021.166529e-060.0000131.259921e-05H-1scatter1.6584880.0097771.6541870.012130
3100021.259921e-050.0001361.360790e-04H-1scatter1.8530020.0073781.8647710.011649
4100021.360790e-040.0014701.469734e-03H-1scatter2.0507730.0124842.0568930.008555
5100021.469734e-030.0158741.587401e-02H-1scatter2.1317590.0078212.1388330.015180
6100021.587401e-020.1714491.714488e-01H-1scatter2.2137100.0151592.2072090.014853
7100021.714488e-011.8517491.851749e+00H-1scatter2.0119250.0094061.9994070.009053
8100021.851749e+0020.0000002.000000e+01H-1scatter0.3712800.0039490.3687600.003373
\n", @@ -1576,26 +1578,26 @@ ], "text/plain": [ " cell energy low [MeV] energy high [MeV] nuclide score mean \\\n", - "0 10002 1.00e-08 1.08e-07 H-1 scatter 4.62e+00 \n", + "0 10002 1.00e-08 1.08e-07 H-1 scatter 4.59e+00 \n", "1 10002 1.08e-07 1.17e-06 H-1 scatter 2.03e+00 \n", - "2 10002 1.17e-06 1.26e-05 H-1 scatter 1.66e+00 \n", - "3 10002 1.26e-05 1.36e-04 H-1 scatter 1.85e+00 \n", - "4 10002 1.36e-04 1.47e-03 H-1 scatter 2.05e+00 \n", - "5 10002 1.47e-03 1.59e-02 H-1 scatter 2.13e+00 \n", + "2 10002 1.17e-06 1.26e-05 H-1 scatter 1.65e+00 \n", + "3 10002 1.26e-05 1.36e-04 H-1 scatter 1.86e+00 \n", + "4 10002 1.36e-04 1.47e-03 H-1 scatter 2.06e+00 \n", + "5 10002 1.47e-03 1.59e-02 H-1 scatter 2.14e+00 \n", "6 10002 1.59e-02 1.71e-01 H-1 scatter 2.21e+00 \n", - "7 10002 1.71e-01 1.85e+00 H-1 scatter 2.01e+00 \n", - "8 10002 1.85e+00 2.00e+01 H-1 scatter 3.71e-01 \n", + "7 10002 1.71e-01 1.85e+00 H-1 scatter 2.00e+00 \n", + "8 10002 1.85e+00 2.00e+01 H-1 scatter 3.69e-01 \n", "\n", " std. dev. \n", - "0 4.01e-02 \n", - "1 1.12e-02 \n", - "2 9.78e-03 \n", - "3 7.38e-03 \n", - "4 1.25e-02 \n", - "5 7.82e-03 \n", - "6 1.52e-02 \n", - "7 9.41e-03 \n", - "8 3.95e-03 " + "0 4.40e-02 \n", + "1 1.09e-02 \n", + "2 1.21e-02 \n", + "3 1.16e-02 \n", + "4 8.56e-03 \n", + "5 1.52e-02 \n", + "6 1.49e-02 \n", + "7 9.05e-03 \n", + "8 3.37e-03 " ] }, "execution_count": 38, @@ -1628,7 +1630,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.11" } }, "nbformat": 4, diff --git a/openmc/summary.py b/openmc/summary.py index 9609a866b..b8f92664f 100644 --- a/openmc/summary.py +++ b/openmc/summary.py @@ -565,7 +565,7 @@ class Summary(object): # Read in distribcell paths if filter_type == 'distribcell': paths = self._f['{0}/paths'.format(subsubbase)][...] - paths = [path.decode() for path in paths] + paths = [str(path.decode()) for path in paths] new_filter.distribcell_paths = paths # Add Filter to the Tally diff --git a/openmc/trigger.py b/openmc/trigger.py index ad2e9d681..b8383bd27 100644 --- a/openmc/trigger.py +++ b/openmc/trigger.py @@ -2,8 +2,9 @@ from numbers import Real from xml.etree import ElementTree as ET import sys import warnings +from collections import Iterable -from openmc.checkvalue import check_type, check_value +import openmc.checkvalue as cv if sys.version_info[0] >= 3: basestring = str @@ -87,13 +88,13 @@ class Trigger(object): @trigger_type.setter def trigger_type(self, trigger_type): - check_value('tally trigger type', trigger_type, + cv.check_value('tally trigger type', trigger_type, ['variance', 'std_dev', 'rel_err']) self._trigger_type = trigger_type @threshold.setter def threshold(self, threshold): - check_type('tally trigger threshold', threshold, Real) + cv.check_type('tally trigger threshold', threshold, Real) self._threshold = threshold @scores.setter