diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb index 610e82ec1..a97a0c02e 100644 --- a/docs/source/pythonapi/examples/mgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb @@ -376,6 +376,7 @@ "* `CaptureXS`\n", "* `FissionXS`\n", "* `NuFissionXS`\n", + "* `KappaFissionXS`\n", "* `ScatterXS`\n", "* `NuScatterXS`\n", "* `ScatterMatrixXS`\n", @@ -1162,21 +1163,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 2", "language": "python", - "name": "python3" + "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" + "pygments_lexer": "ipython2", + "version": "2.7.6" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb index fd8d09052..7b313da74 100644 --- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb @@ -34,16 +34,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/matplotlib/__init__.py:1350: UserWarning: This call to matplotlib.use() has no effect\n", + "/usr/local/lib/python2.7/dist-packages/matplotlib-1.5.1+1178.ga40c9ec-py2.7-linux-x86_64.egg/matplotlib/__init__.py:884: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", + " warnings.warn(self.msg_depr % (key, alt_key))\n", + "/usr/local/lib/python2.7/dist-packages/matplotlib-1.5.1+1178.ga40c9ec-py2.7-linux-x86_64.egg/matplotlib/__init__.py:1362: 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", - "/home/romano/miniconda3/envs/default/lib/python3.5/importlib/_bootstrap.py:222: QAWarning: pyne.rxname is not yet QA compliant.\n", - " return f(*args, **kwds)\n", - "/home/romano/miniconda3/envs/default/lib/python3.5/importlib/_bootstrap.py:222: QAWarning: pyne.ace is not yet QA compliant.\n", - " return f(*args, **kwds)\n" + "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:9: QAWarning: pyne.rxname is not yet QA compliant.\n", + "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:9: QAWarning: pyne.ace is not yet QA compliant.\n" ] } ], @@ -443,10 +443,11 @@ " 888\n", "\n", " Copyright: 2011-2016 Massachusetts Institute of Technology\n", - " License: http://openmc.readthedocs.org/en/latest/license.html\n", + " License: http://openmc.readthedocs.io/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: df280b60eb1c6d7b7f842e05ede734a4883a0fc8\n", - " Date/Time: 2016-05-05 15:00:51\n", + " Git SHA1: 7b20f8ad4aa9e6f02f8b1d51e002f9f56ba7aa15\n", + " Date/Time: 2016-05-09 13:34:05\n", + " MPI Processes: 1\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -522,7 +523,7 @@ " 48/1 1.21610 1.22612 +/- 0.00251\n", " 49/1 1.22199 1.22602 +/- 0.00245\n", " 50/1 1.20860 1.22558 +/- 0.00243\n", - " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10056\n", + " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10050\n", " The estimated number of batches is 73\n", " Creating state point statepoint.050.h5...\n", " 51/1 1.21850 1.22541 +/- 0.00237\n", @@ -548,7 +549,7 @@ " 71/1 1.19720 1.22444 +/- 0.00195\n", " 72/1 1.23770 1.22465 +/- 0.00193\n", " 73/1 1.23894 1.22488 +/- 0.00191\n", - " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10056\n", + " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10050\n", " The estimated number of batches is 74\n", " 74/1 1.22437 1.22487 +/- 0.00188\n", " Triggers satisfied for batch 74\n", @@ -561,20 +562,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.8600E-01 seconds\n", - " Reading cross sections = 1.1000E-01 seconds\n", - " Total time in simulation = 2.3697E+02 seconds\n", - " Time in transport only = 2.3690E+02 seconds\n", - " Time in inactive batches = 1.5640E+01 seconds\n", - " Time in active batches = 2.2133E+02 seconds\n", - " Time synchronizing fission bank = 3.0000E-02 seconds\n", - " Sampling source sites = 1.9000E-02 seconds\n", - " SEND/RECV source sites = 1.1000E-02 seconds\n", - " Time accumulating tallies = 0.0000E+00 seconds\n", - " Total time for finalization = 1.0000E-02 seconds\n", - " Total time elapsed = 2.3743E+02 seconds\n", - " Calculation Rate (inactive) = 6393.86 neutrons/second\n", - " Calculation Rate (active) = 1807.26 neutrons/second\n", + " Total time for initialization = 3.8900E-01 seconds\n", + " Reading cross sections = 8.3000E-02 seconds\n", + " Total time in simulation = 2.2066E+02 seconds\n", + " Time in transport only = 2.2061E+02 seconds\n", + " Time in inactive batches = 1.5872E+01 seconds\n", + " Time in active batches = 2.0478E+02 seconds\n", + " Time synchronizing fission bank = 1.9000E-02 seconds\n", + " Sampling source sites = 1.2000E-02 seconds\n", + " SEND/RECV source sites = 6.0000E-03 seconds\n", + " Time accumulating tallies = 3.0000E-03 seconds\n", + " Total time for finalization = 1.1000E-02 seconds\n", + " Total time elapsed = 2.2111E+02 seconds\n", + " Calculation Rate (inactive) = 6300.40 neutrons/second\n", + " Calculation Rate (active) = 1953.28 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -785,6 +786,7 @@ " group in\n", " group out\n", " nuclide\n", + " score\n", " mean\n", " std. dev.\n", " \n", @@ -796,6 +798,7 @@ " 1\n", " 1\n", " H-1\n", + " ((nu-scatter-0 - scatter-1) / flux)\n", " 0.234115\n", " 0.003568\n", " \n", @@ -805,6 +808,7 @@ " 1\n", " 1\n", " O-16\n", + " ((nu-scatter-0 - scatter-1) / flux)\n", " 1.563707\n", " 0.005953\n", " \n", @@ -814,6 +818,7 @@ " 1\n", " 2\n", " H-1\n", + " ((nu-scatter-0 - scatter-1) / flux)\n", " 1.594129\n", " 0.002369\n", " \n", @@ -823,6 +828,7 @@ " 1\n", " 2\n", " O-16\n", + " ((nu-scatter-0 - scatter-1) / flux)\n", " 0.285761\n", " 0.001676\n", " \n", @@ -832,6 +838,7 @@ " 1\n", " 3\n", " H-1\n", + " ((nu-scatter-0 - scatter-1) / flux)\n", " 0.011089\n", " 0.000248\n", " \n", @@ -841,6 +848,7 @@ " 1\n", " 3\n", " O-16\n", + " ((nu-scatter-0 - scatter-1) / flux)\n", " 0.000000\n", " 0.000000\n", " \n", @@ -850,6 +858,7 @@ " 1\n", " 4\n", " H-1\n", + " ((nu-scatter-0 - scatter-1) / flux)\n", " 0.000000\n", " 0.000000\n", " \n", @@ -859,6 +868,7 @@ " 1\n", " 4\n", " O-16\n", + " ((nu-scatter-0 - scatter-1) / flux)\n", " 0.000000\n", " 0.000000\n", " \n", @@ -868,6 +878,7 @@ " 1\n", " 5\n", " H-1\n", + " ((nu-scatter-0 - scatter-1) / flux)\n", " 0.000000\n", " 0.000000\n", " \n", @@ -877,6 +888,7 @@ " 1\n", " 5\n", " O-16\n", + " ((nu-scatter-0 - scatter-1) / flux)\n", " 0.000000\n", " 0.000000\n", " \n", @@ -885,17 +897,29 @@ "" ], "text/plain": [ - " cell group in group out nuclide mean std. dev.\n", - "126 10002 1 1 H-1 0.234115 0.003568\n", - "127 10002 1 1 O-16 1.563707 0.005953\n", - "124 10002 1 2 H-1 1.594129 0.002369\n", - "125 10002 1 2 O-16 0.285761 0.001676\n", - "122 10002 1 3 H-1 0.011089 0.000248\n", - "123 10002 1 3 O-16 0.000000 0.000000\n", - "120 10002 1 4 H-1 0.000000 0.000000\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" + " cell group in group out nuclide score \\\n", + "126 10002 1 1 H-1 ((nu-scatter-0 - scatter-1) / flux) \n", + "127 10002 1 1 O-16 ((nu-scatter-0 - scatter-1) / flux) \n", + "124 10002 1 2 H-1 ((nu-scatter-0 - scatter-1) / flux) \n", + "125 10002 1 2 O-16 ((nu-scatter-0 - scatter-1) / flux) \n", + "122 10002 1 3 H-1 ((nu-scatter-0 - scatter-1) / flux) \n", + "123 10002 1 3 O-16 ((nu-scatter-0 - scatter-1) / flux) \n", + "120 10002 1 4 H-1 ((nu-scatter-0 - scatter-1) / flux) \n", + "121 10002 1 4 O-16 ((nu-scatter-0 - scatter-1) / flux) \n", + "118 10002 1 5 H-1 ((nu-scatter-0 - scatter-1) / flux) \n", + "119 10002 1 5 O-16 ((nu-scatter-0 - scatter-1) / flux) \n", + "\n", + " mean std. dev. \n", + "126 0.234115 0.003568 \n", + "127 1.563707 0.005953 \n", + "124 1.594129 0.002369 \n", + "125 0.285761 0.001676 \n", + "122 0.011089 0.000248 \n", + "123 0.000000 0.000000 \n", + "120 0.000000 0.000000 \n", + "121 0.000000 0.000000 \n", + "118 0.000000 0.000000 \n", + "119 0.000000 0.000000 " ] }, "execution_count": 19, @@ -995,6 +1019,7 @@ " cell\n", " group in\n", " nuclide\n", + " score\n", " mean\n", " std. dev.\n", " \n", @@ -1005,6 +1030,7 @@ " 10000\n", " 1\n", " U-235\n", + " ((total - scatter-1) / flux)\n", " 20.611692\n", " 0.104237\n", " \n", @@ -1013,6 +1039,7 @@ " 10000\n", " 1\n", " U-238\n", + " ((total - scatter-1) / flux)\n", " 9.585358\n", " 0.013808\n", " \n", @@ -1021,6 +1048,7 @@ " 10000\n", " 1\n", " O-16\n", + " ((total - scatter-1) / flux)\n", " 3.164190\n", " 0.005049\n", " \n", @@ -1029,6 +1057,7 @@ " 10000\n", " 2\n", " U-235\n", + " ((total - scatter-1) / flux)\n", " 485.413426\n", " 0.996410\n", " \n", @@ -1037,6 +1066,7 @@ " 10000\n", " 2\n", " U-238\n", + " ((total - scatter-1) / flux)\n", " 11.190386\n", " 0.028731\n", " \n", @@ -1045,6 +1075,7 @@ " 10000\n", " 2\n", " O-16\n", + " ((total - scatter-1) / flux)\n", " 3.794859\n", " 0.011139\n", " \n", @@ -1053,13 +1084,13 @@ "" ], "text/plain": [ - " cell group in nuclide mean std. dev.\n", - "3 10000 1 U-235 20.611692 0.104237\n", - "4 10000 1 U-238 9.585358 0.013808\n", - "5 10000 1 O-16 3.164190 0.005049\n", - "0 10000 2 U-235 485.413426 0.996410\n", - "1 10000 2 U-238 11.190386 0.028731\n", - "2 10000 2 O-16 3.794859 0.011139" + " cell group in nuclide score mean std. dev.\n", + "3 10000 1 U-235 ((total - scatter-1) / flux) 2.06e+01 1.04e-01\n", + "4 10000 1 U-238 ((total - scatter-1) / flux) 9.59e+00 1.38e-02\n", + "5 10000 1 O-16 ((total - scatter-1) / flux) 3.16e+00 5.05e-03\n", + "0 10000 2 U-235 ((total - scatter-1) / flux) 4.85e+02 9.96e-01\n", + "1 10000 2 U-238 ((total - scatter-1) / flux) 1.12e+01 2.87e-02\n", + "2 10000 2 O-16 ((total - scatter-1) / flux) 3.79e+00 1.11e-02" ] }, "execution_count": 22, @@ -1166,81 +1197,81 @@ "[ NORMAL ] Iteration 0:\tk_eff = 0.574672\tres = 0.000E+00\n", "[ NORMAL ] Iteration 1:\tk_eff = 0.679815\tres = 4.253E-01\n", "[ NORMAL ] Iteration 2:\tk_eff = 0.660826\tres = 1.830E-01\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.658941\tres = 2.793E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.643012\tres = 2.852E-03\n", + "[ NORMAL ] Iteration 3:\tk_eff = 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1.629E-05\n", - "[ NORMAL ] Iteration 219:\tk_eff = 1.223098\tres = 1.571E-05\n", - "[ NORMAL ] Iteration 220:\tk_eff = 1.223115\tres = 1.508E-05\n", - "[ NORMAL ] Iteration 221:\tk_eff = 1.223132\tres = 1.429E-05\n", - "[ NORMAL ] Iteration 222:\tk_eff = 1.223149\tres = 1.394E-05\n", - "[ NORMAL ] Iteration 223:\tk_eff = 1.223165\tres = 1.330E-05\n", - "[ NORMAL ] Iteration 224:\tk_eff = 1.223180\tres = 1.299E-05\n", - "[ NORMAL ] Iteration 225:\tk_eff = 1.223194\tres = 1.241E-05\n", - "[ NORMAL ] Iteration 226:\tk_eff = 1.223208\tres = 1.167E-05\n", - "[ NORMAL ] Iteration 227:\tk_eff = 1.223221\tres = 1.151E-05\n", - "[ NORMAL ] Iteration 228:\tk_eff = 1.223234\tres = 1.073E-05\n", - "[ NORMAL ] Iteration 229:\tk_eff = 1.223246\tres = 1.051E-05\n", - "[ NORMAL ] Iteration 230:\tk_eff = 1.223258\tres = 1.000E-05\n" + "[ NORMAL ] Iteration 166:\tk_eff = 1.219938\tres = 1.222E-04\n", + "[ NORMAL ] Iteration 167:\tk_eff = 1.220076\tres = 1.176E-04\n", + "[ NORMAL ] Iteration 168:\tk_eff = 1.220208\tres = 1.131E-04\n", + "[ NORMAL ] Iteration 169:\tk_eff = 1.220336\tres = 1.088E-04\n", + "[ NORMAL ] Iteration 170:\tk_eff = 1.220459\tres = 1.046E-04\n", + "[ NORMAL ] Iteration 171:\tk_eff = 1.220577\tres = 1.006E-04\n", + "[ NORMAL ] Iteration 172:\tk_eff = 1.220691\tres = 9.681E-05\n", + "[ NORMAL ] Iteration 173:\tk_eff = 1.220800\tres = 9.312E-05\n", + "[ NORMAL ] Iteration 174:\tk_eff = 1.220905\tres = 8.957E-05\n", + "[ NORMAL ] Iteration 175:\tk_eff = 1.221006\tres = 8.615E-05\n", + "[ NORMAL ] Iteration 176:\tk_eff = 1.221104\tres = 8.287E-05\n", + "[ NORMAL ] Iteration 177:\tk_eff = 1.221197\tres = 7.971E-05\n", + "[ NORMAL ] Iteration 178:\tk_eff = 1.221287\tres = 7.667E-05\n", + "[ NORMAL ] Iteration 179:\tk_eff = 1.221374\tres = 7.374E-05\n", + "[ NORMAL ] Iteration 180:\tk_eff = 1.221457\tres = 7.093E-05\n", + "[ NORMAL ] Iteration 181:\tk_eff = 1.221537\tres = 6.823E-05\n", + "[ NORMAL ] Iteration 182:\tk_eff = 1.221615\tres = 6.562E-05\n", + "[ NORMAL ] 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3.661E-05\n", + "[ NORMAL ] Iteration 198:\tk_eff = 1.222515\tres = 3.521E-05\n", + "[ NORMAL ] Iteration 199:\tk_eff = 1.222555\tres = 3.386E-05\n", + "[ NORMAL ] Iteration 200:\tk_eff = 1.222594\tres = 3.257E-05\n", + "[ NORMAL ] Iteration 201:\tk_eff = 1.222630\tres = 3.133E-05\n", + "[ NORMAL ] Iteration 202:\tk_eff = 1.222666\tres = 3.013E-05\n", + "[ NORMAL ] Iteration 203:\tk_eff = 1.222700\tres = 2.898E-05\n", + "[ NORMAL ] Iteration 204:\tk_eff = 1.222733\tres = 2.787E-05\n", + "[ NORMAL ] Iteration 205:\tk_eff = 1.222764\tres = 2.681E-05\n", + "[ NORMAL ] Iteration 206:\tk_eff = 1.222795\tres = 2.578E-05\n", + "[ NORMAL ] Iteration 207:\tk_eff = 1.222824\tres = 2.480E-05\n", + "[ NORMAL ] Iteration 208:\tk_eff = 1.222852\tres = 2.385E-05\n", + "[ NORMAL ] Iteration 209:\tk_eff = 1.222879\tres = 2.294E-05\n", + "[ NORMAL ] Iteration 210:\tk_eff = 1.222905\tres = 2.206E-05\n", + "[ NORMAL ] Iteration 211:\tk_eff = 1.222930\tres = 2.122E-05\n", + "[ NORMAL ] Iteration 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NORMAL ] Iteration 227:\tk_eff = 1.223221\tres = 1.138E-05\n", + "[ NORMAL ] Iteration 228:\tk_eff = 1.223234\tres = 1.094E-05\n", + "[ NORMAL ] Iteration 229:\tk_eff = 1.223246\tres = 1.052E-05\n", + "[ NORMAL ] Iteration 230:\tk_eff = 1.223258\tres = 1.012E-05\n" ] } ], @@ -1780,9 +1811,9 @@ }, { "data": { - "image/png": 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s//SZaStbolLd0ZyNidDkt7hJQUQeUNXTRGQ2zn0J4e0AqGonmuHGpEqwvEub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\nRK5CYII=\n", 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N73Q6ue++BxgzZhyPPfZos+WNVcYlBxEpFZGPReSUVMdu397DrFm1dO7s5uSTS1izRjuq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\n+KP1PP8hsU6nNWku3KqsyVy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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1896,21 +1927,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 2", "language": "python", - "name": "python3" + "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" + "pygments_lexer": "ipython2", + "version": "2.7.6" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb index c39f21dfa..21aae7e40 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": [ - "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/matplotlib/__init__.py:1350: UserWarning: This call to matplotlib.use() has no effect\n", + "/usr/local/lib/python2.7/dist-packages/matplotlib-1.5.1+1178.ga40c9ec-py2.7-linux-x86_64.egg/matplotlib/__init__.py:1362: 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", @@ -459,7 +459,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -543,6 +543,7 @@ "* `CaptureXS` (`\"capture\"`)\n", "* `FissionXS` (`\"fission\"`)\n", "* `NuFissionXS` (`\"nu-fission\"`)\n", + "* `KappaFissionXS` (`\"kappa-fission\"`)\n", "* `ScatterXS` (`\"scatter\"`)\n", "* `NuScatterXS` (`\"nu-scatter\"`)\n", "* `ScatterMatrixXS` (`\"scatter matrix\"`)\n", @@ -722,10 +723,11 @@ " 888\n", "\n", " Copyright: 2011-2016 Massachusetts Institute of Technology\n", - " License: http://openmc.readthedocs.org/en/latest/license.html\n", + " License: http://openmc.readthedocs.io/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: df280b60eb1c6d7b7f842e05ede734a4883a0fc8\n", - " Date/Time: 2016-05-05 15:06:49\n", + " Git SHA1: 7b20f8ad4aa9e6f02f8b1d51e002f9f56ba7aa15\n", + " Date/Time: 2016-05-09 13:39:11\n", + " MPI Processes: 1\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -811,20 +813,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.1500E-01 seconds\n", - " Reading cross sections = 1.1800E-01 seconds\n", - " Total time in simulation = 5.3686E+01 seconds\n", - " Time in transport only = 5.3657E+01 seconds\n", - " Time in inactive batches = 4.3970E+00 seconds\n", - " Time in active batches = 4.9289E+01 seconds\n", - " Time synchronizing fission bank = 3.0000E-03 seconds\n", + " Total time for initialization = 5.0300E-01 seconds\n", + " Reading cross sections = 1.0400E-01 seconds\n", + " Total time in simulation = 4.8096E+01 seconds\n", + " Time in transport only = 4.8074E+01 seconds\n", + " Time in inactive batches = 4.1080E+00 seconds\n", + " Time in active batches = 4.3988E+01 seconds\n", + " Time synchronizing fission bank = 4.0000E-03 seconds\n", " Sampling source sites = 2.0000E-03 seconds\n", - " SEND/RECV source sites = 1.0000E-03 seconds\n", - " Time accumulating tallies = 0.0000E+00 seconds\n", + " SEND/RECV source sites = 0.0000E+00 seconds\n", + " Time accumulating tallies = 1.0000E-03 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 5.4118E+01 seconds\n", - " Calculation Rate (inactive) = 5685.70 neutrons/second\n", - " Calculation Rate (active) = 2028.85 neutrons/second\n", + " Total time elapsed = 4.8613E+01 seconds\n", + " Calculation Rate (inactive) = 6085.69 neutrons/second\n", + " Calculation Rate (active) = 2273.35 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -950,8 +952,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1996: RuntimeWarning: invalid value encountered in true_divide\n", - " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.1-py2.7.egg/openmc/tallies.py:1996: RuntimeWarning: invalid value encountered in true_divide\n" ] }, { @@ -965,6 +966,7 @@ " cell\n", " group in\n", " nuclide\n", + " score\n", " mean\n", " std. dev.\n", " \n", @@ -975,6 +977,7 @@ " 10000\n", " 1\n", " U-235\n", + " (nu-fission / flux)\n", " 8.055246e-03\n", " 2.857567e-05\n", " \n", @@ -983,6 +986,7 @@ " 10000\n", " 1\n", " U-238\n", + " (nu-fission / flux)\n", " 7.339215e-03\n", " 4.349466e-05\n", " \n", @@ -991,6 +995,7 @@ " 10000\n", " 1\n", " O-16\n", + " (nu-fission / flux)\n", " 0.000000e+00\n", " 0.000000e+00\n", " \n", @@ -999,6 +1004,7 @@ " 10000\n", " 2\n", " U-235\n", + " (nu-fission / flux)\n", " 3.615565e-01\n", " 2.050486e-03\n", " \n", @@ -1007,6 +1013,7 @@ " 10000\n", " 2\n", " U-238\n", + " (nu-fission / flux)\n", " 6.742638e-07\n", " 3.795256e-09\n", " \n", @@ -1015,6 +1022,7 @@ " 10000\n", " 2\n", " O-16\n", + " (nu-fission / flux)\n", " 0.000000e+00\n", " 0.000000e+00\n", " \n", @@ -1023,13 +1031,13 @@ "" ], "text/plain": [ - " 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" + " cell group in nuclide score mean std. dev.\n", + "3 10000 1 U-235 (nu-fission / flux) 8.06e-03 2.86e-05\n", + "4 10000 1 U-238 (nu-fission / flux) 7.34e-03 4.35e-05\n", + "5 10000 1 O-16 (nu-fission / flux) 0.00e+00 0.00e+00\n", + "0 10000 2 U-235 (nu-fission / flux) 3.62e-01 2.05e-03\n", + "1 10000 2 U-238 (nu-fission / flux) 6.74e-07 3.80e-09\n", + "2 10000 2 O-16 (nu-fission / flux) 0.00e+00 0.00e+00" ] }, "execution_count": 30, @@ -1178,6 +1186,7 @@ " cell\n", " group in\n", " nuclide\n", + " score\n", " mean\n", " std. dev.\n", " \n", @@ -1188,6 +1197,7 @@ " 10000\n", " 1\n", " U-235\n", + " (nu-fission / flux)\n", " 0.074860\n", " 0.000303\n", " \n", @@ -1196,6 +1206,7 @@ " 10000\n", " 1\n", " U-238\n", + " (nu-fission / flux)\n", " 0.005952\n", " 0.000035\n", " \n", @@ -1204,6 +1215,7 @@ " 10000\n", " 1\n", " O-16\n", + " (nu-fission / flux)\n", " 0.000000\n", " 0.000000\n", " \n", @@ -1212,10 +1224,10 @@ "" ], "text/plain": [ - " cell group in nuclide mean std. dev.\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" + " cell group in nuclide score mean std. dev.\n", + "0 10000 1 U-235 (nu-fission / flux) 7.49e-02 3.03e-04\n", + "1 10000 1 U-238 (nu-fission / flux) 5.95e-03 3.52e-05\n", + "2 10000 1 O-16 (nu-fission / flux) 0.00e+00 0.00e+00" ] }, "execution_count": 36, @@ -1299,12 +1311,12 @@ "[ NORMAL ] Computing the eigenvalue...\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 = 0.761745\tres = 6.349E-02\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 = 0.683469\tres = 1.277E-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", @@ -1317,11 +1329,11 @@ "[ 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 = 0.803272\tres = 1.611E-02\n", - "[ NORMAL ] Iteration 21:\tk_eff = 0.815414\tres = 1.566E-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 = 0.849846\tres = 1.390E-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", @@ -1343,8 +1355,8 @@ "[ 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 = 0.990741\tres = 3.290E-03\n", - "[ NORMAL ] Iteration 47:\tk_eff = 0.993545\tres = 3.053E-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", @@ -1358,63 +1370,63 @@ "[ 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 = 1.038E-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 = 7.561E-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 = 6.444E-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 = 5.488E-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 = 1.024947\tres = 3.377E-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 = 1.026577\tres = 1.904E-04\n", - "[ NORMAL ] Iteration 83:\tk_eff = 1.026743\tres = 1.753E-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 = 1.027493\tres = 1.067E-04\n", - "[ NORMAL ] Iteration 90:\tk_eff = 1.027586\tres = 9.824E-05\n", - "[ NORMAL ] Iteration 91:\tk_eff = 1.027671\tres = 9.043E-05\n", - "[ NORMAL ] Iteration 92:\tk_eff = 1.027750\tres = 8.318E-05\n", - "[ NORMAL ] Iteration 93:\tk_eff = 1.027822\tres = 7.654E-05\n", - "[ NORMAL ] Iteration 94:\tk_eff = 1.027889\tres = 7.041E-05\n", - "[ NORMAL ] Iteration 95:\tk_eff = 1.027950\tres = 6.481E-05\n", - "[ NORMAL ] Iteration 96:\tk_eff = 1.028006\tres = 5.960E-05\n", - "[ NORMAL ] Iteration 97:\tk_eff = 1.028058\tres = 5.480E-05\n", - "[ NORMAL ] Iteration 98:\tk_eff = 1.028105\tres = 5.043E-05\n", - "[ NORMAL ] Iteration 99:\tk_eff = 1.028149\tres = 4.634E-05\n", - "[ NORMAL ] Iteration 100:\tk_eff = 1.028189\tres = 4.266E-05\n", - "[ NORMAL ] Iteration 101:\tk_eff = 1.028226\tres = 3.920E-05\n", - "[ NORMAL ] Iteration 102:\tk_eff = 1.028260\tres = 3.604E-05\n", - "[ NORMAL ] Iteration 103:\tk_eff = 1.028291\tres = 3.316E-05\n", - "[ NORMAL ] Iteration 104:\tk_eff = 1.028320\tres = 3.047E-05\n", - "[ NORMAL ] Iteration 105:\tk_eff = 1.028347\tres = 2.800E-05\n", - "[ NORMAL ] Iteration 106:\tk_eff = 1.028371\tres = 2.576E-05\n", - "[ NORMAL ] Iteration 107:\tk_eff = 1.028393\tres = 2.367E-05\n", - "[ NORMAL ] Iteration 108:\tk_eff = 1.028414\tres = 2.176E-05\n", - "[ NORMAL ] Iteration 109:\tk_eff = 1.028433\tres = 2.003E-05\n", - "[ NORMAL ] Iteration 110:\tk_eff = 1.028450\tres = 1.836E-05\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 = 1.553E-05\n", - "[ NORMAL ] Iteration 113:\tk_eff = 1.028494\tres = 1.427E-05\n", - "[ NORMAL ] Iteration 114:\tk_eff = 1.028507\tres = 1.309E-05\n", - "[ NORMAL ] Iteration 115:\tk_eff = 1.028518\tres = 1.202E-05\n", - "[ NORMAL ] Iteration 116:\tk_eff = 1.028528\tres = 1.107E-05\n", - "[ NORMAL ] Iteration 117:\tk_eff = 1.028538\tres = 1.015E-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" ] } ], @@ -1556,7 +1568,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 43, @@ -1565,9 +1577,9 @@ }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1575,6 +1587,10 @@ } ], "source": [ + "# Ignore zero fission rates in guide tubes with Matplotlib color scheme\n", + "openmc_fission_rates[openmc_fission_rates == 0] = np.nan\n", + "openmoc_fission_rates[openmoc_fission_rates == 0] = np.nan\n", + "\n", "# Plot OpenMC's fission rates in the left subplot\n", "fig = plt.subplot(121)\n", "plt.imshow(openmc_fission_rates, interpolation='none', cmap='jet')\n", @@ -1589,21 +1605,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 2", "language": "python", - "name": "python3" + "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" + "pygments_lexer": "ipython2", + "version": "2.7.6" } }, "nbformat": 4,