diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb index 8db4cd4df..f1db27133 100644 --- a/docs/source/pythonapi/examples/mgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb @@ -146,8 +146,6 @@ "\n", "import openmc\n", "import openmc.mgxs as mgxs\n", - "from openmc.source import Source\n", - "from openmc.stats import Box\n", "\n", "%matplotlib inline" ] @@ -342,9 +340,11 @@ "settings_file.inactive = inactive\n", "settings_file.particles = particles\n", "settings_file.output = {'tallies': True}\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", - "settings_file.source = Source(space=Box(\n", - " bounds[:3], bounds[3:], only_fissionable=True))\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings_file.source = openmc.source.Source(space=uniform_dist)\n", "\n", "# Export to \"settings.xml\"\n", "settings_file.export_to_xml()" @@ -518,10 +518,9 @@ " 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: 5f252e2df51930b9175fd41bafa8db01f3eaeb92\n", - " Date/Time: 2016-03-23 14:42:51\n", + " Git SHA1: 9a6ecd72597338b40d2b72378e5ad6dd65df2364\n", + " Date/Time: 2016-04-08 11:43:10\n", " MPI Processes: 1\n", - " OpenMP Threads: 16\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -606,20 +605,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.6200E-01 seconds\n", - " Reading cross sections = 1.3100E-01 seconds\n", - " Total time in simulation = 2.4000E+00 seconds\n", - " Time in transport only = 2.1340E+00 seconds\n", - " Time in inactive batches = 2.6400E-01 seconds\n", - " Time in active batches = 2.1360E+00 seconds\n", - " Time synchronizing fission bank = 2.0000E-03 seconds\n", + " Total time for initialization = 5.2800E-01 seconds\n", + " Reading cross sections = 1.3400E-01 seconds\n", + " Total time in simulation = 2.4026E+01 seconds\n", + " Time in transport only = 2.4011E+01 seconds\n", + " Time in inactive batches = 2.9230E+00 seconds\n", + " Time in active batches = 2.1103E+01 seconds\n", + " Time synchronizing fission bank = 3.0000E-03 seconds\n", " Sampling source sites = 2.0000E-03 seconds\n", - " SEND/RECV source sites = 0.0000E+00 seconds\n", + " SEND/RECV source sites = 1.0000E-03 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", " Total time for finalization = 1.0000E-03 seconds\n", - " Total time elapsed = 2.8800E+00 seconds\n", - " Calculation Rate (inactive) = 94697.0 neutrons/second\n", - " Calculation Rate (active) = 46816.5 neutrons/second\n", + " Total time elapsed = 2.4570E+01 seconds\n", + " Calculation Rate (inactive) = 8552.86 neutrons/second\n", + " Calculation Rate (active) = 4738.66 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -914,7 +913,7 @@ " 6.250000e-07\n", " total\n", " (((total / flux) - (absorption / flux)) - (sca...\n", - " 8.881784e-16\n", + " -3.774758e-15\n", " 0.011292\n", " \n", " \n", @@ -924,7 +923,7 @@ " 2.000000e+01\n", " total\n", " (((total / flux) - (absorption / flux)) - (sca...\n", - " -9.992007e-16\n", + " 1.443290e-15\n", " 0.002570\n", " \n", " \n", @@ -937,8 +936,8 @@ "1 1 6.25e-07 2.00e+01 total \n", "\n", " score mean std. dev. \n", - "0 (((total / flux) - (absorption / flux)) - (sca... 8.88e-16 1.13e-02 \n", - "1 (((total / flux) - (absorption / flux)) - (sca... -9.99e-16 2.57e-03 " + "0 (((total / flux) - (absorption / flux)) - (sca... -3.77e-15 1.13e-02 \n", + "1 (((total / flux) - (absorption / flux)) - (sca... 1.44e-15 2.57e-03 " ] }, "execution_count": 23, diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb index 9798b6f07..3ca02ccb2 100644 --- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -34,8 +34,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/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" + "/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" ] } ], @@ -46,8 +50,6 @@ "\n", "import openmc\n", "import openmc.mgxs as mgxs\n", - "from openmc.source import Source\n", - "from openmc.stats import Box\n", "import openmoc\n", "from openmoc.opencg_compatible import get_openmoc_geometry\n", "import pyne.ace\n", @@ -64,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -87,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -121,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -147,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -175,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -212,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -237,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -264,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -281,9 +283,11 @@ "settings_file.inactive = inactive\n", "settings_file.particles = particles\n", "settings_file.output = {'tallies': True}\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", - "settings_file.source = Source(space=Box(\n", - " bounds[:3], bounds[3:], only_fissionable=True))\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings_file.source = openmc.source.Source(space=uniform_dist)\n", "\n", "# Activate tally precision triggers\n", "settings_file.trigger_active = True\n", @@ -302,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -327,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -358,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -382,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -419,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -444,10 +448,9 @@ " 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: 30641c5d37646212ab0540a1064ef6590065f0f0\n", - " Date/Time: 2016-03-23 15:00:26\n", + " Git SHA1: 9a6ecd72597338b40d2b72378e5ad6dd65df2364\n", + " Date/Time: 2016-04-08 11:47:45\n", " MPI Processes: 1\n", - " OpenMP Threads: 4\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -562,20 +565,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.9500E-01 seconds\n", - " Reading cross sections = 1.0300E-01 seconds\n", - " Total time in simulation = 1.1163E+02 seconds\n", - " Time in transport only = 1.1148E+02 seconds\n", - " Time in inactive batches = 6.6440E+00 seconds\n", - " Time in active batches = 1.0499E+02 seconds\n", - " Time synchronizing fission bank = 2.4000E-02 seconds\n", + " Total time for initialization = 5.6900E-01 seconds\n", + " Reading cross sections = 1.4200E-01 seconds\n", + " Total time in simulation = 3.7697E+02 seconds\n", + " Time in transport only = 3.7690E+02 seconds\n", + " Time in inactive batches = 2.4323E+01 seconds\n", + " Time in active batches = 3.5265E+02 seconds\n", + " Time synchronizing fission bank = 2.8000E-02 seconds\n", " Sampling source sites = 1.6000E-02 seconds\n", - " SEND/RECV source sites = 4.0000E-03 seconds\n", - " Time accumulating tallies = 6.0000E-03 seconds\n", - " Total time for finalization = 1.3000E-02 seconds\n", - " Total time elapsed = 1.1220E+02 seconds\n", - " Calculation Rate (inactive) = 15051.2 neutrons/second\n", - " Calculation Rate (active) = 3810.00 neutrons/second\n", + " SEND/RECV source sites = 1.0000E-02 seconds\n", + " Time accumulating tallies = 5.0000E-03 seconds\n", + " Total time for finalization = 2.6000E-02 seconds\n", + " Total time elapsed = 3.7766E+02 seconds\n", + " Calculation Rate (inactive) = 4111.33 neutrons/second\n", + " Calculation Rate (active) = 1134.27 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -593,7 +596,7 @@ "0" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -620,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -639,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -659,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -694,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -748,7 +751,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -790,7 +793,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -920,7 +923,7 @@ "119 10002 1 5 O-16 0.000000 0.000000" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -940,7 +943,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "collapsed": true }, @@ -962,7 +965,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -1001,7 +1004,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -1084,7 +1087,7 @@ "2 10000 2 O-16 3.794859 0.011139" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1110,7 +1113,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -1129,7 +1132,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -1174,7 +1177,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -1185,169 +1188,169 @@ "text": [ "[ NORMAL ] Importing ray tracing data from file...\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ 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 = 0.658940\tres = 2.793E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.643012\tres = 2.853E-03\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.625810\tres = 2.417E-02\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.606678\tres = 2.675E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.587485\tres = 3.057E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.569029\tres = 3.164E-02\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.551707\tres = 3.142E-02\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.536035\tres = 3.044E-02\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.522275\tres = 2.841E-02\n", - "[ NORMAL ] Iteration 12:\tk_eff 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1.247E-05\n", + "[ NORMAL ] Iteration 160:\tk_eff = 1.220976\tres = 1.160E-05\n", + "[ NORMAL ] Iteration 161:\tk_eff = 1.220988\tres = 1.080E-05\n", + "[ NORMAL ] Iteration 162:\tk_eff = 1.220999\tres = 1.004E-05\n" ] } ], @@ -1370,7 +1373,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -1380,8 +1383,8 @@ "output_type": "stream", "text": [ "openmc keff = 1.223474\n", - "openmoc keff = 1.220923\n", - "bias [pcm]: -255.0\n" + "openmoc keff = 1.220999\n", + "bias [pcm]: -247.4\n" ] } ], @@ -1405,7 +1408,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -1445,7 +1448,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "collapsed": false }, @@ -1456,237 +1459,237 @@ "text": [ "[ NORMAL ] Importing ray tracing data from file...\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.495816\tres = 0.000E+00\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.557477\tres = 5.042E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.518301\tres = 1.244E-01\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.509212\tres = 7.027E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.496489\tres = 1.754E-02\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.488581\tres = 2.498E-02\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.482897\tres = 1.593E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.479775\tres = 1.163E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.478835\tres = 6.464E-03\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.479872\tres = 1.960E-03\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.482685\tres = 2.166E-03\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.487085\tres = 5.861E-03\n", - "[ NORMAL ] Iteration 12:\tk_eff = 0.492901\tres = 9.116E-03\n", - "[ NORMAL ] Iteration 13:\tk_eff = 0.499973\tres = 1.194E-02\n", - "[ NORMAL ] Iteration 14:\tk_eff = 0.508155\tres = 1.435E-02\n", - "[ NORMAL ] Iteration 15:\tk_eff = 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NORMAL ] Iteration 212:\tk_eff = 1.222848\tres = 2.042E-05\n", + "[ NORMAL ] Iteration 213:\tk_eff = 1.222871\tres = 1.964E-05\n", + "[ NORMAL ] Iteration 214:\tk_eff = 1.222893\tres = 1.889E-05\n", + "[ NORMAL ] Iteration 215:\tk_eff = 1.222914\tres = 1.817E-05\n", + "[ NORMAL ] Iteration 216:\tk_eff = 1.222935\tres = 1.748E-05\n", + "[ NORMAL ] Iteration 217:\tk_eff = 1.222955\tres = 1.681E-05\n", + "[ NORMAL ] Iteration 218:\tk_eff = 1.222974\tres = 1.617E-05\n", + "[ NORMAL ] Iteration 219:\tk_eff = 1.222992\tres = 1.555E-05\n", + "[ NORMAL ] Iteration 220:\tk_eff = 1.223009\tres = 1.496E-05\n", + "[ NORMAL ] Iteration 221:\tk_eff = 1.223026\tres = 1.438E-05\n", + "[ NORMAL ] Iteration 222:\tk_eff = 1.223043\tres = 1.383E-05\n", + "[ NORMAL ] Iteration 223:\tk_eff = 1.223058\tres = 1.331E-05\n", + "[ NORMAL ] Iteration 224:\tk_eff = 1.223073\tres = 1.280E-05\n", + "[ NORMAL ] Iteration 225:\tk_eff = 1.223088\tres = 1.231E-05\n", + "[ NORMAL ] Iteration 226:\tk_eff = 1.223102\tres = 1.184E-05\n", + "[ NORMAL ] Iteration 227:\tk_eff = 1.223115\tres = 1.138E-05\n", + "[ NORMAL ] Iteration 228:\tk_eff = 1.223128\tres = 1.095E-05\n", + "[ NORMAL ] Iteration 229:\tk_eff = 1.223140\tres = 1.053E-05\n", + "[ NORMAL ] Iteration 230:\tk_eff = 1.223152\tres = 1.013E-05\n" ] } ], @@ -1702,7 +1705,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": { "collapsed": false }, @@ -1712,8 +1715,8 @@ "output_type": "stream", "text": [ "openmc keff = 1.223474\n", - "openmoc keff = 1.223258\n", - "bias [pcm]: -21.5\n" + "openmoc keff = 1.223152\n", + "bias [pcm]: -32.1\n" ] } ], @@ -1759,11 +1762,23 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'pyne' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\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# Instantiate a PyNE ACE continuous-energy cross sections library\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mpyne_lib\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpyne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLibrary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../../../../data/nndc/293.6K/U_235_293.6K.ace'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mpyne_lib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'92235.71c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# Extract the U-235 data from the library\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'pyne' is not defined" + ] + } + ], "source": [ "# Instantiate a PyNE ACE continuous-energy cross sections library\n", "pyne_lib = pyne.ace.Library('../../../../data/nndc/293.6K/U_235_293.6K.ace')\n", @@ -1785,32 +1800,11 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "(9.9999999999999994e-12, 20.0)" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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N73Q6ue++BxgzZhyPPfZos+WNVcYlBxEpFZGPReSUVMdu397DrFm1dO7s5uSTS1izRjuq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\n+KP1PP8hsU6nNWku3KqsyVy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GugNni0h3YC/ge+9p2bHgi1I7oVMnz05tMxruRl5S4uHw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\nCqHJQSmlVAhNDkoppUL8Pzlt5uQccjZkAAAAAElFTkSuQmCC\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", @@ -1846,7 +1840,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "collapsed": false }, @@ -1878,22 +1872,11 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create plot of the H-1 scattering matrix\n", "fig = plt.subplot(121)\n", @@ -1941,7 +1924,7 @@ "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 023efcc10..7a575b544 100644 --- a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb @@ -50,14 +50,9 @@ "\n", "import openmc\n", "import openmc.mgxs\n", - "from openmc.statepoint import StatePoint\n", - "from openmc.summary import Summary\n", - "from openmc.source import Source\n", - "from openmc.stats import Box\n", - "\n", "import openmoc\n", "import openmoc.process\n", - "from openmoc.compatible import get_openmoc_geometry\n", + "from openmoc.opencg_compatible import get_openmoc_geometry\n", "from openmoc.materialize import load_openmc_mgxs_lib\n", "\n", "%matplotlib inline" @@ -393,9 +388,11 @@ "settings_file.inactive = inactive\n", "settings_file.particles = particles\n", "settings_file.output = {'tallies': False}\n", - "source_bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n", - "settings_file.source = Source(Box(\n", - " source_bounds[:3], source_bounds[3:], only_fissionable=True))\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", + "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings_file.source = openmc.source.Source(space=uniform_dist)\n", "\n", "# Export to \"settings.xml\"\n", "settings_file.export_to_xml()" @@ -421,6 +418,7 @@ "plot.filename = 'materials-xy'\n", "plot.origin = [0, 0, 0]\n", "plot.pixels = [250, 250]\n", + "plot.width = [-10.71*2, -10.71*2]\n", "plot.color = 'mat'\n", "\n", "# Instantiate a PlotsFile, add Plot, and export to \"plots.xml\"\n", @@ -469,7 +467,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -735,10 +733,9 @@ " 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: 5f252e2df51930b9175fd41bafa8db01f3eaeb92\n", - " Date/Time: 2016-03-23 14:44:19\n", + " Git SHA1: 9a6ecd72597338b40d2b72378e5ad6dd65df2364\n", + " Date/Time: 2016-04-08 11:57:08\n", " MPI Processes: 1\n", - " OpenMP Threads: 16\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -824,20 +821,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.7900E-01 seconds\n", - " Reading cross sections = 1.3600E-01 seconds\n", - " Total time in simulation = 6.5400E+00 seconds\n", - " Time in transport only = 5.8520E+00 seconds\n", - " Time in inactive batches = 6.1600E-01 seconds\n", - " Time in active batches = 5.9240E+00 seconds\n", - " Time synchronizing fission bank = 2.0000E-03 seconds\n", - " Sampling source sites = 1.0000E-03 seconds\n", - " SEND/RECV source sites = 0.0000E+00 seconds\n", - " Time accumulating tallies = 2.0000E-03 seconds\n", + " Total time for initialization = 5.7200E-01 seconds\n", + " Reading cross sections = 1.4400E-01 seconds\n", + " Total time in simulation = 8.3367E+01 seconds\n", + " Time in transport only = 8.3321E+01 seconds\n", + " Time in inactive batches = 6.3610E+00 seconds\n", + " Time in active batches = 7.7006E+01 seconds\n", + " Time synchronizing fission bank = 1.0000E-02 seconds\n", + " Sampling source sites = 7.0000E-03 seconds\n", + " SEND/RECV source sites = 2.0000E-03 seconds\n", + " Time accumulating tallies = 4.0000E-03 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 7.0410E+00 seconds\n", - " Calculation Rate (inactive) = 40584.4 neutrons/second\n", - " Calculation Rate (active) = 16880.5 neutrons/second\n", + " Total time elapsed = 8.3969E+01 seconds\n", + " Calculation Rate (inactive) = 3930.20 neutrons/second\n", + " Calculation Rate (active) = 1298.60 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1329,124 +1326,124 @@ "text": [ "[ NORMAL ] Importing ray tracing data from file...\n", "[ 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 = 0.761746\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 = 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1.028031\tres = 5.042E-05\n", + "[ NORMAL ] Iteration 99:\tk_eff = 1.028075\tres = 4.637E-05\n", + "[ NORMAL ] Iteration 100:\tk_eff = 1.028115\tres = 4.264E-05\n", + "[ NORMAL ] Iteration 101:\tk_eff = 1.028152\tres = 3.921E-05\n", + "[ NORMAL ] Iteration 102:\tk_eff = 1.028186\tres = 3.605E-05\n", + "[ NORMAL ] Iteration 103:\tk_eff = 1.028217\tres = 3.315E-05\n", + "[ NORMAL ] Iteration 104:\tk_eff = 1.028246\tres = 3.048E-05\n", + "[ NORMAL ] Iteration 105:\tk_eff = 1.028272\tres = 2.802E-05\n", + "[ NORMAL ] Iteration 106:\tk_eff = 1.028297\tres = 2.576E-05\n", + "[ NORMAL ] Iteration 107:\tk_eff = 1.028319\tres = 2.367E-05\n", + "[ NORMAL ] Iteration 108:\tk_eff = 1.028339\tres = 2.176E-05\n", + "[ NORMAL ] Iteration 109:\tk_eff = 1.028358\tres = 2.000E-05\n", + "[ NORMAL ] Iteration 110:\tk_eff = 1.028376\tres = 1.838E-05\n", + "[ NORMAL ] Iteration 111:\tk_eff = 1.028392\tres = 1.689E-05\n", + "[ NORMAL ] Iteration 112:\tk_eff = 1.028406\tres = 1.553E-05\n", + "[ NORMAL ] Iteration 113:\tk_eff = 1.028420\tres = 1.427E-05\n", + "[ NORMAL ] Iteration 114:\tk_eff = 1.028432\tres = 1.311E-05\n", + "[ NORMAL ] Iteration 115:\tk_eff = 1.028443\tres = 1.205E-05\n", + "[ NORMAL ] Iteration 116:\tk_eff = 1.028454\tres = 1.107E-05\n", + "[ NORMAL ] Iteration 117:\tk_eff = 1.028463\tres = 1.017E-05\n" ] } ], @@ -1479,8 +1476,8 @@ "output_type": "stream", "text": [ "openmc keff = 1.028263\n", - "openmoc keff = 1.028538\n", - "bias [pcm]: 27.5\n" + "openmoc keff = 1.028463\n", + "bias [pcm]: 20.0\n" ] } ], @@ -1588,7 +1585,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 44, @@ -1599,7 +1596,7 @@ "data": { "image/png": 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RuQumBWIiI11eOTuwnsCAKlve3sE1/1Zn+YuBdawJxGwMxESeu2YNrnkhEBOZKCE1wARi\nkyBEBtdEBgSNDcQEUpI5gZjUwKLp3d28RoNrRETyp6ItIpIRFW0RkYyoaIuIZERFW0QkIyraIiIZ\nUdEWEcmIiraISEYi59sPycx6gQ3AALDF3efXipt5UmJFP0+3dVhgVoltf5ieVcJvT7f1yrXpth6+\nN91WZCDK3ontui8wU0ZkVo7uJs04MzXQ1sGBtgYmptsKjXYYJdHcrtfFdYF2zgnsq88EnpeI8wJt\nXdqkfDs30NYlgbYiA5Qi29WsmX0+Gmjrq4G29k4sr/duuqGiTZHQPe6+vsH1iHQa5bZ0pEYPj1gT\n1iHSiZTb0pEaTUoHbjSze8zs7GZ0SKRDKLelIzV6eOR4d19rZntSJPhD7n5bMzom0mbKbelIDRVt\nd19b/n7KzK4F5gM7JfbCR7bf7pkOPTMaaVVezpY8V/yMtmhuL664PRcIXLBSpKZlwP3l7Ym9vUPG\njbhom9lkYIy7P2tmU4C3AhfWil14yEhbEdlRz5TiZ9CFkevoDtNwcvv9zW9eXqbmsf2f/rSuLr6y\ncmXNuEbeac8CrjUzL9fzDXe/oYH1iXQK5bZ0rBEXbXdfARzdxL6IdATltnSylsxcM3BC/ZjeW9Lr\n6Toy0lY6xgMDNm4LzJLz+gPSMXSlQ/w39Zf33pFex+OBrmwIxPRMSMesCYx2OKIrHWORURMHB9Zz\na3tnrrm+zvLI4JrIQJWIyMwskRlwpjfakVJkRp7IDC8Rkf28WyCm0bMyBgVeRsn9PK27m+M0c42I\nSP5UtEVEMqKiLSKSERVtEZGMqGiLiGRERVtEJCMq2iIiGVHRFhHJSLPOJ68vcYGo/QNTvPQ+mI45\nIDGIB4An0yHbAquJnK2/MTAwZmVicM1Bk9PrWL85HdO9Tzqmd3U6Zk5g5MAzq9Ix9wV28luOSce0\nW2RQSz0vBGIiL9LIekJ5HRC53EukP5H17BmIiWxXpD8TAzGR5zsyuCa1nnrbpHfaIiIZUdEWEcmI\niraISEZUtEVEMqKiLSKSERVtEZGMqGiLiGRERVtEJCOtGVwTGNCSchAXJGOuv2VRMuZVgZlr3hho\na9uGdFtLEgNnAE5JtHX15nQ7kRlAxq5Ob9NjpNuamhgoBTBubWD/HZ5ui93TIe1W7wX0fODxHwrk\n2j8HnpfA+CrODbR1UZPaWhRoa0GgrcgER+cH2rok0FagNPDhQFtfDbSVGk9Y79203mmLiGRERVtE\nJCMq2iIiGVHRFhHJiIq2iEhGVLRFRDKioi0ikhEVbRGRjJi7j24DZj6wdyIoMK2EB2aK2bQ2HbPb\nvumYZ1akY2bMTsesDswEsyax/JWBmWueD+y/rQOB9aRDQrMMrduYjtnjjYHGZqVD7Ovg7hZYW9OZ\nmf+ozvLAbgjFjA/ERJ67SMzMQExkrFxkuyJjpyIz10T6E3gZhWau2RKIiWxXqpxN7+7mNUuX1sxt\nvdMWEcmIiraISEZUtEVEMqKiLSKSERVtEZGMqGiLiGRERVtEJCMq2iIiGUnOXGNmVwBvB/rdfW55\n3zTg28D+QC9wqrtvGHIlifE7v1iX7uhR6RC2bE3HrH40HbMy0NbxqQFDwG6Bs/63JKbm2BSYJqQ/\nHcLyQMwpgQFM96xPx8yfF2gs8FwRGOTUiGbkdqNTP01q8PHDWU9kl0dGKUUG4ETaigyciYgMPooM\nnIk8l82a6is1S06jM9dcCZxYdd8ngZvc/TDgZuC8wHpEOo1yW7KTLNrufhtQ/f7qncBV5e2rgHc1\nuV8io065LTka6THtme7eD+DuTxD7xCSSA+W2dLRmfRE5uledEmkf5bZ0lJEeV+83s1nu3m9me5G4\n0NbCTdtv90yAnl1G2Kq87C3ZUPyMomHl9uKK23OByHewIrUsA+4vb0/s7R0yLlq0jR2/WP4BcCbw\nD8AZwHX1HrxwarAVkYSe3YufQReuaniVDeX2+xtuXqQwj+3/9Kd1dfGVlbXPY0seHjGzbwJ3AIea\n2eNm9r+Ai4G3mNnDwJvKv0WyotyWHCXfabv76UMsenOT+yLSUsptyVGzzhWvyxKtHHVkeh3jHrwg\nGfMAi5IxkW+V3kCgrbvTbT0TaKs70damyel2egMDcN4T2Ka+jem2EmOBABi7LN3Ws1PSbU2OjKjq\nYJGZYj4QeF4uCeR15Hk5P9DWpYG2IrO3nNek7UoNQgH4y0BbFwXaihTDcwNtfTXQ1vTE8nqDnDSM\nXUQkIyraIiIZUdEWEcmIiraISEZUtEVEMqKiLSKSERVtEZGMqGiLiGTE3Ef3ImZm5gOvqx/jgWlV\nnvl1OuanA+mYyGVQAmNVePO0dExqUBHA6qfqL58VmE2mf2M6pjcdwuRAzCsD/bk90J+ew9IxFphG\nxZaDu0cmXGk6M/Mf1VkeGYSyJhCzKR0SmikmMlBlViAmMmgoEhPJt8iMM5GZm7YFYiKDayL1Y04g\nZkJi+fTubl6zdGnN3NY7bRGRjKhoi4hkREVbRCQjKtoiIhlR0RYRyYiKtohIRlS0RUQyoqItIpKR\n1sxc04QZSGb0pWNO7k3PKnF9YFaJyIn4169Px7wpMBBlcmLEw/jZ6XVMei4dMzcwsmJsIBsmbkzv\n4/UT0vv4Fw+n24oM9Gi3SQ0+PvICjMwCE5mZZXyT+hPZ5sh6mtWfyHoiIvv5S4H9nBo4A+lBQ/XW\noXfaIiIZUdEWEcmIiraISEZUtEVEMqKiLSKSERVtEZGMqGiLiGRERVtEJCMtmbnGuxNBgVlg/IF0\nzCOPpmP2Dwx4mRQYQHJB4CT7yMCACxIn9D8caOeJQDvdgYEDz04JbFNgo8YfE+hQYLAUgUFDY1a3\nd+aaOxtcR2R2m4cCMZGZa84J5MBVgXyLzErz4SYNVInMXHNGoK0vNOn1ekQgphmDfaZ2d3OUZq4R\nEcmfiraISEZUtEVEMqKiLSKSERVtEZGMqGiLiGRERVtEJCMq2iIiGUkOrjGzK4C3A/3uPre8bwFw\nNvBkGXa+u//7EI93Pz7Ri8CAF1akQ/zwdMzmG9Ix39+cjnnfwekYXkiHrFhdf/kBkXYiNgRidg3E\n7J0OsWMD63kyHcIjgbbuHfngmmbkdr0JeCIDZzYFYtYFYiJtRdbT6Ew8gyIDcFrZ1vRATGRQzIxA\nTORllGprUnc3+zUwuOZK4MQa93/W3Y8pf2omtUiHU25LdpJF291vA2rNiNiWocMizaLclhw1ckz7\nHDP7uZl9xcx2b1qPRNpPuS0da6SzsX8RWOTubmZ/B3wW+MBQwQsf3367Z/fiR2QklmwqfkbRsHL7\nsorb84FXj2rX5KXsLuDu8va43t4h40ZUtN39qYo/vwz8sF78wv1G0orIznqmFj+DLlzb3PUPN7c/\n1tzm5WXs1Wz/pz+pq4tLV66sGRc9PGJUHOczs70qlv0BELhwqkhHUm5LVpLvtM3sm0APMMPMHgcW\nACeY2dHAANALfGgU+ygyKpTbkqNk0Xb302vcfeUo9EWkpZTbkqORfhE5PLsklh8SWEdgag5bno6Z\nfEo65n03pmMig0y2LkvHzJpSf7nNDPQlMmriwEBM5Fu0ewMxgRlnWBWISeybTlBv/FRk1pWIZg1C\nmdOk9URmyYkMZomsp1kFalsgpln7OTJIJzXubmydZRrGLiKSERVtEZGMqGiLiGRERVtEJCMtL9pL\nal3pocMtebHdPRi+JZEvAzvMksiVCDvYPe3uwAgEvivvOLn1+a4mr09FOyDLoh24vGynyb1o/7Td\nHRiB+9vdgRHIrc93p0OGRYdHREQy0prztA85ZvvtDX1wSNVJzvsE1hG5yvvUdAhdgZi5VX8/1gcH\nVfU5sp6AMakTSA8NrKTWO9T/6oPfqehz5MrskechcrGmyLVmBmrc91wfHFrR53onqw669WeBoNEz\n6ZjtuT2ur49Je2/v/4TA4yOnokd2Q+Tc4FrrmdDXx9S9A4MOKkTOeY70eaTrGUmfa6VbtdRwkpHG\njO3rY5eq/qau/bvLoYfC0qU1lyVnrmmUmY1uA/KyN9KZaxql3JbRViu3R71oi4hI8+iYtohIRlS0\nRUQy0tKibWZvM7PlZvZLM/tEK9seKTPrNbNlZnafmTX77J2mMLMrzKzfzO6vuG+amd1gZg+b2fWd\nNG3WEP1dYGarzexn5c/b2tnH4VBej47c8hpak9stK9pmNgb4AsXs10cC7zGzw1vVfgMGgB53f5W7\nz293Z4ZQa1bxTwI3ufthwM3AeS3v1dBeMrOgK69HVW55DS3I7Va+054PPOLuK919C3AN8M4Wtj9S\nRocfRhpiVvF3AleVt68C3tXSTtXxEpsFXXk9SnLLa2hNbrfySZvDjldRXk3zLvE7mhy40czuMbOz\n292ZYZjp7v0A7v4EELkyd7vlOAu68rq1csxraGJud/R/2g5xvLsfA/wP4KNm9vp2d2iEOv3czi8C\nB7r70cATFLOgy+hRXrdOU3O7lUV7DTuOldunvK+jufva8vdTwLUUH4dz0G9ms+C3k9U+2eb+1OXu\nT/n2QQNfBo5rZ3+GQXndWlnlNTQ/t1tZtO8BDjaz/c1sAnAa8IMWtj9sZjbZzHYtb08B3krnzs69\nw6ziFPv2zPL2GcB1re5QwktlFnTl9ejKLa9hlHO7NdceAdx9m5mdA9xA8c/iCnd/qFXtj9As4Npy\nuPI44BvufkOb+7STIWYVvxj4rpmdBawETm1fD3f0UpoFXXk9enLLa2hNbmsYu4hIRvRFpIhIRlS0\nRUQyoqItIpIRFW0RkYyoaIuIZERFW0QkIyraIiIZUdEWEcnIfwNw3TpV8WgtIAAAAABJRU5ErkJg\ngg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index 27812f4d6..718ff8f79 100644 --- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb +++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb @@ -24,10 +24,6 @@ "import numpy as np\n", "\n", "import openmc\n", - "from openmc.statepoint import StatePoint\n", - "from openmc.summary import Summary\n", - "from openmc.source import Source\n", - "from openmc.stats import Box\n", "\n", "%matplotlib inline" ] @@ -305,9 +301,11 @@ "settings_file.output = {'tallies': False}\n", "settings_file.trigger_active = True\n", "settings_file.trigger_max_batches = max_batches\n", - "source_bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n", - "settings_file.source = Source(space=Box(\n", - " source_bounds[:3], source_bounds[3:]))\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", + "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings_file.source = openmc.source.Source(space=uniform_dist)\n", "\n", "# Export to \"settings.xml\"\n", "settings_file.export_to_xml()" @@ -382,7 +380,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nYII=\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nYII=\n", "text/plain": [ "" ] @@ -567,10 +565,9 @@ " 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: 5f252e2df51930b9175fd41bafa8db01f3eaeb92\n", - " Date/Time: 2016-03-23 14:45:30\n", + " Git SHA1: 9a6ecd72597338b40d2b72378e5ad6dd65df2364\n", + " Date/Time: 2016-04-08 12:01:24\n", " MPI Processes: 1\n", - " OpenMP Threads: 16\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -597,46 +594,34 @@ "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \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", + " 1/1 0.55921 \n", + " 2/1 0.63816 \n", + " 3/1 0.68834 \n", + " 4/1 0.71192 \n", + " 5/1 0.67935 \n", + " 6/1 0.68274 \n", + " 7/1 0.66339 0.67307 +/- 0.00967\n", + " 8/1 0.65835 0.66816 +/- 0.00743\n", + " 9/1 0.66697 0.66786 +/- 0.00527\n", + " 10/1 0.70498 0.67528 +/- 0.00847\n", + " 11/1 0.68596 0.67706 +/- 0.00714\n", + " 12/1 0.68481 0.67817 +/- 0.00614\n", + " 13/1 0.68369 0.67886 +/- 0.00536\n", + " 14/1 0.68785 0.67986 +/- 0.00483\n", + " 15/1 0.66145 0.67802 +/- 0.00470\n", + " 16/1 0.71831 0.68168 +/- 0.00561\n", + " 17/1 0.68428 0.68190 +/- 0.00512\n", + " 18/1 0.67527 0.68139 +/- 0.00474\n", + " 19/1 0.68166 0.68141 +/- 0.00439\n", + " 20/1 0.65475 0.67963 +/- 0.00446\n", + " Triggers unsatisfied, max unc./thresh. is 1.07581 for absorption in tally 10002\n", + " The estimated number of batches is 23\n", " Creating state point statepoint.020.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", + " 21/1 0.64538 0.67749 +/- 0.00469\n", + " 22/1 0.73275 0.68074 +/- 0.00547\n", + " 23/1 0.71674 0.68274 +/- 0.00553\n", + " Triggers satisfied for batch 23\n", + " Creating state point statepoint.023.h5...\n", "\n", " ===========================================================================\n", " ======================> SIMULATION FINISHED <======================\n", @@ -645,28 +630,28 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.5300E-01 seconds\n", - " Reading cross sections = 1.3200E-01 seconds\n", - " Total time in simulation = 1.9780E+00 seconds\n", - " Time in transport only = 1.7780E+00 seconds\n", - " Time in inactive batches = 2.1000E-01 seconds\n", - " Time in active batches = 1.7680E+00 seconds\n", - " Time synchronizing fission bank = 5.0000E-03 seconds\n", - " Sampling source sites = 5.0000E-03 seconds\n", + " Total time for initialization = 5.4700E-01 seconds\n", + " Reading cross sections = 1.4200E-01 seconds\n", + " Total time in simulation = 1.4279E+01 seconds\n", + " Time in transport only = 1.4263E+01 seconds\n", + " Time in inactive batches = 2.3020E+00 seconds\n", + " Time in active batches = 1.1977E+01 seconds\n", + " Time synchronizing fission bank = 1.0000E-03 seconds\n", + " Sampling source sites = 1.0000E-03 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 = 2.4480E+00 seconds\n", - " Calculation Rate (inactive) = 59523.8 neutrons/second\n", - " Calculation Rate (active) = 21210.4 neutrons/second\n", + " Total time elapsed = 1.4854E+01 seconds\n", + " Calculation Rate (inactive) = 5430.06 neutrons/second\n", + " Calculation Rate (active) = 3131.00 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\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", + " k-effective (Collision) = 0.67952 +/- 0.00434\n", + " k-effective (Track-length) = 0.68274 +/- 0.00553\n", + " k-effective (Absorption) = 0.68095 +/- 0.00369\n", + " Combined k-effective = 0.67994 +/- 0.00349\n", + " Leakage Fraction = 0.34133 +/- 0.00332\n", "\n" ] }, @@ -709,7 +694,7 @@ "statepoints = glob.glob('statepoint.*.h5')\n", "\n", "# Load the last statepoint file\n", - "sp = StatePoint(statepoints[-1])" + "sp = openmc.StatePoint(statepoints[-1])" ] }, { @@ -722,7 +707,7 @@ "outputs": [], "source": [ "# Load the summary file and link with statepoint\n", - "su = Summary('summary.h5')\n", + "su = openmc.Summary('summary.h5')\n", "sp.link_with_summary(su)" ] }, @@ -783,13 +768,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.12916959]]\n", + "[[[ 0.1508711 ]]\n", "\n", - " [[ 0.06336943]]\n", + " [[ 0.05389822]]\n", "\n", - " [[ 0.33288738]]\n", + " [[ 0.19633 ]]\n", "\n", - " [[ 0.14666158]]]\n" + " [[ 0.12963172]]]\n" ] } ], @@ -845,8 +830,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 2.37e-04\n", - " 3.06e-05\n", + " 2.34e-04\n", + " 3.54e-05\n", " \n", " \n", " 1\n", @@ -856,8 +841,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 5.78e-04\n", - " 7.46e-05\n", + " 5.71e-04\n", + " 8.62e-05\n", " \n", " \n", " 2\n", @@ -867,8 +852,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 7.00e-05\n", - " 5.15e-06\n", + " 7.03e-05\n", + " 7.05e-06\n", " \n", " \n", " 3\n", @@ -878,8 +863,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 1.85e-04\n", - " 1.28e-05\n", + " 1.87e-04\n", + " 1.76e-05\n", " \n", " \n", " 4\n", @@ -889,8 +874,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 4.04e-04\n", - " 3.09e-05\n", + " 3.67e-04\n", + " 3.61e-05\n", " \n", " \n", " 5\n", @@ -900,8 +885,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 9.85e-04\n", - " 7.54e-05\n", + " 8.94e-04\n", + " 8.80e-05\n", " \n", " \n", " 6\n", @@ -911,8 +896,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.00e-04\n", - " 5.08e-06\n", + " 1.04e-04\n", + " 5.36e-06\n", " \n", " \n", " 7\n", @@ -922,8 +907,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 2.63e-04\n", - " 1.34e-05\n", + " 2.76e-04\n", + " 1.40e-05\n", " \n", " \n", " 8\n", @@ -933,8 +918,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 5.82e-04\n", - " 5.00e-05\n", + " 6.04e-04\n", + " 5.57e-05\n", " \n", " \n", " 9\n", @@ -944,8 +929,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 1.42e-03\n", - " 1.22e-04\n", + " 1.47e-03\n", + " 1.36e-04\n", " \n", " \n", " 10\n", @@ -955,8 +940,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.38e-04\n", - " 1.03e-05\n", + " 1.41e-04\n", + " 6.69e-06\n", " \n", " \n", " 11\n", @@ -966,8 +951,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 3.59e-04\n", - " 2.54e-05\n", + " 3.72e-04\n", + " 1.82e-05\n", " \n", " \n", " 12\n", @@ -977,8 +962,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 6.88e-04\n", - " 4.25e-05\n", + " 6.45e-04\n", + " 4.59e-05\n", " \n", " \n", " 13\n", @@ -988,8 +973,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 1.68e-03\n", - " 1.04e-04\n", + " 1.57e-03\n", + " 1.12e-04\n", " \n", " \n", " 14\n", @@ -999,8 +984,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.62e-04\n", - " 7.43e-06\n", + " 1.82e-04\n", + " 9.37e-06\n", " \n", " \n", " 15\n", @@ -1010,8 +995,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 4.22e-04\n", - " 1.93e-05\n", + " 4.76e-04\n", + " 2.47e-05\n", " \n", " \n", " 16\n", @@ -1021,8 +1006,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 7.62e-04\n", - " 5.69e-05\n", + " 7.28e-04\n", + " 7.49e-05\n", " \n", " \n", " 17\n", @@ -1032,8 +1017,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 1.86e-03\n", - " 1.39e-04\n", + " 1.77e-03\n", + " 1.83e-04\n", " \n", " \n", " 18\n", @@ -1043,8 +1028,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.80e-04\n", - " 8.16e-06\n", + " 1.81e-04\n", + " 1.04e-05\n", " \n", " \n", " 19\n", @@ -1054,8 +1039,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 4.71e-04\n", - " 2.08e-05\n", + " 4.72e-04\n", + " 2.67e-05\n", " \n", " \n", "\n", @@ -1064,49 +1049,49 @@ "text/plain": [ " 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", + "0 1 1 1 0.00e+00 6.25e-07 fission 2.34e-04 \n", + "1 1 1 1 0.00e+00 6.25e-07 nu-fission 5.71e-04 \n", + "2 1 1 1 6.25e-07 2.00e+01 fission 7.03e-05 \n", + "3 1 1 1 6.25e-07 2.00e+01 nu-fission 1.87e-04 \n", + "4 1 2 1 0.00e+00 6.25e-07 fission 3.67e-04 \n", + "5 1 2 1 0.00e+00 6.25e-07 nu-fission 8.94e-04 \n", + "6 1 2 1 6.25e-07 2.00e+01 fission 1.04e-04 \n", + "7 1 2 1 6.25e-07 2.00e+01 nu-fission 2.76e-04 \n", + "8 1 3 1 0.00e+00 6.25e-07 fission 6.04e-04 \n", + "9 1 3 1 0.00e+00 6.25e-07 nu-fission 1.47e-03 \n", + "10 1 3 1 6.25e-07 2.00e+01 fission 1.41e-04 \n", + "11 1 3 1 6.25e-07 2.00e+01 nu-fission 3.72e-04 \n", + "12 1 4 1 0.00e+00 6.25e-07 fission 6.45e-04 \n", + "13 1 4 1 0.00e+00 6.25e-07 nu-fission 1.57e-03 \n", + "14 1 4 1 6.25e-07 2.00e+01 fission 1.82e-04 \n", + "15 1 4 1 6.25e-07 2.00e+01 nu-fission 4.76e-04 \n", + "16 1 5 1 0.00e+00 6.25e-07 fission 7.28e-04 \n", + "17 1 5 1 0.00e+00 6.25e-07 nu-fission 1.77e-03 \n", + "18 1 5 1 6.25e-07 2.00e+01 fission 1.81e-04 \n", + "19 1 5 1 6.25e-07 2.00e+01 nu-fission 4.72e-04 \n", "\n", " 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 " + "0 3.54e-05 \n", + "1 8.62e-05 \n", + "2 7.05e-06 \n", + "3 1.76e-05 \n", + "4 3.61e-05 \n", + "5 8.80e-05 \n", + "6 5.36e-06 \n", + "7 1.40e-05 \n", + "8 5.57e-05 \n", + "9 1.36e-04 \n", + "10 6.69e-06 \n", + "11 1.82e-05 \n", + "12 4.59e-05 \n", + "13 1.12e-04 \n", + "14 9.37e-06 \n", + "15 2.47e-05 \n", + "16 7.49e-05 \n", + "17 1.83e-04 \n", + "18 1.04e-05 \n", + "19 2.67e-05 " ] }, "execution_count": 25, @@ -1135,9 +1120,9 @@ "outputs": [ { "data": { - "image/png": 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ExEZgZFW9CdmJ3c8ltRwuvNB7oBFxT3ZfoVauLG9m3WrQ+zGl5JZdM28+BoyL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\nTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1259,72 +1244,72 @@ " 10000\n", " U-235\n", " scatter-Y0,0\n", - " 3.77e-02\n", - " 6.49e-04\n", + " 3.86e-02\n", + " 1.11e-03\n", " \n", " \n", " 1\n", " 10000\n", " U-235\n", " scatter-Y1,-1\n", - " 2.54e-04\n", - " 1.81e-04\n", + " 2.75e-04\n", + " 2.96e-04\n", " \n", " \n", " 2\n", " 10000\n", " U-235\n", " scatter-Y1,0\n", - " 3.65e-05\n", - " 2.70e-04\n", + " -5.55e-05\n", + " 4.33e-04\n", " \n", " \n", " 3\n", " 10000\n", " U-235\n", " scatter-Y1,1\n", - " -1.70e-04\n", - " 2.19e-04\n", + " -4.22e-04\n", + " 3.51e-04\n", " \n", " \n", " 4\n", " 10000\n", " U-235\n", " scatter-Y2,-2\n", - " 7.47e-05\n", - " 1.54e-04\n", + " 5.88e-05\n", + " 2.04e-04\n", " \n", " \n", " 5\n", " 10000\n", " U-235\n", " scatter-Y2,-1\n", - " -2.35e-04\n", - " 1.34e-04\n", + " 1.00e-04\n", + " 2.49e-04\n", " \n", " \n", " 6\n", " 10000\n", " U-235\n", " scatter-Y2,0\n", - " -5.51e-05\n", - " 1.79e-04\n", + " -8.09e-05\n", + " 1.59e-04\n", " \n", " \n", " 7\n", " 10000\n", " U-235\n", " scatter-Y2,1\n", - " -1.27e-04\n", - " 1.54e-04\n", + " 1.93e-04\n", + " 2.14e-04\n", " \n", " \n", " 8\n", " 10000\n", " U-235\n", " scatter-Y2,2\n", - " 1.72e-04\n", - " 1.40e-04\n", + " 1.12e-04\n", + " 1.86e-04\n", " \n", " \n", " 9\n", @@ -1332,71 +1317,71 @@ " U-238\n", " scatter-Y0,0\n", " 2.34e+00\n", - " 7.62e-03\n", + " 1.34e-02\n", " \n", " \n", " 10\n", " 10000\n", " U-238\n", " scatter-Y1,-1\n", - " 2.46e-02\n", - " 1.71e-03\n", + " 2.32e-02\n", + " 2.97e-03\n", " \n", " \n", " 11\n", " 10000\n", " U-238\n", " scatter-Y1,0\n", - " 1.15e-03\n", - " 2.17e-03\n", + " 7.50e-04\n", + " 2.55e-03\n", " \n", " \n", " 12\n", " 10000\n", " U-238\n", " scatter-Y1,1\n", - " -2.39e-02\n", - " 2.15e-03\n", + " -2.73e-02\n", + " 3.28e-03\n", " \n", " \n", " 13\n", " 10000\n", " U-238\n", " scatter-Y2,-2\n", - " -3.92e-03\n", - " 1.38e-03\n", + " -2.36e-03\n", + " 1.21e-03\n", " \n", " \n", " 14\n", " 10000\n", " U-238\n", " scatter-Y2,-1\n", - " -1.19e-03\n", - " 1.58e-03\n", + " -1.80e-04\n", + " 1.49e-03\n", " \n", " \n", " 15\n", " 10000\n", " U-238\n", " scatter-Y2,0\n", - " 3.22e-03\n", - " 1.45e-03\n", + " 3.23e-03\n", + " 2.25e-03\n", " \n", " \n", " 16\n", " 10000\n", " U-238\n", " scatter-Y2,1\n", - " 1.27e-04\n", - " 9.70e-04\n", + " 3.75e-03\n", + " 1.97e-03\n", " \n", " \n", " 17\n", " 10000\n", " U-238\n", " scatter-Y2,2\n", - " -2.70e-03\n", - " 1.21e-03\n", + " 2.07e-03\n", + " 1.60e-03\n", " \n", " \n", "\n", @@ -1404,24 +1389,24 @@ ], "text/plain": [ " cell nuclide score mean std. dev.\n", - "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" + "0 10000 U-235 scatter-Y0,0 3.86e-02 1.11e-03\n", + "1 10000 U-235 scatter-Y1,-1 2.75e-04 2.96e-04\n", + "2 10000 U-235 scatter-Y1,0 -5.55e-05 4.33e-04\n", + "3 10000 U-235 scatter-Y1,1 -4.22e-04 3.51e-04\n", + "4 10000 U-235 scatter-Y2,-2 5.88e-05 2.04e-04\n", + "5 10000 U-235 scatter-Y2,-1 1.00e-04 2.49e-04\n", + "6 10000 U-235 scatter-Y2,0 -8.09e-05 1.59e-04\n", + "7 10000 U-235 scatter-Y2,1 1.93e-04 2.14e-04\n", + "8 10000 U-235 scatter-Y2,2 1.12e-04 1.86e-04\n", + "9 10000 U-238 scatter-Y0,0 2.34e+00 1.34e-02\n", + "10 10000 U-238 scatter-Y1,-1 2.32e-02 2.97e-03\n", + "11 10000 U-238 scatter-Y1,0 7.50e-04 2.55e-03\n", + "12 10000 U-238 scatter-Y1,1 -2.73e-02 3.28e-03\n", + "13 10000 U-238 scatter-Y2,-2 -2.36e-03 1.21e-03\n", + "14 10000 U-238 scatter-Y2,-1 -1.80e-04 1.49e-03\n", + "15 10000 U-238 scatter-Y2,0 3.23e-03 2.25e-03\n", + "16 10000 U-238 scatter-Y2,1 3.75e-03 1.97e-03\n", + "17 10000 U-238 scatter-Y2,2 2.07e-03 1.60e-03" ] }, "execution_count": 29, @@ -1455,8 +1440,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00121338 0.00761835]\n", - " [ 0.00013952 0.00064888]]]\n" + "[[[ 0.00159927 0.01341406]\n", + " [ 0.00018637 0.00111048]]]\n" ] } ], @@ -1524,13 +1509,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.03284934]]]\n" + "[[[ 0.05767856]]]\n" ] } ], "source": [ "# Get the relative error for the scattering reaction rates in\n", - "# the first 30 distribcell instances \n", + "# the first 10 distribcell instances \n", "data = tally.get_values(scores=['scatter'], filters=['distribcell'],\n", " filter_bins=[(i,) for i in range(10)], value='rel_err')\n", "print(data)" @@ -1569,141 +1554,141 @@ " 558\n", " 279\n", " absorption\n", - " 7.14e-05\n", - " 8.26e-06\n", + " 8.19e-05\n", + " 7.82e-06\n", " \n", " \n", " 559\n", " 279\n", " scatter\n", - " 1.25e-02\n", - " 5.75e-04\n", + " 1.33e-02\n", + " 6.19e-04\n", " \n", " \n", " 560\n", " 280\n", " absorption\n", - " 8.50e-05\n", - " 6.16e-06\n", + " 1.00e-04\n", + " 7.93e-06\n", " \n", " \n", " 561\n", " 280\n", " scatter\n", - " 1.38e-02\n", - " 4.58e-04\n", + " 1.40e-02\n", + " 5.61e-04\n", " \n", " \n", " 562\n", " 281\n", " absorption\n", - " 1.04e-04\n", - " 7.54e-06\n", + " 9.52e-05\n", + " 7.08e-06\n", " \n", " \n", " 563\n", " 281\n", " scatter\n", - " 1.55e-02\n", - " 4.15e-04\n", + " 1.51e-02\n", + " 6.50e-04\n", " \n", " \n", " 564\n", " 282\n", " absorption\n", - " 1.22e-04\n", - " 9.98e-06\n", + " 9.85e-05\n", + " 9.47e-06\n", " \n", " \n", " 565\n", " 282\n", " scatter\n", - " 1.68e-02\n", - " 5.73e-04\n", + " 1.53e-02\n", + " 4.63e-04\n", " \n", " \n", " 566\n", " 283\n", " absorption\n", - " 1.14e-04\n", - " 8.02e-06\n", + " 1.08e-04\n", + " 1.34e-05\n", " \n", " \n", " 567\n", " 283\n", " scatter\n", - " 1.66e-02\n", - " 5.44e-04\n", + " 1.65e-02\n", + " 7.04e-04\n", " \n", " \n", " 568\n", " 284\n", " absorption\n", - " 1.06e-04\n", - " 8.37e-06\n", + " 1.13e-04\n", + " 7.91e-06\n", " \n", " \n", " 569\n", " 284\n", " scatter\n", - " 1.64e-02\n", - " 5.14e-04\n", + " 1.67e-02\n", + " 5.51e-04\n", " \n", " \n", " 570\n", " 285\n", " absorption\n", " 1.23e-04\n", - " 9.19e-06\n", + " 9.53e-06\n", " \n", " \n", " 571\n", " 285\n", " scatter\n", - " 1.70e-02\n", - " 5.34e-04\n", + " 1.88e-02\n", + " 7.25e-04\n", " \n", " \n", " 572\n", " 286\n", " absorption\n", - " 1.14e-04\n", - " 6.70e-06\n", + " 1.44e-04\n", + " 1.34e-05\n", " \n", " \n", " 573\n", " 286\n", " scatter\n", - " 1.75e-02\n", - " 5.68e-04\n", + " 1.90e-02\n", + " 7.07e-04\n", " \n", " \n", " 574\n", " 287\n", " absorption\n", - " 1.14e-04\n", - " 8.10e-06\n", + " 1.26e-04\n", + " 8.66e-06\n", " \n", " \n", " 575\n", " 287\n", " scatter\n", - " 1.72e-02\n", - " 4.93e-04\n", + " 1.97e-02\n", + " 7.23e-04\n", " \n", " \n", " 576\n", " 288\n", " absorption\n", - " 1.06e-04\n", - " 1.07e-05\n", + " 1.25e-04\n", + " 9.59e-06\n", " \n", " \n", " 577\n", " 288\n", " scatter\n", - " 1.72e-02\n", - " 7.73e-04\n", + " 2.01e-02\n", + " 6.75e-04\n", " \n", " \n", "\n", @@ -1711,26 +1696,26 @@ ], "text/plain": [ " distribcell score mean std. dev.\n", - "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" + "558 279 absorption 8.19e-05 7.82e-06\n", + "559 279 scatter 1.33e-02 6.19e-04\n", + "560 280 absorption 1.00e-04 7.93e-06\n", + "561 280 scatter 1.40e-02 5.61e-04\n", + "562 281 absorption 9.52e-05 7.08e-06\n", + "563 281 scatter 1.51e-02 6.50e-04\n", + "564 282 absorption 9.85e-05 9.47e-06\n", + "565 282 scatter 1.53e-02 4.63e-04\n", + "566 283 absorption 1.08e-04 1.34e-05\n", + "567 283 scatter 1.65e-02 7.04e-04\n", + "568 284 absorption 1.13e-04 7.91e-06\n", + "569 284 scatter 1.67e-02 5.51e-04\n", + "570 285 absorption 1.23e-04 9.53e-06\n", + "571 285 scatter 1.88e-02 7.25e-04\n", + "572 286 absorption 1.44e-04 1.34e-05\n", + "573 286 scatter 1.90e-02 7.07e-04\n", + "574 287 absorption 1.26e-04 8.66e-06\n", + "575 287 scatter 1.97e-02 7.23e-04\n", + "576 288 absorption 1.25e-04 9.59e-06\n", + "577 288 scatter 2.01e-02 6.75e-04" ] }, "execution_count": 33, @@ -1806,357 +1791,357 @@ " \n", " \n", " \n", - " 0\n", + " 558\n", " 10003\n", " 0\n", " 10001\n", - " 0\n", " 16\n", + " 9\n", " 0\n", " 10002\n", " 10000\n", - " 0\n", + " 279\n", " absorption\n", - " 1.30e-04\n", - " 8.67e-06\n", + " 8.19e-05\n", + " 7.82e-06\n", " \n", " \n", - " 1\n", + " 559\n", " 10003\n", " 0\n", " 10001\n", - " 0\n", " 16\n", + " 9\n", " 0\n", " 10002\n", " 10000\n", - " 0\n", + " 279\n", " scatter\n", - " 1.98e-02\n", + " 1.33e-02\n", + " 6.19e-04\n", + " \n", + " \n", + " 560\n", + " 10003\n", + " 0\n", + " 10001\n", + " 16\n", + " 8\n", + " 0\n", + " 10002\n", + " 10000\n", + " 280\n", + " absorption\n", + " 1.00e-04\n", + " 7.93e-06\n", + " \n", + " \n", + " 561\n", + " 10003\n", + " 0\n", + " 10001\n", + " 16\n", + " 8\n", + " 0\n", + " 10002\n", + " 10000\n", + " 280\n", + " scatter\n", + " 1.40e-02\n", + " 5.61e-04\n", + " \n", + " \n", + " 562\n", + " 10003\n", + " 0\n", + " 10001\n", + " 16\n", + " 7\n", + " 0\n", + " 10002\n", + " 10000\n", + " 281\n", + " absorption\n", + " 9.52e-05\n", + " 7.08e-06\n", + " \n", + " \n", + " 563\n", + " 10003\n", + " 0\n", + " 10001\n", + " 16\n", + " 7\n", + " 0\n", + " 10002\n", + " 10000\n", + " 281\n", + " scatter\n", + " 1.51e-02\n", " 6.50e-04\n", " \n", " \n", - " 2\n", + " 564\n", " 10003\n", " 0\n", " 10001\n", - " 0\n", - " 15\n", - " 0\n", - " 10002\n", - " 10000\n", - " 1\n", - " absorption\n", - " 2.24e-04\n", - " 1.44e-05\n", - " \n", - " \n", - " 3\n", - " 10003\n", - " 0\n", - " 10001\n", - " 0\n", - " 15\n", - " 0\n", - " 10002\n", - " 10000\n", - " 1\n", - " scatter\n", - " 3.00e-02\n", - " 8.80e-04\n", - " \n", - " \n", - " 4\n", - " 10003\n", - " 0\n", - " 10001\n", - " 0\n", - " 14\n", - " 0\n", - " 10002\n", - " 10000\n", - " 2\n", - " absorption\n", - " 3.16e-04\n", - " 2.15e-05\n", - " \n", - " \n", - " 5\n", - " 10003\n", - " 0\n", - " 10001\n", - " 0\n", - " 14\n", - " 0\n", - " 10002\n", - " 10000\n", - " 2\n", - " scatter\n", - " 3.90e-02\n", - " 1.25e-03\n", - " \n", - " \n", - " 6\n", - " 10003\n", - " 0\n", - " 10001\n", - " 0\n", - " 13\n", - " 0\n", - " 10002\n", - " 10000\n", - " 3\n", - " absorption\n", - " 3.78e-04\n", - " 1.45e-05\n", - " \n", - " \n", - " 7\n", - " 10003\n", - " 0\n", - " 10001\n", - " 0\n", - " 13\n", - " 0\n", - " 10002\n", - " 10000\n", - " 3\n", - " scatter\n", - " 4.86e-02\n", - " 1.24e-03\n", - " \n", - " \n", - " 8\n", - " 10003\n", - " 0\n", - " 10001\n", - " 0\n", - " 12\n", - " 0\n", - " 10002\n", - " 10000\n", - " 4\n", - " absorption\n", - " 4.21e-04\n", - " 2.14e-05\n", - " \n", - " \n", - " 9\n", - " 10003\n", - " 0\n", - " 10001\n", - " 0\n", - " 12\n", - " 0\n", - " 10002\n", - " 10000\n", - " 4\n", - " scatter\n", - " 5.52e-02\n", - " 9.85e-04\n", - " \n", - " \n", - " 10\n", - " 10003\n", - " 0\n", - " 10001\n", - " 0\n", - " 11\n", - " 0\n", - " 10002\n", - " 10000\n", - " 5\n", - " absorption\n", - " 4.86e-04\n", - " 2.62e-05\n", - " \n", - " \n", - " 11\n", - " 10003\n", - " 0\n", - " 10001\n", - " 0\n", - " 11\n", - " 0\n", - " 10002\n", - " 10000\n", - " 5\n", - " scatter\n", - " 6.30e-02\n", - " 1.35e-03\n", - " \n", - " \n", - " 12\n", - " 10003\n", - " 0\n", - " 10001\n", - " 0\n", - " 10\n", - " 0\n", - " 10002\n", - " 10000\n", + " 16\n", " 6\n", - " absorption\n", - " 5.30e-04\n", - " 1.92e-05\n", - " \n", - " \n", - " 13\n", - " 10003\n", - " 0\n", - " 10001\n", - " 0\n", - " 10\n", " 0\n", " 10002\n", " 10000\n", + " 282\n", + " absorption\n", + " 9.85e-05\n", + " 9.47e-06\n", + " \n", + " \n", + " 565\n", + " 10003\n", + " 0\n", + " 10001\n", + " 16\n", " 6\n", - " scatter\n", - " 6.93e-02\n", - " 1.30e-03\n", - " \n", - " \n", - " 14\n", - " 10003\n", - " 0\n", - " 10001\n", - " 0\n", - " 9\n", " 0\n", " 10002\n", " 10000\n", - " 7\n", + " 282\n", + " scatter\n", + " 1.53e-02\n", + " 4.63e-04\n", + " \n", + " \n", + " 566\n", + " 10003\n", + " 0\n", + " 10001\n", + " 16\n", + " 5\n", + " 0\n", + " 10002\n", + " 10000\n", + " 283\n", " absorption\n", - " 5.86e-04\n", - " 2.02e-05\n", + " 1.08e-04\n", + " 1.34e-05\n", " \n", " \n", - " 15\n", + " 567\n", " 10003\n", " 0\n", " 10001\n", - " 0\n", - " 9\n", + " 16\n", + " 5\n", " 0\n", " 10002\n", " 10000\n", - " 7\n", + " 283\n", " scatter\n", - " 7.57e-02\n", - " 1.40e-03\n", + " 1.65e-02\n", + " 7.04e-04\n", " \n", " \n", - " 16\n", + " 568\n", " 10003\n", " 0\n", " 10001\n", - " 0\n", - " 8\n", + " 16\n", + " 4\n", " 0\n", " 10002\n", " 10000\n", - " 8\n", + " 284\n", " absorption\n", - " 6.30e-04\n", - " 2.35e-05\n", + " 1.13e-04\n", + " 7.91e-06\n", " \n", " \n", - " 17\n", + " 569\n", " 10003\n", " 0\n", " 10001\n", - " 0\n", - " 8\n", + " 16\n", + " 4\n", " 0\n", " 10002\n", " 10000\n", - " 8\n", + " 284\n", " scatter\n", - " 8.09e-02\n", - " 1.49e-03\n", + " 1.67e-02\n", + " 5.51e-04\n", " \n", " \n", - " 18\n", + " 570\n", " 10003\n", " 0\n", " 10001\n", - " 0\n", - " 7\n", + " 16\n", + " 3\n", " 0\n", " 10002\n", " 10000\n", - " 9\n", + " 285\n", " absorption\n", - " 7.10e-04\n", - " 2.23e-05\n", + " 1.23e-04\n", + " 9.53e-06\n", " \n", " \n", - " 19\n", + " 571\n", " 10003\n", " 0\n", " 10001\n", - " 0\n", - " 7\n", + " 16\n", + " 3\n", " 0\n", " 10002\n", " 10000\n", - " 9\n", + " 285\n", " scatter\n", - " 8.94e-02\n", - " 1.37e-03\n", + " 1.88e-02\n", + " 7.25e-04\n", + " \n", + " \n", + " 572\n", + " 10003\n", + " 0\n", + " 10001\n", + " 16\n", + " 2\n", + " 0\n", + " 10002\n", + " 10000\n", + " 286\n", + " absorption\n", + " 1.44e-04\n", + " 1.34e-05\n", + " \n", + " \n", + " 573\n", + " 10003\n", + " 0\n", + " 10001\n", + " 16\n", + " 2\n", + " 0\n", + " 10002\n", + " 10000\n", + " 286\n", + " scatter\n", + " 1.90e-02\n", + " 7.07e-04\n", + " \n", + " \n", + " 574\n", + " 10003\n", + " 0\n", + " 10001\n", + " 16\n", + " 1\n", + " 0\n", + " 10002\n", + " 10000\n", + " 287\n", + " absorption\n", + " 1.26e-04\n", + " 8.66e-06\n", + " \n", + " \n", + " 575\n", + " 10003\n", + " 0\n", + " 10001\n", + " 16\n", + " 1\n", + " 0\n", + " 10002\n", + " 10000\n", + " 287\n", + " scatter\n", + " 1.97e-02\n", + " 7.23e-04\n", + " \n", + " \n", + " 576\n", + " 10003\n", + " 0\n", + " 10001\n", + " 16\n", + " 0\n", + " 0\n", + " 10002\n", + " 10000\n", + " 288\n", + " absorption\n", + " 1.25e-04\n", + " 9.59e-06\n", + " \n", + " \n", + " 577\n", + " 10003\n", + " 0\n", + " 10001\n", + " 16\n", + " 0\n", + " 0\n", + " 10002\n", + " 10000\n", + " 288\n", + " scatter\n", + " 2.01e-02\n", + " 6.75e-04\n", " \n", " \n", "\n", "" ], "text/plain": [ - " level 1 level 2 level 3 distribcell score \\\n", - " cell univ lat cell univ \n", - " id id id x y z id id \n", - "0 10003 0 10001 0 16 0 10002 10000 0 absorption \n", - "1 10003 0 10001 0 16 0 10002 10000 0 scatter \n", - "2 10003 0 10001 0 15 0 10002 10000 1 absorption \n", - "3 10003 0 10001 0 15 0 10002 10000 1 scatter \n", - "4 10003 0 10001 0 14 0 10002 10000 2 absorption \n", - "5 10003 0 10001 0 14 0 10002 10000 2 scatter \n", - "6 10003 0 10001 0 13 0 10002 10000 3 absorption \n", - "7 10003 0 10001 0 13 0 10002 10000 3 scatter \n", - "8 10003 0 10001 0 12 0 10002 10000 4 absorption \n", - "9 10003 0 10001 0 12 0 10002 10000 4 scatter \n", - "10 10003 0 10001 0 11 0 10002 10000 5 absorption \n", - "11 10003 0 10001 0 11 0 10002 10000 5 scatter \n", - "12 10003 0 10001 0 10 0 10002 10000 6 absorption \n", - "13 10003 0 10001 0 10 0 10002 10000 6 scatter \n", - "14 10003 0 10001 0 9 0 10002 10000 7 absorption \n", - "15 10003 0 10001 0 9 0 10002 10000 7 scatter \n", - "16 10003 0 10001 0 8 0 10002 10000 8 absorption \n", - "17 10003 0 10001 0 8 0 10002 10000 8 scatter \n", - "18 10003 0 10001 0 7 0 10002 10000 9 absorption \n", - "19 10003 0 10001 0 7 0 10002 10000 9 scatter \n", + " level 1 level 2 level 3 distribcell score \\\n", + " cell univ lat cell univ \n", + " id id id x y z id id \n", + "558 10003 0 10001 16 9 0 10002 10000 279 absorption \n", + "559 10003 0 10001 16 9 0 10002 10000 279 scatter \n", + "560 10003 0 10001 16 8 0 10002 10000 280 absorption \n", + "561 10003 0 10001 16 8 0 10002 10000 280 scatter \n", + "562 10003 0 10001 16 7 0 10002 10000 281 absorption \n", + "563 10003 0 10001 16 7 0 10002 10000 281 scatter \n", + "564 10003 0 10001 16 6 0 10002 10000 282 absorption \n", + "565 10003 0 10001 16 6 0 10002 10000 282 scatter \n", + "566 10003 0 10001 16 5 0 10002 10000 283 absorption \n", + "567 10003 0 10001 16 5 0 10002 10000 283 scatter \n", + "568 10003 0 10001 16 4 0 10002 10000 284 absorption \n", + "569 10003 0 10001 16 4 0 10002 10000 284 scatter \n", + "570 10003 0 10001 16 3 0 10002 10000 285 absorption \n", + "571 10003 0 10001 16 3 0 10002 10000 285 scatter \n", + "572 10003 0 10001 16 2 0 10002 10000 286 absorption \n", + "573 10003 0 10001 16 2 0 10002 10000 286 scatter \n", + "574 10003 0 10001 16 1 0 10002 10000 287 absorption \n", + "575 10003 0 10001 16 1 0 10002 10000 287 scatter \n", + "576 10003 0 10001 16 0 0 10002 10000 288 absorption \n", + "577 10003 0 10001 16 0 0 10002 10000 288 scatter \n", "\n", - " mean std. dev. \n", - " \n", - " \n", - "0 1.30e-04 8.67e-06 \n", - "1 1.98e-02 6.50e-04 \n", - "2 2.24e-04 1.44e-05 \n", - "3 3.00e-02 8.80e-04 \n", - "4 3.16e-04 2.15e-05 \n", - "5 3.90e-02 1.25e-03 \n", - "6 3.78e-04 1.45e-05 \n", - "7 4.86e-02 1.24e-03 \n", - "8 4.21e-04 2.14e-05 \n", - "9 5.52e-02 9.85e-04 \n", - "10 4.86e-04 2.62e-05 \n", - "11 6.30e-02 1.35e-03 \n", - "12 5.30e-04 1.92e-05 \n", - "13 6.93e-02 1.30e-03 \n", - "14 5.86e-04 2.02e-05 \n", - "15 7.57e-02 1.40e-03 \n", - "16 6.30e-04 2.35e-05 \n", - "17 8.09e-02 1.49e-03 \n", - "18 7.10e-04 2.23e-05 \n", - "19 8.94e-02 1.37e-03 " + " mean std. dev. \n", + " \n", + " \n", + "558 8.19e-05 7.82e-06 \n", + "559 1.33e-02 6.19e-04 \n", + "560 1.00e-04 7.93e-06 \n", + "561 1.40e-02 5.61e-04 \n", + "562 9.52e-05 7.08e-06 \n", + "563 1.51e-02 6.50e-04 \n", + "564 9.85e-05 9.47e-06 \n", + "565 1.53e-02 4.63e-04 \n", + "566 1.08e-04 1.34e-05 \n", + "567 1.65e-02 7.04e-04 \n", + "568 1.13e-04 7.91e-06 \n", + "569 1.67e-02 5.51e-04 \n", + "570 1.23e-04 9.53e-06 \n", + "571 1.88e-02 7.25e-04 \n", + "572 1.44e-04 1.34e-05 \n", + "573 1.90e-02 7.07e-04 \n", + "574 1.26e-04 8.66e-06 \n", + "575 1.97e-02 7.23e-04 \n", + "576 1.25e-04 9.59e-06 \n", + "577 2.01e-02 6.75e-04 " ] }, "execution_count": 34, @@ -2169,7 +2154,7 @@ "df = tally.get_pandas_dataframe(summary=su, nuclides=False)\n", "\n", "# Print the last twenty rows in the dataframe\n", - "df.head(20)" + "df.tail(20)" ] }, { @@ -2209,38 +2194,38 @@ " \n", " \n", " mean\n", - " 4.15e-04\n", - " 1.71e-05\n", + " 4.19e-04\n", + " 2.24e-05\n", " \n", " \n", " std\n", - " 2.41e-04\n", - " 6.82e-06\n", + " 2.42e-04\n", + " 9.14e-06\n", " \n", " \n", " min\n", - " 1.78e-05\n", - " 2.81e-06\n", + " 1.90e-05\n", + " 3.44e-06\n", " \n", " \n", " 25%\n", - " 2.06e-04\n", - " 1.16e-05\n", + " 2.02e-04\n", + " 1.56e-05\n", " \n", " \n", " 50%\n", - " 4.03e-04\n", - " 1.71e-05\n", + " 4.05e-04\n", + " 2.20e-05\n", " \n", " \n", " 75%\n", - " 6.05e-04\n", - " 2.19e-05\n", + " 6.07e-04\n", + " 2.89e-05\n", " \n", " \n", " max\n", - " 9.35e-04\n", - " 4.54e-05\n", + " 9.19e-04\n", + " 4.95e-05\n", " \n", " \n", "\n", @@ -2251,13 +2236,13 @@ " \n", " \n", "count 2.89e+02 2.89e+02\n", - "mean 4.15e-04 1.71e-05\n", - "std 2.41e-04 6.82e-06\n", - "min 1.78e-05 2.81e-06\n", - "25% 2.06e-04 1.16e-05\n", - "50% 4.03e-04 1.71e-05\n", - "75% 6.05e-04 2.19e-05\n", - "max 9.35e-04 4.54e-05" + "mean 4.19e-04 2.24e-05\n", + "std 2.42e-04 9.14e-06\n", + "min 1.90e-05 3.44e-06\n", + "25% 2.02e-04 1.56e-05\n", + "50% 4.05e-04 2.20e-05\n", + "75% 6.07e-04 2.89e-05\n", + "max 9.19e-04 4.95e-05" ] }, "execution_count": 35, @@ -2292,15 +2277,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 1.39844745394e-41\n" + "Mann-Whitney Test p-value: 0.607166663014\n" ] } ], "source": [ - "# Extract tally data from pins in the pins divided along y=x diagonal \n", + "# Extract tally data from pins in the pins divided along y=-x diagonal\n", "multi_index = ('level 2', 'lat',)\n", - "lower = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] < 16]\n", - "upper = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] > 16]\n", + "lower = df[df[multi_index + ('x',)] > df[multi_index + ('y',)]]\n", + "upper = df[df[multi_index + ('x',)] < df[multi_index + ('y',)]]\n", "lower = lower[lower['score'] == 'absorption']\n", "upper = upper[upper['score'] == 'absorption']\n", "\n", @@ -2330,15 +2315,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 0.902458041178\n" + "Mann-Whitney Test p-value: 1.2077327566e-41\n" ] } ], "source": [ - "# Extract tally data from pins in the pins divided along y=-x diagonal\n", + "# Extract tally data from pins in the pins divided along y=x diagonal \n", "multi_index = ('level 2', 'lat',)\n", - "lower = df[df[multi_index + ('x',)] > df[multi_index + ('y',)]]\n", - "upper = df[df[multi_index + ('x',)] < df[multi_index + ('y',)]]\n", + "lower = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] < 16]\n", + "upper = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] > 16]\n", "lower = lower[lower['score'] == 'absorption']\n", "upper = upper[upper['score'] == 'absorption']\n", "\n", @@ -2376,7 +2361,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 38, @@ -2385,9 +2370,9 @@ }, { "data": { - "image/png": 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55+WkkxYPmzcz0phHfNG57rrrE0Fkbrj4JwNGrzc1tTocErq75nqUdZZ3ONST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\n3ZOTVou72vr7+xP7LA5lCks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FZ+rUquM7FInV/jpBX8QxvgORZFOCkKRSB3UVNe8iONp3EJJsShCSVOp/qKK+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\nT3mAAb9yzi2J3NjMhgDrnXN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"text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/docs/source/pythonapi/examples/post-processing.ipynb b/docs/source/pythonapi/examples/post-processing.ipynb index 130e44cf4..e735003cf 100644 --- a/docs/source/pythonapi/examples/post-processing.ipynb +++ b/docs/source/pythonapi/examples/post-processing.ipynb @@ -20,9 +20,6 @@ "import matplotlib.pyplot as plt\n", "\n", "import openmc\n", - "from openmc.statepoint import StatePoint\n", - "from openmc.source import Source\n", - "from openmc.stats import Box\n", "\n", "%matplotlib inline" ] @@ -273,9 +270,11 @@ "settings_file.batches = batches\n", "settings_file.inactive = inactive\n", "settings_file.particles = particles\n", - "source_bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", - "settings_file.source = Source(space=Box(\n", - " source_bounds[:3], source_bounds[3:]))\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", + "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings_file.source = openmc.source.Source(space=uniform_dist)\n", "\n", "# Export to \"settings.xml\"\n", "settings_file.export_to_xml()" @@ -350,7 +349,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN\nAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAADFBMVEX///9yEhLpgJFNv8Tq\nQYT7AAAAAWJLR0QAiAUdSAAAAAd0SU1FB+AECBAFHJ/0NHcAAALKSURBVGje7dpLcqQwDAbgHHE2\nYeEj+D4cwQucBUfo+3CEXoSp8OhuhF70T4qpKXmdr21LogK2Pj7A8QmNP+HDhw8fPnz48Kf6VH9G\n+66vy+je8k19jnf8C5dXIPv86ms56lPdjvaYbyodx3ze+XLE76cXFiD4zPji99z0/AJ4n1lfvJ6f\nnl0A6x+578efMSg1wPr172/jPO5yFXM+Ef78gdblM+WPHyguP//t1/g6pA0wfln+ho/fwgYYn19C\n/xwDvwHGc9OvC+hs37DTrwuwfWanXxdQTC9Mvyygs3wjTL8uwPJpn/tNDbSGz7T0SBEWw4vLXzbQ\n6b6RoveIoO6TvPxlA63qs7z8ZQPF9F+SH22vbX8OQKf5Rtv+EgDNJ3X58wZaxWd1+fMGiuFvir8b\nvjp8J/tGy/6jAmRvhW8fwL3vVT+o3grfPoB7r/IpALI3tz8FoJN84/NV873hB8UnM3xzANtf8nb4\ndwmg3grfFEDJO8JPE0i9Ff4pAYL3pI8mkHor/HMCeO9JH00g9SafEsh7T/ppARBvp48UwJnelT5S\nACd7O31TAlnvKx9SQCd7B58KgPO+8iMFuPWe9E8F8BveWX7bAjzX9y4//Jve+fhsH6Ctv7n8PTzj\nvY/v9gEOHz58+PBX+6v/f/wPvnd54f3j6venE/yl769Xv7+j3x/o98/V32/o9+fl389Xnx+g5x/o\n+Qt6/oOeP6HnX+j5G3z+h54/ouefV5/foufP6Pk3ev4On/+j9w/o/Qd6/4Le/6D3T/D9V67Y/ZsV\nQBq+s+8f0ftP+P41axXguP9NWgDuu/Cdfv+N3r/D9/9TAID+A7T/Ae2/gPs/0P4TtP8F7r9J3AIO\n9P+g/Udw/9Oygbf7r9D+L7j/DO1/Q/vv4P4/tP8Q7n9E+y/h/k+0/xTuf4X7b+H+X7T/+BPuf3aM\n8OHDhw8fPnz4w/4vzcvgeY10sY0AAAAldEVYdGRhdGU6Y3JlYXRlADIwMTYtMDQtMDhUMTI6MDU6\nMjgtMDQ6MDCheDXLAAAAJXRFWHRkYXRlOm1vZGlmeQAyMDE2LTA0LTA4VDEyOjA1OjI4LTA0OjAw\n0CWNdwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] @@ -460,10 +459,9 @@ " 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: 5f252e2df51930b9175fd41bafa8db01f3eaeb92\n", - " Date/Time: 2016-03-23 14:49:42\n", + " Git SHA1: 9a6ecd72597338b40d2b72378e5ad6dd65df2364\n", + " Date/Time: 2016-04-08 12:05:28\n", " MPI Processes: 1\n", - " OpenMP Threads: 16\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -490,106 +488,106 @@ "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \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", + " 1/1 1.04359 \n", + " 2/1 1.04244 \n", + " 3/1 1.03020 \n", + " 4/1 1.03630 \n", + " 5/1 1.06478 \n", + " 6/1 1.05450 \n", + " 7/1 1.02369 \n", + " 8/1 1.03614 \n", + " 9/1 1.05193 \n", + " 10/1 1.02886 \n", + " 11/1 1.05011 \n", + " 12/1 1.04597 1.04804 +/- 0.00207\n", + " 13/1 1.07035 1.05548 +/- 0.00753\n", + " 14/1 1.06150 1.05698 +/- 0.00554\n", + " 15/1 1.07094 1.05977 +/- 0.00512\n", + " 16/1 1.05131 1.05836 +/- 0.00441\n", + " 17/1 1.04733 1.05679 +/- 0.00405\n", + " 18/1 1.08130 1.05985 +/- 0.00465\n", + " 19/1 1.02559 1.05605 +/- 0.00560\n", + " 20/1 1.03399 1.05384 +/- 0.00547\n", + " 21/1 1.04617 1.05314 +/- 0.00500\n", + " 22/1 1.06981 1.05453 +/- 0.00477\n", + " 23/1 1.05270 1.05439 +/- 0.00439\n", + " 24/1 1.02487 1.05228 +/- 0.00458\n", + " 25/1 1.05905 1.05273 +/- 0.00429\n", + " 26/1 1.07658 1.05422 +/- 0.00428\n", + " 27/1 1.03455 1.05307 +/- 0.00418\n", + " 28/1 1.00971 1.05066 +/- 0.00462\n", + " 29/1 1.06111 1.05121 +/- 0.00440\n", + " 30/1 1.01777 1.04954 +/- 0.00450\n", + " 31/1 1.04718 1.04942 +/- 0.00428\n", + " 32/1 1.03340 1.04870 +/- 0.00415\n", + " 33/1 1.04570 1.04857 +/- 0.00397\n", + " 34/1 1.02728 1.04768 +/- 0.00390\n", + " 35/1 1.02852 1.04691 +/- 0.00382\n", + " 36/1 1.03242 1.04636 +/- 0.00371\n", + " 37/1 1.01479 1.04519 +/- 0.00376\n", + " 38/1 1.06045 1.04573 +/- 0.00366\n", + " 39/1 1.03810 1.04547 +/- 0.00354\n", + " 40/1 1.05281 1.04571 +/- 0.00343\n", + " 41/1 1.03941 1.04551 +/- 0.00332\n", + " 42/1 1.04049 1.04535 +/- 0.00322\n", + " 43/1 1.04586 1.04537 +/- 0.00312\n", + " 44/1 1.05437 1.04563 +/- 0.00304\n", + " 45/1 1.03445 1.04531 +/- 0.00297\n", + " 46/1 1.05104 1.04547 +/- 0.00289\n", + " 47/1 1.00773 1.04445 +/- 0.00299\n", + " 48/1 1.06879 1.04509 +/- 0.00298\n", + " 49/1 1.06625 1.04564 +/- 0.00295\n", + " 50/1 1.02641 1.04515 +/- 0.00292\n", + " 51/1 1.05701 1.04544 +/- 0.00286\n", + " 52/1 1.02868 1.04504 +/- 0.00282\n", + " 53/1 1.04592 1.04506 +/- 0.00275\n", + " 54/1 1.05757 1.04535 +/- 0.00271\n", + " 55/1 1.02329 1.04486 +/- 0.00269\n", + " 56/1 1.04116 1.04478 +/- 0.00263\n", + " 57/1 1.01990 1.04425 +/- 0.00263\n", + " 58/1 1.06202 1.04462 +/- 0.00260\n", + " 59/1 1.03550 1.04443 +/- 0.00255\n", + " 60/1 1.01383 1.04382 +/- 0.00258\n", + " 61/1 1.04111 1.04377 +/- 0.00253\n", + " 62/1 1.02061 1.04332 +/- 0.00252\n", + " 63/1 1.00456 1.04259 +/- 0.00257\n", + " 64/1 1.02277 1.04222 +/- 0.00255\n", + " 65/1 1.04544 1.04228 +/- 0.00251\n", + " 66/1 1.04487 1.04233 +/- 0.00246\n", + " 67/1 1.02699 1.04206 +/- 0.00243\n", + " 68/1 1.06160 1.04240 +/- 0.00241\n", + " 69/1 1.02989 1.04218 +/- 0.00238\n", + " 70/1 1.03107 1.04200 +/- 0.00235\n", + " 71/1 1.06571 1.04239 +/- 0.00234\n", + " 72/1 1.03444 1.04226 +/- 0.00231\n", + " 73/1 1.05059 1.04239 +/- 0.00228\n", + " 74/1 1.03352 1.04225 +/- 0.00224\n", + " 75/1 1.03707 1.04217 +/- 0.00221\n", + " 76/1 1.02994 1.04199 +/- 0.00219\n", + " 77/1 1.05416 1.04217 +/- 0.00216\n", + " 78/1 1.03794 1.04211 +/- 0.00213\n", + " 79/1 1.04652 1.04217 +/- 0.00210\n", + " 80/1 1.05715 1.04239 +/- 0.00208\n", + " 81/1 1.08146 1.04294 +/- 0.00212\n", + " 82/1 1.02159 1.04264 +/- 0.00211\n", + " 83/1 1.01968 1.04233 +/- 0.00211\n", + " 84/1 1.05577 1.04251 +/- 0.00209\n", + " 85/1 1.07808 1.04298 +/- 0.00211\n", + " 86/1 1.03943 1.04293 +/- 0.00209\n", + " 87/1 1.03431 1.04282 +/- 0.00206\n", + " 88/1 1.02414 1.04258 +/- 0.00205\n", + " 89/1 1.02316 1.04234 +/- 0.00204\n", + " 90/1 1.03342 1.04223 +/- 0.00202\n", + " 91/1 1.02781 1.04205 +/- 0.00200\n", + " 92/1 1.01293 1.04169 +/- 0.00201\n", + " 93/1 1.04347 1.04171 +/- 0.00198\n", + " 94/1 1.05357 1.04186 +/- 0.00196\n", + " 95/1 1.04740 1.04192 +/- 0.00194\n", + " 96/1 1.05215 1.04204 +/- 0.00192\n", + " 97/1 1.06667 1.04232 +/- 0.00192\n", + " 98/1 1.04926 1.04240 +/- 0.00190\n", + " 99/1 1.05386 1.04253 +/- 0.00188\n", + " 100/1 1.05088 1.04262 +/- 0.00186\n", " Creating state point statepoint.100.h5...\n", "\n", " ===========================================================================\n", @@ -599,27 +597,27 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 5.3200E-01 seconds\n", - " Reading cross sections = 1.7200E-01 seconds\n", - " Total time in simulation = 4.5299E+01 seconds\n", - " Time in transport only = 4.3964E+01 seconds\n", - " Time in inactive batches = 1.2390E+00 seconds\n", - " Time in active batches = 4.4060E+01 seconds\n", - " Time synchronizing fission bank = 2.3000E-02 seconds\n", - " Sampling source sites = 1.5000E-02 seconds\n", - " SEND/RECV source sites = 8.0000E-03 seconds\n", - " Time accumulating tallies = 2.7000E-02 seconds\n", - " Total time for finalization = 3.0800E-01 seconds\n", - " Total time elapsed = 4.6175E+01 seconds\n", - " Calculation Rate (inactive) = 40355.1 neutrons/second\n", - " Calculation Rate (active) = 10213.3 neutrons/second\n", + " Total time for initialization = 5.3700E-01 seconds\n", + " Reading cross sections = 1.4300E-01 seconds\n", + " Total time in simulation = 4.3618E+02 seconds\n", + " Time in transport only = 4.3609E+02 seconds\n", + " Time in inactive batches = 1.5047E+01 seconds\n", + " Time in active batches = 4.2113E+02 seconds\n", + " Time synchronizing fission bank = 2.4000E-02 seconds\n", + " Sampling source sites = 1.6000E-02 seconds\n", + " SEND/RECV source sites = 6.0000E-03 seconds\n", + " Time accumulating tallies = 4.0000E-02 seconds\n", + " Total time for finalization = 2.5600E-01 seconds\n", + " Total time elapsed = 4.3701E+02 seconds\n", + " Calculation Rate (inactive) = 3322.92 neutrons/second\n", + " Calculation Rate (active) = 1068.56 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\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", + " k-effective (Collision) = 1.04214 +/- 0.00161\n", + " k-effective (Track-length) = 1.04262 +/- 0.00186\n", + " k-effective (Absorption) = 1.04338 +/- 0.00158\n", + " Combined k-effective = 1.04278 +/- 0.00122\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -664,7 +662,7 @@ "outputs": [], "source": [ "# Load the statepoint file\n", - "sp = StatePoint('statepoint.100.h5')" + "sp = openmc.StatePoint('statepoint.100.h5')" ] }, { @@ -719,18 +717,18 @@ { "data": { "text/plain": [ - "array([[[ 0.41161103, 0. ]],\n", + "array([[[ 0.40945685, 0. ]],\n", "\n", - " [[ 0.41135796, 0. ]],\n", + " [[ 0.40939021, 0. ]],\n", "\n", - " [[ 0.41058715, 0. ]],\n", + " [[ 0.410625 , 0. ]],\n", "\n", " ..., \n", - " [[ 0.40919256, 0. ]],\n", + " [[ 0.41130501, 0. ]],\n", "\n", - " [[ 0.41057119, 0. ]],\n", + " [[ 0.41228849, 0. ]],\n", "\n", - " [[ 0.41225079, 0. ]]])" + " [[ 0.41420317, 0. ]]])" ] }, "execution_count": 20, @@ -766,30 +764,30 @@ { "data": { "text/plain": [ - "(array([[[ 0.00457346, 0. ]],\n", + "(array([[[ 0.00454952, 0. ]],\n", " \n", - " [[ 0.00457064, 0. ]],\n", + " [[ 0.00454878, 0. ]],\n", " \n", - " [[ 0.00456208, 0. ]],\n", + " [[ 0.0045625 , 0. ]],\n", " \n", " ..., \n", - " [[ 0.00454658, 0. ]],\n", + " [[ 0.00457006, 0. ]],\n", " \n", - " [[ 0.0045619 , 0. ]],\n", + " [[ 0.00458098, 0. ]],\n", " \n", - " [[ 0.00458056, 0. ]]]),\n", - " array([[[ 1.92422804e-05, 0.00000000e+00]],\n", + " [[ 0.00460226, 0. ]]]),\n", + " array([[[ 1.64748193e-05, 0.00000000e+00]],\n", " \n", - " [[ 1.58028832e-05, 0.00000000e+00]],\n", + " [[ 1.70922989e-05, 0.00000000e+00]],\n", " \n", - " [[ 1.56204065e-05, 0.00000000e+00]],\n", + " [[ 1.67622385e-05, 0.00000000e+00]],\n", " \n", " ..., \n", - " [[ 1.98926652e-05, 0.00000000e+00]],\n", + " [[ 1.69274948e-05, 0.00000000e+00]],\n", " \n", - " [[ 1.70440988e-05, 0.00000000e+00]],\n", + " [[ 1.57842763e-05, 0.00000000e+00]],\n", " \n", - " [[ 2.05592499e-05, 0.00000000e+00]]]))" + " [[ 2.06590062e-05, 0.00000000e+00]]]))" ] }, "execution_count": 21, @@ -869,7 +867,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 24, @@ -878,9 +876,9 @@ }, { "data": { - "image/png": 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P0/D7ON9+i0f8bzGiLBMNVrkrThHzynzJ+jPm5P3kpAwJitzgyIdn3tlwH/Cd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Eei7MyE6V/fZEPv5XuDZzAFsdudUAyjYNDEz2VOUCfAiLzCp4Jfw3dQZ35jkn9+/n+lMBTDM9ok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J4x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\nUDD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beJ79IpbdSb3hZT0TQA80GRLmSamb+KlSwc8q/WTMTvRGm5wWQ9Xa9LHGsLCILUgM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\n87qEMGb9LAAAAABJRU5ErkJggg==\n", 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431v/KSvvnyIUKzN6foP7nTPs1saoVSK4GYlSLsHtd69S9MTwBRt85u//OatDkxztPkv1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gX7GZGgWKCNSpoMJhJbpGihMtW+xxcK/4FXok+T9ZzlhHiHDGnuyDE2Av10BIU9IlznHCe5TYot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\npdPVKecjDFa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DIGkecBvwt602SudQvwZ4KXAfsF3SZyNivKbOUmBeRCyQdB6wDlhcs5u3AfeSPMDRzMwK\not3Huf9sMnmkvkfyQMWpLAImImJvOu3tRmC0rs4osAEgIrYBsyTNBpB0OnAR8LE24zSzPhgebj4D\n39BQv6OzvEx1FdYl6ctvSdoM3EwyBvI7wPY29j+HZO70SftJkkqrOgfSsoPAB4B3ArPaOJaZ9cnh\nw8Xo17femqoL6+Ka1weBC9LX/wKckktEqXTc5WBE7JRU4ZG5SBoaGxs7/rpSqVCpVPIMz8ysVKrV\nKtVqtav7zPVGQkmLgbGIGEmXVwMREWtq6qwDtkbETenyOEmiehvwWuBhkmT1BOAzEbGiwXF8FZZZ\nHxXlyqIyKEpb9XJO9JOBNwLnACdPlkfEG6bY7jHAd0gG0X8EfBNYHhG7aupcBLwlIl6ZJpwPRsTi\nuv1cALyj2Y2LTiBm/VWUL8UyKEpb9XJO9OuBXyeZofArJDMUTjmIHhHHgFXAFuAeYGNE7JK0UtLl\naZ3NwPcl7QE+Cry543dhZmY91+4ZyI6IeJ6kOyPiOZIeC3yt/kyhX3wGYtZfRfmrugyK0la9PAM5\nmv4+IulZJFdF/fvpHNjMzMqt3RsJ10saAt4FbCKZofBduUVlZoUzPJxcrtuI7/U4Mflx7mbWlqJ0\nvZRdUdqxl8/CerKkD6XPpLpd0gclPXk6BzYzs3JrdwxkI/Bj4FLg1cBPgJvyCsrMzIqv3auw7o6I\nZ9WV3RURz84tsg64C8ssf0Xpeim7orRjL6/C2iJpWfpo9RmSfhf44nQObGZm5dbyDETSz0genijg\nNOCX6aoZwAMRUYhHrPsMxCx/RfnLueyK0o65z4keEU+Yzs7NzGxwtXsfCJJeBbwkXaxGxOfzCcnM\nzMqg3ct438sjMwPeC7xN0nvyDMzMzIqt3auw7gQWRsQv0+XHADsi4jk5x9cWj4GY5a8offdlV5R2\n7OVVWABPqnntGQLNzDIYGmo+/e/wcL+j60y7YyDvAXZI2kpyRdZLgNW5RWVmNqAOHWq+TtM6H+i9\nKbuwJIlk/o+HgRekxd+MiH/OOba2uQvLLH9F6XoZZL1s417OSFiYu84bcQIxy58TSP7KlkDaHQO5\nQ9ILpq72qySNSBqXtFvSlU3qrJU0IWmnpIVp2eMkbZO0Q9Jdkq7KcnwzM8tHu2cg48AC4AfAgyTj\nIDHVVViSZgC7SeZEvw/YDiyLiPGaOkuBVemc6OcBV0/OdCjp1Ih4KL3q6x+AKyLimw2O4zMQs5z5\nDCR/ZTsDaXcQfUnG/S8CJiJiL4CkjcAoMF5TZxTYABAR2yTNkjQ7Ig5GxENpncelsfrja5YjTxpl\nnWiZQCSdDPwBMB+4C7g2Ih7uYP9zgH01y/tJkkqrOgfSsoPpGcztwDzgwxGxvYNjm1mHDh/2WYa1\nb6ozkE+QzIf+NWApcDbJHek9kd64+DxJTwT+r6SzI+LeRnXHxsaOv65UKlQqlZ7EaGZWBtVqlWq1\n2tV9TvU03uNXX0maSXL57rlt71xaDIxFxEi6vJpk7GRNTZ11wNaIuCldHgcuiIiDdft6F/BgRLy/\nwXE8BmLWBR7n6K+yjYFMdRXW0ckXHXZdTdoOzJc0V9JJwDJgU12dTcAKOJ5wjkTEQUn/TtKstPwU\n4OU8euzEzMz6aKourOdK+mn6WsAp6fLkVVgt5wOJiGOSVgFbSJLVtRGxS9LKdPv1EbFZ0kWS9pBc\n4XVZuvlTgE+k4yAzgJsiYnOmd2lmZl3X1mW8RecuLLPucBdWfw1aF5aZmVlDTiBmZpaJE4iZmWXi\nBGJmZpk4gZiZWSZOIGZmlokTiJmZZeIEYnYCGh5uPCe3n7hrnfCNhGYnIN8wWEy+kdDMzE4ITiBm\nZpaJE4iZmWXiBGJmZpk4gZiZWSZOIGZmlokTiJmZZZJ7ApE0Imlc0m5JVzaps1bShKSdkhamZadL\n+ntJ90i6S9IVecdqZmbtyzWBpNPRXgMsAc4Blks6q67OUmBeRCwAVgLr0lUPA2+PiHOAFwJvqd/W\nzMz6J+8zkEXARETsjYijwEZgtK7OKLABICK2AbMkzY6If46InWn5A8AuYE7O8ZqZWZvyTiBzgH01\ny/v51SRQX+dAfR1JvwEsBLZ1PUIzM8tkZr8DmIqkxwOfAt6Wnok0NDY2dvx1pVKhUqnkHpuZWVlU\nq1Wq1WpX95nrwxQlLQbGImIkXV4NRESsqamzDtgaETely+PABRFxUNJM4PPA30bE1S2O44cpmtUZ\nHobDhxuvGxqCQ4d6G49NzQ9TfLTtwHxJcyWdBCwDNtXV2QSsgOMJ50hEHEzX/R/g3lbJw8waO3w4\n+TJq9OPkYd2QaxdWRByTtArYQpKsro2IXZJWJqtjfURslnSRpD3Ag8DrASSdD/w+cJekHUAAfxwR\nX8gzZjMza4/nAzEbUJ7zo3zchWVmZicEJxAzM8vECcTMzDJxAjEzK4ihoWQcpNHP8HC/o/tVHkQ3\nG1AeRB8s3f739CC6mZn1jROImZll4gRiZmaZOIGYmVkmTiBmZpaJE4iZmWXiBGJmZpk4gZiZWSZO\nIGZmJVDEu9R9J7rZgPKd6CeOLP/WvhPd7AQwPFy8vzzNoAcJRNKIpHFJuyVd2aTOWkkTknZKel5N\n+bWSDkq6M+84zYqq1dS0zeY8N+uFXBOIpBnANcAS4BxguaSz6uosBeZFxAJgJfCRmtXXpduaWQOt\n+sWHhvodnQ26vM9AFgETEbE3Io4CG4HRujqjwAaAiNgGzJI0O13+OuC/scyaOHSo+dnJoUP9js4G\nXd4JZA6wr2Z5f1rWqs6BBnXMzKxgZvY7gG4ZGxs7/rpSqVCpVPoWi5lZ0VSrVarValf3metlvJIW\nA2MRMZIurwYiItbU1FkHbI2Im9LlceCCiDiYLs8FPhcRz2lxHF/Ga6U2PNx8QHxoyN1R1tqgXsa7\nHZgvaa6kk4BlwKa6OpuAFXA84RyZTB4ppT9mA6vVlVZOHlZUuSaQiDgGrAK2APcAGyNil6SVki5P\n62wGvi9pD/BR4M2T20u6AfgGcKakH0q6LM94zcysfb4T3awAfNe4TcegdmGZmdmAcgIxM7NMnEDM\nzCwTJxAzM8vECcSsR1o9VdfPrbIy8lVYZj3iK60sL74Ky8zMSsUJxMzMMnECMcug1XiGxznsRDEw\nT+M166XJZ1eZFcHkxGLN5PVZ9SC6WQYeELey8yC6mZn1jROImZll4gRi1oRv/DNrzWMgVjhFmZ3P\n4xw2yEoxBiJpRNK4pN2SrmxSZ62kCUk7JS3sZFvrrm7PmZxFq9n5miWWVmcLw8O9jb9WEdpzkLg9\niyXXBCJpBnANsAQ4B1gu6ay6OkuBeRGxAFgJrGt3W+u+ov8Hnbxcsf4Hmicd6PyejW51UxW9PcvG\n7VkseZ+BLAImImJvRBwFNgKjdXVGgQ0AEbENmCVpdpvb9sx0PrjtbjtVvVbrG61rp6wf/yGnc8zP\nfKba9rzhk8c5dKhxYtm6tfG+Jsvr9zmI7dmvz2azcrfn1Ouz/l9v57idyjuBzAH21SzvT8vaqdPO\ntj0zKB+qpUurj/or+8ILq1N29WS567rVT+0xO/3Lv5N/h6zt6S+8zus5gXS27aAkkFwH0SVdCiyJ\niMvT5dcCiyLiipo6nwPeExHfSJe/DPwR8PSptq3Zh4c6zcw6NN1B9LwfZXIAOKNm+fS0rL7O0xrU\nOamNbYHpN4KZmXUu7y6s7cB8SXMlnQQsAzbV1dkErACQtBg4EhEH29zWzMz6JNczkIg4JmkVsIUk\nWV0bEbskrUxWx/qI2CzpIkl7gAeBy1ptm2e8ZmbWvoG4kdDMzHrPjzIxM7NMnEDMzCyTgU0gks6S\n9BFJN0v6g37HU3aSRiWtl3SjpJf3O54yk/R0SR+TdHO/Yyk7SadK+rikj0r6vX7HU3adfjYHfgxE\nkoBPRMSKfscyCCQ9CfiLiHhTv2MpO0k3R8Tv9juOMkvvDzscEbdJ2hgRy/od0yBo97NZ+DMQSddK\nOijpzrrydh7SeDHweWBzL2Itg+m0Z+p/AB/ON8py6EJbWp0MbXo6jzyx4ljPAi2JvD+jhU8gwHUk\nD1Q8rtWDFiW9TtL7JT0lIj4XEa8EXtvroAssa3s+VdJ7gc0RsbPXQRdU5s/mZPVeBlsSHbUpSfI4\nfbJqr4IskU7b83i1dnZe+AQSEV8H6h/i3fRBixFxfUS8HThT0tWS1gG39TToAptGe14KvBR4taTL\nexlzUU2jLX8h6SPAQp+hPFqnbQrcSvKZ/DDwud5FWg6dtqek4U4+m3k/yiQvjR60uKi2QkR8BfhK\nL4MqsXba80PAh3oZVEm105aHgP/ay6BKrmmbRsRDwBv6EVSJtWrPjj6bhT8DMTOzYiprAmnnIY3W\nPrdn97gtu89t2l1da8+yJBDx6EEdP2hxetye3eO27D63aXfl1p6FTyCSbgC+QTIo/kNJl0XEMeCt\nJA9avAfY6Acttsft2T1uy+5zm3ZX3u058DcSmplZPgp/BmJmZsXkBGJmZpk4gZiZWSZOIGZmlokT\niJmZZeIEYmZmmTiBmJlZJk4gNrAkHZN0h6Qd6e8/6ndMkyTdIuk30tc/kPSVuvU76+dwaLCP70pa\nUFf2AUnvlPQsSdd1O26zWmV9Gq9ZOx6MiHO7uUNJj0nv5J3OPs4GZkTED9KiAJ4gaU5EHEjnZmjn\nDt8bSR5D8afpfgW8GnhhROyXNEfS6RGxfzrxmjXjMxAbZA0nxZH0fUljkm6X9G1JZ6blp6YzuP1T\nuu7itPw/S/qspL8DvqzEX0m6V9IWSbdJukTShZJurTnOyyR9pkEIvw98tq7sZpJkALAcuKFmPzMk\n/bmkbemZyeR0wht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JMGgaEUcfxvr1YoIe8SPHmGHbXHxXe9Hk65C+aHyKhD3PoqvNhoJaQvxe/H4d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\nGt0ITZQ1NDQ0uhH/DznvrI6ebgS5AAAAAElFTkSuQmCC\n", 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M+mzzwav3wOavwB4F2+bA/LFwRRi8eT04mo/+YTXUQexRunz42qDkZRj2FcSN+/nrQX8s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\nF5W9GoMAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/docs/source/pythonapi/examples/tally-arithmetic.ipynb b/docs/source/pythonapi/examples/tally-arithmetic.ipynb index 0ab708e09..2f1dc820f 100644 --- a/docs/source/pythonapi/examples/tally-arithmetic.ipynb +++ b/docs/source/pythonapi/examples/tally-arithmetic.ipynb @@ -34,10 +34,6 @@ "import numpy as np\n", "\n", "import openmc\n", - "from openmc.statepoint import StatePoint\n", - "from openmc.summary import Summary\n", - "from openmc.source import Source\n", - "from openmc.stats import Box\n", "\n", "%matplotlib inline" ] @@ -289,9 +285,11 @@ "settings_file.inactive = inactive\n", "settings_file.particles = particles\n", "settings_file.output = {'tallies': True}\n", - "source_bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", - "settings_file.source = Source(space=Box(\n", - " source_bounds[:3], source_bounds[3:]))\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", + "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings_file.source = openmc.source.Source(space=uniform_dist)\n", "\n", "# Export to \"settings.xml\"\n", "settings_file.export_to_xml()" @@ -366,7 +364,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -570,10 +568,9 @@ " 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: 5f252e2df51930b9175fd41bafa8db01f3eaeb92\n", - " Date/Time: 2016-03-23 14:50:46\n", + " Git SHA1: 9a6ecd72597338b40d2b72378e5ad6dd65df2364\n", + " Date/Time: 2016-04-08 12:15:26\n", " MPI Processes: 1\n", - " OpenMP Threads: 16\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -600,26 +597,26 @@ "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \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", + " 1/1 1.03471 \n", + " 2/1 1.03257 \n", + " 3/1 1.00600 \n", + " 4/1 1.04547 \n", + " 5/1 1.02287 \n", + " 6/1 1.05752 \n", + " 7/1 1.04283 1.05017 +/- 0.00734\n", + " 8/1 1.05189 1.05074 +/- 0.00428\n", + " 9/1 1.01645 1.04217 +/- 0.00909\n", + " 10/1 1.04978 1.04369 +/- 0.00721\n", + " 11/1 1.03459 1.04218 +/- 0.00608\n", + " 12/1 1.04019 1.04189 +/- 0.00514\n", + " 13/1 1.05985 1.04414 +/- 0.00499\n", + " 14/1 1.02111 1.04158 +/- 0.00509\n", + " 15/1 1.04774 1.04219 +/- 0.00459\n", + " 16/1 1.00733 1.03902 +/- 0.00523\n", + " 17/1 1.02224 1.03763 +/- 0.00497\n", + " 18/1 1.03263 1.03724 +/- 0.00459\n", + " 19/1 1.01611 1.03573 +/- 0.00451\n", + " 20/1 1.04692 1.03648 +/- 0.00426\n", " Creating state point statepoint.20.h5...\n", "\n", " ===========================================================================\n", @@ -629,27 +626,27 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 5.0400E-01 seconds\n", - " Reading cross sections = 1.5000E-01 seconds\n", - " Total time in simulation = 2.1570E+00 seconds\n", - " Time in transport only = 1.9760E+00 seconds\n", - " Time in inactive batches = 3.3600E-01 seconds\n", - " Time in active batches = 1.8210E+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 = 0.0000E+00 seconds\n", + " Total time for initialization = 6.6400E-01 seconds\n", + " Reading cross sections = 1.8900E-01 seconds\n", + " Total time in simulation = 3.0445E+01 seconds\n", + " Time in transport only = 3.0423E+01 seconds\n", + " Time in inactive batches = 4.4900E+00 seconds\n", + " Time in active batches = 2.5955E+01 seconds\n", + " Time synchronizing fission bank = 2.0000E-03 seconds\n", + " Sampling source sites = 2.0000E-03 seconds\n", + " SEND/RECV source sites = 0.0000E+00 seconds\n", + " Time accumulating tallies = 1.0000E-03 seconds\n", " Total time for finalization = 2.0000E-03 seconds\n", - " Total time elapsed = 2.6800E+00 seconds\n", - " Calculation Rate (inactive) = 37202.4 neutrons/second\n", - " Calculation Rate (active) = 20593.1 neutrons/second\n", + " Total time elapsed = 3.1139E+01 seconds\n", + " Calculation Rate (inactive) = 2783.96 neutrons/second\n", + " Calculation Rate (active) = 1444.81 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\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", + " k-effective (Collision) = 1.03296 +/- 0.00669\n", + " k-effective (Track-length) = 1.03648 +/- 0.00426\n", + " k-effective (Absorption) = 1.03431 +/- 0.00702\n", + " Combined k-effective = 1.03621 +/- 0.00456\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -697,7 +694,7 @@ "outputs": [], "source": [ "# Load the statepoint file\n", - "sp = StatePoint('statepoint.20.h5')" + "sp = openmc.StatePoint('statepoint.20.h5')" ] }, { @@ -717,7 +714,7 @@ "outputs": [], "source": [ "# Load the summary file and link with statepoint\n", - "su = Summary('summary.h5')\n", + "su = openmc.Summary('summary.h5')\n", "sp.link_with_summary(su)" ] }, @@ -756,8 +753,8 @@ " 0\n", " total\n", " (nu-fission / absorption)\n", - " 1.036847\n", - " 0.009685\n", + " 1.038387\n", + " 0.006141\n", " \n", " \n", "\n", @@ -765,7 +762,7 @@ ], "text/plain": [ " nuclide score mean std. dev.\n", - "0 total (nu-fission / absorption) 1.04e+00 9.69e-03" + "0 total (nu-fission / absorption) 1.04e+00 6.14e-03" ] }, "execution_count": 26, @@ -820,8 +817,8 @@ " 6.250000e-07\n", " total\n", " absorption\n", - " 0.692034\n", - " 0.007217\n", + " 0.693337\n", + " 0.004109\n", " \n", " \n", "\n", @@ -829,7 +826,7 @@ ], "text/plain": [ " energy low [MeV] energy high [MeV] nuclide score mean std. dev.\n", - "0 0.00e+00 6.25e-07 total absorption 6.92e-01 7.22e-03" + "0 0.00e+00 6.25e-07 total absorption 6.93e-01 4.11e-03" ] }, "execution_count": 27, @@ -882,8 +879,8 @@ " 6.250000e-07\n", " total\n", " nu-fission\n", - " 1.202298\n", - " 0.013385\n", + " 1.203042\n", + " 0.0076\n", " \n", " \n", "\n", @@ -891,7 +888,7 @@ ], "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 1.34e-02" + "0 0.00e+00 6.25e-07 total nu-fission 1.20e+00 7.60e-03" ] }, "execution_count": 28, @@ -947,8 +944,8 @@ " 10000\n", " total\n", " absorption\n", - " 0.749151\n", - " 0.009003\n", + " 0.748413\n", + " 0.004723\n", " \n", " \n", "\n", @@ -956,10 +953,10 @@ ], "text/plain": [ " energy low [MeV] energy high [MeV] cell nuclide score mean \\\n", - "0 0.00e+00 6.25e-07 10000 total absorption 7.49e-01 \n", + "0 0.00e+00 6.25e-07 10000 total absorption 7.48e-01 \n", "\n", " std. dev. \n", - "0 9.00e-03 " + "0 4.72e-03 " ] }, "execution_count": 29, @@ -1013,8 +1010,8 @@ " 10000\n", " total\n", " (nu-fission / absorption)\n", - " 1.663435\n", - " 0.019976\n", + " 1.663385\n", + " 0.011253\n", " \n", " \n", "\n", @@ -1025,7 +1022,7 @@ "0 0.00e+00 6.25e-07 10000 total \n", "\n", " score mean std. dev. \n", - "0 (nu-fission / absorption) 1.66e+00 2.00e-02 " + "0 (nu-fission / absorption) 1.66e+00 1.13e-02 " ] }, "execution_count": 30, @@ -1078,8 +1075,8 @@ " 10000\n", " total\n", " (((absorption * nu-fission) * absorption) * (n...\n", - " 1.036847\n", - " 0.023674\n", + " 1.038387\n", + " 0.01316\n", " \n", " \n", "\n", @@ -1090,7 +1087,7 @@ "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 2.37e-02 " + "0 (((absorption * nu-fission) * absorption) * (n... 1.04e+00 1.32e-02 " ] }, "execution_count": 31, @@ -1160,8 +1157,8 @@ " 6.250000e-07\n", " (U-238 / total)\n", " (nu-fission / flux)\n", - " 6.627781e-07\n", - " 7.082494e-09\n", + " 6.636968e-07\n", + " 4.132875e-09\n", " \n", " \n", " 1\n", @@ -1170,8 +1167,8 @@ " 6.250000e-07\n", " (U-238 / total)\n", " (scatter / flux)\n", - " 2.099843e-01\n", - " 2.003686e-03\n", + " 2.099856e-01\n", + " 1.232455e-03\n", " \n", " \n", " 2\n", @@ -1180,8 +1177,8 @@ " 6.250000e-07\n", " (U-235 / total)\n", " (nu-fission / flux)\n", - " 3.547246e-01\n", - " 3.854562e-03\n", + " 3.552458e-01\n", + " 2.252681e-03\n", " \n", " \n", " 3\n", @@ -1190,8 +1187,8 @@ " 6.250000e-07\n", " (U-235 / total)\n", " (scatter / flux)\n", - " 5.554185e-03\n", - " 5.316706e-05\n", + " 5.554345e-03\n", + " 3.265385e-05\n", " \n", " \n", " 4\n", @@ -1200,8 +1197,8 @@ " 2.000000e+01\n", " (U-238 / total)\n", " (nu-fission / flux)\n", - " 7.151165e-03\n", - " 5.480545e-05\n", + " 7.126668e-03\n", + " 5.296883e-05\n", " \n", " \n", " 5\n", @@ -1210,8 +1207,8 @@ " 2.000000e+01\n", " (U-238 / total)\n", " (scatter / flux)\n", - " 2.278981e-01\n", - " 6.424480e-04\n", + " 2.277460e-01\n", + " 1.003558e-03\n", " \n", " \n", " 6\n", @@ -1220,8 +1217,8 @@ " 2.000000e+01\n", " (U-235 / total)\n", " (nu-fission / flux)\n", - " 8.073636e-03\n", - " 4.374754e-05\n", + " 8.010911e-03\n", + " 6.802256e-05\n", " \n", " \n", " 7\n", @@ -1230,8 +1227,8 @@ " 2.000000e+01\n", " (U-235 / total)\n", " (scatter / flux)\n", - " 3.369592e-03\n", - " 8.971220e-06\n", + " 3.367794e-03\n", + " 1.443644e-05\n", " \n", " \n", "\n", @@ -1249,14 +1246,14 @@ "7 10000 6.25e-07 2.00e+01 (U-235 / total) \n", "\n", " score mean std. dev. \n", - "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 " + "0 (nu-fission / flux) 6.64e-07 4.13e-09 \n", + "1 (scatter / flux) 2.10e-01 1.23e-03 \n", + "2 (nu-fission / flux) 3.55e-01 2.25e-03 \n", + "3 (scatter / flux) 5.55e-03 3.27e-05 \n", + "4 (nu-fission / flux) 7.13e-03 5.30e-05 \n", + "5 (scatter / flux) 2.28e-01 1.00e-03 \n", + "6 (nu-fission / flux) 8.01e-03 6.80e-05 \n", + "7 (scatter / flux) 3.37e-03 1.44e-05 " ] }, "execution_count": 33, @@ -1287,11 +1284,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 6.62778145e-07]\n", - " [ 3.54724568e-01]]\n", + "[[[ 6.63696783e-07]\n", + " [ 3.55245846e-01]]\n", "\n", - " [[ 7.15116511e-03]\n", - " [ 8.07363630e-03]]]\n" + " [[ 7.12666800e-03]\n", + " [ 8.01091088e-03]]]\n" ] } ], @@ -1319,9 +1316,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00555418]]\n", + "[[[ 0.00555435]]\n", "\n", - " [[ 0.00336959]]]\n" + " [[ 0.00336779]]]\n" ] } ], @@ -1343,8 +1340,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.22789806]\n", - " [ 0.00336959]]]\n" + "[[[ 0.22774598]\n", + " [ 0.00336779]]]\n" ] } ], @@ -1396,7 +1393,7 @@ " U-238\n", " nu-fission\n", " 0.000002\n", - " 1.338459e-08\n", + " 7.473789e-09\n", " \n", " \n", " 1\n", @@ -1405,8 +1402,8 @@ " 6.250000e-07\n", " U-235\n", " nu-fission\n", - " 0.864141\n", - " 7.363278e-03\n", + " 0.861547\n", + " 4.131310e-03\n", " \n", " \n", " 2\n", @@ -1415,8 +1412,8 @@ " 2.000000e+01\n", " U-238\n", " nu-fission\n", - " 0.082111\n", - " 6.090952e-04\n", + " 0.082356\n", + " 5.560461e-04\n", " \n", " \n", " 3\n", @@ -1425,8 +1422,8 @@ " 2.000000e+01\n", " U-235\n", " nu-fission\n", - " 0.092703\n", - " 4.695215e-04\n", + " 0.092574\n", + " 7.315442e-04\n", " \n", " \n", "\n", @@ -1435,15 +1432,15 @@ "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 1.61e-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 9.27e-02 \n", + "1 10000 0.00e+00 6.25e-07 U-235 nu-fission 8.62e-01 \n", + "2 10000 6.25e-07 2.00e+01 U-238 nu-fission 8.24e-02 \n", + "3 10000 6.25e-07 2.00e+01 U-235 nu-fission 9.26e-02 \n", "\n", " std. dev. \n", - "0 1.34e-08 \n", - "1 7.36e-03 \n", - "2 6.09e-04 \n", - "3 4.70e-04 " + "0 7.47e-09 \n", + "1 4.13e-03 \n", + "2 5.56e-04 \n", + "3 7.32e-04 " ] }, "execution_count": 37, @@ -1489,8 +1486,8 @@ " 1.080060e-07\n", " H-1\n", " scatter\n", - " 4.591022\n", - " 0.043961\n", + " 4.599225\n", + " 0.015973\n", " \n", " \n", " 1\n", @@ -1499,8 +1496,8 @@ " 1.166529e-06\n", " H-1\n", " scatter\n", - " 2.032481\n", - " 0.010876\n", + " 2.037260\n", + " 0.011236\n", " \n", " \n", " 2\n", @@ -1509,8 +1506,8 @@ " 1.259921e-05\n", " H-1\n", " scatter\n", - " 1.654187\n", - " 0.012130\n", + " 1.662552\n", + " 0.010280\n", " \n", " \n", " 3\n", @@ -1519,8 +1516,8 @@ " 1.360790e-04\n", " H-1\n", " scatter\n", - " 1.864771\n", - " 0.011649\n", + " 1.872201\n", + " 0.012136\n", " \n", " \n", " 4\n", @@ -1529,8 +1526,8 @@ " 1.469734e-03\n", " H-1\n", " scatter\n", - " 2.056893\n", - " 0.008555\n", + " 2.080459\n", + " 0.013155\n", " \n", " \n", " 5\n", @@ -1539,8 +1536,8 @@ " 1.587401e-02\n", " H-1\n", " scatter\n", - " 2.138833\n", - " 0.015180\n", + " 2.154996\n", + " 0.011975\n", " \n", " \n", " 6\n", @@ -1549,8 +1546,8 @@ " 1.714488e-01\n", " H-1\n", " scatter\n", - " 2.207209\n", - " 0.014853\n", + " 2.218740\n", + " 0.008528\n", " \n", " \n", " 7\n", @@ -1559,8 +1556,8 @@ " 1.851749e+00\n", " H-1\n", " scatter\n", - " 1.999407\n", - " 0.009053\n", + " 2.010517\n", + " 0.009187\n", " \n", " \n", " 8\n", @@ -1569,8 +1566,8 @@ " 2.000000e+01\n", " H-1\n", " scatter\n", - " 0.368760\n", - " 0.003373\n", + " 0.372022\n", + " 0.003196\n", " \n", " \n", "\n", @@ -1578,26 +1575,26 @@ ], "text/plain": [ " cell energy low [MeV] energy high [MeV] nuclide score mean \\\n", - "0 10002 1.00e-08 1.08e-07 H-1 scatter 4.59e+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 1.65e+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 2.00e+00 \n", - "8 10002 1.85e+00 2.00e+01 H-1 scatter 3.69e-01 \n", + "0 10002 1.00e-08 1.08e-07 H-1 scatter 4.60e+00 \n", + "1 10002 1.08e-07 1.17e-06 H-1 scatter 2.04e+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 1.87e+00 \n", + "4 10002 1.36e-04 1.47e-03 H-1 scatter 2.08e+00 \n", + "5 10002 1.47e-03 1.59e-02 H-1 scatter 2.15e+00 \n", + "6 10002 1.59e-02 1.71e-01 H-1 scatter 2.22e+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 3.72e-01 \n", "\n", " std. dev. \n", - "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 " + "0 1.60e-02 \n", + "1 1.12e-02 \n", + "2 1.03e-02 \n", + "3 1.21e-02 \n", + "4 1.32e-02 \n", + "5 1.20e-02 \n", + "6 8.53e-03 \n", + "7 9.19e-03 \n", + "8 3.20e-03 " ] }, "execution_count": 38, diff --git a/examples/python/basic/build-xml.py b/examples/python/basic/build-xml.py index eb8fbd23f..fbe683661 100644 --- a/examples/python/basic/build-xml.py +++ b/examples/python/basic/build-xml.py @@ -1,6 +1,5 @@ import openmc -from openmc.source import Source -from openmc.stats import Box + ############################################################################### # Simulation Input File Parameters @@ -94,7 +93,12 @@ settings_file = openmc.SettingsFile() settings_file.batches = batches settings_file.inactive = inactive settings_file.particles = particles -settings_file.source = Source(space=Box([-4, -4, -4], [4, 4, 4])) + +# Create an initial uniform spatial source distribution over fissionable zones +bounds = [-4., -4., -4., 4., 4., 4.] +uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True) +settings_file.source = openmc.source.Source(space=uniform_dist) + settings_file.export_to_xml() diff --git a/examples/python/boxes/build-xml.py b/examples/python/boxes/build-xml.py index 2ae3ee612..ea3e81d17 100644 --- a/examples/python/boxes/build-xml.py +++ b/examples/python/boxes/build-xml.py @@ -1,8 +1,5 @@ import numpy as np - import openmc -from openmc.source import Source -from openmc.stats import Box ############################################################################### # Simulation Input File Parameters @@ -119,7 +116,11 @@ settings_file = openmc.SettingsFile() settings_file.batches = batches settings_file.inactive = inactive settings_file.particles = particles -settings_file.source = Source(space=Box(*outer_cube.bounding_box)) + +# Create an initial uniform spatial source distribution over fissionable zones +uniform_dist = openmc.stats.Box(*outer_cube.bounding_box, only_fissionable=True) +settings_file.source = openmc.source.Source(space=uniform_dist) + settings_file.export_to_xml() ############################################################################### diff --git a/examples/python/lattice/hexagonal/build-xml.py b/examples/python/lattice/hexagonal/build-xml.py index d1144cd91..7f92e6602 100644 --- a/examples/python/lattice/hexagonal/build-xml.py +++ b/examples/python/lattice/hexagonal/build-xml.py @@ -1,6 +1,4 @@ import openmc -from openmc.source import Source -from openmc.stats import Box ############################################################################### # Simulation Input File Parameters @@ -126,8 +124,12 @@ settings_file = openmc.SettingsFile() settings_file.batches = batches settings_file.inactive = inactive settings_file.particles = particles -settings_file.source = Source(space=Box( - [-1, -1, -1], [1, 1, 1])) + +# Create an initial uniform spatial source distribution over fissionable zones +bounds = [-1, -1, -1, 1, 1, 1] +uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True) +settings_file.source = openmc.source.Source(space=uniform_dist) + settings_file.keff_trigger = {'type' : 'std_dev', 'threshold' : 5E-4} settings_file.trigger_active = True settings_file.trigger_max_batches = 100 diff --git a/examples/python/lattice/nested/build-xml.py b/examples/python/lattice/nested/build-xml.py index e4ac84839..f54f06453 100644 --- a/examples/python/lattice/nested/build-xml.py +++ b/examples/python/lattice/nested/build-xml.py @@ -1,6 +1,4 @@ import openmc -from openmc.source import Source -from openmc.stats import Box ############################################################################### # Simulation Input File Parameters @@ -137,8 +135,12 @@ settings_file = openmc.SettingsFile() settings_file.batches = batches settings_file.inactive = inactive settings_file.particles = particles -settings_file.source = Source(space=Box( - [-1, -1, -1], [1, 1, 1])) + +# Create an initial uniform spatial source distribution over fissionable zones +bounds = [-1, -1, -1, 1, 1, 1] +uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True) +settings_file.source = openmc.source.Source(space=uniform_dist) + settings_file.export_to_xml() diff --git a/examples/python/lattice/simple/build-xml.py b/examples/python/lattice/simple/build-xml.py index 78ee61eb4..f633fa96f 100644 --- a/examples/python/lattice/simple/build-xml.py +++ b/examples/python/lattice/simple/build-xml.py @@ -1,6 +1,4 @@ import openmc -from openmc.source import Source -from openmc.stats import Box ############################################################################### # Simulation Input File Parameters @@ -127,8 +125,12 @@ settings_file = openmc.SettingsFile() settings_file.batches = batches settings_file.inactive = inactive settings_file.particles = particles -settings_file.source = Source(space=Box( - [-1, -1, -1], [1, 1, 1])) + +# Create an initial uniform spatial source distribution over fissionable zones +bounds = [-1, -1, -1, 1, 1, 1] +uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True) +settings_file.source = openmc.source.Source(space=uniform_dist) + settings_file.trigger_active = True settings_file.trigger_max_batches = 100 settings_file.export_to_xml() diff --git a/examples/python/pincell/build-xml.py b/examples/python/pincell/build-xml.py index aa8714838..2e72d82ab 100644 --- a/examples/python/pincell/build-xml.py +++ b/examples/python/pincell/build-xml.py @@ -1,6 +1,4 @@ import openmc -from openmc.source import Source -from openmc.stats import Box ############################################################################### # Simulation Input File Parameters @@ -170,8 +168,12 @@ settings_file = openmc.SettingsFile() settings_file.batches = batches settings_file.inactive = inactive settings_file.particles = particles -settings_file.source = Source(space=Box( - [-0.62992, -0.62992, -1], [0.62992, 0.62992, 1])) + +# Create an initial uniform spatial source distribution over fissionable zones +bounds = [-0.62992, -0.62992, -1, 0.62992, 0.62992, 1] +uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True) +settings_file.source = openmc.source.Source(space=uniform_dist) + settings_file.entropy_lower_left = [-0.39218, -0.39218, -1.e50] settings_file.entropy_upper_right = [0.39218, 0.39218, 1.e50] settings_file.entropy_dimension = [10, 10, 1] diff --git a/examples/python/pincell_multigroup/build-xml.py b/examples/python/pincell_multigroup/build-xml.py index ff75a64d9..60026c089 100644 --- a/examples/python/pincell_multigroup/build-xml.py +++ b/examples/python/pincell_multigroup/build-xml.py @@ -1,8 +1,6 @@ +import numpy as np import openmc import openmc.mgxs -from openmc.source import Source -from openmc.stats import Box -import numpy as np ############################################################################### # Simulation Input File Parameters @@ -145,7 +143,13 @@ settings_file.cross_sections = "./mg_cross_sections.xml" settings_file.batches = batches settings_file.inactive = inactive settings_file.particles = particles -settings_file.source = Source(space=Box([-0.63, -0.63, -1.], [0.63, 0.63, 1.])) + +# Create an initial uniform spatial source distribution over fissionable zones +bounds = [-0.63, -0.63, -1, 0.63, 0.63, 1] +uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:]) +settings_file.source = openmc.source.Source(space=uniform_dist) + +settings_file.export_to_xml() ############################################################################### # Exporting to OpenMC tallies.xml File diff --git a/examples/python/reflective/build-xml.py b/examples/python/reflective/build-xml.py index 7e4fd30be..01a5c7815 100644 --- a/examples/python/reflective/build-xml.py +++ b/examples/python/reflective/build-xml.py @@ -1,8 +1,5 @@ import numpy as np - import openmc -from openmc.stats import Box -from openmc.source import Source ############################################################################### # Simulation Input File Parameters @@ -86,5 +83,10 @@ settings_file = openmc.SettingsFile() settings_file.batches = batches settings_file.inactive = inactive settings_file.particles = particles -settings_file.source = Source(space=Box(*cell.region.bounding_box)) + +# Create an initial uniform spatial source distribution over fissionable zones +uniform_dist = openmc.stats.Box(*cell.region.bounding_box, + only_fissionable=True) +settings_file.source = openmc.source.Source(space=uniform_dist) + settings_file.export_to_xml()