diff --git a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
index 2eeeffa3c..b15ace951 100644
--- a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
+++ b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
@@ -354,7 +354,7 @@
"\n",
"# Instantiate a 1-group EnergyGroups object\n",
"one_group = mgxs.EnergyGroups()\n",
- "one_group.group_edges = np.array([0., 20.])\n",
+ "one_group.group_edges = np.array([energy_groups.group_edges[0], energy_groups.group_edges[-1]])\n",
"\n",
"delayed_groups = mgxs.DelayedGroups()\n",
"delayed_groups.groups = range(1,7)"
@@ -404,8 +404,8 @@
"chi_prompt = mgxs.ChiPrompt(domain=cell, groups=energy_groups, by_nuclide=True)\n",
"prompt_nu_fission = mgxs.PromptNuFissionXS(domain=cell, groups=one_group, by_nuclide=True)\n",
"chi_delayed = mgxs.ChiDelayed(domain=cell, energy_groups=energy_groups, by_nuclide=True)\n",
- "delayed_nu_fission = mgxs.DelayedNuFissionXS(domain=cell, energy_groups=one_group, delayed_groups=delayed_groups, by_nuclide=True)\n",
- "beta = mgxs.Beta(domain=cell, energy_groups=one_group, delayed_groups=delayed_groups, by_nuclide=True)\n",
+ "delayed_nu_fission = mgxs.DelayedNuFissionXS(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n",
+ "beta = mgxs.Beta(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n",
"\n",
"chi_prompt.nuclides = ['U235', 'Pu239']\n",
"prompt_nu_fission.nuclides = ['U235', 'Pu239']\n",
@@ -436,7 +436,32 @@
" \tName =\t\n",
" \tFilters =\t\n",
" \t\tcell\t[1]\n",
- " \t\tenergy\t[ 0. 20.]\n",
+ " \t\tenergy\t[ 1.00000000e-09 1.26765187e-09 1.60694125e-09 2.03704208e-09\n",
+ " 2.58226019e-09 3.27340695e-09 4.14954043e-09 5.26017266e-09\n",
+ " 6.66806769e-09 8.45278845e-09 1.07151931e-08 1.35831345e-08\n",
+ " 1.72186857e-08 2.18272991e-08 2.76694165e-08 3.50751874e-08\n",
+ " 4.44631267e-08 5.63637656e-08 7.14496326e-08 9.05732601e-08\n",
+ " 1.14815362e-07 1.45545908e-07 1.84501542e-07 2.33883724e-07\n",
+ " 2.96483139e-07 3.75837404e-07 4.76430987e-07 6.03948629e-07\n",
+ " 7.65596607e-07 9.70509967e-07 1.23026877e-06 1.55955250e-06\n",
+ " 1.97696964e-06 2.50610925e-06 3.17687407e-06 4.02717034e-06\n",
+ " 5.10505000e-06 6.47142616e-06 8.20351544e-06 1.03992017e-05\n",
+ " 1.31825674e-05 1.67109061e-05 2.11836114e-05 2.68534445e-05\n",
+ " 3.40408190e-05 4.31519077e-05 5.47015963e-05 6.93425806e-05\n",
+ " 8.79022517e-05 1.11429453e-04 1.41253754e-04 1.79060585e-04\n",
+ " 2.26986485e-04 2.87739841e-04 3.64753947e-04 4.62381021e-04\n",
+ " 5.86138165e-04 7.43019138e-04 9.41889597e-04 1.19398810e-03\n",
+ " 1.51356125e-03 1.91866874e-03 2.43220401e-03 3.08318795e-03\n",
+ " 3.90840896e-03 4.95450191e-03 6.28058359e-03 7.96159350e-03\n",
+ " 1.00925289e-02 1.27938130e-02 1.62181010e-02 2.05589060e-02\n",
+ " 2.60615355e-02 3.30369541e-02 4.18793565e-02 5.30884444e-02\n",
+ " 6.72976656e-02 8.53100114e-02 1.08143395e-01 1.37088177e-01\n",
+ " 1.73780083e-01 2.20292646e-01 2.79254384e-01 3.53997341e-01\n",
+ " 4.48745390e-01 5.68852931e-01 7.21107479e-01 9.14113241e-01\n",
+ " 1.15877736e+00 1.46892628e+00 1.86208714e+00 2.36047823e+00\n",
+ " 2.99226464e+00 3.79314985e+00 4.80839348e+00 6.09536897e+00\n",
+ " 7.72680585e+00 9.79489985e+00 1.24165231e+01 1.57398286e+01\n",
+ " 1.99526231e+01]\n",
" \tNuclides =\tU235 Pu239 \n",
" \tScores =\t['nu-fission']\n",
" \tEstimator =\ttracklength), ('delayed-nu-fission', Tally\n",
@@ -445,7 +470,32 @@
" \tFilters =\t\n",
" \t\tcell\t[1]\n",
" \t\tdelayedgroup\t[1 2 3 4 5 6]\n",
- " \t\tenergy\t[ 0. 20.]\n",
+ " \t\tenergy\t[ 1.00000000e-09 1.26765187e-09 1.60694125e-09 2.03704208e-09\n",
+ " 2.58226019e-09 3.27340695e-09 4.14954043e-09 5.26017266e-09\n",
+ " 6.66806769e-09 8.45278845e-09 1.07151931e-08 1.35831345e-08\n",
+ " 1.72186857e-08 2.18272991e-08 2.76694165e-08 3.50751874e-08\n",
+ " 4.44631267e-08 5.63637656e-08 7.14496326e-08 9.05732601e-08\n",
+ " 1.14815362e-07 1.45545908e-07 1.84501542e-07 2.33883724e-07\n",
+ " 2.96483139e-07 3.75837404e-07 4.76430987e-07 6.03948629e-07\n",
+ " 7.65596607e-07 9.70509967e-07 1.23026877e-06 1.55955250e-06\n",
+ " 1.97696964e-06 2.50610925e-06 3.17687407e-06 4.02717034e-06\n",
+ " 5.10505000e-06 6.47142616e-06 8.20351544e-06 1.03992017e-05\n",
+ " 1.31825674e-05 1.67109061e-05 2.11836114e-05 2.68534445e-05\n",
+ " 3.40408190e-05 4.31519077e-05 5.47015963e-05 6.93425806e-05\n",
+ " 8.79022517e-05 1.11429453e-04 1.41253754e-04 1.79060585e-04\n",
+ " 2.26986485e-04 2.87739841e-04 3.64753947e-04 4.62381021e-04\n",
+ " 5.86138165e-04 7.43019138e-04 9.41889597e-04 1.19398810e-03\n",
+ " 1.51356125e-03 1.91866874e-03 2.43220401e-03 3.08318795e-03\n",
+ " 3.90840896e-03 4.95450191e-03 6.28058359e-03 7.96159350e-03\n",
+ " 1.00925289e-02 1.27938130e-02 1.62181010e-02 2.05589060e-02\n",
+ " 2.60615355e-02 3.30369541e-02 4.18793565e-02 5.30884444e-02\n",
+ " 6.72976656e-02 8.53100114e-02 1.08143395e-01 1.37088177e-01\n",
+ " 1.73780083e-01 2.20292646e-01 2.79254384e-01 3.53997341e-01\n",
+ " 4.48745390e-01 5.68852931e-01 7.21107479e-01 9.14113241e-01\n",
+ " 1.15877736e+00 1.46892628e+00 1.86208714e+00 2.36047823e+00\n",
+ " 2.99226464e+00 3.79314985e+00 4.80839348e+00 6.09536897e+00\n",
+ " 7.72680585e+00 9.79489985e+00 1.24165231e+01 1.57398286e+01\n",
+ " 1.99526231e+01]\n",
" \tNuclides =\tU235 Pu239 \n",
" \tScores =\t['delayed-nu-fission']\n",
" \tEstimator =\ttracklength)])"
@@ -531,8 +581,8 @@
" Copyright: 2011-2016 Massachusetts Institute of Technology\n",
" License: http://openmc.readthedocs.io/en/latest/license.html\n",
" Version: 0.8.0\n",
- " Git SHA1: bc8a346a978644f5b2b21cccb6c5ae7f8397ebee\n",
- " Date/Time: 2016-07-31 19:49:00\n",
+ " Git SHA1: e8819e6a77f2e998dcce937e80fbdd8dd430b667\n",
+ " Date/Time: 2016-08-02 18:51:52\n",
" MPI Processes: 4\n",
"\n",
" ===========================================================================\n",
@@ -619,20 +669,20 @@
"\n",
" =======================> TIMING STATISTICS <=======================\n",
"\n",
- " Total time for initialization = 7.4000E-01 seconds\n",
- " Reading cross sections = 3.9400E-01 seconds\n",
- " Total time in simulation = 2.5930E+01 seconds\n",
- " Time in transport only = 2.5267E+01 seconds\n",
- " Time in inactive batches = 1.5300E+00 seconds\n",
- " Time in active batches = 2.4400E+01 seconds\n",
- " Time synchronizing fission bank = 6.4500E-01 seconds\n",
- " Sampling source sites = 6.0000E-03 seconds\n",
- " SEND/RECV source sites = 0.0000E+00 seconds\n",
- " Time accumulating tallies = 1.0000E-03 seconds\n",
- " Total time for finalization = 1.1000E-02 seconds\n",
- " Total time elapsed = 2.6689E+01 seconds\n",
- " Calculation Rate (inactive) = 32679.7 neutrons/second\n",
- " Calculation Rate (active) = 8196.72 neutrons/second\n",
+ " Total time for initialization = 9.9200E-01 seconds\n",
+ " Reading cross sections = 3.8400E-01 seconds\n",
+ " Total time in simulation = 2.8675E+01 seconds\n",
+ " Time in transport only = 2.8041E+01 seconds\n",
+ " Time in inactive batches = 1.3230E+00 seconds\n",
+ " Time in active batches = 2.7352E+01 seconds\n",
+ " Time synchronizing fission bank = 5.6200E-01 seconds\n",
+ " Sampling source sites = 3.0000E-03 seconds\n",
+ " SEND/RECV source sites = 3.0000E-03 seconds\n",
+ " Time accumulating tallies = 1.0000E-02 seconds\n",
+ " Total time for finalization = 9.7000E-02 seconds\n",
+ " Total time elapsed = 2.9773E+01 seconds\n",
+ " Calculation Rate (inactive) = 37792.9 neutrons/second\n",
+ " Calculation Rate (active) = 7312.08 neutrons/second\n",
"\n",
" ============================> RESULTS <============================\n",
"\n",
@@ -755,43 +805,43 @@
"\tNuclide =\tU235\n",
"\tCross Sections [cm^-1]:\n",
" Delayed Group 1:\t\n",
- " Group 1 [0.0 - 20.0 MeV]:\t5.16e-06 +/- 3.38e-01%\n",
+ " Group 1 [1e-09 - 19.9526231497MeV]:\t5.14e-06 +/- 1.76e-01%\n",
"\n",
" Delayed Group 2:\t\n",
- " Group 1 [0.0 - 20.0 MeV]:\t2.67e-05 +/- 3.38e-01%\n",
+ " Group 1 [1e-09 - 19.9526231497MeV]:\t2.65e-05 +/- 1.76e-01%\n",
"\n",
" Delayed Group 3:\t\n",
- " Group 1 [0.0 - 20.0 MeV]:\t2.54e-05 +/- 3.38e-01%\n",
+ " Group 1 [1e-09 - 19.9526231497MeV]:\t2.53e-05 +/- 1.76e-01%\n",
"\n",
" Delayed Group 4:\t\n",
- " Group 1 [0.0 - 20.0 MeV]:\t5.71e-05 +/- 3.38e-01%\n",
+ " Group 1 [1e-09 - 19.9526231497MeV]:\t5.68e-05 +/- 1.76e-01%\n",
"\n",
" Delayed Group 5:\t\n",
- " Group 1 [0.0 - 20.0 MeV]:\t2.34e-05 +/- 3.38e-01%\n",
+ " Group 1 [1e-09 - 19.9526231497MeV]:\t2.33e-05 +/- 1.76e-01%\n",
"\n",
" Delayed Group 6:\t\n",
- " Group 1 [0.0 - 20.0 MeV]:\t9.80e-06 +/- 3.38e-01%\n",
+ " Group 1 [1e-09 - 19.9526231497MeV]:\t9.76e-06 +/- 1.76e-01%\n",
"\n",
"\n",
"\tNuclide =\tPu239\n",
"\tCross Sections [cm^-1]:\n",
" Delayed Group 1:\t\n",
- " Group 1 [0.0 - 20.0 MeV]:\t1.17e-06 +/- 3.00e-01%\n",
+ " Group 1 [1e-09 - 19.9526231497MeV]:\t1.16e-06 +/- 1.90e-01%\n",
"\n",
" Delayed Group 2:\t\n",
- " Group 1 [0.0 - 20.0 MeV]:\t7.60e-06 +/- 3.00e-01%\n",
+ " Group 1 [1e-09 - 19.9526231497MeV]:\t7.58e-06 +/- 1.90e-01%\n",
"\n",
" Delayed Group 3:\t\n",
- " Group 1 [0.0 - 20.0 MeV]:\t5.75e-06 +/- 3.00e-01%\n",
+ " Group 1 [1e-09 - 19.9526231497MeV]:\t5.74e-06 +/- 1.90e-01%\n",
"\n",
" Delayed Group 4:\t\n",
- " Group 1 [0.0 - 20.0 MeV]:\t1.05e-05 +/- 3.00e-01%\n",
+ " Group 1 [1e-09 - 19.9526231497MeV]:\t1.05e-05 +/- 1.90e-01%\n",
"\n",
" Delayed Group 5:\t\n",
- " Group 1 [0.0 - 20.0 MeV]:\t5.47e-06 +/- 3.00e-01%\n",
+ " Group 1 [1e-09 - 19.9526231497MeV]:\t5.46e-06 +/- 1.90e-01%\n",
"\n",
" Delayed Group 6:\t\n",
- " Group 1 [0.0 - 20.0 MeV]:\t1.66e-06 +/- 3.00e-01%\n",
+ " Group 1 [1e-09 - 19.9526231497MeV]:\t1.65e-06 +/- 1.90e-01%\n",
"\n",
"\n",
"\n"
@@ -799,7 +849,7 @@
}
],
"source": [
- "delayed_nu_fission.print_xs()"
+ "delayed_nu_fission.get_condensed_xs(one_group).print_xs()"
]
},
{
@@ -840,7 +890,7 @@
" | 1 | \n",
" U235 | \n",
" 0.000228 | \n",
- " 1.038855e-06 | \n",
+ " 4.753468e-07 | \n",
" \n",
" \n",
" | 1 | \n",
@@ -849,7 +899,7 @@
" 1 | \n",
" Pu239 | \n",
" 0.000081 | \n",
- " 3.258333e-07 | \n",
+ " 1.885620e-07 | \n",
"
\n",
" \n",
" | 2 | \n",
@@ -858,7 +908,7 @@
" 1 | \n",
" U235 | \n",
" 0.001175 | \n",
- " 5.362249e-06 | \n",
+ " 2.453594e-06 | \n",
"
\n",
" \n",
" | 3 | \n",
@@ -867,7 +917,7 @@
" 1 | \n",
" Pu239 | \n",
" 0.000531 | \n",
- " 2.121977e-06 | \n",
+ " 1.228003e-06 | \n",
"
\n",
" \n",
" | 4 | \n",
@@ -876,7 +926,7 @@
" 1 | \n",
" U235 | \n",
" 0.001122 | \n",
- " 5.119269e-06 | \n",
+ " 2.342414e-06 | \n",
"
\n",
" \n",
" | 5 | \n",
@@ -885,7 +935,7 @@
" 1 | \n",
" Pu239 | \n",
" 0.000402 | \n",
- " 1.605842e-06 | \n",
+ " 9.293122e-07 | \n",
"
\n",
" \n",
" | 6 | \n",
@@ -894,7 +944,7 @@
" 1 | \n",
" U235 | \n",
" 0.002516 | \n",
- " 1.147782e-05 | \n",
+ " 5.251885e-06 | \n",
"
\n",
" \n",
" | 7 | \n",
@@ -903,7 +953,7 @@
" 1 | \n",
" Pu239 | \n",
" 0.000733 | \n",
- " 2.931785e-06 | \n",
+ " 1.696645e-06 | \n",
"
\n",
" \n",
" | 8 | \n",
@@ -912,7 +962,7 @@
" 1 | \n",
" U235 | \n",
" 0.001031 | \n",
- " 4.705745e-06 | \n",
+ " 2.153199e-06 | \n",
"
\n",
" \n",
" | 9 | \n",
@@ -921,7 +971,7 @@
" 1 | \n",
" Pu239 | \n",
" 0.000382 | \n",
- " 1.527096e-06 | \n",
+ " 8.837413e-07 | \n",
"
\n",
" \n",
" | 10 | \n",
@@ -930,7 +980,7 @@
" 1 | \n",
" U235 | \n",
" 0.000432 | \n",
- " 1.971220e-06 | \n",
+ " 9.019675e-07 | \n",
"
\n",
" \n",
" | 11 | \n",
@@ -939,7 +989,7 @@
" 1 | \n",
" Pu239 | \n",
" 0.000116 | \n",
- " 4.621961e-07 | \n",
+ " 2.674761e-07 | \n",
"
\n",
" \n",
"\n",
@@ -947,18 +997,18 @@
],
"text/plain": [
" cell delayedgroup group in nuclide mean std. dev.\n",
- "0 1 1 1 U235 0.000228 1.038855e-06\n",
- "1 1 1 1 Pu239 0.000081 3.258333e-07\n",
- "2 1 2 1 U235 0.001175 5.362249e-06\n",
- "3 1 2 1 Pu239 0.000531 2.121977e-06\n",
- "4 1 3 1 U235 0.001122 5.119269e-06\n",
- "5 1 3 1 Pu239 0.000402 1.605842e-06\n",
- "6 1 4 1 U235 0.002516 1.147782e-05\n",
- "7 1 4 1 Pu239 0.000733 2.931785e-06\n",
- "8 1 5 1 U235 0.001031 4.705745e-06\n",
- "9 1 5 1 Pu239 0.000382 1.527096e-06\n",
- "10 1 6 1 U235 0.000432 1.971220e-06\n",
- "11 1 6 1 Pu239 0.000116 4.621961e-07"
+ "0 1 1 1 U235 0.000228 4.753468e-07\n",
+ "1 1 1 1 Pu239 0.000081 1.885620e-07\n",
+ "2 1 2 1 U235 0.001175 2.453594e-06\n",
+ "3 1 2 1 Pu239 0.000531 1.228003e-06\n",
+ "4 1 3 1 U235 0.001122 2.342414e-06\n",
+ "5 1 3 1 Pu239 0.000402 9.293122e-07\n",
+ "6 1 4 1 U235 0.002516 5.251885e-06\n",
+ "7 1 4 1 Pu239 0.000733 1.696645e-06\n",
+ "8 1 5 1 U235 0.001031 2.153199e-06\n",
+ "9 1 5 1 Pu239 0.000382 8.837413e-07\n",
+ "10 1 6 1 U235 0.000432 9.019675e-07\n",
+ "11 1 6 1 Pu239 0.000116 2.674761e-07"
]
},
"execution_count": 19,
@@ -967,7 +1017,7 @@
}
],
"source": [
- "df = beta.get_pandas_dataframe()\n",
+ "df = beta.get_condensed_xs(one_group).get_pandas_dataframe()\n",
"df.head(12)"
]
},
@@ -1056,8 +1106,8 @@
" 1 | \n",
" (U235 / total) | \n",
" (((delayed-nu-fission / nu-fission) * (delayed... | \n",
- " 9.430766e-08 | \n",
- " 5.356653e-10 | \n",
+ " 9.391610e-08 | \n",
+ " 2.566220e-10 | \n",
" \n",
" \n",
" | 1 | \n",
@@ -1065,8 +1115,8 @@
" 1 | \n",
" (Pu239 / total) | \n",
" (((delayed-nu-fission / nu-fission) * (delayed... | \n",
- " 7.631830e-09 | \n",
- " 3.816602e-11 | \n",
+ " 7.611347e-09 | \n",
+ " 2.278727e-11 | \n",
"
\n",
" \n",
" | 2 | \n",
@@ -1074,8 +1124,8 @@
" 2 | \n",
" (U235 / total) | \n",
" (((delayed-nu-fission / nu-fission) * (delayed... | \n",
- " 1.107191e-06 | \n",
- " 6.288818e-09 | \n",
+ " 1.102594e-06 | \n",
+ " 3.012794e-09 | \n",
"
\n",
" \n",
" | 3 | \n",
@@ -1083,8 +1133,8 @@
" 2 | \n",
" (Pu239 / total) | \n",
" (((delayed-nu-fission / nu-fission) * (delayed... | \n",
- " 1.426298e-07 | \n",
- " 7.132776e-10 | \n",
+ " 1.422470e-07 | \n",
+ " 4.258670e-10 | \n",
"
\n",
" \n",
" | 4 | \n",
@@ -1092,8 +1142,8 @@
" 3 | \n",
" (U235 / total) | \n",
" (((delayed-nu-fission / nu-fission) * (delayed... | \n",
- " 6.713761e-07 | \n",
- " 3.813401e-09 | \n",
+ " 6.685886e-07 | \n",
+ " 1.826892e-09 | \n",
"
\n",
" \n",
" | 5 | \n",
@@ -1101,8 +1151,8 @@
" 3 | \n",
" (Pu239 / total) | \n",
" (((delayed-nu-fission / nu-fission) * (delayed... | \n",
- " 5.434458e-08 | \n",
- " 2.717718e-10 | \n",
+ " 5.419872e-08 | \n",
+ " 1.622631e-10 | \n",
"
\n",
" \n",
" | 6 | \n",
@@ -1110,8 +1160,8 @@
" 4 | \n",
" (U235 / total) | \n",
" (((delayed-nu-fission / nu-fission) * (delayed... | \n",
- " 4.907155e-07 | \n",
- " 2.787253e-09 | \n",
+ " 4.886781e-07 | \n",
+ " 1.335293e-09 | \n",
"
\n",
" \n",
" | 7 | \n",
@@ -1119,8 +1169,8 @@
" 4 | \n",
" (Pu239 / total) | \n",
" (((delayed-nu-fission / nu-fission) * (delayed... | \n",
- " 2.633756e-08 | \n",
- " 1.317115e-10 | \n",
+ " 2.626687e-08 | \n",
+ " 7.863920e-11 | \n",
"
\n",
" \n",
" | 8 | \n",
@@ -1128,8 +1178,8 @@
" 5 | \n",
" (U235 / total) | \n",
" (((delayed-nu-fission / nu-fission) * (delayed... | \n",
- " 1.475656e-08 | \n",
- " 8.381692e-11 | \n",
+ " 1.469529e-08 | \n",
+ " 4.015430e-11 | \n",
"
\n",
" \n",
" | 9 | \n",
@@ -1137,8 +1187,8 @@
" 5 | \n",
" (Pu239 / total) | \n",
" (((delayed-nu-fission / nu-fission) * (delayed... | \n",
- " 1.278385e-09 | \n",
- " 6.393074e-12 | \n",
+ " 1.274953e-09 | \n",
+ " 3.817026e-12 | \n",
"
\n",
" \n",
" | 10 | \n",
@@ -1146,8 +1196,8 @@
" 6 | \n",
" (U235 / total) | \n",
" (((delayed-nu-fission / nu-fission) * (delayed... | \n",
- " 1.190878e-09 | \n",
- " 6.764161e-12 | \n",
+ " 1.185934e-09 | \n",
+ " 3.240517e-12 | \n",
"
\n",
" \n",
" | 11 | \n",
@@ -1155,8 +1205,8 @@
" 6 | \n",
" (Pu239 / total) | \n",
" (((delayed-nu-fission / nu-fission) * (delayed... | \n",
- " 5.385798e-11 | \n",
- " 2.693384e-13 | \n",
+ " 5.371343e-11 | \n",
+ " 1.608102e-13 | \n",
"
\n",
" \n",
"\n",
@@ -1178,18 +1228,18 @@
"11 1 6 (Pu239 / total) \n",
"\n",
" score mean std. dev. \n",
- "0 (((delayed-nu-fission / nu-fission) * (delayed... 9.43e-08 5.36e-10 \n",
- "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.63e-09 3.82e-11 \n",
- "2 (((delayed-nu-fission / nu-fission) * (delayed... 1.11e-06 6.29e-09 \n",
- "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.43e-07 7.13e-10 \n",
- "4 (((delayed-nu-fission / nu-fission) * (delayed... 6.71e-07 3.81e-09 \n",
- "5 (((delayed-nu-fission / nu-fission) * (delayed... 5.43e-08 2.72e-10 \n",
- "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.91e-07 2.79e-09 \n",
- "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08 1.32e-10 \n",
- "8 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-08 8.38e-11 \n",
- "9 (((delayed-nu-fission / nu-fission) * (delayed... 1.28e-09 6.39e-12 \n",
- "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.19e-09 6.76e-12 \n",
- "11 (((delayed-nu-fission / nu-fission) * (delayed... 5.39e-11 2.69e-13 "
+ "0 (((delayed-nu-fission / nu-fission) * (delayed... 9.39e-08 2.57e-10 \n",
+ "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.61e-09 2.28e-11 \n",
+ "2 (((delayed-nu-fission / nu-fission) * (delayed... 1.10e-06 3.01e-09 \n",
+ "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.42e-07 4.26e-10 \n",
+ "4 (((delayed-nu-fission / nu-fission) * (delayed... 6.69e-07 1.83e-09 \n",
+ "5 (((delayed-nu-fission / nu-fission) * (delayed... 5.42e-08 1.62e-10 \n",
+ "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.89e-07 1.34e-09 \n",
+ "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08 7.86e-11 \n",
+ "8 (((delayed-nu-fission / nu-fission) * (delayed... 1.47e-08 4.02e-11 \n",
+ "9 (((delayed-nu-fission / nu-fission) * (delayed... 1.27e-09 3.82e-12 \n",
+ "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.19e-09 3.24e-12 \n",
+ "11 (((delayed-nu-fission / nu-fission) * (delayed... 5.37e-11 1.61e-13 "
]
},
"execution_count": 22,
@@ -1204,7 +1254,7 @@
"\n",
"# Create a tally object with only the delayed group filter for the time constants\n",
"beta_filters = [f for f in beta.xs_tally.filters if f.type != 'delayedgroup']\n",
- "lambda_tally = beta.xs_tally.summation(nuclides=beta.xs_tally.nuclides)\n",
+ "lambda_tally = beta.get_condensed_xs(one_group).xs_tally.summation(nuclides=beta.xs_tally.nuclides)\n",
"for f in beta_filters:\n",
" lambda_tally = lambda_tally.summation(filter_type=f.type, remove_filter=True) * 0. + 1.\n",
"\n",
@@ -1217,8 +1267,8 @@
"lambda_tally.scores = ['lambda']\n",
"\n",
"# Use tally arithmetic to compute the precursor concentrations\n",
- "precursor_conc = beta.xs_tally.summation(filter_type='energy', remove_filter=True) * \\\n",
- " delayed_nu_fission.xs_tally.summation(filter_type='energy', remove_filter=True) / lambda_tally\n",
+ "precursor_conc = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True) * \\\n",
+ " delayed_nu_fission.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True) / lambda_tally\n",
" \n",
"# The difference is a derived tally which can generate Pandas DataFrames for inspection\n",
"precursor_conc.get_pandas_dataframe()"
@@ -1242,8 +1292,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Beta (U-235) : 0.006504 +/- 0.000015\n",
- "Beta (Pu-239): 0.002245 +/- 0.000004\n"
+ "Beta (U-235) : 0.006504 +/- 0.000007\n",
+ "Beta (Pu-239): 0.002245 +/- 0.000002\n"
]
},
{
@@ -1260,7 +1310,7 @@
"data": {
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TT3PppZdy2GGH1e8CW9PVt7rsottrhpn1DN3573nZsmXxne98J0aMGBGbbbZZ7L777nH3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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1269,7 +1319,7 @@
],
"source": [
"energy_filter = [f for f in beta.xs_tally.filters if f.type == 'energy']\n",
- "beta_integrated = beta.xs_tally.summation(filter_type='energy', remove_filter=True)\n",
+ "beta_integrated = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True)\n",
"beta_u235 = beta_integrated.get_values(nuclides=['U235'])\n",
"beta_pu239 = beta_integrated.get_values(nuclides=['Pu239'])\n",
"\n",
@@ -1323,7 +1373,7 @@
"data": {
"image/png": 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Mlaq6KxsjXsZzetV0v6a4TThuWrRoETJD6auvvgp+vvfee7n33nur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\nTB8xJJxvIcPvPx7ZoBgSHTEEwm5Pdh1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"text/plain": [
- ""
+ ""
]
},
"metadata": {},
diff --git a/openmc/mgxs/library.py b/openmc/mgxs/library.py
index 3099ff0b5..a40857df9 100644
--- a/openmc/mgxs/library.py
+++ b/openmc/mgxs/library.py
@@ -18,17 +18,18 @@ if sys.version_info[0] >= 3:
class Library(object):
- """A multi-group cross section library for some energy group structure.
+ """A multi-energy-group and multi-delayed-group cross section library for
+ some energy group structure.
This class can be used for both OpenMC input generation and tally data
post-processing to compute spatially-homogenized and energy-integrated
multi-group cross sections for deterministic neutronics calculations.
- This class helps automate the generation of MGXS objects for some energy
- group structure and domain type. The Library serves as a collection for
- MGXS objects with routines to automate the initialization of tallies for
- input files, the loading of tally data from statepoint files, data storage,
- energy group condensation and more.
+ This class helps automate the generation of MGXS and MDGXS objects for some
+ energy group structure and domain type. The Library serves as a collection
+ for MGXS and MDGXS objects with routines to automate the initialization of
+ tallies for input files, the loading of tally data from statepoint files,
+ data storage, energy group condensation and more.
Parameters
----------
@@ -64,6 +65,8 @@ class Library(object):
The highest legendre moment in the scattering matrices (default is 0)
energy_groups : openmc.mgxs.EnergyGroups
Energy group structure for energy condensation
+ delayed_groups : openmc.mgxs.DelayedGroups
+ Delayed groups to filter out the xs
tally_trigger : openmc.Trigger
An (optional) tally precision trigger given to each tally used to
compute the cross section
@@ -95,6 +98,7 @@ class Library(object):
self._domain_type = None
self._domains = 'all'
self._energy_groups = None
+ self._delayed_groups = None
self._correction = 'P0'
self._legendre_order = 0
self._tally_trigger = None
@@ -126,6 +130,7 @@ class Library(object):
clone._correction = self.correction
clone._legendre_order = self.legendre_order
clone._energy_groups = copy.deepcopy(self.energy_groups, memo)
+ clone._delayed_groups = copy.deepcopy(self.delayed_groups, memo)
clone._tally_trigger = copy.deepcopy(self.tally_trigger, memo)
clone._all_mgxs = copy.deepcopy(self.all_mgxs)
clone._sp_filename = self._sp_filename
@@ -194,6 +199,10 @@ class Library(object):
def energy_groups(self):
return self._energy_groups
+ @property
+ def delayed_groups(self):
+ return self._delayed_groups
+
@property
def correction(self):
return self._correction
@@ -210,6 +219,13 @@ class Library(object):
def num_groups(self):
return self.energy_groups.num_groups
+ @property
+ def num_delayed_groups(self):
+ if self.delayed_groups == None:
+ return 0
+ else:
+ return self.delayed_groups.num_groups
+
@property
def all_mgxs(self):
return self._all_mgxs
@@ -261,7 +277,7 @@ class Library(object):
def domain_type(self, domain_type):
cv.check_value('domain type', domain_type, openmc.mgxs.DOMAIN_TYPES)
- if by_nuclide == True and domain_type == 'mesh':
+ if self.by_nuclide == True and domain_type == 'mesh':
raise ValueError('Unable to create MGXS library by nuclide with ' +
'mesh domain')
@@ -308,6 +324,12 @@ class Library(object):
cv.check_type('energy groups', energy_groups, openmc.mgxs.EnergyGroups)
self._energy_groups = energy_groups
+ @delayed_groups.setter
+ def delayed_groups(self, delayed_groups):
+ cv.check_type('delayed groups', delayed_groups,
+ openmc.mgxs.DelayedGroups)
+ self._delayed_groups = delayed_groups
+
@correction.setter
def correction(self, correction):
cv.check_value('correction', correction, ('P0', None))
@@ -373,12 +395,19 @@ class Library(object):
for domain in self.domains:
self.all_mgxs[domain.id] = OrderedDict()
for mgxs_type in self.mgxs_types:
- mgxs = openmc.mgxs.MGXS.get_mgxs(mgxs_type, name=self.name)
+ if mgxs_type in openmc.mgxs.MDGXS_TYPES:
+ mgxs = openmc.mgxs.MDGXS.get_mgxs(mgxs_type, name=self.name)
+ else:
+ mgxs = openmc.mgxs.MGXS.get_mgxs(mgxs_type, name=self.name)
+
mgxs.domain = domain
mgxs.domain_type = self.domain_type
mgxs.energy_groups = self.energy_groups
mgxs.by_nuclide = self.by_nuclide
+ if mgxs_type in openmc.mgxs.MDGXS_TYPES:
+ mgxs.delayed_groups = self.delayed_groups
+
# If a tally trigger was specified, add it to the MGXS
if self.tally_trigger:
mgxs.tally_trigger = self.tally_trigger
diff --git a/openmc/mgxs/mdgxs.py b/openmc/mgxs/mdgxs.py
index 4d65aaacc..e985b0151 100644
--- a/openmc/mgxs/mdgxs.py
+++ b/openmc/mgxs/mdgxs.py
@@ -115,7 +115,7 @@ class MDGXS(MGXS):
__metaclass__ = abc.ABCMeta
def __init__(self, domain=None, domain_type=None, energy_groups=None,
- by_nuclide=False, name='', delayed_groups=None):
+ delayed_groups=None, by_nuclide=False, name=''):
super(MDGXS, self).__init__(domain, domain_type, energy_groups,
by_nuclide, name)
self._delayed_groups = None
@@ -124,12 +124,30 @@ class MDGXS(MGXS):
self.delayed_groups = delayed_groups
def __deepcopy__(self, memo):
- super(MDGXS, self).__deepcopy__(memo)
existing = memo.get(id(self))
# If this is the first time we have tried to copy this object, copy it
if existing is None:
+ clone = type(self).__new__(type(self))
+ clone._name = self.name
+ clone._rxn_type = self.rxn_type
+ clone._by_nuclide = self.by_nuclide
+ clone._nuclides = copy.deepcopy(self._nuclides)
+ clone._domain = self.domain
+ clone._domain_type = self.domain_type
+ clone._energy_groups = copy.deepcopy(self.energy_groups, memo)
clone._delayed_groups = copy.deepcopy(self.delayed_groups, memo)
+ clone._tally_trigger = copy.deepcopy(self.tally_trigger, memo)
+ clone._rxn_rate_tally = copy.deepcopy(self._rxn_rate_tally, memo)
+ clone._xs_tally = copy.deepcopy(self._xs_tally, memo)
+ clone._sparse = self.sparse
+ clone._derived = self.derived
+
+ clone._tallies = OrderedDict()
+ for tally_type, tally in self.tallies.items():
+ clone.tallies[tally_type] = copy.deepcopy(tally, memo)
+
+ memo[id(self)] = clone
return clone
@@ -143,7 +161,10 @@ class MDGXS(MGXS):
@property
def num_delayed_groups(self):
- return self.delayed_groups.num_groups
+ if self.delayed_groups == None:
+ return 0
+ else:
+ return self.delayed_groups.num_groups
@delayed_groups.setter
def delayed_groups(self, delayed_groups):
@@ -167,8 +188,8 @@ class MDGXS(MGXS):
@staticmethod
def get_mgxs(mdgxs_type, domain=None, domain_type=None,
- energy_groups=None, by_nuclide=False, name='',
- delayed_groups=None):
+ energy_groups=None, delayed_groups=None,
+ by_nuclide=False, name=''):
"""Return a MDGXS subclass object for some energy group structure within
some spatial domain for some reaction type.
@@ -206,15 +227,16 @@ class MDGXS(MGXS):
cv.check_value('mdgxs_type', mdgxs_type, MDGXS_TYPES)
if mdgxs_type == 'delayed-nu-fission':
- mdgxs = DelayedNuFissionXS(domain, domain_type, energy_groups)
+ mdgxs = DelayedNuFissionXS(domain, domain_type, energy_groups,
+ delayed_groups)
elif mdgxs_type == 'chi-delayed':
- mdgxs = ChiDelayed(domain, domain_type, energy_groups)
+ mdgxs = ChiDelayed(domain, domain_type, energy_groups,
+ delayed_groups)
elif mdgxs_type == 'beta':
- mdgxs = Beta(domain, domain_type, energy_groups)
+ mdgxs = Beta(domain, domain_type, energy_groups, delayed_groups)
mdgxs.by_nuclide = by_nuclide
mdgxs.name = name
- mdgxs.delayed_groups = delayed_groups
return mdgxs
def get_xs(self, groups='all', subdomains='all', nuclides='all',
@@ -936,9 +958,9 @@ class ChiDelayed(MDGXS):
"""
def __init__(self, domain=None, domain_type=None, energy_groups=None,
- by_nuclide=False, name='', delayed_groups=None):
+ delayed_groups=None, by_nuclide=False, name=''):
super(ChiDelayed, self).__init__(domain, domain_type, energy_groups,
- by_nuclide, name, delayed_groups)
+ delayed_groups, by_nuclide, name)
self._rxn_type = 'chi-delayed'
@property
@@ -1390,10 +1412,10 @@ class DelayedNuFissionXS(MDGXS):
"""
def __init__(self, domain=None, domain_type=None, energy_groups=None,
- by_nuclide=False, name='', delayed_groups=None):
+ delayed_groups=None, by_nuclide=False, name=''):
super(DelayedNuFissionXS, self).__init__(domain, domain_type,
- energy_groups, by_nuclide,
- name, delayed_groups)
+ energy_groups, delayed_groups,
+ by_nuclide, name)
self._rxn_type = 'delayed-nu-fission'
@@ -1509,9 +1531,9 @@ class Beta(MDGXS):
"""
def __init__(self, domain=None, domain_type=None, energy_groups=None,
- by_nuclide=False, name='', delayed_groups=None):
+ delayed_groups=None, by_nuclide=False, name=''):
super(Beta, self).__init__(domain, domain_type, energy_groups,
- by_nuclide, name, delayed_groups)
+ delayed_groups, by_nuclide, name)
self._rxn_type = 'beta'
@property
diff --git a/openmc/tallies.py b/openmc/tallies.py
index 2073bbd28..58cf34ed7 100644
--- a/openmc/tallies.py
+++ b/openmc/tallies.py
@@ -795,6 +795,9 @@ class Tally(object):
else:
no_scores_match = False
+ if score == 'current' and score not in self.scores:
+ return False
+
# Nuclides cannot be specified on 'flux' scores
if 'flux' in self.scores or 'flux' in other.scores:
if self.nuclides != other.nuclides: