diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb index 94deeec7b..319c6d1df 100644 --- a/docs/source/pythonapi/examples/mgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb @@ -518,8 +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: ea9fb637f63f9374c7436456141afa850b84acf9\n", - " Date/Time: 2016-01-14 07:16:05\n", + " Git SHA1: 34381b40a9445a727e360873aaa6ef892af1cb6a\n", + " Date/Time: 2016-02-06 15:29:23\n", + " MPI Processes: 1\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -604,20 +605,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 1.1720E+00 seconds\n", - " Reading cross sections = 9.0300E-01 seconds\n", - " Total time in simulation = 1.7319E+01 seconds\n", - " Time in transport only = 1.7310E+01 seconds\n", - " Time in inactive batches = 1.9120E+00 seconds\n", - " Time in active batches = 1.5407E+01 seconds\n", - " Time synchronizing fission bank = 2.0000E-03 seconds\n", + " Total time for initialization = 3.7300E-01 seconds\n", + " Reading cross sections = 9.8000E-02 seconds\n", + " Total time in simulation = 8.5810E+00 seconds\n", + " Time in transport only = 8.5650E+00 seconds\n", + " Time in inactive batches = 1.2990E+00 seconds\n", + " Time in active batches = 7.2820E+00 seconds\n", + " Time synchronizing fission bank = 4.0000E-03 seconds\n", " Sampling source sites = 2.0000E-03 seconds\n", - " SEND/RECV source sites = 0.0000E+00 seconds\n", + " SEND/RECV source sites = 2.0000E-03 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", - " Total time for finalization = 1.0000E-03 seconds\n", - " Total time elapsed = 1.8507E+01 seconds\n", - " Calculation Rate (inactive) = 13075.3 neutrons/second\n", - " Calculation Rate (active) = 6490.56 neutrons/second\n", + " Total time for finalization = 0.0000E+00 seconds\n", + " Total time elapsed = 8.9680E+00 seconds\n", + " Calculation Rate (inactive) = 19245.6 neutrons/second\n", + " Calculation Rate (active) = 13732.5 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -794,19 +795,19 @@ " \n", " \n", " 1\n", - " 1\n", - " 1\n", - " total\n", - " 0.668323\n", - " 0.001264\n", + " 1\n", + " 1\n", + " total\n", + " 0.668323\n", + " 0.001264\n", " \n", " \n", " 0\n", - " 1\n", - " 2\n", - " total\n", - " 1.293258\n", - " 0.007624\n", + " 1\n", + " 2\n", + " total\n", + " 1.293258\n", + " 0.007624\n", " \n", " \n", "\n", @@ -896,7 +897,8 @@ " \n", " \n", " cell\n", - " energy [MeV]\n", + " energy low [MeV]\n", + " energy high [MeV]\n", " nuclide\n", " score\n", " mean\n", @@ -906,30 +908,32 @@ " \n", " \n", " 0\n", - " 1\n", - " (0.0e+00 - 6.3e-07)\n", - " total\n", - " (((total / flux) - (absorption / flux)) - (sca...\n", - " 4.884981e-15\n", - " 0.011274\n", + " 1\n", + " 0.000000\n", + " 0.000001\n", + " total\n", + " (((total / flux) - (absorption / flux)) - (sca...\n", + " 4.884981e-15\n", + " 0.011274\n", " \n", " \n", " 1\n", - " 1\n", - " (6.3e-07 - 2.0e+01)\n", - " total\n", - " (((total / flux) - (absorption / flux)) - (sca...\n", - " 1.221245e-15\n", - " 0.001802\n", + " 1\n", + " 0.000001\n", + " 20.000000\n", + " total\n", + " (((total / flux) - (absorption / flux)) - (sca...\n", + " 1.221245e-15\n", + " 0.001802\n", " \n", " \n", "\n", "" ], "text/plain": [ - " cell energy [MeV] nuclide \\\n", - "0 1 (0.0e+00 - 6.3e-07) total \n", - "1 1 (6.3e-07 - 2.0e+01) total \n", + " cell energy low [MeV] energy high [MeV] nuclide \\\n", + "0 1 0.000000 0.000001 total \n", + "1 1 0.000001 20.000000 total \n", "\n", " score mean std. dev. \n", "0 (((total / flux) - (absorption / flux)) - (sca... 4.884981e-15 0.011274 \n", @@ -972,7 +976,8 @@ " \n", " \n", " cell\n", - " energy [MeV]\n", + " energy low [MeV]\n", + " energy high [MeV]\n", " nuclide\n", " score\n", " mean\n", @@ -982,34 +987,36 @@ " \n", " \n", " 0\n", - " 1\n", - " (0.0e+00 - 6.3e-07)\n", - " total\n", - " ((absorption / flux) / (total / flux))\n", - " 0.076219\n", - " 0.000651\n", + " 1\n", + " 0.000000\n", + " 0.000001\n", + " total\n", + " ((absorption / flux) / (total / flux))\n", + " 0.076219\n", + " 0.000651\n", " \n", " \n", " 1\n", - " 1\n", - " (6.3e-07 - 2.0e+01)\n", - " total\n", - " ((absorption / flux) / (total / flux))\n", - " 0.019319\n", - " 0.000086\n", + " 1\n", + " 0.000001\n", + " 20.000000\n", + " total\n", + " ((absorption / flux) / (total / flux))\n", + " 0.019319\n", + " 0.000086\n", " \n", " \n", "\n", "" ], "text/plain": [ - " cell energy [MeV] nuclide score \\\n", - "0 1 (0.0e+00 - 6.3e-07) total ((absorption / flux) / (total / flux)) \n", - "1 1 (6.3e-07 - 2.0e+01) total ((absorption / flux) / (total / flux)) \n", + " cell energy low [MeV] energy high [MeV] nuclide \\\n", + "0 1 0.000000 0.000001 total \n", + "1 1 0.000001 20.000000 total \n", "\n", - " mean std. dev. \n", - "0 0.076219 0.000651 \n", - "1 0.019319 0.000086 " + " score mean std. dev. \n", + "0 ((absorption / flux) / (total / flux)) 0.076219 0.000651 \n", + "1 ((absorption / flux) / (total / flux)) 0.019319 0.000086 " ] }, "execution_count": 24, @@ -1041,7 +1048,8 @@ " \n", " \n", " cell\n", - " energy [MeV]\n", + " energy low [MeV]\n", + " energy high [MeV]\n", " nuclide\n", " score\n", " mean\n", @@ -1051,34 +1059,36 @@ " \n", " \n", " 0\n", - " 1\n", - " (0.0e+00 - 6.3e-07)\n", - " total\n", - " ((scatter / flux) / (total / flux))\n", - " 0.923781\n", - " 0.007714\n", + " 1\n", + " 0.000000\n", + " 0.000001\n", + " total\n", + " ((scatter / flux) / (total / flux))\n", + " 0.923781\n", + " 0.007714\n", " \n", " \n", " 1\n", - " 1\n", - " (6.3e-07 - 2.0e+01)\n", - " total\n", - " ((scatter / flux) / (total / flux))\n", - " 0.980681\n", - " 0.002617\n", + " 1\n", + " 0.000001\n", + " 20.000000\n", + " total\n", + " ((scatter / flux) / (total / flux))\n", + " 0.980681\n", + " 0.002617\n", " \n", " \n", "\n", "" ], "text/plain": [ - " cell energy [MeV] nuclide score \\\n", - "0 1 (0.0e+00 - 6.3e-07) total ((scatter / flux) / (total / flux)) \n", - "1 1 (6.3e-07 - 2.0e+01) total ((scatter / flux) / (total / flux)) \n", + " cell energy low [MeV] energy high [MeV] nuclide \\\n", + "0 1 0.000000 0.000001 total \n", + "1 1 0.000001 20.000000 total \n", "\n", - " mean std. dev. \n", - "0 0.923781 0.007714 \n", - "1 0.980681 0.002617 " + " score mean std. dev. \n", + "0 ((scatter / flux) / (total / flux)) 0.923781 0.007714 \n", + "1 ((scatter / flux) / (total / flux)) 0.980681 0.002617 " ] }, "execution_count": 25, @@ -1117,7 +1127,8 @@ " \n", " \n", " cell\n", - " energy [MeV]\n", + " energy low [MeV]\n", + " energy high [MeV]\n", " nuclide\n", " score\n", " mean\n", @@ -1127,30 +1138,32 @@ " \n", " \n", " 0\n", - " 1\n", - " (0.0e+00 - 6.3e-07)\n", - " total\n", - " (((absorption / flux) / (total / flux)) + ((sc...\n", - " 1\n", - " 0.007741\n", + " 1\n", + " 0.000000\n", + " 0.000001\n", + " total\n", + " (((absorption / flux) / (total / flux)) + ((sc...\n", + " 1\n", + " 0.007741\n", " \n", " \n", " 1\n", - " 1\n", - " (6.3e-07 - 2.0e+01)\n", - " total\n", - " (((absorption / flux) / (total / flux)) + ((sc...\n", - " 1\n", - " 0.002619\n", + " 1\n", + " 0.000001\n", + " 20.000000\n", + " total\n", + " (((absorption / flux) / (total / flux)) + ((sc...\n", + " 1\n", + " 0.002619\n", " \n", " \n", "\n", "" ], "text/plain": [ - " cell energy [MeV] nuclide \\\n", - "0 1 (0.0e+00 - 6.3e-07) total \n", - "1 1 (6.3e-07 - 2.0e+01) total \n", + " cell energy low [MeV] energy high [MeV] nuclide \\\n", + "0 1 0.000000 0.000001 total \n", + "1 1 0.000001 20.000000 total \n", "\n", " score mean std. dev. \n", "0 (((absorption / flux) / (total / flux)) + ((sc... 1 0.007741 \n", diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index 60c2c1c40..16fa16f21 100644 --- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb +++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb @@ -382,7 +382,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nYII=\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN\nAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAADFBMVEX///9yEhLpgJFNv8Tq\nQYT7AAAAAWJLR0QAiAUdSAAAAAd0SU1FB+ACBhQuBnxwGisAAAPZSURBVGje7Zs7buMwEIZ9iey5\n0gyNjQpXKTYudIScgkdQYTfut1idwkdQkQNsYQO2Qj0sPiVK+mlQDmwgwIcgg8Cc4fCTSK5W4OeF\nkM8rHv+2I/rgxPZEPZgR7XtQxKdXYuUXJSUnBQ/9WCgo4vOSJ+WFUvF7E08mlia+rn7VcKXP8sRs\nzFX8b2MdX2y6v1Tw6MZUw4H4ojfIjD8mvn/qRL5p4+vvlMqvp2EhR8WBzfiz20hXORmP9fi/bM9E\neUFvV5H/0yRkeSbiGRfFJErxD9ENdz7Mbhig/h89fvtFdMiI/ePUIXV4lXju8K3DKv9NThOZ3q2K\nmUy6grxFES8rjeyic+FFQav+ncg3fXjH+Ts+/iibztFqOiZuZP/Z3OafPX40NGgST2r+uvQkXXp6\ncKvmr+r0e1Eef5um3+JHP3IFF1D/seNZJgaDmvY0Gav1s+2f1fqpIcublfKGt6apotG/NVx3SInW\ntLX+7Vg/Pv1YqOsnun6JSVdOXT/X7vk75f938QP+8OmSBs0fXtymMhJbf8qlPynYmpKCh7OB1fzN\nalOj1sl0ZAruHLiA+RM73pDe/VjMVP89+aTXwjyc/x5n+u991895/utrJTy8/06TXh0r/5JOa2Jm\nYmqi4r/vUm/H4wLmT+z4anhr05X+q6KUXhtzr/9qSff5L5uMT//V/NdU4YuBTPa/8P67l/6r44ds\n+hYuoP5jx9ciy6XTWlibBrmx8V/TdMfjkP+6pOsu/lvM9N90sf7r+f6m/65n+S8p/itN15v0UkW3\n/+48+PRfJX6S9Joo4g+G/1qYG9KroqP/WypcuvyXPf13wH89/hHef7MB6R3Cqn55U4rv4kfH3zaS\ngQuYP7HjVf89tXrbO+hfLdr+Ozv/SP1dgtQ/Ov8C+i/3+q/Zf2D/HWi6bjT6rym9I/v/03/b+LHS\n4cTg/utTsV7/net/Afzz4f0XGX84/2j9xZ4/sePR/of2X7D/o+vPo/sv6h9B/Bfxr9j1Hz2eN/hO\n8/wfff4A848+f/1A/530/I0+/8PvH9D3H9HnT+R49P0b+v4PfP/4E/wXfP8Mvf9G37/D/ovuP8Se\nP7Hj0f0vdP8tqP9O339cyv7p3P1fdP8Z3v9G999j13/seMax8x/o+ZN7+O+E8zdP/8XOf8Hnz9Dz\nb7HnT+x49PxlCp7/BM+fOv13wvnXBfivt2lMvD8TyH/Hnb+Gz3+j589jz5/Y8ej9h4D+W7qQmf57\nefqv239n3T+C7z+h969i13/seMax+3/o/cMcu/8Y2H9n3p+J6r98pv8m4fwXuH+M3n+OO3++AX9c\nlR+4PhbRAAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDE2LTAyLTA2VDE1OjQ2OjA2LTA1OjAwkB0d7wAA\nACV0RVh0ZGF0ZTptb2RpZnkAMjAxNi0wMi0wNlQxNTo0NjowNi0wNTowMOFApVMAAAAASUVORK5C\nYII=\n", "text/plain": [ "" ] @@ -571,8 +571,9 @@ " 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: ea9fb637f63f9374c7436456141afa850b84acf9\n", - " Date/Time: 2016-01-14 07:08:19\n", + " Git SHA1: 34381b40a9445a727e360873aaa6ef892af1cb6a\n", + " Date/Time: 2016-02-06 15:46:08\n", + " MPI Processes: 1\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -636,20 +637,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 1.2110E+00 seconds\n", - " Reading cross sections = 9.4900E-01 seconds\n", - " Total time in simulation = 1.0453E+01 seconds\n", - " Time in transport only = 1.0440E+01 seconds\n", - " Time in inactive batches = 1.5590E+00 seconds\n", - " Time in active batches = 8.8940E+00 seconds\n", - " Time synchronizing fission bank = 4.0000E-03 seconds\n", + " Total time for initialization = 3.1900E-01 seconds\n", + " Reading cross sections = 8.7000E-02 seconds\n", + " Total time in simulation = 4.8710E+00 seconds\n", + " Time in transport only = 4.8580E+00 seconds\n", + " Time in inactive batches = 7.1900E-01 seconds\n", + " Time in active batches = 4.1520E+00 seconds\n", + " Time synchronizing fission bank = 3.0000E-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", + " 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 = 1.1681E+01 seconds\n", - " Calculation Rate (inactive) = 8017.96 neutrons/second\n", - " Calculation Rate (active) = 4216.33 neutrons/second\n", + " Total time elapsed = 5.1990E+00 seconds\n", + " Calculation Rate (inactive) = 17385.3 neutrons/second\n", + " Calculation Rate (active) = 9031.79 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -774,13 +775,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.21161313]]\n", + "[[[ 0.18257268]]\n", "\n", - " [[ 0.07979747]]\n", + " [[ 0.07111957]]\n", "\n", - " [[ 0.40532194]]\n", + " [[ 0.40880276]]\n", "\n", - " [[ 0.19458598]]]\n" + " [[ 0.16407535]]]\n" ] } ], @@ -806,253 +807,287 @@ "
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mesh 1energy [MeV](mesh 1, x)(mesh 1, y)(mesh 1, z)energy low [MeV]energy high [MeV]scoremeanstd. dev.
xyz
0111(0.0e+00 - 6.3e-07)fission0.0001650.0000350 1 1 1 0.000000 0.000001 fission 0.000202 0.000037
1111(0.0e+00 - 6.3e-07)nu-fission0.0004030.0000851 1 1 1 0.000000 0.000001 nu-fission 0.000492 0.000090
2111(6.3e-07 - 2.0e+01)fission0.0000770.0000042 1 1 1 0.000001 20.000000 fission 0.000076 0.000004
3111(6.3e-07 - 2.0e+01)nu-fission0.0002070.0000113 1 1 1 0.000001 20.000000 nu-fission 0.000204 0.000010
4121(0.0e+00 - 6.3e-07)fission0.0003500.0000434 1 2 1 0.000000 0.000001 fission 0.000375 0.000039
5121(0.0e+00 - 6.3e-07)nu-fission0.0008530.0001055 1 2 1 0.000000 0.000001 nu-fission 0.000914 0.000094
6121(6.3e-07 - 2.0e+01)fission0.0001060.0000156 1 2 1 0.000001 20.000000 fission 0.000107 0.000013
7121(6.3e-07 - 2.0e+01)nu-fission0.0002740.0000397 1 2 1 0.000001 20.000000 nu-fission 0.000278 0.000032
8131(0.0e+00 - 6.3e-07)fission0.0005520.0000608 1 3 1 0.000000 0.000001 fission 0.000564 0.000056
9131(0.0e+00 - 6.3e-07)nu-fission0.0013460.0001469 1 3 1 0.000000 0.000001 nu-fission 0.001374 0.000137
10131(6.3e-07 - 2.0e+01)fission0.0001480.000008 1 3 1 0.000001 20.000000 fission 0.000149 0.000007
11131(6.3e-07 - 2.0e+01)nu-fission0.0003840.000021 1 3 1 0.000001 20.000000 nu-fission 0.000388 0.000018
12141(0.0e+00 - 6.3e-07)fission0.0006820.000054 1 4 1 0.000000 0.000001 fission 0.000669 0.000044
13141(0.0e+00 - 6.3e-07)nu-fission0.0016620.000132 1 4 1 0.000000 0.000001 nu-fission 0.001631 0.000108
14141(6.3e-07 - 2.0e+01)fission0.0001620.000012 1 4 1 0.000001 20.000000 fission 0.000165 0.000011
15141(6.3e-07 - 2.0e+01)nu-fission0.0004240.000031 1 4 1 0.000001 20.000000 nu-fission 0.000433 0.000029
16151(0.0e+00 - 6.3e-07)fission0.0009110.000076 1 5 1 0.000000 0.000001 fission 0.000932 0.000069
17151(0.0e+00 - 6.3e-07)nu-fission0.0022210.000186 1 5 1 0.000000 0.000001 nu-fission 0.002270 0.000168
18151(6.3e-07 - 2.0e+01)fission0.0001780.000013 1 5 1 0.000001 20.000000 fission 0.000183 0.000011
19151(6.3e-07 - 2.0e+01)nu-fission0.0004640.000032 1 5 1 0.000001 20.000000 nu-fission 0.000477 0.000028
\n", "
" ], "text/plain": [ - " mesh 1 energy [MeV] score mean std. dev.\n", - " x y z \n", - "0 1 1 1 (0.0e+00 - 6.3e-07) fission 0.000165 0.000035\n", - "1 1 1 1 (0.0e+00 - 6.3e-07) nu-fission 0.000403 0.000085\n", - "2 1 1 1 (6.3e-07 - 2.0e+01) fission 0.000077 0.000004\n", - "3 1 1 1 (6.3e-07 - 2.0e+01) nu-fission 0.000207 0.000011\n", - "4 1 2 1 (0.0e+00 - 6.3e-07) fission 0.000350 0.000043\n", - "5 1 2 1 (0.0e+00 - 6.3e-07) nu-fission 0.000853 0.000105\n", - "6 1 2 1 (6.3e-07 - 2.0e+01) fission 0.000106 0.000015\n", - "7 1 2 1 (6.3e-07 - 2.0e+01) nu-fission 0.000274 0.000039\n", - "8 1 3 1 (0.0e+00 - 6.3e-07) fission 0.000552 0.000060\n", - "9 1 3 1 (0.0e+00 - 6.3e-07) nu-fission 0.001346 0.000146\n", - "10 1 3 1 (6.3e-07 - 2.0e+01) fission 0.000148 0.000008\n", - "11 1 3 1 (6.3e-07 - 2.0e+01) nu-fission 0.000384 0.000021\n", - "12 1 4 1 (0.0e+00 - 6.3e-07) fission 0.000682 0.000054\n", - "13 1 4 1 (0.0e+00 - 6.3e-07) nu-fission 0.001662 0.000132\n", - "14 1 4 1 (6.3e-07 - 2.0e+01) fission 0.000162 0.000012\n", - "15 1 4 1 (6.3e-07 - 2.0e+01) nu-fission 0.000424 0.000031\n", - "16 1 5 1 (0.0e+00 - 6.3e-07) fission 0.000911 0.000076\n", - "17 1 5 1 (0.0e+00 - 6.3e-07) nu-fission 0.002221 0.000186\n", - "18 1 5 1 (6.3e-07 - 2.0e+01) fission 0.000178 0.000013\n", - "19 1 5 1 (6.3e-07 - 2.0e+01) nu-fission 0.000464 0.000032" + " (mesh 1, x) (mesh 1, y) (mesh 1, z) energy low [MeV] \\\n", + "0 1 1 1 0.000000 \n", + "1 1 1 1 0.000000 \n", + "2 1 1 1 0.000001 \n", + "3 1 1 1 0.000001 \n", + "4 1 2 1 0.000000 \n", + "5 1 2 1 0.000000 \n", + "6 1 2 1 0.000001 \n", + "7 1 2 1 0.000001 \n", + "8 1 3 1 0.000000 \n", + "9 1 3 1 0.000000 \n", + "10 1 3 1 0.000001 \n", + "11 1 3 1 0.000001 \n", + "12 1 4 1 0.000000 \n", + "13 1 4 1 0.000000 \n", + "14 1 4 1 0.000001 \n", + "15 1 4 1 0.000001 \n", + "16 1 5 1 0.000000 \n", + "17 1 5 1 0.000000 \n", + "18 1 5 1 0.000001 \n", + "19 1 5 1 0.000001 \n", + "\n", + " energy high [MeV] score mean std. dev. \n", + "0 0.000001 fission 0.000202 0.000037 \n", + "1 0.000001 nu-fission 0.000492 0.000090 \n", + "2 20.000000 fission 0.000076 0.000004 \n", + "3 20.000000 nu-fission 0.000204 0.000010 \n", + "4 0.000001 fission 0.000375 0.000039 \n", + "5 0.000001 nu-fission 0.000914 0.000094 \n", + "6 20.000000 fission 0.000107 0.000013 \n", + "7 20.000000 nu-fission 0.000278 0.000032 \n", + "8 0.000001 fission 0.000564 0.000056 \n", + "9 0.000001 nu-fission 0.001374 0.000137 \n", + "10 20.000000 fission 0.000149 0.000007 \n", + "11 20.000000 nu-fission 0.000388 0.000018 \n", + "12 0.000001 fission 0.000669 0.000044 \n", + "13 0.000001 nu-fission 0.001631 0.000108 \n", + "14 20.000000 fission 0.000165 0.000011 \n", + "15 20.000000 nu-fission 0.000433 0.000029 \n", + "16 0.000001 fission 0.000932 0.000069 \n", + "17 0.000001 nu-fission 0.002270 0.000168 \n", + "18 20.000000 fission 0.000183 0.000011 \n", + "19 20.000000 nu-fission 0.000477 0.000028 " ] }, "execution_count": 25, @@ -1077,9 +1112,9 @@ "outputs": [ { "data": { - "image/png": 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vRAwAFwIbJB1ZsA42Qb4/bN3G52zrNRsRvhvozaV7yVoMjfIsSHkOr7F+d1re\nK2luRHxf0iuARwEiYh+wLy3fI+khYCFwT3XFhoeH6evrA6Cnp4f+/v5KU7V8Is30NIw1Px1Vf6dn\nVnqs5+t401CiVGr/8XZaurw8OjpKIw3HaUiaDTwAnEbWCtgCLI+IHbk8Q8DKiBiStARYHRFLGpWV\n9EngRxHxCUmXAD0RcYmko4HHI+KgpOOBu4DXRMQTVfXyOI0m/My7dRufs51lXHNPRcQBSSuBTcAs\n4Np00V+Rtl8TERslDUkaAZ4Gzm1UNu3648CNkt4PjAJnpvVvAf5I0n7gOWBFdcAwM7P28YjwaWo8\nv6ZKpVKuCT9132NWi8/ZzuIR4WZmNmFuaUxTvj9s3aZVr9M46ih47LHWfFc38/s0zKyjjefHh3+0\ntJ5vT1lF/tE7s+5QancFZhwHDTMzK8x9GtOU+zRsJvD5N3XcpzHDBCo2ucuEv+f5/5rZ9OfbU9OU\niOwn2Bg+pTvvHHMZOWBYG51zTqndVZhxHDTMrGsND7e7BjOP+zSmKfdpmNlEeES4mZlNmIOGVXic\nhnUbn7Ot56BhZmaF+ZHbaWzsc/kMjvk7jjpqzEXMJk2pNIhfE95a7gi3CndqW7fxOTt1xt0RLmmp\npJ2SHpR0cZ08a9L2bZIGmpWVNEfSbZK+LelWST25bZem/DslnT72Q7XxK7W7AmZjVGp3BWachkFD\n0ixgLbAUWAQsl3RSVZ4h4MSIWAicB1xdoOwlwG0R8UrgjpRG0iLgrJR/KXCVJPe7tMy97a6A2Rj5\nnG21ZhfkxcBIRIxGxH7gBmBZVZ4zgPUAEbEZ6JE0t0nZSpn057vS8jLg+ojYHxGjwEjaj7WE36xr\nnUlSzQ/8Xt1tatULOmaYZkFjPvBILr0rrSuSZ16DssdExN60vBc4Ji3PS/kafZ+ZzTARUfNz2WWX\n1d3mfs+p0ezpqaJ/60VCumrtLyJCUqPv8f/5SdboF5i0qu42/yO0TjM6OtruKsw4zYLGbqA3l+7l\nhS2BWnkWpDyH11i/Oy3vlTQ3Ir4v6RXAow32tZsa3PRsPf+dWydav35980w2aZoFjW8BCyX1AXvI\nOqmXV+W5BVgJ3CBpCfBEROyV9KMGZW8BzgE+kf68Obd+g6RPkd2WWghsqa5UrcfAzMxs6jUMGhFx\nQNJKYBMwC7g2InZIWpG2XxMRGyUNSRoBngbObVQ27frjwI2S3g+MAmemMtsl3QhsBw4A53tAhplZ\n5+jKwX0Nu439AAAE80lEQVRmZtYeHgMxDUm6QNJ2SY9J+oNxlP/6VNTLbDwk/aykeyXdLen48Zyf\nklZJOm0q6jfTuKUxDUnaAZwWEXvaXReziZJ0CTArIj7W7rqYWxrTjqTPAMcD/yTpg5KuTOvfI+n+\n9Ivtq2ndqyVtlrQ1TQFzQlr/VPpTkq5I5e6TdGZaPyipJOkLknZI+lx7jta6gaS+dJ78H0n/JmmT\npBenc+gNKc/Rkh6uUXYI+F3gA5LuSOvK5+crJN2Vzt/7Jb1J0mGS1uXO2d9NeddJ+tW0fJqke9L2\nayW9KK0flXR5atHcJ+lVrfkb6i4OGtNMRPwW2dNqg8DjPD/O5aPA6RHRD/xyWrcC+KuIGADewPOP\nN5fL/ApwMvA64BeBK9Jof4B+sn/Mi4DjJb1pqo7JpoUTgbUR8RqyqQd+lew8a3irIyI2Ap8BPhUR\n5dtL5TK/DvxTOn9fB2wDBoB5EfHaiHgdcF2uTEh6cVp3Zto+G/hALs8PIuINZNMhXTTBY56WHDSm\nL+U+AF8H1kv6Xzz/1Nw3gA+nfo++iHi2ah+nABsi8yjwVeDnyP5xbYmIPenptnuBvik9Gut2D0fE\nfWn5bsZ+vtR6zH4LcK6ky4DXRcRTwENkP2LWSHo78GTVPl6V6jKS1q0H3pLLc1P6855x1HFGcNCY\n3iq/4iLiA8Afkg2evFvSnIi4nqzV8QywUdJba5Sv/sda3ud/5dYdxO9mscZqnS8HyB7HB3hxeaOk\n69Itp39stMOI+BrwZrIW8jpJZ0fEE2St4xLwW8Bnq4tVpatnqijX0+d0HQ4a01vlgi/phIjYEhGX\nAT8AFkg6DhiNiCuBLwGvrSr/NeCsdJ/4p8h+kW2h9q8+s7EaJbstCvBr5ZURcW5EDETELzUqLOlY\nsttJnyULDq+X9HKyTvObyG7JDuSKBPAA0FfuvwPOJmtBW0GOpNNTVH0APilpIdkF//aIuE/ZO07O\nlrQf+A/gY7nyRMQXJf082b3iAH4/Ih5VNsV99S82P4ZnjdQ6X/6cbJDvecCXa+SpV768/FbgonT+\nPgn8JtlMEtfp+VcqXPKCnUT8l6RzgS9Imk32I+gzdb7D53QNfuTWzMwK8+0pMzMrzEHDzMwKc9Aw\nM7PCHDTMzKwwBw0zMyvMQcPMzApz0DAzs8IcNMzaKA0wM+saDhpmYyTpJyV9OU0zf7+kMyX9nKR/\nSes2pzwvTvMo3Zem4h5M5Ycl3ZKm+r5N0n+T9Nep3D2SzmjvEZrV5185ZmO3FNgdEe8EkPRSYCvZ\ndNt3SzoCeBb4IHAwIl6X3s1wq6RXpn0MAK+NiCck/SlwR0S8T1IPsFnS7RHx45YfmVkTbmmYjd19\nwNskfVzSKcDPAP8REXcDRMRTEXEQeBPwubTuAeC7wCvJ5jS6Lc3ICnA6cImkrcCdwE+QzUZs1nHc\n0jAbo4h4UNIA8E7gT8gu9PXUmxH46ar0r0TEg5NRP7Op5JaG2RhJegXwbET8LdlMrYuBuZL+e9p+\npKRZZFPLvzeteyVwLLCTQwPJJuCC3P4HMOtQbmmYjd1ryV59+xywj+x1oYcBV0p6CfBjstfjXgVc\nLek+shcOnRMR+yVVT7v9x8DqlO8w4DuAO8OtI3lqdDMzK8y3p8zMrDAHDTMzK8xBw8zMCnPQMDOz\nwhw0zMysMAcNMzMrzEHDzMwKc9AwM7PC/j9cp/PXFesviwAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1102,26 +1137,18 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python2.7/dist-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", - " if self._edgecolors == str('face'):\n" - ] - }, { "data": { - "image/png": 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33sJ/DJxVuO24MQ9WamvQqt/TnAqsrXm/Dji5hTpTgSktxNabAtTOPrNrX6VG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\nAAAASUVORK5CYII=\n", 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fZB/HtS+yjx0lYsoMFAMwr0TMnSXrGiInTTOzBM8eOL5gyV3D2o7h4KRpZh23p6d7L2o6\naZpZx+0Zq+9IFuCkaWYdN+CkaWZW3J4uTi3de2RmVpluPj33vOdm1nF76Cm0NCJpgaQNkjZKOqdJ\nmYvz7++SNLtdrKQvSFqfl/+WpCPy7dMlPS3pjny5tN2xOWmaWcc9y/hCSz1JPcAlwAJgFnC6pOPq\nyiwEZkZEH3AWcFmB2OuB4yPitcCvgHNrdrkpImbny9ntjs1J08w6bg8HFFoamEOWxPojYjewgmye\n8lqnAFcARMRtwARJk1vFRsQNEfFcHn8bMK3ssTlpmlnHDeH0fCqwuWZ9S76tSJkpBWIBPgSsqlmf\nkZ+ar5H0xnbH5htBZtZxza5Xrl3zFGvX/K5VaBSsotQcY5LOA3ZFxJX5pm1Ab0Q8JulE4FpJx0dE\n0/eenTTNrOOaPad5wvzDOWH+4c+v/89lD9cX2Qr01qz3kvUYW5WZlpcZ1ypW0geBhcBbB7dFxC7y\ndzkj4nZJ9wJ9wO1NDm00J82fpRW/5w/Sq3hfesiL3vdUehBwN69Ojnkp+/xCtbWDSckxO5nQvlCd\nR3hpcgxA/xHpl5ImHbo9Oebw+3Ynx3BSeggr00N2P1iiHmBcmaG6T08snz6eSkNDeE5zLdAnaTpZ\nL/A09j2KlcASYIWkucDOiNgu6ZFmsZIWAJ8E5kXE80OmSJoIPBYReyQdTZYw72vVwFGcNM1srCr7\nnGZEDEhaAlxHNl/l5RGxXtLi/PvlEbFK0kJJm4CngDNbxea7/jIwnmwiR4Af53fK5wHLJO0GngMW\nR0TLWZOcNM2s43Y1eJyoqIhYDayu27a8bn1J0dh8e1+T8lcDV6e0z0nTzDrO756bmSXwu+dmZgm6\n+d1zJ00z6zgnTTOzBL6maWaWYBcHVt2EYeOkaWYd59NzM7MEPj03M0vgR47MzBL49LwS49KKP9O+\nyD7Sx8PguVsPKVERPDDh2PSYmekHdeXU/5Qc82ruTo6ZQMvXc5vq21g/YE0BR6SHbJj38uSYY7/4\nQHpFc9NDxpUb8wU+WCImdVyV/1GijgacNM3MEjhpmpkleNaPHJmZFeeepplZgm5Omm0nVpP0V5Je\nMhKNMbPuMEBPoWUsKtLTnAT8VNLtwFeB6yKi6ORHZrYf6ubnNNv2NCPiPOAYsoT5QWCjpAslvWKY\n22ZmY9QQpvAd9QrNe55Psv4QsB3YA7wE+A9JXxjGtpnZGDWUpClpgaQNkjZKOqdJmYvz7++SNLtd\nrKQvSFqfl/+WpCNqvjs3L79B0sntjq3INc2/lvQz4PPAzcCrIuIjwB8A724Xb2b7n2cZX2ipJ6kH\nuARYAMwCTpd0XF2ZhcDMfN6fs4DLCsReDxwfEa8FfgWcm8fMIpu1clYed6mklnmxyIWHI4F3R8Re\nr0tExHOS3lUg3sz2M0O4pjkH2BQR/QCSVgCLgPU1ZU4BrgCIiNskTZA0GZjRLDYibqiJvw34s/zz\nIuCqiNgN9OczXM4Bbm3WwCLXNM+vT5g1361rF29m+58hnJ5PBTbXrG/JtxUpM6VALMCHgFX55yl5\nuXYxz+veW1xmVpkh3OQp+mSOyuxc0nnAroi4smwbnDTNrOOaPYO5bc1Gtq3Z1Cp0K9Bbs97L3j3B\nRmWm5WXGtYqV9EFgIfDWNvva2qqBozhpTh/+KqaViPleybremR7yogP2JMesY1ZyzO84ODmmh/S2\nAWztuyU5ZuoPH02OOfbR9BGLfvDxP0qOOZjfJcf0nrq5faEGyvwcqvoX3uya5qT5xzFp/gv3dW5f\ndl19kbVAn6TpwDaymzSn15VZCSwBVkiaC+yMiO2SHmkWK2kB8ElgXkQ8U7evKyVdRHZa3gf8pNWx\njeKkaWZjVdnT84gYkLQEuA7oAS6PiPWSFuffL4+IVZIW5jdtngLObBWb7/rLwHjgBkkAP46IsyNi\nnaRvAuuAAeDsdi/vOGmaWcftavA4UVERsRpYXbdted36kqKx+fa+FvVdCFxYtH1OmmbWcWP1vfIi\nnDTNrOO6+d3z7j0yM6vMWH2vvAgnTTPrOCdNM7MEvqZpZpbA1zTNzBIM5ZGj0c5J08w6zqfnZmYJ\nfHpuZpbAd88r0Z9W/MnXpFexIT2EY0vEAPxHeshzEw5Jjjlq0rbkmFfQctSZhvpLDqjyff44OWb6\nvP7kmJt4U3JMmcE3jiL9530YTyTHAPxuXvrAKr1P1Q8QNDKcNM3MEjhpliDpq8A7gB0R8ep825HA\nN4CXk3Ul3xsRO4erDWZWjWc5sOomDJtCs1GW9DWyiYpqfQq4ISKOAW7M182sy+z3U/iWERE3AY/V\nbX5+QqT8v386XPWbWXW6OWmO9DXNSRGxPf+8HZg0wvWb2Qjwc5rDICJCUosRki+r+fw64A+Hu0lm\n+50f/Qh+dFPn9+vnNDtnu6TJEfGQpKOAHc2LfmTEGmW2v3rzm7Nl0AWf7cx+x+qpdxHDeSOokZXA\nGfnnM4BrR7h+MxsB3XxNc9iSpqSrgFuAV0raLOlM4HPA2yT9CnhLvm5mXebZXeMLLY1IWiBpg6SN\nks5pUubi/Pu7JM1uFyvpPZJ+IWmPpBNrtk+X9LSkO/Ll0nbHNmyn5xFRP+3moPRXQsxsTNkzUC61\nSOoBLiHLE1uBn0paWTOrJJIWAjMjok/S68lugMxtE3s3cCqwnH1tiojZDbY31L1Xa82sMnsGSp96\nzyFLYv0AklYAi4D1NWWef3QxIm6TNEHSZGBGs9iI2JBvK9uu5430NU0z2w/sGegptDQwFdhcs74l\n31akzJQCsY3MyE/N10h6Y7vCo7in+fPE8iUG7HgmPaT0T2xCiZgS7btzT+GzjOft7Elv3NOkDx4B\ncFCJQTF+x0ElYtLbNz11kBjgQaYkx+wq+YrhExyWHLPwkO8mRjyUXEcjA7sb9zTj5h8Rt7R8xqnF\nY4h7GXqXMbMN6I2Ix/JrnddKOj4imo6qMoqTppmNVc/taZJa5r4lWwZ9cZ9nnLYCvTXrvWQ9xlZl\npuVlxhWI3UtE7AJ25Z9vl3Qv0Afc3izGp+dm1nkDPcWWfa0F+vK72uOB08geVay1EvgAgKS5wM78\nTcMisVDTS5U0Mb+BhKSjyRLmfa0OzT1NM+u8Z8qllogYkLQEuA7oAS6PiPWSFuffL4+IVZIWStoE\nPAWc2SoWQNKpwMXAROC7ku6IiLcD84BlknYDzwGL24285qRpZp03UD40IlYDq+u2La9bX1I0Nt9+\nDXBNg+1XA1entM9J08w6bwhJc7Rz0jSzznPSNDNLsLvqBgwfJ00z67w9VTdg+Dhpmlnn+fTczCxB\nmbftxggnTTPrPPc0zcwSdHHSVETR9+NHTjZ30KrEqJnpFU3rS495VXpI6bgyM8KXmd/z0PSQo+f9\nokRFcHCJATvKDFQxocQPr3evAXKKKTOYyCzWJccAvIa7k2NWcFpS+R/oXUTEkAbDkBRcXTCv/JmG\nXN9Ic0/TzDrPjxyZmSXwI0dmZgm6+Jqmk6aZdZ4fOTIzS+CepplZAidNM7METppmZgn8yJGZWYIu\nfuTIE6uZWec9U3BpQNICSRskbZR0TpMyF+ff3yVpdrtYSe+R9AtJe/Kpemv3dW5efoOkk9sdmpOm\nmXXeQMGlTj4z5CXAAmAWcLqk4+rKLARmRkQfcBZwWYHYu4FTgR/V7WsW2ayVs/K4SyW1zItOmmbW\nebsLLvuaA2yKiP6I2A2sABbVlTkFuAIgIm4DJkia3Co2IjZExK8a1LcIuCoidkdEP7Ap309To/ia\nZn9i+ZenV7GlxNXq141Lj4Fyg2+UGIOELSViSrjvoeNHpiKAY9NDHnhxesxdd85NjnnxgkeTY+48\ndHb7Qg18vfE84S294cBbStU1ZOWvaU6FvUZO2QK8vkCZqcCUArH1pgC3NthXU6M4aZrZmFX+kaOi\nw64N58hILdvgpGlmndcsaW5dA9vWtIrcCvTWrPey7/lTfZlpeZlxBWLb1Tct39aUk6aZdV6zK18v\nm58tg9Yuqy+xFuiTNB3YRnaT5vS6MiuBJcAKSXOBnRGxXdIjBWJh717qSuBKSReRnZb3AT9pcWRO\nmmY2DJ4tFxYRA5KWANcBPcDlEbFe0uL8++URsUrSQkmbgKeAM1vFAkg6FbgYmAh8V9IdEfH2iFgn\n6ZvAOrL+8dnRZmR2J00z67whvEYZEauB1XXbltetLykam2+/BrimScyFwIVF2+ekaWad59cozcwS\ndPFrlE6aZtZ5HuXIzCyBk6aZWQJf0zQzS1DykaOxwEnTzDrPp+dVSO3fPzAsrdjH/5s1MvVAuRn9\n3lci5qESMZNLxMDI/WN6uETMk+khz9x6ZHpMmcFbACakh6ze+e6SlQ2RT8/NzBL4kSMzswQ+PTcz\nS+CkaWaWwNc0zcwS+JEjM7MEPj03M0vg03MzswR+5MjMLIFPz83MEjhpmpkl6OJrmi+qugFm1oUG\nCi4NSFogaYOkjZLOaVLm4vz7uyTNbhcr6UhJN0j6laTrJU3It0+X9LSkO/Ll0naH5qRpZqOGpB7g\nEmABMAs4XdJxdWUWAjMjog84C7isQOyngBsi4hjgxnx90KaImJ0vZ7dr4yg+PX86sfwIHcrDG0sG\nTk8POWBcesxX0kNKmTlC9QDcWSKmxIhApX6F7ikRU2I0pdJ1LShZV3XmkCWxfgBJK4BFwPqaMqcA\nVwBExG2SJkiaDMxoEXsKMC+PvwJYw96JszD3NM1sNJkKbK5Z35JvK1JmSovYSRGxPf+8HZhUU25G\nfmq+RtIb2zVw2Lpnkr4KvAPYERGvzrctBf4C+E1e7NyI+N5wtcHMqtLsTtAP86WpKFiBCpbZZ38R\nEZIGt28DeiPiMUk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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1131,7 +1158,7 @@ "source": [ "# Extract thermal nu-fission rates from pandas\n", "fiss = df[df['score'] == 'nu-fission']\n", - "fiss = fiss[fiss['energy [MeV]'] == '(0.0e+00 - 6.3e-07)']\n", + "fiss = fiss[fiss['energy low [MeV]'] == 0.0]\n", "\n", "# Extract mean and reshape as 2D NumPy arrays\n", "mean = fiss['mean'].reshape((17,17))\n", @@ -1205,148 +1232,148 @@ " \n", " \n", " \n", - " 0\n", - " 10000\n", - " U-235\n", - " scatter-Y0,0\n", - " 0.038027\n", - " 0.001350\n", + " 0 \n", + " 10000\n", + " U-235\n", + " scatter-Y0,0\n", + " 0.037095\n", + " 0.001150\n", " \n", " \n", - " 1\n", - " 10000\n", - " U-235\n", - " scatter-Y1,-1\n", - " 0.000071\n", - " 0.000383\n", + " 1 \n", + " 10000\n", + " U-235\n", + " scatter-Y1,-1\n", + " 0.000266\n", + " 0.000323\n", " \n", " \n", - " 2\n", - " 10000\n", - " U-235\n", - " scatter-Y1,0\n", - " -0.000579\n", - " 0.000250\n", + " 2 \n", + " 10000\n", + " U-235\n", + " scatter-Y1,0\n", + " -0.000417\n", + " 0.000274\n", " \n", " \n", - " 3\n", - " 10000\n", - " U-235\n", - " scatter-Y1,1\n", - " -0.000176\n", - " 0.000282\n", + " 3 \n", + " 10000\n", + " U-235\n", + " scatter-Y1,1\n", + " -0.000228\n", + " 0.000237\n", " \n", " \n", - " 4\n", - " 10000\n", - " U-235\n", - " scatter-Y2,-2\n", - " 0.000105\n", - " 0.000224\n", + " 4 \n", + " 10000\n", + " U-235\n", + " scatter-Y2,-2\n", + " 0.000026\n", + " 0.000199\n", " \n", " \n", - " 5\n", - " 10000\n", - " U-235\n", - " scatter-Y2,-1\n", - " -0.000077\n", - " 0.000221\n", + " 5 \n", + " 10000\n", + " U-235\n", + " scatter-Y2,-1\n", + " -0.000115\n", + " 0.000185\n", " \n", " \n", - " 6\n", - " 10000\n", - " U-235\n", - " scatter-Y2,0\n", - " 0.000134\n", - " 0.000181\n", + " 6 \n", + " 10000\n", + " U-235\n", + " scatter-Y2,0\n", + " 0.000151\n", + " 0.000159\n", " \n", " \n", - " 7\n", - " 10000\n", - " U-235\n", - " scatter-Y2,1\n", - " -0.000117\n", - " 0.000308\n", + " 7 \n", + " 10000\n", + " U-235\n", + " scatter-Y2,1\n", + " -0.000122\n", + " 0.000280\n", " \n", " \n", - " 8\n", - " 10000\n", - " U-235\n", - " scatter-Y2,2\n", - " 0.000039\n", - " 0.000211\n", + " 8 \n", + " 10000\n", + " U-235\n", + " scatter-Y2,2\n", + " 0.000008\n", + " 0.000181\n", " \n", " \n", - " 9\n", - " 10000\n", - " U-238\n", - " scatter-Y0,0\n", - " 2.340987\n", - " 0.014310\n", + " 9 \n", + " 10000\n", + " U-238\n", + " scatter-Y0,0\n", + " 2.328632\n", + " 0.013107\n", " \n", " \n", " 10\n", - " 10000\n", - " U-238\n", - " scatter-Y1,-1\n", - " 0.022817\n", - " 0.002458\n", + " 10000\n", + " U-238\n", + " scatter-Y1,-1\n", + " 0.024530\n", + " 0.002272\n", " \n", " \n", " 11\n", - " 10000\n", - " U-238\n", - " scatter-Y1,0\n", - " 0.001589\n", - " 0.003051\n", + " 10000\n", + " U-238\n", + " scatter-Y1,0\n", + " -0.000059\n", + " 0.002804\n", " \n", " \n", " 12\n", - " 10000\n", - " U-238\n", - " scatter-Y1,1\n", - " -0.027146\n", - " 0.002511\n", + " 10000\n", + " U-238\n", + " scatter-Y1,1\n", + " -0.027990\n", + " 0.002536\n", " \n", " \n", " 13\n", - " 10000\n", - " U-238\n", - " scatter-Y2,-2\n", - " -0.004146\n", - " 0.001722\n", + " 10000\n", + " U-238\n", + " scatter-Y2,-2\n", + " -0.004861\n", + " 0.001575\n", " \n", " \n", " 14\n", - " 10000\n", - " U-238\n", - " scatter-Y2,-1\n", - " 0.001765\n", - " 0.002474\n", + " 10000\n", + " U-238\n", + " scatter-Y2,-1\n", + " 0.000557\n", + " 0.002018\n", " \n", " \n", " 15\n", - " 10000\n", - " U-238\n", - " scatter-Y2,0\n", - " 0.006038\n", - " 0.001917\n", + " 10000\n", + " U-238\n", + " scatter-Y2,0\n", + " 0.006236\n", + " 0.001627\n", " \n", " \n", " 16\n", - " 10000\n", - " U-238\n", - " scatter-Y2,1\n", - " 0.000167\n", - " 0.001438\n", + " 10000\n", + " U-238\n", + " scatter-Y2,1\n", + " -0.000648\n", + " 0.001551\n", " \n", " \n", " 17\n", - " 10000\n", - " U-238\n", - " scatter-Y2,2\n", - " -0.001684\n", - " 0.001535\n", + " 10000\n", + " U-238\n", + " scatter-Y2,2\n", + " -0.001031\n", + " 0.001310\n", " \n", " \n", "\n", @@ -1354,24 +1381,24 @@ ], "text/plain": [ " cell nuclide score mean std. dev.\n", - "0 10000 U-235 scatter-Y0,0 0.038027 0.001350\n", - "1 10000 U-235 scatter-Y1,-1 0.000071 0.000383\n", - "2 10000 U-235 scatter-Y1,0 -0.000579 0.000250\n", - "3 10000 U-235 scatter-Y1,1 -0.000176 0.000282\n", - "4 10000 U-235 scatter-Y2,-2 0.000105 0.000224\n", - "5 10000 U-235 scatter-Y2,-1 -0.000077 0.000221\n", - "6 10000 U-235 scatter-Y2,0 0.000134 0.000181\n", - "7 10000 U-235 scatter-Y2,1 -0.000117 0.000308\n", - "8 10000 U-235 scatter-Y2,2 0.000039 0.000211\n", - "9 10000 U-238 scatter-Y0,0 2.340987 0.014310\n", - "10 10000 U-238 scatter-Y1,-1 0.022817 0.002458\n", - "11 10000 U-238 scatter-Y1,0 0.001589 0.003051\n", - "12 10000 U-238 scatter-Y1,1 -0.027146 0.002511\n", - "13 10000 U-238 scatter-Y2,-2 -0.004146 0.001722\n", - "14 10000 U-238 scatter-Y2,-1 0.001765 0.002474\n", - "15 10000 U-238 scatter-Y2,0 0.006038 0.001917\n", - "16 10000 U-238 scatter-Y2,1 0.000167 0.001438\n", - "17 10000 U-238 scatter-Y2,2 -0.001684 0.001535" + "0 10000 U-235 scatter-Y0,0 0.037095 0.001150\n", + "1 10000 U-235 scatter-Y1,-1 0.000266 0.000323\n", + "2 10000 U-235 scatter-Y1,0 -0.000417 0.000274\n", + "3 10000 U-235 scatter-Y1,1 -0.000228 0.000237\n", + "4 10000 U-235 scatter-Y2,-2 0.000026 0.000199\n", + "5 10000 U-235 scatter-Y2,-1 -0.000115 0.000185\n", + "6 10000 U-235 scatter-Y2,0 0.000151 0.000159\n", + "7 10000 U-235 scatter-Y2,1 -0.000122 0.000280\n", + "8 10000 U-235 scatter-Y2,2 0.000008 0.000181\n", + "9 10000 U-238 scatter-Y0,0 2.328632 0.013107\n", + "10 10000 U-238 scatter-Y1,-1 0.024530 0.002272\n", + "11 10000 U-238 scatter-Y1,0 -0.000059 0.002804\n", + "12 10000 U-238 scatter-Y1,1 -0.027990 0.002536\n", + "13 10000 U-238 scatter-Y2,-2 -0.004861 0.001575\n", + "14 10000 U-238 scatter-Y2,-1 0.000557 0.002018\n", + "15 10000 U-238 scatter-Y2,0 0.006236 0.001627\n", + "16 10000 U-238 scatter-Y2,1 -0.000648 0.001551\n", + "17 10000 U-238 scatter-Y2,2 -0.001031 0.001310" ] }, "execution_count": 29, @@ -1405,8 +1432,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00153535 0.0143096 ]\n", - " [ 0.00021107 0.00135025]]]\n" + "[[[ 0.00131009 0.01310707]\n", + " [ 0.00018089 0.00114976]]]\n" ] } ], @@ -1474,7 +1501,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.04318886]]]\n" + "[[[ 0.04537029]]]\n" ] } ], @@ -1517,143 +1544,143 @@ " \n", " \n", " 558\n", - " 279\n", - " absorption\n", - " 0.000102\n", - " 0.000016\n", + " 279\n", + " absorption\n", + " 0.000093\n", + " 0.000013\n", " \n", " \n", " 559\n", - " 279\n", - " scatter\n", - " 0.013889\n", - " 0.000964\n", + " 279\n", + " scatter\n", + " 0.013504\n", + " 0.000805\n", " \n", " \n", " 560\n", - " 280\n", - " absorption\n", - " 0.000087\n", - " 0.000012\n", + " 280\n", + " absorption\n", + " 0.000084\n", + " 0.000010\n", " \n", " \n", " 561\n", - " 280\n", - " scatter\n", - " 0.014347\n", - " 0.000652\n", + " 280\n", + " scatter\n", + " 0.014215\n", + " 0.000612\n", " \n", " \n", " 562\n", - " 281\n", - " absorption\n", - " 0.000087\n", - " 0.000010\n", + " 281\n", + " absorption\n", + " 0.000091\n", + " 0.000008\n", " \n", " \n", " 563\n", - " 281\n", - " scatter\n", - " 0.014283\n", - " 0.000715\n", + " 281\n", + " scatter\n", + " 0.014545\n", + " 0.000590\n", " \n", " \n", " 564\n", - " 282\n", - " absorption\n", - " 0.000111\n", - " 0.000012\n", + " 282\n", + " absorption\n", + " 0.000112\n", + " 0.000012\n", " \n", " \n", " 565\n", - " 282\n", - " scatter\n", - " 0.016374\n", - " 0.000865\n", + " 282\n", + " scatter\n", + " 0.016321\n", + " 0.000729\n", " \n", " \n", " 566\n", - " 283\n", - " absorption\n", - " 0.000090\n", - " 0.000008\n", + " 283\n", + " absorption\n", + " 0.000092\n", + " 0.000007\n", " \n", " \n", " 567\n", - " 283\n", - " scatter\n", - " 0.015839\n", - " 0.000795\n", + " 283\n", + " scatter\n", + " 0.016163\n", + " 0.000661\n", " \n", " \n", " 568\n", - " 284\n", - " absorption\n", - " 0.000103\n", - " 0.000012\n", + " 284\n", + " absorption\n", + " 0.000104\n", + " 0.000011\n", " \n", " \n", " 569\n", - " 284\n", - " scatter\n", - " 0.017182\n", - " 0.000660\n", + " 284\n", + " scatter\n", + " 0.017384\n", + " 0.000599\n", " \n", " \n", " 570\n", - " 285\n", - " absorption\n", - " 0.000111\n", - " 0.000014\n", + " 285\n", + " absorption\n", + " 0.000111\n", + " 0.000011\n", " \n", " \n", " 571\n", - " 285\n", - " scatter\n", - " 0.017565\n", - " 0.000862\n", + " 285\n", + " scatter\n", + " 0.018015\n", + " 0.000774\n", " \n", " \n", " 572\n", - " 286\n", - " absorption\n", - " 0.000125\n", - " 0.000014\n", + " 286\n", + " absorption\n", + " 0.000125\n", + " 0.000012\n", " \n", " \n", " 573\n", - " 286\n", - " scatter\n", - " 0.018128\n", - " 0.000931\n", + " 286\n", + " scatter\n", + " 0.018294\n", + " 0.000828\n", " \n", " \n", " 574\n", - " 287\n", - " absorption\n", - " 0.000124\n", - " 0.000016\n", + " 287\n", + " absorption\n", + " 0.000119\n", + " 0.000013\n", " \n", " \n", " 575\n", - " 287\n", - " scatter\n", - " 0.017253\n", - " 0.000902\n", + " 287\n", + " scatter\n", + " 0.017483\n", + " 0.000757\n", " \n", " \n", " 576\n", - " 288\n", - " absorption\n", - " 0.000119\n", - " 0.000016\n", + " 288\n", + " absorption\n", + " 0.000113\n", + " 0.000014\n", " \n", " \n", " 577\n", - " 288\n", - " scatter\n", - " 0.018482\n", - " 0.000861\n", + " 288\n", + " scatter\n", + " 0.018248\n", + " 0.000782\n", " \n", " \n", "\n", @@ -1661,26 +1688,26 @@ ], "text/plain": [ " distribcell score mean std. dev.\n", - "558 279 absorption 0.000102 0.000016\n", - "559 279 scatter 0.013889 0.000964\n", - "560 280 absorption 0.000087 0.000012\n", - "561 280 scatter 0.014347 0.000652\n", - "562 281 absorption 0.000087 0.000010\n", - "563 281 scatter 0.014283 0.000715\n", - "564 282 absorption 0.000111 0.000012\n", - "565 282 scatter 0.016374 0.000865\n", - "566 283 absorption 0.000090 0.000008\n", - "567 283 scatter 0.015839 0.000795\n", - "568 284 absorption 0.000103 0.000012\n", - "569 284 scatter 0.017182 0.000660\n", - "570 285 absorption 0.000111 0.000014\n", - "571 285 scatter 0.017565 0.000862\n", - "572 286 absorption 0.000125 0.000014\n", - "573 286 scatter 0.018128 0.000931\n", - "574 287 absorption 0.000124 0.000016\n", - "575 287 scatter 0.017253 0.000902\n", - "576 288 absorption 0.000119 0.000016\n", - "577 288 scatter 0.018482 0.000861" + "558 279 absorption 0.000093 0.000013\n", + "559 279 scatter 0.013504 0.000805\n", + "560 280 absorption 0.000084 0.000010\n", + "561 280 scatter 0.014215 0.000612\n", + "562 281 absorption 0.000091 0.000008\n", + "563 281 scatter 0.014545 0.000590\n", + "564 282 absorption 0.000112 0.000012\n", + "565 282 scatter 0.016321 0.000729\n", + "566 283 absorption 0.000092 0.000007\n", + "567 283 scatter 0.016163 0.000661\n", + "568 284 absorption 0.000104 0.000011\n", + "569 284 scatter 0.017384 0.000599\n", + "570 285 absorption 0.000111 0.000011\n", + "571 285 scatter 0.018015 0.000774\n", + "572 286 absorption 0.000125 0.000012\n", + "573 286 scatter 0.018294 0.000828\n", + "574 287 absorption 0.000119 0.000013\n", + "575 287 scatter 0.017483 0.000757\n", + "576 288 absorption 0.000113 0.000014\n", + "577 288 scatter 0.018248 0.000782" ] }, "execution_count": 33, @@ -1716,397 +1743,415 @@ "
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level 1level 2level 3(level 1, cell, id)(level 1, univ, id)(level 2, lat, id)(level 2, lat, x)(level 2, lat, y)(level 2, lat, z)(level 3, cell, id)(level 3, univ, id)distribcellscoremeanstd. dev.
cellunivlatcelluniv
idididxyzidid
01000301000100010002100000absorption0.0001130.0000130 10003 0 10001 0 0 0 10002 10000 0 absorption 0.000123 0.000012
11000301000100010002100000scatter0.0173370.0007491 10003 0 10001 0 0 0 10002 10000 0 scatter 0.017805 0.000808
21000301000101010002100001absorption0.0002040.0000212 10003 0 10001 0 1 0 10002 10000 1 absorption 0.000217 0.000020
31000301000101010002100001scatter0.0276310.0013483 10003 0 10001 0 1 0 10002 10000 1 scatter 0.028867 0.001263
41000301000102010002100002absorption0.0003190.0000254 10003 0 10001 0 2 0 10002 10000 2 absorption 0.000318 0.000020
51000301000102010002100002scatter0.0400520.0014275 10003 0 10001 0 2 0 10002 10000 2 scatter 0.040493 0.001269
61000301000103010002100003absorption0.0003880.0000226 10003 0 10001 0 3 0 10002 10000 3 absorption 0.000386 0.000018
71000301000103010002100003scatter0.0485780.0015617 10003 0 10001 0 3 0 10002 10000 3 scatter 0.048576 0.001337
81000301000104010002100004absorption0.0005110.0000308 10003 0 10001 0 4 0 10002 10000 4 absorption 0.000501 0.000026
91000301000104010002100004scatter0.0579030.0019889 10003 0 10001 0 4 0 10002 10000 4 scatter 0.057063 0.001715
101000301000105010002100005absorption0.0004810.000033 10003 0 10001 0 5 0 10002 10000 5 absorption 0.000484 0.000026
111000301000105010002100005scatter0.0612110.001989 10003 0 10001 0 5 0 10002 10000 5 scatter 0.060822 0.001581
121000301000106010002100006absorption0.0005420.000045 10003 0 10001 0 6 0 10002 10000 6 absorption 0.000532 0.000039
131000301000106010002100006scatter0.0708880.002497 10003 0 10001 0 6 0 10002 10000 6 scatter 0.069101 0.002249
141000301000107010002100007absorption0.0005870.000047 10003 0 10001 0 7 0 10002 10000 7 absorption 0.000577 0.000039
151000301000107010002100007scatter0.0781070.002794 10003 0 10001 0 7 0 10002 10000 7 scatter 0.076722 0.002335
161000301000108010002100008absorption0.0006270.000033 10003 0 10001 0 8 0 10002 10000 8 absorption 0.000649 0.000039
171000301000108010002100008scatter0.0820310.001740 10003 0 10001 0 8 0 10002 10000 8 scatter 0.081564 0.001610
181000301000109010002100009absorption0.0006670.000028 10003 0 10001 0 9 0 10002 10000 9 absorption 0.000680 0.000032
191000301000109010002100009scatter0.0885160.002037 10003 0 10001 0 9 0 10002 10000 9 scatter 0.087715 0.001959
\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 0 0 10002 10000 0 absorption \n", - "1 10003 0 10001 0 0 0 10002 10000 0 scatter \n", - "2 10003 0 10001 0 1 0 10002 10000 1 absorption \n", - "3 10003 0 10001 0 1 0 10002 10000 1 scatter \n", - "4 10003 0 10001 0 2 0 10002 10000 2 absorption \n", - "5 10003 0 10001 0 2 0 10002 10000 2 scatter \n", - "6 10003 0 10001 0 3 0 10002 10000 3 absorption \n", - "7 10003 0 10001 0 3 0 10002 10000 3 scatter \n", - "8 10003 0 10001 0 4 0 10002 10000 4 absorption \n", - "9 10003 0 10001 0 4 0 10002 10000 4 scatter \n", - "10 10003 0 10001 0 5 0 10002 10000 5 absorption \n", - "11 10003 0 10001 0 5 0 10002 10000 5 scatter \n", - "12 10003 0 10001 0 6 0 10002 10000 6 absorption \n", - "13 10003 0 10001 0 6 0 10002 10000 6 scatter \n", - "14 10003 0 10001 0 7 0 10002 10000 7 absorption \n", - "15 10003 0 10001 0 7 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 9 0 10002 10000 9 absorption \n", - "19 10003 0 10001 0 9 0 10002 10000 9 scatter \n", + " (level 1, cell, id) (level 1, univ, id) (level 2, lat, id) \\\n", + "0 10003 0 10001 \n", + "1 10003 0 10001 \n", + "2 10003 0 10001 \n", + "3 10003 0 10001 \n", + "4 10003 0 10001 \n", + "5 10003 0 10001 \n", + "6 10003 0 10001 \n", + "7 10003 0 10001 \n", + "8 10003 0 10001 \n", + "9 10003 0 10001 \n", + "10 10003 0 10001 \n", + "11 10003 0 10001 \n", + "12 10003 0 10001 \n", + "13 10003 0 10001 \n", + "14 10003 0 10001 \n", + "15 10003 0 10001 \n", + "16 10003 0 10001 \n", + "17 10003 0 10001 \n", + "18 10003 0 10001 \n", + "19 10003 0 10001 \n", "\n", - " mean std. dev. \n", - " \n", - " \n", - "0 0.000113 0.000013 \n", - "1 0.017337 0.000749 \n", - "2 0.000204 0.000021 \n", - "3 0.027631 0.001348 \n", - "4 0.000319 0.000025 \n", - "5 0.040052 0.001427 \n", - "6 0.000388 0.000022 \n", - "7 0.048578 0.001561 \n", - "8 0.000511 0.000030 \n", - "9 0.057903 0.001988 \n", - "10 0.000481 0.000033 \n", - "11 0.061211 0.001989 \n", - "12 0.000542 0.000045 \n", - "13 0.070888 0.002497 \n", - "14 0.000587 0.000047 \n", - "15 0.078107 0.002794 \n", - "16 0.000627 0.000033 \n", - "17 0.082031 0.001740 \n", - "18 0.000667 0.000028 \n", - "19 0.088516 0.002037 " + " (level 2, lat, x) (level 2, lat, y) (level 2, lat, z) \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 1 0 \n", + "3 0 1 0 \n", + "4 0 2 0 \n", + "5 0 2 0 \n", + "6 0 3 0 \n", + "7 0 3 0 \n", + "8 0 4 0 \n", + "9 0 4 0 \n", + "10 0 5 0 \n", + "11 0 5 0 \n", + "12 0 6 0 \n", + "13 0 6 0 \n", + "14 0 7 0 \n", + "15 0 7 0 \n", + "16 0 8 0 \n", + "17 0 8 0 \n", + "18 0 9 0 \n", + "19 0 9 0 \n", + "\n", + " (level 3, cell, id) (level 3, univ, id) distribcell score \\\n", + "0 10002 10000 0 absorption \n", + "1 10002 10000 0 scatter \n", + "2 10002 10000 1 absorption \n", + "3 10002 10000 1 scatter \n", + "4 10002 10000 2 absorption \n", + "5 10002 10000 2 scatter \n", + "6 10002 10000 3 absorption \n", + "7 10002 10000 3 scatter \n", + "8 10002 10000 4 absorption \n", + "9 10002 10000 4 scatter \n", + "10 10002 10000 5 absorption \n", + "11 10002 10000 5 scatter \n", + "12 10002 10000 6 absorption \n", + "13 10002 10000 6 scatter \n", + "14 10002 10000 7 absorption \n", + "15 10002 10000 7 scatter \n", + "16 10002 10000 8 absorption \n", + "17 10002 10000 8 scatter \n", + "18 10002 10000 9 absorption \n", + "19 10002 10000 9 scatter \n", + "\n", + " mean std. dev. \n", + "0 0.000123 0.000012 \n", + "1 0.017805 0.000808 \n", + "2 0.000217 0.000020 \n", + "3 0.028867 0.001263 \n", + "4 0.000318 0.000020 \n", + "5 0.040493 0.001269 \n", + "6 0.000386 0.000018 \n", + "7 0.048576 0.001337 \n", + "8 0.000501 0.000026 \n", + "9 0.057063 0.001715 \n", + "10 0.000484 0.000026 \n", + "11 0.060822 0.001581 \n", + "12 0.000532 0.000039 \n", + "13 0.069101 0.002249 \n", + "14 0.000577 0.000039 \n", + "15 0.076722 0.002335 \n", + "16 0.000649 0.000039 \n", + "17 0.081564 0.001610 \n", + "18 0.000680 0.000032 \n", + "19 0.087715 0.001959 " ] }, "execution_count": 34, @@ -2135,62 +2180,52 @@ "
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meanstd. dev.
count289.000000289.000000 289.000000 289.000000
mean0.0004190.000024 0.000418 0.000022
std0.0002370.000010 0.000239 0.000009
min0.0000150.000003 0.000018 0.000004
25%0.0002070.000017 0.000202 0.000015
50%0.0004150.000023 0.000402 0.000021
75%0.0006150.000030 0.000615 0.000027
max0.0009010.000055 0.000892 0.000044
\n", @@ -2198,16 +2233,14 @@ ], "text/plain": [ " mean std. dev.\n", - " \n", - " \n", "count 289.000000 289.000000\n", - "mean 0.000419 0.000024\n", - "std 0.000237 0.000010\n", - "min 0.000015 0.000003\n", - "25% 0.000207 0.000017\n", - "50% 0.000415 0.000023\n", - "75% 0.000615 0.000030\n", - "max 0.000901 0.000055" + "mean 0.000418 0.000022\n", + "std 0.000239 0.000009\n", + "min 0.000018 0.000004\n", + "25% 0.000202 0.000015\n", + "50% 0.000402 0.000021\n", + "75% 0.000615 0.000027\n", + "max 0.000892 0.000044" ] }, "execution_count": 35, @@ -2242,7 +2275,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 0.456115837774\n" + "Mann-Whitney Test p-value: 0.414863173548\n" ] } ], @@ -2280,7 +2313,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 4.59783355073e-42\n" + "Mann-Whitney Test p-value: 3.28554363741e-42\n" ] } ], @@ -2316,7 +2349,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n", + "/home/smharper/.local/lib/python2.7/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -2326,7 +2359,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 38, @@ -2335,9 +2368,9 @@ }, { "data": { - "image/png": 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lFF22YsUKr6ur9/Hjp3pd3SkFV0uljlFXV++jRx+RMb7mtnBsTktadVpV1YSs\n9xJUrWWOx8munhvMZxZ9b5VcLZarurDQOItV3Viocvs8c6mUOEmgWqzkCaCYDyWXwRnoApEvzrjk\nlHnBrak53GtqxhV8AW5vb/e6uno3O8xhusOc8Hl2ggiWRV8f5XB62EZzW9976k868cml0Itkrvdb\n6AW2WIM9h3KBz/fDopA4izUgdjDK5Ts0kEqJU8lFyWVYBnsxyhdnXGP8+PFT8ySBdoc5Pn781Nhz\nZ16woDYsnRzicFjMccdHtj3EoSk81wSHpr4EsmLFipwzBwzmIpmv80Ehn3HSf/PhXODzvZdC4iz0\nsyimSrloV0qcSSSXQhr0ZR9U2vvWp24cdhV79sCCBdnnjp9Ovw24jgMPvIienv72m2CihrEE86o+\nDzQA9xPcPwai96RZvnw5J598Mq2tq9i9uws4rq+NJ+lG+ZH8jLNj38rChV/hfe+bVbK50DQf235u\nuNmpnB+o5JLTUH5tDq9arMXNxofVWicMeO74Ls7Bsrq6+jztHXMKalfJjLemZpyPHfuucN/2rP0y\nSyDp+7d4VdWEvrgK+YyT/punn6s9LOkVXv2YdLXYYNughqtSSgSVEieqFlNyGaqkk4t77gb9urpT\n0qqiYNyA585XLbZixYqc554yZYabjR/w+PkuxqlzpS6IuS6+ce8tehHN9Rm3t7f7CSd80Ovq6r2u\n7pS81ZIDdZKIr9IbfKeFXMc84YQPFlRtmrn/SFeVVcpFu1LiVHJRchmyodTRR+McTHtN9oUm1XbS\n30YyderMrGO1t7eH7TYnOJwSllxa8l6k1qxZk7ddJT6m7AthdfU7+5JY+gDOoMdZXd0pOd5bemln\noF/0mYlsoL9RvhJB6m8S19Y12Av7cBvplVziVUqcSi5KLsMy1Ab9zAtPVdVhsaWJlPgqrunhhfr0\nMNnMib2IDfYiFY0x33vrfw8tHvQsy+54kN6FOb37c1XVYQX9Qi/kF30quRVSNdifOLITXfZ7G3qV\n1HCTw0j3IBuoyraU3aSjlFz2kYeSS7JSccZdeMwO87q6+rQ2ibq6U3z8+Kk+deqJHr1PTP+ULZkX\n2PiLc5LjR6IXmubm5rCE0+Lp1WKHe7QL84oVK7y/N1r6xXaw8eVvSzplwEQUJJcm7+8x15/ocr3P\nQi6oxajWGsmLeq6/eyF/n3KIs9wouSi5jKh8ySXaFTiY4+vQtAt2VVXqRmRHhRfHzEGQp6Rd0KPi\nGtNzXQxlRFwHAAAZuUlEQVQG0+mgP7F4eO4TwpjHOqzou+AH+2S3Y6QGg06d+t6CB4bmakuqqRnn\nNTWHp10E46rAgpu6ZbdZ5erSXYjszg2H+9SpMwesWiylzP8Duf7uhZQsy6WEVU6UXJRcRlS0yim9\ngT56kXbP1WNr6tT3enX1Oz2zWieoIgsutDU144bcsykzxswEFJ8Uo+NuoqWXQ726+uDwjpupeDMn\nzGzyzF5ZqYGUdXWnpJXkMt9DZoN+/3nSL4LxJYr4QaRDvTDm/lyCHn6ZveBKLe7/wNKlS2O3HSi5\nqG0onpKLksuIisYZNJpPyEgO+ZNLXDVScHHu7/pbV1fv7rmrKga6GKxZsyZnAoq/iKZKAdnxpi6s\n/ctXeFDyGh8mluzjBZ9JejVbKumkqgnr6uqzLoaFXuTi2n+C5B5f6osazGeaq5qy2AqpooqL94QT\nPpjzePlmUsiV1IulUr7rSi5KLiMqM87UhWDq1Jlu1j+tS1y1WE3N4Tl6NbVkfbHzlU4KSS75ugBH\n4wzaVprC8S3ZbSowJ3xv4z2oMov2cKt1yL4wBUkqvk0q+nmYHZpWjVZo9Ux6R4Q5YdwrBrww5jv+\nQAl/pJJLe3t7OD1Q8OMkVyl2MMklddxUMsmekii7OlLVYkouSi4jLC7OzItdVdUEX7FiRVqDfq5q\nlcGUMLK796afLxpjrobw4Ffqgd5fshrnqa7NQaN9qtNBiwfTxkx1szE5L7pBiS3arpSqHowrCeTu\nkZaa0HPs2KN9zJhJA1ZDpT6jurr6tLna8vXay5dwU8dKVeVFj1lTUzuoMTj5DLR9cP+g9PFGmT3h\nUsfJ/H/z6U9/OuvY+atG+6tlU93gB9o3CZXyXVdyUXIZUXFxJtFltZC2kegx+8exZCeYuGqx9JuW\npSeIqqoJfeddsWKFm431/qqyuITSX12UasRPta30/ypOrxYLYs2sOuzvJdY/6DOopjMbm7drd1R6\n9WRLVomkv/on+8Zv/Z0V0pN7dL9cbRmDbQgfaPv29vawPS59TNP48VMH/H+zYsUKr6nJrobM/cMl\nvlv5UN/bYFTKd13JRcllRBUjuUT1/4o+JbaqIrs6LfsCkdmgH1f1Fk0QqTaelLg6+Oj2qbaZ6upD\n05JK9EKX2aAfXPzGZfwqn+ipdpLsGZ3n9JVCBir95SuRDDQjdSHtDUPthVXI9tH2tejfO6jqa3EY\n56NHHz7ghT13l+3s8/V3K4+f5mco720wKuW7nkRy0cSVMiyF3T1yYJmTPNbUXEBd3a3U1k7oO17/\n+hdJ3RwsNVFjb28weeOiRU1AcOOxxsZG5s5torPzxIyzvQisZvTopaxcmR5rbe2EmOiC7c0u4OCD\nRzNxYivPPVfDpk1nAVvp7PxH4DoA7rvvQmbNmsnKlZf2TdTY0dHB8cfPYseOp9m7dwlvvtmL+1nA\nLqqqLqS394sZ53uZ3t6/5NJLv4H7gcDV7NkD8+d/nra222MmgNwKNIXPjwWyJ7Ls6SH8PIM7eLa0\n9N+UbSDFmoBy06YtHHfcScAoenr+qS/WwK3AtXR33xA7sWk0tocf3kzwNzqCfDfO3bTpYR59dCvw\nbuCU8HyD/78qBRpudirnByq5JCrfQLXh1k8Prstou+ca1BjX6SDzF3x0Pq+4MTT5ts+OJb4bb6oD\nQ9zYmo9//ON+8MFH+fjxU725uTn81R5toG/yoGoufoLPzIGgmVPppEpPA/36LqT6Z+nSpTmrzgZb\nLZbefT2zU0LmZ5gqecTPXhAXf3QqncwpgILP89DY88dV0alaTCUXKQOpUsIInhH4GMFU+4FUiamr\nqysrtly3Yc41JX50+/r689mw4ZFBxjeZnp4vs2zZSmprJ6SVIHp74Wc/uwC4ljfegB/8YClnnDGP\n1atvBr4V7r8U+Bvg+1lH3r37pbSYg8/gS0R/9W/Y0EZ9/Wzuu29ot5WOllSeeuqp2NsQrF+/Luct\nqeM0NjYya9ZMNm26AZhMUGLYBfwWeAVYEtl6CVAbfg6p7bLF3ZahuvoSpk+fxsknn5xxvlkEd0mP\nlo5uYPz4l1m7Nj32fLfblkEYbnYq5wcquSSqmHFm1rtHuy6n1hdy58fBxDjU0dvpyzMn4exvSxk7\n9l0DDNwMXue+qdqBWaWSqVPfO+DxgttB5+5RN9DfIb00kF2qyGynKvS4QaeCzNJDexjnwd7fi++g\n8HVL3pJD7s82KCFOnToz8n8qexxTtDPHSKmU7zoquci+5S3ghsjzfrl+TS5fXrxoct08LPWrfdmy\nlTz77PMcdthRPP30RQS/Z4K2FFiC+wG0tCzmvvvOiNzY7EIgs40lzq+BUQSlkrZw2Zf4wx9+nGPb\noFRSVXUhTz11IN3dZwJXA9Dbu5oNG9oK+qyySwNbgQsiWyzh1VcnFRB/oKOjg2XLrmDz5m309l4T\nHu/88L0Fn1VNzfe47LJlrFvXyY4dT/P661W4nw1spKrqNpYvvzC25JDZ3heUeO4AGunthd/85gZq\nal6kru5WYBSPP34xPT30fU5f/3qLSiTFNNzsVM4PVHJJVDHjTKqHTq6xOHFtQgPVrRc23iY1KHJc\n+Is79ev7YJ86daa7Z3YXbkorjdTUjPOpU0/MaB9I3btmnGf2dMu+N85ETw0E7Z8dYOCBkIMbrZ8+\ng3XcL/6gZFKfNsda/2eUXWoYM2ZSbC+4wfw/SJWGgkGw2Z9V0G7TP2t0scauDEalfNdRV2Qll5FU\nyuRS6IVhoAb9uMbbXMfNV1WXeyqZVDfXlthZCVJdk4O5xaJdrls8mMYlvYtsMJgzvbt13NiWqVNn\nev/sAKkuzid4XLVYXCeDaELI7hZ8UEYya8kYgHmKV1dPiGwTzBHXP7am8IRRaHKJr76Ljk/qH9sU\nN2t0qVTKd13JRcllRBW7zaXQ6Uny9d7JjHE4JaJ805HEHTf4BV3YueJnEoibkDJ3gogmq/ieWPED\nTXO1VaQ+1yApRBNV6p43/YkrfQBm/ESa6feeKezvF9f7rbm5uaCBtvAuDwZgpua7G/zfvNgq5bue\nRHIpaZuLmZ0KXEtQAXuzu18Vs811wCeA/wIWufsmMzsa+B7wTsCBVe5+3chFLknL10MnV9vHcOvL\nBxq/0dq6ip6ea+kfK7K677xx43uOO+44Nm0aTkSnEO0BV13dwoknTqe2diItLZdn9WhKvZ47tyls\nz2iOHOsCYAJwG7CI3t4v8/d/fz7r1t0LsV/7yXR3f5lly67g2Wd3AYcDs4FVBGNI/gDMJ2hPOo9X\nX/2zyN+kLeZ4MGXKUXR3Lw23O5OqqhZmzTqBlStzj1m5/fZ/I72dqYHVq39CamxKqkdfvMnAk0A3\n8OUc28hIKVlyMbNRwPXAx4EXgAfNrM3dt0e2mQcc5+7TzOwDwHeBOQStvRe6+6NmNgZ42Mw6o/tK\n5SlGl+ZcgzxzdUMu9PxxyRBSAz37z1Vffy5z5zaxe3cXsDdMFIv7Yktv7P8e0U4NVVVvpw3GHBwD\nLg2fp7r0fotNm26gpuZxamr6G7f713dGGt5/QtAhYDpB0nsSuAmYCDTw7LP/EcZ5BLAYWBg59xJq\navaycuWdAJHPaE1aN/DMxN7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KlcydOxeAjo4O5s2bx+LFi4Fi/LPZ64VttbS/5557uOKKf2Z4+N3ABcAO4Dja21fxznde\nzJe+9B+h99ILbGF4eOWo4RgeXgkcBSxmeBjWrFnH9u3bw/OtB3Zw551nsWnTv9HT01N2/WuuuSaT\n9y+6vm3bNi688MLM6Km0Hv/sm62n0rru575xP7ds2cLGjRsBRp+XdePuTVkIcicDkfXVwKpYm+uB\nd0bWdwKzgMOAXZHtbwT+I+Eangc2b95cc9slS8502OjgDgMOC7yzs8sHBgbc3b27e1Fkvzts9CVL\nzowdN/b2enU2izxodJfOtJHOdAmfnXU945vpudwLHG1mc4HdwDuAs2JtNgEfAG4Nq8Ged/efAZjZ\nk2Z2jLv/iKAo4KFGCU+bwi+J8dMDPMP8+ZsAOPnkxWzbdj/wwGiLtrZL6ev7PBCEwoaHg+3t7avo\n6+sfV0XZxHU2jjxoBOlMG+nMHk0zLu7+kpl9ABgEpgE3ufsOMzsv3P9pd7/dzE43s0eB3wDnRE5x\nPnCzmbUBj8X2TVn6+s4tMxKLFp0fyaecA7yfIEI4wpw5h4zmUG67rT+SWyn2gUkyOkIIURf1uj5Z\nXpiCYTF394GBgdGQVuF1aahsZri+0VtaDhoNmdV6vrR0NoM8aHSXzrSRznQh52ExMUF6enpKKrpK\nQ1s3AB+nUKI8MjL2mGPx8wkhRL1o+JcpQOlQMNcDf010zLElSzZxxx1fbZ5AIUSuyP3wLyIdenp6\nuO22wIh0d0+jre1SoB/op63tQp577hea80UI0VBkXDJAtEZ/ovT09HDHHV/l/vvvYtOmz4eG5kZg\nP7ZuPSeVOV/S0DnZ5EEjSGfaSGf2kHGZghQMzcyZsyZ9MEshhEhCOZcpTNKcL8q/CCHGIu9ji4lJ\nJqlPjPqwCCEagcJiGWCy4rDRRP+SJZvqnjwsD/HiPGgE6Uwb6cwe8lymOOPtw6IRkoUQaaCcixgl\nPnWypkoWYt8kjZyLjIsYRQUAQghQJ8opQ7PjsIODgyxduiKcgKwyzdZZC3nQCNKZNtKZPZRz2ccp\nDYW9kmACsgBVlwkhJorCYjkmjeR7eSjsElpbP8+JJx7HVVetVr5FiH0Q9XPZh4kn3++6q3fcyffB\nwcEwFLY8svVEXnrpVezcuTNdwUKIfQrlXDLAROKwGzbcEBqWiQ3tUjBOe/b8GXAJhYEuYRVwRcn5\nCjmZU075o8wPfpmXmLZ0pot0Zo8JGRczuzFtIaKxFI3Tx4EvEAzV//cEBqbo/RSM0NDQcu677w11\nD34phNhHqDaTGMH0wxclbD+l3lnKGrGQk5koJ8LAwIC3t88anXGyvX1WySySY80uWTp7pYezVh5c\ndr6kdkuWnDkunbXMcimEyA6kMBNlLQ/o79d7kWYtU9m4uFd+cI9leCq1Wbt2bdn56jEutegQQmSP\nRhmXq4HrgDcBJxeWei/ciCUvxiXtebWrGYSoQUoyJgMDA97Vdby3th7qM2Yc6b29vREDsWrUQNTi\nkdTr9UyEvMxRLp3pIp3pkoZxqaVarBtw4B9j2/+4jmicaALlFWalw7sMDg7y1reu4KWXpgHX8sIL\n0N9/Ab29Z7B79yb27HmWdeuCfi/1VqoJIaY41SwPQc7l4notWLMWcuK5pE2lcFQlT6Kwr7Ozy2FW\nWZvOzq4ST6W7e2FNHonCYkLkE1LwXKpWi7n774GzJt3CiVQZz1D7zz33s9FqsD17PgjsLWvz4ot7\nR9sMDS1n+/aHgQfGrWPNmvPZsOEGli5dkdmKs0LZdZY1CpELxrI+lOdc5qOcS6o0Kg6b5El0dy+K\neCEDDic4dDj0hdsP8K6uE8PXm0c9Feh0WODQV5NH0igvpp572UhPKy+xd+lMl7zoRDkXMR56enp4\n+9uXcfPNlwHw9re/hd27Xwj3DhJ0yFxP4JXchFkL73nPGeze/QKPPRY/2zHAX9PSchFr1vRVzbcM\nDg7yrne9n+HhVwKHAT0MDwd9bbKUpyntmEomNQqRG+q1TlleyInn0ijWrl3rcMDoL3M4IFINtiDc\nNhDJu2z0trZDfO3atSW/6GFm2M7HrACLewPBuQcaUjk2XppR3SZEFqFBpciHATcBA+H68cBf1nvh\nRiwyLkUGBgZ82rSDyx6eM2bM8YGBAZ8x48hwXy1J/76aH8BJD2xYUDXk1KyOlypAECKgUcZlAHgH\n8INwfT/gwXov3IglL8ZlsuOwxYfm7LIHfWvroe7uYQVYR8SDKTcemzdvHvcDOMm4FKrPqmud2AO+\n3nvZKMOWl9i7dKZLXnSmYVxqybnMdPcvmtnl4dP6RTN7KY2QnJktA64hKHn+jLuvT2hzLfAW4H+A\nle6+NbJvGnAv8JS7/2kamqYiQS7h3cAtwIUEOZVvAz/igAP2Y+nSFTzxxDPAa4EfhG0C2toupa/v\n8yXnO/bYY3niiSs56qjDuOqq6v1b+vrO5a67ehkeDtbb21dxyy2Vj2l23qOnp0c5FiHSYCzrA2wB\nDga2husLgDvrtWoEBuVRYC6BN7QNOC7W5nTg9vD1qcDdsf0XAzcDmypcIyU7nl8GBgZ8+vTDQ69k\no8OKSN6lrywHE+w/1uEgP/zwY8Y9rEwlDbV6A8p7CNF8aFBYbD7wHeCX4d9HgJPqvjC8njCPE65f\nDlwea3M98I7I+k5gVvh6NvBNgqq1b1S4Rpr3O3cUjUE01HVmhdfuxRLjExz6vK3tkBJjMBkP/rjh\nGa8B08CYQqRPGsZlzCH33f0+YBGwEDgPeK27V59svTZeATwZWX8q3FZrm6uBS4GRFLQ0lcma46EY\nYjpiHEcdAzwLLGHv3o+VzBGzZ8+zE9JRqWNidDj/oaHlnHFGEAqrtQNo0vEf/ehHJ6Sx0eRlXg/p\nTJe86EyDmmaidPcXgQdTvrbX2C4+1aaZ2VuBn7v7VjNbXO3glStXMnfuXAA6OjqYN28eixcHhxQ+\n6GavF0j7/IEx2AGcS5DD2EHwkV8QXrEV+NuIgosIHMhZwA3A0SUGZf7843jggYvYG3bib2u7iNNO\nu7yq/nvuuYcrrvjn0Mjt4M47z2LTpn8D4C/+4r0MD3dS7PuygzVr1nHvvf9FT0/PmPdnzZp1DA+v\nDI+/geHhTj7xiU9x2WWXTcr93BfXt23blik9eV/P6v3csmULGzduBBh9XtZNva7PRBeC3E00LLYa\nWBVrcz3wzsj6ToInyT8ReDS7gJ8CvwE+l3CNNDzE3FIaYurzlpaDvbt7UcloyGvXrg3Lixd4se9K\nn8NsN+v0tWvXlp2z2qjKcUpDaQMOC3z69MO9re2QSK6ntr4v8RBYcO4VDgd7YbSAlpaDFB4Tok5o\nRM5lshaCn82PEST02xg7ob+AWEI/3L4I5VwqUktOIm6Eokn+SjmPeG6kre0Q7+5eWHadonGJds4s\nL3eupe9LPBfT29sbK0iY5dCnAgAh6iTXxiXQz1uAHxJUja0Ot50HnBdpc124fzsJY5qFxiXX1WJZ\nqH0v7SRZuZ9LgUqdI+MGae3ateEMl7NDw3WmQ/k14n1f4h5Skq6kbXBcLoxLFj7zWpDOdMmLzjSM\nS005lzhmttXduydybBR3/0/gP2PbPh1b/8AY57gTuLNeLaIyzz33C5YuXRHO57KmSj+QI4De0b4p\nAOvWfZKRkQ3AR4B+4OPAKynmfaCl5SJuueXfSuaVic4XMzR0AXBkhWs+AKwIX78SeIq+vqsn/maF\nEOlQr3XK8kJOPJdmUy0s1tZ2iLe1dXg8TFY+Zlj5eGOl3s2imJfR53CkwwLv7l5YoifZK1ro0THP\nksNiB3hLy8smlHNRSbMQRWiW5yKmFvFe8QCdnVcyf/5JPPfcMWzd+j6iPeZXr76SmTNnceyxxwI3\nAq089NBL7N37DNBPe/sq+vr6S8qYg364UU4Evk17+y6uuqq/BpWzgA8CV9DZ+Sy33FI4/7UlukdG\nrmf16itHr93Xd+6YPe7LZ+jUzJpC1E0lqwP8GnihwvKreq1aIxZy4rk0Ow5bmnQ/s8SbKPUiNo9W\nZCV5MvFf/tU8Iujw6dMPT/QSgjzNQSUeSWF+mWg+J9nDOcjNptekr/z9F88z2XmbZn/mtSKd6ZIX\nnUym5+Lu0yfbsIlssGjRyQwN/S3wcoKcCDzwQB+Dg4OxscF20NKykZGRqyl6Mg/wrne9n/nzTyrz\nEgozUW7YcAP33bedPXuWAJvCvX/J61+/q8w7GBwcDPM07wWup6XlEc4++wx2794F7KKvr+hR9PWd\ny513nj3a7yYYDWg67r8h6G+7ZtTT2rnzUXkmQjSSWiwQwSyU54SvDwFeWa9Va8RCTjyXZhP8cj+h\n4q/36K/+8pkrZ0byMx3e3b2ozHspHJeUu0nWUrsXUZwuoDCDZsHbOcgLfWeqVcAVzhEvc66lD48Q\nUxUakXMxsyuAUwjGBfksQZ+Um4E3TIaxE82isqMaHSm4mJ+AoI/rxwm8mEH27m1l69ZzAPjWt87i\n7LOX86UvDYx6DG1tl9LdfSMzZ84q8UCSGSQYJWA3zz03raq2BQtOYWhoN8EA272RvVfQ3r6Lo446\nlj17Kl8p6mEBLFp0PuvWfXLKejqDg4PjykkJMSHGsj4E/UtaCEdFDrf9oF6r1oiFnHguzY7DDgwM\nhF5F1As5pCxXsX79+tH25X1ikvMfQQ/6M8MluYNjvE9LJS3V9Ad9aTaGeSEf9VgGBgYSZ+CMjzwQ\npRE5mDQ+84lUuI13YNBm/2/WinSmCw0aFfme8G9hyP39ZVzSJQv/cAMDA97dvdA7O7u8u3tRYrlx\nW1vp0Cql+5N63R8bC1XNLCs7TnrYdXXNG/PhHn+wFosAVlVI/Bc6cFY2cgWqFThUuv54SWNSs4lM\nfzBew5mF/81akM50aZRxuRT4NME4XucCdwMX1HvhRix5MS7NoJaHYy0PooGBgdCDOdYhOl7YdIfD\nyo4vTKtcbdrkajmSghGMVpO1tBzk3d0Lvbe31zs7u7yzs6vEM0ka32ys2TCreU+1Dn0zmUzUu9J8\nOaIWJt24EIxIfCSwlCC4/nFgSb0XbdQi45JMrb96a30QFc/XF3ow08OQWPIYYq2tB7pZqUcT7YDZ\n3b0wUV/y/DTF81YqWS7VN7PsvEmUFi6UvvdqQ98kFTVMBhM1EvVOIy32DRplXB6s9yLNWvJiXBrt\nKo/faCSHxeJtC1VhM2bMiYSVor34OxyOT/Ro4uOSJXlWRd1JD/fCtjd5YQSAzs6ukknIkjyigiGo\nPOBm6T0qnic6inTh+qXVc9Ue3M0Ki8U/q7E8rnp0NnLUg7yEm/Kis1FhsX7gdfVeqBmLjEsy4/nV\nm5TQH9/5B8IH8WyHl3sw02W559Haeqh3dy+s+hBKHmG5dMh+OMmDoWLKy56T3nexEKCSt1Nanlw+\n5E3BGxrw8iFuygfkLNCshH702FqM00R1NtpDystDOy86G2Vcfgj8HvgxwSiBDyihn2/S+uJXeriV\njztWePgXjErcOAQP6SQd8Uqy0h7/naER6fNCFViwlBuvgsaoriBvE833lHs75fPHxHNIR4b5mYKe\nco8si6Gnyc69KLeTb9IwLmNOcwz0AF3AnwB/Gi7LazhOZJRCv45aphKuRNIUw4UpjAvn7+y8kqAv\nTD/Bv9GognDb9cDfA18APs7w8PqS8cji11i37pOsWXN+eN5vA7cAt4avL+bww2cSjDWWPK1z/H2f\ndNLxBGOcQdCv5rPs2fNBhoaWs3z52dx7771j3ocFC05h06Zb6ez8OnAOsCp8b/0EM3teUfa+6qXS\ntNFCZIp6rVOWF3LiueTFVR5rPpekSrJSD+aAyK/7jQ4HerxSLHqOStdI2l7InQSlyKWeUaUe96X6\nykcoMCutSOvqOr5kBs3kcc6K5ctBeC753jQ73DTRsFitoTiFxZLJi04aERbL8yLjki7jNS7u5cnj\nYFmUEOYqfwBVMiLd3YvCXElpZVhQQlwwCKXTOle6TkHftGmHlF0ryBNF1xdUrAZLNqSBvqRjJvqZ\npxluqsVQRHWO12AUJnmLl4ZPBnn8DmUZGZcpYlzySLz8uKXl4Ak9QKo94AJjUfQUWlsPLhmfrNC/\nJf6Qr1xllvxAHhgYcLMZHq30Cl4fGzMuZ1Z9mMfzQ4FRXVjR2xnrXkzkvUwm4y0EUclzfpFxkXFp\nKvGh8ccc84G+AAAZBUlEQVR6gIy3uqnYmXGBw4LQAKTfcbDYg3//0FuZ7fAyLx0ypliRNp6HedLo\nANGigeh7LS377kg0Ss18aI/HuDTDCDay9HmqI+MyRYxLXlzluM7x9HyvVNpb7WFQ/oBKrgKrprHS\ntcvDbyu8WCbd50EV2sIwXNYZ7h//w7y7e2EFj2hViedV63stjFAQHaYnTeIP6ImGxRptXNavXz8h\no1sM2y5sSOfXvHzXZVxkXBpKZeNSnkCPf0HLHzbJk47Ve0yle5n0q7awravrxAQvpWBgSkNwcYM4\nVigrqad/0B9ms0dLlcvblRuX7u5Fk+q1JBmPeN+mrCb0589/07iNWeB5Hxwa+86GaM3Ld13GZYoY\nl7xSfICM7VFMxAuZiLczfu0bE7UUQnGlD/eFVUNXSaGswHAljSYQfV1+brPpJUPkBAZoYdm5xhoj\nrVo+K54fShrnrR5voxZDVG20gPGEuWqtXoy+5+IPlbH/F/c1ZFxkXJrOwEDysCpjGYpiz/jqX+jJ\nCluU5kLK9Qdjo/V5tLS4OKxN1EBG8ynxcua+8DyFc5VWkUXzOIX3N2PGkWFuqc+hz80O8hkzjvSu\nrhNjw+oUyp2Prdj5tFqFXKXKtvg4b7UUL0z08yjXUexMO1YlYZxq0yoUQonxwU6LhlQdPuPIuEwR\n45IXV7layKmWB0Hl3vblIwtXa1vrmF3VQlZdXcd7MRfSF3swdTj0hn+L+RKzzpgxme2l+ZRoD/2B\n2L4Oh+ne2TkrnDlzhhfyOHGPp/iAj5/jAA8GBY1uC8I6cQ+m2i/5pH2l3lRxnLekIX/SCnlV1pE8\nMna1fN6MGUd4vHCi8Lkne9d94ed3psNar3VMuHrJy3ddxkXGpaFU0zmRX7LRkEi0xDj+sC0fpqXy\nL8uCxvLqq0NKrhEYitKHzYwZR8a0xD2RFQlGKP7AOrDkAV364Dwhsr+Y0E/OyxQekvHtce+p1BjU\nUrI8lnGJVrOtX7++Qjl07fPjVGK8xgVOKHvwFz/n4xLfb/Ea8eKTeJHFH3hX1zwl9ENkXKaIcdnX\nqSUfE89/jPUwS35wLah6jcI5C0avmIOoFPZK0nm4B9MKHJqwb/YYD8DCg2+BByGzmV4++nLSQ/dM\nT3oP8XHUCpVp1cJiYw3eWQgxxR/O8cnUqhENdZZ7bIEX2dvbm7DvwDJDVq2opLe3N/wcZnvgiVbO\nsXV3Lxrnf+3UJg3j0jrp48sIkQItLY8wMtIPQHv7Kvr6+sdx9CDBOGZPha97gIW0tFzEyAhl5+zp\n6aGnp4d169bx93//UYLxygAujJ13IXBBZP0CYAnt7XexZs0F/OM/XsrevYV9lwC/TVS3aNHJDA1d\nQDAmbD/BtEmFYxYC7wZ6w32LYte8hGBstlIK46itXn0V27bdz8jIUWzd+nuWL38nmzbdym239Y+O\nd7Zo0WXceef9wC76+orjzG3YcAPDw+vDa8PwMOExraHG3sgVP1ty/cHBwdHz9/WdO3rOwnhxwXmh\nre1CZsz4IC+8cCDwGoI5Cd/H7t27mDPnMB577HqCseK+ADwTXveYhLtYGK/uCjo7n+VP/3QZ/f23\nUfzsLgBOpKWlj/33358XXig9eubMg8fULsZJvdYpyws58Vzy4ipPls6xOhC2tR3iXV0nhn07qg/L\nXx4WK50gLJ40Hl8/m76ypPBpp53mnZ1dYdL9+LJqp+7uRaO6S3NHq2JTAfRV8Uo2hiG7hSXVXd3d\ni7y1dX8vVLa1tXWUvadavIxKIc1A16oSPYX+NZW8vqTPs3CvA72HejzE+Qd/0Bl6F10e5D82RjzH\n+P3oqBAWW+XRIX+mTz+87NjW1kPH7Ig62SXUefmuo7CYjEsjmUydlZLv8XzMWF/2eEJ/PInhOEmh\nta6uE8NKtwWjRmo8D5/C+5o//02jxwUGYIHDkRWNS1LYZmBgIBY62t9bW0vnpyl9yAYht2nTisUT\nvb29Ze8nOnRNa+uBHjVM0Fdx9IDK962vysyj8TzWAd7aun/EMHbGjts/sTLu1a8+ocTwJw2K2tnZ\nVfY5JBvUyoazXvLyXc+9cQGWATuBR4BVFdpcG+7fDnSH2+YAm4GHgAeBCyocm9KtFs2i3i97tePH\nKkJI+hWb1NdkvA+feCVc8UEdr1orTkaWlNOopeNlcUDOpDl04g/2WaO//qNeZFACXZr7KRinpEq8\n8ntUKYe20ZPmwJkx48jR+xSUZS/w4kyf5V5SNS8narRqGftuso1LXkjDuDQt52Jm04DrgNOAp4Hv\nm9kmd98RaXM68Gp3P9rMTgU+BSwAXgQucvdtZjYduM/MhqLHCgFBzPyuu3oZHg7WC7mVeOz/rrt6\ny+a1KeQtivH3/prnZYnG7RctOjnMaQSv16375Oh1v/WtixgZeS+l+YvLgEMp5iB6mTlzV9n5t29/\ncEwdv//9LIJ8w/FAMX8ScCXBb7fotusZGTkaOAy4gb17j6Wt7Qngr4nOyfPEE89w1VUfpKenJyGP\nciltbReO5puCfFlc2VN0dl7Jiy9OL8t/7LfffkBw/+fNO5mtW8+JaCzm2gYHB1m+/Gz27v0YsLvs\nvR9++KH8+tcfYnj4txx11GxOOeWU6jeLyv8vYgLUa50mugCvBwYi65cDl8faXA+8I7K+E5iVcK6v\nA29O2F6vAW8IeXGVm6FzvDHwSmOLJZfTjv8Xai16StuUeiNFr2Bz7Fd8QUdf6EEU+tRUGxqn0Ekz\nGgqKhrEO8iCH0Veheq3Sr/2jIudd5WYHutn0Mo+q2vTRhQ6vhdBbpdBXtc6Ple53IWwX9BcqXHe9\nx3NLXV3H1zXe2GSUJeflu06ew2LAnwM3RtbfDXwy1uYbwBsi698E5sfazAWeAKYnXCOVGz3Z5OUf\nrlk6x/Nlr1VjPeGPsfSUnrtSmXXRuBQNTqkhMuuoWMBQvMZaDzpSnuDFkuIFXhxsMwh1xYeXSQqL\nmXV4S8sMLw1jbQ5fn+BB0r00PFZeSl1+L5P6xURzSGPN+xItjCidsC2us9AxMighr2XkiEaTl+96\nGsalmaXIXmM7q3RcGBL7CvB37v7rpINXrlzJ3LlzAejo6GDevHksXrwYgC1btgBovcb1wrZGX79Q\nGlxYj2pJaj/W/sWLF9PXdy533nkWe/fuAI6jvX0Vp512cU3vbyw9kS3As7H1I8MS6KuBy2lru4EP\nfaiPO+/cxN13380LL/wNhRCQ+w5aWr4zGqpL1n8usJKgFPhvCNKYHycIH90ErKSl5TNcddXNbN++\nnS996SZGRlqA19DS8nNOOunPefLJTQDs2jWbRx/9XwQpzoLeAseE7+UNBOGxQWA9d9/9S848c0n4\nnoKodHv7Rvr6+mP340TgreHrJ5g5c9fo/lNOOYX58+9nz55nR0Ni8fu5c+dOhodXsmfPJuBj4T36\nGaVl2f8KLAF+Qnv7F+jsPIw9e6KR8h3s2VP8PJr1fWr29ZPWt2zZwsaNGwFGn5d1U691muhCkDuJ\nhsVWE0vqE4TF3hlZHw2LAfsR/IdfWOUaaRhxMUWZrPBHaSin1DuoVgI9VolvNf3l455VrzRLolKH\nxPLhaOLl3Qd4MKXzbIdOP/zwI8tKssdT+hu/P6W6umLeU59Hp0qIenuTXVY8lSHnYbFW4DGCsFYb\nsA04LtbmdOB2Lxqju8PXBnwOuHqMa6R0qyeXvLjKedCZFY2lgyWWz9YZL5nu7l4Y5jWKD+22tkNq\nfhgmzxtTW3+eqI5orqil5WDv7DzczQ4afXgH1WPxkul47qfDiZVp1176Wz6tQvDeCrmoeFn0Id7V\ndbzPmHFE4vw2k5k/mQhZ+f8ci1wbl0A/bwF+CDwKrA63nQecF2lzXbh/O3ByuO2NwEhokLaGy7KE\n86d3tyeRvPzDNSuhP56HQ5buZbVcRHlnz0L+oDji8XiHVCnO2nmCm83w7u5F4x5duLxMOmo09vf2\n9sPD4oAVkfeVVGpcHCOs2vXGHvqnLzRmhQKH4jWi587S516NvOjMvXGZ7CUvxkUkk/ewRpJxiU9x\nnDywYqkhilIt+R03DKXjo1U/b9I5SsN0SSM0r4h4KsnGpTAZWiWPIj6+WOlUDEkDTI49HYCoHxkX\nGZcpTd47tNUyQGS1gRfjD+SxynYrX7f2OVoqz7lT/lm0th4a6eV/UOx6hTDWgBcqt6IdLuPD/RRK\nl0s9rSSPKKhYa2vryNUPjbwh4zJFjEteXOVG65yIccnavSwfYbnwXlaNPmzjeY6k3IG7VxzKJk7l\nEaHHO+99X6R/S/mDvnDt0lLhE8MhZwpJ91LvI3mUg3LjU7nX/QJPykdl7XOvRF50pmFcWiZeZybE\n5NLXdy7t7asIymr7w97S5zZb1rjo6enhjju+yvz5JxGU45bvv+22fpYs2cSSJbu4/fabuf/+LamP\nxNvZ+SxLlmwqG4WgOifS1TWXJUs20dX1G4Ky3/5wuYCLLz5ntHf+1q3nsGfPB9m9++dcfvn7aW/f\nBQwBfwW8mqDHf9CL/4knnolcYxDoZ8+eDzI0tJwzzugFgs/+qKMOo6Xlosg1LwGuAHrZu/djNY+W\nIJpEvdYpyws58VxEZbJW7TNR0sgfTTQsVu1a8TxNteOS8j2VvMvSOeoLowUEVV/d3Yuqhr5mzJhT\nMt5aS8vBYVK/9tyRqA8UFpNxEfkhDUM5Vm/28Vyrlj4mY1HJuFQOzQUGsXroq3xStfgIA3kr7sgb\nMi5TxLjkJQ6bB5150OieDZ215LTG0lnJS0o2LmeWXaO8+GBW6OGU66pmMLNwP2shLzrTMC6aiVII\nMWGSRo4u5HSiowtDIXf2TOLx73rX+9mz5xCKox6/e7RNYWTiwrA7IifUa52yvJATz0WIZjDZ/YgK\nVWRBSXPlEZ6TtETLkxX+ajyk4LlYcJ6piZn5VH5/QtRLI+aLr/Uamrs+O5gZ7h4fNHh81GudsryQ\nE88lL3HYPOjMg0Z36Uy7CnBfv59pg3IuQoi8UcssoCL/KCwmhGgoS5euYGhoOdGpi5cs2cQdd3y1\nmbJEhDTCYuqhL4QQInVkXDJA+QyG2SQPOvOgEfZtnZMxrM++fD+zinIuQoiGUq1vjJg6KOcihBCi\nBOVchBBCZBIZlwyQlzhsHnTmQSNIZ9pIZ/aQcRFCCJE6yrkIIYQoQTkXIYQQmUTGJQPkJQ6bB515\n0AjSmTbSmT1kXIQQQqSOci5CCCFKUM5FCCFEJpFxyQB5icPmQWceNIJ0po10Zg8ZFyGEEKmjnIsQ\nQogScp9zMbNlZrbTzB4xs1UV2lwb7t9uZt3jOVYIIURzaJpxMbNpwHXAMuB44CwzOy7W5nTg1e5+\nNHAu8Klaj80TeYnD5kFnHjSCdKaNdGaPZnourwMedffH3f1F4FbgbbE2ywlmFMLdvwd0mNlhNR4r\nhBCiSTQt52Jmfw70uPv7wvV3A6e6+/mRNt8ArnL374Tr3wRWAXOBZdWODbcr5yKEEOMk7zmXWp/6\ndb1BIYQQjaeZ0xw/DcyJrM8BnhqjzeywzX41HAvAypUrmTt3LgAdHR3MmzePxYsXA8X4Z7PXC9uy\noqfS+jXXXJPJ+xdd37ZtGxdeeGFm9FRaj3/2zdZTaV33c9+4n1u2bGHjxo0Ao8/LunH3piwEhu0x\nghBXG7ANOC7W5nTg9vD1AuDuWo8N23ke2Lx5c7Ml1EQedOZBo7t0po10pkv47KzrGd/Ufi5m9hbg\nGmAacJO7X2Vm54VW4dNhm0JV2G+Ac9z9/krHJpzfm/n+hBAij6SRc1EnSiGEECXkPaEvQqLx4iyT\nB5150AjSmTbSmT1kXIQQQqSOwmJCCCFKUFhMCCFEJpFxyQB5icPmQWceNIJ0po10Zg8ZFyGEEKmj\nnIsQQogSlHMRQgiRSWRcMkBe4rB50JkHjSCdaSOd2UPGRQghROoo5yKEEKIE5VyEEEJkEhmXDJCX\nOGwedOZBI0hn2khn9pBxEUIIkTrKuQghhChBORchhBCZRMYlA+QlDpsHnXnQCNKZNtKZPWRchBBC\npI5yLkIIIUpQzkUIIUQmkXHJAHmJw+ZBZx40gnSmjXRmDxkXIYQQqaOcixBCiBKUcxFCCJFJZFwy\nQF7isHnQmQeNIJ1pI53ZQ8ZFCCFE6ijnIoQQogTlXIQQQmSSphgXM+s0syEz+5GZ3WFmHRXaLTOz\nnWb2iJmtimz/mJntMLPtZvY1MzuwcerTJy9x2DzozINGkM60kc7s0SzP5XJgyN2PAb4VrpdgZtOA\n64BlwPHAWWZ2XLj7DuC17n4S8CNgdUNUTxLbtm1rtoSayIPOPGgE6Uwb6cwezTIuy4H+8HU/8GcJ\nbV4HPOruj7v7i8CtwNsA3H3I3UfCdt8DZk+y3knl+eefb7aEmsiDzjxoBOlMG+nMHs0yLrPc/Wfh\n658BsxLavAJ4MrL+VLgtznuB29OVJ4QQoh5aJ+vEZjYEHJawa010xd3dzJJKusYs8zKzNcBed79l\nYiqzweOPP95sCTWRB5150AjSmTbSmT2aUopsZjuBxe7+jJkdDmx292NjbRYAV7j7snB9NTDi7uvD\n9ZXA+4A3u/tvK1xHdchCCDEB6i1FnjTPZQw2Ab3A+vDv1xPa3AscbWZzgd3AO4CzIKgiAy4FFlUy\nLFD/zRFCCDExmuW5dAJfAo4EHgfe7u7Pm9kRwI3u/r/Cdm8BrgGmATe5+1Xh9keANmBPeMrvuvvf\nNvZdCCGEqMSU7qEvhBCiOeS+h36WO2RWumaszbXh/u1m1j2eY5ut08zmmNlmM3vIzB40swuyqDOy\nb5qZbTWzb2RVp5l1mNlXwv/Jh8PcYxZ1rg4/9wfM7BYze1kzNJrZsWb2XTP7rZn1jefYLOjM2neo\n2v0M99f+HXL3XC/AR4HLwtergI8ktJkGPArMBfYDtgHHhfuWAC3h648kHT9BXRWvGWlzOnB7+PpU\n4O5aj03x/tWj8zBgXvh6OvDDLOqM7L8YuBnYNIn/j3XpJOj39d7wdStwYNZ0hsf8GHhZuP5FoLdJ\nGg8BTgHWAn3jOTYjOrP2HUrUGdlf83co954L2e2QWfGaSdrd/XtAh5kdVuOxaTFRnbPc/Rl33xZu\n/zWwAzgiazoBzGw2wcPyM8BkFnpMWGfoNb/J3f813PeSu/8yazqBXwEvAi83s1bg5cDTzdDo7s+6\n+72hnnEdmwWdWfsOVbmf4/4OTQXjktUOmbVcs1KbI2o4Ni0mqrPECIdVfd0EBnoyqOd+AlxNUGE4\nwuRSz/18JfCsmX3WzO43sxvN7OUZ0/kKd98DbAB+QlDJ+by7f7NJGifj2PGSyrUy8h2qxri+Q7kw\nLmFO5YGEZXm0nQd+W1Y6ZNZaKdHscumJ6hw9zsymA18B/i789TUZTFSnmdlbgZ+7+9aE/WlTz/1s\nBU4G/sXdTwZ+Q8K4eykx4f9PM+sCLiQIrxwBTDez/zc9aaPUU23UyEqluq+Vse9QGRP5DjWrn8u4\ncPcllfaZ2c/M7DAvdsj8eUKzp4E5kfU5BFa7cI6VBO7em9NRPPY1K7SZHbbZr4Zj02KiOp8GMLP9\ngK8CX3D3pP5KWdC5AlhuZqcDfwAcYGafc/f3ZEynAU+5+/fD7V9h8oxLPToXA99x918AmNnXgDcQ\nxOIbrXEyjh0vdV0rY9+hSryB8X6HJiNx1MiFIKG/Knx9OckJ/VbgMYJfWm2UJvSXAQ8BM1PWVfGa\nkTbRhOkCignTMY/NiE4DPgdc3YDPecI6Y20WAd/Iqk7gv4BjwtdXAOuzphOYBzwItIf/A/3A+5uh\nMdL2CkoT5Zn6DlXRmanvUCWdsX01fYcm9c00YgE6gW8SDL1/B9ARbj8C+P8i7d5CUInxKLA6sv0R\n4Alga7j8S4rayq4JnAecF2lzXbh/O3DyWHon6R5OSCfwRoL467bI/VuWNZ2xcyxiEqvFUvjcTwK+\nH27/GpNULZaCzssIfpQ9QGBc9muGRoJqqyeBXwL/TZAHml7p2Gbdy0o6s/YdqnY/I+eo6TukTpRC\nCCFSJxcJfSGEEPlCxkUIIUTqyLgIIYRIHRkXIYQQqSPjIoQQInVkXIQQQqSOjIsQQojUkXERQgiR\nOjIuQtSJmc0NJ2D6rJn90MxuNrOlZvZtCyax+0Mz29/M/tXMvheOeLw8cux/mdl94fL6cPtiM9ti\nZl8OJw77QnPfpRDjQz30haiTcKj0RwjG3HqYcPgWd//L0IicE25/2N1vtmC21O8RDK/uwIi7/87M\njgZucfc/NLPFwNeB44GfAt8GLnX3bzf0zQkxQXIxKrIQOWCXuz8EYGYPEYx3B8EAj3MJRhRebmaX\nhNtfRjAq7TPAdWZ2EvB74OjIOe9x993hObeF55FxEblAxkWIdPhd5PUIsDfyuhV4CTjT3R+JHmRm\nVwA/dfezzWwa8NsK5/w9+r6KHKGcixCNYRC4oLBiZt3hywMIvBeA9xDMcy5E7pFxESId4slLj72+\nEtjPzH5gZg8C/xDu+xegNwx7vQb4dYV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DHPbPqAORFIssKZjZMjObY2azzGxGVHFI6ryz7B36dO0TdRiSrELgkOdiT8eT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\nReuGFnegfMcAAAAASUVORK5CYII=\n", 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qq6/SuHFjrr32WoYOHVpqmfLiLfu4V4feVodv1z4SkXbAS0ALnIObn1PVJ0SkGfA60AHI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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2390,6 +2423,15 @@ "pylab.xlabel('Mean')\n", "pylab.legend(['KDE', 'Histogram'])" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/docs/source/pythonapi/examples/tally-arithmetic.ipynb b/docs/source/pythonapi/examples/tally-arithmetic.ipynb index 87cdc9b66..91514719d 100644 --- a/docs/source/pythonapi/examples/tally-arithmetic.ipynb +++ b/docs/source/pythonapi/examples/tally-arithmetic.ipynb @@ -366,7 +366,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nSwQkewAAAABJRU5ErkJggg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nE12ngwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] @@ -472,7 +472,7 @@ "# Resonance Escape Probability tallies\n", "therm_abs_rate = openmc.Tally(name='therm. abs. rate')\n", "therm_abs_rate.add_score('absorption')\n", - "therm_abs_rate.add_filter(openmc.Filter(type='energy', bins=[0., 0.625]))\n", + "therm_abs_rate.add_filter(openmc.Filter(type='energy', bins=[0., 0.625e-6]))\n", "tallies_file.add_tally(therm_abs_rate)" ] }, @@ -487,7 +487,7 @@ "# Thermal Flux Utilization tallies\n", "fuel_therm_abs_rate = openmc.Tally(name='fuel therm. abs. rate')\n", "fuel_therm_abs_rate.add_score('absorption')\n", - "fuel_therm_abs_rate.add_filter(openmc.Filter(type='energy', bins=[0., 0.625]))\n", + "fuel_therm_abs_rate.add_filter(openmc.Filter(type='energy', bins=[0., 0.625e-6]))\n", "fuel_therm_abs_rate.add_filter(openmc.Filter(type='cell', bins=[fuel_cell.id]))\n", "tallies_file.add_tally(fuel_therm_abs_rate)" ] @@ -503,7 +503,7 @@ "# Fast Fission Factor tallies\n", "therm_fiss_rate = openmc.Tally(name='therm. fiss. rate')\n", "therm_fiss_rate.add_score('nu-fission')\n", - "therm_fiss_rate.add_filter(openmc.Filter(type='energy', bins=[0., 0.625]))\n", + "therm_fiss_rate.add_filter(openmc.Filter(type='energy', bins=[0., 0.625e-6]))\n", "tallies_file.add_tally(therm_fiss_rate)" ] }, @@ -576,8 +576,9 @@ " 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: ea9fb637f63f9374c7436456141afa850b84acf9\n", - " Date/Time: 2016-01-14 07:00:14\n", + " Git SHA1: 34381b40a9445a727e360873aaa6ef892af1cb6a\n", + " Date/Time: 2016-02-06 15:53:27\n", + " MPI Processes: 1\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -633,20 +634,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 1.2510E+00 seconds\n", - " Reading cross sections = 9.7600E-01 seconds\n", - " Total time in simulation = 1.5844E+01 seconds\n", - " Time in transport only = 1.5834E+01 seconds\n", - " Time in inactive batches = 2.2840E+00 seconds\n", - " Time in active batches = 1.3560E+01 seconds\n", + " Total time for initialization = 3.8900E-01 seconds\n", + " Reading cross sections = 8.8000E-02 seconds\n", + " Total time in simulation = 8.0560E+00 seconds\n", + " Time in transport only = 8.0400E+00 seconds\n", + " Time in inactive batches = 1.1570E+00 seconds\n", + " Time in active batches = 6.8990E+00 seconds\n", " Time synchronizing fission bank = 3.0000E-03 seconds\n", - " Sampling source sites = 2.0000E-03 seconds\n", - " SEND/RECV source sites = 1.0000E-03 seconds\n", + " Sampling source sites = 3.0000E-03 seconds\n", + " SEND/RECV source sites = 0.0000E+00 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", - " Total time for finalization = 1.0000E-03 seconds\n", - " Total time elapsed = 1.7110E+01 seconds\n", - " Calculation Rate (inactive) = 5472.85 neutrons/second\n", - " Calculation Rate (active) = 2765.49 neutrons/second\n", + " Total time for finalization = 2.0000E-03 seconds\n", + " Total time elapsed = 8.4560E+00 seconds\n", + " Calculation Rate (inactive) = 10803.8 neutrons/second\n", + " Calculation Rate (active) = 5435.57 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -758,10 +759,10 @@ " \n", " \n", " 0\n", - " total\n", - " (nu-fission / absorption)\n", - " 1.040166\n", - " 0.009069\n", + " total\n", + " (nu-fission / absorption)\n", + " 1.040166\n", + " 0.009069\n", " \n", " \n", "\n", @@ -809,7 +810,8 @@ " \n", " \n", " \n", - " energy [MeV]\n", + " energy low [MeV]\n", + " energy high [MeV]\n", " nuclide\n", " score\n", " mean\n", @@ -819,19 +821,23 @@ " \n", " \n", " 0\n", - " (0.0e+00 - 6.2e-01)\n", - " total\n", - " absorption\n", - " 0.95938\n", - " 0.008187\n", + " 0\n", + " 0.000001\n", + " total\n", + " absorption\n", + " 0.694707\n", + " 0.006699\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " energy [MeV] nuclide score mean std. dev.\n", - "0 (0.0e+00 - 6.2e-01) total absorption 0.95938 0.008187" + " energy low [MeV] energy high [MeV] nuclide score mean \\\n", + "0 0 0.000001 total absorption 0.694707 \n", + "\n", + " std. dev. \n", + "0 0.006699 " ] }, "execution_count": 27, @@ -869,7 +875,8 @@ " \n", " \n", " \n", - " energy [MeV]\n", + " energy low [MeV]\n", + " energy high [MeV]\n", " nuclide\n", " score\n", " mean\n", @@ -879,19 +886,23 @@ " \n", " \n", " 0\n", - " (0.0e+00 - 6.2e-01)\n", - " total\n", - " nu-fission\n", - " 1.090899\n", - " 0.010602\n", + " 0\n", + " 0.000001\n", + " total\n", + " nu-fission\n", + " 1.201216\n", + " 0.012288\n", " \n", " \n", "\n", "" ], "text/plain": [ - " energy [MeV] nuclide score mean std. dev.\n", - "0 (0.0e+00 - 6.2e-01) total nu-fission 1.090899 0.010602" + " energy low [MeV] energy high [MeV] nuclide score mean \\\n", + "0 0 0.000001 total nu-fission 1.201216 \n", + "\n", + " std. dev. \n", + "0 0.012288 " ] }, "execution_count": 28, @@ -930,7 +941,8 @@ " \n", " \n", " \n", - " energy [MeV]\n", + " energy low [MeV]\n", + " energy high [MeV]\n", " cell\n", " nuclide\n", " score\n", @@ -941,20 +953,24 @@ " \n", " \n", " 0\n", - " (0.0e+00 - 6.2e-01)\n", - " 10000\n", - " total\n", - " absorption\n", - " 0.803413\n", - " 0.007031\n", + " 0\n", + " 0.000001\n", + " 10000\n", + " total\n", + " absorption\n", + " 0.74925\n", + " 0.008257\n", " \n", " \n", "\n", "" ], "text/plain": [ - " energy [MeV] cell nuclide score mean std. dev.\n", - "0 (0.0e+00 - 6.2e-01) 10000 total absorption 0.803413 0.007031" + " energy low [MeV] energy high [MeV] cell nuclide score mean \\\n", + "0 0 0.000001 10000 total absorption 0.74925 \n", + "\n", + " std. dev. \n", + "0 0.008257 " ] }, "execution_count": 29, @@ -991,7 +1007,8 @@ " \n", " \n", " \n", - " energy [MeV]\n", + " energy low [MeV]\n", + " energy high [MeV]\n", " cell\n", " nuclide\n", " score\n", @@ -1002,23 +1019,24 @@ " \n", " \n", " 0\n", - " (0.0e+00 - 6.2e-01)\n", - " 10000\n", - " total\n", - " (nu-fission / absorption)\n", - " 1.237053\n", - " 0.011765\n", + " 0\n", + " 0.000001\n", + " 10000\n", + " total\n", + " (nu-fission / absorption)\n", + " 1.663616\n", + " 0.018624\n", " \n", " \n", "\n", "" ], "text/plain": [ - " energy [MeV] cell nuclide score mean \\\n", - "0 (0.0e+00 - 6.2e-01) 10000 total (nu-fission / absorption) 1.237053 \n", + " energy low [MeV] energy high [MeV] cell nuclide \\\n", + "0 0 0.000001 10000 total \n", "\n", - " std. dev. \n", - "0 0.011765 " + " score mean std. dev. \n", + "0 (nu-fission / absorption) 1.663616 0.018624 " ] }, "execution_count": 30, @@ -1054,7 +1072,8 @@ " \n", " \n", " \n", - " energy [MeV]\n", + " energy low [MeV]\n", + " energy high [MeV]\n", " cell\n", " nuclide\n", " score\n", @@ -1065,23 +1084,24 @@ " \n", " \n", " 0\n", - " (0.0e+00 - 6.2e-01)\n", - " 10000\n", - " total\n", - " (((absorption * nu-fission) * absorption) * (n...\n", - " 1.040166\n", - " 0.019018\n", + " 0\n", + " 0.000001\n", + " 10000\n", + " total\n", + " (((absorption * nu-fission) * absorption) * (n...\n", + " 1.040166\n", + " 0.021928\n", " \n", " \n", "\n", "" ], "text/plain": [ - " energy [MeV] cell nuclide \\\n", - "0 (0.0e+00 - 6.2e-01) 10000 total \n", + " energy low [MeV] energy high [MeV] cell nuclide \\\n", + "0 0 0.000001 10000 total \n", "\n", " score mean std. dev. \n", - "0 (((absorption * nu-fission) * absorption) * (n... 1.040166 0.019018 " + "0 (((absorption * nu-fission) * absorption) * (n... 1.040166 0.021928 " ] }, "execution_count": 31, @@ -1135,7 +1155,8 @@ " \n", " \n", " cell\n", - " energy [MeV]\n", + " energy low [MeV]\n", + " energy high [MeV]\n", " nuclide\n", " score\n", " mean\n", @@ -1145,100 +1166,108 @@ " \n", " \n", " 0\n", - " 10000\n", - " (0.0e+00 - 6.3e-07)\n", - " (U-238 / total)\n", - " (nu-fission / flux)\n", - " 0.000001\n", - " 7.377419e-09\n", + " 10000\n", + " 0.000000\n", + " 0.000001\n", + " (U-238 / total)\n", + " (nu-fission / flux)\n", + " 0.000001\n", + " 7.377419e-09\n", " \n", " \n", " 1\n", - " 10000\n", - " (0.0e+00 - 6.3e-07)\n", - " (U-238 / total)\n", - " (scatter / flux)\n", - " 0.209989\n", - " 2.303838e-03\n", + " 10000\n", + " 0.000000\n", + " 0.000001\n", + " (U-238 / total)\n", + " (scatter / flux)\n", + " 0.209989\n", + " 2.303838e-03\n", " \n", " \n", " 2\n", - " 10000\n", - " (0.0e+00 - 6.3e-07)\n", - " (U-235 / total)\n", - " (nu-fission / flux)\n", - " 0.356420\n", - " 3.951669e-03\n", + " 10000\n", + " 0.000000\n", + " 0.000001\n", + " (U-235 / total)\n", + " (nu-fission / flux)\n", + " 0.356420\n", + " 3.951669e-03\n", " \n", " \n", " 3\n", - " 10000\n", - " (0.0e+00 - 6.3e-07)\n", - " (U-235 / total)\n", - " (scatter / flux)\n", - " 0.005555\n", - " 6.101004e-05\n", + " 10000\n", + " 0.000000\n", + " 0.000001\n", + " (U-235 / total)\n", + " (scatter / flux)\n", + " 0.005555\n", + " 6.101004e-05\n", " \n", " \n", " 4\n", - " 10000\n", - " (6.3e-07 - 2.0e+01)\n", - " (U-238 / total)\n", - " (nu-fission / flux)\n", - " 0.007155\n", - " 8.053460e-05\n", + " 10000\n", + " 0.000001\n", + " 20.000000\n", + " (U-238 / total)\n", + " (nu-fission / flux)\n", + " 0.007155\n", + " 8.053460e-05\n", " \n", " \n", " 5\n", - " 10000\n", - " (6.3e-07 - 2.0e+01)\n", - " (U-238 / total)\n", - " (scatter / flux)\n", - " 0.227770\n", - " 1.079289e-03\n", + " 10000\n", + " 0.000001\n", + " 20.000000\n", + " (U-238 / total)\n", + " (scatter / flux)\n", + " 0.227770\n", + " 1.079289e-03\n", " \n", " \n", " 6\n", - " 10000\n", - " (6.3e-07 - 2.0e+01)\n", - " (U-235 / total)\n", - " (nu-fission / flux)\n", - " 0.008067\n", - " 5.254797e-05\n", + " 10000\n", + " 0.000001\n", + " 20.000000\n", + " (U-235 / total)\n", + " (nu-fission / flux)\n", + " 0.008067\n", + " 5.254797e-05\n", " \n", " \n", " 7\n", - " 10000\n", - " (6.3e-07 - 2.0e+01)\n", - " (U-235 / total)\n", - " (scatter / flux)\n", - " 0.003367\n", - " 1.647058e-05\n", + " 10000\n", + " 0.000001\n", + " 20.000000\n", + " (U-235 / total)\n", + " (scatter / flux)\n", + " 0.003367\n", + " 1.647058e-05\n", " \n", " \n", "\n", "" ], "text/plain": [ - " cell energy [MeV] nuclide score mean \\\n", - "0 10000 (0.0e+00 - 6.3e-07) (U-238 / total) (nu-fission / flux) 0.000001 \n", - "1 10000 (0.0e+00 - 6.3e-07) (U-238 / total) (scatter / flux) 0.209989 \n", - "2 10000 (0.0e+00 - 6.3e-07) (U-235 / total) (nu-fission / flux) 0.356420 \n", - "3 10000 (0.0e+00 - 6.3e-07) (U-235 / total) (scatter / flux) 0.005555 \n", - "4 10000 (6.3e-07 - 2.0e+01) (U-238 / total) (nu-fission / flux) 0.007155 \n", - "5 10000 (6.3e-07 - 2.0e+01) (U-238 / total) (scatter / flux) 0.227770 \n", - "6 10000 (6.3e-07 - 2.0e+01) (U-235 / total) (nu-fission / flux) 0.008067 \n", - "7 10000 (6.3e-07 - 2.0e+01) (U-235 / total) (scatter / flux) 0.003367 \n", + " cell energy low [MeV] energy high [MeV] nuclide \\\n", + "0 10000 0.000000 0.000001 (U-238 / total) \n", + "1 10000 0.000000 0.000001 (U-238 / total) \n", + "2 10000 0.000000 0.000001 (U-235 / total) \n", + "3 10000 0.000000 0.000001 (U-235 / total) \n", + "4 10000 0.000001 20.000000 (U-238 / total) \n", + "5 10000 0.000001 20.000000 (U-238 / total) \n", + "6 10000 0.000001 20.000000 (U-235 / total) \n", + "7 10000 0.000001 20.000000 (U-235 / total) \n", "\n", - " std. dev. \n", - "0 7.377419e-09 \n", - "1 2.303838e-03 \n", - "2 3.951669e-03 \n", - "3 6.101004e-05 \n", - "4 8.053460e-05 \n", - "5 1.079289e-03 \n", - "6 5.254797e-05 \n", - "7 1.647058e-05 " + " score mean std. dev. \n", + "0 (nu-fission / flux) 0.000001 7.377419e-09 \n", + "1 (scatter / flux) 0.209989 2.303838e-03 \n", + "2 (nu-fission / flux) 0.356420 3.951669e-03 \n", + "3 (scatter / flux) 0.005555 6.101004e-05 \n", + "4 (nu-fission / flux) 0.007155 8.053460e-05 \n", + "5 (scatter / flux) 0.227770 1.079289e-03 \n", + "6 (nu-fission / flux) 0.008067 5.254797e-05 \n", + "7 (scatter / flux) 0.003367 1.647058e-05 " ] }, "execution_count": 33, @@ -1361,7 +1390,8 @@ " \n", " \n", " cell\n", - " energy [MeV]\n", + " energy low [MeV]\n", + " energy high [MeV]\n", " nuclide\n", " score\n", " mean\n", @@ -1371,50 +1401,60 @@ " \n", " \n", " 0\n", - " 10000\n", - " (0.0e+00 - 6.3e-07)\n", - " U-238\n", - " nu-fission\n", - " 0.000002\n", - " 1.283958e-08\n", + " 10000\n", + " 0.000000\n", + " 0.000001\n", + " U-238\n", + " nu-fission\n", + " 0.000002\n", + " 1.283958e-08\n", " \n", " \n", " 1\n", - " 10000\n", - " (0.0e+00 - 6.3e-07)\n", - " U-235\n", - " nu-fission\n", - " 0.868553\n", - " 6.880390e-03\n", + " 10000\n", + " 0.000000\n", + " 0.000001\n", + " U-235\n", + " nu-fission\n", + " 0.868553\n", + " 6.880390e-03\n", " \n", " \n", " 2\n", - " 10000\n", - " (6.3e-07 - 2.0e+01)\n", - " U-238\n", - " nu-fission\n", - " 0.082149\n", - " 8.837250e-04\n", + " 10000\n", + " 0.000001\n", + " 20.000000\n", + " U-238\n", + " nu-fission\n", + " 0.082149\n", + " 8.837250e-04\n", " \n", " \n", " 3\n", - " 10000\n", - " (6.3e-07 - 2.0e+01)\n", - " U-235\n", - " nu-fission\n", - " 0.092618\n", - " 5.195308e-04\n", + " 10000\n", + " 0.000001\n", + " 20.000000\n", + " U-235\n", + " nu-fission\n", + " 0.092618\n", + " 5.195308e-04\n", " \n", " \n", "\n", "" ], "text/plain": [ - " cell energy [MeV] nuclide score mean std. dev.\n", - "0 10000 (0.0e+00 - 6.3e-07) U-238 nu-fission 0.000002 1.283958e-08\n", - "1 10000 (0.0e+00 - 6.3e-07) U-235 nu-fission 0.868553 6.880390e-03\n", - "2 10000 (6.3e-07 - 2.0e+01) U-238 nu-fission 0.082149 8.837250e-04\n", - "3 10000 (6.3e-07 - 2.0e+01) U-235 nu-fission 0.092618 5.195308e-04" + " cell energy low [MeV] energy high [MeV] nuclide score mean \\\n", + "0 10000 0.000000 0.000001 U-238 nu-fission 0.000002 \n", + "1 10000 0.000000 0.000001 U-235 nu-fission 0.868553 \n", + "2 10000 0.000001 20.000000 U-238 nu-fission 0.082149 \n", + "3 10000 0.000001 20.000000 U-235 nu-fission 0.092618 \n", + "\n", + " std. dev. \n", + "0 1.283958e-08 \n", + "1 6.880390e-03 \n", + "2 8.837250e-04 \n", + "3 5.195308e-04 " ] }, "execution_count": 37, @@ -1444,7 +1484,8 @@ " \n", " \n", " cell\n", - " energy [MeV]\n", + " energy low [MeV]\n", + " energy high [MeV]\n", " nuclide\n", " score\n", " mean\n", @@ -1454,100 +1495,120 @@ " \n", " \n", " 0\n", - " 10002\n", - " (1.0e-08 - 1.1e-07)\n", - " H-1\n", - " scatter\n", - " 4.619398\n", - " 0.040124\n", + " 10002\n", + " 1.000000e-08\n", + " 0.000000\n", + " H-1\n", + " scatter\n", + " 4.619398\n", + " 0.040124\n", " \n", " \n", " 1\n", - " 10002\n", - " (1.1e-07 - 1.2e-06)\n", - " H-1\n", - " scatter\n", - " 2.030757\n", - " 0.011239\n", + " 10002\n", + " 1.080060e-07\n", + " 0.000001\n", + " H-1\n", + " scatter\n", + " 2.030757\n", + " 0.011239\n", " \n", " \n", " 2\n", - " 10002\n", - " (1.2e-06 - 1.3e-05)\n", - " H-1\n", - " scatter\n", - " 1.658488\n", - " 0.009777\n", + " 10002\n", + " 1.166529e-06\n", + " 0.000013\n", + " H-1\n", + " scatter\n", + " 1.658488\n", + " 0.009777\n", " \n", " \n", " 3\n", - " 10002\n", - " (1.3e-05 - 1.4e-04)\n", - " H-1\n", - " scatter\n", - " 1.853002\n", - " 0.007378\n", + " 10002\n", + " 1.259921e-05\n", + " 0.000136\n", + " H-1\n", + " scatter\n", + " 1.853002\n", + " 0.007378\n", " \n", " \n", " 4\n", - " 10002\n", - " (1.4e-04 - 1.5e-03)\n", - " H-1\n", - " scatter\n", - " 2.050773\n", - " 0.012484\n", + " 10002\n", + " 1.360790e-04\n", + " 0.001470\n", + " H-1\n", + " scatter\n", + " 2.050773\n", + " 0.012484\n", " \n", " \n", " 5\n", - " 10002\n", - " (1.5e-03 - 1.6e-02)\n", - " H-1\n", - " scatter\n", - " 2.131759\n", - " 0.007821\n", + " 10002\n", + " 1.469734e-03\n", + " 0.015874\n", + " H-1\n", + " scatter\n", + " 2.131759\n", + " 0.007821\n", " \n", " \n", " 6\n", - " 10002\n", - " (1.6e-02 - 1.7e-01)\n", - " H-1\n", - " scatter\n", - " 2.213710\n", - " 0.015159\n", + " 10002\n", + " 1.587401e-02\n", + " 0.171449\n", + " H-1\n", + " scatter\n", + " 2.213710\n", + " 0.015159\n", " \n", " \n", " 7\n", - " 10002\n", - " (1.7e-01 - 1.9e+00)\n", - " H-1\n", - " scatter\n", - " 2.011925\n", - " 0.009406\n", + " 10002\n", + " 1.714488e-01\n", + " 1.851749\n", + " H-1\n", + " scatter\n", + " 2.011925\n", + " 0.009406\n", " \n", " \n", " 8\n", - " 10002\n", - " (1.9e+00 - 2.0e+01)\n", - " H-1\n", - " scatter\n", - " 0.371280\n", - " 0.003949\n", + " 10002\n", + " 1.851749e+00\n", + " 20.000000\n", + " H-1\n", + " scatter\n", + " 0.371280\n", + " 0.003949\n", " \n", " \n", "\n", "" ], "text/plain": [ - " cell energy [MeV] nuclide score mean std. dev.\n", - "0 10002 (1.0e-08 - 1.1e-07) H-1 scatter 4.619398 0.040124\n", - "1 10002 (1.1e-07 - 1.2e-06) H-1 scatter 2.030757 0.011239\n", - "2 10002 (1.2e-06 - 1.3e-05) H-1 scatter 1.658488 0.009777\n", - "3 10002 (1.3e-05 - 1.4e-04) H-1 scatter 1.853002 0.007378\n", - "4 10002 (1.4e-04 - 1.5e-03) H-1 scatter 2.050773 0.012484\n", - "5 10002 (1.5e-03 - 1.6e-02) H-1 scatter 2.131759 0.007821\n", - "6 10002 (1.6e-02 - 1.7e-01) H-1 scatter 2.213710 0.015159\n", - "7 10002 (1.7e-01 - 1.9e+00) H-1 scatter 2.011925 0.009406\n", - "8 10002 (1.9e+00 - 2.0e+01) H-1 scatter 0.371280 0.003949" + " cell energy low [MeV] energy high [MeV] nuclide score mean \\\n", + "0 10002 1.000000e-08 0.000000 H-1 scatter 4.619398 \n", + "1 10002 1.080060e-07 0.000001 H-1 scatter 2.030757 \n", + "2 10002 1.166529e-06 0.000013 H-1 scatter 1.658488 \n", + "3 10002 1.259921e-05 0.000136 H-1 scatter 1.853002 \n", + "4 10002 1.360790e-04 0.001470 H-1 scatter 2.050773 \n", + "5 10002 1.469734e-03 0.015874 H-1 scatter 2.131759 \n", + "6 10002 1.587401e-02 0.171449 H-1 scatter 2.213710 \n", + "7 10002 1.714488e-01 1.851749 H-1 scatter 2.011925 \n", + "8 10002 1.851749e+00 20.000000 H-1 scatter 0.371280 \n", + "\n", + " std. dev. \n", + "0 0.040124 \n", + "1 0.011239 \n", + "2 0.009777 \n", + "3 0.007378 \n", + "4 0.012484 \n", + "5 0.007821 \n", + "6 0.015159 \n", + "7 0.009406 \n", + "8 0.003949 " ] }, "execution_count": 38, diff --git a/openmc/filter.py b/openmc/filter.py index 54814a6b6..ace98dc42 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -502,9 +502,9 @@ class Filter(object): 2. separate columns for the cell IDs, universe IDs, and lattice IDs and x,y,z cell indices corresponding to each (with summary info). - For 'energy' and 'energyout' filters, the DataFrame include a single - column with each element comprising a string with the lower, upper - energy bounds for each filter bin. + For 'energy' and 'energyout' filters, the DataFrame includes one + column for the lower energy bound and one column for the upper + energy bound for each filter bin. For 'mesh' filters, the DataFrame includes three columns for the x,y,z mesh cell indices corresponding to each filter bin. @@ -719,21 +719,20 @@ class Filter(object): # energy, energyout filters elif 'energy' in self.type: - bins = self.bins - num_bins = self.num_bins + # Extract the lower and upper energy bounds for each result. + lo_bins = self.bins[:-1] + hi_bins = self.bins[1:] - # Create strings for - template = '({0:.1e} - {1:.1e})' - filter_bins = [] - for i in range(num_bins): - filter_bins.append(template.format(bins[i], bins[i+1])) + # Repeat and tile them as necessary to account for other filters. + lo_bins = np.repeat(lo_bins, self.stride) + hi_bins = np.repeat(hi_bins, self.stride) + tile_factor = data_size / len(lo_bins) + lo_bins = np.tile(lo_bins, tile_factor) + hi_bins = np.tile(hi_bins, tile_factor) - # Tile the energy bins into a DataFrame column - filter_bins = np.repeat(filter_bins, self.stride) - tile_factor = data_size / len(filter_bins) - filter_bins = np.tile(filter_bins, tile_factor) - filter_bins = filter_bins - df = pd.concat([df, pd.DataFrame({self.type + ' [MeV]' : filter_bins})]) + # Now stick 'em in the DataFrame. + df.loc[:, self.type + ' low [MeV]'] = lo_bins + df.loc[:, self.type + ' high [MeV]'] = hi_bins # universe, material, surface, cell, and cellborn filters else: diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index f24127458..875a82c46 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -1232,28 +1232,35 @@ class MGXS(object): # Override energy groups bounds with indices all_groups = np.arange(self.num_groups, 0, -1, dtype=np.int) all_groups = np.repeat(all_groups, self.num_nuclides) - if 'energy [MeV]' in df and 'energyout [MeV]' in df: - df.rename(columns={'energy [MeV]': 'group in'}, inplace=True) + if 'energy low [MeV]' in df and 'energyout low [MeV]' in df: + df.rename(columns={'energy low [MeV]': 'group in'}, + inplace=True) in_groups = np.tile(all_groups, self.num_subdomains) in_groups = np.repeat(in_groups, self.num_groups) df['group in'] = in_groups + del df['energy high [MeV]'] - df.rename(columns={'energyout [MeV]': 'group out'}, inplace=True) + df.rename(columns={'energyout low [MeV]': 'group out'}, + inplace=True) out_groups = \ np.tile(all_groups, self.num_subdomains * self.num_groups) df['group out'] = out_groups + del df['energyout high [MeV]'] columns = ['group in', 'group out'] - elif 'energyout [MeV]' in df: - df.rename(columns={'energyout [MeV]': 'group out'}, inplace=True) + elif 'energyout low [MeV]' in df: + df.rename(columns={'energyout low [MeV]': 'group out'}, + inplace=True) in_groups = np.tile(all_groups, self.num_subdomains) df['group out'] = in_groups + del df['energyout high [MeV]'] columns = ['group out'] - elif 'energy [MeV]' in df: - df.rename(columns={'energy [MeV]': 'group in'}, inplace=True) + elif 'energy low [MeV]' in df: + df.rename(columns={'energy low [MeV]': 'group in'}, inplace=True) in_groups = np.tile(all_groups, self.num_subdomains) df['group in'] = in_groups + del df['energy high [MeV]'] columns = ['group in'] # Select out those groups the user requested