diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index 24c5c00a0..384fa7620 100644 --- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb +++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb @@ -374,7 +374,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -562,9 +562,9 @@ "\n", " Copyright: 2011-2015 Massachusetts Institute of Technology\n", " License: http://mit-crpg.github.io/openmc/license.html\n", - " Version: 0.6.2\n", - " Date/Time: 2015-08-11 13:40:43\n", - " MPI Processes: 4\n", + " Version: 0.7.0\n", + " Git SHA1: 36a516ed8125ab8a86d8c9b3aee4bd4bc2db859c\n", + " Date/Time: 2015-09-16 18:22:08\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -590,38 +590,39 @@ "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", - " 1/1 0.60069 \n", - " 2/1 0.62857 \n", - " 3/1 0.69431 \n", - " 4/1 0.65935 \n", - " 5/1 0.68092 \n", - " 6/1 0.64791 \n", - " 7/1 0.65859 0.65325 +/- 0.00534\n", - " 8/1 0.67381 0.66010 +/- 0.00752\n", - " 9/1 0.74149 0.68045 +/- 0.02103\n", - " 10/1 0.68244 0.68085 +/- 0.01629\n", - " 11/1 0.68068 0.68082 +/- 0.01330\n", - " 12/1 0.70394 0.68412 +/- 0.01172\n", - " 13/1 0.68624 0.68439 +/- 0.01015\n", - " 14/1 0.65667 0.68131 +/- 0.00947\n", - " 15/1 0.70080 0.68326 +/- 0.00869\n", - " 16/1 0.69639 0.68445 +/- 0.00795\n", - " 17/1 0.68786 0.68474 +/- 0.00726\n", - " 18/1 0.63698 0.68106 +/- 0.00762\n", - " 19/1 0.62785 0.67726 +/- 0.00802\n", - " 20/1 0.65759 0.67595 +/- 0.00758\n", - " Triggers unsatisfied, max unc./thresh. is 1.20713 for absorption in tally 10002\n", - " The estimated number of batches is 27\n", + " 1/1 0.59998 \n", + " 2/1 0.65473 \n", + " 3/1 0.67452 \n", + " 4/1 0.66458 \n", + " 5/1 0.70093 \n", + " 6/1 0.70726 \n", + " 7/1 0.65977 0.68351 +/- 0.02375\n", + " 8/1 0.68457 0.68387 +/- 0.01372\n", + " 9/1 0.70024 0.68796 +/- 0.01053\n", + " 10/1 0.64895 0.68016 +/- 0.01128\n", + " 11/1 0.68744 0.68137 +/- 0.00929\n", + " 12/1 0.68037 0.68123 +/- 0.00786\n", + " 13/1 0.64865 0.67715 +/- 0.00793\n", + " 14/1 0.71415 0.68127 +/- 0.00811\n", + " 15/1 0.65717 0.67886 +/- 0.00764\n", + " 16/1 0.71598 0.68223 +/- 0.00769\n", + " 17/1 0.67285 0.68145 +/- 0.00707\n", + " 18/1 0.69329 0.68236 +/- 0.00656\n", + " 19/1 0.65696 0.68055 +/- 0.00634\n", + " 20/1 0.65500 0.67884 +/- 0.00615\n", + " Triggers unsatisfied, max unc./thresh. is 1.21110 for absorption in tally 10002\n", + " The estimated number of batches is 28\n", " Creating state point statepoint.020.h5...\n", - " 21/1 0.68391 0.67645 +/- 0.00711\n", - " 22/1 0.69243 0.67739 +/- 0.00674\n", - " 23/1 0.65491 0.67614 +/- 0.00648\n", - " 24/1 0.64021 0.67425 +/- 0.00641\n", - " 25/1 0.72281 0.67668 +/- 0.00655\n", - " 26/1 0.71261 0.67839 +/- 0.00646\n", - " 27/1 0.69503 0.67914 +/- 0.00621\n", - " Triggers satisfied for batch 27\n", - " Creating state point statepoint.027.h5...\n", + " 21/1 0.67090 0.67835 +/- 0.00577\n", + " 22/1 0.69025 0.67905 +/- 0.00546\n", + " 23/1 0.66113 0.67805 +/- 0.00525\n", + " 24/1 0.67934 0.67812 +/- 0.00496\n", + " 25/1 0.67203 0.67781 +/- 0.00472\n", + " 26/1 0.66928 0.67741 +/- 0.00451\n", + " 27/1 0.70271 0.67856 +/- 0.00445\n", + " 28/1 0.70233 0.67959 +/- 0.00437\n", + " Triggers satisfied for batch 28\n", + " Creating state point statepoint.028.h5...\n", "\n", " ===========================================================================\n", " ======================> SIMULATION FINISHED <======================\n", @@ -630,28 +631,28 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 9.7300E-01 seconds\n", - " Reading cross sections = 3.0300E-01 seconds\n", - " Total time in simulation = 5.9130E+00 seconds\n", - " Time in transport only = 5.4000E+00 seconds\n", - " Time in inactive batches = 7.7300E-01 seconds\n", - " Time in active batches = 5.1400E+00 seconds\n", - " Time synchronizing fission bank = 4.4600E-01 seconds\n", - " Sampling source sites = 0.0000E+00 seconds\n", + " Total time for initialization = 5.5700E-01 seconds\n", + " Reading cross sections = 1.8900E-01 seconds\n", + " Total time in simulation = 1.1416E+01 seconds\n", + " Time in transport only = 1.1395E+01 seconds\n", + " Time in inactive batches = 1.4590E+00 seconds\n", + " Time in active batches = 9.9570E+00 seconds\n", + " Time synchronizing fission bank = 4.0000E-03 seconds\n", + " Sampling source sites = 4.0000E-03 seconds\n", " SEND/RECV source sites = 0.0000E+00 seconds\n", - " Time accumulating tallies = 9.0000E-03 seconds\n", + " Time accumulating tallies = 1.0000E-03 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 6.8870E+00 seconds\n", - " Calculation Rate (inactive) = 16170.8 neutrons/second\n", - " Calculation Rate (active) = 7295.72 neutrons/second\n", + " Total time elapsed = 1.1985E+01 seconds\n", + " Calculation Rate (inactive) = 8567.51 neutrons/second\n", + " Calculation Rate (active) = 3766.19 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.68117 +/- 0.00597\n", - " k-effective (Track-length) = 0.67914 +/- 0.00621\n", - " k-effective (Absorption) = 0.67898 +/- 0.00471\n", - " Combined k-effective = 0.67922 +/- 0.00479\n", - " Leakage Fraction = 0.34264 +/- 0.00301\n", + " k-effective (Collision) = 0.68196 +/- 0.00427\n", + " k-effective (Track-length) = 0.67959 +/- 0.00437\n", + " k-effective (Absorption) = 0.67957 +/- 0.00402\n", + " Combined k-effective = 0.67943 +/- 0.00295\n", + " Leakage Fraction = 0.34370 +/- 0.00201\n", "\n" ] }, @@ -671,7 +672,7 @@ "!rm statepoint.*\n", "\n", "# Run OpenMC with MPI!\n", - "executor.run_simulation(mpi_procs=4)" + "executor.run_simulation()" ] }, { @@ -694,9 +695,7 @@ "statepoints = glob.glob('statepoint.*.h5')\n", "\n", "# Load the last statepoint file\n", - "sp = StatePoint(statepoints[-1])\n", - "sp.read_results()\n", - "sp.compute_stdev()" + "sp = StatePoint(statepoints[-1])" ] }, { @@ -770,13 +769,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.14583021]]\n", + "[[[ 0.15044911]]\n", "\n", - " [[ 0.07846909]]\n", + " [[ 0.09149973]]\n", "\n", - " [[ 0.33705448]]\n", + " [[ 0.27611475]]\n", "\n", - " [[ 0.15150059]]]\n" + " [[ 0.12476673]]]\n" ] } ], @@ -840,7 +839,7 @@ " 0.0e+00 - 6.3e-07\n", " fission\n", " 0.000236\n", - " 0.000034\n", + " 0.000035\n", " \n", " \n", " 1\n", @@ -849,8 +848,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " nu-fission\n", - " 0.000576\n", - " 0.000084\n", + " 0.000574\n", + " 0.000086\n", " \n", " \n", " 2\n", @@ -859,8 +858,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " fission\n", - " 0.000069\n", - " 0.000004\n", + " 0.000072\n", + " 0.000006\n", " \n", " \n", " 3\n", @@ -869,8 +868,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " nu-fission\n", - " 0.000183\n", - " 0.000011\n", + " 0.000190\n", + " 0.000014\n", " \n", " \n", " 4\n", @@ -879,8 +878,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " fission\n", - " 0.000366\n", - " 0.000052\n", + " 0.000451\n", + " 0.000058\n", " \n", " \n", " 5\n", @@ -889,8 +888,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " nu-fission\n", - " 0.000892\n", - " 0.000127\n", + " 0.001100\n", + " 0.000141\n", " \n", " \n", " 6\n", @@ -899,8 +898,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " fission\n", - " 0.000109\n", - " 0.000009\n", + " 0.000095\n", + " 0.000006\n", " \n", " \n", " 7\n", @@ -909,8 +908,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " nu-fission\n", - " 0.000284\n", - " 0.000021\n", + " 0.000250\n", + " 0.000016\n", " \n", " \n", " 8\n", @@ -919,8 +918,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " fission\n", - " 0.000540\n", - " 0.000058\n", + " 0.000575\n", + " 0.000080\n", " \n", " \n", " 9\n", @@ -929,8 +928,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " nu-fission\n", - " 0.001316\n", - " 0.000141\n", + " 0.001401\n", + " 0.000194\n", " \n", " \n", " 10\n", @@ -939,8 +938,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " fission\n", - " 0.000144\n", - " 0.000017\n", + " 0.000134\n", + " 0.000011\n", " \n", " \n", " 11\n", @@ -949,8 +948,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " nu-fission\n", - " 0.000376\n", - " 0.000041\n", + " 0.000353\n", + " 0.000028\n", " \n", " \n", " 12\n", @@ -959,8 +958,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " fission\n", - " 0.000830\n", - " 0.000085\n", + " 0.000655\n", + " 0.000071\n", " \n", " \n", " 13\n", @@ -969,8 +968,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " nu-fission\n", - " 0.002022\n", - " 0.000207\n", + " 0.001596\n", + " 0.000174\n", " \n", " \n", " 14\n", @@ -979,8 +978,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " fission\n", - " 0.000168\n", - " 0.000013\n", + " 0.000149\n", + " 0.000009\n", " \n", " \n", " 15\n", @@ -989,8 +988,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " nu-fission\n", - " 0.000434\n", - " 0.000034\n", + " 0.000391\n", + " 0.000023\n", " \n", " \n", " 16\n", @@ -999,8 +998,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " fission\n", - " 0.000738\n", - " 0.000043\n", + " 0.000781\n", + " 0.000078\n", " \n", " \n", " 17\n", @@ -1009,8 +1008,8 @@ " 1\n", " 0.0e+00 - 6.3e-07\n", " nu-fission\n", - " 0.001799\n", - " 0.000105\n", + " 0.001903\n", + " 0.000191\n", " \n", " \n", " 18\n", @@ -1019,8 +1018,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " fission\n", - " 0.000186\n", - " 0.000010\n", + " 0.000185\n", + " 0.000009\n", " \n", " \n", " 19\n", @@ -1029,8 +1028,8 @@ " 1\n", " 6.3e-07 - 2.0e+01\n", " nu-fission\n", - " 0.000486\n", - " 0.000026\n", + " 0.000484\n", + " 0.000024\n", " \n", " \n", "\n", @@ -1040,26 +1039,26 @@ " mesh 1 energy [MeV] score mean std. dev.\n", " x y z \n", "bin \n", - "0 1 1 1 0.0e+00 - 6.3e-07 fission 0.000236 0.000034\n", - "1 1 1 1 0.0e+00 - 6.3e-07 nu-fission 0.000576 0.000084\n", - "2 1 1 1 6.3e-07 - 2.0e+01 fission 0.000069 0.000004\n", - "3 1 1 1 6.3e-07 - 2.0e+01 nu-fission 0.000183 0.000011\n", - "4 1 2 1 0.0e+00 - 6.3e-07 fission 0.000366 0.000052\n", - "5 1 2 1 0.0e+00 - 6.3e-07 nu-fission 0.000892 0.000127\n", - "6 1 2 1 6.3e-07 - 2.0e+01 fission 0.000109 0.000009\n", - "7 1 2 1 6.3e-07 - 2.0e+01 nu-fission 0.000284 0.000021\n", - "8 1 3 1 0.0e+00 - 6.3e-07 fission 0.000540 0.000058\n", - "9 1 3 1 0.0e+00 - 6.3e-07 nu-fission 0.001316 0.000141\n", - "10 1 3 1 6.3e-07 - 2.0e+01 fission 0.000144 0.000017\n", - "11 1 3 1 6.3e-07 - 2.0e+01 nu-fission 0.000376 0.000041\n", - "12 1 4 1 0.0e+00 - 6.3e-07 fission 0.000830 0.000085\n", - "13 1 4 1 0.0e+00 - 6.3e-07 nu-fission 0.002022 0.000207\n", - "14 1 4 1 6.3e-07 - 2.0e+01 fission 0.000168 0.000013\n", - "15 1 4 1 6.3e-07 - 2.0e+01 nu-fission 0.000434 0.000034\n", - "16 1 5 1 0.0e+00 - 6.3e-07 fission 0.000738 0.000043\n", - "17 1 5 1 0.0e+00 - 6.3e-07 nu-fission 0.001799 0.000105\n", - "18 1 5 1 6.3e-07 - 2.0e+01 fission 0.000186 0.000010\n", - "19 1 5 1 6.3e-07 - 2.0e+01 nu-fission 0.000486 0.000026" + "0 1 1 1 0.0e+00 - 6.3e-07 fission 0.000236 0.000035\n", + "1 1 1 1 0.0e+00 - 6.3e-07 nu-fission 0.000574 0.000086\n", + "2 1 1 1 6.3e-07 - 2.0e+01 fission 0.000072 0.000006\n", + "3 1 1 1 6.3e-07 - 2.0e+01 nu-fission 0.000190 0.000014\n", + "4 1 2 1 0.0e+00 - 6.3e-07 fission 0.000451 0.000058\n", + "5 1 2 1 0.0e+00 - 6.3e-07 nu-fission 0.001100 0.000141\n", + "6 1 2 1 6.3e-07 - 2.0e+01 fission 0.000095 0.000006\n", + "7 1 2 1 6.3e-07 - 2.0e+01 nu-fission 0.000250 0.000016\n", + "8 1 3 1 0.0e+00 - 6.3e-07 fission 0.000575 0.000080\n", + "9 1 3 1 0.0e+00 - 6.3e-07 nu-fission 0.001401 0.000194\n", + "10 1 3 1 6.3e-07 - 2.0e+01 fission 0.000134 0.000011\n", + "11 1 3 1 6.3e-07 - 2.0e+01 nu-fission 0.000353 0.000028\n", + "12 1 4 1 0.0e+00 - 6.3e-07 fission 0.000655 0.000071\n", + "13 1 4 1 0.0e+00 - 6.3e-07 nu-fission 0.001596 0.000174\n", + "14 1 4 1 6.3e-07 - 2.0e+01 fission 0.000149 0.000009\n", + "15 1 4 1 6.3e-07 - 2.0e+01 nu-fission 0.000391 0.000023\n", + "16 1 5 1 0.0e+00 - 6.3e-07 fission 0.000781 0.000078\n", + "17 1 5 1 0.0e+00 - 6.3e-07 nu-fission 0.001903 0.000191\n", + "18 1 5 1 6.3e-07 - 2.0e+01 fission 0.000185 0.000009\n", + "19 1 5 1 6.3e-07 - 2.0e+01 nu-fission 0.000484 0.000024" ] }, "execution_count": 25, @@ -1084,9 +1083,9 @@ "outputs": [ { "data": { - "image/png": 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EhKT+8u1AS0TslPR24E5Jp0XEcwXbYWZmwygvaWwDWkrmW8h6DLXqTEl1jqhQ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\nAAAAAElFTkSuQmCC\n", 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BuoCVABGxG9idph+U9DjQDDxYHtTe3t4/3dLSQmtra86uWJ7u7u5GN8FsQM4881i6un7Z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\neLpkfksqK1JnUo3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FJ00za7/qq1HOBLojoicieoGbgLkNdc4CrgeIiAeBsZImNIuNiBUR8XhBe3OB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\nTkSuQmCC\n", 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z0jSz9utr8bWnI4B1De/X52Wt1JnaQmyzqey+NvqAMSP4mqaZjVrbake2Ort1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\nEvx/rHWCrxSlro8AAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1166,7 +1165,7 @@ "\tFilters =\t\n", " \t\tcell\t[10000]\n", "\tNuclides =\tU-235 U-238 \n", - "\tScores =\t['scatter-Y0,0', 'scatter-Y1,-1', 'scatter-Y1,0', 'scatter-Y1,1', 'scatter-Y2,-2', 'scatter-Y2,-1', 'scatter-Y2,0', 'scatter-Y2,1', 'scatter-Y2,2']\n", + "\tScores =\t[u'scatter-Y0,0', u'scatter-Y1,-1', u'scatter-Y1,0', u'scatter-Y1,1', u'scatter-Y2,-2', u'scatter-Y2,-1', u'scatter-Y2,0', u'scatter-Y2,1', u'scatter-Y2,2']\n", "\tEstimator =\tanalog\n", "\n" ] @@ -1217,143 +1216,143 @@ " U-235\n", " scatter-Y0,0\n", " 0.036453\n", - " 0.000941\n", + " 0.001219\n", " \n", " \n", " 1\n", " 10000\n", " U-235\n", " scatter-Y1,-1\n", - " -0.000725\n", - " 0.000261\n", + " 0.000302\n", + " 0.000314\n", " \n", " \n", " 2\n", " 10000\n", " U-235\n", " scatter-Y1,0\n", - " -0.000088\n", - " 0.000408\n", + " -0.000006\n", + " 0.000347\n", " \n", " \n", " 3\n", " 10000\n", " U-235\n", " scatter-Y1,1\n", - " 0.000986\n", - " 0.000412\n", + " 0.000244\n", + " 0.000286\n", " \n", " \n", " 4\n", " 10000\n", " U-235\n", " scatter-Y2,-2\n", - " 0.000098\n", - " 0.000204\n", + " 0.000184\n", + " 0.000211\n", " \n", " \n", " 5\n", " 10000\n", " U-235\n", " scatter-Y2,-1\n", - " -0.000358\n", - " 0.000247\n", + " 0.000067\n", + " 0.000173\n", " \n", " \n", " 6\n", " 10000\n", " U-235\n", " scatter-Y2,0\n", - " 0.000197\n", - " 0.000140\n", + " 0.000353\n", + " 0.000210\n", " \n", " \n", " 7\n", " 10000\n", " U-235\n", " scatter-Y2,1\n", - " -0.000084\n", - " 0.000196\n", + " -0.000266\n", + " 0.000263\n", " \n", " \n", " 8\n", " 10000\n", " U-235\n", " scatter-Y2,2\n", - " 0.000052\n", - " 0.000168\n", + " -0.000246\n", + " 0.000153\n", " \n", " \n", " 9\n", " 10000\n", " U-238\n", " scatter-Y0,0\n", - " 2.325600\n", - " 0.015545\n", + " 2.315893\n", + " 0.008243\n", " \n", " \n", " 10\n", " 10000\n", " U-238\n", " scatter-Y1,-1\n", - " -0.030089\n", - " 0.002460\n", + " -0.022028\n", + " 0.002316\n", " \n", " \n", " 11\n", " 10000\n", " U-238\n", " scatter-Y1,0\n", - " -0.004451\n", - " 0.003663\n", + " -0.003426\n", + " 0.002651\n", " \n", " \n", " 12\n", " 10000\n", " U-238\n", " scatter-Y1,1\n", - " 0.020832\n", - " 0.002831\n", + " 0.026620\n", + " 0.002084\n", " \n", " \n", " 13\n", " 10000\n", " U-238\n", " scatter-Y2,-2\n", - " -0.004149\n", - " 0.001530\n", + " -0.001295\n", + " 0.001627\n", " \n", " \n", " 14\n", " 10000\n", " U-238\n", " scatter-Y2,-1\n", - " 0.000735\n", - " 0.001729\n", + " 0.000759\n", + " 0.001426\n", " \n", " \n", " 15\n", " 10000\n", " U-238\n", " scatter-Y2,0\n", - " 0.003431\n", - " 0.002098\n", + " 0.005513\n", + " 0.001983\n", " \n", " \n", " 16\n", " 10000\n", " U-238\n", " scatter-Y2,1\n", - " 0.000385\n", - " 0.001263\n", + " 0.000431\n", + " 0.001862\n", " \n", " \n", " 17\n", " 10000\n", " U-238\n", " scatter-Y2,2\n", - " 0.000002\n", - " 0.001718\n", + " -0.001962\n", + " 0.001222\n", " \n", " \n", "\n", @@ -1362,24 +1361,24 @@ "text/plain": [ " cell nuclide score mean std. dev.\n", "bin \n", - "0 10000 U-235 scatter-Y0,0 0.036453 0.000941\n", - "1 10000 U-235 scatter-Y1,-1 -0.000725 0.000261\n", - "2 10000 U-235 scatter-Y1,0 -0.000088 0.000408\n", - "3 10000 U-235 scatter-Y1,1 0.000986 0.000412\n", - "4 10000 U-235 scatter-Y2,-2 0.000098 0.000204\n", - "5 10000 U-235 scatter-Y2,-1 -0.000358 0.000247\n", - "6 10000 U-235 scatter-Y2,0 0.000197 0.000140\n", - "7 10000 U-235 scatter-Y2,1 -0.000084 0.000196\n", - "8 10000 U-235 scatter-Y2,2 0.000052 0.000168\n", - "9 10000 U-238 scatter-Y0,0 2.325600 0.015545\n", - "10 10000 U-238 scatter-Y1,-1 -0.030089 0.002460\n", - "11 10000 U-238 scatter-Y1,0 -0.004451 0.003663\n", - "12 10000 U-238 scatter-Y1,1 0.020832 0.002831\n", - "13 10000 U-238 scatter-Y2,-2 -0.004149 0.001530\n", - "14 10000 U-238 scatter-Y2,-1 0.000735 0.001729\n", - "15 10000 U-238 scatter-Y2,0 0.003431 0.002098\n", - "16 10000 U-238 scatter-Y2,1 0.000385 0.001263\n", - "17 10000 U-238 scatter-Y2,2 0.000002 0.001718" + "0 10000 U-235 scatter-Y0,0 0.036453 0.001219\n", + "1 10000 U-235 scatter-Y1,-1 0.000302 0.000314\n", + "2 10000 U-235 scatter-Y1,0 -0.000006 0.000347\n", + "3 10000 U-235 scatter-Y1,1 0.000244 0.000286\n", + "4 10000 U-235 scatter-Y2,-2 0.000184 0.000211\n", + "5 10000 U-235 scatter-Y2,-1 0.000067 0.000173\n", + "6 10000 U-235 scatter-Y2,0 0.000353 0.000210\n", + "7 10000 U-235 scatter-Y2,1 -0.000266 0.000263\n", + "8 10000 U-235 scatter-Y2,2 -0.000246 0.000153\n", + "9 10000 U-238 scatter-Y0,0 2.315893 0.008243\n", + "10 10000 U-238 scatter-Y1,-1 -0.022028 0.002316\n", + "11 10000 U-238 scatter-Y1,0 -0.003426 0.002651\n", + "12 10000 U-238 scatter-Y1,1 0.026620 0.002084\n", + "13 10000 U-238 scatter-Y2,-2 -0.001295 0.001627\n", + "14 10000 U-238 scatter-Y2,-1 0.000759 0.001426\n", + "15 10000 U-238 scatter-Y2,0 0.005513 0.001983\n", + "16 10000 U-238 scatter-Y2,1 0.000431 0.001862\n", + "17 10000 U-238 scatter-Y2,2 -0.001962 0.001222" ] }, "execution_count": 29, @@ -1413,8 +1412,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00171834 0.01554515]\n", - " [ 0.00016768 0.00094081]]]\n" + "[[[ 0.00122163 0.00824348]\n", + " [ 0.00015287 0.00121882]]]\n" ] } ], @@ -1482,25 +1481,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.04682759]]\n", + "[[[ 0.0400168 ]]\n", "\n", - " [[ 0.03205271]]\n", + " [[ 0.05233031]]\n", "\n", - " [[ 0.03592433]]\n", + " [[ 0.03819276]]\n", "\n", - " [[ 0.02417979]]\n", + " [[ 0.02900783]]\n", "\n", - " [[ 0.02524314]]\n", + " [[ 0.03176394]]\n", "\n", - " [[ 0.02390359]]\n", + " [[ 0.03046477]]\n", "\n", - " [[ 0.0274475 ]]\n", + " [[ 0.03864163]]\n", "\n", - " [[ 0.02827721]]\n", + " [[ 0.02455132]]\n", "\n", - " [[ 0.0231313 ]]\n", + " [[ 0.02282716]]\n", "\n", - " [[ 0.01898386]]]\n" + " [[ 0.02162945]]]\n" ] } ], @@ -1552,141 +1551,141 @@ " 558\n", " 279\n", " absorption\n", - " 0.000095\n", - " 0.000009\n", + " 0.000085\n", + " 0.000008\n", " \n", " \n", " 559\n", " 279\n", " scatter\n", - " 0.013611\n", - " 0.000544\n", + " 0.013429\n", + " 0.000449\n", " \n", " \n", " 560\n", " 280\n", " absorption\n", - " 0.000096\n", - " 0.000009\n", + " 0.000095\n", + " 0.000014\n", " \n", " \n", " 561\n", " 280\n", " scatter\n", - " 0.013999\n", - " 0.000568\n", + " 0.014770\n", + " 0.000783\n", " \n", " \n", " 562\n", " 281\n", " absorption\n", - " 0.000117\n", - " 0.000015\n", + " 0.000107\n", + " 0.000013\n", " \n", " \n", " 563\n", " 281\n", " scatter\n", - " 0.015951\n", - " 0.000682\n", + " 0.015044\n", + " 0.000605\n", " \n", " \n", " 564\n", " 282\n", " absorption\n", - " 0.000104\n", - " 0.000011\n", + " 0.000110\n", + " 0.000010\n", " \n", " \n", " 565\n", " 282\n", " scatter\n", - " 0.016057\n", - " 0.000574\n", + " 0.016090\n", + " 0.000795\n", " \n", " \n", " 566\n", " 283\n", " absorption\n", - " 0.000117\n", - " 0.000013\n", + " 0.000121\n", + " 0.000012\n", " \n", " \n", " 567\n", " 283\n", " scatter\n", - " 0.015997\n", - " 0.000706\n", + " 0.017010\n", + " 0.000793\n", " \n", " \n", " 568\n", " 284\n", " absorption\n", - " 0.000108\n", + " 0.000110\n", " 0.000007\n", " \n", " \n", " 569\n", " 284\n", " scatter\n", - " 0.016720\n", - " 0.000639\n", + " 0.017010\n", + " 0.000430\n", " \n", " \n", " 570\n", " 285\n", " absorption\n", - " 0.000116\n", - " 0.000008\n", + " 0.000112\n", + " 0.000007\n", " \n", " \n", " 571\n", " 285\n", " scatter\n", - " 0.017764\n", - " 0.000639\n", + " 0.017499\n", + " 0.000615\n", " \n", " \n", " 572\n", " 286\n", " absorption\n", - " 0.000111\n", - " 0.000014\n", + " 0.000127\n", + " 0.000016\n", " \n", " \n", " 573\n", " 286\n", " scatter\n", - " 0.018101\n", - " 0.000766\n", + " 0.017716\n", + " 0.000690\n", " \n", " \n", " 574\n", " 287\n", " absorption\n", - " 0.000115\n", - " 0.000012\n", + " 0.000119\n", + " 0.000013\n", " \n", " \n", " 575\n", " 287\n", " scatter\n", - " 0.018411\n", - " 0.000655\n", + " 0.018041\n", + " 0.000702\n", " \n", " \n", " 576\n", " 288\n", " absorption\n", - " 0.000138\n", - " 0.000014\n", + " 0.000125\n", + " 0.000013\n", " \n", " \n", " 577\n", " 288\n", " scatter\n", - " 0.019154\n", - " 0.000763\n", + " 0.018212\n", + " 0.000715\n", " \n", " \n", "\n", @@ -1695,26 +1694,26 @@ "text/plain": [ " distribcell score mean std. dev.\n", "bin \n", - "558 279 absorption 0.000095 0.000009\n", - "559 279 scatter 0.013611 0.000544\n", - "560 280 absorption 0.000096 0.000009\n", - "561 280 scatter 0.013999 0.000568\n", - "562 281 absorption 0.000117 0.000015\n", - "563 281 scatter 0.015951 0.000682\n", - "564 282 absorption 0.000104 0.000011\n", - "565 282 scatter 0.016057 0.000574\n", - "566 283 absorption 0.000117 0.000013\n", - "567 283 scatter 0.015997 0.000706\n", - "568 284 absorption 0.000108 0.000007\n", - "569 284 scatter 0.016720 0.000639\n", - "570 285 absorption 0.000116 0.000008\n", - "571 285 scatter 0.017764 0.000639\n", - "572 286 absorption 0.000111 0.000014\n", - "573 286 scatter 0.018101 0.000766\n", - "574 287 absorption 0.000115 0.000012\n", - "575 287 scatter 0.018411 0.000655\n", - "576 288 absorption 0.000138 0.000014\n", - "577 288 scatter 0.019154 0.000763" + "558 279 absorption 0.000085 0.000008\n", + "559 279 scatter 0.013429 0.000449\n", + "560 280 absorption 0.000095 0.000014\n", + "561 280 scatter 0.014770 0.000783\n", + "562 281 absorption 0.000107 0.000013\n", + "563 281 scatter 0.015044 0.000605\n", + "564 282 absorption 0.000110 0.000010\n", + "565 282 scatter 0.016090 0.000795\n", + "566 283 absorption 0.000121 0.000012\n", + "567 283 scatter 0.017010 0.000793\n", + "568 284 absorption 0.000110 0.000007\n", + "569 284 scatter 0.017010 0.000430\n", + "570 285 absorption 0.000112 0.000007\n", + "571 285 scatter 0.017499 0.000615\n", + "572 286 absorption 0.000127 0.000016\n", + "573 286 scatter 0.017716 0.000690\n", + "574 287 absorption 0.000119 0.000013\n", + "575 287 scatter 0.018041 0.000702\n", + "576 288 absorption 0.000125 0.000013\n", + "577 288 scatter 0.018212 0.000715" ] }, "execution_count": 33, @@ -1816,8 +1815,8 @@ " 10000\n", " 0\n", " absorption\n", - " 0.000122\n", - " 0.000010\n", + " 0.000136\n", + " 0.000017\n", " \n", " \n", " 1\n", @@ -1831,8 +1830,8 @@ " 10000\n", " 0\n", " scatter\n", - " 0.018596\n", - " 0.000871\n", + " 0.018504\n", + " 0.000740\n", " \n", " \n", " 2\n", @@ -1846,8 +1845,8 @@ " 10000\n", " 1\n", " absorption\n", - " 0.000206\n", - " 0.000014\n", + " 0.000231\n", + " 0.000031\n", " \n", " \n", " 3\n", @@ -1861,8 +1860,8 @@ " 10000\n", " 1\n", " scatter\n", - " 0.029733\n", - " 0.000953\n", + " 0.029149\n", + " 0.001525\n", " \n", " \n", " 4\n", @@ -1876,8 +1875,8 @@ " 10000\n", " 2\n", " absorption\n", - " 0.000280\n", - " 0.000018\n", + " 0.000306\n", + " 0.000032\n", " \n", " \n", " 5\n", @@ -1891,8 +1890,8 @@ " 10000\n", " 2\n", " scatter\n", - " 0.038494\n", - " 0.001383\n", + " 0.039770\n", + " 0.001519\n", " \n", " \n", " 6\n", @@ -1906,8 +1905,8 @@ " 10000\n", " 3\n", " absorption\n", - " 0.000384\n", - " 0.000026\n", + " 0.000339\n", + " 0.000028\n", " \n", " \n", " 7\n", @@ -1921,8 +1920,8 @@ " 10000\n", " 3\n", " scatter\n", - " 0.048839\n", - " 0.001181\n", + " 0.046708\n", + " 0.001355\n", " \n", " \n", " 8\n", @@ -1936,8 +1935,8 @@ " 10000\n", " 4\n", " absorption\n", - " 0.000457\n", - " 0.000023\n", + " 0.000433\n", + " 0.000031\n", " \n", " \n", " 9\n", @@ -1951,8 +1950,8 @@ " 10000\n", " 4\n", " scatter\n", - " 0.058061\n", - " 0.001466\n", + " 0.056359\n", + " 0.001790\n", " \n", " \n", " 10\n", @@ -1966,8 +1965,8 @@ " 10000\n", " 5\n", " absorption\n", - " 0.000494\n", - " 0.000026\n", + " 0.000538\n", + " 0.000028\n", " \n", " \n", " 11\n", @@ -1981,8 +1980,8 @@ " 10000\n", " 5\n", " scatter\n", - " 0.065874\n", - " 0.001575\n", + " 0.064943\n", + " 0.001978\n", " \n", " \n", " 12\n", @@ -1996,8 +1995,8 @@ " 10000\n", " 6\n", " absorption\n", - " 0.000490\n", - " 0.000032\n", + " 0.000588\n", + " 0.000028\n", " \n", " \n", " 13\n", @@ -2011,8 +2010,8 @@ " 10000\n", " 6\n", " scatter\n", - " 0.072420\n", - " 0.001988\n", + " 0.070231\n", + " 0.002714\n", " \n", " \n", " 14\n", @@ -2026,8 +2025,8 @@ " 10000\n", " 7\n", " absorption\n", - " 0.000605\n", - " 0.000042\n", + " 0.000670\n", + " 0.000041\n", " \n", " \n", " 15\n", @@ -2041,8 +2040,8 @@ " 10000\n", " 7\n", " scatter\n", - " 0.078802\n", - " 0.002228\n", + " 0.075852\n", + " 0.001862\n", " \n", " \n", " 16\n", @@ -2056,8 +2055,8 @@ " 10000\n", " 8\n", " absorption\n", - " 0.000627\n", - " 0.000037\n", + " 0.000745\n", + " 0.000039\n", " \n", " \n", " 17\n", @@ -2071,8 +2070,8 @@ " 10000\n", " 8\n", " scatter\n", - " 0.083684\n", - " 0.001936\n", + " 0.086234\n", + " 0.001968\n", " \n", " \n", " 18\n", @@ -2086,8 +2085,8 @@ " 10000\n", " 9\n", " absorption\n", - " 0.000711\n", - " 0.000040\n", + " 0.000731\n", + " 0.000039\n", " \n", " \n", " 19\n", @@ -2101,8 +2100,8 @@ " 10000\n", " 9\n", " scatter\n", - " 0.088989\n", - " 0.001689\n", + " 0.090448\n", + " 0.001956\n", " \n", " \n", "\n", @@ -2138,26 +2137,26 @@ " \n", " \n", "bin \n", - "0 0.000122 0.000010 \n", - "1 0.018596 0.000871 \n", - "2 0.000206 0.000014 \n", - "3 0.029733 0.000953 \n", - "4 0.000280 0.000018 \n", - "5 0.038494 0.001383 \n", - "6 0.000384 0.000026 \n", - "7 0.048839 0.001181 \n", - "8 0.000457 0.000023 \n", - "9 0.058061 0.001466 \n", - "10 0.000494 0.000026 \n", - "11 0.065874 0.001575 \n", - "12 0.000490 0.000032 \n", - "13 0.072420 0.001988 \n", - "14 0.000605 0.000042 \n", - "15 0.078802 0.002228 \n", - "16 0.000627 0.000037 \n", - "17 0.083684 0.001936 \n", - "18 0.000711 0.000040 \n", - "19 0.088989 0.001689 " + "0 0.000136 0.000017 \n", + "1 0.018504 0.000740 \n", + "2 0.000231 0.000031 \n", + "3 0.029149 0.001525 \n", + "4 0.000306 0.000032 \n", + "5 0.039770 0.001519 \n", + "6 0.000339 0.000028 \n", + "7 0.046708 0.001355 \n", + "8 0.000433 0.000031 \n", + "9 0.056359 0.001790 \n", + "10 0.000538 0.000028 \n", + "11 0.064943 0.001978 \n", + "12 0.000588 0.000028 \n", + "13 0.070231 0.002714 \n", + "14 0.000670 0.000041 \n", + "15 0.075852 0.001862 \n", + "16 0.000745 0.000039 \n", + "17 0.086234 0.001968 \n", + "18 0.000731 0.000039 \n", + "19 0.090448 0.001956 " ] }, "execution_count": 34, @@ -2210,38 +2209,38 @@ " \n", " \n", " mean\n", - " 0.000414\n", + " 0.000416\n", " 0.000025\n", " \n", " \n", " std\n", - " 0.000241\n", - " 0.000010\n", + " 0.000238\n", + " 0.000011\n", " \n", " \n", " min\n", - " 0.000013\n", - " 0.000003\n", + " 0.000023\n", + " 0.000004\n", " \n", " \n", " 25%\n", - " 0.000204\n", + " 0.000206\n", " 0.000017\n", " \n", " \n", " 50%\n", - " 0.000387\n", + " 0.000391\n", " 0.000024\n", " \n", " \n", " 75%\n", - " 0.000594\n", + " 0.000626\n", " 0.000031\n", " \n", " \n", " max\n", - " 0.000919\n", - " 0.000060\n", + " 0.000928\n", + " 0.000061\n", " \n", " \n", "\n", @@ -2252,13 +2251,13 @@ " \n", " \n", "count 289.000000 289.000000\n", - "mean 0.000414 0.000025\n", - "std 0.000241 0.000010\n", - "min 0.000013 0.000003\n", - "25% 0.000204 0.000017\n", - "50% 0.000387 0.000024\n", - "75% 0.000594 0.000031\n", - "max 0.000919 0.000060" + "mean 0.000416 0.000025\n", + "std 0.000238 0.000011\n", + "min 0.000023 0.000004\n", + "25% 0.000206 0.000017\n", + "50% 0.000391 0.000024\n", + "75% 0.000626 0.000031\n", + "max 0.000928 0.000061" ] }, "execution_count": 35, @@ -2293,7 +2292,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 0.378626583393\n" + "Mann-Whitney Test p-value: 0.474494586047\n" ] } ], @@ -2331,7 +2330,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 7.18782749267e-43\n" + "Mann-Whitney Test p-value: 1.364780046e-41\n" ] } ], @@ -2377,7 +2376,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 38, @@ -2386,9 +2385,9 @@ }, { "data": { - "image/png": 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YPOCYT4pyCyLDPks89Li7+284/vgJiTtkPvbYY0ydGmyWesYZ7+Kkk4qzFW3e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\njcN6c0IMkabIiixEE/CEuz8MYGYPE+S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173hBjjI6gc641zYK5+///mzWrr0LuBxnJNMznHXWaRkVSZSgt3+0UV3dM8B1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\noyPFSVKumSgzqnOdDys/V88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i/Yqok0iKRFYUzGyemU0xs8lmNimqHJIiLT6DHzpHnUIqakMTmAFPT3k66iSS\nIlFuKThwkrt3dHd9WmS75trJnLG+gEe/eFSX2qwmou4+0lVWqosWk7STOVPNB8P4aP5HUSeRFIh6\nS2GcmX1uZrq6RzaruQEaz4Glh0WdRCqpX6d+PDb5sahjSArUiPC1u7r7IjPbGxhrZt+6+4fbnuzR\no0dsxrZt29KuXbsoMibFhAkToo6QVKXXb9WqVdBsJixtD1uD4S22bNkSVTSppBtPvhGuheG/Gw67\nOMm5sLAwdaEqIdv+94qKipg+fXpClxlZUXD3ReHPZWb2MtAZiBWFUaNGRRUtJXr37h11hKTatn5/\n/es/WbDHt9vtT6hRowabNkWVTCplncOCM6BtT5hyIeX1/GbC33UmZKwss6r3yEfSfWRm9cwsL7xf\nHzgFmBpFFkmB5t9qf0I2+LovHD486hSSZFHtU8gHPjSzr4BPgdfdfUxEWSTZWszQkUfZYEZ3aP45\n5OkC29ksku4jd58LHBHFa0tqbam5Ger/BMsPjjqKVNWWulDUAzo8C9nVNS+lRH1IqmS5DU3Wwo8H\ngWu47Kzw9UVw+LCoU0gSqShIUq1vshoWHhp1DEmUBV2DQ4ybRR1EkkVFQZJq/Z6rYYGKQtbwnODo\no8OjDiLJoqIgSbO1ZCvrG6/RlkK2mdIH2gM5Ot8kG6koSNIULSuixqZasL5R1FEkkVYcDMXAfuOj\nTiJJoKIgSTNx4UTqrcyLOoYkwxSgwzNRp5AkUFGQpJm4cCL1VjSIOoYkwzTg4Neg1tqok0iCqShI\n0ny84GMVhWy1DlhwHBz8atRJJMFUFCQplq9fzuK1i6mzul7UUSRZplwYnMgmWUVFQZLik4Wf0LlF\nZ0yXzMhe354FLT+G+kuiTiIJpKIgSfHR/I/o2rJr1DEkmTbXD8ZDav9c1EkkgVQUJCnem/ceJ7Y+\nMeoYkmxTLtRRSFlGRUESbmPJRqYtncYx+x4TdRRJtrndoMEC2HNG1EkkQVQUJOFmbphJp2adqFuz\nbtRRJNlKasC0XtrhnEVUFCThpm+YzkkFJ0UdQ1JFXUhZRUVBEm76hunan1CdLOoIW+pAy6iDSCKo\nKEhCrft5HfM3zefYlsdGHUVSxsKthahzSCKoKEhCTVw4kda1W1Ovpk5aq1am9oZD4eetP0edRKpI\nRUESauzssRxaT0NlVzurCmAZvDXrraiTSBWpKEhCvT37bTrUUz9CtTQFnpmiHc6ZTkVBEmbRmkXM\nL57P/nUvwTm0AAAK9ElEQVT2jzqKRKEo+FJQvLE46iRSBSoKkjBjZo+h237dyLXcqKNIFDZAt/26\nMWr6qKiTSBWoKEjCvD37bU7d/9SoY0iELjzsQnUhZTgVBUmIEi9h7JyxnHqAikJ19tuDfstXi79i\nQfGCqKNIJakoSEJ8svAT8uvn06phq6ijSITq1KhDj7Y9eHaqhr3IVCoKkhAvT3+Zc9ueG3UMSQNX\ndLqCoV8OpcRLoo4ilaCiIFXm7rz07Uucc8g5UUeRNNC5RWca1WnEmNljoo4ilaCiIFU2delUSryE\nI5oeEXUUSQNmxtVHXc2Qz4ZEHUUqQUVBquzl6S9zziHnYKZLb0qgV/teTFgwge9XfR91FKkgFQWp\nEnfnhaIXtD9BtlO/Vn36dujLo188GnUUqSAVBamSrxZ/xbrN6ziu5XFRR5E0c83R1zD0y6Gs+3ld\n1FGkAlQUpEqGfz2cvh36kmP6U5LtHbTnQZzQ+gQe+/KxqKNIBeg/WSpt89bNFE4rpG+HvlFHkTR1\nS9dbeOCTB9i8dXPUUSROKgpSaW9+9yb7N96fA/c8MOookqY6t+hMm8ZteO6b56KOInFSUZBKGzxp\nMP2P7h91DElzfz7+z9z9wd1sKdkSdRSJg4qCVErRsiK+WfYN5x96ftRRJM39us2vaZ7XnKe+eirq\nKBIHFQWplMGfDubKTldSK7dW1FEkzZkZ9/zqHu54/w42bN4QdRzZDRUFqbAfVv/A80XPc83R10Qd\nRTJEl3270LlFZx789MGoo8huqChIhd3z0T1cdsRl5O+RH3UUySD3/vpe7vv4Pp3lnOZUFKRCvl/1\nPSOmjeAPXf8QdRTJMAc0OYDfH/N7rv33tbh71HGkHCoKUiG/f/v33NDlBvapv0/UUSQD3dz1Zmav\nnM2IaSOijiLlUFGQuL353ZtMWzqNm7veHHUUyVC1cmvxzLnPcMNbNzBr5ayo48hOqChIXJavX85V\nr1/FkN8MoU6NOlHHkQzWqVknbjvhNnq+2FNHI6UhFQXZrRIv4ZJXLqFX+16cvP/JUceRLHBt52s5\nZK9D6DWql05qSzMqCrJL7s7AMQMp3lTMX7r9Jeo4kiXMjCfOeoL1m9dz5WtXsrVka9SRJKSiIOVy\ndwa9N4ixc8YyuudoaubWjDqSZJFaubV46YKXWLB6AT2e76GupDQRSVEws9PM7Fsz+87Mbokig+za\n+s3ruWz0Zbzx3RuM7TuWxnUbRx1JstAetfbgjd5vkFc7jy6PdWHqkqlRR6r2Ul4UzCwXeBg4DWgH\n9DKztqnOEaWioqKoI+zSO3PfoeOjHdm0ZRPvX/I+TfdoWqH26b5+kl5q5dZi+NnDGXDMALoN78Yt\nY2/hpw0/JeW19Le5e1FsKXQGZrn7PHffDIwEzoogR2SmT58edYQdbNyykReLXuSkp07iqtev4p5f\n3UNhj0Lq16pf4WWl4/pJejMzLu14KV9d9RUrN6xk/4f2p/8b/fnixy8SeqKb/jZ3r0YEr9kCWFDq\n8UKgSwQ5qiV3Z83Pa5i3ah6zV86maFkRHy34iIkLJnJk8yO5otMV9Gzfkxo5UfxpSHXXokELhnYf\nym0n3sYTk5+g16herN60ml/u90uOyD+Cw/IPo6BRAc32aEajOo0ws6gjZx1L9enmZtYDOM3d+4WP\nLwS6uPt1pebxbDwN/rZ3b+OLRV/wxRdf0LFTR9wdx7f7CewwLZ6fQLnPlXgJqzetpnhjMas3raZO\njTq0btSa/Rvvz8F7HkzXVl05vtXx7FVvr4SsZ48ePRg1ahQAnTqdwMyZW8jN3TP2/Pr149iyZSNQ\n+j22Mo/TfVq65EivbMn4v53z0xw++P4Dpi6ZytSlU1mwegE/rvmRTVs20aB2A+rXqk+9mvWoX7M+\nNXNrkmM55FgOuZYbu59jOeTm5PLlF19y5JFHJizb490fT6sxwMwMd69SpYyiKBwDDHL308LHfwJK\n3P3eUvNkX0UQEUmBTCwKNYAZwK+AH4FJQC93V2efiEjEUt5x7O5bzOxa4G0gF3hcBUFEJD2kfEtB\nRETSV2RnNJtZEzMba2YzzWyMmTUqZ74nzGyJmU2tTPuoVGD9dnoin5kNMrOFZjY5vJ2WuvTli+fE\nQzN7KHz+azPrWJG2Uarius0zsynhezUpdanjt7v1M7NDzGyimW00s5sq0jYdVHH9suH96xP+XU4x\nswlm1iHetttx90huwN+Am8P7twB/LWe+XwAdgamVaZ/O60fQfTYLKABqAl8BbcPnbgdujHo94s1b\nap7fAG+G97sAn8TbNlPXLXw8F2gS9XpUcf32Bo4C7gZuqkjbqG9VWb8sev+OBRqG90+r7P9elGMf\ndQeGhfeHAWfvbCZ3/xDY2emNcbWPUDz5dnciX7odhB3PiYex9Xb3T4FGZtY0zrZRquy6lT4eMd3e\nr9J2u37uvszdPwc2V7RtGqjK+m2T6e/fRHcvDh9+Cuwbb9vSoiwK+e6+JLy/BKjowb5VbZ9s8eTb\n2Yl8LUo9vi7cHHw8TbrHdpd3V/M0j6NtlKqybhActD/OzD43s35JS1l58axfMtqmSlUzZtv7dznw\nZmXaJvXoIzMbC+xs4JxbSz9wd6/KuQlVbV9ZCVi/XWV+BLgzvH8XcD/BGx2leH/H6fyNqzxVXbfj\n3f1HM9sbGGtm34ZbuemiKv8fmXA0SlUzdnX3Rdnw/pnZL4HLgK4VbQtJLgruXu4VWcKdx03dfbGZ\nNQOWVnDxVW1fZQlYvx+AlqUetySo4rh7bH4zewx4LTGpq6TcvLuYZ99wnppxtI1SZdftBwB3/zH8\nuczMXibYZE+nD5V41i8ZbVOlShndfVH4M6Pfv3Dn8lCCUSN+qkjbbaLsPhoNXBzevxh4JcXtky2e\nfJ8DB5pZgZnVAi4I2xEWkm3OAdJhTOFy85YyGrgIYmevrwq70eJpG6VKr5uZ1TOzvHB6feAU0uP9\nKq0iv/+yW0Pp/t5BFdYvW94/M2sFvARc6O6zKtJ2OxHuTW8CjANmAmOARuH05sAbpeYbQXDm8yaC\nfrFLd9U+XW4VWL/TCc7wngX8qdT04cAU4GuCgpIf9TqVlxe4Criq1DwPh89/DXTa3bqmy62y6wa0\nITii4ytgWjquWzzrR9AVugAoJji4Yz6wRya8d1VZvyx6/x4DVgCTw9ukXbUt76aT10REJEaX4xQR\nkRgVBRERiVFREBGRGBUFERGJUVEQEZEYFQUREYlRUZBqzcxKzOzpUo9rmNkyM0uHM8hFUk5FQaq7\ndcChZlYnfHwywRAAOoFHqiUVBZFgNMnfhvd7EZxFbxAMe2DBhZ4+NbMvzax7OL3AzD4wsy/C27Hh\n9JPM7D0ze8HMppvZM1GskEhlqSiIwHNATzOrDRxGMBb9NrcC4929C9AN+F8zq0cwHPrJ7n4k0BN4\nqFSbI4AbgHZAGzPrikiGSOooqSKZwN2nmlkBwVbCG2WePgU408wGho9rE4wyuRh42MwOB7YCB5Zq\nM8nDUVPN7CuCK15NSFZ+kURSURAJjAbuA04kuGxjaee6+3elJ5jZIGCRu/c1s1xgY6mnN5W6vxX9\nn0kGUfeRSOAJYJC7f1Nm+tv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v4iipYfl5UA04+t9hJ5E0VC3sAFI2ubm5XHHFtezd+924rQ02UrN5bXbm7wwv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\nCwBdwCNVkoqCSKQ3yR8Hjwc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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2459,7 +2458,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.8" + "version": "2.7.9" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/tally-arithmetic.ipynb b/docs/source/pythonapi/examples/tally-arithmetic.ipynb index 2b1205204..0ff2e5f58 100644 --- a/docs/source/pythonapi/examples/tally-arithmetic.ipynb +++ b/docs/source/pythonapi/examples/tally-arithmetic.ipynb @@ -358,7 +358,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -569,7 +569,8 @@ " Copyright: 2011-2015 Massachusetts Institute of Technology\n", " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.0\n", - " Date/Time: 2015-08-15 10:52:49\n", + " Git SHA1: 36a516ed8125ab8a86d8c9b3aee4bd4bc2db859c\n", + " Date/Time: 2015-09-16 18:34:04\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -595,26 +596,26 @@ "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", - " 1/1 1.00465 \n", - " 2/1 1.05814 \n", - " 3/1 1.05114 \n", - " 4/1 1.09189 \n", - " 5/1 1.03731 \n", - " 6/1 1.03510 \n", - " 7/1 1.09378 1.06444 +/- 0.02934\n", - " 8/1 1.04522 1.05803 +/- 0.01811\n", - " 9/1 1.06557 1.05992 +/- 0.01294\n", - " 10/1 1.05757 1.05945 +/- 0.01004\n", - " 11/1 1.04858 1.05764 +/- 0.00839\n", - " 12/1 1.01832 1.05202 +/- 0.00905\n", - " 13/1 1.05822 1.05279 +/- 0.00787\n", - " 14/1 1.07684 1.05547 +/- 0.00744\n", - " 15/1 1.00349 1.05027 +/- 0.00844\n", - " 16/1 1.06969 1.05203 +/- 0.00784\n", - " 17/1 1.06377 1.05301 +/- 0.00722\n", - " 18/1 1.02897 1.05116 +/- 0.00690\n", - " 19/1 1.00685 1.04800 +/- 0.00713\n", - " 20/1 1.02644 1.04656 +/- 0.00679\n", + " 1/1 1.00279 \n", + " 2/1 1.03320 \n", + " 3/1 1.04467 \n", + " 4/1 1.09693 \n", + " 5/1 1.05008 \n", + " 6/1 1.08426 \n", + " 7/1 1.05363 1.06894 +/- 0.01531\n", + " 8/1 0.97961 1.03917 +/- 0.03106\n", + " 9/1 1.06444 1.04549 +/- 0.02285\n", + " 10/1 1.08345 1.05308 +/- 0.01926\n", + " 11/1 1.06871 1.05568 +/- 0.01594\n", + " 12/1 1.03183 1.05228 +/- 0.01390\n", + " 13/1 1.04486 1.05135 +/- 0.01207\n", + " 14/1 1.06468 1.05283 +/- 0.01075\n", + " 15/1 1.04185 1.05173 +/- 0.00968\n", + " 16/1 1.01268 1.04818 +/- 0.00944\n", + " 17/1 1.04129 1.04761 +/- 0.00864\n", + " 18/1 1.01127 1.04481 +/- 0.00843\n", + " 19/1 1.03738 1.04428 +/- 0.00782\n", + " 20/1 1.04410 1.04427 +/- 0.00728\n", " Creating state point statepoint.20.h5...\n", "\n", " ===========================================================================\n", @@ -624,27 +625,27 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.4100E-01 seconds\n", - " Reading cross sections = 1.1300E-01 seconds\n", - " Total time in simulation = 1.8418E+01 seconds\n", - " Time in transport only = 1.8403E+01 seconds\n", - " Time in inactive batches = 2.1070E+00 seconds\n", - " Time in active batches = 1.6311E+01 seconds\n", - " Time synchronizing fission bank = 2.0000E-03 seconds\n", - " Sampling source sites = 2.0000E-03 seconds\n", + " Total time for initialization = 5.2100E-01 seconds\n", + " Reading cross sections = 1.7200E-01 seconds\n", + " Total time in simulation = 1.5669E+01 seconds\n", + " Time in transport only = 1.5663E+01 seconds\n", + " Time in inactive batches = 2.1160E+00 seconds\n", + " Time in active batches = 1.3553E+01 seconds\n", + " Time synchronizing fission bank = 0.0000E+00 seconds\n", + " Sampling source sites = 0.0000E+00 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 = 1.8861E+01 seconds\n", - " Calculation Rate (inactive) = 5932.61 neutrons/second\n", - " Calculation Rate (active) = 2299.06 neutrons/second\n", + " Total time elapsed = 1.6203E+01 seconds\n", + " Calculation Rate (inactive) = 5907.37 neutrons/second\n", + " Calculation Rate (active) = 2766.91 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.04599 +/- 0.00622\n", - " k-effective (Track-length) = 1.04656 +/- 0.00679\n", - " k-effective (Absorption) = 1.04614 +/- 0.00461\n", - " Combined k-effective = 1.04651 +/- 0.00368\n", + " k-effective (Collision) = 1.04044 +/- 0.00527\n", + " k-effective (Track-length) = 1.04427 +/- 0.00728\n", + " k-effective (Absorption) = 1.04794 +/- 0.00535\n", + " Combined k-effective = 1.04628 +/- 0.00467\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -692,8 +693,7 @@ "outputs": [], "source": [ "# Load the statepoint file\n", - "sp = StatePoint('statepoint.20.h5')\n", - "sp.read_results()" + "sp = StatePoint('statepoint.20.h5')" ] }, { @@ -759,8 +759,8 @@ " 0\n", " total\n", " (nu-fission / absorption)\n", - " 1.042726\n", - " 0.008661\n", + " 1.046353\n", + " 0.00935\n", " \n", " \n", "\n", @@ -769,7 +769,7 @@ "text/plain": [ " nuclide score mean std. dev.\n", "bin \n", - "0 total (nu-fission / absorption) 1.042726 0.008661" + "0 total (nu-fission / absorption) 1.046353 0.00935" ] }, "execution_count": 26, @@ -827,17 +827,17 @@ " 0\n", " total\n", " absorption\n", - " 0.958874\n", - " 0.007146\n", + " 0.95873\n", + " 0.00774\n", " \n", " \n", "\n", "" ], "text/plain": [ - " nuclide score mean std. dev.\n", - "bin \n", - "0 total absorption 0.958874 0.007146" + " nuclide score mean std. dev.\n", + "bin \n", + "0 total absorption 0.95873 0.00774" ] }, "execution_count": 27, @@ -893,17 +893,17 @@ " 0\n", " total\n", " nu-fission\n", - " 1.09186\n", - " 0.010424\n", + " 1.091622\n", + " 0.011163\n", " \n", " \n", "\n", "" ], "text/plain": [ - " nuclide score mean std. dev.\n", - "bin \n", - "0 total nu-fission 1.09186 0.010424" + " nuclide score mean std. dev.\n", + "bin \n", + "0 total nu-fission 1.091622 0.011163" ] }, "execution_count": 28, @@ -966,8 +966,8 @@ " 10000\n", " total\n", " absorption\n", - " 0.802921\n", - " 0.006109\n", + " 0.802012\n", + " 0.006609\n", " \n", " \n", "\n", @@ -976,7 +976,7 @@ "text/plain": [ " energy [MeV] cell nuclide score mean std. dev.\n", "bin \n", - "0 0.0e+00 - 6.2e-01 10000 total absorption 0.802921 0.006109" + "0 0.0e+00 - 6.2e-01 10000 total absorption 0.802012 0.006609" ] }, "execution_count": 29, @@ -1037,8 +1037,8 @@ " 10000\n", " total\n", " (nu-fission / absorption)\n", - " 1.240421\n", - " 0.010978\n", + " 1.246604\n", + " 0.011825\n", " \n", " \n", "\n", @@ -1047,11 +1047,11 @@ "text/plain": [ " energy [MeV] cell nuclide score mean \\\n", "bin \n", - "0 0.0e+00 - 6.2e-01 10000 total (nu-fission / absorption) 1.240421 \n", + "0 0.0e+00 - 6.2e-01 10000 total (nu-fission / absorption) 1.246604 \n", "\n", " std. dev. \n", "bin \n", - "0 0.010978 " + "0 0.011825 " ] }, "execution_count": 30, @@ -1105,8 +1105,8 @@ " 0\n", " total\n", " (((absorption * nu-fission) * absorption) * (n...\n", - " 1.042726\n", - " 0.017538\n", + " 1.046353\n", + " 0.01894\n", " \n", " \n", "\n", @@ -1115,11 +1115,11 @@ "text/plain": [ " nuclide score mean \\\n", "bin \n", - "0 total (((absorption * nu-fission) * absorption) * (n... 1.042726 \n", + "0 total (((absorption * nu-fission) * absorption) * (n... 1.046353 \n", "\n", " std. dev. \n", "bin \n", - "0 0.017538 " + "0 0.01894 " ] }, "execution_count": 31, @@ -1197,7 +1197,7 @@ " (U-238 / total)\n", " (nu-fission / flux)\n", " 0.000001\n", - " 6.985151e-09\n", + " 6.859257e-09\n", " \n", " \n", " 1\n", @@ -1205,8 +1205,8 @@ " 0.0e+00 - 6.3e-07\n", " (U-238 / total)\n", " (scatter / flux)\n", - " 0.209988\n", - " 2.206753e-03\n", + " 0.209986\n", + " 1.966887e-03\n", " \n", " \n", " 2\n", @@ -1214,8 +1214,8 @@ " 0.0e+00 - 6.3e-07\n", " (U-235 / total)\n", " (nu-fission / flux)\n", - " 0.355276\n", - " 3.741612e-03\n", + " 0.355667\n", + " 3.717881e-03\n", " \n", " \n", " 3\n", @@ -1224,7 +1224,7 @@ " (U-235 / total)\n", " (scatter / flux)\n", " 0.005555\n", - " 5.842517e-05\n", + " 5.218094e-05\n", " \n", " \n", " 4\n", @@ -1232,8 +1232,8 @@ " 6.3e-07 - 2.0e+01\n", " (U-238 / total)\n", " (nu-fission / flux)\n", - " 0.007229\n", - " 5.951357e-05\n", + " 0.007165\n", + " 5.625590e-05\n", " \n", " \n", " 5\n", @@ -1241,8 +1241,8 @@ " 6.3e-07 - 2.0e+01\n", " (U-238 / total)\n", " (scatter / flux)\n", - " 0.227642\n", - " 9.496469e-04\n", + " 0.227653\n", + " 8.544314e-04\n", " \n", " \n", " 6\n", @@ -1250,8 +1250,8 @@ " 6.3e-07 - 2.0e+01\n", " (U-235 / total)\n", " (nu-fission / flux)\n", - " 0.008076\n", - " 5.699123e-05\n", + " 0.008089\n", + " 5.080374e-05\n", " \n", " \n", " 7\n", @@ -1259,8 +1259,8 @@ " 6.3e-07 - 2.0e+01\n", " (U-235 / total)\n", " (scatter / flux)\n", - " 0.003369\n", - " 1.369755e-05\n", + " 0.003370\n", + " 1.361116e-05\n", " \n", " \n", "\n", @@ -1270,24 +1270,24 @@ " cell energy [MeV] nuclide score mean \\\n", "bin \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) 0.209988 \n", - "2 10000 0.0e+00 - 6.3e-07 (U-235 / total) (nu-fission / flux) 0.355276 \n", + "1 10000 0.0e+00 - 6.3e-07 (U-238 / total) (scatter / flux) 0.209986 \n", + "2 10000 0.0e+00 - 6.3e-07 (U-235 / total) (nu-fission / flux) 0.355667 \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) 0.007229 \n", - "5 10000 6.3e-07 - 2.0e+01 (U-238 / total) (scatter / flux) 0.227642 \n", - "6 10000 6.3e-07 - 2.0e+01 (U-235 / total) (nu-fission / flux) 0.008076 \n", - "7 10000 6.3e-07 - 2.0e+01 (U-235 / total) (scatter / flux) 0.003369 \n", + "4 10000 6.3e-07 - 2.0e+01 (U-238 / total) (nu-fission / flux) 0.007165 \n", + "5 10000 6.3e-07 - 2.0e+01 (U-238 / total) (scatter / flux) 0.227653 \n", + "6 10000 6.3e-07 - 2.0e+01 (U-235 / total) (nu-fission / flux) 0.008089 \n", + "7 10000 6.3e-07 - 2.0e+01 (U-235 / total) (scatter / flux) 0.003370 \n", "\n", " std. dev. \n", "bin \n", - "0 6.985151e-09 \n", - "1 2.206753e-03 \n", - "2 3.741612e-03 \n", - "3 5.842517e-05 \n", - "4 5.951357e-05 \n", - "5 9.496469e-04 \n", - "6 5.699123e-05 \n", - "7 1.369755e-05 " + "0 6.859257e-09 \n", + "1 1.966887e-03 \n", + "2 3.717881e-03 \n", + "3 5.218094e-05 \n", + "4 5.625590e-05 \n", + "5 8.544314e-04 \n", + "6 5.080374e-05 \n", + "7 1.361116e-05 " ] }, "execution_count": 33, @@ -1318,11 +1318,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 6.63809296e-07]\n", - " [ 3.55275544e-01]]\n", + "[[[ 6.64174599e-07]\n", + " [ 3.55666541e-01]]\n", "\n", - " [[ 7.22895528e-03]\n", - " [ 8.07565148e-03]]]\n" + " [[ 7.16505734e-03]\n", + " [ 8.08949336e-03]]]\n" ] } ], @@ -1350,9 +1350,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00555505]]\n", + "[[[ 0.00555465]]\n", "\n", - " [[ 0.0033688 ]]]\n" + " [[ 0.00337011]]]\n" ] } ], @@ -1374,8 +1374,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.2276418]\n", - " [ 0.0033688]]]\n" + "[[[ 0.22765348]\n", + " [ 0.00337011]]]\n" ] } ], @@ -1434,7 +1434,7 @@ " U-238\n", " nu-fission\n", " 0.000002\n", - " 1.211808e-08\n", + " 1.284890e-08\n", " \n", " \n", " 1\n", @@ -1442,8 +1442,8 @@ " 0.0e+00 - 6.3e-07\n", " U-235\n", " nu-fission\n", - " 0.870360\n", - " 6.496431e-03\n", + " 0.867982\n", + " 7.022256e-03\n", " \n", " \n", " 2\n", @@ -1451,8 +1451,8 @@ " 6.3e-07 - 2.0e+01\n", " U-238\n", " nu-fission\n", - " 0.083226\n", - " 6.367951e-04\n", + " 0.082801\n", + " 6.087096e-04\n", " \n", " \n", " 3\n", @@ -1460,8 +1460,8 @@ " 6.3e-07 - 2.0e+01\n", " U-235\n", " nu-fission\n", - " 0.092974\n", - " 5.921990e-04\n", + " 0.093484\n", + " 5.275039e-04\n", " \n", " \n", "\n", @@ -1470,10 +1470,10 @@ "text/plain": [ " cell energy [MeV] nuclide score mean std. dev.\n", "bin \n", - "0 10000 0.0e+00 - 6.3e-07 U-238 nu-fission 0.000002 1.211808e-08\n", - "1 10000 0.0e+00 - 6.3e-07 U-235 nu-fission 0.870360 6.496431e-03\n", - "2 10000 6.3e-07 - 2.0e+01 U-238 nu-fission 0.083226 6.367951e-04\n", - "3 10000 6.3e-07 - 2.0e+01 U-235 nu-fission 0.092974 5.921990e-04" + "0 10000 0.0e+00 - 6.3e-07 U-238 nu-fission 0.000002 1.284890e-08\n", + "1 10000 0.0e+00 - 6.3e-07 U-235 nu-fission 0.867982 7.022256e-03\n", + "2 10000 6.3e-07 - 2.0e+01 U-238 nu-fission 0.082801 6.087096e-04\n", + "3 10000 6.3e-07 - 2.0e+01 U-235 nu-fission 0.093484 5.275039e-04" ] }, "execution_count": 37, @@ -1526,8 +1526,8 @@ " 1.0e-08 - 1.1e-07\n", " H-1\n", " scatter\n", - " 4.638428\n", - " 0.034134\n", + " 4.620525\n", + " 0.038249\n", " \n", " \n", " 1\n", @@ -1535,8 +1535,8 @@ " 1.1e-07 - 1.2e-06\n", " H-1\n", " scatter\n", - " 2.050818\n", - " 0.010745\n", + " 2.036841\n", + " 0.013203\n", " \n", " \n", " 2\n", @@ -1544,8 +1544,8 @@ " 1.2e-06 - 1.3e-05\n", " H-1\n", " scatter\n", - " 1.656905\n", - " 0.009480\n", + " 1.659916\n", + " 0.010107\n", " \n", " \n", " 3\n", @@ -1553,8 +1553,8 @@ " 1.3e-05 - 1.4e-04\n", " H-1\n", " scatter\n", - " 1.870808\n", - " 0.011883\n", + " 1.861546\n", + " 0.013328\n", " \n", " \n", " 4\n", @@ -1562,8 +1562,8 @@ " 1.4e-04 - 1.5e-03\n", " H-1\n", " scatter\n", - " 2.045621\n", - " 0.011414\n", + " 2.049664\n", + " 0.008215\n", " \n", " \n", " 5\n", @@ -1571,8 +1571,8 @@ " 1.5e-03 - 1.6e-02\n", " H-1\n", " scatter\n", - " 2.163297\n", - " 0.008725\n", + " 2.162157\n", + " 0.010245\n", " \n", " \n", " 6\n", @@ -1580,8 +1580,8 @@ " 1.6e-02 - 1.7e-01\n", " H-1\n", " scatter\n", - " 2.202045\n", - " 0.013500\n", + " 2.224496\n", + " 0.013796\n", " \n", " \n", " 7\n", @@ -1589,8 +1589,8 @@ " 1.7e-01 - 1.9e+00\n", " H-1\n", " scatter\n", - " 1.996977\n", - " 0.010791\n", + " 1.997585\n", + " 0.009161\n", " \n", " \n", " 8\n", @@ -1598,8 +1598,8 @@ " 1.9e+00 - 2.0e+01\n", " H-1\n", " scatter\n", - " 0.370890\n", - " 0.003597\n", + " 0.373472\n", + " 0.003922\n", " \n", " \n", "\n", @@ -1608,15 +1608,15 @@ "text/plain": [ " cell energy [MeV] nuclide score mean std. dev.\n", "bin \n", - "0 10002 1.0e-08 - 1.1e-07 H-1 scatter 4.638428 0.034134\n", - "1 10002 1.1e-07 - 1.2e-06 H-1 scatter 2.050818 0.010745\n", - "2 10002 1.2e-06 - 1.3e-05 H-1 scatter 1.656905 0.009480\n", - "3 10002 1.3e-05 - 1.4e-04 H-1 scatter 1.870808 0.011883\n", - "4 10002 1.4e-04 - 1.5e-03 H-1 scatter 2.045621 0.011414\n", - "5 10002 1.5e-03 - 1.6e-02 H-1 scatter 2.163297 0.008725\n", - "6 10002 1.6e-02 - 1.7e-01 H-1 scatter 2.202045 0.013500\n", - "7 10002 1.7e-01 - 1.9e+00 H-1 scatter 1.996977 0.010791\n", - "8 10002 1.9e+00 - 2.0e+01 H-1 scatter 0.370890 0.003597" + "0 10002 1.0e-08 - 1.1e-07 H-1 scatter 4.620525 0.038249\n", + "1 10002 1.1e-07 - 1.2e-06 H-1 scatter 2.036841 0.013203\n", + "2 10002 1.2e-06 - 1.3e-05 H-1 scatter 1.659916 0.010107\n", + "3 10002 1.3e-05 - 1.4e-04 H-1 scatter 1.861546 0.013328\n", + "4 10002 1.4e-04 - 1.5e-03 H-1 scatter 2.049664 0.008215\n", + "5 10002 1.5e-03 - 1.6e-02 H-1 scatter 2.162157 0.010245\n", + "6 10002 1.6e-02 - 1.7e-01 H-1 scatter 2.224496 0.013796\n", + "7 10002 1.7e-01 - 1.9e+00 H-1 scatter 1.997585 0.009161\n", + "8 10002 1.9e+00 - 2.0e+01 H-1 scatter 0.373472 0.003922" ] }, "execution_count": 38, @@ -1649,7 +1649,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.8" + "version": "2.7.9" } }, "nbformat": 4,