diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb index 211829be6..104fbe759 100644 --- a/docs/source/pythonapi/examples/mgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb @@ -519,7 +519,7 @@ " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.1\n", " Git SHA1: 34381b40a9445a727e360873aaa6ef892af1cb6a\n", - " Date/Time: 2016-02-07 14:10:52\n", + " Date/Time: 2016-02-07 15:58:16\n", " MPI Processes: 1\n", "\n", " ===========================================================================\n", @@ -605,20 +605,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 8.7800E-01 seconds\n", - " Reading cross sections = 1.9200E-01 seconds\n", - " Total time in simulation = 1.3677E+01 seconds\n", - " Time in transport only = 1.3579E+01 seconds\n", - " Time in inactive batches = 1.7900E+00 seconds\n", - " Time in active batches = 1.1887E+01 seconds\n", - " Time synchronizing fission bank = 6.0000E-03 seconds\n", - " Sampling source sites = 2.0000E-03 seconds\n", + " Total time for initialization = 3.2100E-01 seconds\n", + " Reading cross sections = 7.4000E-02 seconds\n", + " Total time in simulation = 8.3830E+00 seconds\n", + " Time in transport only = 8.3670E+00 seconds\n", + " Time in inactive batches = 1.0330E+00 seconds\n", + " Time in active batches = 7.3500E+00 seconds\n", + " Time synchronizing fission bank = 4.0000E-03 seconds\n", + " Sampling source sites = 1.0000E-03 seconds\n", " SEND/RECV source sites = 3.0000E-03 seconds\n", - " Time accumulating tallies = 1.0000E-03 seconds\n", + " Time accumulating tallies = 0.0000E+00 seconds\n", " Total time for finalization = 1.0000E-03 seconds\n", - " Total time elapsed = 1.4578E+01 seconds\n", - " Calculation Rate (inactive) = 13966.5 neutrons/second\n", - " Calculation Rate (active) = 8412.55 neutrons/second\n", + " Total time elapsed = 8.7140E+00 seconds\n", + " Calculation Rate (inactive) = 24201.4 neutrons/second\n", + " Calculation Rate (active) = 13605.4 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -909,8 +909,8 @@ " \n", " 0\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.000000\n", + " 0.000001\n", " total\n", " (((total / flux) - (absorption / flux)) - (sca...\n", " 4.884981e-15\n", @@ -919,8 +919,8 @@ " \n", " 1\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 0.000001\n", + " 20.000000\n", " total\n", " (((total / flux) - (absorption / flux)) - (sca...\n", " 1.221245e-15\n", @@ -931,13 +931,13 @@ "" ], "text/plain": [ - " cell energy low [MeV] energy high [MeV] nuclide \\\n", - "0 1 0.00e+00 6.25e-07 total \n", - "1 1 6.25e-07 2.00e+01 total \n", + " cell energy low [MeV] energy high [MeV] nuclide \\\n", + "0 1 0.00e+00 6.25e-07 total \n", + "1 1 6.25e-07 2.00e+01 total \n", "\n", - " score mean std. dev. \n", - "0 (((total / flux) - (absorption / flux)) - (sca... 4.884981e-15 0.011274 \n", - "1 (((total / flux) - (absorption / flux)) - (sca... 1.221245e-15 0.001802 " + " score mean std. dev. \n", + "0 (((total / flux) - (absorption / flux)) - (sca... 4.88e-15 1.13e-02 \n", + "1 (((total / flux) - (absorption / flux)) - (sca... 1.22e-15 1.80e-03 " ] }, "execution_count": 23, @@ -988,8 +988,8 @@ " \n", " 0\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.000000\n", + " 0.000001\n", " total\n", " ((absorption / flux) / (total / flux))\n", " 0.076219\n", @@ -998,8 +998,8 @@ " \n", " 1\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 0.000001\n", + " 20.000000\n", " total\n", " ((absorption / flux) / (total / flux))\n", " 0.019319\n", @@ -1010,13 +1010,13 @@ "" ], "text/plain": [ - " cell energy low [MeV] energy high [MeV] nuclide \\\n", - "0 1 0.00e+00 6.25e-07 total \n", - "1 1 6.25e-07 2.00e+01 total \n", + " cell energy low [MeV] energy high [MeV] nuclide \\\n", + "0 1 0.00e+00 6.25e-07 total \n", + "1 1 6.25e-07 2.00e+01 total \n", "\n", - " score mean std. dev. \n", - "0 ((absorption / flux) / (total / flux)) 0.076219 0.000651 \n", - "1 ((absorption / flux) / (total / flux)) 0.019319 0.000086 " + " score mean std. dev. \n", + "0 ((absorption / flux) / (total / flux)) 7.62e-02 6.51e-04 \n", + "1 ((absorption / flux) / (total / flux)) 1.93e-02 8.65e-05 " ] }, "execution_count": 24, @@ -1060,8 +1060,8 @@ " \n", " 0\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.000000\n", + " 0.000001\n", " total\n", " ((scatter / flux) / (total / flux))\n", " 0.923781\n", @@ -1070,8 +1070,8 @@ " \n", " 1\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 0.000001\n", + " 20.000000\n", " total\n", " ((scatter / flux) / (total / flux))\n", " 0.980681\n", @@ -1082,13 +1082,13 @@ "" ], "text/plain": [ - " cell energy low [MeV] energy high [MeV] nuclide \\\n", - "0 1 0.00e+00 6.25e-07 total \n", - "1 1 6.25e-07 2.00e+01 total \n", + " cell energy low [MeV] energy high [MeV] nuclide \\\n", + "0 1 0.00e+00 6.25e-07 total \n", + "1 1 6.25e-07 2.00e+01 total \n", "\n", - " score mean std. dev. \n", - "0 ((scatter / flux) / (total / flux)) 0.923781 0.007714 \n", - "1 ((scatter / flux) / (total / flux)) 0.980681 0.002617 " + " score mean std. dev. \n", + "0 ((scatter / flux) / (total / flux)) 9.24e-01 7.71e-03 \n", + "1 ((scatter / flux) / (total / flux)) 9.81e-01 2.62e-03 " ] }, "execution_count": 25, @@ -1139,8 +1139,8 @@ " \n", " 0\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.000000\n", + " 0.000001\n", " total\n", " (((absorption / flux) / (total / flux)) + ((sc...\n", " 1\n", @@ -1149,8 +1149,8 @@ " \n", " 1\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 0.000001\n", + " 20.000000\n", " total\n", " (((absorption / flux) / (total / flux)) + ((sc...\n", " 1\n", @@ -1161,13 +1161,13 @@ "" ], "text/plain": [ - " cell energy low [MeV] energy high [MeV] nuclide \\\n", - "0 1 0.00e+00 6.25e-07 total \n", - "1 1 6.25e-07 2.00e+01 total \n", + " cell energy low [MeV] energy high [MeV] nuclide \\\n", + "0 1 0.00e+00 6.25e-07 total \n", + "1 1 6.25e-07 2.00e+01 total \n", "\n", - " score mean std. dev. \n", - "0 (((absorption / flux) / (total / flux)) + ((sc... 1 0.007741 \n", - "1 (((absorption / flux) / (total / flux)) + ((sc... 1 0.002619 " + " score mean std. dev. \n", + "0 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 7.74e-03 \n", + "1 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 2.62e-03 " ] }, "execution_count": 26, @@ -1182,15 +1182,6 @@ "# The scattering-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", "sum_ratio.get_pandas_dataframe()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index 45868ab76..9e08acccd 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": 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"text/plain": [ "" ] @@ -572,7 +572,7 @@ " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.1\n", " Git SHA1: 34381b40a9445a727e360873aaa6ef892af1cb6a\n", - " Date/Time: 2016-02-07 14:14:45\n", + " Date/Time: 2016-02-07 16:01:57\n", " MPI Processes: 1\n", "\n", " ===========================================================================\n", @@ -637,20 +637,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.1700E-01 seconds\n", - " Reading cross sections = 8.0000E-02 seconds\n", - " Total time in simulation = 4.7680E+00 seconds\n", - " Time in transport only = 4.7530E+00 seconds\n", - " Time in inactive batches = 7.0700E-01 seconds\n", - " Time in active batches = 4.0610E+00 seconds\n", - " Time synchronizing fission bank = 3.0000E-03 seconds\n", - " Sampling source sites = 2.0000E-03 seconds\n", + " Total time for initialization = 3.0900E-01 seconds\n", + " Reading cross sections = 7.8000E-02 seconds\n", + " Total time in simulation = 4.9560E+00 seconds\n", + " Time in transport only = 4.9400E+00 seconds\n", + " Time in inactive batches = 7.3100E-01 seconds\n", + " Time in active batches = 4.2250E+00 seconds\n", + " Time synchronizing fission bank = 4.0000E-03 seconds\n", + " Sampling source sites = 3.0000E-03 seconds\n", " SEND/RECV source sites = 1.0000E-03 seconds\n", - " Time accumulating tallies = 0.0000E+00 seconds\n", + " Time accumulating tallies = 1.0000E-03 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 5.0960E+00 seconds\n", - " Calculation Rate (inactive) = 17680.3 neutrons/second\n", - " Calculation Rate (active) = 9234.18 neutrons/second\n", + " Total time elapsed = 5.2780E+00 seconds\n", + " Calculation Rate (inactive) = 17099.9 neutrons/second\n", + " Calculation Rate (active) = 8875.74 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -825,269 +825,269 @@ " 1\n", " 1\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.00e+00\n", + " 6.25e-07\n", " fission\n", - " 0.000202\n", - " 0.000037\n", + " 2.02e-04\n", + " 3.69e-05\n", " \n", " \n", " 1 \n", " 1\n", " 1\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.00e+00\n", + " 6.25e-07\n", " nu-fission\n", - " 0.000492\n", - " 0.000090\n", + " 4.92e-04\n", + " 8.98e-05\n", " \n", " \n", " 2 \n", " 1\n", " 1\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 6.25e-07\n", + " 2.00e+01\n", " fission\n", - " 0.000076\n", - " 0.000004\n", + " 7.62e-05\n", + " 3.74e-06\n", " \n", " \n", " 3 \n", " 1\n", " 1\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 6.25e-07\n", + " 2.00e+01\n", " nu-fission\n", - " 0.000204\n", - " 0.000010\n", + " 2.04e-04\n", + " 9.88e-06\n", " \n", " \n", " 4 \n", " 1\n", " 2\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.00e+00\n", + " 6.25e-07\n", " fission\n", - " 0.000375\n", - " 0.000039\n", + " 3.75e-04\n", + " 3.86e-05\n", " \n", " \n", " 5 \n", " 1\n", " 2\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.00e+00\n", + " 6.25e-07\n", " nu-fission\n", - " 0.000914\n", - " 0.000094\n", + " 9.14e-04\n", + " 9.41e-05\n", " \n", " \n", " 6 \n", " 1\n", " 2\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 6.25e-07\n", + " 2.00e+01\n", " fission\n", - " 0.000107\n", - " 0.000013\n", + " 1.07e-04\n", + " 1.26e-05\n", " \n", " \n", " 7 \n", " 1\n", " 2\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 6.25e-07\n", + " 2.00e+01\n", " nu-fission\n", - " 0.000278\n", - " 0.000032\n", + " 2.78e-04\n", + " 3.16e-05\n", " \n", " \n", " 8 \n", " 1\n", " 3\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.00e+00\n", + " 6.25e-07\n", " fission\n", - " 0.000564\n", - " 0.000056\n", + " 5.64e-04\n", + " 5.60e-05\n", " \n", " \n", " 9 \n", " 1\n", " 3\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.00e+00\n", + " 6.25e-07\n", " nu-fission\n", - " 0.001374\n", - " 0.000137\n", + " 1.37e-03\n", + " 1.37e-04\n", " \n", " \n", " 10\n", " 1\n", " 3\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 6.25e-07\n", + " 2.00e+01\n", " fission\n", - " 0.000149\n", - " 0.000007\n", + " 1.49e-04\n", + " 7.25e-06\n", " \n", " \n", " 11\n", " 1\n", " 3\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 6.25e-07\n", + " 2.00e+01\n", " nu-fission\n", - " 0.000388\n", - " 0.000018\n", + " 3.88e-04\n", + " 1.78e-05\n", " \n", " \n", " 12\n", " 1\n", " 4\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.00e+00\n", + " 6.25e-07\n", " fission\n", - " 0.000669\n", - " 0.000044\n", + " 6.69e-04\n", + " 4.44e-05\n", " \n", " \n", " 13\n", " 1\n", " 4\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.00e+00\n", + " 6.25e-07\n", " nu-fission\n", - " 0.001631\n", - " 0.000108\n", + " 1.63e-03\n", + " 1.08e-04\n", " \n", " \n", " 14\n", " 1\n", " 4\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 6.25e-07\n", + " 2.00e+01\n", " fission\n", - " 0.000165\n", - " 0.000011\n", + " 1.65e-04\n", + " 1.09e-05\n", " \n", " \n", " 15\n", " 1\n", " 4\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 6.25e-07\n", + " 2.00e+01\n", " nu-fission\n", - " 0.000433\n", - " 0.000029\n", + " 4.33e-04\n", + " 2.89e-05\n", " \n", " \n", " 16\n", " 1\n", " 5\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.00e+00\n", + " 6.25e-07\n", " fission\n", - " 0.000932\n", - " 0.000069\n", + " 9.32e-04\n", + " 6.90e-05\n", " \n", " \n", " 17\n", " 1\n", " 5\n", " 1\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.00e+00\n", + " 6.25e-07\n", " nu-fission\n", - " 0.002270\n", - " 0.000168\n", + " 2.27e-03\n", + " 1.68e-04\n", " \n", " \n", " 18\n", " 1\n", " 5\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 6.25e-07\n", + " 2.00e+01\n", " fission\n", - " 0.000183\n", - " 0.000011\n", + " 1.83e-04\n", + " 1.10e-05\n", " \n", " \n", " 19\n", " 1\n", " 5\n", " 1\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 6.25e-07\n", + " 2.00e+01\n", " nu-fission\n", - " 0.000477\n", - " 0.000028\n", + " 4.77e-04\n", + " 2.77e-05\n", " \n", " \n", "\n", "" ], "text/plain": [ - " (mesh 1, x) (mesh 1, y) (mesh 1, z) energy low [MeV] energy high [MeV] \\\n", - "0 1 1 1 0.00e+00 6.25e-07 \n", - "1 1 1 1 0.00e+00 6.25e-07 \n", - "2 1 1 1 6.25e-07 2.00e+01 \n", - "3 1 1 1 6.25e-07 2.00e+01 \n", - "4 1 2 1 0.00e+00 6.25e-07 \n", - "5 1 2 1 0.00e+00 6.25e-07 \n", - "6 1 2 1 6.25e-07 2.00e+01 \n", - "7 1 2 1 6.25e-07 2.00e+01 \n", - "8 1 3 1 0.00e+00 6.25e-07 \n", - "9 1 3 1 0.00e+00 6.25e-07 \n", - "10 1 3 1 6.25e-07 2.00e+01 \n", - "11 1 3 1 6.25e-07 2.00e+01 \n", - "12 1 4 1 0.00e+00 6.25e-07 \n", - "13 1 4 1 0.00e+00 6.25e-07 \n", - "14 1 4 1 6.25e-07 2.00e+01 \n", - "15 1 4 1 6.25e-07 2.00e+01 \n", - "16 1 5 1 0.00e+00 6.25e-07 \n", - "17 1 5 1 0.00e+00 6.25e-07 \n", - "18 1 5 1 6.25e-07 2.00e+01 \n", - "19 1 5 1 6.25e-07 2.00e+01 \n", + " (mesh 1, x) (mesh 1, y) (mesh 1, z) energy low [MeV] \\\n", + "0 1 1 1 0.00e+00 \n", + "1 1 1 1 0.00e+00 \n", + "2 1 1 1 6.25e-07 \n", + "3 1 1 1 6.25e-07 \n", + "4 1 2 1 0.00e+00 \n", + "5 1 2 1 0.00e+00 \n", + "6 1 2 1 6.25e-07 \n", + "7 1 2 1 6.25e-07 \n", + "8 1 3 1 0.00e+00 \n", + "9 1 3 1 0.00e+00 \n", + "10 1 3 1 6.25e-07 \n", + "11 1 3 1 6.25e-07 \n", + "12 1 4 1 0.00e+00 \n", + "13 1 4 1 0.00e+00 \n", + "14 1 4 1 6.25e-07 \n", + "15 1 4 1 6.25e-07 \n", + "16 1 5 1 0.00e+00 \n", + "17 1 5 1 0.00e+00 \n", + "18 1 5 1 6.25e-07 \n", + "19 1 5 1 6.25e-07 \n", "\n", - " score mean std. dev. \n", - "0 fission 0.000202 0.000037 \n", - "1 nu-fission 0.000492 0.000090 \n", - "2 fission 0.000076 0.000004 \n", - "3 nu-fission 0.000204 0.000010 \n", - "4 fission 0.000375 0.000039 \n", - "5 nu-fission 0.000914 0.000094 \n", - "6 fission 0.000107 0.000013 \n", - "7 nu-fission 0.000278 0.000032 \n", - "8 fission 0.000564 0.000056 \n", - "9 nu-fission 0.001374 0.000137 \n", - "10 fission 0.000149 0.000007 \n", - "11 nu-fission 0.000388 0.000018 \n", - "12 fission 0.000669 0.000044 \n", - "13 nu-fission 0.001631 0.000108 \n", - "14 fission 0.000165 0.000011 \n", - "15 nu-fission 0.000433 0.000029 \n", - "16 fission 0.000932 0.000069 \n", - "17 nu-fission 0.002270 0.000168 \n", - "18 fission 0.000183 0.000011 \n", - "19 nu-fission 0.000477 0.000028 " + " energy high [MeV] score mean std. dev. \n", + "0 6.25e-07 fission 2.02e-04 3.69e-05 \n", + "1 6.25e-07 nu-fission 4.92e-04 8.98e-05 \n", + "2 2.00e+01 fission 7.62e-05 3.74e-06 \n", + "3 2.00e+01 nu-fission 2.04e-04 9.88e-06 \n", + "4 6.25e-07 fission 3.75e-04 3.86e-05 \n", + "5 6.25e-07 nu-fission 9.14e-04 9.41e-05 \n", + "6 2.00e+01 fission 1.07e-04 1.26e-05 \n", + "7 2.00e+01 nu-fission 2.78e-04 3.16e-05 \n", + "8 6.25e-07 fission 5.64e-04 5.60e-05 \n", + "9 6.25e-07 nu-fission 1.37e-03 1.37e-04 \n", + "10 2.00e+01 fission 1.49e-04 7.25e-06 \n", + "11 2.00e+01 nu-fission 3.88e-04 1.78e-05 \n", + "12 6.25e-07 fission 6.69e-04 4.44e-05 \n", + "13 6.25e-07 nu-fission 1.63e-03 1.08e-04 \n", + "14 2.00e+01 fission 1.65e-04 1.09e-05 \n", + "15 2.00e+01 nu-fission 4.33e-04 2.89e-05 \n", + "16 6.25e-07 fission 9.32e-04 6.90e-05 \n", + "17 6.25e-07 nu-fission 2.27e-03 1.68e-04 \n", + "18 2.00e+01 fission 1.83e-04 1.10e-05 \n", + "19 2.00e+01 nu-fission 4.77e-04 2.77e-05 " ] }, "execution_count": 25, @@ -1099,6 +1099,10 @@ "# Get a pandas dataframe for the mesh tally data\n", "df = tally.get_pandas_dataframe(nuclides=False)\n", "\n", + "# Set the Pandas float display settings\n", + "import pandas as pd\n", + "pd.set_option('display.float_format', '{:.2e}'.format)\n", + "\n", "# Print the first twenty rows in the dataframe\n", "df.head(20)" ] @@ -1114,7 +1118,7 @@ "data": { "image/png": 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SqKykhRHxYCr/O8DiiDhb0tHA4xFxUNLxwF3AayLiiap6eZyGmdkUGtc4jYg4AKwENgHb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\nOVVrf+kxqPL6PUB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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1158,7 +1162,7 @@ "source": [ "# Extract thermal nu-fission rates from pandas\n", "fiss = df[df['score'] == 'nu-fission']\n", - "fiss = fiss[fiss['energy low [MeV]'] == '0.00e+00']\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", @@ -1236,144 +1240,144 @@ " 10000\n", " U-235\n", " scatter-Y0,0\n", - " 0.037095\n", - " 0.001150\n", + " 3.71e-02\n", + " 1.15e-03\n", " \n", " \n", " 1 \n", " 10000\n", " U-235\n", " scatter-Y1,-1\n", - " 0.000266\n", - " 0.000323\n", + " 2.66e-04\n", + " 3.23e-04\n", " \n", " \n", " 2 \n", " 10000\n", " U-235\n", " scatter-Y1,0\n", - " -0.000417\n", - " 0.000274\n", + " -4.17e-04\n", + " 2.74e-04\n", " \n", " \n", " 3 \n", " 10000\n", " U-235\n", " scatter-Y1,1\n", - " -0.000228\n", - " 0.000237\n", + " -2.28e-04\n", + " 2.37e-04\n", " \n", " \n", " 4 \n", " 10000\n", " U-235\n", " scatter-Y2,-2\n", - " 0.000026\n", - " 0.000199\n", + " 2.57e-05\n", + " 1.99e-04\n", " \n", " \n", " 5 \n", " 10000\n", " U-235\n", " scatter-Y2,-1\n", - " -0.000115\n", - " 0.000185\n", + " -1.15e-04\n", + " 1.85e-04\n", " \n", " \n", " 6 \n", " 10000\n", " U-235\n", " scatter-Y2,0\n", - " 0.000151\n", - " 0.000159\n", + " 1.51e-04\n", + " 1.59e-04\n", " \n", " \n", " 7 \n", " 10000\n", " U-235\n", " scatter-Y2,1\n", - " -0.000122\n", - " 0.000280\n", + " -1.22e-04\n", + " 2.80e-04\n", " \n", " \n", " 8 \n", " 10000\n", " U-235\n", " scatter-Y2,2\n", - " 0.000008\n", - " 0.000181\n", + " 7.65e-06\n", + " 1.81e-04\n", " \n", " \n", " 9 \n", " 10000\n", " U-238\n", " scatter-Y0,0\n", - " 2.328632\n", - " 0.013107\n", + " 2.33e+00\n", + " 1.31e-02\n", " \n", " \n", " 10\n", " 10000\n", " U-238\n", " scatter-Y1,-1\n", - " 0.024530\n", - " 0.002272\n", + " 2.45e-02\n", + " 2.27e-03\n", " \n", " \n", " 11\n", " 10000\n", " U-238\n", " scatter-Y1,0\n", - " -0.000059\n", - " 0.002804\n", + " -5.87e-05\n", + " 2.80e-03\n", " \n", " \n", " 12\n", " 10000\n", " U-238\n", " scatter-Y1,1\n", - " -0.027990\n", - " 0.002536\n", + " -2.80e-02\n", + " 2.54e-03\n", " \n", " \n", " 13\n", " 10000\n", " U-238\n", " scatter-Y2,-2\n", - " -0.004861\n", - " 0.001575\n", + " -4.86e-03\n", + " 1.58e-03\n", " \n", " \n", " 14\n", " 10000\n", " U-238\n", " scatter-Y2,-1\n", - " 0.000557\n", - " 0.002018\n", + " 5.57e-04\n", + " 2.02e-03\n", " \n", " \n", " 15\n", " 10000\n", " U-238\n", " scatter-Y2,0\n", - " 0.006236\n", - " 0.001627\n", + " 6.24e-03\n", + " 1.63e-03\n", " \n", " \n", " 16\n", " 10000\n", " U-238\n", " scatter-Y2,1\n", - " -0.000648\n", - " 0.001551\n", + " -6.48e-04\n", + " 1.55e-03\n", " \n", " \n", " 17\n", " 10000\n", " U-238\n", " scatter-Y2,2\n", - " -0.001031\n", - " 0.001310\n", + " -1.03e-03\n", + " 1.31e-03\n", " \n", " \n", "\n", @@ -1381,24 +1385,24 @@ ], "text/plain": [ " cell nuclide score mean std. dev.\n", - "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" + "0 10000 U-235 scatter-Y0,0 3.71e-02 1.15e-03\n", + "1 10000 U-235 scatter-Y1,-1 2.66e-04 3.23e-04\n", + "2 10000 U-235 scatter-Y1,0 -4.17e-04 2.74e-04\n", + "3 10000 U-235 scatter-Y1,1 -2.28e-04 2.37e-04\n", + "4 10000 U-235 scatter-Y2,-2 2.57e-05 1.99e-04\n", + "5 10000 U-235 scatter-Y2,-1 -1.15e-04 1.85e-04\n", + "6 10000 U-235 scatter-Y2,0 1.51e-04 1.59e-04\n", + "7 10000 U-235 scatter-Y2,1 -1.22e-04 2.80e-04\n", + "8 10000 U-235 scatter-Y2,2 7.65e-06 1.81e-04\n", + "9 10000 U-238 scatter-Y0,0 2.33e+00 1.31e-02\n", + "10 10000 U-238 scatter-Y1,-1 2.45e-02 2.27e-03\n", + "11 10000 U-238 scatter-Y1,0 -5.87e-05 2.80e-03\n", + "12 10000 U-238 scatter-Y1,1 -2.80e-02 2.54e-03\n", + "13 10000 U-238 scatter-Y2,-2 -4.86e-03 1.58e-03\n", + "14 10000 U-238 scatter-Y2,-1 5.57e-04 2.02e-03\n", + "15 10000 U-238 scatter-Y2,0 6.24e-03 1.63e-03\n", + "16 10000 U-238 scatter-Y2,1 -6.48e-04 1.55e-03\n", + "17 10000 U-238 scatter-Y2,2 -1.03e-03 1.31e-03" ] }, "execution_count": 29, @@ -1546,168 +1550,168 @@ " 558\n", " 279\n", " absorption\n", - " 0.000093\n", - " 0.000013\n", + " 9.27e-05\n", + " 1.33e-05\n", " \n", " \n", " 559\n", " 279\n", " scatter\n", - " 0.013504\n", - " 0.000805\n", + " 1.35e-02\n", + " 8.05e-04\n", " \n", " \n", " 560\n", " 280\n", " absorption\n", - " 0.000084\n", - " 0.000010\n", + " 8.41e-05\n", + " 9.92e-06\n", " \n", " \n", " 561\n", " 280\n", " scatter\n", - " 0.014215\n", - " 0.000612\n", + " 1.42e-02\n", + " 6.12e-04\n", " \n", " \n", " 562\n", " 281\n", " absorption\n", - " 0.000091\n", - " 0.000008\n", + " 9.12e-05\n", + " 8.26e-06\n", " \n", " \n", " 563\n", " 281\n", " scatter\n", - " 0.014545\n", - " 0.000590\n", + " 1.45e-02\n", + " 5.90e-04\n", " \n", " \n", " 564\n", " 282\n", " absorption\n", - " 0.000112\n", - " 0.000012\n", + " 1.12e-04\n", + " 1.24e-05\n", " \n", " \n", " 565\n", " 282\n", " scatter\n", - " 0.016321\n", - " 0.000729\n", + " 1.63e-02\n", + " 7.29e-04\n", " \n", " \n", " 566\n", " 283\n", " absorption\n", - " 0.000092\n", - " 0.000007\n", + " 9.18e-05\n", + " 7.12e-06\n", " \n", " \n", " 567\n", " 283\n", " scatter\n", - " 0.016163\n", - " 0.000661\n", + " 1.62e-02\n", + " 6.61e-04\n", " \n", " \n", " 568\n", " 284\n", " absorption\n", - " 0.000104\n", - " 0.000011\n", + " 1.04e-04\n", + " 1.10e-05\n", " \n", " \n", " 569\n", " 284\n", " scatter\n", - " 0.017384\n", - " 0.000599\n", + " 1.74e-02\n", + " 5.99e-04\n", " \n", " \n", " 570\n", " 285\n", " absorption\n", - " 0.000111\n", - " 0.000011\n", + " 1.11e-04\n", + " 1.14e-05\n", " \n", " \n", " 571\n", " 285\n", " scatter\n", - " 0.018015\n", - " 0.000774\n", + " 1.80e-02\n", + " 7.74e-04\n", " \n", " \n", " 572\n", " 286\n", " absorption\n", - " 0.000125\n", - " 0.000012\n", + " 1.25e-04\n", + " 1.20e-05\n", " \n", " \n", " 573\n", " 286\n", " scatter\n", - " 0.018294\n", - " 0.000828\n", + " 1.83e-02\n", + " 8.28e-04\n", " \n", " \n", " 574\n", " 287\n", " absorption\n", - " 0.000119\n", - " 0.000013\n", + " 1.19e-04\n", + " 1.30e-05\n", " \n", " \n", " 575\n", " 287\n", " scatter\n", - " 0.017483\n", - " 0.000757\n", + " 1.75e-02\n", + " 7.57e-04\n", " \n", " \n", " 576\n", " 288\n", " absorption\n", - " 0.000113\n", - " 0.000014\n", + " 1.13e-04\n", + " 1.40e-05\n", " \n", " \n", " 577\n", " 288\n", " scatter\n", - " 0.018248\n", - " 0.000782\n", + " 1.82e-02\n", + " 7.82e-04\n", " \n", " \n", "\n", "" ], "text/plain": [ - " distribcell score mean std. dev.\n", - "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" + " distribcell score mean std. dev.\n", + "558 279 absorption 9.27e-05 1.33e-05\n", + "559 279 scatter 1.35e-02 8.05e-04\n", + "560 280 absorption 8.41e-05 9.92e-06\n", + "561 280 scatter 1.42e-02 6.12e-04\n", + "562 281 absorption 9.12e-05 8.26e-06\n", + "563 281 scatter 1.45e-02 5.90e-04\n", + "564 282 absorption 1.12e-04 1.24e-05\n", + "565 282 scatter 1.63e-02 7.29e-04\n", + "566 283 absorption 9.18e-05 7.12e-06\n", + "567 283 scatter 1.62e-02 6.61e-04\n", + "568 284 absorption 1.04e-04 1.10e-05\n", + "569 284 scatter 1.74e-02 5.99e-04\n", + "570 285 absorption 1.11e-04 1.14e-05\n", + "571 285 scatter 1.80e-02 7.74e-04\n", + "572 286 absorption 1.25e-04 1.20e-05\n", + "573 286 scatter 1.83e-02 8.28e-04\n", + "574 287 absorption 1.19e-04 1.30e-05\n", + "575 287 scatter 1.75e-02 7.57e-04\n", + "576 288 absorption 1.13e-04 1.40e-05\n", + "577 288 scatter 1.82e-02 7.82e-04" ] }, "execution_count": 33, @@ -1772,8 +1776,8 @@ " 10000\n", " 0\n", " absorption\n", - " 0.000123\n", - " 0.000012\n", + " 1.23e-04\n", + " 1.19e-05\n", " \n", " \n", " 1 \n", @@ -1787,8 +1791,8 @@ " 10000\n", " 0\n", " scatter\n", - " 0.017805\n", - " 0.000808\n", + " 1.78e-02\n", + " 8.08e-04\n", " \n", " \n", " 2 \n", @@ -1802,8 +1806,8 @@ " 10000\n", " 1\n", " absorption\n", - " 0.000217\n", - " 0.000020\n", + " 2.17e-04\n", + " 1.96e-05\n", " \n", " \n", " 3 \n", @@ -1817,8 +1821,8 @@ " 10000\n", " 1\n", " scatter\n", - " 0.028867\n", - " 0.001263\n", + " 2.89e-02\n", + " 1.26e-03\n", " \n", " \n", " 4 \n", @@ -1832,8 +1836,8 @@ " 10000\n", " 2\n", " absorption\n", - " 0.000318\n", - " 0.000020\n", + " 3.18e-04\n", + " 2.03e-05\n", " \n", " \n", " 5 \n", @@ -1847,8 +1851,8 @@ " 10000\n", " 2\n", " scatter\n", - " 0.040493\n", - " 0.001269\n", + " 4.05e-02\n", + " 1.27e-03\n", " \n", " \n", " 6 \n", @@ -1862,8 +1866,8 @@ " 10000\n", " 3\n", " absorption\n", - " 0.000386\n", - " 0.000018\n", + " 3.86e-04\n", + " 1.80e-05\n", " \n", " \n", " 7 \n", @@ -1877,8 +1881,8 @@ " 10000\n", " 3\n", " scatter\n", - " 0.048576\n", - " 0.001337\n", + " 4.86e-02\n", + " 1.34e-03\n", " \n", " \n", " 8 \n", @@ -1892,8 +1896,8 @@ " 10000\n", " 4\n", " absorption\n", - " 0.000501\n", - " 0.000026\n", + " 5.01e-04\n", + " 2.60e-05\n", " \n", " \n", " 9 \n", @@ -1907,8 +1911,8 @@ " 10000\n", " 4\n", " scatter\n", - " 0.057063\n", - " 0.001715\n", + " 5.71e-02\n", + " 1.72e-03\n", " \n", " \n", " 10\n", @@ -1922,8 +1926,8 @@ " 10000\n", " 5\n", " absorption\n", - " 0.000484\n", - " 0.000026\n", + " 4.84e-04\n", + " 2.58e-05\n", " \n", " \n", " 11\n", @@ -1937,8 +1941,8 @@ " 10000\n", " 5\n", " scatter\n", - " 0.060822\n", - " 0.001581\n", + " 6.08e-02\n", + " 1.58e-03\n", " \n", " \n", " 12\n", @@ -1952,8 +1956,8 @@ " 10000\n", " 6\n", " absorption\n", - " 0.000532\n", - " 0.000039\n", + " 5.32e-04\n", + " 3.90e-05\n", " \n", " \n", " 13\n", @@ -1967,8 +1971,8 @@ " 10000\n", " 6\n", " scatter\n", - " 0.069101\n", - " 0.002249\n", + " 6.91e-02\n", + " 2.25e-03\n", " \n", " \n", " 14\n", @@ -1982,8 +1986,8 @@ " 10000\n", " 7\n", " absorption\n", - " 0.000577\n", - " 0.000039\n", + " 5.77e-04\n", + " 3.92e-05\n", " \n", " \n", " 15\n", @@ -1997,8 +2001,8 @@ " 10000\n", " 7\n", " scatter\n", - " 0.076722\n", - " 0.002335\n", + " 7.67e-02\n", + " 2.34e-03\n", " \n", " \n", " 16\n", @@ -2012,8 +2016,8 @@ " 10000\n", " 8\n", " absorption\n", - " 0.000649\n", - " 0.000039\n", + " 6.49e-04\n", + " 3.90e-05\n", " \n", " \n", " 17\n", @@ -2027,8 +2031,8 @@ " 10000\n", " 8\n", " scatter\n", - " 0.081564\n", - " 0.001610\n", + " 8.16e-02\n", + " 1.61e-03\n", " \n", " \n", " 18\n", @@ -2042,8 +2046,8 @@ " 10000\n", " 9\n", " absorption\n", - " 0.000680\n", - " 0.000032\n", + " 6.80e-04\n", + " 3.17e-05\n", " \n", " \n", " 19\n", @@ -2057,8 +2061,8 @@ " 10000\n", " 9\n", " scatter\n", - " 0.087715\n", - " 0.001959\n", + " 8.77e-02\n", + " 1.96e-03\n", " \n", " \n", "\n", @@ -2131,27 +2135,27 @@ "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 " + " mean std. dev. \n", + "0 1.23e-04 1.19e-05 \n", + "1 1.78e-02 8.08e-04 \n", + "2 2.17e-04 1.96e-05 \n", + "3 2.89e-02 1.26e-03 \n", + "4 3.18e-04 2.03e-05 \n", + "5 4.05e-02 1.27e-03 \n", + "6 3.86e-04 1.80e-05 \n", + "7 4.86e-02 1.34e-03 \n", + "8 5.01e-04 2.60e-05 \n", + "9 5.71e-02 1.72e-03 \n", + "10 4.84e-04 2.58e-05 \n", + "11 6.08e-02 1.58e-03 \n", + "12 5.32e-04 3.90e-05 \n", + "13 6.91e-02 2.25e-03 \n", + "14 5.77e-04 3.92e-05 \n", + "15 7.67e-02 2.34e-03 \n", + "16 6.49e-04 3.90e-05 \n", + "17 8.16e-02 1.61e-03 \n", + "18 6.80e-04 3.17e-05 \n", + "19 8.77e-02 1.96e-03 " ] }, "execution_count": 34, @@ -2189,58 +2193,58 @@ " \n", " \n", " count\n", - " 289.000000\n", - " 289.000000\n", + " 2.89e+02\n", + " 2.89e+02\n", " \n", " \n", " mean\n", - " 0.000418\n", - " 0.000022\n", + " 4.18e-04\n", + " 2.17e-05\n", " \n", " \n", " std\n", - " 0.000239\n", - " 0.000009\n", + " 2.39e-04\n", + " 8.82e-06\n", " \n", " \n", " min\n", - " 0.000018\n", - " 0.000004\n", + " 1.81e-05\n", + " 3.82e-06\n", " \n", " \n", " 25%\n", - " 0.000202\n", - " 0.000015\n", + " 2.02e-04\n", + " 1.49e-05\n", " \n", " \n", " 50%\n", - " 0.000402\n", - " 0.000021\n", + " 4.02e-04\n", + " 2.11e-05\n", " \n", " \n", " 75%\n", - " 0.000615\n", - " 0.000027\n", + " 6.15e-04\n", + " 2.67e-05\n", " \n", " \n", " max\n", - " 0.000892\n", - " 0.000044\n", + " 8.92e-04\n", + " 4.43e-05\n", " \n", " \n", "\n", "" ], "text/plain": [ - " mean std. dev.\n", - "count 289.000000 289.000000\n", - "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" + " mean std. dev.\n", + "count 2.89e+02 2.89e+02\n", + "mean 4.18e-04 2.17e-05\n", + "std 2.39e-04 8.82e-06\n", + "min 1.81e-05 3.82e-06\n", + "25% 2.02e-04 1.49e-05\n", + "50% 4.02e-04 2.11e-05\n", + "75% 6.15e-04 2.67e-05\n", + "max 8.92e-04 4.43e-05" ] }, "execution_count": 35, @@ -2359,7 +2363,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 38, @@ -2370,7 +2374,7 @@ "data": { "image/png": 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KlcydOxeAjo4O5s2bx+LFi4Fi/LPZ64VttbS/5557uOKKf2Z4+N3ABcAO4Dja21fxznde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\nEtjPzH5gZg8C/xDu+xegNwx7vQb4dYV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qq6/SuHFjrr32WoYOHVpqmfLiLfu4V4feVodv1z4SkXbAS0ALnIObn1PVJ0SkGfA60AHI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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2423,15 +2427,6 @@ "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 5c77dcf9c..e357e73fc 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": 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+ "image/png": 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"text/plain": [ "" ] @@ -577,7 +577,7 @@ " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.1\n", " Git SHA1: 34381b40a9445a727e360873aaa6ef892af1cb6a\n", - " Date/Time: 2016-02-07 14:16:08\n", + " Date/Time: 2016-02-07 16:05:17\n", " MPI Processes: 1\n", "\n", " ===========================================================================\n", @@ -634,20 +634,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.0900E-01 seconds\n", - " Reading cross sections = 1.0100E-01 seconds\n", - " Total time in simulation = 7.8100E+00 seconds\n", - " Time in transport only = 7.7980E+00 seconds\n", - " Time in inactive batches = 1.3850E+00 seconds\n", - " Time in active batches = 6.4250E+00 seconds\n", - " Time synchronizing fission bank = 1.0000E-03 seconds\n", - " Sampling source sites = 0.0000E+00 seconds\n", - " SEND/RECV source sites = 1.0000E-03 seconds\n", + " Total time for initialization = 3.4700E-01 seconds\n", + " Reading cross sections = 9.1000E-02 seconds\n", + " Total time in simulation = 7.3920E+00 seconds\n", + " Time in transport only = 7.3820E+00 seconds\n", + " Time in inactive batches = 1.0930E+00 seconds\n", + " Time in active batches = 6.2990E+00 seconds\n", + " Time synchronizing fission bank = 2.0000E-03 seconds\n", + " Sampling source sites = 1.0000E-03 seconds\n", + " SEND/RECV source sites = 0.0000E+00 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", - " Total time for finalization = 1.0000E-03 seconds\n", - " Total time elapsed = 8.2360E+00 seconds\n", - " Calculation Rate (inactive) = 9025.27 neutrons/second\n", - " Calculation Rate (active) = 5836.58 neutrons/second\n", + " Total time for finalization = 2.0000E-03 seconds\n", + " Total time elapsed = 7.7510E+00 seconds\n", + " Calculation Rate (inactive) = 11436.4 neutrons/second\n", + " Calculation Rate (active) = 5953.33 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -769,8 +769,8 @@ "" ], "text/plain": [ - " nuclide score mean std. dev.\n", - "0 total (nu-fission / absorption) 1.040166 0.009069" + " nuclide score mean std. dev.\n", + "0 total (nu-fission / absorption) 1.04e+00 9.07e-03" ] }, "execution_count": 26, @@ -821,8 +821,8 @@ " \n", " \n", " 0\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0\n", + " 0.000001\n", " total\n", " absorption\n", " 0.694707\n", @@ -833,8 +833,8 @@ "" ], "text/plain": [ - " energy low [MeV] energy high [MeV] nuclide score mean std. dev.\n", - "0 0.00e+00 6.25e-07 total absorption 0.694707 0.006699" + " energy low [MeV] energy high [MeV] nuclide score mean std. dev.\n", + "0 0.00e+00 6.25e-07 total absorption 6.95e-01 6.70e-03" ] }, "execution_count": 27, @@ -883,8 +883,8 @@ " \n", " \n", " 0\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0\n", + " 0.000001\n", " total\n", " nu-fission\n", " 1.201216\n", @@ -895,8 +895,8 @@ "" ], "text/plain": [ - " energy low [MeV] energy high [MeV] nuclide score mean std. dev.\n", - "0 0.00e+00 6.25e-07 total nu-fission 1.201216 0.012288" + " energy low [MeV] energy high [MeV] nuclide score mean std. dev.\n", + "0 0.00e+00 6.25e-07 total nu-fission 1.20e+00 1.23e-02" ] }, "execution_count": 28, @@ -947,8 +947,8 @@ " \n", " \n", " 0\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0\n", + " 0.000001\n", " 10000\n", " total\n", " absorption\n", @@ -960,11 +960,11 @@ "" ], "text/plain": [ - " energy low [MeV] energy high [MeV] cell nuclide score mean \\\n", - "0 0.00e+00 6.25e-07 10000 total absorption 0.74925 \n", + " energy low [MeV] energy high [MeV] cell nuclide score mean \\\n", + "0 0.00e+00 6.25e-07 10000 total absorption 7.49e-01 \n", "\n", " std. dev. \n", - "0 0.008257 " + "0 8.26e-03 " ] }, "execution_count": 29, @@ -1013,8 +1013,8 @@ " \n", " \n", " 0\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0\n", + " 0.000001\n", " 10000\n", " total\n", " (nu-fission / absorption)\n", @@ -1026,11 +1026,11 @@ "" ], "text/plain": [ - " energy low [MeV] energy high [MeV] cell nuclide \\\n", - "0 0.00e+00 6.25e-07 10000 total \n", + " energy low [MeV] energy high [MeV] cell nuclide \\\n", + "0 0.00e+00 6.25e-07 10000 total \n", "\n", - " score mean std. dev. \n", - "0 (nu-fission / absorption) 1.663616 0.018624 " + " score mean std. dev. \n", + "0 (nu-fission / absorption) 1.66e+00 1.86e-02 " ] }, "execution_count": 30, @@ -1078,8 +1078,8 @@ " \n", " \n", " 0\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0\n", + " 0.000001\n", " 10000\n", " total\n", " (((absorption * nu-fission) * absorption) * (n...\n", @@ -1091,11 +1091,11 @@ "" ], "text/plain": [ - " energy low [MeV] energy high [MeV] cell nuclide \\\n", - "0 0.00e+00 6.25e-07 10000 total \n", + " energy low [MeV] energy high [MeV] cell nuclide \\\n", + "0 0.00e+00 6.25e-07 10000 total \n", "\n", - " score mean std. dev. \n", - "0 (((absorption * nu-fission) * absorption) * (n... 1.040166 0.021928 " + " score mean std. dev. \n", + "0 (((absorption * nu-fission) * absorption) * (n... 1.04e+00 2.19e-02 " ] }, "execution_count": 31, @@ -1161,8 +1161,8 @@ " \n", " 0\n", " 10000\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.000000\n", + " 0.000001\n", " (U-238 / total)\n", " (nu-fission / flux)\n", " 0.000001\n", @@ -1171,8 +1171,8 @@ " \n", " 1\n", " 10000\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.000000\n", + " 0.000001\n", " (U-238 / total)\n", " (scatter / flux)\n", " 0.209989\n", @@ -1181,8 +1181,8 @@ " \n", " 2\n", " 10000\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.000000\n", + " 0.000001\n", " (U-235 / total)\n", " (nu-fission / flux)\n", " 0.356420\n", @@ -1191,8 +1191,8 @@ " \n", " 3\n", " 10000\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.000000\n", + " 0.000001\n", " (U-235 / total)\n", " (scatter / flux)\n", " 0.005555\n", @@ -1201,8 +1201,8 @@ " \n", " 4\n", " 10000\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 0.000001\n", + " 20.000000\n", " (U-238 / total)\n", " (nu-fission / flux)\n", " 0.007155\n", @@ -1211,8 +1211,8 @@ " \n", " 5\n", " 10000\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 0.000001\n", + " 20.000000\n", " (U-238 / total)\n", " (scatter / flux)\n", " 0.227770\n", @@ -1221,8 +1221,8 @@ " \n", " 6\n", " 10000\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 0.000001\n", + " 20.000000\n", " (U-235 / total)\n", " (nu-fission / flux)\n", " 0.008067\n", @@ -1231,8 +1231,8 @@ " \n", " 7\n", " 10000\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 0.000001\n", + " 20.000000\n", " (U-235 / total)\n", " (scatter / flux)\n", " 0.003367\n", @@ -1243,25 +1243,25 @@ "" ], "text/plain": [ - " cell energy low [MeV] energy high [MeV] nuclide \\\n", - "0 10000 0.00e+00 6.25e-07 (U-238 / total) \n", - "1 10000 0.00e+00 6.25e-07 (U-238 / total) \n", - "2 10000 0.00e+00 6.25e-07 (U-235 / total) \n", - "3 10000 0.00e+00 6.25e-07 (U-235 / total) \n", - "4 10000 6.25e-07 2.00e+01 (U-238 / total) \n", - "5 10000 6.25e-07 2.00e+01 (U-238 / total) \n", - "6 10000 6.25e-07 2.00e+01 (U-235 / total) \n", - "7 10000 6.25e-07 2.00e+01 (U-235 / total) \n", + " cell energy low [MeV] energy high [MeV] nuclide \\\n", + "0 10000 0.00e+00 6.25e-07 (U-238 / total) \n", + "1 10000 0.00e+00 6.25e-07 (U-238 / total) \n", + "2 10000 0.00e+00 6.25e-07 (U-235 / total) \n", + "3 10000 0.00e+00 6.25e-07 (U-235 / total) \n", + "4 10000 6.25e-07 2.00e+01 (U-238 / total) \n", + "5 10000 6.25e-07 2.00e+01 (U-238 / total) \n", + "6 10000 6.25e-07 2.00e+01 (U-235 / total) \n", + "7 10000 6.25e-07 2.00e+01 (U-235 / total) \n", "\n", - " 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 " + " score mean std. dev. \n", + "0 (nu-fission / flux) 6.66e-07 7.38e-09 \n", + "1 (scatter / flux) 2.10e-01 2.30e-03 \n", + "2 (nu-fission / flux) 3.56e-01 3.95e-03 \n", + "3 (scatter / flux) 5.56e-03 6.10e-05 \n", + "4 (nu-fission / flux) 7.15e-03 8.05e-05 \n", + "5 (scatter / flux) 2.28e-01 1.08e-03 \n", + "6 (nu-fission / flux) 8.07e-03 5.25e-05 \n", + "7 (scatter / flux) 3.37e-03 1.65e-05 " ] }, "execution_count": 33, @@ -1396,8 +1396,8 @@ " \n", " 0\n", " 10000\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.000000\n", + " 0.000001\n", " U-238\n", " nu-fission\n", " 0.000002\n", @@ -1406,8 +1406,8 @@ " \n", " 1\n", " 10000\n", - " 0.00e+00\n", - " 6.25e-07\n", + " 0.000000\n", + " 0.000001\n", " U-235\n", " nu-fission\n", " 0.868553\n", @@ -1416,8 +1416,8 @@ " \n", " 2\n", " 10000\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 0.000001\n", + " 20.000000\n", " U-238\n", " nu-fission\n", " 0.082149\n", @@ -1426,8 +1426,8 @@ " \n", " 3\n", " 10000\n", - " 6.25e-07\n", - " 2.00e+01\n", + " 0.000001\n", + " 20.000000\n", " U-235\n", " nu-fission\n", " 0.092618\n", @@ -1438,17 +1438,17 @@ "" ], "text/plain": [ - " cell energy low [MeV] energy high [MeV] nuclide score mean \\\n", - "0 10000 0.00e+00 6.25e-07 U-238 nu-fission 0.000002 \n", - "1 10000 0.00e+00 6.25e-07 U-235 nu-fission 0.868553 \n", - "2 10000 6.25e-07 2.00e+01 U-238 nu-fission 0.082149 \n", - "3 10000 6.25e-07 2.00e+01 U-235 nu-fission 0.092618 \n", + " cell energy low [MeV] energy high [MeV] nuclide score mean \\\n", + "0 10000 0.00e+00 6.25e-07 U-238 nu-fission 1.62e-06 \n", + "1 10000 0.00e+00 6.25e-07 U-235 nu-fission 8.69e-01 \n", + "2 10000 6.25e-07 2.00e+01 U-238 nu-fission 8.21e-02 \n", + "3 10000 6.25e-07 2.00e+01 U-235 nu-fission 9.26e-02 \n", "\n", - " std. dev. \n", - "0 1.283958e-08 \n", - "1 6.880390e-03 \n", - "2 8.837250e-04 \n", - "3 5.195308e-04 " + " std. dev. \n", + "0 1.28e-08 \n", + "1 6.88e-03 \n", + "2 8.84e-04 \n", + "3 5.20e-04 " ] }, "execution_count": 37, @@ -1490,8 +1490,8 @@ " \n", " 0\n", " 10002\n", - " 1.00e-08\n", - " 1.08e-07\n", + " 1.000000e-08\n", + " 0.000000\n", " H-1\n", " scatter\n", " 4.619398\n", @@ -1500,8 +1500,8 @@ " \n", " 1\n", " 10002\n", - " 1.08e-07\n", - " 1.17e-06\n", + " 1.080060e-07\n", + " 0.000001\n", " H-1\n", " scatter\n", " 2.030757\n", @@ -1510,8 +1510,8 @@ " \n", " 2\n", " 10002\n", - " 1.17e-06\n", - " 1.26e-05\n", + " 1.166529e-06\n", + " 0.000013\n", " H-1\n", " scatter\n", " 1.658488\n", @@ -1520,8 +1520,8 @@ " \n", " 3\n", " 10002\n", - " 1.26e-05\n", - " 1.36e-04\n", + " 1.259921e-05\n", + " 0.000136\n", " H-1\n", " scatter\n", " 1.853002\n", @@ -1530,8 +1530,8 @@ " \n", " 4\n", " 10002\n", - " 1.36e-04\n", - " 1.47e-03\n", + " 1.360790e-04\n", + " 0.001470\n", " H-1\n", " scatter\n", " 2.050773\n", @@ -1540,8 +1540,8 @@ " \n", " 5\n", " 10002\n", - " 1.47e-03\n", - " 1.59e-02\n", + " 1.469734e-03\n", + " 0.015874\n", " H-1\n", " scatter\n", " 2.131759\n", @@ -1550,8 +1550,8 @@ " \n", " 6\n", " 10002\n", - " 1.59e-02\n", - " 1.71e-01\n", + " 1.587401e-02\n", + " 0.171449\n", " H-1\n", " scatter\n", " 2.213710\n", @@ -1560,8 +1560,8 @@ " \n", " 7\n", " 10002\n", - " 1.71e-01\n", - " 1.85e+00\n", + " 1.714488e-01\n", + " 1.851749\n", " H-1\n", " scatter\n", " 2.011925\n", @@ -1570,8 +1570,8 @@ " \n", " 8\n", " 10002\n", - " 1.85e+00\n", - " 2.00e+01\n", + " 1.851749e+00\n", + " 20.000000\n", " H-1\n", " scatter\n", " 0.371280\n", @@ -1582,27 +1582,27 @@ "" ], "text/plain": [ - " cell energy low [MeV] energy high [MeV] nuclide score mean \\\n", - "0 10002 1.00e-08 1.08e-07 H-1 scatter 4.619398 \n", - "1 10002 1.08e-07 1.17e-06 H-1 scatter 2.030757 \n", - "2 10002 1.17e-06 1.26e-05 H-1 scatter 1.658488 \n", - "3 10002 1.26e-05 1.36e-04 H-1 scatter 1.853002 \n", - "4 10002 1.36e-04 1.47e-03 H-1 scatter 2.050773 \n", - "5 10002 1.47e-03 1.59e-02 H-1 scatter 2.131759 \n", - "6 10002 1.59e-02 1.71e-01 H-1 scatter 2.213710 \n", - "7 10002 1.71e-01 1.85e+00 H-1 scatter 2.011925 \n", - "8 10002 1.85e+00 2.00e+01 H-1 scatter 0.371280 \n", + " cell energy low [MeV] energy high [MeV] nuclide score mean \\\n", + "0 10002 1.00e-08 1.08e-07 H-1 scatter 4.62e+00 \n", + "1 10002 1.08e-07 1.17e-06 H-1 scatter 2.03e+00 \n", + "2 10002 1.17e-06 1.26e-05 H-1 scatter 1.66e+00 \n", + "3 10002 1.26e-05 1.36e-04 H-1 scatter 1.85e+00 \n", + "4 10002 1.36e-04 1.47e-03 H-1 scatter 2.05e+00 \n", + "5 10002 1.47e-03 1.59e-02 H-1 scatter 2.13e+00 \n", + "6 10002 1.59e-02 1.71e-01 H-1 scatter 2.21e+00 \n", + "7 10002 1.71e-01 1.85e+00 H-1 scatter 2.01e+00 \n", + "8 10002 1.85e+00 2.00e+01 H-1 scatter 3.71e-01 \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 " + "0 4.01e-02 \n", + "1 1.12e-02 \n", + "2 9.78e-03 \n", + "3 7.38e-03 \n", + "4 1.25e-02 \n", + "5 7.82e-03 \n", + "6 1.52e-02 \n", + "7 9.41e-03 \n", + "8 3.95e-03 " ] }, "execution_count": 38, diff --git a/openmc/arithmetic.py b/openmc/arithmetic.py index f6094533a..8574c4873 100644 --- a/openmc/arithmetic.py +++ b/openmc/arithmetic.py @@ -430,7 +430,7 @@ class CrossFilter(object): filter_index = left_index * self.right_filter.num_bins + right_index return filter_index - def get_pandas_dataframe(self, datasize, summary=None, **kwargs): + def get_pandas_dataframe(self, datasize, summary=None): """Builds a Pandas DataFrame for the CrossFilter's bins. This method constructs a Pandas DataFrame object for the CrossFilter @@ -454,14 +454,6 @@ class CrossFilter(object): column with a geometric "path" to each distribcell instance. NOTE: This option requires the OpenCG Python package. - Keyword arguments - ----------------- - energy_fmt : None or string - If a format string is provided, energy and energyout filter bins - will be converted from floats to strings using the given format. If - None is provided, the values will be left as floats. The default is - '{:.2e}'. - Returns ------- pandas.DataFrame @@ -480,15 +472,12 @@ class CrossFilter(object): # If left and right filters are identical, do not combine bins if self.left_filter == self.right_filter: - df = self.left_filter.get_pandas_dataframe(datasize, summary, - **kwargs) + df = self.left_filter.get_pandas_dataframe(datasize, summary) # If left and right filters are different, combine their bins else: - left_df = self.left_filter.get_pandas_dataframe(datasize, summary, - **kwargs) - right_df = self.right_filter.get_pandas_dataframe(datasize, summary, - **kwargs) + left_df = self.left_filter.get_pandas_dataframe(datasize, summary) + right_df = self.right_filter.get_pandas_dataframe(datasize, summary) left_df = left_df.astype(str) right_df = right_df.astype(str) df = '(' + left_df + ' ' + self.binary_op + ' ' + right_df + ')' @@ -842,7 +831,7 @@ class AggregateFilter(object): else: return 0 - def get_pandas_dataframe(self, datasize, summary=None, **kwargs): + def get_pandas_dataframe(self, datasize, summary=None): """Builds a Pandas DataFrame for the AggregateFilter's bins. This method constructs a Pandas DataFrame object for the AggregateFilter diff --git a/openmc/filter.py b/openmc/filter.py index d6f604108..a27e17cd9 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -462,7 +462,7 @@ class Filter(object): return filter_bin - def get_pandas_dataframe(self, data_size, summary=None, **kwargs): + def get_pandas_dataframe(self, data_size, summary=None): """Builds a Pandas DataFrame for the Filter's bins. This method constructs a Pandas DataFrame object for the filter with @@ -484,14 +484,6 @@ class Filter(object): column with a geometric "path" to each distribcell instance. NOTE: This option requires the OpenCG Python package. - Keyword arguments - ----------------- - energy_fmt : None or string - If a format string is provided, energy and energyout filter bins - will be converted from floats to strings using the given format. If - None is provided, the values will be left as floats. The default is - '{:.2e}'. - Returns ------- pandas.DataFrame @@ -735,12 +727,6 @@ class Filter(object): lo_bins = np.tile(lo_bins, tile_factor) hi_bins = np.tile(hi_bins, tile_factor) - # Format the energy values, if necessary. - energy_fmt = kwargs.setdefault('energy_fmt', '{:.2e}') - if energy_fmt is not None: - lo_bins = [energy_fmt.format(E) for E in lo_bins] - hi_bins = [energy_fmt.format(E) for E in hi_bins] - # Add the new energy columns to the DataFrame. df.loc[:, self.type + ' low [MeV]'] = lo_bins df.loc[:, self.type + ' high [MeV]'] = hi_bins diff --git a/openmc/tallies.py b/openmc/tallies.py index f98be83a2..d1666694f 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -2,6 +2,7 @@ from __future__ import division from collections import Iterable, defaultdict import copy +from functools import partial import os import pickle import itertools @@ -1244,7 +1245,7 @@ class Tally(object): return data def get_pandas_dataframe(self, filters=True, nuclides=True, - scores=True, summary=None, **kwargs): + scores=True, summary=None, float_format='{:.2e}'): """Build a Pandas DataFrame for the Tally data. This method constructs a Pandas DataFrame object for the Tally data @@ -1268,14 +1269,9 @@ class Tally(object): information in the Summary object is embedded into a Multi-index column with a geometric "path" to each distribcell intance. NOTE: This option requires the OpenCG Python package. - - Keyword arguments - ----------------- - energy_fmt : None or string - If a format string is provided, energy and energyout filter bins - will be converted from floats to strings using the given format. If - None is provided, the values will be left as floats. The default is - '{:.2e}'. + float_format : string + All floats in the DataFrame will be formatted using the given + format string before printing. Returns ------- @@ -1324,8 +1320,7 @@ class Tally(object): # Append each Filter's DataFrame to the overall DataFrame for self_filter in self.filters: - filter_df = self_filter.get_pandas_dataframe(data_size, summary, - **kwargs) + filter_df = self_filter.get_pandas_dataframe(data_size, summary) df = pd.concat([df, filter_df], axis=1) # Include DataFrame column for nuclides if user requested it @@ -1376,6 +1371,10 @@ class Tally(object): # Create and set a MultiIndex for the DataFrame's columns df.columns = pd.MultiIndex.from_tuples(columns) + # Modify the df.to_string method so that it prints formatted strings. + # Credit to http://stackoverflow.com/users/3657742/chrisb for this trick + df.to_string = partial(df.to_string, float_format=float_format.format) + return df def get_reshaped_data(self, value='mean'):