diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index ea71055a7..b88cf9949 100644 --- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb +++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb @@ -370,7 +370,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -552,10 +552,11 @@ " 888\n", "\n", " Copyright: 2011-2016 Massachusetts Institute of Technology\n", - " License: http://openmc.readthedocs.org/en/latest/license.html\n", + " License: http://openmc.readthedocs.io/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: df280b60eb1c6d7b7f842e05ede734a4883a0fc8\n", - " Date/Time: 2016-05-05 14:39:34\n", + " Git SHA1: ae588276014a905ecc6e0967bf08288ecec5b550\n", + " Date/Time: 2016-05-09 23:01:18\n", + " MPI Processes: 1\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -618,20 +619,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.6600E-01 seconds\n", - " Reading cross sections = 1.1100E-01 seconds\n", - " Total time in simulation = 1.1106E+01 seconds\n", - " Time in transport only = 1.1089E+01 seconds\n", - " Time in inactive batches = 1.7090E+00 seconds\n", - " Time in active batches = 9.3970E+00 seconds\n", - " Time synchronizing fission bank = 4.0000E-03 seconds\n", - " Sampling source sites = 3.0000E-03 seconds\n", + " Total time for initialization = 3.9000E-01 seconds\n", + " Reading cross sections = 8.6000E-02 seconds\n", + " Total time in simulation = 1.0830E+01 seconds\n", + " Time in transport only = 1.0818E+01 seconds\n", + " Time in inactive batches = 1.3590E+00 seconds\n", + " Time in active batches = 9.4710E+00 seconds\n", + " Time synchronizing fission bank = 3.0000E-03 seconds\n", + " Sampling source sites = 2.0000E-03 seconds\n", " SEND/RECV source sites = 1.0000E-03 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 1.1590E+01 seconds\n", - " Calculation Rate (inactive) = 7314.22 neutrons/second\n", - " Calculation Rate (active) = 3990.64 neutrons/second\n", + " Total time elapsed = 1.1234E+01 seconds\n", + " Calculation Rate (inactive) = 9197.94 neutrons/second\n", + " Calculation Rate (active) = 3959.46 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -710,7 +711,7 @@ " \t\tmesh\t[1]\n", " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", "\tNuclides =\ttotal \n", - "\tScores =\t['fission', 'nu-fission']\n", + "\tScores =\t[u'fission', u'nu-fission']\n", "\tEstimator =\ttracklength\n", "\n" ] @@ -742,13 +743,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.1501735 ]]\n", + "[[[ 0.1508711 ]]\n", "\n", - " [[ 0.05936257]]\n", + " [[ 0.05389822]]\n", "\n", - " [[ 0.21402727]]\n", + " [[ 0.19633 ]]\n", "\n", - " [[ 0.13436703]]]\n" + " [[ 0.12963172]]]\n" ] } ], @@ -804,8 +805,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 2.20e-04\n", - " 3.31e-05\n", + " 2.34e-04\n", + " 3.54e-05\n", " \n", " \n", " 1\n", @@ -815,8 +816,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 5.37e-04\n", - " 8.06e-05\n", + " 5.71e-04\n", + " 8.62e-05\n", " \n", " \n", " 2\n", @@ -826,8 +827,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 7.43e-05\n", - " 7.91e-06\n", + " 7.03e-05\n", + " 7.05e-06\n", " \n", " \n", " 3\n", @@ -837,8 +838,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 1.97e-04\n", - " 1.96e-05\n", + " 1.87e-04\n", + " 1.76e-05\n", " \n", " \n", " 4\n", @@ -848,8 +849,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 3.52e-04\n", - " 3.39e-05\n", + " 3.67e-04\n", + " 3.61e-05\n", " \n", " \n", " 5\n", @@ -859,8 +860,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 8.57e-04\n", - " 8.26e-05\n", + " 8.94e-04\n", + " 8.80e-05\n", " \n", " \n", " 6\n", @@ -870,8 +871,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.02e-04\n", - " 6.16e-06\n", + " 1.04e-04\n", + " 5.36e-06\n", " \n", " \n", " 7\n", @@ -881,8 +882,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 2.70e-04\n", - " 1.61e-05\n", + " 2.76e-04\n", + " 1.40e-05\n", " \n", " \n", " 8\n", @@ -892,8 +893,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 6.09e-04\n", - " 6.55e-05\n", + " 6.04e-04\n", + " 5.57e-05\n", " \n", " \n", " 9\n", @@ -903,8 +904,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 1.48e-03\n", - " 1.60e-04\n", + " 1.47e-03\n", + " 1.36e-04\n", " \n", " \n", " 10\n", @@ -914,8 +915,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.38e-04\n", - " 6.74e-06\n", + " 1.41e-04\n", + " 6.69e-06\n", " \n", " \n", " 11\n", @@ -925,8 +926,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 3.65e-04\n", - " 1.88e-05\n", + " 3.72e-04\n", + " 1.82e-05\n", " \n", " \n", " 12\n", @@ -936,8 +937,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 6.23e-04\n", - " 5.16e-05\n", + " 6.45e-04\n", + " 4.59e-05\n", " \n", " \n", " 13\n", @@ -947,8 +948,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 1.52e-03\n", - " 1.26e-04\n", + " 1.57e-03\n", + " 1.12e-04\n", " \n", " \n", " 14\n", @@ -958,8 +959,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.74e-04\n", - " 9.99e-06\n", + " 1.82e-04\n", + " 9.37e-06\n", " \n", " \n", " 15\n", @@ -969,8 +970,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 4.58e-04\n", - " 2.68e-05\n", + " 4.76e-04\n", + " 2.47e-05\n", " \n", " \n", " 16\n", @@ -980,8 +981,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 6.94e-04\n", - " 8.68e-05\n", + " 7.28e-04\n", + " 7.49e-05\n", " \n", " \n", " 17\n", @@ -991,8 +992,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 1.69e-03\n", - " 2.12e-04\n", + " 1.77e-03\n", + " 1.83e-04\n", " \n", " \n", " 18\n", @@ -1002,8 +1003,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.75e-04\n", - " 1.10e-05\n", + " 1.81e-04\n", + " 1.04e-05\n", " \n", " \n", " 19\n", @@ -1013,8 +1014,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 4.55e-04\n", - " 2.80e-05\n", + " 4.72e-04\n", + " 2.67e-05\n", " \n", " \n", "\n", @@ -1023,49 +1024,49 @@ "text/plain": [ " mesh 1 energy low [MeV] energy high [MeV] score mean \\\n", " x y z \n", - "0 1 1 1 0.00e+00 6.25e-07 fission 2.20e-04 \n", - "1 1 1 1 0.00e+00 6.25e-07 nu-fission 5.37e-04 \n", - "2 1 1 1 6.25e-07 2.00e+01 fission 7.43e-05 \n", - "3 1 1 1 6.25e-07 2.00e+01 nu-fission 1.97e-04 \n", - "4 1 2 1 0.00e+00 6.25e-07 fission 3.52e-04 \n", - "5 1 2 1 0.00e+00 6.25e-07 nu-fission 8.57e-04 \n", - "6 1 2 1 6.25e-07 2.00e+01 fission 1.02e-04 \n", - "7 1 2 1 6.25e-07 2.00e+01 nu-fission 2.70e-04 \n", - "8 1 3 1 0.00e+00 6.25e-07 fission 6.09e-04 \n", - "9 1 3 1 0.00e+00 6.25e-07 nu-fission 1.48e-03 \n", - "10 1 3 1 6.25e-07 2.00e+01 fission 1.38e-04 \n", - "11 1 3 1 6.25e-07 2.00e+01 nu-fission 3.65e-04 \n", - "12 1 4 1 0.00e+00 6.25e-07 fission 6.23e-04 \n", - "13 1 4 1 0.00e+00 6.25e-07 nu-fission 1.52e-03 \n", - "14 1 4 1 6.25e-07 2.00e+01 fission 1.74e-04 \n", - "15 1 4 1 6.25e-07 2.00e+01 nu-fission 4.58e-04 \n", - "16 1 5 1 0.00e+00 6.25e-07 fission 6.94e-04 \n", - "17 1 5 1 0.00e+00 6.25e-07 nu-fission 1.69e-03 \n", - "18 1 5 1 6.25e-07 2.00e+01 fission 1.75e-04 \n", - "19 1 5 1 6.25e-07 2.00e+01 nu-fission 4.55e-04 \n", + "0 1 1 1 0.00e+00 6.25e-07 fission 2.34e-04 \n", + "1 1 1 1 0.00e+00 6.25e-07 nu-fission 5.71e-04 \n", + "2 1 1 1 6.25e-07 2.00e+01 fission 7.03e-05 \n", + "3 1 1 1 6.25e-07 2.00e+01 nu-fission 1.87e-04 \n", + "4 1 2 1 0.00e+00 6.25e-07 fission 3.67e-04 \n", + "5 1 2 1 0.00e+00 6.25e-07 nu-fission 8.94e-04 \n", + "6 1 2 1 6.25e-07 2.00e+01 fission 1.04e-04 \n", + "7 1 2 1 6.25e-07 2.00e+01 nu-fission 2.76e-04 \n", + "8 1 3 1 0.00e+00 6.25e-07 fission 6.04e-04 \n", + "9 1 3 1 0.00e+00 6.25e-07 nu-fission 1.47e-03 \n", + "10 1 3 1 6.25e-07 2.00e+01 fission 1.41e-04 \n", + "11 1 3 1 6.25e-07 2.00e+01 nu-fission 3.72e-04 \n", + "12 1 4 1 0.00e+00 6.25e-07 fission 6.45e-04 \n", + "13 1 4 1 0.00e+00 6.25e-07 nu-fission 1.57e-03 \n", + "14 1 4 1 6.25e-07 2.00e+01 fission 1.82e-04 \n", + "15 1 4 1 6.25e-07 2.00e+01 nu-fission 4.76e-04 \n", + "16 1 5 1 0.00e+00 6.25e-07 fission 7.28e-04 \n", + "17 1 5 1 0.00e+00 6.25e-07 nu-fission 1.77e-03 \n", + "18 1 5 1 6.25e-07 2.00e+01 fission 1.81e-04 \n", + "19 1 5 1 6.25e-07 2.00e+01 nu-fission 4.72e-04 \n", "\n", " std. dev. \n", " \n", - "0 3.31e-05 \n", - "1 8.06e-05 \n", - "2 7.91e-06 \n", - "3 1.96e-05 \n", - "4 3.39e-05 \n", - "5 8.26e-05 \n", - "6 6.16e-06 \n", - "7 1.61e-05 \n", - "8 6.55e-05 \n", - "9 1.60e-04 \n", - "10 6.74e-06 \n", - "11 1.88e-05 \n", - "12 5.16e-05 \n", - "13 1.26e-04 \n", - "14 9.99e-06 \n", - "15 2.68e-05 \n", - "16 8.68e-05 \n", - "17 2.12e-04 \n", - "18 1.10e-05 \n", - "19 2.80e-05 " + "0 3.54e-05 \n", + "1 8.62e-05 \n", + "2 7.05e-06 \n", + "3 1.76e-05 \n", + "4 3.61e-05 \n", + "5 8.80e-05 \n", + "6 5.36e-06 \n", + "7 1.40e-05 \n", + "8 5.57e-05 \n", + "9 1.36e-04 \n", + "10 6.69e-06 \n", + "11 1.82e-05 \n", + "12 4.59e-05 \n", + "13 1.12e-04 \n", + "14 9.37e-06 \n", + "15 2.47e-05 \n", + "16 7.49e-05 \n", + "17 1.83e-04 \n", + "18 1.04e-05 \n", + "19 2.67e-05 " ] }, "execution_count": 24, @@ -1094,9 +1095,9 @@ "outputs": [ { "data": { - "image/png": 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J50fEc2PFt6sP9v2SHsm7EE5tUx3MrGrDtWIbICnqtrWjHG03sLDu+Vn5viJl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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1176,7 +1177,7 @@ "\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" ] @@ -1218,144 +1219,144 @@ " 10000\n", " U-235\n", " scatter-Y0,0\n", - " 3.84e-02\n", - " 1.32e-03\n", + " 3.86e-02\n", + " 1.11e-03\n", " \n", " \n", " 1\n", " 10000\n", " U-235\n", " scatter-Y1,-1\n", - " 3.61e-04\n", - " 3.13e-04\n", + " 2.75e-04\n", + " 2.96e-04\n", " \n", " \n", " 2\n", " 10000\n", " U-235\n", " scatter-Y1,0\n", - " -2.38e-04\n", - " 4.69e-04\n", + " -5.55e-05\n", + " 4.33e-04\n", " \n", " \n", " 3\n", " 10000\n", " U-235\n", " scatter-Y1,1\n", - " -5.08e-04\n", - " 3.83e-04\n", + " -4.22e-04\n", + " 3.51e-04\n", " \n", " \n", " 4\n", " 10000\n", " U-235\n", " scatter-Y2,-2\n", - " 6.68e-05\n", - " 2.46e-04\n", + " 5.88e-05\n", + " 2.04e-04\n", " \n", " \n", " 5\n", " 10000\n", " U-235\n", " scatter-Y2,-1\n", - " 6.47e-06\n", - " 2.84e-04\n", + " 1.00e-04\n", + " 2.49e-04\n", " \n", " \n", " 6\n", " 10000\n", " U-235\n", " scatter-Y2,0\n", - " -1.41e-04\n", - " 1.75e-04\n", + " -8.09e-05\n", + " 1.59e-04\n", " \n", " \n", " 7\n", " 10000\n", " U-235\n", " scatter-Y2,1\n", - " 1.61e-04\n", - " 2.33e-04\n", + " 1.93e-04\n", + " 2.14e-04\n", " \n", " \n", " 8\n", " 10000\n", " U-235\n", " scatter-Y2,2\n", - " -1.80e-05\n", - " 1.97e-04\n", + " 1.12e-04\n", + " 1.86e-04\n", " \n", " \n", " 9\n", " 10000\n", " U-238\n", " scatter-Y0,0\n", - " 2.33e+00\n", - " 1.35e-02\n", + " 2.34e+00\n", + " 1.34e-02\n", " \n", " \n", " 10\n", " 10000\n", " U-238\n", " scatter-Y1,-1\n", - " 2.53e-02\n", - " 3.23e-03\n", + " 2.32e-02\n", + " 2.97e-03\n", " \n", " \n", " 11\n", " 10000\n", " U-238\n", " scatter-Y1,0\n", - " 7.10e-04\n", - " 2.92e-03\n", + " 7.50e-04\n", + " 2.55e-03\n", " \n", " \n", " 12\n", " 10000\n", " U-238\n", " scatter-Y1,1\n", - " -2.49e-02\n", - " 3.52e-03\n", + " -2.73e-02\n", + " 3.28e-03\n", " \n", " \n", " 13\n", " 10000\n", " U-238\n", " scatter-Y2,-2\n", - " -1.43e-03\n", - " 1.17e-03\n", + " -2.36e-03\n", + " 1.21e-03\n", " \n", " \n", " 14\n", " 10000\n", " U-238\n", " scatter-Y2,-1\n", - " 6.84e-04\n", - " 1.63e-03\n", + " -1.80e-04\n", + " 1.49e-03\n", " \n", " \n", " 15\n", " 10000\n", " U-238\n", " scatter-Y2,0\n", - " 2.85e-03\n", - " 2.63e-03\n", + " 3.23e-03\n", + " 2.25e-03\n", " \n", " \n", " 16\n", " 10000\n", " U-238\n", " scatter-Y2,1\n", - " 3.97e-03\n", - " 2.24e-03\n", + " 3.75e-03\n", + " 1.97e-03\n", " \n", " \n", " 17\n", " 10000\n", " U-238\n", " scatter-Y2,2\n", - " 2.26e-03\n", - " 1.85e-03\n", + " 2.07e-03\n", + " 1.60e-03\n", " \n", " \n", "\n", @@ -1363,24 +1364,24 @@ ], "text/plain": [ " cell nuclide score mean std. dev.\n", - "0 10000 U-235 scatter-Y0,0 3.84e-02 1.32e-03\n", - "1 10000 U-235 scatter-Y1,-1 3.61e-04 3.13e-04\n", - "2 10000 U-235 scatter-Y1,0 -2.38e-04 4.69e-04\n", - "3 10000 U-235 scatter-Y1,1 -5.08e-04 3.83e-04\n", - "4 10000 U-235 scatter-Y2,-2 6.68e-05 2.46e-04\n", - "5 10000 U-235 scatter-Y2,-1 6.47e-06 2.84e-04\n", - "6 10000 U-235 scatter-Y2,0 -1.41e-04 1.75e-04\n", - "7 10000 U-235 scatter-Y2,1 1.61e-04 2.33e-04\n", - "8 10000 U-235 scatter-Y2,2 -1.80e-05 1.97e-04\n", - "9 10000 U-238 scatter-Y0,0 2.33e+00 1.35e-02\n", - "10 10000 U-238 scatter-Y1,-1 2.53e-02 3.23e-03\n", - "11 10000 U-238 scatter-Y1,0 7.10e-04 2.92e-03\n", - "12 10000 U-238 scatter-Y1,1 -2.49e-02 3.52e-03\n", - "13 10000 U-238 scatter-Y2,-2 -1.43e-03 1.17e-03\n", - "14 10000 U-238 scatter-Y2,-1 6.84e-04 1.63e-03\n", - "15 10000 U-238 scatter-Y2,0 2.85e-03 2.63e-03\n", - "16 10000 U-238 scatter-Y2,1 3.97e-03 2.24e-03\n", - "17 10000 U-238 scatter-Y2,2 2.26e-03 1.85e-03" + "0 10000 U-235 scatter-Y0,0 3.86e-02 1.11e-03\n", + "1 10000 U-235 scatter-Y1,-1 2.75e-04 2.96e-04\n", + "2 10000 U-235 scatter-Y1,0 -5.55e-05 4.33e-04\n", + "3 10000 U-235 scatter-Y1,1 -4.22e-04 3.51e-04\n", + "4 10000 U-235 scatter-Y2,-2 5.88e-05 2.04e-04\n", + "5 10000 U-235 scatter-Y2,-1 1.00e-04 2.49e-04\n", + "6 10000 U-235 scatter-Y2,0 -8.09e-05 1.59e-04\n", + "7 10000 U-235 scatter-Y2,1 1.93e-04 2.14e-04\n", + "8 10000 U-235 scatter-Y2,2 1.12e-04 1.86e-04\n", + "9 10000 U-238 scatter-Y0,0 2.34e+00 1.34e-02\n", + "10 10000 U-238 scatter-Y1,-1 2.32e-02 2.97e-03\n", + "11 10000 U-238 scatter-Y1,0 7.50e-04 2.55e-03\n", + "12 10000 U-238 scatter-Y1,1 -2.73e-02 3.28e-03\n", + "13 10000 U-238 scatter-Y2,-2 -2.36e-03 1.21e-03\n", + "14 10000 U-238 scatter-Y2,-1 -1.80e-04 1.49e-03\n", + "15 10000 U-238 scatter-Y2,0 3.23e-03 2.25e-03\n", + "16 10000 U-238 scatter-Y2,1 3.75e-03 1.97e-03\n", + "17 10000 U-238 scatter-Y2,2 2.07e-03 1.60e-03" ] }, "execution_count": 28, @@ -1414,8 +1415,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00185463 0.01350521]\n", - " [ 0.00019723 0.00131654]]]\n" + "[[[ 0.00159927 0.01341406]\n", + " [ 0.00018637 0.00111048]]]\n" ] } ], @@ -1451,7 +1452,7 @@ "\tFilters =\t\n", " \t\tdistribcell\t[10002]\n", "\tNuclides =\ttotal \n", - "\tScores =\t['absorption', 'scatter']\n", + "\tScores =\t[u'absorption', u'scatter']\n", "\tEstimator =\ttracklength\n", "\n" ] @@ -1483,7 +1484,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.05468423]]]\n" + "[[[ 0.05767856]]]\n" ] } ], @@ -1499,7 +1500,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Print the distribcell tally dataframe **without** OpenCG info" + "Print the distribcell tally dataframe" ] }, { @@ -1508,216 +1509,6 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/html": [ - "
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distribcellscoremeanstd. dev.
558279absorption8.72e-058.13e-06
559279scatter1.37e-026.98e-04
560280absorption1.03e-049.17e-06
561280scatter1.41e-026.26e-04
562281absorption9.41e-058.40e-06
563281scatter1.50e-026.92e-04
564282absorption9.56e-051.03e-05
565282scatter1.52e-025.37e-04
566283absorption1.06e-041.49e-05
567283scatter1.64e-028.14e-04
568284absorption1.16e-049.02e-06
569284scatter1.64e-026.00e-04
570285absorption1.25e-041.12e-05
571285scatter1.87e-028.26e-04
572286absorption1.47e-041.49e-05
573286scatter1.94e-027.71e-04
574287absorption1.31e-049.84e-06
575287scatter1.97e-027.93e-04
576288absorption1.23e-041.07e-05
577288scatter1.97e-027.34e-04
\n", - "
" - ], - "text/plain": [ - " distribcell score mean std. dev.\n", - "558 279 absorption 8.72e-05 8.13e-06\n", - "559 279 scatter 1.37e-02 6.98e-04\n", - "560 280 absorption 1.03e-04 9.17e-06\n", - "561 280 scatter 1.41e-02 6.26e-04\n", - "562 281 absorption 9.41e-05 8.40e-06\n", - "563 281 scatter 1.50e-02 6.92e-04\n", - "564 282 absorption 9.56e-05 1.03e-05\n", - "565 282 scatter 1.52e-02 5.37e-04\n", - "566 283 absorption 1.06e-04 1.49e-05\n", - "567 283 scatter 1.64e-02 8.14e-04\n", - "568 284 absorption 1.16e-04 9.02e-06\n", - "569 284 scatter 1.64e-02 6.00e-04\n", - "570 285 absorption 1.25e-04 1.12e-05\n", - "571 285 scatter 1.87e-02 8.26e-04\n", - "572 286 absorption 1.47e-04 1.49e-05\n", - "573 286 scatter 1.94e-02 7.71e-04\n", - "574 287 absorption 1.31e-04 9.84e-06\n", - "575 287 scatter 1.97e-02 7.93e-04\n", - "576 288 absorption 1.23e-04 1.07e-05\n", - "577 288 scatter 1.97e-02 7.34e-04" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get a pandas dataframe for the distribcell tally data\n", - "df = tally.get_pandas_dataframe(nuclides=False)\n", - "\n", - "# Print the last twenty rows in the dataframe\n", - "df.tail(20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Print the distribcell tally dataframe **with** OpenCG info" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, "outputs": [ { "data": { @@ -1737,11 +1528,11 @@ " \n", " \n", " \n", - " cell\n", " univ\n", + " cell\n", " lat\n", - " cell\n", " univ\n", + " cell\n", " \n", " \n", " \n", @@ -1766,366 +1557,366 @@ " \n", " \n", " 558\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 9\n", + " 7\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 279\n", " absorption\n", - " 8.72e-05\n", - " 8.13e-06\n", + " 8.19e-05\n", + " 7.82e-06\n", " \n", " \n", " 559\n", - " 10003\n", " 0\n", + " 10003\n", + " 10001\n", + " 16\n", + " 7\n", + " 0\n", + " 10000\n", + " 10002\n", + " 279\n", + " scatter\n", + " 1.33e-02\n", + " 6.19e-04\n", + " \n", + " \n", + " 560\n", + " 0\n", + " 10003\n", + " 10001\n", + " 16\n", + " 8\n", + " 0\n", + " 10000\n", + " 10002\n", + " 280\n", + " absorption\n", + " 1.00e-04\n", + " 7.93e-06\n", + " \n", + " \n", + " 561\n", + " 0\n", + " 10003\n", + " 10001\n", + " 16\n", + " 8\n", + " 0\n", + " 10000\n", + " 10002\n", + " 280\n", + " scatter\n", + " 1.40e-02\n", + " 5.61e-04\n", + " \n", + " \n", + " 562\n", + " 0\n", + " 10003\n", " 10001\n", " 16\n", " 9\n", " 0\n", - " 10002\n", " 10000\n", - " 279\n", - " scatter\n", - " 1.37e-02\n", - " 6.98e-04\n", - " \n", - " \n", - " 560\n", - " 10003\n", - " 0\n", - " 10001\n", - " 16\n", - " 8\n", - " 0\n", " 10002\n", - " 10000\n", - " 280\n", - " absorption\n", - " 1.03e-04\n", - " 9.17e-06\n", - " \n", - " \n", - " 561\n", - " 10003\n", - " 0\n", - " 10001\n", - " 16\n", - " 8\n", - " 0\n", - " 10002\n", - " 10000\n", - " 280\n", - " scatter\n", - " 1.41e-02\n", - " 6.26e-04\n", - " \n", - " \n", - " 562\n", - " 10003\n", - " 0\n", - " 10001\n", - " 16\n", - " 7\n", - " 0\n", - " 10002\n", - " 10000\n", " 281\n", " absorption\n", - " 9.41e-05\n", - " 8.40e-06\n", + " 9.52e-05\n", + " 7.08e-06\n", " \n", " \n", " 563\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 7\n", + " 9\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 281\n", " scatter\n", - " 1.50e-02\n", - " 6.92e-04\n", + " 1.51e-02\n", + " 6.50e-04\n", " \n", " \n", " 564\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 6\n", + " 10\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 282\n", " absorption\n", - " 9.56e-05\n", - " 1.03e-05\n", + " 9.85e-05\n", + " 9.47e-06\n", " \n", " \n", " 565\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 6\n", + " 10\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 282\n", " scatter\n", - " 1.52e-02\n", - " 5.37e-04\n", + " 1.53e-02\n", + " 4.63e-04\n", " \n", " \n", " 566\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 5\n", + " 11\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 283\n", " absorption\n", - " 1.06e-04\n", - " 1.49e-05\n", + " 1.08e-04\n", + " 1.34e-05\n", " \n", " \n", " 567\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 5\n", + " 11\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 283\n", " scatter\n", - " 1.64e-02\n", - " 8.14e-04\n", + " 1.65e-02\n", + " 7.04e-04\n", " \n", " \n", " 568\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 4\n", + " 12\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 284\n", " absorption\n", - " 1.16e-04\n", - " 9.02e-06\n", + " 1.13e-04\n", + " 7.91e-06\n", " \n", " \n", " 569\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 4\n", + " 12\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 284\n", " scatter\n", - " 1.64e-02\n", - " 6.00e-04\n", + " 1.67e-02\n", + " 5.51e-04\n", " \n", " \n", " 570\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 3\n", + " 13\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 285\n", " absorption\n", - " 1.25e-04\n", - " 1.12e-05\n", + " 1.23e-04\n", + " 9.53e-06\n", " \n", " \n", " 571\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 3\n", + " 13\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 285\n", " scatter\n", - " 1.87e-02\n", - " 8.26e-04\n", + " 1.88e-02\n", + " 7.25e-04\n", " \n", " \n", " 572\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 2\n", + " 14\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 286\n", " absorption\n", - " 1.47e-04\n", - " 1.49e-05\n", + " 1.44e-04\n", + " 1.34e-05\n", " \n", " \n", " 573\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 2\n", + " 14\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 286\n", " scatter\n", - " 1.94e-02\n", - " 7.71e-04\n", + " 1.90e-02\n", + " 7.07e-04\n", " \n", " \n", " 574\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 1\n", + " 15\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 287\n", " absorption\n", - " 1.31e-04\n", - " 9.84e-06\n", + " 1.26e-04\n", + " 8.66e-06\n", " \n", " \n", " 575\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", - " 1\n", + " 15\n", " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 287\n", " scatter\n", " 1.97e-02\n", - " 7.93e-04\n", + " 7.23e-04\n", " \n", " \n", " 576\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", + " 16\n", " 0\n", - " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 288\n", " absorption\n", - " 1.23e-04\n", - " 1.07e-05\n", + " 1.25e-04\n", + " 9.59e-06\n", " \n", " \n", " 577\n", - " 10003\n", " 0\n", + " 10003\n", " 10001\n", " 16\n", + " 16\n", " 0\n", - " 0\n", - " 10002\n", " 10000\n", + " 10002\n", " 288\n", " scatter\n", - " 1.97e-02\n", - " 7.34e-04\n", + " 2.01e-02\n", + " 6.75e-04\n", " \n", " \n", "\n", "" ], "text/plain": [ - " level 1 level 2 level 3 distribcell score \\\n", - " cell univ lat cell univ \n", - " id id id x y z id id \n", - "558 10003 0 10001 16 9 0 10002 10000 279 absorption \n", - "559 10003 0 10001 16 9 0 10002 10000 279 scatter \n", - "560 10003 0 10001 16 8 0 10002 10000 280 absorption \n", - "561 10003 0 10001 16 8 0 10002 10000 280 scatter \n", - "562 10003 0 10001 16 7 0 10002 10000 281 absorption \n", - "563 10003 0 10001 16 7 0 10002 10000 281 scatter \n", - "564 10003 0 10001 16 6 0 10002 10000 282 absorption \n", - "565 10003 0 10001 16 6 0 10002 10000 282 scatter \n", - "566 10003 0 10001 16 5 0 10002 10000 283 absorption \n", - "567 10003 0 10001 16 5 0 10002 10000 283 scatter \n", - "568 10003 0 10001 16 4 0 10002 10000 284 absorption \n", - "569 10003 0 10001 16 4 0 10002 10000 284 scatter \n", - "570 10003 0 10001 16 3 0 10002 10000 285 absorption \n", - "571 10003 0 10001 16 3 0 10002 10000 285 scatter \n", - "572 10003 0 10001 16 2 0 10002 10000 286 absorption \n", - "573 10003 0 10001 16 2 0 10002 10000 286 scatter \n", - "574 10003 0 10001 16 1 0 10002 10000 287 absorption \n", - "575 10003 0 10001 16 1 0 10002 10000 287 scatter \n", - "576 10003 0 10001 16 0 0 10002 10000 288 absorption \n", - "577 10003 0 10001 16 0 0 10002 10000 288 scatter \n", + " level 1 level 2 level 3 distribcell score \\\n", + " univ cell lat univ cell \n", + " id id id x y z id id \n", + "558 0 10003 10001 16 7 0 10000 10002 279 absorption \n", + "559 0 10003 10001 16 7 0 10000 10002 279 scatter \n", + "560 0 10003 10001 16 8 0 10000 10002 280 absorption \n", + "561 0 10003 10001 16 8 0 10000 10002 280 scatter \n", + "562 0 10003 10001 16 9 0 10000 10002 281 absorption \n", + "563 0 10003 10001 16 9 0 10000 10002 281 scatter \n", + "564 0 10003 10001 16 10 0 10000 10002 282 absorption \n", + "565 0 10003 10001 16 10 0 10000 10002 282 scatter \n", + "566 0 10003 10001 16 11 0 10000 10002 283 absorption \n", + "567 0 10003 10001 16 11 0 10000 10002 283 scatter \n", + "568 0 10003 10001 16 12 0 10000 10002 284 absorption \n", + "569 0 10003 10001 16 12 0 10000 10002 284 scatter \n", + "570 0 10003 10001 16 13 0 10000 10002 285 absorption \n", + "571 0 10003 10001 16 13 0 10000 10002 285 scatter \n", + "572 0 10003 10001 16 14 0 10000 10002 286 absorption \n", + "573 0 10003 10001 16 14 0 10000 10002 286 scatter \n", + "574 0 10003 10001 16 15 0 10000 10002 287 absorption \n", + "575 0 10003 10001 16 15 0 10000 10002 287 scatter \n", + "576 0 10003 10001 16 16 0 10000 10002 288 absorption \n", + "577 0 10003 10001 16 16 0 10000 10002 288 scatter \n", "\n", " mean std. dev. \n", " \n", " \n", - "558 8.72e-05 8.13e-06 \n", - "559 1.37e-02 6.98e-04 \n", - "560 1.03e-04 9.17e-06 \n", - "561 1.41e-02 6.26e-04 \n", - "562 9.41e-05 8.40e-06 \n", - "563 1.50e-02 6.92e-04 \n", - "564 9.56e-05 1.03e-05 \n", - "565 1.52e-02 5.37e-04 \n", - "566 1.06e-04 1.49e-05 \n", - "567 1.64e-02 8.14e-04 \n", - "568 1.16e-04 9.02e-06 \n", - "569 1.64e-02 6.00e-04 \n", - "570 1.25e-04 1.12e-05 \n", - "571 1.87e-02 8.26e-04 \n", - "572 1.47e-04 1.49e-05 \n", - "573 1.94e-02 7.71e-04 \n", - "574 1.31e-04 9.84e-06 \n", - "575 1.97e-02 7.93e-04 \n", - "576 1.23e-04 1.07e-05 \n", - "577 1.97e-02 7.34e-04 " + "558 8.19e-05 7.82e-06 \n", + "559 1.33e-02 6.19e-04 \n", + "560 1.00e-04 7.93e-06 \n", + "561 1.40e-02 5.61e-04 \n", + "562 9.52e-05 7.08e-06 \n", + "563 1.51e-02 6.50e-04 \n", + "564 9.85e-05 9.47e-06 \n", + "565 1.53e-02 4.63e-04 \n", + "566 1.08e-04 1.34e-05 \n", + "567 1.65e-02 7.04e-04 \n", + "568 1.13e-04 7.91e-06 \n", + "569 1.67e-02 5.51e-04 \n", + "570 1.23e-04 9.53e-06 \n", + "571 1.88e-02 7.25e-04 \n", + "572 1.44e-04 1.34e-05 \n", + "573 1.90e-02 7.07e-04 \n", + "574 1.26e-04 8.66e-06 \n", + "575 1.97e-02 7.23e-04 \n", + "576 1.25e-04 9.59e-06 \n", + "577 2.01e-02 6.75e-04 " ] }, - "execution_count": 33, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get a pandas dataframe for the distribcell tally data\n", - "df = tally.get_pandas_dataframe(summary=sp.summary, nuclides=False)\n", + "df = tally.get_pandas_dataframe(nuclides=False)\n", "\n", "# Print the last twenty rows in the dataframe\n", "df.tail(20)" @@ -2133,7 +1924,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "collapsed": false }, @@ -2168,38 +1959,38 @@ " \n", " \n", " mean\n", - " 4.16e-04\n", - " 2.42e-05\n", + " 4.19e-04\n", + " 2.24e-05\n", " \n", " \n", " std\n", - " 2.39e-04\n", - " 1.03e-05\n", + " 2.42e-04\n", + " 9.14e-06\n", " \n", " \n", " min\n", " 1.90e-05\n", - " 3.80e-06\n", + " 3.44e-06\n", " \n", " \n", " 25%\n", - " 1.99e-04\n", - " 1.61e-05\n", + " 2.02e-04\n", + " 1.56e-05\n", " \n", " \n", " 50%\n", - " 4.09e-04\n", - " 2.37e-05\n", + " 4.05e-04\n", + " 2.20e-05\n", " \n", " \n", " 75%\n", - " 6.00e-04\n", - " 3.08e-05\n", + " 6.07e-04\n", + " 2.89e-05\n", " \n", " \n", " max\n", - " 9.07e-04\n", - " 5.38e-05\n", + " 9.19e-04\n", + " 4.95e-05\n", " \n", " \n", "\n", @@ -2210,16 +2001,16 @@ " \n", " \n", "count 2.89e+02 2.89e+02\n", - "mean 4.16e-04 2.42e-05\n", - "std 2.39e-04 1.03e-05\n", - "min 1.90e-05 3.80e-06\n", - "25% 1.99e-04 1.61e-05\n", - "50% 4.09e-04 2.37e-05\n", - "75% 6.00e-04 3.08e-05\n", - "max 9.07e-04 5.38e-05" + "mean 4.19e-04 2.24e-05\n", + "std 2.42e-04 9.14e-06\n", + "min 1.90e-05 3.44e-06\n", + "25% 2.02e-04 1.56e-05\n", + "50% 4.05e-04 2.20e-05\n", + "75% 6.07e-04 2.89e-05\n", + "max 9.19e-04 4.95e-05" ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -2242,7 +2033,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": { "collapsed": false }, @@ -2251,45 +2042,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 0.7234916721800682\n" - ] - } - ], - "source": [ - "# Extract tally data from pins in the pins divided along y=-x diagonal\n", - "multi_index = ('level 2', 'lat',)\n", - "lower = df[df[multi_index + ('x',)] > df[multi_index + ('y',)]]\n", - "upper = df[df[multi_index + ('x',)] < df[multi_index + ('y',)]]\n", - "lower = lower[lower['score'] == 'absorption']\n", - "upper = upper[upper['score'] == 'absorption']\n", - "\n", - "# Perform non-parametric Mann-Whitney U Test to see if the \n", - "# absorption rates (may) come from same sampling distribution\n", - "u, p = scipy.stats.mannwhitneyu(lower['mean'], upper['mean'])\n", - "print('Mann-Whitney Test p-value: {0}'.format(p))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the symmetry implied by the y=x diagonal ensures that the two sampling distributions are identical. Indeed, as illustrated by the test above, for any reasonable significance level (*e.g.*, $\\alpha$=0.05) one would **not reject** the null hypothesis that the two sampling distributions are identical.\n", - "\n", - "Next, perform the same test but with two groupings of pins which are not symmetrically identical to one another." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mann-Whitney Test p-value: 3.5054120724573393e-41\n" + "Mann-Whitney Test p-value: 0.303583331507\n" ] } ], @@ -2307,6 +2060,44 @@ "print('Mann-Whitney Test p-value: {0}'.format(p))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the symmetry implied by the y=x diagonal ensures that the two sampling distributions are identical. Indeed, as illustrated by the test above, for any reasonable significance level (*e.g.*, $\\alpha$=0.05) one would **not reject** the null hypothesis that the two sampling distributions are identical.\n", + "\n", + "Next, perform the same test but with two groupings of pins which are not symmetrically identical to one another." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mann-Whitney Test p-value: 6.038663783e-42\n" + ] + } + ], + "source": [ + "# Extract tally data from pins in the pins divided along y=-x diagonal\n", + "multi_index = ('level 2', 'lat',)\n", + "lower = df[df[multi_index + ('x',)] > df[multi_index + ('y',)]]\n", + "upper = df[df[multi_index + ('x',)] < df[multi_index + ('y',)]]\n", + "lower = lower[lower['score'] == 'absorption']\n", + "upper = upper[upper['score'] == 'absorption']\n", + "\n", + "# Perform non-parametric Mann-Whitney U Test to see if the \n", + "# absorption rates (may) come from same sampling distribution\n", + "u, p = scipy.stats.mannwhitneyu(lower['mean'], upper['mean'])\n", + "print('Mann-Whitney Test p-value: {0}'.format(p))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2316,7 +2107,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": { "collapsed": false }, @@ -2325,7 +2116,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n", + "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -2335,18 +2126,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 37, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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MTGiGAH7z1Nx9921cf/3ClHDa9u3bLcxmGMagkveqMyM3JM9TEw+ndXQkhtOam5t9l7e1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\nYxiGYQSKCY1hGIYRKCY0hmEYRqCY0Bi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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2365,7 +2156,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": { "collapsed": false }, @@ -2373,18 +2164,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 38, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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WDt9JxAMVCEm4Wetn0a1FN98xJBo/NYbvgPYTfScRD1QgJOFmrZvFSS10BXWl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\nGTjnFphZFsHWw7sUHaiuJ9D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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2403,21 +2194,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 2", "language": "python", - "name": "python3" + "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" + "pygments_lexer": "ipython2", + "version": "2.7.6" } }, "nbformat": 4, diff --git a/docs/source/usersguide/input.rst b/docs/source/usersguide/input.rst index 2158e1d8c..775407d70 100644 --- a/docs/source/usersguide/input.rst +++ b/docs/source/usersguide/input.rst @@ -1215,11 +1215,10 @@ Each ``material`` element can have the following attributes or sub-elements: An element with attributes/sub-elements called ``value`` and ``units``. The ``value`` attribute is the numeric value of the density while the ``units`` can be "g/cm3", "kg/m3", "atom/b-cm", "atom/cm3", or "sum". The "sum" unit - indicates that values appearing in ``ao`` attributes for ```` and - ```` sub-elements are to be interpreted as nuclide/element - densities in atom/b-cm, and the total density of the material is taken as - the sum of all nuclides/elements. The "sum" option cannot be used in - conjunction with weight percents. The "macro" unit is used with + indicates that values appearing in ``ao`` or ``wo`` attributes for ```` + and ```` sub-elements are to be interpreted as absolute nuclide/element + densities in atom/b-cm or g/cm3, and the total density of the material is + taken as the sum of all nuclides/elements. The "macro" unit is used with a ``macroscopic`` quantity to indicate that the density is already included in the library and thus not needed here. However, if a value is provided for the ``value``, then this is treated as a number density multiplier on diff --git a/openmc/filter.py b/openmc/filter.py index b0e59874b..52560a193 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -1,4 +1,4 @@ -from collections import Iterable +from collections import Iterable, OrderedDict import copy from numbers import Real, Integral import sys @@ -516,7 +516,7 @@ class Filter(object): return filter_bin - def get_pandas_dataframe(self, data_size, summary=None): + def get_pandas_dataframe(self, data_size, distribcell_paths=True): """Builds a Pandas DataFrame for the Filter's bins. This method constructs a Pandas DataFrame object for the filter with @@ -531,12 +531,13 @@ class Filter(object): ---------- data_size : Integral The total number of bins in the tally corresponding to this filter - summary : None or openmc.Summary - An optional Summary object to be used to construct columns for - distribcell tally filters (default is None). The geometric - information in the Summary object is embedded into a Multi-index - column with a geometric "path" to each distribcell instance. - NOTE: This option requires the OpenCG Python package. + distribcell_paths : bool, optional + Construct columns for distribcell tally filters (default is True). + The geometric information in the Summary object is embedded into a + Multi-index column with a geometric "path" to each distribcell + instance. NOTE: This option assumes that all distribcell paths are + of the same length and do not have the same universes and cells but + different lattice cell indices. Returns ------- @@ -554,7 +555,7 @@ class Filter(object): 1. a single column with the cell instance IDs (without summary info) 2. separate columns for the cell IDs, universe IDs, and lattice IDs - and x,y,z cell indices corresponding to each (with summary info). + and x,y,z cell indices corresponding to each (distribcell paths). For 'energy' and 'energyout' filters, the DataFrame includes one column for the lower energy bound and one column for the upper @@ -566,8 +567,7 @@ class Filter(object): Raises ------ ImportError - When Pandas is not installed, or summary info is requested but - OpenCG is not installed. + When Pandas is not installed See also -------- @@ -626,106 +626,117 @@ class Filter(object): elif self.type == 'distribcell': level_df = None - if isinstance(summary, Summary): - # Attempt to import the OpenCG package - try: - import opencg - except ImportError: - msg = 'The OpenCG package must be installed ' \ - 'to use a Summary for distribcell dataframes' - raise ImportError(msg) + # Create Pandas Multi-index columns for each level in CSG tree + if distribcell_paths: - # Extract the OpenCG geometry from the Summary - opencg_geometry = summary.opencg_geometry - openmc_geometry = summary.openmc_geometry + # Distribcell paths require linked metadata from the Summary + if self.distribcell_paths is None: + msg = 'Unable to construct distribcell paths since ' \ + 'the Summary is not linked to the StatePoint' + raise ValueError(msg) - # Use OpenCG to compute the number of regions - opencg_geometry.initialize_cell_offsets() - num_regions = opencg_geometry.num_regions + # Make copy of array of distribcell paths to use in + # Pandas Multi-index column construction + distribcell_paths = copy.deepcopy(self.distribcell_paths) + num_offsets = len(distribcell_paths) - # Initialize a dictionary mapping OpenMC distribcell - # offsets to OpenCG LocalCoords linked lists - offsets_to_coords = {} - - for offset, path in enumerate(self.distribcell_paths): - region = opencg_geometry.get_region_from_path(path) - coords = opencg_geometry.find_region(region) - offsets_to_coords[offset] = coords - - # Each distribcell offset is a DataFrame bin - # Unravel the paths into DataFrame columns - num_offsets = len(offsets_to_coords) - - # Initialize termination condition for while loop + # Loop over CSG levels in the distribcell paths + level_counter = 0 levels_remain = True - counter = 0 - - # Iterate over each level in the CSG tree hierarchy while levels_remain: - levels_remain = False - # Initialize dictionary to build Pandas Multi-index - # column for this level in the CSG tree hierarchy - level_dict = {} + # Use level key as first index in Pandas Multi-index column + level_counter += 1 + level_key = 'level {}'.format(level_counter) - # Initialize prefix Multi-index keys - counter += 1 - level_key = 'level {0}'.format(counter) - univ_key = (level_key, 'univ', 'id') - cell_key = (level_key, 'cell', 'id') - lat_id_key = (level_key, 'lat', 'id') - lat_x_key = (level_key, 'lat', 'x') - lat_y_key = (level_key, 'lat', 'y') - lat_z_key = (level_key, 'lat', 'z') + # Use the first distribcell path to determine if level + # is a universe/cell or lattice level + first_path = distribcell_paths[0] + next_index = first_path.index('-') + level = first_path[:next_index] - # Allocate NumPy arrays for each CSG level and - # each Multi-index column in the DataFrame - level_dict[univ_key] = np.empty(num_offsets) - level_dict[cell_key] = np.empty(num_offsets) - level_dict[lat_id_key] = np.empty(num_offsets) - level_dict[lat_x_key] = np.empty(num_offsets) - level_dict[lat_y_key] = np.empty(num_offsets) - level_dict[lat_z_key] = np.empty(num_offsets) + # Trim universe/lattice info from path + first_path = first_path[next_index+2:] - # Initialize Multi-index columns to NaN - this is - # necessary since some distribcell instances may - # have very different LocalCoords linked lists - level_dict[univ_key][:] = np.NAN - level_dict[cell_key][:] = np.NAN - level_dict[lat_id_key][:] = np.NAN - level_dict[lat_x_key][:] = np.NAN - level_dict[lat_y_key][:] = np.NAN - level_dict[lat_z_key][:] = np.NAN + # Create a dictionary for this level for Pandas Multi-index + level_dict = OrderedDict() - # Iterate over all regions (distribcell instances) - for offset in range(num_offsets): - coords = offsets_to_coords[offset] + # This level is a lattice (e.g., ID(x,y,z)) + if '(' in level: + level_type = 'lattice' - # If entire LocalCoords has been unraveled into - # Multi-index columns already, continue - if coords is None: - continue + # Initialize prefix Multi-index keys + lat_id_key = (level_key, 'lat', 'id') + lat_x_key = (level_key, 'lat', 'x') + lat_y_key = (level_key, 'lat', 'y') + lat_z_key = (level_key, 'lat', 'z') - # Assign entry to Universe Multi-index column - if coords._type == 'universe': - level_dict[univ_key][offset] = coords._universe._id - level_dict[cell_key][offset] = coords._cell._id + # Allocate NumPy arrays for each CSG level and + # each Multi-index column in the DataFrame + level_dict[lat_id_key] = np.empty(num_offsets) + level_dict[lat_x_key] = np.empty(num_offsets) + level_dict[lat_y_key] = np.empty(num_offsets) + level_dict[lat_z_key] = np.empty(num_offsets) + + # This level is a universe / cell (e.g., ID->ID) + else: + level_type = 'universe' + + # Initialize prefix Multi-index keys + univ_key = (level_key, 'univ', 'id') + cell_key = (level_key, 'cell', 'id') + + # Allocate NumPy arrays for each CSG level and + # each Multi-index column in the DataFrame + level_dict[univ_key] = np.empty(num_offsets) + level_dict[cell_key] = np.empty(num_offsets) + + # Determine any levels remain in path + if '-' not in first_path: + levels_remain = False + + # Populate Multi-index arrays with all distribcell paths + for i, path in enumerate(distribcell_paths): + + if level_type == 'lattice': + # Extract lattice ID, indices from path + next_index = path.index('-') + lat_id_indices = path[:next_index] + + # Trim lattice info from distribcell path + distribcell_paths[i] = path[next_index+2:] + + # Extract the lattice cell indices from the path + i1 = lat_id_indices.index('(') + i2 = lat_id_indices.index(')') + i3 = lat_id_indices[i1+1:i2] + + # Assign entry to Lattice Multi-index column + level_dict[lat_id_key][i] = path[:i1] + level_dict[lat_x_key][i] = int(i3.split(',')[0]) - 1 + level_dict[lat_y_key][i] = int(i3.split(',')[1]) - 1 + level_dict[lat_z_key][i] = int(i3.split(',')[2]) - 1 - # Assign entry to Lattice Multi-index column else: - # Reverse y index per lattice ordering in OpenCG - level_dict[lat_id_key][offset] = coords._lattice._id - level_dict[lat_x_key][offset] = coords._lat_x - level_dict[lat_y_key][offset] = \ - coords._lattice.dimension[1] - coords._lat_y - 1 - level_dict[lat_z_key][offset] = coords._lat_z + # Extract universe ID from path + next_index = path.index('-') + universe_id = int(path[:next_index]) - # Move to next node in LocalCoords linked list - if coords._next is None: - offsets_to_coords[offset] = None - else: - offsets_to_coords[offset] = coords._next - levels_remain = True + # Trim universe info from distribcell path + path = path[next_index+2:] + + # Extract cell ID from path + if '-' in path: + next_index = path.index('-') + cell_id = int(path[:next_index]) + distribcell_paths[i] = path[next_index+2:] + else: + cell_id = int(path) + distribcell_paths[i] = '' + + # Assign entry to Universe, Cell Multi-index columns + level_dict[univ_key][i] = universe_id + level_dict[cell_key][i] = cell_id # Tile the Multi-index columns for level_key, level_bins in level_dict.items(): @@ -740,7 +751,7 @@ class Filter(object): else: level_df = pd.concat([level_df, pd.DataFrame(level_dict)], axis=1) - # Create DataFrame column for distribcell instances IDs + # Create DataFrame column for distribcell instance IDs # NOTE: This is performed regardless of whether the user # requests Summary geometric information filter_bins = np.arange(self.num_bins) diff --git a/openmc/material.py b/openmc/material.py index ff690aa9a..e9a74f1e7 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -245,19 +245,25 @@ class Material(object): """ - cv.check_type('the density for Material ID="{0}"'.format(self._id), - density, Real) cv.check_value('density units', units, DENSITY_UNITS) - - if density is None and units is not 'sum': - msg = 'Unable to set the density for Material ID="{0}" ' \ - 'because a density must be set when not using ' \ - 'sum unit'.format(self._id) - raise ValueError(msg) - - self._density = density self._density_units = units + if units is 'sum': + if density is not None: + msg = 'Density "{0}" for Material ID="{1}" is ignored ' \ + 'because the unit is "sum"'.format(density, self.id) + warnings.warn(msg) + else: + if density is None: + msg = 'Unable to set the density for Material ID="{0}" ' \ + 'because a density value must be given when not using ' \ + '"sum" unit'.format(self.id) + raise ValueError(msg) + + cv.check_type('the density for Material ID="{0}"'.format(self.id), + density, Real) + self._density = density + @distrib_otf_file.setter def distrib_otf_file(self, filename): # TODO: remove this when distributed materials are merged diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index 830b6d766..eed0627bf 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -1352,7 +1352,7 @@ class MGXS(object): modified.write('\n\\end{document}') def get_pandas_dataframe(self, groups='all', nuclides='all', - xs_type='macro', summary=None): + xs_type='macro', distribcell_paths=True): """Build a Pandas DataFrame for the MGXS data. This method leverages :meth:`openmc.Tally.get_pandas_dataframe`, but @@ -1372,12 +1372,11 @@ class MGXS(object): xs_type: {'macro', 'micro'} Return macro or micro cross section in units of cm^-1 or barns. Defaults to 'macro'. - summary : None or openmc.Summary - An optional Summary object to be used to construct columns for - distribcell tally filters (default is None). The geometric - 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. + distribcell_paths : bool, optional + Construct columns for distribcell tally filters (default is True). + The geometric information in the Summary object is embedded into + a Multi-index column with a geometric "path" to each distribcell + instance. Returns ------- @@ -1404,7 +1403,8 @@ class MGXS(object): # Use tally summation to sum across all nuclides query_nuclides = self.get_all_nuclides() xs_tally = self.xs_tally.summation(nuclides=query_nuclides) - df = xs_tally.get_pandas_dataframe(summary=summary) + df = xs_tally.get_pandas_dataframe( + distribcell_paths=distribcell_paths) # Remove nuclide column since it is homogeneous and redundant df.drop('nuclide', axis=1, inplace=True) @@ -1412,11 +1412,13 @@ class MGXS(object): # If the user requested a specific set of nuclides elif self.by_nuclide and nuclides != 'all': xs_tally = self.xs_tally.get_slice(nuclides=nuclides) - df = xs_tally.get_pandas_dataframe(summary=summary) + df = xs_tally.get_pandas_dataframe( + distribcell_paths=distribcell_paths) # If the user requested all nuclides, keep nuclide column in dataframe else: - df = self.xs_tally.get_pandas_dataframe(summary=summary) + df = self.xs_tally.get_pandas_dataframe( + distribcell_paths=distribcell_paths) # Remove the score column since it is homogeneous and redundant df = df.drop('score', axis=1) @@ -2727,7 +2729,7 @@ class Chi(MGXS): return xs def get_pandas_dataframe(self, groups='all', nuclides='all', - xs_type='macro', summary=None): + xs_type='macro', distribcell_paths=False): """Build a Pandas DataFrame for the MGXS data. This method leverages :meth:`openmc.Tally.get_pandas_dataframe`, but @@ -2747,12 +2749,11 @@ class Chi(MGXS): xs_type: {'macro', 'micro'} Return macro or micro cross section in units of cm^-1 or barns. Defaults to 'macro'. - summary : None or openmc.Summary - An optional Summary object to be used to construct columns for - distribcell tally filters (default is None). The geometric - 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. + distribcell_paths : bool, optional + Construct columns for distribcell tally filters (default is True). + The geometric information in the Summary object is embedded into + a Multi-index column with a geometric "path" to each distribcell + instance. Returns ------- @@ -2768,8 +2769,8 @@ class Chi(MGXS): """ # Build the dataframe using the parent class method - df = super(Chi, self).get_pandas_dataframe(groups, nuclides, - xs_type, summary) + df = super(Chi, self).get_pandas_dataframe( + groups, nuclides, xs_type, distribcell_paths=distribcell_paths) # If user requested micro cross sections, multiply by the atom # densities to cancel out division made by the parent class method diff --git a/openmc/tallies.py b/openmc/tallies.py index dc431ddf9..9559adcad 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -1538,8 +1538,8 @@ class Tally(object): return data - def get_pandas_dataframe(self, filters=True, nuclides=True, - scores=True, summary=None, float_format='{:.2e}'): + def get_pandas_dataframe(self, filters=True, nuclides=True, scores=True, + distribcell_paths=True, float_format='{:.2e}'): """Build a Pandas DataFrame for the Tally data. This method constructs a Pandas DataFrame object for the Tally data @@ -1557,12 +1557,11 @@ class Tally(object): Include columns with nuclide bin information (default is True). scores : bool Include columns with score bin information (default is True). - summary : None or openmc.Summary - An optional Summary object to be used to construct columns for - distribcell tally filters (default is None). The geometric - 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. + distribcell_paths : bool, optional + Construct columns for distribcell tally filters (default is True). + The geometric information in the Summary object is embedded into a + Multi-index column with a geometric "path" to each distribcell + instance. float_format : str All floats in the DataFrame will be formatted using the given format string before printing. @@ -1588,14 +1587,6 @@ class Tally(object): msg = 'The Tally ID="{0}" has no data to return'.format(self.id) raise KeyError(msg) - # If using Summary, ensure StatePoint.link_with_summary(...) was called - if summary and not self.with_summary: - msg = 'The Tally ID="{0}" has not been linked with the Summary. ' \ - 'Call the StatePoint.link_with_summary(...) method ' \ - 'before using Tally.get_pandas_dataframe(...) with ' \ - 'Summary info'.format(self.id) - raise KeyError(msg) - # Initialize a pandas dataframe for the tally data import pandas as pd df = pd.DataFrame() @@ -1608,7 +1599,8 @@ 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) + filter_df = self_filter.get_pandas_dataframe( + data_size, distribcell_paths) df = pd.concat([df, filter_df], axis=1) # Include DataFrame column for nuclides if user requested it