diff --git a/examples/jupyter/pandas-dataframes.ipynb b/examples/jupyter/pandas-dataframes.ipynb index 72d5d7313..140a0d684 100644 --- a/examples/jupyter/pandas-dataframes.ipynb +++ b/examples/jupyter/pandas-dataframes.ipynb @@ -215,9 +215,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# OpenMC simulation parameters\n", @@ -281,18 +279,7 @@ "cell_type": "code", "execution_count": 11, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Run openmc in plotting mode\n", "openmc.plot_geometry(output=False)" @@ -305,7 +292,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\nMLStB7oAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -333,9 +320,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate an empty Tallies object\n", @@ -396,7 +381,7 @@ "# Instantiate the tally\n", "tally = openmc.Tally(name='cell tally')\n", "tally.filters = [cell_filter]\n", - "tally.scores = ['scatter-y2']\n", + "tally.scores = ['scatter']\n", "tally.nuclides = ['U235', 'U238']\n", "\n", "# Add mesh and tally to Tallies\n", @@ -436,9 +421,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Export to \"tallies.xml\"\n", @@ -487,27 +470,27 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2017 Massachusetts Institute of Technology\n", + " Copyright | 2011-2018 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.9.0\n", - " Git SHA1 | 897db1389dec7871026442bae96d9410ade85192\n", - " Date/Time | 2017-06-07 13:18:50\n", - " MPI Processes | 1\n", - " OpenMP Threads | 12\n", + " Version | 0.10.0\n", + " Git SHA1 | 199126b2fcc5cb094f2cc820ae13e1a972cacddd\n", + " Date/Time | 2018-10-11 16:41:25\n", + " OpenMP Threads | 8\n", "\n", " Reading settings XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", " Reading cross sections XML file...\n", - " Reading U235 from /home/johnny/Github/openmc/scripts/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/johnny/Github/openmc/scripts/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/johnny/Github/openmc/scripts/nndc_hdf5/O16.h5\n", - " Reading H1 from /home/johnny/Github/openmc/scripts/nndc_hdf5/H1.h5\n", - " Reading B10 from /home/johnny/Github/openmc/scripts/nndc_hdf5/B10.h5\n", - " Reading Zr90 from /home/johnny/Github/openmc/scripts/nndc_hdf5/Zr90.h5\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Building neighboring cells lists for each surface...\n", + " Reading U235 from /home/jan/openmc/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/jan/openmc/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/jan/openmc/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/jan/openmc/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/jan/openmc/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /home/jan/openmc/nndc_hdf5/Zr90.h5\n", " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", " Reading tallies XML file...\n", - " Building neighboring cells lists for each surface...\n", + " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -534,7 +517,7 @@ " 18/1 0.67696 0.68367 +/- 0.00625\n", " 19/1 0.65444 0.68158 +/- 0.00615\n", " 20/1 0.69766 0.68266 +/- 0.00583\n", - " Triggers unsatisfied, max unc./thresh. is 1.17617 for absorption in tally 10002\n", + " Triggers unsatisfied, max unc./thresh. is 1.17617 for absorption in tally 3\n", " The estimated number of batches is 26\n", " Creating state point statepoint.020.h5...\n", " 21/1 0.64126 0.68007 +/- 0.00603\n", @@ -548,20 +531,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.6433E-01 seconds\n", - " Reading cross sections = 2.7963E-01 seconds\n", - " Total time in simulation = 1.4715E+00 seconds\n", - " Time in transport only = 1.3171E+00 seconds\n", - " Time in inactive batches = 2.2329E-01 seconds\n", - " Time in active batches = 1.2482E+00 seconds\n", - " Time synchronizing fission bank = 2.3145E-03 seconds\n", - " Sampling source sites = 1.4054E-03 seconds\n", - " SEND/RECV source sites = 7.6241E-04 seconds\n", - " Time accumulating tallies = 5.8822E-04 seconds\n", - " Total time for finalization = 5.0159E-05 seconds\n", - " Total time elapsed = 1.8511E+00 seconds\n", - " Calculation Rate (inactive) = 55980.8 neutrons/second\n", - " Calculation Rate (active) = 30044.0 neutrons/second\n", + " Total time for initialization = 6.5303E-01 seconds\n", + " Reading cross sections = 5.8105E-01 seconds\n", + " Total time in simulation = 4.6015E+00 seconds\n", + " Time in transport only = 3.5767E+00 seconds\n", + " Time in inactive batches = 4.5008E-01 seconds\n", + " Time in active batches = 4.1514E+00 seconds\n", + " Time synchronizing fission bank = 2.3493E-03 seconds\n", + " Sampling source sites = 1.7160E-03 seconds\n", + " SEND/RECV source sites = 4.7010E-04 seconds\n", + " Time accumulating tallies = 2.3040E-04 seconds\n", + " Total time for finalization = 2.6451E-02 seconds\n", + " Total time elapsed = 5.3123E+00 seconds\n", + " Calculation Rate (inactive) = 27772.9 particles/second\n", + " Calculation Rate (active) = 12646.3 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -572,16 +555,6 @@ " Leakage Fraction = 0.34011 +/- 0.00283\n", "\n" ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -630,7 +603,7 @@ "output_type": "stream", "text": [ "Tally\n", - "\tID =\t10000\n", + "\tID =\t1\n", "\tName =\tmesh tally\n", "\tFilters =\tMeshFilter, EnergyFilter\n", "\tNuclides =\ttotal \n", @@ -664,13 +637,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.17581417]]\n", + "[[[0.17581417]]\n", "\n", - " [[ 0.30578219]]\n", + " [[0.06842901]]\n", "\n", - " [[ 0.06842901]]\n", + " [[0.30578219]]\n", "\n", - " [[ 0.12436752]]]\n" + " [[0.12436752]]]\n" ] } ], @@ -693,18 +666,18 @@ "data": { "text/html": [ "
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" \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -897,9 +870,9 @@ " \n", " \n", " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -908,9 +881,9 @@ " \n", " \n", " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -919,9 +892,9 @@ " \n", " \n", " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -930,9 +903,9 @@ " \n", " \n", " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -941,9 +914,9 @@ " \n", " \n", " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -961,22 +934,22 @@ "1 1 1 1 0.00e+00 6.25e-01 nu-fission 5.46e-04 \n", "2 1 1 1 6.25e-01 2.00e+07 fission 8.42e-05 \n", "3 1 1 1 6.25e-01 2.00e+07 nu-fission 2.22e-04 \n", - "4 1 2 1 0.00e+00 6.25e-01 fission 1.85e-04 \n", - "5 1 2 1 0.00e+00 6.25e-01 nu-fission 4.52e-04 \n", - "6 1 2 1 6.25e-01 2.00e+07 fission 6.82e-05 \n", - "7 1 2 1 6.25e-01 2.00e+07 nu-fission 1.81e-04 \n", - "8 1 3 1 0.00e+00 6.25e-01 fission 2.05e-04 \n", - "9 1 3 1 0.00e+00 6.25e-01 nu-fission 5.00e-04 \n", - "10 1 3 1 6.25e-01 2.00e+07 fission 7.53e-05 \n", - "11 1 3 1 6.25e-01 2.00e+07 nu-fission 1.99e-04 \n", - "12 1 4 1 0.00e+00 6.25e-01 fission 2.06e-04 \n", - "13 1 4 1 0.00e+00 6.25e-01 nu-fission 5.03e-04 \n", - "14 1 4 1 6.25e-01 2.00e+07 fission 6.65e-05 \n", - "15 1 4 1 6.25e-01 2.00e+07 nu-fission 1.75e-04 \n", - "16 1 5 1 0.00e+00 6.25e-01 fission 2.03e-04 \n", - "17 1 5 1 0.00e+00 6.25e-01 nu-fission 4.94e-04 \n", - "18 1 5 1 6.25e-01 2.00e+07 fission 6.26e-05 \n", - "19 1 5 1 6.25e-01 2.00e+07 nu-fission 1.64e-04 \n", + "4 2 1 1 0.00e+00 6.25e-01 fission 1.85e-04 \n", + "5 2 1 1 0.00e+00 6.25e-01 nu-fission 4.52e-04 \n", + "6 2 1 1 6.25e-01 2.00e+07 fission 6.82e-05 \n", + "7 2 1 1 6.25e-01 2.00e+07 nu-fission 1.81e-04 \n", + "8 3 1 1 0.00e+00 6.25e-01 fission 2.05e-04 \n", + "9 3 1 1 0.00e+00 6.25e-01 nu-fission 5.00e-04 \n", + "10 3 1 1 6.25e-01 2.00e+07 fission 7.53e-05 \n", + "11 3 1 1 6.25e-01 2.00e+07 nu-fission 1.99e-04 \n", + "12 4 1 1 0.00e+00 6.25e-01 fission 2.06e-04 \n", + "13 4 1 1 0.00e+00 6.25e-01 nu-fission 5.03e-04 \n", + "14 4 1 1 6.25e-01 2.00e+07 fission 6.65e-05 \n", + "15 4 1 1 6.25e-01 2.00e+07 nu-fission 1.75e-04 \n", + "16 5 1 1 0.00e+00 6.25e-01 fission 2.03e-04 \n", + "17 5 1 1 0.00e+00 6.25e-01 nu-fission 4.94e-04 \n", + "18 5 1 1 6.25e-01 2.00e+07 fission 6.26e-05 \n", + "19 5 1 1 6.25e-01 2.00e+07 nu-fission 1.64e-04 \n", "\n", " std. dev. \n", " \n", @@ -1025,12 +998,14 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1098,12 +1075,12 @@ "output_type": "stream", "text": [ "Tally\n", - "\tID =\t10001\n", + "\tID =\t2\n", "\tName =\tcell tally\n", "\tFilters =\tCellFilter\n", "\tNuclides =\tU235 U238 \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", - "\tEstimator =\tanalog\n", + "\tScores =\t['scatter']\n", + "\tEstimator =\ttracklength\n", "\n" ] } @@ -1125,18 +1102,18 @@ "data": { "text/html": [ "
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0100001U235scatter-Y0,03.86e-026.85e-04scatter3.81e-021.65e-04
110000U235scatter-Y1,-16.95e-043.15e-04
210000U235scatter-Y1,0-1.06e-043.79e-04
310000U235scatter-Y1,1-3.63e-043.18e-04
410000U235scatter-Y2,-21.20e-041.59e-04
510000U235scatter-Y2,-13.93e-051.86e-04
610000U235scatter-Y2,01.81e-041.85e-04
710000U235scatter-Y2,11.24e-041.81e-04
810000U235scatter-Y2,22.06e-042.26e-04
9100001U238scatter-Y0,0scatter2.33e+001.10e-02
1010000U238scatter-Y1,-12.90e-022.33e-03
1110000U238scatter-Y1,03.45e-032.38e-03
1210000U238scatter-Y1,1-2.72e-022.76e-03
1310000U238scatter-Y2,-2-2.02e-031.44e-03
1410000U238scatter-Y2,-18.07e-061.49e-03
1510000U238scatter-Y2,0-3.74e-071.79e-03
1610000U238scatter-Y2,16.54e-041.49e-03
1710000U238scatter-Y2,2-1.93e-031.36e-039.59e-03
\n", "
" ], "text/plain": [ - " cell nuclide score mean std. dev.\n", - "0 10000 U235 scatter-Y0,0 3.86e-02 6.85e-04\n", - "1 10000 U235 scatter-Y1,-1 6.95e-04 3.15e-04\n", - "2 10000 U235 scatter-Y1,0 -1.06e-04 3.79e-04\n", - "3 10000 U235 scatter-Y1,1 -3.63e-04 3.18e-04\n", - "4 10000 U235 scatter-Y2,-2 1.20e-04 1.59e-04\n", - "5 10000 U235 scatter-Y2,-1 3.93e-05 1.86e-04\n", - "6 10000 U235 scatter-Y2,0 1.81e-04 1.85e-04\n", - "7 10000 U235 scatter-Y2,1 1.24e-04 1.81e-04\n", - "8 10000 U235 scatter-Y2,2 2.06e-04 2.26e-04\n", - "9 10000 U238 scatter-Y0,0 2.33e+00 1.10e-02\n", - "10 10000 U238 scatter-Y1,-1 2.90e-02 2.33e-03\n", - "11 10000 U238 scatter-Y1,0 3.45e-03 2.38e-03\n", - "12 10000 U238 scatter-Y1,1 -2.72e-02 2.76e-03\n", - "13 10000 U238 scatter-Y2,-2 -2.02e-03 1.44e-03\n", - "14 10000 U238 scatter-Y2,-1 8.07e-06 1.49e-03\n", - "15 10000 U238 scatter-Y2,0 -3.74e-07 1.79e-03\n", - "16 10000 U238 scatter-Y2,1 6.54e-04 1.49e-03\n", - "17 10000 U238 scatter-Y2,2 -1.93e-03 1.36e-03" + " cell nuclide score mean std. dev.\n", + "0 1 U235 scatter 3.81e-02 1.65e-04\n", + "1 1 U238 scatter 2.33e+00 9.59e-03" ] }, "execution_count": 26, @@ -1349,15 +1182,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00136183 0.01104314]\n", - " [ 0.00022601 0.00068479]]]\n" + "[[[0.00958717]\n", + " [0.00016469]]]\n" ] } ], "source": [ - "# Get the standard deviations for two of the spherical harmonic\n", - "# scattering reaction rates \n", - "data = tally.get_values(scores=['scatter-Y2,2', 'scatter-Y0,0'], \n", + "# Get the standard deviations the total scattering rate\n", + "data = tally.get_values(scores=['scatter'], \n", " nuclides=['U238', 'U235'], value='std_dev')\n", "print(data)" ] @@ -1379,7 +1211,7 @@ "output_type": "stream", "text": [ "Tally\n", - "\tID =\t10002\n", + "\tID =\t3\n", "\tName =\tdistribcell tally\n", "\tFilters =\tDistribcellFilter\n", "\tNuclides =\ttotal \n", @@ -1413,25 +1245,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.03500496]]\n", + "[[[0.03500496]]\n", "\n", - " [[ 0.03004793]]\n", + " [[0.02745568]]\n", "\n", - " [[ 0.02536586]]\n", + " [[0.02988488]]\n", "\n", - " [[ 0.03403647]]\n", + " [[0.04474905]]\n", "\n", - " [[ 0.02498 ]]\n", + " [[0.03697764]]\n", "\n", - " [[ 0.01892844]]\n", + " [[0.0409214 ]]\n", "\n", - " [[ 0.02662923]]\n", + " [[0.03366461]]\n", "\n", - " [[ 0.02875671]]\n", + " [[0.03210393]]\n", "\n", - " [[ 0.01945598]]\n", + " [[0.03216398]]\n", "\n", - " [[ 0.02612378]]]\n" + " [[0.04003553]]]\n" ] } ], @@ -1459,18 +1291,18 @@ "data": { "text/html": [ "
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"561 10002 10003 10001 16 8 10000 10002 280 scatter \n", - "562 10002 10003 10001 16 9 10000 10002 281 absorption \n", - "563 10002 10003 10001 16 9 10000 10002 281 scatter \n", - "564 10002 10003 10001 16 10 10000 10002 282 absorption \n", - "565 10002 10003 10001 16 10 10000 10002 282 scatter \n", - "566 10002 10003 10001 16 11 10000 10002 283 absorption \n", - "567 10002 10003 10001 16 11 10000 10002 283 scatter \n", - "568 10002 10003 10001 16 12 10000 10002 284 absorption \n", - "569 10002 10003 10001 16 12 10000 10002 284 scatter \n", - "570 10002 10003 10001 16 13 10000 10002 285 absorption \n", - "571 10002 10003 10001 16 13 10000 10002 285 scatter \n", - "572 10002 10003 10001 16 14 10000 10002 286 absorption \n", - "573 10002 10003 10001 16 14 10000 10002 286 scatter \n", - "574 10002 10003 10001 16 15 10000 10002 287 absorption \n", - "575 10002 10003 10001 16 15 10000 10002 287 scatter \n", - "576 10002 10003 10001 16 16 10000 10002 288 absorption \n", - "577 10002 10003 10001 16 16 10000 10002 288 scatter \n", + " level 1 level 2 level 3 distribcell score \\\n", + " univ cell lat univ cell \n", + " id id id x y id id \n", + "558 3 4 2 7 16 1 3 279 absorption \n", + "559 3 4 2 7 16 1 3 279 scatter \n", + "560 3 4 2 8 16 1 3 280 absorption \n", + "561 3 4 2 8 16 1 3 280 scatter \n", + "562 3 4 2 9 16 1 3 281 absorption \n", + "563 3 4 2 9 16 1 3 281 scatter \n", + "564 3 4 2 10 16 1 3 282 absorption \n", + "565 3 4 2 10 16 1 3 282 scatter \n", + "566 3 4 2 11 16 1 3 283 absorption \n", + "567 3 4 2 11 16 1 3 283 scatter \n", + "568 3 4 2 12 16 1 3 284 absorption \n", + "569 3 4 2 12 16 1 3 284 scatter \n", + "570 3 4 2 13 16 1 3 285 absorption \n", + "571 3 4 2 13 16 1 3 285 scatter \n", + "572 3 4 2 14 16 1 3 286 absorption \n", + "573 3 4 2 14 16 1 3 286 scatter \n", + "574 3 4 2 15 16 1 3 287 absorption \n", + "575 3 4 2 15 16 1 3 287 scatter \n", + "576 3 4 2 16 16 1 3 288 absorption \n", + "577 3 4 2 16 16 1 3 288 scatter \n", "\n", " mean std. dev. \n", " \n", " \n", - "558 7.77e-05 7.87e-06 \n", - "559 1.28e-02 5.09e-04 \n", - "560 8.92e-05 7.32e-06 \n", - "561 1.37e-02 4.99e-04 \n", - "562 9.50e-05 7.80e-06 \n", - "563 1.49e-02 4.74e-04 \n", - "564 1.15e-04 1.00e-05 \n", - "565 1.58e-02 6.18e-04 \n", - "566 1.13e-04 1.01e-05 \n", - "567 1.75e-02 5.66e-04 \n", - "568 1.08e-04 9.66e-06 \n", - "569 1.73e-02 5.40e-04 \n", - "570 1.16e-04 1.44e-05 \n", - "571 1.70e-02 6.90e-04 \n", - "572 1.16e-04 1.02e-05 \n", - "573 1.77e-02 6.80e-04 \n", - "574 1.20e-04 1.36e-05 \n", - "575 1.80e-02 7.80e-04 \n", + "558 6.81e-04 2.84e-05 \n", + "559 8.82e-02 1.86e-03 \n", + "560 6.65e-04 3.46e-05 \n", + "561 8.37e-02 2.02e-03 \n", + "562 5.61e-04 2.91e-05 \n", + "563 7.52e-02 1.79e-03 \n", + "564 4.77e-04 2.33e-05 \n", + "565 6.68e-02 1.14e-03 \n", + "566 4.64e-04 2.05e-05 \n", + "567 6.20e-02 1.61e-03 \n", + "568 4.44e-04 2.93e-05 \n", + "569 5.47e-02 1.53e-03 \n", + "570 3.67e-04 2.63e-05 \n", + "571 4.68e-02 1.52e-03 \n", + "572 2.76e-04 1.75e-05 \n", + "573 3.81e-02 1.28e-03 \n", + "574 2.08e-04 1.69e-05 \n", + "575 2.85e-02 1.13e-03 \n", "576 1.32e-04 1.30e-05 \n", "577 1.86e-02 7.12e-04 " ] @@ -1868,18 +1700,18 @@ "data": { "text/html": [ "
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\n", " \n", @@ -2058,7 +1890,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/johnny/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", + "/home/jan/.local/lib/python3.6/site-packages/ipykernel_launcher.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", @@ -2069,7 +1901,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 34, @@ -2078,12 +1910,14 @@ }, { "data": { - "image/png": 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MwzWPrGbowJrs3ut9raItlS0gjOXWMn4WMKpUoS9yqa6HsEry2sVrAMnZo9u/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\nh1fljxGV4g+sUW44ghV8uSl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+ "image/png": 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\n", 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4bD4cqG4B5NvW0ZA7y07jyOR79E/Mi7ocqQYFgVRu8eTgp4JAKjEudRxL03tx\nfcE4kqSiLkeqSEEglVs8EVoeCM33i7oSqaVKKeB3ZedwQOJjTk+qu+q6SkEgFfvmK1jxuo4GZKde\nTPfmnfR+XFnwDIWURV2OVIGCQCq25CXwlIJAMmDcUXYGbe1Lzki+EnUxUgWRBIGZLTezBWb2jpnN\njqIG2YnFE6Hx3tC6Z9SVSB3wSrobc9P7c0XBsxRRGnU5souiPCIY4O493F1XKtU2pZvgg6lw4BBI\n6KBRMmH8sexM2thqvpucHnUxsov0Vy7ftnQ6lH6jZiHZJTPSh/BW+gAuL3iOepREXY7sgqiCwIEp\nZjbHzC6paAIzu8TMZpvZ7FWr1OVtrnQcMYmnHruXdd6ATg+sp+OISTm9PaLUZcFRQWv7irOS/4y6\nGNkFUQXBke7eAxgMXG5mR28/gbvf7+693b13y5Ytc19hTCVIc1xyDv9M96RUt7SWXTQz3YXZ6QO4\npGASBTqDqM6IJAjc/ePw5xfAM8BhUdQh39bb3qe5refl1KFRlyJ1kvHnsmG0tS8Zlng96mIkQzkP\nAjNraGaNtz4HTgDezXUdUrEhyTfZ7IVMS+tsIamaaemeLEq357KC5zHSUZcjGYjiiKAVMMPM5gGz\ngEnu/kIEdcj20mkGJ2cxPd2Db6gfdTVSZxl/KRtGp8THnJCYE3UxkoGcB4G7L3X37uHjYHe/Jdc1\nSCVWvkErW8Pk1OFRVyJ13KT04axI78llBc+hnklrP50+Kv/x3rNs8UKmqllIqilFkvtSQ+mRWEq/\nxHtRlyM7oSCQQDoNiyYwPd1dt6SUGvF06ig+96Zcnnwu6lJkJxQEElj5Jqz/lElqFpIasoUiHiwb\nwneS79HdPoi6HNkBBYEEFj4LyXpMTeuWlFJzHk8NZI035McFE6IuRXZAQSBBs9DCCbD/cWoWkhq1\nkQY8mjqRE5Oz6WTFUZcjlVAQCCx/DdZ/AoecFnUlkoceLjuRjV6PHxfou4LaSkEgMP8pKGqsTuYk\nK9bQmHGp44Irjb9aGnU5UgEFQdyVfBM0C3U5GQrVLCTZ8UDZEMoogBl/iroUqYCCIO7enwwl66H7\nWVFXInlsFXswPnUMvPM4rP046nJkOwqCuJv/FOzeBjocGXUlkufuSw0FT8Pro6MuRbajIIizDauC\nO5F1PVN3IpOsK/aW0O0smPNI8N6TWkN//XG2YHxwg/puahaSHDnqZ1C2Gd74c9SVSDkKgrhyh9kP\nQ9s+0Kr94jkHAAAJk0lEQVRL1NVIXLToFJyY8NaDsGlN1NVISEEQVyteh9VL4NDvRV2JxM1R18CW\ndTDrgagrkZCCIK7mPAz1msDBuohMcqx1N+h0IrxxD2xeG3U1goIgnjauhoXPBaeMFu0WdTUSRwN+\nCZu+hpn3RF2JoCCIp3fGQapEzUISnb17Bt8VzLwHNn4ZdTWxpyCIm1QpvHkfdPgOtDo46mokzgbc\nAKXfwIw7oq4k9hQEcbPwOVhXDP1+EnUlEnctO0P3c4MvjXW1caQUBHHiHlzV2bxT8GWdSNT6/yK4\n2nj6rVFXEmsKgjhZ/hp8+g4ccbmuJJbaoWl7OPxH8PY4+Hhu1NXElv4bxIU7TB8FjfaC7mdHXY3I\nfxzzc2jYAv7xi+B9KjmnIIiLZa/Ain8FF/Oou2mpTeo3gYG/huJZMH981NXEkoIgDtxh2i1BL6O9\nLoy6GpFv63FecErpy7/SRWYRUBDEweJJwaeto66BwvpRVyPybYkEnPQH2PhFEAaSUwqCfFe6GV68\nHloepKMBqd3aHApHXBF0U710etTVxIqCIN/NHA1rVsDgUZAsjLoakR0bcD002w8m/AS2rI+6mthQ\nEOSz1R/Ca3+Eg4bCvv2jrkZk5wobwCl/hrXFMPFqnUWUIwqCfJVOwbM/Do4CBv8+6mpEMte+b3Bk\nsOBvQTORZJ2CIF+9PhpWvgFDbofd9466GpFdc+Q1sN+xwbUFutAs6xQE+WjZazD1ZjhoWHA/YpG6\nJpGA0x6ARq3g8bPg6xVRV5TXFAT55usVMP5CaL4/nHwPmEVdkUjVNGwB5/8dUltg3JnqrjqLFAT5\nZP3nMPb04PuBsx+H+rtHXZFI9bTsHLyX16yAR04K3uNS4xQE+WLDF/DoUFj3CZz7FLTYP+qKRGpG\nxyPhvL/Bmo/g4cHB2XBSoxQE+eCzd+GBgcEfynnjocMRUVckUrP2ORoueAY2r4EHBsAHU6KuKK8o\nCOoyd5j7GDx0AqRL4aLJwacnkXzUvi9cPC3oM2vs6TD5OijZGHVVeUFBUFd9sSj4Y5hwBezdAy7+\nJ7TpFXVVItm1R0f44VQ4/DKYdT+M7h18GEqnoq6sTlMQ1CXusOJ1+Pv34c9HwMpZMPg2GD4Rdm8d\ndXUiuVG0W9BlykUvBO/7CVfA3b3h9bvhm6+irq5OKohipWY2CLgTSAIPuvuoKOqoE7asD/7hfzgN\n/v0CrP4AihoH9xw+8mrYrVnUFYpEo8MRwdHBoufhjT/DSzfAlF9D+yOg8+CgKanVIVBQL+pKa72c\nB4GZJYF7gOOBYuAtM5vg7gtzXUvGtvZ34g7s4Pm2flF29tyhrARKNgRtnCUboWQ9fPM1rP8E1n0K\na1fC5+/C18uDWZNF0KFf8M//4FOhqGF2t1mkLjCDLsOCx2fvwrtPw/v/CHrcBUgUwp4HBU1Ke3SA\nJu2gQTNosAc0aBrcFCdZFIRFsug/zxMFsboGJ4ojgsOAD9x9KYCZPQmcDNR8ELxwPcx5OHhe1X/c\nUShqFHwh1roH9DwfWvcMPv3on79I5fY6JHgc92tYsxI+ngOfzIXP3wu+U/v3i8HFabvMwlDI4Gc2\nnD026G4ji6IIgjbAynKvi4HDt5/IzC4BLglfbjCz96uxzhZAHboscR3wCfBWtldUx/ZLTmnfVK7K\n+8Z+V8OV1D41/765cWB15u6QyUSRfEeQCXe/H7i/JpZlZrPdvXdNLCufaL9UTvumcto3laur+yaK\ns4Y+BtqVe902HCYiIhGIIgjeAjqZ2T5mVgScDUyIoA4RESGCpiF3LzOzK4AXCU4fHePu72V5tTXS\nxJSHtF8qp31TOe2bytXJfWOuW8GJiMSariwWEYk5BYGISMzlRRCYWTMze9nMloQ/96hkukFm9r6Z\nfWBmI8oNH2lmH5vZO+FjSO6qz47KtrXceDOzu8Lx882sV6bz1nXV3DfLzWxB+D6ZndvKsyuD/XKg\nmc00sy1mdu2uzFvXVXPf1P73jLvX+Qfwe2BE+HwE8LsKpkkCHwL7AkXAPKBLOG4kcG3U21GD+6PS\nbS03zRDgHwSXQ/YF3sx03rr8qM6+CcctB1pEvR0R7Zc9gT7ALeX/XvSeqXzf1JX3TF4cERB0UfFo\n+PxR4JQKptnWtYW7lwBbu7bIR5ls68nAXz3wBtDUzFpnOG9dVp19k892ul/c/Qt3fwso3dV567jq\n7Js6IV+CoJW7fxo+/wxoVcE0FXVt0abc65+EzQBjKmtaqkN2tq07miaTeeuy6uwbCDqgmmJmc8Ju\nUPJFdX7ves/sWK1/z9TaLia2Z2ZTgL0qGHVD+Rfu7ma2q+fE/gX4DcEv7DfAH4DvV6VOyXtHuvvH\nZrYn8LKZLXb3V6MuSmq1Wv+eqTNB4O7HVTbOzD43s9bu/ml4CP9FBZNV2rWFu39eblkPABNrpurI\nZNKNR2XTFGYwb11WnX2Du2/9+YWZPUPQbFCr/qirqDpdv+R7tzHV2r668J7Jl6ahCcDw8Plw4LkK\npqm0a4vt2n9PBd7NYq25kEk3HhOAC8MzZPoCa8PmtXzvAqTK+8bMGppZYwAzawicQN1/r2xVnd+7\n3jOVqDPvmai/ra6JB9AcmAosAaYAzcLhewOTy003BPg3wRkAN5Qb/hiwAJhP8AtuHfU21cA++da2\nApcCl4bPjeAGQR+G2957Z/spXx5V3TcEZ43MCx/v5du+yWC/7EXQPr4OWBM+313vmcr3TV15z6iL\nCRGRmMuXpiEREakiBYGISMwpCEREYk5BICIScwoCEZGYUxCIlGNmbmZjy70uMLNVZlbXLzIUqZSC\nQOS/bQQOMbMG4evjya+rZEW+RUEg8m2TgZPC5+cAT2wdEV4pOsbMZpnZ22Z2cji8o5m9ZmZzw0e/\ncHh/M5tuZn83s8VmNs7MLOdbJLIDCgKRb3sSONvM6gPdgDfLjbsBmObuhwEDgNvCrgO+AI53917A\nWcBd5ebpCfwU6EJwpel3sr8JIpmrM53OieSKu883s44ERwOTtxt9AjCs3F2o6gPtgU+Au82sB5AC\nDig3zyx3LwYws3eAjsCMbNUvsqsUBCIVmwDcDvQn6MtqKwNOd/f3y09sZiOBz4HuBEfam8uN3lLu\neQr93Ukto6YhkYqNAW5y9wXbDX+R4CZGBmBmPcPhTYBP3T0NXEBwe0OROkFBIFIBdy9297sqGPUb\ngns2zDez98LXAH8GhpvZPOBAgrOPROoE9T4qIhJzOiIQEYk5BYGISMwpCEREYk5BICIScwoCEZGY\nUxCIiMScgkBEJOb+H/MP8RDX50XfAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -2150,7 +1986,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.6" } }, "nbformat": 4, diff --git a/openmc/tallies.py b/openmc/tallies.py index 1e4b142fe..5bc6494bb 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -376,7 +376,7 @@ class Tally(IDManagerMixin): def scores(self, scores): cv.check_type('tally scores', scores, MutableSequence) - for i, score in enumerate(scores[:-1]): + for i, score in enumerate(scores): # If the score is already in the Tally, raise an error if score in scores[i+1:]: msg = 'Unable to add a duplicate score "{0}" to Tally ID="{1}" ' \ @@ -390,7 +390,7 @@ class Tally(IDManagerMixin): for deprecated in ['scatter-', 'nu-scatter-', 'scatter-p', 'nu-scatter-p', 'scatter-y', 'nu-scatter-y', 'flux-y', 'total-y']: - if score.startswith(deprecated): + if score.strip().startswith(deprecated): msg = score.strip() + ' is no longer supported.' raise ValueError(msg) scores[i] = score.strip()