diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb index c43fab6ff..43bec06fa 100644 --- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb @@ -34,7 +34,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/matplotlib/__init__.py:1350: UserWarning: This call to matplotlib.use() has no effect\n", + "/usr/lib/python3.5/site-packages/matplotlib/__init__.py:1357: UserWarning: This call to matplotlib.use() has no effect\n", "because the backend has already been chosen;\n", "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", @@ -453,9 +453,9 @@ " Copyright | 2011-2016 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.8.0\n", - " Git SHA1 | da5563eddb5f2c2d6b2c9839d518de40962b78f2\n", - " Date/Time | 2016-10-31 12:29:16\n", - " OpenMP Threads | 4\n", + " Git SHA1 | d2979851f07f4162f0f02d95a847019bf9f2068d\n", + " Date/Time | 2016-11-30 19:38:02\n", + " OpenMP Threads | 8\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -465,11 +465,11 @@ " Reading geometry XML file...\n", " Reading materials XML file...\n", " Reading cross sections XML file...\n", - " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n", - " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n", - " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", + " Reading U235 from /opt/xsdata/nndc/U235.h5\n", + " Reading U238 from /opt/xsdata/nndc/U238.h5\n", + " Reading O16 from /opt/xsdata/nndc/O16.h5\n", + " Reading H1 from /opt/xsdata/nndc/H1.h5\n", + " Reading Zr90 from /opt/xsdata/nndc/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", @@ -531,7 +531,7 @@ " 48/1 1.21610 1.22612 +/- 0.00251\n", " 49/1 1.22199 1.22602 +/- 0.00245\n", " 50/1 1.20860 1.22558 +/- 0.00243\n", - " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10050\n", + " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10057\n", " The estimated number of batches is 73\n", " Creating state point statepoint.050.h5...\n", " 51/1 1.21850 1.22541 +/- 0.00237\n", @@ -557,7 +557,7 @@ " 71/1 1.19720 1.22444 +/- 0.00195\n", " 72/1 1.23770 1.22465 +/- 0.00193\n", " 73/1 1.23894 1.22488 +/- 0.00191\n", - " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10050\n", + " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10057\n", " The estimated number of batches is 74\n", " 74/1 1.22437 1.22487 +/- 0.00188\n", " Triggers satisfied for batch 74\n", @@ -570,20 +570,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 5.0262E-01 seconds\n", - " Reading cross sections = 3.4207E-01 seconds\n", - " Total time in simulation = 1.2843E+02 seconds\n", - " Time in transport only = 1.2831E+02 seconds\n", - " Time in inactive batches = 8.1328E+00 seconds\n", - " Time in active batches = 1.2030E+02 seconds\n", - " Time synchronizing fission bank = 2.9797E-02 seconds\n", - " Sampling source sites = 2.1385E-02 seconds\n", - " SEND/RECV source sites = 8.2632E-03 seconds\n", - " Time accumulating tallies = 1.4577E-03 seconds\n", - " Total time for finalization = 1.3462E-02 seconds\n", - " Total time elapsed = 1.2901E+02 seconds\n", - " Calculation Rate (inactive) = 12295.9 neutrons/second\n", - " Calculation Rate (active) = 3325.12 neutrons/second\n", + " Total time for initialization = 3.0758E-01 seconds\n", + " Reading cross sections = 2.2344E-01 seconds\n", + " Total time in simulation = 2.7832E+01 seconds\n", + " Time in transport only = 2.7708E+01 seconds\n", + " Time in inactive batches = 1.7095E+00 seconds\n", + " Time in active batches = 2.6122E+01 seconds\n", + " Time synchronizing fission bank = 1.4982E-02 seconds\n", + " Sampling source sites = 1.0718E-02 seconds\n", + " SEND/RECV source sites = 4.2104E-03 seconds\n", + " Time accumulating tallies = 4.1146E-04 seconds\n", + " Total time for finalization = 1.5648E-02 seconds\n", + " Total time elapsed = 2.8184E+01 seconds\n", + " Calculation Rate (inactive) = 58495.1 neutrons/second\n", + " Calculation Rate (active) = 15312.7 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -719,6 +719,14 @@ "\n", "\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nelsonag/git/openmc/openmc/tallies.py:1974: RuntimeWarning: invalid value encountered in true_divide\n", + " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" + ] } ], "source": [ @@ -782,6 +790,16 @@ "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nelsonag/git/openmc/openmc/tallies.py:1974: RuntimeWarning: invalid value encountered in true_divide\n", + " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", + "/usr/lib/python3.5/site-packages/numpy/lib/shape_base.py:873: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", + " return c.reshape(shape_out)\n" + ] + }, { "data": { "text/html": [ @@ -993,6 +1011,14 @@ "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3.5/site-packages/numpy/lib/shape_base.py:873: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", + " return c.reshape(shape_out)\n" + ] + }, { "data": { "text/html": [ @@ -1120,7 +1146,20 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nelsonag/git/openmc/openmc/tallies.py:1974: RuntimeWarning: invalid value encountered in true_divide\n", + " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1975: RuntimeWarning: invalid value encountered in true_divide\n", + " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1976: RuntimeWarning: invalid value encountered in true_divide\n", + " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" + ] + } + ], "source": [ "# Get all OpenMOC cells in the gometry\n", "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", @@ -1172,169 +1211,239 @@ "text": [ "[ NORMAL ] Importing ray tracing data from file...\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.574672\tres = 0.000E+00\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.679815\tres = 4.253E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.660826\tres = 1.830E-01\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.658941\tres = 2.793E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.643012\tres = 2.852E-03\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.625810\tres = 2.417E-02\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.606678\tres = 2.675E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.587485\tres = 3.057E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.569029\tres = 3.164E-02\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.551707\tres = 3.142E-02\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.536035\tres = 3.044E-02\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.522274\tres = 2.841E-02\n", - "[ NORMAL ] Iteration 12:\tk_eff = 0.510609\tres = 2.567E-02\n", - "[ 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"text": [ "openmc keff = 1.223474\n", - "openmoc keff = 1.220892\n", - "bias [pcm]: -258.1\n" + "openmoc keff = 1.220813\n", + "bias [pcm]: -266.0\n" ] } ], @@ -1396,7 +1505,20 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nelsonag/git/openmc/openmc/tallies.py:1974: RuntimeWarning: invalid value encountered in true_divide\n", + " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1975: RuntimeWarning: invalid value encountered in true_divide\n", + " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1976: RuntimeWarning: invalid value encountered in true_divide\n", + " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" + ] + } + ], "source": [ "openmoc_geometry = get_openmoc_geometry(sp.summary.opencg_geometry)\n", "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", @@ -1443,237 +1565,347 @@ "text": [ "[ NORMAL ] Importing ray tracing data from file...\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.495816\tres = 0.000E+00\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.557477\tres = 5.042E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.518301\tres = 1.244E-01\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.509212\tres = 7.027E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.496490\tres = 1.754E-02\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.488581\tres = 2.498E-02\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.482897\tres = 1.593E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.479775\tres = 1.163E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.478834\tres = 6.465E-03\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.479871\tres = 1.960E-03\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.482684\tres = 2.165E-03\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.487084\tres = 5.860E-03\n", - "[ NORMAL ] 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340:\tk_eff = 1.223051\tres = 1.002E-05\n" ] } ], @@ -1699,8 +1931,8 @@ "output_type": "stream", "text": [ "openmc keff = 1.223474\n", - "openmoc keff = 1.223227\n", - "bias [pcm]: -24.7\n" + "openmoc keff = 1.223051\n", + "bias [pcm]: -42.3\n" ] } ], @@ -1741,7 +1973,9 @@ "source": [ "It is often insightful to generate visual depictions of multi-group cross sections. There are many different types of plots which may be useful for multi-group cross section visualization, only a few of which will be shown here for enrichment and inspiration.\n", "\n", - "One particularly useful visualization is a comparison of the continuous-energy and multi-group cross sections for a particular nuclide and reaction type. We illustrate one option for generating such plots with the use of the `openmc.data` module to parse continuous-energy cross sections from an openly available ACE cross section library distributed by NNDC. First, we instantiate a `openmc.data.IncidentNeutron` object for U-235 as follows." + "One particularly useful visualization is a comparison of the continuous-energy and multi-group cross sections for a particular nuclide and reaction type. We illustrate one option for generating such plots with the use of the `openmc.plotter` module to plot continuous-energy cross sections from the openly available cross section library distributed by NNDC.\n", + "\n", + "There is a simpler way to plot the MGXS data (using the same interface as is used for the continuous-energy data), however this example series has not yet introduced the pre-requisite information and so we will do this manually here." ] }, { @@ -1750,44 +1984,30 @@ "metadata": { "collapsed": false }, - "outputs": [], - "source": [ - "# Parse ACE data into memory\n", - "u235 = openmc.data.IncidentNeutron.from_ace('../../../../scripts/nndc/293.6K/U_235_293.6K.ace')\n", - "\n", - "# Extract the continuous-energy U-235 fission cross section data\n", - "fission = u235[18]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we use [`matplotlib`](http://matplotlib.org/) and [`seaborn`](http://stanford.edu/~mwaskom/software/seaborn/) to plot the continuous-energy and multi-group cross sections on a single plot." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nelsonag/git/openmc/openmc/tallies.py:1974: RuntimeWarning: invalid value encountered in true_divide\n", + " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" + ] + }, { "data": { "text/plain": [ - "(9.9999999999999991e-06, 20000000.0)" + "(1.0000000000000001e-05, 20000000.0)" ] }, - "execution_count": 31, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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9XXjr7b+y8zNjUla2RCW78zkTk6HJfXETg4g8qKpnisg8nOsWwtsBUNVONCOO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\nSwzGGGOiWGIwxhgT5f8B4ykN0fh7FfYAAAAASUVORK5CYII=\n", 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nc1xDpszbwvb9aSQkHmf81JVcd35bLu7TEuspykCFEMKvQkKwxbhfxsAWaoXo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\n8yvg7vidzrnz8JKizwIvAJcBDzrnQma2PXrYBnoXYJsKPJ/LoEWkcC1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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1795,30 +2015,31 @@ } ], "source": [ - "# Create a loglog plot of the U-235 continuous-energy fission cross section \n", - "plt.loglog(fission.xs['294K'].x, fission.xs['294K'].y, color='b', linewidth=1)\n", + "# Create a figure of the U-235 continuous-energy fission cross section \n", + "fig = openmc.plot_xs(u235, ['fission'])\n", + "\n", + "# Get the axis to use for plotting the MGXS\n", + "ax = fig.gca()\n", "\n", "# Extract energy group bounds and MGXS values to plot\n", - "nufission = xs_library[fuel_cell.id]['fission']\n", - "energy_groups = nufission.energy_groups\n", + "fission = xs_library[fuel_cell.id]['fission']\n", + "energy_groups = fission.energy_groups\n", "x = energy_groups.group_edges\n", - "y = nufission.get_xs(nuclides=['U235'], order_groups='decreasing', xs_type='micro')\n", + "y = fission.get_xs(nuclides=['U235'], order_groups='decreasing', xs_type='micro')\n", "y = np.squeeze(y)\n", "\n", - "# Fix low energy bound to the value defined by the ACE library\n", - "x[0] = fission.xs['294K'].x[0]\n", + "# Fix low energy bound\n", + "x[0] = 1.e-5\n", "\n", "# Extend the mgxs values array for matplotlib's step plot\n", "y = np.insert(y, 0, y[0])\n", "\n", "# Create a step plot for the MGXS\n", - "plt.plot(x, y, drawstyle='steps', color='r', linewidth=3)\n", + "ax.plot(x, y, drawstyle='steps', color='r', linewidth=3)\n", "\n", - "plt.title('U-235 Fission Cross Section')\n", - "plt.xlabel('Energy [eV]')\n", - "plt.ylabel('Micro Fission XS')\n", - "plt.legend(['Continuous', 'Multi-Group'])\n", - "plt.xlim((x.min(), x.max()))" + "ax.set_title('U-235 Fission Cross Section')\n", + "ax.legend(['Continuous', 'Multi-Group'])\n", + "ax.set_xlim((x.min(), x.max()))" ] }, { @@ -1830,11 +2051,20 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3.5/site-packages/numpy/lib/shape_base.py:873: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", + " return c.reshape(shape_out)\n" + ] + } + ], "source": [ "# Construct a Pandas DataFrame for the microscopic nu-scattering matrix\n", "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n", @@ -1862,16 +2092,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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kSVItFg2SJKkWiwZJklSLRYMkSarFokGSJNVi0SBJkmqxaJAkSbVYNEiSpFos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\n", 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