From e2112b86ae1caca7cfddbc5674a6cb595ad7fba2 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 25 Jul 2016 06:33:37 -0500 Subject: [PATCH] Respond to @smharper comments on #684 --- data/get_jeff_data.py | 35 +- .../pythonapi/examples/mgxs-part-i.ipynb | 95 ++- .../pythonapi/examples/mgxs-part-ii.ipynb | 752 +++++++++--------- .../pythonapi/examples/mgxs-part-iii.ipynb | 214 ++--- .../pythonapi/examples/mgxs-part-iv.ipynb | 110 ++- .../pythonapi/examples/nuclear-data.ipynb | 144 ++-- .../examples/pandas-dataframes.ipynb | 577 +++++++------- .../pythonapi/examples/post-processing.ipynb | 78 +- .../pythonapi/examples/tally-arithmetic.ipynb | 152 ++-- openmc/data/angle_energy.py | 4 +- openmc/data/data.py | 192 ++--- openmc/data/energy_distribution.py | 3 + openmc/data/neutron.py | 2 +- openmc/data/reaction.py | 4 +- openmc/element.py | 4 +- openmc/opencg_compatible.py | 2 +- 16 files changed, 1183 insertions(+), 1185 deletions(-) diff --git a/data/get_jeff_data.py b/data/get_jeff_data.py index 59023d9a2..fa9394850 100755 --- a/data/get_jeff_data.py +++ b/data/get_jeff_data.py @@ -18,6 +18,20 @@ try: except ImportError: from urllib2 import urlopen +if sys.version_info[0] < 3: + askuser = raw_input +else: + askuser = input + + +download_warning = """ +WARNING: This script will download approximately 9 GB of data. Extracting and +processing the data may require as much as 30 GB of additional free disk +space. Note that if you don't need all 11 temperatures, you can modify the +'files' list in the script to download only the data you want. + +Are you sure you want to continue? ([y]/n) +""" thermal_suffix = {20: '01t', 100: '02t', 293: '03t', 296: '03t', 323: '04t', 350: '05t', 373: '06t', 400: '07t', 423: '08t', 473: '09t', @@ -26,13 +40,15 @@ thermal_suffix = {20: '01t', 100: '02t', 293: '03t', 296: '03t', 323: '04t', 1000: '19t', 1200: '20t', 1600: '21t', 2000: '22t', 3000: '23t'} - - parser = argparse.ArgumentParser() parser.add_argument('-b', '--batch', action='store_true', help='supresses standard in') args = parser.parse_args() +response = askuser(download_warning) if not args.batch else 'y' +if response.lower().startswith('n'): + sys.exit() + base_url = 'https://www.oecd-nea.org/dbforms/data/eva/evatapes/jeff_32/Processed/' files = ['JEFF32-ACE-293K.tar.gz', 'JEFF32-ACE-400K.tar.gz', @@ -72,10 +88,7 @@ for f in files: files_complete.append(f) continue else: - if sys.version_info[0] < 3: - overwrite = raw_input('Overwrite {}? ([y]/n) '.format(f)) - else: - overwrite = input('Overwrite {}? ([y]/n) '.format(f)) + overwrite = askuser('Overwrite {}? ([y]/n) '.format(f)) if overwrite.lower().startswith('n'): continue @@ -165,10 +178,7 @@ ace_files = (glob.glob(os.path.join('jeff-3.2', '**', '*.ACE')) + # Ask user to convert if not args.batch: - if sys.version_info[0] < 3: - response = raw_input('Convert ACE files to binary? ([y]/n) ') - else: - response = input('Convert ACE files to binary? ([y]/n) ') + response = askuser('Convert ACE files to binary? ([y]/n) ') else: response = 'y' @@ -183,10 +193,7 @@ if not response or response.lower().startswith('y'): # Ask user to convert if not args.batch: - if sys.version_info[0] < 3: - response = raw_input('Generate HDF5 library? ([y]/n) ') - else: - response = input('Generate HDF5 library? ([y]/n) ') + response = askuser('Generate HDF5 library? ([y]/n) ') else: response = 'y' diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb index ea75bec72..b95bea462 100644 --- a/docs/source/pythonapi/examples/mgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb @@ -165,11 +165,11 @@ "outputs": [], "source": [ "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H-1')\n", - "o16 = openmc.Nuclide('O-16')\n", - "u235 = openmc.Nuclide('U-235')\n", - "u238 = openmc.Nuclide('U-238')\n", - "zr90 = openmc.Nuclide('Zr-90')" + "h1 = openmc.Nuclide('H1')\n", + "o16 = openmc.Nuclide('O16')\n", + "u235 = openmc.Nuclide('U235')\n", + "u238 = openmc.Nuclide('U238')\n", + "zr90 = openmc.Nuclide('Zr90')" ] }, { @@ -419,24 +419,22 @@ "data": { "text/plain": [ "OrderedDict([('flux', Tally\n", - "\tID =\t10000\n", - "\tName =\t\n", - "\tFilters =\t\n", - " \t\tcell\t[1]\n", - " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", - "\tNuclides =\ttotal \n", - "\tScores =\t['flux']\n", - "\tEstimator =\ttracklength\n", - "), ('absorption', Tally\n", - "\tID =\t10001\n", - "\tName =\t\n", - "\tFilters =\t\n", - " \t\tcell\t[1]\n", - " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", - "\tNuclides =\ttotal \n", - "\tScores =\t['absorption']\n", - "\tEstimator =\ttracklength\n", - ")])" + " \tID =\t10000\n", + " \tName =\t\n", + " \tFilters =\t\n", + " \t\tcell\t[1]\n", + " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", + " \tNuclides =\ttotal \n", + " \tScores =\t['flux']\n", + " \tEstimator =\ttracklength), ('absorption', Tally\n", + " \tID =\t10001\n", + " \tName =\t\n", + " \tFilters =\t\n", + " \t\tcell\t[1]\n", + " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", + " \tNuclides =\ttotal \n", + " \tScores =\t['absorption']\n", + " \tEstimator =\ttracklength)])" ] }, "execution_count": 13, @@ -513,26 +511,25 @@ " Copyright: 2011-2016 Massachusetts Institute of Technology\n", " License: http://openmc.readthedocs.io/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: 19feb55e6d5e8350398627f39fb55ee8e2e63011\n", - " Date/Time: 2016-05-13 10:19:16\n", - " MPI Processes: 1\n", + " Git SHA1: 3d68c07625e33cd64188df03ee03e9c31b3d4b74\n", + " Date/Time: 2016-07-22 21:03:18\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", " ===========================================================================\n", "\n", " Reading settings XML file...\n", - " Reading cross sections XML file...\n", " Reading geometry XML file...\n", + " Reading cross sections XML file...\n", " Reading materials XML file...\n", + " Reading H1.71c from /home/romano/openmc/data/nndc_hdf5/H1_71c.h5\n", + " Reading O16.71c from /home/romano/openmc/data/nndc_hdf5/O16_71c.h5\n", + " Reading U235.71c from /home/romano/openmc/data/nndc_hdf5/U235_71c.h5\n", + " Reading U238.71c from /home/romano/openmc/data/nndc_hdf5/U238_71c.h5\n", + " Reading Zr90.71c from /home/romano/openmc/data/nndc_hdf5/Zr90_71c.h5\n", + " Maximum neutron transport energy: 20.0000 MeV for H1.71c\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", - " Loading ACE cross section table: 1001.71c\n", - " Loading ACE cross section table: 8016.71c\n", - " Loading ACE cross section table: 92235.71c\n", - " Loading ACE cross section table: 92238.71c\n", - " Loading ACE cross section table: 40090.71c\n", - " Maximum neutron transport energy: 20.0000 MeV for 1001.71c\n", " Initializing source particles...\n", "\n", " ===========================================================================\n", @@ -600,20 +597,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.2300E-01 seconds\n", - " Reading cross sections = 9.3000E-02 seconds\n", - " Total time in simulation = 1.6549E+01 seconds\n", - " Time in transport only = 1.6535E+01 seconds\n", - " Time in inactive batches = 2.3650E+00 seconds\n", - " Time in active batches = 1.4184E+01 seconds\n", - " Time synchronizing fission bank = 5.0000E-03 seconds\n", - " Sampling source sites = 3.0000E-03 seconds\n", + " Total time for initialization = 3.2300E-01 seconds\n", + " Reading cross sections = 1.6900E-01 seconds\n", + " Total time in simulation = 1.9882E+01 seconds\n", + " Time in transport only = 1.9869E+01 seconds\n", + " Time in inactive batches = 2.6590E+00 seconds\n", + " Time in active batches = 1.7223E+01 seconds\n", + " Time synchronizing fission bank = 4.0000E-03 seconds\n", + " Sampling source sites = 4.0000E-03 seconds\n", " SEND/RECV source sites = 0.0000E+00 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 1.6981E+01 seconds\n", - " Calculation Rate (inactive) = 10570.8 neutrons/second\n", - " Calculation Rate (active) = 7050.20 neutrons/second\n", + " Total time elapsed = 2.0217E+01 seconds\n", + " Calculation Rate (inactive) = 9402.03 neutrons/second\n", + " Calculation Rate (active) = 5806.19 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1167,21 +1164,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb index b882e949c..cb4df0fad 100644 --- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb @@ -8,9 +8,9 @@ "\n", "* Creation of multi-group cross sections on a **heterogeneous geometry**\n", "* Calculation of cross sections on a **nuclide-by-nuclide basis**\n", - "* The use of **[tally precision triggers](https://mit-crpg.github.io/openmc/usersguide/input.html#trigger-element)** with multi-group cross sections\n", + "* The use of **[tally precision triggers](http://openmc.readthedocs.io/en/latest/usersguide/input.html#trigger-element)** with multi-group cross sections\n", "* Built-in features for **energy condensation** in downstream data processing\n", - "* The use of **[PyNE](http://pyne.io/) to plot** continuous-energy vs. multi-group cross sections\n", + "* The use of the **`openmc.data`** module to plot continuous-energy vs. multi-group cross sections\n", "* **Validation** of multi-group cross sections with **[OpenMOC](https://mit-crpg.github.io/OpenMOC/)**\n", "\n", "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. In order to run this Notebook in its entirety, you must have [OpenMOC](https://mit-crpg.github.io/OpenMOC/) installed on your system, along with OpenCG to convert the OpenMC geometries into OpenMOC geometries. In addition, this Notebook illustrates the use of [Pandas](http://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data." @@ -34,16 +34,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python2.7/dist-packages/matplotlib-1.5.1+1178.ga40c9ec-py2.7-linux-x86_64.egg/matplotlib/__init__.py:884: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", - " warnings.warn(self.msg_depr % (key, alt_key))\n", - "/usr/local/lib/python2.7/dist-packages/matplotlib-1.5.1+1178.ga40c9ec-py2.7-linux-x86_64.egg/matplotlib/__init__.py:1362: UserWarning: This call to matplotlib.use() has no effect\n", + "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/matplotlib/__init__.py:1350: 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", "\n", - " warnings.warn(_use_error_msg)\n", - "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:9: QAWarning: pyne.rxname is not yet QA compliant.\n", - "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:9: QAWarning: pyne.ace is not yet QA compliant.\n" + " warnings.warn(_use_error_msg)\n" ] } ], @@ -52,11 +48,12 @@ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", - "import openmc\n", - "import openmc.mgxs as mgxs\n", "import openmoc\n", "from openmoc.opencg_compatible import get_openmoc_geometry\n", - "import pyne.ace\n", + "\n", + "import openmc\n", + "import openmc.mgxs as mgxs\n", + "import openmc.data\n", "\n", "%matplotlib inline" ] @@ -77,11 +74,11 @@ "outputs": [], "source": [ "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H-1')\n", - "o16 = openmc.Nuclide('O-16')\n", - "u235 = openmc.Nuclide('U-235')\n", - "u238 = openmc.Nuclide('U-238')\n", - "zr90 = openmc.Nuclide('Zr-90')" + "h1 = openmc.Nuclide('H1')\n", + "o16 = openmc.Nuclide('O16')\n", + "u235 = openmc.Nuclide('U235')\n", + "u238 = openmc.Nuclide('U238')\n", + "zr90 = openmc.Nuclide('Zr90')" ] }, { @@ -444,26 +441,25 @@ " Copyright: 2011-2016 Massachusetts Institute of Technology\n", " License: http://openmc.readthedocs.io/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: 19feb55e6d5e8350398627f39fb55ee8e2e63011\n", - " Date/Time: 2016-05-13 10:13:48\n", - " MPI Processes: 1\n", + " Git SHA1: 3d68c07625e33cd64188df03ee03e9c31b3d4b74\n", + " Date/Time: 2016-07-22 21:32:41\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", " ===========================================================================\n", "\n", " Reading settings XML file...\n", - " Reading cross sections XML file...\n", " Reading geometry XML file...\n", + " Reading cross sections XML file...\n", " Reading materials XML file...\n", + " Reading U235.71c from /home/romano/openmc/data/nndc_hdf5/U235_71c.h5\n", + " Reading U238.71c from /home/romano/openmc/data/nndc_hdf5/U238_71c.h5\n", + " Reading O16.71c from /home/romano/openmc/data/nndc_hdf5/O16_71c.h5\n", + " Reading H1.71c from /home/romano/openmc/data/nndc_hdf5/H1_71c.h5\n", + " Reading Zr90.71c from /home/romano/openmc/data/nndc_hdf5/Zr90_71c.h5\n", + " Maximum neutron transport energy: 20.0000 MeV for U235.71c\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", - " Loading ACE cross section table: 92235.71c\n", - " Loading ACE cross section table: 92238.71c\n", - " Loading ACE cross section table: 8016.71c\n", - " Loading ACE cross section table: 1001.71c\n", - " Loading ACE cross section table: 40090.71c\n", - " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", " Initializing source particles...\n", "\n", " ===========================================================================\n", @@ -522,7 +518,7 @@ " 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 10051\n", + " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10054\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", @@ -548,7 +544,7 @@ " 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 10051\n", + " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10054\n", " The estimated number of batches is 74\n", " 74/1 1.22437 1.22487 +/- 0.00188\n", " Triggers satisfied for batch 74\n", @@ -561,20 +557,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 5.7400E-01 seconds\n", - " Reading cross sections = 1.2600E-01 seconds\n", - " Total time in simulation = 2.6256E+02 seconds\n", - " Time in transport only = 2.6250E+02 seconds\n", - " Time in inactive batches = 2.2890E+01 seconds\n", - " Time in active batches = 2.3967E+02 seconds\n", - " Time synchronizing fission bank = 3.4000E-02 seconds\n", - " Sampling source sites = 2.1000E-02 seconds\n", + " Total time for initialization = 4.3000E-01 seconds\n", + " Reading cross sections = 2.6000E-01 seconds\n", + " Total time in simulation = 3.4077E+02 seconds\n", + " Time in transport only = 3.4068E+02 seconds\n", + " Time in inactive batches = 2.3968E+01 seconds\n", + " Time in active batches = 3.1680E+02 seconds\n", + " Time synchronizing fission bank = 3.0000E-02 seconds\n", + " Sampling source sites = 1.7000E-02 seconds\n", " SEND/RECV source sites = 1.3000E-02 seconds\n", - " Time accumulating tallies = 0.0000E+00 seconds\n", - " Total time for finalization = 1.3000E-02 seconds\n", - " Total time elapsed = 2.6320E+02 seconds\n", - " Calculation Rate (inactive) = 4368.72 neutrons/second\n", - " Calculation Rate (active) = 1668.93 neutrons/second\n", + " Time accumulating tallies = 3.0000E-03 seconds\n", + " Total time for finalization = 1.6000E-02 seconds\n", + " Total time elapsed = 3.4129E+02 seconds\n", + " Calculation Rate (inactive) = 4172.23 neutrons/second\n", + " Calculation Rate (active) = 1262.62 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -685,7 +681,7 @@ "\tReaction Type =\tnu-fission\n", "\tDomain Type =\tcell\n", "\tDomain ID =\t10000\n", - "\tNuclide =\tU-235\n", + "\tNuclide =\tU235\n", "\tCross Sections [barns]:\n", " Group 1 [0.821 - 20.0 MeV]:\t3.30e+00 +/- 2.19e-01%\n", " Group 2 [0.00553 - 0.821 MeV]:\t3.96e+00 +/- 1.32e-01%\n", @@ -696,7 +692,7 @@ " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t6.87e+02 +/- 2.97e-01%\n", " Group 8 [0.0 - 5.8e-08 MeV]:\t1.44e+03 +/- 2.91e-01%\n", "\n", - "\tNuclide =\tU-238\n", + "\tNuclide =\tU238\n", "\tCross Sections [barns]:\n", " Group 1 [0.821 - 20.0 MeV]:\t1.06e+00 +/- 2.56e-01%\n", " Group 2 [0.00553 - 0.821 MeV]:\t1.21e-03 +/- 2.55e-01%\n", @@ -714,7 +710,7 @@ ], "source": [ "nufission = xs_library[fuel_cell.id]['nu-fission']\n", - "nufission.print_xs(xs_type='micro', nuclides=['U-235', 'U-238'])" + "nufission.print_xs(xs_type='micro', nuclides=['U235', 'U238'])" ] }, { @@ -795,7 +791,7 @@ " 10002\n", " 1\n", " 1\n", - " H-1\n", + " H1\n", " 0.234115\n", " 0.003568\n", " \n", @@ -804,7 +800,7 @@ " 10002\n", " 1\n", " 1\n", - " O-16\n", + " O16\n", " 1.563707\n", " 0.005953\n", " \n", @@ -813,7 +809,7 @@ " 10002\n", " 1\n", " 2\n", - " H-1\n", + " H1\n", " 1.594129\n", " 0.002369\n", " \n", @@ -822,7 +818,7 @@ " 10002\n", " 1\n", " 2\n", - " O-16\n", + " O16\n", " 0.285761\n", " 0.001676\n", " \n", @@ -831,7 +827,7 @@ " 10002\n", " 1\n", " 3\n", - " H-1\n", + " H1\n", " 0.011089\n", " 0.000248\n", " \n", @@ -840,7 +836,7 @@ " 10002\n", " 1\n", " 3\n", - " O-16\n", + " O16\n", " 0.000000\n", " 0.000000\n", " \n", @@ -849,7 +845,7 @@ " 10002\n", " 1\n", " 4\n", - " H-1\n", + " H1\n", " 0.000000\n", " 0.000000\n", " \n", @@ -858,7 +854,7 @@ " 10002\n", " 1\n", " 4\n", - " O-16\n", + " O16\n", " 0.000000\n", " 0.000000\n", " \n", @@ -867,7 +863,7 @@ " 10002\n", " 1\n", " 5\n", - " H-1\n", + " H1\n", " 0.000000\n", " 0.000000\n", " \n", @@ -876,7 +872,7 @@ " 10002\n", " 1\n", " 5\n", - " O-16\n", + " O16\n", " 0.000000\n", " 0.000000\n", " \n", @@ -886,16 +882,16 @@ ], "text/plain": [ " cell group in group out nuclide mean std. dev.\n", - "126 10002 1 1 H-1 0.234115 0.003568\n", - "127 10002 1 1 O-16 1.563707 0.005953\n", - "124 10002 1 2 H-1 1.594129 0.002369\n", - "125 10002 1 2 O-16 0.285761 0.001676\n", - "122 10002 1 3 H-1 0.011089 0.000248\n", - "123 10002 1 3 O-16 0.000000 0.000000\n", - "120 10002 1 4 H-1 0.000000 0.000000\n", - "121 10002 1 4 O-16 0.000000 0.000000\n", - "118 10002 1 5 H-1 0.000000 0.000000\n", - "119 10002 1 5 O-16 0.000000 0.000000" + "126 10002 1 1 H1 0.234115 0.003568\n", + "127 10002 1 1 O16 1.563707 0.005953\n", + "124 10002 1 2 H1 1.594129 0.002369\n", + "125 10002 1 2 O16 0.285761 0.001676\n", + "122 10002 1 3 H1 0.011089 0.000248\n", + "123 10002 1 3 O16 0.000000 0.000000\n", + "120 10002 1 4 H1 0.000000 0.000000\n", + "121 10002 1 4 O16 0.000000 0.000000\n", + "118 10002 1 5 H1 0.000000 0.000000\n", + "119 10002 1 5 O16 0.000000 0.000000" ] }, "execution_count": 19, @@ -953,17 +949,17 @@ "\tReaction Type =\ttransport\n", "\tDomain Type =\tcell\n", "\tDomain ID =\t10000\n", - "\tNuclide =\tU-235\n", + "\tNuclide =\tU235\n", "\tCross Sections [cm^-1]:\n", " Group 1 [6.25e-07 - 20.0 MeV]:\t7.73e-03 +/- 5.06e-01%\n", " Group 2 [0.0 - 6.25e-07 MeV]:\t1.82e-01 +/- 2.05e-01%\n", "\n", - "\tNuclide =\tU-238\n", + "\tNuclide =\tU238\n", "\tCross Sections [cm^-1]:\n", " Group 1 [6.25e-07 - 20.0 MeV]:\t2.17e-01 +/- 1.44e-01%\n", " Group 2 [0.0 - 6.25e-07 MeV]:\t2.53e-01 +/- 2.57e-01%\n", "\n", - "\tNuclide =\tO-16\n", + "\tNuclide =\tO16\n", "\tCross Sections [cm^-1]:\n", " Group 1 [6.25e-07 - 20.0 MeV]:\t1.46e-01 +/- 1.60e-01%\n", " Group 2 [0.0 - 6.25e-07 MeV]:\t1.75e-01 +/- 2.94e-01%\n", @@ -1004,7 +1000,7 @@ " 3\n", " 10000\n", " 1\n", - " U-235\n", + " U235\n", " 20.611692\n", " 0.104237\n", " \n", @@ -1012,7 +1008,7 @@ " 4\n", " 10000\n", " 1\n", - " U-238\n", + " U238\n", " 9.585358\n", " 0.013808\n", " \n", @@ -1020,7 +1016,7 @@ " 5\n", " 10000\n", " 1\n", - " O-16\n", + " O16\n", " 3.164190\n", " 0.005049\n", " \n", @@ -1028,7 +1024,7 @@ " 0\n", " 10000\n", " 2\n", - " U-235\n", + " U235\n", " 485.413426\n", " 0.996410\n", " \n", @@ -1036,7 +1032,7 @@ " 1\n", " 10000\n", " 2\n", - " U-238\n", + " U238\n", " 11.190386\n", " 0.028731\n", " \n", @@ -1044,7 +1040,7 @@ " 2\n", " 10000\n", " 2\n", - " O-16\n", + " O16\n", " 3.794859\n", " 0.011139\n", " \n", @@ -1054,12 +1050,12 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "3 10000 1 U-235 20.611692 0.104237\n", - "4 10000 1 U-238 9.585358 0.013808\n", - "5 10000 1 O-16 3.164190 0.005049\n", - "0 10000 2 U-235 485.413426 0.996410\n", - "1 10000 2 U-238 11.190386 0.028731\n", - "2 10000 2 O-16 3.794859 0.011139" + "3 10000 1 U235 20.611692 0.104237\n", + "4 10000 1 U238 9.585358 0.013808\n", + "5 10000 1 O16 3.164190 0.005049\n", + "0 10000 2 U235 485.413426 0.996410\n", + "1 10000 2 U238 11.190386 0.028731\n", + "2 10000 2 O16 3.794859 0.011139" ] }, "execution_count": 22, @@ -1166,81 +1162,81 @@ "[ 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 = 0.658940\tres = 2.793E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.643012\tres = 2.853E-03\n", + "[ NORMAL ] Iteration 3:\tk_eff = 0.658941\tres 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= 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 = 2.166E-03\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.487084\tres = 5.861E-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 ] Iteration 12:\tk_eff = 0.492900\tres = 9.116E-03\n", "[ NORMAL ] Iteration 13:\tk_eff = 0.499971\tres = 1.194E-02\n", "[ NORMAL ] Iteration 14:\tk_eff = 0.508153\tres = 1.435E-02\n", "[ NORMAL ] Iteration 15:\tk_eff = 0.517312\tres = 1.637E-02\n", - "[ NORMAL ] Iteration 16:\tk_eff = 0.527324\tres = 1.802E-02\n", - "[ NORMAL ] Iteration 17:\tk_eff = 0.538079\tres = 1.935E-02\n", + "[ NORMAL ] Iteration 16:\tk_eff = 0.527325\tres = 1.802E-02\n", 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1.964E-05\n", + "[ NORMAL ] Iteration 214:\tk_eff = 1.222967\tres = 1.882E-05\n", + "[ NORMAL ] Iteration 215:\tk_eff = 1.222988\tres = 1.821E-05\n", + "[ NORMAL ] Iteration 216:\tk_eff = 1.223009\tres = 1.763E-05\n", + "[ NORMAL ] Iteration 217:\tk_eff = 1.223029\tres = 1.690E-05\n", + "[ NORMAL ] Iteration 218:\tk_eff = 1.223048\tres = 1.630E-05\n", + "[ NORMAL ] Iteration 219:\tk_eff = 1.223067\tres = 1.572E-05\n", + "[ NORMAL ] Iteration 220:\tk_eff = 1.223084\tres = 1.507E-05\n", + "[ NORMAL ] Iteration 221:\tk_eff = 1.223101\tres = 1.427E-05\n", + "[ NORMAL ] Iteration 222:\tk_eff = 1.223117\tres = 1.394E-05\n", + "[ NORMAL ] Iteration 223:\tk_eff = 1.223133\tres = 1.330E-05\n", + "[ NORMAL ] Iteration 224:\tk_eff = 1.223148\tres = 1.298E-05\n", + "[ NORMAL ] Iteration 225:\tk_eff = 1.223163\tres = 1.241E-05\n", + "[ NORMAL ] Iteration 226:\tk_eff = 1.223177\tres = 1.167E-05\n", + "[ NORMAL ] Iteration 227:\tk_eff = 1.223190\tres = 1.151E-05\n", + "[ NORMAL ] Iteration 228:\tk_eff = 1.223203\tres = 1.073E-05\n", + "[ NORMAL ] Iteration 229:\tk_eff = 1.223215\tres = 1.050E-05\n", + "[ NORMAL ] Iteration 230:\tk_eff = 1.223227\tres = 1.000E-05\n" ] } ], @@ -1732,7 +1728,7 @@ "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 open source [PyNE](http://pyne.io/) library to parse continuous-energy cross sections from the cross section data library provided with OpenMC. First, we instantiate a `pyne.ace.Library` 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.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." ] }, { @@ -1743,15 +1739,11 @@ }, "outputs": [], "source": [ - "# Instantiate a PyNE ACE continuous-energy cross sections library\n", - "pyne_lib = pyne.ace.Library('../../../../data/nndc/293.6K/U_235_293.6K.ace')\n", - "pyne_lib.read('92235.71c')\n", - "\n", - "# Extract the U-235 data from the library\n", - "u235 = pyne_lib.tables['92235.71c']\n", + "# Parse ACE data into memory\n", + "u235 = openmc.data.IncidentNeutron.from_ace('../../../../data/nndc/293.6K/U_235_293.6K.ace')\n", "\n", "# Extract the continuous-energy U-235 fission cross section data\n", - "fission = u235.reactions[18]" + "fission = u235[18]" ] }, { @@ -1780,9 +1772,9 @@ }, { "data": { - "image/png": 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QJiJHAEOBeSJSCOyS3GKpXGezwaRJzc+lXLvWxpIlmb+bbaKXz6irs7XoeUol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\n+KP1PP8hsU6nNWku3KqsyVy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GugNni0h3YC/ge+9p2bHgi1I7oVMnz05tMxruRl5S4uHw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\nCqHJQSmlVAhNDkoppUL8Pzlt5uQccjZkAAAAAElFTkSuQmCC\n", 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s//SZaStbolLd0ZyNidDkt7hJQUQeUNXTRGQ2zn0J4e0AqGonmuHGpEqwvEub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\nMFCsqjsBE3BulItm/z4mpXw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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1791,16 +1783,16 @@ ], "source": [ "# Create a loglog plot of the U-235 continuous-energy fission cross section \n", - "plt.loglog(u235.energy, fission.sigma, color='b', linewidth=1)\n", + "plt.loglog(fission.xs.x, fission.xs.y, color='b', linewidth=1)\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", "x = energy_groups.group_edges\n", - "y = nufission.get_xs(nuclides=['U-235'], order_groups='decreasing', xs_type='micro')\n", + "y = nufission.get_xs(nuclides=['U235'], order_groups='decreasing', xs_type='micro')\n", "\n", "# Fix low energy bound to the value defined by the ACE library\n", - "x[0] = u235.energy[0]\n", + "x[0] = fission.xs.x[0]\n", "\n", "# Extend the mgxs values array for matplotlib's step plot\n", "y = np.insert(y, 0, y[0])\n", @@ -1835,8 +1827,8 @@ "df = nuscatter.get_pandas_dataframe(xs_type='micro')\n", "\n", "# Slice DataFrame in two for each nuclide's mean values\n", - "h1 = df[df['nuclide'] == 'H-1']['mean']\n", - "o16 = df[df['nuclide'] == 'O-16']['mean']\n", + "h1 = df[df['nuclide'] == 'H1']['mean']\n", + "o16 = df[df['nuclide'] == 'O16']['mean']\n", "\n", "# Cast DataFrames as NumPy arrays\n", "h1 = h1.as_matrix()\n", @@ -1863,9 +1855,9 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1905,21 +1897,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb index 5f0acde3f..789366d3a 100644 --- a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb @@ -32,7 +32,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/wboyd/anaconda2/lib/python2.7/site-packages/matplotlib/__init__.py:1350: UserWarning: This call to matplotlib.use() has no effect\n", + "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/matplotlib/__init__.py:1350: 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", @@ -75,12 +75,12 @@ "outputs": [], "source": [ "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H-1')\n", - "b10 = openmc.Nuclide('B-10')\n", - "o16 = openmc.Nuclide('O-16')\n", - "u235 = openmc.Nuclide('U-235')\n", - "u238 = openmc.Nuclide('U-238')\n", - "zr90 = openmc.Nuclide('Zr-90')" + "h1 = openmc.Nuclide('H1')\n", + "b10 = openmc.Nuclide('B10')\n", + "o16 = openmc.Nuclide('O16')\n", + "u235 = openmc.Nuclide('U235')\n", + "u238 = openmc.Nuclide('U238')\n", + "zr90 = openmc.Nuclide('Zr90')" ] }, { @@ -458,7 +458,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -725,27 +725,26 @@ " Copyright: 2011-2016 Massachusetts Institute of Technology\n", " License: http://openmc.readthedocs.io/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: 47ef320ad517612376e181ec6a6bc42ca0db98ce\n", - " Date/Time: 2016-05-14 12:29:07\n", - " MPI Processes: 1\n", + " Git SHA1: 3d68c07625e33cd64188df03ee03e9c31b3d4b74\n", + " Date/Time: 2016-07-23 16:42:32\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", " ===========================================================================\n", "\n", " Reading settings XML file...\n", - " Reading cross sections XML file...\n", " Reading geometry XML file...\n", + " Reading cross sections XML file...\n", " Reading materials XML file...\n", + " Reading U235.71c from /home/romano/openmc/data/nndc_hdf5/U235_71c.h5\n", + " Reading U238.71c from /home/romano/openmc/data/nndc_hdf5/U238_71c.h5\n", + " Reading O16.71c from /home/romano/openmc/data/nndc_hdf5/O16_71c.h5\n", + " Reading H1.71c from /home/romano/openmc/data/nndc_hdf5/H1_71c.h5\n", + " Reading B10.71c from /home/romano/openmc/data/nndc_hdf5/B10_71c.h5\n", + " Reading Zr90.71c from /home/romano/openmc/data/nndc_hdf5/Zr90_71c.h5\n", + " Maximum neutron transport energy: 20.0000 MeV for U235.71c\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", - " Loading ACE cross section table: 92235.71c\n", - " Loading ACE cross section table: 92238.71c\n", - " Loading ACE cross section table: 8016.71c\n", - " Loading ACE cross section table: 1001.71c\n", - " Loading ACE cross section table: 5010.71c\n", - " Loading ACE cross section table: 40090.71c\n", - " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", " Initializing source particles...\n", "\n", " ===========================================================================\n", @@ -813,20 +812,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 5.7700E-01 seconds\n", - " Reading cross sections = 1.3400E-01 seconds\n", - " Total time in simulation = 8.0461E+01 seconds\n", - " Time in transport only = 8.0422E+01 seconds\n", - " Time in inactive batches = 6.4060E+00 seconds\n", - " Time in active batches = 7.4055E+01 seconds\n", - " Time synchronizing fission bank = 6.0000E-03 seconds\n", - " Sampling source sites = 2.0000E-03 seconds\n", - " SEND/RECV source sites = 3.0000E-03 seconds\n", + " Total time for initialization = 4.3400E-01 seconds\n", + " Reading cross sections = 2.7900E-01 seconds\n", + " Total time in simulation = 6.1121E+01 seconds\n", + " Time in transport only = 6.1101E+01 seconds\n", + " Time in inactive batches = 5.0660E+00 seconds\n", + " Time in active batches = 5.6055E+01 seconds\n", + " Time synchronizing fission bank = 5.0000E-03 seconds\n", + " Sampling source sites = 3.0000E-03 seconds\n", + " SEND/RECV source sites = 2.0000E-03 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 8.1067E+01 seconds\n", - " Calculation Rate (inactive) = 3902.59 neutrons/second\n", - " Calculation Rate (active) = 1350.35 neutrons/second\n", + " Total time elapsed = 6.1576E+01 seconds\n", + " Calculation Rate (inactive) = 4934.86 neutrons/second\n", + " Calculation Rate (active) = 1783.96 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -953,7 +952,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/wboyd/Documents/NSE-CRPG-Codes/openmc/openmc/tallies.py:1988: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1941: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" ] }, @@ -977,7 +976,7 @@ " 3\n", " 10000\n", " 1\n", - " U-235\n", + " U235\n", " 8.055246e-03\n", " 2.857567e-05\n", " \n", @@ -985,7 +984,7 @@ " 4\n", " 10000\n", " 1\n", - " U-238\n", + " U238\n", " 7.339215e-03\n", " 4.349466e-05\n", " \n", @@ -993,7 +992,7 @@ " 5\n", " 10000\n", " 1\n", - " O-16\n", + " O16\n", " 0.000000e+00\n", " 0.000000e+00\n", " \n", @@ -1001,7 +1000,7 @@ " 0\n", " 10000\n", " 2\n", - " U-235\n", + " U235\n", " 3.615565e-01\n", " 2.050486e-03\n", " \n", @@ -1009,7 +1008,7 @@ " 1\n", " 10000\n", " 2\n", - " U-238\n", + " U238\n", " 6.742638e-07\n", " 3.795256e-09\n", " \n", @@ -1017,7 +1016,7 @@ " 2\n", " 10000\n", " 2\n", - " O-16\n", + " O16\n", " 0.000000e+00\n", " 0.000000e+00\n", " \n", @@ -1027,12 +1026,12 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "3 10000 1 U-235 8.055246e-03 2.857567e-05\n", - "4 10000 1 U-238 7.339215e-03 4.349466e-05\n", - "5 10000 1 O-16 0.000000e+00 0.000000e+00\n", - "0 10000 2 U-235 3.615565e-01 2.050486e-03\n", - "1 10000 2 U-238 6.742638e-07 3.795256e-09\n", - "2 10000 2 O-16 0.000000e+00 0.000000e+00" + "3 10000 1 U235 8.055246e-03 2.857567e-05\n", + "4 10000 1 U238 7.339215e-03 4.349466e-05\n", + "5 10000 1 O16 0.000000e+00 0.000000e+00\n", + "0 10000 2 U235 3.615565e-01 2.050486e-03\n", + "1 10000 2 U238 6.742638e-07 3.795256e-09\n", + "2 10000 2 O16 0.000000e+00 0.000000e+00" ] }, "execution_count": 30, @@ -1067,17 +1066,17 @@ "\tReaction Type =\tnu-fission\n", "\tDomain Type =\tcell\n", "\tDomain ID =\t10000\n", - "\tNuclide =\tU-235\n", + "\tNuclide =\tU235\n", "\tCross Sections [cm^-1]:\n", " Group 1 [6.25e-07 - 20.0 MeV]:\t8.06e-03 +/- 3.55e-01%\n", " Group 2 [0.0 - 6.25e-07 MeV]:\t3.62e-01 +/- 5.67e-01%\n", "\n", - "\tNuclide =\tU-238\n", + "\tNuclide =\tU238\n", "\tCross Sections [cm^-1]:\n", " Group 1 [6.25e-07 - 20.0 MeV]:\t7.34e-03 +/- 5.93e-01%\n", " Group 2 [0.0 - 6.25e-07 MeV]:\t6.74e-07 +/- 5.63e-01%\n", "\n", - "\tNuclide =\tO-16\n", + "\tNuclide =\tO16\n", "\tCross Sections [cm^-1]:\n", " Group 1 [6.25e-07 - 20.0 MeV]:\t0.00e+00 +/- nan%\n", " Group 2 [0.0 - 6.25e-07 MeV]:\t0.00e+00 +/- nan%\n", @@ -1190,7 +1189,7 @@ " 0\n", " 10000\n", " 1\n", - " U-235\n", + " U235\n", " 0.074860\n", " 0.000303\n", " \n", @@ -1198,7 +1197,7 @@ " 1\n", " 10000\n", " 1\n", - " U-238\n", + " U238\n", " 0.005952\n", " 0.000035\n", " \n", @@ -1206,7 +1205,7 @@ " 2\n", " 10000\n", " 1\n", - " O-16\n", + " O16\n", " 0.000000\n", " 0.000000\n", " \n", @@ -1216,9 +1215,9 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "0 10000 1 U-235 0.074860 0.000303\n", - "1 10000 1 U-238 0.005952 0.000035\n", - "2 10000 1 O-16 0.000000 0.000000" + "0 10000 1 U235 0.074860 0.000303\n", + "1 10000 1 U238 0.005952 0.000035\n", + "2 10000 1 O16 0.000000 0.000000" ] }, "execution_count": 36, @@ -1298,7 +1297,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ NORMAL ] Importing ray tracing data from file...\n", + "[ NORMAL ] Ray tracing for track segmentation...\n", + "[ NORMAL ] Dumping tracks to file...\n", "[ NORMAL ] Computing the eigenvalue...\n", "[ NORMAL ] Iteration 0:\tk_eff = 0.854370\tres = 0.000E+00\n", "[ NORMAL ] Iteration 1:\tk_eff = 0.801922\tres = 1.521E-01\n", @@ -1311,42 +1311,42 @@ "[ NORMAL ] Iteration 8:\tk_eff = 0.683124\tres = 6.142E-03\n", "[ NORMAL ] Iteration 9:\tk_eff = 0.685943\tres = 7.897E-04\n", "[ NORMAL ] Iteration 10:\tk_eff = 0.691322\tres = 4.180E-03\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.698747\tres = 7.873E-03\n", + "[ NORMAL ] Iteration 11:\tk_eff = 0.698746\tres = 7.873E-03\n", "[ NORMAL ] Iteration 12:\tk_eff = 0.707777\tres = 1.076E-02\n", - "[ NORMAL ] Iteration 13:\tk_eff = 0.718040\tres = 1.295E-02\n", + "[ NORMAL ] Iteration 13:\tk_eff = 0.718039\tres = 1.295E-02\n", "[ NORMAL ] Iteration 14:\tk_eff = 0.729218\tres = 1.452E-02\n", "[ NORMAL ] Iteration 15:\tk_eff = 0.741045\tres = 1.559E-02\n", "[ NORMAL ] Iteration 16:\tk_eff = 0.753296\tres = 1.624E-02\n", - "[ NORMAL ] Iteration 17:\tk_eff = 0.765785\tres = 1.655E-02\n", + "[ NORMAL ] Iteration 17:\tk_eff = 0.765784\tres = 1.655E-02\n", "[ NORMAL ] Iteration 18:\tk_eff = 0.778355\tres = 1.659E-02\n", "[ NORMAL ] Iteration 19:\tk_eff = 0.790879\tres = 1.643E-02\n", - "[ NORMAL ] Iteration 20:\tk_eff = 0.803254\tres = 1.610E-02\n", + "[ NORMAL ] Iteration 20:\tk_eff = 0.803253\tres = 1.610E-02\n", "[ NORMAL ] Iteration 21:\tk_eff = 0.815394\tres = 1.566E-02\n", "[ NORMAL ] Iteration 22:\tk_eff = 0.827235\tres = 1.513E-02\n", "[ NORMAL ] Iteration 23:\tk_eff = 0.838724\tres = 1.453E-02\n", "[ NORMAL ] Iteration 24:\tk_eff = 0.849823\tres = 1.390E-02\n", - "[ NORMAL ] Iteration 25:\tk_eff = 0.860503\tres = 1.324E-02\n", + "[ NORMAL ] Iteration 25:\tk_eff = 0.860502\tres = 1.324E-02\n", "[ NORMAL ] Iteration 26:\tk_eff = 0.870744\tres = 1.258E-02\n", "[ NORMAL ] Iteration 27:\tk_eff = 0.880535\tres = 1.191E-02\n", - "[ NORMAL ] Iteration 28:\tk_eff = 0.889870\tres = 1.125E-02\n", - "[ NORMAL ] Iteration 29:\tk_eff = 0.898748\tres = 1.061E-02\n", + "[ NORMAL ] Iteration 28:\tk_eff = 0.889869\tres = 1.125E-02\n", + "[ NORMAL ] Iteration 29:\tk_eff = 0.898747\tres = 1.061E-02\n", "[ NORMAL ] Iteration 30:\tk_eff = 0.907172\tres = 9.985E-03\n", - "[ NORMAL ] Iteration 31:\tk_eff = 0.915151\tres = 9.382E-03\n", - "[ NORMAL ] Iteration 32:\tk_eff = 0.922693\tres = 8.802E-03\n", - "[ NORMAL ] Iteration 33:\tk_eff = 0.929811\tres = 8.248E-03\n", + "[ NORMAL ] Iteration 31:\tk_eff = 0.915150\tres = 9.382E-03\n", + "[ NORMAL ] Iteration 32:\tk_eff = 0.922692\tres = 8.802E-03\n", + "[ NORMAL ] Iteration 33:\tk_eff = 0.929810\tres = 8.248E-03\n", "[ NORMAL ] Iteration 34:\tk_eff = 0.936517\tres = 7.720E-03\n", "[ NORMAL ] Iteration 35:\tk_eff = 0.942827\tres = 7.219E-03\n", - "[ NORMAL ] Iteration 36:\tk_eff = 0.948757\tres = 6.744E-03\n", - "[ NORMAL ] Iteration 37:\tk_eff = 0.954322\tres = 6.295E-03\n", + "[ NORMAL ] Iteration 36:\tk_eff = 0.948756\tres = 6.744E-03\n", + "[ NORMAL ] Iteration 37:\tk_eff = 0.954321\tres = 6.295E-03\n", "[ NORMAL ] Iteration 38:\tk_eff = 0.959539\tres = 5.871E-03\n", - "[ NORMAL ] Iteration 39:\tk_eff = 0.964425\tres = 5.472E-03\n", - "[ NORMAL ] Iteration 40:\tk_eff = 0.968996\tres = 5.096E-03\n", + "[ NORMAL ] Iteration 39:\tk_eff = 0.964424\tres = 5.472E-03\n", + "[ NORMAL ] Iteration 40:\tk_eff = 0.968995\tres = 5.096E-03\n", "[ NORMAL ] Iteration 41:\tk_eff = 0.973268\tres = 4.744E-03\n", - "[ NORMAL ] Iteration 42:\tk_eff = 0.977259\tres = 4.413E-03\n", - "[ NORMAL ] Iteration 43:\tk_eff = 0.980982\tres = 4.104E-03\n", - "[ NORMAL ] Iteration 44:\tk_eff = 0.984454\tres = 3.814E-03\n", + "[ NORMAL ] Iteration 42:\tk_eff = 0.977258\tres = 4.413E-03\n", + "[ NORMAL ] Iteration 43:\tk_eff = 0.980981\tres = 4.104E-03\n", + "[ NORMAL ] Iteration 44:\tk_eff = 0.984453\tres = 3.814E-03\n", "[ NORMAL ] Iteration 45:\tk_eff = 0.987689\tres = 3.543E-03\n", - "[ NORMAL ] Iteration 46:\tk_eff = 0.990702\tres = 3.289E-03\n", + "[ NORMAL ] Iteration 46:\tk_eff = 0.990701\tres = 3.289E-03\n", "[ NORMAL ] Iteration 47:\tk_eff = 0.993505\tres = 3.053E-03\n", "[ NORMAL ] Iteration 48:\tk_eff = 0.996112\tres = 2.832E-03\n", "[ NORMAL ] Iteration 49:\tk_eff = 0.998536\tres = 2.627E-03\n", @@ -1366,12 +1366,12 @@ "[ NORMAL ] Iteration 63:\tk_eff = 1.018721\tres = 8.864E-04\n", "[ NORMAL ] Iteration 64:\tk_eff = 1.019490\tres = 8.187E-04\n", "[ NORMAL ] Iteration 65:\tk_eff = 1.020201\tres = 7.560E-04\n", - "[ NORMAL ] Iteration 66:\tk_eff = 1.020858\tres = 6.980E-04\n", - "[ NORMAL ] Iteration 67:\tk_eff = 1.021464\tres = 6.444E-04\n", + "[ NORMAL ] Iteration 66:\tk_eff = 1.020857\tres = 6.980E-04\n", + "[ NORMAL ] Iteration 67:\tk_eff = 1.021464\tres = 6.443E-04\n", "[ NORMAL ] Iteration 68:\tk_eff = 1.022024\tres = 5.947E-04\n", - "[ NORMAL ] Iteration 69:\tk_eff = 1.022541\tres = 5.488E-04\n", + "[ NORMAL ] Iteration 69:\tk_eff = 1.022540\tres = 5.488E-04\n", "[ NORMAL ] Iteration 70:\tk_eff = 1.023017\tres = 5.063E-04\n", - "[ NORMAL ] Iteration 71:\tk_eff = 1.023458\tres = 4.670E-04\n", + "[ NORMAL ] Iteration 71:\tk_eff = 1.023457\tres = 4.670E-04\n", "[ NORMAL ] Iteration 72:\tk_eff = 1.023863\tres = 4.308E-04\n", "[ NORMAL ] Iteration 73:\tk_eff = 1.024238\tres = 3.972E-04\n", "[ NORMAL ] Iteration 74:\tk_eff = 1.024583\tres = 3.663E-04\n", @@ -1386,38 +1386,38 @@ "[ NORMAL ] Iteration 83:\tk_eff = 1.026697\tres = 1.753E-04\n", "[ NORMAL ] Iteration 84:\tk_eff = 1.026849\tres = 1.614E-04\n", "[ NORMAL ] Iteration 85:\tk_eff = 1.026989\tres = 1.487E-04\n", - "[ NORMAL ] Iteration 86:\tk_eff = 1.027118\tres = 1.369E-04\n", + "[ NORMAL ] Iteration 86:\tk_eff = 1.027118\tres = 1.368E-04\n", "[ NORMAL ] Iteration 87:\tk_eff = 1.027237\tres = 1.260E-04\n", - "[ NORMAL ] Iteration 88:\tk_eff = 1.027347\tres = 1.160E-04\n", + "[ NORMAL ] Iteration 88:\tk_eff = 1.027346\tres = 1.159E-04\n", "[ NORMAL ] Iteration 89:\tk_eff = 1.027447\tres = 1.067E-04\n", - "[ NORMAL ] Iteration 90:\tk_eff = 1.027540\tres = 9.823E-05\n", - "[ NORMAL ] Iteration 91:\tk_eff = 1.027625\tres = 9.039E-05\n", - "[ NORMAL ] Iteration 92:\tk_eff = 1.027704\tres = 8.317E-05\n", - "[ NORMAL ] Iteration 93:\tk_eff = 1.027776\tres = 7.652E-05\n", - "[ NORMAL ] Iteration 94:\tk_eff = 1.027843\tres = 7.040E-05\n", - "[ NORMAL ] Iteration 95:\tk_eff = 1.027904\tres = 6.476E-05\n", - "[ NORMAL ] Iteration 96:\tk_eff = 1.027960\tres = 5.957E-05\n", - "[ NORMAL ] Iteration 97:\tk_eff = 1.028012\tres = 5.479E-05\n", - "[ NORMAL ] Iteration 98:\tk_eff = 1.028059\tres = 5.039E-05\n", - "[ NORMAL ] Iteration 99:\tk_eff = 1.028103\tres = 4.635E-05\n", - "[ NORMAL ] Iteration 100:\tk_eff = 1.028143\tres = 4.262E-05\n", - "[ NORMAL ] Iteration 101:\tk_eff = 1.028180\tres = 3.919E-05\n", - "[ NORMAL ] Iteration 102:\tk_eff = 1.028214\tres = 3.603E-05\n", - "[ NORMAL ] Iteration 103:\tk_eff = 1.028245\tres = 3.313E-05\n", - "[ NORMAL ] Iteration 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Iteration 104:\tk_eff = 1.028273\tres = 3.047E-05\n", "[ NORMAL ] Iteration 105:\tk_eff = 1.028300\tres = 2.800E-05\n", - "[ NORMAL ] Iteration 106:\tk_eff = 1.028324\tres = 2.574E-05\n", - "[ NORMAL ] Iteration 107:\tk_eff = 1.028347\tres = 2.366E-05\n", - "[ NORMAL ] Iteration 108:\tk_eff = 1.028367\tres = 2.175E-05\n", - "[ NORMAL ] Iteration 109:\tk_eff = 1.028386\tres = 1.999E-05\n", + "[ NORMAL ] Iteration 106:\tk_eff = 1.028324\tres = 2.575E-05\n", + "[ NORMAL ] Iteration 107:\tk_eff = 1.028346\tres = 2.368E-05\n", + "[ NORMAL ] Iteration 108:\tk_eff = 1.028367\tres = 2.176E-05\n", + "[ NORMAL ] Iteration 109:\tk_eff = 1.028386\tres = 2.003E-05\n", "[ NORMAL ] Iteration 110:\tk_eff = 1.028403\tres = 1.837E-05\n", - "[ NORMAL ] Iteration 111:\tk_eff = 1.028419\tres = 1.688E-05\n", - "[ NORMAL ] Iteration 112:\tk_eff = 1.028434\tres = 1.551E-05\n", + "[ NORMAL ] Iteration 111:\tk_eff = 1.028419\tres = 1.690E-05\n", + "[ NORMAL ] Iteration 112:\tk_eff = 1.028434\tres = 1.553E-05\n", "[ NORMAL ] Iteration 113:\tk_eff = 1.028447\tres = 1.426E-05\n", - "[ NORMAL ] Iteration 114:\tk_eff = 1.028460\tres = 1.310E-05\n", - "[ NORMAL ] Iteration 115:\tk_eff = 1.028471\tres = 1.204E-05\n", - "[ NORMAL ] Iteration 116:\tk_eff = 1.028481\tres = 1.106E-05\n", - "[ NORMAL ] Iteration 117:\tk_eff = 1.028491\tres = 1.016E-05\n" + "[ NORMAL ] Iteration 114:\tk_eff = 1.028459\tres = 1.309E-05\n", + "[ NORMAL ] Iteration 115:\tk_eff = 1.028471\tres = 1.202E-05\n", + "[ NORMAL ] Iteration 116:\tk_eff = 1.028481\tres = 1.107E-05\n", + "[ NORMAL ] Iteration 117:\tk_eff = 1.028491\tres = 1.015E-05\n" ] } ], @@ -1559,7 +1559,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 43, @@ -1568,9 +1568,9 @@ }, { "data": { - "image/png": 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LwOXDO9vT701ffYEZcxn+ZcZs6GBPxNqmg32+8nV3hzgXM8Tk3IcHB7c162x7tuzzNs8x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\n2ajR7Q6wgAAAAABJRU5ErkJggg==\n", 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VNdtPwnmHuC2E1Cu0bRJH6iVyzU5JR96okOa+NACcdbbdB/qxnmvqYIn9CqQF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\nSUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1610,7 +1610,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/mgxs-part-iv.ipynb b/docs/source/pythonapi/examples/mgxs-part-iv.ipynb index e5c80c192..ae015b0a6 100644 --- a/docs/source/pythonapi/examples/mgxs-part-iv.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-iv.ipynb @@ -59,12 +59,12 @@ "outputs": [], "source": [ "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H-1')\n", - "b10 = openmc.Nuclide('B-10')\n", - "o16 = openmc.Nuclide('O-16')\n", - "u235 = openmc.Nuclide('U-235')\n", - "u238 = openmc.Nuclide('U-238')\n", - "zr90 = openmc.Nuclide('Zr-90')" + "h1 = openmc.Nuclide('H1')\n", + "b10 = openmc.Nuclide('B10')\n", + "o16 = openmc.Nuclide('O16')\n", + "u235 = openmc.Nuclide('U235')\n", + "u238 = openmc.Nuclide('U238')\n", + "zr90 = openmc.Nuclide('Zr90')" ] }, { @@ -432,7 +432,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -578,8 +578,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/mgxs/library.py:320: RuntimeWarning: The P0 correction will be ignored since the scattering order 0 is greater than zero\n", - " warnings.warn(msg, RuntimeWarning)\n" + "/home/romano/openmc/openmc/mgxs/library.py:312: RuntimeWarning: The P0 correction will be ignored since the scattering order 0 is greater than zero\n", + " warn(msg, RuntimeWarning)\n" ] } ], @@ -722,27 +722,26 @@ " Copyright: 2011-2016 Massachusetts Institute of Technology\n", " License: http://openmc.readthedocs.io/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: 826d5a43d85eaec1b6c7b4ce22e1a8f5e9336a4f\n", - " Date/Time: 2016-06-08 19:33:38\n", - " OpenMP Threads: 4\n", + " Git SHA1: 3d68c07625e33cd64188df03ee03e9c31b3d4b74\n", + " Date/Time: 2016-07-23 16:50:57\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", " ===========================================================================\n", "\n", " Reading settings XML file...\n", - " Reading cross sections XML file...\n", " Reading geometry XML file...\n", + " Reading cross sections XML file...\n", " Reading materials XML file...\n", + " Reading U235.71c from /home/romano/openmc/data/nndc_hdf5/U235_71c.h5\n", + " Reading U238.71c from /home/romano/openmc/data/nndc_hdf5/U238_71c.h5\n", + " Reading O16.71c from /home/romano/openmc/data/nndc_hdf5/O16_71c.h5\n", + " Reading Zr90.71c from /home/romano/openmc/data/nndc_hdf5/Zr90_71c.h5\n", + " Reading H1.71c from /home/romano/openmc/data/nndc_hdf5/H1_71c.h5\n", + " Reading B10.71c from /home/romano/openmc/data/nndc_hdf5/B10_71c.h5\n", + " Maximum neutron transport energy: 20.0000 MeV for U235.71c\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", - " Loading ACE cross section table: 92235.71c\n", - " Loading ACE cross section table: 92238.71c\n", - " Loading ACE cross section table: 8016.71c\n", - " Loading ACE cross section table: 40090.71c\n", - " Loading ACE cross section table: 1001.71c\n", - " Loading ACE cross section table: 5010.71c\n", - " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", " Initializing source particles...\n", "\n", " ===========================================================================\n", @@ -810,20 +809,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 1.4220E+00 seconds\n", - " Reading cross sections = 1.1320E+00 seconds\n", - " Total time in simulation = 1.6571E+01 seconds\n", - " Time in transport only = 1.6501E+01 seconds\n", - " Time in inactive batches = 2.1010E+00 seconds\n", - " Time in active batches = 1.4470E+01 seconds\n", - " Time synchronizing fission bank = 5.0000E-03 seconds\n", - " Sampling source sites = 4.0000E-03 seconds\n", - " SEND/RECV source sites = 1.0000E-03 seconds\n", + " Total time for initialization = 3.6600E-01 seconds\n", + " Reading cross sections = 2.1400E-01 seconds\n", + " Total time in simulation = 7.0360E+01 seconds\n", + " Time in transport only = 7.0341E+01 seconds\n", + " Time in inactive batches = 9.6400E+00 seconds\n", + " Time in active batches = 6.0720E+01 seconds\n", + " Time synchronizing fission bank = 7.0000E-03 seconds\n", + " Sampling source sites = 5.0000E-03 seconds\n", + " SEND/RECV source sites = 2.0000E-03 seconds\n", " Time accumulating tallies = 0.0000E+00 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 1.8002E+01 seconds\n", - " Calculation Rate (inactive) = 23798.2 neutrons/second\n", - " Calculation Rate (active) = 13821.7 neutrons/second\n", + " Total time elapsed = 7.0764E+01 seconds\n", + " Calculation Rate (inactive) = 5186.72 neutrons/second\n", + " Calculation Rate (active) = 3293.81 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -966,11 +965,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/tallies.py:1990: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1941: 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:1991: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1942: 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:1992: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1943: RuntimeWarning: invalid value encountered in true_divide\n", " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" ] } @@ -1098,17 +1097,16 @@ " Copyright: 2011-2016 Massachusetts Institute of Technology\n", " License: http://openmc.readthedocs.io/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: 826d5a43d85eaec1b6c7b4ce22e1a8f5e9336a4f\n", - " Date/Time: 2016-06-08 19:33:56\n", - " OpenMP Threads: 4\n", + " Git SHA1: 3d68c07625e33cd64188df03ee03e9c31b3d4b74\n", + " Date/Time: 2016-07-23 16:52:09\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", " ===========================================================================\n", "\n", " Reading settings XML file...\n", - " Reading cross sections XML file...\n", " Reading geometry XML file...\n", + " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", @@ -1183,20 +1181,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.1000E-02 seconds\n", - " Reading cross sections = 5.0000E-03 seconds\n", - " Total time in simulation = 1.1867E+01 seconds\n", - " Time in transport only = 1.1830E+01 seconds\n", - " Time in inactive batches = 1.2670E+00 seconds\n", - " Time in active batches = 1.0600E+01 seconds\n", - " Time synchronizing fission bank = 7.0000E-03 seconds\n", - " Sampling source sites = 7.0000E-03 seconds\n", - " SEND/RECV source sites = 0.0000E+00 seconds\n", - " Time accumulating tallies = 0.0000E+00 seconds\n", + " Total time for initialization = 4.6000E-02 seconds\n", + " Reading cross sections = 8.0000E-03 seconds\n", + " Total time in simulation = 5.5889E+01 seconds\n", + " Time in transport only = 5.5863E+01 seconds\n", + " Time in inactive batches = 7.1040E+00 seconds\n", + " Time in active batches = 4.8785E+01 seconds\n", + " Time synchronizing fission bank = 1.6000E-02 seconds\n", + " Sampling source sites = 1.0000E-02 seconds\n", + " SEND/RECV source sites = 6.0000E-03 seconds\n", + " Time accumulating tallies = 1.0000E-03 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 1.1907E+01 seconds\n", - " Calculation Rate (inactive) = 39463.3 neutrons/second\n", - " Calculation Rate (active) = 18867.9 neutrons/second\n", + " Total time elapsed = 5.5976E+01 seconds\n", + " Calculation Rate (inactive) = 7038.29 neutrons/second\n", + " Calculation Rate (active) = 4099.62 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1383,7 +1381,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 40, @@ -1392,9 +1390,9 @@ }, { "data": { - "image/png": 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D//NevF8+N3ajKzY9OtRM8/6hpr9NK5nej0Hsbyev0AkuhBBCVAAFdCGEyAkK6EIIkRMU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\n0o/WWjagu/t7BYt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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1453,7 +1451,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/nuclear-data.ipynb b/docs/source/pythonapi/examples/nuclear-data.ipynb index 78a71cd29..0c24079cf 100644 --- a/docs/source/pythonapi/examples/nuclear-data.ipynb +++ b/docs/source/pythonapi/examples/nuclear-data.ipynb @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have our ACE table, we can look at its contents. Let's start off by plotting the total cross section." + "Now that we have our ACE table, we can look at its contents. Let's start off by plotting the total cross section. Reactions are indexed using their \"MT\" number -- a unique identifier for each reaction defined by the ENDF-6 format. The MT number for the total cross section is 1." ] }, { @@ -79,7 +79,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -90,7 +90,7 @@ "data": { "image/png": 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Q9u3b+x5jsZ+tlhbH9xiNGh3B+PFdOfbY5yv8QF67tohly9bw8ceN2bXrWw45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\nAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -98,7 +98,7 @@ } ], "source": [ - "total = gd157.summed_reactions[1]\n", + "total = gd157[1]\n", "plt.loglog(total.xs.x, total.xs.y)\n", "plt.xlabel('Energy (MeV)')\n", "plt.ylabel('Cross section (b)')" @@ -124,16 +124,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]\n" + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]\n" ] } ], @@ -166,7 +166,7 @@ "data": { "image/png": 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LgEYFHjdMHPudu68qcP95MxthZvXcfUXRFxs/fnxkgZbkuuvC2oV+/U4o89w+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\n9zC0aV7vrgAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -174,7 +174,7 @@ } ], "source": [ - "n2n = gd157.reactions[16]\n", + "n2n = gd157[16]\n", "plt.plot(n2n.xs.x, n2n.xs.y)\n", "plt.xlabel('Energy (MeV)')\n", "plt.ylabel('Cross section (b)')\n", @@ -222,7 +222,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 7, @@ -252,36 +252,36 @@ { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" ] }, "execution_count": 8, @@ -312,7 +312,7 @@ "data": { "image/png": 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SIwT6Eq2AnkTrK6+8wvjx43nyyScJDQ0lMDCw1hKt9aFVGAyNKDdRSkwk2dER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\ndCg+EzU0itQLUxn90Gimlk2l4JkC9jy/52jtKpM4LCJCWtp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p00f2Wk/QQICAAAl3Uks1EME/oqYGooY5Sc1EwlRcPxZqjrqZ4wNHqltIsTvw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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -476,7 +476,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "While the groups containing reaction data are only labeled sequentially, we can look at the group attributes to figure out what they actually are." + "All reaction data is contained in the `reactions` group under a nuclide. While the group for each reaction is only labeled by its MT value, we can look at the group attributes to get a label for the reaction." ] }, { @@ -490,24 +490,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "reaction_0, MT=2 (n,elastic)\n", - "reaction_1, MT=16 (n,2n)\n", - "reaction_10, MT=54 (n,n4)\n", - "reaction_11, MT=55 (n,n5)\n", - "reaction_12, MT=56 (n,n6)\n", - "reaction_13, MT=57 (n,n7)\n", - "reaction_14, MT=58 (n,n8)\n", - "reaction_15, MT=59 (n,n9)\n", - "reaction_16, MT=60 (n,n10)\n" + "reaction_002, (n,elastic)\n", + "reaction_016, (n,2n)\n", + "reaction_017, (n,3n)\n", + "reaction_022, (n,na)\n", + "reaction_024, (n,2na)\n", + "reaction_028, (n,np)\n", + "reaction_041, (n,2np)\n", + "reaction_051, (n,n1)\n", + "reaction_052, (n,n2)\n", + "reaction_053, (n,n3)\n" ] } ], "source": [ - "main_group = h5file['Gd157.71c']\n", - "for name, obj in list(main_group.items())[:10]:\n", - " if 'mt' in obj.attrs:\n", - " print('{}, MT={} {}'.format(name, obj.attrs['mt'],\n", - " obj.attrs['label'].decode()))" + "main_group = h5file['Gd157.71c/reactions']\n", + "for name, obj in sorted(list(main_group.items()))[:10]:\n", + " if 'reaction_' in name:\n", + " print('{}, {}'.format(name, obj.attrs['label'].decode()))" ] }, { @@ -521,14 +521,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[,\n", - " ,\n", - " ]\n" + "[,\n", + " ,\n", + " ]\n" ] } ], "source": [ - "n2n_group = main_group['reaction_1']\n", + "n2n_group = main_group['reaction_016']\n", "pprint(list(n2n_group.values()))" ] }, @@ -601,8 +601,8 @@ } ], "source": [ - "gd157_reconstructed = openmc.data.IncidentNeutron.from_hdf5(main_group)\n", - "gd157.reactions[16].xs.y - gd157_reconstructed.reactions[16].xs.y" + "gd157_reconstructed = openmc.data.IncidentNeutron.from_hdf5('gd157.h5')\n", + "gd157[16].xs.y - gd157_reconstructed[16].xs.y" ] } ], @@ -622,7 +622,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index 2c222ad6e..412013f2f 100644 --- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb +++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb @@ -23,6 +23,7 @@ "import matplotlib.pyplot as plt\n", "import scipy.stats\n", "import numpy as np\n", + "import pandas as pd\n", "\n", "import openmc" ] @@ -50,12 +51,12 @@ "outputs": [], "source": [ "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H-1')\n", - "b10 = openmc.Nuclide('B-10')\n", - "o16 = openmc.Nuclide('O-16')\n", - "u235 = openmc.Nuclide('U-235')\n", - "u238 = openmc.Nuclide('U-238')\n", - "zr90 = openmc.Nuclide('Zr-90')" + "h1 = openmc.Nuclide('H1')\n", + "b10 = openmc.Nuclide('B10')\n", + "o16 = openmc.Nuclide('O16')\n", + "u235 = openmc.Nuclide('U235')\n", + "u238 = openmc.Nuclide('U238')\n", + "zr90 = openmc.Nuclide('Zr90')" ] }, { @@ -369,7 +370,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -553,27 +554,26 @@ " Copyright: 2011-2016 Massachusetts Institute of Technology\n", " License: http://openmc.readthedocs.io/en/latest/license.html\n", " Version: 0.7.1\n", - " Git SHA1: ae588276014a905ecc6e0967bf08288ecec5b550\n", - " Date/Time: 2016-05-09 23:01:18\n", - " MPI Processes: 1\n", + " Git SHA1: 3d68c07625e33cd64188df03ee03e9c31b3d4b74\n", + " Date/Time: 2016-07-23 16:36:04\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", " ===========================================================================\n", "\n", " Reading settings XML file...\n", - " Reading cross sections XML file...\n", " Reading geometry XML file...\n", + " Reading cross sections XML file...\n", " Reading materials XML file...\n", + " Reading U235.71c from /home/romano/openmc/data/nndc_hdf5/U235_71c.h5\n", + " Reading U238.71c from /home/romano/openmc/data/nndc_hdf5/U238_71c.h5\n", + " Reading O16.71c from /home/romano/openmc/data/nndc_hdf5/O16_71c.h5\n", + " Reading H1.71c from /home/romano/openmc/data/nndc_hdf5/H1_71c.h5\n", + " Reading B10.71c from /home/romano/openmc/data/nndc_hdf5/B10_71c.h5\n", + " Reading Zr90.71c from /home/romano/openmc/data/nndc_hdf5/Zr90_71c.h5\n", + " Maximum neutron transport energy: 20.0000 MeV for U235.71c\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", - " Loading ACE cross section table: 92235.71c\n", - " Loading ACE cross section table: 92238.71c\n", - " Loading ACE cross section table: 8016.71c\n", - " Loading ACE cross section table: 1001.71c\n", - " Loading ACE cross section table: 5010.71c\n", - " Loading ACE cross section table: 40090.71c\n", - " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", " Initializing source particles...\n", "\n", " ===========================================================================\n", @@ -618,20 +618,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\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", + " Total time for initialization = 4.2600E-01 seconds\n", + " Reading cross sections = 2.9500E-01 seconds\n", + " Total time in simulation = 1.1986E+01 seconds\n", + " Time in transport only = 1.1977E+01 seconds\n", + " Time in inactive batches = 1.8370E+00 seconds\n", + " Time in active batches = 1.0149E+01 seconds\n", + " Time synchronizing fission bank = 2.0000E-03 seconds\n", + " Sampling source sites = 1.0000E-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 = 1.1234E+01 seconds\n", - " Calculation Rate (inactive) = 9197.94 neutrons/second\n", - " Calculation Rate (active) = 3959.46 neutrons/second\n", + " Total time elapsed = 1.2431E+01 seconds\n", + " Calculation Rate (inactive) = 6804.57 neutrons/second\n", + " Calculation Rate (active) = 3694.95 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -710,7 +710,7 @@ " \t\tmesh\t[1]\n", " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", "\tNuclides =\ttotal \n", - "\tScores =\t[u'fission', u'nu-fission']\n", + "\tScores =\t['fission', 'nu-fission']\n", "\tEstimator =\ttracklength\n", "\n" ] @@ -742,13 +742,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.1508711 ]]\n", + "[[[ 0.1501735 ]]\n", "\n", - " [[ 0.05389822]]\n", + " [[ 0.05936257]]\n", "\n", - " [[ 0.19633 ]]\n", + " [[ 0.21402727]]\n", "\n", - " [[ 0.12963172]]]\n" + " [[ 0.13436703]]]\n" ] } ], @@ -804,8 +804,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 2.34e-04\n", - " 3.54e-05\n", + " 2.20e-04\n", + " 3.31e-05\n", " \n", " \n", " 1\n", @@ -815,8 +815,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 5.71e-04\n", - " 8.62e-05\n", + " 5.37e-04\n", + " 8.06e-05\n", " \n", " \n", " 2\n", @@ -826,8 +826,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 7.03e-05\n", - " 7.05e-06\n", + " 7.43e-05\n", + " 7.91e-06\n", " \n", " \n", " 3\n", @@ -837,8 +837,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 1.87e-04\n", - " 1.76e-05\n", + " 1.97e-04\n", + " 1.96e-05\n", " \n", " \n", " 4\n", @@ -848,8 +848,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 3.67e-04\n", - " 3.61e-05\n", + " 3.52e-04\n", + " 3.39e-05\n", " \n", " \n", " 5\n", @@ -859,8 +859,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 8.94e-04\n", - " 8.80e-05\n", + " 8.57e-04\n", + " 8.26e-05\n", " \n", " \n", " 6\n", @@ -870,8 +870,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.04e-04\n", - " 5.36e-06\n", + " 1.02e-04\n", + " 6.16e-06\n", " \n", " \n", " 7\n", @@ -881,8 +881,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 2.76e-04\n", - " 1.40e-05\n", + " 2.70e-04\n", + " 1.61e-05\n", " \n", " \n", " 8\n", @@ -892,8 +892,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 6.04e-04\n", - " 5.57e-05\n", + " 6.09e-04\n", + " 6.55e-05\n", " \n", " \n", " 9\n", @@ -903,8 +903,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 1.47e-03\n", - " 1.36e-04\n", + " 1.48e-03\n", + " 1.60e-04\n", " \n", " \n", " 10\n", @@ -914,8 +914,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.41e-04\n", - " 6.69e-06\n", + " 1.38e-04\n", + " 6.74e-06\n", " \n", " \n", " 11\n", @@ -925,8 +925,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 3.72e-04\n", - " 1.82e-05\n", + " 3.65e-04\n", + " 1.88e-05\n", " \n", " \n", " 12\n", @@ -936,8 +936,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 6.45e-04\n", - " 4.59e-05\n", + " 6.23e-04\n", + " 5.16e-05\n", " \n", " \n", " 13\n", @@ -947,8 +947,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 1.57e-03\n", - " 1.12e-04\n", + " 1.52e-03\n", + " 1.26e-04\n", " \n", " \n", " 14\n", @@ -958,8 +958,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.82e-04\n", - " 9.37e-06\n", + " 1.74e-04\n", + " 9.99e-06\n", " \n", " \n", " 15\n", @@ -969,8 +969,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 4.76e-04\n", - " 2.47e-05\n", + " 4.58e-04\n", + " 2.68e-05\n", " \n", " \n", " 16\n", @@ -980,8 +980,8 @@ " 0.00e+00\n", " 6.25e-07\n", " fission\n", - " 7.28e-04\n", - " 7.49e-05\n", + " 6.94e-04\n", + " 8.68e-05\n", " \n", " \n", " 17\n", @@ -991,8 +991,8 @@ " 0.00e+00\n", " 6.25e-07\n", " nu-fission\n", - " 1.77e-03\n", - " 1.83e-04\n", + " 1.69e-03\n", + " 2.12e-04\n", " \n", " \n", " 18\n", @@ -1002,8 +1002,8 @@ " 6.25e-07\n", " 2.00e+01\n", " fission\n", - " 1.81e-04\n", - " 1.04e-05\n", + " 1.75e-04\n", + " 1.10e-05\n", " \n", " \n", " 19\n", @@ -1013,8 +1013,8 @@ " 6.25e-07\n", " 2.00e+01\n", " nu-fission\n", - " 4.72e-04\n", - " 2.67e-05\n", + " 4.55e-04\n", + " 2.80e-05\n", " \n", " \n", "\n", @@ -1023,49 +1023,49 @@ "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 2.34e-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", + "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", "\n", " std. dev. \n", " \n", - "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 " + "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 " ] }, "execution_count": 24, @@ -1078,8 +1078,7 @@ "df = tally.get_pandas_dataframe(nuclides=False)\n", "\n", "# Set the Pandas float display settings\n", - "import pandas as pd\n", - "pd.set_option('display.float_format', '{:.2e}'.format)\n", + "pd.options.display.float_format = '{:.2e}'.format\n", "\n", "# Print the first twenty rows in the dataframe\n", "df.head(20)" @@ -1094,9 +1093,9 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1175,8 +1174,8 @@ "\tName =\tcell tally\n", "\tFilters =\t\n", " \t\tcell\t[10000]\n", - "\tNuclides =\tU-235 U-238 \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", + "\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", "\n" ] @@ -1216,146 +1215,146 @@ " \n", " 0\n", " 10000\n", - " U-235\n", + " U235\n", " scatter-Y0,0\n", - " 3.86e-02\n", - " 1.11e-03\n", + " 3.84e-02\n", + " 1.32e-03\n", " \n", " \n", " 1\n", " 10000\n", - " U-235\n", + " U235\n", " scatter-Y1,-1\n", - " 2.75e-04\n", - " 2.96e-04\n", + " 3.61e-04\n", + " 3.13e-04\n", " \n", " \n", " 2\n", " 10000\n", - " U-235\n", + " U235\n", " scatter-Y1,0\n", - " -5.55e-05\n", - " 4.33e-04\n", + " -2.38e-04\n", + " 4.69e-04\n", " \n", " \n", " 3\n", " 10000\n", - " U-235\n", + " U235\n", " scatter-Y1,1\n", - " -4.22e-04\n", - " 3.51e-04\n", + " -5.08e-04\n", + " 3.83e-04\n", " \n", " \n", " 4\n", " 10000\n", - " U-235\n", + " U235\n", " scatter-Y2,-2\n", - " 5.88e-05\n", - " 2.04e-04\n", + " 6.68e-05\n", + " 2.46e-04\n", " \n", " \n", " 5\n", " 10000\n", - " U-235\n", + " U235\n", " scatter-Y2,-1\n", - " 1.00e-04\n", - " 2.49e-04\n", + " 6.47e-06\n", + " 2.84e-04\n", " \n", " \n", " 6\n", " 10000\n", - " U-235\n", + " U235\n", " scatter-Y2,0\n", - " -8.09e-05\n", - " 1.59e-04\n", + " -1.41e-04\n", + " 1.75e-04\n", " \n", " \n", " 7\n", " 10000\n", - " U-235\n", + " U235\n", " scatter-Y2,1\n", - " 1.93e-04\n", - " 2.14e-04\n", + " 1.61e-04\n", + " 2.33e-04\n", " \n", " \n", " 8\n", " 10000\n", - " U-235\n", + " U235\n", " scatter-Y2,2\n", - " 1.12e-04\n", - " 1.86e-04\n", + " -1.80e-05\n", + " 1.97e-04\n", " \n", " \n", " 9\n", " 10000\n", - " U-238\n", + " U238\n", " scatter-Y0,0\n", - " 2.34e+00\n", - " 1.34e-02\n", + " 2.33e+00\n", + " 1.35e-02\n", " \n", " \n", " 10\n", " 10000\n", - " U-238\n", + " U238\n", " scatter-Y1,-1\n", - " 2.32e-02\n", - " 2.97e-03\n", + " 2.53e-02\n", + " 3.23e-03\n", " \n", " \n", " 11\n", " 10000\n", - " U-238\n", + " U238\n", " scatter-Y1,0\n", - " 7.50e-04\n", - " 2.55e-03\n", + " 7.10e-04\n", + " 2.92e-03\n", " \n", " \n", " 12\n", " 10000\n", - " U-238\n", + " U238\n", " scatter-Y1,1\n", - " -2.73e-02\n", - " 3.28e-03\n", + " -2.49e-02\n", + " 3.52e-03\n", " \n", " \n", " 13\n", " 10000\n", - " U-238\n", + " U238\n", " scatter-Y2,-2\n", - " -2.36e-03\n", - " 1.21e-03\n", + " -1.43e-03\n", + " 1.17e-03\n", " \n", " \n", " 14\n", " 10000\n", - " U-238\n", + " U238\n", " scatter-Y2,-1\n", - " -1.80e-04\n", - " 1.49e-03\n", + " 6.84e-04\n", + " 1.63e-03\n", " \n", " \n", " 15\n", " 10000\n", - " U-238\n", + " U238\n", " scatter-Y2,0\n", - " 3.23e-03\n", - " 2.25e-03\n", + " 2.85e-03\n", + " 2.63e-03\n", " \n", " \n", " 16\n", " 10000\n", - " U-238\n", + " U238\n", " scatter-Y2,1\n", - " 3.75e-03\n", - " 1.97e-03\n", + " 3.97e-03\n", + " 2.24e-03\n", " \n", " \n", " 17\n", " 10000\n", - " U-238\n", + " U238\n", " scatter-Y2,2\n", - " 2.07e-03\n", - " 1.60e-03\n", + " 2.26e-03\n", + " 1.85e-03\n", " \n", " \n", "\n", @@ -1363,24 +1362,24 @@ ], "text/plain": [ " cell nuclide score mean std. dev.\n", - "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" + "0 10000 U235 scatter-Y0,0 3.84e-02 1.32e-03\n", + "1 10000 U235 scatter-Y1,-1 3.61e-04 3.13e-04\n", + "2 10000 U235 scatter-Y1,0 -2.38e-04 4.69e-04\n", + "3 10000 U235 scatter-Y1,1 -5.08e-04 3.83e-04\n", + "4 10000 U235 scatter-Y2,-2 6.68e-05 2.46e-04\n", + "5 10000 U235 scatter-Y2,-1 6.47e-06 2.84e-04\n", + "6 10000 U235 scatter-Y2,0 -1.41e-04 1.75e-04\n", + "7 10000 U235 scatter-Y2,1 1.61e-04 2.33e-04\n", + "8 10000 U235 scatter-Y2,2 -1.80e-05 1.97e-04\n", + "9 10000 U238 scatter-Y0,0 2.33e+00 1.35e-02\n", + "10 10000 U238 scatter-Y1,-1 2.53e-02 3.23e-03\n", + "11 10000 U238 scatter-Y1,0 7.10e-04 2.92e-03\n", + "12 10000 U238 scatter-Y1,1 -2.49e-02 3.52e-03\n", + "13 10000 U238 scatter-Y2,-2 -1.43e-03 1.17e-03\n", + "14 10000 U238 scatter-Y2,-1 6.84e-04 1.63e-03\n", + "15 10000 U238 scatter-Y2,0 2.85e-03 2.63e-03\n", + "16 10000 U238 scatter-Y2,1 3.97e-03 2.24e-03\n", + "17 10000 U238 scatter-Y2,2 2.26e-03 1.85e-03" ] }, "execution_count": 28, @@ -1414,8 +1413,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00159927 0.01341406]\n", - " [ 0.00018637 0.00111048]]]\n" + "[[[ 0.00185463 0.01350521]\n", + " [ 0.00019723 0.00131654]]]\n" ] } ], @@ -1423,7 +1422,7 @@ "# 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", - " nuclides=['U-238', 'U-235'], value='std_dev')\n", + " nuclides=['U238', 'U235'], value='std_dev')\n", "print(data)" ] }, @@ -1451,7 +1450,7 @@ "\tFilters =\t\n", " \t\tdistribcell\t[10002]\n", "\tNuclides =\ttotal \n", - "\tScores =\t[u'absorption', u'scatter']\n", + "\tScores =\t['absorption', 'scatter']\n", "\tEstimator =\ttracklength\n", "\n" ] @@ -1483,7 +1482,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.05767856]]]\n" + "[[[ 0.05468423]]]\n" ] } ], @@ -1566,8 +1565,8 @@ " 10002\n", " 279\n", " absorption\n", - " 8.19e-05\n", - " 7.82e-06\n", + " 8.72e-05\n", + " 8.13e-06\n", " \n", " \n", " 559\n", @@ -1581,8 +1580,8 @@ " 10002\n", " 279\n", " scatter\n", - " 1.33e-02\n", - " 6.19e-04\n", + " 1.37e-02\n", + " 6.98e-04\n", " \n", " \n", " 560\n", @@ -1596,8 +1595,8 @@ " 10002\n", " 280\n", " absorption\n", - " 1.00e-04\n", - " 7.93e-06\n", + " 1.03e-04\n", + " 9.17e-06\n", " \n", " \n", " 561\n", @@ -1611,8 +1610,8 @@ " 10002\n", " 280\n", " scatter\n", - " 1.40e-02\n", - " 5.61e-04\n", + " 1.41e-02\n", + " 6.26e-04\n", " \n", " \n", " 562\n", @@ -1626,8 +1625,8 @@ " 10002\n", " 281\n", " absorption\n", - " 9.52e-05\n", - " 7.08e-06\n", + " 9.41e-05\n", + " 8.40e-06\n", " \n", " \n", " 563\n", @@ -1641,8 +1640,8 @@ " 10002\n", " 281\n", " scatter\n", - " 1.51e-02\n", - " 6.50e-04\n", + " 1.50e-02\n", + " 6.92e-04\n", " \n", " \n", " 564\n", @@ -1656,8 +1655,8 @@ " 10002\n", " 282\n", " absorption\n", - " 9.85e-05\n", - " 9.47e-06\n", + " 9.56e-05\n", + " 1.03e-05\n", " \n", " \n", " 565\n", @@ -1671,8 +1670,8 @@ " 10002\n", " 282\n", " scatter\n", - " 1.53e-02\n", - " 4.63e-04\n", + " 1.52e-02\n", + " 5.37e-04\n", " \n", " \n", " 566\n", @@ -1686,8 +1685,8 @@ " 10002\n", " 283\n", " absorption\n", - " 1.08e-04\n", - " 1.34e-05\n", + " 1.06e-04\n", + " 1.49e-05\n", " \n", " \n", " 567\n", @@ -1701,8 +1700,8 @@ " 10002\n", " 283\n", " scatter\n", - " 1.65e-02\n", - " 7.04e-04\n", + " 1.64e-02\n", + " 8.14e-04\n", " \n", " \n", " 568\n", @@ -1716,8 +1715,8 @@ " 10002\n", " 284\n", " absorption\n", - " 1.13e-04\n", - " 7.91e-06\n", + " 1.16e-04\n", + " 9.02e-06\n", " \n", " \n", " 569\n", @@ -1731,8 +1730,8 @@ " 10002\n", " 284\n", " scatter\n", - " 1.67e-02\n", - " 5.51e-04\n", + " 1.64e-02\n", + " 6.00e-04\n", " \n", " \n", " 570\n", @@ -1746,8 +1745,8 @@ " 10002\n", " 285\n", " absorption\n", - " 1.23e-04\n", - " 9.53e-06\n", + " 1.25e-04\n", + " 1.12e-05\n", " \n", " \n", " 571\n", @@ -1761,8 +1760,8 @@ " 10002\n", " 285\n", " scatter\n", - " 1.88e-02\n", - " 7.25e-04\n", + " 1.87e-02\n", + " 8.26e-04\n", " \n", " \n", " 572\n", @@ -1776,8 +1775,8 @@ " 10002\n", " 286\n", " absorption\n", - " 1.44e-04\n", - " 1.34e-05\n", + " 1.47e-04\n", + " 1.49e-05\n", " \n", " \n", " 573\n", @@ -1791,8 +1790,8 @@ " 10002\n", " 286\n", " scatter\n", - " 1.90e-02\n", - " 7.07e-04\n", + " 1.94e-02\n", + " 7.71e-04\n", " \n", " \n", " 574\n", @@ -1806,8 +1805,8 @@ " 10002\n", " 287\n", " absorption\n", - " 1.26e-04\n", - " 8.66e-06\n", + " 1.31e-04\n", + " 9.84e-06\n", " \n", " \n", " 575\n", @@ -1822,7 +1821,7 @@ " 287\n", " scatter\n", " 1.97e-02\n", - " 7.23e-04\n", + " 7.93e-04\n", " \n", " \n", " 576\n", @@ -1836,8 +1835,8 @@ " 10002\n", " 288\n", " absorption\n", - " 1.25e-04\n", - " 9.59e-06\n", + " 1.23e-04\n", + " 1.07e-05\n", " \n", " \n", " 577\n", @@ -1851,8 +1850,8 @@ " 10002\n", " 288\n", " scatter\n", - " 2.01e-02\n", - " 6.75e-04\n", + " 1.97e-02\n", + " 7.34e-04\n", " \n", " \n", "\n", @@ -1886,26 +1885,26 @@ " mean std. dev. \n", " \n", " \n", - "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 " + "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 " ] }, "execution_count": 32, @@ -1958,38 +1957,38 @@ " \n", " \n", " mean\n", - " 4.19e-04\n", - " 2.24e-05\n", + " 4.16e-04\n", + " 2.42e-05\n", " \n", " \n", " std\n", - " 2.42e-04\n", - " 9.14e-06\n", + " 2.39e-04\n", + " 1.03e-05\n", " \n", " \n", " min\n", " 1.90e-05\n", - " 3.44e-06\n", + " 3.80e-06\n", " \n", " \n", " 25%\n", - " 2.02e-04\n", - " 1.56e-05\n", + " 1.99e-04\n", + " 1.61e-05\n", " \n", " \n", " 50%\n", - " 4.05e-04\n", - " 2.20e-05\n", + " 4.09e-04\n", + " 2.37e-05\n", " \n", " \n", " 75%\n", - " 6.07e-04\n", - " 2.89e-05\n", + " 6.00e-04\n", + " 3.08e-05\n", " \n", " \n", " max\n", - " 9.19e-04\n", - " 4.95e-05\n", + " 9.07e-04\n", + " 5.38e-05\n", " \n", " \n", "\n", @@ -2000,13 +1999,13 @@ " \n", " \n", "count 2.89e+02 2.89e+02\n", - "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" + "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" ] }, "execution_count": 33, @@ -2041,7 +2040,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 0.303583331507\n" + "Mann-Whitney Test p-value: 0.7234916721800682\n" ] } ], @@ -2079,7 +2078,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 6.038663783e-42\n" + "Mann-Whitney Test p-value: 3.5054120724573393e-41\n" ] } ], @@ -2115,7 +2114,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:4: SettingWithCopyWarning: \n", + "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/ipykernel/__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", @@ -2125,7 +2124,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 36, @@ -2134,9 +2133,9 @@ }, { "data": { - "image/png": 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MTGiGAH7z1Nx9921cf/3ClHDa9u3bLcxmGMagkveqMyM3JM9TEw+ndXQkhtOam5t9l7e1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\nyRsDKQZIfYpODDv5Ee+Ily6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DlnoznkkeyJPJ/qpSks3oorJstUPi00rHX03uFWEkUtbX3oa7Sn7FgYX/j5ML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\nlcvmVCblU7lsLlvKpCE+qXx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WDt9JxAMVCEm4Wetn0a1FN98xJBo/NYbvgPYTfScRD1QgJOFmrZvFSS10BXWl\n8S3BldVS7ahASEJt/nEzP+T/QNsmbX1HkWjlAEdNhlo7fSeRBFOBkISavW42J7Y4kRTTj16lkQ+s\nPRXaves7iSSYfksloWatm8VJzbV7qdL59jLtZqqGvBUIM1tlZnPNbI6ZzfSVQxJr1nodf6iUci6E\ntpMgdbfvJJJAPrcgCoCznXNdnHMne8whCaQD1JXUj0fAxs6Q9W/fSSSBfBYI8/z+kmAbdm7gxz0/\nktUoy3cUKY8lfaHD275TSAL5HGXFAR+Z2T7gH865UR6zSBzl5+fz1FNPMX/3fBrRiOHDh/uOJOWR\ncyFc2Qve8x1EEsVngejhnFtvZkcQFIpFzrnPii/Ur1+/A487duxIp06dEpkxrqZPn+47Qlzt79+C\nBQt44ol/see0o6BGC4ZPAajafa+SNnWEfTUhA7Kzs32nqZCq9ru3cOFCFi1aFPP1eisQzrn14b+b\nzOwt4GTgoALxxhtvJDpaQg0aNMh3hLgaNGgQU6ZMYcSIL9neoi58cy1wCfA4MNlzOikbC3Yztf9b\nlfi5rQp9OBQzi8l6vBwDMLO6ZlY/fFwP6Aks8JFFEsPhoMUs3YO6ssu5EDr4DiGJ4usgcTrwmZnN\nAWYA7zjnJnnKIgng6v0HUvbB9ta+o0hFrD4DmsC6HbqZR3XgpUA451Y6504IT3E9zjn3qI8ckjj7\n0vPCrYfYbPqKJwU1YTm8u0RXVVcHOs1UEmJfRp52L1UVOfD2Ep3uWh2oQEhC7G2eB2tP8R1DYmEZ\nfLLqE/L35PtOInGmAiFxt8/tC3Yxre3uO4rEQj50ad6FqSun+k4icaYCIXH33Y/fkbKrNuQ39R1F\nYuSCdhfwzpJ3fMeQOFOBkLhbuGMhqRvSfMeQGOrboS8Tl0zEOd1YvCpTgZC4W7RjEanrVSCqkg5N\nO1CnZh2+2fCN7ygSRyoQEneLdiyixvqGvmNIzNQmJSWFZe8uo+uArpgZGRlZvkNJHKhASFxt+2kb\nG3dvJGVzPd9RJGZ2Aw6WTIEO3QBHbu5q36EkDlQgJK5mfj+TdvXaYU4/alXOd6dDk6VQf4PvJBIn\n+q2VuJqxdgYd0zr6jiHxsK8WLO+pe1VXYSoQElefr/mcTg2qzhDtUsySC6D9RN8pJE5UICRu9rl9\nfLH2CzqndfYdReJlWW84cqrfO8tI3KhASNys2r2KzIaZNKypM5iqrB8Ph43HQZbvIBIPKhASN4vz\nF3Nm5pk+RG9GAAALWElEQVS+Y0i85fSF9r5DSDyoQEjcqEBUE0sugPboquoqSAVC4qLAFZCTn6MC\nUR1s6gQO5m+c7zuJxJgKhMTF/Nz5pKWmkVE/w3cUiTuDJTBxic5mqmpUICQupq2eRoc6unlxtZGD\nRnetglQgJC6mrppKpzq6/qHaWA2LNi1i466NvpNIDKlASMzt2beHj1d+TOe6uv6h2tgH5x51ru5V\nXcWoQEjMzfx+Jkc1PoqGNXT9Q3VyaadLeX3h675jSAypQEjMfbj8Q3q27ek7hiTYBe0vYPqa6WzN\n3+o7isSICoTE3KTlk1QgqqH6tepz3lHnMX7xeN9RJEZUICSmtuZvZeGmhfRo3cN3FPGg/7H9ee3b\n13zHkBhRgZCYmrR8EmdknkHtGrV9RxEP+rTrwxdrv2DLj1t8R5EYUIGQmBq/eDwXd7jYdwzxpF6t\nepzf9nzeXPSm7ygSAyoQEjO79+7mg2UfcGGHC31HEY8Gdh7I2PljfceQGFCBkJiZunIqnZt1Jr1+\nuu8o4lGf9n34dtO3rPhhhe8oUkEqEBIz4xeP51fH/Mp3DPGsVmotBnUexOhvRvuOIhWkAiExsbdg\nLxNyJnDxMTr+IHBtl2sZPXc0Ba7AdxSpABUIiYkpK6bQpmEb2jZp6zuKJIETMk6g0WGN+Peqf/uO\nIhWgAiEx8dK8lxh8/GDfMSSJ/Lrrr3l29rO+Y0gFqEBIhe38z04mLpnIgM4DfEeRJHLVz67io+Uf\nsWb7Gt9RpJxUIKTCxi0cxxmZZ3BEvSN8R5EkklY7jcHHD2bkrJG+o0g5qUBIhTjneGbmM9x04k2+\no0gSuvXkW3nu6+fI35PvO4qUgwqEVMjM72fyw08/0OvoXr6jSBJq17Qdp7U+jVFfj/IdRcpBBUIq\n5JmvnmHISUNITUn1HUWS1INnPchj0x/TVkQlpAIh5bbihxW8v/R9rutyne8oksS6Nu/KSS1O0lZE\nJaQCIeX2p0//xC3dbqFxnca+o0iSe+ish3j0s0fJ253nO4qUgQqElMvyrcsZv3g8vz3lt76jSCXQ\ntXlXeh3di4c/edh3FCkDFQgplzs/vJPfnfY7bT1I1B4991FGzx3Nwk0LfUeRKKlASJm9k/MOS7Ys\nYeipQ31HkUqkWb1mDD97ONdNuI49+/b4jiNRUIGQMtny4xaGvDeEZ375DLVSa/mOI5XMzSfdTOM6\njbWrqZJQgZCoOee4dsK1XH7s5Zx71Lm+40glZGa8cNEL/PObfzJh8QTfcaQUNXwHkMrj/in3s+nH\nTYzrP853FKnEMupnMP7y8fwy+5dk1M+ge6vuviPJIWgLQkrlnOPPn/6Z8TnjmThwonYtSYV1a9mN\nFy96kb4v92Xa6mm+48gheCsQZtbLzBab2RIzu9dXDinZ7r27ufW9W3l5wct8NPgjmtZt6juSVBF9\n2vchu182/V7rx//N+j+cc74jSTFeCoSZpQDPAOcDxwIDzewYH1l8WrgwuU/3m7F2Bic/dzLf7/ie\nT6/9lFZprcrUPtn7J/6de9S5TL9uOiNnjaT32N7kbM5JyPvqZzM6vrYgTgaWOudWO+f2AK8AF3nK\n4s2iRYt8RzjInn17eHfJu/TJ7kP/1/tz92l389blb9HwsIZlXlcy9k+ST/um7Zn1m1n0bNuTHv/s\nQf/X+zN15VT2FuyN23vqZzM6vg5StwQK30VkLUHRkARxzvHjnh9Zk7eGZVuXsXjzYj777jM+/e5T\nOjTtwDUnXMOb/d+kdo3avqNKNVAztSZDTx3Kr7v+mhe/eZG7P7qbVdtW8fMjf86JzU/k+PTjadOw\nDW0atiGtdprvuNWGzmLyYPa62Tz47weZnTWb3mN7A8EfbIcr879lbbtrzy62/bSNbT9to0ZKDVqn\nteboJkfTrkk7BnYeyIg+I2jRoEVM+1uzZk1++mkuaWl9D8zbvXsZu3fH9G2kCkirncbt3W/n9u63\nszZvLdNWT2PWulk8NeMp1uStYc32Nexz+2hQqwENajegfq36HFbjMFItldSU1IP+TbHIO0lmZ82m\nT3afg+YbFlXOdk3a8ddef61QXysD83FgyMxOAYY553qFz+8DnHPusWLL6aiViEg5OOeiq3Yl8FUg\nUoEc4BfAemAmMNA5px2DIiJJwssuJufcPjO7FZhEcKD8eRUHEZHk4mULQkREkp/3K6nNrLGZTTKz\nHDP70Mwink9pZs+bWa6ZzStPex/K0LeIFw2a2UNmttbMvg6npLjxczQXOZrZ02a21My+MbMTytLW\nt3L0r0uh+avMbK6ZzTGzmYlLHb3S+mdmHczsczP7ycyGlqWtbxXsW1X47AaFfZhrZp+Z2fHRto3I\nOed1Ah4D7gkf3ws8eojlTgdOAOaVp32y9o2gSC8DMoGawDfAMeFrDwFDffcj2ryFlukNvBs+7g7M\niLat76ki/QufrwAa++5HBft3OHAi8IfCP3/J/vlVpG9V6LM7BWgYPu5V0d8971sQBBfIjQ4fjwYu\njrSQc+4z4IfytvckmmylXTRY4TMRYiyaixwvAsYAOOe+BBqaWXqUbX2rSP8g+LyS4ffqUErtn3Nu\ns3NuNlD8SrVk//wq0jeoGp/dDOfc9vDpDIJrzqJqG0ky/Gc0c87lAjjnNgDNEtw+nqLJFumiwZaF\nnt8a7sZ4Lkl2n5WWt6RlomnrW3n6932hZRzwkZl9ZWa/iVvK8qvIZ5Dsn19F81W1z+7XwPvlbAsk\n6CwmM/sISC88i+DD+H8RFq/oUfOEHnWPc99GAA8755yZPQI8CVxfrqB+JdtWUDz1cM6tN7MjCP7Y\nLAq3fiX5VZnPzszOAa4l2DVfbgkpEM658w71WnjgOd05l2tmGcDGMq6+ou0rJAZ9+x5oU+h5q3Ae\nzrlNheaPAt6JQeSKOmTeYsu0jrBMrSja+laR/uGcWx/+u8nM3iLYtE+mPzLR9C8ebROhQvmqymcX\nHpj+B9DLOfdDWdoWlwy7mN4GrgkfXw2UdJsp4+Bvo2Vpn2jRZPsKONrMMs2sFjAgbEdYVPa7BFgQ\nv6hRO2TeQt4GroIDV81vC3e1RdPWt3L3z8zqmln9cH49oCfJ8ZkVVtbPoPDvW7J/fuXuW1X57Mys\nDfAGMNg5t7wsbSNKgiPzTYDJBFdWTwIahfObAxMLLZcNrAN2A98B15bUPhmmMvStV7jMUuC+QvPH\nAPMIzjgYD6T77tOh8gI3AjcUWuYZgrMm5gJdS+trMk3l7R9wZPhZzQHmV9b+EewyXQNsA7aGv2/1\nK8PnV96+VaHPbhSwBfg67MvMktqWNulCORERiSgZdjGJiEgSUoEQEZGIVCBERCQiFQgREYlIBUJE\nRCJSgRARkYhUIEQAMyswszGFnqea2SYzS6YLwUQSSgVCJLAL6GxmtcPn51F0cDORakcFQuS/3gP6\nhI8HAi/vfyEciuF5M5thZrPNrG84P9PMppnZrHA6JZx/lpl9bGavm9kiM3sp4b0RqSAVCJGAIxgj\nf2C4FXE88GWh1x8ApjjnTgF+DjxhZnWAXOBc59xJBOPb/L1QmxOA24FOQFszOy3+3RCJnYSM5ipS\nGTjnFphZFsHWw7sUHaiuJ9D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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2207,7 +2206,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/post-processing.ipynb b/docs/source/pythonapi/examples/post-processing.ipynb index de92c2bcb..19182aa7a 100644 --- a/docs/source/pythonapi/examples/post-processing.ipynb +++ b/docs/source/pythonapi/examples/post-processing.ipynb @@ -46,12 +46,12 @@ "outputs": [], "source": [ "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H-1')\n", - "b10 = openmc.Nuclide('B-10')\n", - "o16 = openmc.Nuclide('O-16')\n", - "u235 = openmc.Nuclide('U-235')\n", - "u238 = openmc.Nuclide('U-238')\n", - "zr90 = openmc.Nuclide('Zr-90')" + "h1 = openmc.Nuclide('H1')\n", + "b10 = openmc.Nuclide('B10')\n", + "o16 = openmc.Nuclide('O16')\n", + "u235 = openmc.Nuclide('U235')\n", + "u238 = openmc.Nuclide('U238')\n", + "zr90 = openmc.Nuclide('Zr90')" ] }, { @@ -339,7 +339,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nHczWQwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] @@ -446,28 +446,28 @@ " 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 14:41:55\n", + " Git SHA1: 3d68c07625e33cd64188df03ee03e9c31b3d4b74\n", + " Date/Time: 2016-07-22 21:40:25\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", " ===========================================================================\n", "\n", " Reading settings XML file...\n", - " Reading cross sections XML file...\n", " Reading geometry XML file...\n", + " Reading cross sections XML file...\n", " Reading materials XML file...\n", + " Reading U235.71c from /home/romano/openmc/data/nndc_hdf5/U235_71c.h5\n", + " Reading U238.71c from /home/romano/openmc/data/nndc_hdf5/U238_71c.h5\n", + " Reading O16.71c from /home/romano/openmc/data/nndc_hdf5/O16_71c.h5\n", + " Reading H1.71c from /home/romano/openmc/data/nndc_hdf5/H1_71c.h5\n", + " Reading B10.71c from /home/romano/openmc/data/nndc_hdf5/B10_71c.h5\n", + " Reading Zr90.71c from /home/romano/openmc/data/nndc_hdf5/Zr90_71c.h5\n", + " Maximum neutron transport energy: 20.0000 MeV for U235.71c\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", - " Loading ACE cross section table: 92235.71c\n", - " Loading ACE cross section table: 92238.71c\n", - " Loading ACE cross section table: 8016.71c\n", - " Loading ACE cross section table: 1001.71c\n", - " Loading ACE cross section table: 5010.71c\n", - " Loading ACE cross section table: 40090.71c\n", - " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", " Initializing source particles...\n", "\n", " ===========================================================================\n", @@ -585,20 +585,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.4900E-01 seconds\n", - " Reading cross sections = 1.2100E-01 seconds\n", - " Total time in simulation = 3.4132E+02 seconds\n", - " Time in transport only = 3.4128E+02 seconds\n", - " Time in inactive batches = 1.0748E+01 seconds\n", - " Time in active batches = 3.3057E+02 seconds\n", - " Time synchronizing fission bank = 1.1000E-02 seconds\n", - " Sampling source sites = 1.1000E-02 seconds\n", - " SEND/RECV source sites = 0.0000E+00 seconds\n", - " Time accumulating tallies = 1.9000E-02 seconds\n", - " Total time for finalization = 1.5600E-01 seconds\n", - " Total time elapsed = 3.4196E+02 seconds\n", - " Calculation Rate (inactive) = 4652.03 neutrons/second\n", - " Calculation Rate (active) = 1361.27 neutrons/second\n", + " Total time for initialization = 3.5100E-01 seconds\n", + " Reading cross sections = 1.8600E-01 seconds\n", + " Total time in simulation = 3.1672E+02 seconds\n", + " Time in transport only = 3.1667E+02 seconds\n", + " Time in inactive batches = 1.0782E+01 seconds\n", + " Time in active batches = 3.0594E+02 seconds\n", + " Time synchronizing fission bank = 2.1000E-02 seconds\n", + " Sampling source sites = 1.2000E-02 seconds\n", + " SEND/RECV source sites = 9.0000E-03 seconds\n", + " Time accumulating tallies = 1.7000E-02 seconds\n", + " Total time for finalization = 1.8100E-01 seconds\n", + " Total time elapsed = 3.1729E+02 seconds\n", + " Calculation Rate (inactive) = 4637.36 neutrons/second\n", + " Calculation Rate (active) = 1470.89 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -855,7 +855,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 24, @@ -866,7 +866,7 @@ "data": { "image/png": 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dF1kKr3FWfMAV8zaXmw+QtQElb5BFNrAQ6eDGqfYIjVWYT25zJI9RJ8BZHrBM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\nHip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qUWM9BKc/isdYh6muBNGcDzU6CErINWJzbqCHr4R7tq6hJPpEDCk7MK2qwx/f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qYxdsNyiiu3cKbqMY5G3EC/3cmxqy+nwGQlO3geid4iCp77AqkoG0U6hdrgxxm5BlUvZBMfMkFc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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1107,7 +1107,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/tally-arithmetic.ipynb b/docs/source/pythonapi/examples/tally-arithmetic.ipynb index 56b3cb45c..dfd493f16 100644 --- a/docs/source/pythonapi/examples/tally-arithmetic.ipynb +++ b/docs/source/pythonapi/examples/tally-arithmetic.ipynb @@ -45,12 +45,12 @@ "outputs": [], "source": [ "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H-1')\n", - "b10 = openmc.Nuclide('B-10')\n", - "o16 = openmc.Nuclide('O-16')\n", - "u235 = openmc.Nuclide('U-235')\n", - "u238 = openmc.Nuclide('U-238')\n", - "zr90 = openmc.Nuclide('Zr-90')" + "h1 = openmc.Nuclide('H1')\n", + "b10 = openmc.Nuclide('B10')\n", + "o16 = openmc.Nuclide('O16')\n", + "u235 = openmc.Nuclide('U235')\n", + "u238 = openmc.Nuclide('U238')\n", + "zr90 = openmc.Nuclide('Zr90')" ] }, { @@ -339,7 +339,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -540,28 +540,28 @@ " 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 14:51:45\n", + " Git SHA1: 3d68c07625e33cd64188df03ee03e9c31b3d4b74\n", + " Date/Time: 2016-07-22 21:39:46\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", " ===========================================================================\n", "\n", " Reading settings XML file...\n", - " Reading cross sections XML file...\n", " Reading geometry XML file...\n", + " Reading cross sections XML file...\n", " Reading materials XML file...\n", + " Reading U235.71c from /home/romano/openmc/data/nndc_hdf5/U235_71c.h5\n", + " Reading U238.71c from /home/romano/openmc/data/nndc_hdf5/U238_71c.h5\n", + " Reading O16.71c from /home/romano/openmc/data/nndc_hdf5/O16_71c.h5\n", + " Reading H1.71c from /home/romano/openmc/data/nndc_hdf5/H1_71c.h5\n", + " Reading B10.71c from /home/romano/openmc/data/nndc_hdf5/B10_71c.h5\n", + " Reading Zr90.71c from /home/romano/openmc/data/nndc_hdf5/Zr90_71c.h5\n", + " Maximum neutron transport energy: 20.0000 MeV for U235.71c\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", - " Loading ACE cross section table: 92235.71c\n", - " Loading ACE cross section table: 92238.71c\n", - " Loading ACE cross section table: 8016.71c\n", - " Loading ACE cross section table: 1001.71c\n", - " Loading ACE cross section table: 5010.71c\n", - " Loading ACE cross section table: 40090.71c\n", - " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", " Initializing source particles...\n", "\n", " ===========================================================================\n", @@ -599,20 +599,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 7.2500E-01 seconds\n", - " Reading cross sections = 4.4400E-01 seconds\n", - " Total time in simulation = 1.5547E+01 seconds\n", - " Time in transport only = 1.5527E+01 seconds\n", - " Time in inactive batches = 2.2880E+00 seconds\n", - " Time in active batches = 1.3259E+01 seconds\n", + " Total time for initialization = 3.5600E-01 seconds\n", + " Reading cross sections = 2.3400E-01 seconds\n", + " Total time in simulation = 1.8333E+01 seconds\n", + " Time in transport only = 1.8325E+01 seconds\n", + " Time in inactive batches = 2.6950E+00 seconds\n", + " Time in active batches = 1.5638E+01 seconds\n", " Time synchronizing fission bank = 1.0000E-03 seconds\n", " Sampling source sites = 0.0000E+00 seconds\n", - " SEND/RECV source sites = 0.0000E+00 seconds\n", - " Time accumulating tallies = 1.0000E-03 seconds\n", - " Total time for finalization = 2.0000E-03 seconds\n", - " Total time elapsed = 1.6291E+01 seconds\n", - " Calculation Rate (inactive) = 5463.29 neutrons/second\n", - " Calculation Rate (active) = 2828.27 neutrons/second\n", + " SEND/RECV source sites = 1.0000E-03 seconds\n", + " Time accumulating tallies = 0.0000E+00 seconds\n", + " Total time for finalization = 1.0000E-03 seconds\n", + " Total time elapsed = 1.8711E+01 seconds\n", + " Calculation Rate (inactive) = 4638.22 neutrons/second\n", + " Calculation Rate (active) = 2398.00 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1107,7 +1107,7 @@ " 10000\n", " 0.000000e+00\n", " 6.250000e-07\n", - " (U-238 / total)\n", + " (U238 / total)\n", " (nu-fission / flux)\n", " 6.636968e-07\n", " 4.132875e-09\n", @@ -1117,7 +1117,7 @@ " 10000\n", " 0.000000e+00\n", " 6.250000e-07\n", - " (U-238 / total)\n", + " (U238 / total)\n", " (scatter / flux)\n", " 2.099856e-01\n", " 1.232455e-03\n", @@ -1127,7 +1127,7 @@ " 10000\n", " 0.000000e+00\n", " 6.250000e-07\n", - " (U-235 / total)\n", + " (U235 / total)\n", " (nu-fission / flux)\n", " 3.552458e-01\n", " 2.252681e-03\n", @@ -1137,7 +1137,7 @@ " 10000\n", " 0.000000e+00\n", " 6.250000e-07\n", - " (U-235 / total)\n", + " (U235 / total)\n", " (scatter / flux)\n", " 5.554345e-03\n", " 3.265385e-05\n", @@ -1147,7 +1147,7 @@ " 10000\n", " 6.250000e-07\n", " 2.000000e+01\n", - " (U-238 / total)\n", + " (U238 / total)\n", " (nu-fission / flux)\n", " 7.126668e-03\n", " 5.296883e-05\n", @@ -1157,7 +1157,7 @@ " 10000\n", " 6.250000e-07\n", " 2.000000e+01\n", - " (U-238 / total)\n", + " (U238 / total)\n", " (scatter / flux)\n", " 2.277460e-01\n", " 1.003558e-03\n", @@ -1167,7 +1167,7 @@ " 10000\n", " 6.250000e-07\n", " 2.000000e+01\n", - " (U-235 / total)\n", + " (U235 / total)\n", " (nu-fission / flux)\n", " 8.010911e-03\n", " 6.802256e-05\n", @@ -1177,7 +1177,7 @@ " 10000\n", " 6.250000e-07\n", " 2.000000e+01\n", - " (U-235 / total)\n", + " (U235 / total)\n", " (scatter / flux)\n", " 3.367794e-03\n", " 1.443644e-05\n", @@ -1187,15 +1187,15 @@ "" ], "text/plain": [ - " cell energy low [MeV] energy high [MeV] nuclide \\\n", - "0 10000 0.00e+00 6.25e-07 (U-238 / total) \n", - "1 10000 0.00e+00 6.25e-07 (U-238 / total) \n", - "2 10000 0.00e+00 6.25e-07 (U-235 / total) \n", - "3 10000 0.00e+00 6.25e-07 (U-235 / total) \n", - "4 10000 6.25e-07 2.00e+01 (U-238 / total) \n", - "5 10000 6.25e-07 2.00e+01 (U-238 / total) \n", - "6 10000 6.25e-07 2.00e+01 (U-235 / total) \n", - "7 10000 6.25e-07 2.00e+01 (U-235 / total) \n", + " cell energy low [MeV] energy high [MeV] nuclide \\\n", + "0 10000 0.00e+00 6.25e-07 (U238 / total) \n", + "1 10000 0.00e+00 6.25e-07 (U238 / total) \n", + "2 10000 0.00e+00 6.25e-07 (U235 / total) \n", + "3 10000 0.00e+00 6.25e-07 (U235 / total) \n", + "4 10000 6.25e-07 2.00e+01 (U238 / total) \n", + "5 10000 6.25e-07 2.00e+01 (U238 / total) \n", + "6 10000 6.25e-07 2.00e+01 (U235 / total) \n", + "7 10000 6.25e-07 2.00e+01 (U235 / total) \n", "\n", " score mean std. dev. \n", "0 (nu-fission / flux) 6.64e-07 4.13e-09 \n", @@ -1276,7 +1276,7 @@ ], "source": [ "# Show how to use Tally.get_values(...) with a CrossScore and CrossNuclide\n", - "u235_scatter_xs = fuel_xs.get_values(nuclides=['(U-235 / total)'], \n", + "u235_scatter_xs = fuel_xs.get_values(nuclides=['(U235 / total)'], \n", " scores=['(scatter / flux)'])\n", "print(u235_scatter_xs)" ] @@ -1342,7 +1342,7 @@ " 10000\n", " 0.000000e+00\n", " 6.250000e-07\n", - " U-238\n", + " U238\n", " nu-fission\n", " 0.000002\n", " 7.473789e-09\n", @@ -1352,7 +1352,7 @@ " 10000\n", " 0.000000e+00\n", " 6.250000e-07\n", - " U-235\n", + " U235\n", " nu-fission\n", " 0.861547\n", " 4.131310e-03\n", @@ -1362,7 +1362,7 @@ " 10000\n", " 6.250000e-07\n", " 2.000000e+01\n", - " U-238\n", + " U238\n", " nu-fission\n", " 0.082356\n", " 5.560461e-04\n", @@ -1372,7 +1372,7 @@ " 10000\n", " 6.250000e-07\n", " 2.000000e+01\n", - " U-235\n", + " U235\n", " nu-fission\n", " 0.092574\n", " 7.315442e-04\n", @@ -1383,10 +1383,10 @@ ], "text/plain": [ " cell energy low [MeV] energy high [MeV] nuclide score mean \\\n", - "0 10000 0.00e+00 6.25e-07 U-238 nu-fission 1.61e-06 \n", - "1 10000 0.00e+00 6.25e-07 U-235 nu-fission 8.62e-01 \n", - "2 10000 6.25e-07 2.00e+01 U-238 nu-fission 8.24e-02 \n", - "3 10000 6.25e-07 2.00e+01 U-235 nu-fission 9.26e-02 \n", + "0 10000 0.00e+00 6.25e-07 U238 nu-fission 1.61e-06 \n", + "1 10000 0.00e+00 6.25e-07 U235 nu-fission 8.62e-01 \n", + "2 10000 6.25e-07 2.00e+01 U238 nu-fission 8.24e-02 \n", + "3 10000 6.25e-07 2.00e+01 U235 nu-fission 9.26e-02 \n", "\n", " std. dev. \n", "0 7.47e-09 \n", @@ -1436,7 +1436,7 @@ " 10002\n", " 1.000000e-08\n", " 1.080060e-07\n", - " H-1\n", + " H1\n", " scatter\n", " 4.599225\n", " 0.015973\n", @@ -1446,7 +1446,7 @@ " 10002\n", " 1.080060e-07\n", " 1.166529e-06\n", - " H-1\n", + " H1\n", " scatter\n", " 2.037260\n", " 0.011236\n", @@ -1456,7 +1456,7 @@ " 10002\n", " 1.166529e-06\n", " 1.259921e-05\n", - " H-1\n", + " H1\n", " scatter\n", " 1.662552\n", " 0.010280\n", @@ -1466,7 +1466,7 @@ " 10002\n", " 1.259921e-05\n", " 1.360790e-04\n", - " H-1\n", + " H1\n", " scatter\n", " 1.872201\n", " 0.012136\n", @@ -1476,7 +1476,7 @@ " 10002\n", " 1.360790e-04\n", " 1.469734e-03\n", - " H-1\n", + " H1\n", " scatter\n", " 2.080459\n", " 0.013155\n", @@ -1486,7 +1486,7 @@ " 10002\n", " 1.469734e-03\n", " 1.587401e-02\n", - " H-1\n", + " H1\n", " scatter\n", " 2.154996\n", " 0.011975\n", @@ -1496,7 +1496,7 @@ " 10002\n", " 1.587401e-02\n", " 1.714488e-01\n", - " H-1\n", + " H1\n", " scatter\n", " 2.218740\n", " 0.008528\n", @@ -1506,7 +1506,7 @@ " 10002\n", " 1.714488e-01\n", " 1.851749e+00\n", - " H-1\n", + " H1\n", " scatter\n", " 2.010517\n", " 0.009187\n", @@ -1516,7 +1516,7 @@ " 10002\n", " 1.851749e+00\n", " 2.000000e+01\n", - " H-1\n", + " H1\n", " scatter\n", " 0.372022\n", " 0.003196\n", @@ -1527,15 +1527,15 @@ ], "text/plain": [ " cell energy low [MeV] energy high [MeV] nuclide score mean \\\n", - "0 10002 1.00e-08 1.08e-07 H-1 scatter 4.60e+00 \n", - "1 10002 1.08e-07 1.17e-06 H-1 scatter 2.04e+00 \n", - "2 10002 1.17e-06 1.26e-05 H-1 scatter 1.66e+00 \n", - "3 10002 1.26e-05 1.36e-04 H-1 scatter 1.87e+00 \n", - "4 10002 1.36e-04 1.47e-03 H-1 scatter 2.08e+00 \n", - "5 10002 1.47e-03 1.59e-02 H-1 scatter 2.15e+00 \n", - "6 10002 1.59e-02 1.71e-01 H-1 scatter 2.22e+00 \n", - "7 10002 1.71e-01 1.85e+00 H-1 scatter 2.01e+00 \n", - "8 10002 1.85e+00 2.00e+01 H-1 scatter 3.72e-01 \n", + "0 10002 1.00e-08 1.08e-07 H1 scatter 4.60e+00 \n", + "1 10002 1.08e-07 1.17e-06 H1 scatter 2.04e+00 \n", + "2 10002 1.17e-06 1.26e-05 H1 scatter 1.66e+00 \n", + "3 10002 1.26e-05 1.36e-04 H1 scatter 1.87e+00 \n", + "4 10002 1.36e-04 1.47e-03 H1 scatter 2.08e+00 \n", + "5 10002 1.47e-03 1.59e-02 H1 scatter 2.15e+00 \n", + "6 10002 1.59e-02 1.71e-01 H1 scatter 2.22e+00 \n", + "7 10002 1.71e-01 1.85e+00 H1 scatter 2.01e+00 \n", + "8 10002 1.85e+00 2.00e+01 H1 scatter 3.72e-01 \n", "\n", " std. dev. \n", "0 1.60e-02 \n", @@ -1557,7 +1557,7 @@ "source": [ "# \"Slice\" the H-1 scatter data in the moderator Cell into a new derived Tally\n", "need_to_slice = sp.get_tally(name='need-to-slice')\n", - "slice_test = need_to_slice.get_slice(scores=['scatter'], nuclides=['H-1'],\n", + "slice_test = need_to_slice.get_slice(scores=['scatter'], nuclides=['H1'],\n", " filters=['cell'], filter_bins=[(moderator_cell.id,)])\n", "slice_test.get_pandas_dataframe()" ] @@ -1579,7 +1579,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/openmc/data/angle_energy.py b/openmc/data/angle_energy.py index c20c5f0ff..00e049ebc 100644 --- a/openmc/data/angle_energy.py +++ b/openmc/data/angle_energy.py @@ -102,7 +102,7 @@ class AngleEnergy(object): distribution = openmc.data.NBodyPhaseSpace.from_ace( ace, idx, rx.q_value) else: - raise IOError("Unsupported ACE secondary energy " - "distribution law {0}".format(law)) + raise ValueError("Unsupported ACE secondary energy " + "distribution law {}".format(law)) return distribution diff --git a/openmc/data/data.py b/openmc/data/data.py index 5f83d31c0..21fffc8cb 100644 --- a/openmc/data/data.py +++ b/openmc/data/data.py @@ -2,102 +2,102 @@ # of the elements 2009 (IUPAC Technical Report)", Pure. Appl. Chem. 83 (2), # pp. 397--410 (2011). NATURAL_ABUNDANCE = { - 'H-1': 0.999885, 'H-2': 0.000115, 'He-3': 1.34e-06, - 'He-4': 0.99999866, 'Li-6': 0.0759, 'Li-7': 0.9241, - 'Be-9': 1.0, 'B-10': 0.199, 'B-11': 0.801, - 'C-12': 0.9893, 'C-13': 0.0107, 'N-14': 0.99636, - 'N-15': 0.00364, 'O-16': 0.99757, 'O-17': 0.00038, - 'O-18': 0.00205, 'F-19': 1.0, 'Ne-20': 0.9048, - 'Ne-21': 0.0027, 'Ne-22': 0.0925, 'Na-23': 1.0, - 'Mg-24': 0.7899, 'Mg-25': 0.1, 'Mg-26': 0.1101, - 'Al-27': 1.0, 'Si-28': 0.92223, 'Si-29': 0.04685, - 'Si-30': 0.03092, 'P-31': 1.0, 'S-32': 0.9499, - 'S-33': 0.0075, 'S-34': 0.0425, 'S-36': 0.0001, - 'Cl-35': 0.7576, 'Cl-37': 0.2424, 'Ar-36': 0.003336, - 'Ar-38': 0.000629, 'Ar-40': 0.996035, 'K-39': 0.932581, - 'K-40': 0.000117, 'K-41': 0.067302, 'Ca-40': 0.96941, - 'Ca-42': 0.00647, 'Ca-43': 0.00135, 'Ca-44': 0.02086, - 'Ca-46': 4e-05, 'Ca-48': 0.00187, 'Sc-45': 1.0, - 'Ti-46': 0.0825, 'Ti-47': 0.0744, 'Ti-48': 0.7372, - 'Ti-49': 0.0541, 'Ti-50': 0.0518, 'V-50': 0.0025, - 'V-51': 0.9975, 'Cr-50': 0.04345, 'Cr-52': 0.83789, - 'Cr-53': 0.09501, 'Cr-54': 0.02365, 'Mn-55': 1.0, - 'Fe-54': 0.05845, 'Fe-56': 0.91754, 'Fe-57': 0.02119, - 'Fe-58': 0.00282, 'Co-59': 1.0, 'Ni-58': 0.68077, - 'Ni-60': 0.26223, 'Ni-61': 0.011399, 'Ni-62': 0.036346, - 'Ni-64': 0.009255, 'Cu-63': 0.6915, 'Cu-65': 0.3085, - 'Zn-64': 0.4917, 'Zn-66': 0.2773, 'Zn-67': 0.0404, - 'Zn-68': 0.1845, 'Zn-70': 0.0061, 'Ga-69': 0.60108, - 'Ga-71': 0.39892, 'Ge-70': 0.2057, 'Ge-72': 0.2745, - 'Ge-73': 0.0775, 'Ge-74': 0.365, 'Ge-76': 0.0773, - 'As-75': 1.0, 'Se-74': 0.0089, 'Se-76': 0.0937, - 'Se-77': 0.0763, 'Se-78': 0.2377, 'Se-80': 0.4961, - 'Se-82': 0.0873, 'Br-79': 0.5069, 'Br-81': 0.4931, - 'Kr-78': 0.00355, 'Kr-80': 0.02286, 'Kr-82': 0.11593, - 'Kr-83': 0.115, 'Kr-84': 0.56987, 'Kr-86': 0.17279, - 'Rb-85': 0.7217, 'Rb-87': 0.2783, 'Sr-84': 0.0056, - 'Sr-86': 0.0986, 'Sr-87': 0.07, 'Sr-88': 0.8258, - 'Y-89': 1.0, 'Zr-90': 0.5145, 'Zr-91': 0.1122, - 'Zr-92': 0.1715, 'Zr-94': 0.1738, 'Zr-96': 0.028, - 'Nb-93': 1.0, 'Mo-92': 0.1453, 'Mo-94': 0.0915, - 'Mo-95': 0.1584, 'Mo-96': 0.1667, 'Mo-97': 0.096, - 'Mo-98': 0.2439, 'Mo-100': 0.0982, 'Ru-96': 0.0554, - 'Ru-98': 0.0187, 'Ru-99': 0.1276, 'Ru-100': 0.126, - 'Ru-101': 0.1706, 'Ru-102': 0.3155, 'Ru-104': 0.1862, - 'Rh-103': 1.0, 'Pd-102': 0.0102, 'Pd-104': 0.1114, - 'Pd-105': 0.2233, 'Pd-106': 0.2733, 'Pd-108': 0.2646, - 'Pd-110': 0.1172, 'Ag-107': 0.51839, 'Ag-109': 0.48161, - 'Cd-106': 0.0125, 'Cd-108': 0.0089, 'Cd-110': 0.1249, - 'Cd-111': 0.128, 'Cd-112': 0.2413, 'Cd-113': 0.1222, - 'Cd-114': 0.2873, 'Cd-116': 0.0749, 'In-113': 0.0429, - 'In-115': 0.9571, 'Sn-112': 0.0097, 'Sn-114': 0.0066, - 'Sn-115': 0.0034, 'Sn-116': 0.1454, 'Sn-117': 0.0768, - 'Sn-118': 0.2422, 'Sn-119': 0.0859, 'Sn-120': 0.3258, - 'Sn-122': 0.0463, 'Sn-124': 0.0579, 'Sb-121': 0.5721, - 'Sb-123': 0.4279, 'Te-120': 0.0009, 'Te-122': 0.0255, - 'Te-123': 0.0089, 'Te-124': 0.0474, 'Te-125': 0.0707, - 'Te-126': 0.1884, 'Te-128': 0.3174, 'Te-130': 0.3408, - 'I-127': 1.0, 'Xe-124': 0.000952, 'Xe-126': 0.00089, - 'Xe-128': 0.019102, 'Xe-129': 0.264006, 'Xe-130': 0.04071, - 'Xe-131': 0.212324, 'Xe-132': 0.269086, 'Xe-134': 0.104357, - 'Xe-136': 0.088573, 'Cs-133': 1.0, 'Ba-130': 0.00106, - 'Ba-132': 0.00101, 'Ba-134': 0.02417, 'Ba-135': 0.06592, - 'Ba-136': 0.07854, 'Ba-137': 0.11232, 'Ba-138': 0.71698, - 'La-138': 0.0008881, 'La-139': 0.9991119, 'Ce-136': 0.00185, - 'Ce-138': 0.00251, 'Ce-140': 0.8845, 'Ce-142': 0.11114, - 'Pr-141': 1.0, 'Nd-142': 0.27152, 'Nd-143': 0.12174, - 'Nd-144': 0.23798, 'Nd-145': 0.08293, 'Nd-146': 0.17189, - 'Nd-148': 0.05756, 'Nd-150': 0.05638, 'Sm-144': 0.0307, - 'Sm-147': 0.1499, 'Sm-148': 0.1124, 'Sm-149': 0.1382, - 'Sm-150': 0.0738, 'Sm-152': 0.2675, 'Sm-154': 0.2275, - 'Eu-151': 0.4781, 'Eu-153': 0.5219, 'Gd-152': 0.002, - 'Gd-154': 0.0218, 'Gd-155': 0.148, 'Gd-156': 0.2047, - 'Gd-157': 0.1565, 'Gd-158': 0.2484, 'Gd-160': 0.2186, - 'Tb-159': 1.0, 'Dy-156': 0.00056, 'Dy-158': 0.00095, - 'Dy-160': 0.02329, 'Dy-161': 0.18889, 'Dy-162': 0.25475, - 'Dy-163': 0.24896, 'Dy-164': 0.2826, 'Ho-165': 1.0, - 'Er-162': 0.00139, 'Er-164': 0.01601, 'Er-166': 0.33503, - 'Er-167': 0.22869, 'Er-168': 0.26978, 'Er-170': 0.1491, - 'Tm-169': 1.0, 'Yb-168': 0.00123, 'Yb-170': 0.02982, - 'Yb-171': 0.1409, 'Yb-172': 0.2168, 'Yb-173': 0.16103, - 'Yb-174': 0.32026, 'Yb-176': 0.12996, 'Lu-175': 0.97401, - 'Lu-176': 0.02599, 'Hf-174': 0.0016, 'Hf-176': 0.0526, - 'Hf-177': 0.186, 'Hf-178': 0.2728, 'Hf-179': 0.1362, - 'Hf-180': 0.3508, 'Ta-180': 0.0001201, 'Ta-181': 0.9998799, - 'W-180': 0.0012, 'W-182': 0.265, 'W-183': 0.1431, - 'W-184': 0.3064, 'W-186': 0.2843, 'Re-185': 0.374, - 'Re-187': 0.626, 'Os-184': 0.0002, 'Os-186': 0.0159, - 'Os-187': 0.0196, 'Os-188': 0.1324, 'Os-189': 0.1615, - 'Os-190': 0.2626, 'Os-192': 0.4078, 'Ir-191': 0.373, - 'Ir-193': 0.627, 'Pt-190': 0.00012, 'Pt-192': 0.00782, - 'Pt-194': 0.3286, 'Pt-195': 0.3378, 'Pt-196': 0.2521, - 'Pt-198': 0.07356, 'Au-197': 1.0, 'Hg-196': 0.0015, - 'Hg-198': 0.0997, 'Hg-199': 0.1687, 'Hg-200': 0.231, - 'Hg-201': 0.1318, 'Hg-202': 0.2986, 'Hg-204': 0.0687, - 'Tl-203': 0.2952, 'Tl-205': 0.7048, 'Pb-204': 0.014, - 'Pb-206': 0.241, 'Pb-207': 0.221, 'Pb-208': 0.524, - 'Bi-209': 1.0, 'Th-232': 1.0, 'Pa-231': 1.0, - 'U-234': 5.4e-05, 'U-235': 0.007204, 'U-238': 0.992742 + 'H1': 0.999885, 'H2': 0.000115, 'He3': 1.34e-06, + 'He4': 0.99999866, 'Li6': 0.0759, 'Li7': 0.9241, + 'Be9': 1.0, 'B10': 0.199, 'B11': 0.801, + 'C12': 0.9893, 'C13': 0.0107, 'N14': 0.99636, + 'N15': 0.00364, 'O16': 0.99757, 'O17': 0.00038, + 'O18': 0.00205, 'F19': 1.0, 'Ne20': 0.9048, + 'Ne21': 0.0027, 'Ne22': 0.0925, 'Na23': 1.0, + 'Mg24': 0.7899, 'Mg25': 0.1, 'Mg26': 0.1101, + 'Al27': 1.0, 'Si28': 0.92223, 'Si29': 0.04685, + 'Si30': 0.03092, 'P31': 1.0, 'S32': 0.9499, + 'S33': 0.0075, 'S34': 0.0425, 'S36': 0.0001, + 'Cl35': 0.7576, 'Cl37': 0.2424, 'Ar36': 0.003336, + 'Ar38': 0.000629, 'Ar40': 0.996035, 'K39': 0.932581, + 'K40': 0.000117, 'K41': 0.067302, 'Ca40': 0.96941, + 'Ca42': 0.00647, 'Ca43': 0.00135, 'Ca44': 0.02086, + 'Ca46': 4e-05, 'Ca48': 0.00187, 'Sc45': 1.0, + 'Ti46': 0.0825, 'Ti47': 0.0744, 'Ti48': 0.7372, + 'Ti49': 0.0541, 'Ti50': 0.0518, 'V50': 0.0025, + 'V51': 0.9975, 'Cr50': 0.04345, 'Cr52': 0.83789, + 'Cr53': 0.09501, 'Cr54': 0.02365, 'Mn55': 1.0, + 'Fe54': 0.05845, 'Fe56': 0.91754, 'Fe57': 0.02119, + 'Fe58': 0.00282, 'Co59': 1.0, 'Ni58': 0.68077, + 'Ni60': 0.26223, 'Ni61': 0.011399, 'Ni62': 0.036346, + 'Ni64': 0.009255, 'Cu63': 0.6915, 'Cu65': 0.3085, + 'Zn64': 0.4917, 'Zn66': 0.2773, 'Zn67': 0.0404, + 'Zn68': 0.1845, 'Zn70': 0.0061, 'Ga69': 0.60108, + 'Ga71': 0.39892, 'Ge70': 0.2057, 'Ge72': 0.2745, + 'Ge73': 0.0775, 'Ge74': 0.365, 'Ge76': 0.0773, + 'As75': 1.0, 'Se74': 0.0089, 'Se76': 0.0937, + 'Se77': 0.0763, 'Se78': 0.2377, 'Se80': 0.4961, + 'Se82': 0.0873, 'Br79': 0.5069, 'Br81': 0.4931, + 'Kr78': 0.00355, 'Kr80': 0.02286, 'Kr82': 0.11593, + 'Kr83': 0.115, 'Kr84': 0.56987, 'Kr86': 0.17279, + 'Rb85': 0.7217, 'Rb87': 0.2783, 'Sr84': 0.0056, + 'Sr86': 0.0986, 'Sr87': 0.07, 'Sr88': 0.8258, + 'Y89': 1.0, 'Zr90': 0.5145, 'Zr91': 0.1122, + 'Zr92': 0.1715, 'Zr94': 0.1738, 'Zr96': 0.028, + 'Nb93': 1.0, 'Mo92': 0.1453, 'Mo94': 0.0915, + 'Mo95': 0.1584, 'Mo96': 0.1667, 'Mo97': 0.096, + 'Mo98': 0.2439, 'Mo100': 0.0982, 'Ru96': 0.0554, + 'Ru98': 0.0187, 'Ru99': 0.1276, 'Ru100': 0.126, + 'Ru101': 0.1706, 'Ru102': 0.3155, 'Ru104': 0.1862, + 'Rh103': 1.0, 'Pd102': 0.0102, 'Pd104': 0.1114, + 'Pd105': 0.2233, 'Pd106': 0.2733, 'Pd108': 0.2646, + 'Pd110': 0.1172, 'Ag107': 0.51839, 'Ag109': 0.48161, + 'Cd106': 0.0125, 'Cd108': 0.0089, 'Cd110': 0.1249, + 'Cd111': 0.128, 'Cd112': 0.2413, 'Cd113': 0.1222, + 'Cd114': 0.2873, 'Cd116': 0.0749, 'In113': 0.0429, + 'In115': 0.9571, 'Sn112': 0.0097, 'Sn114': 0.0066, + 'Sn115': 0.0034, 'Sn116': 0.1454, 'Sn117': 0.0768, + 'Sn118': 0.2422, 'Sn119': 0.0859, 'Sn120': 0.3258, + 'Sn122': 0.0463, 'Sn124': 0.0579, 'Sb121': 0.5721, + 'Sb123': 0.4279, 'Te120': 0.0009, 'Te122': 0.0255, + 'Te123': 0.0089, 'Te124': 0.0474, 'Te125': 0.0707, + 'Te126': 0.1884, 'Te128': 0.3174, 'Te130': 0.3408, + 'I127': 1.0, 'Xe124': 0.000952, 'Xe126': 0.00089, + 'Xe128': 0.019102, 'Xe129': 0.264006, 'Xe130': 0.04071, + 'Xe131': 0.212324, 'Xe132': 0.269086, 'Xe134': 0.104357, + 'Xe136': 0.088573, 'Cs133': 1.0, 'Ba130': 0.00106, + 'Ba132': 0.00101, 'Ba134': 0.02417, 'Ba135': 0.06592, + 'Ba136': 0.07854, 'Ba137': 0.11232, 'Ba138': 0.71698, + 'La138': 0.0008881, 'La139': 0.9991119, 'Ce136': 0.00185, + 'Ce138': 0.00251, 'Ce140': 0.8845, 'Ce142': 0.11114, + 'Pr141': 1.0, 'Nd142': 0.27152, 'Nd143': 0.12174, + 'Nd144': 0.23798, 'Nd145': 0.08293, 'Nd146': 0.17189, + 'Nd148': 0.05756, 'Nd150': 0.05638, 'Sm144': 0.0307, + 'Sm147': 0.1499, 'Sm148': 0.1124, 'Sm149': 0.1382, + 'Sm150': 0.0738, 'Sm152': 0.2675, 'Sm154': 0.2275, + 'Eu151': 0.4781, 'Eu153': 0.5219, 'Gd152': 0.002, + 'Gd154': 0.0218, 'Gd155': 0.148, 'Gd156': 0.2047, + 'Gd157': 0.1565, 'Gd158': 0.2484, 'Gd160': 0.2186, + 'Tb159': 1.0, 'Dy156': 0.00056, 'Dy158': 0.00095, + 'Dy160': 0.02329, 'Dy161': 0.18889, 'Dy162': 0.25475, + 'Dy163': 0.24896, 'Dy164': 0.2826, 'Ho165': 1.0, + 'Er162': 0.00139, 'Er164': 0.01601, 'Er166': 0.33503, + 'Er167': 0.22869, 'Er168': 0.26978, 'Er170': 0.1491, + 'Tm169': 1.0, 'Yb168': 0.00123, 'Yb170': 0.02982, + 'Yb171': 0.1409, 'Yb172': 0.2168, 'Yb173': 0.16103, + 'Yb174': 0.32026, 'Yb176': 0.12996, 'Lu175': 0.97401, + 'Lu176': 0.02599, 'Hf174': 0.0016, 'Hf176': 0.0526, + 'Hf177': 0.186, 'Hf178': 0.2728, 'Hf179': 0.1362, + 'Hf180': 0.3508, 'Ta180': 0.0001201, 'Ta181': 0.9998799, + 'W180': 0.0012, 'W182': 0.265, 'W183': 0.1431, + 'W184': 0.3064, 'W186': 0.2843, 'Re185': 0.374, + 'Re187': 0.626, 'Os184': 0.0002, 'Os186': 0.0159, + 'Os187': 0.0196, 'Os188': 0.1324, 'Os189': 0.1615, + 'Os190': 0.2626, 'Os192': 0.4078, 'Ir191': 0.373, + 'Ir193': 0.627, 'Pt190': 0.00012, 'Pt192': 0.00782, + 'Pt194': 0.3286, 'Pt195': 0.3378, 'Pt196': 0.2521, + 'Pt198': 0.07356, 'Au197': 1.0, 'Hg196': 0.0015, + 'Hg198': 0.0997, 'Hg199': 0.1687, 'Hg200': 0.231, + 'Hg201': 0.1318, 'Hg202': 0.2986, 'Hg204': 0.0687, + 'Tl203': 0.2952, 'Tl205': 0.7048, 'Pb204': 0.014, + 'Pb206': 0.241, 'Pb207': 0.221, 'Pb208': 0.524, + 'Bi209': 1.0, 'Th232': 1.0, 'Pa231': 1.0, + 'U234': 5.4e-05, 'U235': 0.007204, 'U238': 0.992742 } ATOMIC_SYMBOL = {1: 'H', 2: 'He', 3: 'Li', 4: 'Be', 5: 'B', 6: 'C', 7: 'N', diff --git a/openmc/data/energy_distribution.py b/openmc/data/energy_distribution.py index 677d31af2..2300081c1 100644 --- a/openmc/data/energy_distribution.py +++ b/openmc/data/energy_distribution.py @@ -52,6 +52,9 @@ class EnergyDistribution(object): return LevelInelastic.from_hdf5(group) elif energy_type == 'continuous': return ContinuousTabular.from_hdf5(group) + else: + raise ValueError("Unknown energy distribution type: {}" + .format(energy_type)) class ArbitraryTabulated(EnergyDistribution): diff --git a/openmc/data/neutron.py b/openmc/data/neutron.py index b6a4f03b5..5b587745d 100644 --- a/openmc/data/neutron.py +++ b/openmc/data/neutron.py @@ -39,7 +39,7 @@ class IncidentNeutron(object): atomic_weight_ratio : float Atomic mass ratio of the target nuclide. temperature : float - Temperature of the target nuclide in eV. + Temperature of the target nuclide in MeV. Attributes ---------- diff --git a/openmc/data/reaction.py b/openmc/data/reaction.py index cadab0bf7..c2e1b2410 100644 --- a/openmc/data/reaction.py +++ b/openmc/data/reaction.py @@ -303,9 +303,9 @@ class Reaction(object): def __repr__(self): if self.mt in REACTION_NAME: - return "".format(self.mt, REACTION_NAME[self.mt]) + return "".format(self.mt, REACTION_NAME[self.mt]) else: - return "".format(self.mt) + return "".format(self.mt) @property def center_of_mass(self): diff --git a/openmc/element.py b/openmc/element.py index f116c43eb..ada5726b4 100644 --- a/openmc/element.py +++ b/openmc/element.py @@ -1,3 +1,4 @@ +import re import sys import openmc @@ -8,6 +9,7 @@ if sys.version_info[0] >= 3: basestring = str + class Element(object): """A natural element used in a material via . Internally, OpenMC will expand the natural element into isotopes based on the known natural @@ -124,7 +126,7 @@ class Element(object): isotopes = [] for isotope, abundance in sorted(NATURAL_ABUNDANCE.items()): - if isotope.startswith(self.name + '-'): + if re.match(r'{}\d+'.format(self.name), isotope): nuc = openmc.Nuclide(isotope, self.xs) isotopes.append((nuc, abundance)) return isotopes diff --git a/openmc/opencg_compatible.py b/openmc/opencg_compatible.py index 967e11f48..93a257f46 100644 --- a/openmc/opencg_compatible.py +++ b/openmc/opencg_compatible.py @@ -820,7 +820,7 @@ def get_opencg_lattice(openmc_lattice): # Create an OpenCG Lattice to represent this OpenMC Lattice name = openmc_lattice.name - dimension = openmc_lattice.dimension + dimension = openmc_lattice.shape pitch = openmc_lattice.pitch lower_left = openmc_lattice.lower_left universes = openmc_lattice.universes