diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index d97d01520..d56061f2c 100644 --- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb +++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb @@ -4,9 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook demonstrates how systematic analysis of tally scores is possible using Pandas dataframes. A dataframe can be automatically generated using the `Tally.get_pandas_dataframe(...)` method. Furthermore, by linking the tally data in a statepoint file with geometry and material information from a summary file, the dataframe can be shown with user-supplied labels.\n", - "\n", - "**Note:** that this Notebook was created using the latest Pandas v0.16.1. Everything in the Notebook will wun with older versions of Pandas, but the multi-indexing option in >v0.15.0 makes the tables look prettier." + "This notebook demonstrates how systematic analysis of tally scores is possible using Pandas dataframes. A dataframe can be automatically generated using the `Tally.get_pandas_dataframe(...)` method. Furthermore, by linking the tally data in a statepoint file with geometry and material information from a summary file, the dataframe can be shown with user-supplied labels." ] }, { @@ -17,7 +15,6 @@ }, "outputs": [], "source": [ - "%matplotlib inline\n", "import glob\n", "\n", "from IPython.display import Image\n", @@ -26,7 +23,8 @@ "import numpy as np\n", "import pandas as pd\n", "\n", - "import openmc" + "import openmc\n", + "%matplotlib inline" ] }, { @@ -40,36 +38,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First we need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the material." + "First we need to define materials that will be used in the problem. We will create three materials for the fuel, water, and cladding of the fuel pin." ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate some Nuclides\n", - "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')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the nuclides we defined, we will now create three materials for the fuel, water, and cladding of the fuel pin." - ] - }, - { - "cell_type": "code", - "execution_count": 3, "metadata": { "collapsed": false }, @@ -78,21 +52,21 @@ "# 1.6 enriched fuel\n", "fuel = openmc.Material(name='1.6% Fuel')\n", "fuel.set_density('g/cm3', 10.31341)\n", - "fuel.add_nuclide(u235, 3.7503e-4)\n", - "fuel.add_nuclide(u238, 2.2625e-2)\n", - "fuel.add_nuclide(o16, 4.6007e-2)\n", + "fuel.add_nuclide('U235', 3.7503e-4)\n", + "fuel.add_nuclide('U238', 2.2625e-2)\n", + "fuel.add_nuclide('O16', 4.6007e-2)\n", "\n", "# borated water\n", "water = openmc.Material(name='Borated Water')\n", "water.set_density('g/cm3', 0.740582)\n", - "water.add_nuclide(h1, 4.9457e-2)\n", - "water.add_nuclide(o16, 2.4732e-2)\n", - "water.add_nuclide(b10, 8.0042e-6)\n", + "water.add_nuclide('H1', 4.9457e-2)\n", + "water.add_nuclide('O16', 2.4732e-2)\n", + "water.add_nuclide('B10', 8.0042e-6)\n", "\n", "# zircaloy\n", "zircaloy = openmc.Material(name='Zircaloy')\n", "zircaloy.set_density('g/cm3', 6.55)\n", - "zircaloy.add_nuclide(zr90, 7.2758e-3)" + "zircaloy.add_nuclide('Zr90', 7.2758e-3)" ] }, { @@ -104,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -126,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -155,32 +129,28 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "# Create a Universe to encapsulate a fuel pin\n", - "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n", - "\n", "# Create fuel Cell\n", - "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", - "fuel_cell.fill = fuel\n", - "fuel_cell.region = -fuel_outer_radius\n", - "pin_cell_universe.add_cell(fuel_cell)\n", + "fuel_cell = openmc.Cell(name='1.6% Fuel', fill=fuel,\n", + " region=-fuel_outer_radius)\n", "\n", "# Create a clad Cell\n", - "clad_cell = openmc.Cell(name='1.6% Clad')\n", - "clad_cell.fill = zircaloy\n", + "clad_cell = openmc.Cell(name='1.6% Clad', fill=zircaloy)\n", "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", - "pin_cell_universe.add_cell(clad_cell)\n", "\n", "# Create a moderator Cell\n", - "moderator_cell = openmc.Cell(name='1.6% Moderator')\n", - "moderator_cell.fill = water\n", - "moderator_cell.region = +clad_outer_radius\n", - "pin_cell_universe.add_cell(moderator_cell)" + "moderator_cell = openmc.Cell(name='1.6% Moderator', fill=water,\n", + " region=+clad_outer_radius)\n", + "\n", + "# Create a Universe to encapsulate a fuel pin\n", + "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin', cells=[\n", + " fuel_cell, clad_cell, moderator_cell\n", + "])" ] }, { @@ -192,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -214,21 +184,20 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create root Cell\n", - "root_cell = openmc.Cell(name='root cell')\n", - "root_cell.fill = assembly\n", + "root_cell = openmc.Cell(name='root cell', fill=assembly)\n", "\n", "# Add boundary planes\n", "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n", "\n", "# Create root Universe\n", - "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", + "root_universe = openmc.Universe(name='root universe')\n", "root_universe.add_cell(root_cell)" ] }, @@ -241,26 +210,14 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Create Geometry and set root Universe\n", - "geometry = openmc.Geometry()\n", - "geometry.root_universe = root_universe" - ] - }, - { - "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "# Export to \"geometry.xml\"\n", + "# Create Geometry and export to \"geometry.xml\"\n", + "geometry = openmc.Geometry(root_universe)\n", "geometry.export_to_xml()" ] }, @@ -273,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -286,21 +243,21 @@ "particles = 2500\n", "\n", "# Instantiate a Settings object\n", - "settings_file = openmc.Settings()\n", - "settings_file.batches = min_batches\n", - "settings_file.inactive = inactive\n", - "settings_file.particles = particles\n", - "settings_file.output = {'tallies': False}\n", - "settings_file.trigger_active = True\n", - "settings_file.trigger_max_batches = max_batches\n", + "settings = openmc.Settings()\n", + "settings.batches = min_batches\n", + "settings.inactive = inactive\n", + "settings.particles = particles\n", + "settings.output = {'tallies': False}\n", + "settings.trigger_active = True\n", + "settings.trigger_max_batches = max_batches\n", "\n", "# Create an initial uniform spatial source distribution over fissionable zones\n", "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n", "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", - "settings_file.source = openmc.source.Source(space=uniform_dist)\n", + "settings.source = openmc.source.Source(space=uniform_dist)\n", "\n", "# Export to \"settings.xml\"\n", - "settings_file.export_to_xml()" + "settings.export_to_xml()" ] }, { @@ -312,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -340,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -351,7 +308,7 @@ "0" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -363,19 +320,19 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -397,15 +354,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Instantiate an empty Tallies object\n", - "tallies_file = openmc.Tallies()\n", - "tallies_file._tallies = []" + "tallies = openmc.Tallies()" ] }, { @@ -417,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -442,7 +398,7 @@ "tally.scores = ['fission', 'nu-fission']\n", "\n", "# Add mesh and Tally to Tallies\n", - "tallies_file.append(tally)" + "tallies.append(tally)" ] }, { @@ -454,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -467,10 +423,10 @@ "tally = openmc.Tally(name='cell tally')\n", "tally.filters = [cell_filter]\n", "tally.scores = ['scatter-y2']\n", - "tally.nuclides = [u235, u238]\n", + "tally.nuclides = ['U235', 'U238']\n", "\n", "# Add mesh and tally to Tallies\n", - "tallies_file.append(tally)" + "tallies.append(tally)" ] }, { @@ -482,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -502,19 +458,19 @@ "tally.triggers = [trigger]\n", "\n", "# Add mesh and tally to Tallies\n", - "tallies_file.append(tally)" + "tallies.append(tally)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Export to \"tallies.xml\"\n", - "tallies_file.export_to_xml()" + "tallies.export_to_xml()" ] }, { @@ -526,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -561,17 +517,13 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2016 Massachusetts Institute of Technology\n", + " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.8.0\n", - " Git SHA1 | da5563eddb5f2c2d6b2c9839d518de40962b78f2\n", - " Date/Time | 2016-10-31 12:45:15\n", + " Git SHA1 | 2d1897a051baaca9fd65ff851bc803512889a06f\n", + " Date/Time | 2017-03-06 19:46:57\n", " OpenMP Threads | 4\n", "\n", - " ===========================================================================\n", - " ========================> INITIALIZATION <=========================\n", - " ===========================================================================\n", - "\n", " Reading settings XML file...\n", " Reading geometry XML file...\n", " Reading materials XML file...\n", @@ -587,9 +539,7 @@ " Building neighboring cells lists for each surface...\n", " Initializing source particles...\n", "\n", - " ===========================================================================\n", " ====================> K EIGENVALUE SIMULATION <====================\n", - " ===========================================================================\n", "\n", " Bat./Gen. k Average k \n", " ========= ======== ==================== \n", @@ -598,59 +548,57 @@ " 3/1 0.68834 \n", " 4/1 0.71192 \n", " 5/1 0.67935 \n", - " 6/1 0.68274 \n", - " 7/1 0.66339 0.67307 +/- 0.00967\n", - " 8/1 0.65835 0.66816 +/- 0.00743\n", - " 9/1 0.66697 0.66786 +/- 0.00527\n", - " 10/1 0.70498 0.67528 +/- 0.00847\n", - " 11/1 0.68596 0.67706 +/- 0.00714\n", - " 12/1 0.68481 0.67817 +/- 0.00614\n", - " 13/1 0.68369 0.67886 +/- 0.00536\n", - " 14/1 0.68785 0.67986 +/- 0.00483\n", - " 15/1 0.66145 0.67802 +/- 0.00470\n", - " 16/1 0.71831 0.68168 +/- 0.00561\n", - " 17/1 0.68428 0.68190 +/- 0.00512\n", - " 18/1 0.67527 0.68139 +/- 0.00474\n", - " 19/1 0.68166 0.68141 +/- 0.00439\n", - " 20/1 0.65475 0.67963 +/- 0.00446\n", - " Triggers unsatisfied, max unc./thresh. is 1.07581 for absorption in tally 10002\n", - " The estimated number of batches is 23\n", + " 6/1 0.68254 \n", + " 7/1 0.65804 0.67029 +/- 0.01225\n", + " 8/1 0.66225 0.66761 +/- 0.00756\n", + " 9/1 0.66336 0.66655 +/- 0.00545\n", + " 10/1 0.68037 0.66931 +/- 0.00505\n", + " 11/1 0.71728 0.67731 +/- 0.00899\n", + " 12/1 0.66098 0.67498 +/- 0.00795\n", + " 13/1 0.69969 0.67806 +/- 0.00755\n", + " 14/1 0.70998 0.68161 +/- 0.00754\n", + " 15/1 0.70092 0.68354 +/- 0.00702\n", + " 16/1 0.71586 0.68648 +/- 0.00699\n", + " 17/1 0.65949 0.68423 +/- 0.00677\n", + " 18/1 0.67696 0.68367 +/- 0.00625\n", + " 19/1 0.65444 0.68158 +/- 0.00615\n", + " 20/1 0.69766 0.68266 +/- 0.00583\n", + " Triggers unsatisfied, max unc./thresh. is 1.17617 for absorption in tally 10002\n", + " The estimated number of batches is 26\n", " Creating state point statepoint.020.h5...\n", - " 21/1 0.64538 0.67749 +/- 0.00469\n", - " 22/1 0.73275 0.68074 +/- 0.00547\n", - " 23/1 0.71674 0.68274 +/- 0.00553\n", - " Triggers satisfied for batch 23\n", - " Creating state point statepoint.023.h5...\n", - "\n", - " ===========================================================================\n", - " ======================> SIMULATION FINISHED <======================\n", - " ===========================================================================\n", - "\n", + " 21/1 0.64126 0.68007 +/- 0.00603\n", + " 22/1 0.69287 0.68082 +/- 0.00572\n", + " 23/1 0.70254 0.68203 +/- 0.00552\n", + " 24/1 0.68198 0.68203 +/- 0.00523\n", + " 25/1 0.67214 0.68153 +/- 0.00498\n", + " 26/1 0.68171 0.68154 +/- 0.00474\n", + " Triggers satisfied for batch 26\n", + " Creating state point statepoint.026.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 5.0551E-01 seconds\n", - " Reading cross sections = 3.6354E-01 seconds\n", - " Total time in simulation = 5.9963E+00 seconds\n", - " Time in transport only = 5.9140E+00 seconds\n", - " Time in inactive batches = 1.1232E+00 seconds\n", - " Time in active batches = 4.8731E+00 seconds\n", - " Time synchronizing fission bank = 2.4961E-03 seconds\n", - " Sampling source sites = 1.7274E-03 seconds\n", - " SEND/RECV source sites = 7.2484E-04 seconds\n", - " Time accumulating tallies = 4.0034E-04 seconds\n", - " Total time for finalization = 2.3800E-05 seconds\n", - " Total time elapsed = 6.5204E+00 seconds\n", - " Calculation Rate (inactive) = 11129.0 neutrons/second\n", - " Calculation Rate (active) = 7695.33 neutrons/second\n", + " Total time for initialization = 4.8645E-01 seconds\n", + " Reading cross sections = 4.1789E-01 seconds\n", + " Total time in simulation = 4.6732E+00 seconds\n", + " Time in transport only = 4.5503E+00 seconds\n", + " Time in inactive batches = 5.6790E-01 seconds\n", + " Time in active batches = 4.1053E+00 seconds\n", + " Time synchronizing fission bank = 1.7410E-03 seconds\n", + " Sampling source sites = 1.2602E-03 seconds\n", + " SEND/RECV source sites = 4.4969E-04 seconds\n", + " Time accumulating tallies = 8.8049E-04 seconds\n", + " Total time for finalization = 4.3874E-05 seconds\n", + " Total time elapsed = 5.1725E+00 seconds\n", + " Calculation Rate (inactive) = 22010.8 neutrons/second\n", + " Calculation Rate (active) = 9134.46 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.67952 +/- 0.00434\n", - " k-effective (Track-length) = 0.68274 +/- 0.00553\n", - " k-effective (Absorption) = 0.68095 +/- 0.00369\n", - " Combined k-effective = 0.67994 +/- 0.00349\n", - " Leakage Fraction = 0.34133 +/- 0.00332\n", + " k-effective (Collision) = 0.67976 +/- 0.00436\n", + " k-effective (Track-length) = 0.68154 +/- 0.00474\n", + " k-effective (Absorption) = 0.68320 +/- 0.00518\n", + " Combined k-effective = 0.68122 +/- 0.00432\n", + " Leakage Fraction = 0.34011 +/- 0.00283\n", "\n" ] }, @@ -660,7 +608,7 @@ "0" ] }, - "execution_count": 20, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -682,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -705,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -717,9 +665,7 @@ "Tally\n", "\tID =\t10000\n", "\tName =\tmesh tally\n", - "\tFilters =\t\n", - " \t\tMeshFilter\t[1]\n", - " \t\tEnergyFilter\t[ 0.00000000e+00 6.25000000e-01 2.00000000e+07]\n", + "\tFilters =\tMeshFilter, EnergyFilter\n", "\tNuclides =\ttotal \n", "\tScores =\t['fission', 'nu-fission']\n", "\tEstimator =\ttracklength\n", @@ -744,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -753,13 +699,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.1501735 ]]\n", + "[[[ 0.17581417]]\n", "\n", - " [[ 0.21402727]]\n", + " [[ 0.30578219]]\n", "\n", - " [[ 0.05936257]]\n", + " [[ 0.06842901]]\n", "\n", - " [[ 0.13436703]]]\n" + " [[ 0.12436752]]]\n" ] } ], @@ -775,7 +721,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -816,8 +762,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.20e-04\n", - " 3.31e-05\n", + " 2.24e-04\n", + " 3.94e-05\n", " \n", " \n", " 1\n", @@ -827,8 +773,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.37e-04\n", - " 8.06e-05\n", + " 5.46e-04\n", + " 9.59e-05\n", " \n", " \n", " 2\n", @@ -838,8 +784,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 7.43e-05\n", - " 7.91e-06\n", + " 8.42e-05\n", + " 6.79e-06\n", " \n", " \n", " 3\n", @@ -849,8 +795,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.97e-04\n", - " 1.96e-05\n", + " 2.22e-04\n", + " 1.65e-05\n", " \n", " \n", " 4\n", @@ -860,8 +806,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.32e-04\n", - " 4.97e-05\n", + " 1.85e-04\n", + " 2.70e-05\n", " \n", " \n", " 5\n", @@ -871,8 +817,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.65e-04\n", - " 1.21e-04\n", + " 4.52e-04\n", + " 6.58e-05\n", " \n", " \n", " 6\n", @@ -882,8 +828,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 6.96e-05\n", - " 6.90e-06\n", + " 6.82e-05\n", + " 5.29e-06\n", " \n", " \n", " 7\n", @@ -893,8 +839,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.86e-04\n", - " 1.90e-05\n", + " 1.81e-04\n", + " 1.35e-05\n", " \n", " \n", " 8\n", @@ -904,8 +850,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.43e-04\n", - " 3.24e-05\n", + " 2.05e-04\n", + " 2.25e-05\n", " \n", " \n", " 9\n", @@ -915,8 +861,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.91e-04\n", - " 7.90e-05\n", + " 5.00e-04\n", + " 5.49e-05\n", " \n", " \n", " 10\n", @@ -926,8 +872,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 7.27e-05\n", - " 4.76e-06\n", + " 7.53e-05\n", + " 7.06e-06\n", " \n", " \n", " 11\n", @@ -937,8 +883,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.93e-04\n", - " 1.14e-05\n", + " 1.99e-04\n", + " 1.81e-05\n", " \n", " \n", " 12\n", @@ -948,8 +894,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.61e-04\n", - " 4.48e-05\n", + " 2.06e-04\n", + " 2.79e-05\n", " \n", " \n", " 13\n", @@ -959,8 +905,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 6.35e-04\n", - " 1.09e-04\n", + " 5.03e-04\n", + " 6.80e-05\n", " \n", " \n", " 14\n", @@ -970,8 +916,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 6.00e-05\n", - " 4.53e-06\n", + " 6.65e-05\n", + " 3.91e-06\n", " \n", " \n", " 15\n", @@ -981,8 +927,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.59e-04\n", - " 1.17e-05\n", + " 1.75e-04\n", + " 1.04e-05\n", " \n", " \n", " 16\n", @@ -992,8 +938,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.23e-04\n", - " 2.89e-05\n", + " 2.03e-04\n", + " 2.78e-05\n", " \n", " \n", " 17\n", @@ -1003,8 +949,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.43e-04\n", - " 7.04e-05\n", + " 4.94e-04\n", + " 6.78e-05\n", " \n", " \n", " 18\n", @@ -1014,8 +960,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 7.93e-05\n", - " 7.77e-06\n", + " 6.26e-05\n", + " 5.71e-06\n", " \n", " \n", " 19\n", @@ -1025,8 +971,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 2.07e-04\n", - " 1.94e-05\n", + " 1.64e-04\n", + " 1.53e-05\n", " \n", " \n", "\n", @@ -1035,52 +981,52 @@ "text/plain": [ " mesh 1 energy low [eV] energy high [eV] score mean \\\n", " x y z \n", - "0 1 1 1 0.00e+00 6.25e-01 fission 2.20e-04 \n", - "1 1 1 1 0.00e+00 6.25e-01 nu-fission 5.37e-04 \n", - "2 1 1 1 6.25e-01 2.00e+07 fission 7.43e-05 \n", - "3 1 1 1 6.25e-01 2.00e+07 nu-fission 1.97e-04 \n", - "4 1 2 1 0.00e+00 6.25e-01 fission 2.32e-04 \n", - "5 1 2 1 0.00e+00 6.25e-01 nu-fission 5.65e-04 \n", - "6 1 2 1 6.25e-01 2.00e+07 fission 6.96e-05 \n", - "7 1 2 1 6.25e-01 2.00e+07 nu-fission 1.86e-04 \n", - "8 1 3 1 0.00e+00 6.25e-01 fission 2.43e-04 \n", - "9 1 3 1 0.00e+00 6.25e-01 nu-fission 5.91e-04 \n", - "10 1 3 1 6.25e-01 2.00e+07 fission 7.27e-05 \n", - "11 1 3 1 6.25e-01 2.00e+07 nu-fission 1.93e-04 \n", - "12 1 4 1 0.00e+00 6.25e-01 fission 2.61e-04 \n", - "13 1 4 1 0.00e+00 6.25e-01 nu-fission 6.35e-04 \n", - "14 1 4 1 6.25e-01 2.00e+07 fission 6.00e-05 \n", - "15 1 4 1 6.25e-01 2.00e+07 nu-fission 1.59e-04 \n", - "16 1 5 1 0.00e+00 6.25e-01 fission 2.23e-04 \n", - "17 1 5 1 0.00e+00 6.25e-01 nu-fission 5.43e-04 \n", - "18 1 5 1 6.25e-01 2.00e+07 fission 7.93e-05 \n", - "19 1 5 1 6.25e-01 2.00e+07 nu-fission 2.07e-04 \n", + "0 1 1 1 0.00e+00 6.25e-01 fission 2.24e-04 \n", + "1 1 1 1 0.00e+00 6.25e-01 nu-fission 5.46e-04 \n", + "2 1 1 1 6.25e-01 2.00e+07 fission 8.42e-05 \n", + "3 1 1 1 6.25e-01 2.00e+07 nu-fission 2.22e-04 \n", + "4 1 2 1 0.00e+00 6.25e-01 fission 1.85e-04 \n", + "5 1 2 1 0.00e+00 6.25e-01 nu-fission 4.52e-04 \n", + "6 1 2 1 6.25e-01 2.00e+07 fission 6.82e-05 \n", + "7 1 2 1 6.25e-01 2.00e+07 nu-fission 1.81e-04 \n", + "8 1 3 1 0.00e+00 6.25e-01 fission 2.05e-04 \n", + "9 1 3 1 0.00e+00 6.25e-01 nu-fission 5.00e-04 \n", + "10 1 3 1 6.25e-01 2.00e+07 fission 7.53e-05 \n", + "11 1 3 1 6.25e-01 2.00e+07 nu-fission 1.99e-04 \n", + "12 1 4 1 0.00e+00 6.25e-01 fission 2.06e-04 \n", + "13 1 4 1 0.00e+00 6.25e-01 nu-fission 5.03e-04 \n", + "14 1 4 1 6.25e-01 2.00e+07 fission 6.65e-05 \n", + "15 1 4 1 6.25e-01 2.00e+07 nu-fission 1.75e-04 \n", + "16 1 5 1 0.00e+00 6.25e-01 fission 2.03e-04 \n", + "17 1 5 1 0.00e+00 6.25e-01 nu-fission 4.94e-04 \n", + "18 1 5 1 6.25e-01 2.00e+07 fission 6.26e-05 \n", + "19 1 5 1 6.25e-01 2.00e+07 nu-fission 1.64e-04 \n", "\n", " std. dev. \n", " \n", - "0 3.31e-05 \n", - "1 8.06e-05 \n", - "2 7.91e-06 \n", - "3 1.96e-05 \n", - "4 4.97e-05 \n", - "5 1.21e-04 \n", - "6 6.90e-06 \n", - "7 1.90e-05 \n", - "8 3.24e-05 \n", - "9 7.90e-05 \n", - "10 4.76e-06 \n", - "11 1.14e-05 \n", - "12 4.48e-05 \n", - "13 1.09e-04 \n", - "14 4.53e-06 \n", - "15 1.17e-05 \n", - "16 2.89e-05 \n", - "17 7.04e-05 \n", - "18 7.77e-06 \n", - "19 1.94e-05 " + "0 3.94e-05 \n", + "1 9.59e-05 \n", + "2 6.79e-06 \n", + "3 1.65e-05 \n", + "4 2.70e-05 \n", + "5 6.58e-05 \n", + "6 5.29e-06 \n", + "7 1.35e-05 \n", + "8 2.25e-05 \n", + "9 5.49e-05 \n", + "10 7.06e-06 \n", + "11 1.81e-05 \n", + "12 2.79e-05 \n", + "13 6.80e-05 \n", + "14 3.91e-06 \n", + "15 1.04e-05 \n", + "16 2.78e-05 \n", + "17 6.78e-05 \n", + "18 5.71e-06 \n", + "19 1.53e-05 " ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1098,16 +1044,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1122,7 +1068,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -1130,18 +1076,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1154,7 +1100,7 @@ "fiss = fiss[fiss['energy low [eV]'] == 0.0]\n", "\n", "# Extract mean and reshape as 2D NumPy arrays\n", - "mean = fiss['mean'].reshape((17,17))\n", + "mean = fiss['mean'].values.reshape((17,17))\n", "\n", "plt.imshow(mean, interpolation='nearest')\n", "plt.title('fission rate')\n", @@ -1172,7 +1118,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -1184,8 +1130,7 @@ "Tally\n", "\tID =\t10001\n", "\tName =\tcell tally\n", - "\tFilters =\t\n", - " \t\tCellFilter\t[10000]\n", + "\tFilters =\tCellFilter\n", "\tNuclides =\tU235 U238 \n", "\tScores =\t['scatter-Y0,0', 'scatter-Y1,-1', 'scatter-Y1,0', 'scatter-Y1,1', 'scatter-Y2,-2', 'scatter-Y2,-1', 'scatter-Y2,0', 'scatter-Y2,1', 'scatter-Y2,2']\n", "\tEstimator =\tanalog\n", @@ -1203,7 +1148,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -1229,72 +1174,72 @@ " 10000\n", " U235\n", " scatter-Y0,0\n", - " 3.84e-02\n", - " 1.32e-03\n", + " 3.86e-02\n", + " 6.85e-04\n", " \n", " \n", " 1\n", " 10000\n", " U235\n", " scatter-Y1,-1\n", - " 3.61e-04\n", - " 3.13e-04\n", + " 6.95e-04\n", + " 3.15e-04\n", " \n", " \n", " 2\n", " 10000\n", " U235\n", " scatter-Y1,0\n", - " -2.38e-04\n", - " 4.69e-04\n", + " -1.06e-04\n", + " 3.79e-04\n", " \n", " \n", " 3\n", " 10000\n", " U235\n", " scatter-Y1,1\n", - " -5.08e-04\n", - " 3.83e-04\n", + " -3.63e-04\n", + " 3.18e-04\n", " \n", " \n", " 4\n", " 10000\n", " U235\n", " scatter-Y2,-2\n", - " 6.68e-05\n", - " 2.46e-04\n", + " 1.20e-04\n", + " 1.59e-04\n", " \n", " \n", " 5\n", " 10000\n", " U235\n", " scatter-Y2,-1\n", - " 6.47e-06\n", - " 2.84e-04\n", + " 3.93e-05\n", + " 1.86e-04\n", " \n", " \n", " 6\n", " 10000\n", " U235\n", " scatter-Y2,0\n", - " -1.41e-04\n", - " 1.75e-04\n", + " 1.81e-04\n", + " 1.85e-04\n", " \n", " \n", " 7\n", " 10000\n", " U235\n", " scatter-Y2,1\n", - " 1.61e-04\n", - " 2.33e-04\n", + " 1.24e-04\n", + " 1.81e-04\n", " \n", " \n", " 8\n", " 10000\n", " U235\n", " scatter-Y2,2\n", - " -1.80e-05\n", - " 1.97e-04\n", + " 2.06e-04\n", + " 2.26e-04\n", " \n", " \n", " 9\n", @@ -1302,71 +1247,71 @@ " U238\n", " scatter-Y0,0\n", " 2.33e+00\n", - " 1.35e-02\n", + " 1.10e-02\n", " \n", " \n", " 10\n", " 10000\n", " U238\n", " scatter-Y1,-1\n", - " 2.53e-02\n", - " 3.23e-03\n", + " 2.90e-02\n", + " 2.33e-03\n", " \n", " \n", " 11\n", " 10000\n", " U238\n", " scatter-Y1,0\n", - " 7.10e-04\n", - " 2.92e-03\n", + " 3.45e-03\n", + " 2.38e-03\n", " \n", " \n", " 12\n", " 10000\n", " U238\n", " scatter-Y1,1\n", - " -2.49e-02\n", - " 3.52e-03\n", + " -2.72e-02\n", + " 2.76e-03\n", " \n", " \n", " 13\n", " 10000\n", " U238\n", " scatter-Y2,-2\n", - " -1.43e-03\n", - " 1.17e-03\n", + " -2.02e-03\n", + " 1.44e-03\n", " \n", " \n", " 14\n", " 10000\n", " U238\n", " scatter-Y2,-1\n", - " 6.84e-04\n", - " 1.63e-03\n", + " 8.07e-06\n", + " 1.49e-03\n", " \n", " \n", " 15\n", " 10000\n", " U238\n", " scatter-Y2,0\n", - " 2.85e-03\n", - " 2.63e-03\n", + " -3.74e-07\n", + " 1.79e-03\n", " \n", " \n", " 16\n", " 10000\n", " U238\n", " scatter-Y2,1\n", - " 3.97e-03\n", - " 2.24e-03\n", + " 6.54e-04\n", + " 1.49e-03\n", " \n", " \n", " 17\n", " 10000\n", " U238\n", " scatter-Y2,2\n", - " 2.26e-03\n", - " 1.85e-03\n", + " -1.93e-03\n", + " 1.36e-03\n", " \n", " \n", "\n", @@ -1374,27 +1319,27 @@ ], "text/plain": [ " cell nuclide score mean std. dev.\n", - "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" + "0 10000 U235 scatter-Y0,0 3.86e-02 6.85e-04\n", + "1 10000 U235 scatter-Y1,-1 6.95e-04 3.15e-04\n", + "2 10000 U235 scatter-Y1,0 -1.06e-04 3.79e-04\n", + "3 10000 U235 scatter-Y1,1 -3.63e-04 3.18e-04\n", + "4 10000 U235 scatter-Y2,-2 1.20e-04 1.59e-04\n", + "5 10000 U235 scatter-Y2,-1 3.93e-05 1.86e-04\n", + "6 10000 U235 scatter-Y2,0 1.81e-04 1.85e-04\n", + "7 10000 U235 scatter-Y2,1 1.24e-04 1.81e-04\n", + "8 10000 U235 scatter-Y2,2 2.06e-04 2.26e-04\n", + "9 10000 U238 scatter-Y0,0 2.33e+00 1.10e-02\n", + "10 10000 U238 scatter-Y1,-1 2.90e-02 2.33e-03\n", + "11 10000 U238 scatter-Y1,0 3.45e-03 2.38e-03\n", + "12 10000 U238 scatter-Y1,1 -2.72e-02 2.76e-03\n", + "13 10000 U238 scatter-Y2,-2 -2.02e-03 1.44e-03\n", + "14 10000 U238 scatter-Y2,-1 8.07e-06 1.49e-03\n", + "15 10000 U238 scatter-Y2,0 -3.74e-07 1.79e-03\n", + "16 10000 U238 scatter-Y2,1 6.54e-04 1.49e-03\n", + "17 10000 U238 scatter-Y2,2 -1.93e-03 1.36e-03" ] }, - "execution_count": 28, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1416,7 +1361,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -1425,8 +1370,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00185463 0.01350521]\n", - " [ 0.00019723 0.00131654]]]\n" + "[[[ 0.00136183 0.01104314]\n", + " [ 0.00022601 0.00068479]]]\n" ] } ], @@ -1447,7 +1392,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -1459,8 +1404,7 @@ "Tally\n", "\tID =\t10002\n", "\tName =\tdistribcell tally\n", - "\tFilters =\t\n", - " \t\tDistribcellFilter\t[10002]\n", + "\tFilters =\tDistribcellFilter\n", "\tNuclides =\ttotal \n", "\tScores =\t['absorption', 'scatter']\n", "\tEstimator =\ttracklength\n", @@ -1485,7 +1429,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "metadata": { "collapsed": false }, @@ -1494,7 +1438,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.05468423]]]\n" + "[[[ 0.03500496]]]\n" ] } ], @@ -1515,7 +1459,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 30, "metadata": { "collapsed": false }, @@ -1529,7 +1473,7 @@ " \n", " \n", " level 1\n", - " level 2\n", + " level 2\n", " level 3\n", " distribcell\n", " score\n", @@ -1540,7 +1484,7 @@ " \n", " univ\n", " cell\n", - " lat\n", + " lat\n", " univ\n", " cell\n", " \n", @@ -1555,7 +1499,6 @@ " id\n", " x\n", " y\n", - " z\n", " id\n", " id\n", " \n", @@ -1567,359 +1510,339 @@ " \n", " \n", " 558\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 7\n", - " 0\n", " 10000\n", " 10002\n", " 279\n", " absorption\n", - " 8.72e-05\n", - " 8.13e-06\n", + " 7.77e-05\n", + " 7.87e-06\n", " \n", " \n", " 559\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 7\n", - " 0\n", " 10000\n", " 10002\n", " 279\n", " scatter\n", - " 1.37e-02\n", - " 6.98e-04\n", + " 1.28e-02\n", + " 5.09e-04\n", " \n", " \n", " 560\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 8\n", - " 0\n", " 10000\n", " 10002\n", " 280\n", " absorption\n", - " 1.03e-04\n", - " 9.17e-06\n", + " 8.92e-05\n", + " 7.32e-06\n", " \n", " \n", " 561\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 8\n", - " 0\n", " 10000\n", " 10002\n", " 280\n", " scatter\n", - " 1.41e-02\n", - " 6.26e-04\n", + " 1.37e-02\n", + " 4.99e-04\n", " \n", " \n", " 562\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 9\n", - " 0\n", " 10000\n", " 10002\n", " 281\n", " absorption\n", - " 9.41e-05\n", - " 8.40e-06\n", + " 9.50e-05\n", + " 7.80e-06\n", " \n", " \n", " 563\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 9\n", - " 0\n", " 10000\n", " 10002\n", " 281\n", " scatter\n", - " 1.50e-02\n", - " 6.92e-04\n", + " 1.49e-02\n", + " 4.74e-04\n", " \n", " \n", " 564\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 10\n", - " 0\n", " 10000\n", " 10002\n", " 282\n", " absorption\n", - " 9.56e-05\n", - " 1.03e-05\n", + " 1.15e-04\n", + " 1.00e-05\n", " \n", " \n", " 565\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 10\n", - " 0\n", " 10000\n", " 10002\n", " 282\n", " scatter\n", - " 1.52e-02\n", - " 5.37e-04\n", + " 1.58e-02\n", + " 6.18e-04\n", " \n", " \n", " 566\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 11\n", - " 0\n", " 10000\n", " 10002\n", " 283\n", " absorption\n", - " 1.06e-04\n", - " 1.49e-05\n", + " 1.13e-04\n", + " 1.01e-05\n", " \n", " \n", " 567\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 11\n", - " 0\n", " 10000\n", " 10002\n", " 283\n", " scatter\n", - " 1.64e-02\n", - " 8.14e-04\n", + " 1.75e-02\n", + " 5.66e-04\n", " \n", " \n", " 568\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 12\n", - " 0\n", " 10000\n", " 10002\n", " 284\n", " absorption\n", - " 1.16e-04\n", - " 9.02e-06\n", + " 1.08e-04\n", + " 9.66e-06\n", " \n", " \n", " 569\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 12\n", - " 0\n", " 10000\n", " 10002\n", " 284\n", " scatter\n", - " 1.64e-02\n", - " 6.00e-04\n", + " 1.73e-02\n", + " 5.40e-04\n", " \n", " \n", " 570\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 13\n", - " 0\n", " 10000\n", " 10002\n", " 285\n", " absorption\n", - " 1.25e-04\n", - " 1.12e-05\n", + " 1.16e-04\n", + " 1.44e-05\n", " \n", " \n", " 571\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 13\n", - " 0\n", " 10000\n", " 10002\n", " 285\n", " scatter\n", - " 1.87e-02\n", - " 8.26e-04\n", + " 1.70e-02\n", + " 6.90e-04\n", " \n", " \n", " 572\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 14\n", - " 0\n", " 10000\n", " 10002\n", " 286\n", " absorption\n", - " 1.47e-04\n", - " 1.49e-05\n", + " 1.16e-04\n", + " 1.02e-05\n", " \n", " \n", " 573\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 14\n", - " 0\n", " 10000\n", " 10002\n", " 286\n", " scatter\n", - " 1.94e-02\n", - " 7.71e-04\n", + " 1.77e-02\n", + " 6.80e-04\n", " \n", " \n", " 574\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 15\n", - " 0\n", " 10000\n", " 10002\n", " 287\n", " absorption\n", - " 1.31e-04\n", - " 9.84e-06\n", + " 1.20e-04\n", + " 1.36e-05\n", " \n", " \n", " 575\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 15\n", - " 0\n", " 10000\n", " 10002\n", " 287\n", " scatter\n", - " 1.97e-02\n", - " 7.93e-04\n", + " 1.80e-02\n", + " 7.80e-04\n", " \n", " \n", " 576\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 16\n", - " 0\n", " 10000\n", " 10002\n", " 288\n", " absorption\n", - " 1.23e-04\n", - " 1.07e-05\n", + " 1.32e-04\n", + " 1.30e-05\n", " \n", " \n", " 577\n", - " 0\n", + " 10002\n", " 10003\n", " 10001\n", " 16\n", " 16\n", - " 0\n", " 10000\n", " 10002\n", " 288\n", " scatter\n", - " 1.97e-02\n", - " 7.34e-04\n", + " 1.86e-02\n", + " 7.12e-04\n", " \n", " \n", "\n", "" ], "text/plain": [ - " level 1 level 2 level 3 distribcell score \\\n", - " univ cell lat univ cell \n", - " id id id x y z id id \n", - "558 0 10003 10001 16 7 0 10000 10002 279 absorption \n", - "559 0 10003 10001 16 7 0 10000 10002 279 scatter \n", - "560 0 10003 10001 16 8 0 10000 10002 280 absorption \n", - "561 0 10003 10001 16 8 0 10000 10002 280 scatter \n", - "562 0 10003 10001 16 9 0 10000 10002 281 absorption \n", - "563 0 10003 10001 16 9 0 10000 10002 281 scatter \n", - "564 0 10003 10001 16 10 0 10000 10002 282 absorption \n", - "565 0 10003 10001 16 10 0 10000 10002 282 scatter \n", - "566 0 10003 10001 16 11 0 10000 10002 283 absorption \n", - "567 0 10003 10001 16 11 0 10000 10002 283 scatter \n", - "568 0 10003 10001 16 12 0 10000 10002 284 absorption \n", - "569 0 10003 10001 16 12 0 10000 10002 284 scatter \n", - "570 0 10003 10001 16 13 0 10000 10002 285 absorption \n", - "571 0 10003 10001 16 13 0 10000 10002 285 scatter \n", - "572 0 10003 10001 16 14 0 10000 10002 286 absorption \n", - "573 0 10003 10001 16 14 0 10000 10002 286 scatter \n", - "574 0 10003 10001 16 15 0 10000 10002 287 absorption \n", - "575 0 10003 10001 16 15 0 10000 10002 287 scatter \n", - "576 0 10003 10001 16 16 0 10000 10002 288 absorption \n", - "577 0 10003 10001 16 16 0 10000 10002 288 scatter \n", + " level 1 level 2 level 3 distribcell score \\\n", + " univ cell lat univ cell \n", + " id id id x y id id \n", + "558 10002 10003 10001 16 7 10000 10002 279 absorption \n", + "559 10002 10003 10001 16 7 10000 10002 279 scatter \n", + "560 10002 10003 10001 16 8 10000 10002 280 absorption \n", + "561 10002 10003 10001 16 8 10000 10002 280 scatter \n", + "562 10002 10003 10001 16 9 10000 10002 281 absorption \n", + "563 10002 10003 10001 16 9 10000 10002 281 scatter \n", + "564 10002 10003 10001 16 10 10000 10002 282 absorption \n", + "565 10002 10003 10001 16 10 10000 10002 282 scatter \n", + "566 10002 10003 10001 16 11 10000 10002 283 absorption \n", + "567 10002 10003 10001 16 11 10000 10002 283 scatter \n", + "568 10002 10003 10001 16 12 10000 10002 284 absorption \n", + "569 10002 10003 10001 16 12 10000 10002 284 scatter \n", + "570 10002 10003 10001 16 13 10000 10002 285 absorption \n", + "571 10002 10003 10001 16 13 10000 10002 285 scatter \n", + "572 10002 10003 10001 16 14 10000 10002 286 absorption \n", + "573 10002 10003 10001 16 14 10000 10002 286 scatter \n", + "574 10002 10003 10001 16 15 10000 10002 287 absorption \n", + "575 10002 10003 10001 16 15 10000 10002 287 scatter \n", + "576 10002 10003 10001 16 16 10000 10002 288 absorption \n", + "577 10002 10003 10001 16 16 10000 10002 288 scatter \n", "\n", " mean std. dev. \n", " \n", " \n", - "558 8.72e-05 8.13e-06 \n", - "559 1.37e-02 6.98e-04 \n", - "560 1.03e-04 9.17e-06 \n", - "561 1.41e-02 6.26e-04 \n", - "562 9.41e-05 8.40e-06 \n", - "563 1.50e-02 6.92e-04 \n", - "564 9.56e-05 1.03e-05 \n", - "565 1.52e-02 5.37e-04 \n", - "566 1.06e-04 1.49e-05 \n", - "567 1.64e-02 8.14e-04 \n", - "568 1.16e-04 9.02e-06 \n", - "569 1.64e-02 6.00e-04 \n", - "570 1.25e-04 1.12e-05 \n", - "571 1.87e-02 8.26e-04 \n", - "572 1.47e-04 1.49e-05 \n", - "573 1.94e-02 7.71e-04 \n", - "574 1.31e-04 9.84e-06 \n", - "575 1.97e-02 7.93e-04 \n", - "576 1.23e-04 1.07e-05 \n", - "577 1.97e-02 7.34e-04 " + "558 7.77e-05 7.87e-06 \n", + "559 1.28e-02 5.09e-04 \n", + "560 8.92e-05 7.32e-06 \n", + "561 1.37e-02 4.99e-04 \n", + "562 9.50e-05 7.80e-06 \n", + "563 1.49e-02 4.74e-04 \n", + "564 1.15e-04 1.00e-05 \n", + "565 1.58e-02 6.18e-04 \n", + "566 1.13e-04 1.01e-05 \n", + "567 1.75e-02 5.66e-04 \n", + "568 1.08e-04 9.66e-06 \n", + "569 1.73e-02 5.40e-04 \n", + "570 1.16e-04 1.44e-05 \n", + "571 1.70e-02 6.90e-04 \n", + "572 1.16e-04 1.02e-05 \n", + "573 1.77e-02 6.80e-04 \n", + "574 1.20e-04 1.36e-05 \n", + "575 1.80e-02 7.80e-04 \n", + "576 1.32e-04 1.30e-05 \n", + "577 1.86e-02 7.12e-04 " ] }, - "execution_count": 32, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1934,7 +1857,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 31, "metadata": { "collapsed": false }, @@ -1969,38 +1892,38 @@ " \n", " \n", " mean\n", - " 4.16e-04\n", - " 2.42e-05\n", + " 4.17e-04\n", + " 2.05e-05\n", " \n", " \n", " std\n", - " 2.39e-04\n", - " 1.03e-05\n", + " 2.42e-04\n", + " 8.32e-06\n", " \n", " \n", " min\n", - " 1.90e-05\n", - " 3.80e-06\n", + " 2.27e-05\n", + " 4.04e-06\n", " \n", " \n", " 25%\n", - " 1.99e-04\n", - " 1.61e-05\n", + " 2.01e-04\n", + " 1.40e-05\n", " \n", " \n", " 50%\n", - " 4.09e-04\n", - " 2.37e-05\n", + " 4.00e-04\n", + " 2.05e-05\n", " \n", " \n", " 75%\n", - " 6.00e-04\n", - " 3.08e-05\n", + " 6.08e-04\n", + " 2.60e-05\n", " \n", " \n", " max\n", - " 9.07e-04\n", - " 5.38e-05\n", + " 9.38e-04\n", + " 4.27e-05\n", " \n", " \n", "\n", @@ -2011,16 +1934,16 @@ " \n", " \n", "count 2.89e+02 2.89e+02\n", - "mean 4.16e-04 2.42e-05\n", - "std 2.39e-04 1.03e-05\n", - "min 1.90e-05 3.80e-06\n", - "25% 1.99e-04 1.61e-05\n", - "50% 4.09e-04 2.37e-05\n", - "75% 6.00e-04 3.08e-05\n", - "max 9.07e-04 5.38e-05" + "mean 4.17e-04 2.05e-05\n", + "std 2.42e-04 8.32e-06\n", + "min 2.27e-05 4.04e-06\n", + "25% 2.01e-04 1.40e-05\n", + "50% 4.00e-04 2.05e-05\n", + "75% 6.08e-04 2.60e-05\n", + "max 9.38e-04 4.27e-05" ] }, - "execution_count": 33, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -2043,7 +1966,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "metadata": { "collapsed": false }, @@ -2052,7 +1975,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 0.7234916721800682\n" + "Mann-Whitney Test p-value: 0.3933685843661936\n" ] } ], @@ -2081,7 +2004,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 33, "metadata": { "collapsed": false }, @@ -2090,7 +2013,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 3.5054120724573393e-41\n" + "Mann-Whitney Test p-value: 7.927841393301949e-42\n" ] } ], @@ -2117,7 +2040,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 34, "metadata": { "collapsed": false }, @@ -2126,7 +2049,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n", + "/home/romano/miniconda3/envs/python3/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", @@ -2136,18 +2059,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 36, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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MTGiGAH7z1Nx9921cf/3ClHDa9u3bLcxmGMagkveqMyM3JM9TEw+ndXQkhtOam5t9l7e1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\nYxiGYQSKCY1hGIYRKCY0hmEYRqCY0Bi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MwzWPrGbowJrs3ut9raItlS0gjOXWMn4WMKpUoS9yqa6HsEry2sVrAMnZo9u/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\nh1fljxGV4g+sUW44ghV8uSl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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2166,7 +2089,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 35, "metadata": { "collapsed": false }, @@ -2174,18 +2097,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 37, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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WDt9JxAMVCEm4Wetn0a1FN98xJBo/NYbvgPYTfScRD1QgJOFmrZvFSS10BXWl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\nGTjnFphZFsHWw7sUHaiuJ9D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4bD4cqG4B5NvW0ZA7y07jyOR79E/Mi7ocqQYFgVRu8eTgp4JAKjEudRxL03tx\nfcE4kqSiLkeqSEEglVs8EVoeCM33i7oSqaVKKeB3ZedwQOJjTk+qu+q6SkEgFfvmK1jxuo4GZKde\nTPfmnfR+XFnwDIWURV2OVIGCQCq25CXwlIJAMmDcUXYGbe1Lzki+EnUxUgWRBIGZLTezBWb2jpnN\njqIG2YnFE6Hx3tC6Z9SVSB3wSrobc9P7c0XBsxRRGnU5souiPCIY4O493F1XKtU2pZvgg6lw4BBI\n6KBRMmH8sexM2thqvpucHnUxsov0Vy7ftnQ6lH6jZiHZJTPSh/BW+gAuL3iOepREXY7sgqiCwIEp\nZjbHzC6paAIzu8TMZpvZ7FWr1OVtrnQcMYmnHruXdd6ATg+sp+OISTm9PaLUZcFRQWv7irOS/4y6\nGNkFUQXBke7eAxgMXG5mR28/gbvf7+693b13y5Ytc19hTCVIc1xyDv9M96RUt7SWXTQz3YXZ6QO4\npGASBTqDqM6IJAjc/ePw5xfAM8BhUdQh39bb3qe5refl1KFRlyJ1kvHnsmG0tS8Zlng96mIkQzkP\nAjNraGaNtz4HTgDezXUdUrEhyTfZ7IVMS+tsIamaaemeLEq357KC5zHSUZcjGYjiiKAVMMPM5gGz\ngEnu/kIEdcj20mkGJ2cxPd2Db6gfdTVSZxl/KRtGp8THnJCYE3UxkoGcB4G7L3X37uHjYHe/Jdc1\nSCVWvkErW8Pk1OFRVyJ13KT04axI78llBc+hnklrP50+Kv/x3rNs8UKmqllIqilFkvtSQ+mRWEq/\nxHtRlyM7oSCQQDoNiyYwPd1dt6SUGvF06ig+96Zcnnwu6lJkJxQEElj5Jqz/lElqFpIasoUiHiwb\nwneS79HdPoi6HNkBBYEEFj4LyXpMTeuWlFJzHk8NZI035McFE6IuRXZAQSBBs9DCCbD/cWoWkhq1\nkQY8mjqRE5Oz6WTFUZcjlVAQCCx/DdZ/AoecFnUlkoceLjuRjV6PHxfou4LaSkEgMP8pKGqsTuYk\nK9bQmHGp44Irjb9aGnU5UgEFQdyVfBM0C3U5GQrVLCTZ8UDZEMoogBl/iroUqYCCIO7enwwl66H7\nWVFXInlsFXswPnUMvPM4rP046nJkOwqCuJv/FOzeBjocGXUlkufuSw0FT8Pro6MuRbajIIizDauC\nO5F1PVN3IpOsK/aW0O0smPNI8N6TWkN//XG2YHxwg/puahaSHDnqZ1C2Gd74c9SVSDkKgrhyh9kP\nQ9s+0Kr94jkHAAAJk0lEQVRL1NVIXLToFJyY8NaDsGlN1NVISEEQVyteh9VL4NDvRV2JxM1R18CW\ndTDrgagrkZCCIK7mPAz1msDBuohMcqx1N+h0IrxxD2xeG3U1goIgnjauhoXPBaeMFu0WdTUSRwN+\nCZu+hpn3RF2JoCCIp3fGQapEzUISnb17Bt8VzLwHNn4ZdTWxpyCIm1QpvHkfdPgOtDo46mokzgbc\nAKXfwIw7oq4k9hQEcbPwOVhXDP1+EnUlEnctO0P3c4MvjXW1caQUBHHiHlzV2bxT8GWdSNT6/yK4\n2nj6rVFXEmsKgjhZ/hp8+g4ccbmuJJbaoWl7OPxH8PY4+Hhu1NXElv4bxIU7TB8FjfaC7mdHXY3I\nfxzzc2jYAv7xi+B9KjmnIIiLZa/Ain8FF/Oou2mpTeo3gYG/huJZMH981NXEkoIgDtxh2i1BL6O9\nLoy6GpFv63FecErpy7/SRWYRUBDEweJJwaeto66BwvpRVyPybYkEnPQH2PhFEAaSUwqCfFe6GV68\nHloepKMBqd3aHApHXBF0U710etTVxIqCIN/NHA1rVsDgUZAsjLoakR0bcD002w8m/AS2rI+6mthQ\nEOSz1R/Ca3+Eg4bCvv2jrkZk5wobwCl/hrXFMPFqnUWUIwqCfJVOwbM/Do4CBv8+6mpEMte+b3Bk\nsOBvQTORZJ2CIF+9PhpWvgFDbofd9466GpFdc+Q1sN+xwbUFutAs6xQE+WjZazD1ZjhoWHA/YpG6\nJpGA0x6ARq3g8bPg6xVRV5TXFAT55usVMP5CaL4/nHwPmEVdkUjVNGwB5/8dUltg3JnqrjqLFAT5\nZP3nMPb04PuBsx+H+rtHXZFI9bTsHLyX16yAR04K3uNS4xQE+WLDF/DoUFj3CZz7FLTYP+qKRGpG\nxyPhvL/Bmo/g4cHB2XBSoxQE+eCzd+GBgcEfynnjocMRUVckUrP2ORoueAY2r4EHBsAHU6KuKK8o\nCOoyd5j7GDx0AqRL4aLJwacnkXzUvi9cPC3oM2vs6TD5OijZGHVVeUFBUFd9sSj4Y5hwBezdAy7+\nJ7TpFXVVItm1R0f44VQ4/DKYdT+M7h18GEqnoq6sTlMQ1CXusOJ1+Pv34c9HwMpZMPg2GD4Rdm8d\ndXUiuVG0W9BlykUvBO/7CVfA3b3h9bvhm6+irq5OKohipWY2CLgTSAIPuvuoKOqoE7asD/7hfzgN\n/v0CrP4AihoH9xw+8mrYrVnUFYpEo8MRwdHBoufhjT/DSzfAlF9D+yOg8+CgKanVIVBQL+pKa72c\nB4GZJYF7gOOBYuAtM5vg7gtzXUvGtvZ34g7s4Pm2flF29tyhrARKNgRtnCUboWQ9fPM1rP8E1n0K\na1fC5+/C18uDWZNF0KFf8M//4FOhqGF2t1mkLjCDLsOCx2fvwrtPw/v/CHrcBUgUwp4HBU1Ke3SA\nJu2gQTNosAc0aBrcFCdZFIRFsug/zxMFsboGJ4ojgsOAD9x9KYCZPQmcDNR8ELxwPcx5OHhe1X/c\nUShqFHwh1roH9DwfWvcMPv3on79I5fY6JHgc92tYsxI+ngOfzIXP3wu+U/v3i8HFabvMwlDI4Gc2\nnD026G4ji6IIgjbAynKvi4HDt5/IzC4BLglfbjCz96uxzhZAHboscR3wCfBWtldUx/ZLTmnfVK7K\n+8Z+V8OV1D41/765cWB15u6QyUSRfEeQCXe/H7i/JpZlZrPdvXdNLCufaL9UTvumcto3laur+yaK\ns4Y+BtqVe902HCYiIhGIIgjeAjqZ2T5mVgScDUyIoA4RESGCpiF3LzOzK4AXCU4fHePu72V5tTXS\nxJSHtF8qp31TOe2bytXJfWOuW8GJiMSariwWEYk5BYGISMzlRRCYWTMze9nMloQ/96hkukFm9r6Z\nfWBmI8oNH2lmH5vZO+FjSO6qz47KtrXceDOzu8Lx882sV6bz1nXV3DfLzWxB+D6ZndvKsyuD/XKg\nmc00sy1mdu2uzFvXVXPf1P73jLvX+Qfwe2BE+HwE8LsKpkkCHwL7AkXAPKBLOG4kcG3U21GD+6PS\nbS03zRDgHwSXQ/YF3sx03rr8qM6+CcctB1pEvR0R7Zc9gT7ALeX/XvSeqXzf1JX3TF4cERB0UfFo\n+PxR4JQKptnWtYW7lwBbu7bIR5ls68nAXz3wBtDUzFpnOG9dVp19k892ul/c/Qt3fwso3dV567jq\n7Js6IV+CoJW7fxo+/wxoVcE0FXVt0abc65+EzQBjKmtaqkN2tq07miaTeeuy6uwbCDqgmmJmc8Ju\nUPJFdX7ves/sWK1/z9TaLia2Z2ZTgL0qGHVD+Rfu7ma2q+fE/gX4DcEv7DfAH4DvV6VOyXtHuvvH\nZrYn8LKZLXb3V6MuSmq1Wv+eqTNB4O7HVTbOzD43s9bu/ml4CP9FBZNV2rWFu39eblkPABNrpurI\nZNKNR2XTFGYwb11WnX2Du2/9+YWZPUPQbFCr/qirqDpdv+R7tzHV2r668J7Jl6ahCcDw8Plw4LkK\npqm0a4vt2n9PBd7NYq25kEk3HhOAC8MzZPoCa8PmtXzvAqTK+8bMGppZYwAzawicQN1/r2xVnd+7\n3jOVqDPvmai/ra6JB9AcmAosAaYAzcLhewOTy003BPg3wRkAN5Qb/hiwAJhP8AtuHfU21cA++da2\nApcCl4bPjeAGQR+G2957Z/spXx5V3TcEZ43MCx/v5du+yWC/7EXQPr4OWBM+313vmcr3TV15z6iL\nCRGRmMuXpiEREakiBYGISMwpCEREYk5BICIScwoCEZGYUxCIlGNmbmZjy70uMLNVZlbXLzIUqZSC\nQOS/bQQOMbMG4evjya+rZEW+RUEg8m2TgZPC5+cAT2wdEV4pOsbMZpnZ22Z2cji8o5m9ZmZzw0e/\ncHh/M5tuZn83s8VmNs7MLOdbJLIDCgKRb3sSONvM6gPdgDfLjbsBmObuhwEDgNvCrgO+AI53917A\nWcBd5ebpCfwU6EJwpel3sr8JIpmrM53OieSKu883s44ERwOTtxt9AjCs3F2o6gPtgU+Au82sB5AC\nDig3zyx3LwYws3eAjsCMbNUvsqsUBCIVmwDcDvQn6MtqKwNOd/f3y09sZiOBz4HuBEfam8uN3lLu\neQr93Ukto6YhkYqNAW5y9wXbDX+R4CZGBmBmPcPhTYBP3T0NXEBwe0OROkFBIFIBdy9297sqGPUb\ngns2zDez98LXAH8GhpvZPOBAgrOPROoE9T4qIhJzOiIQEYk5BYGISMwpCEREYk5BICIScwoCEZGY\nUxCIiMScgkBEJOb+H/MP8RDX50XfAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2203,8 +2126,9 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, diff --git a/openmc/cell.py b/openmc/cell.py index 53a01a6dc..0adc64083 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -80,11 +80,11 @@ class Cell(object): If the cell is filled with a universe, this array specifies a vector that is used to translate (shift) the universe. paths : list of str - The paths traversed through the CSG tree to reach each cell instance + The paths traversed through the CSG tree to reach each cell + instance. This property is initialized by calling the + :meth:`Geometry.determine_paths` method. num_instances : int - The number of instances of this cell throughout the geometry. This - property is initialized by calling the - :meth:`Geometry.count_cell_instances` method. + The number of instances of this cell throughout the geometry. volume : float Volume of the cell in cm^3. This can either be set manually or calculated in a stochastic volume calculation and added via the diff --git a/openmc/geometry.py b/openmc/geometry.py index a25a7ace6..77301934b 100644 --- a/openmc/geometry.py +++ b/openmc/geometry.py @@ -43,7 +43,7 @@ class Geometry(object): def root_universe(self, root_universe): check_type('root universe', root_universe, openmc.Universe) self._root_universe = root_universe - self.determine_paths() + self._determine_paths() def add_volume_information(self, volume_calc): """Add volume information from a stochastic volume calculation. @@ -114,7 +114,6 @@ class Geometry(object): Parameters ---------- path : str - The path traversed through the CSG tree to reach a cell or material instance. For example, 'u0->c10->l20(2,2,1)->u5->c5' would indicate the cell instance whose first level is universe 0 and cell 10, @@ -441,10 +440,14 @@ class Geometry(object): lattices.sort(key=lambda x: x.id) return lattices - def determine_paths(self): - """Count the number of instances for each cell in the Geometry, and - record the count in the :attr:`Cell.num_instances` properties.""" + def _determine_paths(self): + """Determine paths through CSG tree for cells and materials. + This method recursively traverses the CSG tree to determine each unique + path that reaches every cell and material. The paths are stored in the + :attr:`Cell.paths` and :attr:`Material.paths` attributes. + + """ # (Re-)initialize all cell instances to 0 for cell in self.get_all_cells().values(): cell._paths = [] diff --git a/openmc/lattice.py b/openmc/lattice.py index 2d60ecdb3..baefba730 100644 --- a/openmc/lattice.py +++ b/openmc/lattice.py @@ -372,7 +372,10 @@ class Lattice(object): Parameters ---------- idx : Iterable of int - Lattice element indices in the :math:`(x,y,z)` coordinate system + Lattice element indices. For a rectangular lattice, the indices are + given in the :math:`(x,y)` or :math:`(x,y,z)` coordinate system. For + hexagonal lattices, they are given in the :math:`x,\alpha` or + :math:`x,\alpha,z` coordinate systems. Returns ------- @@ -392,7 +395,7 @@ class Lattice(object): Parameters ---------- point : 3-tuple of float - Cartesian coordinatesof the point + Cartesian coordinates of the point Returns ------- @@ -537,6 +540,13 @@ class RectLattice(Lattice): @property def _natural_indices(self): + """Iterate over all possible (x,y) or (x,y,z) lattice element indices. + + This property is used when constructing distributed cell and material + paths. Most importantly, the iteration order matches that used on the + Fortran side. + + """ if self.ndim == 2: nx, ny = self.shape return np.broadcast(*np.ogrid[:nx, :ny]) @@ -889,6 +899,14 @@ class HexLattice(Lattice): @property def _natural_indices(self): + """Iterate over all possible (x,alpha) or (x,alpha,z) lattice element + indices. + + This property is used when constructing distributed cell and material + paths. Most importantly, the iteration order matches that used on the + Fortran side. + + """ r = self.num_rings if self.num_axial is None: for a in range(-r + 1, r): diff --git a/openmc/material.py b/openmc/material.py index 5b5f8947c..7ffa899a1 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -75,10 +75,12 @@ class Material(object): Volume of the material in cm^3. This can either be set manually or calculated in a stochastic volume calculation and added via the :meth:`Material.add_volume_information` method. + paths : list of str + The paths traversed through the CSG tree to reach each material + instance. This property is initialized by calling the + :meth:`Geometry.determine_paths` method. num_instances : int - The number of instances of this material throughout the geometry. This - property is initialized by calling the - :meth:`Geometry.count_material_instances` method. + The number of instances of this material throughout the geometry. """ diff --git a/src/geometry.F90 b/src/geometry.F90 index 7365f5af2..485615991 100644 --- a/src/geometry.F90 +++ b/src/geometry.F90 @@ -1333,8 +1333,6 @@ contains n = size(univ % cells) do i = 1, n - - ! get pointer to cell associate (c => cells(univ % cells(i))) c % instances = c % instances + 1 diff --git a/src/input_xml.F90 b/src/input_xml.F90 index 6d536828d..e7365330c 100644 --- a/src/input_xml.F90 +++ b/src/input_xml.F90 @@ -2020,7 +2020,7 @@ contains do i = 1, n_cells ! Get index in universes array - j = universe_dict%get_key(cells(i) % universe) + j = universe_dict % get_key(cells(i) % universe) ! Set the first zero entry in the universe cells array to the index in the ! global cells array