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OpenMC/examples/jupyter/pandas-dataframes.ipynb
Sterling Harper 98ab232dc1 Allow objects in addition to ids in Filters
2017-04-13 17:36:49 -04:00

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{
"cells": [
{
"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."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import glob\n",
"\n",
"from IPython.display import Image\n",
"import matplotlib.pyplot as plt\n",
"import scipy.stats\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import openmc\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate Input Files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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": false
},
"outputs": [],
"source": [
"# 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",
"\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",
"\n",
"# zircaloy\n",
"zircaloy = openmc.Material(name='Zircaloy')\n",
"zircaloy.set_density('g/cm3', 6.55)\n",
"zircaloy.add_nuclide('Zr90', 7.2758e-3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With our three materials, we can now create a materials file object that can be exported to an actual XML file."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Instantiate a Materials collection\n",
"materials_file = openmc.Materials((fuel, water, zircaloy))\n",
"\n",
"# Export to \"materials.xml\"\n",
"materials_file.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's move on to the geometry. This problem will be a square array of fuel pins for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create cylinders for the fuel and clad\n",
"fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n",
"clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n",
"\n",
"# Create boundary planes to surround the geometry\n",
"# Use both reflective and vacuum boundaries to make life interesting\n",
"min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n",
"max_x = openmc.XPlane(x0=+10.71, boundary_type='vacuum')\n",
"min_y = openmc.YPlane(y0=-10.71, boundary_type='vacuum')\n",
"max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n",
"min_z = openmc.ZPlane(z0=-10.71, boundary_type='reflective')\n",
"max_z = openmc.ZPlane(z0=+10.71, boundary_type='reflective')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create 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', fill=zircaloy)\n",
"clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
"\n",
"# Create a moderator Cell\n",
"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",
"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create fuel assembly Lattice\n",
"assembly = openmc.RectLattice(name='1.6% Fuel - 0BA')\n",
"assembly.pitch = (1.26, 1.26)\n",
"assembly.lower_left = [-1.26 * 17. / 2.0] * 2\n",
"assembly.universes = [[pin_cell_universe] * 17] * 17"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create root Cell\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(name='root universe')\n",
"root_universe.add_cell(root_cell)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now must create a geometry that is assigned a root universe and export it to XML."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create Geometry and export to \"geometry.xml\"\n",
"geometry = openmc.Geometry(root_universe)\n",
"geometry.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 5 inactive batches and 15 minimum active batches each with 2500 particles. We also tell OpenMC to turn tally triggers on, which means it will keep running until some criterion on the uncertainty of tallies is reached."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# OpenMC simulation parameters\n",
"min_batches = 20\n",
"max_batches = 200\n",
"inactive = 5\n",
"particles = 2500\n",
"\n",
"# Instantiate a Settings object\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.source = openmc.source.Source(space=uniform_dist)\n",
"\n",
"# Export to \"settings.xml\"\n",
"settings.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Instantiate a Plot\n",
"plot = openmc.Plot(plot_id=1)\n",
"plot.filename = 'materials-xy'\n",
"plot.origin = [0, 0, 0]\n",
"plot.width = [21.5, 21.5]\n",
"plot.pixels = [250, 250]\n",
"plot.color = 'mat'\n",
"\n",
"# Instantiate a Plots collection and export to \"plots.xml\"\n",
"plot_file = openmc.Plots([plot])\n",
"plot_file.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the plots.xml file, we can now generate and view the plot. OpenMC outputs plots in .ppm format, which can be converted into a compressed format like .png with the convert utility."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run openmc in plotting mode\n",
"openmc.plot_geometry(output=False)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert OpenMC's funky ppm to png\n",
"!convert materials-xy.ppm materials-xy.png\n",
"\n",
"# Display the materials plot inline\n",
"Image(filename='materials-xy.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see from the plot, we have a nice array of pin cells with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a variety of tallies."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Instantiate an empty Tallies object\n",
"tallies = openmc.Tallies()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instantiate a fission rate mesh Tally"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Instantiate a tally Mesh\n",
"mesh = openmc.Mesh(mesh_id=1)\n",
"mesh.type = 'regular'\n",
"mesh.dimension = [17, 17]\n",
"mesh.lower_left = [-10.71, -10.71]\n",
"mesh.width = [1.26, 1.26]\n",
"\n",
"# Instantiate tally Filter\n",
"mesh_filter = openmc.MeshFilter(mesh)\n",
"\n",
"# Instantiate energy Filter\n",
"energy_filter = openmc.EnergyFilter([0, 0.625, 20.0e6])\n",
"\n",
"# Instantiate the Tally\n",
"tally = openmc.Tally(name='mesh tally')\n",
"tally.filters = [mesh_filter, energy_filter]\n",
"tally.scores = ['fission', 'nu-fission']\n",
"\n",
"# Add mesh and Tally to Tallies\n",
"tallies.append(tally)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instantiate a cell Tally with nuclides"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Instantiate tally Filter\n",
"cell_filter = openmc.CellFilter(fuel_cell)\n",
"\n",
"# Instantiate the tally\n",
"tally = openmc.Tally(name='cell tally')\n",
"tally.filters = [cell_filter]\n",
"tally.scores = ['scatter-y2']\n",
"tally.nuclides = ['U235', 'U238']\n",
"\n",
"# Add mesh and tally to Tallies\n",
"tallies.append(tally)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a \"distribcell\" Tally. The distribcell filter allows us to tally multiple repeated instances of the same cell throughout the geometry."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Instantiate tally Filter\n",
"distribcell_filter = openmc.DistribcellFilter(moderator_cell)\n",
"\n",
"# Instantiate tally Trigger for kicks\n",
"trigger = openmc.Trigger(trigger_type='std_dev', threshold=5e-5)\n",
"trigger.scores = ['absorption']\n",
"\n",
"# Instantiate the Tally\n",
"tally = openmc.Tally(name='distribcell tally')\n",
"tally.filters = [distribcell_filter]\n",
"tally.scores = ['absorption', 'scatter']\n",
"tally.triggers = [trigger]\n",
"\n",
"# Add mesh and tally to Tallies\n",
"tallies.append(tally)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Export to \"tallies.xml\"\n",
"tallies.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we a have a complete set of inputs, so we can go ahead and run our simulation."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"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",
" ############### %%%%%%%%%%%%%%%%\n",
" ############ %%%%%%%%%%%%%%%\n",
" ######## %%%%%%%%%%%%%%\n",
" %%%%%%%%%%%\n",
"\n",
" | The OpenMC Monte Carlo Code\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 | bc4683be1c853fe6d0e31bccad416ef219e3efaf\n",
" Date/Time | 2017-04-13 17:23:58\n",
" MPI Processes | 1\n",
" OpenMP Threads | 1\n",
"\n",
" Reading settings XML file...\n",
" Reading geometry XML file...\n",
" Reading materials XML file...\n",
" Reading cross sections XML file...\n",
" Reading U235 from /home/smharper/openmc/data/nndc_hdf5/U235.h5\n",
" Reading U238 from /home/smharper/openmc/data/nndc_hdf5/U238.h5\n",
" Reading O16 from /home/smharper/openmc/data/nndc_hdf5/O16.h5\n",
" Reading H1 from /home/smharper/openmc/data/nndc_hdf5/H1.h5\n",
" Reading B10 from /home/smharper/openmc/data/nndc_hdf5/B10.h5\n",
" Reading Zr90 from /home/smharper/openmc/data/nndc_hdf5/Zr90.h5\n",
" Maximum neutron transport energy: 2.00000E+07 eV for U235\n",
" Reading tallies XML file...\n",
" Building neighboring cells lists for each surface...\n",
" Initializing source particles...\n",
"\n",
" ====================> K EIGENVALUE SIMULATION <====================\n",
"\n",
" Bat./Gen. k Average k \n",
" ========= ======== ==================== \n",
" 1/1 0.55921 \n",
" 2/1 0.63816 \n",
" 3/1 0.68834 \n",
" 4/1 0.71192 \n",
" 5/1 0.67935 \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 0.64126 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 = 2.4022E-01 seconds\n",
" Reading cross sections = 1.9056E-01 seconds\n",
" Total time in simulation = 7.5438E+00 seconds\n",
" Time in transport only = 7.5290E+00 seconds\n",
" Time in inactive batches = 1.0482E+00 seconds\n",
" Time in active batches = 6.4956E+00 seconds\n",
" Time synchronizing fission bank = 2.3117E-03 seconds\n",
" Sampling source sites = 1.3344E-03 seconds\n",
" SEND/RECV source sites = 6.8338E-04 seconds\n",
" Time accumulating tallies = 3.9461E-04 seconds\n",
" Total time for finalization = 1.3648E-05 seconds\n",
" Total time elapsed = 7.7964E+00 seconds\n",
" Calculation Rate (inactive) = 11925.2 neutrons/second\n",
" Calculation Rate (active) = 5773.18 neutrons/second\n",
"\n",
" ============================> RESULTS <============================\n",
"\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"
]
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Remove old HDF5 (summary, statepoint) files\n",
"!rm statepoint.*\n",
"\n",
"# Run OpenMC!\n",
"openmc.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tally Data Processing"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# We do not know how many batches were needed to satisfy the \n",
"# tally trigger(s), so find the statepoint file(s)\n",
"statepoints = glob.glob('statepoint.*.h5')\n",
"\n",
"# Load the last statepoint file\n",
"sp = openmc.StatePoint(statepoints[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analyze the mesh fission rate tally**"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tally\n",
"\tID =\t10000\n",
"\tName =\tmesh tally\n",
"\tFilters =\tMeshFilter, EnergyFilter\n",
"\tNuclides =\ttotal \n",
"\tScores =\t['fission', 'nu-fission']\n",
"\tEstimator =\ttracklength\n",
"\n"
]
}
],
"source": [
"# Find the mesh tally with the StatePoint API\n",
"tally = sp.get_tally(name='mesh tally')\n",
"\n",
"# Print a little info about the mesh tally to the screen\n",
"print(tally)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the new Tally data retrieval API with pure NumPy"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[ 0.22614381]]\n",
"\n",
" [[ 0.3789572 ]]\n",
"\n",
" [[ 0.05763899]]\n",
"\n",
" [[ 0.14265074]]]\n"
]
}
],
"source": [
"# Get the relative error for the thermal fission reaction \n",
"# rates in the four corner pins \n",
"data = tally.get_values(scores=['fission'],\n",
" filters=[openmc.MeshFilter, openmc.EnergyFilter], \\\n",
" filter_bins=[((1,1),(1,17), (17,1), (17,17)), \\\n",
" ((0., 0.625),)], value='rel_err')\n",
"print(data)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">mesh 1</th>\n",
" <th>energy low [eV]</th>\n",
" <th>energy high [eV]</th>\n",
" <th>score</th>\n",
" <th>mean</th>\n",
" <th>std. dev.</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>z</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>fission</td>\n",
" <td>2.28e-04</td>\n",
" <td>5.15e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>nu-fission</td>\n",
" <td>5.55e-04</td>\n",
" <td>1.26e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>fission</td>\n",
" <td>8.65e-05</td>\n",
" <td>9.06e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>nu-fission</td>\n",
" <td>2.28e-04</td>\n",
" <td>2.19e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>fission</td>\n",
" <td>1.83e-04</td>\n",
" <td>3.54e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>nu-fission</td>\n",
" <td>4.47e-04</td>\n",
" <td>8.62e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>fission</td>\n",
" <td>6.91e-05</td>\n",
" <td>6.94e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>nu-fission</td>\n",
" <td>1.81e-04</td>\n",
" <td>1.77e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>fission</td>\n",
" <td>1.98e-04</td>\n",
" <td>2.55e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>nu-fission</td>\n",
" <td>4.82e-04</td>\n",
" <td>6.20e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>fission</td>\n",
" <td>7.76e-05</td>\n",
" <td>9.73e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>nu-fission</td>\n",
" <td>2.05e-04</td>\n",
" <td>2.48e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>fission</td>\n",
" <td>2.32e-04</td>\n",
" <td>3.38e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>nu-fission</td>\n",
" <td>5.66e-04</td>\n",
" <td>8.23e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>fission</td>\n",
" <td>6.54e-05</td>\n",
" <td>4.61e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>nu-fission</td>\n",
" <td>1.73e-04</td>\n",
" <td>1.23e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>fission</td>\n",
" <td>2.18e-04</td>\n",
" <td>3.68e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>nu-fission</td>\n",
" <td>5.32e-04</td>\n",
" <td>8.97e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>fission</td>\n",
" <td>5.92e-05</td>\n",
" <td>6.63e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>nu-fission</td>\n",
" <td>1.56e-04</td>\n",
" <td>1.82e-05</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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 2.28e-04 \n",
"1 1 1 1 0.00e+00 6.25e-01 nu-fission 5.55e-04 \n",
"2 1 1 1 6.25e-01 2.00e+07 fission 8.65e-05 \n",
"3 1 1 1 6.25e-01 2.00e+07 nu-fission 2.28e-04 \n",
"4 1 2 1 0.00e+00 6.25e-01 fission 1.83e-04 \n",
"5 1 2 1 0.00e+00 6.25e-01 nu-fission 4.47e-04 \n",
"6 1 2 1 6.25e-01 2.00e+07 fission 6.91e-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 1.98e-04 \n",
"9 1 3 1 0.00e+00 6.25e-01 nu-fission 4.82e-04 \n",
"10 1 3 1 6.25e-01 2.00e+07 fission 7.76e-05 \n",
"11 1 3 1 6.25e-01 2.00e+07 nu-fission 2.05e-04 \n",
"12 1 4 1 0.00e+00 6.25e-01 fission 2.32e-04 \n",
"13 1 4 1 0.00e+00 6.25e-01 nu-fission 5.66e-04 \n",
"14 1 4 1 6.25e-01 2.00e+07 fission 6.54e-05 \n",
"15 1 4 1 6.25e-01 2.00e+07 nu-fission 1.73e-04 \n",
"16 1 5 1 0.00e+00 6.25e-01 fission 2.18e-04 \n",
"17 1 5 1 0.00e+00 6.25e-01 nu-fission 5.32e-04 \n",
"18 1 5 1 6.25e-01 2.00e+07 fission 5.92e-05 \n",
"19 1 5 1 6.25e-01 2.00e+07 nu-fission 1.56e-04 \n",
"\n",
" std. dev. \n",
" \n",
"0 5.15e-05 \n",
"1 1.26e-04 \n",
"2 9.06e-06 \n",
"3 2.19e-05 \n",
"4 3.54e-05 \n",
"5 8.62e-05 \n",
"6 6.94e-06 \n",
"7 1.77e-05 \n",
"8 2.55e-05 \n",
"9 6.20e-05 \n",
"10 9.73e-06 \n",
"11 2.48e-05 \n",
"12 3.38e-05 \n",
"13 8.23e-05 \n",
"14 4.61e-06 \n",
"15 1.23e-05 \n",
"16 3.68e-05 \n",
"17 8.97e-05 \n",
"18 6.63e-06 \n",
"19 1.82e-05 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get a pandas dataframe for the mesh tally data\n",
"df = tally.get_pandas_dataframe(nuclides=False)\n",
"\n",
"# Set the Pandas float display settings\n",
"pd.options.display.float_format = '{:.2e}'.format\n",
"\n",
"# Print the first twenty rows in the dataframe\n",
"df.head(20)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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AeEkf6+NzuraVNH78eAqFQrdXU1PTDpevrly5ssdnSUyfPp3Fixd3a2tra6NQ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7d809cea90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create a boxplot to view the distribution of\n",
"# fission and nu-fission rates in the pins\n",
"bp = df.boxplot(column='mean', by='score')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f7d7dd40a20>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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R8XhE3AV8C9hNUnmNBayIiKfKjqY2hnMiNTOztpE0GpgE3FQ6FxEB3AjsXuW23bPr5eaU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\nE6mZmVkLnEjNzMxa8P8BkFEtrpI/UtsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7d7de87e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Extract thermal nu-fission rates from pandas\n",
"fiss = df[df['score'] == 'nu-fission']\n",
"fiss = fiss[fiss['energy low [eV]'] == 0.0]\n",
"\n",
"# Extract mean and reshape as 2D NumPy arrays\n",
"mean = fiss['mean'].values.reshape((17,17))\n",
"\n",
"plt.imshow(mean, interpolation='nearest')\n",
"plt.title('fission rate')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analyze the cell+nuclides scatter-y2 rate tally**"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tally\n",
"\tID =\t10001\n",
"\tName =\tcell tally\n",
"\tFilters =\tCellFilter\n",
"\tNuclides =\tU235 U238 \n",
"\tScores =\t['scatter-Y0,0', 'scatter-Y1,-1', 'scatter-Y1,0', 'scatter-Y1,1', 'scatter-Y2,-2', 'scatter-Y2,-1', 'scatter-Y2,0', 'scatter-Y2,1', 'scatter-Y2,2']\n",
"\tEstimator =\tanalog\n",
"\n"
]
}
],
"source": [
"# Find the cell Tally with the StatePoint API\n",
"tally = sp.get_tally(name='cell tally')\n",
"\n",
"# Print a little info about the cell tally to the screen\n",
"print(tally)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cell</th>\n",
" <th>nuclide</th>\n",
" <th>score</th>\n",
" <th>mean</th>\n",
" <th>std. dev.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10000</td>\n",
" <td>U235</td>\n",
" <td>scatter-Y0,0</td>\n",
" <td>3.87e-02</td>\n",
" <td>8.61e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10000</td>\n",
" <td>U235</td>\n",
" <td>scatter-Y1,-1</td>\n",
" <td>6.88e-04</td>\n",
" <td>3.63e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10000</td>\n",
" <td>U235</td>\n",
" <td>scatter-Y1,0</td>\n",
" <td>-3.38e-04</td>\n",
" <td>4.37e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10000</td>\n",
" <td>U235</td>\n",
" <td>scatter-Y1,1</td>\n",
" <td>-4.27e-04</td>\n",
" <td>3.74e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10000</td>\n",
" <td>U235</td>\n",
" <td>scatter-Y2,-2</td>\n",
" <td>1.88e-04</td>\n",
" <td>1.86e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>10000</td>\n",
" <td>U235</td>\n",
" <td>scatter-Y2,-1</td>\n",
" <td>8.96e-05</td>\n",
" <td>2.02e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>10000</td>\n",
" <td>U235</td>\n",
" <td>scatter-Y2,0</td>\n",
" <td>4.03e-04</td>\n",
" <td>2.04e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>10000</td>\n",
" <td>U235</td>\n",
" <td>scatter-Y2,1</td>\n",
" <td>1.48e-04</td>\n",
" <td>2.24e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>10000</td>\n",
" <td>U235</td>\n",
" <td>scatter-Y2,2</td>\n",
" <td>1.11e-04</td>\n",
" <td>2.01e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10000</td>\n",
" <td>U238</td>\n",
" <td>scatter-Y0,0</td>\n",
" <td>2.34e+00</td>\n",
" <td>1.08e-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>10000</td>\n",
" <td>U238</td>\n",
" <td>scatter-Y1,-1</td>\n",
" <td>2.93e-02</td>\n",
" <td>2.81e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>10000</td>\n",
" <td>U238</td>\n",
" <td>scatter-Y1,0</td>\n",
" <td>4.25e-03</td>\n",
" <td>1.83e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>10000</td>\n",
" <td>U238</td>\n",
" <td>scatter-Y1,1</td>\n",
" <td>-2.60e-02</td>\n",
" <td>3.28e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>10000</td>\n",
" <td>U238</td>\n",
" <td>scatter-Y2,-2</td>\n",
" <td>-1.10e-03</td>\n",
" <td>1.58e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>10000</td>\n",
" <td>U238</td>\n",
" <td>scatter-Y2,-1</td>\n",
" <td>-4.81e-04</td>\n",
" <td>1.67e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>10000</td>\n",
" <td>U238</td>\n",
" <td>scatter-Y2,0</td>\n",
" <td>-7.95e-04</td>\n",
" <td>1.53e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>10000</td>\n",
" <td>U238</td>\n",
" <td>scatter-Y2,1</td>\n",
" <td>1.06e-03</td>\n",
" <td>1.96e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>10000</td>\n",
" <td>U238</td>\n",
" <td>scatter-Y2,2</td>\n",
" <td>-7.65e-04</td>\n",
" <td>1.67e-03</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cell nuclide score mean std. dev.\n",
"0 10000 U235 scatter-Y0,0 3.87e-02 8.61e-04\n",
"1 10000 U235 scatter-Y1,-1 6.88e-04 3.63e-04\n",
"2 10000 U235 scatter-Y1,0 -3.38e-04 4.37e-04\n",
"3 10000 U235 scatter-Y1,1 -4.27e-04 3.74e-04\n",
"4 10000 U235 scatter-Y2,-2 1.88e-04 1.86e-04\n",
"5 10000 U235 scatter-Y2,-1 8.96e-05 2.02e-04\n",
"6 10000 U235 scatter-Y2,0 4.03e-04 2.04e-04\n",
"7 10000 U235 scatter-Y2,1 1.48e-04 2.24e-04\n",
"8 10000 U235 scatter-Y2,2 1.11e-04 2.01e-04\n",
"9 10000 U238 scatter-Y0,0 2.34e+00 1.08e-02\n",
"10 10000 U238 scatter-Y1,-1 2.93e-02 2.81e-03\n",
"11 10000 U238 scatter-Y1,0 4.25e-03 1.83e-03\n",
"12 10000 U238 scatter-Y1,1 -2.60e-02 3.28e-03\n",
"13 10000 U238 scatter-Y2,-2 -1.10e-03 1.58e-03\n",
"14 10000 U238 scatter-Y2,-1 -4.81e-04 1.67e-03\n",
"15 10000 U238 scatter-Y2,0 -7.95e-04 1.53e-03\n",
"16 10000 U238 scatter-Y2,1 1.06e-03 1.96e-03\n",
"17 10000 U238 scatter-Y2,2 -7.65e-04 1.67e-03"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get a pandas dataframe for the cell tally data\n",
"df = tally.get_pandas_dataframe()\n",
"\n",
"# Print the first twenty rows in the dataframe\n",
"df.head(100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the new Tally data retrieval API with pure NumPy"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[ 0.0016699 0.01076055]\n",
" [ 0.00020076 0.00086098]]]\n"
]
}
],
"source": [
"# Get the standard deviations for two of the spherical harmonic\n",
"# scattering reaction rates \n",
"data = tally.get_values(scores=['scatter-Y2,2', 'scatter-Y0,0'], \n",
" nuclides=['U238', 'U235'], value='std_dev')\n",
"print(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analyze the distribcell tally**"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tally\n",
"\tID =\t10002\n",
"\tName =\tdistribcell tally\n",
"\tFilters =\tDistribcellFilter\n",
"\tNuclides =\ttotal \n",
"\tScores =\t['absorption', 'scatter']\n",
"\tEstimator =\ttracklength\n",
"\n"
]
}
],
"source": [
"# Find the distribcell Tally with the StatePoint API\n",
"tally = sp.get_tally(name='distribcell tally')\n",
"\n",
"# Print a little info about the distribcell tally to the screen\n",
"print(tally)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the new Tally data retrieval API with pure NumPy"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[ 0.04126529]]]\n"
]
}
],
"source": [
"# Get the relative error for the scattering reaction rates in\n",
"# the first 10 distribcell instances \n",
"data = tally.get_values(scores=['scatter'], filters=[openmc.DistribcellFilter],\n",
" filter_bins=[(i,) for i in range(10)], value='rel_err')\n",
"print(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the distribcell tally dataframe"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">level 1</th>\n",
" <th colspan=\"3\" halign=\"left\">level 2</th>\n",
" <th colspan=\"2\" halign=\"left\">level 3</th>\n",
" <th>distribcell</th>\n",
" <th>score</th>\n",
" <th>mean</th>\n",
" <th>std. dev.</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>univ</th>\n",
" <th>cell</th>\n",
" <th colspan=\"3\" halign=\"left\">lat</th>\n",
" <th>univ</th>\n",
" <th>cell</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>id</th>\n",
" <th>id</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>id</th>\n",
" <th>id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>558</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>7</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>279</td>\n",
" <td>absorption</td>\n",
" <td>7.37e-05</td>\n",
" <td>7.77e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>559</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>7</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>279</td>\n",
" <td>scatter</td>\n",
" <td>1.27e-02</td>\n",
" <td>6.76e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>560</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>8</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>280</td>\n",
" <td>absorption</td>\n",
" <td>9.14e-05</td>\n",
" <td>9.21e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>561</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>8</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>280</td>\n",
" <td>scatter</td>\n",
" <td>1.39e-02</td>\n",
" <td>5.27e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>562</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>281</td>\n",
" <td>absorption</td>\n",
" <td>8.98e-05</td>\n",
" <td>8.99e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>563</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>281</td>\n",
" <td>scatter</td>\n",
" <td>1.47e-02</td>\n",
" <td>6.07e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>10</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>282</td>\n",
" <td>absorption</td>\n",
" <td>1.13e-04</td>\n",
" <td>1.28e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>10</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>282</td>\n",
" <td>scatter</td>\n",
" <td>1.54e-02</td>\n",
" <td>6.58e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>11</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>283</td>\n",
" <td>absorption</td>\n",
" <td>1.13e-04</td>\n",
" <td>9.64e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>11</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>283</td>\n",
" <td>scatter</td>\n",
" <td>1.75e-02</td>\n",
" <td>5.84e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>284</td>\n",
" <td>absorption</td>\n",
" <td>1.10e-04</td>\n",
" <td>1.12e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>569</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>12</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>284</td>\n",
" <td>scatter</td>\n",
" <td>1.72e-02</td>\n",
" <td>7.15e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>570</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>13</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>285</td>\n",
" <td>absorption</td>\n",
" <td>1.27e-04</td>\n",
" <td>1.92e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>571</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>13</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>285</td>\n",
" <td>scatter</td>\n",
" <td>1.75e-02</td>\n",
" <td>8.93e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>572</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>14</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>286</td>\n",
" <td>absorption</td>\n",
" <td>1.24e-04</td>\n",
" <td>1.26e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>573</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>14</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>286</td>\n",
" <td>scatter</td>\n",
" <td>1.78e-02</td>\n",
" <td>9.03e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>574</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>287</td>\n",
" <td>absorption</td>\n",
" <td>1.24e-04</td>\n",
" <td>1.64e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>575</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>287</td>\n",
" <td>scatter</td>\n",
" <td>1.85e-02</td>\n",
" <td>1.01e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>576</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>288</td>\n",
" <td>absorption</td>\n",
" <td>1.32e-04</td>\n",
" <td>1.37e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>577</th>\n",
" <td>10002</td>\n",
" <td>10003</td>\n",
" <td>10001</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>10000</td>\n",
" <td>10002</td>\n",
" <td>288</td>\n",
" <td>scatter</td>\n",
" <td>1.87e-02</td>\n",
" <td>8.24e-04</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 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 7.37e-05 7.77e-06 \n",
"559 1.27e-02 6.76e-04 \n",
"560 9.14e-05 9.21e-06 \n",
"561 1.39e-02 5.27e-04 \n",
"562 8.98e-05 8.99e-06 \n",
"563 1.47e-02 6.07e-04 \n",
"564 1.13e-04 1.28e-05 \n",
"565 1.54e-02 6.58e-04 \n",
"566 1.13e-04 9.64e-06 \n",
"567 1.75e-02 5.84e-04 \n",
"568 1.10e-04 1.12e-05 \n",
"569 1.72e-02 7.15e-04 \n",
"570 1.27e-04 1.92e-05 \n",
"571 1.75e-02 8.93e-04 \n",
"572 1.24e-04 1.26e-05 \n",
"573 1.78e-02 9.03e-04 \n",
"574 1.24e-04 1.64e-05 \n",
"575 1.85e-02 1.01e-03 \n",
"576 1.32e-04 1.37e-05 \n",
"577 1.87e-02 8.24e-04 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get a pandas dataframe for the distribcell tally data\n",
"df = tally.get_pandas_dataframe(nuclides=False)\n",
"\n",
"# Print the last twenty rows in the dataframe\n",
"df.tail(20)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>std. dev.</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2.89e+02</td>\n",
" <td>2.89e+02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4.19e-04</td>\n",
" <td>2.40e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.42e-04</td>\n",
" <td>1.03e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2.31e-05</td>\n",
" <td>4.39e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.03e-04</td>\n",
" <td>1.64e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>4.01e-04</td>\n",
" <td>2.39e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.17e-04</td>\n",
" <td>3.02e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>9.28e-04</td>\n",
" <td>5.88e-05</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean std. dev.\n",
" \n",
" \n",
"count 2.89e+02 2.89e+02\n",
"mean 4.19e-04 2.40e-05\n",
"std 2.42e-04 1.03e-05\n",
"min 2.31e-05 4.39e-06\n",
"25% 2.03e-04 1.64e-05\n",
"50% 4.01e-04 2.39e-05\n",
"75% 6.17e-04 3.02e-05\n",
"max 9.28e-04 5.88e-05"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Show summary statistics for absorption distribcell tally data\n",
"absorption = df[df['score'] == 'absorption']\n",
"absorption[['mean', 'std. dev.']].dropna().describe()\n",
"\n",
"# Note that the maximum standard deviation does indeed\n",
"# meet the 5e-4 threshold set by the tally trigger"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform a statistical test comparing the tally sample distributions for two categories of fuel pins."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mann-Whitney Test p-value: 0.7108210033985298\n"
]
}
],
"source": [
"# Extract tally data from pins in the pins divided along y=x diagonal \n",
"multi_index = ('level 2', 'lat',)\n",
"lower = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] < 16]\n",
"upper = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] > 16]\n",
"lower = lower[lower['score'] == 'absorption']\n",
"upper = upper[upper['score'] == 'absorption']\n",
"\n",
"# Perform non-parametric Mann-Whitney U Test to see if the \n",
"# absorption rates (may) come from same sampling distribution\n",
"u, p = scipy.stats.mannwhitneyu(lower['mean'], upper['mean'])\n",
"print('Mann-Whitney Test p-value: {0}'.format(p))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the symmetry implied by the y=x diagonal ensures that the two sampling distributions are identical. Indeed, as illustrated by the test above, for any reasonable significance level (*e.g.*, $\\alpha$=0.05) one would **not reject** the null hypothesis that the two sampling distributions are identical.\n",
"\n",
"Next, perform the same test but with two groupings of pins which are not symmetrically identical to one another."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mann-Whitney Test p-value: 7.454144155212731e-42\n"
]
}
],
"source": [
"# Extract tally data from pins in the pins divided along y=-x diagonal\n",
"multi_index = ('level 2', 'lat',)\n",
"lower = df[df[multi_index + ('x',)] > df[multi_index + ('y',)]]\n",
"upper = df[df[multi_index + ('x',)] < df[multi_index + ('y',)]]\n",
"lower = lower[lower['score'] == 'absorption']\n",
"upper = upper[upper['score'] == 'absorption']\n",
"\n",
"# Perform non-parametric Mann-Whitney U Test to see if the \n",
"# absorption rates (may) come from same sampling distribution\n",
"u, p = scipy.stats.mannwhitneyu(lower['mean'], upper['mean'])\n",
"print('Mann-Whitney Test p-value: {0}'.format(p))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the asymmetry implied by the y=-x diagonal ensures that the two sampling distributions are *not* identical. Indeed, as illustrated by the test above, for any reasonable significance level (*e.g.*, $\\alpha$=0.05) one would **reject** the null hypothesis that the two sampling distributions are identical."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-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",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f7d809ce2e8>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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3LuXLRRB+vYxCoaCQIyIySbZs2UJfXx9An7tvadV1Gq7JmUSfAm4wsxFgE8Fo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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7d7ddf7d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Extract the scatter tally data from pandas\n",
"scatter = df[df['score'] == 'scatter']\n",
"\n",
"scatter['rel. err.'] = scatter['std. dev.'] / scatter['mean']\n",
"\n",
"# Show a scatter plot of the mean vs. the std. dev.\n",
"scatter.plot(kind='scatter', x='mean', y='rel. err.', title='Scattering Rates')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f7d7dc8d630>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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MgQMHRrhKqQ49BSMSV7oAPYOWLhVvLhLH+vfvT2ZmJsuXL2fQoEGHbq1UZPLkyUyYMIE+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\nExBJklQ5ExBJklQ5ExBJklS5/wD6ovxjH+G+1wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7d7dd0a588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot a histogram and kernel density estimate for the scattering rates\n",
"scatter['mean'].plot(kind='hist', bins=25)\n",
"scatter['mean'].plot(kind='kde')\n",
"plt.title('Scattering Rates')\n",
"plt.xlabel('Mean')\n",
"plt.legend(['KDE', 'Histogram'])"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
Reference in a new issue View git blame Copy permalink