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OpenMC/examples/jupyter/pandas-dataframes.ipynb
Paul Romano bc09d1ef55 adding back files to be reviewed
2019-10-28 11:55:45 -05:00

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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": {},
"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": {},
"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": {},
"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": {},
"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": {},
"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": {},
"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": {},
"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": {},
"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": {},
"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(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": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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_by = 'material'\n",
"\n",
"# Show plot\n",
"openmc.plot_inline(plot)"
]
},
{
"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": 11,
"metadata": {},
"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": 12,
"metadata": {},
"outputs": [],
"source": [
"# Instantiate a tally Mesh\n",
"mesh = openmc.RegularMesh(mesh_id=1)\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": 13,
"metadata": {},
"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']\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": 14,
"metadata": {},
"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": 15,
"metadata": {},
"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": 16,
"metadata": {},
"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",
" | The OpenMC Monte Carlo Code\n",
" Copyright | 2011-2019 MIT and OpenMC contributors\n",
" License | http://openmc.readthedocs.io/en/latest/license.html\n",
" Version | 0.11.0-dev\n",
" Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n",
" Date/Time | 2019-07-18 22:46:04\n",
" OpenMP Threads | 4\n",
"\n",
" Reading settings XML file...\n",
" Reading cross sections XML file...\n",
" Reading materials XML file...\n",
" Reading geometry XML file...\n",
" Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n",
" Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n",
" Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n",
" Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n",
" Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n",
" Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n",
" Maximum neutron transport energy: 20000000.000000 eV for U235\n",
" Reading tallies XML file...\n",
" Writing summary.h5 file...\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 0.70686 0.67461 +/- 0.00910\n",
" 11/1 0.71753 0.68176 +/- 0.01031\n",
" 12/1 0.66967 0.68004 +/- 0.00889\n",
" 13/1 0.67800 0.67978 +/- 0.00770\n",
" 14/1 0.65634 0.67718 +/- 0.00727\n",
" 15/1 0.66891 0.67635 +/- 0.00656\n",
" 16/1 0.66281 0.67512 +/- 0.00606\n",
" 17/1 0.68160 0.67566 +/- 0.00556\n",
" 18/1 0.63835 0.67279 +/- 0.00586\n",
" 19/1 0.66200 0.67202 +/- 0.00548\n",
" 20/1 0.67156 0.67199 +/- 0.00510\n",
" Triggers unsatisfied, max unc./thresh. is 68.3537 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 70089 --- greater than max batches\n",
" Creating state point statepoint.020.h5...\n",
" 21/1 0.67469 0.67216 +/- 0.00478\n",
" Triggers unsatisfied, max unc./thresh. is 63.9814 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 65503 --- greater than max batches\n",
" 22/1 0.69218 0.67334 +/- 0.00464\n",
" Triggers unsatisfied, max unc./thresh. is 64.4829 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 70692 --- greater than max batches\n",
" 23/1 0.72838 0.67639 +/- 0.00534\n",
" Triggers unsatisfied, max unc./thresh. is 65.1347 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 76371 --- greater than max batches\n",
" 24/1 0.68472 0.67683 +/- 0.00507\n",
" Triggers unsatisfied, max unc./thresh. is 61.6163 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 72140 --- greater than max batches\n",
" 25/1 0.66664 0.67632 +/- 0.00483\n",
" Triggers unsatisfied, max unc./thresh. is 59.0208 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 69675 --- greater than max batches\n",
" 26/1 0.65315 0.67522 +/- 0.00473\n",
" Triggers unsatisfied, max unc./thresh. is 56.5216 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 67094 --- greater than max batches\n",
" 27/1 0.63865 0.67356 +/- 0.00480\n",
" Triggers unsatisfied, max unc./thresh. is 53.8991 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 63918 --- greater than max batches\n",
" 28/1 0.68053 0.67386 +/- 0.00460\n",
" Triggers unsatisfied, max unc./thresh. is 51.504 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61017 --- greater than max batches\n",
" 29/1 0.71585 0.67561 +/- 0.00474\n",
" Triggers unsatisfied, max unc./thresh. is 49.3115 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58364 --- greater than max batches\n",
" 30/1 0.67268 0.67549 +/- 0.00455\n",
" Triggers unsatisfied, max unc./thresh. is 47.3457 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56046 --- greater than max batches\n",
" 31/1 0.67027 0.67529 +/- 0.00437\n",
" Triggers unsatisfied, max unc./thresh. is 48.2456 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60524 --- greater than max batches\n",
" 32/1 0.67324 0.67522 +/- 0.00421\n",
" Triggers unsatisfied, max unc./thresh. is 47.1077 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59922 --- greater than max batches\n",
" 33/1 0.66398 0.67481 +/- 0.00408\n",
" Triggers unsatisfied, max unc./thresh. is 45.4352 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57807 --- greater than max batches\n",
" 34/1 0.66373 0.67443 +/- 0.00395\n",
" Triggers unsatisfied, max unc./thresh. is 44.8243 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58273 --- greater than max batches\n",
" 35/1 0.68412 0.67476 +/- 0.00383\n",
" Triggers unsatisfied, max unc./thresh. is 43.7412 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57404 --- greater than max batches\n",
" 36/1 0.66026 0.67429 +/- 0.00374\n",
" Triggers unsatisfied, max unc./thresh. is 43.0549 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57471 --- greater than max batches\n",
" 37/1 0.67283 0.67424 +/- 0.00362\n",
" Triggers unsatisfied, max unc./thresh. is 42.9634 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59073 --- greater than max batches\n",
" 38/1 0.69507 0.67487 +/- 0.00356\n",
" Triggers unsatisfied, max unc./thresh. is 41.6527 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57259 --- greater than max batches\n",
" 39/1 0.68681 0.67522 +/- 0.00347\n",
" Triggers unsatisfied, max unc./thresh. is 40.4174 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55547 --- greater than max batches\n",
" 40/1 0.65886 0.67476 +/- 0.00340\n",
" Triggers unsatisfied, max unc./thresh. is 39.424 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54404 --- greater than max batches\n",
" 41/1 0.63736 0.67372 +/- 0.00347\n",
" Triggers unsatisfied, max unc./thresh. is 40.094 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57877 --- greater than max batches\n",
" 42/1 0.71800 0.67491 +/- 0.00358\n",
" Triggers unsatisfied, max unc./thresh. is 39.0603 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56457 --- greater than max batches\n",
" 43/1 0.67193 0.67484 +/- 0.00348\n",
" Triggers unsatisfied, max unc./thresh. is 38.8448 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57344 --- greater than max batches\n",
" 44/1 0.66680 0.67463 +/- 0.00340\n",
" Triggers unsatisfied, max unc./thresh. is 38.227 for absorption in tally 3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" WARNING: The estimated number of batches is 56996 --- greater than max batches\n",
" 45/1 0.65956 0.67425 +/- 0.00334\n",
" Triggers unsatisfied, max unc./thresh. is 37.2591 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55535 --- greater than max batches\n",
" 46/1 0.64705 0.67359 +/- 0.00332\n",
" Triggers unsatisfied, max unc./thresh. is 37.802 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58594 --- greater than max batches\n",
" 47/1 0.67729 0.67368 +/- 0.00324\n",
" Triggers unsatisfied, max unc./thresh. is 36.9727 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57419 --- greater than max batches\n",
" 48/1 0.68259 0.67389 +/- 0.00317\n",
" Triggers unsatisfied, max unc./thresh. is 36.3752 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56901 --- greater than max batches\n",
" 49/1 0.64395 0.67320 +/- 0.00317\n",
" Triggers unsatisfied, max unc./thresh. is 35.7676 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56296 --- greater than max batches\n",
" 50/1 0.68839 0.67354 +/- 0.00312\n",
" Triggers unsatisfied, max unc./thresh. is 34.977 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55058 --- greater than max batches\n",
" 51/1 0.71108 0.67436 +/- 0.00316\n",
" Triggers unsatisfied, max unc./thresh. is 34.453 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54608 --- greater than max batches\n",
" 52/1 0.66286 0.67411 +/- 0.00310\n",
" Triggers unsatisfied, max unc./thresh. is 33.9781 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54268 --- greater than max batches\n",
" 53/1 0.62666 0.67313 +/- 0.00319\n",
" Triggers unsatisfied, max unc./thresh. is 33.4946 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 53856 --- greater than max batches\n",
" 54/1 0.67124 0.67309 +/- 0.00313\n",
" Triggers unsatisfied, max unc./thresh. is 32.8639 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 52927 --- greater than max batches\n",
" 55/1 0.67741 0.67317 +/- 0.00306\n",
" Triggers unsatisfied, max unc./thresh. is 32.2922 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 52145 --- greater than max batches\n",
" 56/1 0.67182 0.67315 +/- 0.00300\n",
" Triggers unsatisfied, max unc./thresh. is 31.9136 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 51948 --- greater than max batches\n",
" 57/1 0.68764 0.67343 +/- 0.00296\n",
" Triggers unsatisfied, max unc./thresh. is 31.3059 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50969 --- greater than max batches\n",
" 58/1 0.72310 0.67436 +/- 0.00305\n",
" Triggers unsatisfied, max unc./thresh. is 30.8841 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50558 --- greater than max batches\n",
" 59/1 0.67689 0.67441 +/- 0.00299\n",
" Triggers unsatisfied, max unc./thresh. is 30.5895 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50534 --- greater than max batches\n",
" 60/1 0.65890 0.67413 +/- 0.00295\n",
" Triggers unsatisfied, max unc./thresh. is 30.0567 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 49693 --- greater than max batches\n",
" 61/1 0.69128 0.67443 +/- 0.00291\n",
" Triggers unsatisfied, max unc./thresh. is 29.8144 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 49784 --- greater than max batches\n",
" 62/1 0.65469 0.67409 +/- 0.00288\n",
" Triggers unsatisfied, max unc./thresh. is 29.3138 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 48986 --- greater than max batches\n",
" 63/1 0.71839 0.67485 +/- 0.00293\n",
" Triggers unsatisfied, max unc./thresh. is 28.9465 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 48604 --- greater than max batches\n",
" 64/1 0.69556 0.67520 +/- 0.00291\n",
" Triggers unsatisfied, max unc./thresh. is 29.1602 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50174 --- greater than max batches\n",
" 65/1 0.70067 0.67563 +/- 0.00289\n",
" Triggers unsatisfied, max unc./thresh. is 28.9248 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50204 --- greater than max batches\n",
" 66/1 0.67994 0.67570 +/- 0.00284\n",
" Triggers unsatisfied, max unc./thresh. is 28.7841 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50545 --- greater than max batches\n",
" 67/1 0.74539 0.67682 +/- 0.00301\n",
" Triggers unsatisfied, max unc./thresh. is 28.4946 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50346 --- greater than max batches\n",
" 68/1 0.67753 0.67683 +/- 0.00296\n",
" Triggers unsatisfied, max unc./thresh. is 28.1166 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 49810 --- greater than max batches\n",
" 69/1 0.69595 0.67713 +/- 0.00293\n",
" Triggers unsatisfied, max unc./thresh. is 28.0441 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50340 --- greater than max batches\n",
" 70/1 0.70621 0.67758 +/- 0.00292\n",
" Triggers unsatisfied, max unc./thresh. is 27.708 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 49908 --- greater than max batches\n",
" 71/1 0.71027 0.67807 +/- 0.00292\n",
" Triggers unsatisfied, max unc./thresh. is 27.2979 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 49187 --- greater than max batches\n",
" 72/1 0.63710 0.67746 +/- 0.00294\n",
" Triggers unsatisfied, max unc./thresh. is 27.3359 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50071 --- greater than max batches\n",
" 73/1 0.70979 0.67794 +/- 0.00294\n",
" Triggers unsatisfied, max unc./thresh. is 29.5308 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59306 --- greater than max batches\n",
" 74/1 0.65957 0.67767 +/- 0.00291\n",
" Triggers unsatisfied, max unc./thresh. is 29.2344 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58976 --- greater than max batches\n",
" 75/1 0.66611 0.67751 +/- 0.00287\n",
" Triggers unsatisfied, max unc./thresh. is 28.8289 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58183 --- greater than max batches\n",
" 76/1 0.66033 0.67726 +/- 0.00284\n",
" Triggers unsatisfied, max unc./thresh. is 28.4986 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57670 --- greater than max batches\n",
" 77/1 0.68535 0.67738 +/- 0.00280\n",
" Triggers unsatisfied, max unc./thresh. is 28.2548 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57486 --- greater than max batches\n",
" 78/1 0.71920 0.67795 +/- 0.00282\n",
" Triggers unsatisfied, max unc./thresh. is 28.2853 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58410 --- greater than max batches\n",
" 79/1 0.67645 0.67793 +/- 0.00278\n",
" Triggers unsatisfied, max unc./thresh. is 27.9534 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57829 --- greater than max batches\n",
" 80/1 0.68300 0.67800 +/- 0.00275\n",
" Triggers unsatisfied, max unc./thresh. is 27.5813 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57060 --- greater than max batches\n",
" 81/1 0.69810 0.67826 +/- 0.00272\n",
" Triggers unsatisfied, max unc./thresh. is 27.2164 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56301 --- greater than max batches\n",
" 82/1 0.68213 0.67831 +/- 0.00269\n",
" Triggers unsatisfied, max unc./thresh. is 26.8628 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55570 --- greater than max batches\n",
" 83/1 0.68745 0.67843 +/- 0.00265\n",
" Triggers unsatisfied, max unc./thresh. is 26.5172 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54852 --- greater than max batches\n",
" 84/1 0.65239 0.67810 +/- 0.00264\n",
" Triggers unsatisfied, max unc./thresh. is 26.2016 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54241 --- greater than max batches\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 85/1 0.64990 0.67775 +/- 0.00263\n",
" Triggers unsatisfied, max unc./thresh. is 25.9705 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 53963 --- greater than max batches\n",
" 86/1 0.68586 0.67785 +/- 0.00260\n",
" Triggers unsatisfied, max unc./thresh. is 25.7908 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 53884 --- greater than max batches\n",
" 87/1 0.63453 0.67732 +/- 0.00262\n",
" Triggers unsatisfied, max unc./thresh. is 25.5271 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 53439 --- greater than max batches\n",
" 88/1 0.65402 0.67704 +/- 0.00261\n",
" Triggers unsatisfied, max unc./thresh. is 25.321 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 53221 --- greater than max batches\n",
" 89/1 0.69063 0.67720 +/- 0.00258\n",
" Triggers unsatisfied, max unc./thresh. is 25.8769 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56253 --- greater than max batches\n",
" 90/1 0.65729 0.67697 +/- 0.00256\n",
" Triggers unsatisfied, max unc./thresh. is 25.7648 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56431 --- greater than max batches\n",
" 91/1 0.72355 0.67751 +/- 0.00259\n",
" Triggers unsatisfied, max unc./thresh. is 25.5034 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55942 --- greater than max batches\n",
" 92/1 0.63010 0.67696 +/- 0.00262\n",
" Triggers unsatisfied, max unc./thresh. is 25.2708 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55565 --- greater than max batches\n",
" 93/1 0.68610 0.67707 +/- 0.00259\n",
" Triggers unsatisfied, max unc./thresh. is 24.9941 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54980 --- greater than max batches\n",
" 94/1 0.67618 0.67706 +/- 0.00256\n",
" Triggers unsatisfied, max unc./thresh. is 24.7139 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54365 --- greater than max batches\n",
" 95/1 0.68946 0.67719 +/- 0.00253\n",
" Triggers unsatisfied, max unc./thresh. is 25.4371 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58240 --- greater than max batches\n",
" 96/1 0.70557 0.67751 +/- 0.00252\n",
" Triggers unsatisfied, max unc./thresh. is 25.5082 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59216 --- greater than max batches\n",
" 97/1 0.64689 0.67717 +/- 0.00252\n",
" Triggers unsatisfied, max unc./thresh. is 25.2374 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58603 --- greater than max batches\n",
" 98/1 0.70194 0.67744 +/- 0.00251\n",
" Triggers unsatisfied, max unc./thresh. is 25.393 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59972 --- greater than max batches\n",
" 99/1 0.68278 0.67750 +/- 0.00248\n",
" Triggers unsatisfied, max unc./thresh. is 25.5651 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61441 --- greater than max batches\n",
" 100/1 0.67066 0.67742 +/- 0.00246\n",
" Triggers unsatisfied, max unc./thresh. is 25.3552 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61079 --- greater than max batches\n",
" 101/1 0.64907 0.67713 +/- 0.00245\n",
" Triggers unsatisfied, max unc./thresh. is 25.3463 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61679 --- greater than max batches\n",
" 102/1 0.69810 0.67735 +/- 0.00243\n",
" Triggers unsatisfied, max unc./thresh. is 25.1877 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61544 --- greater than max batches\n",
" 103/1 0.70659 0.67764 +/- 0.00242\n",
" Triggers unsatisfied, max unc./thresh. is 24.9371 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60948 --- greater than max batches\n",
" 104/1 0.64152 0.67728 +/- 0.00243\n",
" Triggers unsatisfied, max unc./thresh. is 24.6848 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60330 --- greater than max batches\n",
" 105/1 0.68117 0.67732 +/- 0.00240\n",
" Triggers unsatisfied, max unc./thresh. is 24.4368 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59721 --- greater than max batches\n",
" 106/1 0.71963 0.67774 +/- 0.00242\n",
" Triggers unsatisfied, max unc./thresh. is 24.2091 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59200 --- greater than max batches\n",
" 107/1 0.69488 0.67790 +/- 0.00240\n",
" Triggers unsatisfied, max unc./thresh. is 23.9711 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58616 --- greater than max batches\n",
" 108/1 0.65697 0.67770 +/- 0.00238\n",
" Triggers unsatisfied, max unc./thresh. is 23.8071 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58384 --- greater than max batches\n",
" 109/1 0.70032 0.67792 +/- 0.00237\n",
" Triggers unsatisfied, max unc./thresh. is 23.5788 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57825 --- greater than max batches\n",
" 110/1 0.66571 0.67780 +/- 0.00235\n",
" Triggers unsatisfied, max unc./thresh. is 23.5035 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58009 --- greater than max batches\n",
" 111/1 0.69676 0.67798 +/- 0.00234\n",
" Triggers unsatisfied, max unc./thresh. is 23.3157 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57629 --- greater than max batches\n",
" 112/1 0.68219 0.67802 +/- 0.00231\n",
" Triggers unsatisfied, max unc./thresh. is 23.1525 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57361 --- greater than max batches\n",
" 113/1 0.69025 0.67813 +/- 0.00230\n",
" Triggers unsatisfied, max unc./thresh. is 23.0036 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57156 --- greater than max batches\n",
" 114/1 0.69241 0.67826 +/- 0.00228\n",
" Triggers unsatisfied, max unc./thresh. is 22.792 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56628 --- greater than max batches\n",
" 115/1 0.68646 0.67834 +/- 0.00226\n",
" Triggers unsatisfied, max unc./thresh. is 22.6864 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56620 --- greater than max batches\n",
" 116/1 0.69601 0.67850 +/- 0.00224\n",
" Triggers unsatisfied, max unc./thresh. is 22.5007 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56203 --- greater than max batches\n",
" 117/1 0.68761 0.67858 +/- 0.00222\n",
" Triggers unsatisfied, max unc./thresh. is 22.3093 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55749 --- greater than max batches\n",
" 118/1 0.71356 0.67889 +/- 0.00223\n",
" Triggers unsatisfied, max unc./thresh. is 22.6651 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58054 --- greater than max batches\n",
" 119/1 0.69850 0.67906 +/- 0.00221\n",
" Triggers unsatisfied, max unc./thresh. is 22.4712 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57570 --- greater than max batches\n",
" 120/1 0.70957 0.67933 +/- 0.00221\n",
" Triggers unsatisfied, max unc./thresh. is 22.3266 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57331 --- greater than max batches\n",
" 121/1 0.69643 0.67947 +/- 0.00220\n",
" Triggers unsatisfied, max unc./thresh. is 22.6029 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59269 --- greater than max batches\n",
" 122/1 0.67717 0.67945 +/- 0.00218\n",
" Triggers unsatisfied, max unc./thresh. is 22.4667 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59062 --- greater than max batches\n",
" 123/1 0.68419 0.67949 +/- 0.00216\n",
" Triggers unsatisfied, max unc./thresh. is 22.3764 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59089 --- greater than max batches\n",
" 124/1 0.69221 0.67960 +/- 0.00214\n",
" Triggers unsatisfied, max unc./thresh. is 22.3341 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59364 --- greater than max batches\n",
" 125/1 0.73940 0.68010 +/- 0.00218\n",
" Triggers unsatisfied, max unc./thresh. is 22.1478 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58868 --- greater than max batches\n",
" 126/1 0.66908 0.68001 +/- 0.00217\n",
" Triggers unsatisfied, max unc./thresh. is 22.0085 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58615 --- greater than max batches\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 127/1 0.66041 0.67985 +/- 0.00216\n",
" Triggers unsatisfied, max unc./thresh. is 21.8274 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58131 --- greater than max batches\n",
" 128/1 0.69395 0.67996 +/- 0.00214\n",
" Triggers unsatisfied, max unc./thresh. is 21.6537 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57678 --- greater than max batches\n",
" 129/1 0.68665 0.68002 +/- 0.00212\n",
" Triggers unsatisfied, max unc./thresh. is 21.7739 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58794 --- greater than max batches\n",
" 130/1 0.64849 0.67976 +/- 0.00212\n",
" Triggers unsatisfied, max unc./thresh. is 21.7492 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59134 --- greater than max batches\n",
" 131/1 0.69734 0.67990 +/- 0.00211\n",
" Triggers unsatisfied, max unc./thresh. is 21.59 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58738 --- greater than max batches\n",
" 132/1 0.69482 0.68002 +/- 0.00210\n",
" Triggers unsatisfied, max unc./thresh. is 21.4249 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58302 --- greater than max batches\n",
" 133/1 0.68884 0.68009 +/- 0.00208\n",
" Triggers unsatisfied, max unc./thresh. is 21.2587 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57853 --- greater than max batches\n",
" 134/1 0.63042 0.67971 +/- 0.00210\n",
" Triggers unsatisfied, max unc./thresh. is 21.1851 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57902 --- greater than max batches\n",
" 135/1 0.69209 0.67980 +/- 0.00209\n",
" Triggers unsatisfied, max unc./thresh. is 21.0525 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57623 --- greater than max batches\n",
" 136/1 0.69873 0.67995 +/- 0.00208\n",
" Triggers unsatisfied, max unc./thresh. is 20.9996 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57774 --- greater than max batches\n",
" 137/1 0.70270 0.68012 +/- 0.00207\n",
" Triggers unsatisfied, max unc./thresh. is 20.8455 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57364 --- greater than max batches\n",
" 138/1 0.67295 0.68006 +/- 0.00205\n",
" Triggers unsatisfied, max unc./thresh. is 21.3716 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60752 --- greater than max batches\n",
" 139/1 0.63853 0.67975 +/- 0.00206\n",
" Triggers unsatisfied, max unc./thresh. is 21.2124 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60301 --- greater than max batches\n",
" 140/1 0.66645 0.67966 +/- 0.00205\n",
" Triggers unsatisfied, max unc./thresh. is 21.1279 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60268 --- greater than max batches\n",
" 141/1 0.70730 0.67986 +/- 0.00204\n",
" Triggers unsatisfied, max unc./thresh. is 20.9845 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59893 --- greater than max batches\n",
" 142/1 0.68838 0.67992 +/- 0.00203\n",
" Triggers unsatisfied, max unc./thresh. is 20.8774 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59719 --- greater than max batches\n",
" 143/1 0.64900 0.67970 +/- 0.00203\n",
" Triggers unsatisfied, max unc./thresh. is 21.3772 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 63069 --- greater than max batches\n",
" 144/1 0.64490 0.67945 +/- 0.00203\n",
" Triggers unsatisfied, max unc./thresh. is 21.2531 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62791 --- greater than max batches\n",
" 145/1 0.69221 0.67954 +/- 0.00201\n",
" Triggers unsatisfied, max unc./thresh. is 21.2049 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62956 --- greater than max batches\n",
" 146/1 0.69481 0.67965 +/- 0.00200\n",
" Triggers unsatisfied, max unc./thresh. is 21.0645 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62569 --- greater than max batches\n",
" 147/1 0.70394 0.67982 +/- 0.00200\n",
" Triggers unsatisfied, max unc./thresh. is 20.9156 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62125 --- greater than max batches\n",
" 148/1 0.69482 0.67992 +/- 0.00198\n",
" Triggers unsatisfied, max unc./thresh. is 20.7699 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61694 --- greater than max batches\n",
" 149/1 0.63886 0.67964 +/- 0.00199\n",
" Triggers unsatisfied, max unc./thresh. is 20.6366 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61331 --- greater than max batches\n",
" 150/1 0.69377 0.67973 +/- 0.00198\n",
" Triggers unsatisfied, max unc./thresh. is 20.5819 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61430 --- greater than max batches\n",
" 151/1 0.71045 0.67994 +/- 0.00198\n",
" Triggers unsatisfied, max unc./thresh. is 20.5417 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61612 --- greater than max batches\n",
" 152/1 0.66093 0.67982 +/- 0.00197\n",
" Triggers unsatisfied, max unc./thresh. is 20.4124 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61256 --- greater than max batches\n",
" 153/1 0.68564 0.67985 +/- 0.00196\n",
" Triggers unsatisfied, max unc./thresh. is 20.3025 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61010 --- greater than max batches\n",
" 154/1 0.66961 0.67979 +/- 0.00194\n",
" Triggers unsatisfied, max unc./thresh. is 20.2239 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60948 --- greater than max batches\n",
" 155/1 0.67099 0.67973 +/- 0.00193\n",
" Triggers unsatisfied, max unc./thresh. is 20.0962 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60584 --- greater than max batches\n",
" 156/1 0.72742 0.68004 +/- 0.00194\n",
" Triggers unsatisfied, max unc./thresh. is 19.9753 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60256 --- greater than max batches\n",
" 157/1 0.66458 0.67994 +/- 0.00193\n",
" Triggers unsatisfied, max unc./thresh. is 19.8852 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60109 --- greater than max batches\n",
" 158/1 0.69052 0.68001 +/- 0.00192\n",
" Triggers unsatisfied, max unc./thresh. is 19.7963 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59965 --- greater than max batches\n",
" 159/1 0.70643 0.68018 +/- 0.00192\n",
" Triggers unsatisfied, max unc./thresh. is 19.6991 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59766 --- greater than max batches\n",
" 160/1 0.68576 0.68022 +/- 0.00191\n",
" Triggers unsatisfied, max unc./thresh. is 19.6197 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59670 --- greater than max batches\n",
" 161/1 0.69854 0.68034 +/- 0.00190\n",
" Triggers unsatisfied, max unc./thresh. is 19.8287 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61341 --- greater than max batches\n",
" 162/1 0.65983 0.68020 +/- 0.00189\n",
" Triggers unsatisfied, max unc./thresh. is 20.0243 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62958 --- greater than max batches\n",
" 163/1 0.66316 0.68010 +/- 0.00188\n",
" Triggers unsatisfied, max unc./thresh. is 19.8975 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62560 --- greater than max batches\n",
" 164/1 0.66179 0.67998 +/- 0.00187\n",
" Triggers unsatisfied, max unc./thresh. is 19.895 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62940 --- greater than max batches\n",
" 165/1 0.70881 0.68016 +/- 0.00187\n",
" Triggers unsatisfied, max unc./thresh. is 19.8013 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62740 --- greater than max batches\n",
" 166/1 0.70729 0.68033 +/- 0.00187\n",
" Triggers unsatisfied, max unc./thresh. is 19.6876 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62410 --- greater than max batches\n",
" 167/1 0.71073 0.68052 +/- 0.00186\n",
" Triggers unsatisfied, max unc./thresh. is 19.5695 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62046 --- greater than max batches\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 168/1 0.69610 0.68061 +/- 0.00185\n",
" Triggers unsatisfied, max unc./thresh. is 19.4797 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61857 --- greater than max batches\n",
" 169/1 0.67141 0.68056 +/- 0.00184\n",
" Triggers unsatisfied, max unc./thresh. is 19.438 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61970 --- greater than max batches\n",
" 170/1 0.67727 0.68054 +/- 0.00183\n",
" Triggers unsatisfied, max unc./thresh. is 19.3208 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61599 --- greater than max batches\n",
" 171/1 0.64150 0.68030 +/- 0.00184\n",
" Triggers unsatisfied, max unc./thresh. is 19.2066 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61242 --- greater than max batches\n",
" 172/1 0.68758 0.68035 +/- 0.00183\n",
" Triggers unsatisfied, max unc./thresh. is 19.114 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61018 --- greater than max batches\n",
" 173/1 0.67126 0.68029 +/- 0.00182\n",
" Triggers unsatisfied, max unc./thresh. is 19.1545 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61644 --- greater than max batches\n",
" 174/1 0.65933 0.68017 +/- 0.00181\n",
" Triggers unsatisfied, max unc./thresh. is 19.0415 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61281 --- greater than max batches\n",
" 175/1 0.70572 0.68032 +/- 0.00181\n",
" Triggers unsatisfied, max unc./thresh. is 18.9347 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60954 --- greater than max batches\n",
" 176/1 0.66175 0.68021 +/- 0.00180\n",
" Triggers unsatisfied, max unc./thresh. is 18.8337 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60660 --- greater than max batches\n",
" 177/1 0.68714 0.68025 +/- 0.00179\n",
" Triggers unsatisfied, max unc./thresh. is 18.7329 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60364 --- greater than max batches\n",
" 178/1 0.70181 0.68037 +/- 0.00178\n",
" Triggers unsatisfied, max unc./thresh. is 18.6297 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60048 --- greater than max batches\n",
" 179/1 0.66700 0.68030 +/- 0.00177\n",
" Triggers unsatisfied, max unc./thresh. is 18.5239 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59711 --- greater than max batches\n",
" 180/1 0.68980 0.68035 +/- 0.00176\n",
" Triggers unsatisfied, max unc./thresh. is 18.4186 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59374 --- greater than max batches\n",
" 181/1 0.69586 0.68044 +/- 0.00176\n",
" Triggers unsatisfied, max unc./thresh. is 18.3816 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59473 --- greater than max batches\n",
" 182/1 0.68689 0.68048 +/- 0.00175\n",
" Triggers unsatisfied, max unc./thresh. is 18.2781 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59139 --- greater than max batches\n",
" 183/1 0.69257 0.68054 +/- 0.00174\n",
" Triggers unsatisfied, max unc./thresh. is 18.1773 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58819 --- greater than max batches\n",
" 184/1 0.69926 0.68065 +/- 0.00173\n",
" Triggers unsatisfied, max unc./thresh. is 18.2191 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59422 --- greater than max batches\n",
" 185/1 0.67801 0.68063 +/- 0.00172\n",
" Triggers unsatisfied, max unc./thresh. is 18.1184 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59096 --- greater than max batches\n",
" 186/1 0.67049 0.68058 +/- 0.00171\n",
" Triggers unsatisfied, max unc./thresh. is 18.0484 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58965 --- greater than max batches\n",
" 187/1 0.68164 0.68058 +/- 0.00170\n",
" Triggers unsatisfied, max unc./thresh. is 17.9808 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58848 --- greater than max batches\n",
" 188/1 0.66856 0.68052 +/- 0.00170\n",
" Triggers unsatisfied, max unc./thresh. is 17.9146 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58736 --- greater than max batches\n",
" 189/1 0.71850 0.68073 +/- 0.00170\n",
" Triggers unsatisfied, max unc./thresh. is 17.8551 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58665 --- greater than max batches\n",
" 190/1 0.67095 0.68067 +/- 0.00169\n",
" Triggers unsatisfied, max unc./thresh. is 17.8953 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59250 --- greater than max batches\n",
" 191/1 0.70857 0.68082 +/- 0.00169\n",
" Triggers unsatisfied, max unc./thresh. is 17.8197 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59068 --- greater than max batches\n",
" 192/1 0.65322 0.68067 +/- 0.00169\n",
" Triggers unsatisfied, max unc./thresh. is 17.8199 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59387 --- greater than max batches\n",
" 193/1 0.67888 0.68066 +/- 0.00168\n",
" Triggers unsatisfied, max unc./thresh. is 17.8072 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59620 --- greater than max batches\n",
" 194/1 0.72890 0.68092 +/- 0.00169\n",
" Triggers unsatisfied, max unc./thresh. is 17.7152 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59319 --- greater than max batches\n",
" 195/1 0.64688 0.68074 +/- 0.00169\n",
" Triggers unsatisfied, max unc./thresh. is 17.6252 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59029 --- greater than max batches\n",
" 196/1 0.68906 0.68078 +/- 0.00168\n",
" Triggers unsatisfied, max unc./thresh. is 17.5465 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58810 --- greater than max batches\n",
" 197/1 0.69381 0.68085 +/- 0.00167\n",
" Triggers unsatisfied, max unc./thresh. is 17.4939 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58764 --- greater than max batches\n",
" 198/1 0.70057 0.68095 +/- 0.00167\n",
" Triggers unsatisfied, max unc./thresh. is 17.4414 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58717 --- greater than max batches\n",
" 199/1 0.67868 0.68094 +/- 0.00166\n",
" Triggers unsatisfied, max unc./thresh. is 17.4394 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59008 --- greater than max batches\n",
" 200/1 0.69190 0.68100 +/- 0.00165\n",
" Triggers unsatisfied, max unc./thresh. is 17.3511 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58712 --- greater than max batches\n",
" Creating state point statepoint.200.h5...\n",
"\n",
" =======================> TIMING STATISTICS <=======================\n",
"\n",
" Total time for initialization = 9.3777e-01 seconds\n",
" Reading cross sections = 8.7757e-01 seconds\n",
" Total time in simulation = 4.0652e+01 seconds\n",
" Time in transport only = 3.9022e+01 seconds\n",
" Time in inactive batches = 9.1120e-01 seconds\n",
" Time in active batches = 3.9741e+01 seconds\n",
" Time synchronizing fission bank = 4.0496e-02 seconds\n",
" Sampling source sites = 3.3700e-02 seconds\n",
" SEND/RECV source sites = 6.4404e-03 seconds\n",
" Time accumulating tallies = 2.0272e-03 seconds\n",
" Total time for finalization = 4.0896e-03 seconds\n",
" Total time elapsed = 4.1621e+01 seconds\n",
" Calculation Rate (inactive) = 13718.1 particles/second\n",
" Calculation Rate (active) = 12267.1 particles/second\n",
"\n",
" ============================> RESULTS <============================\n",
"\n",
" k-effective (Collision) = 0.68122 +/- 0.00150\n",
" k-effective (Track-length) = 0.68100 +/- 0.00165\n",
" k-effective (Absorption) = 0.68224 +/- 0.00159\n",
" Combined k-effective = 0.68162 +/- 0.00134\n",
" Leakage Fraction = 0.34047 +/- 0.00082\n",
"\n"
]
}
],
"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": 17,
"metadata": {},
"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": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tally\n",
"\tID =\t1\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": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0.16617932]]\n",
"\n",
" [[0.06455926]]\n",
"\n",
" [[0.32266365]]\n",
"\n",
" [[0.13355528]]]\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": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\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>1.76e-04</td>\n",
" <td>2.92e-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>4.28e-04</td>\n",
" <td>7.12e-05</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>6.67e-05</td>\n",
" <td>6.94e-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>1.75e-04</td>\n",
" <td>1.71e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</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>2.04e-04</td>\n",
" <td>3.80e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2</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>4.96e-04</td>\n",
" <td>9.27e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</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>5.76e-05</td>\n",
" <td>6.97e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2</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>1.52e-04</td>\n",
" <td>1.91e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3</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>1.80e-04</td>\n",
" <td>3.15e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3</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>4.38e-04</td>\n",
" <td>7.68e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>3</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>7.19e-05</td>\n",
" <td>9.68e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>3</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>1.89e-04</td>\n",
" <td>2.49e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>4</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>1.91e-04</td>\n",
" <td>3.67e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>4</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>4.66e-04</td>\n",
" <td>8.93e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>4</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>6.78e-05</td>\n",
" <td>9.81e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>4</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>1.76e-04</td>\n",
" <td>2.44e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>5</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>1.56e-04</td>\n",
" <td>2.32e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>5</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>3.81e-04</td>\n",
" <td>5.65e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>5</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>6.28e-05</td>\n",
" <td>8.06e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>5</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>1.62e-04</td>\n",
" <td>2.05e-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 1.76e-04 \n",
"1 1 1 1 0.00e+00 6.25e-01 nu-fission 4.28e-04 \n",
"2 1 1 1 6.25e-01 2.00e+07 fission 6.67e-05 \n",
"3 1 1 1 6.25e-01 2.00e+07 nu-fission 1.75e-04 \n",
"4 2 1 1 0.00e+00 6.25e-01 fission 2.04e-04 \n",
"5 2 1 1 0.00e+00 6.25e-01 nu-fission 4.96e-04 \n",
"6 2 1 1 6.25e-01 2.00e+07 fission 5.76e-05 \n",
"7 2 1 1 6.25e-01 2.00e+07 nu-fission 1.52e-04 \n",
"8 3 1 1 0.00e+00 6.25e-01 fission 1.80e-04 \n",
"9 3 1 1 0.00e+00 6.25e-01 nu-fission 4.38e-04 \n",
"10 3 1 1 6.25e-01 2.00e+07 fission 7.19e-05 \n",
"11 3 1 1 6.25e-01 2.00e+07 nu-fission 1.89e-04 \n",
"12 4 1 1 0.00e+00 6.25e-01 fission 1.91e-04 \n",
"13 4 1 1 0.00e+00 6.25e-01 nu-fission 4.66e-04 \n",
"14 4 1 1 6.25e-01 2.00e+07 fission 6.78e-05 \n",
"15 4 1 1 6.25e-01 2.00e+07 nu-fission 1.76e-04 \n",
"16 5 1 1 0.00e+00 6.25e-01 fission 1.56e-04 \n",
"17 5 1 1 0.00e+00 6.25e-01 nu-fission 3.81e-04 \n",
"18 5 1 1 6.25e-01 2.00e+07 fission 6.28e-05 \n",
"19 5 1 1 6.25e-01 2.00e+07 nu-fission 1.62e-04 \n",
"\n",
" std. dev. \n",
" \n",
"0 2.92e-05 \n",
"1 7.12e-05 \n",
"2 6.94e-06 \n",
"3 1.71e-05 \n",
"4 3.80e-05 \n",
"5 9.27e-05 \n",
"6 6.97e-06 \n",
"7 1.91e-05 \n",
"8 3.15e-05 \n",
"9 7.68e-05 \n",
"10 9.68e-06 \n",
"11 2.49e-05 \n",
"12 3.67e-05 \n",
"13 8.93e-05 \n",
"14 9.81e-06 \n",
"15 2.44e-05 \n",
"16 2.32e-05 \n",
"17 5.65e-05 \n",
"18 8.06e-06 \n",
"19 2.05e-05 "
]
},
"execution_count": 20,
"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": 21,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x15494902cf28>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tally\n",
"\tID =\t2\n",
"\tName =\tcell tally\n",
"\tFilters =\tCellFilter\n",
"\tNuclides =\tU235 U238 \n",
"\tScores =\t['scatter']\n",
"\tEstimator =\ttracklength\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": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\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>1</td>\n",
" <td>U235</td>\n",
" <td>scatter</td>\n",
" <td>3.80e-02</td>\n",
" <td>1.33e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>U238</td>\n",
" <td>scatter</td>\n",
" <td>2.33e+00</td>\n",
" <td>8.12e-03</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cell nuclide score mean std. dev.\n",
"0 1 U235 scatter 3.80e-02 1.33e-04\n",
"1 1 U238 scatter 2.33e+00 8.12e-03"
]
},
"execution_count": 24,
"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(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the new Tally data retrieval API with pure NumPy"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0.00811746]\n",
" [0.00013266]]]\n"
]
}
],
"source": [
"# Get the standard deviations the total scattering rate\n",
"data = tally.get_values(scores=['scatter'], \n",
" nuclides=['U238', 'U235'], value='std_dev')\n",
"print(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analyze the distribcell tally**"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tally\n",
"\tID =\t3\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": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0.04347272]]\n",
"\n",
" [[0.04671736]]\n",
"\n",
" [[0.04878286]]\n",
"\n",
" [[0.03059582]]\n",
"\n",
" [[0.04548096]]\n",
"\n",
" [[0.04288085]]\n",
"\n",
" [[0.02557663]]\n",
"\n",
" [[0.0419826 ]]\n",
"\n",
" [[0.05878954]]\n",
"\n",
" [[0.04217666]]]\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=[tuple(range(10))], value='rel_err')\n",
"print(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the distribcell tally dataframe"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" .dataframe thead tr th {\n",
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"</style>\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",
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" <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>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>279</td>\n",
" <td>absorption</td>\n",
" <td>6.26e-04</td>\n",
" <td>4.62e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>559</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>279</td>\n",
" <td>scatter</td>\n",
" <td>8.73e-02</td>\n",
" <td>2.14e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>560</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>280</td>\n",
" <td>absorption</td>\n",
" <td>6.15e-04</td>\n",
" <td>3.12e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>561</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
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" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>280</td>\n",
" <td>scatter</td>\n",
" <td>8.06e-02</td>\n",
" <td>1.85e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>562</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>281</td>\n",
" <td>absorption</td>\n",
" <td>6.36e-04</td>\n",
" <td>4.24e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>563</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>281</td>\n",
" <td>scatter</td>\n",
" <td>7.59e-02</td>\n",
" <td>1.93e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>282</td>\n",
" <td>absorption</td>\n",
" <td>5.30e-04</td>\n",
" <td>2.75e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>282</td>\n",
" <td>scatter</td>\n",
" <td>6.82e-02</td>\n",
" <td>1.02e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>11</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>283</td>\n",
" <td>absorption</td>\n",
" <td>4.67e-04</td>\n",
" <td>2.84e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>11</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>283</td>\n",
" <td>scatter</td>\n",
" <td>6.42e-02</td>\n",
" <td>1.81e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>284</td>\n",
" <td>absorption</td>\n",
" <td>4.52e-04</td>\n",
" <td>2.13e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>569</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>284</td>\n",
" <td>scatter</td>\n",
" <td>5.64e-02</td>\n",
" <td>1.20e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>570</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>285</td>\n",
" <td>absorption</td>\n",
" <td>3.85e-04</td>\n",
" <td>1.99e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>571</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>285</td>\n",
" <td>scatter</td>\n",
" <td>4.86e-02</td>\n",
" <td>1.58e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>572</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>286</td>\n",
" <td>absorption</td>\n",
" <td>2.84e-04</td>\n",
" <td>2.16e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>573</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>286</td>\n",
" <td>scatter</td>\n",
" <td>3.91e-02</td>\n",
" <td>1.66e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>574</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>287</td>\n",
" <td>absorption</td>\n",
" <td>2.17e-04</td>\n",
" <td>2.15e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>575</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>287</td>\n",
" <td>scatter</td>\n",
" <td>3.02e-02</td>\n",
" <td>1.71e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>576</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>288</td>\n",
" <td>absorption</td>\n",
" <td>1.50e-04</td>\n",
" <td>1.42e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>577</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>288</td>\n",
" <td>scatter</td>\n",
" <td>1.89e-02</td>\n",
" <td>9.31e-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 3 4 2 7 16 1 3 279 absorption \n",
"559 3 4 2 7 16 1 3 279 scatter \n",
"560 3 4 2 8 16 1 3 280 absorption \n",
"561 3 4 2 8 16 1 3 280 scatter \n",
"562 3 4 2 9 16 1 3 281 absorption \n",
"563 3 4 2 9 16 1 3 281 scatter \n",
"564 3 4 2 10 16 1 3 282 absorption \n",
"565 3 4 2 10 16 1 3 282 scatter \n",
"566 3 4 2 11 16 1 3 283 absorption \n",
"567 3 4 2 11 16 1 3 283 scatter \n",
"568 3 4 2 12 16 1 3 284 absorption \n",
"569 3 4 2 12 16 1 3 284 scatter \n",
"570 3 4 2 13 16 1 3 285 absorption \n",
"571 3 4 2 13 16 1 3 285 scatter \n",
"572 3 4 2 14 16 1 3 286 absorption \n",
"573 3 4 2 14 16 1 3 286 scatter \n",
"574 3 4 2 15 16 1 3 287 absorption \n",
"575 3 4 2 15 16 1 3 287 scatter \n",
"576 3 4 2 16 16 1 3 288 absorption \n",
"577 3 4 2 16 16 1 3 288 scatter \n",
"\n",
" mean std. dev. \n",
" \n",
" \n",
"558 6.26e-04 4.62e-05 \n",
"559 8.73e-02 2.14e-03 \n",
"560 6.15e-04 3.12e-05 \n",
"561 8.06e-02 1.85e-03 \n",
"562 6.36e-04 4.24e-05 \n",
"563 7.59e-02 1.93e-03 \n",
"564 5.30e-04 2.75e-05 \n",
"565 6.82e-02 1.02e-03 \n",
"566 4.67e-04 2.84e-05 \n",
"567 6.42e-02 1.81e-03 \n",
"568 4.52e-04 2.13e-05 \n",
"569 5.64e-02 1.20e-03 \n",
"570 3.85e-04 1.99e-05 \n",
"571 4.86e-02 1.58e-03 \n",
"572 2.84e-04 2.16e-05 \n",
"573 3.91e-02 1.66e-03 \n",
"574 2.17e-04 2.15e-05 \n",
"575 3.02e-02 1.71e-03 \n",
"576 1.50e-04 1.42e-05 \n",
"577 1.89e-02 9.31e-04 "
]
},
"execution_count": 28,
"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": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\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",
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" <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>4.15e-04</td>\n",
" <td>2.29e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.33e-04</td>\n",
" <td>9.14e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.84e-05</td>\n",
" <td>3.31e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.08e-04</td>\n",
" <td>1.58e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>4.10e-04</td>\n",
" <td>2.24e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.25e-04</td>\n",
" <td>2.93e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>8.87e-04</td>\n",
" <td>5.06e-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 4.15e-04 2.29e-05\n",
"std 2.33e-04 9.14e-06\n",
"min 1.84e-05 3.31e-06\n",
"25% 2.08e-04 1.58e-05\n",
"50% 4.10e-04 2.24e-05\n",
"75% 6.25e-04 2.93e-05\n",
"max 8.87e-04 5.06e-05"
]
},
"execution_count": 29,
"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-5 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": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mann-Whitney Test p-value: 0.3531165056829588\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": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mann-Whitney Test p-value: 2.835784441937541e-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": 32,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/romano/.pyenv/versions/3.7.0/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" after removing the cwd from sys.path.\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x154948ffb160>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x154948f326a0>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
Reference in a new issue View git blame Copy permalink