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OpenMC/examples/jupyter/pandas-dataframes.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas Dataframes\n",
"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",
"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",
"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 = openmc.Materials([fuel, water, zircaloy])\n",
"\n",
"# Export to \"materials.xml\"\n",
"materials.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": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAADFBMVEVyEhLpgJFNv8T///98iBL0AAAAAWJLR0QDEQxM8gAAAAd0SU1FB+QIDwgKFMc0xewAAAOeSURBVGje7ZtLkqowFIZPrAsDRz247YAluIosgYEO2h20q3AJDFyAA6wSVnkDCAnkBAK/lH0bTpVVX1mCmsfJRx5E7w6hXn/U64PFWKNkkaIriXNMQWajpNDAO9HJQqJzRmGeUpRLHkVeYOJAEnkut+qT5zxpI/EYtZE26qJI3Uy9x2NY4sOBFDafvNWYD2NSo/H9t6FbJRY+qv///E0GbnowNZGqgkxpX1xf4XkYLzWq+rureyRVpfGYUuBEok/VxI6SxJHFQ4MBi2sEsSoIKktqAlJREUVNhZmscTeMzWVN+4l084h082h6atHApG4/DTrar9AtVdiN1my/Rv9huxLXafJ5+u/G/lIOH1b/3fb9aWdRUKv/GiU9BlUi1vVfYlwm7V5s6j8u2p9UnfHZpsbjGnVOO07CcvxsMm2m06uB93rQbGHajJ++Sb+LW91/s6FBp4sh038njX8hcn3WP/7zP7o9/hf/P9ROEfb7R2ZiVI2fRZletfQkXiieSLSrRCq4u7Dwr28XriGkNt02/rWRLKylN8xaeHKjNJH6pJdHT/+Vbun9f/z3zHbamf13PyS9nP/uLOnlsczKGeO/h0m4RimycrT/fsZt//WQXg55/zWa0oWR3tV/u/7L5S/Wf6389Rr/9ZBeHrXIHliMtfSyuMZOldZX5bS7eBQKheSSXt6fLBWmjrR5Se/qvz7+6y4023/9pJfB0nTpW0uvNyar/5YhZJOpOmj7b2AjRanW29TDf3MD1T08pdelwqv/9viDS3pn89+0i6b/Zl2s/Nfwt9H+++6+8xOi678WPkuKR2IqbVB6o37/3Tv89zLWf33aL9p/5vHf/vzxSv8lxH9v1E7abWSk99pFznRH4eKj8Jevp5McWKxMl8HSX4alt0+Fnf7r4W9bdvwc478v8NeX++/I34+WH1p/cPtZeqD5C82fYP5Gxw90/Pot/jvVX1B/gv1t6YE+P4DPL+jz0+/x32nPz+jzOzp/AM9fCInNnyw90Pk7cP4Qnb9E509f56/T5o/R+Wt0/hydv4fXD5Ye6PoVuH6Grt/N47/+65ezzB+NWL9F14/R9Wt0/Rxev196oPtHwP0r8/iv//6d1X+x/WPw/jUhsf1zSw90/+YZ2z+K7l9d/RfbP43u34b3jy890PMLJ+z8xGv8d/r5kdV/sfNL6Pkp+PzW0gM9PwieX0TPT87rv5P2D/4M//U8v4ueH4bPL6Pnp98b/wAXomzv5H3x5AAAACV0RVh0ZGF0ZTpjcmVhdGUAMjAyMC0wOC0xNVQwNzoxMDoyMCswMTowML6m1KIAAAAldEVYdGRhdGU6bW9kaWZ5ADIwMjAtMDgtMTVUMDc6MTA6MjArMDE6MDDP+2weAAAAAElFTkSuQmCC\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 | 2011-2020 MIT and OpenMC contributors\n",
" License | https://docs.openmc.org/en/latest/license.html\n",
" Version | 0.12.0\n",
" Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n",
" Date/Time | 2020-08-15 07:10:20\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 /home/master/data/nuclear/endfb71_hdf5/U235.h5\n",
" Reading U238 from /home/master/data/nuclear/endfb71_hdf5/U238.h5\n",
" Reading O16 from /home/master/data/nuclear/endfb71_hdf5/O16.h5\n",
" Reading H1 from /home/master/data/nuclear/endfb71_hdf5/H1.h5\n",
" Reading B10 from /home/master/data/nuclear/endfb71_hdf5/B10.h5\n",
" Reading Zr90 from /home/master/data/nuclear/endfb71_hdf5/Zr90.h5\n",
" Minimum neutron data temperature: 294.000000 K\n",
" Maximum neutron data temperature: 294.000000 K\n",
" Reading tallies XML file...\n",
" Preparing distributed cell instances...\n",
" Writing summary.h5 file...\n",
" Maximum neutron transport energy: 20000000.000000 eV for U235\n",
" Initializing source particles...\n",
"\n",
" ====================> K EIGENVALUE SIMULATION <====================\n",
"\n",
" Bat./Gen. k Average k\n",
" ========= ======== ====================\n",
" 1/1 0.53544\n",
" 2/1 0.62631\n",
" 3/1 0.63917\n",
" 4/1 0.67203\n",
" 5/1 0.69300\n",
" 6/1 0.64862\n",
" 7/1 0.63937 0.64399 +/- 0.00463\n",
" 8/1 0.67696 0.65498 +/- 0.01131\n",
" 9/1 0.63216 0.64928 +/- 0.00982\n",
" 10/1 0.70996 0.66141 +/- 0.01433\n",
" 11/1 0.69761 0.66745 +/- 0.01316\n",
" 12/1 0.68662 0.67019 +/- 0.01146\n",
" 13/1 0.64374 0.66688 +/- 0.01046\n",
" 14/1 0.69121 0.66958 +/- 0.00961\n",
" 15/1 0.72125 0.67475 +/- 0.01003\n",
" 16/1 0.72706 0.67950 +/- 0.01024\n",
" 17/1 0.69623 0.68090 +/- 0.00945\n",
" 18/1 0.70953 0.68310 +/- 0.00897\n",
" 19/1 0.69026 0.68361 +/- 0.00832\n",
" 20/1 0.68633 0.68379 +/- 0.00775\n",
" Triggers unsatisfied, max unc./thresh. is 75.24758750489383 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 84938 --- greater than max batches\n",
" Creating state point statepoint.020.h5...\n",
" 21/1 0.68310 0.68375 +/- 0.00725\n",
" Triggers unsatisfied, max unc./thresh. is 71.20148627325992 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 81120 --- greater than max batches\n",
" 22/1 0.68679 0.68393 +/- 0.00681\n",
" Triggers unsatisfied, max unc./thresh. is 66.94650483064697 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 76197 --- greater than max batches\n",
" 23/1 0.67440 0.68340 +/- 0.00644\n",
" Triggers unsatisfied, max unc./thresh. is 63.553590826021285 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 72709 --- greater than max batches\n",
" 24/1 0.67483 0.68295 +/- 0.00611\n",
" Triggers unsatisfied, max unc./thresh. is 60.37873858685279 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 69272 --- greater than max batches\n",
" 25/1 0.71558 0.68458 +/- 0.00602\n",
" Triggers unsatisfied, max unc./thresh. is 60.34535216026281 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 72837 --- greater than max batches\n",
" 26/1 0.71853 0.68620 +/- 0.00595\n",
" Triggers unsatisfied, max unc./thresh. is 59.60875760463032 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 74623 --- greater than max batches\n",
" 27/1 0.67455 0.68567 +/- 0.00570\n",
" Triggers unsatisfied, max unc./thresh. is 57.228951643423976 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 72059 --- greater than max batches\n",
" 28/1 0.69435 0.68605 +/- 0.00546\n",
" Triggers unsatisfied, max unc./thresh. is 56.194065573871285 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 72634 --- greater than max batches\n",
" 29/1 0.67706 0.68567 +/- 0.00524\n",
" Triggers unsatisfied, max unc./thresh. is 53.86022066923874 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 69628 --- greater than max batches\n",
" 30/1 0.69294 0.68596 +/- 0.00504\n",
" Triggers unsatisfied, max unc./thresh. is 51.73413206858763 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 66916 --- greater than max batches\n",
" 31/1 0.69108 0.68616 +/- 0.00484\n",
" Triggers unsatisfied, max unc./thresh. is 49.71174462484801 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 64258 --- greater than max batches\n",
" 32/1 0.68089 0.68596 +/- 0.00466\n",
" Triggers unsatisfied, max unc./thresh. is 50.80993117627794 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 69710 --- greater than max batches\n",
" 33/1 0.67698 0.68564 +/- 0.00450\n",
" Triggers unsatisfied, max unc./thresh. is 50.46659333785448 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 71318 --- greater than max batches\n",
" 34/1 0.68167 0.68551 +/- 0.00435\n",
" Triggers unsatisfied, max unc./thresh. is 48.852656603250665 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 69216 --- greater than max batches\n",
" 35/1 0.67760 0.68524 +/- 0.00421\n",
" Triggers unsatisfied, max unc./thresh. is 48.5685583427197 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 70773 --- greater than max batches\n",
" 36/1 0.67628 0.68495 +/- 0.00408\n",
" Triggers unsatisfied, max unc./thresh. is 47.77661998216646 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 70766 --- greater than max batches\n",
" 37/1 0.66736 0.68440 +/- 0.00399\n",
" Triggers unsatisfied, max unc./thresh. is 46.57810773879176 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 69430 --- greater than max batches\n",
" 38/1 0.71026 0.68519 +/- 0.00395\n",
" Triggers unsatisfied, max unc./thresh. is 45.876107560616674 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 69458 --- greater than max batches\n",
" 39/1 0.67674 0.68494 +/- 0.00384\n",
" Triggers unsatisfied, max unc./thresh. is 45.30341918376721 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 69787 --- greater than max batches\n",
" 40/1 0.69360 0.68519 +/- 0.00373\n",
" Triggers unsatisfied, max unc./thresh. is 44.018056562863386 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 67821 --- greater than max batches\n",
" 41/1 0.70987 0.68587 +/- 0.00369\n",
" Triggers unsatisfied, max unc./thresh. is 42.781078099052785 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 65893 --- greater than max batches\n",
" 42/1 0.68780 0.68592 +/- 0.00359\n",
" Triggers unsatisfied, max unc./thresh. is 41.60877069209228 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 64063 --- greater than max batches\n",
" 43/1 0.69223 0.68609 +/- 0.00350\n",
" Triggers unsatisfied, max unc./thresh. is 42.44932759168626 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 68479 --- greater than max batches\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 44/1 0.69561 0.68633 +/- 0.00342\n",
" Triggers unsatisfied, max unc./thresh. is 41.34743776753899 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 66680 --- greater than max batches\n",
" 45/1 0.67503 0.68605 +/- 0.00334\n",
" Triggers unsatisfied, max unc./thresh. is 40.97332186124358 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 67158 --- greater than max batches\n",
" 46/1 0.67290 0.68573 +/- 0.00328\n",
" Triggers unsatisfied, max unc./thresh. is 40.24678448931756 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 66417 --- greater than max batches\n",
" 47/1 0.67355 0.68544 +/- 0.00321\n",
" Triggers unsatisfied, max unc./thresh. is 40.42620640829592 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 68645 --- greater than max batches\n",
" 48/1 0.71383 0.68610 +/- 0.00320\n",
" Triggers unsatisfied, max unc./thresh. is 39.90662320308606 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 68485 --- greater than max batches\n",
" 49/1 0.68389 0.68605 +/- 0.00313\n",
" Triggers unsatisfied, max unc./thresh. is 39.075369568753906 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 67188 --- greater than max batches\n",
" 50/1 0.73148 0.68706 +/- 0.00322\n",
" Triggers unsatisfied, max unc./thresh. is 38.57054567218653 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 66951 --- greater than max batches\n",
" 51/1 0.69796 0.68730 +/- 0.00316\n",
" Triggers unsatisfied, max unc./thresh. is 38.21572703507316 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 67186 --- greater than max batches\n",
" 52/1 0.70691 0.68771 +/- 0.00312\n",
" Triggers unsatisfied, max unc./thresh. is 37.50971908717773 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 66134 --- greater than max batches\n",
" 53/1 0.69104 0.68778 +/- 0.00306\n",
" Triggers unsatisfied, max unc./thresh. is 36.824732312223716 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 65096 --- greater than max batches\n",
" 54/1 0.74368 0.68892 +/- 0.00320\n",
" Triggers unsatisfied, max unc./thresh. is 36.20814737643575 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 64246 --- greater than max batches\n",
" 55/1 0.67371 0.68862 +/- 0.00315\n",
" Triggers unsatisfied, max unc./thresh. is 35.48607231512293 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62969 --- greater than max batches\n",
" 56/1 0.67846 0.68842 +/- 0.00310\n",
" Triggers unsatisfied, max unc./thresh. is 35.34421893287461 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63715 --- greater than max batches\n",
" 57/1 0.66351 0.68794 +/- 0.00307\n",
" Triggers unsatisfied, max unc./thresh. is 34.67062652878957 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62512 --- greater than max batches\n",
" 58/1 0.67049 0.68761 +/- 0.00303\n",
" Triggers unsatisfied, max unc./thresh. is 34.22135922543247 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62074 --- greater than max batches\n",
" 59/1 0.66967 0.68728 +/- 0.00299\n",
" Triggers unsatisfied, max unc./thresh. is 33.66881484408945 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 61219 --- greater than max batches\n",
" 60/1 0.70271 0.68756 +/- 0.00295\n",
" Triggers unsatisfied, max unc./thresh. is 33.19914799505717 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 60626 --- greater than max batches\n",
" 61/1 0.70035 0.68779 +/- 0.00291\n",
" Triggers unsatisfied, max unc./thresh. is 32.65594936729897 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59725 --- greater than max batches\n",
" 62/1 0.66274 0.68735 +/- 0.00289\n",
" Triggers unsatisfied, max unc./thresh. is 32.15622046485561 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 58945 --- greater than max batches\n",
" 63/1 0.68607 0.68733 +/- 0.00284\n",
" Triggers unsatisfied, max unc./thresh. is 31.601225649282494 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57926 --- greater than max batches\n",
" 64/1 0.66518 0.68695 +/- 0.00282\n",
" Triggers unsatisfied, max unc./thresh. is 31.12129365572805 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57149 --- greater than max batches\n",
" 65/1 0.65999 0.68650 +/- 0.00281\n",
" Triggers unsatisfied, max unc./thresh. is 30.641988019531464 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56341 --- greater than max batches\n",
" 66/1 0.67843 0.68637 +/- 0.00276\n",
" Triggers unsatisfied, max unc./thresh. is 30.320463443580458 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56085 --- greater than max batches\n",
" 67/1 0.69295 0.68648 +/- 0.00272\n",
" Triggers unsatisfied, max unc./thresh. is 30.06051080038397 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56031 --- greater than max batches\n",
" 68/1 0.69158 0.68656 +/- 0.00268\n",
" Triggers unsatisfied, max unc./thresh. is 29.7400907913873 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 55727 --- greater than max batches\n",
" 69/1 0.69825 0.68674 +/- 0.00264\n",
" Triggers unsatisfied, max unc./thresh. is 29.278619659445805 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 54869 --- greater than max batches\n",
" 70/1 0.73637 0.68750 +/- 0.00271\n",
" Triggers unsatisfied, max unc./thresh. is 28.945044018568716 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 54464 --- greater than max batches\n",
" 71/1 0.64301 0.68683 +/- 0.00275\n",
" Triggers unsatisfied, max unc./thresh. is 28.73677928804667 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 54508 --- greater than max batches\n",
" 72/1 0.71506 0.68725 +/- 0.00274\n",
" Triggers unsatisfied, max unc./thresh. is 29.20796537704291 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57164 --- greater than max batches\n",
" 73/1 0.69203 0.68732 +/- 0.00270\n",
" Triggers unsatisfied, max unc./thresh. is 29.56297016014581 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59435 --- greater than max batches\n",
" 74/1 0.69208 0.68739 +/- 0.00267\n",
" Triggers unsatisfied, max unc./thresh. is 29.545241442413783 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 60237 --- greater than max batches\n",
" 75/1 0.65717 0.68696 +/- 0.00266\n",
" Triggers unsatisfied, max unc./thresh. is 29.284013224166248 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 60034 --- greater than max batches\n",
" 76/1 0.70992 0.68728 +/- 0.00265\n",
" Triggers unsatisfied, max unc./thresh. is 29.00034584995327 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59718 --- greater than max batches\n",
" 77/1 0.65590 0.68685 +/- 0.00264\n",
" Triggers unsatisfied, max unc./thresh. is 28.867967845905174 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 60007 --- greater than max batches\n",
" 78/1 0.64439 0.68626 +/- 0.00267\n",
" Triggers unsatisfied, max unc./thresh. is 29.016012595317935 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 61466 --- greater than max batches\n",
" 79/1 0.66295 0.68595 +/- 0.00265\n",
" Triggers unsatisfied, max unc./thresh. is 28.626245464278142 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 60646 --- greater than max batches\n",
" 80/1 0.66672 0.68569 +/- 0.00263\n",
" Triggers unsatisfied, max unc./thresh. is 28.24218910063624 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59827 --- greater than max batches\n",
" 81/1 0.69110 0.68576 +/- 0.00260\n",
" Triggers unsatisfied, max unc./thresh. is 27.917349908027763 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59238 --- greater than max batches\n",
" 82/1 0.67481 0.68562 +/- 0.00257\n",
" Triggers unsatisfied, max unc./thresh. is 28.01946018168837 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 60457 --- greater than max batches\n",
" 83/1 0.72216 0.68609 +/- 0.00258\n",
" Triggers unsatisfied, max unc./thresh. is 27.931394620754766 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 60858 --- greater than max batches\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 84/1 0.68429 0.68607 +/- 0.00254\n",
" Triggers unsatisfied, max unc./thresh. is 27.713137470531738 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 60679 --- greater than max batches\n",
" 85/1 0.65458 0.68567 +/- 0.00254\n",
" Triggers unsatisfied, max unc./thresh. is 27.364539968246927 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59911 --- greater than max batches\n",
" 86/1 0.69966 0.68585 +/- 0.00252\n",
" Triggers unsatisfied, max unc./thresh. is 27.178113974435043 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59836 --- greater than max batches\n",
" 87/1 0.64776 0.68538 +/- 0.00253\n",
" Triggers unsatisfied, max unc./thresh. is 26.941566345072534 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59525 --- greater than max batches\n",
" 88/1 0.62737 0.68468 +/- 0.00259\n",
" Triggers unsatisfied, max unc./thresh. is 26.73959660667411 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59351 --- greater than max batches\n",
" 89/1 0.69779 0.68484 +/- 0.00257\n",
" Triggers unsatisfied, max unc./thresh. is 26.490234865810894 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 58951 --- greater than max batches\n",
" 90/1 0.67312 0.68470 +/- 0.00254\n",
" Triggers unsatisfied, max unc./thresh. is 26.24036001465229 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 58533 --- greater than max batches\n",
" 91/1 0.69289 0.68480 +/- 0.00251\n",
" Triggers unsatisfied, max unc./thresh. is 25.936795778335345 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57859 --- greater than max batches\n",
" 92/1 0.69884 0.68496 +/- 0.00249\n",
" Triggers unsatisfied, max unc./thresh. is 25.695465963215582 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57448 --- greater than max batches\n",
" 93/1 0.71351 0.68528 +/- 0.00248\n",
" Triggers unsatisfied, max unc./thresh. is 25.49001212499821 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57183 --- greater than max batches\n",
" 94/1 0.65602 0.68495 +/- 0.00248\n",
" Triggers unsatisfied, max unc./thresh. is 25.350859463183905 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57203 --- greater than max batches\n",
" 95/1 0.72223 0.68537 +/- 0.00248\n",
" Triggers unsatisfied, max unc./thresh. is 25.157803279393637 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56968 --- greater than max batches\n",
" 96/1 0.67930 0.68530 +/- 0.00246\n",
" Triggers unsatisfied, max unc./thresh. is 24.92205849077747 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56526 --- greater than max batches\n",
" 97/1 0.66201 0.68505 +/- 0.00244\n",
" Triggers unsatisfied, max unc./thresh. is 24.653967027285237 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 55925 --- greater than max batches\n",
" 98/1 0.71110 0.68533 +/- 0.00243\n",
" Triggers unsatisfied, max unc./thresh. is 24.566957281211884 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56134 --- greater than max batches\n",
" 99/1 0.69409 0.68542 +/- 0.00241\n",
" Triggers unsatisfied, max unc./thresh. is 24.581943149247135 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56807 --- greater than max batches\n",
" 100/1 0.71197 0.68570 +/- 0.00240\n",
" Triggers unsatisfied, max unc./thresh. is 24.444112571967217 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56769 --- greater than max batches\n",
" 101/1 0.71713 0.68603 +/- 0.00240\n",
" Triggers unsatisfied, max unc./thresh. is 24.18957776896016 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56179 --- greater than max batches\n",
" 102/1 0.68143 0.68598 +/- 0.00237\n",
" Triggers unsatisfied, max unc./thresh. is 23.97797098066553 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 55775 --- greater than max batches\n",
" 103/1 0.69936 0.68612 +/- 0.00235\n",
" Triggers unsatisfied, max unc./thresh. is 24.253000406602812 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57650 --- greater than max batches\n",
" 104/1 0.65247 0.68578 +/- 0.00235\n",
" Triggers unsatisfied, max unc./thresh. is 24.593482483379837 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59885 --- greater than max batches\n",
" 105/1 0.66517 0.68557 +/- 0.00234\n",
" Triggers unsatisfied, max unc./thresh. is 24.37904760701804 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59439 --- greater than max batches\n",
" 106/1 0.67814 0.68550 +/- 0.00232\n",
" Triggers unsatisfied, max unc./thresh. is 24.142311084988883 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 58873 --- greater than max batches\n",
" 107/1 0.67788 0.68542 +/- 0.00229\n",
" Triggers unsatisfied, max unc./thresh. is 23.935477435724106 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 58442 --- greater than max batches\n",
" 108/1 0.68016 0.68537 +/- 0.00227\n",
" Triggers unsatisfied, max unc./thresh. is 24.532504688648594 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 61995 --- greater than max batches\n",
" 109/1 0.66963 0.68522 +/- 0.00226\n",
" Triggers unsatisfied, max unc./thresh. is 24.354532539671386 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 61692 --- greater than max batches\n",
" 110/1 0.67556 0.68513 +/- 0.00224\n",
" Triggers unsatisfied, max unc./thresh. is 24.16165322902175 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 61303 --- greater than max batches\n",
" 111/1 0.68273 0.68511 +/- 0.00222\n",
" Triggers unsatisfied, max unc./thresh. is 24.00069508298176 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 61065 --- greater than max batches\n",
" 112/1 0.69505 0.68520 +/- 0.00220\n",
" Triggers unsatisfied, max unc./thresh. is 23.791656279909404 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 60572 --- greater than max batches\n",
" 113/1 0.69385 0.68528 +/- 0.00218\n",
" Triggers unsatisfied, max unc./thresh. is 23.667941020219764 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 60504 --- greater than max batches\n",
" 114/1 0.65352 0.68499 +/- 0.00218\n",
" Triggers unsatisfied, max unc./thresh. is 23.469658485546123 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 60045 --- greater than max batches\n",
" 115/1 0.68339 0.68497 +/- 0.00216\n",
" Triggers unsatisfied, max unc./thresh. is 23.259123161328624 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59514 --- greater than max batches\n",
" 116/1 0.65854 0.68474 +/- 0.00215\n",
" Triggers unsatisfied, max unc./thresh. is 23.06250977653337 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59044 --- greater than max batches\n",
" 117/1 0.66907 0.68460 +/- 0.00214\n",
" Triggers unsatisfied, max unc./thresh. is 22.874382198219536 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 58608 --- greater than max batches\n",
" 118/1 0.68165 0.68457 +/- 0.00212\n",
" Triggers unsatisfied, max unc./thresh. is 22.709602691165983 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 58283 --- greater than max batches\n",
" 119/1 0.70967 0.68479 +/- 0.00211\n",
" Triggers unsatisfied, max unc./thresh. is 22.509869996658225 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57769 --- greater than max batches\n",
" 120/1 0.65543 0.68453 +/- 0.00211\n",
" Triggers unsatisfied, max unc./thresh. is 22.422104322874098 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57822 --- greater than max batches\n",
" 121/1 0.67305 0.68444 +/- 0.00209\n",
" Triggers unsatisfied, max unc./thresh. is 22.32834321567902 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57838 --- greater than max batches\n",
" 122/1 0.68206 0.68441 +/- 0.00207\n",
" Triggers unsatisfied, max unc./thresh. is 22.155196965032374 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57435 --- greater than max batches\n",
" 123/1 0.71125 0.68464 +/- 0.00207\n",
" Triggers unsatisfied, max unc./thresh. is 21.96683678913398 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56945 --- greater than max batches\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 124/1 0.65918 0.68443 +/- 0.00206\n",
" Triggers unsatisfied, max unc./thresh. is 21.78216358933223 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56467 --- greater than max batches\n",
" 125/1 0.68122 0.68440 +/- 0.00205\n",
" Triggers unsatisfied, max unc./thresh. is 21.681928420505304 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56418 --- greater than max batches\n",
" 126/1 0.66900 0.68427 +/- 0.00203\n",
" Triggers unsatisfied, max unc./thresh. is 21.60631058168512 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56492 --- greater than max batches\n",
" 127/1 0.66742 0.68414 +/- 0.00202\n",
" Triggers unsatisfied, max unc./thresh. is 21.468291123480988 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56234 --- greater than max batches\n",
" 128/1 0.66971 0.68402 +/- 0.00201\n",
" Triggers unsatisfied, max unc./thresh. is 21.313238544974386 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 55879 --- greater than max batches\n",
" 129/1 0.68183 0.68400 +/- 0.00199\n",
" Triggers unsatisfied, max unc./thresh. is 21.314008888585132 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56337 --- greater than max batches\n",
" 130/1 0.68403 0.68400 +/- 0.00197\n",
" Triggers unsatisfied, max unc./thresh. is 21.159444482258046 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 55971 --- greater than max batches\n",
" 131/1 0.69137 0.68406 +/- 0.00196\n",
" Triggers unsatisfied, max unc./thresh. is 21.24931160989673 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56899 --- greater than max batches\n",
" 132/1 0.67481 0.68399 +/- 0.00195\n",
" Triggers unsatisfied, max unc./thresh. is 21.164512281281944 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56893 --- greater than max batches\n",
" 133/1 0.70390 0.68414 +/- 0.00194\n",
" Triggers unsatisfied, max unc./thresh. is 21.084176856795946 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 56907 --- greater than max batches\n",
" 134/1 0.67961 0.68411 +/- 0.00192\n",
" Triggers unsatisfied, max unc./thresh. is 21.48684646255342 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59563 --- greater than max batches\n",
" 135/1 0.65362 0.68387 +/- 0.00192\n",
" Triggers unsatisfied, max unc./thresh. is 21.41235779526348 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59609 --- greater than max batches\n",
" 136/1 0.63946 0.68353 +/- 0.00194\n",
" Triggers unsatisfied, max unc./thresh. is 21.30299014546295 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59456 --- greater than max batches\n",
" 137/1 0.64818 0.68327 +/- 0.00194\n",
" Triggers unsatisfied, max unc./thresh. is 21.159761745415484 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 59107 --- greater than max batches\n",
" 138/1 0.68975 0.68331 +/- 0.00193\n",
" Triggers unsatisfied, max unc./thresh. is 21.000094566393475 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 58659 --- greater than max batches\n",
" 139/1 0.67280 0.68324 +/- 0.00191\n",
" Triggers unsatisfied, max unc./thresh. is 20.853656171297644 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 58279 --- greater than max batches\n",
" 140/1 0.66857 0.68313 +/- 0.00190\n",
" Triggers unsatisfied, max unc./thresh. is 20.709591767033707 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57905 --- greater than max batches\n",
" 141/1 0.68175 0.68312 +/- 0.00189\n",
" Triggers unsatisfied, max unc./thresh. is 20.560813068689416 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57499 --- greater than max batches\n",
" 142/1 0.72210 0.68340 +/- 0.00190\n",
" Triggers unsatisfied, max unc./thresh. is 20.54814917791921 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57851 --- greater than max batches\n",
" 143/1 0.67361 0.68333 +/- 0.00188\n",
" Triggers unsatisfied, max unc./thresh. is 20.4177880049802 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 57536 --- greater than max batches\n",
" 144/1 0.65862 0.68315 +/- 0.00188\n",
" Triggers unsatisfied, max unc./thresh. is 21.229890183572195 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62654 --- greater than max batches\n",
" 145/1 0.69713 0.68325 +/- 0.00187\n",
" Triggers unsatisfied, max unc./thresh. is 21.34513800240435 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63792 --- greater than max batches\n",
" 146/1 0.72980 0.68358 +/- 0.00188\n",
" Triggers unsatisfied, max unc./thresh. is 21.60165210412777 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 65801 --- greater than max batches\n",
" 147/1 0.70004 0.68370 +/- 0.00187\n",
" Triggers unsatisfied, max unc./thresh. is 21.596734424310384 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 66237 --- greater than max batches\n",
" 148/1 0.68882 0.68374 +/- 0.00186\n",
" Triggers unsatisfied, max unc./thresh. is 21.447240534346236 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 65783 --- greater than max batches\n",
" 149/1 0.70401 0.68388 +/- 0.00185\n",
" Triggers unsatisfied, max unc./thresh. is 21.424993974056104 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 66106 --- greater than max batches\n",
" 150/1 0.72110 0.68413 +/- 0.00186\n",
" Triggers unsatisfied, max unc./thresh. is 21.27792348945665 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 65654 --- greater than max batches\n",
" 151/1 0.65918 0.68396 +/- 0.00185\n",
" Triggers unsatisfied, max unc./thresh. is 21.378637401006184 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 66734 --- greater than max batches\n",
" 152/1 0.67751 0.68392 +/- 0.00184\n",
" Triggers unsatisfied, max unc./thresh. is 21.25974745003047 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 66446 --- greater than max batches\n",
" 153/1 0.69302 0.68398 +/- 0.00183\n",
" Triggers unsatisfied, max unc./thresh. is 21.16055371271148 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 66275 --- greater than max batches\n",
" 154/1 0.67102 0.68389 +/- 0.00182\n",
" Triggers unsatisfied, max unc./thresh. is 21.0227808264386 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 65857 --- greater than max batches\n",
" 155/1 0.64427 0.68363 +/- 0.00183\n",
" Triggers unsatisfied, max unc./thresh. is 20.882547322553506 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 65418 --- greater than max batches\n",
" 156/1 0.68488 0.68364 +/- 0.00181\n",
" Triggers unsatisfied, max unc./thresh. is 20.797424126850476 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 65318 --- greater than max batches\n",
" 157/1 0.67337 0.68357 +/- 0.00180\n",
" Triggers unsatisfied, max unc./thresh. is 20.67015745584828 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 64948 --- greater than max batches\n",
" 158/1 0.66662 0.68346 +/- 0.00180\n",
" Triggers unsatisfied, max unc./thresh. is 20.568519956722266 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 64734 --- greater than max batches\n",
" 159/1 0.62697 0.68309 +/- 0.00182\n",
" Triggers unsatisfied, max unc./thresh. is 20.47085213159483 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 64540 --- greater than max batches\n",
" 160/1 0.68300 0.68309 +/- 0.00181\n",
" Triggers unsatisfied, max unc./thresh. is 20.34586209866351 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 64168 --- greater than max batches\n",
" 161/1 0.68918 0.68313 +/- 0.00180\n",
" Triggers unsatisfied, max unc./thresh. is 20.23505377614212 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63881 --- greater than max batches\n",
" 162/1 0.70939 0.68330 +/- 0.00179\n",
" Triggers unsatisfied, max unc./thresh. is 20.21114215977674 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 64138 --- greater than max batches\n",
" 163/1 0.69681 0.68338 +/- 0.00179\n",
" Triggers unsatisfied, max unc./thresh. is 20.163170350438893 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 64241 --- greater than max batches\n",
" 164/1 0.66454 0.68326 +/- 0.00178\n",
" Triggers unsatisfied, max unc./thresh. is 20.109882525638955 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 64306 --- greater than max batches\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 165/1 0.68804 0.68329 +/- 0.00177\n",
" Triggers unsatisfied, max unc./thresh. is 20.033653897136055 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 64221 --- greater than max batches\n",
" 166/1 0.66078 0.68315 +/- 0.00176\n",
" Triggers unsatisfied, max unc./thresh. is 19.916131324861123 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63867 --- greater than max batches\n",
" 167/1 0.65762 0.68300 +/- 0.00176\n",
" Triggers unsatisfied, max unc./thresh. is 19.85097950868946 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63843 --- greater than max batches\n",
" 168/1 0.69267 0.68306 +/- 0.00175\n",
" Triggers unsatisfied, max unc./thresh. is 19.729436003984436 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63453 --- greater than max batches\n",
" 169/1 0.67859 0.68303 +/- 0.00174\n",
" Triggers unsatisfied, max unc./thresh. is 19.61178427698242 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63084 --- greater than max batches\n",
" 170/1 0.66545 0.68292 +/- 0.00173\n",
" Triggers unsatisfied, max unc./thresh. is 19.495197332905757 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62716 --- greater than max batches\n",
" 171/1 0.66716 0.68283 +/- 0.00172\n",
" Triggers unsatisfied, max unc./thresh. is 19.47044861415963 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62936 --- greater than max batches\n",
" 172/1 0.70008 0.68293 +/- 0.00172\n",
" Triggers unsatisfied, max unc./thresh. is 19.382801191970024 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62746 --- greater than max batches\n",
" 173/1 0.69417 0.68300 +/- 0.00171\n",
" Triggers unsatisfied, max unc./thresh. is 19.270038663528165 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62390 --- greater than max batches\n",
" 174/1 0.66458 0.68289 +/- 0.00170\n",
" Triggers unsatisfied, max unc./thresh. is 19.281533726364312 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62836 --- greater than max batches\n",
" 175/1 0.65867 0.68275 +/- 0.00170\n",
" Triggers unsatisfied, max unc./thresh. is 19.234847030404104 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62902 --- greater than max batches\n",
" 176/1 0.69631 0.68283 +/- 0.00169\n",
" Triggers unsatisfied, max unc./thresh. is 19.13381099709457 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62609 --- greater than max batches\n",
" 177/1 0.71142 0.68299 +/- 0.00169\n",
" Triggers unsatisfied, max unc./thresh. is 19.022563643493143 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62245 --- greater than max batches\n",
" 178/1 0.68640 0.68301 +/- 0.00168\n",
" Triggers unsatisfied, max unc./thresh. is 19.03176453708651 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62667 --- greater than max batches\n",
" 179/1 0.70448 0.68313 +/- 0.00167\n",
" Triggers unsatisfied, max unc./thresh. is 19.088395456136563 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63405 --- greater than max batches\n",
" 180/1 0.70538 0.68326 +/- 0.00167\n",
" Triggers unsatisfied, max unc./thresh. is 18.98864751831452 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63105 --- greater than max batches\n",
" 181/1 0.65591 0.68311 +/- 0.00166\n",
" Triggers unsatisfied, max unc./thresh. is 18.911017891051518 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62948 --- greater than max batches\n",
" 182/1 0.72818 0.68336 +/- 0.00167\n",
" Triggers unsatisfied, max unc./thresh. is 18.808510226366458 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62621 --- greater than max batches\n",
" 183/1 0.67896 0.68334 +/- 0.00167\n",
" Triggers unsatisfied, max unc./thresh. is 18.825142861337717 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63086 --- greater than max batches\n",
" 184/1 0.65442 0.68317 +/- 0.00166\n",
" Triggers unsatisfied, max unc./thresh. is 18.79514029258707 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63239 --- greater than max batches\n",
" 185/1 0.68885 0.68321 +/- 0.00165\n",
" Triggers unsatisfied, max unc./thresh. is 18.76276176864163 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63373 --- greater than max batches\n",
" 186/1 0.68893 0.68324 +/- 0.00165\n",
" Triggers unsatisfied, max unc./thresh. is 18.690155368597363 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63233 --- greater than max batches\n",
" 187/1 0.68918 0.68327 +/- 0.00164\n",
" Triggers unsatisfied, max unc./thresh. is 18.590144288270153 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62904 --- greater than max batches\n",
" 188/1 0.69854 0.68335 +/- 0.00163\n",
" Triggers unsatisfied, max unc./thresh. is 18.61460656150607 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63416 --- greater than max batches\n",
" 189/1 0.66324 0.68324 +/- 0.00162\n",
" Triggers unsatisfied, max unc./thresh. is 18.518608099237504 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 63106 --- greater than max batches\n",
" 190/1 0.69450 0.68331 +/- 0.00162\n",
" Triggers unsatisfied, max unc./thresh. is 18.425351661292233 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62812 --- greater than max batches\n",
" 191/1 0.68953 0.68334 +/- 0.00161\n",
" Triggers unsatisfied, max unc./thresh. is 18.328779429843646 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62491 --- greater than max batches\n",
" 192/1 0.66621 0.68325 +/- 0.00160\n",
" Triggers unsatisfied, max unc./thresh. is 18.28094973389564 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62500 --- greater than max batches\n",
" 193/1 0.71102 0.68339 +/- 0.00160\n",
" Triggers unsatisfied, max unc./thresh. is 18.19949730061142 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62275 --- greater than max batches\n",
" 194/1 0.65341 0.68324 +/- 0.00160\n",
" Triggers unsatisfied, max unc./thresh. is 18.159054737369345 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62328 --- greater than max batches\n",
" 195/1 0.70061 0.68333 +/- 0.00159\n",
" Triggers unsatisfied, max unc./thresh. is 18.082465954324594 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62131 --- greater than max batches\n",
" 196/1 0.69339 0.68338 +/- 0.00159\n",
" Triggers unsatisfied, max unc./thresh. is 18.043133483791827 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62186 --- greater than max batches\n",
" 197/1 0.64411 0.68318 +/- 0.00159\n",
" Triggers unsatisfied, max unc./thresh. is 18.019303623546417 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62347 --- greater than max batches\n",
" 198/1 0.66626 0.68309 +/- 0.00159\n",
" Triggers unsatisfied, max unc./thresh. is 17.968092739058083 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62316 --- greater than max batches\n",
" 199/1 0.67839 0.68306 +/- 0.00158\n",
" Triggers unsatisfied, max unc./thresh. is 17.91968515142146 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 62302 --- greater than max batches\n",
" 200/1 0.66459 0.68297 +/- 0.00157\n",
" Triggers unsatisfied, max unc./thresh. is 17.82970764669685 for absorption in\n",
" tally 3\n",
" WARNING: The estimated number of batches is 61996 --- greater than max batches\n",
" Creating state point statepoint.200.h5...\n",
"\n",
" =======================> TIMING STATISTICS <=======================\n",
"\n",
" Total time for initialization = 2.9309e-01 seconds\n",
" Reading cross sections = 2.8108e-01 seconds\n",
" Total time in simulation = 1.1321e+01 seconds\n",
" Time in transport only = 1.1242e+01 seconds\n",
" Time in inactive batches = 1.6721e-01 seconds\n",
" Time in active batches = 1.1153e+01 seconds\n",
" Time synchronizing fission bank = 2.2958e-02 seconds\n",
" Sampling source sites = 1.8701e-02 seconds\n",
" SEND/RECV source sites = 3.9403e-03 seconds\n",
" Time accumulating tallies = 9.9349e-04 seconds\n",
" Total time for finalization = 5.2200e-07 seconds\n",
" Total time elapsed = 1.1620e+01 seconds\n",
" Calculation Rate (inactive) = 74758.2 particles/second\n",
" Calculation Rate (active) = 43708.5 particles/second\n",
"\n",
" ============================> RESULTS <============================\n",
"\n",
" k-effective (Collision) = 0.68198 +/- 0.00141\n",
" k-effective (Track-length) = 0.68297 +/- 0.00157\n",
" k-effective (Absorption) = 0.68161 +/- 0.00145\n",
" Combined k-effective = 0.68209 +/- 0.00118\n",
" Leakage Fraction = 0.34033 +/- 0.00074\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"
]
}
],
"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": [
"[[[0.04508259]]\n",
"\n",
" [[0.0221707 ]]\n",
"\n",
" [[0.10763375]]\n",
"\n",
" [[0.05107401]]]\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",
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" .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>2.27e-04</td>\n",
" <td>1.02e-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>5.54e-04</td>\n",
" <td>2.50e-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>7.19e-05</td>\n",
" <td>1.82e-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>1.89e-04</td>\n",
" <td>4.69e-06</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>2.35e-04</td>\n",
" <td>9.82e-06</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>5.71e-04</td>\n",
" <td>2.39e-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>6.88e-05</td>\n",
" <td>1.61e-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>1.81e-04</td>\n",
" <td>4.15e-06</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>2.31e-04</td>\n",
" <td>1.13e-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>5.63e-04</td>\n",
" <td>2.76e-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>6.95e-05</td>\n",
" <td>1.76e-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>1.83e-04</td>\n",
" <td>4.53e-06</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>2.07e-04</td>\n",
" <td>9.85e-06</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>5.04e-04</td>\n",
" <td>2.40e-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>6.48e-05</td>\n",
" <td>1.45e-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>1.71e-04</td>\n",
" <td>3.81e-06</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>2.20e-04</td>\n",
" <td>1.07e-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>5.37e-04</td>\n",
" <td>2.60e-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>6.76e-05</td>\n",
" <td>1.78e-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>1.78e-04</td>\n",
" <td>4.63e-06</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 2.27e-04 \n",
"1 1 1 1 0.00e+00 6.25e-01 nu-fission 5.54e-04 \n",
"2 1 1 1 6.25e-01 2.00e+07 fission 7.19e-05 \n",
"3 1 1 1 6.25e-01 2.00e+07 nu-fission 1.89e-04 \n",
"4 2 1 1 0.00e+00 6.25e-01 fission 2.35e-04 \n",
"5 2 1 1 0.00e+00 6.25e-01 nu-fission 5.71e-04 \n",
"6 2 1 1 6.25e-01 2.00e+07 fission 6.88e-05 \n",
"7 2 1 1 6.25e-01 2.00e+07 nu-fission 1.81e-04 \n",
"8 3 1 1 0.00e+00 6.25e-01 fission 2.31e-04 \n",
"9 3 1 1 0.00e+00 6.25e-01 nu-fission 5.63e-04 \n",
"10 3 1 1 6.25e-01 2.00e+07 fission 6.95e-05 \n",
"11 3 1 1 6.25e-01 2.00e+07 nu-fission 1.83e-04 \n",
"12 4 1 1 0.00e+00 6.25e-01 fission 2.07e-04 \n",
"13 4 1 1 0.00e+00 6.25e-01 nu-fission 5.04e-04 \n",
"14 4 1 1 6.25e-01 2.00e+07 fission 6.48e-05 \n",
"15 4 1 1 6.25e-01 2.00e+07 nu-fission 1.71e-04 \n",
"16 5 1 1 0.00e+00 6.25e-01 fission 2.20e-04 \n",
"17 5 1 1 0.00e+00 6.25e-01 nu-fission 5.37e-04 \n",
"18 5 1 1 6.25e-01 2.00e+07 fission 6.76e-05 \n",
"19 5 1 1 6.25e-01 2.00e+07 nu-fission 1.78e-04 \n",
"\n",
" std. dev. \n",
" \n",
"0 1.02e-05 \n",
"1 2.50e-05 \n",
"2 1.82e-06 \n",
"3 4.69e-06 \n",
"4 9.82e-06 \n",
"5 2.39e-05 \n",
"6 1.61e-06 \n",
"7 4.15e-06 \n",
"8 1.13e-05 \n",
"9 2.76e-05 \n",
"10 1.76e-06 \n",
"11 4.53e-06 \n",
"12 9.85e-06 \n",
"13 2.40e-05 \n",
"14 1.45e-06 \n",
"15 3.81e-06 \n",
"16 1.07e-05 \n",
"17 2.60e-05 \n",
"18 1.78e-06 \n",
"19 4.63e-06 "
]
},
"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 0x7fd070ca32b0>"
]
},
"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"
]
}
],
"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>3.81e-02</td>\n",
" <td>4.13e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>U238</td>\n",
" <td>scatter</td>\n",
" <td>2.34e+00</td>\n",
" <td>2.41e-03</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cell nuclide score mean std. dev.\n",
"0 1 U235 scatter 3.81e-02 4.13e-05\n",
"1 1 U238 scatter 2.34e+00 2.41e-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": [
"[[[2.41367509e-03]\n",
" [4.12533801e-05]]]\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"
]
}
],
"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": [
"[[[0.0131914 ]]\n",
"\n",
" [[0.01252949]]\n",
"\n",
" [[0.01241481]]\n",
"\n",
" [[0.01194961]]\n",
"\n",
" [[0.01186091]]\n",
"\n",
" [[0.0127257 ]]\n",
"\n",
" [[0.01358576]]\n",
"\n",
" [[0.0130368 ]]\n",
"\n",
" [[0.014031 ]]\n",
"\n",
" [[0.0141883 ]]]\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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" text-align: left;\n",
" }\n",
"</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",
" <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",
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" <tbody>\n",
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" <th>558</th>\n",
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" <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>7.11e-04</td>\n",
" <td>1.10e-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>8.91e-02</td>\n",
" <td>6.60e-04</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>6.75e-04</td>\n",
" <td>1.02e-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>3</td>\n",
" <td>280</td>\n",
" <td>scatter</td>\n",
" <td>8.35e-02</td>\n",
" <td>6.11e-04</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>6.10e-04</td>\n",
" <td>1.02e-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>7.75e-02</td>\n",
" <td>6.10e-04</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>5.67e-04</td>\n",
" <td>9.88e-06</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>7.11e-02</td>\n",
" <td>5.99e-04</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>5.06e-04</td>\n",
" <td>9.35e-06</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>6.39e-02</td>\n",
" <td>5.53e-04</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>4.35e-04</td>\n",
" <td>8.22e-06</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>5.62e-02</td>\n",
" <td>5.18e-04</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>3.73e-04</td>\n",
" <td>7.90e-06</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>4.76e-02</td>\n",
" <td>4.92e-04</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>2.98e-04</td>\n",
" <td>7.30e-06</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>3.82e-02</td>\n",
" <td>4.17e-04</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>2.05e-04</td>\n",
" <td>5.96e-06</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>2.86e-02</td>\n",
" <td>3.72e-04</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>1.22e-04</td>\n",
" <td>4.12e-06</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>1.82e-02</td>\n",
" <td>2.59e-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 7.11e-04 1.10e-05 \n",
"559 8.91e-02 6.60e-04 \n",
"560 6.75e-04 1.02e-05 \n",
"561 8.35e-02 6.11e-04 \n",
"562 6.10e-04 1.02e-05 \n",
"563 7.75e-02 6.10e-04 \n",
"564 5.67e-04 9.88e-06 \n",
"565 7.11e-02 5.99e-04 \n",
"566 5.06e-04 9.35e-06 \n",
"567 6.39e-02 5.53e-04 \n",
"568 4.35e-04 8.22e-06 \n",
"569 5.62e-02 5.18e-04 \n",
"570 3.73e-04 7.90e-06 \n",
"571 4.76e-02 4.92e-04 \n",
"572 2.98e-04 7.30e-06 \n",
"573 3.82e-02 4.17e-04 \n",
"574 2.05e-04 5.96e-06 \n",
"575 2.86e-02 3.72e-04 \n",
"576 1.22e-04 4.12e-06 \n",
"577 1.82e-02 2.59e-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",
"<style scoped>\n",
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" }\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>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>6.86e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.41e-04</td>\n",
" <td>2.51e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.68e-05</td>\n",
" <td>1.07e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.06e-04</td>\n",
" <td>5.09e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.98e-04</td>\n",
" <td>6.90e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.17e-04</td>\n",
" <td>8.44e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>8.70e-04</td>\n",
" <td>1.52e-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 6.86e-06\n",
"std 2.41e-04 2.51e-06\n",
"min 1.68e-05 1.07e-06\n",
"25% 2.06e-04 5.09e-06\n",
"50% 3.98e-04 6.90e-06\n",
"75% 6.17e-04 8.44e-06\n",
"max 8.70e-04 1.52e-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: 0.47449458604689265\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: 2.499381683224802e-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": [
"<ipython-input-32-c935bd379fee>: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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" scatter['rel. err.'] = scatter['std. dev.'] / scatter['mean']\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'Scattering Rates'}, xlabel='mean', ylabel='rel. err.'>"
]
},
"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 0x7fd070fbd1f0>"
]
},
"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": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
Reference in a new issue View git blame Copy permalink