mirror of
https://github.com/openmc-dev/openmc.git
synced 2026-07-22 06:55:35 -04:00
2453 lines
140 KiB
Text
2453 lines
140 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebook demonstrates how systematic analysis of tally scores is possible using Pandas dataframes. A dataframe can be automatically generated using the `Tally.get_pandas_dataframe(...)` method. Furthermore, by linking the tally data in a statepoint file with geometry and material information from a summary file, the dataframe can be shown with user-supplied labels.\n",
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"\n",
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"**Note:** that this Notebook was created using the latest Pandas v0.16.1. Everything in the Notebook will wun with older versions of Pandas, but the multi-indexing option in >v0.15.0 makes the tables look prettier."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import glob\n",
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"from IPython.display import Image\n",
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"import matplotlib.pylab as pylab\n",
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"import scipy.stats\n",
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"import numpy as np\n",
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"\n",
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"import openmc\n",
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"from openmc.statepoint import StatePoint\n",
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"from openmc.summary import Summary\n",
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"from openmc.source import Source\n",
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"from openmc.stats import Box\n",
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"\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generate Input Files"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First we need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the material."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Instantiate some Nuclides\n",
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"h1 = openmc.Nuclide('H-1')\n",
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"b10 = openmc.Nuclide('B-10')\n",
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"o16 = openmc.Nuclide('O-16')\n",
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"u235 = openmc.Nuclide('U-235')\n",
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"u238 = openmc.Nuclide('U-238')\n",
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"zr90 = openmc.Nuclide('Zr-90')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"With the nuclides we defined, we will now create three materials for the fuel, water, and cladding of the fuel pin."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# 1.6 enriched fuel\n",
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"fuel = openmc.Material(name='1.6% Fuel')\n",
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"fuel.set_density('g/cm3', 10.31341)\n",
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"fuel.add_nuclide(u235, 3.7503e-4)\n",
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"fuel.add_nuclide(u238, 2.2625e-2)\n",
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"fuel.add_nuclide(o16, 4.6007e-2)\n",
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"\n",
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"# borated water\n",
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"water = openmc.Material(name='Borated Water')\n",
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"water.set_density('g/cm3', 0.740582)\n",
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"water.add_nuclide(h1, 4.9457e-2)\n",
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"water.add_nuclide(o16, 2.4732e-2)\n",
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"water.add_nuclide(b10, 8.0042e-6)\n",
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"\n",
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"# zircaloy\n",
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"zircaloy = openmc.Material(name='Zircaloy')\n",
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"zircaloy.set_density('g/cm3', 6.55)\n",
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"zircaloy.add_nuclide(zr90, 7.2758e-3)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"With our three materials, we can now create a materials file object that can be exported to an actual XML file."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Instantiate a MaterialsFile, add Materials\n",
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"materials_file = openmc.MaterialsFile()\n",
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"materials_file.add_material(fuel)\n",
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"materials_file.add_material(water)\n",
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"materials_file.add_material(zircaloy)\n",
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"materials_file.default_xs = '71c'\n",
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"\n",
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"# Export to \"materials.xml\"\n",
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"materials_file.export_to_xml()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Create cylinders for the fuel and clad\n",
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"fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n",
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"clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n",
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"\n",
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"# Create boundary planes to surround the geometry\n",
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"# Use both reflective and vacuum boundaries to make life interesting\n",
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"min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n",
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"max_x = openmc.XPlane(x0=+10.71, boundary_type='vacuum')\n",
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"min_y = openmc.YPlane(y0=-10.71, boundary_type='vacuum')\n",
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"max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n",
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"min_z = openmc.ZPlane(z0=-10.71, boundary_type='reflective')\n",
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"max_z = openmc.ZPlane(z0=+10.71, boundary_type='reflective')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Create a Universe to encapsulate a fuel pin\n",
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"pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n",
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"\n",
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"# Create fuel Cell\n",
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"fuel_cell = openmc.Cell(name='1.6% Fuel')\n",
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"fuel_cell.fill = fuel\n",
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"fuel_cell.region = -fuel_outer_radius\n",
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"pin_cell_universe.add_cell(fuel_cell)\n",
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"\n",
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"# Create a clad Cell\n",
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"clad_cell = openmc.Cell(name='1.6% Clad')\n",
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"clad_cell.fill = zircaloy\n",
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"clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
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"pin_cell_universe.add_cell(clad_cell)\n",
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"\n",
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"# Create a moderator Cell\n",
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"moderator_cell = openmc.Cell(name='1.6% Moderator')\n",
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"moderator_cell.fill = water\n",
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"moderator_cell.region = +clad_outer_radius\n",
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"pin_cell_universe.add_cell(moderator_cell)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Create fuel assembly Lattice\n",
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"assembly = openmc.RectLattice(name='1.6% Fuel - 0BA')\n",
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"assembly.dimension = (17, 17)\n",
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"assembly.pitch = (1.26, 1.26)\n",
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"assembly.lower_left = [-1.26 * 17. / 2.0] * 2\n",
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"assembly.universes = [[pin_cell_universe] * 17] * 17"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Create root Cell\n",
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"root_cell = openmc.Cell(name='root cell')\n",
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"root_cell.fill = assembly\n",
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"\n",
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"# Add boundary planes\n",
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"root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n",
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"\n",
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"# Create root Universe\n",
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"root_universe = openmc.Universe(universe_id=0, name='root universe')\n",
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"root_universe.add_cell(root_cell)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We now must create a geometry that is assigned a root universe, put the geometry into a `GeometryFile` object, and export it to XML."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Create Geometry and set root Universe\n",
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"geometry = openmc.Geometry()\n",
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"geometry.root_universe = root_universe"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Instantiate a GeometryFile\n",
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"geometry_file = openmc.GeometryFile()\n",
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"geometry_file.geometry = geometry\n",
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"\n",
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"# Export to \"geometry.xml\"\n",
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"geometry_file.export_to_xml()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# OpenMC simulation parameters\n",
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"min_batches = 20\n",
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"max_batches = 200\n",
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"inactive = 5\n",
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"particles = 2500\n",
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"\n",
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"# Instantiate a SettingsFile\n",
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"settings_file = openmc.SettingsFile()\n",
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"settings_file.batches = min_batches\n",
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"settings_file.inactive = inactive\n",
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"settings_file.particles = particles\n",
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"settings_file.output = {'tallies': False}\n",
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"settings_file.trigger_active = True\n",
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"settings_file.trigger_max_batches = max_batches\n",
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"source_bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n",
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"settings_file.source = Source(space=Box(\n",
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" source_bounds[:3], source_bounds[3:]))\n",
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"\n",
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"# Export to \"settings.xml\"\n",
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"settings_file.export_to_xml()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Instantiate a Plot\n",
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"plot = openmc.Plot(plot_id=1)\n",
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"plot.filename = 'materials-xy'\n",
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"plot.origin = [0, 0, 0]\n",
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"plot.width = [21.5, 21.5]\n",
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"plot.pixels = [250, 250]\n",
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"plot.color = 'mat'\n",
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"\n",
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"# Instantiate a PlotsFile, add Plot, and export to \"plots.xml\"\n",
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"plot_file = openmc.PlotsFile()\n",
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"plot_file.add_plot(plot)\n",
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"plot_file.export_to_xml()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"With the plots.xml file, we can now generate and view the plot. OpenMC outputs plots in .ppm format, which can be converted into a compressed format like .png with the convert utility."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
|
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"text/plain": [
|
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"0"
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]
|
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},
|
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
|
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],
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"source": [
|
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"# Run openmc in plotting mode\n",
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"executor = openmc.Executor()\n",
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"executor.plot_geometry(output=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
|
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nYII=\n",
|
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"text/plain": [
|
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"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Convert OpenMC's funky ppm to png\n",
|
|
"!convert materials-xy.ppm materials-xy.png\n",
|
|
"\n",
|
|
"# Display the materials plot inline\n",
|
|
"Image(filename='materials-xy.png')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"As we can see from the plot, we have a nice array of pin cells with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a variety of tallies."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Instantiate an empty TalliesFile\n",
|
|
"tallies_file = openmc.TalliesFile()\n",
|
|
"tallies_file._tallies = []"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Instantiate a fission rate mesh Tally"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Instantiate a tally Mesh\n",
|
|
"mesh = openmc.Mesh(mesh_id=1)\n",
|
|
"mesh.type = 'regular'\n",
|
|
"mesh.dimension = [17, 17]\n",
|
|
"mesh.lower_left = [-10.71, -10.71]\n",
|
|
"mesh.width = [1.26, 1.26]\n",
|
|
"\n",
|
|
"# Instantiate tally Filter\n",
|
|
"mesh_filter = openmc.Filter()\n",
|
|
"mesh_filter.mesh = mesh\n",
|
|
"\n",
|
|
"# Instantiate energy Filter\n",
|
|
"energy_filter = openmc.Filter()\n",
|
|
"energy_filter.type = 'energy'\n",
|
|
"energy_filter.bins = np.array([0, 0.625e-6, 20.])\n",
|
|
"\n",
|
|
"# Instantiate the Tally\n",
|
|
"tally = openmc.Tally(name='mesh tally')\n",
|
|
"tally.add_filter(mesh_filter)\n",
|
|
"tally.add_filter(energy_filter)\n",
|
|
"tally.add_score('fission')\n",
|
|
"tally.add_score('nu-fission')\n",
|
|
"\n",
|
|
"# Add mesh and Tally to TalliesFile\n",
|
|
"tallies_file.add_mesh(mesh)\n",
|
|
"tallies_file.add_tally(tally)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Instantiate a cell Tally with nuclides"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Instantiate tally Filter\n",
|
|
"cell_filter = openmc.Filter(type='cell', bins=[fuel_cell.id])\n",
|
|
"\n",
|
|
"# Instantiate the tally\n",
|
|
"tally = openmc.Tally(name='cell tally')\n",
|
|
"tally.add_filter(cell_filter)\n",
|
|
"tally.add_score('scatter-y2')\n",
|
|
"tally.add_nuclide(u235)\n",
|
|
"tally.add_nuclide(u238)\n",
|
|
"\n",
|
|
"# Add mesh and tally to TalliesFile\n",
|
|
"tallies_file.add_tally(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": 18,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Instantiate tally Filter\n",
|
|
"distribcell_filter = openmc.Filter(type='distribcell', bins=[moderator_cell.id])\n",
|
|
"\n",
|
|
"# Instantiate tally Trigger for kicks\n",
|
|
"trigger = openmc.Trigger(trigger_type='std_dev', threshold=5e-5)\n",
|
|
"trigger.add_score('absorption')\n",
|
|
"\n",
|
|
"# Instantiate the Tally\n",
|
|
"tally = openmc.Tally(name='distribcell tally')\n",
|
|
"tally.add_filter(distribcell_filter)\n",
|
|
"tally.add_score('absorption')\n",
|
|
"tally.add_score('scatter')\n",
|
|
"tally.add_trigger(trigger)\n",
|
|
"\n",
|
|
"# Add mesh and tally to TalliesFile\n",
|
|
"tallies_file.add_tally(tally)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Export to \"tallies.xml\"\n",
|
|
"tallies_file.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": 20,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" .d88888b. 888b d888 .d8888b.\n",
|
|
" d88P\" \"Y88b 8888b d8888 d88P Y88b\n",
|
|
" 888 888 88888b.d88888 888 888\n",
|
|
" 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n",
|
|
" 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n",
|
|
" 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n",
|
|
" Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n",
|
|
" \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n",
|
|
"__________________888______________________________________________________\n",
|
|
" 888\n",
|
|
" 888\n",
|
|
"\n",
|
|
" Copyright: 2011-2015 Massachusetts Institute of Technology\n",
|
|
" License: http://mit-crpg.github.io/openmc/license.html\n",
|
|
" Version: 0.7.1\n",
|
|
" Git SHA1: 34381b40a9445a727e360873aaa6ef892af1cb6a\n",
|
|
" Date/Time: 2016-02-07 16:01:57\n",
|
|
" MPI Processes: 1\n",
|
|
"\n",
|
|
" ===========================================================================\n",
|
|
" ========================> INITIALIZATION <=========================\n",
|
|
" ===========================================================================\n",
|
|
"\n",
|
|
" Reading settings XML file...\n",
|
|
" Reading cross sections XML file...\n",
|
|
" Reading geometry XML file...\n",
|
|
" Reading materials XML file...\n",
|
|
" Reading tallies XML file...\n",
|
|
" Building neighboring cells lists for each surface...\n",
|
|
" Loading ACE cross section table: 92235.71c\n",
|
|
" Loading ACE cross section table: 92238.71c\n",
|
|
" Loading ACE cross section table: 8016.71c\n",
|
|
" Loading ACE cross section table: 1001.71c\n",
|
|
" Loading ACE cross section table: 5010.71c\n",
|
|
" Loading ACE cross section table: 40090.71c\n",
|
|
" Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n",
|
|
" Initializing source particles...\n",
|
|
"\n",
|
|
" ===========================================================================\n",
|
|
" ====================> K EIGENVALUE SIMULATION <====================\n",
|
|
" ===========================================================================\n",
|
|
"\n",
|
|
" Bat./Gen. k Average k \n",
|
|
" ========= ======== ==================== \n",
|
|
" 1/1 0.54958 \n",
|
|
" 2/1 0.67628 \n",
|
|
" 3/1 0.70618 \n",
|
|
" 4/1 0.66601 \n",
|
|
" 5/1 0.70876 \n",
|
|
" 6/1 0.69708 \n",
|
|
" 7/1 0.68623 0.69166 +/- 0.00543\n",
|
|
" 8/1 0.69159 0.69163 +/- 0.00313\n",
|
|
" 9/1 0.69908 0.69349 +/- 0.00289\n",
|
|
" 10/1 0.63865 0.68253 +/- 0.01120\n",
|
|
" 11/1 0.65439 0.67784 +/- 0.01027\n",
|
|
" 12/1 0.68518 0.67889 +/- 0.00875\n",
|
|
" 13/1 0.69507 0.68091 +/- 0.00784\n",
|
|
" 14/1 0.70129 0.68317 +/- 0.00728\n",
|
|
" 15/1 0.71336 0.68619 +/- 0.00717\n",
|
|
" 16/1 0.68725 0.68629 +/- 0.00649\n",
|
|
" 17/1 0.72579 0.68958 +/- 0.00678\n",
|
|
" 18/1 0.67149 0.68819 +/- 0.00639\n",
|
|
" 19/1 0.67771 0.68744 +/- 0.00596\n",
|
|
" 20/1 0.68035 0.68697 +/- 0.00557\n",
|
|
" Triggers unsatisfied, max unc./thresh. is 1.09851 for absorption in tally 10002\n",
|
|
" The estimated number of batches is 24\n",
|
|
" Creating state point statepoint.020.h5...\n",
|
|
" 21/1 0.68105 0.68660 +/- 0.00522\n",
|
|
" 22/1 0.67168 0.68572 +/- 0.00498\n",
|
|
" 23/1 0.67520 0.68514 +/- 0.00473\n",
|
|
" 24/1 0.67940 0.68483 +/- 0.00449\n",
|
|
" Triggers satisfied for batch 24\n",
|
|
" Creating state point statepoint.024.h5...\n",
|
|
"\n",
|
|
" ===========================================================================\n",
|
|
" ======================> SIMULATION FINISHED <======================\n",
|
|
" ===========================================================================\n",
|
|
"\n",
|
|
"\n",
|
|
" =======================> TIMING STATISTICS <=======================\n",
|
|
"\n",
|
|
" Total time for initialization = 3.0900E-01 seconds\n",
|
|
" Reading cross sections = 7.8000E-02 seconds\n",
|
|
" Total time in simulation = 4.9560E+00 seconds\n",
|
|
" Time in transport only = 4.9400E+00 seconds\n",
|
|
" Time in inactive batches = 7.3100E-01 seconds\n",
|
|
" Time in active batches = 4.2250E+00 seconds\n",
|
|
" Time synchronizing fission bank = 4.0000E-03 seconds\n",
|
|
" Sampling source sites = 3.0000E-03 seconds\n",
|
|
" SEND/RECV source sites = 1.0000E-03 seconds\n",
|
|
" Time accumulating tallies = 1.0000E-03 seconds\n",
|
|
" Total time for finalization = 0.0000E+00 seconds\n",
|
|
" Total time elapsed = 5.2780E+00 seconds\n",
|
|
" Calculation Rate (inactive) = 17099.9 neutrons/second\n",
|
|
" Calculation Rate (active) = 8875.74 neutrons/second\n",
|
|
"\n",
|
|
" ============================> RESULTS <============================\n",
|
|
"\n",
|
|
" k-effective (Collision) = 0.68264 +/- 0.00405\n",
|
|
" k-effective (Track-length) = 0.68483 +/- 0.00449\n",
|
|
" k-effective (Absorption) = 0.68225 +/- 0.00336\n",
|
|
" Combined k-effective = 0.68275 +/- 0.00346\n",
|
|
" Leakage Fraction = 0.34345 +/- 0.00167\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0"
|
|
]
|
|
},
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Remove old HDF5 (summary, statepoint) files\n",
|
|
"!rm statepoint.*\n",
|
|
"\n",
|
|
"# Run OpenMC with MPI!\n",
|
|
"executor.run_simulation()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Tally Data Processing"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# We do not know how many batches were needed to satisfy the \n",
|
|
"# tally trigger(s), so find the statepoint file(s)\n",
|
|
"statepoints = glob.glob('statepoint.*.h5')\n",
|
|
"\n",
|
|
"# Load the last statepoint file\n",
|
|
"sp = StatePoint(statepoints[-1])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Load the summary file and link with statepoint\n",
|
|
"su = Summary('summary.h5')\n",
|
|
"sp.link_with_summary(su)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Analyze the mesh fission rate tally**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Tally\n",
|
|
"\tID =\t10000\n",
|
|
"\tName =\tmesh tally\n",
|
|
"\tFilters =\t\n",
|
|
" \t\tmesh\t[1]\n",
|
|
" \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n",
|
|
"\tNuclides =\ttotal \n",
|
|
"\tScores =\t[u'fission', u'nu-fission']\n",
|
|
"\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": 24,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[[ 0.18257268]]\n",
|
|
"\n",
|
|
" [[ 0.07111957]]\n",
|
|
"\n",
|
|
" [[ 0.40880276]]\n",
|
|
"\n",
|
|
" [[ 0.16407535]]]\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'], filters=['mesh', 'energy'], \\\n",
|
|
" filter_bins=[((1,1),(1,17), (17,1), (17,17)), \\\n",
|
|
" ((0., 0.625e-6),)], value='rel_err')\n",
|
|
"print(data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>(mesh 1, x)</th>\n",
|
|
" <th>(mesh 1, y)</th>\n",
|
|
" <th>(mesh 1, z)</th>\n",
|
|
" <th>energy low [MeV]</th>\n",
|
|
" <th>energy high [MeV]</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> 1</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>0.00e+00</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td> fission</td>\n",
|
|
" <td>2.02e-04</td>\n",
|
|
" <td>3.69e-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-07</td>\n",
|
|
" <td> nu-fission</td>\n",
|
|
" <td>4.92e-04</td>\n",
|
|
" <td>8.98e-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-07</td>\n",
|
|
" <td>2.00e+01</td>\n",
|
|
" <td> fission</td>\n",
|
|
" <td>7.62e-05</td>\n",
|
|
" <td>3.74e-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-07</td>\n",
|
|
" <td>2.00e+01</td>\n",
|
|
" <td> nu-fission</td>\n",
|
|
" <td>2.04e-04</td>\n",
|
|
" <td>9.88e-06</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4 </th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 2</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>0.00e+00</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td> fission</td>\n",
|
|
" <td>3.75e-04</td>\n",
|
|
" <td>3.86e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5 </th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 2</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>0.00e+00</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td> nu-fission</td>\n",
|
|
" <td>9.14e-04</td>\n",
|
|
" <td>9.41e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6 </th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 2</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td>2.00e+01</td>\n",
|
|
" <td> fission</td>\n",
|
|
" <td>1.07e-04</td>\n",
|
|
" <td>1.26e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7 </th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 2</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td>2.00e+01</td>\n",
|
|
" <td> nu-fission</td>\n",
|
|
" <td>2.78e-04</td>\n",
|
|
" <td>3.16e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8 </th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 3</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>0.00e+00</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td> fission</td>\n",
|
|
" <td>5.64e-04</td>\n",
|
|
" <td>5.60e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9 </th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 3</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>0.00e+00</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td> nu-fission</td>\n",
|
|
" <td>1.37e-03</td>\n",
|
|
" <td>1.37e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 3</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td>2.00e+01</td>\n",
|
|
" <td> fission</td>\n",
|
|
" <td>1.49e-04</td>\n",
|
|
" <td>7.25e-06</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 3</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td>2.00e+01</td>\n",
|
|
" <td> nu-fission</td>\n",
|
|
" <td>3.88e-04</td>\n",
|
|
" <td>1.78e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 4</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>0.00e+00</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td> fission</td>\n",
|
|
" <td>6.69e-04</td>\n",
|
|
" <td>4.44e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 4</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>0.00e+00</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td> nu-fission</td>\n",
|
|
" <td>1.63e-03</td>\n",
|
|
" <td>1.08e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 4</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td>2.00e+01</td>\n",
|
|
" <td> fission</td>\n",
|
|
" <td>1.65e-04</td>\n",
|
|
" <td>1.09e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 4</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td>2.00e+01</td>\n",
|
|
" <td> nu-fission</td>\n",
|
|
" <td>4.33e-04</td>\n",
|
|
" <td>2.89e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 5</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>0.00e+00</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td> fission</td>\n",
|
|
" <td>9.32e-04</td>\n",
|
|
" <td>6.90e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 5</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>0.00e+00</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td> nu-fission</td>\n",
|
|
" <td>2.27e-03</td>\n",
|
|
" <td>1.68e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 5</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td>2.00e+01</td>\n",
|
|
" <td> fission</td>\n",
|
|
" <td>1.83e-04</td>\n",
|
|
" <td>1.10e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 5</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td>6.25e-07</td>\n",
|
|
" <td>2.00e+01</td>\n",
|
|
" <td> nu-fission</td>\n",
|
|
" <td>4.77e-04</td>\n",
|
|
" <td>2.77e-05</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" (mesh 1, x) (mesh 1, y) (mesh 1, z) energy low [MeV] \\\n",
|
|
"0 1 1 1 0.00e+00 \n",
|
|
"1 1 1 1 0.00e+00 \n",
|
|
"2 1 1 1 6.25e-07 \n",
|
|
"3 1 1 1 6.25e-07 \n",
|
|
"4 1 2 1 0.00e+00 \n",
|
|
"5 1 2 1 0.00e+00 \n",
|
|
"6 1 2 1 6.25e-07 \n",
|
|
"7 1 2 1 6.25e-07 \n",
|
|
"8 1 3 1 0.00e+00 \n",
|
|
"9 1 3 1 0.00e+00 \n",
|
|
"10 1 3 1 6.25e-07 \n",
|
|
"11 1 3 1 6.25e-07 \n",
|
|
"12 1 4 1 0.00e+00 \n",
|
|
"13 1 4 1 0.00e+00 \n",
|
|
"14 1 4 1 6.25e-07 \n",
|
|
"15 1 4 1 6.25e-07 \n",
|
|
"16 1 5 1 0.00e+00 \n",
|
|
"17 1 5 1 0.00e+00 \n",
|
|
"18 1 5 1 6.25e-07 \n",
|
|
"19 1 5 1 6.25e-07 \n",
|
|
"\n",
|
|
" energy high [MeV] score mean std. dev. \n",
|
|
"0 6.25e-07 fission 2.02e-04 3.69e-05 \n",
|
|
"1 6.25e-07 nu-fission 4.92e-04 8.98e-05 \n",
|
|
"2 2.00e+01 fission 7.62e-05 3.74e-06 \n",
|
|
"3 2.00e+01 nu-fission 2.04e-04 9.88e-06 \n",
|
|
"4 6.25e-07 fission 3.75e-04 3.86e-05 \n",
|
|
"5 6.25e-07 nu-fission 9.14e-04 9.41e-05 \n",
|
|
"6 2.00e+01 fission 1.07e-04 1.26e-05 \n",
|
|
"7 2.00e+01 nu-fission 2.78e-04 3.16e-05 \n",
|
|
"8 6.25e-07 fission 5.64e-04 5.60e-05 \n",
|
|
"9 6.25e-07 nu-fission 1.37e-03 1.37e-04 \n",
|
|
"10 2.00e+01 fission 1.49e-04 7.25e-06 \n",
|
|
"11 2.00e+01 nu-fission 3.88e-04 1.78e-05 \n",
|
|
"12 6.25e-07 fission 6.69e-04 4.44e-05 \n",
|
|
"13 6.25e-07 nu-fission 1.63e-03 1.08e-04 \n",
|
|
"14 2.00e+01 fission 1.65e-04 1.09e-05 \n",
|
|
"15 2.00e+01 nu-fission 4.33e-04 2.89e-05 \n",
|
|
"16 6.25e-07 fission 9.32e-04 6.90e-05 \n",
|
|
"17 6.25e-07 nu-fission 2.27e-03 1.68e-04 \n",
|
|
"18 2.00e+01 fission 1.83e-04 1.10e-05 \n",
|
|
"19 2.00e+01 nu-fission 4.77e-04 2.77e-05 "
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"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",
|
|
"import pandas as pd\n",
|
|
"pd.set_option('display.float_format', '{:.2e}'.format)\n",
|
|
"\n",
|
|
"# Print the first twenty rows in the dataframe\n",
|
|
"df.head(20)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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SqKykhRHxYCr/O8DiiDhb0tHA4xFxUNLxwF3AayLiiap6eZyGmdkUGtc4jYg4AKwENgHb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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f469ace7810>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Create a boxplot to view the distribution of\n",
|
|
"# fission and nu-fission rates in the pins\n",
|
|
"bp = df.boxplot(column='mean', by='score')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.colorbar.Colorbar instance at 0x7f4696feafc8>"
|
|
]
|
|
},
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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fZB/HtS+yjx0lYsoMFAMwr0TMnSXrGiInTTOzBM8eOL5gyV3D2o7h4KRpZh23p6d7L2o6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\ngg==\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f469a941110>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Extract thermal nu-fission rates from pandas\n",
|
|
"fiss = df[df['score'] == 'nu-fission']\n",
|
|
"fiss = fiss[fiss['energy low [MeV]'] == 0.0]\n",
|
|
"\n",
|
|
"# Extract mean and reshape as 2D NumPy arrays\n",
|
|
"mean = fiss['mean'].reshape((17,17))\n",
|
|
"\n",
|
|
"pylab.imshow(mean, interpolation='nearest')\n",
|
|
"pylab.title('fission rate')\n",
|
|
"pylab.xlabel('x')\n",
|
|
"pylab.ylabel('y')\n",
|
|
"pylab.colorbar()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Analyze the cell+nuclides scatter-y2 rate tally**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Tally\n",
|
|
"\tID =\t10001\n",
|
|
"\tName =\tcell tally\n",
|
|
"\tFilters =\t\n",
|
|
" \t\tcell\t[10000]\n",
|
|
"\tNuclides =\tU-235 U-238 \n",
|
|
"\tScores =\t[u'scatter-Y0,0', u'scatter-Y1,-1', u'scatter-Y1,0', u'scatter-Y1,1', u'scatter-Y2,-2', u'scatter-Y2,-1', u'scatter-Y2,0', u'scatter-Y2,1', u'scatter-Y2,2']\n",
|
|
"\tEstimator =\tanalog\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Find the cell Tally with the StatePoint API\n",
|
|
"tally = sp.get_tally(name='cell tally')\n",
|
|
"\n",
|
|
"# Print a little info about the cell tally to the screen\n",
|
|
"print(tally)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>cell</th>\n",
|
|
" <th>nuclide</th>\n",
|
|
" <th>score</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std. dev.</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0 </th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-235</td>\n",
|
|
" <td> scatter-Y0,0</td>\n",
|
|
" <td> 3.71e-02</td>\n",
|
|
" <td>1.15e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1 </th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-235</td>\n",
|
|
" <td> scatter-Y1,-1</td>\n",
|
|
" <td> 2.66e-04</td>\n",
|
|
" <td>3.23e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2 </th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-235</td>\n",
|
|
" <td> scatter-Y1,0</td>\n",
|
|
" <td>-4.17e-04</td>\n",
|
|
" <td>2.74e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3 </th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-235</td>\n",
|
|
" <td> scatter-Y1,1</td>\n",
|
|
" <td>-2.28e-04</td>\n",
|
|
" <td>2.37e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4 </th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-235</td>\n",
|
|
" <td> scatter-Y2,-2</td>\n",
|
|
" <td> 2.57e-05</td>\n",
|
|
" <td>1.99e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5 </th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-235</td>\n",
|
|
" <td> scatter-Y2,-1</td>\n",
|
|
" <td>-1.15e-04</td>\n",
|
|
" <td>1.85e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6 </th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-235</td>\n",
|
|
" <td> scatter-Y2,0</td>\n",
|
|
" <td> 1.51e-04</td>\n",
|
|
" <td>1.59e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7 </th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-235</td>\n",
|
|
" <td> scatter-Y2,1</td>\n",
|
|
" <td>-1.22e-04</td>\n",
|
|
" <td>2.80e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8 </th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-235</td>\n",
|
|
" <td> scatter-Y2,2</td>\n",
|
|
" <td> 7.65e-06</td>\n",
|
|
" <td>1.81e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9 </th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-238</td>\n",
|
|
" <td> scatter-Y0,0</td>\n",
|
|
" <td> 2.33e+00</td>\n",
|
|
" <td>1.31e-02</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-238</td>\n",
|
|
" <td> scatter-Y1,-1</td>\n",
|
|
" <td> 2.45e-02</td>\n",
|
|
" <td>2.27e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-238</td>\n",
|
|
" <td> scatter-Y1,0</td>\n",
|
|
" <td>-5.87e-05</td>\n",
|
|
" <td>2.80e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-238</td>\n",
|
|
" <td> scatter-Y1,1</td>\n",
|
|
" <td>-2.80e-02</td>\n",
|
|
" <td>2.54e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-238</td>\n",
|
|
" <td> scatter-Y2,-2</td>\n",
|
|
" <td>-4.86e-03</td>\n",
|
|
" <td>1.58e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-238</td>\n",
|
|
" <td> scatter-Y2,-1</td>\n",
|
|
" <td> 5.57e-04</td>\n",
|
|
" <td>2.02e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-238</td>\n",
|
|
" <td> scatter-Y2,0</td>\n",
|
|
" <td> 6.24e-03</td>\n",
|
|
" <td>1.63e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-238</td>\n",
|
|
" <td> scatter-Y2,1</td>\n",
|
|
" <td>-6.48e-04</td>\n",
|
|
" <td>1.55e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> U-238</td>\n",
|
|
" <td> scatter-Y2,2</td>\n",
|
|
" <td>-1.03e-03</td>\n",
|
|
" <td>1.31e-03</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" cell nuclide score mean std. dev.\n",
|
|
"0 10000 U-235 scatter-Y0,0 3.71e-02 1.15e-03\n",
|
|
"1 10000 U-235 scatter-Y1,-1 2.66e-04 3.23e-04\n",
|
|
"2 10000 U-235 scatter-Y1,0 -4.17e-04 2.74e-04\n",
|
|
"3 10000 U-235 scatter-Y1,1 -2.28e-04 2.37e-04\n",
|
|
"4 10000 U-235 scatter-Y2,-2 2.57e-05 1.99e-04\n",
|
|
"5 10000 U-235 scatter-Y2,-1 -1.15e-04 1.85e-04\n",
|
|
"6 10000 U-235 scatter-Y2,0 1.51e-04 1.59e-04\n",
|
|
"7 10000 U-235 scatter-Y2,1 -1.22e-04 2.80e-04\n",
|
|
"8 10000 U-235 scatter-Y2,2 7.65e-06 1.81e-04\n",
|
|
"9 10000 U-238 scatter-Y0,0 2.33e+00 1.31e-02\n",
|
|
"10 10000 U-238 scatter-Y1,-1 2.45e-02 2.27e-03\n",
|
|
"11 10000 U-238 scatter-Y1,0 -5.87e-05 2.80e-03\n",
|
|
"12 10000 U-238 scatter-Y1,1 -2.80e-02 2.54e-03\n",
|
|
"13 10000 U-238 scatter-Y2,-2 -4.86e-03 1.58e-03\n",
|
|
"14 10000 U-238 scatter-Y2,-1 5.57e-04 2.02e-03\n",
|
|
"15 10000 U-238 scatter-Y2,0 6.24e-03 1.63e-03\n",
|
|
"16 10000 U-238 scatter-Y2,1 -6.48e-04 1.55e-03\n",
|
|
"17 10000 U-238 scatter-Y2,2 -1.03e-03 1.31e-03"
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Get a pandas dataframe for the cell tally data\n",
|
|
"df = tally.get_pandas_dataframe()\n",
|
|
"\n",
|
|
"# Print the first twenty rows in the dataframe\n",
|
|
"df.head(100)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Use the new Tally data retrieval API with pure NumPy"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[[ 0.00131009 0.01310707]\n",
|
|
" [ 0.00018089 0.00114976]]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Get the standard deviations for two of the spherical harmonic\n",
|
|
"# scattering reaction rates \n",
|
|
"data = tally.get_values(scores=['scatter-Y2,2', 'scatter-Y0,0'], \n",
|
|
" nuclides=['U-238', 'U-235'], value='std_dev')\n",
|
|
"print(data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Analyze the distribcell tally**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Tally\n",
|
|
"\tID =\t10002\n",
|
|
"\tName =\tdistribcell tally\n",
|
|
"\tFilters =\t\n",
|
|
" \t\tdistribcell\t[10002]\n",
|
|
"\tNuclides =\ttotal \n",
|
|
"\tScores =\t[u'absorption', u'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": 32,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[[ 0.04537029]]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Get the relative error for the scattering reaction rates in\n",
|
|
"# the first 30 distribcell instances \n",
|
|
"data = tally.get_values(scores=['scatter'], filters=['distribcell'],\n",
|
|
" filter_bins=[(i,) for i in range(10)], value='rel_err')\n",
|
|
"print(data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Print the distribcell tally dataframe **without** OpenCG info"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>distribcell</th>\n",
|
|
" <th>score</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std. dev.</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>558</th>\n",
|
|
" <td> 279</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>9.27e-05</td>\n",
|
|
" <td>1.33e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>559</th>\n",
|
|
" <td> 279</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>1.35e-02</td>\n",
|
|
" <td>8.05e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>560</th>\n",
|
|
" <td> 280</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>8.41e-05</td>\n",
|
|
" <td>9.92e-06</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>561</th>\n",
|
|
" <td> 280</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>1.42e-02</td>\n",
|
|
" <td>6.12e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>562</th>\n",
|
|
" <td> 281</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>9.12e-05</td>\n",
|
|
" <td>8.26e-06</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>563</th>\n",
|
|
" <td> 281</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>1.45e-02</td>\n",
|
|
" <td>5.90e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>564</th>\n",
|
|
" <td> 282</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>1.12e-04</td>\n",
|
|
" <td>1.24e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>565</th>\n",
|
|
" <td> 282</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>1.63e-02</td>\n",
|
|
" <td>7.29e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>566</th>\n",
|
|
" <td> 283</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>9.18e-05</td>\n",
|
|
" <td>7.12e-06</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>567</th>\n",
|
|
" <td> 283</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>1.62e-02</td>\n",
|
|
" <td>6.61e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>568</th>\n",
|
|
" <td> 284</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>1.04e-04</td>\n",
|
|
" <td>1.10e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>569</th>\n",
|
|
" <td> 284</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>1.74e-02</td>\n",
|
|
" <td>5.99e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>570</th>\n",
|
|
" <td> 285</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>1.11e-04</td>\n",
|
|
" <td>1.14e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>571</th>\n",
|
|
" <td> 285</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>1.80e-02</td>\n",
|
|
" <td>7.74e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>572</th>\n",
|
|
" <td> 286</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>1.25e-04</td>\n",
|
|
" <td>1.20e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>573</th>\n",
|
|
" <td> 286</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>1.83e-02</td>\n",
|
|
" <td>8.28e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>574</th>\n",
|
|
" <td> 287</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>1.19e-04</td>\n",
|
|
" <td>1.30e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>575</th>\n",
|
|
" <td> 287</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>1.75e-02</td>\n",
|
|
" <td>7.57e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>576</th>\n",
|
|
" <td> 288</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>1.13e-04</td>\n",
|
|
" <td>1.40e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>577</th>\n",
|
|
" <td> 288</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>1.82e-02</td>\n",
|
|
" <td>7.82e-04</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" distribcell score mean std. dev.\n",
|
|
"558 279 absorption 9.27e-05 1.33e-05\n",
|
|
"559 279 scatter 1.35e-02 8.05e-04\n",
|
|
"560 280 absorption 8.41e-05 9.92e-06\n",
|
|
"561 280 scatter 1.42e-02 6.12e-04\n",
|
|
"562 281 absorption 9.12e-05 8.26e-06\n",
|
|
"563 281 scatter 1.45e-02 5.90e-04\n",
|
|
"564 282 absorption 1.12e-04 1.24e-05\n",
|
|
"565 282 scatter 1.63e-02 7.29e-04\n",
|
|
"566 283 absorption 9.18e-05 7.12e-06\n",
|
|
"567 283 scatter 1.62e-02 6.61e-04\n",
|
|
"568 284 absorption 1.04e-04 1.10e-05\n",
|
|
"569 284 scatter 1.74e-02 5.99e-04\n",
|
|
"570 285 absorption 1.11e-04 1.14e-05\n",
|
|
"571 285 scatter 1.80e-02 7.74e-04\n",
|
|
"572 286 absorption 1.25e-04 1.20e-05\n",
|
|
"573 286 scatter 1.83e-02 8.28e-04\n",
|
|
"574 287 absorption 1.19e-04 1.30e-05\n",
|
|
"575 287 scatter 1.75e-02 7.57e-04\n",
|
|
"576 288 absorption 1.13e-04 1.40e-05\n",
|
|
"577 288 scatter 1.82e-02 7.82e-04"
|
|
]
|
|
},
|
|
"execution_count": 33,
|
|
"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": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Print the distribcell tally dataframe **with** OpenCG info"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>(level 1, cell, id)</th>\n",
|
|
" <th>(level 1, univ, id)</th>\n",
|
|
" <th>(level 2, lat, id)</th>\n",
|
|
" <th>(level 2, lat, x)</th>\n",
|
|
" <th>(level 2, lat, y)</th>\n",
|
|
" <th>(level 2, lat, z)</th>\n",
|
|
" <th>(level 3, cell, id)</th>\n",
|
|
" <th>(level 3, univ, id)</th>\n",
|
|
" <th>distribcell</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> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>1.23e-04</td>\n",
|
|
" <td>1.19e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1 </th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>1.78e-02</td>\n",
|
|
" <td>8.08e-04</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2 </th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>2.17e-04</td>\n",
|
|
" <td>1.96e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3 </th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 1</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>2.89e-02</td>\n",
|
|
" <td>1.26e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4 </th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 2</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 2</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>3.18e-04</td>\n",
|
|
" <td>2.03e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5 </th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 2</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 2</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>4.05e-02</td>\n",
|
|
" <td>1.27e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6 </th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 3</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 3</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>3.86e-04</td>\n",
|
|
" <td>1.80e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7 </th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 3</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 3</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>4.86e-02</td>\n",
|
|
" <td>1.34e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8 </th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 4</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 4</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>5.01e-04</td>\n",
|
|
" <td>2.60e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9 </th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 4</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 4</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>5.71e-02</td>\n",
|
|
" <td>1.72e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 5</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 5</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>4.84e-04</td>\n",
|
|
" <td>2.58e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 5</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 5</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>6.08e-02</td>\n",
|
|
" <td>1.58e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 6</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 6</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>5.32e-04</td>\n",
|
|
" <td>3.90e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 6</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 6</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>6.91e-02</td>\n",
|
|
" <td>2.25e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 7</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 7</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>5.77e-04</td>\n",
|
|
" <td>3.92e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 7</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 7</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>7.67e-02</td>\n",
|
|
" <td>2.34e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 8</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 8</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>6.49e-04</td>\n",
|
|
" <td>3.90e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 8</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 8</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>8.16e-02</td>\n",
|
|
" <td>1.61e-03</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 9</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 9</td>\n",
|
|
" <td> absorption</td>\n",
|
|
" <td>6.80e-04</td>\n",
|
|
" <td>3.17e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td> 10003</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10001</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 9</td>\n",
|
|
" <td> 0</td>\n",
|
|
" <td> 10002</td>\n",
|
|
" <td> 10000</td>\n",
|
|
" <td> 9</td>\n",
|
|
" <td> scatter</td>\n",
|
|
" <td>8.77e-02</td>\n",
|
|
" <td>1.96e-03</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" (level 1, cell, id) (level 1, univ, id) (level 2, lat, id) \\\n",
|
|
"0 10003 0 10001 \n",
|
|
"1 10003 0 10001 \n",
|
|
"2 10003 0 10001 \n",
|
|
"3 10003 0 10001 \n",
|
|
"4 10003 0 10001 \n",
|
|
"5 10003 0 10001 \n",
|
|
"6 10003 0 10001 \n",
|
|
"7 10003 0 10001 \n",
|
|
"8 10003 0 10001 \n",
|
|
"9 10003 0 10001 \n",
|
|
"10 10003 0 10001 \n",
|
|
"11 10003 0 10001 \n",
|
|
"12 10003 0 10001 \n",
|
|
"13 10003 0 10001 \n",
|
|
"14 10003 0 10001 \n",
|
|
"15 10003 0 10001 \n",
|
|
"16 10003 0 10001 \n",
|
|
"17 10003 0 10001 \n",
|
|
"18 10003 0 10001 \n",
|
|
"19 10003 0 10001 \n",
|
|
"\n",
|
|
" (level 2, lat, x) (level 2, lat, y) (level 2, lat, z) \\\n",
|
|
"0 0 0 0 \n",
|
|
"1 0 0 0 \n",
|
|
"2 0 1 0 \n",
|
|
"3 0 1 0 \n",
|
|
"4 0 2 0 \n",
|
|
"5 0 2 0 \n",
|
|
"6 0 3 0 \n",
|
|
"7 0 3 0 \n",
|
|
"8 0 4 0 \n",
|
|
"9 0 4 0 \n",
|
|
"10 0 5 0 \n",
|
|
"11 0 5 0 \n",
|
|
"12 0 6 0 \n",
|
|
"13 0 6 0 \n",
|
|
"14 0 7 0 \n",
|
|
"15 0 7 0 \n",
|
|
"16 0 8 0 \n",
|
|
"17 0 8 0 \n",
|
|
"18 0 9 0 \n",
|
|
"19 0 9 0 \n",
|
|
"\n",
|
|
" (level 3, cell, id) (level 3, univ, id) distribcell score \\\n",
|
|
"0 10002 10000 0 absorption \n",
|
|
"1 10002 10000 0 scatter \n",
|
|
"2 10002 10000 1 absorption \n",
|
|
"3 10002 10000 1 scatter \n",
|
|
"4 10002 10000 2 absorption \n",
|
|
"5 10002 10000 2 scatter \n",
|
|
"6 10002 10000 3 absorption \n",
|
|
"7 10002 10000 3 scatter \n",
|
|
"8 10002 10000 4 absorption \n",
|
|
"9 10002 10000 4 scatter \n",
|
|
"10 10002 10000 5 absorption \n",
|
|
"11 10002 10000 5 scatter \n",
|
|
"12 10002 10000 6 absorption \n",
|
|
"13 10002 10000 6 scatter \n",
|
|
"14 10002 10000 7 absorption \n",
|
|
"15 10002 10000 7 scatter \n",
|
|
"16 10002 10000 8 absorption \n",
|
|
"17 10002 10000 8 scatter \n",
|
|
"18 10002 10000 9 absorption \n",
|
|
"19 10002 10000 9 scatter \n",
|
|
"\n",
|
|
" mean std. dev. \n",
|
|
"0 1.23e-04 1.19e-05 \n",
|
|
"1 1.78e-02 8.08e-04 \n",
|
|
"2 2.17e-04 1.96e-05 \n",
|
|
"3 2.89e-02 1.26e-03 \n",
|
|
"4 3.18e-04 2.03e-05 \n",
|
|
"5 4.05e-02 1.27e-03 \n",
|
|
"6 3.86e-04 1.80e-05 \n",
|
|
"7 4.86e-02 1.34e-03 \n",
|
|
"8 5.01e-04 2.60e-05 \n",
|
|
"9 5.71e-02 1.72e-03 \n",
|
|
"10 4.84e-04 2.58e-05 \n",
|
|
"11 6.08e-02 1.58e-03 \n",
|
|
"12 5.32e-04 3.90e-05 \n",
|
|
"13 6.91e-02 2.25e-03 \n",
|
|
"14 5.77e-04 3.92e-05 \n",
|
|
"15 7.67e-02 2.34e-03 \n",
|
|
"16 6.49e-04 3.90e-05 \n",
|
|
"17 8.16e-02 1.61e-03 \n",
|
|
"18 6.80e-04 3.17e-05 \n",
|
|
"19 8.77e-02 1.96e-03 "
|
|
]
|
|
},
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Get a pandas dataframe for the distribcell tally data\n",
|
|
"df = tally.get_pandas_dataframe(summary=su, nuclides=False)\n",
|
|
"\n",
|
|
"# Print the last twenty rows in the dataframe\n",
|
|
"df.head(20)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std. dev.</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>4.18e-04</td>\n",
|
|
" <td>2.17e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>2.39e-04</td>\n",
|
|
" <td>8.82e-06</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>1.81e-05</td>\n",
|
|
" <td>3.82e-06</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>2.02e-04</td>\n",
|
|
" <td>1.49e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>4.02e-04</td>\n",
|
|
" <td>2.11e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>6.15e-04</td>\n",
|
|
" <td>2.67e-05</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>8.92e-04</td>\n",
|
|
" <td>4.43e-05</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" mean std. dev.\n",
|
|
"count 2.89e+02 2.89e+02\n",
|
|
"mean 4.18e-04 2.17e-05\n",
|
|
"std 2.39e-04 8.82e-06\n",
|
|
"min 1.81e-05 3.82e-06\n",
|
|
"25% 2.02e-04 1.49e-05\n",
|
|
"50% 4.02e-04 2.11e-05\n",
|
|
"75% 6.15e-04 2.67e-05\n",
|
|
"max 8.92e-04 4.43e-05"
|
|
]
|
|
},
|
|
"execution_count": 35,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Show summary statistics for absorption distribcell tally data\n",
|
|
"absorption = df[df['score'] == 'absorption']\n",
|
|
"absorption[['mean', 'std. dev.']].dropna().describe()\n",
|
|
"\n",
|
|
"# Note that the maximum standard deviation does indeed\n",
|
|
"# meet the 5e-4 threshold set by the tally trigger"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Perform a statistical test comparing the tally sample distributions for two categories of fuel pins."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Mann-Whitney Test p-value: 0.414863173548\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": 37,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Mann-Whitney Test p-value: 3.28554363741e-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": 38,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/home/smharper/.local/lib/python2.7/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
|
|
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
|
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
|
"\n",
|
|
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4696f3d110>"
|
|
]
|
|
},
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
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KlcydOxeAjo4O5s2bx+LFi4Fi/LPZ64VttbS/5557uOKKf2Z4+N3ABcAO4Dja21fxznde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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f4696faabd0>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Extract the scatter tally data from pandas\n",
|
|
"scatter = df[df['score'] == 'scatter']\n",
|
|
"\n",
|
|
"scatter['rel. err.'] = scatter['std. dev.'] / scatter['mean']\n",
|
|
"\n",
|
|
"# Show a scatter plot of the mean vs. the std. dev.\n",
|
|
"scatter.plot(kind='scatter', x='mean', y='rel. err.', title='Scattering Rates')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x7f4696ec8e90>"
|
|
]
|
|
},
|
|
"execution_count": 39,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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qq6/SuHFjrr32WoYOHVpqmfLiLfu4V4feVodv1z4SkXbAS0ALnIObn1PVJ0SkGfA60AHI\nBy5W1e1l2tq1j4xd+yhgdu2jihUVFdG0aVNWrFhRahwiUVL12kd7gFtV9WjgROBGEekC/AGYrqpH\nADPcaWOMSWpTpkxh586d7NixgxEjRnDMMccEUhD85ltRUNUNqjrfvV8E/AdoCwwGJriLTQDO9yuG\nZBWmPtvyWH6pLez51dS7775L27Ztadu2LStXrmTixIlBh+SLhFzmQkSygZ7AbKClqm50H9oItKyg\nmTHGJI2xY8cyduzYoMPwne9FQUQOAiYDt6hqYfTAiaqqiJTbaTl8+HCys7MB51T0Hj16RI6fLvkm\nk6rTJfOSJZ5kzW+/kulw5Zes09HzTHLLy8tj/PjxAJHPy3j5+iM7IlIXeA/4UFUfd+ctA3JUdYOI\ntAZyVfWoMu1soNnYQHPAbKA5eaXkQLM4W/Q4YGlJQXC9C1zh3r8C+KdfMSSrsH8Ls/xSW9jzM5Xz\ns/uoH/BbYKGIlFzA4y7gz8AbInIV7iGpPsZgjIlDEMfJm2DZbzSbpGXdR8ZUT1J3HxljjEk9VhQC\nEPY+W8svtYU5vzDn5hUrCsYYYyJsTMEkLRtTMKZ6bEzBGGOMp6woBCDs/ZqWX2oLc35hzs0rVhSM\nMcZE2JiCSVo2pmBM9diYgjHGGE9ZUQhA2Ps1Lb/UFub8wpybV6woGGOMibAxBZO0bEzBmOqxMQVj\njDGesqIQgLD3a1p+qS3M+YU5N69YUTDGGBNhYwomadmYgjHVY2MKxhhjPGVFIQBh79e0/FJbmPML\nc25esaJgjDEmwsYUTNKyMQVjqsfGFIwxxnjKikIAwt6vafmltjDnF+bcvFIn6ACMiZfTzVSadTEZ\nUzM2pmCSVqxjCgcuZ+MOpnayMQVjjDGesqIQgLD3a1p+qS3M+YU5N69YUTDGGBNhYwomadmYgjHV\nY2MKxhhjPGVFIQBh79e0/FJbmPMLc25esaJgjDEmwsYUTNJKhjGF8k6MAzs5ziQnL8YU7IxmY6p0\nYGEyJqys+ygAYe/XDHt+YRfm1y/MuXnFioIxxpgIX8cUROQF4Gxgk6p2d+eNAq4GNruL3aWqU8u0\nszEFk0RjCvabDiY1pMJ5Ci8Cg8rMU+BRVe3p3qaW084YY0wAfC0KqvoZsK2ch2r1SF3Y+zXDnl/Y\nhfn1C3NuXglqTOH3IrJARMaJSFZAMRhjjCnD9/MURCQbmBI1ptCC/eMJDwCtVfWqMm1sTMHYmIIx\n1ZSS5ymo6qaS+yLyPDClvOWGDx9OdnY2AFlZWfTo0YOcnBxg/y6gTYd7er+S6fKX37/M/um8vLwq\n13/aaadRntzc3DLrL/38sa7fpm3a7+m8vDzGjx8PEPm8jFcQewqtVXW9e/9WoLeqXlqmTaj3FKI/\nUMLIq/z83lOIZf21cU8hzO/PMOcGKbCnICKvAacCzUVkLTASyBGRHjhb2mrgOj9jMMYYEzu79pFJ\nWranYEz1pMJ5CsYYY1JIlUVBRN4SkbNFxAqIRw4cSA2XsOcXdmF+/cKcm1di+aAfA1wGrBCRP4vI\nkT7HZIwxJiAxjym4J5kNBf4XWAOMBV5R1T2eB2VjCgYbUzCmuhI2piAiBwPDcS5k9zXwBHAcMD2e\nJzfGGJNcYhlTeBv4HMgAzlXVwao6UVVvAjL9DjCMwt6vmYz5icgBN7/X7/VzJEoyvn5eCXNuXonl\nPIWxqvpB9AwRqa+qu1T1OJ/iMsYHfv+Cmv1Cm0l9VY4piMg8Ve1ZZt7XqtrLt6BsTMHg7ZhCRevy\nakzBxh5MMvD1jGYRaQ20ARqKSC/2b0GNcbqSjDHGhExlYwq/Ah4B2gJ/c+//DbgNuNv/0MIr7P2a\nYc8v7ML8+oU5N69UuKegquOB8SJygapOTlxIxhhjglLhmIKIDFPVl0Xkdsp22IKq6qO+BWVjCgYb\nUzCmuvy+SmrJuEEm5RSFeJ7UGGNMcrKrpAYg7Nd0T8bfU7A9hdiF+f0Z5twgQWc0i8hfRKSxiNQV\nkRki8qOIDIvnSY0pKywnflWX3ye9hemkOpMYsZynsEBVjxWRIcA5OEcffaaqx/gWVMj3FMyBavpN\nvvy2qbOn4PceRpj2YEzVEnXto5Jxh3OASapagI0pGGNMKMVSFKaIyDKcC+DNEJEWwC/+hhVuYT9W\nOuz5hV2YX78w5+aVKouCqv4B6Accp6q7gR3AeX4HZowxJvFiOvpIRPoBHYC67ixV1Zd8C8rGFGod\nG1OoXrtY2ZhC7eL3eQolT/IK0AmYDxRHPeRbUTDGGBOMWMYUjgP6qeoNqvr7kpvfgYVZ2Ps1w55f\n2IX59Qtzbl6JpSgsBlr7HYgxxpjgxXKeQh7QA5gD7HJnq6oO9i0oG1OodarT518+G1OozvrLY9tc\n6kvImAIwyv2r7H832bvHBMh+4Sx+9j805YvlkNQ8IB+o696fA8zzNaqQC3u/ZtjzC7swv35hzs0r\nsVz76FrgTeBZd9ahwNt+BmWMMSYYMV37COgD/Lvkt5pFZJGqdvctKBtTqHWqN6ZQ1TwbU6hq/Xbu\nQjglakxhl6ruKrmyoojUwcYUTNCkGI54H45+3Tk2rl472NECNnWHlbBzz04y6tpPiRtTXbEckvqJ\niNwDZIjIGThdSVP8DSvcwt6v6Xt+hyyBq0+E/g/Ad6fCJOCFz+H9MfD9iXAMtHusHbdNu40NRRv8\njSWEwvz+DHNuXomlKPwB2AwsAq4DPgD+18+gjKlQR2D4afDVtTB2jvN3I1DQAdb1gbn/Df+A+dfN\nZ5/uo+vTXbnr47ugXtCBG5MaYr32UQsAVd3ke0TYmEJtFFPfd5sv4bI+8Eaes4dQ0XJR/eNrC9Zy\nz8x7ePmzl2Ham7D0AvYffmljCn48pwmOF2MKFRYFcd5NI4GbgHR3djHwJHC/n5/aVhRqnyo/vBpt\ngut6wfvr4JsaDDRnC5x9NPzUFj54CrZ2LqfdgW29Lgrl/+pZTduVs6Y4BthjWZ9Jbn7/yM6tOJfM\n7q2qTVW1Kc5RSP3cx0wNhb1f0/v8FM65Dhb+Fr6p4Sq+A56ZBysHwtV9IWdkbIdZ+EKjbjVtp+XM\n8yquXI/Wl3zCvu15obKicDlwqaquLpmhqquAy9zHjEmMLm/Dwcsh97741rOvLsy6HZ6ZDy2WwA3A\n4VM9CdGYsKis+2ixqnar7mOeBGXdR7VOhd0c6bvghqPhg6edb/lenqdwuMBZnWB9L5j2KPzU7oC2\n/nQflY41nvV7eX6GjTOkPr+7j/bU8LEIEXlBRDaKyKKoec1EZLqILBeRj0QkK9ZgTS103LOwrZNb\nEDy2Ahi9GDZ3heuPhV/dBo28fxpjUkllReEYESks7wbEejbzi8CgMvP+AExX1SOAGe50rRL2fk3P\n8ksHTv4zzHjIm/WVZ29DyLsPnl4CaXvgRrh92u2s2rbKv+dMenlBB+CbsG97XqiwKKhquqpmVnCL\naYhOVT8DtpWZPRiY4N6fAJxfo8hN+HXH+Ra/vpf/z1XUGj58Ep6FNEmjz9g+nPvaudAVqLvT/+c3\nJknEdJ5CXE8gkg1MKblWkohsc49kKjnsdWvJdFQbG1OoZQ7s+1a4IQ2mTSvTdZSYax/t3LOTiYsn\nctVjV0HbJrBiEHx7Jqz6Lyg81MYUTFLy9TwFr1RWFNzprararEwbKwq1zAEfXtm5cNYAGL2P0sfQ\nB3BBvEYb4ch34LDp0HEG7NjKjWfdyGnZp9G/Q38OaXSIFQWTFBJ1QTyvbRSRVqq6QURaA+WeJT18\n+HCys7MByMrKokePHuTk5AD7+wVTdfrxxx8PVT5e5bdfHrT7E3wNzodVyeM5+x8vNV0yb/905Sd7\nlfN8UesrGx87lsLXneHra5wL8WXW4em8p3n62KehPbAU5wyewsnwXX/YuaSKOKrOp2bxx/p8JfPK\nPn+Jx3F+bNF9NMneX/FMR7/XkiEeL/IZP348QOTzMl5B7Cn8Bdiiqg+LyB+ALFX9Q5k2od5TyMvL\n2/+BE0I1ya/UN9oG2+F/suGJAthZk2+53n07rvKbdtpeaDUPsvtA9lnQ/nMoaA/5ObD6KVi5A/Zk\nlN/Wg1j9WVceTsEI355C2Le9pO8+EpHXgFOB5jiXLbsXeAd4A+c7Vj5wsapuL9Mu1EXBHKjUh+/x\nY6DjTHhzEkF8OFarKJSdFykSeXD4HdCmCXx7FiweCit+BcUNPI3Vv3U582w7TC1JXxRqyopC7VPq\nw/fqEyBvFKw4i5QrCmXnNdoAXSdDt4lw8DcwfxN8tQK2HeZJrFYUTDS/T14zPgn7sdJx5dfkO2i2\n0jnKJwx2tIQvb4AXP3V+80Fwfgti2EA46u0k3QLzgg7AN2Hf9ryQlG9JU4t1nQzLzneuUxQ2WzvD\ndOCxtbDgcuj3V7gFOOVPzlVgjUkC1n1kkkKkm+aqvk7X0cpfEVQ3iqfdR1XNayXQ5yroMhm+PRvm\n3Ajfn1TD9Vv3UW1n3UcmXBqvda6GunpA0JEkzgbg3efhiZWwvif8epjz+4Y9x0HdHUFHZ2ohKwoB\nCHu/Ziz5iUipGwBd3oJvzgu86+iAuBLh52bOZb2fXO5cEeyof8KI1jD0POgxHhonLhQbU6jdAvuZ\nEWMO6Pro8hZ8cUdg0exXtksmkU+d5ly9dcUUaLANjnjfKRBnAHs6wNp+sPEY+PEo+BHYuifwImrC\nxcYUTCAO6KtvIHDrQfDXTc6VS52lCL5v3ecxheq0O3gZtJsFhyyF5sug+RRo3AC2d3QuHLh5Mvzw\nT+fEuV1NPInVtsPUkqqXuTDmQJ2ANadEFQRzgC1HOrcIgfTtzjjMIUuhxWToPRp+/VtYexJ8dS0s\n48DPemMqYWMKAQh7v2aN8uuMc+avqZ7i+rCpOyz5jfPTyq9Mc/a2FlwBJz0C/41zccFqyfM+ziQR\n9m3PC1YUTPBknxUFL+1tCIsuhXH/cgrFr4fBGXc4l+Awpgo2pmACUWpMofVXcMHx8FQyjgMk0ZhC\nTdeVsRkuuAR2HwSTX4W9GTGv37bD1GLnKZhw6PwBfBt0ECG2szm8+j4U14OLL7Kt3lTK3h4BCHu/\nZrXzs6Lgv+J68NYrThfS2VD56HNeYmIKQNi3PS9YUTDBqv8TtFwEa4IOpBbYVxfemATtgB4Tgo7G\nJCkbUzCBiIwpdH4fTvobTMglpfrpk3L9MbZrIXBFc+eqrZFDXG1MIQxsTMGkvo65sPq0oKOoXTYB\nn9wLg69xjvwyJooVhQCEvV+zWvl1nFm7LoCXLL68AdJ3Qc8XynkwL9HRJEzYtz0vWFEwwWm4FZqt\ngB96Bx1J7aPp8N6zMOAeZ1zHGJeNKZhAiIjzy2PHj3HOwk31fvqkWH8N2p0/HAraQe4fy13OtsPU\nYmMKJrVZ11Hwcu93rpd0UNCBmGRhRSEAYe/XjDm/7FzIt0HmQBW0d66TdHL0zLyAgvFf2Lc9L1hR\nMMHIAJqshfW9go7E/GsEHAtk/Bh0JCYJ2JiCCYQcLdDjbHj1vZI5hKafPhVjPVeg8F7Iu6/UcrYd\nphYbUzCpqyM2npBMvsAZW6hXFHQkJmBWFAIQ9n7NmPLriJ20lky24vxiW48XsTGF2s2Kgkm4Hwp/\ngEbAxmODDsVEm3MT9B6D/VRb7WZFIQA5OTlBh+CrqvLLXZ0L+Tg/Um+Sx3f9QQWy4+qSTmph3/a8\nYFulSbjc/FxYHXQU5kDiXP6i9+igAzEBsqIQgLD3a1aVX26+u6dgks/CYVDnA8j8IehIfBH2bc8L\nVhRMQn23/TuKdhc5V+o0yWdXY+eosJ7jgo7EBMTOUzAJNX7+eD5c8SFvXPQGSXm8fm09TyF6Xpu5\ncOFv4IlVdp5CirHzFEzKyc3P5bRsOxQ1qf1wHOxtCO2DDsQEwYpCAMLer1lRfqrKzNUzGdDRTlpL\nbp/A/OHQI+g4vBf2bc8LVhRMwqzctpJ9uo/OzToHHYqpysLLoAvs2L0j6EhMgllRCEDYj5WuKL/c\n1U7XkfP7zCZ55UBRa1gLb/3nraCD8VTYtz0vWFEwCTMzf6aNJ6SS+TB+wfigozAJFlhREJF8EVko\nIvNEZE5QcQQh7P2a5eWnquSuzuX0TqcnPiBTTXnOn29gwYYFfLf9u0Cj8VLYtz0vBLmnoECOqvZU\n1T4BxmHnXtWbAAAOxElEQVQSYOnmpWTUzSA7KzvoUEysiuGirhfxysJXgo7EJFDQ3Ue1snM57P2a\n5eVnRx2lkpzIvcuPvZyXF74cmvMVwr7teaFOgM+twMciUgw8q6pjA4zF+CQ3N5dNmzbx8vcvc0Lm\nCbz++utBh2Sq4cRDT6RYi5n7w1x6t+0ddDgmAYIsCv1Udb2IHAJMF5FlqvpZyYPDhw8nOzsbgKys\nLHr06BGp8iX9gqk6/fjjj4cqn8ryu/POPzJ/4Y/s+fVSlua2YsKOTRQWvkFpeTFO51QwXTKvoul4\n11/V8yXL+mN9vqrW/zglJymkpaXBsdDnlT5Qwchfbm6us/Ykef9VNh09ppAM8XiRz/jx4wEin5fx\nSorLXIjISKBIVf/mTof6Mhd5eXmh3o2Nzu+4407n6/UXwq+fhKeXApCe3oDi4l0k/eUePF9XqsSa\nh1Mw3HlNV8LVfeFvm2Hfge1SaVsN+7aXspe5EJEMEcl07zcCBgKLgoglCGF+U0I5+XWcZz+9mVJy\nSk9uOwy2dIbDAwnGU2Hf9rwQ1EBzS+AzEZkPzAbeU9WPAorF+K3jfCsKqW7hMLAfyqsVAikKqrpa\nVXu4t26q+lAQcQQl7MdKR+e3T/ZB+yWQf2pwAZlqyjtw1pKL4TCgwfZEB+OpsG97Xgj6kFQTcjub\n/gRb28DPBwcdionHz81gFdB1UtCRGJ8lxUBzWWEfaK5N2lzSifVbj4eP9h9xZAPNKRrrUQIn9ofx\nn5RaxrbV5JGyA82m9vipxRZYcXzQYRgvfAu0WAJZ+UFHYnxkRSEAYe/XLMlv689b+TlzB6zpHmxA\nppryyp9djDO20P0fiQzGU2Hf9rxgRcH45uNVH3PQ1izYWy/oUIxXFlwOx77EgV1NJiysKAQg7MdK\nl+Q3dcVUGm9qFmwwpgZyKn7o+xNAFNp+mbBovBT2bc8LVhSML1SVaSun0XiTHXUULgILfwvHvBx0\nIMYnVhQCEPZ+zby8PBZvWkyDOg2ov6Nh0OGYasur/OGFv4Vur0PanoRE46Wwb3tesKJgfDF1xVQG\nHTYIqZ1XRw+3bZ1gyxFw+NSgIzE+sKIQgLD3a+bk5DBl+RTO6nxW0KGYGsmpepEFw+DY1OtCCvu2\n5wUrCsZzm3ZsYuHGhfbTm2G25GI4bBo0CDoQ4zUrCgEIe7/mI68+wsDDBtKgjn1ipKa8qhf5pSms\nOgO6+h6Mp8K+7XnBioLx3OdrPuf8o84POgzjtwV25dQwsqIQgDD3axbtLmJxxmIbT0hpObEttuJM\naA752/P9DMZTYd72vGJFwXjqo5UfceKhJ5LVICvoUIzfiuvBEnhl4StBR2I8ZEUhAGHu13xjyRt0\n29kt6DBMXPJiX3QhvLTgpZS5UmqYtz2vWFEwnincVciHKz7k1A72gzq1xvdQv059ZqyeEXQkxiNW\nFAIQ1n7Nt5e9Tf8O/Tlv0HlBh2LiklOtpW/uczNPzH7Cn1A8FtZtz0tWFIxnXl30Kpd1vyzoMEyC\nXXbMZcz6fhYrt64MOhTjASsKAQhjv+aGog3MXjebwUcODmV+tUtetZbOqJvBlT2u5Okvn/YnHA/Z\ne7NqVhSMJ16c9yIXdLmAjLoZQYdiAnBD7xuYsGAChbsKgw7FxMmKQgDC1q9ZvK+Y575+juuPvx4I\nX361T061W3TI6sDAwwYyZu4Y78PxkL03q2ZFwcRt2sppNM9oznFtjgs6FBOgu0++m0dnPcrOPTuD\nDsXEwYpCAMLWrzlm7pjIXgKEL7/aJ69Grbq37M5J7U7iua+e8zYcD9l7s2pWFExclm5eypx1cxja\nbWjQoZgk8L/9/5e//uuvtreQwqwoBCBM/ZoPf/EwN/e5udQAc5jyq51yatyyV+tenNTuJB6b9Zh3\n4XjI3ptVs6Jgaix/ez7vLX+PG/vcGHQoJon8+fQ/8+i/H2VD0YagQzE1YEUhAGHp17w3915uOP6G\nAy5+F5b8aq+8uFof1uwwruxxJXfPuNubcDxk782qWVEwNfL1+q+Zvmo6d/S7I+hQTBL6v1P/j49X\nfcyMVXZNpFRjRSEAqd6vuU/3ccvUWxh56kgy62ce8Hiq52dy4l5D4/qNeeacZ7hmyjXs2L0j/pA8\nYu/NqllRMNU2+svRFO8r5ppe1wQdikliZ3U+i/4d+nPThzelzKW1jRWFQKRyv+byLcsZlTeKF857\ngfS09HKXSeX8DMQ7phDtqbOeYs66OTz/9fOerTMe9t6sWp2gAzCpo3BXIUNeH8KDAx7kqOZHBR2O\nSQEH1TuIty5+i1NePIVOTTtxeqfTgw7JVEGScbdORDQZ46rNdhfvZsjrQ2hzUBvGDh4bc7vjjjud\nr7++G9j/YZCe3oDi4l1A9GssZabjmZes6wpnrLFsq5/kf8JFb17EO0PfoW+7vlUub2pGRFBViWcd\n1n1kqvTL3l/4zaTfUC+9HqPPHh10OCYFnZp9Ki8NeYnzJp7Hu9+8G3Q4phKBFAURGSQiy0TkWxG5\nM4gYgpRK/ZrrflrHqeNPpV56PV6/8HXqptetsk0q5WfKk+fLWgcdPoj3L32f69+/nntz72V38W5f\nnqcy9t6sWsKLgoikA08Bg4CuwCUi0iXRcQRp/vz5QYdQpT3Fe3h27rP0eLYH5x95PhMvmEi99Hox\ntU2F/Exl/Hv9erftzdxr5jJ/w3yOf+54pq6YmtAjk+y9WbUgBpr7ACtUNR9ARCYC5wH/CSCWQGzf\nvj3oECq0oWgDry9+nb/P/jsdsjow4/IZHNPymGqtI5nzM7Hw9/Vrndmad4a+w+T/TObWabeSWS+T\nq3pexcVHX0zThk19fW57b1YtiKLQFlgbNf09cEIAcdRqxfuK+XHnj6zevpqVW1fy1fqv+GLtF3zz\n4zcMPnIwLw15iZPbnxx0mCakRIQLu17IkKOGMHXFVF6c/yIjpo+gS/MunNL+FLq16EaXQ7rQNrMt\nLRq1oH6d+kGHXGsEURRq9WFFH377Ic/PfJ45neeg7r9CVVG03L9AhY/FukzJc+wu3k3BrgK2/7Kd\nnXt20rRBUzo17UTHph3p0bIHf/mvv9CnbR8a1m0YV475+fmR+3XqQEbGPdSp83hkXmFh4vuSTXXk\nJ+yZ0tPSOfuIszn7iLPZtXcXs9fN5os1X5Cbn8vouaNZX7ieTTs2kVE3g8z6mTSs05AGdRrQsG5D\n6qfXJ03SEBEEqfB+yV+A+TPnM/eIuTWO98EBD3Jsq2O9Sj8pJfyQVBE5ERilqoPc6buAfar6cNQy\ntbpwGGNMTcV7SGoQRaEO8A3Oges/AHOAS1S11owpGGNMskp495Gq7hWRm4BpQDowzgqCMcYkh6Q8\no9kYY0wwAjujWUSaich0EVkuIh+JSFYFy70gIhtFZFFN2gelGvmVeyKfiIwSke9FZJ57G5S46CsW\ny4mHIvKE+/gCEelZnbZBijO3fBFZ6L5WcxIXdeyqyk9EjhKRWSLyi4jcXp22ySDO/MLw+l3mvi8X\nisgXInJMrG1LUdVAbsBfgDvc+3cCf65guVOAnsCimrRP5vxwus9WANlAXZyzhrq4j40Ebgs6j1jj\njVrmLOAD9/4JwL9jbZuqubnTq4FmQecRZ36HAMcDfwRur07boG/x5Bei168v0MS9P6im216Q1z4a\nDExw708Azi9vIVX9DNhW0/YBiiW+yIl8qroHKDmRr0RcRxH4oKp4ISpvVZ0NZIlIqxjbBqmmubWM\nejzZXq9oVeanqptVdS6wp7ptk0A8+ZVI9ddvlqoWuJOzgUNjbRstyKLQUlU3uvc3Ai0rW9iH9n6L\nJb7yTuRrGzX9e3d3cFySdI9VFW9ly7SJoW2Q4skNnPNvPhaRuSKSjL8+FEt+frRNlHhjDNvrdxXw\nQU3a+nr0kYhMB1qV89A90ROqqvGcmxBv+5ryIL/KYh4D3O/efwD4G84LHaRY/8fJ/I2rIvHmdrKq\n/iAihwDTRWSZu5ebLOLZPlLhaJR4Y+ynquvD8PqJyGnAlUC/6rYFn4uCqp5R0WPu4HErVd0gIq2B\nTdVcfbzt4+ZBfuuAdlHT7XCqOKoaWV5EngemeBN1XCqMt5JlDnWXqRtD2yDVNLd1AKr6g/t3s4i8\njbPLnkwfKrHk50fbRIkrRlVd7/5N6dfPHVweCwxS1W3VaVsiyO6jd4Er3PtXAP9McHu/xRLfXKCz\niGSLSD3gN2473EJSYgiwqJz2iVZhvFHeBS6HyNnr291utFjaBqnGuYlIhohkuvMbAQNJjtcrWnX+\n/2X3hpL9tYM48gvL6yci7YG3gN+q6orqtC0lwNH0ZsDHwHLgIyDLnd8GeD9quddwznzehdMv9rvK\n2ifLrRr5nYlzhvcK4K6o+S8BC4EFOAWlZdA5VRQvcB1wXdQyT7mPLwB6VZVrstxqmhvQCeeIjvnA\n4mTMLZb8cLpC1wIFOAd3rAEOSoXXLp78QvT6PQ9sAea5tzmVta3oZievGWOMibCf4zTGGBNhRcEY\nY0yEFQVjjDERVhSMMcZEWFEwxhgTYUXBGGNMhBUFU6uJyD4ReTlquo6IbBaRZDiD3JiEs6Jgarsd\nwNEi0sCdPgPnEgB2Ao+plawoGONcTfJs9/4lOGfRCziXPRDnh55mi8jXIjLYnZ8tIp+KyFfura87\nP0dE8kTkTRH5j4i8EkRCxtSUFQVj4HVgqIjUB7rjXIu+xD3ADFU9ARgA/FVEMnAuh36Gqh4HDAWe\niGrTA7gF6Ap0EpF+GJMifL1KqjGpQFUXiUg2zl7C+2UeHgicKyIj3On6OFeZ3AA8JSLHAsVA56g2\nc9S9aqqIzMf5xasv/IrfGC9ZUTDG8S7wCHAqzs82Rvu1qn4bPUNERgHrVXWYiKQDv0Q9vCvqfjG2\nnZkUYt1HxjheAEap6pIy86cBN5dMiEhP925jnL0FcC6nne57hMYkgBUFU9spgKquU9WnouaVHH30\nAFBXRBaKyGLgPnf+aOAKt3voSKCo7DormTYmadmls40xxkTYnoIxxpgIKwrGGGMirCgYY4yJsKJg\njDEmwoqCMcaYCCsKxhhjIqwoGGOMibCiYIwxJuL/A9SD8Qqr/oxCAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f469a9edc90>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Plot a histogram and kernel density estimate for the scattering rates\n",
|
|
"scatter['mean'].plot(kind='hist', bins=25)\n",
|
|
"scatter['mean'].plot(kind='kde')\n",
|
|
"pylab.title('Scattering Rates')\n",
|
|
"pylab.xlabel('Mean')\n",
|
|
"pylab.legend(['KDE', 'Histogram'])"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 2",
|
|
"language": "python",
|
|
"name": "python2"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.10"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|