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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we will go through the salient features of the `openmc.data` package in the Python API. This package enables inspection, analysis, and conversion of nuclear data from ACE files. Most importantly, the package provides a mean to generate HDF5 nuclear data libraries that are used by the transport solver."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import os\n",
"from pprint import pprint\n",
"\n",
"import h5py\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm\n",
"from matplotlib.patches import Rectangle\n",
"\n",
"import openmc.data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"## Importing from HDF5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `openmc.data` module can read OpenMC's HDF5-formatted data into Python objects. The easiest way to do this is with the `openmc.data.IncidentNeutron.from_hdf5(...)` factory method. Replace the `filename` variable below with a valid path to an HDF5 data file on your computer."
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]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<IncidentNeutron: Gd157.71c>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get filename for Gd-157\n",
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"filename ='/home/smharper/nuclear-data/nndc-hdf5/Gd157_71c.h5'\n",
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"\n",
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"# Load HDF5 data into object\n",
"gd157 = openmc.data.IncidentNeutron.from_hdf5(filename)\n",
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"gd157"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"## Cross sections"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From Python, it's easy to explore (and modify) the nuclear data. Let's start off by reading the total cross section. Reactions are indexed using their \"MT\" number -- a unique identifier for each reaction defined by the ENDF-6 format. The MT number for the total cross section is 1."
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]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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"<Reaction: MT=1 (n,total)>"
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]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
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}
],
"source": [
"total = gd157[1]\n",
"total"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To find the cross section at a particular energy, 1 eV for example, simply call the reaction's `xs` attribute at that energy. Note that our nuclear data uses MeV as the unit of energy."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"142.6474702147809"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total.xs(1e-6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `xs` attribute can also be called on an array of energies."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 142.64747021, 38.65417611, 175.40019668])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total.xs([1e-6, 2e-6, 3e-6])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A quick way to plot cross sections is to use the `energy` attribute of `IncidentNeutron`. This gives an array of all the energy values used in cross section interpolation."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.00000000e-11, 1.03250000e-11, 1.06500000e-11, ...,\n",
" 1.95000000e+01, 1.99000000e+01, 2.00000000e+01])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gd157.energy"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f237f0f0810>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
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},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAETCAYAAAAlCTHcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngX+Y2UlJFkwq0zHHhM5
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f239c356b90>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
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"plt.loglog(gd157.energy, total.xs(gd157.energy))\n",
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"plt.xlabel('Energy (MeV)')\n",
"plt.ylabel('Cross section (b)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reaction Data\n",
"\n",
"Most of the interesting data for an `IncidentNeutron` instance is contained within the `reactions` attribute, which is a dictionary mapping MT values to `Reaction` objects."
]
},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"[<Reaction: MT=2 (n,elastic)>,\n",
" <Reaction: MT=16 (n,2n)>,\n",
" <Reaction: MT=17 (n,3n)>,\n",
" <Reaction: MT=22 (n,na)>,\n",
" <Reaction: MT=24 (n,2na)>,\n",
" <Reaction: MT=28 (n,np)>,\n",
" <Reaction: MT=41 (n,2np)>,\n",
" <Reaction: MT=51 (n,n1)>,\n",
" <Reaction: MT=52 (n,n2)>,\n",
" <Reaction: MT=53 (n,n3)>]\n"
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]
}
],
"source": [
"pprint(list(gd157.reactions.values())[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"Let's suppose we want to look more closely at the (n,2n) reaction. This reaction has an energy threshold"
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]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Threshold = 6.400881 MeV\n"
]
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}
],
"source": [
"n2n = gd157[16]\n",
"print('Threshold = {} MeV'.format(n2n.threshold))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The (n,2n) cross section, like all basic cross sections, is represented by the `Tabulated1D` class. The energy and cross section values in the table can be directly accessed with the `x` and `y` attributes. Using the `x` and `y` has the nice benefit of automatically acounting for reaction thresholds."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<openmc.data.function.Tabulated1D at 0x7f237f748650>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n2n.xs"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(6.400881, 20.0)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
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},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYpA6bdgA554Lv/udkoJ
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f237ef64c10>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(n2n.xs.x, n2n.xs.y)\n",
"plt.xlabel('Energy (MeV)')\n",
"plt.ylabel('Cross section (b)')\n",
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"plt.xlim((n2n.xs.x[0], n2n.xs.x[-1]))"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get information on the energy and angle distribution of the neutrons emitted in the reaction, we need to look at the `products` attribute."
]
},
{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<Product: neutron, emission=prompt, yield=2.0>,\n",
" <Product: photon, emission=prompt, yield=tabulated>]"
]
},
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"execution_count": 12,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n2n.products"
]
},
{
"cell_type": "code",
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"execution_count": 13,
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"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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"[<openmc.data.correlated.CorrelatedAngleEnergy at 0x7f237f766710>]"
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]
},
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"execution_count": 13,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"neutron = n2n.products[0]\n",
"neutron.distribution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that the neutrons emitted have a correlated angle-energy distribution. Let's look at the `energy_out` attribute to see what the outgoing energy distributions are."
]
},
{
"cell_type": "code",
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"execution_count": 14,
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"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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"[<openmc.stats.univariate.Tabular at 0x7f237f766790>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f766810>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f7668d0>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f766990>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f766b10>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f766d50>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6ed050>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6ed3d0>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6ed7d0>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6edc50>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6f6190>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6f6710>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6f6c90>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f700310>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f700990>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f70a0d0>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f70a810>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f70afd0>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f713810>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f71d050>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f71d8d0>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f7251d0>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f725ad0>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6af450>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6afdd0>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6b87d0>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6c2210>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6c2b50>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6ca590>,\n",
" <openmc.stats.univariate.Tabular at 0x7f237f6cafd0>]"
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]
},
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"execution_count": 14,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dist = neutron.distribution[0]\n",
"dist.energy_out"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we see we have a tabulated outgoing energy distribution for each incoming energy. Note that the same probability distribution classes that we could use to create a source definition are also used within the `openmc.data` package. Let's plot every fifth distribution to get an idea of what they look like."
]
},
{
"cell_type": "code",
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"execution_count": 15,
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"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEQCAYAAACjnUNyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9u3D7Nmzm7Ro3hHwKSk
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f237ef48610>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for e_in, e_out_dist in zip(dist.energy[::5], dist.energy_out[::5]):\n",
" plt.semilogy(e_out_dist.x, e_out_dist.p, label='E={:.2f} MeV'.format(e_in))\n",
"plt.ylim(ymax=10)\n",
"plt.legend()\n",
"plt.xlabel('Outgoing energy (MeV)')\n",
"plt.ylabel('Probability/MeV')\n",
"plt.show()"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is also `summed_reactions` attribute for cross sections (like total) which are built from summing up other cross sections."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[<Reaction: MT=101 (n,disappear)>,\n",
" <Reaction: MT=27 (n,absorption)>,\n",
" <Reaction: MT=4 (n,level)>,\n",
" <Reaction: MT=3>,\n",
" <Reaction: MT=1 (n,total)>]\n"
]
}
],
"source": [
"pprint(list(gd157.summed_reactions.values()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the cross sections for these reactions are represented by the `Sum` class rather than `Tabulated1D`. They do not support the `x` and `y` attributes."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<openmc.data.function.Sum at 0x7f237f7a9f90>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gd157[27].xs"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Unresolved resonance probability tables\n",
"\n",
"We can also look at unresolved resonance probability tables which are stored in a `ProbabilityTables` object. In the following example, we'll create a plot showing what the total cross section probability tables look like as a function of incoming energy."
]
},
{
"cell_type": "code",
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"execution_count": 18,
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"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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"<matplotlib.text.Text at 0x7f237eccbf10>"
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]
},
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"execution_count": 18,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAETCAYAAAAYm1C6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nx+PJKx9IIBCAyWSKOw9
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f237ecb7a10>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"cm = matplotlib.cm.Spectral_r\n",
"\n",
"# Determine size of probability tables\n",
"urr = gd157.urr\n",
"n_energy = urr.table.shape[0]\n",
"n_band = urr.table.shape[2]\n",
"\n",
"for i in range(n_energy):\n",
" # Get bounds on energy\n",
" if i > 0:\n",
" e_left = urr.energy[i] - 0.5*(urr.energy[i] - urr.energy[i-1])\n",
" else:\n",
" e_left = urr.energy[i] - 0.5*(urr.energy[i+1] - urr.energy[i])\n",
"\n",
" if i < n_energy - 1:\n",
" e_right = urr.energy[i] + 0.5*(urr.energy[i+1] - urr.energy[i])\n",
" else:\n",
" e_right = urr.energy[i] + 0.5*(urr.energy[i] - urr.energy[i-1])\n",
" \n",
" for j in range(n_band):\n",
" # Determine maximum probability for a single band\n",
" max_prob = np.diff(urr.table[i,0,:]).max()\n",
" \n",
" # Determine bottom of band\n",
" if j > 0:\n",
" xs_bottom = urr.table[i,1,j] - 0.5*(urr.table[i,1,j] - urr.table[i,1,j-1])\n",
" value = (urr.table[i,0,j] - urr.table[i,0,j-1])/max_prob\n",
" else:\n",
" xs_bottom = urr.table[i,1,j] - 0.5*(urr.table[i,1,j+1] - urr.table[i,1,j])\n",
" value = urr.table[i,0,j]/max_prob\n",
"\n",
" # Determine top of band\n",
" if j < n_band - 1:\n",
" xs_top = urr.table[i,1,j] + 0.5*(urr.table[i,1,j+1] - urr.table[i,1,j])\n",
" else:\n",
" xs_top = urr.table[i,1,j] + 0.5*(urr.table[i,1,j] - urr.table[i,1,j-1])\n",
" \n",
" # Draw rectangle with appropriate color\n",
" ax.add_patch(Rectangle((e_left, xs_bottom), e_right - e_left, xs_top - xs_bottom,\n",
" color=cm(value)))\n",
"\n",
"# Overlay total cross section\n",
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"ax.plot(gd157.energy, total.xs(gd157.energy), 'k')\n",
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"\n",
"# Make plot pretty and labeled\n",
"ax.set_xlim(1e-6, 1e-1)\n",
"ax.set_ylim(1e-1, 1e4)\n",
"ax.set_xscale('log')\n",
"ax.set_yscale('log')\n",
"ax.set_xlabel('Energy (MeV)')\n",
"ax.set_ylabel('Cross section(b)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"## Converting ACE to HDF5\n",
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"\n",
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"The `openmc.data` package can also read ACE files and output HDF5 files. ACE files can be read with the `openmc.data.IncidentNeutron.from_ace(...)` factory method."
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]
},
{
"cell_type": "code",
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"execution_count": 19,
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"metadata": {
"collapsed": false
},
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"outputs": [
{
"data": {
"text/plain": [
"<IncidentNeutron: Gd157.71c>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filename = '/home/smharper/nuclear-data/nndc/293.6K/Gd_157_293.6K.ace'\n",
"gd157_ace = openmc.data.IncidentNeutron.from_ace(filename)\n",
"gd157_ace"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can store this formerly ACE data as HDF5 with the `export_to_hdf5()` method."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
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"outputs": [],
"source": [
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"gd157_ace.export_to_hdf5('gd157.h5', 'w')"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"With few exceptions, the HDF5 file encodes the same data as the ACE file."
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]
},
{
"cell_type": "code",
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"execution_count": 21,
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"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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"array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
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]
},
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"execution_count": 21,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"gd157_reconstructed = openmc.data.IncidentNeutron.from_hdf5('gd157.h5')\n",
"gd157_ace[16].xs.y - gd157_reconstructed[16].xs.y"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"And one of the best parts of using HDF5 is that it is a widely used format with lots of third-party support. You can use `h5py`, for example, to inspect the data."
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]
},
{
"cell_type": "code",
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"execution_count": 22,
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"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"reaction_002, (n,elastic)\n",
"reaction_016, (n,2n)\n",
"reaction_017, (n,3n)\n",
"reaction_022, (n,na)\n",
"reaction_024, (n,2na)\n",
"reaction_028, (n,np)\n",
"reaction_041, (n,2np)\n",
"reaction_051, (n,n1)\n",
"reaction_052, (n,n2)\n",
"reaction_053, (n,n3)\n"
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]
}
],
"source": [
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"h5file = h5py.File('gd157.h5', 'r')\n",
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"main_group = h5file['Gd157.71c/reactions']\n",
"for name, obj in sorted(list(main_group.items()))[:10]:\n",
" if 'reaction_' in name:\n",
" print('{}, {}'.format(name, obj.attrs['label'].decode()))"
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]
},
{
"cell_type": "code",
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"execution_count": 23,
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"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"[<HDF5 dataset \"xs\": shape (37,), type \"<f8\">,\n",
" <HDF5 group \"/Gd157.71c/reactions/reaction_016/product_0\" (2 members)>,\n",
" <HDF5 group \"/Gd157.71c/reactions/reaction_016/product_1\" (2 members)>]\n"
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]
}
],
"source": [
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"n2n_group = main_group['reaction_016']\n",
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"pprint(list(n2n_group.values()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we see that the hierarchy of data within the HDF5 mirrors the hierarchy of Python objects that we manipulated before."
]
},
{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {
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"collapsed": false,
"scrolled": true
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},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.00000000e+00, 3.02679600e-13, 1.29110100e-02,\n",
" 6.51111000e-02, 3.92627000e-01, 5.75226800e-01,\n",
" 6.96960000e-01, 7.39937800e-01, 9.63545000e-01,\n",
" 1.14213000e+00, 1.30802000e+00, 1.46350000e+00,\n",
" 1.55760000e+00, 1.64055000e+00, 1.68896000e+00,\n",
" 1.71140000e+00, 1.73945000e+00, 1.78207000e+00,\n",
" 1.81665000e+00, 1.84528000e+00, 1.86540900e+00,\n",
" 1.86724000e+00, 1.88155800e+00, 1.88156000e+00,\n",
" 1.88180000e+00, 1.89447000e+00, 1.86957000e+00,\n",
" 1.82120000e+00, 1.71600000e+00, 1.60054000e+00,\n",
" 1.43162000e+00, 1.28346000e+00, 1.10166000e+00,\n",
" 1.06530000e+00, 9.30730000e-01, 8.02980000e-01,\n",
" 7.77740000e-01])"
]
},
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"execution_count": 24,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n2n_group['xs'].value"
]
}
],
"metadata": {
"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"version": 2
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
"version": "2.7.12"
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}
},
"nbformat": 4,
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