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.
In [1]:
%matplotlib inline
import os
from pprint import pprint
import h5py
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm
from matplotlib.patches import Rectangle
import openmc.data
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.
In [2]:
# Get filename for Gd-157
filename ='/home/romano/openmc/scripts/nndc_hdf5/Gd157.h5'
# Load HDF5 data into object
gd157 = openmc.data.IncidentNeutron.from_hdf5(filename)
gd157
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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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total = gd157[1]
total
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Cross sections for each reaction can be stored at multiple temperatures. To see what temperatures are available, we can look at the reaction's xs attribute.
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total.xs
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To find the cross section at a particular energy, 1 eV for example, simply get the cross section at the appropriate temperature and then call it as a function. Note that our nuclear data uses eV as the unit of energy.
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total.xs['294K'](1.0)
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The xs attribute can also be called on an array of energies.
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total.xs['294K']([1.0, 2.0, 3.0])
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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 for each temperature present.
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gd157.energy
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In [8]:
energies = gd157.energy['294K']
total_xs = total.xs['294K'](energies)
plt.loglog(energies, total_xs)
plt.xlabel('Energy (eV)')
plt.ylabel('Cross section (b)')
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In [9]:
pprint(list(gd157.reactions.values())[:10])
Let's suppose we want to look more closely at the (n,2n) reaction. This reaction has an energy threshold
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n2n = gd157[16]
print('Threshold = {} eV'.format(n2n.xs['294K'].x[0]))
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.
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n2n.xs
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In [12]:
xs = n2n.xs['294K']
plt.plot(xs.x, xs.y)
plt.xlabel('Energy (eV)')
plt.ylabel('Cross section (b)')
plt.xlim((xs.x[0], xs.x[-1]))
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To get information on the energy and angle distribution of the neutrons emitted in the reaction, we need to look at the products attribute.
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n2n.products
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In [14]:
neutron = n2n.products[0]
neutron.distribution
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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.
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dist = neutron.distribution[0]
dist.energy_out
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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.
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for e_in, e_out_dist in zip(dist.energy[::5], dist.energy_out[::5]):
plt.semilogy(e_out_dist.x, e_out_dist.p, label='E={:.2f} MeV'.format(e_in/1e6))
plt.ylim(ymax=1e-6)
plt.legend()
plt.xlabel('Outgoing energy (eV)')
plt.ylabel('Probability/eV')
plt.show()
There is also summed_reactions attribute for cross sections (like total) which are built from summing up other cross sections.
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pprint(list(gd157.summed_reactions.values()))
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.
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gd157[27].xs
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In [19]:
fig = plt.figure()
ax = fig.add_subplot(111)
cm = matplotlib.cm.Spectral_r
# Determine size of probability tables
urr = gd157.urr['294K']
n_energy = urr.table.shape[0]
n_band = urr.table.shape[2]
for i in range(n_energy):
# Get bounds on energy
if i > 0:
e_left = urr.energy[i] - 0.5*(urr.energy[i] - urr.energy[i-1])
else:
e_left = urr.energy[i] - 0.5*(urr.energy[i+1] - urr.energy[i])
if i < n_energy - 1:
e_right = urr.energy[i] + 0.5*(urr.energy[i+1] - urr.energy[i])
else:
e_right = urr.energy[i] + 0.5*(urr.energy[i] - urr.energy[i-1])
for j in range(n_band):
# Determine maximum probability for a single band
max_prob = np.diff(urr.table[i,0,:]).max()
# Determine bottom of band
if j > 0:
xs_bottom = urr.table[i,1,j] - 0.5*(urr.table[i,1,j] - urr.table[i,1,j-1])
value = (urr.table[i,0,j] - urr.table[i,0,j-1])/max_prob
else:
xs_bottom = urr.table[i,1,j] - 0.5*(urr.table[i,1,j+1] - urr.table[i,1,j])
value = urr.table[i,0,j]/max_prob
# Determine top of band
if j < n_band - 1:
xs_top = urr.table[i,1,j] + 0.5*(urr.table[i,1,j+1] - urr.table[i,1,j])
else:
xs_top = urr.table[i,1,j] + 0.5*(urr.table[i,1,j] - urr.table[i,1,j-1])
# Draw rectangle with appropriate color
ax.add_patch(Rectangle((e_left, xs_bottom), e_right - e_left, xs_top - xs_bottom,
color=cm(value)))
# Overlay total cross section
ax.plot(gd157.energy['294K'], total.xs['294K'](gd157.energy['294K']), 'k')
# Make plot pretty and labeled
ax.set_xlim(1.0, 1.0e5)
ax.set_ylim(1e-1, 1e4)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('Energy (eV)')
ax.set_ylabel('Cross section(b)')
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In [20]:
filename = '/opt/data/ace/nndc/293.6K/Gd_157_293.6K.ace'
gd157_ace = openmc.data.IncidentNeutron.from_ace(filename)
gd157_ace
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We can store this formerly ACE data as HDF5 with the export_to_hdf5() method.
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gd157_ace.export_to_hdf5('gd157.h5', 'w')
With few exceptions, the HDF5 file encodes the same data as the ACE file.
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gd157_reconstructed = openmc.data.IncidentNeutron.from_hdf5('gd157.h5')
gd157_ace[16].xs['294K'].y - gd157_reconstructed[16].xs['294K'].y
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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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h5file = h5py.File('gd157.h5', 'r')
main_group = h5file['Gd157/reactions']
for name, obj in sorted(list(main_group.items()))[:10]:
if 'reaction_' in name:
print('{}, {}'.format(name, obj.attrs['label'].decode()))
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n2n_group = main_group['reaction_016']
pprint(list(n2n_group.values()))
So we see that the hierarchy of data within the HDF5 mirrors the hierarchy of Python objects that we manipulated before.
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n2n_group['294K/xs'].value
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