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cleaned up mdgxs.py

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Sam Shaner 2016-07-31 09:56:36 -04:00
commit bc8a346a97
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2
docs/source/pythonapi/examples/mgxs-part-iii.ipynb
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@ -574,7 +574,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports `\"material,\"` `\"cell,\"` and `\"universe\"` domain types. We will use a `\"cell\"` domain type here to compute cross sections in each of the cells in the fuel assembly geometry.\n",
"Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports `\"material\"`, `\"cell\"`, `\"universe\"`, and `\"mesh\"` domain types. We will use a `\"cell\"` domain type here to compute cross sections in each of the cells in the fuel assembly geometry.\n",
"\n",
"**Note:** By default, the `Library` class will instantiate `MGXS` objects for each and every domain (material, cell or universe) in the geometry of interest. However, one may specify a subset of these domains to the `Library.domains` property. In our case, we wish to compute multi-group cross sections in each and every cell since they will be needed in our downstream OpenMOC calculation on the identical combinatorial geometry mesh."
]

14
docs/source/pythonapi/examples/mgxs-part-iv.ipynb
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@ -519,9 +519,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports \"material,\" \"cell,\" and \"universe\" domain types. In this simple example, we wish to compute multi-group cross sections only for each material andtherefore will use a \"material\" domain type.\n",
"Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports \"material\" \"cell\", \"universe\", and \"mesh\" domain types. In this simple example, we wish to compute multi-group cross sections only for each material andtherefore will use a \"material\" domain type.\n",
"\n",
"**Note:** By default, the `Library` class will instantiate `MGXS` objects for each and every domain (material, cell or universe) in the geometry of interest. However, one may specify a subset of these domains to the `Library.domains` property."
"**Note:** By default, the `Library` class will instantiate `MGXS` objects for each and every domain (material, cell, universe, or mesh cell) in the geometry of interest. However, one may specify a subset of these domains to the `Library.domains` property."
]
},
{
@ -1437,21 +1437,21 @@
],
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"display_name": "Python 3",
"display_name": "Python 2",
"language": "python",
"name": "python3"
"name": "python2"
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"name": "ipython",
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},
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"mimetype": "text/x-python",
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3
docs/source/pythonapi/index.rst
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@ -269,13 +269,16 @@ Multi-group Cross Sections
openmc.mgxs.AbsorptionXS
openmc.mgxs.CaptureXS
openmc.mgxs.Chi
openmc.mgxs.ChiPrompt
openmc.mgxs.FissionXS
openmc.mgxs.InverseVelocity
openmc.mgxs.KappaFissionXS
openmc.mgxs.MultiplicityMatrixXS
openmc.mgxs.NuFissionXS
openmc.mgxs.NuFissionMatrixXS
openmc.mgxs.NuScatterXS
openmc.mgxs.NuScatterMatrixXS
openmc.mgxs.PromptNuFissionXS
openmc.mgxs.ScatterXS
openmc.mgxs.ScatterMatrixXS
openmc.mgxs.TotalXS

4
openmc/mgxs/groups.py
View file

@ -1,5 +1,5 @@
from collections import Iterable
from numbers import Real
from numbers import Real, Integral
import copy
import sys
@ -370,7 +370,7 @@ class DelayedGroups(object):
@groups.setter
def groups(self, groups):
cv.check_type('groups', groups, Iterable, Int)
cv.check_type('groups', groups, Iterable, Integral)
cv.check_greater_than('number of delayed groups', len(groups), 0)
# Check that the groups are within [1, MAX_DELAYED_GROUPS]

9
openmc/mgxs/library.py
View file

@ -56,7 +56,7 @@ class Library(object):
The types of cross sections in the library (e.g., ['total', 'scatter'])
domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'}
Domain type for spatial homogenization
domains : Iterable of openmc.Material, openmc.Cell, openmc.Universe, or
domains : Iterable of openmc.Material, openmc.Cell, openmc.Universe or
openmc.Mesh
The spatial domain(s) for which MGXS in the Library are computed
correction : {'P0', None}
@ -184,10 +184,8 @@ class Library(object):
return self.openmc_geometry.get_all_material_cells()
elif self.domain_type == 'universe':
return self.openmc_geometry.get_all_universes()
# FIXME: Change to get tuples of all domain cells
elif self.domain_type == 'mesh':
raise ValueError('Unable to get all domains for a mesh domain ' +
'type. The domains must be set to [openmc.Mesh]')
raise ValueError('Unable to get domains for Mesh domain type')
else:
raise ValueError('Unable to get domains without a domain type')
else:
@ -290,6 +288,9 @@ class Library(object):
all_domains = self.openmc_geometry.get_all_universes()
elif self.domain_type == 'mesh':
cv.check_iterable_type('domain', domains, openmc.Mesh)
# The mesh and geometry are independent, so set all_domains
# to the input domains
all_domains = domains
else:
raise ValueError('Unable to set domains with domain '

263
openmc/mgxs/mdgxs.py
View file

@ -13,6 +13,8 @@ import numpy as np
from mgxs import MGXS, MGXS_TYPES, DOMAIN_TYPES, _DOMAINS
from openmc.mgxs import EnergyGroups, DelayedGroups
from openmc import Mesh
import openmc
import openmc.checkvalue as cv
# Supported cross section types
MDGXS_TYPES = ['delayed-nu-fission',
@ -145,19 +147,23 @@ class MDGXS(MGXS):
@delayed_groups.setter
def delayed_groups(self, delayed_groups):
cv.check_type('delayed groups', delayed_groups, openmc.mgxs.DelayedGroups)
cv.check_type('delayed groups', delayed_groups,
openmc.mgxs.DelayedGroups)
self._delayed_groups = delayed_groups
@property
def filters(self):
# Create the non-domain specific Filters for the Tallies
group_edges = self.energy_groups.group_edges
energy_filter = openmc.Filter('energy', group_edges)
if self.delayed_groups != None:
delayed_groups = self.delayed_groups.groups
delayed_filter = openmc.Filter('delayedgroup', delayed_groups)
return [[delayed_filter, energy_filter]] * len(self.scores)
return [[energy_filter], [delayed_filter, energy_filter]]
else:
return [[energy]] * len(self.scores)
return [[energy_filter], [energy_filter]]
@staticmethod
def get_mgxs(mdgxs_type, domain=None, domain_type=None,
@ -171,10 +177,10 @@ class MDGXS(MGXS):
Parameters
----------
mdgxs_type : {'delayed-nu-fission', 'chi-prompt', 'chi-delayed',
'beta'}
mdgxs_type : {'delayed-nu-fission', 'chi-delayed', 'beta'}
The type of multi-delayed-group cross section object to return
domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh
domain : openmc.Material or openmc.Cell or openmc.Universe or
openmc.Mesh
The domain for spatial homogenization
domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'}
The domain type for spatial homogenization
@ -200,7 +206,7 @@ class MDGXS(MGXS):
cv.check_value('mdgxs_type', mdgxs_type, MDGXS_TYPES)
if mdgxs_type == 'delayed-nu-fission':
mdgxs = DelayedNuFission(domain, domain_type, energy_groups)
mdgxs = DelayedNuFissionXS(domain, domain_type, energy_groups)
elif mdgxs_type == 'chi-delayed':
mdgxs = ChiDelayed(domain, domain_type, energy_groups)
elif mdgxs_type == 'beta':
@ -260,12 +266,20 @@ class MDGXS(MGXS):
cv.check_value('value', value, ['mean', 'std_dev', 'rel_err'])
cv.check_value('xs_type', xs_type, ['macro', 'micro'])
# FIXME: Unable to get microscopic xs for mesh domain because the mesh
# cells do not know the nuclide densities in each mesh cell.
if self.domain_type == 'mesh' and xs_type == 'micro':
msg = 'Unable to get micro xs for mesh domain since the mesh ' \
'cells do not know the nuclide densities in each mesh cell.'
raise ValueError(msg)
filters = []
filter_bins = []
# Construct a collection of the domain filter bins
if not isinstance(subdomains, basestring):
cv.check_iterable_type('subdomains', subdomains, Integral, max_depth=3)
cv.check_iterable_type('subdomains', subdomains, Integral,
max_depth=3)
for subdomain in subdomains:
filters.append(self.domain_type)
filter_bins.append((subdomain,))
@ -275,13 +289,14 @@ class MDGXS(MGXS):
cv.check_iterable_type('groups', groups, Integral)
for group in groups:
filters.append('energy')
filter_bins.append((self.energy_groups.get_group_bounds(group),))
filter_bins.append(
(self.energy_groups.get_group_bounds(group),))
# Construct list of delayed group tuples for all requested groups
if not isinstance(delayed_groups, basestring):
cv.check_iterable_type('delayed_groups', delayed_groups, Integral)
for delayed_group in delayed_groups:
filters.append('delayedgroups')
filters.append('delayedgroup')
filter_bins.append((delayed_group,))
# Construct a collection of the nuclides to retrieve from the xs tally
@ -299,7 +314,8 @@ class MDGXS(MGXS):
xs = xs_tally.get_values(filters=filters,
filter_bins=filter_bins, value=value)
else:
xs = self.xs_tally.get_values(filters=filters, filter_bins=filter_bins,
xs = self.xs_tally.get_values(filters=filters,
filter_bins=filter_bins,
nuclides=query_nuclides, value=value)
# Divide by atom number densities for microscopic cross sections
@ -460,12 +476,12 @@ class MDGXS(MGXS):
"""
merged_mdgxs = super(MDGXS, self).merge(other)
# Merge delayed groups
if self.delayed_groups != other.delayed_groups:
merged_delayed_groups = self.delayed_groups.merge(other.delayed_groups)
merged_delayed_groups = self.delayed_groups.merge(
other.delayed_groups)
merged_mdgxs.delayed_groups = merged_delayed_groups
return merged_mdgxs
@ -490,7 +506,7 @@ class MDGXS(MGXS):
"""
if self.delayed_groups != None:
if self.delayed_groups == None:
super(MDGXS, self).print_xs(subdomains, nuclides, xs_type)
return
@ -500,18 +516,8 @@ class MDGXS(MGXS):
elif self.domain_type == 'distribcell':
subdomains = np.arange(self.num_subdomains, dtype=np.int)
elif self.domain_type == 'mesh':
subdomains = []
if (len(self.domain.dimension) == 3):
nx, ny, nz = self.domain.dimension
for x in range(1,nx+1):
for y in range(1,ny+1):
for z in range(1,nz+1):
subdomains.append((x, y, z))
else:
nx, ny = self.domain.dimension
for x in range(1,nx+1):
for y in range(1,ny+1):
subdomains.append((x, y, 1))
xyz = [range(1, x+1) for x in self.domain.dimension]
subdomains = list(itertools.product(*xyz))
else:
subdomains = [self.domain.id]
@ -534,6 +540,9 @@ class MDGXS(MGXS):
string += '{0: <16}=\t{1}\n'.format('\tDomain Type', self.domain_type)
string += '{0: <16}=\t{1}\n'.format('\tDomain ID', self.domain.id)
# Generate the header for an individual XS
xs_header = '\tCross Sections [{0}]:'.format(self.get_units(xs_type))
# If cross section data has not been computed, only print string header
if self.tallies is None:
print(string)
@ -542,7 +551,7 @@ class MDGXS(MGXS):
# Loop over all subdomains
for subdomain in subdomains:
if self.domain_type == 'distribcell':
if self.domain_type == 'distribcell' or self.domain_type == 'mesh':
string += '{0: <16}=\t{1}\n'.format('\tSubdomain', subdomain)
# Loop over all Nuclides
@ -552,11 +561,8 @@ class MDGXS(MGXS):
if nuclide != 'sum':
string += '{0: <16}=\t{1}\n'.format('\tNuclide', nuclide)
# Build header for cross section type
if xs_type == 'macro':
string += '{0: <16}\n'.format('\tCross Sections [cm^-1]:')
else:
string += '{0: <16}\n'.format('\tCross Sections [barns]:')
# Add the cross section header
string += '{0: <16}\n'.format(xs_header)
for delayed_group in self.delayed_groups.groups:
@ -628,15 +634,14 @@ class MDGXS(MGXS):
df = self.get_pandas_dataframe(groups=groups, xs_type=xs_type,
delayed_groups=delayed_groups)
# Capitalize column label strings
#df.columns = df.columns.astype(str)
#df.columns = map(str.title, df.columns)
# Export the data using Pandas IO API
if format == 'csv':
df.to_csv(filename + '.csv', index=False)
elif format == 'excel':
df.to_excel(filename + '.xls', index=False)
if self.domain_type == 'mesh':
df.to_excel(filename + '.xls')
else:
df.to_excel(filename + '.xls', index=False)
elif format == 'pickle':
df.to_pickle(filename + '.pkl')
elif format == 'latex':
@ -664,7 +669,7 @@ class MDGXS(MGXS):
def get_pandas_dataframe(self, groups='all', nuclides='all',
xs_type='macro', distribcell_paths=True,
delayed_groups='all'):
"""Build a Pandas DataFrame for the MGXS data.
"""Build a Pandas DataFrame for the MDGXS data.
This method leverages :meth:`openmc.Tally.get_pandas_dataframe`, but
renames the columns with terminology appropriate for cross section data.
@ -699,8 +704,8 @@ class MDGXS(MGXS):
Raises
------
ValueError
When this method is called before the multi-group cross section is
computed from tally data.
When this method is called before the multi-delayed-group cross
section is computed from tally data.
"""
@ -719,35 +724,35 @@ class MDGXS(MGXS):
class ChiDelayed(MDGXS):
"""The delayed fission spectrum.
r"""The delayed fission spectrum.
This class can be used for both OpenMC input generation and tally data
post-processing to compute spatially-homogenized and energy-integrated
multi-group cross sections for multi-group neutronics calculations. At a
minimum, one needs to set the :attr:`ChiDelayed.energy_groups` and
:attr:`ChiDelayed.domain` properties. Tallies for the flux and appropriate
reaction rates over the specified domain are generated automatically via the
:attr:`ChiDelayed.tallies` property, which can then be appended to a
:class:`openmc.Tallies` instance.
multi-group and multi-delayed-group cross sections for multi-group
neutronics calculations. At a minimum, one needs to set the
:attr:`ChiDelayed.energy_groups` and :attr:`ChiDelayed.domain` properties.
Tallies for the flux and appropriate reaction rates over the specified
domain are generated automatically via the :attr:`ChiDelayed.tallies`
property, which can then be appended to a :class:`openmc.Tallies` instance.
For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the
For post-processing, the :meth:`MDGXS.load_from_statepoint` will pull in the
necessary data to compute multi-group cross sections from a
:class:`openmc.StatePoint` instance. The derived multi-group cross section
can then be obtained from the :attr:`ChiDelayed.xs_tally` property.
:class:`openmc.StatePoint` instance. The derived multi-group cross
section can then be obtained from the :attr:`ChiDelayed.xs_tally` property.
For a spatial domain :math:`V` and energy group :math:`[E_g,E_{g-1}]`, the
fission spectrum is calculated as:
For a spatial domain :math:`V`, energy group :math:`[E_g,E_{g-1}]`, and
delayed group :math:`d`, the delayed fission spectrum is calculated as:
.. math::
\langle \nu\sigma_{f,\rightarrow g} \phi \rangle &= \int_{r \in V} dr
\int_{4\pi} d\Omega' \int_0^\infty dE' \int_{E_g}^{E_{g-1}} dE \; \chi(E)
\nu\sigma_f (r, E') \psi(r, E', \Omega')\\
\langle \nu\sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi}
d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu\sigma_f (r,
\langle \nu^d \sigma_{f,g' \rightarrow g} \phi \rangle &= \int_{r \in V}
dr \int_{4\pi} d\Omega' \int_0^\infty dE' \int_{E_g}^{E_{g-1}} dE \;
\chi(E) \nu^d \sigma_f (r, E') \psi(r, E', \Omega')\\
\langle \nu^d \sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi}
d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu^d \sigma_f (r,
E') \psi(r, E', \Omega') \\
\chi_g &= \frac{\langle \nu\sigma_{f,\rightarrow g} \phi \rangle}{\langle
\nu\sigma_f \phi \rangle}
\chi_g^d &= \frac{\langle \nu^d \sigma_{f,g' \rightarrow g} \phi \rangle}
{\langle \nu^d \sigma_f \phi \rangle}
Parameters
----------
@ -948,8 +953,8 @@ class ChiDelayed(MDGXS):
# Slice nu-fission-out tally along energyout filter
delayed_nu_fission_out = slice_xs.tallies['delayed-nu-fission-out']
tally_slice = delayed_nu_fission_out.get_slice(filters=filters,
filter_bins=filter_bins)
tally_slice = delayed_nu_fission_out.get_slice\
(filters=filters, filter_bins=filter_bins)
slice_xs._tallies['delayed-nu-fission-out'] = tally_slice
# Add energy filter back to nu-fission-in tallies
@ -967,32 +972,33 @@ class ChiDelayed(MDGXS):
Parameters
----------
other : openmc.mgxs.MGXS
MGXS to merge with this one
other : openmc.mdgxs.MDGXS
MDGXS to merge with this one
Returns
-------
merged_mgxs : openmc.mgxs.MGXS
Merged MGXS
merged_mdgxs : openmc.mgxs.MDGXS
Merged MDGXS
"""
if not self.can_merge(other):
raise ValueError('Unable to merge ChiDelayed')
# Create deep copy of tally to return as merged tally
merged_mgxs = copy.deepcopy(self)
merged_mgxs._derived = True
merged_mgxs._rxn_rate_tally = None
merged_mgxs._xs_tally = None
merged_mdgxs = copy.deepcopy(self)
merged_mdgxs._derived = True
merged_mdgxs._rxn_rate_tally = None
merged_mdgxs._xs_tally = None
# Merge energy groups
if self.energy_groups != other.energy_groups:
merged_groups = self.energy_groups.merge(other.energy_groups)
merged_mgxs.energy_groups = merged_groups
merged_mdgxs.energy_groups = merged_groups
# Merge delayed groups
if self.delayed_groups != other.delayed_groups:
merged_delayed_groups = self.delayed_groups.merge(other.delayed_groups)
merged_delayed_groups = self.delayed_groups.merge\
(other.delayed_groups)
merged_mdgxs.delayed_groups = merged_delayed_groups
# Merge nuclides
@ -1005,22 +1011,24 @@ class ChiDelayed(MDGXS):
raise ValueError(msg)
# Concatenate lists of nuclides for the merged MGXS
merged_mgxs.nuclides = self.nuclides + other.nuclides
merged_mdgxs.nuclides = self.nuclides + other.nuclides
# Merge tallies
for tally_key in self.tallies:
merged_tally = self.tallies[tally_key].merge(other.tallies[tally_key])
merged_mgxs.tallies[tally_key] = merged_tally
merged_tally = self.tallies[tally_key].merge\
(other.tallies[tally_key])
merged_mdgxs.tallies[tally_key] = merged_tally
return merged_mgxs
return merged_mdgxs
def get_xs(self, groups='all', subdomains='all', nuclides='all',
xs_type='macro', order_groups='increasing',
value='mean', delayed_groups='all', **kwargs):
"""Returns an array of the fission spectrum.
"""Returns an array of the delayed fission spectrum.
This method constructs a 2D NumPy array for the requested multi-group
cross section data data for one or more energy groups and subdomains.
and multi-delayed group cross section data data for one or more energy
groups and subdomains.
Parameters
----------
@ -1037,7 +1045,7 @@ class ChiDelayed(MDGXS):
cross section summed over all nuclides. Defaults to 'all'.
xs_type: {'macro', 'micro'}
This parameter is not relevant for chi but is included here to
mirror the parent MGXS.get_xs(...) class method
mirror the parent MDGXS.get_xs(...) class method
order_groups: {'increasing', 'decreasing'}
Return the cross section indexed according to increasing or
decreasing energy groups (decreasing or increasing energies).
@ -1048,8 +1056,9 @@ class ChiDelayed(MDGXS):
Returns
-------
numpy.ndarray
A NumPy array of the multi-group cross section indexed in the order
each group, subdomain and nuclide is listed in the parameters.
A NumPy array of the multi-group and multi-delayed-group cross
section indexed in the order each group, subdomain and nuclide is
listed in the parameters.
Raises
------
@ -1062,12 +1071,20 @@ class ChiDelayed(MDGXS):
cv.check_value('value', value, ['mean', 'std_dev', 'rel_err'])
cv.check_value('xs_type', xs_type, ['macro', 'micro'])
# FIXME: Unable to get microscopic xs for mesh domain because the mesh
# cells do not know the nuclide densities in each mesh cell.
if self.domain_type == 'mesh' and xs_type == 'micro':
msg = 'Unable to get micro xs for mesh domain since the mesh ' \
'cells do not know the nuclide densities in each mesh cell.'
raise ValueError(msg)
filters = []
filter_bins = []
# Construct a collection of the domain filter bins
if not isinstance(subdomains, basestring):
cv.check_iterable_type('subdomains', subdomains, Integral, max_depth=3)
cv.check_iterable_type('subdomains', subdomains, Integral,
max_depth=3)
for subdomain in subdomains:
filters.append(self.domain_type)
filter_bins.append((subdomain,))
@ -1077,13 +1094,14 @@ class ChiDelayed(MDGXS):
cv.check_iterable_type('groups', groups, Integral)
for group in groups:
filters.append('energyout')
filter_bins.append((self.energy_groups.get_group_bounds(group),))
filter_bins.append(
(self.energy_groups.get_group_bounds(group),))
# Construct list of delayed group tuples for all requested groups
if not isinstance(delayed_groups, basestring):
cv.check_iterable_type('delayed_groups', delayed_groups, Integral)
for delayed_group in delayed_groups:
filters.append('delayedgroups')
filters.append('delayedgroup')
filter_bins.append((delayed_group,))
# If chi delayed was computed for each nuclide in the domain
@ -1099,8 +1117,10 @@ class ChiDelayed(MDGXS):
# Sum out all nuclides
nuclides = self.get_all_nuclides()
delayed_nu_fission_in = delayed_nu_fission_in.summation(nuclides=nuclides)
delayed_nu_fission_out = delayed_nu_fission_out.summation(nuclides=nuclides)
delayed_nu_fission_in = delayed_nu_fission_in.summation\
(nuclides=nuclides)
delayed_nu_fission_out = delayed_nu_fission_out.summation\
(nuclides=nuclides)
# Remove coarse energy filter to keep it out of tally arithmetic
energy_filter = delayed_nu_fission_in.find_filter('energy')
@ -1160,7 +1180,7 @@ class ChiDelayed(MDGXS):
class DelayedNuFissionXS(MDGXS):
"""A fission delayed neutron production multi-group cross section.
r"""A fission delayed neutron production multi-group cross section.
This class can be used for both OpenMC input generation and tally data
post-processing to compute spatially-homogenized and energy-integrated
@ -1172,18 +1192,19 @@ class DelayedNuFissionXS(MDGXS):
:attr:`DelayedNuFissionXS.tallies` property, which can then be appended to a
:class:`openmc.Tallies` instance.
For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the
For post-processing, the :meth:`MDGXS.load_from_statepoint` will pull in the
necessary data to compute multi-group cross sections from a
:class:`openmc.StatePoint` instance. The derived multi-group cross section
can then be obtained from the :attr:`DelayedNuFissionXS.xs_tally` property.
For a spatial domain :math:`V` and energy group :math:`[E_g,E_{g-1}]`, the
fission neutron production cross section is calculated as:
For a spatial domain :math:`V`, energy group :math:`[E_g,E_{g-1}]`, and
delayed group :math:`d`, the fission delayed neutron production cross
section is calculated as:
.. math::
\frac{\int_{r \in V} dr \int_{4\pi} d\Omega \int_{E_g}^{E_{g-1}} dE \;
\nu\sigma_f (r, E) \psi (r, E, \Omega)}{\int_{r \in V} dr \int_{4\pi}
\nu^d \sigma_f (r, E) \psi (r, E, \Omega)}{\int_{r \in V} dr \int_{4\pi}
d\Omega \int_{E_g}^{E_{g-1}} dE \; \psi (r, E, \Omega)}.
@ -1233,8 +1254,8 @@ class DelayedNuFissionXS(MDGXS):
The tally estimator used to compute the multi-group cross section
tallies : collections.OrderedDict
OpenMC tallies needed to compute the multi-group cross section. The keys
are strings listed in the :attr:`NuFissionXS.tally_keys` property and
values are instances of :class:`openmc.Tally`.
are strings listed in the :attr:`DelayedNuFissionXS.tally_keys` property
and values are instances of :class:`openmc.Tally`.
rxn_rate_tally : openmc.Tally
Derived tally for the reaction rate tally used in the numerator to
compute the multi-group cross section. This attribute is None
@ -1276,35 +1297,35 @@ class DelayedNuFissionXS(MDGXS):
class Beta(MDGXS):
"""The delayed neutron fraction.
r"""The delayed neutron fraction.
This class can be used for both OpenMC input generation and tally data
post-processing to compute spatially-homogenized and energy-integrated
multi-group cross sections for multi-group neutronics calculations. At a
minimum, one needs to set the :attr:`ChiDelayed.energy_groups` and
:attr:`ChiDelayed.domain` properties. Tallies for the flux and appropriate
reaction rates over the specified domain are generated automatically via the
:attr:`ChiDelayed.tallies` property, which can then be appended to a
:class:`openmc.Tallies` instance.
multi-group and multi-delayed group cross sections for multi-group
neutronics calculations. At a minimum, one needs to set the
:attr:`Beta.energy_groups` and :attr:`Beta.domain` properties. Tallies for
the flux and appropriate reaction rates over the specified domain are
generated automatically via the :attr:`Beta.tallies` property, which can
then be appended to a :class:`openmc.Tallies` instance.
For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the
For post-processing, the :meth:`MDGXS.load_from_statepoint` will pull in the
necessary data to compute multi-group cross sections from a
:class:`openmc.StatePoint` instance. The derived multi-group cross section
can then be obtained from the :attr:`ChiDelayed.xs_tally` property.
can then be obtained from the :attr:`Beta.xs_tally` property.
For a spatial domain :math:`V` and energy group :math:`[E_g,E_{g-1}]`, the
fission spectrum is calculated as:
For a spatial domain :math:`V`, energy group :math:`[E_g,E_{g-1}]`, and
delayed group :math:`d`, the delayed neutron fraction is calculated as:
.. math::
\langle \nu\sigma_{f,\rightarrow g} \phi \rangle &= \int_{r \in V} dr
\int_{4\pi} d\Omega' \int_0^\infty dE' \int_{E_g}^{E_{g-1}} dE \; \chi(E)
\nu\sigma_f (r, E') \psi(r, E', \Omega')\\
\langle \nu\sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi}
d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu\sigma_f (r,
E') \psi(r, E', \Omega') \\
\chi_g &= \frac{\langle \nu\sigma_{f,\rightarrow g} \phi \rangle}{\langle
\nu\sigma_f \phi \rangle}
\langle \nu^d \sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi}
d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu^d
\sigma_f (r, E') \psi(r, E', \Omega') \\
\langle \nu \sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi}
d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu
\sigma_f (r, E') \psi(r, E', \Omega') \\
\beta_{d,g} &= \frac{\langle \nu^d \sigma_f \phi \rangle}
{\langle \nu \sigma_f \phi \rangle}
Parameters
----------
@ -1352,7 +1373,7 @@ class Beta(MDGXS):
The tally estimator used to compute the multi-group cross section
tallies : collections.OrderedDict
OpenMC tallies needed to compute the multi-group cross section. The keys
are strings listed in the :attr:`ChiDelayed.tally_keys` property and
are strings listed in the :attr:`Beta.tally_keys` property and
values are instances of :class:`openmc.Tally`.
rxn_rate_tally : openmc.Tally
Derived tally for the reaction rate tally used in the numerator to
@ -1394,23 +1415,11 @@ class Beta(MDGXS):
@property
def scores(self):
return ['delayed-nu-fission', 'nu-fission']
@property
def filters(self):
# Create the non-domain specific Filters for the Tallies
group_edges = self.energy_groups.group_edges
energy = openmc.Filter('energy', [group_edges[0], group_edges[-1]])
if self.delayed_groups != None:
delayed_groups = self.delayed_groups.groups
delayed_filter = openmc.Filter('delayedgroup', delayed_groups)
return [[delayed_filter, energy], [energy]]
else:
return [[energy], [energy]]
return ['nu-fission', 'delayed-nu-fission']
@property
def tally_keys(self):
return ['delayed-nu-fission', 'nu-fission']
return ['nu-fission', 'delayed-nu-fission']
@property
def rxn_rate_tally(self):
@ -1425,7 +1434,7 @@ class Beta(MDGXS):
if self._xs_tally is None:
nu_fission = self.tallies['nu-fission']
# Compute chi
# Compute beta
self._xs_tally = self.rxn_rate_tally / nu_fission
super(Beta, self)._compute_xs()

79
openmc/mgxs/mgxs.py
View file

@ -14,7 +14,6 @@ import numpy as np
import openmc
import openmc.checkvalue as cv
from openmc.mgxs import EnergyGroups
from openmc import Mesh
if sys.version_info[0] >= 3:
basestring = str
@ -694,7 +693,7 @@ class MGXS(object):
# NOTE: This is important if tally merging was used
if self.domain_type == 'mesh':
filters = [self.domain_type]
xyz = map(lambda x: np.arange(1, x+1), self.domain.dimension)
xyz = [range(1, x+1) for x in self.domain.dimension]
filter_bins = [tuple(itertools.product(*xyz))]
elif self.domain_type != 'distribcell':
filters = [self.domain_type]
@ -717,8 +716,6 @@ class MGXS(object):
sp_tally = statepoint.get_tally(
tally.scores, tally.filters, tally.nuclides,
estimator=tally.estimator, exact_filters=True)
print(filters)
print(filter_bins)
sp_tally = sp_tally.get_slice(
tally.scores, filters, filter_bins, tally.nuclides)
sp_tally.sparse = self.sparse
@ -1159,7 +1156,7 @@ class MGXS(object):
elif self.domain_type == 'distribcell':
subdomains = np.arange(self.num_subdomains, dtype=np.int)
elif self.domain_type == 'mesh':
xyz = map(lambda x: np.arange(1, x+1), self.domain.dimension)
xyz = [range(1, x+1) for x in self.domain.dimension]
subdomains = list(itertools.product(*xyz))
else:
subdomains = [self.domain.id]
@ -1194,7 +1191,7 @@ class MGXS(object):
# Loop over all subdomains
for subdomain in subdomains:
if self.domain_type == 'distribcell':
if self.domain_type == 'distribcell' or self.domain_type == 'mesh':
string += '{0: <16}=\t{1}\n'.format('\tSubdomain', subdomain)
# Loop over all Nuclides
@ -1297,7 +1294,7 @@ class MGXS(object):
domain_filter = self.xs_tally.find_filter('avg(distribcell)')
subdomains = domain_filter.bins
elif self.domain_type == 'mesh':
xyz = map(lambda x: np.arange(1, x+1), self.domain.dimension)
xyz = [range(1, x+1) for x in self.domain.dimension]
subdomains = list(itertools.product(*xyz))
else:
subdomains = [self.domain.id]
@ -1409,7 +1406,10 @@ class MGXS(object):
if format == 'csv':
df.to_csv(filename + '.csv', index=False)
elif format == 'excel':
df.to_excel(filename + '.xls', index=False)
if self.domain_type == 'mesh':
df.to_excel(filename + '.xls')
else:
df.to_excel(filename + '.xls', index=False)
elif format == 'pickle':
df.to_pickle(filename + '.pkl')
elif format == 'latex':
@ -1750,6 +1750,13 @@ class MatrixMGXS(MGXS):
cv.check_value('value', value, ['mean', 'std_dev', 'rel_err'])
cv.check_value('xs_type', xs_type, ['macro', 'micro'])
# FIXME: Unable to get microscopic xs for mesh domain because the mesh
# cells do not know the nuclide densities in each mesh cell.
if self.domain_type == 'mesh' and xs_type == 'micro':
msg = 'Unable to get micro xs for mesh domain since the mesh ' \
'cells do not know the nuclide densities in each mesh cell.'
raise ValueError(msg)
filters = []
filter_bins = []
@ -1919,7 +1926,7 @@ class MatrixMGXS(MGXS):
elif self.domain_type == 'distribcell':
subdomains = np.arange(self.num_subdomains, dtype=np.int)
elif self.domain_type == 'mesh':
xyz = map(lambda x: np.arange(1, x+1), self.domain.dimension)
xyz = [range(1, x+1) for x in self.domain.dimension]
subdomains = list(itertools.product(*xyz))
else:
subdomains = [self.domain.id]
@ -3568,6 +3575,13 @@ class ScatterMatrixXS(MatrixMGXS):
cv.check_value('value', value, ['mean', 'std_dev', 'rel_err'])
cv.check_value('xs_type', xs_type, ['macro', 'micro'])
# FIXME: Unable to get microscopic xs for mesh domain because the mesh
# cells do not know the nuclide densities in each mesh cell.
if self.domain_type == 'mesh' and xs_type == 'micro':
msg = 'Unable to get micro xs for mesh domain since the mesh ' \
'cells do not know the nuclide densities in each mesh cell.'
raise ValueError(msg)
filters = []
filter_bins = []
@ -3718,9 +3732,12 @@ class ScatterMatrixXS(MatrixMGXS):
df['moment'] = moments
# Place the moment column before the mean column
mean_index = df.columns.get_loc('mean')
columns = df.columns.tolist()
df = df[columns[:mean_index] + ['moment'] + columns[mean_index:-1]]
mean_index = [i for i, s in enumerate(columns) if 'mean' in s][0]
if self.domain_type == 'mesh':
df = df[columns[:mean_index] + [('moment', '')] + columns[mean_index:-1]]
else:
df = df[columns[:mean_index] + ['moment'] + columns[mean_index:-1]]
# Select rows corresponding to requested scattering moment
if moment != 'all':
@ -3761,7 +3778,7 @@ class ScatterMatrixXS(MatrixMGXS):
elif self.domain_type == 'distribcell':
subdomains = np.arange(self.num_subdomains, dtype=np.int)
elif self.domain_type == 'mesh':
xyz = map(lambda x: np.arange(1, x+1), self.domain.dimension)
xyz = [range(1, x+1) for x in self.domain.dimension]
subdomains = list(itertools.product(*xyz))
else:
subdomains = [self.domain.id]
@ -4249,14 +4266,14 @@ class Chi(MGXS):
.. math::
\langle \nu\sigma_{f,\rightarrow g} \phi \rangle &= \int_{r \in V} dr
\langle \nu\sigma_{f,g' \rightarrow g} \phi \rangle &= \int_{r \in V} dr
\int_{4\pi} d\Omega' \int_0^\infty dE' \int_{E_g}^{E_{g-1}} dE \; \chi(E)
\nu\sigma_f (r, E') \psi(r, E', \Omega')\\
\langle \nu\sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi}
d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu\sigma_f (r,
E') \psi(r, E', \Omega') \\
\chi_g &= \frac{\langle \nu\sigma_{f,\rightarrow g} \phi \rangle}{\langle
\nu\sigma_f \phi \rangle}
\chi_g &= \frac{\langle \nu\sigma_{f,g' \rightarrow g} \phi \rangle}
{\langle \nu\sigma_f \phi \rangle}
Parameters
----------
@ -4539,6 +4556,13 @@ class Chi(MGXS):
cv.check_value('value', value, ['mean', 'std_dev', 'rel_err'])
cv.check_value('xs_type', xs_type, ['macro', 'micro'])
# FIXME: Unable to get microscopic xs for mesh domain because the mesh
# cells do not know the nuclide densities in each mesh cell.
if self.domain_type == 'mesh' and xs_type == 'micro':
msg = 'Unable to get micro xs for mesh domain since the mesh ' \
'cells do not know the nuclide densities in each mesh cell.'
raise ValueError(msg)
filters = []
filter_bins = []
@ -4732,14 +4756,14 @@ class ChiPrompt(Chi):
.. math::
\langle \nu\sigma_{f,\rightarrow g}^p \phi \rangle &= \int_{r \in V} dr
\int_{4\pi} d\Omega' \int_0^\infty dE' \int_{E_g}^{E_{g-1}} dE \; \chi(E)
\nu\sigma_f^p (r, E') \psi(r, E', \Omega')\\
\langle \nu\sigma_f^p \phi \rangle &= \int_{r \in V} dr \int_{4\pi}
d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu\sigma_f^p (r,
\langle \nu^p \sigma_{f,g' \rightarrow g} \phi \rangle &= \int_{r \in V}
dr \int_{4\pi} d\Omega' \int_0^\infty dE' \int_{E_g}^{E_{g-1}} dE \;
\chi(E)^p \nu^p \sigma_f (r, E') \psi(r, E', \Omega')\\
\langle \nu^p \sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi}
d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu^p \sigma_f (r,
E') \psi(r, E', \Omega') \\
\chi_g^p &= \frac{\langle \nu\sigma_{f,\rightarrow g}^p \phi \rangle}{\langle
\nu\sigma_f^p \phi \rangle}
\chi_g^p &= \frac{\langle \nu^p \sigma_{f,g' \rightarrow g} \phi \rangle}
{\langle \nu^p \sigma_f \phi \rangle}
Parameters
----------
@ -4958,17 +4982,6 @@ class InverseVelocity(MGXS):
"""
# Construct a collection of the subdomains to report
if not isinstance(subdomains, basestring):
cv.check_iterable_type('subdomains', subdomains, Integral)
elif self.domain_type == 'distribcell':
subdomains = np.arange(self.num_subdomains, dtype=np.int)
elif self.domain_type == 'mesh':
xyz = map(lambda x: np.arange(1, x+1), self.domain.dimension)
subdomains = list(itertools.product(*xyz))
else:
subdomains = [self.domain.id]
if xs_type == 'macro':
return 'second/cm'
else:

2
openmc/tallies.py
View file

@ -1293,7 +1293,7 @@ class Tally(object):
# Create list of 2- or 3-tuples tuples for mesh cell bins
if self_filter.type == 'mesh':
dimension = self_filter.mesh.dimension
xyz = map(lambda x: np.arange(1, x+1), dimension)
xyz = [range(1, x+1) for x in dimension]
bins = list(itertools.product(*xyz))
# Create list of 2-tuples for energy boundary bins

2
tests/test_mgxs_library_condense/inputs_true.dat
View file

@ -1 +1 @@
e2cdca7ea5b3532050af5b12fac26d7ef212d2696bb1b73cdd00929b2243c40d100ad02438c7b090555b49815d0de6c48cf1b4ebf437a48bc80c2d2b4bad292e
e2cdca7ea5b3532050af5b12fac26d7ef212d2696bb1b73cdd00929b2243c40d100ad02438c7b090555b49815d0de6c48cf1b4ebf437a48bc80c2d2b4bad292e

2
tests/test_mgxs_library_distribcell/inputs_true.dat
View file

@ -1 +1 @@
2d948f3b12293294eaeca231a3df9d51195379e8bb38dd3e68d3bc512a7d08ed52a1109054ca381684ec127268710f6d6e9210ac8154c9b379608e996627624a
2d948f3b12293294eaeca231a3df9d51195379e8bb38dd3e68d3bc512a7d08ed52a1109054ca381684ec127268710f6d6e9210ac8154c9b379608e996627624a

2
tests/test_mgxs_library_hdf5/inputs_true.dat
View file

@ -1 +1 @@
e2cdca7ea5b3532050af5b12fac26d7ef212d2696bb1b73cdd00929b2243c40d100ad02438c7b090555b49815d0de6c48cf1b4ebf437a48bc80c2d2b4bad292e
e2cdca7ea5b3532050af5b12fac26d7ef212d2696bb1b73cdd00929b2243c40d100ad02438c7b090555b49815d0de6c48cf1b4ebf437a48bc80c2d2b4bad292e

1
tests/test_mgxs_library_mesh/inputs_true.dat Normal file
View file

@ -0,0 +1 @@
a4cd030bea212e45fdb159e75a7fb3d1947e9bf3d0384ac5d37a72298d67dcfdd1b9eb5c6af8ac6e5983bd5b47de9c17a2ea472b467b7222a4909ee070bf1ca3

132
tests/test_mgxs_library_mesh/results_true.dat Normal file
View file

@ -0,0 +1,132 @@
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.640786 0.044177
1 1 2 1 1 total 0.660597 0.128423
2 2 1 1 1 total 0.615276 0.104046
3 2 2 1 1 total 0.646999 0.186709
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.36665 0.048814
1 1 2 1 1 total 0.40784 0.096486
2 2 1 1 1 total 0.36356 0.074111
3 2 2 1 1 total 0.41456 0.160443
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.366650 0.048814
1 1 2 1 1 total 0.407840 0.096486
2 2 1 1 1 total 0.363560 0.074111
3 2 2 1 1 total 0.414593 0.160436
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.025749 0.002863
1 1 2 1 1 total 0.028400 0.005275
2 2 1 1 1 total 0.022988 0.004099
3 2 2 1 1 total 0.027589 0.010350
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.015861 0.002876
1 1 2 1 1 total 0.017280 0.004371
2 2 1 1 1 total 0.014403 0.003542
3 2 2 1 1 total 0.018061 0.010110
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.009888 0.001077
1 1 2 1 1 total 0.011121 0.002456
2 2 1 1 1 total 0.008585 0.001552
3 2 2 1 1 total 0.009527 0.003659
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.026065 0.002907
1 1 2 1 1 total 0.029084 0.006430
2 2 1 1 1 total 0.022596 0.004062
3 2 2 1 1 total 0.025066 0.009687
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 1.938476 0.211550
1 1 2 1 1 total 2.177360 0.480780
2 2 1 1 1 total 1.682799 0.303764
3 2 2 1 1 total 1.864890 0.715661
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.615037 0.041754
1 1 2 1 1 total 0.632196 0.123878
2 2 1 1 1 total 0.592288 0.100439
3 2 2 1 1 total 0.619410 0.177190
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.584014 0.054315
1 1 2 1 1 total 0.622514 0.111323
2 2 1 1 1 total 0.587256 0.084833
3 2 2 1 1 total 0.613792 0.168612
mesh 1 group in group out nuclide moment mean std. dev.
x y z
0 1 1 1 1 1 total P0 0.584014 0.054315
1 1 1 1 1 1 total P1 0.243427 0.025488
2 1 1 1 1 1 total P2 0.089236 0.007357
3 1 1 1 1 1 total P3 0.008994 0.005768
4 1 2 1 1 1 total P0 0.622514 0.111323
5 1 2 1 1 1 total P1 0.239376 0.042594
6 1 2 1 1 1 total P2 0.088386 0.017200
7 1 2 1 1 1 total P3 -0.001243 0.005639
8 2 1 1 1 1 total P0 0.587256 0.084833
9 2 1 1 1 1 total P1 0.245120 0.041033
10 2 1 1 1 1 total P2 0.086784 0.016255
11 2 1 1 1 1 total P3 0.008660 0.004755
12 2 2 1 1 1 total P0 0.612950 0.167940
13 2 2 1 1 1 total P1 0.226176 0.061882
14 2 2 1 1 1 total P2 0.086593 0.026126
15 2 2 1 1 1 total P3 0.009672 0.011995
mesh 1 group in group out nuclide moment mean std. dev.
x y z
0 1 1 1 1 1 total P0 0.584014 0.054315
1 1 1 1 1 1 total P1 0.243427 0.025488
2 1 1 1 1 1 total P2 0.089236 0.007357
3 1 1 1 1 1 total P3 0.008994 0.005768
4 1 2 1 1 1 total P0 0.622514 0.111323
5 1 2 1 1 1 total P1 0.239376 0.042594
6 1 2 1 1 1 total P2 0.088386 0.017200
7 1 2 1 1 1 total P3 -0.001243 0.005639
8 2 1 1 1 1 total P0 0.587256 0.084833
9 2 1 1 1 1 total P1 0.245120 0.041033
10 2 1 1 1 1 total P2 0.086784 0.016255
11 2 1 1 1 1 total P3 0.008660 0.004755
12 2 2 1 1 1 total P0 0.613792 0.168612
13 2 2 1 1 1 total P1 0.226142 0.061856
14 2 2 1 1 1 total P2 0.086174 0.025979
15 2 2 1 1 1 total P3 0.009721 0.012027
mesh 1 group in group out nuclide mean std. dev.
x y z
0 1 1 1 1 1 total 1.000000 0.088094
1 1 2 1 1 1 total 1.000000 0.160891
2 2 1 1 1 1 total 1.000000 0.126864
3 2 2 1 1 1 total 1.001374 0.305883
mesh 1 group in group out nuclide mean std. dev.
x y z
0 1 1 1 1 1 total 0.027395 0.004680
1 1 2 1 1 1 total 0.022914 0.006025
2 2 1 1 1 1 total 0.019384 0.002846
3 2 2 1 1 1 total 0.029629 0.006292
mesh 1 group out nuclide mean std. dev.
x y z
0 1 1 1 1 total 1.0 0.220956
1 1 2 1 1 total 1.0 0.316565
2 2 1 1 1 total 1.0 0.132140
3 2 2 1 1 total 1.0 0.181577
mesh 1 group out nuclide mean std. dev.
x y z
0 1 1 1 1 total 1.0 0.222246
1 1 2 1 1 total 1.0 0.316565
2 2 1 1 1 total 1.0 0.132140
3 2 2 1 1 total 1.0 0.181577
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 3.610522e-07 3.169931e-08
1 1 2 1 1 total 3.942353e-07 8.459167e-08
2 2 1 1 1 total 3.097784e-07 5.252025e-08
3 2 2 1 1 total 3.799163e-07 1.806470e-07
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.025735 0.002840
1 1 2 1 1 total 0.028773 0.006349
2 2 1 1 1 total 0.022306 0.004010
3 2 2 1 1 total 0.024549 0.009379

81
tests/test_mgxs_library_mesh/test_mgxs_library_mesh.py Normal file
View file

@ -0,0 +1,81 @@
#!/usr/bin/env python
import os
import sys
import glob
import hashlib
sys.path.insert(0, os.pardir)
from testing_harness import PyAPITestHarness
import openmc
import openmc.mgxs
class MGXSTestHarness(PyAPITestHarness):
def _build_inputs(self):
# Generate inputs using parent class routine
super(MGXSTestHarness, self)._build_inputs()
# Initialize a one-group structure
energy_groups = openmc.mgxs.EnergyGroups(group_edges=[0, 20.])
# Initialize MGXS Library for a few cross section types
# for one material-filled cell in the geometry
self.mgxs_lib = openmc.mgxs.Library(self._input_set.geometry)
self.mgxs_lib.by_nuclide = False
# Test all MGXS types
self.mgxs_lib.mgxs_types = openmc.mgxs.MGXS_TYPES
self.mgxs_lib.energy_groups = energy_groups
self.mgxs_lib.legendre_order = 3
self.mgxs_lib.domain_type = 'mesh'
# Instantiate a tally mesh
mesh = openmc.Mesh(mesh_id=1)
mesh.type = 'regular'
mesh.dimension = [2, 2]
mesh.lower_left = [-100., -100.]
mesh.width = [100., 100.]
self.mgxs_lib.domains = [mesh]
self.mgxs_lib.build_library()
# Initialize a tallies file
self._input_set.tallies = openmc.Tallies()
self.mgxs_lib.add_to_tallies_file(self._input_set.tallies, merge=False)
self._input_set.tallies.export_to_xml()
def _get_results(self, hash_output=False):
"""Digest info in the statepoint and return as a string."""
# Read the statepoint file.
statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0]
sp = openmc.StatePoint(statepoint)
# Load the MGXS library from the statepoint
self.mgxs_lib.load_from_statepoint(sp)
# Build a string from Pandas Dataframe for each 1-group MGXS
outstr = ''
for domain in self.mgxs_lib.domains:
for mgxs_type in self.mgxs_lib.mgxs_types:
mgxs = self.mgxs_lib.get_mgxs(domain, mgxs_type)
df = mgxs.get_pandas_dataframe()
outstr += df.to_string() + '\n'
# Hash the results if necessary
if hash_output:
sha512 = hashlib.sha512()
sha512.update(outstr.encode('utf-8'))
outstr = sha512.hexdigest()
return outstr
def _cleanup(self):
super(MGXSTestHarness, self)._cleanup()
f = os.path.join(os.getcwd(), 'tallies.xml')
if os.path.exists(f): os.remove(f)
if __name__ == '__main__':
harness = MGXSTestHarness('statepoint.10.*', True)
harness.main()

2
tests/test_mgxs_library_no_nuclides/inputs_true.dat
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@ -1 +1 @@
e2cdca7ea5b3532050af5b12fac26d7ef212d2696bb1b73cdd00929b2243c40d100ad02438c7b090555b49815d0de6c48cf1b4ebf437a48bc80c2d2b4bad292e
e2cdca7ea5b3532050af5b12fac26d7ef212d2696bb1b73cdd00929b2243c40d100ad02438c7b090555b49815d0de6c48cf1b4ebf437a48bc80c2d2b4bad292e

2
tests/test_mgxs_library_nuclides/inputs_true.dat
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@ -1 +1 @@
e4a5f03ab6167e96462c4ef537533fe33b98d7878ae00824c5619356bda8d548b3c71af01ba8c88d5a9b46dd1471d331e6f678a164af922200f2ee3642be6340
e4a5f03ab6167e96462c4ef537533fe33b98d7878ae00824c5619356bda8d548b3c71af01ba8c88d5a9b46dd1471d331e6f678a164af922200f2ee3642be6340

2
tests/test_mgxs_library_nuclides/results_true.dat
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@ -1 +1 @@
e494320a213b5704a2ac915a2ba504857be91961ceb6735b6ad05d81eb31c44c9584d5bd9d40baececf1dcb5b030e6ecec63cfbd20639baf69bcb596c5c46591
e494320a213b5704a2ac915a2ba504857be91961ceb6735b6ad05d81eb31c44c9584d5bd9d40baececf1dcb5b030e6ecec63cfbd20639baf69bcb596c5c46591

2
tests/test_tallies/inputs_true.dat
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@ -1 +1 @@
ca47172a42f6c13b244a763c990cbe4811662708ee03307d810a4542ee34bb5db7cc29d66aea313dad95b9f38a4ff7943ded527cfd0c7c8825372fec40cfc0d0
ca47172a42f6c13b244a763c990cbe4811662708ee03307d810a4542ee34bb5db7cc29d66aea313dad95b9f38a4ff7943ded527cfd0c7c8825372fec40cfc0d0