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Changes in mgxs.py/mgdxs.py from PullRequest Inc. review

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This commit is contained in:
Paul Romano 2020-04-01 10:48:03 -05:00
parent 5d07976924
commit 0a34fcd600
3 changed files with 116 additions and 100 deletions
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16
openmc/mgxs/mdgxs.py
View file

@ -47,7 +47,7 @@ class MDGXS(MGXS):
name : str, optional
Name of the multi-group cross section. Used as a label to identify
tallies in OpenMC 'tallies.xml' file.
delayed_groups : list of int
delayed_groups : list of int, optional
Delayed groups to filter out the xs
num_polar : Integral, optional
Number of equi-width polar angle bins for angle discretization;
@ -70,7 +70,7 @@ class MDGXS(MGXS):
Domain type for spatial homogenization
energy_groups : openmc.mgxs.EnergyGroups
Energy group structure for energy condensation
delayed_groups : list of int
delayed_groups : list of int, optional
Delayed groups to filter out the xs
num_polar : Integral
Number of equi-width polar angle bins for angle discretization
@ -246,7 +246,7 @@ class MDGXS(MGXS):
name : str, optional
Name of the multi-group cross section. Used as a label to identify
tallies in OpenMC 'tallies.xml' file. Defaults to the empty string.
delayed_groups : list of int
delayed_groups : list of int, optional
Delayed groups to filter out the xs
num_polar : Integral, optional
Number of equi-width polar angle bins for angle discretization;
@ -1316,10 +1316,10 @@ class ChiDelayed(MDGXS):
# Sum out all nuclides
nuclides = self.get_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(
@ -2169,6 +2169,8 @@ class MatrixMDGXS(MDGXS):
# Eliminate the trivial score dimension
xs = np.squeeze(xs, axis=len(xs.shape) - 1)
# Eliminate NaNs which may have been produced by dividing by density
xs = np.nan_to_num(xs)
if in_groups == 'all':

196
openmc/mgxs/mgxs.py
View file

@ -15,51 +15,67 @@ from openmc.mgxs import EnergyGroups
# Supported cross section types
MGXS_TYPES = ['total',
'transport',
'nu-transport',
'absorption',
'capture',
'fission',
'nu-fission',
'kappa-fission',
'scatter',
'nu-scatter',
'scatter matrix',
'nu-scatter matrix',
'multiplicity matrix',
'nu-fission matrix',
'scatter probability matrix',
'consistent scatter matrix',
'consistent nu-scatter matrix',
'chi',
'chi-prompt',
'inverse-velocity',
'prompt-nu-fission',
'prompt-nu-fission matrix']
MGXS_TYPES = [
'total',
'transport',
'nu-transport',
'absorption',
'capture',
'fission',
'nu-fission',
'kappa-fission',
'scatter',
'nu-scatter',
'scatter matrix',
'nu-scatter matrix',
'multiplicity matrix',
'nu-fission matrix',
'scatter probability matrix',
'consistent scatter matrix',
'consistent nu-scatter matrix',
'chi',
'chi-prompt',
'inverse-velocity',
'prompt-nu-fission',
'prompt-nu-fission matrix'
]
# Supported domain types
DOMAIN_TYPES = ['cell',
'distribcell',
'universe',
'material',
'mesh']
DOMAIN_TYPES = [
'cell',
'distribcell',
'universe',
'material',
'mesh'
]
# Filter types corresponding to each domain
_DOMAIN_TO_FILTER = {'cell': openmc.CellFilter,
'distribcell': openmc.DistribcellFilter,
'universe': openmc.UniverseFilter,
'material': openmc.MaterialFilter,
'mesh': openmc.MeshFilter}
_DOMAIN_TO_FILTER = {
'cell': openmc.CellFilter,
'distribcell': openmc.DistribcellFilter,
'universe': openmc.UniverseFilter,
'material': openmc.MaterialFilter,
'mesh': openmc.MeshFilter
}
# Supported domain classes
_DOMAINS = (openmc.Cell,
openmc.Universe,
openmc.Material,
openmc.RegularMesh)
_DOMAINS = (
openmc.Cell,
openmc.Universe,
openmc.Material,
openmc.RegularMesh
)
# Supported ScatterMatrixXS angular distribution types
MU_TREATMENTS = ('legendre', 'histogram')
# Supported ScatterMatrixXS angular distribution types. Note that 'histogram' is
# defined here and used in mgxs_library.py, but it is not used for the current
# module
SCATTER_TABULAR = 'tabular'
SCATTER_LEGENDRE = 'legendre'
SCATTER_HISTOGRAM = 'histogram'
MU_TREATMENTS = (
SCATTER_LEGENDRE,
SCATTER_HISTOGRAM
)
# Maximum Legendre order supported by OpenMC
_MAX_LEGENDRE = 10
@ -245,38 +261,37 @@ class MGXS(metaclass=ABCMeta):
def __deepcopy__(self, memo):
existing = memo.get(id(self))
# If this is the first time we have tried to copy this object, copy it
if existing is None:
clone = type(self).__new__(type(self))
clone._name = self.name
clone._rxn_type = self.rxn_type
clone._by_nuclide = self.by_nuclide
clone._nuclides = copy.deepcopy(self._nuclides, memo)
clone._domain = self.domain
clone._domain_type = self.domain_type
clone._energy_groups = copy.deepcopy(self.energy_groups, memo)
clone._num_polar = self._num_polar
clone._num_azimuthal = self._num_azimuthal
clone._tally_trigger = copy.deepcopy(self.tally_trigger, memo)
clone._rxn_rate_tally = copy.deepcopy(self._rxn_rate_tally, memo)
clone._xs_tally = copy.deepcopy(self._xs_tally, memo)
clone._sparse = self.sparse
clone._loaded_sp = self._loaded_sp
clone._derived = self.derived
clone._hdf5_key = self._hdf5_key
clone._tallies = OrderedDict()
for tally_type, tally in self.tallies.items():
clone.tallies[tally_type] = copy.deepcopy(tally, memo)
memo[id(self)] = clone
return clone
# If this object has been copied before, return the first copy made
else:
if existing is not None:
return existing
# If this is the first time we have tried to copy this object, copy it
clone = type(self).__new__(type(self))
clone._name = self.name
clone._rxn_type = self.rxn_type
clone._by_nuclide = self.by_nuclide
clone._nuclides = copy.deepcopy(self._nuclides, memo)
clone._domain = self.domain
clone._domain_type = self.domain_type
clone._energy_groups = copy.deepcopy(self.energy_groups, memo)
clone._num_polar = self._num_polar
clone._num_azimuthal = self._num_azimuthal
clone._tally_trigger = copy.deepcopy(self.tally_trigger, memo)
clone._rxn_rate_tally = copy.deepcopy(self._rxn_rate_tally, memo)
clone._xs_tally = copy.deepcopy(self._xs_tally, memo)
clone._sparse = self.sparse
clone._loaded_sp = self._loaded_sp
clone._derived = self.derived
clone._hdf5_key = self._hdf5_key
clone._tallies = OrderedDict()
for tally_type, tally in self.tallies.items():
clone.tallies[tally_type] = copy.deepcopy(tally, memo)
memo[id(self)] = clone
return clone
def _add_angle_filters(self, filters):
"""Add the azimuthal and polar bins to the MGXS filters if needed.
Filters will be provided as a ragged 2D list of openmc.Filter objects.
@ -2170,6 +2185,7 @@ class MatrixMGXS(MGXS):
if not isinstance(in_groups, str):
cv.check_iterable_type('groups', in_groups, Integral)
filters.append(openmc.EnergyFilter)
energy_bins = []
for group in in_groups:
energy_bins.append((self.energy_groups.get_group_bounds(group),))
filter_bins.append(tuple(energy_bins))
@ -3725,7 +3741,7 @@ class ScatterMatrixXS(MatrixMGXS):
num_polar, num_azimuthal)
self._formulation = 'simple'
self._correction = 'P0'
self._scatter_format = 'legendre'
self._scatter_format = SCATTER_LEGENDRE
self._legendre_order = 0
self._histogram_bins = 16
self._estimator = 'analog'
@ -3748,12 +3764,12 @@ class ScatterMatrixXS(MatrixMGXS):
process
"""
if self.num_polar > 1 or self.num_azimuthal > 1:
if self.scatter_format == 'histogram':
if self.scatter_format == SCATTER_HISTOGRAM:
return (0, 1, 3, 4, 5)
else:
return (0, 1, 3, 4)
else:
if self.scatter_format == 'histogram':
if self.scatter_format == SCATTER_HISTOGRAM:
return (1, 2, 3)
else:
return (1, 2)
@ -3857,13 +3873,13 @@ class ScatterMatrixXS(MatrixMGXS):
energy = openmc.EnergyFilter(group_edges)
energyout = openmc.EnergyoutFilter(group_edges)
if self.scatter_format == 'legendre':
if self.scatter_format == SCATTER_LEGENDRE:
if self.correction == 'P0' and self.legendre_order == 0:
angle_filter = openmc.LegendreFilter(order=1)
else:
angle_filter = \
openmc.LegendreFilter(order=self.legendre_order)
elif self.scatter_format == 'histogram':
elif self.scatter_format == SCATTER_HISTOGRAM:
bins = np.linspace(-1., 1., num=self.histogram_bins + 1,
endpoint=True)
angle_filter = openmc.MuFilter(bins)
@ -3878,9 +3894,9 @@ class ScatterMatrixXS(MatrixMGXS):
filters = [[energy], [energy]]
# Group-to-group scattering probability matrix
if self.scatter_format == 'legendre':
if self.scatter_format == SCATTER_LEGENDRE:
angle_filter = openmc.LegendreFilter(order=self.legendre_order)
elif self.scatter_format == 'histogram':
elif self.scatter_format == SCATTER_HISTOGRAM:
bins = np.linspace(-1., 1., num=self.histogram_bins + 1,
endpoint=True)
angle_filter = openmc.MuFilter(bins)
@ -3903,7 +3919,7 @@ class ScatterMatrixXS(MatrixMGXS):
if self._rxn_rate_tally is None:
if self.formulation == 'simple':
if self.scatter_format == 'legendre':
if self.scatter_format == SCATTER_LEGENDRE:
# If using P0 correction subtract P1 scatter from the diag.
if self.correction == 'P0' and self.legendre_order == 0:
scatter_p0 = self.tallies[self.rxn_type].get_slice(
@ -3937,7 +3953,7 @@ class ScatterMatrixXS(MatrixMGXS):
# Otherwise, extract scattering moment reaction rate Tally
else:
self._rxn_rate_tally = self.tallies[self.rxn_type]
elif self.scatter_format == 'histogram':
elif self.scatter_format == SCATTER_HISTOGRAM:
# Extract scattering rate distribution tally
self._rxn_rate_tally = self.tallies[self.rxn_type]
@ -3967,14 +3983,14 @@ class ScatterMatrixXS(MatrixMGXS):
tally_key = 'scatter matrix'
# Compute normalization factor summed across outgoing energies
if self.scatter_format == 'legendre':
if self.scatter_format == SCATTER_LEGENDRE:
norm = self.tallies[tally_key].get_slice(
scores=['scatter'],
filters=[openmc.LegendreFilter],
filter_bins=[('P0',)], squeeze=True)
# Compute normalization factor summed across outgoing mu bins
elif self.scatter_format == 'histogram':
elif self.scatter_format == SCATTER_HISTOGRAM:
norm = self.tallies[tally_key].get_slice(
scores=['scatter'])
norm = norm.summation(
@ -3994,14 +4010,14 @@ class ScatterMatrixXS(MatrixMGXS):
if self.nu:
numer = self.tallies['nu-scatter']
# Get the denominator
if self.scatter_format == 'legendre':
if self.scatter_format == SCATTER_LEGENDRE:
denom = self.tallies[tally_key].get_slice(
scores=['scatter'],
filters=[openmc.LegendreFilter],
filter_bins=[('P0',)], squeeze=True)
# Compute normalization factor summed across mu bins
elif self.scatter_format == 'histogram':
elif self.scatter_format == SCATTER_HISTOGRAM:
denom = self.tallies[tally_key].get_slice(
scores=['scatter'])
@ -4048,7 +4064,7 @@ class ScatterMatrixXS(MatrixMGXS):
self._compute_xs()
# Force the angle filter to be the last filter
if self.scatter_format == 'histogram':
if self.scatter_format == SCATTER_HISTOGRAM:
angle_filter = self._xs_tally.find_filter(openmc.MuFilter)
else:
angle_filter = \
@ -4105,13 +4121,13 @@ class ScatterMatrixXS(MatrixMGXS):
def correction(self, correction):
cv.check_value('correction', correction, ('P0', None))
if self.scatter_format == 'legendre':
if self.scatter_format == SCATTER_LEGENDRE:
if correction == 'P0' and self.legendre_order > 0:
msg = 'The P0 correction will be ignored since the ' \
'scattering order {} is greater than '\
'zero'.format(self.legendre_order)
warnings.warn(msg)
elif self.scatter_format == 'histogram':
elif self.scatter_format == SCATTER_HISTOGRAM:
msg = 'The P0 correction will be ignored since the ' \
'scatter format is set to histogram'
warnings.warn(msg)
@ -4131,14 +4147,14 @@ class ScatterMatrixXS(MatrixMGXS):
cv.check_less_than('legendre_order', legendre_order, _MAX_LEGENDRE,
equality=True)
if self.scatter_format == 'legendre':
if self.scatter_format == SCATTER_LEGENDRE:
if self.correction == 'P0' and legendre_order > 0:
msg = 'The P0 correction will be ignored since the ' \
'scattering order {} is greater than '\
'zero'.format(self.legendre_order)
warnings.warn(msg, RuntimeWarning)
self.correction = None
elif self.scatter_format == 'histogram':
elif self.scatter_format == SCATTER_HISTOGRAM:
msg = 'The legendre order will be ignored since the ' \
'scatter format is set to histogram'
warnings.warn(msg)
@ -4226,7 +4242,7 @@ class ScatterMatrixXS(MatrixMGXS):
slice_xs._xs_tally = None
# Slice the Legendre order if needed
if legendre_order != 'same' and self.scatter_format == 'legendre':
if legendre_order != 'same' and self.scatter_format == SCATTER_LEGENDRE:
cv.check_type('legendre_order', legendre_order, Integral)
cv.check_less_than('legendre_order', legendre_order,
self.legendre_order, equality=True)
@ -4363,7 +4379,7 @@ class ScatterMatrixXS(MatrixMGXS):
filter_bins.append((self.energy_groups.get_group_bounds(group),))
# Construct CrossScore for requested scattering moment
if self.scatter_format == 'legendre':
if self.scatter_format == SCATTER_LEGENDRE:
if moment != 'all':
cv.check_type('moment', moment, Integral)
cv.check_greater_than('moment', moment, 0, equality=True)
@ -4508,7 +4524,7 @@ class ScatterMatrixXS(MatrixMGXS):
paths=paths)
# If the matrix is P0, remove the legendre column
if self.scatter_format == 'legendre' and self.legendre_order == 0:
if self.scatter_format == SCATTER_LEGENDRE and self.legendre_order == 0:
df = df.drop(axis=1, labels=['legendre'])
return df
@ -4560,7 +4576,7 @@ class ScatterMatrixXS(MatrixMGXS):
cv.check_value('xs_type', xs_type, ['macro', 'micro'])
if self.correction != 'P0' and self.scatter_format == 'legendre':
if self.correction != 'P0' and self.scatter_format == SCATTER_LEGENDRE:
rxn_type = '{0} (P{1})'.format(self.rxn_type, moment)
else:
rxn_type = self.rxn_type
@ -4648,7 +4664,7 @@ class ScatterMatrixXS(MatrixMGXS):
return to_print
# Set the number of histogram bins
if self.scatter_format == 'histogram':
if self.scatter_format == SCATTER_HISTOGRAM:
num_mu_bins = self.histogram_bins
else:
num_mu_bins = 0

4
openmc/mgxs_library.py
View file

@ -10,6 +10,7 @@ from scipy.special import eval_legendre
import openmc
import openmc.mgxs
from openmc.mgxs import SCATTER_TABULAR, SCATTER_LEGENDRE, SCATTER_HISTOGRAM
from openmc.checkvalue import check_type, check_value, check_greater_than, \
check_iterable_type, check_less_than, check_filetype_version
@ -24,9 +25,6 @@ _REPRESENTATIONS = [
]
# Supported scattering angular distribution representations
SCATTER_TABULAR = 'tabular'
SCATTER_LEGENDRE = 'legendre'
SCATTER_HISTOGRAM = 'histogram'
_SCATTER_TYPES = [
SCATTER_TABULAR,
SCATTER_LEGENDRE,