mirror of
https://github.com/openmc-dev/openmc.git
synced 2026-07-28 14:15:42 -04:00
Implemented Tally.summation(...) routine
This commit is contained in:
Will Boyd
parent
bcc9ded29b
commit
efb3c0488f
2 changed files with 126 additions and 38 deletions
|
|
@ -114,6 +114,7 @@ class MultiGroupXS(object):
|
|||
|
||||
def __init__(self, domain=None, domain_type=None,
|
||||
energy_groups=None, name=''):
|
||||
|
||||
self._name = ''
|
||||
self._xs_type = None
|
||||
self._domain = None
|
||||
|
|
@ -141,7 +142,7 @@ class MultiGroupXS(object):
|
|||
def __deepcopy__(self, memo):
|
||||
existing = memo.get(id(self))
|
||||
|
||||
# If this is the first time we have tried to copy this object, create a copy
|
||||
# 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
|
||||
|
|
@ -151,7 +152,7 @@ class MultiGroupXS(object):
|
|||
clone._energy_groups = copy.deepcopy(self.energy_groups, memo)
|
||||
clone._num_groups = self.num_groups
|
||||
clone._xs_tally = copy.deepcopy(self.xs_tally, memo)
|
||||
clone._subdomain_offsets = copy.deepcopy(self.subdomain_indices, memo)
|
||||
clone._subdomain_indices = copy.deepcopy(self.subdomain_indices, memo)
|
||||
clone._offset = copy.deepcopy(self.offset, memo)
|
||||
|
||||
clone._tallies = dict()
|
||||
|
|
@ -227,15 +228,15 @@ class MultiGroupXS(object):
|
|||
|
||||
def _find_domain_offset(self):
|
||||
"""Finds and stores the offset of the domain tally filter"""
|
||||
tally = self.tallies[self.tallies.keys()[0]]
|
||||
tally = self.tallies.values()[0]
|
||||
filter = tally.find_filter(self.domain_type, [self.domain.id])
|
||||
self._offset = filter.offset
|
||||
|
||||
def set_subdomain_index(self, subdomain_id, offset):
|
||||
def set_subdomain_index(self, subdomain_id, index):
|
||||
"""Set the filter bin index for a subdomain of the domain.
|
||||
|
||||
This is primary useful when the domain type is 'distribcell', in which
|
||||
case it can be useful to map each subdomain (a cell instance) to its
|
||||
This is primarily useful when the domain type is 'distribcell', in
|
||||
which case one may wish to map each subdomain (a cell instance) to its
|
||||
filter bin in the derived multi-group cross-section tally data array.
|
||||
|
||||
Parameters
|
||||
|
|
@ -248,10 +249,10 @@ class MultiGroupXS(object):
|
|||
"""
|
||||
|
||||
cv.check_type('subdomain id', subdomain_id, Integral)
|
||||
cv.check_type('subdomain offset', offset, Integral)
|
||||
cv.check_type('subdomain offset', index, Integral)
|
||||
cv.check_greater_than('subdomain id', subdomain_id, 0, True)
|
||||
cv.check_greater_than('subdomain offset', subdomain_id, 0, True)
|
||||
self._subdomain_indices[subdomain_id] = offset
|
||||
self._subdomain_indices[subdomain_id] = index
|
||||
|
||||
@abc.abstractmethod
|
||||
def _create_tallies(self, scores, all_filters, keys, estimator):
|
||||
|
|
@ -274,8 +275,7 @@ class MultiGroupXS(object):
|
|||
"""
|
||||
|
||||
cv.check_value('scores', scores, openmc.SCORE_TYPES)
|
||||
# FIXME : Use @smharper's recursive iterable checker
|
||||
# cv.check_type('filters', all_filters, openmc.Filter)
|
||||
cv.check_iterable_type('filters', all_filters, openmc.Filter, 1, 2)
|
||||
cv.check_type('keys', keys, Iterable, basestring)
|
||||
cv.check_length('scores', scores, len(keys))
|
||||
cv.check_value('estimator', estimator, ['analog', 'tracklength'])
|
||||
|
|
@ -299,14 +299,17 @@ class MultiGroupXS(object):
|
|||
This method can be used to extract the indices into the multi-group
|
||||
cross-section tally data array for a subdomain (i.e., cell instance).
|
||||
|
||||
See also : get_subdomains
|
||||
|
||||
Parameters
|
||||
----------
|
||||
subdomains : Iterable of Integral or 'all'
|
||||
Subdomain IDs of interest
|
||||
|
||||
Returns
|
||||
indices : NumPy ndarray
|
||||
Array of subdomain indices indexed in the order of the subdomains
|
||||
----------
|
||||
indices : ndarray
|
||||
The subdomain indices indexed in the order of the subdomains
|
||||
|
||||
Raises
|
||||
------
|
||||
|
|
@ -319,8 +322,8 @@ class MultiGroupXS(object):
|
|||
cv.check_type('subdomains', subdomains, Iterable, Integral)
|
||||
|
||||
if subdomains == 'all':
|
||||
# FIXME: This isn't correct any more!!
|
||||
# indices = np.arange(self.xs.shape[1])
|
||||
num_subdomains = len(self.subdomain_indices)
|
||||
indices = np.arange(num_subdomains)
|
||||
else:
|
||||
indices = np.zeros(len(subdomains), dtype=np.int64)
|
||||
|
||||
|
|
@ -340,7 +343,7 @@ class MultiGroupXS(object):
|
|||
This method can be used to extract the subdomains for the multi-group
|
||||
cross-section from their indices in the tally data array.
|
||||
|
||||
See also : get_subdomain_offsets
|
||||
See also : get_subdomain_indices
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -348,7 +351,8 @@ class MultiGroupXS(object):
|
|||
Subdomain indices of interest
|
||||
|
||||
Returns
|
||||
subdomains : NumPy ndarray
|
||||
----------
|
||||
subdomains : ndarray
|
||||
Array of subdomain IDs indexed in the order of the indices
|
||||
|
||||
Raises
|
||||
|
|
@ -370,9 +374,9 @@ class MultiGroupXS(object):
|
|||
|
||||
for i, index in enumerate(indices):
|
||||
if index in values:
|
||||
subdomains[i] = keys[values.index(indices)]
|
||||
subdomains[i] = keys[values.index(index)]
|
||||
else:
|
||||
msg = 'Unable to get subdomain for offset "{0}" since it ' \
|
||||
msg = 'Unable to get subdomain for index "{0}" since it ' \
|
||||
'is not a valid index'.format(index)
|
||||
raise ValueError(msg)
|
||||
|
||||
|
|
@ -408,21 +412,20 @@ class MultiGroupXS(object):
|
|||
if self.xs_tally is None:
|
||||
msg = 'Unable to get cross-section since it has not been computed'
|
||||
raise ValueError(msg)
|
||||
if groups != 'all':
|
||||
cv.check_value('groups', groups, Iterable, Integral)
|
||||
if subdomains != 'all':
|
||||
cv.check_value('subdomains', subdomains, Iterable, Integral)
|
||||
|
||||
filters = []
|
||||
filter_bins = []
|
||||
|
||||
# Construct a collection of the domain filter bins
|
||||
filters.append(self.domain_type)
|
||||
filter_bins.append(subdomains)
|
||||
if subdomains != 'all':
|
||||
cv.check_value('subdomains', subdomains, Iterable, Integral)
|
||||
filters.append(self.domain_type)
|
||||
filter_bins.append(tuple(subdomains))
|
||||
|
||||
# Construct a collection of the energy group filter bins
|
||||
filters.append('energy')
|
||||
filter_bins.append(self.energy_groups.get_group_bounds(groups))
|
||||
if groups != 'all':
|
||||
cv.check_value('groups', groups, Iterable, Integral)
|
||||
filters.append('energy')
|
||||
filter_bins.append(self.energy_groups.get_group_bounds(groups))
|
||||
|
||||
# Query the multi-group cross-section tally for the data
|
||||
xs = self.xs_tally.get_values(filters=filters,
|
||||
|
|
@ -465,9 +468,9 @@ class MultiGroupXS(object):
|
|||
for key, old_tally in avg_xs.tallies.items():
|
||||
# FIXME: Need to create Tally.mean(...)
|
||||
slice_tally = old_tally.slice(filters=[avg_xs.domain_type],
|
||||
filter_bins=subdomains)
|
||||
filter_bins=[tuple(subdomains)])
|
||||
avg_tally = slice_tally.mean(filters=[avg_xs.domain_type],
|
||||
filter_bins=subdomains)
|
||||
filter_bins=[tuple(subdomains)])
|
||||
avg_xs.tallies[key] = avg_tally
|
||||
|
||||
avg_xs.compute_xs()
|
||||
|
|
|
|||
|
|
@ -3,6 +3,8 @@ import copy
|
|||
import os
|
||||
import pickle
|
||||
import itertools
|
||||
import functools
|
||||
from operator import mul
|
||||
from numbers import Integral, Real
|
||||
from xml.etree import ElementTree as ET
|
||||
import sys
|
||||
|
|
@ -1677,8 +1679,8 @@ class Tally(object):
|
|||
new_tally._derived = True
|
||||
new_tally.with_batch_statistics = True
|
||||
new_tally.name = self.name
|
||||
new_tally._mean = self._mean + other
|
||||
new_tally._std_dev = self._std_dev
|
||||
new_tally._mean = self.mean + other
|
||||
new_tally._std_dev = self.std_dev
|
||||
new_tally.estimator = self.estimator
|
||||
new_tally.with_summary = self.with_summary
|
||||
new_tally.num_realization = self.num_realizations
|
||||
|
|
@ -1746,8 +1748,8 @@ class Tally(object):
|
|||
new_tally = Tally(name='derived')
|
||||
new_tally._derived = True
|
||||
new_tally.name = self.name
|
||||
new_tally._mean = self._mean - other
|
||||
new_tally._std_dev = self._std_dev
|
||||
new_tally._mean = self.mean - other
|
||||
new_tally._std_dev = self.std_dev
|
||||
new_tally.estimator = self.estimator
|
||||
new_tally.with_summary = self.with_summary
|
||||
new_tally.num_realization = self.num_realizations
|
||||
|
|
@ -1816,8 +1818,8 @@ class Tally(object):
|
|||
new_tally = Tally(name='derived')
|
||||
new_tally._derived = True
|
||||
new_tally.name = self.name
|
||||
new_tally._mean = self._mean * other
|
||||
new_tally._std_dev = self._std_dev * np.abs(other)
|
||||
new_tally._mean = self.mean * other
|
||||
new_tally._std_dev = self.std_dev * np.abs(other)
|
||||
new_tally.estimator = self.estimator
|
||||
new_tally.with_summary = self.with_summary
|
||||
new_tally.num_realization = self.num_realizations
|
||||
|
|
@ -1886,8 +1888,8 @@ class Tally(object):
|
|||
new_tally = Tally(name='derived')
|
||||
new_tally._derived = True
|
||||
new_tally.name = self.name
|
||||
new_tally._mean = self._mean / other
|
||||
new_tally._std_dev = self._std_dev * np.abs(1. / other)
|
||||
new_tally._mean = self.mean / other
|
||||
new_tally._std_dev = self.std_dev * np.abs(1. / other)
|
||||
new_tally.estimator = self.estimator
|
||||
new_tally.with_summary = self.with_summary
|
||||
new_tally.num_realization = self.num_realizations
|
||||
|
|
@ -2085,7 +2087,7 @@ class Tally(object):
|
|||
"""Build a sliced tally for the specified filters, scores and nuclides.
|
||||
|
||||
This method constructs a new tally to encapsulate a subset of the data
|
||||
represented by this tally. The subset of data to included in the tally
|
||||
represented by this tally. The subset of data to include in the tally
|
||||
slice is determined by the scores, filters and nuclides specified in
|
||||
the input parameters.
|
||||
|
||||
|
|
@ -2200,6 +2202,89 @@ class Tally(object):
|
|||
|
||||
return new_tally
|
||||
|
||||
def summation(self, scores=[], filters=[], filter_bins=[], nuclides=[]):
|
||||
"""Build a sliced tally for the specified filters, scores and nuclides.
|
||||
|
||||
This method constructs a new tally to encapsulate a subset of the data
|
||||
represented by this tally. The subset of data to include in the tally
|
||||
slice is determined by the scores, filters and nuclides specified in
|
||||
the input parameters.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
scores : list
|
||||
A list of one or more score strings to sum across
|
||||
(e.g., ['absorption', 'nu-fission']; default is [])
|
||||
|
||||
filters : list
|
||||
A list of filter type strings to sum across
|
||||
(e.g., ['mesh', 'energy']; default is [])
|
||||
|
||||
filter_bins : list of Iterables
|
||||
A list of the filter bins corresponding to the filter_types
|
||||
parameter (e.g., [(1,), (0., 0.625e-6)]; default is []). Each bin
|
||||
in the list is the integer ID for 'material', 'surface', 'cell',
|
||||
'cellborn', and 'universe' Filters. Each bin is an integer for the
|
||||
cell instance ID for 'distribcell Filters. Each bin is a 2-tuple of
|
||||
floats for 'energy' and 'energyout' filters corresponding to the
|
||||
energy boundaries of the bin of interest. The bin is a (x,y,z)
|
||||
3-tuple for 'mesh' filters corresponding to the mesh cell of
|
||||
interest. The order of the bins in the list must correspond of the
|
||||
filter_types parameter.
|
||||
|
||||
nuclides : list
|
||||
A list of nuclide name strings to sum across
|
||||
(e.g., ['U-235', 'U-238']; default is [])
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tally
|
||||
A new tally which encapsulates the sum of data requested.
|
||||
|
||||
"""
|
||||
|
||||
# If user did not specify any scores, do not sum across scores
|
||||
if len(scores) == 0:
|
||||
scores = [[]]
|
||||
# Sum across any scores specified by the user
|
||||
else:
|
||||
scores = [[score] for score in scores]
|
||||
|
||||
# If user did not specify any nuclides, do not sum across nuclides
|
||||
if len(nuclides) == 0:
|
||||
nuclides = [[]]
|
||||
# Sum across any nuclides specified by the user
|
||||
else:
|
||||
nuclides = [[nuclide] for nuclide in nuclides]
|
||||
|
||||
# If user did not specify any filter bins, do not sum across filter bins
|
||||
if len(filters) == 0:
|
||||
filter_bins = [[]]
|
||||
filters = [[]]
|
||||
# Sum across any filter bins specified by the user
|
||||
else:
|
||||
filter_bins = list(itertools.product(*filter_bins))
|
||||
filter_bins = [list(filter_bin) for filter_bin in filter_bins]
|
||||
filters = [filters]
|
||||
|
||||
# Initialize Tally sum
|
||||
tally_sum = 0
|
||||
|
||||
# Iterate over all Tally slice operands in summation
|
||||
prod = [scores, filters, filter_bins, nuclides]
|
||||
for scores, filters, filter_bins, nuclides in itertools.product(*prod):
|
||||
tally_slice = self.get_slice(scores, filters, filter_bins, nuclides)
|
||||
|
||||
# Remove filters summed across to avoid bulky CrossFilters
|
||||
for filter in reversed(tally_slice.filters):
|
||||
if filter.type in filters:
|
||||
tally_slice.remove_filter(filter)
|
||||
|
||||
# Accumulate this Tally slice into the Tally sum
|
||||
tally_sum += tally_slice
|
||||
|
||||
return tally_sum
|
||||
|
||||
|
||||
class TalliesFile(object):
|
||||
"""Tallies file used for an OpenMC simulation. Corresponds directly to the
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue