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Merge pull request #576 from wbinventor/hotfix-tally-agg
Bug Fixes for Typechecking in Tally Aggregation
This commit is contained in:
Paul Romano
commit
40038df9e5
6 changed files with 426 additions and 429 deletions
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@ -1,409 +0,0 @@
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import sys
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from numbers import Integral
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import numpy as np
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from openmc import Filter, Nuclide
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from openmc.cross import CrossScore, CrossNuclide, CrossFilter
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from openmc.filter import _FILTER_TYPES
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import openmc.checkvalue as cv
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if sys.version_info[0] >= 3:
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basestring = str
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# Acceptable tally aggregation operations
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_TALLY_AGGREGATE_OPS = ['sum', 'mean']
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class AggregateScore(object):
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"""A special-purpose tally score used to encapsulate an aggregate of a
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subset or all of tally's scores for tally aggregation.
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Parameters
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----------
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scores : Iterable of str or CrossScore
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The scores included in the aggregation
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aggregate_op : str
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The tally aggregation operator (e.g., 'sum', 'mean', etc.) used
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to aggregate across a tally's scores with this AggregateScore
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Attributes
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----------
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scores : Iterable of str or CrossScore
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The scores included in the aggregation
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aggregate_op : str
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The tally aggregation operator (e.g., 'sum', 'mean', etc.) used
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to aggregate across a tally's scores with this AggregateScore
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"""
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def __init__(self, scores=None, aggregate_op=None):
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self._scores = None
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self._aggregate_op = None
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if scores is not None:
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self.scores = scores
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if aggregate_op is not None:
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self.aggregate_op = aggregate_op
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def __hash__(self):
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return hash(repr(self))
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def __eq__(self, other):
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return str(other) == str(self)
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def __ne__(self, other):
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return not self == other
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def __deepcopy__(self, memo):
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existing = memo.get(id(self))
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# If this is the first time we have tried to copy this object, create a copy
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if existing is None:
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clone = type(self).__new__(type(self))
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clone._scores = self.scores
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clone._aggregate_op = self.aggregate_op
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memo[id(self)] = clone
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return clone
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# If this object has been copied before, return the first copy made
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else:
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return existing
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def __repr__(self):
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string = ', '.join(map(str, self.scores))
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string = '{0}({1})'.format(self.aggregate_op, string)
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return string
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@property
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def scores(self):
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return self._scores
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@property
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def aggregate_op(self):
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return self._aggregate_op
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@scores.setter
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def scores(self, scores):
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cv.check_iterable_type('scores', scores, basestring)
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self._scores = scores
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@aggregate_op.setter
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def aggregate_op(self, aggregate_op):
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cv.check_type('aggregate_op', aggregate_op, (basestring, CrossScore))
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cv.check_value('aggregate_op', aggregate_op, _TALLY_AGGREGATE_OPS)
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self._aggregate_op = aggregate_op
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class AggregateNuclide(object):
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"""A special-purpose tally nuclide used to encapsulate an aggregate of a
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subset or all of tally's nuclides for tally aggregation.
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Parameters
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----------
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nuclides : Iterable of str or Nuclide or CrossNuclide
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The nuclides included in the aggregation
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aggregate_op : str
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The tally aggregation operator (e.g., 'sum', 'mean', etc.) used
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to aggregate across a tally's nuclides with this AggregateNuclide
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Attributes
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----------
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nuclides : Iterable of str or Nuclide or CrossNuclide
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The nuclides included in the aggregation
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aggregate_op : str
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The tally aggregation operator (e.g., 'sum', 'mean', etc.) used
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to aggregate across a tally's nuclides with this AggregateNuclide
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"""
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def __init__(self, nuclides=None, aggregate_op=None):
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self._nuclides = None
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self._aggregate_op = None
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if nuclides is not None:
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self.nuclides = nuclides
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if aggregate_op is not None:
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self.aggregate_op = aggregate_op
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def __hash__(self):
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return hash(repr(self))
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def __eq__(self, other):
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return str(other) == str(self)
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def __ne__(self, other):
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return not self == other
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def __deepcopy__(self, memo):
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existing = memo.get(id(self))
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# If this is the first time we have tried to copy this object, create a copy
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if existing is None:
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clone = type(self).__new__(type(self))
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clone._nuclides = self.nuclides
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clone._aggregate_op = self._aggregate_op
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memo[id(self)] = clone
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return clone
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# If this object has been copied before, return the first copy made
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else:
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return existing
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def __repr__(self):
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# Append each nuclide in the aggregate to the string
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string = '{0}('.format(self.aggregate_op)
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names = [nuclide.name if isinstance(nuclide, Nuclide) else str(nuclide)
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for nuclide in self.nuclides]
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string += ', '.join(map(str, names)) + ')'
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return string
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@property
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def nuclides(self):
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return self._nuclides
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@property
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def aggregate_op(self):
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return self._aggregate_op
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@nuclides.setter
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def nuclides(self, nuclides):
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cv.check_iterable_type('nuclides', nuclides,
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(basestring, Nuclide, CrossNuclide))
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self._nuclides = nuclides
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@aggregate_op.setter
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def aggregate_op(self, aggregate_op):
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cv.check_type('aggregate_op', aggregate_op, basestring)
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cv.check_value('aggregate_op', aggregate_op, _TALLY_AGGREGATE_OPS)
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self._aggregate_op = aggregate_op
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class AggregateFilter(object):
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"""A special-purpose tally filter used to encapsulate an aggregate of a
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subset or all of a tally filter's bins for tally aggregation.
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Parameters
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----------
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aggregate_filter : Filter or CrossFilter
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The filter included in the aggregation
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bins : Iterable of tuple
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The filter bins included in the aggregation
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aggregate_op : str
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The tally aggregation operator (e.g., 'sum', 'mean', etc.) used
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to aggregate across a tally filter's bins with this AggregateFilter
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Attributes
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----------
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type : str
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The type of the aggregatefilter (e.g., 'sum(energy)', 'sum(cell)')
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aggregate_filter : filter
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The filter included in the aggregation
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aggregate_op : str
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The tally aggregation operator (e.g., 'sum', 'mean', etc.) used
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to aggregate across a tally filter's bins with this AggregateFilter
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bins : Iterable of tuple
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The filter bins included in the aggregation
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num_bins : Integral
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The number of filter bins (always 1 if aggregate_filter is defined)
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stride : Integral
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The number of filter, nuclide and score bins within each of this
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aggregatefilter's bins.
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"""
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def __init__(self, aggregate_filter=None, bins=None, aggregate_op=None):
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self._type = '{0}({1})'.format(aggregate_op, aggregate_filter.type)
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self._bins = None
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self._stride = None
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self._aggregate_filter = None
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self._aggregate_op = None
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if aggregate_filter is not None:
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self.aggregate_filter = aggregate_filter
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if bins is not None:
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self.bins = bins
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if aggregate_op is not None:
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self.aggregate_op = aggregate_op
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def __hash__(self):
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return hash(repr(self))
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def __eq__(self, other):
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return str(other) == str(self)
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def __ne__(self, other):
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return not self == other
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def __repr__(self):
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string = 'AggregateFilter\n'
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string += '{0: <16}{1}{2}\n'.format('\tType', '=\t', self.type)
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string += '{0: <16}{1}{2}\n'.format('\tBins', '=\t', self.bins)
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return string
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def __deepcopy__(self, memo):
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existing = memo.get(id(self))
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# If this is the first time we have tried to copy this object, create a copy
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if existing is None:
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clone = type(self).__new__(type(self))
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clone._type = self.type
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clone._aggregate_filter = self.aggregate_filter
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clone._aggregate_op = self.aggregate_op
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clone._bins = self._bins
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clone._stride = self.stride
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memo[id(self)] = clone
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return clone
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# If this object has been copied before, return the first copy made
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else:
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return existing
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@property
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def aggregate_filter(self):
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return self._aggregate_filter
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@property
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def aggregate_op(self):
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return self._aggregate_op
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@property
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def type(self):
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return self._type
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@property
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def bins(self):
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return self._bins
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@property
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def num_bins(self):
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return 1 if self.aggregate_filter else 0
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@property
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def stride(self):
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return self._stride
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@type.setter
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def type(self, filter_type):
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if filter_type not in _FILTER_TYPES.values():
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msg = 'Unable to set AggregateFilter type to "{0}" since it ' \
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'is not one of the supported types'.format(filter_type)
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raise ValueError(msg)
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self._type = filter_type
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@aggregate_filter.setter
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def aggregate_filter(self, aggregate_filter):
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cv.check_type('aggregate_filter', aggregate_filter, (Filter, CrossFilter))
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self._aggregate_filter = aggregate_filter
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@bins.setter
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def bins(self, bins):
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cv.check_iterable_type('bins', bins, (Integral, tuple))
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self._bins = bins
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@aggregate_op.setter
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def aggregate_op(self, aggregate_op):
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cv.check_type('aggregate_op', aggregate_op, basestring)
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cv.check_value('aggregate_op', aggregate_op, _TALLY_AGGREGATE_OPS)
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self._aggregate_op = aggregate_op
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@stride.setter
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def stride(self, stride):
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self._stride = stride
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def get_bin_index(self, filter_bin):
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"""Returns the index in the AggregateFilter for some bin.
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Parameters
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----------
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filter_bin : Integral or tuple of Real
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A tuple of value(s) corresponding to the bin of interest in
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the aggregated filter. The bin is the integer ID for 'material',
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'surface', 'cell', 'cellborn', and 'universe' Filters. The bin
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is the integer cell instance ID for 'distribcell' Filters. The
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bin is a 2-tuple of floats for 'energy' and 'energyout' filters
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corresponding to the energy boundaries of the bin of interest.
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The bin is a (x,y,z) 3-tuple for 'mesh' filters corresponding to
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the mesh cell of interest.
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Returns
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-------
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filter_index : Integral
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The index in the Tally data array for this filter bin. For an
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AggregateTally the filter bin index is always unity.
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Raises
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------
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ValueError
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When the filter_bin is not part of the aggregated filter's bins
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"""
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if filter_bin not in self.bins:
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msg = 'Unable to get the bin index for AggregateFilter since ' \
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'"{0}" is not one of the bins'.format(filter_bin)
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raise ValueError(msg)
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else:
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return 0
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def get_pandas_dataframe(self, datasize, summary=None):
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"""Builds a Pandas DataFrame for the AggregateFilter's bins.
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This method constructs a Pandas DataFrame object for the AggregateFilter
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with columns annotated by filter bin information. This is a helper
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method for the Tally.get_pandas_dataframe(...) method.
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Parameters
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----------
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datasize : Integral
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The total number of bins in the tally corresponding to this filter
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summary : None or Summary
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An optional Summary object to be used to construct columns for
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distribcell tally filters (default is None). NOTE: This parameter
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is not used by the AggregateFilter and simply mirrors the method
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signature for the CrossFilter.
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Returns
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-------
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pandas.DataFrame
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A Pandas DataFrame with columns of strings that characterize the
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aggregatefilter's bins. Each entry in the DataFrame will include
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one or more aggregation operations used to construct the
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aggregatefilter's bins. The number of rows in the DataFrame is the
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same as the total number of bins in the corresponding tally, with
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the filter bins appropriately tiled to map to the corresponding
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tally bins.
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See also
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--------
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Tally.get_pandas_dataframe(), Filter.get_pandas_dataframe(),
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CrossFilter.get_pandas_dataframe()
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"""
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import pandas as pd
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# Construct a sring representing the filter aggregation
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aggregate_bin = '{0}('.format(self.aggregate_op)
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aggregate_bin += ', '.join(map(str, self.bins)) + ')'
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# Construct NumPy array of bin repeated for each element in dataframe
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aggregate_bin_array = np.array([aggregate_bin])
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aggregate_bin_array = np.repeat(aggregate_bin_array, datasize)
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# Construct Pandas DataFrame for the AggregateFilter
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df = pd.DataFrame({self.type: aggregate_bin_array})
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return df
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@ -1,16 +1,21 @@
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import sys
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from numbers import Integral
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import numpy as np
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from openmc import Filter, Nuclide
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from openmc.filter import _FILTER_TYPES
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import openmc.checkvalue as cv
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if sys.version_info[0] >= 3:
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basestring = str
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# Acceptable tally arithmetic binary operations
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_TALLY_ARITHMETIC_OPS = ['+', '-', '*', '/', '^']
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# Acceptable tally aggregation operations
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_TALLY_AGGREGATE_OPS = ['sum', 'mean']
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class CrossScore(object):
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"""A special-purpose tally score used to encapsulate all combinations of two
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@ -97,17 +102,19 @@ class CrossScore(object):
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@left_score.setter
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def left_score(self, left_score):
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cv.check_type('left_score', left_score, (basestring, CrossScore))
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cv.check_type('left_score', left_score,
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(basestring, CrossScore, AggregateScore))
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self._left_score = left_score
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@right_score.setter
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def right_score(self, right_score):
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cv.check_type('right_score', right_score, (basestring, CrossScore))
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cv.check_type('right_score', right_score,
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(basestring, CrossScore, AggregateScore))
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self._right_score = right_score
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@binary_op.setter
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def binary_op(self, binary_op):
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cv.check_type('binary_op', binary_op, (basestring, CrossScore))
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cv.check_type('binary_op', binary_op, basestring)
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cv.check_value('binary_op', binary_op, _TALLY_ARITHMETIC_OPS)
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self._binary_op = binary_op
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@ -214,12 +221,14 @@ class CrossNuclide(object):
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@left_nuclide.setter
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def left_nuclide(self, left_nuclide):
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cv.check_type('left_nuclide', left_nuclide, (Nuclide, CrossNuclide))
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cv.check_type('left_nuclide', left_nuclide,
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(Nuclide, CrossNuclide, AggregateNuclide))
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self._left_nuclide = left_nuclide
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@right_nuclide.setter
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def right_nuclide(self, right_nuclide):
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cv.check_type('right_nuclide', right_nuclide, (Nuclide, CrossNuclide))
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cv.check_type('right_nuclide', right_nuclide,
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(Nuclide, CrossNuclide, AggregateNuclide))
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self._right_nuclide = right_nuclide
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@binary_op.setter
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@ -372,13 +381,15 @@ class CrossFilter(object):
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@left_filter.setter
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def left_filter(self, left_filter):
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cv.check_type('left_filter', left_filter, (Filter, CrossFilter))
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cv.check_type('left_filter', left_filter,
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(Filter, CrossFilter, AggregateFilter))
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self._left_filter = left_filter
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self._bins['left'] = left_filter.bins
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@right_filter.setter
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def right_filter(self, right_filter):
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cv.check_type('right_filter', right_filter, (Filter, CrossFilter))
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cv.check_type('right_filter', right_filter,
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(Filter, CrossFilter, AggregateFilter))
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self._right_filter = right_filter
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self._bins['right'] = right_filter.bins
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@ -472,3 +483,398 @@ class CrossFilter(object):
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df = '(' + left_df + ' ' + self.binary_op + ' ' + right_df + ')'
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return df
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class AggregateScore(object):
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"""A special-purpose tally score used to encapsulate an aggregate of a
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||||
subset or all of tally's scores for tally aggregation.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
scores : Iterable of str or CrossScore
|
||||
The scores included in the aggregation
|
||||
aggregate_op : str
|
||||
The tally aggregation operator (e.g., 'sum', 'mean', etc.) used
|
||||
to aggregate across a tally's scores with this AggregateScore
|
||||
|
||||
Attributes
|
||||
----------
|
||||
scores : Iterable of str or CrossScore
|
||||
The scores included in the aggregation
|
||||
aggregate_op : str
|
||||
The tally aggregation operator (e.g., 'sum', 'mean', etc.) used
|
||||
to aggregate across a tally's scores with this AggregateScore
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, scores=None, aggregate_op=None):
|
||||
|
||||
self._scores = None
|
||||
self._aggregate_op = None
|
||||
|
||||
if scores is not None:
|
||||
self.scores = scores
|
||||
if aggregate_op is not None:
|
||||
self.aggregate_op = aggregate_op
|
||||
|
||||
def __hash__(self):
|
||||
return hash(repr(self))
|
||||
|
||||
def __eq__(self, other):
|
||||
return str(other) == str(self)
|
||||
|
||||
def __ne__(self, other):
|
||||
return not self == other
|
||||
|
||||
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 existing is None:
|
||||
clone = type(self).__new__(type(self))
|
||||
clone._scores = self.scores
|
||||
clone._aggregate_op = self.aggregate_op
|
||||
|
||||
memo[id(self)] = clone
|
||||
|
||||
return clone
|
||||
|
||||
# If this object has been copied before, return the first copy made
|
||||
else:
|
||||
return existing
|
||||
|
||||
def __repr__(self):
|
||||
string = ', '.join(map(str, self.scores))
|
||||
string = '{0}({1})'.format(self.aggregate_op, string)
|
||||
return string
|
||||
|
||||
@property
|
||||
def scores(self):
|
||||
return self._scores
|
||||
|
||||
@property
|
||||
def aggregate_op(self):
|
||||
return self._aggregate_op
|
||||
|
||||
@scores.setter
|
||||
def scores(self, scores):
|
||||
cv.check_iterable_type('scores', scores,
|
||||
(basestring, CrossScore, AggregateScore))
|
||||
self._scores = scores
|
||||
|
||||
@aggregate_op.setter
|
||||
def aggregate_op(self, aggregate_op):
|
||||
cv.check_type('aggregate_op', aggregate_op, (basestring, CrossScore))
|
||||
cv.check_value('aggregate_op', aggregate_op, _TALLY_AGGREGATE_OPS)
|
||||
self._aggregate_op = aggregate_op
|
||||
|
||||
|
||||
class AggregateNuclide(object):
|
||||
"""A special-purpose tally nuclide used to encapsulate an aggregate of a
|
||||
subset or all of tally's nuclides for tally aggregation.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
nuclides : Iterable of str or Nuclide or CrossNuclide
|
||||
The nuclides included in the aggregation
|
||||
aggregate_op : str
|
||||
The tally aggregation operator (e.g., 'sum', 'mean', etc.) used
|
||||
to aggregate across a tally's nuclides with this AggregateNuclide
|
||||
|
||||
Attributes
|
||||
----------
|
||||
nuclides : Iterable of str or Nuclide or CrossNuclide
|
||||
The nuclides included in the aggregation
|
||||
aggregate_op : str
|
||||
The tally aggregation operator (e.g., 'sum', 'mean', etc.) used
|
||||
to aggregate across a tally's nuclides with this AggregateNuclide
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, nuclides=None, aggregate_op=None):
|
||||
|
||||
self._nuclides = None
|
||||
self._aggregate_op = None
|
||||
|
||||
if nuclides is not None:
|
||||
self.nuclides = nuclides
|
||||
if aggregate_op is not None:
|
||||
self.aggregate_op = aggregate_op
|
||||
|
||||
def __hash__(self):
|
||||
return hash(repr(self))
|
||||
|
||||
def __eq__(self, other):
|
||||
return str(other) == str(self)
|
||||
|
||||
def __ne__(self, other):
|
||||
return not self == other
|
||||
|
||||
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 existing is None:
|
||||
clone = type(self).__new__(type(self))
|
||||
clone._nuclides = self.nuclides
|
||||
clone._aggregate_op = self._aggregate_op
|
||||
|
||||
memo[id(self)] = clone
|
||||
|
||||
return clone
|
||||
|
||||
# If this object has been copied before, return the first copy made
|
||||
else:
|
||||
return existing
|
||||
|
||||
def __repr__(self):
|
||||
|
||||
# Append each nuclide in the aggregate to the string
|
||||
string = '{0}('.format(self.aggregate_op)
|
||||
names = [nuclide.name if isinstance(nuclide, Nuclide) else str(nuclide)
|
||||
for nuclide in self.nuclides]
|
||||
string += ', '.join(map(str, names)) + ')'
|
||||
return string
|
||||
|
||||
@property
|
||||
def nuclides(self):
|
||||
return self._nuclides
|
||||
|
||||
@property
|
||||
def aggregate_op(self):
|
||||
return self._aggregate_op
|
||||
|
||||
@nuclides.setter
|
||||
def nuclides(self, nuclides):
|
||||
cv.check_iterable_type('nuclides', nuclides,
|
||||
(basestring, Nuclide, CrossNuclide, AggregateNuclide))
|
||||
self._nuclides = nuclides
|
||||
|
||||
@aggregate_op.setter
|
||||
def aggregate_op(self, aggregate_op):
|
||||
cv.check_type('aggregate_op', aggregate_op, basestring)
|
||||
cv.check_value('aggregate_op', aggregate_op, _TALLY_AGGREGATE_OPS)
|
||||
self._aggregate_op = aggregate_op
|
||||
|
||||
|
||||
class AggregateFilter(object):
|
||||
"""A special-purpose tally filter used to encapsulate an aggregate of a
|
||||
subset or all of a tally filter's bins for tally aggregation.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
aggregate_filter : Filter or CrossFilter
|
||||
The filter included in the aggregation
|
||||
bins : Iterable of tuple
|
||||
The filter bins included in the aggregation
|
||||
aggregate_op : str
|
||||
The tally aggregation operator (e.g., 'sum', 'mean', etc.) used
|
||||
to aggregate across a tally filter's bins with this AggregateFilter
|
||||
|
||||
Attributes
|
||||
----------
|
||||
type : str
|
||||
The type of the aggregatefilter (e.g., 'sum(energy)', 'sum(cell)')
|
||||
aggregate_filter : filter
|
||||
The filter included in the aggregation
|
||||
aggregate_op : str
|
||||
The tally aggregation operator (e.g., 'sum', 'mean', etc.) used
|
||||
to aggregate across a tally filter's bins with this AggregateFilter
|
||||
bins : Iterable of tuple
|
||||
The filter bins included in the aggregation
|
||||
num_bins : Integral
|
||||
The number of filter bins (always 1 if aggregate_filter is defined)
|
||||
stride : Integral
|
||||
The number of filter, nuclide and score bins within each of this
|
||||
aggregatefilter's bins.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, aggregate_filter=None, bins=None, aggregate_op=None):
|
||||
|
||||
self._type = '{0}({1})'.format(aggregate_op, aggregate_filter.type)
|
||||
self._bins = None
|
||||
self._stride = None
|
||||
|
||||
self._aggregate_filter = None
|
||||
self._aggregate_op = None
|
||||
|
||||
if aggregate_filter is not None:
|
||||
self.aggregate_filter = aggregate_filter
|
||||
if bins is not None:
|
||||
self.bins = bins
|
||||
if aggregate_op is not None:
|
||||
self.aggregate_op = aggregate_op
|
||||
|
||||
def __hash__(self):
|
||||
return hash(repr(self))
|
||||
|
||||
def __eq__(self, other):
|
||||
return str(other) == str(self)
|
||||
|
||||
def __ne__(self, other):
|
||||
return not self == other
|
||||
|
||||
def __repr__(self):
|
||||
string = 'AggregateFilter\n'
|
||||
string += '{0: <16}{1}{2}\n'.format('\tType', '=\t', self.type)
|
||||
string += '{0: <16}{1}{2}\n'.format('\tBins', '=\t', self.bins)
|
||||
return string
|
||||
|
||||
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 existing is None:
|
||||
clone = type(self).__new__(type(self))
|
||||
clone._type = self.type
|
||||
clone._aggregate_filter = self.aggregate_filter
|
||||
clone._aggregate_op = self.aggregate_op
|
||||
clone._bins = self._bins
|
||||
clone._stride = self.stride
|
||||
|
||||
memo[id(self)] = clone
|
||||
|
||||
return clone
|
||||
|
||||
# If this object has been copied before, return the first copy made
|
||||
else:
|
||||
return existing
|
||||
|
||||
@property
|
||||
def aggregate_filter(self):
|
||||
return self._aggregate_filter
|
||||
|
||||
@property
|
||||
def aggregate_op(self):
|
||||
return self._aggregate_op
|
||||
|
||||
@property
|
||||
def type(self):
|
||||
return self._type
|
||||
|
||||
@property
|
||||
def bins(self):
|
||||
return self._bins
|
||||
|
||||
@property
|
||||
def num_bins(self):
|
||||
return 1 if self.aggregate_filter else 0
|
||||
|
||||
@property
|
||||
def stride(self):
|
||||
return self._stride
|
||||
|
||||
@type.setter
|
||||
def type(self, filter_type):
|
||||
if filter_type not in _FILTER_TYPES.values():
|
||||
msg = 'Unable to set AggregateFilter type to "{0}" since it ' \
|
||||
'is not one of the supported types'.format(filter_type)
|
||||
raise ValueError(msg)
|
||||
|
||||
self._type = filter_type
|
||||
|
||||
@aggregate_filter.setter
|
||||
def aggregate_filter(self, aggregate_filter):
|
||||
cv.check_type('aggregate_filter', aggregate_filter,
|
||||
(Filter, CrossFilter, AggregateFilter))
|
||||
self._aggregate_filter = aggregate_filter
|
||||
|
||||
@bins.setter
|
||||
def bins(self, bins):
|
||||
cv.check_iterable_type('bins', bins, (Integral, tuple))
|
||||
self._bins = bins
|
||||
|
||||
@aggregate_op.setter
|
||||
def aggregate_op(self, aggregate_op):
|
||||
cv.check_type('aggregate_op', aggregate_op, basestring)
|
||||
cv.check_value('aggregate_op', aggregate_op, _TALLY_AGGREGATE_OPS)
|
||||
self._aggregate_op = aggregate_op
|
||||
|
||||
@stride.setter
|
||||
def stride(self, stride):
|
||||
self._stride = stride
|
||||
|
||||
def get_bin_index(self, filter_bin):
|
||||
"""Returns the index in the AggregateFilter for some bin.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
filter_bin : Integral or tuple of Real
|
||||
A tuple of value(s) corresponding to the bin of interest in
|
||||
the aggregated filter. The bin is the integer ID for 'material',
|
||||
'surface', 'cell', 'cellborn', and 'universe' Filters. The bin
|
||||
is the integer cell instance ID for 'distribcell' Filters. The
|
||||
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.
|
||||
|
||||
Returns
|
||||
-------
|
||||
filter_index : Integral
|
||||
The index in the Tally data array for this filter bin. For an
|
||||
AggregateTally the filter bin index is always unity.
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
When the filter_bin is not part of the aggregated filter's bins
|
||||
|
||||
"""
|
||||
|
||||
if filter_bin not in self.bins:
|
||||
msg = 'Unable to get the bin index for AggregateFilter since ' \
|
||||
'"{0}" is not one of the bins'.format(filter_bin)
|
||||
raise ValueError(msg)
|
||||
else:
|
||||
return 0
|
||||
|
||||
def get_pandas_dataframe(self, datasize, summary=None):
|
||||
"""Builds a Pandas DataFrame for the AggregateFilter's bins.
|
||||
|
||||
This method constructs a Pandas DataFrame object for the AggregateFilter
|
||||
with columns annotated by filter bin information. This is a helper
|
||||
method for the Tally.get_pandas_dataframe(...) method.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
datasize : Integral
|
||||
The total number of bins in the tally corresponding to this filter
|
||||
summary : None or Summary
|
||||
An optional Summary object to be used to construct columns for
|
||||
distribcell tally filters (default is None). NOTE: This parameter
|
||||
is not used by the AggregateFilter and simply mirrors the method
|
||||
signature for the CrossFilter.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pandas.DataFrame
|
||||
A Pandas DataFrame with columns of strings that characterize the
|
||||
aggregatefilter's bins. Each entry in the DataFrame will include
|
||||
one or more aggregation operations used to construct the
|
||||
aggregatefilter's bins. The number of rows in the DataFrame is the
|
||||
same as the total number of bins in the corresponding tally, with
|
||||
the filter bins appropriately tiled to map to the corresponding
|
||||
tally bins.
|
||||
|
||||
See also
|
||||
--------
|
||||
Tally.get_pandas_dataframe(), Filter.get_pandas_dataframe(),
|
||||
CrossFilter.get_pandas_dataframe()
|
||||
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
# Construct a sring representing the filter aggregation
|
||||
aggregate_bin = '{0}('.format(self.aggregate_op)
|
||||
aggregate_bin += ', '.join(map(str, self.bins)) + ')'
|
||||
|
||||
# Construct NumPy array of bin repeated for each element in dataframe
|
||||
aggregate_bin_array = np.array([aggregate_bin])
|
||||
aggregate_bin_array = np.repeat(aggregate_bin_array, datasize)
|
||||
|
||||
# Construct Pandas DataFrame for the AggregateFilter
|
||||
df = pd.DataFrame({self.type: aggregate_bin_array})
|
||||
return df
|
||||
|
|
@ -396,7 +396,7 @@ class Library(object):
|
|||
|
||||
cv.check_type('statepoint', statepoint, openmc.StatePoint)
|
||||
|
||||
if not statepoint.with_summary:
|
||||
if statepoint.summary is None:
|
||||
msg = 'Unable to load data from a statepoint which has not been ' \
|
||||
'linked with a summary file'
|
||||
raise ValueError(msg)
|
||||
|
|
|
|||
|
|
@ -583,7 +583,7 @@ class MGXS(object):
|
|||
|
||||
cv.check_type('statepoint', statepoint, openmc.statepoint.StatePoint)
|
||||
|
||||
if not statepoint.with_summary:
|
||||
if statepoint.summary is None:
|
||||
msg = 'Unable to load data from a statepoint which has not been ' \
|
||||
'linked with a summary file'
|
||||
raise ValueError(msg)
|
||||
|
|
@ -612,6 +612,11 @@ class MGXS(object):
|
|||
filters = []
|
||||
filter_bins = []
|
||||
|
||||
# Clear any tallies previously loaded from a statepoint
|
||||
self._tallies = None
|
||||
self._xs_tally = None
|
||||
self._rxn_rate_tally = None
|
||||
|
||||
# Find, slice and store Tallies from StatePoint
|
||||
# The tally slicing is needed if tally merging was used
|
||||
for tally_type, tally in self.tallies.items():
|
||||
|
|
|
|||
|
|
@ -449,10 +449,6 @@ class StatePoint(object):
|
|||
def summary(self):
|
||||
return self._summary
|
||||
|
||||
@property
|
||||
def with_summary(self):
|
||||
return False if self.summary is None else True
|
||||
|
||||
@sparse.setter
|
||||
def sparse(self, sparse):
|
||||
"""Convert tally data from NumPy arrays to SciPy list of lists (LIL)
|
||||
|
|
|
|||
|
|
@ -12,8 +12,7 @@ import sys
|
|||
import numpy as np
|
||||
|
||||
from openmc import Mesh, Filter, Trigger, Nuclide
|
||||
from openmc.cross import CrossScore, CrossNuclide, CrossFilter
|
||||
from openmc.aggregate import AggregateScore, AggregateNuclide, AggregateFilter
|
||||
from openmc.arithmetic import *
|
||||
from openmc.filter import _FILTER_TYPES
|
||||
import openmc.checkvalue as cv
|
||||
from openmc.clean_xml import *
|
||||
|
|
@ -2680,11 +2679,11 @@ class Tally(object):
|
|||
new_tally = copy.deepcopy(self)
|
||||
new_tally.sparse = False
|
||||
|
||||
if self.sum is not None:
|
||||
if not self.derived and self.sum is not None:
|
||||
new_sum = self.get_values(scores, filters, filter_bins,
|
||||
nuclides, 'sum')
|
||||
new_tally.sum = new_sum
|
||||
if self.sum_sq is not None:
|
||||
if not self.derived and self.sum_sq is not None:
|
||||
new_sum_sq = self.get_values(scores, filters, filter_bins,
|
||||
nuclides, 'sum_sq')
|
||||
new_tally.sum_sq = new_sum_sq
|
||||
|
|
@ -2955,10 +2954,10 @@ class Tally(object):
|
|||
diag_indices[start:end] = indices + (i * new_filter.num_bins**2)
|
||||
|
||||
# Inject this Tally's data along the diagonal of the diagonalized Tally
|
||||
if self.sum is not None:
|
||||
if not self.derived and self.sum is not None:
|
||||
new_tally._sum = np.zeros(new_tally.shape, dtype=np.float64)
|
||||
new_tally._sum[diag_indices, :, :] = self.sum
|
||||
if self.sum_sq is not None:
|
||||
if not self.derived and self.sum_sq is not None:
|
||||
new_tally._sum_sq = np.zeros(new_tally.shape, dtype=np.float64)
|
||||
new_tally._sum_sq[diag_indices, :, :] = self.sum_sq
|
||||
if self.mean is not None:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue