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Added docstrings to tally arithmetic routines
This commit is contained in:
Will Boyd
parent
67fb220ada
commit
f6128ff387
1 changed files with 294 additions and 51 deletions
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@ -803,8 +803,8 @@ class Tally(object):
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nuclides=[], value='mean'):
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"""Returns a tally score value given a list of filters to satisfy.
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This routine constructs a 3D NumPy array for the requested Tally data
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indexed by filter bin, nuclide bin, and score index. The routine will
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This method constructs a 3D NumPy array for the requested Tally data
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indexed by filter bin, nuclide bin, and score index. The method will
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order the data in the array as specified in the parameter lists
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Parameters
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@ -846,8 +846,8 @@ class Tally(object):
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Raises
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------
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ValueError
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When this routine is called before the Tally is populated with data
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by the StatePoint.read_results() routine. ValueError is also thrown
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When this method is called before the Tally is populated with data
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by the StatePoint.read_results() method. ValueError is also thrown
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if the input parameters do not correspond to the Tally's attributes,
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e.g., if the score(s) do not match those in the Tally.
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@ -860,7 +860,7 @@ class Tally(object):
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(value == 'sum' and self.sum is None) or \
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(value == 'sum_sq' and self.sum_sq is None):
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msg = 'The Tally ID={0} has no data to return. Call the ' \
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'StatePoint.read_results() routine before using ' \
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'StatePoint.read_results() method before using ' \
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'Tally.get_values(...)'.format(self.id)
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raise ValueError(msg)
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@ -911,7 +911,7 @@ class Tally(object):
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filter_indices[i].append(
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self.get_filter_index(filter.type, bin))
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# Apply cross-product sum between all filter bin indices
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# Apply outer product sum between all filter bin indices
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filter_indices = list(map(sum, itertools.product(*filter_indices)))
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# If user did not specify any specific Filters, use them all
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@ -940,7 +940,7 @@ class Tally(object):
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else:
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score_indices = np.arange(self.num_scores)
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# Construct cross-product of all three index types with each other
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# Construct outer product of all three index types with each other
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indices = np.ix_(filter_indices, nuclide_indices, score_indices)
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# Return the desired result from Tally
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@ -966,11 +966,11 @@ class Tally(object):
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scores=True, summary=None):
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"""Build a Pandas DataFrame for the Tally data.
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This routine constructs a Pandas DataFrame object for the Tally data
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This method constructs a Pandas DataFrame object for the Tally data
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with columns annotated by filter, nuclide and score bin information.
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This capability has been tested for Pandas >=v0.13.1. However, if
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possible, it is recommended to use the v0.16 or newer versions of
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Pandas since this this routine uses the Multi-index Pandas feature.
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Pandas since this this method uses the Multi-index Pandas feature.
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Parameters
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----------
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@ -1000,22 +1000,22 @@ class Tally(object):
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Raises
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------
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KeyError
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When this routine is called before the Tally is populated with data
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by the StatePoint.read_results() routine.
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When this method is called before the Tally is populated with data
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by the StatePoint.read_results() method.
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"""
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# Ensure that StatePoint.read_results() was called first
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if self.mean is None or self.std_dev is None:
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msg = 'The Tally ID={0} has no data to return. Call the ' \
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'StatePoint.read_results() routine before using ' \
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'StatePoint.read_results() method before using ' \
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'Tally.get_pandas_dataframe(...)'.format(self.id)
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raise KeyError(msg)
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# If using Summary, ensure StatePoint.link_with_summary(...) was called
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if summary and not self.with_summary:
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msg = 'The Tally ID={0} has not been linked with the Summary. ' \
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'Call the StatePoint.link_with_summary(...) routine ' \
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'Call the StatePoint.link_with_summary(...) method ' \
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'before using Tally.get_pandas_dataframe(...) with ' \
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'Summary info'.format(self.id)
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raise KeyError(msg)
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@ -1308,15 +1308,15 @@ class Tally(object):
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Raises
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------
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KeyError
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When this routine is called before the Tally is populated with data
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by the StatePoint.read_results() routine.
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When this method is called before the Tally is populated with data
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by the StatePoint.read_results() method.
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"""
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# Ensure that StatePoint.read_results() was called first
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if self._sum is None or self._sum_sq is None:
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msg = 'The Tally ID={0} has no data to export. Call the ' \
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'StatePoint.read_results() routine before using ' \
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'StatePoint.read_results() method before using ' \
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'Tally.export_results(...)'.format(self.id)
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raise KeyError(msg)
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@ -1433,60 +1433,91 @@ class Tally(object):
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# Pickle the Tally results to a file
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pickle.dump(tally_results, open(filename, 'wb'))
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def _outer_product(self, other_tally, new_tally, binary_op):
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"""
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def _outer_product(self, other, new_tally, binary_op):
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"""Combines filters, scores and nuclides with another tally.
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This is a helper method for the tally arithmetic methods. The ilters,
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scores and nuclides from both tallies are enumerated into all possible
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combinations and expressed as _CrossFilter, _CrossScore and
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_CrossNuclide objects in the new derived tally.
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Parameters
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----------
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other_tally : Tally
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The tally on the right hand side of the cross-product
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other : Tally
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The tally on the right hand side of the outer product
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new_tally: Tally
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The new tally to represent the cross-product
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The new tally to represent the outer product
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op : str
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The binary operation in the cross product (+,-,*,/,^)
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The binary operation in the outer product ('+', '-', '*', '/', '^')
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"""
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if self.estimator == other_tally.estimator:
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new_tally.name = '({0} {1} {2})'.format(self.name, other.name, binary_op)
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if self.estimator == other.estimator:
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new_tally.estimator = self.estimator
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if self.with_summary and other_tally.with_summary:
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if self.with_summary and other.with_summary:
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new_tally.with_summary = self.with_summary
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if self.num_realizations == other_tally.num_realizations:
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if self.num_realizations == other.num_realizations:
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new_tally.num_realizations = self.num_realizations
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# Generate nuclide "cross products"
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if self.nuclides == other_tally.nuclides:
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for self_nuclide in self.nuclides:
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new_nuclide = _CrossNuclide(self_nuclide, self_nuclide, binary_op)
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new_tally.add_nuclide(new_nuclide)
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else:
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all_nuclides = [self.nuclides, other_tally.nuclides]
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for self_nuclide, other_nuclide in itertools.product(*all_nuclides):
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new_nuclide = _CrossNuclide(self_nuclide, other_nuclide, binary_op)
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new_tally.add_nuclide(new_nuclide)
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# Generate filter "cross products"
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if self.filters == other_tally.filters:
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# Generate filter "outer products"
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if self.filters == other.filters:
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for self_filter in self.filters:
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new_filter = _CrossScore(self_filter, self_filter, binary_op)
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new_tally.add_filter(new_filter)
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else:
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all_filters = [self.filters, other_tally.filters]
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all_filters = [self.filters, other.filters]
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for self_filter, other_filter in itertools.product(*all_filters):
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new_filter = _CrossScore(self_filter, other_filter, binary_op)
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new_tally.add_filter(new_filter)
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# Generate score "cross products"
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if self.scores == other_tally.scores:
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# Generate score "outer products"
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if self.scores == other.scores:
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for self_score in self.scores:
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new_score = _CrossScore(self_score, self_score, binary_op)
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new_tally.add_score(new_score)
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else:
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all_scores = [self.scores, other_tally.scores]
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all_scores = [self.scores, other.scores]
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for self_score, other_score in itertools.product(*all_scores):
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new_score = _CrossScore(self_score, other_score, binary_op)
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new_tally.add_score(new_score)
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# Generate nuclide "outer products"
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if self.nuclides == other.nuclides:
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for self_nuclide in self.nuclides:
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new_nuclide = _CrossNuclide(self_nuclide, self_nuclide, binary_op)
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new_tally.add_nuclide(new_nuclide)
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else:
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all_nuclides = [self.nuclides, other.nuclides]
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for self_nuclide, other_nuclide in itertools.product(*all_nuclides):
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new_nuclide = _CrossNuclide(self_nuclide, other_nuclide, binary_op)
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new_tally.add_nuclide(new_nuclide)
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def _align_tally_data(self, other):
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"""Aligns data from two tallies for tally arithmetic.
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This is a helper method to construct a dict of dicts of the "aligned"
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data arrays from each tally for tally arithmetic. The method analyzes
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the filters, scores and nuclides in both tally's and determines how to
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appropriately align the data for vectorized arithmetic. For example,
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if the two tallies have different filters, this method will use NumPy
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'tile' and 'repeat' operations to the new data arrays such that all
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possible combinations of the data in each tally's bins will be made
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when the arithmetic operation is applied to the arrays.
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Parameters
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----------
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other : Tally
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The tally to outer product with this tally
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Returns
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-------
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dict
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A dictionary of dictionaries to "aligned" 'mean' and 'std. dev'
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NumPy arrays for each tally's data.
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"""
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self_mean = copy.deepcopy(self.mean)
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self_std_dev = copy.deepcopy(self.std_dev)
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@ -1551,6 +1582,34 @@ class Tally(object):
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return data
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def __add__(self, other):
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"""Adds this tally to another tally or scalar value.
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This method builds a new tally with data that is the sum of this
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tally's data and that from the other tally or scalar value. If the
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filters, scores and nuclides in the two tallies are not the same, then
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they are combined in all possible ways in the new derived tally.
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Uncertainty propagation is used to compute the standard deviation
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for the new tally's data. It is important to note that this makes
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the assumption that the tally data is independently distributed.
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In most use cases, this is *not* true and may lead to under-prediction
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of the uncertainty. The uncertainty propagation model is from the
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following source:
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https://en.wikipedia.org/wiki/Propagation_of_uncertainty
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Parameters
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----------
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other : Tally or Integer or Rational
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The tally or scalar value to add to this tally
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Returns
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-------
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Tally
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A new derived tally which is the sum of this tally and the other
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tally or scalar value in the addition.
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"""
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# Check that results have been read
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if self.mean is None:
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@ -1558,7 +1617,7 @@ class Tally(object):
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'since it does not contain any results.'.format(self.id)
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raise ValueError(msg)
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new_tally = Tally(name='derived')
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new_tally = Tally()
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new_tally.with_batch_statistics = True
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if isinstance(other, Tally):
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@ -1569,7 +1628,6 @@ class Tally(object):
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'since it does not contain any results.'.format(other.id)
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raise ValueError(msg)
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# FIXME: Need to be able to use Tally.get_pandas_dataframe - filters
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# FIXME: Need to be able to use StatePoint.get_tally
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# FIXME: Need to be able to use Tally.get_value
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@ -1581,6 +1639,7 @@ class Tally(object):
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elif isinstance(other, (Integral, Rational)):
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new_tally.name = self.name
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new_tally._mean = self._mean + other
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new_tally._std_dev = self._std_dev
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new_tally.estimator = self.estimator
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@ -1602,6 +1661,34 @@ class Tally(object):
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return new_tally
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def __sub__(self, other):
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"""Subtracts another tally or scalar value from this tally.
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This method builds a new tally with data that is the difference of
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this tally's data and that from the other tally or scalar value. If the
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filters, scores and nuclides in the two tallies are not the same, then
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they are combined in all possible ways in the new derived tally.
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Uncertainty propagation is used to compute the standard deviation
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for the new tally's data. It is important to note that this makes
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the assumption that the tally data is independently distributed.
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In most use cases, this is *not* true and may lead to under-prediction
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of the uncertainty. The uncertainty propagation model is from the
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following source:
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https://en.wikipedia.org/wiki/Propagation_of_uncertainty
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Parameters
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----------
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other : Tally or Integer or Rational
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The tally or scalar value to subtract from this tally
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Returns
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-------
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Tally
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A new derived tally which is the difference of this tally and the
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other tally or scalar value in the subtraction.
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"""
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# Check that results have been read
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if self.mean is None:
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@ -1609,7 +1696,7 @@ class Tally(object):
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'since it does not contain any results.'.format(self.id)
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raise ValueError(msg)
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new_tally = Tally(name='derived')
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new_tally = Tally()
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new_tally.with_batch_statistics = True
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if isinstance(other, Tally):
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@ -1620,7 +1707,6 @@ class Tally(object):
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'since it does not contain any results.'.format(other.id)
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raise ValueError(msg)
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# FIXME: Need to be able to use Tally.get_pandas_dataframe - filters
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# FIXME: Need to be able to use StatePoint.get_tally
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# FIXME: Need to be able to use Tally.get_value
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@ -1632,6 +1718,7 @@ class Tally(object):
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elif isinstance(other, (Integral, Rational)):
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new_tally.name = self.name
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new_tally._mean = self._mean - other
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new_tally._std_dev = self._std_dev
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new_tally.estimator = self.estimator
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@ -1654,6 +1741,34 @@ class Tally(object):
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return new_tally
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def __mul__(self, other):
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"""Multiplies this tally with another tally or scalar value.
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This method builds a new tally with data that is the product of
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this tally's data and that from the other tally or scalar value. If the
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filters, scores and nuclides in the two tallies are not the same, then
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they are combined in all possible ways in the new derived tally.
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Uncertainty propagation is used to compute the standard deviation
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for the new tally's data. It is important to note that this makes
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the assumption that the tally data is independently distributed.
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In most use cases, this is *not* true and may lead to under-prediction
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of the uncertainty. The uncertainty propagation model is from the
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following source:
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https://en.wikipedia.org/wiki/Propagation_of_uncertainty
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Parameters
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----------
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other : Tally or Integer or Rational
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The tally or scalar value to multiply with this tally
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Returns
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-------
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Tally
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A new derived tally which is the product of this tally and the
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other tally or scalar value in the multiplication.
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"""
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# Check that results have been read
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if self.mean is None:
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@ -1661,7 +1776,7 @@ class Tally(object):
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'since it does not contain any results.'.format(self.id)
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raise ValueError(msg)
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new_tally = Tally(name='derived')
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new_tally = Tally()
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new_tally.with_batch_statistics = True
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if isinstance(other, Tally):
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@ -1672,7 +1787,6 @@ class Tally(object):
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'since it does not contain any results.'.format(other.id)
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raise ValueError(msg)
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# FIXME: Need to be able to use Tally.get_pandas_dataframe - filters
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# FIXME: Need to be able to use StatePoint.get_tally
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# FIXME: Need to be able to use Tally.get_value
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@ -1686,6 +1800,7 @@ class Tally(object):
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elif isinstance(other, (Integral, Rational)):
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new_tally.name = self.name
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new_tally._mean = self._mean * other
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new_tally._std_dev = self._std_dev * np.abs(other)
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new_tally.estimator = self.estimator
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@ -1708,6 +1823,34 @@ class Tally(object):
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return new_tally
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def __div__(self, other):
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"""Divides this tally by another tally or scalar value.
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This method builds a new tally with data that is the dividend of
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this tally's data and that from the other tally or scalar value. If the
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filters, scores and nuclides in the two tallies are not the same, then
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they are combined in all possible ways in the new derived tally.
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Uncertainty propagation is used to compute the standard deviation
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for the new tally's data. It is important to note that this makes
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the assumption that the tally data is independently distributed.
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In most use cases, this is *not* true and may lead to under-prediction
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of the uncertainty. The uncertainty propagation model is from the
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following source:
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https://en.wikipedia.org/wiki/Propagation_of_uncertainty
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Parameters
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----------
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other : Tally or Integer or Rational
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The tally or scalar value to divide this tally by
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Returns
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-------
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Tally
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A new derived tally which is the dividend of this tally and the
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other tally or scalar value in the division.
|
||||
|
||||
"""
|
||||
|
||||
# Check that results have been read
|
||||
if self.mean is None:
|
||||
|
|
@ -1726,7 +1869,6 @@ class Tally(object):
|
|||
'since it does not contain any results.'.format(other.id)
|
||||
raise ValueError(msg)
|
||||
|
||||
# FIXME: Need to be able to use Tally.get_pandas_dataframe - filters
|
||||
# FIXME: Need to be able to use StatePoint.get_tally
|
||||
# FIXME: Need to be able to use Tally.get_value
|
||||
|
||||
|
|
@ -1740,6 +1882,7 @@ class Tally(object):
|
|||
|
||||
elif isinstance(other, (Integral, Rational)):
|
||||
|
||||
new_tally.name = self.name
|
||||
new_tally._mean = self._mean / other
|
||||
new_tally._std_dev = self._std_dev * np.abs(1. / other)
|
||||
new_tally.estimator = self.estimator
|
||||
|
|
@ -1762,6 +1905,34 @@ class Tally(object):
|
|||
return new_tally
|
||||
|
||||
def __pow__(self, power):
|
||||
"""Raises this tally to another tally or scalar value power.
|
||||
|
||||
This method builds a new tally with data that is the power of
|
||||
this tally's data to that from the other tally or scalar value. If the
|
||||
filters, scores and nuclides in the two tallies are not the same, then
|
||||
they are combined in all possible ways in the new derived tally.
|
||||
|
||||
Uncertainty propagation is used to compute the standard deviation
|
||||
for the new tally's data. It is important to note that this makes
|
||||
the assumption that the tally data is independently distributed.
|
||||
In most use cases, this is *not* true and may lead to under-prediction
|
||||
of the uncertainty. The uncertainty propagation model is from the
|
||||
following source:
|
||||
|
||||
https://en.wikipedia.org/wiki/Propagation_of_uncertainty
|
||||
|
||||
Parameters
|
||||
----------
|
||||
other : Tally or Integer or Rational
|
||||
The tally or scalar value exponent
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tally
|
||||
A new derived tally which is this tally raised to the power of the
|
||||
other tally or scalar value in the exponentiation.
|
||||
|
||||
"""
|
||||
|
||||
# Check that results have been read
|
||||
if self.mean is None:
|
||||
|
|
@ -1780,7 +1951,6 @@ class Tally(object):
|
|||
'since it does not contain any results.'.format(power.id)
|
||||
raise ValueError(msg)
|
||||
|
||||
# FIXME: Need to be able to use Tally.get_pandas_dataframe - filters
|
||||
# FIXME: Need to be able to use StatePoint.get_tally
|
||||
# FIXME: Need to be able to use Tally.get_value
|
||||
|
||||
|
|
@ -1795,6 +1965,7 @@ class Tally(object):
|
|||
|
||||
elif isinstance(power, (Integral, Rational)):
|
||||
|
||||
new_tally.name = self.name
|
||||
new_tally._mean = self._mean ** power
|
||||
self_rel_err = self.std_dev / self.mean
|
||||
new_tally._std_dev = np.abs(new_tally._mean * power * self_rel_err)
|
||||
|
|
@ -1818,18 +1989,90 @@ class Tally(object):
|
|||
return new_tally
|
||||
|
||||
def __radd__(self, other):
|
||||
"""Right addition with a scalar value.
|
||||
|
||||
This reverses the operands and calls the __add__ method.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
other : Integer or Rational
|
||||
The scalar value to add to this tally
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tally
|
||||
A new derived tally of this tally added with the scalar value.
|
||||
|
||||
"""
|
||||
|
||||
return self + other
|
||||
|
||||
def __rsub__(self, other):
|
||||
"""Right subtraction from a scalar value.
|
||||
|
||||
This reverses the operands and calls the __sub__ method.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
other : Integer or Rational
|
||||
The scalar value to subtract this tally from
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tally
|
||||
A new derived tally of this tally subtracted from the scalar value.
|
||||
|
||||
"""
|
||||
|
||||
return -1. * self + other
|
||||
|
||||
def __rmul__(self, other):
|
||||
"""Right multiplication with a scalar value.
|
||||
|
||||
This reverses the operands and calls the __mul__ method.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
other : Integer or Rational
|
||||
The scalar value to multiply with this tally
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tally
|
||||
A new derived tally of this tally multiplied by the scalar value.
|
||||
|
||||
"""
|
||||
|
||||
return self * other
|
||||
|
||||
def __rdiv__(self, other):
|
||||
"""Right division with a scalar value.
|
||||
|
||||
This reverses the operands and calls the __div__ method.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
other : Integer or Rational
|
||||
The scalar value to divide by this tally
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tally
|
||||
A new derived tally of the scalar value divided by this tally.
|
||||
|
||||
"""
|
||||
|
||||
return self * (1. / other)
|
||||
|
||||
def __pos__(self):
|
||||
"""The absolute value of this tally.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tally
|
||||
A new derived tally which is the absolute value of this tally.
|
||||
|
||||
"""
|
||||
new_tally = copy.deepcopy(self)
|
||||
new_tally._mean = np.abs(new_tally.mean)
|
||||
return new_tally
|
||||
|
|
@ -1977,7 +2220,7 @@ class Tally(object):
|
|||
# Ensure that StatePoint.read_results() was called first
|
||||
if (self.mean is None) or (self.std_dev is None):
|
||||
msg = 'The Tally ID={0} has no data to slice. Call the ' \
|
||||
'StatePoint.read_results() routine before using ' \
|
||||
'StatePoint.read_results() method before using ' \
|
||||
'Tally.slice(...)'.format(self.id)
|
||||
raise ValueError(msg)
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue