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https://github.com/openmc-dev/openmc.git
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Make filter_strides a property of Tally
This commit is contained in:
Paul Romano
parent
e698403076
commit
e6b5803f70
5 changed files with 71 additions and 225 deletions
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@ -237,9 +237,6 @@ class CrossFilter(object):
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left / right filters
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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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crossfilter's bins.
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"""
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@ -250,7 +247,6 @@ class CrossFilter(object):
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self._type = '({0} {1} {2})'.format(left_type, binary_op, right_type)
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self._bins = {}
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self._stride = None
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self._left_filter = None
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self._right_filter = None
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@ -314,10 +310,6 @@ class CrossFilter(object):
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else:
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return 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:
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@ -347,10 +339,6 @@ class CrossFilter(object):
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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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@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 CrossFilter for some bin.
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@ -611,9 +599,6 @@ class AggregateFilter(object):
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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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@ -622,7 +607,6 @@ class AggregateFilter(object):
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self._type = '{0}({1})'.format(aggregate_op,
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aggregate_filter.short_name.lower())
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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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@ -684,10 +668,6 @@ class AggregateFilter(object):
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def num_bins(self):
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return len(self.bins) 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:
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@ -714,10 +694,6 @@ class AggregateFilter(object):
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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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@ -753,7 +729,7 @@ class AggregateFilter(object):
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else:
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return self.bins.index(filter_bin)
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def get_pandas_dataframe(self, data_size, summary=None, **kwargs):
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def get_pandas_dataframe(self, data_size, stride, summary=None, **kwargs):
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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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@ -762,8 +738,10 @@ class AggregateFilter(object):
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Parameters
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----------
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data_size : Integral
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data_size : int
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The total number of bins in the tally corresponding to this filter
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stride : int
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Stride in memory for the 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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@ -793,7 +771,7 @@ class AggregateFilter(object):
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filter_bins[i] = bin
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# Repeat and tile bins as needed for DataFrame
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filter_bins = np.repeat(filter_bins, self.stride)
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filter_bins = np.repeat(filter_bins, stride)
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tile_factor = data_size / len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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210
openmc/filter.py
210
openmc/filter.py
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@ -86,9 +86,6 @@ class Filter(IDManagerMixin):
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Unique identifier for the filter
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num_bins : Integral
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The number of filter bins
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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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filter's bins.
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"""
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@ -99,7 +96,6 @@ class Filter(IDManagerMixin):
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self.bins = bins
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self.id = filter_id
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self._num_bins = 0
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self._stride = None
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def __eq__(self, other):
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if type(self) is not type(other):
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@ -194,10 +190,6 @@ class Filter(IDManagerMixin):
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def num_bins(self):
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return self._num_bins
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@property
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def stride(self):
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return self._stride
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@bins.setter
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def bins(self, bins):
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# Format the bins as a 1D numpy array.
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@ -214,16 +206,6 @@ class Filter(IDManagerMixin):
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cv.check_greater_than('filter num_bins', num_bins, 0, equality=True)
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self._num_bins = num_bins
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@stride.setter
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def stride(self, stride):
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cv.check_type('filter stride', stride, Integral)
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if stride < 0:
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msg = 'Unable to set stride "{0}" for a "{1}" since it ' \
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'is a negative value'.format(stride, type(self))
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raise ValueError(msg)
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self._stride = stride
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def check_bins(self, bins):
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"""Make sure given bins are valid for this filter.
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@ -270,11 +252,7 @@ class Filter(IDManagerMixin):
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Whether the filter can be merged
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"""
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if type(self) is not type(other):
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return False
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return True
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return type(self) is type(other)
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def merge(self, other):
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"""Merge this filter with another.
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@ -402,7 +380,7 @@ class Filter(IDManagerMixin):
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# Return a 1-tuple of the bin.
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return (self.bins[bin_index],)
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def get_pandas_dataframe(self, data_size, **kwargs):
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def get_pandas_dataframe(self, data_size, stride, **kwargs):
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"""Builds a Pandas DataFrame for the Filter's bins.
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This method constructs a Pandas DataFrame object for the filter with
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@ -411,13 +389,15 @@ class Filter(IDManagerMixin):
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Parameters
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----------
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data_size : Integral
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data_size : int
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The total number of bins in the tally corresponding to this filter
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stride : int
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Stride in memory for the filter
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Keyword arguments
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-----------------
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paths : bool
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Only used for DistirbcellFilter. If True (default), expand
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Only used for DistribcellFilter. If True (default), expand
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distribcell indices into multi-index columns describing the path
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to that distribcell through the CSG tree. NOTE: This option assumes
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that all distribcell paths are of the same length and do not have
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@ -431,11 +411,6 @@ class Filter(IDManagerMixin):
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the total number of bins in the corresponding tally, with the filter
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bin appropriately tiled to map to the corresponding tally bins.
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Raises
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------
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ImportError
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When Pandas is not installed
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See also
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--------
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Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
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@ -444,7 +419,7 @@ class Filter(IDManagerMixin):
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# Initialize Pandas DataFrame
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df = pd.DataFrame()
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filter_bins = np.repeat(self.bins, self.stride)
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filter_bins = np.repeat(self.bins, stride)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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df = pd.concat([df, pd.DataFrame(
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@ -502,9 +477,6 @@ class UniverseFilter(WithIDFilter):
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Unique identifier for the filter
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num_bins : Integral
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The number of filter bins
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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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filter's bins.
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"""
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@property
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@ -535,9 +507,6 @@ class MaterialFilter(WithIDFilter):
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Unique identifier for the filter
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num_bins : Integral
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The number of filter bins
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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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filter's bins.
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"""
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@property
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@ -568,15 +537,12 @@ class CellFilter(WithIDFilter):
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Unique identifier for the filter
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num_bins : Integral
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The number of filter bins
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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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filter's bins.
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"""
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@property
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def bins(self):
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return self._bins
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@bins.setter
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def bins(self, bins):
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self._smart_set_bins(bins, openmc.Cell)
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@ -601,15 +567,12 @@ class CellFromFilter(WithIDFilter):
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Unique identifier for the filter
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num_bins : Integral
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The number of filter bins
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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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filter's bins.
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"""
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@property
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def bins(self):
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return self._bins
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@bins.setter
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def bins(self, bins):
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self._smart_set_bins(bins, openmc.Cell)
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@ -634,9 +597,6 @@ class CellbornFilter(WithIDFilter):
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Unique identifier for the filter
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num_bins : Integral
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The number of filter bins
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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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filter's bins.
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"""
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@property
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@ -668,9 +628,6 @@ class SurfaceFilter(Filter):
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Unique identifier for the filter
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num_bins : Integral
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The number of filter bins
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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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filter's bins.
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"""
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@property
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@ -696,7 +653,7 @@ class SurfaceFilter(Filter):
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@num_bins.setter
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def num_bins(self, num_bins): pass
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def get_pandas_dataframe(self, data_size, **kwargs):
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def get_pandas_dataframe(self, data_size, stride, **kwargs):
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"""Builds a Pandas DataFrame for the Filter's bins.
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This method constructs a Pandas DataFrame object for the filter with
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@ -705,8 +662,10 @@ class SurfaceFilter(Filter):
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Parameters
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----------
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data_size : Integral
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data_size : int
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The total number of bins in the tally corresponding to this filter
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stride : int
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Stride in memory for the filter
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Returns
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-------
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@ -717,11 +676,6 @@ class SurfaceFilter(Filter):
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the corresponding tally, with the filter bin appropriately tiled to
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map to the corresponding tally bins.
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Raises
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------
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ImportError
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When Pandas is not installed
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See also
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--------
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Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
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@ -730,7 +684,7 @@ class SurfaceFilter(Filter):
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# Initialize Pandas DataFrame
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df = pd.DataFrame()
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filter_bins = np.repeat(self.bins, self.stride)
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filter_bins = np.repeat(self.bins, stride)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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filter_bins = [_CURRENT_NAMES[x] for x in filter_bins]
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@ -760,9 +714,6 @@ class MeshFilter(Filter):
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Unique identifier for the filter
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num_bins : Integral
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The number of filter bins
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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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filter's bins.
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"""
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@ -854,7 +805,7 @@ class MeshFilter(Filter):
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y = bin_index - (x * ny)
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return (x, y)
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def get_pandas_dataframe(self, data_size, **kwargs):
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def get_pandas_dataframe(self, data_size, stride, **kwargs):
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"""Builds a Pandas DataFrame for the Filter's bins.
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This method constructs a Pandas DataFrame object for the filter with
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@ -863,8 +814,10 @@ class MeshFilter(Filter):
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Parameters
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----------
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data_size : Integral
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data_size : int
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The total number of bins in the tally corresponding to this filter
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stride : int
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Stride in memory for the filter
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Returns
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-------
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@ -875,11 +828,6 @@ class MeshFilter(Filter):
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corresponding tally, with the filter bin appropriately tiled to map
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to the corresponding tally bins.
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Raises
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------
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ImportError
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When Pandas is not installed
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See also
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--------
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Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
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@ -906,7 +854,7 @@ class MeshFilter(Filter):
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# Generate multi-index sub-column for x-axis
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filter_bins = np.arange(1, nx + 1)
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repeat_factor = ny * nz * self.stride
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repeat_factor = ny * nz * stride
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filter_bins = np.repeat(filter_bins, repeat_factor)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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@ -914,7 +862,7 @@ class MeshFilter(Filter):
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# Generate multi-index sub-column for y-axis
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filter_bins = np.arange(1, ny + 1)
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repeat_factor = nz * self.stride
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repeat_factor = nz * stride
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filter_bins = np.repeat(filter_bins, repeat_factor)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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@ -922,7 +870,7 @@ class MeshFilter(Filter):
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# Generate multi-index sub-column for z-axis
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filter_bins = np.arange(1, nz + 1)
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repeat_factor = self.stride
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repeat_factor = stride
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filter_bins = np.repeat(filter_bins, repeat_factor)
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tile_factor = data_size // len(filter_bins)
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filter_bins = np.tile(filter_bins, tile_factor)
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@ -952,9 +900,6 @@ class RealFilter(Filter):
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Unique identifier for the filter
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num_bins : Integral
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The number of filter bins
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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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filter's bins.
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"""
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@ -1063,9 +1008,6 @@ class EnergyFilter(RealFilter):
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Unique identifier for the filter
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num_bins : Integral
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The number of filter bins
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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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filter's bins.
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"""
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@ -1100,7 +1042,7 @@ class EnergyFilter(RealFilter):
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'increasing'.format(bins, type(self))
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raise ValueError(msg)
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def get_pandas_dataframe(self, data_size, **kwargs):
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def get_pandas_dataframe(self, data_size, stride, **kwargs):
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"""Builds a Pandas DataFrame for the Filter's bins.
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This method constructs a Pandas DataFrame object for the filter with
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@ -1109,8 +1051,10 @@ class EnergyFilter(RealFilter):
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Parameters
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----------
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data_size : Integral
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data_size : int
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The total number of bins in the tally corresponding to this filter
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stride : int
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Stride in memory for the filter
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Returns
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-------
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@ -1121,11 +1065,6 @@ class EnergyFilter(RealFilter):
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corresponding tally, with the filter bin appropriately tiled to map
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to the corresponding tally bins.
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Raises
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------
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ImportError
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When Pandas is not installed
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See also
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--------
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Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
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@ -1136,8 +1075,8 @@ class EnergyFilter(RealFilter):
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# Extract the lower and upper energy bounds, then repeat and tile
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# them as necessary to account for other filters.
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lo_bins = np.repeat(self.bins[:-1], self.stride)
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hi_bins = np.repeat(self.bins[1:], self.stride)
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lo_bins = np.repeat(self.bins[:-1], stride)
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hi_bins = np.repeat(self.bins[1:], stride)
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tile_factor = data_size // len(lo_bins)
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lo_bins = np.tile(lo_bins, tile_factor)
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hi_bins = np.tile(hi_bins, tile_factor)
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@ -1167,9 +1106,6 @@ class EnergyoutFilter(EnergyFilter):
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Unique identifier for the filter
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num_bins : Integral
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The number of filter bins
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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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filter's bins.
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"""
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||||
|
|
@ -1229,9 +1165,6 @@ class DistribcellFilter(Filter):
|
|||
Unique identifier for the filter
|
||||
num_bins : Integral
|
||||
The number of filter bins
|
||||
stride : Integral
|
||||
The number of filter, nuclide and score bins within each of this
|
||||
filter's bins.
|
||||
paths : list of str
|
||||
The paths traversed through the CSG tree to reach each distribcell
|
||||
instance (for 'distribcell' filters only)
|
||||
|
|
@ -1296,7 +1229,7 @@ class DistribcellFilter(Filter):
|
|||
# the Cell in the Geometry (consecutive integers starting at 0).
|
||||
return filter_bin
|
||||
|
||||
def get_pandas_dataframe(self, data_size, **kwargs):
|
||||
def get_pandas_dataframe(self, data_size, stride, **kwargs):
|
||||
"""Builds a Pandas DataFrame for the Filter's bins.
|
||||
|
||||
This method constructs a Pandas DataFrame object for the filter with
|
||||
|
|
@ -1305,8 +1238,10 @@ class DistribcellFilter(Filter):
|
|||
|
||||
Parameters
|
||||
----------
|
||||
data_size : Integral
|
||||
data_size : int
|
||||
The total number of bins in the tally corresponding to this filter
|
||||
stride : int
|
||||
Stride in memory for the filter
|
||||
|
||||
Keyword arguments
|
||||
-----------------
|
||||
|
|
@ -1331,11 +1266,6 @@ class DistribcellFilter(Filter):
|
|||
of bins in the corresponding tally, with the filter bin
|
||||
appropriately tiled to map to the corresponding tally bins.
|
||||
|
||||
Raises
|
||||
------
|
||||
ImportError
|
||||
When Pandas is not installed
|
||||
|
||||
See also
|
||||
--------
|
||||
Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
|
||||
|
|
@ -1418,7 +1348,7 @@ class DistribcellFilter(Filter):
|
|||
|
||||
# Tile the Multi-index columns
|
||||
for level_key, level_bins in level_dict.items():
|
||||
level_bins = np.repeat(level_bins, self.stride)
|
||||
level_bins = np.repeat(level_bins, stride)
|
||||
tile_factor = data_size // len(level_bins)
|
||||
level_bins = np.tile(level_bins, tile_factor)
|
||||
level_dict[level_key] = level_bins
|
||||
|
|
@ -1434,7 +1364,7 @@ class DistribcellFilter(Filter):
|
|||
# NOTE: This is performed regardless of whether the user
|
||||
# requests Summary geometric information
|
||||
filter_bins = np.arange(self.num_bins)
|
||||
filter_bins = np.repeat(filter_bins, self.stride)
|
||||
filter_bins = np.repeat(filter_bins, stride)
|
||||
tile_factor = data_size // len(filter_bins)
|
||||
filter_bins = np.tile(filter_bins, tile_factor)
|
||||
df = pd.DataFrame({self.short_name.lower() : filter_bins})
|
||||
|
|
@ -1474,9 +1404,6 @@ class MuFilter(RealFilter):
|
|||
Unique identifier for the filter
|
||||
num_bins : Integral
|
||||
The number of filter bins
|
||||
stride : Integral
|
||||
The number of filter, nuclide and score bins within each of this
|
||||
filter's bins.
|
||||
|
||||
"""
|
||||
|
||||
|
|
@ -1504,7 +1431,7 @@ class MuFilter(RealFilter):
|
|||
'increasing'.format(bins, type(self))
|
||||
raise ValueError(msg)
|
||||
|
||||
def get_pandas_dataframe(self, data_size, **kwargs):
|
||||
def get_pandas_dataframe(self, data_size, stride, **kwargs):
|
||||
"""Builds a Pandas DataFrame for the Filter's bins.
|
||||
|
||||
This method constructs a Pandas DataFrame object for the filter with
|
||||
|
|
@ -1513,8 +1440,10 @@ class MuFilter(RealFilter):
|
|||
|
||||
Parameters
|
||||
----------
|
||||
data_size : Integral
|
||||
data_size : int
|
||||
The total number of bins in the tally corresponding to this filter
|
||||
stride : int
|
||||
Stride in memory for the filter
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
|
@ -1525,11 +1454,6 @@ class MuFilter(RealFilter):
|
|||
corresponding tally, with the filter bin appropriately tiled to map
|
||||
to the corresponding tally bins.
|
||||
|
||||
Raises
|
||||
------
|
||||
ImportError
|
||||
When Pandas is not installed
|
||||
|
||||
See also
|
||||
--------
|
||||
Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
|
||||
|
|
@ -1540,8 +1464,8 @@ class MuFilter(RealFilter):
|
|||
|
||||
# Extract the lower and upper energy bounds, then repeat and tile
|
||||
# them as necessary to account for other filters.
|
||||
lo_bins = np.repeat(self.bins[:-1], self.stride)
|
||||
hi_bins = np.repeat(self.bins[1:], self.stride)
|
||||
lo_bins = np.repeat(self.bins[:-1], stride)
|
||||
hi_bins = np.repeat(self.bins[1:], stride)
|
||||
tile_factor = data_size // len(lo_bins)
|
||||
lo_bins = np.tile(lo_bins, tile_factor)
|
||||
hi_bins = np.tile(hi_bins, tile_factor)
|
||||
|
|
@ -1579,9 +1503,6 @@ class PolarFilter(RealFilter):
|
|||
Unique identifier for the filter
|
||||
num_bins : Integral
|
||||
The number of filter bins
|
||||
stride : Integral
|
||||
The number of filter, nuclide and score bins within each of this
|
||||
filter's bins.
|
||||
|
||||
"""
|
||||
|
||||
|
|
@ -1609,7 +1530,7 @@ class PolarFilter(RealFilter):
|
|||
'increasing'.format(bins, type(self))
|
||||
raise ValueError(msg)
|
||||
|
||||
def get_pandas_dataframe(self, data_size, **kwargs):
|
||||
def get_pandas_dataframe(self, data_size, stride, **kwargs):
|
||||
"""Builds a Pandas DataFrame for the Filter's bins.
|
||||
|
||||
This method constructs a Pandas DataFrame object for the filter with
|
||||
|
|
@ -1618,8 +1539,10 @@ class PolarFilter(RealFilter):
|
|||
|
||||
Parameters
|
||||
----------
|
||||
data_size : Integral
|
||||
data_size : int
|
||||
The total number of bins in the tally corresponding to this filter
|
||||
stride : int
|
||||
Stride in memory for the filter
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
|
@ -1630,11 +1553,6 @@ class PolarFilter(RealFilter):
|
|||
corresponding tally, with the filter bin appropriately tiled to map
|
||||
to the corresponding tally bins.
|
||||
|
||||
Raises
|
||||
------
|
||||
ImportError
|
||||
When Pandas is not installed
|
||||
|
||||
See also
|
||||
--------
|
||||
Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
|
||||
|
|
@ -1645,8 +1563,8 @@ class PolarFilter(RealFilter):
|
|||
|
||||
# Extract the lower and upper angle bounds, then repeat and tile
|
||||
# them as necessary to account for other filters.
|
||||
lo_bins = np.repeat(self.bins[:-1], self.stride)
|
||||
hi_bins = np.repeat(self.bins[1:], self.stride)
|
||||
lo_bins = np.repeat(self.bins[:-1], stride)
|
||||
hi_bins = np.repeat(self.bins[1:], stride)
|
||||
tile_factor = data_size // len(lo_bins)
|
||||
lo_bins = np.tile(lo_bins, tile_factor)
|
||||
hi_bins = np.tile(hi_bins, tile_factor)
|
||||
|
|
@ -1684,9 +1602,6 @@ class AzimuthalFilter(RealFilter):
|
|||
Unique identifier for the filter
|
||||
num_bins : Integral
|
||||
The number of filter bins
|
||||
stride : Integral
|
||||
The number of filter, nuclide and score bins within each of this
|
||||
filter's bins.
|
||||
|
||||
"""
|
||||
|
||||
|
|
@ -1714,7 +1629,7 @@ class AzimuthalFilter(RealFilter):
|
|||
'increasing'.format(bins, type(self))
|
||||
raise ValueError(msg)
|
||||
|
||||
def get_pandas_dataframe(self, data_size, paths=True):
|
||||
def get_pandas_dataframe(self, data_size, stride, paths=True):
|
||||
"""Builds a Pandas DataFrame for the Filter's bins.
|
||||
|
||||
This method constructs a Pandas DataFrame object for the filter with
|
||||
|
|
@ -1723,8 +1638,10 @@ class AzimuthalFilter(RealFilter):
|
|||
|
||||
Parameters
|
||||
----------
|
||||
data_size : Integral
|
||||
data_size : int
|
||||
The total number of bins in the tally corresponding to this filter
|
||||
stride : int
|
||||
Stride in memory for the filter
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
|
@ -1735,11 +1652,6 @@ class AzimuthalFilter(RealFilter):
|
|||
corresponding tally, with the filter bin appropriately tiled to map
|
||||
to the corresponding tally bins.
|
||||
|
||||
Raises
|
||||
------
|
||||
ImportError
|
||||
When Pandas is not installed
|
||||
|
||||
See also
|
||||
--------
|
||||
Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
|
||||
|
|
@ -1750,8 +1662,8 @@ class AzimuthalFilter(RealFilter):
|
|||
|
||||
# Extract the lower and upper angle bounds, then repeat and tile
|
||||
# them as necessary to account for other filters.
|
||||
lo_bins = np.repeat(self.bins[:-1], self.stride)
|
||||
hi_bins = np.repeat(self.bins[1:], self.stride)
|
||||
lo_bins = np.repeat(self.bins[:-1], stride)
|
||||
hi_bins = np.repeat(self.bins[1:], stride)
|
||||
tile_factor = data_size // len(lo_bins)
|
||||
lo_bins = np.tile(lo_bins, tile_factor)
|
||||
hi_bins = np.tile(hi_bins, tile_factor)
|
||||
|
|
@ -1785,9 +1697,6 @@ class DelayedGroupFilter(Filter):
|
|||
Unique identifier for the filter
|
||||
num_bins : Integral
|
||||
The number of filter bins
|
||||
stride : Integral
|
||||
The number of filter, nuclide and score bins within each of this
|
||||
filter's bins.
|
||||
|
||||
"""
|
||||
@property
|
||||
|
|
@ -1840,9 +1749,6 @@ class EnergyFunctionFilter(Filter):
|
|||
Unique identifier for the filter
|
||||
num_bins : Integral
|
||||
The number of filter bins (always 1 for this filter)
|
||||
stride : Integral
|
||||
The number of filter, nuclide and score bins within each of this
|
||||
filter's bins.
|
||||
|
||||
"""
|
||||
|
||||
|
|
@ -1850,7 +1756,6 @@ class EnergyFunctionFilter(Filter):
|
|||
self.energy = energy
|
||||
self.y = y
|
||||
self.id = filter_id
|
||||
self._stride = None
|
||||
|
||||
def __eq__(self, other):
|
||||
if type(self) is not type(other):
|
||||
|
|
@ -2006,7 +1911,7 @@ class EnergyFunctionFilter(Filter):
|
|||
"""This function is invalid for EnergyFunctionFilters."""
|
||||
raise RuntimeError('EnergyFunctionFilters have no get_bin() method')
|
||||
|
||||
def get_pandas_dataframe(self, data_size, **kwargs):
|
||||
def get_pandas_dataframe(self, data_size, stride, **kwargs):
|
||||
"""Builds a Pandas DataFrame for the Filter's bins.
|
||||
|
||||
This method constructs a Pandas DataFrame object for the filter with
|
||||
|
|
@ -2015,8 +1920,10 @@ class EnergyFunctionFilter(Filter):
|
|||
|
||||
Parameters
|
||||
----------
|
||||
data_size : Integral
|
||||
data_size : int
|
||||
The total number of bins in the tally corresponding to this filter
|
||||
stride : int
|
||||
Stride in memory for the filter
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
|
@ -2027,11 +1934,6 @@ class EnergyFunctionFilter(Filter):
|
|||
EnergyFunctionFilters. The number of rows in the DataFrame is the
|
||||
same as the total number of bins in the corresponding tally.
|
||||
|
||||
Raises
|
||||
------
|
||||
ImportError
|
||||
When Pandas is not installed
|
||||
|
||||
See also
|
||||
--------
|
||||
Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
|
||||
|
|
@ -2052,7 +1954,7 @@ class EnergyFunctionFilter(Filter):
|
|||
# hex characters) of the digest are probably sufficient.
|
||||
out = out[:14]
|
||||
|
||||
filter_bins = np.repeat(out, self.stride)
|
||||
filter_bins = np.repeat(out, stride)
|
||||
tile_factor = data_size // len(filter_bins)
|
||||
filter_bins = np.tile(filter_bins, tile_factor)
|
||||
df = pd.concat([df, pd.DataFrame(
|
||||
|
|
|
|||
|
|
@ -2751,7 +2751,6 @@ class TransportXS(MGXS):
|
|||
# Switch EnergyoutFilter to EnergyFilter.
|
||||
old_filt = self.tallies['scatter-1'].filters[-1]
|
||||
new_filt = openmc.EnergyFilter(old_filt.bins)
|
||||
new_filt.stride = old_filt.stride
|
||||
self.tallies['scatter-1'].filters[-1] = new_filt
|
||||
|
||||
self._rxn_rate_tally = \
|
||||
|
|
@ -2771,7 +2770,6 @@ class TransportXS(MGXS):
|
|||
# Switch EnergyoutFilter to EnergyFilter.
|
||||
old_filt = self.tallies['scatter-1'].filters[-1]
|
||||
new_filt = openmc.EnergyFilter(old_filt.bins)
|
||||
new_filt.stride = old_filt.stride
|
||||
self.tallies['scatter-1'].filters[-1] = new_filt
|
||||
|
||||
# Compute total cross section
|
||||
|
|
|
|||
|
|
@ -415,9 +415,6 @@ class StatePoint(object):
|
|||
|
||||
tally.scores.append(score)
|
||||
|
||||
# Compute and set the filter strides
|
||||
tally._update_filter_strides()
|
||||
|
||||
# Add Tally to the global dictionary of all Tallies
|
||||
tally.sparse = self.sparse
|
||||
self._tallies[tally_id] = tally
|
||||
|
|
|
|||
|
|
@ -78,6 +78,8 @@ class Tally(IDManagerMixin):
|
|||
shape : 3-tuple of int
|
||||
The shape of the tally data array ordered as the number of filter bins,
|
||||
nuclide bins and score bins
|
||||
filter_strides : list of int
|
||||
Stride in memory for each filter
|
||||
num_realizations : int
|
||||
Total number of realizations
|
||||
with_summary : bool
|
||||
|
|
@ -818,10 +820,6 @@ class Tally(IDManagerMixin):
|
|||
# Differentiate Tally with a new auto-generated Tally ID
|
||||
merged_tally.id = None
|
||||
|
||||
# If the two tallies are equal, simply return copy
|
||||
if self == other:
|
||||
return merged_tally
|
||||
|
||||
# Create deep copy of other tally to use for array concatenation
|
||||
other_copy = copy.deepcopy(other)
|
||||
|
||||
|
|
@ -877,9 +875,6 @@ class Tally(IDManagerMixin):
|
|||
else:
|
||||
self._derived = True
|
||||
|
||||
# Update filter strides in merged tally
|
||||
merged_tally._update_filter_strides()
|
||||
|
||||
# Concatenate sum arrays if present in both tallies
|
||||
if self.sum is not None and other_copy.sum is not None:
|
||||
self_sum = self.get_reshaped_data(value='sum')
|
||||
|
|
@ -1538,11 +1533,10 @@ class Tally(IDManagerMixin):
|
|||
|
||||
# Build DataFrame columns for filters if user requested them
|
||||
if filters:
|
||||
|
||||
# Append each Filter's DataFrame to the overall DataFrame
|
||||
for self_filter in self.filters:
|
||||
filter_df = self_filter.get_pandas_dataframe(
|
||||
data_size, paths=paths)
|
||||
for f, stride in zip(self.filters, self.filter_strides):
|
||||
filter_df = f.get_pandas_dataframe(
|
||||
data_size, stride, paths=paths)
|
||||
df = pd.concat([df, filter_df], axis=1)
|
||||
|
||||
# Include DataFrame column for nuclides if user requested it
|
||||
|
|
@ -1867,20 +1861,16 @@ class Tally(IDManagerMixin):
|
|||
new_score = cross_score(self_score, other_score, binary_op)
|
||||
new_tally.scores.append(new_score)
|
||||
|
||||
# Update the new tally's filter strides
|
||||
new_tally._update_filter_strides()
|
||||
|
||||
return new_tally
|
||||
|
||||
def _update_filter_strides(self):
|
||||
"""Update each filter's stride based on the tally's nuclides and scores
|
||||
for derived tallies created by tally arithmetic.
|
||||
"""
|
||||
|
||||
@property
|
||||
def filter_strides(self):
|
||||
all_strides = []
|
||||
stride = self.num_nuclides * self.num_scores
|
||||
for self_filter in reversed(self.filters):
|
||||
self_filter.stride = stride
|
||||
all_strides.append(stride)
|
||||
stride *= self_filter.num_bins
|
||||
return all_strides[::-1]
|
||||
|
||||
def _align_tally_data(self, other, filter_product, nuclide_product,
|
||||
score_product):
|
||||
|
|
@ -2019,10 +2009,6 @@ class Tally(IDManagerMixin):
|
|||
if other_index != i:
|
||||
other._swap_scores(score, other.scores[i])
|
||||
|
||||
# Update the tallies' filter strides
|
||||
other._update_filter_strides()
|
||||
self._update_filter_strides()
|
||||
|
||||
data = {}
|
||||
data['self'] = {}
|
||||
data['other'] = {}
|
||||
|
|
@ -2107,9 +2093,6 @@ class Tally(IDManagerMixin):
|
|||
self.filters[filter1_index] = filter2
|
||||
self.filters[filter2_index] = filter1
|
||||
|
||||
# Update the tally's filter strides
|
||||
self._update_filter_strides()
|
||||
|
||||
# Realign the data
|
||||
for i, (bin1, bin2) in enumerate(product(filter1_bins, filter2_bins)):
|
||||
filter_bins = [(bin1,), (bin2,)]
|
||||
|
|
@ -2861,9 +2844,6 @@ class Tally(IDManagerMixin):
|
|||
find_filter.bins = np.unique(find_filter.bins[bin_indices])
|
||||
find_filter.num_bins = num_bins
|
||||
|
||||
# Update the new tally's filter strides
|
||||
new_tally._update_filter_strides()
|
||||
|
||||
# If original tally was sparse, sparsify the sliced tally
|
||||
new_tally.sparse = self.sparse
|
||||
return new_tally
|
||||
|
|
@ -3010,9 +2990,6 @@ class Tally(IDManagerMixin):
|
|||
else:
|
||||
tally_sum._scores = copy.deepcopy(self.scores)
|
||||
|
||||
# Update the tally sum's filter strides
|
||||
tally_sum._update_filter_strides()
|
||||
|
||||
# Reshape condensed data arrays with one dimension for all filters
|
||||
mean = np.reshape(mean, tally_sum.shape)
|
||||
std_dev = np.reshape(std_dev, tally_sum.shape)
|
||||
|
|
@ -3170,9 +3147,6 @@ class Tally(IDManagerMixin):
|
|||
else:
|
||||
tally_avg._scores = copy.deepcopy(self.scores)
|
||||
|
||||
# Update the tally avg's filter strides
|
||||
tally_avg._update_filter_strides()
|
||||
|
||||
# Reshape condensed data arrays with one dimension for all filters
|
||||
mean = np.reshape(mean, tally_avg.shape)
|
||||
std_dev = np.reshape(std_dev, tally_avg.shape)
|
||||
|
|
@ -3246,9 +3220,6 @@ class Tally(IDManagerMixin):
|
|||
new_tally._std_dev = np.zeros(new_tally.shape, dtype=np.float64)
|
||||
new_tally._std_dev[diag_indices, :, :] = self.std_dev
|
||||
|
||||
# Update the new tally's filter strides
|
||||
new_tally._update_filter_strides()
|
||||
|
||||
# If original tally was sparse, sparsify the diagonalized tally
|
||||
new_tally.sparse = self.sparse
|
||||
return new_tally
|
||||
|
|
|
|||
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Add table
Add a link
Reference in a new issue