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updated to only modify the _swap_filters method
This commit is contained in:
Sam Shaner
parent
ad3043970d
commit
f275b71299
1 changed files with 60 additions and 51 deletions
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@ -912,8 +912,7 @@ class Tally(object):
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for i, filter1 in enumerate(self.filters):
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for filter2 in other.filters:
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if filter1 != filter2 and filter1.can_merge(filter2):
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other_copy._swap_filters(other_copy.filters[i], filter2,
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other)
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other_copy._swap_filters(other_copy.filters[i], filter2)
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merged_tally.filters[i] = filter1.merge(filter2)
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join_right = filter1 < filter2
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merge_axis = i
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@ -1854,7 +1853,7 @@ class Tally(object):
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other_copy.sparse = False
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# Align the tally data based on desired hybrid product
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data = self_copy._align_tally_data(other, other_copy, filter_product,
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data = self_copy._align_tally_data(other_copy, filter_product,
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nuclide_product, score_product)
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# Perform tally arithmetic operation
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@ -1949,8 +1948,8 @@ class Tally(object):
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self_filter.stride = stride
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stride *= self_filter.num_bins
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def _align_tally_data(self, other_old, other, filter_product,
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nuclide_product, score_product):
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def _align_tally_data(self, other, filter_product, nuclide_product,
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score_product):
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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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@ -1964,12 +1963,8 @@ class Tally(object):
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Parameters
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----------
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other_old : openmc.Tally
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The tally to outer product with this tally with the old tally
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alignment
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other : openmc.Tally
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The tally to outer product with this tally with the new tally
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alignment
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The tally to outer product with this tally.
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filter_product : {'entrywise'}
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The type of product to be performed between filter data. Currently,
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only the entrywise product is supported for the filter product.
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@ -1997,14 +1992,12 @@ class Tally(object):
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# Add filters present in self but not in other to other
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for other_filter in other_missing_filters:
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filter_copy = copy.deepcopy(other_filter)
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other._mean = np.repeat(other.mean, filter_copy.num_bins, axis=0)
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other._std_dev = np.repeat(other.std_dev, filter_copy.num_bins, axis=0)
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other._mean = np.repeat(other.mean,
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filter_copy.num_bins, axis=0)
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other._std_dev = np.repeat(other.std_dev,
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filter_copy.num_bins, axis=0)
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other.filters.append(filter_copy)
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# Create a new static copy of the other tally to use in swapping filters
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if len(other_missing_filters) > 0:
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other_old = copy.deepcopy(other)
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# Add filters present in other but not in self to self
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for self_filter in self_missing_filters:
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filter_copy = copy.deepcopy(self_filter)
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@ -2018,7 +2011,7 @@ class Tally(object):
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# If necessary, swap other filter
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if other_index != i:
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other._swap_filters(self_filter, other.filters[i], other_old)
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other._swap_filters(self_filter, other.filters[i])
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# Repeat and tile the data by nuclide in preparation for performing
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# the tensor product across nuclides.
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@ -2046,9 +2039,11 @@ class Tally(object):
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# Add nuclides present in self but not in other to other
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for nuclide in other_missing_nuclides:
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other._mean = \
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np.insert(other.mean, other.num_nuclides, 0, axis=1)
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np.insert(other.mean, other.num_nuclides,
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0, axis=1)
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other._std_dev = \
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np.insert(other.std_dev, other.num_nuclides, 0, axis=1)
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np.insert(other.std_dev, other.num_nuclides,
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0, axis=1)
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other.nuclides.append(nuclide)
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# Add nuclides present in other but not in self to self
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@ -2071,9 +2066,12 @@ class Tally(object):
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# the tensor product across scores.
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if score_product == 'tensor':
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self._mean = np.repeat(self.mean, other.num_scores, axis=2)
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self._std_dev = np.repeat(self.std_dev, other.num_scores, axis=2)
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other._mean = np.tile(other.mean, (1, 1, self.num_scores))
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other._std_dev = np.tile(other.std_dev, (1, 1, self.num_scores))
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self._std_dev = np.repeat(self.std_dev, other.num_scores,
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axis=2)
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other._mean = \
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np.tile(other.mean, (1, 1, self.num_scores))
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other._std_dev = \
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np.tile(other.std_dev, (1, 1, self.num_scores))
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# Add scores to each tally such that each tally contains the complete set
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# of scores necessary to perform an entrywise product. New scores added
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@ -2088,8 +2086,12 @@ class Tally(object):
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# Add scores present in self but not in other to other
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for score in other_missing_scores:
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other._mean = np.insert(other.mean, other.num_scores, 0, axis=2)
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other._std_dev = np.insert(other.std_dev, other.num_scores, 0, axis=2)
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other._mean = \
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np.insert(other.mean, other.num_scores, 0,
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axis=2)
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other._std_dev = \
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np.insert(other.std_dev, other.num_scores, 0,
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axis=2)
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other.scores.append(score)
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# Add scores present in other but not in self to self
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@ -2119,7 +2121,7 @@ class Tally(object):
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data['other']['std. dev.'] = other.std_dev
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return data
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def _swap_filters(self, filter1, filter2, other_old):
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def _swap_filters(self, filter1, filter2):
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"""Reverse the ordering of two filters in this tally
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This is a helper method for tally arithmetic which helps align the data
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@ -2132,8 +2134,6 @@ class Tally(object):
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The filter to swap with filter2
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filter2 : Filter
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The filter to swap with filter1
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other_old : openmc.Tally
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A copy of this tally with the old filter alignment
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Raises
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------
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@ -2158,15 +2158,6 @@ class Tally(object):
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'does not contain such a filter'.format(filter2.type, self.id)
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raise ValueError(msg)
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# Swap the filters in the copied version of this Tally
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filter1_index = self.filters.index(filter1)
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filter2_index = self.filters.index(filter2)
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self.filters[filter1_index] = filter2
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self.filters[filter2_index] = filter1
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# Update the tally's filter strides
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self._update_filter_strides()
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# Construct lists of tuples for the bins in each of the two filters
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filters = [type(filter1), type(filter2)]
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if isinstance(filter1, openmc.DistribcellFilter):
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@ -2183,23 +2174,41 @@ class Tally(object):
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else:
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filter2_bins = [filter2.get_bin(i) for i in range(filter2.num_bins)]
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# Adjust the mean data array to relect the new filter order
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if self.mean is not None:
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for bin1, bin2 in itertools.product(filter1_bins, filter2_bins):
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filter_bins = [(bin1,), (bin2,)]
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data = other_old.get_values(
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filters=filters, filter_bins=filter_bins, value='mean')
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indices = self.get_filter_indices(filters, filter_bins)
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self.mean[indices, :, :] = data
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# Create variables to store views of data in the misaligned structure
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mean = {}
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std_dev = {}
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# Adjust the std_dev data array to relect the new filter order
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if self.std_dev is not None:
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for bin1, bin2 in itertools.product(filter1_bins, filter2_bins):
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filter_bins = [(bin1,), (bin2,)]
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data = other_old.get_values(
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# Store the data from the misaligned structure
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for i, (bin1, bin2) in enumerate(itertools.product(filter1_bins, filter2_bins)):
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filter_bins = [(bin1,), (bin2,)]
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if self.mean is not None:
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mean[i] = self.get_values(
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filters=filters, filter_bins=filter_bins, value='mean')
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if self.std_dev is not None:
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std_dev[i] = self.get_values(
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filters=filters, filter_bins=filter_bins, value='std_dev')
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indices = self.get_filter_indices(filters, filter_bins)
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self.std_dev[indices, :, :] = data
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# Swap the filters in the copied version of this Tally
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filter1_index = self.filters.index(filter1)
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filter2_index = self.filters.index(filter2)
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self.filters[filter1_index] = filter2
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self.filters[filter2_index] = filter1
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# Update the tally's filter strides
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self._update_filter_strides()
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# Realign the data
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for i, (bin1, bin2) in enumerate(itertools.product(filter1_bins, filter2_bins)):
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filter_bins = [(bin1,), (bin2,)]
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indices = self.get_filter_indices(filters, filter_bins)
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if self.mean is not None:
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self.mean[indices, :, :] = mean[i]
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if self.std_dev is not None:
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self.std_dev[indices, :, :] = std_dev[i]
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def _swap_nuclides(self, nuclide1, nuclide2):
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"""Reverse the ordering of two nuclides in this tally
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