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removed num_score_bins from Tally object and removed sum and sum_sq from tally arithmetic

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This commit is contained in:
Sam Shaner 2015-12-14 11:34:52 -05:00
parent 52d879ebd3
commit 50519fe62d
4 changed files with 21 additions and 94 deletions
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4
openmc/mgxs/mgxs.py
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@ -755,7 +755,7 @@ class MGXS(object):
# Reshape condensed data arrays with one dimension for all filters
new_shape = \
(tally.num_filter_bins, tally.num_nuclides, tally.num_score_bins,)
(tally.num_filter_bins, tally.num_nuclides, tally.num_scores,)
mean = np.reshape(mean, new_shape)
std_dev = np.reshape(std_dev, new_shape)
@ -837,7 +837,7 @@ class MGXS(object):
# Reshape averaged data arrays with one dimension for all filters
new_shape = \
(tally.num_filter_bins, tally.num_nuclides, tally.num_score_bins,)
(tally.num_filter_bins, tally.num_nuclides, tally.num_scores,)
mean = np.reshape(mean, new_shape)
std_dev = np.reshape(std_dev, new_shape)

2
openmc/statepoint.py
View file

@ -394,8 +394,6 @@ class StatePoint(object):
# Read score bins
n_score_bins = self._f['{0}{1}/n_score_bins'.format(base, tally_key)].value
tally.num_score_bins = n_score_bins
scores = self._f['{0}{1}/score_bins'.format(
base, tally_key)].value
n_user_scores = self._f['{0}{1}/n_user_score_bins'

2
openmc/summary.py
View file

@ -524,8 +524,6 @@ class Summary(object):
scores = self._f['{0}/score_bins'.format(subbase)].value
for score in scores:
tally.add_score(score.decode())
num_score_bins = self._f['{0}/n_score_bins'.format(subbase)][...]
tally.num_score_bins = num_score_bins
# Read filter metadata
num_filters = self._f['{0}/n_filters'.format(subbase)].value

107
openmc/tallies.py
View file

@ -64,10 +64,6 @@ class Tally(object):
Type of estimator for the tally
triggers : list of openmc.trigger.Trigger
List of tally triggers
num_score_bins : Integral
Total number of scores, accounting for the fact that a single
user-specified score, e.g. scatter-P3 or flux-Y2,2, might have multiple
bins
num_scores : Integral
Total number of user-specified scores
num_filter_bins : Integral
@ -100,7 +96,6 @@ class Tally(object):
self._estimator = None
self._triggers = []
self._num_score_bins = 0
self._num_realizations = 0
self._with_summary = False
@ -123,7 +118,6 @@ class Tally(object):
clone.id = self.id
clone.name = self.name
clone.estimator = self.estimator
clone.num_score_bins = self.num_score_bins
clone.num_realizations = self.num_realizations
clone._sum = copy.deepcopy(self._sum, memo)
clone._sum_sq = copy.deepcopy(self._sum_sq, memo)
@ -252,10 +246,6 @@ class Tally(object):
def num_scores(self):
return len(self._scores)
@property
def num_score_bins(self):
return self._num_score_bins
@property
def num_filter_bins(self):
num_bins = 1
@ -269,7 +259,7 @@ class Tally(object):
def num_bins(self):
num_bins = self.num_filter_bins
num_bins *= self.num_nuclides
num_bins *= self.num_score_bins
num_bins *= self.num_scores
return num_bins
@property
@ -312,7 +302,7 @@ class Tally(object):
# Reshape the results arrays
new_shape = (nonzero(self.num_filter_bins),
nonzero(self.num_nuclides),
nonzero(self.num_score_bins))
nonzero(self.num_scores))
sum = np.reshape(sum, new_shape)
sum_sq = np.reshape(sum_sq, new_shape)
@ -468,10 +458,6 @@ class Tally(object):
else:
self._scores.append(score)
@num_score_bins.setter
def num_score_bins(self, num_score_bins):
self._num_score_bins = num_score_bins
@num_realizations.setter
def num_realizations(self, num_realizations):
cv.check_type('number of realizations', num_realizations, Integral)
@ -1286,7 +1272,7 @@ class Tally(object):
for filter in self.filters:
new_shape += (filter.num_bins, )
new_shape += (self.num_nuclides,)
new_shape += (self.num_score_bins,)
new_shape += (self.num_scores,)
# Reshape the data with one dimension for each filter
data = np.reshape(data, new_shape)
@ -1607,18 +1593,16 @@ class Tally(object):
# Add scores to the new tally
if score_product == 'entrywise':
new_tally.num_score_bins = self_copy.num_score_bins
for self_score in self_copy.scores:
new_tally.add_score(self_score)
else:
new_tally.num_score_bins = self_copy.num_score_bins * other_copy.num_score_bins
all_scores = [self_copy.scores, other_copy.scores]
for self_score, other_score in itertools.product(*all_scores):
new_score = CrossScore(self_score, other_score, binary_op)
new_tally.add_score(new_score)
# Correct each Filter's stride
stride = new_tally.num_nuclides * new_tally.num_score_bins
stride = new_tally.num_nuclides * new_tally.num_scores
for filter in reversed(new_tally.filters):
filter.stride = stride
stride *= filter.num_bins
@ -1726,10 +1710,10 @@ class Tally(object):
# Repeat and tile the data by score in preparation for performing
# the tensor product across scores.
if score_product == 'tensor':
self._mean = np.repeat(self.mean, other.num_score_bins, axis=2)
self._std_dev = np.repeat(self.std_dev, other.num_score_bins, axis=2)
other._mean = np.tile(other.mean, (1, 1, self.num_score_bins))
other._std_dev = np.tile(other.std_dev, (1, 1, self.num_score_bins))
self._mean = np.repeat(self.mean, other.num_scores, axis=2)
self._std_dev = np.repeat(self.std_dev, other.num_scores, axis=2)
other._mean = np.tile(other.mean, (1, 1, self.num_scores))
other._std_dev = np.tile(other.std_dev, (1, 1, self.num_scores))
# Add scores to each tally such that each tally contains the complete set
# of scores necessary to perform an entrywise product. New scores added
@ -1742,16 +1726,15 @@ class Tally(object):
# Add scores present in self but not in other to other
for score in other_missing_scores:
other._mean = np.insert(other.mean, other.num_score_bins, 0, axis=2)
other._std_dev = np.insert(other.std_dev, other.num_score_bins, 0, axis=2)
other._mean = np.insert(other.mean, other.num_scores, 0, axis=2)
other._std_dev = np.insert(other.std_dev, other.num_scores, 0, axis=2)
other.add_score(score)
# Add scores present in other but not in self to self
for score in self_missing_scores:
self._mean = np.insert(self.mean, self.num_score_bins, 0, axis=2)
self._std_dev = np.insert(self.std_dev, self.num_score_bins, 0, axis=2)
self._mean = np.insert(self.mean, self.num_scores, 0, axis=2)
self._std_dev = np.insert(self.std_dev, self.num_scores, 0, axis=2)
self.add_score(score)
self.num_score_bins = self.num_score_bins + 1
# Align other scores with self scores
for i, score in enumerate(self.scores):
@ -1762,13 +1745,13 @@ class Tally(object):
other._swap_scores(score, other.scores[i])
# Correct the stride for other filters
stride = other.num_nuclides * other.num_score_bins
stride = other.num_nuclides * other.num_scores
for filter in reversed(other.filters):
filter.stride = stride
stride *= filter.num_bins
# Correct the stride for self filters
stride = self.num_nuclides * self.num_score_bins
stride = self.num_nuclides * self.num_scores
for filter in reversed(self.filters):
filter.stride = stride
stride *= filter.num_bins
@ -1834,7 +1817,7 @@ class Tally(object):
self.filters[filter2_index] = filter1
# Update the strides for each of the filters
stride = self.num_nuclides * self.num_score_bins
stride = self.num_nuclides * self.num_scores
for filter in reversed(self.filters):
filter.stride = stride
stride *= filter.num_bins
@ -1851,24 +1834,6 @@ class Tally(object):
else:
filter2_bins = [filter2.get_bin(i) for i in range(filter2.num_bins)]
# Adjust the sum data array to relect the new filter order
if self.sum is not None:
for bin1, bin2 in itertools.product(filter1_bins, filter2_bins):
filter_bins = [(bin1,), (bin2,)]
data = self.get_values(
filters=filters, filter_bins=filter_bins, value='sum')
indices = self.get_filter_indices(filters, filter_bins)
self.sum[indices, :, :] = data
# Adjust the sum_sq data array to relect the new filter order
if self.sum_sq is not None:
for bin1, bin2 in itertools.product(filter1_bins, filter2_bins):
filter_bins = [(bin1,), (bin2,)]
data = self.get_values(
filters=filters, filter_bins=filter_bins, value='sum_sq')
indices = self.get_filter_indices(filters, filter_bins)
self.sum_sq[indices, :, :] = data
# Adjust the mean data array to relect the new filter order
if self.mean is not None:
for bin1, bin2 in itertools.product(filter1_bins, filter2_bins):
@ -1940,20 +1905,6 @@ class Tally(object):
self.nuclides[nuclide1_index] = nuclide2
self.nuclides[nuclide2_index] = nuclide1
# Adjust the sum data array to relect the new nuclide order
if self.sum is not None:
nuclide1_sum = self._sum[:,nuclide1_index,:].copy()
nuclide2_sum = self._sum[:,nuclide2_index,:].copy()
self._sum[:,nuclide2_index,:] = nuclide1_sum
self._sum[:,nuclide1_index,:] = nuclide2_sum
# Adjust the sum_sq data array to relect the new nuclide order
if self.sum_sq is not None:
nuclide1_sum_sq = self._sum_sq[:,nuclide1_index,:].copy()
nuclide2_sum_sq = self._sum_sq[:,nuclide2_index,:].copy()
self._sum_sq[:,nuclide2_index,:] = nuclide1_sum_sq
self._sum_sq[:,nuclide1_index,:] = nuclide2_sum_sq
# Adjust the mean data array to relect the new nuclide order
if self.mean is not None:
nuclide1_mean = self._mean[:,nuclide1_index,:].copy()
@ -2026,20 +1977,6 @@ class Tally(object):
self.scores[score1_index] = score2
self.scores[score2_index] = score1
# Adjust the sum data array to relect the new nuclide order
if self.sum is not None:
score1_sum = self._sum[:,:,score1_index].copy()
score2_sum = self._sum[:,:,score2_index].copy()
self._sum[:,:,score2_index] = score1_sum
self._sum[:,:,score1_index] = score2_sum
# Adjust the sum_sq data array to relect the new nuclide order
if self.sum_sq is not None:
score1_sum_sq = self._sum_sq[:,:,score1_index].copy()
score2_sum_sq = self._sum_sq[:,:,score2_index].copy()
self._sum_sq[:,:,score2_index] = score1_sum_sq
self._sum_sq[:,:,score1_index] = score2_sum_sq
# Adjust the mean data array to relect the new nuclide order
if self.mean is not None:
score1_mean = self._mean[:,:,score1_index].copy()
@ -2109,7 +2046,6 @@ class Tally(object):
new_tally.estimator = self.estimator
new_tally.with_summary = self.with_summary
new_tally.num_realization = self.num_realizations
new_tally.num_score_bins = self.num_score_bins
for filter in self.filters:
new_tally.add_filter(filter)
@ -2178,7 +2114,6 @@ class Tally(object):
new_tally.estimator = self.estimator
new_tally.with_summary = self.with_summary
new_tally.num_realization = self.num_realizations
new_tally.num_score_bins = self.num_score_bins
for filter in self.filters:
new_tally.add_filter(filter)
@ -2248,7 +2183,6 @@ class Tally(object):
new_tally.estimator = self.estimator
new_tally.with_summary = self.with_summary
new_tally.num_realization = self.num_realizations
new_tally.num_score_bins = self.num_score_bins
for filter in self.filters:
new_tally.add_filter(filter)
@ -2318,7 +2252,6 @@ class Tally(object):
new_tally.estimator = self.estimator
new_tally.with_summary = self.with_summary
new_tally.num_realization = self.num_realizations
new_tally.num_score_bins = self.num_score_bins
for filter in self.filters:
new_tally.add_filter(filter)
@ -2392,7 +2325,6 @@ class Tally(object):
new_tally.estimator = self.estimator
new_tally.with_summary = self.with_summary
new_tally.num_realization = self.num_realizations
new_tally.num_score_bins = self.num_score_bins
for filter in self.filters:
new_tally.add_filter(filter)
@ -2594,7 +2526,6 @@ class Tally(object):
# Loop over indices in reverse to remove excluded scores
for score_index in reversed(score_indices):
new_tally.remove_score(self.scores[score_index])
new_tally.num_score_bins -= 1
# NUCLIDES
if nuclides:
@ -2634,7 +2565,7 @@ class Tally(object):
filter.num_bins = len(filter_bins[i])
# Correct each Filter's stride
stride = new_tally.num_nuclides * new_tally.num_score_bins
stride = new_tally.num_nuclides * new_tally.num_scores
for filter in reversed(new_tally.filters):
filter.stride = stride
stride *= filter.num_bins
@ -2780,8 +2711,8 @@ class Tally(object):
# Determine the shape of data in the new diagonalized Tally
num_filter_bins = new_tally.num_filter_bins
num_nuclides = new_tally.num_nuclides
num_score_bins = new_tally.num_score_bins
new_shape = (num_filter_bins, num_nuclides, num_score_bins)
num_scores = new_tally.num_scores
new_shape = (num_filter_bins, num_nuclides, num_scores)
# Determine "base" indices along the new "diagonal", and the factor
# by which the "base" indices should be repeated to account for all
@ -2811,7 +2742,7 @@ class Tally(object):
new_tally._std_dev[diag_indices, :, :] = self.std_dev
# Correct each Filter's stride
stride = new_tally.num_nuclides * new_tally.num_score_bins
stride = new_tally.num_nuclides * new_tally.num_scores
for filter in reversed(new_tally.filters):
filter.stride = stride
stride *= filter.num_bins