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reverted to using num_score_bins in tallies.py

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This commit is contained in:
Sam Shaner 2015-12-17 15:07:32 -05:00
parent b52d940857
commit a0160ad48a
3 changed files with 40 additions and 22 deletions
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8
openmc/statepoint.py
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@ -9,9 +9,9 @@ if sys.version > '3':
class StatePoint(object):
"""State information on a simulation at a certain point in time (at the end of a
given batch). Statepoints can be used to analyze tally results as well as
restart a simulation.
"""State information on a simulation at a certain point in time (at the end
of a given batch). Statepoints can be used to analyze tally results as well
as restart a simulation.
Attributes
----------
@ -394,6 +394,8 @@ 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

@ -524,6 +524,8 @@ 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

52
openmc/tallies.py
View file

@ -64,6 +64,10 @@ 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
@ -96,6 +100,7 @@ class Tally(object):
self._estimator = None
self._triggers = []
self._num_score_bins = 0
self._num_realizations = 0
self._with_summary = False
@ -118,6 +123,7 @@ 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)
@ -246,6 +252,10 @@ 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
@ -259,7 +269,7 @@ class Tally(object):
def num_bins(self):
num_bins = self.num_filter_bins
num_bins *= self.num_nuclides
num_bins *= self.num_scores
num_bins *= self.num_score_bins
return num_bins
@property
@ -302,7 +312,7 @@ class Tally(object):
# Reshape the results arrays
new_shape = (nonzero(self.num_filter_bins),
nonzero(self.num_nuclides),
nonzero(self.num_scores))
nonzero(self.num_score_bins))
sum = np.reshape(sum, new_shape)
sum_sq = np.reshape(sum_sq, new_shape)
@ -458,6 +468,10 @@ 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)
@ -1272,7 +1286,7 @@ class Tally(object):
for filter in self.filters:
new_shape += (filter.num_bins, )
new_shape += (self.num_nuclides,)
new_shape += (self.num_scores,)
new_shape += (self.num_score_bins,)
# Reshape the data with one dimension for each filter
data = np.reshape(data, new_shape)
@ -1602,7 +1616,7 @@ class Tally(object):
new_tally.add_score(new_score)
# Correct each Filter's stride
stride = new_tally.num_nuclides * new_tally.num_scores
stride = new_tally.num_nuclides * new_tally.num_score_bins
for filter in reversed(new_tally.filters):
filter.stride = stride
stride *= filter.num_bins
@ -1710,10 +1724,10 @@ 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_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))
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))
# Add scores to each tally such that each tally contains the complete set
# of scores necessary to perform an entrywise product. New scores added
@ -1726,14 +1740,14 @@ 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_scores, 0, axis=2)
other._std_dev = np.insert(other.std_dev, other.num_scores, 0, axis=2)
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.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_scores, 0, axis=2)
self._std_dev = np.insert(self.std_dev, self.num_scores, 0, axis=2)
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.add_score(score)
# Align other scores with self scores
@ -1745,13 +1759,13 @@ class Tally(object):
other._swap_scores(score, other.scores[i])
# Correct the stride for other filters
stride = other.num_nuclides * other.num_scores
stride = other.num_nuclides * other.num_score_bins
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_scores
stride = self.num_nuclides * self.num_score_bins
for filter in reversed(self.filters):
filter.stride = stride
stride *= filter.num_bins
@ -1817,7 +1831,7 @@ class Tally(object):
self.filters[filter2_index] = filter1
# Update the strides for each of the filters
stride = self.num_nuclides * self.num_scores
stride = self.num_nuclides * self.num_score_bins
for filter in reversed(self.filters):
filter.stride = stride
stride *= filter.num_bins
@ -2565,7 +2579,7 @@ class Tally(object):
filter.num_bins = len(filter_bins[i])
# Correct each Filter's stride
stride = new_tally.num_nuclides * new_tally.num_scores
stride = new_tally.num_nuclides * new_tally.num_score_bins
for filter in reversed(new_tally.filters):
filter.stride = stride
stride *= filter.num_bins
@ -2711,8 +2725,8 @@ 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_scores = new_tally.num_scores
new_shape = (num_filter_bins, num_nuclides, num_scores)
num_score_bins = new_tally.num_score_bins
new_shape = (num_filter_bins, num_nuclides, num_score_bins)
# Determine "base" indices along the new "diagonal", and the factor
# by which the "base" indices should be repeated to account for all
@ -2742,7 +2756,7 @@ 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_scores
stride = new_tally.num_nuclides * new_tally.num_score_bins
for filter in reversed(new_tally.filters):
filter.stride = stride
stride *= filter.num_bins