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The print_xs routine for the Python API ScatterMatrixXS class is now working

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This commit is contained in:
Will Boyd 2015-09-13 01:34:54 -04:00
parent da6e953319
commit 925d227d1f
2 changed files with 25 additions and 20 deletions
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43
openmc/mgxs/mgxs.py
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@ -455,6 +455,8 @@ class MultiGroupXS(object):
msg = 'Unable to get cross-section since it has not been computed'
raise ValueError(msg)
cv.check_value('value', value, ['mean', 'std_dev', 'rel_err'])
filters = []
filter_bins = []
@ -593,8 +595,8 @@ class MultiGroupXS(object):
string += template.format('', group, bounds[0], bounds[1])
average = self.get_xs([group], [subdomain], 'mean')
rel_err = self.get_xs([group], [subdomain], 'rel_err')*100.
average = average.flatten()[0]
rel_err = rel_err.flatten()[0]
average = np.nan_to_num(average.flatten())[0]
rel_err = np.nan_to_num(rel_err.flatten())[0]
string += '{0:.2e} +/- {1:1.2e}%'.format(average, rel_err)
string += '\n'
string += '\n'
@ -1118,7 +1120,7 @@ class ScatterMatrixXS(MultiGroupXS):
msg = 'Unable to get cross-section since it has not been computed'
raise ValueError(msg)
cv.check_value('value', value, ['mean', 'std. dev.', 'rel. err.'])
cv.check_value('value', value, ['mean', 'std_dev', 'rel_err'])
filters = []
filter_bins = []
@ -1126,20 +1128,23 @@ class ScatterMatrixXS(MultiGroupXS):
# Construct a collection of the domain filter bins
if subdomains != 'all':
cv.check_iterable_type('subdomains', subdomains, Integral)
filters.append(self.domain_type)
filter_bins.append(tuple(subdomains))
for subdomain in subdomains:
filters.append(self.domain_type)
filter_bins.append((subdomain,))
# Construct list of energy group bounds tuples for all requested groups
if in_groups != 'all':
cv.check_iterable_type('in_groups', in_groups, Integral)
filters.append('energy')
for in_group in in_groups:
filter_bins.append(self.energy_groups.get_group_bounds(in_group))
cv.check_iterable_type('groups', in_groups, Integral)
for group in in_groups:
filters.append('energy')
filter_bins.append((self.energy_groups.get_group_bounds(group),))
# Construct list of energy group bounds tuples for all requested groups
if out_groups != 'all':
cv.check_iterable_type('out_groups', out_groups, Integral)
filters.append('energy')
for out_group in out_groups:
filter_bins.append(self.energy_groups.get_group_bounds(out_group))
cv.check_iterable_type('groups', out_groups, Integral)
for group in out_groups:
filters.append('energyout')
filter_bins.append((self.energy_groups.get_group_bounds(group),))
# Query the multi-group cross-section tally for the data
xs = self.xs_tally.get_values(filters=filters,
@ -1167,7 +1172,7 @@ class ScatterMatrixXS(MultiGroupXS):
raise ValueError(msg)
if subdomains != 'all':
cv.check_value('subdomains', subdomains, Iterable, Integral)
cv.check_iterable_type('subdomains', subdomains, Integral)
string = 'Multi-Group XS\n'
string += '{0: <16}{1}{2}\n'.format('\tType', '=\t', self.xs_type)
@ -1183,7 +1188,7 @@ class ScatterMatrixXS(MultiGroupXS):
string += template.format('', group, bounds[0], bounds[1])
if subdomains == 'all':
subdomains = self.subdomain_indices.keys()
subdomains = self.get_subdomain_indices()
# Loop over all subdomains
for subdomain in subdomains:
@ -1202,10 +1207,10 @@ class ScatterMatrixXS(MultiGroupXS):
average = self.get_xs([in_group], [out_group],
[subdomain], 'mean')
rel_err = self.get_xs([in_group], [out_group],
[subdomain], 'rel. err.')*100.
average = average.flatten()[0]
rel_err = rel_err.flatten()[0]
string += '{0:1.2e} +/- {:1.2e}%'.format(average, rel_err)
[subdomain], 'rel_err')*100.
average = np.nan_to_num(average.flatten())[0]
rel_err = np.nan_to_num(rel_err.flatten())[0]
string += '{0:1.2e} +/- {1:1.2e}%'.format(average, rel_err)
string += '\n'
string += '\n'

2
openmc/tallies.py
View file

@ -1009,7 +1009,7 @@ class Tally(object):
else:
msg = 'Unable to return results from Tally ID="{0}" since the ' \
'the requested value "{1}" is not \'mean\', \'std_dev\', ' \
'\rel_err\', \'sum\', or \'sum_sq\''.format(self.id, value)
'\'rel_err\', \'sum\', or \'sum_sq\''.format(self.id, value)
raise LookupError(msg)
return data