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Fixed MLBW sample reconstruction

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This commit is contained in:
Isaac Meyer 2018-07-06 12:45:27 -05:00
parent e88f8cd364
commit 6b836a199c
1 changed files with 8 additions and 6 deletions
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14
openmc/data/resonance_covariance.py
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@ -36,7 +36,7 @@ def file2contributions(file32params, file2params):
file32params_sort = file32params.sort_values(by=['energy'])
#Add in values (.values converts to array first to ignore index)
file32params_sort['L'] = file2params['L'].values
if 'competiveWidth' in file32params_sort:
if 'competitiveWidth' in file2params.columns:
file32params_sort['competitiveWidth'] = file2params['competitiveWidth'].values
#Resort to File 32 order (by L then by E) for use with covariance
parameters = file32params_sort.sort_index()
@ -237,7 +237,7 @@ class ResonanceCovarianceRange(object):
samples = []
# Handling MLBW Sampling
# Handling MLBW sampling
if formalism == 'mlbw' or formalism == 'slbw':
if mpar == 3:
param_list = ['energy','neutronWidth','captureWidth']
@ -245,6 +245,7 @@ class ResonanceCovarianceRange(object):
spin = pd.DataFrame.as_matrix(parameters['J'])
l_value = pd.DataFrame.as_matrix(parameters['L'])
gf = pd.DataFrame.as_matrix(parameters['fissionWidth'])
gx = pd.DataFrame.as_matrix(parameters['competitiveWidth'])
mean = mean_array.flatten()
for i in range(n_samples):
sample = np.random.multivariate_normal(mean,cov)
@ -255,9 +256,9 @@ class ResonanceCovarianceRange(object):
records = []
for j, E in enumerate(energy):
records.append([energy[j], l_value[j], spin[j], gt[j], gn[j],
gg[j], gf[j]])
gg[j], gf[j], gx[j]])
columns = ['energy', 'L', 'J', 'totalWidth', 'neutronWidth',
'captureWidth', 'fissionWidth']
'captureWidth', 'fissionWidth', 'competitiveWidth']
sample_params = pd.DataFrame.from_records(records, columns=columns)
samples.append(sample_params)
@ -266,6 +267,7 @@ class ResonanceCovarianceRange(object):
mean_array = pd.DataFrame.as_matrix(parameters[param_list])
spin = pd.DataFrame.as_matrix(parameters['J'])
l_value = pd.DataFrame.as_matrix(parameters['L'])
gx = pd.DataFrame.as_matrix(parameters['competitiveWidth'])
mean = mean_array.flatten()
for i in range(n_samples):
sample = np.random.multivariate_normal(mean,cov)
@ -277,9 +279,9 @@ class ResonanceCovarianceRange(object):
records = []
for j, E in enumerate(energy):
records.append([energy[j], l_value[j], spin[j], gt[j], gn[j],
gg[j], gf[j]])
gg[j], gf[j], gx[j]])
columns = ['energy', 'L', 'J', 'totalWidth', 'neutronWidth',
'captureWidth', 'fissionWidth']
'captureWidth', 'fissionWidth', 'competitiveWidth']
sample_params = pd.DataFrame.from_records(records, columns=columns)
samples.append(sample_params)