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Added capability for LCOMP=0

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This commit is contained in:
Isaac Meyer 2018-02-06 18:45:19 -05:00
parent b6082acc59
commit 27b6abee2d
1 changed files with 53 additions and 7 deletions
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60
openmc/data/resonance_covariance.py
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@ -123,6 +123,8 @@ class MultiLevelBreitWignerCovariance(ResonanceRange):
The parameters that are included in the covariance matrix
covariance_matrix : array
The covariance matrix contained within the ENDF evaluation
lcomp : int
Flag indicating the format of the covariance matrix
"""
@ -210,6 +212,7 @@ class MultiLevelBreitWignerCovariance(ResonanceRange):
mlbw = cls(energy_min, energy_max)
mlbw.parameters = parameters
mlbw.covariance = cov
mlbw.lcomp = LCOMP
return mlbw
@ -243,15 +246,63 @@ class MultiLevelBreitWignerCovariance(ResonanceRange):
'captureWidth', 'fissionWidth']
parameters = pd.DataFrame.from_records(records, columns=columns)
# Create instance of ReichMooreCovariance
# Create instance of MultiLevelBreitWignerCovariance
mlbw = cls(energy_min, energy_max)
mlbw.parameters = parameters
mlbw.covariance = cov
mlbw.lcomp = LCOMP
return mlbw
elif LCOMP == 0 :
raise TypeError('LCOMP = ' + str(LCOMP) + ' not supported')
cov = np.zeros([5,5])
# test2 = np.pad(test,((0,2),(0,2)),'constant',constant_values=0)
records = []
cov_index = 0
for i in range(NLS):
items, values = get_list_record(file_obj)
num_res = items[5]
for j in range(num_res):
one_res = values[18*j:18*(j+1)]
res_values = one_res[:6]
cov_values = one_res[6:]
energy = res_values[0]
spin = res_values[1]
gt = res_values[2]
gn = res_values[3]
gg = res_values[4]
gf = res_values[5]
records.append([energy, spin, gn, gg, gf])
#Populate the coviariance matrix for this resonance
#There are no covariances between resonances in LCOMP=0
cov[cov_index,cov_index]=cov_values[0]
cov[cov_index+1,cov_index+1]=cov_values[10]
cov[cov_index+2,cov_index+2:cov_index+3]=cov_values[1:2]
cov[cov_index+2,cov_index+4]=cov_values[4]
cov[cov_index+3,cov_index+3] = cov_values[3]
cov[cov_index+3,cov_index+4] = cov_values[5]
cov[cov_index+4,cov_index+4] = cov_values[6]
cov_index += 5
if j < num_res: #Pad matrix for additional values
cov = np.pad(cov,((0,5),(0,5)),'constant',
constant_values=0)
#Create pandas DataFrame with resonance data, currently
#redundant with data.IncidentNeutron.resonance
columns = ['energy', 'J', 'neutronWidth',
'captureWidth', 'fissionWidth']
parameters = pd.DataFrame.from_records(records, columns=columns)
# Create instance of class
mlbw = cls(energy_min, energy_max)
mlbw.parameters = parameters
mlbw.covariance = cov
mlbw.lcomp = LCOMP
return mlbw
class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance):
"""Single-level Breit-Wigner resolved resonance formalism covariance data.
@ -461,11 +512,6 @@ class ReichMooreCovariance(ResonanceRange):
return rmc
elif LCOMP == 0:
raise TypeError('LCOMP = ' + str(LCOMP) + ' not supported')
# _FORMALISMS = {0: ResonanceRange,
# 1: SingleLevelBreitWigner,
# 2: MultiLevelBreitWigner,