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Code Issues Projects Releases Packages Wiki Activity Actions 5

Sampling for almost all possible File 32

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Isaac Meyer 2018-03-20 12:20:31 -04:00
parent 756690c571
commit 00b12a1228
1 changed files with 80 additions and 14 deletions
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94
openmc/data/resonance_covariance.py
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@ -25,10 +25,13 @@ def sample_resonance_parameters(nuclide, n_samples):
"""
nparams,params = nuclide.res_covariance.ranges[0].parameters.shape
cov = nuclide.res_covariance.ranges[0].covariance
cov = cov + cov.T - np.diag(cov.diagonal()) #symmetrizing covariance matrix
covsize = cov.shape[0]
formalism = nuclide.res_covariance.ranges[0].formalism
samples = []
if formalism == 'mlbw':
### Handling MLBW Sampling ###
if formalism == 'mlbw' or formalism == 'slbw':
if covsize/nparams == 3:
param_list = ['energy','neutronWidth','captureWidth']
mean_array = pd.DataFrame.as_matrix(nuclide.res_covariance.ranges[0].parameters[param_list])
@ -50,6 +53,70 @@ def sample_resonance_parameters(nuclide, n_samples):
sample_params = pd.DataFrame.from_records(records, columns=columns)
samples.append(sample_params)
elif covsize/nparams == 4:
param_list = ['energy','neutronWidth','captureWidth','fissionWidth']
mean_array = pd.DataFrame.as_matrix(nuclide.res_covariance.ranges[0].parameters[param_list])
spin = pd.DataFrame.as_matrix(nuclide.res_covariance.ranges[0].parameters['J'])
mean = mean_array.flatten()
for i in range(n_samples):
sample = np.random.multivariate_normal(mean,cov)
energy = sample[0::4]
gn = sample[1::4]
gg = sample[2::4]
gf = sample[3::4]
gt = gn + gg + gf
records = []
for j, E in enumerate(energy):
records.append([energy[j], spin[j], gt[j], gn[j],
gg[j], gf[j]])
columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
'captureWidth', 'fissionWidth']
sample_params = pd.DataFrame.from_records(records, columns=columns)
samples.append(sample_params)
elif covsize/nparams == 5:
param_list = ['energy','neutronWidth','captureWidth','fissionWidth']
mean_array = pd.DataFrame.as_matrix(nuclide.res_covariance.ranges[0].parameters[param_list])
spin = pd.DataFrame.as_matrix(nuclide.res_covariance.ranges[0].parameters['J'])
mean = mean_array.flatten()
for i in range(n_samples):
sample = np.random.multivariate_normal(mean,cov)
energy = sample[0::4]
gn = sample[1::4]
gg = sample[2::4]
gf = sample[3::4]
gt = gn + gg + gf
records = []
for j, E in enumerate(energy):
records.append([energy[j], spin[j], gt[j], gn[j],
gg[j], gf[j]])
columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
'captureWidth', 'fissionWidth']
sample_params = pd.DataFrame.from_records(records, columns=columns)
samples.append(sample_params)
### Handling RM Sampling ###
if formalism == 'rm':
if covsize/nparams == 3:
param_list = ['energy','neutronWidth','captureWidth']
mean_array = pd.DataFrame.as_matrix(nuclide.res_covariance.ranges[0].parameters[param_list])
spin = pd.DataFrame.as_matrix(nuclide.res_covariance.ranges[0].parameters['J'])
gfa = pd.DataFrame.as_matrix(nuclide.res_covariance.ranges[0].parameters['fissionWidthA'])
gfb = pd.DataFrame.as_matrix(nuclide.res_covariance.ranges[0].parameters['fissionWidthB'])
mean = mean_array.flatten()
for i in range(n_samples):
sample = np.random.multivariate_normal(mean,cov)
energy = sample[0::3]
gn = sample[1::3]
gg = sample[2::3]
records = []
for j, E in enumerate(energy):
records.append([energy[j], spin[j], gn[j],
gg[j], gfa[j], gfb[j]])
columns = ['energy', 'J', 'neutronWidth',
'captureWidth', 'fissionWidthA','fissionWidthB']
sample_params = pd.DataFrame.from_records(records, columns=columns)
samples.append(sample_params)
return samples
### Under Construction END
@ -258,7 +325,7 @@ class MultiLevelBreitWignerCovariance(ResonanceRange):
mlbw.parameters = parameters
mlbw.covariance = cov
mlbw.lcomp = LCOMP
mlbw.num_paramaters = num_paramaters
mlbw.num_parameters = num_parameters
return mlbw
@ -308,8 +375,7 @@ class MultiLevelBreitWignerCovariance(ResonanceRange):
return mlbw
elif LCOMP == 0 :
cov = np.zeros([5,5])
# test2 = np.pad(test,((0,2),(0,2)),'constant',constant_values=0)
cov = np.zeros([4,4])
records = []
cov_index = 0
for i in range(NLS):
@ -326,26 +392,26 @@ class MultiLevelBreitWignerCovariance(ResonanceRange):
gn = res_values[3]
gg = res_values[4]
gf = res_values[5]
records.append([energy, spin, gn, gg, gf])
records.append([energy, spin, gt, 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
cov[cov_index+1,cov_index+1:cov_index+2]=cov_values[1:2]
cov[cov_index+1,cov_index+3]=cov_values[4]
cov[cov_index+2,cov_index+2] = cov_values[3]
cov[cov_index+2,cov_index+3] = cov_values[5]
cov[cov_index+3,cov_index+3] = cov_values[6]
cov_index += 4
if j < num_res-1: #Pad matrix for additional values
cov = np.pad(cov,((0,5),(0,5)),'constant',
cov = np.pad(cov,((0,4),(0,4)),'constant',
constant_values=0)
#Create pandas DataFrame with resonance data, currently
#redundant with data.IncidentNeutron.resonance
columns = ['energy', 'J', 'neutronWidth',
columns = ['energy', 'J', 'totalWidth','neutronWidth',
'captureWidth', 'fissionWidth']
parameters = pd.DataFrame.from_records(records, columns=columns)