mirror of
https://github.com/openmc-dev/openmc.git
synced 2026-07-27 21:55:41 -04:00
added reconstruction ability for resonance samples
This commit is contained in:
Isaac Meyer
parent
0bcb6f8874
commit
84ef96cf8a
3 changed files with 154 additions and 38 deletions
File diff suppressed because one or more lines are too long
|
|
@ -202,7 +202,7 @@ class ResonanceRange(object):
|
|||
|
||||
return cls(target_spin, energy_min, energy_max, {0: a}, {0: ap})
|
||||
|
||||
def reconstruct(self, energies):
|
||||
def reconstruct(self, energies, use_sample = False, sample_parameters = None):
|
||||
"""Evaluate cross section at specified energies.
|
||||
|
||||
Parameters
|
||||
|
|
@ -221,8 +221,8 @@ class ResonanceRange(object):
|
|||
raise RuntimeError("Resonance reconstruction not available.")
|
||||
|
||||
# Pre-calculate penetrations and shifts for resonances
|
||||
if not self._prepared:
|
||||
self._prepare_resonances()
|
||||
if not self._prepared or use_sample:
|
||||
self._prepare_resonances(use_sample, sample_parameters)
|
||||
|
||||
if isinstance(energies, Iterable):
|
||||
elastic = np.zeros_like(energies)
|
||||
|
|
@ -394,8 +394,11 @@ class MultiLevelBreitWigner(ResonanceRange):
|
|||
|
||||
return mlbw
|
||||
|
||||
def _prepare_resonances(self):
|
||||
df = self.parameters.copy()
|
||||
def _prepare_resonances(self, use_sample = False, sample_parameters = None):
|
||||
if use_sample == False:
|
||||
df = self.parameters.copy()
|
||||
else:
|
||||
df = sample_parameters.copy()
|
||||
|
||||
# Penetration and shift factors
|
||||
p = np.zeros(len(df))
|
||||
|
|
@ -653,8 +656,11 @@ class ReichMoore(ResonanceRange):
|
|||
|
||||
return rm
|
||||
|
||||
def _prepare_resonances(self):
|
||||
df = self.parameters.copy()
|
||||
def _prepare_resonances(self, use_sample = False, sample_parameters = None):
|
||||
if use_sample == False:
|
||||
df = self.parameters.copy()
|
||||
else:
|
||||
df = sample_parameters.copy()
|
||||
|
||||
# Penetration and shift factors
|
||||
p = np.zeros(len(df))
|
||||
|
|
|
|||
|
|
@ -113,7 +113,7 @@ class ResonanceCovariances(Resonances):
|
|||
|
||||
# Throw error for unsupported formalisms
|
||||
if formalism in [0,7]:
|
||||
raise TypeError('LRF= ', formalism,
|
||||
raise NotImplementedError('LRF= ', formalism,
|
||||
'covariance not supported for this formalism')
|
||||
|
||||
if unresolved_flag in (0,1):
|
||||
|
|
@ -122,14 +122,14 @@ class ResonanceCovariances(Resonances):
|
|||
erange = _FORMALISMS[formalism].from_endf(ev, file_obj,
|
||||
items, file2params)
|
||||
elif unresolved_flag == 2:
|
||||
warnings.warn('Unresolved resonance not supported.'
|
||||
'Covariance values for the'
|
||||
'unresolved region not imported.')
|
||||
warn_str = 'Unresolved resonance not supported.'\
|
||||
'Covariance values for the unresolved region not imported.'
|
||||
warnings.warn(warn_str)
|
||||
|
||||
ranges.append(erange)
|
||||
|
||||
return cls(ranges)
|
||||
|
||||
|
||||
class ResonanceCovarianceRange(object):
|
||||
"""Resonace covariance range. Base class for different formalisms.
|
||||
Parameters
|
||||
|
|
@ -243,6 +243,7 @@ class ResonanceCovarianceRange(object):
|
|||
param_list = ['energy','neutronWidth','captureWidth']
|
||||
mean_array = pd.DataFrame.as_matrix(parameters[param_list])
|
||||
spin = pd.DataFrame.as_matrix(parameters['J'])
|
||||
l_value = pd.DataFrame.as_matrix(parameters['L'])
|
||||
gf = pd.DataFrame.as_matrix(parameters['fissionWidth'])
|
||||
mean = mean_array.flatten()
|
||||
for i in range(n_samples):
|
||||
|
|
@ -253,9 +254,9 @@ class ResonanceCovarianceRange(object):
|
|||
gt = gn + gg + gf
|
||||
records = []
|
||||
for j, E in enumerate(energy):
|
||||
records.append([energy[j], spin[j], gt[j], gn[j],
|
||||
records.append([energy[j], l_value[j], spin[j], gt[j], gn[j],
|
||||
gg[j], gf[j]])
|
||||
columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
|
||||
columns = ['energy', 'L', 'J', 'totalWidth', 'neutronWidth',
|
||||
'captureWidth', 'fissionWidth']
|
||||
sample_params = pd.DataFrame.from_records(records, columns=columns)
|
||||
samples.append(sample_params)
|
||||
|
|
@ -264,6 +265,7 @@ class ResonanceCovarianceRange(object):
|
|||
param_list = ['energy','neutronWidth','captureWidth','fissionWidth']
|
||||
mean_array = pd.DataFrame.as_matrix(parameters[param_list])
|
||||
spin = pd.DataFrame.as_matrix(parameters['J'])
|
||||
l_value = pd.DataFrame.as_matrix(parameters['L'])
|
||||
mean = mean_array.flatten()
|
||||
for i in range(n_samples):
|
||||
sample = np.random.multivariate_normal(mean,cov)
|
||||
|
|
@ -274,9 +276,9 @@ class ResonanceCovarianceRange(object):
|
|||
gt = gn + gg + gf
|
||||
records = []
|
||||
for j, E in enumerate(energy):
|
||||
records.append([energy[j], spin[j], gt[j], gn[j],
|
||||
records.append([energy[j], l_value[j], spin[j], gt[j], gn[j],
|
||||
gg[j], gf[j]])
|
||||
columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
|
||||
columns = ['energy', 'L', 'J', 'totalWidth', 'neutronWidth',
|
||||
'captureWidth', 'fissionWidth']
|
||||
sample_params = pd.DataFrame.from_records(records, columns=columns)
|
||||
samples.append(sample_params)
|
||||
|
|
@ -286,6 +288,7 @@ class ResonanceCovarianceRange(object):
|
|||
'fissionWidth', 'competitiveWidth']
|
||||
mean_array = pd.DataFrame.as_matrix(parameters[param_list])
|
||||
spin = pd.DataFrame.as_matrix(parameters['J'])
|
||||
l_value = pd.DataFrame.as_matrix(parameters['L'])
|
||||
mean = mean_array.flatten()
|
||||
for i in range(n_samples):
|
||||
sample = np.random.multivariate_normal(mean,cov)
|
||||
|
|
@ -297,9 +300,9 @@ class ResonanceCovarianceRange(object):
|
|||
gt = gn + gg + gf
|
||||
records = []
|
||||
for j, E in enumerate(energy):
|
||||
records.append([energy[j], spin[j], gt[j], gn[j],
|
||||
records.append([energy[j], l_value[j], spin[j], gt[j], gn[j],
|
||||
gg[j], gf[j], gx[j]])
|
||||
columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
|
||||
columns = ['energy', 'L', 'J', 'totalWidth', 'neutronWidth',
|
||||
'captureWidth', 'fissionWidth', 'competitveWidth']
|
||||
sample_params = pd.DataFrame.from_records(records, columns=columns)
|
||||
samples.append(sample_params)
|
||||
|
|
@ -310,6 +313,7 @@ class ResonanceCovarianceRange(object):
|
|||
param_list = ['energy','neutronWidth','captureWidth']
|
||||
mean_array = pd.DataFrame.as_matrix(parameters[param_list])
|
||||
spin = pd.DataFrame.as_matrix(parameters['J'])
|
||||
l_value = pd.DataFrame.as_matrix(parameters['L'])
|
||||
gfa = pd.DataFrame.as_matrix(parameters['fissionWidthA'])
|
||||
gfb = pd.DataFrame.as_matrix(parameters['fissionWidthB'])
|
||||
mean = mean_array.flatten()
|
||||
|
|
@ -320,17 +324,19 @@ class ResonanceCovarianceRange(object):
|
|||
gg = sample[2::3]
|
||||
records = []
|
||||
for j, E in enumerate(energy):
|
||||
records.append([energy[j], spin[j], gn[j],
|
||||
records.append([energy[j], l_value[j], spin[j], gn[j],
|
||||
gg[j], gfa[j], gfb[j]])
|
||||
columns = ['energy', 'J', 'neutronWidth',
|
||||
columns = ['energy', 'L', 'J', 'neutronWidth',
|
||||
'captureWidth', 'fissionWidthA','fissionWidthB']
|
||||
sample_params = pd.DataFrame.from_records(records, columns=columns)
|
||||
samples.append(sample_params)
|
||||
|
||||
elif mpar == 5:
|
||||
param_list = ['energy','neutronWidth','captureWidth','fissionWidthA','fissionWidthB']
|
||||
param_list = ['energy','neutronWidth','captureWidth',
|
||||
'fissionWidthA','fissionWidthB']
|
||||
mean_array = pd.DataFrame.as_matrix(parameters[param_list])
|
||||
spin = pd.DataFrame.as_matrix(parameters['J'])
|
||||
l_value = pd.DataFrame.as_matrix(parameters['L'])
|
||||
mean = mean_array.flatten()
|
||||
for i in range(n_samples):
|
||||
sample = np.random.multivariate_normal(mean,cov)
|
||||
|
|
@ -341,15 +347,43 @@ class ResonanceCovarianceRange(object):
|
|||
gfb = sample[4::5]
|
||||
records = []
|
||||
for j, E in enumerate(energy):
|
||||
records.append([energy[j], spin[j], gn[j],
|
||||
records.append([energy[j], l_value[j], spin[j], gn[j],
|
||||
gg[j], gfa[j], gfb[j]])
|
||||
columns = ['energy', 'J', 'neutronWidth',
|
||||
columns = ['energy', 'L', 'J', 'neutronWidth',
|
||||
'captureWidth', 'fissionWidthA','fissionWidthB']
|
||||
sample_params = pd.DataFrame.from_records(records, columns=columns)
|
||||
samples.append(sample_params)
|
||||
|
||||
self.samples = samples
|
||||
|
||||
def reconstruct(self, energies, resonances, sampleN):
|
||||
"""Evaluate the cross section at specified energies for an already
|
||||
sampled set of resonance parameters.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
energies : float or Iterable of float
|
||||
Energies at which the cross section should be evaluated
|
||||
resonances : openmc.data.Resonance object
|
||||
Corresponding resonance range with File 2 data. Used for
|
||||
reconstruction method
|
||||
sampleN : int
|
||||
Index of sample of resonance parameters to be used
|
||||
|
||||
Returns
|
||||
-------
|
||||
3-tuple of float or numpy.ndarray
|
||||
Elastic, capture, and fission cross sections at the specified
|
||||
energies
|
||||
|
||||
"""
|
||||
if self.samples[sampleN] is None:
|
||||
raise ValueError("Sample of resonance parameters has not been set.")
|
||||
sample_parameters = self.samples[sampleN]
|
||||
xs_array = resonances.reconstruct(energies, use_sample = True,
|
||||
sample_parameters = sample_parameters)
|
||||
return xs_array
|
||||
|
||||
class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
|
||||
"""Multi-level Breit-Wigner resolved resonance formalism covariance data.
|
||||
Parameters
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue