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Restructured ResonanceCovarianceRange class to contain corresponding file2 data as an attribute
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Isaac Meyer
parent
5381ad46bc
commit
e2a27f288b
2 changed files with 141 additions and 148 deletions
File diff suppressed because one or more lines are too long
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@ -115,9 +115,9 @@ class ResonanceCovariances(Resonances):
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if unresolved_flag in (0, 1):
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# Resolved resonance region
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file2params = resonances.ranges[j].parameters
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resonance = resonances.ranges[j]
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erange = _FORMALISMS[formalism].from_endf(ev, file_obj,
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items, file2params)
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items, resonance)
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ranges.append(erange)
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elif unresolved_flag == 2:
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@ -159,7 +159,7 @@ class ResonanceCovarianceRange:
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self.energy_min = energy_min
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self.energy_max = energy_max
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def res_subset(self, parameter_str, bounds, resonances):
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def res_subset(self, parameter_str, bounds):
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"""Produce a subset of resonance parameters and the corresponding
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covariance matrix to an IncidentNeutron object.
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@ -170,32 +170,25 @@ class ResonanceCovarianceRange:
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(i.e. 'energy', 'captureWidth', 'fissionWidthA'...)
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bounds : np.array
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[low numerical bound, high numerical bound]
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resonances : openmc.data.ResonanceRange object
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Corresponding resonance range with File 2 data.
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Returns
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-------
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res_range : openmc.data.ResonanceRange
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ResonanceRange object that contains a subset of parameters
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(maintains indexing of original)
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res_cov_range : openmc.data.ResonanceCovarianceRange
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ResonanceCovarianceRange object that contains a subset of the
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covariance matrix (upper triangular) as well as parameters
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covariance matrix (upper triangular) as well as a subset parameters
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within self.file2params
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"""
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# Copy the objects
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res_range = copy.copy(resonances)
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res_cov_range = copy.copy(self)
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# Copy range and prevent change of original
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res_cov_range = copy.deepcopy(self)
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parameters = res_range.parameters
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parameters = self.file2res.parameters
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cov = res_cov_range.covariance
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mpar = res_cov_range.mpar
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# Create mask
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mask1 = parameters[parameter_str] >= bounds[0]
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mask2 = parameters[parameter_str] <= bounds[1]
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mask = mask1 & mask2
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# Set the parameters for each object
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res_range.parameters = parameters[mask]
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res_cov_range.parameters = parameters[mask]
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indices = res_cov_range.parameters.index.values
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# Build subset of covariance
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@ -212,11 +205,16 @@ class ResonanceCovarianceRange:
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cov_subset = np.zeros([sub_cov_dim, sub_cov_dim])
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tri_indices = np.triu_indices(sub_cov_dim)
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cov_subset[tri_indices] = cov_subset_vals
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res_cov_range.file2res.parameters = parameters[mask]
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res_cov_range.covariance = cov_subset
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# Set _prepared to False to ensure parameter subset
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# used during construction routine
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res_cov_range.file2res._prepared = False
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return res_range, res_cov_range
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return res_cov_range
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def sample_resonance_parameters(self, n_samples, resonances):
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def sample_resonance_parameters(self, n_samples):
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"""Sample resonance parameters based on the covariances provided
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within an ENDF evaluation.
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@ -224,8 +222,6 @@ class ResonanceCovarianceRange:
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----------
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n_samples : int
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The number of samples to produce
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resonances : openmc.data.ResonanceRange object
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Corresponding resonance range with File 2 data.
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Returns
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-------
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@ -239,6 +235,11 @@ class ResonanceCovarianceRange:
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warnings.warn(warn_str)
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parameters = self.parameters
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cov = self.covariance
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# Copy ResonanceRange object
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res_range = copy.copy(self.file2res)
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# Set _prepared to False to ensure sampled parameters are
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# used during construction routine
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res_range._prepared = False
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nparams, params = parameters.shape
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# Symmetrizing covariance matrix
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@ -273,10 +274,6 @@ class ResonanceCovarianceRange:
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'captureWidth', 'fissionWidth', 'competitiveWidth']
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sample_params = pd.DataFrame.from_records(records,
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columns=columns)
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res_range = copy.copy(resonances)
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# Set _prepared to False to ensure sampled paramaters are
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# used during construction routine
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res_range._prepared = False
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res_range.parameters = sample_params
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samples.append(res_range)
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@ -304,11 +301,6 @@ class ResonanceCovarianceRange:
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'captureWidth', 'fissionWidth', 'competitiveWidth']
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sample_params = pd.DataFrame.from_records(records,
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columns=columns)
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res_range = copy.copy(resonances)
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# Set _prepared to False to ensure sampled paramaters are
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# used during construction routine
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res_range._prepared = False
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res_range.parameters = sample_params
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samples.append(res_range)
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elif mpar == 5:
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@ -335,11 +327,6 @@ class ResonanceCovarianceRange:
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'captureWidth', 'fissionWidth', 'competitveWidth']
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sample_params = pd.DataFrame.from_records(records,
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columns=columns)
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res_range = copy.copy(resonances)
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# Set _prepared to False to ensure sampled paramaters are
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# used during construction routine
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res_range._prepared = False
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res_range.parameters = sample_params
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samples.append(res_range)
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# Handling RM Sampling
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@ -366,11 +353,6 @@ class ResonanceCovarianceRange:
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'captureWidth', 'fissionWidthA', 'fissionWidthB']
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sample_params = pd.DataFrame.from_records(records,
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columns=columns)
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res_range = copy.copy(resonances)
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# Set _prepared to False to ensure sampled paramaters are
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# used during construction routine
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res_range._prepared = False
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res_range.parameters = sample_params
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samples.append(res_range)
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elif mpar == 5:
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@ -396,11 +378,6 @@ class ResonanceCovarianceRange:
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'captureWidth', 'fissionWidthA', 'fissionWidthB']
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sample_params = pd.DataFrame.from_records(records,
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columns=columns)
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res_range = copy.copy(resonances)
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# Set _prepared to False to ensure sampled paramaters are
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# used during construction routine
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res_range._prepared = False
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res_range.parameters = sample_params
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samples.append(res_range)
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return samples
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@ -425,24 +402,29 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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Resonance parameters
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covariance : numpy.array
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The covariance matrix contained within the ENDF evaluation
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lcomp : int
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Flag indicating format of the covariance matrix within the ENDF file
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mpar : int
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Number of parameters in covariance matrix for each individual resonance
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lcomp : int
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Flag indicating format of the covariance matrix within the ENDF file
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file2res : openmc.data.ResonanceRange object
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Corresponding resonance range with File 2 data.
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formalism : str
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String descriptor of formalism
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"""
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def __init__(self, energy_min, energy_max, parameters, covariance, mpar, lcomp):
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def __init__(self, energy_min, energy_max, parameters, covariance, mpar,
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lcomp, file2res):
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super().__init__(energy_min, energy_max)
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self.parameters = parameters
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self.covariance = covariance
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self.mpar = mpar
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self.lcomp = lcomp
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self.file2res = copy.copy(file2res)
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self.formalism = 'mlbw'
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@classmethod
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def from_endf(cls, ev, file_obj, items, file2params):
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def from_endf(cls, ev, file_obj, items, resonance):
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"""Create MLBW covariance data from an ENDF evaluation.
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Parameters
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@ -455,9 +437,8 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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items : list
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Items from the CONT record at the start of the resonance range
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subsection
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file2params : openmc.data.ResonanceRange object
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Corresponding resonance range with File 2 data. Used for
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reconstruction method
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resonance : openmc.data.ResonanceRange object
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Corresponding resonance range with File 2 data.
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Returns
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-------
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@ -520,7 +501,8 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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parameters = pd.DataFrame.from_records(records, columns=columns)
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# Add parameters from File 2
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parameters = _add_file2_contributions(parameters, file2params)
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parameters = _add_file2_contributions(parameters,
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resonance.parameters)
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# Compact format - Resonances and individual uncertainties followed by
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# compact correlations
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@ -567,7 +549,8 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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mpar = int(covsize/nparams)
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# Add parameters from File 2
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parameters = _add_file2_contributions(parameters, file2params)
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parameters = _add_file2_contributions(parameters,
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resonance.parameters)
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elif lcomp == 0:
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cov = np.zeros([4, 4])
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@ -609,10 +592,12 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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mpar = int(covsize/nparams)
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# Add parameters from File 2
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parameters = _add_file2_contributions(parameters, file2params)
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parameters = _add_file2_contributions(parameters,
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resonance.parameters)
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# Create instance of class
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mlbw = cls(energy_min, energy_max, parameters, cov, mpar, lcomp)
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mlbw = cls(energy_min, energy_max, parameters, cov, mpar, lcomp,
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resonance)
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return mlbw
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@ -638,18 +623,20 @@ class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance):
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Resonance parameters
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covariance : numpy.array
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The covariance matrix contained within the ENDF evaluation
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lcomp : int
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Flag indicating format of the covariance matrix within the ENDF file
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mpar : int
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Number of parameters in covariance matrix for each individual resonance
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formalism : str
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String descriptor of formalism
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lcomp : int
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Flag indicating format of the covariance matrix within the ENDF file
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file2res : openmc.data.ResonanceRange object
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Corresponding resonance range with File 2 data.
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"""
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def __init__(self, energy_min, energy_max, parameters, covariance, mpar,
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lcomp):
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lcomp, file2res):
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super().__init__(energy_min, energy_max, parameters, covariance, mpar,
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lcomp)
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lcomp, file2res)
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self.formalism = 'slbw'
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@ -680,21 +667,24 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
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Flag indicating format of the covariance matrix within the ENDF file
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mpar : int
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Number of parameters in covariance matrix for each individual resonance
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file2res : openmc.data.ResonanceRange object
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Corresponding resonance range with File 2 data.
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formalism : str
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String descriptor of formalism
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"""
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def __init__(self, energy_min, energy_max, parameters, covariance, mpar,
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lcomp):
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lcomp, file2res):
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super().__init__(energy_min, energy_max)
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self.parameters = parameters
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self.covariance = covariance
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self.mpar = mpar
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self.lcomp = lcomp
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self.file2res = copy.copy(file2res)
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self.formalism = 'rm'
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@classmethod
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def from_endf(cls, ev, file_obj, items, file2params):
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def from_endf(cls, ev, file_obj, items, resonance):
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"""Create Reich-Moore resonance covariance data from an ENDF
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evaluation. Includes the resonance parameters contained separately in
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File 32.
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@ -709,7 +699,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
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items : list
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Items from the CONT record at the start of the resonance range
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subsection
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resonances : openmc.data.Resonance object
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resonance : openmc.data.Resonance object
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openmc.data.Resonanance object generated from the same evaluation
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used to import values not contained in File 32
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@ -773,7 +763,8 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
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parameters = pd.DataFrame.from_records(records, columns=columns)
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# Add parameters from File 2
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parameters = _add_file2_contributions(parameters, file2params)
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parameters = _add_file2_contributions(parameters,
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resonance.parameters)
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# Compact format - Resonances and individual uncertainties followed by
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# compact correlations
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@ -813,10 +804,12 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
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mpar = int(covsize/nparams)
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# Add parameters from File 2
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parameters = _add_file2_contributions(parameters, file2params)
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parameters = _add_file2_contributions(parameters,
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resonance.parameters)
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# Create instance of ReichMooreCovariance
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rmc = cls(energy_min, energy_max, parameters, cov, mpar, lcomp)
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rmc = cls(energy_min, energy_max, parameters, cov, mpar, lcomp,
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resonance)
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return rmc
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