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Restructuring of classes, better handling of file 2 contribution
This commit is contained in:
Isaac Meyer
parent
a85829e22f
commit
3647306d5c
2 changed files with 278 additions and 287 deletions
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@ -803,7 +803,7 @@ class IncidentNeutron(EqualityMixin):
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data.resonances = res.Resonances.from_endf(ev)
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if (32, 151) in ev.section and get_covariance:
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data.res_covariance = res_cov.ResonanceCovariance.from_endf(ev)
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data.res_covariance = res_cov.ResonanceCovariances.from_endf(ev, data.resonances)
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# Read each reaction
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for mf, mt, nc, mod in ev.reaction_list:
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@ -10,203 +10,54 @@ import pandas as pd
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from .data import NEUTRON_MASS
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from .endf import get_head_record, get_cont_record, get_tab1_record, get_list_record, get_intg_record
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import openmc.checkvalue as cv
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from .resonance import ResonanceRange
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from .resonance import Resonances
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def res_subset(nuclide, parameter_str, bounds):
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"""Produce a subset of resonance paramaters and the covariance matrix
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to an IncidentNeutron objecti
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Parameters
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----------
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nuclide: ResonanceCovariance object
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parameter_str: paramater to be discriminated
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(i.e. 'energy','captureWidth','fissionWidthA'...)
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bounds: np.array [low numerical bound, high numerical bound]
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def file2contributions(file32params, file2params):
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"""Function for aiding in adding resonance parameters from File 2 that are
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not always present in file 32.
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Paramateers
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-----------
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file2params: pandas.Dataframe
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Resonance parameters from File 2. Ordered by energy.
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file32params: pandas.Dataframe
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Incomplete set of resonance parameters contained in File 32.
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Returns
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-------
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parameters_subset : Dataframe of a subset of parameters
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(maintains indexing)
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cov_subset: subset of covariance matrix (upper triangular)
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parameters: pandas.Dataframs
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Complete set of parameters ordered by L-values and then energy
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"""
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parameters = nuclide.parameters
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cov = nuclide.covariance
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mpar = nuclide.mpar
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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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parameters_subset=parameters[mask]
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indices = parameters_subset.index.values
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sub_cov_dim = len(indices)*mpar
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oldvalues = []
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for index1 in indices:
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for i in range(mpar):
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for index2 in indices:
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for j in range(mpar):
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if index2*mpar+j >= index1*mpar+i:
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oldvalues.append(cov[index1*mpar+i,index2*mpar+j])
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#Use l-values and competitiveWidth from File 2 data
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#Re-sort File 2 by energy to match File 32
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file2params=file2params.sort_values(by=['energy'])
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file2params=file2params.reset_index(drop=True)
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#Sort File 32 parameters by energy as well (maintaining index)
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file32params_sort = file32params.sort_values(by=['energy'])
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#Add in values (.values converts to array first to ignore index)
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file32params_sort['L'] = file2params['L'].values
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if 'competiveWidth' in file32params_sort:
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file32params_sort['competitiveWidth'] = file2params['competitiveWidth'].values
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#Resort to File 32 order (by L then by E) for use with covariance
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parameters = file32params_sort.sort_index()
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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] = oldvalues
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nuclide.parameters_subset = parameters_subset
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nuclide.cov_subset = cov_subset
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def sample_resonance_parameters(nuclide, n_samples, use_subset=False):
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"""Return a IncidentNeutron object with n_samples of xs
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Parameters
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----------
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nuclide: IncidentNeutron object with resonance covariance data
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Returns
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-------
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ev : openmc.data.endf.Evaluation
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"""
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if use_subset==False:
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parameters = nuclide.parameters
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cov = nuclide.covariance
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else:
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parameters = nuclide.parameters_subset
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cov = nuclide.cov_subset
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nparams,params = parameters.shape
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cov = cov + cov.T - np.diag(cov.diagonal()) #symmetrizing covariance matrix
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covsize = cov.shape[0]
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formalism = nuclide.formalism
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mpar = nuclide.mpar
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samples = []
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return parameters
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### Handling MLBW Sampling ###
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if formalism == 'mlbw' or formalism == 'slbw':
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if mpar == 3:
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param_list = ['energy','neutronWidth','captureWidth']
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mean_array = pd.DataFrame.as_matrix(parameters[param_list])
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spin = pd.DataFrame.as_matrix(parameters['J'])
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gf = pd.DataFrame.as_matrix(parameters['fissionWidth'])
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mean = mean_array.flatten()
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for i in range(n_samples):
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sample = np.random.multivariate_normal(mean,cov)
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energy = sample[0::3]
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gn = sample[1::3]
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gg = sample[2::3]
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gt = gn + gg + gf
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records = []
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for j, E in enumerate(energy):
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records.append([energy[j], spin[j], gt[j], gn[j],
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gg[j], gf[j]])
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columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
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'captureWidth', 'fissionWidth']
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sample_params = pd.DataFrame.from_records(records, columns=columns)
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samples.append(sample_params)
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elif mpar == 4:
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param_list = ['energy','neutronWidth','captureWidth','fissionWidth']
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mean_array = pd.DataFrame.as_matrix(parameters[param_list])
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spin = pd.DataFrame.as_matrix(parameters['J'])
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mean = mean_array.flatten()
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for i in range(n_samples):
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sample = np.random.multivariate_normal(mean,cov)
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energy = sample[0::4]
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gn = sample[1::4]
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gg = sample[2::4]
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gf = sample[3::4]
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gt = gn + gg + gf
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records = []
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for j, E in enumerate(energy):
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records.append([energy[j], spin[j], gt[j], gn[j],
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gg[j], gf[j]])
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columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
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'captureWidth', 'fissionWidth']
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sample_params = pd.DataFrame.from_records(records, columns=columns)
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samples.append(sample_params)
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elif mpar == 5:
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param_list = ['energy','neutronWidth','captureWidth','fissionWidth']
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mean_array = pd.DataFrame.as_matrix(parameters[param_list])
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spin = pd.DataFrame.as_matrix(parameters['J'])
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mean = mean_array.flatten()
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for i in range(n_samples):
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sample = np.random.multivariate_normal(mean,cov)
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energy = sample[0::4]
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gn = sample[1::4]
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gg = sample[2::4]
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gf = sample[3::4]
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gt = gn + gg + gf
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records = []
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for j, E in enumerate(energy):
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records.append([energy[j], spin[j], gt[j], gn[j],
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gg[j], gf[j]])
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columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
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'captureWidth', 'fissionWidth']
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sample_params = pd.DataFrame.from_records(records, columns=columns)
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samples.append(sample_params)
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### Handling RM Sampling ###
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if formalism == 'rm':
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if mpar == 3:
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param_list = ['energy','neutronWidth','captureWidth']
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mean_array = pd.DataFrame.as_matrix(parameters[param_list])
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spin = pd.DataFrame.as_matrix(parameters['J'])
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gfa = pd.DataFrame.as_matrix(parameters['fissionWidthA'])
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gfb = pd.DataFrame.as_matrix(parameters['fissionWidthB'])
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mean = mean_array.flatten()
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for i in range(n_samples):
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sample = np.random.multivariate_normal(mean,cov)
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energy = sample[0::3]
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gn = sample[1::3]
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gg = sample[2::3]
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records = []
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for j, E in enumerate(energy):
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records.append([energy[j], spin[j], gn[j],
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gg[j], gfa[j], gfb[j]])
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columns = ['energy', 'J', 'neutronWidth',
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'captureWidth', 'fissionWidthA','fissionWidthB']
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sample_params = pd.DataFrame.from_records(records, columns=columns)
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samples.append(sample_params)
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elif mpar == 5:
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param_list = ['energy','neutronWidth','captureWidth','fissionWidthA','fissionWidthB']
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mean_array = pd.DataFrame.as_matrix(parameters[param_list])
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spin = pd.DataFrame.as_matrix(parameters['J'])
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mean = mean_array.flatten()
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for i in range(n_samples):
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sample = np.random.multivariate_normal(mean,cov)
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energy = sample[0::5]
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gn = sample[1::5]
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gg = sample[2::5]
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gfa = sample[3::5]
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gfb = sample[4::5]
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records = []
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for j, E in enumerate(energy):
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records.append([energy[j], spin[j], gn[j],
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gg[j], gfa[j], gfb[j]])
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columns = ['energy', 'J', 'neutronWidth',
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'captureWidth', 'fissionWidthA','fissionWidthB']
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sample_params = pd.DataFrame.from_records(records, columns=columns)
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samples.append(sample_params)
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nuclide.samples = samples
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class ResonanceCovariance(object):
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class ResonanceCovariances(Resonances):
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"""Resolved resonance covariance data
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Parameters
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----------
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ranges : list of openmc.data.ResonanceRange
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ranges : list of openmc.data.ResonanceCovarianceRange
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Distinct energy ranges for resonance data
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Attributes
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----------
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ranges : list of openmc.data.ResonanceRange
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ranges : list of openmc.data.ResonanceCovarianceRange
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Distinct energy ranges for resonance data
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resolved : openmc.data.ResonanceRange or None
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resolved : openmc.data.ResonanceCovariance or None
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Resolved resonance range
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unresolved : openmc.data.Unresolved or None
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Unresolved resonance range
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"""
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def __init__(self, ranges):
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@ -223,7 +74,7 @@ class ResonanceCovariance(object):
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@ranges.setter
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def ranges(self, ranges):
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cv.check_type('resonance ranges', ranges, MutableSequence)
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self._ranges = cv.CheckedList(ResonanceRange, 'resonance ranges',
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self._ranges = cv.CheckedList(ResonanceCovarianceRange, 'resonance range',
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ranges)
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@classmethod
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@ -238,7 +89,7 @@ class ResonanceCovariance(object):
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Returns
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-------
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openmc.data.ResonanceCovariance
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openmc.data.ResonanceCovariances
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Resonance covariance data
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"""
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@ -257,7 +108,9 @@ class ResonanceCovariance(object):
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for j in range(n_ranges):
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items = get_cont_record(file_obj)
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resonance_flag = items[2] # flag for resolved (1)/unresolved (2)
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unresolved_flag = items[2] # 0: only scattering radius given
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# 1: resolved parameters given
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# 2: unresolved parameters given
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formalism = items[3] # resonance formalism
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# Throw error for unsupported formalisms
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@ -265,11 +118,12 @@ class ResonanceCovariance(object):
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raise TypeError('LRF= ', formalism,
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'covariance not supported for this formalism')
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if resonance_flag in (0, 1):
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if unresolved_flag in (0,1):
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# resolved resonance region
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erange = _FORMALISMS[formalism].from_endf(ev, file_obj, items, resonances)
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elif resonance_flag == 2:
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file2params = resonances.ranges[j].parameters
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erange = _FORMALISMS[formalism].from_endf(ev, file_obj,
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items, file2params)
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elif unresolved_flag == 2:
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warnings.warn('Unresolved resonance not supported.'
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'Covariance values for the'
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'unresolved region not imported.')
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@ -277,26 +131,9 @@ class ResonanceCovariance(object):
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return cls(ranges)
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class MultiLevelBreitWignerCovariance(ResonanceRange):
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"""Multi-level Breit-Wigner resolved resonance formalism covariance data.
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Multi-level Breit-Wigner resolved resonance data is identified by LRF=2 in
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the ENDF-6 format.
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Parameters
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----------
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target_spin : float
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Intrinsic spin, :math:`I`, of the target nuclide
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energy_min : float
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Minimum energy of the resolved resonance range in eV
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energy_max : float
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Maximum energy of the resolved resonance range in eV
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channel : dict
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Dictionary whose keys are l-values and values are channel radii as a
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function of energy
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scattering : dict
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Dictionary whose keys are l-values and values are scattering radii as a
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function of energy
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class ResonanceCovarianceRange(object):
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"""Resonace covariance range
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Attributes
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----------
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@ -306,17 +143,229 @@ class MultiLevelBreitWignerCovariance(ResonanceRange):
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The covariance matrix contained within the ENDF evaluation
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lcomp : int
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Flag indicating the format of the covariance matrix
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mpar : int
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Number of parameters in covariance matrix for each individual resonance
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"""
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@classmethod
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def res_subset(cls, parameter_str, bounds):
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"""Produce a subset of resonance parameters and the covariance matrix
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to an IncidentNeutron object.
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Parameters
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----------
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parameter_str: parameter to be discriminated
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(i.e. 'energy','captureWidth','fissionWidthA'...)
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bounds: np.array [low numerical bound, high numerical bound]
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Returns
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-------
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parameters_subset : Dataframe of a subset of parameters
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(maintains indexing)
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cov_subset: subset of covariance matrix (upper triangular)
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"""
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parameters = cls.parameters
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cov = cls.covariance
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mpar = cls.mpar
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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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parameters_subset=parameters[mask]
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indices = parameters_subset.index.values
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sub_cov_dim = len(indices)*mpar
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oldvalues = []
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for index1 in indices:
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for i in range(mpar):
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for index2 in indices:
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for j in range(mpar):
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if index2*mpar+j >= index1*mpar+i:
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oldvalues.append(cov[index1*mpar+i,index2*mpar+j])
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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] = oldvalues
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cls.parameters_subset = parameters_subset
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cls.cov_subset = cov_subset
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@classmethod
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def sample_resonance_parameters(cls, n_samples, use_subset=False):
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"""Return a IncidentNeutron object with n_samples of xs
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Parameters
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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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use_subset: bool, optional
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Flag on whether to sample from an already produced subset
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Returns
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-------
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"""
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print('Begin sampling')
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print((cls))
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print(dir(cls))
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print(vars(cls))
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if use_subset==False:
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parameters = cls.parameters
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cov = cls.covariance
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else:
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if cls.parameters_subset is None:
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raise ValueError('No subset of resonances defined')
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parameters = cls.parameters_subset
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cov = cls.cov_subset
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nparams,params = parameters.shape
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cov = cov + cov.T - np.diag(cov.diagonal()) #symmetrizing covariance matrix
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covsize = cov.shape[0]
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formalism = cls.formalism
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mpar = cls.mpar
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samples = []
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### Handling MLBW Sampling ###
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if formalism == 'mlbw' or formalism == 'slbw':
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if mpar == 3:
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param_list = ['energy','neutronWidth','captureWidth']
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mean_array = pd.DataFrame.as_matrix(parameters[param_list])
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spin = pd.DataFrame.as_matrix(parameters['J'])
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gf = pd.DataFrame.as_matrix(parameters['fissionWidth'])
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mean = mean_array.flatten()
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for i in range(n_samples):
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sample = np.random.multivariate_normal(mean,cov)
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energy = sample[0::3]
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gn = sample[1::3]
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gg = sample[2::3]
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gt = gn + gg + gf
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records = []
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for j, E in enumerate(energy):
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records.append([energy[j], spin[j], gt[j], gn[j],
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gg[j], gf[j]])
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columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
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'captureWidth', 'fissionWidth']
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sample_params = pd.DataFrame.from_records(records, columns=columns)
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samples.append(sample_params)
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elif mpar == 4:
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param_list = ['energy','neutronWidth','captureWidth','fissionWidth']
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mean_array = pd.DataFrame.as_matrix(parameters[param_list])
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spin = pd.DataFrame.as_matrix(parameters['J'])
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mean = mean_array.flatten()
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for i in range(n_samples):
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sample = np.random.multivariate_normal(mean,cov)
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energy = sample[0::4]
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gn = sample[1::4]
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gg = sample[2::4]
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gf = sample[3::4]
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gt = gn + gg + gf
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records = []
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for j, E in enumerate(energy):
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records.append([energy[j], spin[j], gt[j], gn[j],
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gg[j], gf[j]])
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columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
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'captureWidth', 'fissionWidth']
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sample_params = pd.DataFrame.from_records(records, columns=columns)
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samples.append(sample_params)
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|
||||
elif mpar == 5:
|
||||
param_list = ['energy','neutronWidth','captureWidth','fissionWidth']
|
||||
mean_array = pd.DataFrame.as_matrix(parameters[param_list])
|
||||
spin = pd.DataFrame.as_matrix(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 mpar == 3:
|
||||
param_list = ['energy','neutronWidth','captureWidth']
|
||||
mean_array = pd.DataFrame.as_matrix(parameters[param_list])
|
||||
spin = pd.DataFrame.as_matrix(parameters['J'])
|
||||
gfa = pd.DataFrame.as_matrix(parameters['fissionWidthA'])
|
||||
gfb = pd.DataFrame.as_matrix(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)
|
||||
|
||||
elif mpar == 5:
|
||||
param_list = ['energy','neutronWidth','captureWidth','fissionWidthA','fissionWidthB']
|
||||
mean_array = pd.DataFrame.as_matrix(parameters[param_list])
|
||||
spin = pd.DataFrame.as_matrix(parameters['J'])
|
||||
mean = mean_array.flatten()
|
||||
for i in range(n_samples):
|
||||
sample = np.random.multivariate_normal(mean,cov)
|
||||
energy = sample[0::5]
|
||||
gn = sample[1::5]
|
||||
gg = sample[2::5]
|
||||
gfa = sample[3::5]
|
||||
gfb = sample[4::5]
|
||||
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)
|
||||
|
||||
cls.samples = samples
|
||||
|
||||
class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
|
||||
"""Multi-level Breit-Wigner resolved resonance formalism covariance data.
|
||||
|
||||
Multi-level Breit-Wigner resolved resonance data is identified by LRF=2 in
|
||||
the ENDF-6 format.
|
||||
|
||||
Attributes
|
||||
----------
|
||||
cov_parameters: list
|
||||
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
|
||||
mpar : int
|
||||
Number of parameters in covariance matrix for each individual resonance
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, energy_min, energy_max):
|
||||
self.parameters = None
|
||||
self.covariance = None
|
||||
self.mpar = None
|
||||
self.lcomp = None
|
||||
self.num_parameters = None
|
||||
self.formalism = 'mlbw'
|
||||
|
||||
|
||||
@classmethod
|
||||
def from_endf(cls, ev, file_obj, items, resonances):
|
||||
def from_endf(cls, ev, file_obj, items, file2params):
|
||||
"""Create MLBW covariance data from an ENDF evaluation.
|
||||
|
||||
Parameters
|
||||
|
|
@ -325,11 +374,11 @@ class MultiLevelBreitWignerCovariance(ResonanceRange):
|
|||
ENDF evaluation
|
||||
file_obj : file-like object
|
||||
ENDF file positioned at the second record of a resonance range
|
||||
subsection in MF=2, MT=151
|
||||
subsection in MF=32, MT=151
|
||||
items : list
|
||||
Items from the CONT record at the start of the resonance range
|
||||
subsection
|
||||
resonances : Resonance object
|
||||
resonances : openmc.data.IncidentNeutron.Resonance
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
|
@ -398,17 +447,8 @@ class MultiLevelBreitWignerCovariance(ResonanceRange):
|
|||
covsize = cov.shape[0]
|
||||
mpar = int(covsize/nparams)
|
||||
|
||||
#Use l-values and competitiveWidth from File 2 data
|
||||
#Resort File 2 by energy to match File 32
|
||||
file2parameters=resonances.ranges[0].parameters.sort_values(by=['energy'])
|
||||
file2parameters=file2parameters.reset_index(drop=True)
|
||||
#Sort File 32 parameters by energy as well (maintaining index)
|
||||
parameters_sort = parameters.sort_values(by=['energy'])
|
||||
#Add in values (.values converts to array first to ignore index)
|
||||
parameters_sort['L'] = file2parameters['L'].values
|
||||
parameters_sort['competitiveWidth'] = file2parameters['competitiveWidth'].values
|
||||
#Resort to File 32 order (essential for use with covariance!)
|
||||
parameters = parameters_sort.sort_index()
|
||||
#Add parameters from File 2
|
||||
parameters = file2contributions(parameters, file2params)
|
||||
|
||||
# Create instance of class
|
||||
mlbw = cls(energy_min, energy_max)
|
||||
|
|
@ -463,18 +503,8 @@ class MultiLevelBreitWignerCovariance(ResonanceRange):
|
|||
covsize = cov.shape[0]
|
||||
mpar = int(covsize/nparams)
|
||||
|
||||
#Use l-values and competitiveWidth from File 2 data
|
||||
#Resort File 2 by energy to match File 32
|
||||
file2parameters=resonances.ranges[0].parameters.sort_values(by=['energy'])
|
||||
file2parameters=file2parameters.reset_index(drop=True)
|
||||
#Sort File 32 parameters by energy as well (maintaining index)
|
||||
parameters_sort = parameters.sort_values(by=['energy'])
|
||||
#Add in values (.values converts to array first to ignore index)
|
||||
parameters_sort['L'] = file2parameters['L'].values
|
||||
parameters_sort['competitiveWidth'] = file2parameters['competitiveWidth'].values
|
||||
#Resort to File 32 order (essential for use with covariance!)
|
||||
parameters = parameters_sort.sort_index()
|
||||
|
||||
#Add parameters from File 2
|
||||
parameters = file2contributions(parameter, file2params)
|
||||
|
||||
# Create instance of MultiLevelBreitWignerCovariance
|
||||
mlbw = cls(energy_min, energy_max)
|
||||
|
|
@ -532,18 +562,8 @@ class MultiLevelBreitWignerCovariance(ResonanceRange):
|
|||
covsize = cov.shape[0]
|
||||
mpar = int(covsize/nparams)
|
||||
|
||||
#Use l-values and competitiveWidth from File 2 data
|
||||
#Resort File 2 by energy to match File 32
|
||||
file2parameters=resonances.ranges[0].parameters.sort_values(by=['energy'])
|
||||
file2parameters=file2parameters.reset_index(drop=True)
|
||||
#Sort File 32 parameters by energy as well (maintaining index)
|
||||
parameters_sort = parameters.sort_values(by=['energy'])
|
||||
#Add in values (.values converts to array first to ignore index)
|
||||
parameters_sort['L'] = file2parameters['L'].values
|
||||
parameters_sort['competitiveWidth'] = file2parameters['competitiveWidth'].values
|
||||
#Resort to File 32 order (essential for use with covariance!)
|
||||
parameters = parameters_sort.sort_index()
|
||||
|
||||
#Add parameters from File 2
|
||||
parameters = file2contributions(parameter, file2params)
|
||||
|
||||
# Create instance of class
|
||||
mlbw = cls(energy_min, energy_max)
|
||||
|
|
@ -554,12 +574,6 @@ class MultiLevelBreitWignerCovariance(ResonanceRange):
|
|||
|
||||
return mlbw
|
||||
|
||||
def subset(self, parameter_str, bounds):
|
||||
res_subset(self, parameter_str, bounds)
|
||||
|
||||
def sample(self, n_samples, use_subset=False):
|
||||
sample_resonance_parameters(self,n_samples,use_subset)
|
||||
|
||||
|
||||
class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance):
|
||||
"""Single-level Breit-Wigner resolved resonance formalism covariance data.
|
||||
|
|
@ -612,7 +626,7 @@ class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance):
|
|||
def __init__(self, energy_min, energy_max):
|
||||
self.formalism = 'slbw'
|
||||
|
||||
class ReichMooreCovariance(ResonanceRange):
|
||||
class ReichMooreCovariance(ResonanceCovarianceRange):
|
||||
"""Reich-Moore resolved resonance formalism covariance data.
|
||||
|
||||
Reich-Moore resolved resonance data is identified by LRF=3 in the ENDF-6
|
||||
|
|
@ -654,7 +668,7 @@ class ReichMooreCovariance(ResonanceRange):
|
|||
self.formalism = 'rm'
|
||||
|
||||
@classmethod
|
||||
def from_endf(cls, ev, file_obj, items, resonances):
|
||||
def from_endf(cls, ev, file_obj, items, file2params):
|
||||
"""Create Reich-Moore resonance covariance data from an ENDF evaluation.
|
||||
Includes the resonance parameters contained separately in File 32.
|
||||
|
||||
|
|
@ -737,16 +751,8 @@ class ReichMooreCovariance(ResonanceRange):
|
|||
covsize = cov.shape[0]
|
||||
mpar = int(covsize/nparams)
|
||||
|
||||
#Use l-values and competitiveWidth from File 2 data
|
||||
#Resort File 2 by energy to match File 32
|
||||
file2parameters=resonances.ranges[0].parameters.sort_values(by=['energy'])
|
||||
file2parameters=file2parameters.reset_index(drop=True)
|
||||
#Sort File 32 parameters by energy as well (maintaining index)
|
||||
parameters_sort = parameters.sort_values(by=['energy'])
|
||||
#Add in values (.values converts to array first to ignore index)
|
||||
parameters_sort['L'] = file2parameters['L'].values
|
||||
#Resort to File 32 order (essential for use with covariance!)
|
||||
parameters = parameters_sort.sort_index()
|
||||
#Add parameters from File 2
|
||||
parameters = file2contributions(parameter, file2params)
|
||||
|
||||
# Create instance of ReichMooreCovariance
|
||||
rmc = cls(energy_min, energy_max)
|
||||
|
|
@ -794,16 +800,8 @@ class ReichMooreCovariance(ResonanceRange):
|
|||
covsize = cov.shape[0]
|
||||
mpar = int(covsize/nparams)
|
||||
|
||||
#Use l-values and competitiveWidth from File 2 data
|
||||
#Resort File 2 by energy to match File 32
|
||||
file2parameters=resonances.ranges[0].parameters.sort_values(by=['energy'])
|
||||
file2parameters=file2parameters.reset_index(drop=True)
|
||||
#Sort File 32 parameters by energy as well (maintaining index)
|
||||
parameters_sort = parameters.sort_values(by=['energy'])
|
||||
#Add in values (.values converts to array first to ignore index)
|
||||
parameters_sort['L'] = file2parameters['L'].values
|
||||
#Resort to File 32 order (essential for use with covariance!)
|
||||
parameters = parameters_sort.sort_index()
|
||||
#Add parameters from File 2
|
||||
parameters = file2contributions(parameter, file2params)
|
||||
|
||||
# Create instance of ReichMooreCovariance
|
||||
rmc = cls(energy_min, energy_max)
|
||||
|
|
@ -814,19 +812,12 @@ class ReichMooreCovariance(ResonanceRange):
|
|||
|
||||
return rmc
|
||||
|
||||
def subset(self, parameter_str, bounds):
|
||||
res_subset(self, parameter_str, bounds)
|
||||
|
||||
def sample(self, n_samples, use_subset=False):
|
||||
sample_resonance_parameters(self,n_samples,use_subset)
|
||||
|
||||
# _FORMALISMS = {0: ResonanceRange,
|
||||
# 1: SingleLevelBreitWigner,
|
||||
# 2: MultiLevelBreitWigner,
|
||||
# 3: ReichMoore,
|
||||
# 7: RMatrixLimited}
|
||||
_FORMALISMS = {1: SingleLevelBreitWignerCovariance,
|
||||
_FORMALISMS = {
|
||||
0: ResonanceCovarianceRange,
|
||||
1: SingleLevelBreitWignerCovariance,
|
||||
2: MultiLevelBreitWignerCovariance,
|
||||
3: ReichMooreCovariance}
|
||||
|
||||
3: ReichMooreCovariance
|
||||
# 7: RMatrixLimitedCovariance
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue