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Condensed sampling method
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Isaac Meyer
parent
1bf5e7b2cb
commit
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1 changed files with 63 additions and 156 deletions
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@ -236,7 +236,6 @@ class ResonanceCovarianceRange:
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parameters = self.parameters
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cov = self.covariance
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nparams, params = parameters.shape
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# Symmetrizing covariance matrix
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cov = cov + cov.T - np.diag(cov.diagonal())
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covsize = cov.shape[0]
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@ -244,165 +243,73 @@ class ResonanceCovarianceRange:
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mpar = self.mpar
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samples = []
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# Handling MLBW sampling
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# Handling MLBW/SLBW 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 = parameters[param_list].values
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spin = parameters['J'].values
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l_value = parameters['L'].values
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gf = parameters['fissionWidth'].values
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gx = parameters['competitiveWidth'].values
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mean = mean_array.flatten()
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par_samples = np.random.multivariate_normal(mean, cov,
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size=n_samples)
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for sample in par_samples:
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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], l_value[j], spin[j], gt[j],
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gn[j], gg[j], gf[j], gx[j]])
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columns = ['energy', 'L', 'J', 'totalWidth', 'neutronWidth',
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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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# 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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res_range.parameters = sample_params
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samples.append(res_range)
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params = ['energy', 'neutronWidth', 'captureWidth', 'fissionWidth',
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'competitiveWidth']
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param_list = params[:mpar]
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mean_array = parameters[param_list].values
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mean = mean_array.flatten()
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par_samples = np.random.multivariate_normal(mean, cov,
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size=n_samples)
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spin = parameters['J'].values
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l_value = parameters['L'].values
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for sample in par_samples:
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energy = sample[0::mpar]
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gn = sample[1::mpar]
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gg = sample[2::mpar]
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gf = sample[3::mpar] if mpar > 3 else parameters['fissionWidth'].values
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gx = sample[4::mpar] if mpar > 4 else parameters['competitiveWidth'].values
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gt = gn + gg + gf + gx
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elif mpar == 4:
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param_list = ['energy', 'neutronWidth', 'captureWidth',
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'fissionWidth']
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mean_array = parameters[param_list].values
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spin = parameters['J'].values
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l_value = parameters['L'].values
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gx = parameters['competitiveWidth'].values
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mean = mean_array.flatten()
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par_samples = np.random.multivariate_normal(mean, cov,
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size=n_samples)
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for sample in par_samples:
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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], l_value[j], spin[j], gt[j],
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gn[j], gg[j], gf[j], gx[j]])
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columns = ['energy', 'L', 'J', 'totalWidth', 'neutronWidth',
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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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# 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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res_range.parameters = sample_params
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samples.append(res_range)
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records = []
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for j, E in enumerate(energy):
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records.append([energy[j], l_value[j], spin[j], gt[j], gn[j],
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gg[j], gf[j], gx[j]])
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columns = ['energy', 'L', 'J', 'totalWidth', 'neutronWidth',
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'captureWidth', 'fissionWidth', 'competitiveWidth']
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sample_params = pd.DataFrame.from_records(records, columns=columns)
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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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res_range.parameters = sample_params
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samples.append(res_range)
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elif mpar == 5:
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param_list = ['energy', 'neutronWidth', 'captureWidth',
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'fissionWidth', 'competitiveWidth']
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mean_array = parameters[param_list].values
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spin = parameters['J'].values
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l_value = parameters['L'].values
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mean = mean_array.flatten()
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par_samples = np.random.multivariate_normal(mean, cov,
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size=n_samples)
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for sample in par_samples:
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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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gf = sample[3::5]
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gx = sample[4::5]
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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], l_value[j], spin[j], gt[j],
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gn[j], gg[j], gf[j], gx[j]])
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columns = ['energy', 'L', 'J', 'totalWidth', 'neutronWidth',
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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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# 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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res_range.parameters = sample_params
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samples.append(res_range)
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# Handling RM sampling
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elif formalism == 'rm':
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params = ['energy', 'L', 'J', 'neutronWidth', 'captureWidth',
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'fissionWidthA', 'fissionWidthB']
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param_list = params[:mpar]
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mean_array = parameters[param_list].values
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mean = mean_array.flatten()
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par_samples = np.random.multivariate_normal(mean, cov,
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size=n_samples)
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spin = parameters['J']
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l_value = parameters['L'].values
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for sample in par_samples:
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energy = sample[0::mpar]
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gn = sample[1::mpar]
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gg = sample[2::mpar]
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gfa = sample[3::mpar] if mpar > 3 else parameters['fissionWidthA'].values
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gfb = sample[3::mpar] if mpar > 3 else parameters['fissionWidthB'].values
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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 = parameters[param_list].values
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spin = parameters['J'].values
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l_value = parameters['L'].values
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gfa = parameters['fissionWidthA'].values
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gfb = parameters['fissionWidthB'].values
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mean = mean_array.flatten()
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par_samples = np.random.multivariate_normal(mean, cov,
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size=n_samples)
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for sample in par_samples:
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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], l_value[j], spin[j], gn[j],
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gg[j], gfa[j], gfb[j]])
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columns = ['energy', 'L', 'J', 'neutronWidth',
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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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# 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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res_range.parameters = sample_params
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samples.append(res_range)
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elif mpar == 5:
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param_list = ['energy', 'neutronWidth', 'captureWidth',
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'fissionWidthA', 'fissionWidthB']
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mean_array = parameters[param_list].values
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spin = parameters['J'].values
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l_value = parameters['L'].values
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mean = mean_array.flatten()
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par_samples = np.random.multivariate_normal(mean, cov,
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size=n_samples)
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for sample in par_samples:
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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], l_value[j], spin[j], gn[j],
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gg[j], gfa[j], gfb[j]])
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columns = ['energy', 'L', 'J', 'neutronWidth',
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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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# 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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res_range.parameters = sample_params
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samples.append(res_range)
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records = []
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for j, E in enumerate(energy):
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records.append([energy[j], l_value[j], spin[j], gn[j],
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gg[j], gfa[j], gfb[j]])
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columns = ['energy', 'L', 'J', 'neutronWidth',
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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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# 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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res_range.parameters = sample_params
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samples.append(res_range)
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return samples
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