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Isaac Meyer
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76475730c7
commit
15ffa563e0
2 changed files with 53 additions and 52 deletions
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@ -269,7 +269,8 @@ def get_tab2_record(file_obj):
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def get_intg_record(file_obj):
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"""
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Return data from an INTG record in an ENDF-6 file.
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Return data from an INTG record in an ENDF-6 file. Used to store the
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covariance matrix in a compact format.
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Parameters
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----------
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@ -278,32 +279,32 @@ def get_intg_record(file_obj):
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Returns
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-------
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array
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numpy.ndarray
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The correlation matrix described in the INTG record
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"""
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# determine how many items are in list and NDIGIT
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items = get_cont_record(file_obj)
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NDIGIT = int(items[2])
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NNN = int(items[3]) # Number of parameters
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NM = int(items[4]) # Lines to read
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NROW_RULES = {2: 18,3: 12,4: 11,5: 9,6: 8}
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NROW = NROW_RULES[NDIGIT]
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ndigit = int(items[2])
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npar = int(items[3]) # Number of parameters
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nlines = int(items[4]) # Lines to read
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NROW_RULES = {2: 18, 3: 12, 4: 11, 5: 9, 6: 8}
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nrow = NROW_RULES[ndigit]
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# read lines and build correlation matrix
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corr = np.identity(NNN)
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for i in range(NM):
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corr = np.identity(npar)
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for i in range(nlines):
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line = file_obj.readline()
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ii = int_endf(line[:5]) - 1 #-1 to account for 0 indexing
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jj = int_endf(line[5:10]) - 1
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factor = 10**NDIGIT
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for j in range(NROW):
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factor = 10**ndigit
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for j in range(nrow):
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if jj+j >= ii:
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break
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element = int_endf(line[11+(NDIGIT+1)*j:11+(NDIGIT+1)*(j+1)])
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element = int_endf(line[11+(ndigit+1)*j:11+(ndigit+1)*(j+1)])
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if element > 0:
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corr[ii,jj] = (element+0.5)/factor
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corr[ii, jj] = (element+0.5)/factor
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elif element < 0:
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corr[ii,jj] = (element-0.5)/factor
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corr[ii, jj] = (element-0.5)/factor
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#Symmetrize the correlation matrix
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corr = corr + corr.T - np.diag(corr.diagonal())
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@ -109,11 +109,11 @@ class ResonanceCovariances(Resonances):
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formalism = items[3] # resonance formalism
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# Throw error for unsupported formalisms
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if formalism in [0,7]:
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if formalism in [0, 7]:
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raise NotImplementedError('LRF= ', formalism,
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'covariance not supported for this formalism')
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if unresolved_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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file2params = resonances.ranges[j].parameters
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erange = _FORMALISMS[formalism].from_endf(ev, file_obj,
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@ -166,7 +166,7 @@ class ResonanceCovarianceRange(object):
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----------
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parameter_str: str
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parameter to be discriminated
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(i.e. 'energy','captureWidth','fissionWidthA'...)
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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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@ -193,9 +193,9 @@ class ResonanceCovarianceRange(object):
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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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cov_subset_vals.append(cov[index1*mpar+i,index2*mpar+j])
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cov_subset_vals.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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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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@ -218,7 +218,7 @@ class ResonanceCovarianceRange(object):
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List of samples size [n_samples]
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"""
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if use_subset==False:
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if not use_subset:
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parameters = self.parameters
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cov = self.covariance
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else:
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@ -227,7 +227,7 @@ class ResonanceCovarianceRange(object):
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parameters = self.parameters_subset
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cov = self.cov_subset
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nparams,params = parameters.shape
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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 = self.formalism
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@ -238,7 +238,7 @@ class ResonanceCovarianceRange(object):
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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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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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l_value = pd.DataFrame.as_matrix(parameters['L'])
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@ -246,7 +246,7 @@ class ResonanceCovarianceRange(object):
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gx = pd.DataFrame.as_matrix(parameters['competitiveWidth'])
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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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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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@ -261,14 +261,14 @@ class ResonanceCovarianceRange(object):
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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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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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l_value = pd.DataFrame.as_matrix(parameters['L'])
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gx = pd.DataFrame.as_matrix(parameters['competitiveWidth'])
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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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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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@ -284,14 +284,14 @@ class ResonanceCovarianceRange(object):
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samples.append(sample_params)
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elif mpar == 5:
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param_list = ['energy','neutronWidth','captureWidth',
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param_list = ['energy', 'neutronWidth', 'captureWidth',
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'fissionWidth', 'competitiveWidth']
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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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l_value = pd.DataFrame.as_matrix(parameters['L'])
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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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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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@ -310,7 +310,7 @@ class ResonanceCovarianceRange(object):
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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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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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l_value = pd.DataFrame.as_matrix(parameters['L'])
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@ -318,7 +318,7 @@ class ResonanceCovarianceRange(object):
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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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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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@ -327,19 +327,19 @@ class ResonanceCovarianceRange(object):
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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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'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',
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'fissionWidthA','fissionWidthB']
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param_list = ['energy', 'neutronWidth', 'captureWidth',
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'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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l_value = pd.DataFrame.as_matrix(parameters['L'])
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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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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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@ -350,7 +350,7 @@ class ResonanceCovarianceRange(object):
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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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'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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@ -451,7 +451,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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# Other scatter radius parameters
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items = get_cont_record(file_obj)
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target_spin = items[0]
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LCOMP = items[3] # Flag for compatibility 0,1,2 - 2 is compact form
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LCOMP = items[3] # Flag for compatibility 0, 1, 2 - 2 is compact form
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NLS = items[4] # number of l-values
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# Build covariance matrix for General Resolved Resonance Formats
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@ -485,7 +485,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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#Build the upper-triangular covariance matrix
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cov_dim = mpar*num_res
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cov = np.zeros([cov_dim,cov_dim])
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cov = np.zeros([cov_dim, cov_dim])
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indices = np.triu_indices(cov_dim)
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cov[indices] = cov_values
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@ -522,10 +522,10 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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for i in range(num_res):
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res_unc = values[i*12+6:i*12+12]
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#Delete 0 values (not provided, no fission width)
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# DAJ/DGT always zero, DGF sometimes none zero [1,2,5]
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# DAJ/DGT always zero, DGF sometimes none zero [1, 2, 5]
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res_unc_nonzero = []
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for j in range(6):
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if j in [1,2,5] and res_unc[j] != 0.0 :
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if j in [1, 2, 5] and res_unc[j] != 0.0 :
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res_unc_nonzero.append(res_unc[j])
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elif j in [0,3,4]:
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res_unc_nonzero.append(res_unc[j])
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@ -546,7 +546,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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#Determine mpar (number of parameters for each resonance in
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#covariance matrix)
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nparams,params = parameters.shape
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nparams, params = parameters.shape
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covsize = cov.shape[0]
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mpar = int(covsize/nparams)
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@ -563,7 +563,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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return mlbw
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elif LCOMP == 0 :
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cov = np.zeros([4,4])
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cov = np.zeros([4, 4])
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records = []
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cov_index = 0
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for i in range(NLS):
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@ -584,28 +584,28 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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#Populate the coviariance matrix for this resonance
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#There are no covariances between resonances in LCOMP=0
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cov[cov_index,cov_index]=cov_values[0]
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cov[cov_index+1,cov_index+1:cov_index+2]=cov_values[1:2]
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cov[cov_index+1,cov_index+3]=cov_values[4]
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cov[cov_index+2,cov_index+2] = cov_values[3]
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cov[cov_index+2,cov_index+3] = cov_values[5]
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cov[cov_index+3,cov_index+3] = cov_values[6]
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cov[cov_index, cov_index]=cov_values[0]
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cov[cov_index+1, cov_index+1 : cov_index+2]=cov_values[1:2]
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cov[cov_index+1, cov_index+3]=cov_values[4]
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cov[cov_index+2, cov_index+2] = cov_values[3]
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cov[cov_index+2, cov_index+3] = cov_values[5]
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cov[cov_index+3, cov_index+3] = cov_values[6]
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cov_index += 4
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if j < num_res-1: #Pad matrix for additional values
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cov = np.pad(cov,((0,4),(0,4)),'constant',
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constant_values=0)
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cov = np.pad(cov, ((0, 4), (0, 4)), 'constant',
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constant_values=0)
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#Create pandas DataFrame with resonance data, currently
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#redundant with data.IncidentNeutron.resonance
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columns = ['energy', 'J', 'totalWidth','neutronWidth',
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columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
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'captureWidth', 'fissionWidth']
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parameters = pd.DataFrame.from_records(records, columns=columns)
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#Determine mpar (number of parameters for each resonance in
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#covariance matrix)
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nparams,params = parameters.shape
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nparams, params = parameters.shape
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covsize = cov.shape[0]
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mpar = int(covsize/nparams)
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@ -653,7 +653,7 @@ class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance):
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"""
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def __init__(self, energy_min, energy_max):
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super().__init__(energy_min,energy_max)
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super().__init__(energy_min, energy_max)
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self.formalism = 'slbw'
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class ReichMooreCovariance(ResonanceCovarianceRange):
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@ -725,7 +725,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
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# Other scatter radius parameters
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items = get_cont_record(file_obj)
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target_spin = items[0]
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LCOMP = items[3] # Flag for compatibility 0,1,2 - 2 is compact form
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LCOMP = items[3] # Flag for compatibility 0, 1, 2 - 2 is compact form
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NLS = items[4] # Number of l-values
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