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Style and change of __init__ methods
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Isaac Meyer
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2 changed files with 61 additions and 99 deletions
File diff suppressed because one or more lines are too long
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@ -75,7 +75,8 @@ class ResonanceCovariances(Resonances):
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ev : openmc.data.endf.Evaluation
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ENDF evaluation
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resonances : openmc.data.Resonance object
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Resonanance object generated from the same evaluation
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openmc.data.Resonanance object generated from the same evaluation used
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to import values not contained in File 32
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Returns
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-------
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@ -214,7 +215,7 @@ class ResonanceCovarianceRange:
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Returns
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-------
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samples : list of openmc.data.ResonanceCovarianceRange objects
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List of samples size [n_samples]
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List of samples size `n_samples`
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"""
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if not use_subset:
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@ -412,12 +413,12 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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String descriptor of formalism
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"""
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def __init__(self, energy_min, energy_max):
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def __init__(self, energy_min, energy_max, parameters, covariance, mpar, lcomp):
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super().__init__(energy_min, energy_max)
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self.parameters = None
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self.covariance = None
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self.mpar = None
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self.lcomp = None
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self.parameters = parameters
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self.covariance = covariance
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self.mpar = mpar
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self.lcomp = lcomp
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self.formalism = 'mlbw'
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@classmethod
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@ -454,11 +455,11 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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# Other scatter radius parameters
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items = endf.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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NLS = items[4] # number of l-values
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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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if LCOMP == 1:
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if lcomp == 1:
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items = endf.get_cont_record(file_obj)
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num_short_range = items[4] # Number of short range type resonance
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# covariances
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@ -501,16 +502,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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# Add parameters from File 2
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parameters = _add_file2_contributions(parameters, file2params)
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# Create instance of class
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mlbw = cls(energy_min, energy_max)
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mlbw.parameters = parameters
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mlbw.covariance = cov
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mlbw.mpar = mpar
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mlbw.lcomp = LCOMP
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return mlbw
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elif LCOMP == 2: # Compact format - Resonances and individual
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elif lcomp == 2: # Compact format - Resonances and individual
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# uncertainties followed by compact correlations
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items, values = endf.get_list_record(file_obj)
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mean = items
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@ -523,7 +515,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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gf = values[5::12]
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par_unc = []
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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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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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res_unc_nonzero = []
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@ -556,37 +548,21 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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# Add parameters from File 2
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parameters = _add_file2_contributions(parameters, file2params)
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# Create instance of MultiLevelBreitWignerCovariance
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mlbw = cls(energy_min, energy_max)
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mlbw.parameters = parameters
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mlbw.covariance = cov
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mlbw.mpar = mpar
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mlbw.lcomp = LCOMP
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return mlbw
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elif LCOMP == 0 :
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elif lcomp == 0 :
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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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for i in range(nls):
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items, values = endf.get_list_record(file_obj)
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num_res = items[5]
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for j in range(num_res):
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one_res = values[18*j:18*(j+1)]
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res_values = one_res[:6]
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cov_values = one_res[6:]
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energy = res_values[0]
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spin = res_values[1]
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gt = res_values[2]
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gn = res_values[3]
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gg = res_values[4]
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gf = res_values[5]
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records.append([energy, spin, gt, gn, gg, gf])
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records.append(list(res_values))
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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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# 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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@ -615,14 +591,9 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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# Add parameters from File 2
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parameters = _add_file2_contributions(parameters, file2params)
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# Create instance of class
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mlbw = cls(energy_min, energy_max)
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mlbw.parameters = parameters
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mlbw.covariance = cov
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mlbw.mpar = mpar
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mlbw.lcomp = LCOMP
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return mlbw
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# Create instance of class
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mlbw = cls(energy_min, energy_max, parameters, cov, mpar, lcomp)
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return mlbw
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class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance):
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@ -655,8 +626,8 @@ class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance):
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String descriptor of formalism
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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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def __init__(self, energy_min, energy_max, parameters, covariance, mpar, lcomp):
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super().__init__(energy_min, energy_max, parameters, covariance, mpar, lcomp)
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self.formalism = 'slbw'
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@ -691,10 +662,12 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
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String descriptor of formalism
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"""
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def __init__(self, energy_min, energy_max):
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def __init__(self, energy_min, energy_max, parameters, covariance, mpar, lcomp):
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super().__init__(energy_min, energy_max)
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self.parameters = None
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self.covariance = None
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self.parameters = parameters
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self.covariance = covariance
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self.mpar = mpar
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self.lcomp = lcomp
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self.formalism = 'rm'
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@classmethod
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@ -712,7 +685,9 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
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items : list
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Items from the CONT record at the start of the resonance range
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subsection
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resonances : Resonance object
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resonances : openmc.data.Resonance object
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openmc.data.Resonanance object generated from the same evaluation used
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to import values not contained in File 32
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Returns
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-------
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@ -729,12 +704,12 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
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# Other scatter radius parameters
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items = endf.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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NLS = items[4] # Number of l-values
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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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if LCOMP == 1:
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if lcomp == 1:
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items = endf.get_cont_record(file_obj)
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num_short_range = items[4] # Number of short range type resonance
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# covariances
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@ -777,16 +752,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
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# Add parameters from File 2
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parameters = _add_file2_contributions(parameters, file2params)
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# Create instance of ReichMooreCovariance
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rmc = cls(energy_min, energy_max)
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rmc.parameters = parameters
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rmc.covariance = cov
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rmc.mpar = mpar
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rmc.lcomp = LCOMP
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return rmc
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elif LCOMP == 2: # Compact format - Resonances and individual
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elif lcomp == 2: # Compact format - Resonances and individual
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# uncertainties followed by compact correlations
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items, values = endf.get_list_record(file_obj)
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num_res = items[5]
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@ -798,7 +764,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
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gfb = values[5::12]
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par_unc = []
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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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res_unc = values[i*12+6 : i*12+12]
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# Delete 0 values (not provided in evaluation)
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res_unc = [x for x in res_unc if x != 0.0]
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par_unc.extend(res_unc)
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@ -825,21 +791,17 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
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# Add parameters from File 2
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parameters = _add_file2_contributions(parameters, file2params)
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# Create instance of ReichMooreCovariance
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rmc = cls(energy_min, energy_max)
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rmc.parameters = parameters
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rmc.covariance = cov
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rmc.mpar = mpar
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rmc.lcomp = LCOMP
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return rmc
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# Create instance of ReichMooreCovariance
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rmc = cls(energy_min, energy_max, parameters, cov, mpar, lcomp)
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return rmc
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_FORMALISMS = {
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0: ResonanceCovarianceRange,
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1: SingleLevelBreitWignerCovariance,
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2: MultiLevelBreitWignerCovariance,
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3: ReichMooreCovariance
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# 7: RMatrixLimitedCovariance
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}
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0: ResonanceCovarianceRange,
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1: SingleLevelBreitWignerCovariance,
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2: MultiLevelBreitWignerCovariance,
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3: ReichMooreCovariance
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# 7: RMatrixLimitedCovariance
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}
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