diff --git a/examples/jupyter/nuclear-data-resonance-covariance.ipynb b/examples/jupyter/nuclear-data-resonance-covariance.ipynb index f830a2cac..9e79175d8 100644 --- a/examples/jupyter/nuclear-data-resonance-covariance.ipynb +++ b/examples/jupyter/nuclear-data-resonance-covariance.ipynb @@ -118,7 +118,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -129,7 +129,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -257,18 +257,18 @@ "text": [ "Sample 1\n", " energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n", - "0 0.031837 0 2.0 0.000475 0.106547 0.0 0.0\n", - "1 2.824944 0 2.0 0.000310 0.101103 0.0 0.0\n", - "2 16.230854 0 1.0 0.000379 0.055465 0.0 0.0\n", - "3 16.764246 0 2.0 0.013214 0.075675 0.0 0.0\n", - "4 20.559124 0 2.0 0.011960 0.076114 0.0 0.0\n", + "0 0.030278 0 2.0 0.000472 0.109151 0.0 0.0\n", + "1 2.826910 0 2.0 0.000347 0.099239 0.0 0.0\n", + "2 16.199761 0 1.0 0.000258 0.082103 0.0 0.0\n", + "3 16.772474 0 2.0 0.012354 0.091428 0.0 0.0\n", + "4 20.553868 0 2.0 0.011185 0.089609 0.0 0.0\n", "Sample 2\n", " energy L J neutronWidth captureWidth fissionWidthA fissionWidthB\n", - "0 0.033447 0 2.0 0.000478 0.103629 0.0 0.0\n", - "1 2.821635 0 2.0 0.000334 0.093337 0.0 0.0\n", - "2 16.246838 0 1.0 0.000403 0.104026 0.0 0.0\n", - "3 16.766217 0 2.0 0.012486 0.079445 0.0 0.0\n", - "4 20.561842 0 2.0 0.011493 0.084187 0.0 0.0\n" + "0 0.033611 0 2.0 0.000479 0.103410 0.0 0.0\n", + "1 2.825707 0 2.0 0.000335 0.101266 0.0 0.0\n", + "2 16.270769 0 1.0 0.000360 0.071230 0.0 0.0\n", + "3 16.773850 0 2.0 0.013402 0.074592 0.0 0.0\n", + "4 20.563037 0 2.0 0.011916 0.086590 0.0 0.0\n" ] } ], @@ -296,8 +296,8 @@ { "data": { "text/plain": [ - "[,\n", - " ]" + "[,\n", + " ]" ] }, "execution_count": 10, @@ -326,9 +326,9 @@ }, { "data": { - "image/png": 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hqGo7cCpwuap+gthe0saYCnTyNxcwdPtDODqEV59Mv2nN4BHfk7rniq65Le6q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\nfD9DMQrsBGqJ7YBXiTECfA+4rRLjU9W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G+GTqhHacpkVsGbkvT/76B36HMyxUeqaN3iqmzRsAcDpLWMZUMtVRXfGurCk4a6cvriUI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\nm/vj46edSJhn6GzZn/kX/zB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"text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/openmc/data/resonance_covariance.py b/openmc/data/resonance_covariance.py index 9a8163f3b..2de619c00 100644 --- a/openmc/data/resonance_covariance.py +++ b/openmc/data/resonance_covariance.py @@ -75,7 +75,8 @@ class ResonanceCovariances(Resonances): ev : openmc.data.endf.Evaluation ENDF evaluation resonances : openmc.data.Resonance object - Resonanance object generated from the same evaluation + openmc.data.Resonanance object generated from the same evaluation used + to import values not contained in File 32 Returns ------- @@ -214,7 +215,7 @@ class ResonanceCovarianceRange: Returns ------- samples : list of openmc.data.ResonanceCovarianceRange objects - List of samples size [n_samples] + List of samples size `n_samples` """ if not use_subset: @@ -412,12 +413,12 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange): String descriptor of formalism """ - def __init__(self, energy_min, energy_max): + def __init__(self, energy_min, energy_max, parameters, covariance, mpar, lcomp): super().__init__(energy_min, energy_max) - self.parameters = None - self.covariance = None - self.mpar = None - self.lcomp = None + self.parameters = parameters + self.covariance = covariance + self.mpar = mpar + self.lcomp = lcomp self.formalism = 'mlbw' @classmethod @@ -454,11 +455,11 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange): # Other scatter radius parameters items = endf.get_cont_record(file_obj) target_spin = items[0] - LCOMP = items[3] # Flag for compatibility 0, 1, 2 - 2 is compact form - NLS = items[4] # number of l-values + lcomp = items[3] # Flag for compatibility 0, 1, 2 - 2 is compact form + nls = items[4] # number of l-values # Build covariance matrix for General Resolved Resonance Formats - if LCOMP == 1: + if lcomp == 1: items = endf.get_cont_record(file_obj) num_short_range = items[4] # Number of short range type resonance # covariances @@ -501,16 +502,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange): # Add parameters from File 2 parameters = _add_file2_contributions(parameters, file2params) - # Create instance of class - mlbw = cls(energy_min, energy_max) - mlbw.parameters = parameters - mlbw.covariance = cov - mlbw.mpar = mpar - mlbw.lcomp = LCOMP - - return mlbw - - elif LCOMP == 2: # Compact format - Resonances and individual + elif lcomp == 2: # Compact format - Resonances and individual # uncertainties followed by compact correlations items, values = endf.get_list_record(file_obj) mean = items @@ -523,7 +515,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange): gf = values[5::12] par_unc = [] for i in range(num_res): - res_unc = values[i*12+6:i*12+12] + res_unc = values[i*12+6 : i*12+12] # Delete 0 values (not provided, no fission width) # DAJ/DGT always zero, DGF sometimes none zero [1, 2, 5] res_unc_nonzero = [] @@ -556,37 +548,21 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange): # Add parameters from File 2 parameters = _add_file2_contributions(parameters, file2params) - # Create instance of MultiLevelBreitWignerCovariance - mlbw = cls(energy_min, energy_max) - mlbw.parameters = parameters - mlbw.covariance = cov - mlbw.mpar = mpar - mlbw.lcomp = LCOMP - - return mlbw - - elif LCOMP == 0 : + elif lcomp == 0 : cov = np.zeros([4, 4]) records = [] cov_index = 0 - for i in range(NLS): + for i in range(nls): items, values = endf.get_list_record(file_obj) num_res = items[5] for j in range(num_res): one_res = values[18*j:18*(j+1)] res_values = one_res[:6] cov_values = one_res[6:] - - energy = res_values[0] - spin = res_values[1] - gt = res_values[2] - gn = res_values[3] - gg = res_values[4] - gf = res_values[5] - records.append([energy, spin, gt, gn, gg, gf]) + records.append(list(res_values)) # Populate the coviariance matrix for this resonance - # There are no covariances between resonances in LCOMP=0 + # There are no covariances between resonances in lcomp=0 cov[cov_index, cov_index] = cov_values[0] cov[cov_index+1, cov_index+1 : cov_index+2] = cov_values[1:2] cov[cov_index+1, cov_index+3] = cov_values[4] @@ -615,14 +591,9 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange): # Add parameters from File 2 parameters = _add_file2_contributions(parameters, file2params) - # Create instance of class - mlbw = cls(energy_min, energy_max) - mlbw.parameters = parameters - mlbw.covariance = cov - mlbw.mpar = mpar - mlbw.lcomp = LCOMP - - return mlbw + # Create instance of class + mlbw = cls(energy_min, energy_max, parameters, cov, mpar, lcomp) + return mlbw class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance): @@ -655,8 +626,8 @@ class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance): String descriptor of formalism """ - def __init__(self, energy_min, energy_max): - super().__init__(energy_min, energy_max) + def __init__(self, energy_min, energy_max, parameters, covariance, mpar, lcomp): + super().__init__(energy_min, energy_max, parameters, covariance, mpar, lcomp) self.formalism = 'slbw' @@ -691,10 +662,12 @@ class ReichMooreCovariance(ResonanceCovarianceRange): String descriptor of formalism """ - def __init__(self, energy_min, energy_max): + def __init__(self, energy_min, energy_max, parameters, covariance, mpar, lcomp): super().__init__(energy_min, energy_max) - self.parameters = None - self.covariance = None + self.parameters = parameters + self.covariance = covariance + self.mpar = mpar + self.lcomp = lcomp self.formalism = 'rm' @classmethod @@ -712,7 +685,9 @@ class ReichMooreCovariance(ResonanceCovarianceRange): items : list Items from the CONT record at the start of the resonance range subsection - resonances : Resonance object + resonances : openmc.data.Resonance object + openmc.data.Resonanance object generated from the same evaluation used + to import values not contained in File 32 Returns ------- @@ -729,12 +704,12 @@ class ReichMooreCovariance(ResonanceCovarianceRange): # Other scatter radius parameters items = endf.get_cont_record(file_obj) target_spin = items[0] - LCOMP = items[3] # Flag for compatibility 0, 1, 2 - 2 is compact form - NLS = items[4] # Number of l-values + lcomp = items[3] # Flag for compatibility 0, 1, 2 - 2 is compact form + nls = items[4] # Number of l-values # Build covariance matrix for General Resolved Resonance Formats - if LCOMP == 1: + if lcomp == 1: items = endf.get_cont_record(file_obj) num_short_range = items[4] # Number of short range type resonance # covariances @@ -777,16 +752,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange): # Add parameters from File 2 parameters = _add_file2_contributions(parameters, file2params) - # Create instance of ReichMooreCovariance - rmc = cls(energy_min, energy_max) - rmc.parameters = parameters - rmc.covariance = cov - rmc.mpar = mpar - rmc.lcomp = LCOMP - - return rmc - - elif LCOMP == 2: # Compact format - Resonances and individual + elif lcomp == 2: # Compact format - Resonances and individual # uncertainties followed by compact correlations items, values = endf.get_list_record(file_obj) num_res = items[5] @@ -798,7 +764,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange): gfb = values[5::12] par_unc = [] for i in range(num_res): - res_unc = values[i*12+6:i*12+12] + res_unc = values[i*12+6 : i*12+12] # Delete 0 values (not provided in evaluation) res_unc = [x for x in res_unc if x != 0.0] par_unc.extend(res_unc) @@ -825,21 +791,17 @@ class ReichMooreCovariance(ResonanceCovarianceRange): # Add parameters from File 2 parameters = _add_file2_contributions(parameters, file2params) - # Create instance of ReichMooreCovariance - rmc = cls(energy_min, energy_max) - rmc.parameters = parameters - rmc.covariance = cov - rmc.mpar = mpar - rmc.lcomp = LCOMP - - return rmc + # Create instance of ReichMooreCovariance + rmc = cls(energy_min, energy_max, parameters, cov, mpar, lcomp) + return rmc _FORMALISMS = { - 0: ResonanceCovarianceRange, - 1: SingleLevelBreitWignerCovariance, - 2: MultiLevelBreitWignerCovariance, - 3: ReichMooreCovariance - # 7: RMatrixLimitedCovariance - } + 0: ResonanceCovarianceRange, + 1: SingleLevelBreitWignerCovariance, + 2: MultiLevelBreitWignerCovariance, + 3: ReichMooreCovariance + # 7: RMatrixLimitedCovariance +} +