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even more style
This commit is contained in:
Isaac Meyer
parent
15ffa563e0
commit
1beab30db6
3 changed files with 106 additions and 101 deletions
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@ -298,7 +298,7 @@ class IncidentNeutron(EqualityMixin):
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@resonance_covariance.setter
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def resonance_covariance(self, resonance_covariance):
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cv.check_type('resonances', resonances, res.ResonanceCovariance)
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cv.check_type('resonances', resonances, res_cov.ResonanceCovariance)
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self._resonacne_covariance = resonance_covariance
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@summed_reactions.setter
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@ -756,7 +756,7 @@ class IncidentNeutron(EqualityMixin):
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return data
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@classmethod
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def from_endf(cls, ev_or_filename, get_covariance=False):
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def from_endf(cls, ev_or_filename, covariance=False):
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"""Generate incident neutron continuous-energy data from an ENDF evaluation
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Parameters
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@ -765,7 +765,7 @@ class IncidentNeutron(EqualityMixin):
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ENDF evaluation to read from. If given as a string, it is assumed to
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be the filename for the ENDF file.
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get_covariance : bool
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covariance : bool
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Flag to indicate whether or not covariance data from File 32 should be
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retrieved
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@ -800,8 +800,8 @@ class IncidentNeutron(EqualityMixin):
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if (2, 151) in ev.section:
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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.ResonanceCovariances.from_endf(ev, data.resonances)
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if (32, 151) in ev.section and covariance:
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data.resonance_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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@ -16,6 +16,7 @@ except ImportError:
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_reconstruct = False
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import openmc.checkvalue as cv
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class Resonances(object):
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"""Resolved and unresolved resonance data
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@ -202,7 +203,7 @@ class ResonanceRange(object):
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return cls(target_spin, energy_min, energy_max, {0: a}, {0: ap})
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def reconstruct(self, energies, use_sample = False, sample_parameters = None):
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def reconstruct(self, energies, use_sample=False, sample_parameters=None):
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"""Evaluate cross section at specified energies.
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Parameters
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@ -394,8 +395,8 @@ class MultiLevelBreitWigner(ResonanceRange):
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return mlbw
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def _prepare_resonances(self, use_sample = False, sample_parameters = None):
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if use_sample == False:
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def _prepare_resonances(self, use_sample=False, sample_parameters=None):
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if not use_sample:
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df = self.parameters.copy()
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else:
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df = sample_parameters.copy()
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@ -656,8 +657,8 @@ class ReichMoore(ResonanceRange):
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return rm
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def _prepare_resonances(self, use_sample = False, sample_parameters = None):
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if use_sample == False:
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def _prepare_resonances(self, use_sample=False, sample_parameters=None):
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if not use_sample:
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df = self.parameters.copy()
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else:
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df = sample_parameters.copy()
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@ -5,40 +5,40 @@ import io
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import numpy as np
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import pandas as pd
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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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from . import endf
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import openmc.checkvalue as cv
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from .resonance import Resonances
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def file2contributions(file32params, file2params):
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def _add_file2_contributions(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. Uses already imported resonance data.
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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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Paramaters
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----------
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file32params : pandas.Dataframe
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Incomplete set of resonance parameters contained in File 32.
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file2params : pandas.Dataframe
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Resonance parameters from File 2. Ordered by energy.
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Returns
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-------
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parameters: pandas.Dataframe
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parameters : pandas.Dataframe
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Complete set of parameters ordered by L-values and then energy
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"""
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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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# 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.reset_index(drop=True, inplace=True)
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# Sort File 32 parameters by energy as well (maintaining index)
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file32params.sort_values(by=['energy'], inplace=True)
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# Add in values (.values converts to array first to ignore index)
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file32params['L'] = file2params['L'].values
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if 'competitiveWidth' in file2params.columns:
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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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return parameters
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file32params['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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file32params.sort_index(inplace=True)
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return file32params
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class ResonanceCovariances(Resonances):
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@ -81,6 +81,7 @@ 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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Returns
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-------
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@ -91,18 +92,18 @@ class ResonanceCovariances(Resonances):
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file_obj = io.StringIO(ev.section[32, 151])
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# Determine whether discrete or continuous representation
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items = get_head_record(file_obj)
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items = endf.get_head_record(file_obj)
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n_isotope = items[4] # Number of isotopes
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ranges = []
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for iso in range(n_isotope):
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items = get_cont_record(file_obj)
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items = endf.get_cont_record(file_obj)
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abundance = items[1]
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fission_widths = (items[3] == 1) # Flag for fission widths
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n_ranges = items[4] # number of resonance energy ranges
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for j in range(n_ranges):
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items = get_cont_record(file_obj)
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items = endf.get_cont_record(file_obj)
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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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@ -127,7 +128,8 @@ class ResonanceCovariances(Resonances):
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return cls(ranges)
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class ResonanceCovarianceRange(object):
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class ResonanceCovarianceRange:
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"""Resonace covariance range. Base class for different formalisms.
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Parameters
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@ -143,7 +145,7 @@ class ResonanceCovarianceRange(object):
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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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parameters: pandas.DataFrame
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parameters : pandas.DataFrame
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Resonance parameters
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covariance : numpy.array
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The covariance matrix contained within the ENDF evaluation
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@ -164,27 +166,27 @@ class ResonanceCovarianceRange(object):
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Parameters
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----------
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parameter_str: str
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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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bounds: np.array
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bounds : np.array
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[low numerical bound, high numerical bound]
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Returns
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-------
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parameters_subset : pandas.Dataframe
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Subset of parameters (maintains indexing of original)
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cov_subset: np.array
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cov_subset : np.array
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Subset of covariance matrix (upper triangular)
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"""
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parameters = self.parameters
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cov = self.covariance
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mpar = self.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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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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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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cov_subset_vals = []
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@ -228,7 +230,7 @@ class ResonanceCovarianceRange(object):
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cov = self.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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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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mpar = self.mpar
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@ -384,6 +386,7 @@ class ResonanceCovarianceRange(object):
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sample_parameters = sample_parameters)
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return xs_array
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class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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"""Multi-level Breit-Wigner resolved resonance formalism covariance data.
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Parameters
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@ -399,7 +402,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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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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parameters: pandas.DataFrame
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parameters : pandas.DataFrame
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Resonance parameters
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covariance : numpy.array
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The covariance matrix contained within the ENDF evaluation
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@ -446,26 +449,26 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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energy_min, energy_max = items[0:2]
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nro, naps = items[4:6]
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if nro != 0:
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params, ape = get_tab1_record(file_obj)
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params, ape = endf.get_tab1_record(file_obj)
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# Other scatter radius parameters
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items = get_cont_record(file_obj)
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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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# Build covariance matrix for General Resolved Resonance Formats
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if LCOMP == 1:
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items = 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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num_long_range = items[5] #Number of long range type resonance
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#covariances
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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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num_long_range = items[5] # Number of long range type resonance
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# covariances
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# Read resonance widths, J values, etc
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records = []
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for i in range(num_short_range):
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items, values = get_list_record(file_obj)
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items, values = endf.get_list_record(file_obj)
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mpar = items[2]
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num_res = items[5]
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num_par_vals = num_res*6
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@ -483,20 +486,20 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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records.append([energy[i], spin[i], gt[i], gn[i],
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gg[i], gf[i]])
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#Build the upper-triangular covariance matrix
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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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indices = np.triu_indices(cov_dim)
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cov[indices] = cov_values
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#Create pandas DataFrame with resonance data, currently
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#redundant with data.IncidentNeutron.resonance
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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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'captureWidth', 'fissionWidth']
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parameters = pd.DataFrame.from_records(records, columns=columns)
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#Add parameters from File 2
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parameters = file2contributions(parameters, file2params)
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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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@ -507,9 +510,9 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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return mlbw
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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 = get_list_record(file_obj)
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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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num_res = items[5]
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energy = values[0::12]
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@ -521,7 +524,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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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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#Delete 0 values (not provided, no fission width)
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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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for j in range(6):
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@ -536,7 +539,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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records.append([energy[i], spin[i], gt[i], gn[i],
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gg[i], gf[i]])
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corr = get_intg_record(file_obj)
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corr = endf.get_intg_record(file_obj)
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cov = np.diag(par_unc).dot(corr).dot(np.diag(par_unc))
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# Create pandas DataFrame with resonacne data
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@ -544,14 +547,14 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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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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# 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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covsize = cov.shape[0]
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mpar = int(covsize/nparams)
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#Add parameters from File 2
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parameters = file2contributions(parameters, file2params)
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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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@ -567,7 +570,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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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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items, values = get_list_record(file_obj)
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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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@ -582,35 +585,35 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
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gf = res_values[5]
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records.append([energy, spin, gt, gn, gg, gf])
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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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# 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_index += 4
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if j < num_res-1: #Pad matrix for additional values
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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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#Create pandas DataFrame with resonance data, currently
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#redundant with data.IncidentNeutron.resonance
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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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'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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# 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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covsize = cov.shape[0]
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mpar = int(covsize/nparams)
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#Add parameters from File 2
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parameters = file2contributions(parameters, file2params)
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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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@ -640,7 +643,7 @@ class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance):
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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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parameters: pandas.DataFrame
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parameters : pandas.DataFrame
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Resonance parameters
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covariance : numpy.array
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The covariance matrix contained within the ENDF evaluation
|
||||
|
|
@ -656,6 +659,7 @@ class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance):
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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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"""Reich-Moore resolved resonance formalism covariance data.
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||||
|
||||
|
|
@ -675,7 +679,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
Minimum energy of the resolved resonance range in eV
|
||||
energy_max : float
|
||||
Maximum energy of the resolved resonance range in eV
|
||||
parameters: pandas.DataFrame
|
||||
parameters : pandas.DataFrame
|
||||
Resonance parameters
|
||||
covariance : numpy.array
|
||||
The covariance matrix contained within the ENDF evaluation
|
||||
|
|
@ -720,10 +724,10 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
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|||
energy_min, energy_max = items[0:2]
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||||
nro, naps = items[4:6]
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if nro != 0:
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params, ape = get_tab1_record(file_obj)
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||||
params, ape = endf.get_tab1_record(file_obj)
|
||||
|
||||
# Other scatter radius parameters
|
||||
items = get_cont_record(file_obj)
|
||||
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
|
||||
|
|
@ -731,17 +735,17 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
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|||
|
||||
# Build covariance matrix for General Resolved Resonance Formats
|
||||
if LCOMP == 1:
|
||||
items = get_cont_record(file_obj)
|
||||
num_short_range = items[4] #Number of short range type resonance
|
||||
#covariances
|
||||
num_long_range = items[5] #Number of long range type resonance
|
||||
#covariances
|
||||
items = endf.get_cont_record(file_obj)
|
||||
num_short_range = items[4] # Number of short range type resonance
|
||||
# covariances
|
||||
num_long_range = items[5] # Number of long range type resonance
|
||||
# covariances
|
||||
# Read resonance widths, J values, etc
|
||||
channel_radius = {}
|
||||
scattering_radius = {}
|
||||
records = []
|
||||
for i in range(num_short_range):
|
||||
items, values = get_list_record(file_obj)
|
||||
items, values = endf.get_list_record(file_obj)
|
||||
mpar = items[2]
|
||||
num_res = items[5]
|
||||
num_par_vals = num_res*6
|
||||
|
|
@ -759,7 +763,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
records.append([energy[i], spin[i], gn[i], gg[i],
|
||||
gfa[i], gfb[i]])
|
||||
|
||||
#Build the upper-triangular covariance matrix
|
||||
# Build the upper-triangular covariance matrix
|
||||
cov_dim = mpar*num_res
|
||||
cov = np.zeros([cov_dim,cov_dim])
|
||||
indices = np.triu_indices(cov_dim)
|
||||
|
|
@ -770,8 +774,8 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
'fissionWidthA', 'fissionWidthB']
|
||||
parameters = pd.DataFrame.from_records(records, columns=columns)
|
||||
|
||||
#Add parameters from File 2
|
||||
parameters = file2contributions(parameters, file2params)
|
||||
# Add parameters from File 2
|
||||
parameters = _add_file2_contributions(parameters, file2params)
|
||||
|
||||
# Create instance of ReichMooreCovariance
|
||||
rmc = cls(energy_min, energy_max)
|
||||
|
|
@ -782,9 +786,9 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
|
||||
return rmc
|
||||
|
||||
elif LCOMP == 2: #Compact format - Resonances and individual
|
||||
#uncertainties followed by compact correlations
|
||||
items, values = get_list_record(file_obj)
|
||||
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]
|
||||
energy = values[0::12]
|
||||
spin = values[1::12]
|
||||
|
|
@ -795,7 +799,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
par_unc = []
|
||||
for i in range(num_res):
|
||||
res_unc = values[i*12+6:i*12+12]
|
||||
#Delete 0 values (not provided in evaluation)
|
||||
# Delete 0 values (not provided in evaluation)
|
||||
res_unc = [x for x in res_unc if x != 0.0]
|
||||
par_unc.extend(res_unc)
|
||||
|
||||
|
|
@ -804,7 +808,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
records.append([energy[i], spin[i], gn[i], gg[i],
|
||||
gfa[i], gfb[i]])
|
||||
|
||||
corr = get_intg_record(file_obj)
|
||||
corr = endf.get_intg_record(file_obj)
|
||||
cov = np.diag(par_unc).dot(corr).dot(np.diag(par_unc))
|
||||
|
||||
# Create pandas DataFrame with resonacne data
|
||||
|
|
@ -812,14 +816,14 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
'fissionWidthA', 'fissionWidthB']
|
||||
parameters = pd.DataFrame.from_records(records, columns=columns)
|
||||
|
||||
#Determine mpar (number of parameters for each resonance in
|
||||
#covariance matrix)
|
||||
# Determine mpar (number of parameters for each resonance in
|
||||
# covariance matrix)
|
||||
nparams,params = parameters.shape
|
||||
covsize = cov.shape[0]
|
||||
mpar = int(covsize/nparams)
|
||||
|
||||
#Add parameters from File 2
|
||||
parameters = file2contributions(parameters, file2params)
|
||||
# Add parameters from File 2
|
||||
parameters = _add_file2_contributions(parameters, file2params)
|
||||
|
||||
# Create instance of ReichMooreCovariance
|
||||
rmc = cls(energy_min, energy_max)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue