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https://github.com/openmc-dev/openmc.git
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Style, changed sampling/subset methods to return new objects
This commit is contained in:
Isaac Meyer
parent
010acb8d1f
commit
5381ad46bc
4 changed files with 848 additions and 239 deletions
File diff suppressed because one or more lines are too long
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@ -71,8 +71,8 @@ def float_endf(s):
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return float(_ENDF_FLOAT_RE.sub(r'\1e\2', s))
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def int_endf(s):
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"""Conver string to int. Used for INTG records where blank entries
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def _int_endf(s):
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"""Convert string to int. Used for INTG records where blank entries
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indicate a 0.
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Parameters
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@ -267,10 +267,11 @@ def get_tab2_record(file_obj):
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return params, Tabulated2D(breakpoints, interpolation)
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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. Used to store the
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covariance matrix in a compact format.
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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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@ -285,8 +286,8 @@ def get_intg_record(file_obj):
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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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npar = int(items[3]) # Number of parameters
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nlines = int(items[4]) # Lines to read
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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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@ -294,24 +295,23 @@ def get_intg_record(file_obj):
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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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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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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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elif element < 0:
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corr[ii, jj] = (element-0.5)/factor
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#Symmetrize the correlation matrix
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# Symmetrize the correlation matrix
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corr = corr + corr.T - np.diag(corr.diagonal())
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return corr
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def get_evaluations(filename):
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"""Return a list of all evaluations within an ENDF file.
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@ -299,7 +299,7 @@ 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('resonance covariance', resonance_covariance,
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res_cov.ResonanceCovariances)
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res_cov.ResonanceCovariances)
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self._resonance_covariance = resonance_covariance
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@summed_reactions.setter
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@ -767,7 +767,7 @@ class IncidentNeutron(EqualityMixin):
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be the filename for the ENDF file.
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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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Flag to indicate whether or not covariance data from File 32 should be
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retrieved
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Returns
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@ -802,7 +802,9 @@ class IncidentNeutron(EqualityMixin):
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data.resonances = res.Resonances.from_endf(ev)
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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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data.resonance_covariance = (
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res_cov.ResonanceCovariances.from_endf(ev, data.resonances)
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)
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# Read each reaction
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for mf, mt, nc, mod in ev.reaction_list:
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@ -12,13 +12,13 @@ from .resonance import Resonances
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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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"""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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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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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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@ -26,6 +26,7 @@ def _add_file2_contributions(file32params, file2params):
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-------
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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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@ -54,6 +55,7 @@ class ResonanceCovariances(Resonances):
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----------
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ranges : list of openmc.data.ResonanceCovarianceRange
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Distinct energy ranges for resonance data
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"""
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@property
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@ -63,8 +65,8 @@ class ResonanceCovariances(Resonances):
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@ranges.setter
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def ranges(self, ranges):
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cv.check_type('resonance ranges', ranges, MutableSequence)
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self._ranges = cv.CheckedList(ResonanceCovarianceRange, 'resonance range',
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ranges)
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self._ranges = cv.CheckedList(ResonanceCovarianceRange,
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'resonance range', ranges)
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@classmethod
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def from_endf(cls, ev, resonances):
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@ -75,8 +77,8 @@ 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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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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openmc.data.Resonanance object generated from the same evaluation
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used to import values not contained in File 32
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Returns
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-------
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@ -88,39 +90,40 @@ class ResonanceCovariances(Resonances):
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# Determine whether discrete or continuous representation
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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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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 = 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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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 = 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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# Unresolved flags - 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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unresolved_flag = items[2]
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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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raise NotImplementedError('LRF= ', formalism,
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'covariance not supported for this formalism')
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error = 'LRF = '+str(formalism)+'covariance not supported '\
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'for this formalism'
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raise NotImplementedError(error)
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if unresolved_flag in (0, 1):
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# resolved resonance region
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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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items, file2params)
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ranges.append(erange)
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elif unresolved_flag == 2:
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warn_str = 'Unresolved resonance not supported. '\
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'Covariance values for the unresolved region not imported.'
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warnings.warn(warn_str)
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warn = 'Unresolved resonance not supported. Covariance '\
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'values for the unresolved region not imported.'
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warnings.warn(warn)
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return cls(ranges)
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@ -146,7 +149,7 @@ class ResonanceCovarianceRange:
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covariance : numpy.array
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The covariance matrix contained within the ENDF evaluation
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lcomp : int
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Flag indicating the format of the covariance matrix within the ENDF file
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Flag indicating format of the covariance matrix within the ENDF file
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mpar : int
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Number of parameters in covariance matrix for each individual resonance
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formalism : str
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@ -155,35 +158,47 @@ class ResonanceCovarianceRange:
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def __init__(self, energy_min, energy_max):
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self.energy_min = energy_min
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self.energy_max = energy_max
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def res_subset(self, parameter_str, bounds):
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def res_subset(self, parameter_str, bounds, resonances):
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"""Produce a subset of resonance parameters and the corresponding
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covariance matrix to an IncidentNeutron object.
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Parameters
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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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bounds : np.array
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bounds : np.array
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[low numerical bound, high numerical bound]
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resonances : openmc.data.ResonanceRange object
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Corresponding resonance range with File 2 data.
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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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Subset of covariance matrix (upper triangular)
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res_range : openmc.data.ResonanceRange
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ResonanceRange object that contains a subset of parameters
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(maintains indexing of original)
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res_cov_range : openmc.data.ResonanceCovarianceRange
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ResonanceCovarianceRange object that contains a subset of the
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covariance matrix (upper triangular) as well as parameters
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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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# Copy the objects
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res_range = copy.copy(resonances)
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res_cov_range = copy.copy(self)
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parameters = res_range.parameters
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cov = res_cov_range.covariance
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mpar = res_cov_range.mpar
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# Create mask
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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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indices = parameters_subset.index.values
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# Set the parameters for each object
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res_range.parameters = parameters[mask]
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res_cov_range.parameters = parameters[mask]
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indices = res_cov_range.parameters.index.values
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# Build subset of covariance
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sub_cov_dim = len(indices)*mpar
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cov_subset_vals = []
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for index1 in indices:
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@ -191,54 +206,48 @@ class ResonanceCovarianceRange:
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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,
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index2*mpar+j])
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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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self.parameters_subset = parameters_subset
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self.cov_subset = cov_subset
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res_cov_range.covariance = cov_subset
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return res_range, res_cov_range
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def sample_resonance_parameters(self, n_samples, resonances):
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"""Sample resonance parameters based on the covariances provided
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within an ENDF evaluation.
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def sample_resonance_parameters(self, n_samples, resonances, use_subset=False):
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"""Return a list size 'n_samples' of openmc.data.ResonanceRange objects.
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Each with an indepentenly sampled set of parameters
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Parameters
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----------
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n_samples : int
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The number of samples to produce
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resonances : openmc.data.ResonanceRange object
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Corresponding resonance range with File 2 data.
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use_subset : bool, optional
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Flag on whether to sample from an already produced subset
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Returns
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-------
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samples : list of openmc.data.ResonanceCovarianceRange objects
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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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"""
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warn_str = 'Sampling routine does not guarantee positive values for '\
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'parameters. This can lead to undefined behavior in the '\
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'reconstruction routine.'
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warnings.warn(warn_str)
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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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if self.parameters_subset is None:
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raise ValueError('No subset of resonances defined')
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parameters = self.parameters_subset
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cov = self.cov_subset
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parameters = self.parameters
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cov = self.covariance
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nparams, params = parameters.shape
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cov = cov + cov.T - np.diag(cov.diagonal()) # symmetrizing covariance matrix
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# Symmetrizing covariance matrix
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cov = cov + cov.T - np.diag(cov.diagonal())
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covsize = cov.shape[0]
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formalism = self.formalism
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mpar = self.mpar
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samples = []
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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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@ -258,20 +267,22 @@ class ResonanceCovarianceRange:
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gt = gn + gg + gf
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records = []
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for j, E in enumerate(energy):
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records.append([energy[j], l_value[j], spin[j], gt[j], gn[j],
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gg[j], gf[j], gx[j]])
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records.append([energy[j], l_value[j], spin[j], gt[j],
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gn[j], gg[j], gf[j], gx[j]])
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columns = ['energy', 'L', 'J', 'totalWidth', 'neutronWidth',
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'captureWidth', 'fissionWidth', 'competitiveWidth']
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sample_params = pd.DataFrame.from_records(records, columns=columns)
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sample_params = pd.DataFrame.from_records(records,
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columns=columns)
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res_range = copy.copy(resonances)
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res_range._prepared = False # Set prepared to False to ensure
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# the sampled parameters are used
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# in reconstruction
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# Set _prepared to False to ensure sampled paramaters are
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# used during construction routine
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res_range._prepared = False
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res_range.parameters = sample_params
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samples.append(res_range)
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elif mpar == 4:
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param_list = ['energy', 'neutronWidth', 'captureWidth', 'fissionWidth']
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param_list = ['energy', 'neutronWidth', 'captureWidth',
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'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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@ -287,18 +298,19 @@ class ResonanceCovarianceRange:
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gt = gn + gg + gf
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records = []
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for j, E in enumerate(energy):
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records.append([energy[j], l_value[j], spin[j], gt[j], gn[j],
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gg[j], gf[j], gx[j]])
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records.append([energy[j], l_value[j], spin[j], gt[j],
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gn[j], gg[j], gf[j], gx[j]])
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columns = ['energy', 'L', 'J', 'totalWidth', 'neutronWidth',
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'captureWidth', 'fissionWidth', 'competitiveWidth']
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sample_params = pd.DataFrame.from_records(records, columns=columns)
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sample_params = pd.DataFrame.from_records(records,
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columns=columns)
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res_range = copy.copy(resonances)
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res_range._prepared = False # Set prepared to False to ensure
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# the sampled parameters are used
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# in reconstruction
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# Set _prepared to False to ensure sampled paramaters are
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# used during construction routine
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res_range._prepared = False
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res_range.parameters = sample_params
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samples.append(res_range)
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elif mpar == 5:
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param_list = ['energy', 'neutronWidth', 'captureWidth',
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'fissionWidth', 'competitiveWidth']
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@ -317,15 +329,16 @@ class ResonanceCovarianceRange:
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gt = gn + gg + gf
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records = []
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for j, E in enumerate(energy):
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records.append([energy[j], l_value[j], spin[j], gt[j], gn[j],
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gg[j], gf[j], gx[j]])
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records.append([energy[j], l_value[j], spin[j], gt[j],
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gn[j], gg[j], gf[j], gx[j]])
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columns = ['energy', 'L', 'J', 'totalWidth', 'neutronWidth',
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'captureWidth', 'fissionWidth', 'competitveWidth']
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sample_params = pd.DataFrame.from_records(records, columns=columns)
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sample_params = pd.DataFrame.from_records(records,
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columns=columns)
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res_range = copy.copy(resonances)
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res_range._prepared = False # Set prepared to False to ensure
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# the sampled parameters are used
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# in reconstruction
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# Set _prepared to False to ensure sampled paramaters are
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# used during construction routine
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res_range._prepared = False
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res_range.parameters = sample_params
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samples.append(res_range)
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@ -351,14 +364,15 @@ class ResonanceCovarianceRange:
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gg[j], gfa[j], gfb[j]])
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columns = ['energy', 'L', 'J', 'neutronWidth',
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'captureWidth', 'fissionWidthA', 'fissionWidthB']
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sample_params = pd.DataFrame.from_records(records, columns=columns)
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sample_params = pd.DataFrame.from_records(records,
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columns=columns)
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res_range = copy.copy(resonances)
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res_range._prepared = False # Set prepared to False to ensure
|
||||
# the sampled parameters are used
|
||||
# in reconstruction
|
||||
# Set _prepared to False to ensure sampled paramaters are
|
||||
# used during construction routine
|
||||
res_range._prepared = False
|
||||
res_range.parameters = sample_params
|
||||
samples.append(res_range)
|
||||
|
||||
|
||||
elif mpar == 5:
|
||||
param_list = ['energy', 'neutronWidth', 'captureWidth',
|
||||
'fissionWidthA', 'fissionWidthB']
|
||||
|
|
@ -380,15 +394,16 @@ class ResonanceCovarianceRange:
|
|||
gg[j], gfa[j], gfb[j]])
|
||||
columns = ['energy', 'L', 'J', 'neutronWidth',
|
||||
'captureWidth', 'fissionWidthA', 'fissionWidthB']
|
||||
sample_params = pd.DataFrame.from_records(records, columns=columns)
|
||||
sample_params = pd.DataFrame.from_records(records,
|
||||
columns=columns)
|
||||
res_range = copy.copy(resonances)
|
||||
res_range._prepared = False # Set prepared to False to ensure
|
||||
# the sampled parameters are used
|
||||
# in reconstruction
|
||||
# Set _prepared to False to ensure sampled paramaters are
|
||||
# used during construction routine
|
||||
res_range._prepared = False
|
||||
res_range.parameters = sample_params
|
||||
samples.append(res_range)
|
||||
|
||||
self.samples = samples
|
||||
|
||||
return samples
|
||||
|
||||
|
||||
class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
|
||||
|
|
@ -411,7 +426,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
|
|||
covariance : numpy.array
|
||||
The covariance matrix contained within the ENDF evaluation
|
||||
lcomp : int
|
||||
Flag indicating the format of the covariance matrix within the ENDF file
|
||||
Flag indicating format of the covariance matrix within the ENDF file
|
||||
mpar : int
|
||||
Number of parameters in covariance matrix for each individual resonance
|
||||
formalism : str
|
||||
|
|
@ -425,7 +440,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
|
|||
self.mpar = mpar
|
||||
self.lcomp = lcomp
|
||||
self.formalism = 'mlbw'
|
||||
|
||||
|
||||
@classmethod
|
||||
def from_endf(cls, ev, file_obj, items, file2params):
|
||||
"""Create MLBW covariance data from an ENDF evaluation.
|
||||
|
|
@ -460,16 +475,16 @@ 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
|
||||
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:
|
||||
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
|
||||
# Number of short range type resonance covariances
|
||||
num_short_range = items[4]
|
||||
# Number of long range type resonance covariances
|
||||
num_long_range = items[5]
|
||||
|
||||
# Read resonance widths, J values, etc
|
||||
records = []
|
||||
|
|
@ -498,7 +513,7 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
|
|||
indices = np.triu_indices(cov_dim)
|
||||
cov[indices] = cov_values
|
||||
|
||||
# Create pandas DataFrame with resonance data, currently
|
||||
# Create pandas DataFrame with resonance data, currently
|
||||
# redundant with data.IncidentNeutron.resonance
|
||||
columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
|
||||
'captureWidth', 'fissionWidth']
|
||||
|
|
@ -507,11 +522,12 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
|
|||
# Add parameters from File 2
|
||||
parameters = _add_file2_contributions(parameters, file2params)
|
||||
|
||||
elif lcomp == 2: # Compact format - Resonances and individual
|
||||
# uncertainties followed by compact correlations
|
||||
# Compact format - Resonances and individual uncertainties followed by
|
||||
# compact correlations
|
||||
elif lcomp == 2:
|
||||
items, values = endf.get_list_record(file_obj)
|
||||
mean = items
|
||||
num_res = items[5]
|
||||
num_res = items[5]
|
||||
energy = values[0::12]
|
||||
spin = values[1::12]
|
||||
gt = values[2::12]
|
||||
|
|
@ -525,9 +541,9 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
|
|||
# DAJ/DGT always zero, DGF sometimes none zero [1, 2, 5]
|
||||
res_unc_nonzero = []
|
||||
for j in range(6):
|
||||
if j in [1, 2, 5] and res_unc[j] != 0.0 :
|
||||
if j in [1, 2, 5] and res_unc[j] != 0.0:
|
||||
res_unc_nonzero.append(res_unc[j])
|
||||
elif j in [0,3,4]:
|
||||
elif j in [0, 3, 4]:
|
||||
res_unc_nonzero.append(res_unc[j])
|
||||
par_unc.extend(res_unc_nonzero)
|
||||
|
||||
|
|
@ -549,11 +565,11 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
|
|||
nparams, params = parameters.shape
|
||||
covsize = cov.shape[0]
|
||||
mpar = int(covsize/nparams)
|
||||
|
||||
|
||||
# Add parameters from File 2
|
||||
parameters = _add_file2_contributions(parameters, file2params)
|
||||
|
||||
elif lcomp == 0 :
|
||||
elif lcomp == 0:
|
||||
cov = np.zeros([4, 4])
|
||||
records = []
|
||||
cov_index = 0
|
||||
|
|
@ -576,12 +592,11 @@ class MultiLevelBreitWignerCovariance(ResonanceCovarianceRange):
|
|||
cov[cov_index+3, cov_index+3] = cov_values[6]
|
||||
|
||||
cov_index += 4
|
||||
if j < num_res-1: # Pad matrix for additional values
|
||||
if j < num_res-1: # Pad matrix for additional values
|
||||
cov = np.pad(cov, ((0, 4), (0, 4)), 'constant',
|
||||
constant_values=0)
|
||||
|
||||
|
||||
# Create pandas DataFrame with resonance data, currently
|
||||
# Create pandas DataFrame with resonance data, currently
|
||||
# redundant with data.IncidentNeutron.resonance
|
||||
columns = ['energy', 'J', 'totalWidth', 'neutronWidth',
|
||||
'captureWidth', 'fissionWidth']
|
||||
|
|
@ -624,15 +639,17 @@ class SingleLevelBreitWignerCovariance(MultiLevelBreitWignerCovariance):
|
|||
covariance : numpy.array
|
||||
The covariance matrix contained within the ENDF evaluation
|
||||
lcomp : int
|
||||
Flag indicating the format of the covariance matrix within the ENDF file
|
||||
Flag indicating format of the covariance matrix within the ENDF file
|
||||
mpar : int
|
||||
Number of parameters in covariance matrix for each individual resonance
|
||||
formalism : str
|
||||
String descriptor of formalism
|
||||
"""
|
||||
|
||||
def __init__(self, energy_min, energy_max, parameters, covariance, mpar, lcomp):
|
||||
super().__init__(energy_min, energy_max, parameters, covariance, mpar, lcomp)
|
||||
def __init__(self, energy_min, energy_max, parameters, covariance, mpar,
|
||||
lcomp):
|
||||
super().__init__(energy_min, energy_max, parameters, covariance, mpar,
|
||||
lcomp)
|
||||
self.formalism = 'slbw'
|
||||
|
||||
|
||||
|
|
@ -660,14 +677,15 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
covariance : numpy.array
|
||||
The covariance matrix contained within the ENDF evaluation
|
||||
lcomp : int
|
||||
Flag indicating the format of the covariance matrix within the ENDF file
|
||||
Flag indicating format of the covariance matrix within the ENDF file
|
||||
mpar : int
|
||||
Number of parameters in covariance matrix for each individual resonance
|
||||
formalism : str
|
||||
String descriptor of formalism
|
||||
"""
|
||||
|
||||
def __init__(self, energy_min, energy_max, parameters, covariance, mpar, lcomp):
|
||||
def __init__(self, energy_min, energy_max, parameters, covariance, mpar,
|
||||
lcomp):
|
||||
super().__init__(energy_min, energy_max)
|
||||
self.parameters = parameters
|
||||
self.covariance = covariance
|
||||
|
|
@ -677,8 +695,9 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
|
||||
@classmethod
|
||||
def from_endf(cls, ev, file_obj, items, file2params):
|
||||
"""Create Reich-Moore resonance covariance data from an ENDF evaluation.
|
||||
Includes the resonance parameters contained separately in File 32.
|
||||
"""Create Reich-Moore resonance covariance data from an ENDF
|
||||
evaluation. Includes the resonance parameters contained separately in
|
||||
File 32.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -691,8 +710,8 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
Items from the CONT record at the start of the resonance range
|
||||
subsection
|
||||
resonances : openmc.data.Resonance object
|
||||
openmc.data.Resonanance object generated from the same evaluation used
|
||||
to import values not contained in File 32
|
||||
openmc.data.Resonanance object generated from the same evaluation
|
||||
used to import values not contained in File 32
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
|
@ -712,14 +731,13 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
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:
|
||||
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
|
||||
# Number of short range type resonance covariances
|
||||
num_short_range = items[4]
|
||||
# Number of long range type resonance covariances
|
||||
num_long_range = items[5]
|
||||
# Read resonance widths, J values, etc
|
||||
channel_radius = {}
|
||||
scattering_radius = {}
|
||||
|
|
@ -731,7 +749,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
num_par_vals = num_res*6
|
||||
res_values = values[:num_par_vals]
|
||||
cov_values = values[num_par_vals:]
|
||||
|
||||
|
||||
energy = res_values[0::6]
|
||||
spin = res_values[1::6]
|
||||
gn = res_values[2::6]
|
||||
|
|
@ -745,7 +763,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
|
||||
# Build the upper-triangular covariance matrix
|
||||
cov_dim = mpar*num_res
|
||||
cov = np.zeros([cov_dim,cov_dim])
|
||||
cov = np.zeros([cov_dim, cov_dim])
|
||||
indices = np.triu_indices(cov_dim)
|
||||
cov[indices] = cov_values
|
||||
|
||||
|
|
@ -757,10 +775,11 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
# Add parameters from File 2
|
||||
parameters = _add_file2_contributions(parameters, file2params)
|
||||
|
||||
elif lcomp == 2: # Compact format - Resonances and individual
|
||||
# uncertainties followed by compact correlations
|
||||
# Compact format - Resonances and individual uncertainties followed by
|
||||
# compact correlations
|
||||
elif lcomp == 2:
|
||||
items, values = endf.get_list_record(file_obj)
|
||||
num_res = items[5]
|
||||
num_res = items[5]
|
||||
energy = values[0::12]
|
||||
spin = values[1::12]
|
||||
gn = values[2::12]
|
||||
|
|
@ -789,7 +808,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
|
||||
# Determine mpar (number of parameters for each resonance in
|
||||
# covariance matrix)
|
||||
nparams,params = parameters.shape
|
||||
nparams, params = parameters.shape
|
||||
covsize = cov.shape[0]
|
||||
mpar = int(covsize/nparams)
|
||||
|
||||
|
|
@ -800,6 +819,7 @@ class ReichMooreCovariance(ResonanceCovarianceRange):
|
|||
rmc = cls(energy_min, energy_max, parameters, cov, mpar, lcomp)
|
||||
return rmc
|
||||
|
||||
|
||||
_FORMALISMS = {
|
||||
0: ResonanceCovarianceRange,
|
||||
1: SingleLevelBreitWignerCovariance,
|
||||
|
|
@ -807,6 +827,3 @@ _FORMALISMS = {
|
|||
3: ReichMooreCovariance
|
||||
# 7: RMatrixLimitedCovariance
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue