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Merge pull request #1598 from nelsonag/mult_matrix_hotfix
Corrected Issue w/ multiplicity matrix for microscopic MGXS data sets
This commit is contained in:
Paul Romano
GitHub
commit
8ae05a0093
2 changed files with 51 additions and 68 deletions
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@ -392,7 +392,7 @@ class MDGXS(MGXS):
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nuclides=query_nuclides, value=value)
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# Divide by atom number densities for microscopic cross sections
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if xs_type == 'micro':
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if xs_type == 'micro' and self._divide_by_density:
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if self.by_nuclide:
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densities = self.get_nuclide_densities(nuclides)
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else:
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@ -860,7 +860,7 @@ class MDGXS(MGXS):
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df = df[df['group out'].isin(groups)]
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# If user requested micro cross sections, divide out the atom densities
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if xs_type == 'micro':
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if xs_type == 'micro' and self._divide_by_density:
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if self.by_nuclide:
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densities = self.get_nuclide_densities(nuclides)
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else:
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@ -1005,6 +1005,11 @@ class ChiDelayed(MDGXS):
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"""
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# Store whether or not the number density should be removed for microscopic
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# values of this data; since this chi data is normalized to 1.0, the
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# data should not be divided by the number density
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_divide_by_density = False
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def __init__(self, domain=None, domain_type=None, energy_groups=None,
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delayed_groups=None, by_nuclide=False, name='',
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num_polar=1, num_azimuthal=1):
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@ -1089,7 +1094,6 @@ class ChiDelayed(MDGXS):
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return self._get_homogenized_mgxs(other_mgxs, 'delayed-nu-fission-in')
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def get_slice(self, nuclides=[], groups=[], delayed_groups=[]):
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"""Build a sliced ChiDelayed for the specified nuclides and energy
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groups.
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@ -1653,6 +1657,11 @@ class Beta(MDGXS):
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"""
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# Store whether or not the number density should be removed for microscopic
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# values of this data; since the beta is not a microscopic or macroscopic
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# quantity, it should not be divided by the number density
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_divide_by_density = False
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def __init__(self, domain=None, domain_type=None, energy_groups=None,
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delayed_groups=None, by_nuclide=False, name='',
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num_polar=1, num_azimuthal=1):
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@ -1838,6 +1847,11 @@ class DecayRate(MDGXS):
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"""
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# Store whether or not the number density should be removed for microscopic
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# values of this data; since the decay rates are not microscopic or
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# macroscopic quantities, it should not be divided by the number density.
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_divide_by_density = False
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def __init__(self, domain=None, domain_type=None, energy_groups=None,
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delayed_groups=None, by_nuclide=False, name='',
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num_polar=1, num_azimuthal=1):
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@ -2161,7 +2175,7 @@ class MatrixMDGXS(MDGXS):
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nuclides=query_nuclides, value=value)
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# Divide by atom number densities for microscopic cross sections
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if xs_type == 'micro':
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if xs_type == 'micro' and self._divide_by_density:
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if self.by_nuclide:
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densities = self.get_nuclide_densities(nuclides)
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else:
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@ -222,6 +222,10 @@ class MGXS:
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"""
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# Store whether or not the number density should be removed for microscopic
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# values of this data
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_divide_by_density = True
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def __init__(self, domain=None, domain_type=None,
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energy_groups=None, by_nuclide=False, name='', num_polar=1,
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num_azimuthal=1):
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@ -1081,7 +1085,7 @@ class MGXS:
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nuclides=query_nuclides, value=value)
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# Divide by atom number densities for microscopic cross sections
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if xs_type == 'micro':
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if xs_type == 'micro' and self._divide_by_density:
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if self.by_nuclide:
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densities = self.get_nuclide_densities(nuclides)
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else:
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@ -1938,7 +1942,7 @@ class MGXS:
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df = df[df['group out'].isin(groups)]
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# If user requested micro cross sections, divide out the atom densities
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if xs_type == 'micro':
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if xs_type == 'micro' and self._divide_by_density:
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if self.by_nuclide:
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densities = self.get_nuclide_densities(nuclides)
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else:
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@ -2101,10 +2105,9 @@ class MatrixMGXS(MGXS):
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return self._add_angle_filters(filters)
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def get_xs(self, in_groups='all', out_groups='all',
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subdomains='all', nuclides='all',
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xs_type='macro', order_groups='increasing',
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row_column='inout', value='mean', squeeze=True, **kwargs):
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def get_xs(self, in_groups='all', out_groups='all', subdomains='all',
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nuclides='all', xs_type='macro', order_groups='increasing',
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row_column='inout', value='mean', squeeze=True, **kwargs):
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"""Returns an array of multi-group cross sections.
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This method constructs a 4D NumPy array for the requested
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@ -2217,7 +2220,7 @@ class MatrixMGXS(MGXS):
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nuclides=query_nuclides, value=value)
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# Divide by atom number densities for microscopic cross sections
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if xs_type == 'micro':
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if xs_type == 'micro' and self._divide_by_density:
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if self.by_nuclide:
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densities = self.get_nuclide_densities(nuclides)
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else:
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@ -4413,7 +4416,7 @@ class ScatterMatrixXS(MatrixMGXS):
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nuclides=query_nuclides, value=value)
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# Divide by atom number densities for microscopic cross sections
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if xs_type == 'micro':
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if xs_type == 'micro' and self._divide_by_density:
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if self.by_nuclide:
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densities = self.get_nuclide_densities(nuclides)
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else:
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@ -4820,6 +4823,12 @@ class MultiplicityMatrixXS(MatrixMGXS):
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"""
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# Store whether or not the number density should be removed for microscopic
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# values of this data; since a multiplicity matrix should reflect the
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# multiplication relative to 1, this class will not divide by density
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# for microscopic data
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_divide_by_density = False
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def __init__(self, domain=None, domain_type=None, groups=None,
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by_nuclide=False, name='', num_polar=1, num_azimuthal=1):
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super().__init__(domain, domain_type, groups, by_nuclide, name,
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@ -4987,6 +4996,11 @@ class ScatterProbabilityMatrix(MatrixMGXS):
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"""
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# Store whether or not the number density should be removed for microscopic
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# values of this data; since this probability matrix is always normalized
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# to 1.0, this density division is not necessary
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_divide_by_density = False
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def __init__(self, domain=None, domain_type=None, groups=None,
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by_nuclide=False, name='', num_polar=1, num_azimuthal=1):
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super().__init__(domain, domain_type, groups, by_nuclide,
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@ -5314,6 +5328,11 @@ class Chi(MGXS):
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"""
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# Store whether or not the number density should be removed for microscopic
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# values of this data; since this chi data is normalized to 1.0, the
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# data should not be divided by the number density
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_divide_by_density = False
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def __init__(self, domain=None, domain_type=None, groups=None,
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prompt=False, by_nuclide=False, name='', num_polar=1,
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num_azimuthal=1):
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@ -5695,62 +5714,6 @@ class Chi(MGXS):
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return xs
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def get_pandas_dataframe(self, groups='all', nuclides='all',
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xs_type='macro', paths=False):
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"""Build a Pandas DataFrame for the MGXS data.
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This method leverages :meth:`openmc.Tally.get_pandas_dataframe`, but
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renames the columns with terminology appropriate for cross section data.
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Parameters
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----------
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groups : Iterable of Integral or 'all'
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Energy groups of interest. Defaults to 'all'.
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nuclides : Iterable of str or 'all' or 'sum'
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The nuclides of the cross-sections to include in the dataframe. This
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may be a list of nuclide name strings (e.g., ['U235', 'U238']).
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The special string 'all' will include the cross sections for all
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nuclides in the spatial domain. The special string 'sum' will
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include the cross sections summed over all nuclides. Defaults to
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'all'.
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xs_type: {'macro', 'micro'}
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Return macro or micro cross section in units of cm^-1 or barns.
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Defaults to 'macro'.
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paths : bool, optional
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Construct columns for distribcell tally filters (default is True).
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The geometric information in the Summary object is embedded into
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a Multi-index column with a geometric "path" to each distribcell
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instance.
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Returns
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-------
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pandas.DataFrame
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A Pandas DataFrame for the cross section data.
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Raises
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------
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ValueError
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When this method is called before the multi-group cross section is
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computed from tally data.
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"""
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# Build the dataframe using the parent class method
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df = super().get_pandas_dataframe(groups, nuclides, xs_type, paths=paths)
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# If user requested micro cross sections, multiply by the atom
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# densities to cancel out division made by the parent class method
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if xs_type == 'micro':
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if self.by_nuclide:
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densities = self.get_nuclide_densities(nuclides)
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else:
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densities = self.get_nuclide_densities('sum')
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tile_factor = int(df.shape[0] / len(densities))
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df['mean'] *= np.tile(densities, tile_factor)
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df['std. dev.'] *= np.tile(densities, tile_factor)
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return df
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def get_units(self, xs_type='macro'):
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"""Returns the units of Chi.
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@ -5892,6 +5855,12 @@ class InverseVelocity(MGXS):
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"""
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# Store whether or not the number density should be removed for microscopic
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# values of this data; since the inverse velocity does not contain number
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# density scaling, we should not remove the number density from microscopic
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# values
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_divide_by_density = False
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def __init__(self, domain=None, domain_type=None, groups=None,
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by_nuclide=False, name='', num_polar=1, num_azimuthal=1):
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super().__init__(domain, domain_type, groups, by_nuclide, name,
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