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Ordered the pandas dataframe
In the get_pandas_dataframe function of SurfaceMGXS, I removed all by_nuclide and micro options. I also ordered the dataframe so that results would be ordered reliably. This was necessary for tests to pass. Since the domain object is a little different for current than for the other classes, I had to treat the reorder uniquely for the SurfaceMGXS class because of the presence of the mesh surface filter. While other classes assume all meshes have a x, y, and z component, regardless of the dimension, this is not true in SurfaceMGXS. Therefore, I added an if statement for each dimension.
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Miriam
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b903775c46
2 changed files with 22 additions and 55 deletions
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@ -6196,15 +6196,9 @@ class SurfaceMGXS(MGXS):
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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
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to '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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Unused in SurfaceMGXS
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xs_type: {'macro'}
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'micro' unused in SurfaceMGXS.
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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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@ -6223,31 +6217,9 @@ class SurfaceMGXS(MGXS):
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if not isinstance(groups, str):
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cv.check_iterable_type('groups', groups, Integral)
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if nuclides != 'all' and nuclides != 'sum':
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cv.check_iterable_type('nuclides', nuclides, str)
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cv.check_value('xs_type', xs_type, ['macro', 'micro'])
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cv.check_value('xs_type', xs_type, ['macro'])
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# Get a Pandas DataFrame from the derived xs tally
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if self.by_nuclide and nuclides == 'sum':
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# Use tally summation to sum across all nuclides
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xs_tally = self.xs_tally.summation(nuclides=self.get_nuclides())
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df = xs_tally.get_pandas_dataframe(paths=paths)
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# Remove nuclide column since it is homogeneous and redundant
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if self.domain_type == 'mesh':
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df.drop('sum(nuclide)', axis=1, level=0, inplace=True)
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else:
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df.drop('sum(nuclide)', axis=1, inplace=True)
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# If the user requested a specific set of nuclides
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elif self.by_nuclide and nuclides != 'all':
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xs_tally = self.xs_tally.get_slice(nuclides=nuclides)
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df = xs_tally.get_pandas_dataframe(paths=paths)
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# If the user requested all nuclides, keep nuclide column in dataframe
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else:
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df = self.xs_tally.get_pandas_dataframe(paths=paths)
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df = self.xs_tally.get_pandas_dataframe(paths=paths)
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# Remove the score column since it is homogeneous and redundant
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if self.domain_type == 'mesh':
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@ -6266,20 +6238,15 @@ class SurfaceMGXS(MGXS):
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if 'group out' in df:
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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' 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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densities = self.get_nuclide_densities('sum')
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densities = np.repeat(densities, len(self.rxn_rate_tally.scores))
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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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# Replace NaNs by zeros (happens if nuclide density is zero)
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df['mean'].replace(np.nan, 0.0, inplace=True)
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df['std. dev.'].replace(np.nan, 0.0, inplace=True)
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mesh_str = 'mesh {0}'.format(self.domain.id)
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if len(self.domain.dimension) == 1:
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df.sort_values(by=[(mesh_str, 'x')] + columns, inplace=True)
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elif len(self.domain.dimension) == 2:
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df.sort_values(by=[(mesh_str, 'x'),
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(mesh_str, 'y')] + columns, inplace=True)
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elif len(self.domain.dimension) == 3:
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df.sort_values(by=[(mesh_str, 'x'),
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(mesh_str, 'y'), (mesh_str, 'z')] + columns, inplace=True)
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return df
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@ -188,14 +188,6 @@
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5 1 1 y-min in 1 total 0.0000 0.000000
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6 1 1 y-max out 1 total 0.2358 0.041204
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7 1 1 y-max in 1 total 0.1724 0.009114
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8 2 1 x-min out 1 total 0.1892 0.011302
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9 2 1 x-min in 1 total 0.2738 0.093735
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10 2 1 x-max out 1 total 0.0000 0.000000
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11 2 1 x-max in 1 total 0.0000 0.000000
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12 2 1 y-min out 1 total 0.0000 0.000000
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13 2 1 y-min in 1 total 0.0000 0.000000
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14 2 1 y-max out 1 total 0.2290 0.038756
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15 2 1 y-max in 1 total 0.1894 0.012331
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16 1 2 x-min out 1 total 0.0000 0.000000
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17 1 2 x-min in 1 total 0.0000 0.000000
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18 1 2 x-max out 1 total 0.1778 0.010514
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@ -204,6 +196,14 @@
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21 1 2 y-min in 1 total 0.2358 0.041204
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22 1 2 y-max out 1 total 0.0000 0.000000
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23 1 2 y-max in 1 total 0.0000 0.000000
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8 2 1 x-min out 1 total 0.1892 0.011302
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9 2 1 x-min in 1 total 0.2738 0.093735
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10 2 1 x-max out 1 total 0.0000 0.000000
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11 2 1 x-max in 1 total 0.0000 0.000000
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12 2 1 y-min out 1 total 0.0000 0.000000
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13 2 1 y-min in 1 total 0.0000 0.000000
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14 2 1 y-max out 1 total 0.2290 0.038756
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15 2 1 y-max in 1 total 0.1894 0.012331
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24 2 2 x-min out 1 total 0.1822 0.011922
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25 2 2 x-min in 1 total 0.1778 0.010514
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26 2 2 x-max out 1 total 0.0244 0.024400
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