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Test passes after modifying get_pandas_dataframe
Since the inherted get_pandas_dataframe sorted values by x, y, z, and the current has a different labelling system (doesn't include z if only 2 dimensional, and then also includes x-in, x-out, y-in, y-out, z-in z-out...) I had to redefine get_pandas_dataframe in the SurfaceMGXS class and remove the sorting.
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Miriam
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676bcefb1e
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2 changed files with 131 additions and 1 deletions
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@ -1964,7 +1964,6 @@ class MGXS:
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# energy groups such that data is from fast to thermal
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if self.domain_type == 'mesh':
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mesh_str = 'mesh {0}'.format(self.domain.id)
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print("HELLO WORLD", mesh_str)
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df.sort_values(by=[(mesh_str, 'x'), (mesh_str, 'y'),
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(mesh_str, 'z')] + columns, inplace=True)
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else:
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@ -6187,6 +6186,103 @@ class SurfaceMGXS(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=True):
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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
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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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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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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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# 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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# Remove the score column since it is homogeneous and redundant
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if self.domain_type == 'mesh':
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df = df.drop('score', axis=1, level=0)
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else:
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df = df.drop('score', axis=1)
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# Convert azimuthal, polar, energy in and energy out bin values in to
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# bin indices
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columns = self._df_convert_columns_to_bins(df)
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# Select out those groups the user requested
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if not isinstance(groups, str):
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if 'group in' in df:
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df = df[df['group in'].isin(groups)]
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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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return df
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class Current(SurfaceMGXS):
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r"""A current multi-group cross section.
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@ -178,6 +178,40 @@
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2 1 2 1 1 1 total 0.032188 0.002420
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1 2 1 1 1 1 total 0.032304 0.002073
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3 2 2 1 1 1 total 0.031336 0.001614
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mesh 1 group in nuclide mean std. dev.
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x y surf
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0 1 1 x-min out 1 total 0.0000 0.000000
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1 1 1 x-min in 1 total 0.0000 0.000000
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2 1 1 x-max out 1 total 0.2738 0.093735
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3 1 1 x-max in 1 total 0.1892 0.011302
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4 1 1 y-min out 1 total 0.0000 0.000000
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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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19 1 2 x-max in 1 total 0.1822 0.011922
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20 1 2 y-min out 1 total 0.1724 0.009114
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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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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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27 2 2 x-max in 1 total 0.0000 0.000000
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28 2 2 y-min out 1 total 0.1894 0.012331
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29 2 2 y-min in 1 total 0.2290 0.038756
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30 2 2 y-max out 1 total 0.0236 0.023600
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31 2 2 y-max in 1 total 0.0000 0.000000
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mesh 1 delayedgroup group in nuclide mean std. dev.
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x y z
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0 1 1 1 1 1 total 0.000007 4.734745e-07
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