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Fixed bug in nuclide number densities for micro xs Pandas DataFrames

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This commit is contained in:
Will Boyd 2015-09-30 18:16:58 -04:00
parent b548e3dcc1
commit 006358dacc
1 changed files with 89 additions and 13 deletions
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102
openmc/mgxs/mgxs.py
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@ -297,9 +297,16 @@ class MultiGroupXS(object):
if nuclides == 'sum':
nuclides = self.get_all_nuclides()
densities = np.zeros(1, dtype=np.float)
for i, nuclide in enumerate(nuclides):
for nuclide in nuclides:
densities[0] += self.get_nuclide_density(nuclide)
# Sum the atomic number densities for all nuclides
elif nuclides == 'all':
nuclides = self.get_all_nuclides()
densities = np.zeros(self.num_nuclides, dtype=np.float)
for i, nuclide in enumerate(nuclides):
densities[i] += self.get_nuclide_density(nuclide)
# Store each nuclide's atomic number density in an array
else:
densities = np.zeros(len(nuclides), dtype=np.float)
@ -1048,7 +1055,24 @@ class MultiGroupXS(object):
cv.check_value('xs_type', xs_type, ['macro', 'micro'])
# Get a Pandas DataFrame from the derived xs tally
df = self.xs_tally.get_pandas_dataframe(summary=summary)
if self.by_nuclide and nuclides == 'sum':
# Use tally summation to sum across all nuclides
query_nuclides = self.get_all_nuclides()
xs_tally = self.xs_tally.summation(nuclides=query_nuclides)
df = xs_tally.get_pandas_dataframe(summary=summary)
# Remove nuclide column since it is homogeneous and redundant
df.drop('nuclide', axis=1, inplace=True)
# If the user requested a specific set of nuclides
elif self.by_nuclide and nuclides != 'all':
xs_tally = self.xs_tally.get_slice(nuclides=nuclides)
df = xs_tally.get_pandas_dataframe(summary=summary)
# If the user requested all nuclides, keep nuclide column in dataframe
else:
df = self.xs_tally.get_pandas_dataframe(summary=summary)
# Remove the score column since it is homogeneous and redundant
if summary and self.domain_type == 'distribcell':
@ -1081,22 +1105,15 @@ class MultiGroupXS(object):
if 'group out' in df:
df = df[df['group out'].isin(groups)]
# Sum up cross sections across nuclides if requested
if self.by_nuclide and nuclides == 'sum':
non_nuclide_cols = list(df.columns[df.columns != 'nuclide'])
df = df.groupby(non_nuclide_cols, as_index=False)['nuclide'].sum()
# If the user requested specific nuclides, remove others from dataframe
elif nuclides != 'all' and nuclides != 'sum':
df = df[df.nuclide.isin(nuclides)]
# If user requested micro cross sections, divide out the atom densities
if xs_type == 'micro':
if self.by_nuclide:
densities = self.get_nuclide_densities(nuclides)
else:
densities = self.get_nuclide_densities('sum')
df['mean'] /= densities
df['std. dev.'] /= densities
tile_factor = df.shape[0] / len(densities)
df['mean'] /= np.tile(densities, tile_factor)
df['std. dev.'] /= np.tile(densities, tile_factor)
# Sort the dataframe by domain type id (e.g., distribcell id) and
# energy groups such that data is from fast to thermal
@ -1849,4 +1866,63 @@ class Chi(MultiGroupXS):
new_shape = (num_subdomains * num_groups,) + new_shape[2:]
xs = np.reshape(xs, new_shape)
return xs
return xs
def get_pandas_dataframe(self, groups='all', nuclides='all',
xs_type='macro', summary=None):
"""Build a Pandas DataFrame for the MultiGroupXS data.
This routine leverages the Tally.get_pandas_dataframe(...) routine, but
renames the columns with terminology appropriate for cross section data.
Parameters
----------
groups : Iterable of Integral or 'all'
Energy groups of interest
nuclides : Iterable of str or 'all' or 'sum'
The nuclides of the cross-sections to include in the dataframe. This
may be a list of nuclide name strings (e.g., ['U-235', 'U-238']).
The special string 'all' (default) will include the cross sections
for all nuclides in the spatial domain. The special string 'sum'
will include the cross sections summed over all nuclides.
xs_type: {'macro' or 'micro'}
Return macro or micro cross section in units of cm^-1 or barns
summary : None or Summary
An optional Summary object to be used to construct columns for
distribcell tally filters (default is None). The geometric
information in the Summary object is embedded into a multi-index
column with a geometric "path" to each distribcell intance.
NOTE: This option requires the OpenCG Python package.
Returns
-------
pandas.DataFrame
A Pandas DataFrame for the cross section data.
Raises
------
ValueError
When this method is called before the multi-group cross section is
computed from tally data.
"""
# Build the dataframe using the parent class routine
df = super(Chi, self).get_pandas_dataframe(groups, nuclides,
xs_type, summary)
# If user requested micro cross sections, multiply by the atom
# densities to cancel out division made by the parent class routine
if xs_type == 'micro':
if self.by_nuclide:
densities = self.get_nuclide_densities(nuclides)
else:
densities = self.get_nuclide_densities('sum')
tile_factor = df.shape[0] / len(densities)
df['mean'] *= np.tile(densities, tile_factor)
df['std. dev.'] *= np.tile(densities, tile_factor)
return df