axis.set_xscale('log')
axis.set_ylabel(ylabel)
axis.grid()
#axis.legend(loc=2)
# In[4]:
# Telsa nuclide energy grid batch
tesla_NEG = ParsedBatch(
'tesla_NEG',
cg_entries=[{
'primary': '__cross_section_MOD_calculate_xs'
}, {
'primary': '__cross_section_MOD_calculate_nuclide_xs'
}, {
'primary': '__cross_section_MOD_calculate_nuclide_xs',
'child': '__search_MOD_binary_search_real'
}],
output_pattern=
r'Size of micro xs data \(MB\):\s+(?P<xs_size>[0-9\.E\-\+]+)|' +
r'Calculation Rate \(active\)\s+=\s+(?P<rate_active>[0-9\.E\-\+]+)\s+neutrons/second'
)
tesla_NEG.clean_func_names('gnu')
tesla_NEG.dframe.reset_index(inplace=True)
tesla_NEG.dframe[
'self_per_called'] = tesla_NEG.dframe.self / tesla_NEG.dframe.called
# In[5]:
# Telsa unionized energy grid batch
tesla_UEG = ParsedBatch(
axis.set_xlabel(xlabel)
axis.set_ylabel(ylabel)
axis.grid()
axes.legend(loc=2)
if __name__ == '__main__':
# Parse batch from directory
tesla_NEG = ParsedBatch(
'tesla_NEG',
cg_entries=[{
'primary': '__cross_section_MOD_calculate_xs'
}, {
'primary': '__cross_section_MOD_calculate_nuclide_xs'
}, {
'primary': '__cross_section_MOD_calculate_nuclide_xs',
'child': '__search_MOD_binary_search_real'
}],
output_pattern=r'Number of nuclides:\s+(?P<nuclides>[0-9\.E\-\+]+)|' +
r'Calculation Rate \(active\)\s+=\s+(?P<rate_active>[0-9\.E\-\+]+)\s+neutrons/second'
)
# Sanitize names
tesla_NEG.clean_func_names('gnu')
tesla_NEG.dframe.reset_index(inplace=True)
# Create self-per called column
tesla_NEG.dframe[
'self_per_called'] = tesla_NEG.dframe.self / tesla_NEG.dframe.called
