Python ParsedBatch Examples

Example #1

    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(

Example #2

File: materials_plotting_notebook.py Project: RonRahaman/openmc_parameter_scaling

    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

Example #3

File: egrid_plotting_notebook.py Project: RonRahaman/openmc_parameter_scaling

    axis.set_title(title)
    axis.set_xlabel(xlabel)
    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('tesla_UEG',
            cg_entries=[
                {'primary': '__cross_section_MOD_calculate_xs'},

Example #4

File: plot_materials_scaling.py Project: RonRahaman/openmc_parameter_scaling

        axis.errorbar(mean_fr.loc[f].index, mean_fr.loc[f][column], 
                      yerr=std_fr.loc[f][column], label = f)
    axis.set_title(title)
    axis.set_xlabel(xlabel)
    axis.set_ylabel(ylabel)
    axis.grid()
    axis.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')
    tesla_NEG.clean_func_names('gnu')
    tesla_NEG.dframe.reset_index(inplace=True)

    vesta_NEG = ParsedBatch('vesta_NEG',
                cg_entries=[
                    {'primary': '.__cross_section_NMOD_calculate_xs'},
                    {'primary': '.__cross_section_NMOD_calculate_nuclide_xs'},
                    {'primary': '.__cross_section_NMOD_calculate_nuclide_xs',
                        'child' : '.__search_NMOD_binary_search_real'}
                    ],
            output_pattern =

Example #5

File: materials_plotting_notebook.py Project: RonRahaman/openmc_parameter_scaling

        axis.errorbar(mean_fr.loc[f].index, mean_fr.loc[f][column], 
                      yerr=std_fr.loc[f].self,label = f)
    axis.set_title(title)
    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

    # Groupby to get mean and stds
    tesla_NEG_means = tesla_NEG.dframe.convert_objects(convert_numeric=True).groupby(['index', 'nuclides']).mean()
    tesla_NEG_stds = tesla_NEG.dframe.convert_objects(convert_numeric=True).groupby(['index', 'nuclides']).std()