fitting

Functions and classes for fitting contours using PISA.

graphnet.pisa.fitting.config_updater(config_path, new_config_path, dummy_section)[source]

Update config files and saves them to file.

Parameters:

  • config_path (str) – Path to original config file.

  • new_config_path (Optional[str], default: None) – Path to save updated config file.

  • dummy_section (str, default: 'temp') – Dummy section name to use for config files without section headers.

Yields:

ConfigUpdater instance for programatically updating config file.

Return type:

ConfigUpdater

class graphnet.pisa.fitting.WeightFitter(database_path, truth_table, index_column, statistical_fit)[source]

Bases: object

Class for fitting weights using PISA.

Construct WeightFitter.

Parameters:

  • database_path (str) –

  • truth_table (str) –

  • index_column (str) –

  • statistical_fit (bool) –

fit_weights(config_outdir, weight_name, pisa_config_dict, add_to_database)[source]

Fit flux weights to each neutrino event in self._database_path.

If statistical_fit=True, only statistical effects are accounted for. If True, certain systematic effects are included, but not hypersurfaces.

Parameters:

  • config_outdir (str) – The output directory in which to store the configuration.

  • weight_name (str, default: '') – The name of the weight. If add_to_database=True, this will be the name of the table.

  • pisa_config_dict (Optional[Dict], default: None) – The dictionary of PISA configurations. Can be used to change assumptions regarding the fit.

  • add_to_database (bool, default: False) – If True, a table will be added to the database called weight_name with two columns: [index_column, weight_name]

Return type:

DataFrame

Returns:

A dataframe with columns [index_column, weight_name].

class graphnet.pisa.fitting.ContourFitter(outdir, pipeline_path, post_fix, model_name, include_retro, statistical_fit)[source]

Bases: object

Class for fitting contours using PISA.

Construct ContourFitter.

Parameters:

  • outdir (str) –

  • pipeline_path (str) –

  • post_fix (str) –

  • model_name (str) –

  • include_retro (bool) –

  • statistical_fit (bool) –

fit_1d_contour(run_name, config_dict, grid_size, n_workers, theta23_minmax=(36.0, 54.0), dm31_minmax=(2.3, 2.7))[source]

Fit 1D contours.

Return type:

None

Parameters:

  • run_name (str) –

  • config_dict (Dict) –

  • grid_size (int) –

  • n_workers (int) –

  • theta23_minmax (Tuple[float, float]) –

  • dm31_minmax (Tuple[float, float]) –

fit_2d_contour(run_name, config_dict, grid_size, n_workers, theta23_minmax=(36.0, 54.0), dm31_minmax=(2.3, 2.7))[source]

Fit 2D contours.

Return type:

None

Parameters:

  • run_name (str) –

  • config_dict (Dict) –

  • grid_size (int) –

  • n_workers (int) –

  • theta23_minmax (Tuple[float, float]) –

  • dm31_minmax (Tuple[float, float]) –