experimental.new_evaluator.simulation_output.factory module

Wrap-up creation and execution of ISimulationOutputEvaluator.

Todo

Maybe should inherit from a more generic factory.

class SimulationOutputEvaluatorsFactory(evaluator_kwargs: ~collections.abc.Collection[dict[str, str | float | bool]], user_evaluators: dict[str, type] | None = None, plotter: ~experimental.plotter.i_plotter.IPlotter = <experimental.plotter.pd_plotter.PandasPlotter object>)

Bases: object

Define a class to create and execute multiple evaluators.

__init__(evaluator_kwargs: ~collections.abc.Collection[dict[str, str | float | bool]], user_evaluators: dict[str, type] | None = None, plotter: ~experimental.plotter.i_plotter.IPlotter = <experimental.plotter.pd_plotter.PandasPlotter object>) → None

Instantiate object with basic attributes.

Parameters:

  • evaluator_kwargs (Collection[EvaluatorKwargs]) – Dictionaries holding necessary information to instantiate the evaluators. The only mandatory key-value pair is “name” of type str.

  • user_evaluators (dict[str, type] | None, optional) – Additional user-defined evaluators; keys should be in PascalCase, values ISimulationOutputEvaluator constructors.

  • plotter (IPlotter, optional) – An object used to produce plots. The default is PandasPlotter.

_instantiate_evaluators(reference: SimulationOutput) → list[ISimulationOutputEvaluator]

Create all the evaluators.

Parameters:

reference (SimulationOutput) – The reference simulation output.

Returns:

All the created evaluators.

Return type:

list[ISimulationOutputEvaluator]

batch_evaluate(evaluators: Collection[ISimulationOutputEvaluator], accelerators: Sequence[Accelerator], beam_solver_id: str, plot_kwargs: dict[str, Any] | None = None, csv_kwargs: dict[str, Any] | None = None, **kwargs) → DataFrame

Evaluate several evaluators.

run(accelerators: Sequence[Accelerator], beam_solver_id: str) → list[ISimulationOutputEvaluator]

Instantiate all the evaluators.

_constructors_n_kwargs(evaluator_kwargs: Collection[dict[str, str | float | bool]], user_evaluators: dict[str, type] | None = None) → dict[type, dict[str, bool | float | str]]

Take and associate every evaluator class with its kwargs.

We also remove the “name” key from the kwargs.

Parameters:

  • evaluator_kwargs (Collection[dict[str, str | float | bool]]) – Dictionaries holding necessary information to instantiate the evaluators. The only mandatory key-value pair is “name” of type str.

  • user_evaluators (dict[str, type] | None, optional) – Additional user-defined evaluators; keys should be in PascalCase, values ISimulationOutputEvaluator constructors.

Returns:

Keys are class constructor, values associated keyword arguments.

Return type:

dict[type, dict[str | float | bool]]

_out_folders(simulation_outputs: Collection[SimulationOutput]) → list[Path]

Get the output folders.