experimental.new_evaluator.simulation_output.i_simulation_output_evaluator module

Define the base object for SimulationOutput evaluators.

class ISimulationOutputEvaluator(reference: SimulationOutput, plotter: PandasPlotter)

Bases: IEvaluator

Base class for SimulationOutput evaluations.

__init__(reference: SimulationOutput, plotter: PandasPlotter) → None

Instantiate with a reference simulation output.

_abc_impl = <_abc._abc_data object>

_constant_limits: bool

final _default_dummy(quantity: str) → ndarray[Any, dtype[float64]]

Give dummy ydata, with expected shape if possible.

Also, set _dump_no_numerical_data_to_plot to avoid future pandas plotter errors.

_dump_no_numerical_data_to_plot: bool = False

_elt: str | Element | None = None

final _evaluate_single(post_treated: ndarray[Any, dtype[float64]], lower_limit: ndarray[Any, dtype[float64]] | float = nan, upper_limit: ndarray[Any, dtype[float64]] | float = nan, nan_in_data_is_allowed: bool = False, **kwargs) → bool

Check that post_treated is within limits.

Parameters:

• post_treated (npt.NDArray[np.float64]) – Data, already post-treated. If there is np.nan in this array, we consider that the test if failed.

• lower_limit (npt.NDArray[np.float64] | float, optional) – Min/max value for data. Where it is np.nan, the test is passed.

• upper_limit (npt.NDArray[np.float64] | float, optional) – Min/max value for data. Where it is np.nan, the test is passed.

• nan_in_data_is_allowed (boolean, optional) – If the test is valid where post_treated is NaN. Use for example with synchronous phases, which is Nan when not in a cavity.

Returns:

test – If the data is always within the given limits.

Return type:

bool

_get_kwargs: dict[str, bool | str | None]

_get_n_interpolate(simulation_output: SimulationOutput, interp: bool = True, **kwargs) → ndarray[Any, dtype[float64]]

Give ydata from one simulation, with proper number of points.

_getter(simulation_output: SimulationOutput, quantity: str) → ndarray[Any, dtype[float64]]

Call the get method with proper kwarguments.

_pos: str | None = None

_to_deg: bool = True

_x_quantity: str = 'z_abs'

get(*simulation_outputs: SimulationOutput, **kwargs) → ndarray[Any, dtype[float64]]

Get the data from the simulation outputs.

plot(post_treated: ndarray[Any, dtype[float64]], elts: Sequence[ListOfElements] | None = None, png_folders: Sequence[Path] | None = None, lower_limits: Sequence[Iterable[float]] | Sequence[float] | None = None, upper_limits: Sequence[Iterable[float]] | Sequence[float] | None = None, keep_nan: bool = False, style: Sequence[str] | None = None, **kwargs: Any) → Any

Plot all the post treated data using plotter.

property png_filename: str

Give a filename for consistent saving of figures.