experimental.new_evaluator.i_evaluator module

Define the base object for every evaluator.

class IEvaluator(plotter: IPlotter)

Bases: ABC

Base class for all evaluators.

__init__(plotter: IPlotter) → None: Instantiate the plotter object.

_abc_impl = <_abc._abc_data object>

_axes_index: int = 0

_fignum: int

property _markdown: str: Give a markdown representation of object, with units.

_plot_complementary(data: Iterable[float], axes: Any, *args: Any, **kwargs: Any) → Any: Plot other evaluator-specific data.

_plot_kwargs: dict[str, str | bool | float]

_plot_single(data: Any, elts: ListOfElements | None, png_path: Path | None = None, **kwargs) → Any: Plot evaluated data from a single object.

_x_quantity: str

_y_quantity: str

abstract evaluate(*args: Any, plot_kwargs: dict[str, Any] | None = None, **kwargs: Any) → tuple[list[bool], ndarray[Any, dtype[float64]]]: Test if the object(s) under evaluation pass(es) the test.

abstract get(*args: Any, **kwargs: Any) → Iterable[float]: Get the base data.

abstract plot(post_treated: Collection[Iterable[float]], elts: Sequence[ListOfElements] | None = None, png_folders: Sequence[Path] | None = None, **kwargs: Any) → Any: Plot evaluated data from all the given objects.

post_treat(ydata: Iterable[float]) → Iterable[float]: Perform operations on data. By default, return data as is.

to_pandas(*args: Any, **kwargs: Any) → DataFrame: Give the post-treated data as a pandas dataframe.