optimisation.design_space.design_space_parameter module

Create a base class for Variable and Constraint.

class DesignSpaceParameter(name: str, element_name: str, limits: tuple[float, float])

Bases: ABC

Hold a single variable or constraint.

Name of the parameter. Must be compatible with the SimulationOutput.get() method, and be in IMPLEMENTED_VARIABLES or IMPLEMENTED_CONSTRAINTS.

element_name

Name of the element concerned by the parameter.

limits

Lower and upper bound for the variable. np.nan deactivates a bound.

__init__(name: str, element_name: str, limits: tuple[float, float]) → None

property _fmt_x_max: float: Lower limit in deg if it is has 'phi' in it’s name.

property _fmt_x_min: float: Lower limit in deg if it is has 'phi' in it’s name.

change_limits(x_min: float | None = None, x_max: float | None = None) → None: Change the limits after creation of the object.

classmethod from_floats(name: str, element_name: str, x_min: float, x_max: float, x_0: float = nan) → Self

Initialize object with x_min, x_max instead of limits.

Parameters:

name (str) – Name of the parameter. Must be compatible with the SimulationOutput.get() method, and be in IMPLEMENTED_VARIABLES or IMPLEMENTED_CONSTRAINTS.
element_name (str) – Name of the element concerned by the parameter.
x_min (float) – Lower limit. np.nan to deactivate lower bound.
x_max (float) – Upper limit. np.nan to deactivate lower bound.

Returns:

A DesignSpaceParameter with limits = (x_min, x_max).

Return type:

Self

classmethod from_pd_series(name: str, element_name: str, pd_series: Series) → Self: Init object from a pd series (file import).

classmethod header_of__str__() → str: Give information on what __str__() is about.

to_dict(*to_get: str, missing_value: float | None = None, prepend_parameter_name: bool = False) → dict[str, float | None | tuple[float, float] | str]: Convert important data to dict to convert it later in pandas df.