desing space calulation

functions to calulate the desing space for the models.

class friendly_doe.design_space.DesignSpaceSettings(responses: list[str], factors: list[str], grid_point_simulations: int, resolution: int, use_model_error: bool, use_factor_precision: bool, acceptance_level: float, factor_values: dict[str, list[float]] | None = None, factor_rank: list[str] | None = None)

Bases: DataClassJsonMixin

acceptance_level: float

dataclass_json_config: dict | None = {'letter_case': <function camelcase>, 'undefined': Undefined.RAISE}

factor_rank: list[str] | None = None

factor_values: dict[str, list[float]] | None = None

factors: list[str]

grid_point_simulations: int

resolution: int

responses: list[str]

use_factor_precision: bool

use_model_error: bool

friendly_doe.design_space.design_space_estimation(design: DoeSchema, design_space_settings: DesignSpaceSettings, models: list | None = None, available_factors: list[Factor] | None = None, responses_settings: list[OptimizationSettings] | None = None) → tuple[ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]], dict[str, list[float] | float]]

Get the design space estimation

Parameters:

design: experimental design dataclass

design_space_settings: settings for design space estimation

models: list of fitted model objects (optional)

available_factors: list of Factor objects (optional)

responses_settings: list of OptimizationSettings objects (optional)

Returns:

predicted factor settings: number_array failure rates: number_array factor values used: dict[str, list[float] | float]

exception friendly_doe.robust_setpoint.RobustSetpointException: Bases: Exception

friendly_doe.robust_setpoint.robust_setpoint(factor_settings: ndarray[tuple[int, ...], dtype[_ScalarType_co]], failure_rates: ndarray[tuple[int, ...], dtype[_ScalarType_co]], factor_names: list[str], acceptance_level: float) → tuple[DataFrame, list[str]]

calculate robust setpoint

Parameters:

factor_settings: array of factor settings

failure_rates: array of failure rates

factor_names: list of factor names

acceptance_level: acceptance level for failure rate

Returns:

robust setpoint pandas.DataFrame ranked factor names list[str]