designs list

functions to get the available designs

Bases: object

condition_number: int | None = None

degrees_of_freedom: int | None = None

g_efficiency: float | None = None

i_optimality: float | None = None

log_determinant: float | None = None

norm_log_determinant: float | None = None

friendly_doe.design_functions.get_available_designs_info(factors: list[Factor], settings: DesignSettings, sort_by: str = 'priority', model_type: ModelType | None = None, unused_designs: list[str] | None = None) → list[dict[str, Any]]

get available designs based on the factors

Parameters

factors: list of factors

settings: design settings

sort_by: the criteria to sort the designs by

model_type: the model type to filter the designs

unused_designs: list of design short names to exclude from the available designs

Returns

list of designs info dictionaries

the dictionaries contain design information and statistics the keys are:

‘priority’, ‘type’, ‘name’, ‘description’, ‘model’, ‘runs’, ‘starDistance’, ‘availableBlocks’, ‘needCenterPoint’, ‘totalRuns’, ‘degreesOfFreedom’, ‘conditionNumber’, ‘iOptimality’, ‘gEfficiency’, ‘logDet’, ‘normLogDet’

class friendly_doe.design_settings_model.DesignSettings(*, blocks: int | None = 1, centerPoints: int = 3, runs: int | None = None, replicatedRuns: int = 0, replicatedDesigns: int = 0, starDistance: int | None = None)

blocks: int | None

centerPoints: int

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

replicatedDesigns: int

replicatedRuns: int

runs: int | None

starDistance: int | None