Uncertainty Analysis

Contains the implementations for the uncertainty analysis methods

Implements the supported uncertainty analysis methods.

class calisim.uncertainty.implementation.UncertaintyAnalysisMethod(calibration_func: Callable, specification: UncertaintyAnalysisMethodModel, engine: str = 'chaospy', implementation: type[CalibrationWorkflowBase] | CalibrationWorkflowBase | None = None)[source]: The uncertainty analysis method class.

pydantic model calisim.uncertainty.implementation.UncertaintyAnalysisMethodModel[source]

The uncertainty analysis method data model.

Parameters:

BaseModel (CalibrationModel) – The calibration base model class.

Config:

arbitrary_types_allowed: bool = True
protected_namespaces: tuple = ()

Fields:

algorithm (str)
flatten_Y (bool)
order (int)
solver (str | list[str])

field algorithm: str = '': The algorithm for the uncertainty analysis

field flatten_Y: bool = False: Flatten the simulation outputs

field order: int = 2: The order for polynomial chaos expansion

field solver: str | list[str] = 'linear': The solver for performing the uncertainty analysis

calisim.uncertainty.implementation.get_implementations() → dict[str, str][source]

Get the calibration implementations for the uncertainty analysis.

Returns:

The dictionary of: calibration implementations for the uncertainty analysis.

Return type:

Dict[str, str]

Chaospy

Contains the implementations for uncertainty analysis methods using Chaospy

Implements the supported uncertainty analysis methods using the Chaospy library.

class calisim.uncertainty.chaospy_wrapper.ChaospyUncertaintyAnalysis(calibration_func: Callable, specification: CalibrationModel, task: str)[source]

The Chaospy uncertainty analysis method class.

analyze() → None[source]: Analyze the results of the simulation calibration procedure.

execute() → None[source]: Execute the simulation calibration procedure.

specify() → None[source]: Specify the parameters of the model calibration procedure.

OpenTurns

Contains the implementations for uncertainty analysis methods using OpenTurns

Implements the supported uncertainty analysis methods using the OpenTurns library.

class calisim.uncertainty.openturns_wrapper.OpenTurnsUncertaintyAnalysis(calibration_func: Callable, specification: CalibrationModel, task: str)[source]

The OpenTurns uncertainty analysis method class.

analyze() → None[source]: Analyze the results of the simulation calibration procedure.

execute() → None[source]: Execute the simulation calibration procedure.

Pygpc

Contains the implementations for uncertainty analysis methods using Pygpc

Implements the supported uncertainty analysis methods using the Pygpc library.

class calisim.uncertainty.pygpc_wrapper.PygpcModel(workflow: CalibrationWorkflowBase, parameter_names: list[str], data_types: list[str])[source]

simulate(process_id: int | None = None, matlab_engine: Callable | None = None) → ndarray[source]

Run the simulation.

Parameters:

process_id (int | None, optional) – The simulation process ID. Defaults to None.
matlab_engine (Callable | None, optional) – The MATLAB Python engine. Defaults to None.

Returns:

The simulation output data.

Return type:

np.ndarray

validate() → None[source]: Validate the simulation data.

class calisim.uncertainty.pygpc_wrapper.PygpcUncertaintyAnalysis(calibration_func: Callable, specification: CalibrationModel, task: str)[source]

The Pygpc uncertainty analysis method class.

analyze() → None[source]: Analyze the results of the simulation calibration procedure.

dist_name_processing(name: str) → str[source]

Apply data preprocessing to the distribution name.

Parameters:: name (str) – The unprocessed distribution name.
Returns:: The processed distribution name.
Return type:: str

execute() → None[source]: Execute the simulation calibration procedure.

specify() → None[source]: Specify the parameters of the model calibration procedure.