agentlib.models package

Submodules

agentlib.models.fmu_model module

This module contains the FMUModel class.

class agentlib.models.fmu_model.FmuModel(**kwargs)

Bases: Model

Class to wrap any FMU Model into the Model-Standard of the agentlib.

do_step(*, t_start, t_sample=None)

Performing one simulation step :param t_start: start time for integration :param t_sample: increment of solver integration

Returns:

property extract_fmu

Get whether the fmu shall be extracted to a new directory or if the temp folder is used.

initialize(**kwargs)

Initializes FMU model

Required kwargs:

t_start (float): Start time of simulation t_stop (float): Stop time of simulation

set_input_values(names: List[str], values: List[float | int | bool])

Sets input values in the model and in the FMU.

set_parameter_values(names: List[str], values: List[float | int | bool])

Sets parameter values in the model and in the FMU.

terminate()

Overwrite base method

property tolerance

Get the tolerance of FMU simulation

pydantic model agentlib.models.fmu_model.FmuModelConfig

Bases: ModelConfig

The Config of FMUModels overwrite the default ModelConfig to redefine the system and add relevant fields like path and tolerance of the simulation.

Config:

• validate_assignment: bool = True

• arbitrary_types_allowed: bool = True

• extra: str = forbid

Fields:

• extract_fmu (bool)

• log_fmu (bool)

• only_config_variables (bool)

• path (pathlib.Path)

• tolerance (float)

Validators:

• check_path » path

field extract_fmu: bool = False

field log_fmu: bool = True

field only_config_variables: bool = True

If True, only the variables passed to this model by a simulator will be read and written at each simulation step (specified by dt).

field path: Path [Required]

Constraints:

• path_type = file

Validated by:

• check_path

field tolerance: float = 0.001

validator check_path  »  path

Check if the path has the correct extension

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self – The BaseModel instance.

• context – The context.

agentlib.models.scipy_model module

This module contains the ScipyStateSpaceModel class.

class agentlib.models.scipy_model.ScipyStateSpaceModel(**kwargs)

Bases: Model

This class holds a scipy StateSpace model. It uses scipy.signal.lti as a system and the odeint as integrator.

do_step(*, t_start, t_sample=None)

Performing one simulation step :param t_start: start time for integration :param t_sample: increment of solver integration

Returns:

initialize(**kwargs)

Abstract method to define what to do in order to initialize the model in use.

pydantic model agentlib.models.scipy_model.ScipyStateSpaceModelConfig

Bases: ModelConfig

Customize config of Model.

Config:

• validate_assignment: bool = True

• arbitrary_types_allowed: bool = True

• extra: str = forbid

Fields:

• system (dict | list | tuple | scipy.signal._ltisys.StateSpace)

Validators:

• check_system » all fields

field system: dict | list | tuple | StateSpace [Required]

Validated by:

• check_system

validator check_system  »  all fields

Root validator to check if the given system is valid.

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self – The BaseModel instance.

• context – The context.

Module contents

Package with available models for the agentlib

agentlib.models.get_model_type(model_type) → type

Return and load the given module type

Parameters:

str (model_type) – The string identifier to load the module.

Return type:

module BaseModelType