core.models package
Submodules
core.models.gyro_model module
Gyro sensor model documentation: https://cornell.box.com/s/6nu08iqfk5i389wlpp44r0yu5h50by8n
- class core.models.gyro_model.GyroModel(parameters: core.parameters.Parameters)
Bases:
core.models.model.SensorModelApplies the gyro bias and noise as specified in parameters.py
- __init__(parameters: core.parameters.Parameters) None
Model __init__ All models will be dependent on some parameters, so we load them in here. :param parameters: Instance of the parameters class gets :type parameters: Parameters :param passed in to be accessible by the model.:
- evaluate(state_time: core.state.statetime.StateTime) Dict[str, Any]
Abstracts the angular velocities according to the model
- Parameters
state (State) – The input state
- Returns
The augmented angular velocities
- Return type
Dict[str, Any]
core.models.model module
- class core.models.model.ActuatorModel(parameters: core.parameters.Parameters)
Bases:
core.models.model_base.Model- __init__(parameters: core.parameters.Parameters) None
Creates an actuator model with the given parameters
- Parameters
parameters (Parameters) – parameters for actuator model
- abstract evaluate(state_time: core.state.statetime.StateTime) Dict[str, Any]
- Abstract method for any model that evaluates the model based on the
current state.
- An instance of State is required to evaluate the model, (because each
model should be dependent on the state of the system.)
- Parameters
state (State) – a instance of a State class.
- Returns
Defined in concrete instantiation of subclasses.
- Return type
_type_
- class core.models.model.EnvironmentModel(parameters: core.parameters.Parameters)
Bases:
core.models.model_base.Model- __init__(parameters: core.parameters.Parameters) None
Model __init__ All models will be dependent on some parameters, so we load them in here. :param parameters: Instance of the parameters class gets :type parameters: Parameters :param passed in to be accessible by the model.:
- abstract d_state(state_time: core.state.statetime.StateTime) Dict[str, Union[int, float, bool]]
- Function which evaluates the differential equation:
dy / dt = f(t, y) for the current state. “y” is a state vector (not just one variable)
- Parameters
t (float) – current simulation time
state (State) – Current state object
- Returns
- the name of each state being updated, and the
value of its derivative. The keys of this dictionary must be in STATE_ARRAY_ORDER
- Return type
Dict[str, Any]
- evaluate(state_time: core.state.statetime.StateTime) Dict[str, Union[int, float, bool]]
- Abstract method for any model that evaluates the model based on the
current state.
- An instance of State is required to evaluate the model, (because each
model should be dependent on the state of the system.)
- Parameters
state (State) – a instance of a State class.
- Returns
Defined in concrete instantiation of subclasses.
- Return type
_type_
- class core.models.model.SensorModel(parameters: core.parameters.Parameters)
Bases:
core.models.model_base.Model- __init__(parameters: core.parameters.Parameters) None
Model __init__ All models will be dependent on some parameters, so we load them in here. :param parameters: Instance of the parameters class gets :type parameters: Parameters :param passed in to be accessible by the model.:
- abstract evaluate(state: core.state.statetime.StateTime) Dict[str, Any]
- Abstract method for any model that evaluates the model based on the
current state.
- An instance of State is required to evaluate the model, (because each
model should be dependent on the state of the system.)
- Parameters
state (State) – a instance of a State class.
- Returns
Defined in concrete instantiation of subclasses.
- Return type
_type_
core.models.model_list module
- class core.models.model_list.ModelContainer(config: core.config.Config)
Bases:
object- __init__(config: core.config.Config) None
- class core.models.model_list.PositionDynamics(parameters)
Bases:
core.models.model.EnvironmentModelThe position dynamics model implementation.
- __init__(parameters) None
Model __init__ All models will be dependent on some parameters, so we load them in here. :param parameters: Instance of the parameters class gets :type parameters: Parameters :param passed in to be accessible by the model.:
- d_state(state_time: core.state.statetime.StateTime) Dict[str, Union[int, float, bool]]
Takes the derivative of a vector [r v] to compute [v a], where r is a position vector, v is the velocity vector, and a is the acceleration vector :param t: the initial time :type t: float :param state: the initial state :type state: State
- Returns
The updated vector [v a]
- Return type
Dict[str, State_Type]
- evaluate(state_time: core.state.statetime.StateTime) Dict[str, Union[int, float, bool]]
- Abstract method for any model that evaluates the model based on the
current state.
- An instance of State is required to evaluate the model, (because each
model should be dependent on the state of the system.)
- Parameters
state (State) – a instance of a State class.
- Returns
Defined in concrete instantiation of subclasses.
- Return type
_type_
- class core.models.model_list.TestModel(parameters: core.parameters.Parameters)
Bases:
core.models.model.EnvironmentModel- d_state(state_time: core.state.statetime.StateTime) Dict[str, Union[int, float, bool]]
- Function which evaluates the differential equation:
dy / dt = f(t, y) for the current state. “y” is a state vector (not just one variable)
- Parameters
t (float) – current simulation time
state (State) – Current state object
- Returns
- the name of each state being updated, and the
value of its derivative. The keys of this dictionary must be in STATE_ARRAY_ORDER
- Return type
Dict[str, Any]
- core.models.model_list.build_state_update_function(env_models: List[core.models.model.EnvironmentModel]) Callable[[float, numpy.ndarray], numpy.ndarray]