Hardaware Loop¶

Inheritance diagram of hermespy.hardware_loop.hardware_loop.HardwareLoop, hermespy.hardware_loop.hardware_loop.HardwareLoopSample, hermespy.hardware_loop.hardware_loop.IterationPriority

class HardwareLoop(scenario, manual_triggering=False, plot_information=True, iteration_priority=IterationPriority.DROPS, record_drops=True, **kwargs)[source]¶

Bases: Generic[PhysicalScenarioType, PDT], Pipeline[PhysicalScenarioType, PDT]

Hermespy hardware loop configuration.

Parameters:

scenario (TypeVar(PhysicalScenarioType, bound= PhysicalScenario)) – The physical scenario being controlled by the hardware loop.
manual_triggering (bool) – Require a keyboard user input to trigger each drop manually. Disabled by default.
plot_information (bool) – Plot information during loop runtime. Enabled by default.
iteration_priority (IterationPriority) – Which dimension to iterate over first. Defaults to IterationPriority.DROPS.
record_drops (bool) – Record drops during loop runtime. Enabled by default.

add_dimension(dimension)[source]¶

Add a new dimension to the simulation grid.

Parameters:: dimension (GridDimension) – Dimension to be added.
Raises:: ValueError – If the dimension already exists within the grid.
Return type:: None

add_evaluator(evaluator)[source]¶

Add new evaluator to the hardware loop.

Parameters:: evaluator (Evaluator) – The evaluator to be added.
Return type:: None

add_plot(plot)[source]¶

Add a new plot to be visualized by the hardware loop during runtime.

Parameters:: plot (HardwareLoopPlot) – The plot to be added.
Return type:: None

add_post_drop_hook(hook)[source]¶

Add a post-drop hook.

Parameters:: hook (Callable[[TypeVar(PhysicalScenarioType, bound= PhysicalScenario), Console], None]) – The hook to be added. The hook must accept the scenario and console as arguments.
Raises:: ValueError – If the hook is already registered.
Return type:: None

add_pre_drop_hook(hook)[source]¶

Add a pre-drop hook.

Parameters:: hook (Callable[[TypeVar(PhysicalScenarioType, bound= PhysicalScenario), Console], None]) – The hook to be added. The hook must accept the scenario and console as arguments.
Raises:: ValueError – If the hook is already registered.
Return type:: None

evaluator_index(evaluator)[source]¶

Index of the given evaluator.

Parameters:: evaluator (Evaluator) – The evaluator to be searched for.
Return type:: int

Returns: The index of the evaluator.

new_dimension(dimension, sample_points, *args, **kwargs)[source]¶

Add a dimension to the sweep grid.

Must be a property of the managed scenario.

Parameters:

dimension (str) – String representation of dimension location relative to the investigated object.
sample_points (List[Any]) – List points at which the dimension will be sampled into a grid. The type of points must be identical to the grid arguments / type.
*args (Tuple[Any]) – References to the object the imension belongs to. Resolved to the investigated object by default, but may be an attribute or sub-attribute of the investigated object.
**kwargs – Additional keyword arguments to be passed to the dimension. See GridDimension for more information.

Return type:

GridDimension

Returns: The newly created dimension object.

replay(file_location)[source]¶

Replay a stored pipeline run.

Parameters:: file_location (str) – File system location of the replay.
Return type:: None

run(overwrite=True, campaign=None, serialize_state=True)[source]¶

Run the hardware loop configuration.

Parameters:

overwrite – Allow the replacement of an already existing savefile.
campaing – Name of the measurement campaign.
serialize_state (bool) – Serialize the state of the scenario to the results file. Enabled by default.

Return type:

MonteCarloResult

Returns: The result of the hardware loop.

property evaluators: List[Evaluator]¶: List of registered evaluators.

property iteration_priority: IterationPriority¶: Iteration priority of the hardware loop.

manual_triggering: bool¶: Require a user input to trigger each drop manually

property num_evaluators: int¶

Number of registered evaluators.

Returns: The number of evaluators.

plot_information: bool¶: Plot information during loop runtime

property post_drop_hooks: list[Callable[[PhysicalScenarioType, Console], None]]¶

List of post-drop hooks.

Post-drop hooks are called after each drop is generated and can be used to add additional actions not natively supported by the hardware loop.

property pre_drop_hooks: list[Callable[[PhysicalScenarioType, Console], None]]¶

List of pre-drop hooks.

Pre-drop hooks are called before each drop is generated and can be used to add additional actions not natively supported by the hardware loop.

record_drops: bool¶: Record drops during loop runtime

class HardwareLoopSample(drop, evaluations, artifacts)[source]¶

Bases: object

Sample of the hardware loop.

Generated during HardwareLoop.run().

property artifacts: Sequence[Artifact]¶: Artifacts of the hardware loop sample.

property drop: Drop¶: Drop of the hardware loop sample.

property evaluations: Sequence[Evaluation]¶: Evaluations of the hardware loop sample.

class IterationPriority(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶

Bases: SerializableEnum

Iteration priority of the hardware loop.

Used by the HardwareLoop.run() method.

DROPS = 0¶: Iterate over drops first before iterating over the parameter grid.

GRID = 1¶: Iterate over the parameter grid first before iterating over drops.