hxtorch

Modules

hxtorch.examples

hxtorch.perceptron

hxtorch.spiking

Classes

CalibrationPath

Path to a calibration.

HWDBPath

Path to a hardware database.

Functions

hxtorch.dense_spikes_to_list(spikes: Tuple[numpy.ndarray[numpy.int32], numpy.ndarray[numpy.float32]], input_size: int) → List[List[List[float]]]
hxtorch.extract_n_spikes(data: _pygrenade_vx_signal_flow.OutputData, network_graph: _pygrenade_vx_network.NetworkGraph, runtime: int, n_spikes: Dict[_pygrenade_vx_network.PopulationOnNetwork, int]) → Dict[_pygrenade_vx_network.PopulationOnNetwork, Tuple[numpy.ndarray[numpy.int32], numpy.ndarray[numpy.float32]]]
hxtorch.get_unique_identifier(hwdb_path: Optional[hxtorch::core::HWDBPath] = None) → str

Return the unique identifier of the chip with the initialized connection.

@param hwdb_path Optional path to the hwdb to use @return The identifier as string

hxtorch.init_hardware(*args, **kwargs)

Overloaded function.

  1. init_hardware(hwdb_path: Optional[hxtorch::core::HWDBPath] = None, ann: bool = False) -> None

Initialize the hardware automatically from the environment.

@param hwdb_path Optional path to the hwdb to use @param spiking Boolean flag indicating whether spiking or non-spiking calibration is loaded

  1. init_hardware(calibration_path: hxtorch::core::CalibrationPath) -> None

Initialize the hardware with calibration path.

@param calibration_path Calibration path to load from

hxtorch.init_hardware_minimal() → None

Initialize automatically from the environment without ExperimentInit and without any calibration.

hxtorch.release_hardware() → None

Release hardware resource.

hxtorch.weight_to_connection(*args, **kwargs)

Overloaded function.

  1. weight_to_connection(weight: numpy.ndarray[numpy.int32]) -> List[_pygrenade_vx_network.Projection.Connection]

  2. weight_to_connection(weight: numpy.ndarray[numpy.int32], connections: List[List[int]]) -> List[_pygrenade_vx_network.Projection.Connection]