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) → list[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(path: Optional[_hxtorch_core.HWDBPath, None] = None, ann: bool = False)

Initialize the hardware automatically from the environment.

Parameters

  • path – Optional path to the hwdb to use.

  • ann – Boolean flag indicating whether non-spiking or spiking calibration is loaded.

hxtorch.init_hardware_minimal()

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

hxtorch.release_hardware()

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]