hxtorch.spiking.experiment.ExecutionInstance

class hxtorch.spiking.experiment.ExecutionInstance(calib_path: Optional[Union[pathlib.Path, str]] = None, input_loopback: bool = False)

__init__(calib_path: Optional[Union[pathlib.Path, str]] = None, input_loopback: bool = False) → None

Parameters: input_loopback – Record input spikes and use them for gradient calculation. Depending on link congestion, this may or may not be beneficial for the calculated gradient’s precision.

Methods

`__init__`([calib_path, input_loopback])	param input_loopback Record input spikes and use them for gradient
`cadc_recordings`()	Return the instance’s `CADCRecording` object, holding all neurons that are to be recorded in this instance.
`generate_playback_hooks`()	Handle config injected into grenade (not supported yet).
`load_calib`([calib_path])	Load a calibration from path `calib_path` and apply to the experiment’s chip object.
`prepare_static_config`()	Prepare the static configuration of the instance

Attributes

cadc_recordings() → _pygrenade_vx_network.CADCRecording

Return the instance’s CADCRecording object, holding all neurons that are to be recorded in this instance.

generate_playback_hooks() → _pygrenade_vx_signal_flow.ExecutionInstanceHooks

Handle config injected into grenade (not supported yet).

Returns: Returns the execution instance’s (empty) playback hooks injected into grenade.run.

load_calib(calib_path: Optional[Union[pathlib.Path, str]] = None)

Load a calibration from path calib_path and apply to the experiment’s chip object. If no path is specified a nightly calib is applied.

Parameters: calib_path – The path to the calibration. It None, the nightly calib is loaded.
Returns: Returns the chip object for the given calibration.

prepare_static_config(): Prepare the static configuration of the instance