hxtorch.spiking.utils.from_nir_data.EventData

class hxtorch.spiking.utils.from_nir_data.EventData(idx: numpy.ndarray, time: numpy.ndarray, n_neurons: int, t_max: float)

Bases: object

Event-based data represented as a list of event indices and their corresponding timestamps. Each event is discrete and carries no magnitude; it is defined solely by its occurrence at a certain time.

idxnp.ndarray[int], shape (n_samples, n_events)

Event indices. If there is no event, the index is -1.

timenp.ndarray[float], shape (n_samples, n_events)

Event times. If there is no event, the time is np.inf.

n_neuronsint

Total number of neurons in the layer.

t_maxfloat

Maximum time of the recording.

__init__(idx: numpy.ndarray, time: numpy.ndarray, n_neurons: int, t_max: float) → None

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(idx, time, n_neurons, t_max)	Initialize self.
to_time_gridded(dt)	Arguments

Attributes

n_samples
shape

idx: numpy.ndarray

n_neurons: int

property n_samples

property shape

t_max: float

time: numpy.ndarray

to_time_gridded(dt: float) → nir.data_ir.graph.TimeGriddedData

dtfloat

Time step size.