dspk.synapse.extraction

Snippets: the waveform around each spike, on its nearest channels.

snippets	The waveform around each spike, on its nearest channels.

Functions

snippets

snippets(
    data,
    spikes: dict,
    probe: Probe,
    sample_rate: float,
    *,
    k: int = DEFAULT_K_NEIGHBORS,
    pre_ms: float = DEFAULT_PRE_MS,
    post_ms: float = DEFAULT_POST_MS,
    sinc_shift: bool = False,
) -> dict

The waveform around each spike, on its nearest channels.

Parameters

• data : array_like
  Signal (channels, samples) in µV, converted to float32 on the host.
• spikes : dict of numpy.ndarray
samples and channels, as the detectors return.
• probe : Probe
  The probe, for each spike’s nearest channels.
• sample_rate : float
  Sample rate in Hz.
• k : int, default DEFAULT_K_NEIGHBORS (4)
  Channels per snippet.
• pre_ms : float, default DEFAULT_PRE_MS (1.0)
  The span [t - pre_ms, t + post_ms) around each spike.
• post_ms : float, default DEFAULT_POST_MS (2.0)
  The span [t - pre_ms, t + post_ms) around each spike.
• sinc_shift : bool, default False
  Realign each snippet to its sub-sample trough.

Returns

• dict of np.ndarray — waveforms (spikes, k, samples), channels (spikes, k), centers, offsets, and kept: the input index of each snippet (spikes too near an edge are skipped).

Examples

>>> import numpy as np
>>> import dspk
>>> x = np.random.default_rng(0).standard_normal((4, 30_000)).astype(np.float32) * 10
>>> x[1, 15_000:15_010] -= 120  # one spike
>>> spikes = dspk.synapse.detection.threshold(x, 30_000.0)
>>> probe = dspk.synapse.io.Probe.preset("tetrode")
>>> snips = dspk.synapse.extraction.snippets(x, spikes, probe, 30_000.0, k=4)
>>> snips["waveforms"].shape[1:]
(4, 90)