dspk.ops.peaks

Peak candidates on the device.

candidates	Local extrema of every channel that reach the channel’s height.

Functions

candidates

candidates(
    data,
    heights,
    *,
    polarity: str = 'positive',
    runtime: str | None = None,
) -> dict[str, np.ndarray]

Local extrema of every channel that reach the channel’s height.

Strict on the left (x[t-1] < x[t] >= x[t+1]): a flat peak is reported at its first sample; the first and last samples are never peaks.

Parameters

• data : array_like
  Signal (channels, samples). float32, int8, int16, uint16 and int32 arrays go to the device as they are and are converted there; other dtypes and lists become float32 first.
• heights : float or array_like
  Height per channel (channels,), or one value for all.
• polarity : {"positive", "negative", "both"}, default 'positive'
"positive": maxima >= height. "negative": minima <= -height. "both": either.
• runtime : str, default None
  A runtime from runtime.available ("cuda", "hip", "wgpu", "cpu") for this call. By default: the one chosen with runtime.set, else DSPK_RUNTIME, else the first available (GPUs first).

Returns

• dict of np.ndarray — "channel", "sample" and "value" of every peak, ordered by channel then sample.

Raises

• ValueError: For an unknown polarity, heights of another length than the channels, or data not 2-D.
• RuntimeError: When the runtime has no device.

Examples

>>> import numpy as np
>>> import dspk
>>> x = np.random.default_rng(0).standard_normal((4, 30_000)).astype(np.float32)
>>> noise = dspk.ops.math.channel_noise_std(x)
>>> peaks = dspk.ops.peaks.candidates(x, 4 * noise, polarity="negative")