SpikeParser

SpikeParser detects brief spike-like events in ionic current traces. It wraps scipy.signal.find_peaks and interprets parameters as fractions of the signal mean when fractional=True (the default).

from ionique.parsers import SpikeParser

parser = SpikeParser(
    prominence=0.15,
    distance=100,
    width=(5, 200),
    fractional=True,
)
trace.parse(parser, newrank="event", at_child_rank="vstep")

Parameters

Parameter	Default	Description
`height`	`None`	Minimum spike height. When `fractional=True`, interpreted as a fraction of the segment mean.
`threshold`	`None`	Minimum vertical distance to neighboring samples.
`distance`	`None`	Minimum horizontal distance (in samples) between peaks.
`prominence`	`None`	Minimum peak prominence — how much a peak stands out from its surroundings. This is usually the most important parameter.
`prominence_snr`	`None`	SNR-based prominence (alternative to `prominence`).
`width`	`None`	Required peak width. Pass a tuple `(min, max)` to filter by width.
`wlen`	`None`	Window length for prominence calculation.
`rel_height`	`0.5`	Relative height at which width is measured (0–1).
`plateu_size`	`None`	Minimum flat-top length for plateau detection.
`fractional`	`True`	When `True`, `height`, `threshold`, `prominence`, and `distance` are scaled by the segment mean.

Parameter sensitivity

The figures below show how each parameter affects detection on the same synthetic spike signal.

height

Controls the minimum absolute (or fractional) spike amplitude. Lower values detect smaller events; higher values miss shallow dips.

prominence

Prominence measures how much a peak stands out relative to its surrounding baseline. This is generally the single most useful parameter for nanopore spike detection.

distance

Minimum spacing between detected peaks (in samples). Increase this to prevent detecting multiple hits on a single wide event.

width

Filter by peak width. Pass a tuple (min_width, max_width) to reject events that are too narrow (noise) or too wide (baseline drift).

Full example

from ionique.parsers import SpikeParser

parser = SpikeParser(
    prominence=0.15,
    distance=100,
    width=(1, 200),
    fractional=True,
)

trace.parse(parser, newrank="event", at_child_rank="vstep")

events = trace.traverse_to_rank("event")
print(f"Detected {len(events)} spike events")

for ev in events[:5]:
    print(f"  samples {ev.start}–{ev.end}, mean={ev.mean:.3f} nA")

Tips

Start with prominence only, then add distance and width to refine.
Set fractional=False if you know the absolute current scale and want to specify parameters in nanoamperes directly.
For very noisy data, lowpass-filter first (see Signal Preprocessing), then use a moderate prominence.