signal op• Data kinds: signal → table
• Call: import fullseye as fs; fs.ledger.signal_features(x, rate=1.0) (to call the implementation directly, import dsp; dsp.signal_features(x, rate=1.0); from the registry, ops1d.get("signal_features"))
A compact acoustic/vibration feature vector for anomaly detection: rms, peak, crest_factor, zcr, spectral_centroid (Hz), peak_freq (Hz), bandwidth (Hz). All finite — a NaN / Inf sample raises ValueError rather than producing NaN features; empty signal -> zeros.
The three spectral entries are built on spectrum, but every one of them is a *ratio* of magnitudes (argmax, a magnitude-weighted mean, a magnitude-weighted spread), so the raw |rfft| convention documented there cancels out and these numbers are unaffected by it. rms, peak and crest_factor are computed in the time domain and never touch the FFT.
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• (none yet)
table as input)—
signal)lowpass · highpass · bandpass · envelope · rms · local_std · quantize · companding_mu_law
*Provenance: dsp.py — ONED operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.