synthesis op• Data kinds: none → table (an op determined by its arguments alone — it takes no image or data input)
• Call: import fullseye as fs; fs.ledger.synthesize_speed_ramp(rate=5000.0, duration=4.0, rpm_start=600.0, rpm_end=1800.0, orders=(1.0, 3.5), amplitudes=None, resonance_hz=None, noise_sigma=0.0, seed=None) (to call the implementation directly, import acoustics; acoustics.synthesize_speed_ramp(rate=5000.0, duration=4.0, rpm_start=600.0, rpm_end=1800.0, orders=(1.0, 3.5), amplitudes=None, resonance_hz=None, noise_sigma=0.0, seed=None); from the registry, opsacoustics.get("synthesize_speed_ramp"))
A run-up: components locked to shaft *order*, optionally one fixed in Hz.
Order tracking has no meaning at constant speed, so its ground truth needs a signal whose shaft rate moves. Here the shaft rate ramps linearly from rpm_start to rpm_end and each component's instantaneous phase is 2 pi * order * revolutions(t) — so it is locked to the shaft *exactly*, by construction, and its order is known to machine precision.
resonance_hz adds one component at a fixed frequency instead. That is the discriminating case: after angular resampling an order stays put and a resonance smears, which is the whole diagnostic value of the transform.
Returns a dict, because a speed record without its speed profile is not
analysable: signal (the waveform), rpm (per-sample shaft rate), revolutions (cumulative, per-sample), rate, duration, orders, total_revolutions, resonance_hz, and max_component_hz.
Raises ValueError: non-real / string / bool scalars, non-positive rate / duration / rpm_start / rpm_end, an empty or non-finite orders, an amplitudes of the wrong length, a length over MAX_SAMPLES, and any component reaching Nyquist at the fastest point of the ramp — checked at max(rpm), not at the mean, because a ramp that is legal on average can alias at its top end and produce a perfectly plausible spectrum.
• acoustic_condition_monitoring family guide
• 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.
• acoustic_condition_monitoring — py -3.11 examples/acoustic_condition_monitoring.py
table as input)synthesis)*Provenance: acoustics.py — ACOUSTICS 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.