signal op• 資料種類:signal × indices → signal
• 呼叫: import fullseye as fs; fs.ledger.peak_subbin(x, idx=None, mode='parabola')(要直接呼叫實作,import dsp; dsp.peak_subbin(x, idx=None, mode='parabola');從台帳取用則 ops1d.get("peak_subbin"))
> 該運算子的說明尚無譯文,以下照原文給出。
Peak position between samples — the vertex of a fit through 3 points.
find_peaks returns integer indices, so the position of a spectral line or a correlation peak is quantised to the bin grid no matter how finely the peak itself is resolved. This refines each index to a float by fitting the sample and its two neighbours.
*mode* picks what is fitted:
"parabola"
a x^2 + b x + c through the three raw values. The classic estimator;
exact for a genuinely parabolic top and cheap.
"gauss"
the same parabola through log of the three values, which is exact for
a Gaussian peak. Requires all three values positive — a magnitude
spectrum qualifies, a signed correlation does not. Non-positive
neighbourhoods raise ValueError rather than silently returning the
integer index, because "the refinement quietly did nothing" is the
failure this operator exists to remove.
Measured on a Gaussian of width 1.7 bins centred at 40.37: the integer argmax gives 40 (error 0.37 bins), parabola gives 40.3553 (error 0.0147) and gauss gives 40.3700 (error 0, to machine precision). The parabola is biased on a Gaussian, and the bias grows as the peak gets narrower — the same measurement at widths 6.0 / 3.0 / 1.7 / 1.0 bins errs by 0.0012 / 0.0047 / 0.0147 / 0.0434. That is the whole reason gauss
exists: for a spectral line the log-parabola is not an approximation, it is the exact model.
The refinement is still an assumption about shape, not a measurement of it. On a triangular peak, whose top is not smooth, parabola errs by 0.0857 bins where the integer index errs by 0.30 — better, but three times worse than on the Gaussian it was designed for.
The shift is not clamped. A vertex more than half a sample away from the index means the index was not a local maximum in the first place, and that is
information: clamping it to +/-0.5 would return a plausible number for a bin that has no peak in it. Run find_peaks first, or clamp deliberately at the call site when evaluating bins that are not maxima (a harmonic comb, for instance).
A peak sitting on the first or last sample has no neighbour on one side and keeps its integer position (there is nothing to interpolate against); the returned array says so by being exactly equal to the input index there.
x : array_like
The 1-D signal the peaks were found in.
idx : int, sequence of int, or None
Peak indices. None means "the argmax", so peak_subbin(mag) is the
one-liner for a single line.
mode : {"parabola", "gauss"}
float or ndarray
Refined position(s) in samples. Scalar in, scalar out.
find_peaks : which indices to refine.
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
• 演算法的正典(作者、年份)與用途見上面的族使用指南。
• poc_multibeam_bathymetry — py -3.11 examples/poc_multibeam_bathymetry.py
• poc_print_registration — py -3.11 examples/poc_print_registration.py
• poc_web_roll_periodicity — py -3.11 examples/poc_web_roll_periodicity.py
signal 作為輸入)create_funct_1d_array · create_funct_1d_pairs · smooth_funct_1d_gauss · smooth_funct_1d_mean · derivate_funct_1d · integrate_funct_1d · zero_crossings_funct_1d · local_min_max_funct_1d
signal)lowpass · highpass · bandpass · envelope · rms · local_std · quantize · companding_mu_law
*Provenance: dsp.py — ONED 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.