whiten_by_psd — ONED `matched` op

• Data kinds: signal × pairs → signal

• Call: import fullseye as fs; fs.ledger.whiten_by_psd(x, psd, rate: 'float' = 4096.0) -> 'np.ndarray' (to call the implementation directly, import gwsearch; gwsearch.whiten_by_psd(x, psd, rate: 'float' = 4096.0) -> 'np.ndarray'; from the registry, ops1d.get("whiten_by_psd"))

Usage

Whiten by the noise PSD (x_w = irfft(rfft(x)·√(2Δt/S(f)))).

> The detailed description below is the original text — the summary and the headings are translated.

Parameters

x : array_like

時系列(1-D)。

psd : array_like

片側 PSD の pairs(psd_welch の返り)。rfft の格子へ log 補間する。

rate : float

標本化周波数 [Hz]。

Returns

numpy.ndarray

白色化した時系列(x と同じ長さ)。雑音がこの PSD に従うガウス過程なら分散 1。

Notes

端の数区間は FFT の循環のため乱れる(重力波の実データでは Tukey 窓で落とすのが慣例)。

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• poc_gw_detector_pseudo_simulation — py -3.11 examples/poc_gw_detector_pseudo_simulation.py

Ops the type connects to (they accept `signal` as input)

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

Same category (`matched`)

psd_welch · chirp_newtonian · chirp_mass_from_track · matched_filter_snr · optimal_snr · coincidence_delay

*Provenance: gwsearch.py — ONED operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

whiten_by_psd — ONED matched op