pl_diffusion_psf — GENERATIVE `pl` op

• Data kinds: none → image2d (an op determined by its arguments alone — it takes no image or data input)

• Call: import fullseye as fs; fs.ledger.pl_diffusion_psf(diffusion_length_px: 'float' = 8.0, size: 'int | None' = None) -> 'np.ndarray' (to call the implementation directly, import pl; pl.pl_diffusion_psf(diffusion_length_px: 'float' = 8.0, size: 'int | None' = None) -> 'np.ndarray'; from the registry, opsgenerative.get("pl_diffusion_psf"))

Usage

Return the carrier-diffusion PSF (the 2-D Green function K0(r/L)/2πL², summing to 1) as an image.

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

Parameters

diffusion_length_px : float

拡散長 L = √(Dτ)[px]。

size : int or None

核の一辺(奇数)。None なら 2·ceil(8L)+1(8L で切ると質量の欠けは

8·K₁(8) ≈ 1.2e-3)。

Returns

numpy.ndarray

(size, size) の float64。中心画素は特異点 K₀(0) = ∞ を画素と同面積の円で平均した値 1 − (a/L)K₁(a/L)(a = 1/√π)にして有限にしてある。

Notes

総和は解析的に 1(∫K₀(r/L)dA = 2πL²)。半分の質量が入る半径は 1.2572·L。これを generation に畳むと、欠陥の無い(k = 0)ときの pl_image_simulate と同じ像になる(境界の扱いを除く)。

Examples

>>> import numpy as np

>>> k = pl_diffusion_psf(6.0)

>>> k.shape

(97, 97)

>>> abs(float(k.sum()) - 1.0) < 5e-3 # 総量 = G·τ

True

>>> bool(k[48, 48] == k.max()) # 中心が最大

True

Detailed usage guide

• generative_art family guide

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_si_wafer_pl_defect_visibility — py -3.11 examples/poc_si_wafer_pl_defect_visibility.py

Ops the type connects to (they accept `image2d` as input)

sem_render · sem_image · sem_charging_series · pl_image_simulate

Same category (`pl`)

pl_image_simulate · pl_groove_visibility

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

pl_diffusion_psf — GENERATIVE pl op