pl_image_simulate — GENERATIVE `pl` op

• Data kinds: image2d → image2d

• Call: import fullseye as fs; fs.ledger.pl_image_simulate(generation, sink=None, *, diffusion_length_px: 'float' = 8.0, optical_psf_px: 'float' = 0.0, noise_rel: 'float' = 0.0, seed: 'int' = 0) -> 'np.ndarray' (to call the implementation directly, import pl; pl.pl_image_simulate(generation, sink=None, *, diffusion_length_px: 'float' = 8.0, optical_psf_px: 'float' = 0.0, noise_rel: 'float' = 0.0, seed: 'int' = 0) -> 'np.ndarray'; from the registry, opsgenerative.get("pl_image_simulate"))

Usage

Build a PL image from an excitation map and a recombination map (exact steady-state diffusion solve + optical PSF + noise).

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

Parameters

generation : array_like

励起(生成)の場 g = G/G₀(H×W、≥ 0)。一様 1 が「欠陥なしの地」。影の溝は

ここを 0 にして作る(生成が欠けるだけで再結合は増えない)。

sink : array_like or None

追加の再結合率の場 k = στ(H×W、≥ 0、無次元)。None は 0。沈みの溝・転位・

粒界はここを正にして作る。再結合速度 S[長さ/時間]の線なら k·w = S·L²/D(画素

単位)。

diffusion_length_px : float

拡散長 L = √(Dτ)[px]。

optical_psf_px : float

光学系の PSF(ガウシアン σ[px])。0 で無効。拡散 PSF の後に掛かる。

noise_rel : float

加法ガウス雑音の σ(欠陥なしの地 1 に対する相対値)。0 で決定論。

seed : int

雑音の種。直接解に収まらない像(160,000 画素超)は共役勾配(相対残差 1e-10 固定)。

Returns

numpy.ndarray

(H, W) の float64。欠陥なし・一様励起なら厳密に 1。u = Δn/(G₀τ)。

Notes

境界は流束 0(Neumann)。だから一様な励起は端まで厳密に一様で、溝の採点に境界の暗化が混ざらない。方程式が線形なので generation の重ね合わせは像の重ね合わせ。

Examples

>>> import numpy as np

>>> g = np.ones((1, 201)); g[0, 100] = 0.0 # 幅 1 px の影の溝

>>> u = pl_image_simulate(g, diffusion_length_px=10.0)

>>> rho = 1 + 0.5 / 100 - ((1 + 0.5 / 100) ** 2 - 1) ** 0.5

>>> abs(float(1 - u[0, 100]) - (1 - 2 * rho / (1 + rho))) < 1e-9 # 格子の閉形式

True

>>> round(float(pl_image_simulate(np.ones((8, 8)))[3, 3]), 12) # 一様 → 1(解法の丸めまで)

1.0

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

Same category (`pl`)

pl_diffusion_psf · 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_image_simulate — GENERATIVE pl op