perpetual_reaction_diffusion — GENERATIVE `perpetual` op

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

• Call: import fullseye as fs; fs.ledger.perpetual_reaction_diffusion(size: 'int' = 220, steps: 'int' = 1600, feed: 'float' = 0.037, kill: 'float' = 0.06, seed: 'int' = 0) -> 'np.ndarray' (to call the implementation directly, import perpetual; perpetual.perpetual_reaction_diffusion(size: 'int' = 220, steps: 'int' = 1600, feed: 'float' = 0.037, kill: 'float' = 0.06, seed: 'int' = 0) -> 'np.ndarray'; from the registry, opsgenerative.get("perpetual_reaction_diffusion"))

Usage

> This operator's description has not been translated yet. The original text follows as it is.

グレイ–スコット反応拡散 ―― 模様が生まれ、分裂し、増え続ける。

不変量: **餌も死も 0(`feed = kill = 0`)なら、総量は厳密に保存される**

—— 拡散は再分配であって生成ではないから。数値解法が壊れていれば、

ここが真っ先に崩れる(:func:perpetual_identities)。

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_illusions_and_perpetual_drawing — py -3.11 examples/poc_illusions_and_perpetual_drawing.py

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

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Same category (`perpetual`)

perpetual_ten_print · perpetual_truchet · perpetual_elementary_ca · perpetual_langtons_ant · perpetual_chaos_game · perpetual_apollonian · perpetual_harmonograph · perpetual_ifs_attractor

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

perpetual_reaction_diffusion — GENERATIVE perpetual op