alife_turing — 2D `artificial-life` op

• Data kinds: image → image

• Call: fullseye.apply(img, "alife_turing", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

Usage

Gierer-Meinhardt activator-inhibitor system (Turing morphogenesis).

A_t = Da*lap(A) + rho * A^2 / H - mu_a * A + rho0

H_t = Dh*lap(H) + rho * A^2 - mu_h * H

Short-range self-activation (A) plus long-range lateral inhibition (H) is the

classical Turing mechanism for spot/stripe morphogenesis. The image seeds the

activator; the inhibitor starts at its homogeneous level. `a` sets the

inhibitor range Dh = 0.15 + 1.05a (with Da fixed at 0.02, so a controls the

diffusion-ratio that decides the pattern wavelength); `b` sets the number of

steps T = 5 + int(25b). Returns the normalised activator field.

Detailed usage guide

• gallery2d_physics_alife_3d 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)

• gallery2d_physics_alife_3d — py -3.11 examples/gallery2d_physics_alife_3d.py

• sim2real_and_alife — py -3.11 examples/sim2real_and_alife.py

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

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (`artificial-life`)

alife_gray_scott · alife_life_step · alife_cyclic_ca · alife_perona_malik · alife_curvature_flow · alife_dla · alife_reaction_bz · alife_wolfram1d

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

alife_turing — 2D artificial-life op