artificial-life op• Data kinds: image → image
• Call: fullseye.apply(img, "alife_lenia", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
Lenia continuous cellular automaton (Bert Chan, Complex Systems 2019).
Lenia generalises Conway's Life to a *continuous* state space, a continuous
neighbourhood and a continuous time step. The field u starts as the image.
A normalised Gaussian ring kernel K of radius R (peaking at relative radius
0.5) gives the potential U = K (*) u as a circular convolution; the growth
mapping G(U) = 2 exp(-(U-mu)^2 / (2 sigma^2)) - 1 is in [-1, 1] and is
integrated explicitly as u <- clip(u + dt * G(U), 0, 1).
`a` sets the growth centre mu = 0.08 + 0.25a, its width
sigma = 0.03 + 0.05a and the time step dt = 0.10 + 0.15a; `b` sets the
number of steps 1 + int(19b). The output is deliberately not binarised
-- keeping intermediate values is exactly what separates Lenia from a
discrete Life rule.
• gallery2d_physics_alife_3d family guide
• 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.
• gallery2d_physics_alife_3d — py -3.11 examples/gallery2d_physics_alife_3d.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
artificial-life)alife_gray_scott · alife_turing · alife_life_step · alife_cyclic_ca · alife_perona_malik · alife_curvature_flow · alife_dla · alife_reaction_bz
*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.