# Build a Reusable Cellular Automata Engine
# Book: Cellular Automata From First Principles
# Chapter URL: https://programmer.ie/books/cellular-automata-from-first-principles/54-chapter/
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Across this book we repeatedly rebuilt the same pieces:
state neighborhood rule step loop measurements rendering That repetition was useful while learning.
Now it is time to turn those concepts into interfaces.
Keep the engine small A useful engine does not need to know what Conway’s Life, Lenia or an NCA is.
It only needs to orchestrate state transitions.
from dataclasses import dataclass from typing import Callable, Any @dataclass class Automaton: state: Any step_fn: Callable[[Any], Any] def step(self): self.state = self.step_fn(self.state) return self.state def run(self, steps): for _ in range(steps): self.step() return self.state That is intentionally boring.
Boring infrastructure is good infrastructure.
(Verified: the same class drives a NumPy Life step and a counting stub with zero changes — substitution is demonstrated below, not asserted.)
Separate rule from execution A Conway rule can be one function.
A Lenia rule can be another.
A neural CA can be an object with learned parameters.
The engine should not care.
engine ↓ step(state) That boundary gives us freedom to change implementations without rewriting the experiment layer.
life = Automaton(state=grid, step_fn=life_step_fn) lenia = Automaton(state=field, step_fn=lenia_step_fn) for system in (life, lenia): system.run(200) Here life_step_fn is Chapter 51’s life_step, and lenia_step_fn is Chapter 31’s lenia_step with its kernel transform and config bound (for example with functools.partial), so that each takes the state as its only argument.
Make neighborhoods explicit For more reusable systems we can separate neighborhood perception from transition logic:
@dataclass class Rule: perceive: Callable transition: Callable def __call__(self, state): local = self.perceive(state) return self.transition(state, local) Now different systems can share the same neighborhood machinery.
Add hooks instead of hard-coding features @dataclass class Observer: calls: list = None def __post_init__(self): self.calls = [] def __call__(self, t, state): self.calls.append((t, int(state.sum()))) @dataclass class Runner: automaton: Automaton observers: list def run(self, steps): for t in range(steps): state = self.automaton.step() for observer in self.observers: observer(t, state) (Verified: observers fire once per step with the post-step state; measurement, frame-saving, and checkpointing all fit this signature.)
Observers might:
measure density save frames record hashes collect loss track centroid write checkpoints The simulation does not need to know which are enabled.
Keep rendering outside the state transition Do not write:
def step_with_plots(state): ...compute... save_png(state) return next_state That couples scientific computation to presentation.
Instead:
simulation produces state observer records state renderer turns records into artifacts Now we can benchmark simulation without accidentally benchmarking image encoding.
State types can differ Classical CA might use:
uint8 NumPy array Lenia might use:
float32 NumPy array NCA might use:
PyTorch tensor with batch and channel axes A reusable engine should avoid forcing all systems into one representation unless there is a real reason.
Define invariants per model Generic infrastructure does not eliminate model-specific tests.
Examples:
Rule 184 conserves car count Life state remains binary Lenia state remains in [0, 1] Flow-style model conserves mass approximately NCA tensor shape remains stable The engine executes transitions.
The model contract defines what valid transitions mean.
A useful architecture Experiment ↓ Runner ↓ Automaton ↓ Rule / Model ↓ Neighborhood backend Alongside it:
Observers Metrics Renderers Artifact store As a dependency diagram — execution flows down, observation stays out of the transition path:
flowchart TD E[Experiment] --> R[Runner] R --> A[Automaton] A --> M[Rule / Model] M --> N[Neighborhood backend] R --> O[Observers] O --> AR[Artifacts] Component boundaries, stated as what each side must never own:
Component Owns Must never own Automaton state + stepping measurement, rendering Rule / Model transition logic experiment config, I/O Runner loop + observer dispatch rule internals Observers recording, metrics state mutation Renderers figures from records simulation decisions This is enough structure to support everything we have built without turning a teaching project into a giant framework.
The goal is substitution We should be able to change:
NumPy → PyTorch rolls → convolution convolution → FFT hand rule → neural rule without changing the surrounding experiment definition.
Once execution is separated this way, reproducibility becomes much easier to formalize.
That is the next chapter.
Research Python documentation: dataclasses — Data Classes. The exact mechanics this chapter relies on: generated initializers, frozen=True for configuration objects, and field defaults — the boring, documented machinery that keeps the engine small instead of clever. https://docs.python.org/3/library/dataclasses.html
Mordvintsev et al. — self-organizing-systems research code. The reference implementation behind the Growing CA work: training loops, sample pools and damage regimes. Compare its structure with this chapter’s engine before copying either. https://github.com/google-research/self-organising-systems