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https://github.com/gbpagano/cellular-automata

3D Celullar Automata using the Bevy engine
https://github.com/gbpagano/cellular-automata

3d bevy cellular-automata rust

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3D Celullar Automata using the Bevy engine

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README 

        
# 3D Cellular Automaton



This project implements a 3D Cellular Automaton (CA) using the Bevy engine. Cellular 

automata are systems where cells evolve over discrete time steps based on the states of 

neighboring cells. By extending CA into three dimensions, this project offers dynamic 

and visually engaging simulations.



https://github.com/user-attachments/assets/b93fdb0c-372c-4d9a-a716-b73793113dfe



This project was inspired by and based on the article "3D Cellular Automata" by Jason Rampe. 

You can read the original article [here](https://softologyblog.wordpress.com/2019/12/28/3d-cellular-automata-3/).



## Neighborhoods



In cellular automata, neighborhoods define which cells around a given cell influence 

its behavior—such as its birth, survival, or death. In a 3D CA, two common types of 

neighborhoods are used:



### Moore Neighborhood



The Moore neighborhood in 3D consists of the 26 cells surrounding the current cell, 

similar to the cells in a Rubik’s cube with the center being the current cell. 

Imagine a 3x3x3 grid of small cubes; the center cube is the current cell, while the 

other 26 cubes are its neighbors.



### Von Neumann Neighborhood



The Von Neumann neighborhood in 3D includes only the 6 cells that share a face with the 

current cell. These are the cells located along the positive and negative X, Y, and 

Z axes, forming a 3D cross or "plus sign."



## Rule System



Rules dictate how cells transition between states based on their neighborhood 

configuration. This project supports customizable rule configurations applied to the 

simulation. Here are two examples:



### Rule 445 (4/4/5/M)



- **Survival**: A cell in state 1 survives if it has 4 neighboring cells.

- **Birth**: A new cell is born in an empty location if it has 4 neighbors.

- **States**: The automaton has 5 states. Newly born cells start in state 4, fade to state 1, 

and eventually die in state 0.

- **Neighbours**: Uses the Moore neighborhood.



### Builder Rule (2,6,9/4,6,8-10/10/M)



- **Survival**: Alive cells with 2, 6, or 9 neighbors survive.

- **Birth**: New cells are born in empty locations if they have 4, 6, 8, 9, or 10 neighbors.

- **States**: Supports 10 states, with 8 intermediate states.

- **Neighbours**: Uses the Moore neighborhood.



## Multi-State Automata



In multi-state cellular automata, cells transition through several states before dying. 

For example, in a 5-state automaton, a new cell starts in state 4 and progresses through 

states 3, 2, and 1 before reaching state 0 (dead). This fading effect adds complexity and 

visual interest to the simulation.



## Color Methods



To enhance the visual representation of the 3D Cellular Automaton, three color methods 

have been implemented:



### Distance to Center



Cells are colored based on their distance from the center of the 3D space, creating a 

gradient effect that distinguishes cells by their location.



### State Lerp



This method uses Linear Interpolation to color cells based on their current state, 

providing smooth transitions between states and visualizing state changes.



### Neighbors Lerp



Cells are colored based on the number of neighboring cells in a specific state. 

This method produces a gradient effect that reflects the density or influence of 

neighboring cells.



## Running the Project



To run the project locally, follow these steps:



1. Clone the repository:

```sh

git clone https://github.com/gbPagano/cellular-automata.git

cd cellular-automata

```

2. Build and run the project:

```sh

cargo run --release

```



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Feel free to submit a PR if you have suggestions, bug fixes, or new features.