Yet another implementation of Conway's game of life using p5.js

Bharat Kalluri / 2020-12-27

I've heard about this game on The Joe Rogan podcast with Lex Fridman (#1537). Lex talks about cellular automata, Hyper-graphs and a bunch of other things. In the same podcast, he mentions the infamous game of life. This is what piqued my interest in this topic. So I thought I should somehow implement this for fun. I thought I will start implementing it using typescript and react initially, but then remembered about p5.js and The coding train channel. So, I've pivoted and started learning the basics of p5.js and implementing the game using JS.

The beauty of simple rules and how it can create complexity

In 1970, A column writer presented the conway's game of life as a column in the scientific american. At that point, conway did not have a computer to fully model the output and planned the rules of the game using a go board. The idea was to have three very simple "genetic laws" for life. And using these rules, observe how complexity emerges based on the start configuration.

The game of life does not have players. It is just a set of rules operating on a initial configuration. Based on the start point, life evolves in different ways. The rules are very simple

The implementation of the Game of life

The rules are very simple to model. So we just need to draw up a bunch of squares and render them on the screen. I choose to pick my initial configuration randomly. Then based on the rules, create a new generation based on the current configuration and then repaint the new generation. And then basically repeat the above steps.

Some basics on p5.js.. There are two important methods we should know about. setup and draw. Setup sets up the stage, the initial frame and draw is called every time there needs to be a repaint. p5.js also gives us a bunch of methods to draw shapes, paint them etc..

Let us start by writing up a couple of helper methods. One to generate a 2D array, one to calculate neighbors and see how many of them are alive and one more to calculate the next generation from the current 2D matrix. A couple of assumptions I will be making. I will assume that the box wraps around itself (like a sphere). What this means is that, for the cell which is at the last column, the neighboring cell will be the cell on the first column in the same row. One more assumptions is that a white cell is 1, meaning its alive and a black cell is 0, meaning its dead. You can flip this and still get the same result.

// global variables
let ogMatrix;
let nextMatrix;
const squareSize = 20;
const matSize = 40;
const canvasSize = matSize * squareSize;
function generate2DArray(rows, columns) {
const matrix = [];
for (let i = 0; i < rows; i++) {
const colContent = Array.from(
{
length: columns,
},
() => {
return floor(random(2));
},
);
matrix[i] = colContent;
}
return matrix;
}

Initialized a couple of global variables. ogMatrix stands for original matrix. generate2DArray is a simple method to generate a 2D matrix with 0s and 1s randomly placed.

function calcSurr(matrix, r, c) {
let sumVal = 0;
for (let i = -1; i <= 1; i++) {
for (let j = -1; j <= 1; j++) {
const rowNum = (r + i + matSize) % matSize;
const colNum = (c + j + matSize) % matSize;
sumVal = sumVal + matrix[rowNum][colNum];
}
}
return sumVal - matrix[r][c];
}

calcSurr goes to the 8 surrounding places relative to the cells and adds up all the 1s it sees. Since we are writing a for loop for this, at the end I am subtracting the cell itself from the result. So that we only look at the 8 surrounding neighbors.

function calcNextGen(original) {
nextGen = generate2DArray(matSize, matSize);
for (let r = 0; r < matSize; r++) {
for (let c = 0; c < matSize; c++) {
let negVal = calcSurr(original, r, c);
let origVal = original[r][c];
// Rules
if (origVal === 0 && negVal === 3) {
nextGen[r][c] = 1; // spawn life
} else if (origVal === 1 && (negVal < 2 || negVal > 3)) {
nextGen[r][c] = 0; // death due to over or under population
} else {
nextGen[r][c] = origVal;
}
}
}
return nextGen;
}

calcNextGen calculates the next generation from the current generation, using the above helper methods and rules of life.

Piecing all of this together

function setup() {
createCanvas(canvasSize, canvasSize);
// Generate original matrix here
ogMatrix = generate2DArray(matSize, matSize);
}
function draw() {
background(0);
// draw ogMatrix on the screen to render
for (let r = 0; r < matSize; r++) {
for (let c = 0; c < matSize; c++) {
rect(r * squareSize, c * squareSize, squareSize, squareSize);
if (ogMatrix[r][c] === 1) {
fill(255);
} else {
fill(0);
}
}
}
// calculate nextGen based on ogMatrix
nextGenMatrix = calcNextGen(ogMatrix);
// replace ogMatrix with nextGenMatrix
ogMatrix = nextGenMatrix;
}

The setup function sets up the canvas, we also generate the initial matrix. In the draw function, the background is set to black, ogMatrix is rendered on the screen as a bunch of colored squares, the next generation is calculated and then repainted.

draw gets called as per the framerate of the screen. You can also set the framerate to one frame per sec using frameRate(1) and watch the generation change at 1 frame paint per second.

Here is the entire code and here is a sample output

Glider guns, stable states and logic gates

It is absolutely mind blowing to understand how much complexity can emerge out of these simple rules. Spoilers, you can make some configuration which could expand exponentially, can make logic gates out of these simple rules and expand and make a full blown computer using those gates just by following these rules. Check out this video to understand how all this is possible.

This is just an introduction to the field of cellular automata, and the bigger idea of emergence. These are very interesting topics and I will probably write up once I am more clear on them. These topics are huge and complicated. I probably won't even understand most of it, but its worth a shot.

I highly recommend the documentary I linked to earlier in the article. There are patterns which have no end and in turn fire particles, these are called glider guns. There are properties to these gliders which can be used to cancel out 2 gliders. There are entities called glider eaters which consume gliders, and there are entities called reflectors which can divert gliders in an angle. Using these three ideas, logic gates (AND, NOT, OR) can be built. Using the logic gates, storage latches can be built. And using all these things, a computer can be built!

Here is a computer built using game of life (GOL) computing the fibonacci sequence! (Here is some documentation for understanding how all this fits together)

Another video showcasing some GOL stimulations

We have just scratched the surface here, I will try to learn more and write more about cellular automata. Wish me luck!

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