Functions for simulating Conway's Game of Life

GOL.R

## Functions for simulating Conway's Game of Life
## Modified from http://www.petrkeil.com/?p=236

neighbour_count <- function(X)
{
    side <- nrow(X)
    # make the shifted copies of the original array
    allW = cbind( rep(0,side) , X[,-side] )
    allNW = rbind(rep(0,side),cbind(rep(0,side-1),X[-side,-side]))
    allN = rbind(rep(0,side),X[-side,])
    allNE = rbind(rep(0,side),cbind(X[-side,-1],rep(0,side-1)))
    allE = cbind(X[,-1],rep(0,side))
    allSE = rbind(cbind(X[-1,-1],rep(0,side-1)),rep(0,side))
    allS = rbind(X[-1,],rep(0,side))
    allSW = rbind(cbind(rep(0,side-1),X[-1,-side]),rep(0,side))

    # summation of the matrices
    X2 <- allW + allNW + allN + allNE + allE + allSE + allS + allSW

}

GOL <- function(X,delta,plt=TRUE)
{   
    if(plt)
        par(mfrow=c(1,2),pty='s')  
    alive_prop <- numeric(delta)                                          
    for (i in 1:delta)
    {
        alive_prop[i] <- sum(X/prod(dim(X)))
        if(plt)
        {    
            image(X,main=i,col=0:1,xaxt='n',yaxt='n')
            plot(1:i,alive_prop[1:i],
                ylim=c(0,0.1),
                type='l',
                lwd=2,
                ylab='Proportion alive',
                xlab='Time')
        }
        X2 <- neighbour_count(X)    
        X3 <- X
        # the rules of GoL are applied using logical subscripting
        X3[X==0 & X2==3] <- 1
        X3[X==1 & X2<2] <- 0
        X3[X==1 & X2>3] <- 0
        X <- X3
    }
    return(X)
}

## Example:
grd_size <- 100
## Random starting grid:
x <- array(sample(1:0,grd_size^2,replace=T,prob=c(0.1,0.9)),
    dim=c(grd_size,grd_size))

## Run GOL 1000 time steps
end = GOL(X=x,delta=1000)