bitfield.r

# There is some explanation for why the bitwise operator functions do what they do here:
# https://inventwithpython.com/blog/2021/08/02/algorithmic-art-with-the-bitfielddraw-module/

# for the colour palettes
library(RColorBrewer) 

# Set the aspect ratio
ratio = 5/4

# How many images do we want
for(j in 1:100){

  # for the jth image, set the seed to j
  set.seed(j)
  
  # open a new png
  png(filename=sprintf("bitwise%05d.png",j),width=2000,height=2000*ratio,type="cairo")
  
  # Set up a new blank plot with fixed margin, white background, exact axis, no annotation
  par(mar=4*c(1,1,1,1), bg="white", xaxs="i",yaxs="i")
  plot(NA,xlim=c(0,1),ylim=c(ratio,0),ann=F,axes=F, asp=1)

  # How many layers per plot?
  for(i in 1:10){
    
    # Choose a palette for this layer
    p = brewer.pal.info |> rownames() |> sample(1)
    
    # What is the base modulus for this layer
    t=sample(25*i,1)
    
    # What is the grid size for this layer?
    N=6*2^sample(1:8,1)
    
    # Set up the x and y axes
    x = seq(0,1,l=N+1)
    y = seq(0,ratio,l=(N*ratio)+1)
    
    # Pick a random bitwise function for this layer
    f = sample(list(bitwAnd,bitwXor,bitwOr),1)[[1]]
    
    # Now plot the layer!  We let the modulus vary along the y axis to make the gradient
    image(x,y,outer(1:N, 1:(N*ratio), \(x,y) f(x+y+64,y-x+64)%%(t+floor(y/10))),
          add=TRUE,col=brewer.pal(n=floor(t/2),p)[1:t])
  }
  
  # Close the plotting device
  dev.off()
}