Shelling Simulation in Julia

shelling-simulation.jl

# using PyPlot
using GR
using ColorSchemes

N = 2000
try
    mkdir("~/Sync/Documents/tmp/gifs")
catch
end

function main()
    # Set Parameters
    # TODO Fix non-square errors
    # TODO Allow for 3 different factor types
    width = 2000
    height = 2000

    # Initialise the Array
    pic_mat = zeros(width, height)
    fill_noise(pic_mat)

    # Apply the Shelling Model N times
    print("|--------------------------------------------------|\n")
    for i in 1:N
        shelling(pic_mat, width, height, 4)
        # Display the plot
        GR.setcolormap(47)
        name = string("/home/ryan/Sync/Documents/tmp/gifs/j", lpad(i, 5, "0"), ".png")
        pic = GR.imshow(pic_mat, color = "blue")
        GR.savefig(name)


        print(i*100/N, " % \n")
        # TODO fix the loading bar
        # Print the loading bar
#        if ((i % (N/50)) == 0) 
#            print("#")
#        end
    end

    # pic = PyPlot.imshow(pic_mat,interpolation="none")
    #PyPlot.savefig("/tmp/pyplot.tiff", dpi = 999)
    # PyPlot.display_figs()
end

function shelling(mat, width, height, t)
    # Ignore the boundaries so (2:N-1)
    for i in 2:(size(mat)[1]-1)
        for j in 2:(size(mat)[2]-1)
            # If not satisfied swap
            if !satisfied_nb(mat, i, j, t)
                j_new = rand(1:width)
                i_new = rand(1:height)
                # Maybe he has a change of heart
                # mat[i, j] = rand(0:1)
                new = mat[i_new, j_new]
                old = mat[i, j]
                mat[i, j] = new
                mat[i_new, j_new] = old
            #    mat[i, j], mat[i_new, j_new] = mat[i_new, j_new], mat[i, j]
            end
        end
    end
    return mat
end

function satisfied_nb(mat, i, j, t)
    like_neighbours = -1  # Don't count self
    # Loop over the neighbours and count the number of
    # similar neighbours
    for y in (i-1):(i+1)
        for x in (j-1):(j+1)
            if (mat[i, j] == mat[x, y])
                like_neighbours += 1
            end
        end
    end
    if like_neighbours >= t
        return true
    else 
        return false
    end
end

function fill_noise(mat)
    for i in 1:size(mat)[1]
        for j in 1:size(mat)[2]
            if rand([1,0])==1
                mat[i, j] = 1
            else
                mat[i,j] = 0
            end
        end
    end
end

main()