Basic julia set

The Julia set is a fascinating mathematical concept that produces intricate and beautiful patterns. In this article, we will explore different ways to generate a basic Julia set using Julia programming language. We will present three different approaches, each with its own advantages and disadvantages. Let’s dive in!

Approach 1: Using Nested Loops

One way to generate a basic Julia set is by using nested loops. This approach involves iterating over each pixel in the desired image and applying the Julia set algorithm to determine its color. Here’s a sample code that demonstrates this approach:

function julia_set(width, height, max_iter, c)
    img = zeros(UInt8, height, width, 3)
    for y = 1:height
        for x = 1:width
            zx = 3.0 * (x - width / 2) / (width / 2)
            zy = 2.0 * (y - height / 2) / (height / 2)
            iter = 0
            while (zx * zx + zy * zy < 4) && (iter < max_iter)
                xtemp = zx * zx - zy * zy + real(c)
                zy = 2.0 * zx * zy + imag(c)
                zx = xtemp
                iter += 1
            end
            img[y, x, :] = [iter % 8 * 32, iter % 16 * 16, iter % 32 * 8]
        end
    end
    return img
end

width = 800
height = 600
max_iter = 256
c = -0.8 + 0.156im

julia_img = julia_set(width, height, max_iter, c)

In this code, we define a function `julia_set` that takes the width, height, maximum number of iterations, and the complex constant `c` as input. It initializes an image array with zeros and then iterates over each pixel, applying the Julia set algorithm. The resulting image is stored in the `img` array.

Approach 2: Using Array Operations

Another approach to generate a basic Julia set is by utilizing array operations. This approach leverages the power of Julia's array broadcasting and element-wise operations to perform calculations on entire arrays at once. Here's a sample code that demonstrates this approach:

function julia_set(width, height, max_iter, c)
    x = range(-2, stop=2, length=width)
    y = range(-1.5, stop=1.5, length=height)
    img = zeros(UInt8, height, width, 3)
    zx = x' .+ y .* im
    zy = x .* im .+ y'
    for iter = 1:max_iter
        mask = abs2(zx) .+ abs2(zy) .< 4
        zx[mask] = zx[mask] .* zx[mask] .- zy[mask] .* zy[mask] .+ real(c)
        zy[mask] = 2 .* zx[mask] .* zy[mask] .+ imag(c)
        img[mask] = [iter % 8 * 32, iter % 16 * 16, iter % 32 * 8]
    end
    return img
end

width = 800
height = 600
max_iter = 256
c = -0.8 + 0.156im

julia_img = julia_set(width, height, max_iter, c)

In this code, we define the same `julia_set` function as before. However, instead of using nested loops, we create arrays `x` and `y` that represent the x and y coordinates of each pixel. We then use array broadcasting and element-wise operations to perform calculations on the entire arrays `zx` and `zy` at once. This approach can be more efficient and concise compared to nested loops.

Approach 3: Using Parallel Computing

The third approach to generate a basic Julia set involves utilizing parallel computing. Julia provides built-in support for parallelism, allowing us to distribute the computation across multiple cores or even multiple machines. Here's a sample code that demonstrates this approach:

using Distributed

@everywhere function julia_set_pixel(x, y, max_iter, c)
    zx = 3.0 * (x - width / 2) / (width / 2)
    zy = 2.0 * (y - height / 2) / (height / 2)
    iter = 0
    while (zx * zx + zy * zy < 4) && (iter < max_iter)
        xtemp = zx * zx - zy * zy + real(c)
        zy = 2.0 * zx * zy + imag(c)
        zx = xtemp
        iter += 1
    end
    return [iter % 8 * 32, iter % 16 * 16, iter % 32 * 8]
end

function julia_set(width, height, max_iter, c)
    img = zeros(UInt8, height, width, 3)
    @distributed for y = 1:height
        for x = 1:width
            img[y, x, :] = julia_set_pixel(x, y, max_iter, c)
        end
    end
    return img
end

width = 800
height = 600
max_iter = 256
c = -0.8 + 0.156im

julia_img = julia_set(width, height, max_iter, c)

In this code, we define a new function `julia_set_pixel` that calculates the color of a single pixel. We then modify the `julia_set` function to distribute the computation of each pixel across multiple cores using the `@distributed` macro. This approach can significantly speed up the generation of the Julia set for larger images or higher maximum iterations.

Conclusion

All three approaches presented here can generate a basic Julia set. The choice of the best approach depends on the specific requirements of your application.

- Approach 1 using nested loops is the most straightforward and easy to understand. It is suitable for smaller images or when simplicity is preferred over performance.

- Approach 2 using array operations offers better performance and conciseness compared to nested loops. It is suitable for larger images or when efficiency is a priority.

- Approach 3 using parallel computing provides the highest performance by leveraging multiple cores or machines. It is suitable for very large images or when the computation needs to be distributed across multiple resources.

Consider your specific needs and constraints to determine which approach is the best fit for your Julia set generation task. Happy coding!