Mandelbrot and Julia Set with PIL

fractal.py

import numpy as np
from PIL import Image

output_image = 'fractal.png'

def evalMandelbrot(x, y, n=20):
    c = x + 1j*y
    out, count = 0, 0
    for i in range(n):
        if abs(out) <= 2:
            out = out ** 7 + c
            count += 1
        else:
            return count
    return count

def evalJuliaSet(x, y, c, n=20):
    out = x + 1j*y
    count = 0
    for i in range(n):
        if abs(out) <= 2:
            out = out ** 7 + c
            count += 1
        else:
            return count
    return count

w, h = 600, 600
# Apply antialiasing filtering through resizing the image
w, h = 2*w, 2*h
im = Image.new('RGB', (w, h), (255, 255, 255))
pixels = im.load()

buffer = np.zeros((w, h))
maxVal = 0

# Calculate the Mandelbrot or Julia set 
for i in range(w):
    for j in range(h):
        x, y = 2.5*(float(i)/float(w) - 0.5), 2.5*(float(j)/float(h) - 0.5)
        # Evaluate Mandelbrot set fractal
        val = evalMandelbrot(x, y)
        # Evaluate Julia set fractal
        #val = evalJuliaSet(x, y, -0.798 + 0.225j)

        if val > maxVal: maxVal = val
        buffer[i, j] = val

# Apply scaled buffer to image
for i in range(w):
    for j in range(h):
        val = buffer[i, j]
        pixels[i, j] = tuple(int(255*(1 - val/maxVal)) for i in range(3))

# Shrink to apply antialiasing to the image
im = im.resize((int(w/2), int(h/2)), resample=Image.ANTIALIAS)

im.save(output_image, 'PNG')