The diamond-square algorithm is a popular method for generating images which resemble terrain, clouds or plasma. Its description on Wikipedia is as good as any and will not be repeated here.
In the code below, the image size is $N = 2^n + 1$. Since
plt.imshow interpolates quite well anyway, $n=6$ gives acceptable results for the image size it produces. For high-resolution images, increase
This code is also available on my github page.
import numpy as np import matplotlib.pyplot as plt # The array must be square with edge length 2**n + 1 n = 6 N = 2**n + 1 # f scales the random numbers at each stage of the algorithm f = 1.0 # Initialise the array with random numbers at its corners arr = np.zeros((N, N)) arr[0::N-1,0::N-1] = np.random.uniform(-1, 1, (2,2)) side = N-1 nsquares = 1 while side > 1: sideo2 = side // 2 # Diamond step for ix in range(nsquares): for iy in range(nsquares): x0, x1, y0, y1 = ix*side, (ix+1)*side, iy*side, (iy+1)*side xc, yc = x0 + sideo2, y0 + sideo2 # Set this pixel to the mean of its "diamond" neighbours plus # a random offset. arr[yc,xc] = (arr[y0,x0] + arr[y0,x1] + arr[y1,x0] + arr[y1,x1])/4 arr[yc,xc] += f * np.random.uniform(-1,1) # Square step: NB don't do this step until the pixels from the preceding # diamond step have been set. for iy in range(2*nsquares+1): yc = sideo2 * iy for ix in range(nsquares+1): xc = side * ix + sideo2 * (1 - iy % 2) if not (0 <= xc < N and 0 <= yc < N): continue tot, ntot = 0., 0 # Set this pixel to the mean of its "square" neighbours plus # a random offset. At the edges, it has only three neighbours for (dx, dy) in ((-1,0), (1,0), (0,-1), (0,1)): xs, ys = xc + dx*sideo2, yc + dy*sideo2 if not (0 <= xs < N and 0 <= ys < N): continue else: tot += arr[ys, xs] ntot += 1 arr[yc, xc] += tot / ntot + f * np.random.uniform(-1,1) side = sideo2 nsquares *= 2 f /= 2 plt.imshow(arr, cmap=plt.cm.Blues) plt.axis('off') plt.show()
Update (January 2020): The Matplotlib colormap
terrain can be used to create pleasing map-like images (here, with
n=9 for a 1025 x 1025 pixel image):
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