22 lines
709 B
Python
22 lines
709 B
Python
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#!/usr/bin/env python
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import numpy as np
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import scipy.ndimage
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def noise2d(width, height, smooth=1.8, filter_mode='wrap', falloff=0.02,
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power=3):
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rng = np.random.default_rng()
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x = np.zeros((1, 1)) # Zero mean
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iterations = int(np.ceil(np.log(max(width, height)) / np.log(power)))
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falloff *= smooth
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contribution = 1
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for i in range(iterations):
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upscaled = np.zeros((x.shape[0] * power, x.shape[1] * power))
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upscaled[power // 2::power, power // 2::power] = x
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x = scipy.ndimage.gaussian_filter(upscaled, smooth, mode=filter_mode)
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x += rng.normal(scale=contribution, size=x.shape)
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contribution *= falloff
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return x[:width, :height]
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