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PythonPerlin Perlin noise in python -- procedural generative art tool to seamlessly tile texture patterns in any dimensions In 1997 professor Ken Perlin won an Oscar for Technical Achievement from the Academy of Motion Picture Arts and Sciences for his invention of Perlin noise for the Tron 1982 movie. Installation pip install pythonperlin

More examples and animations can be found at:

https://github.com/timpyrkov/procedural-art/

https://www.instagram.com/timpyrkov/

Generate Perlin noise import pylab as plt from pythonperlin import perlin # Set grid shape for randomly seeded gradients shape = (4,4) # Set density - output shape will be dens * shape = (128,128) dens = 32 # Generate noise x = perlin(shape, dens=dens, seed=0) # Test that noise tiles seamlessly x = np.concatenate([x] * 2, axis=1) plt.figure(figsize=(12,6)) plt.imshow(x, cmap=plt.get_cmap('Accent_r')) plt.axis('off') plt.show()

Generate domain warping

Add noise to grid coordinates and generate noise again

dens = 32 shape = (4,4) x = perlin(shape, dens=dens, seed=0, warp=2) plt.figure(figsize=(6,6)) plt.imshow(x, cmap=plt.get_cmap('Accent_r')) plt.axis('off') plt.show()

Generate octaves import pylab as plt from pythonperlin import perlin # Set grid shape for randomly seeded gradients shape = (8,8) # Set density - output shape will be shape * dens = (256,256) dens = 32 # Generate noise x = perlin(shape, dens=dens, seed=0) plt.figure(figsize=(6,6)) plt.imshow(x, cmap=plt.get_cmap('Accent_r')) plt.axis('off') plt.show() # Generate noise array with 4 additional octaves x = perlin(shape, dens=dens, seed=0, octaves=4) plt.figure(figsize=(6,6)) plt.imshow(x, cmap=plt.get_cmap('Accent_r')) plt.axis('off') plt.show()

Generate water caustics

Take absolute value of Perlin noise and apply log-scaled color gradient

import numpy as np from matplotlib.colors import LinearSegmentedColormap dens = 32 shape = (8,8) x = perlin(shape, dens=dens) # Take absolute values of Perlin noise x = np.abs(x) # Log-scale colormap logscale = np.logspace(0,-3,5) colors = plt.cm.get_cmap('GnBu_r')(logscale) cmap = LinearSegmentedColormap.from_list('caustics', colors) plt.figure(figsize=(6,6)) plt.imshow(x, cmap=cmap) plt.axis('off') plt.show()

Generate flower petals

Take 1D Perlin noise as the varying radius along a circle

dens = 32 shape = (8,8) x = perlin(shape, dens=dens) n = 8 delta = dens color = plt.get_cmap('tab20').colors[::-1] plt.figure(figsize=(6,6)) for i in range(n): r = x[delta * i] + 1 r = np.concatenate([r, (r[0],)]) phi = 2 * np.pi * np.linspace(0, 1, len(r)) scale = 1 - i / (n + 2) z = scale * r * np.exp(1j * phi) ax = plt.gca() zorder = max([ch.zorder for ch in ax.get_children()]) plt.fill(z.real, z.imag, c=color[2*i], zorder=zorder+1) plt.plot(z.real, z.imag, c=color[2*i+1], lw=2, zorder=zorder+2) plt.axis('off') plt.show()

Generate vector field

Take Perlin noise as the vector angle at each point of a grid

dens = 6 shape = (3,3) x = perlin(shape, dens=dens) z = np.exp(2j * np.pi * x) shape = z.shape colors = plt.get_cmap('Accent').colors plt.figure(figsize=(6,6)) for i in range(shape[0]): for j in range(shape[1]): di = 0.5 * z[i,j].real dj = 0.5 * z[i,j].imag color = colors[(di > 0) + 2 * (dj > 0)] plt.arrow(i, j, di, dj, color=color, width=0.1) plt.axis('off') plt.show()

Sound of Perlin noise

Perlin noise sounds nice and less buzzing than white noise

import sounddevice as sd dens = 32 shape = (1024,) x = perlin(shape, dens=dens) sd.play(x, 22050)

Alternatively, save and play perlin.wav audio

import IPython import soundfile as sf sf.write('perlin.wav', x, 22050) IPython.display.Audio('perlin.wav')

perlin.wav

Documentation

https://pythonperlin.readthedocs.io

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