Examples

This section contains examples of using Perlin noise for generating various pixel maps.

Basic Usage

Generate a simple Perlin noise pattern:

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()

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()

Octaves

Generate noise with multiple octaves for more detail:

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 without octaves
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()

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()

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()

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()

Audio Generation

Perlin noise can be used to generate audio:

import sounddevice as sd

dens = 32
shape = (1024,)
x = perlin(shape, dens=dens)

sd.play(x, 22050)

Alternatively, save and play as a WAV file:

import IPython
import soundfile as sf

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