<h1><p align="left"> <img src="https://github.com/timpyrkov/pythonperlin/blob/master/docs/media/logo.png?raw=true" alt="PythonPerlin logo" height="30" style="vertical-align: middle; margin-right: 10px;"> <span style="font-size:2.5em; vertical-align: middle;"><b>PythonPerlin</b></span> </p></h1>

Perlin noise is a type of gradient noise developed by professor <b>Ken Perlin</b> in 1983 at the Department of Computer Science at New York University (NYU).

He began working on the algorithm while contributing to Disney’s 1982 science fiction film <b>"Tron"</b>, which starred Bruce Boxleitner and Peter Jurasik — both of whom later appeared in the sci-fi TV series <b>"Babylon 5"</b> (1993–1998).

In recognition of his contribution to computer graphics, Ken Perlin received an <b>Academy Award for Technical Achievement</b> in 1997 from the Academy of Motion Picture Arts and Sciences. His technique has since become a foundational tool in the procedural generation of natural-looking textures and environments.

This documentation (https://pythonperlin.readthedocs.io) provides examples and explanation of using Perlin noise for procedural generation of:

• <b>Natural textures</b>: water, clouds, fire, wood, marble
• <b>Triangulated surface grids</b>: terrain, planets

Installation

pip install pythonperlin

Quick start

import pythonperlin as pp
import matplotlib.pylab as plt

# 2D grid shape to seed gradients
shape = (6,4)

# Density to fill space between gradients
dens = 32

# Generate noise
noise = pp.perlin(shape, dens=dens, seed=0)

# Grid size = [s * dens for s in shape]
print(noise.shape)

# Show noise
plt.figure(figsize=(9,6))
plt.imshow(noise.T, cmap=plt.get_cmap("tab20b"))
plt.axis("off")
plt.show()

Tile

import numpy as np 

# Tile along x axis
noise = np.concatenate([noise] * 2, axis=0)

# Tile along y axis
noise = np.concatenate([noise] * 2, axis=1)

plt.figure(figsize=(6,4))
plt.imshow(noise.T, cmap=plt.get_cmap("tab20b"))
plt.plot([noise.shape[0] // 2] * 2, [0, noise.shape[1]], "--k", lw=4)
plt.plot([0, noise.shape[0]], [noise.shape[1] // 2] * 2, "--k", lw=4)
plt.axis("off")
plt.show()

Octaves

# Add octaves
noise = pp.perlin(shape, dens=dens, octaves=4, seed=0)

# Show noise
plt.figure(figsize=(6,4))
plt.imshow(noise.T, cmap=plt.get_cmap("tab20b"))
plt.axis("off")
plt.show()

Domain warping

## Add domain warping
noise = pp.perlin(shape, dens=dens, octaves=4, warp=True, seed=0)

# Show noise
plt.figure(figsize=(6,4))
plt.imshow(noise.T, cmap=plt.get_cmap("tab20b"))
plt.axis("off")
plt.show()

N-dimensional

# Shape specifies the number of nodes along each dimension
shape = (6,4,3)

# Density specifies the filling along each dimension if tuple
dens = (32,32,4)

# Generate noise
noise = pp.perlin(shape, dens=dens, seed=0)

# Grid size = [s * d for s, d in zip(shape, dens)]
print(noise.shape)

# Show noise slices along the z axis
fig, ax = plt.subplots(3, 4, figsize=(6,4))
for k in range(noise.shape[2]):
    i, j = k // 4, k % 4
    ax[i,j].imshow(noise[...,k].T, cmap=plt.get_cmap("tab20b"))
    ax[i,j].axis("off")
plt.tight_layout()
plt.show()

Note that the noise tiles seamlessly along the z axis too:

The noise frames transfrom smoothly and the last frame transforms to the first frame.

Documentation

https://pythonperlin.readthedocs.io