Python implementation for Perlin Noise with unlimited coordinates space
Project description
Smooth random noise generator
read more https://en.wikipedia.org/wiki/Perlin_noise
source code: https://github.com/salaxieb/perlin_noise
noise = PerlinNoise(octaves=3.5, seed=777)
octaves : number of sub rectangles in each [0, 1] range
seed : specific seed with which you want to initialize random generator
tile_sizes : tuple of ints of you want to noise seamlessly repeat itself
from perlin_noise import PerlinNoise
noise = PerlinNoise()
# accepts as argument float and/or list[float]
noise(0.5) == noise([0.5])
# --> True
# noise not limited in space dimension and seamless in any space size
noise([0.5, 0.5]) == noise([0.5, 0.5, 0, 0, 0])
# --> True
# noise can seamlessly repeat isself
noise([0.5, 0.5], tile_sizes=[2, 5]) == noise([2.5, 5.5], tile_sizes=[2, 5])
# --> True
Usage examples:
import matplotlib.pyplot as plt
from perlin_noise import PerlinNoise
noise = PerlinNoise(octaves=10, seed=1)
xpix, ypix = 100, 100
pic = [[noise([i/xpix, j/ypix]) for j in range(xpix)] for i in range(ypix)]
plt.imshow(pic, cmap='gray')
plt.show()
import matplotlib.pyplot as plt
from perlin_noise import PerlinNoise
noise1 = PerlinNoise(octaves=3)
noise2 = PerlinNoise(octaves=6)
noise3 = PerlinNoise(octaves=12)
noise4 = PerlinNoise(octaves=24)
xpix, ypix = 100, 100
pic = []
for i in range(xpix):
row = []
for j in range(ypix):
noise_val = noise1([i/xpix, j/ypix])
noise_val += 0.5 * noise2([i/xpix, j/ypix])
noise_val += 0.25 * noise3([i/xpix, j/ypix])
noise_val += 0.125 * noise4([i/xpix, j/ypix])
row.append(noise_val)
pic.append(row)
plt.imshow(pic, cmap='gray')
plt.show()
Library has a possibility to generate repetative random noise with custom tile sizes:
import matplotlib.pyplot as plt
from perlin_noise import PerlinNoise
noise = PerlinNoise(octaves=2, seed=42)
xpix, ypix = 800, 1200
lim_x, lim_y = 6, 9
pic = [
[
noise([lim_x * i / xpix, lim_y * j / ypix], tile_sizes=[2, 3])
for j in range(xpix)
]
for i in range(ypix)
]
plt.imshow(pic, cmap="gray")
plt.show()
import matplotlib.pyplot as plt
from perlin_noise import PerlinNoise
noise1 = PerlinNoise(octaves=1)
noise2 = PerlinNoise(octaves=3)
noise3 = PerlinNoise(octaves=6)
noise4 = PerlinNoise(octaves=12)
xpix, ypix = 800, 1200
lim_x, lim_y = 4, 6
tile_sizes = (2, 3)
pic = []
for i in range(ypix):
row = []
for j in range(xpix):
noise_val = noise1([lim_x * i / xpix, lim_y * j / ypix], tile_sizes)
noise_val += 0.5 * noise2([lim_x * i / xpix, lim_y * j / ypix], tile_sizes)
noise_val += 0.25 * noise3([lim_x * i / xpix, lim_y * j / ypix], tile_sizes)
noise_val += 0.125 * noise4([lim_x * i / xpix, lim_y * j / ypix], tile_sizes)
row.append(noise_val)
pic.append(row)
plt.imshow(pic, cmap="gray")
plt.savefig("pics/multy_noise_tiled.png", transparent=True)
plt.show()
for tiles to work correctly, number of octaves MUST be integer
import matplotlib.pyplot as plt
from perlin_noise import PerlinNoise
noise = PerlinNoise(octaves=2.5, seed=42)
xpix, ypix = 800, 1200
lim_x, lim_y = 6, 9
pic = [
[
noise([lim_x * i / xpix, lim_y * j / ypix], tile_sizes=(2, 3))
for j in range(xpix)
]
for i in range(ypix)
]
plt.imshow(pic, cmap="gray")
plt.savefig('pics/tiled_with_step.png', transparent=True)
plt.show()
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
perlin_noise-1.13.tar.gz
(6.2 kB
view hashes)
Built Distribution
Close
Hashes for perlin_noise-1.13-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 09f274c8da38f5a2a48b1626032a1b7738990df758a3bc7549ff3464869c6ca4 |
|
MD5 | da609b1495bff001c7b794fd4955dd63 |
|
BLAKE2b-256 | 4db2c7bd5f926f0e2cf3a4cf21c527f6d1b8f43c84bbfcd5aa32f47c7c14cc89 |