import numpy as np
from PIL import Image
from simplexnoise.noise import SimplexNoise, normalize
size = 250
noise_scale = 700.0 # Turns up the contrast
sn = SimplexNoise(num_octaves=7,
persistence=0.1,
dimensions=2,
noise_scale=noise_scale)
data = []
for i in xrange(size):
data.append([])
for j in xrange(size):
noise = normalize(sn.noise(i, j))
data[i].append(noise * 255.0)
# Cast to numpy array so we can save
data = np.array(data).astype(np.uint8)
img = Image.fromarray(data, mode='L')
img.save('./noise_example.png')
import numpy as np
from PIL import Image
from simplexnoise.noise import SimplexNoise, normalize
size = 250
sn = SimplexNoise(num_octaves=7, persistence=0.1, dimensions=2)
data = []
for i in range(size):
data.append([])
for j in range(size):
noise = normalize(sn.fractal(i, j, hgrid=size))
data[i].append(noise * 255.0)
# Cast to numpy array so we can save
data = np.array(data).astype(np.uint8)
img = Image.fromarray(data, mode='L')
img.save('./fbm_example.png')
import matplotlib.pyplot as plt
from simplexnoise.noise import PerlinNoise, normalize
length = 10000
pn = PerlinNoise(num_octaves=7, persistence=0.1)
data = []
t = [i for i in xrange(length)]
for i in xrange(length):
data.append(normalize(pn.fractal(x=i, hgrid=length)))
fig = plt.figure()
plt.plot(t, data)
fig.savefig('1D_example.png')