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Updated so code is Python 3 compatible #2

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Updated so code is Python 3 compatible #2

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4 changes: 2 additions & 2 deletions examples/1D_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,8 @@
pn = PerlinNoise(num_octaves=7, persistence=0.1)
data = []

t = [i for i in xrange(length)]
for i in xrange(length):
t = [i for i in range(length)]
for i in range(length):
data.append(normalize(pn.fractal(x=i, hgrid=length)))

fig = plt.figure()
Expand Down
6 changes: 3 additions & 3 deletions examples/fbm_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,13 +6,13 @@
sn = SimplexNoise(num_octaves=7, persistence=0.1, dimensions=2)
data = []

for i in xrange(size):
for i in range(size):
data.append([])
for j in xrange(size):
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')
img.save('./fbm_example.png')
6 changes: 3 additions & 3 deletions examples/noise_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,13 +7,13 @@
sn = SimplexNoise(num_octaves=7, persistence=0.1, dimensions=2, noise_scale=noise_scale)
data = []

for i in xrange(size):
for i in range(size):
data.append([])
for j in xrange(size):
for j in range(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')
img.save('./noise_example.png')
40 changes: 20 additions & 20 deletions simplexnoise/noise.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
from __future__ import division
import math
import random
from geometry import Point
from .geometry import Point

# Constants to avoid magic numbers
DEFAULT_NOISE_SCALE = -1 # Check noise_scale against this
Expand Down Expand Up @@ -41,17 +41,17 @@ def __init__(self, num_octaves, persistence, noise_scale=DEFAULT_NOISE_SCALE):
else:
self.noise_scale = noise_scale

self.octaves = [PerlinNoiseOctave() for i in xrange(self.num_octaves)]
self.frequencies = [1.0 / pow(2, i) for i in xrange(self.num_octaves)]
self.octaves = [PerlinNoiseOctave() for i in range(self.num_octaves)]
self.frequencies = [1.0 / pow(2, i) for i in range(self.num_octaves)]
self.amplitudes = [pow(persistence, len(self.octaves) - i)
for i in xrange(self.num_octaves)]
for i in range(self.num_octaves)]

def noise(self, x):
noise = [
self.octaves[i].noise(
xin=x * self.frequencies[i],
noise_scale=self.noise_scale
) * self.amplitudes[i] for i in xrange(self.num_octaves)]
) * self.amplitudes[i] for i in range(self.num_octaves)]

return sum(noise)

Expand All @@ -61,7 +61,7 @@ def fractal(self, x, hgrid, lacunarity=DEFAULT_LACUNARITY, gain=DEFAULT_GAIN):
frequency = 1.0 / hgrid
amplitude = gain

for i in xrange(self.num_octaves):
for i in range(self.num_octaves):
noise.append(
self.octaves[i].noise(
xin=x * frequency,
Expand All @@ -78,9 +78,9 @@ def fractal(self, x, hgrid, lacunarity=DEFAULT_LACUNARITY, gain=DEFAULT_GAIN):
class PerlinNoiseOctave(object):

def __init__(self, num_shuffles=DEFAULT_SHUFFLES):
self.p_supply = [i for i in xrange(0, 256)]
self.p_supply = [i for i in range(0, 256)]

for i in xrange(num_shuffles):
for i in range(num_shuffles):
random.shuffle(self.p_supply)

self.perm = self.p_supply * 2
Expand Down Expand Up @@ -121,12 +121,12 @@ def __init__(self, num_octaves, persistence, dimensions, noise_scale=DEFAULT_NOI

if DIMENSIONS_2D == dimensions:
self.octaves = [SimplexNoiseOctave2D()
for i in xrange(self.num_octaves)]
for i in range(self.num_octaves)]
self.noise_scale = DEFAULT_2D_NOISE_SCALE

elif DIMENSIONS_3D == dimensions:
self.octaves = [SimplexNoiseOctave3D()
for i in xrange(self.num_octaves)]
for i in range(self.num_octaves)]
self.noise_scale = DEFAULT_2D_NOISE_SCALE

else:
Expand All @@ -137,9 +137,9 @@ def __init__(self, num_octaves, persistence, dimensions, noise_scale=DEFAULT_NOI
if DEFAULT_NOISE_SCALE != noise_scale:
self.noise_scale = noise_scale

self.frequencies = [pow(2, i) for i in xrange(self.num_octaves)]
self.frequencies = [pow(2, i) for i in range(self.num_octaves)]
self.amplitudes = [pow(persistence, len(self.octaves) - i)
for i in xrange(self.num_octaves)]
for i in range(self.num_octaves)]

def noise(self, x=0, y=0, z=0):
noise = [
Expand All @@ -148,7 +148,7 @@ def noise(self, x=0, y=0, z=0):
yin=y / self.frequencies[i],
zin=z / self.frequencies[i],
noise_scale=self.noise_scale
) * self.amplitudes[i] for i in xrange(self.num_octaves)]
) * self.amplitudes[i] for i in range(self.num_octaves)]

return sum(noise)

Expand All @@ -158,7 +158,7 @@ def fractal(self, x=0, y=0, z=0, hgrid=0, lacunarity=DEFAULT_LACUNARITY, gain=DE
frequency = 1.0 / hgrid
amplitude = gain

for i in xrange(self.num_octaves):
for i in range(self.num_octaves):
noise.append(
self.octaves[i].noise(
xin=x * frequency,
Expand All @@ -182,7 +182,7 @@ class SimplexNoiseOctave2D(object):
unskew_factor = (3.0 - math.sqrt(3.0)) / 6.0

def __init__(self, num_shuffles=DEFAULT_SHUFFLES):
self.p_supply = [i for i in xrange(0, 256)]
self.p_supply = [i for i in range(0, 256)]

self.grads = [
Point(1, 1, 0),
Expand All @@ -191,7 +191,7 @@ def __init__(self, num_shuffles=DEFAULT_SHUFFLES):
Point(-1, -1, 0)
]

for i in xrange(num_shuffles):
for i in range(num_shuffles):
random.shuffle(self.p_supply)

self.perm = self.p_supply * 2
Expand Down Expand Up @@ -277,7 +277,7 @@ def hashed_gradient_indices(self, points_ij):
def calc_noise_contributions(self, grad_index_hash, points_xy):
""" Calculates the contribution from each corner (in 2D there are three!) """
contribs = []
for i in xrange(len(grad_index_hash)):
for i in range(len(grad_index_hash)):
x = points_xy[i].x
y = points_xy[i].y
grad = self.grads[grad_index_hash[i]]
Expand All @@ -299,15 +299,15 @@ class SimplexNoiseOctave3D(object):
unskew_factor = 1.0 / 6.0

def __init__(self, num_shuffles=DEFAULT_SHUFFLES):
self.p_supply = [i for i in xrange(0, 256)]
self.p_supply = [i for i in range(0, 256)]

self.grads = [
Point(1, 1, 0), Point(-1, 1, 0), Point(1, -1, 0), Point(-1, -1, 0),
Point(1, 0, 1), Point(-1, 0, 1), Point(1, 0, -1), Point(-1, 0, -1),
Point(0, 1, 1), Point(0, -1, 1), Point(0, 1, -1), Point(0, -1, -1),
]

for i in xrange(num_shuffles):
for i in range(num_shuffles):
random.shuffle(self.p_supply)

self.perm = self.p_supply * 2
Expand Down Expand Up @@ -412,7 +412,7 @@ def hashed_gradient_indices(self, points_ijk):
def calc_noise_contributions(self, grad_index_hash, points_xyz):
""" Calculates the contribution from each corner (in 2D there are three!) """
contribs = []
for i in xrange(len(grad_index_hash)):
for i in range(len(grad_index_hash)):
x = points_xyz[i].x
y = points_xyz[i].y
z = points_xyz[i].z
Expand Down