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simplex.py
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simplex.py
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# https://github.com/lmas/opensimplex fork
from ctypes import c_int64
from math import floor
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import animation
from numba import njit, prange
class Simplex_CLASS:
def __init__(self):
self.newSeed()
def newSeed(self, seed=None):
if not seed:
seed = np.random.randint(-10000000000, 10000000000)
self._perm, self._perm_grad_index3 = _init(seed)
def noise2(self, x, y):
return _noise2(x, y, self._perm)
def noise2array(self, x, y):
return _noise2a(x, y, self._perm)
def noise3(self, x, y, z):
return _noise3(x, y, z, self._perm, self._perm_grad_index3)
def noise3array(self, x, y, z):
return _noise3a(x, y, z, self._perm, self._perm_grad_index3)
def rand_3d_octaves(self, shape, octaves=1, persistence=0.5, frequency=32):
"""
Returns a layered fractal noise in 3D
:param shape: Shape of 3D tensor output
:param octaves: Number of levels of fractal noise
:param persistence: float between (0-1) -> Rate at which amplitude of each level decreases
:param frequency: Frequency of initial octave of noise
:return: Fractal noise sample with n lots of 2D images
"""
assert len(shape) == 3
noise = np.zeros(shape)
z, y, x = [np.arange(0, end) for end in shape]
amplitude = 1
for _ in range(octaves):
noise += amplitude * self.noise3array(x / frequency, y / frequency, z / frequency)
frequency /= 2
amplitude *= persistence
return noise
def rand_2d_octaves(self, shape, octaves=1, persistence=0.5, frequency=32):
"""
Returns a layered fractal noise in 2D
:param shape: Shape of 2D tensor output
:param octaves: Number of levels of fractal noise
:param persistence: float between (0-1) -> Rate at which amplitude of each level decreases
:param frequency: Frequency of initial octave of noise
:return: Fractal noise sample with n lots of 2D images
"""
assert len(shape) == 2
noise = np.zeros(shape)
y, x = [np.arange(0, end) for end in shape]
amplitude = 1
for _ in range(octaves):
noise += amplitude * self.noise2array(x / frequency, y / frequency)
frequency /= 2
amplitude *= persistence
return noise
def rand_3d_fixed_T_octaves(self, shape, T, octaves=1, persistence=0.5, frequency=32):
"""
Returns a layered fractal noise in 3D
:param shape: Shape of 3D tensor output
:param octaves: Number of levels of fractal noise
:param persistence: float between (0-1) -> Rate at which amplitude of each level decreases
:param frequency: Frequency of initial octave of noise
:return: Fractal noise sample with n lots of 2D images
"""
assert len(shape) == 2
noise = np.zeros((1, *shape))
y, x = [np.arange(0, end) for end in shape]
amplitude = 1
for _ in range(octaves):
noise += amplitude * self.noise3array(x / frequency, y / frequency, T / frequency)
frequency /= 2
amplitude *= persistence
return noise
# Why 3 (and not just 0 or something)? Well I ran into a bug with
# "overflowing int" errors while refactoring in numpy and using a
# nonzero seed value... This is a reminder
DEFAULT_SEED = 3
# Gradients for 2D. They approximate the directions to the
# vertices of an octagon from the center.
GRADIENTS2 = np.array(
[
5, 2, 2, 5,
-5, 2, -2, 5,
5, -2, 2, -5,
-5, -2, -2, -5,
], dtype=np.int64
)
# Gradients for 3D. They approximate the directions to the
# vertices of a rhombicuboctahedron from the center, skewed so
# that the triangular and square facets can be inscribed inside
# circles of the same radius.
GRADIENTS3 = np.array(
[
-11, 4, 4, -4, 11, 4, -4, 4, 11,
11, 4, 4, 4, 11, 4, 4, 4, 11,
-11, -4, 4, -4, -11, 4, -4, -4, 11,
11, -4, 4, 4, -11, 4, 4, -4, 11,
-11, 4, -4, -4, 11, -4, -4, 4, -11,
11, 4, -4, 4, 11, -4, 4, 4, -11,
-11, -4, -4, -4, -11, -4, -4, -4, -11,
11, -4, -4, 4, -11, -4, 4, -4, -11,
], dtype=np.int64
)
# Gradients for 4D. They approximate the directions to the
# vertices of a disprismatotesseractihexadecachoron from the center,
# skewed so that the tetrahedral and cubic facets can be inscribed inside
# spheres of the same radius.
GRADIENTS4 = np.array(
[
3, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3,
-3, 1, 1, 1, -1, 3, 1, 1, -1, 1, 3, 1, -1, 1, 1, 3,
3, -1, 1, 1, 1, -3, 1, 1, 1, -1, 3, 1, 1, -1, 1, 3,
-3, -1, 1, 1, -1, -3, 1, 1, -1, -1, 3, 1, -1, -1, 1, 3,
3, 1, -1, 1, 1, 3, -1, 1, 1, 1, -3, 1, 1, 1, -1, 3,
-3, 1, -1, 1, -1, 3, -1, 1, -1, 1, -3, 1, -1, 1, -1, 3,
3, -1, -1, 1, 1, -3, -1, 1, 1, -1, -3, 1, 1, -1, -1, 3,
-3, -1, -1, 1, -1, -3, -1, 1, -1, -1, -3, 1, -1, -1, -1, 3,
3, 1, 1, -1, 1, 3, 1, -1, 1, 1, 3, -1, 1, 1, 1, -3,
-3, 1, 1, -1, -1, 3, 1, -1, -1, 1, 3, -1, -1, 1, 1, -3,
3, -1, 1, -1, 1, -3, 1, -1, 1, -1, 3, -1, 1, -1, 1, -3,
-3, -1, 1, -1, -1, -3, 1, -1, -1, -1, 3, -1, -1, -1, 1, -3,
3, 1, -1, -1, 1, 3, -1, -1, 1, 1, -3, -1, 1, 1, -1, -3,
-3, 1, -1, -1, -1, 3, -1, -1, -1, 1, -3, -1, -1, 1, -1, -3,
3, -1, -1, -1, 1, -3, -1, -1, 1, -1, -3, -1, 1, -1, -1, -3,
-3, -1, -1, -1, -1, -3, -1, -1, -1, -1, -3, -1, -1, -1, -1, -3,
], dtype=np.int64
)
STRETCH_CONSTANT2 = -0.211324865405187 # (1/Math.sqrt(2+1)-1)/2
SQUISH_CONSTANT2 = 0.366025403784439 # (Math.sqrt(2+1)-1)/2
STRETCH_CONSTANT3 = -1.0 / 6 # (1/Math.sqrt(3+1)-1)/3
SQUISH_CONSTANT3 = 1.0 / 3 # (Math.sqrt(3+1)-1)/3
STRETCH_CONSTANT4 = -0.138196601125011 # (1/Math.sqrt(4+1)-1)/4
SQUISH_CONSTANT4 = 0.309016994374947 # (Math.sqrt(4+1)-1)/4
NORM_CONSTANT2 = 47
NORM_CONSTANT3 = 103
NORM_CONSTANT4 = 30
def overflow(x):
# Since normal python ints and longs can be quite humongous we have to use
# self hack to make them be able to overflow.
# Using a np.int64 won't work either, as it will still complain with:
# "OverflowError: int too big to convert"
return c_int64(x).value
def _init(seed=DEFAULT_SEED):
# Have to zero fill so we can properly loop over it later
perm = np.zeros(256, dtype=np.int64)
perm_grad_index3 = np.zeros(256, dtype=np.int64)
source = np.arange(256)
# Generates a proper permutation (i.e. doesn't merely perform N
# successive pair swaps on a base array)
seed = overflow(seed * 6364136223846793005 + 1442695040888963407)
seed = overflow(seed * 6364136223846793005 + 1442695040888963407)
seed = overflow(seed * 6364136223846793005 + 1442695040888963407)
for i in range(255, -1, -1):
seed = overflow(seed * 6364136223846793005 + 1442695040888963407)
r = int((seed + 31) % (i + 1))
if r < 0:
r += i + 1
perm[i] = source[r]
perm_grad_index3[i] = int((perm[i] % (len(GRADIENTS3) / 3)) * 3)
source[r] = source[i]
return perm, perm_grad_index3
@njit(cache=True)
def _extrapolate2(perm, xsb, ysb, dx, dy):
index = perm[(perm[xsb & 0xFF] + ysb) & 0xFF] & 0x0E
g1, g2 = GRADIENTS2[index:index + 2]
return g1 * dx + g2 * dy
@njit(cache=True)
def _extrapolate3(perm, perm_grad_index3, xsb, ysb, zsb, dx, dy, dz):
index = perm_grad_index3[
(perm[(perm[xsb & 0xFF] + ysb) & 0xFF] + zsb) & 0xFF
]
g1, g2, g3 = GRADIENTS3[index:index + 3]
return g1 * dx + g2 * dy + g3 * dz
@njit(cache=True)
def _noise2(x, y, perm):
# Place input coordinates onto grid.
stretch_offset = (x + y) * STRETCH_CONSTANT2
xs = x + stretch_offset
ys = y + stretch_offset
# Floor to get grid coordinates of rhombus (stretched square) super-cell origin.
xsb = floor(xs)
ysb = floor(ys)
# Skew out to get actual coordinates of rhombus origin. We'll need these later.
squish_offset = (xsb + ysb) * SQUISH_CONSTANT2
xb = xsb + squish_offset
yb = ysb + squish_offset
# Compute grid coordinates relative to rhombus origin.
xins = xs - xsb
yins = ys - ysb
# Sum those together to get a value that determines which region we're in.
in_sum = xins + yins
# Positions relative to origin point.
dx0 = x - xb
dy0 = y - yb
value = 0
# Contribution (1,0)
dx1 = dx0 - 1 - SQUISH_CONSTANT2
dy1 = dy0 - 0 - SQUISH_CONSTANT2
attn1 = 2 - dx1 * dx1 - dy1 * dy1
if attn1 > 0:
attn1 *= attn1
value += attn1 * attn1 * _extrapolate2(perm, xsb + 1, ysb + 0, dx1, dy1)
# Contribution (0,1)
dx2 = dx0 - 0 - SQUISH_CONSTANT2
dy2 = dy0 - 1 - SQUISH_CONSTANT2
attn2 = 2 - dx2 * dx2 - dy2 * dy2
if attn2 > 0:
attn2 *= attn2
value += attn2 * attn2 * _extrapolate2(perm, xsb + 0, ysb + 1, dx2, dy2)
if in_sum <= 1: # We're inside the triangle (2-Simplex) at (0,0)
zins = 1 - in_sum
if zins > xins or zins > yins: # (0,0) is one of the closest two triangular vertices
if xins > yins:
xsv_ext = xsb + 1
ysv_ext = ysb - 1
dx_ext = dx0 - 1
dy_ext = dy0 + 1
else:
xsv_ext = xsb - 1
ysv_ext = ysb + 1
dx_ext = dx0 + 1
dy_ext = dy0 - 1
else: # (1,0) and (0,1) are the closest two vertices.
xsv_ext = xsb + 1
ysv_ext = ysb + 1
dx_ext = dx0 - 1 - 2 * SQUISH_CONSTANT2
dy_ext = dy0 - 1 - 2 * SQUISH_CONSTANT2
else: # We're inside the triangle (2-Simplex) at (1,1)
zins = 2 - in_sum
if zins < xins or zins < yins: # (0,0) is one of the closest two triangular vertices
if xins > yins:
xsv_ext = xsb + 2
ysv_ext = ysb + 0
dx_ext = dx0 - 2 - 2 * SQUISH_CONSTANT2
dy_ext = dy0 + 0 - 2 * SQUISH_CONSTANT2
else:
xsv_ext = xsb + 0
ysv_ext = ysb + 2
dx_ext = dx0 + 0 - 2 * SQUISH_CONSTANT2
dy_ext = dy0 - 2 - 2 * SQUISH_CONSTANT2
else: # (1,0) and (0,1) are the closest two vertices.
dx_ext = dx0
dy_ext = dy0
xsv_ext = xsb
ysv_ext = ysb
xsb += 1
ysb += 1
dx0 = dx0 - 1 - 2 * SQUISH_CONSTANT2
dy0 = dy0 - 1 - 2 * SQUISH_CONSTANT2
# Contribution (0,0) or (1,1)
attn0 = 2 - dx0 * dx0 - dy0 * dy0
if attn0 > 0:
attn0 *= attn0
value += attn0 * attn0 * _extrapolate2(perm, xsb, ysb, dx0, dy0)
# Extra Vertex
attn_ext = 2 - dx_ext * dx_ext - dy_ext * dy_ext
if attn_ext > 0:
attn_ext *= attn_ext
value += attn_ext * attn_ext * _extrapolate2(perm, xsv_ext, ysv_ext, dx_ext, dy_ext)
return value / NORM_CONSTANT2
@njit(cache=True, parallel=True)
def _noise2a(x, y, perm):
noise = np.zeros(x.size * y.size, dtype=np.double)
for i in prange(y.size):
for j in prange(x.size):
noise[i * y.size + j] = _noise2(x[j], y[i], perm)
return noise.reshape((x.size, y.size))
@njit(cache=True)
def _noise3(x, y, z, perm, perm_grad_index3):
# Place input coordinates on simplectic honeycomb.
stretch_offset = (x + y + z) * STRETCH_CONSTANT3
xs = x + stretch_offset
ys = y + stretch_offset
zs = z + stretch_offset
# Floor to get simplectic honeycomb coordinates of rhombohedron (stretched cube) super-cell origin.
xsb = floor(xs)
ysb = floor(ys)
zsb = floor(zs)
# Skew out to get actual coordinates of rhombohedron origin. We'll need these later.
squish_offset = (xsb + ysb + zsb) * SQUISH_CONSTANT3
xb = xsb + squish_offset
yb = ysb + squish_offset
zb = zsb + squish_offset
# Compute simplectic honeycomb coordinates relative to rhombohedral origin.
xins = xs - xsb
yins = ys - ysb
zins = zs - zsb
# Sum those together to get a value that determines which region we're in.
in_sum = xins + yins + zins
# Positions relative to origin point.
dx0 = x - xb
dy0 = y - yb
dz0 = z - zb
value = 0
if in_sum <= 1: # We're inside the tetrahedron (3-Simplex) at (0,0,0)
# Determine which two of (0,0,1), (0,1,0), (1,0,0) are closest.
a_point = 0x01
a_score = xins
b_point = 0x02
b_score = yins
if a_score >= b_score and zins > b_score:
b_score = zins
b_point = 0x04
elif a_score < b_score and zins > a_score:
a_score = zins
a_point = 0x04
# Now we determine the two lattice points not part of the tetrahedron that may contribute.
# This depends on the closest two tetrahedral vertices, including (0,0,0)
wins = 1 - in_sum
if wins > a_score or wins > b_score: # (0,0,0) is one of the closest two tetrahedral vertices.
c = b_point if (b_score > a_score) else a_point # Our other closest vertex is the closest out of a and b.
if (c & 0x01) == 0:
xsv_ext0 = xsb - 1
xsv_ext1 = xsb
dx_ext0 = dx0 + 1
dx_ext1 = dx0
else:
xsv_ext0 = xsv_ext1 = xsb + 1
dx_ext0 = dx_ext1 = dx0 - 1
if (c & 0x02) == 0:
ysv_ext0 = ysv_ext1 = ysb
dy_ext0 = dy_ext1 = dy0
if (c & 0x01) == 0:
ysv_ext1 -= 1
dy_ext1 += 1
else:
ysv_ext0 -= 1
dy_ext0 += 1
else:
ysv_ext0 = ysv_ext1 = ysb + 1
dy_ext0 = dy_ext1 = dy0 - 1
if (c & 0x04) == 0:
zsv_ext0 = zsb
zsv_ext1 = zsb - 1
dz_ext0 = dz0
dz_ext1 = dz0 + 1
else:
zsv_ext0 = zsv_ext1 = zsb + 1
dz_ext0 = dz_ext1 = dz0 - 1
else: # (0,0,0) is not one of the closest two tetrahedral vertices.
c = (a_point | b_point) # Our two extra vertices are determined by the closest two.
if (c & 0x01) == 0:
xsv_ext0 = xsb
xsv_ext1 = xsb - 1
dx_ext0 = dx0 - 2 * SQUISH_CONSTANT3
dx_ext1 = dx0 + 1 - SQUISH_CONSTANT3
else:
xsv_ext0 = xsv_ext1 = xsb + 1
dx_ext0 = dx0 - 1 - 2 * SQUISH_CONSTANT3
dx_ext1 = dx0 - 1 - SQUISH_CONSTANT3
if (c & 0x02) == 0:
ysv_ext0 = ysb
ysv_ext1 = ysb - 1
dy_ext0 = dy0 - 2 * SQUISH_CONSTANT3
dy_ext1 = dy0 + 1 - SQUISH_CONSTANT3
else:
ysv_ext0 = ysv_ext1 = ysb + 1
dy_ext0 = dy0 - 1 - 2 * SQUISH_CONSTANT3
dy_ext1 = dy0 - 1 - SQUISH_CONSTANT3
if (c & 0x04) == 0:
zsv_ext0 = zsb
zsv_ext1 = zsb - 1
dz_ext0 = dz0 - 2 * SQUISH_CONSTANT3
dz_ext1 = dz0 + 1 - SQUISH_CONSTANT3
else:
zsv_ext0 = zsv_ext1 = zsb + 1
dz_ext0 = dz0 - 1 - 2 * SQUISH_CONSTANT3
dz_ext1 = dz0 - 1 - SQUISH_CONSTANT3
# Contribution (0,0,0)
attn0 = 2 - dx0 * dx0 - dy0 * dy0 - dz0 * dz0
if attn0 > 0:
attn0 *= attn0
value += attn0 * attn0 * _extrapolate3(perm, perm_grad_index3, xsb + 0, ysb + 0, zsb + 0, dx0, dy0, dz0)
# Contribution (1,0,0)
dx1 = dx0 - 1 - SQUISH_CONSTANT3
dy1 = dy0 - 0 - SQUISH_CONSTANT3
dz1 = dz0 - 0 - SQUISH_CONSTANT3
attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1
if attn1 > 0:
attn1 *= attn1
value += attn1 * attn1 * _extrapolate3(perm, perm_grad_index3, xsb + 1, ysb + 0, zsb + 0, dx1, dy1, dz1)
# Contribution (0,1,0)
dx2 = dx0 - 0 - SQUISH_CONSTANT3
dy2 = dy0 - 1 - SQUISH_CONSTANT3
dz2 = dz1
attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2
if attn2 > 0:
attn2 *= attn2
value += attn2 * attn2 * _extrapolate3(perm, perm_grad_index3, xsb + 0, ysb + 1, zsb + 0, dx2, dy2, dz2)
# Contribution (0,0,1)
dx3 = dx2
dy3 = dy1
dz3 = dz0 - 1 - SQUISH_CONSTANT3
attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3
if attn3 > 0:
attn3 *= attn3
value += attn3 * attn3 * _extrapolate3(perm, perm_grad_index3, xsb + 0, ysb + 0, zsb + 1, dx3, dy3, dz3)
elif in_sum >= 2: # We're inside the tetrahedron (3-Simplex) at (1,1,1)
# Determine which two tetrahedral vertices are the closest, out of (1,1,0), (1,0,1), (0,1,1) but not (1,1,1).
a_point = 0x06
a_score = xins
b_point = 0x05
b_score = yins
if a_score <= b_score and zins < b_score:
b_score = zins
b_point = 0x03
elif a_score > b_score and zins < a_score:
a_score = zins
a_point = 0x03
# Now we determine the two lattice points not part of the tetrahedron that may contribute.
# This depends on the closest two tetrahedral vertices, including (1,1,1)
wins = 3 - in_sum
if wins < a_score or wins < b_score: # (1,1,1) is one of the closest two tetrahedral vertices.
c = b_point if (b_score < a_score) else a_point # Our other closest vertex is the closest out of a and b.
if (c & 0x01) != 0:
xsv_ext0 = xsb + 2
xsv_ext1 = xsb + 1
dx_ext0 = dx0 - 2 - 3 * SQUISH_CONSTANT3
dx_ext1 = dx0 - 1 - 3 * SQUISH_CONSTANT3
else:
xsv_ext0 = xsv_ext1 = xsb
dx_ext0 = dx_ext1 = dx0 - 3 * SQUISH_CONSTANT3
if (c & 0x02) != 0:
ysv_ext0 = ysv_ext1 = ysb + 1
dy_ext0 = dy_ext1 = dy0 - 1 - 3 * SQUISH_CONSTANT3
if (c & 0x01) != 0:
ysv_ext1 += 1
dy_ext1 -= 1
else:
ysv_ext0 += 1
dy_ext0 -= 1
else:
ysv_ext0 = ysv_ext1 = ysb
dy_ext0 = dy_ext1 = dy0 - 3 * SQUISH_CONSTANT3
if (c & 0x04) != 0:
zsv_ext0 = zsb + 1
zsv_ext1 = zsb + 2
dz_ext0 = dz0 - 1 - 3 * SQUISH_CONSTANT3
dz_ext1 = dz0 - 2 - 3 * SQUISH_CONSTANT3
else:
zsv_ext0 = zsv_ext1 = zsb
dz_ext0 = dz_ext1 = dz0 - 3 * SQUISH_CONSTANT3
else: # (1,1,1) is not one of the closest two tetrahedral vertices.
c = (a_point & b_point) # Our two extra vertices are determined by the closest two.
if (c & 0x01) != 0:
xsv_ext0 = xsb + 1
xsv_ext1 = xsb + 2
dx_ext0 = dx0 - 1 - SQUISH_CONSTANT3
dx_ext1 = dx0 - 2 - 2 * SQUISH_CONSTANT3
else:
xsv_ext0 = xsv_ext1 = xsb
dx_ext0 = dx0 - SQUISH_CONSTANT3
dx_ext1 = dx0 - 2 * SQUISH_CONSTANT3
if (c & 0x02) != 0:
ysv_ext0 = ysb + 1
ysv_ext1 = ysb + 2
dy_ext0 = dy0 - 1 - SQUISH_CONSTANT3
dy_ext1 = dy0 - 2 - 2 * SQUISH_CONSTANT3
else:
ysv_ext0 = ysv_ext1 = ysb
dy_ext0 = dy0 - SQUISH_CONSTANT3
dy_ext1 = dy0 - 2 * SQUISH_CONSTANT3
if (c & 0x04) != 0:
zsv_ext0 = zsb + 1
zsv_ext1 = zsb + 2
dz_ext0 = dz0 - 1 - SQUISH_CONSTANT3
dz_ext1 = dz0 - 2 - 2 * SQUISH_CONSTANT3
else:
zsv_ext0 = zsv_ext1 = zsb
dz_ext0 = dz0 - SQUISH_CONSTANT3
dz_ext1 = dz0 - 2 * SQUISH_CONSTANT3
# Contribution (1,1,0)
dx3 = dx0 - 1 - 2 * SQUISH_CONSTANT3
dy3 = dy0 - 1 - 2 * SQUISH_CONSTANT3
dz3 = dz0 - 0 - 2 * SQUISH_CONSTANT3
attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3
if attn3 > 0:
attn3 *= attn3
value += attn3 * attn3 * _extrapolate3(perm, perm_grad_index3, xsb + 1, ysb + 1, zsb + 0, dx3, dy3, dz3)
# Contribution (1,0,1)
dx2 = dx3
dy2 = dy0 - 0 - 2 * SQUISH_CONSTANT3
dz2 = dz0 - 1 - 2 * SQUISH_CONSTANT3
attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2
if attn2 > 0:
attn2 *= attn2
value += attn2 * attn2 * _extrapolate3(perm, perm_grad_index3, xsb + 1, ysb + 0, zsb + 1, dx2, dy2, dz2)
# Contribution (0,1,1)
dx1 = dx0 - 0 - 2 * SQUISH_CONSTANT3
dy1 = dy3
dz1 = dz2
attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1
if attn1 > 0:
attn1 *= attn1
value += attn1 * attn1 * _extrapolate3(perm, perm_grad_index3, xsb + 0, ysb + 1, zsb + 1, dx1, dy1, dz1)
# Contribution (1,1,1)
dx0 = dx0 - 1 - 3 * SQUISH_CONSTANT3
dy0 = dy0 - 1 - 3 * SQUISH_CONSTANT3
dz0 = dz0 - 1 - 3 * SQUISH_CONSTANT3
attn0 = 2 - dx0 * dx0 - dy0 * dy0 - dz0 * dz0
if attn0 > 0:
attn0 *= attn0
value += attn0 * attn0 * _extrapolate3(perm, perm_grad_index3, xsb + 1, ysb + 1, zsb + 1, dx0, dy0, dz0)
else: # We're inside the octahedron (Rectified 3-Simplex) in between.
# Decide between point (0,0,1) and (1,1,0) as closest
p1 = xins + yins
if p1 > 1:
a_score = p1 - 1
a_point = 0x03
a_is_further_side = True
else:
a_score = 1 - p1
a_point = 0x04
a_is_further_side = False
# Decide between point (0,1,0) and (1,0,1) as closest
p2 = xins + zins
if p2 > 1:
b_score = p2 - 1
b_point = 0x05
b_is_further_side = True
else:
b_score = 1 - p2
b_point = 0x02
b_is_further_side = False
# The closest out of the two (1,0,0) and (0,1,1) will replace the furthest
# out of the two decided above, if closer.
p3 = yins + zins
if p3 > 1:
score = p3 - 1
if a_score <= b_score and a_score < score:
a_point = 0x06
a_is_further_side = True
elif a_score > b_score and b_score < score:
b_point = 0x06
b_is_further_side = True
else:
score = 1 - p3
if a_score <= b_score and a_score < score:
a_point = 0x01
a_is_further_side = False
elif a_score > b_score and b_score < score:
b_point = 0x01
b_is_further_side = False
# Where each of the two closest points are determines how the extra two vertices are calculated.
if a_is_further_side == b_is_further_side:
if a_is_further_side: # Both closest points on (1,1,1) side
# One of the two extra points is (1,1,1)
dx_ext0 = dx0 - 1 - 3 * SQUISH_CONSTANT3
dy_ext0 = dy0 - 1 - 3 * SQUISH_CONSTANT3
dz_ext0 = dz0 - 1 - 3 * SQUISH_CONSTANT3
xsv_ext0 = xsb + 1
ysv_ext0 = ysb + 1
zsv_ext0 = zsb + 1
# Other extra point is based on the shared axis.
c = (a_point & b_point)
if (c & 0x01) != 0:
dx_ext1 = dx0 - 2 - 2 * SQUISH_CONSTANT3
dy_ext1 = dy0 - 2 * SQUISH_CONSTANT3
dz_ext1 = dz0 - 2 * SQUISH_CONSTANT3
xsv_ext1 = xsb + 2
ysv_ext1 = ysb
zsv_ext1 = zsb
elif (c & 0x02) != 0:
dx_ext1 = dx0 - 2 * SQUISH_CONSTANT3
dy_ext1 = dy0 - 2 - 2 * SQUISH_CONSTANT3
dz_ext1 = dz0 - 2 * SQUISH_CONSTANT3
xsv_ext1 = xsb
ysv_ext1 = ysb + 2
zsv_ext1 = zsb
else:
dx_ext1 = dx0 - 2 * SQUISH_CONSTANT3
dy_ext1 = dy0 - 2 * SQUISH_CONSTANT3
dz_ext1 = dz0 - 2 - 2 * SQUISH_CONSTANT3
xsv_ext1 = xsb
ysv_ext1 = ysb
zsv_ext1 = zsb + 2
else: # Both closest points on (0,0,0) side
# One of the two extra points is (0,0,0)
dx_ext0 = dx0
dy_ext0 = dy0
dz_ext0 = dz0
xsv_ext0 = xsb
ysv_ext0 = ysb
zsv_ext0 = zsb
# Other extra point is based on the omitted axis.
c = (a_point | b_point)
if (c & 0x01) == 0:
dx_ext1 = dx0 + 1 - SQUISH_CONSTANT3
dy_ext1 = dy0 - 1 - SQUISH_CONSTANT3
dz_ext1 = dz0 - 1 - SQUISH_CONSTANT3
xsv_ext1 = xsb - 1
ysv_ext1 = ysb + 1
zsv_ext1 = zsb + 1
elif (c & 0x02) == 0:
dx_ext1 = dx0 - 1 - SQUISH_CONSTANT3
dy_ext1 = dy0 + 1 - SQUISH_CONSTANT3
dz_ext1 = dz0 - 1 - SQUISH_CONSTANT3
xsv_ext1 = xsb + 1
ysv_ext1 = ysb - 1
zsv_ext1 = zsb + 1
else:
dx_ext1 = dx0 - 1 - SQUISH_CONSTANT3
dy_ext1 = dy0 - 1 - SQUISH_CONSTANT3
dz_ext1 = dz0 + 1 - SQUISH_CONSTANT3
xsv_ext1 = xsb + 1
ysv_ext1 = ysb + 1
zsv_ext1 = zsb - 1
else: # One point on (0,0,0) side, one point on (1,1,1) side
if a_is_further_side:
c1 = a_point
c2 = b_point
else:
c1 = b_point
c2 = a_point
# One contribution is a _permutation of (1,1,-1)
if (c1 & 0x01) == 0:
dx_ext0 = dx0 + 1 - SQUISH_CONSTANT3
dy_ext0 = dy0 - 1 - SQUISH_CONSTANT3
dz_ext0 = dz0 - 1 - SQUISH_CONSTANT3
xsv_ext0 = xsb - 1
ysv_ext0 = ysb + 1
zsv_ext0 = zsb + 1
elif (c1 & 0x02) == 0:
dx_ext0 = dx0 - 1 - SQUISH_CONSTANT3
dy_ext0 = dy0 + 1 - SQUISH_CONSTANT3
dz_ext0 = dz0 - 1 - SQUISH_CONSTANT3
xsv_ext0 = xsb + 1
ysv_ext0 = ysb - 1
zsv_ext0 = zsb + 1
else:
dx_ext0 = dx0 - 1 - SQUISH_CONSTANT3
dy_ext0 = dy0 - 1 - SQUISH_CONSTANT3
dz_ext0 = dz0 + 1 - SQUISH_CONSTANT3
xsv_ext0 = xsb + 1
ysv_ext0 = ysb + 1
zsv_ext0 = zsb - 1
# One contribution is a _permutation of (0,0,2)
dx_ext1 = dx0 - 2 * SQUISH_CONSTANT3
dy_ext1 = dy0 - 2 * SQUISH_CONSTANT3
dz_ext1 = dz0 - 2 * SQUISH_CONSTANT3
xsv_ext1 = xsb
ysv_ext1 = ysb
zsv_ext1 = zsb
if (c2 & 0x01) != 0:
dx_ext1 -= 2
xsv_ext1 += 2
elif (c2 & 0x02) != 0:
dy_ext1 -= 2
ysv_ext1 += 2
else:
dz_ext1 -= 2
zsv_ext1 += 2
# Contribution (1,0,0)
dx1 = dx0 - 1 - SQUISH_CONSTANT3
dy1 = dy0 - 0 - SQUISH_CONSTANT3
dz1 = dz0 - 0 - SQUISH_CONSTANT3
attn1 = 2 - dx1 * dx1 - dy1 * dy1 - dz1 * dz1
if attn1 > 0:
attn1 *= attn1
value += attn1 * attn1 * _extrapolate3(perm, perm_grad_index3, xsb + 1, ysb + 0, zsb + 0, dx1, dy1, dz1)
# Contribution (0,1,0)
dx2 = dx0 - 0 - SQUISH_CONSTANT3
dy2 = dy0 - 1 - SQUISH_CONSTANT3
dz2 = dz1
attn2 = 2 - dx2 * dx2 - dy2 * dy2 - dz2 * dz2
if attn2 > 0:
attn2 *= attn2
value += attn2 * attn2 * _extrapolate3(perm, perm_grad_index3, xsb + 0, ysb + 1, zsb + 0, dx2, dy2, dz2)
# Contribution (0,0,1)
dx3 = dx2
dy3 = dy1
dz3 = dz0 - 1 - SQUISH_CONSTANT3
attn3 = 2 - dx3 * dx3 - dy3 * dy3 - dz3 * dz3
if attn3 > 0:
attn3 *= attn3
value += attn3 * attn3 * _extrapolate3(perm, perm_grad_index3, xsb + 0, ysb + 0, zsb + 1, dx3, dy3, dz3)
# Contribution (1,1,0)
dx4 = dx0 - 1 - 2 * SQUISH_CONSTANT3
dy4 = dy0 - 1 - 2 * SQUISH_CONSTANT3
dz4 = dz0 - 0 - 2 * SQUISH_CONSTANT3
attn4 = 2 - dx4 * dx4 - dy4 * dy4 - dz4 * dz4
if attn4 > 0:
attn4 *= attn4
value += attn4 * attn4 * _extrapolate3(perm, perm_grad_index3, xsb + 1, ysb + 1, zsb + 0, dx4, dy4, dz4)
# Contribution (1,0,1)
dx5 = dx4
dy5 = dy0 - 0 - 2 * SQUISH_CONSTANT3
dz5 = dz0 - 1 - 2 * SQUISH_CONSTANT3
attn5 = 2 - dx5 * dx5 - dy5 * dy5 - dz5 * dz5
if attn5 > 0:
attn5 *= attn5
value += attn5 * attn5 * _extrapolate3(perm, perm_grad_index3, xsb + 1, ysb + 0, zsb + 1, dx5, dy5, dz5)
# Contribution (0,1,1)
dx6 = dx0 - 0 - 2 * SQUISH_CONSTANT3
dy6 = dy4
dz6 = dz5
attn6 = 2 - dx6 * dx6 - dy6 * dy6 - dz6 * dz6
if attn6 > 0:
attn6 *= attn6
value += attn6 * attn6 * _extrapolate3(perm, perm_grad_index3, xsb + 0, ysb + 1, zsb + 1, dx6, dy6, dz6)
# First extra vertex
attn_ext0 = 2 - dx_ext0 * dx_ext0 - dy_ext0 * dy_ext0 - dz_ext0 * dz_ext0
if attn_ext0 > 0:
attn_ext0 *= attn_ext0
value += attn_ext0 * attn_ext0 * _extrapolate3(
perm,
perm_grad_index3,
xsv_ext0,
ysv_ext0,
zsv_ext0,
dx_ext0,
dy_ext0,
dz_ext0
)
# Second extra vertex
attn_ext1 = 2 - dx_ext1 * dx_ext1 - dy_ext1 * dy_ext1 - dz_ext1 * dz_ext1
if attn_ext1 > 0:
attn_ext1 *= attn_ext1
value += attn_ext1 * attn_ext1 * _extrapolate3(
perm,
perm_grad_index3,
xsv_ext1,
ysv_ext1,
zsv_ext1,
dx_ext1,
dy_ext1,
dz_ext1
)
return value / NORM_CONSTANT3
@njit(cache=True, parallel=True)
def _noise3a(X, Y, Z, perm, perm_grad_index3):
noise = np.zeros((Z.size, Y.size, X.size), dtype=np.double)
for z in prange(Z.size):
for y in prange(Y.size):
for x in prange(X.size):
noise[z, y, x] = _noise3(X[x], Y[y], Z[z], perm, perm_grad_index3)
return noise
@njit(cache=True, parallel=True)
def _noise3b(X, Y, Z, perm, perm_grad_index3):
noise = np.zeros(X.size * Y.size * Z.size, dtype=np.double)
for z in prange(Z.size):
for y in prange(Y.size):
for x in prange(X.size):
noise[(y * Y.size + x) + (z * Y.size * X.size)] = _noise3(X[x], Y[y], Z[z], perm, perm_grad_index3)
return noise.reshape((Z.size, Y.size, X.size))
def _noise3aSlow(X, Y, T, FEATURE_SIZE, perm, perm_grad_index3):
img = np.empty((T, X, Y), dtype=np.double)
for t in range(T):
for x in range(X):
for y in range(Y):
img[t, x, y] = _noise3(x / FEATURE_SIZE, y / FEATURE_SIZE, t / FEATURE_SIZE, perm, perm_grad_index3)
return img
def testing_main():
times = []
slices = 100
img_size = (256, 256)
feature_size = 24.0
simplexObj = Simplex_CLASS()
# for _ in tqdm(range(1)):
#
# t = np.arange(0, slices) / feature_size
# x = np.arange(0, img_size[0]) / feature_size # indices divided by feature size
# y = np.arange(0, img_size[1]) / feature_size # indices divided by feature size
# start = time.time()
# three_noise = _noise3a(x, y, t,simplexObj._perm,simplexObj._perm_grad_index3)
# done = time.time() - start
# times.append(done)
#
# print(np.average(np.array(times)))
#
# times = []
# for _ in tqdm(range(1)):
# t = np.arange(0, slices) / feature_size
# x = np.arange(0, img_size[0]) / feature_size # indices divided by feature size
# y = np.arange(0, img_size[1]) / feature_size # indices divided by feature size
# start = time.time()
# three_noise = _noise3b(x, y, t,simplexObj._perm,simplexObj._perm_grad_index3)
# done = time.time() - start
# times.append(done)
#
# print(np.average(np.array(times)))
# print(_noise3aSlow(img_size[0],img_size[1],slices,feature_size,simplexObj._perm,simplexObj._perm_grad_index3).shape)
# times = []
# for _ in tqdm(range(100)):
# start = time.time()
# _noise3aSlow(img_size[0],img_size[1],slices,feature_size,simplexObj._perm,simplexObj._perm_grad_index3)
# done = time.time() - start
# times.append(done)
#
# print(np.average(np.array(times)))
# t = np.arange(0, slices) / feature_size
# x = np.arange(0, img_size[0]) / feature_size # indices divided by feature size
# y = np.arange(0, img_size[1]) / feature_size # indices divided by feature size
#
three_noise = simplexObj.rand_3d_octaves((slices, *img_size), 6, 0.6)
print(three_noise.shape)
fig, ax = plt.subplots()
imgs = [[ax.imshow(three_noise[x], animated=True, cmap='gray')] for x in range(slices)]
ani = animation.ArtistAnimation(
fig, imgs, interval=50, blit=True,
repeat_delay=1000
)
ani.save(f'./SIMPLEX_TEST_Oct.mp4')
# t = np.arange(0, slices) / feature_size
# x = np.arange(0, img_size[0]) / feature_size # indices divided by feature size
# y = np.arange(0, img_size[1]) / feature_size # indices divided by feature size
# perm, perm_grad_index3 = _init()
# three_noise = _noise3a(x,y,t, perm, perm_grad_index3)
#
# print(three_noise.shape)
# fig, ax = plt.subplots()
# imgs = [[ax.imshow(three_noise[x], animated=True, cmap='gray')] for x in range(slices)]
# ani = animation.ArtistAnimation(fig, imgs, interval=50, blit=True,
# repeat_delay=1000)
# ani.save(f'./SIMPLEX_TEST_optimA.mp4')
#
# three_noise = _noise3b(x,y,t, perm, perm_grad_index3)
#
# print(three_noise.shape)
# fig, ax = plt.subplots()
# imgs = [[ax.imshow(three_noise[x], animated=True, cmap='gray')] for x in range(slices)]
# ani = animation.ArtistAnimation(fig, imgs, interval=50, blit=True,
# repeat_delay=1000)
# ani.save(f'./SIMPLEX_TEST_optimB.mp4')
# three_noise = _noise3aSlow(*img_size,slices,feature_size, perm, perm_grad_index3)
#
# print(three_noise.shape)
# fig, ax = plt.subplots()
# imgs = [[ax.imshow(three_noise[x], animated=True, cmap='gray')] for x in range(slices)]
# ani = animation.ArtistAnimation(fig, imgs, interval=50, blit=True,
# repeat_delay=1000)
# ani.save(f'./SIMPLEX_TEST_slow.mp4')
if __name__ == "__main__":
testing_main()