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### Translating simplex noise code from java to perl

by grondilu (Friar)
 on Mar 03, 2014 at 19:57 UTC ( #1076808=CUFP: print w/replies, xml ) Need Help??

Hi monks,

I've been growing an interest for computer graphics lately, and most especially for proceduraly generated landscapes (the kind of stuff we can see in open-world or sandbox games). So while educating myself on this subject I learnt a few things about noise, and I learnt about the so-called Perlin noise, which was invented in the eighties or something. In 2002, Perlin improved his algorithm by using a better tesselation of space. It's called the Simplex noise and it's discussed and explained by Stefan Gustavson in this document, while providing a java implementation in public domain.

Well, I don't like java so I wanted to translate it into Perl. I've done it for the 2D dimension, and I thought it was worth sharing with you monks. I'll certainly translate the rest (3D and 4D) later. I will almost certainly write a Perl 6 version as well.

I also added a few lines to create a noise image in PGM format. Here is the result:

http://imgur.com/ArVvBvN

And here is the code (the original java code is in the __END__)

```package SimplexNoise;
# see __END__ for credits
use strict;
use warnings;

my @grad3 = (
[1, 1, 0], [-1, 1, 0], [1, -1, 0], [-1, -1, 0],
[1, 0, 1], [-1, 0, 1], [1, 0, -1], [-1, 0, -1],
[0, 1, 1], [0, -1, 1], [0, 1, -1], [0, -1, -1],
);

my @grad4 = (
[0, 1, 1, 1],  [0, 1, 1, -1],  [0, 1, -1, 1],  [0, 1, -1, -1],
[0, -1, 1, 1], [0, -1, 1, -1], [0, -1, -1, 1], [0, -1, -1, -1],
[1, 0, 1, 1],  [1, 0, 1, -1],  [1, 0, -1, 1],  [1, 0, -1, -1],
[-1, 0, 1, 1], [-1, 0, 1, -1], [-1, 0, -1, 1], [-1, 0, -1, -1],
[1, 1, 0, 1],  [1, 1, 0, -1],  [1, -1, 0, 1],  [1, -1, 0, -1],
[-1, 1, 0, 1], [-1, 1, 0, -1], [-1, -1, 0, 1], [-1, -1, 0, -1],
[1, 1, 1, 0],  [1, 1, -1, 0],  [1, -1, 1, 0],  [1, -1, -1, 0],
[-1, 1, 1, 0], [-1, 1, -1, 0], [-1, -1, 1, 0], [-1, -1, -1, 0],
);

use constant p => qw(151 160 137 91 90 15 131 13 201 95 96 53 194 233
+7 225
140 36 103 30 69 142 8 99 37 240 21 10 23 190 6 148 247 120 234 75 0 2
+6 197 62
94 252 219 203 117 35 11 32 57 177 33 88 237 149 56 87 174 20 125 136
+171 168
68 175 74 165 71 134 139 48 27 166 77 146 158 231 83 111 229 122 60 21
+1 133 230
220 105 92 41 55 46 245 40 244 102 143 54 65 25 63 161 1 216 80 73 209
+ 76 132
187 208 89 18 169 200 196 135 130 116 188 159 86 164 100 109 198 173 1
+86 3 64
52 217 226 250 124 123 5 202 38 147 118 126 255 82 85 212 207 206 59 2
+27 47 16
58 17 182 189 28 42 223 183 170 213 119 248 152 2 44 154 163 70 221 15
+3 101 155
167 43 172 9 129 22 39 253 19 98 108 110 79 113 224 232 178 185 112 10
+4 218 246
97 228 251 34 242 193 238 210 144 12 191 179 162 241 81 51 145 235 249
+ 14 239
107 49 192 214 31 181 199 106 157 184 84 204 176 115 121 50 45 127 4 1
+50 254
138 236 205 93 222 114 67 29 24 72 243 141 128 195 78 66 215 61 156 18
+0);

use constant perm => (p, p);
use constant permMod12 => map { \$_ % 12 } perm;

# Skewing and unskewing factors for 2, 3, and 4 dimensions
use constant {
F2 => 0.5 * (sqrt(3.0) - 1.0),
G2 => (3.0 - sqrt(3.0)) / 6.0,
F3 => 1.0 / 3.0,
G3 => 1.0 / 6.0,
F4 => (sqrt(5.0) - 1.0) / 4.0,
G4 => (5.0 - sqrt(5.0)) / 20.0,
};

sub floor {
my \$x = shift;
my \$xi = int(\$x);
return \$x < \$xi ? \$xi - 1 : \$xi;
}

sub dot {
my @grad = @{shift()};
my \$sum = 0;
\$sum += \$_ * shift @grad for @_;
\$sum;
}

sub noise {
if (@_ == 2) {
# 2D noise
my (\$n0, \$n1, \$n2);
my (\$xin, \$yin) = @_;
my \$s = (\$xin + \$yin) * F2;
my (\$i, \$j) = map { floor(\$_) } \$xin + \$s, \$yin + \$s;
my \$t = (\$i + \$j) * G2;
my (\$X0, \$Y0) = (\$i - \$t, \$j - \$t);
my (\$x0, \$y0) = (\$xin - \$X0, \$yin - \$Y0);
my (\$i1, \$j1) = \$x0 > \$y0 ? (1, 0) : (0, 1);
my (\$x1, \$y1) = (\$x0 - \$i1 + G2, \$y0 - \$j1 + G2);
my (\$x2, \$y2) = (\$x0 - 1 + 2 * G2, \$y0 - 1 + 2 * G2);
my (\$ii, \$jj) = (\$i & 255, \$j & 255);
my (\$gi0, \$gi1, \$gi2) = (permMod12)[
\$ii + (perm)[\$jj],
\$ii + \$i1 + (perm)[\$jj + \$j1],
\$ii + 1 + (perm)[\$jj + 1]
];
my \$t0 = 0.5 - \$x0 * \$x0 - \$y0 * \$y0;
if (\$t0 < 0) { \$n0 = 0 }
else {
\$t0 *= \$t0;
\$n0 = \$t0 * \$t0 * dot(\$grad3[\$gi0], \$x0, \$y0);
}
my \$t1 = 0.5 - \$x1 * \$x1 - \$y1 * \$y1;
if (\$t1 < 0) { \$n1 = 0 }
else {
\$t1 *= \$t1;
\$n1 = \$t1 * \$t1 * dot(\$grad3[\$gi1], \$x1, \$y1);
}
my \$t2 = 0.5 - \$x2 * \$x2 - \$y2 * \$y2;
if (\$t2 < 0) { \$n2 = 0 }
else {
\$t2 *= \$t2;
\$n2 = \$t2 * \$t2 * dot(\$grad3[\$gi2], \$x2, \$y2);
}
return 70 * (\$n0 + \$n1 + \$n2);
} elsif (@_ == 3) {
# 3D noise
...
} elsif (@_ == 4) {
# 4D noise
...
} else {...}
}

my \$N = 256;

print "P2\n";
print "\$N \$N\n";
print "255\n";
for my \$i (1 .. \$N) {
my \$x = \$i / 10;
for my \$j (1 .. \$N) {
my \$y = \$j / 10;
my \$noise =  noise \$x, \$y;
print int((\$noise + 1) / 2 * 256);
print \$j == \$N ? "\n" : ' ';
}
}

__END__

=pod
=begin java
/*
* A speed-improved simplex noise algorithm for 2D, 3D and 4D in J
+ava.
*
* Based on example code by Stefan Gustavson (stegu@itn.liu.se).
* Optimisations by Peter Eastman (peastman@drizzle.stanford.edu).
* Better rank ordering method by Stefan Gustavson in 2012.
*
* This could be speeded up even further, but it's useful as it is
+.
*
* Version 2012-03-09
*
* This code was placed in the public domain by its original autho
+r,
* Stefan Gustavson. You may use it as you see fit, but
* attribution is appreciated.
*
*/

public class SimplexNoise {  // Simplex noise in 2D, 3D and 4D
private static Grad grad3[] = {new Grad(1,1,0),new Grad(-1,1,0),
+new Grad(1,-1,0),new Grad(-1,-1,0),
new Grad(1,0,1),new Grad(-1,0,1),new Grad(1,0,-1)
+,new Grad(-1,0,-1),
new Grad(0,1,1),new Grad(0,-1,1),new Grad(0,1,-1)
+,new Grad(0,-1,-1)};

private static Grad grad4[]= {new Grad(0,1,1,1),new Grad(0,1,1,-
+1),new Grad(0,1,-1,1),new Grad(0,1,-1,-1),
new Grad(0,-1,1,1),new Grad(0,-1,1,-1),new Grad(0,-1,-1
+,1),new Grad(0,-1,-1,-1),
new Grad(1,0,1,1),new Grad(1,0,1,-1),new Grad(1,0,-1,1)
+,new Grad(1,0,-1,-1),
new Grad(-1,0,1,1),new Grad(-1,0,1,-1),new Grad(-1,0,-1
+,1),new Grad(-1,0,-1,-1),
new Grad(1,1,0,1),new Grad(1,1,0,-1),new Grad(1,-1,0,1)
+,new Grad(1,-1,0,-1),
new Grad(-1,1,0,1),new Grad(-1,1,0,-1),new Grad(-1,-1,0
+,1),new Grad(-1,-1,0,-1),
new Grad(1,1,1,0),new Grad(1,1,-1,0),new Grad(1,-1,1,0)
+,new Grad(1,-1,-1,0),
new Grad(-1,1,1,0),new Grad(-1,1,-1,0),new Grad(-1,-1,1
+,0),new Grad(-1,-1,-1,0)};

private static short p[] = {151,160,137,91,90,15,
131,13,201,95,96,53,194,233,7,225,140,36,103,30,69,142,8,99,37,2
+40,21,10,23,
190, 6,148,247,120,234,75,0,26,197,62,94,252,219,203,117,35,11,3
+2,57,177,33,
88,237,149,56,87,174,20,125,136,171,168, 68,175,74,165,71,134,13
+9,48,27,166,
77,146,158,231,83,111,229,122,60,211,133,230,220,105,92,41,55,46
+,245,40,244,
102,143,54, 65,25,63,161, 1,216,80,73,209,76,132,187,208, 89,18,
+169,200,196,
135,130,116,188,159,86,164,100,109,198,173,186, 3,64,52,217,226,
+250,124,123,
5,202,38,147,118,126,255,82,85,212,207,206,59,227,47,16,58,17,18
+2,189,28,42,
223,183,170,213,119,248,152, 2,44,154,163, 70,221,153,101,155,16
+7, 43,172,9,
129,22,39,253, 19,98,108,110,79,113,224,232,178,185, 112,104,218
+,246,97,228,
251,34,242,193,238,210,144,12,191,179,162,241, 81,51,145,235,249
+,14,239,107,
49,192,214, 31,181,199,106,157,184, 84,204,176,115,121,50,45,127
+, 4,150,254,
138,236,205,93,222,114,67,29,24,72,243,141,128,195,78,66,215,61,
+156,180};
// To remove the need for index wrapping, double the permutation
+ table length
private static short perm[] = new short[512];
private static short permMod12[] = new short[512];
static {
for(int i=0; i<512; i++)
{
perm[i]=p[i & 255];
permMod12[i] = (short)(perm[i] % 12);
}
}

// Skewing and unskewing factors for 2, 3, and 4 dimensions
private static final double F2 = 0.5*(Math.sqrt(3.0)-1.0);
private static final double G2 = (3.0-Math.sqrt(3.0))/6.0;
private static final double F3 = 1.0/3.0;
private static final double G3 = 1.0/6.0;
private static final double F4 = (Math.sqrt(5.0)-1.0)/4.0;
private static final double G4 = (5.0-Math.sqrt(5.0))/20.0;

// This method is a *lot* faster than using (int)Math.floor(x)
private static int fastfloor(double x) {
int xi = (int)x;
return x<xi ? xi-1 : xi;
}

private static double dot(Grad g, double x, double y) {
return g.x*x + g.y*y; }

private static double dot(Grad g, double x, double y, double z)
+{
return g.x*x + g.y*y + g.z*z; }

private static double dot(Grad g, double x, double y, double z,
+double w) {
return g.x*x + g.y*y + g.z*z + g.w*w; }

// 2D simplex noise
public static double noise(double xin, double yin) {
double n0, n1, n2; // Noise contributions from the three corners
// Skew the input space to determine which simplex cell we're in
double s = (xin+yin)*F2; // Hairy factor for 2D
int i = fastfloor(xin+s);
int j = fastfloor(yin+s);
double t = (i+j)*G2;
double X0 = i-t; // Unskew the cell origin back to (x,y) space
double Y0 = j-t;
double x0 = xin-X0; // The x,y distances from the cell origin
double y0 = yin-Y0;
// For the 2D case, the simplex shape is an equilateral triangle.
// Determine which simplex we are in.
int i1, j1; // Offsets for second (middle) corner of simplex in (i
+,j) coords
if(x0>y0) {i1=1; j1=0;} // lower triangle, XY order: (0,0)->(1,0)-
+>(1,1)
else {i1=0; j1=1;}      // upper triangle, YX order: (0,0)->(0,1)-
+>(1,1)
// A step of (1,0) in (i,j) means a step of (1-c,-c) in (x,y), and
// a step of (0,1) in (i,j) means a step of (-c,1-c) in (x,y), whe
+re
// c = (3-sqrt(3))/6
double x1 = x0 - i1 + G2; // Offsets for middle corner in (x,y) un
+skewed coords
double y1 = y0 - j1 + G2;
double x2 = x0 - 1.0 + 2.0 * G2; // Offsets for last corner in (x,
+y) unskewed coords
double y2 = y0 - 1.0 + 2.0 * G2;
// Work out the hashed gradient indices of the three simplex corne
+rs
int ii = i & 255;
int jj = j & 255;
int gi0 = permMod12[ii+perm[jj]];
int gi1 = permMod12[ii+i1+perm[jj+j1]];
int gi2 = permMod12[ii+1+perm[jj+1]];
// Calculate the contribution from the three corners
double t0 = 0.5 - x0*x0-y0*y0;
if(t0<0) n0 = 0.0;
else {
t0 *= t0;
n0 = t0 * t0 * dot(grad3[gi0], x0, y0);  // (x,y) of grad3 used
+for 2D gradient
}
double t1 = 0.5 - x1*x1-y1*y1;
if(t1<0) n1 = 0.0;
else {
t1 *= t1;
n1 = t1 * t1 * dot(grad3[gi1], x1, y1);
}
double t2 = 0.5 - x2*x2-y2*y2;
if(t2<0) n2 = 0.0;
else {
t2 *= t2;
n2 = t2 * t2 * dot(grad3[gi2], x2, y2);
}
// Add contributions from each corner to get the final noise value
+.
// The result is scaled to return values in the interval [-1,1].
return 70.0 * (n0 + n1 + n2);
}

// 3D simplex noise
public static double noise(double xin, double yin, double zin) {
double n0, n1, n2, n3; // Noise contributions from the four corner
+s
// Skew the input space to determine which simplex cell we're in
double s = (xin+yin+zin)*F3; // Very nice and simple skew factor f
+or 3D
int i = fastfloor(xin+s);
int j = fastfloor(yin+s);
int k = fastfloor(zin+s);
double t = (i+j+k)*G3;
double X0 = i-t; // Unskew the cell origin back to (x,y,z) space
double Y0 = j-t;
double Z0 = k-t;
double x0 = xin-X0; // The x,y,z distances from the cell origin
double y0 = yin-Y0;
double z0 = zin-Z0;
// For the 3D case, the simplex shape is a slightly irregular tetr
+ahedron.
// Determine which simplex we are in.
int i1, j1, k1; // Offsets for second corner of simplex in (i,j,k)
+ coords
int i2, j2, k2; // Offsets for third corner of simplex in (i,j,k)
+coords
if(x0>=y0) {
if(y0>=z0)
{ i1=1; j1=0; k1=0; i2=1; j2=1; k2=0; } // X Y Z order
else if(x0>=z0) { i1=1; j1=0; k1=0; i2=1; j2=0; k2=1; } // X Z
+ Y order
else { i1=0; j1=0; k1=1; i2=1; j2=0; k2=1; } // Z X Y order
}
else { // x0<y0
if(y0<z0) { i1=0; j1=0; k1=1; i2=0; j2=1; k2=1; } // Z Y X order
else if(x0<z0) { i1=0; j1=1; k1=0; i2=0; j2=1; k2=1; } // Y Z X
+order
else { i1=0; j1=1; k1=0; i2=1; j2=1; k2=0; } // Y X Z order
}
// A step of (1,0,0) in (i,j,k) means a step of (1-c,-c,-c) in (x,
+y,z),
// a step of (0,1,0) in (i,j,k) means a step of (-c,1-c,-c) in (x,
+y,z), and
// a step of (0,0,1) in (i,j,k) means a step of (-c,-c,1-c) in (x,
+y,z), where
// c = 1/6.
double x1 = x0 - i1 + G3; // Offsets for second corner in (x,y,z)
+coords
double y1 = y0 - j1 + G3;
double z1 = z0 - k1 + G3;
double x2 = x0 - i2 + 2.0*G3; // Offsets for third corner in (x,y,
+z) coords
double y2 = y0 - j2 + 2.0*G3;
double z2 = z0 - k2 + 2.0*G3;
double x3 = x0 - 1.0 + 3.0*G3; // Offsets for last corner in (x,y,
+z) coords
double y3 = y0 - 1.0 + 3.0*G3;
double z3 = z0 - 1.0 + 3.0*G3;
// Work out the hashed gradient indices of the four simplex corner
+s
int ii = i & 255;
int jj = j & 255;
int kk = k & 255;
int gi0 = permMod12[ii+perm[jj+perm[kk]]];
int gi1 = permMod12[ii+i1+perm[jj+j1+perm[kk+k1]]];
int gi2 = permMod12[ii+i2+perm[jj+j2+perm[kk+k2]]];
int gi3 = permMod12[ii+1+perm[jj+1+perm[kk+1]]];
// Calculate the contribution from the four corners
double t0 = 0.6 - x0*x0 - y0*y0 - z0*z0;
if(t0<0) n0 = 0.0;
else {
t0 *= t0;
n0 = t0 * t0 * dot(grad3[gi0], x0, y0, z0);
}
double t1 = 0.6 - x1*x1 - y1*y1 - z1*z1;
if(t1<0) n1 = 0.0;
else {
t1 *= t1;
n1 = t1 * t1 * dot(grad3[gi1], x1, y1, z1);
}
double t2 = 0.6 - x2*x2 - y2*y2 - z2*z2;
if(t2<0) n2 = 0.0;
else {
t2 *= t2;
n2 = t2 * t2 * dot(grad3[gi2], x2, y2, z2);
}
double t3 = 0.6 - x3*x3 - y3*y3 - z3*z3;
if(t3<0) n3 = 0.0;
else {
t3 *= t3;
n3 = t3 * t3 * dot(grad3[gi3], x3, y3, z3);
}
// Add contributions from each corner to get the final noise value
+.
// The result is scaled to stay just inside [-1,1]
return 32.0*(n0 + n1 + n2 + n3);
}

// 4D simplex noise, better simplex rank ordering method 2012-03
+-09
public static double noise(double x, double y, double z, double
+w) {

double n0, n1, n2, n3, n4; // Noise contributions from the five co
+rners
// Skew the (x,y,z,w) space to determine which cell of 24 simplice
+s we're in
double s = (x + y + z + w) * F4; // Factor for 4D skewing
int i = fastfloor(x + s);
int j = fastfloor(y + s);
int k = fastfloor(z + s);
int l = fastfloor(w + s);
double t = (i + j + k + l) * G4; // Factor for 4D unskewing
double X0 = i - t; // Unskew the cell origin back to (x,y,z,w) spa
+ce
double Y0 = j - t;
double Z0 = k - t;
double W0 = l - t;
double x0 = x - X0;  // The x,y,z,w distances from the cell origin
double y0 = y - Y0;
double z0 = z - Z0;
double w0 = w - W0;
// For the 4D case, the simplex is a 4D shape I won't even try to
+describe.
// To find out which of the 24 possible simplices we're in, we nee
+d to
// determine the magnitude ordering of x0, y0, z0 and w0.
// Six pair-wise comparisons are performed between each possible p
+air
// of the four coordinates, and the results are used to rank the n
+umbers.
int rankx = 0;
int ranky = 0;
int rankz = 0;
int rankw = 0;
if(x0 > y0) rankx++; else ranky++;
if(x0 > z0) rankx++; else rankz++;
if(x0 > w0) rankx++; else rankw++;
if(y0 > z0) ranky++; else rankz++;
if(y0 > w0) ranky++; else rankw++;
if(z0 > w0) rankz++; else rankw++;
int i1, j1, k1, l1; // The integer offsets for the second simplex
+corner
int i2, j2, k2, l2; // The integer offsets for the third simplex c
+orner
int i3, j3, k3, l3; // The integer offsets for the fourth simplex
+corner
// simplex[c] is a 4-vector with the numbers 0, 1, 2 and 3 in some
+ order.
// Many values of c will never occur, since e.g. x>y>z>w makes x<z
+, y<w and x<w
// impossible. Only the 24 indices which have non-zero entries mak
+e any sense.
// We use a thresholding to set the coordinates in turn from the l
+argest magnitude.
// Rank 3 denotes the largest coordinate.
i1 = rankx >= 3 ? 1 : 0;
j1 = ranky >= 3 ? 1 : 0;
k1 = rankz >= 3 ? 1 : 0;
l1 = rankw >= 3 ? 1 : 0;
// Rank 2 denotes the second largest coordinate.
i2 = rankx >= 2 ? 1 : 0;
j2 = ranky >= 2 ? 1 : 0;
k2 = rankz >= 2 ? 1 : 0;
l2 = rankw >= 2 ? 1 : 0;
// Rank 1 denotes the second smallest coordinate.
i3 = rankx >= 1 ? 1 : 0;
j3 = ranky >= 1 ? 1 : 0;
k3 = rankz >= 1 ? 1 : 0;
l3 = rankw >= 1 ? 1 : 0;
// The fifth corner has all coordinate offsets = 1, so no need to
+compute that.
double x1 = x0 - i1 + G4; // Offsets for second corner in (x,y,z,w
+) coords
double y1 = y0 - j1 + G4;
double z1 = z0 - k1 + G4;
double w1 = w0 - l1 + G4;
double x2 = x0 - i2 + 2.0*G4; // Offsets for third corner in (x,y,
+z,w) coords
double y2 = y0 - j2 + 2.0*G4;
double z2 = z0 - k2 + 2.0*G4;
double w2 = w0 - l2 + 2.0*G4;
double x3 = x0 - i3 + 3.0*G4; // Offsets for fourth corner in (x,y
+,z,w) coords
double y3 = y0 - j3 + 3.0*G4;
double z3 = z0 - k3 + 3.0*G4;
double w3 = w0 - l3 + 3.0*G4;
double x4 = x0 - 1.0 + 4.0*G4; // Offsets for last corner in (x,y,
+z,w) coords
double y4 = y0 - 1.0 + 4.0*G4;
double z4 = z0 - 1.0 + 4.0*G4;
double w4 = w0 - 1.0 + 4.0*G4;
// Work out the hashed gradient indices of the five simplex corner
+s
int ii = i & 255;
int jj = j & 255;
int kk = k & 255;
int ll = l & 255;
int gi0 = perm[ii+perm[jj+perm[kk+perm[ll]]]] % 32;
int gi1 = perm[ii+i1+perm[jj+j1+perm[kk+k1+perm[ll+l1]]]] % 32;
int gi2 = perm[ii+i2+perm[jj+j2+perm[kk+k2+perm[ll+l2]]]] % 32;
int gi3 = perm[ii+i3+perm[jj+j3+perm[kk+k3+perm[ll+l3]]]] % 32;
int gi4 = perm[ii+1+perm[jj+1+perm[kk+1+perm[ll+1]]]] % 32;
// Calculate the contribution from the five corners
double t0 = 0.6 - x0*x0 - y0*y0 - z0*z0 - w0*w0;
if(t0<0) n0 = 0.0;
else {
t0 *= t0;
n0 = t0 * t0 * dot(grad4[gi0], x0, y0, z0, w0);
}
double t1 = 0.6 - x1*x1 - y1*y1 - z1*z1 - w1*w1;
if(t1<0) n1 = 0.0;
else {
t1 *= t1;
n1 = t1 * t1 * dot(grad4[gi1], x1, y1, z1, w1);
}
double t2 = 0.6 - x2*x2 - y2*y2 - z2*z2 - w2*w2;
if(t2<0) n2 = 0.0;
else {
t2 *= t2;
n2 = t2 * t2 * dot(grad4[gi2], x2, y2, z2, w2);
}
double t3 = 0.6 - x3*x3 - y3*y3 - z3*z3 - w3*w3;
if(t3<0) n3 = 0.0;
else {
t3 *= t3;
n3 = t3 * t3 * dot(grad4[gi3], x3, y3, z3, w3);
}
double t4 = 0.6 - x4*x4 - y4*y4 - z4*z4 - w4*w4;
if(t4<0) n4 = 0.0;
else {
t4 *= t4;
n4 = t4 * t4 * dot(grad4[gi4], x4, y4, z4, w4);
}
// Sum up and scale the result to cover the range [-1,1]
return 27.0 * (n0 + n1 + n2 + n3 + n4);
}

// Inner class to speed upp gradient computations
// (array access is a lot slower than member access)
private static class Grad
{
double x, y, z, w;

Grad(double x, double y, double z)
{
this.x = x;
this.y = y;
this.z = z;
}

Grad(double x, double y, double z, double w)
{
this.x = x;
this.y = y;
this.z = z;
this.w = w;
}
}
}
=end java

=cut
[download]```

Replies are listed 'Best First'.
Re: Translating simplex noise code from java to perl
by zentara (Archbishop) on Mar 04, 2014 at 11:54 UTC
Wow, all I can say is that is as beautiful as the Mona Lisa. You said you had an few lines of code, which outputs the graphic? Can you post it, please?

I am interested in background noise, as it supposedly clears the mind as you sleep. :-)

My quarters sounds like a rocket flying thru deep space ... always in the background. :-) Full cruising speed ahead!!

I'm not really a human, but I play one on earth.
Old Perl Programmer Haiku ................... flash japh

The code which outputs the graphics is in what I posted (the part which begins with print "P2\n"). Just run it and you'll get a noise image on stdout in ASCII.

To display it, you can use Image magick for instance.

\$ perl SimplexNoise.pm |display -
Re: Translating simplex noise code from java to perl
by SuicideJunkie (Vicar) on Mar 13, 2014 at 20:59 UTC

I've looked at this a bit, with an eye to making it handle higher dimensions, and one of the easy changes is that the F and G constants can be simplified and extended:

Playing around with 2D noise, stacking noise on multiple scales makes for really interesting pictures. I've tacked on a BMP output function to keep it all self-contained.

Example picture: http://imgur.com/aD6cQZ9

Image generation and BMP saving code below:

Finally got it all working the same as the original!

It works at multiple dimensions, although I don't know what the magic scaling factors should be for 1 dimension or 5+

Here's the code I wrote (as a result of the discussion in my thread: Randomly biased, random numbers.), to implement the Improved Perlin Noise algorithm. It also runs it with various scales to produce finer and finer detail:

```#! perl -slw
use strict;
use Data::Dump qw[ pp ]; \$Data::Dump::WIDTH = 1e3;
use List::Util qw[ min max ];
use GD;
use constant { X => 0, Y=> 1, R => 2 };
use constant P => [
151,160,137,91,90,15,131,13,201,95,96,53,194,233,7,225,140,36,103,30,6
+9,142,
8,99,37,240,21,10,23,190,6,148,247,120,234,75,0,26,197,62,94,252,219,2
+03,117,
35,11,32,57,177,33,88,237,149,56,87,174,20,125,136,171,168,68,175,74,1
+65,71,
134,139,48,27,166,77,146,158,231,83,111,229,122,60,211,133,230,220,105
+,92,41,
55,46,245,40,244,102,143,54,65,25,63,161,1,216,80,73,209,76,132,187,20
+8,89,
18,169,200,196,135,130,116,188,159,86,164,100,109,198,173,186,3,64,52,
+217,
226,250,124,123,5,202,38,147,118,126,255,82,85,212,207,206,59,227,47,1
+6,58,17,
182,189,28,42,223,183,170,213,119,248,152,2,44,154,163,70,221,153,101,
+155,167,
43,172,9,129,22,39,253,19,98,108,110,79,113,224,232,178,185,112,104,21
+8,246,
97,228,251,34,242,193,238,210,144,12,191,179,162,241,81,51,145,235,249
+,14,239,
107,49,192,214,31,181,199,106,157,184,84,204,176,115,121,50,45,127,4,1
+50,254,
138,236,205,93,222,114,67,29,24,72,243,141,128,195,78,66,215,61,156,18
+0,
151,160,137,91,90,15,131,13,201,95,96,53,194,233,7,225,140,36,103,30,6
+9,142,
8,99,37,240,21,10,23,190,6,148,247,120,234,75,0,26,197,62,94,252,219,2
+03,117,
35,11,32,57,177,33,88,237,149,56,87,174,20,125,136,171,168,68,175,74,1
+65,71,
134,139,48,27,166,77,146,158,231,83,111,229,122,60,211,133,230,220,105
+,92,41,
55,46,245,40,244,102,143,54,65,25,63,161,1,216,80,73,209,76,132,187,20
+8,89,
18,169,200,196,135,130,116,188,159,86,164,100,109,198,173,186,3,64,52,
+217,
226,250,124,123,5,202,38,147,118,126,255,82,85,212,207,206,59,227,47,1
+6,58,17,
182,189,28,42,223,183,170,213,119,248,152,2,44,154,163,70,221,153,101,
+155,167,
43,172,9,129,22,39,253,19,98,108,110,79,113,224,232,178,185,112,104,21
+8,246,
97,228,251,34,242,193,238,210,144,12,191,179,162,241,81,51,145,235,249
+,14,239,
107,49,192,214,31,181,199,106,157,184,84,204,176,115,121,50,45,127,4,1
+50,254,
138,236,205,93,222,114,67,29,24,72,243,141,128,195,78,66,215,61,156,18
+0,
];

sub rgb2n{ local \$^W; unpack 'N', pack 'CCCC', 0, @_ }

my \$RED     = rgb2n( 255, 0, 0 );
my \$GREEN   = rgb2n( 0, 255, 0 );
my \$BLUE    = rgb2n( 0, 0, 255 );
my \$YELLOW  = rgb2n( 255, 255, 0 );
my \$MAGENTA = rgb2n( 255, 0, 255 );
my \$CYAN    = rgb2n( 0, 255, 255 );
my \$WHITE   = rgb2n( 255,255,255 );

sub r2pd {
my( \$x, \$y, \$cx, \$cy ) = @_;
return sqrt( ( \$x - \$cx )**2 + ( \$y - \$cy )**2 );
}

sub fade{ \$_[0]**3 * ( \$_[0] * (\$_[0] * 6 - 15) + 10 ) }
sub lerp{ \$_[1] + \$_[0] * (\$_[2] - \$_[1]) }
sub grad{
my( \$hash, \$x, \$y, \$z ) = @_;
my \$h = \$hash & 15;
my \$u = \$h < 8 ? \$x : \$y;
my \$v = \$h < 4 ? \$y : \$h == 12 || \$h ==14 ? \$x : \$z;
return (( \$h & 1 ) == 0 ? \$u : -\$u ) + (( \$h & 2 ) == 0 ? \$v : -\$v
+ );
}
sub noise {
my( \$x, \$y, \$z ) = @_;
my \$X = int( \$x ) & 255; \$x -= int \$x; my \$u = fade( \$x );
my \$Y = int( \$y ) & 255; \$y -= int \$y; my \$v = fade( \$y );
my \$Z = int( \$z ) & 255; \$z -= int \$z; my \$w = fade( \$z );

my \$A  = P->[\$X  ]+\$Y;
my \$AA = P->[\$A  ]+\$Z;
my \$AB = P->[\$A+1]+\$Z;
my \$B  = P->[\$X+1]+\$Y;
my \$BA = P->[\$B  ]+\$Z;
my \$BB = P->[\$B+1]+\$Z;

return lerp( \$w,
lerp( \$v,
lerp( \$u, grad( P->[\$AA  ], \$x, \$y  , \$z   ), grad( P->[\$B
+A  ], \$x-1, \$y  , \$z   ) ),
lerp( \$u, grad( P->[\$AB  ], \$x, \$y-1, \$z   ), grad( P->[\$B
+B  ], \$x-1, \$y-1, \$z   ) )
),
lerp( \$v,
lerp( \$u, grad( P->[\$AA+1], \$x, \$y  , \$z-1 ), grad( P->[\$B
+A+1], \$x-1, \$y  , \$z-1 ) ),
lerp( \$u, grad( P->[\$AB+1], \$x, \$y-1, \$z-1 ), grad( P->[\$B
+B+1], \$x-1, \$y-1, \$z-1 ) )
)
);
}

our \$F //= 5;
our \$X //= 1024; our \$Y //= 512;

my @pix = map[ (0) x \$X ], 1 .. \$Y;

for my \$f ( 2,3,5,7,11,13,17,19 ) {
my \$yoff = 0;
for my \$y ( 0 .. \$Y-1 ) {
my \$xoff = 0;
for my \$x ( 0 .. \$X-1 ) {
( \$pix[\$y][\$x] += ( (1+noise( \$xoff, \$yoff, 1 )) /2 ) ) /=
+ 2;
\$xoff += 0.01 * \$f;
}
\$yoff += 0.01 * \$f;
}
my \$im = GD::Image->new( \$X, \$Y, 1 );
for my \$y ( 0 .. \$Y-1 ) {
for my \$x ( 0 .. \$X-1 ) {
\$im->setPixel( \$x, \$y, rgb2n( ( \$pix[\$y][\$x] * ( 512 / \$f
+) ) x 3 ) );
}
}
open PNG, '>:raw', "\$0.png" or die \$!;
print PNG \$im->png;
close PNG;
system "\$0.png";
}

print min( map min( @\$_ ), @pix );
print max( map max( @\$_ ), @pix );

[download]```

The next step to producing good texture maps is to pick sets of scales and combine them to produce variable textures across the surface. I never got around to that as it wasn't my goal for the code.

With the rise and rise of 'Social' network sites: 'Computers are making people easier to use everyday'
Examine what is said, not who speaks -- Silence betokens consent -- Love the truth but pardon error.
"Science is about questioning the status quo. Questioning authority".
In the absence of evidence, opinion is indistinguishable from prejudice.

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